diff --git a/docs/bioinformatics_examples.md b/docs/bioinformatics_examples.md index 0e3bd5508..5d37e1ce0 100644 --- a/docs/bioinformatics_examples.md +++ b/docs/bioinformatics_examples.md @@ -10,7 +10,7 @@ Flyte very much supports running your bioinformatics applications. Dive deeper i :header-rows: 0 :widths: 20 30 -* - {doc}`Nucleotide Sequence Querying with BLASTX ` +* - {doc}`Nucleotide Sequence Querying with BLASTX ` - Use BLASTX to Query a Nucleotide Sequence Against a Local Protein Database ``` @@ -19,5 +19,5 @@ Flyte very much supports running your bioinformatics applications. Dive deeper i :caption: Contents :hidden: -auto_examples/blast/index +examples/blast/README ``` diff --git a/docs/environment_setup.md b/docs/environment_setup.md index 17a8f3a3d..619abc310 100644 --- a/docs/environment_setup.md +++ b/docs/environment_setup.md @@ -170,4 +170,4 @@ image you want to use with the `--image` option in `pyflyte run`. ## What's next? -Try out the examples in the {doc}`Basics ` section. +Try out the examples in the {doc}`Basics ` section. diff --git a/docs/examples/README.md b/docs/examples/README.md new file mode 100644 index 000000000..acc344a53 --- /dev/null +++ b/docs/examples/README.md @@ -0,0 +1,21 @@ +# Flyte Examples + +This directory contains a series of example projects that demonstrate how to use +Flyte. The basic structure of the examples is as follows: + +```{code-block} bash +example_project +├── README.md # High-level description of the example project +├── Dockerfile # Dockerfile for packaging up the project requirements +├── requirements.in # Minimal python requirements for the project +├── requirements.txt # Compiled python requirements using pip-compile +└── example_project # Python package containing examples with the same name as the project +    ├── __init__.py +    ├── example_01.py +    ├── example_02.py +    ├── ... +    └── example_n.py +``` + +These example projects are meant to be stand-alone projects that can be built +and run by themselves. diff --git a/docs/examples/advanced_composition/Dockerfile b/docs/examples/advanced_composition/Dockerfile new file mode 100644 index 000000000..c5e6701a7 --- /dev/null +++ b/docs/examples/advanced_composition/Dockerfile @@ -0,0 +1,33 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM python:3.11-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/advanced_composition/README.md b/docs/examples/advanced_composition/README.md new file mode 100644 index 000000000..ed9696f4d --- /dev/null +++ b/docs/examples/advanced_composition/README.md @@ -0,0 +1,23 @@ +(advanced_composition)= + +# Advanced Composition + +This section of the user guide introduces the advanced features of the flytekit Python SDK. +These examples cover more complex aspects of Flyte, including conditions, subworkflows, +dynamic workflows, map tasks, gate nodes and more. + +```{auto-examples-toc} +files +folders +conditions +chain_entities +subworkflows +dynamics +map_task +merge_sort +eager_workflows +decorating_tasks +decorating_workflows +checkpoint +waiting_for_external_inputs +``` diff --git a/docs/examples/advanced_composition/advanced_composition/__init__.py b/docs/examples/advanced_composition/advanced_composition/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/advanced_composition/advanced_composition/chain_entities.py b/docs/examples/advanced_composition/advanced_composition/chain_entities.py new file mode 100644 index 000000000..30533bcdb --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/chain_entities.py @@ -0,0 +1,82 @@ +# %% [markdown] +# (chain_flyte_entities)= +# +# # Chain Flyte Entities +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# flytekit provides a mechanism to chain Flyte entities using the `>>` operator. +# +# ## Tasks +# +# Let's enforce an order for `t1()` to happen after `t0()`, and for `t2()` to happen after `t1()`. +# +# Import the necessary dependencies. +# %% +from flytekit import task, workflow + + +@task +def t2(): + pass + + +@task +def t1(): + pass + + +@task +def t0(): + pass + + +# %% [markdown] +# We want to enforce an order here: `t0()` followed by `t1()` followed by `t2()`. +# %% +@workflow +def chain_tasks_wf(): + t2_promise = t2() + t1_promise = t1() + t0_promise = t0() + + t0_promise >> t1_promise + t1_promise >> t2_promise + + +# %% [markdown] +# ## Chain SubWorkflows +# +# Similar to tasks, you can chain {ref}`subworkflows `. +# %% +@workflow +def sub_workflow_1(): + t1() + + +@workflow +def sub_workflow_0(): + t0() + + +# %% [markdown] +# Use `>>` to chain the subworkflows. +# %% +@workflow +def chain_workflows_wf(): + sub_wf1 = sub_workflow_1() + sub_wf0 = sub_workflow_0() + + sub_wf0 >> sub_wf1 + + +# %% [markdown] +# Run the workflows locally. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running chain_tasks_wf()... {chain_tasks_wf()}") + print(f"Running chain_workflows_wf()... {chain_workflows_wf()}") diff --git a/docs/examples/advanced_composition/advanced_composition/checkpoint.py b/docs/examples/advanced_composition/advanced_composition/checkpoint.py new file mode 100644 index 000000000..13f8ce561 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/checkpoint.py @@ -0,0 +1,103 @@ +# %% [markdown] +# # Intratask Checkpoints +# +# ```{eval-rst} +# .. tags:: MachineLearning, Intermediate +# ``` +# +# :::{note} +# This feature is available from Flytekit version 0.30.0b6+ and needs a Flyte backend version of at least 0.19.0+. +# ::: +# +# A checkpoint recovers a task from a previous failure by recording the state of a task before the failure and +# resuming from the latest recorded state. +# +# ## Why Intra-task Checkpoints? +# +# Flyte, at its core, is a workflow engine. Workflows provide a way to break up an operation/program/idea +# logically into smaller tasks. If a task fails, the workflow does not need to run the previously completed tasks. It can +# simply retry the task that failed. Eventually, when the task succeeds, it will not run again. Thus, task boundaries +# naturally serve as checkpoints. +# +# There are cases where it is not easy or desirable to break a task into smaller tasks, because running a task +# adds to the overhead. This is true when running a large computation in a tight-loop. In such cases, users can +# split each loop iteration into its own task using {ref}`dynamic workflows `, but the overhead of spawning new tasks, recording +# intermediate results, and reconstituting the state can be expensive. +# +# ### Model-training Use Case +# +# An example of this case is model training. Running multiple epochs or different iterations with the same +# dataset can take a long time, but the bootstrap time may be high and creating task boundaries can be expensive. +# +# To tackle this, Flyte offers a way to checkpoint progress within a task execution as a file or a set of files. These +# checkpoints can be written synchronously or asynchronously. In case of failure, the checkpoint file can be re-read to resume +# most of the state without re-running the entire task. This opens up the opportunity to use alternate compute systems with +# lower guarantees like [AWS Spot Instances](https://aws.amazon.com/ec2/spot/), [GCP Pre-emptible Instances](https://cloud.google.com/compute/docs/instances/preemptible), etc. +# +# These instances offer great performance at much lower price-points as compared to their on-demand or reserved alternatives. +# This is possible if you construct the tasks in a fault-tolerant manner. In most cases, when the task runs for a short duration, +# e.g., less than 10 minutes, the potential of failure is insignificant and task-boundary-based recovery offers +# significant fault-tolerance to ensure successful completion. +# +# But as the time for a task increases, the cost of re-running it increases, and reduces the chances of successful +# completion. This is where Flyte's intra-task checkpointing truly shines. +# +# Let's look at an example of how to develop tasks which utilize intra-task checkpointing. It only provides the low-level API, though. We intend to integrate +# higher-level checkpointing APIs available in popular training frameworks like Keras, Pytorch, Scikit-learn, and +# big-data frameworks like Spark and Flink to supercharge their fault-tolerance. + +# %% +from flytekit import current_context, task, workflow +from flytekit.exceptions.user import FlyteRecoverableException + +RETRIES = 3 + + +# %% [markdown] +# This task shows how checkpoints can help resume execution in case of a failure. This is an example task and shows the API for +# the checkpointer. The checkpoint system exposes other APIs. For a detailed understanding, refer to the [checkpointer code](https://github.com/flyteorg/flytekit/blob/master/flytekit/core/checkpointer.py). +# +# The goal of this method is to loop for exactly n_iterations, checkpointing state and recovering from simualted failures. +# %% +@task(retries=RETRIES) +def use_checkpoint(n_iterations: int) -> int: + cp = current_context().checkpoint + prev = cp.read() + start = 0 + if prev: + start = int(prev.decode()) + + # create a failure interval so we can create failures for across 'n' iterations and then succeed after + # configured retries + failure_interval = n_iterations // RETRIES + i = 0 + for i in range(start, n_iterations): + # simulate a deterministic failure, for demonstration. We want to show how it eventually completes within + # the given retries + if i > start and i % failure_interval == 0: + raise FlyteRecoverableException(f"Failed at iteration {i}, failure_interval {failure_interval}") + # save progress state. It is also entirely possible save state every few intervals. + cp.write(f"{i + 1}".encode()) + + return i + + +# %% [markdown] +# The workflow here simply calls the task. The task itself +# will be retried for the {ref}`FlyteRecoverableException `. +# %% +@workflow +def example(n_iterations: int) -> int: + return use_checkpoint(n_iterations=n_iterations) + + +# %% [markdown] +# The checkpoint is stored locally, but it is not used since retries are not supported. +# +# %% +if __name__ == "__main__": + try: + example(n_iterations=10) + except RuntimeError as e: # noqa : F841 + # no retries are performed, so an exception is expected when run locally. + pass diff --git a/docs/examples/advanced_composition/advanced_composition/conditions.py b/docs/examples/advanced_composition/advanced_composition/conditions.py new file mode 100644 index 000000000..07df3864d --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/conditions.py @@ -0,0 +1,285 @@ +# %% [markdown] +# (conditional)= +# +# # Conditions +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# Flytekit supports conditions as a first class construct in the language. Conditions offer a way to selectively execute +# branches of a workflow based on static or dynamic data produced by other tasks or come in as workflow inputs. +# Conditions are very performant to be evaluated. However, they are limited to certain binary and logical operators and can +# only be performed on primitive values. + +# %% [markdown] +# Import the necessary modules. +# %% +import random + +from flytekit import conditional, task, workflow + + +# %% [markdown] +# ## Conditional with simple branch +# +# In this example, we define two tasks `square` and `double`. Depending on whether the workflow input is a +# fraction (0-1) or not, the respective task is executed. +# %% +@task +def square(n: float) -> float: + """ + Parameters: + n (float): name of the parameter for the task is derived from the name of the input variable, and + the type is automatically mapped to Types.Integer + Return: + float: The label for the output is automatically assigned and the type is deduced from the annotation + """ + return n * n + + +@task +def double(n: float) -> float: + """ + Parameters: + n (float): name of the parameter for the task is derived from the name of the input variable + and the type is mapped to ``Types.Integer`` + Return: + float: The label for the output is auto-assigned and the type is deduced from the annotation + """ + return 2 * n + + +@workflow +def multiplier(my_input: float) -> float: + return ( + conditional("fractions") + .if_((my_input >= 0.1) & (my_input <= 1.0)) + .then(double(n=my_input)) + .else_() + .then(square(n=my_input)) + ) + + +if __name__ == "__main__": + print(f"Output of multiplier(my_input=3.0): {multiplier(my_input=3.0)}") + print(f"Output of multiplier(my_input=0.5): {multiplier(my_input=0.5)}") + + +# %% [markdown] +# ## Conditional with multiple branches +# +# In this example, we define an `if` condition with multiple branches. It fails if none of the conditions is met. Flyte +# expects any `conditional()` statement to be **complete**. This means all possible branches should be handled. +# +# :::{note} +# Notice the use of bitwise (&). Python (PEP-335) doesn't allow overloading of the logical `and`, `or`, and `not` operators. Flytekit uses bitwise `&` and `|` as logical `and` and `or` operators. This is a common practice in other libraries too. +# ::: +# %% +@workflow +def multiplier_2(my_input: float) -> float: + return ( + conditional("fractions") + .if_((my_input > 0.1) & (my_input < 1.0)) + .then(double(n=my_input)) + .elif_((my_input > 1.0) & (my_input <= 10.0)) + .then(square(n=my_input)) + .else_() + .fail("The input must be between 0 and 10") + ) + + +if __name__ == "__main__": + print(f"Output of multiplier_2(my_input=10.0): {multiplier_2(my_input=10.0)}") + + +# %% [markdown] +# ## Consuming the output of a conditional +# +# In this example, we consume the output returned by the `conditional()` in the subsequent task. +# %% +@workflow +def multiplier_3(my_input: float) -> float: + result = ( + conditional("fractions") + .if_((my_input > 0.1) & (my_input < 1.0)) + .then(double(n=my_input)) + .elif_((my_input > 1.0) & (my_input < 10.0)) + .then(square(n=my_input)) + .else_() + .fail("The input must be between 0 and 10") + ) + + # the 'result' will either be the output of `double` or `square`. If none of the conditions is true, + # it gives a failure message. + return double(n=result) + + +if __name__ == "__main__": + print(f"Output of multiplier_3(my_input=5.0): {multiplier_3(my_input=5.0)}") + + +# %% [markdown] +# ## Using the output of a previous task in a conditional +# +# It is possible to test if a boolean returned from the previous task is True. But unary operations are not +# supported. Use the `is_true`, `is_false` or `is_` on the result instead. +# +# :::{note} +# How do output values get these methods? +# In a workflow, no output can be accessed directly. The inputs and outputs are auto-wrapped in a special object called {py:class}`flytekit.extend.Promise`. +# ::: +# +# In this example, we create a biased coin whose seed can be controlled. +# %% +@task +def coin_toss(seed: int) -> bool: + """ + Mimic some condition to check if the operation was successfully executed. + """ + r = random.Random(seed) + if r.random() < 0.5: + return True + return False + + +@task +def failed() -> int: + """ + Mimic a task that handles failure + """ + return -1 + + +@task +def success() -> int: + """ + Mimic a task that handles success + """ + return 0 + + +@workflow +def basic_boolean_wf(seed: int = 5) -> int: + result = coin_toss(seed=seed) + return conditional("test").if_(result.is_true()).then(success()).else_().then(failed()) + + +# %% [markdown] +# ## Using boolean workflow inputs in a conditional +# +# It is possible to pass a boolean directly to a workflow. +# +# :::{note} +# Note that the boolean passed has a method named `is_true`. This boolean is present within +# the workflow context and is wrapped in a Flytekit special object. This special object allows it to have the additional +# behavior. +# ::: +# %% +@workflow +def bool_input_wf(b: bool) -> int: + return conditional("test").if_(b.is_true()).then(success()).else_().then(failed()) + + +# %% [markdown] +# The workflow can be executed locally. +# %% +if __name__ == "__main__": + print("Running basic_boolean_wf a few times") + for i in range(0, 5): + print(f"Basic boolean wf output {basic_boolean_wf()}") + print(f"Boolean input {True if i < 2 else False}, workflow output {bool_input_wf(b=True if i < 2 else False)}") + + +# %% [markdown] +# ## Nested conditionals +# +# It is possible to arbitrarily nest conditional sections inside other conditional sections. The conditional sections can only be in the +# `then` part of the previous conditional block. +# This example shows how float comparisons can be used to create a multi-level nested workflow. +# %% +@workflow +def nested_conditions(my_input: float) -> float: + return ( + conditional("fractions") + .if_((my_input > 0.1) & (my_input < 1.0)) + .then( + conditional("inner_fractions") + .if_(my_input < 0.5) + .then(double(n=my_input)) + .elif_((my_input > 0.5) & (my_input < 0.7)) + .then(square(n=my_input)) + .else_() + .fail("Only <0.7 allowed") + ) + .elif_((my_input > 1.0) & (my_input < 10.0)) + .then(square(n=my_input)) + .else_() + .then(double(n=my_input)) + ) + + +# %% [markdown] +# The nested conditionals can be executed locally. +# %% +if __name__ == "__main__": + print(f"nested_conditions(0.4) -> {nested_conditions(my_input=0.4)}") + + +# %% [markdown] +# ## Using the output of a task in the conditional and consuming its output +# +# Finally, we specify an output of an upstream task in the conditional and +# then consume its output in a downstream task, `double`. +# Outputs are computed as a subset of outputs produced by `then` nodes. In this +# example, we call `square()` in one condition and `calc_sum()` in another. +# %% +@task +def calc_sum(a: float, b: float) -> float: + """ + returns the sum of a and b. + """ + return a + b + + +# %% [markdown] +# Altogether, the workflow that consumes outputs from conditionals can be constructed as shown. +# +# :::{tip} +# A useful mental model to consume outputs of conditions is to think of them as ternary operators in programming +# languages. The only difference is that they can be `n`-ary. In Python, this is equivalent to +# +# ```python +# x = 0 if m < 0 else 1 +# ``` +# ::: +# %% +@workflow +def consume_outputs(my_input: float, seed: int = 5) -> float: + is_heads = coin_toss(seed=seed) + res = ( + conditional("double_or_square") + .if_(is_heads.is_true()) + .then(square(n=my_input)) + .else_() + .then(calc_sum(a=my_input, b=my_input)) + ) + + # Regardless of the result, call ``double`` before + # the variable `res` is returned. In this case, ``res`` stores the value of the ``square`` or ``double`` of the variable `my_input` + return double(n=res) + + +# %% [markdown] +# The workflow can be executed locally. +# +# %% +if __name__ == "__main__": + print( + "consume_outputs(0.4) with default seed=5. This should " + f"return output of calc_sum => {consume_outputs(my_input=0.4)}" + ) + print( + "consume_outputs(0.4, seed=7), this should return " + f"output of square => {consume_outputs(my_input=0.4, seed=7)}" + ) diff --git a/docs/examples/advanced_composition/advanced_composition/decorating_tasks.py b/docs/examples/advanced_composition/advanced_composition/decorating_tasks.py new file mode 100644 index 000000000..c57c1374a --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/decorating_tasks.py @@ -0,0 +1,111 @@ +# %% [markdown] +# (decorating_tasks)= +# +# # Decorating Tasks +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# You can easily change how tasks behave by using decorators to wrap your task functions. +# +# In order to make sure that your decorated function contains all the type annotation and docstring +# information that Flyte needs, you will need to use the built-in {py:func}`~functools.wraps` decorator. +# +# To begin, import the required dependencies. +# %% +import logging +from functools import partial, wraps + +from flytekit import task, workflow + +# %% [markdown] +# Create a logger to monitor the execution's progress. +# %% +logger = logging.getLogger(__file__) + + +# %% [markdown] +# ## Using a single decorator +# +# We define a decorator that logs the input and output details for a decorated task. +# %% +def log_io(fn): + @wraps(fn) + def wrapper(*args, **kwargs): + logger.info(f"task {fn.__name__} called with args: {args}, kwargs: {kwargs}") + out = fn(*args, **kwargs) + logger.info(f"task {fn.__name__} output: {out}") + return out + + return wrapper + + +# %% [markdown] +# We create a task named `t1` that is decorated with `log_io`. +# +# :::{note} +# The order of invoking the decorators is important. `@task` should always be the outer-most decorator. +# ::: +# %% +@task +@log_io +def t1(x: int) -> int: + return x + 1 + + +# %% [markdown] +# (stacking_decorators)= +# +# ## Stacking multiple decorators +# +# You can also stack multiple decorators on top of each other as long as `@task` is the outer-most decorator. +# +# We define a decorator that verifies if the output from the decorated function is a positive number before it's returned. +# If this assumption is violated, it raises a `ValueError` exception. +# %% +def validate_output(fn=None, *, floor=0): + @wraps(fn) + def wrapper(*args, **kwargs): + out = fn(*args, **kwargs) + if out <= floor: + raise ValueError(f"output of task {fn.__name__} must be a positive number, found {out}") + return out + + if fn is None: + return partial(validate_output, floor=floor) + + return wrapper + + +# %% [markdown] +# :::{note} +# The output of the `validate_output` task uses {py:func}`~functools.partial` to implement parameterized decorators. +# ::: +# +# We define a function that uses both the logging and validator decorators. +# %% +@task +@log_io +@validate_output(floor=10) +def t2(x: int) -> int: + return x + 10 + + +# %% [markdown] +# Finally, we compose a workflow that calls `t1` and `t2`. +# %% +@workflow +def wf(x: int) -> int: + return t2(x=t1(x=x)) + + +if __name__ == "__main__": + print(f"Running wf(x=10) {wf(x=10)}") + + +# %% [markdown] +# In this example, you learned how to modify the behavior of tasks via function decorators using the built-in +# {py:func}`~functools.wraps` decorator pattern. To learn more about how to extend Flyte at a deeper level, for +# example creating custom types, custom tasks or backend plugins, +# see {ref}`Extending Flyte `. diff --git a/docs/examples/advanced_composition/advanced_composition/decorating_workflows.py b/docs/examples/advanced_composition/advanced_composition/decorating_workflows.py new file mode 100644 index 000000000..7c6442843 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/decorating_workflows.py @@ -0,0 +1,154 @@ +# %% [markdown] +# (decorating_workflows)= +# +# # Decorating Workflows +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# The behavior of workflows can be modified in a light-weight fashion by using the built-in {py:func}`~functools.wraps` +# decorator pattern, similar to using decorators to +# {ref}`customize task behavior `. However, unlike in the case of +# tasks, we need to do a little extra work to make sure that the DAG underlying the workflow executes tasks in the +# correct order. +# +# ## Setup-Teardown Pattern +# +# The main use case of decorating `@workflow`-decorated functions is to establish a setup-teardown pattern to execute task +# before and after your main workflow logic. This is useful when integrating with other external services +# like [wandb](https://wandb.ai/site) or [clearml](https://clear.ml/), which enable you to track metrics of model +# training runs. + +# %% +from functools import partial, wraps +from unittest.mock import MagicMock + +import flytekit +from flytekit import FlyteContextManager, task, workflow +from flytekit.core.node_creation import create_node + +# %% [markdown] +# First, let's define the tasks that we want for setup and teardown. In this example, we'll use the +# {py:class}`unittest.mock.MagicMock` class to create a fake external service that we want to initialize at the +# beginning of our workflow and finish at the end. + +# %% +external_service = MagicMock() + + +@task +def setup(): + print("initializing external service") + external_service.initialize(id=flytekit.current_context().execution_id) + + +@task +def teardown(): + print("finish external service") + external_service.complete(id=flytekit.current_context().execution_id) + + +# %% [markdown] +# As you can see, you can even use Flytekit's current context to access the `execution_id` of the current workflow +# if you need to link Flyte with the external service so that you reference the same unique identifier in both the +# external service and Flyte. +# +# ## Workflow Decorator +# +# Next we create the decorator that we'll use to wrap our workflow function. + + +# %% +def setup_teardown(fn=None, *, before, after): + @wraps(fn) + def wrapper(*args, **kwargs): + # get the current flyte context to obtain access to the compilation state of the workflow DAG. + ctx = FlyteContextManager.current_context() + + # defines before node + before_node = create_node(before) + # ctx.compilation_state.nodes == [before_node] + + # under the hood, flytekit compiler defines and threads + # together nodes within the `my_workflow` function body + outputs = fn(*args, **kwargs) + # ctx.compilation_state.nodes == [before_node, *nodes_created_by_fn] + + # defines the after node + after_node = create_node(after) + # ctx.compilation_state.nodes == [before_node, *nodes_created_by_fn, after_node] + + # compile the workflow correctly by making sure `before_node` + # runs before the first workflow node and `after_node` + # runs after the last workflow node. + if ctx.compilation_state is not None: + # ctx.compilation_state.nodes is a list of nodes defined in the + # order of execution above + workflow_node0 = ctx.compilation_state.nodes[1] + workflow_node1 = ctx.compilation_state.nodes[-2] + before_node >> workflow_node0 + workflow_node1 >> after_node + return outputs + + if fn is None: + return partial(setup_teardown, before=before, after=after) + + return wrapper + + +# %% [markdown] +# There are a few key pieces to note in the `setup_teardown` decorator above: +# +# 1. It takes a `before` and `after` argument, both of which need to be `@task`-decorated functions. These +# tasks will run before and after the main workflow function body. +# 2. The [create_node](https://github.com/flyteorg/flytekit/blob/9e156bb0cf3d1441c7d1727729e8f9b4bbc3f168/flytekit/core/node_creation.py#L18) function +# to create nodes associated with the `before` and `after` tasks. +# 3. When `fn` is called, under the hood Flytekit creates all the nodes associated with the workflow function body +# 4. The code within the `if ctx.compilation_state is not None:` conditional is executed at compile time, which +# is where we extract the first and last nodes associated with the workflow function body at index `1` and `-2`. +# 5. Finally, we use the `>>` right shift operator to ensure that `before_node` executes before the +# first node and `after_node` executes after the last node of the main workflow function body. + +# %% [markdown] +# ## Defining the DAG +# +# Now let's define two tasks that will constitute the workflow + + +# %% +@task +def t1(x: float) -> float: + return x - 1 + + +@task +def t2(x: float) -> float: + return x**2 + + +# %% [markdown] +# And then create our decorated workflow: + + +# %% +@workflow +@setup_teardown(before=setup, after=teardown) +def wf(x: float) -> float: + return t2(x=t1(x=x)) + + +if __name__ == "__main__": + print(wf(x=10.0)) + + +# %% [markdown] +# In this example, you learned how to modify the behavior of a workflow by defining a `setup_teardown` decorator +# that can be applied to any workflow in your project. This is useful when integrating with other external services +# like [wandb](https://wandb.ai/site) or [clearml](https://clear.ml/), which enable you to track metrics of model +# training runs. +# +# To define workflows imperatively, refer to {ref}`this example `, +# and to learn more about how to extend Flyte at a deeper level, for example creating custom types, custom tasks, or +# backend plugins, see {ref}`Extending Flyte `. +# diff --git a/docs/examples/advanced_composition/advanced_composition/dynamics.py b/docs/examples/advanced_composition/advanced_composition/dynamics.py new file mode 100644 index 000000000..2e5acf816 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/dynamics.py @@ -0,0 +1,216 @@ +# %% [markdown] +# (dynamic_workflows)= +# +# # Dynamic Workflows +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# ```{image} https://img.shields.io/badge/Blog%20Post-Dynamic%20Workflows-blue?style=for-the-badge +# :alt: Dynamic Workflows Blog Post +# :target: https://flyte.org/blog/dynamic-workflows-in-flyte +# ``` +# +# A workflow is typically static when the directed acyclic graph's (DAG) structure is known at compile-time. +# However, in cases where a run-time parameter (for example, the output of an earlier task) determines the full DAG structure, you can use dynamic workflows by decorating a function with `@dynamic`. +# +# A dynamic workflow is similar to the {py:func}`~flytekit.workflow`, in that it represents a python-esque DSL to +# declare task interactions or new workflows. One significant difference between a regular workflow and dynamic (workflow) is that +# the latter is evaluated at runtime. This means the inputs are first materialized and sent to the actual function, +# as if it were a task. However, the return value from a dynamic workflow is a Promise object instead of an actual value, +# which is fulfilled by evaluating the various tasks invoked in the dynamic workflow. +# +# Think of a dynamic workflow as a parent graph node that spins off new child nodes which would represent a new child graph. +# At runtime, dynamic workflows receive input and create new workflows. These new workflows have graph nodes. +# +# Within the `@dynamic` context (function), every invocation of a {py:func}`~flytekit.task` or a derivative of +# {py:class}`~flytekit.core.base_task.Task` class will result in deferred evaluation using a promise, instead +# of the actual value being materialized. You can also nest other `@dynamic` and `@workflow` constructs within this +# task, but it is not possible to interact with the outputs of a `task/workflow` as they are lazily evaluated. +# If you want to interact with the outputs, break up the logic in dynamic and create a new task to read and resolve the outputs. +# +# Refer to {py:func}`~flytekit.dynamic` for documentation. +# +# Here's a code example that counts the common characters between any two strings. + +# %% [markdown] +# Let's first import all the required libraries. +# %% +import typing + +from flytekit import dynamic, task, workflow + + +# %% [markdown] +# Next, we write a task that returns the index of a character (A-Z/a-z is equivalent to 0 to 25). +# %% +@task +def return_index(character: str) -> int: + """ + Computes the character index (which needs to fit into the 26 characters list)""" + if character.islower(): + return ord(character) - ord("a") + else: + return ord(character) - ord("A") + + +# %% [markdown] +# We now write a task that prepares the 26-character list by populating the frequency of every character. +# %% +@task +def update_list(freq_list: typing.List[int], list_index: int) -> typing.List[int]: + """ + Notes the frequency of characters""" + freq_list[list_index] += 1 + return freq_list + + +# %% [markdown] +# Next we find the number of common characters between the two strings. +# %% +@task +def derive_count(freq1: typing.List[int], freq2: typing.List[int]) -> int: + """ + Derives the number of common characters""" + count = 0 + for i in range(26): + count += min(freq1[i], freq2[i]) + return count + + +# %% [markdown] +# In this step, we perform the following: +# +# 1. Initialize the empty 26-character list to be sent to the `update_list` task +# 2. Loop through every character of the first string (s1) and populate the frequency list +# 3. Loop through every character of the second string (s2) and populate the frequency list +# 4. Derive the number of common characters by comparing the two frequency lists +# +# The looping is dependent on the number of characters of both the strings which aren't known until the run time. If the `@task` decorator is used to encapsulate the calls mentioned above, the compilation will fail very early on due to the absence of the literal values. +# Therefore, `@dynamic` decorator has to be used. +# +# Dynamic workflow is effectively both a task and a workflow. The key thing to note is that the \_body of tasks is run at run time and the +# body of workflows is run at compile (aka registration) time. Essentially, this is what a dynamic workflow leverages -- it’s a workflow that is compiled at run time (the best of both worlds)! +# +# At execution (run) time, Flytekit runs the compilation step, and produces +# a `WorkflowTemplate` (from the dynamic workflow), which Flytekit then passes back to Flyte Propeller for further running, exactly how sub-workflows are handled. +# +# :::{note} +# The dynamic pattern isn't the most efficient method to iterate over a list. [Map tasks](https://github.com/flyteorg/flytekit/blob/8528268a29a07fe7e9ce9f7f08fea68c41b6a60b/flytekit/core/map_task.py/) +# might be more efficient in certain cases. But they only work for Python tasks (tasks decorated with the @task decorator) not SQL, Spark, and so on. +# ::: +# +# We now define a dynamic workflow that encapsulates the above mentioned points. +# %% +@dynamic +def count_characters(s1: str, s2: str) -> int: + """ + Calls the required tasks and returns the final result""" + + # s1 and s2 are accessible + + # initialize an empty list consisting of 26 empty slots corresponding to every alphabet (lower and upper case) + freq1 = [0] * 26 + freq2 = [0] * 26 + + # looping through the string s1 + for i in range(len(s1)): + + # index and freq1 are not accessible as they are promises + index = return_index(character=s1[i]) + freq1 = update_list(freq_list=freq1, list_index=index) + + # looping through the string s2 + for i in range(len(s2)): + + # index and freq2 are not accessible as they are promises + index = return_index(character=s2[i]) + freq2 = update_list(freq_list=freq2, list_index=index) + + # counting the common characters + return derive_count(freq1=freq1, freq2=freq2) + + +# %% [markdown] +# When tasks are called within any workflow, they return Promise objects. Likewise, in a dynamic workflow, the tasks' outputs are Promise objects that cannot be directly accessed (they shall be fulfilled by Flyte later). +# Because of this fact, operations on the `index` variable like `index + 1` are not valid. +# To manage this problem, the values need to be passed to the other tasks to unwrap them. +# +# :::{note} +# The local execution will work when a `@dynamic` decorator is used because Flytekit treats it like a `task` that will run with the Python native inputs. +# ::: +# +# Therefore, there are no Promise objects locally within the function decorated with `@dynamic` as it is treated as a `task`. + +# %% [markdown] +# Finally, we define a workflow that calls the dynamic workflow. +# %% +@workflow +def wf(s1: str, s2: str) -> int: + """ + Calls the dynamic workflow and returns the result""" + + # sending two strings to the workflow + return count_characters(s1=s1, s2=s2) + + +if __name__ == "__main__": + print(wf(s1="Pear", s2="Earth")) + + +# %% [markdown] +# ## Dynamic Workflows Under the Hood +# +# ### What Is a Dynamic Workflow? +# +# A workflow whose directed acyclic graph (DAG) is computed at run-time is a {ref}`dynamic workflow `. The tasks in a dynamic workflow are executed at runtime using dynamic inputs. +# +# Think of a dynamic workflow as a combination of a task and a workflow. It is used to dynamically decide the parameters of a workflow at runtime. It is both compiled and executed at run-time. You can define a dynamic workflow using the `@dynamic` decorator. +# +# ### Why Use Dynamic Workflows? +# +# #### Flexibility +# +# Dynamic workflows simplify your pipelines, providing the flexibility to design workflows based on your project’s requirements, which can’t be achieved using static workflows. +# +# #### Lower Pressure on etcd +# +# The workflow CRD and the states associated with static workflows are stored in etcd, which is the Kubernetes database. This database stores Flyte workflow CRD as key-value pairs and keeps track of the status of each node’s execution. +# A limitation of etcd is that there is a hard limit on the data size (data size refers to the aggregate of the size of the workflow and the status of the nodes). +# Due to this limitation, you need to ensure that your static workflows don’t consume too much memory. +# +# Dynamic workflows offload the workflow spec (node/task definitions and connections, etc) to the blobstore but the node statuses are stored in the FlyteWorkflow CRD (in etcd). +# Dynamic workflows alleviate a portion of etcd storage space thereby reducing pressure on etcd. +# +# ### How Is a Dynamic Workflow Executed? +# +# FlytePropeller executes the dynamic task in its k8s pod and results in a compiled Flyte DAG which is made available in the FlyteConsole. +# FlytePropeller uses the information obtained by executing the dynamic task to schedule and execute every node within the dynamic task. +# You can visualize the dynamic workflow’s graph in the UI only after the dynamic task has completed execution. +# +# When a dynamic task is executed, it generates the entire workflow as its output. This output is known as the **futures file**. +# It is named so because the workflow is yet to be executed and all the subsequent outputs are futures. +# +# ### How Does Flyte Handle Dynamic Workflows? +# +# A dynamic workflow is modeled as a task in the backend, but the body of the function is executed to produce a workflow at run-time. In both dynamic and static workflows, the output of tasks are Promise objects. +# +# :::{note} +# When a dynamic (or static) workflow calls a task, the workflow returns a {py:class}`Promise ` object. You can’t interact with this Promise object directly since it uses lazy evaluation (it defers the evaluation until absolutely needed). You can unwrap the Promise object by passing it to a task or a dynamic workflow. +# ::: +# +# {ref}`Here ` is an example of house price prediction using dynamic workflows. +# +# ### Where Are Dynamic Workflows Used? +# +# Dynamic workflow comes into the picture when you need to: +# +# 1. Modify the logic of the code at runtime +# 2. Change or decide on feature extraction parameters on-the-go +# 3. Build AutoML pipelines +# 4. Tune hyperparameters during execution +# +# ### Dynamic versus Map Tasks +# +# Dynamic tasks have overhead for large fan-out tasks because they store metadata for the entire workflow. In contrast, map tasks are efficient for these large fan-out tasks since they don’t store the metadata, as a consequence of which overhead is less apparent. diff --git a/docs/examples/advanced_composition/advanced_composition/eager_workflows.py b/docs/examples/advanced_composition/advanced_composition/eager_workflows.py new file mode 100644 index 000000000..35e38ed37 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/eager_workflows.py @@ -0,0 +1,475 @@ +# %% [markdown] +# (eager_workflows)= +# +# # Eager Workflows +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# ```{important} +# This feature is experimental and the API is subject to breaking changes. +# If you encounter any issues please consider submitting a +# [bug report](https://github.com/flyteorg/flyte/issues/new?assignees=&labels=bug%2Cuntriaged&projects=&template=bug_report.yaml&title=%5BBUG%5D+). +# ``` +# +# So far, the two types of workflows you've seen are static workflows, which +# are defined with `@workflow`-decorated functions or imperative `Workflow` class, +# and dynamic workflows, which are defined with the `@dynamic` decorator. +# +# {ref}`Static workflows ` are created at compile time when you call `pyflyte run`, +# `pyflyte register`, or `pyflyte serialize`. This means that the workflow is static +# and cannot change its shape at any point: all of the variables defined as an input +# to the workflow or as an output of a task or subworkflow are promises. +# {ref}`Dynamic workflows `, on the other hand, are compiled +# at runtime so that they can materialize the inputs of the workflow as Python values +# and use them to determine the shape of the execution graph. +# +# In this guide you'll learn how to use eager workflows, which allow you to +# create extremely flexible workflows that give you run-time access to +# intermediary task/subworkflow outputs. +# +# ## Why Eager Workflows? +# +# Both static and dynamic workflows have a key limitation: while they provide +# compile-time and run-time type safety, respectively, they both suffer from +# inflexibility in expressing asynchronous execution graphs that many Python +# programmers may be accustomed to by using, for example, the +# [asyncio](https://docs.python.org/3/library/asyncio.html) library. +# +# Unlike static and dynamic workflows, eager workflows allow you to use all of +# the python constructs that you're familiar with via the `asyncio` API. To +# understand what this looks like, let's define a very basic eager workflow +# using the `@eager` decorator. + +# %% +from flytekit import task, workflow +from flytekit.experimental import eager + + +@task +def add_one(x: int) -> int: + return x + 1 + + +@task +def double(x: int) -> int: + return x * 2 + + +@eager +async def simple_eager_workflow(x: int) -> int: + out = await add_one(x=x) + if out < 0: + return -1 + return await double(x=out) + + +# %% [markdown] +# As we can see in the code above, we're defining an `async` function called +# `simple_eager_workflow` that takes an integer as input and returns an integer. +# By decorating this function with `@eager`, we now have the ability to invoke +# tasks, static subworkflows, and even other eager subworkflows in an _eager_ +# fashion such that we can materialize their outputs and use them inside the +# parent eager workflow itself. +# +# In the `simple_eager_workflow` function, we can see that we're `await`ing +# the output of the `add_one` task and assigning it to the `out` variable. If +# `out` is a negative integer, the workflow will return `-1`. Otherwise, it +# will double the output of `add_one` and return it. +# +# Unlike in static and dynamic workflows, this variable is actually +# the Python integer that is the result of `x + 1` and not a promise. +# +# %% [markdown] +# ## How it Works +# +# When you decorate a function with `@eager`, any function invoked within it +# that's decorated with `@task`, `@workflow`, or `@eager` becomes +# an [awaitable](https://docs.python.org/3/library/asyncio-task.html#awaitables) +# object within the lifetime of the parent eager workflow execution. Note that +# this happens automatically and you don't need to use the `async` keyword when +# defining a task or workflow that you want to invoke within an eager workflow. +# +# ```{important} +# With eager workflows, you basically have access to the Python `asyncio` +# interface to define extremely flexible execution graphs! The trade-off is that +# you lose the compile-time type safety that you get with regular static workflows +# and to a lesser extent, dynamic workflows. +# +# We're leveraging Python's native `async` capabilities in order to: +# +# 1. Materialize the output of flyte tasks and subworkflows so you can operate +# on them without spinning up another pod and also determine the shape of the +# workflow graph in an extremely flexible manner. +# 2. Provide an alternative way of achieving concurrency in Flyte. Flyte has +# concurrency built into it, so all tasks/subworkflows will execute concurrently +# assuming that they don't have any dependencies on each other. However, eager +# workflows provide a python-native way of doing this, with the main downside +# being that you lose the benefits of statically compiled workflows such as +# compile-time analysis and first-class data lineage tracking. +# ``` +# +# Similar to {ref}`dynamic workflows `, eager workflows are +# actually tasks. The main difference is that, while dynamic workflows compile +# a static workflow at runtime using materialized inputs, eager workflows do +# not compile any workflow at all. Instead, they use the {py:class}`~flytekit.remote.remote.FlyteRemote` +# object together with Python's `asyncio` API to kick off tasks and subworkflow +# executions eagerly whenever you `await` on a coroutine. This means that eager +# workflows can materialize an output of a task or subworkflow and use it as a +# Python object in the underlying runtime environment. We'll see how to configure +# `@eager` functions to run on a remote Flyte cluster +# {ref}`later in this guide `. + +# %% [markdown] +# ## What can you do with eager workflows? +# +# In this section we'll cover a few of the use cases that you can accomplish +# with eager workflows, some of which you can't accomplish with static or dynamic +# workflows. +# +# ### Operating on task and subworkflow outputs +# +# One of the biggest benefits of eager workflows is that you can now materialize +# task and subworkflow outputs as Python values and do operations on them just +# like you would in any other Python function. Let's look at another example: + +# %% +@eager +async def another_eager_workflow(x: int) -> int: + out = await add_one(x=x) + + # out is a Python integer + out = out - 1 + + return await double(x=out) + + +# %% [markdown] +# Since out is an actual Python integer and not a promise, we can do operations +# on it at runtime, inside the eager workflow function body. This is not possible +# with static or dynamic workflows. + + +# %% [markdown] +# ### Pythonic Conditionals +# +# As you saw in the `simple_eager_workflow` workflow above, you can use regular +# Python conditionals in your eager workflows. Let's look at a more complicated +# example: + +# %% +@task +def gt_100(x: int) -> bool: + return x > 100 + + +@eager +async def eager_workflow_with_conditionals(x: int) -> int: + out = await add_one(x=x) + + if out < 0: + return -1 + elif await gt_100(x=out): + return 100 + else: + out = await double(x=out) + + assert out >= -1 + return out + + +# %% [markdown] +# In the above example, we're using the eager workflow's Python runtime +# to check if `out` is negative, but we're also using the `gt_100` task in the +# `elif` statement, which will be executed in a separate Flyte task. + + +# %% [markdown] +# ### Loops +# +# You can also gather the outputs of multiple tasks or subworkflows into a list: + +# %% +import asyncio + + +@eager +async def eager_workflow_with_for_loop(x: int) -> int: + outputs = [] + + for i in range(x): + outputs.append(add_one(x=i)) + + outputs = await asyncio.gather(*outputs) + return await double(x=sum(outputs)) + + +# %% [markdown] +# ### Static subworkflows +# +# You can also invoke static workflows from within an eager workflow: + +# %% +@workflow +def subworkflow(x: int) -> int: + out = add_one(x=x) + return double(x=out) + + +@eager +async def eager_workflow_with_static_subworkflow(x: int) -> int: + out = await subworkflow(x=x) + assert out == (x + 1) * 2 + return out + + +# %% [markdown] +# ### Eager subworkflows +# +# You can have nest eager subworkflows inside a parent eager workflow: + +# %% +@eager +async def eager_subworkflow(x: int) -> int: + return await add_one(x=x) + + +@eager +async def nested_eager_workflow(x: int) -> int: + out = await eager_subworkflow(x=x) + return await double(x=out) + + +# %% [markdown] +# ### Catching exceptions +# +# You can also catch exceptions in eager workflows through `EagerException`: + +# %% +from flytekit.experimental import EagerException + + +@task +def raises_exc(x: int) -> int: + if x <= 0: + raise TypeError + return x + + +@eager +async def eager_workflow_with_exception(x: int) -> int: + try: + return await raises_exc(x=x) + except EagerException: + return -1 + + +# %% [markdown] +# Even though the `raises_exc` exception task raises a `TypeError`, the +# `eager_workflow_with_exception` runtime will raise an `EagerException` and +# you'll need to specify `EagerException` as the exception type in your `try... except` +# block. +# +# ```{note} +# This is a current limitation in the `@eager` workflow implementation. +# ```` + + +# %% [markdown] +# ## Executing Eager Workflows +# +# As with most Flyte constructs, you can execute eager workflows both locally +# and remotely. +# +# ### Local Execution +# +# You can execute eager workflows locally by simply calling them like a regular +# `async` function: + +# %% +if __name__ == "__main__": + result = asyncio.run(simple_eager_workflow(x=5)) + print(f"Result: {result}") # "Result: 12" + +# %% [markdown] +# This just uses the `asyncio.run` function to execute the eager workflow just +# like any other Python async code. This is useful for local debugging as you're +# developing your workflows and tasks. + +# %% [markdown] +# (eager_workflows_remote)= +# +# ### Remote Flyte Cluster Execution +# +# Under the hood, `@eager` workflows use the {py:class}`~flytekit.remote.remote.FlyteRemote` +# object to kick off task, static workflow, and eager workflow executions. +# +# In order to actually execute them on a Flyte cluster, you'll need to configure +# eager workflows with a `FlyteRemote` object and secrets configuration that +# allows you to authenticate into the cluster via a client secret key. +# +# ```{code-block} python +# from flytekit.remote import FlyteRemote +# from flytekit.configuration import Config +# +# @eager( +# remote=FlyteRemote( +# config=Config.auto(config_file="config.yaml"), +# default_project="flytesnacks", +# default_domain="development", +# ), +# client_secret_group="", +# client_secret_key="", +# ) +# async def eager_workflow_remote(x: int) -> int: +# ... +# ``` + +# %% [markdown] +# Where `config.yaml` contains a +# [flytectl](https://docs.flyte.org/projects/flytectl/en/latest/#configuration)-compatible +# config file and `my_client_secret_group` and `my_client_secret_key` are the +# {ref}`secret group and key ` that you've configured for your Flyte +# cluster to authenticate via a client key. + +# %% [markdown] +# ### Sandbox Flyte Cluster Execution +# +# When using a sandbox cluster started with `flytectl demo start`, however, the +# `client_secret_group` and `client_secret_key` are not required, since the +# default sandbox configration does not require key-based authentication. + +# %% +from flytekit.configuration import Config +from flytekit.remote import FlyteRemote + + +@eager( + remote=FlyteRemote( + config=Config.for_sandbox(), + default_project="flytesnacks", + default_domain="development", + ) +) +async def eager_workflow_sandbox(x: int) -> int: + out = await add_one(x=x) + if out < 0: + return -1 + return await double(x=out) + + +# %% [markdown] +# ```{important} +# When executing eager workflows on a remote Flyte cluster, it will execute the +# latest version of tasks, static workflows, and eager workflows that are on +# the `default_project` and `default_domain` as specified in the `FlyteRemote` +# object. This means that you need to pre-register all Flyte entities that are +# invoked inside of the eager workflow. +# ``` + +# %% [markdown] +# ### Registering and Running +# +# Assuming that your `flytekit` code is configured correctly, you will need to +# register all of the task and subworkflows that are used with your eager +# workflow with `pyflyte register`: +# +# ```{prompt} bash +# pyflyte --config register \ +# --project \ +# --domain \ +# --image \ +# path/to/eager_workflows.py +# ``` +# +# And then run it with `pyflyte run`: +# +# ```{prompt} bash +# pyflyte --config run \ +# --project \ +# --domain \ +# --image \ +# path/to/eager_workflows.py simple_eager_workflow --x 10 +# ``` +# +# ```{note} +# You need to register the tasks/workflows associated with your eager workflow +# because eager workflows are actually flyte tasks under the hood, which means +# that `pyflyte run` has no way of knowing what tasks and subworkflows are +# invoked inside of it. +# ``` + +# %% [markdown] +# ## Eager Workflows on Flyte Console +# +# Since eager workflows are an experimental feature, there is currently no +# first-class representation of them on Flyte Console, the UI for Flyte. +# When you register an eager workflow, you'll be able to see it in the task view: +# +# :::{figure} https://github.com/flyteorg/static-resources/blob/main/flytesnacks/user_guide/flyte_eager_workflow_ui_view.png?raw=true +# :alt: Eager Workflow UI View +# :class: with-shadow +# ::: +# +# When you execute an eager workflow, the tasks and subworkflows invoked within +# it **won't show up** on the node, graph, or timeline view. As mentioned above, +# this is because eager workflows are actually Flyte tasks under the hood and +# Flyte has no way of knowing the shape of the execution graph before actually +# executing them. +# +# :::{figure} https://github.com/flyteorg/static-resources/blob/main/flytesnacks/user_guide/flyte_eager_workflow_execution.png?raw=true +# :alt: Eager Workflow Execution +# :class: with-shadow +# ::: +# +# However, at the end of execution, you'll be able to use {ref}`Flyte Decks ` +# to see a list of all the tasks and subworkflows that were executed within the +# eager workflow: +# +# :::{figure} https://github.com/flyteorg/static-resources/blob/main/flytesnacks/user_guide/flyte_eager_workflow_deck.png?raw=true +# :alt: Eager Workflow Deck +# :class: with-shadow +# ::: + + +# %% [markdown] +# ## Limitations +# +# As this feature is still experimental, there are a few limitations that you +# need to keep in mind: +# +# - You cannot invoke {ref}`dynamic workflows `, +# {ref}`map tasks `, or {ref}`launch plans ` inside an +# eager workflow. +# - [Context managers](https://docs.python.org/3/library/contextlib.html) will +# only work on locally executed functions within the eager workflow, i.e. using a +# context manager to modify the behavior of a task or subworkflow will not work +# because they are executed on a completely different pod. +# - All exceptions raised by Flyte tasks or workflows will be caught and raised +# as an {py:class}`~flytekit.experimental.EagerException` at runtime. +# - All task/subworkflow outputs are materialized as Python values, which includes +# offloaded types like `FlyteFile`, `FlyteDirectory`, `StructuredDataset`, and +# `pandas.DataFrame` will be fully downloaded into the pod running the eager workflow. +# This prevents you from incrementally downloading or streaming very large datasets +# in eager workflows. +# - Flyte entities that are invoked inside of an eager workflow must be registered +# under the same project and domain as the eager workflow itself. The eager +# workflow will execute the latest version of these entities. +# - Flyte console currently does not have a first-class way of viewing eager +# workflows, but it can be accessed via the task list view and the execution +# graph is viewable via Flyte Decks. + +# %% [markdown] +# ## Summary of Workflows +# +# Eager workflows are a powerful new construct that trades-off compile-time type +# safety for flexibility in the shape of the execution graph. The table below +# will help you to reason about the different workflow constructs in Flyte in terms +# of promises and materialized values: +# +# | Construct | Description | Flyte Promises | Pro | Con | +# |--------|--------|--------|----|----| +# | `@workflow` | Compiled at compile-time | All inputs and intermediary outputs are promises | Type errors caught at compile-time | Constrained by Flyte DSL | +# | `@dynamic` | Compiled at run-time | Inputs are materialized, but outputs of all Flyte entities are Promises | More flexible than `@workflow`, e.g. can do Python operations on inputs | Can't use a lot of Python constructs (e.g. try/except) | +# | `@eager` | Never compiled | Everything is materialized! | Can effectively use all Python constructs via `asyncio` syntax | No compile-time benefits, this is the wild west 🏜 | diff --git a/docs/examples/advanced_composition/advanced_composition/files.py b/docs/examples/advanced_composition/advanced_composition/files.py new file mode 100644 index 000000000..2f5606e52 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/files.py @@ -0,0 +1,142 @@ +# %% [markdown] +# (files)= +# +# # Working With Files +# +# ```{eval-rst} +# .. tags:: Data, Basic +# ``` +# +# Files are one of the most fundamental entities that users of Python work with, +# and they are fully supported by Flyte. In the IDL, they are known as +# [Blob](https://github.com/flyteorg/flyteidl/blob/cee566b2e6e109120f1bb34c980b1cfaf006a473/protos/flyteidl/core/literals.proto#L33) +# literals which are backed by the +# [blob type](https://github.com/flyteorg/flyteidl/blob/cee566b2e6e109120f1bb34c980b1cfaf006a473/protos/flyteidl/core/types.proto#L47). +# +# Let's assume our mission here is pretty simple. We download a few csv file +# links, read them with the python built-in {py:class}`csv.DictReader` function, +# normalize some pre-specified columns, and output the normalized columns to +# another csv file. + +# %% [markdown] +# First, let's import the libraries. +# %% +import csv +import os +from collections import defaultdict +from typing import List + +import flytekit +from flytekit import task, workflow +from flytekit.types.file import FlyteFile + +# %% [markdown] +# Next, we write a task that accepts a `FlyteFile`, a list of column names, +# and a list of column names to normalize, then outputs a csv file of only +# the normalized columns. For this example we'll use z-score normalization, +# i.e. mean-centering and standard-deviation-scaling. +# +# :::{note} +# The `FlyteFile` literal can be scoped with a string, which gets inserted +# into the format of the Blob type ("jpeg" is the string in +# `FlyteFile[typing.TypeVar("jpeg")]`). The format is entirely optional, +# and if not specified, defaults to `""`. +# ::: + +# %% +@task +def normalize_columns( + csv_url: FlyteFile, + column_names: List[str], + columns_to_normalize: List[str], + output_location: str, +) -> FlyteFile: + # read the data from the raw csv file + parsed_data = defaultdict(list) + with open(csv_url, newline="\n") as input_file: + reader = csv.DictReader(input_file, fieldnames=column_names) + for row in (x for i, x in enumerate(reader) if i > 0): + for column in columns_to_normalize: + parsed_data[column].append(float(row[column].strip())) + + # normalize the data + normalized_data = defaultdict(list) + for colname, values in parsed_data.items(): + mean = sum(values) / len(values) + std = (sum([(x - mean) ** 2 for x in values]) / len(values)) ** 0.5 + normalized_data[colname] = [(x - mean) / std for x in values] + + # write to local path + out_path = os.path.join( + flytekit.current_context().working_directory, + f"normalized-{os.path.basename(csv_url.path).rsplit('.')[0]}.csv", + ) + with open(out_path, mode="w") as output_file: + writer = csv.DictWriter(output_file, fieldnames=columns_to_normalize) + writer.writeheader() + for row in zip(*normalized_data.values()): + writer.writerow({k: row[i] for i, k in enumerate(columns_to_normalize)}) + + if output_location: + return FlyteFile(path=out_path, remote_path=output_location) + else: + return FlyteFile(path=out_path) + + +# %% [markdown] +# When the image URL is sent to the task, the Flytekit engine translates it into a `FlyteFile` object on the local +# drive (but doesn't download it). The act of calling `download` method should trigger the download, and the `path` +# attribute enables to `open` the file. +# +# If the `output_location` argument is specified, it will be passed to the `remote_path` argument of `FlyteFile`, +# which will use that path as the storage location instead of a random location (Flyte's object store). +# +# When this task finishes, the Flytekit engine returns the `FlyteFile` instance, uploads the file to the location, and +# creates a Blob literal pointing to it. + +# %% [markdown] +# Lastly, we define a `normalize_csv_files` workflow. Note that there is an `output_location` argument specified in +# the workflow. This is passed to the `location` input of the task. If it's not an empty string, the task attempts to +# upload its file to that location. + +# %% +@workflow +def normalize_csv_file( + csv_url: FlyteFile, + column_names: List[str], + columns_to_normalize: List[str], + output_location: str = "", +) -> FlyteFile: + return normalize_columns( + csv_url=csv_url, + column_names=column_names, + columns_to_normalize=columns_to_normalize, + output_location=output_location, + ) + + +# %% [markdown] +# Finally, we can run the workflow locally. +# +# %% +if __name__ == "__main__": + default_files = [ + ( + "https://people.sc.fsu.edu/~jburkardt/data/csv/biostats.csv", + ["Name", "Sex", "Age", "Heights (in)", "Weight (lbs)"], + ["Age"], + ), + ( + "https://people.sc.fsu.edu/~jburkardt/data/csv/faithful.csv", + ["Index", "Eruption length (mins)", "Eruption wait (mins)"], + ["Eruption length (mins)"], + ), + ] + print(f"Running {__file__} main...") + for index, (csv_url, column_names, columns_to_normalize) in enumerate(default_files): + normalized_columns = normalize_csv_file( + csv_url=csv_url, + column_names=column_names, + columns_to_normalize=columns_to_normalize, + ) + print(f"Running normalize_csv_file workflow on {default_files}: " f"{normalized_columns}") diff --git a/docs/examples/advanced_composition/advanced_composition/folders.py b/docs/examples/advanced_composition/advanced_composition/folders.py new file mode 100644 index 000000000..45adabfed --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/folders.py @@ -0,0 +1,173 @@ +# %% [markdown] +# (folders)= +# +# # Working With Folders +# +# ```{eval-rst} +# .. tags:: Data, Basic +# ``` +# +# In addition to files, folders are another fundamental operating system primitive users often work with. Flyte supports folders +# in the form of [multi-part blobs](https://github.com/flyteorg/flyteidl/blob/237f025a15c0102675ad41d2d1e66869afa80822/protos/flyteidl/core/types.proto#L73). + +# %% [markdown] +# First, let's import the libraries we need in this example. +# %% +import csv +import os +import urllib.request +from collections import defaultdict +from pathlib import Path +from typing import List + +import flytekit +from flytekit import task, workflow +from flytekit.types.directory import FlyteDirectory + + +# %% [markdown] +# Extending the same use case that we used in the File example, which is to normalize columns in a csv file. +# +# The following task downloads a list of urls pointing to csv files and returns the path to the folder, in a +# `FlyteDirectory` object. +# %% +@task +def download_files(csv_urls: List[str]) -> FlyteDirectory: + working_dir = flytekit.current_context().working_directory + local_dir = Path(os.path.join(working_dir, "csv_files")) + local_dir.mkdir(exist_ok=True) + + # get the number of digits needed to preserve the order of files in the local directory + zfill_len = len(str(len(csv_urls))) + for idx, remote_location in enumerate(csv_urls): + local_image = os.path.join( + # prefix the file name with the index location of the file in the original csv_urls list + local_dir, + f"{str(idx).zfill(zfill_len)}_{os.path.basename(remote_location)}", + ) + urllib.request.urlretrieve(remote_location, local_image) + return FlyteDirectory(path=str(local_dir)) + + +# %% [markdown] +# :::{note} +# You can annotate a FlyteDirectory when you want to download/upload the contents of the directory in batches. For example, +# +# ```{code-block} +# @task +# def t1(directory: Annotated[FlyteDirectory, BatchSize(10)]) -> Annotated[FlyteDirectory, BatchSize(100)]: +# ... +# return FlyteDirectory(...) +# ``` +# +# In the above example flytekit will download all files from the input `directory` in chunks of 10, i.e. first it +# downloads 10 files, loads them to memory, then writes those 10 to local disk, then it loads the next 10, so on +# and so forth. Similarly, for outputs, in this case flytekit is going to upload the resulting directory in chunks of +# 100. +# ::: + + +# %% [markdown] +# Next, we define a helper function to normalize the columns in-place. +# +# :::{note} +# This is a plain python function that will be called in a subsequent Flyte task. This example +# demonstrates how Flyte tasks are simply entrypoints of execution, which can themselves call +# other functions and routines that are written in pure python. +# ::: + +# %% +def normalize_columns( + local_csv_file: str, + column_names: List[str], + columns_to_normalize: List[str], +): + # read the data from the raw csv file + parsed_data = defaultdict(list) + with open(local_csv_file, newline="\n") as input_file: + reader = csv.DictReader(input_file, fieldnames=column_names) + for row in (x for i, x in enumerate(reader) if i > 0): + for column in columns_to_normalize: + parsed_data[column].append(float(row[column].strip())) + + # normalize the data + normalized_data = defaultdict(list) + for colname, values in parsed_data.items(): + mean = sum(values) / len(values) + std = (sum([(x - mean) ** 2 for x in values]) / len(values)) ** 0.5 + normalized_data[colname] = [(x - mean) / std for x in values] + + # overwrite the csv file with the normalized columns + with open(local_csv_file, mode="w") as output_file: + writer = csv.DictWriter(output_file, fieldnames=columns_to_normalize) + writer.writeheader() + for row in zip(*normalized_data.values()): + writer.writerow({k: row[i] for i, k in enumerate(columns_to_normalize)}) + + +# %% [markdown] +# Now we define a task that accepts the previously downloaded folder, along with some metadata about the +# column names of each file in the directory and the column names that we want to normalize. + +# %% +@task +def normalize_all_files( + csv_files_dir: FlyteDirectory, + columns_metadata: List[List[str]], + columns_to_normalize_metadata: List[List[str]], +) -> FlyteDirectory: + for local_csv_file, column_names, columns_to_normalize in zip( + # make sure we sort the files in the directory to preserve the original order of the csv urls + [os.path.join(csv_files_dir, x) for x in sorted(os.listdir(csv_files_dir))], + columns_metadata, + columns_to_normalize_metadata, + ): + normalize_columns(local_csv_file, column_names, columns_to_normalize) + return FlyteDirectory(path=csv_files_dir.path) + + +# %% [markdown] +# Then we compose all of the above tasks into a workflow. This workflow accepts a list +# of url strings pointing to a remote location containing a csv file, a list of column names +# associated with each csv file, and a list of columns that we want to normalize. + +# %% +@workflow +def download_and_normalize_csv_files( + csv_urls: List[str], + columns_metadata: List[List[str]], + columns_to_normalize_metadata: List[List[str]], +) -> FlyteDirectory: + directory = download_files(csv_urls=csv_urls) + return normalize_all_files( + csv_files_dir=directory, + columns_metadata=columns_metadata, + columns_to_normalize_metadata=columns_to_normalize_metadata, + ) + + +# %% [markdown] +# Finally, we can run the workflow locally. +# +# %% +if __name__ == "__main__": + csv_urls = [ + "https://people.sc.fsu.edu/~jburkardt/data/csv/biostats.csv", + "https://people.sc.fsu.edu/~jburkardt/data/csv/faithful.csv", + ] + columns_metadata = [ + ["Name", "Sex", "Age", "Heights (in)", "Weight (lbs)"], + ["Index", "Eruption length (mins)", "Eruption wait (mins)"], + ] + columns_to_normalize_metadata = [ + ["Age"], + ["Eruption length (mins)"], + ] + + print(f"Running {__file__} main...") + directory = download_and_normalize_csv_files( + csv_urls=csv_urls, + columns_metadata=columns_metadata, + columns_to_normalize_metadata=columns_to_normalize_metadata, + ) + print(f"Running download_and_normalize_csv_files on {csv_urls}: " f"{directory}") diff --git a/docs/examples/advanced_composition/advanced_composition/map_task.py b/docs/examples/advanced_composition/advanced_composition/map_task.py new file mode 100644 index 000000000..803f0b47f --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/map_task.py @@ -0,0 +1,171 @@ +# %% [markdown] +# (map_task)= +# +# # Map Tasks +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# ```{image} https://img.shields.io/badge/Blog%20Post-Map%20Tasks-blue?style=for-the-badge +# :alt: Map Task Blog Post +# :target: https://blog.flyte.org/map-tasks-in-flyte +# ``` +# +# A map task allows you to execute a pod task or a regular task on a series of inputs within a single workflow node. +# This enables you to execute numerous instances of the task without having to create a node for each instance, resulting in significant performance improvements. +# +# Map tasks find application in various scenarios, including: +# +# - When multiple inputs require running through the same code logic. +# - Processing multiple data batches concurrently. +# - Conducting hyperparameter optimization. +# +# Now, let's delve into an example! + +# %% [markdown] +# First, import the libraries. +# %% +from typing import List + +from flytekit import Resources, map_task, task, workflow + + +# %% [markdown] +# Define a task to be used in the map task. +# +# :::{note} +# A map task can only accept one input and produce one output. +# ::: +# %% +@task +def a_mappable_task(a: int) -> str: + inc = a + 2 + stringified = str(inc) + return stringified + + +# %% [markdown] +# Also define a task to reduce the mapped output to a string. +# %% +@task +def coalesce(b: List[str]) -> str: + coalesced = "".join(b) + return coalesced + + +# %% [markdown] +# To repeat the execution of the `a_mappable_task` across a collection of inputs, use the {py:func}`~flytekit:flytekit.map_task` function from flytekit. +# In this example, the input `a` is of type `List[int]`. +# The `a_mappable_task` is executed for each element in the list. +# +# You can utilize the `with_overrides` function to set resources specifically for individual map tasks. +# This allows you to customize resource allocations such as memory usage. + +# %% +@workflow +def my_map_workflow(a: List[int]) -> str: + mapped_out = map_task(a_mappable_task)(a=a).with_overrides( + requests=Resources(mem="300Mi"), + limits=Resources(mem="500Mi"), + retries=1, + ) + coalesced = coalesce(b=mapped_out) + return coalesced + + +# %% [markdown] +# Finally, you can run the workflow locally. +# %% +if __name__ == "__main__": + result = my_map_workflow(a=[1, 2, 3, 4, 5]) + print(f"{result}") + +# %% [markdown] +# When defining a map task, avoid calling other tasks in it. Flyte +# can't accurately register tasks that call other tasks. While Flyte +# will correctly execute a task that calls other tasks, it will not be +# able to give full performance advantages. This is +# especially true for map tasks. +# +# In this example, the map task `suboptimal_mappable_task` would not +# give you the best performance. +# %% +@task +def upperhalf(a: int) -> int: + return a / 2 + 1 + + +@task +def suboptimal_mappable_task(a: int) -> str: + inc = upperhalf(a=a) + stringified = str(inc) + return stringified + + +# %% [markdown] +# By default, the map task utilizes the Kubernetes Array plugin for execution. +# However, map tasks can also be run on alternate execution backends. +# For example, you can configure the map task to run on [AWS Batch](https://docs.flyte.org/en/latest/deployment/plugin_setup/aws/batch.html#deployment-plugin-setup-aws-array), +# a provisioned service that offers scalability for handling large-scale tasks. + +# %% [markdown] +# ## Map a Task with Multiple Inputs +# +# You might need to map a task with multiple inputs. +# +# For instance, consider a task that requires three inputs. + +# %% +@task +def multi_input_task(quantity: int, price: float, shipping: float) -> float: + return quantity * price * shipping + + +# %% [markdown] +# In some cases, you may want to map this task with only the ``quantity`` input, while keeping the other inputs unchanged. +# Since a map task accepts only one input, you can achieve this by partially binding values to the map task. +# This can be done using the {py:func}`functools.partial` function. + +# %% +import functools + + +@workflow +def multiple_workflow(list_q: List[int] = [1, 2, 3, 4, 5], p: float = 6.0, s: float = 7.0) -> List[float]: + partial_task = functools.partial(multi_input_task, price=p, shipping=s) + return map_task(partial_task)(quantity=list_q) + + +# %% [markdown] +# Another possibility is to bind the outputs of a task to partials. + +# %% +@task +def get_price() -> float: + return 7.0 + + +@workflow +def multiple_workflow_with_task_output(list_q: List[int] = [1, 2, 3, 4, 5], s: float = 6.0) -> List[float]: + p = get_price() + partial_task = functools.partial(multi_input_task, price=p, shipping=s) + return map_task(partial_task)(quantity=list_q) + + +# %% [markdown] +# You can also provide multiple lists as input to a ``map_task``. + +# %% +@workflow +def multiple_workflow_with_lists( + list_q: List[int] = [1, 2, 3, 4, 5], list_p: List[float] = [6.0, 9.0, 8.7, 6.5, 1.2], s: float = 6.0 +) -> List[float]: + partial_task = functools.partial(multi_input_task, shipping=s) + return map_task(partial_task)(quantity=list_q, price=list_p) + + +# %% [markdown] +# ```{note} +# It is important to note that you cannot provide a list as an input to a partial task. +# ``` diff --git a/docs/examples/advanced_composition/advanced_composition/merge_sort.py b/docs/examples/advanced_composition/advanced_composition/merge_sort.py new file mode 100644 index 000000000..342ff3306 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/merge_sort.py @@ -0,0 +1,130 @@ +# %% [markdown] +# (advanced_merge_sort)= +# +# # Implementing Merge Sort +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# FlyteIdl (the fundamental building block of the Flyte Language) allows various programming language features: +# conditionals, recursion, custom typing, and more. +# +# This tutorial will walk you through writing a simple Distributed Merge Sort algorithm. It'll show usage of conditions +# as well as recursion using dynamically generated workflows. Flyte imposes limitation on the depth of the recursion to +# avoid mis-use and potentially affecting the overall stability of the system. + +# %% +import typing +from datetime import datetime +from random import random, seed +from typing import Tuple + +from flytekit import conditional, dynamic, task, workflow + +# seed random number generator +seed(datetime.now().microsecond) + + +# %% [markdown] +# A simple split function that divides a list into two halves. + +# %% +@task +def split(numbers: typing.List[int]) -> Tuple[typing.List[int], typing.List[int], int, int]: + return ( + numbers[0 : int(len(numbers) / 2)], + numbers[int(len(numbers) / 2) :], + int(len(numbers) / 2), + int(len(numbers)) - int(len(numbers) / 2), + ) + + +# %% [markdown] +# One sample implementation for merging. In a more real world example, this might merge file streams and only load +# chunks into the memory. +# %% +@task +def merge(sorted_list1: typing.List[int], sorted_list2: typing.List[int]) -> typing.List[int]: + result = [] + while len(sorted_list1) > 0 and len(sorted_list2) > 0: + # Check if current element of first array is smaller than current element of second array. If yes, + # store first array element and increment first array index. Otherwise do same with second array + if sorted_list1[0] < sorted_list2[0]: + result.append(sorted_list1.pop(0)) + else: + result.append(sorted_list2.pop(0)) + + result.extend(sorted_list1) + result.extend(sorted_list2) + + return result + + +# %% [markdown] +# Generally speaking, the algorithm will recurse through the list, splitting it in half until it reaches a size that we +# know is efficient enough to run locally. At which point it'll just use the python-builtin sorted function. + +# %% [markdown] +# This runs the sorting completely locally. It's faster and more efficient to do so if the entire list fits in memory. +# %% +@task +def sort_locally(numbers: typing.List[int]) -> typing.List[int]: + return sorted(numbers) + + +# %% [markdown] +# Let's now define the typical merge sort algorithm. We split, merge-sort each half then finally merge. With the simple +# addition of the `@dynamic` annotation, this function will instead generate a plan of execution (a flyte workflow) with +# 4 different nodes that will all run remotely on potentially different hosts. Flyte takes care of ensuring references +# of data are properly passed around and order of execution is maintained with maximum possible parallelism. +# %% +@dynamic +def merge_sort_remotely(numbers: typing.List[int], run_local_at_count: int) -> typing.List[int]: + split1, split2, new_count1, new_count2 = split(numbers=numbers) + sorted1 = merge_sort(numbers=split1, numbers_count=new_count1, run_local_at_count=run_local_at_count) + sorted2 = merge_sort(numbers=split2, numbers_count=new_count2, run_local_at_count=run_local_at_count) + return merge(sorted_list1=sorted1, sorted_list2=sorted2) + + +# %% [markdown] +# Putting it all together, this is the workflow that also serves as the entry point of execution. Given an unordered set +# of numbers, their length as well as the size at which to sort locally, it runs a condition on the size. The condition +# should look and flow naturally to a python developer. Binary arithmetic and logical operations on simple types as well +# as logical operations on conditions are supported. This condition checks if the current size of the numbers is below +# the cut-off size to run locally, if so, it runs the sort_locally task. Otherwise it runs the above dynamic workflow +# that recurse down the list. +# %% +@workflow +def merge_sort(numbers: typing.List[int], numbers_count: int, run_local_at_count: int = 10) -> typing.List[int]: + return ( + conditional("terminal_case") + .if_(numbers_count <= run_local_at_count) + .then(sort_locally(numbers=numbers)) + .else_() + .then(merge_sort_remotely(numbers=numbers, run_local_at_count=run_local_at_count)) + ) + + +# %% [markdown] +# A helper function to generate inputs for running the workflow locally. +# %% +def generate_inputs(numbers_count: int) -> typing.List[int]: + generated_list = [] + # generate random numbers between 0-1 + for _ in range(numbers_count): + value = int(random() * 10000) + generated_list.append(value) + + return generated_list + + +# %% [markdown] +# The entire workflow can be executed locally as follows: +# +# %% +if __name__ == "__main__": + count = 20 + x = generate_inputs(count) + print(x) + print(f"Running Merge Sort Locally...{merge_sort(numbers=x, numbers_count=count)}") diff --git a/docs/examples/advanced_composition/advanced_composition/subworkflows.py b/docs/examples/advanced_composition/advanced_composition/subworkflows.py new file mode 100644 index 000000000..3e483aa38 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/subworkflows.py @@ -0,0 +1,191 @@ +# %% [markdown] +# (subworkflows)= +# +# # SubWorkflows +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# Subworkflows are similar to {ref}`launch plans ` since they allow users to kick off one workflow from within another. +# +# What's the Difference? +# Consider launch plans as pass by pointer and subworkflows as pass by value. +# +# :::{note} +# Flyte's handling of dynamic workflows necessitates the use of subworkflows. +# We provide this capability at the user level rather than hiding it. The pros and cons of +# using subworkflows are discussed below. +# ::: +# +# ## When Should I Use SubWorkflows? +# +# Subworkflows provide a clean solution to control parallelism between a workflow and its launched sub-flows +# because they execute within the same context as the parent workflow. +# Thus, all nodes of a subworkflow are bound by the total constraint on the parent workflow. +# +# Consider this: When Workflow A is included as a subworkflow of Workflow B, and when Workflow B is run, the entire graph of workflow A is +# copied into workflow B at the point where it is invoked. +# +# Let's look at an example of subworkflow. + +# %% [markdown] +# ## Example +# +# Import the required dependencies into the environment. +# %% +import typing +from typing import Tuple + +from flytekit import task, workflow + +# %% [markdown] +# Next, define a task that uses named outputs. +# As a best practice, usually try and define `NamedTuple` as a distinct type (although it can be defined inline). +# %% +op = typing.NamedTuple("OutputsBC", t1_int_output=int, c=str) + + +@task +def t1(a: int) -> op: + return op(a + 2, "world") + + +# %% [markdown] +# Then define a subworkflow like a typical workflow. +# %% +@workflow +def my_subwf(a: int = 42) -> Tuple[str, str]: + x, y = t1(a=a) + u, v = t1(a=x) + return y, v + + +# %% [markdown] +# We call the above-mentioned workflow above in a `parent` workflow below +# which demonstrates how to override the node name of a task (or subworkflow in this case). +# +# Nodes are typically named sequentially: `n0`, `n1`, and so on. Since the inner `my_subwf` also has a `n0`, you might +# want to modify the first node's name. Because node IDs must be different within a workflow graph, +# Flyte automatically prepends an attribute to the inner `n0`. +# %% +@workflow +def parent_wf(a: int) -> Tuple[int, str, str]: + x, y = t1(a=a).with_overrides(node_name="node-t1-parent") + u, v = my_subwf(a=x) + return x, u, v + + +# %% [markdown] +# :::{note} +# For improved presentation or readability, the `with_overrides` method provides a new name to the graph-node. +# ::: + +# %% [markdown] +# You can run the subworkflows locally. +# %% +if __name__ == "__main__": + print(f"Running parent_wf(a=3) {parent_wf(a=3)}") + + +# Interestingly, we can nest a workflow that has a subworkflow within a workflow. +# Workflows can be simply composed from other workflows, even if they are standalone entities. Each of the +# workflows in this module can exist and run independently. +@workflow +def nested_parent_wf(a: int) -> Tuple[int, str, str, str]: + x, y = my_subwf(a=a) + m, n, o = parent_wf(a=a) + return m, n, o, y + + +# %% [markdown] +# You can run the nested workflows locally as well. +# %% +if __name__ == "__main__": + print(f"Running nested_parent_wf(a=3) {nested_parent_wf(a=3)}") + +# %% [markdown] +# :::{note} +# You can {ref}`chain and execute subworkflows ` similar to chained flyte tasks. +# ::: + +# %% [markdown] +# ## External Workflow +# +# When launch plans are used within a workflow to launch the execution of a previously defined workflow, a new +# external execution is launched, with a separate execution ID and can be observed as a distinct entity in +# FlyteConsole/Flytectl. +# +# They may have separate parallelism constraints since the context is not shared. +# We refer to such external invocations of a workflow using launch plans from a parent workflow as `External Workflows`. +# +# :::{tip} +# If your deployment uses {ref}`multiple Kubernetes clusters `, then external workflows may allow you to distribute the workload of a workflow to multiple clusters. +# ::: +# +# Here's an example demonstrating external workflows: + +# %% [markdown] +# Import the required dependencies into the environment. +# %% +import typing # noqa: E402 +from collections import Counter # noqa: E402 +from typing import Dict, Tuple # noqa: E402 + +from flytekit import LaunchPlan, task, workflow # noqa: E402 + + +# %% [markdown] +# Define a task that computes the frequency of each word in a string, and returns a dictionary mapping every word to its count. +# %% +@task +def count_freq_words(input_string1: str) -> Dict: + # input_string = "The cat sat on the mat" + words = input_string1.split() + wordCount = dict(Counter(words)) + return wordCount + + +# %% [markdown] +# Construct a workflow that executes the previously-defined task. +# %% +@workflow +def ext_workflow(my_input: str) -> Dict: + result = count_freq_words(input_string1=my_input) + return result + + +# %% [markdown] +# Next, create a launch plan. +# %% +external_lp = LaunchPlan.get_or_create( + ext_workflow, + "parent_workflow_execution", +) + +# %% [markdown] +# Define another task that returns the repeated keys (in our case, words) from a dictionary. +# %% +@task +def count_repetitive_words(word_counter: Dict) -> typing.List[str]: + repeated_words = [key for key, value in word_counter.items() if value > 1] + return repeated_words + + +# %% [markdown] +# Define a workflow that triggers the launch plan of the previously-defined workflow. +# %% +@workflow +def parent_workflow(my_input1: str) -> typing.List[str]: + my_op1 = external_lp(my_input=my_input1) + my_op2 = count_repetitive_words(word_counter=my_op1) + return my_op2 + + +# %% [markdown] +# Here, `parent_workflow` is an external workflow. This can also be run locally. +# +# %% +if __name__ == "__main__": + print("Running parent workflow...") + print(parent_workflow(my_input1="the cat took the apple and ate the apple")) diff --git a/docs/examples/advanced_composition/advanced_composition/waiting_for_external_inputs.py b/docs/examples/advanced_composition/advanced_composition/waiting_for_external_inputs.py new file mode 100644 index 000000000..9ff31ce65 --- /dev/null +++ b/docs/examples/advanced_composition/advanced_composition/waiting_for_external_inputs.py @@ -0,0 +1,296 @@ +# %% [markdown] +# # Waiting for External Inputs +# +# *New in Flyte 1.3.0* +# +# There are use cases where we want a workflow execution to pause, only to continue +# when some time has passed or when it receives some inputs that are external to +# the workflow execution inputs. You can think of these as execution-time inputs, +# since they need to be supplied to the workflow after it's launched. Examples of +# this use case would be: +# +# 1. **Model Deployment**: A hyperparameter-tuning workflow that +# trains `n` models, where a human needs to inspect a report before approving +# the model for downstream deployment to some serving layer. +# 2. **Data Labeling**: A workflow that iterates through an image dataset, +# presenting individual images to a human annotator for them to label. +# 3. **Active Learning**: An [active learning]() +# workflow that trains a model, shows examples for a human annotator to label +# based which examples it's least/most certain about or would provide the most +# information to the model. +# +# These use cases can be achieved in Flyte with the {func}`~flytekit.sleep`, +# {func}`~flytekit.wait_for_input`, and {func}`~flytekit.approve` workflow nodes. +# Although all of the examples above are human-in-the-loop processes, these +# constructs allow you to pass inputs into a workflow from some arbitrary external +# process (👩 human or 🤖 machine) in order to continue. +# +# :::{important} +# These functions can only be used inside {func}`@workflow `-decorated +# functions, {func}`@dynamic `-decorated functions, or +# {ref}`imperative workflows `. +# ::: + +# %% [markdown] +# ## Pause executions with the `sleep` node +# +# The simplest case is when you want your workflow to {py:func}`~flytekit.sleep` +# for some specified amount of time before continuing. +# +# Though this type of node may not be used often in a production setting, +# you might want to use it, for example, if you want to simulate a delay in +# your workflow to mock out the behavior of some long-running computation. + +# %% +from datetime import timedelta + +from flytekit import sleep, task, workflow + + +@task +def long_running_computation(num: int) -> int: + """A mock task pretending to be a long-running computation.""" + return num + + +@workflow +def sleep_wf(num: int) -> int: + """Simulate a "long-running" computation with sleep.""" + + # increase the sleep duration to actually make it long-running + sleeping = sleep(timedelta(seconds=10)) + result = long_running_computation(num=num) + sleeping >> result + return result + + +# %% [markdown] +# As you can see above, we define a simple `add_one` task and a `sleep_wf` +# workflow. We first create a `sleeping` and `result` node, then +# order the dependencies with the `>>` operator such that the workflow sleeps +# for 10 seconds before kicking off the `result` computation. Finally, we +# return the `result`. +# +# :::{note} +# You can learn more about the `>>` chaining operator +# {ref}`here `. +# ::: +# +# Now that you have a general sense of how this works, let's move onto the +# {func}`~flytekit.wait_for_input` workflow node. +# +# ## Supply external inputs with `wait_for_input` +# +# With the {py:func}`~flytekit.wait_for_input` node, you can pause a +# workflow execution that requires some external input signal. For example, +# suppose that you have a workflow that publishes an automated analytics report, +# but before publishing it you want to give it a custom title. You can achieve +# this by defining a `wait_for_input` node that takes a `str` input and +# finalizes the report: + +# %% +import typing + +from flytekit import wait_for_input + + +@task +def create_report(data: typing.List[float]) -> dict: # o0 + """A toy report task.""" + return { + "mean": sum(data) / len(data), + "length": len(data), + "max": max(data), + "min": min(data), + } + + +@task +def finalize_report(report: dict, title: str) -> dict: + return {"title": title, **report} + + +@workflow +def reporting_wf(data: typing.List[float]) -> dict: + report = create_report(data=data) + title_input = wait_for_input("title", timeout=timedelta(hours=1), expected_type=str) + return finalize_report(report=report, title=title_input) + + +# %% [markdown] +# Let's breakdown what's happening in the code above: +# +# - In `reporting_wf` we first create the raw `report` +# - Then, we define a `title` node that will wait for a string to be provided +# through the Flyte API, which can be done through the Flyte UI or through +# `FlyteRemote` (more on that later). This node will time out after 1 hour. +# - Finally, we pass the `title_input` promise into `finalize_report`, which +# attaches the custom title to the report. +# +# :::{note} +# The `create_report` task is just toy example. In a realistic example, this +# report might be an html file or set of visualizations. This can be rendered +# in the Flyte UI with {ref}`Flyte Decks `. +# ::: +# +# As mentioned in the beginning of this page, this construct can be used for +# selecting the best-performing model in cases where there isn't a clear single +# metric to determine the best model, or if you're doing data labeling using +# a Flyte workflow. +# +# ## Continue executions with `approve` +# +# Finally, the {py:func}`~flytekit.approve` workflow node allows you to wait on +# an explicit approval signal before continuing execution. Going back to our +# report-publishing use case, suppose that we want to block the publishing of +# a report for some reason (e.g. if they don't appear to be valid): + +# %% +from flytekit import approve + + +@workflow +def reporting_with_approval_wf(data: typing.List[float]) -> dict: + report = create_report(data=data) + title_input = wait_for_input("title", timeout=timedelta(hours=1), expected_type=str) + final_report = finalize_report(report=report, title=title_input) + + # approve the final report, where the output of approve is the final_report + # dictionary. + return approve(final_report, "approve-final-report", timeout=timedelta(hours=2)) + + +# %% [markdown] +# The `approve` node will pass the `final_report` promise through as the +# output of the workflow, provided that the `approve-final-report` gets an +# approval input via the Flyte UI or Flyte API. +# +# You can also use the output of the `approve` function as a promise, feeding +# it to a subsequent task. Let's create a version of our report-publishing +# workflow where the approval happens after `create_report`: + + +# %% +@workflow +def approval_as_promise_wf(data: typing.List[float]) -> dict: + report = create_report(data=data) + title_input = wait_for_input("title", timeout=timedelta(hours=1), expected_type=str) + + # wait for report to run so that the user can view it before adding a custom + # title to the report + report >> title_input + + final_report = finalize_report( + report=approve(report, "raw-report-approval", timeout=timedelta(hours=2)), + title=title_input, + ) + return final_report + + +# %% [markdown] +# ## Working with Conditionals +# +# The node constructs by themselves are useful, but they become even more +# useful when we combine them with other Flyte constructs, like {ref}`conditionals `. +# +# To illustrate this, let's extend the report-publishing use case so that we +# produce and "invalid report" output in case we don't approve the final report: + +# %% +from flytekit import conditional + + +@task +def invalid_report() -> dict: + return {"invalid_report": True} + + +@workflow +def conditional_wf(data: typing.List[float]) -> dict: + report = create_report(data=data) + title_input = wait_for_input("title-input", timeout=timedelta(hours=1), expected_type=str) + + # Define a "review-passes" wait_for_input node so that a human can review + # the report before finalizing it. + review_passed = wait_for_input("review-passes", timeout=timedelta(hours=2), expected_type=bool) + report >> review_passed + + # This conditional returns the finalized report if the review passes, + # otherwise it returns an invalid report output. + return ( + conditional("final-report-condition") + .if_(review_passed.is_true()) + .then(finalize_report(report=report, title=title_input)) + .else_() + .then(invalid_report()) + ) + + +# %% [markdown] +# On top of the `approved` node, which we use in the `conditional` to +# determine which branch to execute, we also define a `disapprove_reason` +# gate node, which will be used as an input to the `invalid_report` task. +# +# ## Sending inputs to `wait_for_input` and `approve` nodes +# +# Assuming that you've registered the above workflows on a Flyte cluster that's +# been started with {ref}`flytectl demo start `, +# there are two ways of using `wait_for_input` and `approve` nodes: +# +# ### Using the Flyte UI +# +# If you launch the `reporting_wf` workflow on the Flyte UI, you'll see a +# **Graph** view of the workflow execution like this: +# +# ```{image} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/wait_for_input_graph.png +# :alt: reporting workflow wait for input graph +# ``` +# +# Clicking on the {fa}`play-circle,style=far` icon of the `title` task node or the +# **Resume** button on the sidebar will create a modal form that you can use to +# provide the custom title input. +# +# ```{image} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/wait_for_input_form.png +# :alt: reporting workflow wait for input form +# ``` +# +# ### Using `FlyteRemote` +# +# For many cases it's enough to use Flyte UI to provide inputs/approvals on +# gate nodes. However, if you want to pass inputs to `wait_for_input` and +# `approve` nodes programmatically, you can use the +# {py:meth}`FlyteRemote.set_signal ` +# method. Using the `gate_node_with_conditional_wf` workflow, the example +# below allows you to set values for `title-input` and `review-passes` nodes. +# +# ```python +# import typing +# from flytekit.remote.remote import FlyteRemote +# from flytekit.configuration import Config +# +# remote = FlyteRemote( +# Config.for_sandbox(), +# default_project="flytesnacks", +# default_domain="development", +# ) +# +# # First kick off the wotrkflow +# flyte_workflow = remote.fetch_workflow( +# name="core.control_flow.waiting_for_external_inputs.conditional_wf" +# ) +# +# # Execute the workflow +# execution = remote.execute(flyte_workflow, inputs={"data": [1.0, 2.0, 3.0, 4.0, 5.0]}) +# +# # Get a list of signals available for the execution +# signals = remote.list_signals(execution.id.name) +# +# # Set a signal value for the "title" node. Make sure that the "title-input" +# # node is in the `signals` list above +# remote.set_signal("title-input", execution.id.name, "my report") +# +# # Set signal value for the "review-passes" node. Make sure that the "review-passes" +# # node is in the `signals` list above +# remote.set_signal("review-passes", execution.id.name, True) +# ``` +# diff --git a/docs/examples/advanced_composition/requirements.in b/docs/examples/advanced_composition/requirements.in new file mode 100644 index 000000000..3cfb26e38 --- /dev/null +++ b/docs/examples/advanced_composition/requirements.in @@ -0,0 +1 @@ +flytekit diff --git a/docs/examples/advanced_composition/requirements.txt b/docs/examples/advanced_composition/requirements.txt new file mode 100644 index 000000000..a1a517f1b --- /dev/null +++ b/docs/examples/advanced_composition/requirements.txt @@ -0,0 +1,348 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile --resolver=backtracking requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.4 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.3 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +flyteidl==1.5.17 + # via flytekit +flytekit==1.9.1 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.9.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.9.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.35 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.6.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +importlib-resources==6.0.1 + # via keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +jsonpickle==3.0.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.24.4 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.3 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3.post1 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.9.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.7.1 + # via + # aioitertools + # azure-core + # azure-storage-blob + # flytekit + # rich + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.2 + # via + # docker + # kubernetes +wheel==0.41.2 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via + # importlib-metadata + # importlib-resources diff --git a/docs/examples/airflow_plugin/README.md b/docs/examples/airflow_plugin/README.md new file mode 100644 index 000000000..cc5791010 --- /dev/null +++ b/docs/examples/airflow_plugin/README.md @@ -0,0 +1,29 @@ +# Airflow Provider + +```{eval-rst} +.. tags:: Integration, Intermediate +``` + +```{image} https://img.shields.io/badge/Blog-Airflow-blue?style=for-the-badge +:target: https://blog.flyte.org/scale-airflow-for-machine-learning-tasks-with-the-flyte-airflow-provider +:alt: Airflow Blog Post +``` + +The `airflow-provider-flyte` package provides an operator, a sensor, and a hook that integrates Flyte into Apache Airflow. +`FlyteOperator` is helpful to trigger a task/workflow in Flyte and `FlyteSensor` enables monitoring a Flyte execution status for completion. + +The primary use case of this provider is to **scale Airflow for machine learning tasks using Flyte**. +With the Flyte Airflow provider, you can construct your ETL pipelines in Airflow and machine learning pipelines in Flyte +and use the provider to trigger machine learning or Flyte pipelines from within Airflow. + +## Installation + +``` +pip install airflow-provider-flyte +``` + +All the configuration options for the provider are available in the provider repo's [README](https://github.com/flyteorg/airflow-provider-flyte#readme). + +```{auto-examples-toc} +airflow +``` diff --git a/docs/examples/airflow_plugin/airflow_plugin/__init__.py b/docs/examples/airflow_plugin/airflow_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/airflow_plugin/airflow_plugin/airflow.py b/docs/examples/airflow_plugin/airflow_plugin/airflow.py new file mode 100644 index 000000000..20fba3f7e --- /dev/null +++ b/docs/examples/airflow_plugin/airflow_plugin/airflow.py @@ -0,0 +1,158 @@ +# %% [markdown] +# # FlyteOperator Example +# +# This tutorial will walk you through constructing an Airflow DAG using the `FlyteOperator`. +# +# :::{note} +# The provider source code can be found in the [airflow-provider-flyte](https://github.com/flyteorg/airflow-provider-flyte) repository. +# ::: +# +# Watch a demo of this provider below! It demonstrates an example of pulling NYC taxi data from S3, uploading it to CrateDB, +# and building an XGBoost model. The extract and load steps are handled by Airflow and the machine learning task is offloaded to Flyte +# using the Flyte Airflow Provider. +# +# ```{eval-rst} +# .. youtube:: F2JyA0O2U4M +# ``` +# +# The Airflow DAG demonstrated in the video is available [here](https://github.com/flyteorg/airflow-provider-flyte/blob/master/demo/dags/nyc_taxi.py). +# +# ## Environment Setup +# +# **AIRFLOW** +# +# Astronomer's CLI is the fastest and easiest way to set up Airflow. +# +# Download the [Astro CLI](https://github.com/astronomer/astro-cli) and then initialize a new astro project. +# +# ``` +# mkdir flyte-astro-project +# cd flyte-astro-project +# astro dev init +# ``` +# +# The directory structure of `flyte-astro-project` would look as follows: +# +# ``` +# . +# ├── Dockerfile +# ├── README.md +# ├── airflow_settings.yaml +# ├── dags +# │   ├── example-dag-advanced.py +# │   └── example-dag-basic.py +# ├── include +# ├── packages.txt +# ├── plugins +# ├── requirements.txt +# └── tests +# └── dags +# └── test_dag_integrity.py +# ``` +# +# **FLYTE** +# +# The [getting started tutorial](https://docs.flyte.org/en/latest/getting_started/index.html) should help you with setting up Flyte. +# +# ## Create an Airflow Connection +# +# Hit `http://localhost:8080/`, give the credentials (default username and password: `admin`), navigate to `Connections` and create a +# Flyte connection. +# +# ```{image} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/integrations/airflow/airflow_connection.png +# :alt: Airflow Connection +# ``` +# +# Click `Save` in the end. +# +# :::{note} +# Use external IP as the Flyte `Host`. You can {std:ref}`deploy ` Flyte on an on-prem machine or on cloud. +# ::: +# +# ## Register Flyte Code +# +# At the Flyte end, we'll train an XGBoost model on Pima Indians Diabetes Dataset. +# The source code is available [here](https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/pima_diabetes/diabetes.py). +# +# Register the example on the Flyte backend before proceeding with running the Airflow DAG. +# +# - Configure flytectl config at `~/.flyte/config.yaml` to point to the relevant endpoint. +# +# ```yaml +# admin: +# endpoint: dns:/// +# insecure: true # Set to false to enable TLS/SSL connection. +# authType: Pkce # authType: Pkce # if using authentication or just drop this. +# ``` +# +# - Clone the [flytesnacks repository](https://github.com/flyteorg/flytesnacks) and go into the `examples` directory. +# +# ``` +# git clone https://github.com/flyteorg/flytesnacks +# cd flytesnacks/examples +# ``` +# +# - Serialize the workflow. +# +# ``` +# pyflyte --pkgs pima_diabetes package --image "ghcr.io/flyteorg/flytecookbook:pima_diabetes-latest" -f +# ``` +# +# - Register the workflow. +# +# ``` +# flytectl register files --project flytesnacks --domain development --archive flyte-package.tgz --version v1 +# ``` + +# %% [markdown] +# ## Create an Airflow DAG +# +# Place the following file under the `dags/` directory. You can name it `example_dag_flyte.py`. +# %% +from datetime import datetime, timedelta + +from airflow import DAG +from flyte_provider.operators.flyte import FlyteOperator +from flyte_provider.sensors.flyte import FlyteSensor + +with DAG( + dag_id="example_flyte", + schedule_interval=None, + start_date=datetime(2022, 1, 1), + dagrun_timeout=timedelta(minutes=60), + catchup=False, +) as dag: + task = FlyteOperator( + task_id="diabetes_predictions", + flyte_conn_id="flyte_conn", + project="flytesnacks", + domain="development", + launchplan_name="ml_training.pima_diabetes.diabetes.diabetes_xgboost_model", + inputs={"test_split_ratio": 0.66, "seed": 5}, + ) + + sensor = FlyteSensor( + task_id="sensor", + execution_name=task.output, + project="flytesnacks", + domain="development", + flyte_conn_id="flyte_conn", + ) + + task >> sensor + +# %% [markdown] +# Also, add `airflow-provider-flyte` package to `requirements.txt` under the astro project. + +# %% [markdown] +# ## Run the Workflow +# +# - Run the command `astro dev start`. +# - Trigger the Airflow DAG by clicking the "Trigger DAG" button on the Airflow UI. +# - Verify if Flyte execution got triggered on the FlyteConsole by going to `http://` and navigating to the workflow page. + +# %% [markdown] +# That's about it! With the Flyte Airflow Provider, you get to reap the benefits of Flyte, a full-fledged machine learning orchestration service, +# as an extension to Airflow. +# For more example DAGs, refer to [this folder](https://github.com/flyteorg/airflow-provider-flyte/tree/master/flyte_provider/example_dags). +# diff --git a/docs/examples/athena_plugin/Dockerfile b/docs/examples/athena_plugin/Dockerfile new file mode 100644 index 000000000..4cd8c1621 --- /dev/null +++ b/docs/examples/athena_plugin/Dockerfile @@ -0,0 +1,37 @@ +FROM python:3.8-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/athena_plugin/README.md b/docs/examples/athena_plugin/README.md new file mode 100644 index 000000000..cd4b20fb5 --- /dev/null +++ b/docs/examples/athena_plugin/README.md @@ -0,0 +1,28 @@ +(aws-athena)= + +# AWS Athena + +```{eval-rst} +.. tags:: Data, Integration, AWS, Advanced +``` + +## Executing Athena Queries + +Flyte backend can be connected with Athena. Once enabled, it allows you to query AWS Athena service (Presto + ANSI SQL Support) and retrieve typed schema (optionally). +This plugin is purely a spec and since SQL is completely portable, it has no need to build a container. Thus this plugin example does not have any Dockerfile. + +### Installation + +To use the flytekit Athena plugin, simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-athena +``` + +Now let's dive into the code. + +```{auto-examples-toc} +athena +``` diff --git a/docs/examples/athena_plugin/athena_plugin/__init__.py b/docs/examples/athena_plugin/athena_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/athena_plugin/athena_plugin/athena.py b/docs/examples/athena_plugin/athena_plugin/athena.py new file mode 100644 index 000000000..6fb163f31 --- /dev/null +++ b/docs/examples/athena_plugin/athena_plugin/athena.py @@ -0,0 +1,112 @@ +# %% [markdown] +# # Athena Query +# +# This example shows how to use a Flyte AthenaTask to execute a query. + +# %% +from flytekit import kwtypes, task, workflow +from flytekit.types.schema import FlyteSchema +from flytekitplugins.athena import AthenaConfig, AthenaTask + +# %% [markdown] +# This is the world's simplest query. Note that in order for registration to work properly, you'll need to give your +# Athena task a name that's unique across your project/domain for your Flyte installation. +# %% +athena_task_no_io = AthenaTask( + name="sql.athena.no_io", + inputs={}, + query_template=""" + select 1 + """, + output_schema_type=None, + task_config=AthenaConfig(database="mnist"), +) + + +@workflow +def no_io_wf(): + return athena_task_no_io() + + +# %% [markdown] +# Of course, in real world applications we are usually more interested in using Athena to query a dataset. +# In this case we've populated our vaccinations table with the publicly available dataset +# [here](https://www.kaggle.com/gpreda/covid-world-vaccination-progress). +# For a primer on how upload a dataset, checkout of the official +# [AWS docs](https://docs.aws.amazon.com/quicksight/latest/user/create-a-data-set-athena.html). +# The data is formatted according to this schema: +# +# ```{eval-rst} +# +----------------------------------------------+ +# | country (string) | +# +----------------------------------------------+ +# | iso_code (string) | +# +----------------------------------------------+ +# | date (string) | +# +----------------------------------------------+ +# | total_vaccinations (string) | +# +----------------------------------------------+ +# | people_vaccinated (string) | +# +----------------------------------------------+ +# | people_fully_vaccinated (string) | +# +----------------------------------------------+ +# | daily_vaccinations_raw (string) | +# +----------------------------------------------+ +# | daily_vaccinations (string) | +# +----------------------------------------------+ +# | total_vaccinations_per_hundred (string) | +# +----------------------------------------------+ +# | people_vaccinated_per_hundred (string) | +# +----------------------------------------------+ +# | people_fully_vaccinated_per_hundred (string) | +# +----------------------------------------------+ +# | daily_vaccinations_per_million (string) | +# +----------------------------------------------+ +# | vaccines (string) | +# +----------------------------------------------+ +# | source_name (string) | +# +----------------------------------------------+ +# | source_website (string) | +# +----------------------------------------------+ +# ``` +# +# Let's look out how we can parameterize our query to filter results for a specific country, provided as a user input +# specifying a country iso code. +# We'll produce a FlyteSchema that we can use in downstream flyte tasks for further analysis or manipulation. +# Note that we cache this output data so we don't have to re-run the query in future workflow iterations +# should we decide to change how we manipulate data downstream + +# %% +athena_task_templatized_query = AthenaTask( + name="sql.athena.w_io", + # Define inputs as well as their types that can be used to customize the query. + inputs=kwtypes(iso_code=str), + task_config=AthenaConfig(workgroup="primary", catalog="AwsDataCatalog", database="vaccinations"), + query_template=""" + SELECT * FROM vaccinations where iso_code like '{{ .Inputs.iso_code }}' + """, + # While we define a generic schema as the output here, Flyte supports more strongly typed schemas to provide + # better compile-time checks for task compatibility. Refer to {py:class}`flytekit.FlyteSchema` for more details + output_schema_type=FlyteSchema, + # Cache the output data so we don't have to re-run the query in future workflow iterations + # should we decide to change how we manipulate data downstream. + # For more information about caching, visit {ref}`Task Caching ` + cache=True, + cache_version="1.0", +) + + +# %% [markdown] +# Now we (trivially) clean up and interact with the data produced from the above Athena query in a separate Flyte task. +# +# %% +@task +def manipulate_athena_schema(s: FlyteSchema) -> FlyteSchema: + df = s.open().all() + return df + + +@workflow +def full_athena_wf(country_iso_code: str) -> FlyteSchema: + demo_schema = athena_task_templatized_query(iso_code=country_iso_code) + return manipulate_athena_schema(s=demo_schema) diff --git a/docs/examples/athena_plugin/requirements.in b/docs/examples/athena_plugin/requirements.in new file mode 100644 index 000000000..5d9920308 --- /dev/null +++ b/docs/examples/athena_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-athena>=0.20.0 diff --git a/docs/examples/athena_plugin/requirements.txt b/docs/examples/athena_plugin/requirements.txt new file mode 100644 index 000000000..c4bb4a72c --- /dev/null +++ b/docs/examples/athena_plugin/requirements.txt @@ -0,0 +1,198 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==37.0.3 + # via secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-athena +flytekitplugins-athena==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +pillow==9.1.0 + # via matplotlib +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/aws_batch_plugin/Dockerfile b/docs/examples/aws_batch_plugin/Dockerfile new file mode 100644 index 000000000..b892c9dee --- /dev/null +++ b/docs/examples/aws_batch_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-slim-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install --no-deps -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/aws_batch_plugin/README.md b/docs/examples/aws_batch_plugin/README.md new file mode 100644 index 000000000..02f5e1b2c --- /dev/null +++ b/docs/examples/aws_batch_plugin/README.md @@ -0,0 +1,52 @@ +# AWS Batch + +```{eval-rst} +.. tags:: Data, Integration, AWS, Advanced +``` + +## Executing Batch Job + +Flyte backend can be connected with batch. Once enabled, it allows you to run regular task on AWS batch. + +This section provides a guide on how to use the AWS Batch Plugin using flytekit python. + +### Installation + +To use the flytekit batch plugin simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-awsbatch +``` + +### Configuring the backend to get AWS Batch working + +[Follow this guide to setting up the AWS Batch Plugin]() + +### Quick Start + +This plugin allows you to run batch tasks on AWS and only requires you to change a few lines of code. +We can then move workflow execution from Kubernetes to AWS. + +```{eval-rst} +.. testcode:: awsbatch-quickstart + from flytekitplugins.awsbatch import AWSBatchConfig + + config = AWSBatch( + parameters={"codec": "mp4"}, + platformCapabilities="EC2", + propagateTags=True, + retryStrategy={"attempts": 10}, + tags={"hello": "world"}, + timeout={"attemptDurationSeconds": 60}, + ) + + @task(task_config=config) + def t1(a: int) -> str: + return str(a) +``` + +```{auto-examples-toc} +batch +``` diff --git a/docs/examples/aws_batch_plugin/aws_batch_plugin/__init__.py b/docs/examples/aws_batch_plugin/aws_batch_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/aws_batch_plugin/aws_batch_plugin/batch.py b/docs/examples/aws_batch_plugin/aws_batch_plugin/batch.py new file mode 100644 index 000000000..b41ab48ca --- /dev/null +++ b/docs/examples/aws_batch_plugin/aws_batch_plugin/batch.py @@ -0,0 +1,42 @@ +# %% [markdown] +# # AWS Batch +# +# This example shows how to use a Flyte AWS batch plugin to execute a tasks on batch service. +# With AWS Batch, there is no need to install and manage batch computing software or server clusters +# that you use to run your jobs, allowing you to focus on analyzing results and solving problems. + +# %% +from flytekit import task, workflow +from flytekitplugins.awsbatch import AWSBatchConfig + +# %% [markdown] +# Use this to configure SubmitJobInput for a AWS batch job. Task's marked with this will automatically execute +# natively onto AWS batch service. +# Refer to the official [AWS SubmitJobInput documentation](https://docs.aws.amazon.com/sdk-for-go/api/service/batch/#SubmitJobInput) for more detail. +# +# %% +config = AWSBatchConfig( + parameters={"codec": "mp4"}, + platformCapabilities="EC2", + tags={"name": "flyte-example"}, +) + + +@task(task_config=config) +def t1(a: int) -> int: + return a + 2 + + +@task(task_config=config) +def t2(b: int) -> int: + return b * 10 + + +@workflow +def my_wf(a: int) -> int: + b = t1(a=a) + return t2(b=b) + + +if __name__ == "__main__": + print(f"Running my_wf(a=3') {my_wf(a=3)}") diff --git a/docs/examples/aws_batch_plugin/requirements.in b/docs/examples/aws_batch_plugin/requirements.in new file mode 100644 index 000000000..60ba3259f --- /dev/null +++ b/docs/examples/aws_batch_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-awsbatch>=0.31.0b1 diff --git a/docs/examples/aws_batch_plugin/requirements.txt b/docs/examples/aws_batch_plugin/requirements.txt new file mode 100644 index 000000000..a1722ffb0 --- /dev/null +++ b/docs/examples/aws_batch_plugin/requirements.txt @@ -0,0 +1,186 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==1.7.3 + # via flytekit +croniter==1.3.4 + # via flytekit +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-awsbatch +flytekitplugins-awsbatch==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +pillow==9.1.0 + # via matplotlib +poyo==0.5.0 + # via cookiecutter +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +six==1.16.0 + # via + # cookiecutter + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/basics/Dockerfile b/docs/examples/basics/Dockerfile new file mode 100644 index 000000000..858d7d252 --- /dev/null +++ b/docs/examples/basics/Dockerfile @@ -0,0 +1,30 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM python:3.11-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +WORKDIR /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +RUN pip install flytekit==1.9.0 flytekitplugins-envd + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/basics/README.md b/docs/examples/basics/README.md new file mode 100644 index 000000000..38735f60d --- /dev/null +++ b/docs/examples/basics/README.md @@ -0,0 +1,21 @@ +# Basics + +This section introduces you to the basic building blocks of Flyte +using `flytekit`. `flytekit` is a Python SDK for developing Flyte workflows and +tasks, and can be used generally, whenever stateful computation is desirable. +`flytekit` workflows and tasks are completely runnable locally, unless they need +some advanced backend functionality like starting a distributed Spark cluster. + +Here, you will learn how to write Flyte tasks, assemble them into workflows, +run bash scripts, and document workflows. + +```{auto-examples-toc} +hello_world +task +workflow +launch_plan +imperative_workflow +documenting_workflows +shell_task +named_outputs +``` diff --git a/docs/examples/basics/basics/__init__.py b/docs/examples/basics/basics/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/basics/basics/documenting_workflows.py b/docs/examples/basics/basics/documenting_workflows.py new file mode 100644 index 000000000..476b5a67a --- /dev/null +++ b/docs/examples/basics/basics/documenting_workflows.py @@ -0,0 +1,128 @@ +# %% [markdown] +# # Documenting Workflows +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Well-documented code significantly improves code readability. +# Flyte enables the use of docstrings to document your code. +# Docstrings are stored in [FlyteAdmin](https://docs.flyte.org/en/latest/concepts/admin.html) +# and displayed on the UI. +# +# To begin, import the relevant libraries. +# %% +from typing import Tuple + +from flytekit import workflow + +# %% [markdown] +# We import the `slope` and `intercept` tasks from the `workflow.py` file. +# %% +from .workflow import intercept, slope + + +# %% [markdown] +# ## Sphinx-style docstring +# +# An example to demonstrate Sphinx-style docstring. +# +# The initial section of the docstring provides a concise overview of the workflow. +# The subsequent section provides a comprehensive explanation. +# The last part of the docstring outlines the parameters and return type. +# %% +@workflow +def sphinx_docstring_wf(x: list[int] = [-3, 0, 3], y: list[int] = [7, 4, -2]) -> Tuple[float, float]: + """ + Slope and intercept of a regression line + + This workflow accepts a list of coefficient pairs for a regression line. + It calculates both the slope and intercept of the regression line. + + :param x: List of x-coefficients + :param y: List of y-coefficients + :return: Slope and intercept values + """ + slope_value = slope(x=x, y=y) + intercept_value = intercept(x=x, y=y, slope=slope_value) + return slope_value, intercept_value + + +# %% [markdown] +# ## NumPy-style docstring +# +# An example to demonstrate NumPy-style docstring. +# +# The first part of the docstring provides a concise overview of the workflow. +# The next section offers a comprehensive description. +# The third section of the docstring details all parameters along with their respective data types. +# The final section of the docstring explains the return type and its associated data type. +# %% +@workflow +def numpy_docstring_wf(x: list[int] = [-3, 0, 3], y: list[int] = [7, 4, -2]) -> Tuple[float, float]: + """ + Slope and intercept of a regression line + + This workflow accepts a list of coefficient pairs for a regression line. + It calculates both the slope and intercept of the regression line. + + Parameters + ---------- + x : list[int] + List of x-coefficients + y : list[int] + List of y-coefficients + + Returns + ------- + out : Tuple[float, float] + Slope and intercept values + """ + slope_value = slope(x=x, y=y) + intercept_value = intercept(x=x, y=y, slope=slope_value) + return slope_value, intercept_value + + +# %% [markdown] +# ## Google-style docstring +# +# An example to demonstrate Google-style docstring. +# +# The initial section of the docstring offers a succinct one-liner summary of the workflow. +# The subsequent section of the docstring provides an extensive explanation. +# The third segment of the docstring outlines the parameters and return type, +# including their respective data types. +# %% +@workflow +def google_docstring_wf(x: list[int] = [-3, 0, 3], y: list[int] = [7, 4, -2]) -> Tuple[float, float]: + """ + Slope and intercept of a regression line + + This workflow accepts a list of coefficient pairs for a regression line. + It calculates both the slope and intercept of the regression line. + + Args: + x (list[int]): List of x-coefficients + y (list[int]): List of y-coefficients + + Returns: + Tuple[float, float]: Slope and intercept values + """ + slope_value = slope(x=x, y=y) + intercept_value = intercept(x=x, y=y, slope=slope_value) + return slope_value, intercept_value + + +# %% [markdown] +# Here are two screenshots showcasing how the description appears on the UI: +# 1. On the workflow page, you'll find the short description: +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/document_wf_short.png +# :alt: Short description +# :class: with-shadow +# ::: +# +# 2. If you click into the workflow, you'll see the long description in the basic information section: +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/document_wf_long.png +# :alt: Long description +# :class: with-shadow +# ::: diff --git a/docs/examples/basics/basics/hello_world.py b/docs/examples/basics/basics/hello_world.py new file mode 100644 index 000000000..951d6952b --- /dev/null +++ b/docs/examples/basics/basics/hello_world.py @@ -0,0 +1,49 @@ +# %% [markdown] +# +# # Hello, World! +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Let's write a Flyte {py:func}`~flytekit.workflow` that invokes a +# {py:func}`~flytekit.task` to generate the output "Hello, World!". +# +# Flyte tasks are the core building blocks of larger, more complex workflows. +# Workflows compose multiple tasks – or other workflows – +# into meaningful steps of computation to produce some useful set of outputs or outcomes. +# +# To begin, import `task` and `workflow` from the `flytekit` library. +# %% +from flytekit import task, workflow + + +# %% [markdown] +# Define a task that produces the string "Hello, World!". +# Simply using the `@task` decorator to annotate the Python function. +# %% +@task +def say_hello() -> str: + return "Hello, World!" + + +# %% [markdown] +# You can handle the output of a task in the same way you would with a regular Python function. +# Store the output in a variable and use it as a return value for a Flyte workflow. +# %% +@workflow +def hello_world_wf() -> str: + res = say_hello() + return res + + +# %% [markdown] +# Run the workflow by simply calling it like a Python function. +# %% +if __name__ == "__main__": + print(f"Running my_wf() {hello_world_wf()}") + + +# %% [markdown] +# Next, let's delve into the specifics of {ref}`tasks `, +# {ref}`workflows ` and {ref}`launch plans `. diff --git a/docs/examples/basics/basics/imperative_workflow.py b/docs/examples/basics/basics/imperative_workflow.py new file mode 100644 index 000000000..fae77e506 --- /dev/null +++ b/docs/examples/basics/basics/imperative_workflow.py @@ -0,0 +1,82 @@ +# %% [markdown] +# (imperative_workflow)= +# +# # Imperative Workflows +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Workflows are commonly created by applying the `@workflow` decorator to Python functions. +# During compilation, this involves processing the function's body and utilizing subsequent calls to +# underlying tasks to establish and record the workflow structure. This approach is known as declarative +# and is suitable when manually drafting the workflow. +# +# However, in cases where workflows are constructed programmatically, an imperative style is more appropriate. +# For instance, if tasks have been defined already, their sequence and dependencies might have been specified +# in textual form (perhaps during a transition from a legacy system). +# In such scenarios, you want to orchestrate these tasks. +# This is where Flyte's imperative workflows come into play, allowing you to programmatically construct workflows. +# +# To begin, import the necessary dependencies. +# %% +from flytekit import Workflow + +# %% [markdown] +# We import the `slope` and `intercept` tasks from the `workflow.py` file. +# %% +from .workflow import intercept, slope + +# %% [markdown] +# Create an imperative workflow. +# %% +imperative_wf = Workflow(name="imperative_workflow") + +# %% [markdown] +# Add the workflow inputs to the imperative workflow. +# %% +imperative_wf.add_workflow_input("x", list[int]) +imperative_wf.add_workflow_input("y", list[int]) + +# %% [markdown] +# ::: {note} +# If you want to assign default values to the workflow inputs, +# you can create a {ref}`launch plan `. +# ::: +# +# Add the tasks that need to be triggered from within the workflow. +# %% +node_t1 = imperative_wf.add_entity(slope, x=imperative_wf.inputs["x"], y=imperative_wf.inputs["y"]) +node_t2 = imperative_wf.add_entity( + intercept, x=imperative_wf.inputs["x"], y=imperative_wf.inputs["y"], slope=node_t1.outputs["o0"] +) + +# %% [markdown] +# Lastly, add the workflow output. +# %% +imperative_wf.add_workflow_output("wf_output", node_t2.outputs["o0"]) + +# %% [markdown] +# You can execute the workflow locally as follows: +# %% +if __name__ == "__main__": + print(f"Running imperative_wf() {imperative_wf(x=[-3, 0, 3], y=[7, 4, -2])}") + +# %% [markdown] +# :::{note} +# You also have the option to provide a list of inputs and +# retrieve a list of outputs from the workflow. +# +# ```python +# wf_input_y = imperative_wf.add_workflow_input("y", list[str]) +# node_t3 = wf.add_entity(some_task, a=[wf.inputs["x"], wf_input_y]) +# ``` +# +# ```python +# wf.add_workflow_output( +# "list_of_outputs", +# [node_t1.outputs["o0"], node_t2.outputs["o0"]], +# python_type=list[str], +# ) +# ``` +# ::: diff --git a/docs/examples/basics/basics/launch_plan.py b/docs/examples/basics/basics/launch_plan.py new file mode 100644 index 000000000..1536e37e2 --- /dev/null +++ b/docs/examples/basics/basics/launch_plan.py @@ -0,0 +1,76 @@ +# %% [markdown] +# (launch_plan)= +# +# # Launch Plans +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Launch plans link a partial or complete list of inputs required to initiate a workflow, +# accompanied by optional run-time overrides like notifications, schedules and more. +# They serve various purposes: +# +# - Schedule the same workflow multiple times, with optional predefined inputs. +# - Run a specific workflow but with altered notifications. +# - Share a workflow with predefined inputs, allowing another user to initiate an execution. +# - Share a workflow with the option for the other user to override certain inputs. +# - Share a workflow, ensuring specific inputs remain unchanged. +# +# Launch plans are the only means for invoking workflow executions. +# When a workflow is serialized and registered, a _default launch plan_ is generated. +# This default launch plan can bind default workflow inputs and runtime options defined +# in the project's flytekit configuration (such as user role). +# +# To begin, import the necessary libraries. +# %% +from flytekit import LaunchPlan, current_context + +# %% [markdown] +# We import the workflow from the `workflow.py` file for which we're going to create a launch plan. +# %% +from .workflow import simple_wf + +# %% [markdown] +# Create a default launch plan with no inputs during serialization. +# %% +default_lp = LaunchPlan.get_default_launch_plan(current_context(), simple_wf) + +# %% [markdown] +# You can run the launch plan locally as follows: +# %% +default_lp(x=[-3, 0, 3], y=[7, 4, -2]) + +# %% [markdown] +# Create a launch plan and specify the default inputs. +# %% +simple_wf_lp = LaunchPlan.create( + name="simple_wf_lp", workflow=simple_wf, default_inputs={"x": [-3, 0, 3], "y": [7, 4, -2]} +) + +# %% [markdown] +# You can trigger the launch plan locally as follows: +# %% +simple_wf_lp() + +# %% [markdown] +# You can override the defaults as follows: +# %% +simple_wf_lp(x=[3, 5, 3], y=[-3, 2, -2]) + +# %% [markdown] +# It's possible to lock launch plan inputs, preventing them from being overridden during execution. +# %% +simple_wf_lp_fixed_inputs = LaunchPlan.get_or_create( + name="fixed_inputs", workflow=simple_wf, fixed_inputs={"x": [-3, 0, 3]} +) + +# %% [markdown] +# Attempting to modify the inputs will result in an error being raised by Flyte. +# +# :::{note} +# You can employ default and fixed inputs in conjunction in a launch plan. +# ::: +# +# Launch plans can also be used to run workflows on a specific cadence. +# For more information, refer to the {ref}`scheduling_launch_plan` documentation. diff --git a/docs/examples/basics/basics/named_outputs.py b/docs/examples/basics/basics/named_outputs.py new file mode 100644 index 000000000..692aa88a9 --- /dev/null +++ b/docs/examples/basics/basics/named_outputs.py @@ -0,0 +1,87 @@ +# %% [markdown] +# (named_outputs)= +# +# # Named Outputs +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# By default, Flyte employs a standardized convention to assign names to the outputs of tasks or workflows. +# Each output is sequentially labeled as `o1`, `o2`, `o3`, ... `on`, where `o` serves as the standard prefix, +# and `1`, `2`, ... `n` indicates the positional index within the returned values. +# +# However, Flyte allows the customization of output names for tasks or workflows. +# This customization becomes beneficial when you're returning multiple outputs +# and you wish to assign a distinct name to each of them. +# +# The following example illustrates the process of assigning names to outputs for both a task and a workflow. +# +# To begin, import the required dependencies. +# %% +from typing import NamedTuple + +from flytekit import task, workflow + +# %% [markdown] +# Define a `NamedTuple` and assign it as an output to a task. +# %% +slope_value = NamedTuple("slope_value", [("slope", float)]) + + +@task +def slope(x: list[int], y: list[int]) -> slope_value: + sum_xy = sum([x[i] * y[i] for i in range(len(x))]) + sum_x_squared = sum([x[i] ** 2 for i in range(len(x))]) + n = len(x) + return (n * sum_xy - sum(x) * sum(y)) / (n * sum_x_squared - sum(x) ** 2) + + +# %% [markdown] +# Likewise, assign a `NamedTuple` to the output of `intercept` task. +# %% +intercept_value = NamedTuple("intercept_value", [("intercept", float)]) + + +@task +def intercept(x: list[int], y: list[int], slope: float) -> intercept_value: + mean_x = sum(x) / len(x) + mean_y = sum(y) / len(y) + intercept = mean_y - slope * mean_x + return intercept + + +# %% [markdown] +# :::{note} +# While it's possible to create `NamedTuple`s directly within the code, +# it's often better to declare them explicitly. This helps prevent potential linting errors in tools like pypy. +# +# ``` +# def slope() -> NamedTuple("slope_value", slope=float): +# pass +# ``` +# ::: +# +# You can easily unpack the `NamedTuple` outputs directly within a workflow. +# Additionally, you can also have the workflow return a `NamedTuple` as an output. +# +# :::{note} +# Remember that we are extracting individual task execution outputs by dereferencing them. +# This is necessary because `NamedTuple`s function as tuples and require this dereferencing. +# ::: +# %% +slope_and_intercept_values = NamedTuple("slope_and_intercept_values", [("slope", float), ("intercept", float)]) + + +@workflow +def simple_wf_with_named_outputs(x: list[int] = [-3, 0, 3], y: list[int] = [7, 4, -2]) -> slope_and_intercept_values: + slope_value = slope(x=x, y=y) + intercept_value = intercept(x=x, y=y, slope=slope_value.slope) + return slope_and_intercept_values(slope=slope_value.slope, intercept=intercept_value.intercept) + + +# %% [markdown] +# You can run the workflow locally as follows: +# %% +if __name__ == "__main__": + print(f"Running simple_wf_with_named_outputs() {simple_wf_with_named_outputs()}") diff --git a/docs/examples/basics/basics/shell_task.py b/docs/examples/basics/basics/shell_task.py new file mode 100644 index 000000000..fd468410e --- /dev/null +++ b/docs/examples/basics/basics/shell_task.py @@ -0,0 +1,114 @@ +# %% [markdown] +# (shell_task)= +# +# # Shell Tasks +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# To execute bash scripts within Flyte, you can utilize the {py:class}`~flytekit.extras.tasks.shell.ShellTask` class. +# This example includes three shell tasks to execute bash commands. +# +# First, import the necessary libraries. +# %% +import os +from typing import Tuple + +import flytekit +from flytekit import kwtypes, task, workflow +from flytekit.extras.tasks.shell import OutputLocation, ShellTask +from flytekit.types.directory import FlyteDirectory +from flytekit.types.file import FlyteFile + +# %% [markdown] +# With the required imports in place, you can proceed to define a shell task. +# To create a shell task, provide a name for it, specify the bash script to be executed, +# and define inputs and outputs if needed. +# %% +t1 = ShellTask( + name="task_1", + debug=True, + script=""" + set -ex + echo "Hey there! Let's run some bash scripts using Flyte's ShellTask." + echo "Showcasing Flyte's Shell Task." >> {inputs.x} + if grep "Flyte" {inputs.x} + then + echo "Found it!" >> {inputs.x} + else + echo "Not found!" + fi + """, + inputs=kwtypes(x=FlyteFile), + output_locs=[OutputLocation(var="i", var_type=FlyteFile, location="{inputs.x}")], +) + + +t2 = ShellTask( + name="task_2", + debug=True, + script=""" + set -ex + cp {inputs.x} {inputs.y} + tar -zcvf {outputs.j} {inputs.y} + """, + inputs=kwtypes(x=FlyteFile, y=FlyteDirectory), + output_locs=[OutputLocation(var="j", var_type=FlyteFile, location="{inputs.y}.tar.gz")], +) + + +t3 = ShellTask( + name="task_3", + debug=True, + script=""" + set -ex + tar -zxvf {inputs.z} + cat {inputs.y}/$(basename {inputs.x}) | wc -m > {outputs.k} + """, + inputs=kwtypes(x=FlyteFile, y=FlyteDirectory, z=FlyteFile), + output_locs=[OutputLocation(var="k", var_type=FlyteFile, location="output.txt")], +) + + +# %% [markdown] +# Here's a breakdown of the parameters of the `ShellTask`: +# +# - The `inputs` parameter allows you to specify the types of inputs that the task will accept +# - The `output_locs` parameter is used to define the output locations, which can be `FlyteFile` or `FlyteDirectory` +# - The `script` parameter contains the actual bash script that will be executed +# (`{inputs.x}`, `{outputs.j}`, etc. will be replaced with the actual input and output values). +# - The `debug` parameter is helpful for debugging purposes +# +# We define a task to instantiate `FlyteFile` and `FlyteDirectory`. +# A `.gitkeep` file is created in the FlyteDirectory as a placeholder to ensure the directory exists. +# %% +@task +def create_entities() -> Tuple[FlyteFile, FlyteDirectory]: + working_dir = flytekit.current_context().working_directory + flytefile = os.path.join(working_dir, "test.txt") + os.open(flytefile, os.O_CREAT) + flytedir = os.path.join(working_dir, "testdata") + os.makedirs(flytedir, exist_ok=True) + flytedir_file = os.path.join(flytedir, ".gitkeep") + os.open(flytedir_file, os.O_CREAT) + return flytefile, flytedir + + +# %% [markdown] +# We create a workflow to define the dependencies between the tasks. +# %% +@workflow +def shell_task_wf() -> FlyteFile: + x, y = create_entities() + t1_out = t1(x=x) + t2_out = t2(x=t1_out, y=y) + t3_out = t3(x=x, y=y, z=t2_out) + return t3_out + + +# %% [markdown] +# You can run the workflow locally. +# %% +if __name__ == "__main__": + print(f"Running shell_task_wf() {shell_task_wf()}") diff --git a/docs/examples/basics/basics/task.py b/docs/examples/basics/basics/task.py new file mode 100644 index 000000000..28a3109f1 --- /dev/null +++ b/docs/examples/basics/basics/task.py @@ -0,0 +1,85 @@ +# %% [markdown] +# (task)= +# +# # Tasks +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# A task serves as the fundamental building block and an extension point within Flyte. +# It exhibits the following characteristics: +# +# 1. Versioned (typically aligned with the git sha) +# 2. Strong interfaces (annotated inputs and outputs) +# 3. Declarative +# 4. Independently executable +# 5. Suitable for unit testing +# +# A Flyte task operates within its own container and and runs on a [Kubernetes pod](https://kubernetes.io/docs/concepts/workloads/pods/). +# It can be classified into two types: +# +# 1. A task associated with a Python function. Executing the task is the same as executing the function. +# 2. A task without a Python function, such as a SQL query or a portable task like prebuilt +# algorithms in SageMaker, or a service calling an API. +# +# Flyte offers numerous plugins for tasks, including backend plugins like +# [Athena](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-aws-athena/flytekitplugins/athena/task.py). +# +# This example demonstrates how to write and execute a +# [Python function task](https://github.com/flyteorg/flytekit/blob/master/flytekit/core/python_function_task.py#L75). +# +# To begin, import `task` from the `flytekit` library. +# %% +from flytekit import task + + +# %% [markdown] +# The use of the {py:func}`~flytekit.task` decorator is mandatory for a ``PythonFunctionTask``. +# A task is essentially a regular Python function, with the exception that all inputs and outputs must be clearly annotated with their types. +# Learn more about the supported types in the {ref}`type-system section `. +# +# We create a task that computes the slope of a regression line. +# %% +@task +def slope(x: list[int], y: list[int]) -> float: + sum_xy = sum([x[i] * y[i] for i in range(len(x))]) + sum_x_squared = sum([x[i] ** 2 for i in range(len(x))]) + n = len(x) + return (n * sum_xy - sum(x) * sum(y)) / (n * sum_x_squared - sum(x) ** 2) + + +# %% [markdown] +# :::{note} +# Flytekit will assign a default name to the output variable like `out0`. +# In case of multiple outputs, each output will be numbered in the order +# starting with 0, e.g., -> `out0, out1, out2, ...`. +# ::: +# +# You can execute a Flyte task just like any regular Python function. +# %% +if __name__ == "__main__": + print(slope(x=[-3, 0, 3], y=[7, 4, -2])) + +# %% [markdown] +# :::{note} +# When invoking a Flyte task, you need to use keyword arguments to specify +# the values for the corresponding parameters. +# ::: +# +# (single_task_execution)= +# +# To run it locally, you can use the following `pyflyte run` command: +# ``` +# pyflyte run \ +# https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/01_basics/01_basics/task.py \ +# slope --x '[-3,0,3]' --y '[7,4,-2]' +# ``` +# +# If you want to run it remotely on the Flyte cluster, +# simply add the `--remote flag` to the `pyflyte run` command: +# ``` +# pyflyte run --remote \ +# https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/01_basics/01_basics/task.py \ +# slope --x '[-3,0,3]' --y '[7,4,-2]' +# ``` diff --git a/docs/examples/basics/basics/workflow.py b/docs/examples/basics/basics/workflow.py new file mode 100644 index 000000000..db80c1b32 --- /dev/null +++ b/docs/examples/basics/basics/workflow.py @@ -0,0 +1,122 @@ +# %% [markdown] +# (workflow)= +# +# # Workflows +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Workflows link multiple tasks together. They can be written as Python functions, +# but it's important to distinguish tasks and workflows. +# +# A task's body executes at run-time on a Kubernetes cluster, in a Query Engine like BigQuery, +# or on hosted services like AWS Batch or Sagemaker. +# +# In contrast, a workflow's body doesn't perform computations; it's used to structure tasks. +# A workflow's body executes at registration time, during the workflow's registration process. +# Registration involves uploading the packaged (serialized) code to the Flyte backend, +# enabling the workflow to be triggered. +# +# For more information, see the {std:ref}`registration documentation `. +# +# To begin, import {py:func}`~flytekit.task` and {py:func}`~flytekit.workflow` from the flytekit library. +# %% +from flytekit import task, workflow + + +# %% [markdown] +# We define `slope` and `intercept` tasks to compute the slope and +# intercept of the regression line, respectively. +# %% +@task +def slope(x: list[int], y: list[int]) -> float: + sum_xy = sum([x[i] * y[i] for i in range(len(x))]) + sum_x_squared = sum([x[i] ** 2 for i in range(len(x))]) + n = len(x) + return (n * sum_xy - sum(x) * sum(y)) / (n * sum_x_squared - sum(x) ** 2) + + +@task +def intercept(x: list[int], y: list[int], slope: float) -> float: + mean_x = sum(x) / len(x) + mean_y = sum(y) / len(y) + intercept = mean_y - slope * mean_x + return intercept + + +# %% [markdown] +# Define a workflow to establish the task dependencies. +# Just like a task, a workflow is also strongly typed. +# %% +@workflow +def simple_wf(x: list[int], y: list[int]) -> float: + slope_value = slope(x=x, y=y) + intercept_value = intercept(x=x, y=y, slope=slope_value) + return intercept_value + + +# %% [markdown] +# The {py:func}`~flytekit.workflow` decorator encapsulates Flyte tasks, +# essentially representing lazily evaluated promises. +# During parsing, function calls are deferred until execution time. +# These function calls generate {py:class}`~flytekit.extend.Promise`s that can be propagated to downstream functions, +# yet remain inaccessible within the workflow itself. +# The actual evaluation occurs when the workflow is executed. +# +# Workflows can be executed locally, resulting in immediate evaluation, or through tools like +# [`pyflyte`](https://docs.flyte.org/projects/flytekit/en/latest/pyflyte.html), +# [`flytectl`](https://docs.flyte.org/projects/flytectl/en/latest/index.html) or UI, triggering evaluation. +# While workflows decorated with `@workflow` resemble Python functions, +# they function as python-esque Domain Specific Language (DSL). +# When encountering a @task-decorated Python function, a promise object is created. +# This promise doesn't store the task's actual output. Its fulfillment only occurs during execution. +# Additionally, the inputs to a workflow are also promises, you can only pass promises into +# tasks, workflows and other Flyte constructs. +# +# :::{note} +# You can learn more about creating dynamic Flyte workflows by referring +# to {ref}`dynamic workflows `. +# In a dynamic workflow, unlike a simple workflow, the inputs are pre-materialized. +# However, each task invocation within the dynamic workflow still generates a promise that is evaluated lazily. +# Bear in mind that a workflow can have tasks, other workflows and dynamic workflows. +# ::: +# +# You can run a workflow by calling it as you would with a Python function and providing the necessary inputs. +# %% +if __name__ == "__main__": + print(f"Running simple_wf() {simple_wf(x=[-3, 0, 3], y=[7, 4, -2])}") + +# %% [markdown] +# To run the workflow locally, you can use the following `pyflyte run` command: +# ``` +# pyflyte run \ +# https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/01_basics/01_basics/workflow.py \ +# simple_wf --x '[-3,0,3]' --y '[7,4,-2]' +# ``` +# +# If you want to run it remotely on the Flyte cluster, +# simply add the `--remote flag` to the `pyflyte run` command: +# ``` +# pyflyte run --remote \ +# https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/01_basics/01_basics/workflow.py \ +# simple_wf --x '[-3,0,3]' --y '[7,4,-2]' +# ``` +# +# While workflows are usually constructed from multiple tasks with dependencies established through +# shared inputs and outputs, there are scenarios where isolating the execution of a single task +# proves advantageous during the development and iteration of its logic. +# Crafting a new workflow definition each time for this purpose can be cumbersome. +# However, {ref}`executing an individual task ` independently, +# without the confines of a workflow, offers a convenient approach for iterating on task logic effortlessly. +# +# ## Use `partial` to provide default arguments to tasks +# You can use the {py:func}`functools.partial` function to assign default or constant values to the parameters of your tasks. +# %% +import functools + + +@workflow +def simple_wf_with_partial(x: list[int], y: list[int]) -> float: + partial_task = functools.partial(slope, x=x) + return partial_task(y=y) diff --git a/docs/examples/bigquery_plugin/Dockerfile b/docs/examples/bigquery_plugin/Dockerfile new file mode 100644 index 000000000..61d7c5e3a --- /dev/null +++ b/docs/examples/bigquery_plugin/Dockerfile @@ -0,0 +1,33 @@ +FROM python:3.8-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN apt-get update && apt-get install -y build-essential curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/bigquery_plugin/README.md b/docs/examples/bigquery_plugin/README.md new file mode 100644 index 000000000..f7319f6ee --- /dev/null +++ b/docs/examples/bigquery_plugin/README.md @@ -0,0 +1,30 @@ +(big-query)= + +# BigQuery + +```{eval-rst} +.. tags:: GCP, Data, Integration, Advanced +``` + +Flyte backend can be connected with BigQuery service. Once enabled, it can allow you to query a BigQuery table. +This section will provide how to use the BigQuery Plugin using flytekit python. + +## Installation + +To use the flytekit bigquery plugin simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-bigquery +``` + +This plugin is purely a spec. Since SQL is completely portable, there is no need to build a Docker container. + +## Configuring the backend to get bigquery working + +BigQuery plugins are [enabled in flytepropeller's config](https://docs.flyte.org/en/latest/deployment/plugin_setup/gcp/bigquery.html#deployment-plugin-setup-gcp-bigquery) + +```{auto-examples-toc} +bigquery +``` diff --git a/docs/examples/bigquery_plugin/bigquery_plugin/__init__.py b/docs/examples/bigquery_plugin/bigquery_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/bigquery_plugin/bigquery_plugin/bigquery.py b/docs/examples/bigquery_plugin/bigquery_plugin/bigquery.py new file mode 100644 index 000000000..767eadc32 --- /dev/null +++ b/docs/examples/bigquery_plugin/bigquery_plugin/bigquery.py @@ -0,0 +1,70 @@ +# %% [markdown] +# # BigQuery Query +# +# This example shows how to use a Flyte BigQueryTask to execute a query. +# %% +try: + from typing import Annotated +except ImportError: + from typing_extensions import Annotated + +import pandas as pd +from flytekit import StructuredDataset, kwtypes, task, workflow +from flytekitplugins.bigquery import BigQueryConfig, BigQueryTask + +# %% [markdown] +# This is the world's simplest query. Note that in order for registration to work properly, you'll need to give your +# BigQuery task a name that's unique across your project/domain for your Flyte installation. +# %% +bigquery_task_no_io = BigQueryTask( + name="sql.bigquery.no_io", + inputs={}, + query_template="SELECT 1", + task_config=BigQueryConfig(ProjectID="flyte"), +) + + +@workflow +def no_io_wf(): + return bigquery_task_no_io() + + +# %% [markdown] +# Of course, in real world applications we are usually more interested in using BigQuery to query a dataset. +# In this case we use crypto_dogecoin data which is public dataset in BigQuery. +# [here](https://console.cloud.google.com/bigquery?project=bigquery-public-data&page=table&d=crypto_dogecoin&p=bigquery-public-data&t=transactions) +# +# Let's look out how we can parameterize our query to filter results for a specific transaction version, provided as a user input +# specifying a version. + +# %% +DogeCoinDataset = Annotated[StructuredDataset, kwtypes(hash=str, size=int, block_number=int)] + +bigquery_task_templatized_query = BigQueryTask( + name="sql.bigquery.w_io", + # Define inputs as well as their types that can be used to customize the query. + inputs=kwtypes(version=int), + output_structured_dataset_type=DogeCoinDataset, + task_config=BigQueryConfig(ProjectID="flyte"), + query_template="SELECT * FROM `bigquery-public-data.crypto_dogecoin.transactions` WHERE version = @version LIMIT 10;", +) + + +# %% [markdown] +# StructuredDataset transformer can convert query result to pandas dataframe here. +# We can also change "pandas.dataframe" to "pyarrow.Table", and convert result to Arrow table. +# %% +@task +def convert_bq_table_to_pandas_dataframe(sd: DogeCoinDataset) -> pd.DataFrame: + return sd.open(pd.DataFrame).all() + + +@workflow +def full_bigquery_wf(version: int) -> pd.DataFrame: + sd = bigquery_task_templatized_query(version=version) + return convert_bq_table_to_pandas_dataframe(sd=sd) + + +# %% [markdown] +# Check query result on bigquery console: `https://console.cloud.google.com/bigquery` +# diff --git a/docs/examples/bigquery_plugin/requirements.in b/docs/examples/bigquery_plugin/requirements.in new file mode 100644 index 000000000..0b590068f --- /dev/null +++ b/docs/examples/bigquery_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-bigquery==0.30.1 diff --git a/docs/examples/bigquery_plugin/requirements.txt b/docs/examples/bigquery_plugin/requirements.txt new file mode 100644 index 000000000..e8556d240 --- /dev/null +++ b/docs/examples/bigquery_plugin/requirements.txt @@ -0,0 +1,246 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +cachetools==5.0.0 + # via google-auth +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==37.0.3 + # via secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-bigquery +flytekitplugins-bigquery==0.30.1 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +google-api-core[grpc]==2.7.1 + # via + # google-cloud-bigquery + # google-cloud-bigquery-storage + # google-cloud-core +google-auth==2.6.4 + # via + # google-api-core + # google-cloud-core +google-cloud-bigquery==3.0.1 + # via flytekitplugins-bigquery +google-cloud-bigquery-storage==2.13.1 + # via google-cloud-bigquery +google-cloud-core==2.3.0 + # via google-cloud-bigquery +google-crc32c==1.3.0 + # via google-resumable-media +google-resumable-media==2.3.2 + # via google-cloud-bigquery +googleapis-common-protos==1.56.0 + # via + # flyteidl + # google-api-core + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # google-api-core + # google-cloud-bigquery + # grpcio-status +grpcio-status==1.44.0 + # via + # flytekit + # google-api-core +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow +packaging==21.3 + # via + # google-cloud-bigquery + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +pillow==9.1.0 + # via matplotlib +proto-plus==1.20.3 + # via + # google-cloud-bigquery + # google-cloud-bigquery-storage +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # google-api-core + # google-cloud-bigquery + # googleapis-common-protos + # grpcio-status + # proto-plus + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via + # flytekit + # google-cloud-bigquery +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # google-cloud-bigquery + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # google-api-core + # google-cloud-bigquery + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.8 + # via google-auth +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # google-auth + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/blast/Dockerfile b/docs/examples/blast/Dockerfile new file mode 100644 index 000000000..867647729 --- /dev/null +++ b/docs/examples/blast/Dockerfile @@ -0,0 +1,71 @@ +FROM ubuntu:focal + +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN apt-get update \ + && apt-get install -y software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa \ + && apt-get install -y \ + && apt-get update \ + && apt-get install -y \ + cmake \ + curl \ + python3.8 \ + python3.8-venv \ + python3.8-dev \ + make \ + build-essential \ + libssl-dev \ + libffi-dev \ + python3-pip \ + zlib1g-dev \ + vim \ + wget + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Download BLAST +RUN wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.13.0/ncbi-blast-2.13.0+-x64-linux.tar.gz && \ + tar -xzf ncbi-blast-2.13.0+-x64-linux.tar.gz + +# Set the working directory +WORKDIR /root + +# Install Python dependencies +COPY requirements.txt /root +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy data +# COPY blast/kitasatospora /root/kitasatospora + +# Copy the actual code +COPY . /root/ + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +# Check if BLAST is installed +ENV PATH=$PATH:/root/ncbi-blast-2.13.0+/bin +RUN echo $PATH +RUN output="$(which blastx)" && echo $output + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/blast/README.md b/docs/examples/blast/README.md new file mode 100644 index 000000000..4d1053716 --- /dev/null +++ b/docs/examples/blast/README.md @@ -0,0 +1,62 @@ +(blast)= + +# Nucleotide Sequence Querying with BLASTX + +```{eval-rst} +.. tags:: Advanced +``` + +This tutorial demonstrates the integration of computational biology and Flyte. +The focus will be on searching a nucleotide sequence against a local protein database to identify possible homologues. +The steps include: + +- Data loading +- Creation of a {ref}`ShellTask ` to execute the BLASTX search command +- Loading of BLASTX results and plotting a graph of "e-value" vs "pc identity" + +This tutorial is based on the reference guide ["Using BLAST+ Programmatically with Biopython"](https://widdowquinn.github.io/2018-03-06-ibioic/02-sequence_databases/03-programming_for_blast.html). + +## BLAST + +The Basic Local Alignment Search Tool (BLAST) is a program that identifies similar regions between sequences. +It compares nucleotide or protein sequences with sequence databases and evaluates the statistical significance of the matches. +BLAST can be used to deduce functional and evolutionary relationships between sequences and identify members of gene families. + +For additional information, visit the [BLAST Homepage](https://blast.ncbi.nlm.nih.gov/Blast.cgi). + +### BLASTX + +BLASTx is a useful tool for searching genes and predicting their functions or relationships with other gene sequences. +It is commonly employed to find protein-coding genes in genomic DNA or cDNA, as well as to determine whether a new nucleotide sequence encodes a protein or to identify proteins encoded by transcripts or transcript variants. + +This tutorial will demonstrate how to perform a BLASTx search. + +## Data + +The database used in this example consists of predicted gene products from five Kitasatospora genomes. +The query is a single nucleotide sequence of a predicted penicillin-binding protein from Kitasatospora sp. CB01950. + +```{note} +To run the example locally, you need to download BLAST. +You can find OS-specific installation instructions in the [user manual](https://www.ncbi.nlm.nih.gov/books/NBK569861/). +``` + +## Dockerfile + +```{literalinclude} ../../../examples/blast/Dockerfile +:emphasize-lines: 42-44,67-70 +:language: docker +``` + +Initiate the workflow on the Flyte backend by executing the following two commands in the "bioinformatics" directory: + +``` +pyflyte --pkgs blast package --image ghcr.io/flyteorg/flytecookbook:blast-latest +flytectl register files --project flytesnacks --domain development --archive flyte-package.tgz --version v1 +``` + +## Examples + +```{auto-examples-toc} +blastx_example +``` diff --git a/docs/examples/blast/blast/__init__.py b/docs/examples/blast/blast/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/blast/blast/blastx_example.py b/docs/examples/blast/blast/blastx_example.py new file mode 100644 index 000000000..ce0f14298 --- /dev/null +++ b/docs/examples/blast/blast/blastx_example.py @@ -0,0 +1,164 @@ +# %% [markdown] +# # BLASTX Example +# +# This demonstration will utilize BLASTX to search for a nucleotide sequence within a local protein database. + +# %% [markdown] +# Import the necessary libraries. +# %% +from pathlib import Path +from typing import NamedTuple + +import matplotlib.pyplot as plt +import pandas as pd +import requests +from flytekit import conditional, kwtypes, task, workflow +from flytekit.extras.tasks.shell import OutputLocation, ShellTask +from flytekit.types.file import FlyteFile, PNGImageFile + + +# %% [markdown] +# Download the data from GitHub. +# +# :::{note} +# When running code on the demo cluster, make sure data is included in the Docker image. +# Uncomment copy data command in the Dockerfile. +# ::: +# %% +def download_dataset(): + Path("kitasatospora").mkdir(exist_ok=True) + r = requests.get("https://api.github.com/repos/flyteorg/flytesnacks/contents/blast/kitasatospora?ref=datasets") + for each_file in r.json(): + download_url = each_file["download_url"] + file_name = f"kitasatospora/{Path(download_url).name}" + if not Path(file_name).exists(): + r_file = requests.get(each_file["download_url"]) + open(file_name, "wb").write(r_file.content) + + +# %% [markdown] +# A `ShellTask` allows you to run commands on the shell. +# In this example, we use a `ShellTask` to create and execute the BLASTX command. +# +# Start by specifying the location of the BLAST output file. +# Then, define variables that hold the paths to the input query sequence file, the database we are searching against, and the output file for BLAST. +# Finally, generate and run the BLASTX command. +# Both the standard output (stdout) and standard error (stderr) are captured and saved in the `stdout` variable. +# +# The `{inputs}` and `{outputs}` are placeholders for input and output values, respectively. +# %% +blastx_on_shell = ShellTask( + name="blastx", + debug=True, + script=""" + mkdir -p {inputs.outdir} + + query={inputs.datadir}/{inputs.query} + db={inputs.datadir}/{inputs.db} + blastout={inputs.outdir}/{inputs.blast_output} + + blastx -out $blastout -outfmt 6 -query $query -db $db >> {outputs.stdout} 2>&1 + """, + inputs=kwtypes(datadir=str, query=str, outdir=str, blast_output=str, db=str), + output_locs=[ + OutputLocation(var="stdout", var_type=FlyteFile, location="stdout.txt"), + OutputLocation( + var="blastout", + var_type=FlyteFile, + location="{inputs.outdir}/{inputs.blast_output}", + ), + ], +) + +# %% [markdown] +# :::{note} +# The `outfmt=6` option requests BLASTX to generate a tab-separated plain text file, which is convenient for automated processing. +# ::: +# +# If the command runs successfully, there should be no standard output or error (stdout and stderr should be empty). + +# %% [markdown] +# Next, define a task to load the BLASTX output. +# The task returns a pandas DataFrame and a plot. +# The file containing the BLASTX results is referred to as blastout. +# %% +BLASTXOutput = NamedTuple("blastx_output", result=pd.DataFrame, plot=PNGImageFile) + + +@task +def blastx_output(blastout: FlyteFile) -> BLASTXOutput: + # read BLASTX output + result = pd.read_csv(blastout, sep="\t", header=None) + + # define column headers + headers = [ + "query", + "subject", + "pc_identity", + "aln_length", + "mismatches", + "gaps_opened", + "query_start", + "query_end", + "subject_start", + "subject_end", + "e_value", + "bitscore", + ] + + # assign headers + result.columns = headers + + # create a scatterplot + result.plot.scatter("pc_identity", "e_value") + plt.title("E value vs %identity") + plot = "plot.png" + plt.savefig(plot) + + return BLASTXOutput(result=result.head(), plot=plot) + + +# %% [markdown] +# Verify that the BLASTX run was successful by checking if the standard output and error are empty. +# %% +@task +def is_batchx_success(stdout: FlyteFile) -> bool: + if open(stdout).read(): + return False + else: + return True + + +# %% [markdown] +# Create a workflow that calls the previously defined tasks. +# A {ref}`conditional ` statement is used to check the success of the BLASTX command. +# %% +@workflow +def blast_wf( + datadir: str = "kitasatospora", + outdir: str = "output", + query: str = "k_sp_CB01950_penicillin.fasta", + db: str = "kitasatospora_proteins.faa", + blast_output: str = "AMK19_00175_blastx_kitasatospora.tab", +) -> BLASTXOutput: + stdout, blastout = blastx_on_shell(datadir=datadir, outdir=outdir, query=query, db=db, blast_output=blast_output) + result = is_batchx_success(stdout=stdout) + final_result, plot = ( + conditional("blastx_output") + .if_(result.is_true()) + .then(blastx_output(blastout=blastout)) + .else_() + .fail("BLASTX failed") + ) + return BLASTXOutput(result=final_result, plot=plot) + + +# %% [markdown] +# Run the workflow locally. +# +# %% +if __name__ == "__main__": + print("Downloading dataset...") + download_dataset() + print("Running BLASTX...") + print(f"BLASTX result: {blast_wf()}") diff --git a/docs/examples/blast/requirements.in b/docs/examples/blast/requirements.in new file mode 100644 index 000000000..bec48ef8c --- /dev/null +++ b/docs/examples/blast/requirements.in @@ -0,0 +1,4 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard diff --git a/docs/examples/blast/requirements.txt b/docs/examples/blast/requirements.txt new file mode 100644 index 000000000..8d801fcf5 --- /dev/null +++ b/docs/examples/blast/requirements.txt @@ -0,0 +1,381 @@ +# +# This file is autogenerated by pip-compile with Python 3.10 +# by the following command: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +appnope==0.1.3 + # via + # ipykernel + # ipython +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +attrs==22.2.0 + # via visions +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +certifi==2022.12.7 + # via requests +cffi==1.15.1 + # via cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.0.1 + # via requests +click==8.1.3 + # via + # cookiecutter + # flytekit +cloudpickle==2.2.1 + # via flytekit +comm==0.1.2 + # via ipykernel +contourpy==1.0.7 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.8 + # via flytekit +cryptography==39.0.0 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +debugpy==1.6.6 + # via ipykernel +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==6.0.1 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +executing==1.2.0 + # via stack-data +flyteidl==1.3.5 + # via flytekit +flytekit==1.3.1 + # via + # -r ../../../common/requirements-common.in + # -r requirements.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.3.1 + # via -r ../../../common/requirements-common.in +fonttools==4.38.0 + # via matplotlib +gitdb==4.0.10 + # via gitpython +gitpython==3.1.30 + # via flytekit +googleapis-common-protos==1.58.0 + # via + # flyteidl + # flytekit + # grpcio-status +grpcio==1.51.1 + # via + # flytekit + # grpcio-status +grpcio-status==1.51.1 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via requests +imagehash==4.3.1 + # via visions +importlib-metadata==6.0.0 + # via + # flytekit + # keyring +ipykernel==6.21.1 + # via ipywidgets +ipython==8.9.0 + # via + # ipykernel + # ipywidgets +ipywidgets==8.0.4 + # via flytekitplugins-deck-standard +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.2.0 + # via + # flytekit + # phik +jupyter-client==8.0.2 + # via ipykernel +jupyter-core==5.2.0 + # via + # ipykernel + # jupyter-client +jupyterlab-widgets==3.0.5 + # via ipywidgets +keyring==23.13.1 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.2 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.6.3 + # via + # -r ../../../common/requirements-common.in + # phik + # seaborn + # ydata-profiling +matplotlib-inline==0.1.6 + # via + # ipykernel + # ipython +more-itertools==9.0.0 + # via jaraco-classes +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.2.0 + # via flytekit +nest-asyncio==1.5.6 + # via ipykernel +networkx==3.0 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # scipy + # seaborn + # statsmodels + # visions + # ydata-profiling +packaging==23.0 + # via + # docker + # ipykernel + # marshmallow + # matplotlib + # statsmodels +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +pandas-profiling==3.6.6 + # via flytekitplugins-deck-standard +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==9.4.0 + # via + # imagehash + # matplotlib + # visions +platformdirs==3.0.0 + # via jupyter-core +plotly==5.13.0 + # via flytekitplugins-deck-standard +prompt-toolkit==3.0.36 + # via ipython +protobuf==4.21.12 + # via + # flyteidl + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.4 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==10.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pydantic==1.10.4 + # via ydata-profiling +pygments==2.14.0 + # via ipython +pyopenssl==23.0.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # jupyter-client + # matplotlib + # pandas +python-json-logger==2.0.4 + # via flytekit +python-slugify==8.0.0 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.7.1 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # ydata-profiling +pyzmq==25.0.0 + # via + # ipykernel + # jupyter-client +regex==2022.10.31 + # via docker-image-py +requests==2.28.2 + # via + # cookiecutter + # docker + # flytekit + # responses + # ydata-profiling +responses==0.22.0 + # via flytekit +retry==0.9.2 + # via flytekit +scipy==1.9.3 + # via + # imagehash + # phik + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +six==1.16.0 + # via + # asttokens + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.13.5 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.1.0 + # via plotly +text-unidecode==1.3 + # via python-slugify +toml==0.10.2 + # via responses +tornado==6.2 + # via + # ipykernel + # jupyter-client +tqdm==4.64.1 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +types-toml==0.10.8.3 + # via responses +typing-extensions==4.4.0 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.14 + # via + # docker + # flytekit + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.5.1 + # via docker +wheel==0.38.4 + # via + # -r ../../../common/requirements-common.in + # flytekit +widgetsnbextension==4.0.5 + # via ipywidgets +wrapt==1.14.1 + # via + # deprecated + # flytekit +ydata-profiling==4.0.0 + # via pandas-profiling +zipp==3.12.1 + # via importlib-metadata diff --git a/docs/examples/customizing_dependencies/Dockerfile b/docs/examples/customizing_dependencies/Dockerfile new file mode 100644 index 000000000..cedf44210 --- /dev/null +++ b/docs/examples/customizing_dependencies/Dockerfile @@ -0,0 +1,31 @@ +FROM python:3.11-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt +RUN pip freeze + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/customizing_dependencies/README.md b/docs/examples/customizing_dependencies/README.md new file mode 100644 index 000000000..a31ed9616 --- /dev/null +++ b/docs/examples/customizing_dependencies/README.md @@ -0,0 +1,13 @@ +# Customizing Dependencies + +In this section, you will uncover how Flyte utilizes Docker images to construct containers under the hood, +and you'll learn how to craft your own images to encompass all the necessary dependencies for your tasks or workflows. +You will explore how to execute a raw container with custom commands, +indicate multiple container images within a single workflow, +and get familiar with the ins and outs of `ImageSpec`! + +```{auto-examples-toc} +image_spec +raw_container +multi_images +``` diff --git a/docs/examples/customizing_dependencies/customizing_dependencies/__init__.py b/docs/examples/customizing_dependencies/customizing_dependencies/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/customizing_dependencies/customizing_dependencies/image_spec.py b/docs/examples/customizing_dependencies/customizing_dependencies/image_spec.py new file mode 100644 index 000000000..5a2b718b2 --- /dev/null +++ b/docs/examples/customizing_dependencies/customizing_dependencies/image_spec.py @@ -0,0 +1,131 @@ +# %% [markdown] +# (image_spec_example)= +# +# # Building Docker Images without a Dockerfile +# +# ```{eval-rst} +# .. tags:: Containerization, Intermediate +# ``` +# +# :::{note} +# This is an experimental feature, which is subject to change the API in the future. +# ::: +# +# Image Spec is a way to specify how to build a container image without a Dockerfile. The image spec by default will be +# converted to an [Envd](https://envd.tensorchord.ai/) config, and the [Envd builder](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-envd/flytekitplugins/envd/image_builder.py#L12-L34) will build the image for you. However, you can also register your own builder to build +# the image using other tools. +# +# For every {py:class}`flytekit.PythonFunctionTask` task or a task decorated with the `@task` decorator, +# you can specify rules for binding container images. By default, flytekit binds a single container image, i.e., +# the [default Docker image](https://ghcr.io/flyteorg/flytekit), to all tasks. To modify this behavior, +# use the `container_image` parameter available in the {py:func}`flytekit.task` decorator, and pass an +# `ImageSpec`. +# +# Before building the image, Flytekit checks the container registry first to see if the image already exists. By doing +# so, it avoids having to rebuild the image over and over again. If the image does not exist, flytekit will build the +# image before registering the workflow, and replace the image name in the task template with the newly built image name. +# %% +import typing + +import pandas as pd +from flytekit import ImageSpec, Resources, task, workflow + +# %% [markdown] +# :::{admonition} Prerequisites +# :class: important +# +# - Install [flytekitplugins-envd](https://github.com/flyteorg/flytekit/tree/master/plugins/flytekit-envd) to build the image spec. +# - To build the image on remote machine, check this [doc](https://envd.tensorchord.ai/teams/context.html#start-remote-buildkitd-on-builder-machine). +# - When using a registry in ImageSpec, `docker login` is required to push the image +# ::: + +# %% [markdown] +# You can specify python packages, apt packages, and environment variables in the `ImageSpec`. +# These specified packages will be added on top of the [default image](https://github.com/flyteorg/flytekit/blob/master/Dockerfile), which can be found in the Flytekit Dockerfile. +# More specifically, flytekit invokes [DefaultImages.default_image()](https://github.com/flyteorg/flytekit/blob/f2cfef0ec098d4ae8f042ab915b0b30d524092c6/flytekit/configuration/default_images.py#L26-L27) function. +# This function determines and returns the default image based on the Python version and flytekit version. For example, if you are using python 3.8 and flytekit 0.16.0, the default image assigned will be `ghcr.io/flyteorg/flytekit:py3.8-1.6.0`. +# If desired, you can also override the default image by providing a custom `base_image` parameter when using the `ImageSpec`. +# %% +pandas_image_spec = ImageSpec( + base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.2", + packages=["pandas", "numpy"], + python_version="3.9", + apt_packages=["git"], + env={"Debug": "True"}, + registry="ghcr.io/flyteorg", +) + +sklearn_image_spec = ImageSpec( + base_image="ghcr.io/flyteorg/flytekit:py3.8-1.6.2", + packages=["scikit-learn"], + registry="ghcr.io/flyteorg", +) + +# %% [markdown] +# `is_container` is used to determine whether the task is utilizing the image constructed from the `ImageSpec`. +# If the task is indeed using the image built from the `ImageSpec`, it will then import Tensorflow. +# This approach helps minimize module loading time and prevents unnecessary dependency installation within a single image. +# %% +if sklearn_image_spec.is_container(): + from sklearn.linear_model import LogisticRegression + + +# %% [markdown] +# To enable tasks to utilize the images built with `ImageSpec`, you can specify the `container_image` parameter for those tasks. +# %% +@task(container_image=pandas_image_spec) +def get_pandas_dataframe() -> typing.Tuple[pd.DataFrame, pd.Series]: + df = pd.read_csv("https://storage.googleapis.com/download.tensorflow.org/data/heart.csv") + print(df.head()) + return df[["age", "thalach", "trestbps", "chol", "oldpeak"]], df.pop("target") + + +@task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) +def get_model(max_iter: int, multi_class: str) -> typing.Any: + return LogisticRegression(max_iter=max_iter, multi_class=multi_class) + + +# Get a basic model to train. +@task(container_image=sklearn_image_spec, requests=Resources(cpu="1", mem="1Gi")) +def train_model(model: typing.Any, feature: pd.DataFrame, target: pd.Series) -> typing.Any: + model.fit(feature, target) + return model + + +# Lastly, let's define a workflow to capture the dependencies between the tasks. +@workflow() +def wf(): + feature, target = get_pandas_dataframe() + model = get_model(max_iter=3000, multi_class="auto") + train_model(model=model, feature=feature, target=target) + + +if __name__ == "__main__": + wf() + +# %% [markdown] +# There exists an option to override the container image by providing an Image Spec YAML file to the `pyflyte run` or `pyflyte register` command. +# This allows for greater flexibility in specifying a custom container image. For example: +# +# ```yaml +# # imageSpec.yaml +# python_version: 3.11 +# registry: pingsutw +# packages: +# - sklearn +# env: +# Debug: "True" +# ``` +# +# ``` +# # Use pyflyte to register the workflow +# pyflyte run --remote --image image.yaml image_spec.py wf +# ``` + +# %% [markdown] +# If you only want to build the image without registering the workflow, you can use the `pyflyte build` command. +# +# ``` +# pyflyte build --remote image_spec.py wf +# ``` +# diff --git a/docs/examples/customizing_dependencies/customizing_dependencies/multi_images.py b/docs/examples/customizing_dependencies/customizing_dependencies/multi_images.py new file mode 100644 index 000000000..874f68640 --- /dev/null +++ b/docs/examples/customizing_dependencies/customizing_dependencies/multi_images.py @@ -0,0 +1,94 @@ +# %% [markdown] +# (multi_images)= +# +# # Multiple Container Images in a Single Workflow +# +# ```{eval-rst} +# .. tags:: Containerization, Intermediate +# ``` +# +# For every {py:class}`flytekit.PythonFunctionTask` task or a task decorated with the `@task` decorator, you can specify rules for binding container images. +# By default, flytekit binds a single container image, i.e., the [default Docker image](https://ghcr.io/flyteorg/flytekit), to all tasks. +# To modify this behavior, use the `container_image` parameter available in the {py:func}`flytekit.task` decorator. +# +# :::{note} +# If the Docker image is not available publicly, refer to {ref}`Pulling Private Images`. +# ::: + +# %% +import numpy as np +from flytekit import task, workflow + + +@task(container_image="{{.image.mindmeld.fqn}}:{{.image.mindmeld.version}}") +def get_data() -> np.ndarray: + # here we're importing scikit learn within the Flyte task + from sklearn import datasets + + iris = datasets.load_iris() + X = iris.data[:, :2] + return X + + +@task(container_image="{{.image.borebuster.fqn}}:{{.image.borebuster.version}}") +def normalize(X: np.ndarray) -> np.ndarray: + return (X - X.mean(axis=0)) / X.std(axis=0) + + +@workflow +def multi_images_wf() -> np.ndarray: + X = get_data() + X = normalize(X=X) + return X + + +# %% [markdown] +# Observe how the `sklearn` library is imported in the context of a Flyte task. +# This approach is beneficial when creating tasks in a single module, where some tasks have dependencies that others do not require. +# +# ## Configuring image parameters +# +# The following parameters can be used to configure images in the `@task` decorator: +# +# 1. `image` refers to the name of the image in the image configuration. The name `default` is a reserved keyword and will automatically apply to the default image name for this repository. +# 2. `fqn` refers to the fully qualified name of the image. For example, it includes the repository and domain URL of the image. Example: docker.io/my_repo/xyz. +# 3. `version` refers to the tag of the image. For example: latest, or python-3.9 etc. If `container_image` is not specified, then the default configured image for the project is used. +# +# ## Sending images to `pyflyte` command +# +# You can pass Docker images to the `pyflyte run` or `pyflyte register` command. +# For instance: +# +# ``` +# pyflyte run --remote --image mindmeld="ghcr.io/flyteorg/flytecookbook:core-latest" --image borebuster="ghcr.io/flyteorg/flytekit:py3.9-latest" multi_images.py multi_images_wf +# ``` +# +# ## Configuring images in `$HOME/.flyte/config.yaml` +# +# To specify images in your `$HOME/.flyte/config.yaml` file (or whichever configuration file you are using), include an "images" section in the configuration. +# For example: +# +# ```{code-block} yaml +# :emphasize-lines: 6-8 +# +# admin: +# # For GRPC endpoints you might want to use dns:///flyte.myexample.com +# endpoint: localhost:30080 +# authType: Pkce +# insecure: true +# images: +# mindmeld: ghcr.io/flyteorg/flytecookbook:core-latest +# borebuster: ghcr.io/flyteorg/flytekit:py3.9-latest +# console: +# endpoint: http://localhost:30080 +# logger: +# show-source: true +# level: 0 +# ``` +# +# Send the name of the configuration file to your `pyflyte run` command as follows: +# +# ``` +# pyflyte --config $HOME/.flyte/config.yaml run --remote multi_images.py multi_images_wf +# ``` +# diff --git a/docs/examples/customizing_dependencies/customizing_dependencies/raw_container.py b/docs/examples/customizing_dependencies/customizing_dependencies/raw_container.py new file mode 100644 index 000000000..0021a5bab --- /dev/null +++ b/docs/examples/customizing_dependencies/customizing_dependencies/raw_container.py @@ -0,0 +1,203 @@ +# %% [markdown] +# (raw_container)= +# +# # Using Raw Containers +# +# ```{eval-rst} +# .. tags:: Containerization, Advanced +# ``` +# +# This example demonstrates how to use arbitrary containers in 5 different languages, all orchestrated in flytekit seamlessly. +# Flyte mounts an input data volume where all the data needed by the container is available, and an output data volume +# for the container to write all the data which will be stored away. +# +# The data is written as separate files, one per input variable. The format of the file is serialized strings. +# Refer to the raw protocol to understand how to leverage this. +# %% +import logging + +from flytekit import ContainerTask, kwtypes, task, workflow + +logger = logging.getLogger(__file__) + +# %% [markdown] +# ## Container Tasks +# +# A {py:class}`flytekit.ContainerTask` denotes an arbitrary container. In the following example, the name of the task +# is `calculate_ellipse_area_shell`. This name has to be unique in the entire project. Users can specify: +# +# - `input_data_dir` -> where inputs will be written to. +# - `output_data_dir` -> where Flyte will expect the outputs to exist. +# +# `inputs` and `outputs` specify the interface for the task; thus it should be an ordered dictionary of typed input and +# output variables. +# %% +calculate_ellipse_area_shell = ContainerTask( + name="ellipse-area-metadata-shell", + input_data_dir="/var/inputs", + output_data_dir="/var/outputs", + inputs=kwtypes(a=float, b=float), + outputs=kwtypes(area=float, metadata=str), + image="ghcr.io/flyteorg/rawcontainers-shell:v2", + command=[ + "./calculate-ellipse-area.sh", + "{{.inputs.a}}", + "{{.inputs.b}}", + "/var/outputs", + ], +) + +calculate_ellipse_area_python = ContainerTask( + name="ellipse-area-metadata-python", + input_data_dir="/var/inputs", + output_data_dir="/var/outputs", + inputs=kwtypes(a=float, b=float), + outputs=kwtypes(area=float, metadata=str), + image="ghcr.io/flyteorg/rawcontainers-python:v2", + command=[ + "python", + "calculate-ellipse-area.py", + "{{.inputs.a}}", + "{{.inputs.b}}", + "/var/outputs", + ], +) + +calculate_ellipse_area_r = ContainerTask( + name="ellipse-area-metadata-r", + input_data_dir="/var/inputs", + output_data_dir="/var/outputs", + inputs=kwtypes(a=float, b=float), + outputs=kwtypes(area=float, metadata=str), + image="ghcr.io/flyteorg/rawcontainers-r:v2", + command=[ + "Rscript", + "--vanilla", + "calculate-ellipse-area.R", + "{{.inputs.a}}", + "{{.inputs.b}}", + "/var/outputs", + ], +) + +calculate_ellipse_area_haskell = ContainerTask( + name="ellipse-area-metadata-haskell", + input_data_dir="/var/inputs", + output_data_dir="/var/outputs", + inputs=kwtypes(a=float, b=float), + outputs=kwtypes(area=float, metadata=str), + image="ghcr.io/flyteorg/rawcontainers-haskell:v2", + command=[ + "./calculate-ellipse-area", + "{{.inputs.a}}", + "{{.inputs.b}}", + "/var/outputs", + ], +) + +calculate_ellipse_area_julia = ContainerTask( + name="ellipse-area-metadata-julia", + input_data_dir="/var/inputs", + output_data_dir="/var/outputs", + inputs=kwtypes(a=float, b=float), + outputs=kwtypes(area=float, metadata=str), + image="ghcr.io/flyteorg/rawcontainers-julia:v2", + command=[ + "julia", + "calculate-ellipse-area.jl", + "{{.inputs.a}}", + "{{.inputs.b}}", + "/var/outputs", + ], +) + + +@task +def report_all_calculated_areas( + area_shell: float, + metadata_shell: str, + area_python: float, + metadata_python: str, + area_r: float, + metadata_r: str, + area_haskell: float, + metadata_haskell: str, + area_julia: float, + metadata_julia: str, +): + logger.info(f"shell: area={area_shell}, metadata={metadata_shell}") + logger.info(f"python: area={area_python}, metadata={metadata_python}") + logger.info(f"r: area={area_r}, metadata={metadata_r}") + logger.info(f"haskell: area={area_haskell}, metadata={metadata_haskell}") + logger.info(f"julia: area={area_julia}, metadata={metadata_julia}") + + +# %% [markdown] +# As can be seen in this example, `ContainerTask`s can be interacted with like normal Python functions, whose inputs +# correspond to the declared input variables. All data returned by the tasks are consumed and logged by a Flyte task. +# %% +@workflow +def wf(a: float, b: float): + # Calculate area in all languages + area_shell, metadata_shell = calculate_ellipse_area_shell(a=a, b=b) + area_python, metadata_python = calculate_ellipse_area_python(a=a, b=b) + area_r, metadata_r = calculate_ellipse_area_r(a=a, b=b) + area_haskell, metadata_haskell = calculate_ellipse_area_haskell(a=a, b=b) + area_julia, metadata_julia = calculate_ellipse_area_julia(a=a, b=b) + + # Report on all results in a single task to simplify comparison + report_all_calculated_areas( + area_shell=area_shell, + metadata_shell=metadata_shell, + area_python=area_python, + metadata_python=metadata_python, + area_r=area_r, + metadata_r=metadata_r, + area_haskell=area_haskell, + metadata_haskell=metadata_haskell, + area_julia=area_julia, + metadata_julia=metadata_julia, + ) + + +# %% [markdown] +# One of the benefits of raw container tasks is that Flytekit does not need to be installed in the target container. +# +# :::{note} +# Raw containers cannot be run locally at the moment. +# ::: +# +# ## Scripts +# +# The contents of each script specified in the `ContainerTask` is as follows: +# +# ### calculate-ellipse-area.sh +# +# ```{literalinclude} ../../../examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/calculate-ellipse-area.sh +# :language: shell +# ``` +# +# ### calculate-ellipse-area.py +# +# ```{literalinclude} ../../../examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/calculate-ellipse-area.py +# :language: python +# ``` +# +# ### calculate-ellipse-area.R +# +# ```{literalinclude} ../../../examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/calculate-ellipse-area.R +# :language: r +# ``` +# +# ### calculate-ellipse-area.hs +# +# ```{literalinclude} ../../../examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/calculate-ellipse-area.hs +# :language: haskell +# ``` +# +# ### calculate-ellipse-area.jl +# +# ```{literalinclude} ../../../examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/calculate-ellipse-area.jl +# :language: julia +# ``` +# diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/MAINTAINERS.md b/docs/examples/customizing_dependencies/raw-containers-supporting-files/MAINTAINERS.md new file mode 100644 index 000000000..c02d084fb --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/MAINTAINERS.md @@ -0,0 +1,5 @@ +# raw-containers-demo + +This directory holds the Dockerfiles and supporting files needed to run the example described in `raw_container.py`, split by language. + +The actual example points to images present in the gihub registry (i.e. ghcr.io), so in case we need to update the examples for any reason we should keep in mind to push them to ghcr too. diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/Dockerfile b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/Dockerfile new file mode 100644 index 000000000..8d9679088 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/Dockerfile @@ -0,0 +1,7 @@ +FROM haskell:9 + +WORKDIR /root + +COPY calculate-ellipse-area.hs /root + +RUN ghc calculate-ellipse-area.hs diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/calculate-ellipse-area.hs b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/calculate-ellipse-area.hs new file mode 100644 index 000000000..c31d4a10a --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/haskell/calculate-ellipse-area.hs @@ -0,0 +1,19 @@ +import System.IO +import System.Environment +import Text.Read +import Text.Printf + +calculateEllipseArea :: Float -> Float -> Float +calculateEllipseArea a b = pi * a * b + +main = do + args <- getArgs + let a = args!!0 + b = args!!1 + + let area = calculateEllipseArea (read a::Float) (read b::Float) + + let output_area = args!!2 ++ "/area" + output_metadata = args!!2 ++ "/metadata" + writeFile output_area (show area) + writeFile output_metadata "[from haskell rawcontainer]" diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/Dockerfile b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/Dockerfile new file mode 100644 index 000000000..caaf85a2a --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/Dockerfile @@ -0,0 +1,5 @@ +FROM julia:1.6.4-buster + +WORKDIR /root + +COPY calculate-ellipse-area.jl /root diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/calculate-ellipse-area.jl b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/calculate-ellipse-area.jl new file mode 100644 index 000000000..c26ffdfea --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/julia/calculate-ellipse-area.jl @@ -0,0 +1,31 @@ + +using Printf + +function calculate_area(a, b) + π * a * b +end + +function write_output(output_dir, output_file, v) + output_path = @sprintf "%s/%s" output_dir output_file + open(output_path, "w") do file + write(file, string(v)) + end +end + +function main(a, b, output_dir) + a = parse.(Float64, a) + b = parse.(Float64, b) + + area = calculate_area(a, b) + + write_output(output_dir, "area", area) + write_output(output_dir, "metadata", "[from julia rawcontainer]") +end + +# the keyword ARGS is a special value that contains the command-line arguments +# julia arrays are 1-indexed +a = ARGS[1] +b = ARGS[2] +output_dir = ARGS[3] + +main(a, b, output_dir) diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/Dockerfile b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/Dockerfile new file mode 100644 index 000000000..2174b2d99 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/Dockerfile @@ -0,0 +1,5 @@ +FROM python:3.10-slim-buster + +WORKDIR /root + +COPY *.py /root/ diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/calculate-ellipse-area.py b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/calculate-ellipse-area.py new file mode 100644 index 000000000..7c589da7c --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/python/calculate-ellipse-area.py @@ -0,0 +1,29 @@ +import math +import sys + + +def write_output(output_dir, output_file, v): + with open(f"{output_dir}/{output_file}", "w") as f: + f.write(str(v)) + + +def calculate_area(a, b): + return math.pi * a * b + + +def main(a, b, output_dir): + a = float(a) + b = float(b) + + area = calculate_area(a, b) + + write_output(output_dir, "area", area) + write_output(output_dir, "metadata", "[from python rawcontainer]") + + +if __name__ == "__main__": + a = sys.argv[1] + b = sys.argv[2] + output_dir = sys.argv[3] + + main(a, b, output_dir) diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/Dockerfile b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/Dockerfile new file mode 100644 index 000000000..b1dad09c0 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/Dockerfile @@ -0,0 +1,5 @@ +FROM r-base + +WORKDIR /root + +COPY *.R /root/ diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/calculate-ellipse-area.R b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/calculate-ellipse-area.R new file mode 100644 index 000000000..d2650d826 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/calculate-ellipse-area.R @@ -0,0 +1,13 @@ +#!/usr/bin/env Rscript + +args = commandArgs(trailingOnly=TRUE) + +a = args[1] +b = args[2] +output_dir = args[3] + +area <- pi * as.double(a) * as.double(b) +print(area) + +writeLines(as.character(area), sprintf("%s/%s", output_dir, 'area')) +writeLines("[from R rawcontainer]", sprintf("%s/%s", output_dir, 'metadata')) diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/install-readr.R b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/install-readr.R new file mode 100644 index 000000000..3308314e4 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/r/install-readr.R @@ -0,0 +1 @@ +install.packages("readr") diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/Dockerfile b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/Dockerfile new file mode 100644 index 000000000..856160ba1 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/Dockerfile @@ -0,0 +1,6 @@ +FROM alpine + +WORKDIR /root + +COPY calculate-ellipse-area.sh /root +RUN chmod +x /root/calculate-ellipse-area.sh diff --git a/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/calculate-ellipse-area.sh b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/calculate-ellipse-area.sh new file mode 100644 index 000000000..5096e1403 --- /dev/null +++ b/docs/examples/customizing_dependencies/raw-containers-supporting-files/per-language/shell/calculate-ellipse-area.sh @@ -0,0 +1,5 @@ +#! /usr/bin/env sh + +echo "4*a(1) * $1 * $2" | bc -l | tee "$3/area" + +echo "[from shell rawcontainer]" | tee "$3/metadata" diff --git a/docs/examples/customizing_dependencies/requirements.in b/docs/examples/customizing_dependencies/requirements.in new file mode 100644 index 000000000..f5303d5a4 --- /dev/null +++ b/docs/examples/customizing_dependencies/requirements.in @@ -0,0 +1,3 @@ +flytekit +flytekitplugins-envd +scikit-learn diff --git a/docs/examples/customizing_dependencies/requirements.txt b/docs/examples/customizing_dependencies/requirements.txt new file mode 100644 index 000000000..c09165282 --- /dev/null +++ b/docs/examples/customizing_dependencies/requirements.txt @@ -0,0 +1,356 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.2 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.39 + # via flytekitplugins-envd +flyteidl==1.5.16 + # via flytekit +flytekit==1.9.0 + # via + # -r requirements.in + # flytekitplugins-envd +flytekitplugins-envd==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via + # flytekit + # scikit-learn +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow + # scikit-learn + # scipy +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.1 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +scikit-learn==1.3.0 + # via -r requirements.in +scipy==1.11.2 + # via scikit-learn +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.2.0 + # via scikit-learn +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/data_types_and_io/Dockerfile b/docs/examples/data_types_and_io/Dockerfile new file mode 100644 index 000000000..2777e57c8 --- /dev/null +++ b/docs/examples/data_types_and_io/Dockerfile @@ -0,0 +1,31 @@ +FROM python:3.8-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install --no-cache-dir -r /root/requirements.txt +RUN pip freeze + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/data_types_and_io/README.md b/docs/examples/data_types_and_io/README.md new file mode 100644 index 000000000..2025cdcdf --- /dev/null +++ b/docs/examples/data_types_and_io/README.md @@ -0,0 +1,26 @@ +(data_types_and_io)= + +# Data Types and IO + +Flyte is a data-aware DAG scheduling system. The graph itself is derived +automatically from the flow of data and this closely resembles how a functional +programming language passes data between methods. + +Data awareness is powered by Flyte's own type system, which closely maps most programming languages. These types are what power Flyte's magic of: + +- Data lineage +- Memoization +- Auto parallelization +- Simplifying access to data +- Auto generated CLI and Launch UI + +```{auto-examples-toc} +flyte_python_types +schema +structured_dataset +typed_schema +pytorch_types +custom_objects +enums +flyte_pickle +``` diff --git a/docs/examples/data_types_and_io/data_types_and_io/__init__.py b/docs/examples/data_types_and_io/data_types_and_io/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/data_types_and_io/data_types_and_io/custom_objects.py b/docs/examples/data_types_and_io/data_types_and_io/custom_objects.py new file mode 100644 index 000000000..435ad65f1 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/custom_objects.py @@ -0,0 +1,159 @@ +# %% [markdown] +# (dataclass_type)= +# +# # Using Custom Python Objects +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Flyte supports passing JSON between tasks. But to simplify the usage for the users and introduce type-safety, +# Flytekit supports passing custom data objects between tasks. +# +# Currently, data classes decorated with `@dataclass_json` are supported. +# One good use case of a data class would be when you want to wrap all input in a data class in the case of a map task +# which can only accept one input and produce one output. +# +# This example shows how users can serialize custom JSON-compatible dataclasses between successive tasks using the +# excellent [dataclasses_json](https://pypi.org/project/dataclasses-json/) library. + +# %% [markdown] +# To get started, let's import the necessary libraries. +# %% +import os +import tempfile +import typing +from dataclasses import dataclass + +import pandas as pd +from dataclasses_json import dataclass_json +from flytekit import task, workflow +from flytekit.types.directory import FlyteDirectory +from flytekit.types.file import FlyteFile +from flytekit.types.schema import FlyteSchema + + +# %% [markdown] +# We define a simple data class that can be sent between tasks. +# %% +@dataclass_json +@dataclass +class Datum(object): + """ + Example of a simple custom class that is modeled as a dataclass + """ + + x: int + y: str + z: typing.Dict[int, str] + + +# %% [markdown] +# `Datum` is a user defined complex type that can be used to pass complex data between tasks. +# Interestingly, users can send this data between different tasks written in different languages and input it through the Flyte Console as raw JSON. +# +# :::{note} +# All variables in a data class should be **annotated with their type**. Failure to do should will result in an error. +# ::: + +# %% [markdown] +# Next, we define a data class that accepts {std:ref}`FlyteSchema `, {std:ref}`FlyteFile `, +# and {std:ref}`FlyteDirectory `. +# %% +@dataclass_json +@dataclass +class Result: + schema: FlyteSchema + file: FlyteFile + directory: FlyteDirectory + + +# %% [markdown] +# :::{note} +# A data class supports the usage of data associated with Python types, data classes, FlyteFile, FlyteDirectory, and FlyteSchema. +# ::: +# +# Once declared, dataclasses can be returned as outputs or accepted as inputs. +# +# 1. Datum Data Class +# %% +@task +def stringify(x: int) -> Datum: + """ + A dataclass return will be regarded as a complex single json return. + """ + return Datum(x=x, y=str(x), z={x: str(x)}) + + +@task +def add(x: Datum, y: Datum) -> Datum: + """ + Flytekit will automatically convert the passed in json into a DataClass. If the structures dont match, it will raise + a runtime failure + """ + x.z.update(y.z) + return Datum(x=x.x + y.x, y=x.y + y.y, z=x.z) + + +# %% [markdown] +# The `stringify` task outputs a data class, and the `add` task accepts data classes as inputs. +# +# 2. Result Data Class +# %% +@task +def upload_result() -> Result: + """ + Flytekit will upload FlyteFile, FlyteDirectory, and FlyteSchema to blob store (GCP, S3) + """ + df = pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]}) + temp_dir = tempfile.mkdtemp(prefix="flyte-") + + schema_path = temp_dir + "/schema.parquet" + df.to_parquet(schema_path) + + file_path = tempfile.NamedTemporaryFile(delete=False) + file_path.write(b"Hello world!") + fs = Result( + schema=FlyteSchema(temp_dir), + file=FlyteFile(file_path.name), + directory=FlyteDirectory(temp_dir), + ) + return fs + + +@task +def download_result(res: Result): + """ + Flytekit will lazily load FlyteSchema. We download the schema only when users invoke open(). + """ + assert pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]}).equals(res.schema.open().all()) + f = open(res.file, "r") + assert f.read() == "Hello world!" + assert os.listdir(res.directory) == ["schema.parquet"] + + +# %% [markdown] +# The `upload_result` task outputs a data class, and the `download_result` task accepts data classes as inputs. + +# %% [markdown] +# Lastly, we create a workflow. +# %% +@workflow +def wf(x: int, y: int) -> (Datum, Result): + """ + Dataclasses (JSON) can be returned from a workflow as well. + """ + res = upload_result() + download_result(res=res) + return add(x=stringify(x=x), y=stringify(x=y)), res + + +# %% [markdown] +# We can run the workflow locally. +# +# %% +if __name__ == "__main__": + """ + This workflow can be run locally. During local execution also, the dataclasses will be marshalled to and from json. + """ + wf(x=10, y=20) diff --git a/docs/examples/data_types_and_io/data_types_and_io/enums.py b/docs/examples/data_types_and_io/data_types_and_io/enums.py new file mode 100644 index 000000000..5f74f6b40 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/enums.py @@ -0,0 +1,66 @@ +# %% [markdown] +# # Using Enum types +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Sometimes you may want to restrict the set of inputs / outputs to a finite set of acceptable values. This is commonly +# achieved using Enum types in programming languages. +# +# Since version 0.15.0, Flyte supports Enum Types with string values. You can create a python Enum type and pass it to +# a task or return from a task. Flyte will automatically convert this to and limit the inputs etc to a finite set of +# values. +# +# UX: flytectl will allow only the finite set of values to be acceptable and (in progress) UI will provide a drop-down for +# the values. +# +# **Caveat:** Only string values are supported as valid enum values. The first value in the list is assumed as the default +# and the Enum types are not optional. So when defining enums, design them well to always make the first value as a +# valid default. +# %% +from enum import Enum +from typing import Tuple + +from flytekit import task, workflow + + +# %% [markdown] +# Enums are natively supported in flyte's type system. Enum values can only be of type string. At runtime they are +# represented using their string values. +# +# :::{note} +# ENUM Values can only be string. Other languages will receive enum as a string. +# ::: +# %% +class Color(Enum): + RED = "red" + GREEN = "green" + BLUE = "blue" + + +# %% [markdown] +# Enums can be used as a regular type +# %% +@task +def enum_stringify(c: Color) -> str: + return c.value + + +# %% [markdown] +# Their values can be accepted as string +# +# %% +@task +def string_to_enum(c: str) -> Color: + return Color(c) + + +@workflow +def enum_wf(c: Color = Color.RED) -> Tuple[Color, str]: + v = enum_stringify(c=c) + return string_to_enum(c=v), v + + +if __name__ == "__main__": + print(enum_wf()) diff --git a/docs/examples/data_types_and_io/data_types_and_io/flyte_pickle.py b/docs/examples/data_types_and_io/data_types_and_io/flyte_pickle.py new file mode 100644 index 000000000..c8d2d1dc8 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/flyte_pickle.py @@ -0,0 +1,94 @@ +# %% [markdown] +# (flyte_pickle)= +# +# # Using Flyte Pickle +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Flyte enforces type safety by leveraging type information to be able to compile +# tasks/workflows, which enables all sorts of nice features (like static analysis of tasks/workflows, conditional branching, etc.) +# +# However, we do also want to provide enough flexibility to end-users so that they don't have to put in a lot of up front +# investment figuring out all the types of their data structures before experiencing the value that flyte has to offer. +# +# Flyte supports FlytePickle transformer which will convert any unrecognized type in type hint to +# FlytePickle, and serialize / deserialize the python value to / from a pickle file. +# +# ## Caveats +# +# Pickle can only be used to send objects between the exact same version of Python, +# and we strongly recommend to use python type that flyte support or register a custom transformer +# +# This example shows how users can custom object without register a transformer. +# %% +from flytekit import task, workflow + + +# %% [markdown] +# `People` is a user defined complex type, which can be used to pass complex data between tasks. +# We will serialize this class to a pickle file and pass it between different tasks. +# +# :::{Note} +# Here we can also {ref}`turn this object to dataclass ` to have better performance. +# We use simple object here for demo purpose. +# You may have some object that can't turn into a dataclass, e.g. NumPy, Tensor. +# ::: +# %% +class People: + def __init__(self, name): + self.name = name + + +# %% [markdown] +# Object can be returned as outputs or accepted as inputs +# %% +@task +def greet(name: str) -> People: + return People(name) + + +@workflow +def welcome(name: str) -> People: + return greet(name=name) + + +if __name__ == "__main__": + """ + This workflow can be run locally. During local execution also, + the custom object (People) will be marshalled to and from python pickle. + """ + welcome(name="Foo") + + +from typing import List + +# %% [markdown] +# By default, if the list subtype is unrecognized, a single pickle file is generated. +# To also improve serialization and deserialization performance for cases with millions of items or large list items, +# users can specify a batch size, processing each batch as a separate pickle file. +# Example below shows how users can set batch size. +# +# %% +from flytekit.types.pickle.pickle import BatchSize +from typing_extensions import Annotated + + +@task +def greet_all(names: List[str]) -> Annotated[List[People], BatchSize(2)]: + return [People(name) for name in names] + + +@workflow +def welcome_all(names: List[str]) -> Annotated[List[People], BatchSize(2)]: + return greet_all(names=names) + + +if __name__ == "__main__": + """ + In this example, two pickle files will be generated: + - One containing two People objects + - One containing one People object + """ + welcome_all(names=["f", "o", "o"]) diff --git a/docs/examples/data_types_and_io/data_types_and_io/flyte_python_types.py b/docs/examples/data_types_and_io/data_types_and_io/flyte_python_types.py new file mode 100644 index 000000000..f3a6efe05 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/flyte_python_types.py @@ -0,0 +1,101 @@ +# %% [markdown] +# (flytekit_to_flyte_type_mapping)= +# +# # Flyte and Python Types +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# FlyteKit automatically maps Python types to Flyte types. This section provides details of the mappings, but for the most +# part you can skip this section, as almost all of Python types are mapped automatically. +# +# The following table provides a quick overview of how types are converted from the type-hints (python native) to Flyte-understood, cross-language types. +# +# ```{eval-rst} +# .. list-table:: Supported Python types and mapping to underlying Flyte Type +# :widths: auto +# :header-rows: 1 +# +# * - Python Type +# - Flyte Type +# - Conversion +# - Comment +# * - int +# - Integer +# - Automatic +# - just use python 3 type hints +# * - float +# - Float +# - Automatic +# - just use python 3 type hints +# * - str +# - String +# - Automatic +# - just use python 3 type hints +# * - bool +# - Boolean +# - Automatic +# - just use python 3 type hints +# * - bytes/bytearray +# - binary +# - Not Supported +# - Let us know if this is an interesting usecase that you can currently support using your own transformers. +# * - complex +# - NA +# - Not Supported +# - Let us know if this is an interesting usecase that you can currently support using your own transformers. +# * - datetime.timedelta +# - Duration +# - Automatic +# - just use python 3 type hints +# * - datetime.datetime +# - Datetime +# - Automatic +# - just use python 3 type hints +# * - datetime.date +# - Datetime +# - Automatic +# - just use python 3 type hints +# * - Univariate List / typing.List +# - Collection [ type ] +# - Automatic +# - Use python 3 type hints e.g ``typing.List[T], where T can be one of the other supported types in the table`` +# * - file / file-like / os.PathLike / flytekit.types.file.FlyteFile +# - Blob - Single +# - Automatic +# - Use python 3 type hints. if using ``file / os.PathLike`` objects then, Flyte will default to binary protocol for the file. If using FlyteFile["protocol"], it is assumed that the file is in the specified protocol. e.g. "jpg", "png", "hdf5" etc +# * - Directory / flytekit.types.directory.FlyteDirectory +# - Blob - MultiPart +# - Automatic +# - Use python 3 type hints. If using FlyteDirectory["protocol"] it is assumed that all the files are of the specified protocol +# * - Typed dictionary with str key - typing.Dict[str, V] +# - Map[str, V] +# - Automatic +# - Use python 3 type hints e.g ``typing.Dict[str, V], where V can be one of the other supported types in the table even another Dictionary (nested)`` +# * - Untyped dictionary - dict +# - JSON (struct.pb) +# - Automatic +# - Use python 3 type hints e.g ``dict``, it will be assumed that we can convert the dict to json. This may not always be possible and will cause a RuntimeError +# * - Dataclasses ``@dataclass`` +# - Struct +# - Automatic +# - Use python 3 type hints. The class should be a pure value class and should be annotated with ``@dataclass and @dataclass_json``. +# * - pandas.DataFrame +# - Schema +# - Automatic +# - Use python 3 type hints. Pandas column types are not preserved +# * - pyspark.DataFrame +# - Schema +# - Automatic +# - Use python 3 type hints. Column types are not preserved. Install ``flytekitplugins-spark`` plugin using pip +# * - torch.Tensor & torch.nn.Module +# - Blob - Single +# - Automatic +# - Use PyTorch type hints. +# * - User defined types +# - Any +# - Custom Transformers +# - Use python 3 type hints. We use ``FlytePickle transformer`` by default, but users still can provide custom transformers. Refer to {ref}`advanced_custom_types`. +# ``` +# diff --git a/docs/examples/data_types_and_io/data_types_and_io/pytorch_types.py b/docs/examples/data_types_and_io/data_types_and_io/pytorch_types.py new file mode 100644 index 000000000..a3cd61180 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/pytorch_types.py @@ -0,0 +1,193 @@ +# %% [markdown] +# (pytorch_types)= +# +# # PyTorch Types +# +# ```{eval-rst} +# .. tags:: MachineLearning, Basic +# ``` +# +# Flyte promotes the use of strongly-typed data to make it easier to write pipelines that are more robust and easier to test. +# Flyte is primarily used for machine learning besides data engineering. To simplify the communication between Flyte tasks, especially when passing +# around tensors and models, we added support for the PyTorch types. + +# %% [markdown] +# ## Tensors & Modules +# +# Many a times, you may need to pass around tensors and modules (aka models). In the absence of native type support for PyTorch tensors and modules, +# Flytekit resorts to using pickle to serialize and deserialize the entities; in fact, pickle is used for any unknown type. +# This is not very efficient, and hence, we added PyTorch's serialization and deserialization support to the Flyte type system. +# %% +import torch +from flytekit import task, workflow + + +@task +def generate_tensor_2d() -> torch.Tensor: + return torch.tensor([[1.0, -1.0, 2], [1.0, -1.0, 9], [0, 7.0, 3]]) + + +@task +def reshape_tensor(tensor: torch.Tensor) -> torch.Tensor: + # convert 2D to 3D + tensor.unsqueeze_(-1) + return tensor.expand(3, 3, 2) + + +@task +def generate_module() -> torch.nn.Module: + bn = torch.nn.BatchNorm1d(3, track_running_stats=True) + return bn + + +@task +def get_model_weight(model: torch.nn.Module) -> torch.Tensor: + return model.weight + + +class MyModel(torch.nn.Module): + def __init__(self): + super(MyModel, self).__init__() + self.l0 = torch.nn.Linear(4, 2) + self.l1 = torch.nn.Linear(2, 1) + + def forward(self, input): + out0 = self.l0(input) + out0_relu = torch.nn.functional.relu(out0) + return self.l1(out0_relu) + + +@task +def get_l1() -> torch.nn.Module: + model = MyModel() + return model.l1 + + +@workflow +def pytorch_native_wf(): + reshape_tensor(tensor=generate_tensor_2d()) + get_model_weight(model=generate_module()) + get_l1() + + +# %% [markdown] +# Passing around tensors and modules is no more a hassle! + +from dataclasses import dataclass + +# %% [markdown] +# ## Checkpoint +# +# `PyTorchCheckpoint` is a special type of checkpoint to serialize and deserialize PyTorch models. +# It checkpoints `torch.nn.Module`'s state, hyperparameters, and optimizer state. +# The module checkpoint differs from the standard checkpoint in that it checkpoints the module's `state_dict`. +# Hence, when restoring the module, the module's `state_dict` needs to be used in conjunction with the actual module. +# As per PyTorch [docs](https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-load-entire-model), it is recommended to +# store the module's `state_dict` rather than the module itself. However, the serialization should work either way. +# %% +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from dataclasses_json import dataclass_json +from flytekit.extras.pytorch import PyTorchCheckpoint + + +@dataclass_json +@dataclass +class Hyperparameters: + epochs: int + loss: float + + +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +@task +def generate_model(hyperparameters: Hyperparameters) -> PyTorchCheckpoint: + bn = Net() + optimizer = optim.SGD(bn.parameters(), lr=0.001, momentum=0.9) + return PyTorchCheckpoint(module=bn, hyperparameters=hyperparameters, optimizer=optimizer) + + +@task +def load(checkpoint: PyTorchCheckpoint): + new_bn = Net() + new_bn.load_state_dict(checkpoint["module_state_dict"]) + optimizer = optim.SGD(new_bn.parameters(), lr=0.001, momentum=0.9) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + + +@workflow +def pytorch_checkpoint_wf(): + checkpoint = generate_model(hyperparameters=Hyperparameters(epochs=10, loss=0.1)) + load(checkpoint=checkpoint) + + +# %% [markdown] +# :::{note} +# `PyTorchCheckpoint` supports serializing hyperparameters of types `dict`, `NamedTuple`, and `dataclass`. +# ::: +# +# ## Auto GPU to CPU & CPU to GPU Conversion +# +# Not all PyTorch computations require a GPU to run. There are some cases where it is beneficial to move the computation to the CPU after, say, +# the model is trained on a GPU. To avail the GPU power, we do `to(torch.device("cuda"))`. +# To use the GPU-variables on a CPU, we need to move the variables to a CPU using `to(torch.device("cpu"))` construct. +# This manual conversion proposed by PyTorch is not very user friendly, and hence, +# we added support for automatic GPU to CPU conversion (and vice versa) for the PyTorch types. +# +# ```python +# from flytekit import Resources +# from typing import Tuple +# +# +# @task(requests=Resources(gpu="1")) +# def train() -> Tuple[PyTorchCheckpoint, torch.Tensor, torch.Tensor, torch.Tensor]: +# ... +# device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +# model = Model(X_train.shape[1]) +# model.to(device) +# ... +# X_train, X_test = X_train.to(device), X_test.to(device) +# y_train, y_test = y_train.to(device), y_test.to(device) +# ... +# return PyTorchCheckpoint(module=model), X_train, X_test, y_test +# +# @task +# def predict( +# checkpoint: PyTorchCheckpoint, +# X_train: torch.Tensor, +# X_test: torch.Tensor, +# y_test: torch.Tensor, +# ): +# new_bn = Model(X_train.shape[1]) +# new_bn.load_state_dict(checkpoint["module_state_dict"]) +# +# accuracy_list = np.zeros((5,)) +# +# with torch.no_grad(): +# y_pred = new_bn(X_test) +# correct = (torch.argmax(y_pred, dim=1) == y_test).type(torch.FloatTensor) +# accuracy_list = correct.mean() +# ``` +# +# The `predict` task here runs on a CPU. +# As can be seen, you need not do the device conversion from GPU to CPU in the `predict` task as that's handled automatically by Flytekit! +# diff --git a/docs/examples/data_types_and_io/data_types_and_io/schema.py b/docs/examples/data_types_and_io/data_types_and_io/schema.py new file mode 100644 index 000000000..f70f62cca --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/schema.py @@ -0,0 +1,70 @@ +# %% [markdown] +# # Using Schemas +# +# ```{eval-rst} +# .. tags:: DataFrame, Basic +# ``` +# +# This example explains how an untyped schema is passed between tasks using {py:class}`pandas.DataFrame`. +# Flytekit makes it possible for users to directly return or accept a {py:class}`pandas.DataFrame`, which are automatically +# converted into flyte's abstract representation of a schema object + +# %% [markdown] +# :::{warning} +# `FlyteSchema` is deprecated, use {ref}`structured_dataset_example` instead. +# ::: + +# %% +import pandas +from flytekit import task, workflow + +# %% [markdown] +# Flytekit allows users to directly use pandas.dataframe in their tasks as long as they import +# Note: # noqa: F401. This is to ignore pylint complaining about unused imports +# %% +from flytekit.types import schema # noqa: F401 + + +# %% [markdown] +# This task generates a pandas.DataFrame and returns it. The Dataframe itself will be serialized to an intermediate +# format like parquet before passing between tasks +# %% +@task +def get_df(a: int) -> pandas.DataFrame: + """ + Generate a sample dataframe + """ + return pandas.DataFrame(data={"col1": [a, 2], "col2": [a, 4]}) + + +# %% [markdown] +# This task shows an example of transforming a dataFrame +# %% +@task +def add_df(df: pandas.DataFrame) -> pandas.DataFrame: + """ + Append some data to the dataframe. + NOTE: this may result in runtime failures if the columns do not match + """ + return df.append(pandas.DataFrame(data={"col1": [5, 10], "col2": [5, 10]})) + + +# %% [markdown] +# The workflow shows that passing DataFrame's between tasks is as simple as passing dataFrames in memory +# %% +@workflow +def df_wf(a: int) -> pandas.DataFrame: + """ + Pass data between the dataframes + """ + df = get_df(a=a) + return add_df(df=df) + + +# %% [markdown] +# The entire program can be run locally +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running df_wf(a=42) {df_wf(a=42)}") diff --git a/docs/examples/data_types_and_io/data_types_and_io/structured_dataset.py b/docs/examples/data_types_and_io/data_types_and_io/structured_dataset.py new file mode 100644 index 000000000..85db71718 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/structured_dataset.py @@ -0,0 +1,265 @@ +# %% [markdown] +# (structured_dataset_example)= +# +# # Structured Dataset +# +# ```{eval-rst} +# .. tags:: DataFrame, Basic, Data +# ``` +# +# Structured dataset is a superset of Flyte Schema. +# +# The `StructuredDataset` Transformer can write a dataframe to BigQuery, s3, or any storage by registering new structured dataset encoder and decoder. +# +# Flytekit makes it possible to return or accept a {py:class}`pandas.DataFrame` which is automatically +# converted into Flyte's abstract representation of a structured dataset object. +# +# This example explains how a structured dataset can be used with the Flyte entities. + +# %% [markdown] +# Let's import the necessary dependencies. +# %% +import os +import typing + +import numpy as np +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +from flytekit import FlyteContext, StructuredDatasetType, kwtypes, task, workflow +from flytekit.models import literals +from flytekit.models.literals import StructuredDatasetMetadata +from flytekit.types.schema import FlyteSchema +from flytekit.types.structured.structured_dataset import ( + PARQUET, + StructuredDataset, + StructuredDatasetDecoder, + StructuredDatasetEncoder, + StructuredDatasetTransformerEngine, +) +from typing_extensions import Annotated + +# %% [markdown] +# We define the columns types for schema and `StructuredDataset`. +# %% +superset_cols = kwtypes(Name=str, Age=int, Height=int) +subset_cols = kwtypes(Age=int) + + +# %% [markdown] +# We define two tasks, one returns a pandas DataFrame and the other a `FlyteSchema`. +# Flyte serializes the DataFrames to an intermediate format, a parquet file, before sending them to the other tasks. +# %% +@task +def get_df(a: int) -> Annotated[pd.DataFrame, superset_cols]: + """ + Generate a sample dataframe + """ + return pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [a, 22], "Height": [160, 178]}) + + +@task +def get_schema_df(a: int) -> FlyteSchema[superset_cols]: + """ + Generate a sample dataframe + """ + s = FlyteSchema() + s.open().write(pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [a, 22], "Height": [160, 178]})) + return s + + +# %% [markdown] +# Next, we define a task that opens a structured dataset by calling `all()`. +# When we invoke `all()`, the Flyte engine downloads the parquet file on S3, and deserializes it to `pandas.dataframe`. +# +# :::{note} +# - Despite the input type of the task being `StructuredDataset`, it can also accept FlyteSchema as input. +# - The code may result in runtime failures if the columns do not match. +# ::: +# %% +@task +def get_subset_df(df: Annotated[StructuredDataset, subset_cols]) -> Annotated[StructuredDataset, subset_cols]: + df = df.open(pd.DataFrame).all() + df = pd.concat([df, pd.DataFrame([[30]], columns=["Age"])]) + # On specifying BigQuery uri for StructuredDataset, flytekit writes a pandas dataframe to a BigQuery table + return StructuredDataset(dataframe=df) + + +# %% [markdown] +# ## StructuredDataset with `uri` Argument +# +# BigQuery `uri` allows you to load and retrieve data from cloud using the `uri`. The `uri` comprises of the bucket name and the filename prefixed with `gs://`. +# If you specify BigQuery `uri` for StructuredDataset, BigQuery creates a table in the location specified by the `uri`. +# The `uri` in StructuredDataset reads from or writes to S3, GCP, BigQuery, or any storage. +# Let's understand how to convert a pandas DataFrame to a BigQuery table and vice-versa through an example. +# +# Before writing DataFrame to a BigQuery table, +# +# 1. Create a [GCP account](https://cloud.google.com/docs/authentication/getting-started) and create a service account. +# 2. Create a project and add the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to your .bashrc file. +# 3. Create a dataset in your project. + +# %% [markdown] +# Import the dependencies. +# %% +import pandas as pd # noqa: E402 +from flytekit import task # noqa: E402 +from flytekit.types.structured import StructuredDataset # noqa: E402 + + +# %% [markdown] +# Define a task that converts a pandas DataFrame to a BigQuery table. +# %% +@task +def pandas_to_bq() -> StructuredDataset: + # create a pandas dataframe + df = pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]}) + # convert the dataframe to StructuredDataset + return StructuredDataset(dataframe=df, uri="bq://sample-project-1-352610.sample_352610.test1") + + +# %% [markdown] +# :::{note} +# The BigQuery uri's format is `bq://..`. +# ::: + +# %% [markdown] +# Define a task that converts the BigQuery table to a pandas DataFrame. +# %% +@task +def bq_to_pandas(sd: StructuredDataset) -> pd.DataFrame: + # convert to pandas dataframe + return sd.open(pd.DataFrame).all() + + +# %% [markdown] +# :::{note} +# Flyte creates the table inside the dataset in the project upon BigQuery query execution. +# ::: + +# %% [markdown] +# Trigger the tasks locally. +# %% +if __name__ == "__main__": + o1 = bq_to_pandas(sd=StructuredDataset(uri="bq://sample-project-1-352610.sample_352610.test1")) + o2 = pandas_to_bq() + + +# %% [markdown] +# ## NumPy Encoder and Decoder +# +# `StructuredDataset` ships with an encoder and a decoder that handles the conversion of a Python value to a Flyte literal and vice-versa, respectively. +# Let's understand how to write them by defining a NumPy encoder and decoder, which helps use NumPy array as a valid type within structured datasets. + +# %% [markdown] +# ### NumPy Encoder +# +# We extend `StructuredDatasetEncoder` and implement the `encode` function. +# The `encode` function converts NumPy array to an intermediate format (parquet file format in this case). +# %% +class NumpyEncodingHandlers(StructuredDatasetEncoder): + def encode( + self, + ctx: FlyteContext, + structured_dataset: StructuredDataset, + structured_dataset_type: StructuredDatasetType, + ) -> literals.StructuredDataset: + df = typing.cast(np.ndarray, structured_dataset.dataframe) + name = ["col" + str(i) for i in range(len(df))] + table = pa.Table.from_arrays(df, name) + path = ctx.file_access.get_random_remote_directory() + local_dir = ctx.file_access.get_random_local_directory() + local_path = os.path.join(local_dir, f"{0:05}") + pq.write_table(table, local_path) + ctx.file_access.upload_directory(local_dir, path) + return literals.StructuredDataset( + uri=path, + metadata=StructuredDatasetMetadata(structured_dataset_type=StructuredDatasetType(format=PARQUET)), + ) + + +# %% [markdown] +# ### NumPy Decoder +# +# Next we extend `StructuredDatasetDecoder` and implement the `decode` function. +# The `decode` function converts the parquet file to a `numpy.ndarray`. +# %% +class NumpyDecodingHandlers(StructuredDatasetDecoder): + def decode( + self, + ctx: FlyteContext, + flyte_value: literals.StructuredDataset, + current_task_metadata: StructuredDatasetMetadata, + ) -> np.ndarray: + local_dir = ctx.file_access.get_random_local_directory() + ctx.file_access.get_data(flyte_value.uri, local_dir, is_multipart=True) + table = pq.read_table(local_dir) + return table.to_pandas().to_numpy() + + +# %% [markdown] +# ### NumPy Renderer +# +# Create a default renderer for numpy array, then flytekit will use this renderer to +# display schema of numpy array on flyte deck. +# %% +class NumpyRenderer: + """ + The schema of Numpy array are rendered as an HTML table. + """ + + def to_html(self, df: np.ndarray) -> str: + assert isinstance(df, np.ndarray) + name = ["col" + str(i) for i in range(len(df))] + table = pa.Table.from_arrays(df, name) + return pd.DataFrame(table.schema).to_html(index=False) + + +# %% [markdown] +# Finally, we register the encoder, decoder, and renderer with the `StructuredDatasetTransformerEngine`. +# %% +StructuredDatasetTransformerEngine.register(NumpyEncodingHandlers(np.ndarray, None, PARQUET)) +StructuredDatasetTransformerEngine.register(NumpyDecodingHandlers(np.ndarray, None, PARQUET)) +StructuredDatasetTransformerEngine.register_renderer(np.ndarray, NumpyRenderer()) + + +# %% [markdown] +# You can now use `numpy.ndarray` to deserialize the parquet file to NumPy and serialize a task's output (NumPy array) to a parquet file. + +# %% [markdown] +# Let's define a task to test the above functionality. +# We open a structured dataset of type `numpy.ndarray` and serialize it again. + +# %% +@task +def to_numpy(ds: Annotated[StructuredDataset, subset_cols]) -> Annotated[StructuredDataset, subset_cols, PARQUET]: + numpy_array = ds.open(np.ndarray).all() + return StructuredDataset(dataframe=numpy_array) + + +# %% [markdown] +# Finally, we define two workflows that showcase how a `pandas.DataFrame` and `FlyteSchema` are accepted by the `StructuredDataset`. +# %% +@workflow +def pandas_compatibility_wf(a: int) -> Annotated[StructuredDataset, subset_cols]: + df = get_df(a=a) + ds = get_subset_df(df=df) # noqa: shown for demonstration; users should use the same types between tasks + return to_numpy(ds=ds) + + +@workflow +def schema_compatibility_wf(a: int) -> Annotated[StructuredDataset, subset_cols]: + df = get_schema_df(a=a) + ds = get_subset_df(df=df) # noqa: shown for demonstration; users should use the same types between tasks + return to_numpy(ds=ds) + + +# %% [markdown] +# You can run the code locally as follows: +# +# %% +if __name__ == "__main__": + numpy_array_one = pandas_compatibility_wf(a=42).open(np.ndarray).all() + print(f"pandas DataFrame compatibility check output: {numpy_array_one}") + numpy_array_two = schema_compatibility_wf(a=42).open(np.ndarray).all() + print(f"Schema compatibility check output: {numpy_array_two}") diff --git a/docs/examples/data_types_and_io/data_types_and_io/typed_schema.py b/docs/examples/data_types_and_io/data_types_and_io/typed_schema.py new file mode 100644 index 000000000..7a09a6e99 --- /dev/null +++ b/docs/examples/data_types_and_io/data_types_and_io/typed_schema.py @@ -0,0 +1,69 @@ +# %% [markdown] +# (typed_schema)= +# +# # Typed Columns in a Schema +# +# ```{eval-rst} +# .. tags:: DataFrame, Basic, Data +# ``` +# +# This example explains how a typed schema can be used in Flyte and declared in flytekit. +# %% +import pandas +from flytekit import kwtypes, task, workflow + +# %% [markdown] +# Flytekit consists of some pre-built type extensions, one of them is the FlyteSchema type +# %% +from flytekit.types.schema import FlyteSchema + +# %% [markdown] +# FlyteSchema is an abstract Schema type that can be used to represent any structured dataset which has typed +# (or untyped) columns +# %% +out_schema = FlyteSchema[kwtypes(x=int, y=str)] + + +# %% [markdown] +# To write to a schema object refer to `FlyteSchema.open` method. Writing can be done +# using any of the supported dataframe formats. +# +# ```{eval-rst} +# .. todo:: +# +# Reference the supported dataframe formats here +# ``` +# %% +@task +def t1() -> out_schema: + w = out_schema() + df = pandas.DataFrame(data={"x": [1, 2], "y": ["3", "4"]}) + w.open().write(df) + return w + + +# %% [markdown] +# To read a Schema, one has to invoke the `FlyteSchema.open`. The default mode +# is automatically configured to be `open` and the default returned dataframe type is {py:class}`pandas.DataFrame` +# Different types of dataframes can be returned based on the type passed into the open method +# %% +@task +def t2(schema: FlyteSchema[kwtypes(x=int, y=str)]) -> FlyteSchema[kwtypes(x=int)]: + assert isinstance(schema, FlyteSchema) + df: pandas.DataFrame = schema.open().all() + return df[schema.column_names()[:-1]] + + +@workflow +def wf() -> FlyteSchema[kwtypes(x=int)]: + return t2(schema=t1()) + + +# %% [markdown] +# Local execution will convert the data to and from the serialized representation thus, mimicking a complete distributed +# execution. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running wf(), returns columns {wf().columns()}") diff --git a/docs/examples/data_types_and_io/requirements.in b/docs/examples/data_types_and_io/requirements.in new file mode 100644 index 000000000..cc489e0e3 --- /dev/null +++ b/docs/examples/data_types_and_io/requirements.in @@ -0,0 +1,6 @@ +flytekit>=1.7.0 +wheel +matplotlib +flytekitplugins-deck-standard +scikit-learn +torch diff --git a/docs/examples/data_types_and_io/requirements.txt b/docs/examples/data_types_and_io/requirements.txt new file mode 100644 index 000000000..5496972cb --- /dev/null +++ b/docs/examples/data_types_and_io/requirements.txt @@ -0,0 +1,607 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile --resolver=backtracking requirements.in +# +adlfs==2023.4.0 + # via flytekit +aiobotocore==2.5.0 + # via s3fs +aiohttp==3.8.4 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +async-timeout==4.0.2 + # via aiohttp +attrs==23.1.0 + # via + # aiohttp + # visions +azure-core==1.27.1 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.13.0 + # via adlfs +azure-storage-blob==12.16.0 + # via adlfs +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +botocore==1.29.76 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.5.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via + # aiohttp + # requests +click==8.1.3 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cmake==3.27.6 + # via triton +comm==0.1.3 + # via ipykernel +contourpy==1.1.0 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.1 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl + # secretstorage +cycler==0.11.0 + # via matplotlib +dacite==1.8.1 + # via ydata-profiling +dataclasses-json==0.5.8 + # via flytekit +debugpy==1.6.7 + # via ipykernel +decorator==5.1.1 + # via + # gcsfs + # ipython +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +executing==1.2.0 + # via stack-data +filelock==3.12.2 + # via + # torch + # triton +flyteidl==1.5.11 + # via flytekit +flytekit==1.7.0 + # via + # -r requirements.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.7.0 + # via -r requirements.in +fonttools==4.40.0 + # via matplotlib +frozenlist==1.3.3 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.31 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.20.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.2 + # via google-cloud-storage +google-cloud-storage==2.9.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.59.1 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.56.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.56.0 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via + # requests + # yarl +imagehash==4.3.1 + # via + # visions + # ydata-profiling +importlib-metadata==6.7.0 + # via + # flytekit + # keyring +ipykernel==6.23.3 + # via ipywidgets +ipython==8.12.2 + # via + # ipykernel + # ipywidgets +ipywidgets==8.0.6 + # via flytekitplugins-deck-standard +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # torch + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.2.0 + # via + # flytekit + # phik + # scikit-learn +jupyter-client==8.3.0 + # via ipykernel +jupyter-core==5.3.1 + # via + # ipykernel + # jupyter-client +jupyterlab-widgets==3.0.7 + # via ipywidgets +keyring==24.1.0 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==26.1.0 + # via flytekit +lit==17.0.2 + # via triton +markdown==3.4.3 + # via flytekitplugins-deck-standard +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.7.1 + # via + # -r requirements.in + # phik + # seaborn + # wordcloud + # ydata-profiling +matplotlib-inline==0.1.6 + # via + # ipykernel + # ipython +mdurl==0.1.2 + # via markdown-it-py +more-itertools==9.1.0 + # via jaraco-classes +mpmath==1.3.0 + # via sympy +msal==1.22.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +nest-asyncio==1.5.6 + # via ipykernel +networkx==3.1 + # via + # torch + # visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions + # wordcloud + # ydata-profiling +nvidia-cublas-cu11==11.10.3.66 + # via + # nvidia-cudnn-cu11 + # nvidia-cusolver-cu11 + # torch +nvidia-cuda-cupti-cu11==11.7.101 + # via torch +nvidia-cuda-nvrtc-cu11==11.7.99 + # via torch +nvidia-cuda-runtime-cu11==11.7.99 + # via torch +nvidia-cudnn-cu11==8.5.0.96 + # via torch +nvidia-cufft-cu11==10.9.0.58 + # via torch +nvidia-curand-cu11==10.2.10.91 + # via torch +nvidia-cusolver-cu11==11.4.0.1 + # via torch +nvidia-cusparse-cu11==11.7.4.91 + # via torch +nvidia-nccl-cu11==2.14.3 + # via torch +nvidia-nvtx-cu11==11.7.91 + # via torch +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.1 + # via + # docker + # ipykernel + # marshmallow + # matplotlib + # plotly + # statsmodels +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==10.0.1 + # via + # imagehash + # matplotlib + # visions + # wordcloud +platformdirs==3.8.0 + # via jupyter-core +plotly==5.15.0 + # via flytekitplugins-deck-standard +portalocker==2.7.0 + # via msal-extensions +prompt-toolkit==3.0.38 + # via ipython +protobuf==4.23.3 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.5 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.9 + # via ydata-profiling +pygments==2.15.1 + # via + # ipython + # rich +pyjwt[crypto]==2.7.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.1.0 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # jupyter-client + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # responses + # ydata-profiling +pyzmq==25.1.0 + # via + # ipykernel + # jupyter-client +regex==2023.6.3 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # responses + # ydata-profiling +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +responses==0.23.1 + # via flytekit +rich==13.4.2 + # via + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +scikit-learn==1.2.2 + # via -r requirements.in +scipy==1.10.1 + # via + # imagehash + # phik + # scikit-learn + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # asttokens + # azure-core + # azure-identity + # google-auth + # isodate + # kubernetes + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.14.0 + # via ydata-profiling +sympy==1.12 + # via torch +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.2 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +torch==2.0.1 + # via + # -r requirements.in + # triton +tornado==6.3.2 + # via + # ipykernel + # jupyter-client +tqdm==4.65.0 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # matplotlib-inline +triton==2.0.0 + # via torch +typeguard==2.13.3 + # via ydata-profiling +types-pyyaml==6.0.12.10 + # via responses +typing-extensions==4.6.3 + # via + # azure-core + # azure-storage-blob + # flytekit + # pydantic + # torch + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.40.0 + # via + # -r requirements.in + # flytekit + # nvidia-cublas-cu11 + # nvidia-cuda-cupti-cu11 + # nvidia-cuda-runtime-cu11 + # nvidia-curand-cu11 + # nvidia-cusparse-cu11 + # nvidia-nvtx-cu11 +widgetsnbextension==4.0.7 + # via ipywidgets +wordcloud==1.9.2 + # via ydata-profiling +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +ydata-profiling==4.3.1 + # via flytekitplugins-deck-standard +zipp==3.15.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/databricks_plugin/Dockerfile b/docs/examples/databricks_plugin/Dockerfile new file mode 100644 index 000000000..4e752f538 --- /dev/null +++ b/docs/examples/databricks_plugin/Dockerfile @@ -0,0 +1,25 @@ +FROM databricksruntime/standard:12.2-LTS +LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks + +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /databricks/driver +ENV PATH="/databricks/python3/bin:$PATH" +USER 0 + +RUN sudo apt-get update && sudo apt-get install -y make build-essential libssl-dev git + +# Install Python dependencies +COPY ./requirements.txt /databricks/driver/requirements.txt +RUN /databricks/python3/bin/pip install -r /databricks/driver/requirements.txt + +WORKDIR /databricks/driver + +# Copy the actual code +COPY . /databricks/driver/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows and launch plans. +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/databricks_plugin/README.md b/docs/examples/databricks_plugin/README.md new file mode 100644 index 000000000..80c2fc01f --- /dev/null +++ b/docs/examples/databricks_plugin/README.md @@ -0,0 +1,51 @@ +# Databricks + +```{eval-rst} +.. tags:: Spark, Integration, DistributedComputing, Data, Advanced +``` + +Flyte can be seamlessly integrated with the [Databricks](https://www.databricks.com/) service, +enabling you to effortlessly submit Spark jobs to the Databricks platform. + +## Install the plugin + +The Databricks plugin comes bundled with the Spark plugin. +To execute it locally, run the following command: + +``` +pip install flytekitplugins-spark +``` + +If you intend to run the plugin on the Flyte cluster, you must first set it up on the backend. +Please refer to the +{std:ref}`Databricks plugin setup guide ` +for detailed instructions. + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + --image ghcr.io/flyteorg/flytecookbook:databricks_plugin-latest \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/databricks_plugin/databricks_plugin/databricks_job.py \ + my_databricks_job +``` + +:::{note} +Using Spark on Databricks is incredibly simple and offers comprehensive versioning through a +custom-built Spark container. This built container also facilitates the execution of standard Spark tasks. + +To utilize Spark, the image should employ a base image provided by Databricks, +and the workflow code must be copied to `/databricks/driver`. + +```{literalinclude} ../../../examples/databricks_plugin/Dockerfile +:language: docker +:emphasize-lines: 1,7-8,20 +``` + +::: + +```{auto-examples-toc} +databricks_job +``` diff --git a/docs/examples/databricks_plugin/databricks_plugin/__init__.py b/docs/examples/databricks_plugin/databricks_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/databricks_plugin/databricks_plugin/databricks_job.py b/docs/examples/databricks_plugin/databricks_plugin/databricks_job.py new file mode 100644 index 000000000..1ba6047ee --- /dev/null +++ b/docs/examples/databricks_plugin/databricks_plugin/databricks_job.py @@ -0,0 +1,96 @@ +# %% [markdown] +# (spark_on_databricks)= +# +# # Running Spark on Databricks +# +# To begin, import the required dependencies. +# %% +import datetime +import random +from operator import add + +import flytekit +from flytekit import Resources, task, workflow +from flytekitplugins.spark import Databricks + + +# %% [markdown] +# To run a Spark job on the Databricks platform, simply include Databricks configuration in the task config. +# The Databricks config is the same as the Databricks job request. For more details, please refer to the +# [Databricks job request](https://docs.databricks.com/dev-tools/api/2.0/jobs.html#request-structure) documentation. +# %% +@task( + task_config=Databricks( + spark_conf={ + "spark.driver.memory": "1000M", + "spark.executor.memory": "1000M", + "spark.executor.cores": "1", + "spark.executor.instances": "2", + "spark.driver.cores": "1", + }, + databricks_conf={ + "run_name": "flytekit databricks plugin example", + "new_cluster": { + "spark_version": "11.0.x-scala2.12", + "node_type_id": "r3.xlarge", + "aws_attributes": { + "availability": "ON_DEMAND", + "instance_profile_arn": "arn:aws:iam:::instance-profile/databricks-flyte-integration", + }, + "num_workers": 4, + }, + "timeout_seconds": 3600, + "max_retries": 1, + }, + ), + limits=Resources(mem="2000M"), + cache_version="1", +) +def hello_spark(partitions: int) -> float: + print(f"Starting Spark with {partitions} partitions.") + n = 100000 * partitions + sess = flytekit.current_context().spark_session + count = sess.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add) + pi_val = 4.0 * count / n + print("Pi val is :{}".format(pi_val)) + return pi_val + + +# %% [markdown] +# For this particular example, +# we define a function that executes the map-reduce operation within the Spark cluster. +# %% +def f(_): + x = random.random() * 2 - 1 + y = random.random() * 2 - 1 + return 1 if x**2 + y**2 <= 1 else 0 + + +# %% [markdown] +# Additionally, we define a standard Flyte task that won't be executed on the Spark cluster. +# %% +@task(cache_version="1") +def print_every_time(value_to_print: float, date_triggered: datetime.datetime) -> int: + print("My printed value: {} @ {}".format(value_to_print, date_triggered)) + return 1 + + +# %% [markdown] +# Finally, define a workflow that connects your tasks in a sequence. +# Remember, Spark and non-Spark tasks can be chained together as long as their parameter specifications match. +# %% +@workflow +def my_databricks_job(triggered_date: datetime.datetime = datetime.datetime.now()) -> float: + pi = hello_spark(partitions=1) + print_every_time(value_to_print=pi, date_triggered=triggered_date) + return pi + + +# %% [markdown] +# You can execute the workflow locally. +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print( + f"Running my_databricks_job(triggered_date=datetime.datetime.now()) {my_databricks_job(triggered_date=datetime.datetime.now())}" + ) diff --git a/docs/examples/databricks_plugin/requirements.in b/docs/examples/databricks_plugin/requirements.in new file mode 100644 index 000000000..31becd66f --- /dev/null +++ b/docs/examples/databricks_plugin/requirements.in @@ -0,0 +1 @@ +flytekitplugins-spark diff --git a/docs/examples/databricks_plugin/requirements.txt b/docs/examples/databricks_plugin/requirements.txt new file mode 100644 index 000000000..463dacc1d --- /dev/null +++ b/docs/examples/databricks_plugin/requirements.txt @@ -0,0 +1,346 @@ +# +# This file is autogenerated by pip-compile with Python 3.10 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.1 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +flyteidl==1.5.15 + # via flytekit +flytekit==1.9.0 + # via flytekitplugins-spark +flytekitplugins-spark==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.0 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py4j==0.10.9.7 + # via pyspark +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +pyspark==3.4.1 + # via flytekitplugins-spark +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/dbt_plugin/Dockerfile b/docs/examples/dbt_plugin/Dockerfile new file mode 100644 index 000000000..cd793ee56 --- /dev/null +++ b/docs/examples/dbt_plugin/Dockerfile @@ -0,0 +1,38 @@ +FROM python:3.8-slim-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN apt-get update && apt-get install -y build-essential git postgresql-client libpq-dev + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/ +RUN pip install -r /root/requirements.txt +# psycopg2-binary is a dependency of the dbt-postgres adapter, but that doesn't work on mac M1s. +# As per https://github.com/psycopg/psycopg2/issues/1360, we install psycopg to circumvent this. +RUN pip uninstall -y psycopg2-binary && pip install psycopg2 + +# Copy the actual code +COPY . /root/ + +# Copy dbt-specific files +COPY profiles.yml /root/dbt-profiles/ +RUN git clone https://github.com/dbt-labs/jaffle_shop.git + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag + +ENV FLYTE_SDK_LOGGING_LEVEL 10 diff --git a/docs/examples/dbt_plugin/README.md b/docs/examples/dbt_plugin/README.md new file mode 100644 index 000000000..6d6e9b8b5 --- /dev/null +++ b/docs/examples/dbt_plugin/README.md @@ -0,0 +1,126 @@ +(dbt-integration)= + +# DBT + +```{eval-rst} +.. tags:: Integration, Data, SQL, Intermediate +``` + +[dbt](https://www.getdbt.com/) is one of the widely-used data transformation +tools for working with data directly in a data warehouse. It's optimized for +analytics use cases and can be used for business intelligence, operational +analytics, and even machine learning. + +The Flytekit `dbt` plugin is a Python module that provides an easy way to +invoke basic `dbt` CLI commands from within a Flyte task. The plugin supports +the commands [dbt run](https://docs.getdbt.com/reference/commands/run), +[dbt test](https://docs.getdbt.com/reference/commands/test), and +[dbt source freshness](https://docs.getdbt.com/reference/commands/source). + +## Prerequisities + +To use the `dbt` plugin you'll need to install the `flytekitplugins-dbt` +plugin. + +:::{note} +See [the PyPi page here](https://pypi.org/project/flytekitplugins-dbt/). +::: + +```{eval-rst} +.. prompt:: bash $ + + pip install flytekitplugins-dbt +``` + +Then install dbt itself. You will have to install `dbt-core` as well as +the correct adapter for the database that you are accessing. + +For example, if you are using a Postgres database you would do: + +```{eval-rst} +.. prompt:: bash $ + + pip install dbt-postgres +``` + +This will install `dbt-core` and `dbt-postgres`, but not any of the other +adapters, `dbt-redshift`, `dbt-snowflake`, or `dbt-bigquery`. See +[the official installation page](https://docs.getdbt.com/docs/get-started/pip-install) +for details. + +(dbt-integration-run)= + +## Running the Example + +We use a Postgres database installed on the cluster and an example project from +dbt, called [jaffle-shop](https://github.com/dbt-labs/jaffle_shop). +To run the example on your local machine, do the following. + +:::{important} +The example below is not designed to run directly in your local +python environment. It must be run in a Kubernetes cluster, either locally on +your machine using the `flytectl demo start` command or on a cloud cluster. +::: + +Start up the demo cluster on your local machine: + +```{eval-rst} +.. prompt:: bash $ + + flytectl demo start +``` + +Pull the pre-built image for this example: + +```{eval-rst} +.. prompt:: bash $ + + docker pull ghcr.io/flyteorg/flytecookbook:dbt_example-latest +``` + +This image is built using the following `Dockerfile` and contains: + +- The `flytekitplugins-dbt` and `dbt-postgres` Python dependencies. +- The `jaffle-shop` example. +- A postgres database. + +````{dropdown} See Dockerfile +:title: text-muted + +This Dockerfile can be found in the ``flytesnacks/examples`` directory under +the filepath listed in the code block title below. + +```{literalinclude} ../../../examples/dbt_plugin/Dockerfile +:caption: examples/dbt/Dockerfile +:language: docker +``` + +```` + +To run this example, copy the code in the **dbt example** below into a file +called `dbt_example.py`, then run it on your local container using the +provided image: + +```{eval-rst} +.. prompt:: bash $ + + pyflyte run --remote \ + --image ghcr.io/flyteorg/flytecookbook:dbt_example-latest \ + dbt_plugin/dbt_example.py wf +``` + +Alternatively, you can clone the `flytesnacks` repo and run the example directly: + +```{eval-rst} +.. prompt:: bash $ + + git clone https://github.com/flyteorg/flytesnacks + cd flytesnacks/examples/dbt_example + pyflyte run --remote \ + --image ghcr.io/flyteorg/flytecookbook:dbt_example-latest \ + dbt_plugin/dbt_example.py wf +``` + +```{auto-examples-toc} +dbt_example +``` diff --git a/docs/examples/dbt_plugin/dbt_plugin/__init__.py b/docs/examples/dbt_plugin/dbt_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/dbt_plugin/dbt_plugin/dbt_example.py b/docs/examples/dbt_plugin/dbt_plugin/dbt_example.py new file mode 100644 index 000000000..2e4f0c9c7 --- /dev/null +++ b/docs/examples/dbt_plugin/dbt_plugin/dbt_example.py @@ -0,0 +1,116 @@ +# %% [markdown] +# # dbt example +# +# In this example we're going to execute dbt commands supported by Flyte: `dbt run`, +# `dbt test`, and `dbt source freshness`. +# +# First, let's import what we need from `flytekit` and `flytekitplugins-dbt`. +# %% +import subprocess +from typing import Tuple + +from flytekit import task, workflow +from flytekitplugins.dbt.schema import ( + DBTFreshnessInput, + DBTFreshnessOutput, + DBTRunInput, + DBTRunOutput, + DBTTestInput, + DBTTestOutput, +) +from flytekitplugins.dbt.task import DBTFreshness, DBTRun, DBTTest + +# %% [markdown] +# We're going to use the well-known jaffle shop example, which can be found +# [here](https://github.com/dbt-labs/jaffle_shop). +# %% +DBT_PROJECT_DIR = "jaffle_shop" +DBT_PROFILES_DIR = "dbt-profiles" +DBT_PROFILE = "jaffle_shop" + +# %% [markdown] +# This task ensures that the jaffle_shop database is created and it also contains +# some data before scheduling an execution of this workflow. +# %% +@task +def prepare_and_seed_database(): + # Ensure the jaffle_shop database is created + subprocess.run( + [ + "psql", + "-h", + "sandbox-postgresql.flyte.svc.cluster.local", + "-p", + "5432", + "-U", + "postgres", + "-c", + "CREATE DATABASE jaffle_shop;", + ], + env={"PGPASSWORD": "postgres"}, + ) + # Seed the database with some data + subprocess.run( + [ + "dbt", + "seed", + "--project-dir", + DBT_PROJECT_DIR, + "--profiles-dir", + DBT_PROFILES_DIR, + ] + ) + + +# %% [markdown] +# Define the dbt tasks, in this particular case, we're going to execute a DAG containing 3 tasks: +# +# 1. [dbt run](https://docs.getdbt.com/reference/commands/run) +# 2. [dbt test](https://docs.getdbt.com/reference/commands/test) +# 3. [dbt source freshness](https://docs.getdbt.com/reference/commands/source) + +# %% +dbt_run_task = DBTRun(name="example-run-task") +dbt_test_task = DBTTest(name="example-test-task") +dbt_freshness_task = DBTFreshness(name="example-freshness-task") + + +# %% [markdown] +# Define a workflow to run the dbt tasks. +# %% +@workflow +def wf() -> Tuple[DBTRunOutput, DBTTestOutput, DBTFreshnessOutput]: + dbt_run_output = dbt_run_task( + input=DBTRunInput( + project_dir=DBT_PROJECT_DIR, + profiles_dir=DBT_PROFILES_DIR, + profile=DBT_PROFILE, + ) + ) + dbt_test_output = dbt_test_task( + input=DBTTestInput( + project_dir=DBT_PROJECT_DIR, + profiles_dir=DBT_PROFILES_DIR, + profile=DBT_PROFILE, + ) + ) + dbt_freshness_output = dbt_freshness_task( + input=DBTFreshnessInput( + project_dir=DBT_PROJECT_DIR, + profiles_dir=DBT_PROFILES_DIR, + profile=DBT_PROFILE, + ) + ) + + # Ensure the order of the tasks. + prepare_and_seed_database() >> dbt_run_output + dbt_run_output >> dbt_test_output + dbt_test_output >> dbt_freshness_output + + return dbt_run_output, dbt_test_output, dbt_freshness_output + + +# %% [markdown] +# To run this example workflow, follow the instructions in the +# {ref}`dbt integrations page `. +# diff --git a/docs/examples/dbt_plugin/profiles.yml b/docs/examples/dbt_plugin/profiles.yml new file mode 100644 index 000000000..0a1d04c3c --- /dev/null +++ b/docs/examples/dbt_plugin/profiles.yml @@ -0,0 +1,12 @@ +jaffle_shop: # this needs to match the profile in your dbt_project.yml file + target: dev + outputs: + dev: + type: postgres + host: sandbox-postgresql.flyte.svc.cluster.local + user: postgres + password: postgres + port: 5432 + dbname: jaffle_shop + schema: dbt_demo + threads: 4 diff --git a/docs/examples/dbt_plugin/requirements.in b/docs/examples/dbt_plugin/requirements.in new file mode 100644 index 000000000..d0675062a --- /dev/null +++ b/docs/examples/dbt_plugin/requirements.in @@ -0,0 +1,3 @@ +flytekit>=1.8.1 +flytekitplugins-dbt +dbt-postgres diff --git a/docs/examples/dbt_plugin/requirements.txt b/docs/examples/dbt_plugin/requirements.txt new file mode 100644 index 000000000..e15fb92e7 --- /dev/null +++ b/docs/examples/dbt_plugin/requirements.txt @@ -0,0 +1,434 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile --resolver=backtracking requirements.in +# +adlfs==2023.4.0 + # via flytekit +agate==1.6.3 + # via dbt-core +aiobotocore==2.5.2 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.2 + # via aiohttp +attrs==23.1.0 + # via + # aiohttp + # jsonschema +azure-core==1.28.0 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.13.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +babel==2.12.1 + # via agate +binaryornot==0.4.4 + # via cookiecutter +botocore==1.29.161 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.5.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography + # dbt-core +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # dbt-core + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +colorama==0.4.5 + # via dbt-core +cookiecutter==2.2.3 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.2 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +dbt-core==1.2.6 + # via + # dbt-postgres + # flytekitplugins-dbt +dbt-extractor==0.4.1 + # via dbt-core +dbt-postgres==1.2.6 + # via -r requirements.in +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +flyteidl==1.5.13 + # via flytekit +flytekit==1.8.1 + # via + # -r requirements.in + # flytekitplugins-dbt +flytekitplugins-dbt==1.8.1 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +future==0.18.3 + # via parsedatetime +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.59.1 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.56.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.56.0 + # via flytekit +hologram==0.0.15 + # via dbt-core +idna==3.4 + # via + # dbt-core + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +importlib-resources==6.0.0 + # via keyring +isodate==0.6.1 + # via + # agate + # azure-storage-blob + # dbt-core +jaraco-classes==3.3.0 + # via keyring +jinja2==2.11.3 + # via + # cookiecutter + # dbt-core +jmespath==1.0.1 + # via botocore +joblib==1.3.1 + # via flytekit +jsonschema==3.2.0 + # via hologram +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +leather==0.3.4 + # via agate +logbook==1.5.3 + # via dbt-core +markdown-it-py==3.0.0 + # via rich +markupsafe==2.0.1 + # via + # dbt-core + # jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mashumaro==2.9 + # via dbt-core +mdurl==0.1.2 + # via markdown-it-py +minimal-snowplow-tracker==0.0.2 + # via dbt-core +more-itertools==9.1.0 + # via jaraco-classes +msal==1.22.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +msgpack==1.0.5 + # via mashumaro +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +networkx==2.8.8 + # via dbt-core +numpy==1.24.4 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==21.3 + # via + # dbt-core + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +parsedatetime==2.4 + # via agate +portalocker==2.7.0 + # via msal-extensions +protobuf==4.23.4 + # via + # dbt-core + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psycopg2-binary==2.9.6 + # via dbt-postgres +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.15.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.1.0 + # via packaging +pyrsistent==0.19.3 + # via jsonschema +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # hologram + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via + # agate + # cookiecutter +pytimeparse==1.1.8 + # via + # agate + # flytekit +pytz==2023.3 + # via + # babel + # dbt-core + # flytekit + # pandas +pyyaml==5.4.1 + # via + # cookiecutter + # flytekit + # kubernetes + # mashumaro + # responses +regex==2023.6.3 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # dbt-core + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # minimal-snowplow-tracker + # msal + # requests-oauthlib + # responses +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +responses==0.23.1 + # via flytekit +rich==13.4.2 + # via + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # agate + # azure-core + # azure-identity + # google-auth + # isodate + # jsonschema + # kubernetes + # leather + # minimal-snowplow-tracker + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +sqlparse==0.4.3 + # via dbt-core +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +types-pyyaml==6.0.12.10 + # via responses +typing-extensions==4.7.1 + # via + # aioitertools + # azure-core + # azure-storage-blob + # dbt-core + # flytekit + # mashumaro + # rich + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests + # responses +websocket-client==1.6.1 + # via + # docker + # kubernetes +werkzeug==2.1.2 + # via dbt-core +wheel==0.40.0 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via + # importlib-metadata + # importlib-resources + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/development_lifecycle/Dockerfile b/docs/examples/development_lifecycle/Dockerfile new file mode 100644 index 000000000..2e74fe1ee --- /dev/null +++ b/docs/examples/development_lifecycle/Dockerfile @@ -0,0 +1,32 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM python:3.11-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +WORKDIR /root + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg build-essential curl + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/development_lifecycle/README.md b/docs/examples/development_lifecycle/README.md new file mode 100644 index 000000000..e3d8a6bed --- /dev/null +++ b/docs/examples/development_lifecycle/README.md @@ -0,0 +1,18 @@ +# Development Lifecycle + +In this section, you will discover Flyte's features that aid in local workflow development. +You will gain an understanding of concepts like caching, the Flyte remote API, Agents, Decks and more. + +```{auto-examples-toc} +agent_service +private_images +task_cache +task_cache_serialize +decks +register_project +remote_task +remote_workflow +remote_launchplan +inspecting_executions +debugging_workflows_tasks +``` diff --git a/docs/examples/development_lifecycle/development_lifecycle/__init__.py b/docs/examples/development_lifecycle/development_lifecycle/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/development_lifecycle/development_lifecycle/agent_service.py b/docs/examples/development_lifecycle/development_lifecycle/agent_service.py new file mode 100644 index 000000000..8b847152a --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/agent_service.py @@ -0,0 +1,214 @@ +# %% [markdown] +# (extend-agent-service)= +# +# # Writing Agents in Python +# +# ```{eval-rst} +# .. tags:: Extensibility, Contribute, Intermediate +# ``` +# +# :::{note} +# This is an experimental feature, which is subject to change the API in the future. +# ::: +# +# ## What is an Agent? +# +# In Flyte, an Agent is a long-running stateless service that can be used to execute tasks. It reduces the overhead of creating a pod for each task. +# In addition, it's easy to scale up and down the agent service based on the workload. Agent services are designed to be language-agnostic. +# For now, we only support Python agent, but we may support other languages in the future. +# +# Agent is designed to run a specific type of task. For example, you can create a BigQuery agent to run BigQuery task. Therefore, if you create a new type of task, you can +# either run the task in the pod, or you can create a new agent to run it. You can determine how the task will be executed in the FlytePropeller configMap. +# +# Key goals of the agent service include: +# +# - Support for communication with external services: The focus is on enabling agents that seamlessly interact with external services. +# - Independent testing and private deployment: Agents can be tested independently and deployed privately, providing flexibility and control over development. +# - Flyte Agent usage in local development: Users, especially in flytekit and unionml, can leverage backend agents for local development, streamlining the development process. +# - Language-agnostic: Agents can be authored in any programming language, allowing users to work with their preferred language and tools. +# - Scalability: Agents are designed to be scalable, ensuring they can handle large-scale workloads effectively. +# - Simple API: Agents offer a straightforward API, making integration and usage straightforward for developers. +# +# ## Why do we need an Agent Service? +# +# Without Agents, people need to implement a backend plugin in the propeller. The backend plugin is responsible for +# creating a CRD and submitting a http request to the external service. However, it increases the complexity of flytepropeller, and +# it's hard to maintain the backend plugin. For example, if we want to add a new plugin, we need to update and compile +# flytepropeller, and it's also hard to test. In addition, the backend plugin is running in flytepropeller itself, so it +# increases the load of the flytepropeller engine. +# +# Furthermore, implementing backend plugins can be challenging, particularly for data scientists and ML engineers who may lack proficiency in +# Golang. Additionally, managing performance requirements, maintenance, and development can be burdensome. +# To address these issues, we introduced the "Agent Service" in Flyte. This system enables rapid plugin +# development while decoupling them from the core flytepropeller engine. +# +# ## Overview +# +# The Flyte Agent Service is a Python-based agent registry powered by a gRPC server. It allows users and flytepropeller +# to send gRPC requests to the registry for executing jobs such as BigQuery and Databricks. Each Agent service is a Kubernetes +# deployment. You can create two different Agent services hosting different Agents. For example, you can create one production +# agent service and one development agent service. +# +# :::{figure} https://i.ibb.co/vXhBDjP/Screen-Shot-2023-05-29-at-2-54-14-PM.png +# :alt: Agent Service +# :class: with-shadow +# ::: +# +# ## How to register a new agent +# +# ### Flytekit interface specification +# +# To register a new agent, you can extend the `AgentBase` class in the flytekit backend module. Implementing the following three methods is necessary, and it's important to ensure that all calls are idempotent: +# +# - `create`: This method is used to initiate a new task. Users have the flexibility to use gRPC, REST, or an SDK to create a task. +# - `get`: This method allows retrieving the job Resource (jobID or output literal) associated with the task, such as a BigQuery Job ID or Databricks task ID. +# - `delete`: Invoking this method will send a request to delete the corresponding job. +# +# ```python +# from flytekit.extend.backend.base_agent import AgentBase, AgentRegistry +# from dataclasses import dataclass +# import requests +# +# @dataclass +# class Metadata: +# # you can add any metadata you want, propeller will pass the metadata to the agent to get the job status. +# # For example, you can add the job_id to the metadata, and the agent will use the job_id to get the job status. +# # You could also add the s3 file path, and the agent can check if the file exists. +# job_id: str +# +# class CustomAgent(AgentBase): +# def __init__(self, task_type: str): +# # Each agent should have a unique task type. Agent service will use the task type to find the corresponding agent. +# self._task_type = task_type +# +# def create( +# self, +# context: grpc.ServicerContext, +# output_prefix: str, +# task_template: TaskTemplate, +# inputs: typing.Optional[LiteralMap] = None, +# ) -> TaskCreateResponse: +# # 1. Submit the task to the external service (BigQuery, DataBricks, etc.) +# # 2. Create a task metadata such as jobID. +# # 3. Return the task metadata, and keep in mind that the metadata should be serialized to bytes. +# res = requests.post(url, json=data) +# return CreateTaskResponse(resource_meta=json.dumps(asdict(Metadata(job_id=str(res.job_id)))).encode("utf-8")) +# +# def get(self, context: grpc.ServicerContext, resource_meta: bytes) -> TaskGetResponse: +# # 1. Deserialize the metadata. +# # 2. Use the metadata to get the job status. +# # 3. Return the job status. +# metadata = Metadata(**json.loads(resource_meta.decode("utf-8"))) +# res = requests.get(url, json={"job_id": metadata.job_id}) +# return GetTaskResponse(resource=Resource(state=res.state) +# +# def delete(self, context: grpc.ServicerContext, resource_meta: bytes) -> TaskDeleteResponse: +# # 1. Deserialize the metadata. +# # 2. Use the metadata to delete the job. +# # 3. If failed to delete the job, add the error message to the grpc context. +# # context.set_code(grpc.StatusCode.INTERNAL) +# # context.set_details(f"failed to create task with error {e}") +# try: +# metadata = Metadata(**json.loads(resource_meta.decode("utf-8"))) +# requests.delete(url, json={"job_id": metadata.job_id}) +# except Exception as e: +# logger.error(f"failed to delete task with error {e}") +# context.set_code(grpc.StatusCode.INTERNAL) +# context.set_details(f"failed to delete task with error {e}") +# return DeleteTaskResponse() +# +# # To register the custom agent +# AgentRegistry.register(CustomAgent()) +# ``` +# +# Here is an example of [BigQuery Agent](https://github.com/flyteorg/flytekit/blob/9977aac26242ebbede8e00d476c2fbc59ac5487a/plugins/flytekit-bigquery/flytekitplugins/bigquery/agent.py#L35) implementation. +# +# ### How to test the agent +# +# Agent can be tested locally without running backend server. It makes the development of the agent easier. +# +# The task inherited from AsyncAgentExecutorMixin can be executed locally, allowing flytekit to mimic the propeller's behavior to call the agent. +# In some cases, you should store credentials in your local environment when testing locally. +# For example, you need to set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable when testing the BigQuery task. +# After setting up the CREDENTIALS, you can run the task locally. Flytekit will automatically call the agent to create, get, or delete the task. +# +# ```python +# bigquery_doge_coin = BigQueryTask( +# name=f"bigquery.doge_coin", +# inputs=kwtypes(version=int), +# query_template="SELECT * FROM `bigquery-public-data.crypto_dogecoin.transactions` WHERE version = @version LIMIT 10;", +# output_structured_dataset_type=StructuredDataset, +# task_config=BigQueryConfig(ProjectID="flyte-test-340607") +# ) +# ``` +# +# Task above task as an example, you can run the task locally and test agent with the following command: +# +# ```bash +# pyflyte run wf.py bigquery_doge_coin --version 10 +# ``` +# +# ### Build a New image +# +# The following is a sample Dockerfile for building an image for a flyte agent. +# +# ```Dockerfile +# FROM python:3.9-slim-buster +# +# MAINTAINER Flyte Team +# LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytekit +# +# WORKDIR /root +# ENV PYTHONPATH /root +# +# # flytekit will autoload the agent if package is installed. +# RUN pip install flytekitplugins-bigquery +# CMD pyflyte serve --port 8000 +# ``` +# +# ### Update FlyteAgent +# +# 1. Update the FlyteAgent deployment's [image](https://github.com/flyteorg/flyte/blob/201a8e1450d86b72a67be938ce7502ee2621cadb/charts/flyte-core/templates/agent/deployment.yaml#L27) +# 2. Update the FlytePropeller configmap. +# +# ```YAML +# tasks: +# task-plugins: +# enabled-plugins: +# - agent-service +# default-for-task-types: +# - bigquery_query_job_task: agent-service +# - custom_task: agent-service +# +# plugins: +# agent-service: +# supportedTaskTypes: +# - bigquery_query_job_task +# - default_task +# - custom_task +# # By default, all the request will be sent to the default agent. +# defaultAgent: +# endpoint: "dns:///flyteagent.flyte.svc.cluster.local:8000" +# insecure: true +# timeouts: +# GetTask: 200ms +# defaultTimeout: 50ms +# agents: +# custom_agent: +# endpoint: "dns:///custom-flyteagent.flyte.svc.cluster.local:8000" +# insecure: false +# defaultServiceConfig: '{"loadBalancingConfig": [{"round_robin":{}}]}' +# timeouts: +# GetTask: 100ms +# defaultTimeout: 20ms +# agentForTaskTypes: +# # It will override the default agent for custom_task, which means propeller will send the request to this agent. +# - custom_task: custom_agent +# ``` +# +# 3. Restart the FlytePropeller +# +# ``` +# kubectl rollout restart deployment flytepropeller -n flyte +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/debugging_workflows_tasks.py b/docs/examples/development_lifecycle/development_lifecycle/debugging_workflows_tasks.py new file mode 100644 index 000000000..03209a4ee --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/debugging_workflows_tasks.py @@ -0,0 +1,31 @@ +# %% [markdown] +# # Debugging Workflow and Task Executions +# +# The inspection of task and workflow execution would provide you log links to debug things further +# +# Using `--details` flag would shows you node executions view with log links. +# +# ``` +# └── n1 - FAILED - 2021-06-30 08:51:07.3111846 +0000 UTC - 2021-06-30 08:51:17.192852 +0000 UTC +# │ ├── Attempt :0 +# │ └── Task - FAILED - 2021-06-30 08:51:07.3111846 +0000 UTC - 2021-06-30 08:51:17.192852 +0000 UTC +# │ └── Logs : +# │ └── Name :Kubernetes Logs (User) +# │ └── URI :http://localhost:30082/#/log/flytectldemo-development/f3a5a4034960f4aa1a09-n1-0/pod?namespace=flytectldemo-development +# ``` +# +# Additionally you can check the pods launched by flyte in \-\ namespace +# +# ``` +# kubectl get pods -n - +# ``` +# +# The launched pods will have a prefix of execution name along with suffix of nodeId +# +# ``` +# NAME READY STATUS RESTARTS AGE +# f65009af77f284e50959-n0-0 0/1 ErrImagePull 0 18h +# ``` +# +# So here the investigation can move ahead by describing the pod and checking the issue with Image pull. +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/decks.py b/docs/examples/development_lifecycle/development_lifecycle/decks.py new file mode 100644 index 000000000..563e17d9e --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/decks.py @@ -0,0 +1,249 @@ +# %% [markdown] +# (decks)= +# +# # Decks +# +# ```{eval-rst} +# .. tags:: UI, Intermediate +# ``` +# +# The Decks feature enables you to obtain customizable and default visibility into your tasks. +# Think of it as a visualization tool that you can utilize within your Flyte tasks. +# +# Decks are equipped with a variety of {ref}`renderers `, +# such as FrameRenderer and MarkdownRenderer. These renderers produce HTML files. +# As an example, FrameRenderer transforms a DataFrame into an HTML table, and MarkdownRenderer converts Markdown text into HTML. +# +# Each task has a minimum of three decks: input, output and default. +# The input/output decks are used to render the input/output data of tasks, +# while the default deck can be used to render line plots, scatter plots or Markdown text. +# Additionally, you can create new decks to render your data using custom renderers. +# +# :::{note} +# Flyte Decks is an opt-in feature; to enable it, set `disable_deck` to `False` in the task parameters. +# ::: +# +# To begin, import the dependencies. +# %% +import flytekit +from flytekit import ImageSpec, task +from flytekitplugins.deck.renderer import MarkdownRenderer +from sklearn.decomposition import PCA + +# %% [markdown] +# We create a new deck named `pca` and render Markdown content along with a +# [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) plot. +# +# You can begin by initializing an {ref}`ImageSpec ` object to encompass all the necessary dependencies. +# This approach automatically triggers a Docker build, alleviating the need for you to manually create a Docker image. +# %% +custom_image = ImageSpec(name="flyte-decks-example", packages=["plotly"], registry="ghcr.io/flyteorg") + +if custom_image.is_container(): + import plotly + import plotly.express as px + + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# Note the usage of `append` to append the Plotly deck to the Markdown deck. +# %% +@task(disable_deck=False, container_image=custom_image) +def pca_plot(): + iris_df = px.data.iris() + X = iris_df[["sepal_length", "sepal_width", "petal_length", "petal_width"]] + pca = PCA(n_components=3) + components = pca.fit_transform(X) + total_var = pca.explained_variance_ratio_.sum() * 100 + fig = px.scatter_3d( + components, + x=0, + y=1, + z=2, + color=iris_df["species"], + title=f"Total Explained Variance: {total_var:.2f}%", + labels={"0": "PC 1", "1": "PC 2", "2": "PC 3"}, + ) + main_deck = flytekit.Deck("pca", MarkdownRenderer().to_html("### Principal Component Analysis")) + main_deck.append(plotly.io.to_html(fig)) + + +# %% [markdown] +# :::{Important} +# To view the log output locally, the `FLYTE_SDK_LOGGING_LEVEL` environment variable should be set to 20. +# ::: +# +# The following is the expected output containing the path to the `deck.html` file: +# +# ``` +# {"asctime": "2023-07-11 13:16:04,558", "name": "flytekit", "levelname": "INFO", "message": "pca_plot task creates flyte deck html to file:///var/folders/6f/xcgm46ds59j7g__gfxmkgdf80000gn/T/flyte-0_8qfjdd/sandbox/local_flytekit/c085853af5a175edb17b11cd338cbd61/deck.html"} +# ``` +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_deck_plot_local.webp +# :alt: Flyte deck plot +# :class: with-shadow +# ::: +# +# Once you execute this task on the Flyte cluster, you can access the deck by clicking the _Flyte Deck_ button: +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_deck_button.png +# :alt: Flyte deck button +# :class: with-shadow +# ::: +# +# (deck_renderer)= +# +# ## Deck renderer +# +# The Deck renderer is an integral component of the Deck plugin, which offers both default and personalized task visibility. +# Within the Deck, an array of renderers is present, responsible for generating HTML files. +# +# These renderers showcase HTML in the user interface, facilitating the visualization and documentation of task-associated data. +# +# In the Flyte context, a collection of deck objects is stored. +# When the task connected with a deck object is executed, these objects employ renderers to transform data into HTML files. +# +# ### Available renderers +# +# #### Frame Renderer +# +# Creates a profile report from a Pandas DataFrame. +# %% +import pandas as pd +from flytekitplugins.deck.renderer import FrameProfilingRenderer + + +@task(disable_deck=False) +def frame_renderer() -> None: + df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) + flytekit.Deck("Frame Renderer", FrameProfilingRenderer().to_html(df=df)) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_frame_renderer.png +# :alt: Frame renderer +# :class: with-shadow +# ::: +# +# %% [markdown] +# #### TopFrame Renderer +# +# Renders DataFrame as an HTML table. +# This renderer doesn't necessitate plugin installation since it's accessible within the flytekit library. +# %% +from typing import Annotated + +from flytekit.deck import TopFrameRenderer + + +@task(disable_deck=False) +def top_frame_renderer() -> Annotated[pd.DataFrame, TopFrameRenderer(1)]: + return pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_top_frame_renderer.png +# :alt: Top frame renderer +# :class: with-shadow +# ::: +# +# #### MarkDown Renderer +# +# Converts a Markdown string into HTML, producing HTML as a Unicode string. +# %% +@task(disable_deck=False) +def markdown_renderer() -> None: + flytekit.current_context().default_deck.append( + MarkdownRenderer().to_html("You can install flytekit using this command: ```import flytekit```") + ) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_markdown_renderer.png +# :alt: Markdown renderer +# :class: with-shadow +# ::: +# +# #### Box Renderer +# +# Groups rows of DataFrame together into a +# box-and-whisker mark to visualize their distribution. +# +# Each box extends from the first quartile (Q1) to the third quartile (Q3). +# The median (Q2) is indicated by a line within the box. +# Typically, the whiskers extend to the edges of the box, +# plus or minus 1.5 times the interquartile range (IQR: Q3-Q1). +# %% +from flytekitplugins.deck.renderer import BoxRenderer + + +@task(disable_deck=False) +def box_renderer() -> None: + iris_df = px.data.iris() + flytekit.Deck("Box Plot", BoxRenderer("sepal_length").to_html(iris_df)) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_box_renderer.png +# :alt: Box renderer +# :class: with-shadow +# ::: +# +# #### Image Renderer +# +# Converts a {ref}`FlyteFile ` or `PIL.Image.Image` object into an HTML string, +# where the image data is encoded as a base64 string. +# %% +from flytekit import workflow +from flytekit.types.file import FlyteFile +from flytekitplugins.deck.renderer import ImageRenderer + + +@task(disable_deck=False) +def image_renderer(image: FlyteFile) -> None: + flytekit.Deck("Image Renderer", ImageRenderer().to_html(image_src=image)) + + +@workflow +def image_renderer_wf( + image: FlyteFile = "https://bit.ly/3KZ95q4", +) -> None: + image_renderer(image=image) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_image_renderer.png +# :alt: Image renderer +# :class: with-shadow +# ::: +# +# #### Table Renderer +# +# Converts a Pandas DataFrame into an HTML table. +# %% +from flytekitplugins.deck.renderer import TableRenderer + + +@task(disable_deck=False) +def table_renderer() -> None: + flytekit.Deck( + "Table Renderer", + TableRenderer().to_html(df=pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}), table_width=50), + ) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/flyte_decks_table_renderer.png +# :alt: Table renderer +# :class: with-shadow +# ::: +# +# ### Contribute to renderers +# +# Don't hesitate to integrate a new renderer into +# [renderer.py](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-deck-standard/flytekitplugins/deck/renderer.py) +# if your deck renderers can enhance data visibility. +# Feel encouraged to open a pull request and play a part in enhancing the Flyte deck renderer ecosystem! diff --git a/docs/examples/development_lifecycle/development_lifecycle/inspecting_executions.py b/docs/examples/development_lifecycle/development_lifecycle/inspecting_executions.py new file mode 100644 index 000000000..22b9e717e --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/inspecting_executions.py @@ -0,0 +1,68 @@ +# %% [markdown] +# # Inspecting Workflow and Task Executions +# +# ## Flytectl +# +# Flytectl supports inspecting execution by retrieving its details. For a deeper dive, refer to the +# [API reference](https://docs.flyte.org/projects/flytectl/en/stable/gen/flytectl_get_execution.html) guide. +# +# Monitor the execution by providing the execution id from create command which can be task or workflow execution. +# +# ``` +# flytectl get execution -p flytesnacks -d development +# ``` +# +# For more details use `--details` flag which shows node executions along with task executions on them. +# +# ``` +# flytectl get execution -p flytesnacks -d development --details +# ``` +# +# If you prefer to see yaml/json view for the details then change the output format using the -o flag. +# +# ``` +# flytectl get execution -p flytesnacks -d development --details -o yaml +# ``` +# +# To see the results of the execution you can inspect the node closure outputUri in detailed yaml output. +# +# ``` +# "outputUri": "s3://my-s3-bucket/metadata/propeller/flytesnacks-development-/n0/data/0/outputs.pb" +# ``` +# +# ## FlyteRemote +# +# With FlyteRemote, you can fetch the inputs and outputs of executions and inspect them. +# +# ```python +# from flytekit.remote import FlyteRemote +# +# # FlyteRemote object is the main entrypoint to API +# remote = FlyteRemote( +# config=Config.for_endpoint(endpoint="flyte.example.net"), +# default_project="flytesnacks", +# default_domain="development", +# ) +# +# execution = remote.fetch_execution( +# name="fb22e306a0d91e1c6000", project="flytesnacks", domain="development" +# ) +# +# input_keys = execution.inputs.keys() +# output_keys = execution.outputs.keys() +# +# # The inputs and outputs correspond to the top-level execution or the workflow itself. +# # To fetch a specific output, say, a model file: +# model_file = execution.outputs["model_file"] +# with open(model_file) as f: +# ... +# +# # You can use FlyteRemote.sync() to sync the entity object's state with the remote state during the execution run. +# synced_execution = remote.sync(execution, sync_nodes=True) +# node_keys = synced_execution.node_executions.keys() +# +# # node_executions will fetch all the underlying node executions recursively. +# # To fetch output of a specific node execution: +# node_execution_output = synced_execution.node_executions["n1"].outputs["model_file"] +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/private_images.py b/docs/examples/development_lifecycle/development_lifecycle/private_images.py new file mode 100644 index 000000000..afea40a58 --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/private_images.py @@ -0,0 +1,57 @@ +# %% [markdown] +# (private_images)= +# +# # Pulling Private Images +# +# As we learned in the {ref}`Flyte Fundamentals ` guide, +# Flyte uses OCI-compatible containers to package up your code and third-party +# dependencies. For production use-cases your images may require proprietary code +# and configuration that you want to keep private. +# +# You can use different private container registries to host your images, such as +# [AWS ECR](https://docs.aws.amazon.com/AmazonECR/latest/userguide/registry_auth.html), +# [Docker Hub](https://docs.docker.com/docker-hub/repos/#private-repositories), +# [GitLab Container Registry](https://docs.gitlab.com/ee/ci/docker/using_docker_images.html#access-an-image-from-a-private-container-registry), +# and [GitHub Container Registry](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#authenticating-to-the-container-registry). +# +# To pull private images, ensure that you have the command line tools and login +# information associated with the registry. +# +# ## Create a Secret +# +# First [create a secret](https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/) +# that contains all the credentials needed to log into the registry. +# +# ## Configure `imagePullSecrets` +# +# Then, you'll need to specify a `imagePullSecrets` configuration to pull a +# private image using one of two methods below. +# +# ```{eval-rst} +# .. tabs:: +# +# .. tab:: Service Account +# +# You can use the default or new service account for this option: +# +# 1. Add your ``imagePullSecrets`` configuration to the +# `service account `__. +# 2. Use this service account to log into the private registry and pull the image. +# 3. When you create a task/workflow execution this service account should +# be specified to access the private image. +# +# .. tab:: Custom Pod Template +# +# This option uses a `custom pod template `__ +# to create a pod. This template is automatically added to every ``pod`` that +# Flyte creates. +# +# 1. Add your ``imagePullSecrets`` configuration to this custom pod template. +# 2. Update `FlytePropeller `__ about the pod created in the previous step. +# 3. FlytePropeller adds ``imagePullSecrets``, along with other customization for the pod, +# to the PodSpec, which should look similar to this +# `manifest `__. +# 4. The pods with their keys can log in and access the images in the private registry. +# Once you set up the token to authenticate with the private registry, you can pull images from them. +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/register_project.py b/docs/examples/development_lifecycle/development_lifecycle/register_project.py new file mode 100644 index 000000000..5abe1aa00 --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/register_project.py @@ -0,0 +1,13 @@ +# %% [markdown] +# # Creating a New Project +# +# Creates project to be used as a home for the flyte resources of tasks and workflows. +# Refer to the [flytectl API reference](https://docs.flyte.org/projects/flytectl/en/stable/gen/flytectl_create_project.html) +# for more details. +# +# ```{eval-rst} +# .. prompt:: bash +# +# flytectl create project --id "my-flyte-project-name" --labels "my-label=my-project-label" --description "my-flyte-project-name" --name "my-flyte-project-name" +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/remote_launchplan.py b/docs/examples/development_lifecycle/development_lifecycle/remote_launchplan.py new file mode 100644 index 000000000..8b6bf38cb --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/remote_launchplan.py @@ -0,0 +1,69 @@ +# %% [markdown] +# (remote_launchplan)= +# +# # Running a Launchplan +# +# ## Flytectl +# +# This is multi-steps process where we create an execution spec file, update the spec file and then create the execution. +# More details can be found [here](https://docs.flyte.org/projects/flytectl/en/stable/gen/flytectl_create_execution.html). +# +# **Generate an execution spec file** +# +# ``` +# flytectl get launchplan -p flytesnacks -d development myapp.workflows.example.my_wf --execFile exec_spec.yaml +# ``` +# +# **Update the input spec file for arguments to the workflow** +# +# ``` +# .... +# inputs: +# name: "adam" +# .... +# ``` +# +# **Create execution using the exec spec file** +# +# ``` +# flytectl create execution -p flytesnacks -d development --execFile exec_spec.yaml +# ``` +# +# **Monitor the execution by providing the execution id from create command** +# +# ``` +# flytectl get execution -p flytesnacks -d development +# ``` +# +# ## FlyteRemote +# +# A launch plan can be launched via FlyteRemote programmatically. +# +# ```python +# from flytekit.remote import FlyteRemote +# from flytekit.configuration import Config +# from flytekit import LaunchPlan +# +# # FlyteRemote object is the main entrypoint to API +# remote = FlyteRemote( +# config=Config.for_endpoint(endpoint="flyte.example.net"), +# default_project="flytesnacks", +# default_domain="development", +# ) +# +# # Fetch launch plan +# flyte_lp = remote.fetch_launch_plan( +# name="workflows.example.wf", version="v1", project="flytesnacks", domain="development" +# ) +# +# # Execute +# execution = remote.execute( +# flyte_lp, inputs={"mean": 1}, execution_name="lp-execution", wait=True +# ) +# +# # Or use execution_name_prefix to avoid repeated execution names +# execution = remote.execute( +# flyte_lp, inputs={"mean": 1}, execution_name_prefix="flyte", wait=True +# ) +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/remote_task.py b/docs/examples/development_lifecycle/development_lifecycle/remote_task.py new file mode 100644 index 000000000..131461686 --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/remote_task.py @@ -0,0 +1,83 @@ +# %% [markdown] +# (remote_task)= +# +# # Running a Task +# +# ## Flytectl +# +# This is a multi-step process where we create an execution spec file, update the spec file, and then create the execution. +# More details can be found in the [Flytectl API reference](https://docs.flyte.org/projects/flytectl/en/stable/gen/flytectl_create_execution.html). +# +# **Generate execution spec file** +# +# ``` +# flytectl get tasks -d development -p flytesnacks workflows.example.generate_normal_df --latest --execFile exec_spec.yaml +# ``` +# +# **Update the input spec file for arguments to the workflow** +# +# ``` +# iamRoleARN: 'arn:aws:iam::12345678:role/defaultrole' +# inputs: +# n: 200 +# mean: 0.0 +# sigma: 1.0 +# kubeServiceAcct: "" +# targetDomain: "" +# targetProject: "" +# task: workflows.example.generate_normal_df +# version: "v1" +# ``` +# +# **Create execution using the exec spec file** +# +# ``` +# flytectl create execution -p flytesnacks -d development --execFile exec_spec.yaml +# ``` +# +# **Monitor the execution by providing the execution id from create command** +# +# ``` +# flytectl get execution -p flytesnacks -d development +# ``` +# +# ## FlyteRemote +# +# A task can be launched via FlyteRemote programmatically. +# +# ```python +# from flytekit.remote import FlyteRemote +# from flytekit.configuration import Config, SerializationSettings +# +# # FlyteRemote object is the main entrypoint to API +# remote = FlyteRemote( +# config=Config.for_endpoint(endpoint="flyte.example.net"), +# default_project="flytesnacks", +# default_domain="development", +# ) +# +# # Get Task +# flyte_task = remote.fetch_task(name="workflows.example.generate_normal_df", version="v1") +# +# flyte_task = remote.register_task( +# entity=flyte_task, +# serialization_settings=SerializationSettings(image_config=None), +# version="v2", +# ) +# +# # Run Task +# execution = remote.execute( +# flyte_task, inputs={"n": 200, "mean": 0.0, "sigma": 1.0}, execution_name="task-execution", wait=True +# ) +# +# # Or use execution_name_prefix to avoid repeated execution names +# execution = remote.execute( +# flyte_task, inputs={"n": 200, "mean": 0.0, "sigma": 1.0}, execution_name_prefix="flyte", wait=True +# ) +# +# # Inspecting execution +# # The 'inputs' and 'outputs' correspond to the task execution. +# input_keys = execution.inputs.keys() +# output_keys = execution.outputs.keys() +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/remote_workflow.py b/docs/examples/development_lifecycle/development_lifecycle/remote_workflow.py new file mode 100644 index 000000000..43e1122be --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/remote_workflow.py @@ -0,0 +1,44 @@ +# %% [markdown] +# # Running a Workflow +# +# Workflows on their own are not runnable directly. However, a launchplan is always bound to a workflow and you can use +# launchplans to **launch** a workflow. For cases in which you want the launchplan to have the same arguments as a workflow, +# if you are using one of the SDK's to author your workflows - like flytekit, flytekit-java etc, then they should +# automatically create a `default launchplan` for the workflow. +# +# A `default launchplan` has the same name as the workflow and all argument defaults are similar. See +# {ref}`Launch Plans` to run a workflow via the default launchplan. +# +# {ref}`Tasks also can be executed ` using the launch command. +# One difference between running a task and a workflow via launchplans is that launchplans cannot be associated with a +# task. This is to avoid triggers and scheduling. +# +# ## FlyteRemote +# +# Workflows can be executed with FlyteRemote because under the hood it fetches and triggers a default launch plan. +# +# ```python +# from flytekit.remote import FlyteRemote +# from flytekit.configuration import Config +# +# # FlyteRemote object is the main entrypoint to API +# remote = FlyteRemote( +# config=Config.for_endpoint(endpoint="flyte.example.net"), +# default_project="flytesnacks", +# default_domain="development", +# ) +# +# # Fetch workflow +# flyte_workflow = remote.fetch_workflow(name="workflows.example.wf", version="v1") +# +# # Execute +# execution = remote.execute( +# flyte_workflow, inputs={"mean": 1}, execution_name="workflow-execution", wait=True +# ) +# +# # Or use execution_name_prefix to avoid repeated execution names +# execution = remote.execute( +# flyte_workflow, inputs={"mean": 1}, execution_name_prefix="flyte", wait=True +# ) +# ``` +# diff --git a/docs/examples/development_lifecycle/development_lifecycle/task_cache.py b/docs/examples/development_lifecycle/development_lifecycle/task_cache.py new file mode 100644 index 000000000..eaec4f78a --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/task_cache.py @@ -0,0 +1,217 @@ +# %% [markdown] +# # Caching +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# Flyte provides the ability to cache the output of task executions to make the subsequent executions faster. A well-behaved Flyte task should generate deterministic output given the same inputs and task functionality. +# +# Task caching is useful when a user knows that many executions with the same inputs may occur. For example, consider the following scenarios: +# +# - Running a task periodically on a schedule +# - Running the code multiple times when debugging workflows +# - Running the commonly shared tasks amongst different workflows, which receive the same inputs +# +# Let's watch a brief explanation of caching and a demo in this video, followed by how task caching can be enabled . +# +# ```{eval-rst} +# .. youtube:: WNkThCp-gqo +# +# ``` + +# %% [markdown] +# Import the necessary libraries. +# %% +import time + +import pandas + +# %% [markdown] +# For any {py:func}`flytekit.task` in Flyte, there is always one required import, which is: +# %% +from flytekit import HashMethod, task, workflow +from flytekit.core.node_creation import create_node +from typing_extensions import Annotated + +# %% [markdown] +# Task caching is disabled by default to avoid unintended consequences of caching tasks with side effects. To enable caching and control its behavior, use the `cache` and `cache_version` parameters when constructing a task. +# `cache` is a switch to enable or disable the cache, and `cache_version` pertains to the version of the cache. +# `cache_version` field indicates that the task functionality has changed. +# Bumping the `cache_version` is akin to invalidating the cache. +# You can manually update this version and Flyte caches the next execution instead of relying on the old cache. + +# %% +@task(cache=True, cache_version="1.0") # noqa: F841 +def square(n: int) -> int: + """ + Parameters: + n (int): name of the parameter for the task will be derived from the name of the input variable. + The type will be automatically deduced to ``Types.Integer``. + + Return: + int: The label for the output will be automatically assigned, and the type will be deduced from the annotation. + + """ + return n * n + + +# %% [markdown] +# In the above example, calling `square(n=2)` twice (even if it's across different executions or different workflows) will only execute the multiplication operation once. +# The next time, the output will be made available immediately since it is captured from the previous execution with the same inputs. + +# %% [markdown] +# If in a subsequent code update, you update the signature of the task to return the original number along with the result, it'll automatically invalidate the cache (even though the cache version remains the same). +# +# ```python +# @task(cache=True, cache_version="1.0") +# def square(n: int) -> Tuple[int, int]: +# ... +# ``` + +# %% [markdown] +# :::{note} +# If the user changes the task interface in any way (such as adding, removing, or editing inputs/outputs), Flyte treats that as a task functionality change. In the subsequent execution, Flyte runs the task and stores the outputs as newly cached values. +# ::: +# +# ## How Does Caching Work? +# +# Caching is implemented differently depending on the mode the user is running, i.e. whether they are running locally or using remote Flyte. +# +# ### How Does Remote Caching Work? +# +# The cache keys for remote task execution are composed of **Project**, **Domain**, **Cache Version**, **Task Signature**, and **Inputs** associated with the execution of the task, as per the following definitions: +# +# - **Project:** A task run under one project cannot use the cached task execution from another project which would cause inadvertent results between project teams that could result in data corruption. +# - **Domain:** To separate test, staging, and production data, task executions are not shared across these environments. +# - **Cache Version:** When task functionality changes, you can change the `cache_version` of the task. Flyte will know not to use older cached task executions and create a new cache entry on the subsequent execution. +# - **Task Signature:** The cache is specific to the task signature associated with the execution. The signature constitutes the task name, input parameter names/types, and the output parameter name/type. +# - **Task Input Values:** A well-formed Flyte task always produces deterministic outputs. This means, given a set of input values, every execution should have identical outputs. When task execution is cached, the input values are part of the cache key. +# +# The remote cache for a particular task is invalidated in two ways: +# +# 1. Modifying the `cache_version`; +# 2. Updating the task signature. +# +# :::{note} +# Task executions can be cached across different versions of the task because a change in SHA does not necessarily mean that it correlates to a change in the task functionality. +# ::: +# +# ### How Does Local Caching Work? +# +# The flytekit package uses the [diskcache](https://github.com/grantjenks/python-diskcache) package, specifically [diskcache.Cache](http://www.grantjenks.com/docs/diskcache/tutorial.html#cache), to aid in the memoization of task executions. The results of local task executions are stored under `~/.flyte/local-cache/` and cache keys are composed of **Cache Version**, **Task Signature**, and **Task Input Values**. +# +# Similar to the remote case, a local cache entry for a task will be invalidated if either the `cache_version` or the task signature is modified. In addition, the local cache can also be emptied by running the following command: `pyflyte local-cache clear`, which essentially obliterates the contents of the `~/.flyte/local-cache/` directory. +# +# :::{note} +# The format used by the store is opaque and not meant to be inspectable. +# ::: +# +# (cache-offloaded-objects)= +# +# ## Caching of Non-flyte Offloaded Objects +# +# The default behavior displayed by Flyte's memoization feature might not match the user intuition. For example, this code makes use of pandas dataframes: + +# %% +@task +def foo(a: int, b: str) -> pandas.DataFrame: + df = pandas.DataFrame(...) + ... + return df + + +@task(cache=True, cache_version="1.0") +def bar(df: pandas.DataFrame) -> int: + ... + + +@workflow +def wf(a: int, b: str): + df = foo(a=a, b=b) + v = bar(df=df) # noqa: F841 + + +# %% [markdown] +# If run twice with the same inputs, one would expect that `bar` would trigger a cache hit, but it turns out that's not the case because of how dataframes are represented in Flyte. +# However, with release 1.2.0, Flyte provides a new way to control memoization behavior of literals. This is done via a `typing.Annotated` call on the task signature. +# For example, in order to cache the result of calls to `bar`, you can rewrite the code above like this: + +# %% +def hash_pandas_dataframe(df: pandas.DataFrame) -> str: + return str(pandas.util.hash_pandas_object(df)) + + +@task +def foo_1( # noqa: F811 + a: int, b: str # noqa: F821 +) -> Annotated[pandas.DataFrame, HashMethod(hash_pandas_dataframe)]: # noqa: F821 # noqa: F821 + df = pandas.DataFrame(...) # noqa: F821 + ... + return df + + +@task(cache=True, cache_version="1.0") # noqa: F811 +def bar_1(df: pandas.DataFrame) -> int: # noqa: F811 + ... # noqa: F811 + + +@workflow +def wf_1(a: int, b: str): # noqa: F811 + df = foo(a=a, b=b) # noqa: F811 + v = bar(df=df) # noqa: F841 + + +# %% [markdown] +# Note how the output of task `foo` is annotated with an object of type `HashMethod`. Essentially, it represents a function that produces a hash that is used as part of the cache key calculation in calling the task `bar`. +# +# ### How Does Caching of Offloaded Objects Work? +# +# Recall how task input values are taken into account to derive a cache key. +# This is done by turning the literal representation into a string and using that string as part of the cache key. In the case of dataframes annotated with `HashMethod` we use the hash as the representation of the Literal. In other words, the literal hash is used in the cache key. +# +# This feature also works in local execution. + +# %% [markdown] +# Here's a complete example of the feature: +# + +# %% +def hash_pandas_dataframe(df: pandas.DataFrame) -> str: + return str(pandas.util.hash_pandas_object(df)) + + +@task +def uncached_data_reading_task() -> Annotated[pandas.DataFrame, HashMethod(hash_pandas_dataframe)]: + return pandas.DataFrame({"column_1": [1, 2, 3]}) + + +@task(cache=True, cache_version="1.0") +def cached_data_processing_task(df: pandas.DataFrame) -> pandas.DataFrame: + time.sleep(1) + return df * 2 + + +@task +def compare_dataframes(df1: pandas.DataFrame, df2: pandas.DataFrame): + assert df1.equals(df2) + + +@workflow +def cached_dataframe_wf(): + raw_data = uncached_data_reading_task() + + # Execute `cached_data_processing_task` twice, but force those + # two executions to happen serially to demonstrate how the second run + # hits the cache. + t1_node = create_node(cached_data_processing_task, df=raw_data) + t2_node = create_node(cached_data_processing_task, df=raw_data) + t1_node >> t2_node + + # Confirm that the dataframes actually match + compare_dataframes(df1=t1_node.o0, df2=t2_node.o0) + + +if __name__ == "__main__": + df1 = cached_dataframe_wf() + print(f"Running cached_dataframe_wf once : {df1}") diff --git a/docs/examples/development_lifecycle/development_lifecycle/task_cache_serialize.py b/docs/examples/development_lifecycle/development_lifecycle/task_cache_serialize.py new file mode 100644 index 000000000..9edd1d8e4 --- /dev/null +++ b/docs/examples/development_lifecycle/development_lifecycle/task_cache_serialize.py @@ -0,0 +1,53 @@ +# %% [markdown] +# # Cache Serializing +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# Serializing means only executing a single instance of a unique cacheable task (determined by the cache_version parameter and task signature) at a time. Using this mechanism, Flyte ensures that during multiple concurrent executions of a task only a single instance is evaluated and all others wait until completion and reuse the resulting cached outputs. +# +# Ensuring serialized evaluation requires a small degree of overhead to coordinate executions using a lightweight artifact reservation system. Therefore, this should be viewed as an extension to rather than a replacement for non-serialized cacheable tasks. It is particularly well fit for long running or otherwise computationally expensive tasks executed in scenarios similar to the following examples: +# +# - Periodically scheduled workflow where a single task evaluation duration may span multiple scheduled executions. +# - Running a commonly shared task within different workflows (which receive the same inputs). + +# %% [markdown] +# For any {py:func}`flytekit.task` in Flyte, there is always one required import, which is: +# %% +from flytekit import task + + +# %% [markdown] +# Task cache serializing is disabled by default to avoid unexpected behavior for task executions. To enable use the `cache_serialize` parameter. +# `cache_serialize` is a switch to enable or disable serialization of the task +# This operation is only useful for cacheable tasks, where one may reuse output from a previous execution. Flyte requires implicitly enabling the `cache` parameter on all cache serializable tasks. +# Cache key definitions follow the same rules as non-serialized cache tasks. It is important to understand the implications of the task signature and `cache_version` parameter in defining cached results. +# %% +@task(cache=True, cache_serialize=True, cache_version="1.0") +def square(n: int) -> int: + """ + Parameters: + n (int): name of the parameter for the task will be derived from the name of the input variable. + The type will be automatically deduced to Types.Integer + + Return: + int: The label for the output will be automatically assigned, and the type will be deduced from the annotation + + """ + return n * n + + +# %% [markdown] +# In the above example calling `square(n=2)` multiple times concurrently (even in different executions or workflows) will only execute the multiplication operation once. +# Concurrently evaluated tasks will wait for completion of the first instance before reusing the cached results and subsequent evaluations will instantly reuse existing cache results. + +# %% [markdown] +# ## How Does Serializing Caches Work? +# +# The cache serialize paradigm introduces a new artifact reservation system. Tasks may use this reservation system to acquire an artifact reservation, indicating that they are actively evaluating the task, and release the reservation, once the execution is completed. Flyte uses a clock-skew algorithm to define reservation timeouts. Therefore, tasks are required to periodically extend the reservation during execution. +# +# The first execution of a serializable cached task will successfully acquire the artifact reservation. Execution will be performed as usual and upon completion, the results are written to the cache and reservation is released. Concurrently executed task instances (i.e. in parallel with the initial execution) will observe an active reservation, in which case the execution will wait until the next reevaluation and perform another check. Once the initial execution completes it will reuse the cached results. Subsequently executed task instances (i.e. after an execution has already completed successfully) will immediately reuse the existing cached results. +# +# Flyte handles task execution failures using a timeout on the reservation. If the task currently holding the reservation fails to extend it before it times out, another task may acquire the reservation and begin executing the task. +# diff --git a/docs/examples/development_lifecycle/requirements.in b/docs/examples/development_lifecycle/requirements.in new file mode 100644 index 000000000..c9db52868 --- /dev/null +++ b/docs/examples/development_lifecycle/requirements.in @@ -0,0 +1,5 @@ +flytekit +flytekitplugins-deck-standard +flytekitplugins-envd +plotly +scikit-learn diff --git a/docs/examples/development_lifecycle/requirements.txt b/docs/examples/development_lifecycle/requirements.txt new file mode 100644 index 000000000..beab100cf --- /dev/null +++ b/docs/examples/development_lifecycle/requirements.txt @@ -0,0 +1,516 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +asttokens==2.2.1 + # via stack-data +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via + # aiohttp + # visions +azure-core==1.29.3 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +comm==0.1.4 + # via ipywidgets +contourpy==1.1.0 + # via matplotlib +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl + # secretstorage +cycler==0.11.0 + # via matplotlib +dacite==1.8.1 + # via ydata-profiling +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via + # gcsfs + # ipython +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.39 + # via flytekitplugins-envd +executing==1.2.0 + # via stack-data +flyteidl==1.5.17 + # via flytekit +flytekit==1.9.0 + # via + # -r requirements.in + # flytekitplugins-deck-standard + # flytekitplugins-envd +flytekitplugins-deck-standard==1.9.0 + # via -r requirements.in +flytekitplugins-envd==1.9.1 + # via -r requirements.in +fonttools==4.42.1 + # via matplotlib +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via + # requests + # yarl +imagehash==4.3.1 + # via + # visions + # ydata-profiling +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +ipython==8.14.0 + # via ipywidgets +ipywidgets==8.1.0 + # via flytekitplugins-deck-standard +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jedi==0.19.0 + # via ipython +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via + # cookiecutter + # ydata-profiling +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via + # flytekit + # phik + # scikit-learn +jupyterlab-widgets==3.0.8 + # via ipywidgets +keyring==24.2.0 + # via flytekit +kiwisolver==1.4.5 + # via matplotlib +kubernetes==27.2.0 + # via flytekit +markdown==3.4.4 + # via flytekitplugins-deck-standard +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.7.2 + # via + # phik + # seaborn + # wordcloud + # ydata-profiling +matplotlib-inline==0.1.6 + # via ipython +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +networkx==3.1 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions + # wordcloud + # ydata-profiling +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow + # matplotlib + # plotly + # statsmodels +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==10.0.1 + # via + # imagehash + # matplotlib + # visions + # wordcloud +plotly==5.16.1 + # via + # -r requirements.in + # flytekitplugins-deck-standard +portalocker==2.7.0 + # via msal-extensions +prompt-toolkit==3.0.39 + # via ipython +protobuf==4.24.2 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.12 + # via ydata-profiling +pygments==2.16.1 + # via + # ipython + # rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes + # ydata-profiling +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # ydata-profiling +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +scikit-learn==1.3.0 + # via -r requirements.in +scipy==1.11.2 + # via + # imagehash + # phik + # scikit-learn + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # asttokens + # azure-core + # google-auth + # isodate + # kubernetes + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.14.0 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.3 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.2.0 + # via scikit-learn +tqdm==4.66.1 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipython + # ipywidgets + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.6.2 + # via + # docker + # kubernetes +wheel==0.41.2 + # via flytekit +widgetsnbextension==4.0.8 + # via ipywidgets +wordcloud==1.9.2 + # via ydata-profiling +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +ydata-profiling==4.5.1 + # via flytekitplugins-deck-standard +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/dolt_plugin/Dockerfile b/docs/examples/dolt_plugin/Dockerfile new file mode 100644 index 000000000..ae29ebe42 --- /dev/null +++ b/docs/examples/dolt_plugin/Dockerfile @@ -0,0 +1,45 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN apt-get update && \ + apt-get -y install sudo curl + +# Install dolt +RUN sudo bash -c 'curl -L https://github.com/dolthub/dolt/releases/latest/download/install.sh | sudo bash' &&\ + dolt config --global --add user.email bojack@horseman.com &&\ + dolt config --global --add user.name "Bojack Horseman" + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +RUN mkdir -p /root/foo &&\ + cd /root/foo &&\ + dolt init + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/dolt_plugin/README.md b/docs/examples/dolt_plugin/README.md new file mode 100644 index 000000000..8252186bc --- /dev/null +++ b/docs/examples/dolt_plugin/README.md @@ -0,0 +1,42 @@ +# Dolt + +```{eval-rst} +.. tags:: Integration, Data, SQL, Intermediate +``` + +```{image} https://img.shields.io/badge/Blog-Dolt-blue?style=for-the-badge +:target: https://blog.flyte.org/upleveling-flyte-data-lineage-using-dolt +:alt: Dolt Blog Post +``` + +The `DoltTable` plugin is a wrapper that uses [Dolt](https://github.com/dolthub/dolt) to move data between +`pandas.DataFrame`'s at execution time and database tables at rest. + +## Installation + +The dolt plugin and dolt command line tool are required to run these examples: + +```bash +pip install flytekitplugins.dolt +sudo bash -c 'curl -L https://github.com/dolthub/dolt/releases/latest/download/install.sh | sudo bash' +``` + +Dolt requires a user configuration to run `init`: + +```bash +dolt config --global --add user.email +dolt config --global --add user.name +``` + +These demos assume a `foo` database has been created locally: + +```bash +mkdir foo +cd foo +dolt init +``` + +```{auto-examples-toc} +dolt_quickstart_example +dolt_branch_example +``` diff --git a/docs/examples/dolt_plugin/dolt_plugin/__init__.py b/docs/examples/dolt_plugin/dolt_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/dolt_plugin/dolt_plugin/dolt_branch_example.py b/docs/examples/dolt_plugin/dolt_plugin/dolt_branch_example.py new file mode 100644 index 000000000..fc8d1e53e --- /dev/null +++ b/docs/examples/dolt_plugin/dolt_plugin/dolt_branch_example.py @@ -0,0 +1,138 @@ +# %% [markdown] +# # Dolt Branches +# +# In this example, we'll show how to use DoltTable along with Dolt's `Branch` feature. +# %% +import os +import sys +import typing + +import pandas as pd +from dolt_integrations.core import NewBranch +from flytekit import task, workflow +from flytekitplugins.dolt.schema import DoltConfig, DoltTable + +# %% [markdown] +# ## A Simple Workflow +# +# We will run a simple data workflow: +# +# 1. Create a `users` table with `name` and `count` columns. +# 2. Filter the `users` table for users with `count > 5`. +# 3. Record the filtered users' names in a `big_users` table. + +# %% [markdown] +# ### Database Configuration +# +# Let's define our database configuration. +# Our `DoltConfig` references a `foo` folder containing +# our database. Use either a `tablename` or a `sql` select +# statement to fetch data. + +# %% +doltdb_path = os.path.join(os.path.dirname(__file__), "foo") + + +def generate_confs(a: int) -> typing.Tuple[DoltConfig, DoltConfig, DoltConfig]: + users_conf = DoltConfig(db_path=doltdb_path, tablename="users", branch_conf=NewBranch(f"run/a_is_{a}")) + + query_users = DoltTable( + config=DoltConfig( + db_path=doltdb_path, + sql="select * from users where `count` > 5", + branch_conf=NewBranch(f"run/a_is_{a}"), + ), + ) + + big_users_conf = DoltConfig( + db_path=doltdb_path, + tablename="big_users", + branch_conf=NewBranch(f"run/a_is_{a}"), + ) + + return users_conf, query_users, big_users_conf + + +# %% [markdown] +# :::{tip} +# A `DoltTable` is an extension of `DoltConfig` that wraps a `pandas.DataFrame` -- accessible via the `DoltTable.data` +# attribute at execution time. +# ::: + +# %% [markdown] +# ### Type Annotating Tasks and Workflows +# +# We can turn our data processing pipeline into a Flyte workflow +# by decorating functions with the {py:func}`~flytekit.task` and {py:func}`~flytekit.workflow` decorators. +# Annotating the inputs and outputs of those functions with Dolt schemas +# indicates how to save and load data between tasks. +# +# The `DoltTable.data` attribute loads dataframes for input arguments. +# Return types of `DoltTable` save the `data` to the +# Dolt database given a connection configuration. + +# %% +@task +def get_confs(a: int) -> typing.Tuple[DoltConfig, DoltTable, DoltConfig]: + return generate_confs(a) + + +@task +def populate_users(a: int, conf: DoltConfig) -> DoltTable: + users = [("George", a), ("Alice", a * 2), ("Stephanie", a * 3)] + df = pd.DataFrame(users, columns=["name", "count"]) + return DoltTable(data=df, config=conf) + + +@task +def filter_users(a: int, all_users: DoltTable, filtered_users: DoltTable, conf: DoltConfig) -> DoltTable: + usernames = filtered_users.data[["name"]] + return DoltTable(data=usernames, config=conf) + + +@task +def count_users(users: DoltTable) -> int: + return users.data.shape[0] + + +@workflow +def wf(a: int) -> int: + user_conf, query_conf, big_user_conf = get_confs(a=a) + users = populate_users(a=a, conf=user_conf) + big_users = filter_users(a=a, all_users=users, filtered_users=query_conf, conf=big_user_conf) + big_user_cnt = count_users(users=big_users) + return big_user_cnt + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + if len(sys.argv) != 2: + raise ValueError("Expected 1 argument: a (int)") + a = int(sys.argv[1]) + result = wf(a=a) + print(f"Running wf(), returns int\n{result}\n{type(result)}") + +# %% [markdown] +# We will run this workflow twice: +# +# ```{eval-rst} +# .. prompt:: $ +# +# python branch_example.py 2 +# ``` +# +# ```{eval-rst} +# .. prompt:: $ +# +# python branch_example.py 3 +# ``` +# +# Which creates distinct branches for our two `a` values: +# +# ```{eval-rst} +# .. prompt:: $ +# +# cd foo +# dolt branch +# ``` +# diff --git a/docs/examples/dolt_plugin/dolt_plugin/dolt_quickstart_example.py b/docs/examples/dolt_plugin/dolt_plugin/dolt_quickstart_example.py new file mode 100644 index 000000000..55fd31347 --- /dev/null +++ b/docs/examples/dolt_plugin/dolt_plugin/dolt_quickstart_example.py @@ -0,0 +1,76 @@ +# %% [markdown] +# # Quickstart +# +# In this demo and following example, learn how to use `DoltTable` to annotate DataFrame inputs and outputs in the Flyte tasks. +# +# ```{eval-rst} +# .. youtube:: TfBIuHYkyvU +# +# ``` +# %% [markdown] +# First, let's import the libraries. +# %% +import os +import sys + +import pandas as pd +from flytekit import task, workflow +from flytekitplugins.dolt.schema import DoltConfig, DoltTable + +# %% [markdown] +# Next, we initialize Dolt's config. +# %% +doltdb_path = os.path.join(os.path.dirname(__file__), "foo") + +rabbits_conf = DoltConfig( + db_path=doltdb_path, + tablename="rabbits", +) + + +# %% [markdown] +# We define a task to create a DataFrame and store the table in Dolt. + +# %% +@task +def populate_rabbits(a: int) -> DoltTable: + rabbits = [("George", a), ("Alice", a * 2), ("Sugar Maple", a * 3)] + df = pd.DataFrame(rabbits, columns=["name", "count"]) + return DoltTable(data=df, config=rabbits_conf) + + +# %% [markdown] +# `unwrap_rabbits` task does the exact opposite -- reading the table from Dolt and returning a DataFrame. +# %% +@task +def unwrap_rabbits(table: DoltTable) -> pd.DataFrame: + return table.data + + +# %% [markdown] +# Our workflow combines the above two tasks: +# %% +@workflow +def wf(a: int) -> pd.DataFrame: + rabbits = populate_rabbits(a=a) + df = unwrap_rabbits(table=rabbits) + return df + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + if len(sys.argv) != 2: + raise ValueError("Expected 1 argument: a (int)") + a = int(sys.argv[1]) + result = wf(a=a) + print(f"Running wf(), returns dataframe\n{result}\n{result.dtypes}") + +# %% [markdown] +# Run this task by issuing the following command: +# +# ```{eval-rst} +# .. prompt:: $ +# +# python quickstart_example.py 1 +# ``` +# diff --git a/docs/examples/dolt_plugin/requirements.in b/docs/examples/dolt_plugin/requirements.in new file mode 100644 index 000000000..751eed5f7 --- /dev/null +++ b/docs/examples/dolt_plugin/requirements.in @@ -0,0 +1,6 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins.dolt>=0.18.0 +great-expectations>=0.13.31 diff --git a/docs/examples/dolt_plugin/requirements.txt b/docs/examples/dolt_plugin/requirements.txt new file mode 100644 index 000000000..5ea66fd63 --- /dev/null +++ b/docs/examples/dolt_plugin/requirements.txt @@ -0,0 +1,310 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +altair==4.2.0 + # via great-expectations +arrow==1.2.2 + # via jinja2-time +asttokens==2.0.5 + # via stack-data +attrs==21.4.0 + # via jsonschema +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit + # great-expectations +cloudpickle==2.0.0 + # via flytekit +colorama==0.4.4 + # via great-expectations +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==36.0.2 + # via + # great-expectations + # secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via + # dolt-integrations + # flytekit +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +dolt-integrations==0.1.5 + # via flytekitplugins-dolt +doltcli==0.1.17 + # via dolt-integrations +entrypoints==0.4 + # via altair +executing==0.8.3 + # via stack-data +fastjsonschema==2.15.3 + # via nbformat +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-dolt +flytekitplugins-dolt==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +great-expectations==0.15.0 + # via -r requirements.in +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via + # great-expectations + # keyring +ipython==8.2.0 + # via great-expectations +jedi==0.18.1 + # via ipython +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.0.3 + # via + # altair + # cookiecutter + # great-expectations + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +jsonpatch==1.32 + # via great-expectations +jsonpointer==2.3 + # via jsonpatch +jsonschema==4.4.0 + # via + # altair + # great-expectations + # nbformat +jupyter-core==4.9.2 + # via nbformat +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +matplotlib-inline==0.1.3 + # via ipython +mistune==2.0.2 + # via great-expectations +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +nbformat==5.3.0 + # via great-expectations +numpy==1.22.3 + # via + # altair + # great-expectations + # matplotlib + # pandas + # pyarrow + # scipy +packaging==21.3 + # via + # great-expectations + # marshmallow + # matplotlib +pandas==1.4.2 + # via + # altair + # dolt-integrations + # flytekit + # great-expectations +parso==0.8.3 + # via jedi +pexpect==4.8.0 + # via ipython +pickleshare==0.7.5 + # via ipython +pillow==9.1.1 + # via matplotlib +prompt-toolkit==3.0.29 + # via ipython +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pygments==2.11.2 + # via ipython +pyparsing==2.4.7 + # via + # great-expectations + # matplotlib + # packaging +pyrsistent==0.18.1 + # via jsonschema +python-dateutil==2.8.2 + # via + # croniter + # flytekit + # great-expectations + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # great-expectations + # pandas +pytz-deprecation-shim==0.1.0.post0 + # via tzlocal +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # great-expectations + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +ruamel-yaml==0.17.17 + # via great-expectations +scipy==1.8.0 + # via great-expectations +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.2.0 + # via ipython +statsd==3.3.0 + # via flytekit +termcolor==1.1.0 + # via great-expectations +text-unidecode==1.3 + # via python-slugify +toolz==0.11.2 + # via altair +tqdm==4.64.0 + # via great-expectations +traitlets==5.1.1 + # via + # ipython + # jupyter-core + # matplotlib-inline + # nbformat +typing-extensions==4.1.1 + # via + # flytekit + # great-expectations + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +tzdata==2022.1 + # via pytz-deprecation-shim +tzlocal==4.2 + # via great-expectations +urllib3==1.26.9 + # via + # flytekit + # great-expectations + # requests + # responses +wcwidth==0.2.5 + # via prompt-toolkit +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/duckdb_plugin/Dockerfile b/docs/examples/duckdb_plugin/Dockerfile new file mode 100644 index 000000000..6d101022a --- /dev/null +++ b/docs/examples/duckdb_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/ +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/duckdb_plugin/README.md b/docs/examples/duckdb_plugin/README.md new file mode 100644 index 000000000..fcf209b51 --- /dev/null +++ b/docs/examples/duckdb_plugin/README.md @@ -0,0 +1,26 @@ +(duckdb)= + +# DuckDB + +```{eval-rst} +.. tags:: Integration, Data, Analytics, Beginner +``` + +[DuckDB](https://duckdb.org/) is an in-process SQL OLAP database management system that is explicitly designed to achieve high performance in analytics. + +The Flytekit DuckDB plugin facilitates the efficient execution of intricate analytical queries within your workflow. + +To install the Flytekit DuckDB plugin, run the following command: + +``` +pip install flytekitplugins-duckdb +``` + +The Flytekit DuckDB plugin includes the {py:class}`~flytekitplugins:flytekitplugins.duckdb.DuckDBQuery` task, which allows you to specify the following parameters: + +- `query`: The DuckDB query to execute. +- `inputs`: The query parameters to be used during query execution. This can be a StructuredDataset, a string or a list. + +```{auto-examples-toc} +duckdb_example +``` diff --git a/docs/examples/duckdb_plugin/duckdb_plugin/__init__.py b/docs/examples/duckdb_plugin/duckdb_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py b/docs/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py new file mode 100644 index 000000000..74627cc2e --- /dev/null +++ b/docs/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py @@ -0,0 +1,162 @@ +# %% [markdown] +# # DuckDB Example +# +# In this example, we will see how to run SQL queries using DuckDB. +# +# First, import the necessary libraries. + +# %% +import json +from typing import List + +import pandas as pd +import pyarrow as pa +from flytekit import kwtypes, task, workflow +from flytekit.types.structured.structured_dataset import StructuredDataset +from flytekitplugins.duckdb import DuckDBQuery +from typing_extensions import Annotated + +# %% [markdown] +# ## A simple `SELECT` query +# +# To run a simple `SELECT` query, initialize a `DuckDBQuery` task. +# %% +simple_duckdb_query = DuckDBQuery( + name="simple_task", + query="SELECT SUM(a) FROM mydf", + inputs=kwtypes(mydf=pd.DataFrame), +) + + +# %% [markdown] +# :::{note} +# The default output type for the `DuckDBQuery` task is `StructuredDataset`. +# Hence, it is possible to retrieve any compatible type of Structured Dataset such as a Pandas dataframe, Vaex dataframe, and others. +# ::: +# +# You can invoke the task from within a {py:func}`~flytekit:flytekit.workflow` and return both a Pandas dataframe and a PyArrow table. +# The query will be executed on a Pandas dataframe, and the resulting output can belong to any StructuredDataset-compatible type. +# %% +@task +def get_pandas_df() -> pd.DataFrame: + return pd.DataFrame({"a": [1, 2, 3]}) + + +@workflow +def pandas_wf() -> pd.DataFrame: + return simple_duckdb_query(mydf=get_pandas_df()) + + +@workflow +def arrow_wf() -> pa.Table: + return simple_duckdb_query(mydf=get_pandas_df()) + + +if __name__ == "__main__": + print(f"Running pandas_wf()... {pandas_wf()}") + print(f"Running arrow_wf()... {arrow_wf()}") + +# %% [markdown] +# ## SQL query on Parquet file +# +# DuckDB enables direct querying of a parquet file without the need for intermediate conversions to a database. +# If you wish to execute a SQL query on a parquet file stored in a public S3 bucket, you can use the `httpfs` library by installing and loading it. +# Simply send the parquet file as a parameter to the `SELECT` query. +# +# It is important to note that multiple commands can be executed within a single `DuckDBQuery`. +# However, it is essential to ensure that the last command within the query is a "SELECT" query to retrieve data in the end. +# %% +parquet_duckdb_query = DuckDBQuery( + name="parquet_query", + query=[ + "INSTALL httpfs", + "LOAD httpfs", + """SELECT hour(lpep_pickup_datetime) AS hour, count(*) AS count FROM READ_PARQUET(?) GROUP BY hour""", + ], + inputs=kwtypes(params=List[str]), +) + + +@workflow +def parquet_wf(parquet_file: str) -> pd.DataFrame: + return parquet_duckdb_query(params=[parquet_file]) + + +if __name__ == "__main__": + parquet_file = "https://d37ci6vzurychx.cloudfront.net/trip-data/green_tripdata_2022-02.parquet" + print(f"Running parquet_wf()... {parquet_wf(parquet_file=parquet_file)}") + +# %% [markdown] +# ## SQL query on StructuredDataset +# +# To execute a SQL query on a structured dataset, you can simply run a query just like any other query on a Pandas dataframe or PyArrow table. +# %% +sd_duckdb_query = DuckDBQuery( + name="sd_query", + query="SELECT * FROM sd_df WHERE i = 2", + inputs=kwtypes(sd_df=StructuredDataset), +) + + +@task +def get_sd() -> StructuredDataset: + return StructuredDataset( + dataframe=pd.DataFrame.from_dict({"i": [1, 2, 3, 4], "j": ["one", "two", "three", "four"]}) + ) + + +@workflow +def sd_wf() -> pd.DataFrame: + sd_df = get_sd() + return sd_duckdb_query(sd_df=sd_df) + + +if __name__ == "__main__": + print(f"Running sd_wf()... {sd_wf()}") + +# %% [markdown] +# ## Send parameters to multiple queries +# +# To send parameters to multiple queries, use list of lists. +# +# :::{note} +# Sometimes, the annotation of parameter types can be somewhat complicated. +# In such situations, you can convert the list to a string using `json.dumps`. +# The string will be automatically loaded into a list under the hood. +# If the length of the query list is 3 and the length of the parameter list is 2, +# the plugin will search for parameter acceptance symbols ("?" or "\$") in each query +# to determine whether to include or exclude the parameters before executing the query. +# Therefore, it is necessary to provide the query parameters in the same order as the queries listed. +# ::: +# +# %% +duckdb_params_query = DuckDBQuery( + name="params_query", + query=[ + "CREATE TABLE items(item VARCHAR, value DECIMAL(10,2), count INTEGER)", + "INSERT INTO items VALUES (?, ?, ?)", + "SELECT $1 AS one, $1 AS two, $2 AS three", + ], + inputs=kwtypes(params=str), +) + + +@task +def read_df(df: Annotated[StructuredDataset, kwtypes(one=str)]) -> pd.DataFrame: + return df.open(pd.DataFrame).all() + + +@workflow +def params_wf( + params: str = json.dumps( + [ + [["chainsaw", 500, 10], ["iphone", 300, 2]], + ["duck", "goose"], + ] + ) +) -> pd.DataFrame: + return read_df(df=duckdb_params_query(params=params)) + + +if __name__ == "__main__": + print(f"Running params_wf()... {params_wf()}") diff --git a/docs/examples/duckdb_plugin/requirements.in b/docs/examples/duckdb_plugin/requirements.in new file mode 100644 index 000000000..f4180eb1a --- /dev/null +++ b/docs/examples/duckdb_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-duckdb diff --git a/docs/examples/duckdb_plugin/requirements.txt b/docs/examples/duckdb_plugin/requirements.txt new file mode 100644 index 000000000..4adf1bf59 --- /dev/null +++ b/docs/examples/duckdb_plugin/requirements.txt @@ -0,0 +1,419 @@ +# +# This file is autogenerated by pip-compile with Python 3.9 +# by the following command: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +appnope==0.1.3 + # via + # ipykernel + # ipython +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +attrs==22.2.0 + # via visions +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +cachetools==5.3.0 + # via google-auth +certifi==2022.12.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via requests +click==8.1.3 + # via + # cookiecutter + # flytekit +cloudpickle==2.2.1 + # via flytekit +comm==0.1.2 + # via ipykernel +contourpy==1.0.7 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.8 + # via flytekit +cryptography==39.0.2 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +debugpy==1.6.6 + # via ipykernel +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==6.0.1 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +duckdb==0.7.1 + # via flytekitplugins-duckdb +executing==1.2.0 + # via stack-data +flyteidl==1.3.9 + # via flytekit +flytekit==1.4.0 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard + # flytekitplugins-duckdb +flytekitplugins-deck-standard==1.4.0 + # via -r ../../../common/requirements-common.in +flytekitplugins-duckdb==1.4.0 + # via -r requirements.in +fonttools==4.39.0 + # via matplotlib +gitdb==4.0.10 + # via gitpython +gitpython==3.1.31 + # via flytekit +google-auth==2.16.2 + # via kubernetes +googleapis-common-protos==1.58.0 + # via + # flyteidl + # flytekit + # grpcio-status +grpcio==1.51.3 + # via + # flytekit + # grpcio-status +grpcio-status==1.51.3 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via requests +imagehash==4.3.1 + # via visions +importlib-metadata==6.0.0 + # via + # flytekit + # jupyter-client + # keyring + # markdown +ipykernel==6.21.3 + # via ipywidgets +ipython==8.11.0 + # via + # ipykernel + # ipywidgets +ipywidgets==8.0.4 + # via flytekitplugins-deck-standard +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.2.0 + # via + # flytekit + # phik +jupyter-client==8.0.3 + # via ipykernel +jupyter-core==5.2.0 + # via + # ipykernel + # jupyter-client +jupyterlab-widgets==3.0.5 + # via ipywidgets +keyring==23.13.1 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==26.1.0 + # via flytekit +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.2 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.6.3 + # via + # -r ../../../common/requirements-common.in + # phik + # seaborn + # ydata-profiling +matplotlib-inline==0.1.6 + # via + # ipykernel + # ipython +more-itertools==9.1.0 + # via jaraco-classes +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.3.1 + # via flytekit +nest-asyncio==1.5.6 + # via ipykernel +networkx==3.0 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # scipy + # seaborn + # statsmodels + # visions + # ydata-profiling +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.0 + # via + # docker + # ipykernel + # marshmallow + # matplotlib + # statsmodels +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +pandas-profiling==3.6.6 + # via flytekitplugins-deck-standard +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==9.4.0 + # via + # imagehash + # matplotlib + # visions +platformdirs==3.1.0 + # via jupyter-core +plotly==5.13.1 + # via flytekitplugins-deck-standard +prompt-toolkit==3.0.38 + # via ipython +protobuf==4.22.0 + # via + # flyteidl + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.4 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==10.0.1 + # via flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.5 + # via ydata-profiling +pygments==2.14.0 + # via ipython +pyopenssl==23.0.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # jupyter-client + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.7.1 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # ydata-profiling +pyzmq==25.0.0 + # via + # ipykernel + # jupyter-client +regex==2022.10.31 + # via docker-image-py +requests==2.28.2 + # via + # cookiecutter + # docker + # flytekit + # kubernetes + # requests-oauthlib + # responses + # ydata-profiling +requests-oauthlib==1.3.1 + # via kubernetes +responses==0.22.0 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +scipy==1.9.3 + # via + # imagehash + # phik + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +six==1.16.0 + # via + # asttokens + # google-auth + # kubernetes + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.13.5 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.2 + # via plotly +text-unidecode==1.3 + # via python-slugify +toml==0.10.2 + # via responses +tornado==6.2 + # via + # ipykernel + # jupyter-client +tqdm==4.64.1 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +types-toml==0.10.8.5 + # via responses +typing-extensions==4.5.0 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.14 + # via + # docker + # flytekit + # kubernetes + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.5.1 + # via + # docker + # kubernetes +wheel==0.38.4 + # via + # -r ../../../common/requirements-common.in + # flytekit +widgetsnbextension==4.0.5 + # via ipywidgets +wrapt==1.15.0 + # via + # deprecated + # flytekit +ydata-profiling==4.0.0 + # via pandas-profiling +zipp==3.15.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/exploratory_data_analysis/Dockerfile b/docs/examples/exploratory_data_analysis/Dockerfile new file mode 100644 index 000000000..ed26329d4 --- /dev/null +++ b/docs/examples/exploratory_data_analysis/Dockerfile @@ -0,0 +1,49 @@ +FROM ubuntu:focal + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN : \ + && apt-get update \ + && apt install -y software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa + +RUN : \ + && apt-get update \ + && apt-get install -y python3.8 python3-pip python3-venv make build-essential libssl-dev curl vim + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/exploratory_data_analysis/README.md b/docs/examples/exploratory_data_analysis/README.md new file mode 100644 index 000000000..513e65d70 --- /dev/null +++ b/docs/examples/exploratory_data_analysis/README.md @@ -0,0 +1,73 @@ +# EDA, Feature Engineering, and Modeling With Papermill + +```{eval-rst} +.. tags:: Data, Jupyter, Intermediate +``` + +Exploratory Data Analysis (EDA) refers to the critical process of performing initial investigations on data to discover patterns, +spot anomalies, test hypotheses and check assumptions with the help of summary statistics and graphical representations. + +EDA cannot be solely implemented within Flyte as it requires visual analysis of the data. +In such scenarios, we are inclined towards using a Jupyter notebook as it helps visualize and feature engineer the data. + +**Now the question is, how do we leverage the power of Jupyter Notebook within Flyte to perform EDA on the data?** + +## Papermill + +[Papermill](https://papermill.readthedocs.io/en/latest/) is a tool for parameterizing and executing Jupyter Notebooks. +Papermill lets you: + +- parameterize notebooks +- execute notebooks + +We have a pre-packaged version of Papermill with Flyte that lets you leverage the power of Jupyter Notebook within Flyte pipelines. + +To install the plugin, run the following command: + +```{prompt} bash $ +pip install flytekitplugins-papermill +``` + +## Examples + +There are three code examples that you can refer to in this tutorial: + +- Run the whole pipeline (EDA + Feature Engineering + Modeling) in one notebook +- Run EDA and feature engineering in one notebook, fetch the result (EDA'ed and feature engineered-dataset), + and model the data as a Flyte task by sending the dataset as an argument +- Run EDA and feature engineering in one notebook, fetch the result (EDA'ed and feature engineered-dataset), + and model the data in another notebook by sending the dataset as an argument + +### Notebook Etiquette + +- If you want to send inputs and receive outputs, your Jupyter notebook has to have `parameters` and `outputs` tags, respectively. + To set up tags in a notebook, follow this [guide](https://jupyterbook.org/content/metadata.html#adding-tags-using-notebook-interfaces). + +- `parameters` cell must only have the input variables. + +- `outputs` cell looks like the following: + + ```{code-block} python + from flytekitplugins.papermill import record_outputs + record_outputs(variable_name=variable_name) + ``` + + Of course, you can have any number of variables! + +- The `inputs` and `outputs` variable names in the `NotebookTask` must match the variable names in the notebook. + +```{note} +You will see three outputs on running the Python code files, although a single output is returned. +One output is the executed notebook, and the other is the rendered HTML of the notebook. +``` + +## Examples + +```{auto-examples-toc} +notebook +notebook_and_task +notebooks_as_tasks +supermarket_regression +supermarket_regression_1 +supermarket_regression_2 +``` diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/__init__.py b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook.py b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook.py new file mode 100644 index 000000000..c330ad06a --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook.py @@ -0,0 +1,77 @@ +# %% [markdown] +# # Flyte Pipeline in One Jupyter Notebook +# +# In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering, and measures the Gradient +# Boosting model's performance using mean absolute error (MAE), all in one notebook. + +# %% [markdown] +# First, let's import the libraries we will use in this example. +# %% +import os +import pathlib + +from flytekit import Resources, kwtypes, workflow +from flytekitplugins.papermill import NotebookTask + +# %% [markdown] +# We define a `NotebookTask` to run the [Jupyter notebook](https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/feature_engineering/eda/supermarket_regression.ipynb). +# +# ```{eval-rst} +# .. list-table:: ``NotebookTask`` Parameters +# :widths: 25 25 +# +# * - ``notebook_path`` +# - Path to the Jupyter notebook file +# * - ``inputs`` +# - Inputs to be sent to the notebook +# * - ``outputs`` +# - Outputs to be returned from the notebook +# * - ``requests`` +# - Specify compute resource requests for your task. +# ``` +# +# This notebook returns `mae_score` as the output. +# %% +nb = NotebookTask( + name="pipeline-nb", + notebook_path=os.path.join(pathlib.Path(__file__).parent.absolute(), "supermarket_regression.ipynb"), + inputs=kwtypes( + n_estimators=int, + max_depth=int, + max_features=str, + min_samples_split=int, + random_state=int, + ), + outputs=kwtypes(mae_score=float), + requests=Resources(mem="500Mi"), +) + + +# %% [markdown] +# Since a task need not be defined, we create a `workflow` and return the MAE score. + +# %% +@workflow +def notebook_wf( + n_estimators: int = 150, + max_depth: int = 3, + max_features: str = "sqrt", + min_samples_split: int = 4, + random_state: int = 2, +) -> float: + output = nb( + n_estimators=n_estimators, + max_depth=max_depth, + max_features=max_features, + min_samples_split=min_samples_split, + random_state=random_state, + ) + return output.mae_score + + +# %% [markdown] +# We can now run the notebook locally. +# +# %% +if __name__ == "__main__": + print(notebook_wf()) diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook_and_task.py b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook_and_task.py new file mode 100644 index 000000000..5853d9b92 --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebook_and_task.py @@ -0,0 +1,108 @@ +# %% [markdown] +# # EDA and Feature Engineering in Jupyter Notebook and Modeling in a Flyte Task +# +# In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering +# (step 1: EDA and feature engineering in notebook), and measures the Gradient Boosting model's performance using mean absolute error (MAE) +# (step 2: Modeling in a Flyte Task). + +# %% [markdown] +# First, let's import the libraries we will use in this example. +# %% +import os +import pathlib +from dataclasses import dataclass + +import numpy as np +import pandas as pd +from dataclasses_json import dataclass_json +from flytekit import Resources, kwtypes, task, workflow +from flytekitplugins.papermill import NotebookTask +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.model_selection import cross_val_score, train_test_split +from sklearn.preprocessing import RobustScaler + + +# %% [markdown] +# We define a `dataclass` to store the hyperparameters of the Gradient Boosting Regressor. +# %% +@dataclass_json +@dataclass +class Hyperparameters(object): + n_estimators: int = 150 + max_depth: int = 3 + max_features: str = "sqrt" + min_samples_split: int = 4 + random_state: int = 2 + nfolds: int = 10 + + +# %% [markdown] +# We define a `NotebookTask` to run the [Jupyter notebook](https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/feature_engineering/eda/supermarket_regression_1.ipynb). +# This notebook returns `dummified_data` and `dataset` as the outputs. +# +# :::{note} +# `dummified_data` is used in this example, and `dataset` is used in the upcoming example. +# ::: +# %% +nb = NotebookTask( + name="eda-feature-eng-nb", + notebook_path=os.path.join(pathlib.Path(__file__).parent.absolute(), "supermarket_regression_1.ipynb"), + outputs=kwtypes(dummified_data=pd.DataFrame, dataset=str), + requests=Resources(mem="500Mi"), +) + + +# %% [markdown] +# Next, we define a `cross_validate` function and a `modeling` task to compute the MAE score of the data against +# the Gradient Boosting Regressor. + +# %% +def cross_validate(model, nfolds, feats, targets): + score = -1 * (cross_val_score(model, feats, targets, cv=nfolds, scoring="neg_mean_absolute_error")) + return np.mean(score) + + +@task +def modeling( + dataset: pd.DataFrame, + hyperparams: Hyperparameters, +) -> float: + y_target = dataset["Product_Supermarket_Sales"].tolist() + dataset.drop(["Product_Supermarket_Sales"], axis=1, inplace=True) + + X_train, X_test, y_train, _ = train_test_split(dataset, y_target, test_size=0.3) + + scaler = RobustScaler() + + scaler.fit(X_train) + + X_train = scaler.transform(X_train) + X_test = scaler.transform(X_test) + + gb_model = GradientBoostingRegressor( + n_estimators=hyperparams.n_estimators, + max_depth=hyperparams.max_depth, + max_features=hyperparams.max_features, + min_samples_split=hyperparams.min_samples_split, + random_state=hyperparams.random_state, + ) + + return cross_validate(gb_model, hyperparams.nfolds, X_train, y_train) + + +# %% [markdown] +# We define a `workflow` to run the notebook and the `modeling` task. +# %% +@workflow +def notebook_wf(hyperparams: Hyperparameters = Hyperparameters()) -> float: + output = nb() + mae_score = modeling(dataset=output.dummified_data, hyperparams=hyperparams) + return mae_score + + +# %% [markdown] +# We can now run the notebook and the modeling task locally. +# +# %% +if __name__ == "__main__": + print(notebook_wf()) diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebooks_as_tasks.py b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebooks_as_tasks.py new file mode 100644 index 000000000..a54e15b0c --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/notebooks_as_tasks.py @@ -0,0 +1,89 @@ +# %% [markdown] +# # EDA and Feature Engineering in One Jupyter Notebook and Modeling in the Other +# +# In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering +# (step 1: EDA and feature engineering in notebook), and measures the Gradient Boosting model's performance using mean absolute error +# (MAE) (step 2: Modeling in notebook). + +# %% [markdown] +# First, let's import the libraries we will use in this example. +# %% +import os +import pathlib + +import pandas as pd +from flytekit import Resources, kwtypes, workflow +from flytekitplugins.papermill import NotebookTask + +# %% [markdown] +# We define a `NotebookTask` to run the [Jupyter notebook](https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/feature_engineering/eda/supermarket_regression_1.ipynb) (EDA). +# This notebook returns `dummified_data` and `dataset` as the outputs. +# +# :::{note} +# `dataset` is used in this example, and `dummified_data` is used in the previous example. +# `dataset` lets us send the DataFrame as a JSON string to the subsequent notebook because DataFrame input cannot be sent +# directly to the notebook as per Papermill. +# ::: +# %% +nb_1 = NotebookTask( + name="eda-featureeng-nb", + notebook_path=os.path.join(pathlib.Path(__file__).parent.absolute(), "supermarket_regression_1.ipynb"), + outputs=kwtypes(dummified_data=pd.DataFrame, dataset=str), + requests=Resources(mem="500Mi"), +) + +# %% [markdown] +# We define a `NotebookTask` to run the [Jupyter notebook](https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/feature_engineering/eda/supermarket_regression_2.ipynb) +# (Modeling). +# +# This notebook returns `mae_score` as the output. +# %% +nb_2 = NotebookTask( + name="regression-nb", + notebook_path=os.path.join( + pathlib.Path(__file__).parent.absolute(), + "supermarket_regression_2.ipynb", + ), + inputs=kwtypes( + dataset=str, + n_estimators=int, + max_depth=int, + max_features=str, + min_samples_split=int, + random_state=int, + ), + outputs=kwtypes(mae_score=float), + requests=Resources(mem="500Mi"), +) + + +# %% [markdown] +# We define a `Workflow` to run the notebook tasks. + +# %% +@workflow +def notebook_wf( + n_estimators: int = 150, + max_depth: int = 3, + max_features: str = "sqrt", + min_samples_split: int = 4, + random_state: int = 2, +) -> float: + eda_output = nb_1() + regression_output = nb_2( + dataset=eda_output.dataset, + n_estimators=n_estimators, + max_depth=max_depth, + max_features=max_features, + min_samples_split=min_samples_split, + random_state=random_state, + ) + return regression_output.mae_score + + +# %% [markdown] +# We can now run the two notebooks locally. +# +# %% +if __name__ == "__main__": + print(notebook_wf()) diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression.ipynb b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression.ipynb new file mode 100644 index 000000000..6afed2a87 --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression.ipynb @@ -0,0 +1,1825 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supermarket Regression Notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# reference: https://github.com/risenW/medium_tutorial_notebooks/blob/master/supermarket_regression.ipynb\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# makes graph display in notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "supermarket_data = pd.read_csv('https://raw.githubusercontent.com/risenW/medium_tutorial_notebooks/master/train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_IdentifierSupermarket_IdentifierProduct_Supermarket_IdentifierProduct_WeightProduct_Fat_ContentProduct_Shelf_VisibilityProduct_TypeProduct_PriceSupermarket_Opening_YearSupermarket _SizeSupermarket_Location_TypeSupermarket_TypeProduct_Supermarket_Sales
0DRA12CHUKWUDI010DRA12_CHUKWUDI01011.6Low Fat0.068535Soft Drinks357.542005NaNCluster 3Grocery Store709.08
1DRA12CHUKWUDI013DRA12_CHUKWUDI01311.6Low Fat0.040912Soft Drinks355.791994HighCluster 3Supermarket Type16381.69
2DRA12CHUKWUDI017DRA12_CHUKWUDI01711.6Low Fat0.041178Soft Drinks350.792014NaNCluster 2Supermarket Type16381.69
3DRA12CHUKWUDI018DRA12_CHUKWUDI01811.6Low Fat0.041113Soft Drinks355.042016MediumCluster 3Supermarket Type22127.23
4DRA12CHUKWUDI035DRA12_CHUKWUDI03511.6Ultra Low fat0.000000Soft Drinks354.792011SmallCluster 2Supermarket Type12481.77
\n", + "
" + ], + "text/plain": [ + " Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier \\\n", + "0 DRA12 CHUKWUDI010 DRA12_CHUKWUDI010 \n", + "1 DRA12 CHUKWUDI013 DRA12_CHUKWUDI013 \n", + "2 DRA12 CHUKWUDI017 DRA12_CHUKWUDI017 \n", + "3 DRA12 CHUKWUDI018 DRA12_CHUKWUDI018 \n", + "4 DRA12 CHUKWUDI035 DRA12_CHUKWUDI035 \n", + "\n", + " Product_Weight Product_Fat_Content Product_Shelf_Visibility Product_Type \\\n", + "0 11.6 Low Fat 0.068535 Soft Drinks \n", + "1 11.6 Low Fat 0.040912 Soft Drinks \n", + "2 11.6 Low Fat 0.041178 Soft Drinks \n", + "3 11.6 Low Fat 0.041113 Soft Drinks \n", + "4 11.6 Ultra Low fat 0.000000 Soft Drinks \n", + "\n", + " Product_Price Supermarket_Opening_Year Supermarket _Size \\\n", + "0 357.54 2005 NaN \n", + "1 355.79 1994 High \n", + "2 350.79 2014 NaN \n", + "3 355.04 2016 Medium \n", + "4 354.79 2011 Small \n", + "\n", + " Supermarket_Location_Type Supermarket_Type Product_Supermarket_Sales \n", + "0 Cluster 3 Grocery Store 709.08 \n", + "1 Cluster 3 Supermarket Type1 6381.69 \n", + "2 Cluster 2 Supermarket Type1 6381.69 \n", + "3 Cluster 3 Supermarket Type2 2127.23 \n", + "4 Cluster 2 Supermarket Type1 2481.77 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supermarket_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_WeightProduct_Shelf_VisibilityProduct_PriceSupermarket_Opening_YearProduct_Supermarket_Sales
count4188.0000004990.0000004990.0000004990.0000004990.000000
mean12.9088380.066916391.8037962004.7835676103.520164
std4.7032560.053058119.3782598.2831514447.333835
min4.5550000.00000078.7300001992.00000083.230000
25%8.7675000.027273307.8900001994.0000002757.660000
50%12.6000000.053564393.8600002006.0000005374.675000
75%17.1000000.095358465.0675002011.0000008522.240000
max21.3500000.328391667.2200002016.00000032717.410000
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" + ], + "text/plain": [ + " Product_Weight Product_Shelf_Visibility Product_Price \\\n", + "count 4188.000000 4990.000000 4990.000000 \n", + "mean 12.908838 0.066916 391.803796 \n", + "std 4.703256 0.053058 119.378259 \n", + "min 4.555000 0.000000 78.730000 \n", + "25% 8.767500 0.027273 307.890000 \n", + "50% 12.600000 0.053564 393.860000 \n", + "75% 17.100000 0.095358 465.067500 \n", + "max 21.350000 0.328391 667.220000 \n", + "\n", + " Supermarket_Opening_Year Product_Supermarket_Sales \n", + "count 4990.000000 4990.000000 \n", + "mean 2004.783567 6103.520164 \n", + "std 8.283151 4447.333835 \n", + "min 1992.000000 83.230000 \n", + "25% 1994.000000 2757.660000 \n", + "50% 2006.000000 5374.675000 \n", + "75% 2011.000000 8522.240000 \n", + "max 2016.000000 32717.410000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supermarket_data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# remove ID columns\n", + "cols_2_remove = ['Product_Identifier', 'Supermarket_Identifier', 'Product_Supermarket_Identifier']\n", + "\n", + "newdata = supermarket_data.drop(cols_2_remove, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_WeightProduct_Fat_ContentProduct_Shelf_VisibilityProduct_TypeProduct_PriceSupermarket_Opening_YearSupermarket _SizeSupermarket_Location_TypeSupermarket_TypeProduct_Supermarket_Sales
011.6Low Fat0.068535Soft Drinks357.542005NaNCluster 3Grocery Store709.08
111.6Low Fat0.040912Soft Drinks355.791994HighCluster 3Supermarket Type16381.69
211.6Low Fat0.041178Soft Drinks350.792014NaNCluster 2Supermarket Type16381.69
311.6Low Fat0.041113Soft Drinks355.042016MediumCluster 3Supermarket Type22127.23
411.6Ultra Low fat0.000000Soft Drinks354.792011SmallCluster 2Supermarket Type12481.77
\n", + "
" + ], + "text/plain": [ + " Product_Weight Product_Fat_Content Product_Shelf_Visibility Product_Type \\\n", + "0 11.6 Low Fat 0.068535 Soft Drinks \n", + "1 11.6 Low Fat 0.040912 Soft Drinks \n", + "2 11.6 Low Fat 0.041178 Soft Drinks \n", + "3 11.6 Low Fat 0.041113 Soft Drinks \n", + "4 11.6 Ultra Low fat 0.000000 Soft Drinks \n", + "\n", + " Product_Price Supermarket_Opening_Year Supermarket _Size \\\n", + "0 357.54 2005 NaN \n", + "1 355.79 1994 High \n", + "2 350.79 2014 NaN \n", + "3 355.04 2016 Medium \n", + "4 354.79 2011 Small \n", + "\n", + " Supermarket_Location_Type Supermarket_Type Product_Supermarket_Sales \n", + "0 Cluster 3 Grocery Store 709.08 \n", + "1 Cluster 3 Supermarket Type1 6381.69 \n", + "2 Cluster 2 Supermarket Type1 6381.69 \n", + "3 Cluster 3 Supermarket Type2 2127.23 \n", + "4 Cluster 2 Supermarket Type1 2481.77 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newdata.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = ['Product_Fat_Content','Product_Type',\n", + " 'Supermarket _Size', 'Supermarket_Location_Type',\n", + " 'Supermarket_Type' ]\n", + "\n", + "num_cols = ['Product_Weight', 'Product_Shelf_Visibility',\n", + " 'Product_Price', 'Supermarket_Opening_Year', 'Product_Supermarket_Sales']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# bar plot for categorial features\n", + "for col in cat_cols:\n", + " fig = plt.figure(figsize=(6,6)) # define plot area\n", + " ax = fig.gca() # define axis \n", + " \n", + " counts = newdata[col].value_counts() # find the counts for each unique category\n", + " counts.plot.bar(ax = ax) # use the plot.bar method on the counts data frame\n", + " ax.set_title('Bar plot for ' + col)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot for numerical features\n", + "for col in num_cols:\n", + " fig = plt.figure(figsize=(6,6)) # define plot area\n", + " ax = fig.gca() # define axis \n", + "\n", + " newdata.plot.scatter(x = col, y = 'Product_Supermarket_Sales', ax = ax)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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aTD83OEbEKuAN5cRnZjYcurq6WLp0KRHB0qVLaW1tZeLEidUOa8QM2p0laZakH6ebBu8vPEYiOMtvypQpvcr77rvvAFua2XBqa2t7aYaInp4e2trK+t5ds/J0Z32P7Ga/bwBHkHUz5bmWYv1YuHAhHR0dw37cvnc5P/roo8O6MNXMmTO90JVZP9rb29m6dSsAW7du5frrr2+oLq08yWBCRNwIKCIejIgLgL+ubFg2VHvttVfJso1e++23X8myjW5z5syhqakJgKamppfNQFDv8rREtkgaA6yXdDrZMNrdKhtW/arUt/muri5OOOEEIoKddtqJ7373uw3VL1vLzjrrLD75yU++VG6kb7H1oLW1leuuuw7IZs9ubW0dZI/6kqclchbZlCdnAgeTjbhqrHepBjQ3N7P33nsDcPTRRzuB1JCrrrqqV/l//ud/qhSJlaO5uZnJkycD2QzMjfZ/L8/orMJ4w82kYbc2Ok2ePJkXX3yx4b4J1TqvSlnburq62Lgxu89548aNbNq0qaESSZ7RWS2SrpZ0e5qifY2kNSMRnA1NU1MTs2bNaqh/wGbVtmjRInp6eoBsdNaiRYuqHNHIytOd9QOyEVonAMcWPczMGt4NN9zQq9x3LrR6l+fC+hMR4UWfzcz6UWiFDFSud3mSyPmSvku2gNSWQmXRhIpmZg1LUq/lqBttgdU83VkfBg4iWzWw0JV1TAVjMmsoY8eOLVm20W3OnDm9yr5P5OXeGhGvqXgkZjWgEjMO9Ncd4tkGasfHPvYxli1b1qvcSPK0RG5Ja6ObWQUU7u8ZqGyjX6ELq9G6siBfS+Qw4E5JD5BdExHZpLtvrGhkZqNQJb7Rd3V18Td/8zcAjBs3zrMN1Ji2tjbGjh1Ld3c3Y8eObbjp4Eu2RNJiTx8DZpEtBlW4HuIhvmbDpLm5+aWkccwxxziB1Jj29na6u7sB6O7ubrjljQdbHjeAi9PEi70eIxSfWUOYPHkyu+66q2cbqEFz5sxh3LisU2fcuHENd2E9zzWR2yW9teKRmDUwzzZQu1pbW9m2bRsA27Zta7gvAnmuiRwKfEjSBuBP+JqImVkvhftEiu8XaRR5WiLzgFcBR+JrImZmvSxcuLBkud4NmkTS9Y/9gCPT8+fz7Gdm1ghWrFjRq7x8+fLqBFIleWbxPR/4FHBeqmoCvl/JoMzMrDbkaVEcD7yX7HoIEfEw8IrBdpK0WNLjku4uqrtA0kZJd6bH0UWvnSepQ9J9kuYV1c9PdR2Szi2q31/Sb1P9jyTtlO9XNjOz4ZInifw5DfUNAEm75jz2ZWTzbfX1jYg4KD2uS8c8EDgReH3a51uSxkoaC1wMHAUcCHyw6O75r6RjzQSeAk7JGZeZmQ2TPEnkSkmLgD0lfRS4AfjOYDtFxE3AkznjWABcERFbIuIBoAM4JD06IuL+iPgzcAWwIN0EeSTw47R/G3BcznOZmQ2bXXbZpWS53uW5sP5Vsj/WVwGvBj4bERftwDlPT6sjLpa0V6qbCjxUtE1nqhuofiLwdER096k3MxtRjb6eSN5RVmuBm4Gb0vNyfRs4gGxq+UeAr+3AsXKTdKqkVZJWPfHEEyNxSjNrEPPmzetVnj+/v178+jXozYaS/g/wWeCXZDcaXiTp8xGxeKgni4jHio77HeDaVNxINoy4YFqqY4D6TWTda+NSa6R4+/7OewlwCUBLS0vj3Q1kZhWZxh9g69atvcrr169vqKn887RE/hl4c0ScHBGtwMFkQ36HTNKUouLxQGHk1hLgREk7S9qfbMLH24CVwKw0EmsnsovvS9KF/uXA+9L+rcA15cRkZrYjmpqaXpo7a+LEiTQ1NVU5opGVZ9qTTcBzReXnUl1Jkn4IzAaaJXUC5wOzJR1ENtJrA9kMwUTEOklXAvcA3cBpEbEtHed0YBkwFlgcEevSKT4FXCHpC8AdwKU5fhcza1CV/Db/iU98gg0bNjTkNP55kkgH8FtJ15D98V8ArJF0DkBEfL2/nSLig/1UD/iHPiK+CHyxn/rrgOv6qb+fbPSWmVlVNfIEmnmSyB/So6DQbTToDYdmZlbfBk0iEfG5kQjEzMxqT57RWctJd6sXi4gjKxKRmZnVjDzdWZ8sej4eOIHs4reZmTW4PN1Zq/tU/VrSbRWKx8zMakie7qy9i4pjyO4T2aNiEZmZWc3I0521muyaiMi6sR7AM+aamRn5urP2H4lAzMys9gw47Ymkt0qaXFQ+SdI1khb26eIyM7MGVWrurEXAnwEkvRP4MnA58AxpMkMzM2tspbqzxkZEYVGpDwCXRMRVwFWS7qx4ZGZmNuqVaomMlVRIMu8mmwq+IM8FeTMzq3OlksEPgV9J6gJeIFuUCkkzybq0zMyswQ2YRCLii5JuBKYA16c1PCBrvZwxEsGZmdnoVrJbKiJu7afu95ULx8zMakneNdbNzMxexknEzMzKNmgSkfSVPHVmZtZ48rRE5vRTd9RwB2JmZrVnwAvrkj4B/ANwgKQ1RS+9Aril0oGZmdnoV2p01n8DS4EvAecW1T9XdCe7mZk1sAG7syLimYjYAPwH8GREPBgRDwLdkg4dqQDNzGz0ynNN5NvA5qLy5lRXkqTFkh6XdHdR3d6S2iWtTz/3SvVKswN3SFoj6S1F+7Sm7ddLai2qP1jS2rTPQknK8wubmdnwyZNEVHS3OhHRQ765sy4D5vepOxe4MSJmATeyvZvsKGBWepxKSlJpyvnzgUOBQ4DzC4knbfPRov36nsvMzCosTxK5X9KZkprS4yzg/sF2ioibgL7XThYAbel5G3BcUf3lkbkV2FPSFGAe0B4RT0bEU0A7MD+9tntE3JoS3OVFxzIzsxGSp0XxcWAh8BmyZXJvJGstlGOfiHgkPX8U2Cc9nwo8VLRdZ6orVd/ZT32/JJ1aiHn69Ollhm61YuHChXR0dFQ7jCFZv349AGeeeWaVIxmamTNn1lzMNrzyLI/7OHDicJ84IkJSDL7lsJzrEtJCWi0tLSNyTquejo4Ofn/37UzfbVu1Q8ltp61Zp8CLG1ZWOZL8/rh5bLVDsFFg0CQi6XtkLZBeIuIjZZzvMUlTIuKR1CX1eKrfCOxXtN20VLcRmN2nfkWqn9bP9mYATN9tG59p2Tz4hla2L6zardoh2CiQ55rItcDP0+NGYHd6j9YaiiVAYYRVK3BNUf1JaZTWYcAzqdtrGTBX0l7pgvpcYFl67VlJh6VRWScVHcvMzEZInu6sq4rLkn4I/O9g+6XtZgPNkjrJRll9GbhS0inAg8D70+bXAUcDHcDzwIfTuZ+UdCFQaON/vuhGx38gGwE2geymyKWDxWRmZsOrnGVuZwGvHGyjiPjgAC+9u59tAzhtgOMsBhb3U78KeMNgcZiZWeXkuSbyHNk1EaWfjwKfqnBcZmZWA/J0Z71iJAIxM7PaU2oW37cM9BpARNw+/OGMHr7XYGT4PgOz2laqJfK19HM80ALcRdal9UZgFfC2yoZWXR0dHdyx9h56dtm72qHkpj9nI7FX/+HRKkeSz5jnPRm0Wa0bMIlExBEAkn4CvCUi1qbyG4ALRiS6KuvZZW9ePPCYaodRt8bfc221QzCzHZTnPpHXFBIIQETcDbyuciGZmVmtyDPEd42k7wLfT+W/A9aU2N7MzBpEniTyYeATwFmpfBM51hMxM7P6l2eI74uSLgZuILtP5L6I2FrxyMzMbNTLc7PhbLK1PzaQjc7aT1JrWi/EzMwaWJ7urK8BcyPiPgBJrwZ+CBxcycDMrDH5Hq2RMVz3aOVJIk2FBAIQEb+X1LTDZzarkM7OTv703FhPVV5hDz43ll07OwffcIg6Ojq4Y90dsOewH7pyerIfd2y8o7px5PX08B0qTxJZ3c/orFXDF4KZWR97Qs/snmpHUbfGrMhzd0c+eZfHPQ0otHtuBr41bBGYDbNp06bxYvcjXpSqwr6wajfGT5s2+IZW10omEUljgbsi4rXA10cmJDMzqxUl2zQRsQ24T9L0EYrHzMxqSJ7urL2AdZJuA/5UqIyI91YsKjMzqwl5ksj/rXgUZmZWk0qtJzKe7KL6TGAtcGlEdI9UYGZmNvqVuibSRraOyFrgKLavL2JmZgaU7s46MCL+EkDSpcBtIxOSmZnVilItkZcmWRzubixJGyStlXSnpFWpbm9J7ZLWp597pXpJWiipQ9Ka4mV7JbWm7ddLah3OGM3MbHClksibJD2bHs8Bbyw8l/TsMJz7iIg4KCJaUvlc4MaImAXcmMqQdaXNSo9TSdPQS9obOB84FDgEOL+QeMzMbGQMmEQiYmxE7J4er4iIcUXPd69ALAvIrsOQfh5XVH95ZG4F9pQ0BZgHtEfEkxHxFNAOzK9AXGZmNoA8Q3wrIYDrJQWwKCIuAfaJiEfS648C+6TnU4GHivbtTHUD1b+MpFPJWjFMn+77JhvBHzfX1gSMjz2ffZ/bZ5famS/qj5vH8upqB2FVV60k8o6I2CjplUC7pN8VvxgRkRLMsEhJ6hKAlpaWYTuujU4zZ86sdghD9uc0lfj4GbOqHEl+r6Y232sbXlVJIhGxMf18XNLVZNc0HpM0JSIeSd1Vj6fNNwL7Fe0+LdVtBGb3qV9R4dCtBtTSmg4FhZgXLlxY5UjMhmbEk4ikXYExEfFcej4X+DywBGgFvpx+XpN2WQKcLukKsovoz6REswz416KL6XOB84Yrzs7OTsY8/wzj77l2uA5pfYx5fhOdnb5/1Xrr7OyEZ4Z3unLr42nojOFZC6YaLZF9gKslFc7/3xHxC0krgSslnQI8CLw/bX8dcDTQATwPfBggIp6UdCGwMm33+Yh4cuR+DTMzG/EkEhH3A2/qp34T8O5+6oNsPZP+jrUYWDzcMUK2JsVjW8bx4oHHVOLwBoy/51qmTZtc7TBslJk2bRpP6AkvSlVBY1aMYdrU4VkLxu1FMzMrm5OImZmVzUnEzMzK5iRiZmZlcxIxM7OyOYmYmVnZnETMzKxsTiJmZlY2JxEzMyubk4iZmZXNScTMzMrmJGJmZmWr1qJUZmYDe7rGpoLfnH7WymKaTzPAOrBD5yRiZqNKLa6WuD6tTDlrao2sTDl1+N5nJ5ESxjz/ZE0tSqUXnwUgxu9e5UjyGfP8k4CngrfevDJlbXESGUBtfht6DoBZB9TKH+bJNfk+m9l2TiID8LchM7PB1dCVKzMzG22cRMzMrGxOImZmVjZfEzEbgoULF9LR0THsxy0MEa3EtbiZM2fW5DU+qw013xKRNF/SfZI6JJ1b7XjMyjFhwgQmTJhQ7TDMhqymWyKSxgIXA3OATmClpCURcU91I7N65W/0tatSrUho7JZkTScR4BCgIyLuB5B0BbAAGLVJxP+QzepPI7ciaz2JTAUeKip3AodWKZaqa+R/yGaD8Zegyqj1JJKLpFOBUwGmT59e1Vj8D9nM6kmtX1jfCOxXVJ6W6nqJiEsioiUiWiZNmjRiwZmZ1btaTyIrgVmS9pe0E3AisKTKMZmZNYya7s6KiG5JpwPLgLHA4ohYV+WwzMwaRk0nEYCIuA64rtpxmJk1olrvzjIzsypyEjEzs7I5iZiZWdmcRMzMrGyKiGrHMKIkPQE8WO04KqgZ6Kp2EFYWf3a1rd4/v7+IiJfdaNdwSaTeSVoVES3VjsOGzp9dbWvUz8/dWWZmVjYnETMzK5uTSP25pNoBWNn82dW2hvz8fE3EzMzK5paImZmVzUnEzMzK5iRSJZImS7pC0h8krZZ0naRXS5oh6e4yj3mypH13MK4FktZIulPSKknv2JHj1atR/Pn9Xfr81kq6RdKbduR49WgUf3avlfQbSVskfXJHjjWSnESqQJKAq4EVEXFARBwMnAfss4OHPhkY0j9kSX1ncr4ReFNEHAR8BPjuDsZUd0b55/cA8K6I+EvgQhr0Yu9ARvln9yRwJvDVHYxlRDmJVMcRwNaI+M9CRUTcFRE3F2+Uvt38v6LytZJmSxor6TJJd6dvnP8o6X1AC/CD1IqYIOlgSb9K37aWSZqSjrNC0jclrQLOKj5nRGyO7aMtdgU88uLlRvPnd0tEPJWKt5Kt9mnbjebP7vGIWAlsreDvP+xqfj2RGvUGYPUO7H8QMDUi3gAgac+IeDot0PXJiFglqQm4CFgQEU9I+gDwRbLWBcBOA91dK+l44EvAK4G/3oE469Wo/vyKnAIs3YE461GtfHY1w0mkNt0PvErSRcDPgev72eY1ZP9h2rMWPGOBR4pe/9FAB4+Iq4GrJb2TrEvkPcMUt2Uq+vkBSDqCLIn4mtbwqvhnV2ucRKpjHfC+HNt107vLcTxARDyVLpjOAz4OvJ/t33IKBKyLiLcNcOw/DXbyiLhJ0qskNUdEPU8sN1Sj+vOT9Eaya1lHRcSmHHE2klH92dUiXxOpjl8CO0s6tVAh6Y2SDu+z3QbgIEljJO0HHJK2bQbGRMRVwGeAt6TtnwNekZ7fB0yS9La0T5Ok1w8WmKSZ6eIjkt4C7Az4D1Fvo/nzmw78BPj7iPh9ub9gHRu1n12tckukCiIi0nWHb0r6FPAi2T/as/ts+muy0Tb3APcCt6f6qcD3JBW+BJyXfl4G/KekF4C3kX3jWihpD7LP+ptk38RKOQE4SdJW4AXgA0UX2o1R//l9FpgIfCt9F+iup/73HTWaPztJk4FVwO5Aj6SzgQMj4tkyftUR42lPzMysbO7OMjOzsjmJmJlZ2ZxEzMysbE4iZmZWNicRMzMrm5OImZmVzUnEqkbSv0hap+1Tzx9a7ZjySpPxXTuE7QedKjxNzleRezoknS1pl6LydZL2HKZjX5w+v3skvZCe35kmJrQ655sNrSrS3bzHAG+JiC3pTuCdKnQukd0T1TNMxyvn/83JwN3Aw8MRQxnOBr4PPA8QEUcP14Ej4jQASTOAa9MyAtYg3BKxapkCdEXEFoCI6IqIhyVtSAkFSS2SVqTnF0j6L2WL9qyX9NHCgST9s6SVqUXzuVQ3Q9J9ki4n++N9uKTfKZvG+/eSfiDpPZJ+nY5XmNbikHSOO5Qt6vSaVH+ypCWSfkm25gpF539r2v4A9TMFuPqZKjzvmyRpb0k/Tb/brcrmxULSbpK+p2w68jWSTkj131a2mNi6ovfiTLK1LpZLWp7qit/nc5RNbX53uku68P7dK+k76VjXDzHuyyUdV1T+gbIFz06WdE1qda2XdH7RNh+SdFt6jxZJGpv3fFZFEeGHHyP+AHYD7gR+D3yLbCElyKagaE7PW8gWDwK4ALgLmAA0Aw+R/WGcS7bwksi+FF0LvBOYAfQAh6X9Z5BNqveXabvVwOK03wLgp2m73YFx6fl7gKvS85OBTmDvVJ6dzvVX6VjTgSbgFmBS2uYDwOL0fAXQMsh78rJtyKYUPz89PxK4Mz3/CvDNou32Sj8L8Y1Nx3tj3/e1uAwcDKwlWztmN7KpOd5c9H4dlLa/EvjQIPHPAO5Oz99V9J7uQTaFyLj0Pj5CNjXLBLIE3wK8DvgZ0JT2+RZwUrX/nfox+MPdWVYVEbFZ0sHA4WQLBf1I0rmD7HZNRLwAvJC+UR9CNtX5XOCOtM1uwCzgj8CDEXFr0f4PRMRaAEnrgBsjIiStJfsDCNkfvDZJs8gW5Goq2r89Ip4sKr+OLIHNjawV9QZKTwFejneQzWdGRPxS0kRJu5MluBMLG8X2hajer2xywXFkrb0DgTWDHP/qiPgTgKSfkH0mS8jerzvTdqvZ/h4NKiJ+Jelbkial+K+KiO70vrRHml04ne8dZAnrYGBl2mYC8Hje81n1OIlY1UTENrJvyyvSH/JWek/BPb7vLv2UBXwpIhYVv5D65/tOub2l6HlPUbmH7f8XLgSWR8Tx6Rgrivbpe7xHUoxvJrvWMdgU4BUlaX/gk8BbI5uy/DJe/h4ORfH7tY3sD/tQXA58iCzZfbiofqDPsS0izsNqiq+JWFVIek36tl9wEPAgWTfLwanuhD67LZA0XtJEsu6klcAy4COSdkvHnSrplTsQ2h7AxvT85EG2fZps5ccvSZpN6SnAi6cKH4qbgb9Lx5tNdh3pWaAdOK2wkaS9yLri/gQ8I2kf4Kii4wx0/puB4yTtImlX4PhUNxwuI82OGxH3FNXPSdd6JgDHkc2YeyPwvsJnl17/i2GKwyrILRGrlt2Ai5QNM+0GOoBTybqILpV0Ib1bAZB1yywn68u/MCIeBh6W9DrgN6kbZDPZt99tZcb1b2TdWZ8hW7mupIh4TNIxZMvQfoSBpwC/jKKpwlO3XH9+rmwafoDfAB8DFktaQzayqjW99gXgYkl3k/2un4uIn0i6A/gd2TWjXxcd9xLgF5IejogjiuK/PbVYbktV342IO1IrbIek9+Ze4Kd9XroNuIps/ffvR8QqgPSeX69smvWtZEnywR2NwyrLU8FbTZB0AbA5Ir5a7VgsH2X3pawlG8b9TKo7mWzwwOnVjM2Gj7uzzGzYSXoP2WJOFxUSiNUnt0TMRpikq4H9+1R/KiKWVSOeoZB0MfD2PtX/ERHfq0Y8Vn1OImZmVjZ3Z5mZWdmcRMzMrGxOImZmVjYnETMzK9v/ByWpdPwyaIlAAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# box plot for categorial features\n", + "for col in cat_cols:\n", + " sns.boxplot(x=col, y='Product_Supermarket_Sales', data=newdata)\n", + " plt.xlabel(col)\n", + " plt.ylabel('Product Supermarket Sales')\n", + " plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# correlation matrix\n", + "corrmat = newdata.corr()\n", + "f,ax = plt.subplots(figsize=(5,4))\n", + "sns.heatmap(corrmat, square=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", 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WWdO2gnFFN7KucSd5Ng+tgd5Ek3pZR76xlNtK72HdhghlBd2MyrVh1Ik80oIgCCdiUJ+2+3sQ7q9ne9GiRaxZs4Zf//rX3H///Xzzm988oeOOJTv77Mx393iOPce2rTtCSb4Dl+vYibHy3BYCCZXiItcA1q7X8eo5VIh6nhknej0Mt/f1UQNRdw92fnD+fTT72zAoegrteZj0Rn66+JvUdjUiyzJlrmIK7LnHPVc+bqYzAYDOUBfnls7m1b3v9NknqSZpDLQwdnQFkMpM/f0/rORIfG4z6/mfryygNN+Bqqm8ufVtXtr9JpAabfDzD3/PTxZ/k3PKZqKhsr19LzXeWpaMXczEgiocxjN3HxzO35Wz9fvwUf19Xl3hbgrsecwsnIxe1hOKhajIHkU4HsFTPHQ+34H8t/Z2h1m3q5Xn3+/NvD++3M3d10yistg1YK9zxHD+np6tup+p60H1qVw59iL2dh5kXeMWbph4JWsbNqGTdQSiQW6svoZX3/ayenvvai1Lzq3g01eMw6gfnMfX4fR9GU51heFXX0EYDgY10M/Ly2P9+t75rW1tbeTm9j4g+3w+tm/fzoIFCwBYsmQJ9913H263m0AgQDKZRFEU2tvb+xx3Ijo7AyfWm3UaPB477e3+Y+5zoMHHvAn5+HzHHoqZbTeydXcr2WdgnuKJ1HMoEPU8Pcf6ET2R62Govq8Tcap1TwKxuIrZIIOWWrovHFOxSTmYJAW/L4afGEZsVFnGotPJqDHtpF9LlvWMdY7nPcNKwolIn23hsEZrux9Zknjqrb3pID/PbeKKi9ysaF6O1WtkbE4FH9b2zReQ1FSae9oI6EPs6TxIPBnns9NupsoxhmiPRjtn5t9zOHxXTvd6GOi69Pd5RZUwSTXJxLxqPqhdQ7bZRXlWCeF4ZMh8vgP9b93hj/KvDw72Kdt50EubN4TTOLAj2obD9/RoBrrug3E9BCMaZtnMKJeF+aUzMetMlLtK2Nq6i+qcStSkwtodfUdrvrLiAAunFJJ9BpK4Hs9w+r4Mp7rC0KuvaHQQRopBDfTnz5/Pgw8+iNfrxWw28+abb/LjH/84vV3TNL7xjW/w3HPPUVhYyOuvv8706dPR6/XMnDmT1157jSVLlvDiiy+ycOHCQXwnp0bTNFq7wrgdxuPu63GZOdQydG6CgjCSSZJEc1eYx5bupq6lh3mTCrh8fjm7Dnl55p19IMHNF45h2pgcDIpMLKmxv7Gb5ZsbKfTYmD0+D4Mi47LqT2i4sapqdHfoua5qCY9s/nu6PNvspqPZgNcSxW030h2MHq4fXHO5m8f3/Q1VSy3FZ9IZuWXSVfxt0zN9zm3SGfjZhw+mR1Dt7qjhq7M/Q7Vt3EB9XMIZYsKITlH426anAaihlk3NO/jWufcMcs3OnISmkUhmLi8Zi4vl9UaSYCzJtoOd2LL1/H7DI8wqmsLSfe+l850c8jWwvW0vi+ddxJsre4N9TYP4GVh+VBAEYSQa9B79++67jzvvvJN4PM4NN9zA5MmTufvuu7n33nuZNGkSP/7xj/niF7+IJElUVlbywx/+EIAf/OAHfPvb3+bhhx+moKCAX//614P5Vk6JPxxH0zQsJzBfOTfLzIqtmYlqBEEYeL5QjB/9ZU16KcC319XT0R1BkSV8gVSw/YcXt/ON26YzYVQW63a28ud/7Th8dCtvra1jyYJyGtoC3HLhGCwnkFtjVL6dAy0F3FLxaToTTVgUO1ogi+eWNjG9rAxFgivPKef3z21lXHkWG72r00E+QCQRpScSwGm00x1NNQpWZVewz3soY5rUq/veYeyMKiRV5PwYysJEWbrv/T5lcTVBXXcTBe7iwanUGeawGJhcmcPW/b0rC9gtenLdx57eJgwfSQ0eW7qb5s4QY89pIKEmKHLks6p+Q5/9GnqaOKe877Gji5zknEDniCAIgjDIgT6khuMvWdJ3TeBHHnkk/f+LFy9m8eLFGccVFRXx2GOPnfH6nUktnSGynaYTyi/gcZlp9oZQVQ35DCxHJghDhappeAMxvD1Rsp2mQRmi2dIZSgf5R2ze287Ni6vYsLs3C/qyTY2MKXbR4Qtz9cIKVm9vodUbIhiOI0kSK7Y0MbEim3njcwlGk0gSmHRKv/lJLHqFioIsfva3/fhDJqKxCLFEI1WlLrIdRjQNykYp3HhZMV2dGi3JYMY5wokI/zHvS+zzHsBusFLhLGN96+aM/QyK4aTzmghnnySBXs6crqVIIzcZmS8QY2qVh+JcG2t3tlBR5GLuxHzavCE8g3AvEAaeLxilosjJjHF5bE/sAeBod6Ncl5XZE/KpafAxozqXy+aWoRPPQIIgCCdk0AP9T7IWbwi3/cRapo16BZtZT5svTL7o2RBGsHV72vnjS9vRNJAl+PINU5hemX1Wl9cyGTJvjSaDQvxjQ4onjc5m64FOlm9qJBJLcv7MEiLRBO+ur08npg+EY7y9sZHn36/BqFe49eKxTK5w9/uwatHJfP226by9rp4t+zqYPSGPRVOL0MsSiiKzvPED1vg3MaNkBvOzZlLTVdvn+Ol5k8iWPWTnHM7Sr8H4nLEYFD2xZDy931VVF0Fy5AaLI4VBMnJF1QX89fDQfQCrwUKRI38Qa3VmGXQyVaVZtHmDTKvKRdU0HFYD5gGeny8MHl8wzvPv7Sc/28qN185mffNW9nYeYFrBRDY1b0/vNy57LPX1GjecX4nDoseok9DEqH1BEIQTJgL9QdTiDeGynfgQNI/LTENbQAT6wojlDcZ45F870kG9qsH/vbCNn395AU7z2btd5WebmVqVw+a9vcOHb7ukmrfW9gbWVpOOXLeV/3p0XbrstQ8Pcu15leRlmQGwmHTodQp/fWUnAMFwnP99dgvfvWsWo/P7T/bjMuu5cdForj23gtwcG52dgfS2uBonEAuyrH45U+MTuGHCFayu34hJZ+SasZdSYCzMWPouR8nlewu+xqbWbQTjYWYWTKbAWCSWyBsG4skE0USML826nW2te3CZ7IzzVOEPByg4+wsDnBUWk44/PbWZvfW+dNmHW5r4wefnDl6lhAETS2r86cVtxBIqi6YXs/TtJm6e/SlWtS9nZuFkpuSNZ19nLdU5oxmbVYlVsnPkB0EE+YIgCCdHBPqDqNUbothz4k9r2Q4j9W0BZlaf3AoDgjBUSBL0hBO0eEOYDAr5WRb0Sm/Ptj8Yy8juHE+o+EOxsxroGxWZu5dMoK4tQFdPlCKPjaJsM+PL3dS1+pGQqCh08Nb6+oxjN+5u5c4rxvPQs1tYPKuE5ZsbM/ZZv7uNqiJHak36fmiahiLRZ5pOMqmyoGQ2y2vXALC5ZQd7Omr41oJ7yNF7kJJKv8G7pmlkKx4uLr4QSCX+E0H+8GDUzBgUA09ufYlzS2fTGerid6v/wvfP/dpgV+2M8fZE+wT5AJFYkuaOIHlibvawF4klae0KA6kEizv2d7O/VmHauPOpaYV9dX6uPOccqm0eLCic1aFcgiAII4wI9AdRuy/MxHL3Ce/vcZmpFZn3hWGs1RflR39dQyiSAGD2+Dw+fdk4zPrUMHK3w4TJoPSZH28z68k6wSkuA8msVxhb5ISi3rIsi56sw9esJKWuyY8ryLGS4zDx7TtmUpBtwdsTpaahu88+eVlm1FPonSo0FvHtBV/hzZplJLUkl1QsIls5HOQfRUQK0RRqJhQPU2DNI1uXA9qpzXGVZYkwISQkjJq531wDwsDRNI2pOZPIm5VNXXcTubZsLh9zAW7ZM2I/e7NRwWhQiH4sR4bVPPBLywpnn8Wso7LExf56H8rhRt4ZE11UVkGcMJMm5RDtTqApCVRZQ1bFY6ogCMKpEnfQQaJpGh3dkZMaup/jNLFqR8sZrJUgnDkq8MSbu9NBPsDana1cMKOEqiIHAA6zjm/cNoPfPbOZ7kCMLLuRr908DZtRGXIdO5oGkyqyyXaY6OyJAKn5xVctqMDzkZ7HK+aXsW5nK9HDy4O5bEamjOk/UNPkBF1xH4qs4FCcGdslTabEUMoXJt2JpmmoSY7ZOx+RQjyy+TF2d9akjpckvj7vi5SZKk76/caIsK51Ey/tfgNZlrlx/JVMcU9Cp4kA7ExqibTwy5V/JKmmvj+jnEV8eeZnsTIy13l2WPTcdGEVj72+K102c1wuHpdpEGslDJRQJMEFM4oJRxL4g3EuW1BEIGszzxzszbh/x+Sb+MOWl5AlmdsmXUeuLm/I3f8FQRCGAxHoD5JAOJUYy3wCS+sdkWU30eWPEosnMehFYiJheIklVA40dmeUd/RE0oG+pkFFvo2ffmEegUgcu1mPxTD0gvwjnGYd3//sbOpaA8STKqV5NrJtxj5BfL7LzE+/NI/61gCKIlGaZ8dhyrzug/j5x44X2NS8HUmSuKhiIddbLgN6k+ZJkoSmaSQTJ/aBNAab0kE+pBoY/77lWb4z79/QqSc3SmJPzz6e3PZC+u+/bPon9811UGmpPKnzCCdO1SV4etO/0kE+QG13I4d6aplgnziINTtzugMxOn0hvnXHTDq6wzisBkKROK3eEK6izMYvYXgJhOL85eWdLJpehMWkY/RYiQfW9V1W7+kdL/LVuZ/hgVV/4hcrH+YH5/7HiG3YEgRBOJNEoD9I2nzhPsOR1XAPsW1vIBksGCZdjKT0s6SSLOG2m2juDDHqKIm8BGGoMukVZo3P5/2NDX3Ki3Ksff7WNLAYlPTa80MtyPdHEtS3BYglVEo8VnIcRiaMcqW3f7ynXtM03FYD7oqjT9ORZYk1jRvTGac1TePNmmWMz61kjHksMSLUhxo46KunyJ5PmWMUZu34STlD8XBGWXvIS4IEOk480FcUifcPrcooX9O4kbHVVSQ/thpBNB5Fk5NIqmiQPB1RLUxb0JtR3hP1M1LjHodFT3cgRlNHkGg8SSSWpKHNz5KPL6guDEtZdgM2s5531qXym9zhdnNN+Q1IYReaBoo1wNKGf9ESaGdO0TRW1q+nPdyB1TxCv/CCIAhnkAj0B0mHr3fYvoZGbONLSBYXWriH2LY3MU69ot/jclwmmjqCItAXhh9N45pzK2jrCrHzoBeDTuZTl4ylKHv4rCLRHU7ws0fX0u5LDdXX62Tu//xcCk5zWHFSSrCuaXNG+Y7WvVSPHsvbh5bx6r530uXTCiZy14Rb0KnHXlc835qLhIT2kfH9C0pnYeLkPnNNgwJbLrs79vcpL7Dl9mnYSEoJDgQO8vLmN9E0jauqLqbCVoGiiZ+aU6FICnOLp/H2gRV9ynOs2YNUozPPqJOpLnfz53/tSJfNm1SAXczRHxEsBoWv3zad3zy1iQ5fhGLrKB56egc9wVQDsNmo43M334HRECbfnlom1KwT0zYEQRBOxYAtpPzYY48RCASOv6MAQEd3GLsl9eCidtSiRYMoJZPQjZ5FsnU/aqCj3+PcdhP17eJzFoYnh1nHfTdN5RdfWcCv7j2XieXZ1HcE8UcSSNKpJYg7WyQJdtV600E+pFYEeGFZDZxm1WVNoTpndEZ5hbsUb6KL1/a/26d8U/N22iP93yM+yqP3cN+8u8m2ZCFJEgtKZ3Pl6ItAPbkKq6rG+WXzMel6RwHYDVam50/us0pCQ7ie36x5hBpvLQe66vjNmj9RF6o7qdcSeuklPeM8Y5hfMhNFknGZHNwy6Sps+swkkCNFTzjB40t39ylbta05naldGN40DYrcZn70+bn8z1cWsK/WT08wlt4ejibYsTPOrvb92PQWFo2aS44hZxBrLAiCMHwNWDfL3r17ueSSSzjvvPO45ZZbmDRp0kCdekTq8EVwWFK9cYnaLSh5lalAR9Gj5I0mcXAjhkkXZxyX7TRxsLnnbFdXEAaMIoHbpmdzjZc/vriNaFzFbNTxzdtnUJZrHXJD9Y+QJImOjwT5R7R0Bkmqqfd1qjQVziudz8bm7XSEUkO1q7IrmJhXjdff3W/ivrgaP4ETy1RaKvnP+fcR1xJYJAuop9a+m6Pk8v1z/50GfxOSJFFiL8SOK71dp5NZVrc647j3a1cyZmIliYRYBPtkRZJRHtvyHFlmJ0uqLyIYC/Pynre5YfzlFLhLBrt6Z0QsoWZk3Af6JPEUhreEqrF6Rys7D3WiVzKn97R0hLHmeZlbMp1p2VNQRMJPQRCEUzJgPfo//vGPeeONN5g4cSI//OEPuf7663n22WeJRqMD9RIjSnt3GKfVgKYmSLbXIGf3PrTJnnISTTvRtMwH4xxHaui+IAxXgWiSVTvb2bC7jWsWVXLBzBLC0QQPPrOFcHzoBoOqqjGpInPI9EWzS9HJpz8awSm5+f/mfZVvzf8y/985X+WeaZ8hz5ZDliGLsdl9e/vdZhe55hPr5dI00KsmLJrthIN8SdLwa920J9qIy9H0eZxSFhMcExhvH98nyD+y3aa3ZpzLbrAN2caboc4gGTHrTBTa81AkGbvRitvswqgce8rGcGYx6RhT6upTZjIouAZhiU3hzOgKxlAUCZvFwNQqT8b2c6Z7mJQ3BpfRiQExbF8QBOFUDVigD2Cz2bj00ku58sor8fl8PPnkk1x66aUsXbp0IF9mROjojuC0GVC7GpHNDiR974+ZbLIhm2yoHbUZx7nsRnyBVOZ9QRhuYkmVh57byp/+tZ2V25p56u29xBMq48rceHsiBCIn0Es9iIpzLPzbzVNxO0yYjTpuunAMM8fmDtia5ibJQp45l2JLEfrDWfEVVc9nptzCJaMXkWNxc07pLP597hcxaZlB9UBISknWezfx/eU/5/7lv+Tnq/+XLvX40wSSSZUFJXPQyb0DxRRZ4dzSuRnJ+oQTo1P1fHb6zTT5W3lu5+u8tPtNJuaOpcJVNthVO2Oi8SRXLahg+thcFFmirMDB566agNpPw7cwPHV0R/nrKzvZtr+DcCLM7ZdV47AasJp03HhxGQeS6/jn9pf5y+Z/4idzpRZBEAThxAzY0P2VK1fy9NNPs2rVKi655BIeeughqqurqaur49Zbb+XSSy8dqJca9jRNo8sfxWExkDxwCMmRl7GP5Coi2boPxdM30/CRzPst3hCleSIhnzC8tHSF2VPX1adsxZZGbr5oLJ3dYezmod1TqcgSUyvcVH9xLqoGVoOMOkDxR4/Wxcu732RL2y6qsyu5tvoyPIdTq9txcnX5FVxWthi9ZDjpOfYnoyPezl82/TP9d1OglUe3PcNXp92Noh77JyNPn8d/nvs1dnv3kVRVJuSMJUfnAdGjf0o0SeX9Q6s42JXKUJ5Uk7y+7z2m5I7HbnQNbuXOEFWFB/65iXFlbq5ZNJrmziB/eGEb37h9Jvlidb3hT5J49cODXDDXg7O0nTdaHiXP6uE/7roORQf/t+VPdPpSvxF7Omp4++Byrht9JdoZvOcJgiCMVAMW6P/oRz/i1ltv5cc//jF2e28AWlpayk033TRQLzMi+ENxdIqMQa8Q6WxAya3I2EfOKiCxP3O+K6Tm6Td2BEWgLww7H03cdoSmgV6RuPemqRhPZ6L7WaJpYFRSg6EGKsiPy1EeWvs3GnqaAdjQvJUDvlp+tvhbHLlNayroMZ7xoLkj3JlRtq/zIGE1hA3HMY/VtNRc/mvHj6ajI5Aa6SCC/FMWJcrm5p0Z5Q3+ZkqMowahRmeeLKUaw3cc6GTHgd7v4tC/MwgnQgOMehn3KC+vHkyN9vRFeniw5yEuG3M+naG+DcHrm7dwRcVFYgi/IAjCKRiwoft33HEHd955Z58g/49//CMA995770C9zIjQ2RPBZTOgaSpqdwuSLXPer2RxocUjqOHMYWtuu1HM0xeGpbwsC/kfW05v5rg85k3Ip8g9cjOJH4836k0H+Ud0hbtp9red9bq4DJnBfIEtF5N8cnOkB2o6wyeZJEOpqzCj3G48M9M2hgKnRc+CKX3fc7bDRGHO8FmGUzg6SdO4+oJS1ret6VMeSoRxmTLvPVXZFRikoT3SSxAEYag67R79f/zjH0QiEf72t78Ri/UukRKPx3nsscf4whe+cLovMeJ4e6LYLQa0gBfJYEbSZz5AS5KE7MxH7ahFLpncZ1u200Rdq1hiTxh+zHqZb94+g/c3NrLjQCdzJxYwZ3weJp38iU7YZlAMGevdAxh1BjjLU5PzTHlcWnk+S/e/d7gORj477RZ0qkiGdrYpqo4Fo2ZT391MIJZq3J2aPwGHceSO5pKAmy4YQ3mBgw+3NjOuLIvzp5dg0WdmZxeGpwKXFafJQWuwN/eHpmk4TQ6mF0xiY/M2AJxGO0vGXHzKK4UIgiB80p12oK/T6di7dy+RSIS9e/emyxVF4T//8z9P9/Qjkrcngs2sT/XmW91H3U92eEh21qL7WKCf4zSxekfrma6mIJwRLrOe684t59oF5UhS/8P5B1o8kaQrFCeeUHHbDAOSJX8gqFKSlmgzrcF2Lq5cyBv7l6W3LRw1l2J7PoHugVlWTJLAr/YQSARw6J1Ypf6XMtRpBq4YtZg5hdMIxkPkmLJxyC7RQz8IFFWP2+jiwopz0Mk6FFmmPejFbTj678ZIYDMqzJtYQPUoNzazHrtJ+UQ3BI40Ok3H9eMu5+Xd71JlnwwatMQP4DFl8+kJN3P56AuIqBHsRjt69CBpoA2Ne7YgCMJwctqB/o033siNN97I22+/zeLFiweiTiNeZ08Eu1mP2t2KZDl6diHJ7iG5Z0VGeZbNiNcfIZ5Q0etES7cw/BwJ7s/Gw3s0ofLY67t4aVkNqgbjytx88ZqJOEwDlqLklEgS7A/U8Ns1fwJgZuFkbpt8LQD51lTmfbPBTAD/ALyYxo6eXfxp45NEkzHsBitfnf1Zio0l/f4byJqeXF1++hdC0zTCUpDmYAtJTaXAmnfc+frCwLAZrVRll9MR7sIgGyjPKkWT1BGd+6DZF+GXT2zE2xPBbNTxxWsnMaksS8zTHyEkCZSkFWPTbJ7Y3ALA7InVJMsMyCYFVU5S29WALClsb93NzMIpzMyZjqyJUR2CIAgn47SfdB955BHuvvtuVq1axerVmcnjvve9753uS4w4nd0RCrItqM1tKJ6yo+4nmR1oiRhqxI9s6h2qqSgyLpuRVm+I4lzbWaixIAxf+5t6eOH9mvTfuw55eXNtHTcuGj2ovdQxKcY/t7+Y/nt901a2tOzkG/O+Qr6hcOAypQK+ZBf/t/7vJA8vUeaPBXlo/aN8/5x/x8Tx5z776ea3ax6hOZDKGWAzWPn2/C+TJecMYC2Fj0vqYjT7WvnThn8QS6aWnhztHsXNE5Zg17sGt3JnSDSp8dt/bsLbEwEgHE3wu6c28T9fPodsm5g+MhIkpBi7DwRYcTjIB1i7vY0Z1R4093Ye3fxsuvySykV8ULeGMmdJqvFREARBOGGn3R18JPleVlYWLpcr47/jefnll7n88su56KKLeOKJJzK2v/3221x99dVcddVV3HPPPXR3p5LTvfjiiyxYsICrr76aq6++mgceeOB038pZ4/VHcFgNaIEOsLiOup8kScgOD2pXY8a2HKeZRpGQTxCOqSMQY/uBzCzy63a1EhvItd0ljdZEC6s6VrPZt5kezXfcQ1QtSSgeTv9d7iznhuLP8egzzfz00XVsOeglGI4d4wwnzhv1pYP8I7ojPfTEjz9aQJJgZ8eedJAPEIgFead2BbIYUHRGxYnxxv5l6SAfoMZbS3vIO4i1OrN6QjFau8J9ylQN2nyRQaqRMNAkRWL3/sx7j6oP8fjWF/qUvbl/OTOLJtMZHrnfeUEQhDPltDuNbrnlFgC+8pWvnPSxra2tPPDAAzz//PMYDAZuueUW5syZQ2VlJQCBQID777+f5557jry8PH7729/y4IMP8r3vfY9t27bx7W9/myuvvPJ038JZ1+WPYtOpaIkYkuHYmcYlmxvV2wgF1X3K3Q4jje0BIO8M1lQQhi9Jgt2HvGQ5MpdlGlfmRq/IAzJ3ICZFaQg38OtVf0yPEHCaHHx73ldwSK6jHmfCzKWV5/PMzlcAmOu+kL88VZve/pt/bubbd+qoKjz9IfIuoyMj2Z/VYMGmtx53CLgsS9T7mzLKD3bVoUoqA7F4iyRJxKQIMjKKqj/t840UkibTEmjPKP9oA9FIo9cpOG0GugN9G7msJvG9GCkMGCnJd7BuV99VRcwWlaSa7FOmoZFUk7hMrrNYQ0EQhJHhtJ/Qpk2bxvTp04/637GsXLmSuXPn4nK5sFgsXHLJJSxdujS9PR6Pc//995OXlwpmx44dS3Nzagmqbdu28eKLL3LVVVfx9a9/Pd3TP9QlVRV/KI412Y1scSFJx551KNtyULsaMsqzHSYa2kWPviAcjaaBTifT1RNh0ujeIeZuh4mrFpQPSJAflkIsb/yQF3cvRdM0puSP5+aJV3FhxTm0hFtISPE++8tyKom+fLiNYW7BTG6ddA2T8qo5cCAz6d7LKw4iK5n3CE1WUZUEx7l9pGUpbj499cb0/Uav6PnSjDuwcvzs7cmkxpTc8RnlC0pmI6mnP2c2JkVY17men658gJ+v/V/2hfaiSsnjH/gJoEfH7KKpGeUFds/Zr8xZkkiqXH/+mHT+GUmCKxeUkxjIETjCoEomNXKzzJQV9DZiluTZcBpcGUvsWfRmKt3l5BpG7ndeEAThTDntHv1XXnnllI9ta2vD4+m9eefm5rJ169b031lZWekEf5FIhD/+8Y/ccccdAHg8Hr7whS8wefJkfv3rX/OjH/2IX/3qVyf82tnZZ2duu8fT90G6szuM1azHKgUJ213YbJm9jR+lmgvw7VmO025AUnr/ucqTGuv2tGWcf6DqOVSJep4ZJ3o9DLf3NTmh8sTS3UytyuXWS8aiajBpdDZjy7IH5PyrG/bTEfbijwYZk12O2+ziqe3/Sm+/aeISrq6+CL2ip6HNz5sra9mwu42Z4/JYPKsE2azhNNmZlFdNS3fm7dhpM+JyWVEOrxKQUJPsat/Hc9teoyca4IqqC5hVPOWEllu7KGsBE/LG4Iv0kGNxU2DPRZZOrK3X6BjLLROv4rldr5NUkyyuWMD8UTPIsR79e3Oi35X3D27jr5ufSv/9m9V/4j/P+zcm5VUf46gz62z9PnxUf59Xe6CT2UVT8IZ9bGnZiVlvYknVYgyyYUhdiwNZl3DSzysrarj+/EpUDfQ6mbU7WphZnXdG3vNQ+hxP1tmq+5m4HrJcnVw0u5icLDNd0S66aGDNtjauGnUTbzW/QnOghVxrDjeMuZ5JeWMx6AwDXodTMZy+L8OprjD86isIw8FpB/p1dXXMmzePN998s9/tRUVFRz22v0RY/fVw+/1+7rnnHqqrq7n22lRW6oceeii9/fOf//xJZ/zv7Ayc8WW9PB477e1956EdaOrBZtIRaG1CUywEAicw79Bsp6v+ELKrMF2kR6PDF6ap2Yded3q9av3VcygS9Tw9x/oRPZHrYai+r2Ox6mV++Pm5bDvYibc7yozqXPIcpgF7Hy3dbWxt2cXCsjmYdCae3fFqn+3P7niVKTnjsWhufv74Bg429wBQ1+Jn8942xs5tZVnDcuYUTWPmmFG8vVohGkv1ZiuyxFXnVuDtDKTP15po4Scf/C597/zD+idIJFVmuKed0AAFC04sihOi0Bk9uRFBC/MXMCN3CioadtmOFpJoD/X/OZ7od0VTVF7f935G+bqGrRTqis/oPfp0r4eBrkt/n1dUH+OZHa9g1pu5bvxlRBMx3j6wgmxL1pC5Fgf6vmDSwZwJBfzzrd7leseUuMiyGwf8PQ/He9oRA133s3k9JOQY5qwAxmScxmAnz9Q+CsCtlZ/h8edamD35Ymbl6ujqSpKjK6K7KwpEB+z1T9Vw+r4Mp7rC0KuvaHQQRorTDvRfffVV5s2bx2OPPZaxTZIkLr744qMem5eXx/r169N/t7W1kZub22eftrY2Pve5zzF37ly+853vAKnA/7nnnuOuu+4CUg0GOt3gLpV1orr8EWwWA2qgE8V5YvPrJWs2ya6mPoG+oshk2U00d4YozRM3JEHoj6aB22bghguqzkjwVuwopDvqpyvSzXhPbp858ACqphJORAkGwukg/4gDjT3MlYrRNI3VDRvJt3n43C2j8LbpkFGYXJFLdZk7HehLEuzp3J/RQPra/neZMmciinZm5zBrKlgOD/XXBmgUtYxElsnFQer7lLtMDrFuOpDUVNqCXgKxIDvaegNffzTICcy6GJa6AjHausLcfmk1rd4QboeJUCRBqzeEvWCEvulPGJ1O4Z1dKyiMz0Qy9wbwr9Y/y03XXou3TYcWMzGt2kRBlhk1KW4GgiAIp+K0o+Of/OQnAP0G+sczf/58HnzwQbxeL2azmTfffJMf//jH6e3JZJIvfelLXHbZZdxzzz3pcovFwp/+9CemTZvGlClTePzxx7noootO962cFV3+KDazDs3fBfljTugY+UhCvvKZfco9ThONHUER6AvCCVBVDSTwBeMEwnHcdiNWo3JaAWWRuZCbJ1zF87teI9eag8Nopyfa2yuRbc7CbXThC/c/RF6We198Q/M2Lhrt4oPwG+hkHY74+YyKz0lv1zQw6zKXF7PqzcgDkBBvMGhJiSvGXMiW1p3pJFxWg4VJOeMGdenDoUKHjpmFk3n/0Ko+5dmWrEGq0ZmnyDK7DnkpyLaS6zaTSGqs393KtCqxlONIEUlECEVjFOfkkEi6Ob94Ee83Lqc76uefNX9ngqeKfLuHnOxzCEaSGPXD9Q4nCIIwuAasG7yzs5Of/vSnfPjhh+j1ehYuXMi3v/1tHI6jZ4zOy8vjvvvu48477yQej3PDDTcwefJk7r77bu69915aWlrYuXMnyWSSN954A4CJEyfy05/+lN/85jfcf//9RCIRysrK+PnPfz5Qb+WM8vqjWE06tNZuJNOJzXuTbNkkm3ZllLsdJhraA/0cIQjCx6karNvdxp9f3kEiqeGwGvjm7TMozDr2yhfHotMMLCpYwLS8SSS0JJNzx/PYlmep6aplbPZobp90HUbNQo5DY97EfFZt7103esZ4NzXBHem/Sx2FvHdwJd6wD4BHtzyDx+am3Dg6tYOkUeYahVVvIRgPpYqQuLb6snRSPEmSiEohNE3DLFlPewRDUo4TUSOYZDOKemZGTeXrC/j+ufdxwFeLXtZR4RqFS3aLHn3AgIEiRz7zS2aypmEjdqONS8ech04+/SSIQ5XLquPuaybxxxe24g/F0SkyNy0eQ0G2ZbCrJgwQNa7H2DKTX7y2GYCyglxuX/xpHtvzNybnjeeacRcjJQ08/XILW/ZtZ1JlDp+6eCxui1h5QRAE4WQM2JPb9773Paqqqnj22WdRVZV//vOffP/73+c3v/nNMY9bsmQJS5Ys6VP2yCOPADBp0iR2797d73EzZ87khRde6HfbUNbZHSHHCpJO3ye53rFIZgdaPIIaCyIbrOnyHKeJ/Y3DY7UBQThZXaE4Hb4wDquBHIcR5URTzB9Fhz/KH17cnv67Jxjjf5/Zwv2fm4Ohn8z2J0pTwY4TJECCf5vxBaJaFJNkQj4cHCuSxG0Xj2XW+Hz21HVRVeqkXd7DSwe2AKle7Il51XxYv77PuVfXb6Ry7BiSSZWOZDsbt3dzWd6txAytJKUopbYKzPE8MEFCirPNu51ndr1KIpngqrEXMStvBkbt2Ak/+yNJ0JZo5dGNz3DQV8cYdzl3TL6BbPkMZL7WJHKUXHKye6dtiSA/JZqM47G6WduwiSXVFxGMhdndvp9RzuLBrtoZE4yq/Gv5fq5cUEFS1dDrZDbsbmViRTb5zpP/LgtDT21TiHAIbrukmkRSRQNaDsX54YLvsKFtI4FwhIf+dhCfPzWsf8PuNpo7gnz/M7NP614tCILwSTNggX5jYyMPP/xw+u9vfetbGQG8kBq6P8qmIZlOfG1sSZKQbTlovmbIrUyXe1xmlm3JXN9aEIYzSZLY29jDL55YT+Lw3MxbLqpi8YwiZE79Ia+zO3Pt8RZviEAkjts6cBmdFVWPBX3G+vQWg8LUCjfTK7NJJFV8iUmMys5DklTyrXm8XrMs41wF9jw0TUNWJHa3HOLDDTC1ykM8UciGXW0k1WZuWmwnt9pCXaiOP236R/rYf2x/CbvBxtSsKScdOAcJ8KvVf8AfTY0Y2uc9yG/WPML35n8NoyZ6Vs8WvU5mb/tBphRMQNM0XCYHsWSMWDJ+/IOHqZ5wjHOmFPH467vS1/9l88oIhOIgAv1hT5YlQpEERr3CE2+kOnLMRh13XTme9u4QRboqbIoVn/9Qn+OaOoJ09kQoOI0RWIIgCJ80Axbo5+bmUl9fT0lJCQAtLS19ls4TUrqDUWxqDIwn97As2dwkuxpRPhLou2wGguE4oUgCi2l4JCMUhOPxheI89NyW9EM+wD/f2ktxrg2TQUeJx4pePvmA3+3IDBI8LjPWs3ztRGJJXl1dy8srDgKQ7TTxtVtKKFaqMevXE46nVuJwGO2Md09AVTVkRaMj3E5FUTmJhMobq2vT5/vDC9so+sJc1ndtzXit9w6tZFr2FLSTXJa+M+xNB/lHeMM+vFEfBQYLYYLUBRroivgothdQYCpE0cQ9aKCFkmGW160hEOu7QkKJo5BK84nleBlujHodz7+3v8/1//qqQ8yekD+ItRIGiqpqWC16Vm5rTpeFowleXXGQGeNyeeH9GqZW5fCpi6v4x5u9CShlCYyGkTtlRRAE4Uw47SezL33pSwB4vV6uueYa5s+fjyzLrFmzhrFjx552BUcSTdPoDsQwJ3uQjNbjH/ARss1Nsr22T5kkSXiyzDS0B6gqcQ1gTQVhcMiyREN7gJ5gLGNbXUuAp9/Zy52Xj+P8qYVoJzn/PMdh5K4rxvH313ahamAx6fjKjVMw65WzupRaY2coHeRDajrP8+/tx6iXuWT0bcgWPxIScb8Nm5RKuqYmJapyyjA6PDz81L6Mc9Y0dFOUnxkI5dtPrbHVojcjIfVZRUCWZMw6E1EpzJ+2PMHujv3pbZ+ZejOzc2ae1c/xk8Akm8m1ZmcE+vaT/P0YTqKxRL/XfyAUA8RokuFOksDn7+f+3urnojmlAGze28G06mycVgPdh78L118wZkBHXgmCIHwSnHagf8kll/Rbft55553uqUecyOH1sfVhL5LFedT9ArEgRp0Rvdz7zyPZclD3r0ZDRfpI/lmPUwT6wsgRT6ocbO4hz22h1RtKl8tSahgzwFNv7WVGlQf7SfbEK5LEuZPymViejT8Uw2Ez0tAWYOfBTsaWZlHisZ52HoAT0e7LnEKw85CXz181gYee7e2Vv2ZhNgUeG91dqc9hjGMMzaE2ct1mvD2RPse7bEbKPGOxG23pnnijzsjisoWoJ9mbD5Clc3PV2It4ac+b6bIbxl+BU5dFXai2T5AP8M/tLzF+4VgsnFiCUeHESKrMBRXnULupMb0qQYmzEIdx5K60YtDJmde/LGGziCBvJIgmNOKJzPU5J1Vmo8i9zzY7D3q5/4sz2XsogMdloihn5DZuCYIgnCmnHehfdNFF2Gw2fD7fAFRnZPMFotgtBgh3I7kL+91nS9sOav31aMA5BbPJsbgBkPRGJL0Jze9FsvcuM5TjNFHb4u/3XIIw3GhIFOXYuGlxFU+9tYe2rjAWk47rzx/DO+vqAIjGkyRPsedYQsJtM2AyKPzPExuob+0dnv6laycxd1zuMXulZVk67V5rjytzjunEimwmlmfz7Ttm0NwZojjXRqnHSkeog4PBBvSyjgJLPqMsxdx6cRY/+vPa9NDmIo+VsgIHTknHd+ffS32gCVVTKbYVkqVkn9IydbKmcEHxQiZ6qukMd5FtdpNnzEVSJaLJzN64cCJCQktwGikUhH4kSbCqbgP3zv0sPZEe9IoBVUvSHuyk1FA22NU7IxKqxlXnVvDi8hraP3L9x+Kn0GIlDDkJVWXtjmZuuGAML39wAKfNwDWX5GJxRohGOlg4M4fl6zsYVWzEYpaYJZZVFARBOGWnHejfcccdvPDCC8ydOxfpcG/YkQdLSZLYtStzWbhPKp8/it2iRwv3gCGzdbo52EpjsIlZ+dPxx/ysal7HpWUXoj+cnV+y5ZDsakT+SKCfm2Xmw20tGecShOFEkqCuI8RfX9lJQ5ufORML+PQV4zHqFQLhOG+tqaWpIzV8eda4PJynucxSQ3uwT5AP8NjS3UyqcGPWZ84DjRGhNljHXu8Biu0FjHGNPuXe66IcK9csrOClDw6gaZCXZeZTF1VhVCSqipxUFaVG+3Sobfz32/+bnrNf4ijk89M/RcwY5j/umkB9QyohWyAUZ1+9j1ljc7DjYoLTRU/SRyQZJaaLoNeMp1RPnWag0FBMoaEYVUrSGmulNdiG1WBhccW5vH3gg/S+swqnYFfskNlRJ5wGo2bm8qoL+M3qPxM/nIBvbHYFt02+bpBrduY4LXo272vnvOnFFOXaCIRiLF1Zy9dumTrYVRMGgNWgY3SxixVbGrlp8RiKKsL87/pHSGqpm8fUwgl8957FaKgYVOPHc5oKgiAIJ+G0A/0jS9wdbRk8oZcvEMNq0qF5Q0iGvnMNNQ12dOym3FmOTlLIMrpwm7PY07WfiTnVAMi2bFRvPZROSR/ncZlp6gySVNU+w94EYTjxBmP8+C9rSSRTD3trtrcwszqXNdtb2F3rZeLoHGZNyKe22c9504tOu+M41s/Q0Ug0QTimEo2ruD7akCBrvFv3AS/vfStdNDa7gi9N/QyGUwiiDYrEknllnDO5kHAsQY7DhEn3sWtX1nh1z9vpIB+gvqeJTa3beW3vu9gMNi7Lv5E/PZUa5TAq3870qhw0KcH6tg08uf0lEmqCQlseX551Fy4p+6Trma6KLLG1awd/2PB4umxSXjU3TLicN/YtY17JDBaPWoikikRZAy0pJ3hp9xvpIB9gT+cB2kIdZFtzj3Hk8OUPJ7h4zig27G7lyaW7KStwcNeS8YRjKojR28NeUtPIcZm4Yn4Z7myFJ3c8lg7yATa37KDaM5oCWy4+rQsnWYNYW0EQhOFtwCLDjo4O3nnnHQB+9rOfceedd4rg/2N8gSgWnYpksCB9LGu4N+olpsbJNrrSZaW2Imq6D5FUUz+Ckj0H1dvY5zijXsFu0dPizZz3KwjDRXNHKB3kQ2o5rSfe2M3yzY20dYV5d309KzY3YrfoBmQIb5HHmpHBedH0Ih5+YStff/AD/vnefsKHX6cn2c2r+97ps++ezgO0RVpP+fUlCbJtBordllSQL4E/kiAQSSBJEkkS1HU3ZhzXHenBarDgDXexI7ia6rJU73+W3YQsSbTF2vj71udIqAkAmgKtPL7tOVQ5ccp1DWoBHtv6XJ+yba27Ge0q44cLv8k15Vdg5cSXCxVOXIQQjf7M75k33DUItTk7JEni5RUHWLqqlrauMGt3tvK7pzajqmK4yEjgD8V5YukeYgmVurYu2kOdGfvEk3GW165hZcNaNFn8uwuCIJyqAQv0v/3tb1NfX8+qVatYvXo111xzDT/5yU8G6vQjgtcfwSLH+s24X9vdSJ7Fk57+AGDSmXAYbDQFUsvQSBYnWiyA+rEMzHlZFurEPH1hGPv48pBWs44OX9+Ec/sbusl1WynKPv1uPbfVwP2fm8OM6lzy3BauP78Sq8lATUM3qgZvrqljx6EuJAmSWhJVy3zYTJxKlrt+hOJJnlt+gH//3Qf8x/+uYOm6erSEjgWlszP2zbN58IV7ADjYc4jSYjOKLHHdeaNB0+gIZz407+rYT0Q79YbAuBonFM88PhyPYlTNaKqYmH/mSEzNn5BReiR3y0iUUFW27uvoU9YTjPWbiV8YfnQ6menVuazZ0cqufUEmefp+vyUkDIoBb7ib1kAH0dO4dwmCIHzSDVig7/P5uOuuu1i+fDlXXnkl1113HeGwuEF/lM8fwypFwfjxYfsaTcEWcsyZw2tzzR4O9qSG50qShGzzZPTq57rMHGzuOXMVF4QzrCDbwvSxvUvByXJm8ChJUFnsxKQ//duWpmnkOU18+ZpJ/PQL82jtCvHmmlo8LjNHXnrNjhYURcapczGtYGKf410mB7mWU1u67qMkCTbv7+DVDw+RVFPZqJ96ey97GrqZkz+D88vnI0kSRp2RG8ZfSX13U3rJu4k51YwvyeMnX5xHcXbqnuIyZq7mUeoswiCd2jx9ALtizwg29bKOPOvpv3/h2GyKhWkFE5hWMAEJCYvezPXjL8M5grPu62QJvU5m+thcblpcxfkzSjDqFYz95M4Qhh+7Scf5M4uxmnRs3dfFOOM5jMtOLcXsMNr51OSrWVG3jvNKzmVaziySMUPGCEhBEAThxJz2HP0j4vE48XicDz74gP/+7/8mHA4TCoWOf+AniC8YpdwaQvpYIj5ftBtFkrHoMrNxu81Z7OuuIZ5MoFd0SHYPamc95Fel98lzm9mwt/2M118QzhSjIvP5JROonxugJxhnVJ6Nmnofa3b2Dlu+fH45HvupB6z909DJEnMnFJDvtuLtiTB1jIdwLIHDYkBVNWRV4fbx17Nw1Bwae1oIxoIsKJmDRTv9peQkSWL5pqaM8vW7WplcPo6bxl7HRNtsDjb6WfZGN7MmT2VKTgxvvIMrx1yIQ+o7fzXflMfFoxfxZs0yAMx6E3dNuQmdeupLk0mqwqfGX4PNYGFNwyYKHfncPul6smQ3p5DQXzgJakwhz+Zhgmcso91lSECxowCXPmtEJz6871PTefXDgzz99l4Kc6x8/pqJmI0D9rgiDCJV1ajItXPFgnK27O/g7y/UMa16BtdXnUtZiZkndvyTi0svw1fv4sVlB0gkmrjq3AoumFGcmctEEARBOKYB++W88MILmTdvHuPGjWPixIlceeWVXHnllQN1+hGhOxDDYuxBsvcN6FtDHWR9ZG7+R+kkBZfBSUuwlRJHEbLDQ7J+W5998t0WGtoCqaBEtHwLw5RJJzOmoHeu952XVnPOlELqWwNUFDkoy7OfkdXbwrEkLy2rYV+Dj09fMZ6XVxxgX70Po17hzsurmTrOwdqWTby85010ssINE67EJjsYqHTQlUVO9tb1nXNdVuBA0zRWb2vj/17ozXVS1xzgG3dezphiKzo1s9FDrxlZUnYx84pmEoqHyDFnY5ccpx2Q23DyqarrubbqCvToUVS9CPLPEjNmookosWSMuJpAQsKoZTYKjxRmg8Kz7+zlQFNqlFpTR5BHXtjGD+6eO8g1EwZKMJrgrdW1fOeumRxo6kZGQp9U2L6rh8nyEiIdLp58Y2d6/2ff20+208TccbniviMIgnASBizQv/fee7npppvIy8sD4Je//CXV1dUDdfoRoScYw+z0IRlK+5S3hdrx9DNs/wi3KYvGQAsljiIkmxs16EVLRJF0qQd9k0GH1ayn2RuiKEekJRZGBrNBYeKoLCaVuU9pLfgT1dwZYl+DjwkV2Wzb38G+eh8A0XiSx5fuQZ9n5+kd/0rv/9dNT3HfXCeVlkokWSKWVEkmNZo6g1iMejxOI4p07CYJVU6QJIkRExfMLObDbU10B1JzkPPdFqaOySGpaby1ti7j2K17uphYkk3yKC0NsqYnV5eXvrsP2EenypiwHH8/YcBIMrxft5JVDRuYkj8efzTA79b8lf/vnK+Qry8c7OqdEf5wHF8gxueWjMduNRJPJFm6uhZvd4QCp2mwqycMgEMtfiqKXazd0cKFC7KJRWUO1kXo7NQ42NTDqPzMY95Z38Dc8XkDeEMTBEEY+U470H/ppZe4+uqr+etf/5qxbdWqVXzmM5853ZcYEcLRBBqgj/rA0NsAoqoa3mgXla7yox6bZXJyqKcOTdOQZOXwMnsNKLmj0/sUuC0caOoWgb4wrGhodPhjdPjCuO0mcvoJks9kkA8QP5ztv6o0i5c/ONBnW3WZkxX1KzOO2dC8lfzict5ZX8/qHS2UFzgYX5HN46/vYuG0Iq5fNBpjP8NMNUmlNlzL0ztfxh8NcMno85iVO40ff34ujZ1BZEmiKMeKxaAgSVCab6emsbvPOQo9VlS19zORJI1utZuElsSlcyKrvbd1TVbpSaaOdyhOJHXghr5KkoQkQYwoOvQgkvINuIgWJqkmmF8ykxW1a3GaHNw59Xo6wp0jNtBXZJn/d91k1uxsYdv+Q5Tm2bnh/DFYjGKO/kihKBJTJlhZ1bSNB9Y9Ra45hytGX8beuiRfvGEcBxuCfLi175Sm0nw7kjRgA6kEQRA+EU470K+rS/U47d2797QrM5J1B2PYzXqIBvpk3e+OdWNSTOhl/VGPPbLdF+0my+RCsntIdtT2CfTz3Rb2N3Rz7uSR+fAnjDySBBv2efn9c1vSnTR3XFrNoqmFA5cl9AQUZltxWA20dAYZ9bHAuqsnRqU5l30c7HPMRPcE/vTyDrbuT2UHb/WG2FPXxeXnlPPishrmTMxndF5mwrTWWAu/XPl/6YR6/9j+ItIkiXPz5mMz9U2kp6mw5NwKVm9vIRxNLY/ncZmYPDo7/XnFpRgrmlbywp43SKpJJnjGcuekG7DhJEyQl/e9wfLaNSDB4vIFXFp+ISbt9HrlY1KUhmADUS1GU08LH9avp8iex1VVl5Cryz/phpmEFKcj1k531I/H7MatywZNzMUFMMoGTHoTL+xaCkBXpJu/bnqaby+4Z5BrduaYjDqeeGMPW/al8s4cuba+c9esQa6ZMBBkWSLLaeTtQ0v5sHEVkFo29H83/oEvTP8Sh5q7ySrp5t/vquKBR/eiaalVWS6ZU4qaFGG+IAjCyTjtQL+7u5va2lr+67/+ayDqM2J1B6LYTArEQdL1BvXeiA+74fhJvbKMrtRcfpML2ZlHsn5rn+0F2Vbe25S57rYgDFXdoQSPvLitz0jMx9/YzaTR2eQMeNK9o7MaFf7zM7N5fdUhLp03ivc3NtLWFaK9K0xBjo2p2TPZ0LKZSCIKgNNoxyEVsHX/+j7n6fJHMRlSvY7e7mhGoC9JUNN1KB3kH/FmzTJm581AT2bCvDElWfzsS/OobwugU2SKPVZsRh0JVSMcS9KhNfDsrtfS++9o38O7tSu4puIKtrfvYlnt6tQGDd468AETc6vJNeeQ0FRcOheyepK9pLLG+/Ur2Ny6g1JnIR/UrgWgNdDOjra9/GDhv+OUTnzpt6QU5626d3ll3zupzwiJL8/6NBOcE/qMWvikiksx3jnwYZ8yTdNo8rdS7B41SLU6s7oD0XSQf0RPMEZLZ4jcs3hfEM6MnnCCYMLP6qY1fcoTaoIQXeyvVzjkew2jTs8Pv3Ib3R16inPtuCw6MWpfEAThJA3IHP2bbrqJiRMncvvtt3Peeef1WQteSPEFYlj0GpLcd2h9Z6QLu/74gb7TaKc91E61uxLJlo0a7EKLhZEMqaRMuVlm2n1hwtGEyE4sDAuBSJxYQmV0sZMZY/NQNY3V25vpCcXOaqAPkG0zcPOFlexr9GM366ksLmTmuFzcdgMb93Zw19i78cbaMegU8s0FmHUmZFnKCEaPJMPMzcpMlqZpYDVk9qY7TXYU6egBt9OsxzkqlV1fkqCpK8wfXtiOtyfCRVdlri2+tmkzSyovZU3jxj7l55TOYnPrDt4/tApN05hZOIWbqq/Cyokv1dad9PHy3re4qvoiXt79Vp9t0WSMpkArTvuJB/od8Y50kA+pqRx/2fwU95/79ZOq10iVkOLYjVZ6ov4+5Xrl6CPAhjuDXkGnSCQ+1ntrNIih+yNBKBonmQCrIfN7rZMNFBbKbGjrIqkmqQ3XML9iFlJSBPmCIAin4rTHR37ve99j+fLlXHnllfzxj39k8eLFPPLII/h8vgGo3sjRHYxhUZJIH3vQ90a6TqhH32l00Bn1oaoakiwjO3JJdtamt+sUmfxsSzpTsSAMdVk2I1efW0FJrp3n3tvHS8trmDQ6h2zH2c8oLssS6/e088snNrB6RwsvLa/hf/6+nmRSY/2uNh74634efaKbRx718uDj+7GZdFx9bkWfc4wrc9PYFuDWi8dSmN3/8PjRrjLcJlf6b0mSuHHclcjJ/oOYYDhOVyhGOJ5EkiT8kQQ/e3Q9da1+QtEEVsmVcUxlVhmKJlOdXZkuU2SFQnsu7x1cmR5av75pC+tbNp/USh0JNYmqqcSScUy6zMRoeuXkGhn9sWBGWSgeJpyMnNR5RiwNLqxYgPSR9SZyLG4cxtNf2nGoyrLpue68ShZOK+KmxVVcfk4506o85LlFIsiRwGU1smWnn6tGp1ZlkpCYnjeN28bciUdfRMhYR1JNAtAWaEcvjdxGLUEQhDNtQLp+jUYj1157Lddeey379u3jmWee4dprr2Xu3LnHHdL/8ssv8/DDDxOPx7nrrru47bbb+mx/++23efDBB9E0jeLiYv7rv/4Lp9NJU1MT3/jGN+js7KS8vJxf/vKXWK1DNxGdLxDFLMVA3xvExNUE4UQEi+74DzB6WY9ZMeGL+XCbspAdeSTbD6Ir6E3sV5htZW+9jwnlJ96jJgiDxWqUyXaZeelwArykqvH6qkOMK8tK92CfLaFYkqfe3tenLBCO0+aLZAwj9gWiNHUEuWRWCWNLs9jX4KM410ZhjhW9IuOy6o+aMcqOi2/O/zIHu2sJxyOUu0rJ1ef1u78vFOcX/9zErkNdOKwGvnD1RJw2A8FwHEgl8uxsMjHWPYY93lTd7QYrV1VdjJqQmF0wnQ/r1tEW6iTXkk2TvzXjNdY0bWRR0XzgxHpLXXoX4z1jWFW3gYsrF6bnjgMU2vMotBSc0HmO8JizUWQl/WAPUGjLw6l3jOh14k+UASO+cDefmnw1PVE/RsVAUlP7BP4jTU8wQXmRk7U7W1i+qZEsu5E7rxhPKBLHZRKj1YY7gyJx/oxiDjR7uXfG/0NRLbz0VhN/OtCG1dzFNReVMcpRSm1PHeNzq0gkRFe+IAjCqRrwX80xY8Zw3nnn0dXVxbJly465b2trKw888ADPP/88BoOBW265hTlz5lBZmeqJCgQC3H///Tz33HPk5eXx29/+lgcffJDvfe97/PCHP+TWW2/liiuu4KGHHuL3v/893/jGNwb67QyY7kAUG5H0UHuA7mgPVr0F+QSnOjgNDjrCnbhNWUiuPJJ7PkRDSz/0FeVY2Xaw84zUXxAGnCTz4ZamjOL1u9uYUpFNMtkb6SU1iCZUzHr5jIU4/c0JV2QJRZZIfmybTidj0MlUFTmoLnH2PfY4z6V2nEx2Tj7m/kngL6/uZNehLiA1R/lX/9jIj74wr89+ry9v5bzZ8/jmvEtQSZBnyUXVVA6GD2LVW/jG/C/TFmxHkRVaQ218WNc3r8DY7EokTTnhTNaKquPOSTfxbu0KmvytfH7Gp2gLdJBr9TDGVYGF/nuaNUmlPd5GS7ANq95CkbUQk2bBKbv497l388jGJ/FFeih1FnH3tFvRq2IuNoCs6ajMHoU/GiKWiKHICiX2PIxKZj6HkULVNJ5YupuGtgCQyn3x0DOb+c5dswe5ZsJAyXUY0elyqGnsRrEEmDo7wbTZTqLdVp5+rZHbrz+fmcXtlFvKBruqgiAIw9qABfrNzc0899xzPP/88+Tk5HDrrbfy05/+9JjHrFy5krlz5+JyuQC45JJLWLp0KV/5ylcAiMfj3H///eTl5QEwduxYXn75ZeLxOOvWreOhhx4C4LrrruP2228f0oG+LxAjVwv0G+ifKLvBRluok6qsSiSzE7QkWqATyZYDQLHHxssrDxFPqOj7WdpLEIYSCY3KEid76rr6lJcXONKBsyRBiy/K317bwb76bqZWebjt4rFkWU5gOKckpRrCTiCKtRgUbrxwDH99ZWdvmUmH22Hk2kWjefa9/enySZXZ5GeZSchxOqMdJNQkWWYXnWEvsWScPIsHu+Q45TmlGhCNqew+1Pdz0TTwdke44YJKnn23tz5lnmxGmVM96U2xBn656g9EDycOvKzyfC4dtRidpsdtzKLSXcZ+7yEAcsxuziudh3aSPed2nFxTfgUxougxIGfJx0ycJ0mwx7+P3639c7psnGcMd0++HaNmptxcwffP+XfCyQg2nQ2dOnKD2JMlJRUUScefN/4T9fA/VLEjn3tmfXrErjOWUFUa2gLkZpmpKs2iuTNITUM3gVAMGLqj9oSTE44mcOeHeXDDI8SSqVFKWUYnN15xA6akk/OKJ5BMDHIlBUEQhrnTDvRfe+01nn32WTZt2sRll13Gb3/7WyZNmnRCx7a1teHxeNJ/5+bmsnVrbzb5rKwsFi9eDEAkEuGPf/wjd9xxB11dXdhsNnS6VPU9Hg+trZnDUoeSnmAMsz6AZMhPl3VFfdh0J/7g4jQ62N99EE3TUmtYOwtIth9EPhzoGw0KOS4zB5q6GVt6doc+C8LJSiY1zp9ezIotTXQHUknl8rOtTB2Tk55H7o8k+Onf1hI4PFx94+422rwhvnfXLAxHmVuuanCoLcALy2qIJ5Jcs3A0VnvmfPI+x6gac8bl4rIZeXdDA8W5NhZNK8Jh0rF4ZjFjSlzsa+im2GOlsshJUgnzxM4XWd+0hYtGL6TW18DeztQUBKvewrfO+TLZsueYr/lxCVVjT0M3Ly6rQaeT+fQV43h/YwM1Db3L/TmsBi6eWcLk0Tl4e6LkuEzkOVPvLS5H+fOmf6aDfIAVdWuZXzKLHMWDVbXz5WmfpS3SRlJVybN4MGmnFjhpKuhJ9bqrx4k4I4R5dOszfcp2te+jOdRCmbkcTQMjFoyyRQzX/5ikLsYzG19JB/kADT0t1Pc047RnD2LNzhybSc9Ni6vw+aNsr+mgNN/OomnFOEXG/RGjJ5zg6Xf34qjeng7yAbqi3ehdXogbORg8xChTmUjCJwiCcBpOO9B/4IEHuPXWW3nggQdwOp397hMKhbBYMnuu+1tvub+M/X6/n3vuuYfq6mquvfbafoP6k830n519dpIZeTypzNH+cBy7rhuLqxqdLfVg7q8PUJk9CrP5xHqwzBgwdOpJ6GJkmZ3E8kuJth7ANW1Rep/KEhd1HSEWzCg9pXoOdaKeZ8aJXg8D/b48HvjFV8+ltsWPLEuUFTjIzeq9V9TvaUsH+Uc0tAUIRpMUHaUxa8u+dn7y17Xpv3/++Aa++5nZzJ14/PnjRflOLpiVee0U5TuZP6Uo/fcHh9awvmkLiqSQZXbwVs2B9LZgPMRLe9/gvvmfw3ASQ6xXbWviV0/2ZsrfU9vFZ64cnw70z59ZTNWoLBxWIwV5jozjm/whWgJt6b8vGr0Qo07P37Y8xZjsci6sWMAoVxFFnFwDxKk68l1p8Yfpifgztke16JC9Ts7W78NH9fdZNHQ30Rnqyij3R/14KobOZzeQ/46h5h4ONnWzbmfqd76pI8iuQ16+deesM/J9GarfwRNxtuo+0NdD7a5WzEaJ9nBHxrYWfzuLisbzu/W/4z/P+zdKXUX9nGHwDKfvy3CqKwy/+grCcHDagf5bb7113H1uu+02XnjhhYzyvLw81q/vnTPa1tZGbm5un33a2tr43Oc+x9y5c/nOd74DgNvtJhAIkEwmURSF9vb2jOOOp7MzcMbXafZ47LS3+1FVjWA4jl7vI5xQkAIRNE2jO9qDTjMSDmcukXU0dr2Nem8LeqcRzeQm1vEBXZ0+pMMBRZ7TxNrtzVw0/cR/HI/Uc6gT9Tw9x/oRPZHr4Uy9Lx0wOu/wg2Qi2ec19P302usUCU3V+q2Losgs21ifUf7KigOMyrVyqCXA7touCnIsVJW48AViaJpGnsuM8QSnu8iyxL7OWuaVzGB0Vmm/meMPeGtp7+rGoKUa9VQgEE5g0MuY9XJGL5WsSLy64mDGeWpbevi3m6fitBkpdFuIhmK0h/q/XyiygTHuMvZ5D1HpLiMQC/JWzQYAary1rKrbwHfP+dpZWbbuo98VRTYyp3gaqxt6GzEUScZjyhnU6+R0r4eBrkt/n4Wmk5lVNJV3D37Ypzzb4h4y95iBvi/0BKPpIP+I7kCMTl94wN/zUL1Xn4iBrvvZvB50Muys6eb66XOp636uz7YCYzntHSo3j76T+q52jEkrsjo0llYcTt+X4VRXGHr1FY0OwkhxViZy99dzDzB//nxWrVqF1+slHA7z5ptvsnDhwvT2ZDLJl770JS677DK++93vpnvt9Xo9M2fO5LXXXgPgxRdf7HPcUOMPxzEZFGQ1DrpUQB5KhNHJenTHWD+7P3aDnY6wFwBJ0SPbPSTbD6W3F3ts1LcHCEfF5DZh+DObdJwzubBP2WXzyoklUt9vSeo7mkfTNCzGzPn7hR4bH25r4WePruP59/fT0hnid09v4QePrOb+P63hvx/fQE/k+NeMJKWG+U/OHQdoPL71Bcz6zGkBc4qmYZRS5d3hOL97ditf++1yvvOHVexu6AFJwkQYiy6OSqrxwNZP3gFFlrHZFA42+jDqj327llUdn55yE/m2XCbnj+sTWAN0R/20hAZhipMqc23V5SwcNefwMn95fH3+/8OtjMyh5wNJLylUuEtZWDYHvaInx+Lm1snXYOnnOzdS6HUyOiWzgc9kSP1WnuTgvbSTWUbyZCiSijHRjUGLHLtukoQ2gldLOBl2m8LUsTlkaaO4qHQxJp0Rp9HOdaOvZ/36OK3eEL/9Ww3vvBeiKxQGTuHfT5JQ6f196O/fRpbAlOzBqAZPelSoIAjCcHBW1qo52g00Ly+P++67jzvvvJN4PM4NN9zA5MmTufvuu7n33ntpaWlh586dJJNJ3njjDQAmTpzIT3/6U37wgx/w7W9/m4cffpiCggJ+/etfn423ckq6A1FsRhnJYEl/Fj0xP1b9ya8X7jTY2eVtTv8tu/JJtuxDl18FpB6SinNs7K7tYlrV2RmiKwinSqdLJXI7Wm9RIpFE1TRuv7SaWELFqFfYsq+d6WNz8AZjbD/gJRJLMGl0DvkuE6qqMW9iPq+tSiWlhNQD4nnTivn+I6sAsFv0JJIqNY29c99rW/ys3tHCpbNK+q1LOJ7kUIufpo4go/IdWHOMrKpPBdK72vdz2ZjzebvmA+Jqgin54zl/1AI0NdWT/+jru9m6PzVE1eeP8ocXtvL1uyvY3L6VQCzEjMKJZCfcXDxnFKu3t6Qz/Bt0MsW5Nn79+Bbuu30isYTa7wiHj8qScvjWnK/Qk+zmpd1vktSSfbbL0sm37UalCK3hVhJqKqv/qYwIsOHg5jHXcXXlpeglPXoMKFqCpKxDFfPyj0rVwKwzggqXjTmPUCyMhEQ8GYcRsry4JIFf66Ep0IIiy+Qby7h8fjn/+qB3OkxViQuHzcD2nu10hrsY7Sqj0FSArB3/EcYY70Lz1qNFAugdOSSdpSTkvg0lsqRhCLWiBjqRDGZUewFx+fi/z6Z4F8E1z+HbtRKdw4Pros8Sz65C1T7S+IjGodYgzy+rIRJNcPXCCqpLXOjOUMPDUCdJ0BZu4+J5RYRCYPCOZZGllFhM4+V/dVLoMdB4eMWFdTvaGVPk4rKiGuKNezEUV6PljCagSLRF2jECdpMTC870KClJkmj2hfnXBwdo6ghyy+WjkCxdNAdaKXEUUmIpxoAJQ9JPbMc7dKx/DUlvxHXe7WjF00hIx59upVcjyD1NqOEeZFcecUseqnbq/WaSJB21Q0wQBOF0DPqitEuWLGHJkiV9yh555BEAJk2axO7du/s9rqioiMcee+yM128g9ARjWPQakqF37nF31I9Fd/KBvkVnJqbGiCSimHRGpKwiEjveRtNUpMMP8aPybWyp6RCBvjBkqUCzN8yWmg7UZCo4z7EbMoa0Z9mMRONJHl/aex/IdpgwG/V8/5HVhA73wj/99l7+83NzGJVjJT/LxI+/MI9DLX5kCUblO7BbDcTjqaA3z21NL931UVv3d3DZnNJUdPURcVXjsaV7WLOzBU2DxbNLKavszRuwpWUnbrOLL8+5C6veTK4xF10ylTgsGEmweW97n/NdfamHX67+PdFkagj+skOruWf2nTgMHr77mdms39WKLEtkO8288H4N4WiCUDSGQZE+8jALsiz3WYLwCINmwqMYuXLMYl7a80a6vMCeR74l77jZ2i0GFSniJ5lI0GU08odNj7HPm5pWYDfa+Oa8e3DLOcc+ST8kTcYV8iOFfUQObsZftwNTxTRM484jYnCf9Pk+CZIkCMUjBBNhPHI2VoPEyrr1fGry1Rn7huMqvkCMhKritBpwmvXDInjoTHbwPysfInB4Csx3p/+AXLeF2y6tprkjSI7TjNEgEwjFeXj739PH3T39VqZlTTvmezSqAbTmXYQPbiHR1YJp1ESMRRHU/MnpBiZJAqPvAPHWGhI9HShmO4q9FX3xNOLy0UdO6KQkgVVPE96dakBMdLfS8dz/kHPbT4haekchNXaG+fFHcob8+h+b+Pqt0xlf6jqVj2sEkLCajCxb3cKbqxpYMLWQ0jw7oWiYmxdX0dQR5NUPe6cxrdzWynzfepL7V8P6V9Ff/VW2xn2MDYQw7dmAancjzboCHJVomkZnIMoP/7SGaDzJjAluVrS9zaa2zenzXTr6PJZUXEZy71r8q18EQEvE8L7+MDk3fodEVlW/tVYOjzLRaRGia58muPXdw29HJufq+1ALJxNPnNz1plMjyN6DxFtr0GcVIudWEtVl5l8RBEE4VYMe6H8SdAdjWJQk9FlarxvLSSytd4QkSTgMdjojXRTZ8pFNNiS9CbWrEcVdAkBFoZPnltWks/MLwlASiiV5b1MjK7Y0UZBjZea4PH755Ea+dfsMXB8bvi4Dn76smopCB6u3t1A9KovL55ezeW9bOsiHVGz+/Hs13HfTFNq6I/z4L2sIHt5enGvjO3fNZuG0Yt7f2EBje4CpVeVs2N3W57Vmj8+jLdaKL9KDx5yNU3ahadATiXHZBW4Wna/HbrDTcEimrrETjyWb9lAnF40+l/agl9+s+hMAEzxVfHrSzVixY9DJuB0mvD0RAKxmPX6pOR3kH/HG/mXcPulaYsRZu6OFaDyJP5RqTJBlCZfDmA7y/fSwvW0nB3x1TM2fQKW9HCN97yWaJrGo+BxKnUVsadnBKFcxE7KrMR8zy36EusAh3q1bg0Vn5Pzi6bT4u9NBPoA/GmBpzXvcNvYGNLX/e4tMkmjLQQzeNmSbm5jZg4qCMdhIeMd7RBt2E29P5VGIt9cTrd2Bfck3iEkjdzj6qZIlmX/teYuOoJdmfyvBeJjuSA/N/jaKsnqTRoYSKjsOenl3fT3+UIx5kwuYPT4Pj21oZ6qXZHjv4IfpIB8AGf72yg5UVcPtNNETiBFLqHzrjhl9jn1y+4tUn1uFiaP/jirBdtrffxI1kmrYi7XVYg12Y80pJyynRqYYtDDR+h10r3w+fZw+pwR3dhFx69GT2urifqJ1O3Gdcz1IEpKsI1K/E9XXBIcDfVmWWLcrc7rMKx8eZHzZ9IxGxU8CTdOQ41beWr0dgBWbm5BlCbfDyKTR2TzyUt9cJWOLrcj+duScEhI97bQQZ7y3C231K8QAWg/Rc2g7OTd9hzrs1LcliR5u1K0ea+D5hs19zvdGzTLOLZ6Jadu7GXWL1mzAOKOYqNT7nZIk6Ei2s7JuHa3bO/hs3ozeIB9AU/G++QjOuVeht2SjFYwjcQL3MlnSUHe9Q9eHvSuSGEsnYLv4y8Tkk382FARB6I8I9M+CnmAMsxxH+shQfX8sQI751OaoHpmnX2RLLdUnZxWRbNmbDvTddiOSJFHfFqA0TyQUEYYQSeLFFQd4d109Y0qzCEcTPPnGbq5aOJq6tgCussxM+najjivmlHLJrFJ0CkhI6SD+owKHk9S98uHBPtsb2gJsq+nghvNGk59tYdmmRlx2IwsmF7JiaxMAs8blkV0Y4fvvPwyAXtHz73O/gItCakI7eXz70yTVJHpZx6fL7+XNFa3cef117I9uxG6w8VbNB+nX29G+l/Wtmzm/YCEmvcwXr53E/zy2HlXVUGQJRZf5cJ9QE6xq2Mjmlp1cdO61PPVq/ZGPi9suH41k8LO1u55cWw6v7n2Hjc3bAFhZv54rxlzI5aMuho8F3kbNRLWtmgljx6Oq6nGXqTrgr+FX6x7tfR/eg8wpmpqx3/6uQ8SJoyNziKssQbizhX0NrRhlFdeWZWSNmYpcMZfIvjXobC4C7X2TJcZaagj6DqBmlWPk5Ec5jWRxNc7UvPGM94xlc8t2ssxZVLpH4Q33zcTf3BHkDy9sTceNz727H70sc/GsEob0+mSSxiFf3+9Dnb+ORDJV5w5fJF0eTIT67BeOR1DpOzXl45I97ekgP32eXSuxz7wCTKnfRl3MT+f61/vsE++oJ9nTDscI9CVFR9a5N+F99zG0+OGGvHHz0VmcfHSdEJMx8zHLatYjcdzBNSNWLK71+VqqqkaHL0I0plJe6OBgUw8AboeJ887JZnf7PHYFWhhjWcAEZz7qG4/3+ey0RIx43Q6sagxb1rzecinzd0JDI6YlsWUVEO9s7LNNNphQW/dC/tR0WVfSy89W/I7I4WVL2/We3qalynPweaYRVyV0NjvxV39B1kWfQxo197iXnSHqpX1V3yTV0bod2HuawTX62AcLgiCcoNMO9J944gluu+026uvrKSkp6Xef/pbW+yTxBWKYiYIh1cqraRqBeBDLKczRB3AY7NT7e3+gZHcJib0foo2/AAkJSZKoLHKyaV+7CPSFIcUfjlPT0M1dV05g095Uj/otF41FkY+etBMgkdRo74nS5Y+Q4zQzZUwOz7+/v8/D1JJzy1PzYZszM/c2tAWYM9bDpbNKuGBaETpFZm51LlctLCepJbDaNX70wS/T+8eTcR7Z+ARfnn43T2x/hqSaCijiagIfDeh1Mn95up7zZ06hXtqU8Xobm7dxftG5aAkYU2jnv//ffBo7ewjjp7RAz+sHZJIfWRv9/LL5/GP7v4gmoqyTXuW2m87DIefitptZ27mcZ1emsq5LSNwy6Sr2dNYQjKUCn6X73+Pc4jnYJVe/n11/w/s/TtFrvLx/WZ+yzrCPQnt+xr7ziqZjkAwEpACapmGTbGiH5yR3dgf5yT8O0eVPPRQvmjCXqxv2UFA8jliwG50xr9/Xr/U3s9tfx5Vll2Q0WHySmRUT5Vll/HbNn9JlVoOFb8y7J/23LMOBpp6MzuH3NjawaFohRuWs5Nw9JZImM6toCge66tJlW7zrmDluOut39Y64cdoMeKntc+yc4qlICnCMHJqSLrMxSlJ0IPcmwVUlBS0ezTz4OIGapCboWfdaOsiHVCOCddxHAk0NRhc5MeqVdC+zLME5kws/0YF+jsNC9agsdtf2Nlh5sszUtvipLHYxd2IBJoNCXkmY1w+9xbqmLQCsAO42WKjSGyDS95ySJKGu/Bejb5pKttNEZ3eEHq+ebLObzsMJjAFGuYrJMmRjn3M1kUNb0RKpBmKdw4Ok6Ige3IK+aHr6vlnnb0gH+QA9RhMWSSY54TKeaR3NqrW+VP1dUb5+/j0oa57CWTKNmHTs0TSamgS1n4aIxImvwiQIgnA8px3oP/bYY9x6663ce++9/S6hB6nGgE8yXyBKlhZCOjx0P5KIoEgKOunUPn6HwUZPzE9SVVFkGcniBAm07lYkZ+rBvLLIyQdbm7h6QcWAvQ9BOF2KInHBzBL+/K8d6bJNe9r5j1unU5jd/7ByVYP3Njfx5Jt70mVfv206//mZ2Tzz7n6CkThXn1vBhFFZKJLEedOL+PvrfXN7TB3jSSfZ08kSaBohuYcVnct4/9BqXEY7d069nrWNm9nQlOot94Z9RLUwiY89jL1e9wr/8emv8vryZvbW9rB4fDnrmvoG+5Nyqw8HrBpoYLRHeHTT74gmotxpuJyvz/8Cbx5YQSge5ryyuezpOED08MNkY6CJZwNPMr9wHmVyJcvre5dWK7TnMslRwtRz7iGsJYkn4nSGulBPM2TQgI8nBNc0DVlLckP1Jby49x0SaoJZhVOYWzSDdxqX8dKeN9E0jcsqz+O84nMxYuaf7x1IB/kAy3Z0MeeKieT2dGIeN5/Q5jcxl08hfHBLeh95zHRW9tSxrn0PC0vm4jhKg8UnUUxN8OLuVG+z02gnkowRjIWo8zXhyU4tKatpYDX102ts0p2xTPMDRZNUrAYLt02+FovBjKapWBU7AYsbt8PEtv2dlOTbmV7lYXShnlG5nyOejGNQDBzy1RNNRjEfIzmkbM1C5y4g8ZEEto7ZV5DU9Q6tjhmysE44l+D23oYuSW9Czhl17MqrSeLexoziZKh3BIEsw+5DXr58w2SSKiRVFYtJx7KNjUwd/cnNS3Go2c/UsbkUemzsOuilvMjB+TNK+J+/rydxOMCeVu1mbmEiHeQf8WTNO/x8wQ34X/9jukyxOtHUJJJOT0iX4DM3F4Cqo7kBvj72swRDzURMRmRFh8eai5LQU6fmEr/sx+RIPizdh1CjIXyrXsJ54Z0oRJDjXWiKCYPSt7Hoqaa13HvpZ2jo9rBqeVO6vN0X4dW9Fm7Nq0y15hznlqya3ZgqphE50PvbIZvtSK6CU/1YBUEQMpx2oF9eXs6UKVNIJpNMnz49XX5kfvjGjRuPcfQnQ08wRlHSj2Rwpf6OBU9pfv4RiqRg1VvoivjIsbiRJCnVq9+4E8PhQL8ox0qXP0q7L4zHJYbDCkOD02Jg10FvRvmaHS18Ycl4EonM3mdvIMo/3trTp+zBp7fwP18+h69/aiqaBkoqdkdVNWaPy6OtK8yba+sw6GRuurAKTdP4YHsLo4ucFGSZkWV4/9CK9JD79pCX/1v3OPfO/Ww60C91FuE0OCmw59Ls7+1dVDUVLD6+fO1EogmVpC7E9s7t7GzfC8AoZzFzC2f0yd7fGmxjSs5kikyj8UeT7D+4jhsrL8RqdqCi0NzTN18AQL6pmGiyN2g+t2AKi0pm8H7zFgLxMCvr1wNQaMvjy7PuAEDSYvh6QuhlcDktxNTjZ5AGUOMSSyrP51dr/5ouU2SFQqOL2a6xzM0eQyIRI0dvZmtPA8/ufDW938t73ybfmss4x2R21/oyzt0WUpDs2SQUK+YJi0h21GIqGUfM10rEU8xKrYc1dauQkNA+sX2c/UskNfKseVyQexXNTSpWu4Ipy0/iI0sVaBqUFjhwWA30BHt7A69ZVIluiOdokTSZfJuHlXUbWF67BkWSuXzMYuoPhNh/MExVaRYtnUFWbK/Dbw/wwp7X0DQNp8nBPbPuwK63wzE6QJOWLNwX3Em0cS+J7naMBaPRe0qJ6BypjKBAUpOxT78ExWghtH89uqx8nLOvJGbKTu/Tn4TRgaliKsacEiS9AUnREdq/AZ0rN12lZFJj2tg8PtzaiMtmQpKgvSvMRXNKP7E9+poELy6vYV+dj8IcK5UlLhra/GzY1YrdoqfLH0WWJWZOsxGIZzakBGMhWrI9jLryK0QObUM221DMdnyrXiR69f/jN9uewRv2IUkSV41ZjLI/QCQnlz/vfYWucDejnEXcOeFGNu5KosgSPQEd5+dnY1z9D5T80SgFFXS/8msS9btRrC7GLb6TGXkT2NCaapxuDnbS5HDS1Jr5DBeV9DRXXc+ONa14XCbGlmbh6KcRDiCu6bAvuhN9TjHh3avRF4zGPvtqwjrXgH7ep+pIwtcTmfYlCMLQddqB/kMPPURLSwt33303f/zjH49/wCdQTyiGWfPD4aH6/rgfi+70Ek85DHbaI15yLKleATm7lMTeFWjjz0NCRpYlxhQ7Wb+7jcvmHqdnQhDOElXVMOiVjHKdIvf7MJFQNWJJlXOmFLJ6W3N67m40niQYjuMw6VIPzB851mJQuPn8Si6bO4qkpvHIi9v5++u7gNTDy3c+PYviIoXldWv7vJaGRkugjcm547lw9HyC8TDt4TauL72d7kgA2dpNOBkkx5qNU8mmuStMjsOEUbPxpal30hxoJRiJYUpYUX0qOleCxOFbrE3Kw7uzksJxFmRbN2WuCurCXTiSCeSghXPyp7Cns4Y9naklxWYXzCTmzWLMaDOyJKNqKotLZvDzTf/kyrEX8ub23pwATYFWXq15n+tHLeHDbR2oh6//IleMcYUWNIMt/d71ahhZSxDT2VA/NkS+3F7Jt+Z+nvdr11LiKGB6wQRy9S4ia18ksWEpAD0FlawdU57x7/RB/VqmT5/GtCoPyzc39dlWUuwhps9C0zSSuZMw5I0jRoQ/bX+GnQ3vppaKA+aXzMShOI8ZXH3SmHUGZtsu4X//uTNdlmU38vVPT+qzn9Oi5ys3TqG5M0QwHKe8wEGOyzT0V22XNfZ3HuL9Q6nM9aqm8tKepVw/5mbWbA7zwebU9JRP35jH07ufSh/WHenhia0vcu+cz2Dm6EOklWgPrc/9AslgQrE4CO5aibF4HLYr7iN2+No0SDG63nuMZE8nppLxJHo6aH3m5+Td+n0S1qP/duoTIZzTL6H9tYdRQz2AhGPWFUhy71QJWZYIRWKAxPPv7yOpasyZkI8/FEOSJbTkJy+CkgDd4c+oqSNIU0cqEePMcXncsLiCVl832XkqbzQ/T2V2KZXuMvZ7D6WPL3EUYgkF6Hzrbziv+xrBt/5GvKMBy/m38reGNXjDPiDV2fTS3rcYM+MOfrPpH+mRWbXdjfx52z8YE7ucpSuaqSrNYlz5aCbc9F1ki5Wedx8nUZ8aEZYM+uj51//yudv+kzx7Lq2BNs5xjCLPFyPo7PsM57AamFCeww//1ttDn+c2871Pz8ZqzPzNA4jos5CnX49zyhUkZQMhTaYrEMMfipHtMGEfpJUzQrEkO2u72Lq/gwnlbiaUu7H1k2tCEISh77Sv3J6eHiwWC3/5y18wGod2ht/B0hOMYdZ3Ix2eo98TDWA6zUDfabDTEeoAdyVAavi+rEP19mbfryp2sXpXqwj0hSEjmVS5cGYJyzc1pOcUyxJcOLMkYy55VyjOH1/cxp46H2UFDj5z5QQeX7qbcDSB22Eiy370+42madhNOrYe9LLrI/NANQ0eW7qb735mOm6zKz3P/Ygsk5PzKubym9V/Tj9gzS6aRqk6G2Myi+frniOhJsi35jHTdAUbtgS498YpRKNRXnzPh8NqZMfBgwTDcX54x3hys52okoFVGzu4dXEBPbFa9gbaIZxPJJggt8hJmzeIsb2FL8j5yPMuBZONen+M9mYdf3m6hi9c/1k2tK4nFAmioeGL9GS83x1te1mQE2L1jjZqGrsBGDsqC9fFoynISc0HlVt24nv/UZJBH7bJF2KadjlRnTN9DknTU2qp4prxHt6oeZ8HNzzB7IKJzLLZ0z8USX8HJcaprP7Y65e7SpE0uGbRaGpbAtS29CBLcM3CCooLsvs8rMY0HWDj5vFX80H9GvZ01jC3eAbTPJOQ1KE7n3wwJGM6Xni779z0Ln+U5tY4BbbeMn84QUNbgLfX1qHXyTS0+rlwdgnZNsOQ7jaWFJX1TVszyptjBynIGUV9ayrfhmYIZ+xT191IJBE9ZvrGpLcJNBUtGiIRTV3r0fodZIXaiJlSmfH14Xai9amGwEB378iaRGcjHCPQl7U4Hcv/eTjIB9DoWfcKxqIxcPjfRpYlvD1R3lzT+2+4ensL+dlWpo72wHGSCY5EEhJXnFPOrkO9I7v0OpnxZW6Wrj7IrJlm3m58jY5QJx5rFjdPWsKWll3sbNtLZXY5Mwsnodu2Gl12AX9rWE3x1NkUyAspya9i98p3Ml6vMxHKmH7V5G9lVn7q92ZvXRfvmXRULrRiSyZI1O3sewJNJeFt4QZHJd1bNxA3h/h79xwMJi9XzCvltdV1aBpctaCM59/b3+fQVm+Y/U092C06CtxWzPrM+5uqQkwyoaoaH+5o4dFXd6JqYDHp+NbtMyn1WM5qj3pSg3+8vZcPt6amu6zY0sSk0dl85frJ6If4VCBBEDKddqA/d+7c9BJuH295lCSJXbt2ne5LDGtJVSUcTWAyxpGU1NJhgViAPOvprXHvMDjY6zuQniIhSRJyTimJ+u3pQL80z86rq2tp84XJFcP3hSGi0G3mR1+Yx4otqZ7fBVMKKcjq/X6G4kn21nezraaDadV5jCpwAhrhaIK7r5nIyx8c4O6rJ2LSHT8oDEUzkx31BGJEo3D9uCv43Zo/p4biA0X2fEa5ivnvFb/vcy9b27iJmTOmsX6tyl2zbsJhcGBTnAS6FaxKhHZ/BJsc59bJoESaubbaw76ggw+2tnHzIomonMVNYyPEdr6PO6eSA13j+ceyWlRVozg3yjWLKll+yMB1i+bRFPfR2dlKiaOQuoiKPxin6YCZmbmXYtK1E0lEcZky11me4KmitiGSDvIB9tR2sbPOT2Gek2DTAZIv/yq9LbDpDSSdHnnGDXxkFDgRKcgvVj6c7hV7yd/KoZxKbquaibp3PcmAj0mqkXfNLjoP72M32lhQMotkUsNl1vOdO2fgjySQNI0s69GnDjglN1eVXY5WroIqD4s138+2eFIj2M93OPyRMklKrTghyxI3Lh4DWmokzIGGbkpzbfTflzg0SEgUOQr6JOMDKHEUUTi9iN2HuijIsVLgyPxulDgLMSqGY8bKsilzeLVidSLpepfxlBQdii2LZKDvSgay6VhLUQJqklhbbWZxpHepQEWR+lyTR2zd38E1iyrQPnlxPpIEWQ4D37xjBvsbu/C49RhkEw89u5mrF1Xyf4/vZPbk85hSYGR2VQnrmpezqn4jxc4CNjVt472DK/n+rM9ird2BQZJ49tAKJCT+x+ymKquUPV19/02yTc6MOpj1JuLR3itjy752uLCARHctit1N0t93eplstBKNJ5FNdgK5U1i3zsfoIo3PTjOysDCbWDiM3dXD05H4x1+K1q4Qv31qD3luM9+6fWbGErJHtPdE+esrvY0MoUiCB5/dzI8/PxfjCfzWDZROfyQd5B+xraaT9u4IhVniOVIQhpvTDvR37959/J0+wfyhOGaDgmLsfeAIJAKU6Y6+bM+JMCh6DLKe7qg//eCv5JQR27oUbeJiJEWPLEtUlbpYu7OFK+dnDrcVhMEgAYVZZj51YWo0SvIjw1cTmsZjS/ewZkcLkEoodvc1k/jHm3to9YbwuMzcc8NkClzmEwoMywocyFLf5aovn1/Glv3trN8V5N5LvkhHpA29rGeUs5hkUqW7nx5znQ6yRjfSGVbY5z3Ajra9lLtKGeeYjdXowrprKdrW3nnr42deSyRnOsF4CHuwnbYXfgFotM2fyVPvHUrv19AWZNW2Zr56aT6vNq/lvbp13DjxCv6y40UmyBczqsBBIJzg7bUNnD/Byk2jz6O+p5k5xdNY05AaIppnzeHKMRfwyFMNGfXeU9fFwsm52HQJ9Dd8B5/Pj1q3GXX/KoJb38U95VKicm/XcEu4LR3kH7GlYz/XjFqMeW8qJ4B124d8a+7ltFhtJGUdRdZ8bJITQ8yHHPdjNZgpKC6iw/uxtNj9SOUx+KTOVj4+WVG5aHYJz7zT21OoyBK57t4HbkUBvV4mmdR47LXdBMNxFkwtYta4XKIxFUs/vYhDhQbML5nOlpad9ERTvff5tlyinW6ef3sfhR4re+q6ONRm5qrZl/Ly3jdSo3WMNj416eo+K1f0S9ZhHj2NaOM+FKuTeFcrjllXwkeP0xvJWvQpOl77v3S5uXIGOuexG+M1vRFD/mhiLTV9yhV7b5I9SZIoK8hsmKsszgw+PylUVcNpMeJLdNBmW8W6jlrGuqq5eNF4dh30UpxrZ+WmdtgE5YUulh5YQSIZx6QzYjPYGOMaS3M8zOjR/z977x0mR3Xl779V1Tn35Jyjcs5COQASQiJHg40NDl+ntdde22vv+rfr9e7arMPa4ITBgMEgoghCJEkoZ2mURpNz7O7pHKvq90eLHg0jhL0ILFvzPo+eR3P71q3bXV3V99xzzudM5gp7DvsH6pmeNQ1RFbkleyo/CXsYiviSkQMl88jxDHFlwUxe7dwPJDeX1pRcw0ubho35wmwruvgQ4XeexrTkVvybfpn6LmiqZqD4Bwm88Qes01ZhtpgQhQiLpubjjvsxvP3vGAApLZ+rZ9zKc3v6UuPqNGJS/JWkd/+do92sm18yQr/lXdy+0c/LwaEIgUgCveXP01q5GLzfz+r55jzGGGNc+ly0pBtFUfj9739PQ0MD3/nOd3j88ce55557kKRL2Z/w0eMLxjDrSCnuy4pCNBFDL334NAe73spg2JUy9AW9CdGaidx9Ck3hJABqCp1sPdLF1XNLUpEXY4xxKXCugS8I4I8kaOjypox8gOWzinlo04mUyNjAUJj/fuwgP/zsfCzvk/d4Ltl2A9+5ezZ/3HIaty/KlfNKmD0um3/57V7cvghHG9xkOAzEE3HWL1JYMNVJVXopZ1wt58xNQBQl+qM9xEJxTvQnhQEHQ25O6xq4oeiTjBtoHFHlK37wBebePINQPIR64FVABUFkIDLam3O82YUrlsHLTdu5pmYFTx9/mWA8xNQyH5GYQG6Gmfo2Dz97sY2v3VBLZU6cCAqLi+YSS6hYtVrEkJ9p1ZmcfI/Q4eTKDIRgC8fEADu76yiy5lJUuxxH4QIyG54nIsqpPG6v6qYvODBqfqIgIp3NqdWm5WGbvIRgby81U68krmoRBDD420gMtOA98iZKJIB1wiJMpZOIWQpTOgVj/OXoNAJ5JVHWr8xn32EvdquGhbNtmBwhIPncVxQIR2QefXU4eu7N/e2Y9FLSoLyEF+iiINDt6+VLcz5JX3AQSRDJNefxzz85TjQup+qp1zfL3Hr1JIoc2fiiQbLN6TS728ksuLBgnhqPYq6ejT6vkri7B9vMq4l7uhHOtWYSyTs3+4ZvkvD0IhqtSa+uzwWZee8/eDxK2uJbGXj5F0kPsCDimHstiMP3uKKo5GdZKMqx0t6b3MhwWPXMHp976esnfISIhgiP7/8Dnkgy2mEwtItyZy/j81aPqNqhKgJLcyYzR2PD1tOJYi+gVSlh954EjlkLKfQ1cl/Fl/jDq43I2SI35g3wDes4PDlmTBoDBZllxJuPsianhpmF0xiK+nGaMtmxJ4DblxT602lE1q/Mo0PvJd3iIHxiF+abvoPgdyHqzMhA8IX7AfAf2oJzVSF3rCjlZKsbXY7KOK0eNR5FdnexsKAeyxU1vHHST5bTyMxxOWx8qyH1fk60uFk3v5TzbWym20d7y3PSTFiM548A+KhItxmYWJFOXaMr1VaaayPL8eHSTccYY4y/DhdtBfZf//VfuN1u6urqkgrX77zDwMAA3/nOdy7WKf4m8YVimLUKaJMPyWA8iEFjQLwIRrdNZ6M/PECFc9hbL2WXkWg5iFQ4EQGBgkwzoWiCjv4ARdnvX4ZojDH+GvgiCRo7vQTCcZxWPeHYyFhWk0EzIkwZkiGN/UNhLNkWPggBKMky82/3zWfIG8KglUgoKhkOQ8qDMjgUwWbWUZRjJRgQuH3SdTx67BkaXC3YDTZuHL8WbzhAmbOIZ06+MmJ8fyxIQutDyK2EnnMqA6gKobALU1Yh8XdrMKsKaYbRYdhVhQ5kxYeKikaUCMaTucSbWp/j6gXXkmnWMXdiLo2dQ/z3U01oJJEff3kqW9p2sKt7D5FEFK2k5dtTv8SkigyONQ4CML0mk6kFIltdZ3i2ZTsAx/pPY9cfYLnzFoyz76ZhsJ45mbOJCzF+d/AJ8mzZVKaX0nDORseVFYtJz5kJ15USTygEJC3aolzianIBqpWDJPqbcb/xCO8uYId2P4ddSaAtiJBIrx3xfkVRwK/4iMhR7Fobmj+zMsDlSIQQT535ExNzarn++kriSoLNDc+z0rqYfEOywoogQEd/0ogURQFREEjICruP97BqTjGmjzHs9y9FiYsUOQv4r50Popd0yKqMRpC456Y7+N9Hm1P9rr8yl//Z/0DK6w+wsuIKEsqFY981ZjsDW/9IYijpZQ2eeAf73PWo4shNQjnsY/DlX6b+1mYWk77irguOLWgNDG37I+bq2ckwf0EgdGY/+oLq4T6CQDAcZ3xpOvMn5aECsbhMjytAed7l+3vcF+pLGfnv0uRp5uopEs+84cFs1PKp68qJqUGWp1WSeOkBorHk87rAnk1Z0Z3c/8czfG19KT989BiKotLZH6RnKI+75tupEF1IOh2Df/w+6lmxT1t+Lc6ltxBqPswKpZ/Jd6ymxT+EYAjxZOvv8UZ8fHHKWgq3PkdbwMHvXh2ge7CX0lwLn1n8Vczv/Aw1HkFrTWdqRjaPvr6Hlm4jNXNuQNjzGADSsRdZOCXA0ltW0TiQ4Ad/Oj7CQz5/Ys77RqJlWPXce+0EfrfpBAlZxWbW8YUbJqOXPt4tIY0A96wdz76T/Rw41ceUqgzmTshFJ126z5Exxhjj/blohv7u3bt57rnn2LBhA1arlYceeoh169ZdrOH/ZvEFYxjFRMrQ98eCGDUXJ8/JobfT7G1N5ekDCI481NYjKO5OpLRCBEFgXLGT3cd7xwz9MS4pfJEE//b7fQx6I1hNWhZNK2ByZSbrF5ez61g3q+YkQxzXXVGOXivx7NZGwtEEoihgv0Du9/kwG7WEAiKKoiICt66s4f97aC+yolKSa2PxtAJ+8uRhfMEYcyfm8Jm1n8AfH0IvGUjTO6kfamAo6kESJeT3GBeSIUx3cSFZAzWoZ9WaJWsaddE+csJWqmZcRbQz2Z49eJBlk6bw5rGk591u0bF0eiGmcANOox3pHAPEojNz2LOH1ztf4Vuzv4nFOJGXd7Zis+io957irfbhut9xOc6L7Zv48jUbeKdbIseaiagZQsDNpvbdI+brjfqxp4c4NqBQHz/B/Jw5eGJ+mjxtNHnaWF25mAlZ1bjCHsZlVFFlrwTVgJjpQC+A8h6lcCEWJO4d4L1equDpvZgFCV1G5VkBvmTd9GNDJ3n4yFOEExGK7PncO+12HEL6X3Q9Lxc0gpZ1tSvZ3XGId9r2opW0rCy/AqfBkeqjKJBu03P3mloyHSYUVSUhqxw41XvJL85VFA51Hef6cVcxEHIhCRIOg40Ifdx5ZQ09rhAGnUh2fhRfr3/EsVtbdrOsdMEFx5eD3pSR/y6+/S9jqp13ziRkhnY+O6JPfKAtmbNvef+UNzUWJtx8lHDzyDrvCW8/OJLGviwrHDkzwFsHOkb0KS+ws2Di5VsvXRJHR2MJgoDTauTWVdVkF4d59MRDeCM+0o1O7l17L4b+CGI0iP7MG9SmxZCuyqAloIwIKT/V4edbTwX438+WEX/q31JGPkCi6xSytxdT22ni/W10FGXyXPc7I+bwUu8RPnfVP/Cjh44TjibISTdxxbQiDnniZM/+FiV0oToK0CQMGPUa+txhHjppZ8P8r2BVPFjS00CIInUeIWNwiFVTx7Hl8ACKCgvGpzGz3PK+hr4owOzaLKqLnKmNb4te+quUtrPqNayYns+KGfmgjoXtjzHG3zIXzdDXaDSI55SV0el0aDRjIZu+YBwDsZTifiARwKC5ONUJ9JIOjagZkacvCAJSbjXxxt1Is5KifOOK03h6ayM3LKlAHFNNHeMSobnbx6A3QpbTyNXzS3luaxMv7WihKMfKfRsm8V+PHiAcTRrVZoOG65dW8tKOZu5eM54Mmx71Qyw+ijJM/Pt98+ga8JPuMPH93+5JRTirqsBbuwfpdrupGqfSFt7ClNxaREFkTdUyXji9JTVOVXoFeqz8uO53fGn6BnK7G9HmlhOedSVPnniaL6SXImfWkLH+HwgcfBVB9nL7FVnMmZmLPxIjz5mGKEik6Sv5B7OdHZ5GFpfMJduSiTs8hDvsYUX5QiSNwpysACUb0nhroA5XePQzpC/sRSLC1LI8BsMeorEYqOd/BouigCrGmJw9DllW0Ut6rHoL/miAzQ1b0UpabHoLCwtmo1eTz64Riz1JBUFBkDWoOjOi/nyiZzbkaAjpbK6rIEJvvJcHDvwh1afd28XDR//EF6d9BlEZ+70YhSLS5G7nzNmyi3E5zstn3uQrcypSXVQVinPs7D3Ryx9fO0I0LjO1OpMNiyqS+cGXssihoFJgz+V3h55MiWLqJR1fnv5FTsSinGn3UFHowEo2WkmbKsWYOvwDAuAVebQ4mqrIICeGVz9yAjU2Oj9ajccuPHetEY0zh4Snd0SzaByZk59xHiHcTLuRyzmTTi8amZwzjqO9w+JzS4oW8fZONwtnp/Gj/Q8QPRsJ5Qp7+PnpTcwz3MirW2PcNP8T1BbYeWr/z1mX+4lRYzttenxRL4bzXPtYNISamY94HhFFAFTwx0XC0QSSKLBmfhkPvXQi9exLtxn4TqkRp1nizqvG8cCzxzjW6udYK0wocfL1a/PRuptR7DmI255gTV4zS65agIqIuXsrdjGPKEmHiyBAQgGNdI5khAoOkzYl2PfXvHXHjPsxxvj74KKtrKqqqnj88ceRZZnm5mYefvhhampqLtbwf7N4A1GMRBC0yR97fyxw0Tz6AHa9jYHw4AglbjGrFPnISRRvD6I9l3S7AYtJy8k2NxNKxzxnY/z1kFWVQV+UaFzGH0oupJfNLOIPr5xCPruwaO/189sXTjB3Yl7KExaMJMhOM7JiVjEv72yhayDA3Ak52Az/90eYxRZHK/RwwtvD/7s3FzFq4+GNnZQX2Hl1VyvLr5R5umkTAPu6D7GoZC6zc2Zya2U2vZEuHNp0hvrMvP5mkOm1U3m15zAbrv8y27sOsafuSaw6Mz6XHleGiD1zIqblRahylEA8hKALY9JK6PVR+qK9mDRZOK2FrNDaiRgk/mv3r1OigAe761hbfjVrLLl4Ij1sb9/FN+Z+hhcb3h7xftaVXcHBUA9P7n+JaCLKFSWzKcubwdrC2TzbuoNMUxomnRFvJIAm4UDWtzExYzLhuIwSkfnkpOv52YFHUFWVuBxnYlYNmfqslKNeFAXCiov+6BCbm7YzEHKzvGg207ImYCuegHTkdeTAULKzIGIZtwDVYCeOjqgQZnvHLvLMabyXBncrQTmAVXD8n6/l3ysJYhzuOT6qvT80QIWpHEgaDH2eEM9uHRbsO1w/QIbdyA1LKy5phQRRFDjYfSxl5ANUOMvZvK2fPXXJUndtvX4One5n7dpreLbxmVS/JaXzPlDCUWPPQjRaUcLD0QDWyctQtcP5xqregql6FqHTw0UjBa0eTVoOUd6fhKAlbekdDDz/k5Tn2FQ9B40jJ9VHUVQmlWewZU8bQ4HkaHqtxMq5xR9qo/JvnYBHw8ri5czMnUZfYACDnE5jvcCuw50488IpI/9d/NEAhvQ40bjMH7b28Pk8C7Ii4xM7KM2309IVAJL3wppl2bwxuId1JRORW+tSY4gGM6Ijk05JoajBQI3WikbUjCi9t65iMVIiuYk6rTqLbYc7Rxi8Ll+EhvZB5jr6WZBlouqucs70xSkpzMSBn/BbvyEQ9GKbtpK0lZ/CveV3GLqTKV3G8VcgG9OTmwmRBNuOduPxRagtTaOqwPGBv2URIchAxI1RYyBN40Q8ZxNXq0YQ/X2oiRiCPZuoNFoAcowxxrg8uWhrgG9/+9v84Ac/wOVyccstt7BgwYLLPj8fwBuMYVFDCNqkgm8gFiTPcvFC6J06B72hfiqdZak2QZSQ8sYRO/k2+rm3ICAwrjiNHcd6xgz9Mf5qRBMKz25v5vV9yVJan7pmPKIAsqKmjPx36RoIMH/ysBDWvEm5vLK7jZPNSYGg+nYPB0/38bVbpqH7C3IYIwmFPk8YBJWO2CmePL0R9ay5sLRsPp+/fTbbdg+xYEYab3U+OeLYM4NNlGmm8dATA9jMFoLhAAk5aUDcNq2ak4H9xHVGeqI+5ubOplA7nl/9qYmS3AG+fPMUxJAX10A9v3Ifo8OfDCfOMWdyz5Rb+On+P/CFmZ+gV/Wji2tYUDSDwz0n6D7b7/W2N7jiii8zTnHyI1suzZEQ9029mSdOvkwkEeXKknmYTXb+e/dvUvN9o2kHuQYHywpnUpFby6G+UwRiQdZXr6YwEGRy0TT8UR3RzuNo9j9GviTxvQUbcJlM2AxOHAY7siqjFwVUVaU92EBbsJ+njr+UWhw/cvwFwlVhVpQsI+O6byJ7upPibxodki2dhCkLQYXTnjP4AgPUiKMFndJNTkyS5oKiapcrOo1EkT2fU4MNI9odhuHfEEEgJfR2LgdP97PuijI0l3COPpJKMBYe0VRlncgf6/pHtHn8UdKFUpaWzWcg6KIqvYz+wOAHC9oJImlLbiPccoy4pxdj8Xh0uRUIwvBnomj02GdchWSyEWo4gNaRg33ONQiGC2uAaASFUH8bmeu+hBz0IuoMyCEfaiwEZzOLRFHAH4rx2esmMegNoygqWWkmwpH4payR+JGTiEl4fFBVWsSBXRr2Hu9NfR6SokcQhBEh7lpRg5oYTtfq7I1g1pl4s+MNVsxdyXy5AEk2UJJr5/GGh+kKdDOuZiUV9nSEpqNos0owl0/B3dNCu0nEM3sp1aKWb8/+FO/01OGLBVmYP4OigS5OGAKsnJ9DPKKlvW/0feV1uRna+yuUSBDLuAXYc1fy3YePoNdK3Dr/Wmr7t+B+8xHSr7oP+6JbiTYfxjBhKYHMiQT8ETR6A8+/00h5QRqNHUM89uppJpSnc93iCpzvU3rPpfTz4z3Dm79XVixhVfFStKoerRwgvOMxwvXJjSrJ7CD9um8CY6maY4wxxkU09C0WCz/4wQ8u1nB/N3iDMTLlAOiSYfSBeAijdPHUSx0GG2e8jciKklLGBhCzypD7ziD3NqDJqaKm2MlvXzpJOJrAqL+UfTxj/L3SMRhMGfkAr+5q5bPXTSYaHy2oZTZqiZ3TXpxjY9eW+hF9Gju9nGzzUJlvx/xnKPB7w3Huf+IwHf1J709xrpm1C6/lxdbngGTOb1VaGflZTiJKYISXEaAn0I/dYEFVwRsYDusVBUBQubpqGWrAyXzjDfhDcZ7c1kwsoXCmY4hIKIzm0CucLC1mWv5kZktaBAQ8kSGO9p8mnIjSHRwgGA/yUt2b+KIB5hROY2J2Da81bkNRFTyJIP975FmuK5rDFF0arZKOb87+HFpRQI6ZeLPvzVHveWvnIQos5fz04B9SXrI9nYf5fPVVTPWZICIivv0zFDlpuBtefICC+Rs4kO7mxfrXkUSJG8atYVJGNQ8c/hPLyuaN8IABvNK6k/l5U7EIWqK9rURaDmOYtJK4ZEfTtQtz6WR2dOxjTeYEHKcPsjxvGm90HwKSC/hPj78GqyoS+sArePmRUBNcW7uS1j0dhOPJ8PIpOeNINzlH9MtOH506UZRtRau5tOPDBUTmFU2n3jVcok5FQRKFUZt/vpiPYwOncBrtvFj/OqsrFiMJF97EkL39DL7yINrMQjS2TPzHtsLRt8i+6dugT5a4kyI++l/4CfqCWpyLb0P2u+h//idkrPk8ZGa8/9yjfrw7n4H3aHakr/50KrdfEOB0u5sXtjVjNmiQJBFfMMbkygwmlmWQiI0W57wcyM82Yo4maPW1Mn6qSEVNFk2NCrsOD7Jjr4/rVlzDxtMvAMn0jLWl63hzy3BFkfwMK5G2CCoqW9pfA+CWijuQVSt6TdJY/nXjFgqs2axddiO1PV24Xv89AAvT80nUzoLSfHpDA1yTP52QK4HgjeISFH57+jnGp49nWvEcstPzeXLLyE22SkcCJRIEIHRyBwbHNKIxmWhM5sEt3Xxz7TJy2w8TPLED6zX/iDBuFYHBbuTDL6DrPU4iZwK3zF7M1/9wJvU7sutYDy5vhH+4eSrvvWVlMcETx54fUfb11ca3mZw9jkJ9McJgS8rIB5CDQ/h3P4sp9yt/9vUQRYFwXEYSRC7lfcExxhjjL+eiWXzNzc089NBDuFyuETuxDz744MU6xd8kvmAMo+xD0BqRFZm4HEN3EUrrvYtW1GLSmHBH3GSahhclgiiiKZ5G/PgbSJmlmPRairOtHDjdz8LJFygZNMYYHxH97pGmXPdgkEdfPcW96yewak4xr+1J5k2KAty6sppoXCY7zYRWI1KQdX7v2sBQmMaOIW5YUsYHldTefbw3ZeQDtPUEmeLOxaoz448FUVQFfyzA9Joqfv/SAItmLGZz6+ZUf6PWgMkWZ1ypk5MtnlT7kpl5VOWa8fQb+fWzxwlFEqTZDNy2uobHN58mISsYhBhq1I8jrYCXjj2fUg8vtOexuGQuWZYMBAF+d+hPqXHfbtnFsrIFZJszmJI7Hn80iFlv4f7jz/JPVVdTas5H9nTje/FnDF3xVTLso6N1Ciy5dAQ6RoXCvtB9kHH2YlTfQDJf+SyCzsgZq5GnT75MsT0fWVF45OjTfHH23fiifsTzGFZGjR6TqjL06v+ScHUTWvB5Hjoic6qjlcnlTtZLPZQ7ivCpMTLaT7FCrmRW+SoCyGTIkBeIEnAoCKpMTI2iF4ygXNoG6seFiIaIHOPqymVnDWCJuBxPCkKevRSCAE6LnupiB/VtQwAY9RpWzSkmIatoP2bV7r8ERZVp93Zz04S17Ok8jFbUUJqVxpoFWbywfVh1vyzfTk1BLm7NBAZDbq6tWYnTaCeuJrjQtrmaSIbUxwc6iA+cK4h3zmeSiCL73YRO7SR0aufw3MI+LoikQdSbRqQFAIiaYc+zKAqpMqKxhIJ49v/xhEIicfmGsMTkGFFxiEeOPE3sbNrDxNyJLNZOYechF6WGKVxfmIaki1KRncMjz3XS60oa1+WFVjLT9FyvvYm9AzsQBYnZGQvYsSPCtOowNeIyakr66I10UGQup8CYS2jfr1Lnll1dCDtfoN1i5peNWyi0ZPFZfTE2nZVBu4ViRwHN3iZOuE6wMG8R1ywt5539LqxmHbfMtpF++okRwUcGJTjivR3tUih0ZCGZ7ciKQDTkQ9n6K9SBVuIAAx3E+05zRe0GNu0fLmda3+bBE4iSaR25PoypUepdzbwXV3iIIkMxiaHeUa/FuuvPbkZ88BI/HFfYfaKXl3a0YDFpuX11DZW5tstaQ2KMMf6euGiG/te+9jWmT5/OihUrxuq1n4M/FMMkhkGjIxgLXLTSeufi1DvoCfaPMPQBREcOgiWNeP076MYtZVyJk+1Hu8cM/TH+KuScx+tYUeDg5Z2tWIxavnXXTFp7fIiCwJv727lhWRXfv2c2sqrS7wkzozabA6eGFbRrSpx09gfYXdfD1fNKMGrf3xWhKCpOm4GbllehkUQ6+/1sO9xFZ1eMjKJ0jFoj14+/Co2oIaId4J5rxhFORJmdPZfBoTAIMulpEk6Djc+uz+RUu4emTi/jSh0U5xuRYxq+/+TOlBfS7Yvw7NuNLJlRSLpdT1wyocuv5VjvyRElwjq83aiqgivkwR3yjpr3/q6jfHLqTUQScXrcPhaXzuHkwBnOxLwUxSUGQ6CV4ziGDhEtmEWa0YE7PAQkRc2Wlczn9MBo4amEqiAYbRht8ghPujavEpeg54uVX6WrN0CWTSKnDI54GhiXWUlCkXEYbAyd4126uXolYjBAvK8VZdr1/NcbwVTpwl0nBmjrM/H1O2by4PHH+c6yO3G9/AC2piPYAFPVLOSZVbQGe9nSuJ1GTytTssdxTdVqbDje93peLqiqwkv1b4wodwiQac4gz5aMElPOWh0rZhWzZkEZ8YSC2aDFH4qh14jnK9l9ySAqGsxaI8+f3sKk7BoSSgJtwkEs7uWWldV09gfIchpRVJVwUOJA11EcRjvPnXoNnaTle4sv7LXU2DMQJC2C3ojG4iTm6sJYMgk0wyHSot6CPr+aaNe5EUMCWkc2F5LjUyUdzoU34tryu1SbNqMQyZGd+luWFWqK07Cb9UTjMrKSLJtmNWoQpeFrd7kRjsk8Vf9iysgHqBus41MTp1NankmHr4NHn0kawUumG7npymKaewdwGm1YjDp8PhnBl0P6wFKicZlH33QzrjSdhnYPB0/3YzVpqS6aQsn0XBxxL9GCamKdw9dXnLiQN13JvzsC/QzkTMXhGaIiYuD/+USiOTOp06k82bqNyrR2br/6OrLSFNJ3P058oPWcdyLgFR3AsMGebRNR+6NYJi8jFJdRfX0oI46BRH8LE8oSbDqnTSMJaM/jTtcLBsZnVlLXPzKiLcPoRFWT37n3YiifjmS0QHi0yOS5iKLAvlN9PLY5WRVmKBDlh384wPc/M5eCtIunJTXGGGP89bhohn48Hufb3/72X3zcpk2beOCBB4jH49x1113cdttt5+33jW98g9mzZ7NhwwYAnn/+eX70ox+Rnp70Yi1evJivfOXPD1X6OFAUlUA4jilNgyAIBOJBjJqLF7b/LmkGJ41DzUzKHDfqNU3JVGJHX0VTMJ6y3Ey27O+g3xMiyzna6BpjjI+SggwL6xeV88L2JhQV8jPN3Li8kmAoTkJReWLLaaZUZZGXYeHz108mzaxNLmQQKMowc9dVNUyuzORYwwAF2VYUReWF7U1kOoxI7+O19IbjNHZ5sfX4efilk6k0gQll6SybWURejoaTioEV5Qv51f7HkFUFAYHPT70Pg5zOL546hNuX9IaX5Fm565oqipx2ZlRkMKsqE0EQUBSFM/2+UaHGbl+EadUZFGVYCEQSxMtn03Li0VFz7PH384Upn6cjNNpr4zTYMalp7D3Uw4BLx3VXJ0O2LZIOBC0hWcUO9Ofm87O9v+fqqqWYdSZ0opZsSybZxky8JgWtqCF+Tsj9tRVLEbVpaJwmdLVXEDu1HQBBo0ftK+I/3xhWw55WbuOelbVMyR7H/+x/hOXlC9FLWpwGO0aNnnxjBqo/GSnh0uXg9o30hHYNhggNyXx3/AYirXU4bvwGgrsf0WAibLGxxd/Klqbt+KPJMXZ3HqLL38dXZ34WrfKXlVH8e0NRVLp9faPaA7HAiL/jCZnX97WN8Ojft2EicVlBL166sbgJWcaiMzEus4L9XUcRBIFVOdfz6u5j6DQi1cVp1DUNMuSPUpxjxRPxpuqvq6qC+gFhPHI0SvaN/0Sk/SSxwU5s01chGG0gy3A220eOhXDM34DnnaeI9TQhGq04F9+Kolw4rF5BRFXUZLh/wINoMCfnlTh3eyBpvD2/rZFgJDmeRhL48s3TPjAC6e8VQYBwPEKnb7Qn2qv0s7HpFa4vvhNIRnc1dfpYt7gUfaHIQ88009Kd3Ci1GLXcd/1EXtjazIYlFURjMs9vS6aA+ENxet0h8rU9+J/5EbZZa9HXzEEe7GIoPYtdcTf13adS53WY04ke30+0M2l6S2cOML10Ep7S2WTrJ/Lrp09xx3VZpM++HikaRu5tRDRaMS6+m41bhzUm0m16ppTZyZj4VcKWIlQVEsr57z+bxQgMR4Zdv7Qyqbb/no05UZG4ady19AV+S3/IhSAIXFuzihxDDqigOkuwzrkW/94XQVXQ5ZZjmnYVgqQFLmzoxxIKm/eM3giub3NTmJ5/SRfsGGOMMf48Lpqhn5eXR0dHB4WFo3cX34++vj7+53/+h2effRadTsfNN9/M7NmzqaioGNHne9/7Hrt372b27Nmp9rq6Or75zW+yZs2ai/UWLjqBcBydRkCjP1taLx7EcBHz89/FqjUTk6ME4yHM2pEGvKA1oCmcROzYa+gX3EFNsZOddb2sv6LsfUYbY4yPBp0ksGZuMfMm5hKNy2TYDOgkgTP+pBcBSC3i7ryyhiVT8jl31WPSSkyrTKe+zc2WPa0EIwkEAT65djx6SRi1KPGGE3z/oX3UlqTR6w6N0AI43uxi3qRcIvEEa0qv5ueHHkA+u/K2GaxkmB28vbuPrHQDqxanIwhwqj5CW1eQfIedsBqk2dNEXf9pqtLKKLTUjnq/ZqOWYDh5TqtRw7PbvcytmsZG3ysj+tVm1BBKBDFLdrJNWfSFkkJkoiByRc5yGpvivLUn6TFa4c3AprdSIZjw6rKxRY8hGi20RL0klMSI0n/pJif/PO+rVDgK+NqcL7CjczeeyBDLSuZRbqtAVgTQGLEsuBV1/EKUkA+fo4qNDxwcMb9DTT76+m1U1FTxvXmfwx/zcWqonQf2P4qsKmSY0vjW7M9grJ6DXhxtvQgCGE1Ggrv/RLjtOA1Lr+NP3bsA8MeCXDfuqpSR/y7t3i48MQ9ZmuxR411OiKLAtLyJvNO2d0R7jiVrxN/BSCJl5AOEownePtDJ+LI0kC/d1bqIiE6jI6HIXD/+alRVxaDVsGxGIdnpJrr6A1QXO0nICuk2A+tqVqIRJQZDHjSilKxgcwG3u8bqZOC5+0n4kvdPqH4P9rnrEbOr4ezjQDDYGNr1LJbxC5FmXwOKgnffy6QtPb/T4V2ERBzP9j+ixiIIehNqPAqKTObaL8BZ0XNZVmnp9qaMfICErPLOkS4mlTlJjJYn+btHVcGutzItdyIHuo+OeE0rapmYMYGmRoWp1ZmML02nZzDIgdO9FGc7ae8dfk4EwnF2He1i1uIAJrQ8+czQiLGWLUijwxKjML0A375NWOasQ5l9PW3u05iiWtZYl/FO6z6icoyshIq38/SI4+WWYyyovZpvP9WDzaSjKrOY/954kuL0NUyeqcEdFnj79SB3raogGAwjIVNclI3ZoE9GSamgTQSICgr6ytlEG4bvYX3lbAK2bO68yo43EKUk1051of19o2+cYjr/NO+LuCIeDBo9TskJanIDIS4akCZfQ0b1fEjEUM0ZRND/WVJ8kiiQZtPT9560OrtFP2bkjzHG3wkf2tC/7777ABgYGOD6669n4sSJaDTDw14oR3/Xrl3MmTMHh8MBwKpVq9i8eTNf+MIXUn02bdrEsmXLUn3epa6ujra2Nn79619TVVXFP//zP2O32z/s27moeANRzDrgbCmfQCyIQXPxPVSCIJBmTKMr0EuVc7QBL2aVofQ3I3edYnxJCS/tbmXdwtKLnkIwxhgfhACkW0bmsB5vGhzVb+uhLq6YnMd7fSFGrcRNyyqZMyGXQChGYZaFHKfhvIuShs4hPP4oWWkmDp7uH/V6LK5Qd8ZFQW4eS/KXsbtvB0MRH7dPWs9rLW+QbptKfnY7L3TvQUVlcvlEzNbFNPUMscuzhf29yc2JXR0HWFgwlzuvms2jr55GVZNeu+uXVvL7TSf4xh3TsdvCzJshYzHW0OnrZW/XYSRRYnHBIg4dTDB3qsqBnqMsTd9APGcImTgObSaSbKRxYNhDbpQ0fGvyzRhiBoKGLOzsIhCNkK0brWFQYi9AixZEkVJ9GlVFi4hoReKiBo2qS1UbiAsGSKuENIgGYyTk0cZ6LKGiqgo6nMSEEE8cHw46HQy5efDYU3xtwc0UuPpYPs3MG4eGvdDXzi/CGWzG13ocNR6hvKud1fnT2dSxF7PORKEtZ9T5REFEJ51fgfpyQitqqMkoJ5aIoZE0ROIRytNKRujgiCIMekd77tr7/ESiMqZLWF1L1EJcjpNnzWZT/RtoRQ0Tp87GZNTwx9eGQ5XL8+2MHy+xuWErUTlGnjWbmyZeQyQRwcL7hxgnPL0pI/9dfPtewlg7HzTJVDdVTmCunE6ofh/h5qNoHFnYZlyJqnyQFS6nYu/V6LChpJyjeaHTiee9Nm5fJLnRdpli1OpYXbmIWCKGWW9EQKQirRSblEaNJo9XWnpYNLUgFVIOkO00snZhWcprD9DaHcBQOsChgde584Y76e0W8PiiFBdr2D/0BkpDnHuv+SzuzhNojWkMhvp57vQruMNDmLUmbpqwhuKEgBQYra4PMOgKk5NmYtWcYnyeEEP+KC3dPrYOV+2jzxNmSaWOuN6JfM6SWp/w4XvtF8S76rHNWYeh+FPE+ttQMsqJZ1Ty7QcPIwjJcouRmMw375xBVd77l8XTKUZydWe/6+/5vZNVEVmfCX+h/JMA3LS8mu8/tDdVRjDdZqCywPGXDTTGGGNcsnxoQ3/VqlX/52P7+/vJzMxM/Z2VlcWxY8dG9LnnnnsAOHhwpJcpMzOTz3zmM0yaNIn777+f73//+/z4xz/+P8/lo2DIH8WkUUGbfPoG4kGyTJkfcNT/jXRDGl2B7vMa+oIgIBVNIn56G1lLkjnKDR1DVBc5zzPSGGN8fKiqSl7GaCO1utiBVhKRz2N0mnUS4wo/eFMvHE0uuE+1uJlSlcneEyNDRYtzrZxocfGD3x7FbNSyYcWtTKm18dTJF+n291FRVs5Lx3en+h8dqKMivQQ1Xpwy8t/lnc7dfHveXO7RTUBVIRqTeXVXC9G4TFzfw3d2/J5wPMLSoqVEmyewrngKiiywe8cQ7b19VBamsaP7HVYXWdD788nPNlMfOMyx/uMU5RTzyRtn0HhGpjgjF1GK0uvvpD94mLSaqeRNWk65EqbSdYYGdzKX26Q1sq5qFSoKAXkQxddHV9THH5u3oqKyvnolkzMmo1VHrgwdJi0TytI43jyscG01acnPS8enhugKtBOJhfjOnE9zrPcUL7buAOCMqxmvRsIpB7lrhpXbZ5YyFBVxeUNkB07CoIC+oIpIyzGEuneY1Z7FtPFzMRWNR2stYkHRLHa070ud89qaVTgk52Vfci8cEkg3plHoyOWdtv04DTacJjsWMS3VR1WhINM86tiZ47LPm/N7KaGgsLfzCP5ogJXlC0koMkMRL6/sbB3Rr6nLS1ufgaicdN93+/vYdHoLn5l+Ya+7ek4OeKpNVRAS0dTqR0hEGNrxDM4F12OumQuShmD9XkSdEWwVo45PoTNgn7UGVU4gaLQIkpZw+yl0afmpIANFURlfls4b51QbAZg/KQ9RUpEvQ48+QEKV8UZ8zC6cwisNb6MTtZSZa3n2bRdpFjOfWjuBnz99ZMQxfZ7wqIpBk8fZqPOeIZqI8nbva+TaMmmMt7C7Nfn8chrsxBQjUVs5uxMdvHnkNTzhZOpHMB7ikaPP8G9T70BVvCSuuodONZmmlReTMTedoLA4j38UDmEtykPob+b7V1lp8jr4034fPa4wogClBelEzkZt6pQwgq8bNR5B0htIuLoA8O15AUGjQ182lVDWBP60Lfl9UNVk+Py1K3Lxatqo80GuOZsMTSaoIzeC4rJKXFYw6iWEi+htL8408YN759LS68eokyjJtWEzjFVmGmOMvxc+9N28fv36EX+rqsrJkycpKirCar1w8JB6Hjfcnyvk94tf/CL1/3vuuYfly5f/Wce9S3r6hWvkXgxOd/qwamX0Zgsmi4GQHMZptmDUXXyvfp4hg3pPI6JexaQ9j4fDUoyv9zSGoWam12az/8wgC6YXpV7OzPzbqLk6Ns+Phj/3frgY7ysUTdDe48Pjj5KdZmJ6bTYTjvfQPRDEF4xy0/IqDHoNz+1oYWpVJjUlaZgMw95djy9C50AASRQoyLJiM5//fqotjSMKUN/u4fbVNQTCcU40uzDqNdxxVTWKzkd5kYHFC8rxJlzoxRCK7KDb34dRa6Bh6MyoMY/0H+O60tFaGAAJJYRVkKjvClKSZ+cLG8YxGA7w68N/SJVG0wlajp3xsOvoSANEVUQ0oobN7a+yoGgmx7v7aXIncycHQm5ajC18bcUXaej04vZFCMRVVJvMb+p+z33Tbqbe3UaBPYcpueMxa41UOAqxGq385tCjHO07hV7SsbJiEXm2bE70n+H3Rzfy5bl25hXNGPU+Prmuhi272tlzYoCKPDM3LylBn6nlP3f8iq5AcrNEFES+Pvkm1KI5bGrfQ0VaCWJCR0TnIFH3Ooq3D2f5NLIzi3Ad2o46fh7amVcRH2hHDgwhe/ux+r3YbVno0zK5c+p1zC+azkDQRZ4th7K0Yiy6v56OyMfx+/BezndvxV0yTd1tbDyZTPfoCwzQ4Grh63O/OKJ/6PQgt62qZuNbjUTjMrPGZVOYZUGSRDLTRm8CfBxz/3NQVIVFpXMJRAPIqoIBUJBH6V3AaOG6RncbsiJf8NzhYBaiyYYSGo6KsUxYiKA3pY4TtFrSFt/CwMsPwtm8fPPERUi2jAuOHe0dRJtZiGvLQ2eV9wVsM69CjgTJLB4+zmL0cudVtbx1oIN4QmHhlHxyMswY9DpM9g8XtfJx/c5c7PvB09lHLK7wm0NPpNpahh7llgWf4NGnB7hiaj6x+OhdPptZh04jEpcVZozPwJQzgKs1mee+onwhbUOdLC2bT0JJYNQamWbIQNr7DJahHuyLNrAxPFLwVFZk+mJ+olqR/255g2A8GZlh0hr56oJ7sL/+Kwh7ER1W3G/+AVWOUypp+ObCT/J4fSYzx+dhNopkZlpJ+F0MvPwbwk3J0qGCzkDaoltxvfEwqApqIkas/QTFK3Ujfs/Wr8jlYPQlXjuejDqTBJFvLfp/TMyuOTtHlaNn+nnwuWP0uULMmZjH6jnFGHQSpXl2DBcol/znfj8yMqyMr/honFB/CX9r66Yxxvhb4EMb+n19fXz5y1/mvvvuY8GCBdxxxx00Njai1+t54IEHmDBhwvsem52dzYEDw56x/v5+srKy3rf/u/j9fp555hnuuusuILlhcG66wJ+DyxVIhSp9VAwFomiVCHFVi88XJhQLQ0IiLF9Iy/f/ToYxjfreViqdpefvkFWFr247JbPu5OFXT3PDojL0WonMTCsDA+cPXbuUGJvnh+NCP6J/zv1wMd6XrKhsOdjJ028maxOLAnzxpqlMrsigqtDBtJpsfvqnwwx4kgJHz29r4rMbJjGrOhNVVfGGE/zPk4dp70vOY1J5Bp++ZjxmvTTqXOlmLd+5ezZPvl7P63vb+OQ147l1VSV9kW6ebXmIQF+Ie6beyi8O/Dq16VhoLeBTkz/Bzw88yJSc8aPGrHCU09UTY3xGLScGh8WcZmZPpERnZbA8hqVIizah8M7+Hq6YY8PVOCy4dGBgP0vmXMumt7tTbTqNSGGmjS/mfYat7dspdRaxo33/iPO6w0Mc7Wjl4Sf7MRu1XLekgjOn7CypWkxX0MXbrbtH9P/cjNtpaN/P0b7kHKNyjE31r3PLxHWc6E9uYLzVvIta6zgS5yQKDyku/n3fz8iwprP22lkEEr2ELRn0DnpTRj4kDbSnWrbz+YrVvNV7jFsq7qDjTDO5e36SzFUGYr3N2GatwbnkVnytx3i57yhZc5ezwFaC0WAnYcnFp2oQBnzo/W3k7X+ZfJ0R83gbCU2YAfWjdXd+2PvhYs/lfPeWqouypXnbiDZZVegOdFM4kKyeIoqQn2Vm91s9rFlQikYSaeocIifdTDwmf+TPog/zXBD1MladiceOPJPy1s/Knc6cCRPZc3z4++a06olr3SOOzbFkIkrSBc9tjEXJWHUPoYaDxD09GIrGo88rR43HGRjwk5lpRVUF3FufSBn5AMG6bZirZ19wbAswtOu5c8rrqfj2v4w+vyp1nFYrEorIiILAuivKAYjLMi5vmHA4RtB/YbG0C3Gxf2c+rvtBFAWiUYG32naMeq1faeZT18zhWEM/K+cW8MqO4UgIo15DXp6Gz984EY8vRmZunLe7D1CRVsLy8oUk5Dgtng7OnFOKzlGzhqK2oyjhAPquJoxaQ2rTFUBAQDVa2B8cSBn5AKF4mAO9B1keC2KZcAXuNx4Zjg6REwg7f8/VS/6J/++ZOopyLHzj9losrtaUkQ+gxiIETu7AWD6FcGOy3TRuAf64jivnFLPvRC/haAKD009f83BqmawqPH70eb4y/V5ERYPLH+X7v9ub2vzadaybcCSOrKjMHp/DvHHZ53WaXarrkPfjUpvv2KbDGH8vfOi4vh/+8IcsWrSIWbNm8dprr9HT08PWrVv57W9/y49+9KMLHjtv3jx2796N2+0mHA6zZcsWrrjiig88p8lk4re//S1HjyaFXB577DFWrFjxYd/KRWfIH8GgRhC0BsJyGJ2kO28d6otFpjGNNl/H+74uOHJQE3FM4X7yMswcPjPwvn3HGOOjYNAXTRn5AIoKv3m+jmAkwbNbmzjZ4koZ+e/y2OZThOMyoijwzpGulJEPcKxpkBOt7vPW/BWAkiwz/3jbNO7/8iIy7EbOeBr4zYlf4wp5uKJ4FhtPvTRikdTh76TP72JiVi1ltnIKbQWp1zJN6VSaJ+D1x7kifS3XVFxNeVox66uv4taqZRwNdfODXb/iv3f/hv888AtKJnrRiSayTMNh1oMhN2H7GT61poKSXBvTazP42icms/tEJ2LCxFUF66lwlpw3sikRT7YFw3Eee/UU5blpVNlqOONqGtX3QM9xuvyjVa0DsRBaMbkpmm50ECM6/HkJcMJVTygept3XycYzz7K5+S1ebXwT/3tU3gEGI14kBP71in/gyKkh0uXBlJGfOt/Rt9A4sjiak82JoVbS9DYkvRFioZSugj7YxeAzP8RYVIugyni3PY5y4nX0idHlBi834jEBk2Z0hJbEsEdQVaGjP8jhMwM883Yjf3rjDAdO9/P8tkZ0Fyg5eSmgETTs6TiUMvIB9vUcZN4sM1fNL6Uo28r8SXl88dZxeNVhY0gv6VhVuRjlA+rTKWE/nh0b0eWVY528DEEQGXjpgZQ+BYAajyD7XaOOlQOeUW0jUCE+0D66OTr8/BIEgSF/hGhc5oXtTTy3rZE+V4ghXzT5gLoMiSQUTja7sWpHRwlIioH03DCz52nILw+ybnkexTlWZk5I5/brstjT9w6S1YNqHqSz38eS/KXcOeFmHjm8EV8sMMLIB3i0dTvKxIUA6I68zT0VK5DOWYNtqL2Kg30n6QyN1onpCbmQTHYQhPdUUkimhMR8bhKyQnOXj6HmVhKDo78LscFONOOWIaUVYKqdj2HyahRVINOm59/vm8fXb5uO3jh6Q9MVcpNQkxtPPe7QqAiXw2cGqClJ45GXT+INfTSOozHGGOPvgw+9CmhsbOS+++7DaDSye/duli9fjslkorq6moGBCxuS2dnZfOUrX+HOO+/k2muvZc2aNUyaNIlPf/rT1NXVve9xkiTxk5/8hH/5l3/hyiuv5MSJE3z961//sG/louPxRzGpYdAZCMRCGDR/oVLKX4hd5yCcCOOLnX9XVBAExKwyEu1HqC1ysqOu5yOdzxhjvJfzLUpiCQWdNumRP5/XKBqTUVQVVYWj5xHuq2/3IF6ghJgI2Cx6Bj1hfPLwgj7D5MRztub8uYRiYYrthXj69ZSEl7Gh6HbWF93GZHEdP/tDI1pJ4kyLn77T2WiaFzBZqqElPMiTxzcRTiS9RbKq8FjdswQTQT495Ras+uSiViNqqDQZmD64ic/dmEO8eDetnk5efqeDX/+piUNNXbR0hllWsmjEnCZljaOzY/izUVTQ6yR0ko58W+6o91DqLMR0nlKeRq2euJLAoNFTYM+lJzS8GSAIAv5ocNQx7vAQ+Zb8Ue1LsieConJs8BRtvX7U8/ycCBodDeFB9rqb+G7pKsZ1dRB46UF8+zah87UhigLx7tPYpq3Cu/dFgqd2EetvY2jH04T3bkQjXKZJzGeRVC0rilaOaHPo7WTohq+5Vgsub/i9h9LjChGIXLhE3F8bBZWBkHtU+xH3fmqKnGxYWsGkygw8US++iI+bJqzlunFXcnX1MppcrSOMtvOhsWWQ8PTi3vIQg688wNCuZzAWjUM453khaHXoc8vfc6SAxpp+wbEFUUCXMzp6TjQOG7CCAHmZFl7e0cy40nSmVmWx42g3eZnmy9XOJyGrHDjVz4L8BSOun0GjJ41iBgJe7t/9KywWgf2xjRTNaiKUt5PHGh8i15ZBlq6AfHsmuRkWjp0Mc7ilg3AijHwe8URf1I989nrIgSGK9r/Nl2Z+gs/MuJVbJq5jXFYlnpCP6ozR2kbz7aXEepsRjFYE3chnqaDRMaQkN+D0Wgkx0Icgjo4qE0pm8OOtEd4quIf4nLuJah1AcnPOZtRSnmejIr1o1HFLS+ejJ3lO03ny5R0WPcFwnFhCIRy7vJ+RY4wxxoX50KH7kjT8cDt8+DBf+tKXUn8nEh+8yFi7di1r164d0fab3/xmVL8f/vCHI/6eMWMGzz333F863Y8Vjz9KlhIArYFg1IXxPAvvi4koCGSbs2j1djAp8/x5xFJGMbGjr1JRu4I3DnbiDUTHQpTG+NjItBvRSAIJWWXl7CIynSYi0QRZTiOTKjKQRBG9TkIjCgiCQCAc5+r5pagkF80za7Np6hzp6Z1YlnFe0b73YjBosMaHveu7Ow4xv3A2b7cOh5Ba9Gaq0ioQNHEGE7Bl58ga5majlsx0Hc9vb2LZzCK2Hurm7kUOXLEgQ5GR9eNVVDwRL3a5kH+d/yU8oQHMWjN2Rcugo5qGUCcN7iYmWuYC0OcO88zmpMH25dunkVGZT3+4B4c2A1+/mczsNHZrXcwcn43ZoMWol3htay83rZnL0Z6TuMJJD2S2JZnmsLhsHscHzhBJJL3sE7OrKbTmcvuk9cSUOM+e2szdk28i/6zEgaKoTMyqZdOZ11Pv4YrcyVybNx291sYXZ9zFEydfxBcNsDR/KgvSa3iu/xB7eo9z16Sv0OqGWrMdOTh8fewLrqdd0nB3yWLk4zsJnt4NCCgF5bQONmMxm7HpjZCIjjgOIHhiB8YZ60joMz7w2v69IuniOCnglrK7GEx0YZBMWJUctOqwfkEiAWV5dnQakZnjcjAbNRw41c/cCblYjZpLu7yeBDPzJqf0KN5lbt4chvplLEYNoBLzm1lRtoQnjj9Lf3CQaXkTqU4vQ3m/emRnUWNh0pbdSaBuK3F3D6aKGWjTcxHOPU6Rsc+9lqEdG4n1tyHqTdjnrUf4oAo5chzrxCUMBX3JiABRwjZ9NeqIKAOBky0ublxezZGGAWIJmTULyqhrcjGl6q+fE/3XQFEVplRnYlHT+dKML9DibUYjasjRF9LQoJBRHGZa9lS2tuxhXFY1O8+mMRVYc0mocQ71H+a552JEzxq4t67LQxJENKIWSRDRSTqmZU9DEiQkwuibjvHuSjRRPZ3fHd3InMJp7O44xPXjr2Zp2Tw0osTN49fxcsMbqMCqkmWU2EtQ132Bw2qYqpV3E9/yMGosjKAzEJ/zSZ7amnxe3bkoG8Pp3xO2Z2Cfsw7fgVdREzGkgnE0OedyYncPJ1q9mA06lk3LR1VVBnwxnt/eREuPjzULivnirHt44viz+KIBlpcvZEHB7JSyfn66manVmRyuTzrOBAHWLizjhe1N5GWYcVo+WgfSGGOM8bfNhzb0DQYDvb29BAIBWltbmTVrFgBNTU0fKMb3986QP0Jxwo+gMxAMBNFLH/0DOduUybGBE4xPr0E6j5dT0BkRLemI7mYqC+zsPdlHRenlu5Ae4+PFadby9dumc7B+gJ7BAANDYVDhhe1NrF1YxvHmQf7pzpkcb3YRTyhUFDow6kTeOdLN5KpMFk7O5USLC28gxqxxOWQ5jdSUOP6sc+c4jbS70pidM4u9vftoHepkVflSUGFH517m5s6hSJ3Bzx6rRxQENiwtY+HkXN452oMoCsydlMGVi3JIxAXyMs34gsnohF6PgiVdh9NgxxMZNlYFBPSChfx0MzpEckx2TnZ48Tvj/ODAL1lUOocSRwG9iWZqSgs43ZLcKLBbdLR3xXh+qwub2Yo/5EdWfKxbaOTOq2vZvLuNQCiGyaAhw2Hi+784xT/e+wl8sSEUIUGHv5ONJ1+h1FHIvdNvQydp0GsNBONhXj7zNvWDTaioCAhkm0be+7n6PL429z6ePf0qa3MmU9p4Cq3FTVAUKDXk8NVpn0VVgghxhWc6d7K35zAAjswYx/o1mKfcR16kGUPMjbFiCmRVMVlREN0t9Ncn60jHl9/K494GTre+jqb9bTZULmWhIwtBb0IyWkl4B0BVQJQQPsJUp78FYgkVl9KOlUK0cibIKnabxJDQQT61qX4Om57P3zCFZ95uIBCKs3R6ATPGZROOyhgvYeV9VYWYHOfKyiVsa92DVtRw28QNaGNpaKQILl8EnVZDhsOIUbZwZdViLDoLje5WDFoDWlGCCzg05UgAz46NOOdtQDSYifY249n+FIbiicOq+6KWYNMRHAtvBFVFkDTEXF0o8Q/Inxe1RHoaMVfNQDRaAYFQw0GMJRPP6aQgiSK/23ScigIHWo3IY6+eYkZtNhoJ4qOLAvzdY9JpmFmbzeBQmMEhHXZpHC5fmC6NRCIh86eNfnLSq7hheTl2m0CpoRaTSUKKW/HFPWzre5W7r7yVB19Iao28vdPDrWtvYkvLG9w3/S5cvXo2b+8nIStsWFyKOHMqon0bsZIJtBokFlvnIgoiWlFDMBbioUNPkmFKY7K6gVmaG0EQeOVFN93VIboc21hetoB/b3yNr1z3JXzefjLteQRjelYsjOCwidQOHkPx9hHx9pHwDZK54ev0hLVsPOBlx6s9GPUaZFnh9f3tLJiUSyQm868P7SUYTl783zx/kuWzCvn2iq+QUBIEfBJv7u7HYvQxoSydDKuOT68dT+ecIJ5AlHhC5vW97eRlmPnU2vHopMs1NmSMMcb4c/jQhv59993HtddeSyKR4I477sBut/Pss89y//33893vfvdizPFvFm8gilFKIIgaArEQNt1Hv/Fh0hgxa010B3ootI0OtwUQ0/KRu05RW7ycncd7ue3q0aJjY4zxUVGVb8ds0NLQ5WXP8R4EAW6/spa9dT3cuKKa/3hkP9F4cvWukQS+ess0XtjezDNvN7JydjF3Xz2etw508Py2JkwGDZ+4qpZJpelI57FnBAF8kQTNJ3oRUZlamk9peDXz8mejSnE6/Z2EEyH+ac5X6esV+MkTR1LH/uLpOr54y3gKCjRUlpoYTPTyZP1TyIrMmrVL2LMjgl4r0SXLZEk6rht/Fb6InxfqX0dWZG6ftAFBE0KvlVI6AJkOI12uKJMyq9naspu7J1+HoijULDGxcGounb1hJlU62FvnQlGTgp7vkptp4cFnh8uPPre1iXVXlGEz62hphMc393LTtRm81r2N9UXzWJQ/DbeQoDvs4ckDjxOIhZiRP4n141bzeuN2PjnlRpya9BE1mUVVpNRYxj/MvA9Nwy48k+bzcPsujjScwW6wcfe4NeQpEj868zL9Z0Ouq9JLCalDHFZeRmOaS7ctgzO+fk6deIIfZn6DtGgYUW9C0BkQndm8Gh3gtKcVgISS4Jkzb1Cz4HO0Ll1Pd9jNBFM2OQ112DKKiRku7xJ7OklLWVY2Hlec9kEfFpOGApsdo3E4rFxRIBiK85MnD6V0Dza+3YhOJ7FqZiFy4tL9AFVVpcCeyx+PPc+ikjkkFJl8XSXt7iANHUMcb3ZRlG1lUkUGxUYrG0+8wlDEx8SsGiZm1xCKRzDw/rXHNfYsnFfchGfbEyghH/r8SjKv/TKCdnjTXU3EsFTPInhqF6Gmw2idOdimr0YQRodij0AQsUxYRLjxAMGTuxDNdpwLb4BzqkVIksT48nRe39dGQ8cQkAz1Xj6riMRlGnEtCTDojXC4foCTrW5Kc23UlKSBAI9vPg2ALxjjPx85zL3rJ1CYk8dPD/8UURC5rmYtywpnMLF9Kz+4bx39vW6sCQ9Z0Tj6qiXIfge/f/Zk6ly/eu4kX76mhMyJC9jvaeKtul2oqsqK8oV8YtJ69OEAX6hZQ0lCJKaV6dVp6Bjyc+WSDAod2bQOrqb+qMrCrA0o6EkbcsE7z5OuN7Nq7joGtSKazOzhcophP6rRSeMAnOnp5Z51E/AHY+g0Eg6rHq1GoLk7mDLy3+XN/R1cPbcUf0imsaEZhyTw2gEfT7/VwPc/PYc0s46KXCtgJa6oTCxLx6iT0PyZVarGGGOMy5cPbegvXryYF198EY/HQ3V1NQAOh4Mf//jHzJ49G4BQKITJ9NcrlfTXwheMYbYnrY9gPEiW6eMJ1cs1Z3NmqJkCa/55RcrEtAJi7UcpnGLk1UCUjj4/hkvX6TPG3xmqqjIUjPHIy8MLstOtHu5ZN4H9p/pSRj4k8zm3H+miIMtCU5eXV3e34rTq2X6ki4Ss4AvG+PnTR/nep2ZTfLaWeDgu0z0YIqEopNuN/OejB3B5k965mmInn79uEgUGCwOJfobiRiYIs3jqlW7k84Q4HzjpZtEiif5IF48c2Zhq/9WBR/nUrDuoqcxjc/8TeLuSuhgWnZl/mPtpwokwT9ZtYnHRTLAnyyTphThOc5RA2MT6nKuw6gzM1WbRnvDxTNdO2r1dZFjSmG5bTUmpBMMCzpgNGtz+0XnYu+t6+Pcb89GrQRKLCpC0Ye4pX05FTOVPbe+wvHQesiJTlVHGoZ7j7O86ilFr5KszPkGRPpuIKiIIAmGCJNQEZtGCoIhoEwkUk4WnO3ZzZCDpOfNGfPz00BP865Rb+czk62kK9JGmt1IQitIRj9IXHOCllhdTc9NLOqSwB/erv8OQV4Fj7np8aoJDA/tGvIfVlUt48NAT9AeT+glvALePX8ui7JnElMt7IStpVAb7RPr9Q2TmRUHVsLcuwrxJuXD2J1UUobXHx3uFt1/fl/QgGs63A3aJIKgi6UYnt01az462fegkLYqSFN08eDopvtfe6+dEs4uv3DKVCdnVpBkdnOg/w6tn3uKOydfDBbziqiITPLkT+8yrUBNxEET8x7aStvjW4TloDQT2vUiksx5j8QQSvkEGX3mQ7Ju+dcG5q1o9cXcXGmcOpmoDktFCtLcJY9mUVB9ZVhBRuXJuKaKQTOeRRBEBEFEvFIzwd0soprBpezPHm5P3e3uvn5Otbu5eMzLdMCEruLwRDjd5uHXWerwxH4GEl9K0QuK543j44G9I05pZnTkOvaBjqrWCxw+PFlV87ZiXmwuNvNY4XL3i1Ya3yavSU/3285QtvAFX2zHeyHLwVs8RNKKGCkch4y1Xs2vzEOGYzBpzGs6uJoK7XkheM7+boU3/S9aS24kE/ehW3Ivo7UFfPY+oIYNxJTK5t1ax61gfOw4M4g/FsRi1fO9Ts9CeJ8JGp5HQCDJpPbuYWf8sqpxg0viVbA1WcqrNw4Lx2an7WysKaN+npF4gKtM1GEQjifhjChadeNlqQYwxxhhJPrShD5CVlTWiLN7SpUtHvH7bbbdd8vn0F5toTEZRVHS6pOcgmAhj/IjF+N4lzZBGi68NV8RNhjFt1OuC1oBodICnk5oiJ1sPdrJ6ZsHogcYY4yNAoxHZfqhzVPvJZhf5WaOVmIPhxIhawXtO9FJd5GT/qeH8+dNtbkqyLPjCce5/8jBtvUnD22rScsOyKh7adAJI6li4/TGONHoJyn4q8ks40xHB5Y1QlD064sak16JEDJxxHU61FdrzmF80g5DqI7dAR1Esn7q+02hEDbIis7PjAPWDTbhCHsZlVgMq+mAHHUT49fEXkEQtG8o3cGfZKjyDzfy06RX8sSAbKtczNb+c/tAA40py+OJNdn77wikSCYWrZ2SSZhy9ZHNatEQPvUyoaS8LKmYTTl+HrrMVpaiWVUIxz5x+nRPuZiqdxXxq2k08dvQ59nUe5sqyBUQkA4oic8p7mkePbSQQCzG7YCrXlC4lT9QwoNVwsL9+xPkK7LmcjHt58sjjqbZPVK6g1lxEhimNwXOE1TZUr0B+7WESgx34Smppt5vxqAlKEoXU9Z0e/oy1hpSR/y4b67cwJWcqxks3vfxjIRxJoLNE2NT0KNHuZHTHhMwaIvE1qT6qSkrM8lzsZj3iJe7xUxOwq/0Aeo2ORSVzEASIROWUkf8uHn8Ujz9Ks7udPcHDTM+bSIEtl7gc50LKN2o0hMaWjmfbk0BSRM25+Nak0S+9O4cIktmBuWoW4ZajaJw5pC2+lYTfDeaS9x87HkWNR/G8/Tii3oQSj6F15qDPq+LdScXjCqdbPWQ6jYQiCRRFxWnV887hTirzz6+j8/fOUDCWMvLfZcATJp6QWTI7i7f3Dl97SRQ5ccbL4oXZ/P7on1KCe7MLpnBF0RVEhqwc6lJpcsJkGdLPk6+eaTfQ7O/BpDUSSURR1GSEy25PI5MrpxOs38vQlAXE3APcXPwZZtg1aE69TmLzA3x36iz0ueXEWvcSqTs1amw57EdvMiM6MxHLp+JVVTzxLuoGTvNa4zYMGj3XXruSA3uMnGr2cazRxdwJOZTl22nuGk7zumlZOc5EL9FAF5oZV4KqEDi5i4VVdo7Kw2trSSOCqp53UzoQldmyvx2DTsO+k73YzDquvaKcsuzRv6ljjDHG5cNFMfQ/iPPV+Px7xxeKYdGTFG6R46iqgkb4WD5uREEg35xHvaeRDOOs8/YRHLnIfQ2MK17Ay3vbWTUj/7wlvcYY4+IjYDSOvhesZh1TKzNGlN8DmFKVyRNbhg3Ogkwz7X0jy71lOIyAyslWT8rIB/CH4pxqdVOWb2fuhFxK82x8/3d7UuWK8jPdzBqfS3uvnxWzith/cjiiwKjXUJBlQVWVVMWMEkcBswum8mTdsOf6qvJFfLpmDV5BQUXFKYu0ZVRjMzpI1+SgC/XijQf48fGnCcSSqvY/O/K/3D/rK3hjPvyxIDdUXk+5tZqj9S7Sremckdppip3mH+6aQPpgEM3hRwnbV5Nu0+PyJQ0+URS4cYYV+Z0DACQa9yLnzUSwV6GLhvhp02v0njWgT7ua6Q4MsKxsASf6T/NSw1YUVWFx6Tx+eeCR1HvZ03kIvaRjSfpi0rUG0k1OXKHhMmNzCqby1MlXRnz2jzW9xXcn3cKK8oXIqkx/wMWM/EkUh2IE+1sRy6fwgj7O7hPPIyBw59TrafF0pD4Lw3lESuNKYkQJtMsVSZLY3LaZaGI4heP4wGkWl84DkgaAIEBBloU0mwG3Lxm5Igqwak7xeSO6LiVkUaE2s4Izrmbafd0IQJkxDUkURpUU02nhxvFrQBAIxkKc7D+DJEoXTu0QBAJ1w55cNRHDu+8lsq8fTlcTRC1yJIioM2CunQeA99jbpC+944JzF9UE0d5msq77OnLAg2gwI4f9KOEAOIb7VRU7qW/zYNBpEESBPneYRdMLL1pd+r819FoRQWBUBIpWB770vXzi2mU88nwzRTlWhgJRVl+Rw6PHNo5Q1d/beYRZedNo6YU3dw9QXmRCb/Mzq6KUl3ZpCJ2tNqHViKyeaKVQl2CqbTwJi4NjhHmhcx8L8qcRM2Vh9HsoELWs1OoRDBrU139GxJ/csJQPvkJs6a14KyehKyhHv28z8sA5JYwFgaGdz6Am4mhv+x5PtL1Dri2LTfVvABBORNjY+DQ3z76TU80+3P4IFoOWf7iuipP1XXQPJajOFMjpfxWtsgRf1xkCri4EjQ777LXoPW1Mn76AhKLS2hdg85429DqJ1bOLyU83jfDW97iCaDUiG98a/v081eLm3+6dS7b9oxWCHmOMMS5dPhbL83I0IH2hGGatClo9gXgQo8b4sX4OOeYs9vUewhv1Y9eP9lSKzlwSjXvJGr8CjSjQ1OWjosD+sc1vjMuXzsEglYVOdhzpTi3mNZLAoin5ZNr1fOOO6Wx8qxFZVli3qJwjZwZInFXVt5q0rJxTwvd/uyc1Xl6GmapCB4Ig0NE/urRk10CApdMLqSh0sPHNhtQ5TQYNk6uyGFeaRiRawks7WrhxeRXhaAKLSYuiqBxvGmSSmM700imMM85DFeI8dPrBEeO/0rSNLGsmDx/ZSJ4lm+vGX0klOnSaLGQV4q4uXEYxZdi+i0dRMWstZBidpCnlfO/Bg6nXJlY5yKo18aNDj/IPNdeQ6e1Dv+9RvrngNtpMtcRCMQp1QziOPoxyzgJYFx2i31yIPlCXMvLfxRf1Y9ToWVQ6j8ePPoesyhQ5Rkfy7O06zNLiOahBE58ffy0P1W+m05csxWnSGFIesXeRFZmowUCFNpNsfRYaQUtampnAmf0EgUDpOHa3Jhe+KipP1W1iZcUVlDgKsWgs6DUGjFoD4XPEz1YWz8UZDhA3WlDUy+/3411kNUaHt3tUuzvihnMcdUaDxJdvnkp7n59wNEFJrg2TUeJSt/R1goaEKtPkaad+sAmAOyeauWp+CZveaUn1K8uzkZ6m5SeHNuKJeJmQVc3VVctIKBeu7KOERz8PZN8gajySWv2oqowhtwLv3k0kfAMIkhb77DWosQuL8amIWGrmMrDp56m+5vELsU5ekuqj0QgkEipmo5ZeV4h4QqEw20I0JiNpRBKXYWk0p1nHqjnFbN49XGmhqshJS1eAbGs+FmeQz143ia7+AFsPdfJP947jxR2jSzAGoiF2Bl/l9utuIKZEefzEozxYcQ3fXaGjNZKJrEC5UyZn6AjuXRsRAC0wq2wqeXNu46mTL9MTGKDCUcTt2VMxbnsYwWTFPvNq3G89CoJIZPUneKBzJ70tLiRB5PaZqymNZ+DxxrAbwKYPocaTm3BtnlY0koZD3cdHzbU32k6azUF1kRO3P4K99yjVh/9ArcGM3DCEqXI6nneeIu7qApIbUkM7nyH96s+iMRup7/LzH4/sT423p66H739mLnlOY6pNkkTeOdI14ryyotLY5R0z9McY4zLm43ExX4b4gjFMooyg0ROMhzF8DIr75yIKIvmWXE65zjAnb/qo1wWTAxIR1LCXiRUZ7DzeM2boj/HRI8Bz25to7fFx51W1dA8GEQSBqdWZ5DgNoEJ1vp1v3zkDRU1WZq8ssDNvYi6yopKfYcZh1vGDz86jsz+ITitSmGXBrJOS5eHK0nllV+uIU86oySYv00QkGk+J29nMOm5cVsWzWxt5ZWcL6XYDt6ys5q0DHSycko/dquWnTxzl67dP5/4nDvKlm6Zx/x/2c+Pa7FS5unPxnTXiuwN9/Pbgk3xl9l2U+Nvo1QrkaHWYNRKCIIyIbnqq5WXuyV/NPbW38uNHR4aF1p0Z4o6JFcBOXh08yaeKxxFvqUN/+Cn0K29lutlAvH4/4aGeEcf5teloBAW9oEEUxFFGeZGjgCeOPZ8ykGz60WGd2ZYMAvEgHklgV+chqtNLuXXiNWgSMlatCZPWSCg+rBdg1ZlJt+RhUCyggHzWxarY8zFNXkHgPeOHExFeOL2Fby/4IrLPyb8/eYgb1t5Bv3KaOAlKtXYqBvoZfOPbZN70XSK2klFzvFwQRYFJ2TXs7jw0oj3N6Ej9XxAgGlV4fnsTdY2DaCQRo0HDZzdMwmFR0YqXrrEvizEaXa0pIx/ALuRRH/Rz66pquvoDZDpNJGSFXlc4VdXieH99MjpkynUX9OhLZseoNl1WMcI5aXQC4D/6JglfsnyZKscZ2vUcWdf/4wXnLgCe7X8asSEQPPEOpsqZqU0YjUZDXFZ47NXTqUghUYAv3DDlUq56+JEiCrByTgEZDiPtvX6y00xEYjJPv9HIHRuKcYeGKLcXgRTja3dM5uWtbVRmltPgaRoxTsRnZn7uIt7ufZVFRfMBiJtsGDffT62kQUDAPnsN3n0vjzguWlTJg4eeSEXJNA6182A8yJcnL0I99AbRrgZ0OaUkrGn8aWB4w9SmtxIKFvDtFztIyCqiAJ9clsekjBJUdydeOYI36sdptNPl7x1xTqfBwQ3LKghFEnz15zv44roKyo3WZFlGkt/JoZ3PjP6wVFAReGlHMwAaSWTmuGzsZh2nWtwUpBekIkMcVj0G3eglvUH3AaKSY4wxxt81Y4b+R4Q/FMcgxBB0BkKJIPqPKT//XPIsOezvPYQv6sf2Hq++IAgI9hzkwVamVE7jF88c5bYVVWguYeGmMf72kRXoHggy4Anz+5dOkmYzoKoqGklAQCXdZuRMxxC9riDlBQ6ynUbsJh2VeTbCcZnGLh8NHR4qCpxU5tswvmcRU5pr49aV1Tz9VgOyrLB4WiFLpuXj9ke5/4lDXLeknIc2+Vgxq4gntpwmGEmgkUR0WoknttTz1VunoEhhNrdu5nM3L6WhY4i1C0vZvLsVRQXXoEC2OZO+4EDqnFoxaVRDsqReOBHBFfGR6Q3wL40v8g8Trqeks59bq1YxLq0Mu85MWBSJyDIajQ2jL4EvOHIRCyCqOjbUXsmUjEpM1SL6aatRDWYOBNo4rVGZMGUZCd8g8d4mBEmLMuVaDrsMLBPPYLObubZ4Ls+27kyNt7JsAaFYiGUlcyk1ZdDubkMraqhIK6HR3QqARtSwrGwBgXiYBw48ljp2W9s+7p1xOw8deZIbJqzhpfo3cIU8ZJnSuG/qzZgFK4JGIHGOwntUtGKYuZ7cmI+btPBK+x780aTZX2jPw6nL4N+eO8JQIEY4CA67iaOuBjKtWhSjGVFVCBx8Gf3yzyHLl66x+lEiITKvaAb+WIjj/afRSzrWj1s94nmuqtDnCXHkTPI7KSsy0bjMln1tfPqaS7uiiihI1LuamZoznvnFyc09Q8zC1kOnkUSBLKeRvSd6icRkPnfDeK6pWYHTYKeu7zSHe04Qk+NcSOZXNNlJW/FJEr5BRI0WORzEVDYZdIZUtQlVUYgNdGCdvhpDQTVyKMDQrmdQQn4YLXGTQpXjoDOSde1XkANDCAYjCZ8bJTyce60oMvVt7hECo4oK2490Mb0m87IU4wOIJxSeer2eNHvy+kbPRjYYBRs6vZZYXOH1nd0smlHArsP93H3DMuJKnFZvO2atiQ1V1/La5iHmzXHQ4euiMrOQu6fcSNiSRtZ1XyfcWkc4ECBcsgBL6Rw0wQESES9enRa9I4vv6YwERJU3vW0EBIVSZwFxXTraw28R7W/DUFiDnFPEyeZXU3NekLuYp5/vJnF2h0ZR4aE3u/m3W+/G4GlmfHo5WWjJ09k4Yy/h8fZdOAw25hZOoyatksYzMo+8chpFUXnw5Wb+bdnVaPf+AYCEbxCNM5eEJ7lxK5kdaOwZCNYsBAFEUSQ7zcTaBWW8eaCdUy1uFk3LJxhNYDyrz5Fm0nLDskr+54lhPRmbWUd53pgDZ4wxLmfGDP2PCG8whlGIIGiN+GODGD9mjz6AJEjkW/I54TrN3LyZo14XbdnI/c1kTppHht1AXZOLqVUfT2WAMS5PtJLA8pmFPPJK0oP9bk5xut3I0QYXRxsG6Owf9gFfv7SCwmwrFXk2/vRmI9tToYmtzJuYy11X1YwoMaSTBK6YkkdNSRoGnUSmVY8gwNYjXXgDMfLyBa5blY9R0BCNK3zx9loS+gGavS0UWPNRdAG27/FSVVNGb6yRzMwiinLsHG1Iel627uvn7hvWs7X/ZTr8XaQZHaypXkbb4ADXF95NNCRhMMtk6h24heSC//7jz/DjuV9ggSRxxNNMVKdn0+k3cIU9lDuL+fTUO5hWncWh+mERqvxME5m5CR47toNnT71KhaOQO7OmYDv4GgvnXM2PGzbjLZlLzfJPoQ1F8YRU2mICtRVa4gY7GknLilAa1Y4iXHIUm8nB/r6THOg+Rre/n8GQi2/WrEOnStw+4RpOupuRFQWNKOEN+zjUMzL8NKEk6Pb3EpdjPHV8EzePu4pyUyZ2WcAw0I+oHSLUdhxD4XjU7BrAiiAISGEX0eNbWVo2g0VFTlRJxGMxo7XlElcSzJnmpCo7n82DL3GmPhmqXT/YzDFnMXdPWIDidSFcpp5PSBrCgyE3GlHiunFXElcSvNOyl8q00hH9PP4I6xeXY9RrUBQVrUZid93Z1JhLuM62gMB1465kf9dRfrnvUSRB5FNT7mTZzELyMpIh7lqtSJ8rREGOCdcQNLpbmZhdw4KiWeglHVwgel9VVSSjhaEdT6GEA+hyy7CMnw9yDM7uaasGC1nX/yPe3c/jP7gZyWwnfcntiI7sC85d1JtwzLyK/hd+CmcjZwzFE3BecVOq3Jqqiqm0o3OJRBNcrl9sQYBAKM6sCTnsODIckVRT5MRq1jE0JKK3KHz6xmqOnvQxrjSdeMDM9aU3gymAL+ojGI4xe5odjZSgNqOCN5p3MN+Ui/DO7+jva0GypuFY+glUbzPBw1vwjJvF08FWjg40YNIaua10ERVHdnFrfiVvOM281ridlrQSbljzaTTkcaBPYLA9yp1ln2Wv503qPWeQEkbC0aER70VR4fighie3qFwx2c/aPIHonv+lRGvguzd/gzcGTrHx5CuIvMrV5Yv5zLpyfvFMG5GYjLVkPEbz7aAmPfqmiUsZ2PhD7LPXoIQDxN09JIb6iEp27l2eDQE3bzX1pypsnGh2U1HgIM2mJ91qQCcJjC928i/3zOFY4yDpdgO1xU4cJu3HfIXHGGOMS4mLZuirqjoqB93r9WK32y/L0nreQBSDHAatnWAo9LGV1nsveZZs9vcexhMZwmlwjHhNtGeTaD+CqirUFjnZUdczZuiP8ZGiKCoza7PwBqO8vLMVo17DVfNKOHiqj0mVGSOMfICXd7by5ZumEIgk2FU3Mkx9V10PaxeUjsg/dAVi3P/EIXpcIbQakbuurmVObXbKYxRI+NkReoo7qj7JZzbUciKwi11ndqeOr06vYGnt1cQTesYXWHnixHO840mwcMo6Trd6iCcUHnqqg3lTl3HHvCycfSfoVAyc7ijm0f3D87t+iQEykvnBKioGVcNJTwtuZJ4/+hzxs6HzTZ42frH/d/y/qz/PzdN0aPw9qFojlrIKHm3YTDiR3AhpHOrgUeAzJjO6g6/zlQV38/ahAX7bPsDiSZnk5OvJjLaSQI8rKmKq20vkxDbSFmzgFdXFnWWruD5/Fh6NSFxViMoxwopCUSCMb+9TjF+wnnZfN3pBS1HuOA721I26dloE/jFnLkJGPpl6K57XfkOwp5kggCCStvR23Jt/hW3edahZ69GH+3A9999YrvwiQy/djxoNAaCzpsOae/mXQ39gQ9FcrKKdM+6WEec67WnDV7qCkvJZl3WJvZgS56X6N8kwp6ECCUUmmAjT6e0jJy0fSJbXqy1O45fPHKPPnfyMNZLAF26Ygk4rXlis7q+NCB3ebra1JjU3FFXhlcbXmJd9E79/abj85uzx2Rzs38crLa8DsLP9ABtqV1PqLLzw+PEIAy/+nHfd97GeZtxvPUralfcOT0GR8ex5gWhnshKEHPQy8PID5Nz8nQsOrcpxfEffxrnoZtREDEHSkPC7SfhcYEzOS5IExpdl8PLO1hHHLp5eiHqZfq+jCRWNpOHKuaWoqsLuY31UFDpYMDWXH/9h+LkzrtzO7atqCUcTvL6vjTnmIJuPvJZ6Pcecxfriq1hrWEFd20HSd7+CPJis5iL73bhe+jnpy+9GU1CF1mDh5ng+a8vLedPfxm/ObOZbk69CffVhKlffwSY5zomBBjr9fSyy3MSfNp8V3NsFd6xbQqeuE60hztKZhTR2DNF+VvBVI4koioqiwtYjfRjETNZklqD4+jnlamFz49bkfIDnG97gc+PMfObKEhLRCMq2X+PpPRvJJUpkXftVsm/7PoMv3E/CnfwtCTcfQaxdyhNDU1hTI7F46HnSlq6nYUiLViPyP08cQlGTYpxfuXkqTpOWogwTJVnFpKdbGBgYrVExxhhjXF5ctDjtDRs2jGq75ZZbAHj88cdHvfb3jjcQw6AEQGsgGA+lVLs/biRBosiaz3HX6VGvCXoTgkZP3NNHdZGDU20eAuELFCUeY4yLgFZK1vb96i3TuGJqPpt3t3G82cX5RKijcZlQNMGTb5zhpuVVzKgd6WU711smq/DbTcfpcSWNnXhC4TcvnKDbHWbexDwEAWIBE1adlSNDu7GlJdjdvWfEePWuRvSWGAO+AO7IECcHT3PG3YgpLcTda8ZRXmCnstDJlPJcDvYe5YhWwSoWsW3/wIhxnt3agl7MTf1tEFTeGTiNrCopI/9deoMD2IPN6Lf8EE3DNlyKnZ8+00rvoSrWZt/F+LRkGa6GoQ5C2UWEGw8R8YYoynXyueVZlOeG6Q+dwaPGsJps5GEkenoX4qRFRAsqWVe8CG/Cyw+PPcV3t/2Eh49upD/k4rXm7bgcThKeQfSP/X+Ma2iiuD9IyNPL3MKRuh4aUUOpNRf19UdQnvgB8c56Yj3NiEYL2sxCBFHCd/A1zLVz8e16hoRvENndgTjtOoLHt6WM/ORFi2Ho60YnaalRdRANcz50WSUoWdXnfe1yQVI1rChfiEVn4rmTm9nWuocV5Qsxa0dunncNBFJGPkBCVtm8p41o7FK28pORIvu7jo5ou6poHX98bWRZx70n+jApI+/9Vxu2jtCKOO/43n54T/WGaNeZEd85NeIn2vGe0mmqQtwzcmNxFHICU+kkPNufItrVgO/o28R6mhA0wx5UWVZo6Bjik2vHU1uSRnmBndtW19DU6Rk1r8uFaEJBUOHImT4WLNRx+602rlzu4I+vnRnR72STF7c/zEs7Wpg/w8lbnW+NeL032E9UCdM21Em1IT1l5KeQE6AqCIk46ku/Qtj2FJbX/sC1UT3VjmL6SSCZHVi1ppSTyhvxIRmHvxt6ncT+oz6+OvWr+DqzONPmoSTXxieuHofNrOW21TW8eWBYhX/bCQ/xopnoskvZ6W4c9d4PuBuYlKUypTKDWO856VqKjOedJ5GHelJGfuql01uZUSDxy3f8yGmlTLcOML40nS1721O/mZ39AZ5+qzH1jbpcKzqMMcYYo/nQHv1PfOIT1NXVEYlEmDZtWqpdURRqa2s/7PB/s3iDUQplP2gNhBMRjNJfT/U0x5xNZ18Pg2E3GcaRSYeCLZtYXzOG3KmU5trYf6qPJdNGK3GPMcbFon8ozHPbmlk5W+Z402BKIA9VxajXEI4OG8LzJubyyq5WGjqGOFw/wA3LKjnT7sEXjFGcYyXTbkiVagpFE9S3DY06X+dAgFVzivnuJ2fz4jvN3LrsVnzKIPHE+Uu4BcNxjJYECXV40+s3Jx5kUcFCrls9hUxLOodce3itPuldvLZgdBSMokKGLptcSzYl5gwUVSXHYEcrjn7kLsmfRmDbE6AquGqv4wfPDysnN3b6uHPDAs4MncGkNaILBdE6cwhqJRKWTp4bOIXdZMeoNfJcXTKf9BtzPoN01d3sDfmZQRpmE/xg1x8IxpKGYIe3mxdObWFWwRQOupuYuurrSJt/SPzUVsSKuWiKamnoP8Jtk67lRH8DRq2ByvRSbO6+4fcXCeKYfx0aeyZqIlk+VNSZkEN+YBeQLFuWsGSha9qOLErY56xDm5YLqopkdvDj3LsIhIdInN7LxPQy6lzNqfEnZ9fitJaQUC7vsFMJHQNBNwe7k57OYCzE0yde5uvzPpfqI8tJ8df3MjgUQrqEw/YBJFWiwJZLs6d9uE0xjshpf5dEfOR7iSuJpIF2AZtG0JlwLrkdyWRDiYbQWJwEGvaDNHwfChod2owCTJUzESQJQdQQajyIaBhdrWbk5DUoSoL01fcQbjmKZdw89NllqPI5+fiKit2i45GXTzKpIgOLScvGtxpYOr0A6RIPtviosJm0NHQMYU2P8PN9D6Gisr70hlRJvHOR48kNW0mTjGYBWJg9gSssBRiiUayKEZ0lD53kxqU3jdxQJJnrHtjyuxFt6qE3WH3lnZRbstHUzEHXfIJ/KV3FS/4W9g/UI6oaBAE+tSSbGl0vpvApol1xAh4tnf0BOvsD1Le5+eYnZvJfjx5kyD8szprjNKAJNiP7XZRZKjk9ONLYLzKkEfZ7afCYmDppJbHjb8LZ95XwDoyq9GCqno0+p5SJWj+mWRZkXTWxnmP0m8tHfVZHGwaIxKsxasd0lsYYY4xhPrSh/4tf/IKhoSG+9a1v8R//8R/DA2s0ZGZevmHg/lAMk5QgosTRitqUWNdfA1EQKbIVUDd4ksUFC0ZUXJJsmUS7m5BypzK+xMn2o91jhv4YHynvGvJv7m/n1lU1tPR4aWgfwhuM8eWbp7Jlbxs9g0HmTcojGpfZcXS4vNib+ztYNbsYk1HD5IpMGrt89A+FKc62kp9pJj/TQtfAe3XeQauRKM4088XrJ6ECXZE4asREbXo1p1zD3sNMUzo5lkyqs/XElShfnvNpNKKEVtTi9YjsODBESTZUl0/ihvESFq2FfGM+Dqt/xIIvN91MU3OcCeJ6VpRmENfILMibzHPtu5lfNIOd7QdSfefnTULevRWNM4d97aOX/vsP+aiZUMXS9HKkt57BftXn2B08xcbTL6X6ZJjSuLJqCS/Vv8krLe9QZa9mTtpEDrn2og9ZU0b+u3giXiw6E3X99RTmTiDvmm+jRiKEBDMOs5GrK5bxwIE/YNWbCcXDzLbkod39Usow0WYVE+uqJ1C3jUjKGyqQvuoe7AtuRGPLQEiPoDadJFI0l7TyaQTqtp31sIJotOKYfx22tFwGdHpulqxMLSvmZLCPCc5SJuRPQ7rMjXyAsBJiT9ehUe2d3j5KDCVAMnS/stAxqs+yGUVJI/gStvVlVeWK0tn4ogHK04pQVBWtPkZVkZMz7Z5UP6Neg8Y40ghaXjYfk2S4oLWscWThP/omkZbhqIGMq+5D0FuGxfhEDWmLb6V/0/+mDEXbjCvR2D9g/SJqEBQF1yvD5TYli5PMdV8a7iJCeb6dCWXphKIJtLJIXoaZ2RNykS/XspEKWEw6AkryOW3WmWgJ1jOxcjx1DcPXXKcRyRU9lOdZOXTMz+xps4jH3KzxxlB3JkXsIoBu6e10F1VhuPrTqM/+jHcvrHnCFajqeb4cqkKVJZvgpl+myi/qT+xgw6IbSa/I48ThGNfPzWJS59Oog63EAZEtrJ1yDQ3Z+bT1Belzh+nyDJLu0KWe+xpJ4I55DtR3dqLas5mVO52dPXV4o8lzZBgdTDbl0h4o4O3DneyQalk3fxq59U+h9jdjqpyBKitoHFkkhvoxVc9Kevq3PQlAriDiXPUZBrLGoY+NVtKvLUlDP2bkjzHGGO/hQxv6FosFi8XCL3/5Sx5//HHuvfdeurq6+O1vf8tXvvKVyzI/H8AfjGGySwTjIYx/pbD9c8kyZtLp76Yv1E+OOSvVLtizibUcwKAqlOTY2LK/g87+AAVZo8tujTHGxSDbaUp57h999RQluTYWT81n9sRc2np8LJqaR5bTTL8nxP3nKAgD6LXJ8kKn2zzsONaN1aTjrQPJ7+ydV9Zyx5U1/PRPR1KbCUumF+Cw6Khvc+MaCpPlNJJu0/NWyw7GZ9ayqng1BZYiTg+doNJRzuyCacghHX/c2saaFU5erH+NZk87AgJLS+dTkDOOP24+Q5rNwGduLSXAAP+973+4cd0d7NgdpLHNx8SKdOZMyOVXzx0nIStIosT6IhdpJic3lC7ClQgzM28SkUScTEMG4ZAB58TFRE/vwqQbvfg3m7TcUb0a29AgodX/gNtu54Vjj4zoMxhyY9Qko4bc4SGcjgKGlB5eatnBDePXkKxpMOz6lAQREJiYWU1vaJC09EwcxqxUafYscvinOf8PvxzAoihIW58k7B1AcuYgj5uDrCQQ9eZzjHwAlaGdG0m/6XsIgkBU6yC7opZ+fwyle3/KyIdkffNodwNy63E086+B537GhFiEudNWo8ueQkT9AG/qZYJB0pFrzqIx1jqi3WEcfj4rCtjNOj533SSefrMBfyjGkumFTKnKHKWbc6mhlUQGAi7sBivPntqMKIisrxb5xNWzeXlnKwdP9VOcY+WmlVVoHW5mRibTHxxkcs44atLLiSpxLrQdlBjqG2HkA7jffpycm74NuuRnKKDievMPI7zBvgOvYiieAPpc3pdEFO++TSOa5ICHxFAfmEuAZG3zgaEQV80v5Y197URiMtcuqqCn309p7uX5HVdVBYtJolDMZ0P+3Xg8Kg6NSPZCI1azhoMn3BRmGbljjg3Tjl9w7+zb2XhSwuStYWVZAvW5+0eM59/2JzqW38BufwfX3voNpJ4W4noj9qwaIrKEZHEiB4Y3EDS2THQBH/7wyPx1zcEtzFh7H9Nn2clQ3ERPt46ceN0rXD31q/xyS7KU6lDcTe0sD3NnFiInRGrychjq7CM0/Qucdom8/esWvnHnXYRinRANkZ2ArkgxP9s4LHR6stXDd6+9niLndjS2TDyqiYz13yBSvxuN2Yb79YfO+eAUfO88gX/Ol2hp9TFvUi67jiXD/J1WPTevqLp4ubhjjDHG3w0XTYzvn/7pnygoSHqCbTYbgiDwz//8z/z4xz++WKf4myEhK0TiCha9lo54CIPmrxe2/y6iIFBiK+T44CmyTcMLQEGrRzJaUId6EJ35jC9NY9vRLm5bcXnnxo7x0WEzaviXe+ZwqtVNXFYIR+PMHp+Dw6AhvSIdQRBJJGS0GmFUKP+9Gybxg4f3p8L9RQHuXjueR14+yR+31PPDz8/nuiUVJGSF7DQTJoOGUy1u/vPRgwBoNSJfuGEycSWON+7hTw3PsaxwMSuLl7O3dx89AxF+9cd9fO6GGra170qFFKuovNmyg8/OKEcUBdy+CDrRwFPHNhGVYzx25iEm1UzgmulFFKYJDPS4U/oBb+xv59o8Cd/T/wqihpzSyZBWQqJsLnpRQ8gcRjduIcgy06wyL2mlETW3p042cnioCV2gmIefa+JfPjseRRkZ2iwgkG3J4pbx11OdUYwa1LK7Lylwd6injuXlC3i96Z1U/9WVS+jzDzArfR6H/EeYpSnhvbW+dKqRDMmE2LoN3eI7EBffxNb+E7zU8DbfMFnIU84TahsYQj0nPzSsceLMFImeGhjVN+HpRTRaUeJRtNd/DRt6Elo7EXVsufouMTXGkrK5tB3uTGk7lDoLsehHbqD3ukM88vJJ5k3Kw2zUsv9kMs1i/eKySzoVXFFVegMDKTE+WZXZ1XmIqFiK2xfhynkl9LqCvLi9iczxbQyGPWSY0nm7ZTd1fae5d8btFxxfjYRGtSlhP2oiDrqzfWJhEp7eUf3kgAfSLzR5Jfnvvecc0SYgCiL/+Yf9qXzquqZB7lk3AUW9hC/MR4iqgoLK/sNBXt01HK01fXwaa5ZnsnpBBs6Dm1G27URWEui3/Zw7iybhLrwd2dc9yphV5ThGVeB4fz2F9jx2DRzEG/FxrymXZzZ5+dT8e8loeAG5pwFNQS32SYuRvX28F1VOEIr6SYsqeEKh0WUbZRmtmLxmtWV2euJN7O4ZFnL9SvF9tAUNPPt2UitAFKClX6Uqvwb3kIdjIYl9DaN1H/Z2SZSX1hI2pxGyZxLQ2dFOWovQfWRUXznoxe0Ls+NoH5MrM7njyhqKc2zkpBkxaUd7+ccYY4wxLpqh39rays9//nMArFYr3/rWt7jmmmsu1vB/U/hDcUw6AVFvIBgPov8rlNY7H+mGNDr8XXQGeii05qXaNWm5yINtiM58Jpal89jrZ7hhcQW6sR+OMT4C+n1R/uORAylj/ap5JZh0ye+aLKu8a3GmmXX86z1z2FnXTa8rxJJp+fQMBodz+knmwm871MXU6iz2n+xDVVTmTMihrcfPw6+cZOXsYp7fPpz7HU8oPPLyKb54x0qaQqeQVZkXml4GwKa3UhLRoigqWTkiLx0bLabU6m3HYbHj9kUQBZVwIjkXFZWjA3UcHajjOtNViOJw+ovDqkeTWQCiBEoCOX8yclYNCQb4zbE3aBzqwGm08/+qP0eBLc6Xb0/nRGuARFwhv1CkWz5JubaQicV63sg00twuc0XhfN5uTxruFfYCPl19NS4lik+j8lLD61xVsIYia1K8rMndhkGj56YJ12DWGsg0Z6BXBRxxeL7rIIvKZqGRRz6jJI2ArKgIKBiyi/C0HuaA3cAzp5Oq161Cgny9CQQxVVoMwFgxDVVnHjFWIqFgLJ9O6Pi2Ee2G4glE+lrxCiqFulyiinpJG6V/DQQBNjds49ra1SiqgiiIuMMe+gIDlOiTJfYkCdp6/QQjCV7fN5zrHokluGpeCZpLOJxXVhSO9Y0Uip2ZMY+NG1tIyAqnW4c9sbdPLmOnZwctZzff/NEAsnLhSvSatJyz991wP0PROESzLfVdE/RmtJmFxAc6RhyrdWQzWvngHAxmrFNX4Nv/cqpJNJjRpeenjlMUhaZO7yih0V3HerhiSu6FKgP+XSMnYPPuthFtB0+4mTxJz//P3nkGyFFdafupqs55eqYn56QZhdEo54QiIJCQyDk7YWy8jrv7re21vbsO67C7NraxjTGYaKJACEkoooBy1gRNzrGnc6yq70eLHg2DwQHbAs3zS7p969atmu6qe+455z1a0xCOxrfhwo3EwTbONvbhMOqo1BlRo8OCeZIzm3OxRCnTc4PN5Nmy8IS9CKEU2no6+PcX/cyuWEFl1dWc6gizdiBIjk5E0BpQY8PpIMaZq8nUl3KsbQhJhkkmG0rQm/zcMG4OQa2Nz1znImzs5MmaYSPfYbDhjbpJK1L594rp1HS1Iemj7Oz9A5sHwyzPX05pRiWn2wOj7oXeqOPhcBPHmxPfo/WVV7I4ex46e8ao56s+txKT00VxdpiiLCuTSlJxWfVcontGY4wxxp/Ah7YCiMfj+P3DubGBQAD1En36eANRzBoFUWfEHwv8wxT3340gCBTa8zk9UDNClVWbkoncl/D+OSx6slJNHKzp/WPDjDHGX4wCPL6pZoSxvnFvM+0Doz1vqgppVh3XzC/iM9dMpCLPTuA9qkL4glEsRi2TSlJxmLUEQjH++8nDqKqKxTg6sHfQG4aoiXRDBrdMuoaFBbOY4Crn7om3oxETGw7hMBS+R+muXGs23kAEs0GDisqy4vkjPtdKWrKt6WQZrKxbVIzdrOX2yyuJmrJwXvcvWG/5NoHs6WgCLfyqfhPnhhLGhTvkYUfvbk63xnE5VWTXGbqsu+jjHBMzysi15dASCnLntUXMKrNxQ/E8/nvKHawrXcJnxl3Om93HOOZu5HfHn+dQ5wkeq3mCMmcZpY7EhsPp3jreOLeDAnMWtoAf+2u/Aq2OtZXLydJlJucvIhOJdPBWy5v8+OjD4G1icOtjdKW52Ns+nCv+VPMumq0WUq/4xPlcZgFj2XQM2WWI/mHvqCQJiKKA6irDvuAGBI0ORA2WSYtQVRV5+goc5kwURSUmRGgONbGnbx/ngvVEhPdXVL8U0EsSc/Km8tzpV3n+zEaeO/0qO5r2kW8f3khSFEhzGEcdm51mTpTXu4gRFZHilPyRbWjfs/a8HB+ZhpBjzcSsHX3dFyIYzaSv+RwaRwYgYCyuJmXRjSgXpDSoGi3OJbciWc8L1UoaUhbfgmD8gPQ1RUGfV4nzslvRZ5dhnriQ9LVfQI4NP9tUVUV8j/1yrUa4ZI0zQUhEIL3X9YejccRIGpZldyFoE+sm0WTDP+NuXjo4wBN7BjBf/UU0rsR3RsobR9+8K3mjIxGxVejIJSxHuGncDXh9iRMoKuw96+bX27o4WOdGFASC7XXYr/k8mvFz0OWUIy+5kXDRRMx9Z+gZ9PPYrj56pn8KsXwB2rQ8xGnrGBp3BUXpFqrtPqakuVhdMIcMi4u5udO4umIF5/q7CXr0uENuppXlcsZ/kMXFs7i+/Hp0WHDTzuVz8xDF4e+eTiMyocRJbf+wAv/zZ1+jPzZA1JRB2jVfRLKkAKDPn4B92d1MyjHwjUVRVrOdTPdxtLGxEnpjjDHGH+dD8+ivXbuW6667jlWrViEIAlu2bHnPknuXAp5AFJMkI+j0BCL9pBqcH3zQ3wmHzo5W1NLia6PInnhZahwZKCd3osoxBEnL5JI0thxqY+7EzIs+x3OMjxbeUHyEl+4dBj1hijMsCIKAPxzHE4hiM2uxGrSoqkooqtIzFMSg1yRV9t9h/uRs4rLKmgXFSIJAV38AvU7D1QtKCIbjo/rnZ1jZcbiT+ZPz6OsfpNyUy+G2PjpUHS6HHr1W4vGXmrn75jk0DLbQHxwEoCpjPGY5g8VTjUytsuGJeKiwTMUy3sTulgOkW1JZWboYvWIlnOElrp7lk3eVYZVAlCTcZjs1nlYKVT2BuIcW78gwzl2dbzFj+gS6h/Rck3cZ6KA74iUWg//a838szapmRtYknqx9g7ZgPwtypjLTUoxWUZmTOZG0uMLaOdX0SjInB5t4uWUXn5p0Cz3BXuKqQqo5HVFQSIvKiGu+SFiygzLSiS4GOvlDy3b2dp8i3ZyG6O4GVy5eQWVhwWymZk1iT+tBuv19/OepP/Dpqbcw+7qvosZjxId6ErXIO+vwo9Kt07K/+TCSKDEzewpZE1eRVjYHMR4kqsQIaHWIOgdG1Qiiyva2t3i5drhO9pzcqdxYsQ6NovtQvnsfRaIRkRJHMQ/MvIOBkAeDRofdYIW4Lvn2FoSEUV+QZaOlK+GB1Gslrl5QgqwoaMWL19hXJZV5edM51HE8KVrmVbqZMi6fo7XD6R4Wo5acdDOcj/Q2ag3cMvkawvEIWszvNTQAit+D58jmhACfKBHz9OM/tQdL9WWgtSU6hfwEm46Tuvxu1HgUQaNFDoeIDXRC1vvk6MciyN5+go0nMJbPJDbQSbj1FNr0wmQXQRCYVJzGa281j9i8WDGr4JJ1hICA3aKlKNtGU+ewxzzNYaA4LYsN29pxrswgtu7T5OjTOFsT5LHXughF4uS4zNSLBtKvvBc5EuDQQAMbal5BVVXybFkstBexfDCISZXY5A9TkGmlpXvYEF42Mx+PxUinMZdguItz6TZyi0spTiumoesUc4/spmr8zbx6UObbL3VRklNFnnM6uQYrnZ49VBjm8eNNvZTm2bh8/kLm5C+gqSdET7NMeUoRWNzs7NxCuDPEypJFmKNZ/OjxswTCcUpzbaxd6eaz11dR0zyEJAq4Ukw8t6WemTNnsat9V3KenoiXdE0GUdd4HDd+GzEeRtbZiKMQ3PIw4aZjAARO7cRYMQfjwrt5f7WKMcYY41LlQzP0P/GJT1BaWsq+ffvQaDR88YtfZNGiRR943IYNG3j44YeJxWLceeed3HLLLe/Z7ytf+QqzZs1Kbh50dnbypS99iYGBAYqKivjBD36A2fzHX/h/T7yBKEYxiqgzEPBfHDn67yCcz9U/M1BLvjUXSRQRtTpEUwrKYDuSq4iSbBs7jnbQ0OmlNMf+j57yGB8TBEHgVEM/lYVOTjb0j/jMlWJEEATOdXn5v+eOEwjH0WlFPntdNaFIjOe2ncNk0HD5nELuXzuJrQda8QWjXDG3kOoyF3aTJhnhaDZqWTglh1d2N2DSa7l5RQUv7DhHKBInK9XMitkF/PbVM1QUpLDvWC/1bR4Ks2zIMtS1DvLQzVM4XtdPY42Gz8y4h4FQPwaNgXRDGrKscPViLU+feZkC8ySeeradioIUbllxD7GowE9+cYorlqUhpnSzo3kfWxp3s67yCpzWOfzy5NM0ulv56oQvYVMFzFoTgdjISAZVVdnTu53cbjPdWbkoQg6/rfk1Jo2R2WnlfOfI44TOh5s+6emkr2g+awxFZEU8DHo62WZU2dZxlHlZVdxRugIpHkMyZmI0G9AEhnCHJBqsafQNNWOQTORZs7FKieemRlTo9bSzt/sUoiCi1+gQ9CY6yqt49NhzROQokiixtmIl+9uOEIqFyAlH6Hz2nxD1JhwLb0S1Z6O1Oaj3tvNf5xfgAJsbdvEvCz5HpiYLNA4A9JDcZfDIQ2yo2zLiXuxrP8KyokVkat/H2PqYI0rQH+7nhbOvMxBMbJDNzZvO3LzpQBqQ8OgHwzFuXF7OkC+CLKs4rDo8gQhFGtt75pFfLEgi9IcGWVI8F5veAgj0+ftZvyKXFKuBY3V95GdauWZpPhvbX+DWyesQEPBF/bxU8wb3TbvpfVX31WgIc9l0gvWHEHUG5HAgUeLxglB+QdKgBv30vvCDZJtoMONa+9AHTF5DoGYfkfZaws0nks3p1345+W+NRqR7MMD9ayfS3O0jEo1TWeCksd3DpBLnJVleD1S6BgPccWUlu452cKyun7I8O7MnZfHspkamzY3yk4NPI6sKBo2eT0+7m0+kTsAf8xM3DPCrk4+gonLbpGspTZvEQxkVaEI+nE1n4bkfoqgKfuENlq75Vybl5HGuL05du5cJxan0u0Nk0Q8dRxG6TjM5swgx1E7HJA0VGRPwTskl3aDjs9eM4/GtzXT2h6gotWJ3egj4RHxiN95AlL0nerAaDXS7/RyvGwDg7huzefrUo8ln3s8PP8H60uuSaRvn2r1s2q5l2QKJg2d6kBUFjz+KViMyRTu8zhIFMVkGWVUhKprhfDqUEnQzkDEdg7McY+dh4kO9hGr2YZ5+FZiyGWOMMcZ4Nx+aoQ+wdOlSli5d+if37+np4Uc/+hEvvPACOp2OG2+8kVmzZlFaWjqiz9e//nX27dvHrFmzku3f/OY3ufnmm7nyyiv56U9/ys9+9jO+9KUvfZiX8xfjDUYxEkHVGokrcXTixbXTatNZsWjNnBtqZJwzca8FewZyXxOSqwhBEJha7uL1/S18dn3VP3i2Y3xciCsqWw60smJ2AX1DQboHgogCrFlUgsthwB+N09brZ/msAiBRUus3G06zYnYBXf2J3Maf/uE4d145AVeKkfxMG9Mr0jFppQvTGMlLt9DvCfPG/hYMuhi+YJRPX1tFPK6gkUT+99ljxGWFaFzmtivLeWxDHZ5AhAprCm5flP967BBzJ2WRlWamu0MAexxftJ+Xal6nYbCFDHMaN028mkAkTEGWhZoWN995JGGEiQLoTFGCcgydpCOmxNlQt4ViexEN7hY0ooaYSUYrFHNniYaf1WzEpDUyJXsCZSlFoJho9bQh6gsx6py09PvxRfxcWzifbl9v0sh/h23Ne5k3dTJpLUeozcpga8M27q9YzWxTJu4N/0OsrxVdeiFcdhdNAT2NShdP1T6ZVOAfn1rBnVU3olF19Ed6CZitfHfuZ7CIWrYP1tGa4uTn+39DRE5kHcuKzItnN/HQnHtIHxqC136JCiiRIINbfkPaui8R7qxju25k6paiKuxtO8h1pWuR3yMsOyrHUN6jFFZUjnIpO6pUMc7+9iNJIx9gb9shJmdWwvn9Y40GgpE4De1eojEZs1HLsfpeZo7PJBiJYbmItVZUYFvjHmouCF0GiMpxKgrmUF3mon8ohEdsZUrWeGwGG8FoiOKUfFqGOojEY6NF0y5A0JsR43G0Gi1qLIpkdyXK4mmGo0RUJU7g3CFs0y9HPK87EajZhxwYAtv7DB6PEmmvBUFE68xCDnpQQn6UgAfOB/GJooAgJMRwM1KMgICsqtit+lF5+5cKoZiCPxBj94k2xk8JsWrOFAa8UZ7YeJZlixz8ofG3mLVG/LEgZp2Jl+pe47aJV7Pt9OvUNif0Vq4suwwVaPY1MteYi/Lqr7BNXQnz1iGIQDgSXwABAABJREFUGiKd9egGarDvfpYZJhuLFtzKE2cU0i0CvPXr4dx7azb9BcvwB1wMBvVEfDEm9x9hXMNe/n32Esgdh1EbI7btOSZIGqzLpjLnZhud7hiWNAdf/XVCZ8Bh0dMXbx0VpXGgdy+Txy1h/4l+dBqRcFQmw+IkM7WPAU8YD1GWzsilLfAWACatkXun3ESK5BylV9LrjXDo3AAprlTslmxCqdV4u71MtHlG5PGPMcYYY1zIh2boT5ky5T3DvI8cGV0D+B327t3L7NmzcTgcAKxcuZJNmzbxwAMPJPts2LCBpUuXJvsAxGIxDh48yE9/+lMA1q1bx6233nrRGPpDvghGNURYMGLQGC7K8PcCex4n+k5TZC8ADIj2DOTWY8BlAEwsdvLIhjN0DwbJdF6aJRLH+HDRSAJTK9J57LWzPHTTFLr6A8QVlYOnuzlwuptPrqvi2a11hKMJb5teJ3HLygoMOol1S0o5ea6fqtI0DDqRxg4Piqpy/WXDm4Iq0D4QpK51iDSHgSnjXEyryGDD7kZe2d1IcY6dm5aPQwWcNkNCyO9QN3Mmu3BlqGgFPbuPJ3L8957s4nBtL5+/L5sdrYeIK3FStC7uKLoSd79CuF2gzAr/us7Jd1/p5VybB4tRy/rLs9nd8xLXjV/Da7E3AYgrceKKnCgJllbKppZXSTU6WZQzjW/P+yznAl1sPLeDxsFWVpUs4p8nrsOiCBAO0RK3YNVbCCvxpH7AhWglLYqgQZEk9g01IwgCs+yF9L/4A5SgFzG3As+kufQQwOWUKPPKjHcWcnowoclxZqAGfyRCg+coT55+BRUVjajhsxPXstiQxS5386ioA0VVkBQVNj6CKo/UTIgPdqIvmU6sZfuoucaU0foK75Cic1DkyKdpaFhMzm6w4TKmXdICfbKs0DDYMqrdG/GD9Z0+YDPrSbUbyHSaiMkKJTk2YnEFo07DxZwMrsKoiDdJlJhkn8kfNrZR3z6Ey2Hks8UV1Hh2sv3Y8wAYNQY+MeNWTBoD/PGvFYIkEemoxXckkRIiaHSkXfnpkX1EDc5FNzG094WE0r4oYZ9xJZLh/cvfCRodlsmXJUT7eluQLClIBjPiBbn94bBMTpqV1h4fnkAUWVZItRvJcZnRaQTC76v29/FEp5XodYdZWJ3NkbP9PPr2fjQakeuXlVCUJbE2fhdDbpVxOTby1DY0/bUYAyHurFrPYCCORa/j+XMbeK1+GwD64kVctugmBnc8mSyRaCqbjjY1B8eC60BVCertzByfQiAKqvVKOPgMoquQvfaVPLWhG0hspN10WRFi1xlk/yAcfh4Og37hjajObKyTFuHZ+QyxzjpcgKFsFjfOnc/Te/uIyQpaYXSKkVFjIBpTuWJqKkuyPJh79mJs7eRf5qXTpabh0zhxOGyYLHl4Il5MGiMW0YqiqAgCeIJx+r0hrCYdbZ5ujsdfpb0+kb+SZ82h2ryS77wZ49v3pOIcagRVQbBnEREvjujWMcYY4x/Ph2bov/rqq8l/x2IxNm/ejCS9vyeht7cXl8uV/H96ejonTpwY0efee+8F4PDhw8k2t9uNxWJBo0lM3+Vy0dMzulzKPwpPIIpDDRDAcdEI8b0bs8ZEqtHJ2YF65tmnIljTEt6IsA/RYEWnkaguS+PVvc3cu3r8P3q6Y3wMUBWVJVNy6XWHOFLbx5sHh426rFQze453Jo18gEhUpqF9iHEFKaTZjWQ4zby8q5H0FCNrFpWQmWIiFpcxaDSoKnQOBvnGr/Zz3dJy/ve543z19un895NHiJwfs7HDw282nOZzN1bT0uXj1d2NTB+fidkZZEvXm/QH3Txw2910doeJKXHM9igNQw2UphZytPMsqYFJPLxh2PM4o8zO7ZmH+OJVS6gJuugKt3F46A0WZS+lv9PA/dNu5TdHn2ZWzhRcplTKU4t45tQryeP3tR/iwdl38+ix55Jtvzn2LJ+fdRe17nMs0KSS78xjrXk9G1tfYf6EdWSZU+kKDCT7ry1biUtnIdJ8muLJU4k54sgDHQmPVUk1u4uK2VCfeDabdSYeKlnBvUoqz6Ub2dt7BkEQiCrBpJEPiY2JX57dyL9Pup58YyoWnRl/dFgtWhJE7NEoWmc20d7mEX9jyZlF6NQOluQVc7j71IjP5ufNRJYVRBG0MQ/EY8iGFOKqhEbRcf/UW3i9YRtHu09T7izmmnGrMKqX9oLVJFmoTCvjcNcJtJIWWZFRVAWXebju2zv7yMfq+jhen8hrt5l1PHDdZOKygiRefBvN76CqsKRoDsd7ziS9ofOz5/HkhjZauhK51X1DIRr6OtjevDd5XCge5plTG/jc7Lt5vzesEgkmjXwANR5lcPsTZFz7leFOooT3yObhWuuKjOftVzAUTPyAuSto7elEuhvQufJRoxECdQdx5g6XptXpJEKROE9vqSUYTqjIS6LAZ6+vJhK7eDdg/paIqkpFQQrnOgfYvK8DADkqY7UIvPmWm91HE2Ker9HJiuo0rlL7CDz3XYTLv8x/PNVFtsvCqiULaNV3MBTx0BcN4q09mjTyAYL1hzBVzGZozwtEZ9/NlhMxKlx9xGSBx1qzuGnW7cREPc+8NiwcKooCL+xuYfLK5Rh7f5Fsj/a1YiyaTLj1FJHOumR7uP5tZswZz4s6iUAohlXNxaDREz5fiUVAYGHhTAJGK9Xuw6j7XiMGxBoPIVrTqCm9h1+9eQybWccXbppKgcuVKD143shv7PbzvScOYzPrmFyWhmtcJ+3+4XKEbb4OJjg7EQQd9U3dlO79NqgKmpQsUtb8E8mdwDHGGOOS5kMz9HNyckb8//777+e6667jnnvu+aPHvJcYzZ/i/f5Lj7uQ1NQPUNT9KwhGZbIVPz4lhkVvwmi8OMWkKnTF7G05xKRIOTarBV9aDnp/O+bMaQBcNqOAHz51hJggkJ32t7tffw4u10fj5fVRmec7/Km/h7/2ulzAg9dX881f7x/RbjZqGfCGR/X3BqLoNBJHanvZezIhXtfZH+A3r5zmztXj+d7vD3PX6vFcNj2f/bubUFUIReJYjFoa2j1JI/8dugYCiAjsP9nJbZePx2gP0xxuwaozc3XBeiIRFa2rC0GJU+9uJ9+RzUBoiNmuBfx2S+eIsQ7We7iyvIKcwXae2SJz45WFVFXmElWipOg06OI5/PO8zzIY9nCk5xi7Wkdec1SO0exuRytqkjXSAQ51neKO4qUMySFSpAgafza3lt+FR43w0OSbOONuois0REVqOS5DNs2RTuxXfp5F0Sj9XTuQUnOwTFmBu7CC10//IWmoB6JBfte+nwfiDpa7Stnbewa9pCMQ8yWN/HcIxIL45AhSwM+68at47vRrhGJhtJKWmydejb2vG9Oim+h74QdJr74hrxKN2cnAie24epv44vSr2dpfiyiIrCqaQ7nRScDbR31flLZuD1kWhQLNaTInTkebkokLK5903oovGsCsM6KT/nHPzb/l++GP8V6/rZbBLmbnTGW8eSYDAyo6nUB6uoiqyCP6D9UPJI18SPxuNr/dwv1rJv5dnkV/6TkCkSAtnnYenHUXMTmGKErooy42dZ0c0S8qjK7K0eXrIa7E3/fcnrahUW2ytx81FsaVlThOjQSI9beN6hf39OIqm/ZHxw539yNo9cg+N0O1BxANZuwzr0KOBHEVD8+pttWdNPIBZEVl97EOpo5zkfLXPk//Tu+ZD/P3ICsqASXCE5v6RrSbjNqkkf8Om4/1s3j1PMxtJ5Hqd5GXMZWmTi+PPhfgW/d9iqi2HzsSsV1v8G6UoBfnklvp1OdwVderKAcPIWh0VFavpds6GZPViKIeQ6sRuWZRKaII0bhCMMWI2ZGFMtSFPmcclkmLiXn7CHeOLrdq8ZzjgWvX8bvXa/D1SHxl7mc52HWEUDxMji2TTed28JnyK1DfGjk/xddPoSGRPuANRPnx00f54UMLcTkS0ZP9QyH+97njXLWgGH8oRiQap949+vztwWYynVMIo0/s+KkQd3cRrd0NOUUfuXXIR22+Y4zxUeBDzdG/kIaGBgYGBt63T0ZGBocOHUr+v7e3l/T09A8c2+l04vf7kWUZSZLo6+v7k467kIEB/4gScx8mA+4gk4gyFA8hqVpCoYs1Pk8gx5LNwfbjzMqYhmJJx9d4ilhaZbLHlNJUfv3SST655v29G38PXC4rfX0XfymZi3We7/cS/VN+Dx/WdQkCLJicM0J9v6nTw6rZVRw8MzIy5/K5hQx4wuw/NVKhXlZU/MEYkajMz184SXaaGem8uPjOI+2sXVSC/B7Xo9dJNHZ6uePKCbQM9LOn53kkUeLyvLXIMYHDnh283ZFIN5qQPo5CRz7meBZObRaK2jdqvKgiIKgyn7urkBdrX+dETaImeK4ti7XjllGoddHkVeiPDCAyejNSEsVRkekpBivPN+/m5GADGlHDgtxpFNkqeG5jL0fr+llQncO1y6byTO3LHO15FIBMcwZ3Vt3CnPzpPNF7mNS8bCZaXPxg+j0cDbvJtRTi0AkogQGs4RBWUSLbmsH6ytVYNTYkQUS+IM8zxWDHIepojQc50d3ILVXXEIqF0Wt0bKjdypuChm9klZB2/T8TH+wCrQHZmokSjyJodKhdjaS92sztOeWgqtgcQ/Qd3M1GZSEbD3Qkz7NyShrX6Y6jKTVfoBkn4iECDJcp+1vw1/4ePuy5vOdvSxDwDZh45NnGZAR+eoqR+24sSvbXaklqWFxIY4eXaFz5mz+L/prngmRSsOttbGl8i25fL7KqcF3pdZgMmhHGsUPnGHVsmbMQg8bwvuc22UevC3QZRQjaxHEulxW0BrSp2QmV/QvQ2NPfd2wrAsH6g0Q66wFQwgHcu54m/dovJ48zm3WEIvFRx3oDUVRV/av+Nh/2e+bv9XuQJJFwRCEnwzBCdT8ef+/xo4qIGRDlCJrzD/lYXKGuZ4in2n/F5LRSbiuaSLzh2IjjVEVBDgWw9L9NvCmxzlTjUYRDz5KxLIdaRcBh0bNidgFbD7Qmyq4CLwnwL+vupkxtJNJRT88fvotu1mq0+ROIdo3UkvBZC/ndxrN84aYJNEfPsLX5CPn2LPzeAE8cfwEAb9SP/b2cUxc8+Yf8EXr6A8iROJ3uEJ39Aa5bVoYvEGXTvmYsRi2rSso53Vc7YoxCczlvDAQwGnXoMgqT8ws3n0KJRxlwn48uEMAXjjPgCWMx6XCadVxsGaUX27ppbNNhjI8Lf5McfVVVicViH5gzP3fuXP73f/+XwcFBjEYjmzdv5lvf+tYHnkur1TJ9+nQ2btzIVVddxUsvvcTChQs/lOv4MPAGo5jNIm3RIHbN+6n5/OPJsWRxpO8E3YE+MhxZxJsOJcvsAUwbl86vXztLa4+P/IyxB98Yfz2qCpNLUlkxK5+tB1oRBIEr5xUx5I9w04pxbDuU8K6tnl/IkdpeBATsFj1u30jD751FH0Brj595Vdm8tqcZbyDKtkNtXLOohCvnFfLanuZkv7ULS9hxpB1ZVtDqBC7PXs/goMqvn2ll9VWapJEPcLq3lnJHGZteFnngpmzK8x3UtQ4lP3faDLiUPsL2Ylo9rZzoqUl+1u7t4nR/A0aHgimSg0ZpYWnxfF6re5PbCxdQZs0mbDLjU+L828IH+d2JFzg32IxBo2dqVhWKHKcorZC4HCfXnEZMCbFidgHBcBx/KMobTZs42jOc5jSvYCqt/kaePPlSsm2Tzsznq+9huimLw54aHBEVYcczeMJ+NI5MvrLyLv7l2DNUOsv51LRb+fWxZwnFw9gNNh6YsBYxLtNOjLLUIn59+GkWFc3mbG89fedTB/xyBF3jSUKtZ8FgId5RQ9CRgX3OGoZ2PQOqQrS9Bk1KJpLZgTdjKhtfHjbyAd442s+ighQyfb0IIS96i5WIMfMSLj02Eo1i4vWdfSPS7HvdIXxuCc4LdcfjkJdhxWkzsKA6B71W4vi5PrLSzOh14vuq0v+j0YoaFFVlXeUqwvEIoiBi1Oi54yo7Dz93JtnPYTFw86S1hOUIAolomFxr1gdG8gmCSOoVn0D2DoKqoKoqWlc+SMNLH0GjwzFnHcGm42hTMlDCQQS9EUH3AWl3qkK49cyo5gvF+EBgyrh03tg/Umdh+cz899j2uzSQZQWzVsvy2bkcrXETCCUighxWPXeuzycYktl/eIjWngD56WZSAs0A+HLn0nB4KDlOgcXAzybfTjg4hDbbht6aRuDYVgRJi33uNQiihDG3nMAbj4yagyXYRlyfy2dvqaSlLZw08gEUFZ496OOLs8wEUjPx51+PNncK4a4gzrTjKP2Jv6WQM4GTgQz6hnrZsK8Vt+swzZ5W9gBXll9GujmNgZAbSefCv/RraAI9mOq3ovQ1IZgctIRtrFucTYrNQDQmY8PPgRoPj7xyOjmXKeUu5k7KYu/JLsL9mUxOr+J4b+K5PzWzGpucy+fW28kK1WEun4m5bAa+UzsRZl5PKDb8DWsbCPLd3x0iEI4jCnDLqgoWVmUhnf/9iKJAICKjqCpmvfSh6KLEz6chGLTi33XTdIwxxhjJ3yRHXxAEbDYbFsv7h3tlZGTw0EMPcfvttxOLxbj22mupqqrivvvu48EHH2TSpEl/9Nivf/3rfPWrX+Xhhx8mKyuLH/7whx/WpfxVxGWFcFTG6NTgjwTIMPx5kQZ/b0RBpMJVwtHeE6woWIxoTUPua0KTWQ4k6jHPmZDBU2/W8+Wb3ltwcYwx/lxMOokbLytl1ewCRMBu1rL/bB9PbKph5vhMlkzLwe2NsLGuBV8wyrolpTz++tmkwVOW56BvaDicNxyJo9NK/OtdM9l1rJO4rBAIx8lymrlvzUQ8gSh6rcTeE530DAaRFZXNb7WxdlEJT22qoSTHTlvo7Kh5nh44w40r1nHWe4Q5820UNuRxuKaPilwzV1fbMBgzeLH/OIIojzq2eaiNEksmJekGensyKLS7+Nb4dWjjcfaHe3ny6OvE5BiZFhe3TV5HPB7HorNR725iV/PbtHsTUQyCIPDlWffwzJYuPrF+PN74IC83jYx8yLZm8uypDSPa/NEALcFezJIDezSK/MYTSXXm+FA3bH6UlTPm83zjLuZnz+Xf5z1AIObHIenRqiJ7PU2c7T9Hm6eTTEsas3OmcLTzFHpJx6rc6cid5zAWVRHpaSbScgIQ0Kfno8+twHX15wg1n0DjSMeYP4He1x4mMm79e34XopKJn7zWjqKo3DYtQG6OTHisVBQAceJ4/KMjG4Kh4e+bRpPQuFg9v4jnt9UTisSZW5XNylkFhCLyRa26L6sy+Y5stjbsZn/7USRBYknRHOYVzeTLt03H7Q1j1GtQtX1EQhE21G5N/mZSy1IIRkMYeL91hors9+DZ9xKqHEOyppK64m7UWBTOZ4ao0SCiyUrc3UPg9G5Ek42UhTegxN5H5Q9AlNA4MogPjfwtisYLN8QVBFTuvmoC2w61EY3JLJ6Wi14nXcoak/hVL0Gpjy/dXUlvf4S0VA37+7axq/3txHdg4SKWxcYxwRhGX9uOuPqL/Hx7MFmpYG6lk2KlncEXfwlKnCign3wZmhu+Qooq4jv4GvrMYiLdjWjT8pF9gyPOrzdZaToO9UobBdmjSwi7fRHcWaX899HtePt9mDv3sbZ4PW3Ft1MxJcKgL8rBToEtu3oBaO8KU5jvotmT0J3Z2riHFSULydWO46fPddHZF0ASBa5fcCOLJw0gO/NJjznY/mYd9a1DCAL0zMwnLo/8Vhyt6+OWlRXsPdnFq9u6mVIxkS8vW0pjp5eQR0dZloDpja8Ri4VxA4LehLTqi/zLi91YjLu5aUUFBRkWfnq+ZC0kNjIef72GygInmQ4DcUXlSG0fj2+qIRKNs3J2IZfPysek+8ueGyrQ2O3jiTdq8AViXDmvkDnjMzBcxM+hMcb4OPNXG/oHDx58z/aOjoTnZsaMGe97/FVXXcVVV101ou2RR0bvwP7Xf/3XiP/n5OTw+OOP/zlT/bvgC8Yw6QREnR5/zDtKUfhixGVKpU3bxemBWiak5CB3nE0a+gBVJWkcO9fPsXP9TClzvc9IY4zxZ6CCw5iIHFEVmF6eRl76dNzeCCAQisqU5Nl561gnO4+0c+uqSqIxmQynieYuL6/sTpRamlLuor3Xz7h8BwadxNnmARwWPYVZNgKhGIVZVn71yqnkJoFRr8Fu0RMIxYjFE4Zvz2CQKcZ8jjJSDDTPVESqS+T3x7cQlWMUOfK5+dplpGitqHoj3zn2C3xRP7dPWsuu1pHPwhJnAVaNgc6hGBMzipHkATSttXRm5fLYBaJ83f4+Xji7iZsmrKFtqJ24Gk8a+ZCIkHri9KvMn7oOnzeGLUXHgtypNLkTi0rhvG8wKo82TOJKnICgwRKJjCrBFHd3szSljEP2BlJNTnrahjBl2vivI4/RE+hlcdFsFhXMxmVKo2agnl8c/j13TLyaPH0K1rgAfa1EBzqwzbwScfbVxBQBMR4lHvBjn7QQJX86sqyixt3oXTmkhNvISs2kayCUnEOm04jRnsLxxsT74uGwha8uasdQmvueZfguNSRJZtGUHF7Z3ZRsEwVIv6ASiiwnvPy/2zi8UfXW8U5sZh3XLC6+qD36EiKne2vZ15aIpImrcbY07CbPns2RYyb2nezCbNTyT5/M4fkzryeP6/b3sbvlABPSy99XdV+Jxxja9XTy/7JvAPfOp3Fd/WCyTZS0DO55Pim0pgS9DGz6FRk3fO39J68q2GddxcDW34KcMKKMJVMRtMPaEoIgcrSuj20H25hemYFWI/LyrkZyXRYml6X9qbfp44UA7rCXoaiHpnALTnsKzaEgO1sSGiZxNc6Wlje5o9qJZM2hUbyTl3a0MGtiHnMmQ7owRJnFh2f303CBtknk+DYCOYWYRQOR9lr0mSUETr9F2pWfItJZlxTr02UWIw/1sKTADIKELHgT6e0X2NirZmbxy9NP4o0kQskDsSDPnnuaK9Nu56jHwe83DUdvAVRVWjgyNJxDH1fiTEiZxEubeunsS6TVyIrKUzs7KLh9BmJUZf+pLurPR4ipKmx+u5WbV45DEoURaWfxC56D4bBKmi4dxW5lb1sX1ujbxC8ouapGgsgNB5HEApq7fPznYwf5+r2z6XEPP3PfYcAbJtNhoK0/wM9fHNbEeG1PEylWPcum5vxFnvgud4j/eOxg8n4+/noNoiCwpDp7zLM/xhj/AP5qQ//f//3fAQiFQnR2dlJaWopGo6Guro6SkhJefvnlv3qSHyU8gQgWrYqs0SIJIhrho7GLWWIv4kjvcXLTJ2NuOzEifF8SBRZX5/DU1nomFjnRaj4a1zTGxYl4XgX83S99SRDIcRqJxRX+87GDhCJx7r5qAqcaBmjp9vH462cZX+TEYc0hL8PKjcvLkUSRpk4PR2p7uXZxCQatSHWZi6w0M09uriUSlcnPtHLn6vH0u0MY9BqMeg3Pb08syrQaEUEAfyiG6smiyF5Ik6cZgDx7NrZYEbHz5fEAmoZa+dXQbwC4d8qtDIQSOgOBWIjZuVPY334UgFJnIQW2bDLM6QzEdWztfI3FaWWIGi196mjNjobBFrxRH4F4AFlI3JeJ6RWsr1yJRTKgkXV0h71IREhTYkTN+dw24WrkeISOsBubzsL8gplsqN2SHFMrarCoLtzqIEadYZQGs2i0YtQY+PTU27AIFuwFRoxqgM9Mvoc2Xy8IAlmWNB4+9gi9wX4AOj0xcsQgns0/RI0nrsNYXI0wbhHb2s3Ma3mMlJX3AxA9L4QYkyxYq1cQ3/sCn19YwCt1Fk42e5hYlMLy6dl855nhUNWmbj9uOZ1sYWxBCBCPiei0EtcuLaGx3YskCsydnIXbG4aMRFqYJEFLt3fUsW+f7mbVnIKL2qMfQ+Zo1xlSDHam51QRV2QOtB/jbN85bKbpAARCMQaCQxg1BubmT8Omt1LT38DZvnqi8RjvV/xV9rsR9CYs4+chmWxEuhsJNRxDjYaSqx8lGiLSUfeuI1Xinj6wl48ac3jwGEN7XyBl7jpUVUGQNES6mxIe/vPHqSpYjDryMq3kZVgRBCjNdaCRBCRBuJj3YP5mhKMKTqMdRQqzu/UAN42/hqfOvDiqX8NgM+WWTAKROHWtbupa3XxieRZZR76PZu41o7z0AGIoQNCkRTRaUVUV+5y1CBodKUtuI6BNxavo0che/Dt+gWWSDQQBqXEvX7n6Fp56240vKHP5VCfji0SeOzJSGDAmx1C1Ic41ylw5r4gtb7cQlxUWVmeSWxRDH5tGl6+HY91nmJc9B0+/jpMNwzpVNrOOrFQz8XiMQV+UmubR8x/0hnFY9Qx4Esa7Ua+hssjJqtkFlOTaGV/oRNRGyMiEOwvKCW7YwLsVIKRgH1ZTGT2DiY2N2pZBynId1LcPjeiXZjcgigJnmkbP481DbSycnIXmz4zgFASobxsaVdFz475m5k7MRHsRVwAZY4yPK3+1ob9hQyJc9POf/zzf+973mDp1KgCnT5/m5z//+V87/EcOjz+KSYoTEwSMWuM/ejp/MjpJS6mjiP39p1hqSUXurkeTM1xWryjLxvGGfl5/u5Wr5xX9A2c6xkcVQYAeT4SDZ3sQBJg6Lh1VUbFbdJh1UkIxP6bwP88eSwpY/X5TDVfMKyQv3YrRkCij19Hnp7IwhcdfP4M3EMNh1fNPN0/FrE+MsW5RMa+81ZxU3G/t9vHohjOsnJVPYbadX718CoNO4ubrinE5Be5dM5FHN5zh+Te6uHzBEq6ZZSQo+7FpHDS1RjhTE2Re7mx2tw2X9zJrTeTZM3EYbAyFvbxUu5XPTr2H5YXziSpRrFojQ0Evb3XVsCh3NkfOnOCqkkUwMIBDGJ37m2VJx6jRk6rLxKMMcEf1tTS6W/nWrv/FqjOzfvzlVJry6QjEGDTFKMNISVQi2HQOwexEcAko6eMw64wcaD+G0+BgXs580oQ0tnQfwhMY5KZpK+Dw5sQJRQnHnGtAVXFgJg6IgoQoKxhe+CZlggBrvsCX9yUiqcpTi6nOmEBWLBvx4CPI8eHNilDjMQzli5lXmYJBLoeUPAAUVaXTHeJobR9Lx9mwLb6L5sY+5ldYyc6wk5tupadngLsvy+HJt7rpdYcw6jVYnKl/VJjrUkMSRcaVa+nsCyF1QYpDi6zzkJuRkuyjquByjH7XZKWZMeo0jFp1X0RoEFmQPwN32MOu5rfRSBquKF9CqtHBrzcNR7XYdSlcO+EKNtZvZzA4RFVmJXdWX4deo39fj77GkUHK/GvxHHgN2TeAIX88qSvuRjAMh/sLehOS1TnKcJTMjvefvM6ENiUL9+5nRzTbqpfzzq8jFosxrSKdQW+Y57bVoygq0yrSuWZxCZLwvlP/2KLRiMS8RjSikVJnIUODIkWOvGSE0js4jDb+8+1fc3vVddgtOjz+KDtqAkwsn09ssAtdRhHRnqYLjhCImK34zA5cpTMQVBnvoYTavXnG1WwesPLC/nYcFj2fWf5Z7EIAYmH0/S1k7/sBXyydg6KzYoocJyxdhl7SEZGHn3OiIJLrTEXIEclwGlk8vgqdEEeW23ll4CzHe2sosOfwT3PuQ4iZSFcjlOfaONvqYf2SMiIxmbYeHwPeKA6rnuJcO53vEtEszXFQkm1nyB+lzx1gQaFAcaqGwstKiatxznrO8sTJF/BHAywunM3yBTfTm74Ig6Ti6Hwbtfkg/uzpNB3xJMe0m3XcfdV4vvv4YYb8ETSSwF2rx5Nm06MoKpMKLOjmZ7C/IUBTlx+AHJcF/gIVCVVNVNB5NylW/UVd5nOMMT7OfGg5+k1NTUkjH2DChAm0tLS8zxEfTzyBKEYxSlgE00fI0AdIM6YyFPHSGPNS2npihKEPsKQ6h8e31DF3QiZp77GwHGOM96PXE+HffrkPh1XPqjmFfOs3BwhF4hfUETbh9kdHiO5FYjIv7mjg8zdO4afPHcd/XripMMvG/7t7FrKsYDXqMOnEpD2j10h4g1FWzi4g1W5IRA4IAs2dHvYc7+DeNePRpw4QU70MyXGM6Xq+/bnJ9A8GMZggy27F501ja/sb5JvLydI6SbMtptJZwevNm8iz5rCocDbbm9/iC7M/yZnmfrwe+N/fdSRFnW5bn8muwVf5QtUNOIQoX572TwQiPnBlkSdoWFE0n81Nb52fr56rxi1DK+nRxixkWODMQD27Ww4A4In4+M3RZ3lwxh3o/E7iZi3x1pO4t/8+eZ+EE2/CwodISXVxZ9VNuP1hHKIWc+cBlufMpzPayYngEDOu/SdMsThCyA8CRLrOoc8WiZvzAZCHupL1xMXedlbkz2aVayLnwv1s7jzKOV0jKxdfQ1EkjoQKkpZobytCZABj/wDaycsJkUhXaurx853fJtIZXtwJk0qcXDY9n588c4zVM1xMdjbiP/MKgijx/eVXs2coB43BTKo2yuhA00sTrRZaO8M8/kpzsm33YQ1fudsB5x/BqgrZaWYKs2w0dyU8+3qtxPolZURjMkaNOHrgiwRFTWwyb6jdmmx77vRrPDjrXq6a7yImK+g0IkoszhMnXkyKNB7vPoMkSox3vY/HHRAkicFtw9oU4dYzqKqAvmC4ioyqqKQuu5Pel36c7GcePw/BNDp3+904Fl7P4NbHiHY3IuiMOJfcimA0Jz/X6bR09vcnBUYBDtf0Upafct6YuvTQCBCTFXbtC7Ng2jzcnhizXItpGGyhxdMOQKbFhVbU4o8FefzU8zx026f5/qNnqG3z0j59EaX+g1jzKvEe2Uysvw3RYMa0+C6aI7l0dEVZnZ6He9Mvk+f07/sDc+bey6sakSF/hB+81s1n1lbS09vL7MxylO461JqdCFoDlqseQD36JveULuMXdZuQVQVBELh10nq27hrgWF1iQ8hh1fPNeyfx69N7aRxKbFLUDjTSfugJvjTt06jHt3Hr9Em8lZPC4ZqeZIWBY3V9zJyQwbzJOdS3DiU971PHpeMNRvjdxhoEAZZOy8Up+lE0OkRVpTvcxc8OPZa8pm1Ne1HjOo4ddNLZH+SyyfO57uoVPPu2D1lJjOmw6KkoSMFh0vKdT8xmwBvBYtTiMGsREND7Wsg88iSp/e3MKZ5B3eSZPLKtl+pyFy09Pkoy/3wB5rJcB6k2Q7JcrijATcvHcfE+hcYY4+PNh2boGwwGXnjhBdasWYOqqjz33HPYbBe34vzfAo8/gokIQdSPRH7+uym2F3Ayeor83nZ0IQ+icXixY7fomVbu4onNdXz++sn/wFmO8VFDEASO1vcRjStcNiOfJ9+oTeYeegNRfvjUEb5+zyzONg/itBlGqCALAsiKgigKCAKYDVokSaCtx0d1cSow0mkZl2WWTs/jt6+dSS6uDDqJL94yjZ7BAFm5Kv3hKI8efTpZvz7DlM6dE29ne8dWPE0+VhYvpsq8gP958jRxOVFWb/5UF9fOWcPejn10+bvJtmXyvb0/5c7iz/HkS8eTEQTzq7Moykphet4NpOmcBLAQiLbz01OPEJNjrMyfyVV5s5iVPYmhWACH3k5/yMN3dv0PGeZ0Hpp+H4+0DucVv0Obv5fxOjNqzIb/xI4Rn6mxMBZfK/XxQp6v/TX/PPVWjvuamZBXhaG/h/wIWHKnIYoicqAdyeygK+7H+uYzaMx2RGsBiqIiXhBMLOx7lmuW38NRbxu/qNuUbD/Wc5Z/Gb8O88s/Q9AZSJm7Hm1GPnLAi8ZowehvoT8i4RE83LY+my073HQPhDjZMMgV84r45LpJzDM1Mbjxt8kxh7b+moWrP0NciID80Xtu/q0IxWQ27R4ZQhyKxGnvDpF//tGsKBCOySybmYdBr0FVEr+ZYDiGQWeGizgvVhRhR/P+Ue3Hu89w+lgObT2JHOn773KOqsRwtPMU149f/b7jx909o7QpIm2nUSMhMCSiIgRRIh7wkXb5/aiygqDREPf2I8Q/wN8uy3j2vYTWlY9tygqUSIChfS+RuvwuSM0630XmTOPoMsNvn+ri8tn5jJbw/PgjCAIajYCKyn/8OpHqZNRLPHDTTcjl3fQHB/FFA7xUk/DG+yJ+HPTxneUQ1Ts5Z+rlh/5u5gkpVK98gLaGPgZDAi9uGKIkd4DSPDvBs3tHndfcdYSCrPnUtw0RicoIkobc7BRMVbeji3kQwkFkby/9r/8cJeSnsKeVf5t2GYH0PGyqSF2TxLG64aiDIV+EZs9A0sh/h0A0yPGOThp6Krk9V2B6sZmNe5tH9DlwuocVMwtYv6QURVWxmnREYzI/eeYYkHifbT3UzsTiSVRufRghHqW1atqoazrce5gplevp3B1k2/EB5lblsnpRLhWlXjJSTWQ6jVh0iUg4o1YiN9WEKKjoAp0IUT99L/43auz8xvrZ7Uwo9vLANTfwyKt1LJ2eR1m27c/Oq7cbNfy/u2fS2OEhGIlTkm0nI8XwoSj5jzHGGH8+H5qh/x//8R988Ytf5F//9V8BmDhxIv/93//9YQ3/kcHtj2BSg/iIf+Q8+pAIT6tMraTDsw/72a1kTx2plD2jIp3HNtVytL5vTJhvjD8ZQYBoLLGsVRR1hMAQJIz9tl4/z28/xy2rKnh6cy3+UAyNJPLFW6bSMxhkblU2JTl24rLCobM9DPmjhGIyxnflILf1B2nq8o6o0RyOyry8qwFRFMkpdrCz5e2kkQ/QE+zl3EAzLm0uBzpeJcdUwIE3B0eoIL91pI/LphawvHQ+Xd5eUnQW/nnm3bzddZzPXl9FR28QjSRiswrElCBmYxYR1YjbG8Ej9xKJJxZUr7fs5/WW/Vj1Fr5VeQ2yEOdXZzcxOaOKy/PnY9KBy5xK4IKqAgBmrYFwQMBilVCV98ruVQkGVIqsGchvPY9UUsEXj32PmydcRUGKC1kbp87dwo6Wt7DprKwqXYTNYkPUm5NGlGCwoq+6DGX8TMxWF6GTu9gYGxmZpagKZ2NeZhqtKCEfgzufwrX6M2js6fRv/FnCu6nRMWXeenYYdHz71klovN0Qj6BLCYJDwbdzx6jZh+oPYZ1+BbHBLnC89/foUkNU3rvUlcjwd16rhUFvBH8ojtsbIRKTSbUbqW0ZpCDLikm6uH1pTuNoz7kBC77ABekhgdFhvw6D7QNrgQsa3ag20WRLuBnPo8ZCDL2VKAepzy4n7u0n1teaEOyzvk+amhwBVUWXmk2o5RQaSwrWqsXIATekJmdAZpp51KGF2R88948rwahM/2CYt451IokC1ywuRRQEzp7zM7Esl+Od+6lz1yf7j0spwBr04d/zG8Qr7uOJ2ldQVZUcSyGbnmmmvXc4/P3Q2R6WzspEUPOg5dSI80ZtOQyeG/YyuyItZMU7CWx+i8CVn8Ahxxna82JS4E9xd6Pf+hT5t3yToR1PcFIevakUCIAkSsjKyC0bCS1vn+1hdmkRBut7L7OjcYVoPBGx0tHrZ9+prlF9TjYMMNmVTtzThzUyWtslzZjKQOfwhlSfXyE/y4AvFOft092k2Y1Mr0jHbNTg9iWOL1Ba6Hv+u6QsvmnYyH9nTo2HiaVehjcQJTPV9BeL59kMGqpLUocbxoz8Mcb4h/GhGfolJSW8+OKLDAwkdq9TU1M/4IiPJx5/lAzFj1eJkvMR9OhDIpSS7AkYGo9SP1hPmbMs+ZlGElk2LZcnNtcxvsCJ/i8swTLGpYWiqEwuc/HSzgY0koAojHQ0GvUafMEooUicZ7bUsnxWAVqNSI7LzGt7mkaIGq1dVMKUcemEI3F2He9i+fTcZFigRiNy6GwvBsPoR1tHX4Dls/IwSHp6A/2jPvfFvDg0CSVsm8ZJ35B/VJ8Bb5Dfnfo58fOLwUxzGp+fegut4TZiMTdhVebNnn24mz08NPs+Sk1l6LQiQnT0ql4jSIiIHOir4Z7K+7FrvRzoPclvz57h5qo1/Hj/r5MLyHx7DsW2HI42q2SYapkxZRnubcNVRwRJS782G5tRZZqxjNhbj5BVMI67S5YxrqUJXf8+Dk9bzBOn/5A85kx/Hf+y8FNYtQ4C0ThGDSjWFLZkOXn98KM8NP0O8hUZvTj6XuokHbyzuFUV4t4BooOdRLsTlRDUeBS3Yqc47iL06g9R3J2Ja07JJHXF3UhW56gxJbMdVdSCNDrH81JFRGTt4iIeeWm4XrtBJ5GbPhwtpyhgNWl5clMNQ+dL8QkC3Ltm4kUfLquoCouL5nC0+wyx81UjLDozNjmfIX97sl9Hq8REVwWn+hJq5wICV5QvQfkA/QHRYMFYXE2o8dj5FgH7rKsToQQXYC6fiag3Emo5jS4tF+ukxX9kM+0CdEZ0GUW4dzyZbJJsaaRd8SkuNMmy3pVW4bQZqCpxXczSCX9TYrKC/3ypt6sXFrP7WEcyfP3Vt+BT160mJD9Dm7eTCWll3JQ6AaHjHIJGR8Cch4iAjIpLn8HW3sCo8Zv7+5EyM8ky2VCCiXsuWlJoM1cy4ElEx1yzPJeIM4gwqEOTkoUaN/NKg0LOzC9REDqLdPwlAHQV82kcVLH0NDF9moa976rAagrJrC+cz7ONO5Nt1a7J1NYlrq++O0QmxhF/f4BpFekcPtvDm+dTOpZMy2XWhKwRm9MA5dlmwvWJSjAltsUU2LJp8Z5/looaFmQs5ZGtncn+mU4Tp5rd/M+zx5JtgUgci0HLW8c7yEo1cc0UM8rSryGrQd69ehN0RvwxyE23UJGfgpYYUtSLojEQk8yX7Hd2jDE+ynxohn5/fz9f+9rX2LdvH7IsM2PGDL7//e+TkZHxYZ3iI8GQP0KR7KdFkSnTGVE/omo7OnMqmOw0125BHK+hxD7s2SjItJKdauaVPU1ct6T0HzjLMT4qCILAmcYB7r5qIi3dXm5YXs4zW+pQ1MTm0fXLylHUhKfFF4zx4o6EKv4n100aYeQDvPpWI+uWlPHs1jqWzcxnKBDFaR723GWmmZPRAxcyrTKd/AKB5qYY8/Nm8lLtphGfZxpzafInPEkdoSbKC0qpa/GM6KPq/UkjH6A70E+Dr5OUcAGTrHq+f+q3SeN8X9thxo0fh92kJS2ejVlnIhAd9tKvL14AqoJfMGMMe3mzZx+b2hI57b879jz/NOc+BkNDaEQNWcY0PP0aYimn+dXJ1zFVXs341Q8SOrmdmN6OJ2cuA4qdifoedFufREHFoDNRWXsIueEYsSs+y5bmN0dci6Iq1Ho66A5ZaOpsp7PPz5TyNJbmzyUe9nNqsInC9CKuFjL54QVCWS5zKgssOeiveoAYGgRVRSMoDG55NNlHypvEK61WVjtPJ418AEVnoFkJ0zdxGqaCElJb69H1tmHIn4C5Yg7+znOYC6ouSZGy90KjhaxUC5+9YRJvHesmPcXIlHFpaN9lqA54QkkjHxKhv9sOtjGzMuOiFuMTBIGT3bWsH385MTmGIIhkmTP5w/MjDZ6dB/r4wn2LGJ9RiqzIaCUtHb4epmZVvW/5QFFvROvMxpBXgRqPIWj1CKKEoBmOthNNKaiqgufthLBwtLuRUPMJMq79CpE/NjCAIuM98saIJtnbjxL0wPl9mHhcwW7WcdOKcbh9ERRFIc1hRKeV0ErCJRm6bzNpcdr0CEJig/cdI/8dnt7UyKdvX49fHaBCl0v8D99EO2sNYtF0tr7tY0HJfHa07eKct5aqssmcqHePON5gjvPTc5u5beFqpgoWNP4hQjkJFfob16SjM8gcHNjC4YYAXy1chkU7mYHafew87qJvKMTUsgLumX0jZo1CPGcybx8LsDJvAkXew6yfM5WXD/ShqiqrZueR7z9NcWcjhePX0IKEEjXS1iSy/UQi3as8y8BbdX3MHJ/JhOJUWrq9lOY6yEu38vs3hkv07TjSzpdunU5mqonugcT9mFiYQmn4FNHe8xFVz3yXL9z2bzTJcdzBIDrZTthrYnqlzMGzPayaXUiq3cDPXjiBUa9hblUWmalmQuEYT2+pBaC918/JBol1S0oJukNcll2J2jm8e2GefxN59kK+Wm3AqQ7g3fgbIu01SFYnKSvuJ5o6DvUvEOkbY4wx/nF8aIb+t771LSZPnsx///d/I8syjz/+ON/4xjd4+OGHP6xTfCTwBiKYhAgRRUAv6QnHPrpLViW9iBld9TzdsgM1X6XUUZz8bFF1No9tqmHuxMxLVlRojD8dWVXZd7qbth4fFQVOFFXlphUVGA0aZFnhtT1NTCxO44u3TKNrIEggHMMfiGI2jPbuxmU1GXm79UArCyZnk2rRo6oq7kCUQW+YFIueL94ylWe31tHW62deVTY2k46eUA8uUyFZtsksLQyyo3UPJq2RKwqvwKXPZF/fDgCO9BzjK6sv5zcv1dHU4cVs0HDXmkreHtgwaj6heIQsF3R4o9xffTsaUcdvjj+OWXIQjsloRQGdksKXZtzP8b5T9AYGmJU5gWJDKrv7a8jTVaAzRdhy5khyzL7gAN/b83MeqL4BjWgn7LfR6faQbi5gavoUToZ6yTbl41h2D0HZgGOoG8e2HyUMDUDMKEQ22ZAbjgGgClp00mi1f42g4ZVdTUlv0+GaXlbMzOOGmXMJizGsqkSVRse3Zt7L7p5T2IwpLLfkIwSDDGx7FNk3iGiwYFn+CTQl04gfSdQ6j+ZMoeN0FL3QmDTaJUsK56Yt4hdHn0ief17uVK7LXkZw74vE3N1YZ60hZkyF99iouRSJRFUG5E5+3/Y4BcU51EcDNHVouaHkJjgvepjIx4+zanYBzgvEJw+c6iISlTFpL16/vqKqNHvaGa8rQxREQABVZO2SEp7ZXEdHfwCbWcftl1ei1Q6xvXYv/UE31VnjKU8tJhwPY+CPv39iAx0IkgY1HkMODKFxZBBqOo4+uwyMCWtcCQziP7lzxHFKyE9sqAeMeX988nIsUWJSENE6s5CDHpSQH1UefuebTBrcvgj/88zREWlAn15fRcFfIHT2cUCvlXBYtdxx5fikrsmFBEIxHDonNZ0N6HVFaBb9G5LYj6kyi/Y3giwrn8gnphXii/nIL0/HtFXL/lO9GPUarl6axWH3m0TlGL8+txVjyXJydz9L4+W38mzj5uQ5BATWlqylUS5A9veSaTawYrKd3+8McaTezeq5s3h2RyNNm87xrRsKsESmoOuq5zLPq8xftwIxvYAXOt5A0LlQj5/E2Xgcy7z7+OWRGPUdiWfpksnplKXKWGZl8Z3fncBh0ZOVZmbX0XauWlAyQocmUU3GxxVzCxMbQZJIVu9e4vufueDOqIin9vFc3TjOtXmAxAb4rZdXsGJWAW/sbyYuq2SnmakuT2fTvmb2nuhiQXUOq+YUsmlfM5BIY1NUeOlAP4Z5VzClaCFmxU9KfgmyLZcCQYeWKJ5XHyHamdj4ln2D9L/4fVy3/gdhw6XlvBtjjI86H5qh39zczE9+8pPk/x988EGuvPLKD2v4jwSqquIJxDA5wKA1IHzEk/DillT0nGO5qYgtbbtAECg979m3GLXMmZDJY5tq+dqtUz/y1zrG3xaNKDBnYiat3T7ONg9y9nwN4ZtXjOPZN+uYW5VNMBLje08cBhKKxreuqqSpy4vNrMN7Qb7u+CInjReEOHb2B2jo8DCrMoNed4jN+1vwh2LoNCK3XzEeRVVJtRn48TNHufuWNCBOMCgxPm0cBSnZpBgdRL1mfvC74zx41+UUOU5TnTkJt9vHjVdnoKeYkwOnMLkGyJEyONU/7IkRBZE8RwH/uefHyZz/XEsOD07/JI0dXga8YTIdRrRaAcOAj5VZ1bzQ9ja/PPECc7JnU55WjBqRERAxaQz4oiNDUe26DJ56vZfTDQ3JtrtWL2JRsY3+sJvOcAjL7kexTpyDd8E6TP3dkOKiUS8RDPZRotGhxqNIRzawas41PHLit8lxzDoTBbZCmrtG5rLWNA9gmZ1FbOcf6Gk7i6Azkrn4Jq4uu5wefydqVxvet19C9g8BoIT9eDf+BOu1X8eckc/gtseR/D2EIhaCGZPQNh8DIDZ5Eb9r2j7iXHvajzC/ZCUZZjumwipCJ7cjWU9gKJlBxJwzSoDtUkPSxNneuZl8ezYT08sJxsPsbT3MkNILDGukTChK5ZGXTw2r7uskvnDTVHQXsZEPifSVy8uW8PODjxOMJWotpBpTuLnsdsYVOplXnUMoEufg2W5WuTK4unIFcVlGI0o0uVvRabTvW6NO1BrwvP0KgkaHaDAh+z0IOgOC9oLcfUmDIEqo78qzFoQPuHeSnpSFN4ESI9rTgmR1IplsSJbhtJR4XOVc29AIIx9g9/EO5kzK5CPsB/iLCUXjiNoIFQVO+odCaCRxhGbLyjn5RKOwd5uB5/sS74PKwhQWTsnh5tUCr7W9RG1zwgAVBZH7Ft7CrBkFBGU/AaGVqWIlU3IqURSFDGs2HatuQ7I4WD/+cjbWbceht3Bf2U38/JUuWrqPA4nIggfWZwCJ6KO6zgA1LR6mlDgwnt6Au/kwWlc+xtxxaBu345WWk20s5PdDJ7n6qvsojMgQ9vPQlCD91XYkFDJd8GT/XpoDg3zutmupawijiDGuWJzBi5vbR9yT/AwLs8ZnYtZrzqfbKMRO1xFnJKreRr87PKLthe3nWDm7gHSnCYNWZOXsQr73+KHk55vfbuGKeUW4Uoz0uUPn71vis6f39PGMABOL03moqjiZSy+GPUkjP4kiI3t6YMzQH2OMjxQfmqEfj8eJRCLo9QmvUSgUuuSMv2AkjkYEQSth/Ijm549AEIi48nF1NjBv/By2tuxEU6ih0JbwclSXpnG6eZC9p7qZNynrHzzZMS5mFEVlflU2Z5rcnGzoRxRgwZQcyvMd3H3VBKwmHcfq+xFFAUVRGfJF2HmknanjXNx79UR2Hm2npdtLVamL9BQjT2+pAxIGjdsb4dk360ix6nls49lkCb5oXOE3G05xy6pKUh1GVs8vQvGLaG0SZ8P72Xx6V3J+68dfwU1XFpBucREamo6nRyDFbqDB3cCUAgtDvV08dXIvD8y6k5gcY3frQSxaE/dPv5nnz24cIezX7u+gxd1JhiMLWQrhI4rLqEeO6Al1dzE9tZwCWzaSRosgxekPtVMol3Nj8SIeqdmYHCfLkk4kYOJ0w9CIe/nkG/Vk5JSQZVLQ/v5rKMBQ63GE9V/gYfq4P2MmOQYjcVXFusxE3OfDWDiB2XojVYu+xq7us4TjcSZnVBDzmzAbtQRCwxbHV6/Jx7v/BSJtiZBONRpiYPNvyLg+A5teh6q3JI38JHKcyFA/gV1Pkr7mIVQUbi/N4mBNC/PGLUSt3U1MbyAUG7lIBQgJKpZJixh883fJNuHga6Td+E3Cpkv7uSITZ0HBDExaEzElhgOVu6deTyQy/PdSlMRm14U5wJGozMa9TTx4fRUXc3y4oioc7DieNPIBBkJuznnrOHRWO2KDb8pkI4+dS1SjEASBT0y/9QPz6AW9AV1WCVpnFhpbGuHWsxjyK+FCI14A29SVBOoPYcwfnxDjc/cgGD4oUk1FjUWIDbSjTctBjccI1B1Alz2cziYIiaiFrFQzc6qy0Igih2t6UGQVWb40N7FicZV4TMvW/c0cr+/nztXj2Xuik/6hEPMn5zBtQgqiKrFgci6RaJytB1o52+xmYnEatqwgtYPDBqiiKjx/5jWqMiqZmjWRJ05sptufCJu/uWotPzr5BwZDifQrs9bEnVOvZ5yi5URzlJbuYQ2WUCTOzmOd/L/rimlwyxQX6vjubfmkBLswqOVEjXoCZ/cS60ukMImZU9j5tokv33YdFk8zwVObiHQkwuPfkZYMl02nw67Q6e/iFzX/yx3V1/Fa7Vb2nPVxw+JbcB03UNPiYXyRk2uq9KRKQfANIYQ8SFYnyrQVhJqOJbVQBL0JJbMChB7GFaTQ2u0jFIknBfNefauJvAwLwfC7twcSVR6mjEtn64FWMlNNhC+IpFBVuHpB8UjBPK0B8bzY6oWIhkszCmWMMT7KfGiG/hVXXMGdd97JunXrAHjhhRdYuXLlhzX8R4IhXwSLDqKiiEH6GBj6QNyRiaG7kbRIlNlZ09nUvJVrSq8kw5SOKAosm5bHs9vPMbk0DYtxTERrjD+OUSuxZmExVeVpmPUahnwRdhzpYOfRdlQV8jKs3HZ5JY+9lhAea+wYYtWcAr7/xGEqC51MKU9nfLGTV3YlBN8ynCauWlDMH95MLPy6B4IM+UZm1SoqpNr1FGdbUWSFSEymL9bJ5oZdI/q9UrOZh+bcTzA+xJnwAayBCo7Xicydlke7t5fxrjIuK57HgfajzM+bzaL8OaiqSr27CU/Ew6LsarSiBrcSoWGgGW/UhzfmQQ1HaGxs5rNZiwhu+zW18y/nVwcTeb2CIHBLxU2kU0abX6TUOZN/npFBzVALLqOTUnseB5tGiwZGYjKRWAydexDfLf+GKEqY4jpiqp77Jq1ha8dRtjS+hVVn4TvVdyG0b2Tg6WfR2FzYFt5IbqwYjUNBI4f4xatN3Laqgt+/UYMvmDAeHZoofU3HRp03PtiNvnASgj6IoDOiRi+sdi8g6M0oIT/h9rPU2WajifYyo8yOh9VkVq/EbtZS5mug3j2s4q8RNeQbUgic2jziXGo8SrTlBML47Evaq28QTbgMmbS0Rzhdo+KwSZSXixRmDYvdShL0uBN5vTazLpn33Nbjxx+WsWovXsFUFZVOX/eo9sFIPzZzwQhD3xMc/r6pqsozp17hnxc88L7jK5Ew9hlXMrTvRQJn92Mqm3FefC3GO0pkgiBhKKpCVeJEeloQ9Sacy25/T8X+kYMriAYzSiREfKiPeMiPuXQaaiQM5+0hWVaZNSELQRB4bU8T8bjCguocFk3NSWxq/mm36WOFJICqqPR7Qgx6w/zmlVNMLncxoTiNQCRKMBLjJ08exx+MYrfouHfNRB5//Sz93iAZsm/UeANBN1nWdLr8PfQGEuHsNr2VwdBQ0sgvceRybd4ccnt6iZ3aTXfK9aPGae3xkVYZIMv3NmcDlbzpaWKtsxLj5hcwFk7CVDadYH3CUx7R2rh2WQGdgUGqLalEskuID3Wjyywh7htEn56PVDaN/IEztA51oKLS5u0kpsrMy59OTD/A7AUprJhXwY4DfdiCbYSPH8Z/6LXzN0mD6+oHSV/3JcKtpxBECVFvokubzvzJEm09fq6YW0ggFEOrkdh7oouZ41JIU914TSmjri3VbqQkx07hmolMLHISjsqoioo/FGVeVTa5aWZ0ShAGW1ACbnBkkrr6s/Q995+8swNgnrQExXZpb7yOMcZHkQ/N0P/MZz5DZmYmu3fvRlEU1q1bx7XXXvthDf+RYMgfxaJVCAtg1IzOh/1IIohE0gswNp8gdfIypqRP5pWGTdw4bh1WnYWsVBNluQ6e3VbP3VeO/0fPdoyLmPaBIB19fvQaiVMNA4wrcGIOx5NaYW09Pupa3ZTmOjjXPsSE4lRCkTg2sy4Z7r/9cBtXzC3irtXj8QZidA/4qS53setoOzFZGRXmLwig00j8+Omj3LhsHJ5AFFUaLbEVU+L4IwHSxUzm5s/AojPR0hqhqSWMOcdHWPHTHehn47ntZNsyGYp42dt6mDvGr+ZrmXPRdDejc6YR6W9Bdc5ATSvhP04/yfyCGdxWspLglt8SmzSf3zcN5wJfXXgNG18P09F7EgCnTc+Xb6pggVHDnlALvzjxI9YWrceo1xCKDHtpJpY6eLt3L7HcSn799s8BmJ05gftKV7Gpt4bX6hPh8ZcXLCF6aAPBs3sAiLu7GHzlJ5Ss+Qo/3RahdGovt11Wxk9erOH+tRPZtL+FORPSkXQCutScYRGo84gmKxq3G6/GiXXpPXg3/SxZo1yceT1x7wCgEvcN8np9D/eXdWLwDiHqDHjffBOmXsF9Vdfw+zOvc7yvlkyLi3uq1qE/sZ+YPNoLpcoRBOGi1pL7mxOXQjQ3yzyxYTjUd98xDV+8qzJZgjAeh9IcO7ddXonbFyYQinP53EJiMRmzQQMXsedYRKQ6cwINgyO/a+WOcezqHxbhNBs0YBhp5LlDHuLK+4crSGYb3U9/G85/v4K1+1BjYfS5Fck+giAR6W5AMtnRmG1obGlEe1sw5JS//+QFAVWOoU1JJ3juCBp7OqLBgqDRXNBFpXsgwIbdjcm2HUfayUw1UZpre69RP/Z4gzEsej2zq52cPDeAosLR2oQX/qFbJxEjjMOioyzXzsSSNE7U97NidgHFORYsGgUBAfW88SmJEv888Tpc/b3EBzsoL1zG5mA7reEhBoJuJEHkwfIryGmqQX3zGcgpx1w5h/L30ACpLk+nNS5T0HyKUnsaL0cH+LFvB1+ZsYrg3pdJWXgjwfpDCKVzsOQW4g/UMU2w0//0d3Gt/gyIEuGWU+izStBnFjG44adcnV3K7MlX88OaDaQaU5ieNYkOXw87m/cn5//JmfdgIZP+HcNipshxBrf8BvP4BYh51bjbm9BkTuH/nq+hrTcRiXC8vo+F1Tlkpum4eoqVKvc2lB2Hsc+6g8xUI90DofPnEFg4JYfDZ3u568oKjFoJm0HDmnmFCIKALCto1TChPb9PvisAHItvxXXHd5EHOxCNNhR7DjHh4+HAGmOMS4kPzdC/4447eOyxx1i/fv0Hd/6YMuSPYBJjBFEwaD8+D8SYMwd9TxOSf5BsSya+qJ8NTW9wfflaNILEgqosHt14ltpWN+PyR+8mjzGGKAp09vs5Vt+XXNTtPdnFpJI0Zk/MYv/5GsI1zYNMr8wgFpdZPC2Xl3Y2smRaLrKing9NtFJVmsaeE11sfrsFVYWibBs3razAatTw4PXVfP/3h4lEZURRYN3iUrYfbifbZeFU0wBpDiNRvxGjxkAoPhxGnmlx4TCk8PLWJpo6vEypdDJzsgPR0YtJ4yQiaNnTmlDE/9WRpyh1FnD75HVYu1qI7X0F/YT5DG4bDj0XrU6+sf6r+EQVnawQ9fUT1w2Hrpt1JsKDDjp6hxXpB70RthzvYXqFhxnWHMbNvp+gLPCVu1w8u/kcbd0BJlc6KCqP8IeG4xSnZSePPdBzljvKV7Lr/BwBptlzCNb8cuQfQlXQ+LqobRUZX52GyRbHH4pht+pYuCzGq3WP0dFSzO1z19H/6k8TYmOAoXgyUZ2dxpAN0RFmKMVB9g3fJOYZRDRaiQ52I+98BACpfB7zCkTO6uxowlqKLBkob2/AlleO/PpvudVo4vrcRWg8A0gv/wLrVQ8QyyhgoGtYhwBBRF9YTfgvrOP8cSEWFXjjrZ4RbaFInK6eKEWOxP9VFUwGDS9srydwQdjuF26eSiwqo5cu3jx9VVUZl1bMspL57Gzaj0bUsLx0ISWp+cyZqOPEuT7yMqysu6yIn9f+aMSxkzIq+KBi9HFPH4IgYp64CI0tlXDbWUKNR3GE14MhEWStKFGUgBfvoeG0GY3dhT6r5P0nLwrEelvwn96dPFeks570a76Q7CJJEmeaBshKMzN3UhaSKHKktpcDZ3pYOSf/z7lVHxtMRi07jrVDagN3rx3Hhp2tiILAsvmpnPDvZoFrBqIAJbkOHn99WBHe5TDwL5fbebByNb9r3sVQyMsXKtdi2/J7gr7EppD+1G6unruWH8Z7KUrJY0FqBVnb/0DcnXi/BLz9RHtbKJt4GXddls3Te/qIROPMnpSFTiOy7YyXT6QXoDSeYOLESWzvPIY3PxXHtBVEskoZmP9P7GyIszgk4An2EW9pwFK9DP/ZfQRrE8Z7tLuJUIMD2/TL8ex/mfRwgPvmrkHWanEaHbzZNGxMy4rMKw0bqCxeMeo+yf4hRK0O/P3EMibQ39mTNPLfYffxDj51zUTK3bUozQk9A+3BJ/jS9Btpt06kc0hGp5V4ZVcDve4QS2fkMi7nne+9yjveetHXPcLIB/DsfobUwinEsqb8NX/uMcYY4x/Mh2bo+3w+gsEgJpPpwxryI8eQP4JRiOAnjlMyfvABHxUEkagr4dX3T1zMuJQShiJD7G7fy5K8Bei1EpdNzeXRjTV8696ZaDUXb6joGP8YVBVsZn3SyH+Hkw393LxyXNLQryx0MqHYyeSyNHYd7aSu1U1dq5tZEzJZNCWH7FQzTZ1e3tg/7AFs6kyULHIVOclxmblhWTnh84b+nuOdtPX4uCU/BYfVwO6j7UwqdXH/lDt5tuZFunw9lDgLuKbicrbscLP3RMKo6norQEuXj+uvyOPgCTeTJ5spTy2mpu8csqpg1VsJxoKk1x7GWDkX79EtI65L8Q0S7m3mf7v3ct24VZStuBfO7mZSagknBxpwGh1090R5N60dYfxpdZzoPc2np97L1uatXD9hNflT2yhRTdR7dnC4oY0Ug53g+U0Dm97KnRVXEJUkvjblNnZ2HOGV5t0EVRmdxYHsGxx5Eq2RWDyERtSgKhr0Wgm7Scfh7h7cYQ9+JUq/LZf0675C3N2LaDARNaTyrVd6uGxhjCcP/BZFVdBKWm4tW8NsjQWTSUJa90U0qTnIsQjjI15ebm9kT9cJVhTO4arrv4YSCRLpTGgrSA3HUIE4IPsGCDQcI2XxzQTrDyMaTFinrCBizR2ZN3oJokELKmg1IuX5KfgCUVp7fEji8DNWkhK/gcC7cnOf31bPV2+f9vee8p+FIAo0DLSQZU7nmvGXI6gqJp2RRk8DvoCW+ZNz6B4M8JtXznDndTfx1OnnGQwNMSmjgurM8UgfYOgLehPOxTfjPfIG/tM9GIurcV5228gcfUXBd2zriOPinj6UgGc44fq9iEfxn907okmNRxP6Fef3u2VZobrMhdmoZeuBVhRVZeq4dCoKnWgEkdglGLzvC0Z5aXsDD9yTw07fRqYtyUJRFTb1vk4wFqI8rYgp49J5/bxK/Dv0DYVpj2RRsOOnfKVqIXKqnXRZZMA3svwqh95g+ZKrmWIuxTLQgvu8kf8OsYEOzHKYaY2/ofKmz3OoXeV4XR97T3Rx7ex04t2DaHPHsbxgDnMLZ9E41M6mSBOG2k6W5CwnNy+VHDtkp8zEYOxCDfsZOPR6cnzRYMZcMRddej62GVfiP7mTiZoMWiUDbZHhyA4BgXUFc6kQTWhjMRxz1+E5tBE1mniua115xIZ60JkdOOJ9DMXe+7tSnCahO3lkWJNSkdEc/D1FE5bzxKnipAAfQCA0OnIKeFca1vk2OQbxKHxABssYY4xxcfOhGfpGo5ElS5Ywbty4Ecb+z3/+8w/rFBc9g94IJjWIH5ks6eP1dIym5mA9uwcpMIRsdjA1vYo3W3eTb8ujxF5IeZ6DmlY3L+1u4rolpR884BiXFKqqYjG+9+PmHaetK8VIRaGTHz99DIAblpVz4lwfwXCcA2e6+bd7ZvN/zx1jeuVo1d9j9X3otSIpdgPPbq0bITYEoNUI6LQik0pdxBUFd6eVe8ffgyyFGRyS6WyU2HWkGYC5VVnkZybCanWqkUjIw1BXCnkOI3dUpYIo4zTZEJEQLHYQxWRo8MgLk2nzdvKjg4/y9Tn3k2LJ4Aa7C6vWyNH+c8wuNrP/xMgc/PHjTOzztJBqdBL26VlcPI+f7P8tX5p3Hy/Xvkl3sJeq9EqWlSzg/w48SrbFxb2TruG5+m08fOoFJjmLWZdexeJJd/DLpq18fvEtDGz4P96xmHWZxfRL6VQWeShJs/LS5kE+tW4SNqOWFUULKU0tYEvDbv7z8MPcPv5Wyp1F6CSBTrfM9bNScFqMzM2sYl/PST4z4V4K247jPvgztFllmBfeinvTr5D7WzCUzeSOCQu4LX8ucutZQrufwzhnbcLAUkcuWEWdkfC5Q0SajmPIr8RUPpN4LIaiXrye6L8XWo3APWvGI4kiXf1BjHoJu0WPwz7ytyS/R35DJCajcpHfQxX0Gh3B89E1AjAY8pBhduH2hTnRMPz7ULypTMmaiE1voW6gEav+g8u6SgYTfS8+AefFMkMNR1AVGUPp8AaIgDBKcR9A/SDVfVGDqDeihEZ6WS/M7RcEEa1GxGHRc9mMfGRZIcVmQE0Gn196RGMK0biCd1BDh6+HM30JjZUMi4uVpYsw6/VMLneycW/z6GOlRBlVDm7CNm42SvZ7vFPkOAtdE/Cf2g22906PUF0loN1LS7ePZ7cmoqqcVh0z0gOo7VFC42fy/aNPclnxXJ4/M2zEP1n7e/5pxifo7PSRZQgTaTuDLqMI84R5BE6/hWiykTJ3HUP7XsR7aCOiyYZj/nUcaIvz2K5G7ltfjl1vxRPxcXvxYiYc2YvS34YbEA0WUhbdxOCWR9E6s7BVL2Nw59MM5CxEcPeQoVHIc5lo6wsm57NwQirmjrcRS2YQGxip5K9Jy8frjyIKCT0YVVXJcZnf834I9kxEvQklMjy2LqsExeR8z/5jjDHGR4cPxdCvq6tj6dKlzJ8/n8zMzA9jyI8kbl+EdMVLWGs4XxP4Y4SoIeLKT3j1JyxEK+mYnlnNm627yKrMwKQxsnRqLr99o5Zp49Ipzr408w/H+ONkpZooz3dQ1zqUbCvKtlGWa+ez11fT2OHhdxuHQzX3neqiuszF3pNdCEAwHGPIF8FpG50WU5rrQKuR2HGonXmTs3nzYFvyM6tZR5rDxHd/d4hYPGFkrphVQGefiEGn4aVdDVy3tAyAqxYU09A+xN4TCQXlIzUOrlpQhFfp55HTjyfD/VOMdlaULERXWEHK/k1YJi3Cd4FXX9Cb6NEnHq8qKk3ebpyzrkIb9XOLOJ3LvEEsJiMDC2QktCydZEMwhJFVmWmxmznXodI3FOHMwFuMzygj5O1jZdZaUv3teLpUyLGQaU7j5opV/OjIk/giCWPjSF8tveEhHhSz+ETFCnp1etKv/xrqYCfoTHj1mZzqlbhlVQH9/VGWzs8lPQ1QIabG+dWRp4mdrwP+8IlH+NaMT7P7oIc/7E0Ipul1El+54TKshnSM7hDhgy8BYFh4B54Xv5P0DAWPb0X29uOYfRXuQ6+jxmMIWgPW6qUj7pOxsApBbwZJh1hShZxRTLi9DuO01X/el+tjiirKaCSJ/3nmWFJ7YkZlBtcuHansXpRlQxIF5AtSHVbPK0K62AvfiAIGrYHnzryWTGtJMdq5edJaRGFYSyPVbiAr1YxGKcMfDXDVuGVsb9xH7geIg8U9fUkj/x3CTcdRI0HQOxINBjPWSYvxHX9zeFpGC1pHOqPVPC5AELFNW8XQW39INmmdWYiGC6MaVWKywvPbzyV1NiRR4JPrqlDUS8+bD5Bi1VFRkMKGN7uZtXQmW1q2UpSSR3XmBF6t3crS3OW4m0WWTMtj89vDkVtGvYbMtBRM1/4/tJ5OtGYbsc76UQaqdeoK5ECAeM1uwtmlGCvmEKrZl/xcGD+XBo2elMsfIl/Q8mWTAQHIs6vYAi00rLiVn5x8jqnZkzjQcXzU/A931XLgTQdWg8gD03LRbvol1upl6LNKMBRMxL372eR8lKAX966nkKY9hDcQ5WfP1PD5u+7i8bOPUxaOo/QPv6eUsJ9Iex3p13+NaOc5Qm01OC+7jVdaFSZmZ5It9fCFFSkc7EjldHuQmYU6KpQ65MF+xCmrkU5vRw4MAaDLLsdSMplPr5fpGkiUbB1XkEK6w/CeUVJRXQrG675Nc3s/3mCMrBQ9qdmpRPmYaE2NMcYlzF9t6D///PN897vfpaCggNbWVn7wgx+wYMGCD2NuHzncvgjFsg+P7mMUtn8B0dQ8rDXDXv00Yyp51hy2tuzgqpJVmI1alk7N4ZevnOabd89ErxsL4R9jGK0gcOeV49l1tIP69iFKchw4bXoQBFq7fby2p2lEf6NeQ+S8aNKymQUMeEKYDBr6hkJMHZfOkdpeADJSjEwbl05uhoWv/3I/K2YVsHxmPvtOduG0G7hvzSR+/MwR5lVlk+40EYvL7DzSwe1XlRJkgM/dn0Wq1sK87izSUvRs2D0c6l7XOkRdyxBKZu2InH53yIMvGuDRrtPcMms5pZIVhz2dUO1+cOYSq5zN/5x+OtnfrDPyvbd/QbuviymucdxYsQoiEssm56CNd7NzcC+vN+1BUVXmZk1iec5iDvQ1MtTn5crCK5EUOxv2trL/VMLg7nNHuGHZbShKT9LIf4d2Xw+egomYhwZRsnLptqYhWrIJyAPkac0s03lRA4NkiGEwpHHK58PpcNLp604a+esL5rFU46RzSEwa+ZAo2/bwq018+ZbJRJqbgUSoasTdOyr8M9J0DGXmVehSc4l01hPpbsBYPAV9VgnR/nZ0qbkIWj3RiJ+BK+/gxa5j+HynubxsAZP1ZnSXqsvzAmIRkZd2NuAPxSjMshEIxTh4toc5VVm4LMOL8GhM5q7VEzhU04MvGGVGZQaRmIKCSsJPfpGiwqme2hFlF90hD13+HtYsrua1PU2U5NipKHQiIXG2r55oPEYgGmB6zmR0op73i34X3kMUVzRaES6IgFCDHhAE7LOuJtR6Gq0jA31WKbGhXjDm/fHB5TjR/k5SFt9C3NuPZLSgynGU6PC1qKpCa49vhJimrKjsP9XFzAnpf+JN+nhh1mu4eWUFJ8714dKqXFGm4DDaeLX2TYpT8tEEcthxqJ1lM/O5ZnEJR2v7yE4zU1WWxrH6ftKzZLb3HGCyPZt5Zw/hmH8tkY564p5eDAUT0WUW45csCDYXocZjKOu/QCy3CP3QABFHGvuiAxyrf55PVN5DnuTBsOtbiRrxQN/iG/j+qdfRSVpm5lbja/DT9u75ayyEInEGPHGO+jKYY3bgO76N9DWfRw55Rmw6AKjRMKbz1QJCkTjt/UMsKZ6Lua191EZStLcZTosoQQ9yyMfApn1kzPoKFtnLwNZfIRVMo6poPlOyLTiMIsGXN2BY8zV+sqmLT1/1rzhi/QiiBtWejUc18ptX9+DxJzYINZLAN+6dTXbK6PVpOK7w+PYe9p5MpDkIAnzpFheVeZe2GOoYY3wc+KsN/ccff5wNGzaQkZHB0aNH+dGPfnTpGvr+MFbBh0b3PouDjzLSO1794/gnLAKg0lnO9rbd1A6eo8JZRkV+Co2dXp7YUss9Yyr8Y7yLTLuBxdNy0WkljtT2MmdiJg3tQ9jMOswGTTLPWBDgirmFvH2ym3vXTCQ7zcwvXzrJDcvH8fLOBopy7NxxZSVOqwG9TiLVZiDNpmf9klKe3FxLVpqZhVNz0WtERAGuu6yMjXua2XGkHZNBw/3XjuNEYCf7OhLidZIo8Y1VX+LpV9tHzbmubYjynNGrHXdoCAX4Xu2rGLUGfjzrATRIuDOz+Prh3ybLwrlMqVi0Rtp9iUXU0b5aFFXhU+PX0dTYQygtyKuNu5Pj7uk6Qb4lHW+sn0V58/nF7zvIcXkQL8hHPtM0iG6HxPUr8licu5j93fsIxxPLRkkQ0akqqtGMTiMRV2TavQ0c6TpNsdlFlcaOsOHn50PoBSatfgAlRUR7Pu+70lnEwr4hvCdeZHDxv4+67j53iLgs4RVt2DQ6VFVFlUYbVIJGBxotMXdXIt9TjjOw6ReoKmgd6XgPvYFz6a10pTr5wdHfJoOZf3viBe6YLDErbeYlXVoPIC6rGE1w+41OzvnPYNM6SBdK8L+rskRjh5dn36yjstCJ02rg5V2NGPUa5lZlcDEb+oqq0hPoG9XeH3BTYtNx4/JxyLLCq281ctOaHIpS8ogpMkZJTygW4n2tfEAyp2DIn0C49XSyzT57DRiGQ5gFTaJGOqKIPruMaH8bgbN7SV/70PtPXhAx5o1jcNsTCDoDajSMsaQafc6Fiv4iwffIi/YGoh809Y8vakIIbtATYnyVjlh/FjlWO/Pyp2PTWak/ktjc3XqgFZtZR0WhE71OYv+pbtIcBhr0O2kcaqYr0M3UmSuJv/EYurScRO35eJQhMYUtTWGuXnAz3hf/i6ZgL79u2YZZZ8LvOYaqqmhEDUrcj06nQTI70DrS0eeUEzM5sBtsTM+ZxJPHX+KWSes5238O+Xxqh01vxRDNIBjuAOBUV4wFjgzkwBDRgQ50+RNA0oxM5RI1BDECXrQaEYNRoL6/GX12JZEjI7VdjIWTCNQdQPYNIpntiEtvxZlix3hmL+EJl/Oyexw7XhgE3BRkWnhw/Q/QG8I8sL4YrSQQJQ1IPBOOnO5JGvmQeJa8sOMct6ysJMWsGeHZ7xoIJo18SBj3v3zpFP/xyTkYNB+z6NQxxrjE+FBC9zMyEjmzU6ZMwe12/1nHbtiwgYcffphYLMadd97JLbfcMuLzs2fP8q//+q/4/X6mT5/ON7/5TTQaDS+99BI/+MEPSE1N1BNevHgxDz30AS/mvyFxWSEQiqGzKRg1Hx/F/XcTTcvDcnYPkn8Q2eJEEiWmpk9mR8de8mw5mDUmlk7L5Yktdew+0cmCquwPHnSMS4JwXKGhw4M/HCcn3cK0ynRyUkwcqO3jiU1nWb+kDFVVkRWV7DQzda1uygsSAmRN0TifWl9FbYubm1eOQ6/T0NTh4dmtJ1m3pBSzQYuqqCyoyiI9xcTu4x2ML3Qy4Anx4s5zFGfbqS530drjIxiOMxDtTRr5AKnGFPa3HaIwL59Dp0fOu7IwBbt2dA5vvj2X/e1HAdCKWg6HuihPL8MV8fJvM+6lOTSIXjKSb0vlG2/9dMSxx/vr8cT92MQwbw80UWTPZ4pzNqoi0h1r5u3+Oj458Rr+sHuQIV8f/mCUW1ZVcrKhH1GA264Yz+GaHr72f0dIT0lh/fI72dTzHAOhQa4pmItJY6bJJDDQU0NfyM1r53YA8Daww5zGg7OvRNi3AVDxbH2UtFvLcentzMiezGUpJYRe/gUA6ZbRUTkFmRackX5khx3t8s8iv/UoosGImF2J0jmcemGecx2CoiD7E++EoT3Pk7ribpRoGDngwTZzNYLOSE1P3aiM5dfPbWeqazIa9eOldfLnYjKJTJ4m8/szw9EhJu1+Pjf9MyP6pToMqGpiA+gdqstdKIqQrBd/MSKgMjOnelR5vSrXRJ5//RynmwbJSjVz6+UVeKI9vHBmE4OhIaoyK1hSNJeEH/aPE3d3oU3LxTJhPkosgqgz4j38Bob8ymTovqrKOOZdy+C23xFqSPyedZnF8EGReQJE3T3Y564FVUUQNUR6WhDEYcNIVVVK8xywb+Shc6uyLub9l78pqqoy5A+zYqGLYCRGqlnid8eeYWJKNd0tqWSnDmsveANRDpzu5oq5hZw4189lM7N5uC4h6BmKhflJ+27uveo+suN6woKJkCOdM/5G9kc20tKexieu/VeyNXFUVR0R+TQ9cyrWgTZiMTNpq+7De3gTngOvoXPl8p2F17M7NkhfcICuTlifewcR7QA2owlfn4nnXhuOcJqeryNW046hYCJyziR8HQ04Ft3M0PYnEhupgogy62aeP+BDFAU+dVU5U6Reqps7iPf6sE2/HN+xN1HjMTTlcxHzJ6PtaUF1ZlE/aSZPNe4g2LqVxTlTmeGcxY5fDVcmaen288qBHm5amIXuXTk6giAw6A3zbga9EZ7fUc8VcwpHePYDodiovkP+CJGYMmbojzHGR5y/2tAX3qV6K0l/+qqip6eHH/3oR7zwwgvodDpuvPFGZs2aRWnpcP7hl770Jb797W9TXV3NP//zP/Pss89y8803c/LkSb761a+yevXFkcvpDUQx60WiEhi1H8/QfQBEDdGMIkwNR/BNXgaA05hCgTWXba27WV28Ar1WYs28Qp7Zdo7sVDMlOe8nXTzGpYAgwI6j7dgsBp54/WxSLG/twmJmT8ymqiwtUYKvL0CKzYA/HCMUkdl+uA1BEJg/OZshX4T8DBttvT5kWWHAG2ZSSRo9A0FmVibCYPUakZIcK2ZjAS/vbuRUQ0KR+dDZXiYWpyZL+cUZGWZu0ZnoDvSSZtFQVe7gRN0QAJVFKUwZ56LDLbG8YCk72nehESXWVKygpq8BVVXRSlquqVyJU59ChxrhdzWvMBB0Y9NZuLxgNUFTjPi78oQdBhsGSUefrKHKNQm5awJPvdmDoqiU5OazbtVUYlErJvz85gsTiIoyXmQeqZzKseY42w61cbY5YUD3ukM8+odWvnbf3UhCLxmqnm6jRHyom3K0PN6wa8S5uwL99GVO5Z3AYSXsR42FCQsiTkMqWkTecfeYjz/Dp9dcz683NhCJybhSjHx6ZTbhDd/ArrcQXfZ59Ku+gE0H/sX3EO9tBn8/hswCNCgovl5SL/8kocajaFNzkMwO4vjRF1Qz+NIPEErnY8wZXZLTqjcjXcwW6t+JcDzC5ubNI9qCsRB9kU5yjcOh3zaTjvnV2bx1LCEslp1mZto4F9qLPElfQMRlSkmU12t+O/HbGreSkydUTp/ftOgaCPCTp4/ypbuqSDM7KU9LVL94s3EPt0++9n3H///snXeYHFeV9n9V1TlP90xPzlHSKOdoZUuWo5yzMQYMS1hgDUtewrfssmQWWEy0jXO2bMtWsIKVc5qRNJqcY/d0zlXfHy31qD2yDNhgYc/7PHoeza17b92qrrp133vOeY9ksqKyOHBteRQ57EfjLMY8dSWCcF6ue0lN3DtIxuLbkYMeRK2BRCQ4am1zgcFjKJ1EqL2OUNNhVFYn5omXoZyXEUGtFrEYVHzy+kms39VKLC6zdEYh+VmmpIbn337r/qlR6DTjpZvf1f2Z5WULCCUiiJ5CXtvRzRXzS5lcmcXRM0lPj8pCGzl2A7evrMQf8aMWVcTOzqfjMiaz8ZiBnYcGUBQX02oilE8KMhz2olfredF1hOvt4/jMlJt55PR6hsNe5uRN54qcqdiG2hGNeQy/8QiRs6k9o32txF74OQtv/jIvqnQkiPLIM72oJJHL59rodwVS4WTzJmRRa+iHCfMJV8zjE79q4CvX5FF44lHsy++BRAxFVDFsreTWJaDWG/H5QwzIDvTDAwS7GlDb87DMXEM8ZzwnY1pOe+oYKs5mcelcHtzz+9T92tx5AI3GRrbdTJ9r5Nt1ut1NQioddX9lWWFajZPntzallc8Yl8363a2oJJFPXF5ExN1HFDW5DjuiKJxNuZfElKpMzG8joDuGMYzhnwfv+Vv8jh/H87Br1y7mzJmDzWYD4PLLL+e1117j05/+NABdXV2Ew2GmTJkCwNq1a/n5z3+eIvptbW08+OCDVFVV8Y1vfAOr9f0jlC5fBLMGQgIfaIs+QNRRgGlwN2pXNzF70mI/zl7FGx1vcsbdRFVGBZlWPatmFfHzZ4/x77dPI9dxYbXXMXw44AvHGfZH2X6kO00R/4XtzRRkm5lW5eRXzx5LlVuMGu69agKRWIKSXAtGfTI23+WJ8NqeVmRZoSjHzC0rqnB7I/zXwwf4wm3TcBg1tPcH8AaiKZJ/Dieah7h1ZTKVn1o2px2TELm28iriCZlp+VGuW1JGNBEjIPTxUOOvmeisYbp9PnniOHoGgxRIThIOmTJ7Mhf2S6c3clXl5axv2sxQMEnAvVE/TzU+yTdzv8CM7Akc6Eu6CggI3F1zBSGviGzKQgip2LDreGosTZ1ejhy3UDQ1wlrTfnwPb0Uy2VDNXcOLMTeLspemSP45JGSFgUGFLZ6NZBodzMibxISBAXxazdk47bdAOyIYps6rYlil4ke7f0YgGmTIWcPtldOJnDlIouMY47x9/Nf1dxI15eEwAEdeIJ5diq58Gqe8MtlKF0rvMeJT1iJmFGAyaAjte56gNQ+qLkMy6NEsqsHj6yPc14ZeZ0Xx9OGecS+WQAelXh8WrRlvxJe6P9fXrEGQx4i+KCgkFJlKRym1zmpC8TA72w+M2jjSa1WICNy6sjppMfVFsJi0xBMKqkuY7MeVOCf6GyjLKCa3NhsBKNDU8McdB9PqReMywaCMTqWh1d3B7MKpyIpMJBHhYgl9BUmDe+vjnNu4iva34Tu+BW1BdaqOEo/iPfBqsr5ahxKPgiKjcRZfPL2eIOHe+QwJzwC6gnHE/S76X/gJ2Td9Ja2axx/j9y+eYPq4bNQqkee2nOGK+aXUFI3e4PowQJJEBgJuHj7zZ9xhDwpQk1HNoT3J9//VnS3MmpDDrSurybbrqc7RIO97mrC9jCcaM1g95Qpean4JtajCnqhgw8GuVN+HTg2Qn5vHXVOup8XdQfNwO28oIvMySvmqbTIJoxV1w0Fi217FrdLgvOFLKZJ/DkosjKr9NB+dtJZjfS2ML8+hvsnDKztamFKVxTfunUlvpIO+aAuvxzzU+9uZ4ypEJQn0x0w4Vt7JzqEmgkqY6dm1uAdEBv0Kx460c7hhAJUk8q0bP4tl43eJubrxNh2lM2sKjzT/EV80gEGtJ9cyOrPM3p4DTKy+hr7dI0R/fKkDo16LnBg9x1cV2vnXW6by5MYGQtE4q+aU0DsUIBCKccU4FcNPf4e4uwcEEe3Uq/neRxbx42dPMTgcZlq1kztX1VzqOTvGMIYx/AV410T/9OnTTJs2LfV3OBxm2rRpKIqCIAgcOnTobdv29/eTlZWV+tvpdHLs2LG3PZ6VlUVfX1/q/x//+MeZNGkSP/7xj/nOd77Dj370o3d7OX8z3L4IBlWcoAAZ4gdcqVQQCedUYGjcj2fGVSCKSKLEdOdktnTuJN+Uh1FtoDzfyoJInB8+cYQv3zYVZ8bFlmRj+KAjw6Kjdyg4qtxm1vKb54+nlXkDUfzBKI0dbtp6vdx39QS8gSjhSIJrFpWx61gP7b0+th/uwheM0TMU5LXdbdy2opIXtjZy5YKyC47h3HIo6NZz35Q7ebzuWVYWL6fEWkyXy8MLrU+Ta87Eoc8gEAuxuyNJOHr8/bzZvo/LHbezbksPpYV6nq5/GRhJXRQnykAgfXNBURT8ERe3jLuOpcVz8Ef85BgcCGEjcqSHPHsGm0+OjuE9eHKQ68oCBM/m9054B5Fef5gZq+9iKO7GYtSkVNjPQU4IFJtLafe3kaO1kGjYh05nYGnFVDZ3jczDdr2VfJ2VGKAunID5sjvZ3t9KIJr8bQ70n2Jc2RJmOkuINx9Bm1OO3iwgR1txvfhnjFd9GunIZnz71pGYWkCXtYKpM/IQI31EBzqQHIUYFt1ODDWJE5twzVzOQ4ceptXTSZk1nztMM3G2HgX7NJQdf0KjUvNvl91Ai1ohqMiMy5tIlibvgurQHzqICndOXsvujkO8cOp1TBojqyuXkGsesebLcnKjZ+HUfLyBKNFYgvICG1q1iHLpcnwA1KKKkoxCfnfo8VTZVSXXoNeqCEXiiMJI+k1XrI+jvcnQkJ4z/Swsno1BpYPRXscpxL2DvPVBinY3Jsn82X0kgaRAnxzyoZwnCihI6osPPhEj2t0IQOD0npFz+lxgS/4/FoP61iGicZnd58VAHzjZx5XzSy7e/wcUw8EoUYK4wx4AIvEIkiiQl2Wgoy9J9vfV9bKvrpd7r6mhwRUip3wpsruH62bZcct5fHZaIZI6xo6do+dOm0nD+jNbU3Nx23AXTaFBbh8MQcOB1OOixKPIAQ+CWosSS5fFEzNyMMcKWFCczbDTz/I5eQz7wwg6PweHN+I02djYti1VXydYiScGqa6S+e7eh4ieFTV9rXU3X5zxEZ54to8r5pfSPRigzxVk/ckQS2/6InZBS2enQF9oEF80kLofBvVoY1GuyUmOYgGSa+ACp4kr5pXgC8VJyAoWXfpy/kyHm9++eJxZ43PQqiX21/cyfVw2MyozsDW8TMR99nlUZORDL4CphK/fMwtBAJNW9baRJcrZf5IwJtQ3hjH8M+BdE/2NGze+c6W3wYWEls73CLjY8V/+ciTm9b777mP58uV/1bkdjnfOwfvXIFrfh0EKo6g0GAwjRF+v/+eIMf2rx6krQBzuwjrYRKKsFgCTKQdXrJQt3du5fdJ1CILAwmkGNDo1//P4Eb57/zwKs83v0PHFkZX17tr/o/DPMs5z+Evfh7/1uuyyQlO3j5JcC6093rRjiYRCNDbaidUXjDF3Yh7PbW0kFlc43ebmtT1tqCSRlbOL6BkKJuM2ZxRS1zzE6XY3Ko2aUDSBrChMKs+kssiGShJRSSKDnhDVhTa+dd9s3N4I5qiDT039BOF4FLcnSkv4JL3+PhYUzyAhJ3ijZVfaeELxMJIxwK0rqgnLHsbZa5honkPIr0Krl8nXWjBrTaNU8PXoEFQyleEE3d0BhGwDx8KneK1zH0aVnjXZd4y69upiG8rpDQhaA+bJS5Hyx6PEYxiEGL/v3sZ9V67hJ08dTy205kx0UpirJdQ5iZysAhSVGimnnFjddpbllVNQvpQ9nlYqLAXMyRyPRWsjescPCAkGhhWBeKI77fyPNG9hk8nJV2ZdQWT/6/gOb8A0YSHqjFwUQUBaeD3mmjnkRE00B2SUwVYG33wG66qPIygJ6G/Coy1AmLKCn+z7Xcpa3+zp4ucRH1+2TiRHFyOAghKPotn8GDUqDYJKTe4ds9Bmvr/hPu/19+EvwYXerT5P0uK9r+sIAL6In6dOrOOL8z5BVt5I/XiHj7ZeL0OeMFqNRHuvl6IcM/lZJrLsf39vqr91XvAEfexqT7fe7+zbzsevvw+XO044GkejltBrReqDb6TV29W+nysql1BwkXP7+kdvuktmOyCkxqxIaqxzr8V/fCv6ognEvYMkwgFEg/Wi1xXuGUCdVUhsIF2XXdTo09qV59t440C6yOe4EjuC8O6/E/+o78x7+T6E+3yo0KFX67BqrBjj+WSIk1CPlyhwWnh+ayOyrJBt12HLimDVGHl62xCTykp55LGTxOIyd68tpDm+j4r8pWw/lD53WbOiDPSlb7ge7T/NtaUr0TccSCsPaHVYL7uV4U1/SpWpq2bwtKeBHJ2RzRu85E1tYn/vSDtJEPnS7HtTf8/NnUvjaYVZEzKpG2xIkfxzeLVlG9csXMFLO1q5Yn4pz21pZGg4zIaOY7R5Ovj0tE8Q9o9sgCcUmUgiSr45hy5fUg9ALam5snIVGWIuE8uyURTIsunYe6KXR147RSQaZ/H0Qq5fUsGgJ0wslqB3KEg8obDl4MizN3N8Nh9ZUUz06ROjfhdd1E1MVqgptl/wd4vFE5xoGuLJjafxh2Jct7iCWRNyMBveuzXuP9u6aQxj+GfAuyb6+fn5f3Pb7OxsDhwYmUD7+/txOp1pxwcHB1N/DwwM4HQ68fl8PPvss9xzzz1AckNApfrrLmVoyJ8Wj/Ru0do1jCbuQ9HrCIWSlja9XpP6/6WMv3WcYk41xob9+Kx5yNrkYrLMVMq2zl1sa9zPlKzkBkB1noVYJMaX//dNPn/TZEpyLH/TOLOyzAwM+P6mtv9IXKrjvNhH9C95H97tdVXkW7jzinE8+Pxx+lxBNCqRay4rZ8OeNlbPK+WJDadTddUqEa1GIp6QMerVdA34WbcjmX4vFpd5cXszt19eQywuEzkbCjB3Yi7E41yzqIwXtzdzx6oafvLE4ZTQ0A1LK4nFZU62udCoJDx+iUqzmlZvFxlSLu2BZP+KohCX42gkdUrJ/hzyHCb++HobM2szqDIu5aGnm1PHZo5PcPe8j/B/J36dcq2+cdxqMrVG1rfuZEpGJTWlhWwJdPB0U5K0+CJ+miKHmTm+iP31yXSBGWYtVyzIRXVYjXnxnfh2P0fi4OsYa+aizS7m3rxZiDqBO1aNIxJLoJZEcvJkfnrsRywqmoda0dHmViictpJoyxHY/xoTdEamVM1BKJ6OK9jBx/f/hjnO8dyUyMCbPYs8cw5GtYFAbGTBeVnhbCJvPErCl4yV1lfOhESUE552Xj55CI2o4qrKFUy1aZGNc7FnVRCL+GCgnYi9lLo2mSJtR4rkn8Nw2EuwthKH243ppq8wuP5BEr4hlHg0maoPLd5/wPvzbt+H93osF3q3ImKUvWcFH89Hf2AwVV+jAQTIyjDw5tFu/MEYK2YVYdCpCUfif/e56N3MC2qjglaVThTUogqXJ8wj68+kymZNcGIoT6+nVWlRUC56boPBgmnSEvzHtiQLRAn7srtAkhgY8JGVZUYQQJtTRtzVg79uB2pHLrb5NyBI4kX7NgOWaatwb38cOZTc3DNPXoao0aXaWa1aCrNNVBdncLotGW6TbdczpzYXFN7Vb/Nef2f+Ue+DTgI5rOf6shtQIkb+8EQbspK0UudnGfninRPp9gwQ1vTTEW7AaZ6FvbINp8XJV26fiMqsEJZc2CLjKFMEqgvMnO5M3occhwHHBXingIDWnkvSf+Psdcy+gl+2buWGcauxX/sZDD4vglZHQG9goHMvetFLa7ePqfNy0/qqsBWiimfwhTmfIJZIEPCq8ZaFmZglYg4YGFe8jG61yMOt24nEI0QSMTRqgUgsgV6rYs4EJ2tX2ukNyCwunIEggz6cxxUlV7C+bT2KonCos557Jt+R9EoQEujJoKNJxbozJ8nK0LN4agFtvT4efHGEsFtNGn746EFaur2pv29bWc0fX65P1ZFEAZvNijevkmhHfdp1RTVWLBrpbZ+pLleQb/12T2pz+adPHOazN05maoXjPbHsX2rrprFNhzF8UPC+Km3MmzePX/ziF7hcLvR6PRs2bOC73/1u6nh+fj5arZaDBw8yffp0XnjhBRYtWoTBYOB3v/sdU6dOZfLkyfz5z39mxYoV7+OVwMBwGLvshQ+yEN9bIOuMRB2FGBr24Z+4BABRlJiZM5VtnTvJM+Xg1CfTvdSWOtCqJX705BE+d8NkKsYE+j5UiCsKr+xqRRQFrllUhkmvpqPfz5tHuvD4o1xzWTk3L6/iwMk+bGYtU6ucDHqCHDjZzzULy3nzSNeoPlu6Pdy4rJL/eeQAs8Zns3BSLomEQkmuheuWVPDQK/UEQjHGl9qZXpONVi0iKwqD7hBvHu1Gr1XxacckdCo9kUSIclMNLe4OjvWdpNpewYryRaw7PeKxlGfOxqzXkWnTYlAbeW5Temzn/vo+Lp9byNcWfhZP2INaklCJKnTAyYFGXm/cxo0T1rCjfX9au61dW/nEgo9w5YIZRMIJjGaRkLofy+Tl9D/9/VQ9f912TNIS4nVv4plyFxv29rFycQYJtY+4xsjsgqm83vQGZq2JB6b9C79qfJHFy2+lVNYhCGpEi5O2UAe/OLUORVE4MtTE1bZJZPhbialquX/yxzjQdwhXZIDZ+TMpjyZSJF/tLCE+3Mcpo5pOIlxdsRgSCeyKAsRQh91ETHkI4VMEW4+RyJ7CPHsDroSAgJCmqi8IAqqBTlybHwNJhfPaz9P/3I8QVGocqz9JTGX48KYeewsURSHL6MB/1q33HHRvyQ8vCAI/e/JwasH95KYGbl1ZTU2R7R800r8NKkFkRflCjvWeTD0j87IX8dSzzWn19tX1c2/tRPayL1V2dc0K9JLmos+KKKpIBL1kXHYLSjyGIKkJnDmIbWFZqp2iCHgPvU7wVNL9PtJ1hv7nfkT2LV+7+ODVGgStHvOkpQhqDYKoIjY8gGhI38hOyFCQZWJatRMF8Aejyc3HSzys4u8FSRLIdRjQ+kp5ZMMpzt8/6BoI0ONx8Vz3H9FKGv513n10B9upzM7HYRBQh/zsdJ/CqNIwTjTCS1/lUxNWMjipChnIMyvQ00S1rZjTwyOZHJaVzCFT0KJa+0XisSgDkkK7lKA2loVJ0iHvX4+nKxmGIebVcM2MW/CIVq5Z5MGqG/n2mLUm1lZeyW+ebuWTN5RxxHcYtUrN9Gwnqo2PIBosmD0DVMbCfHbZTfzPqXWsKlnAg3/u4rNXllAlNDBlaR7f3/t/hOLJMBGHPoOFlrXs22vg/is/yQnPASqEBXzr53XIsoIgwD1rHGza354KbdhxpJtP3TApNS6dRkISxRTJB/D4o5xuc1OWb6W5KxkmUV2cQUSWsC2+g8Fn/ws5mKwvjluKuaACvVq8IGkXRYGjjYOjjr20o4XJFY4P66M8hjH8U+B9JfrZ2dl8/vOf56677iIWi3HDDTcwadIkPvaxj/HZz36WiRMn8sMf/pCvf/3rBAIBxo8fz1133YUkSfz0pz/lP/7jPwiHw5SUlPCDH/zg/bwUhrxhSmQvii7rnSt/gBDJLsXYsAfNQCvRrBIATBoTk7Jqebn5dW6ruR6dlIw3qyywIYkiP3vmKF+8ecrfbNkfwz8fBoYjvLanjUybDpWUh1Yj8fTmM6gkgX+9ZRpNXcNUFtow6lV09fuJxBKU59vIzzLTO+gn22HgTMdwWp8luRbCkTj3XjWBkhwzRk0y6Fanlhh0B+no91OWb6U838Yj60fSvl23uILCbDMdfT5+9OdD/Me/TOFI/xEqLNV02tuIyVGK1BNRm4f56LRb6PL2YDdkEIgGeabuVe5cex3uPi3R+GiG0eLq4rnjf2Sco4q11Vfxwz3/y5em3cHSwun8ydPFqcEmrDoLPf7+tHbehIug6CXH7sARhR0DzZTFRmtaBE/vxTRxMcHwEFevsfBYw5+QleQ4ajLLuWnClVSYxjEQ6uXowCmODpxKtXUa7Hxhyi1EznoplGUUohroQvb4iZhq6Iw3YdVacBhsPHHiWUozCvno1Z9G6+pDXzqJ4YMb8I2v5XDjQV4J9KOVNBjVBr5QvQaTOoHQcpBofyumSUuI9R/Hu+UhdLnlXDt+Hs+37UyN4/riBeiP70ryrEQcz751OG7+JoSGcW97FI2zBOP0NYS0Tj7sUAnJjA4/3/PHlJdIia2AHGP6vWnp9oxahG/a387CyXmoL2FNQxmBuv7T3DH5Oto93aglFUWmIiKxk6Pq6kQj1467HE/YS47JyenBJqbm1HIxx+GYq5tQ40FCjenhAdbpl4Pu7PcnHiF4am/acSUeJe7qBUPx23cej+I9sB5j1cxkzL8AgigQH+qGvJG0skfO9LP5QLp7f1uPlwllF3aR/qBDQeClN5tZMtdxwRRwoqxjTdUySjMK2dayO5XCNNeYzaem3sLy3gRyw3ZMk5biUmQ48RqZvAZAXJSwzbmauzS5NJVX0BL14dCWUG7Mx7f5V8QGOpDm3UFnYTZ/OPIEcTnOYOEQKwtrkLoaSdSu4RVvNRsfSm40zZ+cy6KSHP5j7idxh0LEwyb+5w9tXLegkMFQiG2te5AVhcUlK5GqZhDpbEBfNgmV1YnU0cIDs+6lpUnFzQvNTArtJ9xzhg1SeYrkAwyF3MSy+3B5oa1FZGnNFXzzt/tSHhSKAg+9eop7rxzH715KWuH9oRj+4EiIgNWkZWA4PZMMQGe/n4pCK9FogjtW15B/VicpbMjDfuv3SAz3kVDpkM3ZCKImNYdIUlKB/9zfiqJg1I3WrDAbNAjne0mMYQxjuOTwvufOuOqqq7jqqqvSyn7729+m/l9TU8Mzzzwzqt2MGTN4/vnn/+7j+0vh8kbIUHvx6yreufIHCYJIuGA8xoZ9xK3ZyGdzDxea83GH3bzasolrK65APKvfWpZnYeWMQn761FG+cfdMHNYPdoaCMSThPxsaMjgcTomF2cxarl5Yxp/Xn6TXlXQZXzytgIpCKwdP9rNgSh69QwHqWt3Mm5jLkdMD+M+64TusOhTg8Q2nGVdi5+UdLXz2xsno1SJmnYqyPCvl+VZm1+akhQQArHuziRuWVrLrONxweVLUaXb+VHxxHzfWrEUlKRw9ESIhd7G/8wgKCkMhN9FEjBJbAVqVirCuh8oiK2faPal+9VoVsiZpITk51MD6ps3cWHUt/REv0yxFvKS3caL/NHdNuYEzQ80kzhL0DJ2VaCLG48dfZGb+FC4vnc+iaAFIoxduksVBIjBMRo2TTSeeSpF8gFODTayuWII6qiYYHy16OBByJ/McAiaNkVuqV5HY8y3ERR9BNAZ4/sBrqbRVAMf7T3Mot5Yhs8RKnRZRrSKWiDHHXsbkzElkRGKI9iI6/BZOCmryzfmEtj+BsWYugX0vACD3NDFbZ6C6ejVus50swLx/I3Jfa+o88eF+BCCWADkaIXBiG5GuU1iv/yZR8cOdrSOeUHijaSfXj19NXE4giSLukIeBgJsc6wiZNOlHL8JtJi3iJS6bLctxGoZaWH9mK9WZ5UTjUTyBEDUllZxqHU7V02tVuOUu1jdswaDR4w7tRiWquGnCVW/fOSCoR28DiHoTCOfdGFFC1BmQw+leE4LmHb5NiQTR3mZig51oc8tJ+N3EhrrQ5KSnO5Mv4HEgf4hVzPqGQyDFeLV9HYtnLOLlN0cs74IAuVlawppsWt2dKZIP0BPoo8PVTPnO51ESMcSpo5evKrOdRMCLdHQz1Wod1fPu5qvPh/jMimGUaARNdimSCMMhD3qVDm/Ex7b2fWSXLmSWLYfT2glsONKb6m/HkR6qCqvI1QxglFQ4LTJfvLoIQaPHSg+fy5qOLrsEpW4PnuNJcb5wRz2S2YFl5hWcrtMzvthIWfAErjc2oKqeRUegf9S4h6NDmI25dPV5mFdjG7WJLMsKVl263TyekJlQ5mBKZRYJRcFu1mK36Fj3ZhPxsyr88yblsmRKDolwAFOkH1U0SkSXiaJARLKAI7nZda7nuKzQOxxiwB3GbFCTY9dj0qpQFJhY5sCgUxEMx1O/1drF5WOKfGMYwyWO953ofxAQisSJJxJoNTES2n+8iNP7jYTRRiwjB+PpPfhqF6fIxMTM8ezs3se2jl0sKVyQql9ZYMPti/CL547x9btmoJIu8dXoGN41sjMMaDUSkWiC1/a00dTl4d/vmsGmfR0pkg+w9VAnlUU2BAH2nOjlsmkFjCu188aBDm5cXoUkQCAcJxJL8PTmM8QTMtNrnLT1eukc8FOZZ0mm3ssyct81tdQ1D/HW0NJ4QqEo20xBeZhH63/H8EkvDn0Gd0y6nh/v/wPRRJRbi+4nx17ButMb08j00rIFPHtyHXcXXkbZShvPHTCwr76f0jwzl80382zbo6m6RweOs7ZyNXbZT1iU+Pzse+kPDmFUG/jK/M/SFeghJscIRIO8dCqZK31/1xGm5dZSrNIRMppRZRURH2hPdiiImCcvw99wEFEl0v8WhX+AQb8Pp85AhiZzlMv83JyJ6BX40pRbccgShsNbCOVPIJpVhTfRD4LA5yfegSZmR9JKRPVBXFEPJq2RXx97ni9VLiKHMDkNO5A7T3GOFlln3MQPDtmZWGLl5is+CyoNnJdLnJbj2FqOY7n5pzj8Tbhb04WgTLWLGH71lySCHiyrPoV/x2PE3X3g6wfr6BzRHypIcc64WznSlx5Pm1WbbtF3WPXYLTpEEQxaNV0DPpbOKCQhk1KXvxQhiiJLSuYxr3A67Z5uVKJEjsnJiitLWbelh6NnBijKNnPbFeX8qeE3WLQmbHorgWiQhcWz0p7vC0EyO9CXTCTUOpLVI2PBTWnpJQVBxDr3WtxbRt5dTV4FovYdssSotdiX3Ykc8BJqr0ObU4Z5ynLU9jzOKd4kElCWbx2Vo3z+pLx/qAbEpQQRAUkSGAgOclm1GkUuZcvBDmwmDdeuKMCv6kInatjbNULyBUGgwJKLPxpE0OpRgjESQR+anHKivedCqAQs01fj3vE0AJLeTJ8mjwllfoKZMnvmLWOSwYlzoJclYS0TrROoM+p4tGUru11NzJu0BEHK4qZlFkQRthzspM8VZM+JQXTyAU4OnGFpwXRWlMyjz9dB3O8mc3gAWyzB8Ik3064x4RtC0ptZVKrwxIFePrmkAkEQkdvqWbzyRh4aTg9Fy9eX8YZriLXTzIjtBzHp1alNbQCtRsJyHtE36FT4gjGunF/KT588nNKpybLpuXP1OHzBGM4MPXmZRn7w2FE6B/zMrbZxVfkQucVxwvqc0b+LKFDf5uYPL9XhDURxWHXcvLyK2lI7OpWIw6zhOx+bQ12LC38oRkWBjWg8gS8cx6wboxJjGMOlirG38z3AkDeMSScQUUmIwoeTtEZyKjCe2Yu25wyRvCoguYCalTONbZ07sQ1YmZo1MVV/Zo2TrsEAz29v5sYlHzIviA8hrAY1X7t7Jr954ThdAwHyMo0EQnEi0Th3rh5HJJpArRbpHghwpn2Yjn4/A+4QB0/28YXbpiOJAvGEzJbDXaNU+weGQ9itekKREWu0AGRbtKgqs3h+W1NKkA+Slk6zPcaPDzyUEtsbCrl56fQGPjL5Dl5r2sgh31auDF7LF+d9gjead+Aw2pmSPQ6jkInZXMThfh9hfRuOGjefnl1NtsnKd3b8kIQykj0gz5SNLCqEtj2NbdrlJKxOBFnGFXLj0GeQrynjF0d+iSeSfj3xeIwOlcR/7vkjd89czlQxA1U0iMpoQwb0uaUIHjc3T7iSqBLHHw5QN9BAl7cXIhYGxAR2nYPPTL+LP9e/iDvsYXZOLWurlqOWZUrcXlS51QRK56OamIk/0MbB/mN8ddLHWb9zmJjKja64kd09u5P3UhC4deI1tIgy5Qk1vs5TaeNVndrEx6/8BoOeMMd8eoqVKDmXf5zBV381EgOqM+JQhvAZ88lY/hG8e55HDgUwTV6KOqcCTfYEIgdfxLfpt5iWfQzvKz9FUP1zZCz5e0IUROYXzGRD8zZsOguhWIRIIkK+JX2h7vKGue+aWk62DOHxR1m7pILGDjdTqjLfp5H/ZVAUBYvOxE92PUGG3kpCkQlGg3xicg4eX4RVc4rpGQoSFoa5d9rNnBo4gyfiY0HhTPIs2ahF1cX1HOQE6qwi9OVTEUQVcjyCHI8ixGJw1glCARBEMhbfRtL/HuRI6J1j6FUqEn4P3v2voMkpJdx5mmDTYZw3/nuqiiRBNBbnnjXj6XcHURRwZujxBaN8SJcKZFq0OM1WqopX0Otz0dkvcO+tTk4Nn+Kx1peJJKI4jQ6m507ktcatjHeMp9Ywj4bGCO6YAf/0uzHu/R3unc/guPHLKLEIcn8HkjkDlS0X65xrCTkLaRQT7OjaSs5EBwHRzvrW3fiLZ7HUWYC48c9kjJvLbG+QvsKZ6LVm6kPj+c0LJ4gnFFSSyK0rq3ltdytF+RrqAkMIgkBxVjn/sefBVPjTuIwiPuuoHbk4QURlzSIR9CbDP9Z/l/tWfgyfT4th5pVI1hzGDfZxZfFcXu/Yj1pSs7xwBSeOJlgxJYtxUjtS0x4euP5+fvJiM8O+CBajhk+vysOkV3HZ1HyqizMw6tVsOdBBQ7s7RfIh+S2MJxQ27WvHE4gwvSabyVVZNHV52Hp8CG/Qxqc0zagrk3o252PAF+HXzxwjcjYDzpAnzKOvn+JLd8wg16ZDUcBu1FBdlMEPHzvI05uTYpkOi46v3TMTm+Ed0lGOYQxjeF8wRvTfAwx6whhUUeIq9aVsPPn7QhAJFU3E2HSAuCWLhCkDAI2kYV7uLLZ37casMVFx1kInCAIrZxTy0OunmTUum+KcMYXTDzIURaHAYeCbH5mFNxTDH4rTN+SnotCWpgo8tTqL5bOK2HE0afGQFTjRNEhD+zBLphfiC0RHEf2SXAv5mUYKzqZujCYUAuEYBp0ap0XNp2+YzEOv1tM7FCQ/y8RVC0oZ8LvSFPWvLr0Wd4eVh58YpChvHnOmmwkqPoRhI4tL57KhcTu9vj4miJdz+PQAuoo6jrQdPdt6C8XWAm6uvZrHjifDidSiihtqruVXh37P/XOuJPHcL+i8/HZ+Xv8iABpJzf3VX6TSVsHxoTo+XbsWuyEDWVRhVtnZ1LaZuJLg9w2vJ/uT1CzIn8o1Le1IU5bSZ7UgeQP0DvdQZStkqWExZk0GLoMaMaEiNniUstwpfG/avSTiQfoEhc6gF5vWgb10On1tXZwgwBRNCHPPGcxaNVFBQ0FtFFntR69x4pdrON53CkVRWHd6Ew/MuR9hMD2VFYB35r3812PHCJ9dcFqMGr62XEfRqo/jP7ENyWBFlZFDou8MqsFuQjVLsV37JSQS9A142HJcYVfDIKXZy7h2rhqDJGCsvQzZ8OHSO7kQJFnLrKx51Jrm0dzlxaRXUVpgIh5W4DzP8kKnmf/3p32pza7tR7r49I2TkS5xlSxBEDncXcfHZtyajNEX1ThNDprdLQx5dby4PalUv2ZFJe6QC5WoIpaIo5JUDARdZOguLuqaCHhRZxUj+11EBpvRl0wk0tsMnJfOU1EIdzagyy0lNtiFZLYjiBJyOAgXk5EJB5DjMezL7yZwei+GyuloC2qIu7tBX3Cua0x6DWaDQHufj3A0TnVxBiICopI2ig8NFFlhWo0TRbQxEIlT13yE2ulGdnaNpDPtDwxRaC7k8rIlGAOVPPRse+rY6zoV/3HPl+jExZueRrIMdjKLKtB7VRQFh3E7p3G6L0o4GmNF7kqe63yOQq2dn1VfT6hxP6IhjG7NvxDzDyMf3cQ1ORXo8ifS3dHPV1dZOdCj4tVDgzy1qYGbllci2HrZ1uRiSs4EdnUcSJF8gJPudtpLF1EweSm+kIeesvHUB/vJM9gZbzCDojC88Xd0zv4ibnkKU8MdsOM5LsssYP74uQh6K7KYxZzpEND4eMPtR1U7mRkWN99eEidkrcAQHsCSpaEHIwbdMM9vbSIvy8SyGUW8srNl1P3tGvCjViVF9Q6c7MNu0ZLjMNA7FORQ0zDD07NwCiJvffpc3kiK5J+Dxx9Nht3ZkpONIMKzWxoZHD5PY8Abpr7VxfwJ2WNe/GMYwyWIMaL/HmDAHUIrBpHVug8v0QdknYlQXhXmE1vwTF+Dok4qQxs1RubmzmBT21b0ZTryTcl0NUa9mgUTc3jk9dN87a7pCMIlviodw7uGWhRAgV89e5SFU/J59S0LlcOnB1gwKZ/rFlekLAbn3F7d3ghF2WYmVWRyrHEQUYDls4oQBYH9J/uxmDSEHSZ+9exRugcCZGfoue+aWvpcAWrLM1k0VcfgcIg/vVzHFz5aiSAIKIrCpKxaGo4aOXwqmeKpzxXkdIuPu9fUYMh28ePdv0dRFFYUreC511q5Yrmd57uPpo27zdPJjaYr+fb8bxFO+BkI9ZOQw/xL7fWEE1HkSBD7eZbHaCJGGB+VuhncNHM2Gzr2sbnrEIqiMCdvCssLlnN8qI02b3KBG0vE8MVCCKYM6jUCB1p2sr8rOYad7fsZ7yjn7pAWU/V8nurdx9qKxWiDfXhO7uIVk8CbvUnXZYNaz/1T7sNgdbKv+RG29gb4wdQ7uUZn4n8PP0qTeyRe9qrqFfT6BxgIDOGL+Dk+eBJbJEql1oAcSYZbqLOK2dQipUg+gDcQ5fCQjWy5lUTIT6SnCSURx1gzB0mjJXzgBbqKVtAdM3K0BfafHACS1qi6djXf/+hU7LNLiApjFn05oeAelvnRn4+mFtFZNh1fvGtyWr3GzmEWTskj06ZPiWht3NfOuNIMVOKlO6/KssLk3HH8bM8fUM5eoFpS87nZH0U3W0csISMKoFELPHP4VYZCSeK/o30/N0648h1j3VVmOwPr/pe4J/luB+p3YJ13HYp0nvVRENDYnLi3PTHSLiMHR+G4iw9eVCNptAy9ltQUCrccQ6zfSfZ1X+QcFRSEpCL69x/an4qb3nGkmwfumI586f4sf1eIooCsKJxpCaDI8K+3TCEaS/Dx8gdwCa280bWR6bmT0Ismsg25xMUI86c52HkoGaq0YIaDh1rW0+RuTfV5dc0Kqs159HpNfPvZU/jOCtVJosC3772VkvAZBtf9KFXff3QTmdf+G8Ntx6HtOOH6bTjHz0e7dx2rC2pZdfd1RIc6ybD1cUoMIQkiWUY7ezoOjbqejvAwRSYHRwvyePzU+lR5rimb+9d+Dd2WP6KOB0ioMoh5hhCAxGAnwvZkfnuNxYn78tv5zyNPp96B9Z37+ffKK8hW3PzyIESVQSwGL3vqkvoBA8MhGtrdfObGyfzgz+lCk/lZJrYcHBF/PHpmkHEldnqHghh0Kgw2B0L7PnSOQiK67NQ57WYtokBaqJtWLWG3jOwoJuTk9/Gt6BrwE5ezL/mNxTGM4cOID6nz2HuLPncQneIFzYcntd7bIZ6RR9ySieXYZoTEee7SOhszsqeyrmUDrrA7VT6xzEEoGmf/qdECNWP44EEG/vByPQPuEGpJTCOI59DvDlLf7GLFrCJUkkCOw4jLG0avlVCpRLRqiVtWVvOF26YzOBzity+e4GSri7YeP09tamDJ9EJuWlbF8lnFPL7xNLmZJnYd6+apTQ28caCDcEzGJGVwTUVSyGtO1iIOn0qPd9eoRXIcRtrcXamFkCSIxBNJtq5X6VhcOpfbJ17HLTU3MCN7GlLcTI+3jx/v/wW/O/oIPz/wB35bvw6bICFl5OEWRlZQOpWOHLuKUrOeJlcrmzoPps6zp/sIjd7TrC5IFxpbmjuZ2KRFDEW8KZJ/DoKoQjd9JUYlwfi88QRRCO14hv6cghTJBwjGQrzU+DKoBD428WaKLbnICZl2/1AayQfY0LSduYXTACiy5tPt6+PRth34Vt6BqrgW0WBBNW4xPa7Rytm9HhlJb0btKEC7+D7CS75IuPpyYn43neXX850X+/BFVew/mf7eB0IxejwJotKHW4TvHGRB4YnXG9MsZQPDYdp60vNNZ2Xo6ejz0z0QwBuMsft4N8tmFF7yYtiiKLC1ZQ9qUcXU3FomZtcgywlODTSx42gXT21q4Jk3GhkMufFF/SwsnsU1NSsptxez7vRGEkr8ov3HvQPEPX1oC6oxjl+AZMnEu+8VhPjItwlFxnt4A5LRhqF6FpqcMuLuXuSw/+KDV2Q8+15OK5KDXmLDfWllR84Mpkj+Oazf3fqO9+aDClEU0KhETAY1BTkm/vhyPT9/6ih/fOk0esXGVVUrqbDUIIgK2zq38VjjQyh5dcyd4gAgKy+eRvIBXm/cjkeOcWJQnSL5AAlZIREKEWg9hnDl/ahW3I1p4mXo8iqI97cg6pMuGwmfC1GjRzRY0BkM2KN9qPc8gv/Vn1G84yU+XXMVHZ4eZuVPGXU9ZnUuvpJanj2zOa28x9/HMY+P1pq7ECxOLEYNflPRqPaG2deyse9Eav5PjjvB/kAvDX4TR5o91JZnsre+N61dKBLH5YuwcnYxGpWI2aDmvqtrOXpmIG2+yM8y0e9OkvM7L8tBfv1HuF7+BYOPfROtf2RDwG5Sc/ea8SO/kwAfu7YWh2lkw1USBFbMHn0NTruR7gtsAIxhDGN4/zFm0X8P0OcOYZI9SLoPnxDfhRDJqUTXdRLLkQ34ahcja5OL9myjk1p7NS80vcot1WsxqPQIgsCiSXk8s7WJaVVZY8J8H3AEI3FOtiZzs7f1eqkuzuB028jGj04jIQhQ1zLEyjlFzKnNpaV7mLvXjMcbitLdH2D6uGwOnuxDr5E4dHog1TbbrkelEnns9VMoSnJBeeuKakLhOHddMY5BTxhfIEploQ2VLkyG3swDsz+DErKhklpTJF4lCVx7WQUnmoaI2UZMFPv797F83loG+mRunXwt605vZGf7AebkzqRcNZNEWM+BwY1EEtFUm3ZvF40hF7NWfZyWkMB1+XdT5SwkJrk5MdDIkqwKtjSmp94CODhwgqtza6nNrEQRYE3JXAoDEbq1Csp54oBqSc3nZ97NgZ4T/M/RJ5ntqGSuo4L+QAxx2i1YHTo+ay+kYaCZTW3biMtxWjztxIUYatnPJ/IWImnNBL2jXfKj8ShqUUWJrYDLSubw52PPk5ATfO/ki0zJq+SOxTejR8syCU62utPazi5Ro87Mp1dbzP++1kdbXy8GnYqPX3UTe+qT5D4hK2hU4iiFaUmOIJEg8aH2j0oilkjgC8YocJqYXpONPxRl17GeND2Kc5g1IYcNe9vwB6MsmJyPLxRDpRIvabKvKDJOo4OarNXs7zqKSlRx26Rr8YZCrJhTjNcXRa0WkQQPd02+HlfYg4DAlJwJTMudSOIdLPqKouBYfg/B5iNEehoxVExDUGlQEufdv0Qc86QlIEqEWo+hzsjBNGEhcuIdHOvlv8zxPh6XKcw2M6c2B1EQOHJmIC112YcNiYSM2xchN9PI1oOdDHmSG4Vrr3CCOkJrnY03D3WTlaHn5lW380TsjxzuP8INlRPZfQQQRt/3WCJGpamYZkuCaxaVs/1wJ25f0q9C71SxLp7F9jPrsOks3Fq7hvIz9aismahs2URDyVAwVUYO5trLCLYcBY7gWH437u1PIod8lJiyuT2zjGBUxVDAy5H+42hVWi4vWsmGrT5uXa4jfoHnQRFkfvtGH9+/rZi4RmL9Pi2rrv4OUtdhhPrNiBVz8OZNJ3Tm9Ki2EVHglcPJDb14QkarlkZtjAfDMepbhvh/989Dr1URjsXZeqgzddyoV3PVwjL6h/zcusCJ9cijJHzJTW0lFiZwaD2ay+4jIQsICMyfkENNUQZuf4RMq44MoyZt/lAUhfElGayZX8rWgx3otCpWzSlhX10vWVYdON5BwHIMYxjDPxxjRP89QJ8rwAx8SLrcS3lN9Y+DIBDOH4d2oBXb/pcJFU0knFeBotJQbC0iEA/yYtN6bqy6BpUgUZxjxqRXs/tELwsn571z/2P4p0UkLlOSa6G1x8veul7uWDUOh1XHgfo+CrPNLJ1RyJObGhAE6BkK0tTRzcKpeTR1eCgvtLKvvpcr5pUy7I/wVrWsDLOOP6yrSy2gZVnhmS1n+NzNU/npk4exGDVctaCUw2d6ka0JNBqBPx1/lHma21k5u4hXd7UCMGNcNlsOdjC9JhtTIhedSks4HmEo5GY4p455NTP46f4/pRS/3+zcxcJ8ie3rXSxeMpEDHEkblzce4ZjHwi+eOHaW1PaxamEO/cYmdPEQZUYnu99yn6qsJWgkFZ+pXEPMaGBj6x5+0baLUlshs3InUZ1ZzunBJm6esIYHjzzFcDi5WPVEfGSqp9Dc7UdytrFpxzYEQWBp4WI+NflTPFz/ECW2QuSEzKlwD9qWZuwVszFrLanrPIeZeZOYLpiYP+E2vr7vFyTOLmTn5U1hXv5kFI2e4d5mqgWBu1dX8+zWZlSSyC2LcqmyBwkEZR58w01bX1KfPxiO89OnT/CZGyexp66fXce6WTW3hJfebB657iIbWklGHR4kocv+2x+0DwhMBol7rx7PsTMuXt/TitWk5eYVVVQUpm8qy7LCQ6+MaF2s393K1QvLUGQllQXlUoQiQGlGEb858OdU2enBJr4w9xP89+P1qc23/xw/jqGQm83NO/GEvYzLqmRxyRwMah1E3653UNmy6Nvwe5SzoSY+dy/G8fMRDeYUgRG0RuRoGP/xrQDEBjoIt9WRfdNXLtY1aHSYp67Ae55VXzRYUFkcRM6rNqc2KZz4/NYmZFlm7sQ8lswoeGexvw8oFCUZ+202aOgeSs4NVpMGmwWOHdHwxsFkuFKgx8cPHzrOp+68gd+d/DUqbZwlk51MzHPySkf6XPXx2k/wf8+0cqLFjVGv5tpF5ew63o1WI7Gjdx+bW5Lx/wOBIX6x/xG+POsj2J74H+yX3Yqr5wzaolrCnafxHUpqosQG2gm1HsO28BZOG9T86cRTuELD1DjKWZK7mhJxJtEI7HzDS221jpggsKRoJpvb9qbGZFQbiPuNZFujGAM9DLfXYTbW8NXHWzHpndy6/BsU5lh47JUGps6YwfH+k2n3aVrWFF5rSd6LnUe7WT2vlOe3NqaOl+VbqSqwsLwojC/m5acvtNLY6WXl7GJWzi7CrJfIzzKj1UgUO40o+x7D31WXdo64qwsdcmpTVRQgy6LFakz+Nm29PjKtenLt+lQIkFmvYcAdZOnMIiLRBC9sbyIUiXPvle8Q6jKGMYzhfcEY0X+XkGUluTttCiBrx1z3UxAEIs5SYuZMtP0t6NuOEs3IIZpdxnh7Jfv6j7C5fRsri5cgIDCvNocXd7YwtzZnzKr/AUb3QIAr5pXw59dO4Q1EeWT9SR64czo1xXZOtrr4w7o6ErLCgkl5ePwR2vq8rNAU4bDp2X64i7kT88jNMnLX6hoEUSArQ8+AO5lvvtcVGJVKT6uWQEjmEz7ROIjLG2b2+HwiiT40gsidk6+nv11Nc5ePO1bV0OcKMrkykwdfOIEsK7yy2cXVy+9gmA7CcphCXTmD4f5Rab0O9O9nQc1tSJHRLueVpkp+8PDJNMv1a2/2ctdNM3mx/Vm+Nv0uivvrafMlXTOdBjsVploEAYbiFmyBEEvzZlJrm4UsS1gtcexGG6UZRWRoTCmSD7AkdwUPv9TOlVereLphxJX09daNyXCF0rUU2B08d+plJuVV4nbmoq/bTv7ExfzLlPvY1P4G7b5u5mZWswATpqhIU8DP2vK1PNnwJF+cfif1Q038+ujT2LRmbh+/hjKdwpWlWmaa9QixMOr2FwgeaSax7HOc6Up3YwYQFYVbF2STZ4wjGRScV9fi8YfJN0SodKqRFD9yREwTm/uwIhxRaOrwsHl/csEfdgV56JV6vnL3DDhPv7RnKDCq7e7jPayaU8SlHDgrIbKtdc+o8gOdJ8jKyKFnMHld3miAx4+9mHrvTg6cQRREyjJGuxGfj7hnMEXyzyFwcjeWWVeCJinkp8TC+E9sT6sjR4LE3L2gL7xI53ES3iFsC28k0tmQshDHPYNgLgcgkVBw+SJprvo7j3VTkmdhfImN0UEvH3wIYtJCHY4mGFds59iZQVSSiE2dw7bD6fHmCVkh4tchCiL5WRaWzUvQ03uSf5l1Nxub3qTD280V5St4c6efEy1Jr6JAKMajr5/i3qsmUFEm8sMDz6f1qaDQFXaRoVIjx2Po51yPuaia/md/kF4vGmYw08nPDj1M4qwX1amhJmKJl7m/6jqM/iFmjnPyyyNPsO2AizVVS7lp/BXs7jpCsSGTywoW8P9+187Xr8oisPNBDpZ8jGe2JzVpQpE4P3/mOJ+/dSpHzwwQjFq4df6dHHHtRRQllhXOx9newrxxmew66abPFcTjC/HVu6Zxqm2Y3AwN5Q4BUfHSFTbQNhRCI8KVM7M42jLIhr1t/Nvacg7Xe1i3txuLQcvXV06CsxsZ52CctIxgXAQUNJKAokBCUXhpZ0tq4xvgrtU1LJ6SBwqoBLhpeRUPvnCchvZhbCYtn7ljOg6z9pL2HhrDGD6sGCP67xJD3jAaDeiQCai07/dwLjnIejOh4kkI8RgqTx/6tuMYGw8wt2I6GwLtHBuoY3JWLQVZJiwGDXvq+lgwKff9HvYY/k4wGTSEh0Msn1WIWpJQSSI7jnQztzaXmhI7kiRQXZSBQafmjQMdTKnMQqOWePjVeuIJhQMn+9l+WMea+aU89vop7lg9jkgkQUe/l/J8W5or+LxJueRnmXjo5Xr0WhUfubqWnUe72PjkYS6fU8SsmTqa3c2ML8wnGAkRJcCQN8im/UEWTcnnjQMdXLWwjCdePI1Oq8OgNZM5w4omKzLquuw6G+7hGOMKzczNm8XengPoVTpuGncFKowpN9LzEQ1LBGJBfnj4cb46+14GQ8PEEbConahRowgCoRhEBTXdnTLV1jCKNkKvP4DZmEGhMQu9On1zUYgZyctSOO7aO+p8Tf6TrHBex6CvE7NeR6OrjWJtNigKyptPkh2XuWn2zWjKbBhjYSRBoDs6yP8e/T+uKb+Gb837PPu79vBSSzJntD8a4Af7/sC3pt9N1pnDaPNqMWtkhLw8/Kf3ElcpZNp0aQrNAJkmqOz4fTIuVm+matknGDQZyQs1oxJziXmGkLKm/c3P2AcJckxk66H0nNuKAj2DQSpyLKm/zYbRwoV2qw7hEhbiA0goMroLpFGU0BA9TwF8OOIZtblW13+aaCLGxZyFhQv4xwsqDcjnhYsoiaQ7f+yvpd0KMc8AwcaDaJzFhJqPEfcO4Fz7byPXIQkcOzMwquXu4z0snVH4oTTqD3qjaDUSarVARYGVZTML2Xqwk0QMrCYtLm/676BRi9w+6Vpeb9iCrWIJbjnMz3b/jtsmXcvy8gVo4jZ+d+r4qPMYCJGpNmDX2+iKpce361U6SCQQzJl0aCso1sQRVRrkRHpITH88mCL559A03IpbHYacHH619/cMBpOhaK80vEGG3saXqq9C44vx8xf7KHIayNP48BVNZ+OJ9GwxAM1dHqpL7JxscdHYLjC+bAayoiCKEYTdj3JL2WyuuG4BskqHdfAo5q6DFEc8hHccBDmOoNEhLfgsVTYt03IPIPc1srJyCo3TptM4KLPlUBcef5TB4TC/2aVw/4qP4d/5JEosinnWlXToqvjxb3ajVoncuqKa8UU2Bn2RNJIP8NiG00ypzEql0LMb1Dxw6zS8oRh6jYReLX5oQ1HGMIZLHWOm03eJnqEAOk2YmFpzSbtIvt9QVGpijgKC5TMI5ddgObOflTE9u3sO0BNIWv1mj8vm5d2tyG81y47hAwO7WcPWQx08t6WJJzc18Ojrp9h9vIdAOIbTpsdh1dPU6eEXTx2hrnmIDXvb+d+nj7BmfmmqjyFPmFhcJp5Q+NPL9Wzc38ZtK2tQqwTuWjMei1GDUaeiKNvM05vP0OcK0trj5WdPHKa2PJN4QuGVnW1s2R4gy+AkrnVhLRzCmNvH6lUGrlqajVotUlvu4NWdzTxw5xS+cX8tX75vAnl5IuqYjXzTSIiJKIgsyb0ctydOU/gI8/Ln8u2FD/DdOfcz1ZDD7oH95GemW/oFATSGpGiUSWfklfa9/Nf+P7Kv9zgBvwoJhcYeH/sHtzHsgVKxF1+4jW8deogfHniY4wMN/PbIU5x0tVBxnlVTpUng8oQpMOWPuvdZ2hzE4U5yAwJzM5cw6OvHPtSHsfYyoj1NKGqRmFVFe6SbXlWMmFqDyu8hocg81/g8/sgAm7rSLW6KohBUMmjR1aDzdTH00s/pffw7xId7sRpUfHJ1cZqHzhWzC7A2vErCl1wcyyEfwdd+Tr4VIl0NDK77FdqMHJSxTxMAWo2EwzratcFiTCfHOQ4jWbaRTR9RFFg9t+RSNuYDICCwuHRuWsYVtaSmUFuRit0GUMuj6bxDn4FKvLiOgyojG3VWutXfOu9aBN2IO4SgM2Obe216O3suasfodyi9khr74tsQDRYi3WeI+13YFt6EaLKlqsiyQknu6Bx9ZflWtOpL/Mf5OyEal0kIEZo6Pfzq2WNU5Fv5xkdno1VLXLmgNK1ucY6ZrEwVz9S/yomBBh47sxm70Y4gCPz56HP8eNdv2d2zj9wLxIZrjWa8fh3Xj1uT9nwVWHIpELSIOiOnVTF+ePz7fOvob1HNvTqtvWTJQqsfnfbXrDFyZOA0+/vrGQiki7i6Q8O0ClEGHQVMHV/Av6zIQRXzI4ZcOK2jDUEZZh23LS5Gq5FIyArHG13Y9SqKTckNB6V5L8VSP7Zt/wNHX0FlthNu2Avy2ePRMBkNL2Nv30LszB4S3kGUk5uo6nqJCYUm/OcJE+5t8LIjVIH99u/juOt/OGVdyNceqmPIE6Z3KMhPnjhM20CAYHi0/kc8oYzSB5AEyDCo0anGSP4YxnApY8yi/y7RPRhELXpRtGMiJH8pEmYHgcpZmJsOsjwzj1dbNnL7uBsoyjahlkQONgwws8b5fg9zDH8HaNXSKAsvJFOyPfr6KT5x3UR+8lh6CqPB4TB6nXpUm/OPD3nD/NfDB1CJAtcsKiPbbuTpzQ2j6nYP+LFbdLi8Yfac6GHF0nJ+e+Ax+gKDQJJ4fGr23Qx4FLr7w3zpvnG8dOYVDp44isOQwerKxTTF2rm55gb6fC7U2gQZ6kx8PoHiSXW80rKZV85mDPxC1RXkH91NqLyUj1wznd88dxKvP8rnrq2gtNyIX4lgz7gDQZB48OAjFFnyWJy/CKNKgyj5Kc0OM0E1ldb2ENr+vTxjlglEk27ImrPpwdad3sSXZ9+LO+KjbbiLCrsOZboDg+jAoj2EN5IUc8rQ2SjSVWJUhzAMnKEzPI1/KV+JBTWSWo2YWUj97GX8ae9vUvfqtoqlzMueitD8GgigV2nJ0JpTYwCYmj2JXQf93DEhgmvd/6UWoOHmoyixKNWLbuV/7iylP6TBKITIEV0EXtuS9pso8Shy10mCjclNBDkSAI0RLi6o/uGAGOPKBaX88pljqQ3Q/CxjWsorQYDeoQD3XVPLsC9CNJ7AbtHR5woQiVlQqS9dUUMB6PH18cD8TxCKhREEAa2kQQhlkGkbSL77WhVWlZPpuRM52JO03IqCyB2T1yIKF98QkqNhLDNWI571GlAUGUFSoURDoDlLwONRYt4hHJffR6S3BbUt+e1J+IdBe3HNGO+JbdiX3I4iJxBVWgJNR9CGA6mwE1EUKMu3kpdppPtsGILFqGHBpDw+rOwoO0OHTm/hkZcbEQSBqBhA0CvElBh2q5571ownEI6h16qYVG6l27OfWZk17Og9xsnBRjQFc7m/ahVPte/hxurrUcdsTL5SxY/+fCSl6TC1Oov9pwfZdayb735uIv865z4GAkPoVVqK9BlkdjQizb+OiCWbuybfSCQM+5UwC279OsGIH42gQmfMICELLCqcwfaOA0Dy+7B2/BVEAxrsKitlGcU0vyVbiV1vQxcQkWWF5oCe2uxalO4urllYzMn24VQGhiybnokFWrI9x/neCoH+hB2DSsbevwf1oBXVko+glWT8x7aixKMIkjqV1vR8xAfb0WWnb2Yluk6SNdXHHQud1Jh9SEqMQexE9SoigpGooPDC9iOj+tpb18Pay8ox6dUEwjFm1GRTXmBFrZLIvMBGxRjGMIZLH2NE/12ifcCLAW+ahWAM7wxFpSVYOpWcM/uoLCxmY9s2rixbyaxx2azb2cKM6qy0XfgxfDBg0Egsm1nIExvTSXhpnoWVs4vo7PMnmctbFsF5mUZuX1VDPC5j1KtHHQ9F4hTnmJk5Pof6liEURcFm0tJGehoyvVZNJJpkkLetKaXZ3Z4i+ZCM33yufj2fWflR1IKWJ04+x8GeZCq7waCLPx97npsmXMWjJx/jE+PvwSTL9CYUHm3+NaF4+gbGkBzB2dvMdSvuYGezm3+/awqZ8iCn8fBfex9lMOhiZt5k1lQv4xvzPokaG+29EYz2Jh488jSBWJBMg51PT7oBSSimuW9Esu943ylWli/CEI3i2PUK1ulr8RqiPHL6VUxqC7OlRXxm6idxRwdJJBJkaDM51HeC3ze8TrmtgBuLpqG2lNLctocyQx6J1ffwyN7fpo3/8aYt1Bhz+eWKb+GPhvHEAqytXE6mxkiGDMMyhGPZrG9uRvD0pUj+OUQ6TqKNyogvfZtxi2/FvfUx1POuQ1BrUWLnhTII4oiLuSAiGiyEEhJjAZ8QCgkUFEjcf3sRLncClQrysvSgSr/X+ZkG+txhnn6jgUA4ztLphUypzMSgUyeTX1+iUIAKRymHe+rY0rwLlaRidcViKh1als8sQlaS8dw6c4iF2lnUZFUQjodx6DMIxILvyJWVeBxiEdx7XiLu7kVfPgV9ySRUGTkjdaJhtJn5eA++hhKPEWyKYp26Ajl+USk+iEbQ55QzuP43KNHku28cvwAlNmJFVRQ40z7M1Goni6drktcTl9l1vJuS3Oq/9bb9UyORUFChZmZtFpNmRnmp7U8EO0OU2ApZ5rwKf0hCrRaZ7oyg3vYLijtPUpZTyqKZ1/Bo70EESxETNWastRP51ZOn6Rlox2HVce/V49GpJYa8EVq6PWw/3EVFoYX1jZvY33sIhyGDcDyCSlRx3/ir6PZ08+LBhxEFkaUFyyi1lPHHzq0c7D1GjtHJlaVXkRkxssYTZ3b55QR1erIkHbp4Bl96uh1FdnPd0iuxOndxuP8wAHfUXouvXeI/X64jHE2g1Uh8cu0kuqQ5JNqGuXVlDbF4AlEUksddp4n7B9DsXUfBefcoaLBwsOpTTMnwIfQn9TmURAyVJfNsDQFV4XgUvQ2d0Uy481T6TRZEHCaRGZ2PIbuToT8FkpqMG75GXHDQ0DmM1TyauGfaDOjVEl+7ZyatvT7ePNLFExsb0GokNGqR2eOcqP4O67JwXObAyT76XUGyM/TkOQwp8b8xjGEM7w5jRP9doq1/mBIphKK1vt9D+aeDotETzq9hSk8zL+XaOTZQz6T88ew80cOxpiEmV2S+cydj+KeCLCssmJhLNJbglV2tmA1q1i6pYHA4RE2RnWA4yvKZRWzYO2IluXt1De29Xracl4rpjtU1WIwavIEoVy8sI9OqY0ZNdkp5/ODJPu67ppbjzUMpS6jZoMZsUBMIx5FEGF9l4Ejf6Nh5b8RHKB6mNzrAoZ5jaccURSEux+gLDBKSouw97ae82MCErGoO9KTnts8SNKjzq3EpcfSChkzZTafs46cHHkrlTN7bdRgZheVl8wl4Icse5b8OPJKKCx0MuvjFsaf5j6l3MDM+yIbOpGWprr8BWUnwmczpeNtfo3NmlCfqXzx75j4a3GeY4p3IveWr6es7xv87+lBqXE3DnbzcvoEbx60kOxIjmGGjOdKXUtU//1oDYS/WqEJiwEN/rJHfntkIwEerLmeOKgOvMYNAVAGdBeu865HMGQRP7iLcXo+oNyFqdWiXf5Lgme3YFt6Iv343tnnX4d7+FIgiuvxqzFOXEajbCQjYl9yGbHSm5ZT+MMNoFHipbjO7u87TXOiGf535SSBpkZZlCEVlfvnMyPP3ys4WtGqJohwzb+8L8/5DEARa3B282vBGsiAOT9W9zCdn3kFbr8Tu48mwrgmTi/n53j8inxcvPSVnAuUZxe/QPwy98efUJlSo6TBKLIquuHakjtaA78S2ZHnROOLeIdxvPk32TV+5+OAlFd5Dr6MyZaArriXhdxM4tRtD1ay083cO+HjjQGda07J8K+/gjPCBhawoxBNQUCLzXzufSpW3DnewTXgN/cBcSswSqoaHiHmS+gaJ3hbMmx7jUzf+G6e9nVilLPYd66VnICnEOuQJ8+DzJ/jYNRN49LUR0uvM1NIVSBLdoWBSrE8raRhMhHi8acSzqCPUTKPvFPUDyQ3oHn8ff6z7E9fk3E2VcQKZm3+JoXIGhprZ+E68wr9ds4YNR4d5emMT/3rbSmblTSHLZEDXH+WrL7cROevmHokm+PWzx3jgjuk0dXl4ZH1SWX/l5AyWFMUwRtyobKOzi0i2HHqGE9iNNqYsuR0lFkYQVcjRMLaFt9CmKuGlYyH0gp4FVXlY8hdiOvEccmvSK8oycw1SxJci+ZDcKAjsfhrTmi/wys5WFk7O5/DpfmJnNW2MejUzqp3IskK2TcfG/R3Ut7hS1/H7l+oocpoozBwtOPtuEJcVnt7SyJaDI+/InatrWDol78Pq9DKGMbynGCP67wKKojDgijLfFkDWjKWF+1sQt2Wj9vSyNG7kpZ59FJjzmDM+mxd3tDCp3DFm1f8AwqCRWDA5j+JcC3XNLh5df4pAOE5FgY1Vc4uZUGbHZtbS2u3hsmkFHD7dT0PHMLMn5KDVSDy/tYmXtjfzhVunIYmQbdMjiAJbD48sFGQFXtzezL/dNp3OAR9ajYqyPCvtfV7uvGIclUUmWofPkGfJQRCENGK5tHQ+Dx97ivmFM7DpLbhDnrTxS6KEJIiIosKeyNO8ftzHR6beRLevl25/H4IgsKZwNs6eDsIzL6c76KLcaSLhCdGrjY4isQe6j7K0dB6dAx5ycrwpkp9tzGRx6TzicoyWuJ8lObX0hYc5OtiISlQx1TkBhvrROovSvBLOodHdjBsVLsPIwkwSklkITvSf5OqqJWSWTmNj1yFmFE7GrDXhi/hHfie1ngyjE7luA/3aCVQpaqZmVXF4oIGHm96gvGwVWcNnuH5eORLt+M/sB0XGPHkppomLAYXoyW1Yy6eRmHcTUixAZuF4EMSkYJkiE/e78NftRGVxkHPr18FoJyDa3sXT9cFCIBqmzdPKTbVXkpBlJFHEFRqmJ9BLpTkZzyyKSVGvt2L7kU6WzihArbp0GWVCjrO38/Co8mN9p6guXpQi+t6IL43kAxztreem2isv3n/QO8rTJNxehxKPplY/SiSIvqgWORYm1HQEVUY2jhX3JNvaLtZ5FEPFNORwgGDDPlTWLBzL7h7lCVBRYBtF9KdWZV103B9k6NUSPUNBhjUXnrOuLl5CaSJI/FS6iKEc8oGnn9/WPc1XFnyO5vbRIWA6TfqStqHFx2VVk+n2bUiVldmL2d+dLt5XllHIs/Xr08richxZ46fPb6H0slvxHX0Dlc2J1pFH3oFfcbclm+vWrqLBFWB+poOe8AADmhwi0Y60fiKxBA0dw5TkmHFYdBQ6tKwxnYCdm/GSJOVqZxGxs5Z7JBWaOTdR47PjtGnxvPIL5OCIkF9w8Rf4zgu9LDibivi/HzmArMCcCUu4/crVZGiiKI5S4o07Rt2fuLsHIRGlpjiDZ7ac4ZYV1YSjcUQhGWLiMGuQZYVQVGbPiZ5R7bsGAu850e/3hNNIPsDjG04ztSoLmz59m1IQwBOM097vR1EUirJN2AzqsQ2BMYzhIhgj+u8CA54wiAky40FCurEY/b8V4ZxKrI37mDJuBq80b+SWquvYU9fH8eYhJpWPWfU/iPAFY/ziqaOpmEqAxs5hJLGExg4PZzrczK7N5fcv1aVUmNt7fUytzmJqVRZHzgwgiQL59qQAWVxJ9+a3W3Qsm1mINxghP9OIXqfiG7/ZlTp+7fI8DsRe46PTb+GTM+/k5dObGQ57WVwyh+qMGtad3shg0MU9U27hV/v/mFL8rsmsoM8/yNpxV/BSw2t4zsbAP3z0Wf7zss/TGxrGImoxhaPU6Tt5/MST/Pu8j5Mh60jEZAwXmHHtOhsalZq46MWoToYA6VRaVlYs4tFjL6QIzmTnOD4y4VpCiQiipEMjigxFjqPubyPHOFrTYpyjinAATDoLa4vmsTSjApXfTSIWwWuxoUgG/NEAkx3F/GjfH/nMzLv4/eGn6AsMkmmw8/GaNYgaC2FPP0ZbDi4MzLaWcHiggVgihj8eJjMRpVzTycCLv0id17X5YezL7iYR9OE9uB5f3XZci79CYeceQi1HUeJRrPPWQjyK+80Ri57/+Day1v4bWDP+qmfpgwy9SstVNcv5/aEniZ8lrCW2AqZPmJKqI8ujxfkAHBY9qktcjU8lqXAaHDS50uOcs41ZbNnRnvpbrx79fbXqzKglFVwkMkG8QMpbUW9GkFRpf8f9QwTqk/ND3DtApLuR7Bu/xGh/nxEIejNxn5tAfZJQJQIeIn2t5Nz0Fc6n+lk2PdcvqWDdm83EZYWl0wspy7ciIpC4cNcfaMiyglojocU06lim3o7bnWBmpSWZh+8tmzu9ZzciY4kINZUGznQMp47NqrQw0erlC2ur+N3rrXgDUQrsWubnVzMcHWR312FUosSczCo6AoPUn9dvOB7BqDGk6Y8ASIqGSZlxXC8/hMZZTMLvxn98W/KgZwBTTwNT13yNtt4oTo2MS+1Gq5aInJcx4txG24vbm/jcLVOwxwdIvDCS+tS7/1Us0y/HNvsa4vE4Hl0u//FSD5397dgtOj533TfJeP3rZ8NDBDoCGgQBchyGtBC4PXX9lBfWsGJaJbKsYMqvGnV/jROXEhX0LJqSz86j3Tyy/iQqSSA/08Tc2pyU95tGJVKcY+FkqyutfYblvc95GopcWPwvGk3AW4j+oC/Kd/6wF99ZkUGTXs23Pjobh2n0/DeGMYwhiTGi/y7Q1utF1PgQScacj+Fvg6I1EM3IpXJwkC6rnp09e5hXO5FntjZRW+ZAHLPqf+CgVokpkq9VS1y1sAyVSgQEZo3PZt6kHPpdoVGplg6fHuCWFVWIopDm7aGRBFbNLea3L5xAq5ZYu6SCP71cnzrH9Bonl03NZ9vhpCtjQ0uQ4gnFPHliHWsqlnPPlJsQEBETeno9ycVNIBZkU9NWHpj7aVxhFzq1Bq2kJR4TUYsST9e/nOw7bxIV9hLOuNsptuWzoWUX+zoPYzdk8OlZd5GpzmAoMkhMpyY/GqXKUUrDUFKxT0DgptoraXa10yMPEG+s5aaaVQxEvKw/s5WJzipurlyOSlTTHQgy4OrC8spvsF7/AD9teh2nxsSaKUvIj8gsK76MN9q2o6CQa8rm8vJl7GzbyHWli6gQrfg2P0J8uC91XvNVX0TKyuJPp19jOOShs0fhI5NvRo2CDhU7uo+yvvURPltzJUa3GhUKITm5wLLpLNg1JiStmeDJkQ2Ucwg2HsQ4YWHyj8pFJGQFf/1OkOMYxy8gERgmeOYtCv6xCDFXN1gr/+bn6oOGhCKzqXkHuaYcpmZOwx/zsad3D/5oAM5+ckQRCrNNZNsN9LmSREUlCVy7uJxE4mzi60sUCTnB0vL5HOqtIxJP0mqr1szE7GpU0wVau71kZRjI0GmY4Kyirj9JbAQEbpl49Tta8kSzA13JJMKtIyE4GYtuAfXI91qOBAmc3JPWTomFiXtdYCp7276VkJ/AW5/9RJzYcD+Yy5N1FFCrJI42DHD1onJEQeDomQHm1OZ8qJP0xGIyGsHGrJyZ7OvdD4BKVHFb7Y3gzaRP303hrNUoe19JtdFMW4Fgz2Fp2XwSsszEKjNuVzZlNoFMs4px+gGCz32bMlMG37vybqKWfKzqOOx7luvjUa5f8DkMIR+aWIyenBx29hwldDal4pGeem4adw1/PPp46nwTHBNw92tQmxoIA/ryaXj2rUu7DiUeJdLfzo83K9x39XgS6kE+flM5v3mykVhCZsF4B9fNcbKnwYMvGGPb4S5mZfrJSe8F78HXsDnL2eQq5LHXT5M4S7hd3jA/e76Bby26GXY9hGSyobVkkOPw0d6brj8DsOdED8unJrNFaHMrsa/5NMNbHkYOBzFNWY6mZhERJamW/62PzqZnKIgoCuTa9ejPE+2UhKT7/Hd+vzeltj+t2kmR87215gNkZ+gx6dX4QyPaFmV5Fuxv0RAQRYFdx3tSJB/AH4qx42g31y0sHcvWNIYxvA3GiP67QF1nLxZNCFlrGkut9y4RdZZgOr2L6QWr2dh/kILCfBQFdp/oZf7E3Pd7eGN4j5Fj0zFjnJMDJ/u5ZWU1L2xrxONP2sHsFh1XzCu54CsligJ2q45qMYNv/nY33/34XHJtOhRFwaCVuHP1OBRF4dWdLWneAgdP9XP7qprU3/VNwzyw5DJebnuOXx14CEmU+MiUG1ELeuyGHBYVLGR755ucdjXzg92/4JYJ11EglPKN/ztEYY6ZGy7Px6gxUGEvQS2qePLES0DSEv//Fv0rq8sW4A37+NWhx/nc9NvpCbgoMNoR1bl8dNw1dEZc+ONhso1ZvNm2hwJrHju7dzA7J8Y87Uwm5miYaC4ggsz39/+JUCzMyuLZzM2aRWLxpwkICqdcrZwCuqwFrIy0szh3ErPzpxOJx0CQ+Z9dPyMix1igcVAQjaRIfhIK4V2Po1nzb3QGBsjQW4mFJZ6of4HW4XQ3yuOBfmYYZ2NX9fJq51FyjA4+Vnk5GbKKuG8I6QIpqESdEQEQp15DsGAGdrUG7fL7UUe96DPziHTUcUGxvVgEVdtupNxqIhr7X/o4fWCRUGLU2qbS02Tl2S0DWE1Wrlx2J+HoyAaYokA0JvMvN0yma8BPNC6Tn2kkGoujUUuXtLq7hIrhkJf7Z9xOty8Z9lJgyaXL7ebZN1wY9Wr21vWSV20hx+Rkdv5UYnIclSjx8unNfHLWnRftP+FzYZ66An3pZJRoEMnsQBBFlEgItLZkJYGkQGQ0lNZWeIfUfYgqRL0pza06WZxuXTzWOEhjl4fG88IrXtvTSkXh5Iv3/wGGIiv0u+MEmyu5oWY8CSGCRjYz0G5kb10LZdP62YmbVavuxhgOY8jI4UDUxeP7/4Rdb6PSXoKeMPdWDRHY8RRyNII4YQHmGVfQa57An3YGaeg8TnWRjRsW34Q+5ib3+Jt4j2xC0OjImnUVX6+4gk5JQFbpsaichMIyX5x7P+6QB0nWoQsbyc5MoA4lvUmUWBhRaxj9e2u03LisiMc3nMHtCzN/mpPP31NFgRhCdeAFos8fZ6E9n6VX3s3Ptvo4o9KSb8slMTziGq+yZiFmlRLtC6dI/jm4vGFczhmU3DYOjcFEcVRHItFKtmM06R5fYk9p2YoaLfGCmdhvGwdKgrjaTEQe+agaNRIVuW8vIp1r0/H9T86n1xVEp5HIzdCj+TuEAZl1Kr5+zyweef0kZ9qHmVbj5KallaPE+ERRoL3PP6p9e78PURTGiP4YxvA2GCP67wJnOl3kaaIktKNd0Mbw10FRaYna87F0nmJW0VQ2t29nSe0VPLO1iWlVWei1Y4/qBwoK3H55DVVFGXQP+FMkH84ubDxhNBqJsnxrWvzxqjnF1De72HooSUb31/dy7YLkbn5xtoVH1u/j+qWV9AwFRp3ynOgQgCQK2NWZXD/+CpDVyGKUQ90nyNBk8lzPy9w38V6m5EzAG/WSacggX+fgpc0tROMyTZ0eXAOF3DHhFjzxQZ44/lKq33A8wte3/4z7p9zELw8/TkKROT3cRq1zHOGgB0X00jjYQZ6liCxdCVp1lEgiRrevj2m5tezt2cve3r1cXbWcCbZCfr7vj6m+X23ZiVltxNdbwZpcPSWWPFq93TR5Ovm1pxMaN/L56fcRjmn59bFfptqJsRhKfMQKcg5ywIMYjzI7q5rtPceQVAly9HmjiH6BrYB8MQG6PD7ruA2bKKEb7MJz6GUMExaiyStHOH6ekr6kwjhuHvGsCgyZ1QjP/geJgIeoVk8kFiWaXYxl5lWIGh3Dbz6dOo9wliC5X/8NKlsOtrVfIaL6cIuc6lVGvJ1ZbNufjPsd8oR56Ll2vnbf1FQdQUgKnH3zwd0YdCo0aolhX4Trl1RQUWAlmcTu0oQsJ/BEfPz56HNp5Z+a8RGCkXjKymfWmNjcnB5zXGDORSVc/LsgqtT0PfMDOE9oUlc4HvuKe1J/C2ot1llXMbxjJIxEnVmIZH6HEBJJhW3ONbjeeGSkXVYhknUk/j4eV/AFR6v3e/xRROGiUQcfbIgCbm8Em9HII88kn221ystNy6uoa3Yxc1Yum4Y2cXiokerMMkxDgxw8G1c/FHTz4MHH+OrMj+DbNDI/+o9vxTLveh7cFaK1J2ntPt0+zGObGvnytCH8B5Mx+EoohnvbY2Quvh3NwfVkLL2Ldd6DvNicdMm/rfQyph/aTdzvQokEkRfciGSy4a/fiWX65WlzlmjLJWEt5OGH6lP7adv29zGl0IhY9xDRgWT4iezqIrr+R3x5zefoixqx1NxGpH4b4a4GtLkVGMqnImiNFGaPls406lQ4rEYUrYUIYNHBv902jUFPmKN5Vpq7k9/HrAw9i6cVpBFeRVGIiGc3BP7Kh01RwKpXYc23/HUN/0ooCjitWr750TkMuoLo1OIFZ6x4XOayqXkcPNWXVr54agHx+If2TRrDGN4R7zt7WrduHb/+9a+JxWLcc8893H777WnHT548yde//nX8fj8zZszg29/+NiqViu7ubh544AGGhoYoLS3lhz/8IUbje+9W9HaQFYW+wThzc/zI+r/vRPhhQTSrGNPpXWSWTKTGXsn+4W0U5czk6S2N3HWeNXYM/9yQJBGEpEUhL9PAsTOjRZkGPSGy7Xrm1uYwb2IuQ54wxblmeoeCbD7QQW2ZA48/QqZNjyQld/NtBjXf/MhshnxhptckvQWAs3mARUpyLSydUUimTce8ibnYTVrcsUzCcoijfc0c6j3O/VPvpTfUyf7+PexsP0Ce2cl11WuIxLTMn5TDkbYANy2rxKTXMDBgp6bMjkVrJhQPMzO3FrWkYyg4RKEtn58s/TJRRUEvWEkkQOtwElPiOB0lDPn7ybeY6fH3cf24KwhEgyRkgRWli4nFE4yzOnmt5U0q7WXElRgt7uRieGvXIW7Iq8Ed03PP+DX8z8FHCcSS7tqrSxdgFJ080zqy+JUEkYjZhqD4kqnr9CbUWUXEXd2oqxeQ6KxjVclc3NEALjoZZ5lG/VA92aZMZEUmFI8y0TkezUAvsSObya+Zw+Drvyeq1aPOr0QQFGLhCJmrP5F0WVZkNLmVaHLLQVGjN4Hxmn9lePvjaHIqiQ20k7HiI2CwIJkzcFxuJXjmAJLZgaFyOnHvEJrcCqI9jSiuDnB+uIm+P5Bgx+G+UeXdfRHOyZcoCqlYZWeGAYNOhT8YY9vhTi6bWoBJcwkTfUFmd8fBUeUnBk7yvY+vRhAFIlGZhNjD8rIF9PkHKLDkcqS3nlVVl5GQL77AT4rxpUfChzvqkxtf58T4omHCHfVkLL6NuHcQSW9GiUWJD/eDseTtO49HCZzag33Z3ciRIKJGR8LvJjbUBYaRvObTapxs3Neemoca2odZNrPwQ22BtJu0+INRLptSwPhSB42dw0yrzqSzLzmXHTwU5Zpp17Kx43UmZ0+gvv8U2aYs+vxnBfoUiMhxHHmVRHuaQJERtAbi3kGmlVXR2jNidS93apBPbRs1hri7F/P01RxTJ4hHJWbkTiTHnE1MpUW67EbEI1tBkQk2H8W+4l7iw/0Ikoqs675IpL8dt2KiPpzJcGdilNOMNuwjPtCeVqbEIgiDLeSbHQy9+H9o86swVs8mEfQR87lQq3WU52m5bWU1j204DSQ3pD9942SybLpkGI6gkIjLOMwaMi0avnT7NHpcQRKyTJ7DiMWgIZFIJOsCkkoERUn9/U44921O/J2Is0oloiiQuEDKT4NOjV59cY+BqgIrH7lyPE9takABblxWSXXhh/sbMYYxvBPeV6Lf19fHT37yE5577jk0Gg233HILs2fPpqKiIlXngQce4Hvf+x5Tpkzhq1/9Kk899RS33XYb3/72t7nttttYs2YNv/zlL/nVr37FAw888A8be1vfMIoYxRn1EtYXvHODMbwjzln19W11lFfOZDjsIZJxhkMnc5lalcXSrLd3MxvDpQ9BgD5PhDcOdlDgNKHTqNhzopdpNU5ONA+l1Z1a7WRvXQ/LZtjxBaNk2my8eaQbnUbiszdNob5liIrCIvbV9XC0cZBVs4vJc+hp7fWy6UAHq+YUo9eqqCrK4FjjIAlZweOPsGpOMQdP9fOb50+wdE42mgwX29t3k6F18KkpH0OMmlDap9DkjnLzjBnkZJhw9UfZVNdEIBzjpuVVVDg1GBUXBe3bYGMP351wGV7bRHYd7edUd4h5VROJnT6Dzq7nuBwiz1pIsSzi8wxx1N+DTzYg+fI55j/Nklwf7riO1oAVQaWmdzhEQVmEBrkHpzAVsTkXs1bgjlodr/c8T47BwZAngckWxSRb+PbsjzEYHkavNaHCQaPvJLOyx9Ph7WGSvYwbbFVYGo6gHrcA9S3/ziF3CweGW6kdv5JZOZPJ8gwR72njIzVr8CkJtIKF2dobCB/dDJKEcdIqxIF24hotYYMZ776XMc2/ljMGHa/3HmWpUcckSz7xgB85u5qEJBJVawkfP4C6eTsqgw3juDnoq+cQDfjRzLmRwb3rEYc7kaoXMmiuwLl4HELbETx7XkJXNB7T5CVIM1d/KIXK3gqdViIrQ483kG4VthpH4ldjMcjLNPLRqydwssWFLxjj5hVVeP0RTAYpqVR5iUJQJPLNOaPE+Cbbp3Oyzc3BU/2U5VtZMDWPGfkS+zqP0jLcyfLyhWQZHBjUWrhIuntRd4GwEr0ZznevV2mJDrQTbjuBqDcjR0OQiOO84csXH7woYapdhJKIE+lsQGXNRF85HYQRl/9IJEaOw8BX7p7JGwc6iMQSfPamKRQ4jYh8eC36gqhQWWTjiU0NzK3NIT/TxHNbWijMNnH3mvE8ufE0Xb1q7r/yHipCXczrdBF15NBbPZtnuo+wMGs1G7dHScSuZOVCLY5oD6ci2exojlKYnQzhemLjaWJxmUFfHNGeC67utDGoMrJBFKmOKhRkVrNfVjjmamZa1jh61Dn01i7j8NB+TOo8lhLFcngjit+Nbfm9RANeEgUTOXzAR3XxaHKakLTJcJBYupyjkl+B7PeiLa4l0nkKT+kydvlNnKmPMVfoYWqhhsW6OibdUYNbMWEzSJh8rXhe+iOizoCxbCoqtZZw2wnig50YJy6hIncCvWEtr+9r50zHMJMrs5hencWxlnY27W+nKNvM4ukFZJk1bxvFIwjQ4w6zcX87bm+ElbOLqMizvHe57AWFrqEwG/a1EQzHuXx2MaXZJqS/sn+NJHLZpFxm1jhBAYNW+lBvmI1hDH8J3leiv2vXLubMmYPNZgPg8ssv57XXXuPTn/40AF1dXYTDYaZMmQLA2rVr+fnPf86NN97I/v37+eUvf5kqv+OOO/6hRH/nmSaMpihSJIisH3Pdf69wzqofKq5linMiO7v3kl9u4nfr6qkpy7ykc0KP4eJw+aN8+/d7MepVODMM/GFdUiDLatJw+exi3jjYgQCsnldCXcsQhU4LT2xsYOmMQh584USqn90nevnSHdP570cOpBYu++v7+MKt0/j500eRZYX65iE+feMU/vfpI5xbBxi0KnYc7aKu2UWGWcuA0si6gyPiSoW6ch57piGVA/lE0xAP3DE9LTvAkYYBfvnxGvwv/zdyOBkekMieyH+/epKB4WTcdH2Li2WTM7lOfpWq6Ut4tW0nt0iZvBLvo8HbR2FgOQdPdPD9pWG8vQF+djybJTMdPPHKSW650cwj9c9wQ/69/PHZutTY9tcJ3HvztVRka+ntteBxw/efrSMSTaDVSNxyY4SnziTnwzvHX8nnpt1OsT9A9NUHCQMxOcFjDj2H+5N5nE8PNrG/t55/q1pDwO6guec4VW++jGn21bg2P5w6b7hhP45VHyPe2obRZMPdcZJwx0lypy4jIPjJUhsYfvL7yJGkJU7Um7DOvY7gG48QASJAsGEftgU3IBRPw//if6buGx31ZM+7kciBk0Q7kr9vpPMU+oppICtY5l7H6ICDDxdsBpHrFlfwk8cOpWJ3C7PN5NjTVegzbXp+9eyxVHjK8aZBPrl2EhpRuRgPft8RC8G8ohns7zpKKJ58f2bkTGXfgTBbDzYByfdpX30vS1fKvNGyE4BTg41cUbWUa6tWErvIBUom22gxvoU3gd7GuYdLMdrJWHgTQ6//LpnCDdAV1SKZLu66L5usxD39ePaOzCGBhr1kn7dBoCjg8kb474f3p+ahIw0DfOHWaThMH1IBX0ng4KlBHll/kvwsEx39AV7b3QpAXfMQZoOa6xZXsPdENwV9B/AdPivI13GSnIaD3L/mC3zhwebU3O8KWKnML+C1vUmvp/oWF3aLjqsWlvHclkZysqz4S1eiaTueTKsIqKzZoMDwjqcxrPkkD9avo9GTDFlqcXdw60Q9D9U/kxry3q7DfG3F7RTFwH9wPeGOk2iOvM5dhROJl32MnbkmmnuS8eOiKJCRlYlt0S24Nz+U6sNQPRt3PEzYkYXTkkniyq/yg2d7GPL2pca9enoWa0L7MamOsFlcxcqSCPL2X6ayP4RO7cG+7C78h5PpAsMd9aiv+To/XjdIz2Aw1U9bjxdfMMaJ5iFONA+x7UgX3/v4HKz6C6+g+j0RvvnbPSPfuTMD/OvNU5hcZn9PJD563BG+9bs9KVJ+8FQ//37XDKry/npvWFlW0J3VChgj+WMYwzvjfU2w29/fT1bWSDyb0+mkr6/vbY9nZWXR19eH2+3GZDKhUqnSyv+RONLYT5FRJmE4mwZmDO8JFJWWaEY++rbjSKLEnNwZDAltOAtDfP03u+gfDr1zJ2O4JNHe5ycUibNmfikvvdmcKt9ysJOmrmG+cNs0PnfLVA6d7mfHkW5MBjU5DsOofL6yrFDX4sL2loXypgPt1BQnF+eZNj11LUOcvw4ozDZT15xU1J871c727q2pY4IgEPLqUiQfwKhX09DuThP1A4j0tY2QVaBfyk2R/NQ1HRskUDQP/ZkjTLYVMJQI80bXYaY6ZrF1Xx8rJtmIH3qJfm0hjozkNU6pyWDvwA7GZ9aw60C64JMsK7h6NIR8SX/t57c2MbUqmVJv6rgM9vS/mar7SP3L7O85BkfeSJX5CipSJP8c2n29dHl7iMkJCuLJxW+o+SjpUAh3nkKdmY9yXqor+eg2ljsnYPX7UiQfwFA5E9/B19J7iEdBlgkPdKbdNwD//pfQ55enlYUaD6HNT7rvf9jR54mybnsTt11ewy0rqrjt8mqmVGVxpnM4rd7pNneaBgUk03l5LvHpUtLD643buLJ6GTdMWMONE9YwN3sB2w51pdXrd4WQora0so1NbzIQTk//9VbEBztQGa1kXHYLtvnXk7H4NrxHNoOvP1VH8PTgPbKJjMW3YVtwAxmLbkFldRAbaLtIzyB4B/EefD2tTA75iQ6OaFwYjSoO1PfxVj7y2p7W93n19f7BG4zxwrbkuz2nNoc39qe7uPuCMWxWgS+szSZ27C33N+hFcg2mkc/p1dls2J+uK+LyhslxGLljVQ29riDfXTdA58zPoV/xKayrPomxZjbuN5/EUDaFnognRfIBap3VvNm2L62/hCJzPDiEr7WecMfIPJroOI5238N8fpGaB67I5jNXFPKfNxdSLHcQ7WlKPlPzryfjslsBEDsbqBtqItR2gvY+P0PedIv/64cHCZUsIFA4j+GQjLFpc9pxFJnYQAeS2ZEq6uwPpEj+Oew+0cOE8pE6gVCMzv7RujWQtOafusB37pktjbwXHvyiKHDodP8oUv7i9uYxEesxjOEfgPfVoq9cYKvw/JRZb3f8ndr9JXA4/nYrvD8cxjWoYnV1GEG2o9e/fQ7Pix27lHBJjbOoEtXRbYg1U1FMZi43LGJT8w5MFPP9Px/kC7dNZ1r16LzhlxKy/snCDP7S9+HdXJeqfRhI5v0W3+Ky1zUQoKs/uRHQNzSyaJEVZVRdAFEYLSYuCQLRs4WKcvE5QVYUxPM36JTRkmXK25z7rRt7FzyLICAoCogiiiIjnK2nICMIArKc7EdEQVGSsZiyAqIgIcuJC7o0BsIyP3/qKDctrzpbP3mtskz6tQBROY7yF2xAJlNXKsnRKXIyV9uoSxERlLeWnT3/W4cpy2+78XnB30MQk23SC5O/hyS9b+/Ru/k+/K240LUGety4fREeWZ++SXPfNbVp9YULvSNi8rfNyvr7a8j8rb+TL+IjlojxdN1IGrXrSm/k3FN5MYgICAgXPbevG/x1b6aVCZIaQRhpJwDxoW7cWx9Lq6cvrr1o3+Hu3gs+6+f3DSBJF5q/BERJwp6hf9v+/xL8o96P9/J98HYOp+ZVWbnwsxtIBHBH/VgFcfRz8JbqCsoFhQ3d3jCPn411B/jZxkFuv7yacrkV096kgKqiyKPS9yb7u9Acdm4Wf0upKKFr20NBw1402SWorE7EimnEXF0E6tMFJJlzFcnXUka4wBMuCiCgIKCgJG/O6GGIYnKuPm9UF8RbPpAqtfi2z4tK6h9VJkkCZrMO49t4Afw1UEmjr0MlCVit+mRmkLP4Z1s3jWEM/wx4X4l+dnY2Bw4cSP3d39+P0+lMOz44OCLUNTAwgNPpxG634/f7SSQSSJKUKv9rMDTk/5vdfp49uBetLo7Z3Uk4u4xE6MK+g3q9htDbHLuUcOmNU0DrKIQTe/CPXwTA/JxZHB48hk5288NHZcpzbVw+q5DKQtuoD/X7jawsMwMDo3Pcvt+42Ef0L3kf3u11FWQZMerVvLyjiWsvS+a5PwdRFKgqyuD3606wfFYRr+5qZdgXweUJs2RGIafb3Km6KklkXKmDdTta0vpfPquIHz2aFPYaGA5RnGNGJQnEzwoRNXd5mFqdxeHTA+w65GLN1Ut5oel5ILm401pCGHUqAuE4AMFwnMrCDLRqiUhsxNKvzS5GMVpJBJJqx85oO/mZTrrOs6qsnpaJoW0dgZnLqfN1MkHl4PKCmRwY3MOyuavZcGiI5SuvI3O4BZ+vgEXTi/nza6e4Y/ICnm58nJtmLKS+5fxrFsjNNJKQFYx6NWsWFvN/zyTd3Y+cdHHHpEW0Dj+aql/nakU942piXcm84+aWOubmTmB330g4QLm1gDxLLh5vP02SzDjfMLZx8wg1HSFFswQRbUE10d5mJON5okfTlrGh7zgFFcsxGyyplFPBM/uxXnYL7g1/SFUVNHoQQJtZgHxeXQDTnLUE20fCMgCM1bMJd57Csuj2v+t79G7fh/d6LBe6VrtRxbWXlaeFrhh0Kkrz0utXF9nQaaRUzmuAaxaVY1QLf/e56N3OCyvKF3Kif4SQnRg+xPLZS9mwpyNVVuA0EtOkW+9XVS4mW+O46LlNmQVIZgcJ34gGiHnaShKmPAYGfGRlmUlk5GGZfnmaC75ktqNyFFy0b60pB8usNXh2PjvSzmhDnZnebnpNNq/tbk3NQwBXzC8hEYm/q/v2Xn9n/lHvg1UvccPSSn7/Uh27jnWzclYx63aMeHhlmDUUZZtY176dO6Yuhf0jHkKi2U4kIxNR9KXGs7++jzVzi3lxR2uqXrZdP0rw7fo5WRSaEzT3WphssqP4XYSajpA9YT4T7CXUuZLtj/ed5u4p19PoGulPLaowxPLQlhcSOfr6eURbQDd+EZ4XfwiAcdx8PHtfQptfiWXmGgbX/W+qD0GlYciRxXh7EXIkSHasixy7jV7XiDfYmhlZ6JqeAZWGbMvV+IuWo+0c+U4iqlA78kkc3vj/2TvLADuu+24/A5d57zKDWFoxWpJlSWaZIabEduw4TpykaZumSVu/TSFtykkbaMNgiJkZJMu2mBl3pWW+jHMH3g9X2tV6RU5kS7Ln+WLv0ZmZ/8y9d875nfOHwaaqEjfVJdlhpecWTa9k64G+oWfutlJe4Dzh92V0lQ+bVRrm0Xbz4tGkEhlSicxxj/kwTB1dyNMrDgz7DVyzsJ5oZGjMPNfmTeaig8knBcE43vb4x0RPTw+33XYbTz31FA6Hg1tvvZV//Md/ZPLkyYN9rrrqKv7+7/+eGTNm8OCDD1JbW8sXvvAFvvjFL3L11Vdz9dVX87//+7/09vbyne9857Sv/YcOXIZh8Ke/eYYyp5tLwu8Rn7johDtY556APj7npJ26invvauKTFqH6SgBwu6xsbtvN/lAzxblGYv1O0lmN+nIv5YUugl47XpcVr9OC12Ul6LUPWy3+uDjXBqyjnG2hLwjQH1dYt7ubAo8Nl9PKmu1dFPodTB9XjMcuk1Q0OnoTaLrBnsMDLJxaSSKlIAgCOw7243TIzBhXwq6mfhqq/OxsGkBVdS6cVkFpwE5zV5xV2zspDbqYN6mMrKLx/vZOMorGhVPLcTst7GgaYM+hMPOmBrEXRFnbvgm/rYDpxVMQVBfrd3XTH84yb0opBS4n0USOHU39pDI55jWWUeWXcAkRlIMbMEKdWMfMJuYZxeZ9IQ50pZld72KsvR9ngYttRo5iTymVmkg2G2JPuo+QZsGariAbVVhUliCqSDRl/BiynZ5InKo6hY5kO2XSeLbtjmG35pMK9oaSjK0uIJqNILpiuLQyVm3tQZTgohllJMV+1nduwiE7aQw2UuCUcYQ6kQ5sRnZ4SE+cx/5UH9u69zDeW8GUwlEUWPyQ6KMTBdkw8A30YrE7SbfsQpQtOOqnoQsCmtVOsm03Ym8bllEzaXZaWNW7i3mFYxnrKiZzYBN6qAvLmJnogTqUnk6sreuRXT4cdY2o8QjpWBi5ZirZpo0Q6UQeNZc2sZQir4Kzex9a+wFsFaMQXX7kQAUZV9VxvbfOFOeD0AdIqgrNHUnW7Mj/VuZMLKWuxIFyzCvbboeDnWnW7OgikshyweQyass8uOSP/v33x74XsmKKjlQna9o2Y5UszK2ajkerYPehENv291FX4WP6uCJwhdjUuYOueA/TyxppCNTg0U9RAg9wp1pJNW1G6WvH2TANS3kDSUvpMNtduR5yXQdJHdyCtbAC5+gZJBzVpzgzuLVecp3NpA5uxFJQjmP0DJLOmuGdLNDSlWTVti4yisaCKeXUlXmw/JHr0+er0AdIKBotPTHW7OhmckMhkiyyaU8P5cUOGsd5OZTcRU2gCjUZoSIew3J4NxRUo1Y3siPXhy1XzK59aQwNFo1zU+KT2d2WZF1TgtHlHqY2+OmOweHuOO29MeY1OBjnz6A5gzQnrAS0CIHwLqRwG/KEeSQ9XnaFD7Mr0s7k4jEUW+rpz4bZE92O2+qkzjkBSXUytsSOJdJPeu9q0DWcExZAtAOlYz/OmkmoqTiSpwDN5qHbZqEikyK1dw2a3Yla14jiCVDQ2QrecjLNm0kVTWRrv4MDnQlmjC1iQqmE7eAK1HgYrXEZXQmRYqMfW9t6ZLsDW8VYBIuVXHczuVAXjrHzMIrG0JuR2LSvlwNtERobgkysC9IXSfPu1g6qij3MHl9MwHVir01BEOiNZli9s4twLMuFU8upKf7wyfJOfH7oimRYtb2LVCbHhVMrqCpyDYteOdfmTabQN/mkcFaFPuTL6/30pz8ll8tx0003cd9993HffffxJ3/yJzQ2NrJ3714efPBBkskkEyZM4Hvf+x5Wq5WOjg6+/e1vMzAwQFlZGf/1X/+Fz3f6ZTb+0IFrS9cefvRwG58ZH8cXaydTPemEfc9JAX0czlU75UgPtt5DRGZeDZKE220jkciSVJLsCe2nO9VLpb0On16OoLrIZHXSWY1UJkcinSOWVPC5bNSVeZhQV8CUhkICno8+AdK5NmAd5WwL/aOIooAoCqiqjtUqI4oCuq6jqjqiKCDLEoIAuZyGrudd22VZHAzn0zQdi0VC0wz0vA/8sN0bWRbRdWPwfiRJoKDAPWi7JOWvoWk6um5gtUqDx2ha3l3fYpHIZPLZugRBxGoVMAzhiC35cn6CICCK+VrZ+f/qiKJ4JLxIR9Py51MUdfC6druMKIroev4e9CPe8ppmDHPxzeV0BAEcDhvptIIo5s+pKPn7tFjEvEu9kI/fz5cYzNcllyQBQRDJZjWsVmnQtqM2S5KIoqhkjnguCALIxwhCScr3yeW0I+fPP3NJGvoM8mEN+XvVNH3QFTeX0/LJqAIu4vH0kXsSSKcVLBaZXE7FYpGxWCQURRv83ASBI8/DGPzcP2rOF6F/FL/fjqZpxOMnTlHo9VoRRZFI5I/fhTtdztR7we+3YxgG0Wh2WJuiKKRS+e+J2y0jy/KHvj+XS8JisYw47oO2+/12crkcyeSHq/ng99tRVZVEQj1hH5/PhiAIZ+yzOZ+FPuR/84WFHnK5oe/z0Xfp0Lvq6HsNBMEAjHwJuA+gaSOjjgwDMpkcTqdl8G/In0uSGHz3HjsDHir9ZiCK0uA4krdJx+12EIkkcTrzojmT0RAEA1kWj4xHxpH70I+cz8BmkwfHO1XVkWXxyJh3NPRKxzCO2pEf72w2GV3Pjx02m2VwPBHF/Ps2HxUmDKsfLwgCVmv+vWoYBkVFHsLh5LCx8FSIojD4Tv8oODoeHO/859q8yRT6Jp8UzrrQP1v8IQOXpmv85ZO/x5Is5QZxI0pJHaqn8IT9z1UB/UHOWTsNA0frDjSnn+SY2YNC/yjpXJr2eCc96T4G0iFskg2v1YNdtiGLEoYB6bRAIiaSjFnIRJ3IrjSukn68xTFcDgs+m4dCR5ASZzHl7hLKXKXYpD8uX8G5NmAd5VwR+meD89V20+6PjvNN6J+rmLafHc53oX/0mufT8z+f7D2fbIVzz15T6Jt8UjirMfrnG8/ufZtYezHLJuoIHTlUd/DUB5n84QgCmYrxuA6uQ+0KwOjh3hMOi4PRBQ2MpgHD0Enl0qTUNIquoB1J8FXoFJEKJWRRRjQkBgYcdHb76DysUVIi4KrMES2I0ZnYxlutYUKZMB6rh1JnMcXOQgrsATxWNw7ZjizIxBM6rV1p2nsy9A4oxJM6uZyBKBrYHAZen0Z1lYWyYplSn58SZxElziJk0fypmZiYmJiYmJiYmJh8PJjq4zRZ27mJt96LUV9RQkXnu2RLG8zSIB8DhmwhWTcN18FN6FYZgnXH7ScIIi6rC5fVddLzFThhdBUoik5bV4b25ix9Gy0EA6WUF1sZ5ZNByJJJJukMpdmX7iOR6Mt7BMTsANjcGWyuHA6/RmkZWCQRAQlNkUklJXbvhTWrdeyeNqzBjai+NioCfhp8dYwK1NPgq8Vj/fizepuYmJiYmJiYmJiYfDowhf4pSKtpnt/3FitXZfAJhVyob0Wzu1G953Z5t08Shs1FctQM3Ae34O5pI103Hc3xxwllq1WkocZJQ40TVTUYiOQIR3O0dGbJZnU03Y4oOrBaREocIr5amQK/BadDPGUpR7fbRjSaoasvS0dXgI6ddfS6DLJFKXa5NxCRn8Rrd1DjraLWW0W5u4xSVzE+q/dDl4k0MTExMTExMTExMTH5IJ9aoX/c+tjH8FbLSjZ2b+XAdi9aXzXlXpmL1PdISW4SxbUQPXUsUTIpoeQ+XFKfs8H5YmeivBG5px3bmlfQrU4UTwGqw4MmWzEkCUQLWW8AOHX98A8iWaCwEAoRT3C8Sjqnkj5xHqwhO5UsmXQOuwMa6iXq65wMhDV6B2QGWlxksjUkJAi7c2y3D6DKLShiAkHKIVsE3FY7bpsDt9WJ02LDbbdRXmzHIduwSBZkUUYWJEQhv+ggkE++Vu+rwSr9YTVvT/V7+LD9zkXOV9tNuz9+zobt5vM6O5i2n93rnG/P/3yy93yyFc4/e01Mzgc+tcn4TsXnnvo6WU0hs3kJhmrFKaexajq6+R466wiAcIJvbUqW0M7xwcLQRfTchxPktsZ3ER2pk/a5b8btXDJq4R9jmomJiYmJiYmJiYnJJwBT6JuYmJiYmJiYmJiYmJiYfIL48D7OJiYmJiYmJiYmJiYmJiYm5yym0DcxMTExMTExMTExMTEx+QRhCn0TExMTExMTExMTExMTk08QptA3MTExMTExMTExMTExMfkE8aktrzcwkEDXP9o8hIGAk3D45JnSzwVMO88s56qdRUWeE/7b6fweztX7Oh3OV9tNuz86/tjfw5nkfHheJ8K0/exwpm0/G7+H8+35n0/2nk+2wrln78l+DyYm5xPmjv5HiCxLZ9uE08K088xyvtj5YTmf7+t8td20+9PB+fy8TNvPDuez7Uc53+7hfLL3fLIVzj97TUzOF0yhb2JiYmJiYmJiYmJiYmLyCcIU+iYmJiYmJiYmJiYmJiYmnyBMoW9iYmJiYmJiYmJiYmJi8gnCFPomp4UggE1PYDPSCMLZtsbExMTExOTsI4kGNi2OlezZNuUTi/mMTUxMTP4wPrVZ902GSOfS9KrdJHNpihxBPIIPwxjKsGvVEqh7VxLa+DKCzYl/0R1opZPQBAuCIGBVYwiagmrzoRrmV8rExMTkTCEIkCRBb7oPu2QjaC3EYljPtlkmgF2NkN70EtFd7yIHSvEvvpOsvwEDczX8DyErpOlJ9yKJEsX2Qiy6HZsaIbPlFaI7VyL7ivAvvotswSgM48M9Y1EEixJFMHRyVj/ahzzexMTE5HzEVGUfAVY9hRDtIBHK4XT40VQVHD4Ui3+YgD4ZsqAjaUl0yUHuNMSzhIol0YUW7UFw+jF8FeRExymPywkKv9/xCq8deAcAh2znL+Z9iVJLOQCCIJDrO0BMSSAVlKF1NTHw4n9TdPs/IHoroW0LoeW/Rk8ncIyaiXvh7WQsBQiCgCbkEJFANwdUExMTkz+Efq2PX255lNGFdcSzSSyChevHLMNunPr9bvLRIQk62f3v42yYiq2kBsHmRB1ow2ZzkXGUnW3zzhsEAXJShrSe4eebHqYp3ArAmGA9X5x2B2x/g8SWNwDI9bfT99T3KLzju2Sd5ad9DdnIYOx9j4FVT4EoEbz0XmRBxBAkhIIqspbASe2z6GkEQwdGllwTBLBlQ+iRTpBt4C9HEV0ntceqpxASvQgWOzlHERpmxnkTE5OPBlPon2GsWpzkmz8l07oTEBCsNoIX303ouX8jePWfkQ00cDytL4rCYJ1au9JPYt0zZA5uxlpaj3fRHWRdFRgGaIJKj9JDf2oAv81HmaMUp2iBw5voe/V/B8/nalyMde6tqILtpPb2ZLoHRT5AWs3w622P883ZX0HWrYS0Hp7q28TBZAdTR9Vz2bTFZASD9/u3QXg3U0QXPqsT0gnSBzci2pyoC29hc+9Olh9aRbG7kKtHX0Kppey4931WEAzieoy0msZv82PV7WfbIhMTE5MRGKJG80ALU8omsPLQWnx2L8vGLKEv00uVreZsm/epxqrFMQrK6X32v8DQAbDXTMJXWn+WLTt/iGXibI9u59k9r6HqGpeOupASdxGr2zaxf6CZDd1bEQs8jLv0c9hXPImRy4Cho4c74TSEvk2NY4RbEdQM/e88AkDB4s8Seiu/OQEgufwU3PjXZOzFI46XjBxS1y6i7z6CrmTQ5lyDteECFGlIyNuSHfQ/8V0MJZ3/u2oC7ku/hCJ5geFzOwB7ppfwC/+JGukBBDyzlmGZchU58dTzEFk0EPQcqmA77U0jExOTTzem0D/DCH0HsZXV46idiKGpCBYbyUM7sFWMI/TKDwnc8o9kJQ+CIOBQQ5CJQy6LpqTRNRW5pIHI6/+L0tUEQLZtNwNP/TMFt/8TitXPht6NPLT9mcHrXVI3n5udNYTf/vUwO5I7VuCYuAjdXzc40AiCgKzEENBRLF50HSLZ2Ih7aI91kdWz5ASFf1vzU2LZOADvd+2gMx2h3FvK+60bAHhFEPn2gmvwvvyLvL3dzaxtX8dTe18DoCPezc6evXznwm8QEIN/9PPVjyx09CUH8Ng8lDvKsHH6Ql0XdHZEdvCbrU+gaDmKnAV8dfY9FIojB3kTk7OBqum88P4hdreEWTKzmnnjixDMxBifSnQxRygT5oV9bwIQzcb5v40P85fzv3SWLTORUelZ8fCgyAfItOzEM2UpuMxFmNNhV98B/m/jw4N//37H89wz/RY2de4gqykcjrTTFu3kxWyCv1x4PdblvwdAsDpx5ELofYfQlRSWYCVZb+0wd3ybGkXZvQLJ4UZyuHFPXEim8yBKf9ugyAfQkhGUpg1Ik69C04aLZznaSv+LPxj8O/zOIwRkG0LDIgzDQBZU4queGBT5kJ+zufsOYS0aBX0HybbvxVJSh1wxAcPmIrH8iSMiH6yldYiCgCXdS85VfcLnJAgCtmQbifUvooY6cDUuRqqbjSJ78iEJuRggoMiec2dDxcTE5JzAFPpnGEG2kNy3bvBFjiASvOTzYLGRPrgRMnFkpxVrtBU1NgAYSHYXsbROi1hD7ECawulfpsyawK6ESaz4DXo2iZiJkTBS/H7nC8Ou9+ahVVw3cxyGkhlpSzqC0f4Smc4DuBsXE8NFb28YJxmCDhVr3WwKHSPF9/jCUThFG22hvYMi/yjNkTamlTcO/q0bOu/HW7m2qJpcXyv61EW83PTOsGNyukp7opOA948T+oZhsDW0nV9ueWywbU7lNG4fdyOyfnoxqwNqPz/b9Mjg332pEL/Y8ih/MeuB0z6HiclHyRPLD3KwI8qsccW8uvYwPf0Jrr/Q3CX8NKIICu8cXjuszTAMOmI9VBfWnSWrzi8cRhSj7zB6Oo4lUEbWX4Oqj5z6SIKORUuii1YUTu4JB4CaQ4uHRzTruZFjsclIrFaZjU3bRrS/37KBeVUzeOfwGsYVjWJz5w5yukqHTaIOAWv5KCy+IAPP/ye5UEf+IEGk6Po/RyucNHgeKdlHpmXH4KaJtawB7+yrSO1ZNeKaSk8z7s4tWAOVpK2FQH4nXmndPqJvYusbFIyahZ5NIaoZYn0tI/posV607mbi658HBLj27xBCMRSln9zoZTgEJ4HSEnJ9bcS2vEH68A58S+4i66s7rlC3pXsYWPEYA3UX0+O14clo1HQdxl/RQG7vuwyseQ4E8F1wE+Ko+acVtmk1sgipAQSLjaytgIF4Dh0DWRKJJxX8bhteh2wuHJiYnOeYQv8Mo6ZjQyIfwNBJ7N+A74Lr8V94C0KiD7uo0/fmr1FDnQDIYxfwmnARL64eGvRunFfC4uw7+K54ADHWQ/j1/yM29UJUXR1xzWgug724BqV3aMARZCuGmkMsrMNRO422pJV/f3o//ZEMVlnknqVlzO1torhiAp+f/hke3vYsOS1HubuEzzbeiD05gBTpHXEtSRCB4W/+jJ5DkGSQZByFVdh7baQ/MNmxiJZTPjtdVEEAUTv+17I32c8jO54d1raufQsX1y6k3Fp5yvMDDKRDI9raop0k1SQ+0RT6JmeXlu446/b0cOfCIuxEqb90FD9+ehdTRxdSV+Yd1tcwDLTu/aiHt4CWQ26YjVw29ixZbvKRYIDX5h6x4Oq0OM+SQecXLj1C5O1fkzk0NLYWXvll9Oq5w9ypnXoEYj2o0V5EmxtXoISUvezkCd8sNhz1U0g3bx1qE0Rktx/lzN/KJxKPzT2izWf3Ioki86pmYJesyJJMqacYX7CGglseRHAXofbsHxL5AIZO5N3HCNzwVwipEFqoi2y8b1DkAyhdTehj5+GdcQWO2skYuoogWYhvW46jYRr9r/0M1/h5OGbfRAYHhmEgOYe/c0WHG2vlOHJN6wmveATZX4KjdgqJnSuH9ZNsTiSLFclbiOeyBzC695DuOICcioLTT2TMMly9G0jtzS/iKb2H6Xvynyi645+PG0Kgx3rZW3E1P35h6J6n1vt4YGkf6XeHNj4i7zxMgSdIu20sb23pxOu0MLY6gM8xfE7lyPYRefUnKL2HQJKxzb6JN7oqKSou4Im39pNTdRw2mT+/bRqjyrxmmICJyXmMKfTPMGo2NbxBlHCPnUPv4/8EWl6kW0pqcTZMIxbqxFJURWLMFbz4233DDnt2bQ+zls2FFb/FM+c6cgOdeEJ9lLuL6UwMCXCHxY7R345/4WeIrX+JTNse5EAZvpmXE175CIVX3I/a10yJq5Alk3w88X4GRdX5v9c7qb6plEpB4rKGRYz1jSKjZfFb/Vh0G0ZiN77W/cwpHs+63j2D17tm9FI2dg5f5V5SMR0nQVxjZ5N4+2FuW3wDPzlm1z3oCFDpPnE8nSbkOBA/yAv73kQQ4Nqxl9HgbkD6QBLCjJolo44sr5PKpeE0NbrP5h3RFnQGsEv2D65fmJh87Ly85jBT/THEjcvJ2ZzklBRzSufxxPIDfOuOGYP9tJ6DZNY+hpEMI1VMAEEi89ZPsEy+DNuUK8/iHZicSSTNzsWVl/Hb3b/FOPKCKnQW4BdLz7Jl5wd6qG2YyAcIrXiI0tsaiMv5nVu7bKC1HqT/pR9zdBBwNEzHf+GtJCwnCenKZbBXT8ReMwkjl0GQrIhOD1omdeJjTAZRFJXJJeN559Ca/BgOWESZC2tm0xHvZkvXTvb2N3HzhGU0hVv49fanmVQ0hkt9FxHMJkecT0uEEdMheh7/LtaSWgRp5OaCNVhGdP2LZNv25hsEkaJlDxCSCuia/XXK9zyKa0IvuGswDLAU1SC5/Ri6TvDiu8kNdGDoOpZAGYGln0NPRrEEytCSEdKHtiHIVrwzLscwDEJvP0TxtV8nqWpsV2p4o6+YoEvismqZ4vBu4r5Rw1PwaSpapBNbsROSIQSbC8UWRDcgbi3iN+8MnyNubY7SOs1LicM9LBQhtWcVD3UZHGiLABD02vnbe2bjsefnU7KgEV/9BErvIexV43HUT8FQc9wyJ8C3HztMTs2HoqSzKj94bAv/8uX5uGxmskATk/MVU+ifYcTiGkDg6ITB2TAtv9qrDe3E53oO45m0CNHhwT3xQjr7e0acRzcgrUk4Qp0YlnwMurFtJV+6/C4e79/FrlAztb5KPlsxE8sbj6LMvBzB6sA//0bU2AChFY9gqArZriYi7z8FgshVS+/k8Lgg6/cOADCQs1NuiIiiiEfw45GBI+GGmtuDsW8j102+kLmjL6dPy1BucaMatTiLy/BY1oOhcUXNXKpCA0TXvwSAd94NTAqM5a/mf5Vd/fspcPgYXVCPV/QN20E5lsPJFn64fijHwH+v+yV/Me9L1DmGuysXOYOMLWxgX//QKr1VslDsLDrtz6fYWsR14y7jub2vDx7/xel3YMdh6nyTs0ospbCzqZ+7fNuxTL4cwWLDTopxezezqaOOPa/8nrpCGa1zL0ZiAHnUPKSqSQiCCIBcMZ7s6keQiuqQy8ef5bsxORPkchIrVmb4zKy7SQn9WAQ72aiHjg6NmnFn27pzH/2Y2OnBtnQin9TtyOzHkumnZ8VDHLvSm27ajGfKEig8sdAXrE5kb5Dwe0+ihrsQJAve2Vdhr59ypm/jE4vP7ua+GbfRFuvCMAwqfWVs697NW83vA3DVmKU8tesVWqL5newVLWvZ3X+QB6d/lmPnWQDuSYtI7FqNkcuSG+jA07iYTMvOYdczlPSQyAcwdMLvPwkL7uVfXujg76+9kUJhSNQaNi/emVdiLamn9+l/w1AVHHWTMXJZYpvfAF3FUlSFb/bVeKZfimhzkm7bm/9+6RqC08vmQwY/ebVt8Jzr9on8w3XV+H0u9LELEZQk6uGtYOhINgfpDc+Q7TiAGu7Gf/HdCDVzUCwekpmR3pxhw8fBui/TENApbnkD2neguMsIx4c8KgdiGQ53x2mszVcWkNUkmeat2GsbsfiKCa/Mb8oI8ov85ZIH+N6bEIrlj09mVKJJBZfNrPBhYnK+Ygr9M0yTpFN19VdR3n8SPRXDMWkR4dd/PqKfoSoEFt2CakjUlVdQVqjQ1T+0E1Ba4KCqoQ6r+25kqx3B5sTIprC+8ivuGj0NYd5XcaTjJJ75PrquosYGUCM9pJs2D55DsDkxNO3IBXUi7zzKnVd+i71tcS5p9DOmynfce1BFhY3pLqYvu5/Ua7+kbNtKKt0BxIvu5ZtPHqYo4OArn7ua9Z2beLp5JRcXT2Tyjd/A5QiSthRgGFBkk/DZunh2z2sUO4I80HgdgmwlrCq4LG48R4S/LIusOJyPmat0F3Nz2Qy8mQy2aD82ZyXZY+pFO60O7m78DM/sf4VNnTuo9JZx5+Sb8ImB03YtkwwLSysWMa2kkbiSoNARxCv4zDg0k7PO5n191FgGcNY1IljyMcKyuwD7uAVMkcIs79S5x9mDXDMNsagWQRy+yyI4vFgmLCG76iGkm747uABgcv4i2rLUVjppadEoLw9i6CJbdsS56bKCs23aeYHsCRJYehey24+eSSK5C8h0HEA4RrgYqoKWjI44Vk/HR7QNQxCIbX4D0WLFO2sZWjxEdP2L2Gsmnunb+EQiSSKv7V/Jzt59zKuaTqWvnPZoN++1bqDKW8atjdciApNKxjGQjpBQ8rv4Pcl+QnqO6s/8FUrPISA/17GW1BFe/hCWwkosBeUIVgf26olkWnch+0vwzLgcwe5GsNqH5TRSo30Yog3DgJa0m/4Omem1EeyyAEdeoenmLRhqPiDDOWomA2/+avD4XF8byT2r8c2+mv5Xf4phGHinLMUz60qSWYOnV3cPu29V04naSxENg46CRezuVJh/4WWMcsfJHt5GtmM/1uJa3JMuJPzmrym8rRaXs4IZ44rYtLdv8DwWWSSczPHY+/mNovsvvYop2Sgd3on0RzqHXTOjDC0SCKkw1uJqHDUTB0U+5H8Hjo2/4yvLvkFrTOKt9a2E4xn8YhxbrAfNU3bKKk4mJibnHqbQP8MYqo+/eznMlTPvo9Bl4WA/TBk1k8T25cP6SZ4CQm/9Fj2TQLQ5+X/XfZsfvR1if2uE8bUB7r64Gung20S2vInk8BK85PPENr2WjzOTCxDCOVweB7ZlX8LiCaIradyNixgYaKNHyyDaHJQ7g8Rf+Mkxtik4LQb/cEMp6WiYVPM2HH4HmmPeYB9NUGlPtYOmsdNhxX/LN9AH2unWFdJagunjirlssY+/W/mf6EeyDf8s3Mq9025lhmU0hpGvK7svso+Htj/NHQ1LWeqroykb44frnySWjWOXbdw/47OMcY/BMMBr9+C1eXigeDriaw8NZjFOT1mKdfZnhg0uXiHAXeNv49bx12HBiqRbPnT8mGjIFErFFDryuzWmyDc5F1i7rYVRcg9C4IIR/zap1scv34dsdT1u+4kFvFg6BqNpHVrrNuSaaR+luSYfA6IAM6bb2Nm9D8MoQhdyXHiJjsM7MoTJZCSC1Um2Yx/hI7HQCCJFV31l2EtfkC3YayeROZwviQsGCCKS+8S11QGMXAZH9XjUdBw1EcYAgks+h5aMw8gIMZMPIIoGA+kQ08snYbfYGUiFyKoZvjD9FgRB5Efrf0M6l8FtdXHDhMt5dvdrxJUksihTmkrS+8z3MbQcALaaiSCI+OZeQ2r/BrLdTdhrJuKbvQz/wptJN28juuppRLuLwPybSOxZhdKdXyRwjJ3Hqi6Dv7+rEYcsUOpSyW1+gVgihGvyRdiqJxDfnK96IftL0dWRGRgyrbvxzbse14T5GGoO2VOAtXwUiuTAJg/P8zC1wc8o2lFWPMrodIzRYxezoX8iNqsP94ZXAMj1t5PL5bBe9nU0w8BphS9ePZ7HXDZWbeukotjDxbOqeGr5gcHzPvxeL1O++C3a9seAIaEviQIFPgc53cAlKITf+Dne6ZeiHWchS4uHiIbCPPRGL/dcM4liRw5e/mf6kxHc0y7DMvOG0xL7mZxOe38SJadRXugi4LKacf4mJmcJU+ifYZJRiQevCiLuehWjNYRl0sU4GqZhKGmS+9YhOtz4Zl6BoWbRj8Tz69kU7HuXyuK5jK0JkEjlKGhdQeyIO7ya66P/pR9TdPNfsbM9y56QxE02BTUaRxCg54nvYagK6pJb+WVsP4ci7QDUesv4wtwrkN95AgDJXUAfBfz3q/vo7E/jcVq4f2mQaa27ITgBQYD+VBs1oV7KtqxAlC3Yp1/MwUAJD234HXWBar657Cus7lk3KPKP8tL+t5g8bxKyYcVhxFjZshaPzc0STzV9fYf4Ye/6wYRSGTXLjzb8hn9c9Jf4jAAX1VxAhWhHfu/ZYedNbnsbx/iFqN7aYdcSdBE7Ls5ndEEjpA6QzKUptBfgFsyyOJ9m0lmVw71pLh7lPW4pPbtFpK7QwsaWNBeNPfF3XxAE5JqpKLuWm0L/E4CgWkknBYLqWDLEsAoObCkXos+MmT0dtHj/YMIzAAyd0IqHKLn5rxkMkLa78S+8hdy4C9DiAwhWB9aSGkTnyYU+khXR4UXSNJTOg8i+IgwDZPfxPeVMhiPKApePXswj256l60jeoYaCGmZVTuXXm5/gqjEXoxkaoiCy8tBaLqq7gBf3vcnnRl9CcuXjgyIfINuyC8/EhYTe+i1qtBffvOtJHdxMrr8N17h5xDa8DICeSRBa8TAFF99NqPswllGz6R99Nbm2JP/48C5UTWfaKD/3XLSUoNqLFhsg2bobZ20jejqGtbh2WDnFo1jLGohtfIXUvnVHb47g0s+RadrK/7vhRu7/2YHB0MUvLXSTeuGfAbBXTcBRWMDF9JCVC5GmLCGxYyXZhQ/w1H4ZcZvGzeM6kd75HaLVxn2zruaWxQvpCmf4p99sQNWGbElnVfpSAnMnFGMAb61vxe+xMX9KOT97bgf3XDWByWUSaiJE+N3HCF7+xRH3IQWrONBnoBvw7pYO/nR0E7lkBIDEltcpHDtvxHzsgySzGt9/YivNHXkvGZtV4u++MAenVaa1N4Gm6VQWuwm6bab4NzH5GDCF/hlmoidN8pl/HYzJV7p/hmXxnRiigH/edehKmmxvK5LNOWzAsMS76Ygl2NUc4j/vGUfyzXdHnDvXe5io3sDlY1SMZAjZE6Tn6X8HXUVy+dgqqoMiH+BwrIvtwTHMcvkBA+u1f8M//34/nf35uMV4Ksd/vdzBv9xUTGmxQNZQKIn0oe3fiKthKgYG6oHNjBs7G6/VTbW3AgEB63Ey6NslG/bMABbJSXbf+1Ta/UwvaCBzcBOxgsIRWaM1XSOUDuNzBiiRSykvaSScfmLEeY10FLwgScInZlBQBYU3Wlfw8oG3AXBaHPzFvC9RIpedZctMzhb72iKUyjFswRN/B8aVWlnTdHKhDyCVjSO3ewVGJoFgH5nV2uT8wZAVRAFe6XuEaCYGwMySGcIPHXMAAQAASURBVJTkloD9LBt3HqClYiPbEpF8DPXRYUxVUDoPEHr7d4N9bOWjCSz53MlnSIZOrr+d+Lb8e1zpOUz68E6Kr/vTM3cDn2R0gZ09++hNDTC+aDSKptAUaiGbTTGrYgpP734F3dCRRImbJy6jyl1K9ejLmCi4iYe6Rp4ul0WN9iHaXYg2B5lD23A3LiK1f/3IvukEhTd8g560lYQq8ejyoYpFWw5GeL3QydXSJjJ78rkC4pvfwD//RiyFlaQObMQ5Zhap/RuAfCZ+/9xr6Hnq34+5gEq6dTdaKgorf8X373uAlzcPUOS34Yg3kQXkQCm2itGE33l08DBH/VRcF32ef383R28ozr9f40Bb/r8cCcAkc3gnxTd/m4SjGkkUULWhS14wuQxDN3A7LLT2xBhT7SeWUvjVi7vQdYN3NnfQeOMk3FMvJr7uBSKrnyGw8DNE1r6Akcsg+koINd7Om8/nQwFiySz6B8K/9FT0lN4qTZ3RQZEPkFU0WnsSPPHmfgaOxP7brBJ/d+8cSnzmS8zE5KPGDOI80/QfHpZ4DyC+8SW8825ELijHXj0Jd+NFg5ODwT4VsznYln85Brx2JM/I3QTR5uLCiX78FdUkdq7MxxAeKbcn+0vYneoeccyeZDfBy+8jePXX2dim0dk/PCOwrhv05xwYCMgiyIkoRk4h/N4TRN57EmWgA7Jpbh1zCZc0LOBA/CABpw/XB8o7XV8xndjT/wahViLvP8kCRxl+mxul9zBOJYvDMvyFLggCPnt+58MwwCpasZZ8oC70kVJFexN7+O8tP+WH635Dr9bNcTY8zyu6Mz2DIh/yVQN+ve1xNNEsyvRpZeeBbsrFAQR38IR9aoIWuqMq4ZR2wj6QL60pFdWitmw502aafMxoOYl32pcPinyAjT2biOYGzqJV5w+WQCl8QKzYKsYMXwBT0kRWPzOsT7bzAFqsj5OiKcQ/UFbNUNKo8ZElXE1GoqgaWU3hMxOvQhQEvDYPd069EZtk5bk9rw1692m6xrO7X8Nnc1H1+qNkN72Gc/TMEeeTvUHAwFYxhkzr7vyxqTiSyz+ir+j0EHrpf7Gs+G9aukYuBq3eHSIjeYa1Rde9iOjwkutvx9A0Ahfein/BTXimXYaWTozY6ddTcUSbE6WnmUDPRq6LPsLF9h2IYv776B4/j9jGV4cdk27eiuD00t6b4tp5ZVj3D58ngkGmaTOeNf/HX9wxnUn1QYr8Dq6YV8OC8QXU+VREQ8Vlt7BySwdb9vUNbpAEfXZU1cA28WK8c69HS4RIte+j+Ja/wXr9d3in9HN894WeQS+BS2dXYuw7ZsNJEJF8JSOe1bEIAvRFhifAdDssdPUnB0U+5MX/S6sOI4jn+WTOxOQ8wNzRP8PownFcKmUbimFBNHR6n/sviq77U3xzryW+5U0Mw8Az4wr6AtVcOSdHfYmDdE7HN+saBl7+EQDClEXES6rRS2uR3/gFRUvuwlAVRKcHRAl0jdxAB9MmTWd77/5hl57mqSDdtIVM9VwCDvA4LcRTuWF9CooK0DQdWbKBppE+PFQ+T+luJtfXyrxRM9k30M5/bfs9DtnOteMvJZvLoqRjTLIHKdiwHD2VIBfJrwY73v491Rd/FnvdFNStb3PfRTfyk/2vouoqgiBw15SbCUiBwaS5ajqBe9KFJEQRpasJye3HN/saomqGH246kpG/H9a1beb/LfwzgtLpZ9o/14hkRyZ+aot2ktGzuE63TqDJJ4q9h/qZ7zOO67Z/FEkUqC+ysKU1w5JxJ9/VF4vqUVu2Yhm78EybavJxImocjrWOaI6pkY/flvMQQ7ZQdOWXCb3zMFoyiq1iDP75N2IcI8oMXUdPjyzXZuRyI9qGIUgIshXjAwv74nHKupmMRDAERgfr+NnGRwBwWZ1UeEootPvRPiCas5pCNpvGanMiuQPYayaipxOkD29HtLvwzr4KweHFVj4aNR7CNWY26eatpJu3Elx6J9nuZtDzC6SStwjZXYDk9pIL91AaGBlzPqrCjRRt49glVUNVyLbvwz1pEammzYTffRxLURWBBTehJSIjzuGauIDQm79GsNiQqybiKx2FXFBB7vBm5EAZiNJggr9jkQwNQYCZ1RbE+MjvkmBzYi2sZHRqK/dcOZOOUJYJ7gjK6p8TefMQjvop3DLnM+xscrNoehXZnIYsidSUeugYSFEW8CJOvY6CSRejizIpbKiSRnF5mKqSLIl0jqWzqpgzJoB4KICSjCA6PAQu+yKKszifwkKAdE5HFARssjAYdmgY0FDhH2av22khmR75W+rsT6Dr+awYJiYmHx2m0D/DSMV1iB+oa2qZeQNSvIP+N36J7C9Bi4dJ7HoP9+TFSHY3/bqHXKSHZRNLST3/PcRgKX3zvkjwqq8Tctv50Z6XaT+4E6FJ4Ooxc1m44y0CM5ehDHQSuPAWwu89gZ5JMjaeYk7ZZNZ15YX6rNJJTA2OwlXcSEgtIJBs5v6LS/ivlzoG48VuXFhNhRdy5DV3rr9txD2l2/agpuOUZDPMLBrHxr69PLbjBTw2N9+qXoz88i/zel0Qkb352sSGkoZXfo50yT1Yi2qofO8F/nbGxcQ8Piy+Iqpd9eja0Cte8BQTefXH2MvH4Kyfip5Jktj1HpumzRtmS05XaY60EAyev0I/6BiZMXt0QR120T5Y3tDk00M6q9Kf0CipOHXeiYZiK5sOn1roS0W1ZPa8g6HrCKLpuHW+IsvQWDKOte2bh7WXuAvPkkXnF1qom/DKx/AvvAXJ7iTbfYieJ/+Vss/9w2CMvuAuwDFqBumDG4cOlGQswQpOmvLQ6sA/91rCK38/dJi3EDlw8l1PkzyGpLGieTUALouTmyZcyTO7X8UmW7FIFnLHxOC7LE7coV4Cl92LxeElvvkNEEX8C27CULLEN7+BJViFZ+YVCLqG5A1iqxhDtmM/0Q0vE7zk82AYGIaOno6jJkKo8RCizcGogM7kej/bmyNAfgf6lkW1GC8cGmavo2EG2Y69pA/twFHbSPCKLyIIItEtb2GvGEPB4s+S2PUehq7hnnQhRi6LoeWwz78LxVtDRtEQNQOP04dr9EwsBeXYyseQ7RzanBGsdnqEIEtmutHCnTjHzDrinZCfrwmyFXv1BAZe/wVaMkpgSjfljRfR9+i/DeZ8Sh/cRC4W5vNX/Rnf/fVGtCNzvSK/g3uvmUh5gQNdN8iKQ2OIyyYxbVQhoyp9iAL4HFYwDCzXfRsxEwWLA0X2YhgG6ZzGqh3dPP9uE3abzOcuH8fEmgDSkd35qiIn91/XyEOv7SGTVRlXU8DMcSW8uX74guUls6oRBcPMTWRi8hFjCv0zjGjo2Jb9JenDOxDTYcTqKQgeP6okYOSyeGdchuwpQIuHiB/aSdO4u/nZG22ks0kqi3v402v+Dvn9H+HqXEd//WKeb3+R9ng+Hs0wDF5oXcO4qbfi7upEsjqQ/GUUXfN1tEQIi7+EL/iLWBYcixoP42nZg/L+d0lMWULZnBswUjquTb/in5ddR3/Ojtei4Wt5EYd4JzlA03SsVRNJ7l417J7sVeNJ7l2LFuvnsmsfYGNfvg5tPJsglUsNhmx5Z15ByG7HMv4CcnvyA3h0zTNYr/4q4UQPOV3nzZ7tTJGmUuVoGHaNrOQheN03ib33KKl1L2KrmYTv8vtZufMxPoh4nguXYmsxn228gd/veh5N1yh2FXLX5JuRdHMn6NPIoa4YxZYkFs+pS6bVFFh4bWeStKLjsJ74dyDYPQg2F3qoDamw5kyaa/JxYkgsqb2Qjlg3bbFOREHkilFLcVvO72SkHxeSpwAtEWLgtZ8NtlmC+dJrgyhpfHOvQbQ7Se1bhyVQRmDRrRjiKRIeqgrWqokUXfMnpA/vQPaXYK8ej66OrHducjx0xCPi8MLaOTyzJ59V/51Da/nMxGU8s+c10rkMLquTr0y4joJ4kuSON8m07cFaXINn8mLC7z2OnkmCJCM6PShdTRhKCjUZpWDxZ1F6WzAMHdHpJb7lTTKHd2CrnkhgwU34Zi1DtLsIv/Q97pu8jNCCOWTTaUrtadwHXsRx1VdIbHsbpacV55iZOBqm0f/SjwGD9OHtpA9vx1pSh2/ONei5DIam4hw3D9lTgOjwoHQ3EbjoDnKVjazZ0c5TKw8jSyKfvaSe6eU6sY0v456yGNEbJHNwE2JhNfEJN7CzR2bBeDtFokp81cMULP0cSl8bgmzBWlhFqmkL6hHPydjGl0GSEJ3eQaEPgDvIk8sPDop8yLvU90XSjCk/fpC9LEDAcWQOckR957CBvXiwTRAEdh4K8cqawyQzKsmMyg8e38p37p1DTVH+nSQJAvMmFDOpvgBdN/DYZVTd4IEbJ/PI63vJKhrXLWpgyqigKfJNTD4GTKF/hsl17CW77VUcF96NYW+E7S+Sbd+D5cbvINrd+cEoFSdw0R10WGv478eGVo3bexP85JXD/L+bvooc6wR62dm3b8Q1enNJCtY8hyCK2KsnIIgSjlHTGXjzNxRceg/W536MFQbdzhLbVmDUzcHl8mIrKEVf9X9UHfk3z4zLEQtKycZiWLEiVUzE3jCdTFN+B8laUototaPF+gGBIvtQDHGlp4zyonrkBTcjOtxEPH7+bfczNATKWHTF3TS4y9C8BXx380N0xvP5A2p8FUwsHHfcF3zaUYbz0j/BrWXQZAdZZK4bexk/Wv/rwT4O2U697/wWLpIhM694DhMLx5JWMwSsAayGmZTm08qB9gglDCC4Jpyyr1UWqAzI7O7KMqPGcdK+YqACrfuAKfTPY1QVXl8Rosp2CTPKDdBl1q+MU71EpKz8bFt3HiAIeGcty8dCG3q+6s3sq0E3BjMUGapC9P2n0JRsXhz2d9Dz3A8oufbrYD/JQxYEtEgX/a/8FNlXiL5/PZnD2/EvvOXjubfzHVXmslEXsa+/GbtsI57Ne0EOpMO8vH85lzQsZIyvkoKWA/gO7yN2aDu5gQ4AlJ5DhN/twTP9MqJrnsU37wa0gY58YrsjeYusJbUEL7mHyKpnkINl2Ksn4J4wP18iL5fFUlxDLtSFoaRxprooWvMDcgMdWCvHYgTK6H3q37BXjcdeMwEcPiLrXsR/wY3Et69A6TmEtXwUgQWfyVc86m/FVj2RyIpHUOMD6EeSQAqylUMLxvPTF/K79pIo8PiKw3gur6Pc6iQV7ud1cTHuCQs53J/jvWd60PVu/vqORrQtz+Gon0Zo+cPIniCCxYqjdjKJ5Q8Ne4yJ7Stwjb+AeHgoR5PmDDLQPNIfJZ1REYQ/rKSwIEB/PEt3KMXciaWUFrp4a30r7b0Jth/so67EPegpqusGLuvQQpksCswaU0RjfRDdMHBZRXTTe9HE5GPBFPpnGHthGen4AOmX/3OwzTFhAdlkjMy8L2DkMmiGRHLtcwzM/5sRxzd3xglHkxTnElhCCUYHqtnWO1zsBwUr6CqGnk/eAqCl4zjHziKrHC9Rl4FoaPxoeYivLrke55jZ5CLd2ErqCRVX8/TWp9jQvpU6fzU3TlhG2dL7cczohP5D5HpbiLz/NACWCYsQ0HBbXTQGarnCU0v27YdxXXIvv259j1Utr3FR2RSm2QvRtRwJtw+7VMQ3Zt9PT7oPAYFSRzFW48QCJYcFJMtg7P4Yz2i+Oe/LrOvcTKGzgGkljRRIn4CVYEPAKwTwWhi8V5NPJ00tfVRZUwiWU9cnBqgNWtjWdjpCvxytax9MuvhMmGlyNtAktuwNj4hxHQirYAr9U6JGesm07MS/4EbQdQxVIbT8IUpu/quh2Y+hD+alGegcGmu1ZBRO5mSjqXm3fV1FPSKyMq270eID4DIX106FYUB9oIqvzrmbjJrFaXGQyuUTuUUyMV7c9xb/MOUOxPefQVpw06DIP4qeTSH7igheei+W4hpCb/xyUORDvgqCMtBB+tBWhPa9FFx0O5FVz+CZspj49nfQlTSucfnQQNHuGqwr76yfSvjdxwHItO2Btj2AgP+qrxFe/hsKr/wSWjxM+vB2ep76V+w1E7EWlKHH+tEziUGRD2CpGMvrW/JJHZdODnBpTQZn/06kUBRH3WT6kgbPrGofMZ/pjamUhTowcpmh766uoeVyI+L6ZU8QOVA6rE3wFXPB5DKefadpWPvoKv8fPHfqjyv87c/Xks7mn7EgwD1XT+TXL+0m6HOcsiqSYRjYJAEQTijyRVEYXCwwMTE5M5hC/wwjWawEL/8ieiqKoed3ECSXj0y8m4RnAk26QqlbBYcfn3WkKA/67LjtMpF3nkaLh7l+2b0cjnUNZl1eVDmTKqxkPnCctaQOS91U+rJOZE8QPT6UlVkKVhGzFrNq93biisi3bp+PPZdCyYT49Y6n2N1/EIDtvXs4GD7MdxZ+A7eriP44WOwK1obZqKUTsQSLsPa28KB7LFJLM1r7CnKAno2zqnsHt9ZeyJQ92yC0GiOXRZjQizr5apxGmjH2QrKi+6Si1oKCpKbQZCe5I0npJEOm1lFHw5gGCgpc9PXFz3+Rb2JyBMMwaOlNMSt4+nXRa4MWntoUxzBOnrxP9JehNG84E2aanCUkUaa+wsuOg8Oz7BeaZalOC9lTgNLbgtI7VD5N9pcg2IdCHwTJgqWsAfeYWRhqDkGyoPQcRnScojSloaPFwyOa9dxJI/tNjiAIAps7d/K7bU8zNtjAzROX8ej258jpKqIgcufYyyhKJYlaHflVAUkeUdFI9gSI7XwXX0EZuejIKgmGkgZBxN24iMja5wEDNREZdH23+ItBEEk3bcE9/gJim17DOK4KNdAsLgqW3oWRSZE6sGFwkyVzaDv28tFkOg4QWPI5+l/+XwwljWhz4Z9xOV8KJ9Fm+Clyw8ALP0PHQAdigVI8c2+jtiTLoe7EsKvldMhccD/21T8j8t6TCDYnrovvB7sXS0EZuaPlBQUR30V3oHkq8F1fSqSnB9FXwm/XJVk4y8uV82tZvb0Lr8vK4hlVbDnQR3WRa0QCPFGElKIjiQJWSRgxxxIE2NE8MCjyIf+RrNnRxfzGMibWFfxR87KkorF9QysH28KMqymgvtyLXT6/QzRNTM4Vzimhv3z5cn70ox+RSqVYsGABDz74IKtXr+Z73/se2WyWK664gj/7sz8DYM+ePTz44IMkEglmzpzJ3//93yPLZ/92hFyK2LblKF158SzIVgqW3gnlE0g39VCea0WsHkN7493Uu0WunlXEixvyA5QsiXz54hI8WpjeIyV6HG88xLdnXUaowofLV4xTCOAVcxhlDWS78qu1lsJKpJoJbEh2YI2UYZt2HyVtbyP07kcvn0R3xSL6+vI7QnVlPoRMAmX/u3RZpEGRf5RULk1vug+fs55g7F3C9gCZ+oVYO7eSXv8Ijos/DyseGQwLcNRNRXAVcs+0WxkbjRGYejHKQCeh0jn87v0edry/hTGVHu6Y5aK6ooCMbWQSPUEAMhE6Ww5jSXTjCu/Hc8HNZBxDNcU17ez4eSlClt5ML4qmUOwswiN4zYUGkzNGJKGg6Toe9+kLN79TRBShK6pS7j9xXgfBXYCRjmJkkwg2M6b7fCRHmkXzPDS3R0lm8pPsaeOCyK4EcOJSjCZ5DFHGN/daomufB/LJzoKX3I2RS4PtaB8J/8wr6Xvpx4Ml0hyjZyIepyzbMCQLjvopg4IvfwER2R3ALJR6anJCltcO5ssT7htooj8V4ppxlxCw+0jm0oiaTmLLCvzzrkOwWPHPu47I+08NHu8aPx/B6kBPRAgtfwjXmFkkdgwvdyj7S8DQkRxutPgAtsqxwzwD4lvfIrj0TpIHNiLY3XgvuBFDV/O5HY4pkyh5C1GxEH/xhwA4R8/EPXkx6bZ9ZMZcQr+3kmCNmx8v72PG9K9T58lRUuSh9+l/w8imEIFYYSW+udcMfhfVcDd+Ic0XLq/nHx/dQ+aIN+biaWXMK05gi3RjXfZVshrIFgv9BJGVCIULb0FLhNGzKWzVE8l4atENEIrHk7XX09YTZ+PBLsaNSlNa4OLqBXUoRzLkr9vZzeKpFficQ+NGWtFYua2Tl1cdwu20cucV4xlb5RtWe1sQhGEi/yipjMoDN4zGaTn9heoPomg6P31uJzub84uZL606zLL5tdywsN7MyG9icgY4+8r4CG1tbXznO9/hySefJBgMctddd7Fy5Uq+853v8NBDD1FWVsb999/PypUrWbRoEd/85jf57ne/y9SpU/nrv/5rnnjiCW6//fazfRsYSmZQ5EM+/i+x813spROo3Pp/GMkIyv4Sai+8BwSBGydbmV1VTDylUuwWqCg2ULo6h47PZRFWv0ChbMVY9jdEBBdStgP/vOsRMDAMA9Hq4IXeXbzSuZG/nfYn/McThwi65zK2YhE7OjLMKRdJWfcxoS7AwqnlCLEWIu8+gbzoJiyiTE4f/gK3iVY0Q8A+dj7BvStJrn0KS0E5npv/BtHupGDJnWS7m/JZYyvHk3UUM18wUJMKCALWyvH84rUOdrfky8jtOBThX/rT/MMVaYrqXeixPgSLjZyjCA2JSCLND5/aT3NXApfDxX1LLmf0ykfxX3p/3gvgLJEmyW93PM6O3nzyQZfVybcu+ApB8fzN+G9ybnG4O0aJNY3o9J32MYIgUFNgYVdn9uRCXxARfaVo/S3IFaeO/zc597DJIrqkcPtlY4mncsiyiM8rIlk+6NNlcjwEXSXTuofgZfcBBrqSJrLqGQquvH+ok64RWvHwsDro6QMb8UxaBI7KE57bMDTs1RNAlEgf3IzkKcA35ypzR/80kZDw2730JvuBfGz+07tf5brxl/Hy/uXousZ3Jl6K+sbvsJWPxl4/hcBFt+fnRLIVZAsDr/18ULg7GqbjHDuH1P4NSE4P/vk3IfmKKb7pL9HTCfwX3kJ8+zu4x88j07ITADURQRnoxDnvJt4/ZPDKpl7++Y56vLWzoK+JXG8L6WgIrfEqlC0vDdqeOrAR5xV/whp9Nk++2YOitjF7fBET6ov4n5f3MKnWx9dKXsE4JkFerr8dYczsfEnGI+73anwA78ZH+MeLbyLsb8TttOLd/zLZ5984UvFBoGDp5wABZ3QbyvbXCdmcCDNvZsA/kRJHIZZjSttViX24I+v43JLJVBS7+dGT20gcCfsRRYEvXDMRSRIG4/RFUWDtnh6eePsAAMmMyr8/sol/uG8ulUHnoO26bjBlVCFPLT8wbKPjmoX1uKzSH7X50RPJDIr8o7y6+jCLp1dS4DLLDZuY/LGcM0L/zTff5Morr6S0NB9r9P3vf5+WlhZqamqoqsqnjrv66qt57bXXGDVqFJlMhqlTpwJwww038D//8z/nhNBXUokRbblQF+5sHCMZAUByenFlepEtKoaWpTyyE9kVAFVGHXAheQoQrI6829kRpMmXExVcBIUoxurfIlzxJXRNz7sa6hpzi8fyQvtawmqce29sINyvEIumuH2egwJPmoc69/KFm2/DL1rR2sOAgX3HKq6fMZ8nDg2tgk8tnUixPV8rNSv7EBqvwT/xEgzRSsbIr9rKoy/EVT+TpCTQo2aoih6m5+l/G7TXUTeZry25hq/8JsrRcKtIPEtvxotvz0q0+ACGruV3TCZewU+f30NzV/65JdM5/vuVdr571SJ88X7wnT2h35poGxT5AEklxXP7XuXeSZ8FzXQrM/njOdwVp1CIITg+XEmuqgKZ3Z1ZLplw8t+H4ClCD7WBKfTPSzRNYu9OgR0Hm5k2toh4KseG3T18886pcPZejecNWiKC5MpnJBckCTQNLRWDbAaOprjIZdGVNN6ZVyLaHCCI+Soz6dhJzy0IErK/FKfLj2fyYgxDR8tmkE7l8m8CgKBL3NJ4Nf+08ofoRxZZAnYfkiAOltZLFlfiKG/AM+sqBl79v2FzIv8H4vaja57FVj6a0tv/Fj2TQs8kUTr2kWnfS3LX+8hlo7Eu+RJqtA3XuHlkOg/im3Ulsa1vk9jxDo0l9Vxwy+eRQq2EVz2FGupCCpTivfgLxA7vRTu8GUG24pm6FNHuIusoYN3BXhQ1b/v6PX0UBxyUF7qoCFhR+4eXk4N83gfR7kJLKIgOD2gaejqOt287VaNHoyd7SQtZEoJ4ZOHJIHlgI47aKWQ35j0BNCUDK36KZdGfsjkGc8eXYBgGFhSib/8KtbuZeaW7WWO5e1DkQ16sr9vVzfyiKLS0YCkfR8JZwetrW0bYua81TFWhc5iALws4+Nt75vDUigMk0yrXLKxjYs0fHvM/+EyO462pGwyrGGBiYvKHc84I/ZaWFiwWC/feey99fX0sXryY0aNHU1Q0tHtaXFxMT08Pvb29w9qLioro6ek5G2aPIOs+Toak+rlkZDe+uddiCVaQi/QQeuNXgIG1tB7PtEsJvf0b/HOvy5dIyWUpuu7PSTVvRWnfg6vxIuTCarwDu0mmc7iv+Crx3e+R3v4WAHJRNYUX38NNo5by3tY0n5+uE3nv3xFECUNJowNfuOoBMkdK2ci+YkBAC3UyvfkANWOvpFPPUFJQTbW7AosxlBTMMEARHMNi61VDpg+Fn6z7LQ3eUq5vahk2AKcPbaegYQbTRwfYuH8ohtFbWIKYihDf+iaCKOGZvJhIIsXe1siwx2UY0KfYqLM5OVsIAvSm+ke0N4dbUQwFK2aMrMkfT0tPnHK9D8E+6kMdV1Vg4a09KTTdGKxffDxETxBtYOSE0+T8QFHAYbMye2Ipa3Z04XNbuWvZeEJRBcxy7adEDpTiaJiGGulFS0SwBMsIXHTbsBh90eogeMk9hFc+ipaIgCjhm3UlcrDyFC74BoIsk96zGWthBVoyilRQCr7Cj/iuPjlMKBrN3174ZzRFDpFRs6i6xgt73wQg6AyQMjSWN07jitJ6/PNvJLzi4cFjLYVVCJIFQxsSs9nuQ+iZFH3P/2Bw19xePQFj2d/w/M406x7roKHcx62LbqJmThY91o936hIEqwPRYkft3s3AmmcHE+pp4W6iL/6Agks/jzLzChx1U4isf5lsyw4QnuGrU6/lEVcFGw7k+6/b3UfjqEI2H+znM/PmkVj37LD7tVWMJttzCHvVeNyNi+h94X8ovObrKJ0H6Hn4QTB0LEVVFFx0e97LBLAWlJPc+c6IZ+dPHiYpFZNVdaySgKSmUbqbAdCTYeKR6IhjogmF+Pa1aAdWgygRuONfKPI76Q2nh/XzuW0j4/SBmiIX37hlKroB0h+Yvf+DlAScFPnt9EWGvJSmjimkwG3u5puYnAnOGaGvaRobN27koYcewul08sADD+BwjMwqLQjCcbN7niwp1fEIBj+aVfftXQGcc+9E2vIURjaNWD8bY8wiXMlOFIsNLRHCWliJc+xsUvvWoXQ3o3Tsp+CSe+gdiNNjFCIIUHJwH0XFldiKqzAK61ByGhZnAJuURYmHB0U+gNrXSmLHOyyZtYxmhxP98KugKsPy3ulbl1M5fj4Wqx3NN5qCi+8itPxhaNpKUft+xl3xJfqf/jGU1eG/6qtYfCd2T08oSf5jxaN0Jnr4XMMl5Na8O6KPruYo9gx9vS6dGsR9aDnZbGQw9i383hM4bp5M0GdnIDrcFTUQDOAqq8YjD3dNLiryfIhP44+jPlc9om1u5XTKAkFE8eQ7+h+nnWeC0/09nG/3dSznou0dfXGmWtN4AsevbQzgPk78vhsocCUZyEqMKTvxopOiVBHftvys3Pu5+LxPl49qfDgZx3teYiSBJAm88G5+Ah+KZfj1i7v467tnn1PP91yy5VhScY3Y+pfJhYbC4bxzrkYuqKSoLG+zYWgkD2zAO/NKDFVBkGRyoW4MJU1R7YnvK9Mbyo9lWo7I6meRvUG8wXIMPr7n8XFd56P8PUwoHYXVInNg4DAbOrZS5inBJltZVDuX/aFmXm96l9H+auoObBhy3bfYMAQR35yriax+ZvBcgYtuI/z+U8My0ysWL79Zk2Trwfy8Y/P+Afa1RvnXO8egPftfg/1sFWMJLPzMsKz5AJbCCoxsmsTuVaQP78Q39xpco6Zh5BTUWD/LxklsyHu+U1/uocgj0RdJQ810XJEukvvXI8hWvLOuJJXKEpv/NbKGhEuSKLzhb9C79xDb+Mrg9XJ9bWQ69mErH0W28yBCySjEaB8cTcB3hJTo5mBrhKUzqyj0O9GyEvbqCUcqP4SYPNXguQ886ysaXeg7tub/0DUyax/njsu+wP/72ZrBHfTigIPJo4soKvr43oF/+4W5vLzqEDubBrigsYyls6opPcn1M1kVA3DYzhkJY2JyznLO/EoKCwuZN28eBQX5ejZLly7ltddeQ5KGknz09vZSXFxMSUkJ/f1Du619fX0UFxd/qOsNDCQ+kjIeRS6RjGc8vpqvI2gait2LK5ug7/kfYyh5MSu5AwQvvZfUgY2ga6QObcM27Ur+6fl9DMTysUrlwSB/sdRFUOsh/dbPCSy9m5SSRiobS2LbOyOuq7TswD/3Wia4IuQKK/MD4QdiBdMplVg8gVWLg7uAkpu/hZHLIjk9ZLsP45t9JfEtb5Nq3YtSakcihxzrQI/2IDh94K9CkVzEjDCHIu0AhBU7weqpGE3rhl1LD1QyW4CqgjIKbCrFfRtRd63BOeOyYf2kfct54Ibr+d7vNqJq+c/jklkVVFSUMBDOwDH1BYqKPPT1xf+oz+fDUGIp45aJ1/DMnlfI6SqNxeNYXD2fgYHkSY/7uO08XU42KTyd38O5el+nw7loeyqTI57K4SuykEgcP+ba7baf8N/KvBKbDkYpdpw4UaUhuFEGOujtjX3oxdA/hnPxeX+QP/b3cKZtOd7zSuQ0Vm5uH9amG9ATSp0zz/dc/qzt8dAwkQ8Q3/wGrrFz6euLU1TkwTAM7OWj8zXYjx5XPQHBME56Xx5dJ3VwM+mmzQCo0T5Cyx+m+IZvfCzP40w/97Pxezh6D36xgDJPktpAJf3JEBOKx+CyOHmneQ0AO/v2U5OKEX7nURyjZ+EcNR09PoCtZhJFRdWooS5Elw+5sBr1SGm8oySLG9m6ITS8LaPS1tE/rEJltmMfaqwf0eFBTw89V9foWQy8/gsEmxPP5Ivof+knHHVxtJWPpmL61Hw/h4Vrx0Og7VUW3HUZyY0vEZwwC/fki8i07SHlrmJlfyHPvbQdVdVZOLWCOyelMRIjKzdk2vYSvPReVFVjfb+b+jGXI7XuGqw6IHqKOGSUsXZXNzdcNAojl0/k573oTtTnv48a7aHwwIv81a2f57F3O0hlVK6bV8rY/uXD8gZoiQhVQSv//OULaOtJYLNKVBe7sXLy7/6ZxiWLfOmGKfQPJI54Chz/+pphcLAzxjPvNGEYBjdcNIrR5d6TerX9oZyri5cmJh+Wc0boL168mG9961vEYjFcLhfvvfcel19+OT/72c9oaWmhsrKSl156iRtvvJGKigpsNhubNm1ixowZPPfcc1x44YVn+xYAsFizKK/8lEh/fnLmWXoviY5dgyIfQEuEyXYexF4xlkzbbmxl9SRUkYHYkDDvHEizrb+AhVo72c6D6LkMkpKmO5LDU1Ax4rpSxQSU3kMMvPA/yP5SCi7+POF3HkZP52PfPVOXImaiCE3rMNx+1K4mMqkY+MvJuCsRdQfseo2CpXdiiBLOZCt6MkL6wAYSu94HDJxj52K/8G7sop2gI8BAOsxAKo21dDE1qTBa134E2Yo+7QYSthICK/6MAAJHB0XHuLlk2vYOs1u02hlXIvOvD8ynJ5zG7bRQ4ncgfgQv7g+LbFhYVLaAaSWNaLqKV/Yh6ufMT8bkPKetN0Gx00Bw/mE7JxUBmb3dClc0nriPYLEjyDaMZBjBfbKi4CbnIlaLiNdlI5oY7kRusZg5Qk4HQxSwFFbm66UbOrqSIb59BUhDnmKCKBJd/9Kw4zKtu/HOuOLkJ9dU0k1bPnBBHS0ZNQsifEjieowfr/st0Wxe3K1t38I1Yy/BZXWSTStUBSrR6nPYA2VYi2vQYvmNnsyhbYhOH5bCSsIrH0W0u3HUTyW1f/3guWVdwSqLg7H0R7GS44PoSgbvrGVE3n0MANHhyed0ANzjLyC68VWOjWPMdh4gOEPhm1cEKRHCWFf9GEVJ4y+tIZ0eIL71LZxL7yNS4qA94+Xxt4a+L+9sbufWBifO4+R0sJWPIr7lTTKtuxg197P8ZFWAP7/uOyQ6D6GJMk0pH796qxc4UrXoCGl7Kb6b/xYhOYDN48UrePnr2jIM3cCdbKXvA96X7umXk9NEijw2ijw2ziaSKCBy8nCAlt4k//rQpsG//+3hTfzN3bNoKDVFuYnJiThnVMuUKVP4whe+wO23304ul2P+/Pncdttt1NfX87WvfY1sNsuiRYu4/PLLAfiP//gPHnzwQZLJJBMmTODOO+88y3eQR+tvR+sf2oERS0aR3f3OyH6JEAY6kieIvXEp77aOHHSa+jUu8OogiAiiRKZpC64KA6NyEsKoeRgH86vdoq8E57Qr0Hv3AaBGugkv/x3BS+8luXcN9trJyP5iwq//GIuvBEHXiG16DdvN38WBgm3gEIamIi/9PGo2hbJnFcm9awGwltZTuOzLRNc8S2rfWpxTLyPnL+S68Zfxu61P0Zbdz9Y9DRQ7lzFl9tVkVJH9/RZum+LBdtn9hFf8DkNJY6+bgnfmlfQ89t2hZ2Nz5gdtwUrAJRE4zQyrcWJ0JvJubBXuMtyc2O35j8XQwYMPRODsVPgz+YTS1pugwKogWP+w0ncVAZnXdyVPGacvuIPokS5EU+ifd2RzGpfOqeZXL+4anAAXBRx4nWb86ukg+8twjpqZd+/WNSSXn8LL78tnbT+Coaoj3LUBdCU9om0YFhuSJzCsDBuAaCbj+9C0J7qYUTGZQmcATdewSBbWtG5iXtV0dvXuZyAT5fu5dr475ybU9n1EVj+DoSqIdlc+v8J7TwzWlg8suhU9kyDTuhvB5qS00MOtS4v53etD1ZCmjgpQbk9jm3YJksND+tA2sj2H8zmUQp0EL7kHLRVFsNgxjlQlkpzeEZ81gJGMULnud/n/ByRPEEtBOUp/O6qriJawQTjjZnvTyJw/3XqAqtBenKNn5j08Acntxzl6FgOv/QwAcdOT3HDBXxCXfPxki5NDnTGOejounVGJyy7T1hfHIgkU+l0gusDjwlvkIdYXRxYgnFFpS3ipu+YvyKx/BpQMntnXYpRNGm6QkL8HkeOHyJ5NZFlk+ca2Ee1vbWhj7PWTUFVzgmZicjzOGaEPcNNNN3HTTTcNa5s3bx4vvPDCiL7jxo3jqaeeGtF+tjHSw92NjFQE9/h5hI7UvD+Ko34qlniInLectZ1WfN6RH8WY6gAHXdcwsaQW3VmMtXoCNocTixAnNG4x4vgloOewOD3EX/4BwYvvRFj8VWSPH8HQUJ1WnKNmYABGOok8+zMkrEHsq3+GFCjHKSjEXvkh2lHXMUu+xvBRkQ/kcwj0HMIxahYuqw1DSbGzbw8vHniTa8dfhm7ozFtaSGenwOrmOJNHFXLt5CCSJKPVXUCwYgJoCrrdj2LoFF71FXJ9rSCIiDYHQlE9qnH6NVjD+gD/uubHxLNHPBVsbr497yv4xeNvoUiSgK5zzg1aJiatvQkKxMSwxGAfBqdVxOsQaQvlqC08sfATXAXokU6onPiHmmpylrBZRPa2hLjzyglEE1lsFglNNwZrbpucHCMTJ7r2ucG/tWSEyPtPUXTtnw7OfgRBwF7bSObwjqEDRenUgl3LEVx6F73P/YCju7z2mon5ajImHwq31UlLpJ3lzasAEAWRz065gdEFtdT6KvnxhryQRlUIv/fEYClEPZNk4O3f4p64kFx/XgSGVz6Oo2EaRdf9GUpXE7LTzaz4cqqumk5rTKLILTC+yoMr20to4ya0RATXuLn4L7qd3EAnkidI3zP/wdHP1DfveuRAKemWXThHzyC1f8OQ4YKI5MqXRpU8QVxj52CvnsDAG7/GcHjY4LqU3/1mOy67zHWLGkbc9/ttInc3zkLtPkhB9UREuwvZX0zk3ScG+xi5LBPsfUSe/U++tuA+diYb2NUSYVqFhVGjSviHX6+neyDvjj9jTJB7rxyDbLWzryVEPKngcVp44b1mqku9rIvYmdJ4Pw3lHlT7UFZ9QYC+mMIraw7T3BnloumVzB5XjNN6+nOzjxrDAPdxFjg9LssZSQpoYvJJ5ZwS+p8E5KKaYX8LA4fJZRL45l5DbNPrCJIF59wbebXFTUufxK62CN/6zDhsPTu5eFYVyze2IQgCi2dW0dQZ55EdXfzn/bNwSS5sYxeQadmJmEmhvfTvaJKMgDBYikYXJISePajvvYfoLiC74LM4CqoRIp3kUlE69HLEbAZPsBKtZgZ6174hkQ/ILh/Zjv0j7il9eCeOhmmkm7cSGDOPRM8WPlM5FyWT4c3enTwTf5WJxWP4+k1fJK4kOBTbT+dAD3X+KiqdlVhl36C3m14yCdlTArkshruQrOQiZSQYyIRwWhwE5ALEkwj/tZ2bB0U+QDybYF3nZq6ovnRYDKEuaHRkOtjUtR2f3cOU4okEpUJzQDA5Z2jrTTCLEIKt7g8+R4Vf5mDvyYW+6PKjR7v/4GuYnD0MDC6cWsl/PLIJh1Umo6jUlnmZMtr0DT8d1NjAiLbcQAdGLj00+5FlXGNmI0gW0s1bkf3FeGdcjiCf3JVZsNhJtewkcNFt+SR+oowa7QP15Ln6TYZIKikOZw6RUlM0hYbKvOmGzusHV2IbY8Vtc/Ll2XdyKNyKnk0PivzBvqkYou3YxVKDdNNmHDUTiW16DcntR+jYQYNdot5QSG5ch8N3F32v/4KjE5PkntUIFiu52ACixY61ehJKa37hJ7r2eXyzrsQSrEB0eBAkK8m9a5C9QQKL70Cwe/HOWoYgiMS3ryCx+328U5fSF2jk4Ufy95TMqIiiQGnQOSjKXQ4LiyYVEnrp21hLapFcfrR4CNkbxDVuLo66RgxNRXR6UXqaMXIZpBU/ZIa/mKUX3EDIUchLW/oGzwewaf8A8xuj7DzcxvJNec/SQr+dr93UyPcf204kkeWNtVBT6uFbn5uBXcqHAEXTKn//q3Uk0zkkUeCt9a1EYhmuX1h/0k0S4Qxl3j8dNE3noukVLN/YhnqkJJ8sCSyZXnXcEn0mJiZ5TKF/htEcXvzLvkx85e/RUglymQSO8jFE330M74zLMTSVtLuMCkOkKFjMVQu9BLY/imhzcvHUidSWuqkpkHEkO4hlIBV3EwrHKNR7MfyV5CK9pB2NiE4veio2GC0m2l1kZRf67rfzdkS6Sb38X1hu/Bu0lp3YKsZQpzajdBzGMeECtNYD6Npw10QtEcZaNDLTvK20HskdIBfuostIEbA4ULJJagQbXxl9OXu1BKIo05vp5cX9b7KuYygO7dqxl3JJ+TT6Er3YLE4K7MVkHaWDNYz71G7+Y81PSSj5BHfXjL2UpZUXIhvHFy5t0Y4Rba2xTkRRGCb0m5PNfH/tzwf/fnn/2/zNgq8TOMHOv4nJx4luGHT1Jynw9CLY8+6Tmq7RHG0hYPNR6Dy972m5X2ZfT5aLJ5zYK0BwBdC69p0Ru00+XrKKwRvrm/nm/fV0Jjtwyh5cehEH2yJUBM5e+dGzgmiQEKJk1Sw+qxeL6jilyJDcgRFtcqAULMdUqjBADpQgyFb8865DTUYwDH2Ye//xMLJJElveYljtWcBaWgfeD1cu89OIKMJbze/zyLZnuWnilSP+vT85gCEYfH/NLxgTrGdu5TTCkhUGa8wfOY/DjbWoEo7JB+SZeQW6quCbfyOSy4dr7ByS+9YiOTwEFt2Gnknwwc8tuXs13llXkty7HuviL6DF+tEiXfnQSasdA4FM+17ckxdjCZahxUOEN70BC67DHSwn8trQfCOy+llSV84aNid59PV9XHdRA3WlXjRdp9IvYX3pb9FzWbLt+fezd8blJPevRw6UEt+2PB9SIskEFt6CddQs0oUTcHudGHYP6azBrkMjQwkOdCTY0Ty0wNUfyfD66mb+8XIrrx9y89KmAVq643T2p6gvyXutdPQlSaZzzGsso77cR0tPDEkW6U9kCR4npFLVDVr7kjR3RCkOOGmo8OL6GHb/y/x2vnv/PHY09WMAjfWFlPhtH/woTUxMjsEU+meYbCrE65kuihZdi9/qZO3AAa61CASu+CqhjnY8gQCaKpBJZ3m3KcFlSxO0jBnPRF8VXk1gkr4H4cW8m1oQ+OyUq5H9VfTF2pEEO2uNKYwT3cgXfAnnxt+iRXqQfEVo8+4hvXfNcGMMHT3SQ2LnuyT3rKbwqgeIrduGraweu9uD5GsgvuHloe6qgiVQOsyN0RKswFpSS66/neTiW/iXNT8lq+aTBlolC3dMvp7fbnsaAJts45ZJVw0T+i/tfwu3zcUj259FQGBZ/QIurV2MhBtNzPGbzU8MinyAF/a9QWPROMqtlcd9vjMrJrOtZ/ewtrmV04fFZ+miytN7XhnWJ61mOBg5xKyCP07of5wr2CafXPoiaRw2CauWAkt+1Wtt9yYUTWFv6ACzS2dQ4jp1Pe4Kv8y7+1MYhnHCrPqC048e6z2j9pt8PDhtIvPn2/jBhh9jHJnNBh0F3D/5vrNs2ceLLuXYFdnDw9ueJaEkGROs547J11MolJz0OEG24pt3PdE1zwEGos1JweLPkk+6ksdQc+TCPXkXbFFEdgfIhbqw+MvgZGspsvX4Mfq2T9kCzB9Iwojz5M58EsQaXyUCwuB3HOCCqpmsOLQav91LY8k4frftaWq8ZXzpwhsR3svnXBAsNoIXf57oxlcpuvZPUENdIEqkm7cRb9tNwdK7yA10Dpav0+IhQm//jsKrHhhhj+QroquillX2HLUkcM/+EpO8abTuJpJ715Drb8dWPRFDzRHf/DqWy+7hndIC2lre4662kfH3frUPj9NCPJX3uNR0gzfXtXLttRb2xLZzY8GlBKcuJbb+JQw1h3PMLARJRouHEAQRI3tkI0ZTiax+htCiv6R7IMXEbc+S6DmI01/KzPq7aO9NDLtuTZmHNzcMj2Xf3ZZAK+7k4kIP77s9RBJZcupQ+I8sCVQWuwl4bDzy+lDC5A27e/irz83EcUzyT0GANbt7+PVLQ/OwsdV+vv6ZqdjljzZJqGFAsdfGxdMrj/xtmCLfxOQUmEL/DNNFjpdaVg9r0zWVu0Zdh6e0nNThHZDNUFU0hmuXFPPvW3+IpmtUe0r5zpTb6N/w2PD31rYXURsaOECKYjFDY6kTe6KFLmslWyo/T/FolbRgoz5QgvtIQphjEa02LAVl5EKdKN2HsRTXEnrz1xRd/2eIVhuFV3+NyPtPoueyCFfcS7dFxFtSh6N6Aoaho8UjYECm6yDrizyDIh9A0XI0h1qo8JQyxlPGEt8onDmJP51wHT898BrpXAbN0MkcOcbA4KXm95hYNIZq11gyeobDkfYRNg+kw8cV+tFMnMPhNi4ffRErmleDILCk7gI8H0hmZmAMhjMcS07L/cFCPScodKQ7aI12UOoqotpdhf2ks0ATkxPT3pukyCMj6C4EQSCUiRDORJlVMpVwNsqWvm1c6lhyyvN4HRKSKNAX1yg+Tp4PyAt9IxHC0DUE8dyJuTQ5NZqc5dXDrw4TQAPpEANqFxWfotTuXdku/m/Dw4iCiF22sX+gmcd3vsiXZ96JmD1JfgrZipoMU3TN1zA0FTQVNdqL5C85po9MfMubuMbMAsNAkCyoA51QO/nkRuk6BRfdQd+LP2Kw3Fr1hON6EZiMJKNnyGkqNtlKPJvgtsnX8uqBFVT7KphZPpmsqjC6sA5BEHlsx/MAtMS6+LGuce2ltzPOXoDH4UXL5VA6D5KrnUTkvSeHXUNyB4iu+2COJwNBtmAtqUPpOZRvEkTsC28iFo9woehjIDNAzqMQff8V1O7mwSOzrbtw1jXinrqUHVZ4ac+7VHrL0Lz5RKfOMbOxldZh6DoWJ/zNrWP5nxea6OxPU17o5OpLC3my5Xekcxn2hQ7x3Qu+SklNI9n23aQP7SC1fz2SrwQwMI6ZwxhKGlkwGF9kIByMAHmvzfnj+9lX42NPSxSAi6cWUhF0DJYqPsq0Wjdy7z6EdJQF4+9kxc4I5cGheVNFoYuls6p49PXhnl/tvQk6+5M0lA1ltY9nVB59Y3i/fa2RvIdA6ceTiNLMuWRicvqYQv8M050bWftzV7gFay6C1nMAr6xjSDKS2o18TD3T1ng3yWQY4zjxfeFoD+vSbVTrCteWTSP8qx/g/cxfkCruplfy0pHexc62NF9ceBvRF77P0UmHXFyLbJHRlSPXEQXUUCeC1UHE7sSu5Ui8/gu80y4hOW4m/7bl9/SmBri2ai5TZSslkh1HYRWZriYCC29hoG/zCNui2Ti3NFxCcSpOqxojrGaotvl4cPJtPLj5N4wuqKP1A+72Pekw1S6wiw5GF9RxIHRo2L+fyG3ZMHQ2dm7HJtu4dNQiANa1b6HaWwFHPDEFAQx07mi8nv9c+7PBAUESRMYUNIwQ+WkhSVeyC83QKXeV4WJkmRZBNHi3YxXP7Hl1sG1a2STumnALFuPslqQxOT9p640TtOsIWn6ydTjaSrmrGFEQCdoDtMc76Eh24vWOTOD0Qcr9Mk19yomFviQj2F0YiRCCt+iM3ofJR0tOU4hlEyPaM9opMsJ/wuhO9LFszBKcFiepXBqf3cPGju1EshEKKD7hcVoihLN2Mlo6jhbrx1pcm98x1Yd2Mw1Dxzt1KaEVjwyOv+7GRSCdYlFMlNBS8aEYfUlGTycHa52bnByHZOeyUYuoCVTQE+vlnZZ13N54HQklSTKXIqsqqIaVElcRqdzQ97090cuPD77O1ybfxKSeQ9jKRlOw5HMYxsg4bUPX8tnyP1CrPtffjq1yLN5ZV5LO5LAUljKQi9IqZMECtRoUWQMkBjpHnjOnYK8Yw4aB/I52e6yLgbHXUCnb0BJhwu8+DoBod1Nz1QP8/bVFRCQvKwY28vtDLw5uQqi6yqGe3UgHD6GOuQi12oNWfSHOwlIir/7r4PVEh5vYBV/l2S1J2vuSLBp3N3NGtyJvegLrml/x1fm3s23aFCqLXFRn9hFvfY9rFoznxVWHMQyoKXFxRX0W7b2DWErrKS/28Z17xuB1yIPzIadVYmJ98Lix7qo+vE3TDZTcyGSgimomCDUxORcxhf4ZpsBWzA0185kgOpE1jQGblf1qHDGTpMNu5YBVJaPrjLNaqRYszCwax7qeXQCkbTYkb+FgjVgAwWonZBGpsVbQl+xHyB1Z5RVl3mh9Y9i1QyXTCF79p+ipKILFipAMY2QTGLWTkDqbsRZVE13zHOqN3+T7u5/FJdl4YO7VZHMKO5Ld9KbycV3Pt63leWBUQQ1/VrMEa1ENMYvM+OLRrO/cPuyai6pmUoqLn3Yvpyk2JOjvGXclX51+OylD45ebHxt2TJEriCAIOKJdfLZmPv+TjjCQDiMKIp+ZeDXF1qLjumP5HT6uGXspD21/mhf3vQnkwweqPBUApEiwpnMD77aso9xdwjfnf4k3DryLLElc0bCEQnn4eWNGhB+s/zk9iT4APFYX37rgKwTE4S7TUS3K83tfH9a2pWsnVzYspdxaMdLQIxiiTlyLIQkSHtGDbuaLMTlCW2+CMmsWNCeGYdCR6GZK0VBW/Ap3GQcjhxhfdmqhX+qVOdCrMK/hxB4mgsOPnuhHNIX+eYVTdrGoej4vHnhtsE0QBMqc5WfRqo+fUncxbzW9R2t0SHjdMfl67LINTqKrRauD0IqH85VejuCbey2GcKwrskR0/cvDFtkTO1biHDPn5EYZOuH3n8A4ZsEejsTo+07zxj7FOAU3Ra4gv9/2PH8y/TZcNheaoRPJxnhh75uD/W5vvI4LqmawqnXjYJtFshC0uthoi9Mf3kdDoJzaTAZr2SiUrqEyekY2jWfapQwck3hP8gRB14lveg3L4s+RjfYQqqzgn7c8TVbLfweskoW/8txMwbg5pHesHGa3WFqHkU5Q5wxydOvjh81v8a8NV5J+6SeD/fRMguj6V/FOXYxhyRHK9nNt9c3kshKSLcM73W8hegvR46tIZVQ2p+pQNZ19Owf49qVfJPrmLzCUNOmZn+Mfn+8ZrLTxxOoU4amVXFs6Gq37ANmsjuA0KNj1FNG9KwC4as4NLPnKEmIt+/BF9sP7bwICvgW3MK+wBkM3Rmx6FLqtLJ1VxZvrh9z+vS4rQZ8Dg3wGBACfw8oFjeWs2j70W3TY5GEeAiYmJucOptA/w1QaXqp3bUXrzu9SV0oWJn/m23QaOf5lz9ukc/n6p4Ig8O3Jt9Dgq2Rdzy68Ng8xdIRFN+Bf+ypqXxuir4j0gmt5vXsj86pmMtFWgGxoCJIFh1DCXzZ+A13I8GrnG+zqP8CKSBs3ljQSjbRgMew4a8bzWOcm9shh5s69iNk2F5kL/5RWPU13Mr+Y8F7ZRGoC9RiZGLc2XsOKQ2sGhe9AKkK8aRNsfou+W/6cTZ07+cykq1h5aC0GcF3tBTRYCjic6hsm8gEea1rB381/gF2RFgIOH+F03rVsbuV0CmwexHgnkVd/RPHEBTw45kpChobDYscfGIcuGOiGimTIIwaj6UVTcM10suLQakpchSypW0CBVIgh6LzWtJy3D70PQH8qxN6Bg/zdom8QkArQNYaJfEGAnX17Bu8VIK4kWdm6hhvqrxmWRCenq2jH2S1QtBNnV04S57l9r7CmbTNWycKNE5Yxp3gmsmE5ybfH5NNCe1+SSaUJBMlJNBtHEiUc8lCCsAJ7gIORZqKZGBInTwpW7pd5e2/qpH0Epxcj1gfl48+I/SYfD+mMjhyu4a/m/TnxhIZFFrHboa9LouoPL9Zw3pFUUsNEPsDze99gWunJS0bq2dQwkQ8Q2/w6znHzBv82lDRqdGQOCy0Z4eTREUY+tnr0TKwltejZNMnd72Po5s7m6RDX4jyx80U8NjeqqvDI9uf4+tx76Ir3ccukq8npKlbJwrqOLdwx+Tp8NjerWjdR5gxyw6iLeLz5XfYMDLnVf27idcwfNQPXmFmoiQiyrxDR5UW0eyi64RvkelsAA3SdyLoXYNblrA242KQoBNo3D4p8yIclrurfx3XTL8GWjpM9uBnR7sJ16ZfoVtwIoofLyuqpdRSwPd7Be927SB7nO6T0NJFz34K6dx21woU8/HIzhgEWWeTuG26jN30I75LbiPYXIokpQGTmuBJSRoTiG75BLtTFNqWUjDK8PPPb2we4/PL5uBwuGD2DxvVPoDWtH7ruzrepmXslKWsNWlcWveh2rOVjUNyVGPoJ3N4NuHZBPeVFbt7d0kFFkZvRVX6+8/M13HXlBGaPLRrseMvS0RQHHLy3rYPaMi83LR6NzzlyvmZiYnL2MYX+Gcab7CTaPeSKbmg5MntWs7O8bFDkQz7G6KXOTVxUM5fFFdO5sHoW/7v9aULpCBdPms4NFV9gAJWeXJzrRy/FHY9SnooRdpeQveof+f5j++gcSGG3Snzhisu5ZtplvL0lSqiiDH2Mk5Su8Q/rf04smw8leH7/W7SVNbKk8ErCmfzK+OK6eewZOMhz+/KeAaIgcseU63ly50tk1CxL6y5AXvkKKpBIDLCzdx/XlkxjVslsUv5CoskBUuEOsraR4jWdy5DD4Pc7nmdp/QKcFgeiINIa6cAte8kM7MU7ZQmR1c9i5DJ43X48S+6kyXKQp/e9iaIpXDX6Ysb7xw7LwG817DR6G5k8oxHBAF3LP8sUCd45PDw3gqLl6Iz34PMUjLBPFEXaYiPd8pojrSAMH638Fh9jCxvY1z802Hpsboqdx/c8MAyDVR3rWd22CYCspvDojmepuKCUWvunaHb+CeZQtJVoNsrkoomIwodLQJTNaYTjGXzFYQSnh/7MAD7b8JARURApdhbRFGplzCkyeBd5JEIJlUxOx245vi2C3YsW78dcZjq/EASD0dUF/PbpJpraY8iSwLJFlcwcf+pEjZ8kjnXdPrYtqytHC7gcl+OJbiOnIBzz4hac3uHx2kewBEo4WaE8Q5ApvuZrRDe8TOT9pxAdHnxzrkH2FZM9yXEmebJalqym4BdENg/kd+FFRCRB5PGdLw72u2HCFShqjiUVs7goriH0HKYj3D1M5AM8tf81ps38AtLqZ3GOmZPPtRDuArrZVVaGvaYBXclCNkXh9X/CjlyMR7Y/Q0NBDaI4MpFpfy5NRknhmX4ljjGzSfsa+N27nbyzI/89ubCxkGuLBri8cxfXLbkDcjofDNy01E3j8Q0paisv5LGndwy251Sdx19uZ9nVNnqzDh56YRfxVA6Xw8ItF4+hPSGjvPrPFN/w51gGRn6H7VaJVNFYVvQXMz1lIXiMyAew101FsnvIpmWE2kKsWjwfxiiKcBLPQqdVYubYYva3hGnrifP+tvwc6WfP72TUV+ZTcCQDv9smcc38Wi6bXY1VEoGRHgImJibnBh9tisxPIXoqPKLNSCdIHInLsknWQWGQyKWZEBzN2MJR/NO6X9Kb7EfVVV5rW09zJkag7SC1L/+Oihd/SemhvdicPvTdW/nf19voPFI7NaNo/Oj5A5C20dGp4Et2UJmMkMhEB0X+UTZ37SBqbcPnzPsVFrmC7OsfGix1Q+e1/StYVDuXSxsuZF7xONRYfse7WDWwSBYKBBuvq/18Z/Pv+K99L/O3+5+nyF2GVRouIeZVTsdr8bO0eg6vHljB07tf4Zk9r7KocjqG7sTudBB+93GMI4sfWiLCISPDv6/9Bc3hFtpjXfzfpoc4EDvI8TDUvMg/ioiE/diSSUc4apcsi8MGc03TmVoyaUT/BVWzP1imF0m38PnJt7K0bgE+u5cZZZP5y3lfxmkcP/FMQkmyun3jiPaDoUMnzIxucv6wa2Av/7v9VzzX9CovNb9x6gM+QEdfkkKfAzEbQ7A66U8N4LN6R/TLC/2WUyYekkSBYq/M4f6RCSiPIjh9GLGeD22rydnFIkm8uaaDpvYYAKpm8PzyNmKJT9euccDhw/KBMWZWxRRk4eR7FZIngGAdPi64xs2DY85lKFk8Uxbny+4BgsWGf8FN6LmTyfy8V1582/LB0mh6Ok74nUeGJVEzOTF+i48afyVZTcFly4+lGS3L2vbhuYBe3vc2aTXLOx0b8cl2tAObyB1HrWZVBcUA14T5GLqKGupAS0QQBJFCe4CHd7/Mf2x5hP/Y/Sz/uf8VRDn/3TkcbmNs4cgQqcUVU3GpMtmmreRadrLlQJh3dgyVrXt3Rz97hdFo0X7Sr/0Kw10M028AMX9eS+komoMLeWVdO/3RkUs/saRCmb2WXz9zaDAzfzKd45HX95KwBZH9JYRXPkZddSHVJcPnGldeUMd/P9/ES+u7cTpseGYu46hzvaWoGueMqxBkGYueRtj7JgO//SYDv/sm7HwVi35y769IPMuand20dA/NH3XdIJoc/nswdAOrNFTS0MTE5NzE3NE/w+QC+cRAtvJRiHY3mba9KC4vM4omMVl04ImGUe0ODsg6Fk85j+99jXrPWFR9KNBQEASysptY5Sh8lWOxpOKoyQhqpAe1dg7Nb+0Zcd1QJMnXltVi6duMWlqH5TgB4ZIoUWrz83Trm/zp3HsJZ2Ij+vSnw4wrHEVBOol/oIc+8kllfIkE31n4J8SyKd7afoyLmKbw0sE3+Oa8B3h27yt0JHqYXzWTxdXzETU7VzZcwezyqcSzMYpdxXgtRQiArmSG1cKVA6VsjreNsOeN5pVMmD4eQzu5QHYKLm6beC2/2PL7wbYqbzmlrmL2xffyftt6yj0lzCmfQVAqxDCgzlPDzROu4rl9r6PrGpc0XEhj4cTjrkx78HFjw9Vc1XApVqygn3iNzC7ZqPFV0pccGNZe4i4ys8We52i6xqN7n+bSmsUE7QU8vOcJFlVegM82UqifiLbeOEV+B0YyDjYnoXCEMnfpiH4u2YkoCAxkwhQ6RnqlHEupV6a5T2Fc2fGTQwoOH1rXvuP+m8m5S1rR2LZ/5OJxd3+a8WVnwaCzhE2y8tnJ17OqdSM9yX6ml01idLAWWZTgZGseukHw4rtJ7FmNOtCFo2Eq9uoJ+VXiI7n2BF0jtOL3uCfMQ5q4AEPTSOxYid934iR/AIaaId28dUS7Gu6GU3jhmIBmaNzWeA2qlg/bK3QFCaUjI/plNYWuRA8VhozS24LvgutRBSs22TasCtD8ijk49m9CrBhFeOVjg7mORKeXvW6ZzvjQQmdfKkR3op855VNQ1Cx7+g5y88SreK9lHQZwffU8SjeuICHYsUxYhNcusn75SK+S9S0KMwqryHbsw5EOsbd0HrlZoyj0SOzpE/n9S/k5jUUSEQU41mu+KOBAECCdHZ5kIqtoGAboqShyQQVJVea+6xpp70lwuDtGccDJtoN99EcyTBldRNBrR5pxA4XjF4KqYLiLyAgOPIDQs4/oykcGzx19/3EKvEVQOfOEn4vfY8PtsJBIDy1Y2SwSQc/IjRQTE5NzH1Pon2EiHh/lN3yDzPb/z955B9hRXff/MzNvXu/b+652V6tV7w01hCREkWgCBJgSDDbGNrZD7MSJY8f2L7ETpziOY1zBNmCKMVUC0SVAvXftanuvr/f3Zub3x5PealkhSiRbQvv5S7rvzp27897M3HPPOd+zESUSwLHwRjx5xRSE/XQGLbSksrHEFCbEmtGyXTzc+gS1k6Zy29g7aAwfYUJeNQbRiCjDTl8b8wUb1miE4J5XQRSxlMwix2mi3zf8pZNlN5CT7CDQuAeh9RDjZqxkWcks3mjfmemzumQuxEJMyK3hpbo3mFU8ZUTt2jmFkyka7EN/dDvi3OtIrbqPLouZPx17leDm3VxaMY/llYt4vfGdzDG7eg5yc8UCvjz9r4irKfQYEdDQKRE0yUShpQLFmH55aWp6/1d+3yJKS8aw6UYGYdoMthNzPDOqqjHZPYm/uySLRm8LWWYXlY5yDg4c5ff7nwFgT/ch3m7eyrcWfBW74ESvGbm0cBGz86ehomET7em9B0EjoPqIpGK49E4MWnpemiqg58NfdrJOZnX1cg7312XSNca4Sqm0l3/osaOc3+zrP4hNtlBiLUIQBMa6qtjctZ0rK5Z/5DHaeoNkOwxogyFSOj0JJYFJGvm7EgSBfGsubYGODzX0nRaFra39SPadLCm+BINuuMEvmOxoYc8HHD3K+YpRp2NMkY2DDcO/uxznmQLWP314oj4sejNXj12GgoIOiXpPE2WOkjM+kdVEFDUWwVg0Fq2gCkE2EOuoR3YPiRlqoohjzlUIOj1aMo4oGzBVz0SyOM88KUmPzpE7Ir9fsowq8X0UYmoUAYFXGjZyfLCJSnc5ayZciV6SSZwSFVFsL6A72Mc4SzHhI+8hmu04x87mm5PW8FLnbjoig0zLmcbs3CmY+utQIn7s01cQPrYVc+V0BL2R6TGN7OqVPNL6LsFEmFyzm6WmfPQNjQixMJFxM9nk7+Wvp67FONiDsn8j8dbDmGdcjtZzFG/TXiaU3s3e48M378fny6Ta+hBkI3EtwbbABq4pW4GgSeze3p4x7Dfu6eDWy8fxp7cbiMZTuO1GbltdjMUkodeJJFKnOD0kgRyXjBqPkJhwFV9/aAcpRWX2+DyWTC/mF88fZNb4fO5ZPRGbWcYfTpBl1RM3jdwsjhzbPLLtyEZM5XNIfYBKvtUg8eBt0/mvJ/YSCCewmGQeuGkqdrNu1Hk/yigXIKOG/lmmKKkQ2vky5vJJaJqG4ukmL7uEw34r0aRItugngZGDQi1jQ2E0NB6r+x1Xl13D3OKZ/Hz3I8RScQQEVlYvQcyuwLPhX9ESUbKvvp/wrj/yxVU38y9PHMm8HFbPzqPUJdD/+H9l5hFrPsBtN/4tYwxOuuJ+qoxZlMRTvNR/lJQIjd5W7AYb9868lT8eXocvGmBWXi1X63KQ923Esehm+qw2jiX7eWzXY5lx19e/xfLKReRYsjIe6xpnGZaAD9mYhyqYkZM+enoGGfSFcNtkJCe0kqDGOTZTe16zFWC48W8J97dgbD6E0lHPjMJJvNyxk9iJXXpJELmi8lI+qraRpOkoMZRSWlCGpmnEifLcKSXxAMLJCB2hTsbbnOl5qGA+UVJPU0ERUmzv28mTh19EURWyzC6+MvsessSPp1aercvlnxY+SHe4F70kk2/Kz2wYjHLh8mbdfroPTeLH77Uzf7qD6jEVbO3e9TEN/RAzKu0gyviTYayy5QNTOgrteWxt282UnIlI4umjSKKpGE3hPfhCtcRSCV5ufoPrqq8a1kcw2dCiATRVQRA/pGzYKOcNBoPGjcuqaO7cm/GwzZ6YS0HOB1dY+DRiN9rY0bGXN5vShotFNnP71OvRS7ozq+7LBsJd9RjyykFLIpyIIjtVdV80WZEszhPK7GkMpROwTlh4xjkJgoZ99tV43vw9nIjIM42ZiqAbGVUTTal0DUZQVJXCLAtW/eg9qEoqv97xZGYdcbT/OL/b9wxfmvNXPHHgebpDfdRkVXLNuOW80rARuTgfadntaFteIHz4PYwi3FQ8GVNeJfXNMVx7XsRz9N0Towvk3vA3DLzyC9RIOnKxSDbw1Ss/S38iwCTBis4zQEJvIty4F11nPWuu/DzJvRuJNu7GUDQW97I7UeIx/O+my+VNnxbmnTwrbb3pcpclOWamOzwoIS/uZXexN+bhWmspuqf/DkSJ6y75Oj9oTUfjdPaHeHNnG9+9ezqRVAqDGeJqlHwZbl89ht8+34iiaoiiwJ2rxmIUuzFe/Q1+9HqA1ImSdzuO9FJZ5OC798zlly8c4o0daZFJnSTy7btnU5w18pkg55QSPb7zfW3lqGcoAaRpUJ5j4V/um0cgnMBm1mM16EajEUcZ5QJl1NA/y4hBH9YJC1EjftA09NnFBKw5iEIPor4LnSLg2Pk8WdmVqOWrgbTau2iM8eiBVzJGrobGK8ffZqqrHEci7b0XEIk27CZ7oIN/u+0L9EVEbEaRklwr4Td//b6ZaITrdzLZ6mZcwyF0JRPoLK0gTzHyVvNmFpXPQSfqePLgi8wvmU5NThVjRAvG/g5YfAtJRxmH+vfgj79fXgZ2dO5jbvE0Xm3YRIktn9vLF6D1tLNJjFJlK6H1eJKHXmlDVTV0ksCXrihGc/ezwfcW1465Ck1TORCo4/dHniEUDzO7bDI3Lr8Lt87Ft+d/gWPeFpKqSk12NTm63I+9i5x5IQmnP/RM76v+RB+PH3wu8//BiJff7X+aB2Z8Dp360aXMNA2sOKg+6d0ZfUde8PQF/NTtymFGjZX8HCPv7vRh0Nvxpvx4Yz5cRueHjqFqGp0DYVaONyMYzPjjASzyB5clMumMWGULXeEeSmwjS6ppGuzo3kOxw0l3l0SlbSLb+zfREeyi+JT+gighGCxoER+C9YxS4qOcR0QSCZ5+4zg3LqvGbJSQRIEDxz209PjJKr94jH1FSWWMfEhv2K6re5MH5tx9xuM0RUEQRLybTpR4FXW4L70NFCWz+tEiQYKH3sW15Fa0ZBxBkkl6ukgFBiDnDGUMUykC21/CtXANmpJCkHQkeltJBQfBOdTNF03xm5cOc7gpbdAWZlv46tppZFvPXE3j044n4huR3tbm66TD3011dgXzSmfQ4m0nkAgjCSLf3vwQNoOVz175V+g1jUeOv07fkQOM7anggdJLCb3xbmYc0Wwj1nY4Y+QDaEqKUlXE+sYfCSbT6yxT+WSsExcTOrSJZGc9hoJqdFYngmwg3tOMbvIS2q64Aw2NMn2clStlwqFcrDoTFXaBlKcO501/S5fJSCjYS0pV0LvyUb3dFNQ9xXeuXcOObgmzQWRqtZ2E3MfBoxJ/2tjI3HFZ3CGuY+LMZXzutgK8wSRGs0a+zYOzuZ7HOqsxmEzcfkUZ8aSCXicSjCTpHghzpHkowielqPzu5aP87WdmoHvffrGhag7ivtcz10E0WTHWLiT2Qcr7J6+VBmZZwnwicmjUyB9llAuXUUP/LCOarfjefp5k34mSPpIOabWd/zr0NIqmMsZVzgNXfQ0t6EOSkvz1jM8RiYYoNJh4PDoyF9OfiuEgLXiinhSu8/UivPRP5J3oo7/3v4hIIz0EgqQjVDsL49hp6KIJLGhMNDvpCHTjNjl5/mi6NvyGhk1saNjEzRNXsbV3DwNNHr45/2v4Y0EM8sjFSLbRwWWOCuaOMeGW9BgH+zjmtPPowef520lf5hcbWjPl6VKKxkOvdvHtOyr4Td3PuKxsARElyv/u/G1mvO1d+zHJRm7OnoyoJZiid2FoPoLJNY6Y9MnF60yYuW7cSh498KdMm1k2UWIr+sBjBk/zHTR6W4mpUayjmuUXNU9sOojVnqK6PC2MNG+ag03bfYyZX8xxXxOz86d/6Bh93ihGvYRRDZHUm/ElAphPk7JyKnnmXBp9zac19FsDbcSVOOPcY2m2QbdXpNJRzqHBo8MMfUiH76shD+KooX/BoCoSqaRGXrYeXyiOJEnUVtoIhS8uMT7fiQ1nSRDR6/REkzE6At2opyl7OgwlQdLTjWvhTWiqgqakCO5/E2PJUJlJDbBUz8S78Q+c3JE1lk1EkD7keS9KGMvGo0bDCHoDaBqpoAedxTlMrb+u1culM4pZMr0YVdMwG3VsP9TD6kvKUJSL14Ay603U5lQzOa+WpJpEL8ns7T6MhsY7LdsBqM6qYHfnAfb1HAEgEA/SngzwwtFXSZ6Ioqj3NON1jM28nbWapfTlzaRFM5JzSSmWA8+gBgewjJtHvL0O57xr0VQVNRYiuO9NnJdcj6GwCkE2M7jhl5n5WeatYSBmxWEq4F8P/o77Z3yG5w/8iWAiPOzvuM56OS/vf5u4kkAQBL6y4GoKX38Kta8Jd9+/cfXyO/gfzyFePtiBXpL5Su0XuHJMMUZJITUwk+Qrv2F85RRiRZU4JSfmqA7F4uDGOU7eboJHXxnSZFo6sxh/IDTiWnYNhEmlVHTvq7oSM+bivvmf0DwdaJqG5C4hpj9zGtgoo4zy6WLU0D/LpAKDQ0Y+gJKCbeuZUzmeo/4Ovlx1JZ2+etoSAbKSJkqNudgseYRD7dw2+TpiqRga8HrDOwQTYcwGG96bvkp242FizhxEs33YLrW+uIZD/gHGTb6UaPMBMq5jSYepchrf2PwzJrsruLt4HuaDW3mnpIIZRZM53HWYv6u+kixNIipo7I4PcHywGUkUiaSixBJ+JuaO4/DAMXIt2fSFTwjbCCI31awgT7Kh2DRSJivruvawvu5NAIKxtHF/KvGkQigO2WY3gVSQFt/wusYAmzv2UOku4zd7n8akM3LL2OXM8LQjFmShap/M2FdVjek5U3HOsvNO23YKbXnML56FQ3R+oFffaRyZX1lsL8AoGs9YlmaUTzehaJJDdTEmTx96ZLocMsUFBiKdRTTYP5qh39ITIN9tRo35EPQmgokgxdYP3ngCyDa5aQ60jogaiKfiHBw4yoTsWkRBwG2FTi8syCvk9ba3SalKWqzsBILRjhYaAKo/9t8/yl8GSda4+fJK6rt6EUxhxIRMxG9g9sWkxAfY9BauGnsZRp2BSDKK02inM9D9oVVMNFHCWFKL971nQFMRjda00X/qYaJIpGkfriW3oKUSCJKOlK8//e4+E4KApWYu/S8/dOKdLGCfswoMQxt3BoNEXpaZXz1/iO7BtIFoM8t8Ze20E+kDF9eGzalYdWaq3eU8dejFTNt1tZfTF+7P/H98TjXr6t4YdpyqqYzNrmRi7liSagpJEPGpMjmSDmHaNfyhpYjtW3uAdL77P675ArLcyRFZQFY1yiIxDO+8gGi04FpyK2o0iHn8Qrxv/HbYecLbnsW7sIqdbQp3TLyeLMnCgzVXo/MPoAnQLov8tnkjeslAXElv7Wiaxm+bN/F3kxfCjldAEOmXJdr8nYxxlbEgu4bi+g2EjrxHCNC5CnAvugnP24+RnVtO/PBmBlrSZfgki4OFS7+KfkUNGmmRvpc3NzN/XC3vZ+GUAox68bSRg3HZDXlp4360HsQoo1x8jBr6Zxk1Fh7Rpvn7yTNMZlHtHHb7jvPY8dczn411lvCFCdfzXKCJrZ3psjIGnYFbJ12DP5jEGLexP9SCz25gx/7H+OvL1pJzdBdKTxNSxWSaSir43z2/5kez7yPnmgeINu1D0OkxjZnK3riHcDLCvsFGQgUzwFGIJ+bheEMdX8qbiSpJeNQ4RlHmStlKs1HHz4+8iE1vwa03YZCziDljmGUTGqAXZQrMhUQ9JhpSXsqcRgb1el5s25r5e4KiD4MsEU8OLWAsRh1RXYSbJl7ND9/7X9ZMuHLENcq3ZHP0RKm/aCrGw0deIn/GHVSocRLCJ1d71WsGxtlqmThlAqqqoapnrveaZ8hldc0KXqxLl02zyGbunroWnXpxh1le7Gw/0oNo8VPqLh3WXltp4ZV3oii5IythnI6W7iC5LhNatA1kI4HEwId69EVBpMRayMGBoywsmocgpEMrd/cdINecje1E6H+WFRr7wKgzYNPb6A71UGIf2kQQjFa08MiIlVHOX0RNxJca5DXv40T60ilcE7LGMyF5JXD68p6fRow6Awd7j9Hm78y03Tn1RtQPCSkWJR2BXS9n/q/GQvh3vUJu2YShTpqGuWLSCY9+GkNxDeaxsz9kVgLed546ZeNdI7D9RYwl4+BENo4oijR1+jNGPkAwkuS9fZ2MK3GS+JC9hE8zsVScl+qHG/EvHHude6avZdMJj34sGSfPmjNMMT/XnEW/aZCnDr0EgE7Uce/0m8le/RWO+K1sr2/O9FVVjT6Twm+PvpyJ/rDqLXxj8Rr0bzxG5Pgu7POuJYwFyVWA4u0amoymkmuB1/b08fcTJ2CJ9aGt+1WmJHCV2c53rrqX5/sODPsbfLEAqYIczAVVRKYv4U1fCw9Me4DWtgTjY/1Ej7yX6ZvydhPva8V6+T0kBIifMPIBlLAf+egGDvTO5nCrH6fVwBdvnoTRHuTbn6tk/Xs+9hwdZN44N6tn5ZyT9MCkojEYjGOQRZwWPaPFgUcZ5cLjg2uEjfKJkJ0jS/JoNTPZPliPxWTmj02bhn1W72unO+7NGPmQ9tS93bwNb7ubgDdOniWXd9u3E0/F+cHR53kky0DH5bfzlMvAT+tfQUPj3w7/kTeEEObZV2OetpyE0U6BCisLp1PpKkE5+A7akTcZn13L5QVTCFos/EfrW3zn0B/51uE/8l5igAqDE1mSub/mShw6B4ImUWmuYl7+HMZbJ1OpLyIW6Ue29GLIttESMuI2ZTGrcEpm7utaX+LeG6owG9N7SFaTzP03jsPqkGj1daBqKoMRL9VZFZljJFFiWeVCdnbuG3ZtOiIDqKcRNvokpFJqJp3gTOg0PcuLL+V7i7/O387/It9Z+CD58sXlPRtlJG/ubcPo8mOWh+dFW8wS+dl6+jqMGa/OmWjuDpDrNKPFgqR06WBT/YeFCAP5ljyiqSjHfY1omsaRwWMEEyHK7UMbD1lW6ElLg5BjyqIt1DlsDMFoRQ0Nvn/oUc5jUqrKpq43iSSHqqwcHjxCQBn4C87qz08wER5m5AM8c2T9BwpUnkQJ+0e0pbzdaImh6ykIAv4d64f1iXfUoSVH1j4fPniKRF/LGc8pSSId/SNDrdt6QyTOIIh2MRCIh0bkfquailVv4eaJq/ji7Dsx6PTcPuFqpFPFEwWBd1uHSvym1BRPHl7HQOdh/MHhNeInVrnZ2vvusBSPUCJMnZhANJiJdzdyzCPzT8+082rWbSQu+RycOJdkcxMQ7SfOoUc6ujVj5AOokQDO7laODzYPO2etuwJn8RRMs64l4sxlUsEEnm36I43iRozRLt5PrO0IiewCYoMdIz7TehsYk5t2Msyb4WRv+E3+ZdfP+Pd9PyO7tp4f3V3IbbZt2I1nfynviyT5l0d38fc/38LX/3czG3a0kVQu7t/sKKNciIx69M8yqVgE1+K1BHa9ghINYZ60BH/1fMKH/0BIjZ/WGIifZiu23d/B5CyRLKfMEc/wF8DBwUb6Yn5mFU/NtM0rmY6UTNHq07Fuezd93hiXz6rkyvwS4jYZR1cLQs1cauwFWCQ3vzz8Ii3+9EsnqSR5tOENyl0lfKNoAVmOMqKkDWx/OIGgRlFEL39qeYMpBePxBHuwGwaZmD8WWdW4cdxqShxFbOvYTZW7nKoKCz/8wnyCkSQuQxJLx3YSzW00lKW9i282bebSinlMK5iATtAxPqeaF45uyOTcncRtcpFS/vx7yKImkSXlkHUi6nlUh+bips8bwRuIU1Zx+siSqjIzPQfK6Qh2Ueks/8BxFFWlrTfEytmlaK0Boo4cLB/izT+JKIiMz6rh0MAxjnrqMevMTMyqRTxlAWzSg04EXwSyTVk0+JqGjSEYbSgDrR/pfKOcJ4gKHcHOEc2hVOA0nT+9JE7z3owmYygfUpJFNI4UupTdhXDK5pqWSqBGR4rOKonIiLZT0Qxm9AVjSHQPv890NncmRz+VUphSlcPG3cPf4XMn5iNLAsmLOJbaZrBi01uG5bw7jHYUVJ45vJ6vzb8Xo2wgrih875L76Qx7URUZLTXyonmjfuLZDvISwzdnzCYRT2rkdxtMxRD0RsTsCl7YMUBHX4iOvhCH8i18dcLlSP3HCUy6kb3t6XNpqoro6x8xjjrYxX0TruKhY+vxxwLUusr5TMk8zFEfvr2v4rtkGY/sewKADqEbX9llI8pBGksn0BLxYHPljPhMLZnO/uMRLEYd1jwvG1uGFPTfad9Jqa2MCVOv52CvSm3xma72x0MT4Jm3G2jtSV87VdX441sNjCtzU5F38UQSjTLKp4FRj/5ZJmU049+xHsu4eTjnXkNy5iqO9EvMNdxEMGBkVsHkYf2NOgNO00hxrIk545la7GKvfy/55pEiXGNdY5EEiQJbHrdOvpa+8CA11un84yP7ee9AD/XtPv7n2cNsbpPIkSwkSuaSKJhOKiEQA/YNHB8xZl88iLNkFlF9OiohqWqk+hrQpwJs7t7JxPxxPH7geV45/jZPHXqJ/931e7xaEgs2lhddyjfnfIW11Tdg15xYDTrKDT5STzyIb+PjRI68y2TRktmZf7t5K08fWkeRrQAXOVxVtQSDNBQeX+uqoMxR8n/5KkYZ5aywr2EQszNKlsl12s/zsvWgyOxrbT/jOJ39YWxmGZNBhxYLEkLFJH/0kotGycj03CnMyJ3KlOwJp40EyLJClxfcBicDUc+wMkqCyTYaun+BIakmpuVPGtGeZ847Te9PLza9Ffl9v/fZRVM/PEcfcMxZBSe0KiSLA/uMlXDqfWF2YqqcNvxAUYfsGlmX/FSEZAzb1OVI1hPPBUHEPmc12iljq6pGOJrkqksq0OtERFFg4dQiJEFASV3cO8g22c5npl5PoS39Wy62F3DXtBvxxQLcOe1GJESePrSOH+/8LT/c+VvGD/ZT1NmLRbAjvC+IvNxeiKm/E2f9i3zlqiJs5vRvJZmCxWXzR5x7vCkbJJnukuXsaxqKwGjuCdNRtJQ3XWtoSbjYuKeTW1bU8NLmNuJjFowYR59Xjnv9w/ydZSz/OvtePhsz4tQs+DY+jmHcDF5rGgrT1zSNrQkP+kmLM21yVhHqpEvQ29y8FelCmLEi81vVl4ynwTadtt4wZQV2msLHRpx/X/8h6vpUjjQNIunO3nI+llDZd3zkxkaP58ybX6OMMsr5x6hH/yzTZdDhnLyYwM4NoKaIVEzg1YGnmJe3kO6wmTnFM8kyu9jWuZdSeyHXFUzD3NHBmrHX8GLjyySUJGNcZdxUu5LtXbtpDHWxKnsW03KnsLdvPwD51jwuLbsEt2RmRcklqJpMhb2UxuYYydTw0KoX3m1m9tjpGI1pz3QoJSMkFMocRTT7hhsmNqODhDZkbMdTCubWzcjZy7FZsni1YXjaQU+4n7ZgJ2MtNaiqhohuWGxCorseTRnafbe98zzfXHoTWyM9RFNxFpbOodhYDBrk6kv53sIH6A72YNQZKLAWodNGd45H+cuzp76PpKmPbNP4034uCAK5+Sp7jwS4YcppuwDQ0OmnMDvtYdRiIQIkMUgfLzVFFIRhG2Lvx22FLh9MKJYx6Ux44j6yTWkhJsFoHzX0LzAURaNcN42+rAHqBxuRRR0rKy4nGbRdTCn6KJrKbZOvZWv7bvpCg0wtmEC2xf2hZb8ko5VoRx3O+deDpqImYiQ9XRhKh+5lLezDWDQWQZKJNO5BduVjn7acVNALlvIzTCoJqoJt6jJAQxB1IOtRU0PRB6mUijcYY29dH6sXVSKKAnvr+ghFEyyfVXJR67t6ol5+vesJ5pZMZ07xNPrCgzy041EemHs3Jp2Bf3vvoUzfYDzE60KIlUYT5j0v8YUJq3mk/lWiyRiF1lzuGXs56tP/AWqK6sjP+N7CKwiUTmRD90YaPDquGbeCLW27MMlGrqlcToVsI7L0fn74SPOIedktBqbWFlLXHmTx9GJe297KoD/Gnqpi5sy5GWX/OgRJh232auLdx5EsTpx5Y+nsidKom0t17wDWRBSdyYYtMjyi5NWuPRTU3sTMcUshMUi7pPHvu3/LP06+Cbstj19Gj7F8xS2YRYnCvKnUbfYgiQI9gxGWWMs50l8/bLxCcxnPvtHCV9bUntUcfaMsMrbUxb764cZ+jvOT6yWNMsoofxlGDf2zjA2RRwQPC1fcjF6DQp2Mqqmsb0kLx9ACZY4ivjn3XnRb15Hc8hM0NOYvuJWxs+9HJ4oIapRvv/NflDmLMOj0HOvfyTVlVzEjew4qCiXOPHKNzvSDXUmHZRQZiukS+kbMRy9LRJIaxhPP5xy7gRfe6WTt7Gv5r52/JJZKh7rNL5qB2zA89sukl0kmo6jbn6di3krWnyaU8Ux5ydr7AkbU4CCOd19k1epvYTRZUBQ183LSNA2rmEu1I/fkwaOM8hcnlkjR3B3EUO3HeoZ69xXFJrZsj5NSVHTS6T0r9e0+CrLMaKk4aBq+VBSXwXlW5+u2QNOJx4DL6KAv3J8x9DFY0BJhNFVBEEeW4xzl/EOQFOJBE6aueVxbvgglJbB3c5jqZReXOKhVb2Zb+x6MOiPTCidS399IdVYFZsl0xmooajSMc/YqIsd3khhox1w1A31eRdoYP3kL6PT4tr6APqcEx6yrSAUG8bz1GNlX33/mSelNhA69Q7xzuPGVd+PfZf4tyyKVxU7WvdfMM2+lo+hEAb66djpJRb2oxc0SaoKkmhqWbw9g19noDveOWFusa9vG/JKlmPVmZrnHUTa9hHD3ESyeXsyHtyHPXU2keT+yIxdLeSk/OPybTFqAzWDluporMYQKye3vRTfGCZ5WZo91sOXo0Obnwkl55HRvIeCu5U8bW4gnhlJDJLOD7212sWziF0kqGju2Rvn6DWvZsL2TVx8fQFE17l9RgOnwC1hmX42owhV5k9nfdyyjEWDUGShyFvLrxjfZ13dCXV8QkaJhFh3YxcyZy4kaTChqDn/z04Pkuc3csLQaUYDaXAP7+/fTFUoLE+Zb8jDHignH+nBY5PR66iwhALcur6GlK4AvlF4jXjqjmOKci2h3cZRRPiWcV4b+HXfcweDgIDpdelrf+973aGtr46GHHiKZTHLXXXdx2223AbBlyxZ+8IMfEI/HueKKK/ja1772l5x6hhwF1loraNAU+lApVFUeGH8NPz+2nr6IB4fRzk3ll7CrYy9Tpi0mt3o2KRR8goq19zh5RZMJHXiTf6hYRkBvwKovI+mPIqoxNvW+jqqqfC7/9hGGsCgKVBc7yHYaGfANCcYsm1WKTjcU8igJAqsWVhCKJXlw5lcYjA1g0hnJMebh1JuG5aPLIgiTLiP84o8onrWSxeVzeb3x3aGxRIkiywcL1ekKqhH0pmHCR6mJVyNJhrP6UhpllHNFfbsfl0PEZHLAGcKE8xx2MLSyv2GAGTWnEeTUNI62ernlsmq0WBDBYCaUDFNoPXN48MfFbYGtJwT57Hob/bEh8T1BFBH0ZrSIH8E6Wkv5QkCPTI5bRhByaWkPYDPLXDLJicl0cZmIDtnBnOKpdIf6SCpJllZeQomjEDllRjvDrrBoseHd9CTW2kswVkxGCQwSrtuBecFtmQ2CuDkP5/zr8G56gnhX2hjXuQvQuc+c9KymUiOMfAAl7IMT0fzJpMKgL8qtl9cQCCdIplRyXSYaOnxMrcwi9SEaA59mck05OAw2/PGhHPqarEpcsgssaQNYOUVEb0nhVMTDW0nJRpKyFSEC1v1bUAfbCAOCwYxr6e10u7MQk0murlrCM3WvkVSSlNsLqbXmodvyMI5py0nIDlRHITeOb2JmWT4NAwpVhTYqi50I21/DtG8937nqsxzwWglFk0wdV8Cbe7rwhxM89k66dJ/FJKPGwsyf4mJaTRa2eD/WplexzLseiiYhqVEcz/4rfz//KuoSfvQ6PTU6O7kDHfRGhqoIXDduJfl5k1FTAuaWYxjKJtFvs2M1y7R0B2jpDlBRaGfehKl8dcY97G5vI5nS8PbreGpdJ/dcUYnNaiWVOrtrqmybnu9/bi79vihGvY4chxHp4nrsjDLKp4LzxtDXNI2mpiY2btyYMfR7e3v52te+xrPPPoter2ft2rXMmTOH4uJi/v7v/55HH32UgoICPv/5z7Np0yYWL178IWc592hmN/m+PhzNBxEtTrSON3DmlPGt2uvwJULYdAaskhG9q5pIXEefRUe24CdHjZN015KQTdimXYl0dDNefSV+WSLLYuVAQ4xrim+mwqEgdRxHi4eQc8uJO/IJRSUONHo42jLI9Uuq0MsS+48PMKbIgcOsI8sMYipISrYQUePUBevZ3L6LCmcJC0rmUJFdzMDAkAKuKij0JnpoD3RT667GfNWDpN57nuVzrsBeewUbW7aRa8nihtqrcOuyPtD7rloLsF7/LcLHtiJEfMSLZqArrMHwAR7PUUY53zjc7MFoi+E0Os7YTy/pMTj9vLGn9bSGfkd/GFkn4rQaUAa6QW8ikopg1J3dUEiTHhAgEE0b+m3B4SJggsmOFvHCqKF/QaATRNxOHRazjiynHp0k4HboEPnwSg2fJoyKhWJbIU6jnaSawqQzYhMcaB9SSSVmLcW9+BaSni6UkA85qxBDxVTCp5RLTSY1TFVzyHEVEGs7jOwuwFBUQ0jOOePYKdmGvqAS64RF6OxuNFUlsOc1BMuQloeqwvgKN3VtPqJxBVVV0YBLphSSSl28Rj6AWbPy94u/xJ8Ov8LxwWam5I+nKqucOu9RJlgL+ZeFX+EXB/9Ed6iP5aVzuUSwYRljxjBuATEMiGaZ9kl3khNpRg51E3VVY3FXYg6HeHJXkqYuA5fPvh23Q2ZKQQ6ucDfi8r8iZclFFQR0Vjf9EZFJjn6mOv2kiGLTmxCX3kUipZCtJSkrTKBKVpr8STTgqksqiMSSvLK1hfuuqUW//ylMDbswCQKOBWswL7+LMHY0DQRRQqc3YX35YWaZbGiqghaPkLr277ip9jp8cR/51hxy9QXUDcTpj48lu3wKxdlmHJLIP909m+7BCKIokO82Y9SJiJKTiVl6BvxRJEOAv725lqoi5ycy8gUB/JEUHf0hRFGgJNeKRT880suil7DkjnrxRxnlQua8MfSbmpoQBIF7772XwcFBbrrpJiwWC3PnzsXpdAJw+eWXs2HDBmbPnk1ZWRklJWmxtlWrVrFhw4bzwtCPG/MwFqjY3AWgaQgmK9HjuzD0d+BoP0ZKSTEw53ZKYp1IiTDx5hZ8ezaApmGZugxj7VwGIz60qsuoDHSS3PwwymAHc8umYqldS+ilH59S61XAfe1f88t3FA41eADYfKCbBVMKWTK9iJSiUeuMQuNmUrEQOkcuCbuLX+x+HIAxznLqBlt47tirFNsKmZI7gSzZxVF/HW0DHuxKEcdDEWymIlyLHkCQE4wTc5iaPxGbzoasGM8YYq+qGtiLMMxaQzylYJVEZFFAkgQQ+ERiRKIIceLIggzK6IbBKOeWo60etCwvLkP2h/Z1Zys0Hw3hDcZx2Ybn3h9qGqQszwak8/NVnQGdkEAnnN0QekEYKrNXnG3DE/MN/9xoRQ17GQ3cv3DQaSae2VjP3vp+jHqJtStqmFR1cW3UxIQIfZF+mrxtDIQ9jM2uJMcco1AuOWOevpwM4HnrUeKddZm2rBWfRRyTP6zcqqjEiXm6kJ15qIkYaiyMYDxzxZW4ZiBr2Z34tjxPtHEvkj0L18Kb0JzDIwG8wTit3QGynEZUDQZ8UZxWA3l2w0cq+fpppsJVyh0Tb+Tdzm10+LvJFQ1kHT9AYO9PkHNK+NtLb0UwWBGTcUSdjOCIoPq7sFijGOJJrBW5NPnzkAQBh0lkZ/MgeoMVhw0WT3dCCno6k0w19uHb+iSJvhb0FZOJz74cyWijxqYQ2Poywab9gEbSkYNl4iJEnYySXYF3/f+gxkJk5VWxdOwafvjicWbW5vMf98/BWrcORIGsW75LY9zNK4d6CTZ3smiKytgCIwnRiP2yu/E++0PQmwlOupEesiFooaU+zMtb/CyYYsVt7+K5TY2Za3Ll/HKuWzgGkywxJt+GhsZgMMFAUuVYi4dn3jpOUlG5ZHIhsyYUI8ifbBnfH0jw3Ye3E46mdZSynUb+4c7ZOEznjVkwyiijnAXOmzs6EAgwb948/umf/olYLMYdd9zBFVdcQU7O0K56bm4uBw4coK+vb0R7b2/v6Yb9syNpCRKhfnpD/cTVFDmyGYstm5CiQzd+GVanlbAWJBEMoktBxN9DZOnNaIDY3oDY18k/db3J/5uUT/zlf0dLxpFzShBTEZTBjlOMfACNwKY/sGrGF1g1w4HFpNHdr7K3PY5sD9Dma8YXNlBlcWA59C62acvIFw38ev6X2elvpzMe5uF9j2dGe6dtC1+ZdR8dHg9jnWUcCx1iy8BxpubVUmCvJuTp4rcdW+gM9TE5r5bbJ9yIWbDgV330RQaw6S3k6HOQtOHeJp0AOllCEAT6U7282fgufeEBVlZdylhHJaSkj1TCrjfUz2tt77KlYxeljiKuHbuSHF3u/7n8nSYqRNQwsqjHoBlHy+mNAkA0nqLPG8WQ3Y3TWPmh/R0mC0IuvL23g+sXDe+//Wgvc2rT6tJaLEhCEjF9xNJ6HxenGXp8MDbfREJJEE8lMOhOeDAN1lFBvguIlKjx2o429p4QxYolFH677gj/8FezcRkvnjz9sBZCFCTGZo+h2F6A2+TEE/GRMsaRUh98HYTBFuKddegcOUi2LBJ9LXg3PUFe6QRCYnqzxCCl0CJ+4u1HiTbvR7K5kV35mKNdhI0jK96cxCjG8G17ESXixzF3NUrIy+BrD5N7/YPgqAbSOfqhaIKugbTXVC+LHGv1UJxrQ5AkeF9J2YuNFm87wXiYPGsOHb4ufnHoBaZmVbLkpr9GOLydREcdbUWlvNy6DQGBK0tnU9R2mOTxPTjmX4/f10Zh+QTkzmZkX4A5ip994kL2HOujezCdn1+aZ2WBI4G+Ly28pzNYcKkiA8//hF5/P8bSCbgvvY1I4z4MRVXI7gIGXv4VllX34V+wGh1gOX6AwqYXmD/+ct473MPq+UVkmSyoJRPpIRdZC7Jgpow/CruOdJFM5VJeIGHUUjiv/VsaIlb+36MHUNTAiTnZ+Mbts/AEYoRjSSaMycJpNZDlMHKwcYBFU4vItRvQgG1H+/nNS4e47fJa/vDa0IbVe/u7yM8yU1vmQlGhJMeMxSB+pJLEoijw2s7WjJEPMOCLse94P0umFH6oyOUoo4xy4XDeGPrTpk1j2rR0iRuz2cyaNWv4wQ9+wH333TesnyAIp30IfViZnfeTlXVuwpH62w+z3lvHK+070dAotuVz34QbMG15gaTeArWL8TnMmAorkfs7eTrbTEvfHuaWTCN30kzGJ0QemHYnePoRjBaCK+/kXX8Ler2Z65IjxfCUiJ/xpVZe6NjKoD9EicPJovIq/nXbTzLXyW6w8c2V9+LwdOPd9AfinfWMK66hbPblvC7pM6I3fZEBukI9zMiv4VeHHgUEsswuXm/ezNHBJu6qWs4XdZfynaPPcqD3KC1lLTiNDv71vYcodRQyp3gaTUIrBbZcqrLKcZucJ+YYRImH8GkKv9r7OFOyZrCwdCZZLQeIt7+IbeJCzJXT0dk+2EsVTyV4aOfTbGnbBcBgxMux/gb+ZfnfUWT/5KWmOvxd/G7fs+zvOUy22c29M29hSt54RPGTRwsMhD10pLrRNI0SRyE5lvPf+/ZR74ecHNs5nsm54+POfc+xPgpzzXglyHGcOUcfIDvqRFcUYtO+Lu5cNRGjPv147ewP4QslmFyThyQK+NUoil6HVZYwmT7cWPsofU4lz6XS7VWx2fQ4jXZScow8hxOAkNOJqIXI+jN8jxfyb+VcvR/OxOmuV1Onj22HenBa9SyfXUKvN8o7e7vo6A0yd+IH66P8uTnX33Vw0MOBwSaeO7oBRVNxm5x8YdbtJIU4+Tkjy9OeJNARxbXkVlLeXpLeHhwzryQVHIR4mJzSMgD0SgjPjpeItRxCsrpQoyH6X/wJ+bd++4x/V6yrF0NeOUlRh3/nemRHDq7Fa1GiIXKqho5TlAHGV2Tx6rZW4kmFpTNLiCdSiBK4nf+36/bnusfOxf3QHezjQMdRAMLJKDOKpzCjeAqKmuJwdJDsSbMwCzp+tO03mWMO9dfz97P+CvvuN1BsLow9jWivP0Z80uWEC2oQElG8rXH6fUO6QG29IQ4kqpg/9xrMxWNJHt9NYNfL2KcuI9Z+lGjTPhAEDIVV+Le/hD6nFPHmv+HHx1+l0dsGwOLSKawMppimlzHY8tE74nRLl/Doq80cbdvKhDEu5s018GTDoxTbC5hlX8OgP0ZIzmV3vZddx5pRToneaOsN0toT4InX6tA0uPXyGpo6/by+o42ZtXmkVBWX20J7b5DfvHiILIcxI4pXkG2hMNtCY6efzfu76fNGeWdvJyW5Zv56kZ6CLBOmsnRJzg/6ffR5wrR2B0e0d/SHyM7+y4XqX8jvjFFGOV85bwz9Xbt2kUwmmTdvHpDO2S8qKmJgYCDTp6+vj9zcXPLy8k7b/nEYHAydk7C55piXl9uHVGQ7gj2sb97I9YkQStMekvVbcd3wIIfDSVzGPJaWraIpeoTXjr/DuKwapNxaSnVZyHYdoeu/yg+3/pyUmsKut3LTuDUIshE5qxAl6EEJ+7BOX0mTX6Z5fwnRmMKkOcUc6t01bDMkEA+SjAfwbHwcJZC+bvH2o8jeHu677EZ+evQllIwokEZM9bGkYj5NnjZ6w/0srZhPJBllUFRJagmWFc3glbZt1A800ervxKo3M6NwEk8cfCFzzil5tdw96VZSCQ/tfccRkwlyTblcor+Bw4cCHIl6mFlaxXR9P/H1D2GbeRXSzDUo6umNqSA+trbtHtYWTcVo9XSgj5s/0Xeliil+vf9JjvSnBZgGIh7+7d2H+O7ir+MSP3jxeCqCAAHVx0DMg1VvwaQz8J/bf0VvKO2Byza7eXDufdhxfqI5nk3O9BL9KPdDTo6N/v6Ri4MLgU8y9x2HujCZU6h6O6HwB1eXOIleM+BPdlKUXcTjLx9h9SUVADyx4Rjjy1wEA+nFZ8znwS+l0MkmotEzj2sy6T+0z/uxytA5CKFQDJNkon2wF4tmByClGdD6+1DP8fd4IfxW/q/3w9mey+mul07WuP/GWmRbmM5gN7VlRi5fMpnQoO68ub5n67vWGdNq38nYyM88CR/PHHl56P9RH384+AJfmnXHGc9tdefjfecPKCEfALG2w9imrUAw2+nvD5KTY0OLRxAEEdeSW0h5e5EsdjRVJeXpof8M5fXssoFEbyuJvhas4xeghDx43n6MvBu+npmTxaJHUVWeemNItG/95mbWLh9LKqX9n67b2b7H/tz3w6DipcRZSFegDwEQEIin4mxp282K6kXoVAVrMMSXq1eyrvcAzYF0NOPG7v3cc8u38G16glQkROfEu3jk+UEml8a4YQLMjh9n0uW5HAjm8ui76RIkx9r8XLp8Kv4nf4B2ovxhtPkArsW3Eu86TryjDuvERVhr5xP3dLGx50DGyAfY1L2fSeOuotDp5O2659jePok3X9ZljO+DDR76PCZWXXE1zx5/nn2DO+g6UM7qhVUUZFvo90Z5P4mkiiQKpBSNp16v58bLqtl2qId393WSUlT0OpFAOIGqwZTqHBwWmduvqKWzP0RbT5AFUwopyrGwcU8nAO19EY56bUjv/jdZqx7AXjvntL8PQRDYcriHyVXZHG/3DftsZk3uX+y5cr69M0Y3HUb5tHDeGPrBYJCf/OQnPPnkkySTSZ577jl+9KMf8fWvfx2Px4PJZOK1117j+9//PjU1NTQ3N9Pa2kpxcTHr1q3jhhtu+Ev/CQB0vy8nFuCgt4VrCmugox7UFLrOOp72H6TAkssy9xWUmifzudoJ+BMhlJiGT03islo41L2V1InQPoNsIGq10HrNvezqPUKZaQYzbMX0xLP57m92YTPLOCwGfvrUQb64ZgoGafOw8jTuRIJQYGDYvJSQlwmhON+vuoqHe3bTHw+QY8hClRM8v+cPhE6Upmn0tLKofA5mnZGfNr7N3ZOvxWBxMMZZgopGTXYlL9e/PWzs/b1HCdQM8G/bfp0pcVNsK2DtxGvZzxYKDA7sthl0+hZQ0rSV4J4N5Ey6DEU+vfdbREIvySNK7ujFTy5KFVKCGSM/c000lb5IPy7rSENfEVN4EoMIgoBLdqPTdLTH2/iPrb8goaRD4FZWLyHH7M4Y+gMRDzu69rCi+LKLPh/zQuN4uw+9O4xdb/9I/W16G964j5VTCnn09XomVLhBg13H+rj7ytqhjvEQAZN81oX4MvMwQSQBsSRYZDPemD/zmWC0ovY1nZPzjnL2MRpVRJuXH+/8dUZdPs+SzRem3/MXntnZI6WL0hnpobGnFZ0oUekuo8hYDKmhqCpP1D/iuGZvG1ElwZnuolRwMGPknyS4/y2skxbBSYFNSYc+vwLvxj9k+shZRbiX333GeaupBHJ+JbYpS0kFBxENZmwzrkSNDY+8q3+fMQWw82gvV80vI3kRR+5LosSj+/5Es7c903bb5OuYWjieuoFGPFE/Yw1Oane+w+drZvCwKFHva8cg6kn1NJPoPE5wwZf4txc7GFNg5WrXceIb1gOgB+YXj2Pq7Tfz0NuDfGaOHbmnOWPknyS4/01M130bU6iDwI71qIkIhgU3sLfjnRHzbUqGaOjbQ6O3jUmWRfhCw9dTvZ4o1UoZlxVMZf/AUW5bsIB/fmQHBdkW5kzM582d7cP6m406Ukr6nlZUbZiuy/ZD3cyqzaOm1ImsE3E7TOS4LDy+4Sh9JzYNGjp8zJmYz7LZpYwrc/HO3k46/SpTzHZCe17BNnYGkHZGeMJJegbDWE1pj/97+7sozrWyYk4pb+/uQCeJXL2ggsqiM4vOjjLKKBce59TQ37BhA0ePHuW+++7jzTff5Oqrr/7Avpdeein79+/n2muvRVVVbr31VmbMmMHXvvY17rjjDpLJJGvWrGHy5MkA/PCHP+TLX/4y8XicxYsXs3LlynP5p3xkcm0jIwtqHMVIXW2c9JmLKph0Buo8jazKbsZqnsSG9rfY1pn2WE/OG88q89XoDekQKpfBztfHrOC1noOsa0q/gHYAm8xu1o65k/+8vQxXvBviHjRHIZt7otxQvZI/HHsRSO+U641W0v6S4camLrsI3Ys/4f6Vn2NX0IQlHKfDGMgY+SfZ3LaLFeXzSakpdHozL9e/RUJJUmDN5TNTr2d9/VvD+pc5i3mjZVvGyAfoCHZT722g1dfBsVQDO8X93Dfx8+jzyon3NJ+Y3+mxCjbWTlrN7/Y9k2mrcpWRb84/oyDgmdCLemx6y7A5ApjlkRECYYI8evCPHOw9BsCswincWHs1v9zzeMbIB9hwfCM3T1zNob6hXLr6wSZWli5HvYhLKV1oqKpGa2+Q0lwPuR+x1r1RMqBoKgaTxhWzS/n3J/YhCHDl3DJMhqFHrRYL4TdYKNQZzjDaJ0cUwGmBXj9Y9Va8cV/mM8FgRY34PvDYUc4vYkmF5+vXDysh1xseoDvSSY7j/E8J+ih0RXr5jy2/zESVmXRGHrzkcxRIQ6J2dsPIcOJiewGyKMHHfawKIEjyKf8VCOzeMKxLcrATLRaGM+3x6WT0WQX0PvvvcGLu5qoZ2OddO9RFJ5DrGqnFke+2cLEXmO0LDwwz8gFeOPYaC8tmE0lGqcmq4Hf7nuHbUy7F8OYTXH3FnTwc9XF9/nQYaMe15BZ0DhcleSqfW5YLL/9i2FiJjmPkjDnGP60oxr/5YZKTTyPWLIjsaIrSHXQzf9Y9WN74F+JbX2TilOl0B4frPpUKBioiEbrcY9DpRi46RAGMwV5W2IsZkz8OMaWQ5TAye0I+eW4zteVuth7sprUnwKoFY9hb18/qhWOQJIHDTYMUnlKj3mrWYzWKZKsD/Oo2F5Gkh7qEPmPkn2T7oR4mjsmiuSvAynnl1Gar+MI305lycXx/D6W5FgYDcf75tztJnShpvHBKIStml9LWG8JtN3DvtROxGGWMeglZPLMA5SijjHLhcc4M/V/+8pds3ryZnp4e7rrrLn7605/S2trKF7/4xQ885qtf/Spf/epXh7WtWrWKVatWjeg7b948XnzxxbM97f8zRr2FRaWzeKdtJwBuk5PV7hqUrb9Od5B0yLmlxJoOYTNYkS0VeMIJ7FIWRp2BWCrOgd4jjHVVcUnJTIKJMJPshcSNObxy+Olh5+qLeJDNfgxv/xFf95Bq68IV9xDNHsfrls1YZQs3jLuaNiWGbcZytN2vZfqJU5cSsto4fun11Hr7cFONYDeSOsUwOIkkiBiTSeaVziClKcwqmsKOjn10h/p49sgrfGn2nXSF0iF4vliASCJKk799xDiDES92g5VoKkZSTRHUelCTcUzTVpIwODl19aOISfwpPwbRgBUbSyrmkWfOpcHXjMvowGV04E/6yZUNoH38nHoTVu6aejM/3fFIZiE9t3g6+ca8YZsHgiCwr/dgxsgH2Nm1n8UV8xiMjBQ2S6rJYf+fWzwdRRk18i8kOgfCWM16/Mog1YbSj3aQIGDX2/DE/FQW5fHF6yaiqhp6eUjjXtNUtEQEnyYzRjo3YnwALjP0BaCiwExrcOg+FIxWtMhI7+go5ycqSXyxIHMLZlNgGIMmpNg5sJlIcmQo8IWI3gxv121BURVseguqphFORjjcV09lZTmRSNrlbdQZuWzMJbzZtBkAi97MlWOXIglnfu5LVheSzY0S9GTa7DNWogmn3pMKWnJkeoyqJEe0nYqgqHjffixj5ANEGnZjmbgQTCUnxoaCLAtOmwFfMB3mbdBLTKvJAe3iLkqeUEZe83Aygkk28lrDJqbkj0fRVIKChgHI02T+38TbCf/xB4ST6WtJ7VLmT1xC30CAktNspGuqin/T47DweprVBDmyEe2U3BB51mqefKmD1TNcxD0eCq/+ClKwj8WyyEFrLj2hdOj/9OyxlA4OoO3cwFUr7+B5z24unT2bt3f0Zca6YV4elqZn0eeV8bYvjD/6OveuuYf/fuwIoROid3deXcu1SyoREDAadDzzZj3BSJKrF4whkRxa/Nx+eTUTdK30H6knJUnoju3EPvEzI/4+UQCjXoesEzHqJTSTjW8/2YCi+oEWVi2sYNeR3oyRD/Du/i4WTisi12UiGE1yvMPH5v1dXDqjhLJl1YiQrg4RjOMLxslyGHFb9Wdww4wyyijnM+fM0F+/fj1//OMfuemmm3C5XDz99NPcfPPNZzT0Pw0YoyFW+xIsrFhOUhQpdJdi7u8gVj0TQW/EkF9JJ+myWquL7qCuJ4Yi+nBQxu1Vn+WxxoeJJmPs7z/IpaVTEJQUbrmIcKDj9FutSoLEKUY+gP/dp8i66Vt8rWw1csyLlDDSoHl40xBn6eV3YInHiOgNvBPp4gpN5Ff1G/jrqWuZ4HRyONhKS7CbHLOb/sjQ4uia8gWgtxBJxvjvbQ+Tb8nm1snXsq7+TRo9rXhjfp45nA6bK7Dl8aWpN7O3v4l2f9ewuRXbC9javifzfx0awqybUN0lqKfk5wfw8uvdf6DR24pRZ+D2yTewyDmbbKObP3atoy3QiaIqCAg8OP/zVBjHfOzvStM0amxj+c7iv6YvPIBNbyXflIesDfe0ihLs7jkw4viuYA+ljiLa/J3D2gtteYiCiIbGsooFjHeNG90lv8Bo7PKT7zbRlAhj01s+8nFW2YI37qPQkodOEnl/HTstEQGdHgUVWTx3AVUOc7rE3qRSM8HEKXmPshGUJFoqjnCOIgpGOXvImpk7a+7mude6ebvNh04SuWLRFRSZPh3efEHUQINvLvwigXgISRQxSHoO99UjyxKQNvQHIoMklCRfnfdZ4qkEOlHi8QPP89fz7+WM22WaiHvpHcTaj5Dy9WEorMZQPJZT9wcEScY6YSGhQ5uG2vQmdLYs4mcaW0mQ8vePaFYjQ/eboqi09wZZPrsUnSSiAZIosL++n3kT8j52MMKniQJbHrKoI3lK5YHpBZM43FePikZ/2INeknGmVEAgaaoicXAD2kkjX5Rod87kifUNXDLeTXnpZJS2ofe0ZHGipRKkvL3EjCZ+fugVvnj5bWR1NCGFAoTHTKBVdvGlZVCw71eowUF8gKliMgVTV3Dv2NuIew9iMtoQJR2+kAfHguuwJ1WOeo4xI9fCl26ZixrV4VYGye7ciNrbSLhmKh19x5mQNYGnX23OGPlrl49l5+FefrfuKJIocNXCEm681o2g6Nmyw0eWw8jnr5uIy6qnMnmIg6KeP8QaCSbCrJg2m7mGFFVFdho6A5m/ccHUIt7Y0UZdm5fdx/r4ytppiMJQkIvJINM9OFLEuXsgzCPrjgBQXeLk5mVjefL1eq6YW4rbYuC9Q938dn1aKFEU4IGbpjJljHt0HTPKKBcg52ylqdPp0OuH1KLtdjs63XkjCXDOyA1H8O95nZMyHuLYWWgFVSDq0ICQ1c6TndtZM+ZGAkkv6/qeJqEk0Usya6w3cUXZSp5teJ6JrnKebXibN1veY4l9PIbtL3HZuOm81rErcy630UGhZOb9fgc1GkJLxhFf/FcUQHfjv5BrzGHnwHG29h3N9LuycjH+aNoS2TnYSIVzPH+qf52+UD/X1q4kmooxEPYwNX88E21FPF6/ge2d+wFoD3Tz2IHnWDtxFW80babtFIO+O9jLvp7DTMieRWdhL9u6d6ETJK6svpQmbxuqlt5dNslGKs1ZKNYSLOahsnaaqPDkoRdo9LYCEEvF+dWeP1DqLKQnOECzb0gkR0PjyUMv8I3ZX0JSP36+vqCJ5Eh55JxU7j/Ni0xTYXLueOoGhuc26wU99067lf/Z+Qh94QEMkp47ptzIrKLJ/MuSv0MD7JId1E+u4D/KX4amTj9Wm4ZdsCJ+jFr3Vr0Fb9T3wR1iQVTZgEln+tiVQj4OLgsc6gCDZCCppjLPGEEQEEw2tLAPwfHJq1WM8udBVSS27ApyvC29uE8pKi+93cW4onz46PtP5y0qsHLsYp44+CLHB9Plz6YVTGR1zfJh/bJNWZjlTv5728NomobDYOPmiasw6gyMeAGeOn48SP8LP0ayudHZ3Pi3vYBkcZJ7w4Nw4rGspZJINjf2GZcTaTqA7MrDXDkNJRqEMwmQG8wYKyYTaz51E1hA58zlpK9apxMpzLXyi2cPEo2nDVpRFLjvuknEUtpF7SU1iDJfm38Pzx7ZQHeoj2kFE8gxZ/Hc0Q0sKZ9Hg6eFL469AsM7LyIsvAu/ZMEcHPKgy6589nSkv/zNRzxMXr6SGbmlxBt3oc8pxVgyDu+mJzGNmYKsM6JoCv967CXyLNmYXWbaWl7ntorPUtHzNmpwMDNutPkA5pq5bD8scbm+nb1FRfyhaSOqpmLVW/jazNv4mu0GLDoDBRaJyLP/iXLieF1uOXXmdGRmobmIbZ3pTZ/iXCu9nihHmtPOE0XVeHFTG7dnFbK+57fcNvevSIWgoc3PJZPz6TVZ+e99Q5oRL7ZtRV+u44HLajnkL6W+3UdNmQuzUceGra2Zfm/ubKO2IosDDWn9gONtXqZUZ7P/+HA9gZPaAJDWoinNt1OQZUFVYTAU53cvD60TVQ0eeu4g/3b/JdiMn/41/CijfNo4Z3dtQUEBGzduRBAEEokEv/nNbygqKjpXpztvUELDQ7kj9TuJ97Whu+aLxAQRYno+X6ZnQGfgkWOPZPK7E0qSZxqe5r5pn6XElscsycHTgXSouJoIovQ0saRoDIWVl7HN30qVOZs5JbNwpqBf0oEytCturplNpGfo4S/pJIyeIF+ceg9vtL1JT2SAxSVzMEfH4En2YtIZyTbYkNv2MK9gJs/Wr+dPR17GqrfgNNoZ7yhlwBxhR+dwr7aiKphEmetqL+eRPcPTCo742llavpIFzkspFaeRTGmsyDLTIJowawJu2cx01xhyFYmYZXjt+qgWZX/PkRHXtjc8QDQ1UpLZHwuQ0lJIyKTEBHE1jlE0fiLD/3SoqsaM/Kns7jpA04lNhtrsKsZn1WDBxjfnPYAv4cesM2ITHJhkEzbBeeLgszKFUf7MNHUHGVOpYFM/Xqkhq2ylPzrwgZ+r0SBJScZ0joT4TuIyw0AAQMCiS3v1s054gQVjOk9fHDX0z3sULcXeupFe4wFfDIpPc8AFhk6Q2Nl5IGPkA+ztPsSE3LFUOsozbXElzqsNQx53fzzIhoZNVLrLzji+ciJNRQl6MuH7KX8fWiI+tPoRwL/1OSRbFsaScaQCAwy+/gjZV91/5skn4zjnrMaXShJrP4potuNaeBOcsjEYj6cIBON84YbJKIqGoqpYTDJHmwdZMDmf2EUsxtfs7+DZwy/z5bl3AxpH+hs42HuM1eOWoygKC/LnEW/px3j1V3i+dxcz9REipfPRtafXBkrET0lR+ks0G3X0JK1sty+k6tJLcTS/hefN36Mrn4hlxkqU7lburVzGz+o30BseQIgI3Fx7PcF2FbGvfsRrOjnYyfIKM+HcS3ls688z7aFEmF/s/xNft09A2/Uq3rlXUT9vGZPlbAa8AklbHs+1/w6ApmAD4yuncqDeS3WJi0NNI98L/f0qsiTTFm5k46tG1i6vYVfdAFmVgRF93+49xIJJkyi3ONh1tJdHXjpMUlFZMr0Yh9XAtkPdiIIwbC2173g//3zffBJJlaMtHiwmmVtW1PDmzrZhY9e1elgyvRi3TU9rb2iE5z6eUAhGk6OG/iijXICcs7v2H//xH/nGN75BXV0dU6dOZcqUKfz7v//7uTrdeUPclTOiTS0fz4/2P01XqI/PTV5D3X4bM2anhom4QdrYl0WVL0oFSN6+TDmZQT3YZAPi7jeoNZiZklcBQogGRzH2gklkX/91Au88SdLbg7lmLvqplxN48T9BEBHHzaYp3kO5QU84IrLIdiUVxQJqIkogS2NvsA2b0coUzYB/4+9Zfsv/49n6dAh+KBEmnopT4SjBrknYDFYC8eHlTxw6I75UcoQa/szCqWiqRnW+lUJDiER/F4lGL+PLJzBOMKNpGppkImUvGXG99IKeIls+ncGeYe0GnYEsgxtBEIaVD1w2ZiEmwUR3sptH9j9Fu7+LsVljuGPyGlxC9sf49j4YG3YemHkv/bEBREEg25CNTk1HrOhVIy5ZZCDeT3eyl4icj0mwXfQ5mBcq8aRCvy9KqRzDpn08Q98mW6j3Nnzg51osRFwSMEjnNmzeIIOsA18krbwfONXQN1jRRgX5LghESaOi0M6hxsFh7TaL/gOOuLBIqAmODYy8Xxo9rYinZGP5YyMNnzZ/Jyn1zDupOvvI97GcXYJgGrqvBUnGVD6ZaMsBwkc2n2gUkRwjj30/fS/+FNfim7HPvAKA0MF3MFXPBEcVAHq9jrFlLh575RiHmtLfYXGulfvXTCGpXOxx0BqemJ9Dfcf405FXyLFkUeUuRxZlZFFHjydBIixS4e3iRs2OFOqkxVZGzuLbSe56CUSRaeVWXs8KctWCMTz1Rj2BE2VQr7pkClOvreGdvt2oXVv5YvEllG15lm9XL8IngV2TsA4G6CqqhuQU8L8xbGZCbik9LhPJxMhyb33hQSJZMoZokIRO5vdNr3Jz+V38dl0P3/yrMRlnxOGBo9wxZy5ev5WugRBl+fYRZfZcLpFwb5ioEsUgW6hrGeSGaUaOCCPDdbJMTo4O6Nm0p5699UObf2/v7uAzK8ex7VA3S2cV4wuHCUcTSJLA7SvHk+cw8dc3TyUQSaKXRdp6QzR3Db+fxpW5WTClAEkQyHIY0etEEqmhe8tpM+CyjqZ6jTLKhcg5M/Tz8vL42c9+hiiKKIpCPB4nK+uj1Sa/kBm0WLEtvQVp6zrUWBhx3GyO5OXR1XgYgMePvsx3591NRNCjl+Rhxr5ekrErAgZPP6GZy/Dv2QLAQw0b+H/XPUj49UdIebtRlSShOcuQTEb8iSC/793BZYtvxa0z8o6vBXu8m1k3f5M9bbt419PA0QOPc1fttVTas+jpT3AwEMeqlyiKqVyZU8u8pITh7afQkgkMpPjSpBt4q3sfbr2VpSVz8AYk3KYwn6lYxM+Orc/Mt9ZVikUTcNrymF88nS0d6dz7+SUzmZiVzktvT/ZSn+xA5xYBFyF/Aza9hc5AP4utYxlsD2I3yxQ6RATZgqaBTtVz19Sb+NGWhzLXZ0bhJDa37qQ6awxfn38fTx1+CW/Uz7IxC5hfMJugGuTft/48I1JVP9jET3Y8zN/Pe4BgKkRPuA+LbCbflI9B+2TeVFk1UKg/EZVyyvoyKcR5vvFlNrZsBUAn6nhw3ucpM5aN5rRdgLT3hshxGPEkusi3fPhi/1SseguBRAgVFZGRKRtaLEAE7ZyV1jsVlxn6A2DWmfCfukFnsIwa+hcKio5FU4to6Qpkcn2njs3BYvp0eNZkQU9tTvUI9fXqrApSqaGHp9vsGnFsqaMIvSSfTOM/LYLBjHvpHXg3PYGmJJHs2WQtuxMtlYSTAV9KClPlVDQ0Yi0HkWxZOGZfjZo8Y4Y+iBKWcbMZfGVI7V2QDdhnX5UJ3RcEaOzwZ4x8gI6+EFsOdHHjZVVnHv9TTpGtAFmS0YkykiCSa8ki2+xmXd0bxJUEfzNxDXlNzxDYeyJcXxApW/k5eopm0GaT0VCpbdvEN5ZP5yfvtGWMfID1mzvIKshnV99RlhfPwLPxDzgnX4pwcBP6niZ07iIsl32WApMBt2spvsEWEt0NIIhYpy5lhxLgcHsjV5XOGTHvXHMWNr2ZZMk4jgkx1o69hWMHNL7z+fFYzVG+ufDLHOo7BgLE6GfOpQql0iT0OgMN7T58ofTvqnaMg5DcjqKplBireM3bzbQqJ9FQmEJnFrmWbPrC6SgASRBZUrQMU1RHXdtpBIAVlftvmISiQDCkMH5MFuMr3Kzf3IQoCaxeMIZ8pwlN0yjLszJjXC67j6Wva0G2mavml2PSpd9XDrPM1z8zg//5434C4QTZTiNfvXkaZr04up4ZZZQLkHO2Wnj55Zf58Y9/zGuvvUZzczO33nor//zP/8zSpUvP1SnPC2Qxi83GJipXfoZScza/rN9AXWO69JzL5ODzU++iNRnFlLJz89ibear+qUz+7I3VN+GLmLEtuYmAX+S28dfwdvs23EYHPRYrXPVZZCVFj5rAp8QREmHMsondXQfZ3XUwMwebwUrVgvv5ZcPr2AxWJFHi3Z79mLQy9iY2s71zD6Iosqx8MUvitZj3vEUqEcNSO5+UbMCmG8sdZdX4wkkaeyM0xLZQLFmZaHbxD9VX0ksKCxIFqoii02Pze5iQV8PCkpnYjS4ckpMkSXoT3YSUBM8f3ZDx+LtMDm6ovZIxzOWbD+3K1Ja/8bIqFo1XsFrtaBoU6Yv5zuIHOdh/BE3TaPS0sq1jD9s69vCPC77KgzO/gEIKg2ZC06A11jpCidofD9IWbufH236T0QWYmj+eOybcjEE7e4rnPbHejJEPkFJT/GbvE3xr/leRP+Gmwih/OZp7AuS5zbTHvIx1fTyRR0nUYZT0BOIhnIaRtbm0aJAQCqY/gxCe/YTyfk6uaZggn2CwoIZHLhZHOf8wGEAvwz2rJ5JUVPSyRFd/EKv5o+tGnM8oWopp+RMwy0bShe7SRVaLbfnDNCwkQWRVzTLW1b+ZydFfPW45qvYhcnbJOJookrvm62iJOILeRKTlALaJQ6XWNFXB8+ZjmCom4lpyK6mAB++7T5O17E4Yub8whE7GPHY2gqgj0rgX2ZmDdfKlaKeIbGpaWu/j/RxuGuTGpRe3oS9LMvfNvA1V1bh3xq1EU7FM+VxBEHAGvCj+oZx8NJXgrpcpvSwbVTLy0+a3KJ1wB1rSQlPX7hHjx2MS+dYc5pfMQAkqDIQGkJesJRY2kcTANx+rJxRt5fYrqilduJoSTUfIbGZnqJeoEseh2siOJbmt8lKeaNqEqqlY9GbuqViM2ZxD87wrmWywoe/z4Jod4aeHniaSjDIpt5brK9fQMxjCYBTojoX5yUuHWDQxi7+/u5bjvT6sBiNtiWPUeY5z29g7eOOtIKIoMGlsAd94fA+SKHDditUUj1VQhRQW1U5hZyNizzEmll7Cjvrhz2+LUUYQRfYe6+NYm4cBX4x17zVz96oJ/G79EfbU9fO9e+eSYzNg1kt8bvUE+hdXkkqp5LpMGHWnbEprUFVg41/um0comsJuljHJo0b+KKNcqJwzQ//nP/85v//97wGoqKjg2Wef5f777//UG/q9PSql9grWtb7G2poVNJ0IvxdFkS9M/yv+Z+evCCbCXF2+mq2bZC6feTuiPoGWMLD+ZS+3X5dip7eVfEMh1mg1K9wVSBJEI3re6d1EiaMQURCZaq/A1FKHEGvnPyfeziPdWzk4mA6BDMXDCKrGt8esxOTpJZXrJJE/g8PBo2zrTL8QFVXh1aa3GDOjiAnlkxEqJmMadwlPtL5Ha6CTS4vn4iGMX+tnlasUXUcb+sJJjIl6KfV0gs1KMhkhuvFPvDNuPC937eH+WbeTp3fTn/Dx092PYNVbsOmtw8L6vVE/JjWL/37+aMbIB/jTWw3kuycxe0yMpJDO2Zc0kScPjiyh6In7yNcXIiFntPPMupGG+4LSWTy2/08ZIx9gX88RllX0UGGq+D9/1ycJnia8byDiIa7GkRk19C80mrsCZDkNHAuFsejMH/t4m96GN+Y9vaEfC+BHoeQ0v9ezjdMMvX4oKzThifky7YLBghYa/OADRzlviKUS5Lis/OSpffR60urZV15SBqeJFrkQEQUBDY33WndlSplVuyuoykq/904yGPVyqK+ONeOvRNVUoqk4v979BN9e/NUzjp8K9ON750msExehs+cQbTlIrOUAlpo5cHKvTZRwzF2FaLSQ9HQjWZzYZ16BZHGeefLJBJ7XfoOajGMsqUUJeul/4b/Jve5BMKdT0lRVY2yZizd3DY9YmDY2F0kULmrVfW/Mj6IqeGJ+/LEAY7MqWTtpNcqJMnmKKRu9yYYaHXq/qhE/aiyCaf2v+fK1P+BXL7bithupLnFyvN03bPyKHDeRWA3f3/ZrHAYbyWiKosZevlR7A2/t7sdp1XPLdTkMxPZQkiqjT1b5zx3pqkcAl425hH4xxZQ9Wxk7+VIiBjNGczG9AzoGE3oa4w2s8OwjmFvEQwdeBcBldDLesIjvPLSHeELBoJe4dcU4bl1UwBR9K4luAWsOrK9/gTXVNzNGP5VdhweoKNBz02U1/PaEEr6iajyzoQun1cA3bp9B9uBOolsfQwWuX7KAln4jfd70PJfNKqU038Y7eztp7wsyqzYft8PAlgPdbD/czaSqbPbV93OwaZA54/KwGCRkUaDQ9cHvIE0DsyxhPlEadtTIH2WUC5dzZuirqkp+fn7m/wUFBagfkk/3acDgDPOTPb9G0zR+f2gdfzPrTp4+uoFLK5bw7LH1BBNhAN7tfpsblt7Jb59rIRxLYTLo+NLqSiS1kwnuMSh97dj1HszF5bSGehDELFp9XWzr2Mu/TLsX+eWfE6uahmIyot/1Ep+bupK/8bURVxJMK5iAY7AbXktvtOhlA+Ybp7C7Z/+I+R4eOM6syZcRU5P8qH4dA1Ef43KqeLJuHf9wyX3UD1poTCTpyDWxqucYiX1v4ph/PeFUDK+gcGjceDYP1nPDhCvZ2rGXRnMrgUSIzkAPMwon0x8ZKUATjanEE8OXOKoG/b444bgZ/Qnb2CSZT1u+LsvoQhDSu/4nNwuyZDdXVS9l/fG3Mv1mFEzkrRN1l08lnAxz5ppMH48c80gdgNrsKsyidVSM7wKkpSfI7Ml2LHEzovjxPac2vQVP3EcFI4XC1GiIsEHFIJ77HGunGY73gElnIpgYMjQEoxW1r/EMR45yviBqMi++eyxj5AO8vLmVSZVZFNjP/WbRuUbQBHZ1HiChJLi8ajEpVWFHx15afB2Mz6rO9LMbbDR724eF+Bda89Dr5DOq7gsmG65FawnsXE/K34+pfDLuZXdxan09QTYi2rLQokF0NjeCJJPwdCNUfog+h5Ik6enGWDEZQ2EVqYCHRH8ryvvSYmwmmdkT8tlxOK05M7bURVmBjZR6cVtPsijx8/1/IpxI/7bNsplNzVsZiKa91S/qDHzjsrVY1v0qc4xlwiJUQUAuGce7DWGOt/sQRYG7V03AF4rT740iigJrF+Rhlgd44/B7ABh0ej5bey35Xg+Rp77HPEni0qW38qh/K9fr84kf382z5iR3li0iK6mQ0Ol4J9hGf3Yl5Quuw77/XZqzL+ehDUOikfMnFyFUyAyIQ2uZRQVLeeK5jsz6Jp5Q+MOrx/jXz4xBqO9ja7iYsNrELeNuoadL4BfP7qMw24KqaeS4zPS9L4ffF4qTSCooxzZm2qxHX+SO5ffR7knn4ZsMMv/z9D4G/WnDv7UnyIxxucwen4/dqqdvMMK++n5UVaOtL0RtieMsfoujjDLK+c45M/TdbjdPPvkka9asQRAEnnvuObKzz44w2vnMQLwnIxTXHuzmP3b+ns9MuJpKZwWPHvpjpp8/HmSKK8bX76igyedHkSL4xWNM1Zwc9Hcyxp2L0NNCKhrk4T1Pcfe4z7O88Go2dL6AMRrm2Oyl/KHlXcLJCAsrJnNF3Ms1YxbREfVwTdUSAr/9NiCQWngdjWYD2UKIEnsBrb6OYfMtsOXw65aNbDnh6V9ZvgCXyU6H0Y6iyvRG+zHLJo71N7B03DWYAj4G9UUc0nXy+OH1aGjcMfUGHt3/LJqmMa9kBs3etKJr3UAjS8fMp9U33FDPc9lwWg2ZXDUAvU7EZpbRG4cMIJ2q57NTb+HH23+FN+ZHEiU+M/E6DDo97/ZuZjDqY1reBIpNxYiajhWlS5mSNwFvzEe2KYtcYw6zi6awvXNfZkwBgbyPmXf9YWTrcrh/5p38dn86dG+Mq5TbJ61BVD8d4bUXE/GEwmAghmjQYZU/nhDfSWx6KwNRz4h2DQ0tFkSwuc9pab2TOMzgCYNRMhFMhjPtgiGtuj/K+U9KUTnUMDL6onswRE2h888/obOMgoos6ZlXMoN3Wrahk3SsqFpMIjXceldUhYVls3m3dQcARp2BFdWLiKcSZ9yzlWQT/W89Cie8xNGWA2iahrF88imDJxEBz7YX0FLp6DPLhAUQj555Q1jWk3PdX6OGfMQ669HZ3GRfdT+CTp/J0RdFgR1HeoknFG5ZUYMGdPQFeX5TI1OqP/3roQ9CFKHJ03aKkW8irsQzRj5APBXn9UALN4+/hGTLQSxTl7NHq6XEZKZp8nz2vZF2Iqiqxu/XH+GyWaWU5NmociaxHniSDnEpl41ZgNNoJxgP8b/7nsKmt7J2+VqKd75J5NWfs/baB1Be+y2puVex1mzD+Orv0E5oM1w/fj4hTURuOoiubCK6sIooDjkXthzoY+X0SdgSB8mz5uCLBRBTZuKJ4XXr40kFf0JCKZzH/CwJzWPkwPEoKQzIOpGugfSzWa8TEYW000OvE3FYDRj1OkRRQF80DlSFRH8HkYrF/PhPhzMl8tauqMkY+QC5LhM5LjOFOVZ+9sx+bl4+lqJsC9G4QixxEZd5GGWUi5RzZuh/97vf5cEHH+R73/segiAwYcKEi0J1X/8+Ne2kksQmVJBKyIzPqmFvb7pEXbbZTbcS5V/3/XpYf7F6OTOypvBSy2Zm5CxGkCClKgiGKJveCnP57LX0mwP8cs+GzDHvdO/HUmrkhoplaCEvalzBm0qizb6cp1J95CSK6e5rYXrBJA721WUUjIvs+VS5y/nj4bTA3jhXOYt0TjDnYB6zgFgswEt1r2PUGXhgzmdpD8I+/2Tk/T6yHVncWXMPO7ybqBtoymxutPk6qc6qoCfUTygRxh8LsrxyEe+0bEOv03NtzZU0NAZ5YO1kfvr0QTyBGHaLnjWXVVNdaGFIISlNtpTLty75Gt64D5fVRjKh8M+bf0IwHgLgtcZN3D/rTibaJ6DT9BTpSyjSn1DyV+D6mqtIqQq7uw/iNNq5c8pNZOty4Cw6UxQhRam9iO8u/hsSqSQFrhyi/os5KPPCpb0/RLbDiC8RwCp/skLlNr2VzlDPyA9S6QWkXv/x0wE+CbIEZj1E40ZiqRiqqiKKIoLRihYZmTc8yvmHqFMYW27HH1SYXpNLKJrk3X2duFxnp3ToXxpJ0OEy2Xls/7OZtj8deZkvzr4TVdUQhHTYcEJJMhjx8tnpa5ElHZ6on1eOb6RmbuUZn+Wp4GDGyD9JrPVg2pg7+apWU/h3vJQx8gHCh9/DXD37zJMXJJL97fjeG9rAl45mk7P6gaHzp1TGlbv4+bMH2X98SCn92sVjkEThog34EgRxWEqfRW/GHxuZAtcZ7qd16nLU8TNQYsW8vaUDl7SDZn8rs6rWZsL1EymVV7a2cMcV47Af/hOxKasQNRd5sRwCQh2vNb4DQDQZ4yfHXuKbM67C8fIjyN4+FFXBgY7IlnUopwgwake2UFA9g8D+dJTgWGcBty34DI++05vpE9RFwZTFuNQYnEYHtbYCDHrvsIhFvU7E7TTjjg2g9PYAKjYxyePvdXHnVeN5dVsrnX1BBnwRvnTjFNp6Q+hlCY8/xrhyF0ZJ5eXkHHqkWubOFsm2SqSUIefJqVvGK+aUoZMENh/o4kjTILetHMfhpkHWXFbNz589wDdun4U/msJp1o2G448yykXCOTP0T+bl+/1+JEnCav1k3rELjWJLEW6jM5MTe8/Ee9CLCfrDh1ldvYKB6ADtgS7yLFkc8rePOP7N3kPMsRRxbcFsftrwJ1Kqwh1TbqDOe5QrL5vCu9t8MGfkC/G93sNcUbGIzYcjTJxUiH3cXHqLppPXpLJvVwCHVc/EK+xcN+5yQskIAgJuQxb+Hh3fr7yCWDKGrbsFuW8XLL6B+GAXnfH0znQsFScQidJ8ROPKPC/Gug0QlIjbr6S06EZe738tM4/OYA8LymZlQu43tWxj5ZhLuXPs53AZjIwxK4TKQpiEfr7/2Wn0+lPoJRGnVYfFaDjty8eomSnQm8lx2NjYuC1j5J/kj4dfYuz8KmRtpMCZFQd/NeFWbqoNIwsyRkxn7QUnCNCX6uXh3U/S5u+kyJbPZ6fdglVvJsrI72iU85/WniB5LjOeWCdW/Scz9O16G96YDw3thLxYGi0aIiHLmP4MivsncZphMChg1BkJJcPYDTaQjaCk0FIJBN2no0zbpxWzrOO6pWN4c2s3z29qwG4xcNvKGgpzPh2lrhRNYWv7SCG1w311VFsncKjRg8tmoDivkBVVi4kkowTjYfKt2dwx5XoMOgMkTjPwCUTDSJe8aLYjiEOh+5qikPL3j+invs8zO4JUAv/O9cOalMAAKU83mEsBkGWRohwrN1xahV6W0IBkUmFSVTbaxWrlk067q8muRCCt0TDWMY7x2VWZiI2TzC2Zzhst25BEkVlZNpbMyeHh40+hoeEeG6Sq2E5DR9pxMbkqm4pyA/uNa3j86UZi8U7uWTOWLV3bRpy/JRVmqt5EzGhCmzAfKR5F8XSN6Cf6hn4Xqq+bmtqhtUdNmYPm6FHWH3gz09aW18mXVi3lf15oJJFS0etEvnh5Aa5QAwMv/TT9t0sys6/+Co8Dv1t/hPmTCpg7MZ9FtU5C0Tiv7/RytDkdEfbGzjZWXVKGXtax+VAfmw/B39wyhWljE5kSe33eKJXFDgKhBLJOZP3mdHqBP5TgkXWHufeaibR0B7j9ivH88vmDhKNJvv3ZOWRbR5/9o4xyMXDWDf1f/epX3HvvvXz/+98/bXjqt771rbN9yvMKRUvxhWmfp9HbTCQZodzp5r92/5I52WNZ+u46vrT0HgYEBbNkoCU8Mk821+wmmoogbH6NcdU1PNf0Nsc9Tdw2+VoEMcW8KTlokVyuH3Mjr7avJ5xML0YKLTn0Rz3UVkQhksCxcC1bDgeZZPVx2cQeBCWBv0Mkq6CQPGuSnnA/nnCQlF+mELBsfgJV0mFd/QDPde1jSVYN+5Iebp64mreaN0PCxPzsXnTv/SpTzUja9DOyV32d2UVT2daxC+2Ea+Xpw+v4zpzPEu9pQQZs/YPES0A2R/nl8TfYM1BPiTWP28sXMcZazo5OjfycXLSPkLOYUkeGnsWVJNr7fCOSJKJpWtorpEpYsYN2Vh35RAnzX9t/lYmQ6Az28J/bfskPl/8dAp+OhfjFRmtPgBynibq4j9zTaC98FPSSAUmUCCXC2PRDG5xazE9MFDF/AoG/T8pJ5X2z1ZQx9AVBOOHV9yHYc/9scxnl45NUBXYe7GfT3rQHzxOI8cvnDvGP98zg02DrS4JAlslFI63D2h1GBwcbB+nzRvEEY5hsFjr8XdgMVqx6M13BPkrthWgfYi2LJhumislEmw9k2pzzrgN56OIJJhv6gqp0ebVTkB25Z9pDSKNp2KYtR7Kk857DR7dm3oMn6fNEaOrys7cubZhVFNrJcZmoKBgp1nmxoKoqyVSSr867h15PhHc2JZmw2MZXpn2Fjr4QCBrZbhHREOdQXx0AB3vreHDuFzG3mggnIjxR/zhL5ixi5ZKJuE0uTLYEIY/Iw+vrqSp24Asm2HmoH3exE0/UN+z8NlGPLqsQV3YZsexSpGgIQ/E44h3HhvUThOGilxZDek1bnGvlttUl/OuOPw77fG/vYa4YV8U/rxDwazbsQhTT4d+gTlmS6aMpSQIbf8eD13+dLcdDhGMpxhQ5aPb6MduiTJ8mk+3O4d3d6d/L+i2tfPmmafzDX83mv5/cy8+eO8w/3TMXt8PIwYYBYrE4X11VTo8/xc9eGv4b1jSIxlMEwknWbx4K939rdzu3LK1GUS7i3aZRRrlIOOuGvs1mA8DlOlNdmk8vkQj8+yMH0TSQdSLj7uljIOrl3b4jLKiZjvDkP5B1om/NdV/GZbTjPWEo6kQdl5XP5Tt7nuQrk1aw0FrEc01vAyCnHPzpVQ8t3ekFkcmg4/Y1t/Do8d8gizpuqF7KT/Y9TUpV+MeZn8Xb2kaFwYrrvf/J5Jw5BBHjqm/wSMdW9vceBaDcUcb8SbfgsNyNzplHAI1l9jJe6NzJpp6DiILIZ6ZcR6nJhWHfsyN0j7SGrQiTV3LvjFvZ13P4RJ7+dEzdbRjf/AMACuCYuoKfHHyC4750/n5bsIdHmjdx15Qsjmjb2HswzqUV86h2VCMo0jBF/lMptRchizqSpxj8q8Yuw4gZFY2UqtHSF2LboR7y3CZmjsvDF4ojCgJ57y8j83/EE/dljPyThBJh+sKD5ImFZ+08o/z5aOkJsmRqEdu7g5/Yow/g0NsYiA4OM/TVaJCwoGKW/3wiak5T2tAvcRoIJYe8UYLRhhrxIY4a+uc1sViKjbtHehrbeoJUZF/4oloKKtMKJ7Kv5zAJJf12sektVNnG8f9+PyQe+1eGaorK8tnXc4S+8ADjc6oRBJGkmjpjXZPUYCei2Y5r0Vo0JYkgGwgeeAtDYRWYnOlOyRjOuavxbnqKpKcTQW/EteCmdMjWmRBlcq76Ap63HiPl7wNRh2PWlcjuwswGgapCKJrMGPmQrurR0h3gkkkFF7XqvlFzoSY1Eh4Tg742jHoD//O7OsKx9Lu9KNfM2mvysektBBNhVE2l0dPM6opVPFGX9uq/3b6JFkcbV5ZfySN7nuDK7FtZc72Jo8Gd1OpzKNNPIC97JT/x/hLlxKZQriWLansBtlmr6f/jj0BN4ph3Pe5LP8PAhl+Q7G9HkA045t9ApH4owkA0WnEZkvzH7eW8Wq8iJ/3DKvpIopQu5efKRfIdIX//CyhBD7b51xM+vmvY3674+ymUBrnc1Y3fmI9fDLCh53maj6fXR9XZVVy7fCHPv96NqkGvN8JjG1r5/HWT+Okz+zEZdCydVcKMcXn0DwbQbfsd+ZZsXNZavMH4sHMZZIl39nagnLKmaukOjNiQGmWUUT6dnHVDf+3atQC0tbXxb//2b2d7+POevj6FZCr98E8pKtKJOPFAPMirmp/lS27GdGwnqsVOMJng+tqVGCU9kWQUvU7Pr/c9QygR5pG2zTw4424AZFFH2GOhpXsoNywaT7FvX5K/WfJZDCI82/A2/ng6XLw77qFCTZIVrB+Wc4amoh18hcKqSk4uoTpDXRiVECCQGuxCKqzg7w/8gUgqLe6iaiqvHt/EzOljUM2OEYa+ZDDTcChCUrQxrrKaAwNHaOqtp6R0NoogwYk6xwE1mjHyT3J59WJ+uOUXmZflvt4jfHXO3TT5OplVMIUsKXtYmH1Xf4hde2PcUXM3+7zb8cQ9LKtYSK1rXCaf81CLl588vQ+A2y4fx48e303PYDrqoSzfxtdunURU8DIY8+IyOsnV5yBpnyzf1SQbkAQxs4CAdEiiWTZxUa/gLlBSikqvN4rDJpHqSGKSPnmIvV1vZyDqocIxpLyvRHyEUMn9M5TWO4nDDPU9UK0zZip+QFp5Xwv7/mzzGOWTIcsSxXkWJlTkIIkCOkmksdOP1XLOsu7+rAiaiMto566pN+KLBREEyDK72H1g+AZqQbHKr/c8lfkNH+1vYEXVIpaPWXjm8Q0WwoffI3xKm2R1gjT0zNdUlZR/AENRNZbx89BSSeK9zeiyCj5k8gL+HevSRj6kc/23v4ihdDyYM11o7QmMOLSu1UtSuXgNLVGEhBpnS99mfI3jmVmby6adfRkjH6CzL0J3t0qxo4Cj/WlPtUUSCXZlcXPFXUQFD3rMRDwWmv2tGHUGQqYWnq1bd2KE4+yV9/IF1+f41vjr6UwEMIky+X4fyjP/iXL550EALZXA9+6T6HO+jql0Is7FtxJBIajTYVKTaKkksqsAy4QF9K/7KdHZn+O17T6urs5idm4tO/qOIgkin5l8HVvadlPXO4goVVFy2VzyjFFimo7ElmeH/f2GgkqUoxuhbjtuWzbHF11B8ynro+PeBmrKx2Mx6igvdNDU6WfQHyOeVFi1cAzPbmzg7d0dGGSJr9xQi9boQ+s4yi0L5vCD54OctOlz3SaK82zDjHyAS6cXf6QIylFGGeXC55wV462rq8sItF1M6KXhSutZOgcV9vSCYWPPAb7fs4WOxdexrXYy3z32HL/Z+zQpTeW3+5/lF7ufwBtLi2R1hfqICWmz2mqw4vePvJZtPSF2dtfxz9t/w9HBpky7UTYTLKrCoMVHHKPFI9RYh8oefnPK52mPwXazm6PuYkL9A9xVsWTYMX2RQfwpDcv4BSClF5hSySSi8z9PoGIJNoue5ze1EAmY2NdzmHUtm3m+8S3sN/0DCCJyVjGSzohRNxQuWeYopjc4OGxHHGBD4yZSSoIfbP4ffOqQAq+iaTz07AGeeL2en/6ujf7948jzLKPGOgmDljbIEorGE6+lw/xyXCb6fdGMkQ+gCSq7+nfxvXf/i//d+Vv+37s/5t3urWjCxw9fCxNkQ8NGVlZfOqx9eekytu0KEkmMWvoXGp39YVxWAyE1mPbE/x+U8R0GG73R4Xm/iVA/qmxAFP58NdAdJvCGwSAaCSVO0bYwWNBGlffPfxSRm5fV8Oq2Fp56o57HXz1GMpmiLO/C9+ZDWkisxdtBKBkhmooRVxIMRrzkFcfQSUP3iT/pGbZRBfB289ZMFMAHobO7MY89RVRPEHEuuAlBf0regyDg37keTU2lY501lVjrIbTkhwTuJyLEO+tHNKcCQ/e9pmnUlLpH9JlanYNBd+4rb5yvyLJInaee44PNTBiTRUrV6OiJjujn8aYIxtPfu0lnpFrTMUs7SsprY98WC/1tNuzZcV5v28CU/FpeaXxj2PGRZJQQHlxmF0ZBoi8RxGOzo5uxHJ3ZjpYYUqtXwj4Cu18h2dMIR3dg62hAp2no8ytI+npIDnbCrJtpE/JZu3wsg1ED14hu1pQvYEXVYja37mSeeznPP5fg8ed6+eHDh3lxR5yuqB1twWcR9OkNXl1WCeb5NxGp3wmA5M5n/8DI31FnrJVrFlcyvsKdKc1okCVsJpm3d6erJ8WTCg+9cAzDlMvRlCT5B37Hd1e5uHdZAV+7cQL/cOcs8h1G7riyFpNBh14ncuPSaiaNyRoV4xtllIuEc+YWyM7O5qqrrmLKlClYLEMhsJ/2HP3SPD0Woy6zM/3GDh9fnL2ag94WmoI9THVX4bYUIOiNlDoKafN3YdWbR4RRFdsLkEWBFZWLmJk3heaGkV7n+ZMKyXVJbDqlYl6uJRunLhdTtB9LWS2R3S9zama6Zdw8dg+md8eXli0iIKT42f7fZBZMi0vncEXWBOwGG4ETEQIz86fQ0SficJhwL7oFvzGfZw7BxnW9CMIx5k0s4PK5ZRhSZtaUf4Zjob1s6T7AqsrLCF3/Y3R6Hc2DR/nMxNX8el86p21mzmwS4kjBOlEQmZpby8sNb9MZ6sJhS6eAeEIJ9tUPLaDqWv3UtfpZMLGEirx0eLSmQbbLSG2VDYOop713+PgLZjt4vv63w9qeObqeybnjcYsfPR9bEOBQ/1Hea9/BuOwqvjTj83QMetBrFnbvi3LoeAMm2cDCSfkX5WbXhUpbb5Bclwl//JMr7p/EZXRyzDM8XzIZ9iGY/nz5+QCyDowyqEkLQXEg0y4YLKhh7xmOHOW8QBB4bmMjwciQQbu7rp/F00vILvvzRYacKzQ07EYbD+95KqO/YpKN3DX1JgqyLZlnuCiMLFUqIiCK0pmjp0QJ27TlGMsmosVC6Fz5YDDzfh+HY84qgvvfInz4PQSDGefc1QzXMz8NshE5u4TkwHBRXZ3ZmYl8E0WB0jwrs8fnseNIOiKvssjBjNo8khexR1UUJRQS3Fh1E4PdIomkwpyJeXS8NVxod2JlFpZEDfOzxzJeMONqPoI4bgnZfjP5WWaqqkR+c+wxAFRNO+0mqiwJPNK8kf0DxzNtN41dxhJfD4KsR0ukNxgEexaS1Y1kyyJct5Pw4XfSv4U5q9Ph8wVVxPQGOjq2ETCFKc+fjUWdxsJdzxOcvACvMcyrGz1E40NRCW/v6qSm1M3/bpK4bsaXsOsVjg0I5DYozDCYUWMhlIEOpo1dzvHB5mHzHp81ljc2dNHSnb4HinOtZDmN/PrFQ8P6jSl28d6gkclzbkN37DXcx57DPGE1XlM+NkN6iX/plAJm1+aCBhajbtSbP8ooFxHnzNCfNm0a06ZNO1fDn7dYjCFuvSGbjnaBeEQkq1RHg7eLuaYCFpsK8Tjy+a/dD9Md7mdB2SxmFU3FLMpcXXMZr9S/jaKpOI12rqheQjyVZGXRHO7/j4NMrc7huiWVvLKlhURSYeG0IkQBrKkKbq+9nbjgp8iej1u2IUog+EL0GPXkXPE5wgc3oaXiWGrmoi8cy9iIlZimsKRkAT8/xcgH2NS2nbkFkyl1FHK4r56p+ROZm72EQH+Mo5YARQ4Hx7pMbNzfAqSN6y0Hu7nrqvFk2UzsOGLCLExnbpENQdCRG29DCIZx2vLo1Gt8c/4X8ES9FBmr6U21IokSyinljxaXzyURj2bGPoksDdWYPZVTvT4JKUDlrA4O9R9ljKOKa6bOpf7X3kzYmiokh4XZp8+hEU5GcZ9B2EoTUwSVIHpRj1mwIooCRwbTi4ZQIsy7m8NsOxCEU5T2393fycLJ+WdX/W+Uc0pLb5Acpwl/rBPT/zG83iZbiSlxIqko5hNjCbEQOmfZhxx59nGYIRIzE9OdmqNvRQsOnOGoUc4HkimFho6RpRA9gdjIvqpGvz9GIqmQ6zRj1v/5Ikc+OQJ7ug4OE1mNJmN0BropzimivTdIlsNIvqkQp9GO7xRNlKtrLkMvnHkJk/T0onNkITtzUGMWJIsTNR5GS0RAn46KEHR6woffI9mfDp3W4hG8m54kf+3/Z++9w+yqrvv995Tb69zpvWtGo957o0gISQhE72BMcYvjwjcJduI4cWInceKf7diOKzYYgykCJBBIICEJod7raKTpvd3e7z3n/P64YkbDCIGNBGMz7/PoeTT77rPPufe0vdZe67M+YFFCFHEtuZPedT9CPVehxjb5SiRH5kAXRVHpckdQNbh9aRUa0N0f4kyrl+Jsy6e2vJ6iqExxzSUY0EjPCGG2WXAaHFwxvYCtB9rQ6SRWzS+jzKZS+to21EQM2/iFiK5ccDdSaa/gd2d76eg3cNP8m9nYsoE97YdYXrmY509uGNiPTW8h3egYYuQDrD27lWlT7zln5AvY597A2r6TzL/2AYI7XiLRm9JC0mJhPNufwX7rN0gYDPznrp8OXKsHuo7xpWn3MKFiKqLVRXFUY2vn8Hs1kVS5ZnYJz+9sxHsuf37p5Axmmm0pQz/kZaK9gMPpZdSdi8ycllXNeFsehrl6Dp3ppzjHRm66ha7+EAVZNnzB/oHx02wGNhzqZ23cxMKx9xCJa7z9iocbr4hQnu9C0zQ0Dcy6lLNs1MgfZZRPF5fF0Pd4PCxZsoSKigoMhr8CaeA/gaZIH+54kMz0XIyZFuJiP1FJT6zuEIEzu3Cv+RvagynP/vamPQAESudj0Zu4fuwyNCCWjJNrcpGn6mj1Klw3M4sT7TEy7TL/9OA0Gv2N5Mpp/MvjxzEZZL54VwW7+4/x3Mn1OE127p10EzaHjX/d+XOW5k9j0cylyBp0yToyjU6M0ShK43QojdMR6B72HfxRH7dV34iU1Yg1rZzo6f30FGbz/YNP8uU5D7Br2/CV+CNneglGEsTiClfNLCLNkYm1twt3oJmoJGFvqmdM0UR+1XuAEssUzFGFA76j3DFhNU3eNmLJOGMyyujwdhBWE5h0Rgpsg4J2TouOVQvLeXnbYKWCiRUZZKelwvaTYoz/2//kQJ5bd6iPRn8Dn73xVn7+3BkEAVxG57DJolVvId3w/sKRAbw8dfQFjvWcxqI3c8/EmxjvHMvErLHsbz+CJ+pjXPbwMjVjitIQGLXz/5Jo7gowozqLupgPy0etdS8IpBvT6Ax1Ue4oRUNFl4xjtHz8IqV2EwQjRmKmwTQWwWBF7T57ka1GGQkYjSLTqjPZfmioIF9e5tCIk0hSZefxroFKI2davUytziLdMrJLaGmCNqAtcz6BeJgbFldwxYwiDDoJT7yV68cuoz/spTPQzfjsKhrcLSS14VVYzke2Ogmd2EHg4EYABJ2RjBWPpJLE3z2GeIRY5/B7IenvB3vF+w+eTOB+6ylsU5ciSDKCKBNpPEqyvwNyU+lxoihyqqmf/ae62X9q8F1bWehkydT8ix77XzOSJKCoCg3KQTY2bEEURJaXXoXTkc8XbppEutNEc6efYETFak7DMHs5wa4GtJYzKOOuRVRVvnVbFa8d9bFuXRcLZ9zKnKlptAda+fKMe9jdfoQ8aybTzTl4epuG7T+pJvHrdVhXPITZnslp4rx1+I8UGGyUdZwZ1t/T0cvZtMSwqj/r67dQWbqcyHP/TeXNjzG+QuTYmaGRUvGEynOb67h3RQ2/XnccVYPFk7OxhZeiRsPIzmxOJCOsLJqFJXMSSXMmUb+O9rNeKp0iJ2WRDTubmFCejtNqYO6EXBrafQORA7GEwpLphTyz6TTr9w1GPY4tcY1GFI4yyiiX3tB/6623+OpXv4rJZEIURX72s58xYcKES72bEYuZbHZtjQNuyvKSlOZloFP1tNpMZBQmEC5QDuiNxnf4j8VfozfqIRgPU2LNwrjndZTccuwndrA07OOGGVcRyzQQCXczPy2PZ3alXialBVZ2dG/hcM9Rriq8GqdaREO9SEVeGiX2Yja1H2BzxyEyLelomsY/znkYm5DJ/lOHuW52GuNcZZxwNww5niyjg7ONMcbrNEyuBKasdGxxhUcn3ca2lgMU5U/m+HsqAxZk2Xj7cDveYIy9J7u4bn4pexLtPN35Dgk1SY4lg88p5dxffjUvHYnwdl0DN15TyY+PPEeONROdpGNf+xG+Pu1OwpLMwsLZOMW0wVV9DW5aUkFVoZPTzR5Kcu1UFDjQSyKKpuGLxggnhub4dQV7yJyQ4Ot3T6atK8zOAx6um3krW7pfoc3fSZ4tm89OuQOLYL1gvpomqrx4agPHzpX3CcXD/Gz/E/zTwq8wNm0MM/Ins6/9MKZ8H/mZZtp7U4aUy27giqkF71s5YJSRh6pptPeGWDnHxN5mH6X2j77ynmXKoMnXSrmjlHCwn4QgYNF//CW1HCbwBHUgR9C0VOqJYLSijorxjXw0uHp2Pp29Yc60eZElgTVLKrDYh8ard3siNLR5mTspD6tJxxu7m8nJsJJh1Y/oXFxRE1lQPHNY2PKkzAkcb+gnGlPQ60SKS6z85NCvSDM5cBod/P7Ii4iCwLKKRRcdX41HBox8AC0RxfPWH8i68dGBNkEUkewZKP6hES6C4QOiejSFRF8bvveE7lvGzR/4fyKRJM02XNQzzWbgY5TqGHFIkkS/0oZJcbEi414EAUQlQNmYJCeP+Pj1+hM4rAbqS82UT5vPa3XrybFm8PD0G2DnU6gdtcj2DFbOuRdRzGX9W80sn+Qizx0gdHgtFbkVmLMKUQQd+Fuw6i0Ez9N4GJdZiVGxsbdPhz6gUF7o5ItjltMUdiPbM4foLAAkZCuC1v/erwGAV3RhXP0t4j1Rbru6HH/wNM2dAXSyyMr5ZRyo7SaeVHnnaAczanKYPsaFs2M37v0vDoxRPOM65KJqaj1mvv9ky8A9W1Vg5QvXj+XqmcVk2A2EYknOtPn4ws2TUBQVm0lHrsuMompEogk27GzCYtRxz7VjqS5OI+AfrnswyiijfLq45Ib+T37yE55++mmqq6vZtGkTP/rRj/jlL395qXczYokEjNy9vIYzrR4KMm3sONLG0lnptARsbAtN4zYxnXxrFu3BnoFt5udMwBFL8r0jL/DohJtwNpxArpxO/8ZfYRk7B8loSZV5Uabwo1At1xXPZfWcCl7b186EKivre46xpGAJtfucnGlpPzdqK3etXsbsgl5ydGXEk0lkUwyfEiPPpHDljEJe3ePmjnmL+XkyQou/E4Ok587yJVgFE8FAFLkoHffT/4waSa245GSXcu9Vd9Pq0nPklG/AsM3PtGI2yniDqbC0Ew393Lmsih/s3zzwHbtCffyhYz+PZlXg9QWob/NiiZXx2aprWN+2n6Sq8LmalSQSMTY2vM3DU+8dNkF12oxUFzioKXKmjGgBGruDPPlaLf2+CHOmrGByURdvtG7CJBsJJcIYZT2b3Oupdk2h2iQRU6I8MOUOTKIBk2RCVg3vOxGOamH2dhwZ1t4d6iXbkcs9Y29hVcXVaGjMvTeNrr4oqqpRXuhEHK1P+xdFryeCySBj1Mv4Y0Esuo+eS59lyWB35wFUVLr6zmCVdYgfQeDvz8Vhhha3QLZLJpqMYtIZU6H7o2J8I56EGsPrS7JkegEr55ciyyIN7V4SUWmIsrsgaCyfW0ptk4dILMH8KQXYzTpiioZeHLmib0lNoSvYwx0Tr+eN+rfRiTLXVy+nt93EExtODfSbPjaLRWMXsq1tO55IKjx65ZgrUyUwh2vODqCG/Qh6I865NyLb0wnXHyZ0YjtaMjYw+xH0ZtLm30zfxl+CkloltVTPQbYOF9E7H8HsxFw5jfCQ0mkCOlfuQHk9QRAoy3dgNsrkpFvQSSINHT4WTM7/IAWAv2qSShJiNp57sYVILLVoYTHpePDWDAqLNFYUCLjj7YzLLedwVzO+qJ+Hxt5KsusYgsUMgoji70PY9gtuv+kb3D5nHGJfK/1bfoc6dilN9km0nFAoLEgnJ2sOX3dmsMl7lgQCOQYbC3Im4PVLZDssOOx6/P4oxb0BbKVTsVxdhu+l/x64FuSaJbzdKrJwauaw0r7X5U2j26Py2p52TjS6kUSBxVMLuG9pGaqq8fq+DsZmy2TasijMc1GYbSPHoqKsf2vI7xHdt5708qk8/nbHkPnI6bYgXb4kFfmpstUOk44ZYzIQBGHYQsL180q5ekYRkihglEWMBpnhsTKjjDLKp41Lbugnk0mqq6sBWLp0KT/+8Y8v9S5GNHazie89sZ/lc0rIy4hQOaWF3zWtI9ecwc1XLsMdNfD5wgUcjnRzOtzDVFsBVcEo3SEd95Zfif3kHrynduFYfDeOWdfhP/AaSsiHpWoWOkHk0TEr+GPLbnLsedxy5RhKc4ykh9NIo5gzLV1DjsXTJxLtyMAj91GcbqS2N4kwUSVkFLlqRj6BiEJP5ARfLFpEOBZErzMQlWSeb32buyevRnnn96CpSOPmQSJGvP4wlo4GMk/u5NFJU+nJmEhMMnOywc9zmwdVY8vz7BiMcW4bczs5DifeRC+vnd3GaW8zPhSmVmXx1oF2/vUPTXzmijw+X7IGi0nmoKeWp0+9gqZpNPtbGWsbOzBmLKly9Gwv0WiSHJcZk06kxxfj3x7fO5C3v+mdLj53ezm3jl+FO+Il35ZDusHFHeNXU+c5i88YoCK9kkw5A0GT+KAESZ2gJ9eaNSy94d3a6KIqky4N5mOW56QEEzNdFnp7R1+xf0k0dwfITjOhaiqhZHggr/6j4NDb0Yk6mn2ttPfWUWX4aAJ/fy52E/QHoVgyEkqGMemMIBtS5TYTUQTdn19GcJTLi6DpicbDvLKjkY6+EIIAV0wvInqecSuKoJMlvv/UwYEcYEGAr9w2lZx083BhkxGELEjUZFbRGehmftEMNE1DQmLPsc4h/faf6uHvZ89F0iv0hvoZk15KR6CHcCKCnfe/V0VHJlmr/5ZYVwNJXy/6jAKsN/89gjSY0qBG/ETaTpO1+sskfX2IRiuappBwd0J+wfuOrcVCWKctx1QxDSXQj6Azos8qRjtPc0YUJQRB44s3T+KdI51E40m+cNMkdLI4oiMtLjeCIHH8ZHSIcF0okqCpSaXVvJmTfXUsKZ2LN+pjQnY184tncLqvkV95DpHtTOOGlZ8l68gOHNNXkGg7iqYkEGzp2FZ8md8fEdnyTjdZaSaW6yV0qpm8nPEUdeext9ZNYZkDwWXD4GvlcJ3K26f8PHj9OGJj5tHrifLLLSFWTP8K6YKfmGiiX3BxfF8PKypk/r5yOTuD7fiSMRY4SykKRNkdUzjV7CHdYcQfirN5fyvNHV6+cV02d6cfQvK2cbbyTn664QyxuILFKPPlaz5H7v6foIbfTSPUiMViuP3DvVah2NALRdO4YEi+pmmYdeLA/0cZZZRR4DIY+qI4NB5Nkoar5f4109IVYFJlJhPKXTS5e1E1J9FkjFPuBr6751d8e87n2OP3sKCjk7m+fpRwPZHpd+NVdVS43bhzi+kvq8Gl6enf+POBcUOndmKduBijLYcbIhLuZJRXd/Zyi5jH/TUrOdM69HeXJZHSPDulOTF8KAQTARaU2zDENDojSf7jqb187ubxnI728+uOPYxzldAb9dHgbWNG3mR0UpKI2Urtkut5vesoZpOe1dfeh8XtwVQ+ifiutaRPk+lKn0Z2upk7rxnLwdPd5OUYWDqjjN+ur+XoGQ+56TFWLc1iUfFsBAHW7XQzrjiH25dW8ebeFp7d3c9Vc7Po0e9jX/e+gePvj3gQ7KmXWjCW5MfPH+VMqxeAbJeJv7trOm09gSFz2HlTM9jn3cKJ+sGVoDXVy7mqYDHTXdMRBCH1AvyQ70BZ1XPvpFv4r50/G8jNm5Y7gTxz7mjy/V8Zzd0BMp1GgokgJsmQUvP+qAgCY12VbGh8k+mJJHqj42KLj5cNow7QQMZKMBEiw+RCEAQEkx0t5EFwfkC98FE+UXYf76SjLxV2rGmweV8LE8rTBz4XBKhv8w0Y+e/2e3l7PeUFUzFJI3ftWEMlnAgjCALhRARREOmL9nHFghzGlWWjqho6WWTfyW4SSYVsSzr59hw6Az3U9tWzunrpRccXZT2JvjaCx98m6enCVD4ZyZaG5Mwa6CPojEgGEz1r/3ugTXZkkrH8kQ+4XwW0kJf+138J51LyjEXjSFty52APAWJxlR88c2hgBfZAbQ+P3DCBD1T1/ytGUVR63TFMBpm5kzMZV5ZBb38cl91AnByKK/Op7avnrcadFDsLKLTnsqMlNT/wRH38l6+Nf138EMq6n5H0phzxgs6IY/XX2XK0haWTXazIbEM49jsQRMSpq2luzeJEo4cTjW72nLLyd8sc3JBxlLacEn6z/iT/9NnpBMMKda1+6lrfNcAjyJKX6xeV8/LJPtakd3Bt6wlEnR5F7OJkwXXY0jTuvMVJb7wdlz4Lf7eVmEchtvuPaC0nCM59kB+vbySppM5/KJrkRxt7+JcFK5D2Pw2AZHFwolfiodXj8IYSCEBzl5/dx7vIyzjPQSxq+BQvCSVBmt6JpI5sDY5RRhnlk+eSG/rv9SQKf0ao6n/8x3/g8Xj43ve+x6lTp/jmN79JMBhk+vTpfPvb30aWZTo6Onj00Ufp7++ntLSU73//+0PK+H1SuBxGYokk3/71XgDsFj233XAXv6/7DQklQWewh5fPbqF8yiPEm310RvQ41WwmuVR6jcV899Af0IAfFg6fwIRO78WeVw2Ht5BfPZtQJMEbR/pZmq9SVTAGWeoceJmkO4yU5Ssc9p3lpdpNJJQEmZZ0PjPpRkrkXL79BQN/eKWNlYtms6V5K3u6TgCp87WsfAl9ySQthaX85vhgHtl/e1v5xxn3k3VyP8npt/M/B6y09x0D4LaVBVTN7uZA5xEijYXMnDqd081eBBEsJgmHJYNQPERZBegFhZx0M7PG5WAyyqjOOvY17hvyXYudhXjCCfzBOG19wQEjH6DbHWHroXYmnjfZBSgtE3mh5dSQtpdPb2RG7mTsQtqf5eUuNBTy7YVfpzvci1lnItuYhV4bXQH9a6O5K0B1URq+eADzRyytdz651hwWF84np/EoqvzJTMoEAZxmSMQdhM7LUxWMNtSQB3HU0B+xKKpGbZN7WLs3MKi6r2kQjQ+vMRcIx0e8KamREkb7w9GXUM8ZywZJz99Mf4SfbD5D8lwK1D0rxhAwNPL88Q0k1CTZ1kwenHY72gU0b4aMHw3Sv/kJOOeojdQfQkvEcWUWwbmKtZqSJHDojSHbJX29KGEfXExSQ1Nwb/3DgJEPEG05QdLdBbkpIVlN0zjV5B4WZr39cDtzJ3567ztN01i1qJC2aJw9vRvp7JeZnbWQ17dFKcgpoWKKwGtnUuHtU3LHsb72PedHTdIW7KXQOxhtpyWiRA9vZErFXK7OD8Cu5wf88cldT3PNvIfYfkokqaic7QjSGc0i7cx2bpw2ge+tCxAIJQmHL3Q9aZTk2Ag5jWi6GMaSSZztSbK3RaX5RIiaOU1sahwMxa9Mq+DeWcuJPZeaU3mTRpLKUJdRMJIgaMnDAcg5FbjH3kh/xMLm3U209aSqo4wvT+dfHpxNpsMAGiSEGNvbdvLS6Y2omkp5WjGfnXIndpwf6VyMMsoof91ccjmYhoYGVq1aNfDvvX9/ELt27eLFFweNy0cffZR//Md/ZOPGjWiaxrPPPgvAt7/9be644w5ef/11xo8fz09/+tNL/VX+LBRF5dDpQSEXfyjOrj1hajJS6QwGKeVbScohqqpzKSjPZHyJCbOg8lLLLmJKnLgSJ6YfbkzKjgySramXhy6RygIszrNi0huwWhUevWcKxTlWjHqJ6jIrXgI8d+JVEufK5/WG+nnm5KtoUoz/2vN/3L4yDbNF49ay+1lYsID5+fP56owvcqDzMCElSnM4wKKChTiNg7Odo75W4qXTaRELaO9LCb1MH5fOGWUHr53dTE+ojwNdh1jf8Qw3Lsvn5uvTOBHYx//ueZzHDz3L4yd/Q4t6lFgyhiSJCIJAsWks07KnICBg0hn5wpSH8fbqOdjQRkKIUdfiHfZbnGjoJz/LSmGWdaBNZbgCs6KpQ3Lq/lQ0DZyiiyprFYWGogsa+bGkSlNPkONNHnoDsYHJ6Sh/ObT2BMlymvDHAlguQdj++dgNNuRoEE3/ydU9t5sgFrMSSpynvG+0ooU8F9lqlE8ag1GlqnR4pYbM9EGnkSimVNzf61NfNrsYaQTn5wOICOxtOzJg5APElDgHO49jP69igGD28+TR5wee5d3BXtaefA2DdPGqPkrIO2Dkv0u05QSaMtgmoA0Jt3+XC7UNHTyJEhjuhFFjg840TdPQ6YZHB0mSiPYpDgtTNQ2/2M7zZ5+j1ddBo7eFp+t+z+IFVrbt60ELD845IokoFv1w56vhAotIiqeD2eMysbbvGfaZtfMApXmD44opcQvysyDDYSTTacDl0JPtGqrP8rXryyiI1DIuuAuzyYTaUUsmbsZX57NmWS5vNG0d0v+M5ywRQwzE1FzPIceH3Ydmo4w5vxzxpu8SnP8l1p9SaesNDRj5AMfr++nsCw1ED3ZEOllb+9rAvVLvaea1+s0gfnqvo1FGGeWDueQr+h9GeC8cDmM2Dxe78nq9/OAHP+CRRx6htraW9vZ2otEokydPBmDNmjX86Ec/4uabb2bfvn385Cc/GWi/6667ePTRR4eN+XHjDQxXOW1qD7J4YgFBu480ox1ZlOkIuznhPcHigmn0xqPo9Rbag4MOgv3JfiZnl5LsPqdGLErYp16D+43HEQ1memNW0u1+5s+08Ye6ddw77m7WtT9H2awsphsyOenZiic6d9ixNHvb8SshfLEAuzqOkGfJotBZRqhJxmqVQYqiadDdpXJoexqhcJK5024mnFbHjs630csmftP9DlcVLybDaaTPG6WyQs9LnbVD9uOPBcgtULAajLg7vZh0RiKJ1CrUS7Ub+fKMEp54pY1gJOWEmFBRzVevugJJkNiyt4u3D6Tq2OpkkXtXjGX7ofYh48+dkItJFnn0zmk0dfkJRZKUF+nY3G3Df165puqMCtJ0ae+bjy8IfKRcyZii8sTG0xw63cMN1+TQI/hpTuiocBaSJmWM5sr9BeANxlAUDZtZh88XSOWwX2KkSJDoRy3Z9xGwmSAUMRFMDD5jBIMFNTxq6I9kklqCWTOMNLUbcftTz8+5UzIQLX5gMKLJbJD4u7tn8PyWOgLhBEtnFVNd7CRlJYxcY19DG3gvnE80GcGgH3RwxMXgsD5n3U1EkjEMWId99i6iyXbBNkE+z0FgcmKbdMWQVX3RZEWXnn/x0H2jHVP5FCL1BwfbBBHZlTcgxieKAhPK03nl7QaU81b1l80qGuaY+TQRV5K83f7OsPbm8GlyM/KIRwedIztbD7C8cjHPHn9loK3Qmk2OKW3Ya91cPYcaWxjS8qHl+JDPEtYcvGdTZ3RsoY08h0TYkcl2fx2PfnYeLYHjnIo1ctNNlaj+fLbt6ef6Wdnk1z5NsuUoCuAG9NNvQKqahhTqobM/dsF3fDiRxD55FdrBFzHXvsrDV9/Fz9/oRFE19LLIA9eN53ijn7aeEBlOhWVzitmyr3XYOKdbvUyryqShM8DZWMuwzw91Huf6ymsxXESnYpRRRvl0c8kN/ZkzZ35gnzvvvHPIqv27/NM//RNf+cpX6OxMCfH09PSQmTkodpaZmUl3dzcejwer1Yosy0PaRwLZ6cONhClV6UzJFriquIofHn6O2ydcx4a6t+iPeJiVN4mzjWaKxhpYkDuBtfXbAHiqYSuxCXNZMmsVQiyOTBLfrheRXDnIc+8hbMvgxlt62NyxiQnZ1dhMJlr8bbT62/jO9PuZn5OPTxoesJFlyaDWnXIeNHjbmZlVg12Ko8vsIiwJBBMWnFoJP3mmbsAAfnlzOzdcNYZc62n0ssyJ3jpq++u54Yr7eHJtO6oiIgkiynvCKEVB4FDXcZKqwtLyhfhjQd5q3ImqqbSFWrh+tZVk0IZZtJBIiPzvU2d5YE0Fbx9IVSQYV5bO1KosksmUmNGe413sO9XNrHHZzBibyt+0GiTGFw9OCB+d8znW1W3kjLuRabkTubp0EZKmIyqE6Qh3EkqEybZkkSFn0OWOUdvswWyUqSpKw2lOxXJqkkJCTWAQjANRmSH8dIS6EAWRPEsOJi01uezsD7PrWCf3rCngtZ6nCXalVnP0ko7H5v8NmVL2n3spjfIx0dIdJMdlRhAE/HE/lksYug+AqiDEI2gXiNL5uHCYoLvbSPD8FX2DFS144ZJRo4wMBE1gS8erLFo2EZOagSip1AYOEmPaQB9FgWA0yX//YT9zxuditxpY/3YDqlrKgil5n+DRfzACAvOKpnO6f2i91nJbNW/2DQry6bThTrJcaxY6UYKLLLwLkoyxdBLRxsHqKc65a9C0wY20iA9EGces64i0nEDnzMaQW0HS2wPZF/n94iEcM1cCApH6g0i2NFxX3I2mDI4tCJBMqjx8wwTqWrzEEgrjy13n9GIu8sP8lWPUg+0Cjk+jZCYWV8hyWlltXsqGM28RiAXxhUN8fsqDdIU6yNKS5Dpyeb5+GzcsuAlp7wbURBxpwgKSWQWoL/471msewnvaihpNOYhEkw2xfAYlnX5WzchgapERXbiL5ikL6A518GLdyxzpTi1WvN26jylZExlfMZc82Uuy5eiQY4wfXE+naSybTkS4aWkRNdEqTvadHvjcZXRgkLLYEROYNP+L5Ah9THYqfOPuyfSHVAx6iRe2nGHexDx6PGHe3NeCKApcM6eYuRNz2Xl08LofV+KiocPPv/1uH3fdOjyypyK9FD0Xj2oZZZRRPt1cckP/w3AhD+hzzz1Hbm4uc+bMYe3ate/bb0BQ7QLtfwrp6e+/CvBRqPXWc+2iXDa+3YWialQU2lk5L490h8jWjkNMzKlm7anXCcVTE25/IkxlUQEHuz3Myp6LRefij3Xr0Ul6rHobfksOX3niNIvGuRhbcT8N/Uk2P9nO335Gz5NHn2d8Rg1pFGDWVKZlVnFT+RVs7jzAxsZdzMifxLWVV7DhzBYADLKB2ydcx//tfwqASTljSQJGScZ17B1ciQTH5i7F3Tu85Nw7B9x89o6b2NW+l0xzBr3hPhzpMTLTTBw6EmTp/Ct4rf7Ngf5jXOXUec7wRkPKcXG6r54FxTMpcRYQSkTwRH282fAqoiBy94Tr2bBWY940F2gp50RRto2KAgdPvjaYc79yXgk/+toictKtmAwXvnQzsfHl9AeIJCNY9RY8/ji17V280fkyp/pTL2OdqONzY7/Gf/728MD3tFv0/McXFhCSenjmyMu0+jqZVzSd5ZWLiSZj/Ou2HxGIpSYNmZZ0Hlv4RfLtOZxq8+G0GfCKzUPq9MaVBBsb3uKuSTeSabl4maaRwIe9HzIzh6+Q/aXwfsfed6SDgmwbTqeZsBqmyJaH1XrpJk9C0AcGEybzn2fom0wfPbc/K03jQLNGRI3gdKYm2JGMDGIdZy/bOf1LvlYu1/vhYlzo9+pyqywrW8ovD/92oM1ptJNlWDWk/6YD7SSSGtsPdwy0vbG3mbkTc8nMuVii+aXhzz3X3rCfZl87t45fxe62g8iizNzCaWSILjKcHvq8UUwGmXxbNotKZrOtaTcARtnANZWLUTX1ovv2dwSQLU7SFt2GpiQRZD3BY9sw5I8hM+/cdqpC4OBGRKMZQ14l8b5WQqd2knHtIxcdO9rVT9/rv8BSMx/HzGtR41Hcb/4W15X3DNlOlkT6vFHKCxyIokAgFMds0CF8hN/tXT6ue+xS3w89ATdT8yZwuOvkoDaDbCBbKuXqeRLBWAStt5QvTq1E1HSs39zDS6db+cxV+ZSd+l+6561kb28tZ0ydXLNwBUZJx1s9J7nd340D8Gx5AsfMVSCAYHXRZDRwItLELcuKcJw9QvJoC29WVPJs/VY+M+UWfnPo2SHHd6jnKKX5U1Fjw6NNUJPEYzGOne3nRH0/3/jiMnLtGZzoqaPIWsLcvLl85/+Oct+KGr6/pY+/u3MeXa0d/H+vHhoYIifdTL8vytGzfakhVY0N7zTxwKpx7D7ehapqLJqST01ZOj9bewRNg/YmmUkZEznSl3I82A02bh2/iiyX431/57+0Z/Bf2vGOMspfAp+IoX8ho3zDhg309vayevVqfD4f4XBKibevr2+gT29vL1lZWbhcLoLBIIqiIEnSQPufQn9/cJhAzqXAk3BzVt7JLTfPQdBkumJN/PjYm3xl1oMc7TxBnbtpSP8sSya9oW6SYTu7+91IYg5fHf/3ZFn9CILAiVAP3/xSMYKSxEA6Vq/AwhnFGE1evjfnSxysS6IT4/T6WrnBNZa2mJvXG3cCsLf9MNUZFXxzwZfxxn2Igsjvj7xIUk2yuHQO6UYHWjyELJmILLwJQdORRYRe/fDfxWnV4+s10ryvnOricSwqjxInzLcenESb14dOKqBqdjF17kYyjVnYdU5+fOD/hoyxs2U/90y+CVEQeeLw8wComordYOFbDxTwL3t+gjm6CJtZx5wJubzw1pkh27/yThPzJuYR1EV4byCnIEBSA2kgFl/ALYR5YXsDrvzggJEPMCFzHGs3NwxxZvhDcTqDXfzo8P8SS6bC+1478xb9YQ9j0ssGjHxIaR3sazuCIctKhsOI3azDn/AN+836wh42nd3GlQWLkFTdBa+Xj5OLvUQ/zP2QmWn7iy0beLFjP1nfR166Ba83jDfiR0hKBIOXTh9f39+HqDcTicQ/uPN7MJn0f9Z270UHhKICvnAIrzflZFQVPQlPz2U5p38J18pHvR8u9bFc6PcKJZJs3xbntin30ZdswyiZkSOZNDQmyTGm+ssy2MzDnUF2qwEELvt5+Cjn2mgVsektvFS7iYnZ1SRVhaePreOeiTdz57JqRFFAQCCm66Y31M+t41eRUJNomsZLpzbydws+f9F9W125uN/8Lerxwee3deIVCGY7vb0BMjNtCHozlurZhE7tJFKfMsYEnQE5LeeiY9tQcV1xD5GmY0RbTwEC5urZCPrB8qoWix6rWUdNmYvTzR6SisqYojSsJh0q2kc6N5f6Hvs47we9Xkd3oI+/m/95DnYew6IzU2IvQY2ZEPV92OwWnvt9M6oGX7xpPEdOp7QQXtrbz8R5q3AmkxgkPZ6Ij6cbU4sJdoMNl8mBAmjxKN4dzyGa7cjLH+X3G3sJhEw45uiYWjiNPTY9z9ZvZVxmFVmmCzviSwuMmDUXEbP9vDJ4IBXUcKAt5ZxQNTh5Kkaj1Mqa0ttY90Y39SGNRFLlD5tO89lVNWRqfViyDGSlGenxpBwHFYVOjtX3Ddtnny/CP35mBg3tAY7V9/GjZw9z1YxCDtf1sWV3L7MmTeCmiik4HTJjs/PRJS0cPdOLoqqYDTpauwPYLHry0s0U5jpG/DP4fEbaO2PU6TDKXwufiKF/IR5//PGB/69du5a9e/fy3e9+l5UrV3LgwAGmTZvGSy+9xMKFC9HpdEyfPp0NGzawatWqgfaRQIE1n65QNy8Fnh9ou3PiDRxpb+L2qtU8fvI5WnwdmHRG7puwhmjYiaRE6TccY1PnNoySAVvWciyJUpynNjDRkkZ/joA7ns8P19biDcTIcBpZszyLt3rXc0v5Yhp87axtaeCrhVexrX+ocVzbd5adLYeZ4JrA+sZ1zC+egSiInOo5w4K8uRTHAnRF/JiajrPZbmZB2gRqiiXSbH14zpVrEgW4ZbqNSMhNQ7uPhnYfhQ0WbrzOiSrG+OlT9UyqdnDT1XlsPLsNiz2HoOrnvQiCSImzgLWnXiepKph0RpZXLmFn22GKKnPwRv1sbnuDO9bcRcIvD1QQOJ9wbLiwXjSpcOSsmw27mnDZjdy4pIKCdBOBaJLXdzVz681DVyNMsplgaHi8pzvWN2Dkv8u+9iOMzawc1rfF14GYI5BpN3Dr1VW4MbGnc++QPhOzq3mlbgtTsiaQKY+G8I9UWrqDTKnIREUlmAhjusRifGI4gPIJCvFBSrDNagRfWETTtHPl9VKq+6OMXAw6ie7+GI8/48ZptRCNJ4nG2/n8jYP5+YoCeRkW0myGgWe2IMC1c0sw6SVIjlxx0ISWZGreBLqCvexpO4QgCFxVNp9Cawk/fKGOzv4QVpOOrz0whiZvGyd7B99vV5bNQ/6A0H1Vg8zVf0Pw+HYSva2Yq2ZiKpmIFo2APjWR15Q45pp5SDYXoVM70aXn45h1HUrYDxfL4hElNCVB+Mw+FH8fiBKOWasQzisprCgqvZ4IL247y9SqbERR4PnNdSydXUJexien2fFJE1FjVKQXE44mOdBxjCJrEdHOfF55q4VYQqG6JMHf3jaZno4uCtINLJ6UQ3WWgDussr7Dwi3z7DycX8LvjqzFF/WTbk7j4eoVWFvOos1YQbT5OPqccqSaK3jk1/VEY6mL5BevnOHBq/O5onoc8825JEMJNM1IiSOfJt+gDlClqxSzwcRvzmzimitvI/3kPrSeZoTiqZw2T2Pb64Ph9SadkdkZ83n21Q7mTyzgZGMqHSoUSZBv1wg+920QZR5b9rc0RAoIxDQqijN4TWOgbOa7FGTZ+Pff7idx3j1r0ItMqszkyJle9hzp58BxgX95aA5KTMevN5zgQG0q1bE4x8aciXn88NnDzJmQyxdumnTZzt8oo4zyl8OIMfTfj+9///t885vfJBQKUVNTwz333APAt771Lf7+7/+en/3sZ+Tm5vI///M/n/CRpuhsFrmt/D5O+Pfji3uZljETu97Eiw2v4e1cxUR5FbdM1REOC2ze7Ob+FUaafcd5+cxGIKUw+/ixZ3h05oP01UzFZrBgCOr40dMnBkoo9XmjPLOui6uvncIPDj3DY7M+w0sN22krFSiyZg47JkEJ4ZQzmG5bikwQAYGVpdcRiPWxOdxHripiKa7i9YNPUmWaRrkpymMLFRriOYQTUO6Ik3b0dwSrVwyM2doVIhHKJm404A/FcTl1xJIC9Z5mYgmFq/NXkm5y0R8ZVCVeXrkEb7CfpKqwpuYaYkqcrY27MMtGxHMKtZFklCfP/Jo7q+8gN91MZ/9gTrHFpCPTMTT8WRQF9tX28vgrJ1PH1R3geH0f//bIXCxGGVkWkRI2REEcCBE82neUK2dN4ukNjYO/kQBW0/DQaoOsJ8uSPqx9Ss54lHPq+mMKHBxvyeaBKXfy0ukNJFWFhcWzaPN3DXMcjDKyCEeT+ENx0mwGwokwekmHJA5Xyf4oSGEfmuGTn9Q7TJCMO4kmo5h0JjBYIB5BUxII0icfcTLKcDRNY+msYn736km856JMHFY9afbBZ5VOBycb+1m9sJwMpwnQiCdU3tzbQnmeA5vh0l7PlxKDqOOsuxF/LMCamuWAxsHO42RJxXS7U0ZQMJLg+7+u428fvI9d7XvoDvYxPruKQCxINBm7qAyZoCbw7duAEvIiOzLxH9iEqDdhLJ862EeUEQQRJRLCVDoJLZlACbqRMwovfvCqim/3yykjH0BV8O16CUNO2YCDQK+XiMSS1JSm09ThQ5QE8rNshCJxhBFeEeFyoggKO5r3MadwOouKZ5NFFf/zu8E0vdomD5lWiYfmGeiK+Lnd+g7Rg7uQLHbMS+7jqObn9wdfZEHJTMy6VLUUf9iDf//riAYzxrJJhCcuY+fJ6ICR/y6yCOE3nyDe3XCuQcfn7/oW27tPcaKvlnHpNZRbq3n88DO0+Ts51Hmc8RnljClbTKl5IT96fFB80WKUSbMZkUKZPLaoC6ntNWYVZdI6ppIdTRrW2ldIAKhJhE3fZ/riO/AeegGR61g+50pONroHRDaritKQJZHCbBsN7YMRggdre/neF+aR7TJj0InMnZBLttPI/rq+ASMfUiViq4qjZKWZ2HWskxXzSslzjpYCHmWUTzsj0tBfs2YNa9asAaC6uprnn39+WJ/8/HyefPLJj/vQPhCDQc8Pn2qjsrAKq1lm0/4on7nTSFewh7ysQ5Tpp7DzcJTCbDs3XlmJbIyz/cTeYeOc7KvFk4hwpPsk91d8YVidZH8ojk5NQ0OjP+Llhvz7OdOvMaNgLONchzlxLkUgw+TkirypuOkjKvehD6ZRmpXGts4t7G0/PDDe0vJFfG7859HrBJKCAeOhP1IdjyFIMloiigokBD0wmLOWYXGy81hKxfvIKT8zqvKYUzidbU27eFt+k3sn3E69r4GOQCdT88ZT6Swl6e+izd/JiZ7BUPr7pt+N1z+oOKtpGi+eXcuD132FdVubOdHYT3m+g0fWTMRmlIeE3EcTKi9vbxjy2yiqRkOHj7k1Wdx6ZSUbtzZzx4q72Nj6Kn1hNxWuEvJyk9y4LJ939nuwmmUWz0mjyddEhauEs+elV9w2fjX5pjyWVyxhU8N2REFkddUyymwlA2VvJKAs20WbW2RJ8Vx8cT87mvfiifqYlD0Wl971vqr/o3yytPYEyEozpXJnI0Es8qU3yKWwj0R6/iUf90/FZoJAwkEoGcGkMyEIIoLRhhbyINj/tNSnUT4eFEWgrsXDfStraO8JYjHpMBtlAuEYnFObVxQozrXT1BngZ2uPEoklmT0+h7kTcrFa5FRO0wglriR4p2U/9e7mIav1WcbT5GUWDZQbC0US9HVJjMscw4TssUQTMRJKMlVe7yLVU5Wgl0jD4dS+upsA8O56iez8ajA6U50EAf+hTeisLiSrE01V8e5YS8byB7mozpmaJN7VMKxZCQ+NZjMZZLLSzNS3+YgnFCoL0jDq5Y9W7uUvHRXGZ1fx4z2/AeD6zKJhXfae9nBzpZ60/p2ETqUU+pWgl9i+dRwaU04oEeb1M1sH+ndmVvJAbgXx1pOEjm2js6AYs7lmyJiCAFVm76CRD5BMIL7+W2L2mzD5sjhZp1G22Eibf3DV/nh/Pcf76/n8xPHcv7KG9t4QJoOE3WIg6POzMMePf/2PU/sASqxp1Kz8OsFntg7dv86Ic/XXCZnzaWrys2ByPiaDhCgKdPSG+PFzh7nlysohhn5uuhmnRc+dV1WiaRqqqiGJIqeahpd2rG/zUphto8cTIRiOw6ihP8oon3oum6H/bnjo+fh8PhwOxwVL6/21oJdFakpdnGxMPYRdDiPBeIhbxq/k9TPbOJo4ylXl86lMn0ln+CTN3RGyjA5afR1DxsnQ22gIdBGKh4niRxRSYYjn70eQU7m7Fp2ZP7yYeikdKI3x8Ipb8flOklQVsq3ZtLv1uK1dvN35FtWZ5aSLY4cY+QBvNrxNxfRinjr9AvPzp7JkyV14Xvs52rkaxFLBOA50D+aBTh6TQVDzkJOVepE0dQTp90UYa5pBoiDBrvYDdIY6uGvCzczMmEOf2oooSGTpHDxasICzOggkI1QZXBSLFvYEB0vHCAjcOm41SlTj9muqcJh0GHQiuVn2YTlcopCqSet+T6aAQSehqjB3fC65GVaON/RxV/UDONIVmn2tdEXb8Fi6uWnNOA51HeOZlmMoqsKyikUsKplNNBGjyJ5PrjEXSdWxqmQ5S4rmISBgEW28p8AANoPMuPxMCtUpHO8/SYEjl5VjrmJCxrgRkZ8/yoVp6Q6SmZZaEwzEg5gvcdg+pAz9aH7VJR/3T8VuhD6PjVAiRMa5vFTBZEcN9iOOGvojEllWmVCezi9eOk66w0gklsSol/niLRMG+ohiqi77s2/WDbTtPNpJdpqZWFzFOIJXjmVRItPsos3fxYSsKhRN4Wh3LU59GoHwUG0KQRfjVwefGfj7wWl3oJfkixr6WmK4voX6HkNci0ewlE3Gt28DSW83gqzHMWsVSiw0bNshSDK6rCISPUPLnknWQXX0WCyJomr8Zv2Jgbb6dh/3r6wZcBR/GpEkkc0N76Cd+xEybDaMeolJVWkkFThyup/iXBtWp5HAjqFl+NTsItJMzmFj5hrsaOEmAESznSpbHgadAbtFjz+Uug5kScQuhHhvJrjq7cLiUjhwKhV2X1PuIt2cRv97yo/aLTKPbzsLgkgsoWA3SXxljoKoDn1vqEEPuvMWMwa+d2YxMVsJFkFBUwO8vL1+WB/HeUKwsiTw0PUT0IvCQAQhpFJCxpWm89aBtiHbVhY6OVDbgywJ5GV+/IKio4wyysjjshn6a9asGVZC7/bbb2fDhg089dRTl2u3nziCkiAzzcwdYzJR1ZSzIxht57X6rcwtnIZJZ+Rw5wkW5M7i6QPrkQWJr0y+laN9Z0koqZryLqODKmchT5x+DYDD7j3ctWIhT7ySmsgJAtx4TQHvdK+lKq2YPJ0TSK2sn2z0UN9TRMiXj6IodAatRKwtZOks3D7hOnyxIElV4dG5j/DT/U8MqP+rmkp3qI82fyfP+F/FUzyb62/6BoqvG4M1jYSzkMLmKAsViZJ8CwlLJ2+0vcO4tHHccHU+m97u5v+eP81Xb5/CPNdyZmcuRhJkYn6JHls7GxrfpCazktl5U8hKryTd3QaakTguNp5IMsMp8I/zv0RbqIcMs5N4MkFb7CiyUk2eIft9J0WyKHDnsmr+48n9A20Oq56yvJTStChCYaaF6kIHIc3PH0+tY3/HESRBZEnpXPrjvRzsPjyw7caz25iWO4GHJtxLMqkO7FdTwYJ94P8XQlU1rNhZVX0107In0+Bt5mT/KYodhWTqshC04eUO3z2fn+bFnU+Spi4/WY5zhn4iiFG+tKWKhEQMQUmiXeJx/xzsJoh0mgmdX2LPZB8tsTeCiSsKbx/p4N4VNfR5I5gMMpIo0NztoyTjXI65BvVtPpbOKiLDaRoQTdt5tJOrZhSmvKEjFAVYWrGIkrRCdrUeRCfK3DnxeorMpawPDxrH1y8u5URgNyvGXIlR1tPm7+LZE69QlvZ57Lz/woHsyARBHPLQNhSNQ9AP3o+CzkCwdheWqpkIOgOCIBKq20da3nBtliFoAs45a+jf+KuBMm62qcsQziujKcsStRdYed15tJMrphV80M/zV0s8oQ4I3ObbcpB0Knfd7mBHx1ZkUcf90xbgEvIRtRZ0jmwS7sH8eXd2Pi6zGYvePDB/MemMzDPnkuh/DUFnwDnnBrzr/pcMSxqP3voVWroChOJJCgtMBKUukjPuoFvKAU2j1BrFYpAYG0hy2/xsnt/VS0Obn+vmrqQpdJY0k3MgTSRL0vHNBXG6yEBEIzvRjrj7eRKL7h52nWF2YiieQKz5GIKsxzz/DtafTKI3djCtzEp5hoTTahhIyQEozbMTTyj8v7umoaoauelm0qz6C85/qouczJuYyzvnyvFVFjmxWwxIosBj986kOMdOf/97ZYtHGWWUTxuX3NC/9957OXbsGNFolKlTB/PgVFVl7Nixl3p3I45ig5dtwQB/OJjytNrMOh77TBGKqrDx7DbGZVUxv3gmFoNMJBElz5aNoir808RbaQv1ISFQaM1iQ9NObHor145ZgizIpBlC/PPD0wl6I6Q5JIJiJ+Xa1eRE4vS5h57Gbq+fpZMz2N5+jFdbn+WxsX9LW6iV1+u3crqvAZNs5NoxS/jS9Hv53s6fpY7bWUB3qHdgjI3Nu5mdO5WueAn5GTqcsonqPJHSLJGf1r5AR3c3Fekl9Md7OBHfwbJVc8i35RGKxOnuiGLUy4hiAqszxG8O/pqkmuRMfyOCqiPaWsTLb4coyLLiC3bhC8ZxrSojS9NINzvZdPZtuoI9TMubiCkjSiCaxHaBcnrvllqszLfz7Qdnc6y+D6fVwNgSF06znnZ3mN+sP0FTp59FUwvIH9/J/o5UPWVFU3mzYQcPTL0NSRBRzntBT82dMMR7/qfSFejh33b8iN5wyoASEHh07iMUG0uH9IsIIZr8LXSHeilxFJJvzkenffRSaqN8eJq7A1w5NZWL648FMEmXdkVfCntRjNaUN+cTxmGGcNRIMD54nwtGG2pguPrzKCMDs07PmGIbgtmDyxlA1GSCbjP5mUPFPccUOXl642k27UmtLpsMMg9dPx5xBBv5kPJB9Ib6efX0ZqbmTSCpKjx7/BW+NPMzfO2OaXiCMawmmfTsJGf9BextO4xRZ0TTVFZVXYWiXUSJD1CVGBnXPoIS9qPFI4gmO4KsQ0sm4NyjVkvGsVbPxbPjOdRIAAQR+7RlqMnEBx5//+u/xDblKgRZhyDKhBsOYcgpBVs5AJIkYDYNj+iymGTUT/OSvmJgVs5sXm/aiKqpxPTdPHXsDwMf13sa+crsh+kOZdM75YsYIr1k9h9GOL2FuN7Ai6deZ8WYKxEFAQ2w6kxki3Z0Sx9ACXnx7Xk55XyJBrG27iZhHEdeQTeGSBDZXsJLXTJ763zcs7wARfDj3fw0jliY+ZlFzH3w83glPVECnGw8Q3eoD5fJyS3jV4JkQ995jPzmQ0O+jmrJGPK3aLRw0melPfs2Ziz5LIKS4L9fauFUczMFWVYEoZDxuTKP3jmF57fWc6bFy+QxmVQWOtmyv5Wv3T4F67tznve5TMx6ifuWj2XV/DIURSXdYSSeULliWj56URjx9/4oo4zy8XDJDf2f/OQneL1eHnvsMb773e8O7kiWycwcLhT314ZZDXCH6wjXrJxMTBHI1Pqx7lvPV+d/BhWB0/0N7G8/Sn/YwzcX/w2HO07wHwd/T5mzkLvLr8BhsrO1s5HdvbXcPuE6njm2jvi5lX6LzszfzniEwNkuXJqHuGDkpJpObc9Qr21BthGfGuTZMy8DoApxtrfs5XRfKi8tkozywsnX+NqcB3EYbEzKHku+I5dnjq8bMo7eaKTff5rnd+/EpDNyc80qchQ7X3ZOIOAahxmIF1Sxq+0grzW/xq0Vd/LSK6nKALIksOaqAtLtGl+ceR+SIJJUFRJxgZMJlduXVtHQ7mNCeQZOq4HarhA51SZ+8s7viCRTOgAbzmzBF/NzS+Uq0AYvVU1Q6Ix3Udt3FrvBwpi0CgrTnRRnFqNpGpoGvkiC7zy+d0DbIJKIsbdj6MsZoNnbzvLKK3i1bjMAi0vnUOWs/Egr7GfcTQNGPoCGxjMnXubrM74wEMYfF6I8fvRpTvQOhtveXLOSxbkLQBt9QX8cJJIKvd4omefyGAOJIJmmjA/Y6k9DDnpRTSMjhNIgp/JH+8ORgTbBZB819EcyUoKpU3SEYwKxpIBe0mExBXGdVxFMFMEfjBOMJLj5ykoMOokjZ/rYfqid6hInqbM+MtFUjVZfB1eVL2BH815kScf1Y5fR5G3jjdd76DynSv61z5YhixJFznx6Qn2Mzawkkohg0ZnhItUntUSCeHcj/v0bEWQZQZJJW3grqIMOAkHW4z+4MWXkpw4K//7XyCqZ+AFHryLoTcR7mjHklqOEfST6OxDOi97RNBhfls6rOxqJJVL7FAW4akbxCD4rlx+DTiVfquJvZhagkODN+neG9TnSdYJDb+XQ740QSyjMqpnMmutnkDAm0Et61p56jdnZ45hgzSWRUIglwsROvEOsY2jlIZ2/jVnlY1Feex4l4CYBXL/wDsrMgC/LAAEAAElEQVQmp/Fqy3O8JgisXvEZigUzp0Nt7G5dR7GzgGxLBu6IFwB3xMtvDz3HP876POKk1UieNlR/LyDAuKvwGXPJWnArkfoDCK4CerNmsf1MlJKxbv770HZEQeDapcuY1FpGJAxv7mslObWA57bso6oojatnFjG50kU0pvD1O6Zi0X84AU1JgCz74PWmH8HCm6OMMsonwyU39K1WK1arlZ/+9Kc89dRTPPzww7S3t/OrX/2Kr3zlK3/V+fkAQUs+svcVXHXbARAkHeqKr+PQ23j65HoOdqbCEWv7znK46yQ31VxLQk1y2t3IN92/5suzHkT0FvHZMV/gmHvngJEPEEqEOdV/gmnFs2jpzcEixijJcrC7MaUeb9BLXLckn/QMCEaDzMkay5W2YgRN4Wj3qWHH6o54uX/KrZSIDn5z+iW086zbZUWzqPe28fzJV1MNEfjB7l/wzflfIk0/i8y4H1Enc8bfzK3jVxFXEvScNeMNpAzcu28oYHPfWtbvTf09IbuaLEs6VWkVpNut/P712oF9uexG/ubWSXQHzwwY+e+ys/UAKyuWYD9PW7k+1MAPdv9y4G+H0c4/zPkiNtU50NblDg8RMOzsiVJeWjRMC6HEUcCU9EnMyZuOIAg4JAeoFw6x/7C8G054Pr5oAAUFiZSh3xPtGWLkA7xY+zrTciZhw/GR9j/Kh6OtN4TLbkCWUuc7mAhRbLu04bRSsB/VODIM/VRlCYUe/3lGjsmO2tf8CR7VKBcjkQRNE3ni+B/wRlO55bNyZ5Bvz02pgJIS41NVjatmFvLClrPEEgoza3KYWJGRcliOZItSAIfJzlNHBtP8njm2joen3008PphJrTMorDvyBoF4yvA/1XuWq8rno6oXj7wSdXrQVNIW3owaiyCabETaajGcZ8RryTiJ/vZh26phP1y4xPq5DTUyrvksvj3r8b7zApLFgXP+TYjmwfrbggC93jB/e/sUTjT0k0imcquD4finOl1LEZNk5cL/7n2GXFs2Jt3w1KYx5jwWzhfRhyNEDS7ebtM47tNYX/sct024jlJVxrJnI0rbFiRrGpa5a1AmLBpm6DvHzyV48BWUQCqFQpB0tFp1PHXquYE+m1p3UZ5WwpsNbwOp68tptPOF6Q/ys4O/IZaMEU3GcMf8nGwyMnHeV9D8PaAzoMvIwRPQyM4sQQ66SXQ14sDI9OnV/PLo2oF9/ObI7/nS9Af5/s9auW1pFX/YdBpV1Th6to+jZ/vYerCN7zw0G5Nu1FgfZZRRLh0fzaK5CP/wD/+A1+sFwG63IwgC//iP/3i5djdi2Nei8Hb6GiILvkBy/kO0zvxbfrw1jDceHjDy36Uz0I34npDeEz115JQGECwe+sLDc/t6gj2k9R9lkt1DUZqEQw0wfqafO261sfoGmb3xtfz7rh/hMNm501lFuj0bGxL5tpxhY6XpLTxzbB1b+49za/E8Hqy4mqvzp/H5ymUszZrExobtw7Y51ltHa7KdsC2No7EOfnR8LU8dfZFgLEBrR8rALc230Zg4PGRV+1h3LU6jg3avlxfeOjtkTLc/Srffi1EeHrZukPTI5/1GSTHOsyfWD+nji/ppCrQOaTMbh/qwmrsClBonMjNnOjcX38t1WZ/hnvKHGOOsAlXEKbpwCGkf2cgHKEsrQnjP7PrqsgUYtMHczfMdOO+SVJMo6kWUpUa5pLR0B8h2Djoeg/EQpkssxicHPShG2wd3/JiwGcEdHLw2BbNzdEV/JCPAq2feHDDyAfZ07sMdHRQJk6RU6dGnXj9NNK6gabDnRBcdfSEk8bK94i8JmgZ72w4Pa6/tPYvxvHStYCI4YOS/y7amPcTUiyznkxIFjnacxbP9j/j2rMOz9Sn0rjy0+HlRLZKM7gJVMUSj5eIHb7DgP7CRaEvqva6EfLjf+O2QZ38ikSQWV/j1uuO47EYKs608u7mOM21epJHsgLnMJLUEz558mUA8xJn+RibnjEMUBq/V8enlVLc3Ytz2I8R9z2De8VOu0e2j1JrOtaULGWvOwbH7dZS21IKBEvTgfuNx/FYruklLQBARJB22OTegszpJtA0uLOjyKtjqGToHmZwzjrcah0YVeKN+2vv8rCy6AUil4OlkOyXlGn/sfZ79uk7eSh7l1c71OBJ9eF76L0IHNxLvqEP2NrGra3g1pX2dBynKsZNUtAEtjXfxBGL4wwkkSRwNux9llFEuGZdNjK+pqYkf/zhVbsRms/HYY49x3XXXXa7djRiyMw3814YeBrWBoyyYkoP0PssqsiByfd49yDqV06EjpBlcvNGwnSm545hfNJ3avqEvpGk5U1BlF4oYwyiBIup5ft+6IavxAJakQr1Jz69PrmV16QLuHnM1/3nwqQHBv2nZNRRrOjRB46XTG+ktmM5quZgaTw+hhAEtrwCHwUY7XUPGtRpMNPtbONbbwsyM2Xx+yl282fIOq+3V7C6LcaweinJNNPkbeS/uiIcMXQGKMlzN2BsJUmR0kG/LoT0wuM81Y6/BiX0gOlNFJZyIDNs+lhw64ct2mlg0JZ9thwZXapxSBrmROTyxbtDjf+cyM1dMMV/SRa8yVzGPzn2EZ068jC8a4OqyBczJmzlkBSfbnIVVbyF43uR1Uk4NDtkxWorvY6KpMzAQtq9qKlElemnF+FQVKeQbMaH7kCqx5w4N5gwLJjta2IOmKgji6ErSSCOpJmn0Ng1r74+4eTfwR9OgsdM3rM+uY51cO6cYnTxyjX1REHAa7cPaLZKdpbOKCEWT6GQRTR3u9BY/zFM7ESPeOVTZ3Lf/NcxjZp43kIRz3k30v/l4ahVfELHPWoloHn5cQ4j4iTS8Nx1MI+HtBvu7OfoyLrsJh8VAQ4cPnSSBBjWl6VxcXeCvm6SapNGTcs5raLx2Ziu3T1iNjAGzkkWFTiH6/D8P2UY7s4OiiVdiNM8hTfXjaX+Pqr2mEnZ38gddgC/f9R10vl40Xzeet57EUFBF5OzBVLdoiHRd4fCDEoRh+fCqqhH1WzBIeq6tuBolbOFXJ35MQk3S5E3pME3KmoA92QfKeU76WIh0eXh0WJrRyYloAp0sDhPhtZl1qLowT9S+jMvkYlbeFNKljE915Mcoo4zy0blsM4BkMkkwOJg7HgqFhhmjf43oLX6Wzs0ZEDouyrYwYaJEpmZgTsHUIX2LHPnkSjaefqmHJ5/rw9gzhbGuGmr76nn62DqcRju3jl9FmslBptnFgxNvpdDq4rXOHXzr0O/48cmX6In28djUe4cdh58kPzz6PP5YgHe6jlIa1/hW6VL+pvIa/l/Fcu6yVaL6evHHUuGRO9sP4MkppaFoBaH8KWhinFkFU4Z42W0GK3mWTDRRYlfnLkJRldc2qSxLv41E7TtUSc2snl9Ic3uYSufwcmIZ5nTO+k+yeuFQUTqLUcZgDdPo7uTz0+7g/sk3s3LMVfztrIeYkTGZ+HkCdUZMLK9cMmR7SRApcQx9cetEgduurOSb98/kizdN5F8fmkO2y8yzbw4N63t6Uy2e4MVXhS5EfzDOyRYvLX0hEsrQ61oWJYqNpXx9xhf45wWPckXeYoza0JQVq2Dj7+Z+nul5k3CZnCyvWMIdNWsQ1MvmexvlPTR1Bch2pc5LKBHGKBkRhEv3SJTCXlS9EU0aOeUV08wSwYhxYPIoSDKC0YoWGm5IjfLJo5clxmaMGdaebRmqd5NmG14vuyDTijjCl41VTePKsnnI4uBzz6wzMTFrHE++dopn36zjqddrsWjpwxwCV5UvwKy7eCrghQT1tFgE7bwcfVQQjGbsU67GOf8mnPNvQufI+mABTUlGtg/XHTo/EkAQQBJh+dwSmjsDnGzs56qZRRgN0qfagNOLRqozygf+7gn1cbj9NJ1nnPzP43U0t3q4kAqdFo/Q5O6lNtiBZBme4hbT6YgoSdyaGbWnAd+utcTaTmOffi06Vx4Aid5WluRMRHfec/lo1ymWli0eMla6yUXQY0RUjDw04WF8jbk0dgRZmD90/jHBMY2wMtRJGu9pZpGzdMg+jLKBmVljmVBkYefRDm5YXDEwT9TLIg+uqeZHh37CztYDvFL3Bv+244d4lNHn8iijjPLRuGxWxfXXX8/NN9/MNddcgyAIvPHGG6xZs+Zy7W7E4Il10mY9zG23zARVoi/ZxrrmLUzIfITrHBVUpRVztO8MFWlFTNQ58fcNLt9u39/D5OrBGrwHOo4iCzKfm3EP2e5eNHsZz9e9yo7OlHK8N+rnzIFWvj37cwMK9ACVjkJ6Il7Uc0ryDd426nN9FPZ1k2E0gyAQ6TrJyfIxRBKpnHi9pOe45wgbzmzhc1Pvo4hcTnWf5tbxqwjGw8iihCiIKJEgO1sPEk5E2NK9jluuuhFBkIgVVbI91E6dupH5i2cxNncGXeE2znqaERCYUziNeDLG9Y4KHMVWXGIOb9XFKHJJLJuaTU+yjv5IGntbz+DUitm7FZ7raqEo28MXbppIpi210qqqGtOzpqCbpGNTw3ZcRgerq68hQ84cNi8wyCJl2VbITq2otvSFUd/rsdcgFEvisn44tXtBgLOdAb77xP6B0LtFk/O5/epK9NJQI1FSdUjoLiiaq2ngEjO5v+Z2EiTQY3jfsn2jXHqSikpHf4hM57ul9UKYL3XYvr8f5YNWBT9m0swisZiFuBrHIKWuecGchurvRbT99Yul/qWhiSpLSufTEeiiI9iFIAgsK7sCs27QsBcEKMyyUp7voL49tbJvMsismF+KpgkjOkdfAPpDHr446z66gj1IgkihrYif/q6J5HkO1B/87jTf+ML9HOw5RF+4nwpXKcWOfELxEE7e/76VTDYEWY92XsSXqXIaCOcZZloSz5bfk+gbmv6VteZrYLrAyu/A4Docs1fR/8ZvB8qqGUsmDDH0NU1DUTX+97kjA22/ffUkD1w3Dp2YRuy9Y35KUBMCy8dcQX/YQ3eoD1EQWZi9lP/5bSrEvrZfosBVgOoerBMv2Vw0REy87XmV/vZ+/n7eaqQ3fj9Y0m7MdPZHe7kidzm/ePEs/29hyeCSuaZhLKrBUjMPQRCI7t/MN8atok4UCYYT6OMZdDcnuWf8HRzrPUamIRdroog/vtLOI2smUnc6yKs7UsdyzdxSVo+5lu5gL+XmcWzcHOCqGjvTsktJdg9GMmYlDdxf9VnCYh+qCmlyDk11UW5dUkpvRCapaMyoziYUjeNy6Pld7eMDCy8A0WSMRl8zU9IuJhQxyiijjHJxLpuh//DDD1NRUcGuXbuQZZmvf/3rLFq06HLtbsRQakzjCX/rkHDLuyuvxq8FcViymXJ8K1PCPuitgwnX8JVnh4YVdnsHH/QZBjudEQ+/2P8U/1lzM31ijHe6jg7pH1cSdIR6+caYlXSoUayCTE5fL0H90Lzg/+/M63ym/ArGSw40UaDWYeXXp18b+Hxp+QJ2t6VC23KMdhJnD3NN0Uy+u/8JNE1F0VRqnEUsrF5Bz7nc++P9J7mpch75ziq2tyZ4rTWVk9bkbUGoF/jPRV/B392AZDCjj0WRzx7C6swluv9Nxnm7mZQ3BgJu1Df9VF/7CL/dF2f+1Er+89enUM4Z0S3dAX707GH+6b7BUEuDZmJWxkymZU5GRERQpfctQXM+GXYDDqse33kr+E6bgQz7hw/XjiRUfrb22JD8um2H21k0rYCSzA/I6bwQqogOw6e50NInQld/GIdFj+Gc8FEwEby0YfuAzt+DYhpZwop2EyQSJgKxEAbzu4a+A9XfA/k1n/DRjfJeJARiySgLimeRYUpHEAQOdR3FpB90TKpq6jl585WV9HgixJMKOS4Ltc1uirKtjGRLX9U0tjbv5kx/IzpJh6ZprCxcQ7d7qKBpIqnS0h4jz5VFiTMfbzTAU0df5MtzPsPFYuAFgwnXkrsInnibhKcLc9lk9PljEKRBQ19TksOMfAAlEhjWNoR4BN/eV0lbeAuakkQQZeJ9bSQ9XWCvAEAUBQ7VDdfA2H6onfkT8y4+/l8x8YSG36dxW+XdKGIEs0GHt2dw9fuV/X3UrLyLvM6t0HEcXf4YwlUraA1FafKlSkj+rOcgN19zN45olHRXAQ1JA5Y+jadf7MUfihMIWUibdAX+/a+hBD0EDr855BiMDYdYdNu3OaYJtEaCZOerZJrSmWVZyeu7m1G1EF+/czo7j7Wz/dCgiO+be1r59qQ5ZMbDtLT5mT3eThDonHgPFcZ+lJAPxVFISzKTSpcdKKK9L4QS1RhblYtslMk9PwDHYSApxvFEvcN+p091CcZRRhnlknBZ44SvvPJKrrzyysu5ixFHXlLk76pW8Xr/KfrjQa7KqGEcJryRIA1CkiMuI1PK5hHQFDZv7CUUHSq+ZrSk/rbqLUxKr2Ldnp8TU+K87m9mpnUqRtkwsAr/LgbZiPXVHzJGEFPebUnGOXY5VxbPY3NzSmAmqsQQLJnUiy6aI3VUZJbwgPl2usO92A1WjnadIpKI8oXp92HZ+yrh2j3k3/7P/OvUu2gP9mIU9eSbXPTFoggI2A1W7qlaRo5mIKlE2HrOSfAuGhqbm3ez0D4bXd8ZdP4O4gUzMTjN+He/BECi8fDgBooOs1GjPxAeMPLfpb03hC8c5/xpkaZpSNqfFhJtMUj8/d0z+MVLx2js9FOW7+Ch1eMx6z98GGU8qeL2R4e1B0Jx+HMM/VE+EZq7A2SlDa4EBhMhjPLw8OePguzrIVI47pKO+VGRRNDLCdq9ETLMqeghwexE9XR8wJajfBLISQMusxWjTkddXxN2o5WFJbNAGHxgpVT34b9+f4DJYzKxmHSs296Ay27gmllFn+DRfzCiJpJ2LiT/Xf0Yn9pLTnoaXf2Dxr4oCsjGGL87/PxA2z2Tb8Sht6EOl2w5bwcyotmOLqMAfU4ZGqBzZoN03tRHZ0JOyyXp6RyyqWxLv1jlPtCb0KXl4tn2zHmNAtbxCwe2i8USuC7gSHZa9ciSRuJTqr1qM+lo3K8xsdLM936Rqgh01zXVA3nriqrx3XUdLJ68gAXL51JgtLBtbxhzjg5REFE1lbZgDz84k1qsuM/yAD97YnD1f0yhE4PmQwn7U5UQjFZ4d350DtFgRjIZSNOFiBojWPUW0iL9lIePU7xkHMlYlJ5g/xAjH0AnS6iqwo+fPYzJIJNIqiQVFbtFz399fg56WUJEoPi8SUVl3sUju3Sanuurr+GXB/8w0KaXdJQ6Rvb9O8ooo4x8LpuhP2XKFIQL5LgdPHjwAr3/ekiaMinubeHO7iCarEMfbsI8fQVnE26C8RBT8sbREeyl2FHIndfk8PO1x2nrCWLQS9y7Ygz5rgD/mn4/JmMaSFY+M/VWdrcd4pjnLGWZpdxes5LfHBmc7JQ5C8i25KKbtJR47Q5EZw7W+beRkA1MTVtEhW0sChEkxU7IL/JU22+4rWYNp3sa2dt5iKq0cmblTqfMXoZVZ8Yq2NDNLcBWsxCiAWzmDKqMRkRRRE0kyOo5y39OvRdZURBPH0CaNZYkevItmcNK1+XYsrBmZOMxphHJULCadCD0DXvhGvLH4NYcLJkuEY/qgKYh41hMOkyGj36pahpkOwz8w93TiCQUTHoJWRD+pFxJi0FmQnk6x+oHKwoIAgO53qP8ZdDY6SfLeZ6hHw9ilC6doS/Eo4jx6IgprXc+JkOCdp/GpHOeM9HiQuk5e/GN3gelvwXV04Foy0DMKrukGgejAKqIQTTjUfzk2DOQRIlIIkqWLmugi6ZBWYEdo0HmQG3PQPtD108YUrFkJJKMwZVlCzjYeYLkuYojB3v38/mb/5b//eNxejwRLEaZB1aPp6QA7rfcgjvipTStiGxLOsFoFDPv7/DVRBnRZMVcNRtRlFASUQSdnqhgGYgCUyU96VfeQ+8r/4saDQECjjnXIxou7rhNiGac89YAKpHGo0hWJ2mL7kC0DKbfxeMaU8ZksuGdxgGnviwJrJhfRjLx6V2tlRQdC6Zl43UL3HlNNevfbmDboXbuW1nDM5vqiMSS5KSbWTQjE7vnEIZwhHk1M3l8WyeLpi7irda3BsaamjMROe5AFDtQVQ2X3ciDKytJGoLoMwuJx6OIJheuK+/BveVJUBUEWU/68odpTMgIopVKkwN6urARpS8zm5jQQ0bzAURbObkuC53uQW/SbUsrsZsNmAwykdigp+amJeUYdPK5aL8/7dxqGoxLq+FvZn6GLU3vkGlOZ3HxHNKlzE+FttUoo4xy+RC0y/QUaW8fVDtPJBJs2rQJSZJ44IEHLsfu/mT6+4PDyptcCkRRJB7pRx92I2oqCb2NhDUdQQvRFenFIlixxgQOdSis3d7CzHE5jC9Px2XTkyn4UA6vJ9ZyHH3ReJhwNR6dC70hjKap6BMSkmykO9FLs6+Nsa5SzJIeUVWQZT1iLAGyTNxgxqhaEQTwR5L4QnEcVj2q3IsvFmR/1wm2Nu9GOWdsm2Qj/zDny6Tr0od8F0EAqeMw3taj+DOySbNkYFM04t0t6Fx5yPlVxOTUpMad7ODfdv6MaDKVdZhldvH1WQ9iEYaOKQoquu7jeN74NUrIhyF/DI4rP0PMnIuqaiRVjQ27m3lpe8O5/vDV26cyrjiNjAwrvb0fEE75MeANJ/jV+hOcbHTjsOp5aPUEqgsdAwGymZm2EXGc7yUz8/3LvH2Y+2Gkfq8Pw3uP/TtP7GdGdRbF2anfZEPjGzgMdorsF8nJ/RPQ9zZhaj1FuHTKRxrHZNITifzpYpEX481TforSRT4zOyXypgb6iB9ch/X2//rQY2jRIJG3foHa34KYloca6EUQZQzz7kbOr/mLuFY+6v1wqY/lYr9XQozSH3VjkA045TSE95QBDcST9HqiHKvvJxiJM6E8g/xMC+mWD6c98lH4qOfai5uEFicQDyEKAhbZBGEXiqIRjiUx6WU0NDqV0xQ6M9HLBtxhH2f6G7im5Cq05Ps7M0xaCMHfgW/PehJ9rViqZ2MZO4+wuQBVSx17f38QY7QTIRFFCfQjGq0IejOK0UlUfP9rRBDAHO9F0BTUkBfBYEIwWEmIZmLCoBOxJxgjmdRoaPeRSKpUFDoRBY38tI/mHL7U99jHfT8IAiTUGH0+lXBcQQCcVgOg4Q3GybcLGPtqSXq6MOaVIwrQLefSFY6R0Hvoi/WQYcxgrDkDa9hDTDbTlzBj1oKYEn24rVZ2uM/iQGKcvRx3KI0swYdZCYAlDS3sxeG0E1Fl2sJGLERI696HcexcQh21qPVH0LvyCJXOpxuRUEQhzZhGlsOIXhLo9sXYcqCV9t4gV00vYmyRE/0lqHAhyQJoAory0UR7/hKewecz0o73YvfDKKP8JXHZVvTz84fWpX3ooYe4+eabR4yhf7noiffQF++nL+EhGA9Roi/EmohzqOMonoif67On0JrIwuJKcsuNRsCDGZGMuIIc96DLK0fxdhM5uQO5s57sZfcjJgUCITcmgxVV0dPQbqQwt5zdHYfY1LwTWZS5oWIJeUYn/3P4GSpcJXxhygPoNQM2o4ztXE15U1RAEXLJ1XSsyBiHyRbDrbUwPqualmA7fkOIXFMOSlyizxfBapLQ6XX8TOijpek4giCwonAWy0rHoTirhqRGZhry+fbCr9Dub0cSZfJtBZg0G4IAvf4YrT1BdLJIcbYNe/Yk0m77DiRjqAY7EfS8q5IniwLXzi5menU2vlCcrDQjLqthRHm1nWYdX7llEoFIEr1OQhLPCV6NnEMc5SKoqkZ7b5CVc4oH2oLxEDmWrIts9aehc3eRPG9lbyRhNar0BgYnpILZmSqxpyQQPkSFAC0eIfzK9xAcORgWfxZBlNA0DbX7LNEtP0cunY666rOX8yt86lDRUv80FU1TEd5TMKezP8L3frcPm1mH0SCzeV8raxZXcN28EtSPaDBcTkRRIJaMsKftMG827EAWZa6ruopq6xR+te4MDe0+0h1G7lhaRUFxDoF4ACkZQ0NlbFYFYSWEifePmhFDPfRu+D8sY6ZjzK8k3tOMb++r2BfeTkiwnzsG0DxddL/yU2R7OmrYh6lsMra5N1702HWihurtxL3lSZK+XhBlnHNWYyyfQsxYMPD9et0R6lo8VJe4UDWNsy0ebFY9+ekWhqnDfqoQCATi7Djayxt7W1BVjUVTC1g+PYdAOMkLx/tYvbiSbpcLb9RNvslJrq8eacPPAY0SvZG0pQ/hW/tdwgF3KmVx4e305kymVZQ53LOXaudYLFomQUXHC9vO0tDu4+pJ6ZRn+MnLdCCe2I46cQElwSMICLj3vUqs4yxpC28lKeoJurJ4o28/mxvfQS/puXXCKrKlSWianiy7gTuvSjlLP6pRfj5K8k+PCBhllFFGeT8+tlpe9fX19Pf3f3DHv3DiSoInjj4LgoBZZ+KVuk18cea9vNW0m3+b8yW8RKjvOUCFNYsaUyYnvC1Y9D7Wexqp97czJ6uGSfNvIGzQYz36DmIyyXExRpEjk3ZBozPsx5Su0iu4ea3xbQAUVeGZ2tf50tR7yLZkcNbdRG+sh3z94OqkOdpFe3+YhogOq2ylstBAmztBhimbH+/7wUDY5DXlizH7xvHUhnoEAVYtKsRqcgFdaJrGKy27qcmsoPi87+wJJ3jnaAe1zR7mT8qjOMfOibogLruAQSfx7V/vIXnuRZjhMPLN+2ZiN9mQ9I5UBaPk0JekLArkuUzkuS6tCvrAORIi9ERTAklZpkz06p8esi2LArGEwi/XnaCh3ce06ixuuqICp2nklFIb5cJ0usNYTDqM+sHHXzAZxnQJVfd1ns4Rl5//LnYz1HcNipEJkoxgcaF6OpAyii+yZUobI7L1VwjWDHQ1VwykZwmCgJRTiegqJHHiDdp/+TXk+fch5wwvDTfKn0YALyf7ThNLxhFI1Z2vcoxBr6WeW5IEzZ1+TAaZ6WNzsJhk9p/q5u3D7SyZVoBFN3LTKQRZ5Wx3ExvPbgNS77LnTrzKHWOcNHf6Aej3RWnt78NVqONA5zE6At3MyJ9MibMAURZgeAW9AZRwEMeMa4k0n0CyJtE0FVN+JcSCcE4bwBTrw1d/mIxrHkjZV6JA0teX0q3ISH/fsfVxD/2716WMfAA1ifedF8jKLYdzrxRRFDEaZPp8Ef7r9wcAqCpK46YrKpFliWT805mkLwjgU/z0aH24Svp4pDqHiNfKc5uayEs3M6bCSInFzS+OvUmONZMiRx7f3fsbHph0E2U1s1GObsNSNZPAjj+iBM6VoFOShN56EteNJZzutDCvYBk/feEo0Vg/0XiSK6fl89krcjDt+ClGOQt31TK2SrlEek8ytWgshV0d2O/4N4yJID3PfAfRZGXfnCvYdG6eFUlG+e2h58iYk06ZuZxoUkFLqJj0I/f+GmWUUUb5WHL0NU0jkUjw6KOPXq7djRi6w708ULKI/P5exHCAQOUMtnQcZXr+RNxE+OWBp/m7smXwxh8JeboYc9e3+Pbh3+OLpiY1Z91NtBfPYXpaGfGJS7HqzBxs2shp2cSmhtQLZ2n5Qs60Nw3b9+HeOu4efx1vtx5FSlpR9SCSWrEIotBkiFGTEafd30ZTKEBOWgYZhjyuKl7M640pRdrX67fy93MnwoZU3ti6ra3cc/N0TvafHNhPZ9RPgTNJd7QDJWGgtjFAbhFUjcmhpTnJjsPtnG33UZBlpTDbxpSqTA7W9iCKAjMnpdEebQOdHp3PRzIWJs2UDpZckuKlVT2/EEF8/HT/b2n2pVJLih35fG7a/dj408qgBaJJvvP43oG8y53HOunxhPl/d0y95Mc8yqWluctPznmaChoa4UQYo3Rprj8pEkRMxlFNIzP0L80sEIwMdUiJtkxUd9sHGvrJs7tQ3a0Y5t19QQ0WQW9EN3klRn8jvjd+gphRhH7SCqTcqgv2H+XiaKJKe7CTp468OJBqVejII3NyOjlSKmpOUSA33cwtV41h4+4mAuEECybnk2YzYNSLI3txUNTY3XZoWHNr7Cwza8bisBrocoeZOdXG/+z+BaF4SqDvTH8jyyuXsLR84cWHN5oQxByMSoKEtw9z5QwEUULUDd7rmpLAUjmVwNG3iNQfRnZm4Zi1EkF/8dB6QY0R7xyubaEE3HCuIposC7j9UXYd6xr4/HSLh1PNbqpLnBcd/6+ZmBjncOcx/njypYG2WbmzeOCGBRhtMfb1v8ObDTuA1Lm2G2wsr1zCH06+yk1VV1MZj6JzZhM6sWPY2NG+HvadNOPxBPj8XD1W31lUg4NWMYZfdZE39UrasvP47v7fEVcSCIJAjc5GrO4gQt0+YrEImpKAorHs6KsbNv6pvjNEcfGrdSeIxhWuX1TGgom5mHQfXtR3lFFGGeXj4rIZ+q+88srA/wVBwG63Y7WOPGGqS02NbCK5+eck4lEESUZ/dCtrlj9Ia2Yubzbu5JHyZWibnyIxcT4xvZHOZHTAyH+XrS17uLJoLi+dfYsMYyYLi+bw3R0/Hvg8HI9QbMul0dOCSTayNH8q2QY7Rms6dZ42ci25dHoCNHs6yEyzUGq2s9vfTKUtn3d6j2HQm0lIAtFkgDCtXFkwh0M9h+kOpVa5I8pgnpTZKGOSrNj0FuZl1ZBjdFDhKuFQ7wH+eGoDD0+9l7f8T+PrDWDSGbmt+ibyM/KZUp3FwdM9vLy9nqw0M59ZNQ6DLcxLrc/w1iE3WZYMlpUvIpdC3F6VAjmAbDZe1hB9QRA42HVswMgHaPa1c7DnCEtyFqD+CdF3Xe4woWiScWXpjCtLJ5FU2HG4A08oTu5lOPZRLh2NnX4yzxPiiylxBEFA/hBh6x8G2dNBwpaeWrYagdgMOhRVJBRTsRhSq1GCNR2lvwUd8953Oy0eJrbrGfTTViNI7//qEAQBU/F4otYilNZjRLf9CkFvxjjvbqScykv+ff6aSYgRXjm9ecDIB2j1ddAXdpNjSxn6kgRGg8zvXj2ELAlIosjru5q45apKVFVDGqHXIYAsiuTbc2jwtAxpz7ZkYHQaOdvux2LU0RPuGzDy3+XN+reZXzQDPe8vmifIetxvPE6iP/XMDx55E+fcG9Hnlg/2kXT4D24CwDlvDUrQi3vbM2Rd9+WLH7ykR5dROKw0n2gcdPBJkkhtU2rFWZZERCFVueXQ6R6um1968fH/ivFE/aytfWVI257OPRTrxlLhymBv+5GBdoNsIBgPoZN0+GMBQkoCT0kVFr0DKS0HNeTDWjMPyeIg2nmW9oSJMUVOFqZ1IW77GQASUG5xErnia/QmTRz1NhNXEkzNHc/M3Amk+wMkvF2YCqqJ96auRcHXR0FJHh2B7iHH6TK6+PGTR4jGU8mLT2+qwyKrLLS3IGeXEzPnjahUw1FGGeXTzSU39Pft23fB9nfF+WbMmHGpdzmisHp66Zp0M81qFqE4lNoT6Gq3UVPxOTYmt+HSZM4uuo7fNrxFKB7mC5nlw8YQBIGoIrCvM1WhwGYZaoD0hPu5q2gurb4OvpQ3j8TWp1ACboyVM6icdT2nwjEMBo3Hj72I97SfOyfewIu1G/narPtoD/Wyr27wJbqmZjlFtgj3Tr6FXW37OetuIt1gAzpZvbAMSRI5URvmxpIHqBbaEduPY+51k11YxrWVS/jN4afwxVKOgUgiyhMnnuYrE7/MOwf62XIgVe7GF4zT1OnnwTvyiSkx/nXCHdha6tBOHEZXZeb1VgMb9/fw2ZUGQrJCQlNwyg5EVSYuROiN9iEKIsZowZDfQZRSE1hV+XAvVUkSOXkBD/3JnjqKHfkUG0sQtA8XhmcyyNx0RSXNXX6e21yHUS+zcl4phnNhsoqYIKZFMQomRPVjy5AZ5UPQ2BFgxtjBfPxQ4tKG7ev720jaXJdsvEuNUdaj14Xp8iUpz0qJtYmObJLNhy+6XezAOsSsUsS0D1f/W5Bk5JIpSMWTUTpOEdn0Q/Szb0c/5v2dCaMMRUGhNzQ85S0QC8E5e1IQoLU7yEPXT0BRVaKxJJlpZrbsa2Hh5Hx08sg19JOCysz8KZzsPUNZWhFJVaEj0EWVYyx1dgW7N0q2y4IdEwIC2nnhCaIoIQrCRSMWFF/vgJH/Lr59r2KqnAbnFvW1RBRT6USizcfxvvMCsiOTtHk3oUSDFz94VcM5ezX9bz5+Tq0frBOXIJoHFzQEQaA0z0FuhoVEUkVRNSxGHcFwjE9zgYq4EiehDk1bmJUzG6PmYtM2N7Mzbia3TCMaAa9bwGQWyNObmJ49lQxKMOeqvNpxjCVX3Ematw/f3ldQAm70FdPJyMmiOmJAv/tFzt+DEvLiiHRQq2QRTvby0JR7iIUMdPeFOEOErOkLmdbRibliGoFDb5Bsr+OayYs41t9AJJkqqZttzcSi5BCNnxly7G8c7GZy8UnULU+Qceu3iFqGalSNMsooo3xSXHIL5F/+5V8AiEQidHR0UFFRgSzL1NXVUV5ezssvv3ypdzmicBsL+M9dbfR6U6F6ggD/cMO1GKJ+5hVNxydZ+OnupwYmLA5dOlnmTHrCvQNjLMpfSDg+KHUXTcaw6i0E46nJhIaG/eg7PDppOd7n/n2gVF30zF4MmkZjaSG6UBoPT3qA/z34c4rNFdxQdDchNc6+8zzlAK+e3sy0rLG8cvJNeiP9XFU6H52k49HPVPPqWz2cbEytRuw4DHMnZjNr7mx0OoHCuMo8ZynPn9wwZDxFVTAYdGw9NHRylUiqeL0qf1u5HP0r/0c0kXpxxk7v4oorP8froou3eg6w7swmkmqSmswx3DFhNf+37/e0+VP1jWsyK7lvwm1YsNHhibBhVxOhcIJrZhdTnmdHFi8+oVUUhWk5EznWXTukvTStiJ/ufYK/m/sF0qXMi47xLrkZZjr7w9S3exGASCzJc1vOML48nbP9jfz6wB+p9zQzJr2MuyfciEv8cOOOcnlRVJW23iCr5pYMtIUSIUzyJSqtpyrovN3EcoY78EYKkiAh6yK0eWODhr4zD/XgOjRVQRClYduogT4Sp7djXHj/n7w/QRCQ82sQ7VnEdj2N5MxFyir7yN/j04CAyIz8ibzVuGtIe7p5qNBjRaGTHz5zCLc/9VwVBfjandPQjXAfo0GQCcSCXFe9lC0NO9CJOm6qWcHJugh/eK1+oN/hOhsrFl/LKw2vDrQtr1iMQdLDRSKxNFUZ3paMc36MtaA3Emk8SrT5OABJXy/uLU+Sfcs/XPTYNU1FiQZJW3JnqqykpoEso0YGnTCiCIXZVr7/1EFCkZSYgCwJ/N3dMxi57pfLj8NoodCeR6s/VZI335aHyVPDz99OpQjaLXquX1TOExtODWxTlGPltmuuIqp5efHMDrpDfSy2FaZK5p2bA8XP7kNSNI7rl5JTOgc8LwIgWRzoXHmoqoovGGN26WzWb+3mncOpaIyCbCdXXmmgu8hEaU8P1vELCZ7ciX3ner6x7LN0ChESmoKAQGt9hPeS45ARwm60ZJzo6Z34a24gqaikWfSf6vM8yiijfPJccp/y+vXrWb9+PePHj+f3v/8969atY+3atfzxj3+kqKjoUu9uxNHo19PrjQ78rWnw1E436xsPUGRKpzcZHrIqUe9u4saS27m2+Fqm5UxhTdktTHLN4Nkzfxzo80b923xuxj2UOYsREDBIevTV80i6e4fUoweInd3PTEcF6+rXc7zvJPfW3ElHu8CvnmlBUYZPemJKnGgyRrGlgquyrqfImc/RvnqSqjZg5L/LzqPd9Hri/GTvEzTIKjpF4b4pNw+ZdAqCgF4SsV5AlM5i1JMZCKElokPatUMvMbYG1p7eMCAK2BvuZ0fL3gEjH+Bk7xlOuevo8cf41i93s/NoJ0fO9vEfvz/A6TbfRc/Lu+diXHo1swoGS55NyR1PXIkTSoTpj3g+cAyAYEzh1XeaeeaNOmJxhQeuG4/DmjKYIlqA7739M+o9zQDU9Tfwgz2/JCaELzbkKB8TnX1hbBY9Bv2gMRtKhDFJl8bQ1/l6UQwWNPny6038uQiCgMmQoNU9eB8KeiOCyY7qbrvgNrH9a5GLJyMY//z0K9GWgW7sYqLbH0fTRq4S/IhC0Miz5bCkdC6SKOEyOXlg6q28dxm7rTswYORDSsz9xa1n0dSRbWbE1AThZITHD/6RZm87Z91N/GTv7zCmDV1Nb+4KUGocx3XVVzMzfzIPTL0Vm8FKRIlddHxdegGCYWiuvaVmHkn7YIKVFo8NGPmDjSpK4OLvA0FvQrKmoUaCBE+8Q6yrES2RQHZkDPRRVY3TLd4BIx8gqWi8ua9lREsnXG6UiIl7xt/B+KyxCAgszr2CN3YO6hjMn5zPy9vrh2zT0hUkFFb4Xe1vONx1gs5AN/H+9mFzIKXxAAXWJN2ulF6OY871WMbOA0FAF/cxs0RPT2eSdw4PhuS3dYdoOC3hFWUCR7YQ19nxXPEY7+TdzcZDCuvqNvOL/X/gnZb9FBTIpNsH3xcGncTKcXqS7akFhJjPzY+eO8LXf7yDJzfVDVm0GWWUUUb5uLlswWONjY1MnTooTDZu3Diam5sv1+5GDOe/0N/FF0pQZs3n3/f8Crt+aD7h6y0bMBt1TEmfxRVZqyi3jyEk9NLqS3m6K9PKubpwGVYycPYsZGX6A1i759BtzEOyDBf7kmxpGM+JCG1teZtMWzq7j6eMZSlmTa2AnEexs4BIXGL7G3qKM53sbjvI08deojtw4QoJmgaqpvJa3Vb8ssyTR9ZyTeViIGVAXFd1NXZdnPuurR6yXVmejYJ0JxdMhJf1dAQ7hzTl2bI5424a1vWMu5HGTj9ZLjPXzi1h3sQ8dLLIS9vqP9TEySSamJk/mdsnrOamcStIqskBxWen0fHBAwjw0tsNvLitHrc/yslGN09sOMWKeal8S1UXwh8bWgvWHfHijnk/xNGNcrlp7PST6xo68Q/GQ+gvkRCfzt2OYnt/pe6RgsWQpMM39FklpheRbDs+rK/ibiPZchS5bOZH3q+UX4OmJkk2DxdgG2U4ApBvz6bZ287KMVcyJXc8u1oPDnGuiiIEwvFh2/pCcRIjuLQegF6U2dt2eFh7c7iOeRNzWb2wnNnjc5BEgTZ/Nw2eFtJMTl44sYHjPbUfGIkTMuWRfdP/wzxmJjpXLo65a7DNvI5o/Lzykjo9kmX4s180XVyMT1PixLsb8Wz9A9HmYwQObcKz7WnU2KBTV9MEvIHhzojznTKfRhIJjWdf6eTWmpX8y6K/p8I5hgWT8zDqJSaUZzC+LJ1gePhcKqEkiSuD7QmdflgfyZZGX0glhoGM1V8l2nQc//4NRFtOEtzxDNrBF5GSw6sd1DWGyHIUE5j1IC+7K/nnZ5t4YXcfRaUaSVVhdfVSKtNLEYxBPn9XGfevKeVvbqzm28uNpO37xcA4vpxpNHWkFh62HmzjRJNnpMq1jDLKKJ8CLpuhbzQaWbt2LYqikEwmefrpp7HbL65s/sMf/pBrr72WFStW8PjjjwOwc+dOVq1axdKlS/nBD34w0PfUqVPceOONLFu2jG984xskL/Dg/iQoydTx3gjy5VPS6Qo3EYgFOdhziqvK5gNQ7izjyrTb+eETdfzTz3exaW8DZwOncBmd3Dvhdh6o+SzWrnm89EKSXzxXx/xJhZjscQoKRZr9fiSzDWNhzXl7EnDMWEEwlPJUm3RGrPKg6nJHf5Ivz7yPAlsOABOyqnhg0i1s3+2lzxtF0YXZ1rQbAK/WTkH2UKdETZmD1mgqN80d8YAgoagKsUScW8av4tbxq6ix5mFTdUytdPHtB2dz/8oavnr7FP7mtkmENC/teYWoi25GNA6OnRhzJRmWoaHtLd52qtKHhz+PyxyDw6pnQnkGO4500NYT4J5rx5LtMvFhYuSCapBfH3wGnSSzvvYNjnXXIiBwU80KMnQfbKAFIkm2HhgqvhRLKKiqxpgiJy6zFeE9ByIIAqYRvML7aaKhw09W2tB8/GAieMnOj66/fUTn57+LzazR4x/qGpMyS1BajgzrG9v9DLryWQi6j/4bCYKAXDqdxLE3PvJYnwZEUeLFk6/T4Gnm5dpNbG7YwcneM3QFB1O9NA0qCpzDjImrZxRh0I/s2H1FU7G+R91eEASmZ84inlB5fVcTfd4oD90wgXGFObhDHva0HqQivZTFpXNJXiBK7Xw0DUKWEgxXPoLz1n+BiasJ6Ya+a1SrC9cV93D+C8RUPhXJnv0BB5/Av29o6poaCaD4+wb+lmWoLBjuRJgzIfe9C9GfKqIJBYtFwhOM8fqOTr79y73UNnvOpZsIvLDlDPMnDdUCkSWRjLShhv3BuBu5ZMJ5LQLRqbez+biPQnMY1ASx91RGiDUfo8w53PlSXWanPtTKabOII9fEg6uruf2mNNqVU0zMrmbDmbdYV7uJencz9aFTxGyNHIy8So5TRDTZ0WUWIy75PM8c1VDPe7TuO9WNJH2KBRlGGWWUT5TLNgv493//d77+9a/zzW9+E4Dx48fz3//93+/bf+/evezevZt169aRTCa59tprmTNnDo899hhPPvkkubm5PPzww2zbto1Fixbx6KOP8p3vfIfJkyfz2GOP8eyzz3LHHXdcrq/zoUmzRfnGbdX8YXsHnkCca2flMaUoyb+d2gOAKAh44yE+P+M+xGAmf9jQwPIlmUiySnu7Sn9zGg2Gs4xNr+bJF9s50ZAKnz/b5uP/e/oIN95gYq9/BzcXrCAcDSM7s0grm4imJBFkPYEjWwjOuAKA1ZUraG8OsWpeMYgKx6PbeWFPHfOLZrCgZBZn+hqJKAkaW1M5Z9Hk4Mtva/tWblhyE32tOTS0RKkqN2HOdrO+cT8Ai/MmYz03xzJLeoo1HQ6TE1G08nL7cWxmmar0ChZm5BAiwBtNm9hUvx0Ai87MV697hLR9m0mUzIO8akrNIpOyx3Gk+wQACTVJTVYljd4WTvWmXtQz8ydT4SrljRP9bNqTig7xh+I8/spJvnn/TIQPsaSvE3RIosTLtW9w7ZhUHXD5/2fvLOPkuK68/VQ1Mw8zg6QRM1gGWZItk0yxZYrjOOjNZjebBe9u9k12N7vJQmDD4DhmtkwySLZlSxYzjYaZZ5q5q94PLfVoPDJJI3Q/H/yzblfVvdXTVfeee875H1FBha0UUf7kx0GhENBpVeMiN3JdRhbX5aBWyVxTvYwXD7+e+mx11UosCtv5Xebqc0JTj5clU8bWRfDHgjh1p++FF6MhFJEACf2niAw5xxg1Mv6wQCwhozomaik6C4nufQ3JP4xoTG5WxNv2ILl7UE1ZPmF9K7IqiB1cP6afNCcnnojjifpZXbMCAFFQ0OPvwxfxw7EsCllOevXvubqWDTs68AWjLJiSg92iuSDUv2fnTWVP7yGkY5bvvOw5vPhmH0dak6HzjZ1uugb8fOdLlXxhynWE4mH0Sh2vN77DLZOv/RjN/VHiCZE4Iid7CQdCIkZXEZm3/gMJzwCCxoDSmkFImwEfI/QqiIqTV9b4kMaFTqvkjhXVvL2zg2gsweJpeZgNakRR+Dh5gYuaWFxi/lQHO/Z6eOuDZMh+UbaZN7a0sas+uYk1qdTBsjkFbDvUR7ZdzxcuLyZDM0ymwZmqEPRK53amzbuH3KlXEvR46UtYeL9V5ttXOQmYIoiBk/cfkLzMq8vgg739AORlGJk51cAv9j0EgEJU8I+zvsyrHQcotOXy9MFRbYg3m97jC5OvJRALsnOwgTnF85l6wz8iiAKv7epnT0v9mL5qimwkPqVgcJo0adJMNGfM0C8tLeX5559naCgZAu5wfPxCevbs2Tz88MMolUr6+vpIJBJ4vV4KCwvJz88HYNWqVaxbt46ysjLC4TBTp04F4IYbbuCnP/3peWHoo9WRO7ieb+UHkTQW1P3voai6iVA8TLWrDKWoYGvnblpHOrg68y7mLg3xWvuLxKU4hbZ8FtpXMuCPsC/YxsHmsWX3ItEEiqiZBQUz+cmWP7AyfxZL+luR9r+TOkZdMYuI0cq35tyHXqXn3X0+ls2C66/M4d8/eAyAt1tHhZ1m5E5hZk0G7X0+TAorGoWaSCKKLMs81/w01c4yvnPLajpHmtjuG+CWyddgUGgoEjT09B9Bq9QQ8Tj4v3fdrF4p8tDRnxE7FlqnFJX80+K/ZCg8RDyq5LrCmwnj5b2ejfypaT33Lf4yZo0etSggSTJ31NzC4rxOesPdRBNRfr71ISZnVnFj7VXkmDLZ0LyJYW+IN7aOLcUkSTJDnhBFrk9e8ukwcFfdTfx82x95/vA6AKZlTWJe5uyPNcSVSpFEQsaoUXLnimp++dy+1GclORZKsk1oj0VPXFd1BbWOCobDbhxaGxmazE+t5p/mzBGNJegbDpJh+1DofixAnunTKcl/HMqRXuJGOxeCnLZWpUavidPnjZNnS+ppCAoVipxqovvfQDvvVqSQl/B7D6GacuVJBfpOFUGhRJFVTqx5G5oJ3EC4GBFFBbfUXs1vdj5O5NhGbKWzlMWFc1PHCAIcbh3hhXebmFWdiUFn5f293Qj7oOo+Oyrl+ft7PK5X88VpN9Ph7UEhKqgyTOdfW8emkORmaukJt/H0rrVEElFyTJncNuU6dEo1jI/w/sz4lQ5UDidaRy6SoMIfVX6skQ+AQoVlzjW433861STqzaisWZzoLw5FEgy4Q5TmWlAqRQZGglhNms+1SJtJr0IO63h/16i3fVKpg2c3jKrZv7q5le/fnMfVlhHEkX0k1v4C5YIbuL78UnrCbnyRAJMyK3mldSu9vl6+WnIlmWGJ2ypDHDSG+OXWp7ksdzor86uJdYyK+qmKJrM3NoSU18OaysmY1RZQ+/jV/l+mjpmaUcNrH4Soq1rMULyJ64tvIkGMrf2b6AsMsL1rL9mmZOUWURRIiBokSWZmZQYbd3XR0Z/UmCjMMjG9MuOC2HBLkybNxckZM/QHBwf5u7/7Oz744AMSiQSzZs3iRz/6EZmZHx0Op1Kp+OlPf8of/vAHli9fTn9/Py7XaJhdRkYGfX1949pdLhd9fX0nu+RZRzPUi3/nG6l/RwHV9lf5ztx7eadjBy/VvwWAQ2fDZA/w+x2joX9tng7s2i3kmbOJxyNo1AoiHxJysRtNPHwkqdr/asd2MqdeSpl3Mrq+DoI5xbhzy3AH+qkxOBkMDnL5XCMGAZqCrWiVmpTX/qqixSw2l6CPwNSpBjr7Xfzy0VYeuP1L/GnvU/QHBim2FbC65ipGhlpwtB4iblXz5P61yftUavj27Lu5q/JL/PaJdsx6NUe8B1JGPkBcitPsbsEmlbB9wwj9I15sZg333XQfA3ILgbiP/oE4/mCUygIr/e4Y+xqj7FO8x0gomeO2vWsv27v2clPtVRzor2dh7kJsRg2hyNhUDZ3m09VAl2WZKlMl/7z42/QFBzCqDOTos1HLJ8/1DMcljna42XKwj9JcMzMqM5he5uB7986hqcuDw6KjNMeMTjVqCGmVWvRKPX4xmaspp1355wXt/X6cFu04wycQC6KdgBx99Ug3CaPtkw88D9CIajSaCD3uUUMfQFU2l/B7DyHoTMQbP0CRW4PCWTTh/SsySkm07Ya0of+xSHKCN5reo9RWQJWrjEg8yuaOHQyH3OSa8lPHuaw6YnGJzftHtU4mlzrQqhWMiSM+z1CKSrq9faytfxOTxogkSajzrGhUCiKx0blvwWwTj+z/Y+rf3b4+Xjj8OvfPvH3CxhKLycT4DGU2JQkpGsa66CYiXQ0ozU6U1gwSkQAc20uUZZnGjhGGPGGWTs9DEAR2N/Tz3p4uFtVlf/z1L2YEsOn1OCzaVHTcsDeCy6anbzg5b14+xYFx+59IDHVw/JcQ2Pgk5uV38seWd9CrdOzuOcA36m7kP7Y/zFExTFOolUWFs3l0x6MAdEbcdM+4BEd+OZqeVsI5JezXq9EZ7JTGzUTxU2pSQXcrtxYt4lCgjzprEWF/IQ/taCPHbqGxy8G2g/1oVApWLLmGbuc2so1GbGoTV1VcSpe3l0nmSYCMRafi7+6cSe9wEEEQyLLp0JzHG21p0qS5+Dljhv73v/996urq+K//+i8SiQR//vOf+d73vscvf/nLjz3vgQce4L777uMrX/kKra2t4z4XBOGku6PCZ1Q7cThOXT364+g+0j2uLdFxBOW0JanSdqIgclfpcg6Hxm9OHBw8RK7Fyda+7Vx/+Y088eqogOGUSiui3pcy1mVk/tC0HpPGyJ1zbyAcjzEc6CPL6OCfN/8i9T3dULWMEr2Lmyet4uE9z/Dl6puobGtE2vo7olojIf8IX7r6m2x0x+ge9PONWffij/kwa4z889s/psZWxLKyWby/d7QSQCQe4fFDr1AUvJJAKEaGTUcwPr7usEay8ssn6xnxRRAEuPaKTN7oeo364aNYNCZWFKzCrivgd2sPsnh6Hpt2DXHNtVfwXNMzqWuYNaZUWKfZoOKL19byr3/clqqQlJ9hpLLQhsvxaYI4k2RgoZbyjz0mkZB49PUjPL0+6WXYcqCHN7e1829fW8iMmixm1GThDUTpGvATiknkZBiwGDTs6NrHjzf9OjXmS4sXsKbueoyaTz++s82nfR5crvECkBcK/d4IhdlmrNZRj74sy4TiIVwWKwrF6Xmt1Z5+EuXTUOjGC0SdDroJvh6AGT0qVZDhMGO+D9ATu+wOAvXb0FXMQFc67TO/W48z9rpjkQxV9O99BYdFiaj+DMbVWeJMzQ8fx8merXa3lymZ1TQOt/L8oXXoVTqWly9BlqUxxxdkRijMMtHWmxQC1agUrL60nEgsQUHWmU8lOdX3gjvoYX9/Uq3cF0nOHwkxwjWLS1LvXQBRN75qSeNwK+FElMLTfCed6tjDPQN4t72EoFChziwkONhJwjuIc8X9uCpGrzmjKpO9DYM8/uZRRAGKcixcu7gUURSwn6Oxf1Ym+nkYCITxCL2sXJrBb57wIcmwaW8Xd11Vw29fPIAkyZS5RBKtHePO1UciRBJRIomkAKXCPcDXpt9Cf8SPUqGkxd1BKJZMRyyzF/HzIy+jEpVkuVz0jOwh0Bfk9tqbeOJJPwadiswVNsobDjDV08PszCJ81kv46xdayXEa6BsJsu1gMrw/Ekvwwltd3HPTHCbZo/hUSh47so5bJl2D3T52bs/PPj/Sty60+fpCG2+aNBcCZ8zQb21t5Sc/+Unq3w888ABXXXXVRx7f1NRENBqluroanU7HsmXLWLdu3ZjFd39/PxkZGWRmZjI4OCp4MzAwQEZGxmca39CQH+kMeDoUtqzxbbmVZFqLuW/6GsLxMDmGHKSIlnBw/AK+wJJLj3+AgeAQGWVR/uGuGTR0+ch2aWiL76XR24lLbyeaiDEpswp/1M+RgSZUChWd3h5mZE7i3zb9YsxmyHNH3uCfF3yNySoTX637MuWxOG5LBTtzpjDgTzB3mpL8+l14HQqebdnOV41388jBZ/jKzDtx6O3sH25mcu7kcWNt83SxoCDpCW3r8XKZeQo7+/aMOcYgOBnxJUt2Ta2ysy+4kfrhowB4Ij6ebHicO8q+hEGvxmrU4AvG2LtDzy0z19AXb8Gk1aFX6XjhyOtcVXEZWZoc1BlafnD/PNp6fRh1KoqyzCgkiYGBsWr3p4svEuf5d8aW+OkfCdHUMYKYSOCPxPn1iwdSOgpVhTa+enMFv9nxaMrIB9jQsom5uTPIVedN6Pg+Kx83iX6a58HlMk34d3y2cLlM7DnSh8uqw+0eNRpCiTAKQUEoFAdOXdBTjARQxyIE0UBovAL6qaLTqQlN4PVSxEVEpY+m3tCY7wMA0QLVVxAFop7xNaM/DVarfvx1P4RgzqLvwG6U+ePfLWeD030eJnosJ3u2RLWS/sAgu3uSoeyBWJBnD73Gt+fflzperYYBd4jaUicL6nKQJBlRFNh5uI9rFhWf8Wf2dN4LaoNIjaucXv8AdZk1JOQEyoSJPc1D3LGimkg0gVolYlSO30TOMrrQKjSndX+nM3aTIKDOKCTa30akezQEXdQaU9c0mTQEIjFcNh3l+VaisQSluRZGfGFkmXM29o+63kcx0c9DVPTx822/xaqxcuctywn71Gg0CvIydPzVbdMZcIcoyFSibMolPtw15tyQZjT6yqI1oxnsQaNUcXDoKEcGGrmp4iaWFi3kjeZ3GA56KLTkcXSoicbh0YR9g9KEWhXEF4zxs2eP8P/uuY/C4W0EreX0RkzccnkFWU4Dr21qGTf2uEdFTo6B97welpUsIV+bx+CgD1EUSZxHVS4utPn6fBtvetMhzcXCGTP04/E4kUgEzbGXcigU+ljPUGdnJz/96U95/PHHAVi/fj233nor//mf/0lbWxt5eXm8/PLLrF69mtzcXDQaDTt37mTGjBm88MILLF68+EzdymciYc1HPXkx0f1J4TmF2YV+7nW4/SKd9VbC0RjOySL9Ci9GhYVpGVPZ3b8HAL1Kx+Wli3ip/i2uKbmWpgaBhbVqnl5/FK1ayWXz87BYJe6bPpMDAwfZ2rUHq9bEN+bcjTfso9hWSCAeGiOqd5yRWABZ1KPRiPjiFn7wej+BcDI8/t398I3l01ieryUU15LwuJivuANfn4kbK69HJeuQhPGJkLWuCmRzN39xRyU+t5IsrZYvVNzGpr53kWWZuRmLMGt0qRSEihI9aweOjLmGjIyg87NkTg5WnZp7rq7hkXVHONgoMbumnJUrSwnjZcrCWoqcubiHk4ZHtlVHtjXpCRSEpCCVIMBwYojeQD8ahZocQzZa+eNLJH0sMieNHpFlGUGAPQ2DKSMf4EjbCL0eD+6wd9w5vqgPJt4xm+Yz0Nzjpa7MOaYtEA1+YomuT4PS3U/cYDu5ONd5iFqhBtFLt/vcVSsR7bnEe46cM0P/QiAhxdjRvW9ce39giFLtaERSW6+XVz9klNjNWi6fXYBJPXH6ChONIECFowSTxsh7bdtQiUpmlmtQKiP8+bXDqXf7rCl2Lq1eyIaW9wHQKNTcVHsVCekc1ihPxDHVXcbIpmeQgl5AwDTtcuQTNnkFAZDh4VcPEz9mBL6/t5uv3jDlc523HUh4iCSi9AX7ebL54VT7Gs2X+O3jSceAWinyo1vuQHzn50ghPwgi+vnX8+RIclNFp9Ryf/FSVO+vRVNcw/6+Izww42t09vuosE9CXapjJDrI5blX0un9I8FjXv6ZOVNQS2YKs4NU5Nsx6JS4IwrM2gJ+vcHN4Y5k/wpR4Kurp9Da4yUaT/7tVs4vQqXW8eN1A5Tm21g4OZuwHGDn4B4aR1qZkllNuaX09NYdadKkSTOBnDFDf+XKldx9993ccMMNADz33HNceeWVH3n8kiVL2Lt3L9dddx0KhYJly5Zx1VVXYbfb+eY3v0kkEmHJkiUsX57M6fzxj3/Mgw8+SCAQoKamhjvvvPNM3cpnIhgU0E29DkPNEqRYBMGUQSgmAjGOdgwyaXaAn+x+BUmWMKj03Dv5Hsr1UzGbRPp6BLZtjpEtXs6rm90snqYkKodZOiubDdt62LRjiKsWFFM/1MDa+mR5qj7/AI1Dv+PmSavY13+Eq8uvwKG3MRQcSY1JKSpx6F3879bfIsgC1zruIRAeu8B/ausw362uItRWwk/27E+137GyElEQ2Xqol2vmXMO6tteIJmIUWfOZml3LI3ufY1XFFezcZSPLYaQoJwNb/1JEQWD93iDz79Hypeuq+cXTB/B44zh0NoZCI2P6NmhVPHvkUe6bvobCsjD/8KU6unqj9AwEiYQFHMdK76kUSoKxBP5QHKtBRUQI0uHvoC/YT6E5D5PWwA82/pS4lLy3MlshX5l2FzpOLezQpFOyYn4RL78/uoC2mTTkOA0oFCJ7GwfHndPYEqHcUUzD0Og5giCQoXONOzbNRzPsDbO3aYhcp4HyPMsph48fZ8gTIhRJYDeNzcUPxAJoJ8DQV3n6Lwi1/eOIgoheE6O1NzFGef+sjsGeR7xl51nv90JCo1CTY8oc8z4BsGhGvU2SBFknpC2JQjItvzjHjF6rPK9z9CVZIhQP88Qx7ReAX27/E99a9lUONHlSXuRcuwVB1HDPtJuRkYnEI2zu2MlNNVefq6GDSk2gYRumSUsQVGoEhZJg40705TNTh8iyTEu3l6JsMzOqMhCE5Ibw5n3dzJ00Pvrv84JBNd4QVitUuD2jmyTRuMRjeyTuveYfUPq6UWoMPLJPpiinnMV1i3ANNKLe8DSaJXcQVBm4Z9rNPHXkGbr9vdAOi/MWUKNfgFops6xsMQpBgSiItIy00xQ4wPK5M3nsjXoGRpIbADVFNsqKMjjccSyFRJJ5cWMTi6fn8ta2DqZVuBhwh3h1cysAO+sH2HWkj1lLQrzQkPz9bu7YwWXFC7mh9GqQ0rn5adKkOfecMUP/61//OllZWbz33ntIksQNN9zAjTfe+LHnPPDAAzzwwANj2ubNm8fatWvHHVtVVcUzzzwzrv1c0zIYQa83EImYCUXiZElqNuzqYuHUTKbV6Xi++Wlm504lw+igfrCZx488TnX8GgzZVnp73WzancwHK8k1M3uKixb/UYKuo3z19rnIIRM9nhG2NG8c02dClpDlpHCdWqFhzZQbeHz/i/QHBjFrTNw25VqahzsYCXkQBZHYSTwJ8YRMvy/M+3vG6gY4THqee7eR9l4fIx4dy+bcgVIl4TDYeO5oUhxpc8cOls+/jd893YQvFEUhCvQMBHjglqmoRQFrlo9/vL+OnmEvk1zX88sdD6VC26dkVjMScqNUKDg60sgT+9dS5SzlqvLLeWljH50DPr5xwxREAXYc7uV/n9iNxx/lyoXZeKzb2Nt/MDXWy0sXkWfOotWd3JFvHGmj3d9FpbHy1P6YMqyYU0B+hpGNu7soz7eyqC4Xo0ZJIiExrcLFziP9Y04xqLV8efpt/GrbIzSNtGHSGPni1FuxKx3p8nqfko5+Pz9+YjeFmSZe3tzKnJpMbrqk9LSM/SNtI+S5DOOuEYgH0U2AEJ/S208kq+y0r3M20anUmHSMUd4/m4jWHKThF5ClOIJ4ftd7P1dEpRiLCmfT6u5MCZ2W2QtRK0b/XoKQ9N7fd+0kfKEYiYSEUaeiMNtMOJrAcB6LgSlFJRuaN49r3z+4n2/ctJDeoSA6jYKCbCOiCQZDw8QScSxaM4sKZqMQFZyzGnWCAsvsVQy9/nvinn4QldgW34SgHA3dEgSBbIcebyDK0xsakCSZGVUZTKlwIXyaerAXKYqoieUll7GueX2q7dbaG3jhuVEnwLWLS+js9/PlXx5GFAWWzbYQiSfwD6tw5DhoUdqJzZ5JhlGFRQNRKYzTYKPH34eMzMbOTRRWF5FIwAsnlLuF5AbaHcW1KSMf4FDrCLWlzjEiyL1DQb50zSTyM83kOA386x+3jblOY6eHOZGxm/gbWjdxaeFCrGK6dGiaNGnOPWdsdXXXXXfxpz/9idWrV5+pLs5L8rJt/Pdje+k9phwrCvA3d8zgDy8d4o5bHNzhvIENzZvZ3r2XKZlVTC9dRKU1g9eb3iGY6WfNzXXIskCOw4hWK5OnsZPrmANBE79f18ycKXb0Ki3eyNhcJp3CgIDA9q7dzMubypemfwF/zI9FY6LT04v3mNCRJEvEdIPjVI0vm++i3z+CWimydFY2KjW8u72fuCRh0CYXlX3DIZ5+LTkx3rbclCr35DI4KMs38fWbJ5PhVCGqwxjVRRBRMOiP4Ev4eeLAo1xSPAczOdw6eRWReBSFqKDD000wHqbPP0gsEUOSJQ4NNDAS8vDlW+/k3359kHAsQSwu8+8PbU+F0LkyE2zsOMiJbGjexPXVV6YMfQBPxMspOvST36tKwexKF3NrMpElOeVhkmWYUuZkemUGu+qTxn5dmZPpFS4KrTb+YsaX8cX9aBRq9Bg/12Gan4V4QuLXaw+wcHI2k0schCJxHl/fQEGmkbk1p+4BO9wyTJZ9vBcpEA2iVpymRz8RRxHwktCZT+86ZxmNQoNJm6DbfW4MfUGlQdBZkIa7UDgLz3r/FwKyLLO9ay/XVV2JJEuIgshgcBhfNMBxgXhBSIY5v7CxKWW4iKLAd++YiUpx/hr5ADEpjkE1XoxRlLQ8/mY9oijg9ke4ZLaTkHMv+ZZsjGo9B/rqmZJZTUI6d6knxCIIShXGuktR6C0gJ5BlGfmE8pqCAAqFyIYdo6JyO4/0U5ZnRf4cF9iLRRU4IrXcWV5EQowSDSkZatVj0A1iN8uYDCoSkpzaSJckmXVb2nng5qlk2LV876GdKbV+jVrBXTdlIRg8xKQ4d0y9gcf2JUsWdwe6qTQUjetfr9ah14uY9Cp8wdG0xL7hIDaTht6h5Pptdk0WD792mKZOD7cuO7nD4MNzuyzL6Uo7adKkOW84Y4a+z+cjGAyi13++cpXaegMpIx+SUZNPvdXAd2+uIagL8tvND6fUYnf3HCQQDVFoLuCD7uRO8cGhQwAoOhTcWbeaP+5+iivLlpCpy2LSol4qM3LIjFzJnw8/kurDprWgjTu5e9qN6FV6Ojw9vNbwDlfnr2Z/lw9BNjOjqhyXfhsDwUFe63yRO2+6mfp6iaGRCHWT9LRLu1jomMW3vpLN2vrXCcfD3HbbUhLeMMvmFlDfNpyKADXoVOTmKAkeCKFSqFhSNJcX13cxZZKWPx5+nr7AICa1gVXF1zPQZmDBtFKWFM+h3dNNsa2A3d2HODyYVFSekT0Fq8aGU2cjEBvdXe/x9yOow1QX2dGqFPQO+1JGPoAkjM/NTEYJjF08TUR9dFmGRHy828ioVvDla2vpHSkBIMumQy0m+1dIKqyiLZnnn570PzXv7+9Bq1IyqTjpDdFplKycW8hjbzYwpcSBXntqBumB5kFmV44X7PTH/Mla3KeB0j9CQmeACaw1fzbQKDTotFG6RmJQfG6U70VrNomBlrSh/xGoRBULC2bx252Pp1KSCq25zMubnjpGkpJRMCd6JyVJ5tm3G/ir26aPu+b5hEpUsKhoDvv6DpM4FuWlUWooNVVgmqOmc8CPy6qjukxHQjeTpuE2WtwdFNsKUIpKFKLynHn0Za0R387XMFbPR45HEZRqIj1NcIJGjizDgeahceduO9TLivmf39+81x8h02rhcKvEsEcgP9OEx+fnplVOGjz1ZOtzeG3d8Ljz9jQMUJhtSBn5AJFogkOHo2TWjBCMBXm94V2WFM1hffMmLIoMhLCFDL2D/uDo32FZ6WKean6Em29YztqXfQy4wwBMKrVzsGUIUYB5U3LIcxnZtC9ZSamhfYRpFS52Hx1IXaco20xMNTYVcU7uNKxK67mLNEmTJk2aEzhjhr5Op2Pp0qVUVlaOMfZ/9atfnakuzwtO3B0+zrA3QkyhYyjQnTLyj3N0qJlgfLyydUJK4I34mZ8/kzZ3J683vgtAZ6CDGnklt5bczWCsC51SjyLoZGRYolXRwNSsGlo9nazMu4GHnuoldsxAfe39Lv72nvt4ouUPBGNBhqUuLp89nWAswlMtj1DpLEGtEvjR5t+nxvDwvqe5d/qtDPti/OWaqTS2+zDqlFQXW2mN72d1zUoMaj0xjxmrOchrXc8xcGwy9UUDPNnwONdm3s1z69uI5XdxeOgIe3sPc1PtVVxXtYJAQGLLDh+vbQty3aW3sa736VTfClGBKGlYs7wKETDrNSlhJoCQR4NFa8ZzgvBdpbMEnUqLIAgYVXrWTFlNlibrjIXM+yNxHnr1SMqjP70yg7tXVpPOxj81JFnm9a3tLJ2WOybEPsuupzTHzKtb2rjxks8eHh+Oxuns83Pt/KJxn/ljQZw6x+kMG6VvkITuwsnPP45aoUKtCtMxMv6ddbYQTS4Sg63nrP/zHQNGsgwu7qi7AW/Ej0JU4NBb0al1qfeaKILbfxIBVm+EWFxCdR6H7ickMKh0fH3O3XR4u1GJSsptpWzdFuWl90aFW++yFPKO+wX6A0ldlPfatnFD9XLyrae/kXuqBCQ95rpLGXrzIaK9TQgaPfZLbkO2jI5JECDXNT6krDjHnNRSOJsDPo8wGzX85oX9dA0klfAFAf76/iJ+tjtZMSjHlEVB7pU0d3nGnFeSO1pC8kRG3AnsiThmjYk2dxfLjIu4b/rtxMNaBkbC3FlzF32RTvpDvTj0drZ37aXXP8BTjY9z9eK7ePKlLhbNyKBV3sGlyw3MypxJQ4eP3z5Xn+pj55F+1qyooqrUxL56D3VlTmbXZCJrfAjKKIcHG5idO41prskI0oW16ZsmTZqLlzNi6B89epTLLruMhQsXkpX1+RKcybTrxxikAPOnZKPWSOiE8SHCWqUGs9KKQa0nEB2NBMg35zAYHKbYls/mjh2p9jZPBwvLwzy71k1C0hGJxinKCXDLsmysYh3uoIdV5VewftNIysiHpIfnrS3d3D7vXkKxKE2tIb7/zB7WXJtPuaMIo1rP3r6xofAAbzW/z8ycOh5q+AlVzlIm5V7Gr589yBdWFaI0B+juUPL7tS3ceFUGA71jPRcJKYGkCrDtoJt7pk7n8FBy4fZG40Y0zkoEVZT5s01MD5l54qVWll9+Oe3eZN7/qvIrMYs2LPqkt9VhVPPFVbX8fm1yjK+928dff+mLbOh4mzZvG9OzpnB58SJMopkpS2tRCAp0GM5YyHxSdX8oZeQD7KrvZ1qFi+I86xnp82Knvt0NQH7G+IXxvNos/vR6PSvnFn5mr35Tt5cclwHlScKYA7HAaUd9KL2DSLoLrxSPRqFBVHlp7z+9jY7TQbBkkmgYn6OdJkk0KqFTaSm05DES9qBRqjFrjDgUDo5HrUsSlOSM32haNC0XtUo8z7VBJDq9vWzt3E2XtxdJlqjL6kErTBpzVELtTRn5x3mt8R1m5k45m4MdgyxDQJeH4+pvIofcCCoNca2TkKQac0xproWibDOtPclNaYdFy9xJ2eezRuIZxxuIpox8gKpCK+92vJear7t9vSwqS+Co1zDkSW5iFWWbycgQMZrNvL1jbMm9ukl6VFoT77VtJduYgVal5097niKaiGFQ67nZ8gViATMf9L42Ju0xLsXJyZP5i3tK0OolYtFK9EojI4lBnA491y0p5YP9PfQdi9JU6IK8PfQUmdVOptRci1mhBGysLFjGyqIrkBNCOk0vTZo05xUTbug/++yz/Md//AeFhYW0t7fz4x//mEWLFk10N+ctSqXA3VfV8MbWdjz+CAvrcsjPMLJ/ZA+hWIQZWXXs7N2bOv7W2ut47MUuVs+7nU0Db9Hm7WCSq4rLyxbwP5t/y30zbx/Xx0vtz/F3X/wrht0xtBoFzcH9xIQgLkUBIiNsbt+DL5g97rxwOMFzG9o4eCyUUKdRojKE2d1ykLun3kS7p2vcOSa1AZWoIBgL0enpRZ2rZ/ncIlpaIugz/MQkHfGETCyiQK/SpUrYpL4PWYtJr8aus6baZmTOIC8vzsuNb3KgtR6X3s6aW6/D12vhzklfQIzryTUmy+0dnzMFAVbMK6Iiz8qwL4LdrCEaS3BV3nWYzCJmpR4pIYMEBkxnPGReoVCw80jfuPZd9f1cdwpe5zSw5WAv1YW2k4rumQ1qSnPMvL27i6vmFX2m69a3jVCYdfL8+UAshPY0xfgU/mFiudWndY1zgVapQRZ6CUQkglEJvfrse35FcwaSuxtZlhCE89fzfC4xJKyYVSJ2rRURBUJUxYmp6aKYfD/+1W3TGfaGicYTWI1arEYNCQmU53EquCgoGAm5OTrUnGrb0rmTa4sLx+jISIxP1YomYgic29+MjIBftILBmmz4kIs+kZDpGQxw8+UV9I8EkSSZTLuelm4P1YW2z61HP/GhXQ61WkFECqf+PdlZSyIhcf+NVYRDChIJmcOtQ/S43ZjtEe66tpRX3u0ikZC5fEEGVleII+4ewvEIq2uu5hfbRwV/A9EgzzQ9yR0V9xDrGh+9NBQe4MkDLwGwrOQSEh1V5GQYeGZ9E+FonCvnFmI2qBD1fnZ6NzAccjMccrO7dz8r8rOQjmv3SHCe76qlSZPmc8iEG/p//vOfeemll8jMzGT37t38z//8z+fK0A9Ho0iSxE2XlaFUKOgd8lOQY+Sn+9/EG/FxTcWVzMmbTiAWxKV30N2mYlaNgsajPmrtK7hmsh69Vo0oRMk1Z6NWKHEZHAwEksa5RqHmluK7+Lc/7Mbti6BWityyspAjiT1s69rFt6d/m46DcaaWm3h3Z8+YsS2elUl92zAmQwZlhQacToED/m3MzZvOoCdAtbOct1TvE4gdFxIUWVa2hId2PcXM7DquLL6C+vownf1+3tvTyerLiikpUXDN0jze3zbENVddx5MNT6R2tC8vuJwdu0KsWVHBEfdoVMLsgkm8cORVjgwm6+EOBIf55Y6H+Jv538QiTEGnElEKAh/eGNeolbjMGlzmpGHmDcV5e3cnO4/0M6XMyRWz8rHozo6oWCIhUVfhGldib8qH6rSn+XTEExK7jg6w5iMEjwCmV7hYu6mV5XMKUIiffoF/sHWYpTMLxrXLyATjIXSnI8aXSKAI+ZF0p6H4eI7QKjSEEyFcJiVdI3HKM09Pq+BUEFQaBLUe2TuAYMk86/1fKCQSEorEyX+nkgShSJyDzUO8ua0d+VhpvSvmFJLr0sN5LMgXl+McHDg6rr0v2kGGvYiOvqT3VRW3oFNqCcVHjcHLihegFtXndfy7LMvkOA385Knd1BY7USlFXny3ia/fVHeuh3ZOybDqMGiVqTK/h5pHuG/hAo4ONXFZ/uX01Wfy5wNDwDA1xXYWTMlhSpmLQU8Qm1qArG4uWR4h15SNqAqjUWgpiJUyZ85s/MFIysg/ji/iZ8DtZ1XxtTzd+FRqjXJ15WW0uZMODoNKT6eviyvK5/HDh3anzn3+nSZuv6qEN3ufwHdM1BjAqD5zEYNp0qRJM1GckdD9zMzkgm3atGmMjIx8wtEXF5lODf0jIX7xzD6icYnJZQ7qKh1k6F14Iz7WHh0t83LP5DvYti/B/uZhshx6TL1qXtnYwd/cPZX+fgVfKLsDUenn8pKFdHi7aRnpYEXRlTz5XDduXzKcLRqXeOSlFr735fnUDzXQG2lnap2Z5v5OvnFbFRs+6COekFk2Lw+lPsCiuWZea1rPO94u6ow12HU21HEbmWIFbS1evjLrTppGWohLccpsxRgSLv5yzlfpDnTS1NfP428MUJJrZc2KarYd7OVgk8A1i4uYXmOj3dvBnXU3gixgVtrwjiipvUSPStDS59dR7SqjWF+BLAkpI/84CVliODKEw2JFFDQgjf9pDrpD1Hd5cPuiZLsMvLeni/Xbk2rGnf1+9jUM8g93z0RzFha2siwzo8LFtoO9HGlL/sari2xMr0xn6J8KTV0ezAY1FsNHG5tZdj1GnZK9jUNMr/h033MoEqdrMEBhlonAh/KYI4kIoiCgUJz6a1ARdCNpDXABeqNVooq4lMBhFOkYiZ0TQx9AMGeQGO5ETBv6p4xCFHlja3vq3y3dXhrbR5hZ5TqvnYxKlNS4KmgZ6RjTXm4vxlzlRAAKskwUOV38VcmXeavpfbp8vczPn0GRNZ+oFEF7OmVVzjCiKLLtUB83X1ZBOJIglpCoLalm3QetVBRYz/Xwzhkmg4q/vWsWr2xqoXswwMyqTGJuJWuq1xAcNvDygbbUsYdahplU4uT1rW209/rIdRn42s2TyM4bwR8OIccMhDwq8vQOfvnwEVZc6kAQxobQG1R6fD6Bbe/F+OubH8AjDSDLsKv7AHq1lm/MuZt2TxcDgSH2nCC2d5z3dw5QPaeKbT07UterdVSOc0akSZMmzfnGhBv6Hw67VSg+X6IkI94oj77WkPr3/sYhXtvcyvVzruV/d/yM2LGYy3xTHhm6TFYsUqFSKdnfNIhZr+av18xg+4E+HFYF8YSOpq4ImWUibSNd5JgyCQSlVL7YcWQZjjT7uNR5Ixa9BFENpVkZDPlHWHyZjFNvR62IoVIY+NHmn6e8IhuaNzE7dyqa3nyyS2Uee6UJg1bJdZeXI4oCv32pg2sWm9Fn9RAnzhvdb/FXa+7H45P41XP7Uv0faBriL+6oYnPve7R9KPz/23O+RtPRKBs2q8lxzebNPj/FXxAwaYxjdscB1Aolf7Ph+1Q7y/lC7bXYxFHveCQu8atnd7G3YdSDfsvlFTgsWoY8yfvpHPDT2OVFpRDJzzCiU51Z48ukVfKXN09lwBMGAVxmLSrFeRwnex6zr3mIoqxPznOvK3Wyfmfnpzb069vd5DoNqJTj30OBWAid8vRK6yUV989fQ+PjEAQBjVKLSRmnfegcCvIZHUhD7VA845yN4UJGFEl5vk9kT8MgN15ajvI8ficlkJiaVcPe3kN0epMRaFXOMsqsJbSEhsnPNAIyGlOE/3j/l+RbcsgyuljX8A7TsidxXfWVMF6H8LxBECA/y8Rvnt+PRqVAFAVCkTg3Xvr5Tu/qGQoRjcVZWJdLW6+Xt3d0kmHXcceKKl7a3TLu+N1H+7GbtbT3+ugaCPDzJw5w+012ukPt5ClqePSVZgaPKedv3OLhtuU38sShZ0nIEmqFilsqb2b7VonLZhaiVSn59Y6XxuTqDwaHMaoNHOw/yiLD1HH9ZzsNXFt1BRWuZLWHcmsJNoU9beinSZPmvOeMu6FOlm97MdPR5x/Xtv3gAEaFmXuqvswNxTdzc8ntrC65lX2De3l3dwdTyhz885fmUJpn5fu/38qB5iGys7Q48jxkVPTSHexkbv408i05OMxG7ObxxolSKfKLJw4TD+rRaiV2dO7n/3Y8xJ/2PcF/bfkFzxxey0Cob0zoI8D2rr3YHALbD/cxudTJkDfC759r5LfPNDDoDuOyqdnQuol2dzdXV1xKRAqx/VDvuP537vdwbdUyDOrRCgsryi+hw99OTYWeBXU5tHR7sZs1mAUHt0++DuGEUnjTsifRNNKGJEscHKjnf7b9logwmu/fPRQcY+QDvPx+M4um5o5pG/SE+OGfd/D7Vw6NKcd3plApBHLsOnJsurSRfxrsaxyiOPuT69BXFlhp6/PR7x5fqeJk7G0cpDDz5BsIgVgAnfL0ysop/cNImgvT0AfQKTUYdFHazqGhLxgdSCPj9UHSfHrslvFzQmmu5Xx25gMgCvDC4TeocJZwY+1V3FR7NTadmQ96tmA2qsmwGzBoVfR6B4gmYjQNt7G9ay+eiI+NbVvxR4Of3Mk5JBiMYjdrWbWoBASIxhIsnZFHXoaR87gYwhln2BtGkmS8gSg9QwEWTculssBGOJKgssA27viSXAtdA6Nrq56hAImIGoWoJCS4mVHtRCEKaNQK5tbmYo2V8c1pD3BLyZ18ofA++lqMhMJxXtvcynB0cIyRD3B4oJECSy7BWAijI4DdPKrbolaKXLOwBKtg45rqK5hln4lVTBv5adKkuTCYcI9+fX0906eP1u4Nh8NMnz4dWZYRBIFdu3ZNdJfnFSdbcBVkGxmMd9EaamSyZQ5ajUgIN1MMlczMNNPcEuHfH9qeyldr6fLym6ca+e59lXR6d7GlazS/Xa/SseaaL/PrpxqJRBMIAtx6RQXbDvYiyTAwHEZnUPF604YxY2gYbmFl+WXjxqZWqonHZd7Z2cUDt0ylbyRIe68PURRYvbSMhBjG2LOYlqMJ7HVGKkucqJSecdfRa1W41Bl8Zdo9eGJD2PU24ok4//3Bb5md28lVM65FQGDEF+b7f9zK7SvK+YfFD9Dr78eo0dMw1MIrR0fHPBQcYTgyTLY6aciHIvFxfQbCcTSqUU/tlHInTkvScNt1pJ+eBcUUugwf+/dKc+7xBaMMesPkOD75b6VUiNQW2Xlndxc3L/14r5gsy+xtGuKGxcUn/TwQC06AEN8IMUfuJx94nqJVaFGrAvT7lMQS8jnZrBJNTqKtF/e8cCaRZbAaNcyoymDnkWQVEIdFy/wp2SjO8412WQZBkNnQvIlMo4u4FGcoOMLSgiVs393FwEhyQy+j2D7uXJ1Si1qhhPFTw3lDIiHjMGsRZJmrFhSjEAXcvjBZDgPR8HiBwc8LVqOGnqEAf3710JjqA42dbpbOzKeiwMbR9mRKXEGWCbtZm/otABh0KrzxYZ6pfwWAQnM+f/Pl64l4DDz2Rj39w0EkOfkcTK3Q0DXgp3cwyM2rMgnExqeTKgQxJd67tvVZVq64BpcwCUFSkp9hxGnSpPPx06RJc0Ey4Yb+m2++OdGXvKDIduqpLbVysMkNgF6r5KbLS4kLHuZlLiau8vG73Y/Q5x9AEARWlF3C9NKZKSP/OEOeMMGAyFTHdLZ27UxNQqFYGL0xxu03WwkGBIocGbz1/gD1xyZFk1ZPMBIk8SExGofOhjJqo8CcT7t3NB9yVelyEn1Jj+T/Pb2XJdPzWD63iLJ8M6IyTn1PN7PniBjVZnbsCrIvPsyUMhfbDvUllWYBhSgwvcbGG83vkmPIodxRwM6eHXT7k57/7d17WZR1KZv2dTPsTUYUPPRSPT/MX8A0ay5DiQFebfjDmPEKgoD2hLDqHKdhjAozwIyqDDLsei6ZnkemXY/HH2F/01AqnD8cPY9XgGlSHO3wkOcyIIqfziipK3XwxIZGrl9U8rE1wjuPlW9ynCQCBsAfC6A5XUM/4CGc+9ECguc7GoWGcCKATW+n2x2n0HF2xCxPRDDakX39yFICQfx8pXpNBJKUnGdynEYqr7QhSTKRmITJoD6fdfiApCDmyorLmJYzmdaRDpSiklxzFi5VNq+ONKWOU0RM5JmzU+H9AKtrV4J8fm9kADgMKi6bU8CwJ0IiIeG0arFoVZ9rwzGeSGAxqsaVGNzfNER1sYOSHDOrLsmjP9iHqA3R3zrqgRcFuPe6Kg6H30m1tXk7+L89v+AvJ/09g+4wJoOa1Vdl0BdvAsUgq+dNoWskxjPND3PHlNVMzqhif/+R1PnLypawszuZjhiT4mwf3MRfz56CRk5GKH6e/1Zp0qS5sJlwQz8398L1bk0EYTnA7SvKcPujhKMJMuw6RAH+c+vP+ccFf8srDW/Q50+KvciyzKsNb1PmKKYk10Rz1+hkplQIIAAJFbeX30OD/wAgU26cjBYzXqmRspxi3t7Yx44jSUX+hXU5iIKI1aBhSdFctnTsosxegohIlaWW//7TYa5YeDkzSnz44m7syixylLnECj1885YpNLZ7cVi1OFwSL7Q+xfScKTza/Fhqk+H6qhWUmrPYvs/LXSur6ej3o9MoKC1R8UjTbxgKJTcbap01fKFmNQ9u/D4AVo2ZuNrDdder8A84ePq1LmQ5GcYYF6NE4hHunXYLOpWWHl8/zxx6lWsqrsCqsKWEpGwGNf/y5Xn8Ye0BOvr9LKjLoSzPwi+f3YvNrGXTvm5icYkrZhcgCJBp139kyHaa84sj7cPkuT59+LvdrMVl1bKjvp95tVkfedy2w31U5Fs+Mn3IHw2gVZ66oS9GQwhyAvk0rnGu0Sk1DEfcZJiVtA5Fz42hr1AhaE1J5X3rR/8903w00WicWbWZdA/4CUcTlOcbEQSIxSUU53GMuEIUCcfDPLLnudQ8oxSVfHP2F9FplKlILodDwbLMxbjDXvzRAE6dHavWjCyfx5L7J2BSKTA5R9PaPu+GoyAI6NTj3zU2kwZ/MIrJHucPjT8lHI9wZcEKCrJz+O4Xp9A21IdaF2Ntz58osucyI2cyRwabqLCV4464URlDfOnaWgz2IL87+BuiiWRK0obW97mj7gacOjsGpZkpmTXMyJnCSNiDVWtCBi7Rz2e/7gjl9mKmZU5KGflp0qRJcyFzRlT3P8+oBCXPND7D4eHkbrFepeOemnu4d9oXUKpjHDpJKaGh4DBXzCvi18+M7jBff3kBcYWPwVgXKslKXmImggAJKYCsDuGP+PH648ypyaOmKBMQUGsjJNRe+gfVmIZn8I2ayxkIDiEjkWN0UpDVzItv9aBUCOi1eryBQe5erSc7U83vXj1CIiFzw4pMfrb/j9w//U5+v/vRMbXonz/yGjl1JUwudfCff96J1aTh/luL+a+d/z3mfg4OHqLffwnzsuaxpXcLN0+6mhcOv45Tb2NG4WQun5vF/novNrOaI54j/GbHI6kIhMWFc/jXS75LLGigvsOHzaTBadagAGpLHHzntmnEEjJalUhDt494Qk6F9ImiQGGWiZJcCx5/hIfXHWFubRYV+Va05/Fi9/NOfbubJVM/2wbh1DIXb2xrZ25N5kkNeVmW2Xqoj5VzCz/yGv54EKd2fD7op0UR8CSF+M7z8OiPQ6fU4vf5yTQraOqPsaTi3IxDMDqQPD2IaUP/MyMIyf98//dbiZ2gS3LfdZPIcZ7fqUsJKcE7LR9Q5SqjxlWOjMyu7v0cHDhCeX4B+46VLxU0Qf6w/UmUohKdUoMvGsBlcPCd+fef4ztIcyoYdWqC4SjTKzPYVd9Phk3H4rk2ygqM7D7oR6WJEY5HqHVW03XETtTh5aW+5xgJj6YN9gX7uW/6GjJDs9m9y0+mQ4NcrsSZ5yZGlFJ7EYcHGtAo1AiCwLauvXxh0vX8+INfIssy11cvRyGIHOg/Sr4lG61Sy+riW7Dp0mH6adKkuXhIG/oTzECkL2XkAwRjITZ0vsX1VauIS3FK7YUcHhhbWs6iNbOnzc+a5VWIIrhcCt7sfZHXt7fy7Rnf4sDgYY7E9mNQGZmtn8fh/gbe7djMu2zm9uov0N5sYOYsJX3hbo743bj0DqZPLWZz2we83f42CVmi1lnNjcuv4t9/4yaeSIrgqJQiMZUbpGw8/iiyDCZ7mERHAkFSEklEx91fRArx5xcPcsXsAt7Y2oYsnFzEKxqPU2eZT21uEa82vE2Hp5s2dydHh1r46qwvUpLlosPbyxP1L45JMzjYf5QyYRG/eHpzSuzm9isrWXrMEFQIAgqlADKUZBl58O7ZvLGtDZ1GyeWzC7AY1Hz/j9tSxv+WA72sXlrG1XML0uI55yGRaIK+kRCZts8mileaY+bdvV0c7XCfVLypoTO5IPy46waifvKM2Z9twCegCLpJXMBCfAA6pY5gLESmVcH6rnMnbCYYbEjuXvjofZk0H0Nnv3+MkQ/wyvstTK/MOEcj+nSIgoLJmVXUDzbz3KHXEAWBRUVzyDFnUjG3kKoiO1q1gqjUB0BciuM7lpI1EBgiIX1+89wvZBq73GTadOi1Sr5562SihnaePPwor+yOkGvKYWbOjWh7NVSapvDogSFuuTqHkf7x2kCRiMSzryeFPPNynGzq2cT7Xe8jyTLzC2bywJx7aPV0kpAkMo1OdvTsQZIlLitZwPbuvXR4ugHY2b2PeXkzsIcrsRe60muFNGnSXDSk3ZwTjDc2fjLq8HURiHto9XSwovxSrNpRdfH5+TPJMrqoqpUpKFBwVFjPLw7/Fw0jjcSkOH8+/AiTHZO5rugWluet4uhwAy81v5o6/62Ot1gw28Q7He/y5IG1vNG4kUf3Pc97HVuwG3UpI/rg4GH2De/mhktKAMi067j7xnw2dL2OWg3TKzMQj6nWAihkNQ7dWANKpVBhUFjoGw5SUWDjW3eX0x/pptRWNOa4DIOT/j6RQEDmdzsfT02mAL6IH2/My6+f34+giI/ZoQdYkHkJv3vh8JiJ9tHX6xnyja+hpBAESrKMfOOGyXxxZTW5Nh09g4Exoj0AL25sxhtO5+ufj7T0eMm06VB+xmRiURSYXZXJC++PL8UE8NaODurKHB9b9SMYD52WGJ/C70bWXNjhnaIgolGq0WsjjAQShKLnJhRaNNhIpJX3TwlRhMSHk50BhUJM6aicr0iyRCwRZ0f3PmRkErLEOy0foFGoaenxAjDii+DUjxfjq3GVo1Gqz/aQ00wAdpMGi0HDsDeMNzHIw/ufJBJPzvFdvm5ea3mVB+Z+CVEQEATo6IxSbB27CyggEPElN3KVCpGC0hjvdm4kIUvIyBRZc/nNzsdYe+RNXjm6nof3PEuhNekwsOmsY9YlAFs6d6G3xM77ZyZNmjRpPgtpQ3+CyTOPDz2dkT0FhSjy/OHXAfji9FtZM+UGvjzzdq4sX0I8IfNc89N4pH72Duwfc66MjFItodOItIcb2di1ccznkiwRU/jZ13d4TPs7rR+QbRrrzdk/uI95My3cfHkFy+YWEBQGKbeWMzyooDzfymWz8lAJaoqseRwYPMTq0lvINCTrldu0Fu6uvZP3t3kQBTDqVSh1IV5ueItbJl3DspJLyDZmcEnhfO6dcidPvNKGzaRBcRJxrUgo+bMbGRIosuaN+UyUNESi4700nsD46ILjxOMSiUTSQDlZyJ0syyhEgWhC5sLI6Pz80NjlIftTqO2fjNpiO4PuEPuaxpZd7BkKcLhthElFjo88V0YmEAuhU55cqO/ToAh6kLTnd2j0p0Gn1BFMBMm0KGkZPDdl9gSDHcnd88kHphmHJEGO04hWPfZde9msfITzvMCeDGw/JoJ2Igf7j7LjUB9PvXWUl95rJhQLc8ukVRhUyY21YlsBs/KmEj5J1Fma8xtBAKdFhy8Y5aoFxehM4zfxDw82IkWVlLvyWTozly37hphvu5ICc3K9YFQb+OK0W9m0PVlyL8uhpzPUnDrfrrPS6x8gHB+9dlyKc7D/KCW2gpOOS0bGqEsHuaZJk+biIv1Wm2D0CRc3VK7ipYbXiElxqhzlLMlbgkpUUues4ydbfs/MnMnMz5+FUXaSCAu0eBu5a+rNqCQ9mfoM+oLJEkkCAteUXc3PH2liYV0OM6aWo1aoUgIzAJfkLv1I4zYmjfVil1iL6PMN89RbrQBcs6QYfcJKpy/CsDfAvClZILmpdJZi0RowajTcWnI33ogfnUJHw9Ew7+9pZtmcQp544yhL5ji4b/qt/GjzL3HqbBTZ8mhyt+KJ+Pjazcs50uLmKzXfoHPQg0qT4Ih/F3HCNBxNGvJ/eqGFv/3qDTx++Bk6PN0Y1HqqcnJxWMIMecKpcauVIq6TlC08GTlOAzaThpETIgC+efNU3tndxTu7OsnLMHHzZeVkW3XpPLzzgKYuD4VZpyaaqBAFLp2ex8Ov1/O9eywYdSokWeaRN44yuzojFZ1yMiKJCApBRCGe+itQEbywFfePo1Po8EV9ZFuMNPRHqMk5++KCosGO7O0/6/1eLJj1Km67sor2Ph/+YIzKQhuZdn2y/Oh57KFUygrK7UW0jLSPac835bF5ZDSVJBAL8Xrju1xSPA+tUk2nt5dH9z7PDy7967M95DQTQEe/F6dVj1IhMhIaL8qXZchAp9Lw/35xkJULirhjZRX7D4ywvO5WHDUyojrKgb56ptYU0NDmYcQbxqUZdbLoVFoC0dC463ojfq6pWEl/qJdsYwY9/tF3zty86Tg0dtLegDRp0lxMpA39CWbELfHum2qWz7oThVKmpSXGut4BrllcTLEwl0vmzqet14uv10TzSBCNSqS0IpvmoQ4qnTbunXI3LSMdRBJhsgzZtDUJ9AwGeHZDA5n2Or4182t80LOVkfAIk60zOHxApHCeFZvOwkhoNAy+xFbAcHD03xatmZrMMnye0SCO7Qf7+dqNUxj2Bplc5mDIHSbm06DUaHjm4KvIyJRYCvnS1Dvo6YujU8e5Y0U1h1qGaOx009rj5Zv3ZRBLxOjx96cmzQ5PN1+4dDUqQcXvnznIiDdpdF+9aAHL5+dyuCnIW0IfkZjEv/5fAw9++S70phh6pQ6DbOYvbzXzs6f30jccxGrScN+1kzDrP50auFGj5B/unsX7e7tp6HCzckERu44MsH5HsqTgoDvM4dZh/u0r87F9ymumOXO09HiZW5N5yucXZ5spz7Xw4yd2c9MlZWw+2EMgHGN6+cm9NsfxR4PoVJ9NF+BEhFgEIRG/oBX3j6NX6fBEvORa8znQNd67dlbQGpHjEeRoEEF9YadDnAvC0Tj5GUbCkTh2k+ZY7XaQBeG89upHw8lc6u3de1PzV4ElF2UgmxuWaojEEqiUIkZZIhgL8crR9alzLymal9yoSxtmFxSyDA5LshxuKJLAZnOxuGA+G9s3A6BWqFiWezWbd3r4+3un8/SbzfQPhVg6Mw+LTk+z+zBHR+oxarTUFOTyF2tqONzoo8Sip8CSS7uni25vH5cWL2BL564xfS8unIvkcZAluvji5AoODu/j8FADc3KmMtlZiyily3umSZPm4iJt6E8wfcNBOvsDPPVKINVmNqipKrTx2xcPolYpeODmafzsqd2Ej4Wol+w2c9OKIg6291CWmUmpsYqBkRBr17XQ0JFc/Egy9A+HcNkcWIZnkaNTYkloKckKEfJouKnkdva4P6BhuJm6jFqmO+ZgN+r58kwjvogfpahAk7Dxmxc7Ach2GKgrc7J2YxM1xU427urhQPMQxdkWrl0yg69NqkJQJHCY9Tz47r+yKvsOnnyrb8y9CgII0lhjWavUcHPJGp59s5OWLg8Lp+QiyTKvbGrh5ffamVeTx/QyO//xtQUMesKjqvqCABIIosz+pkHqyp1YjVqCkRi/fHYf371jJhmuT+f5tRvUXLewGEEU8ASi/Ndju8d8Hokm6B4MYCuwAsnw0VA0gVopovyUtdzTnD4jvghxScZsOL0828V1OexuGOSptxtxWXVct7AYxSfk/PtjgdPLzw96SWgvbMX94xhUevqDA0zOUfLK/gDxhJws73kWEQQBwWhH8vShcBWf1b4vdEQRWnu8mPQainJMqJQKhj1h9jYMkO00YNKcv8aLJMkYFHq+Pe/L9AcGUYgimdpcfvjbA2Q6DFQUWOno8/HGliH+6t6v8lL96/T4+plfMINyR8kFU14vzSiiKNDV70epEGnqduPw65hTfgm5ykpC8RByxMCTLw5y+ax8Wru93LuqhnAshkohMOyNs/sDNf5QFWUzbcSjElv29FBXZecPh3/HXVV30+NxAzIJt4VbK27j/d53SEhxVpRdzt6dCt7ZtpO8DCPfWF3HlfmXsbzwMqSEnBbgS5MmzUVJ2tCfYCzG8UZLXqaJ7Yf7kWUoyjazYUd7ysgHaO72crQpilHnZO/BMOs+qKem2MY1i0oJReK8s6uTpk4PBp2SDdvbeXd3UrRKFAXuvqqGTfu6KC+wMVRfSXXGFEaOyCjnaznaPYhCNFGdVYBK1vGrZw6R5TBw/SVltPZ4MOrVVBbZeWNLK4dbRxBFAZNBRXOXH6dFx+Z9Xdy8wsbSwsXIgheTXoUvOJo2MLXMiQE7ZdZSGt1NACwvuIpHn+3HH0oe19brY2FdDuX5Vho63HiDUbJtWpwmDU6TBkEQEBUCsiQjSTKhqMTrW9px+8d6Flt6PEz9DArSkiSDJKMQRXRqBYEPifFpjpXb84TiPPFWPdsO9ZHrMnLftZMocOrTk/5ZoKXHS45D/7GCeZ8GQRCYXuFieoXrU58TiAXQKU/do5/Mz784PM96lR5fzI9aIeAwiLQMxijPPPsiZ6LejuTpTRv6n5F4HMryrXQPBmjr9SNJMlq1ghnVGRh0yvM6dB9AL5nRKeJIOgmFoMDnVvDl6yfz4sYmntnQQH6GiS+umsRIv49KRwkLCmbyQccusk2ZWIzlxM/jiIU0J8di0uAJRMl2GnlpYxOvbYYV84voGxLYvL+LJdNzqSq08+Sb9fxxbT2VhTZWLy1n/bZ2qoscyDJs2t7H3MnZHGzoYWatA380QL/fzePPDB37yQ+jUSu4Yu5KFs508p+/O8zwsejCzn4/P3psJ9+/b+740rsCuIMxItEEdpMGVXrzP02aNBcwaUN/ginKMlFZaKO+bQQAnUbJJdNy+dVzScEhh1lLR79/3HmBUBSrUc3z7zSxYl4RI74I//14Muxs/pRsvnXrNBKSxCPrRkv3SZJMQ4ebjn4/s2qymF2TS4ZdhyDG8cmDBCUvcsjAI2vbmVubQ3GOldI8Cz9/ei+QjDQoz7cyvy6H1h4fa5ZXsXF3Fy++28SUcic3XlbBrgP9DA0XUDvXxrfvMPLae520dHuZVZPJ4mm5qJUKyqRLmFYyhwgB7FIx/tDBMfe2eV83N15WQUu3h4wTyp0FIgl2Nwyy5WAPk0udzKvNwqhTkmHTjTP0LYZT874aNAruWFHNr54fFTkszjGT6zIQikv8du0BDrUMA8nJ/wd/3MYPvzof22l6mdN8Mi09XjJs58ZY9seCaBWn/jdWBDwXvOL+cZSCAo1Ciz/mJ9+m4lBP+JwY+oLekiyxl+YzoVRCLCHR3uvjja1txBMy1UV2Vi4oJhqT0J7l6IxTQUgocYrJFB61UeJ/n9hN71CA6iI7/SNB/vfJ3XzvvjkoBBfeSICVFUvJ1WUTj6eN/AsNURQw6pUkEhK/eu0wZXlWYvEEz73dyH3XTWLZnAIEQeDHj+7Ee0yE90jrCO29XiIxiSfePIooClw6Mx9fIMqqRSWoBTX31N5DOBbmKzfW8qeX6gmE4zgsWmbXZuF3kzLyjzPkCTPsi5BzwpokLsm8t7+Hx9+oJ56QKco288BNdVjTaX5p0qS5QDmvDP2f//znvPbaawAsWbKEv/mbv2Hz5s38+7//O5FIhBUrVvCXf/mXABw+fJgHH3wQv9/PzJkz+Zd/+ReUynN/Ow6jmvuvm0T3YJBQJE5Blok3trSlnCqHWoZZPC0Xq1FDTbGdaFzi/b1dzKnN5sWNTRi0SrQaJVs+aE1dc9PeHiaVOGnpGkGWkxPl8RIwCUlCFAXiCQmnVYtKKdLcGeaJNzuPlVxKhv4Pe6Ism1PA2o1JZdpVi0pQiAINHW5GPBG+fds0fvbUHryBpCd+x+F+BkZC3HN1Dc++3cTmvcn8++oiO6sWFVOSa2F/0yAdvX6uXVRCU7cHglH09vGieSqlAoUo8N07ZmIzqEBOplU++mY9Ww70pr6XD/b38Ld3zOD25VX8v99vTZWMys8wUpx9aoJtkiQzo8LJP987h4ZONy6LjtJcMzqVgr0twykj/zixuETfcCht6J8FWnq8VOZbz0nfvqgf7Wkp7ruJm5wTOKJzi0ltYDjspsCRxc62MNdOPftjEAw2JE/a0P+sJBLg9kXZcbiPVYtKUStFDrYMsfNwL2X55k++wHlGIBSjPN9KXYWLo20jzKjKxGnRMeIOU5M/CYzHDkxXTL0gicclTDo1h5uHuXNlNfsaBtFqtNx4aTn+UBSDTsX2Q30pIx8gw6ajbzjE7qMDQHJef2tbO/esqiU/w8i2g72s29KDKApYjSH+4tZpKEQBlVLE541iM2sRBMZE6qmVIsoPefO7h4P8+bVRZ0prj5cn1x/ly6tqOf+3y9KkSZNmPOfeMj7G5s2bef/993n++ecRBIEvfelLvPzyy/z4xz/mz3/+M9nZ2dx///28++67LFmyhO985zv84Ac/YOrUqfz93/89Tz31FLfddtu5vg1CMYnn32nivb3JGq1Wo4Zv3FzHrvp+PIEobn+EqiI7vcNBnlp/FK1ayY2XltHU5cZh0RJPmGnoGBl33e2HelmzohqzUZfKn33+nSaqCu3sPTqAxahGkmQcZi2NsoeEJFOSa2FmdSaiAL5glMIsM5FYghlVGXT2+VKT5sHmIQqPmJk7KYc3tral+mzr9aFUiMyblMUHxwzyYW+Ygiwzbb0+nnu7iVAkzoK6bGaWOxFF6BoOkeM00D04qlFww9JSFk/NQasQUhPtsC+SMvKP097no28kRGGGgX/7yny6BgNo1QryM4wYPkZB/ZNQCAKFLgNFGYZU/+5gjLZeH3qtkuCHwvr1E1hiRxDBF4ojyWDWKUlHmSaRZZn2Pj9L6nLOSf/+qJ9s46mLACqCXqKuwk8+8ALBpDIyFB5mijOPl/bGCUQkDJqzW31VMNhIdB385APTjEEUkxuUC6fm8uqmlmPv+ExyXEYScRmUF5aJolaJeINR1m1JzkUHmofItOt54Oap53ZgaSYEURSIxOPkZZj4+TN7U+1bDvTyxVW1dPb7sRjHRvCV5Vs51DI07lq9gwGaO92pdEZJkjHqVcQTMk+vbyDDpqey0MaOw33ceGk5T69vSJ173ZIy3t/TxQ2LS1Lzcv/IeKX+XfUDhJZL6FXpatRp0qS58DhvDH2Xy8Xf/u3folYnPamlpaW0trZSWFhIfn4+AKtWrWLdunWUlZURDoeZOnUqADfccAM//elPzwtDv63PlzLyAdz+CK9sauX25VWEowlynHp2HO5n28GkkRuKxPnza0e4Y0U1hdlmGjrcTCp1cqBp7KSW4zLy2OtHUCsVbN7fg8Wo5m/WzGBX/QB/ces0/veJ3QTDce65uobKQhsLp+agVSt5dkMDCUlmTm0WB5uHuHx2AZIk8/gb9WPH3etl3pTsMW0KUUCvUXLPymquXlBMJC7R0u3hp0/tZtgzGgY34o1ANoRiMv/75G6WTMtHlmUGPWHyM4xUFtrQiMKY3fSPSssWBEAGl1mDy5yc7BUKgUTi9C3kE/uXZJn39nRxzeJSnjjhu5hTm0mW9dRzt08kmpDYvLeXJ99qIBZPcOnMfK5dVHJamxYXC25/FFmWMerOTUikPx5AqzhFj76UQIwEkC6S0H0As8ZEw0gzKoVAgV3Jga4Ic0om5jn4tIgGG5J34Kz2eTGQSIBRp+RXzzWm2nYc7sNh0aLVKGAC3p1nk1hc4oP9PWPa+oaDSX0X66lH4aQ5PxAECIQSbNjZMaZdkmTaerzsOdrP6kvLWTojj7d3JsWDewYDlOZZ6fxQ2mNehoH8TDNWk5YjbcPUFNspybHw40d2IMnQ0OFmz9F+rl5YQn6mkTXLq4jGJDRqBZv2deMLRrl6QTHqY3n4NtP4FMGyPCvatJGfJk2aC5TzxtAvLy9P/X9rayuvvvoqd9xxBy7XqMBWRkYGfX199Pf3j2l3uVz09Y1VhD9XnGxHuLFjhCy7ntc+aOWGpWVs/tAiBpIbApv2dnPDJaXkuIzsaxhIecVzXUYMWhU7Dvdz67JKNu/vweOP0tbnY1Z1Bo1dHq5ZVEI4muDJN+v5968tYHplBj99ck/q+lsP9uKy6QiF40yvOrmoneVD4eq3XlGJ9VioffaxPLYNOzrGGPkAWY6kwROLSwyMhHlmQwM6jRKzQc17e7p44KY6cm1jjQabUcOSabmpnXiA1UvLiMQkdjclPTg2o5qGTi+HWocoz7cyXaGYsPA5m0HNpBIH2w/1cseKaiKxBHqNkmkVLtQfFuc5RVr7/Dx8QhjgW9s7yLTruWJGXir14vNKW6+PLPvpC/GdKv5YEL3q1IwGRciPrNYlwzUuEgxKA+FEmHA8QpFTxZ6O8Fk39FHrQU4gh/0IWuMnH58GSHr0uwcC49q3H+rj6oXFGCbofXa2iCdkju33jiEWSyvsXwyIokjXSXSKICmsmpDAH4rhD8ZShnlhtgmrUcP+xkGGvWEAqgptuGx6DrYM8e7uTgqyTJj0an7y5J4x+pOBcByjXoU3EOOx14+M+WzVwmI0ChH5mBcgz2kYs8Fg1Km4a0U1F9YTlCZNmjSjnDeG/nEaGhq4//77+e53v4tSqaSlpWXM54IgpF7KH27/LDgcZ2YhWZgdHNdWU+ygsdMNQEuXh/wMI27fWGPZpFdhNqiJxSV+/dw+/uLW6exvGgTA7Yvw9Pqj47zgvmCU9/Z1s357cmfcZlbz4L1zOdQyzKHWsbnnkAzRd1p0vLOzg/mTs8dsOBRlm4knJO5YUU0iIeG06ijLs9Dvi2LQKinMMiMIAquXllGWZyUcS5BISFiNGqqKHBh0KkzROLNqMtl+qI9QJE4oEkcQoCDbjOskpfHuuqqWyWVOdhzuY/G0XN7e0cmzb496pe69ppZn327E7YvwxtZ2plW6+M7tMzCdojDf+P5r2HKgh/f3djOpxMGs2iwKMk9NC+DDuFwmXtzcOq793d1drFpUiuEcebI/ik/7PJzs73gqDOzqIj/LjNV69rzix/tKSAmiiSgOi+WUNhpEfwj0RnS6s6PjcLb6cehseCQ3U4ry+MVbA+iN2tPa9DqVv+2gxYVZ9KF1ZX/ywWeQMzU/fByn82wZj4mFFWWbMehUHG0fITfDgFKcuGf245jIPjzhOIumjt0EznYacFq1Z+Rezsb3c6Y4W2Of6OdBr1UwpzZrjEaOUiGQ6zLwxtYwVqOGigIb0biEVqNg55E+rpidzzdvrsMfjOEPxWjr8fLmtnauWVTKu7s62dcwSGmuBZVSJJ6QMOpUOK06ugeSmwrFOWZuvKwiFeVYV+7iqoUlOJ1j7+3+66ewYn4xwXCMHKeRbKdhzOcX0u/lQhorXHjjTZPmQuC8MvR37tzJAw88wN///d9z1VVXsW3bNgYHB1Of9/f3k5GRQWZm5pj2gYEBMjI+fek1gKEh/xnxqmZbtdx0WTnPvt2IJCVVWysLbTz86mEA9jYM8Hd3z6ahw50qsVdRYCXDpuPy2QUoFQIrFhSj1yo50DzI/sbREP6FdTnsaxgNbS3Pt/Gff94BQF6GkRsuKeO5txtIJGSWTM+jezBAU6cndXxBponmbi8dfT6+96W5ZDsNtPZ4ycswolIq+MNLB5FluPvqGl7Y2MjqpRU8/04jbn+EZbMLmF2bxb8/tJ2hYzvqFqOaf7xnDkF/mKA/2Xb7skoSCZld9f1YTRq+fO0krFolAwO+k35fM8oczK500djjY/vhsVEZT755lEtn5rP2vaSA4O76AZq7PGNUck+XedUZLJiUlSrv91Hj/Cy4XCYGBnxkOwzjPivJMRMKRFLf19nk4ybRT/M8HL+vieBQ0yCFWSbc7vEbY2cCq1Wf6ssb9aFTaAicIPb0WdANDyIpdURCp3b+Z+pLpyZ0FvoBsKjNNA62kZmTSaZJwXsHR5hReGrP2onf92dB0lgYbmtBpT7zhv7pPg8TPZZTfbaUSsjLNPL1G5NaML5glFuuqKAs14IoihP2zH4UE/legKR+x5VzCynINnOgaZDCLDMzqjJQKIQJv5eJHvvZZKLHfjafh/J8Gz2DQb6zZgab9/WgVolMr8okHImxqC6XHYf72XIg6YiYVuFiQV0Ou+oHcVi0eHxRHn9zNN2uZzDAF1fV8tjr9Wza2811S0qJxSVicYmeoQDzJmej06gYGAlx1Zx85tVkEktI2E0aRE4+5zsNKjCoQJbGfH4h/V4upLHC+Tfe9KZDmouF88bQ7+np4etf/zr/8z//w7x58wCoq6ujpaWFtrY28vLyePnll1m9ejW5ubloNBp27tzJjBkzeOGFF1i8ePE5voMkaqXIvEnZFGaaiCUkJBn+7wTBGQQBjz/Ct26dRiSWIBSJ09zl4X+f2APA/MnZRGMJagptrL6knCmlTnqGg1QX2lEpRB5+7TC5LiPXLi5h2DOaJrBsTiE/e3pPKg9999EB7rtuEi1dHiQZ7GYtRTkW3t3dxfzJ2QiCwJvb2ijOsTCp1MnOw30smppLSa6F9/Z0cemMAn7yxK5UmNtT6xuIJSQUJ5Rq8vijvLe3i+sXFqcWAWatkq9dNwl/JI5aKaJTiR9bk16Wkyq8wVBs3Gf+UAydZuxPNJGY2PBNWYZE/MyEhFYX2ijINNHel5y8DDoVVy8oPmlEyueN9n4/s6tPXQzvdPBHA+iUp75ZpAh4kDRnOaz9LODUOmjytBJLxKjIUvNBU+iUDf1TRdBZSHh6Ob/iXS4EBH7zwn5ix95lB5qG+MoNk8l06D9aEOU8Ra0W+ePLh2jocJOfaeKdnZ28ubWNf/rS3HM9tDQThN2kpqXbywcHBugfDhKLS7y9s5Msh577r5/Mr4+Vw8206ynOHS0JDFCWZ+HqBcW8vCkZ7dnZ76el20NehpEcp5HJpQ5+/fwB2nq9AGw72MuSablMrXABApZ0qbw0adJ8jjhvDP3f//73RCIRfvjDH6babr31Vn74wx/yzW9+k0gkwpIlS1i+fDkAP/7xj3nwwQcJBALU1NRw5513nquhj2HIH+Uff7OFUCSp5H7NohKuX1zKhp0d2C1alk7P5+n1DfQNB/najVP41XP7x5y/eX8Pt11ZydEONzMrXTR3CZTnWfn18/spz7ewYn4RPQN+hjxhtMeM4LwMI0c7RsYZ1DsO9fG1G+tQKkT0WiV9w0HWLK/CZdXx82f2cPNllby8qZl1H7TisOpo7HCzcXdXUml/ssSHN/A37Ohg3uQc1p1Q+q+p04MgCpx4sCgkDX7gY438E8l26FEqkiF3x5lUOpryAJCfaSRjAr35ZxqTVsl310ynezBILCGR6zRg1ik/9XdyseIPxQhG4liN56aEoS/mR6c6DUM/6CGeUTRxAzpPUIpKnFoHje5WKjPL2Hg0iDuYwKo/e+KRgsGK7E6X2PssSBI0tLtTRv5x1m5spq7MxYVWF8znj7P/mBhtQ4c71T4wEsR1jt4ZaSYOQYBhTwR/KJYSJT5O71CQ7oEA966qpWsgQH6mkf98ZOeYYxo7PSybO1rxRBRAo1ImU/BKHZTkWVJG/nHe29PFZbMKkKS0zkOaNGk+X5w3hv6DDz7Igw8+eNLP1q5dO66tqqqKZ5555kwP6zPT3O1NGfkAjZ1u7GYN914ziff3dvHQKweJH1NBHvacPHxbkmTiCRmNUmTptFw8wRi3XFHOMxsaaehws2JeMfGExKHmIZbPLWR/8xAKcXwurSgKtHR5cFl15LmcZFp0fO8PW7liTiEDIyH+vO4wC+tycZi1yMjctbIaXzCGzayl/UMTJYDFqMH/oTDixdNykSbAy243qnnwnln8bu1Bugf8zJmUzepLStl6qI8hT5i6cidXzi3iLFf8Om10KgWl2aMhYJ93Ix+SlSkybbpzJsTni/rRnariPqAIeZG049MyLgbyTDnsHzhIkTmPikw17zcEubru7IUwCgYbic50ib3PyskeJYVCRJIluMCkxERRQK0UiX5o40KpuLDuI83JEUSBYDSBSiHyIR8BAGaDmn99aDsAt1xeQTSWGHeNE+31y2YVEI0nuPnyCtp6PASC41OdJBk06o+PLkyTJk2ai5HzxtC/WJA+NJP0jwQpzDbTPegfVzfe44+S6zLQdYJicq7LyIgvzNLp+clJSZYxa5VcMT2XuTVZyDKYdAqOdvl47p1GCrPMLJySQ1G2mff2dKVC6AUBls0uJNuhw6gdrd/+T1+cQ/dQsr9INMH67e1AMt/+kvvno1OJSLKMjIzLpmPgWBUBQYA1V1bR2uNFrRSR5KRibW2RbUImT1mGAqeBf7p7FtG4hE6jQJBh5ex8ls8qQCEmBYHOpxyuNKdGe58P1wSVMDwVfFEfOuWpCToK0TDIMrLi4vQsGpR6co25bOzaQmlmHRsOxrhykhGV4uxsyogGO5IvXWLvsyBJyXBmrVqR0n0BuHpBMVql4oLbXVSIsHxeUUqbBaC2xIEmXZb0okAAQuE4VpOaBXW5vLdnVHSxIMuETju6LD3YPMTMqswx+j06jZKCTCNfXFWD06Jj68FeHnu9Hr1WyZoV1fSNhMiw6cZUQFo0NQeHcWJEfNOkSZPmQiJt6E8wpTkWNCoFkWO70IPuMDOrMjjSNkJehvFDdWBlVi0q4XDLMIdahqkssjGzOpMMi44Mi2ZMfSFJIlV/XZagLNvED+6fR3O3F5NORZ7TwA++PI/393YTT0gsnJpDrl2fjNo84ToWnRJ9tpnLZuWn1PpFAe6/bjJ6tQJZlhEFgZJMI99dM5PW3mSEQmmOmSyrjvIcM3Nrk7nVJu3Eh6ErRQGlWpEasyyDgEw64u7iobXHR8Y5NPQ9UT9ZBtcnH3gSFCEvCa3xgst7/izkm3JQKRQc9e4iIRbx4431zChOMD2zDqfOfmY7V+tAltIl9j4jRp2a7945iy37e/AGo8ypzSLLrkdEHlem7nzHpFPjC0a5Y0U13kAUo07FoDuI6TyrVJLm1EgkZHJdet7Y2sGcSVmU51s52DxIcY6FunIXGpWC265Mivq+t6eLuZOzyXTo2Xqwl1yXkVULi8m2acmyZCWr+mQaWTa7AJNexbAvwh/WHuDWKyrpHQ7S0etjzqQsZlVlXGBxLWnSpEkzMaQN/QnGadLwL/fNZf3OdobcYS6dkU+eQ0+WTc+UMifN3R46ev1kOvS0dnvZsr+Xmy8vx2hQEY1KvLqphStmFZBp1SB8KLlSFJOlBZPGL2RbdWRbdSgUwjFPv5IbLilFqRCQPkZgTqUQuOmSMhbV5eILRsnPNGHSKFIicXFJJhKXsBlU2EsdY86VZRmj5rPl358Koih87mvNX6y09fmoLbKds/79MT86RcEpnasIXpxCfB8mS59Jlj6TQn2CjYetUNTMM0fXsih3LrXOqjPWryAIiEYHkqcXhbbsjPVzsaFXi2zaO0BdhROjTs3GPZ3kZxRdaM58AFQiLJ+bFJcVBYFgOM4Xr5mE3ajigtu1SHNSzBoVl87I44+vHCIQimIxaik+VuL3hw9vx+OPolSI3HZlJbXFdpRKkUVTc9AoFUkHw7HljSyDXqXA6NAjSTI6tYK/u3MW67a24bLquO/aWuxGzQUX1ZImTZo0E0Xa0J9gZDk52UwqdhKKxrGbtSgVIioF5Np05Nl1iFME3IEYU8uc6LVK/um3Wxg6IV+/ocPNv35lPipRxGJUIwrQNRRkf9MQFoOa2mI7Vr0KSYbOoSBbD/ZQVWSnbyjIhp0dZNkN3HRpGTl23bj5TRCSi2mtKFDg1AP6VFkTQRDoHAryu7UHaOv1Mb0ygzVXVmI9iyq13lCcw20jDIwEqS1xUJBhQHERe08/b0SiCUZ8ERyWc2cs+2MB9KpTy9FPKu5/9vrwFyoZJgVZFujuLWVJRSbvdW3BoDZQZM4/Y30KBhuSpw9FZtrQ/7T0joTQalT85vkD+ENRFk3NpanLw8wK5wVpHOvUCm68tIKj7clIuAyrDuT0PHAxodcoWT63iPq2EXJcBrQaFS+824jHn8yxlyQJURR4cv1Rdh0ZID/DxH3X1WI5QdDWF45zpH2E3qEgNcV2CjOMFGca+cb1kwGZREJOG/lp0qT5XJM29CcYXzjOvz68nf7hZH6YKMDf3z2bksxkGKosJ0PXTFoloijQ7w2PMfKPH7OvcZAn3jxKSa6Fe66q4V9+tyUlWmM2qPmXe+cw5A1zsHUEl01POJJg2BumbzhI71CQgy1D/NtX5uM4QaU4GE2wp3GQzft7WFSXQ3WRHYtu9CfgDsb4wUPbiBzL89xV34/HH+G7t09HKY4usgQBIgmZWFzCoFFM2ELSH0nw73/eTt+x7+65d5v42uopzKpwpUvSXSR0DPhxWrQoxHOzaI9JMWKJGNpTFONTBN3EzacW9n+hMqMYXt4NtXlGZmVO443Wt7mr5lY0yjOjU5AusffZEATwBKI8+vqRVNvbOzvRaZRMLXOgvMA2SgWFwPqdnby4cTRHvyzPwgM312FUp5csFwUCrN3UOqaCz6RSB8vmFHKkdYRAOM7cydm8v6c7VXmno9/HD/6wjR9+bQFWvYpgLMGPHtuVSod8/th6YWqZk2g0jkaZ1nRIkyZNmnTa0gTT3ONNGfmQVHt9dN0REifaqQL0jIR4cVMrfcMhrKbxIjFTyp3MnZRNR5+Px9+sZ8axmuMGrZKrFxbT2O1BRuDtnR08/OphfvncPg61DHP9JUkvWCwu0TngH9Pnqx+0sXFPN7Nqsnh9azs/enQXOxqG8B1Tqe0bDjCrJpObL69g5YJiDDoVTV0e3IFRFVsZONrt4x9/s4UH/udd/vjqEXzheLLE3rFogRNxB2N0DQcJxT45yb5zwJ8y8o/zyLojhE6iupvmwqSt10em7dx5xH1RPwbVqdcWVwS9SJrPV+64Tg0zS+CZbWBWOcgyZLClZ/sZ608w2pHd3Wfs+hcbggDdg4Fx7dsO9RK/ANOf+t1hXt/SyqKpucm5aH4RPUNBugaC53poaSaIAV+Ut3e0c+nMfG6+rIJlcwpp6vQw6A6xdGYyWig/wzSmvC5ANC7RN5z8HXT2B8ZoHk2rcDHsDfMPv/6AB3+7ha1H+olNQEWgNGnSpLmQSW+PTzCBUHxc24g/QlySUBwrD9Q5GOR7xzz0Bp2KW6+o4LHX6wlF4jgsWr64qpaX3mvBH0oKEu2u72dmVQZqpYJZNZlsOdDDtEoXHxzowe2LpPpp7/OxoC6HLyyrxKRXY9Sp8IZimPUqvKE467a2cceKav70yqHUOf/3zF4MWiVVeRb0OhWdfX7e39ON1aTh5svKeWNbGxrV6M74gDfCfzy8PRVd8N7ebiQZyvMsvLe3m4V1OcysykCrEtlyuJ8/vXKIeELGbtbyndtnkGn5aOXbD9eBBghF4mkhvouI1h7vOVXc90b96JSn2H8igRgJfi5y9D9MsQsGvPD0Nrh+ViUbOt9hRtZUjKqJLzMoGmzEWndP+HUvZuym8REqGTY9ypOUXT3fiUsya5bX8Oa2Nt7b04XNpOGWyytAuPA2LdKcnFhM4s6VNbyyqYXuwQAum47bl1cRiyeYUuYkGI4Ti0voNMox5YoBtFol/d4wOq2Sr66ezJAnglololYq+MNLo6U5f/X8fv76tunUFFjP8t2lSZMmzflD2tCfYHJdBgRhbFrY5bPyae3xYTFqyLbreG1LW8pQDoRiPLuhkfuumwQyaDVK/vuxXcSP7UQfaBrivusmEYwkaOv1Ek9ILJqaQ89ggOYuz5i+lQqBLIeBXz23LzU5zp2UzbzJWRRnm8jPMNLU5R435pffb6bytun8bu1B2nuT5evcvggPv3qYv14zA4tBRSCcQKcW6RkMjKt7+8H+bnJdRpq6PDR1eWjr9XHF7AJ+vzY56RZlm6krd7HtUC8r5hSMSQM4kTyXYUzFAoCr5hdjOEEoMM2FTVufj6XTcs9Z/96oD/0pGvqKkAdJrQfhwjOeJoKZxfDuEXhlt4byojx29+1jUd68Ce9HMNiRvP3IsoTwOf2uPyuFWeYxpVqVCpHVS8uIy/IFF7pvNqhZt6U15a0d8UV4+NVD/L/755/jkaWZCARBQK0SefbtRoa9ybTFgZEQj647wl/fPp1gOIbDomVGZQZmo5qHXh51TCysy2Hv0UFe2NiEKMCS6flEYwn6R4LYzOOdCOt3djClxEE8no4KTJMmzeeTtKE/wbgDYe65upa3trfj9UeZX5eDKAi8v6+Hzfu7+Zs1M5E/ZCm7/RH21A+QkGWcFm3KyAeYWZ1JPCFj0CnJzzTxwf4e9jUO8p01M/AEorT2eFPHzqnN5sV3m8bsgG850ENZngWjTs0VswvoOLZ4ynIkqwAoRJHyfAv7m4ZTRv5xEpKMLxDj/57fz6HmYRbU5TC9MmPcPdstWtz+0ciCd3d3UlfuBOCGpWUMuUOs29KKy6qjpthBabbxpHn9dqOGf/7SHF54t4mugQCXzcxndk1G2si/SIjFJfpGQjjPoRCfN+JDpzpFQz/oRdJNvAf7QkEUYXEVbKyHvUersGS9y9ycWajEiZ1GBJUGQaVBDowgGB2ffEIa+kYCXDm3iGynAVlO6qfsOdpPfkbJuR7aZ8bti3yoDC3EEzIDI0GyTmLMpbmwiMQlfKFYysg/TigSZ9gboW84QLbTSCwhUZZr4c6V1YSjCTJsOox6Na9vaUUUkmmRb+/s4PblVTR1uSnIMo/rK9OmT68f0qRJ87km7S6ZYNzeKI+tO8z8KdncckUFh1uGeGNbOya9ikum59EzFGDe5GxuubwCk16VCoufXZtJLJ5APMHbfdWCYuIJiT++dJBfPLMPjz/C9ZeUEYrEGXSHEAWB2bXJWrJKhcDs2kxaejzjxhSNSxxtH2HQE2be5GxuuKSMKWVOth/qo6XHSyiSoHc4gPEkdYr1WiXbD/UTCMd5Y2s7fcNBppSNLr5FAa5eWML7e7tOaBOwmjSU5lnoHw7y7u4uItEEnf1+/v3h7Qx4RjcFBEFgyB9lW/0A2+oHUCtFvnpdLd/74myWTs1Br0oL6lwsdA74sZs1qJTn7rXjjXoxnKpHP+BBUn9+DX0AhQhLqsCgVtBwdCHvNDV/8kmngGB0ILl7z8i1LzZEEVQKEb1WyaPrjvDr5/fT2OmmtsRF9GPKrJ6vKBUiBu34zSONOj0XXAxo1QIKgXHzgCCA2aDija3t/OypPfzL77ZypG2Ejbu7kGWZo+0j/Oq5fUSiCe69ZhKmY9WABkaChKMJ8jON6DTKE/pRsHR6Hol0nn6aNGk+x6Q9+hNMaZ6VNStr2LK/B28gyuzaLBwWLQMjIVp7vby9sxMAnUbJX35hGg3tI+S4TABcNrOA7gE/WrUCURTQqhXsOTqQuvahlmFK86xYjRqUCpG17zUzqcTBzZdVIMvQNxRgWoWLQy3DXD67AL02ORHazBoCwRhPvnWUykIb3mCUt7a1A8mwyMYON2tWVHH9JWU8su5wKu1g+dxCNu7pHHN/f3z5EP/9F4vxh6LE4zJ6nZJnNjQQDI9GEVyzqIRch56bLi3nR4/sHHO+JMl0DvgZ8UdQKxWYDCoe/PUHqXB9nUbJv3xpDnaThp6REN5ABINeTSwmEUNALXz6ajkJWWbkWD1eqyFdg/lc09rjJescCvEBuKNecozZp3SuIjBCQj/ea/R5QxRgVgnodVFe2K5lYNjDNXUmjNqJ28ARDDYkdw/k1U7YNS9mlEqRNze2M3dyNsgyfcMhWro8lOaZGZdrdZ5j0qu4c2UNv3p+35i5yGZMe/MvBsIxmeYuD7ddWcWfXjlEUbaZuZOyyLTr6RsKptYS8YTEo68f4Zs3TWXTvm52HukHkhEfzV0erltSyhNvHsVu1uIPRnnx3Sb++d45yahFWaY4x4zDqE5X10uTJs3nmrShP8HEEgn++NJBEscWV+19Pm65vIIMm44hjx6rUcPOI/2EInGef6cJvVbJ0xsauWJ2AfmZRlRKkb+4dRr9w0H2Nw2Nu35Tp5u6chcJSWLlgmLWbW7hQPMQdrOWe66uwWXTM39KDo+9Xs+wN4xKKfKFZZV4g0kvuk6t5O0dHWOuGU9IqJQKlAqZO1ZUo1UrycswoFSI/PjRXWOOtZnUxOIJth/qo6nLw5Jpudy2rIppFRk0d3uYWu6iNMeMUhAozDJhN2sZcI9V0nf7o/zs6b0AVBbauGJOAS+/3wIkw/f2NQ5iNWl5YWMTC6fm8sz6o8QTMjqNkm9/YRpl2eaPDccLxSQ8gShPvHWUfY2DiKLAdYtLWDYrH7UiHcRyrmjp8ZJhO7dCdt6IL6m6fwoog25i9pwJHtGFS3WWAY+0j37fNB58IcTicj1Lqw3Y9KfveRUNNiR31ycfmAZBAH8wSo7LyJNv1iPLSV2UklwzkWgc/QVWZkwQZFq6Pdx+ZRXRuIRKKdLa40GZfndfFEiSTDASZ3f9AN+8aSo9wwGeXt9AQpLJcui566pq/vDSQWQ5ualvNqhTRv5xwsdKAE+vTKb26TRKvnLDFDIsGjJOSO9IG/lp0qT5vJOeOSeYjl5/ysg/zqAnnAyf73DTPxJizfIqirLNdA34yTjm4Vy/vR1RFPnd2oP8x8M7cFh0FGeP9x5WF9spL7AyMBKivnWYW66o5Fu3TGPR1Bx+/sxeDjQN8fw7Tan8t1hc4uFXD2PWa1g2p4Dmbg9G/fgQ/WAoxuNv1CMIAoVZRrRqJYdahpk3OYt7rq4h15UsKfaNm6byb3/azkvvt3CoZZhfPrefjbu7WFCbwZ3LKqktsKI9FpKnVQjcfVXNmEpmxTlmhjyjhn992whGnWrMIs5s0vC7tQdYPC2Xp95KGvmQ3AT4yZN78EfGVzY4jjsU54m36lm/s4N9jYNAcmHx3DtNtPb5P/K8NGee5h4fWfZz59GPJKLEpDhaxSl4BqUEYsiPpP18ldb7OERBIMdkIyeji9tmm+nzJfjnFwf42fphtjb6iSVOfZUtGB1II+kSe5+GRAI4Vmr1uGHT2uOlvm0E1QVm5AN4AnHWbWnjkXVHeOqtozy67gib9vbQPTS+hGCaCw9JFnh1cyvN3R4UCoFnjhn5AL1DQT7Y38O0ilEtoFhCGhOSfxyXTc/yeYUMuMN8/aaplOeax0TtCUIy0uUC06JMkyZNmgkl7dGfYLQfyiM0aJVk2nVjyr40d3m45+oaGjs9HG4dBpLRlf5glEy7nhyXgYGRIHFJoqLAxtH2EQBKci3o1Ep+9+IBls8tpCDLzNPrj3LT5RW8uDGZK+uw6uj6kMceknn6BVlmnnrrKKsWlvDo60dSn+VnGvEGogRCMTz+CAadir/9v02pXXNBgO+smYFRq8IXiuHxR8dc++VNzSydkYvpQ5OxLIPTomXN8mpiCQmnRYtWreSxE/oG8IdirJhfxEvvJe/BadERjiaIJ2SkD22a+EMxfMEoBvV4z/BIMMYHB3rRalQUZJmoKbZzqGUYpUJApVTQ2OGmMteSFuc5B0SiCQbcoXNcWs+LSW3gVFZ+iqAXSa373CrufxQZ+gz2DRxgsrOWS6v0LCzTUd8X5bntI/R7Yswu0XFZlYEM82ebagSTMxm6n+YTEUXo/1DUFCRTvSSJC247X5ZklAohtcE7+sG5GU+aiUVCQqtWMq0ig8GT/G4Ptw6zemk5e472s2pRKRu2t7NyfhHPvt2YOqY0z8KR1mFUSpEdh/vYtLebH9w/D4dRDYA/kmBf0xAHW4aoK3NSW2zHkNZ4SJMmzeeQtKE/wdjMWixGdcoYnlLuYtPe8Z6pvuEgZflW3tuTDE/NyzAypdyJ3aylZyiAWqVAOhbKNqMqAxnIsuuJRBP8wz2zGPJE0KsVXDYzjyFvmC8sq2TTvm7cvggZNh39I2Mn0KJsM25fBG8gyvbDfdy5shqPP4JOq6Km2M4vnkmG0vePBGlod6eMfEga7G9sbecvbpzCkY7xYn+iIOD2RzFrleNC5Ux6FUfahqkssPHEm/UEQnGWTMtlUqmDN7YmdQKsRg3dA37m1GYyd1I2OXYdRdlmlAoBURTGGPsmvQqTXj1uDN5QnO//YdsY9f/br6yirtyJJEE4Gqcs34Ykyekd/nNAa6+XDKvunIbfuiPeUw7bVwTcSDrTBI/owkev1KJTaukLDZBtyECtFJicq2FepYWu/gD7u6L826uD1OVpWT3DhFn36RbbgtaEHIsgRwIIms+3AOInIctgN2vHtZflHd/UvLBeeBajmivmFPLa5tZUW2mehQz7uU37STMxKEWRmy4rx3PCXH0i5XlWirNN3Lqsks37emjt8RKOSnzr1mk0dXlwWrUkEjIPv3qYNSuqWTankBc3NtHc7cVR4SSWkPnVC/s51JJ0onywv4f5k7O5Z2U1igvrUUiTJk2a0yZt6E8wgVCMqxeUEIzECIRiTCp18sbWtnHHOa06NEqRu1ZW4wtFqSq0s/1QH8+/05Q6pjTPQlmelRfebeKS6bnUFtmJSxI9Q0G8vgj6DCPf/+M2ItGkWv/1S0o50DzENYtLeXTdEUKROIIAN15agUmnBJKekqPtIxxtH0GrVlBX4WJ/wwAr5hXx9PoGFtXlcrR9ePx9BWN4QzHyXAYcFi1DntHSOJfNKmDte83cd20tGoU4pnygVily5ZxCfvDHbam2dVvauGZRCS6rljmTsmnsdLPtUB8/+sZC7AYVsgzfuKmOl95v5tYrKnh6fQOxuIReq+Rbt0zDqFGO88q39nrHGPkAb2xtY2FdDs+/m/xOX9zYzHdun0FNgeX0c/cEAW8ohgDJ7zbtbfpYmnu8ZDnOsRBf2INBeWqh90r/CJI2bXCeDJfeRZunnWzD2NKbFr2CheU6ZhVp2NIS5ntrB/niQguTcscbpR9GEAREs4vESBfKrIozNfSLgkQCTDoVM6sz2XG4DwCnVcusmsyksvkFFsEUjSeYNymLkhwLLd0esp0GcpwGotG0evrFgCDL1BTZGPZFWfdBK0um5/HurqTor9mg5rYrq/jlc/voGw6mzukZ8tPQ4WbDjg5mVGXQ0efDbFAjSRIbdnRw25VVqQ38fk8oZeQfZ/P+Hq5dVIIrXZ4xTZo0nzPShv4EU5Bl5JFjRrZOo+St7R18946ZHGoeSokfG3UqBEHgV8/vB2DVwmIEBCwGNXesqEYhChh1KjRqBZIkUZJr4Zn1Dbz2QRvZTgNXLyjGbtXyuxcPEDnmeU/moTfy9RvrCEcS3LWyGoNORTiaYMQb4p9+uwWHRcu910zi9S2t9A4FmVmTSY7TyP7GQVp7vPzV7dPJsesx6lS8sLE5tT5UiALL5hay9VAfk0sc/OWt0xjyhhl0hzAbNLisOnbX9/Ofj+yiPM/KFbPzsRuSXndBEGjocI/7nrYd6uX25dXsOtJPVZGd7Yd6ERldk9r1KtYsqyQcTTCjMgN/MEq2y4gKThp6Py7ME4jEEkQ+VF7qoVcP8YP75qIST31rPxRL8MrmNtZtbUMUYNXCEq6YlZ/SJkgznsZODzmOc2soD0f+P3vnHWdVdS3+72m31+mFGRh6L9KxIKAoCmLDEoOJxpKo4f3y3jPJ85GY5tO8lxgTjekaY2IQFUEQGxYsSBGlM/Q2DEy9c+f2e0/5/XGGy4wzGspQRs/38+HD3H3O3medc+/eZ6+91l4rhMd2ghb9aCOqP7+TJfpikO/MYXXzPjKaiiJ1kBZNEZnY10WvPIUn3g9z1QiN8/v+69+C4MlFbzwIlqL/uYgiBLw23A6Zr1zSD103iCVVvG5bl1yATGd0UmmNaDyNz20jndGIJjK4HV3wZizaIwokExoZVWNAjxw8LoVhffKoaYjj99hx2SW+dtlAook0yz+pQkBg3OBi/rZ0CwZw7tASdh0Mo8giL7y9k1RaY291mKsu7AV89rqW3sUWvCwsLCw6A0vR72TqQgkuHlOOphsk0yq5fiebdzdw96xh1IUSiKKAy6602SO/+P09lBf5yAu6eOWDPWxuWY0uznNzz6xh/PQvq7Ku9IfqY7zw9k5unTGI5ljbvfKGYaaeeea1SsYNLqFveYCA184zr28HoL4pyV9e2sz083pQkuehNhSnosRHjs/Ojv1NHKyLUeh3UpLj5Ie3jmXB2zsZ3i8fp13hYH0Uv9tGPKWSTGs8Mu+T7Au1Z4mP/j1y2H0wzO6DYVZuPsRPbx+XtbwHO1hFz/U7eXttFet31NEYSXLzZYPwfyoFnoSZr9ttkwi6FPLzPNTVRTp87uVFXhRZJNNKsb90fA/e+mh/m/Oi8Qyabpywoi8IsGFXA0s/3AuADry4fBfdi7wMrcg5oTa/6BiGwa6DYUb3L/jXJ59CGpNN9M/JO6G6cixEqrBnJ0v0xUARFXJsAQ5ED9LT3/0zz+uWo3DdKC/PfxxBlgTG9/r8RRfRk4ve2D7eiEVbZBki0QznDy9l/+EIsXSG/t2D2GSBjK4jncSi5pnAZZf5aGstz7+1I1s2ol8+119kLfh8EdANMwPL7xdszJYN6pnL1LHdqaqNcLBe5pPttZTke5g6tjs+j43dVc1Mm1BB3/IAlfsaWLpiX5t3/b7DEXwOBQyDgoCDnqU+dh9szh4f3jefvA62t1hYWFh80bFMkJ3M4cY4zy7bzkvv7uKdtVU89fIW3l57gF1VYcqLzD2+f35pE4lPRY6vCyX45T/WMnZwcXZidqg+xqZdDVSU+Lhxaj++ckk/Zk3pQyqtkkirDO2dy7WT+zDj/J4EvXYqin2U5nu47qK+XDymjKUr9lBd3zZSsarpLFy+G7dDYVdVmJ//7SN+v2AjsaTK/sPNfLj5MJIoUFHo4YapfVmx4RB/XLiRhe/s4qmXt5JIafx1yZY2q+a7q5sJeI8q8+FomuoG0+1OEAS65XvbpFWTJZHxQ4rZuLMOgE27GhjcM+ekrE/5Xhs/um0c4wYXUVHi49YZg+hXHiTU3Nadf9r47jhPIiiPKIq810HMhZWba5Ati36H1IWTGIDf3T62wukknGrGoxy/V4GQTiJoKkYHASAtTArc+ewOt9+i9GmCbomrRniYv6aZnbXpzz1X8OWj1f/rNi1AkgU+2nqYknwPfcsDNDYnqQ0lEbqgST+aVLOBWY/wybY6GlttF7PouiTTGn9/pW1A3s27G6htjPPcmzuQJIHiXDd5fgeyKCILIgMqgtktkMP7FtCzxN+m/rlDizmynmWTRObMGs4NF/elf48gs6f159bLrf35FhYWX04si34nk9+SLi+t6qRbVpxH9Cugf/cgTc0pJFGgT1mgjTu7Iot0L/Yya4ppsTh3aAnvtgTpUzWd4X0LkCSBeFJFNwzmXD8CAYNuBV4WLt+J0y7z9ekDOVAT5ZfPrEXVDMYOKuLcYSXYOlA+R/Yv4EBthE+212XLVm46xHVT+rL0wz2MH1yEQxZpaE6x62Db4HtVtZFs6r7WaJ9ynZdFM5BefSTNr+Z9zKzJfcgLOjF0qAnFWfD2zuxWhqIcF277yUXENQzwORVyfA50A559Yxvdi3z8+1fOYeHyXTSEk0w8p5Qpo8raxBA4/usY9C0LtNsD2Lubv90zsDDZWdVEt3wPwhmMghhLx9ENHYd0/FYdOdqI5vSeULT+LwtBe4Adod2EUxH89s8PWpjnkZk6yM0flof44Yw8vI6O+77oK0APHcQwjDP62znbMQzAgIqSAJquIwoCsiiQH3B1QTXfjLqfymjtyjOatUf/i4Cm60QTmXblqqbTrzxITWOM0nwPqbTK4g92s2N/EzdfNoD/vGkk1fVR1u+IMG1CD0LRJLWNCSYMLWbswMI2QXt9DplLR5cxbUw5um5YmXYsLCy+tFiKfieTSGaYeUEvXlmxB1kWufmyAaiaQTylEvQ72FtZy3VT+vLnlzZR0xjHYZP4t+tH8PQrW6muj+FyyNw4tR+7q8NU1UYpL/RSH04yf9n2rBfAYnE33//aaJa2RCWOJVUam1MsXH40kN+qzYe5bEIPakNxpp9XwdIVe9F1g7yAg6lju7dxizzCvsPNlBV6EQWBjE6HFpSPttYwaVQ3lq0+6lIrioIZ9KmFskIP8ZTGgnf3UJDr4pJx3Vm1+TCfbK/D61K4dkpfgl47jc1JZEngjiuHYOuEaOxb94eyzwRgy95Guhd5GNYnj4oSPz1LvNjFk7uOrhucP7yU99ZV09Cy4FGU42JkvwJrMvEZbNvfRPEZDsRXG2/Ab/edkLKeVfQtPhNREChyF7CraS/nFA75l+f3zLdRFVL524owd00KdqjICzYXgmzDiNQj+Kz4CJ+H3S6RTKv89eUtpNIqE4aVUFroRT7J8e5M4PfY6Nc9yLZ9oWyZ0y5TfIZjfFh0Di6HzKj+BXxUWZstkyWRskIv/boHqA+nONQQQ9MMpo7tzmXjK1ix8RBPvbwFu03m8nMr0HWDr102kIN1USYMKsLVgZeeqfhb72QLC4svN2edoh+NRrnhhhv4/e9/T7du3VixYgUPPvggqVSKadOm8Z3vfAeArVu3MnfuXKLRKKNGjeLHP/4xsnzmb8flUFi95TBXXNCLXt38PDZ/HbGkqaA7bBJfuaQ/b63dz4Uju1FW4EXTdJ55fRvV9TFEUaC8yMt766q5cWo/0hmNdEantjHextVf0w2Wrd5Pv/Ig2/aHyPM7OFgbbSfLxl0NdCvwsGlXA9dd1JfiHBc9S3z86tlP6FUaaBckryTfQ68SL3ZZIBTPIIgCdpuUDfgHUJTj5orzeiKJIu98XEV+wMn1F/VF1w0mj+pGj2I/0XiaR+Z9jNupcM3kPuw+2Jz1HojEMzy5eDP/edNIDtREKMp106vEi6b+a2tNczTF4aYkiiyS67UhiULWii5JHQf927qvif/+2ihkQeg0RTzoUvjRbWOpro8hCAIleS5cipWj97Oo3B/i8nGfvXf7dFAXbcBnO7GI+1K4Ds0d6FyBvoAUuwtZW7ueIXkDjun8Cb2dPLOqmTV7k4yp6HhbhOAvQqvfi2gp+p+JIEA0rvLkki3ZsvfXVRP0Orh8Qvcu54mSSKuMG1SM12Vj/Y46ygu9TB5VRjSRIc9zZrf/WJw8qZRGvx45KIrE2q01FOe5mTq2O16XTCSe4dk3tmWz+pTkuRk9sIiVmw4BkEipPP/WDr5z4wgO1ESY98Z2hvXK61DRt7CwsLA4y/bor1+/nhtvvJG9e/cCkEwmue+++3j88cdZunQpmzZtYvny5QDce++9/OAHP+C1117DMAzmz59/BiU/is9t4/zhpXywoZpPttVmlXww96btrGrCMOBATYR/vr4Nl1PhQE2E3t0C3HzZABw2Ga9LQdcNXnh7J4oiEk+2d3MLR1K4nObChgGMGlDAdRf1ZeYFPfE4FcBMz5dIZjjcECOT0ehV4sPjkBnVv5CCoIvivKMWkj5lAUb1z6dftwCGAU6bTCKRYc51IxjSK5ccn4Pp51Zw2Xk98NgkbpjcmwfuHM+dVw3hpfd28ehz69i+v4n9hyPUh5PohqnUl+R5WFtZ007+mkYzqGBhjvOYlPxwQmXu71dw3+9X8L3fvs/zy3fzzvpDbNrXRFLV0TSDQRW57eqNHVSEInaekn8Et02iT4mP3sVeS8n/HBqbk8QSKvmBM7u/vSZWj0c5MUVfiTaguXydLNEXD7tkI8cRYGd47zGdL4sCFw1w8+yaZuKfkTpN9BWg1R9be19WDAN2V4fblX+wvrrDbCRnO6m0zlNLtxBPZph+XgUFOS6eWLy5XfBZi65JWtX5x6uV1DcluPy8CsqKvDy5ZAuxhEZ9U7JN6t4BFTl8sOFguzb210Q43BCnW4EHv7X4Y2FhYfGZnFWK/vz587n//vspKDCjc2/YsIHu3btTVlaGLMvMmDGDV199lYMHD5JMJhk+fDgAV199Na+++uoZlPwouqYzvFce//GVc9oo+UdoDCeZfm4FF48upzmWQlV1cvwOzh9ewl+XbGHd9jrWVtby8D8/ZurY7ixdsYdzOohWPmlUGTurmgh67cw4ryd/eHEj85dt5+21VVx/cV/KCjz0LvVTkGNG7p8wtBiXTcLQDS4cUYrLLjFxRCnfunooP/zGWO79yjkUB5zZQIAOWWB4v3zmL9uGJImM7F/A2m217K1uRhQFBAzcDoX6UII+5TkoskRVbZRla/ZTmOPKtrN5d32bBYUjuBwK//PNCRQHji2n9kvv72bPITOKrm7A0hV7yag6v5r3Ma+s2geCQJ9ufqaMKsvWG9I7lwlDitrs3TsedKAxlqYpnoGuZRQ7a6jcH6K88Mzuzwc4FKk1XfePEzGdQNAyGCeYlu/LRpmnlJ2hXWS09ouTHVESkOmZr/Dixx1n0xD9Rei1uzs8ZmGiaRDwtM9sUpLn7jBGy9mOxykzpFcelftCvLfuIGu31uByyOQHrWCYXwScDomR/QvYfTDM++uqWbXpMDZFBMEgFGkbPLeuKdHhlo2CgIvDDVH+7brh2LpYVgkLCwuL08mZ93VvxQMPPNDmc21tLfn5R102CwoKqKmpaVeen59PTU17q/GZQDOgsSmBgMGo/gV88KkI7UN65/GTv6ziknHd+cGtY9h+IMwtlw/kzY/appEyDNhxoIloPMP2fSH+/Svn8OI7Zs7YyaPK2LSrnivO60VpgYffPPtJNv1ecyzNvNe38c2rh/HYc+sozHEhigKSKOIrkZEEAYcsMm5gIdWNcbbsDSGIAkGPDX+LJwCYyvXhhjj7DkfYd/joJPyFt3fSqzTApl311DYlKM5zc+BwMzdd2p95r28jmshQG4oT8NppCCdpiqa4bkpfHpn3CWpLMKUBLan4XHaZPO/R1XgDaIymqA8nCXjs5PnsSIJAUtX5ZNvRwIFHCEfTOGwyL3+wl0kjuhFwKdw4pQ+XjjPTG+Z6HSccaTeW1vjnG9tZsfEQogDTJvTg8vE9cHTBifOZZMOuBsoLz+z+dh2dulgDo/KHH3ddOVyL5gp0OffnM4VbcRFw+NlYs42+vt7HVOfcXk6eWhFmYj8X3YJKm2NioIj0+pcxDB1BsPpeR8gylOa76VHsY2/LYqjdJjFtQg8zd3gX++3qhs5Nl/ZjT3UzO6vCFOe56F8e7JQ4LhZnnkRC5YoLejJqQCE7q5ooyfPQrcDDgZpIm+w9ABt31vO9m0ez40BTdo7Ts8RHnzI/I3qPQLaUfAsLC4vP5axS9D9NR+7WwmfstT5ei2Fu7om58f4rqhoTvLlmP5t2NzC8bz43Tu3HstX70XSDyaPK2H0wTFrVWbOlhlEDCskPOKhtjHdoeZFEAU03CEXTfFxZQ3Gum4nnlCJJAqqms21/I4U5ruwL8AixpMqB2ghXXNCTulCCFRuq2X+4ma9eOoAhvc084is3HuKBv64++jz8Dn72zfEIiLidMs2xVJvIuD63jSmjy+jTLchPnlhFrNWxW2cMYsHbO7l0fA+ef2sHAytyiSUzdEuqDO9TwLrttXx9+kAisTSyJHKoIcYbq/cjSyKTR5cTjWfYd7iZww3mnvft+xt5e20Vs1pSBwqiwMCKHFZsPNTmPn1uG8m0imGAYpPIzzcVykJOXrFc/tZ2Nu6qR5ZEVE3n5Q/2MrAilwtGdDum+kdk6Soca384nvvSdYOt+0J88+qhBLxnLodxXbwBp+Ig6D/+70Ta14jgz8HpPHPuoWfy2ifCAKUXHx5YS0WwG0Fn4F+e7wEmDjB4/uMo919d8qmx3EWt3UVACGPLLz9VIrfjVL0fPo+TGTMOb67nuov6UtMYI6PqFOa4qK6L0aPYR37RqR+LOnO8a64K8d66A7z8wZ5sWe9ufm69YtApGVe72ljdmtMle2f2h2ha472V+9oEzh3SK5evXtqfPy7cxNcuH8jrq/YRS2S4YEQpuq7z/ZtHEYqkaWxOUBB0EfA6shmOTjdd6ffSlWSFrievhUVX4KxW9AsLC6mvr89+rq2tpaCgoF15XV1d1t3/WGloiJ6wS/fnURuKs2l3AwDrttexq6qJu2cN45NtdSxbvZ+maIre3QKc07+A/3lqDam0xhXn9eT84aWs2VqTzU8vigI9S/18sr2W/t2DPLF4M5IoUFrg5qOttVx/UR+mjS2nujGeXRA4gk0WCXjsrK2sYW1LZNstexr58V9W8uA3J+BxyDy19GjgJlkSuHJiL349bx2V+0IUBp3cMLU/mqZjt0nYFYlZU/rw4vJdgNBGyQd495OD9OrmpyjXxTevHkKfbn5G9slFEASefWsnW/eG0HSDt9dWtanXs9TPwcNhnntnF2+uMT0a7IrELTMGMWtyH1ZtOczAihzyvXauvrAX2/aHsvv3Rg8opLo+imHAhCHFOGWRurqO3X+PF80AmyJx/vBSvC4baVVj4fJdrN1ay5AeQXQDBIzPTKeXn+/tNFk6k897iR5Lfzje+9pzqBmHTULQdJqa4sdcr7PZ2XiAXGeQaDT1r0/+FP66KpKFvdESZ2Z/sNNpI3GGrn3iiPTL682ynR8wqew8HHJ7t/JP0y9fZPWuDMs3hhhe/qlFIX8xdVvXYyPYqVKebH/obFlOdMyw2Uw3/V/+Yy3D+xbgciq8ueYA11/UF0nklI9FnT3eJdMqr63c26ZsZ1WY5mi60+/lbB2rj4XOlv109QdNN3j1w71tyjbuauBgXYyR/QuZv2w74wYX4bTLVNVEs/Ob5R9X8bXLB1JdH6UwxwVq+xSMp5qu9HvpSrLC2Sevtehg8UXhrFb0hw0bxp49e9i3bx/dunVjyZIlXHPNNZSWlmK321m7di0jR45k4cKFXHDBBWdaXIB2OeYj8Qw7DzSxavNhmloUjfFDinn6la2AmTbIYZeZ/+YObpk+iO0HQkiiyHnDSghHk1w0upx/vFbJBSNK6VbgoSGcZNLIMvL8Tt78+CD1TQnuvnYYNY1xZFnkzTX7mXlBLwTg4221bWRJpTUONcToWeJvo6yfP7yUN1btZ3+NOcjWhBI8/sJ6rr+4LzdPG4BhwD9fr0QQBDIdvFyTaRW3w01tKMH8Zdu5dnJvpo0pR8CgZ6mfREpjzKBCKveGONQQA2B4nzz6lweobohnlXyAVEbjpXd30a97DmMHFdMcS5PvtZPjtvGLOeez92AzoiRwoDbCWx9VMfvS/owZUNhpwSZEUWD11lr+tHBTtqys0MtlEyooznOxYnMNb6+tondZgEnnlJLj/mxrq4FBYzRDcyxNrt+B3ynzZcrAt3Z7HT1L/GdaDKqjh8lz5xx3PUFNI8UjaO4zfw9djRJvIdFEnDcPvMvQ3IEUuPMREYll4kTSEeJaAgCP4qHAlYciykzs62T+R80MLrUjt9pzIwZL0aq3woALz9DdnN2oqjluXnlhb6pqozSGk1w6vgfN8RQOm4ie6VqDjmEIHQYRPLL1y6Jrk1F1OlozqG1KUJDjZOYFPdlT3Ux+wEnf8gAel435y7YTTWT4w4sb+c+bRvL8Wzu484pBVugcCwsLi3/BWa3o2+12HnroIb797W+TSqWYOHEil156KQC/+MUvmDt3LrFYjIEDB3LzzTefYWlNuhe1D/gVjqb57ldH8peXNrPrYLjN1oN+3YOs31HHgZoITyzeTLcCD5puoGk6F4/pzuMvbKRPWQBBEHjmtW3ZekN753FOvwION8SyLnCSKDDn+uE0hJJ0K/LisMlt0vKBaal2KiLTz6vg6VcqASgIutpZ2zOqTiaj849XN3HrjMHZwIK5fieiQJsX9YQhJTjsEq+t3AfAgnd2MX5QMTkehRyfg237GnlvXRWzpw0g4LXjddkozXGhSAI7o+2tldX1Mc4bVsr+wxFG9TsaiyEv4MLImAsN5fluxg8sRBKETrW8RVNqdhHmCAdqIkwZXYbbaePX8z4BYNv+EB9sqOYnt43FY2/fjXQDPthcw1Mvb0E3zD2z371pJD2LPF8KZd8wDD6qrOWS0WX/+uRTzKFYDWNyhh13PSVUY6bVs/aGnxBl3lI8Njc7mvawtm4DYOCQHLhkJ3bJXCCrjh5mbc06+uf0oU9uT/zOFG9Vxpg66KirsJjXnfTKlRiGccaDOp6NSBJU7gvx/Js7yA86cTsUVm8+RNDnYETfQry2rvX7lUSBEf3y28RlCXjseFxdawuLRcccCba4cddRr8y8gIN4IkOtbrDo3V3cMn0gBUEn2/c3sejdXdlthBlVpymaYnd1GFUzUE40CI+FhYXFl4SzUtF/6623sn+PHz+el156qd05/fv35/nnnz+dYh0TAa+NG6f24/m3dpBRdQb3zGXC0GKKg07+4ysjCMfSROJHrelNkRSl+R627Q8BUFUbBUwre1rVuPmyAQQ9dh559pM219mws56Lx5TzcavJkKYb/P3VSi4d150/LtzA9Rf35a+tciv37hbApkjousH4QUXYFImlK/bidsk47e0XBWRJxDAgHE1ht0mk0hqvr9rHrTMG8+GmQ8QSGaaOLcfpkFnw9i5qGk33bF03SGsajVH4n7+uyVpinlyyhcG9cvm3WcM4kpCuo0jKAyty2HWwif49cvA4JIwODDmGbiCAGWyqE9F1g0S6vddC0GPnN8+ta1PWFElRXR+nb2n7xZ36SKpNXutUWuPR59bzwJ3jcSpda+J9Ihysi5FKaxTlnNlo9UktSTjdTK4rSDx+bJHgj6A0VqF6jt8TwOIoQXuAoD3wuefE1QQ7QruojdczofcIXlgbZVxPJz6nOUoILcEQ9aZqpGDpqRe6i6HrkOsztzvUhRLUYXpLFOe5cXUxJR9Mr6p+5TkUBl1s3NVAWaGXgRU5fClWSL8ExFMaE88ppSTfzaZdDfQo9tGnPMDfX6nkhov7ksloROIZ8gJOFr27u828RBDA41CYMqocuyKe1u01FhYWFl2RrjcLOMvZvi/E++uquG3mYOZcN5z8oJNH5plR8R2SSKHPQfd8D5eM6w7A3kPNDOiRg7OVVTjgsdOtwMPzb+2gNpQgksh0OMeJJdsrLnWhBKmMjk2WiCcy3HzZAK6c2IubLulP3+5B5i/bgSCakfcvGFLMDRf3ZXdVM1dO7NWmnUvGdWdA9yDXTelD9yIPd18z1AykVx/jqaVbuPKCnvz310YxrFcuTy3ZyoGao3urenfzk+txcLgx1s7dctOuhjYLHUUBJ3ddMxSHzZzU9yj2MW5wMbsONjGyb36HSv6pxONQmDyybcA9u02iKNeN0IGjoPgZUX8/nSYIoCmaItrBd/ZF5P2NhxjYI3jGLbBV0UPkOXIQxeMc6gwDW8NBVF/eqRHMIotLdjIkbyAAm5vWMKDYxvNrj44ngiAg5legHdhwpkQ8qzEMM95Jeauge3ZF4poLe9MVnd1TaRWXQ2bjrnp6FPuob4rTEE62Czpr0TWJxtP8fsEG+ncP0q97gOr6KH9dsoUpo8uo3BfisnMrcCgSTpvI1y8fmH3HCgJcObEXsiRy/tBiS8m3sLCwOAbOSot+VybgcXCgNsbvXjg6KR3QI4jcKqq+IglcM7En4wcXE4qkiCXSzJrSB0GAoNdBWYGHnz25OqssjhlQwIAeQbbuDWXbyA84yfE5EYS2ho7hffPYsT9EWtVoiqZ4/c0d2BWJtKphGDBucBGiIKBhbg8oyXXzhy0bKcxxMXvaANIZjV7d/HTP96BIAqW55dmtBv9797k0RlIEPDaCbhsC4FQk/vvro3n+7R1U7gsxqn8h08+tQBbB5WibKgvA7VRQWj0LUYDRffPof/e5NMczJNIasUSa+28d2ybd32nDMJh5fk+CPgfvfFxFWYGHayf3odBv58oLevL82zuzpxbnuinN69hineuzt/tu8gIOvGfink4zqqbz4ebDXD/p2NKrnUr2hg+Q7zx+ZV2ONGAIArq9fQ5ni85HFET6BnqxrWknumsLm/f0YUdNmj6Fpru2VNCTzJ612IZOO8OSnn0IAuzY38g91w6juj5GKqNRmu+hui5CftDZZrztCvjcNlZuOsS5Q0tBgO7FXlZvPszYQUVnWjSLTiDoNbdh/HnRJi4Z34MxA12cP6yUvuUBwrE0iiLRHE2xbV8Tb6w5wE2X9MPrshGOpemW76F/me8zA+FaWFhYWLTFUvQ7mR7FXnqV+tl1MAyAIot8ZWr/rKv6EWRBoDzPRY8CNylVJ5nWcDlkJEAzDPweO6FIim37Qvz2hQ189dL+dCvwsnVvIz1KfPQqDSAKBv/vhhH85aXNNMfSDKzI4eoL+/D48+uob0pSnOfBrkikWva1i6LA5RN6oLWysud5bfz8nvPYvLsBVTMY3DOHAp89q6C2jicQcCkEXO0V1VyPjTtmDCKt6tgVEVqqFAedjB9SzIet0uJ9Y8YgvA65zWq8YYDHLne41/1M4LZJXDamjIvO6YYsCwiG6dI/eWQ3you8fLS1looSH8P75ONUPv3NmuR67dxz7TD+8OJG0qqO32Pj364bgVMRv/AeqGsqa8n1OcjxnbmUegA6OnvCezmvdNxx17XV7kH1F3a5HORdGUEQ6BvoxaaGSnoUNfDUCoH7r8hHkQTEvB7o65aix5sQXYEzLepZhaZBrt/Ff/9+BaMHFOJxKTz7xnZGDihgdL8CjC5m+fTaZW66pD9/WbyJ3QebyfU7+MYVgwl6vviLpF8GfE6Z79w4gj+9uIkFb++kKNfF1y8fRFVNhLfWVrHzYJjhffJRNZ1D9THW7ajnqgt6UlbgocBvt5R8iy7FunXr+OUvf0lTUxOGYVBUVMT3vvc9+vTpc6ZFO24effRRQqEQP/zhD4+5zjvvvMP69ev5t3/7t8897/vf/z59+vThG9/4xsmK+bk899xzpNNpbrrpJv75z38SiUS44447OvUaCxcu5MknnwTg0KFD2O12cnLMbaA/+MEPGDVqVKde719xdmhWXyDcNonv3DCcg3UxVN2gMOgk12P7TOVO1w0UUUBxHP0qJEHg5mkD+NlfV6PrBnWhBI3NSTRdp3uRj4M1UfxuO2MHFOBUJH502zgamhOEmlPoms5/3jSK11ft48MN1Xz7uuHUNMZQNYMhPXMpCrRVvgwDehT7cbey+pyIIioAdvmokg9mmr+bL+3HlJFlhGMpinPdFAQcXcLlzjBMz4vW9+OQRQZ3DzKsZy66brRZBPk0AjCidy4/v/tcYokMAa8dt036wiv5hmHw6qr9jO5/fOkuTwWHY3XIoozHdpw5oHUNe80eYr1GnhrBLD4TURAZkNOHT2o3YVO8vPhxM9eN9iNIMlJRHzI7VmAfdtmZFvOsozTfzRXn92Th8l1oukH/HkGmji7vcko+mO/EXK+Nu64ZSjKlYbNJOG0SdqlreSZYdIyhQ0nAyXduHJH1NIwk0ixduZf9h6P0KPYybUIPNuyoZ9I53ejTzY/L1vGCuoXF2Uw6nebOO+/kiSeeYNCgQQAsWrSI22+/nTfffBNJ+uL/rjdu3Eg4HD7TYmRZu3ZtdpHlxhtvPCXXuPLKK7nyyiuB07eA8XlYiv4pwKVI9CnxZfOCnohy16PQzQN3judgXQynXaKs0Mt5g4sJRVNIkkjQrYBhKlYBp0zQ5UUo9mWV6Bsv6o2qGciSyKByPyB8rmJ6qrBLIj2LPMBxKltnMdqxpnkywO9UslsQvuhKPsDH2+vIqDq9StoHKDzdbGmopMxbctz1bPUH0O0uDMtt/4ygiAqD8vrxibqBD3efw4ASO0NKHUhlQ8hsegPb0EsRrEwIbfDYJCadU8qIfvmomoHfZcPvktssVHYlbJJIjsuG6OncrCoWZweSKBB0KeTn51BbH6Uo4GTOrOE0RdPmlkC7zDUXVFjfvUWXJpFIEIlEiMfj2bIrrrgCj8fDhx9+yEMPPcSSJUsAWLVqFT/96U9ZsmQJjz76KDt27KC+vp6Ghgb69+/PAw88gMfjoaamhp/85CccOnSITCbD5Zdfzje/+U2qqqq46aab6NWrFwcPHuShhx7iP//zPxk3bhzr1q1DVVW++93v8uyzz7J7924GDx7Mww8/jCiK/P73v2fZsmWkUikSiQTf+973uPjii3n00UdZt24dtbW19OvXj+7du2fv469//Ssvvvgif/7zn8nPz+d3v/sdr7/+OrquU1payv3338/hw4eZN28emqbh9Xr5zne+c0LPcdmyZTz22GNomobH4+G//uu/GDp0KKqq8n//93+88847SJLEiBEjuP/++2lubuaHP/whDQ0N1NXVUVpayiOPPMLHH3/MW2+9xQcffIDD4aCxsTHrobBjxw5+8pOf0NTUhCAI3HrrrVx55ZWsWrWKX/3qV5SVlbFjxw7S6TQ//OEPGTfu+D1FAW655RYuvfRSrr/+egB+97vfEQqF6N+/Py+//DK6rlNTU0NhYSEPPfQQhYWFRCIRHnjgAbZv304mk2H8+PF897vfRZaPTYW3ZktnKwYU+h2c0zuXAWUBPDYJSYA8r52gS2k3gTNa3Muzn3XTM8DQDQyDM6LkW3y5SGU0nn1rJ+cPLT7jQfiSWpIdTbsp9x5nej/DwLlvI+m8M58W8MuMW3YxMK8HwdxKnngvRE2zihgsBVFC3bfuTIt3VuJSJEoCTspzXfidXVfJb42l6H3xEcwJCjluGz0LPVQUeijqIp5/Fhafh9/v59577+W2225jypQp3HvvvbzwwgtMmDABRfn8rUjr16/nN7/5Da+88gqyLPPb3/4WgHvvvZdrrrmGBQsW8Pzzz7NixQqWLl0KwOHDh7nrrrt47bXXyM/Pp6qqismTJ/Pyyy8zbtw4HnjgAR5++GFefvllPvroI9atW8fBgwdZsWIFf//731m8eDHf+c53+M1vfpOV4+DBg7z44ov84he/yJb96U9/4tVXX+Xpp58mPz+fhQsXsn37dp577jkWLVrExIkTmTt3LsOGDeOGG27gsssuO2Elf9euXdx///08+uijLF68mDlz5nDXXXcRjUZ55pln2Lx5M4sWLWLJkiXEYjGWLl3Kyy+/zPDhw3n22Wd58803cTgcLFq0iIsvvpjJkyfz9a9/nZtuuil7DVVV+da3vsXs2bNZvHgxf/rTn3j44Yf55BMz29mGDRu49dZbWbhwIddeey2PPfbYCd0LwE033cRzzz0HgK7rPPfcc9xwww0AfPzxx/zwhz9k6dKlDBo0iAceeACA//mf/2HQoEEsWLCAhQsXEgqFslsDjgXLom9hYdEpvPDOLvIDTiqKz7w1/+PajRS7i3Aq7dM3fh622j1gaKi+M7/14MtOjiPIgMI0m9R9PPy6wPen5ePrPZ706ueRy4chiF98t0cLCwsLi67LLbfcwqxZs1izZg1r1qzhT3/6E3/605+49957P7fepZdeSl6eGUj42muv5X/+53/49re/zZo1awiHw/z6178GIB6PU1lZydChQ5FlmeHDh2fbUBSFyZMnA1BeXs6IESPweEzv2oKCAsLhMOeccw4///nPWbx4Mfv27WP9+vXEYrFsG8OHD29jOX799depq6vj97//PT6fOdd7++232bhxI9dccw1gKrCJROIkn5zJypUrGTduHGVlpvFl/Pjx5OTksGnTJlasWMHMmTNxOMwtyY888ki23kcffcSTTz7J3r172bFjB8OGDfvMa+zdu5dUKsXUqVMBKCwsZOrUqbz33nuMHTuWkpISBgwYAMDAgQN58cUXT/h+Jk2axM9+9jMqKyupqamhW7du9OzZk3Xr1nHuuedSUVEBwHXXXcfMmTMBM87Bxo0bsynlk8nkcV3TUvQtLCxOmvc2VLN2Wx1fndr3TItCU6qZDXWbmNTt/OOqJ6biuHd+RKLHUCsI31lCsbsQraSazVUHePAV+Lcp3clT1pH+ZAn2kTPPtHgWFhYWFhYdsnbtWj755BNuu+02Jk2axKRJk/j3f/93ZsyYQWVlZRtP20ymberl1vv3dV1HFEV0XccwDObNm4fTaRoxGhsbsdvthEIhbDZbG6VcUZQ23pUdeRFs3ryZu+66i69//euce+65jB49mh//+MfZ4y5X28xS3bt35wc/+AE//vGPGTlyJD6fD13Xue222/jKV74CmLEJOmtffkfeyIZhoKpqO9f1+vp6dF3nqaeeYsOGDVxzzTWMHTsWVVU/16tZ19tvxz1yDSC7kABm0OCT8ZCWJIkbbriB559/ntra2qw1/8ix1jId+azrOr/+9a/p1ctMg97c3HxcXrOW676FhcUJYwbf28fz7+zi6gt64jzDmROSWpLFu1+lf05fXLaOUx92hJBO4t34JuncbmhWVPezim6eEoaVyzg8O/n5q/W875xEctObZHatPtOiWVhYWFhYdEhOTg6/+93v+Oijj7JldXV1JBIJLrroIqqrq2loaMAwDJYtW9am7ptvvkkkEkHXdebPn8+kSZPweDwMHz4867bd3NzMjTfeyJtvvnnCMq5Zs4bBgwdzyy23MGbMGN588000TfvM8/v168cll1zC+PHjswsC5513Hs8//zzRaBSAX//613z3u98FTOX1iMJ8IowbN44PPviAAwcOAPDhhx9y6NAhhg0bxvjx41myZAnpdBpd1/nRj37Eyy+/zPvvv8/XvvY1rrzySnJzc1mxYkX2njqSp6KiAkVReP311wGoqanhtddeY8KECScs9+cxa9Ysli1bxubNm7n44ouz5StXrqSmpgaAefPmMWnSJMB8vn/9618xDIN0Os23vvUt/v73vx/z9SyLvoWFxXGjajqb9jSy5IO9JDMaN07pQ8BjP2PyGBhURQ/x5v7lFDjz6eXvcYwVDWz1Vbh2rkb1F5AuqDilclqcGEWuQlw9omxyVrJ0ezmvG9OZ/PoKRvbfTcG4yxCdZ367iIWFhYWFxREqKir47W9/y69+9SsOHz6M3W7H6/Xyk5/8hP79+3PDDTdwzTXXkJ+fz4UXXtimbl5eHrfffjuhUIjRo0fzzW9+E4Bf/OIX/PSnP2XGjBmk02mmT5/OFVdcQVVV1QnJOH36dF5//XUuu+wyFEVh/PjxhMPhrNL+Wdx3331Mnz6dpUuXMmvWLGpqarjuuusQBIHi4mIeeughwHS1//a3v42iKPzgBz/43DZ/9atftdn/PmnSJB5++GHuv/9+7rnnHjRNw+Fw8Pvf/x6v18sNN9zAwYMHufrqqzEMgzFjxjB79mxKS0v53//9Xx5//HEkSeKcc85h//79AFxwwQX89Kc/bXNdRVF4/PHH+dnPfsajjz6KpmncfffdjBs3jlWrVp3IY/1ccnNzGTx4ML169WrjZVFYWMi9995LXV0dvXv35ic/+QkA//3f/80DDzzAjBkzyGQyTJgwgdtuu+2YrycYX9IobaFQ7JQHe8nN9dDQ8Pmd5WzAkrNzOVvlzM397MwHn9cfVE3nmTe2s6u6mYZwkmjiqItZ71I/A3oEEc+Aq3taS7O+bjMNyUY03VytzXEEyXUG252rKDKZjIpgQKChGmc8ipJJZY8nXF5S9mP3ADhdiJKA3gXzRp8quQ0M4pkktXE7dfEidI66unnEBC4pjV2GbuXdufHSIfjcts9s60T7w6ngbB0zjgVL9jNDZ8t+JvpDV3v+XUneriQrnH3yfl5/ONWcSL56i65DY2Mj1157Lf/4xz8oLi4GYMGCBbz22mv84Q9/6PTrfWkt+sHg6UmddSYHi+PBkrNz6SpyHuHz+kM0nmbz3hA1jWaKGIdNolepH2+LElUXPr7AIJ1FUk1R15hB093YZRsuxYGWEKhNZDo42ywTDB0jLBLR3ICbjCShSRKkMP+dlXTVeAGnQm4BcOETwOuqRTV0whkPTaqXqO4kqjshAwe2NXH9ZeIJ98PT9X5oTVcbM1pjyX5mOF2yn8r+0NWef1eStyvJCl1PXotjZ+XKlTz44IMdHhs7diz33XffaZbo5Pjzn//M4sWLOzz2jW98gyuuuKLDY/Pnz+fhhx/mm9/8ZlbJP9V8aS36FhYWFhYWFhYWFhYWFhZfRKxgfBYWFhYWFhYWFhYWFhYWXyAsRd/CwsLCwsLCwsLCwsLC4guEpehbWFhYWFhYWFhYWFhYWHyBsBR9CwsLCwsLCwsLCwsLC4svEF/aqPsNDdFTnj4pGHQRCsVP6TU6A0vOzuVslTM/3/uZx46lP5yt93UsdFXZLblPHSfbHzqTrvC8PgtL9jNDZ8t+JvpDV3v+XUneriQrnH3yfl5/sLDoSlgW/VOILEv/+qSzAEvOzqWryHm8dOX76qqyW3J/OejKz8uS/czQlWU/Qle7h64kb1eSFbqevBYnRzQa5cc//jHTp09n5syZzJ49m82bNwOwatUqZs+efdxtRiIR7rrrrpOS69e//jWXXXYZl19+OU8++eRJtXW28KW16FtYWFhYWFhYWFhYWFicHnRd5/bbb2fs2LEsXLgQWZZZuXIlt99+Oy+//PIJtxsOh6msrDzh+qtXr2blypW89NJLqKrKZZddxsSJE+nZs+cJt3k2YFn0LSwsLCwsLCwsLCwsLAB4Z+0Bbv3Z61zxH4u49Wev887aA53S7qpVq6itrWXOnDnIsmlvHjduHA8++CC6rrc5d/bs2axatQqAqqoqJk+eDMDixYuZOXMmV199NXPmzCGVSvGzn/2M2tpa7r77bgAWLlzIVVddxcyZM7nvvvtIpVLZa33jG99g5syZZDKZ7LXGjBnD3/72N2RZpqGhAU3TcLlcnXLPZxJL0bewsLCwsLCwsLCwsLDgnbUHeOy59dSFEhhAXSjBY8+t7xRlf8uWLQwZMgRRbKuCTpw4kdzc3GNq45FHHuGJJ55gwYIFVFRUsHv3bubOnUtBQQG//e1v2bFjB/Pnz2fevHksWrSI3Nxc/vKXvwAQCoW44447WLRoEYqitGlXURR+85vfcPnllzN+/HgKCwtP+n7PNJbr/inAAA6Hk+yti1GQ40RVdZrjGTAg4LWRSGnEEhk8DhlFkQhFUvg9djRNJ5rIEPDY0dFRVYOMqiNLAl6XjWRaIxLP4HEqaIaOmtExAKdNQpQEGptTBL0ONE0jltTwuBQkEWJJDV3T8XvsqKpOJJHB61LQdIN4QqU+mkaWROIpFaddIpZQUSQRSRRQbCLN0TQuh0w6o6MbBg5FwqZIJNMqDrtMLKGiajpOh4yq6tgVkWRGI53W8LhsGLpBPKUiSyJel0I4mgYB3E6ZjGqQSKq4nQoOm0giqSJKIum0it9tw2WTME5fTCwLCzKGQTSpEk1k8DoVVM38AUYTZt9LZzRkWUSRBTQNMqqOwy6Rymioqo7LoZBMaRgYuB1mP/O7FJriaZpjabN/twS2kkRQdYNYQsXtkBFFAVXT8bntJFIqoX2NJNMaqq7jdigYhjkmZFSdgMdGWtWJJVXssoTTIZJM6WQ0HaddJp405dUNSGd0DENHlsy+6bBJGDrYFAmXXaIxkkIQBAzDAAHSaR2vSwFBINct86lFdgsLC4tOQRehLpwCHWw2kYxqzoOCXjuaZqDqOhhgkyUEwSCR1tE0c3yyKRINzUlskojDLtMYSeJ2KCiSSHPcbCMcTWFTJGyySFM0jdel4HHZqK6P4nfb0QydVErD77ETS5jHYwmNWCpDnt+BrhtEExmcdpma5noAc75jN+c7qYyGx20jEkvhc9lJqxrpjEbA40DVNUAgHE3hssuIgkA8peJyKmQyGnZFwtANgh4boiDQFM+QTKkEvXbsskAio9MUTeF2KvgcsjUXsjht/O2VraQyWpuyVEbjb69s5cKRZSfVtiiK5lzjJJg0aRI33ngjU6ZM4ZJLLmHAgAFUVVVlj69atYp9+/Zx3XXXAZDJZBg4cGD2+LBhwz6z7Tlz5nD77bfzzW9+k/nz53P99deflKxnGkvR72SakirvfFzFkvf3oOsGvUr9TBvfg8eeX48ii8y5bjhPv7KVZFrjuil9mP/mDppjadxOhesv6svC5buQRIG7rh3G66v38eGGQ+QFHFw7uQ9PL91KLKly0yX9Wb+jjk27GxAEuGBEN4pz3SRSKh9sOMhVE3vz1MtbGNonjzEDi3hyyRYSLS+Payf3YdPueopzPSx6dxeabtCj2Mc1k3oT9Dr49bPr2H84giQKXDOpN7oBkiiwbkcd2/aFEAWYeE43Rg0oxO2Q2bovxKsr9jJtQg/Sqo7PpWAY8PSrlcQSGXxuGzdO7cc/X9/GbTMHs+jdXazeUsOIfvmUF3pZ8v4eNN2ge5GXKy7ohcep8OtnPyGeVAl47Pz7jSMoy3NZLziL00JjPMO+QxE+2FDN2EFFbGhK0KPIyxOLNxOKpHDaZa6Z1JsVG6qZeE437IqEyylT35Tk2WXbSaU1cv0Orr6wN39bupWyQg+zpw2gcl8jTy/dSlrVKcxxcdvMweytDuNz2/jry1uz/fOGqf2QRIFoPINhGLy7rpr311cDMGZAIUN65/GXxZvpUxZg9MBCnntzB5mWNmdPG8DTSzcz7dyePLdsO7Gkis9t446rhrBqYzXlRX5eeHsHybRGjs/BdRf1RRTA77GzdU8jNaE4bqfCW2v2oxvQp1uAa6f0Yd+hDEN75ljuXxYWFp3K7oNNrNp8mIZwkqIcF163jb8u2UI0keGGi/uydW+I9TvqEASYNLIb3Yt8PLlkCwC3zRzMJ9tq2bSrnu/OHs2jz62nuj6GLIlcd1EfuhV4eerlLTQ2J7njyiH8ceHG7Fzr65cPZOveRj5YX82VE3uxesthcv1OZpxXwcZdjfxtqank5AedXH1hb/7y0mYuHFmKIkm8tnIvQZ+Dayb34R+t5jl3Xj2Ep5Zs5aIx5Tz9ylYqSvzMnjaAx19YT1VtFFkSmDa+gn2HmwlHU1w4soz31x1kwtASDMNAkUWefqUSVdPpUezl1hmDeGTeOhqbkzhsEt+8aghDK3LO8Ddm8WWhPpQ4rvLjYfDgwTzzzDMYhoEgCNnyhx9+mAkTJrQpA7KLAqqqZsvmzp1LZWUly5cv59577+Wee+5h5MiR2eOapjFt2jTmzp0LQCwWQ9OOLlw4HI52cu3atYt0Os2AAQNwOp1MnTqVbdu2nfT9nmmsuVsnc7Auykvv7s5a7HYdDPNRZQ19ygKMH1LMc2/uoDaUYMroMv75xnaaY2kAYokM/3i1kotGl1PXlODPizaR4zN/iFNGlfOXlzYTS6oU5bqoDcXZtLsBAMOA5R9XIQhgYKCqBkve38P5I7rRtzzI71/cSCJldo5QJMULb+/kwnPKWPDOTrQWGfceaubddQcJRZJU1UQA0HSD+W/uwOe2UR9OsG1fCADdgLfXVlFdF2PP4Wai8QwXnNONt9YcIJpIY7fJPLlkC7GEue+lOZbm769s5ZpJvTncEGP1lhoABvfMY9G7u7My7DscYcWGag7URPA4TVeapmiKX/7zY+LptquKFhanAlkWOVQfY39NhJrGOHsONdOrxMdfX95CKGLu7UqkVJ55fRvjh5Tw9CtbCcfSKJLE069sJdXyO20IJ3npvd1cMKKUnVVh3vn4IO+srSKtmmbxmsY4z7xWycCKXP6wcFOb/jl/2XZqG+Ooms6hhnhWyQdYvbWG+nASr0th7KAinnltG5lWbb74zk6undKPZ16tJJY022yOpfn9CxuYMKyUf75eSbJFxsbmJAve3kFVXZRkWmXRe7voWepn2WpTyQfYUdXEh5sOsWJDNbXh5Kn/AiwsLL40iJJAdX2MF97eid9tpyjXzZ8WbSKayJAXcBCOplm/ow4w5zlvfVRFIqWiyCIBr52ahjhrK2u54eJ+zH9zO9X1McC0tj/zmjk533uomSsn9uJ3Cza0mWv9aeFGRg8sJJXReHbZds4dWsJHW2vYsLOeaCKTtWTWhRK8smIv5w0voTDo5pUP96Ib5pzsqU/Ncx5/fgOXju/Bi8t3cuHIMhRF5Lm3tlNVG22Ry2Dx+7sZ2juPfYcjbN7dgE2R2HWwCb/HzpNLtqBq5njetzyHx55bT2OzOe4m0xq/mb+O+mjqNH07Fl928oLO4yo/HkaNGkVubi6PPfZYVvl+7733WLBgAb17925zbjAYZOfOnQAsW7YMMBX+qVOnEgwGufPOO5k5cyZbt25FluXsYsDYsWN54403aGhowDAMfvSjH/HUU099rlxVVVXMnTuXdDpNOp3mzTffbLN40FWxFP1Oprou1q5s8+4GencLUJTrZn+LIq3IUvYlcYTWbjIH66KU5XsAcyvAkQl9r9IAW/c2trvG4YYYtY1xyou9HGqIket3oGpGu9y3jc3Jdu44AFt2N5DOmK72rUlntI6v1xhDQKA41w1AebGPeEIl1EH7saRKWYGXTbvMxQlRFLLKTRsZ9jRiGAY9SwPZsnA0TVM03e5cC4vORlYkwtEUO6ua6NXNT+XeBlIZjYZPKbm6bpDRdFTNQNMNmqKpdh4nNY1xgi0LdR9vq6F3WaDN8T3VzcSTarv+2RBOgiDgcihsbOkvrdlZ1UR5kS+7aNCaXQfDAO2ORRMZEimVT6fBrg0lcNll6kIJ3A6FUHN7ZX7DjnqG9c2n3lL0LSw+l5N1Rf2yoepQ35SkONfNgbqIqWC3LERWFPvZtj/Urs6BmigFQRcVxT4q95nzkqI8d9YQ0Zoj47bHaWs310qrOs2xNE67nP0M5uLmkXH7CPtrInQv9FHbOse7QPt5TiKDTRGpb0ric9vo3S3Axp317eRKZ8xrHZkXbtndiF1pm1ou4LVT+ynLqW7Q7l1kYXGquHnagHa/S7sicfO0ASfdtiAIPP744+zfv5/p06czY8YM/vSnP/HHP/6RvLy8NufedtttPPPMM1x11VUkk+bvX5Zl5syZwy233MLVV1/NRx99xC233EJubi4lJSXMnj2b/v37c8899/C1r32Nyy+/HF3XueOOOz5XrokTJzJx4kSuvPJKrrnmGkaMGMHll19+0vd7prFc9zuZgmD7CI0VJX4O1kUp0l0UBJ3UhhIYhoFdkdq8LCRRQJRMl5VcvyM7qMuSgCgK6LpBVW2EHsW+7CrxEfIDLiKJNLu21pDjcxCNp9u9sAA8TgW7rX2+0ooSP7Istnsh2hSJHkW+dgsYuX4nGNAUTSKJAtV1Efp3z8HvsSOJQtZSD+bg0BBO0LtbgA0769F1A0eHMvgQBIGDdUfvzWmXzb3CFhanGFXVcTsVygu9bN8foqzQh90m4XUpROJt+4UiiwgC2fgZnybgtWf7Ut+yIAc/1V+Lcl047e37wJHfejqj0bcswEdba9ocLy/0smrzYYb3zW9XtzjPjdRqrDiC3SbhtLUf6v0eG6mMRkGOi3hKxe+xtzund5mfXQebmDKyvN0xCwsLk8yuVaQ+fAbPV399pkXpMkiCQcBjoy4UZ0S/fDxOBVkSUDWDg3VR+nXPYXfL4uURivPcfLytFlXTGdYnjx0HmmiKpigv9GaNKEfwe8xxOaNq7eZaoijgdSmk0qbBQZFNm1dZgZdYvK1hoSDopCYUz3pYHqnf0TzHMMDntpFMqdQ0xqko9bNjf1Ob9hTFvFZFiY+DdVEqSn2oWttFg0RSxe+xmfGMWhHoYIy2sDgVHNmH/7dXtlIfSpAXdHLztAEnvT//CDk5Ofzf//1fh8fGjh3L2LFjARg6dChLly7NHrvnnnsAmD59OtOnT29Xd968edm/Z82axaxZs9qd83nu+HPmzGHOnDnHdhNdBMui38mUFXkYM+holMaAx865Q4vZsLOedz85yKwpfXHYJN75uIpZU/ogiaZiL4oC10zuw/vrDmJXJG6fOZiNu8zV4PfWVXP9lD6Iguni3rtbgPxW7jMDK3IQBPNazdE0V07sxfKPq9i4s46vXNKPI9tdZEnk2il9WFdZy7lDi7P1fW4bU0aXkeNz4HYeVaovGFFKfVOCPuVB8gNHrzeoZy7d8j10K/AQiWd4b91BRg4oIj/HhW4YXH9xX8SW+5JEgVlT+vDcWzvoWx6kvMgLmKvk4wYXtZHhwpHdKM33cLA20iKvwD3XDsPntBR9i1OPmtEoyffQo9i0mI8aUMjug2G+eumA7ERQEGDmBT1Zvekw107qgyKJaLrOzPN7ZvuZTRa5+sLeLP+4ioDXzvTzK6go8Wev47TLfOWS/tQ0xLn+4r7Zeooscu3kvuQHHSTTKuVFXnqWHq3XvchLUa6bxuYkW/Y0cPGY8jZtXju5D4vf2821k8yxAsz+940Zg1ixsZoZ57WV8ZpJfcjzO3HaJMYOKqKuKdFmASHH5+DiMd0pzfNSGLAmmBYWn4V2aBtGPPyvT7TIYuhQkudh/JBiQOBQQ5SvXNIfUTRd+ksL3BTnHTWcDOiRg8shk2hRossLfZTme3jm1W3cdGl/3I6ji5kXjS7DbZfxuW0sXL6LW2cMOjrXEuDGqf3YVRVGN2Dq2O5s2FFPeZGXc/oVkEgf9Ta02ySuON+cTzXHU9nx8f11B5k1pU+bec43rhjEwnd3cc2k3rzzcRV7qsN89ZL+2a2IABOGFLOnJTbLhKEl7DkU5px+BRyojXLZhB7Z81ZtPsQ3rxqKrdV7Z/al/cnzWeOwxenjwpFlPDF3Ki/9ciZPzJ3aaUq+xelFML6k/mYNDdF2brOdRTRjUNMYI5lSKc7zIIo6tY2mdb4wx00kniYcSxH02JEkkcZwgqDPgaYbhCIpCoJODMN0148nVWRZIM/nIJpUCUfNegamS64AuJ0KsixyuCFOQdCJqmk0R82Is7Ik0NziEpcfcJBWdZoiafICTpJplWg8g99jw65IxJIZnDaZUDSFTRax20RsikxDOInPZSOayLSsWJvXS6YyuBw2wtEUqmbgcSlouo5dloinVOJJ00ooCAbhaBqHTcLvtlPbZLqk5fmdxBIZYokMfq8Np10mFssgygLxhEqe30GglTU/P99LXV2kgyd+dnG2ypmf7/3MY8fSH87W+zoWjkf2aEojllJpiqbI8drJ6Dq6BuFYCr/bTkbVkCQRRRYxdINURsflkEmmVZJpDb/bRjKVIa0a5PodZDSNgMdOfVOKpkiSvIATBNODwG4zI+U3x9L43DYURUTTDLxupSX7hUA4miGj6QS8dgQMIvEMum6Q53eQSGvZAFNep0I4lkbTDJx2mUg8TcBnR0AgmVbRNANJEognM7gdNkQRFEnE6ZAJR1KouoHUEvU/2RJU0DAM8nwO2vsedN7zPlOcbH/obFnO9uf1WViyQ+KdP6Nufx/PrX9EkNt7+JwKOvu5n4n+kJ/vZW91mLqmBLJkZjJJZczxMNfvRBQgmkgjIJgKs2Bu59Naxj9JEqgNJXDYRJx2G7WhOF6XgsMmE2oZaxuakrgdMrIsUh9OEvDY8Lls7KuJkuOzm9mHkhny/E6aY2ly/HaaImmi8QyFeU4M3aCxOY3PZUM3zIwnqmbgd5seUbGkSsBjI55S8TgUMqpGNGnOXzDMrCoN4QQel82cj0XNOVciZWZaUTWdAr8TUYS65hTxRIb8oBOfU6YplqE+bG4FyPXYswu4x/psu1K/PNvk/bz+YGHRlTgjiv5jjz3GK6+8Aph7Ir773e+yYsUKHnzwQVKpFNOmTeM73/lOmzrf+973GDt2LFdffTUACxcu5Be/+EU25+KFF17Yrs7ncTomcmfbwPVZWHJ2LmernJai3/Vkt+Q+dViKfudgyQ7xVx9B278O91d+ieg5tjzQJ8sXRdHvSr+driRvV5IVzj55LUXf4ovCad+jv2LFCt5//31efPFFBEHgtttuY8mSJfziF7/g6aefpri4mDvvvJPly5czceJEampquP/++/nwww+zezYANm7cyPe///0O92hYWFhYWFhYWJwW0magNiMdB06Pom9hYWFhYfGvOO179PPz8/n+97+PzWZDURR69erF3r176d69O2VlZciyzIwZM3j11VcBWLx4MVOmTGHatGlt2tm4cSMLFy7kiiuu4D//8z8Jh639cRYWFhYWFhanFyMdB0HCSFtR0S0sLCwszh5Ou6Lfp08fhg8fDsDevXtZunQpgiCQn380CFRBQQE1NWa06dtuu63DqIn5+fl8+9vfZtGiRRQXF/OTn/zktMhvYWFhYWFhYXEEI5NCcHqzln0LCwsLC4uzgTOWXm/Hjh3ceeedfO9730OWZfbs2dPmuCB8ftSR3/72t9m/b7vtNi666KLjun5urue4zj9Ruso+H0vOzqWryHmEY+0PXe2+WtNVZbfkPv2crvdDa7ry8/qyyx7X04huH16XiOc0PovT9dxPZX/oar+driRvV5IVup68FidONBrll7/8JWvWrEGSJHw+H9///vcZNGgQq1at4rHHHuPpp58+rjYjkQjf+973ePzxx09Yro5iyHV1zoiiv3btWubMmcN9993H5ZdfzurVq6mvr88er62tpaCg4DPrRyIRXnjhBb7+9a8DYBgGsnx8t2IF4zuKJWfncrbKaQXj63qyW3KfOqxgfJ2DJTto6RR4HTQ3hkmcpmdhBeM7/XQlebuSrHD2yWstOpw6dF3n9ttvZ+zYsSxcuBBZllm5ciW33347L7/88gm3Gw6HqaysPOH6HcWQe+ONN7j44otPuM2zgdPuun/o0CHuvvtufvGLX3D55ZcDMGzYMPbs2cO+ffvQNI0lS5ZwwQUXfGYbLpeLP//5z6xfvx6Av//9713+i7CwsLCwsLDoWhiGAWoabC6MTOpMi2NhYWHRKUQ2vcv+R+9k9wPXsv/RO4lserdT2l21ahW1tbXMmTMna6QdN24cDz74ILqutzl39uzZrFq1CoCqqiomT54MmPHbZs6cydVXX82cOXNIpVL87Gc/o7a2lrvvvhsws7NdddVVzJw5k/vuu49UKpW91je+8Q1mzpxJJpPJXqujGHLV1dWdcs9nktNu0f/LX/5CKpXioYceypbdcMMNPPTQQ3z7298mlUoxceJELr300s9sQ5IkHnnkEX70ox+RTCbp0aMH//u//3s6xLewsLCwsLCwMNE1AATZbir8FhYWFl2cyKZ3qX/59xiqqRyrzfXUv/x7ALyDP9sQeyxs2bKFIUOGIIptbc0TJ04EYOfOnf+yjUceeYT58+eTm5vLr371K3bv3s3cuXO5+eab+e1vf8uOHTuYP38+8+bNw26388tf/pK//OUv3HXXXYRCIe644442mdzAjCF3hCMx5ObNm3dS93o2cNoV/blz5zJ37twOj7300kufWa/1wgDAqFGjePHFFztVts7CqzegNh4iciiFJ1iImogj2e1o0TCCKCLYnRjJOILdiWhzoafiGLqKIAjoiSiyPx8tHUd2B8jUVyM63AgON0Yigp5OIHlyQABD00BTQQA9FUe0u0CyASCIAlo0ZJbpBogCkitAJnQIyelFz6QwtAyi00s8JuJMxcEwEBQ7gsODHm1ET8WRffkYuoqhZpCcHjLhemSnFx0DUZRMuZJRDE3FyKQxNA3J7QVRQmtuQHT6Ee121FANgmJH8hegNdVgaBnkQCF6KoYejyB6ggiSgtpcj+zyoakZDLsP1V2IhnSGv1GLLzt2MYUcq0GQbKCr6MmY+ZvXVGR/PmpzA+gqcrAIPRFDkGW0SAhBVpC8OWD3YsRD6PFmRLsbLRYy+7XdhR4NtQTySqKn4ub5igM9GSeRsONoOoygOJH8+ZBJoDY3IHmCYBho0RCSNwdDzaAnIsiBArREFNQMUqAAPdYEigPUNFqsCdmfjyGIoGUwDB3JHUSLNmGkE0hON4YoYyRjoGtIniBavBlBFEEQ0NMJZG8ueiqBkUkheoMYiRhaIoKcUwSZDFq0wRxTkm48CC3nJjG0DJIvDxQ7RqQRLRlFcgdBUkBNISh2jEwKPRVHECUkbw6q7CEp+c70V29h8floafN3LMkYlqJ/3Li1BvRQNYLNhaGlEWUbWiKC5PKjqykEUUZPRMz5gzsIgoAWbcRQVeRAAUYyihptQnL7ERU7ajSEIMkIsoIgyuacSMugNtcheYKILj9quA7J6UFrrkewO5EcbnRVRc8kkZw+BElGjTQi2hzosSYkfx56Mk6kTsXt9mMYOqRTaIkISqAA3QDB0NGijYguP4IgoEYakD05YHejNdciSAqi3YWhZTDSSQxNRXR5ERQ7GAZ6tMmcFwWL0dMJyKTQMylkXx5qcx2CJCN5ctDTCYx0AkM3kILFGKKCEalDMFT0VBLRX0jSVQSATU8gNB/CUNMI/iJSSgBBAFs6hBE+jGBzonuLyAgOJCODHK/FiDchevNIu/JREg0YzbUITh+apwgV5Qz/WixOF6G3/5FV8o9gqClCb//jpBV9URRNT6iTYNKkSdx4441MmTKFSy65hAEDBlBVVZU9vmrVKvbt28d1110HQCaTYeDAgdnjw4YN+8y2W8eQ69Gjx0nJeTZwxoLxfVHxZA7T+M4zJPdsAEB0eCi4+t+pX/xb1HAtAEpeN9x9R5OJNmELFJLYuwElp4TIumVmI6JM/ox7aFjxV5w9hyMm3eixEE0rXgQMghfehKGmSddXofjzCK9abNYTRILnX4fg8KCGa0lX7yB5YCvu/uPxDL2Qmhd+jnfIhTRtX02m3uwQsr8A74iLERU7DW/9jfzpdxNb9ybxyg/NJhUH+TPuIb7rY/RkDO+QiRx+9n/IveRWogcq8Q4+n8SeDaQObid1cLspvstHYMLVNC77K7kXfZ3QBy+gJyLkz5xD46t/JHVoJ86ew5FcfqKblpvXkRSCk24i9O6zGOkkgfOuIbr5AzzDL0LqMxHN+qlanCHskoqxZy0Ei9BTSaKb38VQM8S3rSJ4wfWE3nkGtcnMEqLklZJz0S3UvfQoWqQBAFtRT3Im30ztgl8QPP866hY/mnXxdfUbi72kD6nN7xHfvgYAwe4iZ/LNKL5cDs//H4yUGcnb1Xc0os1F6tBO3P3GZscDELL9RU9Gybv8W8T3bEBPRlFyikHXCK9qWUQVRPIu+ybpmr3IvlzCHy7CP/oy6hY+TOCCG4htXUGmbj9gjg3B82cR27GW2LaVZnXFTs6FNxHb8RFKoIDIumXYUk2oOwABAABJREFUiipwlg8ivHpJ9hrBC67H1q0f4ZWLSO7bCIB78EQEONrnFTs5k2dj6Drx7WuwFXanuaUNW3Fv/OOuwOkrJmE/mpHFwuJsw1BbFH1RxtAsRf94SFRto2Hp73H3HU1s+xp8wy+ibvkz+MdMJ7xjEd4hE2l855nsAop3xMVI/gLCH75I/hVzSNfspvH1JzE00/3WO/wi1EgDiV2f4Ow5HKWoAtnlp3HZUxwZK4MTb8DWrR+H5/3UNJQAnqGTcPYYRrp+P0qwiND7z+EffTn1b/8Dz7DJpD95nVTVNsCc3+Rdeju1C35p3oQgknf5t2j6YIHZ1qBzaXr/hez1cqbMJlV3AHthDzKNh0jX7GnTVsGV/07TB8+T3LfJLHN4yL/i29Q8/7/kTLqJxrf+hhZpBMBW3AtX79E0vWdaGZXcEnxjryBTu5/mj5YelWf6PWTsQ4i9+QeSe9aZ7Tq95F17H4auUf98q/dKv/G4z/sK6e0fUN/SLqJE3mXfIvTuP9GazfeYd8wVyMOmowq2Tv0NWJydqC3f+7GWHw+DBw/mmWeewTCMNoHXH374YSZMmNAuGPuRRQFVVbNlc+fOpbKykuXLl3Pvvfdyzz33MHLkyOxxTdOYNm1a1rAci8XQNC173OFwdCjbp2PIfRE47Xv0v+hkavdnlXwA0e4iVrkyq+QDZOqrMNQM9rxuhN6bj7Ni6FElH0BXaXjjCZx9RiEARiZJ08pFgIGSW4radBhDy2AvKCe8ulXgCkOn6cMX0eNhBMDZ01yxSjdUEa9chaGmMdR0VskHUMO1qKHDxHauxd1vHBhGVsmn5dqhd59FCRZjL6ogvGYp7iETCb39D9x9RxNesxQlr1tWyQfQ480k927EO+Ji4rs+QU9EkLy5qJEmUodMlxxHWf/shB/A0DKEVy/BM+g8wKDpw4XmC/PtvyMn6k7uS7GwOAmU2GGSB3egxkI0rViALa+M+LZVSL481EhjVskHyNQfJLlvc3biCZA+vJtU9Q7c/cbS/MnrbfbxxretQvLmZJV8ACMVJ9NQRej957KTMYD49jUoucW4B07IjgctNbL9RU9EiHz8Op7+44lvW4m9uOdRJR/A0Glc9ldcvc+h6cOF2At7kK7Zi6vvaFDTWSUfzLFB19Sskg9mGrHmj1/DM3BCdsxy9x3b4ThkxCNZJR/AllvSts9nUjR/9IppnVLTSHYXCOYrKX1oJ1qkkUTle4ji52dgsbA4o6gZBEkGSbJc948DSYLEzo/RYk3o6SS2nBIiG99GVOzoyZg5dq15uY2XROSTNxAMDc/gCzDSScIfvNhmrI2sW4azYjgAid3rcJb2o/Gtp2k9Vobefw49HoFWFsXohrfREmHQdWKVK3H1HE541WIMw0By+7KKOZjzm8j6t7GX9mtpUqfhjSfxj7ncnLN8uLDN9RpbLKDh1S8jubyfaitCunZfVskH0JNRwqtfxjNsMqmavVklHyB9aBeGmkSwmUpKpqEaramGWOWKow/W0Gl4/c9k6vZnlXwAPREhunIBiS3vtn2vbPsQobma8HutXJR1jcY3nsAz4Nyjz3b1S0jRQx18kxZfRGRf7nGVHw+jRo0iNzeXxx57LKt8v/feeyxYsIDevXu3OTcYDGZd+ZctM+ccqqoydepUgsEgd955JzNnzmTr1q3IspxdDBg7dixvvPEGDQ0NGIbBj370I5566qnPlaujGHJfBE5K0f/lL3/ZWXJ8YVCb69t8VoJFpGv2tjsv03gIQbGBoWOomXbH9XgziidA+sjEu2UfoBIsQos1ozbVYmgqGG0DVxiZFBg6gqxk21Xyykke2onsyyfT2H6gTtcfAENHCRaixdtHPc3UH0AJFGCoGdK1+7AXdkdPxUHNkD68G6EDF5x03X5s+d1J1+0DwFbYA7XxaFCLju5Za65HcrW46mpqy4vYwIg3tzvXwuJ0oSciOEp6IRgG6Zq92YmnEiggU3+w3fnpmr3I/rZW6HT9AZT8sjaLbNn2O+hzgiSRrt3XrtzIpM2tOLrW9kC2v9AyZpjH9WSs/fVS8RZX/yiizUG67gCO8kGkO5Stfd/LNBxsM7k2/247Bhgt2xDalGmfkrmlLQQRJacILd6MaHdmj6Xrq9CaG5BoX8/C4mzB0NIgyQiiYrnuHweKCOnafcj+fDKN1cg5RaRr9yP7C8g0VCO5A6jh9ov8hppB8edjqGnU5g6MAPpRq5+eTnY4VurJaJuxBsxxXskrJV2zB8nlQ4s0IDpcaNGmdpdI1+1HySk6KlMqjtjign/ES+Bowxp6PIKRSaLFwm0OCYot6/nVpv2aPdiLe5GpP9DumNpUa25haCF1aBeyr+37xkjF0WLtx+7UoZ10tGyqx8Pty1JxBLmtq74ea+qgtsUXkeCkm8y4I60QZDvBSTeddNuCIPD444+zf/9+pk+fzowZM/jTn/7EH//4R/Ly8tqce9ttt/HMM89w1VVXkUwmAZBlmTlz5nDLLbdw9dVX89FHH3HLLbeQm5tLSUkJs2fPpn///txzzz187Wtf4/LLL0fXde64447Plat1DLmZM2cyc+ZM/vnPf570/Z5pTsof+p133uE//uM/OkuWLwS23NI2n1M1e/CNmNrG4g2m4qvFIwiyzRxMBbGN0q7kFJOq3Y+9tC9GOolgc2KkE6QO78I94FxEh8vcUy/b2kwuJLcfEMx9sx7zZZCq2opnyESaV7+Mq/c5xHeubSOLo7QvmVANqeqdeEe0z17g6D6Y5MHtSC4fzp7Die/8GMmbiyApuHqf0+FeG0f5IGK7PsHRfTCxze+T3L+VnMlfzR4X5PbuX7YW1zYA0eHG0FQESUHwnvwKooXFiSJ6comtexPPgHNx9hph7qkE0rX78Q6bTPLAljbnO3oMoum959uWlfQhvnsdjrKB7c6XvUE+jaGDs2I48e2r2pQLNgd6KpEdD7Iy2l3mwh/g7D4Yo8X1TXL5QZTbTH4lXx5IMnKwEC0awtGtL9FN7+LuN4b49tVtZetg9d5RNsBs88iYJYgIktJG+Zc8AURn29zbQgcpUB1l5p65VPVOPIMvaLMw4SjpgxpvRjUkPr2QYGFx1qBlWvboS5C0FP1jJa0JOLoPIrR8Hq7Rw0hWbcPZfTDJg9vwjZxGpuEgtqIK0of3tKknyDZSh3fj6jsGW2EF6Zq2x494BYE5Xgo2B0Y6mS0THW4klw890XoRVEDy5pLYvxVnrxFkGg9hK+huLjx0MAY6uw8iVX00YJjkzSUTbTT33dtdbRY5BZsT0e1D9uQie3PatGNkUii5Je3b7zWC+LbVOMr6t1vwVfJKiW07Ok47ewwl9OGiNudI3lxkf1uFCcDZdwxGB3MvyZcPotRmUUTy5aG1WegVEP2fnfba4ovFkX34obf/gdrcgOzLJTjpppPen3+EnJwc/u///q/DY2PHjs0Gyhs6dChLly7NHrvnnnsAmD59OtOnT29Xt3XwvFmzZjFr1qx252zbtq1dGXx+DLmuzElZ9Lt168att97KY489xpNPPpn992VGzC3BP+FqkMxJrRIsxtFjEK4+o8wTBBH3wPNQoyEMTSX30juIbv+I4MQbzMB5gBwoJOeiW9CTMTJ1VUhuPzkXfgXJHUCLNiHaHAiijJaMm/VarOCSN4fAedch+fNQckuJbFyOoDjwDp2ELb87jh5DUCONuAee1/IyFHD1GYXg8OAoH0Bi7ya0VMJcyWtRZmwF3fGPmYHk9JCqP4C77xjUhmpyL76F6M61uAeeRyZ0yFwgEM17dvQYgpJbSnLXWmz55djLB2Kk4xhqGu85l4AoEd3yATmTZyPYzFV1JacU7/CLiG1bheQJEDxvFrFtq8id+e+k7Zaib3HmSNry8I+4GMnpwTfiIjIN1QQmXG0GVVJTuPqNBQQQRDyDJ2IvqMBRPsCsLIh4z7kEpagCNVyHq+8olPxy85BiJ3D+dcT3biRw/vXZxS9bcW/sheX4Rl2Krdh0YxNkG8GJN5Kq2Utk/ZsEJ96A5A4AplIdPP96Iuvfwt6tP+4BEwh/+BLBiTcS2fIBedPuQHSYSrfsyyfv0juIbv4A/6jLMCQF0eklfXg3hmHgHX5Rdmxw9x+PGm8mcO412fFAye+Oq+8YmlctIfeiryHaXUTWv0Vw8lcRnWbeYcmbS+C86xAdXvxjr8iOC5mmWnIu+nqrtsrxDL0QQ8vg6jMKPZU4+sxGXgqyDblizEkH7bGwOKVoqhn0TZTNwHwWx4SuGzh7DME9cAJaLIzk9uOsGIoSLEZPNIMo4x08EaXFeCLYHORechui20d893pAIDD+ylbHneROvZXoxncQJAX/mBkk9mwgf8acFgMISJ4guVNvQ7A5kYOmRV6wuwhO+ipIEvaCchzd+qEmIniHTjINLtU7W+YtLfOb7oNxDzwXrcUKLvvyyb/sm8S3rTHHwguuR/IEzOu5A2a8pbeexjtsEmq4vm1bZQOR/IXmONkyZ7R36493yERSNbsRZDvOniPMB9YyLgqyvUUhF3APOh8cHoITrswurMq+fPKmfxtbXhn+867LXsvefQjOYZfgHDgRW0lfs0nZRmDSzWS8peRd8Z2j7wl/AXmX30WyxUAl2F3kTP82Gael6H+Z8A6+gPJv/4Ge//085d/+Q6cp+RanF8E4iVnUf/3Xf3VY/uCDD56wQKeLhoYoun5qJpAuBcTmAxhqGtGbhx5rOrqqLEkIgmha3wTRjPaaSoChgSCaUV9dfvR0AtHpQ482mAO7zWFGw9ZUBKfHHOgNHUM3EEQRI5M0FQVBwvTNEjDScQTFia6mzQj5Lq8Z4VuygWCAppkvF0E0vQIEAQEBXD6IN2Nkkogun+kujIFgd6NFG5EcHnTdnNyYGQRiGLqO0JIJQJAVBFlBi0cQbU4E2YYeb8pa5/VII+gqotNrKkupBEJLxgA90WxGgtV0dJubtOzPTvTz873U1bV3cz7bOFvlzM/3fuaxY+kPZ+t9HQsnK7sogjtTD4KAgYGRSgKG6VVjd2OkzEj1oieAkUqY56WTIMoITj+q4kJJNaHFm5EcbrRkDMnmxBAkc8+lpJj9OZNCdLjRBQkhk0BwuNEjjaZy7PAgGAZarMmM2I9guru7faCaiw6iy4+eiIIAojOAkYpitPRrPRk1LVm6ioiEbmgY7nxINCGoSQRJAkRzbFBVsLvM+xIkBFFqiUjtbbGOGSA7QEtnxyxD1zESzeb+UcUBuoGhZRB0FUPXEB0eDEmBVBwjk0Cwu812BNNib6gZMDQERLC7Sco+VOPUBeE82f7Q2bJ8WfvWmaQzZFert5Ja+Sxy9xFodbtxXfqdTpLu8+ns534m+kN+vpfmxkbk6GEEUTbHC9lmjoOKHV1TEUTJnJ+IkmkMESWMeNjMVOQKQMbcJiQ6PGbmonQKQRIxDAFBlkwru2RDizeZi5GyAyMRNsftRMT0qJTtCLqKpqaRbC6QZPR4c4sHVcysp2bMcczmQtcyCIZuZkFyB8zo5KJs7n2XbQgt9SWnF2Q7etL8ngTFYd6joZtzppbMAAaGGWVf1xC9eRjJqDluahlEl9/MfiLZwO6GdBwMDUM30D2FaIKMLVGLYGjoagbDlUdC9JKf76WhvhlbyvQ00J05ZFqi5iukEeONINvIOHLQddOd2pZpglQMnAFSogubkUBIhMDmImMLntIx8WwbRz6vP1hYdCVOahbVFRT6M0E8AzjLjg5cLavJn5mZxNHKdfdIIMgj28c8/qPHXJ9j2bZ3UGb71DGDo7K0ot0AawBO31EZWv9KfG1dzzAAe/s2AfC22jd2xP1eA1zuo+VSKzkBPJ9u37LmWZx5dB0iUitXSOenTmjdN+0t/bn171qFpJQH3pY2Wo8Frc9rHQhWbumbQqs+IQCt3T+PuFK27qPuVrI4WvXNjsYIDbAVtJWhtUyuVvd8pH7re1daySwBiilPdkzpaMxzBNreZ+u2WmN1fYuugJYx3Z5Fyfzb4rhIaQopZ1nbwk+PR58eu1yfGtdaH//0OHLkc2u38yN73L1t97a3aefIXOSILHKrce3IeNt67Pv0tf2FreRtNcf7rHngkXIDsH9KyfS0HtMDR/82wxiRsRd32KRuCCRt7eeNGWzQkoKPlh2jhmGQkv0gH322acEJLueRxj5DcAsLi7OZk1L0P/nkE/74xz8Sj8cxDANd16mqquKdd97pJPEsLCwsLCwsLM5ODO1I1H3ZUvQtLCwsLM4qTmqP/ty5cxkxYgTRaJQZM2bg8XiYOnVqZ8lmYWFhYWFhYXH2ombMfdCilA2IaWFhYWFhcTZwUhZ9QRC44447CIVC9OzZkyuuuIIbb7yxs2SzsLCwsLCwsDgtGLqGkYwgugLHXklXQRQRRKl9ajULCwsLC4szyElZ9N1uc691eXk5O3bswG63o3WQK9nCwsLCwsLC4mwmtfo5Yn//f8dVx9DUlj36smXRt7CwsLA4qzgpRX/o0KH8v//3/xg3bhxPPPEEDz30EJIk/euKFhYWFhYWFhZnEUYsdPyVtIyZOcIKxmdhYWFxTESjUX784x8zffp0Zs6cyezZs9m8eTMAq1atYvbs2cfdZiQS4a677uoU2aZPn05VVdVJt3U2cFKK/n333cfXv/51KioquO+++9B1nV/84hedJZuFhYWFhYWFxWnByCTN/9OJY6+kmyngECXTjd/CwsLC4jPRdZ3bb78dv9/PwoULWbRoEXfffTe33347odAJLLa2EA6HqaysPCnZ1q9fz4033sjevXtPqp2ziZNS9AVBQBRF5s2bx4QJE5g2bRo9e/bsLNksLCwsLCwsLE4LRjJq/p+KHXudFtd9QZRAt7YuWlhYfDF4b99q7lr831z/7Le4a/F/896+1Z3S7qpVq6itrWXOnDnIshkqbty4cTz44IPout7m3NmzZ7Nq1SoAqqqqmDx5MgCLFy9m5syZXH311cyZM4dUKsXPfvYzamtrufvuuwFYuHAhV111FTNnzuS+++4jlUplr/WNb3yDmTNnksm09cKaP38+999/PwUFBXxROClF/4UXXuC//uu/+POf/5x1mZg/f35nyWZhYWFhYWFhcVowUnFAwEjHj72SpoIgWlH3LSwsvjC8t281f1jzD+rjjRhAfbyRP6z5R6co+1u2bGHIkCGIYlsVdOLEieTm5h5TG4888ghPPPEECxYsoKKigt27dzN37lwKCgr47W9/y44dO5g/fz7z5s1j0aJF5Obm8pe//AWAUCjEHXfcwaJFi1AUpU27DzzwAKNGjTrpezybOClF/+9//zvPPvssHo+H3NxcFixYwFNPPdVZsllYWFhYWFhYnB4yCQSX/zgt+mmQjrjuWxZ9CwuLrs8/NywiraXblKW1NP/csOik2xZFEcMwTqqNSZMmceONN/Lzn/+cSZMmMWDAgDbHV61axb59+7juuuuYOXMmb775Jrt3784eHzZs2EldvytxUun1RFHE4/FkPxcXF1vB+CwsLCwsLCy6HEYmiejNh5a9+seEph0Nxmft0bewsPgC0BBvPK7y42Hw4ME888wzGIaBIAjZ8ocffpgJEya0KQOyiwKqenR8nTt3LpWVlSxfvpx7772Xe+65h5EjR2aPa5rGtGnTmDt3LgCxWKxNVjiHw3HS99FVOCmLfiAQYOvWrdkv5aWXXsLv93eKYBYWFhYWFhYWpwPDMEBNIzg8GJnUsVfUM+b+fEEEw8D41B5TCwsLi65GrivnuMqPh1GjRpGbm8tjjz2WVb7fe+89FixYQO/evducGwwG2blzJwDLli0DTIV/6tSpBINB7rzzTmbOnMnWrVuRZTm7GDB27FjeeOMNGhoaMAyDH/3oR19aj/MTsugnEgmcTif33Xcf//Zv/8b+/fs577zzsNvtPP74450to4WFhYWFhYXFqUPLmMq6bMdQj13RNzQVwWYzDR5HrPqi7RQKamFhYXFquXHoTP6w5h9t3Pdtko0bh8486bYFQeDxxx/nwQcfZPr06ciyTDAY5I9//CN5eXns2rUre+5tt93G97//fV544QWmTJkCgCzLzJkzh1tuuQWHw4HP5+PnP/85OTk5lJSUMHv2bJ5++mnuuecevva1r6HrOgMGDOCOO+44adm7Iiek6F9zzTX8+te/pk+fPixatIi9e/eiaRoVFRXtAhtYWFhYWFhYWJxJPu0m2u64mgLZBpIM6eNx3Tej7gOt3PctRd/CwqLrcn73MYC5V78h3kiuK4cbh87Mlp8sOTk5/N///V+Hx8aOHcvYsWMBGDp0KEuXLs0eu+eeewCYPn0606dPb1d33rx52b9nzZrFrFmz2p2zbdu2fynfW2+99S/P6SqckKL/1a9+ldmzZ/Mf//EfzJo1i169enW2XBYWFhYWFhYWnULsmf/ANmwatsEXd3yCmkaQFARJMQPsHSt6W0Xf0FQ+eznBwsLComtwfvcxnabYW5w5TkjR/8pXvsL48eP5/ve/z8qVK/nZz36G0+nsbNm6LHY7KM3VJPZl8Hly0eJhRFnByKRBkjEAEQNd1xDtXoxkBAQBQbahZ5IIsg3BMNAFEUHXwNAQ7B4MNQNaGhBAlrNpfQRJRk/FEWQ7oiSh6TpCixuiICvoyTiC3YUgSeiJCIIkg6iYx+JhEhkHHsMw9xcaBqLLixELAyC4POixZgRJRnC4zb9l2bwDXUO02UDTQRQxMimzvt2Fnooj2t0Ympmj0kjHER0eDEnGiIURFLv5HBQnesJsP+kqRlLjSIlGUBykbUFUwwruaHH2I8sCLj2MHms2J/sYiJKMoWsYzjx0dORMHD0ZMZUJu9NM5aXrCLKMoRtmHcWB0RK520jFSOp+POmE+VkQEGXZHEcAZDuoSQxRRpTtGKmo2eftTgxVw8gkEO0usw8aBoIgYhg6giijq2lEl4+kFEA3dJyZRoxkNNt3BcUOuoaBgKgooGvo6RSiw20eP5I3HANkmzk2qSqIIggCyZoGPMm4OU4pTkg0gyQhuHMwoiEMPYPo9GEARrwZQbGjevJJpq3+bnFqMGKNaIe2wWco+oaaAkkx39HHsUff0DREwQx3JFiR948bURRwpBsgHUN0ehG0DHo6gWh3oqcSiDYHhqG3BD0UQLJhqOY8CQQwdPOZaxqC04MeDyPYnOZcRLabGRQEEJw+c14jCmB3QTIBoohgd5nft5pCsNnNcc7uxkgnzHHT4cHIJDAyaVJCEK+WRk9GERUnhigg6Ob8B1FCT8bMuR4CgihhCCJkUiAKCKJotm1zmV4jmQR6JoPoaBlzbQ4QFYxkBNHmQM+kEZ1etEQUQZIxZAdCKgqKHXQVQ01juPPQVR0ycfP+klFEUcAwBDJNCUTRha6fXHRzCwuLrs0JR92vqKjgn//8J/fddx8XX3wxwWAwe2zx4sWdIlxXxKM1kNy4loYPXiR4/ixilX/HXtwLLdZMbNtKEEV8Iy5GSyVw9hhMunYVkY9exdBUXH1HoQQKSVZV4j3nErRwPU0rF2JkUrh6j8Q9+ALqFz+Go/sgbEU9Sexej7vfaMKrlqAno9hLeuMdcTGi4qT2pd+AoeMeMB7R4cHVZxRN7z1LqnonotNDcNJXSexZT3zrh4hOL/6xM4htW0Pw/Gto/uQ1opveA0HEN+IitHgz8R0f4Rt9OYaWIXVgK+4hE0FTSR3ajXfYZJJV2wivWoSRTuLoMQTfyGnEdnyEs6iChjf/hpZopvCa79L07jxSh3YhOj3kTb+baOUrRDe9h+jykzvlZkLvPIPaXIccKCR4wfVI+b1JSb4z/bVaWHwmiqhjj1ZT/9bfSVVVIjrc+MfMIB06hLvvGFI71+IsG0Dtq39GDR1CKexJYPwVNC57Ci0aQsktxTfyEjJNdRgYuHudQ8Prf8FRPhA9nSC2ZQUIIsGJN2Ck4oRXL8FQMzh7j8CW3x17UQWNq14iXb3T7MvjZiK6vGjNjWQaDpI+vAvvsCk0rXoJPd6MragnnsEXEN+zHv+4KzGSUerffRbfsMk0rVqMFmlADhbhH3UZmYaDGALYCyvQkzHiO9aSPLAFwe7CP2oaSDZkXw6pqm1ENrxjjjkDJyAHCpGdXhrfew7/yEtIHtyOq9841NAKmte+BoaOZ8iF2PJKaXzraQTFTuC8a/FUDCcq55/pr9TiC4ahptv83yFqGmQFRNn8+1jRMx247lscC4aWwV63mYY3nsTQNXIv+hqhNS8TGDmNurf+hhYNkTv1VmKVq0ju32yOO2NnkNi5FiW/O3KwCMHQCa9anJ0DufuPJ7x6CTlTbiZaVUn04zfIv2IO0Q+eJ7FrHYJixz9uJkYmjZ5OogTyCb33HMHzriW2Yw3ufmOJbvmAdMs8JefCm4hsepfUga3kXnIb0S3vkzpgjvPBiTeipVM4inrQ+ObTpGv3Irn9+MdeQaruAM7ygYRXvYRn8AWEV72Enohi7z6IwNiZNCx7Cv85F9O87k0y9QeQ3AH842fSvHopotNN4NxZRD56hVjlh/jHXkGm8RB6MoK9tB/NH72CkUni6DEU/9jpJPdvIdN4iPi21Qiygnf4FOKxMPaKEQjdRqCdXIItCwuLLswJR93PZDL88pe/ZPny5XznO9/hBz/4Qfbflxmt8SChd57BUT6A2PZVZJpqECSZWOUKc+VZU2n+6BVsgUK0SCPNqxa3WL0N4tvXYGgZ1EiI6MblJKu3Zy0L8Z1rSez4CDlYhKN8IOEVC3D3H0to+Tz0ZBSAVPVOohuWk6rdi72kNxg6sS0fYCvqSdOKBaSqzciVeiJKw9I/YC/o0fI5Quidf+LpP47U4b1ENy5vWSVXaV77Kra8bhiGTvjDF7Hll5E6vIfYlg9I7NuCLbcEtamGpveexWjZ15jcu5FY5Ye4ug+i7uXH0SINBM+/gab3XyB16GiQjdT+LdlreYdOpP61P6E21wGgNtXQsOyvCA17EUXLEdLi7MWhhWl6/zlSVZUA6MkYoXfnYcstJbJuGY6iXtQtfhQ1dAgAd9+R1C/+LVo0BECm4SDhVUsw0gkcJX2pX/o7tFgY0eYktvn9oxYrQ6dpxYIWZcUgsfNjjHSCyKZ3SWf7doTQ2//ASMYwdJXYlvfxDLmQxneeQY83A5A+vJvopuVIip3YpuU0fbAAd59RNL47Dy3SAIAaOkzThwtAlJCcPqKVK0ke3E7ywBYAjFScpg9eQMkpQm2qIbJumangGLops6YS2boCV+8RNK1YgLPncARJonnNy9nzohveMu+zJcp56O1/oIUOIQlW1HKLzsVIRsz/U9HPPkdNm55rknJcwfjQtLaKvmZZ9I+V1OE91C15DLW5juDE66l7+XH8Iy6m7mVzfLR360d89zqS+zcDLePOu8/i6jcWxZeLgEFo+T/bzIHiO9diK+hO3ZLfIrt8OMoHkdy7icSudWYbmRRN783Hll+G7Muh8a2ncXTrR3zXJ0hOL7GtH5JumafoiSj1r/wRV8/hOHoMIbZtFakDR8f5htf+jD2/jIZlT5Gu3QuAFgvT+PY/sAWLSOxah2/0dELv/BM9YcroKOxJ3eJHcXTrS/PHr5OpP9BSr4nGt/6Bd/hktGiI1MFKYls/QPbloadixLevwtlzOOEPX8TIHJlrbSC66V1Ep5f4tlWAgaGmzTlmQTmNr/wOOVJ9Or5KCwuLs5QTUvQrKyu5+uqr2bBhAwsWLOCaa65hzJgx2X9fZtL1VQDYiypIVW3DXtybRMtLqu15B9BT8Xblib2bcJT2IblvE/ainp86tgFnz+GozfUAHboXJg9sQRBFHGX9s2WiJJM6sPVTZxqfsm4YIEBi50ft2kzV7kXJKTb/PrwbyZdr3lthdwxDRw3XtasT37HG3ArQIqMSyCd1sDJ73F7Ui+SBo58FUTJdmVuhx5tRm2qQ9OOYdFlYnG4ySRK717crNjIpErvXYwgCWrTp6AFdz25pOYIarkVy+zHScdRwHfainiRb9VnJ7UNtqm13jfiuT5Bsn942ZW7DiVeuND+paXOxoBXpw3tQckuJb1uNo7QvCEJ2oe4IWrQJ0e4kfXg3zu6DSez8uP29q2lSh3a3K05WbUOUbSiBQgAEQSSxe10H51W2GefS9VXYBKu/W3Qu5rtFwEjGPvskNWNa86Xjtei3bFkBc9uOZdE/ZjKhmqPvfV03t/+paXNbIuAoG0Bi94Z29QTZRrpuf8dzoP1bsBf3MtvQdVx9RhHvYF6Trq/K5ue2l/QmuX8ztoLupKp3fOpMc67kKO1Lcu/Gdu2oTYfJ1O3/VBVzjE/sWYdodwBH3ecFxYaeiCB7c8g0fkoJ11UMTcVe0ofEHvO+7aV9SezZgCAp2cWC1iR2rWs3dgNkGg8he3PRwzXtjllYWHx5OCFF/4YbbuCyyy7jb3/7G8XFxZ0tU5dG9uYC5qqu7MtHDdei5JS2P8+Xh6jY25UruaVkwnXI/oK2ygGg5JSSaahCcpmu7ILcPsOB7C/A0PU2SoFhGEi+vHbntqsviNharPxtrusvyFoflUAheiyM5MtDi4URJBnR6e3gPrq17OE1rfF6Jt1GBjVch5JTcvTSkmymNmqNJCO5/OiilcnB4ixGVpBbFNrWCLINJdfs+4KktCpv/3sWbObefMHmRFAcZMJ1KLlH+4eWjCG5/e3qKfllaKlEe5lECVuLAt3R9SR3AD0ZQ8nvRiZc0yJfW8+ZIzLLgULSjdUoee3HMUQJJdj+3pWcYvRUHP3IBFQAW0H3Ds4rIdNqoVD25qLqJ+xoZmHRIUYqhuAOQKaDvnIELX1Cir6hayBYrvsnguT2Zd/7Qst8SFQc2eNqU22bcTCLpiL781v26bel9dxJkBUyjYdQ8rq1P8+bg9BybTXSiBwoRIs3I7XM4VojyApqcwNysP18V3T5O5wDCbKCkluK8WkPD8Hcz2+oGUS7q4N6NjJNtdnxVg3XtbSjdni+klvaMtdqi+TJQUs0IzitrY8WFl9mTmhG9dRTT/Gtb33rc1PVPPHEEycsVFdGyS/H3q0/0S3v4xtzOZnGw9iLKtpM0pXckqyCrBSUZ8tFpwdH2QDSNfsInn8dmaajK7GCzYFn8AUkdq1DT8axFVWQrtmHq/fIoxcXZXyjL0cOFBLbvhoAyRNAT0bJmTzbnMS04B5wLqmavdnPrn5jSB7YgqvPKCR3IFsuB4sQFDt6IoqS3x3R4cbQdfyjLwdRJLF/K7aC7jjKBhyVVbETPO9a4nvW4R87A4CmFc+Tc+FXsjJkGqtx9BiC5DGvFdu2Ct+YtqkyAmOvQMyvQNMt132LsxdddpFz0ddNBaEFZ69zSNcfxD92BqnqHeRMmZ09Ft+1Dt+oaa1aEAiMu5LUod1kGg+RM2U2augwSm63bP84EnjT1sr6LTrcuCqG4eo59KjrMODqNxY9ncTVazii00vq4A7c/ce3upyIf+wVxLavITjhWmz55cS2r8Y36tI29+UfdwWJlr2okt2Nb+SlbSbWju6DMdQ0crAI2V+QLZe8OSg5JXgGTCC6+T3sJX3IhA5jK+jRZkFE8gRQ8suyWxrs3fqh5HUjQ/sFUAuLkyKTQHD4si7PHWGoGdN1X5Tbedx8LrqK0GLRFwQpa422+NfY8rsTGH8lAM0b38E/7kqilR9mx8fY9tX4hl/UbtxJ1+1HkBTUaKiDOdBlRLd8gG/UZSSrdxH5+DX8oy9DaOX5ZC/pg2hzoEYacXQbQGzrh/hHTSO6bSX+MdPbjKfu/uNJHd5NbOsK/CMvaTvO9x6JINvM+VUrQ4V74Hkkq3fiGTKR+I7VuPoe9XSNVa4kOPEGopvfxT/+SlovsHqGXEhi70Yy9VW4B56H6PKROrgNV8VQRIcLNdKAvbRf9nxBceAfOx3R5UV0erLlSn4ZGDrOniMgWHbc34uFhcUXB8E44rvUyVx11VW8+OKLp6LpTqGhIXrKopF6tDrUhmoM3UDxBclEQsguP1osZEbctjlMlzNRQnJ6USMNoGtIniBqtAnJ6TYj+Trc6LEmdDWDHCwEA/RII4auIfly0KJNCDYHgq6jJSJIbj+C3QUGaM31IIot7Tci5xQi6AaZxkOIdheiOwi6itpch+hwIwgtLoeihOTykmk8hCCY1rp0fTWCLCP788k0ViO5fGY0WTVjKv5mjFm0WAhDzaD4C1DjzUhuH4auY6STaLEm5LxuCLqGGqpBdLoRXF5EQTavJUmIed0x4s3ozXWIbj+Gp4CkHMg+1/x8L3V1kVPynXUmZ6uc+fntrQ5HOJb+cLbe17FwqmV3C1GEWCOZcB2iYgNBRlQUdENAcrhAsWPEm1Cb6hBdXiRPEC3aZFoaHZ5s9H1BUsD2/9n77/i4rjrx/3/dNr2pjLolubfEduL0ZtJNnEYCJAtL2QWW7IeFz/JdSgim/rLA8s0C3ywLLGxgNwkEUk2qSSHdiZ3YTtx7t/popOnllt8fVxqVGcmyJdtycp6Phx+Wbpszo3vv3HPO+7yPGyuTwOjvZUp0A5J9nSoaRioGpo7iCdo9/W4/yDJ6bweS6ixE/OixLtRQFUa8G9nhxrJMrGzKTtSXzaAGKsDhxZRUpN4WOxO/6sBI9aK4/XaGfkXry3StYsajyN4gRiKKrDpAdSBJILv9mNk0ZtrOASC7/UiyipHqRZJkFF8Z+e4WZKcbNVhFvrsFy9DRympAUshHDiE7PSihapJaFcfpK6nIeK+HiS6LuLaOn/yuN8lvfwWjZSu+z9xTqJgPLnt+x+vkd72BUj8P4+AGPNfePqZjJx9ajnba5cjBGrJvPIDzgo+j1s4+8o7jNNGf+8m4HsJhP7GONuTeFsxEN2plHVYmbd97JAkjGUPxlyNJ9lAiNAeKO4AR60RyevtmFclhZTOYeg41UIEe70bWnMhOH5Iike/tQtKcqN4g+UiL/bO/Ar2nHdntQ/aWYfR2YpoGqtuHnkmgOr0YyR47qtBfgZmOY6ZiqBX1YOjo0TZ7X4/fzrTvCUE+ix7rQnZ57FlJVAeoToxYB5LTa89ckoqhBiuRnH6MRBdWf2b9RBTF40dSHei9XXYHTT6P4vWj93QgaS5Uj598byeKrwwrl8LMpFDLajFlFT0WQQuUo8ejyBJIDg+KIpN1V5GXT40ZsSbbfWS060EQTiXHLRXnaA9rP//5z3nmmWcAWLJkCV/72tdYtWoVP/zhD8lms3zwgx/ky1/+8pB9vv71r3Puuedy0003AdDS0sJXv/pVIpEIU6dO5a677sLr9R6vt3NUEkoYqsIDNy5PE1kA/9TSO7gHhZX5hq3z2D3+2VLbeopDYQfWDWrF9U8b2N/TOHQ7X3PpG2ytHTaWAagfFDpXVyJ8t5+3aaCsw/8U5VAIhvQ2D103+Jj+cvAPWy8Ik1zS8oHHV3x9DeYrB9+gvBvlowx7cgABCJS6NgffIwZHZXqH3Q98zfa1OEKZhoxuDc0a9PMIZeq/bks9/ziHlqXonlI/6L5VXT309QffX05QJV94f7HyGVAdoDhAz4CjOATaMvJ2b62s2tNFjvXYRaH7Ihnf0chKXgjNhFDfPcE1aGVo0M+D7zvD73X9x4Li5wv3oPvf4PvQ4GepyqqB/f19/w++t7qB8kH3Y9+g1+g/lVyUfsZzD3q+KR903ysfiIIa8lq+YcdwD3qWG3YvLxzLVUsewDVwLw2H/SQmUcVZEIST47gNhhwprH/VqlW89tprPPbYY6xYsYLNmzfz5JNPcscdd/CLX/yCp59+mk2bNvHyyy8D0N7ezm233cbKlSuHHOd73/seH/vYx1i5ciWnnXYav/jFL47XWxEEQRAE4VSVz4CiIamOkgncADDsZHySfJTh94OT8UmyGKMvCIIgTBonPOtROBzm9ttvx+FwoGka06dPZ9++fTQ1NTFlyhRUVeW6664rVOyfeOIJLr/8cj74wYExrfl8nrfeeourr74agJtuuqmoIUAQBEEQBMHSc0iyBooGI0ydZ+l5u5KvqFjG0WTdF9PrCYIgCJPTCa/oz5w5k0WLFgGwb98+nn76aSRJIhwOF7apqqqivd1ORPfZz36Wj3zkI0OOEY1G8fl8qKo98iAcDhe2FwRBEARB6GfpWTssX9VG7tE383ZF/ah79I1C9nZJksX0eoIgCMKkcVLG6APs3LmTz3/+83z9619HVVX27t07ZP1oGf1LHXu07UupqBg+GP74OFUSeohyTqxTpZz9xno9nGrva7BTteyi3Cfeifp+GOxU/rwme9m7VBP8HtJRFyGfimtQefvLHnFIGB4X7pCPbowxv6ckJoGQD8XtIep0EPBp+E7Q53GiPvfjeT1M9nNnuFOpvKdSWeHUK68gnAqOW0X/M5/5zIjr1q5dy5e+9CXuuOMOli1bxpo1a+jq6iqs7+jooKqqasT9y8vLSSQSGIaBoih0dnaOun0pJyKr8mTLIjoSUc6JNVnLKbLun3plF+U+fkTW/YlxKpQ9HUsgOT0Ylky0qwfVZZd3cNkz8SSWbqIn8hj53Jjfk6nnicVzSNkUed0i1pMkfQI+j/dK1v3Jfu4MdiqV91QqK0y+8opGB+G9YlwV/fnz52Oa5pBlLpeLWbNm8YMf/KDkPq2trXzhC1/gpz/9Keefb8/tvHDhQvbu3cv+/ftpaGjgySef5Oabbx7xdTVN46yzzuLpp5/muuuuY8WKFVxyySXjeSuCIAiCILwX6VnwBEFWYaTx90YOSVb7tjma0H1zIBmfLEL3BUEQhMljXBX9T37yk3i9Xj7xiU8gyzIPPfQQe/bsYcmSJXz3u9/lvvvuK9rnnnvuIZvN8qMf/aiw7NZbb+VHP/oRX/ziF8lmsyxZsoSlS5eO+trf+c53uP322/nlL39JbW0tP/nJT8bzVgRBEARBeA+y9ByyoiIpI0+dZ+k6ktMBinJ0mfOtgWR8kiSL6fUEQRCESWNcFf3Vq1fz6KOPFn7/+7//ez784Q9z55138vOf/7zkPsuXL2f58uUl1z3++OMjvtbghgGA+vr6kg0Jk4GPHszeDhIRHV+gHCPeg6QoSKoDSVUx4lEkhwtJ0ZCdLoxELxYWitODkehBCVRg5jLILg9mshdLz6OW1WAke7DyWRRPACOdRPH4MNJxZFkFhwszFUPxhrCMPJaex5IkFKcHM53A0nMogQqMRA+SrCC5PMiSgh7vIhVz43V6wNCx9DyyL4TZ24ll6KihKvSeTmTNgeQvBz2PkehGkhQkzYGkOTGyKWRZAUnCzOdQ3F6MWDeyy4vk9GLlM5ipXmS3H9nhRk9EkTWH/XBkWZj5LJKigr8GMj1YySiSw4PpDZNVA2JqbeGU4bN6MKItSIDsDdmJvxQVfGVIuSzoWcxcGiubQXa6QVGwTANJ0TCTvciaE8lXZl9n6RiplAt3Mobs8iC5/BixLmSHE0vRkDU3WDpGLILs8trXkpFHcXoxkr3I/pB9jfd2ImlOZG8QMx3HMnQUl72NEqzCcASQkx1IsoKl5+w5x/uub0vP21OSWSayomHpOfve5PTYFSJZgWwKM5tCCYYx00ksI0c6G8KTSSIpKpLqwEh0o3iCmLo9hZksS+jxbhR/OagOzHgExeUjG5hC3jjhOWKF9zojZ/fUj9qjPzgZn4FlWUfM/WNZll2x70vGZ8mKqOgfJRkTt94NmSTIElYmAZKM3Pe8occjKN4gkqxh5u17j5lNIkkSZi6DpDoACcXpxsikkDSH/SzkC2Fm02Dkkdx+JM2FlU1gppP2/dATxEhG7WemfAYznUB2uPruVz3IngD03RNlzYWR7CadCuDRc/Z9UXMiyTJIKmayG8UbBM2Flc9h5dNYSCguH0Y8Yj8rOb1Y+Syy0203NuWzmHoO2VeGlUliWqB6Axi9nUgOF7KvDF32IsdbMNMJlGAlluJGj0eQPCFyzgos7PNTJY+SbLdnl/BXk5W9hc/XYWWQEh1YloXir8BMRrFME9UTwMwkkDEw8nnwhck5QuJ5SxDeY8ZV0U+n00PG03d0dJDN2hltjffpFDPefBuxNU+R3PYGFZd9kq6nf4mZjiN7gpRf+nEiz/7WfpCWFcouuRU93k187TMAqGU1+BdeTtcz/0X4+i8SX/cciXdfQAs34p19Lj2rHrUz/Gouwtd/ichzvyPXuhsAZ91MXFPmEt/wVyou/zQ9bz1FYOFlZKJtxN5+BiwT2eWl7OJb6H7p9/jmX4Jl5ElsfAkA/xlX4pp+BorTQ88L95He+w4AWkU9ZUv+hrY//SvuGWfhrJlKz2sP2a9ZPxtX0zyctTPIdBwgufk1yi79GB2P/gQjHrGPu/AyLEkm8e5fqbji0/S88ShGogcA36IrkGSZ+LpnkRwuKq74O7pf+F/MbAokmeDZy3DNuZC0q+aE/f0E4Vj5Mi1E/vJrcu37AHBNmUfgnGvJdexHK68BzU3u0DZ6Vz9uX49OD2WX3IKRTZM+sIXsvg1ITg+VSz9Hz+on8c07n+hLfwRTR9KclH3gY2gV9bQ9cCeBxUvxzD6HyHO/w1FRB7JCcsvrACj+CkIXfRjZ6aL9yf/EiHfjnX0eksNVuN4Vfzmhc6+n66lfEr7hS1iSRObQDtL7N5E9tA0AR/VUPDMWY+bSOKoaSR7cSmLDwP7ha79IavdqYmueAixkt5+yiz5C5K/34mqYg/+MK5E0F10rfopv3oXkOg9iZlP4F11O93P/g2XkkRSNiqs/Q3zjK2QPbaPsA3+DY/Zl5EzlxP7xhPc0S89DoUd/hIp+X9Z9SZJBkuyeeukIj0iWXcnvbxCQJPnowv7f58x8DldiP7mO/UiyQmLTK2RbdqL4yij/wMfoWvkb+++lqJRf9gkcVc1k9qxHdnroeWMFRiIKgP+Mq7AsE2d4CtFX/oT/zKtJ799ErmUnAM6GuYTOu56uZ+/BiNn5oLynXYJ/wWVkD20j9vZK9GgrAJ5Z5yC7vCS3vUnF0s8hKyptj/y/eOech5GKkd61FgC1rBb/osswMynSe9ZjJHspu/TjmMkYPaseJXTRh+l85Y9YOft5L3ju9aj+ciS3l/TOdSS3vIZn5lnILt/AfdkXInT+h4j89X4qrvo7jGQvPa8+VHh+q1z2f8gf3Ep87V8ov+YL5OsWoukpMmseJrnxRbtcwSrKbvgK4MeZ7yH+wq/JHtiC/8yryUdbyezbRMUVnyL29ga0spohz4cVN36VTKD5hP39BUE4/sbVdfK5z32OD33oQ3z961/nq1/9KjfddBO33XYbP//5zznzzDMnqoynFL3rMMnNr+Kdcx6xd57DTNvJRfynXUL3X++1K/kApkH05QdQvcGBfaNt5Nr3ogYriTz3P3aPH+CbdwE9rz1c6CmQHS4yBzYVKvkA2Zad9DfF9rzxGKFzr8fMpoi99RRYdh4FM5Ok962n8M27kPg7z6GV1xb2j69/DiubId9xoFDJB8hHDpPe8w7OKfNJ73obsJAUzX7Nw9vBMIi+9hBGrJPg+dcTe/uZQiUfIP7uX3HVzcA9bRGJra8XKvkAiXeeRw1U2u9x/sX0vPawXckHsEx61zyB0bYDjRGmQxKESUKWJVI7Vhcq+QCZg1vIde4HSaLntUcgl6b3zRUD12M2Re+aJzFinXia5gPgm38R3X+9H9+ss4i++IdCCLGVz9Lz6oPovZ34Fl1BatfbpHatJd95AEf11EIlH8CIR0jtWENy1zqMeDcAjpqphYdJe5tukjvfxlE7jfg7fyWx8RUsI1+o5APk2vdipOMkt72BpDoLlfz+/c10nNiaJwH7vmOm48TeeQ7v7PPI7N9EvvMAerTN7mXSXGT2byJ47vWFSj6AZeSJPPtbQudeB5ZJ9MXfo8UOTNBfRRD6GHZF31JU++cSLEO3x+jD2KfYMwfC9u39ZCzRoz9m+WgrencrmAb5aJv9HAP4Fy+l69l7BhplDJ3uF+7D0rNkDu0gsfWNQiUfIL7+WZy10+lZ/YTdw28ahUo+QPbQVjItOweev4DkplfIdx5AktVCJR8gtWMNWjCMpefIdxwg8txvsfQcarCyUMkH0KOt5Nr2kt69zm4ESESJr38OyeXBt/BSet94zK7kA5gGvW+ssCMRLEhueQ0AZ/2sofflRA+JLW/gnno6ZipOzyt/GvL81v3CvTjrZ4Gi0P30f+LMdkP3vkIlH0Dv7SDx1gpMPYd+4B2yB7YgqQ4Ut4/M3g24py0ksXUVrvpZRc+H0Wd+icNMjeMvKgjCZDOuiv5NN93Evffey9y5c1m4cCH3338/y5Yt48Ybb+Tb3/72RJXxlKL3tAOgharJdx4sLJc0B2Y6MXRjyyw88PbLtuzCUTkFPdqKI9xob6bn6X+YBtDK68i17St67VznAbSyGvKRw0iKUrLnQu9pR/GV2cfNZ4FBoYlmnmzbnqJ9Moe2426aC0A+2maH2/a/ZtdBJElG8ZUha06ygxof+hnJXhzhxpLr+uc0VjwB9FhnyX0VPV20XBAmE021yBzYXLQ817YHyeFEDVZiZoozCuu9nXboaN+1qrj9dkOZReEBrJ+ZSWKm4jhrp6NVNpA5sAUUdaBxbJDMoe0DYceygpkrvoayrbtwhBtRfEGMRJR816Hi8rfvRauox0zFiss+6EG7X77zIFpZtf1zdytgoQbD5COHAft6H37Ps4cDDJTPiEUQhAnVl2hPkpWi829gm/xApV0ZY0I+Qx9a0Rdj9I+Kmc1gpuOYhk6uc6CBT3G4ByrJhY11jEQPjvCUIZ0cA8dKYcQiaOV1ZFt2Fa3Pte5GKxsaHajH+4Y9DaPHu1HcPiRFsYdTegIl70vZ1t1o4SmFvA/Z1t1ggeIOYCR7h21tYRk6+WjbwJJ8cSdGttV+Bhzp+Q1ADVTaQzRTPejdh4vf64HNmLkM2X0bADvKy74fU3gWK3n83nakfLJouSAIp65xD4aMRCLU19dTVVXFjh07ePbZZ2loaEBVj9vMfZNa/xdJvrsVR3VzYbmZy9hjvgaTZHts+iDO+llkO/ahVTSQ69hvb6ZohTGAALnIIRx104te21HVRL67FS3ciGUYfWPXhpWvvBa97wtL0pwMbkCQZBVnbfFxXY3zSO3d2Pf+atEH9djbjRESeiyCmc3gqp9VtL/iDZJr31tynV0GuyVbDVWX2LcMQ/UULReEySSvS7iaFxQtd9bOwMznyce6kN3F0/WoZTXo8e7CtWoke1GDVXb7mzw0fF12+5E9fjKHtpHrOIC7+TQwdHu8/DDuxvn2+GEA00B2uIu2cdXPIte+Dz3RixKoROtrWBxa/unkuw4hD4o8KpTdX1a0zFHdXHig1CrqQZLQezpwVNYD9vU+/L4kaa5B5ZNQgpVFxxWE8bAM3a68SwqMkIwPQ7fzToDdIDCGhHzW8B59SWTdPxqy043s8SMpKs7Bz0vZJNLw+5qiovjKyLXvs3u1i47lQQ1VkYscKrneWT+zcG/qpwYqMTKJom3VQAVGqi+fib/cvi8Hiu9LzrqZ5Nr3I6l2lKOrfhaSBEayp9ChUtD3vKeVDURS9j//DOaqn2UPZdBKPb/VAXYDsaQ6kDwh1IqG4mM0LUBxeHBNXQSAEetCq7D37X8WK/V8qJbXYjl8RcsFQTh1jaui/7WvfY2vfOUr3Hvvvdx3333cd9993H///RNVtlOSWlGPf+EVJLe/hX/BZYWbfWLTq1Rc8alC67GkOii/7BPo6YHeOC08BUd4CmYqTvkVn8SyTEAisflVyi65pRAyj2ngbpiLa8rcwr6uptPspF6qg9D5N9Kz6lFkt4/geTfaCYiwK9yBxR8kue1NAmcvG/jSk2QCZ1+L5HCjVU7BM+vcwnEd1dNwN80nd3g73nkX2g0OfT0drubTQZIIXXQzakUdPW8+TmDxUtRCq7lE4KwPkj68g/TeDXhmn4saDA96zWWF0OLEltcou/gjA40hskrwgptQamaSp/gLSRAmE9O08Mw8G2f97MIy97RFaJVTkIw85RffguTyErroI4XrUfYECJ51DVplA6n9djRAYtsblF/6tyR2rqX8sk8UHsb6x/Or/kqSm17BN/8i3NMX46ybQbZ1F74Fl9IfnaOW1eCetgjvjMWFxrNsy078Z149sE2oGve0M8h1HSR4xhX4TrsIZAX31IHGCmfDHCSHG99pl2Dms0X7S24/oQtvLjRCKv5y/AsuI7l9DZ4Zi9EqG1CD1cguL0YmiXvGYqKrHqPi6s8iaS4AJIeLyqWfJbrqUXsc7pWfJusvfnAVhHEx7CSQKCrWCMn4LEMvXJt20r4RGgQGMw17XH6/sYb8CwA4ymtRy+qQFQ01VIOr0R7CFFu7ksqrP4vU1wAoaU4qrvg7UDRcUxfgmX2O3SAKheeXbOsugmcvs5dZJq6m0wqv45q6EEfNdGR3f2eLhH/RFWiVDWAaaFUDjZzeeReRj7QguzxoVU1UXPkZZJeHfLQN75zzC9tp4UYcVY1455xHYvPrqMEq/IuuxMxliG94kdD5NyK77EqzpGiELrwZ2RPEsix8Cy8HJDIHtuA/86rCPVQNhvHOPofMga3IniBll32i0JCkeIOUX/YJMge3AhLl136RrLMcq7wZ35kfpP/erFU24Dn7eiRVRWlciGv6mVhGHiMRxTPrbNJ7N+KZdQ6ZwzsInnfDkO+jsqX/h5xU3CgsCMKpS7KsY8+xedlll/HEE0/g9RaHPk12kUgC0zw+6UW9JCHejpnP2S3Diajdc686kBQVMxG1W3JVO2u9lYphSfRldu1B9VcUMlub6RiWkUcJVmEme+ys+G4/RjaF4vJhZhJ2Fn3N2Zf0L4Cp55EsAwsJ2enGzCSw8jpqoNwui6zareWSjJHotjPNOjx2KJepI3vLMONdmEYeNVCFHutA1lxI3hDoOcyknbkfRUN2uDAySft3WcLM5VBdXvREN7Lbj+RwYeWzmKkYsttnZ9NPRO3W6v5MxXpfRmR/FaR7sVI9SA43pjdMRho4t8JhP52dxeHPk81kLWc4XNyj3G8s18NkfV9jcaLK7pPimH3jPRVPAFPPgqyBpwJJT9izYeTSkM8iOVx2VmdDR1adGKm+rPveIOg6RiaOojntmTUcHmSPv5CRGUmxe/JN3Q4zdXntaH89h+z02ENevGVIsowe70LWnMhuO8uy1RcFYKRiKMFqdM2HkmzvS1SWhXweS5LtbPtGrm/Mq2mvN3WsXMbugZcVe9aAbBozm0INhO3eMSOP4gna9wVVQVJdmIkosjuAZeaxZAVZkjASUbshVNHs9S4vWf8U8ieonjTe62GiyyKureMnce8XcV70SYyWbSBJuC78W2Bo2ZOPfBtt7geQQ7VkXvpvPNd8FTk0eiJYM95F6s934rrs8wDkd69BUjRcF3zs+L4hJv5zPxnXQzjsJ9IVx21EIZNAkiXMvucJyRMCM48Rj6J4AvaMBnoW2eHBzKXAwr4/KQ7AQnK6MTMpZM2BkYqjeIJY+QyWkUd2+exnpGwKK5tEUp3InoB9D+rPup9NImluJEW178VuP5Is2zMJOdwYyR5klw/L0O1EoqpmP/dIip293xMExYllZEHPYWEhu3yY8e5C1n0zn0bWPHZjk6Fj6VkUTwgzm8K06Bsi0FmYfSUne1ETrViZBIq/EkN12rM2eYLkHOWFDPmqpKOmuuz7t6+KrOQunB8aOeRkJ1gWkq8CKxUF07Tvx5k4smRi6Dp4K8mp/pOWdX+y3UdGux4E4VQyrvj6urq6U7KSf7wl8YJ/2sCNKxQeukH5sIeHwKD1LjvEtdCJrVUNrPNXDNqu7//BkV+llgFog34OVQ1dV149UM7BHeeB0MDPZYNeVwOGh9gPPn5/Y7BzIDwNddByKP48+lmAywOu2tLrBWGSS1h+CA16QOi/pkxAdtq/jxSg4qobup+juvjhZ/C1CKAA5cVDXnAOOlb5oGveV+JeYwLuKUPLO5rhIwU0oD/as+9+VVTu4fc8ANegnvu+cf2IzlDhOLAG9+iXyFdhbzNovL2sYJlj6dEfOkZfkuVCAk1hbEwLknIZePpC3V3DNqgY9DzQ/2yjUVr/ctew//t5GHr/6h8mNPwZpf/eOPg1HTUjV0YH37uHG3x/Hun+Onh5+aAhAhbkvE0w+DE7GCqs66dbKrq7dKNUHgd4B70fz6A36h02nFRMrScI7znjquifeeaZfPnLX+bSSy/F5Rq4o1511VXjLpggCIIgCMK4mX3j72Vl5JD8wZV2eWzJ+CzTGJI/B0mxe3QFQRAEYRIYV0V//fr1ADz00EOFZZIkiYq+IAiCIAgnnWVZYBh25X20MfSGbifrw07GN6bs+SWm1xM9+oIgCMJkMa6K/n333TdR5RAEQRAEQZhYpgGShCRJSLKKOUIyPkzDDr0HkGU7lH8Mxy7sAyIZnyAIgjCpHFNF/1//9V/55je/yW233VZy/a9+9atxFUoQBEEQBGHcjLw9tR6MWhG3zGFZ98fSMz8oCgCww/jHEgkgCIIgCCfAMVX0zz/fnmLk6quvntDCCIIgCIIgTJRCIj6wx+mPNEZ/cDK+QdPIjnrsYWP0JUnGFBV9QRAEYZI4por+ZZddBsCHPvQhcrkc6XSacczSJwiCIAiCMPEM3Z7eFkBW7Yp/KYPG20uyYvfwH4lp2OPy+411bL8gCIIgnADjGqN/7733ctddd5HP21+clmUhSRJbt26dkMIJgiAIgiAcs0HZ9O0ke8UVeMuyhlbaxzrWvijrvkjGJwiCIEwe467oP/DAA8yfP3+iyiMIgiAIgjAhhoTuy0rpJHuW2ZewbyAZ35gq7EVZ90WPviAIgjB5yEfeZGThcFhU8gVBEARBmJwGj70fqad+UK9//3bWGCrslqkPNA7A2LP1C4IgCMIJMK6K/oUXXsgf/vAH2tvb6enpKfwTBEEQBEE46UzdDtmHkbPpG8Mq+tJRhO7Lw0P3RY++IAiCMDmMK3T/17/+Nblcju9///uFZWKMviAIgiAIk4FlDBujX6ICbxWF4B9F6P7grPuyApao6AuCIAiTw7gq+hs2bJiocgiCIAiCIEwsQz/yGH1jUK8/IEkjbDdcyWR8oqIvCIIgTA7jCt03TZN77rmH22+/nUQiwX/9139hGOJLThAEQRCEScDMDx2jX6qnvmiM/th69C1THza9njymsf2CIAiCcCKMq6L/4x//mO3bt/Puu+9iWRavvvoqP/zhDyeqbIIgCIIgCMfMGtxbP0JWfGt4RV8aY1I907DH8xf2E1n3BUEQhMljXKH7b7zxBo899hg33XQTfr+f3/72t9xwww0TVbZTlssFXfkeeiMRKtxl9OZiINkREE7VQdbIA6DJKhaQN3JosgqSRM7I41IcIEkYhomBjiqpIIFu6GiKRtbI4VJdWJYJSOhGHkVW0S0dCQlNVtAtE8myQJIwLRMJcCoO0noWSZbBtPCoLuJ6imR3D4pDJW/kccgqpmWRN3VURUWTVJL5NA5ZRVZksnoOp+LAwsIyLZAsJGQsLEwsZAtMLEBCk1QMTAzLQEJGlWVMy15r9U1n5JIdpI0slmXh13wk8klUSQFTxi15wBxXW5QgFFFViRg9yJJCzsiiKRq6aSBLEoqkYJgmFiaqpKJKMikj27dORZNVsmYOLAtJksibOm7VRVrP4Fad5A0dRZLJmXlkScalODFMUFFwagrRXAxFUrEs+7pwyhqGBHkjj0/zkDGySIBpgSzJ7I5EcGj29aabOk7FSUbP4FAcmJaJbuposoYkSWiSg6SeRJFlLMtCkVRMy8DERFO0vutbI2PkcCkOZBQ0wwmmgqXpJM0EEmBJoCDb160ko0oKOTOPQ9HIGTkkSUZGRjfzVGqV5LLWyf6TCsLIBifak2SwLCzTtL8HC9uUmCbvWJLxySJ0/2hZikFOSpOzdLS+e40kgUtxkjayKJKM/SgjoUgyWSOHQ3GgSQopPY0kyThlBxkji6bY9+i0nkGWFPteiokmq+SMPJIkoUoKhmXa9zDLQEbCksAlO8gYOUzLxK050U0D07IwDAOHqpHVs2R6E1iaSc7I41ScmKaBBch93wUORcW0QLfs5zZFktBN+16vKRo+yU/CiGNgABZBJUBazyMrFnlLR7d0sMCr+FANB2krhYGBV/JiYZGXsuSsHCYWQSkkTjVBEI5oXBV9VVWRB33JORwOVHVchzzlZdUk77Tv5MHNT5LIJrio6WyWNJ/P/657iA/NW8pzu19lc+cOppc38dH51/LQ5qfY1b2PqWWNLJt1Ga3xdvZGD3HVjItJ5lLcv2EFH5pzFc/veZ0DvYeZVTGNCxoXc6i3lbPrF+KQNVJ6hmiml2d3vcKB3sPMqZzB0plLiKR6UGSZZ3a8SEcywqKa+cwOT+e5Xa9w8/xrONh7mL/seoUyV5Cb519DXs9hAg9veQrTNLnt7L9l5c6X2dq1kynBOj487xpe3bcap+bigsbFtCe6OBxr5YIpZ9ESbwdg1cG32dq5i4ZALTfPv4Y/bvgzmqyybM4VRJLdtCY6efPgWnxOH59c+GEiqW7+vO1Zcmaeq6ZfzGlVs/nN2gfoycS4qPFsrp1+FV78J/ePKrxnmGqOrfF9uFUnf9z4OBc3n8OaQ++wrWs3V06/hHJ3kMe3P4dpmnz5gs/x3K5XWde6kbC3gpvnfRBZknGrTmK5JA9tepLebJxzG87g0qkXsK1zN2XuABvatvHKgdV4NQ8fnn8N00KNyJLMG63bcGsuNFnjkS1PE0lHObP2NM6sPZ14LkF7oou54Zls79qF1+GlK9nN6kPr8Dt9fPS064hl4rQmOuhMRji/cTEPb3qKRD7FhVPO4oLGxaiySkcywhsH1xJyBaj0lPP0zheRkbhy+sU0l03hLztfZntkN03BBj624EY8qgtVUWnt7SCjZ3hhz+vs7t7PtLImrph2Ife++whzwzNYNuty9vUc4ontz5PIJri46VyCLj9pPcMHms8naFac7D+tIJRm6oVx9JIkDYTvy44h20jDQ/f7GuRHY5nGkOn1JEkG05ywor/X7YseZGtkFw5ZQzd02pKdPLf7VVRZ5ZpZl7G7ex8t8Q6WzlhCmSvIK/tXs751M0uazqMmEObxbc+hmzqXTb2QZD7F9LImdnbvZdXBtZS7Qiyd+QGqvZWsOfwOqw6uJej08+H5y+hKRgh7K5AkiT9ve46/WXA98WyS1YfWs6l9O1W+Sj6+4EbePvQuU8sb+cuul2lLdLKoZh4LqufywMbHubDxLM6uX4hlmdz37qNU+SqZUzmDp3f+lZyR5/JpF9IcmsLv1v0JWZK5dvblyJLMg5ufLJTNsAwWVM+hO97Lru59PLPzRWRZZtmsy2kK1vObtQ+QzKe4fOqFLK47ndZEJ49tXUkim+CSpnNZOv0yfFbwZP8ZBUGYxMbVXTpr1ix+//vfYxgGe/bs4dvf/jZz5syZqLKdklqSbfxm7R/ozcQwLJOX963mzYPruLDpbB7c/CSbO3cAMKtiGr986z52de8DYG/0APe/+yhl7hBdqW6e3vEiGT3HVdMv5o+bnuBA72EAdkT28MyOFzGxWN+2GROLHZE9/GHDisI227p28cCGPxNwernvnUdoT3ZhYbG+bRMb27fSEKzhN2//gZAriGmZRNJRfvP2H6j0VfC/7zxEMpfivCln8MeNj7O1aycAB3tb+NXb93PJ1PN5/cBbvH14A92pKJWeCloTHeyM7OXZ3a+wtXMXAIdirfzqrfu5sOlsLmo+h8e3Pksk3cPrB97CsExi2ThdqQh/2LiCZD5F3sjz1I6/sr/3MHlDx7RMXtm/mmf2vACy6DEUJsbB5CFUWeFnb95DU1kDz+16lW1du1FkhTJ3gD9teoJ0PsPcyhk8s/NF1rZuwMKiI9nFr9/+PaqsIEkSv37790QzvZiWyRsH1/Lq/tWUuQPs7N7Hi/tWYZgGsWyc3677Ex2pCPFcktcOvIVbdfHrtb+nK9WNZVmsbdnIutaN9GZiJHMpWuJtbOvaTU+ml1UH38awTHoyMX799u8p94R4ed+bXNJ8Hves/SO92TiGafDK/tWsbdlIR7ILr+ZmR2QvTcEGHtu6kqyeJa1neHz7c3QmI+yK7gNgf+8h7n7ztyDB4Xgbu7v38fDmp9ndvR+APdH9/HHTE1w+7ULWt27mcKyN+999tHBfe2nfG6TyaTa2bWNdy6YhnaGCMJlYhg7KoBNUKTFO39CHhOBLYw3BH56MT1bEGP0x0uUc/7v+IXyal4c3P0XayPDUDruSnMqneXjzU8yqmE5nMsL9Gx4jlkuwrnUTsiRR7Q/zx42Pk8qnyRl5Vu56iRnlU9nSuZPXD7yNZVlE0lFe2b+ad9u38tqBtzAtk2iml9+s/QONoQbu3/AYXcluPnrataw59C6v7l/DxvZtWFi0Jzr5/978LYvrF/D7DStoS3QC8E7bFt44tI6FNXN5ad8bbOzYhgV0pCLMr5rFQ5ufJJmzn2dW7nyJw7E2NEUjrWd4aPNTWFjIkkwkHeUPG1YAsK1rNx2pCCu2/YWskSOdz/Dw5qc4GGshmU9hmEbhfvs/6x8s3INf3PcGLx9cNeT0EwRBGG5ct4hvfvObbN68mUgkwt/8zd+QTCa54447Jqpsp6SDvS1Fy944tI76QE2h1xvAo7noycSGbNeTidGTiXF69Ww2tG9FliQ8mptUPj1ku/ZkF2WuAOtbN5PRs6iyUnKbVD6DYQ3tXdjcsYPp5c1YWOSMXGG5hUVrvKPwe7k7RGuiY8i+6bwdOVDmCvLW4XeZXt6EbuaJpnsIe8uL3ntWt8OQ80aeeVWzWNeysbCuylPBgVKf1cF1LK47rfD7qwfWkCFVtJ0gHIu9vYdI5zOk8xkqBp3jDYHaQiUX4LTqObzbtmXIvoZl0pHsoj0ZKTru24c3EHIFWX1ofdG6/T2H6c3GOK1qNu3JTixraMPVutZNuFQXZ9TNZ/WhdzitajZrD28sOk5rvAOf5qWj76FzyOu3bOgL57eYVtbI2tbiGVG2de2mMVhX+D2ZT9GV6qYj2YVLddKd7hmyfSwbx6U60RSNrlS06HhvHlzHadVzeP3AW+TVbNF6QZgUzKGVeGS1aPx90Rh9WbGXHfHYIuv+serJ9eBz+uhIdnHelDNZfeidom329Ryk1leFYRr09j0v1fqr2Rs9ULStYRmsbRl63zutajarDrxdtG1HsguP6gYJckaOGl8l27t2Dz1eX2OtPuw82BnZS1OoHoD1rZvJGjnq/TUc7G0tep0N7VuYXTm98PuuyD6mBGoB+5krb+Q52Nta8ntjd/d+Gvq2nVk+teTz0mv73yYvi3uvIAgjG1dF3+fz8YMf/IBVq1axevVq/v3f/52ysrKJKtspye/0FS2r9JRjYdnj8PvIkoyENGQ7CQm36iKajhFw+u2x9ZI0/HAosoLVd1xZklFKdKcpsoJTdRYtDzj9hUaB4ft5NXfhZ9OyUOXiYRhezUUqn6bSU0ZGz6LICpriwLBMHIpWshyKrNCTiVHpLS8sT+RShFyBou1rvJW0J7oKv4c95agUH1cQjkXI6S9cF4PP8VgmTrk7VNgulo0TdBYPGXFr7iHXSb9KbzmWZF+TwwVdfpyyg55ML64S12S5O0TOyBHLJAh7yvuuleL7qFdzk9bTI99j+sax9mRi1Pmqi7ap8ISKGhc9mhu36kaS5KJ7jYSELCnopo5bKy532FtBT6aXKm8liim69IVJyhhaiZdKjb8vGms/tjH6pbLuY4mK/li4VCeJbBKX5qI11kG1t7Jom3J3iFguAYBDsYdaxLMJytylwtUtKobdf3syMcLe4nuyR3OTyCcLz0lpPYvX4SnartQzlFfzkNHtTpJKTzmarNCT6SXkKv6+qPSU05PpLfxe4SmjNxsv/K7ICi7VQZ2/+H5d7i4jlrG37UpFS973w55yFOv9PVxWEITRjaui39nZyf/9v/+XxYsXc84553D77bfT29t75B3fw5qD9YVWWLBv5DfNXcqfNjzOR+YvKyxf27KRZbMvH7Lv5dMvJOD08nbLBm49/XocioPVh9ZzUdPZQ7ZbOuMDrD28gYubzkGWJBLZZNE2H5pzNV3JCPOrZg9Zfs2sS3n9wNvMKG8mmh546J9W1kiVL0zYY4+1ffPQej4489Ih+17UeDbpfBbdMrh+zlVsbN9Oa7yDWl+YzkQ318y6bMj25zWcyY6uvbQnukjlU1zcdE6hYpXMp6jwhKhwD1RoXKqTi5vPZWuXHf4vSzKfWPBhVNOBIEyEptAU2mMdXD71Qt48uK5wjkczvVR4ygqV+xf3ruKjp183pDFuXngmAaefCk85zaEpheWKJHPd7Ct4Zc+bLJt1+ZAGvVpfFQ2BWiq8Zezq3k+dv4Y5g3p4JEnigzMvpcJTxtM7XuSqGZewqX0bH5h6wZCGuKZQAzX+KmTJTohZ768prFNllaunLwHshsHWeDvNZVPwagMPrgGnn/nhWUTTA/fny6ddhEdz0RisI5PPcOX0i4d8VnbY/iYsy2JKoLbovnbZtAvZ1L7Nvo/lxcOmMDlZpj408V6pKfaGhe6PuWfeEFn3j5VfDnJ2w0LK3SGSepoLGhfj1lyF9WWuID6Hl3g2wYLqOYVnhd5snJArMKSjwK25kJG5euaSIQ2W3ekoH55/LcqgqIumUD0ezc3iutMxTINtnbuQkbh21tDnsTNq5hNN97KwZu6Q5dfMuoxVB97GoWhcMGUxpmV3XLhUV9HzzILquYVIsWDfd0f/PXh6eRO9mThn1p3Owpp5Q+7XQaefOn8V0b5Ggu50lKZQPfWBoff9j8y/FskQjayCIIxMsobHkR6Fz3zmM8ybN4+PfvSjGIbBn/70Jw4fPszdd989kWU8LiKRBKZ5fMZ+99DF4UQbWT1Lnb8GVVLoycawLAuX5qIzGcGjuShzh0jnM0T6Wmv9Ti+RVJSQK4Bbc2GaJi2JDkJOP3nLIJ6NE3D60Q0Dr9ODW3Whm3aW77SeIZVLk9IzlLkCqLLSF7YvkcylyBl5Kj3lJHJJTMsk4PQiSTJdqW5UWSXsKacrFSXsraAzGekLZ6siZ+RpT3YRdPoIugIcjrVR5a1AAnKmjiaruFQHGT2Pburopl1Or8NDyBUgku4hb+Sp8lWSyqaRZZnebBxNVvE7fbhVJy3xdkzLpM5fTd7QC2OP67y1VKiVdhpwIBz209kZH/WznwwmaznD4ZGTGo7lepis72ssBpe9R4qQNbJ910UOp+qkNxPD63ATdAbpTNmh+dXeCnKGTmuiA7fqotwTwiHb2Zct7N6ijJ4h7K3EMHU8Dg+JbBKn6qQ90YlD0aj1V4ElocoqhqXTlewm5A4STfeQzKcpcwfRZI1YNl7o2bewyOg5XKqTaLoXl+qg3B2iI9lNyOUnko5S7Q3Tne4hrWeo8lbgVJw4FI32ZCdOxUEskyTsK6cz1Y2ERLk7SCafRZZlutM9lLmCdu+XZeGT/fTo3STyabK6/bmE3EFkSSKa7u2LSLIb3rrTPZiWQYW7jFg2QaWnnBq1tij/2Klwroz3epjoskz2z2skk73s2bUrMGMdaLPthqzMy7/FvfTLKGV1hbLn975NftNzOBbfCIDRsg0jchDP1V8a9diZ1+8Hy0SdZje0W5ZJ5qm78P/D747re4KJ/9xPxvXgL3Owq2s/FiaJXAKfw0dbogNVVqnyVtAa78ChOnAqDtyai5yRJ5KKEnT68Tv9tMTbUCSZSk85XaluyjwhVEmhPdGFU9Xs5yjLxKk6aYt34lQdVHjKiaVjeJ0ecnqOeC5JfaCGTD5HWs/Qm431NSQE6U739M2CYpLMpQj7Ksnlc/RkY4S9FTgVBzkzRyafJZ5NUuMP05OOkTPzlLmCuFUnHakIumnYve+SQluyE1VW8GoePJobh+xEt3Lopk5bogtJghpfFV7FS1uyg6yRo85Xg4xE1srSkewib+o0+GupUmoY6Ql+sl+Xw0228o52PQjCqWRc3TBtbW3cc889hd+//vWvc8011xxxv5///Oc888wzACxZsoSvfe1rrFq1ih/+8Idks1k++MEP8uUvfxmArVu3snz5chKJBGeddRbf+973UFWVFStWcNddd1FRYfdAf+ADHyjsc7KFqCTkqxxy4wp7auyoPkui2dWMolhYloTkgsFJ5ZvcU7AsO1+QrkNdRR2K0tdxEOiLDLQo3NxN014mueztJck+bn/uIcsC0933WhJI7r5cRCYYhkWjs4nych89PQnqnfWYJtSGarEse73ikJjmmVp4vfrKegzDQpZBlqVCWSzFQlEku5y+gfLVBev7ymmhOCVME0yXvVKW7Qp8TXktpmkhSWDIMMUpIUn29GWm3VZROJ4s272gdhkk6JvKb6C9yl4my2Ca/T9LA59F38OKJNn72I3//euHfmPaPQNWISS6f33/z4oiYZpW3+cw8BkPN/g4gs3plND7OtUUhb7zbSAKdvD5a5/XoGmQy9k/SxLIsoWu2+eKMagTTZKkvr+/VfgZQNPs86lSqgQFcND3twXZb18TigJ1jrq+/cFULKZWNxauyVwOZM3er9nDkL+pLIPRt67J1Ygs22U3TQss+3yvDlWjKFDvrMOyLFRVIp+3UH122SRp4HOQJAjW+4hGE8gyNLjqMQyY6mkGLBrdDYXtdd1+jZCnL/y1L+K01t8wUMC+u/3U/mjU/sq5ARVSDRUO+zNhUARr07Bgmnr/QCRD/4gakWRcmMwso3j8fcke/SNtU4o5dFq+/gz8RdP3CSW5VCfNvrohy6b6Ggr381qtvnAflSSwVIsmx9TCtlXBgR7u2sDAva6m77ljsLpBUVg13r7XVIH+IAKt79+gkVkV7qqBXzyDKqODR2/JfcfpW1Y7LMI+7Ksd8nt1YOj7BexHEAVqgoPu1xbM8ASLtqvyDxxPPFMIgnAk46roV1dXc+DAARobGwHo6Oigqqpq1H1WrVrFa6+9xmOPPYYkSXz2s5/lySef5K677uK+++6jtraWz3/+87z88sssWbKEr371q9x5550sWrSIO+64gwcffJCPfexjbNy4kdtvv51rr712PG/huGiPZdl+IEoifZDZjWVMqfLQ0plm96EoU6oDRGNZehJZZkwJkc0ZHGyP4/c6CPmcuBwK6azOgfY4c5rKSGZ09rfGqK30Ul/pY9uBKKlMntmNZWQyOuUhF/vb4nRF05w2o4KOaJqunjRNNQE8LpV9rTFM02LGlBAH2mIYpsWU6gDxpJ3ApaUrSXWZh6DPQTSexeNS2dsSozzgYnpDkAPtCVo7E0ytC+JxqWzd1838aRUc7kwQS+aYWhegzO/EtCz2tcYIh7wc7oxjmtBU62dvS4yGKh+6YXKgLU5F0EV1uQdVkdlxoAddN5g3rYKWziTRRIY5TeW0R1J09qSZ3VRGKqNzqD1OTaUX58EeWjoTTKsLoqoym3ZH8Hs0ptYF2d/aS1W5l3RW53BngubaACGfg95Ejq7eDMl0nvqwF1mW8bk1DrTHqKnwEkvmUGSJls4ktZVeZjYEcWkyB7uSbN8XpaHajyxLHGiLU1PuIVzmJpXJoxsWOw/1UhlyMbM+RDSRpTWSRNdNZjSEqCpzYRoWOw/3crAjQUPYR33YRzjgsNsm3ockCbpTeTq60yBZWCYc6kygqTLT64PohsXuQz001QY43Jkkm9OZVh+iLOAgGsuy61APQa+TptoAOw/2kNcN6sM+umMZGqr81Ja7ONSZIpbK0dKZpKrMw5RqH/vbYnT17Keu0ockgdetkckZ6IaJZUFXT5p5U8vp6slwoK3vWqvyoesm8VSO/W1xyvwu/B6NnG7QUOUvXFdet8rB9gThkJvyoIste7upKfdQGXLTHUsTT+WZ2ViGW1No706x82AP1RUe6sM+Nu7uIuRzUVXmJps36O7NkMrq1Id9mKaBz+Nk9+rDaKpMQ7WPHQei1JR7qanwkM0bJNI6+1p6CfldNNX4KfM58DpEGKcgDGHkh2bUH2mM/pCEffKYKvqWaSCpw1rD+veVxZCzkcSyeVq6Ury+uY36Kh+xZJ5DHXHCIQ8et0oskaO5LoCum2zc3UVlyE1FwMW+1hhzm8upK3e/b79HBUEQjsa4KvqSJHHjjTdy0UUXoSgKb7zxBjU1Ndx2220A/OpXvyraJxwOc/vtt+Nw2F+C06dPZ9++fTQ1NTFlit3iet1117Fy5UpmzJhBJpNh0aJFANx0003cfffdhYr+/v37+fWvf82sWbP41re+RTB48ucT7Yhn+X/vX0t3LFNY9i8fO5P7Vm7h4oUN3PPEZjqjacr8Tizgwed3FLZrqPJxxqwqKkIu3t7STiZr8MRrewrrZ04J0Vjj54W3DiJL8P98fDEPPr+Td3Z2cu1FU7nn8c0c6rAT13z62nk88NB2sjm7aVxVZD69bB6/fWoT86eW01Qb4OlV+wrHvvzsKZT7XTz0V3s6vbPnVbN+Rydvbx2YKeADZzYwf2oFP/vjemJJOxmNLMHnbjyd2goP1eU+/u3et8jrdhefU1P47A2n8daWdv769sHCcc6ZV41DU3jt3Rauu3gav3p0I4c7E9xwyXT+67GNtHYlmd1YRqQ3w4trB/Y7Y1YYh6bwp+d28HfXzufhvrKGy9x87obT+NG9b/O3S+fw1Ot7SWV0/s/NC3jytT0caE8UjvGJD87l8c1t3HzpDH735BbOmVfNn18Z+IxnN5bx4ctm8K//8xbV5R4uWFDHYy/tKqyfWhfgI5fP4sf3ry0su+2m0/nfp7aSzup9n7XE8r87l0de3MnG3QMZ2q+5cCoXLaijJlic4Of9IJ41eer1vUyrC2Fhce/TW4ecKx9fOgeHpvDvf1g35Lz9+icW86N738Y0Lf7mqtn8v/e/TW/CPv8kCf7u2vn88H/f4vZPnsWOgz2Fa+qiRXW88s4hNg36G1x1bhO7D/Ww9Pxm1u/oZNWGFv7x5gWs3tzGk6/tLZTltptOpyOa4oFnB67Pxmo/S89v5g9/2UY0nuW0aRVDrqE5zWXUlHt59MVdLDmjninVfl7f0MIbG1s5Z14N96/cVth2ekOQaXVBHnx+J1Vlbi4/q5EHntsO2PWEr/7tWdz5uzWFCBSPS+XmS2dy94Pv8LGrZuHQFP7nqa2F4zXXBrjlylnMrA+I2SgFYTBDtyv3/WS5KKN+UVK9MU+vp1M0v5ksxumPRnWovLhqH8+uPsAnr5nL9v09/GnYc9CiWWFeePsgn7pmLq+/20prJMmUKj+LZoX5zm/e5LufO4/6suKkqIIgCMJQ44otu/baa/nWt77FpZdeyiWXXMLXv/51PvWpT3H11Vdz9dVXl9xn5syZhYr7vn37ePrpp5EkiXA4XNimqqqK9vZ2Ojo6hiwPh8O0t7cXfv7iF7/In//8Z2pra/n+978/nrcyYfYc7h1SyQd48IWdfOqa+TgdCp1RO+P9xYvqefr1vUO2O9SRwO/VeOGtg1xz4VRWvrlvyPqdB3uoLrfjak0Lnnh1D+edbodxBbyOQiU/XObmcEeiUFkC0A2TTXu6mF4f5PSZYVa+MfTYZX4XK14ZmF5m5pTQkEo+wMvrD+FyKoVKfn85Xlx7CJB4fcPhQsUNIJs32HWoh237uoccZ82Wdhpr7PEKfo/G4U673F63SmtXEoAz5lTx0rqDQ/Zbv6OT6Q1BDNNi/Y4OZjfaiW86o2laupKcPr2Sv7y5n/NPt0PjHnlxFzOmDM1evvKNfTTV+GmLJLlwQR0r39g/ZP32A1H2t9nDLS5YUMczq4b+jfa2xGjpHGg4KA+42NsSK1TyAXTDojWSHFLJB3j2zf3sb4tRMr7/PU6SJKLxDNFYlt2He9i+P1p0ruxvjRFL5YrO2xfXHaK5NoBDlcnrZqGSD3bo4svrD7FoVphDHYkh11RjtX9IJR/g+bcOcNbcap5fs59c3sDn1nA71SHnwTnza4jEskMagAAOtMdxaDLv7uzivPm1PLt66LmzbV+UurAXgFfeOYxpWZwzr4YFM8JDGvQAdh/qJdz3oNoRTQ+pK8xtruAvb+4fMiY2ldHpjmUIeB3kdIs/Pjf0ePtaYxzqSNAZE1MtCcJglqkPjAUCkNWSPfqSNDR0f/gUfCUNz9YPIiHfERzuSvLEa3s577QaYskcT5V4Dgp4Hew53EtHNMUHFtth+Ac74lSGXBimxZOv7UVW3n/fo4IgCEdrXD36F198Me+88w6SJLFgwYIhlfIj2blzJ5///Of5+te/jqqq7N079GY/eEz08OUA//mf/1lY9tnPfpYrrrjiqMpeUVE8VclEyGwonks1kcrhcigYxsD7cfaF6A9nmhaJVA6tr1JTan2/ZDqPqy9Ud/Byt1Mlmc6XKEcet0sFy2J4Xh1Floa8XqnEO5ZFyTIlUjmQoCeeK1rXm8jhdhafZv3HH/w6Q17TKj2mvX+bRDqPxzVw3Lxu4nGpJFK5wvLh2wxeltdNFFkimy9+INMN+z06VHnU9WD3tCYzxZ91boT98rqJz+/E7ZxcUwaO9XoYT4KaXa1x3C7VPsdLnJ/xVB6/tzjcNZbM4XGqaCP8PRKpPFOq/FiWNeSaKnUO9+eCiKXyVFd4cToUdMMc8jd1O1UkLDIlrs/+81+W7QadUseHgXwQkmRfW7kjXMuD7w1up0o0ninaPpXRcTmUEc9bwzAxDHPSJBGaLOU4Fsfr+2E0p/LnNZnL3qGB4nXjCdmN5N0OjaBPw9NX5nDYT69bIeN2EujbJpf3EJetI76vNlXC4XPjDg0ktsgqCuVlblTf8f9MTtTnPpHXw8FICtO0cLs0JIkRn4MAcnkTpzbwHd7/TNCbyOIPuHGoJ36o0mQ+14c7lcoKp155BeFUcMwV/Z/85Cfce++9TJs2jXw+z6FDh/j4xz/OV77ylSPuu3btWr70pS9xxx13sGzZMtasWUNX18Dc6f1j/aurq4cs7+zspKqqing8ziOPPMKnP/1pgL6kVkf3Vo5XFtmpdUFkWRpy7CvPbeLpVfuYN7UCVZHQDYt12zu4YEEdr75zuLCdU1NQZJmLF9WzbnsHi2aFeWdHZ2G9161hDDruBxY3sGpDC2An+nI6lMKY/8vOmsKqjUMbHc6YXcUfn9tOecDFzCkhdh7sKaxrjSRZPKeKtds6ALtCHA656exJF7Zprg3YlSBpaBKYCxbYicUuWVTP+u0dQ17zzNnhop7PiqCLZMb+cldkGYcqk9NNVEUuNHC0RlJMqw+y5/DAdGCVoYH9zppTXQjdVxWJ+kofK17axSVnNPDWljYArj63iTc3tQx57UvOqOedHZ189IpZ/GX1/iHvGcDrUqnpi5rYtCfC2fNqWLO5rbDe7VSpKht4qDvcmeCqc5t47Z2hr1NX6SPgdQyJfjhtegW1FW6S8QyJWHFF7ng72Vn3K0MuuqJpzpxdRWNNYMi5DTB3annJYIdLFtXz6xWb0A2T8oCr6Py7cGEdL7x1gDPmhLlwYR2vrLevqWzeIOR30hMf6OWe3VTGgTb7+lizuY1IbwZVlVkws5INO+17zdpt7dy4ZBoXLqwfcn26HAp+j4Myv5Pdh3s5bVoFm/YMRAwEvI5CQ0BTjR8k2N8aJ53ROWf+0PPI61ILjW2qIuHUBh5YN++J8PGrZ7P70NCpSptq/by49iDprM6FC+p47d2Bc67/vKzwuyZF5uLJlkG5FJF1f2JM9rJnUhkkxSDXkwJAN6AnGifZGS+UPRdLYuRNevq2MZN59FzuiO8rm86gp3WyffsBWJJMpLMXOX18K6Gnatb96nI3c5vLWLu1navPbSr5HCTLEh6XSk2llz/3RRo6NQWjL/Pn0vOa6Y2mSh7/eJrs5/pgp1JZYfKVVzQ6CO8Vx1TRf+SRR1i/fj0vvPBCIet9e3s7//Iv/8JDDz3ERz7ykRH3bW1t5Qtf+AI//elPOf/88wFYuHAhe/fuZf/+/TQ0NPDkk09y8803U19fj9PpZO3atSxevJgVK1ZwySWX4PF4+O///m/OOOMMFi5cyP3338+VV155LG9lwjVVefj6J87i0Rd30ZPIctnZUzhrThVBr4M3NrXwDzcu4KV1B4n0Zlg8p4rKoIvXNrRQU+Hh4oX15PIGiupk1cYWPvHBudSFvby1uZ2pdQE+eMFUHvrrDmorvFxxTiPV5W6m1gbJ5gze3NTKlz66iOfWHOBwZwLdMPmnDy/kidf2YJgWyy6cyq5DPdSHvcybWs7CmZU0VNkJwWY1lnH23GoivRlqK7y8ubmVjkiS/3vrGTz1+l52HoyyaGYVC2ZU8swbe/jChxfy7Jv7iafyLDmznrnNFagKxFNZPnP9fJ5dfQBJgg+e38z6HR1cdV4z0w5GWbe9g2n1QT54XjPxVI7m2gDrtnfwLx9fzNOr9vLGphb++dYz+Mub+9m6L8KnrpnH+u0dvLOzk9mN5SycVclTr+3jczechsuhEPI7KQs4ue6iaew61M31F09D6psJ4COXzWTmlBDV5Z7C533hwjoCHgfzp1Ww82CU06ZVoCgylUE367Z30Fwb4MOXzsDn1vjI5TN5ad0hbjh9OrXlHlZtbKWpxs/lZzficihcf/E03tjUSl2Fl2n1Qf7PzQt5ZtVe8obJsgumUhFysvzTZ/PIS7vZfbiHhTPDXHB6LfUVnvdtply/S+Xvr5/P1v3duB0qn7xmLi+uPYQkwY2XTEc3TNbt6OCz18/nuTUHMEyLK85ppLkuwN9cPZvn1xxg+/5uvvLxxax4ZTfxZI5LFjWQyub58GUzaaj0kZtu4HVrvL21nUMdCf6fvzmTp17fw+7DvSyYUUlzrT30A8viA4sbqC738OJbB7jxkulUl3lYv6ODqXVBmmuDVAbdlAecrNrQSl3YywUL6tiws5OvfHwxj7+6hzPnVFEX9hbO64sX1vPH57Zz0aI6zp1XQ3c8S6QnzfVLphMOuakKuVm9uY3GGj9XntvEH5/dzrxp5Vx9bhOmaTGrsYxYMstFC+tRFInP3XAaT72+F5dT4apzm3hrcxtXntPIollhZBnKAi7e3GiX7erzmqkud+MU4ayCMJQxbPx9iWR8ljFsrL2sYBljCL83jeIx+pIsQvdH4ZAk/u7a+fx17SF6klnOnleN36Px1tZ26ip9XHJGHeu2dfIvH1sMlkVrV5KFMyu58pxGnnxtL/948wLmNZ38fEyCIAinAskqFR9/BB/96Ef5+c9/XpRhv62tjS984Qs88sgjI+5755138sgjjxQy9QPceuutNDc3F6bXW7JkCd/4xjeQJIlt27axfPlykskk8+bN44c//CEOh4O3336bf/3XfyWTydDc3MyPf/xj/P6xt8Ad7x4bHcjpJuGgRjJpYMmQ1y37GcDsn/JLwp7jSsLEnh5LQrKnDTPsnj4LCUM30TQJQ7fnAjMtqxAZ4HbI5AyLfN7E41LI5c2+aeksNFUim+/vKZdIZw0cmoyhm1hIdk+6YSIj21OUWfY+uZw9LZnLIZHTQTfs/bAgmzNxuxQM00LX7XJIgKzIpDI6XpdMVreQJQkZyJsWigSqKpHN2VPRKbKELNmfh2nZU+E5HQqptIHPJZPO25+JZVloWt/0Y5KEz+egN57FwsLjUEhmTVRFKmTflWX7szFN+1lLxl5l9k2hhmWhyfbSnG7h1GTyhmVP32f2vZe+Y0kS5Pqm8JMle/oyTZWRJYts3sLplEhmTJyqQt60hwFYWGBCVYWXaH9vgwRZ3UJTZBTp5E6xd7J79PtJqkQqY6Ao9gOyaZmosoKsSGRzBk5NRjctDMPCodlTNjo1iUTGwO1QsCyrL2zeQlXsITFuzZ5yEQl0y55uT1Pt88HCXtd/roKEYVmF8yuXt1BlCUsCQ7enTZSRMCz7/MrrFopsR5o4HGBZMlgmubyFz6ORzOgofdM+mpaFbIGqyWRzBpoqo0gSMha6JJHJGThU+3331yM0TSKTNfC4ZFJZ+5yyI3fs60g3LbS+cqiqjIU9Y4Hcd09RZAl333U9WUy23plSRI/+xJjsZU+t/ClK1TSUmlkA5N55Cm3mBWgzLyiUPfv2Y5jxLrTZFwFgJiLk1j2O79Yfj37sJ3+M0jAPpWp6YVnmpf/Gc81XkUM1o+w5fqdqj34/l0elN5lHluz7slF4ZrEK3+uaJpPOGDidMvmcgapIqJJ00r5HJ/u5PtipVFaYfOUVPfrCe8Ux9ejncrmS0+jV1NSQyxWP0x5s+fLlLF++vOS6xx9/vGjZnDlzePjhh4uWn3XWWTz22GNjLPGJZTl1IukOclYesiG68704FA2P5sKwTHqyvXgdHhyKBkgk8ylkScKpOInnkvgdHnTJwCk76cnE8KhuJBNSRhqP5iZv6TjRSJkZlJyCJquYioluOkhZaQzdwO/0Esml8ahueo0Mlm4ScPppSfeiqSpezUPaNMmaOZyqRjyXxKU48Ms+YlKCvJnHlXfi0dx05btR8yoBh58oPbgMF05ZI6/oeFU3sVzCnu9c0knmVdyqk2iml4DLby9Hx8ybyLKMbuo4JQeqpGEqJmDZ7z8jE3AG6M7rGBiYhkXIUYZsaDhlu3rm8zhJJ3OA3ejh6u+97O/EtCw7u+TwFJP96wfFfDsUya7c96+TBxoM+g6F1ve6WKApUiG3gaZImDq4VbvRoL98IIEM6uBxgxZ9vawnt5I/mVi61ffZ9ZH6mmQMC7cigWnhAFAkMO3/LN3Cq8r0x7srsoTdQmOhyIPGuFugIqEq9s9y3x+/riYw6CHCQlMtUnKc9kwMTdYwTQPDMgk6g3Slu3EoDtyai45MHIeqoaKiOVW6MjEcioZbcZNRM/SmDVyaC8PUyRs6EpJ9XRsSGSuDw3LgVbw4ZQc9uR5SZhq/5Ec3ddJGGpfqBB08LjctmRgAFUoZcTOJaRlIyGSNLG7ZhdPhIK5nUWWFpJ7CqTrsITuqk55cmpyRx+vwkNGzeB0eZGRi2TgWFiFXgFgmgcfhplKtID/6bVoQ3jsMfejUeZJcokc/PzRhn1RiCr5SrGHT8vUd3xI9+keUSelUlbs41NtKTzaOX/MQzecwMfErPnTTIJFOEnT66c2mcagaGT2LW3WTN3PkTQOv5iKeTRJyhzCMPA7VQTTTi0NxIEsy6XzaftaSNVJ6mlQ+Q9Dpx8LCqTqIZRIYloFXc6PJKmkjRzafRZEVHKoDh6ySNXKk9SwOWaWnywmyhYxEbzaBz+FBlRTSeoackcfXd/9FknCrLnuogQUhR5DefIy0nsapONEUlUQ2iVNxUa5WIJnHnhfblHWi+R4AyrQyZFNMsSoIwoBjquinUiOPjcrni5NsvZ/E5R5e2f0mz+16hY8t/BC/XfcnOpJdBJ1+/u7MW/jtuj8Ry8Zxqy4+dcaHORxr58ntz2NhMbNiKguq5/Dq/re45fTr2Ny+A0VWqPCEWLH1L2T0LEFXgM+f/bf8z/oHOdDbgiLJXDbtIppD9XQkIzy5/XkMy2RKoI7r51zBht4tPLPzJXRTp8YX5qoZl/DHDY9zw9yrmF7WiCTJ3PPWAxyOtXHDnKvoTEZYddCeOu7MutOZUzmdP2xYgYTEkubzqPZV8Ne9b3Dz3A8S9pbz9uF3KXMHeWTrSuLZBG7NxYfnLyOTy9Ce7KLWX82+noP4HB5e3PsGpmXSFGrgosazqPVX89zuV3m3bQsA5zWcyXkNZ/D/vflbLCzmVs7g0wtuwYcI0xMmji7n6DGi/O/bD7EnegBZklnSfB6JbJIKbxnRdA/zwrN4dOtKejMxXKqT/3veZ3hi+/Ns6dyBhMSFjWehKRov7l3Fp8/4CK/sW1041tUzlmCaJn/Z/TKyJHPtrMtpKmvgV2vuY0b5VC5oXMz9Gx4jq2cpcwW5Ye5VHI61k8gl2Rs9wFUzLmF39wHcqosX9r6GZVlMK2vkyukX41Kd/PfaP5LMp/A5vHz0tGvRZJX/eefhIcdbdeBtLp12If/11v0AnNuwiMunXcSdL9/N359xC7PKpqHkXSf5LyEIJ8CwqfMkWSmaXm94Y4Akjy383jJ0eyjAYLJiv6YwqrycZVPHPv648QkW1MxhV2QfWzrtvDvnT1nMB5rPI51P87t1D7J05hJW7nqZkCvAzIpmVu58ufBMc/WMJfzirfu47eyP85ctr9Aa7+DyaReyYutfSOsZvnze5+jJ9vKHjX8mq2cJuQL849mf4Iltz7P68HoAFtct4OoZS7hn3R9pT3SiyirXzLyU+VWzeHjL0+yM7EVC4rJpF3Bew5ncv+Ex2hKdfGLhhzAti0e2PFP4rvjQ3KW8sm81FzSeRYO/hrZkJ1OCtby6bw2xXJJLp57P87tfY1vXLiQkLp16PtdOvQonntE+rpLSJHlk2xO8cWgdAOfUn8FHZl+HhxOfTFQQhMnpmJoRTzvtNP785z8XLV+xYgULFiwYd6FOZS2JVp7e8VfOrDudV/a9SUfSTvB1QeNZ/K6vkg+Q1jP8Zu0DuFSHHfIN7IzspTvdgyRJPL3jryRzKU6rmsWDm560W4mBGl8lL+x+lQO9diIuwzJ5bvcrOBSNP297FsOyw3cPxlrIGDme2P48et9DR1uik9WH1jO/ejYPbX6KlJ7hxT2rmFo2BZfqxOvwFir5AOtaNtKTieF3eLGweGnfG/idPrpTUZ7Z9SKHYm24NTcPb3mGeNaeci6dz/DHDX+m2h+mxh/moc1P0hyawgt7XsfsK9v+nkMcirWyvWt3oZIP8OahdXSlozhUO/P61q5dvHLwzaIhkIIwHjkpxV/3vM6e6AHAHjrw4t5VTCtv4i87X+aCKWfxWF8lH8CjuVnftoktnfaUdhYWrx14i3J3kBnlzWzv3DPkWM/sfJGQO1j4/fHtz9GZjJA3da6eeQm/Xf8nsn3XczTTy5PbX8DCZEqwlkuazuP3766gIVDD83teLcw8sid6gO1de3hi2/Mk83ZDayKX5I8bH6c10VF0vCnBetYe3sCM8mYAVh96h93d+zmz9nT+6+376cwOnXZQECar9lQnW7t3lJyFZyzsyvigzO0lxuhj6kiDK+yyMrZe+RLT60mymF5vLLrzXbyybw292RhZPVeo5AO8cXAtB3pbMCyThmANqw6upT3RyRm183ly+wtDnmlWHVzLjPIm/nPNvTQEalnSfC4PbnqCtJ6hOdSAosj8zzsPFe6RPZkYv177B2ZUNBdeb23LBnZ07Snc83VTZ/Xhd1jfuomdEXtGKAuLF/a8TmcqwqHeFi5uOpuuVJRH+yr5ABk9y4ObnuCCxrN4aPOTpA07gmB/z2HeOLSOcxoWsrVzF9u6dhWO+de9q9gd33fUn58kwebubYVKPsCaw+vZFNn6fpzBVxCEERxTFerLX/4yP/3pT/n3f/93Xn75ZV544QV+8IMf8JOf/IQvf/nLE13GU0pr3M7gXh+oYW90YB54t+aiNzt0/JHRFy482LauPUwNTWF3936mlTcSzcQKFWSAaWVNQ74QwR7X39P3RTNYT7q3aNn2rj00hxoA6Ex2Uxesod5fQ52/mu19Xz6D7e7ez5RgXeH3vdFDhL0V7O85TEbP4nN4SOSSQ/bJmzrRdC+6aaApGvFcYvhhSeUzbO/aU7R8W+cu6vwDw0LWtm5A5/0dJSJMLAuJdwY1MPXrzcZwaU6yRm7I9dQUaig87A12KNbG6dWzCw0Ag/VH7fSLZ5NoikYsmyiqsHSlugk4/XQmI6iKgkdzEc0UX7sb27cxJVQ3ZFkqn0aVhwZm2cfzsa1rN1PLBnKh7Orez6LaeeRNveS9QRAmm23dO7nr7Z/zwLZH+f224iF8Y2Lqdg99H6lEj7tVKmHfWHrlLQOpRDI+Ebp/ZGk9y8aObTSHGtgRKX4W2NW9D5fipDk0pXD/zRnFzwI7I3tpCjWQzNnDmQzLLDxXTStvoifdW3TPjaSihFyBoter9VcXfp9WNoV327cVvd6e6EFC7iABpx9ZkouevezXt//+3ekeerMJEjm7cdalOEu+121dO5Hlo6udK4rM+rZNRcvXtm5AUUT4viAItmOq6E+ZMoXf//739Pb2ctddd/Gzn/2MbDbLgw8+SG1t7USX8ZRS6SkH7IftWt9AhVU3ddza0FBZSZJQh4X9NYfqaYm3U+uvpjXeQdDpQ2LgC+BwrI2poSlD9rGw8DuKQ7X8zuJlTaF62hJ2Y0SZK0BbvJNIOkpHMsLUsilF2zcG62hLDEyD1hCoIZKKUu2tRFM0smYep+ocso8syYRcATRZJafn8DmKQ9IciqPk600ra6QjOdDbOKdiBiqTa8554dRmSRYz+3q6Bws4fWT0LA7FgVcbOGdb4+00BuuLtq/xhdndfaDkeex3egtROAA+h4e8kcfv8JZ4XT/pfIZydxmmZZLMpwk6ixMBzahoLjQk9nMoWtFDbMDpJ6NnaQ410BJvLyxvDNaxM7IXWZJL3hsEYTLJG3nu2/ogVzddxkdn3ciWyHZ29RQ3uB2RYQzp0UeS7Yr9YGaJcfxj7tEfHrovi9D9MXAoGtPLmmiJd9DU1/kwWGOwjqyRpzXeUehs0OTiZ4Epfc8oTtWJYRqoslJ4ZjocayPgKr6X+p0+Uvl00XE6Bz17HI61l/yeaAjUEsvESeczYFlDvivA7njpf64LOP34HO5Co29Ozxc6WgabVtZ01MkODcMe3jjcvPAsTHPyJGYVBOHkOuag6Pr6er7//e/zxBNP8MQTT/C9732PmpqBLLM/+tGPJqSAp5opgTrOqlvAGwfXcfXMJYUb/Cv7VvO3Cz5U6H2TJIlbT7uejsTAF0ulp5ypZY10JiN8eP41RDO9HI61sWz2ZYUvrm2du1g267IhD+qL6xZgWCYXTDmrsMzv8FLmCnJh48Ayj+bm0qkXsObwu1w69QLK3GWcP+VM1rZsJJFLoska0wb1ADYEagl7KuhO9wAwNzwTVVZQZIVlsy+nOdjA/u6DfGjuUpS+LzZJkrhhzlWkcil2Rw9yzazLaEt0sqhmfuG4QVeAGeVNzA3PpG5YC3qtv5pkX+t3paecK6ctwRLfWcIEUg0ny2ZfPqRHZ2HNXNoTXZxZexqbO7dz07wPFq7V9mQXZ9UtoNoXLmw/s2IquqmzoX0rZ9SeNuRYZ9UtoCcdKwzJOa/hzELF/c2D67lp3gcL17OmaNww50o6kxFi2TirD63n2tlX0JWKsqhmXuGYZe4gi2rmcVHT2ch9PYiKJPOR+dfiVl1Fx3u3dQvnTzmTzR3bAZha1si88Exe2Ps6f7vgQ1S6yyf8cxWEibS6bS3lrhCNgQYcisaZ1Qt5/sDLR30cyxzeW1+iIj68wj7G8Hur5PR6InR/LMod5Vw5/WIyepZqb+WQ++v08iamlzejyjIb27dy+bQL8WhudnXv5cLGswvbeTQ3l0+7kLUtG/nMGbewsW0bqw+t57o5VyBJEtu7dqPIKjfOvXrgHimr/P0Zt/DGoGGKTaEG5lfNKvTE28vquaBxcaHzBmB+1Wwag7W4NTev7l9DmTvEh+YuHfJcd+3sy1lz6B0un3YhPocX3TRoDNUxo7yZTZ3bOa1qNlXeysIx51bOZFZoYNaGsbIsi0VVpw/p+GkK1rO4esEJnTFEEITJ7Zim1xuLD33oQ5M2Kz4c3+mTUmqcjmQnumVQ7g4RTfXgVB34nV5yhk5nMkLQ5cetOEHCDucFvJqHaKaXMneQvJHHq7lpT3bhd/iRJYlYNo7f6cM0TRyqg95MDFVWcfaNaXcqDmLZOHlTp9wdIpaJ43N4SekZdFOnzBWiO92NpjgIOv3opkHOzKHJKtF0Lx6Hh6DDT3emh5yRJ+D04VSdtCc6cSgqZa4QXaluPA43HtVFOp/F5/QSy8RAkkjlM7g1F27VSVeqmwpPOXldx7DshyoLyBk5PJqdqVaW7anCoukeFEmhwlNGXtfJ6FlMy6LKFcZlDbSWT7bpV0YyWcs5WabXOxmGl91U8ySsOF2pbjRZBSR0I0+5p4z2RBcezWXPOJGK4tFcaIqGpqh0JrtxKg5CriDxXJysniPQl8U5kUsiS3KhhyeWi+NSnASdAVRZoScbI55NUuEpI6fniOUShfwXfoePzlQE07Ko84fpycTtiQgwyegZAk4/LsVJKpdGURR6M3E8DjeqpOBUHMRzSTJ6loDTR87I4XN4UWSFzmQECwh7y+lO9eJzeKhwBlHyR5/4aTyf92QkptebGMer7D9c8zMWVy+kOWA3PmeNLL/b/AD/vwu+UdSLOprEA1/BsfhDyD67wpbf9SaS04Pr3FsKZU8/+x9I5fWodXMBuxKVeerH+D732+LQ/FGODZB96xGcZ1yH2rjwWN72mJ3q0+sBKF6DjmQX3alegq4AiVwC0zLtZwFDJ55L2MuzCVyqi0QuWYhYyhk5gi4/PZkYZe4gkqWgKjKdyQgu1YkiK3ZGfleg7x6ZIJZNUOmtQOmbHaUz3Y1pmgSdfhyKRkrPFDo9PJobTZbJGHkSuSQORcPv8GFiIlkSXeluQo4AmuoglU/1ZfT3kTEygIzP4cU0TXRTJ+QMEc/FiefjeFQPDlWjO92LW3FR46pGNZ1H/KxGkpMzdGa6wLIIu8M4TLtz6VS7p0y28orp9YT3imPKuj8Wx6n94JTg0f00O/2FG1e5u6+l2p4enrBv6Py65c6BXu0qT9/QBxV7WhbPQCt3Tf+zTV/HQ9hdPE9vuXNguECFq++4jkHr+8ti2WVBGXaDNcHvKBvYwYSQp6KwT8hdOfCzChjg18oGytz/2p7qomUApaLwK93VhddCHlTe9+8pJBxnsq4RoJyAc1DPtgaYEOw/34Fyz6BpRK1Bv1sQ0iqGnM/Vw5LYV7n6rmXT/udWAtT2X8Mq1A6+NkwIuPrKooNXta+pIdemBaG+16seVs8pc4QHrpv+jkkTgu6Kws8hV9+1K1JeCJNcR6qTnmwvjf6BMGen4qTBV8eWyHbOrjlj7Acr6q0vMb2eqSMPzrovSQO9+soogY8lQvclMUZ/zMo9IYykUnheqOq/h1qAPPB7Zf8zkrtv/aBnmrC77z4rMfSeBwP3WwuCw+7XmOAffP+3IKgMeo1+g5YV7sfSoGccoFxj4NiDTwe5758BbsVHlTIwtLVi8HPPODhMF/WOhgk5liAI7z3HLZ+5JNJ+CoIgCIJwlN7t3My0YHNhmEq/pkADm7q2Ht3BjGHJ+KQSWfdHnCbvCBV20xhybPsFxji+XxAEQRCOMzFxmSAIgiAIk8bGrq00BYqTXDb46tjZs+eoIgYtc1gyPlkpnYyvVEV/+Halji0dQwOBIAiCIJwAoqIvCIIgCMKkkDVyHIwfpsFXV7Qu5AxiWAaRTHTsBxw+1/2I0+sNC8GXFTuR39Ec295RZN0XBEEQJoXjVtF/P4/RFwRBEATh6O3t3U+VpxKHUpzQRZIkar3V7I8dGPsBh0+dV6qnvlQI/hhD94uy7stijL4gCIIwOYyrov+zn/2saNmdd94JwDe+8Y3xHFoQBEEQhPeZnT17qPVWj7i+yl3J3t6xVfQtyypKmCfJCpYxLCNlqdD9sUyTVzIZnwjdFwRBECaHY8q6f/fddxOLxXj66adJJBKF5fl8nr/+9a8sX76cc889d8IKKQiCIAjCe9/unr3MKZ814vqwJ8zGrs1jO5hlV8SHJAcuGbpfeqx90Vj+wftYFlhmUY++VSKrvyAIgiCcDMfUo79w4UJCoRCyLBMKhQr/ampq+I//+I+JLqMgCIIgCO9xpmVyIH6YmsHTWg4TdldwONE2tuGBRnGPe6mK/ojJ+EYba98Xtj98hiFJZN0XBEEQJolj6tFfsmQJS5Ys4ZJLLmHBggUTXSZBEARBEN5nOlKduFUXHm34ZOYDPJobVVbozvRQ4S4b/YAjhOSXzro/tN9DOlLW/RJh+4AdCSAq+oIgCMIkMK4x+jU1NfzDP/wDV199NZFIhM985jN0dHRMVNkEQRAEQXifOBA/TLUnfMTtKt3ltCbbjridZejFIfnKSMn4hvfoHyGpXqlGBABJEln3BUEQhElhXBX973//+1xxxRU4nU4CgQBz5sxh+fLlE1U2QRAEQRDeJ/bHDlLprjjidmWuMlqT7Uc+oKkjKaWS5Q0bo1+qd77EdkfcB0CSRx3bLwiCIAgnyrgq+ocPH+ajH/0osiyjaRpf/epXaW1tnaiyCYIgCILwPnEwfpjwGCr65c4QhxNjeNYo1aMvK3byvSNud4SkeqZhj8cf7khj+wVBEAThBBlXRV+SJEzTLPyeSCSG/C4IgiAIgnAklmXRkmwbY49+iPbUkYcJWqYOyrBURGNNxneEHn2MEUL3ZZGMTxAEQZgcjikZX7+rrrqKr3zlK8Tjcf74xz/y0EMPsXTp0okqmyAIgiAI7wM92V5kScareY64bbkrREeqC8uyirLeD1GqMj6som9ZVsnEetKRkuqNELpfamiAIAiCIJwM46ro33bbbaxYsQLTNFm1ahW33HILH/3oRyeqbIIgCIIgvA+0JNvH1JsP4FbdSJJELJcg6PSPvKGp2xXvQYqy6Vulp8njCFn3RxyjLytYudxY3oYgCIIgHFfjquhHIhFuvPFGbrzxxsKylStXil59QRAEQRDGrDXZRrkzNObty5whOtNdo1b0LaN42jx7+rtBFXhj5KR6o4bgm7q9zXCyLHr0BUEQhElhXGP0P/vZz5JIJAB7fP5XvvIV/u3f/m1CCiYIgiAIwvvDoXgr5a6yMW8fcgbpTHWNvlGpXndZsSv3/Yw8yCX6PIY3CJQ4dtGUfGCP7RdZ9wVBEIRJYFwV/RtuuIF/+Id/4OWXX+a6665D0zSeeOKJiSqbIAiCIAjvA22p9qOq6AccftpTnaNvZOSPPEa/xBR89najZ923Q/dH6tEXyfgEQRCEk29cofuf/vSn0XWd2267jbvvvpsrr7xyosolCIIgCML7gGVZdKS6KHeFxrxPyBmkNdU++nFNvbjXXZLBMrH6ZwgaMXv+EZLqmSWm5MNOxmeK0H1BEARhEjimiv6dd95Z+NmyLCoqKrj33ntZvXo1AMuXL5+Y0gmCIAiC8J7Wm4uhygou1TXmfYJOPxsjW0bfyDCKKuOSJNmh+v2VcUMvGbovSTKWMUrPfKnx/9DXQCB69AVBEIST75gq+qFQaMjvt95660SURRAEQRCE95m2ZMdRhe0DBJ0BIuno6BsZ+dKVcUWx12H3+pdOxqeAmR/52GZxIwJwxJB/QRAEQThRjqmi/0//9E8AfO1rX+PHP/7xhBZIEARBEIT3j45UJyFn8Kj2cSkuTExS+RQezVNymxEr8bJqZ+SHUUL3R+/Rt4cFlGhEkBR7/L4gCIIgnGTjSsa3fft2LMs66v1+/vOfs2zZMpYtW1ZoKFi1ahXXXXcdV111FT/96U8L227dupWbb76Zq6++mm9+85vouv3l3NLSwsc//nGWLl3KP/7jP5JMJsfzVgRBEARBOAnakh2EHIGj2keSJELOIF3p7pE3Mg2kUuPoh4Tu5+3fh5OP0KM/SgOBmF5PEARBmAzGVdGvrKxk2bJlfOMb3+DOO+8s/BvNqlWreO2113jsscdYsWIFmzdv5sknn+SOO+7gF7/4BU8//TSbNm3i5ZdfBuCrX/0q3/rWt/jLX/6CZVk8+OCDAHzve9/jYx/7GCtXruS0007jF7/4xXjeiiAIgiAIJ0F7qoOQ6+h69AGCDj9dmVEq+kYea6Rx9ENC90fInj9aCP4IofvSkZL4CYIgCMIJMq6K/hlnnME111xDfX09oVCo8G804XCY22+/HYfDgaZpTJ8+nX379tHU1MSUKVNQVZXrrruOlStXcvjwYTKZDIsWLQLgpptuYuXKleTzed566y2uvvrqIcsFQRAEQTi1dKQjlDlDR72f3+EjMkqPvmWMMNe9cuTQfUlSjjC93sjJ+EZN4icIgiAIJ8i4ptfrH6s/WCqVGnWfmTNnFn7et28fTz/9NJ/4xCcIh8OF5VVVVbS3t9PR0TFkeTgcpr29nWg0is/nQ1XVIcsFQRAEQTh16KZObzZGwOE/6n39Dj9d6cjIG4wwBZ7doz961n1kBSufHeXYBpJUaoy+LLLuC4IgCJPCuCr6zz//PHfffTepVArLsjBNk56eHtavX3/EfXfu3MnnP/95vv71r6OqKnv37h2yXpKkkuP/R1t+NCoqfEe1/bEKh4/+4eVkEOWcWKdKOfuN9Xo41d7XYKdq2UW5T7wT9f0w2Kn8eY2n7C2xNkKuABXlR3+MOr2STR3bR3z9bqeE7nHiDw1N1tflcBAKaAD4fSqW00Fo2DapiAfdSo147F6PSsbtJDBsP13xEZXME/L3PFHnzPG8Hk618/5UKu+pVFY49corCKeCcVX0f/zjH/PP//zPPPDAA3zuc5/j+eefx+v1HnG/tWvX8qUvfYk77riDZcuWsWbNGrq6ugrrOzo6qKqqorq6esjyzs5OqqqqKC8vJ5FIYBgGiqIUlh+NSCSBaR59IsGjEQ776eyMH9fXmAiinBNrspZztC/RsVwPk/V9jcWpWnZR7uNnvNfDRJflaD8vy7J4Y3Mb63Z0URFwcs15TQR9zuNUwpGN92+9vesAAc1PT8/o0YClyHkH7bGuEV8/E09i5UyMYcc2TIloVy+1DRCLxskbFL2+ntGxkpkRj53rTWLkzKL9zHQOI5877uf/RF9jJ+N6OBXuE4OdSuU9lcoKk6+8otFBeK8Y1xh9t9vNNddcw6JFi3A6nXz3u9/lzTffHHWf1tZWvvCFL3DXXXexbNkyABYuXMjevXvZv38/hmHw5JNPcskll1BfX4/T6WTt2rUArFixgksuuQRN0zjrrLN4+umnhywXBEEQhPc6y7K456mtPPXGfmrLPfQkcnznd2/RGjn1Zp/pTEcIOI8u436/gMNHNNs78uw/hl56jP6g0H3LyJceoy8r9roRjDR1nySL6fUEQRCEyWFcPfoOh4NcLkdjYyNbt27l3HPPJZfLjbrPPffcQzab5Uc/+lFh2a233sqPfvQjvvjFL5LNZlmyZAlLly4F4K677mL58uUkk0nmzZvHJz/5SQC+853vcPvtt/PLX/6S2tpafvKTn4znrQiCIAjCKeGZ1QfY3xbn1stmoqky84HKoIufPfgu3//suTi1EpXbSaoj1UnwKKfW6+dQHKiyQiKfxO8oDi+3jDyS6ihaLikqVv/UeYZecgo+jpQ939Dt8fjFBweRjE8QBEGYBMZV0b/88sv5h3/4B370ox9x6623snbt2iNm3V++fDnLly8vue7xxx8vWjZnzhwefvjhouX19fXcd999x1RuQRAEQTgVtUdTPP3mfv72yllo6kBF8/RpFexvj7Pi1T3cctnMUY4wuXSkOplZNuOY9w84AnRnoiUr+pg6yO7i5bIC+kBF31KOvqI/Uo8+skjGJwiCIEwO4wrdv+222/jBD35ATU0Nv/jFLzjrrLO4++67J6psgiAIgiAM8ujLe1g8K0yoxHj8Dyyq59V3W+mOZU5CyY5NV7qb0DGG7oMdvh/JREuvHGHqvMGVeMvIlw7vl5SBKfhGPHbp6fVGjQQQBEEQhBNkXBV9sBPnvfzyy3R2dtLc3MymTZsmolyCIAiCIAzSGkmyZX+UxbPCJdf73BqnTyvn6Tf3n+CSHRvTMunJ9h7T1Hr9fJqX7hEq+iONv0dWsAo9+vnSU/Apg6bgK2WkkH9JBsvEsswxlF4QBEEQjp9xhe5/+ctf5u233x6S8V6SJJYsWTLuggmCIAiCMODZNQdZNL0Cxyhj8M+cFeZ/Vm7npkum43GN6yv+uItmevBoHtRS89iPkc/hI5LuLr3S0JFKHVtW7Qo+YBk5u1I/jCQdIXTfyCM5XCX2k/p69Q1Qxt2XIgiCIAjHbFxPAZs2beKFF17A4ShOdiMIgiAIwsRIZXTWbG3n766ZO+p2fo+DqbV+Xt/YypVnTzlBpTs2nekIwXGE7YMdur8vdrD0ylEz6vdV4kdsDFCwRkuqN9IY/b59MXRQtCOUXhAEQRCOn3E1N0+bNg1dF2PRBEEQBOF4Wr21naZaPz73kSuPp0+t4JV3W05AqcYnku4+5oz7/fwOP9FMT+mVIybMU8CwZwiy9Nwo4/hHnl7Pzro/UkVftZP1CYIgCMJJNK4e/VtuuYXrr7+eM844A1UdONQPf/jDcRdMEARBEATbK++0cPac0mPzh2us9pF8K8+B9jiN1cc+/v1460h3lc6WfxT8mo9otrfkOmvEZHyqXcEHu9ff4Smxzehj9C1DRx6hR186wr6CIAiCcCKMq6L/4x//mIsuuojGxsaJKo8gCIIgCIO0d6eIxDI014yt91uSJOY2lfHm5vZJXdHvTEeo9VSP6xhu1UXezJPRs7jUYTMRjBCWLykDY/TR86CU2EZWRu+VHynRHxyxkUAQBEEQToRxVfRVVeW73/3uBBVFEARBEITh3tzSzqwpIWRZGvM+s6eEeHzVPj5y6XQ7Qdwk1JWOMKds5riOIUkSQYef7kyUOl/NkHWj9+hn+7bJIY+YsG+UyvqoY/RVrNHC/gVBEAThBBjXGP1Fixbx0ksvTVBRBEEQBEEYbs3WduY0ho5qn3DIjSxJ7G2NH59CTYDuTJSgc/wRB36Hn2i2p3iFOUKvu6JAf+i+ni+ZdR/lSFn3j5SMb5REfoIgCIJwAoyrR//NN9/k4YcfRtM0NE3DsiwkSWLdunUTVT5BEARBeN9q604RT+Wpr/Qe1X6SJDGjPsi6HR1MqxtfwrvjIZVPYVgmLqV4irqj5Xd46c5Ei1cYesmwfLu3vi8Zn5EHuUSCw77Kev9zTZFRevQlRRkYGiAIgiAIJ8m4Kvp33XUX4fDYkgMJgiAIgnB01m7vYOaU4DGF389sCPLsWwf58AdmHIeSjU9XppuQIzAhwwp8mo9Iuriibxm6nRhvGEnRMPW+iriRsyvmw7eRZJAksAyQSjwqjXBswJ6aT2TdFwRBEE6ycVX0b7/9dlauXDlRZREEQRAEYZC12zs5e07VMe1bU+4hldVp705RXV4is/xJ1JXuJuicmEgDv+ajK9NdvMLI2733wylqIXTf0vMjz3cvK3Zov6P4GHbo/giPUJJIxicIgiCcfOMao19fX8+6deswTXOiyiMIgiAIAhBL5mjrTjGl6timoJMkiWm1Ad7dHZngko1fJN1NwDExMwL4HT66S1X0RwqvHzy9np4bucKuaCP3zI80/h/6wv5F6L4gCIJwco2rR3/37t187GMfQ1VVHA6HGKMvCIIgCBPk3d1dNNf6UZVjb5OfVhdg/Y5Orjp7ygSWbPw6Ul34J7CiH830DllmWSaYRsnKuKRohTH6duh+6R59abRp8kZJxifJqt3jLwiCIAgn0bgq+r///e8nqhyCIAiCIAzy7q4uptaML7y9qdrPU2/uJ5PTcZUIQT9ZutIR5pbPmpBj+RxeEvkEhmmg9Fe++yriJXMAKAM9+qOG7ivqiD3zljn6GH3Roy8IgiCcbOMO3d+4cSMPPvgg5eXlrF+/nvr6+okqmyAIgiC8L+mGydb9UabVjq+i79AU6iu9bN1fIiv9SdSVmbgx+oqk4NW89GQH9eob+dIZ9wFUx8D0ekZu5O1k1c7KX8poY/RledSp+QRBEAThRBhXRf/Xv/41DzzwACtXriSTyfDzn/+c//zP/5yosgmCIAjC+9KuQ72U+Z143SP0Nh+Fpho/G3ZNnnH6hmnQm41NWOg+QMDhHzLF3ojT5mGH7lt6zg6vt8wjjLUfaYy+DiWy9dv7idB9QRAE4eQbV0X/qaee4je/+Q1ut5uysjIefPBBnnzyyYkqmyAIgiC8L23YHaF5nGH7/abWBNi4N4JlWRNyvPGKZnvxaR7UkSrYx8BOyNczsMDQkUbqqVdU0LOY+SwojhGn+JNGCN23LAuM0uP/ARG6LwiCIEwK46ro9yfh6xcIBFDVyTMGUBAEQRBORRv2RJg6zrD9fpVBF3ndpKMnPSHHG6+udISgMzihx/RpXiKDM++PNLUe2MstCzObRFIdpbfp265k6L5pgCQhSaUfoUZN4icIgiAIJ8i4Kvq1tbW89NJLSJJELpfjl7/8pRijLwiCIAjjEI1nicYz1JZ7JuR4kiQxtTbApj0lpqA7CTrTkQmbWq+f3+GjKz3w/qxRxuhLkgSqAyPRO3IiPhi5Z3608f99+404tl8QBEEQTpBxVfS/9a1v8bvf/Y7t27ezcOFCXnnlFb797W9PVNkEQRAE4X1n094ITTV+ZLl0SPmxaKzysXHP5Bin35nqwu/wTegxh4/Rx8iPnBUfkFQHRiIK2sg9+pJSukffGi1aAERFXxAEQZgUxhVnX11dzf/+7/+STqcxDAOfb2K/uAVBEATh/Wbj7gjN1RMTtt+vucbP82sPoRsmqjKuNv5x60h30eCrndBjBhx+IoOT8emjZNMHUJ3o8W4k1TnyNrIKeqke/VHG/4+2nyAIgiCcQOP6tk8mk/z4xz/mlltu4ROf+AT/8R//QS6Xm6iyCYIgCML7imlabNkfZWrtxIa2e1waZX4ne1piE3rcY3E8xuj7HT56szFMy7QXHKHXXVKdGPEIjFrRHyV0f7RjK4o9bZ8gCIIgnETjqugvX76c9vZ2vvGNb/DVr36V3bt3c+edd05U2QRBEAThfWVfWxyvS8PvGRpSnjGydKS7sDj2zPmN1X427z254/QtyyKSjhJ0TGzEgiqruFUXvVm7IcPS86P3umtO9FjXqMn4pBFC8Ecb/w/YSfxEj74gCIJwko0rdH/Lli385S9/Kfx+3nnnsWzZsnEXShAEQRDejzbtidBcM7Q3vzsT5eGdTyABNd5qlk27EvkY2umbq/2s2dbOhy6ZNkGlPXrxfAJFlnGN1pN+jIKOAJFMlDJX6Mi97pqTfLQNRmlwsJSRQveP0IigqKJHXxAEQTjpxtWjX1lZSXf3QO9AKpWirKxs3IUSBEEQhPejDcMq+iYmz+x7gTnlM7iq6VJ6szE2dm45pmPXh70c7kqSypy83uaudITQBIft9ws4/UT6M+/ruZHnuQfQXOSjbaOO0ZdkFatEhd3ScyIZnyAIgjDpjatHv7KykptvvpmlS5eiKAovvPAClZWVhfD95cuXT0ghBUEQBOG9LpXROdSZ4IYLpxaW7enZh2EaTA00gSSxMHwab7a+xfzKuajSKBXZElRFpiHsY+v+KItnV0108cekI9V13Cr6fs1HZ9qeWcAyRk/GJ2lujFQMxeEe+YCyYlfqhztC6L6kaCIZnyAIgnDSjauiP3PmTGbOnFn4XYTtC4IgCMKx2bq/m4awD00dCLZb17GRmWXTQLKn2gu5gvgcPnZF9zCnfOZIhxpRU7WPTXu6T2pFP+CY2ESD/QJOP119FX30PNJoofsu35D/SxoldP9IPfoidF8QBEE42cZV0f+nf/qnY9ovkUhw66238qtf/YqGhgYeffRR/vu//xtFUTj33HO5/fbbUVWVDRs28L3vfY9cLkddXR133nkn4XCYlpYWli1bRmNjI2BHFtxzzz3jeSuCIAiCcFJt2B2hqXqgEhzLxejOdHNOzZlDtmsOTGFz97Zjqug31wR4/PV94y3qMWtPdVDtOT6NDEFHgN09e4EjT68necuG/F9yG1kFI1u03DpCIwKKNiR0f/uBKE+u2s/hrgSVQTdXnT2FxbPDSH2NN4IgCIJwPIyron/dddeVXP7EE0+MuM+7777L8uXL2bdvHwB79uzhZz/7GQ8//DBVVVV897vf5b777uPTn/40X/rSl/jRj37Eeeedx9NPP823vvUtfvWrX7Fx40auu+46vv/974+n+IIgCIIwKViWxcY93dx0yUDY/vbu3dT5alGGjTWv9VazrmMjKT2NRx0l9LyEyqCLnG7QHk1RXeaZkLIfjc50hNllR99AMRZBZ4CuvjH6lp4dtaIvl9XjX3w1eX945AMqGlYmXbzcyIEy8rAJSVbsHAHAU2/s47m3DnLRglouPL2Gzp4MD764i417Inxq6RxkWVT2BUEQhONjXBX9b33rW4Wf8/k8zz//PFVVo7fUP/jgg3znO9/ha1/7GgDbt29n0aJFhf0uvfRSfv3rX3PDDTeQyWQ477zzCsu/9rWvkcvl2LhxIzt27OCmm27C5/PxzW9+k9mzZ4/nrQiCIAjCSdPSlQQsKgKuwrJdvXuZGZpetK0iq9R4wuzp3cdpFXOP6nUkSaK5JsCmPd1ULz6xFX3LsuhMRSg7TmP0fZqXtJ4hZ+RAz40euq+o+OZeQE9PauQDKirkSyfjO2KPvp7n+bcP8tL6Fv72qtn43BoAIZ+Tpmofj726hwde2MHHrxTPLoIgCMLxMa6s++ecc07h34UXXsi3v/1tXnrppVH3+dd//VfOOuuswu9z5szh3XffpbW1FcMwWLlyJV1dXZSVleHxeHjttdcAeOqpp8jn80SjUZxOJzfeeCOPPvoon/nMZ/jCF75ALifGwwmCIAinpg27I0ytDRTCuZN6imimh7C7vOT2Nd4qdvXsO6bXaq7xs2F35FiLesx6czE0RcV5HKbWA7sRI9TXq2+H7mvjO56i2ZEBw+k5e+q9kSgqe1NeHn99Hx9eMq1Qye/n0BRuuGgq7+zsYvWW9nGVURAEQRBGMq4e/eGi0SgdHR1Htc/UqVP5l3/5F/7xH/8Rl8vF0qVL2bhxI5Ikcffdd/Nv//Zv3HXXXdxwww2EQiE0TeOLX/xiYf8lS5bw7//+7+zZs4c5c+aM+XUrKkZJwDOBwuHjk3RooolyTqxTpZz9xno9nGrva7BTteyi3Cfeifp+GGzLgR7OnBUmFLJ72fe17qMuUE0gULrXfbpzCu9u3Ywv4EAdrXe5hIUujefXHiJU5h2S+O9YjfVv3dHRSthbXniPx0Olr4yslsQp66h+L54jvNZoZcmmfaQ6jKL3F3VK5DxuAiPsm1YM7oss4vorptPUMHIOgFuvmsO9T2/hgjMaKB8UyTFWJ+oaO57Xw6l2nziVynsqlRVOvfIKwqlgQsfot7S0cMsttxzVMbLZLAsWLGDFihUAPPvss0yZMsUunKpy3333AdDT08MvfvELQqEQ9913H9deey1lZfYXqGVZqOrRvZVIJIFpWke1z9EKh/10dsaP62tMBFHOiTVZyznal+hYrofJ+r7G4lQtuyj38TPe62EieXwudh6McuXi+kIo+Za2nZQ7ykkkSvQo9/FrPra27GOKr+6oX7M84OSN9QeZ21w6YmCsjuZvvb1lHz7FP3q4/Dh5ZC+72g5SnUyieCxyo7xWKOQZtSxG2kBPp4reX6Y3jpW3Rtz3z+tihOQkDeXuUY/v1WTmN5fznw+u57YbTjvCOxtqoq+xk3E9nAr3icFOpfKeSmWFyVde0eggvFccc1O+ZVncfvvtfOtb3+Kf//mfWbp0KX/4wx8KY+/HKpVK8alPfYpEIkEul+O+++7jmmuuAeCOO+5gw4YNAPz2t79l6dKlyLLMW2+9xcMPPwzAmjVrME2TadOmHetbEQRBEIST5p0dnUyp8uFQ7QRvJiYHEoep9oySKA6ocleyv/fgMb3m1Bo/757g8P3WRPtxG5/fL/j/Z+++4+S66oP/f26dXrbM9l31Xm3JkuXeC64UY2PTCRBwQkIeDISfCYEnlCdxaCEmgYAJBhvbGHdb7rYky1bvWvWyve/0dtvvj5FWWs1KWnWtfN6vF7ysM/feOXd27p37PeV79CCd6a5CMrwjDa8fBknRBpLqDWLmCq8NoTdp8ea2HBe7tuJw9OD4wmmVbGmKsrM1dkJ1FQRBEIRDHVegv2PHDq6++mry+TwzZ87kgQce4IUXXuBLX/oS77zzzjEdq6SkhL/5m7/hzjvv5JZbbmHevHkDIwX++Z//me985zvccMMN7N27l2984xsA/H//3//H0qVLufnmm/l//+//8e///u/I8okPPxQEQRCE023ZpnbGVAUH/t2b6UOTNLzakYedR7zlNCWOM9CvDrFuR89x7Xu82tOdlLjDp/Q9wq4QnanufVn3T2yOPopamOt/CMc4/LGfXZtgZp2bgJwDyzzqW+iqwsXTq/jT69txnNM3ikQQBEE49x1Xc/e//uu/8vd///dceeWVPPnkkwA8//zzdHZ28tWvfpWLL774qMd44403Bv77jjvu4I477ijaZubMmTz11FNF5ZWVlTz00EPHU/XTwuOR6IqZ9Df1UxbU6Y3lUFUFRXZwHImcaeNSJWxAcsABbNtBVSUcW8K0bHwelUzegn2v6aqM44AsgSOBaTrYjoOmSCBJGIaN16WQzVtYjkPAo5HIGKiKgm3baKqMokhksiayIuHWFDI5CwdIt0exARxQZImcYYMDXreC40Aqa+HRZQzTBklCUUCVZTJ5C1WRUGQJ24Fc3sLrUsibNpIEtg0uVcYGHAfypo2qSHhcMsmMhSxJyJKEIoNXlzFtSGZNJAmCHg1O49BZ4fRwFIlY0sDvVYinTDRVRlP2f7fAsBw8euE7g+0gSaAoEtm8gwRIFP5PkkCRJBwgb9jomoxHlVFUmWTWJLPvu+jYDjnTJtkaHfg66ZpMNm/h0VUs2yZv2iiyhISEqkhYtoNh2iiKhCSBLMuYpo3jOLh0GceGTN5CU2V0TSKTtdFVGctx9tVfIWdYuDSFnGHj1iTcmkzGcMgbFrqmkMubqKpSuO6VwjXn1mXyeQtFlZGQMCybXFccx3FIZyw0TcalyUgS6JKEiAnODbbtsLKxk7uvmThQ1pxopcJbdtR9S90l9GdjZK0cbuXYEtxVlXpIZU26+tNUnKZl9jpT3SyovuCo21mWw6YdKXY3Z7Ash5pKFzMn+fF6Dr+k3X4l7jDdmV4w8kjqiSfjO1yPPp7iob0dMZN1LVk+c3EI+tVCoD+MxoZpo0tZubWLjbv7mDH26H/3c4EkQ1t3HAfIZC1sx8HrUtFVmVjKQJYlNLVwP5alwnORbYPHreI4Drm8heOA36uSTBsoiowsObh0lWzeIm/Y+NwahmWBA6btIEugyIV7qOM4mJaDJEko+59jbAdr3//Ufcseul2FZyXTssl2JsBxyBkWiiyhqXLh9wPwulRwCr9ZqbyFhITPJZM1bAzLwasrpPMWuiqjiSUVBUE4TY4r0G9vb+fWW28FYNmyZVx99dXIskx1dTXJZPKkVnCkyVmwcn0Pj7y8hUQ6z6Xn1RL2u9iwo5tP3TyNRxZuYcvefhqqAvzVbdPo6svwh4VbSKTyLJhRzZTRpeQMi+7+DFPHlvI/z2wkmTa4am49c6dUEvDqdPSleGNlM32xLDddPIan395JTyzDgunVTGgI84eXtnDxrBrKQm7e3dDOLZeMZVSVn6fe3s3qrZ1UlHi5+/pJPP7aNnpjWW67bBwzx5eTNywa9/bx7KJd+D0an711Ok+8to3d7XEmjyrhklm1vLJsL5++eSqbd/fx3JJdyJLEh68cTyyV47nFu5kzOcK180fzu+c30dGbZs7kCm6/fByvLGvinXWthANu7rxmIv3JHC+/u4ePXDWBoF8nEvbw7oZ2Xly6p5CR+LJxXDClgpD7pOaLFM4QSZLoTuZ4YcluPnDxGH7/4g7e29TBZ26aimFalIY8PPbqNlq7k8ycUM6d10zEsgqNWV19aZ5bsovmziRTx5Ry2Xm1eN0abk3h9y810tKVZNrYMj5w0ShCfjePvLyFzbv7qK/0c+c1k3jo+U3k8hYfuGgM0WSWmeMjhAM68WSerXv7eW7JLrJ5i7uvm0RdhZ8te/t56d092I7DrZeOZVR1kF89tYF01uC6+aOoKvPyuxc287lbp7Nmaxc7W2Lcde0knl20k9aeFLPGl3P9gtG8t7GN8rCX9p4kF82s4ak3t3P3DVN4fXkTyzZ1UBJ0c8/1k1mxuYPp48pZvLaFGy4aQ2dvmmcW7cS2bO7/3IU8+eZ21mztprrMx8dvnIzfo+I4UF/uQzwujnw7WmMEfTohnz5Q1hRvpdJ35KVqARRZodRTQmuynXGh0cf0vpIkMa4myNodvVx3wakP9DNmlrSZIaQHj7hdR0+ep1/txuuWqat2oyoSze1Z3lsb5/pLSpky3nfE/f2aj6yVwzayoOhH3PaoFH3IrPuOkUMeIoB/dl2C80e5cWsyeUXDsQwkPEd9G1mWWDCtir+8vYvpY0oHVl44V2VNm427ehlTF2JvW4JHX91KfzzLvXfMYu3Wbt5Z38Ynb5rK1r39vLexnfKQh5svHcOStW184KLR9MVz/PmN7diOww0LRhGN59i0u5d7PzIL08zwp1e3sbM1xvi6EJ+6aSprtnbzwtLdyJLEX902HcOwiKXyPL9kF5mcxWXn1VJZ6mFcXZgVmzt5fUUTXrfGh68cT2WZl98+u4nuaIb506q4dHYtP398LbIk8ZGrJtDdn+aV5U1ccX4dt148hhVbunjyze1Ulnr58JXj+ePCLXRFM8ydXMmkUSW8saqZL9w2g1ERr2isFQThlDuu8e4HD5Nfs2YNF1xwoIU+lzt84qD3g+buJA8+uY5oModlO7y1qoVkxuCj10zkl39ez5a9/QA0dSTY3RrnF0+sI5oobLtkXRu72mI0dcbp6EuxvSmKRKFF+9XlTWzd28eWvb288M5uNu7s5eoLGvjNsxvpjmZwHFi6oZ3Nu/uY2FDCojWtxJJ5VEXmoec30dKdYtWWThwHOvvS/PyxtVxxfj3ZvMVjr22jrSdJZ3+Gx1/bTjZvccWcev7zz+vY3R4HYMu+gGj6+DK2NfXz5ze2k8tbZHImf1i4hYBXR1VkJo8u44E/rKSjt5CAKJrM8eaqFhavbcV2oC+e5b+eWk91uQ9Jkvj1MxuxTJvNu/t4ZtEuDNMmlTF45OUtNO7pK/TsCiNeyrB45OWtXDe/gZeW7mHJujbmT61iybpWKkp8/OcT62jtLjQSrt/ew2+e3Yimymxt6uc3z22iubPw2ubdfby4dA+rt3TR0pWkpatQvmlXL7tb4/zqqQ1s3t0HQHNnkl88sZarL2ggmTF4/PVtVJX5+N3zm7Bs2NUW49FXt5LMGJiWjaxI7O1I8Je3dpDJmeTyFk+8vp2m9gSGaWNaDi8u3UNnX4bLzqvjnXVtrGzs4voFo/nNsxtp7UkBsG5HD395cztuTaUvlmX2xAp+/MhqrlswmtdXNPPuxg5sB3pjWf7jibXMmVLJY69tY8qYMn791AYM0yadNbliTj1/fHkLa7Z2A9Dem+Inj67GcSS2NUfpjr+/77XnilVbu5k06kBCPNuxaUt1EPEMr2c34imjKd5yXO89tibI6m3HtlLO8epIdVLmLjliELunNcOfnu9k2gQfl88vYVyDh1G1bubNCnH5/DCvv9vPsnVHnssuSRJl7hJsI3vYefTDphZ69IuG1A8xLaAzbrK5Lcfs+n3Z82UVrOEv+zupPkw6Z7Jp3/3rXCVJsHF3H2NqQ3T3ZfjPJ9fRG8syoaGE5Zs6WbyujcmjS9m0s5d3N7TjONAdzfC/z29mwYxqeqJZHn6pceAe/czbu6gs85HOGJiWw38+uX4g38GOlhg/fmQ1hmmTy1vYtkN3NEMmb/HoK1tJpAv3/jdWNpNIG/zvC5vxujVMyyGeyvPQ85tp70nT1V94xnpvYwevLW9iUkMJmZzJwy81MromhAS8sbKZl5bt5Z31bWTzFpfOruWnj62lc9++Kxo72bSrl/KQh+//bjn9KePM/iEEQXhfOK5APxQKsWXLFlauXEl3d/dAoL969WoqKytPagVHmr0d8aKy5Zs6cGkq7b2pgTJVkYmnih8Clm/qwOvSmFhfwuqtXUwde+AB8K3VrYT9bna2FH7E8oZVNLp9VWMn08eVDRxr5oQIpuXQf0hQYNkOpnUgjF66voPmzgN1V2SJVGbwD1FnX5rqMh8bdxUncNq6t5/R1UHMfQHRfhdMqWTJ2tZB2zoOdPamBs7NchwWrRm8DcC2pijpfdMXhJEtlTFYu60bXVN4d0M7AHUVfiRJoiuaJmcM/jvvao2TyZnYVmGI5sGaOhJUlnqL9lEUmabOQ7Jj561Bvd6t3Ul0TSGayA40LEChR01TZLa3RIvqvmFnDxPqwwP/XtnYyYxx5QMNCo7tkDcHN0ntaIkxviHM8s0d+DwajgMhr4vlmzoGbec4kMmZxFN5dFUhlTVR9g3rnDkhQuMhD/2m5dDWk8RybOLp4QcRwtnJcRxWbeti2kHDtbsy3Xg1z7DXmo94ymhJth3X+4+uCrK3M0kyc+qDjvZUJ6Xuwy81V+jJ7+Gi80M01BQvNVca0rjqohJWbkiwfsuRs3OXuMOFufUnOnRfVgqRqT14rr1j5EAdPFrgxfVJZte7can77jiKOqw5+gPvJUnMn1LBM+/sPqE6n+0kWeLNVS1YtkN7X3qgV3vm+HJWNHYCMG1sGau2dA7az3YK01y2NfcXHXPjzh7mTq2kL54lmhj8rNOfyOHdNzJwTG2Qpo447T2pomOsauxiTE0I/ZDlJrv60gQPGm2zdns3kw9aqWL99m7qKwvTOBataWXm+HIADNMuWqlg/76GaQ90hgiCIJxKxxXo/8M//AOf/vSn+fSnP83f//3f4/V6+c1vfsMXv/hFvvKVr5zsOo4oB/8g7FdR4kVWCvN/97NsG10rnm9YUeIlnTNJZQ0qSryDAvTqci+GZeNxFX60VKX4z1cW8hBLFgKAilIvfbEsAC69+L0O3r+uwo/XpQ352oEyCctyiAyxdnB52EM0mS3aryeaITLE/E+PSx04NwmJmkjxcMySoJuTsMSzcBZQVZmgT0dRZCIlhaGsebMwIsTnLn4Yd+sKmqoM+T10aUphDuUQrx36kAaDv8shv4t4OodLVQj6DgRStu1g2w5lQ6xlHSnx0BfPDvy7osRLKmMQ8BbqrSjFPZQel0oub1FR4sXe1/Bl2c7AuR96PgcfZ39901kDn6f4s/F5dGzLQRUJSEe8ps4kOFBVeuAe2Rxvo3yYvfkAYXeIeD5BxsoefeNDaKrM6KoAa7ef+qR8LYk2yg4T6KczFk8u7GLO9ACV5Ycfbu/zKFx2QZg33ovS1nX4ES0legjZMk586D6A6gLjkPcyB48W6E1arG3OMrvhwD1FkodO5HckkxtK6Ivn2NYcPZEan9UkJBqqAsiSVJjXvk9/Ikd5qHD/jSXzlIWK75WyLA1ZHgl7aO9J4/doHDpgRJYKjQsA/fEcAa825HNapLRwnz/0dyXg1cnkDjTYhP0ukgc1slaV+4jtWwKzotRDX6JwHQ71uxD2u0js6+DxecS0REEQTr3jelKcPXs2ixYtYunSpXz6058G4LzzzuOJJ55g3rx5J7N+I86Y6hANlQeS9KiKzBVz6nh9eRMfvnLCQLnjQFnIzdia4EHbSlw5t55RlQGWbepg/vQqGvcUevQ0VebWS8fhdancdvk4ADr700wadeDBSZbg5kvGsHhtK6oicdXcelY2dnDexAh1Ef+ges6cUD7Q+xn06cydUsGkUSUDwcu67d1cOadu0D43XTyG11c2M2NcGf6DApCQX6eixEtPNMvezjhzJh+YW7p4bRsfv2Ey8kHJZ8bVhago8bJ5dy+jq4O4XSrXzmvAfVBjRCTsYeb4cjya+DE8F4TcGp+5ZRoL39vNPdcXvg+L17Yyf3o1Lk3hwmlVg7b/+I1TKPzpHS6aUT3otVsvG0t7T3LQ9wUgHHBx57UTB5VdNLOazXsKI1AqSjyoiszl59UTCuhUl3mpOCjw7ollaagKEA4ceFj3eTSmjC4dmCLg0hTmT6/iqbd2cNtlhetwZ0uMC6YMHsl0zw2TeX7JLq65oIHNe/qYPKqEpRva+PCVEwZdC5MaSmjvTXH1BQ2s3trFdfNHsbOtMGLn0Ze38Kmbpg467pxJESpL3ETCXqpLjz73Vzi7LWvsZGJ9eNBw9uZkK+We4a9tL0sK5e4yWhPH16s/vibEii2nfvh+S7KNsiHOy3EcXnirh7oq15A9+YcKBlTmzgjw9Kvd5PJDT+4q1wNYkoR0EhrDJFXHMTKD62zkBw3df2Vzkmm1LjzaQe+nHNvQfSgEshdMruD5d/ecSJXPapZlc/Xcetp6EtRW+AdGS72zro2bLxmLLMGSda3cfMkYDs5ZN3l0CW09KeoifsL+wffoMbUhtu9rHLlhwehB7/ehK8ezcUdh+lNnX5q6igCaKlFVdqBxzaUpzJtaxeiqIOu3dw+U15T7CPj1QrJYCoM7brts3MAIxHDARWWpl/5EDkWW+MQNU9i8b8Rje0960EgdSYJbLxvH4rWtXDqrhqrTlABTEIT3N8l5n67n0tubLBpWdbL0pQ2aO5PkTYuaMh89sTS6plIechNL5enuz1ASdBP265iWQyyVI50xKQ0VEg+lswZ+rwvHtokm8ximTUWpF1ly8Ooa6bxJMmMQTeSojfhJZgwSqTxV5V5SGZNEKk91uY++WAZFkfG4Vbwujbxp0d6TIuAtJL/b2xlHliSqynzIOJh2Ych+e28K24FRVX5iqTy90SylQTeWZWPZDpUlXnKmRUdvGlWRqY34iCZydEczlIc9+Dwq0USedNakNOTCo6sYlk17bwqPrlJV5qW9J4XjFAJ623HwezVyeYuWriSyLFFX4ScS0JEOGngdiQTo7j7ykM2zwdlaz0ikOEv0fsO5Hk70vAzLpjOWw6VJZA2b1u4U5SE3bl1Flgu9LYmMQVWpF59HxTAsXLpKOmuSSOdJZUyCfh1FlvB7dCzLIp236ItlKQm4KA250RSJnmiWrv4MpSE3JX4XrT1JFFnG59EwDIuQ30XesPB6VLI5m+5oYQhlVZkXTZVJZy16YhkUWaI85EFTZdp6UliWTW2FH9NyaO1KEgm7cekqrd1Jqsq8ZHIW0USWyjIflmmhqgqJVB6XrlJR4qajN41LV5Akic6+Qu9TwKeTy1tYlo2iFFbWcOkyiZSBaVmMqgoSTxu096TwezWqy3xk8wZlQTfuIUY0nC3O1mvgYCd6PZwox3G475dLueWi0UwaU040msZ2bH65/iFuGH01+jH0Rm/r24EsSVxZf+kx1yOXt/jv5zbxwJcvwjvE6JqjGc7f2nEc7lv8He6ZfAe+Q5YMXNuYYPm6ONdcUjowbWU4VqyP43bJ3HxledFriVgbda8/RNmN9x3xGOGwl2j0yEOos4sewnPdV1BKDzR8J37/N7gv+RSS208ia3P/U118ckEIv/vANWlsexe1YSZq7dShDntYpmXzq+c2c9/HzqO+wn/Y7U72NXY6rwdJgmjGwLQdLMuhvSdNNm8yujqIZdu0dafwe3U8LoXeWBavW0NTZeKpHJWlXlyawt6OBI4DNREfHT0pVFWhptyHhENXNEt/Iksk7CXs04ln8kQTOVRFpjTo3re6kEU8VXi+Kgm4UBQJj66SzBr0xrK4dYWAV8fr1ujqT5PKGNRG/OiaRFNHElWVqS730dWbwbAsGir8lAdcxDMmzd1JJKC2wk9PNEMibVAe9pBM53FpCjVlXlyneLjiSLgHH+xsq++RrgdBGElEd+kpUOrVKB1TMnDjqj2o5y0ScDG+avANpO4IPXMN5UMMafdpMGgI8ND7D3XchoNasSOBwgPSoTfY6vCBXpWI38X4yqFveLUH1aHcrzO++sB2NeEjv3dFYIj5px6NqtDRe3SEkUlT5EHfyfrSwQ/8Q31nAMp8OpQdoffjkOspVKUx7qCyiqDr8A8Rfqgv8xSVjSof/H6VwcHf1/qDzmPwdXZIRvGDpqSE60ID/z16iKkqg+yLXfbXe1B9fCeYYEw4K+xqKzS0Vhz0ve9Id+HXfMcU5ANEvOWs6Vp/XPVw6QoNlQHWbO/h4kNGz5wsfdl+FEkpCvLjSZO3lkW58sLwMQX5ALOn+ln4dh+7mjKMbRh8DYckF3lJImvmcA8z18HhSKoL8oN79DGyoBWO+8aWJBMq9UFBPgCKimMde+4DVZGZMynCC+/u4a9vm3681T6rOU5hlNf++9uhzwMH/xaMGvQMdKDho2zcgd7y6kOeG0p9Ogffi8sDOgzVaDLEfbgi6GLsIdtWBArX44H6Hni/ykPqHvSoTGsID/w7dPDvkxiFJQjCaXb2dgkJgiAIwjnqnQ3tTBk1OAt9U6KFiKe4h/powq4gKSNNyihOMjYck+rDLN3YcfQNj1Nzso0Kb6So/OXFfYwf5SEcPPbGK02VmTszwMLFveSNwUP4FTOHraj0ZIoTxx6zQ4buO7YFtg2yStaweWtLmrmjhmigVrRCdv7jMHt8ORt399EVzRx9Y0EQBEE4DBHoC4IgCMJpZJg2K7Z0MWXU4OR0TfGWYS+rdzBJkqnwltOUKF69ZDjG14bY3R4nmjw1SzbuiTUVnde23Wl6+vNMHX+U0S1HUB1xURbWWLIyOqhcMnM4qk53+sSTDEqqC+fgHv18BlQXkiTx9tY09aUaJb7iZLeSohQn8Rsml6Ywa1wZL72793irLQiCIAgi0BcEQRCE02nN9m4iJZ5BScXyVp7udM9xBfoAEU85e+PNx7WvpspMqg/z7inq1d8Tb6LSeyBJa96weWVJH3OmB4fMTn4sZk31s35riq7eA4nvJCOHpLnoSJ+EJIOaPijQd/IZJM1F3nR4ZXOKeWMOM91M0XCOs0cfYM7ECMu3dNKfODWNL4IgCMK5TwT6giAIgnAavbWmlemjB2egb0m2UeopQVGOL3XO/h59h+NLmjZ9TClvr23jZOfntR2bpkQrlQcN3V+yMkpFqXbEpfSGy+NSmDHRx8JFvQN1l/NZVN1LZ7r7KHsfXWGO/oGEfU4+DZqbxdtSVAYVIoGh/16SouMYx77k4X5et8b0MWW8+N6e4z6GIAiC8P4mAn1BEARBOE3ae1M0dyeZuG9Zsf12x5qo8BTPYx8uv+5HkRS6j3Neek25DwfY0hQ97joMpT3ViU/z4tUKici6evOs35pi5tTDZ5Q/VmNHecgbDuu3FpbBlI0squYjbabJmMcfbAOg6ti5A7kPnHwaQ/Hw0sYUF449fHI1SdVw8if23hdMrmDpxg7Rqy8IgiAcFxHoC4IgCMJp8vqqFmaNLUc9aHlEx3HYHW+i0ldxhD2PrsoXYXes6bj2lSSJ2ePLeHXF8Q3/P5yd0d1U+yqBwnkuXNTLjIk+PK7iee3HS5Yk5swI8NayKOmMhZRLg6pT6i6lI3Viw/clzYOTTQ7828mlWBwfRVVIpTJ4hNEXqg4n2Mjg92jMGFvGc+/sPqHjCIIgCO9PItAXBEEQhNMgmTF4d1MHs8YPnoffmSokjQvpJ7Z2c5Wvkp3RXce9/7TRpWxriZ7UbO9b+rZT6yss27dmcwLDdBg36uQvM1Ya0hhV4+a1pX3IuTS25qLUFaY12XZiB9bdONkDS3OmEkle7a3honFHOQdVLyzDd4LmTalk+ZYuOvvTR99YEARBEA5yfJMBhSNToa03w97uJGVhD33xHB6PhCHH8bg0UtkcuuLCZQewbYinc/i9KrZkksyYRIIekExSdhLLdvC5XSRySdyajiqpJPJpfKqfdExHkWXKQjqxVI60kSMQkJAVSGVzeAiiKzrxTBbDMigNaWTSEkgSecPC5Qb0JL1dXaSNLB7NhdvxY8l50mYGTdbI5A0CLg9JI03Q48G2HZL5FJqkozlevG6FtJFGsQLkrCxZJ0nA5UNBw5KzZMzCA2NA92HaFmkji1vxYtommqxgZXykMxZBv0ZWiuJINmGPl95sFL/uQ5FkDNskbWTo7/ESUAMkrSSGbZIzc3g1L9jgVbz4FT9xYsRzCQzboNxdRjqfQ1MlkvkUqqLhVlzYjkXazKJICh7NTdbMkbcMAi4fYTlEys6QNFLYjkWpXkrKSpOxMvh1b+HcZY1yrRzHgaSZwFFs4rkEXt2LjEyst5esncOwDAIuPwoqPgJI9oF2NUmGuBUlaSZxqy48sg8PXk7y9FjhJLHdeRL5BKl8GrfqJmtm0RUNwzJxay5yZh5N1tAVjYyZJW/l8WleQmoY2dSQJMgrGUzJIpc3SebSyJJM0OVHRaMrmsWru7DMwvvlTYtgyKarowPyPjJpCbemoWug6jaZlIRhOYSDKt2xNC5NIRRQ6YvlcByZMp8XSzLJZG2yORu3S0F2p8naaUJ6mGxSxTAdSkMuZGySOQPTBNmVIW2m8Sg+PKqH7miasqCbvCGRSGcJB1zk8w661yBtJvHKQZJxhYDXRSZr4gAS0J3MYTk2uieHbalk0g6qbhMIQjybwrEUPIoXl8smkwUjp6B78qTtOD6XF6/kw4sf2z63LojXVjYzsS5MwDt4bnpj93ZqfJUgnVhiunJPKctyCRL5JAH92IfG6wdle//UjZNPqC5QmJ+/PbqLOZWziSdNFq2IcdWCwUsKnkzTJ/lY+HYf202dcbU6EU+ALf3bTuiYku6BgwL9FzblGOvPUeY/yoiEQ7P1HyevS2XupAhPvLGDv/nwzBM+3tnC51fZ299Kr9GPKiuUeoKkjBzJXIqQO4Bt28TzCfy6H5eiY1gmKSOFT/OhWwEyUpyskcdNgFxKJejXMU0HWZFIpg0syyHo00nnDFyqgmk75PImbpeGY9soqoxtO+TyFn6PTjZvkjdtAj4Nx3YwTBvLdlBkCVWVURWJvr19ZHImjgNuXQEcFEUmmzNRVQXTsvC4VGJJA1WR8LlU0nkTj65iOw6prIlLU7Bth5BPx7Rt4qk8Ll0ll7dw6wqqApm8jWEU7tsOoCtyIfeGAznDJmdYhPw6puUcOAevRj5v4XFrxFM52qJZNFVGAtI5k3BAJ502CPpceHWZ3mSebN5CU2RcukLWsLAtB8t20FUZRZGxLAuQ8OgKAY+KA/Qn8hiWjabJZHMWQZ9OwKUMfnaRoD+ZJ2/alAXcqDLYFMps26E04OLgHJySBG09SXr6M4T9hfod7lnIBvr2TWUpDbhEb6UgHIUI9E+yWC7Pm++0svC9vXzqA1P55V820NWfQVNlbr+mhh0sZnbtJDZ1bufCwM38+i+NZHImIb/OR6+eyCOvbKUkoPPXH53CD5b/mLtn3saijctpjrUhSzLXjLuE7lQvG7u2cfPoW1jxjsK8qbX43Sr//fRGJo0Kcvv1VaSUblKJFlq2B3lteTOOA5PHhLjn+sn85JG19MWzuHSFT94yjrdjf6E53kKVP8InZ3+E57a8yuTIeJ7f+hqGbVLuLeXmSVezuSdGW6KTFa3rAJhbM4vzApdTV1JOc6qVx7c/SjQbx6W6+NSsj7C+s5H3Wlbj07x8+rw7eHjdX4jnErhVFx+aeiN+o4b//ctWookcbl3hIzfWsjT+DHWhGubUzGBn315KPWEW7niLvdEWJEnipglXMbZkFE9veZmmWCuyJHPzxKvpz8S4etwlbO/dxZObXyJr5gi5Anxx7j38ef2L7Opv4kNTbsCtutnUvY11HZu5csxFZM0c7zavAmBaZCK3TL6Wxu7tvLDtDUzbpMJXzk0Tr6Q71UdTrJX1nVsAWFA/h5mVUwi6/fx2+WP0pvvRFI2PTb+VaDbBs1tfAWBW1RRunHgVm2NbmV02Ex03lmSyomsVj258ZuA9bpl0DbX+airV6jPzxRUOKylH6U/EWLTnPSaXj+MvjQsL33NF57Yp17GufTMLGubwTOMrfOq8j/C7NU8MvP7xWR9iUul4clYOy3KIZZK8uP01tvbtQELikroLuaBmDtmEnzc27yXkd7FoTQt33V7O082LmaJeyZMLt5IzLEqDbj5363TWbO3ktX3Dq6ePK2NiQwlPvbWDq+fWkzdtFq1pZerYEj585UT+68n1dEcz6KrMB6+rZX3+FTJWis+f90n+6/EmaiN+br50NJt3d+Ov7eaxVU9hWAZlnhI+PPZOKjwRNmyP8fALW8mbNpGwhy/dOZnfr/0De6LN+HQvn5/9CdauVYkmcry1ugXHgYkNJcybWkFdpY/fPLuB7v4Mn/vIKJZvf4UdfbuRJIlLay9mdtkcTNsink/w1NonSBlpfLqXz5x3B27FzRjPWHBOTVB4uiUzBq+tauGeayYWvbapaxvTS6ee8HvIkkKNv4rt0V2cX3F8QeGciRF+82IjN180mrLQYTLKD1NTogWv5sWv+XjslS4mjPYQOkzyupNBU2UumBngyRWjuLe2n1JPmP6OKDkzh0t1Hf0AQ5B0L3amMHS/vTfFu60Kd489egAvaTpOPouDg8SJfYfnTKzgoZcaadzbX7Qk40ikuEz2JNp5fOMLbO4uNMRcOmoePs3L1p6d3DjxSh5a8zgZI4tf9/G58+9ClVXeaVpBd6qHixrm8qeNzw3cq26q+wivvhDnszdP442Vzby5uvDMM3V0KdddOIpUxuDJN3cMPPd86gNT0FWFpxftpK4igM+j8ebKJmwHxteFuO2ycfz40dU4DkyoD/OxaydhOTavLW9m2abCyhTnT6rgwulVqIpEa3eKZxbt5FM3TeWlpXto60mhKhI3XTyGyQ0lNKWS/OGlRtJZk6Cv8KzXF8+iqzI+rzZon49cNYFtTVFWb+0i7HfxiRunkMmZ+Dwa0WSOx17dSjZvEQ64+Pxt0/nFE+vI5Eymjy3jqrn1NHcmeOrtnciyxDUXNKCqEmVBDyu3dPKxayfx3uYOHNvhkVe24jjwjU/MZcvePlIZkzf2fQbj6kJcOquWspCbN1c3M3N8hPKQm027eln43l4cByY1lDB7YoT23hSXzKxhXFWg0KBt2by8vJlnFu3E3vf5/fUHZ/Dye3t5dUUTjgMzx5fzuZunEnCrIMHGvVEefHI9mZxJ2O/iHz52HvXlxR0fqbzFo69uY+mGdgAunF7NPddNxKefvGlAgnCuEY1hJ1lrV4ZnF+9m3tQqXl3eRFd/4YHAMG2eWNjCzNA8Ht/4PFfXXs+Dj28kkyt04cWSeR5/fRtXza2npSvFn1/dzadmfpTG7h00xwpDD23H5pUdi5hcPh7DMnhq51+48AI/f1y4haBfJ+TX2bo3zqoNMQKaDz1fxavLmgdullt2x1iytgNNLfzZc3mL3/xlGxeWXwZAR7Kbpxtf5qpxF/NU40IMu1C3nnQfb+xaSk2gciDIB1jZto6U2kJ3PM6TOx4nmo0Xjmvm+PXqR2gI1wBw2ej5/GFfkA+QNXM8tvE54imT6L6W2Wze4pFnm7i44ipWtK0hlc/gVnTWdmxib7QFKMzvfH7b6yTySZpirQOfybNbX2V65ST2RJv504Znye5b0iiWS/DrVY8yoWwMEW8pGTNPIp9kXcdmdEWjxBMaCPIBNnVvY2vPDhbtWYa579y7Uj2sbNuApqgDQT7Au82rKPeW8si6p+lN9xf+xpbB79c9yeiS2oHt1nU0sqlrG5X+MprSheCsO9/Fw+ufHPQeb+15lyXNy8hLJ2/IrHDiFEUmaabY1rsLr+7h2a2vHfieW3me2PQCs6qn8vzW1/no9Jv5w7qnBr3+2zWPsSfWRM7MsrptA1t6trO1bwcADg6LW94lkY/x2vJmRlUFeW7xLq6YX84ze59gRmgejz7fRM6wAOiLZ3n4pc2Den037uwlm7coCbh5bUUz9RUBZFkiEvbxv89vpnvfEOy8afPYi83ML7+MzlQPjzc+zV9/dBLLNnWwvSmGO5TlDxsfx7AMAHoz/Ty39ylQbH7zTCN50wagO5rhoae3cdO4GwBI5dP8as3vqS738OaqloF7zbamfty6yqOvbKe7P0N9pZ92aTM7+gpzjR3HYVHLEjqS3cTyUZqyW0gZ6YFj/mbVY3Qmu4nZ0VP1pz3tXnh3DxPrwpQEBgecPZlesmaOMvfJCeBq/VVs7d9x3Pt73Rqzx5fz1OLjnwKw34buzYwK1LF6U4JEymTqeN8JH/NoqiIuRun9PLezFBmFcncZTYnW4z6epHtxsnEcx+F/F25lbkkMv/foqwVIsgqyDCewxN5+mipzxexafr9wC8a+a3EkS1gplresGwjyARbvXY5P9/DBqdfz61WPktk37SGZT/E/qx5FkxWmVUzkgrrZPLzuL4PuVa91PM+Yeje722O8serAM8/mPX3saInyzrpW+uKF4+XyFss3dfLOhnZau5NMqA/z+opCgAuwoyXGss0djKoKArC9OcqabV3saYsPBPkAq7d20d6bRlFknnxzB7MmRHhvYwdtPYXEjabl8MyiXUiyxG+f3UQ6W/i9j6cKz3qW7eD36SzbNHifP726jWljC1N7oskcf1jYiKbKJDN5Hn6pkWy+8HsQTeT4zbObuHJuPQAbd/WyozVGaciNqkjYtsMry/ZSFvSwszVKPm+xYnMHyVQeWS70mI+uDrBqSxd+r85rB30GO1ti7GqL8eLS3Vx7wSgee2UryYzJS+/uHfhstzb10xvPsr0pyp6OON37nuX2diV56u2dA8fa3RancU8fryxvGth3/Y4elmxoR5KgP2Xw0z+tGXgWjiZz/PhPa0jnB3/PJQk27OwdCPIB3tvYzrodvSc6EEoQzmki0D/JWrsLLf81ET972uNFr2fTKrZjE0/YmNbg5spYMo/HVejt2Lijn3r/GLb07Cw6RspIo8iFFkxTLvxAdPRmaKgs/DBt3p5EcXT2NBfP6du8u4+xtaGBf9sO5DPawL+39e5Cl7Wi/ZpireSsfFH5uq6N5JwUvZn+QeWO42DahR8kj+YmlksMet2yLTL24DLbASNbeO8NXZvRVZ0t3cUPrJ2pHjzq4J6mWLYwrNpyBv849GdjeDUPo0vqSeaT7OovBNuV/shAA8qg8+lopCFcO6jMdmw2D1GPrJmlNVG87nT8oAzNADt69yBLCrujTUiSRE+mr2ifnX17CXtCJMxU0WvCmSPpFjkjR1O0lYDupyc9+G+3/3vek+5DU7QhX4/nkmiqjq6prO/eVPQeu6J76ejNDAT0Hr9JLJcgl1aLejQ6etOUBgd/97fu7WNMTeHa7+hLEfa7qCzx0tQ5+PoCyKYK95dtvbtwlML1vHZbN4q7eC5xe7KTtFX8fWzuTCGbB+Yna4pGS3fxdoZps7e9UIfxo7xsHOLce3LtmHmFMu/gIDdjZtFUnb5stGifkaizP83i9e1cNL2q6LVNvVsZW9JwwsP296v0RojnE/Rl+4++8WHMm1zJ+p297B7iN+xYrOneQMhqYPHKGAvOCyHLp+eJfIG+lda4xrq9UOmtYMcJ5C3A5YV8hrdWN5PMGMz0diBpwxvpIOkenOzJuadPqAsR9Ok8v3TPSTnemZS3DBp7theVt8Q7cBxpIIjfL2WkieeTODiY++c3HaQt0c6oejc7W2JFr63b3kMoMPjvNbYuxIYdPfjcGr3x4nvf5l19jKsLD/y7J5Zl8+7i3+1drVH644UAd2xtiM27i1e8SKQNTGvwc0lhuL5Cd3+alq7i+/T+3wKA/kQOt0shkTaKpjL1xbP4PQee17bu6cMwbEoO+o2IJXPsbI0xri7Emm3djKsLDxx/+rhydrb20xsb4jPY3Ut1uZ9s3sLv09nTVvzZNu57nmzvSZNI5ZEk2Nsx+HwiJR62NUeL9l2+qQPLgd5YFuuQ84omcsRSg583FUVmxZbOouOs2NyBoogefUE4HBHon2QVJYUH4N5Yhuqy4t4L3WUhIRHwKRz6zON1qwM/COPqAvTkOhkVris6hkf1YO0LojXHC0BZyE17b6GRYWyDF0vKUVdVnCxobE2Q1q7koDLdfeBHpSFUO9CTf7BKXzm6UtwAMKl8AprkJuAqng+qyYWgwrBMPIc8GEmShFsZ4vPZV5cJpeMwrPyQ51/mCQ/02u8XdPnxaO6iIZI+3YthG7TFO/FobuqChQftnlQfVf7iDNcTy8bSnhicpdl2HMaVjCra1qW6KPeWFpX7de+gfzeEarAdm7pANY7jEHaFivapDVQRzybwqd6i14QzR7IUdEWnJlBJ1swRdBUnS9NklaCrMKd0qNd9ugfTNsjm84wPjyt6vS5YQzigo+8baWPkVDyaG5e7uOeuNOgmmRn8EDy6OjTQwBgJe4in8kSTWSpLi79LuqdwfdUHa1CcwvU8eXQpdr64h7LMW4JbKb6HRErcSOqBOuTMPFVlxe+lKPJAHVo6sowrGVu0TYkeQVFtYtnBD4earGI5FqEhPs+RxnEcHnpxC/OmVAx6KAcwbZPG/m2MLx190t5PkmRGBepY37P5uI/h0hUum1XNQy82FgUpw9WabCeVMVm82Ob8aQEC/tM0U9Ay0RyDSyfBKxtBMavZE28eaHg+VpIk06VU8ZdFu7lhXj1SLgn68JIJSpoHJ5c8+obDqofENXPqeXNNKzuHCLpGElVSGBOuLyqv8keQJAlFGvxo6lJ0/LoPGRlVLv4elXtL6ezKU1dR/BwysSFM7JDlCVu6koyrCxXmrvuL731ja0O0dh+4JwV8GuPrin+36ysDBH2F/Vu7koypKd7G79GKnvU8LhXLsikLeSgPD3GfVg+cv8+jYVkOPk/xeQe8Grn8gee10TUhFFUadL4Br05duZ+WriSTRpXQ3psaOP7OlhjV5T7CgeJpLWNqQnT1pXDrColUnrqK4nvxmNogLV0JIiUevO5Cw3Rt+eDnur54llFVxfvOGFeGIkuE/XpRG6fPrRbdKy3LYcbYwUlModBYYR3nPUoQ3g9EoH+SNVT5mD+tkiVr27jpkjEDPfQA115UydbUGm6adBXvdS/iEzdNHPgBUBWZO66eyJsrmwl4NT5243h+veZh5tXNHhQ8zKmZSUu8MHTpmoZrWL0uw40LRiHL0BPNEilxM//8MKZtYXo7mHFQdufKUg9Xzq2lo7fQwyBJ8OFrR7Gm/10AvJqHD029gcV7l3PlmIsG9nOrLm6YeCU9qX5GHxR4jwrVEmEMlYESPjT2wwOBvYTEndNvYVtvYZju4r3LuHP6LQM/0JIkcdvk6wh63APTCCQJbru6llV9SxkdbqAuWEXeNplfN5uwOzjwnvNqZ1PuKyV4UMPCgvrz2da7i/pgNTdPumYg2Fdllc+dfyerWjfSmuigzFNCqSdMfaiGjJnFduxBD9hV/ggzKiczJTJ+oMyjurmw7jx8uofawIHeuHElDfSl+7l75u2D5n7ePPFq9vS3DPy7LljNjMop7Im1MjpYeK9KdyU3TrjqwHtobq4bfxnnV83Egwj0zyZG3saneZlYNobmWBu3T7kebV+Dl4TEByZexdr2zdw2+VqebHyJu2bcOuj1j0y9iVHBemRkzq+ZwdyaWUS8B67JyaUTKPeUc8ulY2jqTHDJrBreeKeX28d8mA3x5dx8RfXAQ5BLU/jUTVNo7z4QOFSX+6gq89LRm2bG+DKiyTymZbNpVy+fuWXavoRRBddfUsWmxEo8mpuPTf8wf3h+F6Org0wbW0pfh8bNE64f2Nal6Nwy6oPojpvbrhg9UO7WFT592wRe2r0QAEWS+fiMO+iP5Zgxrnxgu/KwG1mCu6+fiFtX2N4UZ7J3LqWe8MA208qmUuWrosQboFyrRd73cK9IMh+f9SE8iosSpbghbaR5fVULyUyeuROLGxa39G+nRA8RcJ+8NeUBxoRG0di3bchRWMM1bXQpbl3lL4uOrzd8cdNyctvOp67KxajaE5vrfyyUbApb9xD2SVw4Dv6ywoVHqmBn7PiWqMvkbX7VfxEXj/dQFipk4Jdcw7xPu7w4meIe2+MV8GpcM6eOXz69kVjyxKcEnCk+3cvFoy6g0h8ZKJtUNhbLsXl95xLunvnBA/cDWeHjsz6EbdvsjjaxvrORmyYe+P10KTo31t3GivVRJo8uZepBwWBFiYfpY8u4cEb1QHArSYUh69fPH4XPrdLZl+G8iQfqURp0c/HMarbsKYyIKQ+7mT+1igkNJQMjpwAaqgLURgrX7WXn1bJsUwdXz63Hd1CAeunsWhzH4c5rJx30rFeYh98byxBP5bny/LpB+1w1t54dLdF928p84obJdEXThLwuPnjFuIHfA02V+eQHpvLmqsLzRnWZj0kNYaKJ/MBUqzmTK+hPZJk9KUJPNMNl59URS+ZJZwsNtY17+rhsdj2pjMHsQz6DGePKuXhWLYvWtnDrZePwedRB20RKPIyrDePzaNRV+IiEC9f46KogCw4aueRza8wcX86McQf+LlVlXq6aW49jO5T6dT53y7RBz8J/c8fswvz9gziOw5xJFYytPfA3GFsT5IIpFTgii7EgHJbkvE+vkN7e5CnL6Jw2DTp6C8ORSgJu+uIZPF4JW0+g6zKZXB5N1vE6YfKGRDSRpSSoY9om8XSOylIfmsumL9uLAwTcHvoz0UIvn6LTn03gVwNk424kR6U85KIvmSadz1MaVtFdBr3JDAGlBJfkoTeZJm8ZREpc5HM2tqWQzpl4PBKKN4UjmaSNLF7VQ0ALkrEyZM0ciqSQyWcJeQIkcxkCnsKNPJqNo0kaPrkEl6qStdIopp+MkyRpxgl7AoUsuWSI51JIEpR6whiWSTyXxKv6MC0DTXFhp/zEU4WMsHmlH1MyKPeG6Ex3EvaEUCWFvGUQzyXw6V7Ceoi0mSZvG6SNDF7Ng4qKR/ES0oL0W33EcgmyZo4Kbzk500CWIZ5LoCsaPt2LaVsk8ilUSSHo8hPPJ8mbBqWeEH41SMbMkDASmJZJxFNOykyTMlKEPUGimTiaolHlrgRHJmHFsDDpy8QIuvwoKEiyRNrMFI7pDSM7EkEljGof6DmwZZPefA8xI4FfL6wm4CNwShOPRSKH7x0dzvUQiQTo7j55D62n04nWPaMkSFlpkrkkHs1NIpfGo7kwLAuP5iJrZNEUDY/mJpXPkDYyBF1+wnoIt+UHySElxTEdG9O06UvHkCSZUk8YBZWuXgOfy4VlOYX/2RbBMpOslYGsn2QS/G4dXZfQdYjFbUzLpiys09GfwKWplIV0OvszOLZMZYkPx7FJZkzSGQuPW0H1ZkhZUUrdpZhpL8m0SSTkQdFMkhkbw7SRPclCpmslhE/109WXpjzsIZOziaWylIXc2I6D4sqSNBL41BCZmAufWyebMzFMGwdwu1Rsx8Tty2MZKvGEhcsDoZBDbzqG5CgEtCCaZpPLKGRy4PJlSVgxQm4/XslLQA4hOaeuLfpEr4fh2NEa42dPrOPuayYWzc23HZv/bfwTs8qnMaaijuRJDtxWdKymzl/DvKrzj/sYqazBH17ZxseumcC8KZVDbjPUtRXPpLjvdy9R5avg4vNLT1mW/aHova149qwjPbZw3lvaYHO7xezJG/jkjA8M2jYc9hKNHn7ZOsNy+NlrfXgynVw9K4JcMZbsG7/CdcGHhlUXs2kDsi+MNvGS4z+hIby9ro142uDvPzIDTT05w5ZPx/VwMNll0JeP0ZXqRZUVIr5S0kaW/myMck8ppm3Sn40SdofwqIVRUf3ZGAHdj4cgCStGOp/Fr4TJxFyUBF3YTqGBtS+RxbYcSoIucjkTWZawHUhlTXweFcl2UDQVy7ZJZQzCPheprEHOsAoJKJ3Cd9+0nEImfFVGU2QcxyGWyg9k9HccB02VSecsJAksy8bn0eiLZ9EUBb9XI50x8Hs1DMsmnjLwugoZ+Ev8LkzHpi+exatrpHIGXpeGrslksiaZvInPraHIoKoKjuPgOIV8RumsWbgX45DNWuQMi/C+LPxuXaEvnkWRJVy6iixDMm1QFvKQyuQJ+nQ8ukJnNFvI0u9RcakKmX33b9Oy8bo1FEXCMm1sBzy6TInfheNAVyxDzrDxuFRSWYPSgJuwV0U+6Bo3bYeuWJa8YVFR4sGrKeQth65oBst2qCzx4D5o1IKDQzLv0NWXoizoJuwtHj26X86y6eovTDWoLHGjK6fmN+JI14MgjCQi0D+FRkpgJOp5cp2t9RSB/siru6j3qXOqA5v23hQ/+uNqrptbz7ja4iG967o3srlvG5fUzMcfcJ/0QD+ZT/J2yzt8cupdeNXjX7e+qz/Nn9/exadumMycSZGi1w/9W8eSOf7vI0swpDQfuHDMaZuXv5+7pRG1v4Nc3ZSBso0tDhtb8/zV5W5m1xxY2eRIgX4mb/PgW/1IElwb2ovi9qBUjsfY8Ara9GuGVRercxdONo7r/NtO7KQOYdsOr6xqQXIcvvzBGQMj407E6Q7097/n2X6fONhIqu9IqiucffUVgb5wrhBD9wVBEAThJGrqTPCvj6zh0pnVQwb5iXySd9tXMqN8yklLwncov+6nIVDHW81LCmtwH6eKEi8fumwsv395C08t2nXYrO+O47C8sZNv/3YZKbWDy+dETnuQD6Am+7APmQoxvU5iUm2WX71p8Ebj0YPWPT15vv9CDx5N4sbpPtRAGDvajh3rBG942HWRvCGcRM/xnMYRyXJh+HfWsPjx42uL8nYIgiAIAsBpyo4jCIIgCOc2x3F4e20bT769k2vm1DGpoXjJPMM2eH73K4wPjyE0RGLOk2lq2STebF7Cuu5NzI5MP+7jVJV6+cR1k3h1ZTNLNrRz+ewaJjeUEPLp9KYMVmxsY8mGDizbJljfSkW5l8AZSqSoxHswaicXlc+qDZJnB69ugTe3prl2qp9Lp+kD6VtNy2FHd55FW9M0duS4fKKXKdWF6RaOP4K94z2wLeTy4sSshyP5QtipfhzLQBoime2JUBWZWxaMZtH6Nr7z2+V89qYpTBs98nNaCIIgCCePCPQFQRAE4QTYjsPGXX08s2QXOcPmzqvGUx4qHi6fNjM8v+sVPIqbSSXjhzjSyaXIKguqL2BR67uYtsmcyllFK5MMl9+j8cFLx9Lem2Lz3n5WNHaRzZv4PDqlARcXTovQmF1B2sgyueT4GxVOhJxNoeTT2J7iRgZZkphVXc8aeR0V2jRW7lH4y+oEjuOgKhKZvE0koDKxUuMzF4dxqQc+J0nTkUsbcKJtyBMuKjr24Uiyiuwvw+5tQqkoXnHjRMmyxBWza6mv8POb5zfTUBng5gWjGVcbPK15EQRBEISz0/s20D9dQwrPxNDF4yHqeXKNlHruN9z6jrTzOthIrbuo9+l3tLrbjkNvLEtTZ4LNe/pZva0bl6ZwweQKpowuKQqy4rkEW/q2s7prPaOD9Uwrm1wUbp+qeXQB3ccVdRezrGMVO6K7uaBqNqND9ajy8fUw10T81EQODI13eWXWNDWyuGMxYT3Egpo5RcujnS5aohvLX4okD52gzqN6mF0xjU29WygpD3Dj+eeTyyhYtoNHl1GP8HfXx10AOHCM5ya5A5BLIp2C62H/McfXhRldHWTdjh7++9mNKIrM+RMiTGoI01AZoCToGpQs7Vidymt5pN0nRlJ9R1JdYeTVVxBGgvdtMj5BEARBOFbrd3Tz//1y6aCyiQ1hqkp9Q27fmepme+8eAAK6H5davG736RLLxTEs8+gbHgeP6sann9nlQb3ZDCXxKBnX0Zfzy1l5PKob/ymus5now1U9Fi1cdfSNTwIHh5auJLvb4oPKf/mNq4ZcC10QBEE4d4lAXxAEQRAEQRAEQRDOISLrviAIgiAIgiAIgiCcQ0SgLwiCIAiCIAiCIAjnEBHoC4IgCIIgCIIgCMI5RAT6giAIgiAIgiAIgnAOed8ur9fbm8S2T20ewpISL/396VP6HieDqOfJdbbWMxI5fMbl4VwPZ+t5DcdIrbuo96lzotfDyTQSPq/DEXU/M0523c/E9TDSPv+RVN+RVFc4++p7pOtBEEYS0aN/Cqnq0Gv5nm1EPU+ukVLPYzWSz2uk1l3U+/1hJH9eou5nxkiu+34j7RxGUn1HUl1h5NVXEEYKEegLgiAIgiAIgiAIwjlEBPqCIAiCIAiCIAiCcA4Rgb4gCIIgCIIgCIIgnENEoC8IgiAIgiAIgiAI55D3bdb908kB+pI5uvozBH0uKsNuVFk609USBEE4Ybbj0JvI0xPLUBJwEwm5znSVzimSBLG0SXtvCpdLobrUi0sRbfSCIAiCIByZCPRPMUmSaGyK8u+PrGL/6jS3XjqWmxeMEsG+IAgjmiTB6u29PPjk+oGye26YzAev8J7BWp1b2qNZvvebZWTzFgCzJ5Tz+Vun4dFElmpBEARBEA5PdAucYsmcyX89tZ6Dl6B9dvEuumPZM1cpQRCEkyCWNvifZzYOKnvk5S20dCbPUI3OLTbwx4VbB4J8gLXbe2jqEp+vIAiCIAhHJgL9UyyTN0mkjaLyWCp/BmojCIJw8iSzJnnTHlTmOBAX97eTIm/a7OmIF5X3iIZi4RyVzhrkDevoGwqCIAhHJQL9Uyzk1amv9A8qU2SJihLPGaqRIAjCyVHid1ESGDwn36UrVJSK+9vJ4NZkFsyoLiqvr/APsbUgjGy5vMX3H17FT59Yh2XbR99BEARBOCIR6J9imizxt3fMZlRVEICQX+e+j8+hzK+f4ZoJgiCcGK8uc9/H51BT5gOgLOTmm5+YS025CERPCgduvmg050+qAMCtK3zu1mnUlIkcCMK55/XVzQR9OqmsyfLGrjNdHUEQhBFPJOM7Dcr9Ot/65BwSGQOPruLVZRzn6PsJgiCczRwHqkJu/umzF5DMmHjdKh5NRpJEotGTJehW+fIHpxNLGWiqRMCtit8P4ZzUuDfK5PowOcNm1dYuFkyrOtNVEgRBGNFEj/5poskSpT4djyaCfEEQzi26IlPq13Gr4v52KshAiU/D7xJBvnBush2HXW1xaiN+xtUG2bynH8MUc/UFQRBOhAj0BUEQBEEQhDOmvTeNW1fwezR8bo2yoJtdbcWJKAVBEIThE4G+IAiCIAiCcMY0dSaoLj2Qe6K6zMvOttgZrJEgCMLIJwJ9QRAEQRAE4Yzp7s8Q9B1IUlxd5mNHi+jRFwRBOBEi0BcEQRAEQRDOmK5ohtBBqxHVlPvY2RbDEUkpBEEQjpsI9AVBEARBEIQzpqs/Q9jnGvh30KthOxBN5s9grQRBEEY2EeifThLE0gZd8RymfaYrIwiCcGpk8ya9yTy9yTziVnfiTNuhK54jmjZArFwonIN649lBPfqSJFFV4mFvZ+IM1koQBGFkU0/lwX/2s5/x8ssvI0kSH/nIR/jMZz7D0qVL+eEPf0gul+PGG2/kq1/9KgCNjY3cf//9JJNJ5s6dy3e/+11UVaWtrY377ruP3t5exowZwwMPPIDP5yMej/O1r32N5uZmSktL+elPf0okEjmVp3NCDMvh7XVtPP76NkzLYXxdiC9/eCZhj3amqyYIgnDSpPIW//v4Wt5e0wrA5efVcseV4/Hqyhmu2cgUy5j855Pr2NESQ5ElPnrNBK6YXYsmi4hfODeYlk08lSfo1QeVl4fcNHUmmD2+/AzVTBAEYWQ7ZT36y5cv57333uPZZ5/lySef5OGHH2bLli1861vf4sEHH+TFF19k48aNvP322wDcd999fPvb3+bll1/GcRwef/xxAL773e9y9913s3DhQqZPn86DDz4IwE9/+lPmzp3LSy+9xB133MH3v//9U3UqJ0Vrb5pHXtmKaRXmm+1oifHU2ztF74wgCOcMSYL1O3sHgnyAt9e0smFXH5K41x07CZ5etJMdLYXs45bt8Ogr22jpSZ3hignCydMbyxL06ciHNF5VlHjZ0y569AVBEI7XKQv0582bx+9//3tUVaW3txfLsojH44waNYr6+npUVeWWW25h4cKFtLa2ks1mmT17NgAf+tCHWLhwIYZhsGLFCq6//vpB5QBvvfUWt9xyCwA333wzixYtwjCMU3U6J6y9t/jBbNWWLjJ5MbBVEIRzg6IoLNvUUVS+orEDRREzxY5V1rBZuaWrqLxNBPrCOSSazBHwFo9urCjx0NQlAn1BEITjdUqfvDRN4+c//zk33XQTCxYsoKura9Dw+oqKCjo7O4vKI5EInZ2d9Pf34/f7UVV1UDkwaB9VVfH7/fT19Z3K0zkh5SF3UdmE+jAuTTz8CoJwbrBtm+njyorKp40pw7JE9uxj5VJlJtSHi8ojYc/pr4wgnCKxVB6fuzjQL/G7SKQNMjnzDNRKEARh5BvWHP2dO3eyevVqPvKRj3DvvfeydetWvv/973PhhRcedd+vfOUrfP7zn+ev//qv2bNnT9HrkiQNuXzKkcoPR5aHHzSXlfmHve2JiEQCAKgujUtn17J4bWFIa9Cn86mbplK57/UzLXKW1ONoRD1PjeFeDyPtvA42Uus+0up96exalq5vGxhyO6YmyMWzaikvPz333JPhdP0+HOxwf+dPfmAKu1pjxFOF7OOXzKphypgySoPFjcdnykj7jh5M1P3oTuX1EIkEsLZ0URJ0Ew57i16vLveRyNs01J0df6eR9H0ZSXWFkVdfQRgJhhXof+c73+GjH/0ob775Jv39/fzgBz/gxz/+MY899thh99m5cyf5fJ4pU6bg8Xi47rrrWLhwIYpyICFTV1cXFRUVVFZW0tPTM1De3d1NRUUFpaWlJJNJLMtCUZSBciiMBujp6aGqqgrTNEkmk4TD4WGfeG9vEts+tT1MkUiA7u4Dw84+ft1Erp/fQDZnUlnqxe9SBr1+phxaz7OVqOeJOdKP6HCuh7P1vIZjpNZ9JNbbJcF3v3ARu1qiAFSVeNAl56w7jxO9Hk52XQ73+ZR4VL7/xQV09qVxu1QiITdWzqC7++yYqjYSv6P7iboPPt7hnKrrYf85tHbEkYFoNF20TanfxYZtXVQE9OIDnGYj6fsykuoKZ199RaODcK4YVhd4Lpfj1ltv5Z133uHGG29k/vz5R50P39LSwv33308+nyefz/P6669z1113sXv3bvbu3YtlWTz//PNcdtll1NbW4nK5WLVqFQBPP/00l112GZqmMXfuXF588cVB5QCXX345Tz/9NAAvvvgic+fORdPO7gz2mixRU+JhbFUAn64wxIAFQRCEEa806GZUxMeoiA+XKqYnnQjHAZ+uMLYqQE2JR2TbF845sWQen2fofqf9mfcFQRCEYzesHv18Pk9PTw9vvfUW//3f/01PTw+5XO6I+1x++eWsW7eO22+/HUVRuO6667jpppsoLS3lb//2b8nlclx++eXccMMNADzwwAPcf//9pFIppk6dyic/+UmgMJrgm9/8Jr/85S+prq7mxz/+MQB/93d/xze/+U1uuukmAoEADzzwwIl8DoIgCIIgCMJpFk3lqSwtHrYPhYR8Sza0n+YaCYIgnBuGFejfeeedXHnlldx4442MHz+eK664gi9/+ctH3e8rX/kKX/nKVwaVLViwgGeffbZo28mTJ/PnP/+5qLy2tpaHH364qDwcDvNf//Vfw6n+aSdJkDUduvrTyDI4IrG+IAgCsiyRzlvggNelnNbh8SOaBOm8jSpLuFRJjAYTzinxVB6fe+jH0UjYQ3tvGsu2UY4hD5MgCIIwzED/7rvv5q677hpIdvfUU09RUlJySis2UtnAlqYov31uM/FUnusvHMUN8xvw6cpR9xUEQThX5S2H5Zs6eey1bdi2wx1XjWfBtCoxtP8oUnmLl97dyyvLmwj5dT578zQm14U4Ql5aQRhR4ofJug/g0hSCPp323jR1kRNLCmh2bMPu2Ys28WIkfegRBIIgCOeSYT1hpVIp/uVf/oVPfepTRKNRfvKTn5BKiXV8h9Lel+GBP66mL57FtGxeeGc3b65uQTREC4LwfrajLc5vn9tEKlNYLuv3L21ha3P0TFfrrCbJEm+sauHFd/dgWja9sSz/9sdVtEczZ7pqgnBS2I5DMmPgPUyPPkBlieeE5+nnd7xH9pX/wNi5jMyrvxhyVSdBEIRzzbDCz3/5l38hEAjQ29uLy+UimUzyT//0T6e6biPSnvZ4UdnrK5vJ5MX4fUEQ3p9UVead9W1F5a+vakYVPfqHlclbvL6yuai8qUMkJxPODemsiarKqMrh7wORkGdguc7jYSd7yb3zMPoFH0af80HseDdWW+NxH08QBGGkGNYTVmNjI1/96ldRVRWPx8MDDzxAY6O4SQ4l6CteAqaixIuqiHGWgiC8P9m2Q23EV1TeUBkQPWtHoCkyFSWeovLAEL8zgjASpbIGXteRZ5FWlXnZNUQnynDl3vsTasNs5FAlkiyjjjoPY/Mbx308QRCEkWJYgb58yLhzy7KKyoSCMdVBGioPrL+pyBIfv34yiphQKQjC+5RtO1w4rWpQQ6jPrXL5eXVYlgj0D0eR4OM3TEY5aEm9UVUBRleKNZ6Fc0MyY+A5WqBf6qWlK4lpHfvISKt7N1b7VtRx8wbKlOqJmC0bcWzzmI8nCIIwkgwrGd8FF1zAv/3bv5HNZlm8eDF//OMfmT9//qmu24jkdyncd8/5NHclMS2HqlIP5UEXnAXPspIElgOqLIls14IgnFalPp3v/dWFNHUlcJxCb37Yq4oM8kfRUO7jh1+6iJbuJB5dpb7Cj1ckdxXOEamMieco32eXphAOuGjuSjKmOnhMx88tfwJ13IVI6oFGRsnlQ/aXYXXsQK2ZfFz1FgRBGAmG1S3/ta99Da/XSyAQ4Cc/+QmTJk3i61//+qmu24jl0xUm14W4cm495YGzI8hPZE1eWNbEdx9awaNv7KA/bZzpKgmC8D6jqRLRZI4n39rBE29spyOWRRKjnY4oa9rsaovz7OLdvL6qhWgqD+IjE84RqYyBexgNV9WlXna2xo7p2FbnDuz+NpSGmUWvyaV1WB3bjul4giAII82wevQ1TePee+/l3nvvPdX1EU4B03H4zfObWb+jB4DmzgSrt3Txz5+bj0cTUzAEQTj1JFni9VWt/OWtHUAhodyqrV18/4sLKBVzzockyxIrGrt46IXNQCHZ69pt3Xz/rxcQCbjOcO0E4cQlswYu/eiPorXlPrY0Rblmbv2wj51b/QzquPlIcnFDghyuxurYfkx1FQRBGGmOeHe95ZZbjrjzc889d1IrI5wa/cn8QJC/X3c0Q0dfmjGVJ7YurSAIwnCkcibPL9k1qCyXt2jpSlE6RgT6Q8kYNk8v2jmozLRs9rTFiUyKnKFaCcLJk0wPr0e/oTLA2+vasB0HeRijgKy+ZuzuPWjTrxvydTlcQ37jqziOI0YVCYJwzjpioP/tb3/7dNVDOIUUWUKSKJoLK5a1EgThdFEkCZeukDcHJ9RSVfGQfTiKBG5dBXKDyjVNzNEXzg3JYQ7dD/p0XLpCW3eKuoqjd1Dk17+CMuo8JOUwj7nuwjGcdBTJV3JMdRYEQRgpjhjpzZs3b+B/EydOpL6+nrq6OqqrqzEMMcd7pCjx6dy4YPSgshnjyqgMu89MhQRBeN9xawr33DA48VWkxEPDMB7a3680ReLu6ycNKgv5dcZUi6z7wrkhmTHwDGPoPsCoygAbdvUedTsnn8bcvQJ1iLn5+0mShByqxO5tHnZdBUEQRpph3V1/9rOf8atf/QoARVEwDIPx48eLofsjhATcfNFopo4uZcvePkbXhJhUH0ZXRI++IAinh+M4nD++nPs/M4/1O3qoLPUybXQp/qMsrfV+5jgwuT7Edz43n3XbuykNupk2toyQRzvTVROEkyKZMWgY5hTCcTUhVm7t4sYLRx1xO2PncpTIaCSX74jbyf5yrL6mIzYICIIgjGTDesJ65plnePPNN/nRj37E17/+dZYtW8Zbb711iqsmnExuVWZqQ5jpo0vE0nqCIJwRqiwxttLP+OoAjuOIpfWGQZEkRkV8jKn0i89MOOekssfSo+/n+Xf30J/IUXKEZJTm9qUoddOOejwpUCZ69AVBOKcNq0u3tLSUiooKxo4dy5YtW7jtttvYu3fvqa6bcAqIIF8QhDPNtkXAeqzEZyaci1IZE7erMEffweGxrU/xZvOSIbdVFJmJ9WGWbmw/7PHsTByrtxk5Mvao7y35y7Cjhz+WIAjCSDesQF9VVZqamhg7diwrV67ENE3i8fiprpsgCIIgCIJwjkrnzH0JJ6Et2UFfLsrmvm2kjNSQ288cW8ZbawrZ94diNa8vDNs/XBK+g8i+UuxYJ45oQRME4Rw1rED/i1/8It/+9re54ooreOWVV7jiiiu48MILT3Xd3leSOYvGlhgb9vTTnzLEci+CIJyVDNumuSfF2p19NPWkMMQooVNLgp5knvW7+9nWGiedt850jQThpLBth2zexL1vFYnWZAejAvVU+yrYHR96SH11mReXJrN6a/eQr5t71yKXH3kO/36S7gFFw0lHj6v+giAIZ7thTYy68sorufLKK4HCfP29e/cyadKko+wlDFc8Y/KDh1fg1lWuOL+Otp4UtRE/Y2sCeMUySoIgnCUc4I3VbTz22raBsjuuGs/18xoO22qcMSyaupIkUgbV5T6qSzzIoh1z2Nr7s2xt6ieXt1AUibaeJBdMqcQ3jCXJBOFsls2baIqMvO+G0JnuotRdgs/x0pJoZXrZ5KJ9JEliwbQqnlq8i/MmlqPIB+48juNgtm/BNebjw66D7C/DjnUgiyX2BEE4Bx010F+6dCkNDQ3U1dXx1ltv8ac//Ylp06YxceJE0et8EkgSbNrTRyptcOOCMfz+xc0D8zDH14X5+4/Owise6ARBOAv0J/M88fq2QWV/fnMH86ZWUebXi7bPmTa/fHojG3ceWBLrKx+dzXnjysRw2WGwHGjpSvKHlxrZP3CiptzHuLowvjLvma2cIJygg+fnA/Rk+xgbGoWDw4rY4fNAja0JsmpbN6+vauG6CxoGyu1YO5KsIntDw66D5AtjxzqhZsrxnYQgCMJZ7IhD95944gnuv/9+YrEYe/bs4e/+7u+YNWsWbW1t/OxnPztddTynSZJEZ1+aS2bX8vySXYOSLe1uixFN5tnbnaKtPyOGyAqCcEalciaH3oYcB1JZs2hbSYLuWJapY8r42HWTqC4vLHX10PObxPDzYTIsm1eX7+WWS8dy17UTuef6yVSWeenoTZ/pqgnCCUtlDdxaob/JxiaZT+JTvQT1AKl8irxlDLmfJElcPaeOZ9/ZQ1c0M1ButW9DLq07pjpInlAh0BcEQTgHHbFH/9FHH+Wxxx4jEonw4IMPMm/ePL70pS9hmia33347X/3qV4948F/84he89NJLAFx++eV8/etf5x//8R9ZtWoVHo8HgL/5m7/h2muvZenSpfzwhz8kl8tx4403Dhy7sbGR+++/n2Qyydy5c/nud7+Lqqq0tbVx33330dvby5gxY3jggQfw+Y68ZurZyLYdZo4vZ8vefqKJ3KDX7rlhMr94ct3AQ93cKZV8+sbJoodfEIQzojzopjTopi+eHSgLB1yUhwYvdWXbDhv3RvnZY2sxLRtFlvjoNRN5a1ULHX0pDMvGow0rRcz7miTB1Rc08Phr2+jf9/tw4fRq/F7tDNdMEE5cKmPg2vc8kzEyaLKGsi+JXsgVpCfTS42/ash9y4Ju5k2u4LfPb+br95yPLElYnTuRw9XHVAfZV4LV23RiJyIIgnCWOuKTlmVZRCIRAFavXs3FF18MFLLwH23Y/tKlS1myZAlPPfUUTz/9NJs2beLVV19l48aN/OEPf+CZZ57hmWee4dprryWbzfKtb32LBx98kBdffJGNGzfy9ttvA3Dffffx7W9/m5dffhnHcXj88ccB+O53v8vdd9/NwoULmT59Og8++OAJfxhnSkPEx+jqAAtmHPiBGlUVYFdbbFDPzcrGTna0xs5EFQVBEPBoMt/4xBwmjQoDMLEhzDc/Mbdovnh7T4pfPLEO07IBsGyHJ17fzuXn1zJ/WhUB9/DWzX6/01WF5Zs6BoJ8gPeOsLSYIIwkBwf68XwSn3ZgOkpQD9Cd6T3crgDMnVRBMmuyaG0bAFbXsQf6A0P3BUEQzkFHDIBuSjoAAQAASURBVPRtu/CQZhgGa9euZd68eQCYpkk6feShg5FIhG9+85vouo6maYwbN462tjba2tr49re/zS233MLPf/5zbNtm/fr1jBo1ivr6elRV5ZZbbmHhwoW0traSzWaZPXs2AB/60IdYuHAhhmGwYsUKrr/++kHlI5UqS0xrKOHDV47nhgtH4dYVZo4vZ1dLcVC/uy0+kLhGEAThdIsEXHztrvP5+T9czn0fO5+KoKtofff+RJacMXh4vmnZhPwuPnbNRMQdbHhyhsW25mhR+cEjKgRhpEplDVz7Eg4n8gm8mmfgtaAeoOcogb4sS1w3t46nFu0ilUzhJLqRghXHVAfJW4KT7BU5QwRBOCcdsVtl9uzZ/OhHPyKXy1FWVsbUqVOJx+M8+OCDzJ8//4gHnjBhwsB/79mzhxdffJFHHnmE5cuX873vfQ+v18sXv/hF/vznP+P1egdGDgBUVFTQ2dlJV1fXoPJIJEJnZyf9/f34/X5UVR1UfizKyvzHtP3xikQCw962HBhTE+LOaybh0hUURebpt3cO2mbymNJTUvdjqeeZJOp5agz3OzXSzutgI7XuI7HepiThcalkcgfm7rs0hZnjIzRUnf3nc7p+Hw421N85b1qcNzHCknVtg8obKgNn1ffibKrLsRJ1P7pTdT2ktnYT9LsIh73YSRO/24vfX5gGVOWUsalrG+HwkZNOhsNeJjdFeX3ZDq4LV1BSeqyfiZdORaHM56D4gkfdeiR9X0ZSXWHk1VcQRoIjBvrf/OY3+fd//3d6enr4j//4DwAeeOABdu/ePfDvo9m+fTtf/OIX+cY3vsHYsWP5z//8z4HXPvGJT/D0009zww03FO0nSdKQLaxHKj8Wvb1J7FOc3C4SCdDdnRhUJskSOcNCU2WkI7x91jS54rxaNu/uY1tTPwDXzW9gVIW/6JiHkiRQFBnLsot62oZbz7ORqOeJOdKP6HCuh7P1vIZjpNb9tNZbgrxpo6tyYR29E1AdCfAPHzuPn/5pDamsicel8pWPzsajQnd34pjvUafCiV4PJ7suh/s7f+jycextT9DclUCW4PbLx1EZ9pw13+eRem3Byau7qsrYtnPWfGeO93iHc6quh1TGwLFsotE0vbEYsq2QTBamqWiWm+5UH/3RFNJRxgCdN66MPyzczMWTIqjR40hU6Q3TvWc3SsW4I242kr7rI6mucPbVVzQ6COeKIwb6Pp+Pf/qnfxpU9p3vfAdFOTAf87e//S2f/exnh9x/1apVfOUrX+Fb3/oWN910E1u3bmXPnj0DQ+4dx0FVVSorK+np6RnYr6uri4qKiqLy7u5uKioqKC0tJZlMYlkWiqIMlJ/tkjmL11c1s3htG6Org9xx1Xiqwu7DPugG3Spfu2s2PYkcqixRFnAddciry4hiNq0ju3cj7rGzUOpmkFOHv9SMIAjvP32pPE8v2sXm3X2cP6mCmy4aRchzYgnfxlcH+eGXLiaWyhPyaQQ9GrbtDHGPmklOPXpP2vuVS5O567qJdPSmcekKY2uC6Kp0wo0xwonT7AxS51bSW99FjzTgHn8BWdfZ/yxytkhmDgzdT5kZ3OqBpJ4u1YWERNpI49OGTrRs2AZb+3dQ5a2g3ptlWaqB646jHpInjB3vPmqgLwiCMNIcc9rjg4N8gOeee27I7drb27n33nt54IEHuOmmm4BCYP+DH/yAWCyGYRg89thjXHvttcyaNYvdu3ezd+9eLMvi+eef57LLLqO2thaXy8WqVasAePrpp7nsssvQNI25c+fy4osvDio/mznAY69v55lFu+iLZ1m9tYvv/XY5nfEcHdEs1mGifVWWqAq5KR9GkK87WRKv/jfR1x8is2MF/a/8D6nFf0Bj6CVqzrSMYbG7M0lzT5r8vqRdgiCcPHnbob0/Q28yd9i4MGPY/Oj3K1myro2+eJbXVjTxiz+vxzyoB8+0Hdr6M+zsSJDMWQxnAJXjOPhdCrWlHvwuFdt2Cveo1w69R/0elfzJOeFzjCxLLNvcxa7WGHnDIpM1eXbxLjqjYo7+mSZJYG1fQu9zPyWzbRmxd56g78/fx2X0n+mqjRjJdH4gGV/aSOFSBq/eUci83zfkvg4OL+x6lXXdm/jz9ueYpLXwTnfwuObaS54gdqL72E9AEAThLHfCqY8Pd1P9zW9+Qy6X40c/+tFA2V133cUXvvAFPvaxj2GaJtdddx0333wzAD/60Y/427/9W3K5HJdffvnAcP4HHniA+++/n1QqxdSpU/nkJz8JFEYWfPOb3+SXv/wl1dXV/PjHPz7RUzml4hmDpRsGz7PM5EzWbuvmT69uY9rYUr5w23QCruP/k0iJTnItjYPfY/sKfPM+CL6a4z7uqdCXyvP/Hl5J974H1pnjy/n8rdOKsncLgnB8ommDn/xpLc1dhaHyN188hg9cOAqXOrh9t7M/Q09scOC4szVGTyJHVchN1rR5/I3tvLW6FQC/R+P+z8yjIjj4oXw4pEQnueZD71Er8c37EOZZdo86G2TyFl6PytOLdhJPFRpD5kyuIJbKUxlyn+Havb/pZpzepU8OKrNSMZy+ZqgsOUO1GlmSGYNIsPA9TpsZ3Io+6PWA7qcn28eoYH3Rvrtje+nPRbmq4VJ2RfdQ1fIeGWsCTX0mo8qObTSS5A1ix0WgLwjCueeEA/3DzY2///77uf/++4d87Z577ikqW7BgAc8++2xR+eTJk/nzn/9cVF5bW8vDDz98jLU9c1RFxqUpZPODM1Hvz6C/aVcf727s4IYL6nEkMCwHHFCPIfXA4Vuyz64xnpIMC99rGgjyAdbv6GF7S5TZY8vOYM0E4RwhwTOLd9HcVZjz6Djw3JLdzBhXzvjqwXMPXUOsZy9LoCuF8tae1ECQD4WH8yXr27h+/ijcqoRyTPlRiu9FsjeIrOlIEmdsvv7ZSlYklqxtozTo5qPXTKA3lmPhu3u4ZJZoFDnTJACneCSayN4+fKmMSX15oXE/Y+aKevSDepDudM9Qu7Kqcx2TSiYgSwpjPZXg2IyJyKzakzn2QN8Twu5pOr6TEARBOIsd89B94fj4XQr33DB5UNm42hA9BwW7G3b10N6f4fE3dvLfT2/k3U0ddMVzw080GKxErx48x8w9eia2L3KYHc4M04LGPcXD8fa0J8TSgYJwEmQNmzXbinuoWrqTRWWRkJuLZw4OHG++ZAwl/kLv2sFLuSmyxGdvmUZTZ4Kv/vRtHvjTGjpjuWEN5QdwApXoNeP3/UvCmP8pVkz8G36ysJNlW7pJH9IQ+n6XN20umlnNDQtGsXZbD8l0nr+/67wzXS0ByGtBAvNvG1Qme/wopcW9z8LQDl5eL2tl0ZXBAXpID9A9xBJ70Vyc3lw/tf4qAFzZFDndjdfXx8q92WNubJG8Iezk0A0KgiAII9kJ9+gLxSQJElmTTGcCt1xo+XccmD+5gtpyHztbYwR8Llq7kjy3ZNfAfh+6fAL/8rsVpDKFOfWrtnTx0asncMnMavwHDenPWQ6d/YXMspVhD0Enht3fApJCyfVfJLt7Lblda3CNm4M2Zi6G7EKVpTOa4fpgqgzzp1Xxl7d2DCqf1FByWrMWC8K5yqXJTB1Txnsb2weV15QXJ7VSZYm7r53ARTOqaetJ0lAZYFSFfyAnSGXpgeWtrphTx6vLm2juLIwU2Lo3yvd/t5zvfeFCSrzaUe8veclD8Lovk4n20q+UsrYpyzPv7iKZMVi+uZPLz6vlE9dPEi3Q+/g9KobpsHZ7JxPrS8ibNg+/1MgXbp9xpqv2vmfb4Jp6FRU14zB6mpE9QdSK0aT10jNdtREjkzVxaQq2Y2PZJpp8SKDvChLLxTFsY9BrjX3bqPPXIMuFRgIlFcP2BIhbTRhWCR1xi+rQ8B9vJU8IJxXFsW0kWdx9BEE4d5yyOfrvV6bjsKKxi4df2kI2bzFvaiX3XDeJgFtFlSVGV/gZU+knmbMGPYSPrg7Sn8gOBPn7vbK8iZnjywcC/UTW5GePr2VXWxyAMTVB7p2bR1vyKwDUcBUlH/w68rTrsSzoSOZZsnIP25ujXDW3jqmjS3GpMs4ZDKgdBy6fXcOutihrt/UgS3DTxWMYUy2WMxGEk0Fy4CNXjmdnS//AFJkr59TRUDH0etgeTWFKfYipDaGiYL2m1MsnbpzMo69spTzk4fXO5kGvJzMGK7d0MXNc+bDm7Tdlffxq4S52tzUTDri44+oJvLh0D519ad5e08rNF4+hzK8f9TjvB6mMhYODZTs89to2PC6Vmy4eQzSZo67Uc6ar974mSUBfE11/+TewCyNRPBPn4b3s0+TlI6/9LhRkcia6JpO1cuiKzqFDgxRZIbivV7/GV+i9d3DY0r+d8yMHGrvkTAzZEyJj9VBXKrGxNUt1aOh73VAkRUXSPTjpfiS/mD4oCMK5Y1iB/iOPPMLdd989qOxXv/oVX/jCF/jc5z53Sio2UrX1pPn1M5sG/r18cyelQTd3XjVuYDqf44BPV/jS7dPpjmWxbIfKsJtNe4uz9RaWICy0MEuSxKqtXQNBPsDutjhr+itZ4AtjpaKY0Q6MPWuxJ15FMmfxg/9dQW88ywcuGs2e9gQvvLOHcXUhrp3XQJnv1D1Mm7ZDR3+GnmiWkqCLmlIPmnKgpTzgVrn3gzPoS+ZRFZkSn3a2pRIQhBGt1Kfxz5+7kO5oBl1TiIRcR51PP1S7rSpLXDm7hvMmRsjlbZ58czumNXhDSZL4/UuNfPXO2di2w7rt3XT2pigPe6gu8aDum5KTNmwefHL9wIiAaCLH719s5M5rJ/LIy1v31UHcCPbTVJmdLVHWby8MK87kTP78xnbuu2fOGa6ZoNkZoq/+ZiDIB8hsW4535jVQOvEM1mzkKAT6CikzVQj0h1DiLqEt2TEQ6HdlerBsi1L3gYSHSiqGGYpQaoWR5STrWxSunTr8QB9A8oaxk73IItAXBOEccsRA/9FHHyWbzfK73/2OXC43UG4YBg8//DBf+MIXuOWWW055JUeSvfseYA+2dEM7t14yBvch2a41WaKm5ECvzJiqIB6XSiZnDpTduGA0pft6txRFYsOu4vlqG9sNLg1XYqWiABjde9Emy7R2x+lPZJk9IUI8lWfJukLW/52tMZZt6uD/fn4BftcpyHIvwaL17fxh4ZaBotsuHcstF41CPijQUCSJSGBfD6B4theEk86jyTREhl6D+pg4EPZo4IWPXTeJh186cG1fNKOazbt72d4cJW/avLaiiafePjAl6e7rJ3H1ebVIQE8sMxDkA7h1BQcGGg4umFJJif/Ys/mfq/KmzeqtxbkWemIZqA+dgRqNXCc72aNs5TBjXUXlTiY+xNbCoWzbIW9a6KpMby6HLg8d6Ec8pTQnWplbORuAxt5t1PtrBvX+K5k4+YrRhG2IOh3s7vZgWA6aMvycP5I3iJPogSrRSCMIwrnjiIG+qqps27aNbDbLtm3bBsoVReHb3/72Ka/cSFQ+xJJHDVUB9EOCfIfCknuKLBH0qNg2lPg0/vlz83lzdQvtvSkunV3LpPow6r4fNMuymTelkjWHPPjNbdAxtrQM/Ns99nzylk2l1+AHN3sJm3uJqhH8UgkL1xZGDcSSedp6kkysPfkPi/0pg0de2Tqo7JnFu7hoRjWR41iSSxCEk0eSJFI5k7xpE/RqxzYf3oFLZ1RTVxFgZ2sMTZXZ3RZjzdZu5k2rJJE2BgX5AH96dRvnT4xQ6tPRNQW/R0PXZL50RZjyXDOOpOCqc+CaiWSyJrF0ntJTONpoJFEUqK/ws+WQ0V4B8fkMm8uMYXfuwIr3oFeNxQqPwpRO/PMzNT/ucXPI7lx1UKmEEq7COOxewn6ZvIlbV5EkaV8ivsME+t5yVnWtJ28ZSJLElv7tXF570YENbBs5l8Z2eQnbGrtjTZT7x7OrO8+kquE/b0juIHZCJOQTBOHccsRA/4477uCOO+7gtdde45prrjlddRrRRlUGmD62jI37et7dusLd1w1OLpXMmTz2+g6WbmhD1xTuvnYSF06rRJMlIkEXH7t6ApIEplkY6y9J0JvM0x3NUl/p5+JZNbyzr3f+ohnVzK6ysddnkVSd4IUfxKmYiEYe/+ZnkDctIgd4gA9MuYr2MdNYtzsGgHKKks5kcuaQSfVSWUME+oJwGkmSRG8yR2t3Cl2TqY8E2NEa5X+e3UQyYzB9XBmfvXlqobd+mFRZYkyln+bOBH96dSum5TCqKsidV08knsoVbW/bDtmcBT4oC7j46DUTGKv1Elj8E7AKo5esNW6mXvx3fO+NDq46v/aknf9IZ1sOV5xfx+72OLl9KxJMHl0ykKlcODLdShF/6efk23cOlIWv+Szy+MtPOPGr6agELr0bLIPsnvXI3iAl13yOvL9GjFAbhkzOxK3vz7ifQ5eHfhzVFZ2Ip5Rt/TvI2walrhL8+oFh+UouhaO5QJJxKy5UWaM8aLO14xgDfU8QO148QkMQBGEkG9Yc/QsvvJDvfve77Nq1i5/97Gf8+Mc/5hvf+AY+30kYEnqO8eoK9354Bu29aUwbIiEX4YOyUcezJi8va+Kd9YVAPZe3eOiFzdRW+BlbWfjxsqzBa/PG4gmMlm3URHdiesu5Yeo4br7oIhRZotTvQlMctE89gCNJGHoYwwZ3qoX0pkWDjuM0vsG1889j3W6oq/APmYH7ZCgLuKgo8dDVnxkoC/p0ImGRPEoQTqeOWJYn39hOV3+Gps4En79tOr9+ZuPA6xt39vLIK1v569umH1PPvipLXHVeDedPjJAzLEoDLjRZQldlgj6deCo/sG0k7KE0VHjg1mSJCyZXoCx9nox1YIqSY2SpT23i6x+9mJD32NbAPpf53Spd/Wk+eMV4cBwUWaatJ4XHJRbMGZZo86AgHyC26FHKGmaRU098NFtWL8N7/d8SyMVxFJ28GhQ5JoYpm7Nw7/se56w8qnL4635SyQSWtL2HA1x2cG8+IGcS2K4DyQ9LXEEsKU5ju8qts4ef4FfyhrB79hzTOQiCIJzthvVs9/3vf59gMEhvby8ul4tkMsk//dM/neq6jVguRWZ0hZ8FM6oJeQ4E+RnD5o1Vzaxs7CzaZ0dLFGmoRFmyhNq0HO/i/4ANL6Iu+z3hZf+Fx4pTHnAhS2DZElmthJwaxrKhqSdFc3txYj+AypDG526dxtfuPh+Pdmp69F2qzH33zGHa2MIyQ+PrQvzjJ+eemnwAgiAMKWNYbNjRQyJtMLomyGdunkoqaxZtt7Kxi0zuONavdyDs1agMudH2JdvzuxS+9ckLmLBv/vjUMaXc9/E5uA9KxOnTFZx0tPhw8R6mshNZEoHSfqmcxdjaED39aZ54fTsvLt3N9HFlRauzCEOTjGxRmZPPIjnH8X0f6viSg5KNYvW3QqIb1c4cfScBKAzd3z8yJWfl0KTDN16Vekq4vO5irqq7lKBrcPCuZBLY+oFAP6SHMKROmvtNzGMYtSF5QmLoviAI55xhdQs0Njbywx/+kLfffhuPx8MDDzzAzTfffKrrds7piqbZ1RanOuKjOzr4gaCq1DtkT4Cai2GvfHJQmR1tx51sQyqrKEou1NKb5p//Zxkfu6SSi4IR7PiB+fxaeT3lY0ZTjvukJiUaSplf56sfnU0mb+HSFNSTnAhJEITDkySJ11e18pe3dgCwtamfVVu6+Js7ZhVtW13mRTtJjX6OAxUhF9/74kX09KXx6Epxa7KZxTtpPtm9GwcVu2rG0/f6/1L+iankxFrkQOHv2NKVZHdbnHtumEw8leePLzXy5Y8U/x2FYoovhKS5cQ4K+L0T5iAdoff4WLj6d9L95x+BXWhA8065GPfF92CI5fWOKpOzBobu58w8qnzkv8nBw/UPJmfi2PqB3Ehhd5BtsR2EPZNo7jMYUz68fAySJ4iTjuLYFpIsOiUEQTg3DOvpTj5kLrdlWUVlwtGpiszGnb3Mm1qF132gjeXyWZWcX2mg9+/AZQzuiVclG8conveqy3ZR4KwoMovXtQLwxLvdtM38LPL4i1ACpfimXkTZB+4l55z6IH8/mULvnTr8xLeCIJwEyZzJc0sGJ8VLZQx6ohnmTa0aKFMViS/cPgNdPvGLVJLAZUTR+3egxdsI6kP/wCiJdjI7VlF6zWfQSmvQyuspueJuUttXgGXASeptPRdIwKvLm9jZGuP3Lzby9Ns76Uvk6OpPn+mqjQhmLkvpFR/D3TANJVBKYNZVaGV1OOaJj4jQnQz9r/x6IMgHSDe+gxRtOcJewn6ZnIlLLzwHZa0cmnJ801GUTBJbPzAtUJM1PIqH0oDFjq78EfYcTFJUJJcPJzX0aEhBEISRaFh31gsuuIB/+7d/I5vNsnjxYv74xz8yb968U123c05F2MOcyRU8+vIWbrlkLJIEoys8TM5vpP+P/w6OjaS7Kb/t/5ArnYDjgKGH8M++huTqlweOI2luXJF6isN/B10ptESbls0Pn21ncsP5XDfzambE3sZ0RMQtCO8HEqDIUlH2b11TqK/0M6F+MrouM3VUKWUB/aQkD3PFdtP71L9h59IgyYQu+xjKpCuwGNxTZ2fiZHatxVU7EVfDVJxcmug7f8Exsngmzcdyl4hkZvsoUqGB+FCqKhrah0MJltP9wi9wVY/FM2YWmaZGXHUTkVzhE/6OyVYOM1o8DU8srzc8Bw/dz1t5Qvrw59MfTM4msUtrBpWVuEPE8nG2dbq4durwjyV5w9iJHuRA+XHVRRAE4WwzrKeFr33ta3i9XgKBAD/5yU+YNGkS3/zmN0913c45mizxmQ9M4fO3TSdvWDRUBJhWkiX2+kPgFBLwOfksfS/+J7pZWGvasiXc591E6LKPoZXW4Jk4n8id38bwVBQd37IcLp5ZjXJQ79zW5jglWh6juwnDVUJrX4Y1O3rZ1ZEkZ9pFxxAEYWSRZako8PO5FD56zeD1oMuCbmrKfdi2TUWJhzkTI5T5jz/IT+RMNjdF2bCnHyuXoP+lXxaCfADHJvb2H1GTHUX7KaFKkGRi7z2LXlaLY+ZR/CUEL/oQvos+humIRHP7aYrEXdcO/jv6PBrja07+sqjnIjufpfTSO5AUjVzLFrxjZ6KFKlDs4mbyY2VpftxjiqdQKKGqIbYWDpXNWej7pgzlrNxRh+4fjpJN4uiDE/2GXSHycjt7eo5t5IbkCeEkuo++oSAIwggxrCcq0zS59957uffeewfK1q5dy+zZs09Vvc5ZXl1h5phSZo8rw7YdpK7mom2sVBRySfAWWrhzShB56g24JlzO7q4Mr73RwZTR7cyeUE7gkOzL1SUefvCli2jpSmJZFg0hCDU+RfgD9/LW9hj//dSGgW0vm13LPdcNfogUBGFkkCToSeRZtrmD9p4Ul8yqYWx1EE2WcBy4aFoVVaVedrfFGFMTQpIkfC6V2y4Zg22dWHdmNG3w/d+toDdemPv81RsqGB0rXprKTvaBv35QWd5TQfnt/4f+V35F3+v/i2/qpZTf/n9I6+XkRE/+II5TWCHlO381n7aeFF6XRiTsHpjbLByZneih77Xf4aqdiF45htS2lViJXsrHzQfXia0CYzgqwcs/gWPkyLVsQXb7CV/zGQx/9Umq/bktkzMOSsaXRzvM8npHIhk5cBycQ3IuhPQgebZiWA79KYsS3/CuF8kTxIp3I9b9EAThXDGsO+uXv/xlfv3rX6OqKpZl8fOf/5w//elPLFu27FTX75y1fw1fKVBOYaDtgSdctbQa2criiu/FClRjSjqW7fDHN5t4Y2Vh/t97G9sZWxvkvrvn4FIO9OAncha/fW4zW5sK88wqSzx84ba7yNk+fvfCO4PqsGhtK9dcUE9NZfDUnqwgCCddf8rgu79ZRnJfBvZ31rfzxQ/O4MLJFTiOg65ITKgN0t6b4l//sAooDAO/7+NzmFgzvGXAVCePEm/FTvUjB8oxA9XYksb6nT0DQT7Arj6HcSVVWP2De/CVYATzkESctiORr5hOyV3/AkYG2x0ihUsM1x+CI0F/IscvnliH4zjkDZvp48r44BXjqBHLlR6Vsm8Idq51G7nWbQBo5bU4rqETux2rjF6O76b/QyATBc2FoYUGftuFI0vnTFyuQkidt/Lox5EgUc4mC0vrHbJikSzJlLrCZH0Gu3ryzPEN71qRfGGcePF0DEEQhJFqWIH+xIkT+Yd/+Af+9m//lm984xuEQiGeeuqpU123c5LLiuP0t4EsQ7iWpFZOyQ1/TfTV/wFVI33h59iVi5DZmGRiOE+9ay/KuIvpyziE/G4aqgI0VAWorwjgALFUjsqQG9MpzMPYvKdvIMgH6OzPsHh9JzdeNIpcvjjJVTpXvNyWIAhnv6bOxECQv9/Kxk5mji3D55KxbeiJ53j4pS0Dr5uWzX/+eR0/+OICvEfpFVYwsTa9TP/SA6t+hK/6JOrkq5kczvF/P+AlL7lYshcWrunjso98GteSX2JXTiZadzGZQD0dUYWevV1MHVOCS1WwAV2W0BTIKQFQjm9e7vtF1nRY1djF33/sPFIZE12VSWUNevozItAfBsdbSviSO4gufQpsE8UXpvSqT5GRPCdlGRhJAjkbw4l3Ibl9yAEdW3IffUeBTM4iFCh8h3O2gXocPfpKNjUoEd/ByjyldGpRdnUFmTNqmIG+N4zVuvmY6yEIgnC2Gtad9R//8R/53ve+x+233863vvUt7rnnnlNdr3OSO9dN319+hJXoBUAua2Dz6LtY3eLj83f+C3FT53u/20A6Gyu8Lkv837vGkGiK8dsXtxJN5PjcbdNZtKaVJWvbAFi02sfnb5/OQ89tpibix+8pbhXf0x7HpSlMqA+zvTk6UO7SFSpLxDJAgjASHRymuHWFe26YzLrtPXz/f1dw6ewaLppRTTRZnHU6nsqTyplHDfTVTDc9S/8yqCy2+AkikQa8r/4rjlk49u1jL8Q1/UKW9/i58Nb/y9PvtPL20+1ADyUBF3/9oZk8+eZO3tvYQSTs4dZLxzKmJkjYp+NS5WGNLHi/sh2bi2ZW85M/rSGaKMwrv3R2DTcsGH1mKzZSxDtIbXmX8EUfBMfGzmfpfeU3hD/ybXLyiffqu6K76HnyRwPXgm/6FbguvBNDFo0wR1PIur8/ebBxXEP35WwKW3MN+VqZuwRT2cGO7uHnTJC9IZxEzzHXQxAE4Wx1xDvrQw89NPDftbW1lJSUsHr1avL5wo/aZz7zmVNbuxEqnjHZ1hIls6GDcXUhqks8KLJEduObA0E+gN3bxIQxzfx+j5dv/THBtfPqSWcP9LDbtkNbzsd/PrkWKATmPdEMm3YdOEZbT4pFa9qwbIeVjZ18+uYpXHF+HR19KbbsKfTsTxtbhs+l8qUPzeDRV7axcksnDZVB/urWaYS9YjaaIIxE9RV+PC6VTM7k5kvG8sTr27lkVg3j68PEU3k27+lnXE0QWYKDRxNXlngIDtEgeCgnm+TQ8fTeiXPof+P3hQR6vhBOwxykbIybJsk8uztLazTA22vbB7bvT+R4+b295A0L07Jp703x62c28Llbp/P0op3ceslY5kyK4BZZ5Ifk0VWeemszOHDZebUk0nneWd/OBVOrqA6JnuOjsbNJjJ4WokueGFQuGTk4weH7+5fX2x/kA6Q2voV78sVQOuGEjv1+kMkVsu7bjo3tWCjS/8/eeUbJUV1r+6mqzrl7pidnaUY55ywESoDIOdnY2OCcLg4YX19n3/vhbIwztgETTBBJBIFQjijHGWk0OYfu6RwqfD9aamkYSYwItoT6WYuF+vSpqlM1XVVnn733u89ed0KMhdD0p74PdKKOXLvEvtokiqr1Eyk+LUYbWjKGlowhnGa/GTJkyHA+cUZDv6ampt/nOXPmnLL9dPz2t7/llVdeAWDevHl8/etfZ+PGjfzkJz8hHo+zdOlSvvKVrwBw8OBB7r//fkKhEJMnT+Z73/seOp2O1tZW7r33Xnp6eigvL+eBBx7AarUSCAT4r//6L5qamvB4PPzyl7/E6/We9QX4oAlEZX7w8NZ0/qogwLfumMKIIivR1uoB/fWBJrIc44glZPpCSURRwGLUEY4lkUSR7r4T6sAeh4m2nvCAfdS19lGYY2Pm2HyaO0McqOuhONfOJ68YxZ7D3SyYVIQEuMx67r5yFB9bOhyDXkQnCBlvWoYM5ylZNgP/c9c0Vm1vxm7RM3NsPo3tQfYdrU/3ueeq0Xz5pgk89OxeonEZj8PEl26cgHEQhrVg8yKarKixE88cQ3YJ4f0bUEYtYaduLK/tCeKx67h+VD6LpzjYVzewBvWRZj8zxxaw79gCpaqlogoC4QR94QQHGnzYzXoKs63vGmVwoRGKJsnNslBW4GDLvnacdgOfuHwUwcjg64NfyByv8HC8qg2AsWQUivH969KIShzZ1zagXYv2ve99XwjEEgpGg0RCTaQU94WzL/8rxULINs9pvy9wZHNIitPWJ1PkfvfFTUEQEKxu1EAXUlbxu/bPkCFDhnOdMxr6P/nJTwD42c9+xte+9rWz2vHGjRtZv349zz33HIIgcNddd/HSSy/xwAMP8Mgjj5Cfn8/dd9/NmjVrmDdvHvfeey8//OEPGT9+PPfddx9PPfUUt9xyC9/73ve45ZZbuOyyy3jwwQf53e9+x7333ssvf/lLJk+ezB//+EeWL1/Oj370I375y1++5wvxQXG0ra+fSJWmwaOvHeI7H5uCZfhMEm21/fpHs0fQui2MrKhMG52H3WqgLxTH4zBR0+THc5LXptsfZd7EogHHHFbqJp5QqG70sedwKuys0xflcJOfWxYP44mVNXzishEkZBVfMI7DasBilM5a/EoQIJxQ8AcT2C16HGbdB5HmmCFDhveApkGOw8iVs8tp6YngdVvIdpqZMCyH5WuOEIwk+etLB/h/X5jN/XdOJSmreJ0mLAZxUPdtwuAi6+qv43/9jyR7WjDklKMvn4Clt5XV2lgeeyslvNfWA4ca9/Gje2ZS6B3oJR1Z7qG22d+vTSeJ3LxoGM++dYRAOGW05mVZ+eZtk3CYM+X1jmM16bFZ9PiDceZOKEQQBdbsaOKmRcP+00M7L4hbcsm++l58K/+EEuzFVDoGx0UfI3oKXXU9CcR4H+hMJHTvLlap6K2YysYQq9/br11y5nJ2Rd0uTGIJBaNeIqFE31PYPhwT4/MUnPZ7j9GF0dTL7pYeityDC+EXrW7UQEfG0M+QIcNHgkE9XVevXn3Whr7X6+Wb3/wmBoMBgCFDhlBfX09paSnFxakH6LJly3j11VcZOnQosVgsXa7vmmuu4de//jXXX38927Zt48EHH0y333bbbdx7772sXr2axx57DIDLL7+c73//+ySTSfT6/2woejQ+UPAuGE4gqxqW8ilYOxsI718Hoogw8hLWtlqRlV4+fukwnltdy47qEyWqrpw7hIJsK3csHUFSUVmzo5kuX4SLJhXx1vaU+v6o8iycNiOiAKt3NPc7biCcwB+Ms+1gB/MmFvHbf+0illDQSQL3XD2WiUOzBn1eggBNPVF+9s/t9IUSGPUSn75qDBOGejJq2Rky/Ac4fk8+8Nh2AuHUPXndgko27Gnhs9eOo6bJz6ub6nn7UCePvHIIQYBrLxrKwknF6KV3955pGsTspTiu+Q5iMoKitxIVjDDpOl7+4+5+fRVVo74twORyK1fPKuL5jc2oGpTk2Vk4tZQfPnyiQsuQQidJWaG5M5g28gHae8LsPtLN3LF5mQXEY0QTMh67iS372nmzswlRgEumlhDLiKgOCk0TSHhH4LrxB4hyHMVoJ6oNnCOY4134V/6JRGsNotmOe+FdyHljUDl95EtS0+OYdwdq/Pck2moRDGbcF388U15vkMQSqRz9UCiB/j0o7gNI8Qia4fQh9qIgkGPXsbOlm8tGD87QF8xO1FOUCs2QIUOG85FBGfpFRUV84hOfYOLEiVit1nT7mXL0KytP5KjV19ezYsUKbr/99n7h9Tk5OXR0dNDZ2dmv3ev10tHRgc/nw2azodPp+rUD/bbR6XTYbDZ6e3vJzc0dzCl9aFScIif2slnlmPUicdWBYfbHsExZhhropiUoUR6EH93sIqHK/K26/8vlpfVHMZsknni9BkkU+NRVoxla6MRtNbB0eimqquFxmPCF4kTjMjpJRFbUfvsQj+WlNXcGiR1T3ZcVjd89u4f/++wsPDbDoM4rmlT55RM76Tsm7hVPKjz49C5+cPdM8l2ZXLYMGf6dSJKQviePG8vxpMLjrx/i5kXD2V7dydGWPu6+egxrd6UWADUNnl51hNEVWZRkW8+0+34kBBOcNJnW9CnRT38o3q+fySChb9rGAt86pl+xiE5zBb6wzM6aTm5fOoJ4QiHXY6GpI8jhZj+hyEC/Z317gPnjC1De8Ry7UDFIIrsOd9HcGQJS75XXtzQyuiL7Pzyy8wdNg4RoBYP1lIvSepL0rforcm8L5vJxyIFuel74Jd7bfkzMcmajPWr0Ylv2dcSoD/QmkgZ3przeIIklFEwGHQkliU54Dx59RQYliSadeQ5TkWVjbU2U7mgv2ebTh/kfR7C4UP0DUzIyZMiQ4XxkUE9Xl8sFQEtLy1kf4PDhw9x999184xvfQKfTUVdX1+974TR54mdqPx2iOHhBp6ysD6aO7jvxeKz88J4ZHG0Nkkgq5LjNjKrIJuukUkhyCHx73yQ3ZygWr4vXquOUFrgH7EtRtfTERFE1/vz8fn711XnkeO3keFNlqZJ9XVjqtxBpruG6OQt4YnVDevvKYhcdvREA9Lr+ua+qqhGKyQwrT3n1vd4zl7mqbfbTe1JKAqQmnbUtfYwo9wzYf79+cgI1EUMy2xCE9ye69W7jPFc4X8Z5nMHeD+fbeZ3MuTx2fzBObYuf3r4Y+dlWKgqdWEwpL9c7x13T6GPV241MHOI65T3psOoJhHWoqsZrmxsGVOLwhxNMGjF4Jep3okQC3HHZCH78t23ptiyniWGlbqIr1qG2H8HUfgTv3C/y0MoQkZjM+Aonlww3UmZNkjvESVmBg25/tF8VEIBpo/LweAa/CPFh8WG9H87EqX6fda1+DtT1DmjvDcTOqd/zuTSWU3HiHWQfMIewCFGSngJMhVXEGg9gyCvHPm4BQrgbb2nVu+5bDsnIcQ1RD3a3GUH89+lM/Luu+wd9P2iaRjypYNBLGMwCZoMBm+3U6vmnQwhFwWTBbDnzdkUGjcQ+kS3tO7h1whXvut94bj7h6i2nvbbn+m/9ZM6nscL5N94MGc4HBmXoH8/VP1u2b9/OF7/4Re677z4uu+wytm7dSnf3idIlnZ2d5OTkkJub26+9q6uLnJwcPB4PoVAIRVGQJCndDqlogO7ubvLy8pBlmVAolF6QGAw9PaEPZeU9GJPZsKeNVzY1oKoa5QUOPA4TWvJEqKUl0Uus6QDJ7a+CILJozFIOMxenzZD2mEMq976xPZj+LCsqXb0RzMfCbiVBJbnpKcJ7VgEwfYhG2RVzqQ2ZMZsM9AZirN3ZwpduHM/KrScWACDl6beGm2l//kmcEy4mYilC1U5vhOtFAYfV0C/U9vh8qbsnjO4UiraCAKZIG8Ety0m2H8U8fAbGURcR1w9c1BgMXq+drq7gu3f8D3OujvNML9HB3A/n6nkNhnN57LKq8fibh9PpOAB3XjaSuWPzyc629Rt3TyjB/X/cxLQqJ1mh7lPek8FoEkkS8LrM7KzpYtmcCrYd7Ej3cck99O1pQMsdTlIcfDSOQQ4gH95AeM9blJZN5IefWMz+pjBOm5FhJS7MOhElv5J46xEA7Pv+xWev/hpGNUZR6xskNq9GAZyFwyld+Gn8Bbn0BmK8uqkBURS49qKhlOXY/m1/p/d7P3zQYznVeRskkWElLnZUd/Vrz3aZzpnf87l8bwkCGMMthDY/S7KrEcvIORhGzCWucwGpscc0HZqm0bf5eQDirYeJHt2N95p73/W8LNEWel74FXJfJ4g63HNvgsq5yMLgIuXeDx/0df933g/xhIJOEpFEgd5ACDSJ0DsihN4Nfa8PUWciGn13YUq3FarbQxxubcRrOXM0jIaFRHfLKa/tufxbfyfn01jh3BtvZtEhw0eFQRn6O3fu5I9//CORSARN01BVlebmZlavXn3abdra2vjc5z7HL37xC2bMmAHAuHHjqKuro6GhgaKiIl566SWuvfZaCgsLMRqNbN++nUmTJrF8+XLmzp2LXq9n8uTJrFixgmXLlqXbIaXiv3z5cu655x5WrFjB5MmT/+P5+QCNnSFe3lCf/lzXGuDF9XV88rKRiGhIgkpo6wskOxtTHTQVbc/L5M+p4LoFlTR3hcjzWDHpJVwOI/94+WB6X0aDhMt+YvVaH/Ph37s6/Vmo3Uh+7SbGXH8fUXcpwajM0mmlWI06nBYdzeUtVJgDCIKG3ZuHuPFvRLrqiexbi/em7xJzlJ32vCwGibuvHsOvn9xFPKkgCnDlvKHoJAGDTjzlJMCQ8NP99I9Qo6mw0+DWF5B7WzFffA9JLSO4lSEDQGdfrJ+RDykBz7FDs3jnlLS2pY94QmFasYi05RE+d8nn+dnLbSRkNX1PbtrTRm1LH7ctGY6iqnT2plTzRQGum5mL+8gr9DRsx3XxxxErLxrUBF4UNRJ7XiP49ssAyLtfo8jpxl4+ga6+GGo0CCY75lEXET20ESXch9zbhiHWi0vuIrF/dXpfSsshktXrsY69gmvnVnD9VA9iXyui0IeAnTjO93U9P0qIIiyYXEJlcWpxVJIEFAXslg/fkPx3IQgQ1AK0htqRRJF8ax4W7YPxIBvjPXQ/9QO0RCryJbDpWayBHgyzP4Z8bGFbSCYI7Vvbbzs1GkQO+cFactp964UE/jf/ljLyAVQZ3+pHycktR3YO+UDG/1EllpAx6FORDwk18d5K68XDqPrBRQFk2QQs2hC2tO3g8iGLztzZZEeT42iJKILBfOa+GTJkyHCOMyhr6/777+fKK6/ktdde46abbuLNN99k0aIzPyz/8pe/EI/H+elPf5puu+mmm/jpT3/KF77wBeLxOPPmzWPJkiUAPPDAA9x///2Ew2FGjhzJHXfcAcB3v/tdvvnNb/LQQw+Rn5/Pz3/+cwC+9KUv8c1vfpPLLrsMu93OAw888J4uwAfNqcrf7avtJpqUCUWSyMkkTnMOiBKoJ4T7zNFOVu5WuHzOEP7w7J503ddbFg/jtc0NhKJJvnD9eJwnK90LQuq/fvN0jURSRlU1nGYdggCJRJwh+k5yGv6BcqwckGh14py6jN636gGNyIG16GdVnDY3VtM0KgsdfO3WiRxt6UMUBZKyypiKrNMaCpq/NW3kHyd65G2sM31g+s+XQsyQ4VwgEpOpKnEzeUQOiqIRjcu8vqWBePL0eeoaImo0SMHOP/DDmz7Pzi4jArBxTxv1bQEAwjGZqy4qJ5ZIMGdCPjlWCVPdWsLOseg9JYS2v4KrfApx8d1D5fXJIN07X0t/dkxbxkGxkp//9QDJY4sMn7x0KNPHFOO+4XtIoXZkBZxZpcS3vk3sHfuLHtmGfexSpHgA3/KfIvelPNaSzY3nmm8RM+Wc9XX8KBKNq0SiSZKyikEvIiCgqHIqiiPL8p8e3gdCj9LNTzf+lnAilWbmtWTxtWl3Yxdc73vfiq8lbeQfJ3xgHZYpVyIbUvnamiAgiOLJFfhSvEsIvj7RR7z18MBj9rVDxtA/I9GEgkmfWmhJKAn07yHdQYyF0QZZ6z7LBr1hD02h7fTFAzjPUF5REAQEmwe1rx3JW37W48qQIUOGc4lBGfqCIPDpT38an89HRUUFV1xxBTfffPMZt7n//vu5//77T/ndCy+8MKBt+PDhPP300wPaCwsLeeSRRwa0u1wufv/73w9m+P9W8k6RX3rF3AqeeesIq3ekNA6Ksr18cdbdGNf9Lt0nZvSwYEoJf3wuZeRDKi//yZU1fOmmCdS29PHM6sN89uqxuCypyIWkyY118mWEt564nqIrnzcOq9S9fYhlsyvYUd3JqrebyHGZuGHSx8je/ifUYA9KuI9EVyP6rEKSPS0MnOUAAvjDSeJJBY/diF4UqCpwkO+xoKoadrOeM8ljC7qBXidB0r/rBCpDhguJHLeZsnw7j79ejaaB227kritH4z6FUOaQQidGg8TqIwk+NnQm6pGNWPY9T01oHtuP+Pv1Lcqx8fK6RrbsT5XBy8+y8rHL5rBpbxt7jui4a8FdjB1s7WpBQjRaUCPHFhHyxvPbJ1pIyqnnhqrBn1ccYVhpFh6HC29VcToM01I4lNju1/vtTl80it2NIQyJEK6RV2Da+xxaqBcl5CN2aD3ixGszomaA1aRDpxdZt7uFLl8UgLkTChlR9u6iYucDgghv1W1IG/kAXZEe9nYfZFbOjPddfeGU7yC9Ae2kd5BsdOKYchl9m55Lt+kcXnSuXM4UFC6KEvrsYpLdTf3bTZmQ33fjZI9+UpXRvQePvhQbvEc/2w4HWgQuGlLEnq79zCmaccb+oi0L1deaMfQzZMhw3jMoZbTjSvslJSUcPnwYo9GIogwsI5cBhuTbmTHmhNCV22Ekx21JG/kAzd0RXm8wIeWkXiLG4bPAVUihS0JW+s9sErJKQ1uQZ986wpGmPtbuakkr6bf64rwWGIZl8ecxDJuFMvlGDlXezuPrO9h4LHz3udVH6OmLcbDBz4+ebyM47qb0vpO9beg8BYCAZdS8ft78pKKxcnsz9/52Pd96aCM//sd2/JEkqqphNUjYTbozGvkAmrMAY9Hwfm2OmdeSNH40JqkZMnwQ+INxXt/SmL6dfME4b2xt5BSyF2TbjXzvrmm4PQ6O5F6MacGn0Vvt3Di3ANdJYlZTR+ahk4S0kQ+paKN1u1oZWuRCUTUeeKGJ1uCJCbYoCkjSiVeCSqrahgYkJBuu+bel+llc9EhewtH+qvmaljqXdyLmDcNYNjb9WXLnEyyayc/+uZMf/6uW+19TaJtwN6I5ZSAlWms4C13VjzSyorFu5wkjH2DtzhYC4Y9IpXZBo97fNKC5OdB+VuK6p8VdhD6vv7HmnHMTCf2J9BBdvA+5rwv33BuxVE3FMflS7OMuQvZ3vXNv/dBUBceEhYimE4v7tjHzOeWNm6Ef0biSNvTjSgJJPPu0SzEePmNpvZNxmiGSgFxTKQd7a1DVM1f1EKweFN/Zi09nyJAhw7nGoDz6Y8eO5ctf/jJf+tKXuPvuu6mvr0eSMl7ZU2E2SNx56QgunVlOMqmS6zHz1o6BL4w9DWEWXfVposEAr++PMNUfpdQewmiQiCdOCuk36lBOeintr+vlqtnlCAJUN/hxer08cbgPf2gGNbt9BCPtFOXYsFsMNHUEcViNaWVuWdFojduoBJB0RCffRl3YimnozUhGKx65D0VvJanpaO2N8PjrNenjNrQHeGrVYT61bCTCIL0sCdGCfdFnsHbWIvtaMeQNRfGUIWccdRkypOkJvDOwHQ41+IjLAyejmqahaQJDC10kBNgu28kdNpZ4qI+LJhdh1EtIokhhjo1QJMFNC6uQRJEVG+vwBeMcbvLhcRiZUOVlzc4W2nrCeB1GorJKW2+Ebfs7mDk6F5tJ5PE3a9lf52NcZTbXL6jEWzSB7Bu+Q7OWw5HGIC67sZ9hL4kCHvfA3Oq4zolt0edwRnuRYxGaEzbu//uB9MJGQlZ58I0u/mfmUqTtT2EZOQdFyTwkAGKyTE2jb0D7qRZUzktUgTkl0zjqa+zXPD5v5AdSYjEh2nBe+mXU9sMofR3o8ytR3KX91qgFTSV8aBNK2Rjso2aT6G7Bv+UF3Es/d8Z9y0Y3kYb92CcsRJB0CKKOaN1uROO/v2LD+UYsIWPQpRZykkoS3XsJ3Y9HBu3RF4SUV98fsmLWm2kOtVLiKDr9vm1ZKJ1Hz3pMGTJkyHCuMShD/7777mP37t2Ul5dz3333sXHjxnMmJ/5cQxAEbJFWpMOrUQJdWMZcREX+QEGfqhI3kijgbt/GZVUjeaspTsVENzcvcvGvN2oIx2SsZj03XlLFs28dSW83c2w+mgY6ZMaXWwi2tzC8wIfBmcOa7Bycbhe1zX78oQQVhU56AzF6D5wwJIw6DQSRxKJv8tPnWujpS33ntBn41iUm3E3rcMy7jc6BFZ3YWdNFPKGmc+v6nzdIkoiiqP0mUXGdEwomIhROIv5+4zAzZPgIkuUY6JUaUebBpB84+RUEgZbuEA89uyfd9qmrRvPE60cJHqtLv3RGGVv2t3Ok2Q+AXify8ctG8s/XDzFrXAGBUIJwLFUFxGkzEggG2bC3nUMtEWaMyMYhhvnBI7XpBcLN+9pp7Ajy3x+fimwppe1oC8OMPsqurOJXz1UTCCcwGSQ+e+0YPHYDwjsCxUyJXpJNe4n3tGAoG0swZkmnJx0nEE4Q0bsomHQpYvG4zGLgMSxGHSMrsnj7YAeFXhuhaBJ/ME5u1kdDJEzTYEzWSC6tXMBrtWvQCRJXj1hCua3slDXv3wtxnROKJiMUC6d8ByUNLnKu+wZKbxuR2p3onF68V3wRzVHAmeIm4qoO58xrSbbVoIT9IEk4pl9J3FH6gY39o0qsn0c/iVM6u9J6aBpiPDLoHH2AbBs090BRQT7VviNnNPQFezZq9fqzG1OGDBkynIMMOke/sLCQI0eOUFRUxA033PCuoU8XKsZIG91Pfg8tmfK4RI/uJP/GXzJjTD6b9qaE8IpybJQXOGlu6aLswBtYeYNl8+6mQy7hYF0zn7pyNKIk4rEbeftgB4Fwal+zxhYwodLLoSYfiaRMpSOGa+sfCUy6g02HY4QViQKjRFNniKMtfew+3MXnbxrB6LEQU6LYRBdl+XnUCd/iSKPEuOFOcvOcqIqODVt9bGgxssTfgf+1P1A8faA3o6rIhSQNDEsMxxX21fWw+0gP44ZmMbo8C6uxv5GiZYz8DBlOSX6WhesvruSZVYdRtVRN+ruWjSKk+alvPoJO05NnzsGgmQknZP7+8oF+2wdCibSRD6l0oeNGPkBSVonGZa6dX8nRtgCjyj3YzHomVnnJtkv85JHdxJJw0QwPAclHr5hHNC73O0ZrV5jatgBWLUJV0/Mk63bgNFn5n1nL6MuZQFw047ZKPPFmLbKiMXVULjaTjmJrEt/z/4fsO5ZCsOt1hsy5hYJsO63dqXB0i0nHuMps8kdVIOlmkNAyoc/HiSVU5k0oZOzQbA43+nHYDFQWu9537vq5hBkrl5cuZn7xTARBxCbYONX0QhbjtERbCSXC5Nty8YjZcBa/ldO9gyRBJd54gL7Ny9NtOlcuWVd+5YwzJFEEpaeJnpUPp4V1zeVjcczLImLIiM2eiX4efTVx1hGigpx63mnS4EP+s+1Q2wlTq/JY27wJTdMQTqNRIljcaNE+tGQM4SwWEzJkyJDhXGNQhv5PfvITHnvsMWy2EyFpgiCwadOmD21g5yty59G0kX8coWkXsuLl5kXD0DSNnr4Yj712iG8szUr3SaoCR1r6CEWTNHeGUDSNn6+uZUKVl+99egZmg4ROEvnNv3ZT29IHQK7bzBeuvI8fPHaAeDI10Xh1Wxt3Xj6SxvYAk8d42B1Zy9vt2wEQBZEs712sPBDhkvluXmx4nU1tbehEHYvmLyLS6kGyZxNv3IcW6mH66Dw270tN0B1WAzPGFvDKpgYun1WW9tnJqsZfVxxg57E6z5v3tTFxmJd7rhqD7qR3qCgKGXGtDBlOgV4UWDKlmKkjconGZbIcJgJCBz9d+1sSSmpCO8pbxZ1jbiaeNPQz6gGaOoNUFbuoafKjk4R+qT+QWiDcUd3JgbpUmM76XS1MGp6DpsErm+pZMrMczdHO8/X/JOqL4fY5ueP66/jHvzrSBr8gQHNniFGGFpJ1OwBQY2Gkt5/A61pD1/z7+Nbvt6BqYDJIdPrCzJ9YjLOv8YSRf4zw5mf4xtXf5xt/P8Sc8YW47EYO1vXyyrY25ozOxmWzoJBJDQOQpFQ+819e2J9us5n1fOOOyf/BUX3waKqAFQdoKWHHdxIXIjxxcDlbW3YBIAkiX55xF0PMQ9910cMoB9C661BCveiyS1BcJcicMBD1sW56t7/SbxvZ34HS0wQFee/cXRpz0kf32if6Vc+J1u3BOrYFcjOG/pmIJhT0aUM/iU44uxx9MR5GHWR+/nGy7bC+Biw6K3pRR1e0mxzLqf9Ogigi2r2ovc1IuUPP6jgZMmTIcC4xKEN/5cqVrFu3Drfb/WGP5/znFCvEpvqNTBv5aX779Ilw2xHFNnJD1akPIxfyh7clalpSpXr2H+1hZLmHr906ieqGXt4+2MGMMfkcbOhJG/kAkbjM7sZo2sg/zoY9bYyr9FJZqfF0w/Z0u6qp/GPvk1w/8042dr1OSzAVYSCrMivqV/D5CfegbNIQDGbquuIkZY1bFg9DUTScNgOb9raxt7abglw7RdlW8t1mugMxdlZ3UVHoZNLwHBRVY9OeNrr7YuS5TIQJcsR/lLZQJ1VZFRRbitFrH50a0BkyfBAIQJbNADYDqijzxI7laSMfYH9XDc3hFrLFcoaVuKk+KW9724EOvv/pGTzyykEO1PXitPUPgy3Js7NhT2u/tu2HOrl50TB2VHfi9sr8Yf+TKMcqb/hifbzY9DQLZlzDy6tTz4gFk0vYW9vN1FnZxKffidl/FLVmPagKol7Phj2tqBpcPtHD/Hw/1ubX0DqLMRdWEnjHuWqKjLtnNz//7Gye39zGv95MPff2He1hza5W/ufqAuw5hchC5jkhIvD0qv4l3ELRJC1dIQrdH43w/cHQHGlJG/kAiqby911Pc9+sL2BUTl8e0qCGCbz+OxLNh9Jt7sWfQiyfnV54FjQNTZEHbvwuCwiaKqfLQvZrj0dO0TvDyUTjMgbdcdX95FmX15MSZxe2D2DSg9kAnQHIsXip72s6raEPIDi8KD1NGUM/Q4YM5zWDMvTLyspwOE5fdzTDCQyeAgSDqV/tXuuo2Uwoz+b7n55BbbOfHIdEftdWhJ0vA+BzDqOmpf90+EBdL+OrcnhpfR0Auw93U1bQ/28giSLRxMDqB0lZQScZkYXwgO980T6KCgzsO3xowHe9sV68xdNxVs1k+Rt9dPtjbD/UCcDCqSV09Kb21+OP8ufle/nxZ2aiaRpLppcSSyg8+9YRdJLIommliBLEhSi/2/63fqrKN4++kjl5s05ZzS9DhgseAfriUZqD7QO+8scDGJQ408fko9eL7KvtIddj4boFQ3HbdHx+cpzwCAmDUM3nlhTx9zUdhGNJ7NZTG8xaer/+tJF/HF+0j4nj9eTrCnHm5LD9UBfTR+Xz0xdr6fApDMkfxV3zJmNd+2tMpWOQuxQKsy0sdNfBpudTZcmaDmBzuRFNVtTYiWeRbeQswoc2EdXlsXJrT7/jdvnjNHUEGGORkO0DtU0uNBKymi5h2K89eWE9QP3x4IC27kgvUSWGkdMb+vib+xn5AP7Vj5FVOIa4LvU+1YwObGPmEdq9Kt1HNFnRe/I4k+ShYDBjKh9LrG7Pya3oHFlnLMuX4Zihf0zrJ6EkkcRBTUXTCLHIWXv0AXLs0NQDhblZNAaamZo/8bR9RXs2Snf9WR8jQ4YMGc4lBvV0vf3227ntttuYNm0aOt2JTT7/+c9/aAM7X4l2t5G99B60RBQ1GUfnyCZcsxVz+RRKLBGyW/+Juq8J14yriVROQQn2ILoLYIDfq39OYUtXiIsmF7N6e3O6zR+KM6LEwYp3hMUvnFrC/qM9VHnzEOoFtJNcEyOyh2IWbZS5ijnqa+h3PDlmQF9SScJgBG0Hc0d5mFMGFiGO1WtkZ7WK1aRDVlQSskpTZ4jRZR5sFj2vbk7tS1EVXlx/lBFlbiK63gGlk545uIIJOWOxkqk1nCHDO2nzxXj4pWrGThrD5rat/b4rsOXh0sz8/PGdDC1yceMlVRRkW6lp8rN6RwvTSu2Mkrph878YYXPxg9mLkIsnsKslSWm+g4a2E8+YqhI3zR0p4ynX4Uag/3PCqrfgtVoZPt5CfchIbpaFR145mI4eqm0L8evVCvfNuYNWUyHzPRJDPALC3r/2c4T6Ny/He+VXCO9fS7KnBdu4hYgGIzp3HiaHGYfFgD800JxSQ72QMfQxGSQWTC7u59U36ETyss5g3H4EybYM/I2O9A7FpDPBKZzxx9ESccxlYzGVjkp9VmWCu95EUE9Kf0mGECQ9zqnLiDbsRe/Kw1hURTLYC+bi0+88HsEyZBKWyimokT4EvRHJ5kGJhd7v6X7kicZlnNZU5FFSldELZ2foi/Ewmu4sBfxIhe83dMP40iy2te9EVpXTKv4LzjyU6nVnfYwMGTJkOJcY1NP1N7/5DVlZWQSDA1fVM/RHsjgI7X6NaN1uAESzHff8W5ARSe5/i2TjXhBENFVBMllBU3B3bGNUeQn7606E446v9PYL00/KKnJSZem0Il7b2oyqwdRhbgqCB/nutUW8ciBOMJJk6XgXQ/I05mQLyJrCl6Z+nL/ufppAPMiwrApuGX0dq7d2clHxYtpDjxBJpgSxpuROQYs4cVY4MUU7+NniJFq0Gclgou/tV1AifXx78RepkfN55JWUh0QniUiSwI7qgeGLO2u6mDNroCcxqcioDIxCyJDhQkeSBF7b3MCR5gBTJoxljDfM3q79mPUmbhl9FfnGPCQkvvWxKby2uZ7CHCt7j/TQ0Rth/9Ee9h+FmSMKuLl0IkrDDsTtT5E0ZdHZaeYr1w7nrd1d7K/zMbLCg0En8fSqw8wcm0+BLZdrh1/BM4deQENDEiVuG30jj7zWw4zRRgqyochrG5Ai1NYTZR+jWLe5lc+VHiCvah6Jff0V97VEjGSgG3HcZThFhcieNwnvXQ2AYDDx4+u+ypcebU17rfM8JnKTLUj2sWdUPL9QSCRVCrxWblo0jJ3VnbhsRsZWetGdQhT1o4xe1PPxCdfz9P6XCSbCVGaVs2z4IrR3KcGn9xQgjp2PFg0iB3vRZxWStfgukiY3nLRpaM9bCAYTpsIqkv52wtWb8S77whn3LRud6JzZdL3wG7RkKoLPPnYB1nELThkJIIoCgiB8IGUDz3eicQWv66Qc/bMU45NiYVT92af25DphXzPoRD12g42uaDf51txT9hUdOaj+NjRFRpDObiEiQ4YMGc4VBvX0ikaj/OlPf/qwx/KRQFASaSMfQI0GCR/ajL1kAqGjbwNgqZxM+OAm4q0pL0181iQmDMthaLGHxvYAQ4tdjK/y8u2HNvbbt9OuZ/JoF3MmCBhDSQwHV6FueJuyOdfzMWMNgseFwzuN+NG38e1ciabIDB23gP8ZcTUJvQGbu4w127t5etURrGY9l86/Bb01ypD8bCJ9RkqzPZiOVw04nnog6rBf8V/0tLVj3f00vdk3EoomyXKYKMm1IQIVBQ6OnrQoAVCca8djsmDVWwgnT+QsziqZgl109JtkZciQAUCg71iFjcdfbGb8sHFcWTUNr8PG1JxSkkkFDbCadJTkOzDpU9E1uR4L37xjMi+sO8rGg70su2wKloYdiDnlOMqLudhxBGpf5dJQA5cuuY71TTqyPRa+dstEXt/SSCymUSyN4p4xRbT5eyFp4enn/CyZXs6fX9jHdQsq08JZJ2PUS/QEElw6OQdl7UPQtAfrtKsIrX8i3Ueyukh6hmCUA8hd9WkjH1KLAGx6lB/c8QWe2dBKZY6OCa4+PBYbSdvpRdAuJPQ6gcONfvKyLIwdko0kCSRlZUBVhI86VslKc187X5h+J7IiowlQ3VVLaVHZmcX41ASBzS+Q6KxPN7nn34rkKUcmpXGgigYcUy6lb9NyIodT72jJ7kG0nVmTSEDFt/qfaSMfILhnFebKSWA+UbpNEFKROpv2tRFLKMwaW0BxthmBC2ux5mRiCRmj/kSOvnTWHv0IiuXs00ntJpBV8EfAY3LTGmo/raEv6AwINg9qTyNSTsVZHytDhgwZzgUG9XStrKzk0KFDDB8+/MMez3mPHOwZ0JZor0WRk6jeoeBrx+Atxl+TCsvVOb2s7XLyzOZq7BY9eVlWVmysZ2SRhQfunkibL4YoGTBYNQ6GdvC39atQVIUJOSO4fvRUhPIZNElO7CMr0UkCcvdRAtteTh87tOsNnCYryv4NaFd/hxWb6gEIR5P865UWAK6a52bSMCsOk474nu399AVQZSJ73+IfvdO5afyNTDDbsHmyGVXuwW7Uoaoai6eVsnlfO6FoygeXn2VlbEUWVnR8Y9bnWHHkTer9TcwsnsL0/MmgDjQaMmS40FEUlcXTStl9uBuAXdW97KqG//7EVJLHvemCwPPrjjJpeC4/+ce2tJGz6u0m7r19MgfqUl5L2yWforWgkD/s/Ds9UR9Ok4NPj7sI78pfMWz6Z/n5yw30BmIYdCKCIBCNavziidpjI0lFFi1fe4S5E4o4WNeL025k2axSXtxwIt3nY0urmOyNYBIVfHISTdMI5Y7HtMiLVreVuDUP28hpJOt3koj2Ip1iYp7saqTMI/HVa4ahhbrRdDkkTdmomRJ7QEpHYXyVl18+voOCHBuhSBKnzcDHLh35nx7avxWH5KDUVcjPN/6JmBynxFnIpybe8q5aL2pfZz8jH8C/8Vlyy8YSNxwTM9QZQADXnOuJt9aid3mRrG7gzO8pKd5HsqdlQLsS7IUTBXXo8Mf47z9tRj7myV+5tZH//sRUynJsA7a9UIgmFAw6EeVYxQJJOLs5gZCIoOrPPnRfECDPmQrf93hctIbamJQ77rT9RVcBSmdtxtDPkCHDecugDP3Ozk6uu+46CgsLMRhOhEu9+OKLH9rAzldO5YkSSsYTwUhgxJW4lBhqLIRkc6OEfIjufPa1pQzkYCRJMOLnljk5kIjw7X8eJZ5UEAX4zJ25vHJ0ZXqfOzsP4jHlsWONh9auRmaMyefmUTLJ3oEiXpGabZiKqhATYewWI93+WL/vDTqJLfs6KLt4CImwf8D2ungfwwttPL0ryJduHE6uJvXzomTbDfzw7hm0dIXRSQKF2VYshlSfLNHL7SNuRNFk9BgyJfYyZDgDw4td3H/nVP7xykF0ksCNC4dR7D2Rjx2JyyiqxqptTWhaKhx48ohcct0WGtsD3LZkGIbsPOLGLH6z/mcE46l84b5YgF/XvMp3Jl6MFO6iN5B6Bnz+hnE8u6aWQu9Ao6PbH8NhNRBPKOyv7eHm+YV890ovQcFOliGBfe9fiK4/gpI/lKylnyJsyuP/vdhET1+M//nkneQmGlETEZI1GxC8JeicAxWujSWj0XQGopoJrMe8oJlHRJpEUiMal/nY5SOxmw2omkZvIEZfOEG+68Kp792V6OLPOx5Pf27sa+Gfe5/js+M/gaiefhrzzlK36Tb1RESEEA3Qt3E5gqRH7y0m0XEUJeQna+k9YC877b4FnQlj0XDi7xD7k076nYuiwNvVnWkj/zjPr6vjy9ePRbtA34fxhIJBL5FQE+gE3SmrFZ2J96K6f5wcB9R3QWWBh/091WfsK7ryUdqqYfTC93SsDBkyZPhPMyhD//Of/3w/Az/D6WlR3HgmXA27XwRVRsobijDmUn75r/3UtwWZNmohS0q95OYNI/TmX7FVTmZGloPqhpQXzaATmVJm5v9WNKdzYp02I0f9DQOOtbdnLyOHXE6eQ8c1Y1LhuHJi4EKDPrsIua8bm17izstHsPVAB8FIks372rAYdSBATJZRVTBVTSO0+41+21uGTmB+42uMn3gJ0aSAUYJoUqXTH8Fs1JNtN+Iw6XAUO099URQBCT3qKWbw4juEBDNkuBCRNY2jbUFe3dSAzaLnU1eOpsBtJsdrp6sriCAKBCJJkorGiFI3m/a147AauPGSKlbvaGbvkW4mDc/hqjllxJMKvYovbeQfJy7H8el1DMvx8rnr3OR6zFhNejbu2cltS0YMGFNxrp3evihl+Xbe2t7EwZYYt1QEUfqO0rf2ufTdnGg7QqKwCrXlTb42YiRi2USEwytJxHowDZ2EaDBjzB9CuHoLzhlXEdi2Ak1OoMsqoqvqSgTZgEEv9BMfzZBCr4OeviidvigbdrfishtZNK30grtWXZGBkXIHu48QVSNYOX0It3SKKjiWysko5qwTC0o6PZLdgxLsJdF+NN1PNFrOOCZV1GEfexFqNEiypwVBZ8A59TIEXf+a8PIpqibIinpBL2jFEnLK0FeS6M9ScR9FBkVBk/Tv3vcU5Dlh1QEwS2YUVSGUDGPTn1rcUvQUk6hZj6ZpCGe5GJEhQ4YM5wKDesI+8MADLF++/EMeykcDm83Kg9UFLJ70FYyCgt6dy6/+UU0klvIgrN7RQrc/xvjKXOYs+TK+V37OmNHLmD4sj83VPnI8ZgRSQlfHCUaSuPTZA441xFXMLUMk1MN7Cb+6hi40PAvvRO8tIdnVCIKINOpigkPnYzYZCCsSe2o6WL29GafNyN1XjyESTfKPVw5y/51TUyJBrjKyrvoagQ1Pg5LEOnIWsaZq5KM7cLQexFX6A+SEQPORevwJA3/Y2sfkEXlcPrMcw1kIRMWSKkfbAtQ0+SnPd1BZ5MRiODtBngwZzidEUSBGBAERg2bsFxVT09zHA4/tSH/euLeN//vcLOJqkERc5mBjLw+/eICkojJnTB5Lp5dQ1xbikVcOEjtWYnP97lb6QnGmj87HlS2gl/QklROSdoIg4LJnobe5meROxRYH4zJ6SWTdrhZuWjQMLexnhCuKUQJnUSlvN6q8uOEoX76siAm5CfSWfOINewecW6K1BslkI7H7RUxJH8meFkIddYT2rsZzyZ2EqzejH7uI2KGNuGZfj+rIZWe3hQefaeJHl/VS4JRQC8eh8N4m7x9VBES6/FFeO1bVJBRN8vBL+/n6bZP/wyP79+IwDqzSkmf1YhCNZ9R7SVjyyb32Xvo2v0CiuxnbiBmYR8wirJ3wBmt6M+65N9G94vcczwWwVE1F5xz4zj0ZMRGi49U/YRs1G+uIGaCqhA5uRHLmgH0IAKqqMXlELi+sO8rJ69nLZpdfcIs1JxNLKBj14nsqrSclImgG01lHARzHYQZVA19EwG1y0RnuwuY6taEvWJyAgNbXjuDKf0/Hy5AhQ4b/JIN6wppMJtrb28nLywgkvRs6LcnH52Tz+r4gXSGFiycJaSP/OPuO9nDZ7HLCrQeQ4hF025/k1rJJ3PGpy9EbVMS+ACNKnRxsSAncyYpKsMvGsKwhVPek8mjtRhtLbMVInUeItdTgnncjmpxES8Rwz70RWYUOKZ/fvlLH4bXVOKwGbl8yjI37OglFk4SiSX7z1C4+e+04/vuT09NhoIqgR8kZg/uyAsJbnyOw7WXUeEpMT42FkRt2ET60hbLKSeQ27eHrs2fwv+s6mFCVQ3nu4HIONeD5DXXpySvAxOFe7r5iNHoxs2qe4aNHghjbO3bxYs1KdJKOG0YuY4RzGJKmR0Ng+Zrafv2vmFPBs6tr2bSvDYNOYtH0UsYMzWb7oU7W7mmnqtDMkKIsZk3MpqktRs2xZ8Xe2h5GlmfxwromrlhyBc8eeTZdkuy2EZfiCQQJZzmQjkXSOEx6rr+kisdePcQ3rsxHW/ssyYOpsahWJ3MWf54512YRevW3+CMBBIMJ57QrgE39xmvIqyByeDsA4UObcE2/gkRHHQCBXW9gHLuYrriJ1+X56OsE1h/0EY51U5pnI8ttw/fqr8i6+usoriEf5p/hvEPTVDbuaXtHG3T5IwwrPHsxsvOVPFMOF5XNoDvqw2N2UdNdx8fGX4tePXOetlEOIPu7sFRNxVI5GQ3QogF01gJkOXVfSPEA/r2rybnqyyjhPkSjhWRvayoNLr/g9DuX9BiyCwntXd2vWTT2Nxrz3Wa+e9d0XtlUTyyusHRmGRWDfFd+FNE0jXhSwaCTiMqRs/boi/Hoe8rPP44gQL4L6jrB5XTQHumkwlV2mr4CYnYpcssBDBlDP0OGDOchg1bdv/jii8nLy8NiORHOlsnRH4gl1IT42q+4IacMwWmjRXfTgD42i54sMzizvURtLjRFITRhBs+3raI51Mms7OHcuXQ8v3lWpqkzjMkgUehwYbdXMTZvBDpRYpQ5G8sbj6MUVmEbOx/fmifS3gj75EsRxy3j948f4EhzygAIhBM89Oxeblk8nEdfTeUUahokkgrjKr10dfUvnagKIpHqLWhK/yJXWjJOvKWaeEs17vm3Et/4KDdM/Ty9gRhl+WaSJDFiOqNIUm8owetb+qci7DjURdfcGAUe81lf8wwZzmUEAQ76qnl077Pptofe/gdfn/lZSk1lIGjopBNiVDluM7G4zMa9KQMvnlR4cd1RblsynB3VnQiA4IBd4VVUGw9SNraMOdOm8I9nm5FEgaSi0tEbZe0qK5+59DMY9X7sfb14uoO058zg4N4ushwmKoucmPUSc8fkMWmkHZq2kGw/seCghPtQatYhGMyokQCC3giahhoNYiofR+xYdRFDXjmS2Y4S7MVcPhZddhGcXONaTvBWdw4KAqXFdp5YWUM0LlOQZeHuWRaiq/+Mc/oVJFsOIbiHXtCezneiauC2GwmEE/3aLaYLK/IhrsWYWDCG146sYV9nNTOKJiKJEogaqGdYHI756dv6Yj/RPOe0K7A4vIRET6pBFLFVTaXzhV+nwsIB64iZGAoqzzwoVcE+YSHBPWsw5pYgB33oXDkI7ygVF0kovLC2lmBURq8TeWJlNV+8fjxO84VZsi2RVNFJIqIokFAS6M7S0BfiYTTd+9OnyHNCTTvMz3HSEh6oa3QyYnYpcuMeDKMufl/HzJAhQ4b/BIN6wn7729/+sMfxkUEX7QUgeUzpN6esnrFDs9hzJJVjWJZr4VuLbChrf0E0GsQ4/Sa6c4r43x1/IC6nhINeaNxEn5rku9dOwS87SBoEHtr/J7paT+QpXlQwnst0BswlI+l65Q+cbFkH316BNnRx2sg/jqpB4h35gjZzasIoCBAlgj/ux6K34Da5ccy+gb41j6X7Ggur+on9RWt3YswfgtsgoytO8NDuv9ESbGN2yRTmFc3EwsBwS0hFKJxqLp88RS5jhgznPaLGm/XrBzRvb9tDxdByInGFy2dXcKgh5REfXuZJK++fTEdvBLfdxLjhDtb3vkx9XyMAPVEfdeZ6lsy9GoNmYf3uVgAa28Ns2xrgmkkO1Jxh7E6GeOrZarr9MYaXuvEsHk6Bx4xFiNOl+dF1NQ44ZqL9KIYFtxGtuhR/SENNJHArPeQMmYl38mVosSBquA9EgexL7yG0dw2Rgxsxl4/HNfMaAnvXEpzzRdRWDUkSMYgyP1qiI5LUYQ83Iqx7A1VOgKoi2nOQM0Z+PzRN47LZ5XT7YohiKv2jLxTHbrmwDP0+OcBvtjxM4tjC80s1b+KL9XHN8MswqacOuwaQA70ke1qQ7B50dg+JzkYCO17DMmwqmFKGvgD0bX0pbeQDhA9uxDpy1hnHpOrtSE4vpuJhhA9sQOfOxVw2GsHc/713qNHH9uqufm1rd7Vw5axS1AvwlXc8Px9Ihe4LZ5eyJ8Xfm+L+yRS4YetRsOqddEX2n/l43jKS+1YOcHpkyJAhw/nAoAz9qVOnsmfPHg4cOMA111zD/v37mTBhwoc9tvMSQ3ZKOVowmBD1JpT1D3PLVQ8wdqiXWELh4pIYsRd+DGho5dN4+YgZNx1pI/84a1u2sxAb9sbDVI+ZNUCM6EBfM0s9uahKEi0R5Z0Y1BguuxF/sP9+T66HnZ9lpTw/NSnplDv41ZY/44v1oRN13D72WqYNm022t5RkZx06k4VEdzPBHa+ltxd0elRVwVrh5SdbfkdcSXmdXqp5E380wM3Drj1lKb1sh5ERZR4O1vem23I9ZnI9ZnSCjCTHUHQWZC1Thi/DRwBNIMeaTW1v/yiWbIuHWFLlLy8dIBhN8tWbJ3Cgvpe8LCtJWaWtJ9yvv9tuIhRJUFQssbmpv1HeG/UxarKBF1/10dEbSbfPGaLHEO2lRfDyx+X7kJWUIX2owcffXj7A+Kps5gxz0i53Ycgp5p3TZ/O4i9kb9PLwC/vwBeMMLXIxf1IRyYiGa/czaI27APBc8nF63ng4LXoW2rsay4iZdM39Jj9+dG/6uJfOKGVaqYArdJTI0W0ocuqZoWkauvwqLqzq8O+OwSDitBp5+MUDROOpqzOu0suMMRdWGHFnuCdt5B9nU9MOLq28GBOnN/QRRdzzbkIO9iL7O1ORI74OEE8slKhyEiXQjSG3DFPxSORgN5HDO1AjwX5l8t6JKumJHtpEaM9qAJSQj67WWnJv+k66jyQJaaHdk9l1uIvLZ5YhXICKfNGEgkmfercnlCQ68ewMfSEeRdO9P3Fokx5cFuj2m1E0hXAyglV/avFFwWBBdOSgtByEvDMv/mTIkCHDucagLKlnn32Wb33rW/z5z38mGAzy2c9+lqeeempQBwiFQlx++eU0NzcD8K1vfYtFixZx5ZVXcuWVV7JyZapk3MaNG1m2bBmLFi3iF7/4RXr7gwcPcu2117J48WK+/e1vI8upyU5rayu33norS5Ys4TOf+QzhcHjgwf8D1MnZyFf8gN0jv8jqorvwXXQfajLOxCovwwptmH1HOC6325s/nVd29CAy8EVn0hkR7Xl050yl2FiK19JfGGhu0WRcZePQknE8C25Hf2yBAQBJh16Cq+YNQTwp5/3GSyoZXurmv26dxBdvGMd9d0zGZtQRiAf5445H8cWOaQKoMo/seYZooo+YrKErHIkur5LQ7lUnjiGImIdOxDJ+Mb1KIG3kH2dD89uE1P7pAOnhCQL3XDWaq+ZWUOi1sWhaCd+8fTJuuZPoygfpeeS/iK16CFO882wufYYM5ySaCosq5qE/SSXabrAy1juSxs4QBoPEtFF5rNnZgtmooyzfzlXzKlgwpRjdMYHLikIHYyucfHlJDiNzdAgMDFe2G/UsnpzP0CIno8o9fP+uqVgMIt3mElq6wmlj+zhHW/rQSRK/X1FLtsnOmngnwqSFcGzira+cTGvWWH75+B58xxYMjzT7eXNbI161B61xF4bcctzzbkY0mPspmwOEDdk8+EJN+rhTKx1cktVCZPMzRA5vwzZ2PpaqqSBKmMonoBzzsGY4gabAi+uOpo18gN2Hu/AFBpaN+yhjlAYadmadCUk88xRG5ykgsON1gjteJ3p0F/71TyOZbcRNJ5V6NDrxXHIn+qxCgntXI/d1kXXxHeg8Z15M0UV7CO1b169NU5Ike5rTnxVFY2T5wNWCySNyOQvt2o8U7/Ton23ovhgPoxren0cfUl79mnYBt9FJV3RgVYd+x8wZQvLotvd9zAwZMmT4dzOoJ+wjjzzCk08+yW233UZWVhbPPvssd911FzfccMMZt9u9ezf3338/9fX16bZ9+/bx6KOPkpOTk26LxWLcd999PPLII+Tn53P33XezZs0a5s2bx7333ssPf/hDxo8fz3333cdTTz3FLbfcwve+9z1uueUWLrvsMh588EF+97vfce+99763q/ABksDIz58+hD+Umog9Ddx99Whm5iYx7/4DYuWkdN9gMvWy6+0wUmQvpDl4Io/w+mFLeXiNyJ7aKEb9AW5YfCM7Da9Q6z/K+JxRzE6a6V35u5QVIYi4Zl9PeP9alFgY1+J7WN2g8uqmBm5eOIykolJR6GTtzmaeevMwdouBW5cMwymFkXHQFwvSGuzodx5fqlqK+vpf0ZWNRhEENKuLnNt/QKxmK1oyjrliPKrNS0LvxhDuLyQGqYUKSdARiim0dIWQVY0irxWXRY+mgd2k48rZ5Vw6owy9BIZkkJ4n/xcllPJ+RA9vI9ndjOva7xAXz1zmKEOGc51cXT7fnfNVGoMt2HV2DMlsGhuSWM0aRTl2/rHiIFaTjonDcvjLCwdo6ghSXujkWx+fSkNbH529UX755B6+fedQDvoOMqd0KmsbtqT3Pyl/HGLSxZ9f3s2oCg/TRuXz22f20umLYjF18MkrRjNnXAHrjoX1QyptJ55QqG7sw95nwuHI5/eRfSxcdBM2yUR+dgk19b4B5S/rWgOIsoghtwxz2RiiDfuwDJ3EO0lYc+jpSxn/ogDXjNSQ1v0V5dj3fZuW47n4Y9jGXUJo1xuosSDWiUtJuitQT7H4eSESlxXq2gID2nuDsVP0/ujiNjspsufTHDwhTHjtyKUI2pmtZbmvGyXY268tsHMl3jGLSepdQCptLda4n0jNVgAS7XX0djWTe9OZUxY1UUI0WlCj/Re0hXeUfRtW5GTu+ELW7kq930eUeZg9Jv+CLSsbjSsnGfoJJOHsIvekeATZmfu+x1HsgTWH4KICB92RHsocxac/Zv4w4hseQVMyMUcZMmQ4vxiUoS+KIjbbCZXY/Px8JOndJ2JPPfUU3/3ud/n6178OQCQSobW1le985zu0traycOFCPv/5z7Nnzx5KS0spLk49aJctW8arr77K0KFDicVijB8/HoBrrrmGX//611x//fVs27aNBx98MN1+2223nROGfl8gkjbyj/PyhnpmVQzDPmo2Okc2ktWFEvaTbYgjCPDK2jauvGQRUyqCRLUAY9x57Ngnsae2DbNRR1JWeeSlWu694wZarG2MNJuIrfzpibx8TcW/4Rm8N9yHYLAgC3ryVCNzJxShodHjj9DTF02rNwfCCR56Zi/e28dit0fIzrWRY8mi81h6QIWziIKOFmyVE/FveBZNTiCabWQvvZt4RwNyVyOJ7mYM8+6iOxony5LLEHcptb4TocmXll1KVxc8+MxWunyp1AKLScd/f2IaOQ7jsWFr6ITUaWjBrrSRfxzZ1wbhLrCXfih/qwwZ/l1omoZbzMLjymJrTTcPPZPyDl09fwivbKwHYNH0Mp5YWU0wkgpRrmvp48F/7eaiSUXo5DCfmW1GaWkl3+WkIdrDJybcQEe4mxxrNiWWSjbv6CUQTmA1G3hyZQ2dvmg6IuDBf+3ic9eNY+nMcrp8Ebr7ojhsBh57tRqX3YgxFmX2kR1Mnns9SWTMB7fBoR1YKi8fcC4OqwHRnY95+Ax8a57AXDYGNR7FkD+ERNuJRb+sLCdVJUlqGv0U59qxte9MG/nHCVdvJTbhRqS6naiRANHaHXhv/A4xZ0Z9H8Bs0DFpeA5rd7b0a89xX1iLn4IgsmjoXGRNoS8WIN+eA5qA8V3K6wnviDQDQAPp5CSRWB+Rmv7eWk1JIve2g7Xs9DvXm3HNuIreVY+caMouQufycvIMwGSQWDqjlBHlHhRVpSTHjvkCLiUbS8gYdCeF7gtn6dFPvD/V/eO4LClBYjWRRQddZ+wrWpyIVg/Ro7vBVfW+j50hQ4YM/y4G9YR1uVwcPHgQ4Vjd0hdeeAGn0/mu2/3oRz/q97mnp4fp06fz/e9/H4vFwt13383TTz+NxWLB6z0RSpeTk0NHRwednZ392r1eLx0dHfh8Pmw2Gzqdrl/7uUAiMVCwJcuuRz26Bd+axxF0epzTr0Q0WrArIb567XB+/3Ity1e2UZ7v4OOXz8AZbGFvrY87b8qnT2nHKJlRg25CQYVX3/Qxe5mzX31sAFSZaCiIPnyUWrGCn7/QTDQuIwjwP3fN4Kf/GBh2Vt+T5NFHN7NgcgmfmfVpHj/0BHE1SYU1F7vOS++qR9OLCWo0RM9rf8E27iJitTsITbiZPz15iCMtQSwmHf915w2EhS7qujtwiF727lQIZnenjXyASEzmxfV13L5kGIZ3lNETTvniFuB9qutmyHAuEYjKPPziyeJPJ7J09ToxbeQfpzcQY2SJHe/2x9E2HwQg257Fx5bejX/1o4wxWBBHL2Rbc4BQNLWtx2GiozfMtYvzMbqDRJUQLimXeCLJEyurkUSBnTVdLJleytAiJ5dMKcFebCBWVoxl3wZ0/k40JUm0dieVYxcxb1Iea7anRDhFAT526QiCSRWLIxvQiDYewFw+FlNBJdahk1BiYSSrE4kkd1+Sz29WJAlEkiiWgSHMkiOLva0yQybejnV9auE2svctLHOLianvLw/3o0AkLlOW76DbH+VAXS9GvcSls8qIRD9CwmCCRrfcRa2/Hp0oMcRZjlvy9BNsVWUFp8lOa7ADSZCIJmOUOAtJykn0nL5Si2hxINlcKCF/us0+4WKEk5acBJ0e0WRFjYX6b2s6Q+4/ICQj6LIKyb70MyR6WpDMNox5FSjxCCfLBrT0RvjunzbjsBqQRJHeQIx7b53EiOJ3n0N9FInFlbShH5cTSGcTuq9pCIko2gdg6AsCFGdBe68Lg7P6XftLBSMI7FmFNDdj6GfIkOH8YVBP2Pvuu48vfelLNDY2Mnv2bIxGI7/73e/O+mDFxcVpLzzA7bffzvLly1myZMmAvoIgnLLM0pnaz4asrA+njm1FngVJFFBOCsv7xCw7fS//BtDQ5AT+9f9CMJhxX3wHhYkObls4FF9EwWrQyE82oAgKSxdb+fuBh9P78JjdfG7cJ/nZjTkIbdUEjZZ0fXsAQW+iwwSaYyIPPnoondOpaeALRMn1WKh/RwiozaxH1eCNbY2MqvCQ3XMJFqOOSaU57O3zY5v5eZwNa6A5VUZLCfsRBAmxYjL/3KVxpCUVshiJyXz/ob3c/4mpvPJ8N9F4JwXZVuzmgXmkbT1hekNxxgzx9mtXnKXEJy4iuOP1dJtzxhXYCkpwnCS84/WeWsn/XON8GedxBns/nG/ndTLnwtgDzX5iiRNGxua9bVwypYSXN9ShkwQEgX4GjigKWLQIWuvBdJsa7CGydzUCAvHmQ9B8iKr5X0DOKUxVz4jJXLe0iG3RF2k/emIB9M6xt1HfFmDJjDJ21nTx6uYGvn3nVPIKVH616xG6wt389/Cr0BtMCJqGpWI8/lUPc82Ey5k7fgL+UAKbA0z6TsR9G5BGzgVRB6pMuHoL1tFziB7djWR1oXfn4l/7OLkzruKrQ2sJOSrIKp+Cr3oVaiylpyLojZjyhzCm7zA1yaEM1xnQ5ARoKrFtz+CefT06m/tD/5ucig/r/XAmTvX7jHcG2XGoA6tZz00Lq5AVjQ27W7lxYdU58Xs+zvsZy8GuI3x/3S9R1NR9Ydab+O5FX6HCXZLuE/LZ0Sf0xOQ4wXgYrzULDY08txfjGYTZIhE9jimXI/vaSPo6MBVWIRjNCDrDiTFLOpxTL8e39on0dsb8IYhm+xnPK9bVR6KzAdFgSs0/NIg112AqGdFvu9febuKOS0fS5Y8iywoFXhub9rcxfUx+OoT9vfLv+g18kPeDztiNzWrE5bKQ6EhgNRux2QZpuCdiIOkwWz+YMryVBSobqgUKLCGsdgN66fRTYnXEBDqf/xUll4JkPnfuvTNxLj0jBsP5Nt4MGc4HBmXoDxkyhOeff576+noURaG8vBy9/uzL+1RXV1NfX8/ixYuBY0rLOh25ubl0d58oJ9XZ2UlOTs6A9q6uLnJycvB4PIRCIRRFQZKkdPvZ0NMT+lBy5LIC1dx/VT7Ld0XoCcksGWPHGW+n7x2F5bVElIgpl5izELEjSKHXyOjkPkIv/BVp3HzeEPvnFfZGfXTFmgnJcYpqtuCacwP+Dc+gRoOIZhuJedfzm4MvsKBgKd3+/vmbPX1xLplawt9e2p8Wxhpa5MJsPPHnP9zYS2N7kOmj8/n+n7dw/NJcO30h8wpVtJa9iGYbWjJOIncUu9/uX7oPoMcfSy8wtPeEuWreEFZvb+7XZ1xlNi2dIfIcAz31+olXkV0+CSXQheTMQXGV0OOLw7FASK/XTlfXqQX+ziXO1XGe6SU6mPvhXD2vwXCujN2sFynJtdPYkRpLW0+YcDTJp64aTVN7kGVzKnhh7dF0/yvmVGDr3DMgOjnR2YDBW0yiM5UuY23eSnVsPndePopdNZ3MGamjfVcHI7OGU+UcSW+im5dqX+HiGVcTO0nYrcsf4e9H/0JP1MdvJ3yc8LO/IHJMCR9RIuear9Fjy2Z363pW1qWEx0w6IzeOvAxzsIusyz5D39719Iy7hZ0+lZzRE/FYdehe/G+0SB+aomCWw7DjMRTr9WQvuZtET1PKMyfp8W96Dr3OSMHMkaljCiLGwip63/gbhrIJJLKGfzh/CN7//fBBj+VUv8+krHDtgiqeWZUSNQxHk1QUOcl2mc+J3zO8v3tLFOHl6jfSRj5ANBljS9NOnKon/TeQhSSP7H4ao86Ix+LilSNvcfWIJRSaiggkTy9MaPR14HvrUXROL5I9i763V6Al4piKR9LVFcTrtaPFo0SObMc9/1a0RARBb0QO9pLsbaHLVnbafdvkBMgJetc8nm7TufMw5A9JXw9RFCjNd/KrJ3YST6bOURTgs9eNIxCIor2P39gH/Uz7d90PXT1hNFXF74+QUJIYZIlQaHDiklLIh05nJBo9RUrGe8BhgGgcJDmHo+0t5Fq9Z+xvLKiibfNKjKMXfiDH/zA5V955g+VcG29m0SHDR4VBGfqKovDEE0+wfv16JEliwYIFXHPNNWd9ME3T+PGPf8z06dOxWCw8+eSTXH311YwbN466ujoaGhooKiripZde4tprr6WwsBCj0cj27duZNGkSy5cvZ+7cuej1eiZPnsyKFStYtmxZuv1cQNXAs+b/+FTpOMyzLyKy6iEEy0KE496qY+jcudTH3fzz5T00tAcZXeEhb6Ibk6RDMZgIRgZWEYjJEZ44spp7h09B2fQctqu/TGNfC82JEM83rSEYD9Gb7CAvK4v2nhPe/l2Hu6gosHPjJcNQVBVRFPA4TDy96nDagzg0C8ryC/n7impOfp8/s7mT8cvm4eyoxj3/VnxrHkdXMYOCrHJaTzoGgMdpIi/LSntPGFWDo6193LiwihfWHiUpq8yfVEQ4miT3NPmlSdECWcMga1imzFaGjyTBSJKF00pYv7uV6gYf+dlWKotdVOboKc/OQjSYcVqN6CQBt8NENC6jaFkDNPZNxcOJ1u1Nf5asLjpaImzd387nbi8nqnZwY+Ut7N4OT1T3kuvJYtkl11HgdvH7pw5TkmenyxfF4zTQ0+JjtLcKjuzq94xCVQjtW4t/+uUk4iLXFn8cnWYiP9vM6y0vsyYZ4boh8xHGf5Kf/XMH4Vjqrl04tYRLFtxLXs9WIoc2o3PnkLXw4yjhIImuBvwbnk3FzR5b/BRzKvDYdRhHzcaQXUTg7VcA0N4RSn0hYjZJmKISeVlWnlt9BIfVwK1LhmPUf0RKj0oavtgpxAYjPkRRSBuX3WEfc8qm8XbLHmq665hUMIbOUDcBjw8bp4/6OC6MJ/d1Ifel8rAFgxlO8twKkg5NSeJb/RgIYvp3mXvd1zmTOSkAge2v9muTfe0pcb6ThlTX0kdViZvRQ7LQgMa2AJv2tDGpsn8lnQuFaFzGoDuhum/RnTlF4mSkDyhs/ziCAKXZEOgroifa+66GvmXIePw73sQw6pKzjiLNkCFDhv8EgzL0f/jDH3LkyBGuvPJKNE3j6aefpqGhga985StndbDhw4fz6U9/mptvvhlZllm0aBGXX54Se/rpT3/KF77wBeLxOPPmzUuH8z/wwAPcf//9hMNhRo4cyR133AHAd7/7Xb75zW/y0EMPkZ+fz89//vOzGsuHhc5bgmB1odTvJBkPYBk6ieCet3Atvoe+VX9FjYaQrC5C877Gr/65l8ixyfG+o738KmDhG6MWoavexqJpc3m67kTpHlEQybfn4rG4iBgM2EdMY3nPAQ51H6Wx74RQ08a2jdx95Rf547+O0BeKM29CEZNH5AIaoiDS0BagosBKPBTiM1MUDApo9lxMkUaazWPSk/WTidiKKL3x+zTFrRgv+x+y5U7uHprHD/+xi6ScmhRdNKmI4mwL37xtEvvqemjpCnHJuGyy8bOwMJu4OZsnN/Yyakg2+Z4PJuwuQ4bzDQ14+MX9TBqRyw2XVNHtj/KPFQf5+u0TcdtMHKlrpTzXyd9fPUxDe5Bls8opGjWCxMX/jSNUh2HPc+hzKpDsWajxMDpnDtZRs5HzR3NPlYeu5iYkUwyd4OS11VH2HUkJXLb3RHn46Ua+9bFc5k0oorEjyPyJRZiMAgIChXYvmr99wHjVaAi9ZqZuVy6v1qW+10kin7hhPo/X/ZXaQJC1Kw/0e26s3NrIiLJx2PauRfZ3QNMBwgc3k3v9N4g1HUSyOU/KmRawzbqePlkmWbkYaePDKP4OQEBy5vERykR/T8hJlbU7mzEYdFy3oBJJEli9vZnL51SQZe0fsh6OKzR1hojLCoXZVrwOI6fIcju3UAWmFo7ncE9dv+aROVX9PMh6nY7Htz2fLuP6+pG1zCqZ/K5V6CVHFobcChIdJ6JkXDOvRtCdMBY1yYBr9vX0rnoE2deOoDfinnMj4iDCs9XEKTzRJ0XvCYKA122mqTPE46+n8sCHl7mZPbYglYb4rmfw0SMSk9MLVXE5gXQWwoRCPPKBCPGdTFk2rKnOpjPSxEiGnbGvIa8cLRFB7a5H8pZ/oOPIkCFDhg+DQRn6GzZs4OWXX06H619xxRVcccUVgzb0V606UX/91ltv5dZbbx3QZ8aMGbzwwgsD2ocPH87TTz89oL2wsJBHHnlkQPt/GrG7Dsuiz5LsbUOJhbGUDMNYPIp1vdlIY75AqVtgb0sStTmZNvKP09odoc9agjOwgkm9fYgV83izfQ8eo4PrhsxH7TZjaZlHYlI+fuMB1hx9natGLOln6GuaRkBXz03XuMjVlfLKpgYeeGw7ACV5dm5bMpxKUw+R7Y+RaD2c3s4+/xbQIC/L0i8aQCcJxDDyak2Ef762DUkU+NilI5g12sn/fnYW7b0RbBY9uU4zeknAYBaZNSoXkjYSm5/Cf2BN6rqYbNxx+b1I2d73Fa6YIcP5TLZdx+zROazd28G2A6n8+aIcG7UtAWaOkJiUFeKxXSEa2oMsmFyEzaLnO39+G1XVsJr0fPXGH2AxCGxrDeOeMZqR2mECG/6FlnwOV+UkvFmFBLtt7LMUpo3848iKRktXiKferAFg8742hpe5+cTFH2NHx07EqjlwcFO/bUwj59LZrVFdFzhpPyqvr+ll0pSJOHRumjsHLhCEIomUd/QYWjJGqLMNQ8Fw3DYPStgPgoCUV8WeLgmbGsSl1BMcdxM2y2u4R04nYS/gArSD+hGXNYpy7SxfU5sWNp07oZB4ov+7IxiX+X+P7aC5MxUFoZNEvvvJaRSe64uqAhQ6crlm5FI2NGxDJ+mYWzoNj7m/UF0gHkob+cfZ3LSDK6oWnXH3sr8Lz8KPk+xqRA72YsgpRdAZUWNBMLuOjUFA0BmwjZ6bLlcrmq0gvosBKorYRs4itG/NidPRG5EszpO7oGoam/edKAt4qN7HiDIPF6pDOBqXcdtTxnpCSaI7CzE+MR5B032whn5KfkDkSHeU+SVn7isIIlLRaBIHV2POGPoZMmQ4DxhU/J/b7UZRTlKpFQQcDseHNqjzGaPDRaT6LbqsAm05DvqSfhI5I6hujfLHla0cjbl5dG3HKcO+dJKIyZOLzp2PrtfHxa5RfNNSxaeDEq7lD1HXEmDbvi5+8I89qJ4KvNZswvEYHxt/PePyRjKjeBKfn/ZxtjXvpaVB4lCDn60HTohxNbYH2V3ThV9woET7h933bX2JbKvG7UtHUOhNhdI5bQbuuWo0WqgHnZpgXGU2iqrx15cO0NkXx2XRM7zISZHHgl46cT7huEK8/SixAycmQGosRGzjP4nHomTIcKGilyNcNUrg+gWVjBmSzaUzy5g1toAnXq/GGGonVH+QHbV+ACYNz+XxlTVpz2Y4JvP75w/hi4n85eVq3MkugqsfQUvGAI3I4bdRo0H0egvZdgcO60CRsuMaHcc5VO8jl0KWFC1hT1KP+fIvoM8bgs5bivPSzxPLriQcGhjl09YdJcvopSF8lCFFA9XDCz2GVHnMk/BHFX6zJkKseApq4Tj+XFfMrQ/V8b9PH+E7z3ZwRM7D07aR2KRbSOaPR9U+IuHp7wOTQWL7wY5+1UvW7mxBeEcyR21LgObOEJIoYNCJyIrKE2/UnKny3DmBIGhsbNyO02jnoopZzC2dhklnYGfbAaST3inmU1RfMelM6DlzZQa9O5fO5b8g3l6HaDAT3LmS8MH1iDZPuo+oJPGvfxr/uqfS/+9++fepEPwzoAGi2YZj8lJ07nzM5WPxzL8VrZ9mgEB1g4/KYhc3LazipkXDmDDMy66aLrQL2NA3HhMhTCoJ9GdRXk/8EDz6ggBl2Rp17bZBRcBIRaORj27rn+aUIUOGDOcog3rCVlRUcMstt3DNNdcgSRIrVqzA7Xbz8MMpVfg777zzQx3k+UTAYOQlr4OChB+bZOStYDXXWxxMrzCweqeG2ahj1tgC9tZ2M3dCYb/6yDdfMoTNzSp5I++hwi2grvwjmi8lZqdNuZEVW1ICeAa9RCLooKhvMTsORRgzwkpVzihCdPN67Tqq7GNZs7WPyuKBM4k9tT30hRNUFN/KJNtKaNoJgBoJImoyFrvEPdeM4WC9j/GFeuzt6+mzWil02phZWcE32gJcPrsCVYNAXMZl0aMeMx4EAYyxTrTOZoRkhHcGNaqddajxMBhMxBWVLn8Mg14i225AvFDdGxkuGAQBmvs0rIKMwyOzsNxBZ4fK06/XomogaQmsVjMjimxsOhgnEB44kezyR1FVFZtZjzXcPOD7yNHdCCMvpqdd4ROXj+CXT+5Ofze2Mpsct5lsl6mfYGcyHuK1jlXs6dyLXtIzc9gYZnhn82ZtDIuwg+FZkwYcZ9IoD2ZdBHryuWpuDn9fcYBufwydJHDLwkrKDb1Is65FTcQIH1iPpsi0a9lsPdDGNXPKiIZg7a7+kQAPr+7iezOdGJJBNC0jhASpmuPVDb4B7b2BE38/QYC+cJwbLqlCABKyisNqYNuBNmRVG1DK9JxCBEVT+duuf1HuKkZWFRr7Wrhq+OJ+i+E5lmxGe4cxNLsMSRDpifopcxVhEWwoZ1jOSAZ6yFpwG31bXiLeWoPOnYe5dAxqJADG1IK2KseJt7yzvJqGHOgG1xlCuTUB0WQjuGslpuIRKEEf/k3P4r3iSye6aBpTRuQC8NQbNagaTBjm5Yq5FRdsZFs0oWA4FrqfUGWkd4ucOAkxHkF2fPDaBkO8ItV78wglwtiNZ9YMEM0ORGcucuMu9BVTP/CxZMiQIcMHyaCXUocNG8b+/an6z0VFRQDU1NR8OKM6j+kVNS4VXUjrX8RUPJKpJaOI7VzFsKxifnf3WI6GBCqLXRRl6bEmfcwdWoEvEMFrSOBp+BeOuTez4pCMVQ1SWTUVoTcftXw69RSwYLKCIKQiKh781560IVDb3MfFUwopHpUkJh/FYXDQ6WtnzvjCAeOrLHZxqL6X9bvDlC67iOxjhr44ZBp/frONuvYIX7plDHalD12gmYe1TvY21CIKIktLpvGdT17Eqxu7eHJlNZIocvX8IUwdkYtRL+FOtNL95PfR5ASuy7444NhC8RhEvYk+Xw//75kjtHanBAcvmVLCNfMqMOkyHrwMH13issZjq46waKGR5/Y8TLQxRpbFzdc+dRvmjjDygbeQu5q4dvZnqG4JYzUNfDx73WZsFgM5bhMJk4d3+rZ03hICgSTjQruJlMzmzmUjCUdl9DqR5s4Qv3pyF9deNJQnVqae3SU5Vgx2hT01KWG/pJJkTcsOWqN9TPMsIc9YQGtXiFsWD+OFtUcJx5JMGZHDpbPyWLuth9e3NGPUt7FgcjGFOTYqChyUqvVE9q4hWL0F0WTBNfNa1KxyfvtwY+oYkQCRxEDjMxBOIJucOExn9tJeSJgNOkZWZKXTPI7jdZ8Iydc0GFbi5oHHdqQXAEQBvnzTREw68d9aPeBsEVSB8Xkj2dW+n6O+1O/DbXZS7CpAOSn6pC8RZGz+CJ7e/zIJJUmBPZcxucMJCX7MnD66UGdzEms8gKlkJHKgC2NuOcm+TgwFlSd1MiLZPSjB/pVuJKvrzGMXVNRoEMeERcTbjmDIKcZcMQ41GoZj1ehkWSUSl3nrpOozO6u7qCp2Iw7JRrkAc1Ni/Tz6SXRnY+gnIqj6gdEd7xeXVcCgU3i7ycdFQ99dHFAqGE7y8MaMoZ8hQ4ZznkEZ+j/5yU8+7HF8ZHBHI8RXPY7ozEHvysH3RirqIQoIttfpq7yLl7b7uX3pCHIibVjW/YXjOq+WqVeh1O9kX20B/6rv5f/umonVsJdH95vYuDdVR9tokPjUlaMHePve2t7KD6aO4ajpKAG1nVEVbnoDMaaMyGXbwdQksbzAQV6WhVVvNwHQmTTjNZhhyHQOWqewd1vKw3a40c+MfB0boyH29tQCoGoqLzdsotA9jHW7u1BUDVlReGJlDS67kX+9cZi7FxWQb7ajBXuIH96Mdeb1hDc/B6qMlF2Cfso1KFue4LngpLSRD/DGtkYmD8+hqjCTDpLho0tSUVk0L5vf73gQ9ZhgV0/ExyMH/sl/GcuJVm8BwLDiB3z/0s9idIa565JC/vZWK7KiYbfo+eyySn79xA6mji6gzaCnPKcCrTMlNCYYTNjHzMcid7NGGUawLspTbwxcjHXajAwtcjFmiIeZjlZq+w4N6NMcaOGKIRKRTju/ez51jy+YXIzJqKPYruJIKLyxrRWAeFLhlU31AHzj1nHEu/YQOZTK9VejIXrf/DuJBV8lGpcp8lrwhOuxuCvQSUK/VIIJQ1xkOSER6kFvc5B8l7DsCwFZVVk2u5zG9iAdvREEARZNK8Vu6V/etqUz1M/Lr2rw0oajjCydOLj8vP8QcjJl2H9iwo1E5RiiICAKEm6jE0U54amPywn+uWd5+nNrsIMVNau4a+LNnEmFQEsmCO15K624H6neimPSYjRVSScuCoKAa+Y19Lz+17SQnqVyMqLpXQw+OUng7RUg6jB4i4i3HUEJ+claene6i8EgcaCuZ8CmWw+0s3ha8Zn3/xEl5dE/obovnVXo/gerun8yOa4ob9cJXDT03ftKeVUkD7yFloimqjhkyJAhwznKoJ6w99xzzynbf//733+gg/koYPB1EAesw6cT2PF6v++0UC+jXGECk0to6QwxZOR4nAs/gZqMI9nchPv8CBYXV42zcPd8D8baN2gsXsTGvbvQ6UTsZj2+YJxQJInDamDZRDdZFo3uuAFRk+mOdLCtdQ/b2MPVk66jr8mKxaTjs9eOwW4xsrOmkydePxGiaPdkUTv5v3hpRy+1rSfCaPuCCezj3Ly9o3bA+TWGGrjxhixUWYe/w8RLb7Wz72gPVoue/3uunh8vvQbjxj8RrdmKIRrEcd13iCRUdE4v2uZHSRiy2NswsHRgU2eQYUWOc18lOkOG94jVKCGLobSRf5yucA/hrHEcLzqpyQnUtX/GMOMqxtWv5kdLlhBSDbhVP+bdDzO6dBGvbqon7/KR2Cd8kizNR7bLiKTT0yNbCOn0bK5tYM5EDyaDRCxxsr4KxBMKDquB2cNs6J7+I7lLPz5grOPzRnHIv5/e2pQx4g/GeWFdakFhSJGTLy8rQycKJN7hLc61aoTXrB+wP1O4jcunFHLxuCxcyTYEuvnRJ8byq2cP09YbYdqIbG6Z5cWsjxJ84vvYb/kh2Irex9X+aKBpsHJLI0tmlOKymZAkgQ17WugLJSjypH4xggCh2MD6BL5gHEXREKVzN3RfEAQUVWH5wddoCabeQSO8Q7lmxNJ+/ULJge+M2t4G3k2tUY0G0kb+cYK738I2am569qPJCfq2vIh77g1oiowg6Yi3H0X2dYC17PQ7N5gx5FRgHTMXQ3YhmqrgW/M4kvWEZoWiaBR4bQM2rShwIolCv6iFC4VYIlVeT0VFVuXBe/RVFUFOoOk+nAXAUq/KhgMGErKGQXfme0bQmxA9xciNu9EPnf6hjCdDhgwZPggGZegvXrw4/e9kMsmqVasYNuzMZUguVHQWV+ofogSqMuB7TVV58o0arGY92a4RTNYbaBOzeW5znIYuK3NHO5lpqUe363UMC+6iszvJNz82hLi+m754H4W2QuSQjh8sMSFu+QNqJMDIgkpso2bTt3cH3xp1FT+vWcFzR59mTvFUbiAf7cjL+IcuYcPubo7Py2eOzsHlieKzxhk53EZdeygd4jlpmJvehEqVs4T2UP9JktVgYvmh5SRVmVJnCUvmLsBpsLHncDeyotItWzieMJBoOohthoIlrwKDHKK3YS86VwFjS6tYsy+OQScyd2IRHoeJ4aUeUpWJL7yJT4bzm7is0tYbIRxNkptlxWs3nHrBSgPPSYrcx7EbrBhN/WuBSxYHqiKj9jZj2vRnjgerat5i8vKs3HhJFh67ibeberh2qETvvs28rZ/MExsaSCQVZozJx+qKctuycv76XG363r5+QSVrd7XgD8ZJKAWYC6rwHNrBTZXzeaZ+PUlVpjKrjCFZpRgiYey5A42U8mwDzkgj184q4PE1J0KS87MsOC06kq5c4pH+tdFdHic3lphBl6Rv+1soHXVkDZnAAzddgi8GDjGKXh+h48kfp85THSgAeCGiahol+Q6iMZl4ImXsjirLTpc1hdRiQFWRC0Gg3+/u0pllGPXneOi+pLKz7QALKmZi0hkRBIHeiJ8jvfUU55emvfoOw8DfYb4tJ5XffSbFQWFgPIOmKmgnGZeCZEBLxvGteaJfP9voeWcefDJO1uI76V39T3xvHkQ02/EsuK1fWT5V1agsdlFe4KCuNXVPZLtMzBpXkNa2udCIJRSMehFFVdCJEsIp/kanQkzGUkb+IPufLR6LGZMxzN6WGJNK391LL+UOQT66LWPoZ8iQ4ZxmUIb+1Vdf3e/zNddcw2233fahDOh8R/aUYMgpI3J4G7Yx8wjueiP9nWiycSRiB8KEo0l+98wevn77JH7z3G6icZniXBu1HRH6rPlc5cgh9PLPGXPLj/jD3ueoC7Sm9/OLiXcSX/G7tGcwfqxMnsHmxvPWU1w3cxE1wTaucFchxy2IE66mOFjH9xYIdOsLMHjz2Nq7lh9tXY/DaOfyYZfwhaG5hIMiBQY7gj1EvT/JzMLZ7O05gi+WEgGszConlIiQPDYJb+hrZMZQBSGoZ/FYJ+OyohTaE0jzbia4bx2OqctQXcVoKiQlK5aRcwjteJXL5iap77ayaFo5L62vo60nzKtWA5+9dixVBZnw/QznDzFZ5R+vHGLz/pQ3UhQFvnXHZIbm209p7Dt1WVw97FKeq14BgCRKXF52NY0BOxVGC1o8gjpyEfvcsykzRjG9Y8EwOuXj9FSrvLX9MJqmMWdcAf7WRrrdE/j7iydU7jfuaSPLU4ylqIHbbsgmHpUwmTWKvRpvbIvxmWvH4pQCZF36WRK97SxAYdLMscTVJOZEAlMyQfitR4lOuJFct4kOXwyvy8Rdc5wMtUdRRDNLRxgoyRnG9toApTlmRpg60XY9j2vmNXQ+93M0JeVlNuSWo0X6UP0dBHetRI2mSsCF969D9neQs/gu4q0tdK15HDQFfU4p2HM/rD/ZeYVJJ+J1m3l1Uz2H6n0YdCKXziqnJK+/WKHTZuTTV43h1U31BCNJZo8rYESZ55w28gEQBMbkDqPO34jFYAFNI6EkGeoq7Fd+zmawct3IS5FEiaQqIwBlrmKEd5Gu17nzES2OlPjeMezjFiAYrek1ZQ0Nz8I7kX3taHICQdKjAYL+XYw9vRnf6seIN6XS6tRokO6Xf0/ujfdxPJ9AEKC3L8anrx5Djz+GoqrkuC209YTRcgYuXnzUUVQVWVHR60QiShydqH/3jY7xYSjun4xVZ8ZsqWPjkexBGvpDiR1cg6bKCGdRIjBDhgwZ/p28p6eTqqp0dnZ+0GP5SLAj2sywSz6JO9yLaDCBp5jEofWQXUyLawp/fqW/h/y4+vVdNxZTn9hHZ6ydLMdYYuZLsW59GkENMcZVSnu0l2gy1TfQ3YjxHeG/8dbDuGZdS6RmKxOM2Yyzj+SVXSFe3d6EKApcPaOAiwojRKy5bOxdx5rm9eglPdeMXMLje55P1yi+uHgKIyxjeealXnyBVj5zw20YrUFsJj3BeIiHdv+r33EtFvA3djLH/zxa9RECAJKOnOu+ScxViaZpSBIY4z6ECQsRdAbY8Ae+uvCr/OTVo7T3psr8BcIJ/t+j2/m/z83GbR38yz9Dhv8krT3htJEPKQ/eH5fv4wefmo7hHSHTsgYvvFXPkomVDJtwFwElTjhk4ZmXO3DbZb5wyZfQDHaaoxbEhMzBoI6qeV/B2bgOKRkkUj6P/X4Lb247mN7n2l2tlC4ehngKhf5te3sY7QqyofVECpG3I4s7rvg4j79ezScuH4VBMGPIspDc9xrKs89iyirAPuMa+t76B2oshHHL3/jGlOtpc09kqLmX2Ev/j9CxhQdD0QjGjJ5DpeUA0T07UWNhxPEX01u9E8c134JQLzqjkWRbDb41T+CafR1qNIQ+qxBL5WS0ZJzQwY0kOuoJbHsZx4SFSE4vYm4VMSGT9wqQUDS27mvnUH1KeT8hqyxfU0tFoRPSyR5Q29LHwy/uZ/LIXGxmPet3t9LYEeQL14zh3A3cB0kQkQQBm8HK+oat6EQdUwrHohd0qOqJd1w4GcEX6+PNoxsAMOtN3Db2anKsWWfcvxzswTXtChKdDSR97ZhKRqIm45CIgj61WCKIEmokcMyjn7L+zUMmYiqqOvPgE2FidXve0agh+9rBmRL7U1WNLKeJPy/fR21LasE8y2nic9eNQxTgXF+H+aBJefMlBEEgqcjopbPNz//ghfjS+xdEsp1RqhsSRBIqFsOZIwcEoxXR5kFpP4yuYMSHNq4MGTJkeD+8pxz9mpoapk7NqI2eiuGWXAz1++nd+CwAiVt/SKPTgyTqWf5ykGi8f0iqxaTja7eX8vtDDxNOpIzewz11ULmAyycsQmus5pLObqaXXMSfOndRF2wjptMNUNuWbC4MOWUYCyqxRsKsbUvw8rbUYoyiajy1rpXiG0ZRc7iXsuF5uO2LUVSF14+sTRv5AG82bWNswWQ6eqPcPieHIXufQmtJVVvwuHL5/KxL+VX1ywDoRR2l7jwmlrcSOnDkxGAUGf/qf2K78pvokiGi21fSuWcVotmOe/Z1WD/2U5rCBtp7+9fZVlSNLn80Y+hnOG8IRQbmRnf5oyRkFYPUP/c0EElw/egkyRe+h1FO4EJAmHIjRZeU44o2Ywn7aInk84836+nyxyjItmKeV0FH0eVMLLdDPEn95t4Bx9u4v4dPXVUKq/qX2yvNt9Ed6+4/tnAPJnMS0Nh2oI0544vwWA00Zc9iyM3jEEQd0VAIJZwySlAVTA0biBurSO54rF90QaL5IGrlRNRYCNfs6/Gvfxp9fiWv1Nn51x/qAfj2Uic5W14EUgZV1tK7SXY2Enj7FQSDCeeUy5DsHnSeAvwbn8U1/1ZkU/aFZwGdBk3T2H2ke0B7tz8KJS4g5TU+/pvbuKf/MzWpqBikc1eOTxUVemMB/rHrmXTbnvaDfGH6nej1OuLH3pdJVU4b+QDRZIyXa1bxpemfPOP+tXiE3rceRZ9ViM6RTXDXm6ixMNZRc+DYa0ZTEoQObcE9/2a0ZBxB0pP0d6BEQ2n1/FMi6dE5c5D7+js9RPOJjSRJoL4tmDbyAXr6YmzZ105ZXiUXWqpa9GTFfTWB/iw84WIigvoh5ecfx2Ey4XUk2dkYY9ZQy7v2F7PLkJv2Zgz9DBkynLOcdY6+IAjcfPPNzJ49+0Mb1PmMIxhEiQYxzbwav3skK9b1smFPL/nZZm5cVMXDzx+k0xcFYOKwbKoKJJp8DWkj/ziv1K5let4MxLXPYJ+wENPuddwwYQ7/G2yj26gnb+QcogfWpToLIs6py+h+5Q84p16OnJRZW53K5yzKsTBrqhtRJ2P2aCSUvfxj9yZ0gsSSyosocuTTGuxfuqkn7CfPY2GcvRvtWElFAMXfQWlLA5WeMhRNZW7ZNOx6K/pkaMB1SPrakLQE8UPrCG5/JbV9Mk73it/jvfIr6PpiWE06wrH+Cx9Om2FArmmGDOcCgggxLYIk6NCpqQlnnscy4Pc6aXgOVuNAgaksQ4zw6r+gyccX1jTcNh2s+yVKIkoQcIg6vrzgy3z72RjZHiNDS+KEkvDczk7Wbu/i8jllA/ZbUmgionRQkWflaHvqvreYdCycmcfLzf1vpNE5Vezyb6Fimkax1YukKRjFJGU0E1r1LJqqYr3o4yRHz0GLhpDr9iLqDFgNAmpfx4Bja8k40aO7SHTUk3P1V4j2dJLvAINOJCGrJAQjx7U3RJsbuaeZwLHngSYn8K15HM/CT2AbPQ9r5STkkA992y7IKiOuG6hncMGhCZTnO9h3tL9ye5bzhGdT02BI4cBrNX9iIUa9dE7XaxcE2NC4jUkFYyh3lwCwv7OafR2HGJ81Jt0vmowO2LY12EFSHbjQdjI6pxdBbyTZ00KypwU4pqhvPCliRNBhqRiLb/U/002m4hGI72ZUSgbc82+h68XfpBfAzBUT0Dlz0l1EUeBoSx8Wk45po/Ix6EW27m+npsl3QSrSxOLKSaX1ZHTS4Bf1hXgYPmRD36q34LIH2XLUOkhDvxS5ei1Mu+FDHVeGDBkyvFfOKkdf0zQOHDhASUkJkjT42qcXEqKmEDq6C9WaxePV+Ww6mPLA1beG+Nkju/jax0bR0uujxOXBYYmQSLYhniLvTBQlBCU1eQjuehP37OtwR2MsK5/LMIMbpXQKtoop6HwNCDo9gZ0rUWMh/JuewzXrWoZk65B0DkZNjrCi4e/IqkxBIIdLhs5hXXPKQ/Ji9UpuG3cN21v3oJyUCqDFrVw02Ysh8NaAiYjWfJhL5l/Jts5D1PY0MD17Enp3Du/EOnIOgk5HMK7SPeNLJFXIUbvQ7XyaRHcjbqeXexbm8YuXW9J5pDdcVEF219sIHTGMJWOIm3MzBn+Gc4IIIdY2buLNuvW4TA5uGXM15eYycpwm7r11En9cvo/FYx1MLZDJtijo493ETV60k37AhmSAQMiX/izZ3CT7OtESJxkxqoyraS03LF5E0l3DdzY+jqZpVLjKuPSiS8hxmijOs9J0zKD3OIyUDlGISyKfuzKf7rCJtkiAhKGXh/b/hk9PvJnWQAeXFk5ghM6By+xidd9RljdvosZyhBnd1zGtLBfpxZ9jLB2HcepS9sndvGwKY7QYuHzkx8nZtgq7044wZDpa9dp+10XQpZ5dSthPrOkQfev/RZXVxWcv+TS/fLWdl/ZG+fzEK5B3PI+oMxCu2Tbg2sq+dnR2D12v/RmU1MKfzlOI68r/Iq53D+h/IaHXiyydWcbR1j4ixxZFRw/Jwm3vH8JclG3h01eN5tFXDxGLy8wZX8j8CYXntJEPKcfBtKIJbGvZzdP7X0ZAYGrReIZ4yvrdOx6za8C2QzylWHUWOIOtryZi5FxzL0lfG1o0hOT0ItlcaLEImD2pMWgqfVtf6rddrOkgjklLT7XLEySj+Dc+i3v29ek87URnQ2pBoTAlSatpMH10HoU5Nt7Y2kgiqTB/UhFFXjuCIFxwhn40IWPQpyJMEmoS/WAV9wEpHv1Qc/QB7HobXcYWaluzCMYU7KYzj090F6AGutBiIQTThae5kCFDhnOfMxr6HR0dfPnLX+aee+5h9uzZ3H777Rw5cgSj0cjvfvc7xowZc6bNL0i0WAhDbjmRyoVs/lv/8nSyotLc46OsTCNXjPBm+x7WNW7lyuEL8Zhd9Eb96b5Xl0xHt2UNCoCmomkqTkcBMzzDkRMqktKJXgvh2/BM/+Mn42hyggUj7RxI6nj86FPp71pDnWxp3sX4vJHsbEt56pv72hibO5Kd7fswSHquGXYl7Y0wfKiGITQav7uKtqQNvaiRn2xCFPt4puZNSlxFXDHkYtTdK/DX7cI97yb6tq1AjYawjJiJecJSfDGJP9WWsKcuZdy4bFa+ueizeIwhel79M2UOLz++7Ep6FBvW7BxUWaE5YMHdvhdp83Nk3fhdYsaBiwgZMvw7EURY17iZF2tWAhBJRvnZxj/w33O/Qo4ujxHFTn716VGEX/styUNH8AOC3kj29fcTs52ola3pTUiuXBR/yjMuGsyosYFlw8RYgKIymYd2rk63HfXXk2/ZT/W2oVx9aRY9fiuaJpDQ97Cm/TWmSuPZEt7L2NwRPFXzONoxE8IX8fHj8Z8i+vLPUfo6iQKzcitwj1/AX2vfpKRSpb47RvGcz6B3ODkQaefBY0KBAId66/jqgo/T2HeAaeOWoFPiyEe2IlrsOKdcRvjgiXBqQRQRTTaiY67GYHXw6auyqWn0E8jJonC+FQQBnSM7lcN8EjqnN1WKVDkR3SP3tqB2HIaiCztFLJFUeeSVg1w+qwJRTHmIGzuCNLYHKfKc8EpLgsC4Idk4rx1HPKlQkmPDbtKd8wulgioQiIfY31kDpITxtjTvZGzuiLTiPoAkSFw5fBEv16xCVmW81iwuq1pATI1j4PT17kWzjWRnA1osjKbKKCEfosmGYD/hrdWUZD+xvuOo8ciAtv6DF0j2tOBb21+t31wx7qRPGpoGj716CEFIxbY8t7qWu68egyCc43+cD4FITMZkSE07k2oS3dmE7scjKKeoWvJBYjVYCcsByrJ07GiIMW/Y6X9bkEpHEj1FyG3V6Msnfahjy5AhQ4b3whmfsv+fvfOOkqM68/ZTVZ1zT86jmZFmNNIo54AiKCEJEIicjI1xWBvba2wva6/D7trez2nXeXEmmCxAgCKgACjnrNHknDvnrqrvj5Z61BqQ5LUB2ernHJ2jvnOr6nZ1d1X97n3f3/v973+fuXPnMnXqVDZu3EhnZydbt26ltbWV733ve/zxj3/8kIb594NgMCH7B5Bbj2Ix2vFdkMNbZM6gsO80WwQ/m+oTq2MvHF/PTdVLQI7T6e9moqWAgroTyGdz/yRbFqoo0SFk8f3/PYA/FOO7d5Zhjw4gaPWosUhy/7rcYcgBL5mxA1jyC7mQ03313FS9JCn08015qB3V3Jg/mXhUpP4wTJ9k5vm6p/lY+UP8x3P7icbPpgFkOXnktpl8KtZP0K9BE1Dw7ngBgLinB8uoWQmH7eKJhBUNZxp6OdI4mJvo9kd4/bSFB+cPAyWO4u7EtO9xvNMe5j+eOk78bLmha8fOYHmRhnjnGRiWFvppPlrCaog3m1Jrw6uotPk6yHHmoaog9jUQ6xz0qVBjEXzvPodpyReIqRIK0B0ykLnkk/hf+zmy30Xc24dj1ioCJ95NPd6IBZzpbxkyjlOuU1TZKzlxMsLECQIHuo9h19qZXTqVl06sR1YVBoJuJheOZW/7YQC0og3/ka3g6UEZvZRO22hcEYk8wcqEnH5seol+UeSlejP3lTWwOdY05Lj7e05xZqCJKTVVaOfdgTx5LjafF8+WPycFknHYWCLdzXhmfIb/fK2PUCQh3MZWZGLPHEbsyHa0znzMlVOJtNcm0xc09hy0WUXELlhRBZD9rqs+jUenFTHoJJ57szalfWJV6nXRH4nzgz8foLU7kUal1Yh88+PTKHBe2aaGCkryXnQ+J3vPMD13ErGzt8/e4AB72g+zcuR1CIA77OOxfU/zb/M+f/EDxOMETu4k3DJ4DOe8u9DYs+HsYq2gN2Esn0Co4eDgdqIGjSOHCBdB0mIZMw//4TeTTYJGh8aendxOo9Fw6EwvD982gUA4hqKoOG16th9oY3pN3sXH/g9IKDK4op8Q+pe/oi9EgyjaDzZ0XyNI6CU9hZkKuxtDlxT6AGJGMXLHibTQT5MmzRXJRYV+XV0dP/nJTwDYuXMn1157LSaTiaqqKnp7ey+26VWLGo8SbjmBtr+d++d+hp+tb0/+rbLEyfEWH5nDi9ha98dke1yJ8/zx1/jkqOUsFLLQaTPweAeIIKArrsI0/QZckoN//dVJVBWGF1rJCjaiqjGyV3yOYN1B/EfewlA+FvukpchBH663n8XhvH7I+ErshfijISbmjicSD1CsLeD376RGHsyYXsH9Y+7g+dcbiJ5Xr7mtL0htXTuVB36MWaPDuPKfiUra5IqId996RIOZzHtGoxdlWtuGmkjVdoZo9Q56HCnV1/G/W/qTIh/gjSMDXHPnUrK1PchX+YN+mo8eSZBwGuxDfDRMZ8tvCQLIvsEcak3+CASTg7irDVGJEpb1vPJ2PRt3t3LTvAqmX/s14t4+9CYLA0YTGYsfxHdgI8gxlJplHAk7GFashwu0fomtlLYjIZZPLWSMqxGLbRg/r9uILzLokXFmoJGbRy1jb/thRmYOJ9BnQj9QR6RqHk93DWfP2+eMwzq5c+lMnIIRfZYZbzCGKacYU8/QPPxySx73OEYiuNvptzn49sGnmJ49knmzb8Dk6UeXUYjW3Uegq5Vn94dSDEeP1PfT1JNDWW8r+mE1CDo9zrl3oESCSI5sNNYsBEHEOmYe7h2p0Una/BFErvLfvqIoLJtZxq/XHEn6E+ZnmXFaUkP369q9SZEPEIsrPLu5lodvHceVa8WXiJYpcxTT6Er9shdYcxFFCRIxbeg1Ojp93aw5sT7Zx6g1JFaEUwvQpCAH3SkiH8Cz+xWMJaOTQh/ANnU5glZHsHYvWmcejmtWw6XCxOU4otFKzqovo8TCCJIOBJD9Hjhb/VCWZWbU5PPjpw/gPVsZQ6cR+co9k5EElfhFdv+PSCgSR6c5l6P/l63oS9HgB+q6fw6rzozF5KXNZcEdlHGYLhG+n1FI/NS2D3xcadKkSfN/4aJX2fPz8A8ePMjDDz+cfB2PX223qMsj5k+sYMsBDyOD+/nszYvodYfRakX63SFee6eRHOtwpubW8Fbr7tSNdRn0O7PI9pzCXDMXY9k4QrlFfOvM60zS3MS8qdkUlapck2MnvmUDgc6EQDdWTiF7xT8hh3z0vvpzMlb8E4LZTkbjSa4tmMgbHQcS/bQG7h59IwcOxmmvdTFxhAOf15yyarZsbh7rG9eRZyykqz97yPvrDyiIOgNKyI9/6x+xrPwyvpe+h2RxEq+4BtGRSZgIer+HEcU2INUFuqYikzePuFg5cRXCwZeJGjLp94SHHMfV7yLo2YUpu5yIxvHXfShp0vwVaBQdd9bcyA92/jqZN1xgzaXYUgQkfju6nDJEkw3P1IfYcDpOZ2ecReMyGafoaenxs3F3K1UlTvo9Yb76+3MTa93cv6yKaYXF6CauRDCYaYxaOR1/h5JwDmOyR3O0NyFSnAY7C0vmEbKZKJA8SDoLSqAnReQD6CQtlZZcHqm5mbrubBRBQSifRJ+cx56drpS+L77RzKSKKajxEMMyNQT2r2fJuFkc7jmVfJ9Ts0cyuXeAvh2/BVVBsmby5Xk38/1Ta3lbPYZJa8QWbeXRojkoioGmk0PDnfvcYcZPuR457MO18xUs1TMxlo8n1HCQgU1/QDJYsM+8EfvMm/HsfiXxev69xG3FQ/Z1tRGNKTR1enj0/qm09vgwGbTkOE20dHkozU6En59z3b+Qjv4AcUVBJ165Ul9QRSqzyjnec5ruQGJiuNxZQq4lK+UZI9PoJM+cTVdgcIFhVfVSRC4uwtR4DG1GAebqGUAiHN93eAvqeTUH1WgIJeDCPGo2lrHzQFESjvt+FxiGRsUl0WgxllTTt/5/kX0DIIg4Zt6EoXzi4PsToLbVlRT5kCiR+Nb+VipLRl/OKfqHIhhJzdG/bKEvx0GWUf8C877/KxatGU/URUW2kwPNYRZUX3xVX3Tko3gTXiuC7sqOoEmTJs3Vx0WvsgaDga6uLvx+P01NTcmSevX19Vit1g9lgH9vaJ35WJd8FhUBxWjntdcbaen2MWaYnfmVBioW5eJXYUHOAhp9XTS6mwEosOTS06GjLeqhuqwAe38jhpM7UPe5WTJvJXaLmYbWbdT19DDdU0a8c3AVPlS7F312CYLOgGX8tfTpdUQnzEe3/vdcF61h2vBFhA0GcpzlPPZyF8ea3AA0dXopL3Azb2IRW/YnSnPll8TYcroRl8nDlDE3sG574gGyKMdCVamTMcUalBMJcRHvbUUrh9Be+1n29Fp4ed8Aeq3EXSaFYRlWLEIvq+eXEg+FKLQLhDAQEQw8uaGV9sI8Vk77Eg67iZElbk61uJPvRxAgWx8hWLsHbXYp4pjlScO+NGk+CkqNw/jO3EfwhsJoNSJOvQ2NoKUt3Ioogt1uJbLoUf79idNEYolVyF+1ebhX0aHXJcTI+Mpsnn+zliXX5JJdEKPAnkOBu5vomp8R0+qITrwdyWlnTtYSPBE/K4aNZ5R5Cg6blsYmmf/3WC2RmILNrOM7tw0ja+AEk7Iq2d83GNZ9+6hlZG1/GVVRCU6/k9+f/D1S0VQyIxYgVehHYjLxjlqKCjNZWq0l8toJMnwDPDrjepqJk5NRQEVExvf8D5LbyL5+HPveZObw0bzTeZRALMj0grGYjDasVQX8qChOuzvGvsYQGw/2E5cVCnQ++jb8L1nLPo2hoAJtdhGh+gN49yW8AOLREP0bfkPmdQ/gmHEj+sqZhLQZ6UgewGzQMLzIyeG6PswGDfG4yqHTPSy/pjylX16micXTSyjJs6GqEInG8QVjCILwPnu+MpBlFafRzoSCMVh0prNh+V4cBhvyYCVH+oL9XF+1kGxzBqFYBLPOyNvNexmTU3XR/WsyCjBVTcO96xWQ40jWDLIWfwJBd95qvaTFtfMlrGMXoLFmgKLg27cOxzUXd1IXVBXX9mcTIh9AVXC/+yI5BSPgrN7TaDSJUogX0DMQQlau7M/mgyB4Xnm9qBxDc5mmzlI0iKozJB4OPmBsOguNnhYqc3XsbgxdUugLooToyEfurkNTnPatSpMmzZXFRYX+pz71KW688Ubi8Tj33HMPdrudNWvW8OMf/5h/+7d/+7DG+HeFas4k+PazyH0tiAYLSyZ+maDPyNToTpS9O0CUME5cxsu7yxmbvYLlk8N4QyEqrZkMxGV+c/Rx1h30IYkSd0+dz6hDO8nXmPHr3ZwcOMW0vNFIjfVEJ6zCo8vFKEQwnd5IzNOLbtJiXmndxWhV5un2Hdy75H4c2iyiMQuqKOLym5MiXyMJLJ6QRWWmSlmJDW8gh/JcPZI+IQb6ggPoSjqZPzWXHJudXneIQ7W9eL1mblr4FQr8p5BsmcR7Wzium8Ift5wEINtppM8V4xfPH0evlfj2ygy0e55Cru1ElzMMcfb9PK+VON3u5wftfrLsRh6+bSy/e/UkTZ1ezEYtDy7IxXbySRQgeGoH1tHXofDB5ualSXMxQlGFY6dirN/ZhsOi57brrBzyb6bAkctbDe/S6ulgdf6nkyL/HC9tq+er90wGEs+o183Opc34NtsbG/lazWeRdj+JrCoEZn6a1xv0aLr8bD1wCkiUmnz4tvH8/pUT+EJRvrKykOx8iR45gFcTJrt6Hre3HOKaERV4RCjIKMJkyGTvaB9ZJgcHXXtx6KwMyFFGluRg1HemhNWPLnOSZQYG2nHYMhkwWLBUTMQWjqHPKeD/HXiWr+ZMRe8sJFS1mKCqxxHrQT36OiPHz+AdjpJnzuSm/ImET+zEdzBhVlg8cjrD8vKZdmMlTW6V7NaNCFo9giBgm7iYwOm9BOv3DznHMVcX/qNbySgZj6rJ+GA+yL8zIjGV+HmmdLKqkptlxuuPkn9eib0Mm4HGDi8bdyVC4I16DV+55+8hZ1jlYMcx7AYbcSWe+I7orTQMtFKUX5LsZZIMROQYDa5W4kocg0bPrOJJyBeL2wfUSJBoVz3OWbegyjEEUcJ7dCuZOaWDTz/xKJkL78O15SmiPc2IBjPOeXfBJZIe1FiYaFfDkHbZ2wvOkUDiN19V4mTT7tTUhGk1eYjCucSEq4dg+HyhH0WnubwVevFDcNw/h0VrwRcLUOQU2Xg8Tr8/Tqbl4pEHoqOAeFrop0mT5grkolevefPmsXbtWlwuF1VViZlzh8PBj370I6ZNmwZAMBjEZLp0vdGrhVhfK3Jf4qauhP2MM3UhiW7CO84abilxQvvWMnfmg/zrehd3ObLJc6oYg7DTtQtvxAeArMj8qe4N/m3C9UTjxQz4E6sGrf4+XBPv5wcvNTHgDaCRRO6bdw+z8/zsD3UzMnccBjWLfEs+LTj539e8dPYlXK4/t3oskHBufnRlPjnHnkJuaIUjJh6efQvBrBE0iI7ke9nYsoGFpfM4c6SYA6cTIZP9njAnW7R8+9psNG/8AVPVNEzZg87h8ycV8/Sm08TiCg/MyULa8jPks+XDoj1NaN74BT/4xL/w0p4+LCYdVpOW//jDPmaMyeezSwqQ2g+jO/IicsANgK6wEkXUXTQPM02aDwpBAlGA3Ye7eXx9QoB3DwT5j9/v5csfm8yLdU/R4UvktcvvUedLp5VwWHQsnFzEkbo+5szVsv1EA1pRg15VkX39aPKGs74WhhXZeHLDqeS2Hn+UZ9+opbosgyXlUWK6Xr5zaFPSK6AmawQ3lt/EwOleRLOTk70yp5o6qCipxGSIMs0qs9IVwm4u5U/Na7n3lulsecdDa1eQcVUOVs0tRduxh76MsciKhpx5d+J++1nkgAejNZMvz12FLBk5UnYPf9jYRVwOkGHL5MvLHmG0LcZXKxZTYM1F23IK14GNyXEHTu7APv0GsmpfpLRiEu6DB7HPvYPe13+JfdoK4r5eNBYnsjfVw0PUG0FnAKPtb/45/r0iCCqSJHLwdA9tPX5EARZOKUHSDK5sqmriO1l7XlRUKBLnle31fP6WcXAFR0MJEtT2N9DgakFASFaLmF0yBalI4Fz0vk6j50jLHna3JQzztKKG+yasJteSDdH32zso8Qii0TbojC9pyJh3V8IM8uzTj2Cw4N78B6I9ieg6JRygf8NvyL396xcfvEaPNruEWG+qiJfOc4aPxxWsFh33Latm3c4mYjGFBZOLKc2zIgrCVSn0cxyJcIeYEscsXl7OvRAJoGo++Px8AFEQsWjNeKNeRuTo2dsUYknNxSNYRWcBcttQU8k0adKk+ai5ZPJeTk5OUuQDLFiwICnyAe6666733dbv97N8+XLa2hJh4Tt27GDFihUsWrQoafIHcPLkSW6++WYWL17Mv/7rvyZz8zo6OrjrrrtYsmQJn/70pwkEEoLS6/XyyU9+kqVLl3LXXXddUcaAcsCT8lrbeQSlYfeQfqb+0+RlmomERH5zdA04NJzsrRvSr89kZ1dthGxDFgICozJG86sNbQx4E3ntcVnhd2920CXmUqizc/xEnG/8/AAVzKHhhJbOvsGc2d0nephcncO8mgxyTj2P3N8KJFY9XG8+Tmenl81vBnlw4l0sKV3MzUX3M0ycTGWJE4txcObdH4rRHbeBqhA8tZMy9x7uXVDEv11vZ072APfNyUavk8jWBlJrhANxby+ZYoC75+RiESM8/+YZVFUl1xgnW+7B2LE/KfIlsx3T+CXIaZGf5sNGUGmPtvK/R/7IG027eO3dppQ/Kyp0dCrcUn4dq0cu5Y4xN1CUa8JhSV11unNRFWadxO0LR3Dvkgp0+sSjfUyJM4CKNGwCii2Php5IivHlOc60uFk4MRdT525eGziZYgh4rO8M/bEOjvcI7D7Zx1MbTrP/VA/PbWpi4xsBCgYicOgtfEYDuzqP8mTd78gaW8uSlXH8ee/QG6mjzTKGr/zpFPWN3Qy8+afk9Uv29WN661n8QiG/eaMjuao84A3zy7f60QkWynx+OPQW4fbaIeMOt51G0ujQmG1kLLgXVZETpT/lOOG201hGz4bzHLc19lwMJaPJnH8PcV1a6J9DEAR2HOmgrSeRLqWosHlPC8HQYGSGKCaE/oW0dPkJRa9sKSkIMCG/BiAp8gFGZJYhSYOPJ56INynyIfH7WXtqM+H4UH+X1P2LBE6cVzFDjuPZ8yrCee7tashLpPPCe69K3N3FRVFknHNuQ5tdiqVmDobSGqwTFiEYB0WhJAl09gZ4YUsd40ZkM2NMPlsPtLHvRDdx5eq7sQUj8WQqU1SJXnaOvhQJfmgr+gBWnYXe8ABVeTp21V/8OwZnhX5fE+pV+JmmSZPmyuavdulR3yeR8vDhw9xxxx00NTUBEA6HefTRR/nlL3/JunXrOHbsGNu2JZxKH3nkEb7xjW+wceNGVFXluecStd+//e1vc+edd7JhwwZqamr45S9/CcB///d/M3nyZNavX8/q1av5z//8z7/2bfzN0GaVpryO9TSjzRsxpJ8uuwRvIEJ2ho6pheOJqDLie5gmZZizUZQ423f6+dbcL1PpqKal2z+kX7c7gk1fxIw8Ld+73sDsUjNH6gdS+uw53sX0mnyWTchA7q4fso8MvBw8NUBWvJzjuxw8uaaLnz97jFe2N3DHoqqU9DidOHhDi9e+w3xHK5k7f4a88ceMq/1fvrosh6g41JhGkLQIBhN6Xyez2p/g+9fJfO/aODNymlkXauTdUWMJLvkY5hu/gPP27xA25A49yWnSfMB0x7r43rs/53D3Cdp97ViMQx9InSaRiBzgeH8d7pCHkNTH5++p5J5llSyZXsLXPzaFseWJXPO4GKYp0kCRuRirPlFzoi3cBZNWo5FgTpU5GdIKMGaYnXvm5nLznBKydDFiGg0NvqHCoyfYx8SRORw43ZPSXtfqYUBbgGbCtej1ZvItOSiqwsHuQ2xo3kC9uwmz3sqhthiyrJAhBVLKdAIoQS8uz9Dl0rYeP56gjHvHS0Q66tBm5A/po88rQ4mEiPa14d65JpH7DPiPbsM54yY8e9fjnHMbjtm3kLn4E1jHLyRweg/B1lNoYkOvb1cr8bjCqWbXkPZzE70AigLFuVYEAUaVZTClOhejXsOkkTkY9JdfvuyjQWRM7kgWDZ+DRtRg0Oi5edQyypxFKb4soXgEAYExuSOZO2w62aYMegJ9l/RxUCJDJ0Bk30DKd13Q6JCsmUP6ScZL+BAJEPf2YaqcTLjtdCItr2wsaui86gexOK3dPgKhGG/saWH9ziYGvGHOtLmRrnD/hA+C0Hk5+jE5ila6PKEvRoKomg8vfc+ms9IX6qPIqSEQVWhzDY3WOh9BZ0LQm1Hc7RftlyZNmjQfNn+10H8/s5/nnnuOb37zm+TkJOr9HjlyhNLSUoqLi9FoNKxYsYINGzbQ3t5OOBxm/PjxAKxatYoNGzYQi8XYu3cvixcvTmkH2Lp1KytWrABg+fLlbN++nVjs4hfiDwutzYl50WeQzA7Q6LDNvBlL5WQki2OwT2YhtrxCvvXgMI5793O0+xQbWnbxmcn3YtGdc1IWuHPU9Vh9EUaWm5g02c/zJ17BYbCQl2FCEgWWz8vnzluyuPuWHPJKMwn3dmJ54/uYdv0W/UAdY0qGmsi4vBH21fuQbFlD/ma2W5FEga7+ILWtg5EJoUic3ce7GFOR2GZ0qZ0s/5nB9+PIRe4cfK0EveS0b+V4nwbGJEr8CeXT8Mz8LH3XfZ0+0YQmswBDfhm6Qy+ir93MLk2EV5t38mLTO3yn7nU+f+xpWtShD2lp0nzQiKLA0d6TyUnM/T0HuPaazJSJrkybAYfTQFdAoN3bzfq6rTx37FXCml5OCRuYPktDxNJKb7yTuBijzy1zaI+WXz1Tz1L7x7l5xM14OzP4xrONdFXcwIyxBahynOtnDeOrKwv4ZNYeppz+GQvca1ACbkySnimZQycMS2xFOHVDV5HGVzvozwjzE3r40YmXuWXkYh6pWc3c/PEIgsCUvNEcOq6y50Q3H1s+mqhkStQ6Ow9Bo8NiGXqLyMswYrEasa/8HLbrP4Wpcioa52BNcMmaiTazEMe0FfiPv40S8iOZHUhmO7LfhXf/Riw11yAabQgaPd5963Ft+zOBk+9iHj4BoacOnRwYctyrEY1GoLLEMaQ9yz44iSqKkJ9l4st3TUKnlRjwhVm9cAQLJhUTCl/Z1XEEQeVETy3VmRV8btrH+Kdp92HXW2n1dCKKgz+4Amsu9024hbgic6znNOPyR3HX2JvQXUL8aSxOIPUZRZdXgaA9bxJaqyPz2vvhvNVly5i5iO8h/lN3riPa24rn3ReJu7sJNx6md+1PEY2D911RFBlR7Byy6cSqHK7Gxd+E0D/ruv8XlNcTwwEU3YcTug9g19kYCLsBGJmnY2f9UEPFCxEdBe+5gJImTZo0HyWXX8T0L+TCVfaenh6yswfLteXk5NDd3T2kPTs7m+7ublwuFxaLBY1Gk9J+4b40Gg0Wi4WBgQFycz/61d8+IYRQVIZl1dcxECO4dy1KJIilZi6CVocgiMgBN25/H7+se512f2Ilblvzbs70N/KVmZ/heN9pFFWl3tXGqGElBKKNPHd0AxXOct7a083ya8qJazy81f8SvS2JPNcj/jI+ZqtElWMgaVDdnVw/vJgznUY6+hM3qZlj83H7wuw46mLSorvQb/9loj9gHjULk9XK7PEiA/6hkyZtPX7uWFrB6FF6NGY3MakA3TERBBHbtJX0vf6LlP5CXwPXLlxJpzoWx4iJrN0VZMdrvYCPwuwuvnitHXNvC7ZpK4hZ7GxpfjNlexWVZk8buZlDVwvTpPkgUVUVvTQYJlqdMQq9QeWRe8bT2hnEaNAQDMf5/h8PoZFE7rrhDl7ueAJPxEdPYIBlVQv55Z7Hk6XvphVOQNc7ll3HEtevxg4vqxeOYFiuiew5NsyhLqSdzzBvxDQC4+eifet/iHUnTL6irScQe39M1q2PMr+vka4sD0f6zqARNdxUeR15YRHh5POsnLmQ1t4gxxvdKCpMmSLx2OGnku/hZ/ue4J+rlnNDYxO3zPsMm0+FefatxOrTi1vqWDGzhPzJqxH2PpvYQBCxX3c/YafI7ddV8Owb9ahqwuTts8vLCb/0HwSDXiRrBtq5d2GqmIBktKKiokbC9G/+I865t6EEvYn30dOMbfJS5IAHOeRH68zFd2Qr4eZjyTFqbNkE6/bjP7gZc81c9DPuIiZc3SacigpLZwyjqcOLy5dYhZ49rgC7dfC8KAr4AjF+/OcDyGdXwevbPHx85Whm1eRd0Tn6CgrDM4fx2/3P0HO2vN7wjFJWj14xpO+fj7xM9Oz96o36d5hfNoOJeRc3P1NVBefc23DvWosaCaLNKsI64VpUJUayMp8gEmg4hHP2zaiKjCBqCHfWIaiXUOKxCP4jW1KPF48S9/SB5byqCILK9bPKWL+zCUVRmTEmj5wMEwhX7ufyQZGyoq/E0Umay/LfEaNBVG3epTv+jdBJWnSiFk/ES3W+hTUHfKyaeIk8fUc+ctcZqJ734QwyTZo0aS6DD0zoX8h7hfgLgvAXt78f7xX2fjEyMy1/Uf/Lpb1ZzzPrWpDjAp+eZ0Xnd6ExO/DseiWln3/V55Ii/xwd/h58wX6ePfZqsq3B3cLEgsTDzBjHBJ7d1EVbUYiRM7rpDQ6aWZ0ZaKTWWU61zogaDSEZrei3/ZqvjF2Gy1iMobCEtribBs8pri2ycjxuoWDWI1Q6goiehADx7VvHhHHjKdDkUL3chk6J0IuDP7ztYlKNg63uF2h2tyKrCoedpTy84jMYjA68EW+izu15GEpGE9PIPH7iReY6bmbHsUEfhfbeAJtOGlmpCri3P4tjwT0U2/I53utL2YfdaCU7O/XmeuHrK5W/l3Ge43J/D39v7+t8/pKxT5BG8dLJ9dRk1qC0j+JXr7cALXxs+Sie2nCKSCzxdBqLKzy/rpMFS+eyoXk9DoON12vfTKlvv7v9IKvLq5OvRVFANASJWrrxSn0MZJbiX/4AR7tPMSvcAN2pTt5K2E9TUyfvtNm5b9oywlUKCjqMsoz6yg9xTFvJ8tY3UYxeoivn4jWX8OrA+iHvaaenmdt0JpT2WqbllTNymZXTXiPP7ehF0Gg4LY1j4i2jiIe6aVH8/LJrF07XMe7OGc+keyvxxwSyHQbUV76NEkoIeNk3gBxw4d13wfEEETWWCPvXOHJQQj68h98i54aHibt7UKJhTMMnEumsT/h4iBrs01fS++rPAQgc24Z9yjIcBcMv+zP7W/JB3R8uxnt9P5s7PXT1B3nwxjH0e8IY9RJ6rUgspqT0f+tQR1Lkn+P1dxuZUp1LYc4H/5v9v14X/GE/+zuOJkU+QN1AM63eDiYUjMZsTqziHqrrTor8c2xr2s3C8tkUZ7//ZLC33YX3wCZs469F0GiJe/ro3/g78u/+FtkFiTGr4QCBw28RIPX8mUdMIXvCqPfdd7izF1FnRAmnppoIopRyPhwWA5uaWrhlwQhEAU40DqCRRDQaEbvj4qXbLsWHdT3+W/0eIjGFnGwrRr0GmTgaUYPFdOnceykaQme1gv7Dm/jLNDvwyB5G5+XiMIdodENmBjgc720+HSspx7PrlSvqHnkljeVy+Hsbb5o0fw98aEI/NzeXvr7Bm3lPTw85OTlD2nt7e8nJySEjIwO/348sy0iSlGyHRDRAX18feXl5xONx/H4/DofjLxpPf7//A6nNPjCgMmuahSOe3TzW6WXhmMlMsZYSPLMPlIQYFrR69Lr3vlno9KntvcEBrLrEw4BGEtFIInarlnrP0BCxpqiXWfPvJtJ+Gv+Jd8iYezue/RvIlmUaCu7g98dfSPbNMDj4/MRPIHV2o4oisZ4WIo2HKRw/G8vux9E3HwFgmKTlOzd8iR1xN+80NCe3P+NqxmUuR++L0+vMIm/KUmL7N4EiYyitgap5bDylct/wT3O0dmiO6dHWECtqqqC3Cf++Ddx3w2f5at+Z5ARPviWHIlMBveeJ/+xsa8rrK5UrdZwXu4lezu/hSn1fl8NfOnabmMm35n6Fnr4o333tSLI9EI4nRf45PP4oBuw4jXY0kobm98jTDCgedBqRaFzhxmsL2BN8la6jZyf66uCGkYvY036Y0kIdZaImea04hz8m8fq+Hl7fl9hGpxH5xYMVWK69n561Px2caGs7ybCln8ShGeqPYTc52DuygLd7jjFKE2Sm1cKs2pcpv+UhOgLwh/Vn+FVUZtKoTIaPidPi66LFB2NtRYzZ8iecpWPQlo7CfVbkAwh6E5I1A0PZOMKNh5Pt1gnXogoCptu+ixANooQ95Nz8FQY2PEZsoAMA0WAme+XnkAMeBFFiYMuTKe87Egzi+wC/b3/t7+FvPZb3+n4a9RKFOWa27W8jJ8NEe28Mh1nPuMrsZH9BAEkcOgmu10qoCh/4b/avuS5IJoXa/sYh7U2uVqLRKB5PIorBqB36fTZqDOgk7UWPbbI4kX0DeHavTbZpMvIRtAZ6e33J74BoMA8V7JLmovu2avU4Zt7EwFtPDO7bmY/GmZvcTqsVOd7YT327h/r2wXQ4WVaZWp39V302f+vr8Qf9e1BUlXA0TigQIRKKEo5H0Yoa/P7IxTdUFTKiYUKyCKGLlFj4G2MRLbS6Oyk1l1CVq2HdQReTy8243e+dUqhiJebtp6etC0H/103g/C34e7tfX2njTU86pPlH4bKEvqqqQ1bTPR4Pdrv9skvrjRs3jsbGRpqbmykqKuK1117j5ptvprCwEL1ez/79+5k0aRIvv/wyc+bMQavVMnnyZNatW8eKFSuS7QBz587l5Zdf5lOf+hTr1q1j8uTJaLWXV4/1g0ZvifDbQ38ifvaBtcHVglhzE1NXfo5IZz0CKqLRhvfgFubmj2Nb5+DD8dziKeRFYUpONXt7EnXptZKWYmsiJUHUR1g8O593D/RzzahqGl2tKcceqXcysPmP6Iuq0OVVENU7qB/3WSpH2Hlmz69S+g6E3fT2HseyZz2GYTX4j79NfOlXMblDRJsHhY0qx9Dse5ZwTWqIZJYpA53PRSirjH2nVGZMnEhp2XgEbz+xaIhexca+k/W89k4j9ywbOeQ8jRzmJFQ8nayyShRACAX5n3lf5qinHYPGQLGlEDPpC22avx0ajYgsq+9rIHqOQFRmz4kO3j7cQVmhlfuXj+LpjaeJxGS0kogopEZDO616ch1WFlnnsLluO+NyR/Jua2qdeKc2i2i8m1FlGRSVKqw/nBrNs6l+OwvKZrGu8yifnboEdr2W/Ju+cia9qpOxw0XyLCpTCxVKHCK6jqNE4tEh0TTe3a8yb+Ed7Og6lrwO6SUdxc5iHtv/ZwCa3e0cMmfy+eFjkCSJP75+AoC5EwopzLHgiBfw+fGf4jfH/sgxfxcTM/LRldYgGlIfYB3Tb6B/428xj5qF8ZpbiQfcGIqrkayZeFULoZ3PoDbsQdAaEKcsS4p8SJQx8x/ZgqVmLq5tT6eU29M4c8Gac9HP6WogGJY5VNtHXpaFdw934LTpGTbORmefn1zb4EpoWaEds0FD4Lyc/JVzKpBElQtz1K8ktKLE6JwRNLpSS9SVZZQQO29CzawzUWjNo/08Q8olI+YhqBd/b4KkxTZ1Od6960BVEA1m7FNXgDj4vCAYrGTMv5u4NxF1Jmi0xNy9ie/gxYiGCDYcImP+3cQ8PYnUFUUm1tcORUXA2egdQUAjiUyozEarFTl4uhdJEq461/1wJI5OIyW9F+JyDK2kRb5EkUExGgZJO8RD5IPGYbBT62lAUVRG5ul554ybXm+M93vSFEQxkaff04Cm+OIpJWnSpEnzYXFZQn/VqlW89NJLKW133HEH69at46mnnnqfrVLR6/V8//vf53Of+xyRSIS5c+eyZMkSAH74wx/y9a9/nUAgwKhRo7j33nsB+OY3v8nXvvY1fvWrX5Gfn8+Pf/xjAB5++GG+9rWvcf3112O1WvnhD3942W/4g8Yjdycfrs/xfP0WRhbNgYEufHmTcAUlHBljWRLpZNz4O2jzdVEo6inoaCGw5ZvcO+9uxo6cRd+ASo7FRl5M5bPjbyUkQIvtGNcvrMFmKGRcTjuHe44jIDBv2AxKRSOi2U64+Ri66uv45vogC6foCLb4Cb1HGaI4CoKkwVgyGipn899vePnYSA8X+jTHBzqYYvkEurICXm18GUmUeGDEdWRJVn70ph9Rq8HR78W1/rcASM4C3s7IZcAb5oHbCvAo9cydWMi2A4mVztI8KwXZFrzBAKYjLyAPdCa2y6tg0tJPIWuzSZPmb4U/EufdbXXsO9nDxKpsJozIwqJ/70ufCry4tZ6tBxIlQZs6vTgsfSyfXcaLW+rYfqidT9w4ij+8eopYXMFi1PLQ6mqeb/gDhaZSbitciUHtosvbTb2nDUkQuXHEAmqMRh64toiM3FzEeJgbCu9C0IXZ0rEZT8RLNB5FJ2lp9XXxosHODcs/iTMUJyDaaBWyefWNNm6ZlsmorteJ7jxICIhaM8i87oGhb0LSkKNIfL36RurVEIIcp8ReyP8cfi6lW2egH1feFBq7EsZ3y2aV0dDuZtvBxO8002bgkTs+R6v/MPG8DF5utDNnZh7i9OWou15H0BtRwgGUSBDfwc1YxsxH0Bvpfe0XoCiYxy7AYrXiAySTFdnbP2SosYFOkCQyF30C3+E3CbeewFBag3nSckLiR78q9lEjnRWKz72RKGHY3uvnZOMAj9w9OaWfIAg8eOMYVFUlElNwWvWEIzG0Ggn1Cq5PGo+rZBgcjMkdydHuUwgITC+egF7UIZ837r5APxMLapgmTcAfDZBpdHK46wQT82u42FKDHPCgzSjAOe9OUFVQE/c8NeSFc/Xu42EQBDx7XkeNJe6T1omLUKIhuIT/W6TjDOGmo4gmG2okhCrHyFr+2eTfVVVlyqgcnFY9bx/qIBqTuWFuBdWlTkRBvKTI/UciGIljOFtaT0EhrspoRAkuJfTDAeQP0YjvHFpRi0kyMBBxkWXMYFSBno1HPCyvef9vnOjIJ959Ji3006RJc8VwUaF/3333cfToUcLhMBMnTky2K4pCdXX1RbYc5K233kr+f8aMGaxdu3ZIn5EjR/LCCy8MaS8sLOSJJ54Y0u5wOPj1r399Wcf/sNEIQ0+pXtKhhkKcyF/JU282U5xvoNclc8f08YzTahnevAdUhVDjEVQg+M5zKOMf5plN3UA75YV2bltUgRqTKXT6WXPqD2SbMxlmL+LWmhUUWHOo7TlDvUHCNXUOk3xhvB4v0bjAnOIocvtJ/MNmsqZu0DhIK2rIjyfchfs2/hbfpPs5Wh/AOyqTCz2ChbIpPP5mF3FFy6NLP47e04Rh01P0x6JMHvVpnIVONPvXci6oTs0u53BjiBuuzePFpqcIxIJ8rOrzFGRVoQI9A0He3NvCv9S0JkU+gNxVT6z5MNKIay9ZNilNmsshpqj8Zu1xjtYnRObhM73sH57FP60ag+Y9wp09wRjbDraltLn9EfQ6ibkTCqkosoOg8uV7JhAKxWnuCrDx3RbuufZetu0eIKx2Yd75cx4aPQtf6UK0ioLp8F4swyKMLx3Pu80+nn+zloLsRPWMGyfcxQstf2JSwRgOdiaM6fb2nmZS8UwO9troc4XZsOs0GkmgWIgRbRysJS77Bgic2oW+uJq4uxtD0UgUk4VI+Vh2yT5QI8zKrCLeXotbNKT4BpxDVBWyM8xoNSIWo5ZTTS4kUUCvlfAEIryxp4d7FozDFWllnDGGN+xiTbyLZUvuwYKI1ZfYp6DVo7Fn4n5n8DoeOPwG9hk3IehNxL39aDMLhhzfNGIyos5IxFKCbu4nMMRDyKKekPrhrt5dqaioQ76PsqLS7wnB2RxzSSJhsnqkgz0nEn4r2Q4jNy8Yjj8cw6y9cOr2ykEUBTJNTuaUTuOa0qkIZ6MPLNpUMZVpyuDxw2vIMWeSZ8nm7aY9TC4ci07SwkUKC4gGSyKKRI4Tc3Whyy8n0t2ELr9isJMs496xJinyAXwHNmEoGQ22iwxeZ8A2YRGe3WuTppOSxYlkSb2DBkJx/vDaieTrlk2n+dzq8VQU2a4imQ/B8KDQj8mJ/Hwuo8SgGA2gfgRCH8BhcNAV6CHLmMH4Yj3P7vVxbZUBg/a9r0+iswC57fiHPMo0adKkeX8uKvR/8Ytf4Ha7efTRR/ne9743uJFGk+KUn2YQh5iLWWciEB3M45pXuBCv6qSt28+8JTHq/DuYYBpGQJuNGgmeresbxj51OZGuekJ1B6jMNzFvYhFbD7TR0O6hpSPEUxtPsWxOPneNvZEdrftRUTFodPxm/9MICHxu7C38tmkNgeKpjJPy+PyCKLG130WVY0yeeC3G8vls7T1JjsnJ8sJJFLg8BBv2oUZDiMgIAjx7KMInZ34MzYHnUMIBdOUTOZUxm1O7E4I82ilg3fnnpFHu5AIFr9UGggZx1EyUrAL0uTV8tcyLNljPuMJZ7I97CMgent7UnTwnkyoz0fef5sLsvHhHHfrqxcRiV9MjUJoPij5vJCnyz3Gkro8+b4Q8x9CHR1EUkESBuJw605SbpaO+PcbvXx18YF+9cATvHm4nFIkT9whs2dfOwmVGUBWUY29jPmsoHwc0E5fQHbOw9u2j3L+6iJbocfqi3XgZz2cnfxyjVseak+sZmTWcOUXXYMCGRhTZsj8h8iwmHTp/JxcSaa/FtOLT6OsPEzq9B7IKCMVCSBqR/IxSXus+xEjzZF5e38K06ins7tqb3HZUZgW5ooGsPBMTR2Ti9oW5d1k1k3Mi6D3NCJKGblEHkRjavU/ijIYxlX6JT4xeCdEwvQIEdYnQcG1WEZGOuvcY32n0OcMIt54g0l6LfcaNePeuQ5XjmEfPRjLZ8R9+C9OsOwjJRhCMkJ7kSyIIAia9Brcv9UqpO09oyHLi3zmRD9DrDnH4TB8Tq7Kv6PMpIxNXZHqD/exuO4RW1DC9eCIGjQ6dTkMkkrgP6EQdX5jxALtaD9Lp6+HG6sWUOopQLmNG2H90G3F34twETu7APv0GVEVOFhdWVTX59/NRo0Oj4FKIhIgNdOKcezuRrgY01kwkkz3hum9LmEjq9Vr2newZsunG3c1MHHl1PUMFwnEMusQjZ0yJoRUvL91SDIdQNJc27PsgcOodNHtbqMkaicMkMSxbz/baIItGv7c5oegsJHrwtbPVG67cCbY0adJcPVxU6FssFiwWC7/85S956qmneOihh2hvb+e3v/0tX/ziFy87P/9qwtev5c7h99GnNOONeKlyjuTgfpmKcUY6dNs5crac1Jn+RvItx6l2l0D7aSBRgsp5za3EQyFePuIjEJaYXJ3LvpPdGA3wmY/lMEWnpSUuYdGZ6A70saf9EACrKuahRSDblMEb7QfILp7MhOCxpFOxeOANplVOY9a4pciihIKZsLuecGvCC8DSuoOFY6/jjcP9/LvHwMqJn6UwQ0tHUM8f1g8+wF/4WBWUIuyt72bUvJt5+uTrdHbvYroUYpFgQ377GQzA7Irx7B3uZOWCQl7b2omiqLj8MTRTJg8RB9rS0WmRn+Zvx/sIgfeu6gGSMcyKOcN4acugQVhxnhmzSWT3sdQH9lffbmDZrDImlKl4gglRYMrIRtDoUOODplH6wko8difuthg3Ls5jbfufCcQSE4Gn+xpYqVlMlloGjZNBlonbrYSdLgrKLMydmsOmdzvx+iOErcVcaElmqBhP5Mx+IrvP5vQPdGBoPoFh/iqOhVx0+3qJtXlo7Qxy4+zpTMofwemBBipsBVSE41hFHYrnNB+b5qRDzMXga4N1PyF8dvx2nRFp/l3Y5t5BrVnPU8dfwhP1sTB/PDMNuRwnxpgVX4ZT29A6sgk1HEoZnzZnGKH6RBRC3DeAYfhksu74FsGYgC7YjeetP6HPr+CKVqMfIbKqcPOCETy14RQTqrLxBqI0dnjIyxhMa5Ak6B4IDNn2dLOLeFxBI1250REaUcAd9vDcsUFPirqBJj495R7OL6SjkSR+vPNx/NHE+6ztb2DlyOu4rnz2RSO/lbBvUMSLEigy3n3rMY+cnuwjGEzococR7W5K2VayZV107IJGh6DR4Hr7ebSZBYRbT6GEA+Su/loyuk1RwKQfKvjMRg1aET48a7mPnmA4hv7sin5Ujp4N2780YsSPqv1oVvRtOgv+eJBwPIJBo2dWpYU/7+hnbpUZvWZoNIKgMyIYbSgDbUhZpR/BiNOkSZMmlct6AviXf/kX3G43ADabDUEQ+MY3vvFBjuvvlpI8lcOunbzRuJ1jPad46vizLJhhBZvKkd5jKX07/T30Oy4I86vdg3nuPeyt87H3ZDdVJU4EAUqtMWqMWqTuOoYFIyzJn0C7pxOtqOHfxtzKvI4e7Juf4p+1JXxi+LUMz7cRyBmDmF8FgC63DI0tA1evnzV7o3ztqWYeayontPjrCDojStsxVhT184WbRjC53ILRbKRXtvGHDYNCvDzfQna0nXPmTkLJePo0ZsorRH6w9w80eTuIyFG2te7lpXAH0rBEnppaf4gy4JS4ia/cX85/fmwMD49zIWYXoCkff3bvAvrRs9EWjPggPpY0VylZdgNjKjJT2sZUZJJlH/rg2Cf38s1t/48BywHuurGQuZNz+diKkXx85RhCoaFCNByVGTFcy9Otr9CmtnHv3Dx0x9aSMf9udDmlIGkwVU3DtuA+nmzYTkGWGcnkS4r8c2ys20JOpoksp5nrF+TwSscT/Hrf4/xkzy9xOXaybG4uigpvtegQxy5JmlLpC0ZgGTmTyJ51KftTI0HyYgrvtuzjrvI53JzVzC9WiozWeKk6soM7jSVUblmD3efB9fZzaPQmdB2HCAfD2FrfTZmkUKMhYn3ttBm0/Hj/U3QFegnFwrzWsovdkV4q0HJMlPGMvBFd8Sg09sFVSsmagaGwEsvoWThmr0abM4w+UxmPvdXHI0808Lv9ArHrvoJx1DX0h68MM9UrDUlImD8unl7KkTN9eHwRHrxhDN7A4GckCJDtHOpKP3KY87JCoz9SVJKT1edzqq+O873quvw9SZF/jvW1WwhEQxfffTyGdeIinHNuxz5tJc55d6LLK0c9b+dqJIh17Hw0joT5nqDR4Zh1M0rkEvuWRKwTF2Ounkl8oAvJaCXnhocRjefH+6uMq8zBeJ4niCgKLJpaSvwqm9sKnBe6H1Wif8GKfgBF99Gs6IuCiFNnpzuYMGrMc2jJt2t46+TQNKjkNhmFyF21H9YQ06RJk+aiXJYZX1NTEz/72c8AsFqtPProo6xcufIDHdjfK/1yD3s6DwEkc2LXNb7BipHXISCgXrByJVzgiCzqjER2v8AXr13Iv73UjSjCl5cXUug6jNzQgevULgByc8v46ey7iAORjb8j6D9bwq6/ncry8bwo5/DWoR6WTFrOdSOLsZWPICoaeHKnyoEziZXJAW+Yky1evrP8IXQn19OhKSQ31sZIZRtq9i106BXuWF6CKktkWvVU2gVMrW1olzyEX7SypU5G7w1gs4eGGBDu7znFDcPmoW06CkCBzsoduWPIaXiTzJoFdO9+Ch9gvmY1xnELEHV6RJOdoKsDi9CP4CgkqnOkc/XT/FVoRYEHV47mcH0/+0/2MOGsGZ/2gvx8UYI36rcTiofZ072b/cJesm2ZlGfPxBMppV/uQa+TiEQHlw+njsqhI3KKRk8bPaEBvlu2ktDrR+hvO4G5cgrG8vFE3b14ZR33F04nuutXZExbOmSMoigSD8icbOwjmNlEf2iwHOWx3lPcN2YiNwrlOK06egqziBXlUa21Ezu9m2hvSyKCIJYaZiyLAiaNgdiBN3Ed3Q4kSn9lzLuD3rU/w3HNatw71pC15JMEG49iKK7GoIponXkoehNqZHAyQg75aAm7h1y7tnQdZcaIpWhkN7Z9r6LWzMYyZh6CRoug0aPLKcVfdwD/noQvi27R5/jhy0209yX2ve1ohDMdQR69rZLGLi8TSq1E+fDqZP89IArQ3uPnhS2JCdded4j/emIf3/jY1JR+ZqOWhZOLcPujmI1aet0hJlblXMF+++cQMGuHRgZadOaUcm7vFYEjihLKJSJBtBkF+A69ge/ApmRbxvy7Ec4rYysg4Hr3BcyV05As14Ci4D/+Ds7MwosPXZbxH92OHPSSsegBZG8/fRt+S87KfwLd2UkDAeKyzMO3j8fljYCqYrfoE9FDgsCVa5P4tycYjqPXnlvRjyVy9C8DKRIgbr9EBYQPEKfRQae/m1JbopLCzOFGnt/r5ZoRZiyGoWtlorOIePsJdDXXfdhDTZMmTZohXNaK/rla9ecIBAKXLFF1tdIbHBjSdsbVTCQW5pqS1IezMkcJme7za8wLmKqmEWo4hLNzJ2OHZzLR6aXkwC+QJJHgWZEPEO1uRDmzH31/J2gMRKY/gGvap1EmrSbWdppxeYkSYOv29dJfPIe4r5/ufj8HzqSOzx+KcSycT93oT/LHd10YiqvoHn0njTEbBz17kZ2N7Iq8wE7fWtzRRkKnd+Le8GvkthO4Y1paQ/XYdEPdsc06E5qzKyKiyYbJ76Gwt5/ouCUc9bbhu/WfESctJrDjJeKNR4iKIp71jxF59ZeEa3ci1+/CFBmaN5kmzV+KRa/hxrnD+dJt45gzJj/FcV8QICIrBKJx2rznGUOqCl3+Xlq97VgzonRE6/nWxyfxseXVLJ5eyjWjs/jY/HyO958BIBANEiOCOnsVwvh5BBsP49n1CqHaPZgG6tB0NhIbP5siWx52Q6rD102ls8hr3s200U6a/U1Dxt/qaefwmT6e3nQGTzTA/9Su44new3gKywm11+KYeVNKfyGzkDOEuaVkBuqpPcn2uKsTOehD0GhR4zHUaJi4pwfD8Ik064ezbm8n/3UknxM1n0OtnJfczpBXjtU41JUsw2BF0WjJiSnEOs8Qaj+DsWIC2swS4n4XMVcnmtLxiGdFVZ+YlRT55+joD+Hyx6mOnyJ+bAMmXyOikL63nCMqq7yxN7WMqqKotHT7znsNRr1ETUU2gVCMM61uJlRm47ToudJTIuKqwtSi8YjnlU7TSVpK7UXJMmwAVr0Fp8Gesu2CspmIl3iEkQNuIu2pq6vuHWtSo1Y0GmyTluI79Abud17AvWMNgihdMnSfeBRBq0OXWYh396tEOs7gnH0zcb8n2UVRoLbFlZywjisqoihw4FTPZfkL/CMRCMcGhb4Su/zQ/XAARTc0YuXDIkPvpDvUm5x4yjRLVObpWHv4vWu+i5nFyF21qOrVNI2TJk2aK5XLmlK98cYbWb16NUuWLEEQBDZv3syqVas+6LH9XZJvdAxpG59ZQSwskxkax+rKEhp9Zyg0FyMF8pCLbDg1epSQD60zD9/BzaAqaIN9PDjbhrrue4jWjEQZqguIdNahKR3H0eH38/tNnWTaVeZPGs+oJdPJJIog9KCqoBdVPLvWIk28Ha1GJBZPvQGFo3H++PoJHr59HL+te4Y7KxcySRIo8hdyMNCJO+Sh299L7UATj85YhnnDn1CPrmfFLRM46rWTp7cxIrOMM/2Dec13j7gO3TvrkMrHY5u0lEavju4Claf2PZYMXR6XW83KO75Kh3+Akt4W1L42Mhfci/fQZgLH30Fz+E2cSz5FxDE8PbGU5q8mfsH3PiKEEqvxXg/51mxmFU2l4YJ63kW2An6193EeGvkw//3MUbpdIXKdRv5p5XB46+dMGDeFk7113F5zAw1iHt0mHV1SjMmL7qBg92bMueWgMxDPLKDLrOPo7l6uy1pNUN9GX7iX4dZqRgp64gf+H2WzR+ESq+ny96aMYXRGATUTNGTZM7B1HeHzI5fzSscBXhIl7pl2PZo4OOeJxHx9yPZsXI5Mhuv1OHa8jnzBSn8kriKNnIMgiuhyy4j2tNAjFvFvL+9PGhD+ut3Dx6+dweSCdsxjFyJkF6JV/ORasuk+OzZRELm3cjFOtGTGXRju/jaRzgb6NzyGZLJjHbeAYPMJBJMb+7QVqKpC9H08XWKqyC8PGLltXDGmV39K9o1fImgq/qs+638UBBUsJi1uf6oZ37lcZzhnQyHw4ltnmD4mH40kcLrZhU4jMazAcqnqZR8pWlHCrDXyhekfxxcNIAgCVp0ZrahJWdHXCBLXDb8GT9hHX9BFubOEYCyEQaO7aKL7e4XfK5EQyHGSBdHjMSKdDTjn3UlsoBPJ7ABUZG8fmEouMnhDwgVRK2EeNQtBFPGd2Ilz9uCzkSjCmIosXn+3id3HuwCoLHFy+3WVCO9R9eMfGX8oNhi6L0fRCpcRuq/ICPEoqvaji/TRSzoMkp6BiAvbWZeUGeVG/rTDw5xKE0XO1PchGm0IWgPKQDtSZvo6liZNmo+WyxL6Dz30EMOHD2fnzp1oNBq+/OUvM3fu3A96bH+X5Gqt3FQ6i1dadqKoCuX2QpYVTsIdy0Crd9HqayYYD9Dmb6XanM/Bnn6uySpH03kSVY5hrpqGoDOAvYBg09vIWUXIfW1ox8wbcix9XgVdipP/3VTPmIoshhfbWbOljqdjMmOHZ/G1O8fx23W12PQKqhzHeGYzq2fexZ+3dyX3UVXqpLMvgKKotPd5eWDsSiwndtO1+zVAZZLFScX81Xzv1FpkVaHFoKN62vUI+zZhi/kZv2cb6sJiyp0lTCuagC4aJTcmM0zrID5sDLG+Vnqe/x72kdN5JUOXkp/c4GpBKdKTF81Ea3FimrwEz+5XiXsTYiLu6aVvzQ/Iuvu7hLUZH/RHl+YqIiKE+P2RpznWewpIhO9+fvoDPDjpDjwRHwICDoON3W2HmFuwkP/58zF8wYSxZbcrxH89d4rvzJ3F6ICPu0beQl9TJk9sayYuK0yorqBxbADrNTciGGyY+7sJF1bi7+9l58EBbl2ZQ3OgB3/MR6O3jpL8qehUlVyrRLU4hp6sDk711SEIAovK5pBhqqDbFids0yNmzqXk5Jv8a+UKBG8v8um9qBkFxH0DhGr3oaoKZkFAMmdgKK7C23xeqSdBpCliRy2sosbUh8aZT+/rv6TReM2QKgOv7Otn2qpbca//CSgKuTNWsKR8NkE5hqzKjM8cTk7jKdzbnwFFRjRYcF6zmpiri2h3E6GW4+Tc8AUCZ/bhOroVAGNFC4smz2fTvo7kca6dmMexRg8H6t30+818dcK1xAc6IS30ATDoRG6aN5yfPXco2ZblMFCUM+j6LYqJNKwJI3N4ZXs9sbjC2OFZqEAoomDSXLlmfCoqcUWh2dPG28170Ioa5g6bQamjEM15ZmcaUYtW1LK//QhmvYlObzefmHwHYTmClqERZeeQzPYh5pjG8nEgDYozQRAJNRwkVLcfyZqBEvajxiJkrfjcxQevyBiKRyIHPCiRACBhGzcfNT6YxiaKAk2dvqTIh8QK//5TPQwvtr/HTv9xCYRi5GYkJvuicgzpMkL3pUgQVatP+pJ8VGQYHHQGuhmWnSgRatSJzKgw8uQuD19dkolwgReGmFmK3H48LfTTpEnzkXN5SVLAwoULWbhw4Qc5ln8IXHKUJc7hTNQ6iCoxsrUWgoqC0yzwbOMGmr2DYZh1+no+Oe4B6jpjlPW1E2k8lPybed59rM91EszQs7jkXmS0GDvOEKrbD4A2uxhtZj49PW4Axldm88T6k8ntj9T1kW2G7yxU0TjMMGExwf2vM9W+jdLls2n2SDizM2noDLB+ZxOQCHGmr5Xw7leT+5H9Lhz732RmxWje6TxKQI3zk1AD/7TyU8RO70V2daLEgjgMNp48vIZ/qlpGTlsj0dhpAkYjrqpxyFXjyDQ5aTwzaBo2zFbKZMtifvhEK95AhOk1+SyYuICsXDeCd3BFU42FUby9kJkW+mn+dnSGupIiHxKCo9HdSru3k/0dCV+JqqxyqrIqEPxWfMGhKS/d2ZMoat1EhqOC3751OPm3gycHyMnIo2WUj6f2/JZHZz6ETaNBEuH6hbm82PzkeRNedbhjA3xyzq2IdZuoCniYsOAuusvnIeht+Dx6/vOxw/iCMbQakU/fPIbSkddiOP4a/kNvJI9pnrwUXUk1oePvIBqthMfMxJA/BikmoJ7aimB2EhpzE3/YHmDK6AD5LX9AY8/COWsV2tDQEFqDViJcu3MwV3/78xQsuY/v1a1HQGDauLtwb30q2V8J+/Hsfg3L6GsSUUlynNhAB8aycQSObgVArt/N9ePzmHTHTJp7QuRmGHH5Yzj0Mv91TzkZQgCh9QyxvhCGjCIi5sKrPpInGInz1r4W7rt+FAPeMMaz5clau3wUOAbDmSVR4NW3G5Kvj9T1ke00MntcfiKH6wpFUVR6An28fHJjsu3ZY2t5cNKdKf0iSpg3G97hmmHTEAUBV9jLq6c3c1vNJbyCdHqyb3gY97svEnN1YR4+GVP1dITzLf11RqwTrsW3fyOyL1GKUzRY0GYWDCn/moqIHPAw8Nbjg7vKK8M57+7ka1WFhnYP14wvpCjHggq4fRFONQ2kRCxcDaSY8ckxNMKlQ/fFSBBF99E47p+PU++gwdOU0jamSM/xjig76oPMGp462SRllRJvOYxu7JIPcZRp0qRJM5TLmiadMGECEydOHPIvzVAKYjLul/8HR+sZ8vv7iL77ErbeDmKqJ0XkA3giPtzxPnI0/hSRDxB891nKNVbead3Pf+7+HdvcDWgnXkfW0odwXLMaY/kEVFkhp7gIjSTiD8WGjGXXaTeCNQep+RBtWTOQp9yJPtRLQf9eagp19HgiSZFvMmgozxHA5x6yH7mjnoW5YxjmKEJRFbqD/aztO4GmeCTOm77EnkgfRo2eB2ruo12V8E1dRrhmJr+XfPxXw2Z+2LCZ53qPMSWvJrnP6ZnzeeLlFtz+CIoKO4528vbRHtoL5iNckI8nGK3/tw8jTZr3IRhLDek1agwIkBT5kCh9B1Ce72Tx7DwclkHnZ1EUaOkOskWYxukW75D9Hznlw6bNQFYV2oL96CNBCm0OsnJjQ1z3T/adoSt/FGHJSrS9Fs8z3yNT1LK2/k1eaH2Se27PYNW1xcTiCr9bewKn7EsR+QCBfRvwVk6gZfFdHJy9hO93vEt7TMfzAzXsrPg0ay2r+de1Xtr6guh1GuSCMYRbThDtaaGqNAOrKTX89I45hdizsnHMvAmNPQcAazhxzlRUDKGh5dzi3l4k02Auv6DRgRInY8E96HLL0GYW4rSbKKt/jtFWD0dqO5nn7KC68VkyGzZhDPcSrt+Hd89r9D3zLfSB9vf9/K4WtBoJjz/Kn14/wZt7W1iztY7n3qxFOq9kniBAZ39wyLYHT/cSjl7BcfuAIAns6zgypP10X33K64GQh97gAG3eTvzRILV9DRzsPH5JCwIB6H3152hsmVjHzCPa14L/8BYQzlvjiEcxFFXjnHsHurxyLDVzyF75OZTwxV33UWU8u9emNEW7GpH9g5OCkUicmWPy6XUHeXrTaZ7ZdJrDZ3q5ad5wtFduoMUHQjAcx3B2oioqR9FIlyH0w34U7UeXn38Oq85KSI6kVHkQBYGF1SZe3O/DH05NCxOzSpF7GoaYpKZJkybNh81lrei/9tpgjdtYLMamTZuQLuMifTWi9HaixqMEjr8DgMaRi8GRizYaQhRElAsMWnSiQE6GGXnqcgJn9hF3JUL81FiY8fYi8kyZdAX7EUUBb+cZzAO96LIKEbUqStiP+dga7lq0FJ1uaL5beYEVuX437ozhfPf5Ogw6I6NLbqC7KUrjzhY+d1sNC6fnkme3MsbQTTRUS1Cv50LbLX3+cCy1R/mivYItvoR54PG+OsKV1cTX/y9jVn+R7Z1HGGmcTLv3OJsa36TUUcTM8mm0HushFAvT5u/mtuJpdPq6Oe1qJuAd+tXbe6KL4pzhDCsZR7wuYTxonXETsinnr/1Y0lylCAJ4Q3EajnUiAnmZJgwakVxTTsrvMc+aTasn1QejOnsEVp2FJ088Q1yKs3j5XJpPZbLrUD83zCnn3SMddPYFuG951ZDjlhaa8EQTvxW7zkQ85KNQ7yQiDTVo0ooaTrSEGOUYhZO3UGNherrOEAn7+FhGDeajbzDWZGfK6qn8YKML+T0EiKDT49TbaQi7OOnrZmnJcp5/o4GVs0bwn3/ci9mg4c45+VRafWQYuhCLpxNt3U+o8QhZU27g64uMnHRnUplvIF/jQQqdQVUUvIfewjZxEcEze3GfLXGVY87EYLJx4Sg0tmzkYGLSQzTZ0GUX49r2NNGeFozl4xG0BtR4jFDdfmySiTsrK/Fs+F1y+2DtXhzXrMa19c+o8SjR5qMIo67uVf24orBoeil/ePU4wXAiJDzbYcR+3qSTIIDTOrT8WGm+FY14ZatJQQWncWgIe6YpteysRWvinnGr2NN2mP5QIkd/ZsmkIZUgLiTu7sExezW6rCJQFYwVE/Du24AaDYDBAYCqxAk1HiZ4Zh/6ghFEupsINhwi+/rPXnzwqpL8vqc0y4Oh+0ajhj5PmFNNg6a7nX0BTre4GDM8c8i2/8gEI4M5+hE5clnl9cSQH1X70a/oi4JAht5Bh7eLfEN+sj3XpqEyV88L+73cP8uRbBe0ekRnAfG242jLJn0EI06TJk2aBJcl9AsLU8vMfPKTn2T16tV8/OMf/0AG9feMakoNMc9YcDe+EzswigIrS2fyctM7yb9NzxvNqCh4Nv8MJRLCOm4++qKRDGx5EtGajdzZzMeLZ/Kfp1+lWp+BTRdBRkE02RH1RtRICMOIKSyMenF1dDGxws6B+oTjr1Gv4fYFxXhtTsxuEUWpJxiO0+uL4z/7wNgf6WfA+Q63l1xD8PlfYJp7O+/qYlw3bQWBszn6ksWJddwCXO++gOwbYMb05Ww3OqhwFCNmFiIuuJPWQB/Zpkze6l7Hmf7EKuiJ3lqa3W0sqpjDK6c2Mbd0GmLDUR7wqPiL59ErDDXmynGakBUV++xbUKunI5qdyNZ84pefYZImTQo93gjf+d1uRFFg5TUVNHZ5cVoNFOVY+fTk+3j+xKv0BvvJM2dT4ijkQGdiRV8QBMbnjeKpIy8l97W2YS33TbqTyoJqdh7tpKUr4brsdEiMKLFzpiXx27OZdcydls3PjvyEMTkjKZcs6HpbkKwyBYLKuNzRHO4ezJ1fWHItO7Z6GDl5cPI0LkncZh+B5o0/oymuRvX0Yjr53zx87ReQrFlItiw0Fgei0Yo2dxhaaxaeDb9hZMDNmNHz2RvU4/P4KLDK/MsnR2JUXTh2vIh8qJYIIEhaMhbcg792L1GzlbyyCkqibvrX/g/egBsA0WDGMXMVA289QeayTyFnF/D5zFyKRSPxkwlR7n53TTJHP/O6+wm1nMA553b0JdV0P/8D1LMlRoO1e0DS4Jy9GgCTViS8f3ACGUCVYyjhQDKnWpUjCAJXdYlNnUakZyDIvctG4fFH0GslZEVNmfxQFLAadVSVODndkhCUZqOWGTX5XOn19RRVZWHZbA52HCN2tkSrWWtidE7q5JlFZ6LV20mZs5iBkJt8ay6qqibE4kXMzTUZ+aCqDGx5gvhAF8aKCdgmL0744JxFUFT8x95GsjjQZicmBGK9Lcjh93ZVTyLpMI+amZzUB0DUoHXkJv0BVRVONydW+Ifl29BqROrb3Byr7+PmeRWXfZ7+EQiG4xi054R+FLvm0pF6Uth/RYTuAzgMdlq9nSlCHxLl9v60w0N9T5SKnEHTQCl3OPHGvWmhnyZNmo+U/5OCqq+vp7+//289ln8IuqVcrIWjUduP45i9mmhPC7K7C609l+uyR1FhcNIZ9ZGtMVFmzMD34o+T23r3rsOu0WGfsQpyyqH1KI6OJu6vWoJl+0t4OxKlvIK1exGWfw23NRs1rsXc04t2/wvcWzaD60fWENPbyZa81IcOs6e9hSUFK/jUTaPJt6hktrxFVJEYyBxLlzXO7aZZ6LpaCAL+eAazMzOJn3oB55zbkOzZ+EwmXu49hXbqXMZq7dj2vcniybMx27L50tv/w7TCcZTqixFFISnyzxGIBdGKGlYNm0WVrRBXXMS+dwPG03sonHAzIwpyyMw2Ul6mQRQESpx5mLR6ojoT5F+itFGaNJdCgJe21RMIx3lgxWj+vPFUMpT506vG0izVMipnBE7DZE701pIRdVKZWU5tfwMF1lya3G0pu8u35qKKEQzmKEadBqtJSzgq0xFuYdX1mWgHsojF4jgLHfREW/jipLvJH+hH19mMGnDRs+m3ANxz8yPMyR9Lf6CPAlM2asDOMU0vmf5EuLKufBwxi4O8mIjvpm/S2BMABIon6SkN9+CL9RBY/iDmcIiMuIoaj9K/7teci2OOHtrA+DEikxePI+Q6xe+aNvOlvGnI55UZU+UYviNbyFjyIMf8XVSYKwie2YEc8CDqTSiREEo4QKS7EW1mIe54mB8deoa7qpehvv4YPnc32qwinHPvQOvIQTTZcGGnwe4kpogUR3ToLQ7ikcGyrLYJ16HLqyD7xi8gmhxEOure4zMTE+pIEDGWjCZ4leUxX0g0plJR5CAYjmE2Jm7XOVZjSmk2UQSNRqQo18Kk6hwURUVWVGQFUJWP3MjsYgiCgCQIPDrn84Tiiag3ragZslIfk+NsbdyJK+RGp9Gxs3U/K6quRVEvkZoQDdO34bGEyz6JCSc1HsU5fzCPHklD5nX3o8ajBOsOoM0sJPeWr1w6h16V0WYWYhk7n8CpXWjsOdgnLyEeCsBZDasoML4qh5ljC+j3hJEVhVsWjKDXFUQShYvNUfxDoaoqoah8Xo5+BK14ad8dMexHNl8ZpoUZeif7eppRstSU0o96jcCcEUae2OXhG8uzkM7+TcodQfjtP6DGo4k0pjRp0qT5CLgsoT9hwoSkq6iqqsRiMR555JEPdGB/r3jQs1O/mNkLr8fiPop//3ogkbsnNh1hxLw70ehsdMghKjoakFd9gU6tQFiOkq+zotv7BlpPH2JvM0JGIY7i0czQG3GdFfkAkZu+wrqu/ezp3EeWKYNPVt2Mc+WXUU9sQdjzCwRBRHPLl9jd0Ex2YDr/8btEDqQgwOeXTWKyqR1HrJGyiJ0+OY9O+3hM9i34BQvGllqU1pNEuhpQbv8qLb31GEUN6zsOsE6O8S8zrmeCs4BfHX8ZRVXY2XaQiBxnQdkMNKKGuBJPOR/DLDnkbHsddWSUP6p9rF72cWy1B9H1n+KTqyfwfN0mXupImKJNVcawuno5EVEgrsQwSxaQr9yH1DRXNlFZpaHdQ0meldpWV0q+8r6T3dRMy+WZE2uSbaf7Grh37K0sHTGfgZCbgdBguO3cYdPRShqePrYWrajhgSV3Iss5yMTQigqKv5WqnGJ8G/9IbGsrpWe3s825HU12Cb1v/AEAXU4p+roDFB5+iyKtDjUWQbJl85VVX0Ts9qLNvhskiQp/kD5jGd9/oYXOgUSeZ7bDwOfvrOLHh/6bcDyCUWvg08MXU2qw0L30XnzIZKkSzhN7EM5sx5KVQb1BxBfxo4sOtRWLubqIBn1k6nVoRJWg0UjXsntpj/op0FrIa28kPtCNZHHSLSh0Bfr4yf4n+fdZN5AV8COIGgSdnkh3IwOGYv7fli66zuaKazUi37778+TWv0rM3Y2hZDSSxYEaDSNqjcRdXVgnLUKNRfEe2Ijs7UPQGdHYMjGUjsZUPh7v/vUY5z9IjKFh6VcLkgjhSJzfrz2OfFZ4FudauXvJyJR+GhGmjMpj+8F23L4wcycWodOIiYv+FYwkiITlKK+eWJfMy59WNIFrSqainKeCvRE/q6qXIooiwViITKOD/e1HUS4hleO+gaTIP0eo4RCO2avhbACNKmmI9bfj3Ze4V4ebjxE8vZvsVf988cGrCuGOOhzTVmKqnIqg0RL3exDO8+CQJMh1Gvnli0do60lMejkser54x4Qref7lb04oEkcriUlviYgcQ3MZrvti2I+s++hz9AF0khaT1sBAxEWWMXWSoipPx/GOKG+dCnDdqERFDMFoRbTlEm89grZs8kcx5DRp0qT5y3P0BUHAZrNhsVgussXVi14nIRptNLtDGA9uTvmbEgkSFkX+69TL6CUdldMf5I8nX6N+oBkASZT45+kfo+Dd9QRO7yL7jm8Rbz+NcJ5xkK60hs0DJ9jVsQer3sJni2Zh3Pi/+Dw9aLOKyVxwD979m9jae4LRtsk8vmGwrI+qwmNvdFGyUECz69nE/rQGAjM/j3vWVxEE0McaEfLKsU1bgeetZxjWXkuF2cH42Tfyi669nFZDTHz5Zzw0bi6/QaDe08bBzmPU5FZyXcU1rD+zJXm8yfljyW9rJubpRTjwJqtXfY7/ObaGbIeTHHMxBZ5ajp7nfL6n8yhjcypZW7eN3kA/U4vGs6ryeixDXAPSpLk0eo3ArLEFnGlz4/KmCt29J7tZce1YCiz5dPgTuflzhk3DabLS7Gkj15xDobmQrY0JrwiHwcYrpzYBMG/YDF6v20yjq4XlRVOY4wmjHN7GgAC2KctRZn0Mvz9Eri5M+NR2TDlliZVVwDR8Eu5dawEVNZYYk+ztxTdQT4duFGfO9JObaWF8Tozd9YGkyAfodYd550gPFp2ZcDxCKBbmV3UbuXvMjfzmzPpkv3tHLWSCRo8c9CIb7Ciqgs889Fekq5pKYP8GHCOn4QpLbBa8bK7dkfz7nLwx3Fo0j0BfG5vdidV3rajBodHj2vbb5HvSF1RSN2wcXedF9MTiCi+83cYnC7WIWgP+Y9uxT72euK8Xz+7Xku7miBqyrv8U0d5WDAUjCLWdQomE6H/jT4CKefpqMOb+Hz79fww0ksjmPc1JkQ/Q2u0jcJ75qixDJK7w02cPUZBlxmTQ8ttXjnHvsuqz0RFXrthXUKnta0gx39vddpBR2SM4314gy5zJmhPrONZzGkg8h3xq8t1Il3Buf6+VVNFgQRDP2y4awnvBvVoOuBNlHgvfvzyaKmlxTF6Ke/szhFtPIppsOGbchK54ZNKtX1XheONAUuQDuP0Rthxoo6yw+qJj/0fCF4phMgw+x0TlKLpL5eirCmI0hHqFCH1IeEd0B3qGCH1BEJg/0sSze71MHmbEaUp8v6SCamKn304L/TRp0nxkXHROee/evezdu5eOjo7kv/b2dk6ePMnevXs/rDH+XRENRblltMKUciOCZug8ijuWcKsWBZHWsCsp8gFkRebpE+uIFyZy92KAz2RCElQ0tmwA4hVj2dWeKLF3a9E0DJufQPH0JPr3teLetRbrdfdxYKCJaHToxxuKxAmpgw8/aixMRvduTrZ4qevw0+Kcyr7iu/Ds20jsbKivHHAjbXqcWwsmE4lHQInD22tYmTMGAJveQpnOwTUaJ49Mvpd7x97Ew1M/xm3lc4jveCl5HPPLv+AbwxbyiZobuaNyEUe6a4eM70hvHSatARWV3W0Heb1+M4hXd/humv8bqgLzJxbhtBoYOzw1FaQo1wxinBsrr+fhKZ/h02M+hRY9fzz4POZYIaK7iB/9ponFWXdy96g7ONo9OCGVYbTT6GrBqDEwQzGiHHwz8ZuQ43h3vYzW341bsHNMKcM78R7292jROPMSGwsC72UVbsDMD586xSt7enlsfSObGrWc6R66Ct/eESHrPB+QUCxMf9iT0ueZxu1EZ9+IxplHflxFJ2lZ03sMdcEdiEYLIKCvmoZ++EQiDQeRvW7OROJsbtqRsp/tXUcZyMrDUziMvkjCdGx+/jiC259PinyAmLuLfv/QEOpOdwzZ6CDUdARj2Vh8h94CRR4U+QBKHN+hNzBWzSDa0wTRMJGuekBFNFjgCnrI/ygIR2V6XUPNF33Bwbrwogh9rhChSJz6dg9H6/uIxRU2727hSk/SlxWZo2fF+/mcusB13xV2JUU+JCILnz32akoKw3sh6k3oi1KjH+xTrkc9/7QIIsJ7nCfhEkvuAuDe9Qrh1kRZWyXoZeDNx5HPelwk9gHt54n8c7R0+VIiFv7R8YdimPTnCX3l0iv6UjiIqtFdUaknmUYnncGe9/xbhlliXJGBp3cPXo+lgirkzlqUoPtDGmGaNGnSpHLRK+13vvMdAEKhEB0dHQwfPhyNRkNtbS0VFRW88sorH8og/54Ya/XRf+QdfPmTyJt6I/53nkn+TbJnUy8OGg75YkNLVPUE+pELpmOdsJhXeg4zwpaLtb8X68TriPW2Eg35yTZnEnAHyYqDGk0t3yL7+mn2d7OgfCaxoIJeKzGtyk5xhpZd9UECoTjWUGrZKo2/F0O2SHaGme//+TCfW5xLrP1USh9UBXs4wpjcUuSAF1AxRRLHvnv0cswv/QIl5CUTyMku5dTka9AUjUUuqiTedAwAU8VEtJ4Bwid2oeZXMzZrOLUX5PVXZwzjeO9gmsKO1v3cMHwpBoaa96VJ834IgkAwKqPViDywrJpgLJEfumZrHcV5ZqbPifP/dv03sqqgk7R8esq9ZCg2bi67jc1bfWTYOvEGojz1ShsVRXYKJ2bTQAuCIBCVE6upwxyFGJtOEL3w2E17KFd3E8wbT31oOL6IDuvizxDa9TzB+oNYaubgPzIY+SJanBztNwHuZNuG3S18fFkloiozIlfP4ZYQR5s8jKw08pZn0DtAL+mGuNJH5RhuOYLh9C6KRs/mX0avYlPvSf4caef2FZ8mIxRCzC3Fvf43AIRzRqPI711KLNB5Bucbz/CZuav5OVBlL0b2bkzpowS9VBcMNcxaMNqCI1fEP2wMpooJhFtPIr9HWT7Z5yKwfx3+Y9vROPPImHcXA28+jvO6TxDV2K9qNz6dRmR6TT6b97SktOc4B6+HigIxeahqfK+2Kw1JEBmRMYxGV+r7K3MWpbwORYeWKRsIuYekil2IaLSgLxiOdcJ1iJKEfNZ7QtBZkvNtglaPddJivHsGIxclixNNZv777PUs0RDhpqMXNKrInj44r2jA2BFZbD2Q6vcxfXTelRxo8TfHH4xh1A9GUUSVKFrp4iv6YtiPor+y7vtOow1/zE8kHkX/HtEiU8sMPLnLy4HmEBNLjQgaPVJBFbFT29BPvOEjGHGaNGmudi46Vfrqq6/y6quvUlNTw5NPPsnatWtZs2YNzz77LCUlJR/WGP+u8IYVnuobw7++2MPjdTlE53wG09iFMPV2rNc9yOlwIu+3LzRAjnmo4dy0wnE4tBaUUddQ626m1duFlFNKl6jQWlJBNKuQ2yvmIwkiUe3QG6UgaemVw7xwYh01WVZ+fX8htyqvMe3ML/hi6TG+e8cwhBObUrYJlMygutTB0xsTKyZ9AZDMjiH7zrHnkt3SgMaeDQhkZZfxxWkPUHbgHZTQYJkhubeZatXIgZYDOMYuQLJmoM0oQDSYcL/zAuHGIwR3PMuEqECZfbCiw0hnKSNVHVa9OdmWa85CK6Rd969GVMAViNHQ5cMVjF3W4qQgCHhCcU61eWju8VPb6uJEi4uXttVRlG3hPx+ayd03FvDn4y8gn12Vjsoxfrv/aWJKjIDQR1WZHVlWWb20kNtvymTqRDM19skYNQZUVcV4ttxTf8hD7D3EgNaRQ7SnCWHHHylUOohEZVRVRZORj2PmTZiqZ+BccDf6klHoJy3HuOIRHt+WukokIDApL8p9xreYWvszPpm1mx/dW86oEm1yokEjavjEhFvZ3XowZdtyZwmGcJBQ4xFkv5uMQ29zpw/u7w2ifeUXBHe8hE4B1/i5GCsmEnEa6FfayLemhshnmzNxelygKgjbX+Aro26iPHc45sqpF551SoUuHr6pCodFj04jcuO0HCZJtbjfeQFjaQ0D257BNmkx+oLhQ86XqXIKoaaEj0jc1UWo8Qg5q7+GWjDqqi6tBxCXFZxWPXMnFqGRBDLtBu5dVk00PhhBIYqJspEaKfUHMm9iEZcsNP8RI6sqU4vGU2jNS7ZVZZVT4RyW0i/HkpX0CTrH2NxqDNqL+zeoioKhsArvvnX0rn+MSOsptBn5qLHQ+Z3QZuSTcd3HMJaPxzZ1OZmLHkAJB99/xwBaw9l7YSrieeZxigJ2s46V15SjkQQEAa4ZX0hZgQ3hyv5o/qb4QzGMZ1f0VVRichSNePG0CzHsQ7nCInpEQcKhs9MT6n3Pv2skgetGmfjzbi++cOI3qikZT+zEFtRLTEqlSZMmzQfBZSmoxsZGJk6cmHw9evRompubL7LF1UtT0Mzu2lYA3j7pJirlM71mOVpJxHjwTyybuByjaOVI3xE6PT18YuIdPHv8VfyRAFMKx7KgfBbf2P04PcF+Hpx0FxlKAe9EG3mibRuyImPUGPj29E/wb9WrCIoq5ukrCexamzy+bu6tvNp5iFAsjCMYwPfKT1HjiTVH+dQ24lEvmUsfwrX1KVBkhHHLkTOKyFO7k2GQrx0YYNLiO9Fu//VgbnHlVOSTO9EVjUZVVZwL7ibwziuU2Zzoi0fiqtufch6EkIxqMeN55wUsNXPRlo2j/9n/TOkjbX6SL6z6Ah2+HkTA0dWK1NFARE6MVxJE7qxZhaSkHWuvOgQ4XD/AL144jKyoaCSBf1o9nrFlzvfVLoIgUNfl4ydPH0iUctJJfHxlDd5giLEV2bz6biPTRudgLugZIiADsSCiIPLsiZe4r+pBqqp1/O7I44T7I0iixC32m/nnaf/EQKADjcbMA+Pv4vmTr9A5rISCWgfK2XBdyZqBZHYi+xOvLT2HOdY7jQWji4g2HMZ/IDHJZptyPfGaFXxvoxfhcBvzJhbx+o6m5HjuuSabwKs/Qjlbp1tt2IPR30PGzFv5t6J5+HQasvMq2X5Q5ubKm3m14TVafe3UZI1iXtkMuvqbGCZpiftcKOEAMVcXWmceitaIduadPPZWL6PKK2BsDdvrN7K7/QC3j1lJbX8jZ/obGZk1gmU54+DFHyYGpMiY+jsRvP1IY+clztnp3WisGThm34KKwvRMF6NW5yAjYgl3EzpRS8SfOH58oANtdgneg5txzrsL36E3UMIBLKNnoy8YnrKaGm45jr5gBJKj5EqPPP/AMRu1KKpKc6eX1QsrcfsjvLytjodvm5DSLxKN8893TsIbjCLLCg6LjvoOL6J45YQ9vxeas2HZ909YjSvsQRREHHrbkJB8SdDw6Sn38PTRV3CFPIzNrWbFyOuIxmNcrPiaGgnS++rPkvdA/7FtyEEPjrl3ntcnRP8+HwRQAACp90lEQVSG3yBoDZgqJhBqOIR3z2tkLnkQ7CPef+eCQMbC++h5+SegJESdcfgkJNvgBL4ows6jXZxpdXH30mokUWDn0U6efeMM33hgyl94tv5+8QVjGHSJx82YHEcUJMRL+CtIIR/qFVJa73ycejudgR6KrYXv+fdCp5aR+Tp+/46Hzy10ItpzEYw24o0H0FZcOEmaJk2aNB8slyX0DQYDa9as4YYbbkBVVZ5//nlstrRB2nvR4088oAgC3Hv9KHYc6eQnTx/EZtbx8cU3US4aGC5OxWEeTaxLg2Az8+iMzyMTx9slc3SPh/tL7uNQcDdv1L/Ngvxl/OnUi0lhEoqHebZhG3d7JPSHNyGX1uCcewdqPIroLOClSDudgV4sOjMavyv5gHOOUMNBDMUjMVVMwjhyOlGfC+nQc+gyC7hlxnR+s6kVXzDGT97WcO/Mf6bS4kUKe4h0NRB3daMbOYfMRZ+g3S/SMrwalBglmgD2qmmEm46inA2x7FEzKNRlEHP30m8pQ3vupKSgog8FMMkyiiAiRiPEhl3DteFqZKKIMSvPv+Li4dWl6KSr/In/KsMdiPHLF48kTcjissovXzjMf312Fnbje4d8+iKxpMiHRH7zYy8f5aZ5w/nd2gPMnVCIKEqIETuSICZX9AFseivheCIn3ukU+N2Bp5KvZUXmudrn+ZLtYTLkElTJz97eQyyumEubAFx7G1nhMDmqBtnTi+udF5L7jZuy0GsF1KAb++SlDGx5ClWOEWo8jL10LF9fZMLt8hLJMKHXVbD3ZA8FGXpmlaq4DwxGyQDEeppQVQO67OkMdx+lPa6lolLA75a4Lms15mEC67d30GzwctR7kjtvf4Qj/Q3MqrwNtXY/0Y5ajOOuZX+XxFsHOnjrQBfzJxURKYggqwrrz2zjtuF3U67Oor09SltYoax0CkLDTgSNDjngRomE0BeZMJSPxzJuPoJGD6JEuLsZ2d1N+N3Ee3cBtsnLMJSOIdh4CNFgRgm4CZ3aRbjhMOaqaYh6I8Ez+9FmFqS8T11eOZI1k5hovNIXpD9wPP4ogiiyav5wGto9ZNoMfO7WCfQMBCnLOevuLUBuppkTjQO88OYZIjGZqaPyuGFuOZGYjFZ7cUH1UaKicrDzOLvbDjKlcBxxRWZX2wFuql5MdeZgnfmBkIvXat/kExPvQBJFevx9/M/O3/H1uZ+/6P7lwHvdAw8nXPfPXkYEnQHJloXs7SNwameyn2RxclHiUSI9LeTe/Ahxbx+i3oRoshHrbYGiwe90tsPIG3tb+ONrJ5JtM8fmX1WTWL5QNFlaLyJH0EmXnrwXQz5ko/WDHtpfjNPg5HDv8Yv6XM6sMPL8Ph+vH/GzYpwVzbCJRI+sTwv9NGnSfOhcltD/7ne/y5e//GW+/vWvA1BTU8OPfvSj//NB7733Xvr7+9GcNav7zne+Q0tLC7/61a+IxWLcf//93HXXXQDs2LGD733ve0QiEZYuXcoXv/hFAE6ePMnXv/51/H4/kydP5tvf/nZyfx8leVmJG9PEqhz2HO+itiURqu8NRPnvl87w8K3j+PkLg3l9NrOOb94/kR88fZKugcFwwi/dOpUmqRO9ITxk9XF/x1Hun/wZoiffJtx8jHDzMUS9Cc3Kf2b9qWcxa018ZuT1iIGhebeC3oQSDqBEQ4Rq9wBgLB2FoDUwum07n73perYd6SYvw4TNIiIGmhGtDozDxhI1NhEIx2n3CXxnbV+yXJnJoOGr991KQ0kx+Xo7wxyFBOUgCG5sd/4rp3tjvHXiBT4/YQHs3ZAci27ZQ7wc72Nj6zuoqsqkvBqGuQWefKXvbI+E0BnwRchzXHkz+2k+OLyBKPELcoyjcQVvIIrdqCUSV+jzhNHrJDKsOkQEugfCSZF/jlhcSYrFjr4AgiBwens/Ny9azcsNa4jKMcxaE3eMvYEnDydK7cXjCr5oai65qqr0BQYIuPWUFRvZ2b4XUq0u+NXEj+Pd8mTCmI9EfnC7uYrlMwtQlS7cu9fimHkTqqqgzSzCf3wb0ZM7MAEmUWLFjV+lalgV4ywDxPtah54UUeJMZ4isCoG9iptXdic8BoY7yxlnWMAfXulm1bzhGC39zM6Yxnf3/oFbSmfhX/cYiqcHyexArd/LKEsLU6umsue0i60H2vjM6Okc7D7KooIV/PSPdcTlxAnbDHx68VxG99Zin7wUz/4NCKKEYdgY8Ifx7t+ILqsIUWdEUFVcO9akDNe7fwM5t3wFrTMX0WRLLG9KGtRoCP/RrYNvyzD4MC+ZHdgmL0PNKL2aU/OTWE1asuwGfvznA8m2TLthyIq+xxflyfWDviq7j3eR5TBw68IRyNFL1Jr/CJFVhRO9tfQFB1IqtjQMtEDZYD+DRsfkgrH8dNfvichRCqy53Dx6Kap6cR8C0TC0OpCoNyKcH/IvSjivuY3+zb9Hm5GP7B/AUFKT8r18LwSdIWHAt/XPGIeNQfb1E2o5Qc4ND6f4dlSXZWC36PD4E616rcSCycVDUhH+kfEFB834EkL/Eo77gBT0EndceRU3jBoDkijiiXhwGOzv2UcSBZaPs/DMHi+ZZokZFSNQT21H7q5Dyh2avpQmTZo0HxSXpYwrKip46aWX6O9PuCVnZmb+nw+oqioNDQ1s3bo1Kcy7u7v54he/yJo1a9DpdNx+++1MmzaNoqIiHn30UZ544gny8/N56KGH2LZtG3PnzuWRRx7hP/7jPxg/fjyPPvoozz33HHfeeecljv4hoMS5flYZFpOW5988k/InVYWO/lTxHQjF6BwI0edJddh+YnMT/3zPTQj9pxAEIUXs2/VWtgabmHHTI2gHehEFUDIL2BNpY9WopciKTMRoYk8sSHVxNbGzrsAAzlk3owLx1pOIOiOiPQtjfgXevevQeAfo1gwA4LBpKM7Wo8YNhM7sR9AZMZSNB0ce297pTalJHgzHeedoN6d1x+gNDjAhvwaA/oCba4uWoreFaK/v4c2CIq6ZfzvmppPoRk7llEZhQ+3byf3s7zpGwYhishwG+tyJyACNJGLUXbkrUmk+GBxWPXqdROS875lRr8FpNeAKxvjBk/vpGkjk0C6fNYylM4bR2e/HoJNSvpsaSUiW6ZpQmcMLW86gKCpWsYCbqpcQU+LE5BhHOk8yuWAsHZ4+dIoVq96CLzLoli0IArLWzwsdj7PUPJ+RWcM51VeX/Hu2KYO2oAXflIcpM3gQJQmvIR+310CVTSSoOjBWTMX19nMAOOfdSejkeS73ikzgjcfIuP4h3FufQzRaMVbPTO0zdjmvHg5wc1kXaxq3J5vrXA1kF+SQaS9CURXG2Sy82HYMh95GmWBA8Q3gnHMbcsBDzN2DKa+Q1SNs7DntQlUh15hFriULV58mKfLP8fJ+D1Nv+xKh3c8je/swFI9C8bvp3/x7AEJn9iFZHDjn3JHixA+gyylBjQRwvfM8StiPdcJ1ZC19iL7XfpHsYx41G0FvwjnnNlRFRpc/nGhGFbJ69YigixGJKrz6dqphab8nTFd/gKKMwfzlxg7PhZuy82gXi6cPw6K9csP3JUFkdHYlja7Uia2KjNKU1xpJw5oTgyUkO3zd7GzZz5jskXARrS8areiLqoi0DTr222euSnFyVyNBBJ2BjPl3EWw4hLFsDIaSGmT/AJguUl4vGkE0mNHnlOI7sgWtPRvnrJuJB31w3hzBvpOJz8GolxBIRCdt2NlEVfH4i5+cfyB8wSiZtsRkfUSOor1kaT0VKexH0Zkv3u8jIsPgpDPQ/b5CH8CiF7lpgpUXD/hQVJXpwyYSObwO06KLR6GkSZMmzd+SyxL6fX19/Mu//As7d+5ElmWmTJnCD37wA3Jz//LZ1oaGBgRB4MEHH6S/v59bb70Vs9nM9OnTcTgcACxevJgNGzYwdepUSktLKS5O3GxXrFjBhg0bGD58OOFwmPHjxwOwatUqfvrTn14RQl+ORZk/ykqrW+XWBSPQ6SQ272mhsy+xQqjTDD5gzJ9URF6mmRPNHm5ZMJwBb5hNuxPuw/5gDIvPh7r3DT4+dRF/rHuTuBLHqDVwQ/UiXjy+nn2mDBbbbiTP1MJ39v4sZRxWvYVrSqcyMKKSyaOm4ZRBo9XjP7KVSPtpTJVTMVdNJdR8jGhnA7bJy4iGglRFDGRWCBwY2IK7vxL5zceT+wyc3kXWss/S5Rv6EO5yx7EUW+gNDnCw8xgPjL+dptMmfvV4G3fdkigHtqnjAFslLaU5RTxgMHBy4MyQ/RzuOUJ58fyk0L93WTUO81Bn8TT/2NiNGr50x0R++uxBAuE4ZqOWL9w2HotBw2OvHk+KfIDX3m1izPAs1u1o4tZrK3l602licQWNJLB6YWXS8ToUiWM2aCjONbPP9TaHe46kHPOTE+8h1lJJR6vADaW38GLjM4RiYTSihhtHLmJLY0J0rz+zhTvG3ECprYSjvcepdpayoGAezZ0qbQELHbKTg7U9HG84g6rCo/dPZkwmdI9bTGzEIkxCFNWX6nGiy6/ANHwidllhoCPxuzAt+gzhrAnoIm782gw2nIEuV4BO/wWhBMDJgROMKasi7PMSa/dSqExFjYyhP24nZ9E/4dvxFHFvwkAqVH8Ax+QVDMsrItNhRuf38LXc6RzwDV391GkE/Ac2Ys0pJe4bwDblenpfG7zWCFo91rHzkSwORJMt6SkAYKmZQ+/awb6eHS/hmHUzuau/RmygE9FgJtx6Et++11FiUSJtp7BNXoaQWX1VO+2fT1yR8QdjQ9ovjFxx2oZGPBVmW9Bc4XOkAjA8cxhlzuKk2B+TOzKlhCTAQGjoREZtfyMRJcbF7Nri/e3oskswlU9AiUUS6SK1ezAUjYSztdAFg5nIiXfx7lsHJCav/MfeJvuGhy8+eFEk1t9K8HQiMi7a00z/m0+Qe/OXU7qZ9FpK8q00d/qIxhWGFznocweTaUlXAwkzvsSXMSxHLin0xVji/q9KH32U5nuRYXDQ4munOrPyov0yLRK3TLLy0kEf3aXlLBnYgeLtQbTlfEgjTZMmzdXOZV1F//3f/51x48bxox/9CFmWeeKJJ/jWt77Fr371q7/4gF6vlxkzZvCtb32LcDjMvffey9KlS8nOHnSvzcnJ4ciRI/T09Axp7+7uHtKenZ1Nd/f/Z++tA+S4rnz/T1U1c/f0MLMGJI2YGSywZFsyM2XD2SS7yeLb9zZ5S2/z22yym2ySDZhZZsuSQWTJYpZGmhkNMzdzV/3+aLlH7ZFlr+M4sj2ff+y5ulV1q7rgnnvO+Z7+//FY/hDkOTT84zMN9F303IsC3LehhodfO8f8KhsOfeLjPrnUSTga58k3xjwN0yrSmV2TxaGzfayfmw/v/Bp5pJuK/W/yd3VLCGg0BB0Z/PbUC/ijAW7K30Bxy0H8aeXMz57M6ZHWpBfSG/ahlTQ837GD54HbSu+jxmnHPt+KPhxGDPkYfOU/k8cWDRYs01aht4d4tOEV5mTWIB7fQUrQZzxGpL+Z5ZWTOXmBFKoqdbzQc0nZL9HAa7t7AOjplKhOq6R+uIGYHEcSVFhjCgW68TmQkxzF1BTkUlliojgzjXy7ZcLI/wKiKFCRY+Gfv7oAdyCCxaDBrFMRjMY5fWFoXP+BkSC1JWm8vr+NTUvL0GokKgrsbNnRSO+QH7NBQ21pGjkZBlr7hzjlaRu3j0HfCPtPCOSvSEPwp3N/5VeRpQBmg4aeQDc1GRW4Qh78kQBPnn6JByd9hTmTa0kbHuWXW9to6g0xtdjCgDtKTVk6Q64gapXIhS43GWYnP3nuDK29XvLSTfzvzYnKFaCgL52OyuzAtXcL1tnrEbUG5HCA0DuPMjT9G/z7mz6i8YShs7rOQZZqfEpOvqmAnsYIy+Y6eexQkEONifN7m35Wz8xmgzULPGNK0YHjr/ON9f+HvogBkz2KoTdCidiNXqsiGB4zIm+aaUU5dAC3LJN5018mPKGXFAC3L7wJ18GXUY5ux7H4VjzH3yA61IUqpwI5mhqlBOA7vQeVM5eRtx9OtulL6ngvv0KTU074C2QAfRhGrYqVs/J5bufYC1cUBQqzxzRyZBnyM0yU5llp7krcJ3qtivULilGrVFzNBdujiswr598k15LFjJwpCEDzSDsHu44zLbMm2c+iGb8IlW3KQPqQ8HfRYMF7/M2Lf0ggxxENlpTQfSUUwHviLSSjFW1uJTHPEJGLmjQYCj9gzwAKgcYjqU1yjLjfnSyvpygK1SVp/ONDh5KRRqIAf3XvbCTxisEInyt8gSj6i2J8kXgEzYcZ+kEfca3xg5Pg/8hYNRb80SaCsRB61ZXTCh1GidtmWXjttI/22FoeOPYmaUvv+JRGOsEEE3zR+UiGfltbGz/5yU+Sf3/rW99i/fr1H+uA06ZNY9q0RH6hwWDgxhtv5J/+6Z/4yle+ktLv/eHqH6X9f0Ja2viJwyfB6Qt9SSMfQFZgx5FOfnhbCfqmN6FH4HubVxIS9PzHc6k1eI83DnLPumrKc0wsLBaINowAArJ7EFXvEMHMeXS0qNiQdTsZTigwmbBrLXhP7eTG4W42lNVxSAcvdOwnTW9HK2nZVHwzCmDVmtjXW8/rbdvYVLSQeQd2phxbDngQJDWNYReKolzUCRp/nUWNnkl6D19eV8Kze3sQBFizKIvG8LtJcTOH3oYqNnZ9394/wIp5c/jTacuIB828daCXLpOeslA35dZ8mtwJT45Tb2dZwSyeOPMcZ0Za+NvS75GVkSr6mJ5+9YnzXI7Pyjjf46M+D5/2eb2/AGU4EmNymZMDZ/pS2nMzTMyqycLlC/PK3ha+csNk2vs82Mx6vnHTFKxGLQ+9Ws8N1zqZZNYj+6rY13kwZR/59mxuXGEi02Hk/3viKLG4wnfvq2B7y1uc7K/HrrNe9OzvZ8A/jNUqcaH/NAFfDsVODXeUDqFvf5NYVjYux2KmbawkwxCm0Rfn5f1dtPZ6mT8tjeLKMM/0H2bT2geJ7XoKXV4lo7ufBMB3di/WORsY3fM0ctBLwdmH+Ieb7qM/IGJSvDh6DyB3mZmWWc3x/oS4l1lrYm76Iow2A1H/IIcaXSnn9caRXpZeuwBj5yXvG0XBblKR3n2YWLQA38m3MQY8/N2a+zkxpMMVkJlb5SDt1BNJMbNw1zmQ1JinrcJz+DVU9mwiQ51JL/7IjkcxVs3DtvBmokiI/vELMpLJNs7wNFbOYfith7DOvR59bjkWxx//2flDfR+uxOWera4BD8U5Vm5dVcGOI53YTFo2Ly8nGoun9O8ZCXHXmira+zxEYjJ5GSbMBhWKLH8qz+zHPYYn6CXPmsPutgMp7TfXXotGoyE9PSHa5hqysrx4PjsuRtVoJQ131W1GK2lJv8L94nep0RdPIdh66qIyvoB19rUoipIcsyAImKYuRxAlgm2nUduzMNUsAlF1xfMK9Q0jGSxoskrQZhUjh4P46vciqNQp2x1vTE11kxXYuq+NioKpv/dv82m9j3/f58EXipGTacGoVyP4ZPQ6LSZTYrHlvf9eijQaQDSY0Ouvvoo7740p0+hgKDpEle3Dc+5NJrhnsZ43T4r882H4wdwgBfl/eK/+Z20e8lkb7wQTfBb4SIZ+LBYjHA6j1SZeyMFg8GMLyRw5coRoNMq8efOAxIp3bm4uQ0Njk8KBgQEyMjLIzMz8SO2Dg4NkZPzPXprDwz7kP4DnyBuIjGsb9YRxqEMIip9gyymq65bQHrt8bleRQySveSvKKRnL6vuJuweJGpw8fs7Ejhc6kv2+e0M5VscgA6/9HCWSWFgQBtqZV7ecxvRylhUv5NgRhZ0H+5AVWDDVSHGNkakZlWSY0i9b01VRZHKdRdxkMKMA4ZyZiM/+aCz3VlLRabfRqZY5F97Hl+9cSFyJ44kPoR+SsOkslKcVMyN7MgMDqWJmOw4MUJk7lZ8/dxKAjn4t319ZzJcUGHJWoehM5BidRJ78J+7KLiE27W5cHi2DGm9yH+npZgYHvVztXK3jvNJH9KM8D1fLed24rJyWbjcDo4n7fuWsfIx6FRpB4UsbaugY8LPzaCdpVj07j3ZiNqg429LOiDdMt7+dbe3b+OrMu+jx9tLq6kAQBJbmL0EdN1NYCLv39xKLK0yrdLBv8G1ODSQM6tGQmydOvcTNtRtQFIWn6rewIncaks7EYu1RlMNbEyJcA+1YO09gWvdtfM/+G9XzruNkJAeHRUduhZvtnTuY5CjkIXcD92/+DvSNPddx3yj+hoPYl91JWGPF5EhD9o2Sq5ERRBWu851otIVsnnIdZc4SBEHAprGhESPEoxECqnxgYNw1iwmpHjSpegVe0UqGLROVMZ1QLIYc8mN45z9YZLIhavQY3HPwDCTSCCSjDTkSQmU1o80sQjJYifldRPrG8seVeBTfuXfRF08hfHYv2tyKpJo5AKKEafJiRK0J46R5KIqMafISBFEk44bvItsLccV18CndY7/v8/BJj+WDni0FsJm03LehBllWaOocZUpZerK/RgORmEJ92zA9gz70WjUt3W7WLygiEpP/4M/s7/Ne0BhgSdEcjvacJhANoqDg1NupSi8nEongvqhd448FGQyMcHPttcTlOArwTttBNtesu+KxdSE/ot6CfcmtKLEoglqL7+xedEVTGBz0Ju4BgxklGsZ7KrH4HR3sINRxlsyb/uqK+zYrMZzrvor70Cu49m1BNFiwL7oZ0WBJbme1avEHx88JAqEoisLv9dt80u/jP9TzEI3JhMIxIqEI0XAUl88HcQGfL4zJpMXnGx/5YxgZRlTpCF/m2v0x0es1BC+Oyaqxc2GojXx93kfefkG5CZO3g7/4+bv82e2zKcz6wxm2V8v3+qNytY13YtFhgs8LH8nQX7duHffeey+bNm0C4Pnnn+eaa675WAf0er389Kc/5amnniIajfLCCy/wr//6r3zve99jZGQEvV7PG2+8wQ9/+EMqKytpbW2lvb2dvLw8Xn31VTZv3kxubi5arZajR48yY8YMXnzxRRYvXvyxxvNJU5KuQRBSU0xXT7UR3fnfaHPL0V7/13z/6T6mlMUpybXQ0j2W05rjNJLuEIl0aTE5CnAfeZ1oXwvu+V9nx4kxD6YoCmTEuokODSeN/PeQT+/h3tv/htM9Wt4+MKbCHPb7WYSaBR2DCP0HMa9+APeep4mO9AIgqDTgyOadrmMc7T0DwD5zJl+546+wHdiGojMyVFTJk73HKHYU0OnuxVakxyeP8vDR5yi25zMtu5YeTz/+aJCKgmxWzRZo6fEyf3IOVqOaow1j6RXD7jD/66Uo962ZQo0jivbIm4SaDoOkQuUZRnrnOfLWfOcT/W0m+GwiigKiKBCLyYRiMn3DPm5eWYEoCEiSwJsHO/irn7/L/75/DsFwjLY+D++e7uWutVUA6LRqmrsTIc1mKSEk6vOoyHQtY2oOIEscPujlZKiDr91Riu/iRO6GNdn80/4nsOutzM+fgVpSc7z3DBatkW5PHzNzpyDqzBTZTHh2vZkyZiUSQvL0osQi+N55lmuXfg2N3Y5WaeJv0megbzqBmJaDYbAbRZaRTHbitQtRJIlIPE6HxUiJ1sTglv+HcrFkpb5oCo6V9+I3Wvj7vT8hesli3fdnfZcDZ/xI4jBZaQb6hsc0DEpzrQj2XITatWjdbQhl89neZSW3L4BTF8Wz61GMNQtx738BgLjPRVzyJVT6YxHUjhyMa75Mt1qDKziKU47hFEVio33oiyYT7hp7z5inLmf0nWeJufoJtp3BOmdDQuVcpUZldiLY84h1n0VXNBk5HGDw5f/EUDUXU8VsGGjCYMkgpM/6VI3sq5l4TEGrFvnxS2eSOd2FWWamVaSn9HP7wjzzVqrmiUGr4tZVZVd1iUJFVBj2jfDd+V+i3dWFSpQosOXR5xqg3FaU7DfoH6Z5pJ1MkxOL1kzDUAtnBxpYX7Hiijn6KquTYOsJgq0nkUw2osO9mCYvRrikPrsS8OA7sydlOznkJzraC1cy4iQtnmPbCLUnvpdywMPw9t+Qectfp3SbMSkzqb3zHitm5SMJwhcidN8biGDUq5LOoWA09KE5+lLARczkuGKfPzYOrY3G0Wb80SBG9ZXuwlQmV2ShPnOef3taw1/cMZ0c59UpODjBBBN8PvhIhv7Xv/51srKyeOedd5BlmU2bNnHjjTd+rAMuW7aMkydPcv311yPLMrfffjszZszgO9/5DnfffTfRaJQbb7yRKVOmAPDP//zPfPOb3yQcDrNkyRLWrFkDwI9+9CP+9m//Fr/fT3V1NXfffffHGs8nTZYpxF9szOOx/aO4fRHWTEtjrqaRuGeQgGcQl30Gg64QO492cevqSioL7JxoGqK21MHsaRYOes4QK8hnVnolqneeAiAcT1VNznQYMIycR3Cmjzu+oNLQ4urk2Dlbsk2jErm1JkZo2y+SbeGWUziu/1P8B15GsqajL6zllBBOGvkAvd5+9g2cZ0N6Iacz0vjV2Ze4b9otRHw6BKGC5pYodd7D3DPlRp448yKto53cNXUTx3vP8OjJLSzKn881BQt4dW8Lm691Iojjy6Wd7vJR6PSjNB3GWDUftTOXyEA7amsGuriLAKmh+xN8cRBFAU8oxrHzA5xuGWHR1Bz84Sj//eLYPVpZaCcrzYgsKxw610//SICibAsalciJxkE2Ly/DH4iSYdczMBpE8dm4pmQ50aCGnYfaxx3zbE8bk2usBEM2AgGFeXnTyTSnUz/QhCiIFNnySdM72X5hN1pJS4WzhDOREIUqNUo8VTRNiIWS/28YOU+OfTl17iDxw68TBuhtJtR4BNtN3+fMsut4rnkXUzOrCcdCaIebyG1uTxr5AMG2U+hKphLX16XYboIg4BpWs31/O5IocMeaSbT0uGnuclNd5CDDYeDvHq0nw15Ihn0SdYF0thw8z/8qzwREom0niWq12FfczahKIqTVkp5WgFsWMd/5DwxHdex3n+O5hpeTx/tS3W1UNvqRPEOYpy7He3oPgiiizS7De+yNxMDkWHLxwLHqAQS9mVj/BVTWdKIjvUT6WrAvvBHJ6qT/+R9BPIagNZB+7TcIOasn9PgAtVrk6bcamDPVSWGBhBwT2Xc4Ec2SbRszLtr7EgvGBVlmjDo1TZ2jHG8cZOPiEsxXcdUSQQGrwcY/vfMzohefH6PGwJ/P/5OUfnadjU3Va9nauIORoIvJmZO4u24zGpUaxgenJYn7PTjXfY1I7wUiwz1Ypq0GQUQJB0FruzgIEUFSocipZQgF8crXTYmFCDYff38rMfcAWMtT2r596zS2vttGJBpn1ZwCTHrV1bz+8oni9kcw6cYM+2AshFVzZW+pFPAQSfvonvI/BqIgkqF30ubuoMZZ+dG3MzkoN4eQTQo/fvYkf3fPTMyGqy9FYYIJJvh88JEM/XvuuYeHH36YzZs3fyIH/fa3v823v/3tlLYNGzawYcOGcX3nzZvHyy+/PK590qRJPPfcc5/IeD5JVEqcNKed9fMsRGIK2doAYkdv8t/1ciKkXVbgie0NfOXGapYtsNE0eoH/OPkQ0XiUQlseDoOdyqlL4dibOOMD2M0WRr2JEDe3L4xiyUGOeFBZ04m5x0S2jPM30REPUpSXxeGL9lBdqQ3Dha2kmCGKjKv9NPunzcGoNZGtMnHOnTB8rDoLq7Pr0IkqTvv6iKfX0R7u4sHptxGKhSCUwSs7mtBqBii/eQFH93lZW3o3DpuG80OHOTOQEBg0iTYefe08m6918stTv+L26gc4cFpIeutEUaCmSosvMogtvQBRa8D1zrPJIfouHMO86XvExfGifRN8vnEFo+w82sXp5mEmFTkozrHw708fZ938InKcRnouVrFoaB9lxqRE2s6QK4jTquPQ2T6+cdNUugb9lOZa6Rzwcf+GWvae6GLUJZOjnk5GmgZINfStJg1BxUu97yT33XAz5zuGmJM/naFBBWNvBv6ATGatgSGvm3XFS7FKKh5v3M7BeIS/W7CJwI7HkvuSLE7kS4x0yZHH7CINocfeTjmmEgkSHOnh4aaE4neuJYtnz77KbSVLkYf2jbsuSjhAyB1gYe5CdnXuItuYxXVlG2lpTIQ8xmWFR7aeS3p9DVopKfjZPxIg06HnXNsoBRl6nKNnkLOzAAgN91JfVs3DzW8TjIXIMqVzTflSXqzfzldn3c2WY6+MjUFReOjUs/xwyf1Y/V7UFifmaatAkIgH3LwnMngpKrOd/qd+iKF8BnLQT6jjLAD+i6H+hpI6Ak1HUMIBhl7/Bc7bfkBQsn34jfI5JxyNs3iunUOe7bzY24ZKVLFy6UpQpVq3hdkWHthYw/m2UbzBCLeuqiQYiWEyqCB29ZqUoijwdvNeFEVhcuYkYnKM80PNnO4/T7m9ONlPo1Lz+KkXkvo8p/vPoxJUTMmsvuL+BZWK4e2/SRjfQKDhAJYZ16DJLr2kE1imrcJ96NVkk8qWiai7sqdVQBz3/QUQ1anibAfP9HHkfD+blpWjUYm88k4rOq1EVfHHL1P8WcLti2DUX2Lox0OkS1c4dzmOGPIha69+T3eWMYOzww1UOSoQxY+eziplV1DZd4SR7FX86uWzfOeWOsSrVHhwggkm+GzzkQrser1eAoHAh3ecgN6Alr99pJ5fvlTP7147xz8+3855x1JEfULMxvO+yataDUORAR47vSXp0Wh3dXG4+ySRosQkRjrxAt9fbWZGRRpGvZqaMhtyQQneC0cxTV6Cdc5GTLWLsV3/HQ4YJM4PXSCjwM/GZbkARGUQ1eNXjPVaIzs6DqEIAs+07iLbnMEMZzl/mTadWXu3U/v289wTMyEbcpiRM4UWVwd7Ow4TNrdy/apsotE4XUE9h88N8fSrPRjUGo72nkzuXycaiERl/AyioPBG74vcf0sOy+ZksmJuFn//4HSGoufY6WrGOHs93lO7UsYXH+khOND8sfUgJvjsIQgCEVnhR48f55W9rbT1eti2v42zLcPMm5xNQ/sIt62u5OYVFdy2upKsNAPvRXnPqspk+cx81swr4um3mnjmrUYe336eNKuO7kEvS2bko1ZJ/OqFM4TwcM3CrORxVZLA5jXZ7OvdQ8NoA974EBqrh7DHwO+e7uXdE4OcbBzm4ec78Q4ZmTQyTObu5/kz8yQerLme9ows5HUPop66HPPyO7HMWo/70GsASPZsfFl5HOo9hnCZclER5VKhrkTUy/HRNoTiyeP6quxZnOpTCHfnc+eku1iXfTMtrjYynanPd3ufF78vwJwCidpiOya9mtWzcrl5aTGTM+GbM8KIJ19CsDoRzen4pi/llw2vE7wYhdDnG2RHyz5qMytpGW0bJ4AaiUeJabQIwOAr/0HPw3+Da+8zyOEA5umrU/qa61bgbzwMchxtRlHSyH+PYOsptFklY9cg4IHLlFP7IqLRQGv8GM2uNgBicoxtbdvQ21I1UCxGDY9sPce+Uz2cahrisW3nybAbiF3FRj5AXAaDWs9NNeuJxKNIgsRdUzcRiacuZPT7Bsfdg8f7zhCOjc/vvhQ5HEwa+e/hPbkz5ZsiSBrkaBT7ktsw1izEOvc6TDULExUmroRKwjrv+oSa/0X0xVOQTKkL0wVZFjz+KA+9Ws+vXjxD77Cfinw70tUbaPGJ4vaHMejG3nvhWAiN9MGh+1LQi6zRp1zXqxWT2ohepaXL1/M/2k6w56JEgizIl3H7I2w/1PHhG00wwQQTfAw+kkdfr9ezbNkyKisrMRgMyfZf/OIXV9jqi0nrqEQ4mhoCuOWwi+q6pZgys3n4yNgEZlqVHYsjwnBovABJ/UAj6qq1qJ35xP2jpPkb+NKGlezra+Tk8H7+9kQrt0xfxHS1DUtcwVM2hb878hDheCK/uHmknRuq1vLArXnIsozZsJxwx1ne87QJKg3a9ALs0U5KTZnsjEcRBZFbnLXIr/4ymTsYPvgqFmMaWyPtDAZGUIkqttS/xpSMyaxfPAdXMHE+eRlGLCY1WaYs7Bo7k0zTsYhpPHidCo+qFYDBwBCPN/+GTKOTPEs2OaKAvbGeSOkUVAbrmOjfJXhDXiQxghS/ck7fBJ99vKEYxxoH0agleoZ8yfY0q46KAjtTytO40Onmx08eQ1ZAEgXuWluFICh8ddNkaovtROMKT2w/jy+YWDTr6PPyzJsNfPOWOmIxhZIcC1+7cTJqnZeA4yR33VxDui4TdD52db2NN5IwoIJyEK88wkiPhVg89b7csX+QaTcUc6jWx0jMT37YxaOnXkBAIN2YRrC/jS/X3Uzm6nuwxuIEsor4i4O/wKazMHn2Gnjn+eS+RLMDr9mW/DsmxzCqDZwfbaOrcj35fjfx7iYElQbrvOuRwwFmmgcIRzMJD6kIG0Z5o+MNri3SMrksjdMXhgGwm7Wsnp9PRriFLzsPEC/JQd29E9VxCzm2dLwH30Qz7zq8IR/iqm8xEu9GeZ8XvtPdw6zcqUTlGBpJTeSS1ASL1kxGOMzgyz9NPreBpiMosoyhcjbp13+HmHsQlcWJaLAyuutxBI1+3DHe49J20WAB/eXFSr9oxMQApwfOjmsfCvVTYR7zeDd2uIjGUu/Tl3Y3U1PiQC19pPX8PwqSIFCZXsIvDo9Fw5wdaOQ78x9M6aeRxi9UO/Q2REG8sgbBZb4pihxPvQ/jEfTFkxnZ8QggEIycxjZ347hQ/nGIaiRbBo4VdyMHvQgqDZI5LaV0H8CkYgcVBTYaO1wAZDj0LJ6ei6SIxPiQY3wOcPsjGLRjU81QPHzZ3/M9pIALWffpV734uOSbc6kfaSDPlPORvfqCICBlVSK3HGTd3I08/mYjk0vSyEv/7Jz3BBNM8NngQw39xsZGVqxYwcKFC8nKyvqw7l945Mu86EVBIFq7kH61yJKlXqqn6NBrVWSmafnNqUe4a+r4lIhCWy6qU3vRpOcjlU3jhDMNU3yY55q2JPs82bqbkpJriL/xGC1r70wa+e/xVvMe5hfM5NzwBeaOZuJYcReR/jYElQa1I4dA/bvcNmslQsBLj7efTFM60oU94wSCQmd2MWfRBoRYOgePeVlSrefA6NvUTpEYHExEeiyd66Spa4TrCjdz6NQoD2/tB/pRSQJ//eUa0g0OBgMjAAwEhrm+/DqaIhbyDTak3c/izSnHOGku/nPvJo8rGW14jSbSlI+0HjXBZ5hwXOEnz56gpdvD7deM5TtWFzuoLXHy2r4WjHoVz+9sSk7R47LCU2828E9fXYDNoEJRoHfEnzTyAcwGNRsWlfDjJ48zOBokK83AhoUl7H7Hz5qVi+jx9tLmP8a5tiYKbXlMza6mYbAFi9YIZNB1medZEkW29p9mT9c+lhTNZVtzQshLQWHgYkm5RncXZlFH/K2HaL/2AWRFZiTo4iXzMGtW34W+qxljegH1Bg2jcR9WrRl32MsbzXu4bfJ1HOw8xq9bdnDb9BXMXnoH6oAX97HthC8Kf81a/9ccHbYQISHG+WrbKyycvIDFc+oQZDUFGUaO9uzEcnQ/sqsfgUQqcwwwrLofy+xrESrnEjjxDvZpKxlV8sedp11vxRv2c26gkW/PuINfntyCO+wlTW/nT4qWovS3jTOkgs3H0WaVoKuaRyzoRw4HkEM+VLYM9IU1qCzpaDKLiPS3JbfR5JQTG014XQWNDufarxBS2a5qEblPCxUaCm151A82prRbdak5zqrLfXdE4aoPB44pcfa0pZa5VFA43d/A9MzaZJvTYGdqZjUnL5aUFASBm2s3oJbUVyxGL+pMiAZLsgQkgKl6AXCpR1+N79w+9MVTkQwWUBQ8p3bhXHnPlQcfCeI9+iam6vmIkhpBrSMe8hId6oK83GS3UCjGrKosZkzKRAFEAYKhGPEvhBQfuLxhjJfk6IdiH2Lo+9zIWsMH/vvVhk1jRSNqaHG3UXZJusmHIWYUETt2BgteFk7J5tev1PO398xEdRUvzE0wwQSfPa5oQW3ZsoV/+Zd/obCwkI6ODn70ox+xaNGiT2tsn0mc6SJ6rYpgeMxzf+3SXC6Eu3B53ORqSvC5RRZPMTM00gbAmYEG5uXPYH/nUQB0Ki23Fywk9sJ/ErvoVSja8FWCKiOZRif9l9Sn1kSjgIJ0mQmdWlITk2P0evvx5lUSevsRVLZMsOfRYZpMl3MjtiEtGN1MSitlyD9Mnj0rNZcfiFud7Ow8RFRWWDdnPW2uDqZm1aDXSEzNt/KnN1rQWQX6PMMM9InsPjwWKhmLK/zXE8382QMP0Ovrp8/tRh9P4+nnRxgc7eXbd11LXuV0vCEvjoxSRGc26uaTRJ259BeUYbIXgHx1T1Yn+HjEFQVZTohF9o/6GXGH2bSsDItRy/0bqnluxwVmTMrk0dfPUZ5vIyvNyPqFJeg0Et5AlFf3thCKxPEHo1j1iVeZ2aBOqXqxek4hj287jz+UeB77hgM883YjS6fnEfFpOdpzKqkp0ebqYkZWHatzN+AZiuJwGBFyI2hUIpFLvKUbF+fxUHtCZC4aj6EWx79G0zRm9FsfRQaMhrHSn/sHznFwsIHa9EruqJzPqeY3ONRygnUVy9GptFi1ZvJCYapHY8Qd04hZstk9WI8/Hqa6Zja2oBtloBNcPVQXFXC2z40gCCiKwt7ufexlH9nGdL4dmcqinDrirpfGjU0RJEKd59BLaqz5iVzlHLWVtSVLeL1lNwAqUcW1lSt55fybPFC8nNLeHv7SXEUgw4jBaKMnHsJrGJ9DK5msKIqM/9y7hDrPIekt+BvGaqRrssuwztlIuK+NcOdZNNmlGMpngUaPvnIOoiWDgNo5YeS/R1xiY+U1tLu68EcTi6rTsmtI04zdU4oCJXk2tBqJ8CX12q9fUopaJVzREP5jI4kSqss8PyoxYewIgoAggISKNIMtWV5PJaoZCYwifohhJYcD2OZeR7i/ldhoH7qCmkSZvUsqVsjxGIHzB8ctWsX8bj5MCzbcVU/wwhHUjmziAQ9y0Itz/ddS+rT2unl8+/mUtmvmFlKe/8WIWnH5IhRmJjzVsiITk6NorqC6L/lGiJtsn9Lofn8EQaDEVsipwbNkmzIxqj/aIoUgqpCyKoheOMCUqetp6nKz9UA7Gxd89MWCCSaYYIIP44qG/qOPPsorr7xCZmYmx48f58c//vGEof8heJRebtuURme7iNcnU1mu4WRgB2dPnCXLlMFtkwtw5kRp6ItS0X+BvzBNwi3rMNnyWZo9HXc0jlkxoBkcRNQZL3oiBHyCQsilZU3edTzd/Bihi7mJKlsOCCI5wTBmrQlveCzk+dqKVbzcsJ2oHGPYkkPB9PXEGvbSnLWaf3+5G0iE+RbnmLl27RKerX+Wyik3I5odyN6E911Qa3FXTqfx/EvcNXUTvzr930ktAbWo4m/n/QnlxgFc2ipku5+BwPgZul6rwj0qMdRjx2lIJ8cYI3dOmF0tWprbo5inZ/JWcxNFPjUFxVWkVcxiNOzFrnNgl5zjcjOvRgQB/OE4sgKO+FU8s74KkBVo6vHw5BsNhKNx7l5XRbbTwN3rqhj1htBpJOS4zNc2T6G5243FqGF2TRb/9sSx5D6Ksi2snVfE3pM9WE1j3iGHScvf3T+HQXeIcCQxmX/PyH8Pty+CVqMiLoSSRj7A9Izp6Iem8PevHEdRICfdyB3XZfOdeyZx8pwXly/MorpsNCYXkZbEM3C09zSbqtbw5Okxg9qg1lNgy8O/8DqMp/Yiu1QszVvKrq5dAGgkNXW2eZzsbKXfN8T9U2/EJKqwxmXsgpmAKg3F4yaUX86/HHuMQDThtX8ZgT+bsxHna78DrRFDqB+7Jo2bym7mldaXCMZCOA0OvjL1Juw97XT5zFhLZxJtPnzJ2QsIKjX6gmp8p3ciqPU4N/8leu8gM4UiJk8rIxJzY3Hk4A16+Pa02zDtfQEKpyIceBVzaR2vZKfxTt9prs2fzaK8SSjJ8noC1lnXokrLZfDFH2Odd12KuCZApPcCSixCsPUEamcefpuT4ZgfW1oBkj4PbdyHZrgB5DiCPZew6othDH0QcVkhEguzsnQhKlGFJIoM+odTQoQFAYxaiW/fOo2TTYO4fRFmV2eS6TAQDMcxqa/eXGdZkVlRsoDT/eeT4fRqUcXkzCpON4/w6LbzVBbYqajzsqP13ZRtVaKKuuwatHxwuLNkcuA9/iZxvwuVNR3vibexzr8BQXNpOTQBVXoB5qp5KLEIgqQmMtCOeBk9jRQ0eqyzr2V091NEh7sTY3fkoLI4uVQ5oL13fGpeY8foF2Yta9QXpqYooVsQjIUS3vwrRJqo/C6izvERRlczRpWBPFMuh/qOsSR3wUcO4Zcyy4iceA2l0s3qmfk8+kYD08rTyc+YCOGfYIIJPhk+NCY6MzMTgGnTpjE6OvoHH9BnHacuk0daHuOr09bjDXh5e/AsZ4cTxkSfb4AuTy/PNrxKgTWPr9XegtnThWWwA6/JyX8+3s3AaEIMS6uW+F8bvox517+in72esLWUrTv6kERYlXsnkj6IpGh56ViMe9Z/i+DeJ/jzWas5K8UYjofI0ZZz/jgszb6JgkoVfd1qnj5byJ0rlvDQS6n1llt7vIyOmFlWPJ99w4PMW/MdrKEBhv1DDGjVPNTyFlmmdNpcXUkjHyAqx9jZfZxYPEa12kb92Thza50pHlW9VsU18wr559+dSLYVZhj41tRRbrF00WpfQf3gBXQqLVvObgUBluVOZ5VPxhSJop2xgZD66lbdj8QV3j3Ty9NvNxGPK2xcXMKK6Xno1RMheJeja9jPS3uaWTErH6dNT8+Qj51HOzlybiwSZNOyMkx6NeX5NopzrPzyhVMp+2jr9bB4Wi7fu3MGCgmnZVxROHCmj4e3nkOWFVSSyF/dMwtRgEvLsqskAVEAo06LgJA0MEp1dTzy7pioUs+gn13vepg7V0tuuZdq0YnPLeMajZFtymRZ+iSskpa+oIevz7qL84MtaEUTOZoS3MMG3vR2MmP6fHLCEdpPZLK57j7iQph4UM/efUGy69rI01goOrYH5UJiEcMriMSWfI239BvJ1o0mjXxIhDS/1HeCL09fg2RLR9t5iKrsaRwazuPBqq8SI0xzW5j/81/t/N1aE9qhvQwUXkNaLILcfhLRYMG07F6arWZCFj25JTXw2n8ju7qJDnZidAcJiXU0hjrYenqsosmXqtYwpb0dy6z19NjsvNOaKJ/3auchpIq5TK2aSbZkQGuwgCAQHe1DiaWmEV1KPBQgHovQXlrFb1rext8RIMvo5Osz7sD46q+JDCQ0PSSjFcfmvyaky/yf3WCfI1SaOE8dfYkeb39Ke0VaCRlqZ/Jvlz/Cb18+iygKGPVqfvbcSf7khslMr0hPvfmvMgQZTvWd4866TbS7ulCLKrLNmbSOdDBwXqa5K1Emsmp6xrhtVR9BrE2ORbDMWkd0pAclEsJQPhOVPRs5FoX3nMqiiG3WWoa2/jLp1deXzUA0fMgiUzSINq+a9Ou/DfE4SFKiJJ8q1VtdUWBj17GulLYp5U6+KHFqo54QFmNiMTYYC6JT6T64czyGGA58pkL33yPPlI17xM2Z4XNMSb9yNYj3ENQapMxSok37sUxZy+KpOfzq5bP83b0zUauu3gW6CSaY4LPDFS2R96udS18Umdjfg+CQyF/ZZ6Dd8hPSXvoFt/V7+VL5qpQ+xbZ8lhXP5ZS/hQNSlJMFxZwYFJJGPiTKKr18XiF+57+wPTCZYMBAtgk2ThLYvX+EhjMqYl4zMyuyiTqKaF16F8M6Pf1BF3Z1OtPVBlZkeJkW76f4wmH2nBqkpddHfU8Yr3/8JDweEygxVPPO22q+/csmDgfyeTXq5T+atuGN+NGr9QQiwXHbjQRdLCicSV+wk9JiLUPeEb5/1wwW1eWgUYmsm1/Iy3taUmpitw8E6FEXoDTto8oaBBT2dhxGQUFRFHZ0HeVCWhqB8+8SOPQiknB1e8hbej088vp5wpE4sbjM8zsvcKp5CEFS8OEhKPiu5MD4QiGIiUWg2lInL+5u5uk3G8l1mlKMfIDX9raiUou8c6KH1h433sD7E0rAbNDw65fO8K1/283PXzjDkCfMY9vOJ8s3xuIyz7zVwK2rU2scX7+kjHSbHu+ImjnZswGQBBG/d/zr8EzzCJ3NGnoabYS8Rjz+KM1NIf46cyF177xO4bbHWFR/hmopDVdDJcGOYvr643QGWyl3lhDWGbGWZDCz2MITL/bx5JYRdh8Y5Z51k0jTW1mSVoPSfGLsgIqM9sjjOE0Sw4Hxz1sgFiZes4hoyx5EjQ69FCA3O0J9i4tfP9XF2+8O8A+3FpItjuA0qynUewjWbELc/I+w8e/46dAR/uXQf/OTI4/yg5NPE934NeI+F76z72DVgcYpsbXzUMoxH2nZSWjqAsitQMksSPm3lzoO8IPWNzhpM1EfGmbw8GsoYT+i1kCkrxVd0ZSU/ip7NmpHFsHZ1/CfDa/hjyTC0fv8Q/zs6ONEc8fU9+N+N8HTOxC/wOtlMSL4IwGq0svYVL2WdRXLseksBGKhlH69Q35GPCHyM83kZ5gw6NS8ebCDcPTqfncKAnR7+3j0xBbODzZzvPcsj518npGgC1mWWVSXS3m+DbvOSpohdcF3bfmyKxuNgBCPEmg+htqRgzanHEFjwHv8rVQjW44zuvvJlND94IWjxP2uKw9epUUOuvEc2cbgK//B8PZfE3MPjeuWZtWxcVEJt6ys4NbVlaydV0RFvv0LUU0mGpPxh2LJHH1/LIBW0n5gf5V/lLje+OEVD65CBEGg0l5Gh7ebXv/Ah29wESm7knhPA3LQTW2xA5NBzdM7LvwBRzrBBBN8kfgfqZx9ET5Mvy9lOheB7Q8n/443n6DcYKHAks1kaz5LZT2L5DS8Q0OcUck81raHTKOT2tgmtOpUxf7e4SCvduxFn24gqjhYUO1A/cr/5nvrvsdTR4Ns29/OTSvK+fmrHfSNBFg4LZu55dW0dPlRZQTJ6HiLcM8F4s4CirOm0dzl5njjAPOmZHOkvp/VdWlkWiT2NfnJzzTR0BihZzChOj40EkEKVLKppJDuYDtZulwyrTaO9p5OOd+ajAqahttwhV00+XZRq1/Mtj1NiKLAVzZNITdbw6v72sZdp1A8sWikKEGahsf/+zFvF7XOPPz175A+5wbiKttlr7cgCAQicaJxGbNOdVkvSTgm0znop3fYT3aakfx0I1qV+L79wMfJEJAkkcPn+se17zzWTdTRzFNnX0Qrabi5+lpmpE9HpXyxqwf0u8KcaBrkpT0tAIx6w7T3jQ9tDUfjSKLIOye6Kc+3Mbc2i3dP9Sb/XSWJePxj2x5rGMDlDTFvcjZ7jncn+zV0uLjtmkn8r/vnMDgawGLSYtBJaNUSO452IYoVbCopoDvYRo5xfEJuTamDwmwLT73RwOv72zDp1fzrbbn4X/xxsk+0rxn3cJhgJEZDxyjTq+2k2aO83LgNWZEJVQZZm59B9VoN9vR0tL4ewudeYJa1lDP9BkqWfg/74f9G9iXSZeK+UayaGGopL5l//x5z8qfx94d+zjfLr0HRWni6+xDHBs5j1pq47/brmKZLw/Ps/8UVSRiCKks62Ytu5se7tWRP7uH88NgE0hfx885APTel1TA8fQk+rZlweGT8bxELc9Dfy772w9wx9XqyTBn0+cYmsrnmLPRqHT9qfI0v166hbN827EtuJdh5HsvkpWicuYS6G9FmFKKyZTD8xm9xr7s7WUrwPfr8QwQr5qE/pUa5GDkU6W3ChIz80SrBfu5QBLirbjOD/iFisoxRbeD6qmvIMqWn9BMFgXuvrWbP8W66B3ysmFVAOBwjIXZw9X63ZWBBwSzODzUnhSwB6rImc7Jb4HTzAMU5FpSAlY2VKxn0j+AOe8gxZ5OmtxGJRdDywfXWBbUGQ3Ed4Z4m4t4R1BmFidJ5l14TRSbuc43b9kpRKYkOCp6j2wlfTF2Rgz5G3n6YzBu/DxczA2RZQVEgEovz6r5WZFlhdk0WkiR8YAWKzxOjvjAWgyYZyh6IBtFeQYhP5R1F1n+IMMJVjFpUM8lexpG+46wqXIpO9cGLGu8hqLVIWWVEG/airVvPNbPyefSNRsryrMytnhDAnmCCCX4/rmjoNzQ0MH369OTfoVCI6dOnoygKgiBw7NixK2z9xURyd45vvHCc2699gOzms3h3/izRD6jLrWC0fCZxrZ5iSwhtNpiEdI4ei3KmycX0WjMHhuvxhL3k2zM5etrAxurF7GkJ0djp5s41k/j1S2eSQmFb3m7D5Ynj8UXQCTamVyzBXjYDRZZZne7gYP0AzV1uFk3J5PbqCPEDjyK3DDOreiEaay5b21wArJnmpDpXyw8fG0I8LJBhL+CgJ0RVSZj7FtzFG61vgaCwuHAO5wcvUGDLZV/nYW7Me4CHtozVg/3pMyf4qwemsmh6BqcbXSxf6EBRB1ArevI1iQWNUSVKriWTc4Op6QTlBicx90kkkw3lAyYGsgKnWob53atn8QWjzKnJ4rZVFZgvKeUjK/DyvjZe39+WbFs7r4jNi0sQBYgLUXpCvbS5O3EaHBSZ8tFfIedz3BhkhdzLlMQpyDaiUcW4btJqdrce4JFTW8ian0mhrghIHHc4OkIgGsCpT8MsWD8TWgS/D5Ik0tztZv/p3pR2RWGcgOWkQgdqlYhWIzG5zElprhWLUcO7p3rJtOu5fmkZv30ltexYS4+HObXZKW1mgxpREPjJs8cZ9SYyZzVqkfuvraYw28SvX+xAfVgkw16Iku9h6fRc9hzvprzAjs2kZfWcAn75wmkGRhPedV8wiq+/k0vvyOi0m/m/z3cl1f637wsyo8rJg0vuYDA4yOtNO1kw+0HyIj48+x7G/V4+L1A37QZ+vC+bL8++DePexLtBlVfLsY4oMzNN3D/tFvZ2HCYQDbK0aB4jwUQ4v6w380LXUY4NJIwMb9jHr04+zt9NuQVDZMzbG/MMEg96WTvZQZZaz/KCFQzoNPyubQ/eiJ8Obx+RjCn8Y8ubAPz13C+hltQpKTqZpnSGA6P0+gb46YHf8b0FX+GN5j20jHRQllZEib2A471nyTKl82L3Ef5207cx6K0opYuJdxzGf/4AmoxCQl2NxNNzGbr2AVQqNd+b9yVa3N282fwOnrAXk8ZIu0GLbvWtFHQ0I5x9F0P1QmKKxBdVnU8tqNGIKvIsOfgifjSSGklQJcrKXUK208j//e3BZJT+C7sucMeaSrRq6aoO3RcRaHN1cUvtBg50HUctqlhaNI8jR6Js25d4TnqH/ZxpGeZP7sjFoAkhiRKiILCn/eBlK9akoNIyuuc3RIfHUnJsi25C5cxJ/i1IKrR5k5IGe6JRRDLarrzvWJjQxSoYKc2+UXBcPD9RwBuIsm1/e/LfD53tozDLTFWh48r7/xww4h4L24dE6P6VDH3JN/SZKq13OaxaC5nGDI4NnGR+zuyPtI2UXUnkxGvIviF0JifXLSjm8TcasZu0VBZc3amLE0wwwdXNFQ39N99889Max+cGxTR+NVpy5mIRNQSPv53at7uRxTNW8rv+o7zZ/E6yfeOMjVQUFhAyX8DTnfBYHuo9yqyaWcjmtYhn/SyfacYXjKaogQPsPtbN9UtK+dUrDXxjXT4Vx/4N0ezA6sjkh/dWU98VZbIjQPSVf0+GKgbP7AJkrl98A4tK1VR3vwjv+vnyqrv41Vu9dA/60KhEppRmseWFNsoLV7J2YS6j8R7K0oppGm6lxFHIyTOBcee+63AfN68sY1JtEw+deYi4HEdAwFCxitqVd/Hq4BlmFMxMqSaQa0pnclyDEothX/MAEdFw2Xl+32iQnz5zIvn3gTN9mPRqbl9Znuw/7AunGPkAr+9vY+n0XDIsWhq9TfT6BhAEgd3t+9ke3cVXp92LDj2iKBK/KKwnCBDEjyvixqQ2YRYsKAp4Q1EkUcBp0zHkShhYJr2a4rI4D594Fq1Ky43V69jauIOmkVaKcouJEGZb21tsa94FgF6l48/nfYUsdQ6fZwQBtBoJm1mbNJy1Ggm3L8Rf3D2TJ7c30NHvZWq5k/J8O02dLr58/WQe3lrP8zsj5GWY2LCohIIME50DPobdqeHLWo1Eaa4Vg05FIBTDYtTwpetrae1xJ418SCwsuHwR7FYVs2rSOXx2kO5BH4oic9emPGbV1rHzcA/haBxvIIrVNDZegLBoTDH0+0nDFxxOGcvRc0NUTrGwq28/6yuW4+9twmLKSIp2vYd4+jXWTPlTeqJqygF1fg2aBXewJqbjpe7nON/QTE1GBVatmefqX+P6SdcgIGCRtBwaODfuGvdFvJS8ry3u6ie/dx/h3mZUQK5ayzevuZt/PP8Si3Pq6Am7k30fOfsq3519D78+uYXhwCgF1lyWl8zneM8Z7pq6iUxTOnFFZmHBbApsuRztPsXBruNUOkvZULmSwcAI2/pOMTmjgjyPF03YTzzgJdhygugN3+LHF15npC1xvOr0cvKtOdxQvYZnT7/CdZNWseXcdjxhL5uLFrLMfD2qopmEP+cLYFdCIY5WpeU/Dz2cFFqdmTOFtRXLUvq19XrG2fNvHepk0dQcNFdxJJ4gJHzrL57bzuTMScTkOKMuOWnkv4fHH2F4JMYzHa8k226quRblQ0oKxF0DKUY+gPvAy+iL60CXMKCUeBzb/Btwvfs84a4GJJMN++JbP9zjrtGjsmYQc6eGaUvv80g3tI+Pkjlyrp9rvwDq6sOeEGbDWBSbPxq8Ymk9lXeYcFbZpzG0PyiF5jyODpyix9dHjunDvfKCSoMqp5rI2bfRzbmFDLue9fMK+c/nT/O1GyZTVThh7E8wwQQfjysa+rm5uVf65wkuw6DRhDGnDLknESIrqLW46xbjcveQo4yflOg1JmamLaDOOp/OYBMH+w6zvWM7N1Rdw+undyT7FdnyUQSZ1/v24xkpY8/x7pR64+9h0KmoKLChkkS2nvRQfstf8XbPMQ60bKPQlMH12TOweHy4FAVVXjXoLMidpwie3YuuaiaVRRLy0UTN5irlEf5hzXr81iIUvZWt+9rIcRpZUlfI0bMDNLbGmFKdw/rSKh6rfxKHfnx4rUGrod8zyhP1zxC/WCpQQeHRpjf5P3Me5IYBEbXGwobKVbjDHiRRosxWRm+vRP+UaooUO/lxBfVlVGy7h/zj2t452cMNS0rRXwzNv7Tc1KXE4zKDITf1A83sbN+LoihMy66lwpnLaHSEltEOuj191GXVUmwuZCA8wE8P/RZv2IdW0vDg9NuotlTTNejn7cPt3LG+hHhMIhaTCet6GaGd1WWLOdR1gi31W7mmbAkZxjQURaE/3J808iGhRPy7k0/zvdlfRyV/8CTos443GONc2ygbFpbw4yePkZ9pZvnMfPpH/IQjcVbMykenVWExanhtXwtNHW5uXVWB25cIoZVlhWA4RkxW6Bv2M39Kdko4/+2rK3n6zQbWzCuissBOQ/sovkCE/tHUXPdoTMZs0PDY6+f5+r15VNbIyHEBD70Mexz85unGZBrHicZB7t9QQ3O3O5n7v79bzcaymYQuHAFALY43CNQqkbgSYzToRhIltDYnfaN9vD+jWInH0EigNVvIuOHPEDQ6PEEvKrtM/cUol9P9Y55GrUrL12fehx4tWcZ0enypaSNG3fiFRm12Cf6TO5EqZ6NodNB0DHvTSb5UdxMVtmLOeseikLq8vTx6+lW+O/cBhoNu+v1DpBvSQBB44vRLFNnyWFQ4m/qBJqZkVVFgy6XN1cX07FqaR9qTyuivNe3grtIVTKs/StqKu/D3NvPKSAMjwbFFhfrBJqozKnijaQ9fmnkHT5x+EU84sbD5SudB5i39SwQltV78Fw0FeOn8mynVVI70nGJ2Xh1ZpsT3WZZBpxn/KTfp1QhXcdg+JAQ067JrONxziiM9CcHNqvI6VFJi3JkOIyOeEMFwjKgSTtl2W9NOZuVMvuL+lct8c5VYlEuFHwSVmpG3H0Vlz8C2YDNyyMfIjkdJ3/itKw8+EsQyax0jOx6Di+X69CV1KYrysgwF2eOfycoCO6KocPmv0+eHEU8Ik/5SQ9+PUf0BqRbxOJLfQ/wzHLr/HqIgUmYr5sTgGTINGUgfQWhEzConfup14n1NSFnlFGVZuHZ+Ef/14hk2LihixYy8ifTZCSaY4H/M/yhHf4IPZ0CW6KycxJTaOaRLNnwaJ79se5ZScxY3FNYQbx8LNxYmLWdPq5lndlwgFleoKCjhT1ZN55enf5EyQcs3ZzMtuxpFFijSV/E3z+4HwB+Mku000nuJwbtuQTHPvt3Iiln5GPTwVMtOTvQnjukKeWh0d/HvlZtxL/0er56JMDAY45qaxVTHz3N6pBlXLMSG7FLivc3Ioz2Y6l/CMXUV3py53Lmugjhh/uG/TxEMx5g+KYO29iher4vpBVMwOjUcOi0Qi18sk6QSKcyy0DY4SPB94lGKonBhNIxBnkzPyHFeaUxEj8zInM7J/UaO1Y9VeFi3KJ/V8zNIJzHpD0t+hsOjZOXosBg1eC4RF8xJN9If7kIVF8jSZeG06shyGOgbGYs2qCm2E1XinO1tZEfbWCTF8d4zFNnyeOzU87S6EsbPrvb93D75eg50HUtOtsPxCP915FF+uOT7mO0KkxeNsKV7J1mGLNZXLKfV42Ff02FkFJYVz6PH049RY6DUmvDguC7xoL5Hl6cXn+zFRtqH3GGfXS50u3n7cAc5TiN/fucMYnGFwdEg5fkO/t+jR4hfNKRVksB3bptOLKaQn2GiPN9GXoYJnUbFa3tbkSSBm1dUkJ9pYl5tNj1DflSSSP+In3lTcnjmrUYMOhXP77rAzSsrSLfpx41FFOFLN5WhEeDFjv8mLsfJMWficuWO02o43jjApEI79a0Jz5xosNCdex3FtSsZGhpFY8mkPF+mqdOV3OaahVkcHnot0R+BfllNhrUSUW9CDo4ZbWL5fI52yWwydjLwws9Qp+cTmnM/w26FHHPmOLX1UCyEFHByLqrl7sm34vcroIrhivWhVWuJiCpCN30Xw7uvwlAXhgU3E47K9K+7mxd7jhGID7N26UaqXV7qrIUIbWexOZ0px1hbsZQfH/gNQ4FRlhTNZVfrfrq9fQA0j7TT5xtkZclCOt09FFpzWVGygB5vP1mmVGX0lzoPUVUyhcjbj6JbdReNvfvH/Q6jQReBaABREFhWPA+L1owr6CYUjyAwIf4ak2O0u7rGtY8ERnkvw0gUIdtpwGrSJBfFBAE2Li4lHpdRq65ifQMBXmvcwbrypWSa0gGFaFThy9fXYjRqUUsisqww6gkyrD7L9VXXIAkiQ4FR9nUeIX4ZQ/5S1I7sRJna0Ng30jR5CYLWmIz6UsIBosNdRIe7CF4YS0eMeYbBdoWdiyq8Z/ZgX3gjihxDEFVEBtpR5DHzPR5XSLfqKcm10tKdeO+nWXVMm5RB/PNu5QMDrmBK6L4v6sepv/w3TuUbSQjxfYRqCp8F7ForRpWBxtFmqtLKP7S/IIqoimcQOfMGOmcBgkpLYaaZ21aU8+r+No41DnLPmklkOj57FQkmmGCCPx4Thv4njENvo1Gt5reDJynyreV4fQ83bFzNsxeeomrScsrTc1G31UNmBV1F1/LEoyeS2zZ2uMg6pueBWbeRocvlK7PuQhIS+alvNx3C7KnFqBIQBYG4ovDK3lZuWFKKJIkXvZRqjpzrp6nTzYyqTMorYdvBsYWFTKOTqoxyBi0l/N9HjibD/n/Z6+GeNTM4PPws/f4h1pQvQuxrJTLvAXYNWjl9JELlaBczapwEvJCXYWZKuZN3TnQjAKV5eUx1zmYw2s6f31dNR2cMXyCK3aLj+V0XWDTLgVVrxh0eE11TiSosGhv9Lh97+xIq36W2Imbbl/Jv9fUp13Tb3k4mTzIiqjoIKVH++8DjdHl7EwJkd97I08+56BsOoZJEbr2mlP5wI4MjI1Q5w9g1Dv7snhoaW3089cYFls3MYcpUiZ3vdiHntoz7/U721WPRpXoRnz/3Ot+ccx8ROcpIYJShwCjbL+wmEA+wq+dd3u1KeHaHA6O0uFpZV7Esea4vn3+Tm2uuZbJzEsaLCxWXm+iUOgrZ332U1fnLkeTP32MpigLn2hKG8padTfztfXPoH/HTNeClqdOVNPIBYnGFQ/X93LC0lAvdLjYuLqG918tzOy7qOEThodfq+dNb6vj5llNEL0lfqS1JY0qZM+l9V0siu491ce+11bx9uBN/MMqiulxK8swEpAFGA16+VvcnvNX+NiqVhME/PqJCFBVWLzez1JdBNCTxzske3H4DD/d6ae8LAue4dmExMyZlEI7K5KTrwTjK2/X9CAiUW8o5cMLPv7/byvfXfZ303r1Iox2oK+YTyJ/M3HiQmCGT0PX/j+hQLw7fAPXdFu6cv4mfHPwt4VjCkzknbxrNI+0UaPRkqUt5+LkLtHR7sBg13L2hjNcGnqDXlzDIb5m5Hosi0hUMMzOzmH/b/7Pk+fzuwls8OPkGJH8PpQMtFBjNSIJEXIlTbMtDp9IyFEgstDkNdna3HUi5Hv5IAJUocX6omXRDGpOcpahEFYIgckvtBsxaI0aNkX7fIK0qPYU3fpumsIfazEp2tqYa+2kGOw69nfODF8g0p/N8/euMBF2YNEYqHaWUGEoSinRfUNSimpqMCg51n0hpz3zfoorHH2Hd/GIi0TihSBynTU9bj5vqYisfEt3+R0VQoC6rGovWdFGcUcCkMWJJM3KsYZAzzcPkZ5pYWJeLSrLx2OkthOMRcsyZfG3mXQgfItIox2Okb/wWvlO7iAx1Yaycja54KkokPFZeT29B7cghOpIa4q+yOLmiHJ+owjJ5CcNvPsR7qwbanHIk/ViOuSCAWi1SlmdlTk0WChAMxQiGY4iSgDy+mMjnir7hADMnjd2rvmgA/QdUSlB5h4jrP6Sk4WeMYmshJwZPU2ItQPsRhPlEaxaCJYPI2R1op64FwG7WcsfKCo42DvDDR46waEo2GxcUo9d+/uYJE0wwwSfPxJviE6bH302Pp597pt7Irt1hXN4wW18XuG7RvQgGhbNKCZ7ITIwWM6NdvnHbHz0/xKpFlbzaujXp5frZwYe5LvduHtnZTWmulaUz8nj7cCeyrLBl5wUKs0ysmlvAr19MGMiCABUFVobCDWgkNZF4lPUVKwjGQhzvPcOAb5j7bl3M0y8O4vIljIiX93WxYPVkwvGjqAQRefI6/uOQio6BRP7hhS4XLZ0B7tlYzrTKdB7ZOpYf/Ojr58hIm8p+z2GWZK9gcqmTHz12lGFPwou/491B7rzpZl5qewZ32IterePG8ht55PlOCnIMpOWlMStjLsMtGXTLMd6PrIA3GOJA53kOd5+ky5sI1/aGffz6xCP81b3fovGcQG6mjneGXudo3wnMWhP5ljze3dfH0XMjTJvk4H9/aRZxjYfzA2209YjMLM0BjqYcqzytmJN9qQsNWkmDK+Sly9NDpbMUBfju3C+jEfTs70zdPhG5kGqYnB+6wIr8Rbwn4pyuSeeOKTfwzJlXiMoxMk3pLCyYxaMnn2dO9gwc4ufPqx+PK+RlmKgrT2f9gmKaOkfJzTDRM+THoBv/GgpHYoy4QwRCcfIz1Jy6ML5s1YmmIaZXZnDwbF+y7UzLMLesrMCgU6NVSzR1uchKM/DkGw3Mrs7CYlAzrSKdh19twGAQ2LAii+YOL+bB+cQVmcI8G5LYnVx4EASYNtXAr07/CgWFmwru43zbKFPK0lPU/V/d24pKErjtmkn8/LnTVBRZWTN7FRUZ+bhH9Wx95xyxuMIPXuylLLeOPMdsZjizePb8QxjUOlYVrOaVbR7aevxkOfQ8cG0Wfd1eNlauQlbiSKJEw1ALJ/vqmTl5ES++0UxLtwdIGHk/f7qeO29ZwbO+xwF4pn4rN1av5/XWN5icW8p9025Gq9KgVWkZ8bs4PthITUYFkdxyDF1N/NPyv8If8xORI/T5BlOusyRKybSbS9vyrdkIAvz62NOEY2GWFM3FE/YxJ6+OR09sYSToYnZuHfsifuoHm7ipZj3ljmKaRloRBIHFhXMYCbjYWLWKC8NtvHBuO+5Q4px8ET8/PfgbfrDk+1gE28e44z4fKCisKV9Kv3+Qdlc3kiCytnwZae+r8d4z6OfptxrRqiXUKhFfMIrDomPZzDxM6qvXQ6ooUOIo4MJwG7vaDqAWVXy59ss8924bB84knusLXS5OXRjiwZuLCccTL9Eebz+vX9jF12bdxRWt8WiY/mf+CU12Keq0HLwn38Z/7gDOG74z1ifsx770NoZe/2Ui2kYQsc27flxViHFjj4bwHH8L+9LbUGIRBElFzD1EdKQPzKXJfkfq+3njYEfKtpWFdmZUpL9/l587+keDOMxjhr0/6v9gQ989gGz4fBn6epWODH06Z4cbmJ455cM3AFSF04ie3k6srwFVViI9UxQFZk3KpKrQwTunevnrXx3grmsqmf4FuIcmmGCC348JQ/8TRq/WsKpoBe1dYSaXOciwGXnt3VYef6mH21ZX8uQbDQDcuLwcw2VWZEtyLQxF+vGEPSwsnE2Ptx+9Rk9fX2Ki3dztJi/TzK2rKmjr9VCUZ0Cw9hOOjQn+3Li8HKNRIBq3sblqPYd6jjMSHGV/ZyIscTTopmmklQ0r7+bxFxPGilYjEZEj3Fi9gYGYhMqWQceuUyljO9fiJugTUatEzAZ1Sm3zd0704Mt1E4nJ/Oa1M8yZnM25tkHmz7KQYTUT9KnZnHcfRksMu97C//vNOUa9YYbcIb4xazVtHRH2Huth4yIzDouOEU9CrXfFrHysJi3ZRgvWqA3MWUx3utjW8RqReBRZkenx9dHSo+WschBFjHNL7UYi8QjusJtJk3LYeSTC7qN9DLki3L05m95AJ7WTasBjIt+cR6c3ERqbbkhjQf4s3mkfqyO+LHc59sgktm8dJT+7iD4lxJbWV6hKq2BF3ko0Kk3S4/oe71fEtuusDIVHsAkJA15SVJTZirm2ciWiIDASdPP4qRdRiRIa8fNZfm/IF8Zh0VNRYMMbiNDW48Fp09PZ7+WmFRUcb0g1LqeUpdM95GfIFaS8wEZNkR2jXkVrtye5OOWwaAFtSq6+1aShJNeKrCjcva4KtUpMGJV1uWg0ErKs8OOnjrF6UTqd0mH++cDjaCQ1K0tW0XLayot7mrlnfTV9Iz784SDl5Rp29L2cFOaKionFOY8vnCLACIlIBOViOa2GVjerFpXS5W6jUleYErFwodvLhW7IKVMz6E+I+P3a9TtuW3Ifv33ST99IkHfr+1kxK58Bj4bh4BBDQhen+s6Ra8qGsJELnal1lmUFIoGxe0dRFOJKHLWkJibHeOn8G4wEXWgkNRsnrWZy5iRMGiOKTkU8FMQsmej29/DIiS3cVHstRrUBfzTAwa4TrC5dzOtNO5P7npM3jS53H9UZ5fR6+5P3/+62A9xSu5GHTzzHypJFvNLwJoW2XJ49m0hheO7sVmbn1XF77vWUOQoQZYUICv/67i/ZULkSd8iDRlQTuejmjMoxhkOjWPS2j3fTfQ6Iy3H+++iTrCpdhEVrQhJE9nceo3mkgzRHQuRLliEzLRHOG47GkyVay/KsiW/MVay6jwDdnj6ePjMmsuf1x2nv9XLHNZOIXkw9ONk4iN+TumDRNNyKLxLAcYUqKfGLJSsjvc1EepsTbbggGiaZGSIIDG//DbZ5NyDqjICA5/ibWB05cAUNNEGjBxRGdz2R0p6x+XvJ/5ckAc1lFlp0Gon451xkMhCKEonFMekT85xIPIKiKKg/4Bundg/gT8v7NIf4qVBgyeVI/wnK7MVYNB+uOSKo1KjK5hE5uQ3RlI5oGqvOYNKrWTungM4BH0+81cjRhgHuuqbyshodE0wwwQQwYeh/4jjUWTz+ahv1zW6gG4NOxYPX1fLmgXZqih3oNBI6rYpj5/tZNaeQaRXpHG9MGDkmvZprl+QxKrckSw5FQxqWp1vINWVypsFLz5Cf3ce60KhEVs7NJa8kyM+ObOHrs+/lh9+tgpgKDQbqW0bZf9JLfradW2bewb8d//9SxhmNR1G0Y6H0m1cWoTVnsme3h5pJOnJslw+JjClRIuphvnl7Fb/e0pRUIzfqRTyKzKgryoUuN3VVDuYvD3N++F1aT87k3RNjHtmaEgdzarPZtr8NWVY4ezbGhc7EWLbtb+POtVU0dYxSVezgxd3NLJvvoGG0lwybGa9LYvdhmT+55ev0uUeJx8GpcjK9Js6e0Qh55hyePvMyAAIC99TdnDTI2nu8NA6F2N1+kOV5JnwDBZRLq5hfEsduVZFvz8AuOPirBd9kR9teIvEYsZ5iHt+fCPFvaAfbGS1r1q/jldaXWFa4kPUVy3m+/vXkuRXZ8rBqxyaeerWOfFsuPf4+bKYxT71dbafL3cvhnpPJtrumbMYsWvgQR9JnkkgszrYDbSydkUckGqd/NMDDr9WzfkExB8/2cs/6ag6c7kUUYeXsQtQqEY1axB+IYNZriMoKvkCUxdNyUYC9J7rJsBsQAJtJy8EzfciKwgMba3HadJxoHCQzzcjDr9Yz6Erco9XFDm5dVYFRp8Knv8DJrsRCViQeZWvbVm6Zehft3QrbD7Tz3btqeLLhKR5vbkg5D7WiB7zsPt7N/Rtq+NWLp5OpA6tmF3CmZUx93yCZEdROzAaJxdPy2H1sLNfaZtIS04ym7Hsw2onNZMTlC1OW6+BHj53AdbFaQHFOGt/e8B00KhWE9djM2uS/JcemGbtx1FJiMr2kaC5PnHopaeTr1Xq2nN3KzbUbsOotvNtzlkpzFpmyi1AsjCRIOHRWvjrrLs4NNiKJKiall1Joy8UT9uLQ29FJGjSShn2dR8aF9UflKMFoCI2kYnnJAjKMY/n/CgoHu45zsOs435n3IMFYCJvOiqzIVKQV8+WZdzISdOE02GkYamZn636sH2Fi/HlGEkRCsRCPnHgupb0qYyznVxShKMvCtMr05IKZxajh+iVl4/Qmrkb2dRxJ+VuQoqyZV8jDW88lU3CumVOYkusNkGVKR6/WwRXC38XLlGpTO7JT8sAVtQHTlGWM7Hg02SaZ01ClXbkKihIJYZm2mpE9T6GEExowptrFKRViYjGZyWVOth9oTy7AiELiXfF511XrHQmQZtElBeQ8ER8GtYHLnbgU9IIso2g+f/nnalFNnimXU4P1LMyd85G2Ec1pqPInEz70LLpF9yCoU6Mg8jNM3L26kh3HuvnBQ4f51o1TyZrI3Z9gggkuw4Sh/wnT2x+5aOQnCIRi7DzayeblZYwGfHz9/hwah5sxiSpsBpEVs/OYPikDQYTMDInuYAtpYgFOg4OhAZFHXmjF44+gkka5aUUZB8700drjQaOWyC2M0xvoQy2p6fP1s2twPzeV3spbB3p581AiVLCxA46cHWb9hmt5oXlLyljTzAZWzjUypcJOJCrz7KsDLF4O+/rfZr1zLTUlds62jBkjc6ekg95Hz2g9uy+8ypduvYN/+kUDKklk8ZQi/PFb0BpibFqdgyMtxqvte7iz+jb29wfQaqSkAv7ZlhE2LinGZk7krMXjMnkZcdr7vERiMs/taOKuNZN4dNs5br0uiy3tjxEYvGisOSuZP2MuR076yMkw0tbjZdvbF/jS9TUsyJ/Fb48/nRyvgsLTZ15i+dzbeW5bD9ctLuFk/zYAdnS9TZYpg3lZ88k2FxMOSnglAYdNwCSZWF+2imBQ4C9eOJhyzVy+MKpwwpPmi7tpGm7ltsnXMRQYxaI1EoyF0am13FSzHoNaj1FlJB5Rk6bKSORkvidUqGi5rWoTiwvn4gq5yTRmkKXN/Fwa+QAalYpTF4bYuKiE7Qfa6RrwMb0yg3hcZk5NNjq1yOYVZdhNWiLROCOeMKOeEAvqcvnRE0cZvLig1NTpYm5tFveuryYQihGOxbEYNPzF3TMZHA2S5TDw5BuNHGsYoDjbwq2rKugZ8iNJIm5fmL7hAJtWFvJi785xYwyph1g2oxSrSYvbDcvzl9E00kLkYk35ZQWLyTZls2SamtwME2aDmltWVhCJyWhUImajmoaOxPMyqzqTfceGmTcln399/hjfuKmOTIeeI+cGKM41UVOt5uGG36QcXyfqCEfjFGVbaOt1M3+6A4tVJhQU2bl/mP5egbJiDYN+F/ddW81Pnj6RNISWz86hN55Q5zdrTdxSu4G9bYdYV7mCHS37uG7SakRBxBP2kWFMI01vZ8A3RIt/gOKMct5q2UsoGuTuaZvZ2rQTd9jD4sK59PuH2FL/OmvKltLvG+aJUy8BMDu3jpGga/zvLKmxas3kmLM42HWCmByj0JaXIihX6iik3z9I03AbGknNXy78GrvbDrC343Cyz8qShXx11l3YVY7Lltb8oqAA68qX0zLaTo45C1mROdZzhmxTasiuN5hYELv9mkpkWSEcleka8OK0fXhe8B8TIS5g1aaqrPeH+nhux3Dy3gbYfrCd2ZPHFo20kobbplxHNBZlvNTmJftXa7HMWo/nyOugyIh6E5ZZ61NU9xVBhaA1kHbNgwTbTqFJy0PtyEaOhOEKl0+QVHhOvIll+jUIkoQgqgi2nUaJX7ryoBCKxPjWLXWcbR0mFpOpLkkjHIkntHY+6oX6DNI/EsBhHruAnogH0wco7qtc/cRNjssuAnweyDVlcXTgBH3+QbKMHy3cXsosRQ56CB/egnbOzQhSaiSERi2xZk4BJy8M8Q+PHuHLG2uoLf78pf1NMMEEvx8Thv4njMczflba1uOhoX0UXV4Hzx17OdleaM3jrkl3M+CKYDDGaOwK887BGIFQE39y+208/FxPUlE+Fpd58o1G/tcDs+hyDxBSDbKl9Qk2V6/ltskbOd3bRH5kIcfqR3n7SGfK8d2+CJnCJFSiitjFHPgCay5RrxmNKsjzb3Zxw/JibltbTPfoKAXetfR3qVmz1ERVhYGu3jBTSzOJx2X+65FWJhVXsap2Ent6dvKVG1eikTTI+lGePvMYQ4ERDGo9d5fdyCr9Yh479yR6m547blrJrl2JMOdF802MSs2oHQKDPRoaLgT52ubJGPVqcpwmht0h4opCUa6JI6PvEIiOlUerH2pgc0kdB/aF0BV34s/u48F5qzl7ZoTJk1PVwyGRMz+p1My3b51Gul1PTvQa+nyDDAdHcIXcmJQMfvpwE/5QDFGA29eW0yLu5vjAKe6puueiNyL1N31vLqIV9ZzuP8/p/vOYNUYCsRBxOc6NNetpHelkWdYa9h0eoaXLQ3WJmvJ8mfIcKwZNwpukVfSU6EtJzlQ/xwaNSafCadVdjGiRWDW7gL0nezDoVCydnodGI/GPvztMXkZCSf/ld1rYvKyMsy3DSSP/PQ6c6WPh1Fza+71UFtjoGvBhNWkoyrbyX8+fprUnsdDmCUTwh2K8eagDjz9Chl1PTUkaF7o95FtzGQ6metSlqJnn9jRjMWpZMj0XnUbNd2d+m+HgMDpRT/OFOF4xRnmVjN0o86unzzDiGQvdFwT4+o1TCURCiJYhhkMDjAY1lOTaONc2Snamhg2lWgQxTFTxEb1Ej8KsMaKNZBIMdzOlLI3CsjhbWp5ntM+NUWNg8/Wb6Gnxs/dkD8sXWrCnhfnqpsnElRh6vUgsHqdnuIavz5xEq6eNXk8/U7NrqB9o4Pqq1ezrOEr/Jbn399TdRJmjiAxjGr888jihWJjbJl/HT/b/Jqlk/qhrCxsnrWLQP8LPDz3CN+fex572g0QvGjJrypfy9OmXGQyMIIkSa8uX0jjYwldn383ZgUa6PL10e/r46uy7uDDcRvNoOyX2QkwaA0+ceolbajfyfP1WZufWpRj5AG+17GVhwSwE5SpWjP8UEAWJ8rQiDnYdZ3/nMSRR4oaqazBpjCnviwudLvacSK09n+00Ulvq5GoW3ZcVmF8wg+N9Z5I6EDoseAN94/r6/DLfmnsfwWgIvVrH9qZd3F13E1e0luU4iKqLyvhxlHgsYeRfms4Q8uDa9ThIKjTOPMJdDcT9btLWfhnMH1zrXkHAPGU5I28/QlKML7cSUXupZ1VEkeHFfc1MK09Ho5F4ZU8za+cXp6TzfB7pHPDhsIx5ot0RLwb15Zdl1KM9xEy2T2lknz6iIFJiLeb4wClWFy77SOX2AFSFdcSaDhA+9jLamTcgCOO3m1rmxGHR8auX67luYRErZuR/0sOfYIIJPsN8pg39V155hf/6r/8iGo1y7733cscdd/yxh3TZ0id1FRlY7ApbmraltLe7uxiODPDIq+PLJ5kFJ4OjzePaLwx18mLvQwBsrFhNtiGX/zz6a67NvYlHXunmxuXliIKA/D6rcWg0wvdnfp9z3qM49U4GOvX88pmmZGjnK7vaqC1L48XdY5PFgmwjMxb5KEpT89aeMM2dXkQB0iw5REe1TDaswWbUEiXIwxeNfIBANMivjj7O5up1jAbdjOLmSe9j3LHkPmJCkKebHkoeo9RWzK3rbuLJN5qoLUnj5XdaGPGE2LysjIIcPUd8qZNXAH/czbSaPHb3n8Ib9tE8+iu+Oe3rhH1q1KIqxYBKN6Zx5JSbbe80YNSruXPNJJZZb+Gw+jVKrWW8sG0QfyjRX1bgsa1N3H3LFI5zire6tnPNgmt5bc/Y75Nh1xFSDVJuLyPkNlLpqKBhpBFvJFG+qTytmBxzBrXWqfzLb+uThmBbr4cZkzIQgKnFaSifhZjaTxCjTuLrN00lHI1Rnm/j8e0NXLeoFFESCEXilORauW9DDaIATpuem1YkQpPTrJcpjSdAZ7+Xbfvb2LYf7llfzWOvn+cv7p6ZNPIBVs4q4JGt54jFE4brwGiQR7bWM6cmi4VZS2gYvkAwmvh9iqyFeAYMxOIeZlZlcvBMH7esquCHPz+RLBc5f3IOBaVxfnfyd1xXcHOKkQ8JYbGYGOT5gV8Q7k0s0OUYs7h28m10dkVwKkb8so9Hjz9Ppimd2ydfhyvkIU3vIFOXR8Sv5YFboSxPz08P/RrXRWE6fyTAsxee4htTvsEbB9pxvR5i1nIPjngJj7wwtqh3zYIsXmzYliyFd0/dTbzecZQ76zalGPkAW+q38p15D6ISVYRiYTSSGl8kMK5c2b6OI0zPqWVX634ah1v4i/lfJhoJYNIYGQx7+Nrse2hzdWLVmXHo7JjzzFg0Fl48tz1xTVDo8w1ytPc0eZYsjvWcTi6wROUoGkkzrvTmewRjYRhfBOELhSgIbGvazYWRNiCRs//c2a3kW3Kw6xOewcSrZLwnVC2JyIpy2X+7WlAEmd2t+7mldgOesBdJkMi2pJPj7KbnkrKxkihgMWr5fwd+d/FviQen34b0ISUYBZ0ZYhHQ6oE4okaHIseJa81jCyUqLZLZQdw7QqS/LbmtqPuQtBE5hvfE26lifK5BYt6RZFm+eFymtcdDbUkaKpVILK4wqzqLQ/V9zJz0+RZSa+31MqVkzMPsCrkxqC5j6CsK6pE+/GUzP8XRffqk6ewM+AepHznPZGf1R9pGEARUZbOJNewlcvxVNNOuvayxn59h4vaV5bzwTgsd/T7uuqbykx7+BBNM8BnlM2vo9/f38+Mf/5jnn38ejUbDrbfeypw5cygrK/ujjsuoU3P9klK27mslEpOpLU1jclkaw6HhZAjwpShCHEkUUlb3J5el4Q1Ek6J0l5KXZmOTfS0qUcISLeBMbwvBaIhwIPFTHjzbx+o5BWx9ty25TabDgMsX4eCJELNmTCLkU/PUa2dS9jt9UiZbdjaltHX0+lkUy0KRozR3Joz465eWcfBMH92DCVEyrVrizx4sYzAwkrKtrMjElVRXi5tuTvSfTmlrdrXSY+/hWMMQlYX25PnuONLJ7WsqCUVr2deTWpIr15xDu9yOd8SXPNZguJd396q4ZeHtvNr+Iq6Qh2xTJrdU3sy//TqRY+8PRtlxpBO7RceGmbeCoPDaaOp4ACLBxLXs9vaRn32Wr2yez6nGEQqzzZQU6gkpbgZaHPxs+wU2XbOQsuJyWnyNFBhKqE2rpT/QyohXO+63O3p+gCnl6cQU5QtXITwWV4grCv5gjLcOdXLrqkpe2HUhGbFiM2m5YVkZcVnhJ08dJ3QxzWPtvMKLoeye5L4W1eUmdS0Adh/rYkZVJrKsoFaJyZz5eFxJGvnvMeQKYdRrePrlXr563dcYDA1iUGupPx/llb0JAzkUibFgag6Pvn4uaeQDvHu6h/JJ2Yk+uLGZdElhQEh49AMMJ5XBAXr8fXjlYYpy8mjpHeG8OpEK0u8b5PFTL6JX6ZiXP4PXmt5m+GJJu/usNyeN/OT1k2MMB0eJywr9I0HMQhqZmSIbF5XQNeijokzNkNRAd++YJ1Sn0nBd1eqkB/5SgrEQMSV+sXwnyIqC6jL1q3UqLeGL5SIMKh1bzm/n3GDiPTE5cxIxOca5wQs4DQ7+Zv630cg6ohGZ2XlTOT+UEAxsG+3EabBzsu9cyr61koZQLIRTbyfd4Eh5h2QYnWTq06/srf0CEIyFON1/flx7r3eAMn0FkLjvSnKt6LUqguGxRc51C4rRXuUvGgGRAlseT5x6Ea1Ki6LIzJq9hOsWl/LSnmZ6hvxYjBpuWFqGWhK4d9pNBKJB0g1pjARcqJQri5dGdQ70pXVER/tQgjFUlnREcxpBjLxn6Ye1adiX3sHQqz/jvdwpQ/lMBEfuFfcd0znQZBSkivGJKjKrFyYLAYTDMaZWOPmH3x1KvpcEAf7irpkIn2OPvqwodPR7WT1zTFxvNOwizzT+mkq+ERRJQtF+/nPMS23FHBs4SbYxC6fe8eEbAIIooapcQOz8O0ROvIambv1ljX2bScvtKyt4/UA7//TYUf72gblX8RLfBBNM8GlxFQf1XZl3332XuXPnYrPZMBgMXHPNNWzbtu3DN/wD0zPk5+DZPjYuLuXWVRXoNSoUGerPB5iRNS2lr16tQ6/YefC6WrLSDAgCTK/M4OYVFYx4wnx50+RkrVRRFLhhVS5bO1/i+frXeebMq5hMIiptYkqh1ScmEZ39XiRR5P4NNcytzea6xSUsnZ7Ha/taudDl5pUL2xHUMbSa1BmgIHDR+5OKooAoyYl6wCoRlSgmjXxIqDwPDEYToaTvQy2mriNZ9IZkfe5LiZIwli49+qg3zDNvNjErYx6TM6oS+5PU3Fx9Ha54H291vpmyD62kpaHdzRPPjTJd3Mx1mfcxR3sDO/d6kyJIAK09HnLTTQwMRYlFlctGYGj0Y5PlsyOn6XWPMqXcyeBokLfe7WegW0demoOZVZmEvBosgUlMYQNnD9jp7lKodFRgv0w9YFEUMGhVqMQv3udXLQloJIndx7ooyDLTNeBNGvmQ0D7oG/bj9oaTRj7AtgPtrJtfxE3Ly5lbm8WD19UiiiKNHWP3UTgaR6MSGXQH2bCoZOyY6vGvN4NORTQmU5BlxufSYgjnsXtPmMGBsQWBg2f7KM+3pSjqv0foYtPe3ne4cX1WyvN597UVHB7eO24bu1lLhl1PVYllXD5yMBZCp9YSuqRyg0VrRve+mssCAkF/4pl1WHQEcGNSm6ioEJi3NEx9fAf7eseOLQgCrpCHR08+j9OYhuZ9+Z2LCmYRl+MY1XoMaj0xOYZKlMY9x0uK5nKs5zRGjYEyR3HSyFdLaqbnTOb8YDOiIHJf3c1olUSYrqIozM6tY0b2ZACO951lVeliimyJkFK9WsdNNes53X+eO6ZuYnvTHh6YcStTM6sxqg1Mz67l67PvQRu/fD7vFwmtqKHAOl4UznFJGbJoFCwGNX96Sx1r5xWycGoO37hxKoVZpqs+5zkWgtrMSsodRYRjYSLxKHFZIS4rTC5zcvPKCpbNyCMSixOLyxztOU2/b4hnzryCw2BDq1zZOIwIemKWXMS0fNRZFWDNJmzOT4moiscV5OxaMm/+GxyrHyD9+u9gWng7QekKkvtAKK7CPGsDxtrFCBo9moxCMq7/NhFLQUq/DKuW7905gw0Li1k9p5Dv3TmD7DQj8fjn19AfHA2i1UgYdGPvneHgKBbNeHFEzVAnMcvnO7rhPTSSmnJ7KQd7j6a88z8MQVShmrQIxT9C5NjLKPLlV0C1aonrFhZTmGnmOz/ezdGGgU9q6BNMMMFnlM+sR39gYID09LGPQ0ZGBqdOnbrCFp8OCgq9Q36e2zHmHXd5Q9ywrAyVIYdsczqHeo6Sbcqh1jSH/3y0iZtXVfCl6yejyAqSJHKmdQhHtp/fNv+KL91xG2LQjl/sZ3ffNjq9iVD2KZnVWLU2Gt528t3VX8MfDbBuUT5b3+lk+4E2blpRQc+Qj7MtQ8kyeHMmp5NTMp9QdJi7ri3hty80JVMVnXY1C6Zms/dEb3LcNrMWUe9HpYLVcws4cLoPT2B80eJdB4a5Z+Ot/OLYQ8k8y01VaznRezbZx6o1U+Yooi6rigNdx5PtkighRc1AIi3gUq/UoCvIyTMBrPI8rs+aSywm0HtOonpq6kfOprWSb85n5iQVh+r7eW1XLwadij+9ZRpPb09VdK4tTeNCp4t5sw282v8y9228n58/e+ai4KHAzWuKOTK8FUgYV+sLNrDlxSGuXWgmM81I3/AAJ5uGONc2QkGWmZZuNyOeEHNrs5Blhcp8G5WZ6RhlN6W5Vpq7x0LJl8/IpyTHgvI59uR8ELIMkWicE01D/Pnt03l9f/u4PgPDAQqyUw1hRYF3TnazbHo+GY7EYtie46mpLoum5vLau63UVaST5zRy19oqwtE4VqOGzcvK2LIz4VkWBdi8rJwTjQNcv7SMeDwOCCyZnseIJ0Smw8DJpiHK8q1YjBomFdo53z62oCAI4LCpoR/80QCv9jzBphuuIU9XhlYLOh10dWbQ5hk7N6fBQY7FSXefC1kVYGXpQs4ONiafE7PGiE1nxh9JqHZbtWZcQQ+3T7meh44/m6zlvbZoHe++60Wrkdi8NhOtxY/PLfFW/1uYtAZuqb6eR089S7u7C7PWxK21G0jTOyiw5vDy+Tf5swVf5rWGt+n3DzEnt45p2TVE4jEcKid/teCbHOo5To+nn2/MuZcLw234I35qMisZ9A9zQ9UaytKK0UkavjH7PuJynHSTA18kwJ/MvINcUxZpUnqKwnuGycm9NbexseIaBATsagclswoZDbsxqvVIopRYCBAEJjurMAkm7q+7hWA8hFmyQPQqd0V/Sth1Vq6ruoaOgz3Je2RqZjXZpoxkH0WBgZEAdquO+VNyEAQBARh2B0m36D5gz1cPaeo0NlSuYigwgihKxOMyT7/VwKrZhQDodWr2newhx2lkZfEifBE/i/PnkqnJ+kgpUBHRBJYPLsEHEEVL1FqKYCsj+j9Iq/LrstEtvBPzzGtRVBpCKnuKiCCATiVRlm0hw25AURQsehXC5/wTcKHbTbZjbKEuEo8Qjocxqi+zsD7YQTinfFz755U0nR2fwce+nkMsyZt/2UiqyyGIKlSVi4g17Sd88Bm0s25AUI1/vgVBYHZVJpOKnTz5VgPHGge5bWUFJv3ns3TvBBNMcGUE5TOaLPyLX/yCYDDId77zHQCeffZZTp8+zQ9+8IM/6rhONg7y9785kAzTA3hwYw0IAm8daifdrmfVvGwsOgPBsIyiJJT5Q9E4/mCIvHQrWo3E8ZZOHJlhgtEg5eZqXNFRmv31tPmaqcucTKW9gqE+NT9+8jhGnYo1SzLISFdj1zjw+WUy7UZ2Hetix5FOFEVh8bRc5kxNoztejyLGKbYWoIo4GBgNYdKpOX5+mLLcNFzeMPtP95KXaWLlrHxEdZSRUQWHWYfbHyQWg588fSLlnNfNL2JJXQ6D4SGGgyPY9Rby7U78uOhw9aCVNORb8jlyapSKShVH+o6xv/MomcZ0bqm5gS2vjFDfMopRn1Axv9DlomfQz+IZWRRlm3nstUYudHuYVpHOLSsrON7UR05hnDZ3Kwa1kWJLMfXnQlQWOWjr8aCgkJ9pZueRTjLTjLz+biuxuEJBlpmVswroHHQxc4YGq1GHOmonEJDxh6MYjQrpDg3Ng710jYygjpvZsdeNHIe71lVxrm2ErfvauHVVBY9tSw2n/cqmyRRmWaguTkO86LHvHfJzrKGfC51uqoodlOfbKM4Z7+n/otDQPsIPfnMQu1nLqjmF/Pql1PSRW1dXoteq+N0rZ1Pav3lzHb968TThSJwMu56Ni0s50TiAyxthYV0Oo54QpXk2CrPMtPS40agkXN4wZoMGjz9CMBxDpRLITjOi10p4/FF6h/2gKGQ7TUSiMUrzbSiKTCym8LtXzzHqCXHfhhqeeauRC11uTPpESk5GFhxx7eZE32kKbHncNOk6ets1vLqvFUkUuHdTCV2hJg73HqfYUsSc3BkUpmURi8dp7naj0UaIq4O0u7pQiyqKrcVEwjAY6ketlrDqjSiKjEZUE5VlfJEgJsmCTrYy7AljMoFWp6CRTcRVfoLRMCa1GUEVQpIkAtEgalGDTqXBabDjjwbxRwLYdBYUFPyRAGatCbWkTpQmm+Cqp320i9GQh6HACFpJQ5rBRrrOQbplTHy0q9/L3//6ALWlaVjNWnYf6+YbN05lRlXmH3Hk/zNGAi4kQcTjgR89foyWSxZJNSqRH3x5HjUl4wVXJ7j6+NdHj2C36JhTk6hQ0+7q4rWGHaytXJbST/CMoj74GrG6ZXCZcPTPK4qicHawgXg8zvKyheOiH6+4rRwn0HiYqGsAx9LbUVs/OBoiHI3z5sF26ltHuH9jDUun5yXLHU4wwQRfDD6zhv4LL7zAkSNH+Id/+AcAfvazn6EoCt/4xjc+0vbDw75xK++fBLKg0DUY5K3DHQy5g6yYWUBlgY1oNI5KElFrRPyhKCpRwuOPcLi+D5tZh8mgIS/DhEYtYtKp6B8J8tBr9XQP+tm0tJSibAtdAz5kZCwGLRWFNhQZLnS5eWzbefzBKJUFdu7fUE1MiaPVKESjEh5fFFlR0KgFDBo1g+4guVkajFod7d1+bGYt/lCMvmE/eq2KzDQjZoOIIkA0Ar3DAboHfGSnGUmzaTHqJc61uHl8+3mC4RiLp+WyclYBQ64AHf0+0u16SnKtZFl1BMKJeukIkJNmIBSW8QajOGwSihhFUtS4PHFCUZnOfh/D7iA1xWnkZ5qIywq+iB9FFrFo9cRkhUynEZ8nRNdwgKffaiTbacRq0jBjUiYCYDdraOp089SbjZTl26gqdnCmeYjaEifpdj16rYQcV0i36VBd4WMXkxU6Bv3Ut42QYddTnmcjzaRm2Bfl1y+fRaeRqC5JY9fRLiRR4KYV5VQV2JJ5MOnpZgYHvUDCCyyKwlURppme/sHiUh/lebj0vD4OiqDQORTkv7acorLQjsWo4c2DHSDADUtKsRg1HGsYoLbUyVuHOojLCuvmF1FVZKejz8fbRzow6jQsm5GHQasi3a5HoxKQJBEllkgvGfFHaev1EIsrlOVaiCvQPehDIaFVoRLBG4wRCscxG9SoJAEFAV8witOqIxiOopIkIjGZNLMGURTxBCKoVSKCAJIEag3IQhhkNZKgQkQgLgvo1AICEpIqjkalQlBE/P7UCBhJEhBF4eK1Voh/SA7673vN/1h8Fsb9+z4Pn/RYrnS9tEYRT8SNJKrQxY3EYuPrcHpDMVp7PQTCMYqzLWTadJ9KJY8/xG/d7wnz+LbznGkZJifNyP0ba5hUYCES/mTrj34W7tMP4pMe+yf1PMiKwrd/upc7VpZjNSVSkI4OnKTX38/U9NqUvtaOE8R9HsLZFR9/4J8ier2GYHB8VOPHQVYULrhbCMVCLMiZMy5d68OI97cQ6zyJunIxqqI6hPdl4tpsBlyuRBRQz5Cft491odOouGlpKZUFtqvO4L/ansUrPQ8TTPBZ4jNr6Pf393Pbbbfx3HPPodfrufXWW/nhD3/IlClTPtL2f8iJnCBA5KISslpQLjvZklQiAuAJJnISrQYV70+7iiqJeshmnQoUhUhcQVFAqxKTIYuiKOIJRonGZUx6NbGYjNmgAgViMRkZEiJlkggoSW/zpYZnerqZ0VE/ipJQCX7/ucgKxC/uQ1ES+whE4kRiMma9ClEQCMdk4rKCQS3yce+oMQPo8lz6IYjJCuGYjEEjpQjOCAJE5cSKuUZKLFgI8LHC5S83nrii4A3F0GskVJIAioD0vu/l1fbBeo8/tqEPiWvqDcfwh2KYDWrkuIIgCCgCSIJALC6jUYvIMoCCRiUiCgIqlYgCqEQBURAJhaIXF1A+2sT/av1NPoyJcf/h+CwZ+lczf6ixx4REaViDToVJI37ootjHYeK6p+7vg/ifPA/NPW5++dJZ7l9XlWx7uWUbaToHBZYxcT7icRwHtuArnYGi/WzocXyShj4k5ikdvm56ff3MzKoj25jx4RtdghxwE285DJIaTe1KRNuYnselhv57x6pvG2V/fR9mvYal03KYXpGO2XB1lDa52p7FCUN/gs8Ln9kc/czMTL7zne9w9913E41GufHGGz+ykf+HRlFADaSnmz7wxRW/6JExqC+qXl9mEqMWBNQaKWmkqi8a6ZeuzciyjEkrwUUdd7VaJBYdM35EEsbVe9t8kGf5ch6i985FIGFgvbcPWVbQqUR0KhGUi0a1KIAofGwj/739flRUooBKMz63TVFAJQDCxfFefp3lY49HEgRsl+a6XV2L4lc9sqxgVEsYL97348oPSJe2C4kfT1GIXRTou/QxuRqiJCaYYII/DCoF0owJI+QPYeRP8Idhz4keqovGhAxlRabL20OVI9Vrr+u7gGKyfmaM/D8EgiBQaM7DqrFwfOAk7ToHU9Nr0F8m9/5yiAYrQs0K5IFWwoeeS4hOVi5GNKWN6ysIAjXFDqoK7bT0ejhY389TOy6QZtFRnGUmL8NEdpqRXKcRh0V71Xn8J5hggo/HZ9bQB9iwYQMbNmz4Yw9jggkmmGCCCSaY4AuNLxjlSMMg962dlGzr8HZj1pjQXWK8CrEI+vZTyOXT/xjDvOqwaS1Mz5hKp7ebN9p3UWYtosJR9pFy9wVBQMosQXQWEO9rJLTvUaSMEqLTlgPjRShFUaAs10pZrpV4XGbAFaRvJMiFbjeHzw0w4AoiKwol2Raqi+zUlqSR6zROGP4TTPAZ5TNt6E8wwQQTTDDBBBNM8Mfn+T3NVBXaUhTeTw/Vk2/OTelnaD5KzGRHMNvhEwyF/ywjCRJFlgKyDBm0e7t4vfUtCi0FCY+/1vKhlTIFSYUqtxops5x4XxPDb/4WjGlIeTVIaYUIRuu4PH5JEslOM5KdlhpV4Q1E6Rny0dTl5o3Dnfz/7d19XM33/8fxx6lOJUWaNJvxc7VyMbNhGD8JW6lOJNeN2dfVMMZoQpiLMDO+aczFZia2XCXKxZD1/W5qMRuzfZdhrqISRaE6dfr8/ujb51cqinTldb/d3G7OOZ/z+TzPp/f7836/z+f9+RyNRkObps/wcrO6ODS0xtxUhg5CVBVSW4UQQgghxCOL+U8iv/x1g7ed7dXnrt1J4NrdBJyf+e/d9hWFGlf+QJt8jXvNX0N+96MwcxNz7Os0IyM7g/h714mKjyE7x4CVqSU1TGpgZmyKuYk5NU0ssDKtSS1TK4zz/USfxkSLSYOW1HzxZdIun8dwLZbsMz+gZGWg0dYAYy1ojNAYacDIBLTmaMytMLKsg6amDUZWdbG0qIN9w9x/iqJwMzWDv6+lsufHC6y5cZfn6tak6fO1aFy/Fg1sLbGzscBMW/hSSiFExXtqB/p5N6WrLtt5XJKzbFWVnHlKmreqfa78qmp2yV3+KiK77K+KIdkfbzupd/Xs+uFvTpy5jpdjUyxqaEFRuHD7Egcv/YtX67VBqzHC5HYi5pd+xyg9jfSm7cAk9/4LVW1KeHnlraGtQZPajWhSuxGZ2XruZaejN2SSlZNFmv4ON9Jvci8rnXvZ97AwsaCOeW2szWpRy7QWlqY1MccaY9tGGNs2yl2hIRslKxNyskHJyb2HUY4BJVsP+nsoqUkYki6g3LuNknEXTQ0rjGpao7Gog3VNa9rXsab9c7UwmNqRkAbXbt4j6vcEbtzKIDktAwtzLXWszKhd0xQrC1MszEwwNzVGqzXCxNgIE6PcX8gxMdZgYmyE8X2PbW5ncO9OJiYmuc8ZG+UuY2SkQaMBDRr1nkhGmv9esmCU+16tSe42hBCFVdm77gshhBBCiIrz7XexfHPwDPWfqUnN/07ZzzRkkngrHjt9dqHls41kQFbWFJRS3XRYa2SCkeZBfwcFDAaUou4SXeTSGu7kmJOmmGModIfdysemljlfzX6zSn/JJ0RJyUBfCCGEEEIIIYSoRuSrVSGEEEIIIYQQohqRgb4QQgghhBBCCFGNyEBfCCGEEEIIIYSoRmSgL4QQQgghhBBCVCMy0BdCCCGEEEIIIaoRGegLIYQQQgghhBDViAz0hRBCCCGEEEKIakQG+kIIIYQQQgghRDUiA/0yEBYWhqurK2+88QZbtmwp9Pqff/6Jl5cXzs7OzJo1i+zs7ApI+fCceaZPn05ISEg5JivoYTkPHz5Mnz598PDwYPz48dy+fbsCUj4856FDh9DpdLi5ueHr64ter6+AlGWjpGWnonz22We4ubnh5ubG0qVLAYiKikKn0/Hmm2+yYsUKddnKUh/z+/jjj/H19QWKz3ft2jW8vb1xcXFh3Lhx3L17tyIjc+TIEfr164eLiwsLFy4EqtY+Lw937tzB3d2duLg4AEJCQnB1dUWn07Fw4UJ1P/z22294eXmh0+kYO3YsSUlJAOj1enx8fOjduzeenp6cP3++XHKXRX2qqPJamux57m/zqkL24trBynacKE5Ftyn3182yKt+pqamMGTOG3r174+3trdblR/Uk62JZZwUICAjA1dUVNzc3vvrqq0qfV4hqTxGPJSEhQXFyclJSUlKUu3fvKjqdTjl79myBZdzc3JRff/1VURRFmTFjhrJly5ZKmTMhIUEZO3as0qZNG2Xnzp3lnrEkOdPS0pQuXbooCQkJiqIoyj//+U9lwYIFlS7n3bt3la5duypJSUmKoijK5MmTleDg4HLPWRZKUnYq0tGjR5VBgwYpmZmZil6vV4YPH66EhYUpjo6OyuXLl5WsrCzlH//4hxIZGakoSuWoj/lFRUUpHTt2VKZPn64oSvH5xowZo4SHhyuKoiifffaZsnTp0grJqyiKcvnyZaVr165KfHy8otfrlSFDhiiRkZFVZp+Xh5MnTyru7u5Kq1atlCtXrijnz59X/vd//1dJTExUFEVR5s6dq2zYsEHJyclRHB0dlejoaEVRFGXv3r3K2LFjFUVRlC+++EKZPXu2oiiKcuzYMaV///5PPHdZ1aeKKK+lzV5cm1fZsz+oHaxMx4niVHSbcn/dTE9PL7PyPW/ePGXt2rWKoijKrl27lPfff/+Rcz7puliWWRVFUWJiYpTBgwcrWVlZSnp6uuLk5KT8+eeflTavEE8DOaP/mKKioujUqRPW1tZYWFjg7OzMgQMH1NevXr1KRkYGbdu2BaBfv34FXq8sOSH3G/aePXvSu3fvcs+X52E5s7Ky+Oijj7CzswPA3t6e+Pj4SpfTwsKCI0eOULduXe7du8fNmzepVatWuecsCyUpOxXJ1tYWX19fTE1N0Wq1NG3alIsXL9KoUSNeeOEFTExM0Ol0HDhwoNLUxzy3bt1ixYoVvPvuu0Dxx4usrCyOHz+Os7NzgecryqFDh3B1deXZZ59Fq9WyYsUKatSoUSX2eXnZtm0bc+fOpV69egCcOXOGtm3bqo+dnJw4fPgwKSkpZGRk0KlTJ/X5H3/8Eb1eT2RkJB4eHgB06NCBlJQUrl279kRzl0V9qqjyWprsUHSbVxWyF9cOVrbjRHEquk25v27+9ttvZVa+IyMj0el0ALi7u/Pvf/+brKysR8r5pOtiWWYFeO2119i0aRMmJibcvHkTg8FAampqpc0rxNNABvqP6fr169ja2qqP69WrR2JiYrGv29raFni9vDwsJ8CoUaMYMGBAeUcr4GE569SpQ69evQDIyMhg3bp16uPKlBNAq9Xyr3/9CycnJ1JSUujatWt5xywTJfmsFal58+ZqZ+HixYvs27cPjUZTZObKUh/zzJkzhylTpqhfAhWXLyUlBUtLS0xMTAo8X1EuXbqEwWBg5MiReHh48M033xRbTirbPi8v/v7+tG/fXn3s4ODAqVOniI+Px2AwcODAAW7cuEGdOnWwsLDgxx9/BGDv3r1kZWWRkpJS5L5LSEh4ornLoj5VVHktTXYous2rCtmLawcr23GiOBXdptxfN0t77HrQfs7/HhMTEywtLUlOTn6knE+6LpZl1jxarZaVK1fi5uZG586dK+2+FeJpIQP9x6QoSqHnNBpNiV8vL5Ulx8OUNGdaWhqjR4/GwcEBT0/P8ohWQElzOjo6EhMTg5OTEx999FE5JCt7VaXsnD17ln/84x9Mnz6dhg0bFnpdo9FUqs+yfft26tevT+fOndXnistXmXIDGAwGoqOj+eSTT9i2bRunT59Wr3XNrzJmryiNGzdm6tSpjBs3Dm9vb+zt7dFqtWg0GlauXMnatWvp27cvaWlpWFtbo9Vqi1yPkVH5NNuPU58q+m9ekuzFqUrZ728HKzp7SVW2nKUtx6XN/7h1tjzrYlkcXyZNmkR0dDTx8fFcvHixVLkqIq8Q1ZnUkMdkZ2fHjRs31MfXr19Xp4MV9XpSUlKB18vLw3JWFiXJef36dYYOHYqDgwP+/v7lHRF4eM5bt26pZ+gAdDodZ86cKdeMZaUqlJ0TJ04wYsQIpk6diqenZ7GZK0t9BNi3bx9Hjx6lT58+rFy5kiNHjrB9+/Yi89nY2HDnzh0MBkOF5waoW7cunTt3xsbGBnNzc3r27MnRo0cr/T6vSJmZmbRp04bQ0FCCg4N57rnneOGFF4Dcs1NBQUGEhobi6elJTk4O1tbW1KtXr8ANp8pr3z1ufarI8lrS7MWpKtmLagcr23GiOJWtTSnL8l2vXj31PdnZ2dy5cwdra+tHzvYk62JZZz1//jx//vknADVq1ODNN98kJiam0uYV4mkgA/3H9PrrrxMdHU1ycjLp6ekcPHiQbt26qa8///zzmJmZceLECQBCQ0MLvF5ZclYWD8tpMBh499136d27N7NmzaqwswAPy6koCj4+Pur1tPv37+fVV1+tkKyPq7KXnfj4eCZMmMCyZctwc3MD4OWXX+bChQvqFPPw8HC6detWaeojwFdffUV4eDi7d+9m0qRJ9OjRg8WLFxeZT6vV0r59e/bt21fhueH/ryNPTU3FYDDwww8/4OLiUun3eUW6d+8eb7/9Nnfu3EGv1xMUFISrqysAM2fO5LfffgNgw4YNuLi4YGRkhKOjI7t37wbg559/xszMjOeee+6J5iyL+lRR5bU02YtTFbIX1w5WtuNEcSpbm1KW5dvR0ZHQ0FAg98vc9u3bFzs752GedF0sy6wAcXFx+Pn5odfr0ev1REREMHjw4EqbV4ingUYpap6MKJWwsDDWrl1LVlYW/fv3Z/To0YwePZpJkybx0ksvERsbi5+fH3fv3qVly5YsXrwYU1PTSpczj6+vL6+99hr9+vUr94wPy5mQkMDEiROxt7dXl2/dunWFnNl/2P48fPgwAQEBaDQamjVrxrx587Cysir3nGWhqM9aWSxcuJCdO3cWmNI4ePBg/ud//ofFixeTmZmJo6MjM2bMQKPRVJr6mF9ISAjHjh1jyZIlxea7evUqvr6+3Lx5k/r167N8+XJq165dYZl37NjBxo0bycrKokuXLvj5+RETE1Nl9nl56dGjB5s2baJBgwZs376djRs3kp2djbu7OxMnTgRybwY2d+5c0tPTsbe3x9/fH0tLSzIzM5kzZw6///47pqamLFy4kFatWj3RvGVVnyqivJY2e57727zKnv3w4cPFtoOV7ThRnMrQpuSvm9HR0WVSvm/duoWvry9XrlzBysqKZcuW0aBBg0fK96TrYllmzbNy5UoOHDiAsbExb775JhMnTqyU+1aIp4UM9IUQQgghhBBCiGpEpu4LIYQQQgghhBDViAz0hRBCCCGEEEKIakQG+kIIIYQQQgghRDUiA30hhBBCCCGEEKIakYG+EEIIIYQQQghRjchAvwLFxcXRokUL+vTpo/7z8PBgx44dj7Ved3d3YmJiHum9aWlpDB8+vETLRkZGMmjQIDw8PHBzc+P9998nISEByP25sLFjx5Z6+/b29iQnJ2MwGBg3bhzOzs5s3ry5yGUVRaFXr16Eh4cXem3BggUsXLiQgIAA9XdXi9OnTx9SU1MfmHnWrFlERUURFxfHK6+8AsC3337LunXrANi+fTtbtmwpxScVQjwtTp48ybBhw9DpdLi7uzNq1CjOnj1b0bEeSWBgIPPnzy/VeyIjIwkICCjRsleuXGHKlCn06tULDw8P+vfv/9ht4oNERESwcOHCMl/vyZMnefnll/nPf/5T4PlvvvkGFxcX7t69W+bbrOyqc5+nKvL19eXLL78s1XtK2te5c+cOfn5+6HQ6PDw86Nu3L9u3b3/o+2JiYnB3dy9VJiFE8UwqOsDTztzcnN27d6uPExMTcXd3p3Xr1jg4OJR7ntu3b3P69OmHLpeYmMj06dMJCQnh+eefB+Dzzz9n8uTJBAcHP3aOxMREfvzxR06ePImxsXGRy2g0GoYMGcLOnTsLNAwZGRmEhYURHBxMkyZNHrqt/Pu/OP7+/kBuRyXPkCFD1P+fOHGC5s2bP3Q9ouLExcXxxhtv8OKLL6rPKYrC8OHD6d+//yOv193dndmzZ9OxY8dSvzctLY0JEyawadOmBy4XExPD6NGjady4MRqNBkVRMDY25r333qNHjx6Flv/2229JS0tjzJgxpc4kypZer2fs2LFs2LCBVq1aAbnHnNGjRxMREVHs8a06OX36NLdv337ocnFxcbz11ltMnjyZ5cuXo9FoSExMZNq0aVy6dImpU6eWebaePXvSs2fPMl9v27ZtGTNmDB9++CEhISGYmppy4cIFAgIC+Prrr6lZs2aZb7MqkD5P1VbSvs6nn36KhYUFe/bsUevxoEGDqF+/Pl27di2HpEIIkIF+pWNnZ0ejRo04evQo8+fPJz09HUtLS4KCgli1ahV79+7F2NiYxo0bM3v2bGxtbTl37hwzZ84kPT2dJk2acO/ePSC306TT6fj111+LfLx27Vp27dqFiYkJjRo1YsmSJcyYMYOMjAz69OlDSEhIsZ3QlJQUsrKy1G0BvP3227Ro0UJ9nJSUxJgxY4iPj8fY2JhPP/2Upk2bkpaWhr+/P3/99RdZWVl07tyZDz/8EBOT3OKYmZnJqFGjyM7Opl+/fgQGBtKwYcMic3h5eREYGMjVq1fVxnf//v20atWKJk2a4OvrS/PmzRk5ciQrV67k0KFDaLVa6tSpw+LFi6lXrx729vZER0ermUeOHMn169d5/vnnWbBgAba2tgwbNgxvb29at26tbjswMJCUlBQ6d+7MkSNHOHr0KObm5mzatInZs2erjZmfnx/Nmzfn7bffLn2BEGWqqnYyARo2bFgge2xsLEOGDCEiIgIbG5sCy+b/EkpUrPT0dNLS0gocKz08PLC0tCQ6OpolS5aos5JiYmJYsGAB4eHhBAYGcvbsWW7cuMHNmzdxcHDA398fS0tLEhMTmT9/PvHx8WRlZeHm5sa7775LXFwc3t7eNG3alKtXr7JkyRKmTZtGp06dOHnyJNnZ2Xz44Yds3bqVv//+m9atW7N8+XKMjIxYs2YNhw8fJjMzk/T0dKZPn84bb7xBYGAgJ0+e5Pr169jb29OoUSP1c2zcuJFdu3bxxRdfYGtry+eff87BgwfJycnh+eefZ+7cuSQkJBAcHIzBYMDKyoopU6YUu6/WrVuHu7s7np6e6nN2dnasWLGCnj17MmzYMH788Uf27t1LTk4OiYmJ2NnZsWTJEuzs7B7Ytrz00kuMGTOGo0ePcv36dYYPH86IESMICQnhu+++Y+3atQwbNoy2bdvyyy+/EB8fT7t27fj4448xMjIiJCSEdevWYW5uTqdOndi0aVOhs/X3GzduHFFRUaxYsYJp06Yxffp0PvjgAxwcHDh//jz+/v7cunULg8HAsGHD6N+/Pzk5OSxatIhTp05x9+5dFEVh4cKFtGvXDl9fX27dusWVK1fo3r07Pj4+j1osK43q0ufJX47uf+zr64tGo+H8+fMkJyfTpUsX/Pz80Gq1xZaDmJgY/P39sbCw4N69e/j4+PDZZ59Rr149zp49S40aNZg4cSJBQUFcuHCBN998k5kzZ5aq/OS3ePFiYmNjWb16NVqtlmXLlnH8+HEMBgMtW7bEz8+P6OjoAn0db2/vYv+uSUlJPPPMM2RlZWFqaoqdnR2BgYFYW1sD8P3337N27Vr0ej3Jycn07duXyZMnF1iHXq8vMoelpSXffPMNwcHBaLVazMzMmD9/Ps2aNStZoRPiKSJT9yuZX3/9lcuXL5ORkcG5c+cICgoiKCiInTt38sMPP7Bjxw7CwsJo3rw5vr6+AEybNo0BAwYQFhbG8OHDuXbt2kO3ExERQUhICFu3biU8PJwGDRqwefNmFi9erA6GHnSmycHBgYEDB+Lp6Ymrqyt+fn58//33Bb6pvXLlCrNmzSIsLIz27durU8QWLVpEq1atCAkJITQ0lJSUFL766iv1fWZmZmqHavfu3cUO8gGsra3p3bs3ISEh6nNbt24t1ADFx8fz9ddfs3PnTkJCQujSpQu//fZbofVduHCBOXPmEBYWxosvvqieyX+QN954gx49ejBixAi8vb0ZMmSIOkXtzp07REREFOi4isojfydz6NCheHp6MmzYMABWrVqFq6srOp2OSZMmkZSUBMC5c+cYOHAgOp2O999/v0AnM++yjqIer127FhcXF9zd3ZkwYQJpaWkFOpkGg6FU2R0cHDA3N+fq1asEBgYycuRIdDod06ZNKzC9+sKFCwwbNgw3Nzd0Oh379u0Dcr/kmDBhAv369UOn07FmzZpH35GiWLVr18bHx4dRo0bRs2dPfHx82LlzJ6+//jparfaB7z116hQrV65k//79mJiYsGrVKgB8fHzw8vIiJCSEHTt2EBUVpf5dExISGD9+PN999x22trbExcXRo0cP9u7dS6dOnfD392f58uXs3buXn3/+mZMnT3L16lWioqLYvHkzYWFhTJkyhZUrV6o5rl69yq5du1i2bJn63Pr16zlw4ABBQUHY2toSGhrKX3/9xfbt29m9ezeOjo74+fnx8ssvM3jwYFxdXR84yIfc9q9Dhw6Fnq9bty5Nmzbl1KlTAPzyyy/MmTOHffv20apVK/U4/aC2Ra/XU6dOHYKDg1m5ciWffvopmZmZhbZ1+fJlgoKC2LNnDz/99BPHjh3j3LlzLFu2jI0bNxIaGoqlpWWJ6quRkRGffPIJu3btYubMmTRo0IBBgwaRnZ3NpEmTmDp1KiEhIWzevJkNGzZw8uRJTp06xfXr19m6dSv79u3D09OT9evXq+vMyMhg79691WKQD9Wrz/MgsbGxfPXVV+zbt4/z58+zdevWB5YDgLNnz/Lpp5+yZ88eTE1NOX36NOPGjePAgQM888wzrFu3jrVr1xISEsI333xDYmJiqcuPoijMmzePq1evsn79emrWrMm6deswNjYmJCSEPXv2UK9ePZYtW1aor/Mg7733Hj/99BOdOnVi5MiRrFq1CktLS1544QUURWHDhg0sWbJE/ZusW7eO5OTkAusoLofBYGDRokV88cUX7Ny5k4EDB3LixIkS/R2EeNrIGf0KltfJBzAYDNSpU4dPPvmEmzdvYm9vj6WlJQD//ve/6devHxYWFgAMHz6cNWvWcOPGDc6cOUPfvn0BaNeuXYmmVUVHR+Pi4kLt2rUBmDFjBlBwavrD+Pr6MnbsWI4dO8bx48dZunQpQUFB6vVbbdq0Uc/+tGjRgkOHDgG517mdPn1avS4vIyOjxNssypAhQ5g8eTLvvfce586d4/r16zg5ORVYxs7ODgcHBzw9PenWrRvdunWjc+fOhdb1+uuvq5n79+//SFO6+/Xrx6pVq0hOTubAgQN0796dWrVqPdqHE0/U/Z3MI0eOYGlpWaCTaWFhQWBgoHo947Rp0/D29mbAgAGcOHHioR0e+P9O5rZt26hduzaLFy9WO5k6na5El4/c7+DBgxgZGdGsWTMiIyO5evUq4eHhmJiYEBgYqC73wQcf0L9/f7y9vYmPj2fYsGF069YNHx8fRowYQY8ePcjMzGT06NE0bNgQV1fXUmcRD/bOO+8wYMAAjh8/zvHjx1m/fj3r169/6GDNxcWFunXrArnHo0WLFjFx4kSOHz/O7du31eve7927R2xsLG3atMHExIS2bduq69BqterlHQ0bNuSVV15R25V69epx+/ZtXn31VT7++GPCwsK4dOmSejYwT9u2bdUZV5Bb9pKSklizZo16bPv+++85ffo0Xl5eAOTk5JCenl7qfZWdnV3k83q9Ho1GA0CXLl1o3LgxAAMHDlTb0Ie1LXlT9Fu1aoVery9wdjaPk5MTRkZGWFpa0qhRI27fvk1sbCxdunTh2WefBeCtt94qUMce5LnnnmPatGksX76cgwcPAnDx4kUuX77MzJkz1eUyMjL4z3/+w9ChQ6lduzbBwcFcuXKFmJiYAtP827VrV6LtVlbVuc/zIJ6enurfsU+fPkRERNCpU6diy0HTpk2pX7++OlMRoEGDBrRs2RLIrctWVlaYmppiY2NDzZo1uX37Nq+88kqpys/GjRu5efMmoaGhmJqaArn1KC0tjaioKACysrJ45plnSryfIPeLkQMHDvDHH39w/Phxjh49ypo1awgICKBHjx6sWbOGyMhIwsPDOX/+PIqiFDpeFJfD2NgYFxcXBg8eTPfu3enSpQs6na5U+YR4WshAv4LdP5U4T0hIiNrAQe63rvnl5OQU6BDlfz2vQ5Z3LW+erKws9f/GxsZqpwkgNTWV1NTUEueOiIjg1q1beHl54ezsjLOzM1OmTKF79+7qdMb8HcP8WXJycggICKBp06bqtvNnKa02bdpgY2NDVFQUkZGRDB48uNA380ZGRmzevJnTp08THR3NokWL6NixI35+fgWWy/8+RVEKfIaSqlWrFi4uLuzZs4ewsDDmzp37aB9MlLmq3Mm8fPmymj07O5tnn32W1atXU6NGDaDwYAzg1q1bxMbGMmDAAADq16/P4cOHuXfvXrGDRRnol60TJ07w66+/MmrUKJycnHBycuKDDz5Ap9MRGxtb7DEaCh6PcnJyMDIyIicnB0VRCA4OVv/2ycnJmJmZkZKSgqmpaYFyoNVqCxxfi5pF8McffzB+/HhGjBhBly5d6NChA/PmzVNfz98WATRq1IjZs2czb9482rVrR61atcjJyWHUqFEMHToUyB2Yl+S6/PxeeeUVYmJi6NWrV4HnExMTiYuL46WXXuLo0aOF9kve44e1LWZmZgDqc/e3q5DbJufJfz+M/MuW9r4KL7zwAjY2NurxxWAwUKtWrQJt/40bN7CysiIyMhJ/f3/eeecdevbsSZMmTdizZ4+63P1/i6qmuvZ5HrTtvO3nz25kZPTAcnDy5MlCf+u8gfj9nzu/0pafDh068OqrrzJjxgy2bt2KVqslJyeHmTNn4ujoCMDdu3eLnP1SnOzsbObNm8fUqVNp3bo1rVu35p133mH16tVs3bqVTp064enpSa9evWjfvj1eXl4cPny4yL95cTmWLVvGX3/9RVRUFOvXr2fHjh18/vnnJc4oxNNCpu5XEV27diUkJEQ9AxEUFESHDh2oW7curVq1UqeK//HHH/z1119A7oAzKyuLc+fOAahn1CH3zPWhQ4e4c+cOkHu9+caNGzExMcFgMBTZAcqvZs2aLF++XF035A5azMzMHjjVPu+zbNy4EUVR0Ov1jBs3rtg765fU0KFD2bVrFwcPHizyLHxsbCzu7u40bdqUsWPHMmLECM6cOVNouZiYGHUa4Lfffku3bt1KtH1jY+MCnRBvb282bdqEoii0adPmET+VKGt5nczdu3cTHh5OUFCQ2oko705maQb58P/X6O/evZu9e/fy5Zdf8vLLL6uvFzUAyJ8tz99//63W8eDgYHWdW7dufaRfyhAPZmNjw+eff87PP/+sPpeUlER6ejq9evXi2rVr3Lx5E0VROHz4cIH3RkREkJaWRk5ODtu2bcPJyQlLS0vatm2rTklPTU1V79XwqI4fP652xl977TUiIiIeODXd3t4eZ2dnOnfurH4h0LVrV3bs2KG2KQEBAXz44YdA4eNjccaNG8f+/fvZtWuX+lxCQgIffPABQ4YMwc7ODoCffvqJxMREAIKDg9UZXE+ibclbb3R0tLrNktw9/EEaN26MmZmZOsCLj4/H3d2d33//naNHj+Lk5MTQoUN56aWXOHz4cKkv66kOqlqfx8bGhrNnz5KZmUl2djbff/99gffv378fvV5PZmYmu3btwsnJ6YHl4FGVtvy0bt2at956CysrKz777DMgd99v2bIFvV5PTk4Os2fPZvny5UDJ6rKJiQkXL15k9erVajuYnZ3NlStXaNmyJZcuXeLOnTtMnjyZHj16cOzYMXVb+RWXIzk5GUdHR6ytrRkxYgSTJ08usj8nhJAz+lVG//79iY+PZ8CAAeTk5NCoUSP1esnly5czY8YMgoODadiwoXqneSsrK3x8fBg9ejQ2Nja4uLio63N0dOTcuXPqTbuaNWvGggULqFGjBi1btqR37958++231KlTp8g8nTp1Yvbs2UyfPp20tDSMjY2xtbVl9erV6lnL4syaNQt/f390Oh1ZWVm8/vrrjBo16rH2j5ubG0uXLsXR0bHQjckgdxpZ79698fLywsLCAnNz80Jn8wFefPFFZs6cyY0bN2jSpEmJf0aqW7duLFiwAICxY8fi4OBA7dq1GTx48GN9LlEx8jqZ7u7uWFhYFNnJ9Pb2LraT2axZs0KdzKVLlzJq1CgsLS0JDAxEURRGjRqldjIfZ1ZLcSwtLWnVqhWhoaEMGDCA+Ph4hgwZQnh4uDpYHD9+vDpYnDBhAh4eHmWe42nWuHFjVq1axYoVK0hISMDMzAwrKyvmz5+Pg4MDgwcPxsvLC1tb20I3yKpbty6jR48mJSWFDh068O677wK5Z7MWLFiATqdDr9fj7u6Oh4dHqb88yuPu7s7BgwdxdXVFq9XSuXNnbt++rQ6KijNz5kzc3d3Zt28fAwYMIDExkYEDB6LRaKhfvz5LliwBoHPnzkycOBGtVsvs2bOLXV/9+vXZunUr//znP1mzZg0mJiaYmZkxcOBABg0apC5nZ2eHj48PSUlJNGvWTD1OP4m2BXL/hjNmzGDkyJGYmprSokULdTbFozA1NWX16tX4+/vzxRdfkJ2dzfvvv0+7du2wtrZm2rRp6HQ6jI2Nad++vXqDw6dJVevz5M2E6d27N7a2tnTs2LHA4NPc3JyhQ4eSmpqKs7MzXl5eGBkZFVsOHvXnAgcPHlzq8qPRaFi0aBF9+/bF0dGR8ePH8/HHH+Pp6YnBYKBFixbq/RHu7+sUJyAggE8++QRnZ2dq1Kih/hzyhAkTMDIyonv37vTu3ZtatWrRsGFDmjVrxqVLlwrMWiguh6WlJePGjWPEiBGYm5tjbGz8RH4iU4jqQKM87GtMIUSpXb58mWHDhnHgwIHH6hCKsnP/HZjzu/+OyTk5OQQGBqodpEaNGvHRRx/x7LPPcvnyZWbMmEFqaioNGzbk8uXL+Pn50bFjRzZu3MjXX3+tdjJXr16tbu/LL78kNDQUKNjJ9Pb25tatWw/sZOa/G3tR8n4BYs6cOYUeX7p0iXnz5nHjxg00Gg0TJ06kV69exMXFsWDBAq5du6YOFidOnPi4u1mUkfv/piLX/XW1PFy5coXdu3czfvx4jIyMOHjwIOvXr3/sM/vi6ZD/13+EEKI8yUBfFGvo0KEFbsiU35YtW9TrDZ+WHCUVEBDAtm3bmDVrllzvLIR4JNVxoP/TTz+xePHiIl/r2LFjgZuSFaciBvpZWVksWLCAEydOYGxsjJWVFR999BF//vmn+msy99PpdGUym0CUnyfV16iOA/09e/ZI2ReiCpCBvhBCVAJV7QstIYQQQghReclAXwghhBBCCCGEqEbkrvtCCCGEEEIIIUQ1IgN9IYQQQgghhBCiGpGBvhBCCCGEEEIIUY3IQF8IIYQQQgghhKhGZKAvhBBCCCGEEEJUI/8HSGoDDoYCn7oAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# pair plot of columns without missing values\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "cat_cols_pair = ['Product_Fat_Content','Product_Type','Supermarket_Location_Type']\n", + "\n", + "cols_2_pair = ['Product_Fat_Content',\n", + " 'Product_Shelf_Visibility',\n", + " 'Product_Type',\n", + " 'Product_Price',\n", + " 'Supermarket_Opening_Year',\n", + " 'Supermarket_Location_Type',\n", + " 'Supermarket_Type',\n", + " 'Product_Supermarket_Sales']\n", + "\n", + "for col in cat_cols_pair:\n", + " sns.set()\n", + " plt.figure()\n", + " sns.pairplot(data=newdata[cols_2_pair], height=3.0, hue=col)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Low Fat', 'Ultra Low fat', 'Normal Fat'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FEATURE ENGINEERING\n", + "# print all unique values\n", + "newdata['Product_Fat_Content'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3217\n", + "1 1773\n", + "Name: is_normal_fat, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fat_content_dict = {'Low Fat': 0, 'Ultra Low fat': 0, 'Normal Fat': 1}\n", + "newdata['is_normal_fat'] = newdata['Product_Fat_Content'].map(fat_content_dict)\n", + "\n", + "# preview the values\n", + "newdata['is_normal_fat'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# assign year 2000 and above as 1, 1996 and below as 0\n", + "def cluster_open_year(year):\n", + " if year <= 1996:\n", + " return 0\n", + " else:\n", + " return 1\n", + " \n", + "newdata['open_in_the_2000s'] = newdata['Supermarket_Opening_Year'].apply(cluster_open_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Supermarket_Opening_Yearopen_in_the_2000s
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" + ], + "text/plain": [ + " Supermarket_Opening_Year open_in_the_2000s\n", + "0 2005 1\n", + "1 1994 0\n", + "2 2014 1\n", + "3 2016 1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# preview feature\n", + "newdata[['Supermarket_Opening_Year', 'open_in_the_2000s']].head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# get the unique categories in the column as a list\n", + "prod_type_cats = list(newdata['Product_Type'].unique())\n", + "\n", + "# remove the class 1 categories\n", + "prod_type_cats.remove('Health and Hygiene')\n", + "prod_type_cats.remove('Household')\n", + "prod_type_cats.remove('Others')\n", + "\n", + "def cluster_prod_type(product):\n", + " if product in prod_type_cats:\n", + " return 0\n", + " else:\n", + " return 1\n", + " \n", + "newdata['Product_type_cluster'] = newdata['Product_Type'].apply(cluster_prod_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_TypeProduct_type_cluster
4980Health and Hygiene1
4981Health and Hygiene1
4982Health and Hygiene1
4983Household1
4984Household1
4985Household1
4986Household1
4987Household1
4988Household1
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\n", + "
" + ], + "text/plain": [ + " Product_Type Product_type_cluster\n", + "4980 Health and Hygiene 1\n", + "4981 Health and Hygiene 1\n", + "4982 Health and Hygiene 1\n", + "4983 Household 1\n", + "4984 Household 1\n", + "4985 Household 1\n", + "4986 Household 1\n", + "4987 Household 1\n", + "4988 Household 1\n", + "4989 Household 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newdata[['Product_Type', 'Product_type_cluster']].tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transformation of Product_Supermarket_Sales feature')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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frtV6VCQhIQHz58/H5s2bUVpaCrVajaCgIMhkMkRERCA8PBzNmjXDU089ZZhm+PDh+Pnnn+Hj4wMXFxf06NHDcCT09ttv45133oFarYYQAlOnTkW3bt2g0WgwY8YMKJVKzJ07t9J4YmNjkZiYiOHDh8Pa2hparRZ9+vQxjBRMmzYNUVFR2LdvHzp06FBuuPmXX35BSkoK9Ho9EhMT4eDggAEDBmDy5MmYOHEiZDIZ7OzssHLlyjIjADU1a9YsTJ8+HQ4ODmjatCl69epV7fe3Q4cOeOWVVxAREYENGzYgKSkJ8fHx+Pjjj6HVavHaa68ZhuSfe+45hIaGIikpqczFffdr3749PvzwQyQmJuLq1auwsbGBvb095s+fjw4dOlT5vb3H2tq60ji6dOlSbX970JgxY5CdnY3//Oc/kMlkaNWqFRYvXgwHBwdMmTIFo0aNQosWLdCyZUs8/fTTuHDhAry8vPDss89i1qxZiIuLq/YzNib2qshEZcfzRERU75588kkcPHgQjo6O5g6FJIxH1kRE1Th06BAWLVpU4Xu9e/cuc7EQATNnzqz0hjaJiYno0KGDiSOyfDyyJiIikjheYEZERCRxTNZEREQSx2RNREQkcUzWREREEtfgrwa/caMAen35a+icnOyQl5dvhoiMxxjrTl3EaWUlQ8uWVd+us6GqrB9JiaV8F+9niTEDtY+7Mfehh9Xgk7VeLyrdyEh94wMwxrpkKXFKUVX9SEosIcYHWWLMgOXGbak4DE5ERCRxTNZEREQSx2RNREQkcUzWREREEsdkTUREJHFM1kRERBLHZE1ERCRxTNZEREQS1+BvilKZEo0OzR1sq6yj0+lRkF9sooiIiGqvmV0TyOVVH39xm2a5Gm2ylgGYs+pAlXXipvU3TTBERA9JLrdCDLdpDRaHwYmIiCSOyZqIiEjimKyJiIgkjsmaiIhI4pisiSQuPz8fgYGBuHz5MgAgPT0darUaPj4+SExMNNQ7c+YMgoOD4evrizlz5kCr1QIA/v33X4SFhcHPzw/Tpk1DQUGBWdaDiGqPyZpIwn777Tc8//zzyMrKAgAUFxcjOjoaSUlJ2LVrF06ePIl9+/YBACIiIjB37lzs3r0bQgikpKQAAObNm4fQ0FCkpqaiW7duSEpKMtfqUC01s2uC5g62Vf7JZOaOkupTo/3pFpElSElJQWxsLN566y0AwPHjx9GuXTs8/vjjAAC1Wo3U1FR07NgRxcXF8PDwAAAEBQVh+fLlGDNmDDIzM/Hhhx8ayseNG4eIiAizrA/VjjE/y4p/hT/LasiYrIkkLD4+vszrnJwcqFQqw2tnZ2dkZ2eXK1epVMjOzsaNGzdgZ2cHhUJRprymnJzsarkGpqVS2Zs7hBozJuZSjQ5KZfWb6+rqyGR110aW2NaWrF6TdX5+PkJCQrB69Wq0adMG6enpWLRoEUpKSuDv749Zs2YBuHuuLSYmBvn5+fD09MS8efOgUCjw77//IiIiAnl5eWjfvj0SEhLQrFmz+gyZSNKEEOXKZDJZjctrKi8vH3p9+XlJiUplj9zcO+YOo0aMjbm5gy00Gm219aqrIwTqpI1q29ZWVjKL2fGTmno7Z81zbUR1z8XFBdeuXTO8zsnJgbOzc7ny3NxcODs7w9HREfn5+dDpdGXKiciy1Fuyvneu7d6G4f5zbQqFwnCu7Z9//il3ri01NRUajQaZmZnw9fUtUy41xlz40cyuibnDpAaie/fuOH/+PC5cuACdToedO3di4MCBaN26NWxsbHD06FEAwNatWzFw4EAolUp4enpi165dZcqJyLLU2zC4VM611Tfej5dMycbGBosXL8aMGTNQUlKCQYMGwc/PDwCQkJCAmJgYFBQUwM3NDePHjwcAxMbGIioqCqtWrUKrVq2wdOlSc64CEdWCyS4wM9e5tsrOjxhzwYYxF2PU1XwqYwkXcVhCjIDlxFmRPXv2GP738vLC9u3by9VxdXXFxo0by5W3bt0aycnJ9RofEdUvkyXrhznXJpfLa32urbILYxxaVH/BhjEXYxhz4UdtL+qwhAtmLCFGoG7i5MUxRGQuJrspCs+1ERER1Y7Jjqx5ro2IiKh26j1Z81wbERHRw+G9wYmIiCSOyZqIiEjimKyJiIgkjsmaiIhI4vjUrSoICDR3sK2yDp8hS0RE9Y3JugpWMhnmJPEZskREZF4cBiciIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJrJA27ZtQ0BAAAICAvDuu+8CAM6cOYPg4GD4+vpizpw50Gq1AIB///0XYWFh8PPzw7Rp01BQUGDO0ImoFpisiSxMUVER4uPjkZycjG3btuHIkSNIT09HREQE5s6di927d0MIgZSUFADAvHnzEBoaitTUVHTr1g1JSUlmXgMiqikmayILo9PpoNfrUVRUBK1WC61WC4VCgeLiYnh4eAAAgoKCkJqaCo1Gg8zMTPj6+pYpJyLLojB3AERUM3Z2dnjttdfg7++PJk2a4JlnnoFSqYRKpTLUUalUyM7Oxo0bN2BnZweFQlGmnIgsi1mS9bZt2/DRRx8BAAYOHIjIyEicOXMGMTExyM/Ph6enJ+bNmweFQoF///0XERERyMvLQ/v27ZGQkIBmzZqZI2wiSTh79iw2bdqEn376Cfb29njzzTeRlpZWrp5MJoMQosLymnJysqtVrKamUtmbO4QaMybmUo0OSmX1m+vq6shkdddGltjWlszkyfre+bbU1FQ0b94czz//PNLT07Fw4ULExcXBw8MD0dHRSElJQWhoqOF8W0BAAD788EMkJSUhIiLC1GETScaBAwfg5eUFJycnAHeHtj/55BNcu3bNUCc3NxfOzs5wdHREfn4+dDod5HK5obym8vLyodeXT/xSolLZIzf3jrnDqBFjY27uYAuNRlttverqCIE6aaPatrWVlcxidvykxuTnrHm+jejhuLq6Ij09HYWFhRBCYM+ePXjmmWdgY2ODo0ePAgC2bt2KgQMHQqlUwtPTE7t27SpTTkSWxeRH1qY+31bZXlxdDSsZU+dhhp4sYajJEmIELCfO6vTv3x+nT59GUFAQlEol3N3dMWXKFDz33HOIiYlBQUEB3NzcMH78eABAbGwsoqKisGrVKrRq1QpLly418xoQUU2ZPFmb+nxbZcN3Di3qZljJmDq1HXqyhGE9S4gRqJs4pTSEN2XKFEyZMqVMmaurKzZu3FiubuvWrZGcnGyq0IioHph8GPz+823W1tYICgpCRkZGtefb7i8nIiJqTEyerBvj+TYBgeYOtlX+NbNrYu4wiYhIokw+DN4Yz7dZyWSYk3Sgyjpx0/qbKBoiIrI0ZvmdNc+3ERERGY+3GyUiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjiz3BuciIjuambXBHJ51cdNMpmJgiHJYrImIjIjudwKMauqfipf/Ct8Kl9jx2FwIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiePvrImIGgkBgeYOtlXW0en0KMgvNlFEZCwmayKiRsJKJsOcpKpvwBI3jTdgkSIOgxMREUkckzUREZHEMVkTERFJHJM1ERGRxDFZExERSRyTNRERkcQxWRMREUkckzWRBdqzZw+CgoLg5+eHuLg4AEB6ejrUajV8fHyQmJhoqHvmzBkEBwfD19cXc+bMgVarNVfYRFRLRiXr6OjocmUzZsyo82CIGqq67EOXLl1CbGwskpKSsGPHDpw+fRr79u1DdHQ0kpKSsGvXLpw8eRL79u0DAERERGDu3LnYvXs3hBBISUl5qHUhItOr8g5msbGxyM7OxtGjR3H9+nVDuVarxd9//13vwRFZuvroQ99//z2GDRuGRx99FACQmJiICxcuoF27dnj88ccBAGq1GqmpqejYsSOKi4vh4eEBAAgKCsLy5csRGhr6cCtGRCZVZbIePXo0zp07h99//x2+vr6Gcrlcjh49etR6oXv27MHKlStRWFiI/v37IyYmBunp6Vi0aBFKSkrg7++PWbNmAbg7hBcTE4P8/Hx4enpi3rx5UCh4l1SyDPXRhy5cuAClUomXXnoJubm58Pb2RqdOnaBSqQx1nJ2dkZ2djZycnDLlKpUK2dnZNV6mk5NdrWI1NZXK3twh1JhMBiiV1W/TTFVHJjOuHS2xrS1ZlZ+au7s73N3d0bdvX8Ne/MO6N4S3YcMGODk5ITw8HPv27UNsbCySk5PRqlUrTJ06Ffv27cOgQYMQERGBuLg4eHh4IDo6GikpKTwqIItRH31Ip9PhyJEjSE5Ohq2tLV555RU0bdq0XD2ZTAYhRIXlNZWXlw+9vvy8pESlskdu7h1zh1EjKpU9hAA0muqvIzBVHSFQbTvWtq2trGQWs+MnNUYdol68eBERERG4detWmc6/Y8eOGi+QQ3jUGNVlH3rkkUfg5eUFR0dHAMDQoUORmpoKuVxuqJOTkwNnZ2e4uLjg2rVrhvLc3Fw4Ozs/xJoQkTkYlaznz5+P4OBguLm51Wqv/H6mHsKrbC+uVKOziKEnSxhqsoQYAfPGWZd9yNvbG5GRkbh9+zaaNWuGn3/+GX5+fvjoo49w4cIFtGnTBjt37kRwcDBat24NGxsbHD16FD179sTWrVsxcODAOlorIjIVo5K1UqnEhAkT6mSBph7Cq2z4zqGFreSHnixhWM8SYgTqJs6HGcKryz7UvXt3TJo0CaGhodBoNOjXrx+ef/55dOjQATNmzEBJSQkGDRoEPz8/AEBCQgJiYmJQUFAANzc3jB8/vk7iICLTMSpZd+rUCb///juefPLJh14gh/CoMarLPgTcvXBt9OjRZcq8vLywffv2cnVdXV2xcePGOlkuEZmHUcn60qVLCA4OxmOPPQYbGxtDeW3Ot3EIjxqjuuxDRNT4GJWs7/2Mqi5wCI8ao7rsQ0TU+BiVrDt37lynC+UQHjU2dd2HiKhxMSpZ9+nTx3DB170LvFQqFfbv31+vwRE1FOxDRPQwjErWZ8+eNfyv0Wjw3XfflSkjoqqxDxHRw6jxU7eUSiUCAgKQlpZWH/EQNXjsQ0RUU0YdWd+8edPwvxACJ0+exO3bt+srJqIGh32IiB5Gjc9ZA4CTkxPmzJlTr4ERNSTsQ0T0MGp8zpqIao59iIgehlHJWq/X45NPPsH+/fuh1WrRr18/vPzyy3xUJZGR2IeI6GEYdYHZ+++/j0OHDiE8PBwTJkzAr7/+iiVLltR3bEQNBvsQET0Mo3brf/75Z2zatAlKpRIAMHjwYAwfPhzR0dH1GhxRQ8E+REQPw6gjayGEYSMDANbW1mVeE1HV2IeI6GEYlaxdXV2xcOFCXLx4ERcvXsTChQt5+0SiGmAfIqKHYVSyjo2Nxe3btxESEoL//Oc/uHHjBubOnVvfsRE1GOxDRPQwqkzWpaWliIyMxKFDh7B48WKkp6fjqaeeglwuh52dnaliJLJY7ENEVBeqTNbLly9Hfn4+evToYShbsGABbt++jRUrVtR7cESWjn2IiOpClcl67969eP/99+Hk5GQoc3FxwZIlS/DDDz/Ue3BElo59iIjqQpXJWqlUokmTJuXK7ezsYG1tXW9BETUU7ENEVBeqTNZWVlbIz88vV56fnw+tVltvQRE1FOxDRFQXqkzWgYGBiImJQWFhoaGssLAQMTEx8PHxqffgGhMBgeYOtmX+SjW6Mq+b2ZU/QiNpYx8iorpQ5R3MwsPDERsbi379+qFTp07Q6/X466+/oFarMX36dFPF2ChYyWSYk3SgTJlSqYBG8/+OvuKm9Td1WPSQ2IeIqC5UmaytrKywYMECTJ06FadPn4aVlRXc3d3h4uJiqviILBr7EBHVBaPuDd6mTRu0adOmvmMharDYh4joYRh1BzMiIiIyHyZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJrJg7777LqKiogAAZ86cQXBwMHx9fTFnzhzDg0L+/fdfhIWFwc/PD9OmTUNBQYE5QyaiWmCyJrJQBw8exJYtWwyvIyIiMHfuXOzevRtCCKSkpAAA5s2bh9DQUKSmpqJbt25ISkoyV8hEVEtmS9Y8IiCqvZs3byIxMREvv/wyAOCff/5BcXExPDw8AABBQUFITU2FRqNBZmYmfH19y5QTkWUxS7LmEQHRw3n77bcxa9YsNG/eHACQk5MDlUpleF+lUiE7Oxs3btyAnZ0dFApFmXIisixGPcijLt1/RHD27NkKjwiWL1+OMWPGIDMzEx9++KGhfNy4cYiIiDB1yESSsmHDBrRq1QpeXl7YvHkzAEAIUa6eTCartLymnJzsah6oGahU9uYOocZksruPw62OqerIZMa1oyW2tSUzebK+d0Rw5coVADwiIKqpXbt2ITc3FyNGjMCtW7dQWFgImUyGa9euGerk5ubC2dkZjo6OyM/Ph06ng1wuN5TXVF5ePvT68olfSlQqe+Tm3jF3GDWiUtlDCJR5bn1lTFVHCFTbjrVtaysrmcXs+EmNSZO1lI4ISjU6Se3NVlbn/jJj93hNTYoxVcRS4qzOZ599Zvh/8+bNOHz4MBYtWoTAwEAcPXoUPXv2xNatWzFw4EAolUp4enpi165dUKvVhnIisiwmTdZSOiJwaGErqb3ZiuoolYoyZcbs8ZqapRzN1EWcUj8qSEhIQExMDAoKCuDm5obx48cDAGJjYxEVFYVVq1ahVatWWLp0qZkjJaKaMmmy5hEBUd0KCgpCUFAQAMDV1RUbN24sV6d169ZITk42dWhEVIck8TvrhIQELFq0CP7+/igqKipzRJCSkoJhw4bhyJEjmDlzpnkDJSIiMgOTX2B2D48IiIiIjCOJI2siIiKqHJM1ERGRxJltGJyIqKFrZtcEcnnlx0SlGh1q8YtUaoSYrImI6olcboWYVQcqfV+pVOCdyX1MGBFZKg6DExERSRyTNRERkcQxWRMREUkckzUREZHEMVkTERFJHJM1ERGRxDFZExERSRyTNRERkcQxWRMREUkckzUREZHEMVkTERFJHJM1ERGRxPFBHhZEQKC5g22VdXQ6PQryi00UERERmQKTtQWxkskwJ6nyJ/gAQNy0/iaKhoiITIXD4ERERBLHZE1ERCRxTNZEREQSx2RNREQkcUzWREREEsdkTUREJHFM1kRERBLHZE1ERCRxTNZEREQSx2RNREQkcbzdKBFRDTWzawK5vPpjHZnMBMFQo8BkTURUQ3K5FWJWVX2ffgCIf4X36qe6wWFwIiIiiWOyJrJAK1euREBAAAICArBkyRIAQHp6OtRqNXx8fJCYmGioe+bMGQQHB8PX1xdz5syBVqs1V9hEVEtmSdbc0BDVXnp6Og4cOIAtW7Zg69atOHXqFHbu3Ino6GgkJSVh165dOHnyJPbt2wcAiIiIwNy5c7F7924IIZCSkmLmNSCimjJ5suaGhujhqFQqREVFwdraGkqlEk888QSysrLQrl07PP7441AoFFCr1UhNTcU///yD4uJieHh4AACCgoKQmppq3hUgohoz+QVm929oAJTb0AAwbGg6duxYbkOzfPlyhIaGmjpsIsno1KmT4f+srCzs2rULL7zwAlQqlaHc2dkZ2dnZyMnJKVOuUqmQnZ1t0ngtjTFXejfkq7wFBJo72FZZp0SjM1E0dI/JkzU3NER149y5c5g6dSoiIyOhUChw/vz5Mu/LZDIIIcpNJ6tFpnFysqt1nKakUtlX+X6JRgdj1v6dtYeqfn9yHyiVxm0+jaknpTpWMplR619dW1PdMttPt0y1oalsI1Oq0Umqg1RW58Gy6uYjIODQouq9YgHARimvNh5jWUqntZQ4jXH06FG8+uqriI6ORkBAAA4fPoxr164Z3s/JyYGzszNcXFzKlOfm5sLZ2bnGy8vLy4deX74/SolKZY/c3DtV1mnuYIs51fzkKv6V/tBoqr82xpg61dW715/ranmmrFNdW1fEykpmMTt+UmOWZG3KDU1lGxmHFraS+/I/WEepVJQrq24+VjIZ5iRVvTGKm9a/Vh2tIsZsIKWgLuKUyobmypUrmD59OhITE+Hl5QUA6N69O86fP48LFy6gTZs22LlzJ4KDg9G6dWvY2Njg6NGj6NmzJ7Zu3YqBAweaeQ2IqKZMnqy5oSF6OJ988glKSkqwePFiQ1lISAgWL16MGTNmoKSkBIMGDYKfnx8AICEhATExMSgoKICbmxvGjx9vrtCJqJZMnqy5oSF6ODExMYiJianwve3bt5crc3V1xcaNG+s7LCKqRyZP1tzQEBER1QzvYEZERCRxTNZEREQSx2RNREQkcUzWREREEsdkTUREJHFmu4MZEVFdKtHoqr2ndUO+pzc1bEzWRNQgyACjbiVKZIk4DE5ERCRxPLImIskz5rGVRA0ZkzURSZ5cboUYDnFTI8ZdVSIiIoljsiYiIpI4JmsiIiKJY7ImIiKSOCZrIiIiiWOyJiIikjgmayIiIoljsiYiIpI43hSlERIQ1T7wQKfToyC/2EQRERFRVZisGyErmQxzkqq+G1TcNN4NiohIKjgMTkREJHFM1kRERBLHZE1ERCRxTNZEREQSxwvMqELGXjFORET1j8maKsQrxomIpIPJmojMqpldE8jlVZ+Rk8lMFAyRRDFZE5FZyeVWiFlV9ShO/CscxaHGjReYERERSRyTNRERkcRxGJyIiGpEL/h8AVNjsqZaExAo1eiq7LTssA2XMReG8fNvmPhrEdOziGS9Y8cOrFq1ChqNBi+++CLCwsLMHRLhbod9Z+0haDTaSussmNaPe+ASUdf9yJgLw4z5/HmlN1H1JJ+ss7OzkZiYiM2bN8Pa2hohISHo3bs3OnbsaO7QyAjcA5cGc/UjYz5/XulNVD3JJ+v09HT06dMHLVq0AAD4+voiNTUV//u//2vU9FZWle+2t7S3qXZ6c9ZRKBXQauSSiaciLextysVY8/lUf/5Lr9ejsKCk2niqUtV3wRTTm1N99SOpfR+lVqe6egqlwuQxmbLOg98bS+5D5iYTQghzB1GVNWvWoLCwELNmzQIAbNiwAcePH8eCBQvMHBmR5WA/IrJskv/pVkX7EjKe5CKqEfYjIssm+WTt4uKCa9euGV7n5OTA2dnZjBERWR72IyLLJvlk3bdvXxw8eBDXr19HUVERvvvuOwwcONDcYRFZFPYjIssm+QvMXFxcMGvWLIwfPx4ajQajR4/GU089Ze6wiCwK+xGRZZP8BWZERESNneSHwYmIiBo7JmsiIiKJY7ImIiKSOCZrIiIiiWt0yXrHjh0YNmwYnnvuOaxbt85ky83Pz0dgYCAuX74M4O7tH9VqNXx8fJCYmGiod+bMGQQHB8PX1xdz5syBVnv3IRn//vsvwsLC4Ofnh2nTpqGgoAAAcPv2bUyZMgX+/v4ICwtDbm5ureJbuXIlAgICEBAQgCVLlkgyRgBYtmwZhg0bhoCAAHz22WeSjZNMZ8+ePQgKCoKfnx/i4uLMHY7Rtm3bZuhz7777rrnDqZKx2y+qR6IRuXr1qvD29hY3btwQBQUFQq1Wi3PnztX7co8dOyYCAwNF165dxaVLl0RRUZEYNGiQuHjxotBoNGLixIli7969QgghAgICxK+//iqEEGL27Nli3bp1QgghpkyZInbu3CmEEGLlypViyZIlQggh5s2bJ9asWSOEEGLLli3itddeq3F8aWlpYuzYsaKkpESUlpaK8ePHix07dkgqRiGEyMjIECEhIUKj0YiioiLh7e0tzpw5I7k4yXQuXrwo+vfvL65cuSJKS0vF888/b/j8paywsFD06tVL5OXlCY1GI0aPHi3S0tLMHVaFarL9ovrTqI6s73+Yga2treFhBvUtJSUFsbGxhjtGHT9+HO3atcPjjz8OhUIBtVqN1NRU/PPPPyguLoaHhwcAICgoCKmpqdBoNMjMzISvr2+ZcgDYu3cv1Go1ACAwMBD79++HRqOpUXwqlQpRUVGwtraGUqnEE088gaysLEnFCADPPPMMvvzySygUCuTl5UGn0+H27duSi5NM5/vvv8ewYcPw6KOPQqlUIjExEd27dzd3WNXS6XTQ6/UoKiqCVquFVquFjY1xDwYxNWO3X1S/GlWyzsnJgUqlMrx2dnZGdnZ2vS83Pj4enp6e1cbxYLlKpUJ2djZu3LgBOzs7KBSKMuUPzkuhUMDOzg7Xr1+vUXydOnUyJLWsrCzs2rULMplMUjHeo1QqsXz5cgQEBMDLy0tybUmmdeHCBeh0Orz00ksYPnw4vvrqKzg4OJg7rGrZ2dnhtddeg7+/PwYOHIjWrVvj6aefNndYFTJ2+0X1q1ElayGRhxlUFkdNyytjZVW7j/XcuXOYOHEiIiMj0bZtW0nGCACvvvoqDh48iCtXriArK0uycVL90+l0OHjwIN577z2kpKTgxIkT2LJli7nDqtbZs2exadMm/PTTTzhw4ACsrKzwySefmDsso0hlO9rYNKotkVQeZlBZHA+W5+bmwtnZGY6OjsjPz4dOpytTDtzdq703jVarRX5+vuGZxTVx9OhRvPjii3jjjTcwatQoScb4119/4cyZMwCApk2bwsfHBxkZGZKLk0znkUcegZeXFxwdHdGkSRMMHToUx48fN3dY1Tpw4AC8vLzg5OQEa2trBAUF4fDhw+YOyyhS2Y42No0qWUvlYQbdu3fH+fPnDUN4O3fuNAyF2djY4OjRowCArVu3YuDAgVAqlfD09MSuXbvKlAPAoEGDsHXrVgDArl274OnpCaVSWaN4rly5gunTpyMhIQEBAQGSjBEALl++jJiYGJSWlqK0tBQ//vgjQkJCJBcnmY63tzcOHDiA27dvQ6fT4eeff0bXrl3NHVa1XF1dkZ6ejsLCQgghsGfPHri7u5s7LKNUtm2g+tXo7g2+Y8cOrFmzxvAwg8mTJ5ts2UOGDMGXX36JNm3a4ODBg1i0aBFKSkowaNAgzJ49GzKZDGfPnkVMTAwKCgrg5uaGRYsWwdraGv/88w+ioqKQl5eHVq1aYenSpXBwcMDNmzcRFRWFS5cuwd7eHgkJCWjTpk2N4oqLi8OmTZvKDH2HhITgf/7nfyQT4z3Lly9Hamoq5HI5fHx8MGPGDEm1JZnexo0b8fnnn0Oj0aBfv36IiYmxiNMXH330ETZv3gylUgl3d3fExsZK9iIzwLjtF9WfRpesiYiILI30dz+JiIgaOSZrIiIiiWOyJiIikjgmayIiIoljsiYiIpK4BpusL1++jC5dumDEiBGGv+HDh2Pjxo0PNd/AwEBkZGTUato7d+5g/PjxRtXdu3cvxo4di+HDhyMgIACvvfYarl69WqvlSkFUVFSN79C0YcMGo56Mlp+fj5iYGKjVagwfPhwjR47Ehg0bqp0uIyMDgYGBNYqpMWEfkhb2ocZNYe4A6lOTJk2wbds2w+vs7GwEBgaiW7ducHV1NXk8t27dwokTJ6qtl52djcjISGzevBmtW7cGAKxatQozZ87E+vXr6ztMyTh69Cg6depUbb33338ftra22L59O2QyGbKzszF27Fi0atUK/fv3N0GkDRf7kGVjH2o4GnSyfpCLiwvatWuHtLQ0zJ8/H0VFRbCzs0NycjI+/PBDfPvtt5DL5Wjfvj3mzp0LlUqFP//8E9HR0SgqKkKHDh1QWFgI4O5Rh1qtxq+//lrh6zVr1mDLli1QKBRo164dFi9ejNmzZ6O4uBgjRozA5s2bIZfLK4zzxo0b0Gg0hmUBQHh4OLp06QIA2Lx5M3bv3o01a9aUex0VFQWZTIa//voL169fN9wkQqlU4q+//kJ8fDxu3rwJnU6HF154AaNHj0ZGRgbi4+Nha2uLwsJCREREYOXKlXB2dsa5c+fQtGlTzJgxA8nJyTh//jx8fHwQHR0NvV6PhQsX4rfffkNBQQGEEIiLi0PPnj0RFRWFmzdv4tKlSxg8eHCZ9Vu0aBHOnj2LpKQkKJVKJCQkIDMzEzqdDm5uboiJicHBgwexZ88epKWloUmTJggLC6v0c83NzYWTkxM0Gg2sra3h4uKCFStWGG4V+tNPP2HNmjUoLS3F9evXMXLkSMycObPMPEpLSyuMw87ODl999RXWr18PpVIJGxsbzJ8/Hx07djTuS9fAsA+xD7EPmYnpn8ppGpcuXRIeHh5lyn755RfRq1cvsXLlStGrVy9x584dIYQQGzduFGPHjhUFBQVCCCGWL18uJk6cKIQQYsSIESIlJUUIIcSRI0fEk08+KQ4dOlRu/ve//uGHH4SPj4+4efOmEEKIhQsXiqSkpApjqsyiRYtE165dhb+/v5gzZ47YuXOn0Gg0QgghNm3aJKZMmWKoe//ryMhIMXLkSJGfny9KSkpEWFiYSE5OFhqNRgwbNkycPHlSCCHE7du3hb+/v/j111/FoUOHhKurq7h8+bIQQohDhw6JLl26iFOnTgkhhHjppZcMz7vOy8sTXbt2FVevXhW//PKLmDFjhtDpdEIIIdasWSOmTp1qiCM8PNwQY2RkpFi7dq145513xPTp00VJSYkQQogVK1aIxYsXC71eL4QQ4v333xexsbGGaT7++ONq2+rMmTPCx8dH9OjRQ0ycOFGsXLlS/P3330IIIfR6vRg3bpw4f/68EOLuM827dOki8vLyxKFDh0RAQECVcWi1WtG1a1eRnZ0thLj7nOv169cb9RlaOvYh9iEh2IekokEfWd/bAwfuPp2nZcuWeO+995CXl4cnn3wSdnZ2AID9+/cjKCgItra2AIDx48dj9erVuHbtGn7//XeMHDkSANCzZ0+jhpQOHjwIPz8/w6P6Zs+eDeDukYOxoqKiMHXqVBw+fBiZmZlYsmQJkpOTjTr/NGrUKDRr1gwAMGLECPz444/o06cPLl68iOjoaEO94uJinD59Gk888QRatWplGC4EgDZt2sDNzQ0A0LZtW9jb28Pa2hqOjo5o1qwZbt26hR49esDBwQHr16/HpUuXkJGRYVgucLe97vf5558jLy8PW7duhbW1NYC75xXv3LmD9PR0AIBGo4GTk5PR7QTcvc9yamoqTp06hczMTKSlpWH16tVYtmwZhgwZgtWrV2Pv3r3YuXMn/vrrLwghUFRUVGYelcUhl8vh5+eHkJAQDB48GP369TM887oxYB9iH2IfkoYGnawfPN92z+bNmw0bFaD8I9/0ej20Wm2F7997DvKDj1vUaDSG/+VyeZn75N6+fRu3b982Ou4ff/wRN2/eRHBwMHx9feHr64tZs2Zh8ODBOH36dJXLvrf8+2O3srKCTqdD8+bNy7THtWvXYG9vj2PHjpVpDwCGDcGD632/vXv3Ij4+HhMmTMDQoUPRoUMHbN++3fD+g/Ps1asXnn76acyePRvffPMNlEol9Ho9oqOjMWjQIABAQUEBSkpKjG0qaLVazJs3D2+88Qa6deuGbt26YcKECUhKSsI333yDPn36YNSoUXj22Wfh6emJ4OBg/PDDDxV+5pXFkZCQgD/++APp6elYu3YtNm7ciFWrVhkdoyVjH2IfYh+ShgZ7NXhN9O/fH5s3bzac30pOTkavXr3wyCOPoGvXroarIk+dOoU//vgDANC8eXNoNBr8+eefAIDvv//eML++ffvi+++/R35+PgBgxYoV+Pzzz6FQKKDT6Sp8Huz9mjVrhqVLlxrmDdw9orCxsUHbtm3h6OiIc+fOoaSkBFqtFj/99FOZ6f/73/+itLQUJSUl2LJlC7y9vdG+fXvY2NgYNjRXrlxBYGAgTp48Wet2S0tLg7e3N0JDQ+Hu7o4ffvjB8OjJinTr1g3jxo2Dvb09Vq5cCeBu269btw6lpaXQ6/WYO3culi5dCuDuBvP+DX5FFAoFsrKykJSUZNjgarVaXLp0CW5ubrhw4QLy8/Mxc+ZMDBkyBIcPHzYs636VxXH9+nUMGjQILVq0wIsvvoiZM2fi999/r3WbNVTsQ7XDPkTGatBH1sYaPXo0rly5gjFjxkCv16Ndu3ZISEgAACxduhSzZ8/G+vXr0bZtW3To0AEAYG9vj4iICEyePBmOjo7w8/MzzG/QoEH4888/8fzzzwMAOnbsiAULFqBp06Zwc3ODv78/vv76a7Rs2bLCePr06YO5c+ciMjISd+7cgVwuh0qlQlJSEhwcHNCvXz/06tUL/v7+UKlU6N27d5kvf5MmTRAaGorbt2/D19cXwcHBsLKyQlJSEuLj4/Hxxx9Dq9XitddeQ8+ePWv9M5qQkBC8+eabUKvVkMvl8PT0xHfffVeuE99PJpNh4cKFGDlyJAYNGoRXXnkF7777LkaNGgWdTocuXbogKioKADBw4EAsWLAAADB16tRK57ls2TK899578PX1RdOmTSGEwLPPPovp06fDysoKgwcPhr+/P5o3b462bduiY8eOuHDhQpkjn8risLOzw7Rp0/Diiy+iSZMmkMvliIuLq1V7NWTsQ+xD7EP1i0/damCioqLQqVMnvPTSS+YOhcgisQ+RFPHI2kxCQ0NRUFBQ4Xvr1q0zXLhDwPbt2yu9GYRarcakSZNMHBFJAfuQ8diHLB+PrImIiCSOF5gRERFJHJM1ERGRxDFZExERSRyTNRERkcQxWRMREUkckzUREZHE/X9SNep1POp1jwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# transforming skewed features\n", + "fig, ax = plt.subplots(1,2)\n", + "\n", + "# plot of normal Product_Supermarket_Sales on the first axis\n", + "sns.histplot(data=newdata['Product_Supermarket_Sales'], bins=15, ax=ax[0])\n", + "\n", + "# transform the Product_Supermarket_Sales and plot on the second axis\n", + "newdata['Product_Supermarket_Sales'] = np.log1p(newdata['Product_Supermarket_Sales'])\n", + "sns.histplot(data=newdata['Product_Supermarket_Sales'], bins=15, ax=ax[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.title(\"Transformation of Product_Supermarket_Sales feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transformation of Product_Shelf_Visibility feature')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# next, let's transform Product_Shelf_Visibility\n", + "fig, ax = plt.subplots(1,2)\n", + "\n", + "# plot of normal Product_Supermarket_Sales on the first axis\n", + "sns.histplot(data=newdata['Product_Shelf_Visibility'], bins=15, ax=ax[0])\n", + "\n", + "# transform the Product_Supermarket_Sales and plot on the second axis\n", + "newdata['Product_Shelf_Visibility'] = np.log1p(newdata['Product_Shelf_Visibility'])\n", + "sns.histplot(data=newdata['Product_Shelf_Visibility'], bins=15, ax=ax[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.title(\"Transformation of Product_Shelf_Visibility feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value Count for Product_Fat_Content\n", + "Low Fat 3039\n", + "Normal Fat 1773\n", + "Ultra Low fat 178\n", + "Name: Product_Fat_Content, dtype: int64\n", + "---------------------------\n", + "Value Count for Product_Type\n", + "Snack Foods 758\n", + "Fruits and Vegetables 747\n", + "Household 567\n", + "Frozen Foods 457\n", + "Canned 376\n", + "Dairy 350\n", + "Baking Goods 344\n", + "Health and Hygiene 307\n", + "Meat 264\n", + "Soft Drinks 261\n", + "Breads 137\n", + "Hard Drinks 134\n", + "Others 100\n", + "Starchy Foods 81\n", + "Breakfast 66\n", + "Seafood 41\n", + "Name: Product_Type, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket _Size\n", + "Medium 1582\n", + "Small 1364\n", + "High 594\n", + "Name: Supermarket _Size, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket_Location_Type\n", + "Cluster 3 1940\n", + "Cluster 2 1581\n", + "Cluster 1 1469\n", + "Name: Supermarket_Location_Type, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket_Type\n", + "Supermarket Type1 3304\n", + "Grocery Store 724\n", + "Supermarket Type2 500\n", + "Supermarket Type3 462\n", + "Name: Supermarket_Type, dtype: int64\n", + "---------------------------\n" + ] + } + ], + "source": [ + "# feature encoding\n", + "for col in cat_cols:\n", + " print('Value Count for', col)\n", + " print(newdata[col].value_counts())\n", + " print(\"---------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_WeightProduct_Shelf_VisibilityProduct_PriceSupermarket_Opening_Yearis_normal_fatopen_in_the_2000sProduct_type_clusterProduct_Fat_Content_Low FatProduct_Fat_Content_Normal FatProduct_Fat_Content_Ultra Low fat...Supermarket _Size_HighSupermarket _Size_MediumSupermarket _Size_SmallSupermarket_Location_Type_Cluster 1Supermarket_Location_Type_Cluster 2Supermarket_Location_Type_Cluster 3Supermarket_Type_Grocery StoreSupermarket_Type_Supermarket Type1Supermarket_Type_Supermarket Type2Supermarket_Type_Supermarket Type3
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5 rows × 36 columns

\n", + "
" + ], + "text/plain": [ + " Product_Weight Product_Shelf_Visibility Product_Price \\\n", + "0 11.6 0.066289 357.54 \n", + "1 11.6 0.040097 355.79 \n", + "2 11.6 0.040352 350.79 \n", + "3 11.6 0.040290 355.04 \n", + "4 11.6 0.000000 354.79 \n", + "\n", + " Supermarket_Opening_Year is_normal_fat open_in_the_2000s \\\n", + "0 2005 0 1 \n", + "1 1994 0 0 \n", + "2 2014 0 1 \n", + "3 2016 0 1 \n", + "4 2011 0 1 \n", + "\n", + " Product_type_cluster Product_Fat_Content_Low Fat \\\n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 0 \n", + "\n", + " Product_Fat_Content_Normal Fat Product_Fat_Content_Ultra Low fat ... \\\n", + "0 0 0 ... \n", + "1 0 0 ... \n", + "2 0 0 ... \n", + "3 0 0 ... \n", + "4 0 1 ... \n", + "\n", + " Supermarket _Size_High Supermarket _Size_Medium Supermarket _Size_Small \\\n", + "0 0 0 0 \n", + "1 1 0 0 \n", + "2 0 0 0 \n", + "3 0 1 0 \n", + "4 0 0 1 \n", + "\n", + " Supermarket_Location_Type_Cluster 1 Supermarket_Location_Type_Cluster 2 \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 1 \n", + "3 0 0 \n", + "4 0 1 \n", + "\n", + " Supermarket_Location_Type_Cluster 3 Supermarket_Type_Grocery Store \\\n", + "0 1 1 \n", + "1 1 0 \n", + "2 0 0 \n", + "3 1 0 \n", + "4 0 0 \n", + "\n", + " Supermarket_Type_Supermarket Type1 Supermarket_Type_Supermarket Type2 \\\n", + "0 0 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 0 1 \n", + "4 1 0 \n", + "\n", + " Supermarket_Type_Supermarket Type3 \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save the target value to a new variable\n", + "y_target = newdata['Product_Supermarket_Sales']\n", + "newdata.drop(['Product_Supermarket_Sales'], axis=1, inplace=True)\n", + "\n", + "# one hot encode using pandas dummy() function\n", + "dummified_data = pd.get_dummies(newdata)\n", + "dummified_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Product_Weight 802\n", + "Product_Shelf_Visibility 0\n", + "Product_Price 0\n", + "Supermarket_Opening_Year 0\n", + "is_normal_fat 0\n", + "open_in_the_2000s 0\n", + "Product_type_cluster 0\n", + "Product_Fat_Content_Low Fat 0\n", + "Product_Fat_Content_Normal Fat 0\n", + "Product_Fat_Content_Ultra Low fat 0\n", + "Product_Type_Baking Goods 0\n", + "Product_Type_Breads 0\n", + "Product_Type_Breakfast 0\n", + "Product_Type_Canned 0\n", + "Product_Type_Dairy 0\n", + "Product_Type_Frozen Foods 0\n", + "Product_Type_Fruits and Vegetables 0\n", + "Product_Type_Hard Drinks 0\n", + "Product_Type_Health and Hygiene 0\n", + "Product_Type_Household 0\n", + "Product_Type_Meat 0\n", + "Product_Type_Others 0\n", + "Product_Type_Seafood 0\n", + "Product_Type_Snack Foods 0\n", + "Product_Type_Soft Drinks 0\n", + "Product_Type_Starchy Foods 0\n", + "Supermarket _Size_High 0\n", + "Supermarket _Size_Medium 0\n", + "Supermarket _Size_Small 0\n", + "Supermarket_Location_Type_Cluster 1 0\n", + "Supermarket_Location_Type_Cluster 2 0\n", + "Supermarket_Location_Type_Cluster 3 0\n", + "Supermarket_Type_Grocery Store 0\n", + "Supermarket_Type_Supermarket Type1 0\n", + "Supermarket_Type_Supermarket Type2 0\n", + "Supermarket_Type_Supermarket Type3 0\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fill-in missing values\n", + "# print null columns\n", + "dummified_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the mean\n", + "mean_pw = dummified_data['Product_Weight'].mean()\n", + "\n", + "# fill the missing values with calculated mean\n", + "dummified_data['Product_Weight'].fillna(mean_pw, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Product_Weight 0\n", + "Product_Shelf_Visibility 0\n", + "Product_Price 0\n", + "Supermarket_Opening_Year 0\n", + "is_normal_fat 0\n", + "open_in_the_2000s 0\n", + "Product_type_cluster 0\n", + "Product_Fat_Content_Low Fat 0\n", + "Product_Fat_Content_Normal Fat 0\n", + "Product_Fat_Content_Ultra Low fat 0\n", + "Product_Type_Baking Goods 0\n", + "Product_Type_Breads 0\n", + "Product_Type_Breakfast 0\n", + "Product_Type_Canned 0\n", + "Product_Type_Dairy 0\n", + "Product_Type_Frozen Foods 0\n", + "Product_Type_Fruits and Vegetables 0\n", + "Product_Type_Hard Drinks 0\n", + "Product_Type_Health and Hygiene 0\n", + "Product_Type_Household 0\n", + "Product_Type_Meat 0\n", + "Product_Type_Others 0\n", + "Product_Type_Seafood 0\n", + "Product_Type_Snack Foods 0\n", + "Product_Type_Soft Drinks 0\n", + "Product_Type_Starchy Foods 0\n", + "Supermarket _Size_High 0\n", + "Supermarket _Size_Medium 0\n", + "Supermarket _Size_Small 0\n", + "Supermarket_Location_Type_Cluster 1 0\n", + "Supermarket_Location_Type_Cluster 2 0\n", + "Supermarket_Location_Type_Cluster 3 0\n", + "Supermarket_Type_Grocery Store 0\n", + "Supermarket_Type_Supermarket Type1 0\n", + "Supermarket_Type_Supermarket Type2 0\n", + "Supermarket_Type_Supermarket Type3 0\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check if filling is successful\n", + "dummified_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data is (3493, 36)\n", + "Training target is (3493,)\n", + "test data is (1497, 36)\n", + "test target is (1497,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(dummified_data, y_target, test_size = 0.3)\n", + "\n", + "print(\"Training data is\", X_train.shape)\n", + "print(\"Training target is\", y_train.shape)\n", + "print(\"test data is\", X_test.shape)\n", + "print(\"test target is\", y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.11222151, 0.77329048, -0.10167541, -0.05882353, 1. ],\n", + " [ 1.03420733, 0.64446093, 0.29696892, 0.58823529, 1. ],\n", + " [ 1.10512931, -0.19777034, -0.09898964, 0. , 0. ],\n", + " [-0.94948062, -0.03939268, 1.10116383, 0.47058824, 1. ],\n", + " [ 0. , 0.5364253 , 0.00690625, -0.82352941, 0. ]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import RobustScaler, StandardScaler\n", + "scaler = RobustScaler()\n", + "\n", + "scaler.fit(X_train)\n", + "\n", + "X_train = scaler.transform(X_train) \n", + "X_test = scaler.transform(X_test)\n", + "\n", + "X_train[:5, :5]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.model_selection import KFold, cross_val_score\n", + "\n", + "\n", + "def cross_validate(model, nfolds, feats, targets):\n", + " score = -1 * (cross_val_score(model, feats, targets, cv=nfolds, scoring='neg_mean_absolute_error'))\n", + " return np.mean(score)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "n_estimators=150\n", + "max_depth=3\n", + "max_features='sqrt'\n", + "min_samples_split=4\n", + "random_state=2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE Score: 0.4078268922230158\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "gb_model = GradientBoostingRegressor(n_estimators=n_estimators, max_depth=max_depth, max_features=max_features, min_samples_split=min_samples_split, random_state=random_state)\n", + "\n", + "mae_score = cross_validate(gb_model, 10, X_train, y_train)\n", + "print(\"MAE Score: \", mae_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "outputs" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "literals {\n", + " key: \"mae_score\"\n", + " value {\n", + " scalar {\n", + " primitive {\n", + " float_value: 0.4078268922230158\n", + " }\n", + " }\n", + " }\n", + "}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from flytekitplugins.papermill import record_outputs\n", + "record_outputs(mae_score=float(mae_score))" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3.9.9 64-bit ('flytesnacks')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + }, + "vscode": { + "interpreter": { + "hash": "93d1c4f33f306e18e1c08a771c972fe86afbedaedb2338666e30a98a5179caac" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_1.ipynb b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_1.ipynb new file mode 100644 index 000000000..b5e6b61dd --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_1.ipynb @@ -0,0 +1,1732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supermarket Regression 1 Notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# reference: https://github.com/risenW/medium_tutorial_notebooks/blob/master/supermarket_regression.ipynb\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# makes graph display in notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "supermarket_data = pd.read_csv('https://raw.githubusercontent.com/risenW/medium_tutorial_notebooks/master/train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_IdentifierSupermarket_IdentifierProduct_Supermarket_IdentifierProduct_WeightProduct_Fat_ContentProduct_Shelf_VisibilityProduct_TypeProduct_PriceSupermarket_Opening_YearSupermarket _SizeSupermarket_Location_TypeSupermarket_TypeProduct_Supermarket_Sales
0DRA12CHUKWUDI010DRA12_CHUKWUDI01011.6Low Fat0.068535Soft Drinks357.542005NaNCluster 3Grocery Store709.08
1DRA12CHUKWUDI013DRA12_CHUKWUDI01311.6Low Fat0.040912Soft Drinks355.791994HighCluster 3Supermarket Type16381.69
2DRA12CHUKWUDI017DRA12_CHUKWUDI01711.6Low Fat0.041178Soft Drinks350.792014NaNCluster 2Supermarket Type16381.69
3DRA12CHUKWUDI018DRA12_CHUKWUDI01811.6Low Fat0.041113Soft Drinks355.042016MediumCluster 3Supermarket Type22127.23
4DRA12CHUKWUDI035DRA12_CHUKWUDI03511.6Ultra Low fat0.000000Soft Drinks354.792011SmallCluster 2Supermarket Type12481.77
\n", + "
" + ], + "text/plain": [ + " Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier \\\n", + "0 DRA12 CHUKWUDI010 DRA12_CHUKWUDI010 \n", + "1 DRA12 CHUKWUDI013 DRA12_CHUKWUDI013 \n", + "2 DRA12 CHUKWUDI017 DRA12_CHUKWUDI017 \n", + "3 DRA12 CHUKWUDI018 DRA12_CHUKWUDI018 \n", + "4 DRA12 CHUKWUDI035 DRA12_CHUKWUDI035 \n", + "\n", + " Product_Weight Product_Fat_Content Product_Shelf_Visibility Product_Type \\\n", + "0 11.6 Low Fat 0.068535 Soft Drinks \n", + "1 11.6 Low Fat 0.040912 Soft Drinks \n", + "2 11.6 Low Fat 0.041178 Soft Drinks \n", + "3 11.6 Low Fat 0.041113 Soft Drinks \n", + "4 11.6 Ultra Low fat 0.000000 Soft Drinks \n", + "\n", + " Product_Price Supermarket_Opening_Year Supermarket _Size \\\n", + "0 357.54 2005 NaN \n", + "1 355.79 1994 High \n", + "2 350.79 2014 NaN \n", + "3 355.04 2016 Medium \n", + "4 354.79 2011 Small \n", + "\n", + " Supermarket_Location_Type Supermarket_Type Product_Supermarket_Sales \n", + "0 Cluster 3 Grocery Store 709.08 \n", + "1 Cluster 3 Supermarket Type1 6381.69 \n", + "2 Cluster 2 Supermarket Type1 6381.69 \n", + "3 Cluster 3 Supermarket Type2 2127.23 \n", + "4 Cluster 2 Supermarket Type1 2481.77 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supermarket_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_WeightProduct_Shelf_VisibilityProduct_PriceSupermarket_Opening_YearProduct_Supermarket_Sales
count4188.0000004990.0000004990.0000004990.0000004990.000000
mean12.9088380.066916391.8037962004.7835676103.520164
std4.7032560.053058119.3782598.2831514447.333835
min4.5550000.00000078.7300001992.00000083.230000
25%8.7675000.027273307.8900001994.0000002757.660000
50%12.6000000.053564393.8600002006.0000005374.675000
75%17.1000000.095358465.0675002011.0000008522.240000
max21.3500000.328391667.2200002016.00000032717.410000
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" + ], + "text/plain": [ + " Product_Weight Product_Shelf_Visibility Product_Price \\\n", + "count 4188.000000 4990.000000 4990.000000 \n", + "mean 12.908838 0.066916 391.803796 \n", + "std 4.703256 0.053058 119.378259 \n", + "min 4.555000 0.000000 78.730000 \n", + "25% 8.767500 0.027273 307.890000 \n", + "50% 12.600000 0.053564 393.860000 \n", + "75% 17.100000 0.095358 465.067500 \n", + "max 21.350000 0.328391 667.220000 \n", + "\n", + " Supermarket_Opening_Year Product_Supermarket_Sales \n", + "count 4990.000000 4990.000000 \n", + "mean 2004.783567 6103.520164 \n", + "std 8.283151 4447.333835 \n", + "min 1992.000000 83.230000 \n", + "25% 1994.000000 2757.660000 \n", + "50% 2006.000000 5374.675000 \n", + "75% 2011.000000 8522.240000 \n", + "max 2016.000000 32717.410000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supermarket_data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# remove ID columns\n", + "cols_2_remove = ['Product_Identifier', 'Supermarket_Identifier', 'Product_Supermarket_Identifier']\n", + "\n", + "newdata = supermarket_data.drop(cols_2_remove, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_WeightProduct_Fat_ContentProduct_Shelf_VisibilityProduct_TypeProduct_PriceSupermarket_Opening_YearSupermarket _SizeSupermarket_Location_TypeSupermarket_TypeProduct_Supermarket_Sales
011.6Low Fat0.068535Soft Drinks357.542005NaNCluster 3Grocery Store709.08
111.6Low Fat0.040912Soft Drinks355.791994HighCluster 3Supermarket Type16381.69
211.6Low Fat0.041178Soft Drinks350.792014NaNCluster 2Supermarket Type16381.69
311.6Low Fat0.041113Soft Drinks355.042016MediumCluster 3Supermarket Type22127.23
411.6Ultra Low fat0.000000Soft Drinks354.792011SmallCluster 2Supermarket Type12481.77
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" + ], + "text/plain": [ + " Product_Weight Product_Fat_Content Product_Shelf_Visibility Product_Type \\\n", + "0 11.6 Low Fat 0.068535 Soft Drinks \n", + "1 11.6 Low Fat 0.040912 Soft Drinks \n", + "2 11.6 Low Fat 0.041178 Soft Drinks \n", + "3 11.6 Low Fat 0.041113 Soft Drinks \n", + "4 11.6 Ultra Low fat 0.000000 Soft Drinks \n", + "\n", + " Product_Price Supermarket_Opening_Year Supermarket _Size \\\n", + "0 357.54 2005 NaN \n", + "1 355.79 1994 High \n", + "2 350.79 2014 NaN \n", + "3 355.04 2016 Medium \n", + "4 354.79 2011 Small \n", + "\n", + " Supermarket_Location_Type Supermarket_Type Product_Supermarket_Sales \n", + "0 Cluster 3 Grocery Store 709.08 \n", + "1 Cluster 3 Supermarket Type1 6381.69 \n", + "2 Cluster 2 Supermarket Type1 6381.69 \n", + "3 Cluster 3 Supermarket Type2 2127.23 \n", + "4 Cluster 2 Supermarket Type1 2481.77 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newdata.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = ['Product_Fat_Content','Product_Type',\n", + " 'Supermarket _Size', 'Supermarket_Location_Type',\n", + " 'Supermarket_Type' ]\n", + "\n", + "num_cols = ['Product_Weight', 'Product_Shelf_Visibility',\n", + " 'Product_Price', 'Supermarket_Opening_Year', 'Product_Supermarket_Sales']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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q+n7V94Cd+/vLgWtHHnZd37a+dknSEllw6CfZFjgTeH1V3TG6rqoKqEkUlOToJKuSrFq7du0kdilJ6i0o9JM8iC7wP1JVn+ybb+inbej/vbFvXwPsPvLw3fq29bX/jKo6sapWVtXKZcuWLea5SJI2YiFX7wQ4CfhmVb1rZNXZwNwVOEcCnx5p/63+Kp4DgNv7aaDPAs9NskN/Ave5fZskaYlssYBtfhV4FXBZkkv6trcA7wTOSHIUcA3w8n7dOcChwGrg+8CrAarqliRvBy7st/vTqrplEk9CkrQwGw39qvoykPWsPmie7Qs4Zj37Ohk4eTEFSpImx3fkSlJDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGbDT0k5yc5MYkXx9pOy7JmiSX9LdDR9a9OcnqJFckOXik/ZC+bXWSYyf/VCRJG7OQI/0PAofM0/7uqtq3v50DkGQf4HDg8f1j/ibJ5kk2B94HPA/YBzii31aStIS22NgGVfWlJCsWuL/DgNOr6kfAVUlWA/v361ZX1ZUASU7vt/3G4kuWJI1rU+b0X5fk0n76Z4e+bTlw7cg21/Vt62uXJC2hcUP/BGAvYF/geuCvJlVQkqOTrEqyau3atZParSSJMUO/qm6oqvuq6sfAB/jpFM4aYPeRTXfr29bXPt++T6yqlVW1ctmyZeOUJ0laj7FCP8kuI4svAeau7DkbODzJVkn2BPYGLgAuBPZOsmeSLelO9p49ftmSpHFs9ERuko8BBwI7JbkOeCtwYJJ9gQKuBn4XoKouT3IG3Qnae4Fjquq+fj+vAz4LbA6cXFWXT/rJSJI2bCFX7xwxT/NJG9j+eOD4edrPAc5ZVHWSpInyHbmS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSEbDf0kJye5McnXR9p2THJukm/3/+7QtyfJe5KsTnJpkqeMPObIfvtvJzny/nk6kqQNWciR/geBQ9ZpOxY4r6r2Bs7rlwGeB+zd344GToDujwTwVuCpwP7AW+f+UEiSls5GQ7+qvgTcsk7zYcCp/f1TgRePtJ9WnfOB7ZPsAhwMnFtVt1TVrcC5/PwfEknS/WzcOf2dq+r6/v73gJ37+8uBa0e2u65vW1+7JGkJbfKJ3KoqoCZQCwBJjk6yKsmqtWvXTmq3kiTGD/0b+mkb+n9v7NvXALuPbLdb37a+9p9TVSdW1cqqWrls2bIxy5MkzWfc0D8bmLsC50jg0yPtv9VfxXMAcHs/DfRZ4LlJduhP4D63b5MkLaEtNrZBko8BBwI7JbmO7iqcdwJnJDkKuAZ4eb/5OcChwGrg+8CrAarqliRvBy7st/vTqlr35LAk6X620dCvqiPWs+qgebYt4Jj17Odk4ORFVSdJmijfkStJDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUkC2GLkDTbcWxnxm6hAW5+p3PH7oE6QHBI31JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkM2KfSTXJ3ksiSXJFnVt+2Y5Nwk3+7/3aFvT5L3JFmd5NIkT5nEE5AkLdwkjvR/var2raqV/fKxwHlVtTdwXr8M8Dxg7/52NHDCBH62JGkR7o/pncOAU/v7pwIvHmk/rTrnA9sn2eV++PmSpPXY1NAv4B+SXJTk6L5t56q6vr//PWDn/v5y4NqRx17Xt0mSlsimfjH606tqTZJHAOcm+dboyqqqJLWYHfZ/PI4G2GOPPTaxPGm6+EXzur9t0pF+Va3p/70ROAvYH7hhbtqm//fGfvM1wO4jD9+tb1t3nydW1cqqWrls2bJNKU+StI6xQz/JNkm2m7sPPBf4OnA2cGS/2ZHAp/v7ZwO/1V/FcwBw+8g0kCRpCWzK9M7OwFlJ5vbz0ar6+yQXAmckOQq4Bnh5v/05wKHAauD7wKs34WdLksYwduhX1ZXAk+Zpvxk4aJ72Ao4Z9+dJ0mJ4fmR+viNXkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDVkyUM/ySFJrkiyOsmxS/3zJallSxr6STYH3gc8D9gHOCLJPktZgyS1bKmP9PcHVlfVlVV1D3A6cNgS1yBJzVrq0F8OXDuyfF3fJklaAlsMXcC6khwNHN0v3pXkiiHrWaCdgJsmucP8xST3NnXsz8myPydnWvryUetbsdShvwbYfWR5t77tJ6rqRODEpSxqUyVZVVUrh65jVtifk2V/Ts4s9OVST+9cCOydZM8kWwKHA2cvcQ2S1KwlPdKvqnuTvA74LLA5cHJVXb6UNUhSy5Z8Tr+qzgHOWeqfez+bqumoKWB/Tpb9OTlT35epqqFrkCQtET+GQZIaYuhLUkMMfUlqiKE/IUn+69A1TKskj0lyXpKv98tPTPLHQ9c1jZIcnOSoJCvWaX/NQCVNpXRenuQ3+/sHJXlPkv+YZKpz0xO5E5Lku1W1x9B1TKMk/wj8IfD+qnpy3/b1qvqlYSubLkn+DHg6cDHwQuCvq+q9/bqLq+opQ9Y3TZL8DfAIYEvgDmAruvcUPR+4oar+YMDyNskD7mMYHsiS3LG+VcDWS1nLjHlIVV2QZLTt3qGKmWIvBJ7cvx/mOOCjSX6hqv4T3RjVwj2jqp6Q5EHA94BdquqeJB+j+6M6tab6ZcoAbgP2rqqHrnPbDrh+4Nqm2U1J9gIKIMnLsD/HsUVV3QtQVbfR/RF4aJKP0x2xauHm+vHfgAv7TwWm798fD1nYpjL0F+c01v9BRh9dykJmzDHA+4HHJVkDvB547aAVTafvJHnm3EJV3VdVRwFXAL84XFlT6XtJtgWoqkPmGpM8ErhnsKomwDl9Dar/Yp2/qKo3JtkG2Kyq7hy6rmmUZGuAqvrBPOuWV9Wan3+UFqMfo9tU1Y1D1zIuj/TH0J/Nf+XcFTtJ9kiy/9B1TaOquo/u5CNVdbeBP76q+kFV/WC+8YnfWzGWdfsSeDiwYsCSNplH+mNIcgLdvN6zquoXk+wA/ENV7TdwaVOp78/lwMeBu+faq+qTgxU1xRyfkzOLfenVO+N5alU9JcnXAKrq1v6jojWeBwM3A88aaSvA0B+P43NyZq4vDf3x/Fs/Fz13tckypvyM/pCq6tVD1zBjHJ+TM3N96Zz+eN4DnAXsnOR44MvAnw1b0vRKsluSs5Lc2N/OTLLb0HVNMcfn5MxcXzqnP6YkjwMO6hc/X1XfHLKeaZbkXLpLXj/UN70S+PdV9Zzhqppujs/JmbW+9Eh/fA+h+/avzfDduJtqWVWdUlX39rcPAsuGLmrKOT4nZ6b60tAfQ3/51qnAjsBOwCl+QNgmubm/LG7z/vZKuhO7GoPjc3JmsS+d3hlDkiuAJ1XVD/vlrYFLquqxw1Y2nZI8Cngv8DS6E2ZfAX6vqq4dtLAp5ficnFnsS6/eGc+/0l1m+MN+eSvAdzuOb7eqetFoQ5JfBQz98Tg+J2fm+tIj/TEk+RSwH3Au3ZHpc4ALgOsAqur3BytuCs33sb9+FPD4HJ+TM4t96ZH+eM7qb3O+OFAdUy3J04BfAZYl+c8jqx5Kd+JM43F8Ts7M9aWhP55bgM9U1VS/SeMBYEtgW7pxuN1I+x3AywapaDY4Pidn5vrS6Z0xJPkw3UnHM4GTq+pbA5c01ZI8qqqu6e/vANxWDsyxOT4nZxb70tAfU5KHAkcAr6ab6zsF+JifErlw/eVwZ1TVt5JsBfxfYF+6L7D4d1X1uSHrm2aOz8mZtb70Ov0xVdUdwCeA04FdgJcAFyf5vUELmy6voPuCD4Aj6cbjMuCZTPlb3Yfm+JycWetLQ38Rkry0//dFSc6iO6nzIGD/qnoe8CTgDcNVOHXuGZnGOZju6Om+/m3unm9aJMfn5MxyXzq9swhzlxEmORU4qaq+NM82B1XVeQOUN3WSnA/8NnAD3RH/L1fVVf26b1XV44asb9o4PidnlvvSo6kxVNWRG1g3dYNgQH9A97J5GfDukcA/FPjakIVNM8fn5MxiX3qkvwhJvg+snm8VUFX1xCUuSfoJx+fkzHJfeqS/OFcBLxy6CGk9HJ+TM7N9aegvzj1z15NLD0COz8mZ2b706p3F+eehC5A2wPE5OTPbl87pa3BJLgJOBj5aVbcOXY80yzzS1wPBK4BdgQuTnJ7k4CQZuihpFnmkP4YkW1XVjzbWpsVJshnwAuAE4D66t7v/j6q6ZdDCpozjc3JmsS890h/P/1tgmxYoyROBvwL+G92HW/0m3adtfn7IuqaU43NyZq4vvXpnEZI8ElgObJ3kyXTX7EL3+e8PGaywKdfP6d8GnAQcO3IU9dX+G7S0AI7PyZnlvjT0F+dg4D8AuwHvGmm/A3jLEAVNu35K58yqmvcD1qrqpUtc0jRzfE7OzPalc/pjSPIbVXXm0HXMiiSrqmrl0HXMCsfn5MxiXzqnP55/TnJSkv8LkGSfJEcNXdQU+1ySNybZPcmOc7ehi5pijs/Jmbm+9Eh/DP0AOAX4o6p6UpItgK9V1RMGLm0qJblqnuaqql9Y8mJmgONzcmaxLz3SH89OVXUG8GOAqrqX7hJDjaGq9pznZuCPz/E5OTPXl4b+eO5O8nC6r04jyQHA7cOWNL2SPCTJHyc5sV/eO8kLhq5rijk+J2fm+tKrd8bzn4Gzgb2S/DPd58G/bNiSptopwEXAr/TLa4CPA/9nsIqmm+NzcmauLw398dxK9z2uj6W7fvcKui/01nj2qqpXJDkCoKq+78cwbBLH5+TMXF86vTOeTwA7V9XlVfV14Gl0Hxim8dyTZGt++hJ6L2Bq3+b+AOD4nJyZ60tDfzyvBT6V5JH9V/u9Fzh04Jqm2VuBvwd2T/IR4DzgTcOWNNUcn5Mzc33pJZtjSvI04P3AD4HnV9XagUuaav3JsgPoXkKfX1U3DVzSVHN8Ts6s9aWhvwhJ/o5+CqK3D3A93bwfVfWiIeqadkleAny+qm7vl7cHDqyqTw1Z17RxfE7OLPelob8ISZ65ofVV9Y9LVcssSXJJVe27TtvXqurJA5U0lRyfkzPLfWnoL1KSzYHPVdWvD13LrEhyaVU9cZ22y6b5XY9DcXxOzqz2pSdyF6mq7gN+nORhQ9cyQ1YleVeSvfrbu+iu29ciOT4nZ1b70uv0x3MXcFmSc4G75xqr6veHK2mq/R7wJ8Df9svnAscMV87Uc3xOzsz1pdM7Y0hy5HztVXXqUtcyS5JsR/dBa3cNXcs0c3xOziz2paGvwSV5AnAaMPdxyjcBR/ZvhpE0QYb+GJLsDfw53WVcD55r95Mhx5PkK3QfXfuFfvlA4M+q6lc29DjNz/E5ObPYl57IHc8pwAnAvcCv0x2lfnjQiqbbNnOBD1BVXwS2Ga6cqef4nJyZ60tDfzxbV9V5dK+Urqmq44DnD1zTNLsyyZ8kWdHf/hi4cuiippjjc3Jmri+9emc8P+q/0PvbSV5H91HA2w5c0zR7DfA24JN074L8p75N43F8Ts7M9aVz+mNIsh/wTWB74O3Aw4C/rKrzh6xrGs3qG2CG5PicnFnsS0Nfg0tyHvDSuc/ekXT/cXpnDElWAn8EPIqRPlz3owS0YDP3BpghOT4nZxb70iP9MSS5AvhD4DL6L0wGqKprBitqis3iG2CG5PicnFnsS0N/DEm+XFVPH7oOaT6Oz8mZxb409MeQ5CDgCLpvePrJ1/pV1ScHK2oKJTkM2K2q3tcvf5Xui6cB3lRVnxisuCnm+JycWexL5/TH82rgccCD+OlLvqK75FAL9ybg8JHlrYD96N6YdQrd95Nq8RyfkzNzfWnoj2e/qnrs0EXMgC2r6tqR5S9X1c3AzUl8R+74HJ+TM3N96Ttyx/OVJPsMXcQM2GF0oapeN7K4DI3L8Tk5M9eXHumP5wDgkiRX0c3zhe4jgaf2Mq6BfDXJ71TVB0Ybk/wucMFANc0Cx+fkzFxfeiJ3kZIEeAbwc5dsTfNlXENI8gjgU3T/mS7um3+Zbm7/xVV1w0ClTS3H5+TMal8a+mPw+1snK8mzgMf3i5dX1eeHrGfaOT4nZxb70umd8VycZL+qunDoQmZBH/IG/eQ4Pidn5vrSI/0xJPkWsDdwNd3HBkz9PJ9mh+NzcmaxLw39MSR51Hzt0zzPp9nh+JycWexLL9kcQ/8L3x14Vn//+9iXeoBwfE7OLPalR/pjSPJWYCXw2Kp6TJJdgY9X1a8OXJrk+JygWezLqf6LNaCXAC+i/xjgqvpXYLtBK5J+yvE5OTPXl4b+eO6p7iVSAfiRAXqAcXxOzsz1paE/njOSvB/YPsnvAJ8DPrCRx0hLxfE5OTPXl87pjynJc4Dn9ov/UFXnDlmPNMrxOTmz1pe+OWt8lwFb073su2zgWqR1OT4nZ6b60umdMST5bboPBHsp8DLg/CSvGbYqqeP4nJxZ7Eund8bQf2/mr/Sf/U6ShwNfmbXP3dZ0cnxOziz2pUf647kZuHNk+c6+TXogcHxOzsz1pUf6Y0hyGvAE4NN083yHAZf2N6rqXcNVp9Y5PidnFvvSE7nj+U5/m/Pp/t+pftOGZobjc3Jmri890pekhnikP4YkX6B/h96oqnrWAOVIP8PxOTmz2JeG/njeOHL/wcBvAPcOVIu0Lsfn5MxcXzq9MyFJLqiq/YeuQ5qP43Nypr0vPdIfQ5IdRxY3o/sy74cNVI70MxyfkzOLfWnoj+ciunm+0L3Uuwo4atCKpJ9yfE7OzPWl0zuS1BDfkbsISfZL8siR5d9K8ukk71nnZaC05ByfkzPLfWnoL877gXsAkvwa8E7gNOB24MQB65LA8TlJM9uXzukvzuZVdUt//xXAiVV1JnBmkkuGK0sCHJ+TNLN96ZH+4myeZO4P5UHA50fW+QdUQ3N8Ts7M9uVUFz+AjwH/mOQm4AfAPwEkeTTdyz5pSI7PyZnZvvTqnUVKcgCwC93Xpt3dtz0G2LaqLh60ODXP8Tk5s9qXhr4kNcQ5fUlqiKEvSQ0x9CWpIYa+JDXE0Jekhvx/kPZ9WP5S3/UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# bar plot for categorial features\n", + "for col in cat_cols:\n", + " fig = plt.figure(figsize=(6,6)) # define plot area\n", + " ax = fig.gca() # define axis \n", + " \n", + " counts = newdata[col].value_counts() # find the counts for each unique category\n", + " counts.plot.bar(ax = ax) # use the plot.bar method on the counts data frame\n", + " ax.set_title('Bar plot for ' + col)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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LWibppNS+taQbJd2ffo5J7ZJ0oaQOSUsl7Z3zWsen7e+XdHxO+z6S7kz7XKiuewCbWdGuWbyStnNvyhsik7bdgnOOeEMVq7J6V3CQSJqXry0ipvew20bgSxGxOzAdOFHS7sAsYF5ETCa7LmVW2v5gYHJ6zAR+mN5na+BMsmnr3wSc2RU+aZtP5Ox3UKGfycxe1v7QGv7j8sV5x6j/6+ip3PSl/atWkzWGQubaGgZsAWyTfnl3/cW/JTAu377pzK7H0vI6SXenfQ4D9kubXQL8GTg5tV8aEQEskDRa0o5p2xsj4slU043AQZL+DGwZEQtS+6XA4cDv+/7oZtbljN/eyaUL/pl3m/MO35PD955QpYqskRRyjOSTwBeAnYA7ctqfAb5f6BtJ2gV4I3AbsH3O6cOPA9un5XHAozm7rUht+dpX9NDe0/vPJOvlsPPOOxdattmA17FqXZ8hctIBk3xMxHrV59BWRHwvIiYCX46IiTmPvSKioCCRNBK4GvhCRDzT7fWDKpzxFRGzI6ItItq23XbbSr+dWcP46q+X5l1/dNt4vjjjdVWqxhpRMWdtzZF0GrBzRMyUNBl4XURcm28nSa1kIXJZRPw6Na+StGNEPJaGrroumV0J5Padx6e2lbw8FNbV/ufUPr6H7c2sD2vWd3LrA//i9kee6nF9C3DFJ6fTNnFsVeuyxlPMWVtzyM7Mekt6vhI4N98O6Qyqi8nm6PpOzqq5QNeZV8cD1+S0H5fO3poOPJ2GwG4AZkgak47TzABuSOuekTQ9vddxOa9lZr24bMEjTP/GPE7Kc8HhOUfs6RCxghTTI9k1Iv5N0ocAIuK5Ak61fSvwEeBOSYtT2ynAN4ErJJ1AdoOso9O664FDgA7gOdJcXhHxpKRzgNvTdl/rOvAOfAb4OTCc7CC7D7Sb5TH75gf4+u/vybvNSQdM4th9fUzEClNMkLwoaTjpeIakXYHOfDtExC30PnXKgT1sH8CJvbzWHLJeUff2dmDPvJWbGZD1RPoKER8TsWIVEyRnAn8AJki6jKy38dFKFGVm5Tdv+eOc1ss08ACH7bUTnztgEpO2H1XFqmwgKCZIFpHdIXE6WS/jJMD/4swaQG93NczlELH+KuZg+++ADRFxXTpTa9vUZmZ17P/+/aE+Q+TQvXZ0iFi/FdMj+TrwO0mHAFOAS4FjK1KVmZXFB35wC3f88+m82xw2bUe+d4xvJWT9V3CQRMR16ZqQG8mGtI6IiPsqVpmZleTDP7k1b4i87w078IV37eaeiJWskLm2/ptXXnm+FfAA8FlJRMTnK1WcmfXPx392G7c88GSv6yU4+7A9fY91K4tCeiTt3Z4vqkQhZlYeH5uzkPn3/SvvNuce7hCx8ukzSCLiEkmDyGbl9TERszqWhcjqvNuccsgUX2xoZVXQMZKI2CTpNZKGRMSrbmBlZrV39A//zsJH1ubd5pRDpjDzHbtWqSJrFsWctfUg8DdJc4Fnuxq7zaFlZjUw/bwbeXxd/r/xzjt8T08FbxVRTJA8kB4t+EJEs7qx77l/ZNX6Db2un7L9CC77xJt9TMQqppjTf8+uZCFmVrzXn3o9z2/Kfzsfh4hVWsFBImlb4CvAHsCwrvaIOKACdZlZHw749p/6DJELj5nmELGKK2Zo6zLgV8D7gE+R3Uck/+khZlYRe5xxHc/mOSQyamgLf/7PAxwiVhXFzLU1NiIuJptv6+aI+Djg3ohZle1xxvV5QwRwiFhVFdMj6Tqa95ik9wL/A2xd/pLMrDcfuOgWnn0x/3DWeb7Y0KqsmCA5V9JWwJeA/wa2BL5YkarM7FU+/rPbuOPR/BMwnnLIFJ/ia1VXzFlb16bFp4H9K1OOmfXk2NkL+NuD+aeCP++IPX3FutVEMWdtTQQ+B+ySu19EHFr+sswMoGPVOo796a2sWtf7dSLDBsPfZr3Lw1lWM8UMbf0WuJjsZlabK1KNmb3kK1ct4Yr2FXm32XvCVvz6xLdVqSKznhUTJC9ExIUVq8TMXjL75gf6DJEDpmzDnI/uW6WKzHpXTJB8T9KZwB+Bzq7GiLij7FWZNbE16zv5+u/vybvNW1871iFidaOYIHkD8BGya0e6hrYCX0tiVjZr1nfy5q/flHeb3bYfwWUzp1epIrO+FRMkHwRe62nkzSrjsgWPcOpv78q7zY5bDuWPX9yvKvWYFaqYK9vvAkZXqA6zplZIiAxrgVtPeVeVKjIrXDE9ktHAPZJu55XHSHz6r1kJ1qzv5PQ+QmS7EYNZePp7qlSRWXGKCZIzK1aFWRN75/l/yns+/aghLQ4Rq2sFBUm6Z/uPI2JKhesxaxpr1neyz7n5D6y/dpst+NOXPZGE1beCjpFExCbgXkk7V7ges6Zw2YJH+gyRkw6c5BCxhlDM0NYYYJmkhbzynu0+RmJWhEIOrP/X0VM5fO8JVarIrDTFBMnpFavCrEmsWd/ZZ4iMHjbIIWINpZjZf2+W9BpgckTcJGkLYFDlSjMbWDpWreOw7/+1z+0Wn3VQFaoxK59iZv/9BDCT7GZWuwLjgB8BB1amNLOB42NzFjL/vvx3pt6iVSw/55AqVWRWPsUMbZ0IvAm4DSAi7pe0XUWqMhtA3v6tm3h0bWfebbYaNogl7olYgyrmyvbO3OlRJA0mm2vLzHpx3rXL+wyREa1yiFhDKyZIbpZ0CjBc0ruBK8nuTWJmPZi3/HF+cstDebfZadQQlnk4yxpcMUEyC1gN3Al8ErgeOC3fDpLmSHpC0l05bWdJWilpcXockrPuq5I6JN0r6T057Qeltg5Js3LaJ0q6LbX/StKQIj6PWcV84KJbOOHSRXm32WaLwfz91HdXqSKzyik4SCJiM3AJcA5wNnBJRPQ1tPVzoKc++3cjYlp6XA8gaXfgGGCPtM8PJA1KV9VfBBwM7A58KG0L8K30WpOAtcAJhX4es0qZccF87nj06bzbbD18MO1neNoTGxgKDhJJ7wUeAC4Evg90SDo43z4R8RfgyQLf4jDg8ojojIiHgA6yg/tvAjoi4sF0jOZy4DBJIrsXylVp/0uAwwv9PGaVsOus67hv9XN5txk7YjB3nOkQsYGjmKGtC4D9I2K/iHgnsD/w3X6+72clLU1DX2NS2zjg0ZxtVqS23trHAk9FxMZu7WY1MeW069jUxzbjRw9lkSdgtAGmmCBZFxEdOc8fBNb14z1/SHYdyjTgMbKAqjhJMyW1S2pfvTr/+fxmxZo46zpe2Jh/mxGt4pZZvp+IDTzFXEfSLul64Aqy034/CNwu6QMAEfHrQl4kIlZ1LUv6CXBteroSyJ0XYnxqo5f2NcBoSYNTryR3+57edzYwG6Ctrc2nLVvZvP606ws6D95nZ9lAVUyPZBiwCngnsB/ZGVzDgfcD7yv0RSTtmPP0CLI7LwLMBY6RNFTSRGAysBC4HZicztAaQnZAfm460D8fOCrtfzxwTRGfx6xk+5//J57fmD9G9txhJA9/871Vqsis+oqZa+tjxb64pF+Shc42klaQ3RxrP0nTyHo1D5OdSkxELJN0BbAc2AicmKavR9JngRvI5vaaExHL0lucDFwu6VzgH8DFxdZo1l+Tv3odG/roimw3opVrv/DO6hRkViPq+wzetKH0M3q4kj0iPl7uoiqtra0t2tvba12GNbBdZl3X5zbDB8Pd57onYgODpEUR0dbTumKOkVybszyMbFjqf0opzKwRveGM3/e5zYghYtnXfEzEmkMxQ1tX5z5Pw1a3lL0iszr2xrP/wLoX891hHXYeM4y/nOxJsa15FNMj6W4y4Nl/rWlMOeU6XsifIbS24BCxplPM/UjWkR0jUfr5ONnBbrMBr5BjIjuOauXWU2dUoRqz+lLM0NaoShZiVq8KCZHB4BCxptXndSSSXiNpq5zn+0v6nqQverZdG+imnNp3iIwfPYwOXydiTayQCxKvAEYApOs/rgT+STbFyQ8qVZhZre0y6zpe6GPyrA/usxO3zPIxEWtuhQxtDY+IrtN8P0x2QeAFklqAxRWrzKyGChnO2mfnrfj2B99YhWrM6lshPRLlLB8AzIOX7k9iNuAUEiKHTd2Bqz/ztipUY1b/CumR/ClNXfIYMAb4E7w0Z9aL+XY0azSFhMjEscP53v/epwrVmDWGQnokXwB+TTYv1tsiYkNq3wE4tTJlmVVfISGy3+RtmP+fB1ShGrPG0WePJM2ye3kP7f/IfS7p1oh4cxlrM6uaQkJkmxGt/PyEfatQjVljKWYa+b4MK+NrmVXFmvWdBYXIUXuPo/10Xydi1pNSpkjpzjeLsoZy2m/u5P/e9s8+tzvviD05dt/XVKEis8ZUziAxaxht5/yRfz27oc/tFp32LsaOHFqFiswaVzmDRH1vYlZ7n7tsUUEh4rsamhWm4GMkkr7VR9tHylKRWQV9bM5Cfnfn431u5xAxK1wxB9vf3UPbwV0LEXFXD+vN6saMC/7M/PtW591m+CCHiFmx+hzakvRp4DPArpKW5qwaBfy9UoWZldObv34Tjz3T2ed2d5/nEDErViHHSP4f8HvgG8CsnPZ1EfFkRaoyK6PJX72ODQWcU+ieiFn/FHJB4tPA05K+BzwZEesAJG0pad+IuK3SRZr1166zrqOPCXwBh4hZKYo5RvJDYH3O8/Wpzawu7eIQMauKYoJEaboU4KXZf30ditWlQq5WB4eIWTkUEyQPSvq8pNb0OAl4sFKFmfWXQ8SsuooJkk8BbwFWAiuAfYGZlSjKrL8cImbVV/DQVEQ8ARxTwVrMSuIQMauNgoNE0s/oYWLGiPh4WSsy64eJDhGzminmYPm1OcvDgCOA/+llW7OqcU/ErLaKGdq6Ove5pF8Ct5S9IrMiOETMaq+UG1tNBrYrVyFmxXKImNWHYo6RrCM7RqL083Hg5ArVZZaXQ8SsfhQztDWqkoWYFcohYlZfCpn9d+986yPijvKVY5afQ8Ss/hTSI7kg/RwGtAFLyIa3pgLtwJsrU5rZKzlEzOpTnwfbI2L/iNgfeAzYOyLaImIf4I1kV7mbVZxDxKx+FXPW1usi4s6uJ+mOiK8vf0lmr+QQMatvxQTJUkk/lbRfevwEWJpvB0lzJD0h6a6ctq0l3Sjp/vRzTGqXpAsldUhamntsRtLxafv7JR2f076PpDvTPhdKUhGfxxqAQ8Ss/hUTJB8DlgEnpcfy1JbPz4GDurXNAuZFxGRgHi/fdfFgsmtTJpNNBvlDyIIHOJNsksg3AWd2hU/a5hM5+3V/L2tgDhGzxlDM6b8vSLoIuInsOpJ7I2JDH/v8RdIu3ZoPA/ZLy5cAfya7HuUw4NJ0z5MFkkZL2jFte2PXbX0l3QgcJOnPwJYRsSC1XwocTnZbYGtwhYTIIMED33CImNVaMRck7kf2i/9hsrO2Jkg6PiL+UuR7bh8Rj6Xlx4Ht0/I44NGc7VaktnztK3potwZXaE/EIWJWH4qZtPECYEZE3AsgaTfgl8A+/X3ziAhJr5pRuBIkzSTdP2XnnXeuxltaP3g4y6zxFHOMpLUrRAAi4j6gtR/vuSoNWZF+PpHaVwITcrYbn9rytY/vob1HETE7nbrctu222/ajbKu0QkJkaItDxKzeFBMki3o4a6u9H+85F+g68+p44Jqc9uPS2VvTgafTENgNwAxJY9JB9hnADWndM5Kmp7O1jst5LWswhfZE7v26Q8Ss3hQztPUp4ETg8+n5X4Ef5NshTTW/H7CNpBVkZ199E7hC0gnAI8DRafPrgUOADuA50hlhEfGkpHOA29N2X+s68A58huzMsOFkB9l9oL0BFRIiAh5yT8SsLik7SaqPjaRBwLKImFL5kiqvra0t2tv705myciskRHYaNYS/n/ruKlRjZr2RtCgi2npaV9DQVkRsAu6V5KPUVjaFhMjrthvhEDGrc8UMbY0BlklaCDzb1RgRh5a9KhvwCgmRPXcaxbWff0cVqjGzUhQTJKdXrAprKoWEyIhWOUTMGkQh9yMZRnagfRJwJ3BxRGysdGE2MBUSIsMHwbJzDqlCNWZWDoX0SC4BNpCdpXUwsDvZXFtmBfvx/Pv5xg339bnduYfuzoffMrEKFZlZuRQSJLtHxBsAJF0MLKxsSTbQvP6063l+Y99nB/pCQ7PGVMhZWy9NzOghLSvWeb+7q6AQueqT06tQjZlVQiE9kr0kPZOWBQxPz0U2XdaWFavOGtrnf3kHc5c81ud2b580lraJY6tQkZlVQp9BEhGDqlGIDSxHXHQL/3j06T63u+qT0x0iZg2umNN/zQryulOvo3NT39v5mIjZwOAgsbLyNPBmzaeY2X/N8nKImDUnB4mVhUPErHk5SKxkDhGz5uYgsZI4RMzMQWL95hAxM3CQWD85RMysi4PEiuYQMbNcDhIrikPEzLpzkFjBHCJm1hMHiRXEIWJmvXGQWJ8cImaWj4PE8nKImFlfHCTWK4eImRXCQWI9coiYWaEcJPYqDhEzK4aDxF7BIWJmxXKQ2EscImbWHw4SAxwiZtZ/DhJziJhZSRwkTc4hYmalcpA0MYeImZWDg6RJOUTMrFwcJE3IIWJm5eQgaTIOETMrNwdJE3GImFklOEiahEPEzCqlZkEi6WFJd0paLKk9tW0t6UZJ96efY1K7JF0oqUPSUkl757zO8Wn7+yUdX6vPU88cImZWSbXukewfEdMioi09nwXMi4jJwLz0HOBgYHJ6zAR+CFnwAGcC+wJvAs7sCh/LOETMrNJqHSTdHQZckpYvAQ7Pab80MguA0ZJ2BN4D3BgRT0bEWuBG4KAq11y3CgmRIYMcImZWmloGSQB/lLRI0szUtn1EPJaWHwe2T8vjgEdz9l2R2nprfxVJMyW1S2pfvXp1uT5D3SokRFoF953nEDGz0gyu4Xu/LSJWStoOuFHSPbkrIyIkRbneLCJmA7MB2trayva69aiQEBnRCsvOcYiYWelq1iOJiJXp5xPAb8iOcaxKQ1akn0+kzVcCE3J2H5/aemtvSu0PrSkoRIYPkkPEzMqmJkEiaYSkUV3LwAzgLmAu0HXm1fHANWl5LnBcOntrOvB0GgK7AZghaUw6yD4jtTWdGd+Zz1E/XtDndkftPY67zzukChWZWbOo1dDW9sBvJHXV8P8i4g+SbgeukHQC8AhwdNr+euAQoAN4DvgYQEQ8Kekc4Pa03dci4snqfYz6sPvp1/Pchr5H62764juYtP2oKlRkZs2kJkESEQ8Ce/XQvgY4sIf2AE7s5bXmAHPKXWOjeN0p19G5ue/tLjxmmkPEzCqilgfbrUSFXiOy6LR3MXbk0ApXY2bNykHSoHyhoZnVi3q7INEK4BAxs3riIGkwDhEzqzcOkgbiEDGzeuQgaRAOETOrVw6SBuAQMbN65iCpcw4RM6t3DpI65hAxs0bgIKlTDhEzaxQOkjrkEDGzRuIgqTMOETNrNA6SOuIQMbNG5CCpEw4RM2tUDpI64BAxs0bmIKkxh4iZNToHSQ05RMxsIHCQ1IhDxMwGCgdJDThEzGwgcZBUmUPEzAYaB0kVOUTMbCBykFSJQ8TMBioHSRU4RMxsIHOQVJhDxMwGOgdJBTlEzKwZOEgqxCFiZs3CQVIBDhEzayYOkjJziJhZs3GQlJFDxMyakYOkTBwiZtasHCRl4BAxs2bmICmRQ8TMmp2DpJ86Vq1ziJiZAYNrXUAjOvpHf2fhw2v73G7LYWLpWYdUoSIzs9pxkBRpymnX8cLGvrfbdmQrt582o/IFmZnVmIe2ivD2b95UUIjsMMohYmbNY0AEiaSDJN0rqUPSrEq8x6d+cTuPPtXZ53aHvmEHFpzqEDGz5tHwQSJpEHARcDCwO/AhSbuX8z06Vq3jD8ue6HO7fSeO4cJj9ynnW5uZ1b2BcIzkTUBHRDwIIOly4DBgebne4IiL/trnNld9cjptE8eW6y3NzBrGQAiSccCjOc9XAPuW8w3WvRh51/v0XjNrZg0/tFUoSTMltUtqX716dVH77jCytdd1DhEza3YDIUhWAhNyno9Pba8QEbMjoi0i2rbddtui3mBBL2dgOUTMzAZGkNwOTJY0UdIQ4Bhgbrnf5OFvvvelnskOI1sdImZmScMfI4mIjZI+C9wADALmRMSySrxXbz0TM7Nm1vBBAhAR1wPX17oOM7NmNBCGtszMrIYcJGZmVhIHiZmZlcRBYmZmJXGQmJlZSRwkZmZWEgeJmZmVxEFiZmYlcZCYmVlJFJF/ivSBSNJq4JF+7LoN8K8yl1MtjVp7o9YNrr0WGrVuqP/aXxMRPc5425RB0l+S2iOirdZ19Eej1t6odYNrr4VGrRsau3YPbZmZWUkcJGZmVhIHSXFm17qAEjRq7Y1aN7j2WmjUuqGBa/cxEjMzK4l7JGZmVhIHSYEkHSTpXkkdkmbVuh4ASQ9LulPSYkntqW1rSTdKuj/9HJPaJenCVP9SSXvnvM7xafv7JR1foVrnSHpC0l05bWWrVdI+6bvoSPuqgnWfJWll+t4XSzokZ91XUw33SnpPTnuP/37SLaJvS+2/SreLLgtJEyTNl7Rc0jJJJ6X2uv7e89Rd99+7pGGSFkpakmo/O9/7SRqannek9bv09zPVVET40ceD7Ba+DwCvBYYAS4Dd66Cuh4FturWdD8xKy7OAb6XlQ4DfAwKmA7el9q2BB9PPMWl5TAVqfQewN3BXJWoFFqZtlfY9uIJ1nwV8uYdtd0//NoYCE9O/mUH5/v0AVwDHpOUfAZ8u43e+I7B3Wh4F3JdqrOvvPU/ddf+9p+9hZFpuBW5L30+P7wd8BvhRWj4G+FV/P1MtH+6RFOZNQEdEPBgRLwKXA4fVuKbeHAZckpYvAQ7Pab80MguA0ZJ2BN4D3BgRT0bEWuBG4KByFxURfwGerEStad2WEbEgsv8LL815rUrU3ZvDgMsjojMiHgI6yP7t9PjvJ/31fgBwVdo/9zsoR+2PRcQdaXkdcDcwjjr/3vPU3Zu6+d7Td7c+PW1Nj8jzfrn/La4CDkz1FfWZylF7KRwkhRkHPJrzfAX5/2FXSwB/lLRI0szUtn1EPJaWHwe2T8u9fYZafrZy1TouLXdvr6TPpuGfOV1DQ33U11P7WOCpiNjYrb3s0pDJG8n+Qm6Y771b3dAA37ukQZIWA0+Qhe4Ded7vpRrT+qdTffX4/2uvHCSN7W0RsTdwMHCipHfkrkx/JTbEaXmNVCvwQ2BXYBrwGHBBTavpg6SRwNXAFyLimdx19fy991B3Q3zvEbEpIqYB48l6EFNqW1HlOUgKsxKYkPN8fGqrqYhYmX4+AfyG7B/tqjTkQPr5RNq8t89Qy89WrlpXpuXu7RUREavSL4vNwE/Ivvf+1L2GbPhocKXqltRK9sv4soj4dWqu+++9p7ob6XtP9T4FzAfenOf9Xqoxrd8q1VeP/7/2ykFSmNuByenMiyFkB8Xm1rIgSSMkjepaBmYAd6W6us6qOR64Ji3PBY5LZ+ZMB55Owxs3ADMkjUlDBTNSWzWUpda07hlJ09P48nE5r1V2Xb+EkyPIvveuuo9JZ+JMBCaTHYzu8d9P6g3MB45K++d+B+WoU8DFwN0R8Z2cVXX9vfdWdyN875K2lTQ6LQ8H3k12jKe398v9b3EU8KdUX1GfqRy1l6TWR/sb5UF2Rst9ZOOdp9ZBPa8lO2NjCbCsqyay8dV5wP3ATcDWqV3ARan+O4G2nNf6ONnBvA7gYxWq95dkwxEbyMZ1TyhnrUAb2S+WB4Dvky62rVDdv0h1LSX7n3jHnO1PTTXcS84ZTL39+0n/HRemz3MlMLSM3/nbyIatlgKL0+OQev/e89Rd9987MBX4R6rxLuCMfO8HDEvPO9L61/b3M9Xy4SvbzcysJB7aMjOzkjhIzMysJA4SMzMriYPEzMxK4iAxM7OSOEjMzKwkDhKrKUmblE0JfpekKyVtUcJr/VlSWz/2Gy3pM31s06JsmvS7lE2bfnu6UKxhSPqopO8Xsf0X+vrvIenj6ftYmr6bvBMIKpsK/suF1mCNwUFitfZ8REyLiD2BF4FP5a7MmVaikkaTTeedz78BOwFTI+INZFdWP1Wpgsr9ufv5el8Aeg0SSePJLpp7W0RMJZsufWm/CrSG5iCxevJXYJKk/ST9VdJcYLmymwX9LP3l+w9J+0M2BYWkyyXdLek3wPCuF5K0Pmf5KEk/T8vbS/qNshsPLZH0FuCbwK6pZ/TtXmrbEXgssnmeiIgVkU2pnu+9fi7pR5LaJd0n6X2pfZCkb6dezVJJn0zt3T/3fpJulnSNpAclfVPSscpunHSnpF3Tfu9XdlOkf0i6SdL2qf0sSb+Q9Deyq8LJqfO9km6VtI2kGWn5jtQrHCnp82TBOV/S/F6+k+2AdcD69J2sj2zKcyR9In2+JZKu7qlnI2lXSX9QNnv1XyVNSe0fTL2bJZL+0st7Wz2p9aX1fjT3A1iffg4mm3/o08B+wLPAxLTuS8CctDwF+CfZ1BL/kdM+FdhImtaj63XT8lHAz9Pyr8hmk4XsJkFbAbuQc+OqXuocT3YjscVks86+sftn6OG9fg78gewPtslkU6wMA2YCp6VthgLtZDcv6v659yPr9eyYtlsJnJ3WnQT8V1oeAy/NUvHvwAVp+SxgETA8Pf8o2TQmR5CF9hhgG+AvwIi0zcm8PK3Hw3S7cVq372QQ2Txc/wR+Brw/Z93YnOVzgc/l1PTltDwPmJyW9yWbZwqyaVDGpeXRtf436kffj2oMG5jlM1zZvRsg++V2MfAWYGGkv27J5l76b4CIuEfSI8BuZHcvvDC1L5VUyLDKAWSTCxIRm4Cn9fJ9LXoVESskvS7tfwAwT9IHI2JeH7teEVkv5n5JD5IF4QxgqqSuSfy2IguaF7t9boDbI907RNIDwB9T+53A/ml5PPArZZMaDgFy958bEc93+/xtwIyIeCb1knYH/qbsLrlDgFv7+j4g+/4kHQT8L+BA4LuS9omIs4A9JZ1LNmw4km4TgSqbIv4twJV6+e68Q9PPvwE/l3QF8Gus7jlIrNaej+zeDS9Jv1ieLfF1cyeRG1bia2UvGNFJdjvZ30taRXaXu3l9vFf3yeyCbHLEz0VE91+u+/Hqz92Zs7w55/lmXv7/97+B70TE3PQaZ+Xs0/31um7TuhtZT0hkdz/8EP0QWbdhIbBQ0o1kPZOzyHpjh0fEEkkfJetd5Wohu9nTtB5e81OS9gXeCyxK4bSmP/VZdfgYiTWCvwLHAkjaDdiZbEbUvwD/O7XvSTa81WWVpNdLaiEbyukyj2z4rOtYxVZk4/yj8hUgaW9JO6XllvRej/TxXgAfVHbG165kv8DvJfvr/NPK7rmBpN2U3Qqgv7bi5XtSHJ9vw1TzkcClkvYAFgBvlTQp1TIifcfQx/ciaSdJe+c0TePl72QU8Fj6jMd23zeyG1U9JOmD6bUkaa+0vGtE3BYRZwCreeX9N6wOOUisEfwAaJF0J9kxjo+m3sEPgZGS7ga+RnY8oMss4Frg72TTwHc5Cdg/vdYiYPf01+7f0gHe3g62bwf8TtJdZGcmbSQ73pDvvSA7frCQrCfzqYh4AfgpsBy4I73ejyltdOAssiGiRcC/+to4Iu4h++V+JbAl2bGTX6ahwVt5+Y5+s4E/5DnY3gr8H0n3pOHJfyP7fgFOJ7s97t+Ae3rZ/1jgBEldt0LoOnX42+lkgrvIvtMlfX0mqy1PI29WIcrO3ro2Iq6qdS1mleQeiZmZlcQ9ErMckt5At2sugM6I2LcW9dQLSbfx8llVXT4SEXfWoh6rLw4SMzMriYe2zMysJA4SMzMriYPEzMxK4iAxM7OSOEjMzKwk/x8mIH5xf9d9EAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot for numerical features\n", + "for col in num_cols:\n", + " fig = plt.figure(figsize=(6,6)) # define plot area\n", + " ax = fig.gca() # define axis \n", + "\n", + " newdata.plot.scatter(x = col, y = 'Product_Supermarket_Sales', ax = ax)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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c5Ugle1AIttRnzJgRm7zUoXJR1VeAi3ArUV4NHKuqS/KIuxX4b1XdBzgE+L6I7ANcCjytqmOBp/0+wInAWP87D5gMThkBVwEHA58FrkoqJH/OtwPXnZCHXB0Stu17us4r1wWZopilHkWNMwrLrvQVGMMac4ljjbMcqWQPCsGWemtra2zyUi7fYv8UkWkiMg24DOdPrAW404flRFWXqeprfns98DYwAjgFuMufdhfwZb99CnC3Ol4EBorIcJwbmidVdbWqrgGeBE7wx/qr6ovqqpl3B+IqirCtm44++uiU/TBmqUdBFLPUo6hxRmHZ1bt36jBir169io6z0scJwqSSPShE0VLvDHK1XH5Duz+xG4FzgZ8G9vNGREYDBwIv4cZvlvlDy4HkYhwjgGCLaKkPyxW+NEN4pvufJyJzRGTOypUrO5Q37K6M9Fnq55xzTijxhk0UPruiaBGMHz8+Rc4wvCKnr8AYxoqMU6dOpa3NTQlra2uLTY2zHIkib8aFuM7ByuVb7FngeeBqVX02/ZfvDUSkH/Ag8ENVXZd2D8XPn4kSVb1dVWtVtTZ9cD0TkydPZt68edx6662h3D84S338+PFlOxs4Cp9d6YQxljFx4sQUi74wui+j+IBnzpyZolzKdZwgDuNCnZE3y5W4zsHKOeaiqm3ANhEZUEjkItITp1juCTi7XOG7tPD/yZRqAHYLXD7Sh+UKH5khvCgSiQSzZ88GYPbs2aF9cBMmTGDnnXfmK1/5SijxRUEUZtjpVldhWGFVVVVx/PHHIyIcf/zxoSjrFStW5NwvhPRWW/p+uRCHcaFKniIQRUu9M8jHWmwDMM87r/xD8tfRRd5y607gbVX9beDQNCBp8XUW8HAg/ExvNXYI0OS7z2YAx4nIID+Qfxwwwx9bJyKH+HudGYirYCZPnpyyH1br5b777qO5uZmpU6eGEl8URLGO/Lhx4+jevTsA3bt3D61QCNvooqqqKmV/yJAhocQbpBwNOeIyLlTJ3sonTpy4/RsKq6XeGeSjXP4O/Ax4Dng18OuIw4FvAONE5HX/Owm4Hvi8iCwAjvX7ANOBeqAO+CPwPQBVXQ38EnjF/37hw/Dn3OGvWQikurYtgGSrJdt+IUTVGgqbKNaRD34Y3bt3D+3DWLNmDQsXLmTt2rWhxLd8+fKU/eTSzMWQ3koLYxwnbOIyf6SS3eNXVVUxbJgbmh42bFhsnj0fU+S7Mv3yuO55VRVV3V9VD/C/6aqaUNXxqjpWVY9NKgpvJfZ9VR2jqvup6pxAXFNUdU//+1MgfI6q7uuvuUDDmpwQMlG1hsIminXkoyoUrrnmGpqbm/nFL34RSnxREIeunDjNH6lU9/hRfJedQYfKRUTGisjf/GTI+uSvM4QrBSNGpBqcjRw5MsuZ+RNFaygKolpHPmwnk3V1ddtbFsuWLQvFy3RtbW3K/mc+85mi44xDV04cFGCSSnWPP2XKlO3f5bZt27jzzjtLLFF+5Luey2TcpMhjcPNJ/hKlUKXk8stTV3C+4oorSiRJ5xNVLfahhx6iubmZBx98MJT4rrnmmpT9MFov6c5ElyzJZ55wbuLQlRPFOJsRLrNmzcq5X670yOOcPqr6tIiIqi4CrhaRV4ErI5atJOy5556MGDGChoYGRo4cyR577FF0nCKSYoJbjgO74Gqxjz/+OK2traHVYhOJxHYlNXPmTM4999yiC9n08ZAwxkfSPVen7xfKxIkTWbRoUcGF9uTJkz/SMkvKFmxl77HHHpx//vk7HH9VVRVDhw6loaGB6urqslSARjzJR7m0iEg3YIGIXIAz9+0XrVil5fLLL+eSSy4JrdVyxBFHpHSFHXnkkaHEm0gkuO6667j88stDG3yfPn064JrfYdRip0yZkjJgfOedd3LJJZcUHW/Y9O3bl40bN6bsh0GyKydMNm/eHFpcmfrzy0XBpCvWTEoVClesEP43FEWcQ4cOTalAxWUSZT7K5Qc41y8X4ay2xtFuStwlGTRoEGPGjGHgwIGhxHf++eenKJdCP4R0pkyZwrx588q2wIbMTfpiZR08eHDKoGa6GXEhpBfYYRbgxZApryTT74Ybbig6/mB/vqqWdV6K4p0E5/hceGE4y1SFHWcUc7A6gw6Vi3dcCW6+yzejFac8SM7Qnzx5ciitl6qqKo488khmz57NUUcdFUptJorupqlTp6ZM1grj44jCaWcUfsCi8opc7kSh/MMiXbGGqVTho+sCnXHGGUV/Q+nzhsKIM9nyz7ZfruRjLVYrIg+JyGveFf5cEZnbGcKVgkQiwfPPPw8UNydl8uTJXHLJJdt/8+fPp0ePHqxcuXJ7WLqJ8o6QqbupWKJwV3LooYem7IcxSz2KMZe4eK4Om0p9bohmXSDzJ9dOPtZi9+Asxv4L+GLg1yWZPHlySjdBMQogyJYtW+jduzc9e/YMJb4oLEiiMEsNq5URVNZJ894k3bp1K1pZJwuEbPtdlbh47I6CKLwNx8WfXGeQz5jLSlXt0MV+VyHZaklS6JyUqJv0UdQ4J06cyBNPPAGEZ5YahW+xKLoJ+vXrx4YNG1L2K4FzzjlneyErImXrsTsKhg4dyqJFi1L2i+Wwww7jqaeeStmvVPJpuVwlIneIyOki8pXkL3LJSkRc+t6jqHFGMS9j3LhxKRMJC20NnX/++dxwww3ccMMNnHzyySnHTj75ZG644YaiDCXS5zf99Kc/LTiuIHV1dUyYMCGUiZ5RUFVVxa677grArrvuWjaWYp1BZ3gbDqPSl3w/SYYPH150nJ1BPsrlm8ABuFUek11iJ+e6IM6km6CGZZIaNuecc07K4HtYNc6wXWxMnDgxZT+MeINxhuXIb/To0Sn7u+++e9FxAkyaNInm5uaPTPwsFxKJxHbroxUrVsTGtUihBLtX+/Tpk3Js5513Lrp7NQp/cj/72c9S9q+8Mh5TDPPpFvuMqu4duSRlQhz73sMchI1iXkbYVFVVbTdHPuGEE0KpbU+dOpVu3bqxbds2unXrFoqlXF1d3fa5GQ0NDdTX14cyKTdM0g1DpkyZwsUXX1xiqTqHYcOGpSjTMLrFxo0bxyOPPJKyXwiZJs8C9OzZc7vyK2Z+T2eQj3L5l4jso6pvRS5NGXDsscemZI5jjz22hNJkJ1kYtrW1FWU2nM9EtWIycRSFNriCoaWlJbQW1syZM1MK2ZkzZxYt56RJk1L2r7nmGqZMmVJUnGHzzDPPpOzPmjWrSyuX9Hx8+umns3r1ak4++eRQ8uVhhx2WUn4cccQRRccJsNNOO7F58+aPtLDLmXyUyyHA6yLyPtACCM6J8f6RSlYiJk6cyIwZM9i6dSs9e/YsW19LmaxSwvg4wpioFlRY8+fP315ot7a2Mn36dBYvXlx0ratnz56MGTMmtDGCKFzfROVSpliC7ydpipuktbV1u/FJudeMwyDsSsptt92Wsj958mRuv/32HY4naoOgziCncvGLcH0HWJTrvK5EcpXDRx99NLRVDqMgrMIw6kw8cODAlK6HQYMGhRJvGAQL2a1bt24vaNva2li4cCGXXHJJUQVsHHzKDRw4MGVNnHJ6P51B2JWUoPVZpv1KIqdyUVUVkVtUdb/OEqgcKNbZYGcQhdlwWAQL40QiwRlnnIGq0qtXL26++eayVNg9e/akR48etLa2Mnjw4FDmI0XlU65Y0t9P0kCiW7du3HrrrWX5fuLC7rvvnqJQwjIMiSP5dIu9JiKfCbiB6fLEZVD7uOOO49FHHy1bd+7g5Bw0aBCrV68uOznTWyQ//OEPWbx4ccEKMH38auvWrSnHk94Zyqm7qaqqanvrZdy4cWX1fuLId77znRSz9nJ5z6UgH1Pkg4EXRWShd/0yryu7f4kTcVmZb9iwYfTt27fs5Qy7i6Rnz57b5/j0798/NO8MYTN8+HD69u3LueeeW2pRYk/6jPwwZv3HlXxaLsdHLoXRpQm70C5XMtVSk62h2267rWyfv1LeT2dQadZ3ueiw5eIXCNsNGOe3m/O5zoieoMt9ozyxgruyiKsH4yjosOUiIlcBtcDeOAeWPXHLHB8erWidR9RzPaIgCpf7hmEUx7Bhw1K8dA8bNqyE0pSWfFogE4AvARsBVLUR2KWji0Rkioh8KCLzA2FXi0iDiLzufycFjl0mInUi8q6IHB8IP8GH1YnIpYHwj4nISz78fhEpflEPz+bNm8tmsahsROFy3zCM4ugMf2VxIZ8xly3eJFkBRCRfZ1t/Bm4G7k4L/52qpphiicg+wGnAJ4Ea4CkR2csfvgX4PLAUeEVEpnlvAb/ycd0nIv8DnAsU5BQojhOWynmRJ8OoVOLi+LYzyEe5PCAitwEDReTbwDnAHzu6SFWfE5HRecpxCnCfqrYA74tIHfBZf6xOVesBROQ+4BQReRu33HLSg+FdwNUUqFziQrD7LpMPtHIzczWMSqOSF19LJ59ljn8jIp8H1gF7AVeq6pNF3PMCETkTmAP8t6quAUYALwbOWerDAJakhR8MVAFrVbU1w/kVQfrM6oEDB5ZMFsPIZ9wSym/sMmzi6Pg2KvJpuQDMA/oA6rcLZTLwSx/PL4EbcS2hSBGR84DzAEaNGhX17SIj18zqyZMn24C+UTaU+5hlVFTqonOZyMda7FvAlcBMnNPKm0TkF6q6w+5dVXVFIN4/Akn3oQ04c+ckI30YWcITuG66Hr71Ejw/031vB24HqK2t7RKdoMGZ1ePHjzfFYpSUOI5bhkWw1TZkyJAU5VJdXV2x3dX5WItdAhyoqmer6lnAQcBPCrmZiASXUJsAJC3JpgGniUhvEfkYMBZ4GXgFGOstw3rhBv2nqRslmwWc6q8/C3i4EJniTHJmdSUtTWsY5Uz//v23b3fr1o1ddunQsLbLkk+3WAJYH9hf78NyIiL3AkcDQ0RkKXAVcLSIHIDrFvsA53EZVX1TRB4A3gJage+rapuP5wJgBtAdmKKqb/pb/AS4T0SuAf4DVJwtrk3QM4zSk94i+c53vsMHH3zApEmTOPDAA0skVenJR7nUAS+JyMM4pXAKMFdEfgygqr/NdJGqnp4hOKsCUNVrgWszhE8HpmcIr6fdoswwDKMs6N+/P/vvv39FKxbIT7ks9L8kye6nym3vGYZhGDnJxxT5550hiGEYhhEdne3mKh9rsVm47rAUVLX4dWANwzCMkhC1uXg+3WJBf9E7Af+FG3Q3DMMwYkJnm4vn0y32alrQCyLyciTSGIZhGF2CfLrFgnau3XDzXAZEJpFhGIYRe/LpFnsVN+YiuO6w93EeiA3DMAwjI/l0i32sMwQxDMMwug5Z3b+IyGdEZNfA/pki8rCI/CGtq8wwDMMwUsjlW+w2YAuAiBwFXI9b+KsJ7wTSMAzDMDKRq1usu6qu9ttfB25X1QeBB0Xk9cglMwzDMGJLrpZLdxFJKp/xOJf7SfJdB8YwDMOoQHIpiXuBZ0VkFbAJmA0gInviusYMwzAMIyNZlYuqXisiTwPDgSf8GirgWjsXdoZwhmEYRjzJ2b2lqi9mCHsvOnEMwzCMrkA+K1EahmEYxg5hysUwDMMInQ6Vi4j8Kp8wwzAMw0iST8vl8xnCTgxbEMMwDKPrkHVAX0TOB74HjBGRuYFDuwD/iloww6hE0lcLzMTChW7V8eR6HJkIc0VBwyiEXNZiU4HHgOuASwPh6wMz9w3DCJH6+nrmvvM2UpXdfV9yVsC8lSsyH0/Y52mUnlzzXJqAJhH5PbBaVdcDiEh/ETlYVV/qLCENo5KQqsH0OPn4gq9vfWRGiNIYRmHkM+YyGdgQ2N/gw3IiIlNE5EMRmR8IGywiT4rIAv8/yIeL97ZcJyJzReTTgWvO8ucvEJGzAuEHicg8f80fRETyeWDDMAwjevJRLhKYnY+qbiM/32J/Bk5IC7sUeFpVxwJP097ddiIw1v/Owysv79r/KuBg4LPAVUmF5M/5duC69HsZhmEYJSIf5VIvIheJSE//+wGQe8QRUNXngPTO31OAu/z2XcCXA+F3q+NFYKCIDAeOB55U1dWqugZ4EjjBH+uvqi96xXd3IC7DMAyjxOSjXL4LHAY0AEtxrYjzCrzfMFVd5reXA8P89ghgSeC8pT4sV/jSDOEZEZHzRGSOiMxZuXJlgaIbhmEY+ZLPMscfAqeFfWNVVRHRjs8M5V634xc4q62t7ZR7GkZXpiOT6XzMpcFMprsyHSoXEfkT8JECWVXPKeB+K0RkuKou811bH/rwBmC3wHkjfVgDcHRa+DM+fGSG8w3D6AScyfR7dK8anvH4Nu0OwJsr12eNoy2xLOsxI/7kMzD/SGB7J2AC0Fjg/aYBZ+GWTD4LeDgQfoGI3IfrdmvyCmgGMCkwiH8ccJmqrhaRdSJyCPAScCZwU4EyGYZRAN2rhrPzFwvtIYfmf9pq6V2ZfLrFHgzui8i9wPMdXefPOxoYIiJLcVZf1wMPiMi5wCLga/706cBJQB3QDHzT33u1iPwSeMWf94vABM7v4SzS+uAmez7WkUyGYRhG51DIcsVjgaEdnaSqp2c5ND7DuQp8P0s8U4ApGcLnAPt2JIeRShh95dZPbkSBjeN0LfIZc1mPG3MR/78c+EnEcsWOuBTa9fX1vPP2XIYMzHLCNve3atncjIdXrY1CqlTMv1ZlUl9fz1vvLKBf1aiMx7dqLwAWr2zJGseGxOJIZDN2nHy6xXbpDEHiTn19PQvems+oAX0yHu/V5j6IloaFGY8vbtoUmWzpDBkIXx5X2FI+/5i5LVxhMlBfX8/8d+bSpyr7OS3exGThysxKcFMiAsGMyOlXNYpPn3JZwde/9vB1KftxaQ3FpXK6I+TyivzpbMcAVPW18MXpHKLKcKMG9OGyI8YWJNN1zy8o6LquSp8qGHty4R59FjySauBoraHKpL6+nnfeqaN68O6ZT/CtocSHW7PGsXL1oihES6G+vp66txYwapfM0/V6tbqiesuS5ozHF68vP2PZXC2XG/3/TkAt8Aaua2x/YA5waLSiRYd7kW8zakBmz7O92lzBtKUhs9dZgMVN5nkW4lNoO9PZ+TBkpxxnbQFg7qq6zIdXbQ5fsE4gLrX3qKgevDtfPemnBV//1+nXhChNdkbtMoLLPnNhQdde90r5Gcvm8op8DICI/B34tKrO8/v7Ald3inQRMmrAYH565HEFX3/N7CdClCa+1NfX8/bbcxkwKPs5bb4nrXF55i6spjURCJaJITvR/ZQ9Cr687eEOvR6VJU6xvoNUVWc8nvQcOG9l9r5ETZhnC2PHyMdabO+kYgFQ1fki8okIZTI8cemHHTAIjipcT/NcTPV0XFptAFJVTe+Tv1rw9S2P/DVEaYxKIB/lMldE7gD+4vfPADJXQY1Qqa+v572357LrgMxjD9189926xnkZjy9vMk83UeJaBG9BVf/sJ2krAHNXLs18PLEuAskMo/SV03yUyzeB84Ef+P3nyGM9FyMcdh0gfOuongVde8dz2QcpjZCo6k+PLx1W8OWt02zFcCMa3Njyu4zqn3laYq9WV2ndsjRzv/TidR9mDM+XfEyRN4vILcBTuHku76qqlVqGYRhlzqj+Q7nisDMKuvbaf91T1L3zmUR5NG7tlQ9w1mK7ichZfr0WwzCMiqLSre/yJZ9usRuB41T1XQAR2Qu4FzgoSsEMwzDKETcWWseIAZk9CfRoc3NnNjZuyRpHQ1PX9ySQj3LpmVQsAKr6nogUNghgGIbRBRgxYBTfP/Lygq+/ZfakEKUpT/JRLq9msBabE51IhmEYRtzJR7l8F+ex+CK/Pxu4NTKJDMMwjNiTU7mISHfgDVX9OPDbzhHJMIxyp7GxkbZ1G4pa8KstsYzGrdlXqjTiTU7loqptIvKuiIxS1a4/AmUYJaaxsRFd10TrIzMKjkMTq2nc2haiVIax4+TTLTYIeFNEXgY2JgNV9UuRSWUYRllTU1PDmp7ri17muKbaVvToquSjXH4WuRSGYQCu0E707E6Pk48vOI7WR2ZQUz0sRKkMY8fJtZ7LTrjB/D2BecCdqt5RkmHEjMbGRli3uTjPxqs207ilMTyhDKMLk6vlchewFWcddiKwD+3+xYxOoLGxkfVrtWAfYcvWKhtILQwbGxtZ11T4ipKr1sIWtQLWiAcuvzcXtSbLysQiWlp3DlGqj9LY2MjG9RsKXpdl0fql9G3sF7JUxZFLueyjqvsBiMidwMudI5JR6TQ2NrJp3UdXk9wRNiWgcWu7EqypqWFVr+ai13OpGVJT8PWGUUnkUi7bq8uq2ipS+JKz6YjIB8B6oA1oVdVaERkM3A+Mxvkx+5qqrhF3498DJwHNwNnJJZZF5CwgucTcNap6V2hClgE1NTWsI1GUV+T+NamFYU1NDb1kFV8e162gOP8xcxtDhlsBa8SDmpoaevfYWvRKlFVDo3VKUlNTw5a25qJWouxVk9q6amxsZOO69QU7oFy07kP6Nm4q6FrIrVw+JSLJxSYE6OP3BVBVzbGIRV4co6qrAvuXAk+r6vUicqnf/wmuS26s/x2Mc/d/sFdGV+GWYFacJ4FpqtpZ6xoauAy8tqm4Bb/WrgG2pbYyNvVcxdiTC6/QLHhEqak2JQhJ8+Z1RS34pYmVNG5tCVGq+NLY2MiGtRuLcuHSsHYR/egbolTlR65ljrt3piDAKcDRfvsu4BmccjkFuFtVFXhRRAaKyHB/7pOquhpARJ4ETsA51cxJY2MjG5uailqqeFHTavqKzSUwDKM8qampYcu2NUW53O9Vk2P98g7IxxQ5ChR4QkQUuE1VbweGqeoyf3w5kLSlHAEsCVy71IdlC/8IInIecB7AqFGj2HvvvcN6ju04hbWJ655fUND1i5s20VfiN1BeU1MD3VYVvcxxza7xa2U4C7R1xS34lViXMjYUBc68uXfRyxzXVFeFKNVHaWxsZP26jbz28HUFx7E+sZjGrdG2CGpqatjIlqIdV/at6RWiVOVHqZTLEaraICJDgSdF5J3gQVVVr3hCwSuv2wFqa2u1pqaGLdqdnx5ZeIl4zewn6FVjcwkMwzAyURLloqoN/v9DEXkI+CywQkSGq+oy3+2VXGOzAdgtcPlIH9ZAezdaMvyZiEXPSk1NDS26icuOGFvQ9dc9v4DeNfGrvVcyNTU1rOq5rehljm1syFFTU0NrzxY+fcplBcfx2sPXUVPdO0SpjEIpzGSoCESkr4jsktwGjgPmA9OAs/xpZwEP++1pwJniOARo8t1nM4DjRGSQiAzy8RTukMkwDMMIjVK0XIYBD3nT5h7AVFV9XEReAR4QkXOBRcDX/PnTcWbIdThT5G8CqOpqEfkl8Io/7xfJwX3DMAyjtHS6clHVeuBTGcITwPgM4YpbTyZTXFOAKWHLaBiGYRRHp3eLGYZhGF0fUy6GYRhG6JTKFNkwjCxoYnXOxcK0ya3eKAMyr4WiidVgLveNEmPKpcxZ3pTdK3Jig5sKVNUvs5uU5U1Kf7NyjRV77NGxY82F6zYAMCabAqkellc8hhElplzKmI4KiJULFwLQv2ZMxuP9a/IrrIzy4fzzz+/wnEsuuQSAG264IWpxDKNgTLmUMR0VNF25kNmUyO1yv6XJ/fcekP16qtMCV3WwWFjTFvc/IItbjlWbYUj2yw3DaMeUSwWyam32xcKaXI8LA7KsO7RqLQwZHolY28mva8i12sZUZ261UZ0aT15xNvk4h2SJc4i1BI3oWLy+IetiYSuaVwIwbOf0GlP7tXvyUe8gi9d9mNXl/oqNzoH8sL6ZnVMuXvchexI/x5UlZ3HT6qxekVdsdAOmw/pmHjBNXr/niGFpYdkdV3640bkrH9o3s2uKxU2bGJvR7Wa4dFQ4NvmutiHDMxewQ4Z/NI6mNbld7m9wyUm/LMnZtAZqdm3fj6JryLqbKpeVqxdlXYly7brlAAzsv2vG48nrq4buGYlsSTr6LrcsdCvM99ot84qYezL2I3F0HKebc95rZGYFsieDiqpMVaRy6TjRXfW914jsFjd7jhi2QzXjLb7Q7j0ic6E9dkTn1IrD7mrLq0Ww0T17za6Zn71mV2sRRI0mVmZdz0Wb1gIgAwbmvJ6IvSJHQUf5au161xWaazGwqqF7Rp4/o+gCL3W3ekUql674IkuFtQjKn44KxoXr1gIwJpfyqK7qlArAhsTirC73m5ucL9udBwzNeT3V7d1DUX2XDU2Lsy4WtmrDCgCG9MteOW1oWsxeNdG2hkpNRSoXwwiNRAfruTRtdP8Dsqwxklj3UcODkIlLxadjJehaGaNyeT2u/mj3UNh0FP/yhU7OXOu17FUTfWuo1JhyMYwC2THDg5GZT6iOb5dgW2IZzf+8PeOxbU0JALoNyN4aakssg+r2gbi4KMG4yFlqTLkYRoFUcpdgx60M14U1pjq7UQzVu8RWsRodY8rFMIwdxmrvRkeY40rDMAwjdEy5GIZhGKFjysUwDMMIHVMuhmEYRuiYcjEMwzBCx5SLYRiGETqmXAzDMIzQif08FxE5Afg90B24Q1WvL7FIhlFxTJ48mfr69rVyFnpHrcn5Lkn22GOPvCafGvEn1spFRLoDtwCfB5YCr4jINFV9q7SSRUM+H7B9vEY5sNNOO5VaBKPEiGr21f7KHRE5FLhaVY/3+5cBqGpmt6pAbW2tzpkzJyUsW6E9Zky7i/gdLbQ7I86GhgYARoxoXwimHOWslDjT44tLnJni29E4oyAKOeOQj8o1ThF5VVVr871frFsuwAhgSWB/KXBwsZFGUesKI87O+NDL9dktzujijEsrIw5pWelxBol7y+VU4ARV/Zbf/wZwsKpekHbeecB5AKNGjTpo0aJFnS6rYRhGnNnRlkvcrcUagN0C+yN9WAqqeruq1qpqbXV1xItnGIZhGLFXLq8AY0XkYyLSCzgNmFZimQzDMCqeWI+5qGqriFwAzMCZIk9R1TdLLJZhGEbFE2vlAqCq04HppZbDMAzDaCfu3WKGYRhGGWLKxTAMwwgdUy6GYRhG6JhyMQzDMEIn1pMoC0FEVgL5zKIcAqwK+fYWp8VZrnHGQUaLs7Rx7q6qeU8UrDjlki8iMmdHZqNanBZnnOOMg4wWZzziTGLdYoZhGEbomHIxDMMwQseUS3ZutzgtzgqKMw4yWpzxiBOwMRfDMAwjAqzlYhiGYYSOKRfDMAwjdGKnXETkChF5U0TmisjrIpJz5UkROdKf/7qIHCoiJ2U572gRaRKR/4jIuyLynIicnCPeL4nINh/vmyLyhoi0iEi3wDnXi8j7adfViMjfduB5PxCRIVnC5/nfWyJyjYjsLiL3ichCEXlVRKaLyF7+fPW/Tf73loiMzvNe3xWRM/322SJS04HMf/YLueU652wRWenT73URuTvtePI9L/HnHRw4NlpElgbT2ocvF5E/BPbbfNxviMhrInJYrmf18c4P7K8Skbd9flgpIodlyn8i8oyI3CgiO2d4xpvTwjaIyP/x1yfzzR0iMjBwztUicnGu9MuSpkeLyCOB51YRWZ18zyLSwz/HIx3Ek/6MXxaR+T59JuYpS5uIbPR57d1g2ud5/YbA9tki8pSI3Jz+jgL3el1E1vj7/T7t+NHJZ/bbwXzwZxG5WLKUC2nxZH0vQXkDMt+c7Xi+1+WD/0YeE5HFIrJWRH4vIr1E5IDgcyXlz3WPbHIWQqyUi4gcCpwMfFpV9weOJXWZ40ycAVynqgcAewO5MtFsVT1QVfcGLgJuFpHxGeTooarTgGZVPUBVPwl8Huf2/6pcwqhqo6p+pOD1H/6Oeqk+RlX3Az4L7IFb3+YZVR2jqgcBlwFJRbAR2KiqffxvH1X9wN9b0gvqNJn/R1WThf/ZgTiL5X6ffgeo6pnJwOB7Bn4GPEzgPXu5FwNHBq75ONDLhyfZ5OP+FC4trttB+foBF6jq3n7ymJI9/50O7Jw5mhS6Az8CTgT298/4L2DYDsqWi00+v2/EpccKH/55MiymFyTLN9boD48G8lIuwFbgXlXtg/uWPpL2BeT3bGwCTgASwJeBAzKcI/7/aCBd0Y0md7lQtoiIAEOB6ao6CqjC5dtrcekQ2nOJSPcdukBVY/MDvgL8M8ux8cB/gHnAFKA38C1gNfA+cC/uQ1sJvA58Pe36o4FH0sLOAR7y238G/gd4CfgtrpDdEjj2B6DN/07FZdglQAvwGvB73Mf+HtAMzPfybMZ9/E1ezm8ATwBvAnXAh8CQDM/7QTAcVyC0AoP9s8zGLZy2AHgaVzC2AVcAzwCzgG0BmTf6/Zv99lbcR7vAy7bVP4v6+2zyz93on2GzT+un/PEHgTf8+5jj382/gL29vHcAC4HH/T1+7cN/jJsx3IbzpPBHL9OPfZpt8dcswRUmv/Nputyna0Pgp8BS4B2f1i0+rWf6+D/wcb4NvOtlXQ8MB77nr1/i38N0XB7a7NMimT5XefnbfPqpj+N9nx5rfRxv+fuol3uh32/w17X4604GlvlrlwH1uMJ5vN9vpD1/XwtM9s/3Gi4PPgJsAD7nZVrhZTkMl/eX+d8goC/wD3/+JuDfuPz3lH9fzf53j0+DdT7OVh/HXFx+esW/6wdxCvYA/0zJb+0Mf848L8sSXN58D6esVvr7fAh8x+eDzT5so0+LN3D5YLRPtwf9/Tf6c+fSng83eHnr/fYqf94e/vg2/1sC3O3TPRm2Ejgl8F1d4eVMeDmW45ZMv8i/0zbct9oGvAgM89dd7NNoHnCTT7O5uG9xVKDc2BS41wZcuTIFeAGXJ+bjKlH9/b1f9O97rY/vfi/3z72c64CDgDVpz9qIyyvP+efd6tP7Ntx7X+LTSX0a/BWnpDYAN/r0PwK43j/3XOA3OcvrUiuMHVQu/XxmfQ+4FficD9/JJ85efv9u4IeBF3iq3z4buDlL3EfzUeVyAPB2IJ5HgO6BuILK5a+0F9Qt/mV8iPuYvobL3KNwH8cmXEF1ts8MbwB9cAXCQuBKYIDPTEp+yuUif7+D/bNsBD6GW7Onv49HaS8AXwyEnQc84Ldn+4w512es33sZLwZ+gCvc/w+u1t2E+4C/4tP/fFyrRnGtRYD/B1zpt48FHgwolzZ/nzdwGfskXMGxFJeBW3z4TNxH2hencJbhCtv1uA9+Z1wBv9i/hz/5NG7z8m7EFVTP42p2I/yx83Ef2xag2r+bxcDfcIVrC66QqvNpshvug92Cy4PT/PazuMJjiX/GBp929+He/9s45XSjT5sbcYX+tV7OrwG7AGN8HDfhFPFfcIVbwocfg1Mid+NaP/X+XmNxNfMHcHm0zb+bbbjC7CGffgtweeM9/14m+ef9HDDQ3+c2/z5a/HUTaVfcp/jnuQlXAPYCRgTy4DXAhX47WVC/g8s/y3waj/fynYfLKwkvRw9chacO1xJXXB54g9SKz2hcwXq6f0fHB85t8c99EDDBv5eNwN9xhfR5/r38Aqec/+Pf9Sv+uffxaVrn0/Mgn247A7v78Et9XMt8HIpbATepyJfhyqhm4EmfFvOAzYEK6z8C5UabP/91v70Yl0+vwOXjr+Dyxo9x30Vff/9bcOXEk/66C/27nYf7tpb7tH/Jv9slXr4FuHLiLp/2zbhW5Qx/bAOubL3fx6/A17y8VbhKWNLKeGCu8jpW3WKqugH3ws/DZdz7ReRsXHfX+6r6nj/1LuCoEG4paft/VdW2LOf+A1dQJGsFR+Fe4igvT3fcS38c6InrFjkWl5n+rqqbVPVZ3Ac3Hffx/AVXqBUi78uq+r4Pm+TDtnnZnsMpi1YvbzPwqj9nd9wHcj8uMyYz9Em4zAauq2q6Dx+Bq9n2A2pUtdHfJxnfe8A5vp/8d8AnAzK+o6r7q+u2egU4DqdsZgL7eRk+xNWY6lV1Iy6z3+/jWY0rUPaiXal/EVcg9sJ1+27D1bam4ZTtah/nNpzi3BP3Pmb5ZxqOezdn+Ht9B9cCfgFX6PXEfYC74gr77oF73a+qT3lZevnn2R3nv+lwXLcUPi274fJxD+B/ca2OPXCFdwJ4FFe4HIQrMBtUdZY/9gKudbwQWKiqC9R97X/x8W/CFRiK61Ici8tXf/XHl+Ly54n+OX6Pa8224GrJp/j0OQyXdwfjCtIbcd133/bvZHdgTxGZLSLzfJol3+8Wnx4fxymdPrS3SBcAHwc+49PyVFzrdgxOAZ2Ey5szcfl3jX+WZLdjf582OwG/8sdO9Wn3uk+fG3EVLQUOxRXQx/n0/r9ehsE+rv/4c+7zzzTCP+eRuJ6LZuCbuArfRbhKxge4fN+KU+ib/LFH1XVJbvP3wKd9q9/+X592Sbao7xr2cVyJy5/f9HFcqKrrfZr3xVUK98Llvd1p/87+7v+bcAq4Py4/jcW922T6veV/6/19evh3U4v7PvriFOA4H38brpWYjHszcKeIfAVXbmQlVsoFQFXbVPUZVb0KuAD4rwhvdyCu1plkY45zW3AfQlL5/MifvwLXbO6Nq3mdgKtFLMAVQGNJ7atfg/u4v4mrHeZLPa6rI6lgk7KegftgwWXeNbRn9KSy6RGQuw730SXzRiOuAKj2cq72cf4KuAGXiWfgalOH+2uCk6e+CqxQ1X1xBf9OgWNtadvb+3S9El+E+xDqgE8Ezk3K/w6u0DjNP+8S//zf9tub/f+3ce+hn3+OM3Dvahbu42zBdUedhCugXiC1AMCnx624QvN6XNfo4z6e9EpId9pr4i04pdUNV2sHuFTdGM7F/v7NuFrzXbgPHX9dkm2Be9yBq63vjlMcGVHV630cfYCRuLxxb4ZTVwUKtyNxafZdXL4YjkurHj6ub+EK/E/i8vJ0XIF8gbqxv5+T+n6TvIvL/8l82JJ2/EJ//58Dd+IURDd//kHAr3Hpnswfiqvd/85fl+y+G4YrCJtw39YGXMF6l49PcAr2dlyrIdlFuYdPp4Nw38iK4HOIyNG4iuC9uPf+H1zL4hYfxyv+1DZSV/cNfgeZaPUy4cc8e/nwRpzyfxE4REQm+Wd/HPeOlqkbMz0XV8EQ2tNUcWndyz/bI/79jqL9e09+P8nvrzvuvT6EG5fdSVWrffybkxVqVW3FVbD+hvteHs/1cLFSLiKyt4iMDQQdgCuA3gVGi8iePvwbuCZxOutxhVE+99ofV0O/JU/x+tOuQMDVcjbgXvr/+rBd/f9gXGE43f8PDcTzKK6JC64AGZSHrP2Ar+MUx1fTnmF3XA0FXCbKNXCcrN204rqMhuEK5x64gnkKrok9ENfdMhHYF9dN0wYcKCLDCSgJH1+yhnN2B48yF9diOVZEjsR1bezh5RrsrbEE+AKu++5vPv7TcF0lH8flhctpV5j34Aqp5bgCRHGFbTdcwdiKe28Hehm2+OvP9Pfq58O7+edajqvlnYrrGgx+qKeJyDh/b6W9X/vLOOV7mw//uYh8A/dxjsJVDJpwimY0Ln9Aez5uAUb6/P0QTknthHsfo0VkjD//9GRC+rBtqvornLJcg3tv+Od/FngM6OXTGuAS/w6G4fLuYbguUPHPPtqn5WLc+38Y9z0tE5GeOEWUidW0F5zdcDX5Z4GXffr+QER6e/kXeBm64brTwCm1oNJajysMz/TWa8lyrHvgfzDtSvlYHz4DVzg2qeqVPv7+/vpWVd3qr93dn/8c7t1V+3NP9NuHAEN9S3IL7ltPZwGu4giupZhUOmfg8i641k9S9i/hWsXg0nSFqv4R944uwo09Ho5rBa8Xkc+JswT9mL/ma4F7j6G90rQfgIh8C/cdtpJKUhEf5NNJRGSwiHzcx78dX84MULe0/I+AT2V47nZKPY6yIz+fAP+ifUDp7/hxBzIM6Af6NZNjLoNxtYzXyTyg3+TjeBeXAb4YOL49Hm0fc9nm41qDyygtuMyyAZexPsRlvl/R3ldfh6sdJWvaCeAngXircN0HjbgMtYjsYy7zcIXWW7j++4/h+t0bcB/go7iuh3/TXtgtwhUMR3h5E/5ZLvYyNdE+VrEYp6wUlymbfbzJAf13fdgmH7YK1wfcGkjzywPpeg3wQWDMZW7geR7x7+DHuJrjNi9DcvD7Z7QP6F/hr6n1Mm/C9c2v88/+Au1jS0ljhc1ejuW42myyZfQn/0yNPh0341o6A316NdI+oH+Hf7Zt/n+jP/aiT+PggP5Sf79l/hkacC1Y9WEb/L1afViLl+0E2gfdkwP383GKPJm/3wFu8GlwAu0D+r+nfUD/JtrHtO7F5YMXfVoux1Va+uAUdLOXZbl/B695ebaRmm9nB8LX4JTjf+Naey/7e/5ZU8dcXvfv5le0D+jXBd77RNx3stmfPwtXWLf499qMy2cbcC2Q0bjehPt9umzy90qOubT4Z5zu03YNrpxYgvs2F9I+yL0CV/m4gHajhlYf/2gv3xU4RbHav8/5OMU4z//acOMwye695PPnM6A/zF+fTJ/kgP5T/j7/8e9sMy4/jsOVXwu8rPW4d74ZpziX+vClPg2n0p5fN+AG9O/z8d/s7/ECLl8u9s+oPs3rcApvQ+BdDffvea5/rrNyldfm/qXM8LXzeThT0KZSy9OVEJF+qrpBRKpwH8nhqro8gvh3xtV6z1PV18KK39+jG67w/6qqLujofCPeiJsvdoqqfiMQ1k/d+DMicikwXFV/UCoZsxGWnbkRAiJyLK7P+XemWCLhEXGTFXsBvwxTsXhuF5F9cF04d0WgWPbBtUweMsXS9RGRm3BdcelzVb4gIpfhyu9FdNzdXBKs5WIYhmGETqwG9A3DMIx4YMrFMAzDCB1TLoZhGEbomHIxDMMwQseUi2F4Aq7b54vIXyXNhf4OxvWMiNQWcN1AEflejuP7SfsyBatF5H2//VShshpGFJhyMYx2ki7698VNHPxu8GCILuJzMRDnkTkjqjov4K5lGnCJ3z822zWGUQpMuRhGZmbjnDIe7R0zTgPeEpGdRORP4hZp+4+IHAMgIn3ELdT2tog8RLuPsPSFr04VkT/77WEi8pC4BcPeELeI1fXAGN8auSEfQUVkjIi8Ftgfm9wXtyjar728LyddJIlItYg8KCKv+N/h2eI3jEKwSZSGkYZvoZxIu2O+TwP7qur7IvLfgKrqfn6Bsie8D6bzcYvHfcL7dMtnAuUfgGdVdYJfiKkfzpXIvr5lkhequlDcKqoHqOrrOKenfwqc0uTlPRPnav9knNuQ36nq8yIyCuc+5BMYRkhYy8Uw2ukjIq/j3L8vxnlLgPblC8D5ZPsLgKq+g5shvRfOi20yfC7O/1JHjMP5e0p6+y7GK8MdwDe9kvo6zq9UknsD/4f67WNxK62+jute6+8dExpGKFjLxTDa2ZTeYnCryOZcaiEfgm4wMrmkD4MHcatizgReVdVElvsnt7sBh6jq5ojkMSoca7kYxo4xG+9a3neHjcJ57X0Ov768iOyLc8efZIWIfMI7nZwQCH8a152GiHQXkQHswLIQQbySmIFrCf0p7fDXA///9ttP0L60AyJywI7e0zByYcrFMHaMW4FufuXF+4GzVbUFV6j3E5G3ccvovhq45lKcw8l/4dywJ/kBcIyP61VgH9/ieMGbQ+c1oB/gHpx79SfSwgeJyFx/vx/5sIuAWhGZKyJvkWYZZxjFYo4rDaOLICIX4xZz+lkg7AOgVlVXlUwwoyKxMRfD6AJ48+cxOCMBwyg51nIxjDJFRPajfYnsJC2qenAp5DGMHcGUi2EYhhE6NqBvGIZhhI4pF8MwDCN0TLkYhmEYoWPKxTAMwwid/w92MOT2IQsS5wAAAABJRU5ErkJggg==", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# box plot for categorial features\n", + "for col in cat_cols:\n", + " sns.boxplot(x=col, y='Product_Supermarket_Sales', data=newdata)\n", + " plt.xlabel(col)\n", + " plt.ylabel('Product Supermarket Sales')\n", + " plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# correlation matrix\n", + "corrmat = newdata.corr()\n", + "f,ax = plt.subplots(figsize=(5,4))\n", + "sns.heatmap(corrmat, square=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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Q09R3SFGUYWdQe9RlPxNDRT9X0pNOOolFixZxxx13cNttt/HlL395n/bbk9LSwN43GiLKy/c8Z3Xr2mbmlIxidaugONxzs15tldCW7aKoyEeNNootm9/e67GGm/fb+xlsh/J3MRQ/qwOtUxnH8T/hv9CVbiHoLiHsq2FCUQ1bEl2UuL1MCldQ6tlNzoyyIEyoo8ax+Yp9GqH7vtn7eTuP1rGB0vBksr9txmno6Sn331AOs3Xefv6ntDcsAqC9YSHxrnX8vzN+zuNbF7Mx0UxTtpNvzryaiWUDN9zz/fT57a99/V0MtXNkd8W5kVKc0in4zRCd+Thn+GuY2NlGcNrMQ/76B3I+7IYmck8HkSt2DNcPoJ1qUnxOBr24fI/7DgdD7TtyoAb7Hkraeb4x9lSMrUvxv/EoP7nkS/wiugK/8NFhx7iz+HrMn0XB3p7ZwCso+fIItFGHb4m/ofxZq7opyvAwqIF6ZWUlb731Vvfj1tZWKip61rqMRCIsX76c448/HoDzzz+fz3/+85SUlJBIJLBtG13XaWtr67XfvujoSBxUhuXDpbx8z8NzpZSsi25jrqccj1FCItEzpNHveHkvvoFIJIXIB2lLbKO1NbbfjRpD1d7OzQfVwfyRO1S/i6H4We1vnbJajjw2AVkYQpkTENRqMHJ+EtksVQQY4Q+iaYJ83KEtvvdjTzJD4A1ANtGrPC40ApsyyO1BujbRgz7Dh7M2g0gZVAdOoUO8hdy+rFw4MIUJa4v53MsLwKvBaQHSQgzYOX8/fH479jkQ+/K7GIrnyOdkqEv4+H7+Kpw3s4gKHf0YSV6+e8jreqDnI9huIFf0zu/uvGiRP8ZLZ35ond/9NRS/I4fyN3EoeZwUpako/qIT0aYdz/hYBXdum4WzKoeYLNBsg/zOKU3Sktx7CRJ+q99OooE2FD/rHVTd9q0eijIUDOrQ9/nz57Nw4UI6OztJp9M888wznHjiid3PSyn50pe+RGNjYc3Xf//738yaNQvTNJkzZw5PPvkkAI8++miv/T5Iuqw4jpRgx3oNewcImwE6rRhSStx6IYlVOh/r7zCKouyGIxyWZtdxy3s/47rF/4+H2l5kdfIN7nr9Kn755nVsTLyKFBZtJPl70xJuX/UMryc20EEzjrbnucCeTI78qTf2fr1wFa9qLuzc9uH+IR1tkgfrwU7sl+Pk7++i/PHZTB73GQA03cMk7/U4v00g12WRS9PIO9vwtb4/GuSUA2e7AhA5jvwDMZzNWew3U+R+m0H3zh/squ1W1u5nlQIHMv2tXqB8IEkBq2WGLfYEnD96kO2l5J9JYz+dQG7N4TyTxX4xjj5vl17/mKOGvyuKMqwMeo/65z//ea655hosy+LSSy9lxowZ3HDDDdxyyy1Mnz6d73znO9x0000IIRg/fjy33347AN/61rf4yle+wq9+9Suqq6u54447BvOtDJr6dCuVnhKSuVY8RqjXcy7NwCUM4naKkOEn5CqnK9OIz+w7d1ZRlP5tyTfx5Xd+0/34nnVPcM2Y43HpHrrSTdz3zte58fi/8eW3X6I9W1hl4bXWzXx41ATKrDc4ZczH8dL/iJ9MeDTrNR3j0tupaN1I1htiWaiSH9Wv5eRp8zFdAuMYP/kXdmlga5MU5wtzjysqj8O1MITceak1CazMoC1wD4uRQ8qh4U4EyT/f3LswI5GNAkr632ewZSslZkiHWE9eCmecQarEGfz1ZJUhYUu+i0+/8Qh/CX0IOizEcQbyjd6jkmR9DnGUr1eZONKrroeKogwrg/537/zzz+f888/vVXbvvfd2//+0007jtNNO67NfTU0N99133yGv31C3Nd1CuStMMrsan6vvnVfYDNCWjRAy/ARcpXSlG6kJHjEINVWUgZXUJNtSaTRNMNLjwesMfFeJELAiurlP+TPNK7i04nje2VrINNyUTnBKzRiklLzQuJmObJJH6jfy2bFjWdH6AkdVn4sjHdwU9bpRzOk+QsUjuWntm2iYZOIxstEuzqudQqY0TfzGFOWdYcj2XZbO565h4pxbKCqZDkv7Do4Srr6Z4pUPFgFgCMj2/iIM5SWll7CF6o8HGPtaELEhh3Okm7dnNOByEozXage7esog03WNVV1t3DBpASUtPiC62+l8VrWGVmmCS6CdFyZVdXjrqiiKcrAGPVBXDs7WdAtlrjCpRBvFvjF9ng8bftpyUcb5awi4SujKNA5CLRVlYHVg8/VFS2lIFeayTggH+ebMKRQxsMNjpYSwq29SuFJXkHSusEb5lBFnsDXTzJMNz6IJjYtGncz6aI632hoo8ZaQz7XxizeuJW/nOGX8NUwvPxNT9ox+KcbPXUdfxN+3vMfaaBvn1E7mxNJxtCXXcs/mz3B8zWWcc8JH0V7M9VTAJZAj/BSHLgFAOwvstW29n5/sQcqDW3deGd6igRihs0LYD0V6CkM6+WqHQZ75tlslZoh8ZQf/PPof+I7ykzLTTB9xDrbmUku0KaRxWNspeKmhhXOOqMRtgL05izbVi7NTbgNruslr4QZmfm4EfuEiJxzUF0hRlOFGBerD3JZUC7PDk9hsdeDpZ0h7wPDRmYsW/u8qoTO97XBXUVEGlKYJntnW3B2kA6yLxnmro4vTy8oGvBd5anA0VZ4SmjOFpQ51oXHJyBm8uPQ7mLqHQPEx3LXmn93b/2H943xi0qXUetwIJ8ITq37V/dzjq/4XnxnmiKIzuuspJVSLIJ8bewJ54VBdEqatLU5SOkgkrzQ8QGhcCbO9JxN4ywOVBtpZYeJh2X3fmRwh8H+hAvluGnwaTPeQKJHqvvQDznJyrKpZwuSPzUGstKBUJzfFZr3nXUZy9GBXr19+zeJ3i75M2upJKLUx8i5XzfkJO8/uUD6YNqVSPLetjYtHh/lby1e5+PrPUP6yjmtMADHVhV2fg4luxDgXx3iLkA7kUA2WiqIMTypQH+YaM+2cUTabtXYCl953uZSQ6aUlGwEgYJbQlFhzmGuoKAfBMUnFBAKBL+wghQWaYElHpM+mK7qinFlRgW0P7E1ZiSzizlmfZm2ynrSdZXyghmItT8WRXyfsreDnGxf22WdVZB1nl5SztXNFn+cWbn2YKSWnIe1dejQdMHbq5Sz1jqLEO4LOdCNPNP+aV30PcdbVNzKt6jQsx+kVhNtCEqsE7ZxCRnpnl+eVDyaXLKLJtZUHk3cw79gLaUlvYvXGN7h53r1733mQdCTrewXpAM2x9XSlm/AbxbvZS/mg6MwWRhaN9KZ4Y+sS7uy6gclHz6PErGJVfBFHL7ieqsARjNKrUfG5oijDnQrUh7GckydiJXCRxW2EEP1MPAzoflblthb+7yohkmk53NVUlAMiLRcL/50nESk8DhXDsWe7kMLi5OoKVnRGe20/t7JswIP0HYpliGN9U3eqHEwI1qDrGqP8G3mro3cD2Ch/BWNCE7DzUWjofaxyfx37MuzY5RTx8Tk/5d2mf7Oh4x1mVp/G6LLZWE7ffSURYsn1WPkkocBo3EYdUu77nH2Pk0ezbbIuF/Z+7KcMbVLCUZXnMi40nq7oBkYE5nD6xGvwihFDNn+Bz+y7LJKhmXiNvlNQlA+eau/2xkjcaEJndNFUplfMgnyakUUXYhs+AsLARGKhrmWKogxvKlAfxlqynZSYIbJWFx6j/0zuIdNHxCpkQ/WbxcRz7TjSRhNqqRtl6NI0Qf1G2R2kA8S6oHkzjJgA88tLWVUT5YWGVjTgwjEjmRYKHvZeZNt2OLdmHk81vkkyX1iKLWT6OLVyNmHKmFTm5mX3X0lkuwBw6R6OH/1hnJ6E1khN0G5bOFJSZpi9ju9nBCfWXM+JtQ7S1vpd/9ehk1ffupW27b33mjA444S78Hum77X+OpJAcwvOYy9CNIF/zjTs+UeRdHkO9JQoQ0wmsZYXX/vv7nwFVWUzOW727UDRoNZrd8pdY5lRcyZLG57uLjt5wn9Rro0pjBRRPtCKE34+OW4SLzVv4+JJnyGci7Di3f/tfn7cmPOp6szjbXwbz5nHE/f1HWmoKIoyXKhAfRhrzLRT5gqTtjpwGX17IQC8mgvLscg6Fm7NxGMESOQ6CLn7Xy5KUYYCTdNoa+gblHY0OtROFoTyGp+dOJ4rx45CE4Iy3UAM0j18jSjn18d8kY2JBoTQGOcfQaksAiBADZ885jc0J9dhyzxV/vEEtdrugDslHB7c2sBDG+pxgHmVpXzxqCNwbz+2EKKQJd4R7K4VIpZY1x2kAzgyz6Ild3Hq3P9FSu8e6+6LRnH+8Gh3eni58D10QF8wD3uI9rgq+07Xkrz23k97JRVsbn+PWGwNoeCxg1iz3bM7ijjK/UmmzT6VRLaNYm8Nuc0TyQYMTF9u7wdQ3reEEMRbBOE15Vw9tZiaQBkvvf7xXtts2PQYo+edSe3ra+AvT+D5+IfI6OpWV1GU4UldvYaxxkw7xa4gqVwbbr3/QF0IQdD005mLUe0pJegqJZJpVoG6MqTZts3I8S5at/WOvkeM07DtPACaA5Xa9h7owxyk6wi8naA12+AVeKtLKfdvnz+7S4DrpZIx/sruxzv3iq9OJPnHhvruxwtbOvj3libOrRzJhi5BU1QyqkSjNmhjku+3LtlcrE9ZPNmAQxbBngN10dbVp5deLl6G+7hZpMxCc4HQJKlcDKGBPARL4CmHjuNkSCab+5QnrTihfrYfCjwBcFaVMa69GDPjkPXqbIzpuCZKlXbhA05KSXGFxso3bZKvG4SOtziq+hpq7bmYWQ+JUDuLOu9mY6oL//yZlLzwJkY8DkUqt4GiKMOTCtSHsaZMB8VmkFRyFR5j97ddQd1LxEpQ7SnFZxYTzap56srQJiWU1UhGT9HYvNJBCBgzTaekSg6JubX+Bon9i7buXmdRa+K7roSUd99bDDRNsKSjq0/5Cw3NFOVG8Oh7O96ozdlTdM4cp0E/y62Fg6P7lE0YfS66COPs7Vx5XH3LwkEcrTA1JkUTi7Y+zOrWhUwqn8fc2kvwUb2XgypDRVYPUVd3Bpu3PLlTqcDw1Q1anfbGY+SZ3pZE/qswZcsNzLqkmLRukh8Cv31lcPmL80w9VmfVYptSrZbSV85G21i4LnpEDadc/S0aijK0h2xKNA1c/VzjFEVRhokBW0j1vvvuI5FIDNThlH3QlOmgxAyStjpw7yFQ9xteot3z1MNEs62Hq4qKcuB0i8lHS067wmDBVWmKp6+gzVlKXhvc64zH1rD/Eendc15vYTTau9ulX44jmRDuOxJmRkkJb27uXfbUKptYvv92Va9rDKfM+3/4veUIoTNxzAVMHvsRnH3o/c5XlMHInt5+BGjnn0xGN7C1BH9Z8g1e2fQAbcl6Xt38APcvuRV7kM+/su+EcFM58njqRi5ACA2/r5JZsz4LLt9gV223XB0SubD3d8z5VxfuuBrNoQDCZuRkm5M+GqNEWt1BOgASfP/yEe/YSHGJjjh9Hmn/0P2uK4qi7M2A9aivXbuWM888k5NPPpkrrriC6dP3nshIOTgt2S7ml0yj0erEtadAXffQuX25G59ZRCTTdLiqqCgHxZE2lruVlzb/gXcbniFnZxgRmsiVR34HH1WDUifNBrr6GYaelrCfWYZnhMMcWVrUvdxcpdfDWbUj+cmyXYajS7AcoJ8ckFIalITmctaJv0eSQxNFSGffkkWmXG58Hz4Xo7UdMllkeQmJcCExZSTXSGNsXa/tm2Lr6co2UGZO2q/3qQwO24lw35JvMzI0gSnTryWei/CPdb/lgqkllIdGDnb1+iUyTt8h7nkQanq6sl1C1vOX977FddrtBOndYy6jNuQcMpUZMuVT1CoWiqIMawMWqH/nO9/hf/7nf3jssce4/fbbkVLykY98hPPPPx+32733Ayj7RUpJa7aLIiNAxorg3k0yOYCA4aPDKixl5TeL2RJdcriqqSgHRdJBW9urlKQSXDT6SlrtKC9tfoglzU9z3IhrC4nWDrOsW+I7LoDz/E5rPQuQVQb7O1k+hMatM46gMZPBciQ1Xg9BT4BiX4auVM97m1ghKHY7uz18YTpAENH9/z3QkmSzLRiGH0OrIOVyw8iaPpvpmtnPzoWlspThwRQCrxmgyl+HKTRCrmJC7lJMfegOB86WZjC9GqR3+rLXGGTCKUCtRvBBJzRJa2QFx5cfh5RZ0Fy9rovyGBfusInbW0ROze5UFGWYG7Ch7wCBQICzzjqL8847j0gkwl/+8hfOOussnnrqqYF8GQWI51NoQqBjoQlztzfVAAHDSyTXs0RbTA19V4YBIdK8tfwOFr1zB1vrX2DF8t9hRpqYVnkca9rfALF/Q80Hii0l9gl+tAVBMAWUG+ifLCdVdmCNBl4pqHZ7GOfzEZAaZQGNz55kMG+MRplfcPpknWuO1tGc/pPJ7Q/L2cpzr32Cx1/8OI+/cA1NHU8jhNXvtmFjBLNrzupVNqvmTMJm36BeGZoMJ8THZ34LrXUV65b8lq3L72NBxQLqAuMHu2q71eHZRvK/ksjRGujgTBXEL48R1dXfLQWkiLJt/eOsWnkfy9P/JneTC6oMMEDOM9k6403eWvxtli//NY5sG+zqKoqiHJQBa258/fXXeeCBB1i4cCFnnnkmd999N5MnT2br1q1ceeWVnHXWWXs/iLLPWrJdlJgh0lbnHuenAwR0L9H89kDdVUwsp254lKEvnWtga+Orvcoam15n0sybSCNA6hz2hdO3S3lt9LO9uBf4cXRImXa/a5zvTVw4PNPUwr82N1DmcXPTlPEUO36K9CxXTNewpIYpbOQArB8ttAxvvPUjovGtAOTtDK+99QPOXTABjzmu7/aOm9PH3cyUyhNpiK2iJjSZmsA0hKNGSA0XQuRYvfovRKIbAbCdHMtX/oHairmYrvJBrl3/ZC7BI2s+wbjjzqDCN4X62EK2Ln2d8yp+N8BdC8pwlE63EE/UM272z/jbNsnPu9Zzy1V1zPTa/GfVbbRuXA7A1sZXKC+ZytiRVw7KyCtFUZSBMGCB+re//W2uvPJKvvOd7xAM9gzDrqur4/LLLx+ol1G2a8tFKDIDpHNdew3U/bqHeD6NIyUuzYuUDtl8ErfhP0y1VZT95zj995j7zCDjiucO+s2XLSUp9/Y6HkBVhCZ4clszf163GYDObI4vv/Ee9y44lnI0pONg4AxYlvu8HaW1c1mf8kSqAU+4b6AO4JJhxviP45jRZ9HWFh+sdhHlADkk2Nb0Rp/yaLIBn+uIQajR3mlSx5F51jU/yTp6stULuW95F5T3N8exGTHx03xnRZLs9gbMe+obuC74PK2R5b223dzwAmNrL0MtcKQoynA1YO3TV199Nddcc02vIP2ee+4B4JZbbhmol1G2a812ETYDZKxOXEZgj9vqmoZXc5GwUwgh8JslKvO7MuT5PDWUFvVOWlZRNp2xZSfiZWj2Bu6PBA6Pbt7Wq8yWkg3R+G72ODiGFiAUqO1T7vOUHZLXUwZfTngoLprYp1yYJYNQm30T8I0i6O+d6K5uxAm4zcFJHqkMLX5vLTF9RHeQDtCeyaL7+07nqCqfDVIF6YqiDF8HfQX761//SiaT4Q9/+AO5XE9aVsuyuO+++7jxxhsP9iWUfrRkuwgZPlJWEy597z3jO+aphww/fldhLfUK/5jDUFNFOUAywAlHf4fN2/7NtuaF1FYfz6iaM8EOD3bNBoSJoNjlImH1nnvuMw/NjaWUfo6b/TWeffXz5O0MAFMnfASfZ7TqKX+fcqSHERNvJvnu18ltX6KzpvZcLNfQXUcdGeaU+T9mw5bHaW57m9EjT6G26lSkmnKhAMggZcFqoKu7KJW3adTGU1F2JK3thWS5QX8N40ddwADMGlIURRk0B31HaBgGa9euJZPJsHbt2u5yXdf5xje+cbCHV3ajNdtFnbeSdKoT3z70jvgNL9F8Eigs0aYSyinDgS4qmTDq40wcfTVSGgM+3N1lmcikQPgkljt/QPPMD0Qa2Jy0uHrSOH7wzrLuOLnW72VyUQjSB5Yozy2zmIlGEBpZfzWW6J3d2++Zynmn/IlkqhGXK4THNRKkCoDer7xSYHnGY07+f1SIDhzhY1UmxFR3yZBunHHpNcwY8WmmF0tEQJDTcgM2BUQZ/kZ5w8yvLONodymljptmLUNJ0MXcmh+QTG+BfJqwUYxpuUm792ElDEVRlCHqoAP1yy67jMsuu4znnnuO0047bSDqpOyD1mwXM0Jj2WZ1UuzUokXzOCOM3U5m8OseItt7VHxGSA19V4aNQnA+sInjhIDEujwt99vYCdA8UHGZC7smd+iDdU3wzy1R/rq2nTFhN5+cOpWMnWWk38PEoJ+agI+29P4Pf/fn2tGf+glyXWFOsnf66Rgn30zaVdy9jZQSXVQS8lcWqmJrGHGDfASMENhhG1sbnGz6yqEgmCYyTDHHk4tMRfM4HBOOIu1c4Us/BAkh0La4aHrYwsmAHhRUXuHGKs2qgEvZTufzvqmkHrVx0jA6ECJ8mYHLb+PpGEu+uQPD6EBb+2uCC64hXjZNfXcURRmWDjpQv/fee7nhhhtYuHAhb7zRN2nNrbfeerAvofSjPRclbAZYn43gW2+iazlExMae1n/vmE/3ELEKN/8+s4hotvlwVldRhhSRNWm8L4udKjx2MtDyV4vqm01y3tyed97bscWee3Da85IH1rUDsCma5Z6lWeZVujmpPEjRAV6ShRDoa17qDtIB5LJnMccdQ2b86f3WR0NDrjZofKxn6H3pGQbGURJ7P9eDV4aqHJ5tGg2P+MAG0PGPC1J0eoxseGgG6q6kScPfrO61se24pPVveSpuMMmZ/S8lqHywiJhG8h82cvuly05A9g0HWarR9aoP8AG1VJ0xEu9TX8F7+f8jZRbv6ZCKoihD0kEnk9uRPK64uJiioqI+//bmscce45xzzuH000/n/vvv7/P8c889x4UXXsgFF1zAzTffTDQaBeDRRx/l+OOP58ILL+TCCy/kzjvvPNi3MmzY0iaWTxLUvWSsCEZxmPxkF/q2PKT6jxACRk+Put8sJppRPerKB5OmQVtnrjtI30HmwYkd+HEzmKyNunm13sXWpJu86D9LtSUl9vafaZVX57tHxpln3ck7i25iW8s/Sabb9/u1DU3C6pf7PrF+EZrW/2XeSBm0Pdl7fnzHs3n0hEq+9H7hsaDt5dLtQXpBcoMbp2vPCUgHUy4Gu7YT5eMSJyUGpT7K0GOmRHeQvoOvTqfr1d6jgVpfLsOZ+CG0ZMdhrJ2iKMrAOeg7siuuuAKAT3/60/u9b0tLC3feeScPP/wwLpeLK664gmOPPZbx4wvZOxOJBLfddhsPPfQQlZWV/OxnP+MXv/gFt956K8uWLeMrX/kK55133sG+hWGnK5cgoHuR+RzC0dAqvWAInDIDfWsOe3LfXnW/7iViNQDgM8PEc22Hu9qKMiTEkazJxpjkKkbu3HmugXaA8UtSk9y/2GFlU8+N4qUzDU6ok33WQC83dWaW+3mvLcl145OsfecLONvvOt9476dILOqqLt2v+fi2FDD2GNi6y/Jro4/C2U02JZmmVwBXKAQnBRzAeRBC4LbjSM0gy9Dsrf3AkR5y7X2/R05uCOcl8OdAaL1mumgeyB/kSBfl/cP2OoVupp0ubf2t5ulkQbrKwFd0uKqmKIoyoA66R/2oo45i1qxZu/23J6+//jpz586lqKgIn8/HmWeeyVNPPdX9vGVZ3HbbbVRWFuZTTpo0iaamJgCWLVvGo48+ygUXXMB///d/d/e0fxB0bB/2nm1uw20HwFXoaXDKdbQGu9+pvH7dSyzf06Mez7UftsRZijKUaMCDnVtxzrF7roACwufp5P35Pe3ar04R5e3WdlY2SWrDghuONPnUZJOKvCBrmT2vq0k03cKUks/PqOLSCcX47U3dQfoOS1f/GYee65nQBJYmQNt9j6LjSOypZ0DVhJ7CUTOxxxy722H4Iiwxgr2PqXlAP4Ck+h47TnDFo5i/ux7X/Z8h3Pw2Ovt/LpWBlXbpBKb1/d7I0qE7aiJmrMd/ahfsqLYOvrObSRn1g1ovZeho96bhTLvnO6KBa2QOscvX2iyTGONGk3IP/+U8FUX5YDrov9aPP/74Ae/b2tpKeXnPBbSiooKlS5d2Py4uLu5OUJfJZLjnnnu4+uqrASgvL+fGG29kxowZ3HHHHXz729/mpz/96T6/dmnp0B36t6vy8mCvx8vyOcp8IZzmCB4tiNdbCAakF+x1OfzSRAR7D7v1OCaJxjShsAdN+DA0E3/Ywe8uOlxv45DY9dwoB+dQ/i6G0md12YQ6vrXqPf7r8vGEMi7yPpuicUFKw3tf6nBXz2xYhGONIuCGS+oM1r3YM482M1pj/ll+Etm1LFn9V9q7VjNxzLmEK4+hMryNgN33EuwygwQDATzuIJuiGR5a186i5jjzqoJcMqGM0budWxzEue5uZNtmhNAQFaMR/mJ8e6i7ea1N0wNZci0Ss0xQfZkL/0gD6NvjuqfPL//6k+T/vX36UbQZ5/4vEr7pXvQxe26sPViH6zu1r7+LofQdB2iOZDHnufCkLTLrHXQfBE81iXocxh6Guh7I+Ug32yzOf5Wpl/4XejaE5WnnP42/5IwpP6W8eGid3wMx1L4jB2ow76GikXYeCm7lY58YiejwkEFjZeq3jLzkFLLP1JGPSMwRNqFzIrhG1VG2m+k/h9pQ/qxV3RRleDjoQH3r1q3MmzePZ555pt/na2pqdrtvfz26QvRt/Y/H49x8881MnjyZiy++GIC77767+/nrr79+vzPOd3QkBnypp0OhvDxIW1vvDNAb25rxOG7ibW2YFQHS6Z7AQA9rpDencMaaux4Kt+ZiW0cnIcOHzyhmY+MmqgLjD/l7OFT6OzfKwf2RO1S/i6H2Wc0Jhig76gheb2qjuszL7NJijJy933XUdY2lnRsZ63Vx+tg66hf3TnbVvNmhtTHNC+99gmyuMAH+jXfvom70uTyT91A6chY+TxmpTM+89GNnfoZEXKMtkeTri+rZHMsC8OD6Dt5rT/L9o2tw7/YzckFwYuG/KSC1l/fjg9JrDERGB49DSkuT6mdWzJ4+P7fM4H79b33K8+vfois48ZCN3DmQ79SB/jb25Xcx1L7jAB5h8fJ/3IRKdaonm6TzkmUrLWZWaIe8rgd6PrxGKbrLxUvrv9hdNnn8xWgyPOTO7/4ait+RQ/mbOFRiMsXc6iytkWWseH0Wjm1z5OnH8PzqTzLp5EsJGqNoza3AV3wu7R3JQanjUPysd1B127d6KMpQcNCB+hNPPMG8efO47777+jwnhOCMM87Y7b6VlZW89dZb3Y9bW1upqKjotU1rayvXXXcdc+fO5Wtf+xpQCNwfeughrr32WqAQ8BvG0B3KN9DacxGCho+M1YRp9u4BlEGB1m73G6gHDC9RK1EI1LevpT6cA3VFOVCmFMyvLmeSy4uU8oCX7rFth+PKp/GTlX/nl1Ons+Q1V59tshmrO0jfoX7LU5x+5Be5fdXj/GDWl/GkGshmI9RVHsvIyplEuiyas/nuIH2H9ZEMzRmbUa6B6yGytDx77HbfC0foECiF9q29n/CF1fSaQWbZLpJxSERtGrf2TOLN5YbuEnyJVBMlReOoqZxDItVKKDCCjsg6MtkWXMaowa6eMgRE83E6YmsJZKu656ave2MSc46+i1j2LSxPC5OLjsQ0R2Pb6hqkKMrwddB3e9/97ncBuO+++/r8+9Of/rTHfefPn8/ChQvp7OwknU7zzDPPcOKJJ3Y/b9s2n/jEJzj77LP5+te/3t3b7vP5+O1vf8uSJUsA+POf/8zpp59+sG9l2GjPRQnnvCS9EVx67+FnTkhH69r9PPVovtC67DPDxNRa6soHnONIJA5J2UC7tZqcFqGfQT17NCM4jnNr5nLPtr9QPrp34jYhwBvsmwRL10wsKck5eb6x8l8s0Yr5UXsz/+pqoDOXBsDczZz03ZUPFgsTTr4etJ2m2/iLcWqPGrxKKQCk3FnKx/ezpFkwc/grs480zWBr4+tkhEQPjSCZT9LY8jZC++A0xit71pmNMDI0AjscYdKp2wiUOKTiGktfGE/bsisYF60m6B5BTOYZpFHviqIoA2LA/vJ1dHTwve99j9deew3TNDnxxBP5yle+QigU2u0+lZWVfP7zn+eaa67BsiwuvfRSZsyYwQ033MAtt9xCc3MzK1euxLZtnn76aQCmTZvG9773Pe666y5uu+02MpkMo0eP5kc/+tFAvZUhryMXY3yygnZvlBLRewQCLoHUBCQcCPb+C+XX3URzhYRyPjNMNNtyuKqsKEOSFDmWdfybf638OY7ME3SXcu2cH1Gs7ftIE7/j4+Mjz6HDiRIYCx5Do369QyAMR51s4glYhIOjiMa3dO9TN+Eyftm8ovD/QAXr4g00Zzr508ZnKHYHOKf4OMrdORbUBnmxvmcY4Bl1YSpcGkJCUhSStQUw9nsIqq3ZJEjhxoXHOfgM4ImyI/Bfdw+iYSW4vTjVU0l4qw/6uMrBMYD0uA1UyHG0rTfxBgTlx3TS7u6ijNGDXb1++X21jJx8GY+s/jWWk8VrBrh8zpdwm1X9ZvZWPnjGeTT+/s63SWS70IXBWbM/h7HsbMibTD9RkqGWT23YSmNqNZePnciZVaMISNXQoyjK8DNgV65bb72ViRMn8uCDD+I4Dn/729/45je/yV133bXH/c4//3zOP//8XmX33nsvANOnT2f16tX97jdnzhweeeSRAan7cNOZixGOuak3o7j6WU9KBgVaxMHZJVD39VpLvUgF6sqwFxdZ6jNR3JpOjTuMx9m/S1okv5VHV9zRc7xsBw8s+S43zP4lmrPv48GFo1FGMRgw9XjJEccaCN1BkgUZYsHcH9PS8RZdsY0YRRN4vKuR+lQbbs3kvNp53LXiwe5jPbL1VU4rm8262CZ8bsH10yvoSMOokAuXnsbB5uVIC79ds4y84/DRCVNYUFaDV/a/bvuu2kUXv1z7KK+3raDWX8GXplzBJLPugIf/AzhSIx4aB6FxB34QZcBZjoUrZPOP4t9xwmmz2WR18VquiUv0Ewa7arvVme/kzaYXmT/5S6QdHZ/I8MKGB7jiqBl4KRns6imDzNaSPLPyfzlq1NXktGJcQrIx8gYLFownkffyZGoLD6/PEM0Vpg39fu0KvLrBeRWjh0VeIkVRlJ0NWKDe0NDAr371q+7H//M//9MnAFcGRpcVp6jLQ6Yyilv0E6j7NUTEhtreH69f99JhFZZ98plFbI0t7bOvogwXLST478X/oiObAuDo0lr++4iTCcp97yGOZJr7HjexiYwTxXeAE7cdxwbd7jX7RBeV1JSfy8hKaJVdnOpv5cSaufgMN99fej952TNkfqSvHEtG2JqM05bRKXVJ3mtL89yWGGeMDlDpy/CjpYu7t//lyvconulmXrByr3WzdIsfr/grS7o2AlCfbOWLb/+S3x37ZcpVEPS+o+uwuG01cyomkpARgm4DV8IkJ4fu0nnxfBxf6eX8v9VrANCF4LOTP0bCiuLV1Hf0gy5jR5gy6mP8dM16ElYXAMdXHMUsx6GdBBNCI8nme3fwPLplA6dV1uI6+NmeiqIoh9WABeoVFRXU19dTW1sLQHNzc6+l15SBkbUtso6Ft1NiVWUwRd9gQvo1tIa+N2J+w8OmVGPh/2YxsWw/6Z0VZRgQus1f1y/uDtIBFnfUs6ZjAwtMH8nQKBy597ncYU/f4LbcX4dbD8EhGGb7dnINty35A1nHwqUZfGHqZdR4y+jIFpLNuTWTy0afRCYbwyUcZpaM456lke79H1oXpcxr4jcMkvme3/hjWzcw/8gqZH7PPUZddqw7SN/BcvI0pNso8Vh0Rldi5VOUFk3GY45BSnVjO5yl7DyPb1vYqyEIYGbxBKYO0aTGUi/jTxsXdj+2peQ369bwy3kTwdnDjsoHgsss4tGGd0lYPYk2X21t5LSaiXxrya8ZHxzBLdPP50dLVnU/X+3zYahrmaIow9BBB+qf+MQnAOjs7OSiiy5i/vz5aJrGokWLmDRp0kFXUOmty4oRMvxkc3Fcwo8Qff/4SL9AizuFm5qdng7oHqJWzxz1RK4TKZ1+j6EoQ1km38aKSHuf8o2RFhY8/ysCl32HWNHerz9FRh3nTL6Zf6/+NRIHrxnkw0feim7v/3rqe9NBlNuX/pGsU0julXPy3LHiH3xz5jXMS07FkRJDaBi6QZFRSYlRz8stfVsLXm1IMa24nEVtTd1ltf4gwuk3h2QvHs1NwPCSyKd7lQcMg2de/RSpdKHxTgidM0/8OX73tIN708qg8mgeqrwlbEv1/q2ETc8g1Wjvkrlcn+9xMp8jnbcHIP2tMtw5ts2aWN9rfzxnoSFYH29kfXwFM0oqWNrZjiE0rp0wDU018iiKMgwddKB+5pln9lt+8sknH+yhlX50WnFC+EgFY7j7mZ8OgC6Q7u0J5UI9dzZ+3UssnyoEBJoLU/eQtCIEXGo4oTK8yPhWjisp5YFUtFf5VNOEeDu8/leM875F3tlzr7om3cyp+BCTyuaRsiIENTeZxmUkvQ0ESmcgRdmA1bnLipOxe2eAzzl5NsWb+b91TwIwMTiS8+rmoSVMjiieyuZ033nno4IutmZ6AnivbnDhqPH7NP+yiCBfnHI5ty/9Y3fZGdVzCFnR7iAdQEqbd1b8hpPm/BTH6bvknDI8+O08N40+jttWPYa9vVd9RriO2iG8nGm1AENovUYBlHn8VDqWCtQVirsaOb64iufat/Uqr3V7cbY38bzZuZ7vTz+e1fGxjPGHqda9e2/FVBRFGYIO+q/16aefTiAQIBKJDEB1lL3pzMUJOV7SgRiufuan7yD9Ai3m4OwUqBuajkszSdhpQoaPgFlCLNuqAnVlWBECpL+CU3Q/m9JpFnc0oQvBR6vHMWXNqyAdZLwNDQfYhwRr0iCsj0K2LGPZoh93FwdLJjDt+B/jiKI+u2ia2O/110vMIF7dTdruGbLp1kyOKZ1MQPdS5g4zOVBBNL6WaCpCua+OBSPK+dfGBJ2ZwjB3n6Fx0eggpj6LjckotpSM8Yeo1Dz7VBfHkRwTmMK9x/4329JtlLiC1LmraGt8vM+26XQ7UuYBFagPV1LmyG58mN8cdRWRbAdu3YuRS0KiAYomDnb1+jUi0c73x0zju/VriFlZKjwBvjtiDCWZOMmAWkngg85IdHCdqdMYLGVDMsK14yYy3p3D42zj+nEn8cdNr3F0US3FQmd+cHtDqwrSFUUZpg46UL/66qt55JFHmDt3bvc653L7HaMQglWrVu1pd2U/dVkxAnk3aW8Ml7b74bnSpyGiNozs/REHdR9RK0HI8G3P/N7KiODkQ11tRRkQUjisyG7izlX/oC0T5VMTzuWz42aiRdqoWvI05pKnANDmXkrO2bcs6ADYHax755e9iuKd68gkNuIKzuouc0SWtuwa1nUspshTydjiOfio2qeXKCHMbUd+jNuW/JG0ncWjm3xzxscYpVUzqngEeRHjn2t+zIrm/xTegzD42NE/5GszfTh2iK5Ylhp7K+G1z+Gfeh2V/lLciSQkcmSDLqx97G7UpU6tVkWtv4qYyLA62UHSN4OxR32L1nW/I5GoB2DK+MsBP+oud/jSRJgxR1zBHxb/D/b2BHKTK+ZywcSbB7lmu6eFSpn3t9v40/yPEAtXUtyynqJFD5K/6MuDXTVlCHDKxjLy3uu4Y+oCOuafzR+X3caSTGE0UNhTwc9nfRsjn0UXIVSSd0VRhruDDtR3LJG2u2XUlIHVmYsTzLhJuRsxxZ4CdYHW2nd+q9/wErES1Hor8Kkl2pRhZpvdyn+//WskEg1BGpO/N23mrY4GZo+ezUcmn0B5ZCP5qpr9Oq6UefK7zNsGIBfHn+sg7SlDSsnG+EL+8u63up8Oeyq46ei78VCxD68BMz0T+d2x/0OnFaPYDFJKuLsnvDWzoTtIB3Bknn8uvxNX5YU0Jzu5ztGR7/6FDs2geMKFmM+vRL6zEgDvtAmYZxxPyr3vc4+jIsPXljzJpngnUMiuffvUL8GyH3DE+EupqTylu9FVGZ5skeGxVb/sDtIBVre+wdzRH6LOPWoQa7Z7W7U8ictv59H6NSytX82JJdWcfPanKXMS+DWVoPaDbp3HQL/sKxject5uf5lYpmfKTjTTyuqWVzmi9Fz8iQxx38DnGlEURTmcBmzGV3t7O88//zwA3//+97nmmmtU8H4IdOZihNIeUmYE1x4Dda2QUG6X+2y/0ZNQzmuGiWZUoK4MH1tSLcjtX+qzaubxfEMjj9WvoikV4/GWzXyprZ63w5DV9q8NUjNKqRl3Xq8y3fDib9mGdu/HCG55BVvEeHzVL3ptE8200pxct8+vIyUUyxDjjJGUyDAZkaedJBk9T8qK9tm+K9VIpTvI650b2FA0EqG70T0h9GiqO0gHkMvXYazd1D2qaV+sS7Z3B+lQyK79hy2bOOWk3zF6xGUIwvt8LGVoSos47cn6PuXRTGc/Ww8NceHhG8tf5emWzTSlYvx92xru3rCUhH5gyyUq7y8bE428oEt+1dpAW3x9n+ejyY38q3k9qQ0bMdVoIEVRhrkBC9S/8pWvUF9fz8KFC3njjTe46KKL+O53vztQh1e267RihGJu0np098nkoDCtVALZ3n+o/LqbLite+L9ZRCTb1HdfRRmiAkZPj3GNbwTrYx29nm9MxSE8DY9r/3rUbUenbsrHGDfjOryBaiqqj+GYaTfjefkByCRw/vENdCtCrp9ed8vJ9XPEPRMCtjoRPvfOP7nmtb/y2bcfwe/tO4R+fMU8FnduBWBNJoLpL6Nq/mfR3t7c96Ar1mHo+x6ox61Mn7LWTBxLmvuUmE4Z+iQeJlae0Kfc7xm6c72jlk17JtmrbHlXCxl7P6ayKO9bfsOLKXy809FAden8Ps9XFR/L2x0NZNNpzNz+X5sVRVGGkgEL1CORCNdeey3/+c9/OO+887jkkktIp/sZSqoclM5cnFDCTYbIHpPJgcDxa4h47zVJAoaXrlwhUA+YxcQyrYewtooysMb5apgcqgNACEl/YWmxvxYp3ft9bEeUUj72auae+FOmN9j4n7gXjO2J1KQk2N7BSeOu6rWPqbmpCozb79eKiSxfefsJtiS7AKhPRvnFmnVcNft7BFxFAEwsP5bR1efwVmeh12hGyVhGnPwN9Mr5UN3PUPvxddj7EWCPC5T1OX8X1k3Dr5LHvW8E8TNl5GWML58HgN9VxGlTvozbtW95FQaDTzNxaTrn1YzhkxOP4KTKWkyh4RMqUFdgnLeGgOkmbVusTYeYM/rDGJqJobk4Ydw1rEz5+fDYGTA+TNKTZT8GGSmKogw5A7ZGi2VZWJbFK6+8wg9/+EPS6TSpVGqgDq9sF8nFCVhuMjKGa0896gBegYg7yPKeG5yA7mOVVeih87tKiOZUoK4MH0EnwHemX8eGVAOa0Lho1DQe2bK8+/lTqsdTbRbBAa6ZKyVILcDWmRfy1lE+dLePCeEA/vbNjAiWMStwHjXhibQntxJJt3Jk1WmEtLr9yv4O0JpNEsn1bsh8r6sF/xHzuWrW7WyLrmZz5zI6I29zRd2xxG3BtOIZ5B0fSLAnjEYbWQXbmgs7V5ZhT52wXz3htUaYH84+j5+veoWObJKL66ZzbvUUpOpNf99w8gaVvmpKKz9CSdkFOOiUhEZSoxcf8G/kUPNpJt+dMY3X1v2Sd+s3MqpkJj+afSOahH5b5pQPlKD0M7d0LC+W1PPPbZsZF6jktAm3MTZYylONTZxaVUFD6+P8sOFpAu5iLpr6RUb5j0HIobskoaIoyu4M2JXr1FNPZd68eRxxxBFMmzaN8847j/POO2/vOyr7TEpJNJ/E4yoMhND30vMlvQIR7duj3j1H3QiSySfIO1kMbf97IBVlMAQdPzM9haWlxteO4piyUayNtTI2UMokfwXm/mR770e97uaWlhTHVAZxWzHuem8htpRcobs5WW/mX8t/SjTdxjF1F+A1ivc7SAcIGi50oXWvbQ2gIbByW/j9m5/vte05R3yK2ZUfQtg9A6CSHi+ej5yHGYmCI8mXFJE2zP2qg5CCGd4q/nf2xeRxCj3pQzR4Uw5cCIdwbjlpqeM4acLSBXLoDn1362meWPpNsvlCQ/+WzvdIr/wRV825C/rmR1U+YIQQLO9K8NFxx9AxMkpDMoZueKjPwPTSKhra/s27254EIJZp509vf5VPz7uXEmNoLkeoKIqyJwMWqN9yyy1cfvnlVFZWAvCTn/yEyZPVsl8DKWlnMNDJ+1O4tMBeE0dJv4bWYfUq8+keMk4OS+YxhUHALCaaaaXUV3soq64oh4RPmhzpqWamd0QhQ/kAdAYvj3SQyucZFfRy75q3u8vrvDn++OY3upPZLdzyMJrQOH30ZzAsA1cOaMtDWCMdlNiib2V0ITAthxojwKePOI6frXyl+7kvTDuJzZ0v9dnn7fonmF1xAdA7o3vGMMmUlR3Ue5US3NJANdO9PwnN4eVN97Gu/S1mFy2gw2rhdxvv51Pz76FIGzvY1etXItNMhWcUF1bdQlCW0cEWHmz4KRmrnaBWPNjVUwZZUuR5vmELxZ48n6yeRlCMpLUry7v5Ftp9Haxreq7PPq3JzZSEVaCuKMrwc9CB+j//+U8uvPBCfv/73/d5buHChXz84x8/2JdQtuuy4oTwkvbGcGvBvW4vvQItsT142R7Ta0IQMLxEcgnK3UX4XSVEss0qUFeGvAwmzQmBQFAZcPDQ0wg1UMuICQEZO0+138/meFuv5zS7oztI38GfK8V+z6DrTYkZhNIjdLT72/BfUERyio690/b+tI3+RgPOO02Y1QEuPXs80+ZV05SJUe72M9IIs0au7VOnisAYNOFCAo6WIpFvw9S8+EQFUoLQBJ1OnpzjUGqaGAfR6yiEwCCP5lhYulcllRvmskSosmu4IHUBxsudUOLh9AWX0ZRpocg3NAN1v1bGJyp/Tfwtg1SzpGz0CD4/4z6a9bYBaYhThjcHycdHzWBqOzj3rYV4lppj66hxjcIOjuVPJUexovW1Xvv4XGoFC0VRhqeDDtS3bi3Md167tu8NpjKwIlacYN5L2hPd49Js3XSBNAWkHPD3DJsNGX66rO2Bulms1lJXhry44+Ln/8nTVpi1QXkAbjnRRVAb2Ky+UsLUolL+uG4F1b6RvZ8UvXu0y/11TGu9hI7/FMaLW+2Q3gojTyuFP7fi+WolyWAhsjAB7akNOEsKvzUZ74QtbzPxlmOo8Ye2vziMLppNmb+2e0ktl+5hwbiPIm2NJNv467u30xhbi0v3cOG0LzCu6FReaO3it6s2kHUcZpUV87lpEymW+zf8Py3SbMk2MSHehf+1f6B1NuCeczHWpFNIm0V73d9wBN5OIGIjSw3SRf2PKFAOL7fwcdyWY5AvbysUxLKU/EkQvGUa2cGt2m7V5cbT9KhDfnsi1EinJNfhoeZDI8mYam7GB50p3UyKSJzfvdHdcOM8sw799PFoT/u5bsGtNJVs4qdrPoMjbUYVT6PSN0E18iiKMiwddKAejUbZsmULP/jBDwaiPsoeRKwEQctDyhXFJfZtTVnpF2hxB2enQD1g+Oi0YsD2JdoyzYekvooyEDRN8PZW2R2kA7Ql4O1tklNGiwHv9a01/Xx39vFsTnUyt3wUUSvD+lg7HXaQ2vAR1EdXAXBsySXkHu39O5Q2WGi4bQorLmwf+OJK5pFLdmkQy9po7SnYEagDPir59PG/YnPHSmzHosI/hrBeBU47j628i8ZYoUE0Z2f4x5Lvc/kxx3D3ip513NdHE6yMdTLa7yZseAhK117n0Duaw98aXuAYW6fooTsgnyvc0z79c8x0lNyxex4VZUiBb2EW+18968D7ry4hMV1HdcgPLl/WhXytsXehLfE022SHaCej3al1B+k7pLZISqIGlKnltj7outI6ZQ2xPtc1e+FW9OOORK7XKX+rim9c/w/qXZuo8I3H5RQNSl0VRVEO1oDMUb/88suZNm0aH/3oRzn55JP3OndaOTBdVoJA1kUq0Iy5x6XZekivhog5sNNqPAHdQ2eucFMdcJXQlW44FNVVlAGhaYL17ZKAG44el8XrydPU4WFju8ZpYwc+UMeBcWaQynAUT7qBlvhGbpx+KiN8I4iHrqYt00zetpgSnEvGI3CyvV9fALgEMqyxIzub1AXCrUN2l3HpZt+e71L/CJxUIcJ32a20L/oe+YoxrG1f3Gs7QzPZkuhZWaPE7eKjk8v51Zpn6cymKHX7+NbMM5hglu9xakCb3cUDm1/i0uJjId87EJIL/477qIvobnHohycisB+L9iqz/9aFd3QFyYDqAR1MmpNH+k2I9u4/17QhvNSZu5/7BwGOS91XKNCREox393PrGnCBW8PZkEW2OPhWu6mZPw2R9/TdVlEUZZg46HXUb7311u610++55x5OO+007r33XiKRyABUT9lZVy5OIOMmrUVx721pth28FAL1nYRMH+3dgXopXZmmga6qogwY23Y4fiycPKuBf3T+g7s23c9y7d8smB7Htg9NIJimhXsWfYrn1/2e5c0v89CS79KUXM9v3/oa/1z+c55Y9St+8e4NFJ3ZO/A2wuDKWejXl5HeKUjNeHW083onMxJji7DKvb3KdCFwWi38CQ23gM73fk9swwsQbaLSP6rXtnnHotzTkwbu3NFl3LPmZTqzheC9I5vi6+/8mwi9l4HbVU5aSCR5vZ/gze1Hir205yadvsNKLYlIq+70wadjnDC+d1G5H+nz9r/5EJAts/BPEZQeq1F9sqDoKI2i43WyJUN1sL5yOIU9kKgKQ8n2ALwmiH7GEZgXH4kImjjLC9c7sU1giqH7PVcURdkXBx2oA7jdbi6++GL++te/8utf/5q2tjYuvvhivvrVr+5138cee4xzzjmH008/nfvvv7/P88899xwXXnghF1xwATfffDPRaCHAbGxs5KqrruKss87ik5/8JMlkciDeypDWlYsRSLnJEMG1zz3qemEI7k4Cup/OXLzwf7NEzVFXhjQpIRiOcNfqJ7uD0BWRRn674SUscWgC9ZbUBhK5SPfj0SXTeWv7kj87pK0473gfYsR1JiWn6FRcaFJzhYE8yiQ+WvQa9u1ISE8pRXxyNtoFE9E+NgP7w1PImD2XYFdew/9Gjsw3tsF3W3A9lMRnHFF4rTXPc0HtZZh6T+/QsXUXMjEQ5LiqQuZ3XbNJ7tIjHreytOf2fG2sNEs4IlzHe24Dp7j3sl3amZ8hY4R2s2eBLNb79IKKIh07PCB/XpSD0OUBu8uFcd5M9AXj0c+cgj51Etkh3KHeojXgn5Eh+EYrroebCa9qR46NE7FVg7IC1b48S9GxrpmF/l/HoNVOIv+4JPezDpyVGfR52++NZnjIW6qxUFGU4W3AlmfbYcKECZx88sl0dXXx8ssv73HblpYW7rzzTh5++GFcLhdXXHEFxx57LOPHF3oAEokEt912Gw899BCVlZX87Gc/4xe/+AW33nort99+O1deeSXnnnsud999N7/85S/50pe+NNBvZ0iJZBNUWyV0yCiufexRl14KPVsO3c0yYbNnjrrXDJPJx9Va6sqQ1pSO4OzSbftuRwNRJ0MpPoQAjyURjiTr1rEPNgv8Lvtn8ynC3vI+m6VlnHxFDr0cHCnpDon7eXlLF1iVPkSVr9954+5GB/vhSPdjZ1GKYHgeZrAKK96MeO333HDkVWTKR+HzVFPlVOBu7OSrY2rZMKYWR8v1WZvd1HTC5p57lVy2i1unXsPD9f/htbOu48hYjGAyjjZqNsnSCX2GzfuTGnqTjbQlstogFXYIfLIc508dyE4bKg20j5WSdNsqgdMgkwIa5zjUbgtAxoPQBPlxgg0lCUYM0UX5SmJhvPdGkTuCrEaL4j9J7M+GGKJVVg4jHZuZVV0sjMc4SYxCD+cR5xQhTEF+YQJ9lh9xXhBrlJsdU48URVGGqwEL1JuamnjooYd4+OGHKSsr48orr+R73/veHvd5/fXXmTt3LkVFRQCceeaZPPXUU3z6058GwLIsbrvttu612SdNmsRjjz2GZVksXryYu+++G4BLLrmEj370ox+AQD2OX6siJ+P7lvUdQBNIt4CEA6FCpO7RXDhSkspn8RluAq4SIpkWynx1h7D2inLgwmbfeYZlHj8ezcCwJb4NMZx/rYF0Hv9Jo7DmVJN299+jq+sSSX6Pr1fpH4/PDJHa3qBVH1nFaROvZVXz69iysK9L9zC98iSc3Fby2ThFTEC0m2AKrHKx2wzV/QXpLs2ApAVeATsNGRdLTILHHUdn/CHyqU7cW5cydvTFeOsjOH/9OzgOAQFHnb+A+LRJfHHqSfx4+YtIQEPw39NOpkzz7fV+tVQWcWPd+aTJAS6SUut3XnsgriP/tw07sn3Iv0vg/3wF8REOns+XoWXA9gmShgrShwKXoxOO+bHu7+j+POQYkzHXhoZs1vdg1AVWElHrgmoXclMG2ZYnnAiTVYG6AniyIRZEfNiPRbG2bB9FJMC8tIT8ujTZa4NYWRWkK4oy/B10oP7kk0/y4IMP8u6773L22Wfzs5/9jOnTp+/Tvq2trZSX9/RSVVRUsHTp0u7HxcXFnHbaaQBkMhnuuecerr76arq6uggEAhhGofrl5eW0tLz/h29HrSSmy8YUXjSx72MXpW975vftgboQgmIzQHsuQp1RSdBVRlemUQXqypBV5yri7JrJ/LthNVCYy/2lqQsISBe+tjTOfT3XDefZjZgeg+zRVb0SzQkhyFjrWb7qfmLxLRwx7mIqS49HUNTn9XyikpuOvZt3Gv9NU2wdc2rPY4R3Gp+afy8bO99BFzpjS46C1tUsWvRj5k79BfLPMWSqcHNoHOHBe0WYtGfPN4saAqPNRddLeZyUQfE5lbg3xOG9Qt+8qDMpmf0RfGNnormCaKGxmFkD58FnwNl+bE3DXrUO74RaTiwey6T5FbRnk5S7/VRqgX3uVJKOwLO9y3LX9eIL5w/EmiwystO8/JyEF+LolwdIuxxw7dtrKYeH3xLw994ZssUmC2+DJDtu8Oq1Jx6/gXVFBV2NGplmCMwMEAzkcXt0sqqH9ANPCDBWdyHSXuSWnab6SMi/EEO7LEC6fTFmaDZSqgSEiqIMbwcdqN95551ceeWV3HnnnYTD/a/3kkql8Pn6LifWX49Nfxnj4/E4N998M5MnT+biiy/uNyjf30zzpaX7mIxtCCgvL2RcjskULq+Fxwjh9Zr7vL8TkoiMQA/09EqWeEMkjTRFRT5KgxXk9Pbu1xlOhmOdh7JD+bs42M/qC8GTuWD0NCK5NLX+IsaFyjA0DWtRc5/bd7lwGyUn1CH8PZFjR9c6nnruZvJ2BoDX3v4xs6ZFOHrGTQjRt/e9jKmMrZjaq6yKaiZUHglAV9sKXnvjh1RUHYfv1UpI9dRCrsrgaQ4ROHrP7zm1yWbL7zPdvZ3NzVC1IIh7TQqEwDy7CE91FcHqMd372JsbsbKFG9TcuEo2zfPzcuxp3JsXsWDCR5lSNhNDG7Hnk3kAysqC5Dra+p7rljxFAR9iNyMYDqXD9fvf19/FULse5bclyEX6jh6RqcNT1wN5jUw8QeMiQT5a+FF0tkNmrEHVUfaQO78H4v3wHmDw7qFkNk/2jW3IKRP7PteVh2IvS165naPP/SVl5VMGoYY9hvJnreqmKMPDQQfqzz777F63ueqqq3jkkUf6lFdWVvLWW291P25tbaWioqLXNq2trVx33XXMnTuXr33tawCUlJSQSCSwbRtd12lra+uz3950dCQGflmnQ6C8PEhbWxzLyZNxclh6AlP6SaetfT6GMB20dod0ItNd5pNutkZaOcI1GlOG2dq2nrZw/FC8hUNmx7lRejuYP3KH6ncxUJ/VKIoY5SoCC7o6Cr3OwWDf0SWy1E0snSOX6hng2x5d2x2k71DfupR80wbe6WxhvL+I+Rq44l3IYDkpfzWO3H3wmYk246+aQ9HET8BzfRsK8y05Iu1xhNCwcwZCSISR726gFEJgrzH6DBHvXAm1NxYjQwZdAQvZ1vu37nO50AM+SGfZND/Abzf1TDFa0fIqN8//DaX6pN3W+0Ds+PyCUzzwQu/PUTshQEc8iYwN6Evuc532d58DsS+/i6F4PQpqDvpRfuy3dkooKECEtUNe1wM9H+6YRr73an+kNkqslEZsiJ3f/TUUvyOH8jdxKBiawFvmRZTphbUwdx4tMitAolUw89h76IpuwxaVCGdwhvkMxc96B1W3fauHogwFh6ULZHdr+M6fP5+FCxfS2dlJOp3mmWee4cQTT+x+3rZtPvGJT3D22Wfz9a9/vbvX3DRN5syZw5NPFrIwP/roo732ez+K5ZMEpIeMK4ZL28f56Tv4tD6Z38OuAC3ZLgCCrjI61VrqyjAVH5lHluw0edXQaDtOkBIRNK0ngNa03u2SLlcQp/YyPvvm47zSuI6pW9/B9Zv/gj9/DnHPxwhuXYi2m4E6QoAvUEPz6Eu5fevbJGf0MxVlpAuZN1m7WOO5v+Z54QGblk0murDxuDIYWgrN0/cFNLcgHdAh0dzvfPa0y4248jycaWN4KdI7E71EsqL55V7veyBlagT6NaUQ0sEj0C8Ik5ts9ltPZfBlPFnEZDf60X4wQJQbmJeVkPXvOUfDYHI8/XyZBOTddt/yfgjBQX//dZFCkx3oWt8GcV2z0bUs+zmITxkgNpLMiTXkt7Uirq6ACqPw3T46QLouSL5LI/KnGlyL5kAmj6aJff6sNE32+mx33U/TBBpRdLrQ1KIWiqIcBgOe9b0/uxuWXllZyec//3muueYaLMvi0ksvZcaMGdxwww3ccsstNDc3s3LlSmzb5umnnwZg2rRpfO973+Nb3/oWX/nKV/jVr35FdXU1d9xxx+F4K4MmYiUI5j2kzAjmviaS2066QeQkWBLMwmdRZARZHtsEbA/UM9sGvM6KMlCEAF3XsG3Zp+Fvo1hP9vw2xqbHo+c12kKdrMy+xBmNt5DdrOEqFRh1UBSYgN9bSTJdmDozou48/nfLBgCuL6ui/MHbwNkeDNh5nEe+g/emP5J0V3a/lk4aGV1LtmMNuVHHc8+WdSSsLG/O6OT49mLM1RYYhQA2Wy1oXA8bVxQayfIWtOY6WNq5muXRdcwpGctJo49G85g4Ozr6BYSONGh9yKb0Yi+aJvr0WkkJ8bJSPGefhLHm+T7nStf2rQfJZ3Whd25GCA2rZDRpo/+pSzuzNEl+mo5nYjkCyHgcsAvX+N01yCqDJyocWqoT1K3XMU4PI5M2aZfFan+MMezfKLRDTThtpOMb8XuOxTtGJ72pp3E5fIyBqafobP0PodLpSL26/2PYDWSiW3HyOdyhKnTPOOT22xxNdpJNbkVKB4+/Dkcr67u/gHxqJcsX/ZhkdDMVtScybuYncLRqhAAns471K+4jnWhg5MSLKao8AUfs/XejDJyUZtHsamXKUePIx91kZpRjBiG2TpJb5FB6oo7MQfIdHW+tjzbfX9A0N8VVR6MbYbLJrRh4MLxF2HpZdyOjsBuoX/UPIq1LGD/zOux8jkyqnVDpFEz/BIR0iDS9yPp3f41j5xgz9RrKRp+HZM/LV2qyk0x8ExIHb3AMjuj7vdsX6hqrKB9MhyVQ35Pzzz+f888/v1fZvffeC8D06dNZvXp1v/vV1NRw3333HfL6DRURK0HQ8pB2N+IWxfu3sxA4PoGIS2RJIVAvNgN05mLY0iHoKiOWbcN28ujaoH8lFKWXtG7REUuwOFaPx+1iXukoimTPkmMV/jH8Yul3sbYPay/JVXOj68+0PCyBQuDtqoSyq0Zw3Im/YUO8lbSdZUS4Bu2d5wAIZ5Pg7NLLaGUQqQhsD9Q1IclseJyWN36J5vJjVh9JyirMFf9B40t89bzTmXF2BQGfG4oEjtTYsron2KialuEPuftYvnojAE81vcOKkfXcctVVpNeCkwNXmSDypkW+VZCRHsztQbquazhOTyOFlJAWLk4adxVr2t7oTv5maCZTK0/sd0iqEAK3K42VjeFLpeAvX4FoMxIwKsbgv+yHJD2VffbblZQSPQtGxMFYlMRptPDM9WPNcJF2q2RfQ4mNzjvau/xtWgvz9InEtDSPpt7gi+aHAHCEQ4eTwknb4NIocnlxO4f/b4DIN/DOC58hl+7k5PkvEZis4xurk49KzFKB5gKZ1Fi58Pu4PEXMOu2XSL13HgbdboNNWYoXTkKL6eSOiWFPbsQO1qE5rdibmgk0lSMcQWZEC9oYC2fXgD/fyDvPfw7HLvyuW+tfxsrFmTzv+zhWK289+6nu51Yt+gnjj0pQNuoKNaLkMHIEjM6PZtvvBa5Si/Bsg1y7JDC90HPe8WLPKIjkCkHHiIV0tS5lzNSPEBBjKW6aivmuH6fSQjs1Q7LMg3C6ePeFL5BJtTB6yuWsf+//SEQ2dR9n6ryv4fFXseqNH3WXrV9yD25fCcHKs/p8/jtGdAi7ifde+iLpRCMALm8Js0/9GZh1+zxtwJPVcNXbyKYc1JrkanSyu1lRRFGU9x8VlQ0TUSuJP+ci7YkQ0mr3/wA+DRFzkCWF8VqGphM0vLTnolS6i/GbRSrzuzLkeLICfVGO8jdhbEUdm0/K89PW//C1KafgdQoJFYNaLZ+YezcvbfwzbYktfGTcd0j8X+9e5VwLWO3wjfb/sDbWBoBXN/nWrDP46uInaHP7wXBDfqdFq7xBEh5oza2gxFNHMJskWDyZigU/wAqVsGTtA5xaeQzPt9TzrXEXkny1nP/EJULA1LkGIyc4hMsE8Ujhhixb0cry+o296vXItkVcP/lDRN8y0AxBPr49MPcDfkhrGVYmN/NK61LGh2qYVzKVUlnUvX+lawqfnPcrlja/gEv3Mq3yZIr1sX1uHHW6iLctZv3mFwiXTmZsSwYZbe7ZoHUT+obXEdMu6dNr48s52KvaCDoSq8SDkTHQl2exno52J9CzH8xhdAQxzvKSV+uyDRmGTHPPxmdI5jMs9W8imkvSlYvTkmqhLjySZCRL6UKb0FKHTK0ksiCLXi0wnH1fVeRgaZqgecvT5NKdADi2Sdu/LYQBul+Qf0eChJE3Faa35DIRYu1LCVb2DtRd7Sba74Jg20hszK0etA9JxLwcZrOG9ocRhVUKAL9egfNJQW60wLZ7vq/pRD0lJUcyuvRCjJxJxh1l9Zb/w7E6SEbWdwfpO2xe8Wcq6s7GRvWqHy7FuovYKpAWZJslrU9YGEGBd7yG1S5xdrqEu2otnLyNNzACf2gspW/NRHsZJDnENpArExR9zqBLrCeTKoy0cnmKewXpAGvf/gWTj/lcn7psW/cvplfNw5I9n7+wG2nZ9Cyb3q0nWDSmO0gHyKU7qV/7AP7QGIqr5u12ZMgOblvDeDCGvTTd857OCGGf6lHXWUX5gFCB+jARzSfwZ9ykw1FcYv+zrUqfQMRsdv7Ii10hmrOdVLqLCbkr6UjXq0BdGTIMBPrzSfRXUoixbtxpmPA7uOTjk2nIxhhvlgKFHt5ibTyXTPomEgt3KkiD1XcObiaX7w7SAdK2xWNbV3D7rLP45+ZlHHXR1yh57MeQTYAvTNd5N/LzpV8mkYswqnga14z/DOYjPyTf1UD+pI8QaV3IeVMnMXvScVjvlZKM9/R2L1+Yp7TKZNJR0LzVIZ8DZzdLSy3WVjFhznjyr2wfJaBB4CLJe/kWSOZ5cMtLLOlaD43wsPc/3DXrM4Sc7dcAqVNmHMHpo6YgJdt73XsfX9M0OuqfY81bdxcKHAe2pvpWpGEl2owP9QpcgikbfW0HTiaPLoFX42jjx4Mte2W5B3D+E8c7P0gibKkexiFCdxw+VHkSHxank1stMEohMTnOGtaQc/IU/yuHuSyHBNztULnOJv5FN/RdpOWQ0TSNWMeq7sfZbAdQhMzTnfkdIB5d2/1/K9uBEPT6nun1OtLuPafceMlEP1qHZRrkdprjboO2UMcYK7F3Kva7ypiWuQb9rxEgh9/wc/SHb8MxTAytb+OFYfhA6H0SQiqHjiEdnFTvzyIfl+SjDmapIFNfKPOMF/jH+hm9/CegQ5nhQr7a2ftgaYmzKkOwbhS66cO2UkjZ9zqdt1KY7r5D3L2BSuKdK/AWz0dK0Jx23nnhs2RTbYRKJuJsbzVweYqZNPo6vFYpmtdDc2whS9f+D0cu+AXOHhp5XB0SZ6cgHcB5Nob7aC/5kPrSKcoHwUGnw7j//vsBqK+v3+02/S3NpuyfSDqB33KRcaK4tQMJ1DVEtPcfoBIzREO6ELgE3WV0pLcOSF0VZSC40xpafR7zQyXg0sCvYV5YzPhsEU4/kaAvnSNY34E71UJ4du8bOc0N0XCyzz4bYh1MD1TzzWln4Bp3Ms6Nf8S+4bdEPv4z/hZ7lkQuAsCWruWsjbyHTBUemyteY1TdGXS+dzfTM110NfbNw5FKSAxflgWX6hx1lsPE8jJG+XsPLT+6bBLPd7zFz4vup+mKjWgfTpC6NsInWx/gW+8+zbfeeZ6jSmYz0lcOQGO6g83p5j6vZdtyt0MpNdnGpuX3dz+Odq4hN+7IPtuJicfhSbXiszrRBLgd0N5uIv/PNeSf2kD+pc0Yk8sgke8/74ghSK12MJNqMfWhwmOEuCJ+Fu33Q+xtSeczEvuvQY41jsITA3NZ7x5iEg5m674lbRsotm0zYmRPMtjO9IsYu8RE3rGCprZHtz8S+MOj+xxHuPpZ/cENaBoi1Xc5U5HQkU7vWyBvogj9P5GegryD67E4rpyXoKsMt7f3/OJJEy/DPIyjDxRI5wThaX3PefE4KNHT1CyAmtPAfyI0/t4ischPYpGf5Gp3d46enQkB8qGtTJx88/bHGpru6bVNzYTz8QZG9fr8DdNPceVMtq17FF0vfI/S8U1kU4V7qkR0M+HSyeimj6PHfZeKh0sIPpjH/8cEo9tOxu+qJpvc/X0zgLT6uaZLEHkVpCvKB8VB96jfd999XHnlldxyyy39LsEGPcG8cuCi6Th+oRHDQce99x12If0CLe4UWv63/60qc4VZlywkkQu7K2hNbtr9ARTlMJOaRJ/jx3qgpxfEWZ6m8pOl6B6bnTuog8k4/P4RiCeRQPEZZ2KcNpHYexJ3lSB8os5Kb7rPa5xVMxmPoyNlIVHak2mN362KkLFtPjT2vzmzegNPr/gmAFvi6zk6VI5sS0L7VkbG56BPvYZUbAXFFfPoat3lpt8vkBIatG3cuOEnjPJX8L3pV/BU49u8E6tnXtkR5KTDfRueAWBRdCWfmHQ596xaCoCG4EtjT+QCz2Q+OXIuMufQ7IuxjpaDOq9WNkqzJ0ftzLNgydMgNLRjLwMrjfbLK8AwCZxyE6LqTOznd7omZG3yz27EuHg2zloLUW4g23pGLogFRXQukRQVAX2XOFYGgZ5x0/p8IfDWgwInK7HjEtFk4C63CncAuww+cRmCvr+UQ1hHXaco62bS0V+iyB6Nx1ONfiZ0rRKkmyAwGoqnQmX+ZiZVfZyEv55EphGfpmHbPReBfE0Gza9BsqdMngVZMviP9mO/3ntpRu1EHzm7d+O1iDt9O8djWbSsjqe9hXkTP4U0xiMzEi2URVv8B2TN6WB4d91LOUTcOR391QgjzgzRuRykDSWzNNxdKZxnIxiAONJLZ3uo19+IruWSwJlFOP/s6i4TpToym0BUh6jyH0l41jEkXZuZe8rvsGNRXFoRmu6CUi96MshxU35HzhWhw34XO59i/bu/ZcLMG/EndcgKUu5adqwZ59g5EtHNzJz1Tbx/TyGz2xvAJOgvdDD6IxcWplvtgVOmIYp16OppPBPj3VhFGjvyryiK8v520IH6mDFjOPLII7Ftm1mzZnWXSykRQvDOO+8c7EsoQCSboETXcGvB3WbR3yNdIE1RuIkJFAKKMleYlzveQ0pJkbuazZF3B7jWinLg8n6BvmyXIdoS9JVZgmN92NtvqXVNIN5agYz39JiLZ54mfKmD+78mY2sOWZmnThTxxakn8+s1r5POW5w1ciJzil2si71ITXAaG1M+fvJezxDc+9Zu5RNTJ1ERGEVrYgtTS49GWsu7nzdff5ixF32V5JS5VI118epjebLbI5xJs3S8wcIswo2pRs6uPpLpviJWdKzi+PKpfKR2Lk22xfVv/KzX2zNFoTdaQ/DQxCspX2tAOk3u+Sg4UBrUqL5pEukyDaI6aOCE8uR2jbZ24ogyxk6/htWLf95dtmHrvyk7/efIuefj4BBsaUI+8v3Ck7k88qm70C6c3+dYsiuD48shp3jRywyI2siojRzjpWOjjtUpkUN35a8PHMMWeKqhZKyD1pKBgE7G5QJbIjxJOMuHfLznNybHmZhFKdIcvlERjuOQKT2Jka9L5H/igI0+L0VZQMMucTDLTPJ/jyObLDSgaO5EvGeO7xNk58PguzYEaxxk0kFMdJEbkcDBS75Mw/WxUvIvxMGWGCcGsWvc7NpKoZcEyNeEcI4ai+Po6FYabX0DTsCLJqdgvuXCNsJIU0NblcU88jPEXWF2M6tFOQTMTgfnzRTmOymqpvrAFMgHUmhnhbs/ho7jNewlO/VKAPkuSdztoei6UpzlSUSRBt504SuQH4G4I4IPgX/MJIwFIeS2MvIvxcBy0OeAdowkty2IK+OjvMpkceMXqKk7h9rIuVj/1wxxB88Rfhac8zAvLrwYgKaNzzKu6iPIrt7L34q6IopcE5BvCkTYxh5nkvT3/RKl3A7Bm8vhuThyfRYxw4s8wU9WG5wgvbD0oYbjOGp6k6IcJgcdqN999900Nzdzww03cM899wxEnZR+RK0EhuHGrQUP+BjSL9CiDs72QN2nu9GFRkcuRpGnko50YekaIdQCocrgc6QsNC7tyhC9bhJcMoNWbiBnj8dZuhl2zE/f1Ig9eWJ3r5spdU4vmcAx82qJ5Vt5btWP+fOiJQCU+mo5bvJ3+rzUW20Rrp35Xbalu2jUDOJXf5tALo0Z24Tl8xMO1aHJBAF/gPmXmnRFbCzNIumN0aH7KXG8TPUE8KQ2UOSfQtzuIJPKsTRXRm1oKp+cdBH3rv0Xeekw0lfG1OIafMYSTikdT8UaA2EKrGcjPRWKO1h/6YCLysl0ShBghHQ8NRoZdw40SYIUhjDwOu7ueevFNacyw1NC85YXqaw6jqLyGTzX8ggvbfwbR1ecwoff3NLnvQsj3reswo8dcpFwSVwjTLRWL+3P2mT+LUFKhA6uWsj12VMZFO4UlRNc2H9p7y7ylBoYN5YRdxcTm91JcW0Yc4uDU6nTXp0mHDi8Uxc0TVC8Ucd5saen0341jnFeEaI1i2M7yKaeuefOGyncU0qxJvderspsLyJ/dwcENIRHQy5MoJ0RQDtNYG60sf7aiT7NC7rAerQL44IixByj17XExkVq9mxanyuMPtM8AWourcLMJpGpUhJ+L22vO8gc+Eb5KU97ceez5PdxSUTl4HXfnuRBLtmpkWmkSfRMFy21Of6360U+Nf1kPCt73y+ZxQL7vRT6FIf8k6tAgL5gNs57PaO25KYczoQcztup7uSD9uIkTtCgaYsHx9KpLK9lzjG/xpMKYN3Z0b2vsyqDbnqZfc5dbN7wNzxV04kHPbgqfYiW7XX1GOjTxmP9eqfra7GO/zNl/Qbr8ZCN8aEAhhXAMiW6pWG26wgX2CEb+zD1rGdoZ2PXm2zoeIcJ5ccyJjwbtyw5LK+tKB9kBx2ox2IxfD4fv/vd73C7939ItrJvok4SzeCAEsntIP0aImpDTeFjF0JQ7SllU7qJo4sm49L9RDLNFHtH7OVIinLo5W0H7ZRgr4y36MBRPhyncEMTzDTB87/EXv0Komw0xnnXk39mPSTTMGlMr6GxUAha/ZrNfe99lfad5gd2pOox7Hp2viTqQvCh8R4+u/hXZLYnqZpWNJqL6o5H+Pw8v+y7hDxlnDj2w5QGpvBiR5r2bBxbSt5o2coZIyfx8bFH0rTh34ydcAEtqQYERTTmbHwySdKKUGKO5cczPkudz0Pa0PjOkj/x1SMvYlKuHOfFNNrkvkNqZaOFE5O0P1Ook6dOo+x0k4gvxt+3vsjj2xZS6g7y+SMuY7p3PJqj4cgwwaIFVEVPxn40CgLmnngO20Jracu2kCuvwWztnZHe1lrRPjwV5+FVYDkQdqN9ZCpRV+FOOYeDUWoTPt5AvJFHDwpC83WsUE4l1xoiXFkv+cfbexd25BFteVIleTqMDN9L/odMqcDKWnzSnM0IytjPBUAPitttwLvRPuXOpiz6FB/2wn4ajOottClGd+JDIUButQr9pwkHmdj+u1+UxnN6AGdpAnIS+52ewM55K4V3XgmpXE+vupX20PpsT+DjZKDpCYeaq9zYlkHrSz3PpbZIOr0a5ePcoKkv/OGSKEninWDCup7GG22Wl7f9LTQdneL+DW/TkIyyrGIjF310Fl0LCw2axXMErkgGrdxN/rE4+jFTkJOC2K/0zV3irM4gRpjIjp1GXCxL4p/mJbJE0vi0Q9nHwnhb+w4fcpalCV84ndc9v2Wct5wvLX2B319yNmV/2QDRLNpRNeRf6j0Ngy6bfEOaTeM6qDHLcDm9G37y0iFvgDvhpuW+fHeSxfCxOt7jBXnj0A5jcrQUDy//Aes73gLg3cZnOXLEaVww4csIR933K8qhdNCB+ty5c7uHYu+6rI8QglWrVvW3m7KfYjKFNAQuceCJ+aRfoO2SKKjSVcyGRANHF02mxDuCluR6FagrQ0ayGvxfqEAuToEh4GgfyVIHBDRYKRY3RTGPuIL5My9hTNtKZLYe/byjcFptsiOq+j2mjUUy19WnXCNDuaeUtkwhU+/FYyv5w/onuoN0gOWRzVw55mTKZDFfn/MbDMKknEbyrjJmlsXZlsrhSIdzak/kkc3ryOaTVNSdwJLmV6kuP5W7Vy2nKRXDEBo3TCrn3GAZZfEOZEMjb9eOpiXTSUMqwgnWKERlGuHqJ/lRtUmqqedxZqtDeqvNO+61PLL1FQCa01185Z17+fWxX2CUVlgCyLtNkv91T9b74k0+Lr7u0/yk5Tqap3+B2vXvFjLeA5SNwq4cR9pTgnfUsbhtyHh0EruMcMjrNoyyCY/RkTjknLwK0ocSW0C6by+dzIEjU7zY1sjFo44m4GTJCRfL4xHKXV4wDrxBeH85jkSvccHqXeaQ17pwpIM20YP9xi7B1Aizu7FuB7uob3ZcWaVjGzbuqn6SyY0wwZC9hn9YmT6bke+SZKWO0zeeI7FeEkTNFz6cYlon9uVFhFf5kWiIsI7YkmHi6x4emLCM6SXVnFozgQuMKRh3NFIxupAYTv46gzi/qDAiK+GQfzKN4/EjqjwIen+42mRPoUd9Z1Uucjvak2wQaUHe2zdAFmUGnXqOSTXn0mn7iVoZXnK1M+OGEUSa26gucjFyUd/fZGcixs1v3sk5Ncdy3ajz8MneCe0MdCLP2b1WQogusvFNNqHq0AbqXbn67iB9hyWNz3HymKsJa6MP6WsrygfdQQfqq1evHoh6KHtgS5sUWSzDwSX8B3wc6dPQYlavhHI13nKeaFlYWOLKU0NjYi2Ty07c43EU5XCxkcQqQb/Qj5Sy++Z8Sz7DZ19fgi0l84p9nJHYhP3KnyGXRptwLJz5ObJm35tzADdhjht9Oc+t+113mSZ0QtkU35o+hc05NzkHZpSZPPFm78Rt80vGMaJtBSXOTDJLxmI1e/BPrULUvUeLu4X/3fAcAB7dxXeOuo6c3c5v3/wSk6tO5cn1a2lKxYBCD8mvVr/JrJmnEvznD3n3nBvZkOlkQrCWmmwR4k9d6FeXkn8phnFqiPwLscLvNqChXVxK9MHeN3qZrZIp42t4YMatbE42c3/bCyyJbWRTsolRwWoMQ4NXYn3ORfF7RYyYPIF763/DTR/5IuFYkqC3inzpeIT0EoxGwetGG1lCqrOfJd22yzsqUBmKbF1DOzGE8/xOPdamwCkxyeQtTg2HSa7+NbHG1/AVj2P+tJvJ0f/v5lDRNEF2po75to6MFL5HolhH+DXkm0n0uQGcjVnk9t5L/Sgfzsg8mubpHjEjJdSXd1JX58KICIRXQ3blqT81gwcXNVO9OG8YPT2kQQ1trrfPSglGcZZdb4vMEnB8GYxg3+HtripBxp1Ds1Xm98PFr5fjyuukdA+dSyXChNIZAXyvZ7lg2iT+p/5pdKFxpW9SoRGm1YIiHX2WH9mYQ8x0Y5wUwl6cwCjRSbRr+I/wIlcVRm6JWhfaDB/Oyp1abbwC+8gQqSd3CpJdDbQazYyZMgG5Y1sd+FCYj6/6LsWuADeOP5cqT4BzDZ2vNDzO253rqY6Xcu+JNxH+905vSodtxV3IZskTDW9wWtVsjnCN6fW+tbxGelPf62y+S0L/bdIDxulnybpCubruK8qhNmDrqDuOw+9//3vWrVvHrbfeyv3338/111+Prqs/YAcrnk/hd9xkjE6KtLEHfiCXQGpASoK/EKkXmQFMzaAh006Jp4atsWUDU2lFGUA7bsiFACnaeWBNG/b2ETzXhrKUPNSTKM1ZtwgRuh9jwRfJy7490o4Dc6ovwG24eGXTgxR5qzhr/Mdoff0u0vG7CLrDGKaPWPE4Tqo6isfqF3bve0FRHd6oi+TTR2CnACTRV3V8k2Zy0qmruHP7dhk7x39a3uNIvZO8Y1EanMyq5vY+dWnsasZ90hV8YfOTHFU6gdGBKspNH0TzWA91Yl5RikzYuKZUQl5CQCPaYiDzvdeL9k/QqF5fgb00Re2YsZwwchLPB5eh6TpCCFplJ1Ve0ScEs30OOTvDyPBk1spWxo2aCfpkQp2daNs2Yi9cAlKSP+4owmNGEgsEVRKhYSTvspGj/RhnaIj34lBi4hwdIu/WcesmTUvuItFeSJCY7FxL5vWvMOH0e4DDN/dUSnjavZhzbpqLq6XwG8ctyN3XDkmJ7U9hfqgEGc2DSyCKM9jtL+OEz+h1nG16A4FLxlOx1o2MSbLTPDyvvczl8nhS8efwX38GstkGRyKqDdKd/0aOPKfXMTQ2U3HOZFqfzoMNuh+qL9TJ57bi8voITB5JYvX2a5ELKk+zsfOdZEX54TpdH3jCcSO7XDQ/39OL3NgoGXlWMYZdmCYhpQSvwLy0BBnJIzMOWp0be3MGx+sQnxkn5AliP9qB+9xyuqrCBKaGAIleYyK3pTEvLgKrkBzTDprUP9ATlJbM03ClgzyV+Tvhi8ZQckIZWlaSL4UfpR6mIxujIxsjYkX5RtUo/P/8IWPmn8LbQFOmgwdHLuaiC2dT9oZBvgS2nJTiB13/6D5+Zy7GrvkcHcPBN14jsaJ30GyUCHr/NRh4xe5aRoYnsy3a0zE3oexowmaNSqSoKIfYgAXqP/rRj+js7GTZsmVIKXnllVdoa2vj1ltvHaiX+MCKWkkCeQ9pPUrlQcxRB5ABDS1i4/h7PvpR3irei65nQclk3mx8qDtjv6IMFUJkSWbW0961El03Oa50Cq80F+aRl8WawPT0GrcqV7yE+4Qbyeuhfo/nksWcdcSNzKw4H02YWNHVbIk3AoXlywx3EL1iHpfUHUtXNs5rrSvwGx5GCA13fhyJXTqXU2t0yk/s3a3RnolieQq3UNHUZkYHatmciPTaptzOsdXUyUuHxe1rKHIFcSol6fkevK9kyf2kGdet1divJnBWZ5DNFt4LS/GNd5FaX7hD8k/S8NlZrL8XEiI5qwrzK08+8QjiegM5X5wvvv0rvjfraia85e4ZpWuAMT/EBYHP4DgOYU85Rdo4PJksWkML+cdf7q6n/dhLGOeciH98HQl/IUGTLgSehAZ5iRWEnKbu2IYay7ZpfQKKZnoInuvGtgWtrzsUn+KgBzu7g3RNd+PYOex8BplqBFfdYatjJpPjdDkL8fNOLJ3uqRPmWUVYj0fQj/CS+1Vrzw6GwLxxGpomuueoAxyXn4T+mxykC9M33K/Cx647AS2fxeU6ntzPWiGzfXsDvJ9cQGaXIeue3Ci051upmx/GEQI9k0c8msR77Qi0jEmpHafoVB+OLfj/7L13gCVVmf/9OZVuDp3zzHRP6MmZSYQBhoEhR5EcFAXURXFlVfwZd1dd1+wiK4uKAVFQQHKUOAwzTM49ubunc7i3b763wnn/uE339PQQlGHE1/v5q7vuqapTVadOneec5/k+mmOjbc4glutk388bVGAEuqISWT16JTfRo9DU2E+NJ8TttSfjkW6cHUns1UlwwCaB/qFizF9G8J/jx/5z3stE/VMX4ZPCOOVuVCmR93VhDXpvKOMNtDPDOPf1UHdxGbkuG1VI1M1ReDzD8nMup73iIIG9LrQXkghVcOPyUzEqVf7Ut5K0YzM9l0R27eE87VKe0twkrAz39DzLS4HN/OCWm3is83V+0foMhyYGrD4kX/ubWNiETnWR7XIwewEB4RNUZOn7v6qtOX4um/UNNnc+w66e1UyrPImpZaegOO533rlAgQLviaNmqK9atYqHHnqIiy66iEAgwC9+8QvOP//8o3X4f2oGzCT+nIuMZwBDee+GuogOC8oBTPTX8mTn66woX4AQCpFMG8We2vda7QIFjgqKIujsfZlX1v4HuuZlas0VTPGm+e7MyfRlO0jbvbSv+BAhJYzvlQehvw0qxmNqnneMl1ad/Pvk9tdTVDGXSNd6guPOZGfxefzuQAx7326+tfh8rh5/BoaiUpLsxOw/QlyiCt5MGR8vP5O7uvM+jfOKG2j06Ly6/362tj3B1dP/gx80pUmY+WH9VfWzmBBtY4dveOlkd6yV72z/DV84/nIai4pRN2dxtmawXzxEUOvPfVRcWIxzipeMjOK3A1jf6xlRH9luomsqRQdttpdG6Ej3cbvzK7728SuoOZCfvHBN90OJizHKAiBv9DgOqI6Fs+/gqGt0djejVpSAL4BuK3jWZrEfGQBLYkx2YVxaRMJXcIX8IKEJSc3pCtqLfcincmiGoOaMIkyviktVqK5ewfiaj6GYYdAydAw8hts4tjnBhRC4tjho54TzrukCRJGGrUjUK0uwXk+M3MGSyM4QzriRE0OuVg0rfVic+xNZ1Ckl2FtTw0Y6gAX2ShN1opdDU7TJXgEdJuLPvbzpCygBJVaE7DZhdQJ1dWLoN9sQ6CdWgLvQ7o8ZCiiB0R27WiwYHy7lnp5JGD9J4OS6EWMN9JvKybU5KK1p7P0Z9OvLsR+JDn8aEg480Y/rutJ8Ro3c8LGdvbm8MGGniX4wDX8eqW3ifU5j4rjJyOeiYAi0FSFKJHwmdjanVM3EE/ChxvcjgfGP/pi7l3+U3V4PiqpSVzqZiu3NLAuU8aq/hp2Jg7hVnVtqV9Cglh3x05X1Zim9VoeYyKu++y2sY7Sk7ZUVLKm6huNrrkI6yqiwkQIFCrw/HDVDXdM0FGVYysUwDDTtqB3+n5qYlcSX08l5ku8pRh0GV9TbRhoaxXqAIiPA2uhOyn0NtMa2Fgz1Ah8YbBlhzeYf43aFOXXs/5B9ehxWVFJRDONPT7Jx28+wzARCqBy37LOENq1COfUGcvLdx9o6BJm88Cuk43vYo47h3g37yNoOCyuCbIxuwZZZJJKAlJxRWoVnrCDdPDxQKT5Jg9UDXKLOZePEfTQW11Ad301/JMmNi3/Cy/vuo63rUX5y3CeIZLK4dS+lqkFA15gQ7+JD5Qs4SyyhdqsfKSSy3EPn4oPsG7eepevPG1Vf2ZTCNVMlaXVC5i0MK00gpUnAKMKt6nRmI9zUcQdFgQASyXdLbqZWVgyusEtQJbawcVwulOAR+hmfB0wLIcDTaWE/GB2uz84syosJ1HO8QyEJBf7+6FKFDVFky6BiWk4iH+3H8y8V2FqQxtr/R+dTkly3RPXrVCy7EqH1HdMVYiEUtFI35m96821RBXlJGbGYTnqrg7++CF9DFvHYISm0jhBHbx3BcBBm3jNXiR9BUC/uwGGhMSJwhNSkQRXpEeA+grBjWM0LKhY4ZjhC4l9sktqpD7ldKy7Q/CpL+mpx/jCsKyKbc1gvxenzhLEtjcrlKtYfe1CKjjA21cQII30I++37M61LwQT0s8KYzw/AYFubpQZRby3HLtdRSsdCbzN1j/6IOkXFue57ZJNpZNjHmPue4cfzZtBVuxiPCVUrezAvUciIwbzlIgO4sJ18+zM181hGpozAcSQ4goJiaIECx46jZklPmjSJe++9F9u22bdvH/fccw+TJ08+Wof/p2Ygk8Br2ejC+7Y5zqW0yWV2IWUGwzUe5Qhuv9In8oMWhxESuceFJ/N09xrOK67jQHQDsypWvA9XUqDAu0cIiWl3YDkxcmaSRQ1fIP3nsTiDK2NmP8QeG8u4ZR9lT9OPqK48HU90PrLnRJynIXiyQrxEjspG8VaYSoAWXxmb+jZx/TQ/db5aejIJNkc7aEv1sjWyH4DxE27guBNVUpMEVkyiFwnSzQ7uej+eBxJ87YQLeb7pVvbHW5i74Duo+lQum3wrLdKm10wyIJMUC0iINF0VFfi9IW5NjMX+QT/IwWjDlXFct1bzk30f55Tpl2K/Oryirkw1UGdlyd2xDb/pwMkutA8VYz0QzytZDxo7BByoKaMml+VzjRfyH9vvByCSi3Pd+BVUKiUgwe/k2GV2ck/rC7Ske7iwejGnLZlMeN12yA3WR9dQxlaTKw2Rja3Hf3B03+5sSGGc7iOtFwZxHxSEJXG2pkf/ELHwlhRz8EmH3KAzhp2A9kcdxny0BIrfXxXpQ1FVgb0zPRyScWqY9jcUrEFhuWynJNNgUDrbCxvz2R9EZV574dB32xkLisahC+SIMzwklDjBOV5YM1LZW13sJW7lGFJWBcyKNOoJfuxXB1fxVdAvLSITHEDUeFCrdWT74DshQD0/RNSXRuSOrRfCPzMx26LfvZeaj1SjtBbjZED1CHqeylE3b3S7lTvS+M8P0/WSpOdlhzK/hjLZg709PWyYBxSwJcp0z8j3xZNvG+piP9LJa4SQGJ70Uc72Y6VM8CjIlDNkpANgg/PUAL2XdKNeeiu+vm4U3UuypIxdzU/R0v4qE8acxsQbjsd/1ys0vJHfTVy6gqyioTg9dB94mlS6G3/5NEqLZ+KIqiPeE0WAJ5VCiSeQfi8pn583560Mx8I1EIeciV0UJGUU3NULFPhH4qgZ6l/60pf45je/SV9fH5dffjknnHBCIT79KBFLJXArFrpy5HhbAClN4pE/gbRQFD+ZxGu4/Sfh9s4YWVAVSI9ADDjIomFLvcwVZmZoAs/176TWaWH8wD5idoqIGccQGtODDVS5S96vSyxQYARCSPoGVvLSmq9RWjSJ+rpT8Fg12IelT7IT4JH1GO4w4+XVqPcqOG+uB67vxn9bOfHQ2xuOlhKnP9tC3E6ysr+F+5tX4yCZGhrLnJIJjPVX8HTbG0PlDdUgutEhtsNB9YCdBCQ4pkJ5gwtJCo+ngqoJN3J3i4HRsZsLxgsOJLq4a9dj2IMKujc1noc3uZETxl1E+XbgjCLEziTyQD4Pub5a8NkLXmQgmSZ0RQnmoxGwJNoiBfO+4bSX9jN70a70on+8DGdjCgyBMt9DX3gvpakkzov3cVo6woQlF9ERKKI4UEGV5sMQkkAizv6+A9zc8TuyTt4A+cGuhxgYt5yPfepyZGc3tpAYvgBOMECCTnr2v0jAW4F2mNqRqNNwDKew2PIBQmgganTkwZFyU8IHZkYj13PYarADVlQc0xU7TVMgNWzgWEEDKzqyTHKfpOTcAJpLoIxzYQ7kgGHjWAiIBA8iP65TtCqIOqAysDBOriFCsawlWRPDd00R9nMJsCTKKX7SDWmQAQ5tsN16B6kZ+xk7YwlKQsEqyrHB/gPjxWls93RTe0URld1FiIwkWyFZFdrDRKeKgpl+7NBRuL/d5otjbRwPdL9o4Qx2+Y6hjErRR5VOpj//jJN7HUpP8GA+HkE/O4y0JSKczzZg3tuHtjyECKo429NQo7PnlDhlIkVYUVBcAuO6UuymDE5HDmehwfbyJkiGmbIpiDxCGkQiDnv2PMKujieYM+0jRPtbOLD5L9RWLWLapEtZs+kOrAlZ5l+0HGVbM7HTF9Hrc6GQwWxbhRkI8WpyLZ17X2Z6bBEnNVyJW9aNOIWiCPytB5H3PZH3eFIVAhefTmJCPXouh/7cStiYF4FT/V6C118IZYGj/FQKFCjwfnHUDHW/3883v/nNo3W4AocQzcQxFBPjbYTkUvFXEKi4/EsQQqDbk0gnXkXKHB7fvBFlpV9BROwRhjrAzGADIdVHS/c+frb3VxhGGX7Ni+lY3NX8KMcXz+CT9ReiK4WQhgLvL5bTxctvfAPHMenu20Zl2Ry8riDxw7zuhAqOFqO26hz0J93IQ8WhTEl2f4TsHBuXPLLlkRbd/G7D/6Mt1gRAqX8cn5x4GT/Z/Tz7Eu2cVbeIgdzIlbj7+1/m855JYOcnCt5ENSTOKV6yOTc1pV/lrrZmtg7EuKLRz+7YQbb27Oe/qj6JN+0l7o1zT9vjfHrqefg6q+jYbpDrkwQaXRTNNuHhPjAlLzSp1BSFmDlVEm5QUFUNZ+WekRchgKwb8zfDcer2qgTyBoukPIBr9yp0YOJjd1By7g28uutnrI3upqZyIYvHXM8eJTZkpL/JIx1rOL1iPCudP5LM9XN8yXlUxzVMmaaofDrtbX+hbvIKxJsiwF4F50xwlBTY3nfxhAscCxRbolxUjPm/3UOrh8p8H4RVFEOieMA5bMFd9R9bIVEhILZY4t88+P8RCwG2xGnOYW9Ikfy0AocIWQkBzfGd/KHle9RPm4FPC7Mr8gaV++r5+HHfYUff4zQPPMmSSz+LInReb/kxRR3TWTztM6TSwx1KNN7Cql3fGD5vXl+Sstp59GQTfGbPT/GoLnyai97WGKXdQe5a8Omjfk8KvDWGA9eMr8V1IEjWrVB8Uj4MIvKaSbxXJTTbBxsH+2yXQJ5azMBT+WeslwARE+IO5kMRENB8i0N1fxA9J7EejyIqdZT5XlbPbOWz++5CV1TOm7qY2d4GiorH4lSqSKFhmLt4NbqHP7at5d/Ou4RlzgyUV0bWNb0wyYG+/MYN237J/Jk3sr/1OVrbX8M004ypPp6m/Y8zZfmlDEwZR1PfGrZtfQBVMThh/GW8tPseWqLbAHj94JPEczEunvI1cIYnST2pFPIPT4E56E1gOzh/egbPJ69EicaQGw9JoZxIwbOvIa97d/pRUslikcFFCKegFVqgwN+Fo2Zx7du3j1/84hf09fWNcEf73//936N1in9aotkEATWLoRw5Pt22IpiZHbjUFWApoEsUNYAnsJR0/EWEMEasrMuAQOmzcRpGx/mN9VXgBKcx2eVnSuWw+3vazvJA+4t8a/dv+fKkawuq8AXeVzLZfmx7OFJ2887fEqvsYPYZXyHy1PAEU8mZJvsHniPoawR1dJtM23E2dr7IosqrjphWrKn31SEjHaA3cYCGTDNl7jA9mSgV7iJ60lEmBGvYE2sD4JWerdw+00SsVxlKI6tAeIGG8ucIZXuzlKnwn6eO42dVXRhqjlItzKeSV8AjeSM2KCq4/ayPEMzo9PxKH4qDjG2VOI06JZPcMMPNhLoMdz6nc8V8nQnlbvZFHufU0pFu52JcEfaGwxL02ODbWYQ5WeIa3JQ88SKe3HUniqJTXjqDnr5tPJ/6DkUTPjnqvlxfv5i7Vt/C9KqlVAbGc/+27+PWfZw94XqKLYPm9vsxZw1QseAkFEsn6WpGMQL4mYXXGiCrB7DlW4fpFDhGCI0UOs75FeimjdQFSanhRUGKKJUrQrQ/bA1NfhUtUkFNcRSHBu9cRSG4T1vJOdfMpfJFA01YeMYaIzQgwgtUZCxJolHj4DSdTKCHyVoJlpUvIyWk7PyMw/7ocIrRaLYHRXETHdjDQKKFJ7d8Zug3VTXgsEwFqjraLVhT3SiKCynzqR5MxyQ1aBOZjo1DIQXtsUTTFOoyXno2KiT25LUXhAHlZxp0PZojeGUYo8FA+hSyhk7HMxLs/KRu6Bybnu445TU6lk/StjTLf/b9gRumXkVt0Ev5dgenVqFnapJ/2/NzJJKcY/HHnlfYFNjP2OQKXmnv519mNdKTDNJjZanzlfPNjj/wqH81t197CRVPaygZSfakLNu8D5EbeDNsSeIcMiHa2bOBudNvoC+yC4GLloH1PLn1K0O/7+9fxwXTPzNkqANs617JisZefFQPbROpNGRzI2+S7SASSYjGRt0/2dyOTL+9CoUQkm5zO4/t+Al9qXYW1J3LgpqLcMuCV2WBAseao/Y1/tznPse8efNYvnx5wYg7ygxYSYJqBv0thOQyyfWI3ATsrBvTFrirHYQhURQvHv+JpOMvoQgdw5Mf4MugitqczQ/OjvCoSv1T2N39BJMrzh+KifeoLq6oOY3/a36UP3e+ygVVJ75fl1ugAG5XKZrmwbKGl/sO9qykaPbzBK8cR3m6End6D8reB5mx/FpsVxDNNjB/c0iMokfQUdzMygMPMK/qPDQ5OnQkbFdzY+UvEFKhy7WDxw7+kL5YE6dXnMWswBJc6SKWVZazonwGlTEXSsQi5VVIlCSpurGETJNEOhLXJIm2cQBn7+AAyIbAs2kuvraSg74EY9Ju+MshK80SlKcDBK7RiNsj4yoTuySl15Xg2DlaEn0E3WV0JyRhr2BDMsZptQInZMDA4ODMpSIPG6cBKFLDkxhe8k94DU5c8AVSqT5yZpwZky5jIN5CUPdT5S6iIzOsaFylCTRFJ+Qu5fndvwYgnu3jno1f56Mzv8zkBbew5dX/pJk/AlA36QIa7aXIe29D9B/EN+tM7OMuJWmMTjNU4NhhSo3eZ01yXZJhURKbqjKVWKkgEEtQd3UAKwWqG5SDSSJeZ2hy51iQTufw+kJc3fEDli6fyZfKP0R5IktmnItsBDylYPQnGTjO4ZM7dtN+IM0tgXomHeJvLiVUBqYBEPZU4NZ8dMUPsLDufNKOh4rKJbS0Pj/ivJW1J4MzMqjd46ukvHQm3b2bh7bNmPYRVDVImcvg1vHLqHASSDOB7a2iXylBoeBBcixJOxHcvcUk9gxP5MgcDKyzqLrMINNhobwYQ/bbqNeU4D6nDdX04w75MC2dvaVx/nTeTg5me1jZu5Vz607hzuZWOpIZ6qf6uWh8CRWeFC5Fx3SG28ZZNcv4+dYoDvB6Ry8X1gjmxS36iOJtWMwLiSjX9P6Qb1z/MeqNItZt+yq9HcOTwJrmGbGIZeh+LCvNgpmfwNSCbDr44KhrbY3uoMw/hp5ES34f1Y0q9BFeZdLnRbhdkDnE+FZVZMB/RO8UMbkB4fNA7gjaFYMM2C387PVbcGT++l/c+1tyVorl425BOoXxfYECx5KjZqibpsmXvvSlv3q/Rx99lDvvvBPTNLnuuuu48sorj1ju85//PAsXLuSiiy4C4OGHH+a73/0uJSX5Gb6TTz6ZW2+99W+/gA8wA3aSWiWJS4x235XSIpfZiZZbjl6cxk4Z5HpVXNWDbo5qAHfgRFLxF3BkDrd3JtIQSEXkRVGOoHLrNypQFYPO2CaqQnOGtmuKysXVS/nZgUdYWjKbIqMQ51Tg/UFTyjll0X/w4utfwbSS6JqXExb8P3RRRFFzN37rIIR05OmfJOYpR0qwpwrEzcDaNGbIpmPiQe7r/CZFnmqUQ1SihYA22yS6N0bRY4sxe/IDj9rABK48t4qD7t2kUmfyi5ccwOJTC03mJATyN9vAkQQA/2ljaF+sUbTAg6IIjKzAeS016jrGRlUUn4dwzE/0sBV9aYEwR79/qgcUIXEeHOCEC4Ks1gRuLV/Hi9012Pddj3b8J8AzFVtzISvdWFEbddshBxGgHRdAcyZhKSo4Nkb1VF5c8zXSmf7B+6Bw4nG3U2IJfuQ6nf1jBIbbIah7cOwEk8oWsKXjpVH1a023IovnM/2072HH29BdQUJqKfziM4jBSQf5+v2o2STass9iycKK498LR+QV3Q/HzkgGpAJFHpK/MYcG/r7FbnqVJHWj9ng/UXCJGpaUz+LZznV8Xf0w1lN9uH0K3joDZ1UGshCaWUp7Mm9cpG1lhNEjBMTNyVy7+GFWdUXpzVqc3xhCy3lRFEi5G5nUeAV7dj+AlA5jx50NwfmM0mu3M9RUHEdd1SKy2QG8nlJQdJAOxcKmdc+97E20DhWfv+A/cKQNoyOjC7xPWE4GOzW6TWe7HbIHHVRH5uPFXQJlTQLflZXEXzKIrc/3oVXKeMZeVsN/K7/m6vEX0Jcspjme7xObojH8SiV/XvuvfGbiDfQ6HnqyGWq9DTzXkiU76P99armgedPtpNOD4UZtL3DylGvx1SzmjUSWzfRzzrSP8sam/2Eg3oLfW8m86R9j9ab/Garv/Jk3URyYgNs1nm4riqGOXohx634se3gV/qwpn8DDyNRtKbeH4OVn4dz3OGRyYOgol5xB3OtFc7nwLFuE88JqcCTUlCOXHod4h4xMPakDQ0b6m7ze8ggnjbsKg8KqeoECx5KjZqhXV1fT2tpKXd27/8R3dXXxgx/8gAcffBDDMLjssstYuHAhEyZMGFHmq1/9KqtWrWLhwoVD27ds2cIXvvAFzjnnnKN1CR9Y4jKFrSaOmEPdzO5HmGHC+jZcA63Yio/+3Ck4GQPFne/OVTWUd4NPvIZttuMJnIwMqSh9Ds4RDHUhBLXhhWzv/BPlgemoyrCRU+4qYlZoAn9oe56b6i943665wD83UkLYfxxnn/orstl+3EYxobgBd/wOGBaIFnNN1BVlWAhyiiQzLsXjuR/RFttFtLULgcI5cz+J4gwvv7VYOf7fG5v4b30+Ss/w6oAdl5TunUFwaT1fezw/IKsKwFS/g/zNAVgwBru4CMU2EWv3UTm1lG3BBC+09zHR4+PMehdsHLlK0V+UQgiD/UqEYqNsxMq3GhDIoIOnDtLDY3/KjldQWjLYLTmKd7jwe6EsIFAVGz3aBmYW+5UfDJUXgLjlQcSngoiXc6CDWOonWa4gxXwCH7sb0d9KT/zAkJGev8cOTfseoWTObRR7Aqzu/j2rtv8ZgPOnfxpFKARcxXQnmkdck657WXXgfraJOnbFOzAdm7sDs6g9zDNAbnwS1wnXYxVW1f9u6GovvsnFJHcclnO8KEu16aX38ZHif8lVDnUzfOA6dqrvtm0T1gMkUtP52IS5KLYCpxeR8Q+uqE8HQ7WQLhe3j53GQ5FWpoRD5Fv+sOu732dy62u7SFn53uHp1h6+Mm8ymCZJ28Xr9imcefwyBJJXehXGZFxMzo28L1amnw3bfo4QKrrmIWcmUBSNc06bB6kW4ocY6QBN237KmUtnYx8hXVyB9wePUoIeysJhfh++CSrZbofSCgtOKSJtGGSTAk+HipZzGPpqOOA87uHGq67h5VgPTzYP929jAz6iOYdzGm/h91u+znG1Z3PhmE+xt9XiipDgJb2LF3q6qVG76Uj3jDh/8+77mTHnf/jPzd383+J6Xn3tW4ypWcL4MctJZyN09W3luNm3kcllCQfHEHKPRQ6mEDXNfhbXX8zOrleQgznnNMVgRvVyPN56Mtk+asKNjPNNRx4WKy6lJFZVhfcTVyASSaTPS9LjxZGQUzXshXPwTJ8IpoUV8JNRNN5J991QR8sj+l1hlMMERAsUKPD+854N9ZtuugmAnp4eLrnkEmbMmDEif/rbxai/9tprLFq0iHA4DMAZZ5zBU089xac+9amhMo8++ijLli0bKvMmW7Zsobm5mbvuuotJkybx5S9/mVAo9F4v5wNJTKTJKfEjisnlUrvxWl40pZeYbzHu3AGCrrXEYks4NAuHogbwBk8ll9pCrPeXePyL8HRPhXFHbgLF3gn0JLaz8eA9zKm9HuUQAbmlJbP40b4/clXdGfi1gt5tgfcHx5GolOF1lQEguveNEhSXm5owTl2EpecHbW7KOHvSp+lM7iJtJagKTCSsjhsyRlRV8PC+g4RcBkaXyuHmiNPuxsh5uHyawpoOm/NmHaClez8Np8+nc42P3CYQOpSfUIQnl+AzK7cM1WnskplM3adCbDCt1AyVJ40NLHcmURPqI3xFJd0POvmUbsWC4hN1+vuylEyzkI0Gti3wlEiUdA67Ke/GKFSFy+YbCCFpSu5E1/VDohMHCZaDomFXZdAvSaEIBVMX2PhBCgb89YhAPZmDvx11jzPZKI6U7Jb7WdX656Htj267g6vmfR1VUdnfv2VodSXgKsGtednR9TLLpn4Gt6LzSs8OnCOl/PGGkErBgPl7IoVJ6fQmEJPQDbAz4B2fxZDbsdLTcdKj+38laeEUHbs6ut0GqT6TyUVF/GHXQa6ZIunucZFem3+zokBonkFul8PE1iK+fmIJXdk4wsUI3YmmaHTISH+Te5pamLe4iCpxkFq3h9vXR7GkwyVjQjRoEYRSCfYhk3WDMcRS2uTMxODfEltaZM3RHjOZ7AAZJ1cw048hQmpoRf2Un6uT2K5iBAS2lITnKiidWei06Gx3k+2SgGRgnUN4sYZRIQZDQMAakLT0pWnLpbl11mR2RAYodbsQQvC9TXv40cIpXDzxY4wNnoi+0sX41/0g4aOTQlx+Sj0p+9VR9ZLSIZrJcc2ksRi2QyYXZdf+x0eUKS9bTFnpyXgVc6jtRux9/GbNLQRdpVwx9+u0RHeiay7KQzO4t3Uzz7Svxa0apA9s5b/nljHDNdqTUUpJ0uUB1+B47JD3wpaQ8L61EPGRKPdOoCbYOEK/5fypn0WXgUJSjwIFjjHv2VA/44wz/uZ9u7u7KSsrG/q/vLyczZs3jyhzww03ALBu3boR28vKyvj4xz/OzJkz+f73v883vvENvve97/3NdfmgkrZySOmQFTFcysgOWkoHM7efElQyrkkgVDLGOALWWkQqipQhDpULEELH5ZuLZtWTTW0ip23Fkzkf1T16VCaEYGLZmezqfoK/7Poy44pPpq54MS4tSEj3M8lfxzPdb3BR9Unv9y0oUCCP5wjGYEMt9mFufG7KGOcb7ldGjCyEoDed5WAiRWqcibFp5BDbM1XhtcctMkmbS5YobI88zDj3NHpW+YbyTUsTul5QqfloaMShP9e6je/cOJWKWH5y7SHzdc6vrmfVyttIZfpZUfEHAtMrUDwCOy7pfjKHVqyw9vQUkwZMKlQ39osxnKiNOsuHONGPnO3C795Oc2Q7lZpB7biZyHnnINY9lj+poiHO/wKuTASR6MFp2Yz9+gOo5fWEz7iFgaIpSCnzMbxl84D/G3G9syZejy8yifE9E/namJX0enfxs903Y9oZnt55NxeO/yjfmPVDbMeiXcTosHqIZfoAeGH7DxlbPIvb6hfhDo6F2mlwcNj/XjnzVhJ6mCOq+BU4JnTqbqr9BpWhCPYaG1GmonpSREMlKEYSvTyE2X3IDgrIcBqOYcIxIeC1rn5aEinOr68lm9NJN49cNhxYb1N6Wn578iWH8movIjTyGIe6CL+JJSWWzNDV+iTz3LM5d9wykIJe53V2736IiYsXYDF8rqLAGMaUL6AxNBfDNIlpDv0iiWqEcfvGoghthEtw3bgLcRQfFBSxjxmKIhhwOigvc+OzJXJTDmWGC7EniexNkJs2geyGkX3OwBqL4qU6fV35NqKNhbWpPl7u7kZIiY3k5fbuoYkeVdGp9Dfg7igmumr4G2HuglC1gTWrkdMa/wcjXoVUTSLGRuJGN/6wQcBuY0y/SfHEW4mRYn3Xs/TF9qJpHooCY9CEiWqpiAEVmQFLc3CrPnqSLdy7/suEPRWcPPGjtOQsnm7PpwUN6V4+NeFEzPROmu0eKnwTccthTyUhBLqpIaTANCycw5fd/0pcspirZn+LjmQTiVyEKv9ESvSGQldeoMDfgfdsqF944YUj/pdSsn37dsaMGUMg8PYxzPIIb/27FaK74447hv6+4YYbOO20097Vfm9SUvLXzTD+vehI9RG2dRRFw+8dKVqTy7bjtnWEUBB6EZoQgIJlVON3dmFai9GPpD9nlOHxLCMd30bywO8onXIlhq/yCAXdzA98mEiyhfbIZnY3Pca8hiuZVHUqp4hZPNy8ko/PPOvvKh5YVsgHelR5P9+Lv+VZyayD055DRmxEsBZryTzYtRcr5KJvYRFtcg++/ieoq1pEUagBgFS6j2jsAEKoFAXH4XaHRxzz4vFjWN8b4SnauOD4MTivK+BAYL7AHh9laU0Rsg8UAaW+a2hJbSHdPLpuyehIwyBj27yYijLBNZ50Di4bX8f+XV8lnuxA17zIjCDy2mFr+CasjUfxGCGK744PeWdazw6gnRNik28b+/q7UfCQ7ttIOJ2mM2xSfd7N6I5Aq56O3rId+95/A2mjTD0Zdd652Kvux/7NrRR/6rco5eMASGcbOe2Eb7N+68/J5eLMnHwVFT1n0vbI8HX4JjXy2ZN/z4+brubWiV/A1XkAe/0fEG4/jdOXkXTZVFbMojo4kfbYbpr7N6EJBaV2KcaV30Ee3IpM9CMqJ6LUTqFUfycny/fGsXr/3+178UHrj2QkgbO1HOfpvPqz7LFwdmnonyulz70f5ewkxpM15Dolqh+C52ZpUzczq+yv+56+Fe/2fpxRV4Ho06iL+jD9CqMs38OGCkanRvG8kW1rcs6NoSjkDskj9eFxYfwazKq8gcRDlfR3579VRugUllw6DbdPJxgcduftODDAKWIW4qH/A+lQ5vJTd8ltZM04pu5j6vz/on3vL8mkO6moPZdQxYloao6yojLeLR+0NvK38vcaQ9mOhR6tw/zfdF5nB7BftFCmGODNYZsmhw9tpZOXGgDQaqH9xCTP7+kE4PSqKty9OteHJmK6bcqKIpS8cCfCNun3Lh51fmsHFE2oIvtANeZgf+0rqqHs8hQvrL+OFdWX4zz2vwRyaQKKSumya1hfNIWKuiXg8VKk+Oh61CS2Nv8tEOokrv3Qj/jfto+QszNE010kMh20DHp3qELhEw2LeHbLN7EHJ4nKfGO46fgfU+4fg52VxLdYdD2Ww8lA0UJJaFo3RpGKUjbuLe/jO7fDALWlb73/+8n/X96RAgWOBu/ZUO/q6uIzn/kMN910EyeccAJXX301e/bsweVyceeddzJ9+vS33LeiooK1a9cO/d/d3U15efk7njMej/OnP/2J6667Dsgb/No7iGMcTl9fAsf54E8PRvUEAUvicvlJp0caBqn4PrymQs4ox3Lybl4AtlqBX7xBIprF0d9ayEnXJ4DjoWfn7wg0XIOiH/nDa1DBuKLllPvmsWH/A+SygurQAgZySVY3NzHeV3PUrvevoawsQE9P/J0L/pPxXj5y79d78bc8K1UKvOtMnAcG1cgFcMVUdpy5D4/Pz8tvfG2orMddwhkn3glIXlpzO5GBvQBUlc9n0ewvojC8+jDZ4+X2OVP5ze4DiFqHaz9ZTsfAbp7u+RWXp75Nz2/MobRrWlERjZcvoa9MDonOvYkSlNA2/P+/Nk5haXE1mZSEgCCdNens2QSAaaXIlbaDUjzCBvGdqOANqIzt1MEemTLHeiFOpHozW/v/yKJJNzG9Yhn7n/ksUtq8GSnr6xjHAjENdVCd2Nn2AuoJV4CvCJIRzM59xEQJQgi6Iy+yct23mTB2BUF/LWGm0fPUyD4lucumYkYln538DVz7NmI9mw9dkgB732DWh7/OdjvL2eOvJpbppjTcSJFrArppYPZsh9UPISvGQek4Mv0JTGX0KufR4m9pU3/ru/Fu3osPYn8UMgPYfxkZS4sl8XYI0iUhjGgVmSqFwDQFJyORewVVlZOOynW82/vh8xlMyhbhPKzgZMA+AbSwwDpEfdHboJBpG35xjLCgvz+Jbee3CQFup4Vvzq3gybYUfTlYXqkxwR/HsktRWyrIdQ+/v9YAONtKyVTlGMgOv3fBjIV47u7hymUTGI/diXLNf6GIbrrMPkKT/pUQEocs3Ym11ITPetf364PYRt7Pd+L9QBFR/Ad92ImR/aWzI4fzuQa6XRFUfxn2cMILfNNzOFMy9JbbiCDc17yfmaVhrphQzsSWANEH823DhYo9PowMlULTn3Et7gUqRpxHGwPKRoWMPXztVgRkc47pVafhf/qeYUV1x8b13D2Mu+IL/GDdlzhl/BWcbHyS2NrhEA1pA8/Ucvypl/JCez7DxrjwLMa5anni4GrmFI1je+tDQ0Y6QE+yhT3dGxDpIvQeFx2/H+5n+1cKFDwE9n8aLvoacd+YUffwg9gO3+SDUrfCZEGBDwrvWar029/+NkuXLmXBggU8/fTTdHR08OKLL3L33Xfz3e9+9233XbJkCatWraK/v590Os0zzzzDSSe9syu11+vl7rvvZtOm/CD4t7/9LcuXL3+vl/KBJJKN4zdtDGV0p2GlW/CQxtRGijVJxcBWgujpzrc9tvQr6JFqNN84Uu1PHdHD4VC8RjGNFeexue1eTDvBzOB4Xuzd+FdfU4EC7wZPTOD8cThlGBL4Q4YqdS7rt/18RNl0po9ofBf7Dz41ZKQDdHSvpbtv7YgQEEMKloTC/PzUhVxar/NK5Nf8fPctBIxi0i97hnOjA1ZEYh1w4T/PQhyioyPmZekPdPPbhaUsLC/m+/Pms8RXzfoXTV55JMcrD2dpfl3l+JnDfeDqg/+B90P78TaCq1qh9EITUdfG0nIfYf/ohFjCo5B0ogxkenhl54+xHRMpR8bgJgcOkCsd6Q3jHNiIUpNPxZgdjN2XRFmz6UfYdpamfX/mjc13YDihI6Z1c1JQlBDYW5477AcbtXU7AcVLX6wdxTMWFT8eM4GvfzfOc/9DZPYiNtDE+u13ko6uQfD2+XoLvL8IW4Jn9GdeCoVgopreJySxDTZ9z5tEVlpE10g8kXeeLD+aWJaNvlXFyeT/j66yKFqsEZyvYFQIipdqeCeoJLbn275nnIJwD09MQz664mBiP+ubf8x5FQf52JguSD/Pg1u/iaK4ybSO/ralD2qohw2BtFRmdAUHOtEsQdo0KTLSbDjwLV7f8xV6I48jZXposqDAsUHtd1COtMakgYIHtx0gcMEBvLPTuCoFgZOipBueYCC9kl7XeppT27iqQeXWqT7szjeIPTFyAja514VVtgzMDB57FZ66YSNYDUFyhkl23+jT23FBSAlCMjLyBylxYl1IJGtaHycRHZVrAKtfMKPoVEp9tVw4/XNUuCdTI8q4a8HnuKZ+GfFMz6h94tk+FEWQbR7d/gZ2hqFqPmLjo6hqISNBgQL/yLznN3jPnj3cdNNNeDweVq1axWmnnYbX66WxsZGentGdy6FUVFRw6623cs0113DBBRdwzjnnMHPmTD72sY+xZcuWt9xPVVV++MMf8rWvfY0zzzyTbdu2cdttt73XS/lAEk3EcWOOUnyX0kG1O3BwIZXR7qWmXoJLHES+zYKW9AiUnER3T8bJ9mMljvD1OQy/q4JSXyO7uh9nRmA8L/dtfEcDv0CBv4nESEVqFEBK9JwH0xqdA9ZxsrR3rRm1vat3E4oysquTEoKGQdoc4GB0JwBl7rFYA6OrYUfhL6xj+yWbiFzYRteH9/N/tffzTHQ9nRvvZHFpinolSMcBm77O4UFTz0GHbGQy5aUzAYin2tmdu4+B5a+zf/n9NPXdyoYXrkXuvodsqBmKRtYxcy6s6n0EgFRuAEuMVuLWXWG09EiRK1E6FhnpwJq4iKdME0eVg+JYyRHlenObcI8deU5hgF4iEB0b4Qhu69LlIyoNthvlfGfvi3xt12O8bvZhdjURX3wmazf+kL6uDfT3bGbdK18ml9g6+oYWOGbYegZxenjkxlINWSxwMuAcYR7FTh7b/lxRFET/sLEkbeh50kTokqILMoiwhZOVlC7XKT1dRw8LUnrfiJArRYHqwDT29G3gt5u/yT2bvs6LB/7AgrHnoUgf/obRBnhwYgzUkUaadSTvsOJaTL2UEleIZ3fdQ1VwPNMrT6I5sg2X0PBo//8Usf2gIpLg6ArUj5zcFCeFSGZsXnnUTX/WT1voS0RmfJOdiY+yu+kHeL0VjA+6mOhPks3s4IXdd2HgG5ogOhTHyfd9YuX3qah/gLIrein+MPgvU3gotg9z9mhj2ynvJmL1Q+CwLBdCIeHO+92HPNVEvaP3ddVbtHY+zEXjrmZmyXJU6UFKKBZhql31LB574ah9xoSn4zgSLTQ69NAImxBvg/YmlIKAQoEC/9C8Z9d3VR12rd6wYQOf/vSnh/63rHdO8XLuuedy7rnnjtj2f//3f6PKffvb3x7x//z583nooYf+2ur+w9Efj2GIHIYyMtjctvpwWSqWcmR5Xkstwa+uI5kG7a0kaYXACSioEdCLZpDuegnN3/COMec14ePYdPA3TC4/H8uxaU53Ms5b9bdcXoECb4ksUsGdb4v6mR6kkwYhcblUpk68hI3bf4nHVUwml7euQ4ExTBi7gp7+bSOOU11+3FuueglUakKT2Nu3gTXdj7Bg7nVEnh3+3VsvCE4R9JsD/PLg/SP2vbl4OdlkFyEnjpmDSM/oc3S2OBy39DvY2SZU1UBVPexP7mdXdDUzBNhWmpamh+gNrGXBVb9E2S8gZeOUuUgOZFlUeREex02v2YaermPslCtp3nFvvu5CpeG4z6K+8KfhexYogxmn0dVwHI9JeLR9F4tqZuJTwkwZfwnbdt8HQEXV8bS7kiw5SyPykk2iycZVoVB8okZGByFK8U5fhtV6iKFteLDrZ7Mm1sYvdj+JR3VxQu14OmJ7aJsyG3Pbg6Ouv2XnH2lcNI938Sko8D6QNlSMqhx8uBylO4v0adhhFdUdw/H5cFXpIME3RpCNSFIHJGqJw/sXsDAaw1CxZiRh1/CHqmiOTfEEE+JghSXxiKT/ZQvFLVBO6SId6MFHeKi840CnrXHmzC+ybv99JLMRZtWdS7coxVRNjHKTovlJIut9ICE4PYMxbnSjjIgqAqd+HtdL3wfbBH8x8aVfxZEGTrSNS+uvY0tsKy2R7RxfeRqenoM4lb2oFFIQHjOKVJztNuLcMGpfDtltIeoMYlEdVwDqGwS7NpbRMHUurU13AVA382NsTKd5dsvnsaWF1wixcMw57Bh4iVPGH09un45/osAIC5KtDrpycOh0ZgjWbr2GsVO+xsbHF3DpJaWkylMUOwFSqySKW+A5PUumGgZypxG/YAmBP30ZUgOguYidfi1P9D6NKjRm1n2cn+zr4tZzq1GeBScDRrWDs3gLu3c+wq6DD7P8hHKC3rl0S4v797SwrT/Gxycv4dQJKV7dfz8+I8R5Uz9NmWsi0gatDvQSMPManwgNiqe3wBOvwfm3YxXs9AIF/qF5z4a62+2ms7OTRCLBgQMHWLBgAQB79+59RzG5Au9MNBFHVbIYYqQ7om124LUVbOPIs/lSceHgQkn3Q7D4LY8vgwqiz0atqsaMbsNK7EMPjH/bOrm0IEFPLe2xtUwJjOO1/m0FQ73AUSflcwjcWIbSncR6ZTvUhBCxDPpfYky98Rym1J8LByycckmmQaE5uY9A0XSmjP8QffEDhMqXEnD5KC2a+5bnCBm1VAcnUBeeQmt0B80NrzD2pKXE1ttUnypQ1w8g/zfL9dMWMXV+HV9o+wVzihs5q+ZEJvtK6NAfICNVUCRF5QrdB0eOiirGqAgRQvgmsTG+l/uaHmFpWQPtiQMsGX8F0a4tgGRMzdUcfMAACYqhY2+E8hM9nNl0ObyRgQqV9CnFbO+7ltknn4KZjdDvCvOlXTEuPO4mzj3VwlA1Oh3J9zoP8Fp/OwAfrp+DBx3HgUn1l+J2hYgrKitzCscbZfS6N1Ny+hTCJ7qQUtIdlax+ymTGtBOZaD2LftancTr2gL+Y3OSTOBjw8ZetT+BVXXy+cRlrdv+M5nQn2/cGuWTKJwn078TKJUgn8ufXdB9SDue7LnBsyToGSStCUXGUrD+EIlIogSxdZhmW2s3Y82pxXogjV6YIVBtoV4bZ52+jhCOJi74/SAlv+PYya3k96koXwamS0qngRDRkDnS3TfFEB/dsOJjdyI7YBmYrH8K2R7aplkwvj7du5l8absSrqDzas5s1nas5teYEepLFlE49SN3MDEjIyRz7+itpqDusXbqyrOw4k8mnz8ZwYgzYFfR0+JlcH8URCm2v/ZAxReOZ4C5i4MBvkd4ybEVFLRhDx4yML4l3fBFOZxYpQamWyESUYMqHdUeSOWEV+/wg8coPEwjW43KHSSbHsCGymdMm38jTO+4glRtACIWY3Ufk+PVMnbMA/hKFbSah+T5E3UTseWeTm7yQqIhTo52JUCShapv/aX6UV/s28dmpH8KschhwkjzU+zKXBlZQok/mppY4X7/yv0nHmqkoqaVXpJmUcDPfU80T+yvZGk3x+ewBrrugggW+BLu7fsuunX9G132YZoqDHa8wZtJsblu5kf5sPjbpK+tSnDvmFD574kXkpGBDNMmrrfuYV1LOeE+QsmtcyC4JqRR6bivqyu8gTrqO3NiFBaX2AgX+wTkqedQvuOACLMvi6quvJhQK8eCDD/L973+fr3zlK0ejjv/U9GdiKEp6VA51M9uOIXPE9bd2u7O0MEa2C4u3MdR9ArVNInKgBSeS7Vv7joY6QKlvMq2RVTRWXM7K/i1cUXt0VIILFHgTKSWJWoEvI0gdN4+BXRp6kaTo+BzGNhP7iTfVggTKJI2VJwoeibzKjZPPIOLz8N9NLVT7PNzqN2h0c1iaNpPuvu3EB9oY459E6fTPEs/2Y0qFtcHtnDJ9CuIn3ciBvJuieD3DovZKfnfDV9hmDfBI8ybus7dx0ewvEM9laHa1ERuTJVChEIgW07naQ1mNQqSkmZASxu24cKs6x1dM55d7n+U/Z9xGxOxmwslfIde7h1BgNr2D8eK2CZ4xAu/BOHLdoFt7wsHT3M3YKyto2jgZKwdyRpR/m6VTqmUR+1twnrmL8nScb81czlOTTuSVXIYL6qYNKckrFDO+7grWxTZwPTYHNv2SrT1bcfsqmbLgFtKpBtaszGdpb48W8+rEJVTJKMqUmTzYnSa7J8U3F1bi170sKWng9V0/ZWAwdtKWFm22St/kG+lJRpnr86DtuZ/axktGGVQFjh2mliZTYxBP7yXR+wyap4RQ6SzcOIREAPvP/bB90P+9KYPV0k35bWHsI2ULeZ9wHEldsYd/bf8jF547k+uLp5Nr1xhohXRnfrU/qDq4XGlaX/syM2d9gj76KdVCmGa+bSkKzA5WsbC0gZZX/52EmeacsUu5YMpFaLhwF+0iMtDGlp0/Qzo21eOvJ1yVQtIw8n65o0w/yUvT2ipSsRrK6mzGzxvAVh2U0Fh8ReNJRN7UwBDUzr2RrDAwKHCsCOTqyHYL+nd4sFJQ1Cjxlvmwn+yBuIOM2Sh39BL7lIEdrqX6ruspqpvNR077BK2qzs2Lf0Z3XzeGXc7s+svwJRT4VQ9k823JeSEOEY3MxZfStPHH9HWsw+UpZdK8uUxenMC2pjGjdBpjvWEsrYPudIJPhk+h1u3mQO8DXFIdQPPMxe5ppumVn6F7ipg16xqaUZhUIljdBb1pCxmQ7IncS0Z2c8rsf8VMdmN4S9C8FbQm00NG+ps82tLBWeNqeLplK7VuiKZzfOGNXdw2cz4nhKqQdRJFGOi5cTiNd5BxFWPLv19GngIFChwd3rOhfvLJJ/PII48QiURobGwEIBwO873vfY+FCxcCkEql8B6WWqzAu6M/l0BVU7gOi1FXsu04uEC8lV872FoYPdeFaU9BvJX4+5vu7702smoM6f7N2LkoqhF+23oVeRvY2/sMc4wAB1KdxM0UAb3wjAscXRRFIdbho+9VAEm2A5L7DOpO0BAMy/pqu0xOPKGO+6xNfH/rS3xiyjIk0JZM8/nVm/jZCfMpUVU6rBjd2QRhNUfL1h/Q37cZgIWzb6Wy4hL2pHqpMX3Emm1CA4fFEraYeKIe/nPPk0M2/39veZHbZpzKt7Y+S08mHwNe5QnyzcvO46Xu1Ty69xXuPu42hCqocBdT7AriUjz43PWsS2j8rOsACTPE1f4486qryLXnjxxsAPnQyJhyLEmlNKlo1NnVD9nQHlKZDtx2FYZdg5x5Gc7Ge1E2Pc3p3iCVx51DifBiKmnSThRD8aGKAGOFZN+6nxHr3wVAJtlJy5aHmTH+i5w3zyTrUegttfj6uubB68yHFkwK+Wnd+zBX1J9MLHmQA4cIHC2e+Cn+Z08XA2Y+j92fgG/O+Qy6Zwz/AMk1/n+LTxGk4+sI9+vUZU/HstJ09v8FdcoyQrEK2H6Y8FXaQe9Rj6mhbts2pW7Bl6Yt4bG23djmbNpesrGi+d+znZJMj6DqDB9j6y5m/+Y7mXjiV3Gc+qFjSAklmT6iHa3MG/sthKWRMA6QaF+DCI9HoYM9G75BeeWJKIrOgW3fp3HRv8JhhrqiqTT3PsOcsoW4VBcxf4Q9A9uZ51tCREliTTyJankaimOTNVz0ag5jlfc8jCrwVyAjKq2PWkPZMzrbofwUhcAJAawnB0VGHHB3Sv4oerj5wp+gd0PpARXv5Jl8bNtjzA+OY76rilw2SGXcQWYP62s3WVhLovR1rAMgm+5ly8ovUbLwZtriuygtPYOVTd+nLbIVl+ZldtlpBKpOZnPbUywYcxbmwdfo3frboX13vvhVKhZ9klkhk+SERmaVBZnqM3AVX4bsjdDT/gqtBx/CMpMUV8+net6kUdetCoEmUzT0/oL+vi1MCU/ipGk38pPd25hzXBleqeJISOmDCzOH9LtCgGm3k0wdxCOKiGqlCFFUWG0vUOAfgKPyhSkvLx+RVu3UU08d8fuVV175TxFP/n4QtRKERRJDDIcRSGmh2TEsUfE2e4KlhvCKHSTSDqr/rXUDZUhB6bYR1S40Xx256DY85ce/7bFVRSfkGUt/cjsNvmo2xHZzUsmsv+7iChR4B5S0Qv/qkQazzEEOlcN10jV7ePWgJxMloGvETQtbSlpTadbn2vnJjleHytw4/jJCVpKAu5qQcQbduxVqwqW0NUnKAkcYwSgQJTM0/rm26jjmO+Pwdhl8beyZ3L7vMUxp05dN8mzvJh5ue47PTL2YfjvKvlgn/9f0GCtqF7C0YiGb+ju4Z/daTiqdwL82nk42plB8sSCx2iS2xkFqgFeB5EifWhFzsH7dSeOKEJEx1bjS9XjWuzBXJRDeMWhnn4W99Stom56lcdFFRK1mHtr2Xfb3b6TYW82HZ32BgJUeMtIBwuHpTLduRfmBBfTjAWqu8DMp6KcpNjwZckmtTsf+VykrmknYXYtb85GxkqhCY0CGGTA7RtT1rt3r+OGcOozCYPDvRtZMUpeYDfdkwZSo+KmfcxGxughZFTy6AHPkA8rqx/aBWZZkZ+fzkDO4sfR8cgNiyEh/k9R+iZVVGff4+VSvWE5PbAcyOLKe7qSH8VuvRNme/9/nryR4VS+KYzHQ3cyShrsxXg4ibGg44Srau55D1BwWlpFQmPv0EsQBC1w5itIeZl24iL6qFLFUCwFtKv7cDMycis8T57nWH1AfmgujeqMC7xeZTsnh+miRTRL/RX54KQ6p/I85QzLHLoOH/JideT0CIxznvz96JgdSQEyjlQhjXEcI0XQJVM2DbgQwcwlAgnQoVgLs6nqJiWXzmBFawQWl36I26kN5JgYCbj/9t2zXVtK5+2cjjycdvJZga0ZhxTgoUhRkPItvpR/5soI/cDE1Z53B5r5v0t++ljHTDjCnJMyGvujQIS6fWEuk5QGqqhZQWbOQ1EArLZv/gwsav4KDRAjQFIEDo7yYclYL8QN7qdwzFWO7C2ucibEsSzJYaLcFCnzQOSZTwQVV8L+dAStGUJjowjO0zbZ6cdkazhFSto1AaNjCh5rpB/9bi91InwALRFKi+seS61uLu2zJO4rKFXnq6RjYyPjACayLNBUM9QJHHSEEip53Bx+x3X1Y2wyq7PL0weACYUj3krL6h35264Kfbl45Ype79zVxx5zbCCSmseoRAIuKOgXpwAFLEJrtRd04rKhuLw+wVcvHXt9adzLzX52A3ZqfADMMHw9f+zFUTaJhE1PTzJtdRokrRHuml//c9FscJPfseZrZxbPozSSZECjl69WnYzflKBYSRQMtlcR/OWzQX2LqeXPw3zfsMSPqDGQ0P+C0nhqguKYM2WdhvRgFQGZtzN/a6Ff9C/bOH9EZ3cwjrQ/QHtsNQH+qnZ+v+Tz/Nvc7gwPQfK7ahoorUO8Z+SlQ7k/y3dsm8XC0k4iisKjUoHP7fxKe8BG+uPkNStw+bm78F57f/t8IoWAeYdk8bVs4otD3/z0JWD54KD3CGHc2pAkvqqV5fBr/mQahR4al33OTNWKlNsdSXUZRBI3upWQ6x6NEJZQdYVJZAIZAXF6Oe0ucqpKFZA/rAgIDY2H7IdkgEg6elWWYk9zUqSvQ1ugoM/Nq3p7tBmPmnIt9mMVX1F+CNlfDWuHGToHul7jWxggnPajqDJJdpRRLB01K4qlSzq76KnDYamyB9xXhGj0uETrEmiXe66sQP23DLlPYFurn+LYK6DykTUQdqtYLlAl+1m1zWDC7Gz3sxRpn4BwYdjXXVoQJWsUscd+FWZukNfsIKauXIvcSvjhnGTKtkkm4CYcl4t4OZC7/fumrNGaecgrxhkpaI4/S0T6sTDogdb6/YzuX1GtICddum4R8Pn9O2Weh/8bN5OtvZnX0FnJmjE9Pn8UbPZL2FIzxWbidNcS9bp7b/Sssx6Qu2MhJU69CMSxKLYmxZw+s3w7V5ci504j782+xogiSva3UvjgTsctBYqJ2Ak0JfJ/1kjRGq9AXKFDgg8MxMdTfyeAr8NakiGLgG3EPbasHryPJGMF33N9Sgqi5Xuy3VaUVyCIFpctCaShGOhZOtgfV/fb5dMPecTRHXmZu5aX85uCzSCkLz7rAUUWkBeHFOn3PD1vqWkhgNKgoZwSR61PIeoNtC5L8pO11ACaHyknlDOzBCcLF5SWEDDH0/5vY0sGr17H91eFVNbcqmdYo6O2VtISCVEz2oQ1YMEajrSJDrVHMw/VX4hsI0tE6PMCROeh93kRRQTpQdqKXxZ6JPGdvIWomcQ5ZtdOFQpHh4Xt156Dc2YnSlzcXHJfAuLAY8dM+6m9o5Pfie1x68+co7ixDOCCTNtbzseFzdllH7MFltJz48efSn+seMtLfJGenySoqk4/7NC1NDxLr24Wa847WerMk/ozkyroaMoZB78BL9KoqT3T3I4HeTJJfNqtcMuXfqdAtigITuGfvLiw5bPhcOW4aPiuOLY6cmaLA+48r58PqiI3aLmMQNjWcWRpqVQg6TESxSrYKvCIKeEbt8/7hUJKsx7NpAGVbGvHRctxjVTKH5IcOzVcZ2GoTW+dQdmIQrzJSsV1RgL4jfHuaQeRU9FgIPBmsR6L58pPdGJkiTKGRY/hYmtfNQEql7/eD2hQa1FwYBpHBlyqh3LKg30LGbXxVOumEgVPGMVXJ/2dHGwuKB5xD7O/QPA3FEPSttwh/rpRn47uJKlmCrQqHm6GyOUe5E2f5bDfqG99CzvtPlAY36iwfMuUgKjScLhPrpxFUNFRCTLjxM/RuU+i7U0H1QfGJOtZGi864pOZD5Yh7u9CWBXE6TeSPo/ipYurSTzFx7g1k+7ux/GneMFQcuih2e3GiNoHVo7MOuCKlGJ5ShK+M3tR21u/6Kl4jyKpMP2dPvZnHtt89VLY11sRmTxlnTVyEsXoL8oXV+R/2t8HGnfg+/mGSbg+KIvCnyhC7DndDcFB6bag+Sg+mQIEC7wuF4KoPMI50sJQ4Lmfk+oaV60STNo72zjHhthbCyPSQYjJvZ0I7IQVtn4kYp6N5a8jFduN5B0PdpQXQFS8uJ4HpWHRk+6h2F9LUFDg6aKj0PWshpKDsTJ1sl4PmF3gmKCR9GdTT3OhLvdi6JCAFXyldjkvRGOctJpKTTAwWEdR1xnq8mGQJ6i5i5vDqYchw40Mhk85bqQvnCKrWDSAeT1OjCZzTQ+wJujGmK+y39rOnK8O1nkkYP+8nebJ/VH3NHol/ikp0jUVXTlK9TDDWUzLCeAVY07uJ2UUzKNoKsu+QYWRWYm9PodS7KF5bTHZ6ih903cg3av6EfX8C2X3IwE7kV5aOuF5d7OGZ/b+ifvw5Q+7pb+I3woQi1bg21FMRWYhcALJUgDsDmUOOFlSRJX6Sqg22pKRoMsWhSWSzw9dyMBXjhztjXDJuBp09z/Bvs45ne18vCJXJLkHZwcfZsauLyYu+gcPo+1Xg/cdRFMQ0L3JrauQPIQ2vGUdpCWLf2wMqkJO4TwvjPs5N4hiqo7lcBmJ/AmVb3vJy3BqeOoFvvIrZ72CUK+T6HAJjVaIrHbqetam9TMflskil8m1WSshW2aMc0K0ZkPM4uLMK1obhe+DszKDUu/IG/iE4OZ2+14ffM2lB59M2VdcbeLIO1hNRGNSusAHPRUWYNSpmYSR1zIgEEtRcHiSxy8HJgKtCENtkI21wjxUIWyFQFeDMjWMRVaO9M5xGH06ZjvpAL+qSRQjaMNdU5MOMDIG+Ioz97PDklmhwEdlqkNqR7/vsBPQ8aVJ2pk7PkyZ9uwWlMzygC5zBNizqDFSPB3lHDk1WggozrjIoce0iZ9v0WHGcIgUlcVhok1+lZtqn+E37JvYmezm9/nJe3/srFKGStQ57h4Gm3nVcMMmFfGVtfkPIj5xWjUhbqL39UFuDZTl4/eUgzNETsmohI0eBAh90Cp+XDzBJK4PPcXAdpviuZDqw8DBqlHEEbD2IJ9sEWd4+jE4XOF6B0mujhqrJRba8Y5w6QMgzhr7kTib4atk4sKdgqBc4aghHYPVKzIhDaq+DXiSwU5KcAanSfezvXgO2ycTwDEpcNYR9VezL9vPQwS2Uuv3MLaqhjHzeZCHcfGve2Xxz83O0pWLUekN8adZJdGz4LxpmfoOuA4KKA0nEjnTeCC5WUV6I0XCDw4ZgO3P7I0h/Ge41WZyEg8s3enDja1RJ7c8P4jOtEsdRKB1IscfrcNGYE/hk6Rn4bQNsG8tRkL1HSC4eVtGWB9FVwS3OnUSVLvoDaUquKMX+TS+yzwKPQD8zjBUSaCh5I+tNe79MY291BM/Yf8Xl8nGBJ8TvN//X0OFvrvsB+v/YOLlB430jJD/qJ3qtTu39NjJiQ5mKeW0YxyMR9qBQV3g8U8dfwA05mz+26Lze20HKMhHA1KJS/rzlYW4pOo/jDtRjdXrQJ/UQUZs40PUkuVQLmnfqUW0bBd4djpZDnBpGZGzkniy4BeqFxTg+BS0Xxn6wByzJm4vK8tko6tRyCB3LwbtEa0rDZDfa8T4yKEReNREGaAFBbIsNNoRnKFQss+lbr5Fuk2iH6MBJCQPVJkUXq3gyBqhgJyXpRQ5uVcM5kEHUG2inBECA9VoSpymDXDFSNc9KSsqWQrDYhpSNDOt0rhWIDIg+a8hIHyr/Qgx9dgloBWPnWBFQIddpElsvUVyCgbWDyv8eKFthoHWlmeYrgsfiyLle1BMC2Kvi4ICyyE8MA9EHvpykf+aF7HQf5Piby5B7cji9WcQUMKYWQcJCJnUIGZSmHMKVNjmhMdAs8Vfb6IF835vpAmWJF6dpeIlfm+vDfCQybAPbUHG/yR2fOJecxyGS7YcVAr3Pi7Uqh1KuIRpVcg0Oj0Z6+HNbXsTupNJT0FU3pp3B0NzDx1cMakITCXsqcUlPXhR4QQOx8m569t2JZgSpcoVRRAWO1HCKvXByFl7IDB1DTHGRKykY6gUKfNApGOofYKJWAp9tox/i4i6lRLWi7+DKPowUBlIYiEwMXG/vKi+LVESnhVJWimPGccw4iv720YpBdw09iZ3UF61gfXQXZ1Uself1KlDgnbA1m8B8nf5nLZBg9g8OKGoy/Oy1mzCd/Oq4KjQ+Mesb7FVr+MqG54b2LzI8/GTBRRRLD1LCBLWE3068FCvr4BQrpJNr2NSxmuJTOpgSrkD7TQr14jAioOHsyaCUaihSozPTS72qML2iDJHOD9S1TTHKl4bpfd3ByUFghoqvUUEvFvmJAQMUt0PH/oeZPmE5Z3nOhAMp7G2tOFu6wK2hX3Qchybg0S4owmnJkvt+FxgC7ZQgoUQQZ6Gb+5IOF9xYiqvPwck5tGkqVs0ARTKA76YylF4L2yvYFG7hT9HXWNvXRL2/in9pPJePTPkcA2YEn+ql7GA1cjA2/U38j2f59fm7GHdVEWe4q9CdHozkBuT2LI7jYDcswrbdpDL9qNEdfLSikquKytA8VeRctXTGEtzb+BXM3/nJxCUgSe8vJbTgUoLFq3CcgmPw3w1FkugW5CqK8c2S2CZEN0D1OAVMG6VcR13uReYkQhc4HeaggOGxC2GybdDO8CM3ZTDv6UM9u4TgPBeuMgXHBEWHbI+N2pvB358icLqLtOHCPCSURUpQVBO3IbGf2wk5GzG9FMMYg23lEMd7UJoUzF/3g5SoJ/hQZnrI2CNXND0VNnJtDOuhYaOr+upizJBEto2+JzIjsVQbeOdJ8wJHB3e6H1lRjZ0Ce9CjwlUlKDpRJ9stcYI+/MICJYEo06FSwzjOi4w7OEkbD2DaQLHK1lQz0zZUYD7XnW8XJ/oh7WA914Q6azzW0zFkn4Wo0DFODSKe7KHmEh/W/WuhMsC48xtBB7GvG1Gno9YHMB9PI2052v5NS+qaXNgr40xYXo71+k6sviT6jfOxn9mL/WgE10sGt1y4lP6SJH/p28ZYTzkzGz8Bjk1ZeDKRsRcj0lFOYgLh3VtQKuuwUm20/MupRHJxvPubsJK94Nh0DmyFgI7XXYtXlGMv82BMyiH3mqjj3OTqFLKac/jtLVCgwAeMo2aoHyk+eWBggFAoVEjN9jcykEvgcSwMbXjWXzpJdEdii3cn92NhY4oAaqYXO/QOhrpfoHSCkhRonkrM+D5cxW8vEBd017K39zkWVVfybPcbhTj1AkcNx5G4p0uKMgrR1xwUD7AsxxaeHDLSIZ/De1XPS/R7V6AKBXvQ1TySS7M70cNC3xgMS+B6LYvz5ACGA2KcgfvyRkDSlNvNk7lX+OJZF0JMYv6pN39cQJRqLPpEI+0lOf5r/aPcN+tiWJuCnWk8PSZ1C4OIyR5ytkLXQznsQZF0oYH7WpWDbY9z+sRPYG/oRFgOzuaufIG0hbV2N8otM8h0SaQioFoges28O2JWYj01gH5REfL3Ayz8qMVXOh9nacUsalxjsDIKhuHmI6+9zlWTy/CO72VmeDJjMjV8NnAxLTX9KIbCgVQnc0rmEu7ZiKJ6sS19tEkh4bTqesZn+9DuuQhpDUbthipR550Nj32L+Pm30Nb5KrZjsmbT/wztOm/6J6juuYwiRaUjPnLQl1jrpe6Sq3H76g4XaS5wrMgZ9L9k4akVSAmOnV9pzvZJXGNt1CkezAeHU7Qp0zyoZRqMiux9/9A0AT0W9kv5l0ckTCQuep4anuApXabhvDCA02rCa2k8N5eArpIdzH2tqlDe7sJ5eOvQPnJrL94iF8kzyxEDGtZTwxNU9ktJRLmGNlFgHjKPpERMTK+O+aEQtiPQVQfWxdDq3VAqQBN5D4Q3636Sl5yeAlkI7ThWWLkYbkqousRPzzMWji0JL9LovH942lOtlYz5RCXWL7rQV4TJ/bx3yHBWDYH7Y+V0fkgwvreM0FPDvZP9YgLh98GJjaQiHuyTgxiqg/J6BPOJKNpxPpwNOSj3QesAPLAO7bR6rJcP5A/g0dDPmonUBGKBD7pMZPNgvYIqZBxkxMZ8IIl+8Xic5k7sp/bg7I/my8RyiF838e83ncGcklr6tv+MPdG8zoii6Cxf+j9Ubl+PeO6ufPmmVWjrHkWuuIR0oJTfuFzcfPy/kPW7+M3uO0l3/BqX5uXy2V9lrHchmQYVZYJGSYmfaM/ICdsCBQp8MDlq08AXXXTRqG2XX345APfee+/ROs0/FbFEAhc5XIeou9tWL4aj4Khvn+jWxGan7GajbGe/bhOnhaw8gqvtCAZF5TotFE8FZmLfO9bR0PzoqhfNiaMJldZ097u5tAIF3hVS68Wq/D2t17Txwtlb+B7P0pLYPKpcLBdD2mXc2Hg680rGDG3P2fk27+qSOI8PDKX1kQdyKC+qFJXMYpKnmOd7N6JWG1jPDYw8f69FeaePpw42cTA5wCp/O9pVxSjjXSgBFa0YEkYzuW5nyEiHfGxr/+tZymtPQmQkwrRxdvSOOLZTV0bb0w6RJoGtqnQ9L+lMBDA/VAnj826OcsBG9tvUZIu4omEZXt3Fxthm+rQWujIRqnwuxvhhYXAGY7d6CP9XnNC/x5n0sJvOlk78mo+9GZufRIppcU0mUWSAMXIizVwW5KmeV5Av/gKsHKKkFlFcAwOdYFvIg9uQvc2Ul85g++4/Du1nGAF273+EccEsIjPaFBcCSqqOwxHF7+JJF3hfUBwqTlYoS8VwPdiJ7+VuahfZqIZEjStYz4xs7862NESPrQq0ogicDcMr2E6jn/jGkXXofdFCzg3l/0lLnP25ESmoHAeUg6MND2dTD37Hh9ySGf3bugya0EduUzT6Ml661koS7dD2EmSmhFEsjWysH/1SD8o0F6JGRzvLi8z1YJv6qGMXeP9wDUQRaQWP16Z2vsmYswR9z40c29gHBdm4QKkysDemRq5u5ySp/TGe1bdQsmO0GIOtuOha66H9WUnXCw6tz0F2cUn+GH4FqSiI0sHxV84ema/co2GNdRORbtpjAfrHlCAvL4dSDf3sMNbKwY+EBJlTUWoDw0b6m0hQWns4zjsByx6ekHYcE7N7B+KlX48sn+inKA37Nv+YOaFqBqon8fvdd5I28+fKWinuXf8VkrJr8DgFV/cCBf6ReM8r6tdeey1btmwhk8kwd+7coe2O4zBlypT3evh/agaSCTSRxTgkRt02e9GkTfptDHUbh4PZVk6IpGlIZvGaWSRZtkZexayYxEC4Kj+KPgJOWEHbbaLWVZDr34SUDkK8/XxO0F1Nf2ovDb5qNsX2MMb79vndCxR4t9gDe+h94y5yJ9dwd9s2BHDW1BPY1vHciHJjys7kO9sOYkvJx6dNZcdAB6bjMDFQli/Qnh11bGdLlhkrvkRz+x/57znX4UScEatlQ1iSnkFBtn/b/xRnlU7iiotm49Z0Eqxl0/YfsyT9c2Ck55AdNfBOrkdoCjJrIcq8yOigwSAg6yki1wvlZ6l0Pz68rNfeAjUrwmj7O8GXj4Psd6V4uOVVwoafEleQb23+HZOCdXxqyiKK9BRV3SrKr4eNLtc6m0WeMXwxeT+3Tb2QNT19JG242VOLvLKCQFMSJW6Rnu6nvSKFTGUw0DEvuBdLGYudEmjeHC6xBdyPoygappk3hCZXns+C0E3oCTcioCJ9AsuUaCGBdYjdV3SChukhL4Nf4O+DZuHelcLZNihEFXfggV5ct1YiHCB3hPZuSo6l63smY6PX6LAz/44enooxvxGkEMO1skenfRUhBXHlAnL4cCww/DbKtt1Y5FCrVNRFfpQKHSklMmojpQ2H2dim0AktEni6HbJdkoqzNBxTYuk6Wa+Cdu96REMxSq2BvaYPs9pLylWFVshwdcwQLi+kQT4VQe7MwLnFWInRkyUyA+hiMJQDmO7FnurHshS0UsGH9GUY1UnsEQFIYJa4SL06ss/qWelQe1kZiYRK2uvBU+bBHQwgXtkNikA0lKBOGYPTJWCPxOWWRPoluW5Jcr+g7tIyzF92QXr4uEIDmbQg6ILYYd8nNUtnOs6kmZ9h8+ovYw5+f6zDBOVE41koVWcRtGo4tWYSe7QEA5m+ISN96JqcLLFsD15X5V9zqwsUKPAB4D0b6nfccQfRaJTbb7+db33rW8MH1jTKysre6+H/qYllEgglg6EMG+oy14FEBe3Is/hCOoxp3855fRG6/CF6vX6yWphwpoWAIxi7ay3S5Wdt4wKSniO466kCJyjQ+wyymgc73Ynmffv8HX5XFX3JXdQHl7JhYDfnVr6zCF2BAu8Gx0wg7Rx1ex7n/004h993d7AhJrhs7r+zct/vkFIyuebDPNsVxpb5wcyzrX18bcZZzLJqcHYKhCYRxaONRWesJK1lqJrwERrsARQlCov82K8eMsjxCMwawc2+hXylYjHBtACPQeaNDJoOxTOmEJr2NQKxMOm1Iwd86VkDPOUrYqJbQ20sBcvBaRmArA2qguNouKsVUvtH121gv6BsoQ+SDuLSEB9v+ia92bwScakryJUNp/FQy6uUGm464nspP1CK/7Du3Ldesnj6ZCK5OD89cRkZy6YM2LVaEFN9GBUCXaZZlXqRl6N7uXnB7Vh9xXQ/8aY6sIp3whzKF3wMpaweV6qZKdUXsGDvx5CvJTFJggD9oiL0Gp3a0yDRJshEwF8vUKdKsgUj/e+KzKg4G0arRctuk8jMDKEJLpw9hxgJLkGu8tga6ooCynwvsiuHUq+hFI1Ov+WqEKjdmXyzVICJ7sG55mHVd6u6nvY/2uS6821OqIK6a6aRMhIUTfdi3R/BfH3Qvb5aR7+6mJQ90sJW/Aqd9+cw+/L/x7Y4FC9RsXTJQLmCPL4S//4k6Ap4dZJnjiGr5tDswqr6sSJtuPB6FZydGTAEYleK4NQiYtsO6WsUMMJ5dX/9nDCOhHhtkP7n8hoaICk5VWBMVlEOuHE2DU6gBhSkPrrteyeq9GyQpPbmV+5jaAQaqyhdLpGRDOrEcZh/fDO7RgojpFJ+ahldL0nsFORMBf0QryNlooHT14fT3It64STsX28ZWplXZoTobdBZ1/5L0maEudMux+zbQ+vBF+lRbWqOvwzXi/cgJp4B6Wsx71cAi3IxltD1XprGN2OoHnL28AukCo2Aq+SoPYMCBQocO96zoe73+/H7/fz0pz/l3nvv5cYbb6StrY27776bW2+9tRCf/h6IpGNIkcYQw6vnitmL5XiPGLSgODYL923GdLK8WteAdYgxb0svcVcRa0pKmBMd4JSNL7By+vFEAqPdUmWRitJuo44tx0y2vKOhHnTX0N61juOqLuep7tWFOPUCRw09OBaEit38KuPa1/O1SSsorppNJHmA8yZ+jHZZx9fX7SNr5wdJPlXlB2NmEW5ycKIp7LBB11qwJugE5rpg/aBRElTJrbDpsFtRM73U2hpazw7UOacigiHsLWmUCg11qR91X47JxSrm77dBIgculcAlUzGfzMBGgWfZdKI7LUpP14mutpCmpOgEjd/6XuOJ9jc4LX0BjdUhtHQW/ZpZyGQO4dbQveBslSjG6HdF8YE2L0BWWNzS9r9DRjpAbzaGrmqcXzObnJ3ioS3fY1LRPXBYCjS7XKFTRjnRrmZcT4KB0jLAZvrxKqkMZEjz7X2/YE9fK9c3nIrZU0TkVWuEK2dqj8RcchbBMi/JHou5RR9BvnaIBSXBfGoA7dQgeqOG74EIgeO8yIk+Evo7hdoUeL9RNQWnTIOukc9C+FR0kUW9qASeGsDZlkFU62gXhEl5ju3ysBCCdETiroxi/6UV9hdRd9kcul+wyRyU+CYplC5REQ9kEVNdaMd7yQxmI3gTRYFshyB3SOSVtKH7eYvyK7yYu9PIfcMTErLdxFqfQavzkj1EQcGKyCEj/U36V9sEZuv0G3sonjMNkg6y28JZ6uags4lqZ/L7dWsKHAHpKUNaCvqlxchY3vW8ZBIoumBgu0QPC8rng7I2hnJGCLs5C2eX0H/fyHbd94KNjzha5QDiuFrsfnBNUBBCINR8+3kTb4NK10MjJ2LjTQL/0hr03ADKLw8LrRiwcWORT8kBZlrAhyrQsjZ4BH3j4uyM9NIyNcrW3J/56k3L8RzsByVNf73Ot7d8EmewAm0Duzh70kdpcJewRwtTNm06E0rGIZJjMH99yEBQgusPGfiEl6WTP8kLO36M5eRQhMals76EX6ksODcVKPAPyFETk/viF79IbW0tAMFgECEEX/7yl/ne9753tE7xT0fU6kVBRx2Mo5NSoppxHI7gWi4lxzXvJIPFhsoafIfF3lmKB8OKU+mtZWXYRHH5OX7rSv4y51RS7pFu9NIjQIDqlGElDkDZ2yu5e/QSTDuJV0gMRac53ck4b9V7uvYCBQBsz1gaz34Aq6UbWyTIGm0Yqp/S0mUIAWYuO8Kw/FHDLIJ39OEMujuqClRdXk7L06BfHCA1txkXYah289OmW+ltagWgvmgGV1VeTvC3F6PMvgRtxQIw3OTursD1cT+5Xw0a6QBZG/O+regfPY7cbo3e502cLGQ7HQIztLyQnNtBksOlGKQTkj+9qmO4dcZOgPKZcVKuFIo0qTy1GifLUAoqABRw12kkkrC+vJ2dqZYR92SMr4y5RRPBitKf3APANvdqltSfhrp/8GZogrYzsyyunkrlgQGcutqh/R1pIz0Wr/c18dVpF9GT3MW6g08jlBVYsdGu0FY2/5lQqUPP2Tj0jywweK+lJcl9NgQuHfMYipEVeGuklHB6Mfyue7h9TXRj+TX8yWKsP21AuDW0M0uRvX1Yv9mO52OLyB3DLJuqqiAi/dgr8+1cCXiQT/VSrqkw1wX70ihrVOSEAWiPY97bj375HGw1wKEL4lb8CG03AqqpIg/kRv3G3gzCOezbd6T4XQeQkrHJcbh+nEQOCtiJvTnqzxuLU1mwfo4lilqMaLcw/xyBwWfB8zFKP16Ot1HD6M8if9+HY0mcoIq2yIeFM1qFXZJPXfbyAZzzimh9KUi5LfCs76FmRTHdb4DZJwlMVVA9R65LWmYxTBcye4T2NdiWjAqBGZH0rAJQ8y7vN+X4QtfvcZAIBH8ub+C4GePZ2duKmVk3ZKS/yZq2Z1g85TZqVQ85V4BY/ckE9klgpO4JSYdoPM5d0S1c0ngrdW4f4/0T8IkqpFPITFCgwD8iR81QP3DgAD/5yU8ACAQC3H777Zx33nlH6/D/lMTtPgJy2CNBOik0x8E+RFzuTcb1dRDIJHiouohyMVogJad48Fr9qEKhQvGz0pOiuLSGRdtf5y9zTj0sZj0vKqf3h8m41yAdG6Gob1lPIQQBVzWR1D7G+2rYNLC3YKgXOCr4+1Xsn4IWK0GjBM+p08mN82APplWqUg1+tGQuz7V3kTBNxrdryOQhA2cHlDUxfOOLyLYrZI938dTen6N26fQmW4eK7Y9sYU/5KcyrmICz7j5Ydx/q6X+AARuZykH8sIGYIyGTBV3HGfzJTkF0dX7l0n26AAO6MhFckxKw0YfhkSiz2zjgpPn51ifYHm3msYnfIVTmpfoyg9QBBxwwigVmzMYqN6n2eblgzAn8fv8LaELl67M/jlv1sy8ex7ZtJrrzHjGPddyJuSzLTPsE/KIMq0In4tnDlJSGx1dNzOsdGqgaLguPleEy26Ilvp971t+OIlRWTOzC11hOcuchg0QBFGeAfJ8iS/SRedsBUWcgUw5oYLp0KGi8f2BwkHS8Lii7uBItZyFVhcSAgism0TQL2mNIwN43PPkioykoPXYq5rbtIJo6hv5XKkswVw2ufm/Ku+3be0A7z4e9rzm/vTsGBDnU9d1drXC4Wn1whkI6EMPT6EKuS474jWkezMPaqlbuoPoZIQwZmq2QLYri2xaAQzxbALzPa7DYR7JgAx0zhOrg7MsNG+kAlsR6I0FUDVLmMoe1RmI2TlcOtSSL6g+PeK5aUKCm8s9TGUiguIK43Ta05lDv66Jqnh/qNcSuFNbUUlyVgmzn8Dk9YwVen474dQR1sR/r+UPahi6QVTplFyjYAwr9Lw4LL7iXwIvZLn604BZaY7tQnSS7Wn5D0l3F+KpzkObo7Dxuzc/6/r3c3/IahqLx33NvYl5Jw+i+eJzOgDdDnVVOwFVFQ3ASPsdXSJVeoMA/MEft82JZFonEcC+YTCZHib0U+OtIyQiuQ4Xk7AiGVHDEyHACw8oxvWM/r5YV41HcKEeIL7RVDwYpkA5B1YUuBGvDQQSSCW27R5V3ggpqVEPRA9iZznesq99VSX9yD/XeSjYM7PobrrZAgZEYUsF5MAqx4ZGI85c4Rveh+ZMlNarBR8aN4TONExGpIxiJSRvVBe5Gyc9Wf5aQu4z22J5RxQ5m21CXXo265MOIhuMgMBjT59bAc9icpgC8BmJHgtCckZNYigtcVQoVJcUUuwKYwSgnX+Iw9fwOIlacu5oeY3s0b3D8Z8+vSEctFLfAijtkO2yspMRbr3IwMIBtwyfLzuXJ6V/nF9NuI20pbIl08N0tL/KTHWsJeWqYW3M6AE93/oIf9t3MwfpddPubiEQ3EfbXM1Bbm1/cERHi6TdYs/G/0Dt3woFNdESa8vdV2vyx8+t4j8/gn6aAAK1I4Lm0l23y2fx1KYJsEWifKEMU5a9ZqXehnRRAnaxhBhWMjIGyz4DtBkbUQCnkl/67kvVFCUxTaX9O0vKySusLguhmiVohES6Rb9uHofiObbx1LmcjxoSH/pfOEcKmHBgxXKn0YprD77qUkCwboPICDdWfLxqep+CZ42BK6J+QhXmHfDenuInOtDncgtnr3kvFFYLwcQruMQrly1XM4+IkSGMfKZpLE2QLYV7HFMeJI9NH8NiJ2YSmK6ghMSSxIKo11MkOPLmVmnNs3LX5H9w1UL00hXgtn9lGBn24JwqyocEJIkvC6jg8GUE2Z7F6bIqXahSfoOKuUyhdJChdBlu69yA7LGTKQTszhKg1UKZ5EDeV8GjRKlobWzEakvgm2hhlAnFajk31+7FI05NMMCc0F685wFkN1zCj4kzcVprxRVPw6CMnyk4YfynrIwcAyDkW39x6L33hFOohfbGY4EJcVcyS4hBX6jaVkXVouQ6EKEycFijwj8xRW1G/4IIL+NCHPsSKFSsQQvDss88eMWVbgXdPTsRwH7J67ph9qNLB0bwjTPEpnS10+0O0uSQ1jF5NB0BRsXChm0lMI0C54qfZHqCpqoE5+7fSXDEOUz9kX1Xg+AWqXYKZaEHz1rxtXQPuKrriW5hZeiaPda7CkQ7KO6jFFyjwdjimRO4drdYuog5OrUKvY5KzHcoMA48DabODRL3BKNWFBQF841R6Q024VC/TXGPQim064yPTD1YFxmNvXos6Zjpm/Syi2dcprpiGvctBO78R6487wHLyRux5jVjtYJ5j4HUJtKBKbLODUSYoWaIS8fdTmeviPyYvpdyAfqMfSwr6szGaYsMr+aui27jF+j7fariJ4uV+kIJ+LcknNj3Ed+VZ1DS7sVdHCIZUik8po8YdpjOQ4+IFJyGEws7IAIsbrmRO7WmkcnFKfDV41ADJXIy5VReRcNwgJUJY9MU289LqbzCmfAFK1wEIVjFVm87NpdPodrXyRPdd/PvO5Zxz3L+w6IzzsGWMTZFniKQsYrui+NZbkHaQx3mRny1DTwKORKoWmYCCY7no/rWF9WZabgFV1+g4laOfYYFjQ9KxCEwS1Faq0G8hPApOUKPX6KNS+tDPnYL1wJah8spxtTjut/iGvE8YhkpmdjHGunaIZsBKQdAYMUGn1BvI7kE33xnlNFeblGvKCGN9p9PBn/UH+OJ1H0aXBs9mXydpSs6Ts3kgtooLT1pI6YJycCDqSfC7yF/4ePXZI+oSMU2CLV1URD0o493Y21K0lEYxwipqlYewX4HE8DnNc7xEXFkCuaM2lCrwDphmBteCMKwZKZKoLAjg7kqhjHEhPl6O4nGwtx7EeqwZHIke66P87BBqRkVuOgh/bAFHosyqBJ+HUhEnXgx6uYLaPfyM1VODuIM2qu3g7kgSKlGxxqmsdR/gL5H1zJywHFYlwKegTnIj0zarnIOE3FX8+8ZXaQyVce3586hK+jH35SiJFKMGK1BfzGBEBLUnfZhXRQdf3vMcQggurINrjvsRTV3Pk7YSVAYaWLnvAU6vPJs98XYg76kVc9K469y4P1uKkoOcFwas7Tz54s3IwWD0bU33cebJ/4tbn3isHk+BAgWOMkft63LjjTcyYcIEVq1ahaZpfO5zn2Pp0qXvuN+jjz7KnXfeiWmaXHfddVx55ZVHLPf5z3+ehQsXDhn/7e3t3HbbbfT19VFfX893v/tdfL63zy3+j4SUEksk8CjDsaVvKr4LbfixucwcY/o7+UvdGDxYqG+zgmXiwZWLYxoB3IpOwDHYrdmMCRYz8eAuttdPH1mHsIo2UIzpbgUWv219/a4qmrofI6B58Gke9ibbmeivfdt9ChR4O3aYUaZNMtB3jHQ7t4tV/tB2kIf2tZJzJOMCXr5eH6L2T1/AGn8i8sbr4YksSgY4LYAzxY3pyiJth5NqVtC85qeMmf4hppQtYEfPGhShsmTchfSnO8jlBtD/9A3SZ9/Apl2/5ITLfoO614dUA+g3zkPGsoiQi3RJlpTooS+RZcODk6gYozDzYo2MKXnkWZMxx3eT86f57ZYf8JETfs/t65/mi7OPR1VUQrqPAXPYDXdnooXdxgESuTRBvYTvb1jJNWVzqNmhYz2Rd0mWrZDbkcb/yQrKbEFL70N0Nb/M7PnfojTegGQ86XCGTruZtB2nP9NGf/oglYEJhEQDltNDb/9OHMcCoSKCY3EONOL+RZZxso5xvrGMvXoKd3R8miJD55WnzyRYOoWpc2/CE61F+W4E50130lcSqLeUE615czUy3+coLQwb6QAS+p+2KL5GwxQFYbm/Bz6rCL3bRjzYM2T4KicGKZ9fRLwsRbEeQLtyPjKVQbh0cHmIqzZHcWjwjqiqwImoqLOnIoosUBSMqwJYryeR+7LkZhj0LZBkEy6ys8fycGY/s6RGmSwfcZysGeNKt5+B1sfRhJeJ4iDNxeOwVMGF9gLCP0ljkRdC9AOX3XD84GTy8ITA1HQtvvviSJnCXp03BCf1+Ynd4maT0UvR9QZT9wVReh1i0+F+fRvnU0hDeyzp1VT6KjOM+2gxzpNxsEE9KYCM5FASDuZPI3lHiYCCfkMtA2MU+gImv0o+xbp9e7m9/EIWzxuP0hhGSavYG2zsX0YQQFD6SF3tw7VP4hzMkZuuky61qXgtjdNrgSog6zDQk6WiuoSl6jTMi73oz5rITWnMnhyRCwwOyCibW7uYHC7nL+27+Qiz0e/qQXfyWTLM30bBknl/jntzTD8nTJnPR1c6wR9bdjExUIRlZ2jqXs3q5kcAqKganlRqDNYRUn1IG9KGAwboukpfrIUpMz5CLtFDT89GYolW9rU+xYyJk7DtgodrgQL/iBzVr/GyZctYtmzZuy7f1dXFD37wAx588EEMw+Cyyy5j4cKFTJgwYUSZr371q6xatYqFCxcObf/617/OFVdcwdlnn80dd9zBT3/6U2677bajeTl/VzJODoMcHm04XkmYvdjSPcIDsKG3jR5/mG7NwvdWq+mDWIoXw44BeRX3UsXHfjvCvrJaFuzdxK66xhFK8dIn0DuKSafWv2M+dV31YKh+4pl2xnur2Tiwu2CoF/ibUVWFZ3taUE+tYXqviuixQQHnTC+x0gzHHwhxkVJEzi/ZGEjx284ebguWo617AHY8Q9Pl3+SZ6D4W1E1jqtEAQFivpcY3jn1Wmsiupzh76ue4oPzTxJwmsiLD3du/Q+Os2xmXSqCiIqXNK29cQXH5fMa4zsPtrqNT20lz28sc3PA6AV8NMydfzfwzy1j7ZBHPtgymhRJgGT286X/ZksrQm0myqrOdk6tquWnyuXxnyx+o9pRwSmguFYEwfs3DQC5FLGfRmY5zgjYG+/WRuXCxQbblcI0tpn3vs5y+9Dnk+hy5v+Slrt2nBamdN46vb72QlJmPl/QbYW6ddw+lA7WU5G5k4fSbkV4BEQvr8UOEiJIOFQ+Xcct1/8XuDd/GsXNEuzZxYPv9zIh9bthIH8R5Job+kRDmITLC8ggL51ZCIhzlTfHjAscYX9aF81JvXh37TV6JoU/xcLAyQdatU74SlGofTq9FaoLJtqoBJh9JsPR9QlEErlezWJsOUc42sijXleKcH2RlYj9znQoqs5VIXXBJUZBXks2oxQJrsF0KAY24KEsvQzf9SFtS7bGpsHajo+LfqKDM8KA2uPN51NtNilZayDkj3da9fUreyPMoiJCK7DERHRa+pIYXncZMMW6vQI79/9h77wA7rvpg+znTbq+7d+929d6bLUtykXvvgKmGYEwzLW+SFxKS0N6EXgIEJyEQIDgUd2Nw712S1azeV9vb7W3a+f64q12tVgYby7K/+D5/7c6dOXNm5pyZ8+uCuhKsDLVRcV3+95gI3vxkbZt/eekw/2zMIvmBBLICzqYCWljBeiIDAQWKLiKgYv0+R+5dkvdv+hG2dPhE8gpWFGfh9ri4ZQ3lgIU6049or2aLczYWCDlenM1F3PN93Bc+yFn7WnCeriqLRZuBtiJAwpQ0HNCYrsZwbs2CKlCviOFO04k+nOa6w6284x3zcXXJR6Ysp2GrguuaIEBaciyGfoToYzZXvnsu/1Z6gbZAlIOlMqtjC9g/vJV0qY/WyGwOlqrfg0mBJJ+d9y4MZ2y9J4XN3swziIyXhebleFU/YJNt6aZX3071W1QT1GvU+P8jJ0xQX7JkyXFLcr344osve8wzzzzDypUriUajAFxwwQXcd9993HTTTaP73HPPPZxzzjmj+wBYlsW6dev44Q9/CMDVV1/Ne97znv9VgnrGyuN3bbyeMUFdtbPYbnRUUBfSZepQD5uaJmG5JabmE6iuoC+cwVEmvpRN1U/Q6qkG9AmBoaiEpYe9VJgRjDG59wB7W2eOHSAEhLwo0o9T7kPz/fEEcSFP00hCuVY2ZHbztpa1J+JW1HgL4jguS+oa+Nttz/MfNy5E6erB0krsyNzJmQc+x5z/HBvf5zeoqFc3kLrwJho2r0NofkKFXn7V/zhnTl4GgO6mqQxtw/V6aGq7hCb1BnK31lGsgH9+Eu+qLlShsc8I4Fz4UdSiQ5McpGf3bQz3r8cIJ+hL34ffX0+AFq6ecT9ufwCtT0GLwqmrJM8/Y+PxweTTenlo4D85b/b1ANiuy8cbV3L24VYa7lOZN6OOCxd9nfQuhcp9Clq9JHQuHFT7MHSFpC+EqwCeicmx0AQVJcOcRX+F3Gtj35dBNOpoZ4YgrGIUVD4+9fv8T+c/0VnYxcL4WcQOxDD/qxdsqm77F0eh/jiv/l4b+gYojbhXAihSQ1jHiVm2J75f9CYmrAejqzRs3arll3uDUBwX5+BxwkeKNlFVw5tWkGdGKFcE6gxQ92dpESdX7HRd4Ois2mEV54oE3Y9IrGHJGVdOQbl9ANIOApg+20vbVfOx7bFBJQQky9OQz1awto7UVwsq1P/FfEynhD7Nj7O+UM0UDijTPGinBXGOqVel+zTsy+ooCp1KWhA4XWL0FzE0wfJCEgqCnKPilMEbliw2k+SEWROBTiIqPgwUwtEoh26xsDMS3xQ/TXPAvKYRsywwGlWcoovMuCTKQW5b/HfojkHoHhs2ZkEB/X31uH0O1h0jbkAegf6uOqQr0RIaIunlQnM+SiOwKI/oKKMu8WPdPuY2pJ4SAJ+C+1IJd3cZdIHxrjrkah151xCiw6QeECsCqKcFcZ7NI5SJ62ThUUAV/Muka6jbE8Ho03AXlFg5ow1Xs2jx1WHIAJe3nE2dHsHreBAC0rj0lsr4dYu2vmmEfqKCaeJiop0VIry9gfCyZggNQcXCiUcp6ic3tKVGjRqvjRMmqP/ud78b/duyLB544AFU9Y+bUfr7+0kkEqP/NzQ0sGXLlnH73HDDDQBs2LBhdFsqlSIYDKKNuIAnEgn6+vpe8zW8mciU8hhYeLQQ2CClg+pUkGIsZr0xO0xZMxhQVS7vWIThqjiKy/TBJOva91MyrHFtSlXDtTR0p4ilVRdj9Yqf/fYwh+JJ5nbvY2/LjHEZ4GVYQcvGsQudf1JQD3oaGS7uZVbzqfy2+zFM18ZQarF7Nf48FoTr+MSsGTy77bPkctXSTQta34l+93hpUOl3OE1G8XcInKcuQhZcGs/w8J0lk6gzgmjSJbP1l1T8AX7Wfyt/Pfk3DP18LFlPcauHUHQy5y/8Bj87cJAr26fywOFhLmq8hGlr5mIW+gjUTcPX/ThWucTcoU8zeK9GVYh2CMxWCTfBqqsH2Z5+mhcKz7By8qUorqQlPIOFwQbq/+Di2WDiYqHsBmV9Gd+yBIWsi52Fyk9g0V/MJB0q8d7py7g/tZf3njkH61dHlUILKIgWg77sk0xrfA/uY2VEk456WhDrt8NVYVgTNF3Zxk3Gt7k59Fkuin4Q64dDVSGd6m2zf5/G+Ph4t2EAmjUy5V3jNk1Nfg5L0VBEZpwArpwbwkUSKKogJZWgxI5YNL3fYPh+GycPkZUqxnyJdbySVzVOCm7AQZnmwd1bAV1UFSwSRFQjmddwQ1EO3mKN6oPqzgyRLJYoek5eHx3HgVM0WEe1H2ujdD0okSZ4WwXKizlIH5VQcmcZX6efYkIZrf4AoAzq2FvTYw3nXexHc6htYahYuC+VxtrYV0GZ40VicLQWyfGp9HUIgm0QqJeUBsCMBQlQQHF8dD3nYKXHxnPTxSrBkkKu9pk7aTR6fXypfTHdPzVHx21siWBgsyT3kqT6onIJL1GxUgLrJY3mxUHEgym0NSHkUoHzYhGkxFl3VCWAisT+QwZlugdrWpDcbokvLLBMSNeHiC8JYP9qYGx/FZwXi+iXRsfGlgQz6EHbkEV2jIVsOesK6FfEcFSQjoSwOi4Hg3p2iPP98yj+TMNxqAZobPJR/7YGbuj/J3yahy8t+gBzjanV44EOu8LfPL+ZgKZxU/s05vxaR5pjbdqP5apu9ndl0M7uxn3qKdR4hOB7LwcmVg6qUaPGm5MT9nlpaRmfbOzGG2/kbW97Gx/84Adf9pjjZYU/nlX+RB13NHV1J6/8zJ/DjmELTVTwqUEMXccyswipYCpBNLVqUp+c6mMgEmd2XxNlxWZjfTdBVaU1E2Vp5xSem7oH9yjLugBMAnidHNJTvX4NjYj0sUsTzBWC5sIwQ/Ext0dpgDMYx8l3Epx8+h/tc1KZzLaue2mui9HanaBb7WdF/awTf3OOIpGofXBOJK/nvHi1zyoBRIwcv1k3Vkfcr9VB/pj5r0FM17H+zR2TJR8qM89o4Ka+/+RD0y+ivuM5PC0LSARayXVOjJcubVZJLmzmbxYkeXE4y9y4xdTwFPrTJro9hYDlwbLKzIpfR+7341+bhZ0OvnYdfzHIWaELMOzLESWBq0Jy7lRCeS/qhvT4E/bZeH0OR9zjpQ1NqQTTHJsFQcmLzb1k6iFyYwJ3VxkRUVGme+iK7EdvWIksgljiQ4sKrHuz4FOg7IItse5NoZ8Z5pIpN2JUvFA6xqIqQZZdtCui2Pekq3JKWEV9d4By15g7fCg2k+IOD6X9Do3vSqJuzYHpwvIQSoNOaJuJ7K4gggq6T6AuCaDM0wlN1ZG2RAsdCZXxvoKn/edxsub/K50Xb7b3kdlRQTkrjLrMQaYdhFdBxFVcB1Q3SPe91jinjaHHHQJT/SQSJ0ZSf6X3I99r4fmLBLiSsu5BmiNlDhMCsakysQR2nz3Oyw6gMjDMschDJoqt4u7MTfjN3V5GuyhMIjFmzs/1lUjOtuH3Q5BzMVoNnLVxHLzYGYGVHt/G4LOSltkGicT4Six/jDfbGPlzeaPWUIX+3VQyk8k5Y4YIxRDkXhrvfZTd5FB/ns7gAy6WpmMUXOz7M+jX16OuDCDTzrFNI3steE89IiWJ7ByGThMjpMDFddgxA6UswRBoF0aq701XIhp1RFxF2qBdFEEKCXtLE9p28w7qxVGcl4oYH23A3V5EDjkozQbOrhJeO0rBGe/h4Xshxqql83liaDN/v+kn3HLm52kJ1JM1Tb79+FaumtpG3rLwVwQydZzrMSVYEicRrX5phjOo619CtjW+qcfhm7lvNWqcbF43PfC+ffsYGhr6o/skk0nWr18/+n9/fz8NDcex9BxDPB4nn8/jOA6qqjIwMPCKjjuaoaE87pvY0tPR2wM46MJPqWRhlgeIuJKi8CIcF82xSWRTHGhoYWk+xgtNB1EEDLsmTjBFoOJlan8DexrGexpY+PCUU+Q8jaPb4sLHAXuYrmgDLZ176Q7WjTtG8dRTKmwjlyv9cYWIDFMoDzEwNMgkTyMPH3yRybL5RN6WcSQSIQYGJi7A3uq8lo/c6zUv/txnpShB2ppWc7jnaQB2Dt7DtNPORHtkZByqoF9Xh9w90b3X/6zNyrfP5nMbf8K/LrkO96nvc97aj+MMloDouH1FvUW6+AIHSi080tPJJ2ZfSHO/h9Btc3HLoPhgxTV/j6ZKuo53eyT4nTDim51YpgQVtMtiJGa2kFYtAq8gRFDkHez7hlEcOO2SJA8Yv+SZ4dv5xEX/zsbDD/DIgf+mZOW4YfpX0KwW3N5B3IIH/doYSB2pKQi/iuyV4EJStOOEXdRjrDeoIHQFqQmcm4J05LYRjS9g0B2idd51NE0+B6uSIRCZjLMOrGE4fB94W8KIAHgLCrEncjiPjT1PdVkAN6iQk2VG9ahlXlf+nDH1586NVzIv3ozvo7DQEGkb69Yxd13RqKO/PY5VUXAKExf3do4Tch2v9H6Ew14MLYDzXB552ERcWMcRBVaxSxKb50M+Mb4dZYqHVKow6v6uqhBsmejSqy7wUTEsfDO9OBuPyRI+y4uDQ3pgbKCGUHF/OzBmZO80UR8dxpkeOzYIBQCnKKkogvwrvF9vxjHyes6J1wOtXEL1jd8mj/dwjhjXGf/qdV7IgyVRF05UrijTPFXh+/6qkA5AzoXfDqB+rAnZoqOfEsR6MDOW/V8F/fp6ZK+NfW8adYUPMcOL0z8+x4jwK7i7S+iX+XGf3wOhAEpDEOvW4aqFfdLESxCuRB3JDZS3S3TlBlHyw6Tye/hYS4WyofPV/Tm89YJFjUGU3vFKaGEIcmcqZP052o7cln2HwTQZyFWvr6AU6akM4lU9JLU6dPeNdQ95s8yRmrKgxpuF1yVGXUqJZVl/MmZ81apVfP/732d4eBifz8cDDzzAl7/85T95Ll3XWb58Ob///e+57LLLuPPOOznjjDNOyHW8WRgqdiOkZ+yeWtWEUSjVxUhTdoi0L0DrUJL94YFqQh4ECoKMsDgQG2BRTzud0eFxLvCWGiTk9IJ0YeQDYAiVkOJhS1Bw5f4OVMfGUceGhggHEQUV1xxG9YwX4o9GESpBbyOp4n5mBFu5r+95Pjjp0hN9a2q8hXBdnWXzb8I0c/QNbaFUGaKyZBiv2oD9PKgrfdjPZlHnTIyrteOCbmsYW7r0RyfRaJfRn/sl4VXzKbRIzK7q3BIGWKcd4u5d3+N9p34XheX095SI3+5DjqzV3BIM/Fal/XoFT7Ok0j229NNiAoREzVlgjmx3wL47hf7uOnxTdZyVftQ8uKsiKA0aTlmgeCFUtMg972I0CrRJOiVvA4omkDE433kvydZJ3HfwP1jf+XsAzm16N3OH5mDdumn0/M7jGvoHFiNMDfu+Au7mMijgXxuie8YjtL3/Ysz/Gq4K616Bfk0cqUMqNMAdQ9+n3+7krNLNmMUEpcU7uXPnd2mPzGGg8xY+OusniOe9SBvKnRIUaF4L9rfGL6ScDQX0mV4iuYM4UlAIT8KVtfrSbzSu4eI+lB23TfZayJKLGlfR68DTqOJpVHDykuxmGz0iMF+mvdcDIQBVwgUxHEdF6FB/ocvgfTZCAWd2EMWv4ng1BKA4DpZPZyyeoxrnLgxQ3xHHthSkFKiOjdKgITQF0SBQTgvg1leTsSo9JcQUD8cOUSXrVGPmj6bTxFMWKHGqSRGPEgpjK1WcWg6Gk4uvGXSb0CKV3ObqwxA+qL9QR1pQ3OtQOuTiaRRYKYniAcM5SoCd6cWaEqKSc9E+5kPcPwT7KogmHS6OVxWNncfMABeUoo11XRK5Jz+uRB/OiPA/1Yt7fhwxTUcplRB7VGT/SKWFeT5k2sHdVUHOVXFe6KxqD1rCqKdPw91uEZkl8ehgWQpDG1zcMhRPSbE8OItrvGtJk6NOUbjv8RupmNU5rWt+/nLRV/nn7V2cddVCZv5KVi3rhkC7NEo5ZKK29tGS24d2yXysPXnypy0nrBmASZ8Y4m823ExfuarIu7JtDde3XYBPVjUhHllGM3PYRpCKOEY78iqQiqREGR9ehFv7LtSo8Wp4XWLUhRCEw2GCwT/uGpVMJvnMZz7D+973PizL4tprr2XhwoV86EMf4pOf/CQLFix42WP/8R//kc9+9rP86Ec/oqmpiW9/+9sn6lLeFAyb/ahyTOMrzD4cPAit+pJrTg/SbcSYnQnxdKwPfSStso7Ag8qgWqEvlGH6QCNbW8bqNruKiuMYGHYeUx9LVFcvAhxQUqQDYZqHujnc0D76m/QJ1HQce/gwatPLC+oAQaOJ4cI+ZiTn0m+mGTIz1BmRE3JParw1UUUzpy//BqYziKEYhDY+iHvoXpwPfQVT0zEesGCmRCQ05MDIgkyFw+eWebR/MwBF16D17C+Q3/8IdN2P94pLKfdaqI5Oyt/B3V1fQ1U1ck6Qf99+iK81LxkV0o/gVsDJCJKLHDItGoXdLt42hfAiFS0o4LvdxxwAMuNglHQOn20Q7w+iehV6fm5i5ySKFxIX6UQ/puIWFQ7/tDKaOV0NQst7DOZ3ncpTxm2jTZ4SPBfnkc7x5ynbyKEiMhWqCukj53YeztHQtprH913Hqr/8LVrKBY9CyVfikd6f8FT6NqR0effc77LnD0ECcZc27zRWTbmKh3f/Alfa3JH6Mtd94KuUdwtcCwLTFRgsHd87QHNwH/wh2twzCTgVctHXN+ylxp9GMxWs/HFcYisS4dg0Xe2h/z6b/DYLPSZovNpAOSa3yeuNEAKrJUhqg0N2Y3XSRVdotH3Ig3QkpT5Jrs9H+ZALCkRXePDmBao6Puu7bWikczrp522Q4GnykJyh4ymYmIpGNhEl/ayNlBBZFiViCLRjhAYROE5lk5CCooNRsmm9RGFoi8TKQHS+IBS1ERWVrH48k26N1wNh+BjIDNI8s57QPAMElA67DD9hIV0ILVBJXq2DFJQOObScC+L2kbCIyR4KoSD9Px97uTdcVE/oSpt8l4Ldp6JJB29IqVrSj0IaKunNDvUTNDkg0y4Fw8/AIw487hJZaFD3rhjioIOQ4B6q4DxZVW7KIqApYLnQlUVZ5iIaQ9j/0oduSvSwSuDt9WQiNh4tjv/OUzAHIKqA/0yHpuipHOx/EADLLuIOPkxT4Bw+1bmV917TxnnBKPvdw9QHh5h291fR+w8AYCsauet+wBe2J5ncV+aKBQa/7HpoVEgHuPPwU6xJLGC+Zxrh/AHk774BXdsxmmfjufRvyIWncpzI0z9KvxjiZ/vuZ8PwLpbXzeb6KReQkPFX10iNGm9hXrOgvm7duuNu7+rqAmDFihV/9PjLLruMyy67bNy2//iP/5iw31e/+tVx/7e0tPCLX/zi1XT1/1fk7AH0owR1xRrGkX5QQLguyVyKYd8sOgMpVGX84sKLQhaLg6EUp3ZPxmfqo1Z1oYJpBfFa6XGCuqGoBF2DPaEgU/sOjRPUQaCqdTipQ9C0+I/2O+RtYjC/k9lCYWaglXWpnVyYPPWPHlOjxp9CSi+60ooEyguvwtO+mL6gillKMbk+iH1/Bu28CMKrIF2JO13lr3v+GUe6XNm2lmd60qyppGjuzXFg2WyG1TyP5L9GX37/6DnOmPoOugtVd8WcYZFUx7tUCg2UlAW/GiA220vsgijoEqFZmH0CTTnGv10FEVKxDYWw5UMNCPruqArpAG4Z+u62aHmPh9wWe1x5MycPxf2S0I4Kq6+8mr2D1WSamquCdRyhwNBxNk2Mi1T2KIRaZ9LrO8w2N8jXX9zOojofaxKLuaB+Ps3BBjYcvJNVV8fRPYIDud00RZfz4VWnUrYLeLUQLx78Jmsu/D+USqDt6Efu8yKSOrJvTKATMRURGULuX499aDPaxZ/GiE7B/BMlI2u8vjiaglgZQj51lAeECjKu46gqffdYVHqr49FKSXp+bdL2FwYTqg28jkgpKR6WZDeMnTP9vI2REGgJF7OPqpAO4FZ/a3qHgW0f3QaYRZX0c2MCWKVHktrgkLhEpZxTSD01Nl4z6xz0mE5k5nipwzJ0ODUEz4/cLwHikjosoaMNFFD/Z5jkPD80qvB4EbciMT7bAMcpjFDj9UHqFQaLaSYNhLB0iQzrDD06NhhyWxy8cQgtEuR2SqySwHd5Ha4pcZq99P9qvCKq/34b7wd0Bp6wiJ6qMLjeZdI1dTg/Gxh7nZ8aYvAlCC/XsHuqK6yjUVaGGHpmZPxKyGwW+BrB31/CfmZ8DJAIuVUhfQS3TuL8KDV2rqwDtw0SuSHEwGM65sDIDy6kH1WZdc27RgV1ALPUSczQOZgr8EI5xekzI2zt7eCazmHEiJBePd4m8Ox/MrX9q2zs0tkz4HLa4rk8wPg1fFdpgFPUOuQtfw25kZwl3TuRv/w/+D74E4p67BU8pSoFpcRnX/wPukvVdh7q2cDu7GG+u/gT+N0/30Jfo8ZbidcsqH/pS18CoFQq0d3dzfTp09E0jd27dzNt2jTuuuuu19zJtyJFmSJ+VHVWxS5gO02gQn0uQ0Y3mJpLsLG+A4WJrkQ+VDJKhb5ghilDCbY3Va19QkBZBolYvWRpH3dMneLnBX+ZFb1D6LaJpY0tslV/A2b56T/Z77C3hb0D9yGly6xgO8+mttUE9Rp/NkKAoozP7myqfsp1Tex66Vv09r9I/RX/SvDnIez7MwDItX66vfu5su1CFGHwQn+Wdn+AcHI+e0MxPtpziErnPm6Y/hfMpBfH7CPmr+dQahsJbxYQ/Kx/P1+8eBHiXrXq1qpC4+UqTwnVugABAABJREFU4tF0NfRxZxl29gKgXh2jb6PB5OvrsX46CCUXDIF+WRRadIq2gyiVca3wqJA+igPmgIvirSoCGi9TCTaC1MEuS9yGAInQJC6ddROKpWD6JeqyZuz79o61oQgIqCiTBE7HeDcAu8VixoqbyCpB5gUKfO20RRzOl6jzGnhkF//29F+wdvp76Le28pt1/4wjqwveRa0X0dJ4ATLXQQgFTfNQqRSQTWVkp4pxZghnbxl3d7la6up0E/vuG0auyUKWcyiuNRqqU+MNwlEoJgMELlCgy6x+8VeEKZUVREWOCulHkA6YGQkn0eClKILCbhs1CJG5oirobJMU9rjEmjUK+0w8jYLoLIFjSVJbJJVeF+9McJwjbUBlwEULCWILBYouyeyD4n4HaRsU9k1UPOR3OkTWjK9MY2YgJwJE3+ZDmC6OR2NgvaRpigQdRL2G2mZUlR3Tvbj7y8jjlEKt8foxmO8gH3KQCuitOuktEy3c2V0S/wIVs9uhd4ckfqZB6hmb+BlM1EG54BRc1KBA2pK6pQI3ouPe2Iguqs82n1LIPewSmAvDOwQNb0/AE2lExUWcESFrGrjl8f0odisEprooGQ/utgp4BdrlYSR5lJWTcLd0g6FQNlz0I0NIVGu1i6iKHNQoHpo4toxyA0fXwZw86TKm5UNc0N7MsnCUupLKB+suROu7ZUJEhpbrI2ZYgE7RhDpl8oT22/wNqNmeMSH9CIUUarYb6l65oN5rDo0K6UfoKPTTZw4zRWt5maNq1KhxNK9ZUL/nnnsA+PSnP83Xv/51li5dCsC2bdu4+eabX2vzb1lMMniVaoI8KS0018YeKc3WmB1mSE/SUoaybuE9jjpfR6GCS0coxYqeyexJ9GFp1S+ULXworo3mlLHVsYzMXkVDVz30+IM0D3ZzqHHyWIP+ELJs42bSKJHoy/ZbV/3oaoBsuYtZwXbu7n2aimPhUWsmhxqvDlcO0Dv4AvlCH8HmtdhKiIQewefqDAxv4HD3UwDc1/tXnPr+m0i4c1EjQQaNbVgiz0uDAZ4b6GJhvJ5rJ8+gInQeK2QpjWQL/ve9u/CpOpe2reBw938R9yWZEvTz6YWN/GDrQb7EFm64bjpztDCup4C6bwh1YQP2vqNM3yq4jR7sjOTQg4JJn0oicg4YCjKgYP9qCOU9Bo6QKGVQvFVL+igCtLBA8SpMe4eD/dsBzF4LZYYH/bIYrk8lnm7n7FQr7pYSxDXEMoHmVXE29iKCBupprUhVoDSZuFEFmR5ZnrUbFNoH8BDHb/kxc1sYfvTzLL3kB9z8wmdwXIvlbZcwK3km9+/811EhHWBz5x+Y03gW/mAjraFJ2E6FQvkleguHiJ6+lB2HDxFrDtJwgZ+Gg/cy7AbxLzwH7YWqYlZEmzC1ILyJE3a+FRCqiadVw+3WUbpNZEzHcRS0OgXHV0bxKuPHIyCCJ9eNW0qIzBd4khY8VVW2hc6IYNarZF+0aVgjMHbnkXfnwKsQODeG3a6Ni76QEnwtguBiCx5KQdHFtyyAdXYYPEW8TV7yW8ef19usgDI++ZZa75DfI8nvFlQD0iWeRoETyYLiRV0WwH4wA5ZEmWygX19PwW+CXavPdrKQrqROKVBscFD6bTyNR0pljmE0CHpvs6g/V6fvLpPCLofQAg3pVBODukc5Hyle0CgRmeshLEs496WRtkQ/PQxInMdy+NsMWi6Igw9KndDRrxCZW4eigV0E1TiOsaRFgZKKeroX7ZIgMmsi+8B+GIQaRD1/CaUZFlQkYKNM8VSTcu4pg0+g+gTB6YLc9vHvUH88wtm+f6EgDlHx5QgEk3ygrh1vQUH9XR736Xy1lOc1V6JdnkAWeiA/jLPuTtILr2V9/5inZksgwuRAkoOFPgSCd045m6meFqRMUVp1FYpVwbvlcagUQAgs76tLsOZVj6+ofbntNWrUmMgJ+7ocOHBgVEgHmDdvHocOHTpRzb/lcEWBgKjGdjt2Gq8UVJQAAmjIDtEvltDrS6OJl69V70VlWK2Q8hVoS8XZn6jWABUqVAjhNQfJ+1rHHVOv+NkaNFgy0DlOUBeKgkoCp+/QHxXUoWpVHyrsYWp9Gy2+ejZmdrMyPu/Pug813qKIHE+t/0dsoVFoeQf/uf4xTNehyR/mWysup3/gxdFdi+VBHt3/Bepjs/GbdXR0P40iND6x5ut8aNZ5xFUPuqvgKgqDVoWrJi8g4Q3iyqpAuyvVx8fmfZLBoSGa+ubRWvJywZx2MpEips9lwO3mxy/9jo8nL2FSWaB/vAH7mRzYYJ7tQYlD/Xk60oWOu2zMPoFQJW1rTMTFfryihGYamJpLw2UGvbeP1P8VED9TQ68DzXSxvtM/mozO3VPB+vUQ+jvrMBwN69e9UKj213lMoH2yAa29DscrcPtcFL9EGgW0K72geBE+lR3uH4hEWrGH+wjUrWbvjtuQ0sYtDrK45WqmNr2L5/tT/POWFAvrP8MNM+P40gZCt9ieu5tUaQA7GMNrtHKo8wkeevpzAIT8TXgW/w3/sO1mvrfkHfzBv4X9PZuYmlzEeR//Dm3PPoL0j3+v1HhjsA2JureIvDc1Yl0robyQR/1oI3lvJ4lLp9F365grcHi5ihnuQ5xMkzrgUyo4vzuqvNrdwwRuSKDXgz5kYT874opecOGuIfRP1GEes3zxShPrrrFKM3J9AU9MpbCogjFDR18vsIar80sNQnCJxJLjbY7CU6bp7QH67rZwS2AkBcnLdfKiiJoD8Ycxl2f3oIn9RJbiO220Y6pI1Hj9iHsm0bn/+7RHmvGXPXh0iXu6wuCTIxUAAuBrU8htdhh+yiK2RmX4MYfg3KqQn7jAYOhRCzsj0SKCxstVlO4eookk1n+MxWs7j2XRLohAUEEeNtE3ZyifEsc3WaF00CW9SSI0aHuHBlmLfExgjRzuaxd48xb23SWgBM0a+rkR7Duq41MC9q+z+D4cp095gujlc9A9Aazfjs0BZ12Bho8mKXZUQ6EAIstUitvB2bCYQGgJDVensfQMqiJRnyniPjmyY87F+q8s+nVJnEe+hKifhHLdP/Pw0ByyI4q5hc2CZp/gu4s/Sa81hFc1aFDiOLLAM4XneER7AsPj49Krrmfms09SbFvKwwWV80MC+QoVsI1qnKva1nDH4adGt71t0pk0qPFaAsYaNV4hJ0xQ93q93H777VxxxRVIKfntb39LOBz+0wfWmICUEiFKBNUoAK49hColrvDht0x8lknSrWdLrBON4yS/GUFDoCE4GBpm0UALB+oHqlluVSjbEUJmL3lvy0ja3So+RacrGOa8vgNotoWtjVnCVb0eO9+BzqI/2v+qoL6TqfVnMyc4iaeGt9QE9RqvikKpg4HhbUxe8o98c/eO0e09xSxm93aajCZ2H3NMMrGI7r716FqASLidF7f+iLWnfhfc6hxxXZeLW2fy7W1PsTdbdcfzqTpfX3Ex5XwvTc+eQWr0VA4Nlwd5ILaNAmX+OfUu1J/ngRSWgOJ7fWyd2sP69B4u7LqIuJ4n+4dAdQUmoP4ikJPKVAIm4S4V6yfrUEIGnncup/2GAFZGovoEtmeAl3b8P5bWfRXHPMYNucuCvItQXERERY4I6pgStprIkILiLyGaXRgOIe04SrtBxZslbR8mprdTSR3CNrMEY4tRtar3zMD220ku/CK/2N3BxsHqynJPJsfzvUPc5FtC/1YvC1Zdj/D3YXt87LFc6mQTV07/JPsr+9hy+F6idph/XfFx7tr41wwXewDY1vc0vbkDfGDtV9BTOU5MJe4arwW9oGM/cqwLqwv9Ft66RlwBjW83kLZE0QROWaKZMcyT+PCEAPlcccJ25/k8epuB80Jhwm/KAQc5ZfzyRXZMzFXvvFAgsLYOJyNpXuZghasXphdMSIHSonJ09nhvWmLd+STtZ8zEDflROgYQd6eIvW0+DNrYx7Tvbi4TuTpOoWZQP2n4KkVOjdyA+FcbuzQAAoIL/QSuj1HoFkgbBh6oKp+sIUm4TRK8wEX1ZZDP7UUuaqfxmgSlDoFTkHTfapM8txHfloklwZwdJZRpXtzNReS2Er7zJcYZDu6ZBm7aRcuYiJ8NQsGheW0Ue6UXJaGiDVRwfzmmNFKiOs7T+Qntyw0VEnI58gIX+9fHjHMH3P1FWi/2YZUVRFwlt80lu25EYZuTFO+IIt52kJ7iDiY9u3Ri+5kY6F7k4CE4vJVFi1YTqRe0x1Viuo2Ohe56mKw244oKaWsfXbkd3LntO6Nt/Nf+H/KBC7/Orw562bSvm9WNzfiPE255PFRX431tF7A6sYCOYh/t/iTTfC2o7ssbmGrUqDGeE/Z5+ad/+if+6q/+is9//vMAzJ8/n29961snqvm3FHm7hIGF1xhRdJi9uGgITdCQT9FvxGnI6xSMCoE/sRz2otJvFClrFo3ZKD2RNEIB0/UhFGck+/t4d6awHuawz0sy1UtXom10uxJIUDGfw1eW4H35F3XY28qh4aeQUjI/PJXv778N23XQlNrLucYrwx3J4pY6pqbrpGCMpu2PIXwaU5KrOND3DLoWYO2sb+FPT2e6fiPeWQb5XS5uJIVe8mEdie6wuzic6hsV0gFKjsW6gUNcZc8it2PcqRh6yGL1ddM4lO1GveeoRZYE/2/KnP7x2azpnYqICKxJfpz3HsRK28hAmZ64iR5uZVI2gnugr+oCnqkgbn4aAXjbwqgXTWNj1zeYPv2DCPM488kQYAhERY5L3Fa9QSBtEKXD0LMTe+O9lN7xT3iyCdS7IN7TiFhuUJodIcVmUL00z7gUNT6L6NTLeLK/NCqkH6GzUMJsL2GZATY9abPqkkb+z7bn6MyXqPMY/GDxxZyS87Jc+yvICQqeAqli7/h7VuxmsNyH4aZoEUv5k8Xja7yuCHFUMelx24GSQXG3i7dJwcpItCA4BYkaNyB08jK/SwkiOnH8i4iKO2Qj6jTk4DEickjlmByqiIllsVHqVRTA3V1G3p/BM8NbDVfZXUacEkLOG1+ZRmZLqMunQFZH2V9BaY3ihssoZRvpndi+aNGqCR5r4/ykoRcy2PfFqrlAlgexpgap5ASenMDOSNIvjI2V0DwF7h5GX67j/GYDOBI5bzKdP7XHPbL+xwSTl3vh+fEJOZWEjts/koi3RUds3Y8uHURTHJn3Yd+dHvO6fyiNMcWDWOBFbhsfTyLzDkpi4nJbhFScDWX0+UEcWZ4wigRA2WTI44KtU1k3vg07Kwk5STJqB7JeII7VNfhMsEcUWAc2EDstzdbKdn7de4Bl8VksD0wjmauAdHnOfYFdQ+sYKnZN6OemwXXsKa2iPejHeIVC+mgXpI/5nmnM90yrbqhZ0mvUeFWcMEF92rRp3HHHHQwNVbWIdXV/vIxXjZdnKJ9GlxaGVk0mJ8wBbDwoCtTnUpg0MejL/VG39yOoI1b1jlCKycP1VUF95D1b1qIEKn0TBHW/onMoFKCl/8B4QV2NIBUL2TOMmPLyz9ejRRBCIV/pI+ptJOGJsjGzmxWxOX/G3ajxVsQXaCEYaCasjP+q1xk+/Klu2LCe1XNXs2jaR/BGz6XvV0lSR9ZYCrS+wwN9cUovqhingqXaFLMHGbAnxsZtGu7jOt9yjl3jOEXwOBqB8nFek2WJu6uM/bs0AOrqIMraGE8qT9BZyfKH7Vuo80T45ey/RFWPs7BRBWgKS6f+P9y9FezhIuqqAM4zY1YV7aIIrnQRYXV8CKZWFWLwSWT3o7gVlzvO+hRnDEfx/HAIt1xd7sm7LfyZBNYlp1JmCE98JvcMH+SCwiAQOiod0VFNj5R/dB0oZEET1VSVf9cyi9YDXsxbhkbd8/2nBLhx5bf4t/1/eUwbPoLB40hNNU46UgPtwij2HUcpZfwKSoOKogi0oGDgvjGh3NuuEJirYXHyBHUhQF0ewNlQgpGxi0+gNBlYtw6jX1eHe6AyOu5o0LBjBlKOj0sWdVXBWXaNCGq6QD3dAwhwJfrb4zibi2CDflUct9dEHiN0iLog9r0Osrdq4XfWgba2EelXECEbZYGBu3VE8PEKtLWeV12uqsZro6yE0A7biMkeMrEQqYdcqm8yl8AshfAyleyLDsEZgvg8gXy0grw8VE266UgcV5vw4nNyIBuMqlJoaGT8+BSU6R6cDQXwCLSzwuCA7BxC1HlwByTaxVHIOdiPVb8e6pkhrDtTaCuDuEflMpEdJuolUZytpbFxHFAQMRWyDs7zZdSVQdz94/OfqO0+nJv7aQTkygDD00Lk9411XvXDBht+Oxjke5cH8P0wO+ogIloE2BvhSHjHnDP51p7beayvWrI0VxhklbcP9/k7QFGZe+rFNDdfzIOHb+dYUd1n1GM6Lh+eM52gXUCtZHE8EcpK7T1fo8brzQkT1AcHB/nc5z7Hs88+i+M4rFixgm984xskk8kTdYq3DP2pTiQaiqi6nSt2BnekNFt9Pk3RXUi3LztaO/1P4UXlkC/D9HSCaNFP2l9EKFBSYsQre1GdCo463jKfCSc5bd82XnJtpFIdJkIIVLUBZ/gg2h8R1IUQRLxtDBZ2EvI2siA0lUcGX6wJ6jVeMQcqBfyzP0DIyfKO9pn8uqPq6H4wn8ZcfD7G/vVo258memAL+cUXjUsOhAvDz9k0FLIE/Rru3AhWFDRjMZOsIrB53LkWxpPoIbeaOvoovYB/qkJKK9DvLzFbC4I9tkAScRWOqk/tPJ2ncXmSs73n84HOr1B2LLqKg+zXU8ybFsPRlXElebTV7TgeDWV3GfvWqhClXhTG+EgDMuNUF3ABgeyzkV0WxgcT2E/nEH4FZbYPGbKRXb9Fbv0NpbM/xH91dnNFcM6YoHOkX0/l0dZ4+E3fN4j7mnhP+zlIyybl1Tm7NcnDnX0ANPm8fKF9NrPtAJwrSUuVnN+l+1CJs+obWNrpxX42M7rIFO0GSqvBzOElfDrxI7aqzyBxqY+0E9DrqHi84zL113hjcIWL2qqjv7ceZ0Me0aijzPaBApqjjStnBlDucMeVCTwpfXQlcm8F/cIo0pGIkAolF+t3aZBg3ZtGvyKKo6k4FUHJ0vCgIMQRAa1qlceroC6uIE4JIF2BUC2Ez8E2bLTpXqx/7R87554y+g0JTHGMxJZXkL3jrff2k0WMNWHwAWcGESs0ZFmixAVSM7GDkpOo13jLU/I5KItU/HUhUk+Of8cUdrk0vcPAONXG12Oj7ndwZnhI7daJrJoKj+9Fc8sII4A8auirQXAOmGjLA4iYWo1giqgwZKNdFq2Ow7tSaBdGkL4sztMmImJg35dBme1FuyyKiGmIehV1lhdZcFFPC+K8kAch0M4N4Wwrol8eBUUgSy4IsH6fBkCZoeFsLaJfG8fZWUJ4RpQEw0eVwHyuQOQjQfL7ATlSavfiCj/q2kiqUuH6yhP85/85i8CASUFkSUf78BYckp4gdusMijMW8dimHzM92Mxl4VM531dH+FefBqtq/Q8efgnn6k+xpGE1uwfXY7vVcweMKHMaTuObzS1MyR6CO74Eg4dQktMwLv9bcpFpNWVVjRqvIydMUP/yl7/MokWL+Na3voXjOPziF7/gC1/4Aj/60Y9O1CneMvTlD3O0n51qF7GderxuBcOy8VphXor14nmFxVtVBJoQHB6xqm/yd4ACrqNS0WOESp2kg9PGH2P4GfJ4EIMHkQ3Tx7Z7EliFg2jWUtBf3gUq4mtjILeNKXVnsTAyjW/v+zVlp4JXrUWu1vjTDJhp/t/uB2jzJzijYQE3zVtC0bZZGgjRPdxF89t+i0il0MQhnHxkwvFOGfCr8GIefXWASkxl6FCERUaQWye/i9vL2/lV7ybOa57FyvoW9piHmPXOOfTfZ2ENSwIzVGIrVXZYWX4w9Byz3n8xyd/YkHUQCQ3t3AjWbUclv1rgp6wZBPp1bmv6Kk8rm/jHAz+hQIV0pEj0xmW4W/uQRQt1UZJCzMZf8uJsTI/1+Q9ZHLIo763HiXoxu2xUW6D3WwjNRr02inAFcmAj7n1/B6UseAJsT8wmd7AbIcREB1xdkCkPsK33SQA2dj/E3y6/hcmFAOfE63lbcyv39vdwk9GOvw+s2/ugLAk3aMTeV4+hKKz2xVCGwR5xvxcxFW1JAOv2qoKhiQQty9+J8AjcbhP32hBDjeVje1LjDUC1gEEH6/ZhlDYD96US8qCJdlkUicRICMKLNVwLFA1KHS6ycnIVLIoisDosnCk6pkdH+FU8jRL8uap7s0fgJj0cul0iFKg/DYx0BfMojzJFAXe/i9BDSDMPFQviEezHHbRWDXvrxDh3++kc6uLxpabcI3KRT6mWyOq3qt4sFlTKHrrudHCKMOKUTPJcH6LBqnrI1DgpqKrB4GkVfL3qcSMOFF2SEQUMJYw9VaDN9WI/B8W2NvTrW3HyLi3vNhh6wqK0T6LFBE2rgFvT2CWJaDVQVwSw70kju4/SwKwIUon7cSLT0SIW+kgVHXdnGXVNCOuWQbTzo9WY9g4TN22jvT2O0gxuajPu79txnpBo54Zxuy3c7VXtsjLbQMwo4exUcW8fRpnswU3ZoIBMj1cadTp55Pu9NDg+9ilD/HPHc6QqVc3aQKXEN4Y2k7T+k47UdugDnx7kho98jb3964jJIT7TeBXn75xD8Nlq3hP1nH/H2fxFGNwHQHDvFvoSWa6d+WGyTgG/J8bk+Cn4ZRMBcxD++9NQGvE969uH/NX/xfcXP6aoRU/kI65Ro8ZRnDBB/eDBg3zve98b/f+Tn/wkl1xyyYlq/i1FqtSL4voAcF0TzXUoEaGukGHIaCTk2rjqq1Nh+lDZF0hxTvd0fKZBQTWRFhQDdcQK+zGs3AQX+IFgnMRgB90N01BHFiaqp5GS9yX8PTay/eUVBRHfJA4OP46ULiHNz2R/E08Nv8S5iWWv8m7UeCvS7K0H4HBxgF8efASAJbEpnF5ZhPHCWXTt9gBJtNhsGi+xGH5yvHdJdLZA/r7qvpov5CmUXKZsU1DWVxfsH54yg+uuWkYKi72FvcwyE/iyRSatro5zUS+w7hmkeZWXq6YsoBwpkvnrOrZ39rAqMQX7v4bHXBibdYoL4wz8dMxEs3j5Qn56yufYUTjAtECCfaYH/7KZdOxyKB2sMC3i4lcF+I4JtJ3vZ7hLJ3PvmFkzfnqI+FyJyDo8ww6mRCskT3sHQtOxGqbyrc4MjpQM1znEExpyYGxxp14Q5s6BL4/+/3+n/wLPwwrWY33gwJxTA8w5bwpqr8S8ZWB0P9lv4/zPEP/zoVN5cnAAd7BaMs7dU0E9JYj1QHpct931RdQbGqg0hyg8L/C3+9Cng+mbmOCrxklEgP1QBmWSB2WmFwou9gt5ZM5BTaqEl2sM/G5MGAktVjGSgpP51FzXRZ4Tp/NWG6dYLVWlBqHxnY243RaVvED0Kky53EbkXeRwGbfNi32Uh4uUIDwq1q1F8Kqga5ArI9oMXI9EaMeJgTdE1R36KGREh6vrKNo6lbQgsFpiSAvbq2INMCKkjzH4vKRloY6tHptmrsbrRUY4xEM+7JyOt92h3HGUp1JYYAQkzo+DdFeqVuu6VUo18/uTLvkdLlUli0nDFQaJVS5qZwm1KJHnRBBeAQ069FloZ4WRWQf7oQxc10C6SyXz2yPPWdB0hRdPVEWmHWS/hTAUyDpYd6YQSR1lthd3W4mKYSN7/4D31Fko9ZfjDgjUZX7UmV6Q4HpcHir/HM9ZdSxdvhblkI0zTeDvV3DXHeXektBITg4gPaAqJg/uPUBvefyAjOiScn5MKVWy8uxJbaIjv4cFrRcy8wkfvseq813221i/8qFf9wmchz8NgOvxky/uomfjD1AUjSXzbiRAMxKJku0dE9KPkBtEyfVBLHpiHm6NGjUmcMIEddu2qVQqeDxVi2mpVEKImpb5zyFt9aNTjf1x7DS6hKLwE8/14cgmhrx5tFf56BQEiiLoCmaYMlTP9qZunApIVPLeJLHCXgbCC3CVsXaHI/WccmAbz5rDTDWqru6K4kcoXty+TkT7lJc9n0cLYahB0qWDxPxTWRKZwX19z9UE9RqviDa9gY/Oupb/3HMnpmszO9zOF9vfRbQ3iNXqZSgtMfvBTkGx0yDwDg37KQe3IonPF3gP5KAkQYWXvB207IqhrD9KKD5gEnjRQi5XOMU7mbpKEOv2AciNLPrCKsb76ogYFksDRaYPleiPuHTGivxj94P809vPw/5dulrz9rJ6Bu8Y7/uaXe8waW4zqXAKnXaadR+yIJlbryEdFbcssDw2xqog5o7SaAy6OydI5uHxFs3hp2xC7X10BLv5zI5fAPCVxZ9ibiBBXUnle1Gbx4f7ifi86B8PQNnBebGI0uKhp7mHoV3ViMOFibMIdPqxHhrLRuw8V0BrNJCRiWE0sstCDtsE6z04S70YQxJZdKueNKVjFIVNOplBjaEnHEDCSy56AhLv1TH1ml/wG4Yj0M6LgEE1djWiYryvHmm5yLLC0EPjn01uk0N0iQInMfRUCIXcPmecEOzkobBPkt9ZTRCG4hBY7SLuGkQ06jDTj6q644R1EVEhqaHO8iF8Cs7WItqaEKYDxlQPzvN51Hl+UAXO1iLq4gCuM34c25rKwAGdYKsgUO9SSimUQ17CAqQANSSILxSouiR3GMo9NZ/fk00JD8nuMod/Y1F/vo4RFxQPuHhbFOKrVFTXpnGlpFJWGd7gMvS0i3eKRn7H+JwGgw+YJC/X0AsO1n0ZAPSrYli/HILMyL4hBf1jDRRKOpkN4+dK3/027e9OoPaWEFN0rGSScl5iXOuB+4dwHq8KtY4f7l1icHnjEjzrc9j3HOOxElCQ7/ZyR893uVf9N648+zPcufW7XD/ti0xaMRV9l4I1w0U9s0JOT3OoVEZVBBe0TeKBzkOYbrU9j6KyLK6wOWPQEplJV2asLsqOvmdYGbmMWc/OGH9uCbKYGEkUoZOZPJ3czt8BIIRKc+JU5Ihfu/SGq2v6o/3cVQ1eZW31GjVqvDpOmKB+8cUX8/73v5+rr74agNtvv50LLrjgRDX/liLvDGFQTSRnl3vQEUhVp76QxrUXsj+YQv8jZdleDi8Ke0KDrO2Zzt5EPyXFBgdMLYzhFInndzEUmoMU1bYLHi+2puOkD2MnoqPJ61SjEStzAMOa/Cfc3yfRl91KzD+VuaHJ3N37NF2lAVp8iVd/U2q8pVBdjWXhxViTWqnzqayqNBD/XgaZTmOo0HRejH6vQakDclvhyYBLcb7gkhYFzx+GYFsZEVXpvNrmlvITfKXjWsZnZAN9T5HYeVEYFLibS2NCOlQT/LxUorxW0JXZQTQwh5Rmkrd6+HLduVRQUS6NoxtQcVQUwyQwX0XxgTkgKe53cfOS2W4jypCXwiGH4kGXSrcLKiROU1AaLaSmYPxlI+7OEkITVJoNJgS8SnDtJgphi3859RP4VQ8z8jH0OzpwN/fR9JE1vG1jEueZfqywinZFDDHXx8bgIfbJXuZM+zB7Dv6cNQ3X4D4y0SXdebGA/q6JdbNFTKWoufxw6x6WLZxejQF+ewhhGIjtBnL/mN1Vrggz/Mz4+2sNgDuoQNOrffo1ThSOX6JGFOi2cfaWETENZZKBaNVxy+CWAQX0mMDJS9xKtewTDSevj0JAuW+iu72VlmjhEUHdBamIagLEXgt1yEROGr98cU0X/fwI9u/S1RjhNSGklGhSxYmraG+rw30+D0i0y2M4CQNxjMu6NCUN023YVUCENIyUjdscAUfFk4S25TbyvmEouHjn+5FXRykEimC+sjC0Gq+duEo1Z4+EwfstPI2CwAyVSr8LwxbSstDvGMJo1vFfWc/hO9zRBJtH45ZA8wmcddWKHiKqIlN2VUgXIBb4Uc4I4zyTQ7QGJx5fBmkLOGgiKyn0koUZaObwCxqtlydQflnN/2G1OcxsWEW3ZpBoCuDV8ygzDXBBxBSYpiB0EAhMp0Sm1Mek+hXsDBY5fG43sy9p4ZmeXzA/eiWfXfcIw5WqRuu0hna+vnING4f6MR2XaYEgp+UjrN77NYQLw0uGuS3/PVRFZ1bjGUhvEKIqHJODQUT8qOd8GHfaKUjDZJZ5JYYeYlLL2Xj0ZorlnWTzh8h4YyTe+UU8t/zD2LHnf4Kir/GEPdsaNWpM5IQJ6h//+MdpbGzkySefxHVdrr76aq699toT1fxbiopMExEtAMhyD47Q0aVNqGyCFSJb1/2K49OPRqGagKTbn2HyUD07Y724djU2MW8kCVZ6qM9uZyg0C1eptt8firMqXeCJ6DBzjKqArRlNVIJb8Pacgdv+8kMo5p9KZ+pZZjdegSZUlkVm8ru+Z/jw5Cte/U2p8ZbCZYjKoQd4h7gG/XkbI2ijrA1XE0xZEu5LUfeOJJ0doM1QGbAcNndJnu2Cd58VZPJFeaSeZY+zH29KIdtqEThGuSVmabDzNgheieyZ6OwrB2x04WNNw2UMMUhfpcB0pwGeKSAOlHHe2cTAEw6x0yF2uo5iVMvlaBFoutbAiEg8u3R69lmE5mtVIR1oWiswHh7AHbKruesSGp4P1GNvKaLsKKCFPdjZsYWlkQBle4FOevlSzy386rR/RPvDQdyX+hGrJ+FsrIxab+SQjfWTAYwPNzCnr4H+WVl+sPcBPj3jg5iyhEhOfG9Ua68X0M4K4YxkL0YTKFfFuHlgH+mKSd6s8Jv0d2kMT2YoNcj73/632HdlcHdUEA0aymwv8pmJ7r/SrVkc31BsiTxgYo9YDAGcjQrGDQmUOpfYahU1qGD2uegxBQkYMU6q67vjuISnC4p7x2/3tSsMPVpVWnmbBUr3mJJJDNs4zngvEOFXsH48ljDOeSSLdlGEik9iFCSmpVJoj+I6gqBwMTI2jhz//dJxsD0qQ74IlV4IT4VQxUYRGjJrIW8bK+0oXyoi/Apqq/cYFWCN1xOleBhPdAGqv+qFUemVVHptvK0CLa7AXhcujlP0GJj7oOFiHSMGqIzqao16QexMndwuF2VpgkC9RN2SxR22IaDgXt3A8B6B+QdJaEaIUFJBqA5HFxrwJAVK3sY9bKHGA7BlG4F5LtnYFFL7oX6qB8uxsOf4MByVH+zbwiXBOZz9thhy0yEI6KgzG7AfPsDp9fNpWf1lfnzwH4gEJpMptfKLnWMT4jNzz+bhocFRIR3g2f4O5kTjtAb9ZEsB1hbbUf6lbzRuv2F9lA9+8p94QjzOFreJW3bdws8u+QzNPxGj+7jNOk/oddRPu55mbwUvsGjW3FEreu/Qgzy5bix0qiW5kjM//hM8/d3ISCPF8CScP8NoVKNGjVfOCRPUr7/+en72s59xzTXXnKgm37I4IodfGUmQVRnAwUudmSOjN2BIE/kaypF7UdgbHuT03qnsjw5QsUe+PEKQ9zQRMPtJZF9iKDQbW/XRE46x9PBeftE6zCQtil/RUbR6XLWI7B6E9pfXpoa9LRTMAUpWCp8e45TYXP71wB1c33YRXnVimawaNY5QLvcya+hMPLdVBQwHcMIq2gWR0ZJoStnBaFfpmN1NobSfq1rnsn53iOZEJzt2fJ3+wS0AnNt+HoOtGZoWNsGW6mJfNgl2zt5CoL6V+FCZ8GI/7o7x1mZljpfyeoPSwXoiqwNMnZRDBdx9Zei3Uf7lMPUfbqRwSEFa1Uzzdqa6wMlucEhcpBNZFIBDzugCUYsKjP4SDB0l1A7YFLfn0Rb60TOSloUag0/YlLokgUmCWLsLv83in2XgUXQSBR25rSqQaAtaMP89PeH+uX0m2uYKvkkap9TPwlFDPNt5B/PmrkKs15G9I1b7sIpY5WeftoGG01uoW9CAzEnsuMLNmQ6eHB7gc8sns9XtJJQ8H0GFtwWuwbljC/gMtCviyHQW+Wg/0eXzSb8wtpJVA6DWH+vHUONkolhgP35MXGnBRQ7bELEQqo/B+494cDh4mgXhmSe3j6qq4GmVxFZr1RrYAuKn60gpETqE5irEEjbcOnYdIikwDEFpJARDCHA7KxBQ0FYEwKfgbCniPJtHrPXgOCqHH3TBqdaVTwtou1LDEOOFDFcodD4yZoEd3gDmLI2G2Q4M2siIinZKsJrgbn8Fd2MBz6UBip5ahYOTRTg/CbGnSMtVAQafcyl3uwSmqXjbFKwyaBGN3h0qlb7qs2ajS3yVSuvVCn0PuVhpiJ+l03ubOSqwDqvQdn4YT97EXRmh826JHMlBMvwC2EVJ6zUqPfe72BmJt0XQsMBF3jeMMtePs7uCaIsgX+omcfVkzJRK7xrJPZl1TJF1RNUIfZVulrg+nDteGr0Wd8cg2uUzkXfuYlo5yIeu+QZpPcbdnY+Ou+abd23iAzNXTLgXfeUi64Ze4B9nfQzuKExIrmc8Leg728f64WqyuM+WfsFf3Xgls/OtZKTBNl3jlj2S5j6LvzxTQ3Xt0WodkmGe3/Sdce119T3H0Mx3E2xf8xqeYI0aNV4NJ0xQz+VyFItF/P5aXcXXihAlwko1JlyYGRw3QqyUpiKS5LwFjFdYlu3lUDXoDGSYmmpgR7in+nIXgBAUPEkcxUN9dhvDwZlkvSEUYElFYZvazwpPC0IINKMF09mNXk6C9/ju74pQifun0pvZyJT6s6kzwkz2N/Lw4AYuSZ72mq6hxv9uwrIZzwMu4+qlZZ1xrqpmG3TNz/Cbnk08P3iQp43dfPnUC+k+dOeokA7Q1fEgzclV7L4kg3aKQHEV9qmbeejwL1gmL2RSZBLLiwb6hRHsR7IAaGeGsBNeUr9xQUL5114a3iMwIybKTAUnUwBD4Hg0spttwou0USH9CEOPWARUl/bTBEVVgqi6GIvuysRkxYcsPhK/m6Bm8Hf6MhJlH3KWjjhURj5TwZmq8qy7i8saT8Mv/SifOhM5mEWaFiKmIQvjbaDCr4IpCWsB9uV7OC3WyN6hDaxPvsDSv1iJ6LOQUmI2WqxTHuDWF76O7ZpMjS7m/Y1fIpAKcaOvnY+tmoxVrrDfsvl611MsDk3jEt2DfSANgLNjzIIZu8nEDniwd7vozYLAUhXTU8v+/kZSMVxUJBgCZbIHmXWqShoNHKky/NR4L4hKt8QsCIie3H6Wu1zCAZvw6pGZobrYUY32JRaqF2RHBUcBPAraOT6kXsFxxiqIOA6IOrXq+v5oFpl3UFcEEU0aiipIdTBeYyQhtUOSWDpewDaLI+EAR5HfLYmfraDWq+hnhbEey4ItUSZ50K+JkfGZ4J6wpVSNP4IQwF4FKhKz1wGlWrWg3OmSe8nC2yqoW6pROSaUYvh5h+Aam+YWG3GWl8Ht9nih1oF8n4J3qQerV4wK6UfIbnOJ1dm0TnVglg9eyCJ/VUBM8yKHbJQpBsop02BQIO8Zxldwsc8ULJ05nSlaA168/LtvKt7HdoydNqCjrpgE3hDqOTPh6f3MMBez0e2shngAuqLyycbTmFesozkbwWiC7/c+hzNSG701EGT9UIZixqH+2FKDgOs6hA3f6P8Hij18vPgj3tl+Obu3nUXhiDKiCLYrxq0sHVnBtPIT2rTsiRUUatSo8fpxwr4uPp+PtWvXMmvWrHHC+s0333yiTvGWwHJNVGESMqplY1S7TMVuJ17qwWPPpDeQR3mNrkYqgsPhFCt7JnMwOEjFsRBHjYSyHsUVGvH8blLBGfSE45w2nGWLVzDgFEioATSjjXJoE0bXSuS0l7eOxwMz6Ey/wJT6swFYFZ/PrV2PcVHDqSii5jJV4/gYagSs4QnbpawKvJnz/HxzeDdP7x/ixnmz2DR8mIxZJmcNkx18ccJxA4NbyfoNnuz91eg2RWi0h6YR0g6xpaGXGS1z8CyKIRUodRv0/7aaoCg4V0U6IMoqImhRWR7DmRVFqGAEQfWLCZYMoOomaUrMmwfwfbKRhksNsi9ayJl+2D1eGhiY43AgN8yNs1cyaBg4Zw5Sf38U2WVhr/Cw7tQuLtCXseRgC/bPB0AVaGeFEK06+uU+zJv7R3UaolFHhFWKp2uU/BZnNy5ldnQWhbmf42sHH+L8RIErJ59DtFxB6xUst9cyZ/oKHs39mku9N+L8YAjntDL67ADWzSlEzmHmmiQ/mfdxnH02or26QOYYt3bNKvH8IZX6NpXGqQqm5VJLM/TGUgqViF1fB6qC7LcRAQX8AuIKtuqO04MdwVFOrnXYcSQ+xcbNu1h6NTTDKFroho3YnsfebyLaDYxPJXC39+Ds6cJePL6cqKKA4lUx7xirXOA8m0e9MIxUHeRx3NCkc6TI2lHtHCfnitAAXSJ1iSUVyuckcMrgDUvIldFUF6smqJ8kBK4F9pM53EuCFPfYFPe4aCFIniHQDAkBjQkD2wU0BZ7IoE0zcM2Jaw9XFbg7S2BMLCGrGCDqNNSSjXPrALLfBkOgLg9g35dGXRKFgxLrN2PfrNA9cMpVk1CaDGTRxe02UVbPxn58O5RstLPmY91bgnwewjr65YuRO10WbkvwlQvP4+8OP8j/az6PU27xIdIukOHK+kbq3nY2X+h+hEvbZ7Ezs4vrpp5N306d5hUu+vOM+xYNLR8iKCcqS+v1BJst0FWwHDhjuopXcZBH3TZDraO9eQ0d3U+NblNVD6FA+yt7VDVq1DghnJCvy+7duznnnHNYs2YNjY21xBKvhe70YVypohkG0q2gSZeiDFFf2IOwIuTig6gnICZIUSWHQimmp5K8ZHSOE9QBTC1IzttCLL+H/uBkFvZ0M7m1na1mL2f6po64vzu4vZ2IaVNf9jwx3xT2DtxPyRzGZ8SZ6m/GUDSeHd7G6roFr/k6avzvpOxR8F0YhDuPctv1CCrTdDZ8SOd/sgfZnK66xT/Tm2VRvIUXBjtIWSbB+DxSmf3j2tMCM2iPRIAxQf3KWTfStfM2dhaqWdH7Gtfgm34dv+54is913Yh/igejQWHwQQtk1ZW78do6+u6qYI+E/CoBaL5CpzwAine8NS62UkM+PlxdJA5YRCYLwlMVLCGQRQMlGUJO9+OWIeK1+ZxyDg9376WtOINM81YOXPYSsuQSa13G7fue4VtD12PfkRpt374rjX51DGeqhvHpJLLHAl1BRBVKlQrD8y1SFZsFkaX0VAb45+13A/DjQ3dztbEccbeDu6vaYV9M5fK/+CjO7zNQkejzA5g/6B9d9Nm/y6DJCMokkLoHdVUrzlOHR/uizKrDDftoblLQNBfbUojXPgVvOOFCEKG4mD8dhGJ1Fa6uCKCdGyYVqhBc4CO/dWx1roYEpZjkZAYmKYrA9qh0bVBGyrOBGtBovUBwRJcrIirSKSPb/aSW1rGleBenxd8z2obrgts5MbLefa6Adrqf0CxlXFgGQHSpMkFSNzQHb7Og3D0m7dSdoqC7Lrar0PWSgp0e+63pAj/eYgmrlkvupCClpDJTwfc7F0NWvUH0GDQvdeCeIbi8nkKqOo6d3NhzCs5VUedpqKF6QBKbJifkRAjPU7D/LYu+MozR6MXsHTs+tlqHgEtllYK3PgKOrHoyVSTqqUGse9JoKycmnHOeyiOneZG9FspML9bPs+jXzUaWilh3FMdKfGYdrDuKaGeHEfuKrNpWxyWz5zB3VwiRHhvXYtBhdXcjn1+yhuf7NzM93E5UayA8a4B9yn5aPtZG8AUfwhUMLB3g19mvc2bbjUT0ABmraglfUTebUxJNNJ26gYJ5mPpgG5PCs5DueG9Y1zVYvODTBCZfTldxiLjuZ2ZkMrraOhrDXqNGjdef1yyo33bbbXzta19j0qRJdHR08M1vfpPTTz/9RPTtLUnP0AGk9AICxxpAlRByJJYSAcXB1dwTIqgD9IXSLOuZhK/opeKfqHW1tAB5byPB8iGQKrNKFgNenT3mILONBLp3MmVjK77sZAgfv0+KolEfmMnh1LPMTF6CEIIz6hZzS+dDrIrPr5Xwq3FcXAnpZQaW7iW5XlKsB9YG+Hn5EL/q7Bq3b0BTyTsWjb4wOSuHFltAaHATuVxVkGxpWoUamkMl/QyXz7qRJzvuIepLoOQHCfiamdX2UUDSn3mcqBLj7W3XEUp68GY0en49tkhyCjB4v4l/ikp2U3XR7xag2OkQaISmtxvktjqYg5LwAhVftgg9I/G/hsD8ag/KFA/GVXHMNQnskkrfr02sQYkagFMvmUZrQz3b79VYMXsh33n+UgBas/P5v4v+CW4+pogz4B4yERWXjrMrqM05FKeeeDmCm/ZSfyjM2kgTD2S7OMQOAGaFmvmrmWcT3W7g7BoT+mXKwX08B60GouLiHjIneAnYT+YxFsaQHRIxswm9PYrbk0NJBpHBEHQLlmwaAksiLohQUdSTmpSsxkS0ioL1u2GouIg2A3IOzroC6mI/RkBBTyroYYXiPgcjqeBrVXBMG7wnN7NArkuMCulQnWv5PoXIPD/aDA9iEriGH/dFibHQz4yWFRz96VBVIDDxGyTqNNAUtJ1ZWi/yk9ojcB1JbCYY27KI+ZFx+6sVh2SzRWWWB6so8IZdtAM5FCeIWVSw0+Pvy8ALkqYFNc+wk0k+aRH6VAKxrUTL28OQs+GOIcR8P6l+lex2i8QFOqVOF7PPHfWIcgaAx3OoC/1oW/K0XBBleDcoqiA2Cwy/jRPXYGueuiuDmClwihI9KijscZBZSd2pPsxbukcN9tpl0WqixvOiuKGJXhsipCILDu7BCsocL3gEzosO6jlRMFPjdy64o+9cdbPJO86oJ/KwMsHpRR6s8HxLH8OVMI/17OGmOQl25m5j/aFbOafpg0xZtQzVo9Alt6FVvBwslfj8ovciXYlP8dDureeFzv/m0b2/HG1z9eRrOXvShxHumIpOCMlTmQ6+tu23o9vePuks3tvSin7inHFr1KjxJ3jNs+0Xv/gF99xzD8lkko0bN/Kd73ynJqi/BvrynQjpBUBUunCERp2doyAaqXjyaK8xPn0cChyIDrJwuIVnI3vQ9IkLDlMLU9YrpHwZZvR30jV1NputPpqdECHPVIq+P+DrGIb59S97mobQfPYM3MeMhosQQmFuaDIPDaxnfXonK2JzTtz11Phfhe5RGFyu8XhbH0/0d3GZt4k6RcWrqpSd6oJZFYLz2urozedYGp/O4ZzFz7t+wZq2K5np8dEWbKCCJOwP8uS6X6AqOqe3ncWsSW+nkO1DHfoQux7zYnggdsEy/vKFLThScm1LnveXp0/oU6VXEpyncHTAa+mwJLwjjd5uYDRoKIs82P/RPVruTTTqVcugA+7eCtavhjDeX8/BO0ys4erKzClA360WrdfH6J9RZmexZ7T9zvRLbD18D+ef8W6UVgP7hQKURtqOKMgZPhozEcqd9RT2uBRbHNwKDD9etTidcW4zL8xqQxUKH516Cg/t+jfm9n97wrW5B020FQHs3WUwjuMCHKpaIGXGxnmgiCy5iIYI9oMVjOs1zB+PuR3Lnw3i/VA95vSaIu4NxQEZ0zFXxsl3gR4SBEM2sugSLfnpeMhCCwu8bdXM77nNDs3X6ZjhkyeoCwGVoYnbK8Ognu5BFk0qloY5pCNmRvFZNmomggiM399t9EEyD30jyjEVODuKo7sIKVBv6SUx3QsqyN+UEUsCyGPc/KUiUHMWnodTePwKFF30q+PVce+Mn/cAblHionHc2JcarwsCib0uj3F6EGV9FjkvQPGCJMQUik+4SBv6762WbdPrFKQDmRcdfF6J2FcBQGsz4De9JGd4QYKmBhjebVB3RRT7/gyZTQ6lAy6Kt/puBhAzFCi7KDM9uI6AGT5kUoewQsnnxZcYKfF2RJmjULW2/6Y6uGW/XbXCl1zQ9WoZnqOH31HvXHsqbBt6jNZl70HdM/76B+a6PNqzD00ovD25hFVGG3/IdvGByd/De8987IzEFTDz9KlMXXImIqQTdAK4I6FKWbuDx/beMq7Npw/eysrEmUQ8s7FFVVgfJsv3dt46br/fHHqMc5LLmKTWam7WqHGyOCFqsWQyCcCSJUtIpVJ/Yu/x3HPPPfzoRz/Csize//738+53v3vc7zt27ODzn/88+Xye5cuX88UvfhFN07jzzjv55je/SV1dNenaWWedxWc+85kTcTlvKMPlHjQ5kvzD7MMRHqLlNIo7i7S/hJgQVfcaz+cv0JiPUD8UI9WYPm77RaOeIV+BuQOdPDdlLpPUKBvNHk73TUY3plJKP43PvYKXM/QHPU2oik5fbiuN4UUoQrC2fgk/O3wfy6Oza1b1GhMQCMy8QTjl4ZxylEsaZpPSU3xk+228f+5qvMJPybJpCEgGytuY6VGwd/+ElknX85H2D6F7MljWIW7Z8hWWT/8gs31TuejMm9l/+Pfodct4JJ1nSyrL0qYuGj0tKNLgO7t34oy49F0ebIPj5MzxNCuYg+MX9+FJIO+t4Owqo96URNguxnvqkYMWBFVQwPzJUWWdDpuQcbBzEF2poQaqMe7ZzTYyJ9lb30/noElbbAmHUxu5vv3LzHpsLtbWQURERb80iv1kDonEWhGlknIpbLDJb6suEPPbwNuuEF5ctfwPPWxx+qxFXNW+ik0dd9GZ3ok7aeKcU+b7kLpERFSUVgMRU5GpI1UhqJ73gSzuxgr6O+LV2tZpG3FpBGvjxJvlPppDnxnFcmsZsd8o3CCUZoTpf+jIM5BkAgrt76ymJNUi1TrlR8YOAjTPyS3PBhCeq1DcO36chNok5jf7UdoMvFfE6LnfQpoQW6Xjj6k4cnwivOxhgXZGPQGfDbbE9mp0PyNpma2jTHdxnhDIPSOeY5pAWR5AusfMAw1kyUG/PIa0JEIVuL0mco6BJyDHlfgCiC5RMKSggkWNk4Pf9qH6K7i2gjMtQGqzQNMkIbdMoNUgPRImXi3b5lB/vo5QJNpQdVS7+yqggv7eehDg+jWsqEbhTofCTkHrRTEi+ep4dI56rYVnK9hPppBXNTD0qEV+ncTTKGh4TxOFTS667uBdE6qOEaUatmE/lBkdL6JZR24por89TslXRF6ro//WGk3mq18UxX46BxEV7dwAjQc+xAFXZ8oqFfW5XDVMY62P7Y39LEw18VHvGXgfDJJPSc6b/QXC8wz6syMnk5B7QiHaXA/B0qiQDmDaJeRxFEuVri0E1GGyU9YipaTgVCg7E8d13i5xIu1FNWrU+OO8ZkH9WCFLVV/5DO7r6+M73/kOt99+O4ZhcN1113HqqacyffqYJeuv//qv+cpXvsLixYv527/9W37zm9/wrne9i61bt/LZz36WSy+99LVewpuKjN2HRjXWSbGGcaSPunIWnxUj7+k88ScUcCA+wJK+Vn4XyRDwHUkBf/Q+goFwC5XhvUwb2I+TnMaQW2S3OciswGyKlfsxOvajTj5+rLoQgqbwMvb0/57G8CIAFoSn8fjQJp5Lbee0+LwTf101/n+NWTQod4HzmE0pJckCvukhvnTmhUQGBVP2eymEbAam5Vlf7OI/BrbxxennMsszSC43CDkfBdlFS3Q+A47KGiWEUP0EW8/hGzu2sye7FYAn2c/5yTlcElzJwK7K6PkNW6Gw2yG2WiP1rA0uaCFB4kKd4gEHxQNSQvwUBW93HioSdbEfN6DT/QeHcqeL8BjUrdEI7EiNN7j5BHgFDZfqDD9mYaUkQoP4mTpqneQaT4KQlURxvgEzNczbMogt1QWTTDtYtw5jfKwBK6Jj9jh4vdC3bbylr9zhEpytUzWpgiyqTPWdRnfqUSQuT+h3cObaK5CPVUv6KNM8qNMO4Lx4O+raD+F6w+g3JHAPmlBxUSYblKNF9EleFEfFlS57p+bYIvaxO32IvwpdNEHFJyLqcReENU4ebllj8NnKuG1OAcpZBX+9S+Pp0P0wuCVAheTpCpo4+QX11JColmdbZyMExFcoGHtzYEvcAxWsWwZpfU+Cgaclg49YNL3dQG8dO15K8CUF2u4yzl1pMCXKbB+J06K4UlJxdOTlSfSSCS7YIQOzIvAdM2hdVUFbFcJ5No/ba6HO8aEu9mMJgao6tL1NY/BZBzsLkXmC0GROuPK8xh/H61FhRYChF1S0uEJ+t0XbYgvuGib8tgTFBgVzpBhFaIGK0CUtl6rw08xoG+riAPYDGWR3tQKCODeG5vVQGRZkDgkCTTaJczQGn6wqg+rWqHgVE3V5iM7f25QPV99rlS5J5+02TdcYyD6JfW+6quy6MIJzoIIcqJYbVJcHkA7ol0XBAO0nJda9/WmaPjYVfzGIDIFPM7CagwxFHYaUDI7RwNaNku5EgBnvChBrrrBN6eCJfpOP15+L8l869ogAXt6poliSwAyVwu6x+SsKAkMaOEeZ7qOeZhKBNgYKYzlG4t5G6oazuLt+jpw0nYHKICFPjMWx6WxKjQXze1WDJm+85kBSo8ZJ5IQHmrwa6+gzzzzDypUriUajAFxwwQXcd9993HTTTQB0dXVRLpdZvHgxAFdffTX/8i//MiqoHzp0iH//939n5syZ/P3f/z2RSORlzvT/HwruIH7ZDIBm53HtZsKVIq7UMDXnhMWnH01Jt+gKpDitezJPTtpNRJuYSsgVOl2RGPO797IvMYlpWpzNVi+NaoiQtox87vcEK+9C9cSPe45EcDad6WcZyG0nEZqLIgTnJpbz047fc2psTi0DfI1RFEWQ6XfxH4RKamxFUOl0WdZfh/OzQaBIAAgEvXzu3afx7kSU9bZGa/9c+h4HxwZf8BTOOfdy1PReYo9t5OCaKL3lCnuy4/1sH+zbwZV1y1kYj7JlOA3AYKjM/As9qHaJ8Bw/bkmgZSzkHb1Ezo4QebeOu7uM1qCCoSGuiOEUHYaetil3VvssKzD4sI33ygjqS2Px5frlMeygTvreqpAOIG0Yetiitc0g9SuVwbwkstCgbo6F2HyMVUOC2eNiCQWfKGP1wfHcWZRqBA1qAHYrOX7w0n7+YeE17B/exL1dN7OrZR0X3/Qhkt52FNmJeOQHpCcvJhfKcNBTJCgKJFZ42d7zFLsGX2CusZoFK08nNi9IMXMAI7uLUxMzWFbXTMrZT9zbBOUjQZbA2uDoQrLGG4QAXPBPEYQmgV0WpDa7SEfiqALDtWlb6OJ4VBQpUdIlXP3k5+ov7HOIJBwia11EQse9ZQAGxyzmst9G6yiTGCpSd0GEUtpFOUqfIAR4HAv7vqPyLuwsoSd11LkhhvdKshslQtWrbuy2xDdJ4F8+fr2iSIn5r/2ISQZKs4GzsYCztYhyUz2KJVFu7iI51w9JFZ4owiEP8h3H/+bVeH3QUiqlfoX0Bpv68xT87QrKtgIuIG4boGl1GGeOB6VRx9IUzE4H5WAe9ZwI1l0plDlenK3FqpAOYIO8L0Xy4404O0qIF0soLQaRlQH8p9kIQyAfH8bttRCnh5DW+JgLaYJrVvMsJNZGkI9ksH+fQV3sR7+hHmQ1lwjdJtaOEtp5EVAEpxmX4m6tIMoSucjDs/69HDYkd+8r0Jkvc25zljNmzGBgD9iui90ywFe39ALwLkfiPcZRqbjPpe4cfZygbsSV0broo/fPDfGB5f+PP+z6MXuHXmRabBEXRc8m8Lsf03PBO3j0sQ9jWnkMPcinVnyOn2o+nhjYyqRAkr+Z907qiNUUsDVqnERes6C+a9culi5dOvp/uVxm6dKlSCkRQvDiixNLJR2hv7+fRCIx+n9DQwNbtmx52d8TiQR9fX2jf994440sXLiQb3/723zpS1/iW9/61mu9nDcciyx+ZS5SSjTXwlM0KKiNlNQ86usozHaF0yzsb6V1uJ7++mF8ysSh0RltYNpQmrbBTRxKLGeKGuNFs4szQ1PwHp5Nfv8t+JrWokfmII7pqxAK7bHVbO35FWuDX0AIhTnBSTwxtJnHBjdxdmLphPPVeOviNQR253jLXmSugnvvMcGseReZa6P1mb+k4Zovsq9LsHwBdA/B4Q7J3scCnB2uIDdt5+DcILp3oveGBHJZixtmTuVfd+7hxsYZzDdV1N2duENltKZ2nIdyuCOxh3LfAMpMD2pABdfAfiADloRzoxT2T1zAWLaK72MNyIyDqNewAhpuCSo9E/ctHZaj9djTGwWBSQZ69CgX9NEbpFQXS50m6pCNf3KI4sGx9jzN1VJFRlKQuFznYzuew3Ild3SGuXbxV9h6+A50zUs2nsOtSHp3ziVxxRf5v9sf5vxSgYjt5azgQspOifbIDBYlV6MVBXUbGnDuyhE0k/hXJ+mZ/Bhu0s+Dh/6Zs973eaJ9reAqeObWUYjXFnNvOMKl9SoV8XQa7ihCWCV4YRwZFwjXRTS6aBEVNeUiggpysoJ7khfhrusSa3Rwf9KPBLQrY7hHhPTRgtICShK5t4LY10/o440Uj5oSUgJdExOiuhsLqBcF0CNVgVwedYweZ4LAcSS+WB4ycQ6NBQAoJZBDVc8aeZTSTeZLGKYLPmqcJJSMQ2Ff9e9yp4sWA4EGByvVmO8ns9WwjitjlCJ+tJhGpT6Mx+uifSyJYoD5/b5xbaqL/chH0rCjjAScwybu3jLaQj/2I9nR/ZxNBYJnBqn0jh83qg8Sq2yk5UWbpkPGQrgVnK370Ja04Tw00oYKQhUY18QxfzAwlvX9yTynfngqfz/wD3xs9vv44eYKD3X3c/kKP3PmmZSF4LO7xkq/VQwX7zH3RQ0J5BHFqIDoGg0rWWSYLFE1gOZUSxMoZDj41DeYo2gsTVxCeWgvQh+kvPoqHt79b9h2CQDTyvPCC1/hPau/xUdnXoUPD17XMyHjuyoEmiOwVTkaOlajRo0Tx2sW1B988ME/+9jjlXg42iL/x37/4Q9/OLrthhtu4Nxzz31V566rm1hK402BKFDva8CjFVAlxEwbWzaQ9ZZRXsdYbqnB7lg/KwZaudufxQ3DhIozikJ3NMbMwRSZSCd6cBKZcpldcohFoWloMko59SLlvkfxhKfgCU/GG52B5gkDEAgsYrC4jc7cwyxovwKAq5U1/Hz/fVw9ezX6cZQDf4pEolap+UTyes6LV/WsLJvCVDAHxqxqQgNpTnwnuEUVlr0HX7qVWY8NwpBN0ywvU1ZFeXKdpDKtGU3dhm2VqfebLI41srShCYmJJgzKJsyKmWRFL59c0My07jBqoR/7of2oF8yHytgCfvScuyvo76vDHbbRL4siKxIRUEiuVei5zx3nGqhFBE6/ja2qOJbOwB02/mmgx8VoMrkjKBoEF6iEF6kItWpprL8ygvVfw6NtKnN9OPUasiIQSR35QIbExR6KzQb5LoG/FUKzFfArJN8juSfXSd6u3scXB4tsGdJY0/hhrq+fzcbbBR4/LD67gt8H31l+BrtyBfblMjTt3cDhZUEUq4SWtUnkZmP9aixhnPIENIZOpz/xIpZd5P59f4Om+VCEylr/F5mSWPvKn/efycma/690XrzZ3kf5gSLquhxy84hwmXHgNwNon2xE8bnQW8LZpyCjBqKjgtomEZNKJBKxE3L+V3o/yi+OKeCcnSW0t8UQdnW+C49A+gTOgyPCjgQOV4gtHd9HKzTMsSjNGgiXYJtKNg7+qRpCgcJem+gcgZByXB8rhyfmWhBxFakqiMhxsnpPNnA151U99zfbGPlzeaPWUGZvAV8csioEEi7BmIOIeFFaPdiPZJFDNiKmosRU/M1QOgz9jzrYGUniEh0FB1+7gTxwVNmzdgPn7vS488hBG4LjDQ5Ki4F3sgrPjFmpg3NA37QLrdmHzFZwXuiCwpgXlFwwUqdSA/2SGPbeMqrljgnpR/rwUJH3X3IBD/c8wZLEatb3ZxhOdzCw4fPMv/jn41RKTzl9XDqzHbl7ZE0oIH6Zg9paonVWEM2rsVs/yP/d9O8MlNMsr5vJp+Zew1Ali7SKqIn5pLb/mkzfJgCKqf0sOePL2B2lcX2y7RL9uQ5mJRcR1Cdqo9yuCvZDWZwdJbwL/WhrwyhNr7244/+WOVKjxongNQvqLS0tf/axyWSS9evXj/7f399PQ0PDuN8HB8eSMA0MDNDQ0EAul+O2227j/e9/P1AV6DXt1V3K0FB+XIKNNwMVuwg4+AhRzuzGJxRiZgaPM4ecZwj3ddZWlg2T/aFBzu+cyV1TXiLh0Tk2Xv1QJMbKjgz7UzsoKmHatShbzB6SAT/Jg2GMZWfgukWcch/5ge2kOx5GC7Tjb1yLYkSYHDuXLYd/iV+dTF1gBg3UEVND/OylB7miac2r6m8iEWJgIPend3yL8Vo+cq/XvHi1z0rRFcLLDZx+QfmABAElryRyXhj3tqMSVmoCO6DjDa7E+rELI25+YleZuJVixpI4us+BfJFZ9Wt57OD/8PYpb+MLW39J2bGI6gG+vvBKdvU+RtRMMKltMm7OhlwZ5MjoV46jIBOAV6A0Glj/OTAqRHuadJJn1NH3eHVDcJ5SvZ9TDURFo+vnFrhgpWwaLjTov89EjqwXY6drGI2Cwl5J9y9NvK0K0ZUaZsSL5y8TuLsthCqgToVNWbxxHeb5EFMM5O+H8YdVAtM86FNDuIMW9n8Nkrohz6RJDYR0jZxVFdZtKZkVjbPzcYW6FpPWNds5VDjAC5vvwZUuZ017Jxc1ToFEPQ3P/YLC4suJpVpx95Ym3AZto5f2qZfwnsVn8auX3o5pVp+xpsZf97n558z/P3duvJJ58WZ8H4UrGs6GY4RPCWLYhqDADkZIhxRyLzl4mjwk6lV0s3xCruOV3o9YzIfQj5pjhkDuq2C/OGa51s6PgHaUEl8VFAoFikdqw6sQblARs7zIXSOWdZ9AvSCMREFTHOrW6gw9aiMdqDtDRzNspKqM66PfayDOj8GDI3klvAL3knrQBFrIQT01gPP8yP30K+iXRTAVyLzC+/VmHCOv55x4PQiGNXwRl+bzFfT7BrCHRpS5KhgfTCD7bWTZxTlYQW3x4LWHaboyhqpItLKJU5GIS+M4P+4frZ5BXBvz3jgK4T9KUPcK1CUBPGqBtrfpWEUN1SMxtAo8moW4jhwoImJe5BFB3aNCzIv18QCm5pLJVvBc7KWxX0W7LIrQBPYTuaq3hiXxqQYFu8ApdRH+T3IusZQO8x5HTcP32lv5TbqD3/Z08MBwL+evTjJpqR/XlFTiWTbJh9i1+1naY3OYGT2LG5/9IbasXt+q5Hw+9cIPGK5Ux96MYCM3Lb6B9Kb/AMCsZBGeCKpi4LhjCgxVMagLNFHJOpTc8ePWZyoo3x+C/pHvykNZnL1l7BtjlNU/P4Hom2WO1JQFNd4svKHFEFetWsX3v/99hoeH8fl8PPDAA3z5y18e/b2lpQWPx8OGDRtYtmwZd955J2eccQZ+v58f//jHLFmyhEWLFvHf//3fnHfeeW/glZwY+jIHMYWBx/AgC93Y6NSXinhsPyWj9/XvgAL9gRxh28uZ3dN4pm0f0WPi1UuGwVAgQKKgYasb6IyvYqoaZ5PTy9mBNpQBFxoDKPpUCE3FcG3s7G5y+3+Ov/VyvMFJzEhcxAsHf8jqaX9F2NvKeYkV/LzzPi5oWIFX9bz+11njTU8wNUx6+zb2XpQknp9Ef6XMvdkuvtI6D+OKOGJjHiIazsIQthxGDlngjB87Yn+F6dc4GDs7UN5xIU0kOa3lXXxi2y3YrstfN1/OWncaocMG2tAs7LuKcJ6k1KBCzAeKwOnoR5kaQpnuwd07lpRLPSOESOhY/zk4bnEneyxCcQf93R4EEiPgIi2Hgz+V1J/HaDkeacLgIybxM3W8jdUMwWpE0v3f1qiVvXzYZSBlElmuIdo1XJ/AshU0KVCdCiKokutWkIvihM9zEZYLdRqurmD/qB8kGMMesvHH+Ifla3mse4D+Uom1LUnazSC7cpJpF26nO7ebIbPA3Gkfoew6lI0QeSkZFGnqMoNopWRVQRCd+LkQdRrO7zPQZfLOj93KzzZfyCmLPobf00ot2fubAKlCvTa6mB7dbCigafSvdynur3qLFPdKDnfaTHqfl5Od911ZHcbZUAQX1KlerDvHV4+xH86gXRTF7k6DX0Gd4hkVQo5QFn7yrTqBBS7CkVhCpdCrEpxcDT/pvW3Mytl3j0XTtTq+Y914Awop1Yv/2kYUq5oHAq9WLe1V1pF2Cf3KGNKRYEtkr4XdbDCh2HWN141DoTQz6iKoBypjQjqAUxUWCSq428voH2sg3yvQI0EMv4PcWqwKtS0hem+3SVzYgI6DVBWckIZ2Sgj5/JiAqEz3INs9qDc2IJAIWbWyqwEP7m2DKJ12NRHdqUHUM+eDZqLU+7Bv3VFtQFPQrpnH4T8YGFdJHqjsYk4ywvx7/DhlgTQlsttEvzaOdUcKzvLz1PBWvhf+CCEZpvN/HHryI4ooP8TP0rlyZzvzTo+wRPoI/TAN5WqCPH2VwcDM/ewcfJad/c+yq38dlzSv5q6uDUwPNbMjfWhUSAfYk+9lf/0sGvQAtlWgeeqFKHoLa1b8HU+u+zKua6MoGguWfJpEYNpxFTLqsIs89r1y0ERLSXj5ar01atR4lbyhgnoymeQzn/kM73vf+7Asi2uvvZaFCxfyoQ99iE9+8pMsWLCAb37zm3z+85+nUCgwd+5c3ve+96GqKt/97nf5whe+QLlcZvLkyXz9619/Iy/lhNAxsBfpehFCICoDGJYX1Q2QNooTErG/XggN9oYGWZhqZm5fMweb+zDEeJe/ffF6VnR2cDCWpCm9DhlbyaAw2B5LsbAnjptUR/srFA09OhfFW///sfff8XYV570//p5ZZfd2elVvqIKQkOi9GEx3AXfH5eskjpN7b/K914m/SZxbcn83ual2Etux4yS2gzEGG4MxGFNsTJUQIISEeju97LP73qvM/P5YR+fo6AgMGCQZr/frdRB77bXXnj1r1sx85nnmeagevpvEnJvIxeczr/kifr7nL9kw/zN0xxcwN9bB9wd+znt7LjkxPzTklMUSGh5+iszgCOXFEe4Yr/P8aJHPL1qOOaDw58dxklGcvCamwW4fw3fbgGMsvglJLV5m18alzIk0kXAUarBCwa3wv7vfx7l3eoiJvQB4qQjWDStw7ykQ+VSSRjVJ5LolqL4KckEEkTORp/voMQ/ZbaFerkNJoQverPLrIRfj0TGs63P42xuYc6KkV1ioY071y1Dc7JJ8h4FlKJyKOcsV3i+DkYLamDGZYksDmuzaJOl5FqNfbaAaMIIgiOCm6Xq/gTXpVmk0WewZeRzlmaTjNm0pm3/cdRtnNa3gfVfczKhzEG1muHvgMLv23UbUsOmINfHhxVciohH+dvky/qxg4f7zKOY1WUSrGUQyhsDCtCKOe1vgtiyfUFx5zRe4b+832O8XWN99A1HdSsjJQ1sC88ZmvK8MTYvJRVF0q4XviCmRfgRVB6cAvDme76+ZiUOC2C3tyMN1dPI4sVh8EK0m5pUZRFyi+xowd3pzltZQO6zIb9HkERwxj5ppn8Rag9IxWREAJjZ5RNfMHFjdMY1yBK5poABpQ2mTT7RToAZd1OYqavO0pZ+UxFyVoWGc1KnUrxWtozb+nhJ4s+tcF3zM81MYZyYY2y7Ibw4WZ1oukWSabfT2GqWaRtWY9HoK2lpsviK9IEmkM4LsqyPmRXBzEfKPQWWHoOt6i8gjI6h9DuJJC/OiDO73x7Gua8J7pIj/8zJEBdZ7mjA/uh6qDTwzwsGHI0S6DHbIbXxx5z/z3c7PEj0tib+9hswI5DlJvGfLGL/Zwj1s4vP++0h9s0bp/DR+efp3+VVwhjW6IDi9miX1wNh04E5APu5w8apbeZzvAXAgv5VzOq8CoCvewr7ybEPPfqfC/HQv2bY1dC1+N75v0Jq9gGsv/TeK9SHsSI6E1Yvwg3qWUqC1Zmpty3yFSal5HNeEkJCQN8xJH12uvfZarr322hnHvvKVr0z9/7Jly7jjjjtmfW7dunXcddddb3n5TiSDpf0YOg6A6RXJ1NPURDuFyOwgOW8lwtRsyw6yZrybxpDHWEeeo1cKKpEII4kkC8d9drck6Mw/hZtdx7N6jLlmgmTBQmdnTraMaBt281lUDn2f1MIP05pchiEsntz3N5zR81EubV3LPx+4h2s7zyFuHBsmJeTXCcP3YWQc8iXO2lMntmoO7cRZsbeFQ48rwCU6T9B+nkD+dILhTAm/ySO1tpn4s5NqRED9hjpP7/4LRluvZm+8i8uyi2jp7GVJrYeVBxKIieGp7xSlBio/jnhXDyICOpHEzUWw57vQAPe2cURSgqfxHw2CSolVUYyzk/gPHeWmJ0DYAkoK95tj2L/dhtrdINFhUitr7FaBMzId8Cd3tsWhO11aNkhiSwjmjcdY56ysZOC2mRbOiWcVyTWgZmbeAkA1JiPJr7cYatrPIvtK/mjrD2YEzvrR4FNcf8ZyDNeiRJKLoqv4X7yfjmIaI2qjD2v8DJyXXYZ/RzBj9O6bwLwsg5xro4s+1DXuD46Ksj3k4UkbhOThPd9gvDrA9Us+C2pWtIuQE4Wv6d8kyL2rHbPho01BpWgQKUmiuSAt4DHpyJGRE59urHoIRndDdE6cJg1WQkJl+kEQPTZqXwP/qTLUNOb1OY4N2WIcm2uNID6EhKlgckdjZcWs1Gra0RS3+FjNCisnqPepIHWdZyFewWpu+jb8Eq6+Ia+PRJ9A0IDW2Xuh5dlJ/LTJ+AtQ2Drd340+7BG/EOSOGtZF6VmfszKC/IsKd9Qg0pMk7hgwAl7VI9IjEVGg2YT9DnrAxbtvAuuGJrzHSujBSU+Nusb9tzGM3+mgoRLYMei40Mdt9UkNZPhyy2fpquVw7zgqHsPzVawbm9hjv8TibITYdx1ExqQxepygpHmFmRLIEQFjsxeeItWZ0ehbIhk+13krq8u9JI0oW7oO8OfDtwd50IGN7atY1nMuVrVOtJinlsngaQtTdNMUm9zS6oMnYMwfZ6g8SMRM0xbrJKcNnCZB5Iw4asv0wpU4L0EjE4r0kJA3k5Mu1EOmGW0cxtJBgBbTd0hXwFQtVCKzA9y8pUhQps/Wpn5WjXWxR0gG2mcG6tnd0sI5+/exu2U5rjHKvPxTlNIr2NI0yoUDEfzsbBd2M96Jqs+h2v8AyTk30pRYyGnmTTx3+F9Z3nEzCxPd/GDg8dCq/muOY1rYZ66A+39O80NbWdeWZXXLSgr3TE+G6/s1g3Xomh+hdf9CxrMOg9c6dKzT6Ik61XSep0f/ibHSyyxtXc+f7zzEmWf10Jxo5bMrbyF1+zHR402Jv3YBww9raj90g7zmF1l4JUFTj4N5QRLvnuk8vEQEstmCjAmGwH+shMiYmBek8H46Kdw16DEfucYkYpuoUUFypYE7rnFGNdKGwmYPVYORxxQLl0PzRSZjD00rp/RaA1XT6GMytEEgyCPdkkbfUYLGDKJZO78Dlewwd+76Ky5d9TlydoovzPlNuqtZrIhFo9jAGaritWcxGg0ueTRD5GWFooBick/wtipXfmIdbmZi8gvBe6CA+YkWhClwvz+zT5AbEnzl6f/EufNvJhXJ8czBe7ls0SdI0PlGmkHIm4BGUDugqe2DwOMiONo+R2Noj+YNkrGnFHarwCtoIq0C2/CP9U15y8mdIWlepJCHKjAoMX+jDf/+PHq/g1wew1gcxd9UQWRMtOMi22dOXbSGaJvAahW4RxbCDGi+0ESZDolOST4aeAxA8JxklhyxvE9jNQu6rxJYB6uIgQZqTQInF8GM+IiMCTE5va8ZMM9LgQrzqJ9IhAVEJd5zezE/tgT/7jy6opDnpnDnJnAdSWHr9MKmmRIYiWD9U0z4JHugEA+s1AAyAtEeg+JzPkjInWVReN6ldbUg5zQCL6JRH3ptjIXRYE/5gAs5E3NtAr06Dp7Ge7QENYV30KPvZ5rYXOi+VCK2eyynAwcD/5ljxh0fdNEjl7TYXTuMtOaiDjukLpOUd8xc/InPNxh71CW3UiK6bHRxpgGnlvNgct20O7OUJc4KWr8JFH2gwQWxTuZ98rfZZmyhO9nGkFfjphe+RXcsx++2ruOM8jiV3o0z3NyFgEHnZe56/k/J1/qJW2kuWPr7LG06l4w0kTemsM6MIQ42kN0KnR0iUjXxknOOGww6JCTk9RMK9VOIkj9ERDeh3AkEkGvUiXkZypHZ0WzfcgxwtccLLX2sHOsi5dvs7Bycmtc0TIvdLa2cv28P9y5bji9t1kw8Rz0xl0N+iq5aCxzHwmFlV1AbuB+3vA8rOZ9UpIOVne9h2+B3WNF8DXcOPMoNnecTMUIr3K8rSmnclUuwSlX0k8/jqhqi4tBxcYyI8kBrXNti6AmNMiTcWyO9zcT5GDw8/seM5XfCUfMhr7SXjL0QR3n4AnqMTszTLfS2o56ry5eQ3wy1vcHkSHtQ3eXTfKlFcZuiaZWJlTDwX64jF9iIJVFUvxtkofAU1rubEGkD9878tIUFEEmJerkPY2kbWidxxjVmVDD2sDvDct75DoF6oEjS1cTe14RTASMqkDGwpCK1VhDpNPDLmuLzPqoaTFjbrrYYfciltkdhtwhar7HRSYdh62XseDvvW/snPDvh8J05/43IXTXMcwzcL49iehoT0KtsVl/ZgTgq9Q+2wO2M4sxJo/ZozI0tmEsaiO+OQF3j3z2B+eEWzOtzeA8WwNOYV2coLCpwVeUT3L3r7/nAuj/jub4HMYQZekGeTKQivToQIUYa/Brggd0kEL4gbdRIX26gd9QRi2xEi4msnHjhGbM93NuGEGkDrcF7dALjM53UhiHRo2HYQ/RaCFtinJZDHbMOLATYdYeuxQ7u+igYYFR97HINrWykcuk5W+NGTDQCy/WI+AolZ44zlvTRPxwJxrlmEx7KE1ufRJ+WCp7zd2ZRhSDwl8yYqJKHHwkb+InE64XIYAyycQ5vkrTc3E7jsE9hh8bd7NF0gYmRDHKbd14osEZqiIKHmYjhr4yh/mOIOZ/qoDEhcPIQ65H44z5dN5vEDBf15BjRpMQo2rjbKpgr4hCX6GEPNdnX+U9XULvq+A9MLt4mJNYNOdzvjKEsiYxB21KF+1fBlhMDSNyYQx/lBiKazSDone3ywk8/y9L1n8Y5J048FyUyUqXlnAhjm4K2lV1v4owpmtZL7MMVzDPiuGUfDrtgCfLvFORbC1zkv4/5ybUcNj2sLQqKR7XNmmbO0xGGct9lvHSY9hW3cFZuLo+M7uT3Kj/ka52XMK8xRtVqmv6ILHHXC4FIB6i6RR7Y9md0bvgqGWse9YjC7BmEZ/4U/4W9oDyEFSX1kS9STC98q5tCSMivBaFQP4WokycrFyLqe1Fa0tKIUbLq+PKNTQReLSCrEL9427swwfUUm1oPcfpYF2ccmMsLvYfwJ938DmWytJbLnNl3mE1z5uIaMc6q7GF3bAI9uBExf3Y6DyEN7NwaaoMPYy6cixCSmN3E8o6beXHgdubFN/LA8NNc23nuG/rNIW8PanYU56KNpNYtx9dlmqsR9LeGoRS0PcsW9H64HTY10IBx0CM6KunuOjcQ6keRaV7JdWI+rVYc4QyjlEZ1tCPOXQBP7QetUfM7qHwncCeUEWi5wqayy2foew6JxQblsiQ6RyLLCj3ho34wgbE8jvudYEXApwwSrHc34X47EL3y9BhaV5EdKbzvbafWvYbii4LOW+zAypOFtitttKsxEgLVL+HHJYwtVWIGYAmsj7cgogbtrS5qZwnZY9N8lYVIGSizgnJt2t5po2qBtVD7GqNm0xW7iL7Kw4xH5pEzokQLUepnJyhPaCI3t2E+X4QdNcRWMM8WHO39rG9qpe8h8CuBVUrY0PaOCNYHWqnvGkOfKXjJ+D5zli+le/kitKGpbM+T/CfJ2amLWXzJGgbcg7xj6aeIi9ZX7YdC3lqkUDSfJWhZLtAHG4iUAb022nXxchIx5uN/byI4eXMF0WEhP9ECzI698Fbib6tgfaAFNeCCANlh4m2pULUTJBI+CIFWAmEKdE0jzZlbq4QAxj2sbgvjYA1d8DBWxvFfrCGX2PiHHOy4gdVfQSuQ7Rb+yw7i9Jnu03rIw3pXE+qwg9rfwLyxCV3yMco62HvbasKEj57w0HMiyC4L4RgQCV3fTxT1JoWDR7RtIY1/cHGKgtHHpzuZiac82m+wkSUX8/vBmKEB99kK8jc7cOoG7lZFtAnSbRrfkZhxhSz56AEXeWYSta+OaDWxNqRQe+rI+RHk+Sm8Bwr4W2sYl2aolwXcHME4WIVnyniPFjHe1czYfknTGuC+8RmLseoHE1gfacH/SRHjzASq30HEJf4iH29LlW2P//+wzp7HeXPmo788TKLVJLEuBa0WGhdzkUTsa6ALPjoqULdGGaqPkLdsZJPmtHIPc372XqzDijlrJYnuJJr8jLqTQwbRrjZKE7sZfeHrXLfx93lkdCeeVuwzNAu8xoy8vBV3jHy1f8Y1lPYoNwbBmocQArnnSfTAUWOuW4cn/wPzqj/CC71NQkJ+aUKhfoqgtUKJEhmjDdnYRNKxqIt2CtHXtz/dV6C0CKxYv6CPPOKZJAQYQiOPE8NHmhpceLL1IGsnuti4ZyGb5+2nbrsgBFs7O9lw8ACFaJRdbe2MplfRXt5J1PkJbuU8VCI765pGrAu3sAO3+DJ25jQA4nYLC1suY8/YI9zZD1d3nI0hjlOgkF8bfA01WnG+l0N31KdEOgCORm4pIlfFcJ+rgIaJej+Rjnl0dJzN4OATCCFZMP86tvtNtEU8hnf9Gwdf/CZaK85c/3WqffPIXtEJaETNJNLuUy0rcudZjD7oBHtTCSZ+VsZAP5LHe6kWBIk77OJvrc4ssAI/KVGfnYM7phESrIzCLIyjDxZIbFAUnjPwSprWd1pIQzBwu4M3oZExaL8+i90Tg38ZBp9gpS1i4D9ZwX8scKf3n6oguiyMlXHEIgFpTel5j8YgVHZMLjTEoOvWCEZyCXfum+BPFi1n4HGoD0zva2y7KEt8yEHnfaqVOvYRl960QaVu4lemz9UOVPf4OCsb/Jeef+Z9Yg4xx2Hz+P1EidI81k7kboVGwQg070uS+c/rqLbLMPL7SUYogRh08b4+Mn2w3cL8WBuy4OPsraPe3YrnSwypMXaU0cM+nOAU2cayOO6XJts94Btg/U4HGVuix8H76nT5/ajA+lQ7s1w1OmzcLwxCLTiuHi9j/kZrML512bj/Mgpe8J4vwfpQy+yUpy0W7peGIB8UxNtRx7gojY4AnsT9h2GYTAmntlQx39OE7JqdXz3krcN2HZ5Tz3PagUvoeJeFtAQdNwcLLhNPu8TmBW3ZtwzUO1qQT0/AfgcuztL/MDSGpheh2i+SJBZp1N0TeDum51rWb7fh/bSEej4YBPxNVeTSKMYFKdyvjKCeqSBubefQw4LE4hS538rgDflYHSb+Ph8zqqFwzD5yT+P5YF6cxv3aUc/jE5ILPv4tnt7+e3SJJP6ICqZuIx7cFwht47QoJA28Z6a3QYrdJrsv38y6MUn6UDPuPSnMWrAoEX/QR5xRRyyLoo76XY3Ti+RHn5/+ncXDZO0EE04Fcp08pMeYR4R20YzWmpSZIm6lqbrFGT+lORaEdZcSyPfNvkljhzF0CY/Z8QBCQkJeH6ESOkUoNkbwMchEM0hnhHTNANVO+dUCyWmI+A4pt0KmUSLeqBBRDQyhMAwdTLxe5c80gj8pND4C1xNH0lDPQJpgK8mWXD/5RIWNexaRqgUB31zD5NnuHs7oO8z80RGUtCgklrLfjpM69DCR0e1wTBodIQRWZjn14cdn7GNqTiwhF51Dq7uHp/Pb35R6DfnVxcJg7F4P7YCYOE509TE/yJV7YQp/qcFz/sN4hsVj8SXMXf8nnLbxr/m2s5Av73mJdOUgdiXC+rl/xVnz/4ZGfR9Wt8fQwyZDD1uM3ueRO88KXM3TTIl0ALtVkGzX6JeCg0e3WbkwgnlTDuuDzRjvzuGkY/R/s8HgHQ4Dtzv03+bjJFsQy1uoDUmEDakWHzMBQ98PRDoE3zfwHQc/FwmsdoA8JwmDLv4TRwWriwjk2UnkmfHA970IZkJOifQj1xp7xKXTnwuAP+hTH5gpSEafVOgNGQD2pEZ4+TeqqOU2ot3ELc42gbtFTVM6wt+3fYqrvXexIXYNlx++leYdLajHZ+fptg+YmMz2qAk5wSiJ/71jtk4NuegBFy3BOb+FkZ0GriMo9EsKbekgddsJxt9cASGQK2PIFTEQ4D1ZQUrw7y8gOizMKzMYF6bAEKhDs9PHqYMONDRyRQxjfQISEu+HE2gkal8DtEYujyFXxYK4Es9VEMcsButxf0qkT5Xtp0W0L1HD3pRIP4L3owI6tBqeWLwai621GHFJ5WVF/7cchn/oYJRdula4NDU7mMpn/GmfQw8Kamc2Q6uJ2xShMTSzbxt5UiFrCr1j5jxLuAQeTR9qxnhXDuPsBDrvIazJe61B7q0SXyQwbHBrgsFHNIf+zSW5wKDhCMT8Y4LdCbBaDbz7JmYeryisPQnO7PxfpO0cnmVOh5OYxFifxH9mZj8rdnqs1eeT+Nm30V7PjNgJAGpLDe/iZBBXwRbodxgcsu7Bc6evo+MtlN06i9LdvFg8xB8//3U++fRfclANABDRLbx7zR8hxbRN7/Kln6TNmhxbfI1YOtv7sbHibMqlvbOOh4SEvH5Ci/opwtD4fhrCImnFMN0ymWqEmJ+jHD0w61yhNRmnRM4ro4GGsPARxNHYnoehFWUjxridwpG/eK+3EGCgwQBfCZQPlnHUgCYgIiUNrdiXGqdheqzft4Bn5+5nIlGlakfY3NPL+kOHiHgeOzo6Gbda2enmWFQ8jF06TKVrAyoyvbpqxDpwJ17EK+/DSi2YOj6v+UJGD32V7x36Pmc3rfil6jTkVxvhGtT3eSBArY4hninPeF+uiOE9WMD6cAvuWTbtfjdtdjPntS6gYjTx+ReewdeaRekWFqsFJO5vRQwHk5l4VxfO+4bxFuQwDtlEegz6kmO0/UYac3B6wiOjkN1golz/SManIHesqzDfmcP/eQnv+3lEu4V5SQar3MBMSvxy8Px4JU3tsI+86nQiI9C9WlKe8DFj4BWOEcQ+eCUwz0ohKx7GuSn03vqU+6Q8J4GxJIb3cBG9tYZxSQqdNYk1S7o/bOMWNGMPufhFggBzjuB/Np9GtTR79U01gLhEfjDFF8q3s7W8n/M2rOTCzCou0hspPjfzM+0XgtXnY3/fg2KeZJOB9Z5m/C0ViM8WdjriEawDh7EmTio+6LJC9NroFQlEwUNvLoOr8OwIQrp0GCX0g1WSXTb+uTncuMWUafsEIVLBHl//2UogaG5swi96aCmQq2NoW+JpCVGwrrfwjw35DmAStMlNZdSYh3lBCt1QgRXd0lg3NQXt1Qfr2ixqwj1OwKvj7NMQBO49CkTOQG/MoG2J3FdF76uHQv0EM2FmSTQSCAGlF4J22nmeICpd1AEHf3MZ0WbR+84c++8LAnXOOTuD1rPThsmIwLcl+voWxIEqPF8Nuq1M4GHkfnMM0WNjXZuDpIToZF8nwFoRpW1nFfbUEEaMrsvj9D+kaYwoshstjNWt+N8eRe9pQEpiXZnF31oD5zhtTGnMe2OkPq0plgXZ3+qCw3V4ooSYY0P2+F4byYaA8hjCOM5WFVvwM/I0fzrKskySijjA4R/fM/V2unUlyZbl/NfsQnaX+rl93yMA1H2H2w48xB8suBWtYF78LH7vvH9loj5I0m4iY/TMyORRa5mDuOqT2I/fBW4Db/01HNQDJGs9WCfYMyck5O1IKNRPEQ6OvgwqDr6DVD7t1SRlq44v1YyxxVYunbVRfCEZN1PUsVBMCm1EEERHKxJenTm1YapGhJFIFle8tlttSI3S4PpipliXEFMGJeUxFi+jhGbtgXlsmr+PYqxGORLl6d45rO0/TLZW4/E5c9gVy9MmTyNujZDa/xPqzctoNC8FIRFCYKYX0xh9eoZQt4wYvdmNvDjxNAeqg8yNd7w5FRzyK0fRcDC7JV4fjO8zaLulGfWjCbSrMc9JoYfcIJfsoAcFk4Vzz8RCEavux48ppJD42kcAid2diOFpK5zoV1i7bP5n09cYyU3wuz3vJWKl+Na+zXx2+ByinTb1AU36dJPiCz5qiSBxVgqeKuE/Wcb6cDPeTwqolwJLjO53cW8bw7omS+sqm8OTaWtlFKyUwaGvOFNpsLIbTazVAnlUFOojyCiIc5NUdvrkv+cz5yITY00c/6UaxqIY7tdHg+9bFqM6YSKRjPzYwctrot2SjhsjTDzpBiniGh7S0UQiFsL20UcZIdOrwWzbxuZcgxe272N901KunbOSlJ2g5I3SfGOOiQdBe5rc+Sam5wZ77ycnmXrcx/nWGOaFScx5EdxD49P9VEpS7hzD8wSm0ftmN4uQ14GIAh9pY2KvoPCcxkrbtL43idkFUimsp/KBkAD03gayfwj52yc+Sr+cH8X9yrQ7sNrTwPxEG4fu8ui+MMbYS4LiNoW0oWWjJLUA/KM2ACsFss3C/dvBqYUt774C5o05dExhzI/h/MN0Oka1r4H1sRb0MfvDZNYIgnyNTQsfY2MSohLdbVO7tI2RxxWqDvH5aVo/ksWPhkEYTiRSWWhPTHn+yBjYGY3aVEM9G2xH0gcc3C8N0fWRDg7eqVEpk0irgbC9qX4w2i1Jn2Fw4D9cVN0gPi9N67sSmA0X91tj6D536lrOV4exPtiCd38BY00cUhL3gQk4NHlOv4s1r073e9oZfdjl4D80sJoFHZc2Y6+pQdHH3VFFL09hXWLgfecoLxcDRM7EujCFd79LYmWC/h96+FWLzFltZFYK1IPjyMUR1K6j8nF2mhgD96EAXX4GMeed6IPTbdF/Z5o1bWNUZJVPbr+foXqF3zzjsyw1XZriWVLxuZgiyv19D/HtAw/PqOODlWGUUEgMtJYk6aHZbCcyPAblPnRTmlouhyckvpnjoBgmes75SMPm4OGHqVcGWb/0pjf3xoeE/JoSur6fIvSX92DqBLJ+gIgHQrUzfsz+9KjfoKc6TMWMMS5TOJgoBFIwGU1HIzyNryRFK85AtAmFZG51iCan+AuDxx1BChBC4/rHpK6RAlMblDyfQrzK/uYRztw/j5gTrK7WbJsne+eSqde5bsd25jkGh+sF3GQ31Y612KU+Uvt+jFELBikz0YvfGMVvTIbo9kEUFF2xNaRocNfBO36JGg35VWd3o0zlEgcjDuW9mtEBC+OqDObGJP6mMv4zFeTyKKrmE9tcpH1wHrlvd3D2xLtZG1/LLQtWA5AyIxh7jgk+1Wxg2THWJOezMrGIuIjzUn6AGxauRFZc2nobdFwkiM8VNPoVICkmE+gbW2BDCh01p0T6FJ5GK41RcBCTXo+ZtSajD7gzclVPPOnh5TWt77Bm9MDNl1mYWYFyITbXoOcqic6ZGBemsN7fjPfo9D5Bb00aXxoM3hmIdIB6n2LkRw7ZDSZN55iIH4yiRjzEUxP0fFARX6Qw04Km9WWaOh+GJoPbx1/i86tu5JpclJ9s/Z/88LnPc8h9mtFlBR69Zit73nuIf2n7eSBcjrUEFX1EV4SGMDF+qwPjqjT61jT5T5uMR/ahxYm1yoYcB1cxcdBg4jmNdsAZhb77FK4jka6CPY2Z59c1snRiA8kB+Md4y0DgDp85XVI4ICi+GCxYqwYMP6polGY+z1KCPuzMCN4F4D9aRDoS//lj4kkA/uNl9DGBWlXewzwvhXlRCrk8hnlNFjSIusL1DIYeUlOLa9V9mvw2wAwDMZxILMcn4mnsbBARVxiT2TW2HHOPPTAdH6sZzEUWXt6j51aL+EKJmRHkzjMZvsedvp/7NWOHDcTi6JRIn6Kmoa5R22rI0+OIlfEpkT7F/BhD97k0JrcZuWOavh/4uIuTNNZkKPQ2cfAnktFBG3FzM2KOjVwew/5EG/6OGv6eBt7cBP3fdXHzGtWA/GMepR0atcdBLo5iXpxGzLUxzk/h3Zqi5AUu6urZr2Ns2ITx0TTq6hR8vAV3rk3Z7+LlukkuEuOCjjncPjTIx1/ezS6VJvrdP8P8pw9yTrxtVh1f33MuphdFisCSH1E+kQd/jv6Xu9Df+RF86XYSL+9BCvCVQe+yW5moDbDz5dvw3Qorz/tjjNj8X/5mh4SEhBb1U4Ux9yBRslDfT7puoHQHpcj0RtmIcumqjZK3UjS0hRbgIZEiCFoVIILt4FoHLo+GoGjFqRoRcl6ZVLXGYLSJxmtwh5cCFHqWZT0mJUXtEnMMJhJVbG+CM/fP54lFu/ClxjcMnuvqZu5Eng/t2su9HTmcSBq7OUatdRVmZYjkoZ/ipHqpt63CTC6gMbaZVPUijN0u2hZYDU3v/LVsG/0xdf8TRI3ZOdlD3v402RBzPHpWC7yIiZQKbRKkkFoSRSyIUjNtCrsge14Se3cJ/Xwd83kwrjBRy8f5reVncaBUorbGJDIZmNa8NAZiAv+xPXy8uZv6eYt45N44qzdmOFDuZ/kZTcgXHSK+g9hdJbk4jlfSuFXJoRcMzGyC5qIgEpez9qwKQyATweRGWBCdK8k/Plv4uOMat+TT+7EIXkFjpEQQeTjqYxYb+MMa2WTChKZuWKi4gR2VgdE6InCqEpljxgIAgDOk8asaO1/FOD2B/3IdDM1P3edZeFmUXqMJ5Y0wFu3lBWc/t/ScTqH8Mj94+Z+nrnHblv/Ob53/bc5q7mWObGat6sH31JTr/xS2oFKQDDygsZo0mZsiPGA9TWd0Phm3nSYjHqZmO8n42qCw5Zj93ArcAljtAmwxewHGmu0i/JYTme3aqy2JjAomds4WwrURTeKYY8fzhicmEZogYvuxWEGKuhnfGTfxvj+CyBqIJjNIP5gx4JIs3ujscpRe9Gm6wIJYuCh1oqg19mJXlxFJCtqutRh7yEVpCQkJ5WPukS1oOs9mYosinVTwhcO0vbcV/2wbpzS7jZd3atTFVjAzPqZvFZHAY1F12IjjuK/7GXtq0fQIqgGqqPCVYOKpoI0UtmlKu006rmgmWqvjv1BFLLTw7yvR6M1w7GpT4Vmf5MoE3g8LiJyB6LFRu2s8smKcR7tWcMOis1kUMXihsZXl/iDNj6dgokQUaD0jTvUdGRJ2nk3jz3JG0zIu717L4Hg/HH4JvAbLf3YHf7TxWr548GHqvsMH5l/OotJqHrrfp7lDsmJDBLs6gN4yM26QuvdRYvN6qERjKNnOkrM+zyJ3DCGjYDTNyMceEhLyxgkt6qcIFYZJiFaMRj9NZQtbpaeEuqEVXbURClaSBlawZ1cIxAyRPokEjMnJhz+5T1YajNgZqmaE3towrc4E8ljTw3GQk5fxjjpVAjFhUBAuyoXhdJGq5bDicM/03E4IDuSa2NQzh4tHi2zY9QLxcg2EwEt2UOlYj/RqpPfcR8K3cMdegv4q3soI3uoI7hkR5o2tIqmq/PDwD99wnYb8atMtNN0/asAPxzHvGkZ+dwT/G+PQYeCtTLH3EZP+exWVnYq+7/s0ehNTQXgSD/n0NnJ8eee3SNoFBpaUqK83Ed0W2hvD//k+mKjDnjzRb27mzGUNdj8Yp237YpQVpdaZpJqI467M0HSBiV/wkQmY89sRut5nE1lmIa9rmlFeuSSKKnroTpvej0WY86kIZhPYLceIBBEEaCw+rdAFH/ufDmG/UMBwfWShiqoIBAL1fB3nC8O4/Yq+b/sYF6cDsezoIKqwnC0+ZCRYLGhk4rimgbEkilpos6m2jxZ3BfXBHvzGWiw1n3yjSq12gGcP3z/jGpd3fpKuobn0HGhh5Gse+X8U+FiY78jM+A3Gzc2MPj1pPRqHwt2S1fYKPv/0HiaMniPdT8jJxAq8KI5FxCR+TCCuyM48vjyGypz4uAJifXJmAC0D1KI4pRcVduvs8lu52VMXMdeG1Mzj5tVZfEsj1yVminUJxkUZlHWMsEoLzPc2ocsKtbcBEYn1/hZ0BGRq9nfabRId5lE/YQgBo+UXiSQE+q/6iR8s0n2rjYoacMXM/ph5EfyMxdAPHPKPeYicCR7ob4wg/64PozZ7AdVuEyhLYL4jO+O4sTaOKnjIizP03asYflIj1sRnnpORHG+HoSh4M4J9QiDgxzaD7rY5cLZg94LtiHYT4zjbKMyswFgcGCt03ke9WENckuFpr5+fjg9yp1PmztKDlB1ouisJE9PfFdlSxRoa5+HBZxms5bmv7wmeGn2aJekm8AJvmvjOp7j6e1/gG2f+Dt/c+P+xdMdl7HwohlOHgf2Kx37g0TCOs9nccRHutFeBr2y00YkSuVCkh4S8iYQW9VMATzXwRZVm2YrpFGirdDFhV1FSIzV01Eapygg1YYMCZQqULzCMV+kMDRHEA/L01ASlYkSpS5usW2F+dZBRO0PBPNYuccxlpA4CzKnp9G0RJK5QlH2PpGdysHmU0wa66crn6G+azttZjkZ5as48mscGuHjTJvb09rB7bi+eadFoWoJbKxIb3oUtBNWebVjRjcEHTYFeEqVpcBmbDn6Hm+be+MtUb8ivKFGVRIznZx1vFOqU8gn0MV67+e3QvjQ2GZ092H3qKI9cJMUHt/0fLl1zBv/pkmvI/sPhmR/0FKl6BYhw+hKbge84uGNHchdC53ttmpa4uIk4fV938Eqa5HKD1PIIsU93IEZdREygLEllWDL07z4oHysnSCw3aLvWZvB7DtKG7hskpgGeq7Deb2H8aCSIQn1uEtc1UI6JlQVjpIL34wIAhlSgoH+zoOsz7aiXakSzmolBn8x6g8Iz0xOzposszLimvF0hlkYxGw0ejLzIpwbew/B3PI5Ya1ouj/LOZdfzSPErpCPNHEmwk421sd69FVURDN8zbYk9fJui65YEic9E0eMeusni0AMad2K6Gt1hODDg0/AVQ7UGyxIZjNDQeFIReLRdaND/fX9qITXaJYjEFaqqcXsSRD5hIydcdMrAS9r4JQWZV7/um02pIEl9uhOjGgSP9FMmA/conFFFx8021f3O1N5iu0Ngd84WzZ4v8d/TRkQq0BpPS7yoRHgG5VGN/cF2jME6eBrVG6U4JEmddswiQEMyMmTR8v+0Q13hRwwGn9W0XeZhpm3iiyTV3cEzJExoudxCnODAe7/OaA2trasC13RfIx2F1XBwTInbEcW4pR1ZdiFpYC6wqO5VRDsE9X5NaVSSuqkF/UwB8/pmpClJLtOUd0zfz9wGi0P/4tD73gRmhw15F5E2ggwCBY2/yKTjNIkzoXCjGWIXpIJo67ZAtRi0XSkZundavDadbSBsn9QKSfWAxjsqo0ZiMRTtCvVxn9a2+ch3xrE9idWkcMcnz5PQfKFJaVhgv7sd6QRtcnSz5qNXnc3yZpN66Vm2HHyIM3r/cCpY6tFEKzO9VZ7P78bs3Mi2d/2/9BvQ4sOifdtIewaVWoKBfTPnlfWqpuzFidgWKIWzbj71VgtbxjFSYbS4kJC3mlConwKMlg7iYtNkS+KOwvQ7GUsF1vS0W8bUinErhZh0Z/cVSPEaViwNwBczxLovJGN2iojvknVKNDlFxq00JSuOeoVd7IbU+L4AoaeMeHFhUjJcLE8Ckr2twywb7CSfqFCLHBW0Sxrsa25mMJZiw2iBK/r62Nfdxf7mLpyRGPXoakRkD+nxJ3F1BC99erBsLgRLc+cw1vgKL+55jpULT3/jFRzyK0nDEiTOi6PuLc047rWAV/A42gRnZqBlDRilKGJxFKdZ893yNxEIar7D/zPvPOb5RSZqz5CNZ6A4U+X7hsG8xZJGv5oW6QAaRn/s0nFTgv5/bkyJnfI2H2EY5CcErVfG8SYUA9/xONpl2M1rjKhAa03PLQZW3cd/qICzqYLImsRvzKHfm6PuSty9gpEfNdAO2O2C9uuTyOYCjPqYuypk1qXJtPuoQQfRZqAeL5ButlErkiRPi+CVFFZSIEfriC+MklkVh6VZDjcZnM0ZjP54pvVo7GGXri6bK5s/ilkV6DYNTRInoalss2lMzJ7wDd+r6L0S1Pfy+O9owRme2V8YKcGQDjZ85iIWtg63rJxsRMMk1u7T+06BUxYYpibSAsL3kEmTSF3BIyW8bVVEp415XRNkT/y0INUG4qUqzqMlEGBekqbnpiReVYPw6b1O4I4H+5GtZoFX8KF1+vNag+sIjIKH9+M8VHzE+hSNBQniczS+Mpg4rKjsjiCEIFqTxObKWe7NvqPJUcf/YrBIhiVoflcrft3GzWvMlKDlSitYBJeQ/7lD27UWHJOJK+Stw4i0YSYF4uYmvKfL+I+VISawb2imYkWRzRZx1cD/xyHsok/7uhS18xLIjMBvi2J1W3hfHYKKonlDksytWRqjgAjSWqZWmRy+w8crCaxsBL+mibQLOs9Q6O+OQN4jtiGJcXoc9+4J9EEHsSgClzURjXn0XqZxtcRoM1HAwP0Kv+bRfLFFcYtHY0ATmQfJXB3x1xUWORqiAvG+GL6tSK0ykBEBKuhT3bIi/6TCK0Dg0xiMMZF+aFtc5e6D3wFga+UxVi8/C/mSh8gZyAVR9JjLSHoUjgqnYgrJuG3xB/0PTR17z8Jz+FgkjrFvCCHaOTYZgogYiN+4mXx9D4/s/3sm+vcTjeQ4v/r/kUmcgdZvjnOuLypsH3qR0VI/2Wg7rdFFGPrVjUkhIW93QqF+CnB4YDs1ESHZGCBbA1QXhdggpvJoahQYiWQRisDlffIzs1zeX4lJsS48jT7K9a9hWIwYWaK+Q8Yr0+pMULASFKwEjpjt+ignLety0oovgYQwKZku0rOpSIeBzASrD/Xy1MI9HK35UzJCX9ThxUwH2ZpP5/AYlx58hkIsQV9rG/2JuUzYY2RKWzDqB2i0vgNkBCsax67P4b49X2Xlgr97hU2IIW9XlAbvrDimo1GPVhBJCe/MUHPGyS22qTzG1IS55wqBfGYCb5cDJQXtBjfeeA5/Xrqdddm59G39IntHnqXPztB+xf8mdse0UNetCYZ1kgXLNWrf7HL4FY0zqmZt2y1vC/an+oMeVnr2wpndIvDKGmEIzIqH/0QZ/6kgh60e83C/OoL9mXYwTIbvni6PM6QZvd+l7cPt8H/74dkyzTda+D8pY9yUw/nCkejVdeRPixjtJtErs3h/Ozr9mzZVMOZGaD8tilczmBHynWBvu18GM2uhag7ef4wjEhLro60oX2MmJS0XGUQykN+mqO7WmM0Cr1Qlcm0O9lRpWp9k/BmNmRMkFkjqp3vcsesgl/Z0MC+RmBXYK+QkEAH13QLyxRrRyUMKsD/djo9A3T2K3jsZ9f2gg/+VIczfOwlR3wcbuPcVpl57P5jAyhoY86PoJ6roH0/M0MLWx9rxjxkDLddD3XHUM/BECTsq8VenEEawnzy1VCKkprTTJ9IlZuWrtpSP/2gBuSKGbDHxd9fhvjGMz3TSqCmKW3yOTl0nTF5zkNaQNwctDERM4G+tog9M9ms1jf6PUSLvaQcJ6rbpDAI8VSJ+PrAkilA26u4xqEx2Tk+VEZ0xRn863ZiMuJhKnXnEst20RON/bXhqDBApA+fro1PX0bsaUBihck4LyUIDK+9CNM7ooEn7FQZjj/uM/NCl5wMWSvvoZA3xFxPTC0V1jfcfBWK/HWHg+x6qAenTBNlehRHVlJuNSaE+jZd0Ud583rfgq5TFAWwy1C7Pkj7NR4x5+AMO5gVpViQSfLj9cr45/BCe9rl1wWX8y76HZlzr9sOPc1XHBpY8+QynLb+Wl7ZNzwHnLRHYCY9iyuShx/6GYvlQUORGnp88/v9y7SX/him739jNPBrp8vjhb/Hwnm9MHbpowfu5oPejM9LBhYT8uhEK9VOAPeNbMf0UhrOHtmIWX0DdcOlu5ClbcXwM0IHQVr5AytdgTT8aA/QRsW6IGTOLumFTN2xM5ZPwa8ypDlOXNuN2iqoRnTpPCBDHBJczEcSFQcFySHgWA5EC2Wqc+SOt7Gs7MlBqGkpjYbInVkREY0COqM6yuN5g7sQYpx3eRylisLfZZDwtiPX/B/X2G9FWhsXJs3lR3k55yxDJtWGqtl836jGFOruM2dSBm4eJBzWq3k70vT691+2l3NdJbomApw6gCxXM8zvQlQT+o3Uuq61i4YY55PwCz408C0DDKfCz6pdY//4Pkiu2o9Nxii0JiiWHipVnaWtXsAp1lMjMrDM4XjxDKydwC5pYrIq8/2XarlvHyL0u2g8sIbnzLIQdCH0hNP7myswLaNBDLn5qchIioGWjIBb1Eb6Padh4nRYMuMEaVb+LLhzHzdaS6O21qWsEi3Ogt1SQK23sRLB3XR3lRGCmBFZWoLbUEBkD88IU3oNF9KYK6Y0ZrLKD99MJqCs6z0nhro3ipSUHmwpkSlGMBRaWbDB3bQQrr1DbyugHfO7ccCb5eXVsTHQYSe6kI+saf8DFujGH9oJFIzXqosY8RExOifQpGhpGPWg6/vXeKvxnK7OPPVfFWBLF2zI7IrwcaKBOn34ohWBG+sWp41vKmJcl8QuaORcoeCKP8DXZjRmKZQn+MUq97mPdEuRi956tYCyPIdqs4Hhy9up4aqnECFekTigxfJgQqB31We9Jx0Pq2T2P3lzBbC6i5nWiD81sJ0bFxUxHp9zSG4OK6BxJ/eD0fTVr3syFWktMi/0jDHtEYhqjI4r6UR1Z9Wmp11G31WjrjeBfncavayJNE5AXswKBUtPoXQ4974gwsglysgq3FVECmj/QQe3gdPBQux3iLQbLjTZ2HkgwtHsxAF63YON4DTHsYqyJ4942huFoPt61gevfv5G9qRGaImn+fc8Ds+qupBqIUonFfY/RdtbpFJ0YCbtBrtumToSGMzYl0o+glEe5NkA28csL9aLXzyN7vjnj2KN7v8XpnVeSlnOnjgkB8bLE6A/GQr/LoJpUs7wAQkLeLoRC/RRgwNlFlCbM+g6aq0sYiJVJqhqm8piwUoHruhQoFaRNe0OGZQO0EkGAOSlmhRH0pEFBJilaCeJ+nY7GOL4wGLUzVCYFuxTgHyPWLSRJBBXTpaoEL2YGWD8yl/2JPEORCnUUJoIIFi4eOUMQFxauhj1xgxfjUTrb2plbLTNv9DArhyrs6OpgZOA/aHTcTIvdgSbH9/Z/jQ+s/uzxo/eGvG2RUuIMRhj78cxRePReg44bXZpbn8H7dwPqwQzG7y8hz5qD6MngTjjMjbRSrk67zkcjWRJzLuPnokLnXPjp6JN8d8fDzE20c5paz++lsnS+J874ox5eSZFZa5KKjCBqFrE5aWpHJm8Ssmdb1A54mHv64NAE8VSJrg+lwQNpC5yiZvRHLh23WuhikCtXjxwzO4tJZDx4GNvOFcSeGYOBYI+jZwvsT7cHKYjSBur5KiI7Ozo2Joh5NuaCCDJjBCIsGkRBVoZA/dsYXbe2MXiXi1fQWM2CtndYmHYD/9kK5mUZmBQhesDBbrg4/zidc9r7Xh7rhhw/lbfz0M5vcN3Kz5D3lzIxEuOWiRbcuyeCfPYAz9fIfTBJ4wxw3dlFDTnBRATmVRnc28amDMFyYQRjXQJlHT/qu4hKOMH7rkWbBdtnCi/RbuFvKkGrFSweHP1ek4E6SidpHVg5Z9FsgiHItHr4Xw4WjzXAXaPkPtgK5szPGFkD599Gp9qz/1QFuTyGsTGB7fp0vNNk+CceqgaJpZLcCo0IhfoJxRImQjUQbSZ6eGa70KaBkdDH7mhANJvooQlUezNicQy9Y3JhMyqw0oKuG00Gf+jijEBjSNFxo83IfS71wwojDkaTMeOJEMZx5iEG2FmB9+VBjDVx1HNV1JH0h+NVjH0NYh9shmoaLRWuUZ35mEUFuAprsEbnJTH8v51Mx6lBfn+E3subcJpstK2JDNQRf1EAQ7DuBpPN3Qn6+zTz2oAfVzBvzOHeeVR8l36X1rsiZD++hKJRZV6inf2Voam3k2aMjlgL8qbLMf/t+zRve4lmy0ScuYL6krOCejeT2FYSx525cBa1c69+w14jDb86a3FXo2n41Rnz1eSERP3tCP6RhZKEJPm7rZQy4XMY8vYkjPp+ktFaUxIDtIksuaoLupexRJXWRp6CmQjSrQnQEpQWxwv0HFznqL9XRBKoba3R/vE/oxFUjBiDkSYqRpS2xjhzakPE/GDAMURQnqNzrBsI0ljEpKRieWzPjLDx8Hxi2qBFWjRJi4Q0yMgYeRXkOo1ISbNp02bYDCmX7fEEWzvTvNzewpLBEVYNx0j034FsDNEdPZNdLY8inpi5Vznk7Y/vK0wmo+tOWpx7L1Z0n+ljJpaiUsumRPoR1OZDGGssrLngU4VIM83NqxHCoGf1Z/mz/eP8n927+E/P3M+uCZ+b5lxMf22M3mSKTxy4g6HoEJ3XCuZ80KJp3gTi7hfgts20ra/SdatNx00W3R+0sVs1TXofPHcY45xezOE8ZlxQO6To+1aDsftdWi6zMJI+xAXmDbkZ3ixijg0pA+36pNcbxIQLRQ/rN1ux/2sn1u+242+rgg2N/zuAsT6JP+Ri3pSbXmJNG1jX55BzIggBzldG8L6bx/3mGN738hgFjR5yiVZH6bmiQu9vmHTdKLCefAH9dz/DPM8O8kRPdixyZQx/52xLlf9MmeWRs/G1j8Dkh31lVjbiUNXTIv0IP6xjNsKh5ZTA03g/LiB7bcyrsxjnpVB9DrqmQPoY186cZIvVcUie+MVQY30CYke1mYTEWJtAKDAvTc9coG01A2F/FEKA7LQQHUcdN8G6Oou2Nbw42yqvnyrN2p6hx7xZ7Vm9VEMUPaQJ0cdH6V3pMOd8j9bCBJbnh7OoE4wmg394BOua+Ix2YaxPYC4yoeYjuo9qBwZY12YRbSnk4ChcmkWsjmFencH+WCveE2XE3/fRvbTBwlsFvTcZOOOK6BzB/E+azLtZIHIy6K8n8bfXMC5IzSzYpTnIe+CD6LanRfoRij7qoIP3sxIiYmB9pCVYKLMFxgVJ7E+2oUZc9JCH6GvMnJgVfcR3R3DTeYyHhhF3jgfvexrzjhJr5gWKX5iAIdDHSR/H7gZWTRD1o/yvdZ/gzOYlACxO9/B/z/wtsjpNqaMd/dvvhw9eB79xE/ULN+DIYLAxZSvnnvmHiKP2Xa457aNE7Z7XfO9ejWyki1xsptdkNtZONtI19VpKAZtqM70ZKgo214L3QkLehoQW9ZNMsT6Ej6Lbb9BeTKJ1FJtdOMKkYdiIyUBw6kgAORH0zz4aD4VCvaKLqUAgJ/8MJCDwJagjAUk0IETwD2CiMZjWElUjQtWIkPDrdDXGcITJmJ2hakRQBGLdkNMB5iwkloRiokytnmBD/zye7TkwdUEbgwgG46pCi0yBAFtIOgybYd/FFCkwx9m64AzmD+zhzL46W9SdLOm5mQPC4if7vs0l6z42c0IX8rbH7jAQJrRuFMQ2jUH/pKn2XoH3qWzQ/ma4JRp4i3wePvRHLE/fSiRzBs6cq1m94Aa+NjBCzZ829b6YH+bs9rV4yqe/dohVze18cPd30MD3Vq+lNbYcY30X/jP9iG88hQ2YNyxl5FAnTYsVjEwgz5+DOlBAP34IIXaSvnEFqU90M6om2Bsfwyu7rC31ojtt7N/tQA+7QX7nZgPnb4ewF8ewrsoiBxXmJ9txvzuOPuggF0Qwb5wUUhWFe8cYxoYkrIpi/14HuqoQaQPnm6NYl2fwHinNqAc97KFHXOSyKBSrcNeOY7fkQtxH+OC9XMc4K4GcFwn2Kh9zmogbCEtyyxmfIx5dzNntku5aHMaO0/doTWR8P2RS1Kzs67/hIW8ePhjrk+hBF+/+CUTOxLouh3Y00gC/18L4VDt6zEOkDESTgdQn1jKllEZMuFgfbkYPuAghoNNCD9eQi8C9M491cw4tBEKCnvBQ22uIedEZ19GjLtYNOfSwi3Y1osXEH3VgrgGJ2dZ2kTJmjZzCOs5kPyKCTCSHPPTuBuxucKTL8UZcrE+3hjOpE4ivimhD4e3tw7w2C76FiADNErW5jP9sBWNNArE+iTZBNpu438+jhyXmJUmQdXSbhfdAASyJeVEKWk301hreDyYQTSapG3Oklxh4383j7Wogeiys63PoUQ9MgUgbwbVXxFAFHxUz8U2J5XnTaz+TW5CORnZaqJdqOH8ziJwfwfrNVkRN434vj//TcuC9cXkKf1MlSCXaNz1WiayBHbOxdlRn1Umk4XPue02yUYFxdfr4Fv82E39yrWFpppfPL/sNKtSIiQi2b6N1YKgpJ5KQmB3NXSlNc2Yj117yr5RrA0TtHLHIXHiTgoZaKsNHzvwL7tv5D+wZfZaFzWt5x9LfwlLTKSikFOj+47hqDbhIGQvTwoW8LQmHl5PM4b6tVGWEZHUfHcU5jEWLZLwyI9EswtdTAeSUDgK5Ofi4+AgEJhJzUoofS9BdqeCzgIsPKAQSicDEQGpAKTQCbYCPwCMQ7Ec3jIoRpWpEJ13ix1AIimaCohGnoawgntekS74QQaC7PU3DrB7qYeFQG3s6pt1oUzLKuKpQ0nVS4ohLvaTdsBnyNQnt4ukKe7oW0zt8gDP7h9kk7qSzdSk/n/s9Lnvk3ahjcpyGvL3xUwa53/CJ79EzB2lHI35ShbWdsHlg6rC6vJN7Dv0e+cIe3O11Lj17Pcl4N09VRthbnph1/arnYUuTew8/xv9Z+zHePec0Xij24XkFHnryXVxwztexV7aiyw6iOUZhMEnhGY07Jum+YiG6v4T+2cHgYhq4cxtiziH+9YKdPFXexX9f+xv0U6arP41f8DDm2GBL3G+PYV2Vxb0nj3ixivnZTpwvDAXB8AC1t4H7LyOYH2rGODuJ/1gZ/+dl+Hk5WOT6nXbcByYQVQ0KdHG2u7Kua6griJoQMaBxzDkZGy8q4eoUebuC40/QmU5BQk5bLQSYF6Wo5Ar83fbN7CnfiyVN6s0X89HURYGQaUxPkIyLFP53Po21YD3+Fb+PI2YKqpATh44p9Ig7FR9Bj3q43xkPghgKgfr+OHq/g8gaqLIK2tXvtZ3YMmqBqDu435jpMWW9P4d2PIx2C/fb4zPf+3AL3lGONEoBUuD80/CM84wLU6i1YC2N4v+0NN1ODTDOSsx28M+ZyAWRIIf6JOZlGbyowvBmL2DoUpia7UTj+z751lFacwtwv7k1iDqasDDWrkI95yBXxvEeKoIA68YczpemA8t595cwE1n8x8tBIDdP4d1XwHp/M95z1cB+MeahdtRRu+vowWC80YddnK+NYF6cARO0q/Buz4MtMK/IwH0TyKKPvjiN6LSC+ArnpoI2N4lcFsXfWZ+ytKu9DYw+F+eu/JSgVy/VcD0F10msM3z8JyX+yyDnasy1fTjReageA3loZrszMgaJiIerQW6IEBkF49xkMF5AYLV/fxMVc/pzpjLJcIxXwC9AawPLmEMuOWfywOv6+C8kJebw8Y1/wWhhFIsEQs1Mp+B5Crkxgf9ibcZxcVYC7zjPZ0jI24GTLtR/8IMf8I//+I+4rstHPvIR3v/+9894f/v27Xzuc5+jXC6zbt06Pv/5z2OaJv39/fzBH/wBY2NjzJ8/n7/8y78kkfjVS+OwfXgT+Alai8NY3nqc+H7qRgwPA6EDa7qvwDc86vgYSCKYiF8Qa3bSkRVNINQtQGBM2eAbOAgpMREYvoH0dRBDS4InAsEemcxFDUF/XDGiVIwoUd8hphya3BJaCOrCwhEmjrRwMHCkSUNYvNA8yNrRbhx8DjaPIazAyp+VccYnXeBTIhrswRWCNsOm4KdRjQOk46s41DYXoTVr+8fw5W7uzSge77+Tsyc+jD7eXt2Qtx1CCAwJLyT2sK7eNcsiLAZ9Bt7ZSrzbJjPuUp+reNG9m2gjQ1vTCnLZxUgh2JhcwbzEGMVGjB8cfAlTSFbk2qn5HguTWW6cczaXd55Fh2FRVB59lT5oXUIqs4C9o3dRHNsOQrBw7keJdq4jtsAld46BGi4j5mSQy1pQfUXEmg7wFRQafHrh9bzPrpNVaSLZAvnWBgkkDQNG6nXaP96MMexjf6YDXfcDt1spkMuiqH4Xij563Ic6GGck8J+sBPEqBFjvakLbAv1sMGHRBhjrEvg/LSHm2ghToPY3oNNCTthoz8S8fAHeT/YhutMwXkOsaeMOtjAn0cG3hn7CYC3PpW2n86HkRUTe3wyjHtpRyPlR9HyLVquT9827lOcKe5FIOjIt+AssrJZW1LNVdMHD2GBD5EWIpdAvPkhk4604mYUnvuGEAKDr6vhBDMc8hGmi9zkQl9BmIaSHHvPQIz40n0g3Uo2/eXYgOH+/g3lDGl33UAfriGUxKGtoKERkdhIQNXbszuQgIB1Xx/Gfr2Jdk0VXFCiNyJi4j5cQK7Izzx9xkHNsjDMT6LpCxCT+C1VYYyLaLZDB3nlSBnp/A+P8FK4VivUTiSDNeHocw0/Q9Km1qJfHEb1p1CETPVpF2BJjfQJ12EGXfOSSaNAXOpOGj34XeW4S9WAx2PbTbaEqKtjzPhS0IZE1pkT6FDUNBsgFEbyfFJBnJhBdFnSYyNUxKPioww7G2QlEwgStkR9vRfc5YAlEm4X7zyMzLqnrepbVXe9s4PsG4vaboXsV5vnr0d4Y/qjAXP2bqPfG4AsTU1s05PlJmGMRMXWQplD6+D1gdaYwzokjKgrdatLIgeEpfH8yGLAp8H39C4OwSSmQEnw/2Kr5ZmEYAiHELIEdMePY6pX3vTfmGdjvyqHuDcLgy2syNOYZhGlGQt6unFShPjQ0xF//9V9z5513Yts2t9xyCxs2bGDRokVT5/zBH/wB/+N//A9OP/10/vAP/5Dbb7+d973vfXz+85/nfe97H9dccw1f/OIX+Yd/+Af+4A/+4CT+mjfG/sbzzPHizJnI4QsDwxinZOUCl3cR9OENGeRnjmAc13p+PDTgEgh2i2l3dgM5KXYMfBQeGtdwMDCwlEQqgY1CIWgYAlNPWtePEyk+byUxlY+lPUztk1R1DO1jej6m8qlLm4OZMssnlpDyoryUG0DbCsOSNMkYE6pGDYdWnSLXSGBqSU1GqIutFLwyGTPJwfZ5LOx3WT1Ypijh4UXf5qz7rkLe2kXI25tEvYbx8l76u+IcFBV6O12Ovev19RauUuRyHQhdJP7kIGctfjd+bweVYZvKZk19VJBeoVi2a4BMcyvNC1fRbRc4OPwgiXiWNdYCFjkFqs99jWTrb5N+wubTtQvQV+WIpb9E9Vmfpl5BZjnUXvLI7/OIz5NEUyCGm/HvrsKSRfgXpshv8kBBdg3Ed7vUUlGMjIs1niGz30FmTfy6R3NLHFdJPGHiFjTSNsm0ephnJlAHG5jrEhAVeD8qIKICHQf799rRE34wiUxIvK8OI7IG5lVZVJ+DPDOGsSSK/0wF7Wmsj7cyGHexLvVpnkghjAQmzfjP1RArbJw1HmviozT23sdHamNk513Iz0Ye5fC8LsyqQaq5iUasRlM1g/jeMImSw7kb2mjJ5FlsrMX4uYs4WKC+XsAVURoDCcqbJMk5pxG/9E+R3gHwZ4unkBNHIwpmzgxcdo8mHrQpLs9Si0cpHRLE5mqSaQ+Rns7VfCJwXYXVbcP2SSu2BOvaHLrq4305j1wdx3pvC+4DBUgamBdnUHGF709PzLUGmmaPjbLNxDM9zJwZBNeKTmY9qWmM85M03GOCV6UVxooY/ks19JCLXBbD2JDEsxz8iA3/uYf8Fh93QpO5VRKbA060Cm6YPupEIYVmnn0W5jMT+LVB5JJ5+I9XQbpY72nC+2kJkTGwbsoFCzWAeWUGXfZxuhOM7RNQguyH4ggpKOwFrw8yF0WxdxTh+cmFLROOjUon59j4eRd9ZTPF7YpaH6QykvQaA/VCFfocQKAONfAfL2OcFYdeCyklFH2m9kxMclwX9bhEpizEiovQ+55Dnn4Z6tAhGM9j7HucsUwU4zezZGvzMGIJyCv01/LYKUnhbIN8ZDP53XcSS/XSteg6Ck09/KT/MFv2lrikOcfp7REefe6fOFx4mXU917AgcxaWTh+3rqsM8sLAj9k9tolV7RezrOV8Irr5l7p/SjQYqL3I4we+S8xKcvbcm2k2l4B+bYuDDUvhbrCIrmkFBJWoQqlQpIe8fTmpQv3xxx9n48aNZLNZAK688kp+9KMf8elPfxqAvr4+6vU6p59+OgA33XQTf/d3f8e73/1unnnmGb74xS9OHf/ABz7wKyfUHa9C1Rhmfq2ZzsIiavYgE1YCVNCbuwbUmRTRs3eXviI+TLqwv3qcmyOiXU1a2uvSRWJgIZEKLKXxRHCtiK+R+khfOjnZkUG0eO84ZRNoIr5LTJWYSD1DZ20x8w4u53CiQMV0MIUg7UbJ1uJYyqBk1fGEIuKbJPwFHEwO8HjHOEZEUuroZcPBXawZFVRljbutL3LTnj+B1tde1yG/WkSUj3HPQ9Sa03wh3s9Phw/wXGaE/3LzWTT90IG6prbBZmIFzHtSokd2og5MRrndk0fOHcXrWkVtv6S2H8rPQc/qCB13PMqGD87lX7f9TwAunHMDe578Gyr5PWw47YvILzigQCyMMPEMFLcGVhVhScZGBZXtAlD4ZUgV6uifFIIFtfVZDv/btFWwvAN63mHSfKgK8yKoH0xgnpfCuydP5YoORFni1zUTz7g0bbRwDnh4P8tPu0XubiAXRLA+3IL2FF4/7L9X0nZVhOGvuyB95t3ciqV9nH8eDiLNZ0yc28enJoJqW42OW5oY6Z3A/5GPiBj4myYnobsaGJsNMp/IcijXydiO7zK8/2EuuOqvuOulL3BwYhsAn5rz57T++yGYtHrYO8Y5/VMb8L5WCSxIeBi7QJxnMjIRodGvqO1NEl8wn/b2hxE9q97SdhLy6lgZE3F904wc0My10a1RVFqTV5LizxSgqe2HYs6gd6UNzA4o+FZRqzlEV8Xxn6hARWFekMJ7soweCp49tauBXBVDSIF6poK7pYL5u50odUwk+NYITKYzBIJsCFdk0dJDLo5C2gjEEkBMYpyZpOHMNGea8Rju14bQE8Fx9VId49wkYk0aNSw59E13Ks1hdZ9P6xUGifNjOG64IHWiMGsl5JdeQpUczGvX4n55bOo99VwN671NUNc4Xx2ZsjqrnXWMG3IMPg7eRNCXxRZYjP7ERU82l+oeaL84TXRPDX/AQV6dQ909HTndODuJ93gJNmbou8vDnXwr1atx7xmFI21mVwPj3CTWR5tRW+v4/zGBHxMYl6Yxrkrj31ecLm9KI0+Pop6bft7Mq+M8X36C1tXraF97Fd5tfwru5Pt7N5G64qM81HiGs9T/plkouGM6m0FqE4iPJNk9sAkGNlGPdvCXYxMcrgSeV8+PTXBRKUO7t4+9Y8+yZ+xZrlz6Cc7u+ADHal1PFvnGs59lsLQ3+Oqx59jX+Tw3LPksHOOS/nroq77Av2z6/anXz/U/yG+f/WVyxqJX+dRMlNJU7SMD3RsuSkjIrwQnNSrX8PAwra3TaqutrY2hoaFXfL+1tZWhoSHy+TzJZBLTNGcc/1XjwKHncHSUVeNFTL+HfGyEmhEBpWiYijou1qRwfi1owGHa1f213lwJmEhsLATQwKUuXXypMYM4OjQMiWuKaX9DrQOrv6+P21FqxJTVvS+e5mD2AKPpLWT8Ii2ORdy1KcoGLzUN8FT3Pl7q7GdHxwDPdx9ic9deYlrx7t1LWTCaZZc7wR3tTbSVHJbmTXT8KXY88kO0E/bQb1fsUgm96yCHT+/lp8MHAHissJ+vdz7P9k++zN7fh/+66DHcoQaiWaEP5Gd8Xh+YINE8PfnxCuBEO3CXdfPQwHemjnfb7VTye4gmOojtaZlqy3pliuKL06aP+DyDyvbpSX12uUD/NJhwiV6bwv7Z1oDCgclJmyGgplADLnptiuJWn8aQCvLBzjEoPu+RaNKzogSrvQ1ExkAdchEH6qSWS5zxyefNg4N3K5z9LnhB+iF12JllCPU3V2nyOpDzorNdoMd8IsMWg84wufbT0dqnpmpTIt02YrQOp6dE+hSHPDgmn7t+okR2yfTr6l4LL7UWle+bVS8hJw49atD3lMB7Tztc34y+uZXy8ibKQ+CPGxSfm3lvvTw0Rl7hYm8RuVwM77ES5iVpzOuyiPmRKZF+BLW1hlw4GbTKA7WrjnWMEbt8QFA9M/iN+oYW3Js66P+5IKJj+M+UMc9PYV2fw7wui3l5Gu+RAun0TMGh+50pkX4E/4ky5pCmMaSnRPoRxh/zkaPhNqwThRACOVSDooOYk8F/6diw/eDvb0CHOSt6v//jAtnlky8MUA5TIv0IY89p+GA7peY0Q4ci+O9tw/xAC9aNOagpRMTAyespkQ4QMfwpkT71XU+U0XkV7IUHqGn8ewroDs2uj9cZei80PmUhUwcQiVGsm6KY18ax3hXBf24nmYk4gxRxh3ZNi/RJYk/ey1lzfwsxkkE8XZj5A3yIHWrDjgau4xOR+VMi/QiPDhToabls6vVDu/6Nmh7jWMbqB6dE+hFeGHiIojc469zXijB8Htn7jRnHlPbZMfJYGLU9JOQVOKkW9ePtdxFHbTx7pfd/0edeC83Ns6Nanmju+PmDtLgWS0bm4Zgj5GNRhK+pmQofjaUtjucZdTxeqxX91QgivwcB6hQKDx8XhYGBRAbB5qTAQmNO+sILReB36BGIeOPIlWZSM2LU4ppYZJy0ewhX2pSiWRpYaE+ALzBMEAb4FhzOjdCfKLJ0bCnzammemjPAPV2K6w4domLCC11fIHtHFyved+kb/LUhx+OtfC5aW1974BpVLxM4Ec4k36jymPsAMns9zxcH0BmOc9Ykx/QJAhBKz0gvM4VW6BmH9Sw3xRmvNdMPmgoCKM7+ejEzB6IIvp+j1ruOZF5AHN/VWO9z0KN+EMBIHfOTjs6tqDTHzd14dLmO/T1Hva+nVttmfoE+Xr96vOo+zrWFANM0X9d9f728ldc+mtf6XJyo8rxWqodd3Dz0/Rimh3tNx82TXlHHaxO8eb/jtV7H9zXeDyYAsK5/DXmZJWQyM689LhqMPH70g6mxW44sLAu8eydmXmJlbFYZvV3HSQEqjvo7TjkEr6++TrU28kY5WXOo8sFJo8zRt/ootJZo/xX6QnVUgz9e1yZgYreksDnwkKgeFnS/N4r5H4eDDApnvcY4SAKOt/lbFx1+hy9R8ev8jbyYjaUsaotC6cPHfN4Kxo/j9b9SorQfPM3H7fP1UXPkV8oINH1cCEksbtMcn9ku88PHlwfRiEVr9o21YddvYBxnsDQMa0Z7ers8IyEhbwYnVai3t7ezadOmqdfDw8O0tbXNeH90dHTq9cjICG1tbTQ1NVEul/F9H8Mwpo6/HsbGyic1lYPWipedJ7kwD8n6EvZkt+MiqRleENFdmcjXoLiP7EUHjiRdCzQz0+PQK80xXg2JxAb01F52f2oy7yMRk+HspJy+toYgUM/k4BF8r8Q4KvRd1YhQlTZJr053dYiilWDcTqO0RHmgHYE0NY5MEzUHeLL9ACvzXVy8s5dnFkR4sstgQ/8+fCl4JPGH9P3w05y+7vrXvVDzduaXGeTequeitTXFyMhxJsGvQCQSI7J8Ib3P7OXKxQu5v38PAE+O9HP5aVdTVx4RafBSYoz5+7oQi1rQu6f7CrG4mfLQdNoYq1ljVfsQOw5zxdqb+OdC4Pq+v3aYzuZllMd2UD9tmITRAT7I50tk1zQzsSVo85U9PqmVktLW4HX+RU384iz6R3n0YYf0hZriS0zPiwSk52hkJoanNWQMZLuF/+MCmXcmQQr8KtT6FLmNJqVDmtziKOyatp7IJVHUoQbGxhRuXlP+saL1HQbCAO2DqoOXjWDaAp33kR0W/jEpgYx1CYbMPlr2ZTDOSuI/eVQ+6Q6TeqtDl9fOyOC3EdIkIm0Wtaxl9+izOH6dodYx0hFzRrR43WuimyVibNqaJc5PM7Fj+tLJJQ2MwmYa6fcy8Tru++vh9bapI595I7yW5+KNlOetJtls0nSBxej906ZDIw52OxhNHpkzDQrPTN9bq0Vgt/Gm/I7XUx+5cxP4W2qgQY17s1JTGWvjqJcnnw1bIBdFZ1072RNBRphh9W4+36CsGqTXxvF/PjNvunlBinzRIfBDmyxHp4Vombmn3zg/hdsBESGQMVBHGShbLjBxm1wKI8eY2l+BU7GNvJXPxFtBpC2K0RRFHypgXidRLzDd70pgdRwq3szMFYB5ZZb8pskTfZAms9pL9iyTsUem2112g0lln0f2nDT6Z0X8F6rY61LYLRpncripuybxVhNGptuMe7GB2XJMgERbUJrjUdlXpzfWzIKxIUbbsqQ3dCCfnLZSq84oo8kR7OIwpVg7zZEkNKb77drGd/L0vr9jQ/v/RXekYf9RLjCmoNo9iLt9AoBMbTcLUyvZU5r2prqiJ8v+4WmvsiuWfAxdTTFSmdkuU0Y3PZnlHC68NHVsXc/V2H7LL9WGL1r4IXaPPTtdZGmxpPnsqWueKs9IuFgQcqog9JsZxvF1MjQ0xK233sodd9xBLBbjlltu4b//9//O6tWrp8555zvfyec//3nOPPNMPve5zzFv3jw+/vGP88lPfpJrr72Wa6+9ln/8x39keHiYP/mTP3nN332yhfqB/Zu5c89/4z8/vwzLz/J86wg14QW2a20ERrZX+KwC/MnUa0fpguNyrEHwSF71N2p1n0wcNZX27ehrzyiDnk4td0TgGxiYRwt3X5PzK8RUg3E7TcFKBnHmFSglsEUZQ1boE10sL+eYU07y7IIhIk4/S4b281S35EDcpNDUwwWLPsTiprMxZBjU5+0g1AHiTh1z70GGmyyetGs8OXKYFdlmzmvrwWv0MeBZbMmXuMpcxuJKEjFSQB0eh4XN+K0tVAs2tb2KaK8gsUhh7n8R9h7GXbuIAy0FNg/cT9zKsrHjUkpDL1DP72Nx88eRz5m4NR/jgiy1MZPqy4potyS5UNHY71E5YBCbK0ktFMgDDfznK8iFUdz5SQpbfVCQnq+xpMdoUpDIeMTGJRz2kCkTv6ZotMbQfvAU+mWFsCWm72MX6rC3jpwXgbSByJg45gSeFaX4XBQR16SW2FRfclAVn+xaE9txUS/U0EJgrE7gP19FVDXyjDhut2JIbSFR7yEz0owogNpTh/kG1dOqDMX7aBzahPCqJLvO5Kmxx1nbcyWHCjvYM7aFJa1nsY4Lkc/nEUUH74wcO1M7WSjXYLzgYx4U+KdL3B6JczCGu9ci0Vsk2jYIzXGqkc43vS0dIRTqr414w8Dpsyi/5GM1CVIrDKqZQG0mqjFqBxWVnYpolyCx1KCaqv2CK742Xk99JJTAHtD4T1dAgHFeGrWrhtrrIFfGEB12ECQsZSDXJnDm+tRqM+9HJAJmf4TSNh+vBKmVBlaXQ9nSRCM+sX0W/jMV8DXGugT1Xp/acax7uXET9WI1iOC9Mo6YHyGfcpES4mMRStsUTl6TWiGJdmmK0ePkdX4T6uRE8asm1KUEu1zE3lZFFxyMRT34zzdACsSaBMr1EZbCqAvUIQc95iFXJdAxgesZlA4IlA7ahykUpR0at6xJrzSQKaju1TQGNbG5EqtJYMQFKu9h5R3EzipiVQzVHqWyV1Mb1iSXGiQSLuyqowYdGqsUh7sO0Z7uIH0git7sQlqg1tr8tXM/nUaM80kScRscaOlgkeggs78BL01Q7zWpLpYMJDTdboPS4CZaY73ED+1GTwyjV17MUEqy/eB9LO64jmZ3HdG8gG0VRFqSXw3F6FZKBx8gnuymdc4lFI1Onhga5cWJIuc2ZzmtxWbX2I/oK+xkTcdl9CRPx9TH9xSoMcLO8Z+zd+w5Tms/l/mZdUT0a/B4eTWEx7Cznc19PyJmJlnTeQVN5sIpB4RT5RkJhXrIqcJJFeoQpGf70pe+hOu6vOtd7+ITn/gEn/jEJ/jMZz7DqlWr2LFjB5/73OeoVCosX76cP//zP8e2bfr6+vhv/+2/MTY2RmdnJ3/1V39FJpN5zd97soX6v/zkv3D2wE7WHbqMrc3bGI8o7CNR3fVMj6cgxZrGQx8jvCWS1+bqfrTAPnLNQLi/9mscjyPet+qY169UCj15tsAIcrkrQUR7NPllDO0zZmcom/HA5VZBRI3gEmfQzLCglmLFeI5tvWO47GX50CE2dUXYnbTYk46CKDI/ewaLcmcxP7uW5tgcFIqtxb1sntjJ/uoAdd8hayVZkZ7PeU2raYm89jbzq8LbRajD5G4KQ6KUwrJMhGAqnUskYuL7Gtf10VpjmsZkGhmFYcjJ1DN66t8j1/J9hdZBmY78XsMQmKYx+VmBZZkopae+1/c9fJ+p79A6SC8DGiGCNDdSHvkXQOB5Pp6ncRwPwxBEo/ZkSpoZoR6m/j9Im6OJRiM4jgtoHCfYy25ZweJd8DuC7zEm98V4nsI05WR59WR9QaXSmPHblNKYpkTKoGxaT9eVEGLq3CP/L0TwvhCCTCZOrVYHJI2GO7l9QGPbJo7jTZ1vWQa+r3Ec/01N5XM8QqH++shmo3ieR7k8O/BZNhvFcRyq1Tcv7scbqY9MJvCCKRQaxGKCSCRCsVhHqaCMSimKxdmp3I4mnbaRUjIxMTsgXiYTQQhx3PeOJh6X2LZ93PNSKQvDMH7hNY7HqdhGftWE+hFs2yCTieO6k0EH1RFv86AvCvpjxZH9HYYxe5ajJrcTHT3fqtc9otHA2fRIF6a1nrGHWqlgwUCpIK+7lHJqbJIyGHuSyQiFQpV43EIpTaPhYZrG1Hlac1RqMk0kYk2OV2pyPJNTAd601lN17fsa2zaQUlCve0QiBlqDUgohJJ7nI6WYGg8g2IZl28ZUv9zamiKfr7ym3OPHjptvFsH90FPp4o5wqjwjoVAPOVU46UL9ZHEyB5nC2CCPPPY+bt1xCUV7jBeaStjKACnRk1tNNYHV3GdaVB+xW08GXH/d7uxHE0gCPSWdj1z9lxXux2XSs1Ib09/todD4k7b9YA980m+Q82uY2idvJinaSbSAqD9IjXZGZZSmRpQN4y0MZiv0ZXZxev8+9uRiHEwLHsmtYNSq0WFWEf4gvvYZJ40f6aE3czpd0Q4i0qLoVdhfHWRbaR/rssv4UO+VdMdeWwj5ipNnrHaYul8Oymw30RzrxTKib2aN/VK8nYT6W8mpWCY4Ncv1dinTr7NQP9GE9TGbU7FOflWFOpya9XmEsGxvjFOlbKFQDzlVOKl71H9d+fETv8ctu9ehUbyUKRDxLbSUgTu5UAS2tGnhDIHF+s0Q6Ec4Ivnl5NWmLe5qaourOOq8I3vSX4lpS/qRJYDp40dnbztyzenlgCNh66BomJSMDHHfI+tXaaoUqRgxylaKGIO00sZ4VPJAWx8bxls5vbCGHR1NtBa30l106S4/w3O5LI+lsgyZc7C0pl16tHp9OMMvUkutojm7gQXplZyZXcrV/kaezG/jd7f+HRe3nsH7e64ga80MkNPwKuzOP8XO8cfZP7EFx6+SibRjGwk0ippboOiM0Bqbx5Lmc1jeejGt8Xlv7KaEhISEhISEhISEhIQQCvUTiu/U+Om9n+KWvXOJuBm2Nu8EGaEhpvdxB/I1kLNH9oEHNuc3R6C/EnLyv9PxcgO5fcTq/moO7cfjlQLlTgfBVkcdk5P/+mg8yoakYqQwlSLtOzQ1Klha48hBmgybcbOJx1p9eqopVgz04hldDKf66Sjv5AMjea6L5MnHbcbTrQxGU/QZOUq+RaS4h0L/C0wIB2klMI0o84XBPK2p7nuOu/Z8mbgZJznpel9WFSa8AvF4Fy2ZFVyS/RixfIrBsRH66+MMMcGEWcMVCxkoVxgb/xk/3/8dMrRwVvwaVvZcRqy96fiRWUNCQkJCQkJCQkJCQl6BUKi/xfhasWv0AC+/cBsX7tnGNeOr0aLC1tYd5M3YMRbmI8HXpq3nJyvR/bST/YkSmRo1VQcajYcrNWPSZAwLUysSvkfKrzPXG2ShAldKBjMGtttGe7GNhHsZZbuI54+SLo+TrUywiEFMPISarFMtkEpgqQpaVKiaUDGhZMGEDUW7xoSdp2RZNIwolkxDPU9t7AH26gcoiQg1HccVMYQy6XIMLARKQFUKJpwO+qwK39Xf5J5dX0a+1Mm88kbO4GxOa1lEpC2GbjPQaXn81CshISEhISEhISEhIb/2/NoKdfkqVk6tNbuKDcqTAT+0Duy/SoPSGl+DpzVl1+WF8XFqvk9D+fhumXkTm7C8CbqrY1w4XCfj2Jzh5livWoB1VOwD9CfyWK5Fmzs7qE/IsRxlzRcShzgOGgOF5WsiaKQYoxodox4xiHg5Uk4ay+/EVHHEpN+9wkPJBr5s4AuHmnTRwkP6iqSvSdUFvSqIuC+VhaEthLYQWASPyWvPUK9RCFzARQkHJUfw5B14exs40sUxvOBPahwJnpT4wkCZUYrRNkwzRcKMUmyKkrJStFkZhDAhaULOBASJzEKsyC8ZffU4vNpzcSpf+41yKpYJTs1y/TqX6bV+z6lYRyeTsD5m83apk1Pld5wq5TgeYdneGKdy2UJCTjS/tsHkXo3BisNNP9j+C89Tchxt9k+9Xlnby6dHvwdAx9gacuVlM87XVN/Ucoa8Vo4I7l8OPZkYb9rvAWb7Pxi/YDf/a2dL76OMpQaP+17P4ms4/cI/e1O+JyQkJCQkJCQkJCTk1CIU6iEhISEhISEhISEhISEhpxAnawt0SEhISEhISEhISEhISEjIcQiFekhISEhISEhISEhISEjIKUQo1ENCQkJCQkJCQkJCQkJCTiFCoR4SEhISEhISEhISEhIScgrxa5uebWysjFKnfhy9XC5OPh9Giz8eYd0cn1wujmkab+izb9VzcSreq1OxTHBqluvtUqbW1tQb+q7X8lycinV0MgnrYzanYp28lc/EW82pWJ9HCMv2xjhVyvZGn4uQkDeb0KJ+ivNGBdevA2HdHJ9TsV7CMr12TsVyhWX6xZxq5TnZhPUxm7BO3lxO5foMy/bGOJXLFhJyMgiFekhISEhISEhISEhISEjIKUQo1ENCQkJCQkJCQkJCQkJCTiFCoR4SEhISEhISEhISEhIScgoRCvWQNw0pHSR5pHROdlFCQkJCQkLeEIasI8ljSP9kFyXkl0AIMEQFgwIynO2GhIT8CvJrG/U95PVjaAOjaKBqYGTBibloHUR9Fd5Bdm75IvmhLTS1r2XRGb+FNucgpYf2xhDCBqPppEeJDQkJCfl1Qgiw6jYqDzIKKuPjiVCAHg8hBH5tBzs2/TWliX20z72YeSs+ipLtJ7toIb8A27NQeYEQApFTeGaNWuFZdm76W5z6BHOWvYv2+TegRO41X1NKF+2NI2QELXJT852QkJCQE0Uo1E9RTN0gVjyENzhONpZGYeElmqma2Vf9nBDgiyo+LrbO8Grjit2wUSMCrTRGG7gx5xXPN32D+lOSiZ95wffY0PkhG6e5gUGBQ9vvIRNfQz0xxGj/k7hOkVXn/xm7t/wTg/t/gmknWLrud0m1XYgUFqZfxzNi+OoNVlBISEhIyC/EnogwcrdHfK6BX9KIpCB+nolneCe7aKceXh/V/n2smvvX6FYDma3Tv/du2he/B1/bJ7t0IcchIiVG1WTsQUVlW7AAFekWNN/ss/WxP8V3g1Rfe7f+K0JatM7/wKvOi44g1RC7N3+BkcM/Y86CD9Hd/H5EI4XZBF7GQ3H8yYs2Gni6hqeis96L+iXs8f1ot45qmkM11v6KZYmqCmaxH2GY1FPduITtLyTk15FQqJ+CmLjEXrgTHvwSnhCgNeIdn8Hc+hPSV/8+xdT8GedLKdAaFC4Hq5u5Z/vfU3UKnL/gvZzR/k4iZImXJcaAj0ajOk18aTH4dQ+vGIwSMgadvxGhkWwct0xiwmTiZ+7Ua+3AyF0urR8z0WNpUi/8Fl5esmjljagLy3y/+BI/P7iTdd2XECsepja+g+1P/h8uvrgXuek+OLCFyLIL8E+/jkqk7a2rzOP9FgExZxxZzaNiWWqRlnClPCQk5G2HpU3cfk1ivkHxeQ8rK2haacGEguaTXbpTD8uRWC9dwcC2I1OjKB03vw/UCIjQqn4qIQRUd9fIPyho9Hsklhm032Ax9H2XRp9mbKdNZd0f01neTX7rPwNweOddtM2/Hs0r58iOu+MY5QG2HbyLkcM/o6v3BtL738/wD2NAsLjV/l4LMd+ZMW8QQjDq7eAHW/+WofI+zui+nPPnfoCYDuY3MTePefefofdvAUBGEqQ+/PcUUwsQIvj8EY/DZH0QcdfnoX978NnVV2Je9JvUrOyr1okhgtRmvg49ZkJC3i6EQv0UJFI+jB4ax73s63hOGiuSx9r3HeTpV6Lu+T9EbvkrXCNKURfx3Dymn8dplPBjGb6+6f+dus79L38FS0a40L4F9TfD+JXJFeAOk9r57VMiHUDVoLRJkbjcmBp87Po4aEXDzuGXZwtZdwyMksnhryuOjAvlpxKYTpTnuspsLQzwXeB/LP8NxON/yOLFNyNu+1N0YTA4+bFvYPS9ROTG/0VDzF59/kXomk9qUCDGfXRG4rRJGtarm+iFEKRGnkff8cdQmcCIpUnf/KeUOtYSeuWHnEyeHN/G7f0Pc83cDVyaWn+yixPyNsBEUhvSTDwVdNB+WdN/u0PvR62TXLJTE1FoorRt5rRo+IcmPZ/M4L3+ISrkLSRRkRz8ukLVg9fFZ328vCJ9pkFxk48+KPmeMUxSpPnAwncysece4qke7HoGZ0CjPI3dIXBTTM15Eo0RzC2PoNsvose8GntOhmz8CsovxWZ898jdLp2/adKwpo0XZd3Hl5/6DK4fFOjJA9+nVBvj5tP+FJSFNbh9SqQD0KjAw1/BuO6/cHj0GQrF/XS2rac1uwb59H3oSZHuLlhHsWURifIQ5LLHrQuBpDxmsWNzsJCw7MwIyWYXhCbmTSCURz2Sw1fhRv2QkF81QqF+CuK7gpL7MQo/ik8eaaHlnE8TbT+M1f9XlLXP7nqF0brClDkWG030FAXGAZP/OfdeRpsH2e9u477tXyYiDPTTJahMC1hhCNyR2YLWGdAMH7wTw69xmrkQhveAUphWDKfr5lnnR3oE3rDm2MVb73mDq5YtZ2thAIDbhwb5aNdGsqTgiEifRO97Fqs8SCM173XVkSEE7kNF1HfzU8fsi1KoK2O48pUVd7wxir79j6BWCg7Uiqhv/yGxT/4rlWhoMQk5OfTVRvjL3bdxXce5/PueH2P12lzQvOZkFyvkVxzDVRQ2HdPXK3DHNLScnDK9lcQcEIfr0NDQZdPIGviTw4EQgqpQaCCJPG68FFWfrcZVDbRrQCjUTyncYYmqz2zb1X2a7g2S4iaf2CLBoWKequfyrgVrEHt/xIqV/5uBrwr8EoBAmND5MYWTDT5vFTTe5ovRBzUpekkuvJXaJSnKx3y3qkGt2IeV0fhmMG8YrR6YEulH2Db8GFcvH8PVDqn8wVm/QQ/u4vCBH/H4y19hfs/l+JF57B2rwLIrSLd1gk7x1VqGn4+XWXrI4TczDr2GPctdvpK3GN/lcWarj9VQ1Po1MqJIDD+Ivv8L0KiQ2PBu3HXvpma98h59IUDpcRruBBE7R8IFWc1TSNqM+yXiVoak7ECHgj8k5IQRCvVTEE/1UHhx5n4kv5bBLiQQF95GJJ/kG4df5IWJIktTSf5vaRHGDytAcENzZ6fYvKqPT2z4C/Zuvw36LplxLT3okrhEUHxh5vemF4FIXUS3HkTd9efokf0gTYwNN2E3P0frjesYvcdDu2A1Q/s1Ft6e2RHejTgUVHXqdd1XCMPGiCRn/1ghQB6/GQohMH0DbWi8Y1YDoiWB9738jGPqkRKRDXHcplcW6qI8Oi3Sj+DWkaVhCIV6yEni3w89wHnNq1iTWURXrokv77ybs3MrsF7h2QgJeS0ILTASYob3FIC0xUkq0VtHpO4i/qmEGJgcKwyI/m6WSruFJzVmuU6qX4MGv1PgZWKY/sx6MVM2GA4cNdxEeyWGHQdcQk4dpDH7mDBBSEivkETMBlk7xoVNi+huWkrLNf9CY3sMvzR9z7UH+Z9q4jcUGCu9TGLXcjg4LbbFHk3iXUnsy32Muo82BeUJg1oeXhq6jdrBPs5f8znqMkVczvRSiVtp1vVew0O7v8Hmwz/gP/d8ms5jyusuv4jh3CI6Os5m2YLPsecJidaKSqGJ9nVZRlITPLT5AAAv5Ev8/hPP8Y/nnkmO6R9vmpLGqM+SHQXk9hoANmDcmkJv+gpUJ4Lf+vi3MBMZti5dy44dh1iS6GFJbA4xHaxACSEo1Z7j0af+mHpjgmgkyyVLP0UFxb+89A/U3DKGMLl59X9lWfYSUOHYFBJyIgiXxU5BHHdmB5hdLcgMFPD/YRTvOwaxvxjhD82FtEQj/GHvIuL3V2acH3lCcpo+i2f7fsz/n73zDpC0qPP+p57QOffkvDnnALsLLOySlpyRIEFEFA/P7J3n6XnnnXd6ZsWA3IsiKjnnJcOSNsDmvDs7OXdP535CvX/0MDO9s6AoYYH+zF9TTz3PU0/op+pb9QuZfILU7MHiE1ig+WzKj1VQnIXOLbJY4O5IUdassU620H7OZ5Fl9WCbWC/ehhrfj2NqiuprNao/rVF+pYqazKL35XBWFg/6AicIbut5bfj/S+onMzEwHY+pIcbNL6orjjifrLdqzD1w5HTMlzV6b5Qk7xc4E07EqNOIrCwaTA2TeWvTd+kJguYsLlQ0pDfylvuVKPFukTKzvBLbyuLQNAAmBeoI635e6N/0PresxAcdRbUpX1b8fXaUg9P74YviqezPj4h0AAvkvUlcmsAVN/H+JIXzhiTO3ybx/CCFu2+s8LZSJlVn6VSfKKhZDlUnKwTmqshMyS/qcEOL2HjHF7/b0aUqmjCJdMdQuw2+5z+by148BsetlagdE0COHfLaMY2du+5i584/o2wr3iYadbTOPNptXYh7e1Hu7CHYHqf6OIVy3+Wg+EmlWgGoEhGmhObgcQS5dOF/ctSEi1BVF0fXnMs/Nv0aS59O9syvgV4QxvnJR9E+exX/tOVO5s76N1KxVmT5z4h5Pk35zLuhz6DS9uPXR8aDKdOkPZuhQ/bSandhqgZSQjn2sEh/A+vuJOqcK4sWQsT6+3m59RVu3PkQX9vwG25tfwJbKXwLLNnNk2v+mbyRYurUi2icegFdXgcbzX1kjORQHZPbX/8vBs22v/GplShR4u1SmhI7DHEFLVSPgjW0KB2otpFPj/oIS6i4J8N/fHYS/X3t1FvBMcfQ8zq7E+tZUXEcW3rvYs6pH8PxROFxm8eF6MsLql/vwzPHC6qAdQnoNMgETe5O/ARddXL5hV+n/hdfLJwyn8VCw3TlR0wAgxb6MzGqVoTJz3Ri5gVOj01P7SBTKCNrmVxYN5WFm1fjXvMnRNNclFWfx+5uhq6d2HUzyFVNxzzoNdRQSTwlSbxe6ECMfkl6j03Vpxzk3YUVfDOkoFVqyK5RkYu9CnZE5dAKvkDGXYn/rK9j3/UfYJsgFMTpXyXtOXiuu0SJ94aXB7Yy3lODVxvxg1wcnsb9nWs4tmze+9iyEh90TGngeCFB3Qlh8lkFRZc4U3mUHhMqPjzz9KoKIjH2epQ+iWVkcWy2CgH03iBt43ghi3ZeiExmpA9xhCRsyyAf6Ie8RERU5DnlKN4MlKJuH1bE/DF8x+oEJvswkwJnGJy6ifnzbhBgrajGvtWm4OwAXbeYVF+ig2IwOmC7Z6HBrpa7EUIlM2UQ566R56wu8GLeOwCj5mnktgzqPDetXVFqp1yFVAsLIQ6tjI/J+XQv+jQ3vvRF8laGC2q/SsUjAdRtNggwllbSddH3CXg09uFlSw5WVR2Ble/n1Z3Xkcn2A9DTv42J9fup5zOcWllNazbDi319AAglwU17H+Hlvl3MCo3ji1MupF4EGDOVlJV01U2k89wraegaIPDsHeQitbQaIxaFt+5/mlXVR1JJlEyuC8NMMWfedTy8/3YGsgU3xcbwDI6fdDlP7Po9cugvnusm4G58h55kiRIl3oqSUD8M6dX34boojPlUGfkWga7bjEmkM2gx1SwnrUfg7Dzc0zfSmfgUqhomcUH+nzjQcy+7ux6m1fMSMy+9gMaaFTz9mBM5IImMd6M/Umw+PlgdI901CAa80P4IFx79cVQjgKg7AtMemb2WiuRZazsrPjkdcVMfjpzEEVBJXeLmvw78js9MPJp9PS+zufkRwpNOZMrsXyM8dWQVL80NLm40d9Pa8yzfKAsxPedESzux/BoZl42SVklsNEGB8iUKLt0EKVFTKvkhLZPVLCLXVpK/tQ+5LYsY50A5P0zCYzG2xxrBloJk49G4r7kJMdiD9JeR9tZglYxLSrxPvBrbziRffVHZNF8jd3c8S18+TtQxdiKuRIm/BksFrdqBoz2NM6xCDqzXU1jnhXnLD+UHDNsGo97CfVUFCMCQiIBKvjuB9KnQnh2zj9JmI5TiFVnFMrDu60Od5UGUadi7cvBAH7nPe8bsX+L9pSuzh9/t/BpLys9kQdWJlPVUYv4hjxjvQD0zhLPXJrJQ0L9hxPoutdtk3GUSq9lGqgpGyKYnshPZLakum0W/v5nKqZNRtkuUJidKvQM5zom1MV00/y+FYKDHRnfVUNmQR6WVvKqTa5jMzp5XyVsZQu5KZrcsQ902ZLkhQX9B4ptQxR7+H7GO9cysX8aSpjNIZfcPi/Q3cFk64wZ1ru2rxujM0TWrge7K3by4+3+otLJ8acJJPNXfzVM967mseiU4BORHftPWFIXHBv7Ehr7VTA7O5pIjz6C1aS4vH3h45DqQGLaJokHGThMJTWZXYuewSAdoHtjCnJoVnD7jOvb3b2RTxzNEpQN/Yh8ZXz2mKMmIEiXeTUq/sMMQS9g82vd9yhc2MfnYxVjW7IKTwqhZYGWKC6Nf0H23jaNCo/qicritB6XJiXZaCHFjP3W95TQe/Y80TD6SJ3d+i6wniWa4OX6+hdNngGnDnCjsKcw55+sMHo39cPgcM11HowWWIPsl1iZJYKIgWSOxAXcyx8r2ShRzAHmVh5ThoTeh0p/JcdW4Rdz00j8MH2fvwEYuX/BdGsUU4mKQL677BUdHJ/NvU8/Gf6AOeUsKO5NE+BX8V5WRixbM8SuPFjif6oG+wjSF9Cv4rysnESzcCLXeSe6yIHo+hKFL8or9V409LRSSnjrw1L0DT6tEib8dKSUbYju5pumMonJd0Zjub+L5vk2cWX3U+9S6Eh90LGHjWOSHTcnCt9GSKEf7MBwqjJ3+/cCiKKB7vdh3xZDNQ3FTnALHtZX0Wn24Ztq4XineJ7PIQBfF98AaNNDPj2LuzqAMmIg6HbVMx0rCW2T0KvE+kDEHmRiYz1RlMWZfhp2BDbg+G2Dy3hmYP+lGscBfpeM5MUrrIxLFAZFqC/snPQirMJ/jnK8ROjPEEeM/Rl/zs1iufahnTkc7I4j5bJr8r3oQ1Tr6BVGMBwYgMbQyXung9IhEE2ClQmzZ9UPc3grCk8/CaCu4LB1fcyWOu8SYIYlvr48ZUz6LpWaQmoWRTOH21xbVqQzPZrHzU1i3xpADFto8D3XNCoZts7evEDk+m0/y6YnfZGN+ACss0L9SiX1LP7LVwJwDu47YymsHngBgZ3wjHYsu57/3vIAc1aKZgQbqJaTtHu7e/ktOm3YZD+y9ecy9bovvpHlgC5PKF3LpnG9Qds/PoGM33jO+RnLySViHcCl4A1MxaTW66c4NUOEMU6dVoMmS9ChR4q+l9Gs5DDEwOTn4CSpeq0LdJRGzTPRrKzD+2Ifst1Cmu1HnuIkPuQnluyGHhvf4IMpMN/kfdA4LVuuJJI3qEo5a+M80mEtxJnTkgVbMJ/ehzKuDTBnW+sJqg+JXOP7jl7J94EWmRZYwO7YM465C5yTKNbSAhsetIT0GeoeCvduD3WygzlYJTJE8txE8fhcJ//ox1/Ts3j9x+dxFNKdaSJtZPls7DzoyyJvSI+PFhI11Qy/a1yqIrtJwtqeGRfob23k5jbrKjTUUBMgQNoZzzOkOK5ymgqNfIi2JVaaQ+Qsp5Ep8dGjP9iKEIOIIjNk2zdfIi/2bS0K9xN+MAw0lZSDLNMhKhEsBXcEpbTJ/efcPDJYFWovAah4V3DQnsR6MEbw6gKNahc9I6DCRUqJU6jjKFApBw0eEi+ZzQJ+NIgV2p4E6xYXUQLgtSsOlw4sabQKT276A66khkeiuQb8qirUtiX5GGGnKgmXFpkG8EwK4osA9/cWecetN3It19m38LZPGX0nDloWQ68YaFMidhXdJ7s9jtPejnRzEei4Bl5ahv57CenoQ2wJ1ppvZp/wLiUwW6xmTE8IXU1c3noicBA1OaCkOuCuqHchbBlCyElBwnVONp7eVU+b8Lw9v+ypS2hxX8y3MnxVM7tVFXpQ6BxJoktNZGF1FnXMSc/cehfcxH5ObxmPe1Y/lFqhnh8hFsvzf9q+x58CGovMeyPXzzZkX88fdj7A+0coxgUbOMz34/t/nUa74Hh3JvTzb+iDTKpbQMbinaN8KfxOvtT3By833s2zWUtSO3QDYD/4Qd91cku5Duw5KxebBnjX8cud9w2Vfmn4BRwar6U7uRRUaVd5JuKh428+/RImPCqWe5zCkwqzCc4tEdOeQgGzNY0w1UE8MQtLGbs6SVzRir48IPisJ5ktJdI8yZlXZejFJ45JjcTU7EN405hP7AFCqyjHuHDVcS9hUP1nFrCOWc0755zF/1DfcqckeE/OxONqlUbAF5qMZ1Fke1LAONlgvZzhmmZ8t+ySG5h1zTbruISm7yOZ7mBOZQLJ3O8H45LGLOikbMWjjnCFQtxhjvc335dGEF0X9YJhtejIKyp9j2NtzAChVGr6ry0j63tyPvsRHh23JAzS6D51tYJK3jtvbnyZtZfGopdxQJd4+el5DSBPjofhwik71CC9K2SEycHyAURSgf6yFgOw00DJORMoi//vekTSlToHj6grUmmJTNcVQyT/UX5gUBszWPOp8D9oMP2ilCdbDiWBvBP2pFKJGRwRV7H05yAtEUMW4e8ilz6ugnxEmUmmT0WLIh8f2u1Z/EkXRadRORK7fjXrSdMx7Dspmk5dYlbD3GoWJ3RbWEyMBeu3NGbQ6B75XTGS/BVjMiS4gc0GEfL2KXpaB3qF3s8mJjOiQHRm/GKszaEe4qXgqxwmn/S97u1fjGgxgyzjKLDdYsrBgMsQZp30SFQ3tORP1VCfGHSPbrK1ZHF8KIRzF8RQ8jiCaHqEpmeZra18kXTUB76bVKN17AXDH40yIzmdH76vUR6YzqWwhu3rXIlBYUH8y3YlmLFm4hrw56t5YBmQG4U2Eepc9wK933l9U5hcpfr7mk8Op7AKucj616Gd4xsTEL1GiBJSivh+WOHudiO7iQYHcnkXWKOQaLKyjw7Q9J7Bzo/Zx2wi/Aq6xj1SENVy6D2tdGhkb8tVTBDI7NkWPul9yUv1VeNK+MTHZZJ8JlkSmBNp8L+ajccwHYphPxNFqnfgFeOYPUBmejz5KWAgUJlafyMaOp9jZ+iArKmeSih/AcCUK9mejcQlsr8CWNsq4sUvlylwPjueyuK6PYTwex509vF9hbbeB3D7qQXWaiJdShYFliY882wb3U+cuP+Q2p+qg3l3BpsG973GrSnxYEFJgrB4R6QDWyylIfrhEpxAg6scGe1PmeRE+ib0lU3QPyEmsl5OoBw2B5KA1LNLfwNqQRs19+NLZfdBxZBzoF0RQah2Ql2gnBhEMvd9vkLKxXkxgBga5t+fHMP2gd0SAEUzg9tchWobGRqYFrrHPO+8x+VrzL7H3jLVFsV5Po4wbNZnaZ+F2aHQ8LUktLsM6pwLr3ArSCyLYaw/Kyp6xwedC7hmgLeXgzwTJ6IVrUCe4sNani6o7H5a4FC/q0X7MV4sz/iDB3prlpAlXMq/hTELuCqZUHsVp875Dla+RnO5CbduGf+39BZEuCu+/pumcM+NrLKo/nef33kl9aBqfXnYDp07/DD3JA6xrfQSAhvAsoi37Rs7ni2AH3nw1PGmkh4P5AcwKNXGg68mifPOD2R529b9SlNWnRIkSI5RW1A9DLMUa+2AEpBxJPt+xj1/65uCfpBLfaKN6BRUrVfRYGvM4Hb1MQ1TqyB4D9UgfokxDGedkIL0Nv1GF8A11VLZEuMauSpvTBHtyr1HuOoT/tlsgQypKWpC/v3dkNTwnMR4YQL+unG39m/n9gX18ato3MTO7UDHQPY28EuvDN/gy+wc24dKDhKuWsHvjTcw7+9tod+sFKwAV1MujJL02mq1g95tox/oxn08Wgskt9iHcAvnQIHLAwt6bQ1vqRTvTh8nhN/BUVQW5Pz2mXG7PoZ3gIX8YtrnEe8v25AFOqFj4ptsneGtZH9vBEeHpAHSl9uDTo3gdofeohSU+yEhs5EGmtwAybQGHSET9AUVKEB5Qz41gPTgAOYky14Myx40UAgYOsdo+YCJl8TdYaIeYQVUPmdWrxPuMGnZi/KELMhJcAmWcE5ka26fazXmyhkVQi6It8GHnU9i7c+BX0E8OYpcZ5HcPIGoLQQis9c1oK2dgPjgigu0ZOtt83Qy0JrCqlTErXEqNA7u7ON2f1pal9gQn3Wuht1vgnaRQvlQi7yseE6hHe7C27wGXxsRoA19qqKW7u4XaxlqkdQjLQQtQBUqjE7k3N8YHXnHqlO1r58S6j7GvahVSdfDbXQ+zPd7C0vLpXHvVj6na/jyJsih5mcPnrcHX34I/NIlTxn+R48dfjS7cGLZO0qfg0rcQcJUxvuwojm06D8+NXy2cKFKHOOebZPQwAoHLHERgk9VD2HahVRXOMCGHl1i+cC+jTi+JZMeYS+pPtyHKBFJ+MCwlS5R4LykJ9cMQs0KgTRKou0aZRy1R6PZbHFUTRXl4kGDcIniEHzWkIPpyEFS42309K8UlVJxfC0Jg3DOAXJMEAe6jg4gTXYCNemQt1kttWPva0VbUYj6dBhtErY7n2AgLvCeS6swQWOnHfGIolYcA/ZwIphOIybEm6xlJNjVAOP4A88PH8r2t2wg73Fw6aQE/2H4zZa4gV1TOZf/AJrZ2PE6Du5Laymls6Po2k664HLddhVYZIF9uYNsODMXGVa5iPZ1EOzEAisDamEapdSAHRpb67RdTOFf4MA/DQD+WZcNkFzxfPOst5roxhfwwBV0u8TdgSYuWbDc1zuib1pnkreXujucBeLr5/1jbcS+2tLhyzs8p9zS9Ry0t8UHF0CyUKS7s7cVRz0VE48P2AZK7ctjrUmiXlIEmsDelMG/qRflqFHW+F2vtQQLpSB9ps/geyDIFUa0jO0ZEl7YiiOn7cN2rDwP53ixKRoJDoJ8dwXgohr5ibKwPZaqLcM9aziz7NPJlA8Iq2mkhyNoYj8QRn4wyb/6NoPoRUyuQ27ux9+9Dv6gJaTvp8Se5T6zFYTlREKQbJP5Gx0jQwoCKutiL9cvukZOqIGxQ/9RF9WIfykl+2JxE6ckiLvBgvWogB2zU2Q6UKQr2izEy54/HEfASslPEQpI1J67mSPepaF6l2BqkViPVYOHdK1EXerH3jjatFGh1bjy/noMnqhC9oIpP9P2UnclCrvdnuzex0F9Fo7WN2ObNw7vNnX0tESNGSougU8gyogPj3BMpm/ovmGQIqgE0S2Be+WvU3CCmM0hW9aNbWdzNa5CPXw9mDt9Rl5GfcTJZ1U9Q+vifeZ/mv7fcwr5kJ/25LKvqz6At/oOiZzSlfMmwuC9RokQxJaF+GBLT+vFf5EPZYeJoUcmPtzAm2XQbcSaEXDDPDbcMoB8D5nODhQ5DF5x71nU8KP8fx0cvxvOUPrKSIkF5VqBMUJE5FWVeHcrkKDJjQp0DfaoH2WIi+0zyP+3CuTyAPl/Hbs6inx1GGhKhC8w1g1hTo8iQKCzGjDaNdwla8q/Q2vECpx55Nk92HWAgn6E/l0FTVAZyScZXnMOurjV0JPbwyN4/cFTj2Ry16Goy8RY6ep5A6fLSWP6FQpMl5Kc7cGS8mI8PIpwC7fQQ1gsHmY0JxprPH0YYTRr6Ui/2moJYF1OdWPNd2HZpNf2jTluml5Dmxam+eX7mGlcZffk4u2Kvs7b9Hk6d+GWaB1/nwV0/4Io5P3sPW1vig4iCjbYqhNHfi+w2QQXtlBDyMA/A+bcgqrRCxOsbe4bLlIUeLHcaoi70S6KYD8aQlkQ/MYhdp6DqEkbHnzMNXJdFkbtz2J0GymQXVGrkrCyo+vtwVSXeDFNIHIC62Iu5Og5xC2tbBu34AOZTg2CBqNDQTpRY62ZivhRD+BW04wKY61LI5jzqEh+hHh2RCCKRaMdOw15Yj0haJMokP0vey329L3LxuJU0eWr4bvUV+H6RRjkliHpSEJmzQRMgZWF88lISwirKKi/mH+NgSliTwH4xgTjOx56aDOFUhtAc0Dw6Impgtx1AO8dJdrLBfZu+yd7YRubVrWJ6w5H8cf93Of2TnyHycAC5P48yyYU6x02KLPkJKurtKfTzI9hteYRTQVTpWDuyhTRtHRby53185upVfCF5w/B9q7VzxHo2F93Lrbv+zJGNy8fcY2FDAAfgALMwtZdRA+AZmRBx9+1APvEbSMXANpGP/wKHJ0hu0olICY1KNT+eex1JO4Nf8SDsDLkpSZ7c/XscmotTp36WKte0D9u8YYkS7xgloX4Y0pvu4PrXvsnJUz9N9ZxJ7Oldz9Ov3sT5C39E2BFEidmIj0WwtmZHZnUNCbcnOP0fP4W0BPa22JjjymYDc20Spc4JHh2CTqjWsa/vKapnPT2IPq+cfGIQ++6R2Vr1mhCWAMsHjiuiWL/rL3REDkH6wjRr238NQJWzkOxcADPDVXi0k6h0BUnHD7A4MButeiWVkekYHZvZsPrLADjdZcxb8WOsUbnasw6b/DIHzgUVSAUsCcpjxQJXOc5P5jBe7cg6bawzvTiP9YElyYdEIY1ciY88e9LtVLvefDUdQBEK473VrN5/I9PLjsOt+5kcWcLmntV0p/ZR4R33HrW2xAcRIcF8PIYyxY04omDqbq1Podd++ESncCqoC71Ya4cmRcs11Lnewlxus4Vxfwx1ZaAw6fz4IKrlR6svnrHQUTEfiWPvyCICKtZLSfQLo6jTVYxDnLPE+4dVq0JARYQ0ZE/BxM/enkUmLLRTQogqC9l7P9amY7CeVwCJHLAw7hpAPzeM0ZpHmefGvD8+sqihgn51GfdHbkQ6A0zzBDmu6RJqXOX0GTmmDFRDKoXISvI39oAF+vkR8g8MIJwKylQ3Mm+TsdqRZxg4HwhBlwXTnaSWKAyKPbjCHsLP/xo7m0D2F1L3JI67iF+89hiD2V4AtnU+R2+qmXDFKtpyJlGvinpsAHt/DuOP/XgDKlsu30TToil47zQQYQ2pgV4VxHo2MeomQU08WHTfdGusG0g+G8MUb98VxpNXEW11UPETlFlpZGI19uu3wKt3oU9aQX5IYjgtJ06cQ4s7Do6suph5VacgUNBlsGTyXqLEW1AS6ochUU81YPPA1p8Ml/kcISLuKrIo5BMZXOUB7M1j/Z/ptFGjOnaTA/lacdAT4VchYWNvHSp3CByLvYzxYLTBMvPYnwC1xYmICdRpPuQmA+VPPYioCueFkP/kJd7eTFxpZm3HDaSzvYyrW4EpFSb4wlzaUI9suY+jGlayddMvyQTqcPprAYFDalQ0nEV5dD62beDyNWCLyJjLsW1JxjGydO+9OoKyLQ/7cmjzvGQalMPSP300BhIj+EZHVOqQShTYn+qgwhn+i/XqnUF6YltZWX8RAIpQmRBaxMbuRzh+3Gfe7WaW+CBjK9hbsiCLTd9lwubDFkvW3pdDJi30M8NIW0LCwrizH/0rFVi7UoX0n/fEhutbmzKIE9wURX0flBivF/pHmS0IGuOeAVzjKsjqpUwdhxPCp6FdXYbotxBhddglTrYZmG0x9AtbIZvEWqdzcGRcadgon3JD3CqO4WCB+eAglefU81rvamb6JtHX9ixrQrO5v+N1/mx9keiouqJCw27LQ0YiM1ZhRR1wh6O87PkiEy/7NK12gD/3P8xzWzZz1cQTmJ/dS7WVhyGRDtAb9DLY3kvUXcclka8T3VkGvZAPOumKJbA3pItHOYMW/myYG13/yglXfxx3zos7EqTqz6IoojyA0+dkdC7GQKiBmKIh7RHBXjXuBIQWHhNA+K1wWArKXXHMDYVviYUPdeFZiKZ94PdiC/VNhzu2DTqhwrM4RCVFESVT+BIlhigJ9cOQLE4umvldwn0SZ0on48/TV6aCqmLmeskujuJstRC1DuSBYpmtulSMm/vQV4WQzfnhzkuZ78HuMYo+nEqDoxAkJ6DC4MgXWjQ4yLoHuWP9pZy6/NcErSnIJ+NYTxVSkshBC+uH3ShfDZGs6WZgoIVodCpTI+cQDowjmdzLx527aV//aywrR210CrU1S3mu+ykG+l5AVXSWKTA76iOqRUk7y7EOEdhIUQSurA1Ick4VS0pSXhuxSEM5wkEk4iXbkxizX4kSHwT2ZzqY6D1E0MaDCFjddCtBHKMyKdT5p7O2896SUC/xlghFRdTqyNbi9WDh/3CJdClBVmrY98eL/PGVOW5yXgutWkM7yYPw5AtdYN6BnVCwD17Jyx9CHKRtpHV4TwZ/FHH3CswfdkG5hn5aCOO2fshJEJA73UXW0Yo32YESBjtevK9RZvIMd3G8edGY4xay28DcssVs2vBz6ud9hdt3Pw1AW1WcqBpAdhooTc5CoMHYWHUru23mXvALdvT2cnfyaZ4Z2AjADbsf5T8nHodxyrXof/42ZBJQNY16//F8NTyTitBE5NMW9qbCuM71jIHrukoQ8WLR6xC4y6pJx+Lc1Pav6IqTs2u/RNUpTfDbkXRtYrwDX4Of/63/BN3pXnx2hr6eDYxb/m26Xr8JKzeIu2oBu8tn4bQGqKKs6DpsNYslszgJYtvFPoaOAYncULwYZK0F/cIzsSeWYdpv3ycxLwZY2/I8e3tfpyE0k3r/bBzyL09mlyjxYeawEupPPvkkP//5z0mn0xx11FF84xvfYM2aNXz3u98ll8uxatUqvvCFgg/ztm3b+MY3vkEymWThwoV8+9vfRtMOq8v5m8lksox7LYj/qS6gYHruO6uWlhk9JBPbaQ5PwWu2MvGU2Rg39Q7PoCqz3QiHgISNcdcA2rF+cCsoVTpENazHR3J/4lcQx/pIJdN4VwWxNmewW/Kok1woE53sG3wc2zLwJSeT22/ifO4gQWyB0qnS69mF0+GnoXopvf072LjtD8yZdiktBx4qnMZbg99XS5eV4JgJHyNtJFGE4Ildv+PpvX9mZfVpLHLPQitfhDnqdXTa4G5LQFsCOZBFCzjJL6gi61CQcihQ27uJsEjY7cSyHfgdZQS1OoR8c1/iEiXeLgfS3SyLzPqL9TLpnfRJN1krh0stmOpGPY0k8r0M5noIOA+d3q1ECQzQlgcw7hgSMYC6wFPIZ/Yhs+7prEpRudCDPRQ0ToRUzJO89MoUVdN1rJu3Qv+QiPc5UK6ch3lwVO2AUhgVjbIOVsY7kS6LD5sFwgcdpd3AkkC3iXFvDO34IGjQ1pTkCXU9WBpLPbOZOENg38DwarGcoqH5TI67axpiaXbscY/w8Hz/nSyJHgFAatRkzn/GbuO/r76cuudyeJYGsVqyqGWOESvFIdRZHrpv0fF1V3PltPOYMLuGXquXuUo9453lrNf6GDzzs8xU/NQOTkK9fgdlgM0GlEUNKFND2NvzYINzp4l9rgdxx5AFpQDjYj8vsJ8lM75HuZYnbyWo8tbToe+h9jO1iE6JcEvMKSFMh81MMYW85mLzzpsxA5O4M3aAI2afRzLdTtZVwY5EP950J1WeIaEuJF35zdz32o/oS7ezuP40ljZ8DJccJeTNQ38/ZO1cEkFXYfbsbWArOR7ddT0b2h8bKrmNeTUncOqkr6DYH8KgGiVK/JUcNsq2paWFb33rW9x+++1Eo1Euv/xynnnmGb71rW9x8803U11dzTXXXMMzzzzD8uXL+cpXvsJ3vvMd5s6dy9e//nVuu+02Lr744vf7Mt4RJmXDuJ/aWFQWebADOa6B/9nyExqj8zh+yjVsT/Qz6ZIISrdV8LurVDCfHgq2ZkrM1QVhrl8SxXAlyC238B0RQUgL6VRZq+6jNd3Hqc9MRAnoqHM92PtzKOP7qbJynHDkjxl8xUZTJC6/UhRtHQCnYELDmextuZ+tu+4gFBjPScf8gGSqk4lNJ+P1VFJVNpdyyqjMVWIPprAj4/hD683EMoUIqfc334Kn8RMckajGyvmwfV5ybjfuA4OYd2yDZB5R4UU7pgHH7gFyM6Jv9/v/thEC9iTXcMv6byKHDM5OnXot8yvPQdgfPt/OEu89edukNx8n6gi+ZT3DyhDL7MXjmkdzppspvnqg4Lte4RlPy+AmZpSveC+aXOIDSMadx6MZ6Oc5kAkQDsAPUrU5rKNw/g3ogz76nBrhK/wIW5JJqcTXmYRqXbCjZ0SkAyTzyHUdKPUTGG36bhkm+vlRzNVxZI+JMs2FOtMzNDFcEuqHFa5Rz2PQKgQKrNK4teJZbu95ji9MP5fLmu/i6okOjv10NYG+IKZu4izX8P/6ANgSc8M+9HMmYqzOQtJCXezCWmry6fwnEdkE5ZOuokPRURDYSHrzcf6svsjR582hQraxt2wtJwQvw10Vwu7KY++0UBpcGH4do7swULF26FyweCHafdsQ7SnwdlB+7niuHnyCi8JLqbprd9Fl2a8eQFsVwd5e+F925Xl59m8Z98ljqNBm0eY1+Gbbg+zZ0g/A8qomTqgKsHXnr9jc8QwV/nEcM/uThJ1NVKhvBH1T2JDVcNacQURJUNX7Ivdv/NXwOafXHE+FYymiEBePuNXMDS//I7YsjPme3387pp3npHGfR9qF+25FFJQqDTpHZrVEk4NcmXtM2sO/hkGjbZRIL7Ch/XGObrqYkDr+bR+vRIkPC4eNUH/88cc55ZRTqKqqAuBHP/oRzc3NNDY2Ul9fGJyefvrpPPLII0ycOJFsNsvcuXMBOOecc/jpT3/6oRHqeuYQStSwIV1YXXfaTsKJOmzbzSPhA1gOA6lCqNzL4glhtG2j9nMJZJngkczNHOM5AyG9kNUQwmKmq5yvH/gdK6/6MnqLg1zcJjHTRc43SP2aV6k85Rw69tuYSYn/+DDc3jty3Bods1pFoYzJjVcyseF8hHAhbQ1XeDLl0fkM2g4i6Rjijm9htxUapfgiXHbW1/h+ro3uVAsAL8TXMF+dib63EyWexLF0CcbNGwvR4wDZncJ8phllYQ06jPWpf4fJ0MPtG/9rWKQDPLj9eiZGjyCoNL3LZy/xUaAt00PUEUD7CwF8+lI78DtrUJzlNKc7h4U6QNTdQMvg5pJQL/GmZPUUzq3tyI4UyuQKZCKDvbUbPj0HOAxzWv4diF4V1WNi57MIxUYIF1arjtNUoDOBmFGJWl+BRCK7+pHtg2gHdbXCYyOzeZTzfaBoyHwGoZhY+odrUuPDgFmrolRohWwGAAI6TjG4v/9lAMqdAZq8lTS4vfx8+z8M7/cVfoz/Df/n1jjmwEa0pQ2IBdXIRD9aSw/a4D7sV25k/KQV1B1xNN+ZEeane5/g0vGn82DzXp7teIFqt59/n3426XX70J7oAsNCWVSLnNJE6+0S1QPR+QJ30EbrzGNlh8YTKQPXzTv4l0+dTcbIQm6Ag5GjAr7lFxrs2vsQnvE1+CJHsDvehk8bcYMS0mL7gVvZ3bsWgI7BXdy14ZvMn/olZkdUqkU5vTLDf218AtO2+O7ceWxsfajofFvbV3NE49k80PcCs0MTyKX3D4v0N3il5QGWj7sc55CXfsZh4/tkGeL5FHJbFjHLhVzqJav+bbEcLDk2yB2AKUthHEt8tDlshHpzczO6rnPVVVfR09PDcccdx6RJkygvHzHrrKiooKuri+7u7qLy8vJyurq63o9mvyvE/FDmVCE3ym+83I0MBPjKtD/g6xxP1w0W2DCnaQLtx3WTzOWp2RElE/Li+ccA2pMDmF6JvtzNdt96FqRWEtweJX9/XyEISkjFfUWEbzd9jH1eyef61uNUFdL7LRTgp0dfwzQth3u8m/grFl3bNMourEQdzKNWaGTHS/KuQsdTyDTmG1nplho7Mw6+99o2/hjqQraNmjlI9qO89hhnzv0YN+z8PgBhVwVWIIyy9mlEVRT6MsMi/Q1kTxoRdGHz7ptsZsxBcubYQH3JfD9BV9O7eu4SHw1as92UO0N/sV53YisBVz0eZ4i9qfaibeWeJjb1PP4utbDEhwF3RqBEgkhPHda6PEo4gHZmHblEAio+PEJdSvA1mCiOPJl+F1ZG4KnMU7MKLE1FW9yAvTqLMZTFRJlQhrLKTV4t7kuEYpG0Q+R3Sxw+yPTphOZKVJKHOm2J95G0xyb6xWqyO+PE4ylaywb5YeIeslYen+bGKZycWftxxntz6IoTwx569p6DXNhShajp5v+thVjB6kLUTUY7+y7Mx/Kov4KjF1awaPlC0imFlfp8RESQiWYx2roIPDzyXbZfaUOJeIgsriNQJ7GfiGFvyWLqAv3U6ZgbtkN3CiRUp9ysdncwt9KD6Bo13tAVhN+FmGwilvtZl/0+5y94lNiDLnrbJeNEHV89sopbK1/mvt4tzAx6WLt9bdElGXaOSapCZ6qDal85MSODYRfGk53pgxz2h9ge28vPdj2LKhR+OuesMdv9zggaxfcu6bdQT3GjneTFVG0s+bcHXAw5aqgNTKZtcOdwWW1gMmFHLYd5vOASJd5VDhuhblkWa9eu5eabb8bj8XDttdfidrvH1BNCHDKVgxBvb8Y7GvX9zW19t3lV9pK8uJLG+/oRfVnscQGUU2dRtUMiUzYikMN1js6BuyXZ/ZIJe6rRAiAtATak9kniK6G3vA+/auOQbmrSEzDuGUnDJmMWxu0xjrxqKl/euRNLStJm4SNrA08O2iycFEZZYpFptsm2S1rbwTfXyUuBtdy55xm+t/AaGnwVY9rflkzzjec3UeZyoHbtGfONlV17CKpHAaArTiZVLKY93kJjNofc346cNjjmmLg1lDo/4bLi51Ze/s4PNvVMLSF3xbB5PoCmOKgK1lMe+PAMbg/Fu/m7eDee1d/L+9Wm2MAgNf4ooZDnkNvfKO/fvYOp1SegO8p4tu91AkEXiiiYHnp8k3nqwA1Eom5U5d3/lH+Un99f+7s43O5RasBGphSs5wtCwI5b2C15nJ+L4nsP2vpe3o9kT4K21R6sIc3Tj4uaM21M20brs1Gb3KgTPUgpEUJgtps4Zyr4ykd+g+m4jjNv4XqtD7pN3NPcmLEQapnrHbuWw+0d+Vs5XMZQKbWf1+M7eaBzF1KDaYE6PtYwnwNxg+u37aFypo9zJ15JBhNdaOSUBNb0EOrWGAAi4saO54ZFOoAys4H8DdmR4IJPpvHEIKkH6NtVGCdpQZ3q8xqAUabr5V4UdwjvI93YYQ11mR91pgfyErs5h7ZgHObDhRzmwu/kxpbHWfWx63Df2YJoTSHCLuwzZpP1h5CnS2xnjgXKP5N8QZBrf8PJHqwXNU45Zxb3swWEjkvzkjVTRfclONDLJI+fyDg/mYSJS9XIWiYHsoJyXxM9yf3DdSOeWtrzhZVrS9ocyBs0hGdyYGAk3/p5c75KVbTm739gb4qfKxZ/lzX772Jr1xqmVy5ladM5VAWqD1lb5gojS+EsuaSU+HBz2Aj1srIylixZQiRSSNG1cuVKHnnkEVR1xDS0u7ubiooKKisr6e0dMcPu6emhomKsYHwr+vqSh236hxCCeK1F91Vh9KxC2NGA8lAWY93Ih1g7NUjZMjc9z0pS2y0ix6l0PlD40OpRQXlllHvtZ5gYqmW5HoC+sVOSss1AyWscVVNBdybH/sTI8S0E8XiaAfc+5Pk2FelGVE2Q0HfizXSjA891bOTU0Fxc/fsQA63gi5Ivm0iLpZEyTZb7fCjumjFCXZmyjJw/yMlTr0ZVdB7f8X+cV37l8HZ7+zbUFbOwnmwuFAjQzptOPKhjjYryXl7up+ddifru4uPzv8stG/6V/nQ7PkeIj839Jlq+7F063zvL3zMQfLd+F+/es/rbeT/btLX3AFE9SCw21nIjFPIQi6XJmymS2R5UO4LMgUPR2d3dScWolXi3FmRny3bKPI3vans/LM/vb/1t/DW/i8PxHvmzJtZLxYGusED2i3e9re/l/RACRJeKlS5+Rr0vqVRNV9F0MJ6LjWQ3cQv0CyIYhiQWH2ljIK1j/blrOJic3JZBy9nIxnfm2384viPv5m/i3aa83I+QNUzwxjgpsJ/OZJzxkbn4HFF+trlgBdFjqOzd+Cuqq4+kuulEDpg9OM+owbukmmTMJNQYRr+v2E8c2wn54t+NvSGN/5wAqV2F/824JN2sEJgcRe7sA0A7YgLGnxKIcg1lsgvj9yPjVHWBF+kvLD4ll0S5Mfks35x7Gbf1b+D4j08h15cjrvQxw/LTc0ceMy7xz9XwNClk9o01C6+Le7mzYQpJbwWRyZ/ksVHpfGdFFlO5fy/u5GYGao7EY2t8e+7J/Pvrj3Fvyz6+Mv2zJAaeYlfvKzRF5hGKHsn/bh+xzrqv7XX+e86/05XcTTofp8I3jog+7l1/d1XKOGvWFzi67nIU6cTOMeacmiXwHLCxHyks6CgnB0g3KpjKO/suflgm1Ep88DlshPpxxx3H1772NQYHB/F6vTz33HOcfPLJ/OY3v6G5uZm6ujoeeOABzj33XGpra3E6naxbt44FCxZwzz33cMwxx7zfl/CO4VYyvNh8D6+2PAjADxufLhLpAOZjg/g+6aYHcNUVzygafRKjT5KsyPB4+1oWTTyPQGCsxYGo0unTTH722g7OHV/PxKCf1a2dqEKwpDJKR+wFeqwYe3peZaFrGm5TQbUChPY+wjfHr8KjT0NryWJ3HkCs/w0i2Ys+60TqV16HJgSWlFjxHtRFZ2Otux9sE2XSkdA4i/977d9I5weRSAQKUTMEFPKKyn0HUI9bhD1zESTyyLCLhN+BdXCE3neRsDKRTy/6DWmzH5cawEn4XQ9iV+KjQ2umh6m+hresM5Deg99ZgxhaQa90hjmQKRbqYVcNXak977pQL/HBRKhOhE8Zm0JKe+vYCB80pAQhBe4mgXdyIX+zEZek9lhIRSC7zKIUpGQk9s4cLD4omvSgURTxHUDuzaHmJXjf/eso8fbJM8CtG/+HrsQ+ADa2P8n8hguo8iwjnjco95ZR2XQGdmQcmX6bgNHAbn07gWCIxv2zUO9NohxbgbW7f/iY4lCizymwD3KXthISGoIwJNRlXgeZR13kw3ys2MTcWpdCXRJl42Vh7sqvZXX3RvaZPUwO1PHH7le5rHoG9Zkjaf+1ORyh3uiT5P0SV61CcrD4N+xy9RG8/VsE66ezb+XHuG7Gv9GbasEvHdQcOIB7/UOIaccABSvU2e5qbjjyAmJGhupAAHd0EfkJKbqsBFe99IOiY1/QeBxOK0qDOwpvGLa+R+bnilDAcr7p6TztEutXIxMg1q968fxDOYP1b7JDiRIfcA4boT5nzhw++clPcvHFF2MYBsuWLeOiiy5i/PjxXHfddeRyOZYvX87JJ58MwP/+7//yjW98g1QqxfTp07nsssve5yt454gbfcMi3aV5IXcIs35DgiXRIwL/LI3E1uLRhdEv6Qr1U+YK0hmLEo5mcawMYD45WHDx9iro50foT6cwbMmfdx/gq/OmoQiYXxbGmVhPzquweudNnD/p01Q4oqTiB8ilu2iacQmugfl0/0FjMA+K82SqT1uG47VvwKbHqFhwJl8eX89P9rdyedUUal+5BfXYy0EoyNZt5H1hIp4aUvk4PmeYCyZ9gfI7W4fbrkxswHaoJMIuiLh4K3zpNpTefaA5McsnkNYjf/f9fwPN9hNQ/CA/bImMSrzftGd7KXOE3rJOX2onfueI2V/UEaQ53cXC0NThsqCzis7k7lJAuRKHxLIdcHwY7hgVCLRSx/TpwIcrSJOjEfROQe/jBkhwlAuqztCRzhyyd+yKpOw2EAd92RWvwhgvW7eCcH247tWHid7sfia5J3JxYBWuXI5Br5sHB1ZzXNUxVDqr2NpSyfGTLsbdrGHcX4nMg88J5WdJXM/3QcJGxlwoi+qx1xXGITJsIyboyD0jz109PUwuqRBdqZDrsElut3BPhKwjjFebhcRGBJxAqpAgwBg7atiX6uLagZsAmOir5MroeNTWNdSEj6OstY6cdMKoQHLZNhv/bBU9pJBttzHjhWP6ZwgcvQ+CtHEc2My0RIawv5aGO74D9qjYRkd9Fk+7hbBtjHIHUnMR0lyUe/30pBME8hq+GPxw9ie5Yf9jpK0cl4w/nnm+ycODHkURGMJAkxp/QzD3dxRNU5AvjHWNlGuSaJcEMM2SM3uJDx+HjVAHOO+88zjvvPOKypYsWcJ99903pu7UqVO544473qumvadkjRFTH011gkeASwznSwcQtTqWV6N8lUKuxyLXUvyB8tbDT8o+xeZQC3ZSohga1Ng4PlOBzNiIoIrxUIxxJwS4Y/JipJCszya4rMJJ3mMyYFUAKQbSXVS6q9m59hcMdBdSxk2d/i0G79d4IxinnYP2+/00nnsd+qSXkAN9rHx+G1OWziHh0jGP+RTattXgiyKXX0U2MIEr5v6IlNmHQ/VSnnGiLK1CDiYRTgfS6yHjD/CXsJpfR/z2WqRR8C1Ty5vwXvh9UgfllVaUwkTH+22mV6IEwKCRwpYSr/rWk1B9qd1UBeYM/1/hCLE2tqOoTshVRWdy58G7ligBgNQs+jt1QhdWosbzSJdC1tbRLJUPk1BXFLAHBIPrR0RKvkcyuM4kWGejTHBivVJslabMcmPZxZPgtr8dZZ4fe8OIS4p2QQRL7APGvavXUOJvI4iTk/dmcG79JQAR4NJTryFWVUZAVnHnWoVz6kOk7/MWjVm67xbUHxVCPNCH9VwW0RhCO7WC7qYOcs5dWMv2Ub5oEWrCgVGVRq+oJvegJN5q4GpQqL3cQczZS2S3G+PuwrulHm2gjHcie01EjY5sH/Ubcwl2uDrwai7OjS7jnOgs9mz4Fo1V51D24Cxkcxb1yoOEpg3JnRbhhRrhpSqoAkdI4HT2IP9823C1SNrJsxuns+zcX+Ha/xC5fAp7zhW4b3PBrsJqv1KjEPpEkLzDibk1TVBIjH272FaVZ0umgytqltMUqiWklA1bD8bEII93reWprg3MDU/k7LqjKZfv3GLI20VKwHsIayCfWrJ4LPGh5bAS6iUKhN3VuHU/GSNBKhcjm0rivqQM49EYstVATHfDcSH610oyrXkqLtZxdSvkuiwQEJ6v4NyVgDV5pl/dxLZgB1ZXBG4eABVQxXCQFO1oiPw+DhJWHRcgN6ec7YN9CLcDl1NQ5q0lm+wYFukAXnUqsYPGeDIPplWFsu1Z1JVfwligUpHtwPNKN6K1F7HsOPqPnsregfUMdL5IQ2gmFc4pKLaTuBNcU8ajxROga+QCfhyZbhwD3UhvmIynGovij7MuTKzVN4AxKjduz37Utk0wvrC6KIQgZ+6jpfVZDCNJQ82xeF2TkbL02pd4/2jP9lLuDL1lAEwpbeLZA0wqP3m4rMwRpCc3gCmt4bRuQUcFWzJPvOttLvHBRJNZQvN8tP4pj+JxYGfB0yiommq962ku30uEAKN7bHlqr00k7QBvHu3kIOazCbAk2jI/IqSiHDTmt2QN8bDAf54XxbCxnBqxVoXA1Nr35kJK/NUIIWiJbSecSODY+kLRtuATt5CvXcDRSgfz5odxZcaTOsSYxdaU4ZGFbDbIVtisr3qGsv27MJIDWOEMSU8zATkf/jAZa2iuJ3vAputuA/tEP2kHvBFWz3ougXq0H1GvoS7SsVankFvzUK9jXxDC1A9wU+Yfqb5DQyYtwsu+i24FsJoLizPqa4ME5wWIbxgS7AKCM1X0J7txDLmrCFUic5A952dsyW4iKBUCzokMDig8/PQUKuqmULl8gAlbXLBrZPVZttvYrxooO1Lk9xd+/eKY8WypWseN7U9DO4Qdfq5fcB1hIliKyU933sWans0IBHnbZPPAPv5r9tX47EP7gbyRh/3dwrJsWOKFF5PD7gFowJHewrYSJT6ElBTLYYgLjcsX/Af3b7uetvhOdvjWMePRuSjVDsQ8L3ZEJyUt5PQ4/hWCQV+Y8iaDUJ2K8Cio/XmEV8PcmyEfz+EOZ4jLNNFqHdlhjKQ+Uyms1CcKHzjz/hiucBmqqCAUjZLxb2RRw6nYVnEPp3vdCA1Gp70UOgiHhmg4Cau3Fi0RIluZovfU2WQG/DgDKlua7+XhvT8c3ue8WV+jwjkbS9pUu8vJRiIIAYGu17D//E9gZBGKiu+UL5GcfCKWGHldVSuP7G0ee/PiHcOZAXLmPh5++hpMqyDmt+y6lROP/gl+99x35DmVKPG30JbtJep4a4uRwWwbTtWPNmrVXVc0grqPzmw/de6C1YjfWUYs24ktTRRR+pyXKEbN6WjP9DLhggCy0wCvgnDYiBYLoh8cP3XbkaHXjKELlQghpFWcJsq2wVE2dqDuaRCgJyHsRO620Zb5QBHYgwZauXNMBpl8n0rsNYsYCgX7ZQCbwBEeCB46z3OJ94eYvZdfPvNpPlf2cSoP3phNUrX/Naxnf09oyT9jVoyjXxNjxizKaKMmr8LA0jhNkVmUG7PxtTSgPKci68BYEaSj2CADMyZxZhzsT5lMOzWA+sggWGDtyaDMd9OdWoPjzCYyR06iZwCyPRlWls3CcVMMORQIQXlZRThGTdhuSBFaAP5zgpiqgu6XKPf1ws7csJOGenoI89E4ewYVPjfwHAAfr/Ow+MgILlca1ZNBsxOIPWMjtMudBsKrjjh8PJvm2Ckz0Gc4uH7bvQzkE2xPtrDEF6HbirGmZzPzIlM5qnw5e2I2QadCj2HhO+jTIYSBmd5FvHczDneUQGQWtvL2Ajz/taTKbLxfrkTsyAICOcVJKmqX/BNLfGgpjewOQ8z8IJ3xnUyIzGNJ49lsj7/KuEtnkT0QxyP8ZMoybDVfYlPH0xyvX04qmaDKdyz2Y4PYmzOFLkAB/ZIo/c4MOVtiewXa8UHMp+LIVgMCKvppIeyWXNG5rbUpsjOctO6Bacc0EXb34HZWo6gu7CHBG0+9RtkJJ9H7mIG0QGhQtlIHaxDr1aOR7Sk0wIcX54VBXt6jkstAtOEUjqzv4KX2PwHw4PZf0DjuU/zf3uc4NjqDz008mzJLYt/1byMr5baF/eD38dTOIOEbCZhlOLzIuSfheOr/Fbe/fuZw+p2O7peHRfobbNr+e45aMAvbfnuD1Hd7prjER4e2TA8R/a2F+kB6Lz5n1ZjyMkeQ1mz3sFDXFAcePUgs20nEXfeutLfEBxfLJdBmuDGu7xouE1U6+qVRGOuNfVjSp3Tw/3Y8wROdr+NUdT4x8UROrJiPyyz+DTn8bQTn1BJ/fchaLASRxSkUlwK9diG1abUOgJAgu21EU/G5VNdYKxfFBYrj7aV/LfHuoiiC11ofJ29lcZbNAFWH0QsKjfOQXXtQj/o2xt2N4B2gckU5XU/Zw2OWwCqddsUkeI0gle6j3buPu/b/mMtdPyTwVC1szwM2Yhtos/RDNAJERSu73bfhqFpBw7Qp+PbZyA4D6+cJyjxziF1QwevbJGUnt3L97lv5/r6PEx19jJSN8B80FlmXwgqn+UP9/3CK49PU1FQhdwMC1CU+ZI8JeUlGH7GJubn1YSbPcuEwunho668QQvDlphsIvlKc4lgZ78Q6KDCxGpP8YXA15zUt55a9q8kORcxThUJI93JEZAW/2tQ5XH/1gb4i7L0AAQAASURBVBg/WTKfyJAtghCQGXiF15/5l+E6bn8dc4/7CbYoutp3BBtIRGzE0kIgSClLIr3Eh5uSUD8MySqS+3b8BnvU9K9XD3Js9cVY2xN4dunMqF3IhKbZ/N/r/0ws08WsxscQm0elE7HBfDDGc5fspKYiSDQwgFmuo0x2IuZ6kYpEhBSsu4oDc4hyjYBTsm9AIrM+Hmr5FZ+Z9x0WrPwfdm64ASOfwKx0YqtZose5CtF2BeR7kvh9Oaz24i+m/lA/c86txO3W0CwdGfok+2Iv0ZXeR87M4BrK//x03xaWlM/gVK0cUrHiGyIlJHphlFAfsAd5sSzIyYtOx7f+YXB66Tn2UnoDAeootMm0iichAAwrUxil/ZU4LBNXTx909kAkiFFdQUZ3/uUdS5R4E1qzPVQ539rPryDUx6wTEXUEacn0cGR4pCzorKQ301IS6iXGIsF8uDj6tOw0kHELyt9kn8MITRc82LKW1Z2v4VIdmLbF9TseoMlbzmznjOF6QkDeZRDx/JrgRecjLR0t8xq2rZHLz8PVY2K7NQzdgQQ0H7A3hzyiWCQpbgPfdJXk1pHV+cqTdfJKGvjgWCB82NE0hVi2B4BBOQ39tB/hff5/SDVNo33abPpVQbWrgsYXmyBjQ8bE+VQP9ctC2AENtcam307Q25vhV52XkLeyMFA4dnVuHGxPFp1PWT9IcGmY+JqR98K/wuCAvpGwL0JXehMT9tRi3j1qzJGw8W9PMeHUPF/e9Fv6cwlyHgtQYLobe5q/YNwYEmgXujHvzUJOYiwQbJzwIjvbXmFnzytct+I3NE2dBK0W1utpZEue5FEKd+TWjNwPoRJUK2jteh1ddZI2BtkQeIZlC05GX1eoI6Y6EJXamAwQA4E0PX0xPJoTTahM9heykZQrIT437SL+b0usqH4sb7AnmSLiK0yUKSTZ8epPiupkEq2k4ztxhZa8jaf69jjYGqZEiQ8rJaF+GJI200UiHSBslePdqIMdAafEmwyR25kjpFcQy3QR6+8gfFD+GNlv4bYcJMw0GcVkp3s3+8bvpVodT7u9h1PdV4MiGJ6OdCso01zEhgLTSWHi0cK4sn7cv/8yC2YcQ9uylVz/ypeo807n+Og1+NJVhMIBgsF12NkZQPEKNmmbujKFVLvETEr0hJfP1P6On7Z+jNrIbJ7t2TNc9aWBHZzSNBXhLysI8zcQCjJQbEZlI/lZ+4s8HK7k7PO+QMI2+FPvRr5szaZuaHtF7YlEOtbQP7BteL9Zky/BtgqvvRACR0rH7AXhFIiohaGN3HdVgOv1rchHR/zf9KnjsM9YSU49xAx7iRJ/Be3ZXmb63zow1UBmH+Mix44pL3MEeTW2vajM54gwkGl7J5tY4kOCaUlEcqxJuGVYfBCEZ0L0szPWwY/rPk95XxRTt9gTaGZbvIUFtbMwjKG+SsILtsLcqecSOSAgnSc/fha32ns4T9cwyxQ69yrkNhf6Oj3ipGapE2EpjM7HJmyLqCdPcKUTyxLomo0eT5ERkg/C/fqoYFmSyZXLGec6CivnZPXa2Sw49Wc0m89SnppIRdaHFc1gJXVgSDz3mIh7etEnOVFX+Knwm8QqLZxxT0GoD2GrgCbAHCUEd2dJrYqRqLMZZ4URYYOk3Ufl3gWU6wbpsk7ULsagd+Tps2L05goLIusD+6k+ZjaDio/YU4XjCwfUXezB9dk4LSR5oPsGdra9OnyM+3b+jFPcP6OyIYXhTsIpGnerr/Fc+2YAxntq+JfQlQTvjVKVnsqChRfysvMPPNz5W1pn7OTMYz6NjhefSEDCgkoNukwQMHiCwp+t54GCf/rPFn2OWqUcJAhbYaZ/PDnr9THXZdqjvinSxMiPjcRum9kxZSVKlHj7lIT6YYjfEWZWxTLmBGejGnlMh4vJcnlh2cCWyKSNcCk4FRcnll3Bbwa/RMzfR1h4i0yAjGkaz2e3cnSuDOFqQFdcPNF58/D2en0ycy4+BtJv5OGANIKcQxKIQJ+ynpOi/80TD4U54YR/xRGweWL3HzFtg/2J1/lt4loATqv6PHV1K/F0KlRqFOWhVZb6SbRQSJkzhHeqxqdX/oY7kk+wuev54fJF4Ul0a3G8Z/wD/rt/BOk4aE6UM/+JpLfY3yqEnwvGHcsf9z7Bd5PthTKHl3GeKnpkij/v3cC6vlaOrLyU46e66N5zC9MmnE84MH/4Hun9Dtr/zxj2W3ONE0TO0sk7Cm11pVPI1S8WnVdu34fj6Di5srKxD04xSdu9KELFI8qxS7FNShyCjmwfUUfwTbebVp5UrhuvY6yPX9QRoCc3gCVt1KH86j49Qn+mdUzdEiUy/iS+ZT7sZ0YyiaCCKNd5zxIj/x24FJ1/Cl5O9mY3Uhak8jT/bGZe2li0oiYETDKCBG/IYw0WhJkqDFZcNZ60zGHmfeQ6R67X6IfBAYXQQQEdRdpGPhZDY2RwZGkCZVbo3b3QEm8Ly7Jp1JbxyvMq409LEVmZodfdR9PaleRf9hcqCeBsA14qtqxTF/sK7n+2Rt0UD6dP/TJ/fP3rw9v3KZuZfuI8tIcGhstyUzXuTW/hicE93Fg1Hs1aROamqpE1Dn8V5jkm2pric9mLPcStLnRFw7BN/rfrThbPmUfmlpF3V+ah80GNsvMMdmbXMc+zkpPE5RjOPC9m78N0SmKmiRE12OVZQyrbRVN4HpX9YbqyA3wxfBHOP0eHkgJJeLicI1Z9nI36arYOrGFu1WLq5Cw8NzwMqoJy7mm0uVzsyXdwS/JpNsf2s7xyDsvCs3CazqIxZAgnl0xq4hdbdg2XORWFRp+KUGykrSCVEI3TLmTvpt8N1xGKhic44W97uCVKlCiiJNQPQzRb4QhRS8vLvxouW7j0Qsz1cez9hY7AArRj/dQ1TAZgl/M1aq6ahvPWDCRszMka61d20+QIYeY6EPlyytQa/n3yfZi2wTPZ27il9TuEJ/2C8lwVIquSq3fzg9guWl0ZLj62npCjli1PhslnYXv3Iua5kpyVr+G4qkt4InML2+IvAZDIdLM32cHv2jbzH59cQePjAnps5CIBiwL03lBsHZDablF7ZBmv9OwfLpsfGMeccB03r/s682pPYNrFX8OdSZPRNcoqlqEbOdA8DAf2tAUfn3Aite5yHm57iSmBBk6vXYpDuPnK+ntoSxdmeO9r2c62eBnfX/g9HKY+ItKlRv/DZlFwmew+id2twJAFsTAtOFQkUWNsUKGs6OXx3b/htbbH0FUnJ0+5htllq1Cke+z+JT6yJIz0X0zNNpA6gMdRhqKM/TzrioZf99KdG6DaVfD/8zmitCQ2v2ttLvHBxZn2k64SBL7gKUzwOgSWUyGTVOAQwdcON5xmkNSzFAUIsRKSSHsYGbYYrSoqOjUYHOX+JaHqURtrhpOB9rHXmmmBwEHms7ZtI7wK2hE+8CgFf+PXUujW8LpsicMARRF079TJZy06tV7+dddj/HHiBXjyASqPsxCmxHKpxJodRK+KYt8fBwvUFX7sPVmslwp+2o6HYeY18zllzjeJDe6gzFOD4g+RPkLHMy5KsiVJImDytOsAW40+rp1yInf05zj1Sb1I0FoJSaLPJHiGB+WhDEiJcp4fs2qQY5Jels/8Bi/27eOu5Bpig2mcFH//jR6wtAqWydNRfp8vmOsDNUuvpfm4GHuUrQjppdecRM5RToMe4j9mnklfPkH9jkoSB1mBi7XlzF++isbKmTTaTQSvf3CooTbc/yjhr1yCnnSzLDGTi73HM9nbUBDpB2HbkuXlZfjmatyzv40KNxxTYXD7uk9w+vTrmOA7BtuGynFnoWoeWnfdjdtXw4Q5n0I46ktxfUqUeAcoCfXDEM22aNl2e1GZTNjDIv0NzOcTuBdHWDnpcsrD87hg8/f53Zc+BzmVA45eTGLMtJx0xJLU5afDvSZycxangNOWXclxx3yMu3t/xopZH6c1V85XX3xp+NjfXreV/1g0AzMPVVWCGYNpzJ+lCOAgQDUXnPF5bvD9K+3JXUyKzGNNqpfm1AB76najn9BCMDCVhLGXMuPcQwf6yBn8oG8OLXVLEEB9a4b96X0sbTqbR3fcyKNGgqCrgi9M/iecj/wPomUrjukrsOadQcpZWGmscIc4PriQ4yMLEbbAtiW7c33DIv0Ndg320pFL0aiGhsuEJcj3HMIkNDnSWMPnxTmhHrmnZaSC140dCRXtIwRs6HiQDW2PApC3sty39SdULB5HjXPeIS6+xEeVtmwPZc7gW6Zm60vuxXeI1fQ3iDoCtGf7hoW631nGQHf7O97WEh98BCr+ahvjtn5kmwG6QDsxiGu69wORRV0xbayEMqbcHgRdlVhD7rZSgpYd29GIpI0wJJ5JFqmNxabrnukG5kHm7Fo4i3JaCOPhOAxaiEYH+lVlGI4YEKbE4YGiKMT7JEJAcyqNJSV+vHhb4/BEYbJGExC6sJxcHbiW+pG9RiEq+0sHBVO7P4vj8ikIPcI8UyL27yUXyeHY9zgsOplcqoN5qpsFnkX8w8s7Oau6Dpkc5TL4BlmFwUVurBovZaEs9kNplFstnEJHXaywQp3IUTQhlwc42FHJ2SjJxDbjuXsiMjMyLnGuUQjNquCAq587d3djSkmDP0y5S6M3v5mIpmLrOQ4eyguPZI7/cvp7PaSyuwiOaqqY3ETYG2Km4WShowrVyJC39Tf9HrilYF4wS6fnTpLZHh7btAmAW1//Tz6/dApuKrFFmLKmj1HeeBoIB5atl0R6iRLvEGN7wBLvO0Y+NaZMvmFHrQL60CDfBITKsymFz62/hf58gs/t+i2d7l2s3vEdntnyfV7cdzvzq09FWa8iNw/5DEmwn0/i2+9HSBWP1cgTrT1jznlvcwdNs20mV0uUp4rFr+dBjVOiV3PF1C+Rtj3siO9ldqSa2U5Jxujk5eevZevL/0tG2YWzpliUaAGB058l8NIOZty+num3rydUXolL9/LQtl+TMQpmmidWrML7p+/AxtXIgXbkC39AffRHOEZlAJaykCbOtgu9gvPgxLgULOCcioqmKShKoS2WbuGfM7auVj7S1pxQsc9YgVgyF/xexPQJcOU5pF3Fs+GmkmZ92yNjjnUgtuktBVmJjx7t2T7K9Dc3ewfoHdyN1/nmQj2i+2nPjvxefY4o8VxXIfptiRKjcCCxHokVRDqAITEfjKEenFT6MEWqktCssd9Q90FxFoUAtcYqfOxHlx/nBD1Fxvca3nmZ4e3uqTny0fWoB9c33Ri398NgYQZANucxH41jqiWRfjhhmhbjphT6f79SiBejJEzYUWxRIR7tJxPfQa5CYL2cgvTYb6TISGboFawyYmQHt9Gb34Wa2svgnCN47cnPc2DNv9P93D9j7r6Z4yu9PN3XhTF/rFWdo9ZBPuWiK5XD3DgIG9+YRQLr5RQiqqG+kkPflsFzVhwxFOZGL5c4lmzBTPQjW8ZmYgik3dy6qwtzSPkeSKR5vs0k4B3PPZt/jHscqJ7RFwSO+Q42POYn16tS5vcWfiAAFRHksYtBVQj2bET/f59C+dl5uB/4Nv5sx5ve70Sul62dT3Igtmm4zLCypIzY8P+2LbGkF8suxe8pUeKdpLSifhhiucJouhfdHcYTaCDVvwsjlMJxbhiZk2BKhFfBasvR40vwws6tw/vuS3VxwHRw2ezvMJgxcNrleOJR7I2HiHSy1+Cs074KqkLY6Riz2a9rlI1vIdA+Nk0UFkzJ16IZMZ5lgAOpDr4z4RSCIs+2Pf8xXG3tS59k6an3kF4XJLXTwt2oEjpOJeUz8V97EfTHIOAlHQ5j5teSt0Y62tq8A1IDRaeVu17Ekeoi760/5L2r0gOcWDOFx9p3DJed3TiLPqOXG/bcweRAPcdVzqdCRvAt0bDSCsnNNooLyk7RsCPFA9iky4O6Ygn60QuxVA0DMWYiXZNOagJT6EsXr2pGPfWlyKQlimjL9BB2+N+yTm9yL43hFW+6PeIIsj3RPPy/rjjRVRcpYwCf451Ph1Pig4vI29g7DhHUacCExsM/OJqlG7grcpQf5WVgk0R1QXQ+CG8cwygOnpr37MX+RAj9KT8iAcaSLHJcCy6rjq7+Z8n7/kj12WcDCt0Dj2B2ZIlOXlx8wj5zjOu+bDZwDubJhcf2kSXeH4SAqHMvR55RRUAPcuPsk8k35znY0UzGLBAufpI/wKUXVFErtcLy1KhnrCzX0M1+tjbfSV9vwYWofvIZ9O2+FXtU5pjePfdx2lErMewQTzs7OfaEarRXVFQPuJdqvLLOZPJ8QW29hvaMEzlqQQFAthuIcg1eySI/k6TvvPU0uKYS67yfnRtvpbxiGRWTF8GO4hcwHRLQV3xdr/cm+MTU8QD06NuouWIa2f1B8oMgywWv7TCwbagbp2FFa5D/cAnkDcygn6yq4+jag33LF8EqTDjIHc8jzByOM/+TvBj7ngec5eiqC2NU0D2PHsDvKCulRitR4l2mJNQPQzJagPrj/4utHU+ybnAHU6evxNIdyJxEqAJUgcxJlCUeNmee4IszLmBPvI2V6hzq8hFceUEq40JN+9m6wWDKsa1Ex3lHVlWGENUOfBsk9tpBrl1UxbyJAb62u9BRqUJwVHUF/eY6usVW5nkXQmqkAxFRDU1pwW7ZytSF1/D75BEoP0wgHE6WnPArtmV+Rk/vS0grzwvPncKi035P1coJWKpNXklj2zlS0TB2OIRlFb70IWcVmqJjDuXxtNRDDCRVDfkWEdd1W+Hq8UewvGo8+xL9TAyUkbQG+NK6XwDwQs8W7mtdwy8WfJ6QM4DvFJXgCg2pSEyXgY2BYXZiWVnczmqk7cWywXqLc0pbZcWEy9nV+ypZs5DWpT40nYbA7FInVqKI1mwPlc43X52z7DzJbDdexyGCFQ4R1QN05vqRUg5bbPgdUWLZzpJQL1FE3pVBqXMgW4pFA6EPhjGdMBK0pO6nselC/HUeEGCovbTnXqBMOXO475AS4kY/bsWFc0YUYWsQyNDe+yoNDQ246lZQofQQSNSCFDiCJ9BX7hxZaXwD3yG+8z4F4Sp9yA8nNGGR8niIrE3gfAomOTyIi93YIlPU58pZCjE9zmO9WZ5Uujm/uo4zP12Bf3UabVCiHmmTn6rh6QwwOfAP9IY24FEridrzaQyeR6r2AJv3fQ/LzDJvyn8SeHk8c/bZ9M/WeKWhlwUfdyMyDl59yYnDoWDkYesOnaUTFThQ3GZRriG3pFGngTfZQbQ6jGV30vzSH0HadHU+Q8PyM/HHG6HTAg2SpznoCGc4mCkhH8Lsoyk0g8TeJ3h+5xeZMPvrpDiZ7p0wbwIEbQstJ8mbKklf8eSw3ds8LNKH79WeV3Fke8m7iwP3AnhFJZfN/y/++Nq3yBgJfI4Ql87/T1xES0OcEiXeZUpC/TDErxrcs+smpkbnsrB8KZai4RjQwbIKIl3Kgglf3GZP3yPUVy7js5mT0W9KgpkDFbyXeFm9L4+iCmLaFgYXz8C3RYWBgmmV0uRAqdXJ/7wbALM5z5HL/Xxj2Qy2JxJUeVwENLht3X/j1gNMvWoR7js1aDMR4zT0o9uxt92DOObjeDY5Ue7tBwp9pHarg0mXXUVPb8Hn3Rtsojs3QIU/i2U009u7FcNI4fVUAYKAtxGH1ohf1PPZudfT193CAWM722Qn9U1zEftfG743ytGXk3ZXvqUA9koH89y1zPfUEReDfPzFnxZt78sNsj/TyVxXABML0zVk5ihT7Nr/RzbtuAWQREKTOGbRv6OKsR3XwYTUcVy35P/oyTSjK07KXOPQZeCvet4lPjq0ZXuY7m960+3xbCs+ZzmKePNPs0ctBP1JWBkCWsHm0auHiee6qGPGm+5X4qOH4ZH4zg2T/1U3Q2GhUY/wIqMKH4RZRFt1URldgbw5jdVXcIkScwXhk2cOuztBQW8HrZk4tnhRynUkElesnDrvSWQtk2p7Ou7bYzCUqs7hmoL7ujAH28rbuoK6wIu1bsj9TIB+cpC8UjiXoiRJZveRN5IEvA1oSm0pu8f7gNA1vNs0HPcXJvUlwG2DaJ8MY90xiIxZyNkq5ok5QkqQn06ZwA2drdze0YpSAQtOC9EQcaL2xnH/II00JG6ijLvkfMy74pA0cODG4ZrKkdf8Ci3rQu/yI0ICIzZI+IE8Jx/hZvPC+9nefCcNU06jseJMXrg3AkBqRQDvRhPZWxDDot5RaKQiUI8RNCu9/P6l79EQmsrRE0+mZ9dDgGTt1q8w5dTPUB85BVPLI9ydlOt+Tq4P80hLwbowoOtcNimILrtY7ptL58Y/ArBn438xZWGeWa7TsG7sKbg4AvpSH5zqIaeN+r24DzE28YSw9UMHv5VSUOdeyHVLfkfGiOHVwziJlPzQS5R4DygJ9cOQRLaTk6s+hr8ngCPlQUZB8ytI3yBYAmIa5vo8+vkRKlzjaMpWo9+cGkmLZoF9Sx8zLqqiR8vz7L47uC3+n3z2yp9TOViPEAIt4iP/0+6i81rPJzhqSQV397cwJVCJR7Ti1YNcHfofXH+WqONccIwPewLY/d1YR19A3JpMcF1szJDPuS+Ex18LQjBhzhW8ZmQJZbezZdet7G99BgC3K8LS+V/kldd/xJHzvkZ4oBbPLRXUtIaZVTsfcUkAeUYXSttW6GuFullky6Ziyb/O77swoXHouvIQg9REaiebdvxh+P/+2C4277yZedO+gm2/9QqUlOCmkoY3nCdLHViJQ9CR7aPsLVKzxdP78bsP4WoyCiEEZY4gHdk+Ar6CUPfoIWLZzne0rSU++DgHnRj3xtDPDoMuEKrA3JRG6bDAf/ivqmu2xPFSBLsvPVwmXpP4F9aSiooi1yJnJozdlcJYU7Bqwi1wnVeOMEBszaAdE0DoQ/tIcL2aRTQVm/kqAxLblOhnh5GGROgC8/kk2qQA0tXHaztvYtfe+wBwOUOsPOp7uNQp7/6NKFGEaluoL4iiblZ2m2T3DbDn/GdwygBxq5lZHefjv9uBP53lf5Y3YE5147wthuxPo56sYu/WkUbBvF2EVGgzhydzANRZHpRnXdgbMpjEQBfo50Uw7uhHUVWm505lnHcJKXrpij/IuDPOpTmVJR0IEVjiAwEirCEcArvTwPGZcoxkOxXZOpaVn87zPfdSV9vE5IVXI/sPEAqOxxduoM+1j/ue+gwLp11JbMfDnHXkl5gb0kibNn41TrXmILX/FZL9B3Ad8X36pYeAyOHFi/WnvmKrgjVJHEd6yI2K66BUT4IZK2DLkyNlp32ZpB7hzdS3lBIXUVxaFGRpiFOixHtFSagfhpRRhvdlN+ozI59Ce1UGa+s+0BXk6fMxZujYDsHx4cvxt+cxcgcFIbHAZdlkDYMUMQw7z493fmp48w/Np4dTgAyjCjyK4F9mV+NQ8/Tk3JxZdh3R3/rAtLG6U/Ay0KDx+kltbFz7PU475nfYIS/iQLFppVLuoqHhfCzNxQ5UPJkWEjI4LNIBMtl+tu25l0njVqElwfpl73AQH9pM5PUxzH9UGYh4MP0NIBN4Ndfb6iGC+Lh0/AncuPuh4bKI0884T3WRn5oQEE/sG7N/a+caZk9NIiitjpf4+xg0Un85NVt6HwHvWwt1gLDupyPbyxRfIVaDVw8x8BbBgEp8NFFtBQsbERbIuAW6gjJRH15dP9zRLA9yV6zwj1OAJcEE0SZhlD62bSAFtlPBPrccaQvUwRzW+iTarABq1IVxZwziQ/2LR0E/P4x10Iq6DAjkoAF2wWpFZmxEjY50SAZTe6lzLmbelOuQeRU7GOf13TewYObnMI1SGs73ElsRiGqBPt8DIodQBHLQQc4TY0PLTeTzCU5vvB7lDyPufs6gE+WGPuTQKyCyIDuHtjc5sY4KkZMK6vlu1NcHYWcWZYIDYwDMs/0oikR5PYH51CDaOWHs19OI32bx4MEjGqj6xEzMYIJX0t2obgfm/WODAmtnuOHxXZQDJ888DjFL5bm2u3hZcXD61GvYsfMeBre3MG/yJzh/4X04szq9dRXsfvGH1DYuxRGsxauGMVLdBGsX0RY+if/c0g3EAbhh8hRCqfSY8xbcFkfedeENkz/+CzjnnwnpAWSkgWSgoRRTp0SJw5CSUD8M8cXDKM8U+yWZj6fQzh5HflwluQ6BEZeobtAzZZjhQewLnVh5gSYsxDMxGLSQjUmCisZS83zu2/rD4WMJBLJSQZRpw6ZZANqKAI/lX+OxPev5h8kX8+e9e/iyMhPteC/CryJVEHmJ+eQgIbsBw0yTNtsQCxfg25YBY+gj71cwpmtY3tkMZPvJtT5Jx4HVhGdeNeZaewe2M3PqJeT7PHgGD/KjTNrkWrrZsPvrBKNTmTz/08h8O+iHDiR3KKQNp1Yuoc5TzqMdrzDZX8/xlQsI2cXCW0rwHyJAXVX5fBThLZl4lfi7ac32UOEMvWUmgFhmP7Vlq8YEtDqYkO6jI9s//L/XEeFAfOM71dQSHxIMXeI8PUTugI2pOFFyEi1toc/W+Ysv2WGCssCNEXGTTasomsQpTZSwhaIwbHauKGA7NWJlQeJPFT7WeshF9TIXSBvRZ6Ad4y8IOikRisA6kMNaWDwEMrw5HLO9GPcPFG6PR8FxeRkD7kEiiQkkXiyja2g+V2hlLLrkq+SsHsSYMGYl3k1SpknZcgfmDevhjUWKCh+cW0H+9QSKouPpL44FIm0J0zxYU33YtkC6bNTFAnt/jsz8KN0PWRQeuqByVQTtSJtUToWIwEpJBp41Cc8LE+gdRPGrmNtHBWmUYN+ZQl9qcM2Ecga8WfSPRZBpGyEE0rAxH4sjHCMLKo7NSRbMXsZz3IVlGzilIJ9PMnv85UxrORXtwTzIPBXeIwhcPIkNB/6N6vIreHLtf2LbhXHb9GlXc+OUWZiJGHsdUR5J9PLZcUHYNyoekQqyTAOKF3Nymp9cxZx37qGUKFHiXaEk1A9D1Iw2dtHYBHtCGZl9gt7HR80SVyuUrwrQ+viQyFVValaV47HyBITgmwe20ugdx7GTr2Nj6x149CCzG8/jJ/H7+PzlZyK3ZJG9JsokJ+lGyXd2/hFNqGQtyYbeJJ5JETAs0BSEYUNARbsggimyOB1BXI4AqaiGeVkl3riB7gW7OkmvXAs5Fz27b6et82UA3O7ImGutrVzEplwOd66Fo5TK4rGjAEMt+CXG+7azc/2vmL7kn9728NJju1nincXRU+di23aRb+Nogr4pTGo6lV37HwTA665g9tQrkfbhHx25xOFPa6aHMkfoTbdbdp5Uvgefq4JMuhB9WukykU4FGSk2Uw7rAXanRrLxevUQifzYFIslPtooUiGbVGh9yUbmAQT+qW4qch+MmUdLgj3ZT8ufrMKsK6D6VOrPU1FVMIfmmW0bcrpOfPPIxLMRg4H9CpHFoEQdhbRriaHewyXQL4wWFP6oHkXmNMx7YyMNSNvkb+tH+YIPtT9IepTRlTSh7zGNqksjpA5/L4IPFW7bifHsVsRoS8LuJEqnH011YVo5bH/xSEGW68QTLmJPFt59oSnUXRRAnW7Rc+vIcbSgwMwrdD1h8YY/obNaIXSExsBLJp6zAuguC1FfHKRRxi1EbRhx/1ZC587FuKMfzKFzhVX0q8qwnhpJbwbgybupD05lad3JOJQKaub9kMp4DdoDiZFKKRvX/WXMvvZnPPDi5cMiHWDrths4a9xnCN33K6b4y+g993rkWQH0NgvrsQQIyJ1vISJyxDXy70AIsImRzfXg0ANoSmVpEaNEiXeZklA/DBE+DelWikzTRbmKrWr0P1ccuT3XYWOOTnFuQdezNvWzbURcZXN/nM398KKziaOq/pVFVRV8Z/PPiRspHtPXc/tR/4LLdJHU+jmQ2MwVdYtZl+xBj73O8XVVOGyBbDMwnhoEE5RxTrTTQvjcdSya8xkcehWOijyyLE42EyNptGHkBvE4K7Etk1lTLhkW6vtbnmHWlIvZvPNWpLQIBycwbfLHuGjdTTS6K5l56scJ3T9yzdYJFnu7/zT8f7xvO7aVKeSS/xswzbE5SovuOwFmT/kck8edg2Vl8brrQIb+tpOVKHEQrZluoo43d6GIZ1vxOKKoigaYqJtyKAMW5CXWNCd2/cjnOuLw05OPYUuJIgRePcxgrvc9uIoSHyQUS9DxohwS6QUS2yXB2RoclD7qsESX9LxgF7k7WUnI9GvI6hHloapg9I3dPd1sE07ZyA5jRKQDZCX2zgxycXGUdzV2CNXRZ+LKSvLpg/J6AfkeCYYDnB8M64QPDdJA6UiNWdBQBzXGL/w6DiSq5oJxAvYVnk1easQ2jpqUMaHzfoPqk9Rhc3iAwByVgRfGjrP8swoDD8sQmHcNoNQ4UI70YdwzAIZEme5G5g3wuxCPppDmSOvkgIXsyiO7kyMHFRCsqeZK5Rr6PCovdzzDvt6XmOv8MQcHOaTTxEw5yeUHOZi0yBNSdbSl3yZykwbdfZgugbjQx273apSonwqz/K++tW+GEJDObeepl/6ZTLYfTXWxbOE/Ux46GilLixklSrxblOaBD0PsjIV+VhhRUxhEKJOc6Gd7IZnDzo2tL63i7spKgu3WiHlHOqX+XJ77muOsbo1T4ymkcFpaMYUBbQ/39v6c32z/FzrtTlZVTOGfJq/CZcY4tbEKBm3MxweHZ2PtfTmsFxKU+6YRDc1C2l6ajSR2rpXO5nvoSh1gp9HBhtg6krqKxxFmxZL/pjwyg1x+kPrqozlj5e84c/kvOXXiZ1Ha2xEo7Ei28l+he9h0dYKWi0z4vJtt7p8TGxiZgXa4I2j6m6e2ekeQLlz6RLyumSWRXuId5UCm+y0DycXS+/E5Cv7potdC6bEwpzmwpjrRtuRGXEsAh6LhVhz0G4WBm1P1Yto58tbYVD4lPrpYCuS6x4pP8wOg0QEsW8dKHqI8VRwnVEo41E/L0yBQFR3ZN3Y5UXabiIMCk8rIIdxSylVMr8ARGTtc8k9XUNxvPQFc4p0nq0mS80NjyhONTr69czUxZ5SW3MvkLpOIz3hQLtXIa2N/B0YMkBItMMp/WxeHHGe9MSugZi1ku4G1NoXxaBxtRQBllht1vgd7YzvKhCj0jn0nZFIipgyl3fQ5yJ9bRlzbz3aXk3u3/5K1B+6kL91Gn7NtzL5Ua+whj89bXVQshIrP1lFmnoPxRDV0D503K5G/T1AfOJr1eYOMkh17zLeJLeM88/K/khlyuTKtLM++8m3yZvvffewSJUq8OSWhfhiS9+cx7u5HqXegXxJFO8uPeaAbLRUnMKd45lLowEGDDUcFaOM1nrDHrrBNDRdW9C5pWsbHG47kt2v/hef330lrbDsPbPslT7bcTUh14q1eQcLugv6xAxxrWxbV0tFEHXEMdiXjGLk4VriGP+2/mfv33MRDe27m+le/woAdJ+xfwrFH/JTli36I2zEdPyEit/0v2k1fomz1jXyiahEALya2cW3P9fyD/RtiNRm0wKjcn0Jh+uLPY6vVY9pTosQHgdZMNxVvkUN9IL0Xr7MCAHVnHrtOLaRj9AjskILaXLzKE9b9dOUKKXuEEPgcEQZz3WOOW+Kji1BsfJPHdvN68INhr6rZFuFZB4lnAe4yOUao6yJPaM5IoSMKkXE2eTuLMtsz5tjqET7yZvFKeMyxB3mOOmK15VPIn5MgI7rQUnkqVyooQ+7o3omCcLmJki4No95rNEsjN9tJ7ogQKAJcGunTyklUm3xi0iqqNBcvrP0etz5/Ls8EX+F3vseRvv4xx3HVCZTXElQfDY4hDZ3rtgjMPmic5QAERBYLtO2jzNIHLUSlhoiokE8gExmEP4B6iPdNCWqQrkI9fQHJizx0hTbScWA1EdWkNb5tuN7q9B9InWGOvIMBFesSN0/E4tRM+xpeTyF8u0P3sXLqZ/CveRARmIZsP2hyQEK8rZupvjBdxthrf7vkzQFSmeL+RUqbVKaUbaREiXeTkun7YUiP8wD+i0IEbhWQs6FZhYEgOBUiM0w0n87g6xZ6hcBxbBYxmEFxubGzoJdD1dka30/cxjGVxzMx6Gd3vNCxzCsLMycaYlnVlXhtm47kFgazxX6tG9pWc/S48wjalXRl+iEyNsCaUq9jDWW1GbQM/rR7G0vH6zTnOsmaI8sfljR5dt+dnDtlJpY18qo5+vdhd+8t/NPfymm7t1Ez+XgezXUx0V/HsZXz0GWEppnXUj3uRPLZfjz+ehTXhDf1Ly9R4nDGtC26cgNvvaKe2ceEshORCQtl0MaYNGKWa1dqqPsNrIkj6aSCDh/duQFmDOVl9+gh4rluyjyN79p1lPhgkZdpIhM8GEmFXJtE6FB+tIowUoDjL+7/fqNIC4+wKD/aycBmieqEsnngyOfIy+LhS86fJ9iRI3CsH1uC2p8HSyJ8OmZ/Hu20EOYzg2CBtsyPPWiiqTqjF0/TZNk8YRD1kyE8OUGPx6TCP0i9FUAYFu7H+6k/IoB0qSi707A6hz1/bOyVEu8ulsjy+93/hqPWyZLpq8jZGZ7q+zHL01cwzTuB7S9/a7hubPet1E+7mp/0PMYVZ52J+bALOweOSqiYI5G3JFHWpqhZ7EMe40V0ZJGKQF3gJLFT4qgQRI7SMN0Wzj/3w95icxThU1GaVOSAghKZgHFLEnWpD7HKi3wqjXApqCcGsLZmsB0CozHLQG47Qqj0tK5h/NRTi463c3AtN/q+yaVf+gGunErW0c629Vdz1rG/4L83J1jS8C1qHBkmBqLU7l+HiHUh8x0QnD2S1WCItDtFKtNGmavp777nDi2Ayxkim4sVlbtdZX/3sUuUKPHmlIT6YYhfwkPqDYz/+DzKlFqmJOdjPJ/E3gKQwV+pEzo1zJ2Rl/jRrjs4IjqZb33ifHTTw+uyh8/teY5GX4jV7a9z7ax5dKSyOISkyeekWjiQxtAA7RBWfkKoGFYeNJjsbWBPuJum2R7sjUMmtR4F7ZQQcYcFNuiqQiyXR/c1kR5YM+Z4iWzvUM7yUaZlB3mWBTY8yrGbnuL4T/yCeHDysBi38KN556F5C/XskhtgiQ8oHbk+wroPXTn0J9ewMqTzfXgcZdjNOewytci2V/pFwfR90IZAYQUvpPnoHB35XQ+VVtRLFJF2DaLl41S5g9jHuBCmRKzvJ/6xDBqHv3WSoQo8FQqee3vxjndD1kK8asLpPixrlL+xhE73NgInNRF6NoEWs0kdpRKr7yBs1CISAvlyBm2hD1Sw1iWxGx1IqzhVYsI1jn9btwXJiDlvg8/N9xeVEavsxSc1eHQAQcES2vy4i4zajSIr3qM7UgJAYpLKx2jP9rA/vnm4XJEmXVt/S3JUoM3YwDYCLasJBE5jd9UAS86tRmm1UAfyaIaGPc+L3WtArYK6PYn1fGGxIVCpEzomgBWVPGfuocwZob5Jwz9KqNtNOj3+PYTWlSFfHpnyyfUmeWbmQ7iv8JG1EnSa+zn9rCvZu+ln9L2wllD5DCJV8/GFmjDVKmbVnMGm9vuG9w/6aslGTV6794zhMrXvOT5ZIVGCCwm4Kmje8Ut2JPcy+4xLcaoqtZf6sH4dH3ZTzKyweDZ7JzOVJZQr4b87yYMiohyz+N94Ys3XsKwcQigsnvOPOLW6v+/AJUqUeEsOK6F+2WWX0dfXh6YVmvXv//7vHDhwgF/+8pcYhsEVV1zBJZdcAsCaNWv47ne/Sy6XY9WqVXzhC194P5v+juKzXcxzNDLoTLLdWk9j3Wwcx/ixXkiABUqFBhHJZN3ke3MvwSugPfMSWf8cnu04wPnj5tDk0VDsBLaxn7K+Z2jvepGyxf+NlL7h81R4G5lRdRRbOp8fLlvccCpOtaCMy2SYgcoYmbNceJd5ISsRZRrJCMNiulx1cc64SXxrRyefnXAsL7U9UnQtS5suwLaKTQONSBNq3XRk69bhMmXSkdi6p7RiXuJDSXO66y3N3uOZA/iclShCxWrNYtccHJxHICMqapeJFShMtIV1H3vSIwNStxYgkS8FlCsxgke62TNhK1FPGcFdQfIRg45VLfj8QQIfAKGetxT0yjTacT4YlAhVxS5zkA2nAH9RXbfuYHf3j/GedBJCcZCPbcObcBKNTkWZn4enUphPDQXjUkC5wkMyV6xe+nLWmABlB5IZsiYMOHZjXWwxsXsW6oDKwPh+XuAWVqifekciapf469Hxc/S4C3lg28+HywQKdYFpGE1+2rpeLqrvKFvM3dv3sryykX5XhopHEsisxABEgwNxoYf9zt3U6k2oTgE5ifBCbqLFJ/ffxxdmLKdR5tg+x6ay1ku4WWJVq+iTJas3fI3JU05h0vgT8BwIYI9T2Rp6kTXtd5HMxwDw6AGMSZcybvaF1OROIhnbB7Zk6uKvsz4t6RIncNy0eSTSu4l4J7A3U0UqM5IPvWrciZRXHkGy/RnsgaeorltOi9HPwOA+nhnch9sV4ZRjViC/ZJNo6yXrzvJs5g6a01s5KXAFiv33u2dIKQl45nH6it+TznThdIZxajVIeVjJiBIlPnQcNr8wKSV79+7l6aefHhbqXV1dfOELX+Cuu+7C4XDwsY99jCOOOIK6ujq+/vWvc/PNN1NdXc0111zDM888w/Lly9/nq3hnkI4KxlFJa/dWArVHc/XebXz/yBmUTy8HS9LntxB+hRq5HJ8ENZfG9I7DdHnIV6nkc62Y6X2UeWtJ5DoJlC9krv86jG0Kml9AjcVetZUnWzeQds/njIXnERt4Ba/Dj1cLEFCDKDKFtNPQ/DTb2l6isuE4yiYdh2X7sKyCf6A/lUBp7eKkSbWEHE4e7Y1xyfzv8OTumzCsHCsmXk6TfwEHj3xSSpDQaV+DHc8gO3chqv4/e2cdZcd15evvnKq6TM0kZgZbki2zDLIMMseYOIlDTjLhZGYyM3l5GXgOTWbCNMkkcRLHEDMzyyDJli2LLFZLjbf7MhSc8/647W61Wo5Rtmzdby2tpS6uuqfqnH323r89BcbOIxtofnceeJUqB5jthQ4a/IlXXd9f2EbY3wQlDTmFjo78NKu4RPZ4MLnyd8IXpaecrtSFFoKgFSddqnrUqwzh6TB1kRqK412cxhLKdInG64i6/veMQk0pFMA3I4WRD4BUeBGHshg56RUKTWH25L9Hd4RRtsBonE2pvlip9lEnCX0ljl7ngasRMy1KjXpE39Tg8zOtJsonxo0nqk12qDzXbGsnZAYIRyfzktrA44FHybW6TAhFmZM4HeGOzEeucmBRSjO7YSmWYfHwlr8Q89dy4phzMPt3UVczkaVHf4/HV38PKQ2mTP8kq0oNfO/IwxhrhUk1Oqy9QjJqu4VR0hSmGQSiu3i570mudf+dEz9yKQnZSP2YiezWXfy/w46l3qgHT3G4fxOl+hAqEManPAIiyLmLr6FEO0HbRE0w6DQc7u1P0zTmwyywNI9t+DnnzvoyDbf8DL1tNcw6iab5y3CaZlHSQer9ee7YleNuESThP4x02SHhL3FWayOTj/wawXAT4VAbhcx2mnzNhOPjkcZ4jj7s38kXd+Apm0hoDFCHVVsmbAYo2DuZkpjHspqPEJET31AJNYGknDcpZCEYFvgjLohKSL3WYIhmoqHmwb+rVKlyYDloDPWtW7cihOATn/gEyWSSCy+8kHA4zJFHHkkikQDg1FNP5e6772bRokWMHTuW0aMr+dPLly/n7rvvft8Y6iUjSrh5AVNC09E6yjenh7g7k8Qfk6zsTPK1ljl4WUlj2YE/90LSxVdnYlxuEW304QuaPLJnNbe99BMmNyzgMvNH7PmrwyuxT/5Rkj0n9XPN9ocBuGXXCr5/2EdpUkWi2iC54z4Mw0co2kbHtnsRZpBcopanNv8C0/Axu2UJ4+0xOM8+Q3acwNfbzon+GN6oUbhelEtn/SN+K4alX73GZio8Fv9hF+Nzs3iYFK0EpqwEyLve6//6l6WLhYFU+4njr1LlIGFLfjfjQq/uwewrbCbqb0X2eBVhIjGyPeuYRG52wAMMCEgfpjDIegViZpiQFWdXcecBvIsq7zmESdyeir5DUtwKMgCx0wVyYp73jFZ5oY+NbGVj+mkCVpTp1kLGGkHy1vCw9WDOT8cfLdwUgAbhp+3DYcpNNpoyXXoVotVFYOAom5heBESGHaMpGOLfGmdTuk7iJmHa5CDfX1aPT2t2qTyrutMcLedjOT726C5eNLo5uXYi2quWp3qnsXScJZMuZYyoIdO9hpjRQDb5POtXXEW0dgqnLvw3HDmDkrA5N5ZD514GoggBDePr2NaiCJgGZZ0h5KXIZgucU/PPSNeiHEuxMfME9738O4p2liPGLuewhqWMYibOekit0aiygRHxqD0ujAxNQa9PwWN5moOSD597PN+Qf+EBL8u3j/49fStbeXHqyQTP6+Lp3l2sTfVypG8bC2paaLYSfGzaWH67YQcX145iUTlKi/SxsX03VyVf4uwWGPfcr8j2bRq4c8G8Jd8lEF9IODATUyuCmSxKKXruD1HYEsTnr2HeGYdDnYvSrz/mXQhJ1zaT5x8dChGZepjBuFmg3ztfjCpV3lccNIZ6JpNh8eLF/N//+38plUpcfvnlnHbaaTQ0DNV/bGxs5IUXXqC7u3vE8q6urnfjsg8IQggoNMI2F1HWTAgKwpPr2OWVmDxuDD2hPBN1AP6UxFwUBkOAp9HXpBhzZZA/bPstW5LPUR8exbmtX6f39/vUBG1XHOPO49/mf7SSZ5e3WdA1Ff9OhY4qsnUh+qwtGFYThy34TzJWmu+uuBw1YOiv2H4znzriB+j5cVY4BR7sep6psTGcEqmjkN5I+7pf49g5Tj76+5jh6ewqpSl5Dm3BOLUEB433Mj7KZh0+zyW6fQc8tQbqaxBHH0Y+HP6bBntWlLl140vcvH0to8Jxrph0BOPNmjc8wysE+N0shlfC9tfgVMO4qhwAthU6WFw781XX9xe20ho/vFKWLfEqg35DoIMCkfLQdZVtEr4IPeUUMTNM2EqQLffsf98qhySGdsk84Ke4tfLtViXouVHT+vEwXt3BP/D2Cc0O0cFMdxFz9BEIBFkvxy7fbmqZOGxbZ5fATYGvSWAEBMV2RfI+j/rLLZz0Hpo2LcS7MwNKI0+KUgz2oWLDDXWRlhSuEUSnCvyzILdDULxVY10CuuDngs2nEZMBrDC07mij//guMvVpol5VUO6dJuDaZPbsoaQNVieO4eZNm5gUaeWSU36F2HE/u2SQh/vuYlVyPfNqp3JS3VR67/8i4xZ9mowpGBubwjPpl2n2thEwRnHES5/B3lQR8PSHIHjeBjKlXgJmhKOj51J/Vy1qRSexWoPosjpS3RJ/BKRf07/Cpe24KCJuQUnRcGOWT3/4NP6u6+fsLCUpjctwRGw8pbTBWdZElgcm80y2h2tz67ls9CwulEnOWrCA6P8WYFcBKDBThvnDp74M6RKF+tHsCd2NCI9CKZsta37N7ONmIFSA0Oq16Jd30Rc+g8KWygBIlaH7Rpe6j5fZGVxFY3gccdmC/RpOEK9s8sITw/M4Nq72aJtoYlbLEFap8q5w0Fgl8+fPZ/78+QCEQiEuuOACrrrqKq688sph2wkh0PuxxsR+PFB/i7q6yGtv9C5R2JPDetnFuS0FtoaQpPXSOhqfAdFsIOYHEBI4M4FzbRJjTghRZ2ItTyDSHrtSG7hyzA9oXT8G/8RWssWRRXO1C891v0zI9PPZvlNxr04OptmFW5uIXDoB7+oMYmuZmikx/vH0P/O9zR/GUWU87bIzs5nHC4pVvZuYFh7NM70beKz7Bf5t/scYN/crrH/qn3js2X8lPOuf+e3W9fxj42gC+T5C0ToCY2Yja9tw3CKFYi9mRxZWb8RefASbwjn+d9dfmB0fx9IxixgfHemF9LTiT2tX86ctqwHoLGZ5oa+D/z3+EqbEG0Zs/2poz0VtehLn5m9DuovQtGMxT/8CsnH8G/q9MnaBzZndpJ0co8KNTIy2IMV7JK50Hw7ke9HQEH3tjd5h3olrKrgl+p0skxpbMfbTLvLlJBqXungTbjIFo/wEg/tvP6pGYeYFxtiKN7E+EycrCyQSIaxQM/mdfQfsng7V3w9e/3txsD2j3O4iufUKMw7xww3sHk32RYXqMWiYduBDtt/q80jlepmRnIfz5z6cXZV+LHR0hLEnTCIwcfhvklxbYtRpAnNzHrIu6vgw6aSFYbv4Olvwbu6FkAQpUPdkCdU2IZcEsMyh6go9HQVGnawRDyWh0yE4M4QzI4aTh5Z8E/FGAx7ogX6P4III8Z4mSqMLb+g+D7Y28mZ5N8dQWmncZzbh0sik3XWMaRKcPWY2W/1JXszvYP6U5fxw/fW8lNoOwKZMO8/3beIfjvw69c44/C/EMPtznH7kZFR8KoW0oq996JvrFSD87HS+cOLvaGlvwrjRQ9QKrAtqcW7rh2u7abi4DueaJOLICKOm+3B/2VfRKogZWOfW0pxXfLp1OUc5MzD3eIhf5sAF85gQOuWxZF2MIz86hs1OkV3xMUzc5sKuvZwqCoxbcshRPiLPtDD1jC+wIh0ilYIpc89C45Eo5bHvexJ12jJy9xsgIThWIISgsF2R7cjw+/5/wG+G+Mykf2Os24acXKkKsr922LMlj9qPPa6V8Y622/fLO1KlytvBQWOor1y5EsdxWLx4MVDJWW9ra6O3d0gcqbu7m8bGRpqamva7/I2QTOYOWuGyWL/EubmfwUijgsL+SxJzcRT33jRiYwnzrBpU2MT4u1bUH7ugz0MogTU7wDdmX4vvv1zIKzgWojMNiu0KMywodSgSswThXIkvP7EE6k3EVCAqIVvxuhgzgri/ziLSAyFTG2wS3X7OvfQLXLf9uwAk4rNZkM3xmdQSEiss0lNcVk1up6eUQZlhGhsPo7t7FZad4btNo5lww/8FrzIVYNeOwb70R9i70nj5MqHmZnTkGKyHPaY7fj675HS+2P5r7upYyX/P/zvCaviAMidtrt+2ZtgyW3ls7O+mxh4eDvm3iOZ2wB++AgOhYWrDYzhaUzzzmzhYr7qfEIJQTiD7FF5Yc5vzND/YegMApjD43uFXMsM3/l3L33orndyBei8aGqL09GRfe8N3kHfqmtZmttLsryWbLu13/e7UWiK+Vgq9ZSxVmZgrFp39biuCGtlhUxxTGVSGCLAj1UkqOBmtDYpOjo6uJKZ8e0tvvV9+vzf7brye9+JgfEYRYTH2UonP8VCdBUSrQfMiHwWbA36tb+Z5GAFACLxi5VmHrCDe4zn0rqHJZu+JHNa0AP21OVy3sp2UEG0wcH/ShbYry8S6IrUX1+FGJXJDCeuCWnTKBQWizsTbUEQdE6Snf+gawz4J13Yjx/sRR/pRm8tYxX6YFSdc9qGv7RzKa38qS0CCnB1+3fd5MLaRA/lOHCjCro3la8ayJUpqgiWDEBazrw8z6+TD0Wi+GjyVraEkP+29hz47y6bMLmrHfYLQL4oY0yUiJHH/1Id1SpxIThOapvEa/HQ+BW4adF7TtrYFfUt2UDDdC0vMZXHcu9LgFxgLw8hWH851e9Upz3i4D6QZdUULl/7OwjoKnBszg6vd+zKY59fAugLB36UoflySS3Qhi20j7lP3uYiZwcr/78ww6YMhHmvXrHmiHh3uQ4RzhIBcCwRnaOpbQT6fBgXq5BjpOgv6oewWuLv7Wi7fcCyW6+E/bPqIdhhQLv4tWwnHppPPDP2ulg9Mv0dPT/Ft+/3+FgfLO1KdLKhysHDQuP2y2Szf/e53KZfL5HI5brrpJr73ve+xYsUK+vr6KBaL3HvvvRx33HHMnTuXbdu2sWPHDjzP4/bbb+e44457t2/hbUPlPEakA+UU+CtRA3qHDUkX73fduCkP77JmSsfWUWgMk88YmE60YqQDpBxqFhnEZhuYCUHjmRY1kxTu1b2VbTaXca7twzp3SJxHtJgj6nHqPo9F2VO5YsxVGMKkVY5iyb3jiN+t0TtsYvcpjrt1NBNo5L7dq5gz5cOcPuXvmBNqZvzKWweNdABx+IeQ13qEfh0i+ucajB/bmBODUFaY44NM7Krjy7XnsqvQza7SyJQGA0HU8o9YHniV0levhujbOWikD97nxsfxlfuHbyeKlJwt2O4OhLSJdgDf7kb9pAfxnV5Oem4SJyXmAeBqj2+v/TMFuX+jrMqhx8bcTkYFX30iMZl/mWigBdnnoWNyf1UTh4hIZNobNBYSZoTuAWVhISQhM07WTr5t117lvY2wNFbOwf5FN+7NKZxrkth/7iUUOrhqXSqjjOjvhxU74ckdiFQ/yrIxi6DWjzQQ1G4Hn2+vkp8CVEepEoG2F/rBNMr2MGaGcG7tx70/g/tgBufGPozDIggxfHsj42Jc2YR7ch2lyTH0JY2Yh4cxSg5GyhkhPqdX5vBX9VHecQzhh6TG/lUP7m0pnD8nsf/Shzq5FnZJfC/BtN1tnHpPgJ/UfIioVZnsD3ZJsEHUGLgPZbCWxHFu6Efd1g/3pzCu62LMaYLoXEnryRJ9d274ifMKEibmp5pw782gdtiD47K90d0ueo+LMARqe3nEevV8HuvyOBQVEzD45vP/w45Y74iSucb8MN5LA+1fQywwNI7q3REiFSiAgJ2ll6ifZSOu6UavL6E3lhB/6abOCXHJ2I8xOjaVPYVt2A0h9GOr0E5lIjhkJ4l2riLW9Rz+ch++hx7l6Fk91DVWGnq8Bo4/VSOtkVGZVapUeWc4oB71u+++m/Xr13PllVfywAMPcOaZZ77qtkuWLGHNmjWcc845KKW49NJLOfzww/nSl77E5ZdfjuM4XHDBBcyZMweAb3/723zuc5+jXC5z/PHHs2zZsgN5K+8obi0YkuF1L4MS3KFRgghKSHlYKZv+DknfYxVPgb9F0HBaCJrSyISJ0oL2a2zUwLc+95JH0ykmsS80oXbbYAhkvYUuuGABDoiG/eTICjBqLKZeO53PfewXJFIBjC35YZsY7R7NmTg1vgjRTS8Qf+A3NNW0YBz/Edwdz4Nb+djr0BHIl/bqvByNc0ca2Wzh3pNGjPcxd+y4yrb7GQOFtI/PTj+Gf33+3sFlo0MJJoTqRgyk/ibB+Mhl0XqUOeSV93Q3K1b/B129zwNw4ux/J/SnWcMGhLGHNBd+/GgeoLJNV6mfgioR5PV796u8f3kpu53RgVc31Pvymxhdcwxip4cO/+25U+2j8l0oawgI4laYXjs9uD5kJciWe6kJHPylt6oceMySqERn7c1uB93jQuJduaT9Eugt4fvpOnAHOj1LYn9mFl6tH2NSAO+Z4X2NbLJQe/WPSoFUcmSpaCHwCQu1rTjciPfAez4PhyeGb1/rJ/m0Jv3cgEEkoPWCEAGfA4H9dC41JlpUc3ffaWTJwLl9n1KUu2zodOjaYtF2tIH32xTmmWMYc/Nmrjh/CTP9E4j3WXBmAmEJjOOjqF4XnOHtQq3IEp4bR2SdEYYzgGcL+tZL4mc3IG/pgeLISS9RZ1JQFr75EUR+ZPsQCYn3/Hasy8ZhhEocXTOjYtR/NIEvLfAeziAnBRBNFkbEgNngrSvgNeeBGACBqIvtr2fF587Db2i4a6RzQD+a4/AxaWYYM0jOOJrdmWl0jQlRswemiz2IP34RMt1oQNeOxjjt83RaO9AL9jDOqKUeQcgKUNKvXlq0SpUqB5YDZqj/6le/4oknnqCzs5OPfOQj/OQnP2HHjh189rOffdV9vvjFL/LFL35x2LLly5ezfPnyEdsuXryYW2+99e2+7IOCbLid2EU1yOuKFc+6JbDOTODcUxmQi7E+tNAwJYCb8CERhMZLClsU5Q5NeqVH3YcbMXIOpaJE7dOR9K7wCE4to+9LVRaEJb5PNiDnBNEZBXYe4+gA3hNDH37j+BBEXKwT4sTTGh0aOUsMlcs9J7GYmNcDsQZ0fwfe43/GvOCb6O5tgMYrmMDw/XWng5g1EOK1zaZGNzAm3EhrsH6wRq1n5Mk7SQJmhIXR0fz86PNZ3bubmBWiLVRL0ZPEjf1rGOwPu3Y8vunHo9c/UlkgBGL531MyYxXRYAHb2+8aNNIBVM6GnpFFcyP5IQ//3JqJxGVkZFRElUMOrTUbsjs4umbWfte7XolcuYuIvwnR56DGvXrKRQWBGvCqq4BJ1AyRcfK42sMUBiErTq5aS73KAFoLdMGDI6OoZj9SaXgqPRAefnB4gmOxANyzFeUqCFuVydaCQ+D5FCytxTssBlvL0Dvw3Z0dwq3xofZKphUCZItV8W6Wh77/5tIY2pPo3H4iCHIKuU9X4eQl6ef2SjvR0HWHw9grTNwo0GbB7oH1Aji5BqVMMPafqlLlAKEYihrcC+Fpyh0aR5hIF1ASMmU+EDkW/fM0brYSom6dWwNSQGY/nXTRI71WYS72YR6fQN+z10RXVOJYJmZYUOwVBC5swsiWME+N496brrTdgECdVkfXA4r6hX5i9faw1EICAjkZ3Fs60a2t1N0O3xv7Qbzb06BTuGN9cEUTjtRYSRfviT7IKuQpYfI6BcTwBSA02eaeLb3UyiDrnSTTjYnsG2fo+QW3j1eM0bVMtBXf2Rgnb0Nj1OFb5l34MnuV8+zbheduZWe0jqJnc0v3ozzbt5X/OvwzTKcGU3kEM1nIF9CJKIVIBLU/T0qVKlXeVg6YoX7HHXdw/fXXc+GFF1JTU8N1113HRRdd9DcN9SoVym4BlY/hOyOBiBgQM1CdNnK0D9lqQUTiuZL8YXXQJ0BDeKqBr1GSWuFS2OxRe7wf4Sp0/34GKGqf+pd5hVpfovcsP46nGbUrjy73Yp3fjLYlwq/wtu8BbzzuXWnin6rnhuQPOH3eZVjP73XYGX7iJYvIr0NoPRbj+J+gc39Er78N3b0V78HfgC+IuuDcEZdkzAqiXh6aGJBIvjjjAhJeDI0mo7fz55XfpDu3nZAvzoVz/olodBF3bd1GspzDVp34DckPjzqMtteZn1syonDqV/EtPA8KaXTtaPLRsYPPRgibnXseG7ZPZ/kFxoydDzuGd/CZRBmKMC0+mq9MvwjTO2jkH6q8i7SXetBAnW8/0RtUyrJF/M0YykIWbLzwaw98dFgiUh40mRhCEjND9NlZGv0JAma0GvpeZRDXJ9FXtND1iKa8SSNMaDiqgXCjAA4O49IwBK6j0Z8+HrtkIQRYfhv93FbwJLsfUjSe24QwQBiCYocHOwWBcUPHUAq8gAEfakJsyEPGQ88I47X6kKbGmBvCe3J4GLNxVASth0eweIWRk7xeAVRZ4OTBnldH6EgP4SrcgEXfBmiecdBkEB4ylBMaa1EY76m9Ii1McH0moNHFSsQR2oGptcj1Dt6AoSwn+FHby3jPF7AuqMVbNTxaQ8+OUnpSU+oBJ+8ncV49YlMeXefDHRWEOovMozbuQB53dLpBwxEhGBfC61foRotyL9QeC8Vul3B3EfP4GCJqoPEQ5SzufS9BzA/9GmNiEOemgcmAOWFyo2Ikf1dJfwyMM6m7qAVZcBHPpKivreOwpeBP9CCTNZz+aC1eLxwxazTeAg+eKg05CAR0H97N49tvBGDpmAtZNHoRD20xqA9p/DteGBGAqNvXUih1kezfyBljTmVs23z+sO0+vjP9Y4RWrkXft6JyaCmJXHYm2dGjXrdjpEqVKm+OA2ZNmKaJzzdkMMViMUyzary8HhqK45C3ZnCxQYB1Vg2kPPAJtKORQYkd8lFc75F/eaA2equkbolJ5nnwt0mclMLqc/DZEumXqL0c2HXHW4g7eod9pHWfy596t/ORwGREbRC9sRv3pc7B9cYxo9G2Rqc8KGhe7HuYmgWNzJlyDOFtIUrjytSOasT5r6EZWvcOiXXRh/E6V0FhQEzFLuLK9eiLxmHdKqCokVMDyPH+wc5KTguggjDBakNrjSfz/HlVxUgHKNhpblv3Q+pbv0dHcci4L3uK23fu4TMTJ+B5ry8Hs2REKTXMfZW1ftqajqAv9fLgkk2dt3P4hR/D+L2Abhd8AuPCWlrHRfn9uK9TY8Twea/lFa1yqPB8+mUmhdtetSpFb24j0UBbpeRaWOy3fvq+6LBApobad9yK0GOnaPQnCJoxMuXuv7F3lUMJgSb5HJQ7K1977UL3o4q2cT4OFkMdwDtmJr33uRS2DuTOTjJoOHka0q9oONGi90EHu2fAMJplEFsgh4W+CwHlPsHuGzVWXRgjICjdr4hMUTSfLVFJF+sDtXjP5tAemAvC6L6RkVG+GlFR7tnr2P5WgQwrfEXoXqfpTUsqG2lalkpMxzuIZHkPDcqqn8DxNRCUeM/kEXUm6rgauh/X+JrALNmY54RxnlqPd9kEfA8OTazLqYFB77e3Mo/14XrchzMVB8YRMXq3GQg0vhpB772azEYDf2sCd5fGVxb4C96gkQ6QXa+JzjQop8EImPT+1UaVQZjQeIaFnBRGoqFG4P7kqYGLEFiXzMO+poy5ZMgP7k6PkLxvqPGVtiuyCUG5R1B7TAOm43JD4WU+GRlH3x+NQaPce16StQTlv4tRvx5wFPYMzW6xEp8RwPZKPNh+MydPvBhoYHs/9E06lZodw0V5C2Onkup4GoDdO+9hwdwvsDGfwioWBo10AJRC//Vegp++hIKvmuJXpcqB5IBNBbe0tPDwww8jhMC2bX7+85/T1jZS1bLKSHzpvQw9Dc4t/ahNJcwlUcTxUezmIHaKQSMdoLxHUdimiMw2ic40Ke7Q6IJC3JVk1AkQnysIjRc0nyyIjlEVg3sv5NwQ9+7aQR8OqmxgfXA2cnItoiaAccoEaI4g0gPNJenx91P+yB2dv+Q/y5/gJ5O+xs3xX+I9PFKp01vjwzz1c3hr7h66v7/+K7k5fey+Yj39f9eLcXoMXVYY80KYp8cxTozhBSCgKx1Y3k0OGumvELDCdBZGigx1FIqVEiWuQ7hcxBJvfrZXKc3EMWdSEx+q19vadASFeg/vC/WIv2+Ef2wkN0fiJ0ATdVUjvcownki+yOTwqFdd35NbRzw4GplSr5mfPkhQIrJD737MDJMcyFMPWTEy1VrqVV7BG95PDC7OHjxeMK0hv1lR2Dp0nYXNivxWwJHkN3uDRjpAdq2Hl68ovb+ClGD3VbZxkprSbgUKiu0K5Uj0tnJlIjhuIhtMnDtTqLVF9D5Z7Vq7tJ1nYsYqE2aBUYLmZSau62EVHdqO17ReYtF8rsWYD5r4d2ZR/oPnWR4qBHIW9n91IWZYmH/XgHt6HT0bJPHpHi0z+/G1OmjlYV86nRdFN3LcUGPR/R6isdJPK0tSLkmc5Y2Uz2ik/VkDOw1tZ5v4HYfoNIH2oLRLgQeJhRbZ1SPD5e2kJjJO0PuAPegU0S503ebglos4f30avbMf87R5mB+Yi7l0Hl4mAHmFsEQljSIoKOdHTtTmtyiCbZLcBo/uZzTLfG2UuuWI1LrSSgFRPyIhUGtLGD/Nc/gfj+DKth8ghYlAkBvQhCvY8EzgaFh0AUgDDBP3iLPZoNvxvCGvjp3ZxkcmLsOX3EdUD6BQQpb2nwJZpUqVt48DNg/8jW98g7//+79n48aNzJs3j7lz5/L973//QJ3ufYUIaQhWvM2DuBpvZQFvRQ7rH1tIdY4cHBS3K2qPNcht8HD6NeJYPxQU4poualosiBrIpIWeFsE6rwbn/gzCAPP0BP2jXPIdDg8WtjDZPwfd4yCObEOGLSgrcP2oDoVxRBhvdZ5IrFK6wvZKdGa3curkjyE2j2xOos5CbXgMinsZ8Q3jiVij8VockpmXcXFomTyrIhBkuaiEQyFsDgrDBYwIIV+cwl6iWR2ZrZw1uY7724cbJWeNbSO8vR198wOQzRGcMw3fiUeQD7y5msGGaOHEI39IodSOECbBwChQQUqmh6xLU85tQ6UVwehYlKh7U+eo8v4k5WTZmN/F+a3H73e94xXIlvYQ87chUi468vry/XQARFlXRJAsQdQM0VNOARC04uzKrH27bqHKex0B/mZBuWN4fyEOIieYEFDcNnIyobhDEVtgUNgycp3drQhMG77M1yTZ13oJT5SooIc5J4j3bB61pjC4Ti4KV7Re9sLJCdLPebScaSAAZWvab3Bo/ZDAa7ZIr9CkVle8/kYERl1cg+eW4e2thljlNbCUD9fJQXsO794XMCM+mifXISM14IBXEggvQGgzHO5vQrQZWJclcO/O4W0qYp1fi7OyQLo2Ruo+BTgYYWg+309ug0ffSo+WhVCbz5A4MYJSYPoUyvQITZRknh/ezvx1Am3rQdHeQRS4rolVcsExcO6xMY+N4q0tIMdV2pG7Iod1Tg3OXSms4Mh7DTQL7KRCGAJpChp7A1hNI7czglBT0LjXDY2TdMqj5a5mFpywFF+slqZAguaoYGaLYvaUKOUZn8FaeAFCCDYlH+bFtT8bdszW+lk0+ceg4yWEFLB3Ob7aOG74zY2rqlSp8vo5YB71pqYmfvazn7Fq1SqeeeYZfvKTn1Q96q+XgId1XgjRXDF8xVgf5slxvBU5cDT6/jTBcSN/uvBkSX6LR2CUpLRT0d8uMT9UDxFZEWtLGJTaIji2hdexDeuKGOblUbwtm4kWHH59zAlcvur3eFuvRjRZCEdBTxFhe2izhJxmgAtqQwld1iwYfQbja+dw5ozPUhNqQU70QWiv6woImOPDnTAHrIGRYcM4OP1r5J2d5HfuZHT3NOKdQXaVH2Vd3c301nRTzobxdQQIlQNIBH7quGjOPyPFkBr9ic3nM2fdNr4xbzotoSCNwQBfnTuNBYaBvvoWyORAg16zAf3cOjpUgWdzPWyy05TeqEqvjhLyTyfomwyq0pMK1cWLj3yR1Q98kecf+jKr778S4e1+Y8et8r4hayfZmHycsjtkCNze+SSzoxPwyf1HWfTkNhALjEJKE5FS6PB+qi3sDyFQIYEY8IrGrDC9AyXaKuXZqmJyVSp4KBJHWsi9VKYiMw2MkdUt3zWU0oQmjOzPQuMFtukQmjhynb9ZYuzzulgJTe3RxqBGnr9FUHOkiYsLtsZcGquIzZkC44RopX/YR1DPqoHiHs2uP7vs/LNL+w0eobECfA7llCS1emjSwMtBzwMOtlUV1HqncSIScUQIcgYiEUA0R5GJVtxbNe6dGpE3cW7sx72uH+9qG+f3ORhnYiwuY34gzJ4XTZwjakg9t9fvmYfkgw5eXlPugkzKQEwIIu/qxbypG2N7AV9QUXuUxN828JsLqF1sYO3KY9gecl9DW4JZ60NMrkcXLKxzalBRDy4NIo7xY55fg7EojPYJjOVx3HE2wWlD7ckIQXi6SWGLIjRJUtylMKWgZNgExg9vdw0nu4jekR5uuU1xfOuHCdUs4pnynUyYcTdjRj3Nreu+xDM9N9Mf9JPzN9HaciyxyOjB/Ua1HE1rzSIsbVGIRBGXngmhgXFcbRxx8emUjGoEYZUqB5oD5lG/8847+e///m/uvfdetm3bxqWXXsp//Md/cOKJJx6oU75vcKxOLF8U4wwToRKoNQWca5OVfPXPNFZ+tSKEpkoKGwdy1NskZkwQm2Ni9wmC4yXZ9YrwOUH4WAvC0xTLEg2Ut7pEXXB/OZRzZDSEqD82itW1BfxxdKaAer4TtSEJlsQ4cTyMt/DWFSAoEA0mCxNnAIqOzBbW9zxJoq6N6NIYQleU14VP0J/oY6e/hsAHf06DdAnHWzDsAuGsoOaWeshU6qS3Bv04Hz+PjqsFmVJlpjk8zaB2SYByosSY8EK+uuB/SO5ZR1RHaHguhe/lZzhpWxeHX3gaLoKAFkQ2bx0ulBcO8tSYWv75ifsHc/JPbhvDlZPmEFBvbp5KCEh1PU0utW1wWbnQQ8fW22mdeiVKVUMhDyV25zZz9QtfIOyrR6kiH5nzY4pY3NzxOJ8ed/ar7teVWUMiNBbKGuFo9H68Ka9KqBL+rmslcTNM0q5oQISsGHm7/zV2rnKo4AZcQNJ8roW2NdInKHYozDfS1g4wQggiYx3kSQaW5yEEOIZBcJSD4Qgi0ySlXQq7eyBHfa6BEdB4+8y3yoxNrNkg/BEL7YJhKMSGHOIkidpYQm0tY54SAynwns0j2m3EccMfhCFsms8PkVnlUe5WhCcZRGcJLOmQze8nim2XJqb1wSKgf8iQt6D7OB+Tkxpz2nR02sD5YyU82zgijHtfZlipVt2jYLdC1vtwHlpP87kLKHSNPG5pt6JumkVsrkFhu8ILB4hc2YJpKbqe1OR+oWhYKmid4aCW+LGkRu8pIcoC1VWm5aQAe+5VaLuSo950gsTy2aiT4rzoyzLWdQndsAlxexGvLYZ51izsHNgBl4fUWq56+S/872n/hDffR4uKY+QNMmtcGpZZ5DZ4CBP80iTzv5rEEQbROQJtaIxQgRvcbSwo1zN1n3vyxpn0+lwe3rOedenNdJdS+KXF16acxq1rryI1oZcTx3wSvFZOOfqn5IvtSGkR9I8CXfGYKw3ZMaMJXnkJslzGDYeqRnqVKu8QB8xQ/8UvfsEf/vAHAMaPH8+NN97IZz7zmaqh/jowiv04fuh7aTxNUzy81RUvne/LzTh/SqKTDuLsZqQhqV9aKWdjJzVuv0JvSsNxfsLzAyAErquxewU4gmCrwMqVoLeMnDcJL+xDP70TAN1XpLa7BzlzCcy6FP1sX8VIB3AU3j1bkB+dh3FkBGNqkO7yUzyXFLQmGujI7qApcTS3lvs5Z0YMe0caywrQV9fNX3f9Ny1Nn+Xp3jKfmD6BGZ1rUHd8H/+cb6Mze83+TmwgeZ+BKg3NcOc3eIQmSfwBHyVfmcZ2QcP1u4Y/rJd3ECuXyVsBNBqCw11FySNm8b3dLw8Tzrt/907OHD2BSVbsTf0+UkoyyQ0jlqd7XmDUNA914AJVqhxk9JbT/GzNV3CMRnbqevxuOz987u9Yb87kpIbDqfcn9ruf1oqu7AvMbLkQ2eehIpI3MtrXAYHIeIBJxAyRdQs42sOSQRQetlfAZ1TDEg91gkUDaj28/+lG93sgILEsjlLRd/vSBhECLM8l9GQ/eqD0mTnawhoVxSVA990ukUkGsbmVXN7CNo/iDoVv9D4H2uWiH0jiOzpaERlbnUf3e/gWNyJmCuQYPzrlgqcxj4miLEYoVjtFP3v+aBMcKwmOluQ3eeTWwaiPWFixke9naILEMou4qmq0vJN0eQ5t3m4MEUZtKCBGJbA+Wgt9lbRBOdpXKWe7l5aHznu4D23C/PBRuL/uxjipfsRxg+MlZlzQdauNMATa0+jpkuhkgRSaxrN8qIyLbJQYnSWcG/oqwoMSrEvqMJ/tY/SCCCpqYtZJZNKGdIbOaJan+zNMvxNEsgS1Qcy5k3F+3I9wNH6/4PAPjedLU09nZefvGd9yPv+96SG+0zybxnic9PoQwWaIz4/Qc1flHUk9XRFDrL1M87388zzStYetNW18aekUIveVKhMVMQPvogS3dW6mMz2DExoOoylc4Kadd7O5mCMWqOeJbdcyq+lC6s0E6DjhwECFkn3mpbTWFPwB8B9EeTNVqhwCHDBDXSlFc3Pz4N8tLS0oNTLXrMpIlDma3Xc34GY0vqhBzaV1eDtKeC8W0F2Vj7S/t0DRF6b33srfwdGC2pMk1pQwyutBj45SzBXxFZrpvctm9HkS45YkjPYjYgbeU3nkuFa8ln7oyGLMrMH945V4ysOceQnu2pHK0aozh1zcQnvv3ax99ipii/+D329+kX+ZeR5bkpuYHXH5zgtX8eFF/8mWvnWUlaCh4XPUacUHzRW07NmOuvFHiFgDJMvD+4GmGOWVI9uHKgAFAT7QiZGDSzFhFK7PjxQCpTROQz3mxNGwpWLQl6JB+pMjw8Eytg1vcmzleYr61qPYs+XOYcubxy3FUwYjergq70u01vz3hh/hFx5LRp2LEJKknWFz100sjcY5rHb/tdMB+gtbMaSfoFWDSNsVxfc3cu6QQHZXXIqGEMTMEP0DJdpCVoKs3UddsGqoH+poaaBv7qkY6VBRur4rjTUxAIl39dKGoTY46KzCPKHyjXdXF1CbXRhTEdJ6xSh5hfg8C8vycAaE67UGmk2sE+M4D6ahoDAODyOOiOBoha/GQO0oI6MmWmt0ysWYEcTZ57XzsgLph1CLxl8HugTZjRptW/jxqF0k6XtWgQarFupmA14A3mg6VZW3RJtTILKqgPN0OwDG0gje42l0cuB38IlKHvo1A84GAaLZRC4ei3tbCt3nYb6Uoe7IBMlnKsKDVq2g7niL9t+VB0reQtNCCS/m8H6epXZiAG9iDdKzYbeHuyaPdXYN2tEIS+A+lsG8sB71bAZLmbjXpPGoqCbUL27h2EUJRHJT5XoXjse5JQ+vNOuypuFPisWfbeWFSIat2d18aPIcrK2rMO78EXUN49AzL2DHHctRpWGPAp1yWVeo1Id/sH83SxbV45tYxFc28DVE+O7W59mZryTPb8nkOKYpzjcbZmHEGrirtw7HK7GxGxJtJqYeWQmhSpUq7y4HzFCvra3lL3/5CxdcUBGquOmmm6ivHzmDWWUkxWLtYPmP5FOadK3JmMsS6KuHck8NS1M/2SM+WqLyHlbRxeiU6AgEohEC/UHs3+Upf0ARm2dgSQe5KIJ7fwaddJFTAoigxDxpKhSyeNv+DE3TMWZ8Du3GkU0RVKZv2HWJuJ9Hnr2IUr6LcHwc42pH80Tepd+DnHCxyklOnPQhAqVGOjNhrtmziyvGaayXvkWy2Mv4qR8Du4BO7sKYWx6ut7u1h/DUNnIv7qNGHxwQ1wNKtTUElx6Nvv9JUBo9ppmO86aytvNqXOUyo/FY6vwTCZy7FKu3D0pl6toaOExkWd07NPFgCEFbKPKWfqNw7RzGzfwgO9b9Ba092iadQaL52GpN0UOItdmtlPJrmRw/HCEqURR1vhiBxtNY1/lXnMaTsYz9xxjvTj1LXXgKACKlUIk3GIURksjcUHmtV5TfG/2JSp56uZe64KurzVc5NDBshb15P8rMfS6MPngif7SSmEdHcJ+ohC+bx0TRrka6FqGJFaHUV/rE0EQJkmEpTkKAsCTOLUNpH94zeYy4iedXkDVRG0p4eyrvjKgzEaN8wPBEd1+NZvwFArYU0RtcQuP81B3mR/od2O4SW5clclwMpQVGvw1XZ5Bfb4FA1VB/J4lkPLxnBsYnfgOcIDo5pA+CrfFeLCDnBFB9HtayODpgYIxpxbtjYCywrkg06xE+NoZq8mEXJGYhQ3yej+hoA2ttBu8XBUSLhXVRHc5t/ch7ejEmBaDJxJgTxrm5vzIvL8A8IwFpB+EzEBED0WiiuyuGr1hRpmlBDBpCFe0fLHD3eS+Lmga7keaaEpOcIo+m1hEPhZipNbp7G1j3Epl4CpmXKh5tMy5QJY0T6eb4mlGsSfaitObR/me5q6NSBu6TtVcMGukAc+MJZlo1+HyKnQ//PccvuoJOL8QTm+tY2FbN36hS5WDkgBnq3/rWt/jKV77Cv/7rvyKEYObMmVXV99fJvuI0vlqg5CFnBFBbKx93Y1YI5/o+xG4Hg0r0lQpLfJ9swNvZCzOjMDOEZbm4OYnhl9g39AyK4qpNJXA1xgdrKZY343v4ToxjfoW3phbpCIzFo1DtGSgOdDTjEuhGP6WNXQTCzYybfgGmm2dx4zjaiyn+34b7+J85n2fSljqsbfChWC2njqkl49vEtmJlgqEoHRL+CJRzqO03Yi67CPexDLgKMTVKfHoJt99PqV0hTKg5yqwoFgdKoMCRBurwOQSnjYeyQ2dNPz968lO4qjL4emjz1Xx68c+pt6ZRbBmK5vjCjPn8dP0anunppDEY4quzF9AkA2/J8W15CSa2fJJxYy6lZGYQRj1KV4vpHkrcuPsh4ipJY3TGsOVhfyOJ4Dg299zN9OZzR+yntEt76mlmtnwAqBjqtL6xtqN9VLwxryi/W0Ml2oJWnFxVUK4KgAA5vpIOJKcH0RkP79kc4vWWAnyHkPUmzh+Tg3+7d6awPlyHkpB5ziF2mIG/QaBVpdycygzPUdca2G2POK73TA7/SfXozhJ6z9DElk66qK1ljIXD5dotw8G5PoXurGzrPZvHPCmGXuIgaqPonTZiZ++geS+nB3D9VSP9nWeoKgwBE72fcoO618W7sBGnqJBr0gghcMsKMdGPfrkyjtK7bMSuXqxPNkHQwzIlDVMU3n1p1MAEl95h43T2YS6N496WQhweRiRMnL/2DF2DBveuFNbHGnDvy+AJKsb9tcnKNlGJ3xcjs/AIyLnUTDDA7BzyqAMEBP5wDbP2SApOOzMaQ/x36n7+9Zyv0vLQ1dC5jpoLupDhcURjCtlRRDRKClGT2Wo2loRZo21uT/5x8JBioKqBIQT/MXYGc56WBNbbMG4MTcd8n6S7hRU9p7CwzaO20E4h1ITSB9e3oUqVQ50DZlm8kpeeTqcxDINI5K15MA8lRDyLNSOGsw6Q0HIsiIJGTA8it9qodUVw9WA+3yB5hc4qjNEt7LpRERwdIx5UxCfZ6LwYUXdTbS2D7bFelpk3/0IIjAaVw3u6gDwjiHn6ZCg4YAjKddAV3MHk+Z+gXOxj8wu/Y9rx/49MschLqZcIGX4ad8QIGEG8dB61vUATIZpqZ7ANAWhe3n47tcs+jv+uX6G3PILX9zJ86BuYkUlQ9hPKavznGtjFSicswppyqMzeGRMekAtHkVHB6vbrB410AI3iiR3Xcd6Ub+J5Qx13nfbzzzMXkfFsAtIgpA3eiuM7ljJQf+pD77SRTSbRy1rJNmuqIe+HDjm3yObUaub7GvEZ4RHrR9ccxQu7/8j4uhMJWPFh6zozawhaNYR8dVDSCKXRbzjtb0D5PafRNYKoGaT3FUPdjJC1k6+xf5VDARVyMc+pwXskg3tXClFjYl1Uh64zGNEhvIt4q/Mjl60pYk7w03qqxM1p3D4PaULtLIlICPY2y4UAERtpYMhGEykNVIczYp3eZSO84d9s3asHjfRXcB/JYC1qRETAOCuBFAKtNHgaOdGPEB7FqprcO0o27hAaG4MdGURdBGNKEO+x4bXRxIIIySc9/C0GgSMTqJ/uqYS4f6wBt6cPnapoNshlcYwg6Ov7sDscrEvrBo30QcoD7cQnoM6qeMr3fX08BlMT0eC9VECO96O2llHL6+m42qMSWS7JbtWMuaQe95okuLoSqn9mDXqri7jWJkwjC+b7eHHhLD7S9ygfOPkCmswws1pdRif7kX8uv3IagnUGMz7h8cftklU7LC5ZcjSPdb0IwJ7SNqYmRjMrEOfw2zTGroG4+XU24V3NqE9EmRByWNxzM+KWXxK97D9JN857u36mKlWqvA287Yb6r3/9az7xiU/wb//2bwgxsvP6l3/5l7f7lO87kiJJy5IIaqofM6CRWbuiYhqRGMdGME+KVsqg7WesJYIS+8Y+ak+oY8+tLqUuQdvxJiS9SmmQHgdvILyQiCRDgb5gCDH/Q6gHihhTg6guB7UTxKhadL3Ny+J5On17yGcyNKV20bPtAaYv+iKrix5jo7U4jGJypI3GjhjO9X2VjgdwH8hg6Cgt40+mY9t92MU+ntl6NXMv/jrthTxpn8Wi2BTELolzZx+6y0HOChI8Nkq2JUsxVMTnNez3GQkhKLkjB3clJw8Ds8hCVITfPE9heFCDD9RbM6eDjkT9qhedHJgK73LxftZN6O8byYerhvqhwtP96xhj2NSGxu93fcCK0xidxbqOv3LYmCsGl2utebn7TppjcwGQ6TcuJDdIUFSU32skMTPMtkJH5dxmjEy5540fr8r7DuGaeCsyeKsqYcE66eL8oRfrS82vsec7TK0FDE++FfUmanMZUWdh9wssP2gtKOzWRMMKuY9dLuosRKs15Dk3BeaSGEo7yBlBvJWFYdvLeUFcc59vtgLRYGIsilReyYLCfTILSHTahX4X57FK/ylqDEStiVFvQqCqv/NO0iNdOGscE9s91E4fjmNgfqYJ9pQrVWdqTcoBi8KTHoWtLoVtgpYT4ugH0jh/6MU8OQYNFqLeQlgK+wfdiBYLOcGP7nMrBrk9vG0IKbDOr0E3mJVJoaCA4l7bBPfZp1yJdhJT/RRKJtodGqw5SejZbdFwYS1kKsvdh9IYRw3p8IjnbD648Eh0S5aUZ3NE4yya06348hqWB/GezlUmDJIeDbvgqqMN/nONx8at4/mX2Rfzl+2P8nJmC5+bdSQN3SGMXfuMl7KKWKGGZY99Avr3AKDu+D6Bj/ySkhg5+VylSpV3h7fdUI9GKx+ampqat/vQhwymk6DnOoHX69B6iYH7eBa1vYx1QS3OLysDcDHOwjwtgXt7anA/44gw7nM5rCMimHEFBjRM0zg/7B40nuUEP+bxUdxHs8hz4vxz5/+gpeC4mqmY5vAcPzHah7gkwi/W/QMAi8eezeFzPkjb+KWkAs0Ix+H3m2/m+f6XuXz8KWhbDZ7nFbzHc0z7+j9gBRrJpzbSOHYZ3f5JfGLzfxKzQtwnl2L/YSgETHc56LwidIskWApiHCsoTxAU9xEi9DzFgtGn8dSOW4YtP2bcB/BcsNxuSnueoti9ATnjYjpkM56WjApZhN6CO91I6yEj/RVKGtGvoNq3HTKs6HuJsEoRDy581W1G1RzJ8+2/ozu7bjA8fk96JbaX3ys/3UO/yTBkHZCIXEX5/ZUcdYCQGaOvtPtNHbPK+4ySwHsmN3yZBtXlQtPB4wVW08KwMgelgW9zUCITJs7VSawr6gnbCm9VAREy8M8N4toW+2rTqk0ljOlBxMIIWmmEIXDuSGF9qh4SHsbREbwnc6BBHhZCtvkw1D6z3Q0WxuII7l1pcDQiYeD7YD2eXyBd8B4bepa638N7Oo+cVgNUDfV3EltH6I52MDExBe/WXuScOO4fuiE38DvUm8gLGjBjAjejKe/RuMdGMB5Ig61x70zDKB9ieS2G7WGdW4PaWKoIwyUMzItrcf8wFJUkZwURDSbaErjf7UAeG8H38Qbs3ycrhnbMwDo9gXPH0PjJWBhGOwrZ5sN7cW9BBYhNk8TaNCjwVubRnQ7mmQm8VcON6WAaPmIWcOqb8W+PIa5O4doajEpOvPdMZV9VUBT+4PGZDxn86HnB8tnzaZ1ah1IpfrLxz1weOpEWo2mEY8dM78AbMNIBSHVguGWwqoOZKlUOFt52Q/3iiy8GYOfOnXz3u999uw9/SBDLJujvqQyi3Bz4TVEJpVqVx/pALe7zlY+51+rH97lGVIeDCBuorSXUYzlUuIjvC03EpoF8NIney3hWW8sYx0cozY3y89JtrO7eDIBVZ+E+MVw8Tu+ykdnK/6UwaXHHE+9owS7VsT3Uzfd3/I68V/GC/GHbfXymdunImwlKyv0BzNiH2Rn8H9ZmH+fYhlamx8diK6di9O5l95onxXH+0Dvo9lZri/ivbOThppeYHZ5IaCBG2PKSxHt2cMWMv+exrvvxtOKEiR+kJTgbS6XZ88A/UU5uRhzxD/xgrWZztqIOO702yOfntdCVtwlIyZiwRfgNGO46CJhixISECEmqg7VDA6UVL6Y3MNtNE/G/umfSlH4mNSxj1c5fsXDslQhh8MLuPzK16axB8TnRr1C1xqse42+hgwI5oOYdM0OknRxKa0JWnB2Z59/UMau8v9CmgFYf5txQRfnaFHibS4iA5KBK1YlLrIvr0P1uJRw5YeLttBGT/JBXONcN9U3emjzWlU3DQt+1BhE3cG5LDTusnBIAU0LOQec8zDMTAKj2MjqtEPtEsghb49w6dAyd8nBu6694awv7qUqyrVwpMl3lHUWYJe7ftZKj0+OR0wPwXH7ISAfodZHtJfxtgUERQiUlXNSEkXPQfklJmQR2OVhjDezf7jXmeKmI9YmGivfc1gi/QO1xsH/Tg+8zjZVtHsuhRvkxz4gjEibCFGi7YpTjaIyFYVSvg/BAOTaRJj+pSgYgLSdKfOv6MZMBMMA6I44OG+isM0xHAUCyC+u+3xA45ms4d7pDHnsP3DtSWGfW4NyVwrUsVEkTSmlOnaH5x2dfoD1f5MNT20jZeX7nPcDsUz5I/O6hZyQXBFG77xj+XGcvpeyLH1SfhipVDnUOmGrExo0bqwrYbxJTDj237PMK86goiIqR7dzYhzgmTm9DLdtvBF3WuH/tx/l979Bsf15BSeNLgO4emZvXke3j1J3f4MbuJwC4uOkEpBiZww6g7MrCL0z+BceY5+Hv9RF1ohyRmci/N18+bFu7TiPqK3M/cmYQ86wEvkvrsF3N8/cHaDLm80LHQ1y96hv80/SloNQwUSMxykJ12iM6Ce/hDBv7d3Bn95MICYb0SL34Bzoe/Dbc918sTZqcka1hvDkGqf142R2Uk5sxAnFeMKexOVu5Byng1LE1fOmR7fzfp9v5xxU7+daqPWReZ9ixEiWeKt1C7uzhz9Q4JorocrG8qgjLocD2Qic1FIkGWpDibxvZieBYJtafzOpdv2Hlzl8yof5kYoG2ykoNMq3QkTfZboISkau8LKY0CBh+0m6OoBkjZ/e9xs5VDgWUqfCdU4N7fxr3thTOTf0Iv0C0HFzCl3JPueKZtDWUNTrjIeIGxsIw7mPZ4Ru7oHcMzyHWGkSLhRi7lzicX2CeHMPza1SnU8lJfqVvyWnUlhJqn0+/2jdaCtBdLmQ9RGLkuy6nBMBXGrG8yoEl5WS5b88qdFyCp6FjP5UNehxUofKDR6ZLrFQRWxh07fGTSVkEAgq5KoO3tjhyzPFoFsb6QIPOKmSrD2N5HL1XE9Cbinir8qj2MurlilihnByAoMRbW4CQQEz0Q0DgS5cYdZogvkDib89izQnjPpzBvS2F/ZtevCezyIQxKPyIT2CeE0f13j9wtvqhaJPBiwTtF6gLG+l6aqAfMASPp7fSPqD0fteOXs4ecywbc7v4UfM9bL6iRN85AvfTCbyzIzD/BIjWgxCIWSejjv0orn5zE8dVqlQ5MByw3rq+vp4zzjiDuXPnEg4PhdFUc9RfGxXx8I8TlLcLijs1zjES65MNqLVFiEhksyRuSgrbFcIvGdBqGyIqwQRrlIExP4S3Yng4VaHBRXUM7fDpxGmgNHJuELVmL0GWmEEpWmRceTZjnCnYv+utTAJQUbtddMoUxoQb2ZnvxhQGyUiB5uOiyDoT9+kc7q0pEBA9JsrUiWFMaxZHTLiMZ7ddS6HUy9enfoW+cprYnCDqhSLG3BC6vJ/JHQOOS8zmC2t/xtKmhdTbRfrX3zbwsFyKu54FwJ20DOqbQHn4W+dhtc6jwwkBlftf0BThofY0JW9oVnljf5FNmTILYr59zzqCPns7N637T3Y1nsbFH/sqoqtSP9XbWEL9Lkvg8w04ba95mCrvcdZmt9FseESs15fnWxueRG140sgVRV15d1+76e0XHQBRqoRPIiFmhemzM4wO1JGzk2it96sTUuXQwfB0JZ1pr0G+t7KAXBCB6N/Y8R3G8Aucv6aGPIY+URH1ynmVGdZ9EKZg76YtBChDYJ1bg97jVDyhzSbKAg+FGTaQbT7cO1OgwZgfQjRaaLVPOdD4SCNF1BjIoET5wDgphvdgpiJ2WmtgLotje8UDOJKqsl+04NK6JRgOiAYfjBKo7cNV/41ZQfw7BfEJ4C+UsAwDq8EjvKeAjJu4f+mHOrNi6O+LAd6DGdTzA+MhAdaH6tDtQ5P0YnwA8WIeGTIG3zExxod5ZAQ1xsKwwXsqV6muc2QEq+BRV+uh0wrv6dywCA21soAe40fUmpizguBV9jWPmYXHTaC6IDRpeFSHCeWgj/bbBtIaY6AabB55diiUfWpthJKjuWzCMh7oeJrtZjdnzjye0UGP6VYTou0IAh/7LdIrY/trcapGepUqBx0HrHuZP38+8+fPP1CHf1+jjSLmUZvwTRqL6G2g3N+NWWdgHJ6AsIn9025MYPQxcbTPwlpeg3NnqlKqKSSxltegLInPrxANFnJmEPVSsSJAd16YXEsXn0ucR5ubYKzVRMDnR71Uwjwhhqox8TaWkK0+5Pwgu6Mvc2n4n3FvzQ4a6QBqfQnfkRHGBBqwlcunp53NM/ZG5k+dwOg1YdQLAx2cBvFYlskfFmyIdPLQdpczp38BnxmkKx0gZwWZeYFN5KgwImEguj28fdIGjSMiZNzdaDRagxYG0vSjnH1UXk0fCGiPN/KHttlsKXZzUtNaLpg6jRs2lmkM+XhqT2bE8+4qOoi4b4QSvJCSnGeiNURNl4xd0QeIyTrUDZmKh2bv7Xc6iFHWW1KUr3Lw81JmK0GVIeIf+5aOI1NvQUgOQAi0X1QGbxFJzAjRa2eYEGoFwPYK+M1qruGhjHAqKUwjyHqARAhNXneRKncQsWqIGm0Ibb3j16k2lYYLcdkata2M6nAwT4rh/H6vcoM+gZjgHyw9NcgeG1p9iAYTbI2IGqjdNmaLH+EXuE8O5Zd7qwtYY30IYTAs9ypuYC6NVcRbdeVc1kV1KL9GdCj0jjLWWTUV1fe8wns+j3FSmINJQf9QYKLVzPxsM0ariUpoZJOFcXwU7/EsSIF5ShwKLok1/cgxPuToikicDEnodMAUGEdG8J7NVXLI9x1zHB3F+c1egpwavCdyyKMq1YvERD8kDMwlcexfdA9tttPGixYxx/mwf9w56EDxVhewPt2IWxvAFOA8N5TLPogUiEYT967UUJPUle+3t+a3mJccjfvnAQE7n8D9YBw7pgnOB6Ne4xvdyxZ3uGBiXcDi1p0bMYRkcdNx5Bybn730PJ+fGWW61YTWUDRiFWHi6rilSpWDkgNiqPf397NkyRImTZqE3+8/EKd4X+M52+jveoTRwTOJNPsRXg/Cmo798x6sS+oGVUK5sw+vPYiY7Mc8daAElKshZiDQiJ1lvN02xoIAxtIYerfNxrYXefzlP/Dh8LcIXOui+z3cuSnMxVHs3/dinhrHnOCvCOmEJSosCGajqJ0jB3uq2+XKE89GqDKpci8/an+MWc1+vLWjR2yrN2Z5yP0eZ7SeQlIHOcw/no1F0AXNxi6D08e41PV56DUFrAtqUdvL4IKc6EftKvNkZD2Xjj+JuIjimtC48JN0PvnDweP76yZixCfQSYrPrfw5ebcSjvi7rXdxWluaGbWLeL47xzFtMW7eMjwseGoiMMK4tjF5crvgjpdcPAUnTDY4dtI8ADrKW3DbNEZqn5usN6tG+iHA+sx2pjndRAMtb+k4ov/NC8m9gg4KZE6hIpLogKCcEIKQlSBrJ6uG+iGODlQUz9VL+0xq1hsIoem0X2RTup289hOSvTRYm5kWP/4dN9Z1ZqShq7Me1gdq8DaXsD5cj3qpABEDOdaPai/jtAxdoxAgGyzcO1KoFwfuNWbg+2g9WoDaMDI83VtTRJwcGr6w10VtGjDGBzyt9k1JfB9rRPW7qM3l4aW7AgLfsaGqR/0dJpYxCNRYOH8ZqBYzwY9xcgw53oeoNVEZD5H0EEeG0TV+3PU5zJkhNGCeGkd12ainS1ifbkLnXXyfa8JbXQBbIeeEKimD+0gS6LxCxA2sTzQgohL7B12Dmgd7o14qohdHRobTP5ZFzgwgxvqRUwND7XRwR423IlfJO7+5H7koCLVTkEf9EVHnY3vddsqfG0Mo76cccVglXkALkyMXOuQe/z7+/BiaJ53O0qZa7u2qTEqt6c2wbNR0/rJ1Nbfv3Dh4qinxxqphXqXKe4S3vXt56KGH+PKXv0wwGERKyc9//nNmz579dp/mfY2pg0zcdRnyJQN1mIHRMAnd4WGdVYMIDPe+qReKSKUxTk2g+j1kVGJnNX2PaIrtPsKT/NTUuViZPuSkMM903slJoUvw/7KMnhzAW1pPyZEYBfCdmMC9NglxA2EKjCPC9Md3kyl1MGf6ArynhofQi2aL/pLJzHKIsV2N/Dg0iWCpHTmqjLdz+HUWW4v05ttpX/8Tls79Fqt313DzGgfLgFNnWISTJurmJOYxcZw/JxHNViX8bFUe6+MNHNswj/H+5sHyav5xy5hQP4HMrpX44mPwN83DNaIU3R3MSYzlqd5N6IGe6N49T/GDBceyvs8hajkc1xbh8T05QqbkytnNjAsYIzqt7WmDm18YCnN7cJNHS6yWD8z5Oje9+H26ju+idWsTFCu9uZjkxx1tUBWUe3+TdvIU3TQCjc94a7HDok+h2t7iJzggBvPUY1aQrnIKgJAVJ2v3Uh8a89aOX+U9jdKV8Gwn7VbCdk2BeVai4pEz+tmYd9hdbGZ5YgYvFTp5rryLlugeEuKtRYu8ERzHw1wYrqR27YWcFyazTWD5/Bgr04iMBztsvIezGGcnME2BN2BMaw263x1u/GQ83IcyyI/EEa0jJx7EGB+I4RNluuyhtpcrE8V74SkXGRg5qSbCBm7Apmqpv7P4bT/22gLOcbV4rsSyFPqFAnKihffLHkTCQC2ppXeToPycZvz5MbwH05X2EZJYy+IYi8J4GRevIUDvIx6aCLHZklACjP6RE0fmsRF0zoOUhzB9mJfVIYoj+3vRbKGL+4mw0CAn+6FfYS6N42Q89A4bTDCXxPDWF9EpD53zECeEEXGB82sPqDi7Rp04jYeP7OYnyV/TubvibLCkyZjDPsWck/4NEWpB53dzhezl9Lax5EWI1kgMn3RxPJdbdq4l4Q/yhenHMiPRSKa/qq1Qpcp7gbe9d/npT3/KNddcw7Rp07j33nv50Y9+xK9//eu3+zTvayKFMZgzfOgJHqKocfeUMWeGUFvLiKyBnBlAvTT0kZV1FiooEI7AtQV7HhP4myXxhRK7S7HnHknrmCKmqzi/7VNkt2ag3qQ4o4bu+xSvGJfROT4avtqCTiu0JXECBvX+Nq5/+SrmLToenXRRL5crHctxMbINLjP6o1g/7UMXFUFATm5CnB+GDWnoq3RW3hjY1LCG0u6KoR/A5rb1lVgrx4PbX3Q4c4aH3ulCUGCenaiU0UFgXVDDykgnm9J5pjUGBu/ZI0jt+GNwY/NQSpMSe3hw87dZ3/UkExPTOWXmGfxw0yPYykEAxeJ6Eoag5Fqc0ubjg1OnEJZRYgLUPqq9hiF5cffIjvbJbZovHXs6Y4+ZQ9krov4+gdktwRI4jZKSVTXS3+9szrczxjIJ66a3lv+tQGYVXvit5ZDrgBhUO44aYdbbOwEImjGydu/f2rXKIYD0BDrlYi6JgSXAEng7y0hPkHFLnCoXYeYVeqNNc91YTh43mXVqB/vRTTtw1yhlJUT9ohq8hyrCceLEBF0vGOS2Vr7D9UclCK/uhR4XTIGYGMTbS2tECNC9LsQlxpk1EDRQT2ZQ221EUcPkEDTnK2HPADUGzIug9T7f+SajEga812I5wY9bp/CXTESdWamNHTVQ28uYy+K42qQ6QfvOomxBnw6TvV/zyrOvPzpCZJSkcKIfxxb4pATtUXOEQO8pIyJGJf0vr3AfzCA/0Yz2SzquczADmvppCrmrCHkTNcGPdWkt3otFhE9UHAcTAxRTBnKcwJIuhlHJSTcWhvCeHQg5twTm6YmKTPM+hWCMo8Lo54s4t6dBVsq3yZNiiIiBuyqPqLcwTvEhWizM+gDe3cPT9PSDBabNraGzNBQR6CiXv2x/gGnTrkB6EsJTiUSmMWkgTbDysEw+NmYRF46Zi4kkpC38pgVUDfUqVd4LvO2Guuu6TJs2DYClS5fy4x//+O0+xfsewxfEe6AP85gIYrwf3V7GubYfOd6HcVIEOS2I11RAd1ZCvrSjMYTAfTiLvShB7dEm6edcci85BNokNYt8uImx6AfWEGkcj5uQ6CPi9KzYa6Djg7qJCnVrP8asEOQURqOPRnMKV07/EY8V2pl9cj3xw8NoC7bX5nissIvLHhiLMio13HVBodYVEe1Bnr14BZPsWVi+IE/Zt3P/7j8A4DdDTLEO48oZkA9rnt0teGoLFOsFEb/A+X0v5qkJzHNqwBI84d/NP2y5C41mcf1YmkUUUwkCKYG7J08oKsjFC/x59f+hI/syAB3p9cxqWsxXJ8wmY+cZXzuHFn8TG3ufwsVmTHgBCRkGrfdbWUcpxeiakYOv8XUCFIRoJWRAMQSMG9zrbW8HVQ4+NufaSQiHsNXwlo4j0godFGC8RUM9KJH9lYTGmBWiz6kM7gJmjGy5aqgf6mgEwqnUjdZJFySYx8fQtqKRZszVJdRdqaHtJwaYeekYMpGR6ucH7Bq1xphmoLs85MKBnFw1PMip92lF+Ixa2FDAmxCm0AnW6L2PAUzwY7bVV3LVCwoxN4xcYiICBsmNmpoL6jBSLlprdK2PnrWaxskme+eoa9fD9/km9C4bXarU1KbJQuQc0Brrolq8FwroXhfrwlqISExXglH9/r+TuJhkNwyvvpJ8SkHUoG+VouFIgS/rEF4kMcoK984s2BrzhFhFKb0pgLu5DELQfJKF2W+jrumFuFFpg8qDKX7EbqcSWh83oayh32XP45rwZIO6xX6svjI4YJ0zkCohKm1Ia7A+UFtJk/B0RQ3eJyv126OyUj/96TzeS0V8VzRAj4uYHcRbmce7r/INNw4LYRwdwXtiSFtBFkcOWPYUk7jaxTegSrrfaksKogOe+WrEe5Uq7y3edkNdyuHhYYZRVZF8o4g0FRG2Fh9qaxnZ6kdtt1Frijg7baxPNiDbLLwdZbyV+UrO1bYSxqwQRoNJ118c3HTlc1zarXDSNq2X+smPm45VLnCDW+CDdfXovfq52vkCo+gglyXQGQX1AsKSgCfwbhLMXFzHx/puYVq4npRb4rlte/jO1NMRdRbmaD/e6jwiYmBdUIvOKzoj2wj46xnjzGeGWk7DhMn0OTs4LnQ+5k9K6N5OnFl+xEkGM060SBkG2U/4abzLhQfSFOdYrFyQ4htb7hvsWDJOmVFmjNAzNt5NqUodXQMiH4+Ts5OD93LKlI9y78bfUnIrHdxTwuTTi3/O/PpzgYFB3d/orbSGGU3QHIPOgUntWACOnyhRamS5uyqHDhvzuwiqDCHfflTc3wCi30NH33o5Px0QyAEl4ID0obSm4JUJWTGyds9r7F3l/Y5A4j2ZqxjpAArchzL4JvsJ9Vu49+0zmbOlhOhREHnnrtEwJLosEJ4JxUoddatG07RYY49SeJZBOQdGTFCeGCb5IiTmCfYeamhNxdO+w8Z5OAt5D2NBBBE10AJqRmnUzzsZFHkXUH9FE/uKwAmfD/e+1JDaN2BdVoeYaoHW2L/tHlTQVxtKmKfFEaPfefG9Qx3PrfwGgTGCuvkSaTvokoCEpu1EgfxrNxQVxgdqca4f8kC7d6SwLq/H+3MXIj2QttZgIo+Owswgcqwf95EMOBrz6Ag4GrWhVPmtl8UJoGk5LsTuezwShxuIJ3KotUW854dE3IyjIhA1KpoGnTYYAv1SAZmIYJ1fOzhhJuIGGrB/1Y2IGegupxIK/8o9ri5U0lRemUsKS0SDj383PknIDtLr7+dXPbdwzqhjCOBHoQl4EjOt0ZagFNV4VbO8SpX3PG+7ob7vbN6bCQ/9zne+Q39/P9/+9rdZv349//Iv/0Iul2PBggV861vfwjRN9uzZw9e+9jWSySTjx4/n+9///rAycO9ltAHGjCD2fw6ohhpgnV2D+1AG3e+h+z28l4rIxRHYbSNiEgrg/LYX9dngoJH+Cl4O3Kym90lJZFqE3738PKcuqCMwNkJpR2Xb8DiQlolzcz96pw0GmMdFkQvDqHPriP2xi4+dfDj/3v4QAJPC9SyIjUIkypUybIDudlHbyvg+08iC0Lk4V48mZUPN/CBt/hpYNRsRczFOieM+miY4P8LcvMDtUYgawY5GxW+WbmFxbCx/6X6Wx9u3D95D1PIz2vAT6XZwbkrtdXOgr86y9EMf5a+7/5NYoJ6+QsegkQ7gaZfHt1/LOVP+BeW9vvYYkTZfPM6iMydRStMchZDYT63WKocU2wsdTHP7Cfvemkdd9nro2NtQOs1HZRDnVEoFxgcE5YJmjJ7Ctrd+/CrvbTxQW0Z+t3ReQZyK+Oi+6/az7EAiBGBLnJt6h+xmE6zzajFu7iNwTg2BTQW8u8qYAlqWxBD1Psp7lZKSEkTaw7lpSE3beyqHERCI8THYVBhuk2tgZRbm1TJM9b3PG2akAzg39eP7UjMqWR5Ry9p9PIt1RAiqtvo7ilGjGX2mwFybQd9uI6cGETED99Z+zONiuEWFaDRRO0e2fe/xLHK0H5Ue+J17XCgpjOkBnBuG2o/7UBbztHglXL6gcB/O4PtoA75NRcx4ELeoMfcXkmeAHGOhW0ysBWHUbhu1rYwUAvuvfUPBdz6B72MNUNLImf79v6e9LiJhokOC3WdLjF1Bxt0+DRQ0+cby6wtnoBsclKeJ5Ay4ug+9vTJ+C50Wp7zYj21WjfUqVd7LvHWXzj5s3bqV5cuXD/7b9+/XYsWKFdx0002Df3/ta1/jG9/4Bvfccw9aa6677joAvvWtb3HppZdy9913M2vWLH72s5+93bfyrmH4Be496SGvrwfOnSmMIwdKg1gC3W6jJwSRR4ZR9Qbujf0VsZKCO7LakwBVAhTYA4bqTqOH5mUm0RkCYYFVJ/Eez1aM9IFzug9lIemy5wYbdV4Ty2KTOXvsLC4aP48f1JyPlTbxOh04Pg5tAyMVDbrTIZFuRdtgJgQxWULf2Y/uclAvl3CuS2KdXwftNs7/9KL/3If6aZIxj/qoC7m8yFbOmDCVcZEaAC5omcvV4z6Eu8mPk9lPky0oJgcPAyqh9QVnZAm2dKmHNxr0FcBhXKTMhJhNSAxXvc8Lm3XlLp7Nt9Olc2+6wlaV9w5lzyFZ6sd2+wlatW/+QBpkn4eKvR3RRgIdFIj8K4JyIfrsDCErQaYa+l7F0shxIyuviLiBigrEjODwFWGJaHiHFd+1rqjS721Iu6B22ohZAVSnM2TEaFAPZpBZ8PbaXikqSt37oJ4vIDRQ3k9ouq3Q+4as70ccjIKq1GU39/ORFwJtVkuzvdP4bIW8qRu1uoDucfEez6K2l5EJE3odiMhK6kJ4P9/YsBxejxzAEKj9lDH01hWRkwbeHyHAAp11iR1mIv0C45go7N0u5oWxZ8Tp7/ZRMII4aY0c5UfODeE+kx+eIWdrvC1lRI2B2mMjx+7nPZ1g0v0Zk10fh6ThUb7NHDyGtqF0o49EKYYpBOL+bMVIh8oE3e1p/J1VI/1Qpr29nenTp3P22WcP/jvrrLO44YYb3tJxzzzzTJ5++uk3tW82m+Xyyy9/ze0+//nPD17z1KlTWb58OWeffTYf+tCH3tR538u87R711yMcVygUCIVCI5anUin+67/+iyuvvJINGzawe/duSqUS8+bNA+C8887jRz/6ER/4wAd49tln+elPfzq4/IMf/CBf+9rX3tZ7ebdQOWekTVnSYAjkvCDaUejpYbof8Kg9zsRnK/SASqm4v4+6Y+pJPjY0eKhZbJJe5WLGBc+T5tMzWvGXdmCsbKK2o0zt/BCi7MfdNFJcRPW62D0GqZUejbNgdDjM0dYU/LstbAlpYpS2a6JTgsQWOYibkmAIIu0O3hFmpXN7bB/D2QPd4+B1OogWC91RGWDpB3JMmdrIZ/b8DJ80ObllEV+ffQ51q+rJXC+BAPZJAt8+Yj+ixiBW38on635Mb34n8VA9q9vvHXbKo8d9AOXtR7VX8IZLquVEmW+vexDcEp+K1ZLI9hCoaUM0TqNo1byxg1V5z7Cz2EWLZRGQCaR8859OkVVoS4Dv7ZndqRjqCp2QRMwQSSfDhOAYclUxuUMe4WqMxRFUrwPZygjfOCpS6V60g1xeA00W6rk8jPFhLk3gvcP51kIIdGk/5ywp5Bg/6un8iFVql4M5y8B1XzkGiMTId1LUmXjSwTg8UhEo3etbL4+P4e7b0TZa+xWTUwmJocSgd/UVzGWxA+DuqPJa6F53sD2/glpXxFyeQO0oI+pM9A4bUWNAUA5NwBgDNdL3qn0OoFt87C/4U9aaqL5KIzNPiKIVqPFhQqMkdq8iu9sifHkr/lQJmXJIxyMkr38lQsMjOMGkYZlVGWjn91OCsOjhTjQwVtrIxRFUk4XuqoyHxEw/HU072Oo5FHskCyMzyNSD3TPUZr0C6KIiZ+/C/2JwxPHpcGFUtSLBoUwgEOCWW24Z/Lurq4szzzyTWbNmDeqJvZOk02lefPHF19zuRz/60eD/p06dyu9//3tqa9+Cg+Q9zNv+Bi9atOg1t7nsssuGec1f4f/8n//Dl770JTo6OgDo7u6moWEoxLShoYGuri76+/uJRCKYpjls+fsFEbUqs7R7hyDGDOQkP3KiH+fhLMVxMXKPKUKTDKxJGjlzoFbuTptIpJ/gKTHciIXyIL9Rgda0nikJWFGcF6K0zBqH6sjBjjLWaXEwQY71DVOTBxD1FtqGcodGLTI5OjyaxnyMckHTc4+DGogeS60Be7xFw5ERcDXenWkiJ0AuGkIEJXrfTtWUdIUS+JshdoJC3NwLRUUzNXxy6pk4nsu4SDORbIDCw5WRUPxwEzcg0B9qwyq5yEf7EEWF/Hg9JUvRzByafXPQwuHDC77NPRt/hatsTpz0EcZFDydJP9vynQhgSrgFo9hBT98LBPw11NfMwRDNKCkoowgh0UqTE4rthSKeVowNhUhgsLXYx458il9HwjTc8M2hmxp/GMGzvknRjB+AVlHl3WZbYQ8NhiYk69/ScUSvh469faN77ZeIfOX9iplhesspQuZs8k4KrRVCVC2JQxYhcO5LYy6Ogl8gpMBbV6ikSmiTroc1TiZI7LgwxS5N8RrF2MvfhfayIAov7FOebXIAb2UeOcqHl9xH3K7ZN+xPpcBJ+KDZGlJ2N0GfkEAIH+luRfCSRuRzmcpE8fwoqU6T+GzJ3qHvRQShTzXiXNeH7nWR0wIYy2soljUh18G6MIhql+heF2O6BTEP1xsw7qu8Y4j9lMqjUkgGY3YIb30BvQOce9NYH6pHpTy8nEKOD5DerQkfE0M/lQFLIk5OkC+aGHEDK5GF1IBBHRAYiyLoFVnMS+sgKNBaUJQmmbttyh2V8VlmNSQW+EjM8ZH803BjvLhVUdyhKLYbNB0bQ708XDfEmxfkRauL+qkB2oo29qUCafvxhMOz3p3U2RMZc98R2O2QNDQ1R5mU4orC5sr33owKknIb927/BR8c+xWMl/Z5JvXVhlllOE1NTYwdO5YnnniCf/3Xf6VYLBKJRLj66qv56U9/yh133IFhGIwfP55vfOMbNDQ0sHnzZv7pn/6JYrHIhAkTKBQqmgzt7e0sX76c5557br9///KXv+Smm27CNE3Gjh3Lt7/9bb7+9a9TKpU4++yzufHGG9+wjtmePXs444wzePTRR4lGo2itWbZsGT/84Q/5j//4DyZOnMjatWvp7+/n7LPP5vOf/zwAq1ev5vvf/z7FYhEhBJ/73OdYsmTJ2/hkDxzvylTb/lQpr7/+elpaWli8eDE33njjq24nhHjV5W+Eurp3UC3nDZLvLiPOr4fbkpXZ+7hREUAJGRQ3KTK+GPnHKs/ATmrEOBt5Tg1MCqLu6UfssQnMc9A9JdwuRbDJh9hTRv88R/HiFrTPocsr0zItgHFqDLWqgPdoFt8nG7F32oMz1fKwEKlOA7QmPFUiLE1Y+RBuxQ2tho+rKGzT8MEY3kP9lU7z2SzGsiAsqYFr9+qgEgb5vElxm6YIZDYKRi2rRdzfx+NqPf+z8Q4MafCDeVcQ8PzkFcQOM3D6FelVAwa/gNaLm4hOksi4yb4Zw40sZUbzYrRWZDyPl1I7+Lc1fyDjFAgZfv5j4tGsW/WdoUuKT2TGET/lNy/tZGMqw4mjmlk+vo3/8/QL7MpVPkoxn8WPj11A1itzWUMbDXd8f/hJt61G922ifsaSt1a6613kQL4XDQ1vre74geCNXFN3dx9x0yHmayASCbz2Dq+C01tGNvrwBV/98xsMvv7wYx2r5BwHIwEaibOmr5e62jg+M0Aw5hENvD0TR+/13++t8Hrfi4PtGRV7bYyTE7jX7BVdUWci6n14ZUH+5YpR29s1NJFqpyQNk9+e+3i9zyPZZRD9QAPi6TQIME+M4T2VQ20rY11ah9pRRg8YUGJaECdsEY0GiO51+ORaG72wnqDPRSiFY1n0rYSWKYJyt6ZnjSA4NoEwBIUHFcExmoQYfo25LpstdwjaLm7C8mtynZC8zmP0R00cn8To0MhmG0YBeQeV8pGfatOQeP2enoOtjbxZ3s0xVKErjRznH1bv3jw2ildS6GlhvIDEvyRObit0PKQJTQuQ2+QRDxskH3XINIWILw+hXUH/Wk18gaT7EYfGJQ34hYv0gRkXuH0u5skxnAcziEkh2h8TxA8Tg0b6K6RWK2IzzEq5gn3QNuTWeIiyQcPlDaiH0ghLIE+I8fBag0jLeH4a+D1XzFzEb1d8FqUrE0eTEws5Y/v52O0Dx/Gg7zGXhtMsCpsVRgiaLhb8esd32dG3lo7jL2HUztah8dvCML4pIRr2E2kCB3c7PJiv7b3Oc889x86dOymVSmzevJkHH3yQSCTCX//6Vx577DFuuOEGQqEQP/7xj/nHf/xHfvOb3/DVr36Vyy67jA984AOsWrWKyy677DXP88ADD3DjjTdy3XXXEY/Hueqqq/jjH//IVVddxfLly4d5+d8Ira2tLF68mFtvvZXLLruMp556ikQiMRgdsGfPHq655hqKxSIXXnghs2fP5rDDDuPrX/86v/nNbxg1ahRdXV1ceOGFTJ06ldbW1jd1He8k74qhvj8j5s4776Snp4ezzz6bdDpNoVBACEFv79AAo6enh8bGRmpra8nlcnieh2EYg8vfCMlkbkT97IMFv+Ojc4Wk7uRGTENj24K+v2qazjHoeHD4jG1wrKTY56fULRChIL6PhwiGPXBKiEwJMTWBhw8xKYBcmqBJCNKil04jg7ZakSmF93Cldq39vz2YH2+o5AdKSXIdZJ7WxOYbWLUCWVZIv4EMCsR+BEqESWVioaSxzq3F217CP9uk6OXxf7keb2OZQNCiIH10Pzy0vyqBHbRIflTzi913oNDMi45ilvbxv5lHOL3+FHwNkszqvXIQNXTd5sCnDGx7aMZACHClQmiBVAItNX/peIB+O0fGqRjcy5pnsWvjn4dde92o8/jyky+Scyqd5I1bd7Ezm2d0JDRoqGdshxs27+TU0XE6pIRSdsQz2NO/i2LPVtrEG2uPbydvpZM7UO9FQ0OUnp6Rz+vd5I1e07rendTZKUzfFHK5N1mD1gVf0qU01oDi/isIBIMWxVdZtz+EVMiUQylXwnJ8dBdSpFIFQmaCbR3baIm89Zzj98Pv98o+b4bX814cjM8oUgrS9bJJ7YWNyO4yRAxKPh/0CALNlW+23sdZLQLibbmP1/s8amqCyLjBzgcUkUm1oKH4KLSdWYtxiibbBXpJIz5ctCHJ9Qh8fQIvnce2B8L5DfC3Btl1twPeK0WsNY1nWrjBEtGZfjLPexS3D5X8iB9mglbDrjFmmCSOMCh0CUCgbE3TcgtDKVKJ3STmjEFvdSGnEOMsCo1pXB143c/rYGwjB/KdOFD4PI/Q+Qn0dg/R5yDH+dAJk1JWQk6QfNQgOtukf4WDtsF73qVmsVUZowDlLk13F4AmMEZidym0C12PVNR7Wy/10fGgg9YGdXET/wkJOh9SqKLGqt3PJLwCgpLQRElhy5CxboSG3q/sBk1gsh99eB1Rt0gp65HslfR0Cz589rmUvTTHT7yMhzb/HoBp0aNxNu8nb92niZ6/kYK3gVUv/JVTZ57PdcVefrn7ayy/9NO0liZQVzMaf0ME3VNG7ChihF1Uag8EApTraoi11B507fAVDpZ35P0yWfCK9xrA8zxqamr43ve+RzKZZOrUqUQilQm3Rx99lPPOO28wLfnyyy/nF7/4Bb29vWzcuJFzzjkHgMMPP5zJkye/5nlXrFjBsmXLiMcrjoKvf/3rQMXr/la57LLL+N73vsdll13GtddeyyWXXDK47qKLLsKyLCzLYtmyZTz++ONIKenp6eGzn/3s4HZCCDZu3Fg11N8I//u//zv4/xtvvJFnnnmGq666ijPPPJNVq1Zx+OGHc/PNN3PcccdhWRYLFizgzjvvZPny5YPL3y+IGpABQffjr3SCmtBEgRmH8DRJfoMCA2qPs5AhcHokqadctFMJjZLjTXyrHZgepdAu6HnQRjsQGC2JzjZo62mkdl6Y9GhFfNVeSqNljXdHGvusRpIPOUSmmNQtgcJWj3idQDmKhwsvsNRbTGCcRXCipLhXp5Q40kR5HrxcQr1cwvhYA10ZQbr7B3gzv8LPuzbzg9hMum7VIwaHXlzx2e6fU/DKzI+P42uhSYQ6t3J914PMPW0yieTID4OXo+Ldf6XzFS6rsu1cs+05YmaAD09aSIPPx9rUdoy9wn+jhh/HGd4RZI1mcs5wMZmVPX1cMX0iT3YOTRZtSme4YtJYVN0oytOOxb/+0aEdTB+7QkF2pV5mVG3jG859r3Jws6vYTY3bT8iqe9PHkN0uKiLfcv30vdFBgSxo0BAxg+S9Io72CFlxMuUeWiJT3rZzVXmPYWhK3ZpdWwVGNIgqa7StablQYARc6hZLeh8b+oaHxoIvrHmT01BvmtAkgd1tkHmuMhEdX2BQSEmS9znUHm/R+4jDYDK41IyZB2qv6GfPA8NyaPugn/QzLm5GEzvcIDhaoEoO/pRHy0Uhkg874EHNcSaBUhHFPiH0foE0K+fVHhhRQaDNQhmQ6DXxbkljTAqCX+A9nic8z8aYoyjz5iNsqrxx3LjGa8/QuT2BtEzCJZPktQ6qpPA1ChpPs3CzeRpPDmCnBDIgcPKKyDSDxrN99N5jo0rgbxbUHmfi9GvcrAajoumTfMilPCDE1nGjQ/PFFnUnWrg5jREAMw5ueuh6orMNyj2a0CQDMyYobFH4myXhaQY9dw+NKywfmGEP/UgBcVEMta6yPECYK1a8yAktC7j4sJn05zczOj4TqwXsHcPv3fN18fyLnxj8u/DUDzll4ce47uVf8NfdPyBoRfjy1D8SXlFC3Z5Ga1ARiXVOCO/aG/HNmYI+7+QD9ttUObjYN0f9FW688cZhWmH7RiorpXBdd7/rX0k73jfC2XGGHAyGYQxzymYyGTKZkULPb4ajjjqKYrHIihUrWLlyJd/5zlB07CvX9so1SynxPI+JEydy/fXXD67r6up6z+S8H/TJi9///ve56qqrOO200ygWi4Nqgd/85je57rrrOP3001m5ciVf/OIX390LfRtx05rE4SbxhSaBNkn9SSYNTTbeDgcZFLR+yEfrxT4KO1wEgu5bHbysRpUg+aCL3aNxj4njBAJ036MH66WXdikKWxV2ryK8McxGqw/ZNHyuRm8vY3kusTkmRgiMqKb2eItyh6a/TzDVHs96sR03owm0SuqXWtQcXRFMsXs0pb4hkSz1ZJZ4LfhDDXhKs64/w4tmjvojhjc7IwSh7JP8r9fMtYkj+N6uPsbc+F3wBYn5wnxlx4/Z0dA+orVGZki8wEA4pIBV2Xb+fc39bMkkea5vN1965maSbpnNmd3Mrxuqe31v9wZaxp0+7Fg+MTJkzS8lrhq+/NRRLVieYLYMIBdfiLvgLPCH8dqmsfX8f+B73c++cXW6Kgc9Ja9Mxs5QdvoJvAXBQLnHRde9zZ9dk4qT0NEYQhAzQ/TbWYJmnGxVUO6QRgpNzeJKRIWX1ZVKHHGBEQb6FeGtaUZfaNB2scWoS0waGsqwe6T69YFEKbA7wElq6k+yqD/ZotylwQblQHGHouE0i+BYQWSqYPQygbkzN0L8yyub9N5j4xU1ZkyQvN/BToIpA9Dix+33CLRJAmMlXr9CNvmR+3yq3YJB790VIx0qz6z3PgftCXSpDmNWCLW7jN5tgwXCqQMv9s48qCqDGL05dMYhPt8gfrhFz71OpbINYHdreh90iLT4kKYg84I7OEHjJjV9DzkkjjCpP9nC3yJx+jS99zhIv6D+BIPM8x7lPUP9vq9J4PRo2n9fpvOvNruvtmlc7qNm8cD47GST6FyDrhsdeu9xKO1RROcYxOZKum+10QOvk1ULfr+Hz+8hx5kYK8ucvBBqmwQ7vEo52Yc7cvzTcxpb1HPDuv9H/TIDudccUHiageEPYfkTg8u0cmnzt7Bg9OmcMuVjXHnEz0kka1G37VU5KKdwHzSQs2fBc+tRHcNz5atUOeaYY7jxxhsH88+vvvpqFi5cSH19PTNnzhw0cl966SU2bdoEQCwWw3EcNm/eDMB99903eLyjjjqK++67j1yu0rZ//OMf87vf/Q7TNPE8b78pzK8XIQSXXnop//zP/8yZZ56J3z8UeXLrrbeilCKdTnPXXXdx4oknMm/ePHbs2MGzzz4LwPr16zn11FPp7u5+tVMcVBw0HvW9Oe+88zjvvPMAmDZt2n5LCbS1tXH11Ve/05f2jmD5TTrvLWPVCnwNkvgUhff7HJaA+LE1FNYrzISk6WST1AvuiP0zqz3MuIG9n29x4WWPmmMtsi+4zJrZimgE4/go3iMVD7NIGFhSIaMKuw9EvY/dV5cHBy7BQCOJjxQRDR6FhzXlPQrpZ7CTjLbtJYIXkHjuHpJ7niU64UNMjEX4+ta1fGfyHCafn6C8SWM1CGJTy1hbt1D7yJ94ZX5LtE1Dx+pJt1de8n/t/C3/femX8O4M4vZDZJak8TQ/GSqKwK5U/GXb88PuVQPPJTu4fOJSXs7u5uLxS7hl55Nk3AKibikzZkbYtf12/MEmmmItzK3LsSY5VEf1iukTCBkGfkPiKs2540exuL4WrTX5YDNRt8Rzh53ExvET2Gqnubv9XqQQzK+ZUrXV32fsKvbQ7PPhc6IY8k2Gkjsa2ePhzB8ZzvjWEKigQOQ0ulYQG6ylHiVTfm90RFUODMKD0h6PhtMs7F6NERZIC1QRRJ2Bnheld4WiuMvDqoGmxQGMWhheK+3AIiVk1rj/n733jo+zuvL/3/dp00dT1CVL7h0bm2IMGNNrgGA6bChJCNlNIFmSfHdTdn+btptk09tmN9lsOoQkkNBDr6YaDO7dsmXJ6po+85R7f3+MLVmW6JYDZt6vl18v62n3zlPvueeczymLbrXtGzYs8Ddq5NZ75Ld4tF6po/eWoN9DTLRGlGfTdbD72OMFHX75Dj7tUtdsUEopchtdqiYBQpFpFxghneDkkda+lxv94nb6FNLT0E0fIiQBgexy0A8L7nnPvyOHUYc0VjBKxxoLu8uh+nRzVJWcUofCzpp03jHs4fMykNrl4aYV/Y8Nj5uiqlxGNrfRw5cAY7/U++hhBr2PDB9HedDxG5sJ5yiqdmRQKyXOMcOeObtLYXe5JE/SaTpHI98JZlgRkA4ir+HcnYKucvuRJzMs+WQt561bN9x3T5KMHMblh88mva2d+LEtCA3Qodgu6ftzgAknXcbWTT8Z2icUaOLc6s+WSx1KBQNjKMx3uIhj9qTkFYpQ0bytsA8XXXQRnZ2dXHzxxUgpaW1t5ZvfLOswffvb3+azn/0st9xyCy0tLUyePBmASCTCZz7zGa677joSiQRnnnnm0PGWLl3K5s2bh8LSp06dype//GUCgQCzZ8/mrLPO4uabbyYef2uOjwsuuICvf/3rXHrppSOWF4tFLrroInK5HFdccQWLFy8Gyiry3/jGNyiVSiil+MY3vkFTU9NbavtgM25fGKXUqFz0VCpFVVXVmKXZKgwj3XII+8BTLk6fh3e4wDg2jLcyD7/rIjrDjzk/hvvAAMbE0aEbRkzg9CuEGh1ea9VpOL0SIybw90js/+nGODeGeW01atBDqzOQJQd1bxZzbpD0dg21j1NZFkFuNPhs7L/4l9M+ROlX+pCRbsbBV7DL+VoCxIkR1j13A9nBrdTnd/CBGZO4Y1s7n9r4MlOiYf7xrJn4dYe8KDEhnMQ499OovnaoqkUEqnix1E/RK38gXeWSbeikdKliemA60pL4qjXYMxmhIYiaow2gsGFxUvXRNAe2szG9g+8t+hhdhQHaC/00VR9Lj72Ujrzge0+XOGdyLYtq6xCax/RImIkBPz40Fi6JI5UipunDNUwVpMMTmaS7FH0hXm5/mnMnHMtZDYto1iph74ca7cUeqjXeXtj7ThcZ10bW3T1Q7C3RltCImGVDvc6MkSodOtUwKrwFdAhPEOy+18GoEsiiQg9C0/sNXEuj6yVJqaP8snIGYNf9igkftjiYhjqAmdAQpiQ0TQcFuU0eRlRg7xG5ExqoDUXcu/sRMR1jTi36fmXkvPwYRna/Kufh5z1q9SzqtnKJtuq5QUpuFUKNVBw2wqOfTaNKYOgKTQrsP/QPTUS7u1IYp0TxCZMSoyOyKowfTjGA3VW+R30JQIfQNB2hl+8dPSCQ+11KX41CCCjuGrlcDwvknnvHjAvMWo3UCm84PU8w5uMgs6CtKuvj+GaV0Pzm0Fho747m/d1UOQqVkzDBR2pqjPBZSfjFnveyB8XnMyTqfOTdsiezyjJpCTYQFCXsUp6+h/fTK9HA2ieqq3XWZeiBCXj7CtklRitpi2YD1bcbDB1R8+4I+a3w9mhubh5SYN+ffR2iAJqm8YlPfIJPfOITo7ZtaWnht7/97ZjHueaaa7jmmmuG/r7uuuG0jA996EN86EMfGrXPLbfc8kZ/AgAbNmwYteyJJ57g2GOPZdKkSSOWn3vuuSMmDPZyzDHHjAh9fzcxbob6smXLRpVgu/zyy7nnnnte9YJXKKOZgmK7pOYME+kCusT5Yz/a7ADG+2KojIfqdZEvFgieqDEYLudrAwirLJKz+7YSoWk64Tk62TXlr4zmg9hRBt332jRe4YOt5fx0965BzI/UoGyJM5Cj2JpFnNeClwHTgKZrdLpud3EH9wzo0pJOXz9/n/tP/uOaj5LoD2EEffirBKUX0oiTA/TOkmz2P0Pd3AsQ6+5m44MfofW0n3Pl9FYumdqCpWuk7D7a+u8hbw+wdPJ1xLs3Yugmri/Mal8Yt6qBbx1+NcItUeevQnU8TzxSj2smkWpkuKHwBFdNOYqX+nYNTa6HDIuFiWZMYTAt1MS86GSeT63nW2t+T9YtMjFcz0UTLuNP20p4Cu7YkkIT8LVjW5juM0CCQhHdG3M/xlhM8wzmWFNZNGkiek8fat0Aqt6gkEjgVspiHTLszHcRwn7rYe8S9K0O3tS3L+w2FuUSbR5gEDGC9NgpJodr6Miue919KxzC2Arfs/00n1ZFISMw/OCzHbTdHo4VQLlQc6aJdEAzIL9d4vRz0L1tsQUaVrVJ5mUPBFSfYaIFIPWcAgEN5+qoQQnvS8DzGeRWG9kwcvjirx9tZEcP11G6wu/Y5VzdC2pQgLG7iD9dBEIjttcNSeIkg/5HXVDlb2bNmSZCSNSAN7JkKuAuz2KeEAZ/xVA/mMhS+ToIAwzPpf4Ci8HnXJQDyRNN/EmJro+8VgHDI9gqyG5iKB1Qjwj0kMDLQ6AFfH15uCfNhA82kNup0DQINoO9UMdXoyHdskNC2iBMCafF4NEUdNmED/NhVekoBbmtLsGpOkVRjem6KEMjN6Ax8JwisEwfUc0vqDQmRoO05/IkfBY3zG/hD7vuYXHNPKbUJxE6QxGNAKEjBDurpzH7hK8S9CcxAxORaqRGQjEJgWUxvD8PlsctVTrmSS7usx2Iay9A1CWhN3vAr0uFCm+GK664glwuN+a63/72t0NCd/vzgQ98gN7eXn7wgx+MZ/feMRxwQ/3qq69m1apVFItFFi5cOLRcSsmsWbMOdHOHJMpRBCZq9NzvlL0LEwQNS6PIR9KoHhfthDDEdLAE2vYc9ctC5Zw+CWa1IL/TI7bIRGhg1QqqjjTwChIjrOGmJPUXWihHodbk9zQIjp7ngcbfM2fqSdR2zGTwRY/cBg9hQGyxQf1FJu0/KydbZSYPsmN3OaT22q3/zv8e8wmm9OVJV01g7ZIcf25bxVM7t/PpmVO5d913ObxuCQsXfADsEIH2AjHTZHtwB/g7eWzzrwFBY/0H+OctL1MfiGBpGXbkBpkXb+BT02dxT98mHt+6kVmRBi4P1hJw+8EYaahrmmCaleTHx1zIC307CRo+Fsab0DSHL66/hed7N/CZuZfyn6t/j9pjym/P7uaO9ttY2nwxD+8sz4zrQlDte3OPRcApIX5/D3Ln7qFloUvOJD118p7IElB2G6neVwCoqpmPMFvfVo5OhYPLjkIXAZUlYL61UCl9q40KCFR4nCZv/CCye9SsjRBb8x0EzWmkS5VcxPcyMiARE8qTQ0ZEoQEiLxHVPnS/ouoIg577nKHQ4egCHTMGb7zmwIHBTSv6HnGIziw/H30POTRcZFG/zMRKCvqfcvFyOsrTCR+VJOL3RuWoWyWbxgt1nLyGcsvaJ36nhC4tXFOnlzC5PdVG/I0h6iZJxH7vYDsjyG/0qD7NLBtHCrrvtmm41sA/RiSM8ItRYdcVxh8zvCdSQoGnG+y+ZVhXodQpqb/IBJ+g+kwTAaRedEFKtDt6mXB6ElvoCAFWTCADiuaTFPquAty3R2T25i6qLk3i7nBxu0xCUw16H3aILdARDgw84eIMgK85QMNNIaQu4DlJ70MOQoPYsQaaruherpDFvWZ5WaxO9+1zwwgwW/18uTgR++jJrKGDL6z+IaZuUqVJamvnUXttgMH7ddw+8B8Oy5u76c77uHTCcWhyTP8BjqaQiyz8s+oQBQUxgS3yeLOWUdQNat6l5WMrHFr87ne/e/2NxuDV0p4P1XToA26o/+hHP2JwcJDPfe5z/Md//MdwQ4ZBTc3+1a4rjIkB+c2SmtPLHnVhQDobJP6pIHQ6eC/mkDttrL+vo/NFRfFFh8QSnUCjhmZ5OL0ag8+WlW8B0KDxcovirj1vdV0R9DmwaU+cliVwYkUefOUXLG69lOwGSW5DeQpXueWPkr/RwmyA4uJB/if/56GuXltzEpNfAPV4H9FwhiPOmUx/9SRmxeqIBvxMr13Ei7sf4fDAMgJ/bsDIgb9JY9GCGojPYdrhR9JhdWBoihvnLGFbpp+S5/L+iYfRkRvgx9se4enesldwV76XFwe385OFH2Kvme5zJL6OLLRnoCHMzOYqJtYlAEVeFLlx5Y/ZmStPKvSV0kNG+l42ZnZwxjTJwzuhytL5pyOaSJYr+7xhzN5+1D5GOoC861EC/9BI3vQhS1t44f6/R3rlwYSm+zny9B8jrMlvvJEKf1PaCz1MdVMEzMPe9L6i10Pf7ODOPdC56cMov4boKptXUTNIr50iZMbJ2L1jpiFVeG9gawq1IAqehiiC8EEhoBOMChSCvidsYvMEwRqw89D/kkfVwtFhs+NNqcNjwpEuYnm5jnrVcTFyOz3yXYpwqyDZ4CJWZyCk41WFcZMWsK8iMShXkV2vSK8rG0t6GBqP08CRFKVJbuvw9sUORXaqQXQ/r6tmgt0ncdMaekhQ6pIoCQKFqDURMX2onjuAcWYVXuDgpglUAM9XpPmSII6rk9sx2lQdfM5DDwrymz2UB/XvN/GFwPVA/KUX/9FhZI2F1ulh1hs4f9pPdNMnKKR0dj+uUJ7ETNhMOA3kfT2ofo/mRWG0OUG8V/KI20qI2QH8QR9pVfZ+DzzhEmg0qT9do/M+ibLL47i6UzRE2IY5foQu0A8Pooo22u7dWA90saAuwG1n3MizvpdYveMvbNIG+emm3zFnwVKagjOob1xAi6rjeCuB9jpBHB6KXFRRHiwpqFQmqFDhXckBN9TD4TDhcJgf//jH/Pa3v+X6669n165d/OxnP+Mf//EfK/npbwBh2gSadXr+OuzXaLjMRG3N4d4+LHbmrcxT95E6tqxWdN/l4qsX1C8zEBbDRjqALIvqJE/ScbIKM2ojbh0stxXXEcsSSH+BS5o/SVjG2LVp9MCj1CXx/12Oz6/9XzZnykletf4Yf9c/F+4qKz6qVAnjpy9z1g1H8mI8hyEEsUnXMK3uZBIvziWfK9d990/Q6Lm77MURVozYMo/ddQP897rllGS57ft3beCbi07n1udHCgn2lzLsKPQzN9CMKrlYj+1APrlzaL0xrxbr/dMpaYIed2DISAew9NFhx7X+GEvqkixc6iNiaETE6DIVr4s9hv+pUEJIia5r7Nhy15CRDiC9Iru330fzrI/heRV3zDsdpRSdpT5a3H4C5uvk9ikQaYnISHBBDHjoXS7udAvlHz9jWQUEWq5coi2qh0g5WTTNRBcGBTdF0IyNW9sV3rkYjo6n6XT+oYS3J4Cq6kgdX4OGcBUTztTg3j54xMas0olemMQuHPww7kjIRf6mb3h+9PZewlfVonSToFtC/aF3aJ22Mof2kYZyufR9sE2LQI1LPOmCo/AiJpkejejRivyu0e/Z3HZFdL9EZst0qT3HovdBBzdVrrHdeImF5TnIQRfzqmrkxiIq7aFN8aECAml7Q5VOKhwcXEuR26DIbnSIzN1vYkmD5DwwizYqJvGiJr0rXHxNJcxlceyARefjEns9WDUG9VNNREBAYZ975JQEu+9wh0LOa48QeP+zeyhXXRQk3q29qF1O+b7cVCQ4N0Dd2Qm67ilPCOU2eyQWCFrOUngZIKQoNtvkVJrIpByqPYPsi0J/Ablyz0R/xsa3I8us65pRDcdz99r/AuDFnnt5kXup7p3AR478Ccb+CfgVKlQ4ZBm3HPXPfvazNDc3A2UJfyEE//Iv/8K3vvWt8WrykMEdLBJtdwieFsFTGqbw8Cck9m/2q0FYVIjePcmFsqx4qzyBzI8eaDkphVFy8QUgZ3iUpiexDgenKBi4V9JwjsUidylKK9f/dPdTDTUTgsjtHj9cdD33NLzEpnwHFyaOQftpx8iGFBhtGRJH6nzlld+wPr2T8xoWM63TAiThWXo51HLv5jaE7qnGf5k9ZKTvZXc+gyE0XDXy9/i0cu1b1ZNHPbVzxDr1SjfW0lZKST8B3T9i/5V9m7m49QSmWRYRN4fSDCZUz6dK+oka5c6/lWh0WR1HGHp5un4PYsEsSn4/QkApP1p5u5Tv3ePlrBjq73T67DRBIQCJqb/GRGNRYTxfRJQUKizAECi/wJnvA3OcB1a6AB0oKXS/RsgIMGBnCJkJUqXuiqH+HkVTGl33O0NGOkDqBY/QdJ1AtcS9qxc69ryPUx7eL7vxf6rxoNdRVytH58uKVVnM+THU/amRK1wQu4owa7gGuhBgCInxSM+Q4rUuIHplLSprEKxXZF4ZeZjQhNH1aaVpsPt2eyieuLhD0vugQ8NlGpoG9v/2YJwYQUy28JZnUa6i8HE/yPGLlqkwGqPXT/eK8kUy4wJhMVQGrfZ4gXFvD6qnbDDrAuourYW4H88v2PUnb0jTx+6BXX90aLm2HvmLLshLtNl+3LCBcofHKUbRGSEopyUMvCdGTtCr1QV8R3qEZurk1nv4koKS3+Hr+mO0Jqp4pdjF1d48DvtlO3Kw/ITpk2K4L4+MxsP2iKaCuNHRZRJ7czspeoPEzCiuW9FFqFDhvcC4qV1t376df/qnfwLKEv6f+9zn2LRp03g1d0ghoiaszWP8vgvfrZ1ov+8Gvax8O3pjqD7ZJHmyib4nHtzXMHrD2NE6RkQgDEFQ+EmvV+x+TNH3rETaEKxyYfsgPLCR2FEja3f6J2gYVRparyT0wxxL7dn4NZOpVi0iPIY4ls/AUS43zF7GlZNP5cqmWWgzy7mycgzns5tWRN3AqOWr+nq5cvLJI5YtiE2iJVgNgHK9se3cPR+wGhHjI9POHVr8fO863pdopn/lt9j6yvfZtvLbrH3xa3iqb4yDvHHy4QjiQxfCxEYIBRBLjsA78WhcJXBdSdO094/ap3HKOZUP7buEXcUe6gyNgJl49RByF8ynC6iohnu4hTfVwptoIuuNVzXS7eJmMv2/J91/C6XCuretWSD9ZeV3gJgRptceJGTFK3nq72GUB8WO0e8ZN6sgJ4eN9L14IPtGl/wcb0TVGD6DmIEREaCNkRuul+uv74s+aI8sS6VAPDGIIRTBRghNG/4uBiYIolNH5/fa/d6ohcV2hcoqhM9Bm2yi2h1Um4PKehgn+zGdg58q8F7Hywxfy/5HHapPMak516DhMotIzIOefe5hBTw6gFkjcIU2ZKQPHSsPTo+HeVEc8xNxtLkFtLbdCGufQ7zBSh1uFgItGlatIBjz+GN6NQ90b+Jnu1/gucGd1GY1GNxnGsyRMIYmjjTB0K1Ry2OBWraX+vnqpl/zfH4tBXGwp9QqVKhwsBk3j7rrumSz2SHVvlwuVxHPeoPoLpjnx3HuHoSSgpCGQmCcXoVza//whgENrd6k9xcOmgUNl/godLpkVigaLrXoe8jBKyriiw0iCQ/n//rAVuiLw0y8PMDmn5Vz1o0EqM4M3jO7EMkAap6k+tSyEjACNAvkgIcaKCvhJnf6OXbBXAKODouacXatGzaYq3yIKh/rBrfi4pF3iqT610DVRuoWn4dmjjbs9YTCC5bQEMh9LO+FyTiW08d/LriWVYM7mBRqZG6kBZ9XVt7WakLQWgVt+3hckgG8xB6jXwnOqj6G2VUT2V3sZ2qomk0vfR1PDs9U9/avYzC9nkT0uNe9Lq5I01Pchu0VqAm2EhKN5bqlCtKJJNZl56J7LrZpsW9Euz96GPOWfImtq34OaEye90H8kbmVgj7vEnYVe6kS3muGvesbSuUw2KY39kotZJ+jVFiJLzAPEBRzy3GdToKRk956RwNauZZ6EqLG3jz1GKni7tfft8IhiW54BCZqFLaNfNtYVQIsDYIa7B+BFTz4kT76ohDy+SzYe9r1CZyGALtudWk5oQr+sM9kkyXQmswRhrpSIIpj5IqnPXRXgbKprVe4TQYgMFwbQwmkGjlBrAdGG2R6EDQdlOZhHB/GvS+LaiuhLw4hogLL1ShVbPWDihkfvk5evlySzarR6LnbpvXYMe6DjIcYcNHyWtk9te8tL0DzFCplowVLuHdvREQtGi+sp/M2B1mCojQI1huo3eUJANnjICZaqO37eL3nB0ntgOgRgoYzdLTVaVpmRIbGNT7dQPOPHP+4Kzoxlrbi/nXL8MLWMIPxHGt3LueEyZfyxNZbUSgsPcBx0z/Kv626laxb5JHdL3HdtHO4qPYkVGUwUWE/7rvvPv7nf/4H13VRSnH++efz4Q9/+DX3WbNmDTfccANNTU1ce+21tLW1ce21147Y5rbbbuNrX/saDQ0NKKUolUqcfPLJfPrTn0bXR78IP//5z3PZZZdx2GFj6/u0t7dz1VVX8fDDD7/1H3uIM26G+vvf/34uvvhizjzzTIQQPPDAAyNq9lV4dYwsuI+kMU6OluO2bIW3JocWNTCvri4LmCQM9Gl+vD3KtLIEfY86mDFBcZek0OYx4RoTd5eLpbvY3x+up+zeOYjh16g+2Ufvg5JAk45cVx4Iqb4CZm8/+XSiXFNWQH6rR6LWKZenAfpEiS+ueIoHF0zG29CLcd4MyNpgauXxXcHmh7tu59ppZ3Fa85GEM1voK23ghdCnmFP9AWouOJreO2VZmTciKJzTS4fWyTeOPpvbtr1M0fM4o7GJSQHYnU/TQoziynmk+hSvBAVHnWbgi5QQIQsunY14Zhes6YFpCTi+hfw+HkxTmUw1JjA1PAFEmhWZtlHnu1DsZUid7lWwxQB/WPNltvStKB9X93P9oh+Q0Kfvs40GhjVqjKvwE0yewLwTjwIEEj+yMmf1rmFnoZuAKuK3YmNvUFToO12ceW8s/NUpbaVUeIlA5CQ0rWwo6EY1hexjlPIrCQaPfkv9VH6ByJZLtEXNEN2lQaabMQZLnW/peBXe/XjKpWapRWdK4fSrcv7uUgPLcFA+0M5PIm/pGX5nHRuBqM7YWtLjg5QgBvIYH6pBdbsIU1CQJl2PK5QDPZt0qi+vwdiYB59Am+ZDecWhZwfKoe9aDeWa1/u8W/VFATAUss1B/iUzFEIoAXl6CDVtpMCWZUqiswXptXsOIqB2iYZmgMCP/T+9QyHQ7r1p9FIIdYafg3m+3usIAZrPJnmiRd/j5QiI8Eyd7rvK0SFu0MTY7z4wjgnjPpuFDpfqYxL0Lh9eWX2MQGvLQkMeikb5HjI0AtkCzUeANDQ0V6IfF0H4NVSPiwhriEYT5gaRbTayxU9OWtibwDAV3NyF1+1y3IYo/3LJKax0OmkNJ/jfnlXctKgR69k9k6eDRbyATu8H55LsySGjilS8H8/McP7sGym5NvMbTqHoFFBmmE+/9Buy7rAX/Vdb7ue02iOJEjkIZ77Cu4Wuri6+/vWvc9tttxGPx8nlcnzgAx9g0qRJnHLKKa+63yOPPML73vc+brrpptcsfXbyySfzta99DSg7YT/2sY/xgx/8gE9+8pOjtv3qV7/6tn/Pe51xM9Svv/56pk6dytNPP41hGHz6059m6dKl49XcIYUKKdAF7r3DnmLt+BBM0FAbPJRU0OOgZgTY9dDwB6fYIQlOMYGy4mmpV2IlNbzHRuf/ec9miX04gFI6ZgLE7iisKyufirtfpuqkWTihJPhMQpkS/HmPJz+isaEmRX6Xw6DhEl/UjPOrl8tfT1eiTUugz0yS7ynxo/V/5p/mXE4VklfW/xaA3X0v0lR/LK0fvAknD17E5dHci2zasZOTIxYtzkvomo8nVq8kMfeT1JhxcrsFA93l35nLKJbf43DiheWZ6WzQQD9lIubSVlxjVJnbEWgiyrSJ72PVht+MWB6vmva616Qzv2HISAdwvCL3rP8xVx72DYQcHaK2P0qBx+jw/grvfNoL3VTJDAGzecz1+nYbWa2/IUEpJUvk0vfjDx490tDQLPyhxRQyj+BEJ/NWClkrv0AbLBsMVWaYjdkdLAy20JXb8jp7VjhU6Qrnadrs0rRQw4370IREWzGIPduHP2/iKoX4cD2yz0VEdDS/QOsrQXTcsuLGQOE9byNXp8EQOOfX0PHI8Is8v0OxY6fGxKujGN0p5Nad0BTGdUca2TLoYV4Swn3SRmUkxlE+RKOL5zOhbbSXVW52EWrkMyv7POL5ApFTQkhPYOge2mP9eK0JtC53RJ4ygLc8j7EkDBWN3INKn7aJZO1srItMdL9E7hMB3r0CGi+vRTwxiEp5GMeGERNM5E/LGj+hwCD+U6J4nobRrGOmM1AYwHt4O5zQir6oCZUqIdcVEc/lhmqeu4BYGMA8NYLckEEIiTcrSjYUJPWKxN8kaDhDIH7RAek9EzftLkfJOnzRFBszu1kQ0+k8sp8Jc2ci+mzs6iBtcR/hsMVAU5pMbgd+K07MPxcIwV4HvA9WFjfQb48cy8lRdWwqvNtwV6zFvedxGEhDPIpx9gkYR8x+W8ccGBjAcRyKxfKDEQqF+NrXvobPV3YmrFy5kq9+9auUSiXi8Thf+tKX2L59OzfffDMAlmVxyy23ANDY2MiFF174qm2FQiFuuukmrrvuOj7xiU/wwx/+kJUrV9LZ2cmVV17Jfffdx8c//nEA/vu//xu/38+WLVuYMWMG3/zmN0cc669//Ss/+tGP+MUvfsFTTz3Fz372M3Rdp7m5mf/8z/8c6v97jXEz1AFOOeWU15y9qTA2RX+e8IkR5E4budNGm+JH1Blomovbvhl9QQteMsKW35TLp+0lNEWnuNNDD0Jwsk77L0pM+qiGiI4ORxERnWKfou9hFwRMu7oa+UIHqr8IjkR7bguhS310WatIzFiIMqIMmjara/r5Zs8T/OOcpax81mTpbDCvno/qzSN8BiT8PGFvxNQMzmlexNzERNpW/WhE27t2Lyccn04pcTivpNqYFGmgRfSzfNP/jvjsuJ5NemA7ctf7RuxvF6GY3yf0TSk8ndeN1pQSprZeQLGUYnPb3fisKEfP/wRB/7TX3Tdd7B21bHdmCy4FTF7fUK/w7qWz2EfEHSRgxkevVKDtcPFmvrEPSCH3DLpRh26OLlWp6WEs/ywG++4lVHXpmy+p5tcQ2fILIWaE6SmlCFsJNvQ/9eaOU+GQwe8E2DC9l6mdCXybMxDX6TtV0RXOMD2XhD/0QtJAmxqAl23klhLi47UczNB315Vo8T1DEVdhSInQ9SHFbaCcq762HfexTRC20I4/CnefWVkpwQuEMEJ9aNNTYOgoXx6ZbKIo0gSn+mBFfkS7YoYPl5H5+LIGeDmL8eKwQSQmWhSqioT9Y3xHQzqev+JNP5goBYUwZMwOsrc04PQras7UhqIpnD7Y8aAgfnSSYLNAcxy0DgcMUZ7J31RE31REB8y/C+P+6YWhY2u1IWRHBq0lCqaF91xuRNta3MQzHHI1RbSooqitIDBxBrXJCJal4X13jOglr5+tz36SgBFkt5NjN4qjFn6KiQvPRyloVOVqHeitJKOtr/q7WwMNxKwwg/sY65dPPJkYkYqx/i7FXbEW99b7wNnzHhpIl/+Gt2Wsz5w5k1NOOYVTTz2VWbNmsWjRIs4991xaW1uxbZubbrqJ7373u8ybN497772Xm266iT/96U9cdtllAHz84x8fSlV+LSN9L9OmTWNwcJD+/rJDz7Zt7rnnHqAcgr+Xl156iXvvvZfa2louueQSnnzySaZPL0elPvnkk/zoRz/i5z//OYlEgu9+97vceuutJJNJvvOd77B161ZmzZr1ls/Ju5lxM9QXLFgw5kDzxRdfHK8mDxncmEemIUtgbTkHXfbblI7TecBYxeJTW4h1ZDE0HzWnhel5wEW54GvSSJygQ9FBi1hIFOE5Oh13KCac7oeH9slF1ME4NUrHYwrNB5HDdNxgAO1909BsCQiU59FTepld1Yfx7a1tpCM2V81sIW5U8YNJy6ghjL3AoLPooz6aR/MJpKnzrNyOE7b4SvyDrBrYiuO6xKOT2cZDI39kqZ+F0VZmGT4sK8a9PQ+MmhuuDbeQqFrC48+MzOsSGlhvcWJNo5rDZ/4jc6dfgxAmGrE3pJ1QHx5d83xB0+n4iFaCHg9hpJJ0FfuZ6A3gH0M5XfR7YApU8PWNaullKRVWEYye/qrbGL4plHI7sIvr8AXe3Ida+UEUVDkU1AhQ8IpYRoRUqev1d65wSKJLMPQqHq/poBBPY+omfpFghkhQjGYInlaFvC+F6s6Ud2ixyNeV4CBOPhYKDoFjAvBMDhyFeHyQhjNr2P2oRBbAqBI0XqijtWlwwRxojbE7so2QmDJUpUMpSAe6CcXq0Y+IIYTELZl4VhHp+chPGcQ3w4faUCrvMMGkON9GlkZGOaWq+4ldl4RbB1H9HkzzoS6MgFXAmaAjanRUzz511JdV0WuBcfD1997ThP1TKOjb8d6Xx3pgMqmXXGrONOl7uJxTblQJjJmw3FvLwuAkwssLGCdGcB8crpyjzfQhBwaH/z62GTkxhhYPIosuIuFDm+lHri97JUWjiVrk54VQBxOmC4qDWxFOjk5fGxF7KsGNIWLzg/Dy8ISQtjCAHetBKYntlA1sXbNoiM9Hvsn8t7iK8r0jbuCvnc+yPr2TMxuP5oiqGZX89Hcx7j2PDxvpe3Fc3Hsef9te9S9+8Yv8wz/8A08++SRPPvkkl1xyCd/85jeZOHEi0WiUefPmAXDWWWfxr//6r2Qymbfc1l5bb6/He++x92fatGnU19cDMGXKFFKpctTwwMAAN9xwAzfccAPV1WWx6JNOOonLL7+cU045hTPOOOM9a6TDOBrqd91119D/Hcfh/vvvH1NooMIYuFVkJ7STv8jDKJk4QRstHGWCU0fBDFDlqyG1XpLd7FF9monVIPAFHbjvZWRbCnFYNaVja6lakkC4GrlQieANtdBmQ0JHRAw8TRK7yMZzNAr+PP26QTAUw5e2wZN4kQw9xuF8bvkqCp7EU4rPLF/FuROb+NDUiUhHYgRsrKDH5s2/AuVgBZIEauayNrUToRucWLeAJlGH07CE9VtvJ18oe6UtK8L0SecjvARBX1mg69Qp11F0sqzrfpqAGeH8OZ+gMTAfpI/Djzd46fHhl9nhSwx0nwu8NWtdSh2NalC84cCxpDWFi+d9ljvX/oCim+Ww+pM4tuVSZKWe6SFNjz1ITAeTELo22njROlxk8o291wq55zCtiSNC3vdHCEEwvIBM6gks/zSEGKOqwquhCZRVrgeshQRVZpi05+HKEiU3j8+oxOe+F0liEgg00V2qJmjo1OgmQgKEyB8lCCQt1JYi1Fu4M0zsYBHtIBqeUirSNe2EP1OHL1PW+BBJj8ZJAlHQkEGXPl8Xdr2PvHR4tms7C2SS4P6vblOA1wF374KsjTiuHrsmhPAS2FVZrEtqMPvCoMBNehQi3ejuyNxenwrQNXWQuuvj6HkoxT26wikSMs7WkM2k65KYO1xURiJaLbYkstTIUGWy9iBjSR2fbyqlmgHCZ+cxHAMV9WiYJig5FoVghvuzL+DoLjuqBFMuriPY68NsqUb1uqh6E5EwUMUIYk6SkpVju7YVI+CSGXiBoJWkhVMQS+P4TpCgFCImIJNlplHHRtWO4wtRcjqZGZ7CZv2/8bWEmbTgBKpPmoLICggKSkmBPzCRkxb/Bxu23kYoUMuMycuwjNa3VAq2jiTXNJ2DmADSUxUj/d3OQPrNLX+DPProo+Tzec4++2wuvPBCLrzwQm699Vb++Mc/ctNNN43aXimF540hwvgG2bBhA/X19UPi4X6/f8zt9g1dF0IMOcmEEPzoRz/i05/+NOeccw51dXV84QtfYP369Tz22GN85jOf4eMf/zjnn3/+W+7ju5lxM9SbmppG/P2Rj3yEiy++mA996EPj1eQhQ07Ps6G3QGNhEvl+g1CNg6dn+d32h/hK7HrsAUmgVcOICvSQAA9Kgzpi9mR0Xyc81UZwdx7nLI2S1s0WZTLXiWNEQG3U8R7tAU0QPiXK2lk9fOjl7/Cl+deyKDQHp6psjPTqebIlm2vm1JB3bBK+KFODGdKFDezKdlITnESvoyh5WZKNR9D25Fcp5bvRdD+nLPwIkegsNOpQCgytlbOW/oSB1HqkksSi0zG1Bnqky5ZMDglMDie5ePaXyM/ow9B8+EkiPQVIaie6nFxrUMhBIAxGwEUd5C+UpnzMiZ/F5GOPwqOEqQcxZKBSBv0Qp6PYR40uCGivEvbe7eFNe33vo5R57OLa1/Sm78W0atD1BMXcCgLhY95Uf1VAoGU9ZMggbkboKQ0SsWoYKHZQH576po5V4dBBUcAT/UjhR5FAoOFIyNwNzg4NMxbC3a4QzyqiH/GhBQ/e+1UIgZevwnhR4jzYAwKM0yLodg/eU9vQZyRJnj4fe1AjpiSn+qrYLgcQghHGjtVroP9hM2rhRJRloq3twFIacpFDeLAJ9euXcEsuQhMgFbFrjiCz32M9oDRiLwRQf+zDcxVGlU7gGj9Oi0dg0CT1nEZiuoEISIoZgZEKkz9MMfawtMJ4UnRKJLeacM+qcsmzCVXwvpnsqsnT3u3jrPhxuF0CUgZmlYP3++Vlzb/qIEZwFs4vestVBiIa5gerqPe3stEpEQ6dyZRSHAYMep5ReAVB5DATBiEUM4imPRrMVlYbaZ7e+huSspkj/Z9CGOBrL+H+uR9Ke8TnTg2CCpKIHMuSI45FKQ3Pk2/JSN+LlKqiXXioEI+ObZTHX0fd+HXw+/18+ctfZt68eTQ3N6OUYvPmzcyaNYvJkyczODjIK6+8wrx587jnnntobGwkFouNOIau65RKpddtK5PJ8L3vfY8rr7zyLfc3FouxePFiLr/8cr7yla/wne98h7PPPptf//rXXH/99TiOw7p16yqG+nizZcsW+vreXr3q9wolx0V7ZQprOwyCEUHuFY36mRqfm30VWkFD5iSuXq7X6WYVxXZJbpPE3xgicNg0AosaUY9txu/pGGoaMzsFpYEiplK4D+4t76Zw700xu7aB1mAdP9t8NwsWTMP0LLpFH+mMxMqGWaolCPgVT9s72ZHt5YFVZQXHWXXHMqnpfL7wyh9p9Cf47OEfpLT8a0ivyPoXfshRp30HABuDzf0aT25toKGqgQUTbTbkVtMYCPP5Z9eTtstKrQFd57vHLqRJb8TQtRE1xhUSI2gTOUAOQYFOKa/j2hAMgzDtN/ThlFIhhcsjW37JK7sfpSEyhfNmf5KkMaNSevAQpaPYS1R4+I0xDPWsBE+h3sB9Wcy9iGlOeE1v+r5YgbkUMg/jCxyGpofeeIf9AnLlezFqBOm2+4lY1RVD/T1MH4P07ciRyCYoGSW2VHfQEq/FSvtxtoO/RSPQquH0KbLrPegXB1UczefTCb8SQt43PD5w70ljnB8Hqw150lzsAZ3Uiy56SJBcGCXi97OvspsQ4Bv0KCw+kly3ge4DVR0nzgCuq8PmfuQpc3DMMALQ3TzGuh58J0+gVByuJZ/sNbFu2WeckvJI3FIi/QkftbaGpeXw/isDCswag5Yrk6QdA6XtV4++wriS1wrUDEQpZYuoU4/GF5FoT61D/8saJl2xiPptioFHFIEWDaFBx32C5suPht89h3FYC86tqWFjNyPhF2niH65mkRK4vxtAFPtRJUnDsRGKEyP0PO5Sc7pJIR0htVMSn6wzq3oOJx92F6knFbte9JhwqsK9dfje8Z7IoEc1zBN9mEUN6ULJfOteywqHHsbZJ4zMUQcwDYyzT3hbxz3mmGP4+Mc/zkc/+lEcp/xuWrJkCR/72MewLIvvfOc7fPnLX6ZQKFBVVcV3vvOdUcc46qij+Kd/+ieqq6v5wAc+MGLdww8/zPnnn1+eZPU8zjjjjNct/fZG+MhHPsJ5553HY489xo033si1116L3+8nGo3y9a9//W0f/92KUONkYeybo66UwnEcPvOZz3D11VePR3Nvmr6+7JvOETpYpDMO9mY/TSUbo8PGbvGxzTOZPsOi6y6b+DEmvQ85JE80yG+VZF7ZJ2cuJmi8SMNPHicQoPNPimKHInqkRnJbP3LTyBkybUGQVe8fYEVuI2fVLyboBMh7g8Q7EwR9ArXTRpUUWr1Jusbjf7ffwI6B1QBcc/TXuP7F21AoTqmZzTk9K8l0vwLAnDP/i04jSGFwMr6SQTwgyAi4f6PL7Gkb8HkROgYc/jrYjVQel0zw0RJwaI4G2V3oJGBESPon4VPJVz1PNTURenreZF6NNNj8EmxdU/5KWz44/jwTI/D6M4dKL3HL6s+xqXdYfMbS/dx47C8JUv/m+jGO1NS89VIt4/VcvKVrNc68kT79z/Y76Ou7j+ZAHY1VR4xYp2130bpdvMmvHZ6upE2q92cEIiei6eVroxR4aYEsgR4BPTB8zn2WQcl2KeVfRgiLUNXre+GH+rS7rEztzfexObeLzmIfc80SjZGZHNt8+Rs+zv68W6/fWPu8Fd7Ic/FOPEdSc7HbBNmbzaHoH18LiGU5kiqKvUmj0CbJbfLw1WlULTQwqz2K1W/f8Hyj5yMUshA/7EOtL45Yrs0PoB+nkSkl6Lp9n3rVOjRf48OpK+F5wxO6gd06lgtsK6CyEm2anyI65hyFbINMm8ngMw5KQuxIg8hUD2OqQTo3/O6PbFDIn48WDlWfrcNKSZwf94xYrs0J4P1dhLz2xt6Z78R7ZDyfiXEjYzL4aw93z6nUfFC/zMKKSHbd5uLscwlD03XQIFRvUxXpRw6YuHeM/t7La+sx2vLoCQPZ5SCCWrkcW5OFq2l0PqiwuxXChNqzLFIvu4Rn6vT+1UEPQvP0IjycGnFMkTTQz43h/q6vXFXh4hj5STru39gl/k68D/fyTunb2xlHvRnGQ/W9wqHFQclRF0IQjUaH8hcqvDZVmkloxQBqcwl8AuvZLDPPjJDdqpM4zqTnfofo4Qa+eo2uv4wcULmDCjev4z5lUzoyRLGjPFMnCyCaLNhUQsR0OCaKsjS0gOKwDbU01cd4vH8VjYFqjtzWihUTyDUFhE8rew27HSKawaUz/x//+fRVAORKg/h0E11opKRLVfMSJsRPRY9U84eBbczzzSOyQ2PrKoc2oHaCxgULdSblpiL+PIAYMFi2eDq5w/t44KUPsx5Bb2IWM2ZczTaZxFOSZqOEcA9cSYZCWmfrmuFzZpfg5Sc9jjrdQKrXTszMuT1s6n2B+ug0pjechVKwsfNu+go7CQbeOYZ6hQNHR7GXoMwQMEcLmWg9Lir6+hoFpcJqdKN6hJFud2rl0Hmfwu7SsKolenjkoNfyzyKf/iuuswBjDJX4sVABgdZVnriLmxFeTm0iHGqhL9/+hvavcGihS4PSfdqIFJ3SDqjvjuKr9uhfKylsL4uKFtsldrdNyzUHszQbaBrQaI0y1JngRxoWg0/sN2ngQaHNw9eo2JtWKQT4NIHzi66yhxTwHs3gv7YGx7MopgXpl11iiwwQguxqFyNqEJ460mASodFDIhHX0SwDNbinfzqgC7AVckMR06kCX8VTerDw+XSyK8SQkQ4gS5BZ7RGZpw+XahOgWZDbWNby8fI63o4U+uyJIEpQa8KRkXL++focwi/QEzrOH/uHDxzVMa5IIoMGdreNv0UjfqyBm9qj1O4Nt09kjOF0nYF8Jgu2QvW5eD/pJXdjiE3JNFMD1fjVQQtqrfAOxThidsUwr/CaHPC3xPPPPz/m8l27dgHlcIoKr00krfBqfTiHx3EKYAYVZqmsZqqEInakQf8TDqFpvqGSJCNQ4K3M400ersXsDCq0Y/x4BUl+aozeR12UDbHFBpHZgtgum2QizPLu1Rx1dwKWxfHWFFA79ngydDAvT1JXN4GzZnyEZ3fcSSxQy8cbLuSwgelEt8YIN2oEsBHPFfjQTEG2Nsrjq4YHMN07JUdOVWg/7h4KOwv9NY/P8TNz3nV0Bpt5tG8jnbk+FiSr+e7q5fzr3LMJFSwMU6FbLv6MhhjwIKyhqoYHWaauIVF43mvP8Bfyo9cPdEn68haxoES8Ru67ofk4ovVyOsQMvr15G0IIzp9wFT5fHVJTZLGx0Ako423loFV459BR7GOSM0ZpNgVan4fT9NqTSEpJivkV+EPD7z2nr2zcG4nyTWJaErtHw+/3EPu8kYVmYQVmk08/SCRx2Rsr1xbQENmyYRMzwww6OQJmnK7cC6+zY4VDkaBt0jM4hhFZELiOjh6G+kt0nH6JEdVwBiVOWkHs4PVR13XcOSFYmYMWPwKFmhwgFwpQXCWJzNXwNypSzw9PpAoN9v34CQFqZ2nISN+Le/8gxsw6vJwiOk9n8BkX5UHVUQbSUXuOMYwXMuC0GDw4WD60T8D51UihIYI6nJOgFLDwbIEvKNF35JDCZFSB9Qrjhq7reP0jr3N4lk5gokZmjUd0vkFgoobdq/DyCiMs0ENgBDW0qRNxNnlo19WR3qnR/7xE6BA/IUggrOE+XhjZWNqDtIerQeJEA6tGw+5RKA/89QIzKRAWKBsKyiTQaELHnoklS2AsDOH8dr+Uz50O/7LjXq6YspCL6+dX8s0rVKjwmhxwQ/1LX/oSAIVCgY6ODqZOnYphGGzcuJEpU6bwl7/85UA3eeiha/R7QTIPDVt7iaN8BA/XUEXB7gfLYVvKg+gCnfSK4UGCVVv+KKHAMrw9IxgQCLyX87hHJ+j+3XAY4cCTLnrQxBk0WFS3gKaqKPKsBCrjDhvpAB64D6cRU6t4duednDfl40wpLCTyrEFxiyKHIveyR3i2RWyBRcCvCDpylOCP1uWM+jAZyzWMI4/k62vL9daf6FrFxN0r+fqUT7H6Dp3coEswDGceKVA/60KVFAhwLogTXGChb3NRT6QRNQYsCZNNqlfNGQ9FRhs7iWaN29e6XDDfIG7YY+xVJihqmFB3Fm3dO4j7AvQUc/yxbTMnNMzlN5sf5+HOzdQHI9w0Zymz/XWVD/C7HKUU3cUeWmQen7GfuEtOonQB1msbz05pE5rmRzfKKRxeQeBlNaya4ZtDmKAHJc6AwKoZed8a1mScUhulwir8wbFLnozoswXCBRyFbmpUmSFc4ae/UPGovxcxlCI6WyO9ep+XkQArBMLS8DfA7luH33nBKRrhGQZw8HKuhYCio8MFDQw+54IQRKM6mZc9Cm0SXi57SgOTNArbJMKC4ARtRAV0KSnXyN6fokIohR7W6L1/+HcOPu1SfZYJYqSB7RUU3tQw+oQAOgoXgSN0go7CrfbR+ZjA6VPsnSBovCSGv1L446AipSQ0G7J7Kv0aMYFVK+i+s3zPRhfoDDzlUtg+fM9Xn25g1Qq6XrAoDSiqYgZ9z5a3Vy70PeBSfYaJ74g4xsbd5TKBSQOR0JGOQhgKPSDYfZsNHmgBqD7Nws0oas+2GHjaofspRcPp1YQiHuQ9RMLAuT81ypFSDEicrORXm1ewpLaJkCoQ0etBvokKHxUqVHjPcMBj3O68807uvPNO5s6dy29+8xvuuOMObrvtNn7/+9/T0tJyoJs7JHGFRWbjyLd7/wqJpiu83PDHp/chm8hcg+rTTMJzdBJLDRInmuhry/U6tScHaDxTw4gJil0SjgqT2zraeky/XBbp6brdpVm10n4/MEYUuOp1yXt5/mHSz5nVuQRvwE9xy8h+Ztd6FAc0Ou5XYOhMPj9FpHYfo8Q/+pYTEZ1nU+tHLFsYmcW6R3Ryg+W/p7Ro8NteKO0tnAvubQNYuyTy532oTSXk8hzyO92EUq8+cvJHXOYea5TDLYFwQhCYZvBKp2Qg/6q7gabY4OzgF1vu4NnexzmxMcF5rTNYWN3M77e9zIOdm5AoOvJp/umFu+j03l55jQp/e/qdNBHhEjATCDHyvtX6JWqMSZ99UUqVS7L5ppX/BtxegRGVo968egi8rIbazzGnezliKkak52GCbT8g2PYjfF13IpzBV2lVoAICkS0/JwkzyqDrUXDT2N5r3eAVDkUUknizS3SBBhqYcUHjBQam64Ar6HtkpEGe3yLxcn+Dftqw+3aH4q6yOGr3HQ7hufqQwzuzyqPqCJ3YYoPmy0yMHaM7KSZZo5+rkyIQ1yhsGe3xzq31EIwsrWhFQTmQ69HoWSUo5XUMC/SgwkmBs59ztOdBlz217iocJDxPko1tpe5cEz0I0cPLkRJ7sWq0EUY6QN/DLrkujcwaiREUZNeMvh+K7ZL0dhBHhlEX15A+opquqgT5YBAjpNFzrzMc6l6A/sfKz05mlUtikUbrWQrfmhR0Ozi/6cP5bS/mCRH2vcVkvc6Lke5y9J+SrOt5lu88+XfcveXblET/qD5VqFChwrglo23bto2FCxcO/T1nzhza2trGq7lDClkcwzMggUEb3Tf8ASruUNhdLtFWRXKxIDBVw9eXR901gKgxMGYFCAZdJpzo0Xq0g+i1sZKjjQsrqaGZ4A4oZL+BzIOMjw620BeFWb0ySqQYo/0hQenVRPwVlDoVxUHBtkIHvoXlDUNRgWqyoH7ksb0LQ9ze+9SIZdOsSaT3OX7EUqgxQjhVpwvT/XBqDDHDD7ZCa3+NXHPNI928i+oTTGqWmPRM0vnZyrKnpeo1BLl3ut188vkfsjbVRldxgN9vfwBTz3J83SSWd20b+XuUYmc+9SpHqvBuoXNvaTYzMWqdGPBQ4dd+fbr2DpA2utkIgMyWn70xhd/1cr66lxEgXcz8dsI9jxDueRjDK1Hy1zLgD1OsPg2lBwh03oxW2j1muyogEHsm9GJmhN2lfqp8dfRVvOrvOaTpIpM+7D5InmgSnKaTWu0hkxbSKXsTR+1THL1sPPE8SXb9fsauBpQUDWdoBCeVo8Is5RLvHED7Xjt60sDY7xOVqZOYH69Fm+FHNJoYlyZwDvfhugqrevR3z1cr8PYzslVR0fuQy8BTLvktkt77HfLbJKIoUfbo77KXU6hK6NRBxTR1agZqCIRdmq7xEZurkTxKYCYgNE1gjCGFpNzhJAc3rTATo+8Hs0pQ6lWwtIpdz+n0v6DIb5V03emQWeth1Y3cx00pjKggOVPhW5GC33RDykXmPYwzqtAXR/ByHuoTUdyrQnRcZXLn+V18q/NJABYk6uhLrQBgRfs9bBl4ljeS3VShQoX3FuNmqPv9fm677TY8z8N1XW6++Wai0deuDfi9732Ps88+m3POOYf/+7//A2D58uWce+65nH766SNKCKxbt44LL7yQM844g89//vO47msLgb2bMEMKYz9vna9eYHgljJc3EZkrQIOmMwTB5/rwvrEL8b9dBLoKmJNNzCsT6POCuE9lcW8bQHcVRgREXmHVCsz48LGFBaEZelkMBdD9GuiQd0yMD9aUhec00BeHyC32U9+qMbha4uXLee+++pH99LdolLr3iPl4MCiLPJFbx1GnWEybb5D36ZQ+WoX2oSTapXG4KYZT5fK/1Z/gUw3LCOjlnN+8yOEPDh97sAjU7DcyM8CdFKQvEWPnWh89VTHUFbWokU6SEeQo8O2NPycb7Obm9Q6PbCvrr16wQBI3X/0e2pxtx9svf/2BjmdJ+CyS/tHls8LGgRPAq/C3oaPYS5gSfjM2at0bMdQLuacx/TMQQpS96X0CPfLq4gVGwMafXYW//Q58uW04vjry8aOxQ1MgMAOFoFTahBuagR1ZgK/7DvAKo46jAhoiW57USlpR2os9RH219OYrE6XvNSR+uu53KO6Q9D3skHrOJbdRUewvp0gFp+4XKeIHs/rg9lHTBPo+5eA0P0w4SxB6sRfrj7upLaVpuVBDrMogt5cwL0sgDcX+n3xPwWCTovShKrxPJkkfoZM3PbLZEsEZYOwj4qwFILpQI5cZGVHgZLWyUNg+DL7g4UodK6RGjZhi8zU0WbGuDiZKKbSnJdKvIzYVkD/uJPhgN62nKGoyKXzdBbT9CtsHp2oU28vfb7tb4W/SRkyYGhGBERFEZ2o4KYmX2e8eWO4SmTNy/GFUCSxfCV8xhdGkMK9OoB8ZQlga7uNp3LsHUWuKDJjdLK+7g/VNq3iZLmoCYd7fMo3TqyVrOx8aOt7qrkfR9YMr5FihQoV3PuMmOfnv//7vfPrTn+YLX/gCAHPnzuVb3/rWq27/3HPP8cwzz3DHHXfgui5nn302ixcv5nOf+xy//vWvaWho4Prrr+exxx5j6dKlfOYzn+ErX/kKhx9+OJ/73Oe49dZbueKKK8br5xxUNEo0nmPR/7KgsFMSnqYRm6FQT25GbOqj5qoaknP9cH8Wrd6HmBkAT+HeM4B5YQKZV7gPlUOvVUHi/KoX85IEKqjhpRWReTqar+ylQED/4zbhmQah6RpuXlK3zGTwWY/uHo3E8bUYIUhvVXhaEVO3KPWUP2KpF1yqTzUJTFQUd0kCLRqaT9D3sAMCjCqNu3atZXHNZPwhCFfDg9lNOErnqKnV1Euw7inge6pEI/D+6EwWfnAqf1JPM6OunvpTdZ65y0VK2LBZMfGSBL7f9EHKA1Mg/j7JrrvdoXDE3GZFqVfQ/AE/MHauuSVMwmaQ/97xAy5aeD6mjINeoCZmodH6qtckOIbhHbNCzIrU8ak5J/K5FXcPpaIdXdPCRH9stMhfhXcVuwq9+FSegNk8coUHWl7hhV59gO7YO5FeBiM0AdjjTddAvMr8jeH0EC4+g0OcUnQhrm6NWC8EWP6ZFAsvoesxDH8L0unBGngCu3pk+TYVEGiD5UFp0oryRF8fx8Qb6M6NjPyocOijPIFXgoaTBYbronSNTI+GKkkM16FmmiIV0slsUfhrBclZ4HNLHMwkCSEEoek66Zc98KD6aIF2WzdqT1SIWp3HqNXRjwyhFoSg4KE2ldCnjh0CZY8hCJqOD1J3TQTZrYMErVYyEB/A5+xXMP7VJnl1MHSP5rMEvSvBTUPVbEEk7uJa1qvsVGE88PtNXE2h5zzk73tQ7CmT91i6XDlgW4nmi2rpW19O+YvM1okdodN19/CkTO8DDg2XWXhZhVco6/2ogkekFezMGO91Af4GyhM1sjyZVH8WiF89jZt3wKejX30EIqPj3DU4tJtcmSeeiOE7OsTdq77A9cf+D7FpS9jS9wB/XPXtEU1MSR7xumK4FSpUeO8xbob6lClTuP322+nrK1tRyeSr18MGOProo/nVr36FYRh0dXXheR7pdJrW1lYmTCgPds8991zuu+8+pk6dSrFY5PDDDwdg2bJlfP/73z9kDHXPr2N12CQH8jDZQrQXMaZGcVMF9GOaUSt3oTcmEUfFcR9Kobpd8AnMM2MoR+E9OboGpcp4uM9nMZdodD2nI/dxxFWfYWLGITzbILPOIzLXoLij/FHrXT486IksMljxosvx84zy7LQqf/CMuKDu/SaFnR5CF1SfaWJVa5T6XaJWgGmxJC9p2/nmc9tw9tRdvSPYyX/XTsfUfMjzI2gFD55O0XKfn2uuXUrITODFbU64yGZDj01eL/CVzBoOu6yayW6MhkSAwOBmnL4ZI36nOwilArDf+GsvhmfwD9Pezydf+CH/tfWXAEyLNPGV+g+PKf62d4Z7eqiF+kCC3YXhPLKPzbiAiBtkXiDAfy++mPZ8iojpY5I/QVBVhGHe7ewq9qB7aQLmyHeXSElkUPBqcYpKKQqZJ7H8MxFCK3vTB8So8mt7Me0Owvnn2WE1sV3XEU4v1TJEsxFF36cNoZlY/tkU8y8R0EIQmo2/734cuw9l7dNHv4bIlt2NId2PROEzE+zObXp7J6TCuw5TeLScAvJXXeCU77/Y3CBaPIbIKfhtD1XNFlVTA4guG/WbAuraBMRfIyxpHNAclwnvExS6IVTlIffRYjHOjSHXF7AfzoAO+vER9KNCuOKNGzWGG8QOeYhZJTwlMRzfaCMdMGJgxMEdGF4WP0YHU1FSOsZAnnq/jUoYiA4Hp6YKlyLjGJxYYT+kVOhHBJA7h2uha5N8uHsN5IJEX5mibmYAL+TB6gI8L2m6LElmssDJCgITNYwXU/hNUK0+lAN6XOB8vxvj3ARG1MTdR2YmsVjHfHGAliUBPDSsCQr1u2cgXx4niSObaEsHabFKGGfHQAe5oYjcWEQ8bxM4LIQmDByl4/N0JsYXUB+Zwu7MFgAaolOZVbP0VUVwK1R4s7S3t3PmmWcyZcoUAIrFIjNmzOBf//Vfqa4eO2yqq6uLL3zhC/z0pz89mF2t8DqMm6He29vLZz/7WZ5++mk8z+Ooo47iP//zP6mrq3vVfUzT5Pvf/z4///nPOfPMM+nu7qamZrh+cG1tLV1dXaOW19TU0NXVNV4/5aBj5jXs33WXhUvWFlCA096H9dGFuPesQm1PYRw1Cff2dNlIBygpnL8MYH2sFhHTUT37xQX6NEhJxL19NJ9fQ7Zfw84J/K06/maB37Why8M/UcczQBjlvC49ArVnWygP9AC0TvWwqyC+WGfgWQ+hQ+xIHWfAwwzrdN1pDwmuxI7V+eoxp/CdbU8T0iYOGekAs+Ihglacjk4PZw1YtQZ1H2xEdJUIZbrYrT1Flb+WHVvv5sd2I217VOUeZzMAN0VnsZixy9OJ17GRp5rN/HTRp2nLdxHQLSYHm4jIkeHruhIEe4DnyqJFLUdX8YMFN/JKZguDdpbZVRNpNRvKs+sIkloRpe/GwIeJH6ga3XCFdxUdhV4a3UEC1sgcdTHoQejVB+auvQ2l8hhWOUJD5srGtvCP3lZ3Bwjnn2O1r5687qPJC4MLA4Ecr9hdzLFqsMSw0aTrYSz/DAq5pwhGTsUNTsFMPYddc9bQNioAoqBAgtAENVaMPBbdua1v53RUeBeiu+Dc24+YHkBODSAKEp5Jow840GCAX0C7De320GtURA6ukQ7g6y3i3T5AKKyhnxMbmjMV9Saq10Vu2mOUeeA9lkGb7IPWN9dPpUAVDATgvUq4k5ZzaZrvkS2YFPsh3KTw5/JobgA7C307fVQfHwRNUOyU9D/kkZyt9q/yVmE8kR6OT8OI7XMNs1553LNHx0af5sf5w/CkugK8+1MEgwLVZqMe9jA+Wo/7y26YEMAO6Pju6QUFRs6lcbFOdsCgNACRZoWvM4MxN4TT5WGFJU4OjCMmIroHUNPqaQ/H6e2CFqVw70+Bp9DnBzFOr8LZmiMYq+XkWTfRUTJpNiCo6vjgwu/SV9wBQNLfgiFfOzW0QoU3S21t7VClLaUU3/72t7nxxhv53e9+N+b2dXV1FSP9Hci4Gepf/vKXmT9/Pt/61rfwPI9f//rX/Nu//Rv/9V//9Zr73XjjjVx33XV89KMfZfv27aPWCyHGnHV8QzWG9yGZHENx5B2CszEzuixrTqIGPdT2FGgC6fOjdg2O2ld1OehHhpFbS0PHEIk96rmOAgfE77uJVhuI06soVgXw9Zdwft49ZPAaF8Rp/kCAXTd7NFzko/NPJYyQwMtCzXSNbMMOcvNTTD1yBlK4PLD67zm++sukf18zot+Dyz0SM/zkXRe5Xxmc+YFquu7ycPrB36QRnqvT8ScHL68R3FFLaGEn+UQPg9vv46qj/j++vHH4t0ZMHy0yTUf3o0w69hgGnxr2vsSONQg3W+i+184RryHKPCa/6npvfYHStzuHJwGezNL6/xqYPON4inaWrux2Mt5masItpIrd/PDJv6fglNX2W2NzuHbR10iGml6zD+9ExvO5qKmJvP5GB5lX65NSin57N626n1h05KSLm3MRMRMrMHpGSClJpu9xQuF5WD4TBWR2gVUF+v5vW+USSj3DdquOkhFhmhfDUIIB26MlWkWXl2Od28OCQCP6PpaAadQgRJFS7kn88RPROu9DN20whieb3KBDSJmIsE5DLkFak5S8LKEqRdB6awPCd9P1O9C80efinXaOnJ0F1OIqenca5J5Q6EGoPasWQ7gIwLwkifOr3qHttWPDqJBOTc2rhCS9Sd7o+XDW7Anxykq8oAGHl+uqa5N9yA2j1e1ku01oSTWh0fIgbwt7YwZu6yUS04kmDORLdlmgdKEff0JDzdLpedgFDYyoRsP7LTQcwm/iur/T7pG3yt9qDFW0C3hBHRnU0JIG9Lm4z+Uwz4nh/LEfFGMK/8mtRYzTqnBfLt9rdpcH1zag9doEOgrI/J5xREBD3NxDNK4j4sP3gN3qZ8eTBpF5OlZSI7W+Cau2mVCz4Ln7PU4/zMO7eXCoPW9lHuPkKPb7HEr+GP+7bjVfOTpBTWLv9Y/QwN9ujPBOvg/fyX070Lgv3Yv71x/CYBfE6jDO+DjGgrNef8c3iRCCG264geOOO47169fzm9/8hk2bNtHb28ukSZP44Q9/SG9vL1dddRUPP/ww//zP/8zg4CBtbW186lOf4mc/+xm33HILALfffjsrV67ki1/84gHvZ4XRjJuhvn37dr73ve8N/X3jjTdyzjnnvOr2W7ZswbZtZs2aRSAQ4PTTT+e+++5D14dnzbu7u6mtraWuro7e3uHBRU9PD7W1tW+qf319WaR8Z4YZxeP6UC7UEEENUWNhnD8TURem5OlocR01sJ9F74H70CDW9bXILgfh11C2RHU4YAnEYUEQoFbl0asMQmEP5zvdI7zS7l8G8N/kY8JVJun1HonFJoV2SXCSwIgK9FyS72+/lCvnf4E6K0Eq10ZfagMiMzqcppDVWDu4m4/MmMeK7uF4wmYZxNkz4R2Zp5dLn+whv14naM4heOIGNM0ksuVm/n3GZbxQ8Ihrirl6gfyqnzL3mE8RCEoCU02c/rKSq6r26E9n39b513WN4GPZkZ56Bc4TaXprU9y78fus2HUfAAsaT8NVzpCRDtA2uIaNXS8yNfK3mSF/Ox+58Xouamoi9PSMTsn4W/JafUo7OSwvR8BMks2ONBTMPhtvooUqjK41XcytRGEiRR0l28XLCZQrkKZC7hfkEsivIq9Z7NIDXNzWwvSeCFJTZA2Xx/VeqmMBSq7LxkIvU/dTnhdaI4g06dRLRHxNyJ6VOLGjh9brPih0FZC6QZUIsym1i1b/BFZtf5HJ8SMO6Ln6W/FW+vRWn4038ly8E89R0K8z0GWS27xH4DMPnQ9IWq7xYWqK3ZsMYpfWoRc9pKkx0KWR8AwGD8DveKPnIx4PoM/w47aVEIcFcU2dTDJC5OIgml+VFdf7Rj48WoNFKpXFHsMgeztEw+UhkRr0hryzImkgDR2KIHQITNZx04rABI1ir0ekWb7h6/5OvEfG85kYDwJ+RW6bzcCDAWqXVOPXXISn8JottI/Vo3pcRNXoiCdtog/ZsUe7RgMPHc0DfteDd3IVxpIo7p/6IaxBQEMNeCPGV3rcACXJvOwRP05QexIEwkVUKs+Z8yHk8+MZAtzhc+K1lbBPUwwW0nxyzonUyzD9/TmUUn/T8ec78T7cyzulbwdjssB96V7c274Czp4xxuDu8t8wLsa6ZVm0trby4IMPYpomv//975FScvXVV/PYY48xZ86cEdvHYjF+8pOfoJTiP/7jP9ixYwctLS3cfvvtfOpTnzrg/aswNuOWWOW6LqXScA5RoVB4Ta93e3s7X/jCF7BtG9u2eeihh7jsssvYtm0bbW1teJ7HXXfdxQknnEBTUxM+n48VK8qlLf785z9zwgknjNdPOejIsMC8NDEsbGMJzCuTENRwe6LYbpRSTqBdXA3G8DnVl4Txuh304yJ464qoLSXc+1KQU3BkGOfyBjoLETpzEewrG/GSJuS80TXTRfmf0EFo0PNXh+waj4HlLv1POgStCB9t/g5zNyymfvUsls35BZNqFo8qX4KAwVCBrx1xOtMDJT63cDKTo0GmVoWojQ+XqZJj6L4V1vrw08TsE/4FN72T0lP/zNKO27moroFp8cksPOXHNEw+FUeXOPUlmG3jNpRwDRclbPq9jWzJPkaPsxZPjFbGfi2EoCwhvD8e9BS2DhnpALpu0JHaOGrT3vyONx3lUeGdQ2exj6Su8JvxkSu8cvUENYYnT8oCxdzTWIF5w0rvA2MrvWteDr+9lVfMCBfsnERDxs8TU3p4dEoPndEiZ62vJ56zqDfCDMoiA/vVzBICTN80XLcXW/djZNeWY3v3oAIaIlMeZNZacdoLPSQCzbRn1rztc1Ph3YPnemT2L30GFNMKNy/IbVDsegB2PKHT/rAgvUbh9B9cA8LzQDVZ2Bc30JmL0P2Uwqwz6N1isO0eHWdWFKLDE/baTB8E93ykDjC21ODM+PDIKKAhz0rieRpKaPQ/5jD4tEt2jUfPfQ4oDfVqCpEVxgepk18VAg+6H1fseFSn7QmD3pcFux6A7Q/odK/WECcMT5SLmI6+KIxcVSiPq85N0rdOoRddOD9BsTVCMRlAuzCJq+uIZckRwoL6uXGKaMQWaDSfpAj7HYL14N26Avm7lwnc9jLe3Ssxz9szqBFgnB1Dn+EnenOQIzfWc5RWS0fhRe7a8g2e7vw1GbWzUo7tPY771x8OG+l7cYrl5eOEEILZs2dzxRVX8Nvf/pavfvWrbN++nXx+tITovHnzhva54IILuOOOO+jo6KCvr4/58+ePWx8rjGTcPOpnn30211xzDcuWLQPgtttu44wzznjV7ZcuXcrLL7/M+9//fnRd5/TTT+ecc84hkUhwww03UCqVWLp0KWeeeSYA3/zmN/nCF75ALpdj9uzZXHXVVeP1Uw4+KQ/VamJ9sh6V9hBxA0+XiLwiWxel71YH5XmYCWj6hwb0QRuR8ZB5ieh3cR9Ooy8JQ5VArXSRm4vYzSE67hy2yDv/7NJ4tobZrJdnkLPDgznziiTOz3tRV9Ux+MxIK97LgpeDSf/bAm45fz704RrcewepPylB57Pg9JVVUUPnKL7Y+QAzijWck3qciTXTuLS+SGeundu2/Ii/P++XdPzBQRvjLjSrFbaWp2D4Wbz4ixTcDMX+zYhMP3r8SCSCVKGHlNxNyIyStbvYNrganxEmFqjh1yv+hZJbfvGcPPUqjmv8AEK9MXVe15WI48PwwsgXlzg2xNa+F0cs29b3CjPrFvPUtj+NWN4am1cRhnkX01HsJaxKBMyGEctFRqICYwvJFTJPYFjN6EYMAFkQKG/suumBwmp6jQQTc9XM6IuyfFIvjl6+XzoSBRypOGVtHXce3kGDHmGr08/hvoYRIfBC6Fj+meTyq/ArH5rdg/TtiSwKgEiXjxcy/JiagWlW055efQDOToV3C9JyMastnO79loclWlBHD5Xf5/tGcOnRgyuMpuuQM3x03jM8Y9vT6VBzjkmhTbLrIUXrVbXl75wCuaUIJYVhgD12cY+3jBbU2L3DR3JZPcKTOEoj9Qo0TZXYvRpaUBA7xkBoUOqRDD7nEplvglF6/YNXOCBI3cPXoFHqGLncrCpPPAFktyl8J0eIfCKEl5ZoUYHaVsS4KIEdsti9XJE41kQom4wI0HeHAwrMGoOGCyxK/RJxQT26W4406d4A8RaNqm29sLV8rd1qA+OkGbh3rQJApEuoQgqifvT5QbxX8qj28g3qewU4OsszM25jzUC5jvoT237PPxzzPwQZ+Y2p8B5i8FW0tV5t+dvEtm22bdvGzp07+d73vsdVV13FsmXLGBgYGHO86vcPC+tccMEFfPjDH8ayLM4///xx6V+FsRk3Q/1jH/sY9fX1PPHEE0gpWbZsGRdddNFr7nPjjTdy4403jli2ePFi7rjjjlHbzpw5kz/+8Y8HtM/vFDRX4LQ7FGuCuCUdqyDQN+RhQYTeBx3CM3R8dRqFdo8dt3lMuNBCWzOIXFE2LMUkC32awHu+A/OjE1FtHukxvCqpzeAvFrAuTeLc3o/q98r57IpyqOH2IkqOcYu43ojwLhSoXQ7ilm4aF0eQsy00W5IKOGzp6mNnYZD3zzmbHY98kgnH3MCKwZVcfvi/8VLhZRZcMwetpAhO0chvKfdR6BA8YSebB55gsNhLy2PPE5t1PM6keWy8//NMvPDXdIkUv330X0gVe1h22E386ZVvofaMNINmlFOnX83da8t6CA9v/hWH1Z5MlT7pDV+DXD2EPlmLejRTLqt1QoRcHTQVRqrM9+V30RqfS95Os3LXgxi6jzOmX0etf3qlPNu7mI5iL5bKEtw/5DwtUcHRRrrr7MYpbSEYLZdKU4DbP7bSu+ZlsdxO1vmbuGFzMxtrMkNGerkR6AwVqVIGx2+q5qFZ3fiEQaebptkYmS+v6xF0sxZbZdFzG4cMdRXU0TqGrZh6X4IMFh3pNSglEaKiUv1eoE8vkT/ZxvxDeEg/RJ8m2RkZZKZbTcOFJqVukCXQrLJ4KNoY5S/GmdSL+4uyQLHNo/lcDU1KDJ9CuQo0gXZ4WcxNjkM3TeESX2TQebeDcgVGFBovMEC30SyTyFyD/scdlAv+Zo3kUgNx8E/Xexrd84jPssitK6dyAJhJgTAEsgCaD2rOtOh71KEnpdBDUHeqhrWhCBfU4PQqkscqfHGFHjJRg2AmBE6fwumBwRUeXgFyaxX7Bp3qr3jUSDWUkah6XeROP9SFoKssOuulsvRcU0VNyYQn9gvffq7EogVnDxnqeSdNR3YDU8MVQ/09S6wOBnePvfwAI6XkBz/4AfPnz2fnzp2cddZZXHjhhXR1dfH888+zePHi19y/qamJ+vp6brnlFm6++eYD3r8Kr864GepXX301v/zlL7nwwgvHq4lDlnytIrPKR/qh4RzY5Akh/J6g7hyL1Isu2Q0OwYka9eea6OsyqKLCPD9enhWr0iGoox0xFdXjILfb6BMj7F9/TA8K1LoS3kwwr61GZSReUuBt2dPukykSx9TQt49Ym+YHy3UQjQb6fD9yl8uQKLWr4In00KfNm1aOD24IRCHSyqwz/0i0GOQzhy2jgMsGsZoL2r7At2ZdSuKofiKHNYFj4vo72LDzJxSmLsYSBhRSeE/+DrOqHjNST0Gl+NVLnyVd7GVGzdG8tOtB1D6/Le+kydtpAmZ4KHe84GaoehMiwZ5QpBvA+LsooHD3hMI3hGZzTMv7eWbHnwFojE6jMTSLqVNP4JTJH0ITJkFRPS6DyAoHj/ZiD3gpAtZI3QWR8lCBkUauUpJ8+kGswFyEVo7akHmBckEbQ5MrUFxPn5FgRraagKPTGR0tliU02BjPcmx7kindIewaj23eAHV6BHM/I9u0JlKwnyWaW4cTPw6EKCu/F1XZONOhzpdgZzFFyIiwO7eZhvD0t3eCKrwr8JTO93MPc9ElC6jJRXAsjyfVZqaJWmb545S2G+UQ7j2EZmhUn3rwVd+1sSoimGA+O4C+IIT7UAE1MwQlBY9lsM6Po43DhEIq0EFU1tByhoY0NPSMg6FJdlttxMzp9D86PPlVbJcYUUFoRuVlfzDRlIW+K8+E8wLYaYXwFEZAsfsZFyMiiB6p0/uwg5cpf7O9HHTe7dHy4Rp2/tIeKk0rLGg+DQI+gZyuo1xIPe9SbJcEWkdPZBohoNmC7cP3gNzpoTVFkHsM9fbpPq5q+z3fT5zNHMZ4jvabt5Vq9ARVhfcOxhkfH5mjDmD6Mc74+AE5fnd395D3W0rJrFmz+Na3vkVXVxef/vSnue+++7Asi8MPP5z29vbXPd7ZZ5/N/fff/5rVuyoceMbNUM9kMuTzeYLBA6Me+16ikIP0KyM//n1PerTMNtj9QGloFjm/RZKYrvAeSIECuWY4F9t8fxwCAmwQTRbByRqpFeWSa7DHaz1ZR8yMktYL3JlbybFeM4nlOlU1IYzzYrj3DBLuyWKcGCK9HcxaSXHKGsyqKTA/g7dyM1p9GKKT0BeH8J7ODbUvjgry2+IqdKFxxdRjCPZZyL+E6e0V6EGoO1Fxpj6B2JTLuL3vFS4LJ2lb/fd4bhHTijBp8T/yx+2/5cromZBPASDXPEzdMZczgE26WBYT9JthurNto85hyc1j6n4KTha/ESbmb3hLHm7XG3kdTBXllMn/wMwJy7ClQ7O/Dr8bAUk5hE3BO1SjsMKboDO/i2olsfSRyegiLZH1IwdgpcIqQA2VY1OA2zd2brqQJSx7JxuCTVzVVseOeH7sVFsdlAdrmlIcuT3BrniBqG6xy80w0RzpVReaAb5WyLUhnP5yTXUhUH6ByElUVKPJn+Serk2cHpnGloHnK4b6e4QwBme3zOPfXrkHb09o49xYA0v80/Bsjd4HRwoi5jZIYosNOMiC3tHDDLKrPPbaLcKA6CwNUTAgrKEtiuCtLZQVuZfG8DLjYxynzUHa/E8wO/p+zIJGqbbI8u6fMqfpWuyB0c9zbqNH9RkW6BWD62DhOSCKEiNto20rIYIa1BhMWGrgbSnhxoP0Z0ZeK+VCqVsNGekAyobUDo14skRmtUXVQh09DEIXhKbqpF/0hsdLBlh1Oj2DYZJnWXBvWQlXm+tDrUuDTyd7chO3iDZcJVlh7mZmYwt6x/B9oRZYvFC6f+hvU/fTWHkPv6fZKxg3Hqrvzc3NrF49dqpbLBbjzjvvHHPdww8/DMDXvva1Ectd1+Xpp5/m4osvftt9q/DmGDdDPRAIcNJJJzFjxowRxvpPfvKT8WrykMFX0Bg99QpeQQ0Z6XtReTm2AVpj4N6XQu20wRLI6iDVp5t4GYUCjKgg2+WwZVEnD3e3cVn/DKpvtQEPDxsR1zHOj+PenyJcpRE91WKgtJ6c1Yt62EC+XM6hkYNF5LYB9I8dhTYpgeh0ES0mnU0OMTvOjckzmKJH0P4Sxu0tWyReHjruM2g52cfC3VX8S+YVXjACfGHpV0igWFNM8cWO1VzQei2hl54c+kkiMYFgcgYFK4TPCFJy82zseY4lky+lM71lxM9vqprB8u23UxeezMXz/pkANQckEj0nCvyy7T7u2PkUADOjLfzL3KtIqtgBOHqFdwqDxV1MsKpHCgIq0DISb9JwWTYpixSzy/GHjxvaVuZEedsxctP9pc1kzDgRN0RzJshjNT1jti+0sshiOuTSWVXgiO1xMtMctroDNKnRXnXDasQubEfLrMRLnlLublAgUmVDPWqEynoS/hbW9z7G8ROufHsnqMK7gqBmMSWc4JOzz2TQyWJqJg2BCNWGBRkd5Yw2MGXh4M40KgWm4dBwqYXdLdF08McV4lfwp+oQAAEAAElEQVS78fISrcnC+8Pwc6ICGlxfhxyHsKWwbwqFmt38fsUFlOwUsehkjjn68xhuFXpg9Iyar04DrTIzezDRRRG92sT5Zd/QMvOKBM7/dJfFPt9nICwDta9+gQC00dfPySi0qMQIQX6bR8MZGqamyPc5NH3ApNiukEXQI4K+R8teet8iH5G4jl0veHL6FuwZkiOr53L9mvvo6EsD8H+7VzDhvCqOb6/H2ugiFgRwZgvmuieRUYNUhyZwbMsyIlpLRcvmPY6x4KxxUXg/kCilWLJkCcceeyynnnrq37o77znGxVDfuHEjp5xyCscffzz19fXj0cQhjRlw0ENGWeRnD1atQB8jN7ZY0okkjRHla0RMB0+VjXQAW2ENlOjuALuv/FHQTEHkKoe1uc1cXrOA5P+NVEZXAx4iIjCviCDb+3D+ayv65TW0BhYiX3lpZCdKHqory5U8ROu0BGc2zaQrZzI/1shD3U8SS09iUm9s5D4SXM/C92wvC4+dyvK+tWxybVJOjllVLVwVbeb2tieIzFjA0lWPgG6gzVqCHWwloHQunvc5fvfS/0fBydKV2cZ5sz/O8ra/EDCCnDH9w9QH5/KZE47A0sLoXpAD9S3ckN8xZKQDrE/v4M+7nuRDze/bP7OgwruUvFdE99KEg/uVGyyq8oDPGn4Oi7lnMcwGdKOsDj+Umz6GNx0l8Ze2sN5fz6LuWrrDRTxdjdrGlAoHgRQaeLC5Nsfxm6tpzATpCeXodDO07O9VFxquVYcvv34fQ11DpD3AQAhBs7+GfmnQX+xgsNhZjjKpcEjj2JJaDIxojO5igKBh0GD6CGk+9IBHoFWj0LZPapMPzJhgdOHB8UMIMHYWEasKmNttQKFN9KEdHUL1urjP7ldusyBRbSXURHPM470dNFenOn4iJ588A9fJ4PfXYrpVKKWwIorQTJ3c+vLkhuaDxFIDfayyJRXGDVdp8EB66G/RYiHXFYc0GMTyQepOq2X3g8NOjJpTNAz/6A90bIrA00yctKLldIX6XS8yJ/FbAvPiBEXbZPD5srG+l8x2hX19jDYzB2aUmBHClhk+Xn80z2Q7uKd3Pa6S3Ods4flmye6EzQdnTGKS4WcqJzJj/lJQGp6nKkZ6hXcFQgiefvrpv3U33rMccEP9T3/6E1//+tdpbW1lx44dfPOb32TJkiUHuplDGtvqp+nkCL2rAhQ7ITRRkZiWQ+/1qDoqTur5fbwgUR25rBrx2CBsLSIm+zBOiIIz8gMg7h+g7vJqvPoAaILV2i76rRSXD4Lo34xdahzVDzVQxLt/j8p5yKRb28JkmsDSoTTSEyMMwY7sII6UTGiCOfTSm+nlwc7lNNZUMTXIqGgA3fCg2ofQBZMjDUyJNvJgxwq+8OLPCRg+rppyOr2agbjoX9E9Fyc5BUeWw44nh4/jMyf9lp50O2ErQtgVzFlwDIYVQzjlUPQAwaGP94FA0wTr06PD7J/uWc0Hmk/H4o2pyld4Z9NR7CMuXAJmcsRyLe0hg8OebOllsAurCURPG16WLRvxYoycW8tpx9b8ZDST2f0x2uLDM3GxQp4pA70kCnmkEGhKkjX8tEeidCRjbKzLcMyWBB3z8myVAzSpKPr+yvP+iRiFTor2bnSrHhUQaL3DD8CEQC3rsjtYULWQFzrv4NRJ17+d01ThXYKBRaMvQI2/gIaBsjWkB0bRoeYUP8V+hcyV88TNJFilIqML9YwvqqjKxtYe5LoiWquvXJFk9xgpJJ46YJOv+6O7kiB1oNdRnrEoN2SkCiQX+AhNN5ElsOJg7czhNh/8nP73NEoraxXsQZgCtfdvAUwPYgqPlqsspOcierOYugupMPVnWfQulyAhcYTAikj6VuskFwrUH7ogt8eYtxXOLX1EPlJP734SIl6j5HPb17AxneG4+iQfTMRJPurnuJUljmuczQfOn8cD/l0MFk3+tKUsFPZgKMhHp0zC8ySeK6gozVaoUOGNcsClf3/9619z55138oc//IGf/OQn/PSnPz3QTRzy7DZ2kg60UTc/w4RlHtVTBrGqPOStq4jTRtNlOnXnGTRfoRGZIcl26+RmJ5AfaUS7tBq5s4TwayNqz3JBNV2rDNp/YdP+fyWSK2PE7RB9VWGKbgfGifvN2VgCrxY4qhl53kzarmnmIb+NHfBhnDRxxKaiOUI+aXFC3WS+NHcp6Z5uvrSziLKm8L05V/Jk/mXsswZH3G2JIyX6pnbU4mo2pNuZG5vIit6N3LnzaVzlkXHy/Gj9n4n7IqiG2WRal5Dd13BSGs2x6TQHjiJsTGeNivC5tS/z2VceZ3WpC28cpHilVEyLNI9afmRyJj4OvHenwt+GjmIvQQoErZGGusiosu7DHgrZpzF8k9D2xLgr9eq56QCB4kZ2GVEm2BGiRYO+kA1KMb23iwW728lYPl6qb+KlxmZW1E+gIxyhPpdhybYtSK1cX2t2TxVBYdKzb7jN3v5pJq4eQKXK6SIqpKGlh71KTYEadhS6mRhfxEu776TgpEcdo8Khiet4CNtC2cMvYSEFqs+l70GH3gcceu51cHdLcF/jQOOEXD9aUFHutFF9LvqRI3Ui0ECbYh30GtQiKtBu7SL4Uj+hnWmsmzsxEgpUpRj2waRkSrTTI0N/y20l9Nl78owuqGZ3NsTO+wU7fuWQXSMxm/y46MikQUi3aZ5RZMJpknCDxDUNwtM1opMk7K974IGWdfHXD19fPSIozHfZnM6Q8FnMDUSZdIcf8UKp/NzscKj97yKTsnVDRjpAwm9VvOcVKlR4S4xL6PteRcAFCxYwMDAwHk0c0lT56zF9E9hxr8LLKMxEnIZzBZpS8PgWzMe3YALa1AT28XNIrdAwqzX6n5e0XqIjak0ICMwzqpDbSkhbksqbFHft+RAp8F4wqJ/exEO7/5UJC7+NVVVgYjSC/+kiXp1G7iQD9CD5zFS8TlAiw+JJc7F2g5hcjXGpH9WZRcR8iNYYhmmxsCbJ//dSO1P9Yf6fOYH6mx00X4TvnHodK4PPUH9tC5F0NX5Lx3W6yS4MkQt10xSs4aSGhXx7zR9GnYs1g9tZ2NyC4Y2cU3JFljW7X6Ens5Ogv4772rtZs6fMxf/rv5PvHn0+082aA35tZoUmckrDQh7qLEcatITquHDCUpSsDNYOFTqKvRhehqA18v7ZtzSb56VxSpsIRs8YWu9lRbmUn2/0MXW3HyELbNdjnNufoCdcQqE4rLuDsF1idU09nrbPxJoQpAMBBrUAVXqJmd1dDPolC9umsTYxyA6Vos4Ij9Kh86w6jGIbStoIyywb6SUFfoGlGTT6k7SXcrRUHc79W3/M+TP++QCdtQrvNjy/QefjEm9PZLlyoeuvHv7rLODghXMrBWKmHzaONNa1ST689QWIaRhXVeM9moawjnFqFdLOoOsHWfGuawDjJAGYqJxEnB/CfXY9YvJcKsFUBw/lGjAL9MuSyKcyENHxaiz4UB2pNo1Sx/A4Z/AlgdXkp/teHWEoas/wEZ5vIAY9cCV+6aBFbeTWDAR1yO9jrAsQaY+6Ggf3tDBuXuFlNbSnFX9YfDyivZ2aVBhnv/sWW9GQGX6XBw2d42qrkRWV2QoVKrwFDrihLvab5tb1SljYm8Wfa6H7Fg+1J1HQ6YeO26H5lBlwz9ryQlNDX9KCWzQBD6dfkjzZpFAAf9SCrXmEDbLLQRwTIbty9EeisFlw8pzvk7PydNX6eDTUiTlVoyYcZmI2Qv9PNYbiC+8PU39qA6pKYn8rhb7Ajza/HrndwftDGv3Tcf7a9RifOfxipm5PEP3fsrdOAcbaIpM+2sw3dv7diPY/0vhtvrzhUaJWmFUD22gMJmnLdY3Yps5fxfeXX82SSZcwv/ZsTBVFaSUe3f5zlrf9aWi7I1svpaeqkbWpshr8fTvXcFSygVK4kaIe4UARVkE+MfliLm05GUe6NFhJgnIM1bAK71p2ZNsQjFZ8J+OhEuXIiWLueQxrIkIrW+VKlXPTjdire9N7zARR4WfiQIiucJGp/T1ESkU2JuuQY7gH9y7JGj7W1tUzua8XpfVxbHs1OyekSckisf3qWklfDf7iDlKFtfhChyNDGlrKQ/rLr/qJwQZWpjZxZfPZ3Lfl+zyx41ccP+EDo97bFQ59XEfHHdgvN0iBkxYQP3j9EAL06QayxUDtKLvzxRQfpTo/uVIAn0+j+x6X0KQEni3I/04y4eoYjn2QXf+RAO4fXgBDA1ODgos2vxFn3CR5K4yFbmZI93QQzkzBPq2a1DpJ6V6oX2aR2z5aXcHuVughgZdRdN3pIi4ysXQHs7MT74kdeHkXwhbmefNwbsuBrUAH49wYXo+DOd1ParNk8GnJ3lw6dwtMODGKGsyDXyvrl+xDc3WYG5tnoCnF3HgV9Zo5bqkaFSpUOLQ54KHv+1MZAL55ZL8YMtL34qYVckINxvumYZw1FfOSOaisSUjYNH/AIn6sQf/jLv2PuHgRA2Q30t2NcYxCs4oEW0a3Y1YJum/2E8xG+MHah/n3lx/ii2sf4NMr7qTQLkalUeWej+AmJGjgvVTE+UUK79E8Iqqz2nmaD09ahM/touqx/WaYFcQ3VtNUNWNo0ZLJV1ASUziqZg6b0u08unslV0w+Fb8+7JpoDlbTYECm1Mc96/+LzYNPIwSknF0jjHSAFW1/4NT64VDliK6jbv8K1iM/xlIjhfLeLpY0adHqmWI0V4z0Q5Ce3DZ8ZnLku0uCllOogEDKAnZxHaZ/2tBqLysQWrk27/5oMo/pdLLRsKj1wtRkfSjRT3N6kM3xmjGN9L0IHaQET9PYVF1D3mpn5u4QE0sROr3MqO2VHgBh4mZXoJRChQQiPewlmhisZ2uhA0cJTp10Pau6H+C3qz9Nf+H1a6hWOLQQohzKuz+a7+B/s90nNqNNTGNeYGFeYKE19WNmugnMNOh7wkOWILNekd9azi8utMHB9gHYARPj7Nng06HoImbVImY2UbAOpvReBc9Ns2bdN2BmgMx2iVWjEzlcB6UITByj/nmVwMsND2a8AuT7A8jmWoiVv98iaEKtxLo+jnllAvPcONQa0OXg5GDwuZFh8coFWwXwXmzDOG1kCWKxIICo0bli+kROSVRTJypGeoWDS3t7OyeffPKo5TNmzBhj6zfOySefPKre+rPPPsuCBQs4//zzOe+88zjjjDP4/Oc/Ty43Oj0P4Hvf+x4PPfTQa7bzdvt5qHHA54I3bNjAwoULh/4uFossXLgQpRRCCF588cUD3eQhhx6SZXfaPi93YYGQIOqr8UI+7JKB0AR6WLHrNw7unhqvXgbSL3kkc0XUis6hdMPYBQvJxyM4ezIRQjM13LRCFaEwoLN2sHuoLVdKSrrL/vF8WkCyvetWJl95AerWUjmkNqpTvDbArfltpFJ5PjL5XLBGfyx1n8nFM7/PDns9ESuIz6rla3cluO6482iYMof1Azm++1I//3nkDXQVu9FRmE4Pd63816FjPLPjdmYnT8EujTa8FRJtz+yGqeksqJmMFBri5XvxHXkBdqxSr7TCGyNX6mSifz/F97xEWQJ0QSn7CobZNJybzmt70/3FDaTMJKbwMTETIu23md27i22xJO7rWRsaZSeOCQjBluooh9mdXLQhyn8cNoBteFhi5DE8M4nP7cFzu9BD1YiB4UGmTzNpDdTzUmojxyfnccbkG1nX9xg/W/lRjm2+nOOa/3/2zjtKjurM28+t0DlOzjMa5VFGGYFEziCTMTbYXqfPa/Du2sYBY6+zvQvY2MYJr3dtcAKTMyabKJBAEsppRpPzdE/nrnC/P3o0o9EIEAJJI+jnHJ2juV1161Z1ddX93Td9GCEO+fptnnGAotgUnaTT/Ug2V8pKQMFxGop2ZMpX2K82Iwt7QILsT8G8SpKJMlS3wNhn1VjxCg5Bdba3JFqYIdDkwnHMLHCC3SdJVQow8/lJDieqHqawYgW9r9jEN0r2JFUYeA6qPuEk2WgPz4e8UxTMqBxdkUVC7+MGfaqT4Kx5FK80sFalMH6TgmwSgirCr6LO9uSq6VQ4UV0m+6YFUYSEgTT2jkb0i2qQLg92UMUoU8gcod9QnjxHgpkzZ3LbbbcBYBgG1157Ld/+9re5/vrrx2z7b//2b4d7eEc977lQf/zxx9/rLj9w2P4BgieGiT419LAXUHi2jiyMkk2FaL/bwOg1QEB4mUbR6TqdfxuJKUw126jHF2O/1jEs9sUDr1P92aXEujwgQS8StN86tI979EulzO0nWy5x+nPuYnvGEDjJYOu6P9PhfIhJH/4Efr0eu6qalp4evmJdSDZo8X/NT1B/0jm4NqdHFhp0AdVuvKtV5k5czF9jjzLBk8tj8NwOm0CFwhOt/QB85fkmfr98Ebt77+XBzTcj93rDFntrEFIl7Kok4CpiMN076rOgu4KL64KUe8L8cvMWps47j5LHfwGZw53DOM/RSsJMo1pRAs7aUe3KUHy6lDaZ1Fpc3mOHP7Pfwpou7DTOzG7WuasowENlxI0mexhweYg595Mafh8UBSyD3G9JAEKwuSzL3JYSzmnXWF0dH1OqzXKEcaciRFJvoHtPQWkeHW88xVfNi/0bWVYwC1XRmFl8MnXBY3i+5Ta6Ejs5f+q1KCLvz/t+R9UMVF2l4Pic0BQqCNtC19/DUhkHiDiuDnNyLfFOJwjwlabRChQif7WouNRB+1+zw+8T1QuOEoXDnRZEV7xElqRwDViQtrEKNTSnC8y8ufRw0m1bFFVcROTufUtbQrrFpmC5hlByVWYcxYKOO0aef75pCpojV2ZTWhBZKwg16LA9BirIS0pIxFRMQ+CfpGCEPPQ9YFJ4qk73vSOeE3oR6IncnEU29WM5IHtpAxkV8nVa87wT+rc8QvuLN2PEutD9pVQcexUF0w5tXfV4PM61115LV1cX3d3dLFiwgP/+7//mlVde4frrr8e2bSZPnszXv/51rrnmGjo7O5k4cSKZTOZt+9Z1na985SuccMIJfPOb3+SJJ57gnnvuIRKJcOKJJ9Ld3c2iRYtYtGgRV111FZMnT2bz5s0UFhbys5/9jFAoNNzXa6+9xte//nVuueUW2tvbh4V/MBjkxhtvpKCg4FBdonHFez4bq6ysfK+7/MCRSbvIzhqgvLYAOQgiDD3BdvRkiNhzFkbv0AtKwsDzJhWXO3AUCbJD7dkuieX3oJ0/DdkUQUpQJoSwbDsnJmzo+HPu5eVcbhALpJkdLqczFWOmv5yFpfV8ccPr/NvZ05gQ8aNkBNGSDGYpzFnwReJ923GEwW33IHbWM+02N8gkXuDL55zNtoX9VH2qAsfWJJpHwQi76HhEYsVNlFfgI1eeyTdfyx2/NCAoCPZw1ewS/ndTH0nTwlbAFzyTFbOXE3YIOvseZk3znzmu7jJsS6AT4pMLfsqj237Nrv61TCpcSEnx+fx2cwuGtBnItOFQFHAqiAnHYBfVHf4vMc9RSXu6l4BI43Puk0guZiHdCkZmJ4riQdVCwJA1fSDn2bI/3KlNxPQCDEXHi4OaiBOVLrYXhQ58UILc3G/IcG6qgvZQhMUdlawp2IkMBUcllbO0IC4rg5naiu0/Ac2UuRhKV26rCmchEpsdyTYme3NVDHyOMKdM+H88s/t/eXTnLzhr0n8c+PjyHJUYWR23N43udGL02qh+gSNgYWUPb/koKSFj+Gl7ZESMDyhuKi534K41cAiTqtMhPShQNHD5bIRpHYbAvbE4LTcyAARyh7fzIv2wo6GhOF3oIYExsM/1V0BmoPtRA0cRlC2F2stVss0WChJ1dwwezVB6fCH9m8BZotC72absfDeG6qH1XrDTOaEdfc2i+CwdtdiipawVx0ecBLqDWD5JuiBBvDVDYFYxYnIh5pQCMmo+zDPPO6N/yyM0P/l9pJkLFzVinTQ/+X2Ady3Wu7u7Wbly5X4/e+aZZ5g+fTo///nPyWaznH322WzcuBGApqYmnn76afx+P9/97ndpaGjgd7/7Ha+++iqPPPLIAR27uLiYQCBAU1MTAF1dXTz88MNomsbXvjaSwHbLli388Ic/pKGhgauvvpoHHniAK664AoDNmzfzjW98g1//+tfU1tZy3XXX8e1vf5vZs2dz6623smnTJo477rh3cYWOHvJmk3FIxpPG+WIWxwPtuQYBrstVnBM9dO0aGw9nRCSOEoXsUM3kghUayZiGJiV6pQII0vgwhZdHw20sEkU4L5AkvQM4FQfl3V5+rp+F4XdixQSiFyZNruArO18jY1o4VIXYbpPvFdQz+S8/pnjGybDjGcTsz2H8LTJqTmc/OMi02jLaVTBmBnBvtIntFd9lZyC90+acKRp3bDCpKe/ijraXOK1iAf8yvYS04WJN7wC/fGPbcLdXN5zBd+Ydj97Zg3SbpIM1+JUaPrnkevqivWRx8f9efpaBvVb7PlxVjcMbZYNVgLPxTkprT0E46vKxYnnektZUFw47sZ+M7xLpF2SS69AcE4bb7ZQAe/910xUrhtNoZp27mkI8BNIKLhN2FDrfMi59X4QCtgV7J4XvCCYoGazkIzsNHpqTwq/tlStB0bBUP07ASO9A800clVBOCMEM/wSe7V07LNQBNMXB8pqP8cjOm9jUM5eG4hMPeIx5jj4UVWC/lIGX+4aLS5qFKuqnyjicWd+FgMF1Fu5qQXBiri2yHWIbLcKLNXi6H3VDEq9HyVmvsxLlw0Vkq/OJaj+IBBUL21QIH6fT/VAWxQnBYzT0QoHiEBj9uflG6QJQnhxAKdfRXx3tt+7VDDKTXKR22/gaVMxgAJGwCM2Ggb0S70ZfMdl53ha+vukWzqpcQX3tTLb0m1RkHQSrTRbM8VGuluQzuuc5KNpfvHlYpO9BmmnaX7z5XQv1kpIS7rvvvlFte2K/zznnHNavX88f/vAHdu3aRSQSIZnMeZ5OmDABvz+XgPmVV17hxhtvBGDhwoVUV1cf8PGFEDiduWS7DQ0NaNpYuVlYWEhDQwMAkydPJhqNDn/2qU99ijPOOIP6+noATj75ZK666ipOOeUUTj75ZJYtW3bAYznayQcjjkNcAyqhB/dyn5JQeKcNto2raj/JUvyCggkW5ScLqi7XyfTYKC6VVMZP+5YSuppLyLr9PJnp5BebtvHlXa/hKrWY1BeieJ0DdauBQ1Mw2my6HzDovsegbIebH9bPIWPbxAwTj6ZSIEyQElE3B3vHy2AX5jKk7oOISYpb0xiGxOgfe352BqoVOHHBVn687SbW9e/kJxv/TrnfYk6hj99t3DEs0gMOnaWxbWi/+TTyr1+B//0M7pf/iMNO4dTc6HYIr+3ixoUruHziNOYXlXDt3EWcXuJm9QvfoqPpCZo2/onV//gc0mh5j76hPO9XmmLbEIoTTRldY03EbExXAsvsQnOMiFtzQKB49zNJkxJf4lX69TJMoRHAybx2A0ONEnG/fQJCG0kGmwQWSdUkKS1S0sKQNiCRAtpCg3iyDUxoaxqzv6WHcEsnmdQGpE9B7JPde7K3irZUD+17hY8AOFQ3x1Z9mId33kTSiJLn/Yti2tir4qMb+yyU2OEvpO6thlI9jvPuTpx3d1LqTuAtlzh8NnQMLRok7eH3jYiaKPnZyweSATNDShhEXzcoOUen7AIHg+stuu836HnMwFmm4qoWKI1JlCINu2OsccNuzZLYbmHFbHy6gfxNJ/KXnfg39lN/saBmvkHN3Cwli2FXqp2pgRp0eyq/3dDBs+09/HV7G/c3RollMyAO/+8lz/sDI9b1jtrfK2677Tb++7//m4KCAj760Y8yceJE5JAVy+UasToIIYbb4cCrePX09BCLxaipqRnT597sEfL7O9YNN9zAP/7xD7Zs2QLAxz/+cW677TZqamq4/vrr+fWvf32AZ3v0k3/VjUNcSXWs52FGkrHThI/T0AIj1jj/HBWtCKQiyESh7U6DxCabrnszOIoFwTMUMksNbleauGFX7oZ3KQqFnRrWbX2Yj0cxH4li3NpLsMIejt0aeM6kLpOrU1vtc/OZmdVsH2yDQDFKyQRwehGODCK8zw/XIRBeBeWFOFbhAL6l+9xiAvQJCu2tKf7edg8xI7eKZ0mb1ngHsXSCzF5Zgi4v9VDx+E/AHnkZypf+ijPaPKrbEuHio1VT+eHsZRwfCNH88vdHfW6ZKWJ9Gw/4O8jzwaQ9vg2HXji60QKRlmTkRjRHNWIoeZvMCuysQPGM7ced2giYrNd0SvHhtAxqBlx0+cycCfFNsJBEbZMessQwySKxBJhCkrYtIrZBt5UlLk16/YMIGWJ+l4U7OdpiZOkhnGYC0+zC8sYR/aPjJjVFZXZwEv/ofnXMGIo9ddQEZvFU0/8c2EXLc1QiLWA/t6J9+EPU8RoZ5Kvx3HtPAqtieGUWxbZQ5o39gSk1+cLlH1RcqpMOu5XQsSpWDDrvyg7n0rFiks67MxSs0HMVM5oyqFPHigS70oUxIClcIODOHhjM3fT6Eh/mb7vgoX54dAD1r10sk5M5rmQxjzX3jeqjPZFmIKuBzDum5jk4dH/pO2p/r3jhhRe49NJLOe+88xBCsGXLFuz9ZOdcunTpsFV+/fr1NDc3j9lmX7LZLNdffz3nn38+7gMwSrwZS5cu5Utf+hLXXXcdtm1z8cUXk0gk+PjHP87HP/5xNm3adNB9H23khfo4RBTouQRsexNUeSq2iz51gNIPaRSfrVN2kYPwcSr/F3+RwS6V/tUyl70XsOKQ3GWRKUjyL9te5tbWxuGuLimvwv5nbHTOk6QNTWn0vZJd2wOSXy6r4YraVkyjmZRDkrjk60gzi7rsMuztd6F9KIwoGXKpDanolxVivBaHAo03sm1sLNpG0YXgLBe46wRlVyqs32bgLEvSkRz94vMIlbCMUOgamYSVKybEx5rlRWJgTJttSwzDImePHPvQUR0+TCWOUPLJXvLsn2iqBe++bu9xG9sF2fQmNMdIkjljEFSPPVrsSIkrtRVntomNjlICwo1b6MxvawFZQp/nzV2KU9j0DbkcB9Dxo+FBwYWCSyp4pEZQ0fErGlnbptvO0hGIkhQzmNm0jb3jOmzNj7AyONUy0toWlKg9ZvFvuq+G5lQXrameMWOZU3IGm3ufoTe5+x1cvTxHE5ZTQSzxj24s1bEDh194WGvHlvKx1yfBEogCDWXm0IRPF2inBLAN+VbrXXnexzjtFHW2SSrQjatIYu9TDdZOg52RqPO9yJgFAtR5ntxzWgPltBDRHhVs0G1rxEujSMNuz472EjQlVa95KXT6R1n79uDSAnm39zwHTcWxVyG00QtJQnNRcexVh/S4H/vYx7j55ps5//zz+c53vsO8efPGlF0D+MIXvkBLSwtnn302v/vd797U9X3Dhg2sXLmSlStXcsEFFxAIBPjGN77xrsf5oQ99CLfbzW233cYXv/hFvva1r3HBBRdw++23c/XVV7/r/o8W8kuB45A+VwsVny5F3paAmI0oUOETXrzeAT658U4Whqo5p2IaZQU+NsS7mV1YiXOGRFmXcysHUJzgb1BRfhXjlxfP4bOb1w73X+5xIRNjzSYyJcEaSSRkBRKkUht5fPMNLK27EL/iYp1WygJfA872bai1s5GBFtSLqhGDElwKsjmLXJ0keUUp0x0a6wZu5sGep5mxeDkZO8XWnS/zL8fcguITfNG9BBB0m/BwxwYqkp30briRb6z4H65f30RHMsUu6WJ5+WREx/aRgQoFGap40+tn46F+1ifY8ML3hjbXqFp8FU9HnmPHjpuYVnIsx9d+GA/l7/aryvM+QkqJbfQQDo6u4SliNoa/CxAoaji3rQVWTMFRPLLoo1pRPMk3UO0Yu1wTGFQM6vBSkExQG5FkVBtD27+5MoFFAhMfGrpQsPeZFAold0yhgorAp2hkpc0Wby8nRSehKpLygT46CoZW2oTIub/jYTC7CdsxFxGzkYGRtVld0ZgXmMKDXS/y2drzRtWNd2pephet4Omm33Nxw3ffzWXNM06xfCnsqX60YgdKYwpZ5sQodqF7D68CNgwbMcmB3DpacSmVDmRTBuv5GKJQQzs3BJbEWpNEcSsYRt6q/kEkY8URukWwZRWq48xcks29H6sqaC7B7ockVZ8ow96agCoH+gkBTLdNrEUluxlAYusq6p4pj1uBxNhFfNegilsxOa2mmEebRxY1S9xO6jzBw5l3Mc/7jD1x6O911veqqiqeeuqpMe1bt24Fctbqxx57bL/7Ll68ePj/Pp+Pm2+++S2PtXjxYl5//fU3/fyCCy7gggsuGP77xz/+8fD/9x7j3sJ7zziB4bJvAPfff/9bjuX9Sl6oj0OcMssL26+k7vxLcItSYlYjHRufoOH467hh0Rm0JSOoUqWtyaS2vIhXB3ZTVhig7MNhsu250iOuYoH6TB80W9Q0Zbl2dj0Pt/dT7XdRrKloy9wYf9vHUj3RhTGUSMU9VdLuX88LjXcDsKnzn1y56Hp6MpJtMsLMRSsx7/oBJH8NwRLUS39Hps2HGdCIf6SU1VugvijB662PYkuL17ofBUARKl4/vNR4N2u6HsWw0hR4Kvj5gh/R/OQ3yKT6MJ64nO8s+TZ2eB4hzYks/Qbc/T1E105wB8ie+2XS/grerLiVlOAvOpY5K35E2/Z7CdefzF+b/kBfsg2AVc330TywiX855iZU23covsI8RyEDRgy3TBB2jV4EEoMWadeWIbf3nIix4gLFaaPKARzJFhxGB4rMkNHL2eoooF9kqCGMAhzb0ki/swEhU/s9bmpIpPvRUPbniwy5bMbm6GzcDqGAatPuGSSjTmPm7jfoChVgDwXvWnoIhxEH1cII9qD2V40S6gBT/dVsiDeyJd7MdP/oknRTC4/j3q0/pDvRSHHx7HdwJfMcDahpA7XARVrxYGguVLfAXSkQqQi437504HtFIpHBMxdYpcJQQlRRoqHUO7E7DdQVAcy/92NvHhLyAuxJCqoqsKy8SvqgoWsBXmy/i+XVS+hv/gulZ15J10PmcAnL0uMVtL4Uhcc5aXvawlHiJVStktBbeab3bxT6apm9/Ax8M31kEhLneSHs+yLI1izKIi/W2tHlXI3jnExyuZlY7WZ6sJ6nOvqZFQ5yckUpfpl3Ss3z7iiYduYhL8eW5+gmL9THIWYqQjrRzZYdo1eyNvTt5MfNL3B99bXseNyHlNCuwLzjnPR4WtFD/ZQbtRA1kX/qh/7cpEdtspgyK0a1NhuXmkSXPVjpIrSLwlj/jIFTQTstgFGqoF/STkbE6PJ30Gs10xnbSamvjvLgZHbHo2xu/Tu7+1YzqWghF1/5AxyxAWLuMC+kdmGvmYe1V14V1ZnB5wwP1zuv8E3h0sIfIO4uY0n2iyye/xGeMH7JxoFn2NnzCotO/iWpwV0IoeL01WHjRkFwRzpF79KPMEdT6UHh1q4Wvl0epYTwm15DGzeu0FKmLl1GR2rDsEjfQ0dsO5FsB4XaZBy2hrAUTIeJJfNu8R9UdsZ2oCJxaoHRHwwaZP07cDlOotOK0W0l0K1+GpTdOOMGMS1El6OYiOJiQKTxYlNLGE0oTOntQpE2ulnMgGese6+BJDZkSX9TkT6EUOSwVX0PDqHQ4RtkVm8VUf8W6jtb2VGRS+Bi6QU4kk1owcmk3Zvw95Zh141+5KtCYUmogfs6n2eSrwp9r851xcn0ouU813wrM+pueIdXM894R8aziI1J1AluHBNVzJSElgy2LwsFh0+oSwndwSy+q114OwUaTuQOA2N1ElnmQJUS7YIw1nMx8Arss91sCaylOjv/sI0xz/ghYaXxOPw0D2xApDqprElQfZ4LMyLRFBvl5X6sGRYca1NcV0lWz2CmDIwXS1nc+R8wM0LvhB2s8z5GeflEquKTmHD+VKQhwaugX1yA+UIuNDB5us6Nxl08/uprnFO1lI/VnsGpJRVIW+Zd3vPkyXNYyAv18YgjOKZJUV1EpMV5pSfQ9tJIvJS0Yfs/3Zx27kTSr2Ro2WxTuFDivcyH0mlhPpIkNjnOYK+L7atswEVJdSmeaR34Hgmi1rvAtEnHMrSV7SBZFKMgWMyanS8gheCUKR+nM9ZEbXg2Vno727ufx637CboLeWFwFQXuMvyeUtb0reX0GdPpWDcywUtGHKyYeBkPbMwtOKws+DqZv5UNGQUFtJRx8vn/yjblJVoim1hcdhkOfwghcqWDsCEtTO7dvYG2ZJTb97oe6wc6WFJe97aX0jTtMRm8c0cXuDQfepeTnvtNjIiJf7ZC4ASNjPPwlSbKM37YHlkHWmiUCziAkW1GKj6ez/Yipc3cdC8V6Q66neU0q0EsIZHkXNJrCeMcEruebIZjOlpYU1bL4iYPuwtGZ1i3gQhZ3KiobyPSYY/7u0CooyeIlsvAUCx2uqpY3L6D5uIysroDqTqRihOXEiQm1iD6loF0jUkgVuMpYUt8N8/0vMapJQtHfTalYBn3bvs+vYlWYOxzKc/Riww6MCZ6kZEs9CRA18gGPThKQrCfHB+HEq/0Y3otdtX2YxlbqXXNo+8xhcwLEk+dIHS6yeCcOI2Db/B8+9+5sPbbeZfjDyiakChCpdp1FvFNXloeV/HUQtF0ibi9F3Gcl2ytl97/E9gpk8AiJyIkCRSkodAm3hpARCfTXvgrir3VbEmvovL5MkTP0D3vFKizPcRO17hwy3eJD5XPur/1Rep8ZZxdsGx0JmyRxsx0IlQdRSvDlvmygXny5HnvyAv1cYjDXU3JzI/RveGPw22Fcz/JbzrXc0npWSRjo2co/qDA2KyT2qxQtSKOeOwNZCyLFXCgfbSBwXCKnjWTh7fvbnHQOrEPz7ntmJmJVIb9bBbttMS7CBOnv+0xjq89jZd7nuPRLb8DoC/RSqmvjjOn/z8UIXih8W4yZpLjJlyMp7eFSxxBPFNjhDxOmjcqeAM2ZSWFJNJuzp1xFYpQ8W+cQHyfyZW6ppwpcxczq+xETNMmIwbYMfASa9v/QV14LnMrTqXaG6QtObpUVLHLe8DXM6hXMr/qLNa0PjzcdmzdRQTi1XTcagzPSWNrbaQNvrNUTHkEUh/nOaK0x7bi1ApGNxqSmGczuxQXRaqb6ck2vOk+urVZoOtv7tMhJcc176IlGEa3Ckg5slj7JDGMY6IicBxoTk8FpMmwi+fe9PpilMfL6Qj0ML2liXX1UwAw9TCO7ACKGibta0SNzYbA2OMtLZjBPR3PMTs4iVLnyFk5VBeTwkt4atttnFh1aBPc5DnMGAEc2Tji2XXQlwK3hjx9BnZBEYezjvoeVEulRBQjpJ/O2wWKA9wTFDKdNj13qzg/6kB4XFx8zHcJ2QdezzfP+wuPBbPcpxD5fQDhyCWpTXfYdAwKyr8SJJVW6P4DDK/kRLP4zDbkc41gS5yTi0hVTOGUsk+j+m0iMk7mo05cf8pCjwWawDjGye3JZ4ZF+h4eaX+FM4oWo1g5Ma7YXWx+5UcMdK1FCJW6GR+htP4SpMiH1OXJk+e9YVwJ9ZtvvplHHnkEgBUrVvCVr3yFF198kR/96EdkMhnOPPNM/uM//gOAzZs3c9111xGPx1mwYAHf+c530LRxdToHjW67KZ98ISUVx5BOduPzV3D97hdpS/XzXHQ1F06cjU+qlIVMkoaCpTmJP2tStNBEPLgO0kP+54NZrLs24L5oAbFeffRBJHTE11BbHCBu7GD9K18FYE8u9aKKBby0+z7KPPVcWPRtnBuqwBb4Ftn8sePfiaRydR4f3/a/nDvlM7D2r3Skf43LU0zNpJMJVB9DQgociX7CRdPpTbSjOMdaDaXTZHrZMmr880BY/LPpVl7anYuL39n3Oq+1Pcq/Lvgpa/raMIbqBlV6gswIlB3w9VRsF6fVf45ZZSfQGdtFuX8Shd5pdG0ywB69+h1/wyZwkgbOvFD/oBFLN1PsqR/Vlokkybp343QsZkJmAH+6gw7ZgHBqb2kDn9Hdgds0eKOkgkk9Pgadoyd8BpI0Fn70N+lh/wy7v+/zqOvzxjmmrZYHSv2c095KY7KSQY8Xy1GAM9mIFppOyr+JQE8DdmBsEi6/5mFBcCp/a3uCqyZciCpGxPy0wuN5aOf1LC69Ao+et6q/X1BNAx5/A7U2hFhYgUwYWP/YiPaxRRA8clZBEXFTcLyOGZdkumyCCzWkCa50KdP8pYfb2J9nnKHpfowulYLjdYyIJNtjU3iihsNvkVFsIrvSDHv/CAiWx5EP7BrpYHsv7mI/5bMms3WTRev6EO0azDxNobYyjuEYZEB9nQJjrDFghrcMl22QRUVRJG1b72Sgay0AUlo0briVUPFsHIEFh/5C5MmT5wPBuFG2L774Is8//zz33HMPQgg+9alP8eCDD3LDDTdw2223UV5ezmc/+1meffZZVqxYwTXXXMP3v/995s6dy7XXXssdd9zB5ZdffqRP471BMdnZ/zx3v3E9EhtVaJw3+yvsTvbzemQb354+SOi+FtgdAb8DeeF0euqCaCIzItL3kDLx++IYJzYTwIOnsxizW2Eg9QJTqo+lIm2wIb4bIRQqqk7BEZ6DQ1HQHIVIbC4o+hby9gmkhxan05vgvEu/xs2xK4ZLoL3a8Thn1p1C+5Y7SSd7MMwOnB43W7f+hZb2F1k2/+vMLf0QMQTiOZDG0NgEFKzQCRaejrQFSdnBy7vvHTX8gVQH2UwHv11yEY2JflyqxgRPISH5zmIodRmgxr2YWs9ipIQd6RSqauBn9IRU8wtQD96nUqqSATuNiiAkXORD3o8epNlDkeeEUW0bI69Sio9yNArjW+l2TAd0hHjze6QsFmVWdwcvV9YhhaAo4ac9MFJOUAJRDFyo77g+plDBNgWqNvr4pmoTcaYIJ4M0FWSY1bSDF6bPxtYCCCuFroTJqq8je/ph4v4Xuab7a2lJd/Nw10ucW7ZsuN2jB5lQMJfVHfexvObKdzjiPOMWI4l+XA3mqlbkax0QdKKdMQkrkwYO3GPpvUb1CiJrTbI9uXs8tcvGO03F6diPK0meDxxxK43fW0rvKybmQO4eSe6yCc6RhBMxjAoHe9Lkan6B0hsZ04fY3kV3aTWt64eSg5qgBrNs2v19bDtLuHoZVbqLCd5SGhM5o0TI4eUiEcbZu5Ns4QyETNLd8tyYvgf7t1AcXDimPU+ePHkOhnGTsrK4uJivfe1rOBwOdF1n4sSJNDU1UVtbS3V1NZqmce655/Loo4/S1tZGOp1m7ty5QC79/6OPPnpkT+A9JGa1D4t0AEuaPLrxZ3xp2nncMvdLhB5ozol0gFgW8cd1BBbGsVUdlH0mMprCmkwjST3C93c9xGO+56hdtpu6imMpfOQPGH++Bl+ghMlzv8a99kyu29nJN3d181Jc54zp/4FzW8WYWEBtXSW14ZnDfxe4K6mdfiXzT/slU477Kll/IQ8/8wVa2l8EoHdgC32WydXbX6X7ogSssBHH2gxemqYrnEHaI2PeNz4YQKBQJvws9dVyjKeSQsV90Nd2T2iZLSUv04tSt9fJCQifq2Loxv53fhtiIsNvG1/iyuf/wsdf+hv392wio5hvv2OeI05PqgOkRYFzRMS2pQZxmU3osoKS6HoG3ZVkDQ843lykB9NJTmjawfqSCtK6jmapeDNO4ntZ1JNDtYScB/P4FSBErlTbvvR740yKFrPO78OdzVAx0Jsr0+YowJHpQnPUkrY3gLn/8QshWF44l/WDO3l1YMuoz+ZWnsSr7Xdj2Qf328gz/rC9YL7QgmyN5RqiGcz7tqL4juy0wErawyJ9D4ktFraRD0rPA5ZiMtDbMyzS9xBdL5AJDXcmjlace0BaSYkM7ccNvTZA4TSbimlQUilYdKrBYO+N9LQ8T1/bK+x4+SaKjBT/WXccN1Wdwg2VJ/J71zQm3PdTRLwv14dwEyoZWw3DG5yw35rrefLkyXMwjBuhPnny5GHh3dTUxMMPP4wQguLi4uFtSkpK6Orqoru7e1R7cXExXV1dh3vIh4xYpm9YpO8hbSZojLbT3hmBxtHx2kjo7Wtnc1UnyoemjxgdBPScW8pv+59EilyB9We7dxJ3esnGWmk+ZhGJRWcRTjn4R8zJ+kiuRmjGMrll6yq8gUWo2ljX3JyWzr2INMXBSZM+hm0HcHhnEs1G2bjzLuRepuTykgXEDZOedIavNL7Ol1jNNc41/FvTanqzI7GQHqWE4+ouGf67wFPF6bN/SKMRZH26g6jop7X7ft7Y9jOiiVdJZ/a5Du+AKpebF2I9PHFMO8kLMljnmFhXGFBtcjDvWEURPN2zg/tbNmEjyVgmv97yIttTvW+/c54jzob+V7EUH+pQaTMpYVXvTsqsAfzSRmCTUEuQFihv4hXsz6Q5fccWthaW0O/JWSQLEz5irjRy6DdpIUlg4uHgXYuFIrHNsQtaA54k5ckQCctkR0k5s5p2oloWpl6AnmpDd9WT9m9GdGXetG+36uD0kkU83PUSr0VGapkWeasIOsvY2DO2NmueoxMrayM74/s0Ssx4ev87HCay7P/+NGQ+yWceSGTjJM39vPuHHoliSz+9Zz+H+FAz2lldWA0e7El7VfIIOLCXhbm9fQu/19exbXoTPc1fo6f1ob06k2QiLZjR7Sx89Pcs/fv1lD36WzDSyFAlAJatUNdwBU5P0fBexVXL8IQaDsFZ58nzzonH43znO9/hnHPOYeXKlVxxxRVs3LjxsI+jtbWVk0466bAf9/3CuHF938P27dv57Gc/y1e/+lU0TaOxsXHU50KI/a5W7s8S+1YUFo7fZB/mYBWaomPuZb3yOcMMWjqGnYSAAwZHT1qybvhN/718YepJpD4RwpdQiHhMbok+SFOyC2WvNZndsR38c1eu3NJHG35CkWseb3Tczr5sivYSmBzAvTowKi7QtyDJQnUZ80uOozA0mWJRiMNK4gqFqdTOpt8zlUgmRpgMQauPmoqFJPFR5nHRmUzTn8mN3aOp1IZ8FPtH3CxP83ycmoLpbOx8jpqKD3Pda89gyc0ATAmEuSKYoGnnXWzeeRfHHvMlZk//yEFf5/9aOpfHWzq5pXs7J1WVsryihCKv56D6ShgZ/rF225j21/vbOHH25P3sMT45lL+L4mL/Iev7YNkzptbtG3A4CvH5ciEV26I9FFs9CCNAgWwiEpgKaQXFMVSRYB9CqSSnbN/MroIiOoPB4bWy4rifQXdyeEk0bls4UNHFW6+RKm/xPJMqKDZgi1Gl2mxVEnEnmRAP0VSYpsLrY2ZbE5vq6tES23E7vBhqELtvO/6pb+6a6cPFhe4V3N/8PD0ywodqjgNgfu3JvNp6JyfNuPAdP28PFYfrnjrQ38V4vMffjI50kpBbAyEQlX7oTyH7UsRcgtL36DwO5nq0mFG0Mh2zc2R64pydIe1PUrfXAv3RytF0j7wVR2oOFY+EaXKvobJoElbvyHM0NEeibGwmOztMr6Od9oI1ZJUiBtMezrt8MsHOLNmEg+6Mh/aNkkvnh7mv9VVCDsGcoeexqrnxBKpIxTvIqm4Kqk9CuJ5GDvaAw412zpdQa6fh0veE3jWw7Nw/kIjuRtGc+EL1OF0jeTzG83edH9v7G9u2+fSnP83ixYu599570TSNl19+mU9/+tM89NBDhMNvXt44z/hiXAn1NWvW8IUvfIFrr72Ws88+m1deeYXe3hGLZHd3NyUlJZSWlo5q7+npoaSk5B0dq68vPm7rYGqiiI8e833+tvZ7pM04PmeYZVO+xE+2NaEgOOGcE6j4WxMMjV+ZX069VcZPA2fTmdpBczjIv7XdAZFcfxXuQhJDFrig7sLOtqAIjc9NuInq16eg7orz+znn8FhZMzd3vjQ8Dqeq8v2eR/npx89DWe9AGiba5DY2bfshsehW6o+9hkBrEe5/qMi+NNljB9g2fZDrdq4BQBWC/1pwDsmEEykNvjt/Fjeu38LWaIwan4dr5kzHnZH0pGN7nb2Les8Kaqcu59/X3Iu116LMtsEB4mXTh/9+df1vKCs6DsE+mboPEC9wQVkZF1aUY1s2MmnRk4y97X77QyiCyYEidsX6RrVXe0L09BxcnwfLu3nJHarfRXGx/7Bfh7dj7zG1DWzGqZcTj6dBwosdjSw1+vBbCmlHkLTwYKZBdUvsffIOlMWinNC0gy2FJXT6gyOLWhKKEn62FXeADVlsMlgE0LHfwm1DEeItP89tBJYhUJXR2w24E9QOFrAz2MSO4jLmN26nJVxEUg9hx1pQnfUk0msQ0Vm8lVHfjZPzSo/jpYGNfH317zipYh4Nzjri6Siv7XyJmuCstx7fYeBg7qmD/W0cyO9iPN7jb8WA1yZ0xTzUbRb2dgsxQUE9W6M3lEZ5D87jYK9HVDepWamQ2SJIdwg8dRJ1kkKHqh5V13d/jMd75FD+Jg4FXqUIX3E5pefbZHc6SLfa+CYIPGUW1oDGC8UJvrupGa/m4Julx3DWQDH6LkGqOMCLm2BwyBjf3y348akz+OYbmymYcQmBYCW6M0h8YCfFlUtRyxaxJRal5Mpfog92Ix1eoq5i7IgB7B0C5APnDGxgMAbEct/vePyu95Af24GN43DQuuMRtq6+mVS8C7evlKkLrqJq0pnvut9Vq1bR3d3NF77wBZQhT8ElS5bwox/9CNu2ue6669i+fTu9vb1MmDCBm2++md7eXq666iomT57M5s2bKSws5Gc/+xmhUIjjjjuO008/nTVr1qCqKjfddBPV1dWsX7+eH/3oR6TTacLhMN/5zneorq5m06ZNfOMb3wBg2rRp7/p8PsiMG9f3jo4OPv/5z3PDDTdw9tlnAzBnzhwaGxvZvXs3lmXx4IMPsnz5ciorK3E6naxZkxOE9957L8uXLz+Sw39vkYLp2jy+NO2n/OvcnzOx7qv8ZFsTMSND1EjzKzaT+vxE1I/MQjtrDsRLkL9L4fqfAOHuAItVBzdM/ggfql7Mv0+/gE9NPp9HmnewuLiKa2fNYXXjHzm7/DPU/LUS8fgg9s4MgbvTnPdaFUvDtQAsKq5hdzyCKS22Olbxt5J/hZO30Gbfj6uwhoolV+FKT6TgfzzIRgMGLXg0wYw1Pib7cq5glpRcv+EZkiIXp12u6PzomFncumIxP1k4hzrN+aaxXGnLpCM1OKY9aQvE0Oq3ZWfHhAi8U2xbYpn2Qbm77420JZfWzcWvj9Rsr/cXMDdU8e46znPIkdImk+0g7K4CoC0dxbaz+DKd+IkSc1UijZxnpdhH3Nb39eRi0ksrciJ9L/wZFxJJWjOwySWQ8xxQxfQDQAEhZK5c2170e5JUJUNotk2/kOwsq2DBji2ghHCkWlBdldh6Eru15W0P4VadnFR0DKeXLGJ3vIubdt1JHwH+sfsP78UZ5DnCFEsXyiob85Ek9o4M1nMpzL8nqTOPbGb/ipQX7eY+vE90U9zZj/veLhx/ilFq5OtT5wFFKkxNz8TTJPH+o5Pizn6cd3Vg/bQLlk/np92rAfjP0hNZ+kcP+t9icPcg7lu6OH6WzR5noFRcosZ6+fSEZtRACZl0lJ3r/o+u5n+y641b6Vh7C2FVkFYU4v46Es5ixqltJ89RSuuOR3jjue+TincCklS8kzee+z6tOx55131v2rSJWbNmDYv0PaxYsYJdu3ah6zq33347jz/+OJlMhmeffRaALVu28IlPfIIHH3yQQCDAAw88AOQMokuXLuXee+9l4cKF/PnPfyabzXLddddx4403cs899/CJT3yCb37zmwB89atf5ZprruGee+6hqqrqXZ/PB5lxY1H//e9/TyaT4cc//vFw22WXXcaPf/xjrr76ajKZDCtWrOCMM84A4IYbbuC6664jkUjQ0NDAlVe+f7IRuywT5Y5HCDV3ENI1Mp87m/XhGJsjPSwqLOZkv+Slll9w4tprYPNe8XxJG29nNTuyf2JZ2SdQ1UYe2PwDGsqWc1X9dNr7X8VOxUmbCSZkZuRqhu6Fc3WWa5avYOfEfkwriZGIcXndRER8N416gBe7nqLXbmPA7KBn2+N8zXcr7JPUyvtilpWfns4N8Vw21K5UjIw0cA/daroNYdS3LbHjQ+fsqgb+3rRuuE0AJWqGpqH495lTLkNTCsZYOI8U5cLPbxZfxO7kALqiUusK45NjS2HlGV/0ppoxpUKJK5dIbn20gwY1jc90kBY+LNWJnRKIfdI1zOhqZ0ZPJ6sraog7nGP6LY4FiLiTIEZqpuvv4dqoUME2BKoqh+MzTdUi5khTkfDT4U8R8gcJJuLMaeljQ/EgQproaj3pgVfw1B5YLeoiR5C6glIW+KaxLdZIS9/9/HLHn/jcxMtR3saFP8/4JdinYq5KjGqT/RZ6hw0Tj5wodvYCsdxD3Y4Nvd+aDFwRN4mDc57K8z5Cs/rRB/1Ym9IwaGEP7jUJeTnJjStPpT3eyYKtRZDcKweDBMezg1RMKKCtOXd/ZeUgqxpvYX6gmq6mJ0cdJ96znukygyrd2Ptm1M2T5z1g6+qbsazROUEsK83W1Te/a6u6oihvaghbuHAhoVCIP//5z+zatYumpiaSySQAhYWFNDTk8ixMnjyZaHQkH8Txxx8/3L569WqamppoaWnhc5/73PA28Xic/v5+uru7OfbYY4Fcwu+77rrrXZ3PB5lxI9Svu+46rrvuuv1+dv/9949pmzZtGnfeeeehHtYRQR+IQHNH7g/DJNnRxNllQc7Smhjse4FtOzYyqfwMRFwZ8/oQqdwEK1leytTM8XSYbby4+14cnc9yUv1l+C2F2WXLkfvL/CxARF1M0PoJ+93oW59i58AqjFgny6vnozhDaAVTiZQpZBSBrzMIjI6VF36VPis5/PeykgkEhGtM5vi3Q9pwQdUsTNvigZZNFLo8/Nv0Y3H3Pk1heCpT6s5lYu3JpJLjRyhICWHchN1DWenz7/ajgh0Dr5MUbnyam5iRoTsdZ6Hdhs/M0OuaBBLsLKjekS90TkcrkwZ6WVVZS2Y/CRcBSuMBOgIRMtgHVTP9bRGgqBLbAGWv9aCIO0VVPESzP0HGtmkqLWdGcxOT+3XWB1uR/nqSA49iRfpRQweufDRFpSE4iW3ZmTT1/ZNfCBf/NvGi9/ac8hxGhhZ4xtlzytAlY5Y3Vcjq42ygeY4IdrILU/fh2E+uEEWFra9+g3RmACvzS9R94ntEzCYQhDagZloWv7ufD9VdhkxH9nssl6KO2xDJPEc/qfj+k2C/Wfs7YebMmfzlL39BSjkqp8xPfvITZs+ezS9+8QuuvPJKLrjgAgYGBoZFvdM5YnTYNyfYns/2tNu2TVVVFffddx8AlmXR29s7Zj9VzXtDvRvGj8rJM8I+q2AhW2cg1c/uHX+jvz+XsbG593lSS8bG8WRrByirOw3DsFGUYk6u/TxfPv5v/Puxf2Jm4RLWrr2ZkmQaR5mCrNznJXacD98k8BU3kHVOgEWX4Q7XA5BuWUPypf9l8PEfUZN2MCVwCna5jV02+mVpXOjnidhOAJYU1/L/pixFsQ/O2TcoXXy6dgl/Pu4j/HrBRczz1DC99kpOWvIrKkvOw+ctPah+8+TZm60Dq1G1MIoQbI51Ue3yEoy1krUDmA43tgFCyf2DnCV90kAvr5bXvKlIdxoanqyTiDM55PKuHZqHrQpSCvYqskC/J0FtrBCHlPTbBlIobK6qoShuM7NxI0LR0akn0/rCQR2yMjCXsN3B65EtPNGz5j06kTyHm3Q4jbp0dEIwUapjlx5ZYdIRjJCaM/rXMniqQqf34Kt85Hn/oDgCtLv6EDNdjIojEpCen6Y/up1kupdk7cCYfTMnOvGVCBafkcTw/56XVn+blt1PEqaKopIFo7b1hyfj8kw4xGeT54OM27f/Oeybtb8TFixYQGFhITfffDOWlfM6ee6557j77rt57rnnOPPMM7nwwgspKiri1VdfHd7mnVBfX080GmX16ly4yV133cWXv/xlwuEwFRUVPPPMMwA8+OCD7/p8PsiMG4t6nhGMcBC9rAg6cwnzqlc1seusWgITzqN51z0AZI042wNPM+Xy83E9aYILzFOyWFUKDm/DsNaXtoKLXKZcoYc4+8Tf0RfZTFJ0wycno2xWYGcGZrkwJuoYij3slh53lFO44FPEO9ZiGzkruat4OnrRTAwJpj+M/v8MlBYb4gKqnFgl8BPlPDK2SUh1o1jvMiLXBj+5VTxJTpTkbtv8Knee94bO+Ca8zqnYUrI13sPxuoHfFETs8pwQToy4vddG+pjR08krlbVktTd/fJbGggy4E/SJLC5U9PcmMn2/5KzqAtWZ+02kdQMpJOGMgw6XQQlOUDU21NYzY/cWjt34HK/UzyOafBor1o/qf2f+xF5nCS4twKnBMm5pup/F4en4tYOrlpDnyGGrLswVGfSqMPbmNEqljpytEfdHQR45H3PhdvC3xWs4ee5MfAMOBorSPKK9zqX6KWNCrfJ88LCc5QwYG9gZ7mbF52airstgKxCdZ5GuzFKpXYvLTvN6919Z/IlP4n06CCkYWCFRpiuENQNFceIrXsnkiScTUMI4f/0ADceeS9eUmXQNrKaoeD7FE8/Clvns43kOHVMXXMUbz31/lPu7qrqYuuCqd923EIJf/epX/OhHP+Kcc85B0zTC4TC33HILqqry5S9/mUcffRSHw8HcuXNpbW19x8dwOBz87Gc/4wc/+AGZTAafz8d//dd/AXD99dfz9a9/nZtuumm49Haeg0PINwtieJ8znrO+A3hTSdSN26GxFaZNIDm9jl51gOTgNnq7X0PxVbJLuCkPNdDencZWLNbFO5jgL+aSqjm45YilT1HEmHNVVQXbltiqBYpEMbQ3KXsHWrYTc3A3QnWiBuowlBCKItAzOlJITId5yK/l/s5hvGQHHW/ks74fGMXFfpramrnxlYsJFa7Eq4ZYM9DCmfE1BJMJ+uyZyKCKERGoPkk4neTMHZtYXV5DzOl6y74XNtWz09vPoCeF8x3a0g8o6/s+SHPI6j+0dlDXV0iHc5DNxX0UqDohNfc80JMtVEcsCpIZ3igIsbPAi2vOBW/bv8/nymXEH6I7toFIqpku9wIqXSV8uu6cdzTe94J81vd3hxDQaPSwrrubU8NT2JLsJuVIsTQ48aC9oPbmoK+HkGzK7OK/N92OV3VhSJNvzryCaqX8XY/pSDMe75GjLes7gC3SPBNZy227nqLWV8pUfxXzi4/hP9c8RtRI41BUvjJjKcs81UjLTVJNIZwKbss93EfOGzgX++EbHES8thFiCVg8i2xREel9M4e+Q8bjd72H/NgObByHg0OV9T3P+4e8RX2cktUCyIpF2N7FqIUShElIeAgFK6kuPIk+O0oFkj83vsEjrVuG93ulr405hRVUu0KU4cTd2QO7WqEohFVTyYDHxY5kkg0DA9SHM9zR9A8i2RiX1J7IsaFZeOSIABFCIJXcCrYsLEOSM7brhkb6dUHPcyZCh8IzdLRJFqby3ps7dFNDdqikW20cpQKtSpJ1GG+/Y548B0DL4BtkhI9iZ5jXI11UOd0Eu/tIqBNAEbn4bxV02+LExm1sKSzdr0i3kRjYZJBgKPgyLhJFmXcs0g+W4cRyWm7iHPGkqIsWsqukl37LHBbqprOQ1kALHcUN1HXtZlbvLtoG76VjyjH0FVcilQObnBZ6p9LY9wzHll3Cb1ue4LKqk/JW9aOMhDDI9CosXzWD5DaF+pJq4qcM0hdIUoz3yA1MCubYDdzq+Q7ZLomzWoBlYyn5536eHCpuzlRWcCLLkQPgDAie6dpG1MgtJk71FjOpcwIDT3gwk5LgohDeJZB1GAhBTpjvagHThPoa4gVhxEnLUAQY5jjJTpvnA0HVpDPzwjzPW5IX6uMQzVaJPQXxtSMTk/BJKo6FEgsby5SECJBSTVb1NI/Zf+1AOz9ueYoPVU7jko2DBNdsBUCpq2Dt2SsYTLaw2BOlc3CAOpef+wd2csOm2/lSg+S00GKklAwKgxd7O1jT18UJ5eXMCBURslwIITC2Kww8nasLJU3oucek7GM6lI0I9YwiyUiJT6goB7nqrqKQekkQeXHkOnimCIIrNQzFfIs98+Q5MHZGXiMqdTyKm/bUIAvUQTSpkDYLwAkyLUCXLGjfTczpomOoBJuFJI5FChMDiSRXllxHYW60lD5PHO1gjZJSIqREinfQgQCh5Mq1CQ2irhRTe8pQTDBUm4xt41QUpOIGBCndZNOEmegDzRQPtjB7dT+udILusjraa6bSWVGPre7zerBB9FmIlET4VYq8U4nEXmO6v5ZHul7mksqTDvKE8xwRTCh9sgizNXefmd3g/nsA12ey4HubfQ8hmqUTud8i1TgimIJLNNwnaph2/rmfB7SYTvvvDOSeqYECx18whSvLBri1czWfCixDud2NORQiF33JQnGo6McKvNEo3HIHMju0s/Iyns9fQcIbRKr52Io8efKML/JCfRwiBlXia0dPSAaesaiYoWF5shiKwY5MK7v625kdLuOZzl3D27lUjeNKqtkZ6ebWprVMn3Esx72+nYFjp7JlRgE1jiZeWf11Vpu5mPMZJQtxVy7g9rbV/LXpSZYfMwcdJ3/YvgmXblEZiPI/O5+gyBXiM5PPoUGrI7rJoniZguqQJDogtlWS3m7jqMi57G7PpPjJ+i20JlIcV1bMp6fVU6wIQGFPvgpTGSSa7cSpevGpZWCPteSpCY3IS6OvQ3KbJBhRIV+mJ897wPaBV9H0IhpT/ZS6/BR0v0LCFULEBLZXYKegyoxSE43wYlUusdAgJlEMnCi4UfGjjNjNJdRHCtlR0POOxqHZFkEjhs9MoQ+JEVsopFQnAw4/aXVs+bd92duqLoUk4kpSFPdjhZL02wblihOEwHIUoKXasfQw2VANjWo/rdkKvDMmUNDfzuTNrzD31X/QNGEmO6cvJKN5sTYkcWxNYbsEuARiu6Q8OImtpc+ysObfuafzn1xUcUK+XNtRhDvpINk6ejFIGuCI6OA7cmFhokcl056lYJGCwyNJRwTR1SbeeToE8kL9g46iCFJv2DgKBKHpubbodkhutDll0TRuV14nNDDWI2RwjUXZQg2xtXFEpLscxM69kHWrfPT3WlRMUJgyT0Po2TH758mTJ8+RIC/UxyFyf+8IGzBycVWvxrbw3fV/BODfGy5nS9RHZyrO6WVVnBWUdK3/ERd6K7ls5gp2ZSXJS87gPtd2prsy7Nh0K4Y5Uj4t0r+By2r/ncuLz8TjcONMeGh3p9gY6WVBWZa/NeZqi3ak+viPV2/m/iU/pnQi2A/0Q8LGOcmJ/7QCsnquHEOPbfKVl9diDsXYPt/ZQ8rMcpH3ZaxMD9MmXkJW0/jDK19hINWBIlTOmPoZjin5EIrcx6XYYr8542R+0TvPe0AiEyGe6Sbkn87WwR4anDrebIqu8BQYBMsU6MJmWUsjm4tKMVSVPrJksAijo+4nQVxRMmeKjDnSYz7bH0JKCrJRgkaClOokovswFR0bULFwW1kq0n0kFQfdrgLstxLC+1jVI+4ktbEC+sMJ+m2LEpyogKWHcaW3kLEzCMWJ7p1ChrXoO0ronFBPZ+VEnKk4Fc3bOPmB/2WXYy5N5QswG5xI99A5S4l/dxkiaeG3YihCYWOskVmBie/wW8hzpJAOG8WpYmdGtyvuI5u7RUiLqhMlyiPdyAELZ7mO//RCbGv85pTJc/hwuARKQBIsScF9UZDgWuQnXe3Dq7r4XP1kzAFrTDFMPSxQDAWjYiqxjzvoTTZSHFrCc08WkB16XO/eYhOPShaepmHL/KJQnjx5jjx588c4RIZM1H1cD53lgrSzhYzdyq27Hhtu/+WWOziuPMQPF5zGOb4E61/7MZ09r7Oz6UF2rP4mx+tBnt9Qz4zWU6hWS+mP7BzV75n1N1L0SzcFv8vi+mUU5W8Rylo9XFgzgcfaXhy1rSktPF0C+2+9kBhyS9yRwbEugnOCICGytCVTwyJ9D2t6o5jOKnbsfoQ3tv+J+zf9hIFUB2F3GWeVfwYzmiZijHXhl34b14TRYkgLgRLKT9jyvHu29byK1MK4VS9p22BCZAMZzYltOZEOkFnBrGgbCd1Bj9dPP1kMbApw7FekA0zsL6bDN8iBJHl32AY1yU7cVoZuV4iI7iOr6MMu7xYqcdVNpzOMAKqTXWhv4/orVLDN3P4D7gRViTBpO4sLhUHLGN7I1MM4Uu0AKKoH3V1H0v8K6tYk2uo05madlmgDG8LLKVWaWNJ/N04R3+tAArtOpyQ1hbYdzzEnMIkn86XajiqEy6bwjNFTAN9sgRo6sEWmQ4XLIxF39iAHhlZkOwyUh3vRfPkY9TyQIotHGPBsNLeYbwMvx/CaGZwphYmDOymsU9DL9tpJhdBSnZZfGLTc5iG5ZhItIsHOeP+wSN9DX4ckm8pPjfPkyTM+yD+NxiHphEHhSQ48kxQUN/gaVALzNHrbd/LYM5/lM9Xzh7c1bJM7mp7AKSNs3fqn4fay8DxOqP4mhTEv1aXQulWhaVU1lRXHD29TXbSE8POVEBuJBZSbUyitWU7R6ihyBlEQnFAwm3+vOp9vzb6S1H6yccpNKWKZBJ98+Q5SMjPm84BDRxq5mqb+YB07+15nefElfCH1M47/86mc8vfzqNxaib5PKTdDmBSepxI6XkUvEgQWKpR+VCOr5ydsed49m7peJCIdxEyLSmeAYKyFhKcUkZVIVeBJZ5jR35lLIIdJGovQGDvNCN6sg6Kkj27v4Nse22umqEp2k9Dc9DmCWLx5EjeJIKL7SGouqlPdaPZbuJTsZVXPahZpzcCfdONTFPotY9hBxXQUoqU7YKgvVatA6F7iZasx6zTsag1zqk6yJsjGKccRDxSz4pW/Eoh1jzpYYVEDbbzObFnN831vYL7V2PKMK9RshnQTVF3poOajKtVX6mheBSV+6EoJHgiiz4DsPouxfSZa35EZT57xRdo0kBtSY9rl2gR60mS2djHeTAblzA24P7Qb34d6qPmEwGg1URy5bbMbnUwePAnVMXbhU1FBfXcJ3/PkyZPnPSPv+j4O0TOC7geyBBcqBGcJYjskPQ8b+D5SzMmV3yPQUclvK+u5OfoAW5OtCASaoqEMlROZUnIO8zuuRL9LAWkytbSP4rMKefYVlWMWfJhEqouentcp8c6C3WMznIqYSaZ7gB9NP4tMe4jap9xo7ZLEPBDzdGCfJegSjVfTLUSNNC/1bOXYsiJe7ByZVX16ko+OzbcDEIs2MrloAcfHVuJ5UCWXLxuUW5O4rvJiVIOKxBOJoLR1Id0utIXluJd6sVWbTD6ZUJ73ACklm7peJEqInmSc0/QsipRkPOWIQQtLU1ncv5vmQJhBXSVChvBbiHSAqb1ldPij2Mpbe3wEs3EKs1H6HQEyylv3uTdx1Y2QUJnqpsVT+qZu8Hus6qomibiTTIgVst3XjYkkbdu4FQVUJ7bmQct0YrorEQJ05xSyqQ2kMqtw+xYzvI4rBB2VDcRUD8e+djcvzTufaKAUAJcrhDsZRm7aSWFNkPWDOzkmNOWAzynPkUNPefDPkciX+rE2p1ErdIpPDpJMe8gEj9yCi3Ds5752CNAV8oXU8xjSRFY7YR8HHlHjxHoqBruzOHRB+IIQG5z3Mtu6COUPvfgB37FB+tp14jskWmMRvSVPUFxXT0/TSNjdjMUaqsNkHFfvzZPnLWltbeWMM85g4sTRoWi/+c1vKC8/cmUuTzrpJFwuF7o+Mu+56qqrOPXUU99Vv7/4xS8AuPrqq99VP+OVvFAfhziCWarOttF2NCM2R3DWFxO6sALRVIzyQAiABjz85tLPkvBmsIoVmkUvlZMvYdv6XzJdPw/9mb0mO10GodcGKS0P0NivsSo4h/mVp9KphZg+24nyWhqOD2K5NRTLRgY1smovWusgVf9XApmcOPY+C9ZgBuMMN/qjQyvaKkQvcnPj7vsAeKJ9Mytr5vCzZcdg2hIng2xf+5+k0v0A9PS+wUXH3Uzw124ko4PxxfoEQfdOFMWDdefL2H05671SEIRPnkO31YFmQEmyCB0XtuvtE2xpWLhS3WAbZL2lZHn7ffK8/+lN7iZrZXFrRei2g7L+10g5A4BAZKEoHacsFeP54nr6yeJFfVN3dwBfxkl5LMiait1vflAJhdkoATNBjzOEeRB1emOaG1ValKb76XAV7d/FXuT+SRv6PQnq+4p53W4hoLjpsbLUKLlJqekoxpFqwXRVgBAIIXC4Z2CkN5OMPYPbuwShjJRc6wtXIYXC0tfv4bkFl5LwhgEo9EyhefAVZujL+GffurxQP0pw6zrWkwPY5S6skzxg22TvG8BzeSFj/aIOH0YI1FP9WI+PeG/pF4RIBU0OKKYkz/ua1lQP5VNrEYUqMi1zcxefjlKhIV9P5DYyJM47deZ//ONwawLJ0AL/vX0UXFxMvFHBqhrghda/snSCyrEzLyMVU/CHBO6AiS3zJdryHN2UlJRw3333HelhjOGWW26hqqrqSA/jqCIv1MchKSVD8Mk3oD+dc1XtTqD3RBF1E2GZir3TRHaa2HdF8Z8RxPxThJrPFtBZOpnCJd/Ft7mYfS0PyvY0tfODbPcZPL15E092baTUFWbhCYvxTg3R8Q8LK5GzZpScrdFu/JNj5IchM1pMq2uzyGvcNFZnCRgeXBV+vtH6KNmhdO6aUJgcDPNy72bWD3Rwavlk5h7zdbqa7sXvq6Si5FjcajmUxGH3PkLdFcX++7ew03G0U7+C+ZQJiRS2S2VV70OoCVj8yjS0rZsAyNaF8F7aQMI9VvAIYaMa7bjXPAQv3gG2hWvyEvTTv0TCWfyefVd5jk629b8EjmKyhsJEXcObHqSraBZkJFKBxd272VlQxKBiYwHut3BNB5jZVUlrYABT2f8ET0gozvTn4tGdIex3EXUU1b0UZyKEjDgRx/7raClD7u9xRwanpeHIqrjdChE7S9a2cSgKUvMiFQdapgvTlQvoFEJBd83AzDaTGHwch2sqDuck9rwq+kMV6FaGpWvv4dlFl2PoLoocU3gt9EcWN6/kf4r/iS3tfPb3owARtzHnBml/VmLFQWgKJSsK0WMWFB65cUlHGhlvQ7+gGGkKhMPG7mohfUw5Yt+Eo3k+cEzNhBG/fxV1WT3Z6kra77OwkiAcFmWnleB4qgd6TESVA/Gazb5PZGVjHP+8EB2V29HaHUyrnI1PMfENVZLJG9LzHE62Nz7MqrW/JJ7sxOcpY/HczzN5wlmH7Hhf+9rXiEQi7N69m2uuuYaCggJ+8IMfkMlkCIfDfPe736W8vJyLL754eJ/W1lZWrlzJt771LW655RYeeeQRLMviuOOO45prrqGtrY2rrrqKyZMns3nzZgoLC/nZz35GKBQ6oDHZts0Pf/hDXnrpJYQQnHfeeXzmM58Bcl4A999/P6qqsmzZMq655hpUVeV//ud/uOOOOwiHwwQCAWbPno1hGFx77bVs374dgMsvv5xLLrnkPb+Gh5u8UB+HOPos6N/Lvdyloc0tw9qyG9kWQ5laglhYhPnAUPZ2Cb77DZQTXmb37jupb7hzbKcTnYhKixfeqONrk7+O05GmszfMy+0eZjyVE+mQyzjfdZ/J9Cs/jGO3F3MfqzdOgXv70zjLdHbXNdARt5ngnc5xpbNZP7CTGn+A23aspiuVSzy1vr+d86ob+Ny0fwMrlxnesCXeE9wY61IjsYgBFRHeBskIAOZzv0Kd/+9Y/9xM36Jynmy6ni/qP0TbGhkeimyKoKzvRllagb2Xn5oQJt39z1ESjcPzfx3ZfvvLqOUPoiz+BHZ+wfwDzZa+Z2k3FOImTE9tJqs5sB1eRMSiPBPFZRm0+AMMkCH4Ni7vZbEAwYyLnW9Skk1Im/J0P6q06HGGkO/SKigR9DsCFGciJDQnxv7c5xWQlgAp6fckmDRYwi5XLwGh0mtnqdhjVXeWoKeaMZ2luZISMOQGX4OqF2Fmmoint2Bka1HUGlStgK7CCXhSgxyz4VFWzV2JQ/Hi00qguwlHicb2RCtTfTXv6hzzHHosl0rHixbWUI5AaULXUzaujzvgCNrUnV1ZjLXtmGvbR7UXLCxn4AguIOQZHxR0W1gJAyvoHhbpkJu7dDxlU3N8CHF3L6RtRGCsB50sUBhc1oqjdQ2faPgyAX3CqEoymqZgWRIp85I9z6Fle+PDPLvq+5hWbr4fT3bw7KrvA7xrsd7d3c3KlSuH/z733HP51Kc+BUAoFOI3v/kN2WyWM844g5tuuonZs2fzyCOP8MUvfpG77rpr2Bq/du1avvrVr3LVVVfxz3/+kw0bNnDnnXcihOCaa67h/vvvZ/78+WzZsoUf/vCHNDQ0cPXVV/PAAw9wxRVXjBnXZz7zmWHX9wkTJnDTTTfx17/+lY6ODu6//36y2SxXXHEFU6ZMQQjBU089xd13342maVx99dX87W9/Y/bs2dx1113cc889CCG49NJLmT17Nq+//jrRaJR7772XgYEB/uu//isv1PMcGtza6JeLtqIW45EdkMwlUZOvNCOmpFAWVUF6SHEO2ng81QQKp9Jc2EvpkkI8L+fePiKk0n/aIC/23smEwkswEuU8+KpB1oL/N0VgDu7zQrJBiwaR3WlErQO5l+VbO0PB2vhnqra62Hjmt7lx89bhz/5l+jTqg5K/7nx9VHcPtmzm4to5FMqR2qampx39/FakUYMIBLG9nVjP/M/ITskIojYAikJKzVDgKcU7OmF9js29qEsrRq2at1j9PJfUEGoByy75PlOf/h1qXxvoTtj4FPqCy8gI99t8C3ner8SzfXQnd9NLHRXOMKH+lxn05VyxRNJmYW8L28LFxISFjkB/C2GtWypzO2vYXtCFFGMndpptUZ7uxRIKfY7gW1pr5Jj/vDmmUIlrHkrT/bS6S/frEawoEmlBnzvBhFgR64paKFeDtFlZim0bXVGwVS+gjrKqj+zvweFuwLbTSLubdOJlQKA7J9JUPp1ZO16gvvl1dtUeQ6FjMrtL19KQPYZV/ZvyQv0owLTBHNjnZpNgpo+wQNnf4QXkg4bzAAgF0BRsn3dYpA9jgSUVNAXUY7yIIg3LKSAzdO84BJtm9/CTnffyeWOAzme/x/zTfgdqGQmRZd1gOy90NzEzXMaSwlrCMj9PyHPoWLX2l8MifQ+mlWbV2l++a6H+Vq7vs2fPBqCpqWnYGg1w5pln8q1vfYtYLIbf76erq4svf/nL/PznP6egoICXXnqJ9evXc8EFFwCQTqepqKhg/vz5FBYW0tDQAMDkyZOJRqP7Pfb+XN9XrVrF+eefj6qquN1uzj33XF566SUUReHss8/G5coZFi688ELuvfde0uk0K1aswOvNaYozzjgD27aZPHkyjY2NfPKTn2T58uV8+ctfflfXcLyQF+rjEFt1ok4sRO4cSsimiGGRvge5rQf9UxPJ/i73Y1BWuBksDzDgOwZbbWTzsh5KZhfiMDWa3D0sKCpmhrmEQbuFApfEcKZQbQ/pTAjVw+gXngDdqWI9F0M7KYCY60UmLEShhl28BV7sBKeXweTot+RftjXzn4smjDkfIcA0o6DuJdRdYWT0z/TWVrN1x91YZpr6hWdR3j0dffXDiMJqZNtrqGfMo7C6iuyWDIk6CK7bp/OGolFO/m32IFeveojMUPbpPyG46fxvMRDppNe2afCHqVUdjPGHy/OBYVPvs7gclcisizlGx3ASOWyo7+/FFArdPi+Db5dATsK8jmr63XGirrFZiJ1Whsp0H3HVRUzz7G93JGDbAimHDdpDHwoQObH9Zk7kMc1NSSaN30oQ07xjN1DANgRRd5KpvaV4DAcZxcIvVHr2WNWFwHCV4Ug2YTpLhmbB+3SjuNAdE1DUGmx7ECvbSiyzlc0V05nXuIruoloK3ZN5zfMCM1pO4R+eTVxZc8abX7c84wINC9UvsGKjBbDqOLIJ22TYiVIfxt41MNymLqrEDuZTcecBK2yizC+DWALFFcTeW+cI0Cbo6OeGMV+NI6MW+tkhpAIkbGKTbb7W+UumBSsJlUwi2voiycGduApL+cvu17mn+Q0Anu7cwcP+zfzX3HPw2Aee8DNPnndCPNn5jtrfK/YIX3s/rqVSSizLIpPJ8K//+q9cffXVwwLcsiw+9rGP8YlPfAKAwcFBVFVlYGAAp3PEwCiEeEceKfuOY88Y9odpmgghRu2jaRrZbJZwOMxDDz3ECy+8wLPPPsv555/PQw89RCAQOOCxjEfygYTjEGFrKMU1qKc3oCyqQRTuZxKuKVhCQa4sQlzqZ/uUN/j969/k8R23cee6H9AbfZQntDU0+rZxemEBdiqJEu8iaQg+99Id/Hznvfyi6W/0BhpRztYRe95FAgrOAcOV84c0nxrEuH8A8/kYqVg36zr/RPb4S7Dnr+SRvtFu8YZtU+L0UOcNjWpfWVlPqvs5VH3EnTKteIktOIn1b9xCJtWLacTZtu0OesuLoXIa6nEfwXr578gyHdtdweVzvkNTcRvGdP/IdaoPYc8qGXZ7VxTB8z27hkU65HLK/6VjJ3fFo/yidSuf2/wyq+Mdo0XRgX4vApKKpEeaZN4ms3ee8cv6rsdotTTcipeygQ0kXQWgCNSkybxoO9vCJUSFifNtEsjVDxQRyLhpDO1TN0rmMrtXpnqJaL5RIl2SWyMyLYFl5/pWFImmSlQl909TQFUlytA9ZtoCS+5/HFHdR3E6grK/5EdDpdpsG/o8CSbHSonaKQKKStS2yAy96HKx6i70VPvYPvbuToCqBnC4G3C4Ghiwt7Gr0MsxGx7DIVz4tFK8qV7aU330Z9++RF2eI4uqZSlfkUUMlaxCQPFxFg5l7KLT4WSzN444YyLqRTNQjq9F+/AszEXl7HQdyRR3ecYLg24b+8QalPZeyk40EXvMTQqUniEQj/Vh3DeAbDcgYWM3ZbDXJTGfGGRLqpmYkeCU4on8rfEPlC/8DEIIeu0k9zVvGHWcnbE+WtKRw35+eT44+Dxl76j9vaa+vp5IJML69esBePjhh6moqCAUCnHttdeycOHCUe7zS5Ys4b777iORSGCaJp///Od57LHH3vU4lixZwr333otlWaRSKR544AEWL17MkiVLeOihh0in05imyV133cWSJUtYunQpzzzzDLFYjEwmw+OPPw7Ak08+yZe//GVOOOEErrvuOjweDx0dHe96fEeavEV9HJItULAGbNTnbITfD7aCmBBGNo5YGDh+Iq2P6WR7ofgcH0Kv4ouT/4A74ka4HXT4W5iQieN84KcQ66WqbBL9Z/8bX968CmNIyJrS5udbH+Om2Rcz6eMO+uMStz/Lizu+iYhLjv/oV/A86kUmbaxlJj3lr2KkAsTLp+EKzaZn9a7h4eiK4NyaErK9L/G96Yvw9oZw9Cg4/SrZcB8v7bqfzu5XmTHlI/jdM1EUB52da/Y9dZq7XmTHqddhpVMsK6kftmcGlXoCteVkKw30qAaWgrMqSG9q9OQtYWbH9JmyTJzKiDXm55uf47eLLsZtHfjtLwRsy6T40dpN9KQyTPB7+drc6VTsKcya56igJ9lEf7qdNquCE902XiNLV7gWgIbWDiK6m36vizhpCt/Cml6Y8DGtt5z1pS2jXN6FtClND+C0s6Myu9vkLOcwZCVX3z5Sfc/nqiKR5MS9oo62sGcUnbTqoCA7SK8zNLYPJVeqrc8TZ2K0mNcLWjCxCSoaHWaGWocbAWRdZTgTuzBcpXAAJeNUNYDLcwzNbKBkME5dy1rayybSUv4GU42JrIls5dSShW/bT54jh+mN48hkqDlWxxQuVMVE6elFBg7PJPHNSJo2yZQPLe1G9QQxkhpZE1J2vpB6HujSvfhTMZT6GlyDGWovVTF6M6h2Fr3WiQz4UWZ7sNYkEGENUaRjvRaBBS7ihQm+Wngir+34LZFMN5sSOziz9EQG2VModjT5OPU8h5LFcz8/KkYdQFNdLJ77+cNyfIfDwU9/+lO+973vkUqlCAaD/PSnP+W1117jwQcfZObMmXzoQx9CSsmkSZO48cYb2bJlC5dccgmWZXH88cdz/vnn09bW9q7Gcemll9LU1MTKlSsxDIPzzjtvuGTb5s2bufDCCzFNk+OPP56PfvSjaJrGxz72MS666CICgQAVFRUALF++nMcee4yzzz4bp9PJaaedxtSpU9/1dTrSCPkBfRL19cVHJSAbT3TIGC/u2MFJ6Vp8vQqREovBghTzYhqiK4XhKyGy28vg1j11jqHmcg19awzzH0OWrBkOtOp7sV/7v+F+ZXEdv1l2JX/p3DXqeL+omIhXd6IHA7zy+n+STPcC4NB9TCtdydSK07AJ0hfV2aSk6VUTnFRZTcq2uGtXEzPDJgEakZk+JolCqhOnYP4uOhxraNcJNp/wGGtabgHgjOU343PPItJ2H1te/emosYSqTuBW/QLWD8S5pd7FxLIJJPXwfq9TcbGfnp7YqLZdZh9XrbpnVNu/Tl/G77a+PLxA4VBU/rTsI/jsAxfZ/Vh85vlXyVgjlstSt4tfLJmH2x5fJYOKi/1vv9GbcKh+F/v7ro4Ej+78OesGNrE+4+SK+Facts5A0QycWYPzX1nLi0V1dPjBBPxvkundm3WwomkK2wq7iOzl8q7bBhWpXgxFY0D3YSNGxLmQOQs3jJjV9yD2+gcIBHJ/k0ZGxL62l0eHik1JeoBmTymmMnbxyTZyNdUXtU/g0foNDGhJStUAHVaaYtVBSM0Jcy3VjlQ0sr6x5dV0TcUwx7qiSWmjR9eyoC3FP5ZcxkvGXZR1fpy22Rmum/qx/V6/94qDuacO9rdxIL+L8XKPHyjedCN6pBIxmMVujyIKvYgiH6a+nVjR9Hfd/8FeD2dCQX8gPlJqCxCnh0kt13gHa6vjkvF4jxzK38ShQKRieP/PQu4eCgcUoH+sgJQlUQJu9NVRRKkOZRpRXxbv1gyZQC/pOsmNm68iY46E7FUGJ/PJeb/GRuMXO5/nsbaRnDuVniA3zV+J9x3ME/ZmPH7Xe8iP7cDGcTg43Fnf8xx9HOWvvfcnESPO77vX8AfxGj6fk8FIGhmBh2cdhzudpuWRiaN3kGC2Gqg7MqhLfFgvx2FjFhaeinXydGzhQ8vuRLz8a451edAmLcWwLVZ17+DMUAOT9CJcHotE+9PDIh0ga8Rpi65mrv0RuD2FN2tSG1bZflkxv9myg6/MnMonK9LcsvF7RFJdAJxe/kkq/h4flRBIaZJUWwtZQ06ob2u6n4Uz5xAunIrTXUQmlTumorqQtStZvzHndt8cqKb8TUT6m1HnCPPTRSv50841pC2DiybM4uGWrcMiHeCC2hn4cbyjMixd6fQokQ7QlUrTl81SpeVrsx8NpM0Y67oeZZOs5hgM/JksAwW539K8XS20u4IkPPpbWtN1S+XY5ok0BwZGiXSPmaYs3UdU8zIoXGAJhJJzXxcwVC1RImQu/DzH0H+kBJkr4Sb3NCsjH++99f6s6xYKcc1NcSZCh7tozJgVVWJb0OOJMWWglOeLd5CSWQoVB11WFq9Q0RUF01mCM7Ed04hi68EDuqZCKBiB2bQNrmL2tqdZN6mQomyChwYasaSFehC14vMcHoQsw94ksWqC2JMLEJZEWTWItnzsQs3hRB+wR4l0APnkAO65JcQLxufiep7Dh7tdjIh0yM1/7o+SWlCMA4Gs9KKWqEgJXqeDgcXdbHnu60yY/TOOn3AJT2z/w/Cuc8pPRZEOsCWfmLCIhlApT3ZsZ25BJaeWTTlokZ4nz4EyecJZeWGe5y3JC/VxSFgHp6qRsUyi2ZxLTInbRyoYxh3sRfUxXFIHwD9d4C4FGtyIoJoT6g1uBjPlOKlC7csiHVNRLzoVp6ebv69vp8zt4obyi0jfJ+iJSfSwoPSsMwkFHiUy2Djc94KiT8Hvk8PCWw5Y1N+VpvZcN93ZLFGjdVikA4S0IuTgWMublh0Rs7rmwWX0wp3Xs/iUr5N11pKSGbbag/xw+8gELeQam4Dr7VBsBb8u8Tr70C2D/9l2O5+aciEZK0tHKs5ZZTWcXTWD/YX0vhVBfaxwc6oKXi3/EzpaeLn17yh6OaQl0xLbsDUXWYePcDxBbV8//yysZ1A1cKGi7McxXUjB4tYJRFwpOv0jGU0D2QRF2Qg9eoC0cAyJ86EfjAVCypw4FwK5X92aO5aEnKVdAvZeol4Ro0S7YEh82wIpQBWSuOamLN2Py8qQVvdZOBoq1dbtHcy565e00mcnqFCD+BWVFjNDncONomgYznKc8a2kgvNBOTCRLRSNlrKZLNqxjhmJGbSWbCFkFrA13kKDv+6A+shz+NGyXjIzHViDNooU2DaYC0IoWRM4cgnlhLkfJ2QThGWz3/IGeT5QiPTY1EoyaqF7BUpAoIVUsv2S/nVgZWxKPzSJhkV/5JYdHRxXcRzH1yd5btcdzC0/hZklJw97BQSkk9PCUzijaCrSZtx6XObJk+eDRV5ljENkqon/mNbALTt24FccfKNwBfVRL/pWwRa/iwkXGvTcqWMloPxkgXNjFPM3KVBBOzmIOtdDdrIPt2Ih/to97GZrehS8nykja7dyefEEkreDNHIvI2NA0nF/Aadd9h3uf+MLpDMRVMVBsTWRfVWt0m0xTfGiYxLJjI4bfCX6CLMWLEZ/Za99FIj7u6EXFKExNbwCtXkDZsOVRFfNJ75ZQXFD5SkWNa7NDGSynFheRI3Lc0ClqvbGVE1+ueke1vRvH2773vpbuGn2FQQzg5SGp2Lb+0nO9zaUaA4+PrWeP2wdCRv44uxpFIjcyn2e8U003cWq9rvYaFVwotmLz1JJhaoBWLK9ke3+QrIOhTjZ/VvTJRzTXoOQ0Bga8ToJZgYJG3E69RCWqqIOx3uAsHMCXSriwNN2Dm+XywSfC24fMrULwR5v/LHWdRjUvRRlIvst16YokjhZsqpJbayQrb5O+uwExYqPDAbtRppK3YXtCCGtGI7EDrL+dxDb5Qiyq6iI43ds5acNClN7LmVV/6a8UB/HmE4dxYSeVyzSLRItKCg7V8PyqRxJoU6RAh4FkiPvEFGlY4ZNeKsqDHk+EOglHgwlMSp8SF3kwz1RoHWnkAMWbqBquZN4RqfjdgMr7uHySRNJF2UoDS9iadFMwpVzSMnQqL6lBMvMv9Dz5Mkzfhh3Wd/j8TjnnHMOra2tALz44ouce+65nHbaafz0pyPxzHsSDJx++ul84xvfwDTNIzXk95ygu5DXtv0XH6swuaXoDKb+GvQ/J+D3cab8yaLTmaT9kp1UXwju3iRyUwoUUOqc2NvTiHleKHegrB4cHQubtCnZDQFdpyjjRI6u+IYVkziaI6z0n825tZ/kgmN/itPoYl9ESMUbclIgY7g0H2Kv26hxcD39J0ZguRccAlmhIT/vp8uznVnTruCcKd8l/PeNCFNloHMJ8c25fe0U8IDKteWTuW66xifrXbjfJNM1gKnYbI1002ZHMVUbVc1tm5YZNkabRm8rbdqsLKWFy7AZ6xp8IGgSVpaX8ctl8/nugln89rgFLAmF8iL9KMCyTe7Z+gMiSinTzSgFloGKIOMuZHJHN7pp0ewMM+h4c2v61N4yQmk3Wwu7QOS0sz8dI2zE6XYEsdW98sNbEqTMCXT1HYj0fREgVZCqyPWFRJhylIba27oexYUqbbzWfrJ2q2Bbgg5/lMm9pfiFi6w06beTFKkaGWnTbqSRgOGqRDWjaKnWdzTcnqJpaJbNsj6VSjPDK92bDvLE8xwWBHQ+aJBuGaouEJW0/dVAmEd2WiDiEn1lGKXeCZpAmeFGO9aPlsyHUeQBGbPRLy5AlOngEKiLvSjVDrSYiXHXAOYDEcwHImT6JZ33GsPeh+YO8DznxGP6KL53E1ok/dYHypMnT55xwLgS6uvWrePDH/4wTU1NAKTTaa699lp+9atf8fDDD7NhwwaeffZZAK655hq++c1v8thjjyGl5I477jiCI39vCWQULp71H+ipQZz3Z0fHe/daFLc7+Nbu39MYbMNen0Spc6BfWABOBXwKQhlK3Jwc699tx0ycmoIzICg/SVB+EgQacreB0EGxI7hfe5Ji7yQ8ehl2011oJ++lDByCzBUhqkrceKQLUnE+uuAH1IRmEnaXcdrUzyDDkDzXw+A3AnR+rJW/bb+A7U330965ChQQiQy2ezKxLe4x41N70qR2/x8B7c1jZGMiw807n+ejz/yZT7/0d7678QleG8jQnHTiEl4WFTWM2afcVcR+Ska+IzQJNZqTeV4/FaoDJV+L/Ygipc3OgVd5fNeveXD7DTzX/CeaImux7JEVqJQR4++bv0lzohNfMkK9FSdoqsR9FXjSGeY3tbAxVIalQlxYePfzSKyOFDAhUsim4g5sRWLa4MsmKDJj9DiD2HtcxG1yQpp3J9AtIAukEGT2/FMEaVUhq+Wc6vcW7Hus64oKA2rOqi7347apqJIeRxyXqVGc8FOgeEnJLL12giJVJyNtmrMpLBSy7jocqRa01IFncxWKws7yela0pLAD2+jODtCXjb79jnmOCDIBmc7R94m0wOg/wg+2JBi39yECKtrJATAlxh39iFReqOcBYhLjngGUOifaiQHsdgO7O4tszkBi5N41DGWMR152CwSzpeDQsUNHd23lPHnyfDAYV67vd9xxB//5n//JV77yFQDWr19PbW0t1dU5F9Vzzz2XRx99lEmTJpFOp5k7dy4AF1xwAT//+c+5/PLLj9TQ31sUHZc6kROqZyGiY10QfQmV22Z9jbXpDdTVTsE10Yfx9/7hz+0NKZR/KUUsDSBbekft2z4BTjSKmbjGxPFcJ0hwTHbhPiFEyj/A7kA3/ov/HU+wHosi/Cd9DLY+h35lA+jlyIogKb+C24aUFqSq9GS+vO5ZpgVXEg5q/Lqli68EPcz2CLwiTby7i+ll57Kx7W7iiQ6eNn7JuQs/iWPrbhxFZWT2Kd2s+1VWzPwOgjcX6usHO/hH+7bhv1f3NTPJt51dW2fz4flOPj3xbFoSXTTGO1GFwicnnUWds3y0d8FbIARkRZSkOYBbDeIknLecjzPa+tfw9KYfgpHAo3pRVBddqsa2tnvpNwfxeaoxgMHkbqTtpjxr4NVc6L5paH2bSTkLOfGNzTQWFRCVLgadWdz7saYXx/3M6q5kQ0kbGcXCsgQ+O0WZEaHHEcLakyzNyt0g78jNfR8sILfEIFCRw+kOJRITGxuJhSSrSgQCBYFmCVRFQQiRK7OmObCsFAEjTr8aAEXmvOVFLuTcNgTNgX5mdlXyjHcrBYqXuMzQZkXwCSeWVNllJKjS3OCpx5FqQjHjyOAUDiQ+OB6oYNDVzKzetWwxLmBV/ybOKlt6cBckzyFF0SWKC+x9DIuK68iMZw+yQAGfirUhhWjOIKMWSr0TM5h/COcBUaSCJbHWJRFOgYxYcG4IuXp0AkJVGXu/aGFwt/UjPvYhku53ngMnT548eQ4340qo/+AHPxj1d3d3N8XFxcN/l5SU0NXVNaa9uLiYrq6xLtpHKwNOH2t7W5jgK6RheSHqg3u9gAQYuoPsnzQWXz6d/pP6KX14n8ykEuSGJMkJfvyXFWI9FQWXQuepgu2F/VzRXYbrn3tZurancdUP8rLrBsxBDYcRYbqm4/OXEfPWII9x0tO/jkRqLSX6XLxMAXSEkGyNR+hMxelM5fzLHIqKJ+pCblVJ9gYoKVxOxe46auYs5OHtXyWWaCNdocKOdgJnZOj9oxM5tBah12VpTN1DeeYYPM4SAOJKlpZUBCGg2hUiIFys7msZc83WR3cxNTyLu9eZXHNCCTfNvZouYwC34qBICSMOsISaENBrbOXPa68jkurG5whx2dxvU+malxfr44B0pp9VG35EV9+r+JQySsxFKLoL05VlkF4yyUEqDRMttRNbKJRLm5hiofomIr01FLc+x6CvikW7dmMLQZOnACNtk1ItChn9Owql3Cxsr2NLYQcxNYttC5wyS2Wmj37dj7HHkm7KIbP2wSW6kuQEuo1AQ6Iic6HpSAxMJBIVFRWBGApSNwEbG1OVZDBQUdCHtonqPoqzEeK6FxMFCwH20DAFtDsSlMWD1PUV0VjUi1+48AidpDRIkwVgl5khJJyUeSbizHSi976IcJRgOQqwND8Ix5vq9qaKBhbuWMsmdxcvtb6RF+rjFEWzKTlRpfORkcXg4CyBwy3ZT/DEYWPA10vB50pJtkCmG9yVAneFTbe7E7csfvsO8ryvGfC14//3OhKNNlYSPHUKCBu92oH1yshcSWtN4ZvmJb5laIVegYKVClZxLQkln+sgT548RwfjSqjvy/5KvAsh3rT9nVBY6DvocR1qurs7iRoZrlr9GJ8pX8gFZ0zE/VwG/CqcEKJnnUD3qvgjQeKODqS+n+uhwMAam8TpKnecM8gJ1UV02W1sGOjjxMbQ2O3XG1R/+pPc2LiW9t44ZzoNLi7K4pdJHnzqP4jFcy6wmurivCW/o8g1GYMBUkZ2uI/jC+q5qng51t8ddA/a5EyLLspPq6fgn28w/Zjz2B15CRdeOo510M59lF1YjStVidQydFj/5I3mPxCq+DbVvmNINSdJJTIUBHR+3fMK3WaCGxafy9zCylH1TgGm+2vo6BbYEjxeJ0G3m6qDiEcfSHZy29PXYkkTgUI8G+GPa77GV0/6C6UfgMRYh/J38W7rknb2rOPJl/6DPiNJRf+x1LgbkC5BWzrKy+Y2Ol0xPFoFimMCGcPAqUqK3GEqvWFUReDv3Y5UNab3pqiIDLJm0gSsXog4M3hR0fZ6hvgyTo5tmcjOcA99jgxIga7YVCd7GdQ9ZFQnwpa5YPWDtKJLcoLb3MuCLiBnNSeXc0NDQdlP5w7AREECTlRsbDIYgIJTUUmpToqzEbpdhaOqwdsy5968NdjD3J5KuhxJIu4UqqoR1DSCgIFNyjaI2ikiZpICR5gKdyl6NoKaakaxkoDA1nxIRwGWuwz2yjQvfWFaQy5WDKzijlKJN6zj0Q6NmfZw1bo90N/F4RrPe4GxJYlXZKk5X8eIgOoBh1ciogbFU96b8ziY6xFrStH5uE2yKff3wGsQWgDB5U6CFUfP9X0zjqZ75K04UnOo3kQ5LbeaWEPl0Ptfsam83IGyLYW+Moz5fAxp5pIjlkyF0HydtJEh4utga1Dj2NJ6Dtc3MJ6/6/zY3r+0trZyxhlnMHHi6FLOv/nNbygvLz+gPr7xjW9w2WWXUVdXx1e/+lV+9atfHYqhjuGkk07i1ltvpaqqCgDbtjn55JP5yU9+wrx584a3a21t5bLLLuPpp59G309VpndKLBY7oPOcOnUqW7duHdN+xRVXcNVVV7F48eJ3PZZ9GddCvbS0lN7eEdft7u5uSkpKxrT39PRQUlLyjvru64uP2/IbKcvm9zvfAOCWrlfJTIQP++aT7ZdEn5GEZkAgkYBfxKiYWoVyQgBrfXokHksBa4IHS7W5I9nCQz2dtMgUC9xP8pHa89FjLtjHZmJPUvnvXWvZMtgDwF8aN5KyBBeF7GGR7nKGOavyRny/9pLuaUXU6Zx+UT2/VF/n8pIFnLR5Bv60i97B0Vnqel7RqZpQyILAx5lT8wnsYJCugUdo6t1ET//j9A9sGbV9mTiG9E0diGaDIqA4rHLj+afysr+dZ9p2sLy4nnkFlbzenxtXna+QGm0KbyQkH12gYSSS9MQP7rtN2F2cVXsR6WQPTk8xzckWXmp9iK5oKw6jBFva4/a+2cO7eckdqt9FcbGfnp7YQe/f1fsqq9Z9h3jGSVDMpHjiDNZY3byebSNNLw4cVJsVuAc1dLeGs8iFrg1Zn00bJTNIoGcbtbEg5dEeVk+oIZuEhGpiqRI3KtbQAqAv42Rp8yQaQ310OhMIJIoCZek+sopOXHGDNWSlUcWBeISPIefmLlCQ6EMCXQJZLCysIQt6TojvHbGxt2TXGHGX11Fw2goWNhnFoFN3MCEdI2qmSe1Trk2okJYGO8LdrGiv54XqnfQ5UiRNgSIkui7w4cCnOEjYBv1WigErSZnqI+woRkgJ0kSxUijZCI7EbgxnMVlPPSi5V0pL6QyWbFvNmZ2DPLplNceXznnnF+ltOJh76mB/Gwfyu3i39/jhxu9VEW1Z1KY4+kQn9g4Tu9dAfLzwPTmPg70erpifZNPoOKXIGvAvCB1V13d/jMd75FD+Jg4FdpsDKzk6JLD3KYPyMwswftOJOs+LUMFqzqAoAkePgZqwSJwrycYGiXRHMcShT880Hr/rPeTHdmDjOJopKSnhvvvuO+j993g4t7a2smXLlrfZ+tChKAof+tCHePDBB0cJ9fvuu49zzz33PRHpANFo9Iie51sxroX6nDlzaGxsZPfu3VRVVfHggw9y4YUXUllZidPpZM2aNcyfP597772X5cuXH+nhvmcMDmVf3sOD3Zs4WTbAWidCA7/fgCdzDzJzbpCOZySlHy5F3ZFAKgLlGC9b9RQPOdqZ6gzyi8BCXN0avklTWD/QT7wgxZRLChBpO+cSuybOwBLJlq09o8Zxf8smzgvUDf+9pOIqfH8MQjY3iZJNBo5bbf7w6dMxWoqwNqhQNvZ87IwEvwt1o4pcl4WrBAFnMVkrzbEz/4UXV/2AzFDSqdnTrsC104PdHBneXw5YKJszLEwG6D8pQ0hx860Zp9Irk8QyFtFogFd3OvjUUpWphfZ+PS4OBCGyNO66l627Rh5u1VUraCheipotZNWzgrI6jYp6gdCyY/bXpIIrAiRt7AKFlMfOu8u/B/QOrGPVuu/g6p9Ef2E3Vf6Z3J3eBNLEoI8atYRytRDhEOCUiH4L0WRhVwqkW0HPppi37WUm9QniriyvTKzFtFWyKUnUZRDaq+RTIO1iWfMkmgL9tDvjKEIiBISzMZy2QbcjlItH36tU2jthJA4d/j97Zx5nRXEt/m9Vd9995t47KzPsIJsialRcEsVd0YdEiEZNMCbPl/iiJi8mRGKMccFEo0Zj3F5+LjExUeOCuIG+aFxRUCMiiAqyb7Pfmbvf7q76/XFnZVCRddD+8pkP91ZXV59b3VVdp+rUOSa6U/EurqIXVXcDAwW4qB5zAB2PkgAkEoMuEWzAkmBoieGCa0CdFaAq38SqUL8ekRkAMDSNvgw61sARa/ZiVbSZlWWNtBp58gWBYWgsA8LSIohJm7LZ6KZJuDkGW1EMaaGkhbJKcfz9MPN1hBJvkSsZi7IiCCvM++UBvl33Pjd9vHMUdY/tQxgS/BI5NohanUdWWTDI1+71ffeFZ9PKoJdDEQ0oC8jvBok8+hJGvvfMqMpDpk4Q+XoZ+v0MYpAfo0QiSgzsZxPgQkX/GL7+LQTMNuxobJfL7eGxJd5c8wxPLrmNluwm4sF+TNrnAg4edPJOu96MGTNIJBKsXr2a6dOnM3PmzM4V7Pnz53Prrbfy17/+tXOF+N5776W+vp4LLriA6667josvvrhzofSCCy7g2GOP7VH+ggULuOmmm8jlcrS2tjJ9+nQmTpzIjBkziEQiLFmyhLq6Oi644AKmTp1KIpFg+vTpbNq0ieHDh5PP9+7jp0yZwllnncWll16KYRRHPU888QS33347jY2NXH755WzatAkhBD/96U85/PDDSSaT/PznP2fNmjUMHDiQTZs2ceutt1JTU8Pvfvc7FixYgOu6TJkyhXPPPZeZM2d2/s7bbruNm266iddff53W1lbi8Th//OMfO7dc/+pXv2LRokXE43F+85vfUFtb20PeP/3pT8yZMwfXdfna177G9OnTSafTn1l3n0SfVtT9fj/XXnstF110Efl8ngkTJnDSSScBcMMNN3DZZZeRTqfZe++9Oeecc3aztDuO2lAZfmmQV8XBUlM+zZsDV3LsEWPIfwxybVGRFzGDbBMU6mHtXPBVRdAKxAvw3Nc20d8fYr8XynAbBDbQ8rrJ+LOrsB5rwt3Q7nwuKLEvLOffwZW95Ij6AkRLhiOlhVI2JdlqKGymeda7lDc10bq6H2lAmMUVO91tnFf2FYURD+I8m4OsQq51qBpVQb9sntfeuIr99z6XYKiSYKCS0uAoeKe3EqzWFzCiBofI/jiOwo/J/pX9aWhIIqslh/YDVGG7FOO8vaGHkg6wdt1LHHH4tax8tZbWekXTJqhbIzjoOBNFV0hAyxUEXs2hnilOOIigpOSCStoqPdfw20NbejWvv/MrBq8/ipcH/pPBoRN4Ivshg4wwK90NDDX6MTxvUZ1cT2kuTcC2MbSLLCisDS5hspTlkyT9Fh/VVNNQEgEtcFs1Tb48IWFgtqvDFekI49cPZXm0gbpgGkMU954H3Tzldht1Vqz4XG+Dkt7dUZzZvoLetYpedBUn2qOwFzeNbMngvZhfo1EoXGhX1os5bcASRQXMcBUFAjjCIVpoZqO/lIA2O/MW99RrmvwZ3q5cy8B0jKNWjUADbf4crVaWFn+OlkiKbDBPmekn4xq06jwf2c0MMaMEO/Z5ShMn2B9VSBBoW0S+dAyuFWdT+TCGtC5l5MZnybnfJrDZyr7H7kW0aJy5reATiH4Wzso8JBVGhQVlu08uGRCYUYHT2tWZB4dIhOHNen7ZEQJCNe1bjbq9Wkv2M2l5wya4l4vYJ0yuxEdgdRr7iZbOOSdjYZ4Ktw1VVsGn+Kr18NhlvLnmGR54Zya2W/To2ZLdyAPvzATYbmW9vr6eyZMnd36fNGkS5513HgCxWIw777wTgJkzZ35qOZdddhnnnHMOt912G7NmzaJ///786U9/4uOPP+aRRx7ppWzef//9zJw5k+HDh/P666/zm9/8hokTJwKwadMm/v73v/PRRx9xzjnnMHXqVG655Rb23ntv/t//+3+8+eabzJkzp5cMAwcOZOjQocybN48jjjiChQsXEovFGD58OD/5yU+YOnUqxx57LPX19Zx99tk8/vjj3HbbbQwdOpQ77riD9957jzPOOAOgM0LYrFmzKBQK/Od//idjx47t8TtXr17NihUrePDBB5FS8vOf/5wnn3yS733vewAcfPDBXH311fztb3/jmmuu4bbbbuuU9eWXX2bx4sU88sgjCCGYPn06TzzxBEqpz6y7T6JPKuovvPBC5+fDDjuMJ554olee0aNH88gjj+xKsXYZgwoBfrPvMcxc+iothSz9giXsUxmmZHCW8IEK8b6Jfht0VmGGu84r1BcHMuG9BeNqNKOaq8g3dM0+Cx/4VtuwoVvM+ayCf7USOKXAXiVxlidbOg+dN3QEQTPOxAm38+/370KHt2Dj6xPYuXp8VTWkqaRlnkPVKT6SSx2cNk3sAEEk4uA+XFTSAUTOZd7b19HYvASABe/eimkG+Y+j78N1TBgjYd5mHlxHBnDeTlMSivRaU1HbG3etHVf1niAAUG1RWuu7mkrjBk0ubeDrVveBRjqVdACyCvf+ZgIXlZEzPWV9WyjYbbz29s8ZtuloFtTOJ+4fy8v5TextlvFB4UOmNsGhdYvwOzZN4VIylp+CaeIIH8oEw58h52RYXhonGa5EWwYiq1EpRbPfQRqCEAZo2Ku5kpFN/VgSryPhz3Z6DDa1ojbfRLMRwUV+rv3omuIY0WlXyU1AdDNzt3FxcNtXu4uav7nFKO5dFBX8osf3osM5hYvq9FdfNINvXy11Fc1GmP52KxnDpsUs/iafNvBhFP16GJqCdPi4tJGPI00EXJOQ6yPkWAxMRdm3qYaC4bC6vJFVsSYsKUmoHCucBP2NUmLdlG/li1GQJv6298mX7kPIrOCtKsmZa+t5celLHDH2hK2+9x47HxEQEBSQ1eg1XX2fiBjszhV1qTXxr5nk1iryGxXBoQZWTGyrr0aPLxhGU47+ZwVped3BzWgi+5gUGhS+uESsyaHCEWSJxnkm0eM8e4iFNFxUzNPSPfoGTy65rVNJ78B2czy55LbtVtQ/zfR93Lhx21TmAQccwO9//3vq6uo46qijuOCCC3rluf766/nXv/7F3Llzeffdd0mnu8byX/3qVxFCMHLkSBKJBFBcgb/xxhuBogLcEeVrc6ZOncpTTz3FEUccwezZs/nGN74BwLx581ixYgW33HILAI7jsHbtWl577TVuuOEGAPbdd19GjRoFwOuvv87SpUt54403AMhkMnz44Yf069dlDjx48GAuueQSHn74YVauXMnChQsZNGgQAIFAgFNPPRWAyZMnc/PNN/eQ8/XXX2fRokVMmTIFKIYZr62tZerUqZ9Zd59En1TUv+zIpgTjH3qBe046mNZ4kPJNSWK3z0H/51RSsTi+URIGpWGNQ0DbmDETJ1E8V1hQNrqVbGI1hjOA7lqFERaIZnvz0KL41mtits258RytNXvT5ihqLUVy5X20Bv+TaPgASkb8lNdakxwzwU/4pS5V2Ti9FCecIVBdILgCsis1dU8WKPmKpOJUC3NhCvdPXco/AtwBmsZ/L+khg+NkSWXWEQ1XYw8x8R1XgvtCEhTI/ULF0NRHlpArEewse/JgoIZ4dBgtrSs600ojA8k0DuqVt5fvwsQWBrabbIw8XivbBpRyeOPdy6lo24umYJa8T7HEFuznq0LXvcMNK4MIXU3BGENammjHwTGztFmN2EYjAacZDEE6UotDGAoakXNxETT6HRxDUSZ8hPIWB2wchM8xebtiHXnL7lQIBJraXANp4SMnfUUlfSuUhe5e3CW61wp6dwW9w4u78Qkr6J+GpLjy3qGw0/69u7KuXEWDGWZILoEKVZITEkcokjjF8G5aYgkDw5BgaHKWTU7bNHf8EAUlhSADW+Ls1VDNezXrMEpbaVMF1rtt5HWEajPY9dvNCHZoMP6299HRcWQiA9kYWcfABX8ET1HvU+SiBXxTy3Hv7/L3Ig6NoMp3r+m7zDqIFBSaBP4aSWalS+WBApHX7DIvYB59EsOQsC6L2ZYnNCRCvl7SMs9GmIL+J0v0HIUTNPHHNO4oH+LD9gmoUoPkEUH8oX3JBAK94qt7eOwOWrKbPlf6jiIQ6OnctWO7qOM4W8reyZAhQ5gzZw6vvPIK//rXv7jnnnuYM2dOD2feZ599NocccgiHHHIIhx12GD/72c86j/n9xYn97vk3dxDeYdq+OSeeeCI33XQTqVSKF198kZ/+9KdAcaHuvvvuIxaLAVBXV0dFRQWGYWxxG6zrukyfPp0TTiiOR5qbmwmFQj38ni1evJif/vSnnHvuuZx44olIKTvLkrJrpKa1xjTNXuV/5zvf4bvf/S4AbW1tGIZBOBz+zLr7JDwVoi8SDEAmR8Vjr3T5LTcNtK8YPiodUgS+XwoNioZsnvwIhW6WlFk+oiU5zAcf5YSSIIWvW+S6xcl1Ehr91SC81tNRR8sBDhvcBuo+uBvQSGmxxi0q44b0oTXUhGL8bvFC3huc5LT/HEJpRlLevwRRYWDKSdhaE/uGJJaQoEDENRkjR2BfHz4nhvpXEqIGcnKMtvJmTCOAs9lMos8qjsJyPoVzQpDQ+DAyo9FZFxWQ5KoFrt6Jq9OqhAnjr+H95Q+yvu4NaqsOZp8R3+L9V6N0f7PXDpX4Qm7Pd31576Ykhviwd3NM4j2VRR/djtuapd/6I3h09J/YqEdxKFUc+1Y9A1OH0BbM0xhxcEQrppsh5EBtS4QRdcNw5RDqSlvYGE1jG7n2Td2CTMGlRRQwJAwqRBjeUkn/1jhrS1pYE21FGBrZrc+szrWAhjYz/Jmh17pWz4EtOIlz0bi47SbuxX3oLrSvpW+fY6MOhd1tX10XmynrOeUnYSgGZxtZFazEwMRPcQ+8KzRZbFw0RrvibrRvBxCiaD2QNLO8H8xRkg0ydmN/alviLBqwllYpaVQpcgWHQb6SzjkMZUawA7UE2t4jWrovCyrWMGl1kro3/0rVwdO267d67DjaMjbhmjCh/+mHbnQQJQa6zGRdOkNpcPcNDWQEgu80E/hKFFcL4lGFmJ+Ec2K7TSaPvoHjKNgrALfXEx7rEhwVprRC4KsSCMPFnVKOU4A6eyUNhzzN4MMOx+ffiwVSM7pK0iainpLu0WeIB/vRkt24xfRdJkM8zvLlyxk4cCDPP/98r+OmaXYq8Pfffz9r167lF7/4BUceeSRHH300yWSS0tJSABKJBKtWreLvf/87fr+fP/7xj7jup0/6HnbYYcyePZvRo0ezaNEi1qxZs8V8wWCQI488kt/97ncceuihRCLFqBOHHnoof//73/nhD3/I8uXL+da3vsXzzz/P4YcfzpNPPsno0aP58MMPWbZsGUIIDj30UP7xj39w9NFHUygUOPvss7nyyisZPHhw5+988803GT9+PGeddRbJZJIrrriCo48+GiiuwD///PMce+yxPProoxx++OE95Dz00EO55ZZbOOOMM/D7/VxwwQWcdtppZDKZT627T8NT1PsgudISgkceiH757c40efKRZMLhzpdMzjKg1qDU9KNcB6ta4Ncu1vK16FQGM5VBLX2X8OSDcd5zcRsV1miD+oCm32kx1NOtYGv04QFeG7KU2Rvf5VvDTmP1x4/gtivp/Sq/Qjg4GDRU4Ofm8UextK2FD500Y2JlxEzZHu+pKJMtnV57G3N+ReFwH74DK1GGpiAVUsYZv9+Pmffv6zrzjRx6KiH/oM7Fckdr2qJu+16yDnVn579hDVHLfqN+wr4jM0gRRCnJ2MMltUMFDesUVQMkZTUaTc+Zx2yZJnhGHPVocU+ciBvIM8vIG7tvZWpPZdX6Z9iw8RXGLz6XJ/Z/gBYqmdo0kKM+ssiZQeZVb0QZzZS5CXKGScoXJCt8FISDoRLE7SD9sqWMXVuO3zVpCqRJWDmUVEQcH+W5MJYyqAsnmV+1FttwO03dOyjPJQi6eeqt2Kcq6R2r5w4gO1fO3XZncLpzpVsgkQgsrPYd6du2iv5pdJi/K1T73neJj2KoxqQIIm3NkGwDawPl5KWFgaS47bc4g11U9DV54ZBtd2RnYmBqA0tKkqEMb/rWMbytnKOWjWZB7UrMiKCFDMsLiqFWaWeIO+WL4WibcPID/KFKFlSmOHTePTijT0KUeLGw+wLhgJ+XnnOxTEFttUlLPWx8xeHoM7bBS+IOJBtPEfqPOM4DTYgmBwZYWGeWk4rY4Ox8b90efRu3JocxNY77ZAK5JINxcARZG2FDUjDvbRdfAPof9Bbvrf87+eEFIgNGMsYXoNTT0D36GJP2uaDHHnUAywgwaZ+tN4veXn70ox9x9dVXc+utt/K1r32t1/Hy8nJqa2uZNm0ad9xxBxdffDGTJk3CNE0uvPDCHopmLBbj9NNP55RTTiESibD//vuTy+XIZDKfev0ZM2ZwyimnMGzYsE80fYcup3L3339/Z9pll13G5ZdfzqRJkwD43e9+RyQS4Yc//CG/+MUvmDRpEoMGDaKiooJAIMCZZ57J6tWrOe2003AchylTpnDIIYdg23bn77zhhhu48MILO73Kjxo1inXr1gFQWlrKP//5T/7whz9QXV3Nb3/72x4yHnPMMXzwwQecccYZuK7LEUccwWmnndbpTO6T6u7TEHpbXWTv4fTl8GwAPuXgb27BzGRxSiJkYzGcrQgpEs5lkX95HJoS4LNoPe0Mlm4owxKC5mbFPoeaxPvlMdqS5DLNJKx15GNDeHTtG0QNwddCMfKtyymLjqA8th9yZ3kVEgWy+ZUk0+sJBiooCQ0HHf7s87qxK8N4CFE0eVHqkz25GwgCSYHIadyoJOvbPUr6nhyeraF5IW8s/BUHLvsO7w3/iPfE60z7eAJDmwWrIqtYFnUZ6DSTl34yRila9AzNobTG1aDQxRjjrkGpHSDs+jC0wJYuaV+BNiOPQiBEz1V0lKas0EbMTVNvRVHtZlgajUtRCe4wNdfdBn6icz1ZtP/r2E/epYw7dHlyN3eoit4bp11ZF0hMunzfRewscTdLg1VCs+/T4yAXf6+LI3T7PniBpYuKe2WmhJGJSt6Pb2J5rJ4WXxowGGxGCXeYhmmNmduA1gWWWWkObBL0FxUY3/lbZxi37cELz7Z9SAH5lGD+c5JMUmP64MCjoarSIS+3//ncnvowSBBMh5E5EzeUJxuyUXrPt3vvi8/InhaeDcDSLchcBJk3EdLgo2WSpe8r/EHBwcdk2Zj8G/GyMcRK9kawdYPhHU1fvNcdeLJtnRy7gl3t9f3LwuzZsxkwYAAHHnggGzZs4Nvf/jb//Oc/e5iu7yl4inofZ1s6rXA+i1HXCLaDrionHSknnxWYfo1hOZ2/u7g3QhcVTwm2sPFrCxBf2Lr5MrCnKuqtyY95+a2fMHbjabSGA7zte4jvvT+BkJPgrbJ1SJ9LWOVJmmXYcts8iCsNrioq1YZBl78DDTiKCqeVUpWlzhelIDtM1rtWxTs2qXeESOtK+ZRrUlSc4ZO9ue8MFBoX3TlZUHRmB5brUOakMbVLwgyRNgPkpYkrPmUlVYCri6byDi4gKC0E2K9pAG2+HG9XrqYh0IYSkioZocL0FetFa3zZ1WQpsEr6OHlTEt+QryEnXrUFRw+fD09R335M5WLkNWnbIGBqrIAiuwMmUWDH1IeUe8a7aGvpi8/InqiodxAvD9HY2oYv58dpswnoDD6zQC4Wx9a71/tgX7zXHXiybZ0cHnsu7733Hr/+9a9RSiGl5OKLL96ixcCegGf6/gUk7Q/CoO7mIzZWqPipu4P0HnM0CiysdnXiizMw8tgzaEut4pW3f8aI1pMo6HLW249zwQfH4MqVvFaZoIoswjURyk8/J4GlXYRWaARKCGxhkjcs8oafrPTjyJ5Kp1Lgtg/cOsKuSQRKFw8KZVPjJDC1YlUgjEK1G5ILjPZ20aGUb61y3rXyXlzR3nUqehHZvrbvtodyU+0SuIbJJiOK37UJuwVK8gks5SLRKCFRCLQoOsNTQmJjYAuDvDTJGT7ywsIVmoyV59V+y9m7pYbj1o3mjX6rWR1uoEG3kbRDDLAC+ExJITiIYHoFtTrJE9UlnPHxq/DaHfC1H+7S+vDojSMNnCCYwWJ0AmcXP6Ofxe5WBD36NqY0MBwT13QRZZI87VFhvMfGw+NLzb777stjjz22u8XYIXiKuoeHx26luXUp8/49g+HJ47FbawgU3mDq2rG0+RezsSTFcMdGKsgZJgVDkBRBXGmg2pUKqRWmcrG0Q7SQpFK14ApJ2giQkgHS0o8SRqeCrtE4WqG0Au1Q5mapsTO0Gn7qrUinQt1dObfo6SlB0TEW7Ihs3jU27Pi/qNSLzwi4tnPpMLPvCOOm2icfHKBgWKQMq8toX6v2PesaqTUGuugUTytM7RJ1C1TZSaRWZAwfSSNASvhZHq2j0Z/i8I1DGBCJ8lblKtJWhuWOQ2UhSJnPohAaSiSzjMEqyQsVFRz3ziPguKgJF273yrqHh4eHh4eHxxcRT1H38PDYLWitWbnuKZYs+xOjm0+lNWVwcMMKgsqgOfQWjs+mVElazRJyhr/dVVtvlDCwDYMMxQgFWoGFQ1AVKHfa6O/aOFKSFQYFWfS27tOaoHLwKZe04WO9FSVvmL2U86JSrrG77UjfXIqO77L9W8ce9b6EbJ8u6HAYZyAwED0XnoSkfVcALlDodkjRZU1gKUVIFSh1s1SrNgrCotXIsLAiTf9UNd9Y8RXeLV/HkvgG6o0kzbaffm6QEv9IAvkPsYxG5pfXcOCSx7GaVuKeciUEPDNDDw8PDw8PD4/ueIq6h4fHLqc1+THvfngr2bZ6Ri3/OjWZRvbLGbjGMhpLmslakDQi5I1gj9Xqons+3SN2ePc1bS00GJCD9tViH0JrfErjx8XQGlODLQQp009emp0rusVQaV0m6x1+/TtWxvuiAv556Fpdp32FXXeGh9sag+cOK4KClORkAEkAqTVhVSDiFqhUKexAI2t8MYYlB7J/00A+itaxNLaRdf5mpOujlKFUiFWE5QbeiZczrOVd+t11Bs6EC3D3OWmHOJnz8PDw8PDw8Pgi4I2KPDw8dgm5fAt1TQtYvWYO/nUbGVM/hNrUOPxuC465kabIBtr8mjYjSFoG2ld3u69lAz3UZdEjveh3vzdaQMGAAl0e4mW3/4sqflEx13R4a9/x4dP6Ch1x14vWAqqbh/guT/Udnzvo+N5RH51bAISg1fDTavgx2q0UIipNNrwI1++nf64/I9eMxRaKNZFm1oXa2BTqj2luop9qYn1E0OR3GfzK7wm/fDv5McdgjJ6Iqh4Fhvd68vDw8PDw8Pjy8qUdCUm556yN7Umy7mq8utmxfFp9rs0kuX/VB9jtHglzWcimdTEkGpo2J8n3Gh7jaxsrCNrF3dlSGwhtktU+KglRCYxlFDAKKODKerL+BLbUWG4F8Ywgvkt+6eZ8EVXyrWFH/24D6PDIrzFIYFutmCrCyLYy9k4M6HWGEgWUKAA2kQVZ5IKH6XTHJ2xyvkXkzAIJs4y0UcJTNUfzUXQv4uUmoDDaLSLK/QHOGTKGEsu3g3/T1vczXn/UE68+evNFqZO+8jv6ihxbwpNt2+jLsu0pzJ07lz/96U84joPWmsmTJ3PeeeftsPLXrVvHOeecwwsvvPCJef74xz/y4IMPUlFR0Zm2995794o9vjOu/UXiS6uox+OfL2b37qS8/NPjHX+Z8epmx/Jp7eKtTCOvNGzomdht6TUmk4xryxHN9f/EMjQZlEyipN25Tm66MczdE3LeYxeiZBsF2UbRp5+FVBGEDiK1D6k/QbnWEHBWElD1xAoNQAOvRGtZHotCc+/sZ47em/LYju8TtvZ94fVHPfHqozdflDrpK2OovlyfnmzbRl+WbU+grq6O6667jscee4x4PE46nWbatGkMHTqUY489dpfKcuaZZ3LRRRft0mt+0fjSKuoeHh6fjxMHDuHEgUM+I9f5u0IUjy8VP+/x7X/a/zw8PDw8PPZk5qybz61LH6cu20x1sIwLx3ydiQMO2a4yW1pasG2bXC4HQDgc5tprr8XvL1q7HXPMMZx66qm8+uqrZLNZrrvuOsaOHcuCBQu46aabyOVytLa2Mn36dCZOnMj69ev5xS9+QXNzM4FAgJkzZxKJdE2mPPvss9x22238+c9/pqysbKtk/Ne//sXNN9+MUoqBAwdy1VVXUVFRwcKFC7nmmmvI5/PE43GuuuoqBg8ezPvvv88vf/lLAEaPHt1ZzpNPPsldd92FYRgMGDCA66+/vvN3flH4stp7enh4eHh4eHh4eHh47HLmrJvPzHfvZ1O2GQ1syjYz8937mbNu/naVO3r0aI499liOO+44vvGNb3D99dejlGLw4MGdeWKxGI888ghnnnkm//u//wvA/fffz8yZM5k1axbXXHMNt99+OwBXXnklJ554Ik899RQXXXQRd9xxR2c5r776Krfddhv33HPPFpX0Bx98kMmTJ3f+rVixgqamJi6//HJuu+02nnzySb7yla9w1VVXUSgUuPjii/nVr37FE088wZlnnsnFF18MwCWXXML06dOZNWsWAwZ0baG7+eabueeee3jssccYOnQoK1as2K6664t4irqHh4eHh4eHh4eHh8cu4talj5NzCz3Scm6BW5c+vt1lX3nllbzwwgucddZZbNiwgTPOOIPnnnuu8/gRRxwBwIgRI0gkEgBcf/31LFu2jNtuu417772XdDoNwJtvvsnkyZMBmDBhAn/4wx+A4sr9RRddxNe//vUe+9C7c+aZZzJ79uzOv2HDhrFo0SLGjRvXqXB/85vf5I033mDVqlWUlpYybtw4ACZOnMiaNWtYv3499fX1HH744QBMmTKls/yjjz6as846i+uuu46jjz6aMWPGbHfd9TU8Rd3Dw8PDw8PDw8PDw2MXUZfdgqOVT0nfWl588UWeeeYZqqurmTp1KjfddBOXXXYZjzzySGeeDvNwIboc95199tksWrSIsWPHcv75XdsYTbNrl7TWmuXLl3eee/vtt3P33XdTV1e31fKpdofE3ct0HKdXesexUCiE1l3RfwzD6Px82WWXccsttxCLxZg+fTqzZ8/eajn2FDxF3cPDw8PDw8PDw8PDYxdRHdzyfu5PSt9aAoEAN954I+vWrQO6lOtPW21OJBKsWrWKH//4x0yYMIHXXnsN1y16+T3ooIN4+umnAZg3bx6/+tWvgKL5/GGHHcZZZ53FzJkzt1q+/fbbj3fffbdTvoceeohDDjmEYcOGkUgkWLRoEQDPPPMMtbW1xONxamtrefHFFwF46qmnAHAchxNOOIF4PM4PfvADJk+ezNKlSz9HTe0ZfGmdyTU1pVBKf3bG3Uw8HqKlJbO7xeiTeHWzZeLxEKZpfHbGLbCz2kVfvFd9USbom3J9UWSqrCzZpmttTbvoi3W0O/Hqozd9sU52ZpvY2fTF+uzAk23b6CuybWu7+DxcOObrzHz3/h7m7wHDx4Vjvr5d5R566KFceOGFnH/++di2DRRN3S+44IJPPCcWi3H66adzyimnEIlE2H///cnlcmQyGS6//HIuu+wy/v73vxMMBnsp5d///vc59dRTef7557fKq3xFRQVXXXUVF154IbZtU1tbyzXXXIPP5+Omm27i6quvJpvNEo1Guemmm4CiWf4vfvELbr75Zvbff3+guNL/ox/9iO9+97sEAgFKS0u57rrrtrHW+i5Cd7cn+BLRF14yW0NlZQkNDcndLUafxKubLbM9L5id1S764r3qizJB35TriyLTzlRK+mId7U68+uhNX6yTPVlR74v12YEn27bRV2TbFYo67Byv7x5fLL60K+oeHh4eHh4eHh4eHh67g4kDDvEUc49Pxduj7uHh4eHh4eHh4eHh4eHRh/AUdQ8PDw8PDw8PDw8PDw+PPoRn+t5HCYcN5CaT9KY8pVE/hnChWUFE4hoSJy0QEQEuaBdMS0ObgyyVoDUip9GuhpiELOgC6LDEtQXaVciARiiNzmtE2MJJaswwGKqASGvcoMAwJKQ0bqmFkxUIodAhh9WBViqzAfwZQT6SoRAwCLQJIjqEzAi0X5KLuDQFNmFgorLNGL4gecAwQqhCCqEd8JXT4OQISMFe2Wp8LaBLNI4BTsaPFVCYVhN52sjpHFilCKsapcRn1p+Hx47A8Gv8bWClXGTAwsHEyRkIA4TQIAWYCtfR+ITAdQUaiSFcZE6ho9AadIm1CkTSRZRKio+vRBYEOqsQEYlDDjfiYOT82B9niKUkIixBgM5qCAJCQEpBSCAEuI7AsUy0q/EFNDoLWoB0FEpokuECbaEEQRyibTEsJ4SrDRQ2+dImCPhw7churmEPDw+PbcN2HZSrMTIWIVODY+FmQUQL5GQKX6oUywJL2pAzIAdEZbHfTrioUonjmLg2GCUCt03jC2tkykEHBcIBsgriBgXXRNsav6WgzUFEJSrvIkImIuGCBDeusZMFTMNHLpkgZhugHPBLKJjYhs2maJKEzBEQUdJ2niqfwNGa0mQEf9bAjto0ivVYhgFa45h+bNdHhRED8rTZG7Gkn7DRjxySxoJNyDQokybsAX6XPDw8Ph+eot4HCfvBXmyy6QkblQMjAjVHS4zHGtCnVbLxdUVwsAEK2t51QUNoiKCi1ka81IJ1biWFvzYia33I/hbOC21wcAmtpSUk3naK+QcLKscqzIY8zsYk2f6lWKUC/7/b0B/lQQCHRGB8hHUPOBQaQRhQ8TWDvfYqw7ltE2Q0ZtQg+o0gIqewH2/GTSsIS4JTyyit6k9d8iEKpQGaDJdYaG9aNj7PimX3o7RDPDaCklHncGTmcOR9LdglBrnx5dS/otC2ixkX1B5XQUK/x8LFv0IaPkYd9CNi/U5A4dvdt8njC44jc5R+bMLCtch9B5G3LRpfV2Q+KnpoLRlrYJQK/NUCf4Wg4Q2X5HvFOKAlowRl4RwsSVN2VjmF2+sgpxFxA+vbFagNOQqzE+CAqLaw/iOGETOhUWE/WAd5jYgZmP8Rw12UQVZZOP9qAxdErYX8jziJNZLmtwqgwN9fUnmsgfFME+qjLAiIHBxBHFxGJKNQdZpNjZBe7gCCkrEVyPEfQ2ULFgN3XyV7eHh4bAMFs0DzRwbuWyYlAxzyAT/1TxdAQOWJPpr/GcFNKWQA+p3sx3qiDhocrG+X4zzfhh4XJhUJ0/RSAVzwVQr6nebDvacOXW4gB/mxn0kU++h+Fr4zylk1W+OvFFRU24jFKazT4jivtKLmpQEwDgphBQ0QWci52AsyGEeUQNbFfavoybxm3wBNJ6R52l3M6kQ1Qfz8Wg5D/i0BeY0VM/CdaXNXw8+YsNeZLN74KiMGfp0mX5w3lv0vaxPvYwiLMw7+MzctbmRtKoMlBT/cewRHVZRjam8hw8Pji4Rn+t4HEXV+Nj5aVNIB3BRs/JeC0yqo/7fAToCvXNK2sKikA2RWaVKOBT6JfW8D5vFRjNEBnP9rA6AwKEzirW75V2uSdRJtCcTqPJGQjX99uqikA2hw30ih6l0Kze1JLjS8pMjXaYyRwWJiq4v7WApdLyBdVFJIK+x/NBFu00QrJrGg+U0KSpJJN7P8oz+jtANAS2IZB7lDMR5IoVtc1CFR6l5Q6GI0CZwWzaaXFHH7QAKhKpRbYOn8G3Byq3di7Xt4FAk2acSaBKKyhMJ6SK6AzEeq83hysYsRFDS9aJNdR6eSDpD8UJMNBaDVxZ7bihweAEC3uNgPNKHrXSg2A3SdjfNSG8KR2H9pgnyxkeqEizOnFfPwCM4/i0o6gN5gk88bNC/Q0H7J/HpF27suotCRCfSCFCUJULNayRgB0su7VluSi8FXN5TVa57DNLvk9vDw8NgTUBlF60Oa0goXFfdTN7s4ZooeaNL4fwXcVHu+HGyc7aC/GkOOCeK+lUZvsHGGRWh6wensVwsNmsYXbOReAYyvhHGeSHT10Zts3LkJ+h0rSa/QpJQf2lzcV1MY7X07gPtWBlFmIEoN3AUZ8AlE1OhU0gH0eznGfFzNC5ve4IAqwSn+KuS9LT36/X4PV3JY2SSefv9OxlQfyrsf30c2s5K1ifcBGFV9FPd81MTaVLFcW2n+sPgj1hXyO7fSPTw8djmeot4HcVo02u2Z5qbADZrkNmismKDQ0HtwnV4DYrC/aB4bMdCFYscvYibZlt6zrKnVoAsaMcCHzLuwLNtbmLU5zNKe5zopEAO6VrR1i1s07epOTmO0OCg3SMAXQckI+cy6XsUHMhF0XVEzd9zej2OhHsiFicX36So6vbG3nB4eOxAhwJc10ZtaECVhKLfIru7d5vIbFaGhBunlbq9jqY0CWWOhl2aRQ/yd6brRQUR7xrlXy/Potq6JtM68TU6vvgC/6Jw86056uYIhwZ7nr8gixwRJre+dP/uxge1mcHVr74MeHn2ML2kkWY9PQKR9aBtkm4OThfb5f6Rf4KZ75tUuuEjkEB/qoxz4BHaqd5mZjxVqZKg4htoMtSyHFSg+g6m1wEA/7rIcBDYbt6QVen3R6kpUWeh1BTbHfFcxPj6avEpSljK22O/X6uEo7eAqm7rUSrTqUsKrovuzuHmzHwlsynmKuofHFw1PUe+DmKWiaHreDekHw3axysFJaqyy3rcu2A/0xgIEBBQUwioWottc/KW9BzmhGhBWcUVPWRIG+nvl0dV+3FTPc40g6Aa7KyEswdmsfBNU1EAYBXJ2GlQKX6C6V/l2IIeIFZUWQ/aW0YwB/hzJ5Medab5gea98Hh47Eq3B9itERSnk84hWh0BN7zbnqxRk17kEB/U+FqrUqAYHMciH3tStvZQaxX2P3RD9LURkC91xiUQYm6UVNFZp76yB/hI29hyoiQF+9Mocwaot5B/oAiZS7Jp4sR4e24LSiuuW/Z2vL7iU5xve3t3iePQVgsV94TpsYgbpHM1qtzhe6oEoji/0Rhsx0AcFXTxnM/w1ErEhXxzTbF7EAB+uUxxThfqB3lRADvSBvdm4JSgR1VZRlian83N33JGCxW2r8MswydAWLJpKJM2yDoHANHzEgtUI2VVOS3o5Q0t7/4Byv7cl0APmz5/PAQccwOTJkzn11FOZOHEid9xxx2eeN23aNObPn98j7b333uOXv/zlDpGrubmZX/7ylxx//PFMnDiR0047jeeff36HlA0watSoHVZWX8JT1PsguixP1Ylml7JuQPVREvFEE9WHFh1JqYImOLjr9llxKC13od7GPLsC9+kEal0B4yshsDWB1hyhoV3avxmD6ECFNCXaJ8jiwx4ZgbIurUCOCCAHW4hungxi+wn8VeD+u92UywTrtAii1O16miRYp8ZJlUiSzXP5StlBhAxJONKP2v7Hd5YV8Md5P7AWzoyCT2AsSVJ2cJeMwgf9jpQk/e+RblsDwJB9zsYXGrpD6tnD49NIl7swMI774UbMMigZLfFVdD2fgf4ShCB6gEV4qMRf03XMVyUImzY4GmtSDPfd9tUPS2CdXdZzdTAoMY+NQkBjTo51tXtLYJ0cw30vW2zHHYQkgZimZGTX9YwIlB1mopudzjQx3E+uwkQcFiYStPFVdhXhrwU9cBPDh5yI63iuSjz6LvOaF7MstZb/HHgit6+YRVPBswDxACKa6GmCVJuBbMpTeYIFAtr+7VB+jAUdQxkBVccZiDcSuO9mML9WAmGJ1ZKn9IBu450gVE200C+3opblML7azdFmUGJOirPpZYVVBiUxG/Iac0JJV98OiMHFSQA0iKG+zglZMbibAl1jsW7fJENLBvJxwmRWrg51WmlXv28KWk5P88/Gv3L0iG+zZOOrHDLiv/AHB1PiLwNgyYanOW9knLDZ1Xd/Y9hABvm7zPA9vtyMHTuW2bNn88QTT/DII4/w4IMPsnz58s9dzr777ss111yz3fIUCgW+853vUFNTw9y5c5kzZw433ngjM2fO5IMPPtju8r/ICP0ltSdrakqh+rCHzLAWiGYfTpvGLBMY0kHUuYgyA0eaFFrBiEtUvmgm7wtqRGMBWWaCcBHpdrP2CgOd0eicQsct7IxEOwozrDGUQqUVxCwKCbCiGkvZRVP2oIkISHSrg1sWwG4FaWpkxGWDv5VYxo+VVNhRGyck8bU4RHQE0gKCknyJpim4ASld7HQTBEI4UiBECW6uCa0KWKF+rLXzlBp+9knWYNQrKJcUhIGTkpgRjc/XTEZsIuO0YQTKMPz9Ubo4XV5ZWUJDQ3L33qg+SGXltq+Q7qx20Rfv1dbI5Mo8sVYfMpVHhkLYwqKQBGlIhE8jKFqlKNvFlAI7L0GD5VPINgfKBdkIhBoVJBxEmYE2BEgDkdbotIuImmjLxvUrjIJEFEx0q4uIGmgDaFWIaNFqRScVIla8huNKHGGilMYXKXp9FwJkxkFLyJa61EeaCeo8FS2VSB3EtiVauKh4K9lQGkP1tnLZ1rra1WyLTNvaNramXfTFOtqd7Kj6+Nni2xhhaRobn8AVfqr7fYsfDDtzB0i46+mLz8jObBM7GyssyDWAP2sQsARuwcDNaIwyTUFkMFqDWAGwrAIiKdGZ4pgIKdCNDqrcopA3UHmw4gI7pQkGFSRsKDGQ+fY+usIkp0x0vhhhQzYXEGUm2lbFcVKjgzAFlBkUEnlM0wCfRmZAK4UoMSAJtqloKU+xwmwhJMtozeepDVr4NJQ0h/ClJSrusNZchmWaKO2ijCB5HaCfVQ46TVNuHX4jQNw/iDbXYmMuT8Q06Wf5MLfydvTF57CDviLb9oyjPg9z1i7l9vdfoy6bpDpYwg/3/ioTB47ZrjLnz5/Prbfeyl//+lcAmpqaOP3003nggQeorq5mzpw53HvvveRyOfL5PDNnzuTggw9m2rRpXHjhhey111585zvf4X/+538oKSnpLGvatGnsu+++vP322zQ3N3PZZZcxYcIENm3axM9+9jNaW1sZOXIkb775Ji+//HIPmZ588knuu+8+HnnkkR7pL730EhUVFeyzzz4sXLiQa665hnw+Tzwe56qrrmLw4MGsXLmSyy+/nEQiQSgU4pe//CXjxo1j3bp1TJ8+nUwmw3777cdDDz3Ehx9+yOuvv871118PQDQa5cYbb6SsrGy76nR3sluWUm699VbmzJkDwIQJE/j5z3/OvHnz+O1vf0s+n2fixIn85Cc/6XHOJZdcwiGHHMKUKVMAePzxx7nhhhsoLy+aQR911FG9ztmTSQsN5XkqR3frtEqg6PnEhVjP/HmAKEC7iW3ncaf9PIACbG4yW96V3mk0GzcobppyIS6KpXc7r5QIygf5GIAfCTiVkOjMUXxbhKgFIBDezKt0oF/nx2i7NVcySjFsCu2/Me5SADKUAqWdD2ofnlvx+AJiKD/JEqAkQNFzW753G9oCOYBu74VCJVDZsXqjAadbOUVP7GBAECorQ+1tvn1zehldn6ug04Mcig5vR3mAXpHWBPFiAydVQWfeIhEM5YVm8+jbtNlpVqTWUqnfZ0z1FDamPuCjjY/gDj0do9eeEI8vG7FQhIZQEifkUNxy3m2LESZu2KZ9tziUdAwe2vvS0o78xXMKAJH2/6PQs790Or/nAOLdj6v2str79UjxuaysLG3vx9uf0ziAxKKUUe2d/8BASef+dF0G+TIAg36M7f1jFYCfmvZVddyimNFAu7WVNzba45izdim/WfhPcm7xWdqUTfKbhf8E2G5lffHixUyePBmlFGvWrGHixIlUVVWhlOLBBx/kzjvvpKysjEceeYS7776bgw8+GIBkMsn3v/99LrzwQo477rhepvC2bfPQQw/xwgsv8Ic//IEJEyZwzTXXMHHiRL71rW/xf//3fzz11FO95Hn33Xc7r9GdCRMmAMUV94svvpibb76ZcePGMWfOHC6++GIeffRRpk+fzve//31OOOEEFi5cyI9//GOeffZZrr76aqZMmcLpp5/O448/zkMPPQTA7bffzhVXXMG4ceP4y1/+wvvvv8/Xvva17arP3ckuN32fN28er776KrNmzeLxxx9nyZIlPPXUU1x66aXcfvvtPPPMMyxevJiXXnoJgLq6Os4//3zmzp3bo5z33nuPGTNmMHv2bGbPnv2FUtI9PDw8PDy+7Lzd+hEjfZqQVU40OIAR5UcSUQler399d4vm4eHhsV3c/v5rnUp6BznX4fb3X9vusjtM35988knmzZvH+vXr+dOf/oSUkttuu41XX32VP/zhD8yaNYt0umv7xq9//Wscx+GEE07YYrlHHHEEACNGjCCRSADw2muvMXnyZACOP/54Sks/ezXjhhtuYPLkyZx44onMnDmTVatWUVpayrhx4wCYOHEia9asIZlMsmbNmk559t9/f6LRKCtWrGDBggVMnDgRgFNPPRXLKq78HXvssVx44YVcddVVDB8+fI9W0mE3KOqVlZXMmDEDn8+HZVkMHz6cVatWMXjwYAYOHIhpmkyaNKlTMX/yySc59thjO29GB++99x6PP/44p556aqfJhYeHh4eHh8cXg3dblxN16qiIFJ0EGdKHPzCUeRse+YwzPTw8PPo2ddktm/h/Uvq2Eg6HOe644/j3v/9NOp1m6tSprFu3rtPcvTv/9V//RVlZGQ888MAWy/L7i1tPhejmI8cwPjMqx9ixY3nnnXc6v//sZz9j9uzZ/OAHPyCVSqFUb6eKWmuSyWSvsrXWuK7b+blDng6Zzj33XP76178yaNAgrr/++q1ypNeX2eWK+ogRI9h///0BWLVqFc888wxCCCoruzwdVVVVUVdXB8B5553H6aef3qucyspKLrroImbPnk1NTQ1XXXXVLpHfw8PDw8PDY+fzYXIVbmE98dDwzrQBpfuTSi9B6y14y/bw8PDYQ6gObnkf/Celbyuu67JgwQL23ntvVq1ahZSS888/n0MPPZSXX365U+kFGDNmDL/+9a+59dZbO/Wwz+Lwww/nySefBIp7ztva2nrlOfnkk8lms9xxxx3YdnG7SSqVYv78+UgpGTZsGIlEgkWLFgHwzDPPUFtbS21tLQMHDuS5554DYOHChTQ2NjJixAgOP/xwnnjiCQCee+45CoXiRpfTTz+ddDrNueeey7nnnsv777+/jTXXN9ht7n6XLVvGD37wAy655BJM02TlypU9jnefrdkSt912W+fn8847j+OOO+5zXb+8fM/Zn7mrnFrsiXh1s2PZme2iL96rvigT9E25vswybW276It1tDvZnvrIuQUS2dWM9JcRj8Y700eER/Jhg8EqeyHj+0/YEWLuUr4oz0hfGUP15fr0ZNs2+rJsO5If7v3VHnvUAQKGyQ/3/up2l92xRx0gm82y77778l//9V/4/X7GjBnDxIkTCQQCHHzwwWzYsKHHuUOGDOFb3/oWV111Feecc85nXuvSSy/lkksu4R//+AejR4/eoum7z+fjL3/5CzfffDNf//rXMU0TpRTHHHMM5513Hj6fj5tuuomrr76abDZLNBrlpptuAuD666/niiuu4I9//COWZfHHP/4Rn8/H5ZdfzvTp03nwwQfZd999CYfDAFx88cXMmDED0zTx+/1ceeWV21udu5Xd4vX97bff5kc/+hGXXnopp5xyCgsWLOD222/nz3/+M1B0FDd//nx++9vfdp4zY8YMxo8fz5QpU0gmkzz66KOce+65ACQSCSZOnMjrr2/9vrW+4LF0a+grHjD7Il7dbBnP6/vW0Rdlgr4p1xdFJs/r+65je+vjw9Qa7lxyDXsHQgyrOLbHsZfWPcLA8CD+a+yeZUnXF5+RPdnre1+szw482baNviLbnuz1fVfzl7/8hcMPP5y99tqLJUuW8Ktf/YrHHntsd4v1hWGXr6hv3LiRCy64gJtuuonDDjsMgP3224+VK1eyevVqBgwYwFNPPcXUqVM/sYxQKMRdd93FAQccwH777cf999/P8ccf/4n5PTw8PDw8PPYcVmc2UUqGiH9or2Ox4FA2tf17N0jl4eHhseOYOHDMHqeYb87gwYO5+OKLkVLi9/u5+uqrd7dIXyh2uaJ+9913k8/nufbaazvTzjzzTK699louuugi8vk8EyZM4KSTTvrEMgzD4Oabb+aKK64gl8sxZMgQfve73+0K8T08PDw8PDx2MiszmzDdVsK+ql7HBpTsxZLWl2nL11Pq733cw8PDw2PXMGHChM4wax47nl2uqF922WVcdtllWzzW4RRgS3RX7AEOOuggZs2atUNl60uUuAZmA9gftBEvN1FSI23QKYUIS5QCN2Bi+ICkg0Cg0wpRZgIurjCxc2CFBaLVRjgaWWZAQaO0wHYkriuwQhphAEIgmwrIoEbrPE68BLtV4jNdZMpBmxI3bGA4Dm6+QHNVgbJcHKdJI8MC6dMYBQcREOSTBkaJIifayJe6yJQkkq/DyDXilkZI+CFZaCESqaE104DPCDMoOxZZr9BRiW0ZOEmJWQKW24oqZCiU5smYbVjBQSjCu/nueHxZkaYk666hIjMIt2Bh5BQi7aDLfdhtIAyQUYGRU9BiowOQqXUIJk2MrMDGxLXBKJWovEL6JU4ChB/MEjBchb0mTSwHotqCnIMqgAxIdJuLiEh0XiOEwI0YFPICIyQRykXlJG5aY5UJhHawgUxYsUK0YmAzyvFjNfkxhIEMGcWAwQWN1i6UmqhWF8oCFBIgAxq/1Kjm4rFspkA8LyCh0YCoNCGpijJFTZSlQUtsaeCkNDKcRZcmsK1SFNZuvWceeyar0msJqTRBX3mvY2VWKRkR4b3GV/lq/ym7QTqPvkBjJsUKp4WI7acqU0pEg87aOBETXPDnFCKnIC5QERO3RaI0+CIa2epCRkG5ibI1IqNQJRa2YWAGXFRK4CY1VlwgpYOhJKrNRWvQcQvXATPvICOSgiuQSmOmXPCD0GCvSBKPSjAFNCm01uRqNEmylNf7kT6BNCWqxYYKQX18E+vzaxgoRlOajGLlJeQdiKbIRjPIbH9Us4kICIi3skGto04XGCmGEGqQSFPglvloFOtpc9ZT4iuhn9UP0drIKkvQpB2qQ1WUGxXYyqWOJupyLZT7S+gnKzCQpNR6ErlNhH1xYuZAJBa2u5FUdgMBX4ywVUMo2QCpRnRpNdlwLW5HnHgPD4+dwm5zJufxyUS0iXwjS+GJBABisA9j/xCF2cXvSLAmxzHKHNyXkxjDg9jPtlIcQYM5rQK9Mo38Sgl6ThvqnTTmMaXoMgM7pWluDZD8sOi8Qvqh/0kC1wYzrxF/aoCf9WfDgy414x3Uw/WoXPs+tP1CpEeWEkhpSnWc1f+wod1ZZNlhBlZcEmjIYo30s2GWoOa4UgKb8qjU0xj/+j0AhjQJ/sf51EcF77x3J/lCkrOGPYz7/xpx+1mkx5XT+JoCXJBQc3wA/4tLsFDo/8jzcdsDDD/gpygR3WX3w8MDQAhIZBYxIDOOTKOFL2uj/lGPnlrJ+rkObrLYToKDJVW1OXi6pfh9dADjoDBN6y0S7xbbnTCh5iw/Gx/O46aK5df8hyTwTgL7vWwxISSxzihDtLkUHm+BdifX5nGluMvziEoT34QoqVUOdhO0LCh6UhUG9P+mhfV+K1Y8zMCRURLpNNafc9CWxQXk6WU4C1Lo1UUvqSJuwLRq1j/oYAQF/QbksecW5UeCeXYF9ttJ9NI8cu8gosrEfbF9H6EpsM4oA1shhEndfAM3aRE/PI7c9wOM2AhPWff43DSmVzHCjCNFb0VACIHPV8Wixlc8Rf1LStZweGz5ewSyFscsGktouEI/1oCaWkV6KUQb23DfzRQzBwTGd6vZ9C+X/qdIxBsp7H+2e6aWYJ1djv1kAgoaeV4/WhdBy/yuvrr/VBPr3VbUvPbOutaCKZWkkwJ3jcYQisCCJlQ/H2iN++/26/oF1pnl2H9rAkdjRA2qvl2O/UQLYt8Q+W7jtspvVZPq10r8jQDCKeC80X4tU2D9z1DWPejgZoovgdDwEpYclqHGjRC9vwCtxYGYHGGy+qg3eXzjLQB8fcT3SYh+/Ob9OQAYQjJz3LlYeR+XvP0n3PbICReNnsJXIqXcPf/HKF383aeMvoBRJSP556vTcVXxPbHvXqez39J1mB+8gRCS8GmXkRp2NEp/uvNnDw+PbWeXh2fz+Gyseo3zZKLzu3lAGOeJru8osOckEDkw9w7jdHT2ABqcB5swRgexUg76nXRxOqZUojMau8RP8sMuBzAqDw1vgU+5ZGwLsZef1EoIVgvkSy2Q6+Ys5t0MPu2ixpaw6SmnU0kHaH7dxYwa2KUBxDspjBBkNgnE/DT+QrfHTDmE/u8+qoxyWpNrmDDiUtSsNOQ16qAojfO6hdxRUPeiiT54GCQKlC4vI9O2lmzbRzuimj08PhcFsw27pQWVN8ivdhHPNSFqLBLrjE4lHSC7WpH3+4srKYD+IAdSkHivK492oP6pAiX7dM2VWmkH1aGkQ3G1J6uwn0l0KukAzvNtGAeEcN9MI5ts/HFJy4KuDNql2D73jxDIFXBekQxfa0Jbe4P1C3Ta7VTSAZCCthVgt0DZGNDPtnQdU+A83IQxNACAMSbQpaQDOBpnTgICEvF6K+X7FZNb5pn4c0NR9rrPV9EeX3ps5VBwmoj4Kz4xTyw4mMbUB58Zv9fji8nqXAsPrvw3hxaGURIyEf/XDPuGqX8LSspc6FDSAXIa9XQzVScaGE0FnH92Cx+lwH60BfPwCGQV2ha0zO8a3GgHNj3rIPbrZsm3wUYsz2KWmzS/qQhkC7DeRg7ydSnpAHmN83+tGAeGit9bXdy305jHleI8t9m47aFWhrn7YFT6cDuUdIBhfppedXG7FZv5WHOkOIBRb5Z1KukAYpnDmNaDsGQx1vXsZXeRNbv6eVcrrlnyAEsSqzqVdIBbP5jFitSaTiUdIJ2r49U3r+lU0gHeW/4wraP3b5dZoZ+4jmB260J4eXh4bBvbpajfeOONO0oOj27oVrerAwe0o3t8ByCn0VmFdrdwrKBBCki2d+BhA13Q6CYbV/W+5blNxTIcG+RefrKrFYFyjV5f6JVXFhSqQA/FpAOV0dg5gVrvEBwoydVpjFo/yLKeGbPJ4h8QNQej1xdXAl0lev0WlQdl+gAw1jlEIkPIZTb2uraHx87GcZLEA0NxHYG/REO9g+7nJ1vXuy3k20CUdLU1Ve9gBDcrr0VjRIrKvDBBZBw2R+d0z8kyAA3aLqbpZM8BXGfZbRqNRNQX0EnA7lqVFFED3bjZtSpNshuKZUrb7d2n5Nv7FGBL4at1iwu2RkQkhtltIjAlKeSae5/g4fEp1OVbiAmXoFX2iXn6BQfgaJfmrDcR9GWkOZ8hbPrwt/oIxDTU2VBtka/TyHzvTkqvL2BZoNvc3oVlFQSL/bWzeX8LOAlQYrOx06YCaLBiAlGXL17D7n2u3mAjKrssitTqAoRkj8lXAGwNadW7jGofuU29RTZzktDq3uO5QH2AsD9WvDYKoXI9jqecLHKz36LRJB27R1qpL0o6W9+r/AzdxoVOHpFp7S2ch4fHDmO7FPUXX3xxB4nh0R1RYXauxhUT6PkdIGogwu23z7fZsRIJGRfi7S+HNhcZkMh+Piyj90sqMlygBfgCRZOtyGiD9FoQo4O98roBA8On8VVuLjQYEYEvpDBG+0kuVYQHC9yVOXR+Vc+88RrccDHO4vrsm8jRxZU6Uys2t3I0SwVGJg2APcqkpeU9wtHeXoA9PHY2llXGpvS7GKYi2wQM98OKLCVDeucNxkEnutqaHODrpVAH+ksKDcXRmnZARXqbh4uwKJqld8cEYbQr+OUmZqko9hHd8FULDNdFDQggazQ61G3FvclB9Ot5Lb22QMlexUIcw+jd35QaxcEsICS9rif6W+CTqJQin2nvlwQYMRt/qKbX7/Lw+DTW5xooIU/oUxT1Kn+cJEGWJ97ehZJ59BVqQ1FSdoF0eZb0RmC4H7EiR3iYwA303tUpxoTIpSj68dmsSxVxAxLFyUsr0tuMO1ArkM5mY6chAbSCQpNGDSqOYcTmYzFAjgygVuY7vxv7BlHNTu9xW0SiSwXC2ix9RZbIXr3LzQVtWvftPZ5LDUzRlmss/hbpxxaBHserA3EKqqdS7pMmUbPnO6EuvYHy+KjNSheUKH/X11AUVbL5YNCjLzB37lymTJnCqaeeyqRJk7jrrrs6j91yyy289dZb232Nxx57jBkzZmzTuTNmzOCoo45i8uTJnX8dcdO3h/nz5zNt2rTtLqcvsV2K+oABA/je977Hrbfeyr333tv557F92FUFrO9WQKR4e9z30vi+1/VdxA2sk2Non8DdVMA6LV5UzgERM7C+W4nbpikETcSkMrBE8UUREVi2TdWREtH+HgsMEJSNE9iGSTCZg6QiXO0gSyT2flHEoOJqNj4Bp8Rx/Qbi/5qpmezDavfxIwNQ83ULp9XFl8xhDwgSGiQIhFzUpCiZfiMhWFTMideQOOHbrE4sZtig43n9oz+gTjEQA32Il1uoOUFitFuJmTFBzYQcvLECNbaUxsplDB7zTXyhvXbJffDw6I5lB4nVjIFAnpKvWKgjim2rxG8THt4+kJJQdqSJlckXV6UNME6OoRsL9DtG0m6RiK9SUHmSRaGxXfn1gVtuYU6KdSrJcrgfHI15UgxR1j6yDEus08pw3khiTo3jhE1Sq11qTjWR7eMxqwJqJlm4y7JkpYl9kMO8yhRibLcBVlhiTCjpfAPIoX5CUZfSfSSN/9bwjcoe/Y3x7Up0+15K5/UU1tnlEGifLKi2sCbG0M0OHFdGy0KFDEC/KQ758AYwPUXd4/OxMdeET2cIWPFPzGNKA8MsY0nja7tQMo++wgAzyi/3P565egmp6gLuYXF0wqZihCLZJODkOLQrvWKwD3l8lMYXHJyYD+vMcmhf6BDlJtbUMpzXUoihflRB0W+K1a2vhuqTTPTH7TOtAsRhJehBAUg71JxikspbcGgJ7sIM5imxTiVcDPRhHF2K+qC4pUmODSL28uPObes1bjP/M8br2aex8wWsyfGu/tXVxA+C0JD2d4wB5UcbPOW+wrwRK7HHmp3pnBTincCLKO0S8cX47r6XspcuJWoVzfb7Bcu4Ztx3OabfAVQHim0r5gvzmwP+i31i+1AeGgBA0CphTL+j+OqBlxJtn4m2zDBHH3QppYvfKF6vpALxzd+S9X3yZJrH7qGuro7rrruOu+++myeeeIIHH3yQZ555hueffx6AN998E9fdgmXJLuZHP/oRs2fP7vz7yU9+srtF6pMIvR0bvH7xi19sMf23v/3tNgu0q2hqSqFU393bFgwKAnUmpFxE1MBRDoY2IKcgIFGuQFkSYYHIuUWv7/mi6alwFI5loHIgwyBTLgKNDkqErYvOToSJ64JpaZQUSECmHYRfgWvjlvhxMiaGBKPgoKXE8WkM1yWPTX04SY1bCa0GMiAQhsIoOLg+AzcvwG+TtZK4QQVZHyE7jWU3Ywd9ZCzIORn8gTIyhTYkgn5qGEazAUGXgl+hUkHMoMJQaVxVIB9K4wiJMKtQqvjCqqwsoaEh+ekV+SWksrJkm8/dWe2iL96rbZFJCLDNBCWFMGY+AnmQBRciEicvEKI4cYUDZFzsgCJf4WKlFYGUDyUNlKsxghInD9IvcDMgLDD8Cg2YGQ15hY7LoglnHkRIotqKER/Iu2AKHJ+BtkGEQBcUQhXbvBnRKFeRMzV2WFFvp5HCplopAulSkAIpBSIviqvj2kUFJDoDhExU1kBYCtNQ0CZRIQMRtCGvMXICjUDFBEaa4j76kMQ1bISwcJRE2QoRSGOX5HHc6E7bQ7wt929b28bWtIu++IzvTranPu5c+Tj16//IIYN/iCn9n5hvXsMCZOZtLj3sKYTo+w6t+uIzsjPbxM6moiLCR831KBcqs1GCCFQhhxuUKFcTcgyMgkaFXfJhG1+iBOUKZASMlFuMelFajIYjHI3rM1BSgqWRheJ2PjOikY5CG6DzAq1BhQ2EozEKLjosUC7gSgzbBanBEEXnmn6N1hKRL5qXt1QWyNoZqpMRDEBYAp1ycUsVTZEWEk4rERmnLFOGWTCQTgGCSdpCNpZTjtkWBJ8mW9JEwU7ShEuZVUFJwkQaBm7QIiMSZHUdQSNCiVlLILWeOkPQKiDqj+N3Q1RWlrC6qZ4WJ0mpGaZUR9Ba48gUabuRgFlCgAq01giRJmc3YBkRLLMSf74ZmW3FDcbJWVF2dPfeV9rI9oyjPg9z16zkjiULqctmqA6G+O999uekQdtnNfrBBx9w3nnn8fDDD1NTU5wo/+ijj/D7/bzzzjtceeWVVFRUcOutt9La2spNN91ELpejtbWV6dOnM3HiRGbMmEEikWD16tVMnz6dQCDAtddei9aa2tpabrzxRp577jkefvhhTNNkw4YNHHbYYcycOZPp06dz0EEH8c1vfhOAadOm8bOf/Yz99tuvU8YZM2Ywfvx4pkzp7Qz00Ucf5d5770UIwT777MOvfvUrwuEw//rXv7j55ptRSjFw4ECuuuoqKioqePXVV/ntb3+L3+9n6NCh1NfX89e//pV7772XWbNmIaVk3LhxXHXVVdtVr7uL7fL6vico5Hsq2awmW2pTObx7p+VAKfTa3LTFcYwDHZbrnROeblcaW5hNC3V8KIZ4o7R7nu6fTaLEyUgHKrrtdQ22y9Z5jQjSLhaXMwPkgsUleAmEzKKIJbIagKQEqnT7UQllHaZZgfa/4or8lvbHenjsKrQG046RFUAgX3w0O+i+U8QPnVEE84K8aZCPufRoR4HN/m+nsn9Hm3fBB5RQ/Bxq/79z/NCtLAuKDWozgV2oksWG7RqQLu12rLO9G93kcCHQrdygApyuwVO426islG79kShev3sFOH56b3b38PhsNmU3YArjU5V0gMpgLXWp+bTk1lMWHLCLpPPoKwghKFMhEKBDNhko7v8uHiXZOVaSoPzkS7vtr+401ug+LurWh/mAyGYX7PQnZ/NZdCmcbmc5QpmEjFKSse5lFofhPreaKlENGnJB2mXyAcVxk22CXdYun47hM2PUAGgodAuCYxHDoniBnAO5wAD8QFX7T+0gpIKEZBBUcRIBwFQRokak6AelPU3rMH6z+MNdV5Mx41DSXnle975dzF2zkt++M59c++r2pmyG374zH2C7lPXRo0dz7LHHctxxxzFmzBgOOeQQJk2axODBgxk8eDCPPvooF154IaNGjeJHP/oRM2fOZPjw4bz++uv85je/YeLEiQDEYjHuvPNOCoUCRx11FHfffTdjxozh97//PbNmzSIcDrNx40Yef/xxQqEQxx13HMuWLWPq1Kn88Y9/5Jvf/Cbr16+nubm5h5LewS233MJ9993X+f1vf/sb69ev58477+Qf//gH8XicK6+8kltvvZXzzjuPyy+/nAceeIABAwZw1113cdVVV3HDDTcwY8YM7rvvPoYPH84vf/lLABzH4X//93955ZVXMAyDK6+8krq6Oqqrq7e5XncX22X6/s477/Df//3ffOc73+Gcc87h29/+NkcdddQOEs3Dw8PDw8Pjy0ZLdgM+M/aZ+WoC5aREkFWJd3a+UB4eHh47kDuWLOxU0jvIuS53LFm43WVfeeWVvPDCC5x11lls2LCBM844g+eee65Xvuuvv55ly5Zx2223ce+995JOpzuPjRs3DoAPP/yQ6upqxowZA8DFF1/cuQ/8oIMOIhaL4fP5GDRoEC0tLRxyyCHU19ezbt06Hn/8cSZPnrxFGTc3fY9EIrz55pscffTRxOPFyaBvfvObvPHGGyxatIhx48YxYMCAHukffvghVVVVDB8+HIDTTjsNANM0OeCAA/jGN77Brbfeyre+9a09UkmH7VTUL7vsMg444ABSqRSTJk0iEolwwgkn7CjZPDw8PDw8PL5kpAv1BK3YZ+aLmEEKIsL7Ta/vfKE8PDw8diB12S2EbPmU9K3lxRdf5JlnnqG6upqpU6dy0003cdlll/HII4/0ynv22WezaNEixo4dy/nnn9/jWCBQNPezrJ6OBpPJJJs2FUMRmGaXYbYQon27hODrX/86Tz/9NHPnzv1ERX1LKNXTbFZrjeM4n5guhOhxzDC6PEXefvvtXHHFFWitOe+881iwYMFWy9GX2C5FXQjB97//fcaPH8+wYcP4wx/+sEM8CXp4eHh4eHh8+ci5eaTKENoKRR0gEujP2rZFXjx1Dw+PPYrqYOhzpW8tgUCAG2+8kXXriqErtdYsX768c0XcMAxc1yWRSLBq1Sp+/OMfM2HCBF577bUtOpkbOnQozc3NLF++HIC77rqLBx544FNlmDJlCg8++CD9+vX7XCvZ48eP54UXXiCRSADwj3/8g0MOOYT99tuPd999t/M3PfTQQxxyyCGMGjWKpqYmPvjgAwCefvppAJqbm5k4cSIjR47kxz/+MV/96lf58MMPt1qOvsR27VEPh4v7VgYNGsSyZcs48MAD+4QnQQ8PDw8PD489j4ZCKyXCxW9unTOnikAtqey7NOfWU+7tU/fw8NhD+O999u+xRx0gYBj89z77b1e5hx56KBdeeCHnn38+tl30p3DEEUdwwQUXdH7+9a9/zXXXXcfpp5/OKaecQiQSYf/99yeXy5HJ9FzR9/v9XH/99fz85z/Htm0GDRrE7373O5599tlPlKGmpoaamppOU/StZfTo0fzgBz9g2rRp2LbNPvvsw5VXXkkkEuGqq67iwgsvxLZtamtrueaaa7Asi9///vdMnz4d0zTZe++9ASgrK+PMM8/kG9/4BsFgcJtk6Stsl9f3a665hoaGBn784x/zgx/8gGOOOYYFCxbw2GOP7UgZdwp9wWPp1tBXPGD2Rby62TKe1/etoy/KBH1Tri+KTJ7X913HttbHO63LeHTJL9iv8kjKQsM+M39DPsHCTbM4feh3OLDm1G0RdZfRF5+RPdnre1+szw482baNviLbnuz1fXejtaa+vp5p06bx1FNP4fP5drdIezTbtaJ+6aWX8u677zJ06FAuvfRS5s2bxw033LCjZPPw8PDw8PD4EtGYT2Dq3FavqJf7orRpPx80v97nFXUPDw+P7pw0aOger5hvzrPPPssVV1zBFVdc4SnpO4DtUtSFEEgpefDBB5kyZQrRaJRhwz57BtzDw8PDw8PDY3Ma861IlcVvln52ZkAKQYl/AKtbF6K1Qojtcr3j4eHh4bEdnHTSSZx00km7W4wvDNv1Rnv00Uf5xS9+wV133UUymeSHP/wh//jHP3aUbB4eHh4eHh5fIupymxBCfmYM9e5UhWpRmGxKL9+Jknl4eHh4eOxatktRv//++3nooYeIRCKUl5fz2GOP9Qhe7+Hh4eHh4eGxtTTlNmIY4c91Tm2gnBQhljfvmeF3PDw8PDw8tsR2KepSSiKRSOf3mpqaHjHsPDw8PDw8PDy2llShEZ8R+eyM3ajwxUjgZ2nTqztJKg8PDw8Pj13PdinqsViMpUuXIoQA4IknniAaje4QwTw8PDw8PDy+XOTsBIGtdCTXgRSCeHAw9emPyTm732O0h4eHh4fHjmCbnMlls1mCwSCXXnopP/7xj1mzZg1f+9rX8Pv93H777TtaRg8PDw8PD48vOK5WuG6SoPn546H3D/ajOR9nefMCxlYduxOk8/Dw8PDw2LVs04r61KlTWbZsGcOHD2f27NnMmjWLe+65h7lz5zJq1KgdLaOHh4eHh4fHF5w2O00IB7+1dR7fuzMoWE29Mnm/8aWdIJmHh4fHnsO6desYNWoUl19+eY/0pUuXMmrUKB577LHPXeZDDz3EU089taNE9NhKtklR//a3v820adN4+OGHMQyD4cOHM3LkSCzL2tHyeXh4eHh4eHwJaLLbCAr3c+9RBwibAaRVzfKWBTiqsBOk8/Dw8NhziMVivPLKK7iu25n2zDPPUFZWtk3lvfPOOxQKXt+6q9km0/ezzz6bww47jBkzZvDGG28wc+ZMgsHgjpbtS41pQqTNxFmaIV5iovMaKQTaBjdo4CiJEArL0IiUCwEBtgJLggZXSFxHYAQ10tXYroGUYLouCo0yDLStkT4QBoiMi2MqhJS4roIokPAhpMDwO8i8AFuj/YqkzNEcyVKbiSJTAkpBpFyEKVE+gZGTuL4caX8d2gwg7EpC+QLaEbjhPCnZQN6oQKJwCw2ErQixTDUyDW44i+1X+FojYAjsUoGRLKCwsUs1tvKeM4++iS9sYDUZUJBggJQgyOPEQGRdrBYTEQBlGDi2RAqQaFwJZkDj5gxcV1DI5ollTZTfQAiNLLhgSlAaZYF0Nbqg0SEJrkA4CkyJdjUCAUKjEbgBm0JIYTrgpiCAhShosDRKuNgmpH1JhMoScCuRBQufECBdkAKRFYDA9dvk1iQocTQ6YCCToKMCI2MgCqBKBTk3Ba7AiVooV6L17r4bHnsizYU2LF3A9zm9vncwODyYrLOWj1veZFT5V3ewdB59EcfM0eCkCJsmEhfbcRmUrsCwDXTAwHVNtKsxLYW0cyjDRJk+pKNwXQkmmFqhCyACgADbNsAAn+FCm4KQBK3BEaiwwFUCnRcYpkI6CmEKyGm0X+CaBioDWbtA3DEhpdFhiS0kaBsZEBhtAoQgH1BkfE0EtEBkJfh9mOkA2jRIxvIY2QL+nMQyLVShgCqRrJYFtJAIFCEDMo6DgUUMk7RqpERGCbVaaJ9GhARGqpF8qJSNwkEIidQWRs5z+txXmLtmA3cuXkZdJkd1KMD5Y0dw0qDaHVJ2OBxm9OjRvPnmmxx66KEAvPbaaxx++OEAvPzyy9xyyy04jsOAAQO4+uqricfjzJkzh3vvvZdcLkc+n2fmzJnYts0LL7zAG2+8QWVlJUccccQOkdHjs9kmRR1g6NChPPDAA1x66aUcf/zxxOPxzmNPPvnkDhHuy0pQQOBjgf1YA+YxUZwFScwDIxSebUUnXKi14KRyZBDsR5tgk42IGZgnRnHeSWNMjLN2jovdqLHKBeXHWCReL1A9yqWwJk+6IkLTmzbahdIxkvK9FeKtJPqdDPIrYcQxcRoedcl8ZCP9UHG0SXBhMyzNIvcNUjIqQEk8hvNgE7rNRfS3MI4oxX6iEfP4UnTSxTANQvEom6IvUxGZyPrHAzgJjVVuUT4lRFvpCt5beD0BWcqJpb/B+UczbquL+R8xfK0u7qv1mMeXYjW7uG+nEYbAPE4jD8+Q95fv7lvk4dEDVxTQi0pY+7SNykJgkCT6FQMj5CeQzqP+2oZdb8O3q6l7V5Fb62KEoeoIiV/bZMqDtMyzqRru4sxtgpTCmlaO80oSZ2UBwhLr1Bg4UJjdArZG7hvEPKIEtd5Gr8sjSkzsV5LgaoyDwhhVJqEhfpzFKXz9AjjPNqATLqLGwjy2FL+jESUB2prCbHjVKvYH+wgqxpuIxRns59vA0Rj7hZCjA+hcK2ptI2L8cMSHGvuZRiho5P5BAgN8OE+1Yu7rozAlhBO2PGXd43PTXGjD0AV85udfUQcYFqrlXy0+FtU95ynqXwLeb17FLUveYEMmxY/3Gce7G1fw3w3HoZ5sRg32k9u/jIZXCqg8hEcIKgamMB0bFSinZZNJYrELAsq+IihpSyMNyA+PUPdigaGTBPZDjegGB1FuYp1RRmFJhtyQKE2v2NR+FdS7rchhQQrPtUJWIUb4cb4aZ/0zmoEnCwrPNcGG4vjM+mY5TpWBeKwN+600mAI5IYoIlWOWp7HaMriLFerDNohKyr5dgT03hXVQBOe5BLS4iH4mzhSXn9bN4Yd7H8nS5iTPrl3NT/epYema/+Xk2Pfo92wQsTwLIYmaatJY08Qt6+byQtNSAoaPqYOPoSVnceqAcQw1414/vRuZu2YD1769hJyrANiUyXHt20sAdpiyPnHiRJ599lkOPfRQFi1axKhRo9Ba09zczH333cdf/vIXotEoDz74IDfccANXX301Dz74IHfeeSdlZWU88sgj3H333dx5550cc8wxjB8/3lPSdzHb7PXdtm1uvPFGXnrpJX7yk5/wq1/9qvPPY/sINBsU7mrA2CeE/XQCY0wI+9GWopIOxY7/iUbEsgxssgHQCRd7VgvGqCDOA42UDC5mtZs0DXMK9DtMwKON2IPDNL6u0A6goe19RdtaiRwVAEC1OCQWKjIfFTsOlYf6uQ7uvqWgQS3KolsV7uxmaCvKo9fbOC+1YewXxpndijHUj/NcK5YdpDJ4HBsfMHESul0eQcMDBjQ00tS8lKP6XYpzTwu0uhCWYGvcV5KImAEOuG+lQQOORs4FtTyFQdsuuxceHp+FEBBpKmHjI0UlHSC3RpF8z6V1kYtd8KHrbcQBYereEeTWFtuCm4aNzyrccj8b/1EgvpeGRxogqTD2C+G+mkKvbDczSyvsB5sRjoaC7myL7oc53HlJ5EA/zr+KijUa3DfTkNM4DzVjjov06D/0Rhtnbitqk42jSki86OvqD5YodIODM7cV7PayFmbQm2x0MohOZJHKwHk8UZQDUAuzxQmAagveK+B7IY9juHh4fF5aCm0InccyQtt0fqkVwvD356OW18k7mR0snUdfwjVy3PPh27zdtJHzR+/Prxb+hXM5Cv1IG+Q17gFR6v7povLF/OllmrZsDJ0Jkc2aJBZpUIALzW9q8v3DqDdSWI15+p8kse+rRzc4AOgmh8KfG7DGR6h7yqFsX4GY1YA5Iog9uwWyxfGSXpbHXNBK9bES8VQjbOg2Pru7ASulin2zpti//jNByHJpWBzGtSOoD4v9vXlgBOdvTZhjQtizWtAtxf5UbHIY+aDk25UHsCmT54nVKzimtoLFq+8gblQy9F/DEcvb+96MQv61gJ2v5YWmpQDk3AJ/WzGXIaUhfvH207SS3zU3y2OL3Ll4WaeS3kHOVdy5eNkOu8bRRx/Nyy+/jFKKOXPmMHHiRAACgQAbN27knHPOYfLkyfztb39j9erVSCm57bbbePXVV/nDH/7ArFmzSKfTO0wej8/PNinqH3zwAVOmTGHRokU89thjTJ06lfHjx3f+eWwfusEtDoKDsqjAtiuqPWh0EL7Nbl/7AJ56B39JV3431W6aHjfJNIle10uu1CglEGUm7B8m+X7vQXYhLzvtL9S/08ihgZ4yb7QR5cUMqs6GgEA3O+jWYOeLsrs8YbsGADPhh/bLycF+1LJc8fOwAO7SbC85zJVBXKe1V7qHx+4iEvHjNPVOz3ysCFRL7CSIchM1IEhu/WbtWIOdBu2AmW9v64AY4EOtyPfKq+2e56t3MshxIdSG3vvG3A+yyIE+SKle/YdudBBlJpm1PdPNUoFe1Xvw5i7NQU5gjK5GrbV7HVdLssi9/MUv/85jZp3eFeLh8Rk05esQwocU226aO6JkGAVZytLGF3ecYB59jjaVZl7DegAcVZyUCWxqHxMZUCj0HutgG+gEJNf0PpbeCKLGQn6YxlQuJHsqUGR152SnZShIK3Sh93K0fj9LMKKhcbM+0NGdCnd3ZF2OQhMo2c3ANSS7FmY2u4Zudhknqlnc3AzA0LBgU9tHjA0fgfF+b3lkQ++0hlwTGk1DIdXrmMeuoy6T+1zp20IkEmH06NG8/fbbvPHGG51m767r8pWvfIXZs2cze/ZsHnnkEW655RbS6TRTp05l3bp1HHzwwUybNm2HyeKxbWyTon7mmWdy8skn85e//IWampodLdOXHhHtdltMgbC28MIJCLZksyQsAUGB63Y7xwCCEp108Zf2PidQIRASdNJFbyjgq+r9WBg+De3vHTHAh27abLAelpAvli1iRvHlUmIgwg5sJr4wwDaLsW51uOtlqJud4qocoBptZL/ezgndqgJCBnqle3jsLnI5B7mFLbVWmcBp0xihYtsSKRuzpHdbNgLFNGV1a3cpFxHvraxs3heIAT70xkJxkm0zZD8fqtFBhLbQzQcEFBT+ip7luRmNqOjd7mSNhZYaVZdElG9BrhofumNgWmvi+rbQZ3n0CRyV56llN7Bg/aO7W5ReNOfqMLZxf3oHw0O1NOow8zb0vd/nseMISIshkSgAQSNIQTk4sfaDLphb8G2shEYEILCF3XOBOOgWB93Pj/YV/Yz0QICIFBOVUTy+xbFZtYWjRLGP3byISO++WMV8SEsj1GYTA5Yo7n3fHJ+gQWYYGCluD0k6kogvRp29Cl3Zu3x3C80p5isl57qUWN5YandSHdpy/X9S+rYyceJEbrzxRsaOHYtpFscK+XyehQsXsnLlSgBuv/12fve737Fq1SqklJx//vkceuihvPzyy53O6AzD6OGYzmPXsE2K+n333cd///d/I8QnD8buueeebRbqy06hQmGeFMVdkMI8JYa7OItxRElXBgFycjluzOqhBJsTSnAXZTCmlNP8bld6+ZEWresl7BMikC/gr+46yQhBbISGFqeoXC/NUHmcifB1nR8aIvDVt69uByXm+DDU+nvIY50YxZmfQg4vnigG+NBCkWIh5cf1nFmuOAXyMRspTD6yn8U4qvjC0ZtsZH8fRA306gJyZKA4AdBBfwEjTDAqt61iPTx2Arbt4lTmKNm3a2QnDIh/zcKMgmU5kNPwepKqr4oevW50rMBszFJ2hEnbBgkHFduCMz+FeXy0x2DRODiMauv2kgxKzK9F0OniAE/UdhuZRiRyuB+5TxD3gyzGhJ79h3ViDJVRBII2vn5dh6QP5GAfYkC3ssISOSaAMUijVzQh+hmIod06iKDA2C+E+jAHfoH6epgeHYhHn+Jfq+6iIbOKV9bez+rWdz/7hF1Iym7G2k5F3ZQGQ6LjaMysoy798Q6SzKOvEVJRLhg9Hr80eHr9Wr4z9BjmRZYjRhT7Hqs+S2hI11hH+KC0Xw45QFFS6dLdDYJVBkHTBlNgDw5RN19jfr2nZ2zz5Bhug01ouKBpMXBCHLWhgBzbzcGtCfr4Mja9rODk8h7jM+PYUlSpsdmYxiJnWFQdamPGbGif4HTnpzD/I4a7KIN5VLe+G2icYnFb43yGloSpDoZ4cm0Th4/4MfMaHqf56209PE+5BwisGkXE7FL8RpcOJlkQXDD6cCrFtm0x8dgxnD92BAGjpxoWMCTnjx2xQ69z9NFHs3TpUk4++eTOtIqKCn7zm9/wP//zP0yaNIklS5ZwySWXMHr0aMaMGcPEiRM57bTTCIVCbNiwAYDDDz+cO++8k7lz5+5Q+Tw+HaH1znElcdpppzFr1qydUfQOoakphVJ914tGuKDxNUh0TiMiEl3QCEBnFDpmkcdE+sBnO9BiF/M4utPzuq1MnDSYMZAGFBJgBTVWzsH1G9iORBU0/hIw/Boa7KJpbVjiuhod9VNo0AhTEIgrjCYbXVCIEpOsypOO20QTQUSbwiwzcdM2EgGlBrRoVKki499EIWgTsPthJAVuWiLimqbwetJWHJ/bQDq9loHWOKItZdCqoEKggwI26uKMcoWJ05hGCwdVrciFQmhdfBNVVpbQ0JDcnbepT1JZWfLZmT6BndUu+uK92tEyWa7AbPThZARGWIBUWKYNQYWRFNDgQJmJbfmwW8GwwDQV2hAQFjhJgVAKn+tCxkXHTYQGkXAQfoE2QYQMaHPRWRdRZhUVeUcX+wlLQF6jdXHbjDYgX5lBpC2MVoGFgU4pRFiiTY0rNelwAlfl8aVqEAUTfwAMn4twgTaFdjREDTAcVCaDEQ7iNgmMWgsa3OJ1+1m4uQKFbBZV7cOO+orn7WS25f5ta9vYmnbRF5/xzXFUnt/Pn8rE4f/D+uT7pArNfGPMFTvlWttSHz9acAF7mQ77VE/armsXlMNzax/gK9GRTNvn6u0qa0fSF5+RndkmdjZl5QHeb1rHmnQLtcESbJWmulBGTUMpIi/QlQHyaYFywFfqYqZa0aYFwRCuI7DTAmFJLJ9CJIrb95QhySfADIFfOcVtQjEDbQJZjVthYackKIVfKoSrEK5GZxUiZlLwm+SbwB8X+Gy783w7YKJNjaV1ce+7IXEjBplQmgA5aLGxQnFUM2AZuP0kdqoNK2tiGT5UpkCh3OS9YII0BSKWRUCaNOYyBKWk0sqRzTUzxB6B1WCggwpZaWM2rWB9RRUr3QxIg5AsoTQQodwNYuhtdlO10+grbWR7xlGfh53p9d3ji8E2e33/LD5N/7/11luZM2cOABMmTODnP/858+bN47e//S35fJ6JEyfyk5/8pMc5l1xyCYcccghTpkwBYMOGDUyfPp2mpiaGDh3KDTfcQDi8fTPxfYm0T5Dur7fQaQmKNujFVeoMQJWgc3MrHeZTxZW3zp2r0W6f6TKx6kwr2fxYDiLd8kQ6ru0CJgYmqSBQI4vnVHYs/Wkog+KyYS0SKFhAt4nbCEOKxZlR4tG90EAiBPTvtnzYaSLsQkk3M6C+O7fi8SXHNjR2dXF/d8fGkM6dZn4NFQbFBzjf3kY2o72NRDrbfLslShV0PfgOxKHYvjY3QeveODracaDYdiMdx7v3FQKIIwC7RAM2PXa6V3TkUVRWRmloaB/UlVO8dmef4LTL097/7gIl3WPbWJH4N2FfBX9e9yLDQ5W0Nb+B7eaxDP9nn7wLKDhJ/P7tt5jySZNh8UP4uPFpErkGYgHPCuuLiCEt+lFBv3BFMUECJrQO7shRaO8v28cxZR1jxPa+tf2xyHV+bu+528dLGYDqYh/YRaFzbNRzJ3HH+MiFGMQrS2hoyHU7v1vvGqM9TQE+Mvg6yyz2r7pYTiBM1ybDYhsdSU+7/YHBWNeX0JDi6DDaLUPNgZQA47oZSVXG+4Yy7FH07u4p5h6fxk6bTvsks/h58+bx6quvMmvWLB5//HGWLFnCU089xaWXXsrtt9/OM888w+LFi3nppZcAqKur4/zzz+9lanHllVdy9tlnM3fuXMaOHcvtt9++s36Kh4eHh4fHHs/Klrepdw0qfDH+3bqSgK+cdcklu1ssAGzlIFWOwDaGZtucMSUjcMwq7vrguh1SnoeHh4eHx65ml9u9VFZWMmPGDHw+H5ZlMXz4cFatWsXgwYMZOHAgpmkyadKkTsX8ySef5Nhjj+0MKQDF0HBvvvkmJ554IgBTpkzx9kx4eHh4eHh8Ch+1zGeTozkguhdjS4eRIsCqxL93t1gAtNppgsLFv4MUdSEEB1SfSDL5Do+sfXaHlOnh4eHh4bEr2eWK+ogRI9h///0BWLVqFc888wxCCCoru0zTqqqqqKurA+C8887j9NNP71FGS0sLkUik03thZWVlZ34PDw8PDw+PnthunkRuA9WhIVjSZFiolnW2zZrW93a3aAAknBR+3G2Oob4lYv4KqiKjeXXd/fx5zRxcrT77JA8PDw8Pjz7CbtmjDrBs2TJ+8IMfcMkll2CaZmeIgA4+zaP8lsr+tPxborx8x8za7wp2lVOLPRGvbnYsO7Nd9MV71Rdlgr4p15dZpq1tF32xjjpY1bwabYQZXjaASCRAhACBhgo2ZT6goiLyud+hW8PnqY9l2sXCJlYSJxLYceGJxvqPo+2j/2VR60J+kVnJpeO+xYjogF752gppnt/4DvMb3ifr5Plq9VhOG3wkltz2mO5boi8/I5+HvjKG6sv16cm2bfRl2Tw8djU7TVH/z//8z0889vbbb/OjH/2ISy+9lFNOOYUFCxbQ2NjYeby+vp6qqqpPPL+srIxUKoXruhiGQUNDw6fm3xJ9wWPp1tBXPGD2Rby62TKe1/etoy/KBH1Tri+KTF9mr+9LN7xDm2sQ16WkUkU3WGW+Suz8+3y8fgVR/+d7h34Wn7c+VjfWI3UeO2+RcnKffcJWY9Gv5ADKVT3Kvzfff+33DAn1Y3x8DGVWKQk7xcLWZbyXXMHI8EBGRgZQ5Svj0RWv8OK6RVw+6lwMsWOMD/viM7Ine33vi/XZgSfbttFXZPMmCzz6CtulqO+zzz4o1dOULBAIMHLkSH7zm99s8ZyNGzdywQUXcNNNN3HYYYcBsN9++7Fy5UpWr17NgAEDeOqpp5g6deonXteyLA466CCeeeYZJk2axOOPP86RRx65PT/Fw8PDw8PjC8uKtsU4Mki4W0zlfoFyNqTDbEot2+GK+uelpZBAaAdT7rjV9A76x8azcN2fOaB8AgftdRZLU6v5ILmGjJsjaPgZGqrh5OpDCXbzfr93yRDuWf00sza+zDdqj9rhMnl4eHjsTObOncuf/vQnHMdBa83kyZM577zzPnc5GzZs4Hvf+x6hUIi//OUvRCLbbk0zY8YMxo8f3xnBy+Oz2S5F/ZxzziEcDjNt2jSklDz88MOsWLGCCRMmcMUVV/DXv/611zl33303+Xyea6+9tjPtzDPP5Nprr+Wiiy4in88zYcIETjrppE+99q9//WtmzJjBHXfcQU1NDb///e+356d4eHh4eHh8YdmQ+piwr6JHWoUvykdK0phZzajyr+4myYo0ZesRMrBTTPANaTGs4jjeWfdnjhl5NeNKhzOudPinnmMKg9NqjuTOVbM5qeoQImZwh8vl4eHhsTOoq6vjuuuu47HHHiMej5NOp5k2bRpDhw7l2GOP/VxlLViwgH322Ycbb7xxJ0nr8Wlsl6I+f/58Hnvssc7v3/ve9/jGN77BzJkzufXWW7d4zmWXXcZll122xWNPPPHEJ16ru2IP0L9//y1OBHxRCBkC2egjtSFHNGRB0kH6BIQMVMJFmxIdlphKoVIK4ZeogkJEDEDhYOEkwRfXmG15tJK4YROlwco5CBTaEsWYxwEDkdEQUAgBulWhwxYFaaIcgRVRGGkXlVVQBvXB9dhmhH6tlThtGqsUhNuI0Jp0mYVsASuncUqzZCI51rkGMcNGu2lcWUVjziEaiFBi2KQyyykxS6nKDcZo1WRLXNp8eUpbI5iGiSx1EC0uWrmock3OH0brXe4D0cNjqwggCdQLSCuIG9iWjevPYxHGzDiQNlAuqLAJEpQrcXJgREEokI7CXpcmpiVEDVTGxbAEqkQiMxrV5iDDBjqrICBRrkLHfLgJ0EoTiGh0wkH7JQUhIaTJhOsx8gHCmVKcrET4BCYOyAL5UDOWE8Rp85ELCozSLKFsGDvhQ0ZMlOuCq0lnUkilCTkCIwPSbxSjsStR7H8qJIVCBtA4FRa210b7HKn8RkpL9+qRVmKGyGKyPrVsN0nVRaLQiNwJq+kdxENDiWUG8+66v3DgoO9v1YRApT/GyMhAnqtfwJTaCTtNNo9tR8sEja5LqWoj0tqEFAZudCShpggipyFmom2QPnBsA+EqZNYFv0T5BYajUUJi2wLTpzELLjqvIGaghUBmFDqn0FETxwCNCWjcFBh+sJSDzLroiEQLG2EAyTxOsyJeMFGuwAkY4BdYbXkQinzcpdnKUtbkwycNMEx0MoOI5HFKbP4/e28epkdRLm7fVb2968w7ayYbSQiEkEASBBPWRMMawo4siojnk6N4oR71gKCiokdBEZUDnHOUnysgIjsGBEQQCBAIIFsQQoDsmcy+vHsvVd8fPWsmQJKZCQH6vq5cMNVd1U/321VdT9WztOlafKNAXaeJUTQQmThuXoItyKY30OqvoTJWT9oYi9MWoHKKoMYE0yfeWSTIVFCwQusQSyti3d3g+gSZdF+5iU8s34godpGrHsdaykigTgQEqkRe5TFlgkprAlJFi1SjxQNr2vnlS5tpLnjUJyzOm9XA0ZOrh91uR0cHnudRKoVuRMlkkh//+Mc4jsNLL73E5ZdfTqlUoqqqiu9///tMnDiR5cuX84tf/IJSqURXVxcXXnghkydP5qqrrqJQKPDd736Xb37zm1xyySWsXLkSIQSf+9znOOmkk1BKcdlll7Fs2TKEEJxwwgl8/vOfR2vNj3/8Yx555BHq6+sJgoC5c+cO+/4+TAxLUS8Wi4P8yZubmymXywAEQTB86T6kJFC4L8doecxn4oEB/u2t4GmsM2rw729Fd4bP1jgsTVAOCJYXwADzmAzBvwqYx1Sx8X6X1G4Cs5jDXZ4DwDwhg361hLcq7Lhyso2cEUe9WcY8OIHOunh3F9FtYfty/xTZihS+J6gWBVjajagyGPP5iRTaDNb9xUP7IEwYe0IN9mPPkqxNAmMInixhVRhYn8wzZkwX/9j8dyY1fIYfvPAGBT/AEIIvzJhI0PkPjs2dBbd0EwRg24KGU6vw/9qF+dEkar2LWtkj70SJ/ckCbm0tWo9sgJ+IiOGSUAbmwwW8v3eDBtIS67P16EIZs8rFvzuPWhWOj0y28Q6uxg0EXf/0qfyoiSN9rIfb8DZ5AMi9YxhHVOLd2451dBXu7e1Yh6Rx724FV4d9/lM1tL7g0/2iZsIigff7lr7xQR6YptNOUn3AGFRLwManFG5b6KqUniGpTioS08fgXteMUSyStID/GM+6P/sEeQ3CIzPXxGtXGA7UHijQd3XDtDh+dxGRNAnu7wzvNSlxzq3H+20z1r4WYnEC1xy1ECgR20nRyxIolzF23aByKQRxq4bG/JvvkWT95Nx20iMY8X1rTK7+GC9v+hNvtv6NPeqO3qY6H81M596mZZw8dv6o7PZH7DiNra/zctZlYvkNMo/cjbnmJcR+n0IX9iJY2gyAqDKwPldHbq3E0j7izmZUToEA88hKggabxn8apCZCal0X7ivFsN4EC+u4Ktz/1wwBEBMYZ9fhZSSb7/Tx2kP//IrZkqpSCbG2iHVmEu9PL2DMnkCwOolaGY73YqJN+aBqAtvAuKMV64AYY2fHCf5WhN0c/L+3huNoXGKdVmTMhFb8p2LwcA69uJqm9VDcEI7rzqQxtB34HC+W/8Enur8Mt+eRGsy4wP98krYNr1Pz7FukP3MSbiyG89gz6KdfBMDIpEl/5kR0hUHi5bvQD/4vrYeezs9bJSuLLVw4YV9KusRDG/9KUy4M8LxPw3yOm/Z1bF21E3/ZDwcPrGnnx89soByE71JTwePHz2wAGLayPn36dA4//HCOOOII9t57b+bNm8fxxx/P2LFj+fKXv8wvf/lLxo0bx9KlS/nOd77D73//e2688UZ++MMfMnXqVJYtW8Zll13GkiVL+MpXvsLy5cv5wQ9+wBVXXEFVVRX33HMP7e3tnHbaaUyfPp3nnnuOxsZG/vKXv+C6LmeffTbTpk2jVCrxr3/9i3vuuYdsNssJJ5ww7Of2YWNY2x7//u//zsknn8xFF13EhRdeyCmnnMJ5553Htddey0c+8pGRkvFDh2yPs/len5pZoO8MJ+Vy7zjBP/N9k3CAYGkWOb7Hpy4A/6+dmLOTeNe3MGahJF0XQI+STlJCSaNX9QfpUWtcKGtUo4cGgue8PiUdQD+XI10dkH1V4TYkwADdEeA3ajb/xUf7Pef50LgkgCP2RT3fiGzwQQDdAfEHa+l8cznzJ53Oz1Y0UfDD9gOt+d9X1rGo5nzSt1jhhxDA1fh3dmDOT4Mp+pR0ANYrxPMaEbSM8BOPiBg+9iZF8GCPkg6QVfh3t0M6hVrl9yvpAGtcrJYSqqSpOsSk+58ezsYCukdJB1CvltBrypgn1OAv6cCclcC7pyNU0iHs839sI1kNyakC85muQeMDT2WpHK/QKwt0rhe4bf2Hsv9SuBUx/Ee6kRPCMUQenWHzfQFBvuckDZ1P+ySmGnSvEJQ2BhgHp/Dv6UTunSC4r7P/XvMK789tGKfXIJ4sY67vv4+I95624no8EaPaqRhyLG3Xki1tftdMLaNN0evCHmVF3ZAW08ecyBstD7C69R/bVGdKYiz5oMS6YpQCdldCigJrij6e30T1xjWYa14Ky8efiVqa7ztPdwT493ehyhrxUBvkeuIqafD/1gWWpNyqScU9dI+SDqA3eAQriojqngXHkia4uRVT6z4lHaD7RYU3MYHICIJ/vAElH2RFn5IOoNe7OC1FWl8WcGAFwWNFaHbD8XTgN6Oo8O9NI7tj8LALaUlB2BQ39F+vvFYwqf1g5lrHwG35AXU11vVF3Gl7QKGEePAJYt3dfUo6AJ1ZxMNPoTatQv/tWjAsnh+3G492vM4nx8+hafVdrM6v6VPSAVZsfox12QFtRIwYv3xpc5+S3ks50Pzypc0j0v73v/99Hn74YT75yU+yadMmTj/9dK677jrWr1/PF7/4RU488USuvPJK1q9fD8BPf/pTVq1axf/8z//wu9/9jnw+P6TNp556ik984hNAGNj78MMPZ/ny5Tz99NOcfPLJGIZBPB7n+OOPZ9myZSxfvpyjjjoKy7Korq6O4ontAMPa8jjllFOYPXs2S5cuxTRNzj//fCZPnsyGDRtoaGgYKRk/dPhdGhSYUkMp7MRyvI3/aPeQc3VZh8stCtCgfQ15he1oVM7vO0+OsVAb3CH11XoXOcaCgkat9occF90e0nYod4GdMqArwM9ptD94Z0F7EHgmBqCzeUjEIa8QqxXpBVPQXpmWUnlI+7JL9ivpvZQ0osJArSgOOd96M0F5fhvYQ59bRMR7ycBFrr6ydS6WI1BvDFVcxboSweQkdkwQq5Wo14e+77rJQ8yMo9a4yH0SUNxCmVJg+AGJMSb6xaGRso2Sjw40hY1D5S13C5xWH2PPGGpVCT0uRvnJocqa6hHd65ZQGU5ydXYr+ag3e4ik0SO3j9gd3mPdL6KH5sJaihhUmMkhxyrsKrIoin43CavyPZAuxA2y2NbWFXXZ5CPX+6ixJmr88Cw1YlYlM8eexqub76Qt/zrTG04k5YTzFa01WgcIYfTtngshmJmezBPtK5iUiOY1uwo6yNJa9qkwPJIb3goLpYnusoDBcw31RonYocCmoeOw7vSxKi3EpjJbDldqbRkxzka39MyNsgoKQ8d5tyiIT7IInupEVMfRm4aOj2JNCaM+gU5bCEAXQBhDz9NtCu05QDg3y28WsIVkxtoMcWfoDrfuCDA9C5FOot/agDxw9tBz3lyP3i/sZ6KijhXlDgCS2sNJ7cbzXa8OqbO2YwXTKj7+nkf5/6DRXNj6gvbblW8PjzzyCIVCgWOPPZZTTz2VU089lVtuuYUlS5YwYcIE7r77dUL+jAABAABJREFUbiC0fu7NuvWpT32KefPmMW/ePA466CAuuOCCIe1uuaCrtSYIgiGBxXvLhRCDjpmRpd12M2xHwra2NsaPH099fT2vv/46f/vb35gwYUL0YwwDMyNAgh8ISIQ/kVpfRk51hpwrHBEq6QAChCkgLSkXBSrd/xuozR5y4lDtVk6yUZtdSEqMqUN/M11poVxwKoBs+IEy0wJhbSGHDYYZDi4inYRCj1B7SrLlNxGWQ0NisP+hAIJMMHS5KCHRnT5i/BYXAbw984goqE/ELoioHeqOISc7+GWNnDb0XdaT4sg4qKKm1KKQ04cqKaLBAl8jd3fA06FlzEAMCEyDQqNGTB3q3xvETDAkyaFpo3EqNKLeQq0PF/DEuhKx8UNNe2WP6FZGgdmjvKS38ukYZ6F7xggazEhJ34VYl3sDw0git2K6nbHTBCJOe3Erqzk7kSDIEzeGRhMWLQHGi2V0XGC85iLXDH8SG7eqmDX+00hhsPSNy/nrK1/mvle+ypKXv8A9K77I/f/6Kq803krQs0o1LTWRZztfG/Z1I0YOaVRQ71jkAovcxJ7AgMpHVA7dkJB7xSm0C8RW5kCiysTr0OjxQ8dPOcVBD9zgqDTQiaHjvB3XqDc95NRqdFsBMX7o+Kh2j+OXQXb1zJOSgDP0PFFvIOxw0VVt9kiOHXrvweQOuuLtQw/UGrimi+7KIfachI5tZc645yRE9TgAdHczs+xQ4c8Ji+7sWqZW7TukzuSqWZGSPgrUJ4bOC96pfHuIxWL87Gc/Y8OG0JRea80bb7zBnDlz6Orq4tlnnwXg9ttv54ILLqCzs5M1a9bwH//xHyxYsIAnnnhiqy7MBx54ILfddhsA7e3tPPTQQ8ydO5cDDzyQu+66iyAIKBaLLFmypE/hv//++3Fdl66uLpYuXTrse/uwMSxF/Rvf+AYXXHAB119/PTfccAM33HADN95440jJ9qElqC0z9kST9hUaTq6FuEC9VsKYnUDU9Wi1AowjKlAbelaOTYF5fAb/hTzWOXU0P6robjQQh6RDjbigwBGImf1KrtwzBlIgpzjgK+RHLERDf/sclKar2aBitsTeVAgXBOpMzAbB2JMtRM83Tzow7kQT/vYyxrwJqI0GaBA1JoWFTVTteQgPr/0T/zlzDJV2OABZUvDVfSfzl6af032mC3bPBDIuME+uwn8sC0WN3LdfXr2HQO9noI33No1QRMTWKI8zMI+t7BtVRZWBcWIGOrPIPUzkgL7HnjHc2hjSErQ/7lM516LUEEdM7p9IGrMTiMkxvLs7MI/PELxSwDouA/GevmILrLNrKXYL8m9p/P0qYcD4wGEVdG2UiL3jVE5QOA39SlrlbInTXcJckEY1hxNH9Y9OxhxjYFb2nCeh6hCT/KqAzH6a2DgD//Es5klVqBV5jBOqoHfOWmFgnlFDcGsr+mMxgglDJ4gR7x1N+TVYxlCzd4CMmaKIQUfpvVPUy4GHoV0ccwtFXYO5okywu4UaYxLsZWO+5kJ++EqDKW12qz6UA3Y7jznjP8us8Z/mwMlf4eDd/5N9x32SjvxbPL3mGrRWTEmM5a38JkrBUKuwiPeGQMeZFLeIWQ10jN8db9o8ANTaGzCOSIVjICDqTMwjK7CSEHy8GpHpGbQkmMdloBgQHy/I5kzkR/oXS+UUGzkzju7qUVaSEuPMWnwtsOt7G4fM/gbWmhy6DMZhuyMqHHA7kbMGKP67O5SrY9Tto9FPd2MenoB6m2BpFnNRZf84mpaYx3YRVJYRixzIKxJ+mcTu/WN3bE/FW5nHeC54ED6VGlTXOztO4rXXIZNGH34QpXQacdj+fc+Cuir0x+Yhx05DHvufoBX7bXiLRbX78McN/2TM1E+wW2wcEyun911v/wnHMDE9a/g/WMQQzpvVgGMMXjx1DMF5s4ZvuXPggQfypS99ifPOO4+jjz6aY445BqUUX/7yl/nv//5vfvzjH3P88cdz55138qMf/YhMJsNpp53G4sWLOemkk2hra6NUKlEoFAa1e/7559PZ2cnxxx/Ppz/9ac477zxmzpzJGWecQUNDAyeeeCInnXQSCxcu5Mgjj+SII45g7ty5HHfccXzxi19k6tR3zrYRMRShh+GYtnDhQpYsWUIyOdScblenrS23S68QJh2QzQ5BUWMnNWR9hCXRcRmanUqJSgpMpdC5ngjQZY1MGWgCfG3h58Gu1Ji5nqjvcQuNxiz5YXR3U4dm57ZElAEzQBiguzU6aeEJiQokVlIhCwGqqNFViqZ4I76ZYFx3PX6XxuyN+o4gW21i9kR999J5CkmPTYFBRvroIIeWNbS6mkonSUL6FItvkTST1JV3w+iGUsqn0ylT0ZXGMg1k2kd0Bmg/wK8B10qidTiw1dWlaWnJvqe/065IXV16h+uOVr/YFX+r0ZApZhjEmoG8RmcMfMPFdzxsEcMsBmHUdyVRCYkQECiJXwazQoMWSF9h5H2EFui0hKJCWIIgJTCKOrRqSUh0SYFjoPwAnbFR3QIdaOyUgm4fbcsw8npSk002Y5ZtkvkMQUkiHYGpPLThUYq34vhJvJxNMSaQ6SLJYpygO4ZImmgVoAONSChKSpEIBGYBZMxAK9BaoPMKagSuW0RIUFU25a1Yxo80O/L77Wjf2JZ+sSu+471csfwsXGs8B9UP9Q9UWrNkze85ccKxHD75cyN2ze15Hs3lDq589v/jo3ULqE72R6aXzQHGK2X8fW16tQ250Ud4Gv+A0YsQD6C14pXGWxmfmcee9cdw3Zq/8LlJx/GRzLQdbnNXfEdGs0+MNnV1aVrbN9EWeCRVllS2A4mBXzGFRGcFoqCQGSt0CTRBeQZCaUQpAFuEwd0ChRbg+xLDBNP30SWNqDRQEkReQVmjKw0CKdE9VilBHgw7jPouSgqdhAAPwxCQ9xCOBZ5FEEDgmOBorKwLKErVPm1mnrr2OJYw0KYJ2QIiWcJPatpFFWWzyJhOA6NkQDqGXzTAFnQn19Pub6AiNoYKswGr3UPlAoJqCyF9nGyJoLKCohFuiphoYrkcuC5+RQUl06KuLk17SxfxYiOimKVQ1cB6XNBQJxV+UKCgi5gySYU5DqF2nq/hrtJHhjOP2h5GK+p7xAeHYdmnjxs37n2ppL8fyJeByjJ1e/QMWmkIteoAUr3/30MF9Nu/B/3/rejx1Er0Hu/ZDRjihriFeUulAPr91YsAAzYDE4wFBblUuUeWPiFAgVcZ/oMEloZJvXYbPSu/Vb2bbQpSzpzwfhO9cjokSOLX9kpgQ80A2XbdtZWICEpBQKmGnndWESoXNmUUpASkFP19tZ+B+3R1U3snKkFPv+/Boqeb9fZXv792T3rsAkCy95ye7BCqGm1BLrNlDAoDGIPrhHVCT0iLXBJI9teHcNJSbMmSZ0sZ6O+fyZ7J3E5Q0iO2j7LXTjo51F8VwsjvppGiMb96q8d3Bl1eHhsfa4tgcnKDj6o36N8SBDXWwHq+HPoLb80FY4QQQjK19khebryZyTXzmRiv51/ZNcNS1CNGHh2kqQaQ1XiVk/rKs1UKqgAGukpsOQYOjcvTT89Altzi7162OvXtmeSknC0Uzp7xMgGhyZVNEpvCwLlNRbKvUQdwSFHMDLh2Mry+zRgajDFhMDwP/LQ54Dvh4FUPFsxHkEsNVToDJLn4+L653Tjo62aWMWDKF43no8rRk6sjxTziHRmWov6Rj3yEr33ta3z84x8nFutf3T7qqKOGLVhERERERETE8AiUj1JFquyatz3HMStoL23aiVINpsvLYWgP0xiwIqxANvt4s7Zwo5CCoMHEeMMj2G90XSzidjWZ+GTWtD3GbvG9eDn71qheLyIiIiIiYiDDUtSff/55AG699da+MiFEpKhHRERERETsAnS7LQRYpK23N+WMW1Xki2t3olSD6fLzSO0O2lEXHQE6JvrjlwxAjQl31YOyDc7o5jZvqJjFW60PMXfqYdze+Cha6yifekRERETETmFYivoNN9wwUnJEREREREREjDAdxU2UMUmZb+/TnbaryGazKB0gxdCo1qNNR7kTgcYQ/b6wssVHVb6NLKZA1RgY6z2CPUbXfzbtjCfQHtpvxxQGTeUOGmKRqWpERERExOizQ4r6j370I7797W9z3nnnbfX4L3/5y2EJFRERERERETF8NuRXo2UMQ7y9P3eFlaZbWGTdNiqdnZ9Vo6PcgpCxQTvVok2hxrz9ooGqNzDe9Aim2gNd2EccIQQ1iT3Y1PUsE+J1vJHfGCnqERERERE7hR1S1A866CAAjj766BEVJiIiIiIiImLk2FxYj7FFkLYtSZlxPGy6SpvfE0W9y21FygE7/gpklyLY4+3zCeuUBCP0Y1djhmUc+K5UJXZnfccyGjLH8mZ+A4fWDM01HRERERERMdLs0Ndt4cKFAJx88sm4rkuxWGQYWd4iIiIiIiIiRoG2UiOWkXrHc9JmghKSrnLzTpJqMDm3nbjsDyQnsgrtCDDeeatc1RvItaOvqKdj48i5TYy34rxVeO+C7kVEREREfLgYVm6T66+/ngMOOIADDzyQgw46qO+/EREREREREe89uXIzjvHOOYEdaRFg0lp8b5TQot+NNSDiu+hU6NS727OrGgPZHkBhdDcKpDCpiE0gFrSzprB5VK8VERERMVyefvpp9ttvP0488UROOOEEFi1axP/93/+NSNsLFy5kw4YNg8qCIOBzn/scixcv5umnn96u9v785z9zzz33jIhsH0SGtQx9/fXX86c//YmZM2eOlDwRERERERERI0TZ7yQZm/qu5xlGks2FdTtBoqGU/Sy2PSDie1eATm7DPoIhULUmxlqXYO/RTdWWdsbhlTfS7uYpBWVixuheLyIiImI47LPPPn1Bv/P5PMceeyxHHnkke+yxx4hfq6mpiZUrV/L4449vd93nn3+euXPnjrhMHxSGpajX1dVFSnpERERERMQuShDkSNvvHvzMNlN0lBp3gkRDCYI8jlnV97foUqjx2zY9UQ0G5ooywTQHRjFgfUVsHBs6n6be2Zv1xRb2TE0YvYtFRER8KHjiTY9bnvNozWtqk4LT97c4ZOrbx+bYUUqlEoZhkE6nWbhwIbNmzeLVV1/lpptuYunSpfzhD39AKcXMmTP53ve+h+M43Hjjjdx9990Ui0WEEFx11VVMndq/6Lt69Wq+8IUvcMUVV/Cd73yHzs5OTjnlFG655RYuvfRSVq1aRWtrK1OmTOHaa6/F932+/vWv09raCsD5559PPB7n4Ycf5qmnnqKuro7DDjtsxO/9/c6wTN8POeQQbrrpJpqamujs7Oz7FxEREREREfHe4gUl0D5pK/Ou58aMCnJu6+gLtQVKK7QqEuv1o9cgcwqd3LZQ7jomUBUSuc4bRSkh5TTQXdpIvVXBhuJ748sfERHxweGJNz1+/YRLaz503WnNa379hMsTb47MWLZixQpOPPFEjj/+eBYuXMjcuXOprw+Dhc6fP58HHniA9vZ2brnlFm6++Wbuvvtuampq+M1vfkMul+Pvf/87N9xwA/fccw9HHHEEN910U1/bjY2NfOlLX+Lyyy9nzpw5/N///R/19fXccccdPP/881iWxZ///GcefPBByuUyjz76KA8++CDjx4/njjvu4Kc//SnPPvssBx98MAsXLuQrX/lKpKS/DcPaUb/uuutwXZcf/OAHfWVCCF599dVhCxYRERERERGx43S7rXhYpK13jvoOELMqcYtdO0GqweT8Ig4Kx+yRsaDRBu8aSG4gapyJ8YaHmmQNc/vh7TGkTdyqwpY+6yJFPSIiYpjc8pyHGwwuc4OwfCR21bc0fT/vvPO47rrrAJg9ezYQ+rKvXbuW008/HQDP85gxYwapVIqf/exn3HvvvaxZs4alS5ey995797X91a9+lX333Zf9999/yHU/+tGPkslk+OMf/8hbb73FmjVrKBQK7Lfffvz85z+nqamJj33sY5x//vnDvscPA8NS1F966aWRkiMiIiIiIiJiBGkpbMLHwhbv/qlPWRlaVYlA+RhydKOoD6TLz+MIH1OGirrMBejE9mnbOiXRMYFc66GmjLzZaC9JZwy+zrO2GAWUi4iIGB69O+nbWj4ckskkRxxxBE8++SQAjhPG2AiCgEWLFnHJJZcAoUIfBAGNjY2cffbZfPrTn2b+/PnU1tYO2oT99re/zbXXXsujjz7KggULBl3roYce4uqrr+Yzn/kMp5xyCh0dHWitmTx5Mvfddx9Lly7lH//4B7/97W+57777RvxeP2gMa+1ZKcVvfvMbLr74YnK5HL/61a8IguDdK0ZERERERESMKpuL60E6CPHuu9MpK4ESNjmvfSdI1k+3l8fUfl/Ud5HVEN/23fRe1EQLc5UL3uhFgE/a9Ui/jQ3FllG7RkRExIeD2rdx73m78uEQBAHLly9nxowZg8rnzZvHgw8+SFtbG1prLr30Uv7whz/w8ssvM2nSJD772c8ye/ZsHnvssUH63axZs7j00kv5/ve/T6FQGNTmsmXLWLRoEaeeeiq1tbU888wzBEHAjTfeyDXXXMOiRYv43ve+R3t7O9lsFsMwIt3xHRiWon7FFVewcuVKXnzxRbTWLF26lMsvv3ykZIuIiIiIiIjYQVqKm5AD8pO/EykjjodJtrxzldBuP4+B15+eLavQO6Co66RAVRsYK9wRlrCfpF2H6zWzudyO1qObEi4iIuKDzen7W9hbBMC0jbB8JOj1UT/xxBNZtGgRsViMf//3fx90zvTp0/nSl77EOeecw+LFi1FK8fnPf55DDjkEpRTHHnssp59+OuPHjx+Skm3u3LnMmzePq666alD5aaedxr333stJJ53El7/8ZebMmcOGDRs46aSTWL16Nccffzyf/vSn+dKXvkRFRQUHH3wwv/zlL7n//vtH5L4/aAzLvm3ZsmXceeednHLKKaTTaX77299y4oknjpRsH3oqPAt3dZmKlI10fYQvwRToQKC1QMUluCCURpgK4QKWBgnC1WhDoIVAeJrAMtBa4DllSnYnhg5I5CswfBPfkkghMXyNbxhIT+HFPUTgIH2BMDUQIMs+XWmPVlmiGpvqrIUWBuUYmAWNtgFtIwIIrBIlq4uiYWAHaQyK2F4KHUgKtodnCUo6wBJ5qso2Mc9GaMDophyXWNlKBJJiugsdZBGYaFIEetsmnRER24NtC3K0U52rQWoTtAYkviUQgcYQCimAkkBbAmyFLIOSIF16RlIdpovSmkBZaKCcdsnjUmEInE6JYVjggjIFGoH0NDqpEWVFICwEGnddiUrLQHkG0gR8D2EYCBcCS4Kj0CUDhAjlNDQqkEgjlMH0FVopylU+2gdfSuKlAHybQApMoRCexouXcK0sdrkGPJtCPE+n7KDGrCcov2c/RcQI0lFqwjTe3T8dIGnEKGtJV7mZCey8bC6dbjdoH1PGABA5hcrs2NQkmGhivuyiN/rbHDV+e0jadeTLm3GsSbR73dTYlSN+jYhtR2uNZ3WSKlVg4YTzHkDFBbgSIQRSawzhIlwDJChHIgsaDAhsgXYlRtxHlAUE4byJQKDt3nmUJECC1ISNC4QRIAwBZZBaQUygAwg8CTYIX+E2lqjyTfA1Kq7wAg/P8YmVLCQSJSV4AmEIPKeI8H3K0sWLO+igjKsLICxSMoUse5TtAOnbWKUEZQekobE9G+FLAjtAag9HgVG0CWzIOUG0mLSL0+uHPhpR3+fNm8fzzz+/1WMPP/zwoL9PO+00TjvttEFljuPwu9/97l3rD9yc7S3fa6+9WLJkyVbr9vrID2Tx4sUsXrx4q+dHDFNRN00TKfs35W3bxjR3nm/bBxXbhvg6E/+WFtjoIfZwkCdVo5pd1Jtl1LIcYm6Kwp4ZWh/20B5UHSDJTFaop7OYCyrx1xQwHJPgkW50o4eYHsefl8GTFumJtZgbO1BvlvAfL4IjkMdm8GodeL6L4JksMmnAUVW0rDNJzzBxXuhCvVwgfUCS9MIUPPQ6/mutmCfPwVoWoF4vI8ZbmCdW0vyqQXpqjAQlcsajmLUfQ7ZW0fHPgMKbCrveJDgq4H/aV3GRPYWka+EvzaLWuhgL08SrTbx7W9GBJrkwTdsem3jun/9JRc10ps+9EOlMJfr+RIwUOaMV0ZFn7AvjkKZL8EY3alUJfeYYmp8Dr10z+RSBd3sbbPRgTwfzuGpwQN3Tif9KEVFnYp1UhZc0ya6TtD9RRgioOsykeu8kcuUGjHF1+I93oZ7NIyoNWFSNnzKQWtHdapJKuYiH2mGTh9g7jrmoEv/hTqy5FXh3tqBbfeQ+cYwjK/EcTduTPhX7WrT+3cdt9knsIak9UKJ+uxnKGvvwCvhonFhO4eZsmp7QlDcr4pMEdfuCfCBP7OyxtDyqyL/pY9c5WMdU8ntxL4snfITqYOx7/dNEDJOc24pjprbpXFMaKGnTUtw0ylINpsNtQwgbIcK5hMwrgtgOmn4agmCajbmijOcIdO3I5mszjRimdBhr2WwstkaK+nuIL7I888bjTC8dhuoyEZsLqIe6QIBcmKG928ErCKoPNbFfL6Me6sY4KIlIGLgPd4MBYmGGQswhpVyC+zsh0JiHphD7JQmWdKBfL8M4C+OUGjo2SZw6A3dzgDNBYhc8eLCdYIOLnObAkdUUi5Lu532KaxXOOEH9/iDvbsHcP4kzJ47xr03oNzsRs6ehHihAPsA4MIUzxcO/40XiNXHkSeP4v9bv0JJfz5xJJ+EkZzKneg9qmzI4d/jIVa1YEy2MT9Sw6W+K8mZFeh9JwzyDYGkeb3kbJA0qT6kiv7fEI5os7cocMnV00rFFfHAYlun7tGnT+OMf/0gQBLz11lt897vfZfr06SMl24eWeKuN/8umUCkA9Btl/Ac60Otc1BM5ALy90jTf66GKoH1of0qRaxSYu8fwbmrFnBrHu60d3djTxmtFzH+0UXrLp9Qs0E2S4JEC+BryiuCxLMbKPDyVhQDoDuC2VqqmaDb/xcOfnoYA1NN5xOMFdFMeuf9E/Ptd1Ovh9pve6OH9ro3afQI23umjnUpqzPmUX6yg88lQSQdwmzXqZsnFY/amdj14SzrRa12QIDMm3m0dUFTgavz7u6lumolpp+lue43nH/46QrXt/B8l4gPLxq4VNKydgF5ZIni9hHqpCPMzbHxIU9qgaVgo8a/r74+sKqOey+Lf1YFaUQQNutnH/U0LQkHbP3y0C6oMbX/3KW0EMaGG4Ik8ankeFOiOAP2nFlRW45o2tlSIW5phU09/fbWI/+c2rAWVuL9pRrf4oEG9XCT4aycir6n6qEnjbS5uczgRK7yhaPp7ADOT4Gn8+7sQq1y8RsXG+xTlzeF5xbWaxicEHFnF5vsD8r39sgVKN8fZK5jKXzf+E8NSO//HiBhRil4HjpHe5vMNGd/pinpnuRUhw8BGuD27lsOYt+qEwN/TxnquhFzjMdJ6SsKuIyN8NpZ2fiq7iH7e6l7OFHc2zX+VxEou6q+dUNZQ0ui/dpCp9ymtV7Q84KJyGkwQKRP//q7wPStq9H0dpNM+wZ3tfXMOXdL4f2oLlXSATR7Br5uoGA/FtYr4VIHa7MGfm2FD6GahXi+jVpdoX+pRXBuOm+VNmo1/13BIJf5D3ajXyphTxmLOnoJ3azacYwUQPJFDrZFQm4TWIs5v32JR5tN4qswzq/8M7gbaO1zivy0jV4XXE+s91HXNVO4hQIOVgODpIsGyfN/8zft9K8lNI+/rHBERsXMZlqL+7W9/m1deeYW2tjY++clPks/n+da3vjVSsn1o0S1e+NEYgNFgEzwVKumi3iK3cegA3P2mQKcN2Oyhc2pIUB29ziVZowm6IXhhsG2rMT1G8ExuSJtGl4uwoNQlIRW+LsE/Sxh7jUFUpNFN/uAKRY3o8pE2uJ0gChVYCUlp4+D70T7YnQbEJHQFffel1g31L9TPuIybeDgAXrmLcmHnTiQjPrgkkza+n0UvLyD3jKFeKQLgJyyCnu5gyQBKg/uSrDRRr5UGNxaA2rxFfwBy/wqQjk3wbH7wAQ1GwcfrAjMIwknmwMMbvXBCuUWMFfVaCekHBEWB3qK7lDdr1Fin729/WQ7fMgkGx3rB69D4KZvSpi2u6cFu5bHcv+k5CnKLShHvO/wgR9La9l1fUyboLO/c1GPdbnu/2Xteh2bLDE/B0BUSf4aDscbDfLyIaAlGTGGPW9UkKNEYKervGaYpWLH5EayuFMkpEv3s0LmLXFXAaZC4LaBqHeSUGOq14qBzRL2FfnWLshoTvWmLPNYljcz65P7lo32BpQIoDJ7TBLaJ1zb4JVNF8O3QylS9VkRLE50dOu0OXihj7FXfU0lTncv0HVvbvJQJxRpo2eJDkFfYMpQhPQmCf27xfQH0+tGL1xAREbFzGJadeiqV4rLLLhspWSJ6EImh5nq6O0DUmugNHjoXsDWLO7sShKfBFAhnKxOdmCAIBMKmr62+9rsCZI2Jyg4e2HXcQPtgxnWfsiKqDXR3ERGPhW/QFrqJiEmUB9IBLB/VbSKdcIdxUNsxEFKEczINOhcgMlsxVRxjkM2u6fvTtLbNlDMi4t3wfYVCIMZakAsQlQa6M8AQuscHHLS5lfXMsoKkhPzgyZrYSlopu1aghQongM2DO4s2JTIW+kkOrSjCf1uSliAF0h56SNggvH6ZZIOF3JqGIkFKvdV+WbRKjI1XYw9nWzNi10AVSVmZbT7dNtNk3Z2rgOa9Tip6I77nQn/fkUDHBf5MG9kWYL5UBgnBJAs1wdx6v9pGElY1ZnEDmyJF/T1Da6hOjAetKLdqRIONfn3wwqmusfDXaoQJQil0l48cZ8Nb/QOezgWI2i3GOQVYYuhGhyOxMgIhQZmSLWcqktDvfcuFVSl7500moBGxofcTzqn65Xft/u9ERWI8BculZittKzN8j30X7K3M36gY1l5cRETELsCwenFLSwv/8R//wf7778/cuXO5+OKL6erqGinZPrQE9SAPqxhcONbGPLEq/BDkFHHTwxxwinQgMx3UyiLmSVVoqTFmbxFE6OhqcpsFTr3A2FfCAGU+2FhGHFM1eOlmnEXJN7GqwSl7oZm8AdbxlagVmwleWIt5RHLQJYyD4hRKBrGxAsdRFM2VkC5Sfdjgj6E1C/5ldlJuLGHO7zHNzClE0hisrMcEal5AR/MLAEyeeRbSGb8dTzMi4u0pl33GVk7H+6jAX1HEPKISBIgXuqneP+wfHa+BPGyw+XBQYWKdVj2oTM6MIxtMBsbuMpKQ2ttAv7Qec3Fm8Ig72cZPmjhxn+5GAfsPXoAyF2cI3iwiZw4IoCjAOq6KwDYorg+omDN4ulh/qEQ81R3+ERMY89NYvk/VRwYrJrWHGYhH2qlbMLh+cjbcW36Cz087Buk6RLx/KfsFNJqkue2m7zEzRcnrHD2htkLJ68bqiUwv8gHaGUHlQghUrYk/2yaYZCFbfOyHCpjPlREdO+baEbdr0F4HjaWdm8Yuop8g0Ow//ljWGS8QrwdmJ8OF017SEm9MHL9LU3u4hXihG73RQ05xBqf+MwRqchwGzDmCF3OYJ1UNup6Yl6LQJanc30T7UPYNxMGD+5U0NdWHDd77yswWGK/kIC6R+8TR61vRpW7EuAHnGWAeaqNWbAbA2zvN8ywDIGYmaag9mK6KLMEJg78P4rA0nW+G99L0qMY8NjNo/ibGWajdosXWiIj3O0IPIyzk5z73OWbMmMHpp59OEAT8+c9/ZuPGjVx99dUjKeOo0NaWQ6ldN8hGqmwiGxV0BYgaE9ICWj1EpYVu9tCmJBjr4LVptKtxqsAo+yAEIg2iXaEloT9sXqEqTFzDxEgJXDuL42vsbgPVJcCQiGoTH4F0NXR4iJjET1q4RYldBVZbGV0KCOokb1a2s0c2gWwpQWUCiYNqdRFpA1VhUu42sTOKMpsoJLpRsgonn8EqJvCyGlWhac0Ued3rZraIMyZrYZVMyPtQWYRUCjZrtAI9HtrMf+IW24ilxmHGp6IJNaG6ujQtLdn39ofaBamr2/aJ+ZaMVr/YFX+rXpmEgA6xgXFd4zBbDaQp0N0BqsrCkyZ+ThOvUchsgO4MIGNC2kBKhcgrdIsf7qTXGOBrXMum3AYIsOsF2XSWqm4DygFGYKPaArQt0ZUWohxgJMANeixXSj6iEEC1iW9KzGIQ7pIXNSobjgVB2kRpCAoSDIUKJCqnMdISy/YxWz1QGtFg0lrXTkU2jszG8YoGQQmsjMAslpFa4dUGqGwcr1ugKzVrKtchEh6TzIkEvhzyrHYldkSmHe0b29IvdrVn1FJYy1XPncuCyV/GkNum/K7oep2u9vv47qEPDvv62/o8vvrU55gWq2CvusMxnyujk6BqRzEobaCRzQGyKUA7AjXNQtWb22xt7wVFnlv//3jenMtt8364XZfe1d4RGN0+MZoIAWWrGasjRixbjWMKaPEQEnStRaHTQMZCBTpm+NDsgmOEVlObPbAlKmNRzAsSVRrR6oIHotJAGYAv0G0+pA2CChM/MBCWxu/Q2DVhNh2jy0MUAkStQRAzcXOAIfHzGjsFduAjOj1ErYnvgAyKiNY8IpOGvIEugagy0E6ZoLWbcsKlNEayNlhFyS+TiDXgGGlqjTie51LROgbaNEHGQNUZmB02Kg9WDVhmF1Yujm4OICbxx5vkElvPTb0rvoe97CqyDWceFRExkgzra7h582Z+85vf9P190UUXceyxx75rvWuvvZb77rsPgAULFvCNb3yDJ598kssvv5xyucyiRYv42te+BsCrr77KJZdcQi6X44ADDuD73/8+pmly1113ceWVV1JTUwPAxz72sb46HwRyjg+Toa6uom/QEhmJlAGiQRLOu8pQA1KAR+jOChohQNVA/9xMIoRC4BJokNomkFCoAT/U7cN2e7I9+RMkQmik9DAMKPtQrNYIYWAYMEHXUEwCDTGU6qk/yUZrjVIaUe8SCIGUY4jrMQQByDgoXKQGoaFBWIyTNQSBxq0SlBQoZSGEhWGArqNnEiCw9QHEM+EsSqnwfnsnCKYp8H2NaQqCQCOl6KsnhEaIsNw0BVqH96eU7rtXIaB3hjZwzUqI3lmb7jkWlmmtMQzR87fua3Pgcle/DBEjgW2Lvufb+7tpHb7fUobvRBCExwwj/H/oP+b7/b+FlD1+Fj2/uWWFbdeJiXjV4FVD2IckoZ1hgCGgpEDXaUxTorVCKRXKUgtMkoTvm8IwwPPK6NrwnSt5YCiTbCVYloVhaPyJEsMA33f73qXQ3hICARUVFXR35wBFyQfL6nnvpYHva3y/x7wxM/g5+UAZoIae+wsw/ErycSAe0Gs32e/wIsN/aR/GhSUTe/4nGOpqH/E+o7nYSCCsbVbSAVJmBZ3axwvKWMbOsagIggKO2ZNhoKDQ1aOcOcYQqLEmqsFAtgcYK1xk3MPf1wndSt4Fy4gjhYHWRfJ+iaS5FVvmiFFHaxhfOZWckYPaIBzXJgqUAq19GOOjBbiBxkWg+jIABFDb+zuHo2EXQKb3mz/A0mK86Pl7gEl51QBvv1oIx9qecwZsxKfq0rS0lGGCHNCmA7Vb9isN2FBTC4Sj8hQahpximuA1wMBD/phQrjJQJg5JYMyA+4z40PL0009z3nnnsdtuu4VpDD2PE044gS9+8Ysjfp1rr72WG264YUTbjehnWF/EMWPGsG7dOnbbbTcAmpubqa+vf8c6Tz75JI8//jh33nknQgjOPfdc7rnnHq688kpuuOEGxo4dyxe+8AUeffRRFixYwIUXXsgPf/hD5syZw7e+9S1uueUWPvWpT/Hyyy9z8cUXc9xxxw3nFnZZKnICudbDbWohM8FCjzcR6zsQpoMu2eiOgGBakmKHxM9q7DoZ+j554OcVyXQAa0qIpIEca6FWlULzq0kWCI1a7aESJqQcSk2aeJ0mJnz0ujJkTLwai2yzINEgsQJNqU3gC0UwPoup2kk21kObQs9MUcoKyk0aZ5yBFGEu6SCrcbsV8QZBskKRt7pwSwq5sRoBxGrAKLiUHYdCi4+oLOCMjcF6Fzp8YlMS0OihKyw8y6G8WRGbZEC3xutUxOuh+FInVgqseJz8WxqnDrz6dl43X8Qz6lmZK3JARR21XokXinlalM+UdIpqmSehXKQKiLvjMFvGQS6GPdFA12RpK/yTruw69jSPxF4bQwoTf7cEm+rWsrrjn+TdTqZXz2Nsx2TstxQiLjB3CwhiCdQan6DVpTzJoDRekDDtKJXcDpLwfaw2B08beGWJ26QwKwWJOo2xuQSGQDd5qCoTOdFEtLSj24vI3cagNil0GfRYhyImNAR0lVuoXGvjuCbBHnGa/5VD502ceok2fBwRYBUg6PTQkxKUWwRupyY+VRIzFaLZQzS5iDoz9H+tdHBbA2KmQjaWIWUQ1DiUmiDeILB0Aacji55STbnLprQh3IkxUoJioyI+VqKUprwJzBTEqzVGl4u7qY3YGIeSYWIKgVOnkR0+ak0ZJ22gJpmUqgWejKKyR7w9TYWNCLF9ynbSjKGERc5toyo+bpQk6yfQAUKXiBuhWa8sKIKtxVcZDYRA1ZioagPZHGA/UcSfbqMmv7u5cNyqoV4YNJXb2d0c/ecU0Y+UkOh0KGyG4qYijg96gwumRI2zKRUMnDqB50JpbUB8rMTqKiFjhN9lK/ogR3w42GefffoU6Hw+z7HHHsuRRx7JHnvs8R5LFrE9DEtRF0Jw0kknceihh2IYBsuWLaOhoYHzzjsPgF/+8pdD6tTV1XHxxRdj22EkpKlTp7JmzRomTZrExIkTATj++OO5//772WOPPSiVSsyZMweAU045hauvvrpPUV+7di3XXXcd06ZN4zvf+Q6VlR+MnKYpT6Dv7MJb0R/0xPhYCqbb+EtK6I05WFzNpr8EeO39q6Z1x1i4nYqqep/gmub+KLdpibWwEu+WdkSVgfXpGvwncuT3q6V1iYeRgNTcAO+O/uA4os7EOqgWr03Q8je/L7p0/aEpnOUl/M2dsG+C9i5F9rXeCwVUHGCgyprcy/1KxNhjJda4ahp/5/Vtd0oHxp4SZ+OfeleqEyQmCepiCmu8g/d/rTDWpnuPOB3PeVQdYtJyn4fb1P+Rrf+YQ+LeZpgdkG+O075UUzGziqoDKniw6w80mvP53evPccG+C/jJyqf76v3H9H2JFZ9jbzEL/rYbfrNB+LB8MifAUy1Xcvj4H2D/VoGfRwHS6sb49xz3rLsSU9rMrTsM57ddfc9YHFMBKzqRGzwkYXYh7zSHtrk+1XqLWAER74ptFLFei1FGUCoJWh/o39GwamHCfBP166b+Cns5iLoO5JgM7v/r6A/yJsD+VD1BTlK/REFXAX1SLY03QZALd50RAfWLbdrWCWq6ujEOTdN4v6K0UWOkIDZWoB/r6IsID2AcWUlzl0H9pAB1a0v/Hky1gVxQx7qbNBMWx3B0nu6XofWx/n3s2HiJM06y4UGX+uNs2h9xSU4RxFUOf0D0YvOAFO2JNHaXi39Lf0pC0WARP7OKYLxEqUhZj9g6baXNSCP+7icOIGHE8DDJ7iRFPesXcFDYZiKMgRIAWwmSOKoIgRpjoisNjJUuoqQI9nLe0RQ+bmXIBAFN5Q52T0aK+s4k0emw/gaPurkCs+jh3dLWv8VtC4xP1LPpdhh3skXzS5q2f/jUHe6Q+FsL1l4OwbEJPBEp6xG7Bqtf9XhhqUchq0mkBXMOs5iy98jHFiiVShiGQTqdZuHChcyaNYtXX32Vm266iaVLl/KHP/wBpRQzZ87ke9/7Ho7jcOONN3L33XdTLBYRQnDVVVcxdepUHn/8cS6//HIcx2HKlCl91/jd737HnXfeiZSSWbNm8YMf/GDE7+PDyLCithx33HF85zvf4eMf/zjz58/noosu4pxzzuHoo4/m6KOP3mqdPffcs0/xXrNmDX/9618RQlBXV9d3Tn19PU1NTTQ3Nw8qr6uro6mpqe//v/zlL3P33XczduzYD9QLYW5WqBWDQzEHj+aQThK9MfwiuQkbb4tYNh3LfGK1Apb2K5AAZBW6rMAWYf7m1gBxaCVty8NJfmZfCQ91DG6sxSfuBJSb9KAUUDEzgJ4UVGpacoCSHtL9bEBs7OAAVa1PaUqbB6eZUmXIvxXukPZSWKsJdkugW70wF+qcNB3/DGU0kmKQkh62q9DzKtBLu6ia3nP9VxRj/Zm80fw4syttAq1Y2rSamVX99mI3rn6LEhbVwX49SvoA+R+Ms9/uX6RiRX04cezF01S/VEVdajfm1Cyk6u+Vg56xcAzYMDilS9U9Hl6Hh9hJG0QfJGKFGMHKMqWSpOPJwXbYXiuUugc/VLWyjBxbi87ZgyOxa5DLu7AtBV0KTEFZmn2p13rP6fqnj1AC5qQodwlKG8Mft2KOiez00K8MTuETPNRF/UcE4pEt+k17QEz4oKDjNY1qqKbtycGyljYqrKowonzXcz7J6QYVu+mhKYaezVEzXaPuHXwNvdlDrvOwi9GLFfH2dJabMeVWFHUFcrWHbB5qGhs3HFwt6Sq37AQJocvLY+NjGgkoaLQz/NRsO4qO9USJ3xQgV3vveG7MzBDXJZrKUUC5nYmUUNoEqgQx20etLA3OOuNqzI1FtBa4LZrMR8O9qNZHA/RHK1CP5Yh1R+NmxK7B6lc9nv6bSyEbzjcKWc3Tf3NZ/eo7jz/byooVKzjxxBM5/vjjWbhwIXPnzu2zep4/fz4PPPAA7e3t3HLLLdx8883cfffd1NTU8Jvf/IZcLsff//53brjhBu655x6OOOIIbrrpJlzX5eKLL+bqq6/mjjvuIBYLXX983+dXv/oVt99+O3fccQdCiD59LWJ4DGtH/bDDDuOFF15ACMGsWbMGKdXvxqpVq/jCF77ARRddhGmarF69etDxXn/gLen1Hf6f//mfvrJzzz2XI444Yrtkr6nZdVN8uW9tJXK+Bj0gXcjWzKmDosaIAbmt7LK5Okw54obB53TcQPfM0wyHITlBey+iiltcaIDvtVJv88HbokpQ0FuXt6SHpJhSgO7JIa8R/W1tpb5yAVuGm6IDV8g9iUCGSaGBrFsmafZfKOeVsMw05IeuU6kyxKxKZHZo3jkzaxIzk6SMDDI/WCS9NZ/0soJAU1v7/glKMpr9YnuCs7hrc2EwRCGGvoNs/f3XQqBLWzmQV4jeX8sE5fX6qfcTFDUyJtGGQA/IZy5tsXVXPwVCanR+K/3G1yAgKIAWEu1vpYGeS6iixhgrEW8T00AEGl0aeg3ta0QgdmrAm10xuM7Okmlb+8Wu9IxKupOYkyKVGuxD7T+RReUV5D3MuSnk+MGDsJAOBaN9RO7l3drY0LYZE59MOoNdNFFJg3j8vY1UrWebiOcKmONiyC1Td/WQ8evZXN5El8xt93Pald6R4fBezaHayuUw5ZoGXRg6topcgIwJlKfD8RvQPtCTyszQO2/c3JV/60i2954XlnpD4sEEflg+ErvqW5q+n3feeVx33XUAzJ49Gwh9zNeuXcvpp58OgOd5zJgxg1Qqxc9+9jPuvfde1qxZw9KlS9l7771ZuXIl9fX1TJ06FYCTTz6Z//7v/8Y0Tfbbbz8+8YlPcPjhh3PWWWcxZsyYYd9DxDAU9Z///Odcf/317L777niex4YNGzjrrLO44IIL3rXuc889x1e+8hW+9a1vsXjxYpYvX05ra7/Zda+v+5gxYwaVt7S0UF9fTzab5fbbb+ezn/0sEAYBM83tu5X3OmLpO1HZYCKqJHpA+hgx2UIkCXPMljS2VAhD9CnbAJX7mXSvCqg7uAJ9Z7+pLIIwcnxegQGywUQszZGcWkH+TU3na4rEgWn00u7+OqbAM0xikyTZFf0X8W0TqyfHqNldxqqK4Q3Y8LMbBF7n4OdaNUduNe97YndJ9qX+to0kWDkPuZtD8HQeualEbHwy3N0UYY7ogbv7FTMlYkUOvYdDvikM2GJVQyndRkW8ljY/nKAePGYyv1/1TF+9xROm0Nn9MF7mIIQ1pVefByC5f4ln111PZs7XSb48eCLSuV8XG9tep7PYxGGHnkTqrv53TsienNdu/70X59mIjNjpEUw/CFHfY6kCdqWFafhU7GfS9Uz/10yYYCe2kDEtoVBANiSG6NX6gDRuIDEkUNLEkkFfjvReKmYZ5N/wycgy9h5xZEyjSpB7LSB+oAmVBnT1tyz2dOhaI6g8MI1+eMDCmgF+3AKtyUwHI5cjNS1O7vX+U2QMdE/XTs8y6HrOx5hhkK43YWCe9QaLfJckMS+NfnLAs7MFosHET+28d2tXicQ7kCjq+zuTK7ZhOhPJ5frzM4sOhdnk4c+xEd0C/VwOryIxaBNbyhjrWtcM+1625XmsbWtBag+3JNHtZaSpcYsjs5s0HOQkA70si7cgsXW7wyCJ8rp4q2PTdj2nXe0dgfdf1PfEeAdVBg+D2N5x1KuD86eraQnchxV2nUXrE+GYndpbIl/vRk+wKKehcyf8Brvib91LJNu2yTHa9O6kb2v5cEgmkxxxxBE8+eSTADhOGL8kCAIWLVrEJZdcAoQKfRAENDY2cvbZZ/PpT3+a+fPnU1tby6uvvooQYpDLnWH0W6X+7//+Ly+88AKPPfYY5557LldeeSVz584d8Xv5sLFDpu+33347zz//PA899BB33HEHS5Ys4f777+eFF17g1ltvfce6jY2NnH/++Vx55ZUsXrwYCFd2Vq9ezdq1awmCgHvuuYf58+czfvx4HMfhueeeA+Cuu+5i/vz5JBIJfv3rX/Piiy8CcOONN3LkkUfuyK3sknQlFNbn6zA+EkNUGRgHJ7A+WU3w1JvYpyeQM2PIZzqZ8AmT+BSJVSWoOtTEGStITrXodi2MU2sQ9SZyio31uTr8FQXkVAf7vHqUraHTp3ZKQNWBBqosKO+WxFicQdSYiOkxOKee9tfC1CZjTzZxGgR2vcCtKsB5DmJ6DPFynnHHGlTMlpiVgor9DaoPNYlNlKFc1YK6+QaVMwVmpkT1yWXsOoEzVjDuOIMYHlVzjTBA2MyAcZ82CZoKBCsLGGfXIBrLjJmlyMw16H7Jp+EUm+Q0iVklqD5YUpVxEZNs1MJqChs1FftKak/xebx4G4fu9U2eaG3nWzMP5aOxOPtW1DI2UcG/7TGLj1YKDp24iKbgSZKnryO2R4BZJcgcblBxCGQyk3iZ2/E+I2GcCeNN9GdrKe0OEzMzsY04m6c3o8+sQNSZMNUmmBRgfLUWPdNB1JgUjopROMKh1vhwrAyPNCWVgI84xG1Fcmr4fltVgsRUyYRPmpi+h3FoClFlIGYnsL9Qh87nCV5bj/XZSsQkG1FvIk6opmA5MMGk/TwbPdXCeLGb8WcaxHYTWDWC6o9bGElNw8cMTFMjVmSZcLpJck9JUNAElkR+th65fyLsjwelMOdXEJ9oUMjEEYuqELUmYpoDZ4+h4w1oOEqSqPdRL62n9mNQdaiJmREkp0vqj7fJvepTe4yFWS0QpqDUCfLT9cgDe+5pbgr/yFrK7WDMT2MeW4moMZHTYljn1uJONPDUe6/QROy6eH6WuFkxqEyu8VBjDZACnZHhAmjb4KUtw0jQWd45JosdbgegkcJCFFSP6ftQfK+RUn45vte4U+RSNQbaFMh1W+9jMStD4HfRXOrY6vGI0UM2eEw8x6arURKMsbE+UYUYayHGW8hP15HtNJj4KYtAadxWTdXBktpJHiIjEf9WRdGIoqFH7Bok0lsf796ufDgEQcDy5cuZMWPGoPJ58+bx4IMP0tbWhtaaSy+9lD/84Q+8/PLLTJo0ic9+9rPMnj2bxx57jCAI2GuvvWhra+O1114D4N577wWgvb2dRYsWMW3aNP7jP/6DQw45hJUrV474fXwY2aE86qeffjrXXnvtkAjvmzdv5vzzz+f2229/27o//OEPuf322/sixQOceeaZTJ48uS8924IFC/jmN7+JEILXXnuNSy65hHw+z4wZM7j88suxbZtnn32WH/3oR5RKJSZPnswVV1xBOr3tStGuvKPeS8qWyBwEKUHRD7DxcFwTZUkCrTG0hRSizwRdS4UWCrREyADhGUgDQIRW3JYGz8dLgHRBKEK/dQ98qZCmRBYUOuFT1gFSO7gSHNfEtKCgXXwjwNIFLJ3AdE08R6AV2GUT0/LRyqQsBFgapygRpsK1FTndgmXYGF4CIzBxlMTHx00EGCUD3ywgDIHwLbzAILDLJJWD74PnQMw1MZRGWeF9CSlBQlZ2EZOVGHkDHfPpEl3YwgGt8dA4lIljUlIBRTOOoVwclUTpAlLYKFwgjuXb+IaPUhopNZBHS4ntJ9FK4BuglUTF8pRVGduI42mfdCmOLXwK0kIpjY1EBxrXUQj/vfGF+yDsqPcSs8EumaFJeiAIjCKeHZAoxAicAMoC5Qg6ZIm6wMIoKgoVEqdgo2SAwiQwNG7go02FqTwcbZKLKYSncLSNADyjCIEmph2kgqIlsHwLMzAJrCKWbxMIAyMIrVKUBqwy0o2hTY3wBVJplCkJ0HiWhyxrDNOgJAIME6yChbKgaHgIpYljIQKB0BLQYdI4KTBdjbY0KpBIqUJ/EEPgqwBtgGtIfH/n5k/bVXY5BhLtqL8zlz5+FJPqT2a3ZBikFQX2A3m82U5o/QPIRh98CPbrjw6/dPNDVOpuvnbAb7bW7DazLc/jhrV3sHrDdRwy+UuYy0uojERXD44bUiq8TCn3OKY9Ad/biBPfj3hq3rBk2xZETmGs8vAO3/qu+tNr/oeXzH25ed4V29zmrvaOwPtvR72XSmmDF84DhNJoCVqL0CZeKlzpIfxw8VW5mrIF/k6Ud1f8rXuJZNs2OUabXh/1gebvhgnzjrKHbfo+MD0bQLFYZN999+W//uu/OO6447j++uuZMGECALfeemtfMLm9996byy67DN/3+dKXvkRTUxO2bTNr1ixWrVrFn/70J5555hl+8IMfYJomM2bMYN26ddxwww38/ve/589//jPxeJyxY8fyk5/8hFRq13Uzfr+wQ4r6SSedxF133bXVY8cffzxLliwZrlyjznv9kXk3kkkDe5NCFxQibRGUShiOgbZtaAvAFvhpC8PVUPARTqhwixigwcdABxpLKigqVMJEyfAcFEhLoQ2BUIKgCCIhMF0PaYV+tyopCQI7VMITAcp38QwPXdB4tsSJS6x2H21Z6JQBbT7ClqgY6JKgXOHR5bQiSOCVJJWlDEpAPlWmOyjgmA4eRUTgMjGXIV62EY4gmyjgo6lsT4AUFKtdjGAjpcDDjNWiRFWff/KuMqDvanyQFPXRZEuZLEfhtIFRtFC2ia8Flu1CvoxMxCEbICxBkLbwugVCghVXiO4AHZP4pkQVBcISmLZCdPvoOBRrCzjtDmYWyBjoLGgp0Y4ES+B1gawEAjADjcz7CAtUSiECiSgCCpQj0LZEdAdIA7QBOqYIlI2fAzMNhpenlPRQ8QSeN3K/4/vh99vWOjvC+01RVzrgvx4/nJkT/p2qHr8j2RIg/1UmmNmvlIuyxlxRxj0q2Wf+vrzlKSi9yjcPfPsF921hW57Hta9fR2fbXzlot3MxHy2gJtnoVP8CZ+C3k23/E/H0x5FGGqVKFLOPkEgdih3fe1jybQvGqy5qioWaMNS17qWNN7Hcr+J3835ObBtzzu9K70gv71dFHcCzA2JtJrY2kNpAlzVWCpTS6ECFvp2BDv3ZDYEvTJQHIh7GPjEqwNAuQdkm8AS2raAQIOICbQvIagg0pMPxWuQUuqghJRGOhywoVMkMx/KYRGmQhQChNCoNBStLEJhYWYGdlmhfYOQF0pagNcKTBCnFZruENMtYRowurxmhyiTM8VjdCSxpYKQ03g7kRZciQHmNaOVhOg0EOgwuuSu+h73sKrLtLD/5nRX1PeL9yw75qBcKhbc95nmROeZwSThg/dPFvbsL68gM7m+aIKuQZ1bjP9aC3uSBAeaiDKrRRT1XgJjAWpzB/2ce8+gMdLkIF7wl7aHf9LFVtOfjZF8OB/uKfQQVs00a7/YIcqHf7OQzJN4NbX3tszBDe3cMq96gcrpNrAvc69uI/39JvNtfJugug2NgHrM3/iOgmnyMj6cgJrHyED9oIq+6m9jtsbG0vhX6mVsH2JT3D3hdrKel9S0+uXIu9qNdBBrk7g6VR1fiP55FvRxGHU4ckKD9o8089+I3sGNVzJr/I8zEjCg3ecSI4osCFcstglu78D0dxnQ4tpaONk3NeIn3y6YwSOORGTo8SfdLoY9Wck9BTZ2LGuPQ+GBPX3JgzOES+/5W6PBJHJtBrcnCYZX4f2hHr3ZDk+NDK8jXJSFpEKyFVNpHLWklaPPBEpgnZAg6fdRD4aTFOCCJmGjh39kJAoy5SUhIgomSjfdrjISg4eQU1gMvoheMxR+fRPvGO9x1xAeVgtdFgEHC7E8NKZp9dGbw+6AdgTYFokuFpvBAwqykO8jvFDmzbjuGDGOJyKImGBz3jmLuSazYNGSPC5GUMWLJAylk/4FpT+grHy1Ug4Fc7W1VUY9ZGao0tLhdTIzXb6V2xGjSHRRJPJailDcoeJrsSx5oSOwuGPNxA9nso94sQUkTNHmUDqyh+TEP7YFZKaj5mEXHXzzqjo2x+c4y4w8F/y+tUFIYX2lALc2il2ZBg9jdwTy5Gu+qxjC4aIOF9cka3P/XGn4XkhLjnDrka0WCf3SHdabYpE+rwfu/JpidItgzjrmsE/VaMTSS+kgCnTQQBozfN86zVhGVeo7H3vgDi6b8ELE0Rm6FABSJfQSZIy1ce9vn10Ln2Pzmzax+5SbQikzdvkw/8Nto2fDulSN2GlP2jhTziHdmh3zU99lnH+6+++4h5XfddRezZs0atlAfdpxN4F3fjrl/Cm9JR6ik7xUjeKkYKtEAAfj3dGJM6lnJL2m8uzowZyXx/9yGVWWgb28LlfSYoBhz+pR0ALPKoPEury9NVXycQD3QOah9HuykapKi4wmfcrdB0OJhnpjCv3MFdPekjysH+H9ZgXmQBRqCh3PIMSbysW7MpoCpq8dSfqtHq9bgPQO1rQ63rv0Lhxf3JfaI1x8Bu9VHrS2jXu5PhRU8W6C6YxaGGcctdfDSY99G6MgvMGIEMTTVTSbBTZ3Qswut23yMR9pJNhh4N3WGk7GYoJSK9SnpAPlVGn9igsYHVV9fUmVofEChD8uE/XRJJ8YBKYIXC6GSDmEWh6XdxAwfVdbofID4exu6rccGztP4t3dgVPYrCMGzeXABJwyEFzydR1aYyIfayMyS+N2apiUeeuEMxPX/QuTffkE14oNNW2kzPia2GBDwsjVAVwz95OtKiRzgp56wK1CqhNKj78ub97qwZDzMlKAYtHWggiy+uxbLmTqojmFmMJ3dKWQfGXX5dEYiiwqyQzMvOGYFFSLMpR6xcylbHvYmi64nNFaVIPti0DePKLyl6X5VIZMCUWUSPJtHzauk6SHVFzjW79J0POERn2TQ+jePMR83EH9phe4AY34aWnz0Y9m+NvVbZYJnc4iJPRkSNnsE93Uid+uZfxUUcrNL8HB3f53VLsGyHKLexN0tibG+iH5twNzmnwVEpUHwZA7aAuYUUyxZ8RNmTDwNc3UFwYr+RbXCCk35DbYr1Ws5t5LVK27si1za2fIyG1fdFrpTRUREvG/YIUX9a1/7Gr/4xS/42c9+xqOPPspDDz3EZZddxs9//nO+9rWvjbSMHzp0e+g3iC36ckLLyQ5qVWnouQOijBOADnSYK33AHEuOtSlsGjzCC1swcNMkOV6gXx/aviz15G1vU4ikiay30J1bnKdBDwhspZt8iEkcT+O+NvT+jA2ShGmT2Tg4LZAcb4V5Ube8x5U+qao9QjlKHXiltiHnRETsKCVZho6tpIBb72IJ1dcHxRiL/Oah9f0Cg/OyQ6ig6wHDqyPQ/xqqOMs2l+JaRbIB2DB0t2RQ/wbU+jKivn/1XTV5iLImVhGe57VrAs+AvIfIRtZNH1aaipvQ0u5LZ4qnkXk9yKy8F50WgwLKpcwEGpOCt5U0oSNM2e/Gepsc6uXiCkx7AkIM3W2yY9PxvU347sbRFVAIghoDY+PQvhQzK4hRpiVS1Hc63X4R2WpiZQTl5qGKZ261RleY6M3h7+aroVNdt1VjVgrKjQrLUtAd9gE52UFtcIecr18pYsyM9/2tVpWQk3rmMCkZztu2rLOigLFfEj8QyDeKQ49vdBG1JrrFw8gLqhIN2HYN8dXxIecWXlGY5rZZSAkhyLa/PqS8Zd1joIbKERERseuyQ4r6xIkT+eMf/0hXVxdXXnklV111FeVymVtuuYWxY8eOtIwfOkTGDOcrSoe7Z4Da7CEn2kPPtQZMvAQIU4T+UwN3Jlo84vVb5kMPTXR7KbZoxG5D21dO+GGwMgKdD1BdASSHTpyE0V8magwoKTxTYE8een+6QZPzSuTGDJ786CYPMXmor5+YYlLoXgeAaaWwtpbrLSJiB4lpG12xlQN1JoGQfYG3dKtPvHaoomPEQ9eRQQgwjQF9ztNbfbd1xiI2VlLqCK+3JYP6N+Gi28AJoagNxwq32JMjOC0wLAWOgU5E5nQfVlqLmxFigC96p0Kl5Fa35FTaQLb370gmjBieMMm5o78g6vl5HCOOLA6N+O6WXsO0J221nhAGdmwGheyj7ECYne1C1xjIjf3PpxfHymCqPM3lzlG9fsRQEoaDrgrwujV2zdBpbHycQBSCMCsLYMih74hZKQgKGqtGEGgJ8Z5xvtFFjNnKHGd3B7W63Pe3nOj0LQRQUIjKrYzfU2OolUUMU6MmbmVuM8ZCd/iIKhPlaDqLzfh+F+UJQzcsYlMlQbBtu+Faa5KVQ/tO1Zj9EEM+VhEREbsyO6SoA4wfP54f/OAHLFmyhCVLlvD973+fhoZ+35cf//jHIyLghxGvRmKdWkmwLIe1OBNGmX6pgDEvBan+n8w4MIna1LPyK8A8uhL/+Tzm6TX4zT7yqB6FNqeI4xEb3183v8ZnzHFW6IsOZF/XyMVVkBzwShyQorvJILW3QawGjHoL/44s1in7gCn7rmss3JPgn6HyIGfFoKxRM+K4NZJNe7dhVvc3ae4ObWNcTpl0LEvTK/FmDvi4aTD2jiPGDlD6J9nkxq3DK3chpcU+h1wCZuQPGDGC+JLuhgB59AB/15hAH11D5xsB1plVYT/JK+K6TGxCv0Jh14HdWabhyNDXEAAB9Qsk4ulwR9I4OIV6oYhxaAVioI/wzDhFZWLXSbyShMU1EOtv2zi8gqCpf2dHTLLDyWSvlc0eDpQV6shqOl5UCBPqF1uIJ1fCmdMJKqIJ2YeVjnJLn+83gOwK0Mm3sZu1BVoChVCZiRsOrjbI7gRFXak8MTMNRfoWpSEMIqdVCWlUv21d056E1iU8961RlVGnRBhQLDdYSYqZFaDyNJXbR/X6EUNJ+g7l8S6JaQLlamK79c9brCrIzJLoVj+0JNndwXipm5p5/ecIG6rnW+Re8ak9ymLzIwpOqAUT/H9kEeNsxN4DdrWrDIxD0qh/9SjQSYmxOEOwot9KSo2xEbMG1MkYGB+rQP2rhN1aItg9CfX98x052QYNco8YYozJm5UlPrbnubyy/jbcqTnkuP73zR4rSMwQ2xW8L1E5g/rdFvT9HUvUMWnGpwlUFLckIuL9xA5Ffd8WTj75ZO68887RaHpE2BUilr4TKSWxWhWqEGCkbYK8i0ybYJnoJg8cSVBlIMqEUaATAlXWiIREowm0gfI1tqHQuQCdtghsI7R68jVmXIcLAEoSZDVGWmCWPaQNusuDpIWLgVISK60x/DKB8PG7fIKkQcwWiLYyOmYjUja62UPYEp0SqDyUqzQd8c34IoYsxEhl02hDUKh0aQyypGybks6RcjUTu6pwSiYiKcnFiwhhEG82EIakXK/w1UrKfgErMRbMsWEKFnad6KC7GlHU921jS5mEKJJqjSFzEp208LTEirnQVcBIJtCdCpGQeGkbrwOQ4KQ1otVFJw1828TPgowJLDtAtnuIhKRY6xLrMBHdAdTY6A6FlgKVNMAxKLdqrGqBVhrLD5BdPsIWkBZh0rZujVCgUwbaktDuIQ0BDui4wAsM3C6BlREYQZbA8SlWWOhgh2KFbtOz2hWIor6/Pf/38rdoKbVyyLgTADCfKaHTAlW79XfCeN1FTbZQ48Lj963+NadMOYeDx5+8wzK82/Nwlc83njiVA+sPo2H9FHBBjQ+vX8o/h++ux0nu/47X8N2NuKXXqKg5p9/MfxQw1njojCTYo9/qTGvFk6uvojtzElfu+5VtamdXekd6eb9GfTcMQSHwSLbamIaBcA20q7Arw6jveBpL9bh0eKAsiW+Y+CUwUhK/EGBVSkzDx8uZKE9h2xqRDaBCgG1AexAu0tQYaEsguhU6ryBjQtzHyCpUTkDCIEgZ4GtkNoz6TrWkYOXRroROhVPtgA+yS2FYBhgCUdboSmiJFcmaZWxTkHU3o7VPlZiK2eFgSRO7WuDK7XdlkqKIV1yPCso4iQkoUQXsmu9hL7uKbDsr6ntExLsxcjO5LRhtc7QPOjmpoB7q6jLhoFVjENreeZCCMPKOgiQwaNOhdxU2/EAVAOoI6+HB1kx8q7b4u1r21A/b6DfCMqEifGVcgFTv66NhyoBXqRJAkGJcKHIciIdtpZGkzZ6dfhGHGJRjUO5th3AXKDu5tzEJ7I3Zc3RL88OIiJFC6zjZGqBG0/OGUwRIxwDV008UUIKeYNpl6OmPfviv59UuQU9fCwCDco2GGhme09cHeyaRybB39l6vbvoWE5U+T48ek/cKGNwRAsj0tmGF/0Y/DljELkzebQ99v3sQXQrVMNS1qRedlIjOAHoUdWnEaC01jqqMWT9PTChsI4EogE72H/PKqzHt8e/ahmGNg9JK3NJrOKOYrk1lJLIpgD36y4SQWEaSznLzqF034u0JAs2EuipayOKj6B0BhxqN99Vg0MCY6R3lgZjfX7dmwPl9KaB75lV9G+Zu/9/1mr7xv6ddgLq6JF0tfjilqeg7ChlJOH73j+EWMaqJgYaUNWCSVg/gM9RjfttQOo4Rm4bRfwcRERHvM3bY9P3dGM3V7YiIiIiIiIitU/a7cIweLcPTCFej38ETQicForN/Km/IOB2jrIB2eXksfEwjDkWFtsPpiNY+vt+IsQ0uTkII7Pg+FHOPo/XQYF4jhU5LZLcKo9MPIG5lKLit0cZERETELsOGDRtYuHDhkPK99tpr2O09/PDD/O53vwPgmmuu4ZprrtlxQbeBt7vGwoUL2bBhw6Cys88+m6effnq7r/GnP/2JP/3pTzss42gzajvqERERERERETsfP8gRN8OdOdGtQ//0d1g81wmJzPWb1lpGgmy5dVRl7PbzGHhYMoEo6r6gjYHXhJRphHx7C4CBmFY9npGmVHiBePKA0RHWEKhUmMZOjemfNsWtSpJ+iS4/T8ZKvUMDEREREe9/XnnllfdahBHnk5/85HstwjsSKeoREREREREfILQqkbBCnwmRVej4uxjP2fR4dWiICWwjScHbNKoydrlZhPaxZBxRLqJ7gmJ77gYMs+adK2+BE9+XYvYRnNjeSCP5rufvCLpCIloDGKCo20aaClGgpdwZKeoRERHbTddzHs33efidGjMjqF9kUbn/6GZsCYKAK664guXLlxMEAaeccgqf/exn8X2fSy+9lFWrVtHa2sqUKVO49tpr++q98cYb3HzzzQCMGzcOgJdeeokzzzyTpqYmTjnlFL785S8PulYul+Nb3/oWTU1NNDc3c8ABB/Rd+1e/+hWxWIw333yTvfbaiyuvvBLbtvn1r3/NLbfcQlVVFRUVFcyaNWu77u+qq65CKcXXv/51AL75zW9y2GGH8ZGPfIQLLriArq4upk2bxjPPPMNjjz3Wt2P/5S9/mccee4yrr74a3/eZMGEC//Vf/0VVVRULFy7khBNO4PHHH6dYLPKTn/yEffbZh7Vr13LppZfS2dlJLBbjO9/5DjNmzNjh32ZrRD7qERERERERHxCU9hHaI92rqHcH6MS7uaIJVFKGSn3MIGamKRW7R1XONrcVISxEWYQLBT07/r63EdNqeOfKWyCNCkxnCoXsw6Qyx4+CtD3m7xsGB/RyrAribKDV7WJPJozKdSMiIj6YdD3n0Xibi+4ZVvxOTeNtYUSC4Srrzc3NnHjiiVs9dssttwBw55134roun/vc59hnn33QWmNZFn/+859RSnHOOefw6KOPMnPmTAD22GMPzjzzTABOPfVUrrnmGtra2rj55pvJ5XIsXLiQf/u3fyOV6l+0fOSRR9h77725+uqrcV2XxYsX9+3KP//889x3333U19dz+umn8/jjj1NXV8ftt9/OnXfeiRCCM844420V9c9//vNYVv9zWrduXZ9s55xzDl/72tcoFossW7aM73//+/znf/4nixYt4qyzzuLBBx/knnvuGdRee3s7P/vZz7j++uuprKzk5ptv5sorr+RHP/oRAJlMhttuu40bbriBX/3qV1xzzTVcdNFFfPe732XGjBm88cYbnH/++TzwwAPb/Xu9E8NS1K+66iq++tWvDir74Q9/yCWXXMI3v/nN4TQdERERERERsZ3k3U58DBJm6JQusgo1dhs+9XERKup1BkmrkoIqvHudYdBZakVIB1HSqB6zd601gbcZOz5zu9uzYzModv8dt/gq9igEltMpgczpMCpYz+N0zAosXaTF7Rzx60VERHywab7P61PSe9FeWD5cRb2+vp677757UFmvj/qyZct49dVXeeqppwAoFAqsXLmSs846i0wmwx//+Efeeust1qxZQ6Hwzt+Bww47DNu2qa6upqqqiq6urkGK+nHHHcdLL73E73//e9566y06Ozv72txzzz370npPnTqVrq4uVq9ezYIFC0gmQ8uoY445BqW2HgrxuuuuY8KE/gXSs88+G4CJEycyfvx4nnnmGTZt2sSCBQuwbZsnnniCyy+/HIAjjzySiorB0bVffPFFGhsb+cxnPgOAUorKyr5ovhx22GF9cv/tb38jn8+zYsWKQfpuoVCgo6ODqqoto3TvODukqF999dV0d3fz17/+lVwu11fueR4PP/wwl1xyCfPmzRsxISMiIiIiIiLenZbSZgJMLNkTwT2nCGLvHtxV9yjqAAkzjdYBXlDGMpxRkbPTbUXKGKKo+nKoK9UNCISIv3PlrSCEgZOcRyH7D6RZg2m9ezC67UIKVFIgOgN0bZiL2jErkKpAS7ljZK8VERHxgcfv3Lrl8duVjxRBEHDhhRdy1FFHAeFOciKR4KGHHuLqq6/mM5/5DKeccgodHR3vah1tmv1qpBBiyPk33HADDzzwAKeffjoHH3wwr7/+et85juMMqSuEGKSYm6aJ625/3oNTTz2Ve+65h02bNvWZ4xuG8Y73EwQBH/nIR/jlL38JQLlcJp/P9x3vlbc3WLpSCtu2By2IbN68mUwms93yvhM7FPV99uzZZDIZpJRkMpm+fw0NDaMeATAiIiIiIiJi6zQXN4HsmQC5OvQ934a4bDouEd3hBClpxgiERc5rHzU5824HpoyFfvFWOBUJvM0YZs3QrDFaY+dXk2x9hGTrI9j5t2ArEy7DzOAk5pDrvAPfa9kmOZQq4pXfolx4kXLxFQK/823P1WmJbO9P8eWYFWhVZHOpbZuuFREREdGLmdn6AurblY8UBx54ILfccgue55HP5/nUpz7Fiy++yLJly1i0aBGnnnoqtbW1PPPMMwTB4FyvhmHg+9ueYeOJJ57gjDPO4IQTTkAIwWuvvfa2O+QABx10EI888gjZbJZyucyDDz64Q/d4zDHHsGzZMlpbW5k9ezYABx98MEuWLAHg0Ucfpbt7sHvX7NmzeeGFF1i9ejUA//u//8sVV1zxttdIp9NMnjy5T1F/4oknOOuss3ZI3ndih3bUFyxYwIIFC5g/f/52O/lHREREREREjA6tpc0I0bPyn1U9/unbtqMu8wo0JIwYnjbIuW1UxcaOipxFv4ukkUAUVF8gOd9rQhqVg0/UmnjX85huC258NwDs/JvYhbXkqw9Gb7Hjb9oT0UCu41ZiyQNxErMQYvBUJ/A7cMtv4pVeJwjaMIwapEyi8SlmH8W0dyNZcSRCDm5bpySyo3/iKoWBYcRpLzeNzEOJiIj40FC/yBrkow4grLB8NDnzzDNZu3YtJ598Mr7vc8oppzBv3jwymQwXXHAB999/P7ZtM2fOnCEp0D760Y9y0UUXUVtbu03XOuecc7j00kv57W9/SzKZZL/99mPDhg3stttuWz1/77335pxzzuETn/gEFRUVfUHrtpdYLMacOXOYNm1aX9m3vvUtLrroIm655RamT58+xPS9rq6Oyy67jK9+9asopRgzZgw//elP3/E6P/3pT7n00kv59a9/jWVZ/OIXvxjx9ORCDyPqW3NzM5dccglr167lpptu4hvf+AaXX3459fUjbHI2CrS15VBq1w94V1eXpqUl+16LsUsSPZutU1eX3uG6o9UvdsXfaleUCXZNuT4oMu1o39iWfrGrPKMbV/6M1zteYMHEM5DrfGSjRzB18Ja61gqlskhZMWBSobGeLeN+PIG24f7V1/GZvb7KrPrDd0iOd3se//n055nixNl3zWGoSomuNsi234LpTMa0+hcHrPwaYrlXKVbMhh5zfrTGLqzG8LvI1XxsiLIOoIJuysWXUH47htWAkAm0KhL4raADTGsshj0ew6xDCGPAs/Fxiy8T+B2kq89AygEJ6F2N9XIZ96hk39rH8xtu5HUxgevmXjnsZ/JeMJp9YrTZFZ9nL5FsO8auIttw5lHbw3sR9f2DjtaafD7PGWecwe9//3vq6uoAuP766zn44IPZY489eOWVV/jOd77DHXfc8R5L++4MK5jcD37wA4444ghuvPFGKioqmD59OpdccgnXXXfdSMkXERERERERsY10l1sxjdDHW2QDdGywh5tSJXIdt6KCbqRZS7rqFISwAIGKC0ROQY2BkA7NxcZRk9MPcsSMOigqqAt9BwO/BTux3wBhPeLZlymmZ1BEYAMGgBC4iSlYxTUk25eSq/lYvxLfgzQqiKcORakiym9HaxdhVGHHpiNk6m13PYQwseNzcIvPU+i6n2TmxP5zbYEWQEFDMiyLW5WUSx19/pURERER20rl/pFiPtK8/PLLnHvuuZx//vl9SjrApEmT+PrXv46UEsdx+K//+q/3UMptZ1iK+saNGzn99NO56aabsCyLCy+8kOOPH53UKBERERERERHvTN5rx+rJJS5yGlU1WFEvdP8daVQSSy2gnF9OIfsYyYqeXfO4ROQ0ugYM6dBWGh2Tbq01qCIJswJR0mhboFUOEIN2sJ38G5TMCp4NsqigGw3sYVVTLeMgBF58MjK/imTH0+SrD+5L8TYQKeNIe/x2ySeEwI7PoZh9CK/8OnZsr37ZUxLZFaCS4fQpZlaQoJkuPx/lUo+IiIh4j5k1axbLly8fUt7rtv1+Y4eCyfWyZXS+XC73jkECIiIiIiIiIkaPst9FzAwVRpFTMGBH3fda8N312PF9EULgJObglV4jCLoA0DGByIU+2KaRoNttHhUZ80EJGx/HSCJcwALfb0EaA1LaaIWdf4MVhkOdTDLNqmU3o5I3vHbaVTE8RwjKyT0QQZFY98sjKqMQEic+h0L2UbTu90vXSYnoGhhQLk1KBLSUO0f0+hEREREREcNS1I866iguuOACstksN998M+eccw7HHHPMSMkWERERERERsR34fp64WQGKcLd6gIt1ufA8ljO1L7iakA6mPYly4UWgJ0VbLvQ7to0kOXd0opl3eTlsAkw/jrYBIQi8FqTRH9zHKjWSFyaWVUmlEd5EXFp9ynq5V3kWklJ6b+zieuz86hGV07DqkDKFW/xXX5lOCkRH/4aEY1YQw6U1yqUeERERETHCDEtRP++885g/fz777rsvTz75JGeccUZfvrqIiIiIiIiInYwukrIqoajRlgAZmoNr7eGWX8d0Jg863XKm4Jb+hdYKHInIh0qobaYoeV2jImKXn8fEw3Zj6J4c6oE/OOK7zL/JBjNBbY8Zfy9xaVEt47w1MHWctCimZxLrfhmzPLJWAFZsGqXCs335d3VCIrvD6PgQKuqmLtHqjs6zioiIiIj48DIsH/W2tjZOOukkTjrppL6y+++/P9pVj4iIiIiI2MkEykdon5RViexQ6Hi/z7ZXXoNhVCFlfFAdaVQghIPvbcKKjUcUw9zrcbOCriA3KnJ2ut1I7WGXY+CECwOB34ZpTwlPUB6W20qQ3gu5ldRytUaCN712OlWJTI9PuzYTlNPTSbQ/Ra52PsrKjIishlkPWuF7G7HsCWCLMOJ7SUNc4JgVCFWITN8jIiIiIkacYe2on3vuueRy4Yc8l8txwQUX8JOf/GREBIuIiIiIiIjYdnJuOz4GCTMeRiaP9Su5bnkVhrX1nLSmNQ6vtAqkQNsCipqUlUGrYrjTPsK0lVtBmBglGQaS034Yhd4IUyLJ0kbajRiVxtaDswkE9TLJGq+TgQnCAitDOTmVVNtSpNc9IrIKITCdyZSL/T7wKimR3aHpvSEdBNBcGh1//oiIiIjtZcOGDSxcuHBI+V577bWVs0eWiy++eLvSnt1xxx1cfPHFQ8qffvppzj777JEU7X3JsBT1E088kc9//vM8+uijHH/88ViWxZIlS0ZKtoiIiIiIiIhtpKnUSICFISQiH6BjvWbvGr+8dlB+8oEY1lg8N/Tv1jGBzAUkzQQag6I/MgrvQDpKLQjhhKnZLIHyO5BGqi+fuSqsI2dWYIi3n6JU9OROb1OFQeWBU0c5PplU26PIETLdN+2JeOU30doDegPKhQsYQghMI017adOIXCsiIiIiIqKXYZm+f/azn8X3fc477zyuvvpqjjzyyJGSKyIiIiIiImI7aC5sBGkDYcR3VR0qvoHfihAmcgt/716kEe6eq6AbIx6HAiRqYnjCJOu2kRghM/JeOtwWDCOGKGpURhL4bUjZE0hOK5JeG83JvXDesRVBnZFgvddNjZMYZCAfxMZQFoJU26Pkqw4mcGqHJa+UcQyjCq+8Bju2J8TpU9Qh9FPPlVuGdY2IiIgPH96yLP4dHeg2H1FjYp5ShXVQelSvqZTisssuY9myZQghOOGEE/j85z/P008/zbXXXssNN9wAhDvjc+fO5aijjuLrX/86ra2tAJx//vkcfvjhrF27lksvvZTOzk5isRjf+c53mDFjBgCPPPIIN910E21tbZx33nmcccYZFItFLrnkElauXIkQgs997nODXKcBHn/8cS6//HIcx2HKlCmj+hzeL+yQov7DH/6w7/+11tTU1HD99dfz9NNPA3DJJZeMjHQRERERERER20RbqakvD7nIaxgb7kj77rrQ1/ptEEJgWGPw3HWYznRELiBmOHjaoLvcypjk1BGVM+u2Y8lEuKNeb+D7bYges3fttlIUBvFtyEmekjbNQYEOVQxzqw8gcOopC5Nk+5MUK2bhJScPS2bDGodbXoUd2xOdMJAb3b5jCauScr4TrTViK7ncIyIiIrbEW5bF+0MruD2BKtv88G8YEWW9ubmZE088cUj5n/70JxobG/nLX/6C67qcffbZTJs2jXg8vpVW4MEHH2T8+PFcd911vPnmm9x2220cfvjhXHTRRXz3u99lxowZvPHGG5x//vk88MADALiuy6233sqqVav4zGc+wxlnnME111xDVVUV99xzD+3t7Zx22mlMnz697zqu63LxxRfzhz/8galTp/Ltb3972M/gg8AOKeqZTGbQ32eeeeZIyBIRERERERGxg3S6zRgyBnpwajbPXYdhvb2iDmCYtfjuenRsb2RbgCEEWto0FTex5wjLWfA6qTUSyLImsAWq0Io060JZixsoGqmtBpEbiqBWxtngdVHlxIfUCOxqihX7Esv9C9Nro1g5B3rM67cX0x5Hsfvvoc9+XCDKGgLAgJiZJkYrOb9I2krsUPsREREfLvw7OvqU9D5cjX9Hx4go6vX19dx9992Dyvbaay+efvppTj75ZAzDIB6Pc/zxx7Ns2bKt+rQD7Lfffvz85z+nqamJj33sY5x//vnk83lWrFjBN7/5zb7zCoUCHR0dABx++OEIIdhzzz37yp566ikuu+wyAKqrqzn88MNZvnw5qVS4KLty5Urq6+uZOjVcGD755JP57//+72E/h/c7O6Sof+lLXwLgG9/4BldcccWIChQRERERERGx/XSX2zCNeJiazSQMDqc1gbcJOz7zHetKs5ZSbhmkJCLvh2UyRlupccTldL1uHCcNLmD1RHx3wsmZ7baQt2uwt6iTKRSYvWkjtfkcvpRsqMzwakMD2nZoVnm6VYlKGRtyLW0mKVbMwcmvIt3yd/JVB6KsyiHnvRtSJhAyQeA1Ytrjw5zz3QpdJXHMClIioMXtjBT1iIiIbUK3+dtVPlIoNThAqNaaIAgQQvSloQTwvDAmx+TJk7nvvvtYunQp//jHP/jtb3/Lrbfeim3bgxYCNm/e3LeRaxjhguhAC6OBbQ+8bi9CiEGy9bbxYWdYweRWrlw55MFvC9deey2LFy9m8eLFfYr+k08+yfHHH89RRx3FL37xi75zX331VU499VSOPvpovv3tb+P74Qu8adMmzjrrLI455hi++MUvks/nh3MrERERERER72sKXhu2kUIUFDoWft5V0AHCGpKWbUukrEDrMoGVR5TCFG2mjNNeahpxOf0gT4wU2hFoFCrIImUKrX1Sfg5hVvWfrGBCcztHv/YviobFP8dP5F/1DSRdlxNWvMzMxkZqRYL17xT0TpqUU9NxnbGkWh/Byb4GOxDN3rDG4JZ7gu4lBCIbtuGYFTi4tLid291mRETEhxNRs/W90rcrHykOPPBA7rrrLoIgoFgssmTJEubNm0dVVRXr16+nXC7T2dnJc889B8CNN97INddcw6JFi/je975He3s7WmsmT57cp6g/8cQTnHXWWe963dtuuw2A9vZ2HnroIebOndt3fK+99qKtrY3XXnsNgHvvvXc0bv99x7DehtraWhYvXszs2bNJJvuD1LyTj/qTTz7J448/zp133okQgnPPPZd77rmHK6+8khtuuIGxY8fyhS98gUcffZQFCxZw4YUX8sMf/pA5c+bwrW99i1tuuYVPfepTfP/73+dTn/oUixcv5n/+53/43//9Xy688MLh3E5ERERERMT7lrLfTaWzGyKn6Y3E5nuNGGbNu9YVQmCYNfj+ZmxrApQ0lpEi67aOqIxKK1AFkkEKbYMKOpEyiRAG5eJG8tLBMsL9dB1ApjnPIZve4rm6iXQacUQAMg7d8TjrMlXss7mR8V2d/H5SDTnT/f/Z++84Se76wP9/fT6fqurcPT15dzZoJa1ytCImiGCDQAEsm2DOAXxfDDZYZ/uO3wlbGMEZGxsZc5wJ9tfha+uAM9kyCIEJgiMqJyStpM07uXs6pwqfz++Pmpmd2dmVNmpntZ+nHi1pqiu8q7qrut71SWTlvmXxiztIlByl4/aRaD2F29lNp3gZ0SF0lOc4o/jdnwEvwqQkohEBDgknhzJdSv7R6WXesqznP+eG4rI26gB4AueG4oEXOgre+MY3smPHDl772tcSBAHXX3/9YmfgV111Fddccw1jY2NccsklALzuda/jD//wD7nuuutwHId3vetd5PN5PvzhD3PLLbfw93//97iuy1//9V8/Yx8d73znO7nlllu47rrriKKId7zjHZx77rls2bIFANd1+chHPsK73/1uHMdZ7JjuZHdEifrFF1/MxRdffEjLDA0NcdNNN+F58Y/paaedxo4dO9i4cSPr168H4LrrruPOO+/k9NNPp9vtctFFFwFwww038LGPfYzXv/713HPPPXz84x9fnP5rv/ZrNlG3LMuyTlo6apFx84h2tFiiHvrjSHVwN35KFQmDcUxqA6Kl8VSGTrDrqMbYCDskREQiyEBCxD2+z3ckF/WmaDt7q46bhuGlM0+zZXCERjaFAqIu6E6crHc8j3vXb+D00iy/t2UPX9wEFDc84/aNStLNnYfqTZMpfR8/fQrd3Dkgn/12SDoDRFEVrTuItIeciattek4OobvMdOcO+7hYlnVyWWiHfix6fV+3bh3f+c53VkxfSIoPVKD6gQ98YL/T/+7v/m7FtNNOO22xh/ilPvShD+13m9lslltvvXXF/DfccAM33HADAJdddpkd5nsfR5SoL7RVX6rdbu9nzr02b97bLc2OHTu44447+PVf/3WGhoYWpw8PDzM9Pc3MzMyy6UNDQ0xPT1OpVMhmsziOs2y6ZVmWZZ20dJesW0Q0DSYbl2yE4RSJ1AUHtbh0Bgh72yAhEC1DOl+g0Tm646hXgwYeIQk/hfEEUTiHkPGNqRfM0XGKSCDqwDmVaXzHYTKbX1xeJeP3TADCBSMETw0NU08mecPWXTw6BhOj6+GZel9fKF33+vFa28jNfpN232VEiaEDLwMIIec73duDSp+ObMZtOKVQSJlkpjN+xMfHsqyTh/uC3DEfjs06sR1Rov6tb32Lj33sY7TbbYwxaK2pVqs88MADz7rsU089xdvf/nb++3//7ziOw/bt25e9v2+nBgcz/VAMDDz70C+rxdCQPYkPxB6bo+tYnher8bNajTHB6ozrZI7pYM+L43mM/LADaEYKg8huF7XWg4ShElVJpQYRB9HbuesMUW39FJVRuAEM5Aep19qHvV/7W25naQLHBKSDDF7GoWXm8BIFPFeRDhr42VORUmDaERdWxnlgbD1CLf99V6k4WfdcFnvaKRXyTCckl05McEq9ziOnn0k3sbJzueVSaO9c6JbIVn5CmD2VsHg+iAN336MTwxg9TqJ4DlHUI+N6iIQk4eWphjPPeqxW43l0OFbLPdRqPp42tsOzmmOzrOfaESXqf/mXf8nv//7v89nPfpa3ve1tfOtb31rWVv1A7rvvPm688Ub+6I/+iGuuuYa7776bUmlvO7iZmRmGh4cZGRlZNn12dpbh4WH6+/tpNptEUYRSanH6oSiXm2h96B3hPdeGhnLMzjaOdxirkj02+3ckP3LH6rxYjZ/VaowJVmdcz5eYDvfcOJjz4ngfo5n2TkJcoq7BaUZ0RUTYnELKPH5ggCU9CesQgcasaM+tQHh0VRm30odan8QY2DM5TcI5tN7MD3Q8ts7sATTUJb1+TeCXEWot9dYMeaEwePgtw5mNWWrJFHU3Cfvp9004ELRALr3l8DL8+9gAL66HvPiBe9g+soato2P47gHarS9wiviFi0k0t+B0S7T6fx6ku99ZjRig136IRDrASQvaUx3MgMKVOWrt6Wf8Dhzv78j+HMtz4lhbjcdzgY3t8KyW2OzDAmu1OKJe31OpFK95zWu46KKLSCQS3HLLLfzkJz95xmUmJyd55zvfya233so111wDwIUXXsj27dvZuXMnURTx1a9+lZe85CWMjY2RSCQWex78yle+wkte8hJc1+XSSy/ljjvuWDbdsizLsk5Gk+1xtHARIXFi60IYTKNU37L5vMbTnLL9djbs+BrJ2iOwTw015RQJ1Syirck4SQLhHNUO5UrdKYRIIDpgXEEUVZEyR9CdojX/MEB34NzaFLsKB25bL935jtuXjWQkyKkUP+pL8uCm08m3W/zig/dw5ZZHOWV6gr5mAzcIVuwzgJEe3dx5GOGSLd2F0P7+t6uKaF1H6858h3LxutJOHj+oHtZIOJZlWZa1P0dUou55Hr7vs2HDBh5//HGuuOIKfH//P24L/uEf/oFer7ess4E3velNfOhDH+L3fu/36PV6XHXVVVx99dUA3Hrrrdx88820Wi3OOeccfuM3fgOA973vfdx000188pOfZM2aNXzkIx85kl2xLMuyrBPWdGccIZOIjsGkBCCIgmmk07c4T6I1zs8/8RAJHZcwn1J5mp9uTtLN7+07Rqo+QjONaJ9KSqbwjaLWm2Ew/cydtB2sue4MQqWQXUPgNhE4COmhgjKBSmMCGOk2UcYwl3zmUnzhxVXgVRaYrx2flR71sMuso/HXrGPH8Br6m3VGK2VOmxonGfioKCKSikhJfMel7SWoZnJM9g9Sy5yG195OpvwDmoNXwT5NBoSQKDUQj6ee2rDY83vK7SPBHpphx46lblmWZR0VR5Sov+IVr+C3f/u3+dCHPsSb3vQm7rvvvsXB7g/k5ptvPmBvg7fffvuKaWedddbiuHtLjY2N7be3QcuyLMs62ZS7U0iZRDQ1JrHQkdz03o7kjObip+5GqzQPbjwVAZy5ZzsXbH+Ye87bgFHxeG5S9cUdyklQPhiZYLo9zmnFS49KnFV/FkekQEPEHFLFHcVlgjr11EZMT3BGc4aJfP6ZO4QDpAItQfsgEwtTBf0qzVTYJOsmQClmC0Vml5bOG4PSEY7WOFFE0vfJdtpcueVRWskUD51yGr7YQ7p6H+3i5Su36/QT+ON4qY2LPb8nnBwZETHrV22iblmWZR0VR1T1/R3veAd/9md/xujoKJ/4xCe49NJL+djHPna0YrMsy7Is6yDUejO4Mo3oGPAExkTosIJUBQCGZ3/GUEvz9NpTMELQNZotazcw2hQMzj68uB6lioThLFEKRNsgZZLZ7sRRi7PlV/BEGpMQRFEFKbMEukdG9xBODnqaU5pzTGYLB7U+4YHxWdaOPSlcksJhKjpAW1chiJRDz/VoJVOU8wV2jqzhvtPOpJrO8OLHHqbf70f5Jdz2yuHplDNIGOzBpCWyGW844eRJmB6zfvUQj4hlWZZl7d8RJeoQd/z2ve99j9nZWU455RQeffTRoxGXZVmWZVkHqdkr4zgZaGlMMh72TKosQjhgDOfuepo9A/10pWCrP8f2sMJjUZWt/UXO2bMLTFwyLGQCIRx0qoloaRyZZq47ddTi7IY1kiYzPzRbGaGydHuztGUCQsW6bo2ml6Dr7L8zt30JEddO193l04sqRVP71HXv4IMTgqn+QX62/hQu2vE0feEoqfqDiGj5OpTTTxTMYtwIIsA3JJ0C0nSY7VUPfnuWZVnHSKvV4v3vfz+/+Iu/yPXXX8+b3/xmfvzjHwPwr//6r3z1q18F4KabbuJLX/rS8QzVegZHVPX9D/7gD7j33nuX9bguhOCqq6464sAsy7Isyzo43bBKOnkqoqXRQ4oonF0sTe+r7SLbi3h83Sg7gyop6VCQSbom5MdZza+Voa/yNNX+M4G4+nvglUm0+kj0Zan3Zo5anGFYJ6kHISHQ4RxOYhO6PUVXpdG+YFOrzEz60HpclvNt1QlZvKuRSAZVhvGwQcJxSMhnH55uQSuV5vF1p3Dxjh38+NR+gsajdPouWXxfCBepskRhCZMuIhoG1Z9AAFOdyUOK3bIs62gzxvCOd7yDs88+m6997Wt4nsdjjz3Gb//2b/NXf/VXPPDAA1x++cpmPdbqc0SJ+qOPPsq3v/1tPO9Zhj6xLMuyLOuYCcMGGTePaBtISKJgZjFRP218C7O5DLN0kUJQkElAkBQuOSfFzlyCUyee5v7FRL1A6M6QbJ2GN5Cj09t6VGI0xoBuk/WzcYl6VMGTF+AFjxOqNKZjWN+s8pOxUw553cKFqDvfsdy8hHAoyhQ7wgqb3H68ZxgffV/NVJpdg8NcOF7lnrUVepnNaDe/+L5S/YTBJCY1gGhqxICDUllm2nsOOXbLsk5O4d3jhLdvwVS6iGIS5/ozcS4fO+L13n333UxMTPAv//IviPm+Ps455xx+53d+h7e85S3kcjl+8pOfMDQ0BMBdd93FZz7zGcrlMu94xzt44xvfSKvV4gMf+ABPPfUUURTxtre9jWuvvZYvfelLfPnLX6ZarfKyl72MzZs38/d///copVi3bh0f/vCHSSQSzxSedQiOqOr7qaeeShiGzz6jZVmWZVnHjNEdMm4R0TWYBERhnKiLKGKsUmeyb4hS1KFPpljsIh3IyQQP53Osq3WQYQcApQpEchbamrRbwA/rRyXGRtgmQUiyl8N4PkZ3ETJNNmxiRJ7hboOu49B1D67a+1Jyvthh35ruWemRkwm2B3N0dXBI65wqDmCQrG3lSDYe22d7/YT+BCYp5nt+B8/JU+1NH3LslmWdfMK7xwk+8wimErfbMZUuwWceIbx7/IjX/cgjj3DeeectJukLLrvsMlKpFC9/+cu58cYbefGLXwyA7/t8/vOf52//9m/567/+awA++clPcu655/KlL32JT3/603zqU59i9+7dAExPT/PlL3+ZP/zDP+SjH/0o//iP/8iXvvQlNm3axLZt2444fmuvIypRf+Mb38j111/PxRdfjOPsXdWf//mfH3FglmVZlmU9u1D7SEIKUR/G1RgBUVjCS1/Emtmn6LqSCc8lqSM8sW8VcIFJ5umoMqOzTzKx5kKk6qNnfoZsaXJekYruYIxGHEKJ9P7MBXU8QhKdJOFAHRnmCXSXnPap6AwbO3sopTMr9w9NlZAOEQJIosjj4O1T1iAT823VXZYVQ+RlEoVke1hjUKXpV2kOqiK8EGwfXcu5u7YxnarTzTfRTlxkL51+gt6TmLRY7Pk95RRo947emPOWZT1/hbdvgUAvnxhowtu3HHGpuhCCKIpWTA+C/T+sfMUrXoEQgs2bN1OpVAD40Y9+RLfb5Ytf/CIA7Xabp556CohL5xfyvpe97GX86q/+Kq94xSt41atexdlnn31EsVvLHVGi/pd/+Ze86EUvYsOGozO+qmVZlmVZh2auO02AQ7rrYpIBRrcBgxBJ1s/spJxJU47a9KvUfpfPSpedGY91s7uZWHMhQmYxpoM2PbJk0UjaQY2MV9zv8ger3KujjE+ilaI3tBupcnT8Em2ZhECxvlVhy+DIsmU6RJTwSaEoEpe099BM0yOHooC3WD9AiPkq8G1QGZZWHCAjPRJCMac7zEVtCjJFVrqkhIN6hgcQ7USSSjbP+kZAJ/sUnb6LAZAyj9YddKKHasbLZ9wCUetpIqOfcZ2WZVkLJekHO/1QXHjhhdx2220EQYC7pIbSgw8+yPnnn79ifqXiR5dLS+C11nz4wx/m3HPPBaBUKlEoFPj3f/93ksnk4nw333wzTzzxBN/73vd497vfzbve9S5e+9rXHvE+WLEj+iVxHIdbbrmF3/qt31r2sizLsizruTHR2okWHrIDJMV8R3J9CGCk1mAy14fBkBQHejYv2JUrMlrvgg4RQsTjqefmyPU8fBxqvdkjjnO6OwVInI5DyFz8QMAv0VMpkl2fdBBQTex9mOCjKeFTwCWDQiFQCNIo+nFpo5nFXzoyG9KJE/Z9e4EHcIRiWGUZUhlCIqbCJluCMo/5szzlz7EzqDIdtujoALNkuT0DQ2yca5Ns7gQdN/cTQsTt1OU0IgQCQ8otkhYh1eAAw8JZlmXNE8XkIU0/FJdeeimnn346f/Znf7ZYiv7oo4/yyU9+kt/93d9FKbXfEvelrrzySj772c8C8Qhf119/PZOTyzvLDMOQV77ylRSLRd7+9rfz2te+lscff/yI47f2OqIS9Ysuuoi77rqLl770pUcpHMuyLMuyDsV0Zxwhk4hWFI9PHpaQKs9gZRxfwR7XIStgsYg5BO3Hf8oEIEEns/SUYKi8ldmhM1EqT5CcI9E5hUi4lDrjrM2dcURxlrqTCJkCF7QuI50BvGArgUhzSq9KOZ2Js2ziYdFn6ZFB4RrBmkaB4VYOZSQNr8tkroZM9KgRMEuXIZKLJQ8yEfcCr3vz+7cPTzh4wpkvqjBoYwjRBEbjE7ErrKGQjDk5UtKl6yWoZnKsbfq0OrvwM6fG21FFwmAKnV6LaBqSqTwpfKZ7FQa8gxsH3rKsk5Nz/ZkEn3lkefV3V+Jcf+ZRWf/f/M3f8Nd//ddce+21KKUoFAp8+MMf5oorrqBUKvGRj3yEXO7AI2y8613v4pZbbuHaa68liiLe/e53s2HDBu699969++A43Hjjjbz1rW8lmUySz+f5i7/4i6MSvxU7okT9Jz/5CV/4whdwXRfXdTHGIITg/vvvP1rxWZZlWZb1DErdSZRMIVoGkxWE8x3Jjc1up5pOUDc+ozJuW617YPy4ijgGolZcTdyTiqmUy8jsLmaHzkSoAqFXItnSCC/JZGc3FxxhnHPdKRRpdCIe5115G8mETepyhHXtKcqpve3TGwQoBAN+iiv2bEIgKKea+Cqk2E2zeW6YarLDz4Yn2JWsr0zWU6A7oA3IZyygEkgh8JB4AjJAUSZp6YAdYY0RlaFfpZjoH+SM8Z3s7N+6mKgrp0gYTEDqEkRDk8jlcU03Hkv90EaYsyzrJLPQDv1Y9PoOkEwmec973sN73vOeFe9dc801XHPNNQBcffXVy97bsmULANlslltvvXXFsjfccAM33HDD4t/XXnst11577VGJ2VrpiBL1W2+9dbFrf8uyLMuynnvV7gyOykBLYwZdol4JJzXGaPUhnhrII4mrfRPGSbpMLhZco8V8sp4VTGSzXFyq8Chxz+++ehyaBjWYptQ+8p6I671ZUiaNSRh0VMMXHkXtU40yrG3X+WlxIwARhhoh6/0ML9l5BuPZKpO56rI259v7yow087xw1+mcmq3y4+FdlByfofk264L5ZL0HURNUkkO44xGLbdqno2Y8KZUmVA7DjRbtYg3tFpBOP1H7QXQq7vndVRmEiZjq2J7fLct6ds7lY0ctMbeen46ojfpNN93E2NjYipdlWZZlWc+Npj9Lwski2wadMOiwQjp0SQYhu5IZ0tID4urgwtubpMN8m24ZV4WvpYukQkOqXUWqAhFz0IrwVJZKb+qI42wHcyR0hshrImQSP6jQlgn6ej1CKem4cZw1ArLa4YW7T2M8V2EyX12WpAMYYZjK1bhvzU5crbhh2wVsnhukbPa2LxeASoBw4n2PGnHb9YVaBbo3/+rOl753wQSw0OjdEYoRlWU6atHWAdN9/aytO3jtXfH6RQowRMkWoqHn2/ZnmGztPOJjZVmWZVlHlKiPjY1x//33o7V+9pkty7IsyzrqekGFtIzbRGtRQcg0o3Pj1BOSitCkhRsnoALkfsYlE16cuHrSpZyQDJaeRkgPITzoVUk4eVr+kQ875gc10n6OKFFFyjymN0dXphjr1phLpYG4NL1JxGUz6+g6AZPZ2jOuM5KabcUSjw6Nc2ZtiOu3nkuxkkfqvbc30gGVivcTA0Rgwvi/hOCGCi9yEFpg/LiGge7E8zpCMSBT7A5rTOcLDLR8Mo2dMN/UTzr9RO4Mohk/HnCdHOXukdc+sCzLsqwjqvq+detW3vzmN+M4Dp7n2TbqlmVZlvUci6ImOd2HSQqisIxSBUbnxqmmkoQYPKGIuvOJ6n4IAUKB9gWz6SSjc1Ps3gDSKRDKMmmnj3r70SOKURuN0S1ynRxhuoJUWZxelZA0azs1Kpk4UW8SMtrNsKE+wP2jO1eUpB9I2/P52fAEhW6KjbV+BmfWU0m3qCbbdDyfSGqkESRCh5Tvke0lSQceycAlkgaDwdGSluczla+yI1vGb0aoDKSlR8v4TJsec9k8o80W1WCOyBtAqT4CM0Xa3wBhPJb6ZG/miI6VZVmWZcERJuqf/vSnj1YclmVZlmUdImMMwnQp9IqYpCAMZhEyx1B9kvtGi6SEA2Gc7e6vNH2BcOJq4DOZHJvHp8EYpMoTpMvko81UdQtjNOIwxwevhy0SBGTaOaLsHqTKkY320I42MtKZYuvgIIY4Ub9qZjO783OE6tBr69WSHWrJDo9FhoFuhiE/TX87gzQCLQyhivBVyFy6yYQb0HMCzPzDAGEg7ScYbOW4qnImjw1OME4VlYGiTDEZNZnKF9hYarO1s4fIG0A6/YS9HZiUQDQ0Ga9I0Nm+WHBhWZZlWYfriKu+P/LII3zuc5+jv7+fBx54wLZRtyzLsqznSDOooJHkO2lMUhKFsxSCBMJo9ngeSeGiF3p5fwZCAgK6TlyFPtucRaoCYaJEoZdBo2gFlcOOs+zXSZiAZCtLZOYIRZJc1CPfUfiOQ89x6RIx3M5S6CWZepYq78/GU4KZTJMH+yZ5YnCKp4em2TY4w65imal8jWq6Tdfdm6QDGAGtRI+d/SWeHJrkzPIom+vDRG1wUGSkx5NJQTqIKNR2zT/MKBKGM+gUiKYm6w6QMF0aYfuI4rcsy7KsI0rU/+7v/o7Pfvaz3HnnnXS7Xf7mb/6Gj3/840crNsuyLMuynsF4aweh8FAtAQmIwhJr6i0aCUFdGFI4mCguMX82wgERKMpJxUBpK0r1ETplsm2PHg6V7uRhxznTLSMJcEQSHVXoRSE96bKm12IuuVDtPeLnSmPsyVWXJdCHK4UkhaJKQI2QaLGbuWfX9nweH5lgXaPIptogugd9IkHV9JjJFRhpRqigipRJhHCJUg1EIyLhFkiLeCx1y7IsyzoSR5Sof+1rX+P//X//X1KpFMVikc997nN89atfPVqxWZZlWZb1DMabOxAyhWhqdCLE6A4j1TKVZAKFRIYSIZc39T5QhXLpgImglEwwWi0hZBYtWritCC08Jo6gN/PJ9m4giUn2AIj8Ol2RZqxTo5JKE2Hwei5DnSwzmfphb2dfLpIcLgYo4VMjpIc+qJQ9VBFPDk1xWm2Y/noWqSUZ6bEtk2S0IXC7EwAoVSRIzCDqmqRTwNVdJrtH3vmeZVmWdXI7okR9oRO5Bfl8Hsc5ombvlmVZlmUdpOnuHqRMIzua0C0jZZ7BRo3pVIqknO/tff5nuUPEOB1202GcDi2iFesTEmZTfYy0eggEUhSIOiWkTDM5PyzZ4Zhpj6NIz3ckV8ANqwQ6xUinQSWZok3E+ZURprI1tDz4ku+DIYlL1/PE9f/rBMzQYw6fOiEdInz0fh9g+E7I9v4ZLiqtR7QkeZHgyYQhGWry9d3x+lUfoYp7fndkEiEE40dh3HnLsizr5HZEifqaNWu46667EELg+z6f/OQnbRt1y7Isy3qOVDoTeDIDIUSUyIdphNFMeC5JHIyOO5FrElLCJ4vDMB5ZXObwaRAuX6EDPZMjlJCrTyNVHh3N4jgZZtu7DzvOud4krk4TelWkzJEJG+S6Hj2l6DkuPa05vTbIVPbolabvSwLJ+YQ9j4uHxAAdNLX55L2MT3OfavL1VIdqqs3Z5TU4kcKTiolslpFaGxG24w7l9CTCN4gQlMoy2dp+zPbDsizLOjkcUaL+3ve+l3/6p39iy5YtXHjhhXz/+9/nT/7kT45WbJZlWZZlPYNGb5YkOUxKEIYzrGkZmp6gKiWJ0EVI8NFUCCjOJ6cAHoIiLlUCuktK1qUCjKSSkBTndiDdAoE3S1IWqPWmDj9Of5ZUmCNSJSKZphB1GO5oKqk0IYaxepFGokvPCZ99ZUeBJK4Wn0SSQZHHpYhLAkWAoTRf2r5QSX68b46RToFMLUVGJNiadhluS9zeJEoViaISUdogmgbPKTBnx1K3LMuyjtAR1VMfGRnhn//5n+l0OkRRRDabPVpxWZZlWZb1LHphhUGzEZMSRMEsa2odqkkXFwcRCHDittkZFGq+pbrUAi9y6DoBeeFQxmcNSeT8+0JCOZlkTbXEtnWnEiYeJ6PPpxVuPew4fX+OTHcdYWIngR7EFy5rO00qmTRtIi6vDjN9FNumHy4XgYsiiaRDRJmIPjyQmvHCHOeU19DIt9mRcnnJdESmuRs/cxpCJInSNWQjSTpVZKJrx1K3LMuyjswRJeqtVouPf/zj/OAHP0Apxctf/nLe/va3L2u3blmWZVnWsaGjJn1hPzohiKIyQ03DQ0MZEiLu7b2ViEuoUyiEEZwzs4ZN1UG00GhheHhknMfzszQIKcy34UZBKZHjrMo0ShboOhXyvX7qNA5rfHBtNOgm+WaByJsjinJ0SDLSbbB9cADhKwq9FE8OHn6J/dEmEWRw6KGp4FPEo5RpMFrvY6CaozrQZSqdZrQyR2U4QjlFgtQsyfog+cIgUXM3kdGowxx33rIsy7KO6Bfk5ptvZnp6mve85z28+93vZuvWrfzpn/7p0YrNsizLsqwDCHUPYXz6m/3oZJ1MoFCRZtzzSBiFUVAjIIeDMHDl7k0MdDLct2Ynd4/tYMvANBdMj3Fhec2y4cuEAx36CCVkWnUEHsWOQSNoB9VDjrMSNEjikw49BBIVNEj1EkRS0HQcNtUGKGWaR2VItqMtgSSNooJPJAyThQqbKyNkjRtXf29JnN5MPJ66M4WoazJeP2l8Sn71eIdvWZZlncCOqET9scce4xvf+Mbi31deeSXXXHPNEQdlWZZlWdYzm2ztIsAlVXfo5cqMVpM0vR4VpRj1XVpOiIvAQXDe9Bhe5PDY0MRiQtxIdHlkeJwLp9dRSnSYzdbpx4srwAtJNSHpL29nrtiH16njp1zK3XEyXvHQ4uyUcY2P8kKkKpDt1Rnu5JhLpmmbiDNqg2zrX71VxV0kCaBCgEg3WVstMlTPszXd4EWlGon2OL38Kfj+z5ANTdItkqTLRLfMSKL/eIdvWZZlnaCOqER9cHCQubm5xb/b7TbF4qH9gFuWZVmWdeh2NreCTKHahkDOsrYZUk86KFyIBE0ZkMZhoJVlXb3IloGpFaXWPSdky8A0L5o8hSBisfM0oaCcSLCmOot0+jDhLJFMsrvx9CHHOd7eg0BiEnW0yFCIuoy0fSrJNKmuhzKChtc7GofkmEkikUBThEzla5xaHsZ1k8wlPEYqk0hZIIrKGBPhBRmEidjTsh3KWZZlWYfviErUBwcH+eVf/mWuvvpqlFJ8+9vfZnBwcLH6+80333xUgrQsy7Isa7nJ1nakSIOCKJphpNHj8YEECRy6KkIIgWcEF0+tZ1txllDtb6RwqCU7VJJtLp9dx8OjExRwEA6UvBxnVGeQbp5Q7ULJDHua2w45zon2dqTJELolQiPxcRjtNtk+0M/GubjaO6uw2vu+0jjUCZjI1LisdgqDnRzb0mXWNVrs0F2kyuPn51DNDErl2NV8Cnjp8Q7bsizLOkEdUaK+efNmNm/evPi3rfZuWZZlWc+NmfZuXLLolMTpzpAMIya8BIlI0VABaSQbqwOEMqKcaj3junYWylwyuZFH+6cwHggBbVkgkjPkuoJSokTCnMpsZ+dhxLkLN8oSqRI6yuEEKbToMOcorqgP8MTg5OEegueUANIoqtJnJlPntNIw9w3NcNFEE6cTD9MWpqZx6mN4qT5m2od+rCzLsixrwREl6u9617sOa7lms8mb3vQmPvWpT7Fu3Tq+9KUv8fd///copbjiiiu46aabcByHhx9+mPe///34vs/atWv50z/9U4aGhpiYmOCaa65hw4YNQFyy/w//8A9HsiuWZVmWdUKp9yZJRf3oZJeRRpeGB3PKoc93CLweRe1wVmmULQNTz1piHSrNRK7Kz82s4+F1u+Ne4pVDLSHor0xQygnyUZp679Crc9e6U4z2skRyEhUqBtqSuVSaTCeFFpq25x/mEXjuuUh8NDtzVS6f3Mh9oxkCKRmo7qY1fCqBN0mqpsnk+5nqThzvcC3LsqwT2BEl6tddd91+p//7v//7AZd56KGHuPnmm9mxYwcA27Zt46Mf/Shf+MIXGB4e5pZbbuG2227jLW95CzfeeCMf+tCHuPLKK7njjjt473vfy6c+9SkeeeQRrrvuOj7wgQ8cSfiWZVmWdcLqBXP0904nSJVYW3VoJEIQHm0ZkhSK9dV+2k5AI3Fw7b8ncjUumdjIw70JSADz7dTHKjNszRfp7wl2irlnXc++ukGZQncQmc2S7TUYbXvMZbKsrxcpp5uHvL7jLYWi4nSpJTpsro6yIz3FaK3KnjUFeuJJRC2icPowUXOHHaLNsizLOmxH9Ovx3ve+d/F10003cemll/Ka17zmGZf53Oc+x/ve9z6Gh4cB2LJlCxdddNHi3y972cv41re+RaVSodvtcuWVVy5O/8EPfoDv+zzyyCM8+eST3HDDDfzGb/wGW7ZsOZLdsCzLsqwTijEGogYDjQFCt8RoM6KUTJAwDm0nImUkZ5RHGM9XDnqdkdRM5WqcNTdKiEEqKLk5RlsdlCyQ63UwJqId1A56nb0oQEUNclqiZZZs1GO002Y8meS0+gClEzBRlwhcFLtyFTZVhtiTTjPYEnhBD00X02uTdYbI0GOmd/DH37Isy7KWOqJE/fLLL198vfCFL+RP/uRPuOuuu55xmQ9+8INceumli3+fddZZPPTQQ0xOThJFEXfeeSelUolisUg6neYHP/gBAF/72tcIgoBKpUIikeB1r3sdX/rSl/jP//k/8853vhPfP3GqzlmWZVnWkWgEFTTQX8kjo3EyfsRkMgFaohCMNQtooaklOoe03slsjdPq/YRh/HfXyeMr6OspHD1HF4/Z9o6DXt9Ur0wKH8frojW4QRJfKVSQJ5ARHTc4pPhWiySC8WQDpSUuI6QCQ665B6X68XPTpLt9JEyXXe2p4x2qZVmWdYI6oqrv+6pUKszMHNpYqJs2beK//tf/yu/8zu+QTCa5+uqreeSRRxBC8LGPfYy/+Iu/4NZbb+W1r30tfX19uK7L7/3e7y0uf9VVV/FXf/VXbNu2jbPOOuugtzswkD2kOI+noaHc8Q5h1bLH5ug6lufFavysVmNMsDrjOpljOtjz4rk8Rlv33EckkrjGIV+fopkQlKWD1oakkJw+N8xUroaUh9adeuRoSukmGyoDzI7MYVyPWkIw2KoxUyhhRI6paDuXDr3oWdc1NJTjwd2PoogwbgUiQbElmUunWFcrUs40keIE6O59PySCBIrxbJUzKmsZTz/Nmsok5VM2E2anKfTORUiPqWgnQ0NXLi63Gs+jw7Fa7qFW8/G0sR2e1RybZT3Xjmob9YmJCd74xjce0jp6vR4XXHABX/nKVwD45je/yfr16+PgHIfbbrsNgGq1yic+8Qn6+vq47bbbuPbaaxfHbDfG4DiHtivlchOtzSEtczwMDeWYnW0c7zBWJXts9u9IfuSO1XmxGj+r1RgTrM64ni8xHe65cTDnxXN9jB4ZfwghsoTZLqP1Lg1PEIkEgTKs7aXJ91JsGZiOq8gfoslsjbNn17B7sISnJLOJJGNzJR7PJ8iaJE9MPcIV/c+8rwvH4+GJB5FRmlDM4fkpRjuG3dkkF8z187PhcfShxmdAopFGA4JIqBVjwz9XEgi2ZSq8fPJ0frwmz9n1EkIn8Z1x/JkezkAfW6YfYXYobhK4Ws+jw7Ea7qFW4/FcYGM7PKslNvuwwFotDjtRN8Zw00034boujUaDJ554gl/4hV/gzDPPPKT1tNttfvM3f5Ovfe1reJ7Hbbfdxhve8AYA/uiP/ohbbrmFCy64gH/8x3/k6quvRkrJPffcQ7fb5W1vext33303WmtOPfXUw90Vy7IsyzqhTLa24ekcYXqWsVnJ1oKHMArPCE6rDDKdqWHE4SVSbc+n4wYM1PM0C03KXo5z52ZIRIMUQ8Fse/tBr2u8tZVckAIEg0GToY7gyewQvgoPvtq7gUzUIR+0SEU9hDEYKcEYpNEE0sGXLm0nRVt5BMJ9TsZllwhwDOVki2y4kb7OLJlehzazUPVJj/YzeRjD2VmWZVkWHGai/vTTT/Pbv/3bvPe97+WSSy7hl37plxBC8MUvfpH/8T/+By984QsPel3FYpF3vetdvPGNbyQMQ6699trFkvpbbrmF973vfXQ6Hc4880w++MEPAvDHf/zH3HTTTfzbv/0biUSCv/qrv0JK26uqZVmWdXKodveQ9geQcg+pQDOZ8ogMFIzD+lo/D47uPqL1T2XrbJ4b4r5Cg8Ap0PJmGOgKym7IXO/ge36vtfcw4jsYL0m+16GecBlpDhx0b+/JqMdwt4LA0HKS1FQfIQozn4kLY3CJSJqATNBmoFdFC0FbJecT9wT6GPa6nkSwM1PlzOoayimHNeU9lAdz6GCGoreGHbWHMMYgTtAq/pZlWdbxc1iJ+l/+5V/y+7//+7zsZS/ji1/8IgBf/epXmZ6e5g/+4A8OKlH/zne+s/j/r3/963n961+/Yp4LLriAL3/5yyumj4yM8E//9E+HE/oJJRemaO8MySeTqFaISAiMANMTGEegXYOIBDLUaCURBqTRGAVGC7SRKKXBhESei/RBR4IgHRGaEM8XOI7C9CQkwPgCoyASASbdRTbTKBRKabQPuBHlRAvHOPT5HtLXGFcSJDReQ2JcCZFBez7VpCbRVjjS0EtHJGsphGNopWdxuzkgRTsZ0NcTyK4HjsHPlOkEXQKVRaLxZJpsJyIIHfyUAASHUYvTOkE5jiAKQ7IkUA2JSRpEBAaB1hLhgTQhodPA0Ul04IGRSKMJEwbf7RH6LqHQ6IQmKyO8mkIYSa0QUZsok9cSoxXG8zC6jXATyI6L1AbtCQQaHSkM4MiQSEh0KJGegQgIBSqhEd0I4wpC4xLPHCGMoQNEyZBUKPF6AoNEhAadkGgEJhJIpRGhRiuF0JpeqUdfy8MAtWITiUb7yeP6WVirT8+fZbh5JoP+vTQSgrLwiKRkU22ARqJLzwmPaP1zqSanVgZwuh5RIqCakIw2m+zOBBjdoRe2STjpZ1yHMYYgmCWLB5FhoKWYSWY4tdTPQ8/2IMFAv1+nL2hQdTM0SIIQCGEQAgR7fwxCFE0UdZ3CGEOCiJTu0efXGYkCQqnoqARdlaAnXXzpYI5S8i4RVFItEpU17PJGGK2O88TIqfRyk/SFp5I2bUp+jaFE31HZnhVLoujONMmHCXQgUJ4GD2TdYBxJ5AjQIKTB9ATS1ZDQ6I6DMSCTGtMFpQxIgxYS0QHhAtIQGYmJBCiDkQah43ssjEFKjZYaYQQmcjCAViEdNyQdSkQPZkUFT0SkmmlwNJVkm3QniXAcum4EQE9U5velSA0fKUAJTaBD8mQx+rgdXsuyVonDStQnJye5/vrrAfjpT3/KK17xCqSUrFmzhmbzxBtqZbVJJECMp5j4mk9vypDaKBg6R+MOSqIfNTH3NREFhfOWIcJv1Yge6cCwA68ZQDsCd6pLw00x+72Q1EbB8Cs9xHTA3JMCvwnJdYrKjwQmgMKlAqMN3d2avssdavcFDLzCo/UjSfXuCCej6X+ZQ+2eCAyMXNNHQrQI73iEcLyO89qLkd/pEezykZsSqCuziMhQrGuib9fAEWSv7mN2GnAVqfVrmflOgAkNY/8pTfWhkPrDEU5WMPSKAdr8GxONuxm64G2srWYxn63jjIdwhkvvlzPoooM8Xg0SreeMZzSTOxSn5pJE36wT9SLkRSn8Yorpb0f0JkNS6wXDr3ZxQpfGuGL2+xEmiOi7SNI3GqLSSSa/FCFcyeBrFYlZTfi1CviG/EvSQBnz+BTqpRuJtMKcOYbY5hP9+xRROYTfGaXbc5n7bkBQNWTPVWTOkEx/yccbEfS/yMWUAlLTLUxoaI3mKN8dj1fd/2IHkRSohCRLiNndRm9IEn1lFmZDxPkpzIv7aDcUvamQ3NkuE1/wGbtW4T7dJPh+HRKS/Gv66J7tMZXZxYDYYB9UWQBEOgDdYqQyxGg0R8MDX3oktWJTZZDJ7MEPn3ZAAiazdTZWBti+ZorpZJpzKg3uHRGExmOi+RSb+i58xlWU/Bop08FRPiqSrGlFbMmO0nb9Z3yQIAwMd8sktc+E208kJFKaZcn5/igZvx+iaKg0NS0QypAgIKFDsmGbog5xdUgkFb5w6CkXX3rzCbyLOYyS76RQ7MpUyfZOZdAfJxUp2qlxMo1LSNBhW3O3TdSPolxNIgX4QZLSfwR0dmrGrpN4D9cIt/eIXjfMzI81/iykT5dkz1LorkE4kvJ3fYyBvssdopYh6mpGXqTQ36xiHmwh+hTqjYNU90DlxxHeqGDoVS5hHSr/N8CfNaQ3CYbOMzhZw/T9Ia0tmsSYZPQXE8ivzaK39Mie4uH+YgH/f0+DK+m/oUj4vTrUIrK/2sfd+W/xb499hHPWXkMv/QI+u+1RpBD80innMNHeyvrMINeN/DwZ88wPwyzLen47rEfKS6uZP/DAA1x22WWLf/d6vSOP6iTnlFNMfLpHbyq+6ejsNEzeLdDlCHNPEzTIUxJE/1bBPDI/9M5MCP97mqgBkVSkez1UOl526ushzSlJ5X5N+lRF+dsBugMmhOpPQlQKwpqm9B8+6dMUE5/toTISNIQNw8ztAfmLFL0pw55P+wTVCLN1DvXzpxJ8voXZFQ+Np7f3CL9ZQ+YdojvrEBjoaPSX5+gfi0itk8zeEaC74A1Jmo9F1B/U8Xbqhskva4aS19CafZDcZAn1N2UYj2/mnCcDUv/UxO+dmEP5WAdPSkGlohjJOejv1DH3N1Gbk+i+BONfCulNzp8Xuw3jnwvoNhJMf4PF73TlXk2z4iB3tHFyoLuQLGnCz1egrSE0RN9pIbx+TBAR/tsW1EgKMxGi/3k6TqQvydLtOEx/xSeoGDDQfDSi+WhE+jSJP22o/NAn3exg7mnib8xS+oHG+GB8KH87RLmCue8F6F095Olpon+cgdn4+2we6SC+U6G3KyQx7FD6dkjhYoU33UF/pw4h0NJEn58jOSHY0WrSEYc2oob1/LWr8TQBHik3YG3TMJ1KEiEZ7qVJBx5zqdZR2c5Mts6mej86grpbQOmIgU6BrHZ5ovrAsy7/ZGMbKd3DCJ+xThtPG/rbI5TSz9BZlIHRbgnXhEw5RYwSKGkOqcm5mH8paRAKfOVSU2lmZJ4pr8ie5BAlr0BHJZHGkA3bjHbLnNYcZ117mr5eHUdHB709hWA8U2WsOUzTcVlTnSNQkzhzBqEybKk9egjRW88k7St4sEOoHaa+7NPZqcmcLnDvqaIfaGNeVmT8jjhJB2g/rak/EOENKWbvDNG9+Bpd+UGI2y/JX+gQfaOKeaAFBkwlIvzbadKZuHAif75Db9Iwc7uPPxv/9rS3G6bvE0T3tcivi6f1xjV7Ph+gN2UAMDt8/H+dw3lRHuoRwf9XwnlpHjMXoT9Rpn8u7gxZZi7hn556kG4U0g4DPv30Q2zOn8H/3vYf/KT2M2yLCcs6uR1Wol4oFHjiiSe49957mZ2dXUzU77//fkZGRo5qgCejoGzQ3X2nQdje+7dY56G37fNQJATVCwkchXikSXpDfIX3BgT1RzRuUdCbXlmXqvVkRGqTImqDdAXGZ8WPg/YBESc9gZ6vhuskobV8faYcYmorb3Dkrg5Rc29pSO48Sf3hlfMFsw4DA5fhVhzoLi89kVMhonLwN0/WickzEVO7ICMF+r4WYthFTwcELYjaK+dvj6+8jNWeNoh2RO5UgVsUyD0rx5KOHg2Rpw6AgchJYkpBXJ0d0GNJoiawz+nSeioidYoCILNOoO9uItd71HetvJtqPBbh5AVmNIkuh4vrXmCe6JLfFNds6U1qsmdI9N0rayTpp7pscjbQCcr7P2DWSWdL7WEgQyqxHQlMeg7CSDbXB5nJ1I9aR2qBiign2wxX+wjdDPUkjDQ1xVCwo/bIsy7/VO0RsmESQ5ahpmAyVWB9q0g5fYAHCQaGe3M4OmJGFUAd+a4sTdqVMiAgMoKudmgpj7qTpuLmmEkUmUgN0FYpUtpnY3uKNZ0Sicg/uA25mqrXoS5PZWxuDkmKqDlNwulnZ/2xI9wLa4GqasRFWcJ6fK8EkFsH5rH4Gh8KhdmnskZ3XBP1VtbGaG+NSPYBD+3zfTTgdOdXooGIleucMESjSZxg7xtRC8LUkoqq9QiSe7/Bph6BitdfrA+zvng2Py5VV8T1s0qZ03Jr+fc9P0IrW//dsk5mh5Wo/+Ef/iFvectbeMtb3sLv//7vk06n+Yd/+Afe/va3c+ONNx7tGE86Mr3y1kQoEN6SCT0DqZUfn3EkUgKDLsF8oUXUNLgDgqhtcHIr1+0WJWF9/kdsYZVqn+07sFDrUDl6Pqb9VEOUIFL7ubUquuDu/dOvGpy+lfPJlKHTm8Ak9/Pj5AhE0nYa+HynhSBbMBgFDDiYZoTISFRy5fdF9wxu38p1eIX4v34DorbB5N0V88gBianFN3fCaFjy3RK+3m/DICcXn0cAQQPEoIOpRSQKK88FbzA+r4SvEfs5V8lIgg5xMuLFD+LE0Mo4Rb9D1TRRKrFyHdZJaWf9MTxdYLC7nVpCUFEJspHH+lo/U9n6Ud3WdLbG5sowRiqmkx6bGk0KUY/yQfT8vqv2GPlQISIYbQk6rKeaaBOo/T9w7fPrpKIeM04+/s07qnuyPGmX879fkRaEWqCJ+7/oKI+qm2Uy2U8oFWOdWdZ0Sjj6mRMmF8meTJVcbxPDLUNKZgncPRTUCLXOjqO8JyexpIRahEzE90UAQUdAPv5DypXXYpmICyH25fYJoii+xu5LO/PXbMWK+6HFdQYa4yy/ti/bvgAh925XpOTiA1s/0aXRLTOWXtn/yEgqzZzf4NTsWqSx9zyWdTI7rCvARRddxPe//31+9KMf8Za3vAWAiy++mM9//vNcfvnlRzO+k5JT0BQuW/7LMPhCiTskFz+x6O4m7msKyxc8P03oKtTWJtE5Obrj8Q9GepOk/zwwEUhPLEuQZQLSpyu6uzWFyxxaWyLylyg6O/felCTWysXS8OLlCkfExZrRE+OoF6WWx/7yfPyjtjSpKip6uQRSCZxCPL3xQMTgK9xl38DkGoEYnKNWfpzmkIN5aWbZuhvXJ9FF+6P1fOdrwdA6wyNbQpwb+qGtESmFavj0X7b88+9/oUMq08Ip7p0mPCiebtAb0zSfimunBEUP+pecU0mBPFdgdtUQpxQQu2cRaz04N/4+ix/XSOQNqU1Ltieg+CKH+oPzJSgpCS8rYmoh2b4ItaQpoUrH513uPIWY6IIj4Lzl54r65X4aO+JmH/0vcSl/K0S+vADeknNnwEGekaRrZsiKsSM5rNbzSKm9jWxngLFmmVpC0BMepzUHaXi9Z2z7HWkIIkEvFPjzr0ALnmk47EaiC8KQa6UoJwvk/ZBCz6D8OYKoe+AFgXprBxl8ir0OeV/T317LbGb/1d7TYZf+oMGMUwAlDpyk6/nXEfbXsCxplwZjBEEkFivRGARNlWIq2Y9GsKE9STZcWTNnqWqqTTpM0xMF1tZ79LK7GfLXIaM5OpFtFng0dPIapjp4GU3/S+MEu/qIhqv7QYB6ukXurOXfnv6XuOiuQS0pqJApSK6TVO/RiOv6lz0VEpuTtJvx70VvXKN13EfJUkM/L5H1gMb03t+I/p+XyAf3fr/Vi3NED8f3S2K9G5eoA5zt8YR3LzPNnVyUF+TcvQ9h+xNphtMu7bDLL6+/CmMrEVrWSU0Yc3J2T1QuN9HPdHdynGW6SYJyXBruFcBt+4isBARmOoCkxIy6yGYE5RCTUuicQvoa5Qm6oSRogFsQeMkAXEnYVPg1gzOgCFtxW123oAiqESot0fNVvFTeAAJ/xqCSAicL/rTGzQsohLhhiOz46LkOcjALoQtzISLnoI3BZCR+QhNO9XBdhTuQpD0DwhXIwYhuRaMjh+SAwO1JgrJBeiAHu4z7P0ClR+i4QwyTJj2tiKoR0YDEH1U4Sx5tDw3lmJ19hraOJ6mhodxhL3uszotD/ayEEERhQML3yDQkeiZADjoYT+K3BEET3KJA5cDVPUJf4DdcdACJHLheRM9RdCqajggxQyEjgUSOm7jH3xFFVKvGHROmPCIviU57SF8iSgF0IsRal1A5+FUwflwrBW3wy+D0CZQLQSUinTdQ8Yn6k/SaEqPB7RfonsZog8xqktUQk3Ew1Sh+8DDo0pXxTabUBuVq/Ebck30qpdETflyDZI3L9r4J+mQGGR2/ToVW47l2ODEd7rlxMOfFc3mM3veDV3H2nit468T3+d7aNDvcQX5p1wVMZWv7rVauTVxyDKCEWZaUGAPaCBDg7qc0EmCwmSPfS/Ho2FNcNLOVHeks39jQ5doLP8Tm/kv3u4xbENx8+y9wbsdw5XREtjXCUPPnuXftDvbtD9TRIRva08w6eXzHXZ6kRwAGYVixHLBkuogf/B5hMbwhHh1FSINasi5PB/QHDRpOmpLXd8DtjFaL9AUNOtl7+c46yJvf5IfZf+aXLvgUv3D6JavyPDocx/Meyo0EqbYGmcCvSYK6ITEIngkx0yHRgEugHaKOwcnG12InATIr6c3EoxG4RUEwF+JmBKoIsmpgNkCkJWbEpdeURA2DTAlUXmD8+KGvaRvcjMFVETqp8JuSsGVwc+AmI1QrQs+FMKQQCQkTAaQk4SCEkz2cjEc06lDxJplub0UKRTK9md2tNkYYigmXaq/CxvQoQxSPSQeiq/F6vmC1xHYk91GWdTQdVq/v1rHXSnZhbO9FK36OP/9odVgQ3074kAPWCPYWMywXwvyyGjIhjEAAMBC/v/D/++2irS/eog/QH68LoIOAvgSsWXgKbGBsvuHVwrYACon57YewpPBfZEEREgBBDhhceEfSz0viReendMeAsbg6sP2ynjyMMUjlEKQ01ZSe/85H8as/nmd5uaGBgbgt6d7vcgR5iCsWOtRTQH7vEkNnrd/nhiCANNAH8V14GL/69tne0JLm5gNQAxiWgA9D+4sNuvn5qQP7rHup+fMgP5Rjtn9hLwL6zdCK9u3Wyavhz4HxOTso0UgISq7Hmm6edOBR3k8ncpGOE/F9E/QFQsTJuzZxabuSBrnPfOVMk021Adwww0za5fRGl59GggdLPzxgon7f7KMMhAHC5BltNKmxielMY0WyLYxhTbdMTaWXJ+lR/J4RcZBmP9WPIX7QwMJLm+WJ+wGWeSaCeMguA4SRQCqDBHzpMpMo0u/XWdMtMZUc2G8P8VPpGhtnNlDQ95EiC41pZD7Fo3P38wtccugBWSsEyhDkBENDCeqzDVgLbeIXQ8uvrwtX2cWeBrLsnb7kvoZRYHTJvVV2ftr+tr/4f9Hi70MEdOfXyXq1N+EcWPIl7EsS16vQpBjhlPR8n04GhtP9e+dLjcYd2x3MwbAs63nN1iO2LMuyrBPEQ+WfEpBhXWcXtYShLpOcVR1hKltbkYiHC0m63H+SvpQUIIUhilZWhTfCMJ6tsqE8xGyyQDKK2NAK2Fm5+4Dr+8nuuyhEioF2j2QgGW2tiTu628eAX0MbaDqpOEQNIowzb6MEKPHMdyqC+H0FKBEvw3zCFc6/Ivb3HPsZCUCquEp8qOOB4TSCspdHGs3azmzct8U+tKcpJzr4nMLGRoSvdpJ0htlae/DQArAsy7JOejZRtyzLsqwTxJbK/aR6Bda2mkxkXJJBivXN4opO5EINZiFJP0hCxG22o/20W5/K1lnf6AMGmUvB6SWPRHucUO+/V/Q9kz8laSLOr/SYc8+knujSdZfX3UqHXfJBizk3F480EhkwBiPnE/QDPFwwQIQhRBMSESx5hSIiVIZIgXGI1wVgTPwAIDr4xF0QP7yQC8eEuO36nJcHIVjbKSH2qZssgfFMDRmdycZSCz+9nYFwLbXWVk7SloaWZVnWYbK1iS3LsizrBDHVeIJLyoKOC5NuivPm1jCXai3rSX1/SbokHi88E3ZI6BCBIRSSjkpSd9L4cn7EAQGKODFlSTV4rTQT2Rrr5tYwkd/BRTM9hnqwZe5uzh180bIYa0GLfn8cL0iytt6jq09je3H58ILKaEa6ZWadHEbKOIGGuGh/Pwl6OJ+YayLiXlQW/lnJEGHm68MvdJotEMiFlwZlJCKcL3t/lqryC53OLSTrjjTMubnFavCTycFl1eBbiS6hcBhqDZGJari1PNtzNaY6czh4+9+IZVmWZe3DlqhblmVZ1gnAGE3gT3BJtUY1AXWR5szaCBPZ6uI80X6S9D6/wamtCfqCJqF0qDhZ5tw8LZXC1QEbOjOMdWfx9HyJ93y79X2rwU/mamys9+PLNcwlBReW4Kc7/8+KOL8/9T2Go5DzygENtYlIGOqJJT2mGxjplGnJFIHjxdXTWVmKHrcWjmjh4xMiAQ9FEpcEDh4Kdz8vD0UChwTu/LwuHhJJnGgH0tBRIS0noOWE+EoTigiiJaXu+ylxX+ghPoziqvBz89Xgh7tzyxoUSwW7MlUCczZnVAyyPY0r4AcTPz6ET9uyLMs62dlE3bIsy7JOADvqW5CRYGO7wXjG4bT6WnwV0kzMd1FllrRJJ+4Fe2NnmkLYZtbro+QVaKkkgXIIpKKrPGoLY4YLhw2dGfr9+Sr0C9XglyTrkYrYnauyoXw6UxnB+SVBovQokV5epf3J7f+HtC85e07j+eeyu1BZloD3+XWUiai76TgxFstLsxcS9DY+GjOfdDuo+TLxQxW3WpcoJC5yMZFP4uIi0YAvNS0V0HEiQqkxZv9J+0Lb9YXS9TkvT1IHDPjVZducyzSRushp5RRB8kkSYph7x+865Ngty7Ksk5dN1C3LsizrBHD3zHf4uVKShgeTbpoL5tYxnqsC80OwRfO9uwOZqMuG9jQd6TGbKBDIA3eBbhA0nBTTiT5yYYf1nRmU0fGIZ/PJ+kLz6qlclcFOFm1Op5SUvHgCvr/17xbXNd2eYk1vJ5dNC9riTHqOoZZsL76fCHv0Bw3KTg4RmbjK+JLQQvSSBN3FRR2ggvvRIeeT94USeIkgEIa2Cug6Oq4Wb5Z0Ssd8si4NWgsiBKVEgVzYps/fO4qEULA9V8H1z2Fdu8Nod5Dp6qO2nbplWZZ10GyiblmWZVkngG1zd3PVTJdyCgrtDSgjmJsfki3SAjnfu3uf32RNt0zZzdN0UntXoJnvTG2+tHhp52oGIqGYTRQIhWRjZwpPB4j5ZH2h53MjDU8XZzhz5mxmMy4b64LEQ5+n1tyO1gHfuPu/sKEm2FRNku6dw85iabE0XemItd0yc04OY+R8p3HxewboEuITkZivwn7s0vP9E4BaLHF3MUBbBnRVhFbzUYZxCftCu3WtBaERlL0C/X6dTLi3in8p3UToUc6bydLf6JCO5tjeHn+O98qyLMs6UdnO5FapbNZBTzk0SgGFpIdohwhXMN+LDiYEnVdEHYnRBsczUI8QRYdICsIGuCmDbEXgCkLHwQQGV2lkN4R+hZkLEUJARmE6EcKToAS6piGliNIg2wYZaEQivmUyXYP2JH6fRtQlLiA6GtKSQINKSWQjbuvX62vRSEekex6prgMNMFmJSZYxvX6idhIna3DDENMx6JTER5KQApohpAW9SBMlYCo9hcAwGA2QqypEAJ2ohasD0g2DCAN6fYJyokbOGWJON2j5M3hC0u/20dQBs1FEykmQEB5eO4sTumQSLm69h1ABYcFQU5LIn8IxPslEkVKvQtrNYfwmKaFIJNfQjmr0tYp4VQeRhiC5m/GEYiby6fMyFBG4FJm/s8M1kmRDo4Mufr5NlMiite1Q6ETkelCOetTCHmt7BTItD08ZpNGEQoErkFoTdgQ6o2kVOjiRQ0eFDPc8oo5H1AKZETgJg6mHaE9itAIMnogIqx2KXQlKYrICERhM1yDSCt2KEEkJRkPbYPoVoVHoblx72BURIjCQl/TqYAZ6tN1ZEvUMiagP3Ra4KYOQ8x1y+UAPTN5Ba43saqQjiRISHQhkL0I4Ed36LH1OEuE7mI5G5AV0gMBAVhG5QEdjhEMUgMga/IQmkPuOKG8drjAK6Kvupt/XPN7vcun4JnYX5kDE458LYRACBv0ahbDFjNdHuFCKHu0zvrgg7mRtoXB32RjkgpqbJYi6bOjMMJkcoKWSSOK22Y401FMdyp0Wa+ZexPbCd3jJHsMDt7+V3X0uZ9d9XjSh6JoXM5Ov0nbjXuGFMazplGipJF3c+fHg5vcNQ48AhSJxgB7dlg2Xvs+0Z7MwYNsCuc9/9/dAQADufGl7hKYjApSSJIxC6PmHG0osdjLXxaHsFRjpzjGRGqSrEhjXsDU3x+nN8+gzP8URGb43cSenbv5/DiJq65lklYv/VI9CSyGKKr6eVUPIS4QPpq0RRUWoJKIVgoxo9YWE3SSJjkK5YAjp5dqke0mcjgNtDXmFbwxIgdPTyCBCpEICN0TrNMaXKM/gdjQiBJ2TaBRhDxwBipAoo/B3dOlrKYwQ6IREiQh0D4GLCcCkBKIJQoJJgfEjRNKBlsYIRegoSAmkMYQdgSs0Jggg46A1OK0I4Qi0YwiTIZGRJAIH2dJxXw9pIDDoXoROKybTPfKRJFM1+JU66UTAlLsdx00y2DH4iQyhmyXoTRAZB6XGQEzTNS16uodyMkiRxol6SEKSqoASQyRbAtXSqESTgAnCdAHfGcLM9+IohKBGnWrYpM/NUTA5tOhQD6eQQpFTa0A7i/OWWxPMhdNk3CJJBvdbA8U1kkQt/v9eAQJxiGMvWtYJxCbqq1AiYeg96lD6ps+aKwzhv5WgpSElcK8tEm3pwM8XKN2taTwa3whnzxAM5LqIexuotwzjzxjMD+YId/rxfdmL8lRNms6sYc3PGdQPaqgXZPH/Zhrnqhzy4gymqgk/XcJUI/AE7q/0E/6wTrQzgLzCvbaP4Etz4AjcNwyiI030hYXYJPJXBjGtgPD2CoSghhz635LB9JJEXyzDeAAS5Kv6KNcVnUmfdVdo/K+UoGMgJ0n+p0HC2yuYiQBcgXp1P+WdLkOXrKUzWCH/FARfK4NvEH2K7K/3E3zhbqj2UEmHoTdtZsv63dy947M8OvENAM4YupLh0au5feJh3rjuFyjsOZ3x+z2uvMKl+qMQf0qBVOSvgrkNd3HPI7dgTMRA8UwuOP9tPPjI/2J69gFAcM7mN3BF+h3of64RlkNwBOqX1vDPhS/y9dI9jKb6+W/n/iqZ4GnWZ84l2y3gfrOJ/kFc6uWdKWm88gmcsXVo0X+8vmLWYTAyZJvf5SeT23lN/TyqdwgqfoCTF6x5MajJBr3NOea+F9GbMiBh4KoUzctqjM1laddcpv7NR3dBpWH0VxL4JYkJBL09PoP5DhQdgq9XMZX4AZv7S0WC+5qYrT7kJO61RYKvVnFenEU/0UVflKU04dF6Mr5RyZ8rKco2ouyTvLaPnf+kGH3dGL1Jwc47Q0wITlEw9loH9UCd8K4GGJCnJHCu78P/+DQkJc5/Hobv1okeiassyxdn4AyN/5kpnKtyRDMh+v75985N4bw0R+Aqdn4miLfRJxh9rYssSHqp/Q/fZR2ae2a/y0snDbMZSHQ2IxGUUy0CHSfeUsBwr0Im6jLt9aHFfE/qBhACo1iZkYrlfxjN/DBm0BZJIleypltm1i1Q87Jxsq7j5HR3scwZs2sYrL+YLcUfs7Hmc/FMQMfxaPFiuo5gcr5avpnvPE4LQZ3kYpJugB4Rer4UfWn784WEfOEFKxPshV14ppL3/SXzesl/l657YV0L/79AzbdvD9G0RYCrFG4kF9vXK2XQRtA2LtLNsbZTYk96GF+6zGSanNpYx+XjfTyVT7Ol/AOwifoRyXQTyCe7hJ/fey/gXNuHnvURriT4eg0iEKMu7psH8DsCvt/EnFtk7q4IE2rcoqD/JS5hKYtT9An/z+Tedb1pCD0doL86hw4Mol/BW0Yp/19NIqfJt1oEd8fXTnFGkt4lRSa/oREODF3tkhuMiO6sYh6La1eIS7P4l+fwQgf/0yXc1/QR/bAR3+cocK7KIy9ME365gn6yF3/5Ls/RXpvFGXbwql2i2/feJ7mvLRJ8uQJdjfOqPoLRNGknRP+0RvDQ/HX5whRyrUv09Tpi0GH9a/swsyHh7VXCpMC7po9+M8BXC//IORtfyGlbpihnI+56/OOsG3sJ+TWX0SPgG1v+nl7YJpso8qoz/x+QeSZrT7I5v5Fzyi+Cf26jOxqdkbiv8xB7/hfRldcTDVwAJHm09zS3PPzPNII2eTfNxy75Tb77xCd5qnQPAsGVG17HS095Kx4Fxrv38+kfv5dO0CDl5vhPF/8PxpIXszRXT3cl6vYG+r54P5OXpHGvz9FO2mTden6yVd9XITWbZuoLPsXzBOIr84kwQMcQfKWCPC1JuyppPLr3wtR80tBOJzG+IfxsiWTRYHbO3yAbMP+3Tn5NhF+C0tMSXdeYyQC5OUn4nQZ0NcHnynGSDuAbgs+WcX4uG/9djwjvqOL8fA6aGv2vJZxSb0lsGvOvsyjfwEIh2mwId0fwvXqcpANo0F+v0rdWM/hzAvP52fjHB6ChCf53CXVeOv47MHB7mf6zDDNf0Qz7fQRfqYAfz2+qEcFn55Bnr4nn74aI/72FYk0uJukAT87+BLe7m2vXv4BUs58996YYGpXIxzX+lFmMq/5d6Gusw5j4GPR6NUrT988n6fGBPCX5QvTnGpjy/E6GhujzFW5MXQfAVGeOT235CjK5jun2E7g7wsUkHYAtmvSWIZqVhxHPdHdprTo1E/JoZYbNwRCdf1OY+dMrrBumfmQwOUXzZ/NJOoCG8ncjitN9+E3F5BcDdDd+K2rD5Od7eAOS0n8E9I1pRDUi/G49TtIBAkPwuTmcCzPx3w1N8LUqzhUZwjtqqCsytHruYpIOUP+ZpjuQwjzZRT/WIXeuxC9Jpr8aJ+kAYcUw9c2IqM1iBqN39Ijua6FenounPdXBPLK3XbEsugT/XAYEpNVikg6gf9ZBb+2h6iGJkfhLHVYNM3f6hOOghP2ZORoe23Y7F1YMk0mHi0pnsKOvHHeWbuIe2ke7c6SjHrOJPrSZTyIXelJXcWl6PPZ4hL/kFQ95tjA0GqDE/NjjBt+4zLh9DIR1hnuVeEzx+d7gIwNbhqaISHLm9Ksx+mLK7sWk29fQcBXbBmdBxG3nhztzeCaiIrOgJEjQGDoEgMHDRSDnx0eHAPCJk2gJuIBHXLLgzE9beD3bZVSwfH65ZD3O/Hpd9t4M6flt9+bjWHr77yBJ4Maxq4DQmf8AQoMk7hG+KRLU3AxjnVkcHYJreDJfYqB5MZsrs3j+bsbb04f+BbAAyGQcVDUi+kxp2b1A+PUa6tw04Vdri30JmKmA8GtVVMKgr+pn5lvR4nUwqBiqd4fkRjThbcvXpb9QQpX9+B4EMBG0nta0d2hyhRB+2li8dponu3jjbdxiXNNx7v+GmCc7i0k6gLm3iWs0wb+UkGMe0WOdOEkHiCB6pE30SDtO0iFe908bpAhobYsQ99SW3yd9pYLzwhxEEN5RJZ0IYU8P/dCS6/JDHXAkZCWmFBL+Rx2RV1BQ0I3vJbNRHy8Q1/KZBz7A7KlnMVzt4DppNmx4BU3d5KuPfYJeGK+z2avwzS3/QKnxGKcNXQkVD/6hBZ35M6SlCb6cw+m/AnfLT9HdPcxR4+YH/5FGEK8jo5I8MP4NnirdM7+bhh/v+jJ7mo/QMTPcdv8f0Qnifh46QSP+28ws7pMQoJ7wF5N0AH1fG/WEb++nrOctewe1CgUVg4nAkTquirXsTYPISpo7VlYHao0L5FoXpgJEtOJt5FyATEJ7p8FsSqJ39pDnxe0XjW/ixHopAybYux1TjSA9/5VpaVZcGf2VMUlXYrZ0Vk5vhzhRtDepX9DU4C1fr+zGO2Mq0YqiEVOKENn03gmhxqv1Vmzv6dkfkVEeXjcHwPCgpLdt5UFymkWkiCuaDBTPYGL6nmXv95n1mD0rSwhTlb2n0tONcZpRQDMow5MrY3EeT9OrTqOUPf1OJO0oYkezRF8ns+J7GFRADyVoPb3yqX5YgrAnFm8QF+gO6B5xLtUJEQMOZjJYsbxZel7VI0jG3xuTVDT3rLw76UwLxJCDfrRD/nRB0FgxC70JgxlZ3vxCb+kiNySRa1z09n2+t1JASPzek90V69PbewhjSI/tjcefjseuVsGBOzGzDk4YBVy69VHKaci2LqDj+sx5HYwROEKztlsiYQJmvQI6AozByLgU3RcRbULa+HQJ6WEIiC+9AdBD0yGkhU+bkIAorhY/n7CHSGZUgaT2Wd+ZwSWKq3sbQWBg28AsPxucpiuKdEWRR4cm2dlfjkvEtWG0UyJpfEoqi5lP0n0iOgQ4SFziasv7JucLifnBJONHaiGZV+xN3p356RHLk/a4Snw8BJxPREfNH6/IIKK4PX9dpGio1HyyHlHOduhJxUt2r6UQJfj3XZ87xnv0/KVw9n8vUA6hvfIeRD/ZRSkI91OxpzepUT29sspFKYyT2nlynUdzF7gFgZxe+ZsunmqTGouvy8lRgX64vWIeaiF0DfKUBPqp5ddQuSGBfmzlfZKY6tGb1pj1qeVv7HOfJELQu1buoH6ii1wbX+fNLj++hq+bv+5HYCJDrl4giHo0dYPkri3kc+tp+DUiHaL3+dFq9OZIOmmavTmyrcLig4xFbQ1iBHf7w+A3Kfk1OtHe43V2YYw95XtXxLlt7gE6UWPxocCCXtim7pcW/1ZKYR5aeZzMwx17P2U9b9lv9irk5OOqjBFyRdKKArqG9NqVty7pYYOeCRFFxf6a+ZmCi+5Cco2AiR5ijYveGl9EhScgv3Ih4S7ZTkbuTcYT+7l12k/VSt3TiI3JFbPqtEI7auUdWFLE1TWXzuvFcYn9xEdeQXfJD6cUhLmV7b/X919IOwoJvPiHYK5qcMdWfv11prH441St72Rw4Jxl77dkGTG0ssWIn98b87rUICnlkHb6EKesjCU6tYubLRJFK28qrNUrKSVj6SLNxMpEVeUEsh6QXLfyvHD6wEmw4morPJAecSdeSYVpRHEVy30sOwdTYn7MaRA9TXpk5XcoOWji/idOT9KaACezcl/cARCV5Q8F5AYPPeujZ0Pk2PLv7ULTZj0bIDclVqxPjnkYAd3ZvfE4xfnhwhxbJfFI3fPEbVxWgjmvwGm1TTxdKKGNwBURY90Sjokoqfx8SaIgUNCW8fBmIQaJwMXFwyGBIoHEQ87/V+HioOIeR/AxtPFpExFKE/dbIhQlkcPH4ZT2FIWwhZIGgSEMBXUnYE+uyniuSssJ4ur4QcDGzgyuiSg5BYyj0MLQJiBEk5gvx+4Rh71Qcr6QnD+bfavGRwd46X1eB3vVXZq4L5S4LyTt4fwcCRwkgrYM8J147WK+dL0m0nRkgrHODK6IeKhYIts9n6v3OGwrfZtQ7+dpuvWstAj3fy+Qk/H1cR9igxd3tLifhp7uQNx+fIW82ltSTFwynx6Na0+ZwZW/6WZDku5M/M3qzWrkGSvvecjFfZjoSR+5fp+HpJM+8tSV11WGPLyihMl9Hg4k56uqLGxfxg9R9yVPTWBm4uu8GHUxCsz0/HVfgHAE7WwLKRRplaW35hSarUkybg5HuitGW0i5WUIdkPH66KRaK+8zPQGiSjh2BsZN0efmcMTembY1ZxgtnLsiznWFc0ioDI5cvg+OdMl6xcW/o0gjzlx5bMWZSXs/ZT1v2UR9FTIDHYZe7VJ+yMD1A3svhhKcV/cRPtQiuw6SS5J1b1iQcQPoaNSbB+m1BKJvyVX0vDTNhkKlYOgCkK0IeWoC/WAbdVEakgL3TQN7HwwIcF5TIFp4MuwI3Nf0Ef64AQrELw0S5ZxlsckbBoiWlv6lBOJSD15RiH9EF2a9LEOjpCg9AuK6/r3JugLnjQNE9zYX5xUvLVDdJuh/maSe7uL8Qn7v/K7AfWMf0QO7FmMWv7yZUkGzrnjB4jqGMxtJZs/hu5P3U8/MMHRmj4ldEernHNSSJCZzMVRSTy7+7QdNNqx/GbnsusVpj1Y/j3pDX/xDubA/v5DjX8LvApBWCd5+1uswvUlGMmcSnu4iztj7o2yGJO1zp8kPXmSH6TnBFKXD+cUR7pO7SP+CWfweCgdGXyxge4fiZS4qu3eZ3IWS2poGrhcy/Bp32Xd95LUenamQ4s87NGYUOgTnlX3LHs45r8wTLZS0OOBeUyT8URP1wizRQ21yhRBvaO/8qTFByvehz0FdnKb8/Qhv0DDwkr3XAuHB6KsdZH1voi6KCvWiHNGddWhFcHYKxvbeNIU7u7hvKMYlOY5AbNj7nRZrXcQZSXTOob3dLG5j5DUe7loIsQnJETGGDT/6LDMZxYbyC9lWKNNREZ4IWd+ZxSAoqTwa6DrQUiE+wXzJr4uHwkEesGR6oTQ5LkmW8z2ux599j5AWca/nkSupqwyzKkcxqHNKe5Ji2MKVEVKYxWrrjg4Y7c2xqTtNR7iUnTxaCXrzpegSicRZLD1fWp193/g0cVIcovHnX735l48mQBPOv6IDvMIlr2A/69i7HrOY2O97ZV56jFwWxnmPS9nFfHX4CENbhUQKhI6T9YrK0JIJ1nWmIdFjPDPF5eOXs7lV586JO4/kW3HSajZDTL+DvKa4/F7gNX3oPT7qBUsuwJm4PXfUBeehGv2XLbkPSUD/C11qO0G9dvm65K8MEC4poTXNiNzZCrdP0Apd2LwkWRx0CE7L4M8n6rnzHOQFGRhZ8mRgU4JAKNw39KMf76Auz0J2SSyjLuqyDGJgyTKnJ+lIj+zZCs5ML/vtcK/pI/pxfJ+kXpil01WwMYFY8gBAjLmQEnFNyKTA+cUCoqMxpTC+v3tVgU63yT3im/zy+f+Vgae3MDuQp+fXKc08gIgiXnHGby72G+FIl18847fw3CH2VH5Gr9CEN6X2ZhEK3Ou7hDvvwD/7SlRijCHZx3vOe/Ni86c97VkuXn8tg+n1i3GeMXgZpxQuJiNHeMMFf7xYo1EKh9df8Mdk5Ojez8EY9PlJWL8kod/gos9P2vsp63lLmJP0210uN9F69e56UmjEXAbd0SSTGtEIEQmJxiClAN8Q9TsEjbjXdy9loBwgBl1CB4KqJJnRiHoY9/qecIl6Btc1OO0ABhxMOYifmObjXt9JxS34TDmEjEJnJaKpkb35Ku8CaGl0UhH2SXTDkNBAO4SMg2/ASUtkLQRfEw4GVPIN8p00XjsBVYPICEy+TtDtJ2oo3ILADQJoReiMQyAkrjCIWojISrqRQScEE7kpjAhY2xshXZPQNYTDCiN8vEYI3YCgqJhMz5JODlCNmjS6kzhCMJwcphoGTIc+OTdNmgxeK4cKXYoJF1XpIZRP2G8ouyFhdw/K9MikB5nuVujz8vidCknpkk2P0Y7aDNWGUWWBzAp62WmedtvMaJ/+ZJ4BoUiJPFLH1dWSocQth+igS1jsEWYcInLH7LszNHT46z5W58XQUI7Z2f3UwT6ODicm4YRMRz4dP2Bjp59Ew8FNGFQUEUqJcRUKTVAH0tAcbONKh6bssLaRJeh6BA2DkxOoVISoayLloLVAonEJkXq+x2JPYHIC4YNua0RWoZsRaqHqeyOCIRdfOERNg5DEozp0QmRR0W4KRDGkkZwiWyvgBDmipsHNguPFbTVFG0xPw4CL1gbRiJBJQZh2MD2QzRDpGHSyh1QOwnfjkv9BBQ2DCTSiz0G7AtPWRDhEPVAFiDKarlpZlf9oOZzP73DPjYM5L47Vd3z3D/+Gdfd+ga64kkj38Xhxlgw9xnolWjJJRaYIFETzaaaaT8yPloU27AaNQMVpdgRZ7ZPVXZLaJxAOWghcE1dJbqsEDSdFKCU9NBERAgXzDwwWXkvtLfHWyzqAW8hP5Py/n63zuIO1dxtm/p/l7y1sO37tfdCxdNtLS+rjCmWakAiJJBGp+arzgpzu0he1GE8McsHsCJGs857LtvGBF3/huPfhcCzPiWMpG3iossY0569HCsTM/P+3dHwNHXAIkyruDV5HtIcgaLt4bYlKxsPn6HyPRMdDdQSmGWGKDoGSiAicXojwI0QmIkgYdJQi6gi8FDjNEAIN/Q6hUQRtcBQ4MkKnJSrUmFrcWELnJNIY0D2kUZgeiIzC1CKEEpARaD9Cph1MLYp7fU84mLRECENUh4SIoBtiCio+E6shwgXjCYJk3FwyoRWiHjfFMAWF7Gh0J0TnFLO5kEwAmbJBeoog5TPl7UAnDQNdD0d5tNJpOs3dGDwSzhoCOUtHd+hEbRJuDimzSB2hdJuM00dCrCFVc5H1AJlqE4rthNk8fnIN2sQ1BLTQlEyFkl9n0CswJIqEos5cdzdKOhS9DSg933xRaAJ3ltnGJHlvkKxcOz88xXLJQOKUNEIIggFB1z36tbaO5D7Kso4mm6ivcqsxwVkt7LHZP5uoH5zVGBOszrieLzGdaIl6OP0Uyc++jYY6n0RvIw8MTNJv6gwEdUoqS8310AdI0Jcmu3q/U2Fp6ilXTFkuLjHXi2m7mE9NpYGE1kgMkZD4QqDFQuq7N7lfmpwvlFrrJUn5wjaXJsbHUxzX8iR+aQIvEcgllYMXSuPjuOOHEw4KL4pT/IT2GYwaVMhyduUMyundfOHVl/Drm9/6HO7VSidqog6r87q0wMZ2eFZLbDZRt1YLOzybZVmWZa0yvfJu3P/zu3S5gHR3Az8b2MOGcBZpNLu8Aj0JEM6nwS4aCPYpjYZnHpHNLJl76djkS+eLE+f4H7mYci8k2RotoK32PhIQ80vEg625i1uIq6GvLK2Wi2tfXRb2e98jqOf/refL/g1L92XvAHJifvz1tgpwUYDLpOxjKKyzp/Az1tfO54avP8Sj/U9w3sBZz9l+WZZlWScOm6hblmVZ1ioyvfU+Rv79FlT4AkKRYk9hC+uCOmWVpOK58yXacUlt3GmaXpb4smxE8kO3b7VwvWzKUmL+32Kf5VgW18KcqzUpPxQLVfCXHt+FKvtLBrpbcmTcuH28itvnT1CgILvM5u9nqH4ehX/8P/zk2ldz6dlXPKf7YVmWZa1+NlG3LMuyrFVgtjzD7B2f4OemZpDRK6ilJukktqClyzY3SzRf3Xxp2+mj1WZ7qb3rPPDa960OvnLZk4dc8m/YW21eL/4fgEIDPRUxoxwqyqHh/IwNtbW89Cs/Ycd3b6dx/e9wytgYwg4KbVmWZXESJ+pSnjg/hCdSrM81e2yOrmN5PFfjZ7UaY4LVGdfJHNPBbudg5+uGholGxFOzPuM7n2T9xHe5Yu5pTukU2GRG6HiauewDlBKGhsyhhZrvdVyumiRY7PNfa6+FavNLP624yrxBzyf0gTTsSWSYHJzjtPY065sbULd9kZ5TYVeux66hs8mf/0JOO20TSXc/w5EdZ6vlerBa4tgfG9vhWc2xWdZz7aTtTM6yLMuyjoQxhvf+aCff2V074DxaljDOFAAvr9/HG2rfAyDXWs+60s8vXx8+gZrFyPCgx/u2nj+UVrjRAJBa8d6u4e/TSk0C8Nm+l/O93EUAiHANUg8ccJ3XbCryx1dsOBbhWpZlWceYTdQty7Isy7Isy7IsaxU53iOgWJZlWZZlWZZlWZa1hE3ULcuyLMuyLMuyLGsVsYm6ZVmWZVmWZVmWZa0iNlG3LMuyLMuyLMuyrFXkpB2erVxuovXq70evWExTqbSPdxirkj02+1cspnGcwxvO51idF6vxs1qNMcHqjOv5EtPQUO6wtnUw58VqPEbHkz0eK63GY3Isz4ljbTUezwU2tsOzWmI73PPCso42W6K+yh1uwnUysMdm/1bjcbExHbzVGJeN6dmttniON3s8VrLH5OhazcfTxnZ4VnNslnU82ETdsizLsizLsizLslYRm6hblmVZlmVZlmVZ1ipiE3XLsizLsizLsizLWkVsom5ZlmVZlmVZlmVZq8hJ2+v7iSYZSNwZDb5Bjzi0MxFm9Xdab1nWCUYKSNclYjaCtMQfFMc7JOsICAGZukLMhpCS+EOCnqOPd1iWZVmWZT0Lm6ifANJdibytin66F09ISXL/ZYh60d5sWZZ1dGUnBdH/msGE8ZNA77I0+j8ljnNU1uHKTUmi/zWNCeY/z59LI34pR9ezvx+WZVmWtZrZqu8nALUrxCwk6QAdjb6jjms/PsuyjqJkKIk+Mwfh3uo6+p42Zpd/HKOyDlcykkT/OgfBks/z/jbutE3SLcuyLGu1s5neiaAUrpy2y0eFtkqqZVlHjwoFTK+83uh6dByisY6UCgVMBCumi4ZN1C3rQCITsb01SSNsH+9QLMs6ydmq7yeCDd6KSeKyNIGrwbZTtyzrKPEThsT5KcwjnWXT5bALrEz4rNXNTxiSF6bRDy5POMyQwv54WNZKD9We5q+e/lcA2lGX/775zVxWPPs4R2VZ1snKlqifALprJPKX+8CLS9DlRWn0CzNEtjc5y7KOokBoeF0BsXm+TXpKot4ygNzPw0Jr9QvQmOvyiDMWPk+B+o0BOgPHNy7LWo2+V3qQDz75L1wz8gL+6+lv5NfXv5IPP/1ZqkHjeIdmWdZJypaonwACpdFXeCTOG0Zo6GYNAbYqqmVZR18zG+H9Vh9uE4wnaKY1A559pnuiamYjvLcu/zy1tg95LWupe6tb+Pj2L/NbG65hTTJ+knVKeg3n5U7lixPf5z9vvOY4R2hZ1snI3n2dICJjaKc1rawmsFUWLcs6hnypaeU17WRkk7rnAft5WtaBTXXL/MVTn+bN635hMUlf8MKB8/nGzN1ExhaOWJb13LOJumVZlmVZlnXS0Ubzoac+w1UDF7IpvWbF+4NegT43y8P1bcchOsuyTnY2Ubcsy7Isy7JOOl+f/gk97fPC/gsOOM/Z2Y38dO6x5zAqy7KsmE3ULcuyLMuyrJNKza/zuZ2f55qhS5DiwMPdnppZy4O1p57DyCzLsmI2Ubcsy7Isy7JOGk1/jk/c/1Y2hU/y6I4PM9c+cNX29akhpnpzdlx1y7KeczZRtyzLsizLsk4Kxhi+9MSfMhsJzlv7a5w29Ep+uv1j+GFzv/MroViXGuap5p7nOFLLsk52NlE/gQgh6IkylWgrvqjwDDW1LMuyjgopYaaxk2q0jUju/0bWWt0i2aIabafNFELaXt+tk9u26r1MtLbipc6h6OXoT5/GYPZMfjb5+QMuM5Yc4Mnm7ucwSsuyrGM8jvr//J//k2984xsIIfiVX/kV3vrWt/KjH/2IP//zP6fX6/HqV7+aP/iDPwDg8ccf5+abb6bZbHLppZfy/ve/H8dxmJiY4N3vfjflcplNmzZx6623kslkqNfr/Lf/9t/YvXs3/f39fPSjH2VoaOhY7s5xJQSMd+/jMw/eQtuvkU0U+bWL/5QR7zyMve+yLOsYMNLnkblvcfvPPkqgewxlNvBrF3+QnNhwvEOzDlKTPXz2/vcx2XgaR7pcc9Y7OX/wNUiTON6hWdZx8X93/QuzYoALC6cuTlvXdyUP7PlHzuhdSyax8l5ybXKIJ5q7nsswLcuyjl2J+t13381PfvITbr/9dr74xS9y22238cQTT/BHf/RHfOITn+COO+7g0Ucf5Xvf+x4A7373u3nve9/LN77xDYwxfO5znwPg/e9/P29+85u58847Oe+88/jEJz4BwEc/+lEuvfRSvv71r/P617+eD37wg8dqV1aFlpnmn+97D22/BkCzV+Ff7nsPXcrHOTLLsp6vKsFOvvjIXxDoHgCzrV18+We3YmTvOEdmHRQZcMeWjzPZeBqAUAf822Mfpezboaask1OpvZOp1naassDaxN4x012VYiR3AVtL39zvcmuTA2xrTTxXYVqWZQHHMFG//PLL+Zd/+Rccx6FcLhNFEfV6nY0bN7J+/Xocx+G6667jzjvvZHx8nG63y0UXXQTADTfcwJ133kkQBNxzzz286lWvWjYd4K677uK6664D4Nprr+X73/8+QRAcq9057uq9GYKou2xaO6jT8EvHKSLLsp7v5jorb0x3VB6ia2rHIRrrUPk02DLz4xXTK12bcFgnpwem7iB0Rzgzewpin/aDo/mL2F35MUHUWbHcgFegEjToRv5zFaplWdaxbaPuui4f+9jHuOaaa3jBC17AzMzMsurpw8PDTE9Pr5g+NDTE9PQ0lUqFbDaL4zjLpgPLlnEch2w2y9zc3LHcneMq6xWRQi2b5qokabfv+ARkWdbzXj4xuGLacPYUPJE5DtFYh8ohzbq+s1ZMz3srP1fLer4zRvPI7H+wI4TN2XUr3k84OQqpdUzU7l3xnhKS4USR3Z2Z5yJUy7Is4CDbqG/dupX777+fX/mVX+Gd73wnW7Zs4YMf/CBXXnnlsy5744038ra3vY13vOMd7NixY8X7QgjMfhpZP9P0A5Hy4J87DAxkD3re421oKEdRn87rL/zvfO7BD2HQSKF408V/zPrBU5Hi5O0TcGgod7xDeF45lufFavysVmNMsHriyvhn89LT3sxdWz8DgKdS/OrFf8zowOhxjiz2XB2ngz0vVsvntleOX7nw/8cnf/h7dOd7tH7BKb/EKUPnkksc+1hX3/E4/p4vx2S13EMdyvHcWnqASEiGsxsYLhT2O8/66CLGKz/kwlNftfK93BBzTpWhobOPemzPNRubZZ0YDipRf9/73scb3vAGvvvd71KpVPizP/szPvKRj/Cv//qvB1xm69at+L7P2WefTSqV4pWvfCV33nknSu0tFZ6ZmWF4eJiRkRFKpb1VuGdnZxkeHqa/v59ms0kURSilFqdDXBpfKpUYHR0lDEOazSZ9fX0HvePlchOtV38vbENDOWZnGwCcmX8Fv/+is6n3ShQSw2TlWsql1nGO8PhZemysvY7kR+5YnRer8bNajTHBaotL8qKxt3DJ+ldRa1UoJteSFqOrIr7DOU6He24czHmxuj63vfLiVG58wf/HXHcPSTdHn7uObt2hy7GNdbUej+NpNR6TY3lOHGuHejx/tPVr1EyWUxJraDa7+50nJddTaX6VyZk9pLz+Ze/lyfDo9E6uSJ531GN7LtnYDi4Oy1oNDqoottfrcf311/PDH/6QV7/61VxxxRXP2h58z5493Hzzzfi+j+/7fPvb3+ZNb3oT27dvZ+fOnURRxFe/+lVe8pKXMDY2RiKR4L777gPgK1/5Ci95yUtwXZdLL72UO+64Y9l0gKuuuoqvfOUrANxxxx1ceumluK57uMfhxGAccmIDY8mfIyvWgTl5S9Ity3puSJPglP7zWZu8mBQj+63pZK1exkCSIdYmL6ZfnY7UyeMdkmU954zRPDp7F5PaYWNq5IDzSaEYyJzBnurdK94b8ArssVXfLct6Dh1Uibrv+5RKJe666y7+9m//llKpRK/3zL3+XnXVVTz00EO87nWvQynFK1/5Sq655hr6+/v5vd/7PXq9HldddRVXX301ALfeeis333wzrVaLc845h9/4jd8A4tL8m266iU9+8pOsWbOGj3zkIwD8l//yX7jpppu45ppryOVy3HrrrUdyHCzLsizLsqznoYnmFgKjWZ85FUeqZ5x3ILOZidq9bB6+etn0Qa/AD+ceOZZhWpZlLXNQifob3/hGXvayl/HqV7+a008/nZe+9KX87u/+7rMud+ONN3LjjTcum/aCF7yA22+/fcW8Z511Fl/4whdWTB8bG+O2225bMb2vr49PfepTBxP+CcvILuVWA1QCIu94h2NZlgXEfYUEooYmIkEfRh+47xDr+BMyokcNJRI4OoOtFGGdbB6bvYsKaS7MrOxEbl+F1AaenPka3aBKckmHvYNeganeHP9/9s47yqrqbNzPPuX2O3OnV4beO9IUBRuWIHaNJZqo+TRqTGISjTGa/MyXRPOFNGNJTDMx9l6i2DuKgPQ+wAwwvd+5/ZT9++PiDMMMOCADo55nLdZi3nvKe/p+99uklPusl+Tg4OBwsOiVoX7RRRdxwQUXdBRre/rpp8nKyupTxb7MCAEtVjlPr/4tO1vXMyJvBvNGXUeAT//AODg4OPQlUqTY0r6I59bdScKIcMyQrzK9+Gxc0vkm9EcSooG3t/2bJTteJMtbwJnjfkCJZ7KTOuXwpUFKycqG14kpuRS4P/09pQiVLN9gasMrGZQzp0PuUV24FZ2mVJhcd8/F6BwcHBwOJr36UkejUX7xi1/w9a9/ndbWVn7/+98TjX55i5j1NXEa+dtH17OjdR0SycaGD/nP8p9gKZHDrZqDg8OXnIbUZh5c/jPak00YdpI3yv/N6obXUBTHw9TfEIrN2xUPsHj7c9jSpClWxT8++iGtVsXhVs3B4ZBRF91CzIwxODCq157wkHcwteEV3eSfeNUdHBwcDgW9MtR/8YtfEAwGaWpqwu12E4lE+OlPf9rXun1paUlUETPCXWT1kQrCqdrDpJGDg4NDOuS9omVFN/ni7c9g4kze9jeStLJkx3+7yCQ2DdGKw6OQg8NhYHndKzTjZWSwrNfrZPkG0xjdiGV3LZycpQcdQ93BweGQ0StDff369Vx//fVomobX62XBggWsX7++r3X70uLRuvcnVYWGS/MfBm0cHBwc0kgpyfR0r5ic4ytFFU4djf6GKtxk9VDh2qM7rYccvhxIKVle/zKZ3qF41d6/o3TVi9+VR3N0cxd5SA9Qm2g62Go6ODg49EivDPVPctM/wbKsbjKHg0emVsqsged2kZ066moCyt5bijg4ODgcCsoyJ5DjG9Dxt6a4mDv8cqTVq5InDocQzfZz5rgfIHb71A/KGk+Bd9hh1MrB4dBREV5DzIwzNmvSfq+b6Smjvn1NF1mWHqTGMdQdHBwOEb0aWU2bNo3f/OY3JBIJ3n33XR588EFmzJjR17p9aVGkmzkDL2Ns4RyiRhMZrkJyXEOQ9qGbHBFCYCoGqlTBqejs4OCwC6/M55tT/0BttBzDTlAQGEKGKHMqifdDpIQSz2S+M+vv1Ecr8OhBCnzDcdmhw62ag8Mh4YWKfyNcxeS4MvZ73ZBvIJXN7zJ2N1m2HmRDZPvBU9DBwcFhH/TK8vvhD3+Iz+cjGAzy+9//npEjR3LjjTf2tW5fajQZoNA1gZmDTidPH40i3Yds3+1KlBea3+c7K+/kt1sfoVrW43QicXBw+IQUXtYmbX5f8TH3VbzPTtt5R/RXoiRYHG7gj5UreKhmE3VWwmkt5fCloC0VpiG8nFHZMw9o/YC7iGiqgZTZWcg3yxWkPtlysFR0cHBw2Ce98qjrus61117Ltdde29f6OBxuFHii6i0erXgTgC3t1SxqWMt9039IDqHDq5uDg8NhRyiSp6re5uGKN4D0O+L9+jXcN+MGcp13RL9CKPDfug/4R/lLAJS3V/NBwxr+Mv2HFJBzmLVzcOhb/lX+V1Q1SKHvwFrbKkIlw11CU3QTRZlTAMjUArQY7VjSRhVOCqiDg0Pfsk9Dff78+ftc+fnnnz+oyjgcftpo58nKt7vIomaCilgNOd7Q4VHKwcGh39BGhMf3eEfErCQVsRpynXdEv6KdGA9te72LLGEZbIvWUOB3DHWHLy6NyTZ2NL/D2Kypn2k7Gd5SGiLrOwx1TVHxqx5aUu1OL3UHB4c+Z5+G+q233nqo9HDoJygI3KoL00x0kWvCKRTl4OAACgoeVSdiWl3kulAPk0YOe0NB4FFdJKxUF7muONfK4YvNI9ufJUiM4oxxn2k7GZ4SKpvf7SLL0oPUp1ocQ93BwaHP2WfczvTp0zv+jRgxggEDBlBaWkpRURGGYexrVYfPKZkEuXL4aV1kA3z5DPYWHSaNHBwc+hMZBLhqxOldZKW+PAb7ig+TRg57wy99XDvizC6yAk8WQ30lh0chB4dDQNSMs7bhVXL8I1EV/TNtK+AuJJqsx7DiHbKQHqAh2foZtXRwcHD4dHrlJv3jH//IfffdB4CqqhiGwbBhw5zQ9y8gti05LnsKpUfks7RpAwP8+UzKHE6G7N7b3cHB4cuHbUvmZE2i+IhcljZtoNSfx+TM4WTYzjuivyGlZGbmWP4w9Vo+atpAkSeHKVkjCMn9r4Dt4PB54Z3GlRTIJkoyjvnM21KERsBTSEtsC/nBtHc+Q/c7BeUcHBwOCb0y1J999lnefPNN7rjjDm688UYWL17MW2+91ceqORwuXLaLsa4hjC8ZipTSabvk4ODQBecd8flBt3VG6YMZUzzEuVYOXwrernudoFAJuA9OJGDAVURTtLzTUNd81KccQ93BwaHv6VXJyuzsbPLz8xkyZAgbNmzgjDPOoLKysq91czjM2LYzqHNwcNg7zjvi84NzrRy+DMSsBG2RNRQERh20NoRBTxHNsfKOvzOd0HcHB4dDRK8MdU3T2L59O0OGDGHp0qWYpkk4HO5r3RwcHBwcHBwcHBx6xerwVvJoI9c/4qBtM+gppjVWgZQ2kG7R1pBsO2jbd3BwcNgbvTLUr7rqKm699VaOPfZYXnnlFY499lhmzpzZ17o59BJFtpJsX0as6W2kUYEi7MOtkoODw+cIhThmbC3Rxjcx4+sQJD59JYfPBYqwkUYFsaa3SbYvQ5Gth1slB4c+Y1nTx2jSJOAuPGjbdKl+NMVNJFkPQKbup8lwDHUHB4e+p1c56scddxzHHXcckM5Xr6ysZOTIkX2qmEPvUGQL6z/8GToqQ3OPxF1diZ4zBCN/AgnNKRjk4OCwbxRhULflUbauvr9DNmzSVeQNOg9bdn4ihAB/dCdK/WYQKnb+cKK+Iiecup9jxTZA9VqyYglsXafdW4Gv7ARsETrcqjk4HHQqW5eT7y45aGHvnxDwFNEa30rQU0hQ89FuxjBtC81pdejg4NCHfKqhvmjRIsrKyigtLeWtt97ikUceYezYsYwYMeKgvwgd9p94ezmpeDOTso5De+6eDrlr3AnIuT8gqfgOo3YODg79HStVzdbV/+oiK1/5V3JKjgGttEMWCG+D+69BptJtioQvE/+ldxHxDzik+jr0HlWJoVeXoz97D5/MqLiKhmHkjiHlDx1e5RwcDjKWtEkkKsnJGn/Qtx1w5dMS28aArKNQhUJQ89FshMl3Zx30fTk4ODh8wj5D3x9//HFuueUW2traqKio4Lvf/S4TJ06kurqaP/7xj4dKR4d9YKbCDB0wF+2dx7rIhQzirYVADXhTvcpwcHBw+BJiGhFgD7e4tDGN6G5/StQGC2XaHajH/i/kDIZYG8qGt1AUZ8K2v+I2o+jvP4161PdRjv4j6rH/h+rOx9Vce7hVc3A46OyM1xMkSpa39NMX3k8C7iKaY1s6/g7pARpTTvi7g4ND37JPj/rDDz/Mo48+Sl5eHvfccw/Tp0/n6quvxjRNzjzzTK6//vp9bvyuu+7ipZdeAmDOnDnceOON/PjHP2bZsmV4vV4Avv3tbzN37lwWLVrE7bffTjKZ5NRTT+3Y9vr167nllluIRCJMnTqV2267DU3TqK6u5oYbbqCpqYnBgwezYMEC/H7/wTgnnyt8wYEYLbVgJjtkyvRrsDcdj/1mFIiiFOkEvplDJGAdPkUdHBz6JW5fMS5vNql4c4fM48vH7S3EIj2bay2JYvw9E8xMUIvRT/8l1vqboKVqV2SVE//eHxG2inbETaSeyoVIunaJeuR3UbWmw6yZg8PBp7x9G7pM4nflHfRt+935tCeqsaWFIlQyNL9jqDs4OPQ5+3S1WpZFXl76hffxxx8za9YsIF0F/tPC3hctWsR7773H008/zTPPPMPatWt59dVXWbNmDf/5z3949tlnefbZZ5k7dy6JRIKbb76Ze+65hxdffJE1a9bw9ttvA3DDDTdw66238vLLLyOl5LHH0p7j2267jYsuuoiFCxcybtw47rnnnn2p84VF8QzGUzIFBoxJC3QPKDOxN+02cK4xEB9EUR3Pl4ODwx5IkcXk4xaQmZt+h4TyxjPx2P/DIhMAb1Ql9c8GMHetYIHxnI46/nIYeyKW5RSv7K/Yai7Gu/kdRjqA9YGCMIceRq0cHPqGLa0rUbQQQhz8KEJNcePWMogkagAIaj6aHEPdwcGhj9nn28y20x93wzBYsWIF06dPB8A0TWKx2D43nJeXx0033YTL5ULXdYYOHUp1dTXV1dXceuutzJ8/nzvvvBPbtlm1ahUDBw5kwIABaJrG/PnzWbhwIVVVVSQSCSZNmgTA2WefzcKFCzEMgyVLlnDyySd3kX8ZkVJF+kfD6T9GjDsBkV2KbAh0X25DEtV2DHUHB4euSAnoQxh79O848vQnGTNrAeiDOn4XERtSe3jMLSA0gUTBuEOpqsN+oqQEssLsJrfb9cOgjYND31IX24JHz+mz7QfchbTGK4G0od7o9FJ3cHDoY/YZ+j5p0iTuuOMOkskkOTk5jBkzhnA4zD333MOMGTP2ueHhw4d3/L+iooIXX3yRhx56iI8++oif//zn+Hw+rrrqKp544gl8Pl+H5x4gPz+furo66uvru8jz8vKoq6ujpaWFQCCApmld5PtDTk53Y7a/kpcX7MVSY5AX/C8y0oK1zov1QdfQRnWSj8y8L15qQO/OjUNv6cvnoj9eq/6oExwuvYJA95BRWxokPAISuxnrLoE6qIjMAtehU68HDtV56u1z0d/uJ5mwSI7yYK/v2m5PK3CRl+ft8/33t/PRH/iinJP+Moba/XxGkzWUhIYQCHj6ZF9Z8SLi9k5CIR9FqSx2xhr2eT3787V2dHNw+HywT0P9pptu4re//S2NjY386U9/AmDBggVs27at4+9PY/PmzVx11VX86Ec/YsiQIdx9990dv11yySU888wznHLKKd3WE0Ige+j7sy/5/tDUFMG2+39eZV5ekIaG9o6/hRBIEUGgIW13D2v48ZQp6Ef5sReli0GJEW7MIzy07badPdE0BcuSPZ7b/sqe58YhzWf5yPXVc9Efr1V/1An6Ri9FkdgygiJ82Pb+tRMSiiD0rQJSf6mHuA0egXpZLi1aEppSKIrANA99+PuBnKcDfTZ681z01/speHYI7muEBhMUUOeHaA9ZGH2sa389H71BCIGqHvz7uj+ek758Jvqa3c+nlBJpNhNQZxKJJD5lzQNDl1nUtC2ntTWGZuhUtTft9Xr2x2v9CY5uvdPDwaE/sE9D3e/389Of/rSL7Gc/+xmq2jnQ+8c//sHll1/e4/rLli3jO9/5DjfffDPz5s1j48aNVFRUdISsSynRNI2CggIaGxs71quvryc/P7+bvKGhgfz8fLKzs4lEIliWhaqqHfIvOpJ2qureYO3mh3G7Q0wZexVB3wSQXQfeCbeNdbof95wA2BIjJEgqPReS8xqt6NuXwsb3oGwC1rBZRD0Fh+JwHBwcDgGWrGN9+RNUVL1BXvZYJo66HJc2qNf9z6WUaBN8JH+Uh9IusQOCSEDii1ahbHgLajfDuBNIFk8iqfYPL5tDJ20ZDSQvq4YmE+FR0Ipi6NpAp/7fXgjEa1A2vws718GYY0mVTiGhZRxutRw+hZZUOx6ZIMPdd+MXvzufcGInUkoyNb+To+7g4NDn7HfFjd2NdIDnn3++x+Vqamq49tprWbBgAfPmzQPSA75f/epXtLW1YRgGjz76KHPnzmXixIls27aNyspKLMvihRdeYPbs2ZSUlOB2u1m2bBkAzzzzDLNnz0bXdaZOncqLL77YRf5FRghBdcNbfLjid7RHa2hsXs8r715PKrqRQCyCKrqOugwhiYRsItmSpNKzV0ATJvqi+5HP/AK5/i3ky3eiPPETvFb4UBxSj1hKhHpjHbXJ1RhK62HTw8Hh84IubYLhNgKtrbjtrhNyQkmyZNUC1pU/RizeSGXV27z87newZHoCNCmaqEmupNHYgKXsu+5IzGsTyZfEfDbeRD088B3kG/ch172JfOwW3KufR3E6QfYrFEVSsfMFKpPLKM9YTYVnHUs33UfK3H64VeuX+IwWxMM/RL56T/qb+OT/w7X0sW7fV4f+R0V0KwIFXe27lA5d9aEqLmJGIxmaj5ZU++cqCtHBweHzxz496r1hby+pv//97ySTSe64444O2QUXXMCVV17JhRdeiGmanHTSSZx22mkA3HHHHVx33XUkk0nmzJnTEQ6/YMECbrnlFqLRKGPGjOHSSy8F0p79m266iXvvvZeioiJ+97vffdZD6d+IKOs2P7KHUFJX9RE5L32E/7jpxMaOxFR6H9bqidYhlz3bVVhXjtZSCbnjP7vO+0lCNPLYqtuoaFkFQI6vlG8c8Rv8FB9yXRwcPg94U0n0Nz5ALl+PAFyDS9DOPJGoxwdAyqijqu6jLuskU21EYtsR3hh/++h7tCfT9SzGFs5m/ojv45JZn7pftXErsr2xi0y+8y+8Y08i2ofFnBz2D1u2gj+b5zbcS8KMADCl6HiiqQZC3rLDq1w/RGvehmyp6iKTHzyCZ9J8J9Ksn7M9sgWp9n0dHr+rgLb4Dooz81CEQtRKEND6vt6Dg4PDl5PPbKjvLTf8lltu4ZZbbunxt4svvrib7Mgjj+S5557rJh81ahRPPPFEN3lJSQkPPPDAfmr7+UWg4XFnEY7s7CJ3Kz5ItSFfeBtvaSHhglxSUuKyJOJT0+skPcW/isMwQywEbG1Z0mGkAzTFdrK06nmOG/Ctw54L5+DQH9F31CCXr+8UbKtCXbURZeYUbFuiKC4URcO2u1b+drlDLNzyzw4jXSBI2dBkJcjXQP20d8ee7wjNjSge2SdtkRw+Awq8uf0Zcv0DmTzwPFqiO/io4kEmF59A6HDr1h/p8TMj9/aDQz+iLlaJdghSb3yuHMLxHRRnTiFTD9CUanMMdQcHhz7DGVV9TrBtF1PGXtllIOz1ZFOQLALDxB5YyEZ/Dc9t+DUvrr+ddeHltAljn9tM+goQE0/tKswZgJl16D0tiqKwo21dN/mWpmVIZd/H4eDwZURVBWzb0f2H9VvRZNrS1pR8Jo+5osvPJQUz0VwhKltW71rGxUnj/48N5nlc/V4F/2/VemrsfT9zVu4Q8IcAiB5xEivP+hr/mhDgjeaXiFL92Q/O4aAgLYsZg7/B5LLz2Fz3JoaV5KJpf8CU+1dQ8MuCmTMImdG13o2YdhZJb/duCA79i+Z4Fa5DUEvA58qlLZFOHcnU/TSlDl+qoIODwxefz+xRd+gbhABDhKluq0NRAqi2n4B3HF859j6amlejx6EgkkfgxQ0AbDt9CH9f/G0k6QH6mpqFnDn5d4zJmIJqg6VY1FpNtKUiFHqyCFoRmiKV+GadQva42ejvPwqDjsAeO5eUJxtVgmUdukrOlmUzLGcai7d3jaoYX3g8wtaRjkfDwaELliVhYDEsWdP1h+FlmELsCphRGFRyJjmhMTS1bSTDX0ooYwyazGRc4Rzer3iCyWUX8o9ylepoOjR6RVMLN320kjuPmkSm3fMnIurOJ/j1u4mmGtiqh9nU8CHrGz5idd17fLj9Ga6adi9u6YTAH250PUQ4WUdt6woGZo3AMBO8vG4BZ0742eFWrV8S07Nxf+P3tLVtIhFvJCNzED7/cGdi43NAItVArrekz/fjd+Wxs/VDAIKqzyko5+Dg0Kf0WY66w4EjBNSl1vDIyp/TGq+jIDCYr068lZAyFI8+nNLC4QRbWpDPPgOGiSjMYWnjqx1GOoBEsrHmBQZnTsYtLJ6rf5e/bv4vAB5V5+bhJ1G3YgEpI8KIwWcw6au3k7C9rI2089zq9QwIeDlnQAE5qhuj54LxB52y4ERmDTqXRRVPIpGMLTiG8QUnOmHvDg57wSwrQRs5CLmxIi0ozMWePAZ79zk26SPom0SGf1JHxLptw1Fl51MT3oLLPZTqaLLLdpuTKZY072BGVj5B2T3vU1Fgg9rIk5t/SWu8noLgYM4Y9x2eW3sXrfE6GuJbKfU4hvrhJmxH8GuCmBHm5Q1/w6sHOHbYxUQT9eQ5OerdEXGWVjzKpm2f1G4RHH/kr8jOmOWMdfo5ltlKQJ9w4BuQQEKCW+wz1tSrZ5Mw2jDtJBm6z/GoOzg49Cm9Cn1/6KGHusnuu+8+AK644opuvzl8NmKyjn8s+QGt8ToA6iLb+PfHP8ZQ0h8EKaE9Kwv7WxfAFefAhachZU/ebxtdUag2GzqMdICEZXBXxfsUDz4dgE3bniUS28HS1lZuXbKaQUqKr7atoPSR7+FZeDsZ4S373ad+XyREA5WxD9gafZcoVXyyaV1mcuLAq7n+6Af53qwHOHvUT/HI3IO2XweHLxoxt4fkmXPhW1+F/zkP49IziXh7Lqi0p53hlQVcNP4ORuVO6vFDEDHDbI1XEZE7WbpjIdtji0mIdE57s13Nv5fcQGu8HoC69m28Wf4g08vmHczDc/iMaCpsaljC1qYVAMSNCC+t/wua4hidPRFLVOxmpANIFn38a2zZfNh0cugdwo6R4T6w8YJosdHfiOF6J4brlSjKTnPvywoFnyuH9kQVQdVHQ7L1ADV2cHBw+HT26VF/+OGHSSQS3H///SSTnR4XwzB44IEHuPLKK5k/f36fK/lloyVZQ8pKdJG1xutoT9WTvSsHS0qIur2Qny5iMm3AaXy888UuIeJHDDgLr0WPM771iVbwjOz4OyVc/HtTBWUBH1+LbyDz9bvTW6paj1z/Nv5v/o2Ir/QzH1uMWv625DsdkxAu1cvVM+8lUxmcPi5bIyBKQACHLvLeweFzS1LRSGZlH9C6iu2hWPNw6ajB3L9hW4f8pAHZLGl6j/HByfzpg+swdr2PcnwDuHzqb6mLVGHY6W+CQMGrB2iJ1RFwhchw5VDkLvrsB+bwmbFsiw11H3aTR5JN4D4MCh0ihOixTuqnkuwhjDmRbMW0oujqp3dDcDg8xM0EKiZB/QCuUdhGXxzHHKojs1RETKKuSwFgl/Y8RPbpuYQTVQT1YsqjVT0u4+Dg4HAw2KehrmkamzZtIpFIsGnTpg65qqrceuutfa7clxW/Huom01UPXi3Y8bdAw0opqJoE1SRfH8mVM+/mw+1PYUubaWVnUegZg21LCj1ZCEQXI36gLw87kv7ACKHicYW4eGgMr7WFNbE2Bp97HYVvPIXSXAVGAqW+HAZ9NkNdCNjctLjDSAdIWXHerXiY04f/GNs6eF57B4cvK0IAto5lCDSXjc3evUMAwob5RYUU+SU1sTi6arK2bTkCk2UVT3YY6QBNsR1Utq7A7xsCwBGlp1CYMZhwopEMTy4DgsOZmOslq76ecMFnn9hz+Gy4pU1+cCB17du6yL2a7zBp1LfEqGV72yriZjsDM8eTrQ+F/cgvD/hKUISGLTufmdzsMbj0HHoMWnPoF1RFK7HRUPejPS0ANujLEliDNGRWel3pE1ijXGhrkqSyVfB1H5d4XTm0xXeQmTXcyVF3cHDoU/ZpqJ933nmcd955vPbaa5x44omHSqcvPRlaMaeMvJKFG+/rkJ097of4RH7aS2C4WfaWSWONidsLU451kZEnKNDHcs7IdP9z07SR0qbKrqPdinHT+Iv4/brHSVgpCjxZfG/osWxcfCs+by6zjrgZU0b5cNNNxHZ9dBShcvXJ32PQI38AaYPy2esOCiFoinavUl0X2YbEwqlt6OCw/wgB9bRQEa3Bp3kYkhzJx6/ZRNslwWyYdoIbzZvc5zZctmBU0M+zO59lZcsWFAS3jr+AFZve67ZsS7yW0uw5zBtzIztbl/Lfdfd2/HZk0cnMX9sERw4/6MfpcADYklmDz+G5NXdi2mkv4ci86biVL547PUYt9y25lnCiEUi3HLxi+u8pdk/u9TZcWiknzPoN7y/7JbF4IwW5E5k5+Uak7bTf6s9UxSqRime/11MqDKQusHO7GvjSJ7CKNLR1Scyp3bfrd+VS176aAbqf5lT7Aevt4ODg8Gn0yjKaOXMmt912G1u3buWPf/wjv/vd7/jRj36E399zLqTDZ0PYLqYVnsuw7OnE7GaCWj4Z6gCkFHgNkw/eVmisSS+bjMMHLxkcf56G5k1hmulpfyFgh7GNpuY1GO0V5ARK+eOkr2GrmRTo2fhRGH7cv9BUH4rIZEXjsx1GOoAtLV5teZPLhx2BWrMFK/+zD7xtWzIy7yjeq3i8i3zGgDNgL9WlHRwc9s0Ou46/lb9AwkpyQsYs6t62MHd1V2tvhg9eMpl9pv6pbQ6zZSb/O+YKGsxWXIpGvpqFu+wMXlh/V5flBmdPJmB5GJg1kv+u+78uv31Q8zIzjrudAt+gg3mIDgeIpubT2FbBSSMvx5ImmqJT07YFn5J5uFU76OxsX9thpEO6oOpLG+/l8kl/Qti9m5iQUpDpn8Kps/+ZDnfXspG9XNfh8NEYr0Io+xklYoFWnsIc5Sada9cVu0hFW5FEtNnIzK5VPHyuPNoTNQQ1H21mBEvaqMLpduzg4HDw6dWb5Ze//CUZGRk0NTXhdruJRCL89Kc/7WvdvtQI2022NpzJJSeSqQxGSA2PZWJsaaC+hzbFsT3S0JNqnMbtC9my4nds3/IUG1feydbV9+ATFj7bi7TdaEoRgiDx1GZaYzu7bbPdbMOedibykruIegoOynEVekdzzvgf4dWD6IqbE4dfzojsYw4on9DB4UuPmsIIr2dubAOnpyqZFLTwZ3YNdY9HJMl479JKXLaLEiWfPLKRlmBc3okcN/QSNMWF3xXiqxNvpWBXbQtp9RxSX2/UE9edQWt/wDJVJmWfRUO4glc2/IP3tz3F5IJjsaMjDrdqB5240T0EOZpqxWbfE1R7YhGnMVVFVaKCsFWHUJyY9/5OS7IWTd2/qAdlh4EdUJA9hLanFxDYhRrK1u73j0sNYEsDy4rhUz20GZEDUdvBwcHhU+mVG3P9+vXcfvvtvP3223i9XhYsWMBpp53W17o57IHWFkbW1+ENlBKPdLVsXZ6uHxvLaGDblie7yJqbVmMn6sDX2ZYnltzMi29dxfgp3+m2v2MGX0AqdDSJg9geTZVexmfPY8RRs5BYeMju2krKwcGh17RH1/Hx4s6e2DW1HzJr0r2sem1ch0xRQXcd2PZdMovZpVcwZ9hXSSUkusxE7nofBNwZ5PoH0LhbOkuefwDRtgoivgoC3s/QKsnh4KCkaGh4H39rFReP+jaJeAOrPv4Ds6cNAvq+5/ShpNA/AIHSpU3pjJKTcQkXve0waos4b27/O+/vivoSKHxj6h2U+WY67dn6Me3JBlRlPyI8JajbDKyB+j4XswtU9I+TWElXum3bLoQQu7zqVWRqfppSYbJdGQeqvoODg8Ne6ZXbQ1G6LmZZVjeZQ98jNBXXsmVMm9DOJ1FWbi+M/EqY7d7N1NKIvWv2X5UgZffhye6fJUURbK9+G5Ds3Poi54/5HiUZI8j1l3LamGsZljWjT3qY27ZEtzNx2Y6R7uBwoGiawsYtT3WT14ZfIJTbOaicMkdDde+fVxEAIWmgmY3J7USkii4zOowVRRGsrXuXs8Z9h0lFx5HpyWNS0XHMHXAmW7c8j2UfwP4cDjqqbGP9tvtpaFzN8uV3sn7Dw8Ti9UQTmw+3agcdEQvz1bHfY0DmaLJ9RZw09GL8yST2ftyLzUZlh5EOILF5fNWvSAqnPVt/Jm604NYCvV5eNFlgg8z4lEgjVWBnKyhV3aOHfK4cwokqMvUAjU5BOQcHhz6iVx71adOm8Zvf/IZEIsG7777Lgw8+yPTp0/taN4c9SASDeMcOI+/1ZznpqDlE9SDVA1v4wZr7iJgJFATXjDyTU3Jn4FEKKS06ip01izrW93pyyPEPYbfi76hq2tXW0rqZ8Ip7mVA8i+ys4WwMb2VCnlOF3cGhP6Mq3V3lqqYzbUqEWKOJvywDkWHu94SbLWzeDi/nt2sfxZQ2HtXFryZ9k7HuoUgpURRBZcsqtjYtZZx7MGXZR9LaspmVW9/E4w4R9A88WIfo8BmwhYbSQyFQfX+rY38OCAbLWPT2tYwpnIHLN4Sa8oWUjL4UCNDlo7cPoqnWbrJIqpWUFcUlnPZs/RXDbCfTO6TXy6vbTewClZ5y0/fEztPSYfJDunrffXoO4cROgtoQp/K7g4NDn9Ert/gPf/hDfD4fwWCQ3//+94wcOZKbbrqpr3Vz2ANDKBjHHYk49WiC7dW4i6P8YuMDRMx0+yQbyV0bn6babMS2XUwb/30mjv46mcEBDB80j5OO+SNC5nRsz7YlA4pmoyjpD5BlJdm+4w2E7qMgYxTbjTDvR1ay0aggIfZdNdrBwaHvUVUFVU2/tk3TZtTQcxG7FTESQmVU/olkVm+maICFkmF3CQXeF1JIdso6FkVXUW5u5zdrHsHc1ZMqYaW4bdW/aBPtHfseX3QCGxo+IurxoAgN04gwpGwuJx1zJwq5B/nIHQ4EhUyOGPetLjKvJ5uM4BcvR3271Bg65QZSRoSWxnWUDDuXjWomtuh92Fa2twRFdJ3EKM0ciU/N2csaDv0B24ri03tZINGQKHVmt0rve0MGBUpCQrTrZI/XlbvLUPfSkGzdT40dHBwcekevPOqmaXLttddy7bXXdshWrFjBpEmT+kovh70Q113EywagDCqj0WqkNt7SbZmGZCtl3kIU8hgx8HLGF56EVr4U8dp/YMIpxPPHYIh0JVuPPoTz5vwV2bAF20ySzMpnVXwz9a4yfvLRHzq2edaAo7m8bB66ve+cLgcHh4OPioW/eROsWgiqBuNPJpI1DJ97BKfO+TNtTWsoUnLx2TrCnUfkiCLs/cipFQLWJLdy47I/YyO5YvhXsPfwQrYZUcJmlAwlHWI6OOMIjh16MS9veZBsbyEnjrmEYdlHoVhOrmZ/QUpJRvZEvjrzTmTzdoQng2RWEUk1iO8LlHYkBGxqr+Leja8wI3c0mZkeFlVvQFM2c1TuTHz0rtBYhlrC14+4g8dX/4pIsoXSzFGcP+EWFKc9W79GyCQBPdSrZZUqCzukgNbLiEEhsHNU1GoDa3hnBJPPlUN7oobcbB8NPURiODg4OBwMemWoX3PNNfz1r39F0zQsy+LOO+/kkUceYfHixX2tn8NesG1Jpuqn1JfHzlhDhzyo+xiAQiC6g1igFF/7TsQ/roRUPD3sXvUyvvN+QXjg0UgJgUQj4oW7kRVLAQhkFnL0+T9n7vp/dNnf0zve49SimZSphYfwKB0cHAD8zRuQ93+bjvYIS58hcNk9hEMjydAGUrDzA+Q7f0r/pnsIXLyA9txxe+2m4LITuFu3IdobkKFimrKK+e26xzqMc11RUYWCJTutuRJfLtl6kE8qc+kykzml/8PU4jMQgE/JQ1pO7ZL+REo1KKqpQj75v6BokIrjH30M0WMuwtyPUOH+jpQw2F+EjeSDxk0d8lOLp+Ol9/21pa1Q5pvBt2f+k5QVxaflolj735/b4dCRsmKAjV8L9mp5pTKFXbJ/7WDtLAWl2oLdutS6VD9CKPgVSbkT+u7g4NBH9OptNWLECL7//e9z3XXX8aMf/YjMzEyefvrpvtbNoQdiSpwdyTpM22KwN5efjbuIG1f8A7/m5uxBs4mlYqxu2YoWNxiUm8A0w9izzkWp20bTsLG0KwYhq4GgSBHFh1q1CmuXkQ5AWy36sucZmlfAhvaqLvuOmHH44qU2Ojj0azRVwEdPsLvVbecPI9XSiJo7Fk9DJfKd+ztXMBLIZ36J+xv3kVC7D151mcSz5EHkew90+syv/iunlx2FaVu4FI2d0Qa+OWIez1a8x/kDj2Cgx49bC7IqvImxgSJypECzPSSVDHykWzfKL5CH9otCZrKVRPmH1F3xa9rsNtyql1BjK4VNNbSVfnEMdYDReogLiqbxaM1SJJIh/gIuLTkKYe9frZUUKaqNOG1GjCIRI1+4QTr1WvorzfF6DHRcPdRi2BMRthFJuvVF/zRkhoKyyYCEhN067PhcuWhW2Ckm5+Dg0Gf0ylD/8Y9/zM9//nPOPPNMbr75Zi6++OK+1suhB1pFmNtW38/68HYActwZ3JV/FPcXzGFrQSk/XHZfhwdsWmgINxl+ylfch+7NwTjiaJ7beB8SG01xcW7uABaVz+Sq6Bb2LEcltq9i1vBzuhjqAc1LsSentzV5HBwcDhoC7F1ubEWlft7tvJqYwNrtfiZagvPcTd2LjbTWoKUi4O1uqHvaq5DvPbCbIMDWeCN/3/wi5q5OEScVT6Up3sYvx57KA8tuZqkVRyCYO+JyGra+yPqKNyguOZqhw8/B5y0moufs1XvvcPhwSZvtM47n/o9/TGyXMXHUwLOYXTi/23v/805W1XquWvYBp004loSA4vodhF64k8RZvw+PN5QAAQAASURBVCQle5eylVIMHq56jYcrXgdAEwp3TLmK8e6hzv3dT6lLVGELF0J8+mSKUmFg56vpXIn9QQjskIJSb2GXdQ6bfXoOmC00p8L7q7aDg4NDr9inof7Pf/6z4/8lJSVkZWXx8ccfk0qlALjsssv6VrsvMSnRRk10HR9v2EGBfygF3lGsDJd3GOkATckwjyZr+W5VA49EN3YJUz0/Zwhr3v85AKExZ/DvjX9B7rKyTTvFc2tvY3LRv2gNTqZwfASZiiLL3wLLRBlyFCeVzWZtvJHFjesZlVnG9aPPI4sMx053cDjEmJYN086F9e8QnXYJ99ROZ0JZE3PyFxM3aon6R9PNHC8ajuHeS3GlRNdBZcu00/jl9tcxpcWIQAmTvcNY1b6NH46ex5PLbyJlxQGQSF7Z9HcuHX4NcusrVO14C8s2GNfmJ2PIkcQHziSlOLm8/Ylmn5cXlt2Nrrg4puBcmowaPqh8ltH5Mylh6OFW76AhBNBWj6tiBYMrVnTIZTAXxUyA2jtDvcpo6DDSAUxp86s1/+G+qT8kIPejT7fDIaMpUYcQvZh2MiRqlYkx0X1A+5GZCkq92cVQ97pyiCZrkEDUTODXnDQJBweHg8s+DfVNmzZ1+fuYY47pUb437rrrLl566SUA5syZw4033siiRYu4/fbbSSaTnHrqqVx//fUArF+/nltuuYVIJMLUqVO57bbb0DSN6upqbrjhBpqamhg8eDALFizA7/cTDof54Q9/yI4dO8jOzuYPf/gDeXl5+30C+iO2EuelTX9kZU3ngOH4YZcQcXev1Ls6Xk8yt4xmcyeaUPBpHgzbwhutI7ZrmQRGh5H+CTEjzDCfJLh9EtbaYQiPjXrc17CansGYdhqZdohbR36d2Mg4Htzotu54FBwcDhOx3DH4vv4n6hOZjDVaqW7/Xyp3LAdgZ2AE3zr9B2gv3Q1GArJKEKf/hKTi63FbdmYxissHqfQbIhrMoal1C3eUXMakFUUENghiw2ehDHDTnujePzpmd3aAqK1axKBpt1JuxAhH1lGUMZBCkeOECvcTDLOdcZ5jmNp8PIG3PZi5No1zmqlLbmM/Urf7PVICRaO6ycXEUzD0IL1sfEBLqr2brDnZTsxKEBCOod4faU02IpRPN77VSgM7SwHXgb2b7JCKvj2ZjirctQm/K5em6CZC+igaUq34NaeGj4ODw8Fln4b67bffDsBvf/tbfvCDH+zXhhctWsR7773H008/jRCCb37zm7zwwgssWLCABx54gKKiIq666irefvtt5syZww033MAvfvELJk2axM0338xjjz3GRRddxG233cZFF13EvHnzuPvuu7nnnnu44YYb+MMf/sDUqVO57777eOaZZ/jlL3/JH/7whwM+Ef2JltSOLkY6wJvlD/KNI+/ptuxJwUEE3/kvN593Pe1NGxHxZsxgCQHdQ9OuZbzoKELDlmbHenn+MoZWZ+J+rAVJ+ttjV/qpv+Zr/KvhLS4ZeBbtUS9e3Yumm/R2pOOyFVytEhRBMlNiCMe6d3D4rJhCI5o3gQFtJiWRVsK+S6jPP4alLUtZ3bCIu5Rn+daVf0MkTJSMXBK7KrP3RMxTQPBrv0U++yto2kFWLMr3ys7iyKeKUHZaSMDbBGxNcuZ53+XJyt92WT+gePjEnHF5Qixxq9xS+SZUp0OFFxxxNaNdg52JvX6AXwly1PZT8SR8iJkqKlD4okrWhfkYh1u5g0wseyi+M3+CfPlOSEQRk07Fmnwm1q5Pl1CgWcaRErIVb4+ftGJPDgqiS8eD4cFSMtXeG/sOh5ZwqhFV+ZRZJ1OibjUwRx2YNx0Al0C6BKLVRmalk418ei6RZC2h4BE0ptoY5HMMdQcHh4NLrypqvPXWW/u94by8PG666SZcLhe6rjN06FAqKioYOHAgAwYMQNM05s+fz8KFC6mqqiKRSHS0ezv77LNZuHAhhmGwZMkSTj755C7yT3SaP38+AKeddhrvvPMOhvHFGHoYdqKbTGLjEzrfGHoy2q4+r3Nzx3BSSwRz8gm0LrublqV/onntg4Q//D/ciTaCs2/DP+MHGM07OWfElbjUdFhqwBXi7BG/wfN2ZM+d4N4seL9hHR/V1/B/r5v8/GWTd7erWL2oIueLKbj+1Yr8dT3y9jrcT7fjddqvOzh8ZnRL4F+cRLmjGe2PNtn/HkRpZDhH+sZy0ujv0Rir4rVGD9e8N4THt2SR2sccrJSScNZojEv/jH3to+hTvso81zSUnVbXBestJusnkuFJ90TXFTdnjbqG8OZXOhYZOuVqFlS82fG3KW3+b+2jxJXu7zCHQ48e9+DLzsReH8d8vhXzxVbU4V687V8877Ap3ESGn4T1P//GvvYRYsd/n6grfe/GhMHDNSv4+vsP8/VFD/G37YuJiO4fpwIlh/+ddDkZejoaZUigmJvHXYzb/qJl9H9xiKSa0dR9G+rqxhR2poL0fbZIH5mhoDR1Ojw01YMqXGQokkanl7qDg0Mf0KticqWlpVx++eVMmTIFv7/zA7+vHPXhwzv7WFRUVPDiiy9yySWXdAlPz8/Pp66ujvr6+i7yvLw86urqaGlpIRAIoGlaFznQZR1N0wgEAjQ3N1NQUNCbQ+rXZHsGkOHJJZxo7JCVhcaSpRdzQeFQjis4gtpIA2WRdlStjeZsP+Et93XZxpbl91E/5RrurHibswYcyTfyZ/HdguOIGW349TzsVAEy0LDnrjG9NknLIGpFgXRI4VMrLUbmaRR4rG7Lf4KiCNTlCewNnYMfuTiOMlKgjA9g2457zcHhQBAi7eG2ntytsnCjjf/1EsypL5MaMJHTJt3O6ytzAHh9o8WEIp2BAXMvW0yTUPzgSb/PA7rSY/2JDfEwI4bcxCi1CRWBDx2Gnox3+OkEowbrpJvWVNcJv+p4Iwk7heeLFFv9OUVRNKxlEWTDrnvBAvP1MPrwnMOrWB9h25KYntVNvjZSy3/KlzEuuxBVKDy7fS2DA9mcmDW8S+SHkIKpvjH8bfqNRK0E2VoGbssx0vszcaMNTdl7azal2kSpMjEndPWmS2kTj7xPKrEWVc3Gl3kKqpqxz33ZmemCcgzrlPldeUCCBsdQd3Bw6AN6ZaiHQiEAqqqq9r1gD2zevJmrrrqKH/3oR2iaxrZt27r8LoRA9hAjuS/53lCU3rfcyMnZe2jo4SfItTPvYn3LDlpTBrluF6NyBlOQmZ6EqGpsZ10kjJGZRSKQwaDYjm5bsMwEXiXtBX96xwecUDKNI/PGArAlXM0Ldc9z+vFHULCJzkruPoXy4nritUn85HbZXltKYdyAvX8MpWGTWNHaXY8NcdzTFAL+/O7r2BJicXC7EPr+9TX9hLy83vVOdegdfflc9MdrdTh1klIiqxuwa+oRmoYoLUDJzeqiV1W0jXdrt3By4wD2DNoUW2wK553CVnsHT1atIF+b2PFbU1wwdXDvj832JjHmBLB2j7KZ5uXZ5DJe3bCZuydM4uPFN6GpbkqGnYOlexlRNolcw0AgutTAODp/HKVZeXjVvjdwDtX16+1z0d/u8VhVC3Z5d8+xHTXJy+t7Y/1wnA+ZNMA0Ef7OwoYba+u4btwUPqhfjiktvjVmMhvb6zh36EQ0pWu0mC1tItEIpCQur0JeD50TPgv97R45UPrLGMq0IwT8JQQCXScGZcrGXhPDqkihTvSjBzrHh1JKWptewjYbyMw6nlRyJ9HWx8krvgxlH/nu0qVjlbfj97oRanosmuHPR5CgTUS6Xdv+fK0d3RwcPh/0yjr6JFd9f1m2bBnf+c53uPnmm5k3bx4fffQRjY2dXuL6+nry8/MpKCjoIm9oaCA/P5/s7GwikQiWZaGqaocc0t74xsZGCgsLMU2TSCTSMaHQG5qaIv3Wy6ths6FJ41cbIGoKst2Cn4/RcJlhTFuQkhFerH6Zhq2tuBWdu8ZfiKr7sIxYxzZCZbP5b2tlx9810WYaGtqJKFG+9/FdVMebeNe7ipuuPJdB1dngFqzM28Fdbc9z46greW9l10FcdriR5GuVWCMHE/V0L1KlKOAbrcL2rvLUgDjJSJJ4rGs1aH8ijrpsDXL1JkRpIXLONCIZGfuV15qXF6ShoXvxny87n+Uj11fPRX+8Vodbp4yWZuTfngBzV6RKVgb218/EV1ZIQ0M7hmJxx4Y3KPb7qXb5GUzXytX2AJ23yUPxCNbs+JCLspNsqE0/m/lWhMSWKJHMzH0+U5q08VXXIF/7EGXIcPTLR2LXAiUumkpNXlu2GYDVMZOZk6+n1VfKz9c9Q0tqHV71A24ccza/OeJb/GL1A7SmIkzLGcnVw84g0pwkQt/mvRzI9TvQZ6M3z8Xhvp96Qrht/IN05LauaWFGCMJ9rOuhPh+KgEBDA/LVRYhIDI6aTGrEEJIuN2OzQtz8cWfnk6VNG7llwjcIt8axrM7ragub99tWsGD94yRtgzxPiNsnXk6pUnxQdOyP90hfPhN9TV5eEMOMoEovkUhnuo1otNA/TmCHVKxxLgzVgnhnRGAqvoFkbDvejOMxbQ1FH45ItdLS8A6+jGP3uU/NK4jtjCFz0hM8OpkY8Wpq7Pou17Y/XutPcHTrnR4ODv2BXrmgly9fztVXX83Xv/51Lr30Ur72ta9x7LHH7nOdmpoarr32WhYsWMC8efMAmDhxItu2baOyshLLsnjhhReYPXs2JSUluN1uli1bBsAzzzzD7Nmz0XWdqVOn8uKLL3aRQ7qK/DPPPAPAiy++yNSpU9H13rVg6e80pRL8bF05UTMdrticTHHrunKiRoqEkuT/Vj9EQ6IVgKRt8IN1TzDk2F8zePT/MH3UnRw99iFyyq5gbbgzAqLQkw3AjkQ91fF0mblt8VquqruL8z2/pW5GiuJBRfx+8nVM9I/i5JI2fjZ8GT8f+iG/ml5NyZIPkC++g/L4y3is7iG1tg32dB1Z2BnxYA+HxvxyXFrXUEQXNurCd5DvLIWWMHL1JvjHk/gS8YN6Hh0c+iu6kPDWR51GOkBLGLWyuuPPOjPChw2VZHlUHjLfIXrsbp4/v8K201zctWUb2yNuZucfwfYGDwI4eaDBwOUfwl8fx9++7wGPr6ER+e/noLoee81KhHcn6sAICi1k2xZXjzoKgFdrq4hqAX65/tmOythxK8kv1jxKvivE45O/zdtDL+QPgfGUxVP73abYoW+IaVB7hg9tfghtfgj9zCyS5wTZkdv/KqMJu4FE22KSbR+hyMZPX2EPAq2tyL89CRXVyMZW5HNv4tqwBVUVfNS4tlvnk1erFyP3KHZab9bzy7UPkbTTExsNiVb+d82DpFSn2Ep/RdpxPOpu3v2oRF+awBzmwhqid6vybtsJYu1v4vZPRYhOX5XLO45kYg22tUftnj2wM1WUxs4xkM+Vi2U2OqHvDg4OfUKvPOq33HILZ5xxBi+//DIXXHABr7/+OieddNI+1/n73/9OMpnkjjvu6JBdcMEF3HHHHVx33XUkk0nmzJnDKaecAsCCBQu45ZZbiEajjBkzhksvvRSAn/3sZ9x0003ce++9FBUV8bvf/Q6A7373u9x0003MmzePYDDIggULDugE9EfqEimMPWaqBeCO6rijNt/MOJm/2QvZFk/n64eNGFVJP1M2no7rvfSs8sCAmz9+/SqurbmbS4eezEBXIdigiO5zM+1GDClN8kQBbpnEG1lJ2YaHkBvfTy/gzUA79seYG4CdteitbSRyuodNxoMuxFU2ie2NWDJBk3cbhWUzkHZXD7wrGkWu39p15VgCpakVip0+zA5ffBTbhvYoyqyjEa5sUC3sTSuhdTfDetcrQEGwsGUp/lEezhh3ErF2k0pPkj9Xr8GUkrVNSX40eRaJvGXkuArxVsVJCok7nINWVQcje/YMqKqANZs7/tbmH4v5pgd7TdqIE1kRLvrWCCbMLKbViqOpSRqTXXuwW9KmId7A0Ofvhh2r0/q6fAS/fhfhjCEH8Yw5HAgpS5A0DTA10ARIUE1oT1nk9qMSAsLcycdvfIdUPN0O0O3LY/Lxf0Sq++HJ3lnLnuEj8r1l6ONH4la6T+J7VB0hBexmwNfFu9dtqYzW0Z5qI0ftnr7lcHixbBOkiU/rfMdpa5JYxRoyo2c/VCK6GE0vQtWyu8gVxYvuKiMRW4EvePRe9ymDAqXOgpHpv72uXBKpRhqtVqSU+0zPdHBwcNhfemWoCyG48soraWlpYciQIZx++ulceOGF+1znlltu4ZZbbunxt+eee66bbNSoUTzxxBPd5CUlJTzwwAPd5KFQiD//+c+9Uf9zR47Pi0JnN5hMl86fssfi/20zJCSz1ULGnHsZN/oeYHMs7TUf01aI671o50YiNiNeCfLz87/Bg1WvMyNnDKWigBJPLsODJWxu7/S2f6VkMm9u+APzhlxMyUsPYtZuRhk+E+XYy7De+ifEw9gVryNKRyN31ncZDAnFJmrXkTSjZLgK0fz5uEZ7sewoA9SxSLt7vpdUVISqgrVHcboDzFN3cPi8kVI0fCfMw/h3DNlqgQB11jGIEZ3PQIEe4Mj8QVRFE4zKKOPlxqX4S8fycENXY2JCdoDHl1xPfXQ7Pj2DK8bdzJrUB4QnJji1aOJew6akBALpSTQxsBDZ6MZes9vvLRJeizLs/GwyC/1saaohQ/cR3i3FRiDwqVAxdSb5ZcPxLXom3Z/97b+hz78Ngy9GlNPnlTzpxhvRsD5o6bjP3LOCjEn4ie+7btYhQ1EEtZWvdBjpAMlYAw073iR/yNd6H17t6qEmgs+LLSUn5Y7hicq3MWX6myMQXFByZJewd4D8HqqH53tCZOEYX/2RWCqMjYrnk+sWlygtFsbgnvPMbaudVHwN3oy5Pf6uu4cSj7yLN3AUogenBoAMKiibDLAAFTTFhUsLEMCkzYwS0vtH7r6Dg8MXg16Fvn9S6b2srIzNmzfjdrux9jSyHA4a2S43148b3nFxLssvo+jRGCR2DSosyH1McmXGKQgElw2dR7C9+6UUWw0amltY1bqNuzY+jaWYVMbrmF92FNeNOp3jCsbx7eHHUybr2FS/iKfW/Ymk1wPtjdgfv4Cs3ogydBoAsmkLItsPpfkYWZkA2CLJiobn+MP7l/CnD67gvqVX0y53gAyiisIejXSAhM+HOGFGV+HgUoys0ME4fQ4O/R7dFhgLU2njCUCC9Z6BTAR2W0blOyOOpsyfx2mlJ3DJ0FMpDdqMzer0HpUFfAzybKU+mi4OETPCPLrpHo4KTmN921LeaVuIUCRCgKYpCJHO5fWmVFyGQI4ZBi4da1AJZnt3y83eYqGZaSMlUwa5dfylHd5JBcFVw0/ktQ2/4+6q+7jXtYzmed9MH05tOarthAsfbtwpif1O+x73WTuu9v5Tn0VRFMJN67vJ25s3oii9N5Ct0oKOiaeObZ80C0PVGLnmA/5SeiIXF0zlvPwp/LnsFMYuexVN7br9wZEIVxcfhdhlmHtVNz8rnEV2IopD/yOaasVExbOrcKW608DOUdMvuR6IRz5Acw1GUXqO3FPUDITwYaYq9r5TVSD9AtHSOQb26bnkqjb1yZYDPhYHBweHnuiVC3PChAl873vf47vf/S5XXXUVFRUVqOqn99V2ODCEDbPz8hhzTCZthsHYiB8ie4TkSZgih3PL8B+zbEsIWZDqNuefGqWyOLEJgI3hHSQVg0WNq3GpLoZpSbLb32V1zQ4C7iyG5kymqm0z8eJZuFal+yLbmxahHv9NBEEY/y2SZgZ6rouU0o6i+GhMbuPZdb/v2F9DdDvPrvs9F427A7GPvrOWhMSksXiKC9LhinlZmMUFJDTH++bw5UBLCmQP1bhp6Zo7nJICnQy8iodcVyED9QjluduZUVyET3FzkigmuaOOCbn3slpdxOt1D1Af3Y4dbWFy9gyWVi9k8qALaTZjLG/YzEneiRQt92K/GYWAijg7hH31RTRozfjLG/Dt0VJNGecirtr4SVdKHu8Zyr+PupnaZBOG2cqijXdRG06Hz9dGK1gWqmduqBAxbi6GFugMC3I4LFi2gdjW/T6TMYtefv77HNO0KBpyKk01S7rICwaegGn2/gaKev0ErjgXZUc1xBIwsJhIdhZCCES4nlEfP8vQYy9Cqm5cr92PCBVj7JG37vEEOf+tNzlyylxahaQ4kaBo8UukzvjZQTlWh4NLa6IRExVdpMejot7CLuh5bGqZLRjJzfgyTtnnNjVXKcnEBnT33lN37AwFpdnCyk3vy+fKISPRTl2ymRGBAQd4NA4ODg7d6dWX+uabb2blypUMHjyYm2++mUWLFn2hcsL7I6pZj7bzTaj5CGvYdejBILTvNmgR4NdcHL0ywPAyjTW6zbj5AZQXI2CBXaCy7rgW3t2Zzhs9pmA8fsXD8YUT2N66kUzNw+whX8WlutnStIL6SCXHDr0Q2v2d+3B5wVsInnlY95motGMDyrkGH2f8hcFDTkURGrbsLKyypWkZKRnGvUd7N1VVkFJ2hDGmVI1UUSGiuKjHNnwODl9kTJfENcKN3NDViJJZXQeZ7ZbN/Rt2EjMtRoQCDAitZ2H1ElyKxj/yvkfg7zECZhAIkjf8DAInZLIltQJvNEK7FuHSSbdR27SI5bVvMShrLPkrp2I/vysPPmJj3dOAckMeVcaz5BSOxHv8UMSbKZAgBuqI4zI6Cm4pgL9BJbjKRYlWRONQldf3KKC0MbaZE6eegT36RCzHSD/smF4b91A39tYkokBHRixot1Ey+4eR/gnBnKkMGf8NKtY9BAgGj7sEf84U9vfLEPH5YeRwhNgtQ8uyMaefgTn8AlgHwhIkj5+Ekt2OsUdgYHvGENSTv4naWo5bRhGZOchTriUm/N325XD4aYjVI4Wezgu3QGmzsYb1POEfj7yP7h6OUPbdNlJzDSAWfhkpzS7F5nZHBnf1Ux+R/tvnysUdr6HO8ag7ODgcZHqdo15SUkJ5eTmlpaWcf/752LYzCusrFNpZ895PaG9Je6rW8wemn/M7jEebIS5BE2hfycRaFiHQYuF7vY327xSycEwbk8cF8SVttvsa+NH6fwBwfP5krs+9EGuzRnEgk5qWHQwxjyNzQyZKjk5ocA5/afkhNeEtjCn7NVnHPYCw2rGLBC1qEcFNoM0PASDrDdzPxyi6ZCLLVtzFEcXHs6TqlQ7dSzJH4FY86fwtQJMGvsb1sHoh+LKQY08kkjGkwzh3jHSHLyMpxcZ9Vgj+3Ihs2ZWjflE2qGAuiRDIVEhkw/MV1cR2VYbP97rYEqkB4LScGQz4rxt2dYbAI/CODzFr+2kc3XYqYryPeZ52EvE2Him/naOzzmJscha6dGNmpqCt00KRW1JsyNnK8vI/M6xoMsdceR6DfaPxeMLIj/6O1+WlaexsPMkx2L9r6Ki9laN5uPSb/48/7bwWVWiUZY1h5oDTSIVOJGU7EVf9AT3uQj3RhdpsYVckEZkqYpAb2c8+37bIoGDY1ykYfFpaoObyyRBDCAi0qyg1JsKlYBQpRF37Tr3b87MiI7nod8fhkznlNwX2dQWIQNdlDZp4vv45lta8lt43gosCP2KEbwCW3atMQYdDSHO8ocPwFi0Wtl+A2j3s3TTqMVM78GXu25sOoCgeVDUTM7V9r151GVRQNhvpiCElbagrZis1uzrqODg4OBwset1H/cEHHyQQ6MyfFELwwQcf9JliX2ZSsR0dRjpAvL2GZKIJ17GZ6Y+QAOuDCMpAN3bEQjs+g1B5gnmbXLQOjbJk0HYebniLa0efweiMQZRWllJ/jwm2heLN5ZQ5lyMf+qRwj0Gpv4gLLrmJHFFEwb05mEkJeJDDddTTvagjwXyxFSwQA1zop4XwWJk0tWzgiJHndRjqXj3A/LKvQtVilOITsW2Jr+Zj5MM/6jy4j54kcPlfaA8MBNKFhA53L1YHh8NBe8jG+/081BYb4VPgoxjWg83pOS4BvstyCNud/a/XNkc4adBkNod3MkgvSFce3oV+cgjj5TaI2mk7+v04obNyqVmfxU0nPYj4Vzu0WZjeNvR5WZhvhZG7WgyZPotVtW8AUN62nG3h1dw04Ht4H/wtElBzB+K3beyqgXRxcZpQuLGEo4eewyzjdLKXZaNs1eBIN2aejd3frMEvIYoikC0m5mOdhdrIUNGvyqNjNrWfYNsCxK5IrN1uncwGFeOuOux4+uYTZS6C38im3d9df38ihlpVh0imsIvziYVCKLoGHxudRjqABOUdE9fXFZKpzp01xis6jPT0YpJnNvyZ62aMw4UT0tzfaEk0ooi0oa602hDoeTIlHnkH3TNqrx7yPVH1IlKJ8r2Hv6sC2ysQLTYyR8GrZyOtCDWJ+gM6DgcHB4e90au31quvvsq7775LVlbWpy/s8NnZo71HIlpHZHQlGQ8NhviuQYVLoAx1I1M2dpWBvS7dgzxzI5wwYBCDvjaX5a1vkq0Op/Eps2PgExorYGFb1/1FbYYyAddrQLJz8CM2GwSaMzDfbOoYoMsdKewcDfdpeejVPuptnSPH3IiwDaLoiNY2qpf8neHHDURkFSPf/icEcpATLka6slG2v4qo/Bj3qBLcNXWwvQYKcjFLC4m5+1G/IAeHQ0DcZUEBBBpt7Fd2a30mwXqomWuuK+Gj+rSR1ZJMkTQKOWfgHN5sWcmpU0bjWWyCAGlLiHY1jMU7reSfmddhpKd3KDGebkY/LQvj2RbIVokPNDmn9RbidhtvNzxAWWA4mUteAbefutOu4CNzM7gVTtuyZydq0E3BGdZV2P8KA0lskvBhDPWaAmSJiuxnxuCXDQUwX2jtKgxb0GixR3ZSv8QtVKyX2tKRZLuQ21OoWw0Y39Uo88djKPc/hWxtT9+nQhC4/GzipcUoKZWuljqIpNqtlVbcjLEn0VQrhkzt2Y7boR/QlmhGVdJFa0WrhQx0v0hGshLbbMHtm9br7Wp6MfHIO/tstyaDCkqziZXjQhEqLj2Lqvj2AzsQBwcHh73QK0N90KBBZGT0k14uXwKErxhf9ihizRs6ZJXJN5hww42ktobx2n5EXGK82Ip+fGZ6wL37+jtMXHUmS+ufYHTqSILJzp7nQhXIruMVADTVjVKb6K5LswW6gFTnQMnamMA41WTwmG/w223vUZtI77/Um8uRwoNtxLCrd+KKgJU1hMToX9Lwbh5WDPwjjiYvtxH38jXIVzsjMrShA/CcczIJ1Sko5/AlQ0isSKJ7C46EpCDaylcHhHiqqg2XojI+I4sM3yyytBrqijSKEyr6mp6rq0tTomqyS5g7kK5hEYLUBbkYhR4S75vkrj4BxQOXnzCH9b5nEfYSmk+8kLtr/0HcaEcRGjOnn0rOuj1aD022kI/Fu8pMCVtTpLK86D7HUD+cKIaavh57YuyK2e3n6EmBrDG6/1BvoCjeLil4anUdsrW9cxkpsV9+H+XSM7Cm6qjvdX1OrDkaqT2K1WW7h3SruzIgcywBpbBbOL3D4SeSbEZV0hP8ot3Gzu+afy6lJBZ5G5d33F7brfWEogYRaFhmPZpe0OMyMkNBabRgePrvoDufRLwOW9oo+7EvBwcHh33RK0P9kksu4Wtf+xozZsxA0zpX+fa3v91nin2ZKU+amKOuZ2YqA7UthZnt4jXZSK7HZGcsgNmqMbnYxluk7zXU6xOa1G1kBWdg7mrH07ZeEpyVgXxpN+PeJWjzNZF9RBbira7GuijWuxjpANYgSLhNFoVVRmQO4OyBx5C0TSZllCBfvx1PznDU2iT2O4uwz/kOtf/ubJkT3eRCCxSTXb+4yzbllh3oLW0kcj8Hbh4Hh4NIo7GRqKxnjDamq1FVoGFTydzN/+Kk0edhho5i0Y4gLUnBiJIAd9WuQp9gc+LMUo7L0BELBRi7rT8rg0QLeLyii0cSAalCg2bDhbraJrYq/Zsdh9QLOYy++GSMo8qotmuIN6QNH1uaPGfcy1nf+DZZizIRpRrKaA2rPYo6O4j1SltnC7Bd+0hEQO/aLcvhEGN4LLSjg5iv7xat4RGIbI3PQ0l+22OhTfBivdneRa6UubrXyYknCE8fTuWQTFKqpKRVUvjRNlRF8I54gbHfnEr2eyEwBa2z2lim/JfZ2mXYu922SlshV479K2pLO2pMxQiZmK4i7JgH0XNHL4fDSDzVhqYG02l5cYnc4xoZyU0gJapest/bVvVCjOS2fRvq5Z156n5XLpnxrTSlwuS5Q/t/MA4ODg490CtD/U9/+hM5OTm0t7d/+sIOnxk/MLi8BN+zcZAabhWOumgICdXNzo0SX9BmY+EKNs1ZSL67jNlTvoL4uNPrYA0UrBcfAfBGzT+ZdN48Ik8EMcMS25CERyQRIYPgYj92lo59TJCqrBbUY3xktQnk8nh6MHdWiHWBKAOmuPF8vMsbEVCoP76BPGUQ3zdHkaiKUZdo5xn1I64vf5lfzrySmS1uxONLIeAjGQmyZy5keBVkHTUSsb2664GbjvfN4cuFokre2fwQVa0bKPzG/5HzZEa6uNwADfVruWjBUWS0TyGSMYVXtoYwLFhbY7O22sMZR0zknm2v8mFLLUW5Mxn+vSy0txKIJomcEKC1UcdoAt/XsrD/0Zx+DAVYZwV4KNrIvAIP0XXdddKbM3nRv4xhOVO6yDe0LeZO10ZuvfAJtPdtzLvTxp+pt6CfvSuUPiHBJWjP0vEEum/b4dCSFAbeUjfavBD2+jgiS0MZ6SG178LX/QbLsnBPUKDFh7UyBrpAOzEDGYwhhL+Ll7txeAHtBVmMqPYjYtBeZrLzooHkqRqtqUYW1F3BiOnTUBWNjc2LGaHOBAS7F17ICGrkLSpELAymBZrAvjKEUaBh7tqZogiEEFhOW4PDTtKMoOt5iKiN9IguaYNSSuKRRbu86fuft6DqBRiJTXgDM/eygEB6BKLNRmYp+Fx5BFlHdaLRMdQdHBwOGr0y1OPxOH/961/7WheHXQyNZ6A929o5frAg7/E4LZdlApLiKVU8vPEHWNLktJLzUEcvRykehr0tgFKWJDHO5M31DwGQNOM0eDaRe/5QbNum0viYf+28k5LgWI4+4TYsVWOZWs5/Fq8jaVlcNWc8XzkpE4/Xy693buONtQ2cMq6AUybn4bMU8ss0gh7wPwP2R814gIHoXHfs8UyfWcKfq95hdGwmObaNcsrRCI/Cnoa6K18g9hwoBv1Y2aG+PK0ODv0Qm0iymZZEHX9svJbjzruQXIpRQ24GZR+NYrrxFE3DZRRxbrONVEGfqfL3LTbLtnqYmT+A04rLSLYpvG40s+aIek4pKiQj2kTW0GxC23dSWRBn8JU5UBtF6BGU8sc4tuhCHqiUfDVvEMk90irj7lbys8ZitY0n4M4islvLoW+M+hX6ZgXztdbOFQyJ+Uob2plZJKsMIkN9xDI0cnzmfrfXcji4SEXQWqcSyJCoozzYLoWE1LGsz8ekaEpRcEe3oxyVgzInD6FIrKqNSE8BUnZtmeZOeci6Dwino8KyBGRcHSIVshibdyIfbH+cDc0fdiw/Y8BXkWZXA84VNhALo50CU6I+FIYfhjB1hXpp8n5NIzujMY4tymekz49LOsnrhwvDjOL1+BGRXYb67r8lywEFVevZI/5pqFoeCfMDbDuBovRcP0cGFZQmEyvLhd+Vh263U51oZGLmsAPap4ODg8Oe9MpQHz58OBs2bGDUqFF9rY8DoIeV7vlwCUnQlXaJxdQqLGmiKTrjUyF49TZsdwCRV4a9PQYTfs6tg58H00TJ9tP2XzdtlQKhQemJRzJvuJeQbzB2TKdc3cHfVq7u2M2fy1cTHjqMKcR5ozrtRV/YWMdC6gC4Ln8EZ8fzsD/qWt1UeTvJ4OEm15RMImoVkDvwHGL5OQjTxjdMECvfVbFXhZyvaNj+gaipabBmMwwsRs6aQszl7qtT6uDQL7EtlaMHf5WKltUkzAgvVacnRL9Vdg+2LbEbloGcQ8u/rY6Ju+RHFldcpnLvJptvl0wg/pIgVQ7ZaiZjjsnjX/HVfDWUScYDD2OOKKK4rAD7ycu67LfstTpCc36Cd65N6l9KR90KV5lElnkIiTLKXyvkknl3sb79VZoilUwoOpbMuhCEu3sSZYuFOdxNZLQXRYEct4l0qr4fdmzFwDPcw47HVdz5OmZEoucIcofaxD8HOepSglE6iuaFkugmCSrkzBmHOyjZfQJYCPBWynShvI6VQX0+jvIdnTqZwQnjbqe+5T1s26AwezZVViaD9gj/t1qT3c6KbLUw40madBfXf7iccCodvfbKjlpunDiK2Vk5TpvRw4RlJ/DofkRTd0M9EVuG7hl+QN50ACFUVD0PM7Udl2dEj8vYn+SpDwOXGkAgqYxUQMFevPAODg4O+0mvDPX6+nrOPfdcSkpKcLk6XaHPP/98nyn2ZUb6VNBE13zVDBU9KJgyVRBW08XhfK5MPE27DOZkBNlcTeP59xJ8zIVnWwIx1E1ToZdk5a6e5SbEFvrQvzqG75Y/x83j5rGxsXnP3fNmTQ0zRxahihTWHgOQuG2RMCy6mdQSvEoW9Zv/zuhZfyBs7Ip71Q1CZ2hkNGnIpETNASOQIiW9KLOmos+YjKWqPdY7cnD4MjAkczpXzbiTlzbei0Rw6uj/IV8fiaYKErEmEqs0kBLFA5ljFRQN4ttsLpxmIz/USJWnjRNpgXxL4+xzR7BDVjFSKNTO8JHRuIk9SzSKlirKNJNyrZzQRSlCiSE0E+c1Pub+FS9xbMFELjuhmK3xMP+pa+AXU75Nc/MyErF2RE85m6U6UV8KTUmbOY7d0j/wxFWI25SdIhHe9HURLQZWu4Dg4dbu01EUQfvHguimXQa1BU1v2BQO0KFwj6iAePcoARGRuG2FN2p28Hp1HWWBKWiKwtbqRsZlSY7OGQRW581q52h7RsNDkYYRgK2RaIeR/gl/27CVqUdl4XW86occKW2kncKj+CEiwd15DSyzGdtsQfMf+Zn2oWoFGMltezXUZYaCsiWdpy4UgUvPoTqy6TPt08HBwWF3emWof/vb3+5ioDv0LVGRwn9OLjzfBDEbMlX0r+Zg/LWJQSbY8wdyzvAfUR7fiK1Nhg+fA6Dx6POxKjT0belBjRzqI7Kq+4g5GPbyldLhaHqEIcHMbr8PDQaIN33At8fNpzXWSG3C5o2adsbnhNgWjhArzsOdq0FjZ2VcUaKS3b6Vo4vPQLUMIsKkJhkloOnk617UgvSyFnQMgmwbkorKnvGxiiKQUjqDfYcvNEKATFVSs+E52lu3cO6wc8nIO4Kc3DIam8I02G24S6fCcoFvIOQNMRFvNSENScaxGQSlQtOa7l5Rf6OL0IQMxJXnsK7yV0zKmMGePTusolE0Kx5CtW+glIziAWsNT+74qOP3t+pWcpw7nzkbljF+6ln8Y+s6hgYLKS7yYT4ZRT8jC2NhKyQlIk9DPyWEbQriLueh7U8IxYXWnETWGljLY4hsFe2YDBRDIf7pqx92VFMlsrZ7ZEZqh41aJDo82VKCHKimU5R3uwXNOQLpVhkcTFc13B7prLMzLMOH2OPjszMrTMaFKnlPAQmJzFGpODdBwK1gR+mGKbu3LHQ4NCTMKKDiVj2IqI2d2TmcTcbXobnK9qvSe09oegHxyPt7b9Omdc1TD7jyqI3v/Ez7dHBwcNidXhnqCxYs4JlnnuljVRw+QQtJajcrZJ6Yj+aSeHPA+Hc9xNODAnF/CxOvPYHFrmz+Fm3jiunnEFzyNJGsAvKWa3T0i61P4c53k9jZdSgRynHxfuO7PFhRy9cGn8bwzBCb21rJ8Xi4tGAcR4dy0AKDEJVPEq94gTH+Is6Zci3vhTP4oK4Z2y9QrsqB18LITSmUYSbK0A1Yry8gxxPA/J/JrG7fTKspebEuwbjsIs4tHYZb7vujqUoFpUknXm6jZQjcgyHpTfXNSXZwOIQoiiBCFFWoeGx3ehLKrGXZa9dipiIAhJs3MWnGbwkn81mXqOTWjX8naaV4cNrPya/3wGONHUaB/VwLvoI82osEZrjr863mQI7bIhoowFObxdKG1zn+1CuRRpK2gAtF8+ApmMjIxk1ooSxUXz4rK5d203lVooG5O9aRt30dJcf9D39av4Q3MvP482mnYX2YRDs1E5GhgVuQur+RTVfF8JQFyJPZfXw2HXqLFDayKoW1OG1lyiob47Em9Gvy6TZD2g+xVRtPqUKkpauueq7oFu2VzBd4rwshXoil0zOO8yInSgxDMim3mKId1dTE0n3Ss90ejisdxJ6p+tWxGn5k/ZcrLjuJLNPPBqWKB6vf4M8l1zAkUIRXVYnvttKlIwYTQMH+HJzLLxpxM4wlNDyqjhKzsXaFvkspMZIbcPumf+Z9CCUISGyrBVXr+b22e556yFOIjC7DsHvogevg4OBwAPTKUPd4PNTW1lJYWNjX+jgAUWMr3rmZRNZl4E5m4Xq1qWt7JUAsT2AcYfNsww42Z5Vxw7f+hlsqKOMEvLdroVVR8i4OUtUA9q6i7fpom3eV5YzMGMjJxXOwJVw0IpcsZQKDduYSfsamPQ6ewW4KZ05lvfEIqdbNfLjoh8ycdS8nFI3BYwvaM2yC82KooWewaz7GemNXz/d4mJYdH9Ko7aRYz+HcQIqXwoLtyUKGu/b06+12PAKo1Kl5pDO0UAtC/uUuUh7HWHf4/BJV4rzasIRHKl7Hr3m5ZsSZTPYNJxne0mGkuzwhxg+/i8iTA2ltsSjKHcyCk6/jBzV38rfkM/w0fibKUQFko4m9KV0sS77cRui0AhLbU9i73KPuYZLW0HoK9RIsSzJ66Pm89NbVtE3+Lm9/8FNidQ0A5NSNYMLEq7j7oxvIri5hQv58ytu7dmEY785GtjWgmEkm7Bp4rm1roKEwQl6bgtQE1odRZK0BOSrxQIxfr36S3068FrflRGD1B1RbYC3dwxVsg2yzYED/z1G3pE1gtkqqBnylAjspSBk2oqS7lz1CG9v8qxDnuRGWguWKkquMIFsbwds7NnPF6FGYtoIEfKrg2cpV3DT6OFK7hb4XKB7qki38qubRDlmxJ5scqeFSNf4waxLPVVaxM5LgtIGFTMoIYduOkX44iJthTBTcUgfDhF2vHNtqRkoLRQ195n0IIdC0fIxU5V4NdTuzM0896C4kSIKqRCPFZH3m/Ts4ODj0uur7CSecQGFhIT5fZ2NcJ0e9bzAxeGHpRQT8hQwsnMOU2osRW3dbwCUwR7k5PjSMD+t28IPs2ZQ8D9o2iTkelP8JYf+jNd1bdGMrrksC1DcauLyCZH6SzU1VNMZ9vLpzJQB+zcVDIy6l9fHOwU9im4LHNZnSgUezs+E9bGniNarJpjNXy5YSVj0OVte8vTgG71Y+TVnmaCZ4BnJy7miSdgpTaUfHh7TVbsesWxr1L3edhTbbwaoTMPCzn1MHh8OBEIJ3m1fy503p9JTWVJSfrPgbd037LqW7hWUOGXwt4efKsJPpQb/ZKMh8rohL551KsRJEtNvp9lrFLvQLcjCeaYZMjdYlBllH6qgZEPbvpN69mSz/ULbWbiQvO5sc9zBOO/Up1m64n1iioWN/Ta2bqG9cjVvzEku1MtPjYniwiM3tNQAcnz2SyTU7YcRcpDcLsVuvtY899Rx9dDHeh2IQsxE5GjXn17Ok9m+MzphCg9FKqZJ/KE6vw6dgoaBkqOmWf7vj7v9GOqSfn0atjYKJOvpbEfArxL8SoFaPkbtHkn3MaOCJjfcQTbV2yC6c8CNC/jJGhgr41+bFVEbSHQwKvAEuHzGjW+h7UdLg+6VzWBprZZCriBWJTVybNRbdNDDUKBt23o/a+jFj3Nks3VTFmCm/wiMG9fVpcOiBmNGGgYI7pSHdu3pPAqnkVjS98ICLyO2JoudjJCvw+Cb3+LsMdvZT9+k56DLOtsgOpjH8oOzfwcHhy02vDPWf/OQnfa2Hw26krAQgiURrWLvlEUYfczG+FQIMCQNdGEfn0PaBpFDz8/gpFyH/0gBhCwmo71jImjjyhkwMLO5rquOpNZ1V3b/pHcjAwCCeqehsUxM1U0TqTDqmpHeR2ORiwMRj2dmQdtHrmh8hoJk2Wo0oBf5MCo/7JvK1ezvWMQZPZJVVCcD2tvXMyJ9NZtDLyrr7ebVuEYOzJ3LisMsJMKDrQdsCmeyhmrRTZc7hc0xKSfH09ne7yZe3bGZQ7lhc3mxS8WZcZgnRZNdlrHbJNPcIBr2qYq9Je95lSxy7Iol2YiZGyE3sRUlsi4E+TpKY58ZcPpOlazVUtQj/1BSZw8I0GNDasqabDsK0uWDmAzQnY9iylR+PmUhReyG+KtBJkcoPUL1Bx4oq5GUY/KgslzIzQEm1l3hWkq1XrEGLq1SJLbxZ/RApK8HR+cfiU12fh6jqLwUSFXlaDlprOipJqAK7zcQI6ICx75X7AaZmElqjoD3emr6lGsBzbzM538tC7JajrihQ276FaKqVXH8pXj1IVdsm3tz6OEPyjqQ+Ee0w0gHq4hHKw03MzRvB7uchFQhxRuIkjl7jI1kH5489hUBePS0+jdZoOe9VPJZecFeq+3833MWF434FTgTJIact1YxERU2A3K2QnJmsRHOVHrT9aFoBydjHSGkhRHcnw+556kqWhqIG2dy2Bjj+oOng4ODw5aVXhvr06dNZtWoV69at4+yzz2bt2rVMntzz7KLDZyfoK0ZV3Uhp43WHWBr5N6XXnEWg1kVGXjZ1D9mgQtkpoG5MYoa7ekvk5iT6Wi9WABalulZ13xqOMiyruzelXU/g28NQ13MkjcltAORkjSIYGMbH8Y3876p/E7OSBHUf90y8gqFFo7Br19LkgdVaI29XPdmxjVBwAC9t+jM729Kh8atq3qCiZRXXzPgbLrszNMx0mYSO1ml6ZbcCdSq4ikAYClIIkrrlFJhz+FyholLgyaIyWtdFHnIH+e6yGq484nYGpraiS3+3atNChWw86GsSXTcatZE5OtVvdorCo9tpaUtRuzr9SjdtWPeBC687SXWWQmb+HJpaOqsR5xXOpsJ/Cn9/v5yEZTGrKMT3swoJ3BWDhMS4rIidj9q79JFoa3VOG1KG/VwrkASvIOOyI/jY9QZLGxbumlyEIk8W2SLkGOr9BOmWqEEF89E2SO0yaqf4Eb7PR5Vyl6HjfTvc9XaS4Co3sYr1ju+BbYMidL456F6CWwYj2t2kRteyRD6KW/Wyqa2BTN3DGTmj0YXKM83rWddai71He7bM1ACqHlE6IltaP5QYbfmEzk5Smej6DANsa15JSkZx4Rjqh5q2ZBNCcSFissPHIKWFadbg3ov3+0AQihtFCWIZtWiukh6XkRkKSmM6T92j57IzsuGg7d/BweHLTa8M9aeeeoq///3vJJNJ5s6dyzXXXMP111/P+eef/6nrRiIRLrjgAv785z9TWlrKj3/8Y5YtW4bX6wXSFeXnzp3LokWLuP3220kmk5x66qlcf/31AKxfv55bbrmFSCTC1KlTue2229A0jerqam644QaampoYPHgwCxYswO/3f4ZT0X/wxFOcPe0ORNUGtKYaDLsAOyvM9gIf7sXp0XzGaIHyVjNiSg/HrAAlbpRGjRsHjuT7G9Ih7vkeN5eHBuJvsxlWmMF/oqtYvisvtTqjhVFjMjDX7dJhgEL2V1Q89nxycqeRm19Gs4SfrfwnqV35qhEjzvONq5meOxpv7tG0tK7kpbX3dagxrmAWiubpMNI/IZxopDmxk0JXp6Fu2xLXeJs8j0bbYgstS5B3rIK6Job1ShvCreA6K0RsqIIpHCvA4fOBYil8Y+ipfNy8GVOmJ9Ty3CFy9BK+WZDB0PIgon4M+nSFkstV2pdbtK+ykCYE50mSHrt7q0ZAekELgXekQnCiYJstKTGzqd1j/011Gbj9W9mqTaK45ASqq15HERoZg77Jb5ZXdixXEzEJbJBgg3ZGiHBCgd36oGePBvuJVgDEABfajADiiTDTY7MYf8x0FhW/xEfhlxiaMXr39tYOh5mUbaA+12mkA9gfR5FHeSGz/xvrqmZgBxVEfVe54heYe8zajtNPpuEJN8lPSpqsK+XEc68Dw8XZBWO4pm0i+U9YSAtOO2EwlcVJLKOroW7Xadh7RHZF19tkN7vI8hZ102943jRcn4c+d19AwqkWFMWFiFlIV9r5YJn1KEoAoRzciRNVyyOVrNiroW5nKCgNFgyHTE8hNe0VB3X/Dg4OX156laj2wAMP8OijjxIIBMjJyeGpp57iX//616eut3LlSi688EIqKio6ZGvWrOE///kPzz77LM8++yxz584lkUhw8803c8899/Diiy+yZs0a3n77bQBuuOEGbr31Vl5++WWklDz2WDr07LbbbuOiiy5i4cKFjBs3jnvuuecADr9/okkL31uP4nvxPlyLn8f/3N0El76CFdmMsquBuScbZHUKuzqFMtLTZX3luEx2vi/Y8YZK8aJMfjBwNBkunT/njKP4d+1k/jnC1Pvc3NYyi2nBUqblDqBVVXh8+DZazo0T+LpAK4LqvxpEHyghq3oGiqXTlAp3GOkAlw8/lcr2GnY0r6eqYTlxAlx+1N+YWXYGF0z6CV8ZeTV+VxGih9tMV3XqUqvZEnmbFmsLUpgYmgljDHK+Icg8S6JWxrGebIF2G9loYv21Ed+eloiDQz9niFbMfTN+wK0TLuWv06/k56OPZ4ipM+iFDJLvCzKG64RfkVT9PUVip03uxSqNFzfwXt4aNlnvY5zaNdxSDNdRdvybwrEvo+txqu+3cN+fifcdF8fM0dk9NdMfjKPveB5TyeB1ziR7wp8oPeIetsS7dlbPcbvQ6iz0s7MxIxJpdzXiFMPq8JJrMwIYTzQj602I2HhfUpnTdDanjPs1v15dz4pYOylnMq1foBkKVHUPcdfCn4/rE1OiGKe6P0k/ThNUSAxOdm+9tdOP3KPuaPQtN4qpMK4hQN4jKWSzBW0W2U+lmFQTRLDHfa52n7wQrnTXhlz3UE4acUXH9yzXP4BTR1wDdq/8HQ4HmYjRgqq4IQbsGheZqRpU9eB3nVD1AsxUxV5/lxkKSpsNNmR7StDtNlqSkYOuh4ODw5ePXhnqiqIQCHQWEyoqKkJVe8jV2YPHHnuMn/3sZ+TnpwsLxWIxqqurufXWW5k/fz533nkntm2zatUqBg4cyIABA9A0jfnz57Nw4UKqqqpIJBJMmjQJgLPPPpuFCxdiGAZLlizh5JNP7iL/oqAlTcTmxV2FK15ilBxPoFSg+iCyA8QYH9aSKKJARz8jC+3YIPr/5NGU9JKqlShuiG+yObI2i3+MGUjokUgXz1zmiyluLzqOW8Ycw/BEJnOtLHCn0HYKIh+lPzp2AlpfMjFqveS4PeR7QgDkujNQgfNC+aTW/ImGlb+jbdXvEIl6WuN1vL3lEVpNSCl5HDvsa10OZWzhsVS0ruIvi7/Ng8t/yp8WXc6W8Nsd/dMNTIQlsd9qZ0/k+gSK0v89QQ4OHUhBschnoj+HJ5bdxANLf4xRE8MKQ2iaRtNbBsnqtBcvVS9petQi6NEZ1pTLlMhRVAzZTPv/GJhfUYleYqPMrcZe9i9SjKH5QzfYoHggVm7jrrA4Z77kK0ebTD8CXJ7VhLe/yrGerVwxpowit0qpXU+23tWiWd3SRvwEH9aqGCJmI3TQMnbL+1RVcAnwKcjW7q2H9HdtssJFLG1o5tYlq1nV3v3ZdTj0uDUVMdbb/YdMvbusH+KWPipyV9F6nY1xpo/UhT52XtFAQ7AS296jpkkPHbGkBJemoizv3jlEec/Are8RqZIdx7tHenPuLIVYMIyGn8GFp/CVyb/i+PE/Ye74W1FEzmc9RIcDJGa0oakeRNxGutLvKtOoQtlLdfbPgqrlYlnNSDu5lwUEtlcgWiyC7gJ8Ms7a1q09L+vg4OCwH/RqKjgUCrF+/fqOKprPPfccmZmZn7reL3/5yy5/NzU1MXPmTH7+85/j8/m46qqreOKJJ/D5fOTl5XUsl5+fT11dHfX19V3keXl51NXV0dLSQiAQQNO0LvIvCinD/GSCuAPtuF9j361DsoaSeTkkdRcUhBAxC+uddiyvQMzLJhrVcRUrZBeCNCRqUBDdahIapkF795hUV0scb7ub0D8TkJIoF+Swc2V3b4tZ4eLHDf/iutFn8dLOxYTcQQbrOpuX3Im9y8ueTIVZvvTXTBh/Ga3KUH76cQM1sR3MHziTr02byLaW1eiuLPJ8RTy+9MYu23967e/47swRuHYVmZMKkKN19wZlqh0FhBwcPi8oimBNzRu0J5vSgl23sOoTmG1d72c7AUN2htAeTgIGw/OGoc+vxq65G73sLJI7NuIK5hFvysI/UsU3WMFsl6h+gTQl8sMofilxL28ldmEBrqnXkZE9jKVVTzNAyaB6xb8ZeexvGRUKsKE17fURAihWsXckYVsStcxP5rT0+1WaEjsTlCtzsR9u7rFiuAipDNmhcUXxIP5aXcGD5RVMnTLG8TYeZuykhjEhE1fEQlakwCPgpCwSpkp/ylEQQqCHdYydAgS4SiWpoIFipPDrQbZkrCA8tAGEIDdQRn4qC+HrLCYnBHhy0hNMcrdPRs5MBcs20XJcSLoaWSJXxRQKu5+HStd6hp0yltROF0YEPDkSs7CRZhkjTowrF/8eXdEI6j5q4q9w9YjTOStvjtOi7TCQMNvR1AAiKaEj9L0O3T3koO9LCBVVzcFI7cDlGdbjMjJDQWmy0HI8CMXD8rpljCwafNB1cXBw+HLRq1HUzTffzHe/+122b9/O0UcfjdvtPqBQ8wEDBnD33Xd3/H3JJZfwzDPPcMopp3RbVgjRo0G2L/n+kJMT+PSFDhMRIwcyC6EtHectyqZgrhoAu4rGiaca8WjA+bkYp+Yi2iyEDrpu4fap1D5tYH4S2iig8FwXUZFHKLcRGndzOwhIZAVw39uZwyhcAlcWGI1dddIzBPXRFu5Y/TA/HTUXl4wT0EEbfzmamWTThkcxrQSJZCsBPZvb1yZp3pUs+HxlG6/uVLlo9Dju2/QYN484ptsxx40IkVgVIweO6ZBZX1FIrot3ekoyVFzj/eTmduaf5eU5+YEHk758LvrjtTqUOtVuKu/8v2c9pcFB6T96sJmU1G7ewgYLe1M+JNoRT/0K11f/F5a9gKcoCR5Bw8JOy8Q7SCFYpIIioc0i8E4xxvwI5Yv+yFEDf4Kr3MWQ0HGEt5fzw8Ea9a4xhI0U2R5BiwzjGqegfmDiXhtGmZhJ21bQc6DNs5b3s8I0nBflwsyjEYt2a/mlgjotAAmbKVb6fOpIPNuXEBg3G6F3Tc05mByq69fb56K/3eOJmhRNq8BdloN/io1lCZpXS/LLlEOia2/3Ea002XFfErnrXS9cUPYtD76yAEblFtq9w6i1g3gUnUxPHjmmRUZu12uSqm2n9ASFcI3AjAkyB0ncIkXMZeMe7Mb2RSG267lyCdSJfhSXRtDf2XLWtcOLtuVldGU6dtKFkgpjbfmIzGmz2RCr5VvDZqOn6omnmgiVHc/bzZs5feAs8r2hg35O+juHewxlyhi6modIStwZLqSaRNoxPJ5Q97QIACkZvGMTA3eWgxBsKRvJjuIh6VkeaaPEqlDjtUihYgUGId1dPfO2pwBp78DrHd2jPjJXYFcbeAMe/J58NjSuIG/CZX1x6AeF/nwf9mfdHBwONb0y1IcOHcqzzz5LRUUFlmUxePBgdH3/Q+c2btxIRUVFR8i6lBJN0ygoKKCxsdMyrK+vJz8/v5u8oaGB/Px8srOziUQiWJaFqqod8v2hqSnSb2fB7dgO5InnEdy8Dq1qI8qEszCf3mMhExTDRvgUbKHg2d6O9UIr5kUlnUY6gISW9w2yzmgifnYOvscb0wNsj6DhHJ3aaCPjo51pDKLRJHuESqySjnw/Vw4Y+TES5QZgkOnO5JV191Pbng7tCrqzOX3it1jx8R9w6QFsLUTzHvlZCcvCsvxIJG5XNpqiY9qdBsaAzFHYLTtpyYhimunBlJINvhsKUKpM0AVWiUqTKwkNac9IXl6QhgYnxHZPPstHrq+ei/54rQ61TuMKZ7Oq5i0Ant/5Wy6en0uw5Qhy5rhpeqNzAi3rSAVlRVuXdeVON6JkCDTtRCx9nvC5N+NXJLUvmei5guA4DSxJeJWFMcSFmko/I/bWFJG6SsaV3oZ2ZxSkgQAyfUPxfS/A99beQUuqHbeiM7tgDDef+FVIJpHHZGFFBKGZCtIfZdG6Oxla8gMej3/IxAIXE86YgWiRSEsi3ALztTZESCXwFTfUwTfyXehP3Egi669E/GV9cj4P5Pod6LPRm+eiP97jmaZC3slumt4xSCk6VlziHSJx+S0aGhKfvoHPQG/Ph6oqRBcrHUY6pL89zcsMksEk5bqbn678DzNyhhK1Uty3pY7/m3gxyd22LQQEKuMoT0bIKtLT38WlSWSGindCiNSydlxzM9OT/BKELrDejyDHaTS0dm4nI+LC+mgacocNJLBwoc09En8sQqGmsWjr3wgnPhmTPMvcMd/DiJs0RHp33fvjPdKXz0RfkjQiuLxesCBhpjBiVQglRMqwYY9q/kjJ1I1LCEVb2Vw0FAXJsG3rcEUjbCgdhK/lA4S0MV25IFO46t4i6R9JMjiycxMij2RsKXHfXtoauiV6s0mkLUGGq5CK9i3U1beh9DRpcJjpj/fhJ/QX3ZzJAof+Qq8MdcuyeOSRR3jvvfdQVZXjjz+es88+e793JqXkV7/6FTNnzsTn8/Hoo49y1llnMXHiRLZt20ZlZSWlpaW88MILnHPOOZSUlOB2u1m2bBlHHHEEzzzzDLNnz0bXdaZOncqLL77I/PnzO+RfFBShsXjln8jMHsnkk69EX/oKyqiRWB/sFjVwRICw8NL8QAopIWeqF/90CyvcvRe5FYVI20YiahnB67Ipr6qnSU/wz+ZlnGePZ3xGUYe33loawTUTBhypYagaAglegwVNzwGQ7cqgLba9w0gHaE82s7FtLbnZYxgwaC5VsUbcio/kbjmECuDddbf9Y+sH/GTyz3hu/b00xaoYlj2JY/OOxa/ldRjpALaESMiG0Ccfuu7H5uDweUBVFSKpNmYP+SofVj6LBLYpS8gc40GoxbQWJilMZiOCBp6EC/7b1YhShkext6wFwI408YpuMklxEZqgorgFLe8bCBVCM3WUQhvr/SQipCIG6ehZ+ajPp7q2TIvZyE0GNxWdx9C2PII+FxvURdxVfg3XnfwAtY+amK3pFfyj3Jw06z60hxv5i+sbmHkKidow2kIz/WDveiyVUhfhgMkfhocY/d6fwUgi4u3wxWjG8bkknpFAS7nQg4KmNw20DEHeV3Skp/9MUmua0i39A8Bokyi6wcrqcq4sHc6G6oVk6hmcMPh0tkbqycsY2BFdJyXY3vQ3TNYYnbd6ABAKySEG4sGWzqJ0EpJnCPQ9ovNEJAe5I9ZFZr6loc3MxLA+IujO5qhBZyGRtMUb+LjiMWbkz4VuyWoOfY1lJfDg29VDXWCZjahqzymZw3duIivSyqoh47GVtGNi1ZDxTN78MXGrnJqcAZieEj6pxGm6C/CGV2FrPgxvOh1PUUNIO45ttaOoPRhxqkD603nqWb4SguH1VMRqGeIv7pPjd3Bw+HLQK0P9F7/4BeXl5ZxxxhlIKXniiSeorKzsaKHWW0aNGsWVV17JhRdeiGmanHTSSZx22mkA3HHHHVx33XUkk0nmzJnTEQ6/YMECbrnlFqLRKGPGjOHSSy8F4Gc/+xk33XQT9957L0VFRfzud7/bL136M3agmMKSWdRWvU9d2waKm7ahH9WCbCjELk+BS5Aal0nj850uiMZFNtrxfjy6tSuUq3N7WTMUymufJmfkFSxLruUXDRUdv93f+DHTLziNgQ8qoIF6VAAKVIQnjh6xibkVWrwGF9R/hXOzT8JVKljW9M9uOldHKrhoxv+jNpVisCL4zgQ/C1aUd6jxrbHDmJHrQYizCaeiBH1lnDbmO3g1H0q4Hhmuwjf4RKf9ssMXlqq2jVS2rOGYIecjhGBl9Zu0J5sYVHol7ryd/HXVteTHy/if4jvQv5YD7TbmsigiZCIyV0L2INQpV2MPGsekbJPFDZWclpdN08vp94A0oPktA88FLnRdgUk+lKluLFwose6tzT1xjaMeLoRGC0gxsWwaGV/No3WR1WGkA0Q3WPiH6WgpAWsSaMv/P3vvHWbJUd/rv1UdTk6T88zmvKtdrVZplXMOCFBABANCBBv7Gv8MOF1fY4N9AfvaRoBtkslIQkIIZZQDytqc4+R4cuzuqt8fZzSzs7PgBbSLgPM+jx7t1OmuU6dP1+n61DeB+uMIPJ6D0uup4IHzwozY21icG8HXuwECUVS05bhc2xpHxij5ST3v4Qtp2s/UaLO6qVN/ngVBcKVLrzPMQHGMhB2hx9dGUB+7UIUj4XkKb1UFdswsqaVWlKlQoUlkeGzHl6fae5Obefvaf8QwwD3ECl/sEQTiAl6/dwV4VwTIGg7MFfhPD8CzxepEWO2nstwB97DybJ6PahrxQ3A1ZWz8Msjc+lU8tOOrKO3RGO7irPk3IvURstjVOKZordG6jM8LoCedOz1nFGFEZx0bKuZY0rudV+avnhLpAK5Q9EY9Fo9K+traOLRchjZ8lCKLCaRexbUb0YYfIQSG1YxTOYgvsOyI41JRAznmEl7Qgl/n2ZDeVRPqNWrU+LU4KqH+zDPP8JOf/GTK3f3KK6/kyiuvPGqh/uijj079+6abbuKmm26adcypp57KPffcM6t98eLF3HHHHbPa29vb+eY3v3lU7//bxpaKR2L+21jVfgalXD/q6j9FJ5sQXT70mXV4SDLbZkvazAFBs8jTfkmU8dc0bgESaw1km8ODmXdymZVnX/9DnNNyHo8N9QKQc8r8U+lp/umPziCejuN8bRyKCgwQbwlSTlh4Xw8jFdiA0aBZ//abeP7ATF/8+U1n8nI6w2e3fJOg6efzJ36Y/zjjJIaLJRr8PlosG0PB5Q2n0lvayBefvQXHq1oNT5v7Lk6ddz1aBQ//SDVq/E7geYq17RfwSt+D/HTXfwMgEJyx6IM0W1m+98rn0Fpxa/wfUV/I4eSzIMG8PIZYbsLw+Qj7LJw782AKei4w8a/zk39stpdJbrOL3V9B9zt4z+VIfLCNyrlBrG8d4k4vwGiwUWPTLobGQU2nu5Sxg0ewbqY0+Cc9WzTIHYqJDxcJ9AYIiCC6R7Kr8hpdqS5GQ034LvwA9V0nUvDVz94hqHHckBWbWKuD8eNRdMoDAU3ro3ilCELAM+mNfHrzd6aOv7B1LR+eew0+dXwtxPm2NOaVYfzPBkFA8bQ8hZY8cRVgc99PZhyr0SSzu/GCJ0+1SQlPihEu+HAbcp8DRQ09Fi8Hx+lS9dTrRmRbBXlFtdSbMiRBbYPUuIfeoBEDgnI6lh0wVgYncyJKntp7+1T7aO4gW4ee4YT4ZUdKOF/jGFLxCmgMAq4NvqrA9twxLP+iWceu2LuRvoYOyvYhG1DKxZ/ZyHismYZSlq6xYQ40tc44T5kRXF8j/uw2ivHVQLWeulPe/3OFuo4K5KCHtTiAaQR4efxFrmk76w361DVq1Ph95KiCZxKJBJ43ne1ICEE0OnvnssYbQ7MvyJcGDvKiP8KuxjnsDvopuDbe0zkqrmD4cYUVm508z9cApD2s7RnaLpd09BQJPjjE2HCO+wdS7M1XSBX6uCzh54+XrOLE+nZu6FnAX809hfAOP+53J6oiHarJrX5QoLHgw9cy/V7emCA41sQ589+JIUwEghWtF9AdWc2e0RGu7T6HZCXL32/+JglTsyoUod2oinSAUT3InRs+NSXSAZ7d+w1GCvuP3QWtUeM3jBBglktcu+bvWNp6Dktbz+H8FZ8k7ZQ5OP4SycIgN3R/Av39POQnJ4sC9540YkKjDyi8Z/NV0etoxH2axmEwmmerYDsKejKUhbLGN9jMrs5RKjcFEW0Wep4Nf9iI+9zsOr96oEhg4ezx+1sEektx6m/XqdAfPkBpTYk7su/kmy9dxNMb/4gtBz+BMSKYWHAJ2fhCagUafrOYpsJ4JlkV6VC9f57KYHsuSTL8y7Y7AfAbNgLBQ4Mv0V8ZPa5jNAzJxqFvsrd9I69csZ2XL9/OvtaNbBz6NhIfIWu2O3PIjs64t5SC1bKbBx8KsE9oBupcnnjVT3y8jSZfGHu/i7p9AveeFO6PUqgfTmBuqyCMmUsg13GxrkwgVwYQTSbGWRFEm4XQmnRxgqZQDze2/wXvaf17Tm++hj1jr1IQaWocX4puBiVMfBUTzGqCYc+bmOX6Hs2naciM0d9wiFVba/zZLSgjjOdroK+hiYUDBzn0hvJKgsq4JF/qxiocREyuVwyzGbdy8OdWntFhicwo8KAu2MFAdgcVVdvGqVGjxq/OUVnU586dy4033si1116LYRjcd999JBIJvva1qgv0e97z5s1s+dtIwh3n2uYy2bFXSNhz2Tb2IG0tf4BlQyBQJNBmY9WbGEHwJr30ZAACc00KdXX4Qhr9tVEYdiBqsD1cQiZhkbuEi5L/jv0zg+WLS6xcItjBSxj97RiVOG7SmzUWcbBMS70mFQ+Q3lp9OJVzMK/rSlz/Slb6fHQfHMTcMcKqYJB0UxeBuR6nRZsxvEEMsxtv0hs/o3upVNKkiiOz3meiOEJPeDGDzhhFr0KLXU9IHaH+b40av4UonUVKk72ZEudG3oNjaP5v34+5sm0uPitG1N9AvW5Fj86eg7oA3rbirHZ7WwDf6S7FTQZqct/LDEO4R8CrBjpbFfxupcQPtt8CwAmXnM/81ovYWshww5x6vJ2HlKyKSHzNYew6RaVfUh7QICB+soGPMsYlQdRmB9XrUFri8oPX/o63L7iFQnla2E2ktzG/bQdu9gTMiIHjvHlKgP0+Iise7r7ZtZ9FzqWsKqxMzGdt/SpSFYeIZTFcGiTnFo9yZfDGIIRESIuHN/wZ7fFFoDV96R2cPvcdmMLPeQvfyaa9P2W5vQ5HODxfeISO+NJZ/Tgph8tWPw4/+w5CQ2fXCga9m1FuI/II80dsyGOeM9PNP1WXp+67Chk1kXN8qM1FvBMthkMlWvV8bi19htBXLHA1C7uXsPbai1HSX0ufcpwpulkUBnbZRNsCrQoIBEJWPUFc7bGpMsxl+7fyQizMLi9NC2FCwsKX34XQCidQtaBnAkE8YdCcmmAoUY+XEaiiRAYUCItysQkruYdKwzKkEQJhotwxDKtx9sAMgZqMU48HW2ks7mFzZi9r4kfY/axRo0aNo+CoH8eLFi1iy5ZqMqOOjg4Adu7ceWxG9XtOUWoW5xbR+HAPxrwWToydg7dBYVweR4Ysmubl8OqiBK4xqy6pJYXlOBj7S5TOEAxsrtDeaVJe6yO5VDHMbj6/ainz/ttF9ntoPAIHJfP2zqd4TQ4za0HWQ9QZ6ImZC2thCXgiRewtNultVevDwcg4f/7CT/iHE85n3q49eC814h7Ig/SIn6O4cmGIzY9+kgHl0rHoKlrnX01Benzh2Vs5tftqmiNzGT4kGR1AS3QuX9n3I+7oewaA9mADnz7h/TTp+uNz0WvUOIbkinupr7RywyNp2HMQpOCL513Fq50RvnKgn3cs/nN2JF/i1KZz0CMzLTAiYSLn+vD6Z2YbrrTAuLGL7nMW4pSrsZdWyUF/eQTrijjOD5NgwGDzXop9Vev5c8N389rEI7xr9b+RWbyP4Hgz4mUNfoH5zjrcL40jnAytp0bwVgYRbQZGKom+czNeycVY04Z4SzffGvkTHK+CoyqzPqun04RtG9etifTfNG4A5Dy7mtvkEGRM0GDGWRxbwRe3vTLVfkJ9C9d1thxX4amUR3v9egIDD9KX2g5A2JegLXEyDmVimTBveflyxK4USB/Lzv4gg7lKNVHcIXT5dyNHo+j6v0fnDGQ4Rav7EmVxIbLTxHv5sDfusiZd2qcZNg8y8JYxenoXExjwk7kww8boc8wXZ9Gc6sL60QSvx3LIA4rWx9txbgrjvIlq0v8+UHQyuEh8JQNs8LzkVHy6RvNCuZ+o67E2leX++SsQuGx3xugpT9Dm5sgF52AJUc0tKATDiTp6Rgbplw2ossCIqanEgy6tBMubKXtLEYbANJtwKgePLNQBPRmnHlvcTmR8I88lN9eEeo0aNX5ljkqof/rTnz7W46hxCNFihPB3xxFdcZiI4j6aB8B9oYic40M0mxh1BUTWQzxVfY2gpHCLwQOj27ktqfn36+Zy+8EneWDX8wB8seFDyP7DMpXudmgvz2cs0kdiUwTrojjOPcmq660B5uVx3Jer/YvRMr7uIPm1Ob6Yeoqy57I7Pc4pBxrQByaq/SmN99NBQg1RlOeitUvvtjvxhZrZOfYiVy+6hbu2f4l3nPSP3L3xH0kWB7ENP+ct/hMmcpkpkQ7QXxjjG3sf5E/nXY9Ub77yJjVq/DJIJIGXy7Bn0k1WacTDB6hrWMi2ZIbPFiz+evHpmJfFce6YgOzkHLwkhrutiNHtQ/VX0Hurgku3SMrzXYYnFD3fHsU+7P004K32oc6x+e7Bz8x4zZQ2xthLPP/SbdS3raV9xcU4Kkd75nyoTGbRfjaLfDaLdb0P90cbps71XurHi0EmOMbqzkuwmV2mM+Sfiz+SQ2MCtbwTv0mUV8G+MoHztcmynAKMC6KogCTjlfjBnk0zjn9tfIhUuUL9L1999VemUvFI+No5f/mnkd4oQghc2UDMbsbzciRe1VWRDqA08tEB6jq7kAmJ4xxSWaRQh/NDazLBocZ7MYb19uX4KznUHAvRYKDHJgV11ECsNEDP3JEoVVJ8r+9vCfsTxJc2MZzdj5Mt86E5p2KMz969EJvKGEUXx54dilbj2FF0Mzha4CsY6DqBcieQsrpz0+ukcbTigok8w5EEwvLRgI+eSoqgm+WAr5Gcl0V4WRJGgDoZYCwSo2d4ECvq4Cas6eoAgLJDqIqNTA2j61swrCacyn78oROPOLbX49SjgVZwUzwztpEP9lz9pizTVqNGjTc/RyXUb7311iO2f+lLX3pDB1OjSiBtQMlFzm3BvWum26LaV8Y+N4oarKDXmCQXFvHpAIV4jkez32Zlx9v4p5N89JZGeGDgeWxpEjYD1R1/n8BYF0bEjWo1mVSFrBrkm31/y/uv/yfaXzawz4siYtWEOu6PUuihqhXP6RHcFXiBH41spuhV25p0CG/H+Kzxh9JNrG//bwrhg2zc9xlG9z+GGY0z3vcM8+tP4NsvfYIrln+MkK8ZnxWnbXOKB+JDs/p5aXwHxXllQtRc4Gv8dlPn60Zs3jGrPT5SImJZTJQd9Igf53sTmGdGwBIgwXspj3lSGOfb49gfbkR7ZQhZaB/kKkl8AfuInjD7msf5csNDXBpZT6o8M+Z4ZfN6hvc+BMD42EuMj70EQMvcM5ghN4ISnZodx26+PM7bb/5Txvw+5sgu9DKP17b+BwF/gtXL/wR/tJ0HHr2Si8+8jYBdsyT9RhEa9+EMxoUxREgipMB9Loucb+Noj7KabQkueg5H2H85dkMUYLsVvrXbY0equuW0NKH4s8UVgl4UY1thdsWCQU1p/vTfhgF6JAylmWUN3ScCiDU+9Iv7ME6KInwhQKC9IvrxXYiFq2Ycn/A10BDs5L3z/p6ol2Avm7jnwBcJWlHKYTXrSaQ6TRxbAQY1jh+5ShpXSMwiuLbEcyeQRhiNZpc7zlyzjsUjO9jR1A5aESscxO+mSAe7iQmLGJqK9siqMmNekVYdYcyO0e2OsEe0z3o/12zCLh6kRAuG2USp8BJauwgxewldjVN3MLRFwKojTJEduV6WRLqPx6WpUaPG7xhHJdQvuuiiqX87jsOjjz7KokWzs2vWeGMwgr94laTGHNyHMhgqgl4hMJ7XNG6O8LY5HyEve9k28DkKy9/Bd+b+OZ3DcWRWo3oszBvAuzeFHnMRLSbmBT7ad/m5dt6H+feDH2HRwnVc1f4uAnmT4Deapmqri4U+zITBacU2dodGeSnTB0BbLIzoKKG3HeZWKQJYt7vErPmseden6K3cz2B2OxPp3cxb9V62jT7PnRv+jltPvY15yQD85F66bloDgCVNrmtYzyp6CIT9hMTxLRVUo8avQkWkGC3uxVVlGoM9hETrzERqdhjdFUCkZgqJYHOUwtAAABYSigr3wUOSU4Vkda5eFge/Qu0EaSnch9I0ZxUtCxuw3hmn8oURcCat4eeF+K/CnUQjIR7sLXP+0r/m6Z2fpeBkqAu2ckLruRSyFbITu2eMxWspY0Z8VWs+QFkhmo4w/5qDzPXPpfsn+xD7t7Ji8RoWnX03+60UwoiyY9s/o5SDUs7sc2scV4QLxgI/KudBQaEBuTKIdhX1RpB1DV28MHZw6viw5aMjEDuumfpN0+C58Tw31nWwOuQHIXjRLvDieJKWHo9AdxwmZm7kysYoQkwPUinQWMDM+YUHCgPREsD7ya4ZL4nTW1GH+fjXixCfTHwD5wtpyJRZ1LOMj1//DcacAdJtNmKNhf+Vyfs6IMhe7UObHpZbE+rHk4wzgRAWlEGb4JWTmFYL4141Vr2zVCHglJkIBKjPbkeiSAW60eL170lgC5N6wyTsugyTZU/cZmlymD1Ns4W6YzcSdl6kUFAYQRspo7jOIJbdOXtwk/XU9bhDxNfCHGHyxNirNaFeo0aNX4mjEurXXHPNjL+vvfZa3vGOdxyTAdWAZNwgcXYbemAcubARtXNaCIsWq+rC6Gq8BzI0xdpwn5mAOoEsSUL3tNFx0aWsFKuxv1OBoRQKMN0Y7qOZqbrHesjF/bHGd3KY5S938Y5T/5j5uo1INozrc8i/+yBmOkbY7kFtLqG/lOTEc+o5ofkCetuypONp6gKK/IXtBHvzkKuOUSxoQI2aoF2oaIJbWmm78Dqi/WXsSAOe57GiYR/LOtbTaC3Ei3gYQT+Lt6e5fv4pnGWtZekPo8gBDwIS4ybILxB4tRpPNd6klMQY33rtEwxkqjk7fGaQD5z8BeJy7vQxniRy7gLY+9r0XJlfjz3h45LGFiIJl4JvHNUZQ/ZWY9RFwsC8NE7lO+OQVYifmVhvq6PytdFq+SlA7yzjyjTWO+rQox7CEngNkop2ubHrDHwqyECxgZvXfQlbZfGbFndv/QKntZyJ1fssrlNk/sKPEhFrqRSbMf5QYm7PoocVaqnHuLmDWHsI+idDbCyJub4e5z82ItKTv0uvDGEP5ej8g0U8ufkvGRp5mVi0h1Cgs1aa7TeMtDTETdRP8+gDFfAJzAtiyDYfFpr3zl9EvS15bmyYeeEYN8xZQEzI4/q9CQHn2x20bpHQaCEQXNBrMLA8gNQS85QeVEMUhB8hQTlFZDyK1oLXByoEiK4AmDlwpwdvXBCl5HdQCyT2ZctxZBitwaaAM6+APCx7dyLdjvOfE1Mx+nq/g/edLNEPNrBX9CGuacE7MwwljdsIB+19NOsOrFnBJzWOJZnKBFL4qqE6FigvhfDNp9/L0GiEmDs8xFAkRkNuG64RIG03z6iTPoUCs2jSbEfYGcxw0kiBQKVE0Z65QamljSfCGLlhCLZW3d/LB44s1AEVkehhl3BDK5XSIE+Ob+CWnitr7u81atT4pfmVcrsqpRgZmZ25u8Ybwz6nzL5VsLJSj1/5kYv8qM1lRLeNjJnVOPJJ9L4y1tvqUb1l9ICDuTxIc/RM7EIQvdrE21hA9zsgxZRInzo35YE/gLUpzYlnXYh7Zwl3j4KoQfzaBrJtCuf/joEL1vX1OD9JQlbRAbQvDdF3dYmbd32Of3/Xe5mbixKQPtSEifv96drM0hfCeTWM81SA1+1rV1/2d5hRDVJT9A9h/OFFRH+6iw+PL4IX4jAweWRR4X1ljMCfN5NL1Fb8Nd6cHMxsnBLpAGW3wKN7vsZbFv1vtKpacPrdCrGsR9dbV6FLZYQ08LZp1INFPvSRbnqFi+Moxm/w8G+A2E4D47woztfHpoSHHnNxvjOOeV4U995pq7vaXkLO9+Pem6o22IL3/9HbebB3jKu66ijqnbz3he/xg6U3syH5KLvGX6Y3vZ0bT/sT2tQKMne0kp6oLmKlH5puMtkh/5axLc8DgnU3fgdzXBIuKUR+KzrZD+kKtEUwlrZBsYL3Si/7B3rZVLeOixe9g05/C+haCc/fNMLx4/0sXRXpAGWNe28Ku6OJoh7juy98iESojZtbTyKZ28ydL36WD536ZerN4+cxp7WmtRRAxDy8F6tlTIwTgrSVLDzHRI+7eE9Z6GT1M8jlYWg3Ea36kD5AjxexrqtDbS+icx7GsiDeYAXL8TGOTeHlerzJaWMEfQTmuCS0AYdUQRcjelYiPX3QwU5GaIqH+Y+9Azw2UA33Cg4Y/MPaHqLCrqWSO87kKymksMEWaEB5GYQRYtgZZZnZzLyxQYZCFSpGlILvyEnf0NWqOcKslghsIsq+UJpEaoBi09xZh7tWPXapnzKtGGYTTmkbsP7IXUclatQh0tFGf/oF/L42tmYPsDw65427CDVq1Pi94FeKUd+5cyfr1q07JgOqAT1miJd9o7xl37+xINTC5xregzwvAnmF872JGdYOY3UQ584keqy62FB7yxjjQfRKhfBL5CI/YlEAYYhqgpRD9a4Jsj2Eag6h9huoPZMrlIyH8w0I/XkCcWkJETLx9pamXWIBsVVTd2qCj7Vcwdy7JzD7+6pCvDWCefkC3B9XF1zuCRFy35i5jBm/X9E+V7Mn+wNe2/ZNAFYtv4GloVNQd2RmXgwNYtyDRC1ZT403H0IIJgr9s9oHM7vxRBn5ejK1okPblhGcn1XDRphfh3X6EoTfRh6EoBHgxU2avRasf4uLsc5XLa/ozt5cE20zrXciYaCzh8yxiiZSMvjJwUFW1SdYagX40km3ok0/W/ZXM3yX3DyjXprIARt3YnpuqRIUnouwovHjjC56ljG9g0/138Oewihfy4aIvnA3xrmfwnr7CXh7Ddz784hwEPOiNSxKFPnLXbvZkh3isys/iFFTL795NKgjlCbTGY+Kl6fk5hhM72QwPb3RVHAy1B/H8mw+n4nIl3G+OzHVprYWsd7XiDAkamOh6kX2+mubi8hVAdThKdvTHs6d44huGxGUOPenECGJebEPY38jjasFtnYBjWNa5Lc3oDsPq3ISPMJzJiyRPk1vxZoS6QAF1+M/dwzzqdVRzONZz64GBSeNiQ98Aq1yCGGTUx4SSZ3jEinl2V4fomA3/Nw+VLFqZJeTkYYmklQoRmtqhD1NnQQOS9TgWvUEKxsol8Gw6yl5SZQqIeXs8CAdkehdZQJGHY5XZFmwmcfGXqkJ9Ro1avzS/NIx6kIIbrjhBtavP/JOYo1fn5zKsmngRT5bdxILQuvhvhLurjJyro11Y301K3RZI5pNtGJKpL+OeqWAbLdxHkpjXVOHHqrg7S1iXhibEf9qXhjCuW8z1mULcb5XAAOMk8OIehPRZqBe7cN76gDYBsaZ82CxH7V92g3fSMMpE/WY/YPTbz6YhUUZRFeA0XUC35FsDQpKuQzPb/j3qaYXNn2JjpNPIRKPVS39hxKV1Hxoa7xZkFJQIY3AwNQhuuMrZx1zYselmDqEmrxvF2Qc9Osi3WdgLp5D5UvTi/62hMFJlzeSaDKIDgIjJXT0CG6SPgGuRtRXLY1IMK+N4U1WZwAQcYPWXsk3I6voc138vjCDL/8rReVxwpxz6fQvpL+wB1v6EMnZiRrLSY3M2zTJ9aSvXMLKYh+7949SijUQBVRIwF6J92T1PXXKw/l+BvUhxfe7L2ViZA/RgY3QsJiyEZnVf43jh2e4iHYL3XdYvoCIJGQ20BZdOMMbxJQWdYG24zpGISTey3nksgByXrUOttpdRr2ax10awTg4uwSgnnCR0kKp6edCoVvgE6APVKaeFqVLLKyAIhq2EPeOTG02236BdWMz1SRwh/SfkBgnhfBenJxPAqxrE3jBEmNpB1tKTm+Nk/AbvDCcY1e6QAGo+Y4cX0peDkPHELbE89JII8ywKhCTPnpG+8n4IB9oObK7O6DK1YT/xmEaOxOIsmZkmD5nkHlWJ/KQ9JpKBtDCROQnwFeHYTbgVg5i+4+QMNMQEJTIlCbq76DRVPxwfCMfmnMNRs39vUaNGr8Ev1SMutaarVu30tXVhWHUkqccKwpellv6homh8F52UCNV4ap2lFGDLva7G9AVVV2sjx2h4O3rz5ayRu0oolyFsSZEqaOMOTeE2echfApv424YzuM+she5eC6yLYD7TBb9bK5aGuqcesSCAnrnKN7DOzEvW4naPv022Q6HpifKs97eG0zy47fk+Grfi3yt7iZkQKAOMeqYMUFO7uOkVR/C88pIabFz77283P8NVrztj6n7isfr+l6c42e4/DBGOkIgtgx1ePHcGjWOI67Ismn0IR7b/d9Yhp9LF3+I7ugarl3+MV7se4B5bdfgGfXUhVop4uKbzAYt09MTQK5swz183mQ8uhIa7xvDOGMuWALzyjjmdQncOyZDXQRYl8bxJlys6+vQOQ9yGdwHXsU8cyGyI4jwO4hmH96LRRIvlIm8L84rXR4hX5RFXe/E2qIQGycod15CriNIqScLL4ZmDCU6V6AfL0JO0bsuzddHHuB9Cy9jQzbJBXXtGP55VF46LGkX4BuOon70LpqdyddWXQzn/hFlWSvR9pvCtTSBi+NU/ntsqvSeXBGolpBSQa5f9df8cPM/sj+5iUSghbeu/CRh0YY+jvuiWmvMZQG8TUXce1LVMa4KIhb7MXwgl/nwnpy5GS07LDxvepBCwMZYH6v+VyehARccj0qTj4N1KTqkj3B/Ce8QjzBKGmNbDtbMlNh6xENXNNbVCbSrEZbAfTSD0RGmKxLmAyuj3HPwfkZTKc5uOZmF4RUEpKbm+358qbh5bN0yGZ+eRsgQ46pARPiYM3aQ8WAQJY6clFeVQVdAHqGYjJKS8WCYZZkSW+vGmS9mWuRdWYdZGsKlrur+Xt5/ZKEOyLiJnHCJNLZRKfcRMYNszuxlVWz+EY+vUaNGjSPxC4X68PAwf/zHf8ytt97K+vXrufnmm9m9ezc+n4/bbruNFStWHK9x/l7RXSpjjPZSWPcH2PcftgLIeOich9MVYPQRj0CjQbjDgkMsJsa6EGpLVRjopItY7KewQFJ2i8QyDt4jO2d0qSeKmFdHcH+cRb8eH+6B+0gB67p23J3V8k66VICABRWNd2GIp8UGrlg9n8C8dnTFQhganc8wFC3zw5HX+MSq8/h/u4d569VNRH8qcUfA1y5ovNpgW2Yjr2z+TwCkMFm76lbSpTT/u/A93n7LGcwvNhELhbCNEnbBYseuL9E052ya592MqtVVr/EbQAjYnXqOH2/916m2b7/619xy8r+yquEqir6V/O9XH0azH4Aru5bxB10nYWsTN2FNOVIKn4UuzNxgM9aE8O6aDmHB0bh3JrE/0oR1TQLtTIqGF3J459SRGzEIZQfgiWrmdl3K473SB8kSCJArWjBOaUE8VmDvVRmuWXgj5l0TsCOLBuxhqNtVpPLBVjIXlyg85ke5kFglCeaKVcujT5DwB/lcww20j8VIR9tIXX4r9U4AUafR2ZmfQfhdcA+xTm54AHvN1ZTrFr+RX0ONXwLtSCp3jGNeFANAGAJ1oIwecaHBIK67+FDLv4HpQMTAlRYlfYTN32OI5ykMR6M2TW9mqQ0F5FI/5F2MFSbqgIU+4FTrwJ8aQIRcLMukMrn5oDV063qCQ2UomWjPwh6v0BUN4RM+GMvPfuMRB3mYxVVrgdpQQG0ozDxWgJZp/t+2r+NphSEkP+p9lOvngCfPPZ7V7GoAjipgKX/Vou6mETJAShVpl0Gac0We7e454nlTIt0PPy+YbjQYYlW6yAt1ZUZ1lkYx7RXkWgnsYi8OSzGsZkq559BaI45guRcxAzFUJtbZwb7xx1lWdzVPT2ysCfUaNWr8UvxCof6Zz3yGs846i3Xr1vHggw8yODjI448/Tm9vL5/+9Kf5+te/fpyG+fvHXSefR5s3xHrRPNvru86i/7sebkZT2AvmmfUEVlaQo+VqVvhxF7W3arGTSwPoThv7b0YJXplAhMu4h3VnnNCGHvVQO2dbyXR52nNC1AcwLwhVk+0cKLDmxEVIFcP5xgRM1lYXHUFi727gY7JEqDjKz4ZD/Ezv47JT61jg87O0I0BK7OKVZ/9zql+lXV7b8nXWnvr3vPrqtwjETP5iZD2Bx6vm+7iQrHnL/8cLe/6Wpu6LQTb/2te3Ro1fGuny7IE7ZjVvG3mGcNcSPrf5yRlT9Z6DW7iwdRHz7RjFRhfj4jbkQ4N4WwYxT12C+/C0GBAtFvrF2WJC9TsIW1QTammBe1Y9Q09p3KyHOLeVYPAAFByELdGexrpsBbpsVcV6lx/3uQIdwQjecBKx47Ca6Oky9A+SXl1PeK5Lwx4/xtNZdG9VbBdv9LFoo0fo2SSQpF4K9LuXUAiXCZ4exulzpj1fWk1EZKTqT3oo5dl12GscPwxTV2un/zg1o90+KYgUENzi4f33dAiGeUoI35VhysbxE+tSCryNs+Po1eYi4qQo7r2vITsSiNVxEBq1bT/qQD1ex3QZLSGgPRPDfSSLHp7sy4Dwu2LkWiqEVwTxXpg5v+TJYbSYuRGebfYIBMRUVQUATvCTihQ5kB3gcz1/wPJ0O0YRsk0uf5+8k0JHAT+1MqLHE88rEnD9YAmUSuGKAJ7WzEv34hiCoh2o/jZNfo1agXKqf0v/z/WIB2A0GGbJ2DDdai57ZZowPgKTWf09M4YptiKKFaQ/CtpDeSkMMzG7o5iB3KYIWc0UnDFWh5r4Vv8TfLDn6lr29xo1ahw1v1Co7969m3/+538G4LnnnuP8888nGAyyaNEiRkdHj8sAfx85EArx71ufYXmkj1Mufjfm/dOLGGNdCHWgjL/JTy4DaBh+QiMDFp3X+TGHS3iPFcAnEOdF0Ats1Dcmqm7yWiMcP+bNp6IODKOe3Ydc14Fe2Yp+pIjstFH7ZrrkihCIZc3gOODzT7km0mXT5TYh7x5HH7LW0X0OI/vzfCjzDDfNWcZ185r5zs4sd/VXF4MfT7QzR0xwOBUnx4H8MADXB04m9vgh95cG6ycpVr3rz9E1H8MavyEEBvXBdvrSO2a0JwItFLwKGac6d9qDMZYEG9maHyHjFNgxdA/bdt7OmlM/h3/BAryCQ0NCEAkHkE86iJiBnO9HNJiz8k0IgIom3x1h4lmFs2VaQKS3GgQXNCKSeXTOwTpvGc7txepcBQiXMD9QR1e0TKDgB7kbDonpFYsaCfk7aBxy+VrmYdqj9Zx6xnyCKYtKl0BYWZqeHZsejNKI2/eQ+9BC/BGN9XY/Oi8RbX5EyEKVDlv9Wn5UouPXvu41fnV8rkRfFMT5XnZKtIh2E8IKf8HC+8HYjOP1z/JYpwcpNx2/MRqGRHX7YcdhG8WdfrAk+A3UK31A3/Rra5rxvEOSmwpgSKGHD5k/HniPFwnNj+E1mhhvqcN7IAWexjg/hur0oQ/bBd9sDtH+wTjdhRhUNMov+Kmxnx4R5UxjEfHve4iB6jgTAv7x1psYF7Vn0vHEVWVA43N8CL9AeWkKVpQw0JXaQ9IfQOWr4vz1SoNCVLO7y6OI2HQMk4zPT3c+Tz4SZoceZYVoxUCCkLgijiwMowKdGFYzTuXAEYW6sAXaFhg5QdTfiXCGsaTFnvwAC8K138UaNWocHb9QqB8ah/7qq6/y0Y9+dOpv1z3cLlvjjWJcldBoNmX3M7qsTFcggS5rhE+g9pfx7knR9IFmcrunz1ElwJIUEgGMdwSojCnyC3I0fC2JeUYEOdeH91wOZ9KqIDpCWO84nfRAnq3uCCdutbCuq0eNOJCvLoCMU0Joz0KPNGOs8UNu8n6oNymtq8fdAOGx2feBvyAxpcG39m3hs+vmEd4nOa3FpM7vMT8qqeRbkNJEqelzQ4EmFvhbCRg+wqUj7DYXXbxsHqMuT61kbY3fBMoTnDnnRrYMP42rqlbnsC/BgrqTsc0Ai2NN3BhZyZodCaI7Ib0ILM/P7Vu/zvxVH+NPN71I1iljCMHfmRexrqsHfbXEjAjSBz0Sb6nD/foolKviwTg3Ch02IihQ2zROcqaoMOMCsbYdwzFRvUOoLQqcQ47JKdTeCgk7hXy6BXlSJ+r5gwDIU3ogHafyrxkiwIfmnMGTlxzkpuLnCYT8mBMG34y9d/ZFyFbwl/1438jDBSHk3DDuPRPoXge53I+86suoez8MDd2Iy/8/CoFWjmvAc42ZVBReehz7wx2oIQcRkIh6gR5NogMJRGn2d+PmHTiOztyuq9BzfMhWCwYnN5naLZwum4IuUXf+XNyvvjZ9QsBEdYawbUmlMi3WdRGY60OfEEVpMEdL6K0FtDRQwx7lpiC+WwKAplQWWKMuomumcmsP1NO+1Ydz52jVIhuRrH9vF6NWicSQCQPTyVjRYNxZov5Po8z2RatxrCg6WbSw8FcsiAhUJUvalIQRNGdzDPpaqiESR4hBP1pGg2G60hP0R+dS0BX26YmpeHVXxrHKQ5TpnKynvhd/8IQj9qOjEjHuEatrZyS7hcXhhTw3sbkm1GvUqHHU/EKh7vf7GRoaIpfLsX///qmSbHv27CESqWXzPVY02yE+1nkllzorCeQiOD+cgLDEPDmMaLMxghKBpv4ck/HHqrv5detNjL4iQb+kUDGYeFlj9Acw3xrA/eooli8+w/VP9zl4rxQprxCEEmCcDc7dE5hnRSEkkW0m7jN5vNtTALgHKxinhRGdNmplhJGnFVYMIqtD6KezVVfbJQFEwiDVWsTprY5rJD/Ap9Z5PLz9NvonRhgOv5eu8GpOW/u3DAw9TjzahVIe3UYr8e//I9885UrCdWEwknBIsiAa/SRLr9L/xL+y+oL/RBE7Xl9HjRpT1JkL+OjpX8PNFxGGwAxESLp+3Mown1l8AbEvlZC91azT0V4QuxXLz3wbvaKOhF3i5vlrOcvsJrI5ysBTitgKTbQHwh0GFWFgv68JUfAg6eFtKeA8moGQJPQHLUxsEHjZ6pwQJuhTKpTKBr67c5hXNuNtPkIZrnFF2JuPfmEUTkpgXhaDmAnlIM5jh7g87/NYu6OT5qY6+otjXN99DnWhduQ7m9HlMmrzAHrLMKo9xKgvR8cJAURXBO8/hqbchNVrRbxUEwM3/wPhWDOm2V0T6b9hymGFnWihstvFDVjIgsbYXMC6sIF0MEt8jQ97YahqdjQE3pYC6UQZ87gKdQ8jLsitqcfvUwgBxaIkGNeEHM3Y7hB1H1qPLIIwBWUPyqNlaJxZR10tCJLJBkk+oUCDrzlE641hckEHSjbFfR6BpRo0FPcJSpZBXM70AukYCaEO9TLIKqxvp6n/aASjIGf7c6U9pMtRpuWt8UZQdDNoYeGrmGhbo7MFJrSm0y0SL8KmcBj5a27mjwbDnDjYy3MdmmYi7GOCMfI0EMIz6wiWN1DWGtNsplx4Ba1dhJh9E+iIQI57xNt72DnyExZ3nc9Doy/yzq6Lf70B1qhR4/eGX/h4ufXWW7n66qtxXZebb76ZWCzGD3/4Qz7/+c/z13/918drjL93NHhhrtmwEvWkg15fQSzyYa4K4TyQhoyHaLaQK8M4KWh/hw1ZF6tV4H1+Akoa2xa0X1XP4BYDL6nAlKhRF+OkEKLVQgiBt6OI2lMmdJHN4rHtUHgO8603IfwRcB10TqFeqcbQinYLY3WoWgpqRYBKxUBXNJVRKCwKETwTzA4b78U8aluRtojFl5Zdyi61jwWROF999j1oFCe0nIU7sZtHXv5XTl3zJxRLE+w9+DDBQAPN894NUtB6/xchdjfypn/Gu+sAZCvQFCB/icG+LXeivDJueRzpqwn1Gscfy5G07mhDPZhBW1C6NEhv0x4e3/N/uTb4ERK9nTOO1/tdOs45iUGpuKZnBV/Z8TxXtS1h+BlFYpUgKovkxwOM3etW4yd90HaxhflKBvOyOOaFoAsK4VN0XAaVUYk2BXa3QTn1UwL50/BSHt64xjgtjNo/M3SlvMJAFx38EYmI+xAtAdRQGX2ghFzgw1gaRCuNHnWJ7SizvKeHcyIruHXv+Xj3j+G51SzhxrJ56EXNbKobRGSLiNPacfdWMIozhbjY75JNlkn6e5ljdh/z76PGL0ZUBBXbomALbKFwhaQ4J0q0oJBxF/OECM7Xxqfc4uU5EVRwdiWPYzxK5OYcIb+fiqhauEN+F7Gtgldvk1hkIQ6W0CUNUmAFBbo+gCPg0AQupbyB9lw6ztagNA4GyV2CxEILFYfwYA7vy1lQkDgphLc2OnMzGDDGqilYDkWPuoQyNiKu8MSMt8Q4KYQrLGafVeNYUXAyuBj4SwaezCFkgAwOXfkJlJCU/Ra/bgR43rJRQlBfLDAeDNGuoxzQSWLCj2UHoCwR5Qz4Y0gZw60MYPm6ZvWjIwbGwTJBqxFXlWkwBEOlCZKVLAm7ZuyqUaPG/8wvFOpnn30299xzD8lkkkWLFgEQj8f53Oc+x8knnwxAoVAgGKyV33kjqR+zcZ+simTvuRz2n7RQ+ZchXs8Cp4cdvO+PYZ7UgBpShOsVlUOT1FY03DVO07ubEZuK6JyHsSqIe29qqj6ssSaIcW6UlDmBr7GHoP4pImPhfCcDLljXVGOujBOCiLiBe38KPJDL/JgXx7GawBmBkac1HZeH0N8dnnK7DTzgsTjjo5D4DuWBOq5b9AFu3/FFFseWs+m1L9DVfgZ7DjzE8NhGAArFMR7a8nmuuurT2K8+yWj3an7gbeL6P1hEYHg76cpGDmz5EcqrYFhBDCtSq6pe4/hjCPy7Zibf8v9Hmvm3xvh+dg9e+MixquFIG93BJP+wYyMR248sSFAQrvdQ+Bh7dPpuVmUYekLTdV09enMB56FJV1sB1jvqMRolMq3wHigQiq7GWGUhLhfIFgvvhSLmRTHc53IIS2CeHWG0MYctPMJXxKv5JR7OIFotrKvieK8WcO5JggbRZWNeGmN1wwrOH1+A/vG0i6/aVETWmThBi10NSa68owlnrYs0j7ActgRlWaTeP+8NueQ1fj0MbeNKTei5URh1QYJ9bhyvO0h9KY77vbEZwlM9lqXphEZSLcdvjJYlEXsqsD2L73UDtwa90o9xvg+z5FK5Pw25qhiWc334rohTOcRbQwgwPEWsLwWPVR3RjaDE99YmdMHCzhZwHs9OHe+9kMdss/BWzly7iOjsIGZRZyB8BtqpYN3cgPtIGp30ME4MYawOIFQRDN8be1Fq/FxKbgYXgb9s4sksQgYpa4+u7ARJ288RDNszEErQkUrQlkkQcGxKZoXexAT9seR0KnghGAmF6UxPMB4M4RcWCR1gv55ggWjE0XWYhSEcfwzTasYp7z2yUPeJaox8QZMIzmE0V3V7fzm9g/Mb177h16ZGjRq/e/yPG49NTU1TIh3g3HPPnRLpADfddNPPPTeXy3H55ZfT11dNAvPss89yxRVXcOGFF04lqQPYtm0bb3nLW7jooov4i7/4i6n494GBAW666SYuvvhiPvjBD5LPV0VmJpPhlltu4ZJLLuGmm276nUtsp/MzE+LopMesVO3jLv6AppIVOD9OYkfEVGwrAK4GO4/b4SHqTLztpalM8ADeKwWwBLJk8H9eej/l0/4U90f29GZAXiEaTOQcH+7j2anszmpLifJrGbJXpIidZtB4kYVnGKirG6Ft2l1SPK/obLqc0d6nsPo38ydL/oalxQjnL7yV9sY1UyJ96jNrRdIo88iai/jQ+DA/GT3I4+VB0oEh9u27HeVVQEiWnfoJhHkcMx3V+L3HEbCxmON7+3vxHsvMej2yNcKtp30Bs8WPWjXTZVieGMRuayURamSklGO4kKUUdzAjAukoXDU7/bCb0nhlgfvwzHhY5/sTGCED59vjVTfzJx0qX55AYKCTCu/5PN7zOcw1QeSSAM79aerzfvoHtuB+f2JK6OjhqseM93x+OivywQrejjwn211YO2Zvg3nbS5gZwTXWaRgHPEypyQ4KWHWY0LkqgmxtImDXYjDfDGgpEE8kqyIdqobfR1IYRQ8KAgqzLcE6fXy3QQ1DkjpxcimimbonMydIzJzEfS43de8CqL1l9LCDPMRtXSmwyw7sOiRavKCQL6axgqC2HiGr/MYixuHPVR+Yl8Sq3ianhBEdJtZ1dSg0Iq9xvjuO7LAxTwujdpdwfpxEi8qsvmscO4puFkdLbCyUl8YVPkJIGvIFklbo55ZdAwiVfZy+bwHt6QQj4QzbmwYYjqTpGW9gTW8P8pDf49FghK50curveoLkKJOlhGvEsSpDABhWC05l7899Tx2VyAlFIjiXwfQrzAu182Jy+699HWrUqPH7wa9dI0L/nBjEDRs2cMMNN7B//34ASqUSn/zkJ7ntttu477772Lx5M0888QQAf/Znf8Zf/dVf8eCDD6K15gc/+AEAf/u3f8uNN97IAw88wPLly7ntttsA+Jd/+RfWrl3L/fffz1vf+lb+/u///tf9GG8qdKMJgemvRhwpKYpf4JmCQLeADj84amYZNwOkF6ZixTDfWY/aUpjVhdpTIuKL87GGrxJMJWac7/40jXFWBJWfbSW0XqtwoDyGh2L0QYfBH7n0PiqonNGAiE9aJPwSR1VLM40ffIaGwT4Cd/wjHXd9hXnFEInYbIubZYfYtPtTvK9T8MlFbZzuz1LKDbJo7UdYevL/4pRL/pNg3WkoVbOn1zh+7Cjk+YsXNrJxIo0Tmb0MNMM2c+/uoefRHrjQDzdHkGcEke+pw70yQkWPIitl/teCM4n7AmxUg7RdZaCiFubrZbAEhBYbJM4wCa+UGI47uyxjRaPzalab8gfRrTa0WOiUh/t4Fu/pLGhNxeeQKDYwI7g2YqAOzBYXeouDka3g1M2OT5ZdNlpqdKr6uyRfzmAnIF0XRV3XCFfWoT4U57uh/4sw/AyWaq7AbwZkBdgz25VdZD28kIdosxBxo+o51W6BARyh0tSxRCmNf56BvCgKlgBbIC+J4p8j0IZEDzgQkXBKBE4IgQk6NfO5JASI1OzEprqvgnJcRPvsoGXRaaMPi1Gn7CDn+CBkoHaVkEtC6KJC6Ao6Dzga74U87k8z6CEH3eeiCb2Rl6PG/0DeSeEAtmHiOhkqwqZJOURLknTg53t3xgtBTj4wj5Fwhp2NQ6QDBSqmSzpQZHvzAFILVvV3Tf3upvwBQk6FUGWy1K0QNBLmgE7iGnFMNQHKRRoJtCrhuakjvq+OVBPKxQM9pIoH6PEneC296+eunWvUqFHjUH5toS5+TkHKH/zgB/zN3/wNTU1V6+fGjRvp7u6ms7MT0zS54ooreOCBB+jv76dUKnHCCScAcO211/LAAw/gOA4vvvgiF1100Yx2gMcff5wrrrgCgMsvv5wnn3wS5/WSRL8DDNaBeWsA0WEgEgZEDMyzD4lnEmBe34DRZDH6lGIgGSI7ZsKacPV1E6wb6jHrKoTVIGpHL3KhvxpjfnYE88o41lWJquveBo+G5+qgpJmxFe1VxbqsP8It0mUzz2oi9+whDxoNw08p9KnV2HHvco/9fd8DwB9qxshO7kwrF/Mn/845i29FCMnClsu5uOuzXD3vqySM+SxuPpsDY08Rd5Psf/qzDO6+nx0v/Ttbn/88ezZ+BSlqAqDG8UOakh/u6wVgUypN78lmVcy8jl9gdPuR0kA/U0L+cw4r5EMuCuDZFWSuQijXQP29cS77bgPfda/ipGI3gw+4mF0Cux6aL5A0X2mjSpr0Cy4ogQpa4D/stzVqIOxD2nwCfWMzI3sDHLgD0ic2oT/ShjgxVJ3L18X4/wa+RaCxdWY/Oa/6u3I48w0SRoTQEgPReYiwCUvk0gCsipLcBmJFEPaWCG2cIJrw8PyC/fN38w9Db+PV8YfJl3sxvG14YrYVs8bxRVsa0XMEkRqRjAaHsW6owzgphBpzke029keaGa+b7TVyLDEMQaUXKj0hrFuasN7fRLkjhNMvUIZCXJageH4TA+Mhhr0I7vWtiIX+WXkKZfNsn2djVbW+tlgSgJZDNqDiBvKkEEoc1knQoPK1MdRrBfS4i/dwGm9zAe1XEJs9Z+QyH65Zs6gfTzKVCcBC2Aael6aASbNTIOwo0v4j17MPl3ys6ethX90oY+Esh5vdtYB99SMEHJue8YbJNsFIaKZVPYofD0XadnBVFLMyghACw2r9uVZ1HTGQEx6GtEgE51Iq7MSSJvsLQ2/I9ahRo8bvNscsV+nhVu6RkREaGxun/m5qamJ4eHhWe2NjI8PDwySTScLhMKZpzmg/vC/TNAmHw0xMTNDc3HysPs5xxSvCrpY07beYhNL1qC0l1ICDdU0C7WiEJShpk4HvTi8Qhn7o0PrWOMbcIB6S9Lgg7s/Bj3dgXrkaLBN5XR3uE1n0cHVTQ3TZGGtCEDNwX81jXh7HvS9Vtb75BdaVIXQ6hZxrofZOboREJOL0GJFChcOX4aoEdPiw3pIAcjTE19FfepBlC96O8eOvTB9YzBJy4C2nfpfg3X7YNNl3h6br2gvZEokTjK8if9jDdLTvWeadMAbGYcKjRo1jhYagVV2g1/ltNiTSRD+aoPGggVGuZqGufGMM2WRiXhrDfSCNEBLnO2OISbdiuSZIQJp4fXn8fRXkeoOOqyPw6j605+FbvYj+b1XwJp1ecps9nAlBx/ubcb8xWk0gWWdgvbMBNV5BLg2g0y56dYT+J8GbfJ/Ucy6VEYmvM0b8ggQfH/4yr07s4VPenXzmirdh/rg89ZlEj41cHUC9OjmLGwzMrhDOP03ANQlkjw+5JoRWGjyN871xnKuayG6D0CURjCVZrD5B2uxjg/8pfrLzS1OXzJYWX3/xT7hq6UdZ3Xgtqra39htDCzAvjuN8c2zKfdw4PYyKGEAU5/Es+uXJfCh9FbwdJeQfh49rFnOtIV4XwfvOGM5keTarw8L39gYQHgVtM/JE1VruAP0Dmq53m1iWy+tVYrUGbZYxLwjiPlYEVyMX2hgLNHhQLBqU19QT8Cs0mrJnYGZM7MNqoOukhuLMG1a9VkRcGkHrPNZb4jg/TkNFI+f5MNZZUPHVSoYeRzKVJBIfmOC5aXIEWZZLkzctOELeDNOTrOnr4WB8nHRgtmfh62gBe+tHWDbUzkg0Q8GuMBIM05MaZ1tjNWmDEFCvQwyIDA2qDrM4iOtvw7RacUq78QfXzO43CMLRUNI0hBbSm3yWeaHT2ZDZzZxQbS1To0aNX8xxexwfyc1HCPFLt/88pPzlnAPq68O/1PHHk4mxMTb238cjqa28z/s7bJ8PtbOE2lmNvxNNJrnls5PXJJ938XeYpJ71AA2rAySWNKD6QO3IIE8ITYl0qMalsjiAd7CCuSxQjW+9NA4dFhUhmdinMGwfwQsUVqYEkRDZMQPfuEe026TuAjBKHsqWpHollaRG7C7gPFaNrZ13y/tpW34R0Tv+Bcq5qfcViVa0FaCyo0Jw0yFWij6XhZvr+GxLHy+M7+b/rLiZiVf/c+pl0woTDIUJHlIasLGxljn1jeRYzos343d1NGN6+/weXhge5+bFzXxh6wN8zbD4tryayIOlauJGQO2vIOf5MU4OVxMvHhL7671SwLomgfdSHk4OU2oLUdousFvnYUYEzrCeEumvUx7QOJ7E94HGar4Jn8DbXkIgoKSQnTbljgDeszPdfQt7FJEVFvmDig/GL+Vs/wq+knqI0ZV5GuZGEEWbV0WR/xh8jU9cMp/u9SFsz0Zs93DunABAKHCfys4sRSVAKwFCU4r0Ua6rYCR8hNwIjWY7YV+CXDnJwsZTGErvAuD+HV/mhI7zaDyGNYOP1z11tPPizXaPlwaSZNN+wjc3woSDCEoqpolWgvpUEP3yYfld0h7RIZPAyW/M5zja61Hanp2uoQ7Q5yB2l6ApwMTzh4VfaSgcUDQuCxE+5GupPD+Mt7Mf89IukAa6fxjn3iTGslPJbFFkX9NUTanV1O2BHo+2VXLGGN19WWZhCSQCggHcn6Uwz4+CIdADDnoYygsVjfXxo70kb7p75FflN7WGqmzLYAofht/Ac7OkjQht+RwZy484wjJwyUA7WX+JiUhu9otU0zaUlYcHWJZiMJZm8XAbr3bvZyIUYsXIAEHPoWRVd2MSBBhVeXJmlMbydrRlYFvtJAsv4rM9pDFt1Q8Eql4cXsIjkJd0di5l7/hPWdgaZXNhD+9vvPSNvjxHzZv5Pnwzj61GjePNcRPqzc3NjI1N1ycdGRmhqalpVvvo6ChNTU3U1dWRy+XwPA/DMKbaoWqNHxsbo6WlBdd1yeVyxOPxX2o84+O5N22sc8EdZomzhEv3X43c68BZfuSKAGpT1fqlyxojIuGwqq5GWOJvmf5M2R2C+In1iKKFaNDo/bNjFdWIA5fWoYfKyCUB3J+mcd/WQv89itcjI2TAoOtGHzLpYfsU8qkkATuC/sEE6KoncMPpETgnhP6v6QRYlf1lHjef4PLz3ofxo3+EYhbiLXhX/RW7QvNYMOACMxVKdJfJ0rldPDT2Mulw+4zXFq39I4qVKPnR6mKqsTHC6OgRFla/5/w6D7ljNS/ejN/V0Y6p07D4+hlr+Y/dz1BWHvNjjYReVlMi/XVUbwVjbQjnhdkLQu1oxBwfqWiE1COK17M2Bnok8VOP8DNsAP0OlR+NgS0wr4kjigr3wUm35L1lRNfshbIwwF+pIJ5PM/fUBrp3h7hg1SrEv2UgU0R1ugSvMDlQKPC+1zbw93NPYk1W4Hv0EHfniEQuD6AOqcsuz4wQmCeY3+Chg604t2cQu6oePUvspXzsw99gV2wTvRObeXzPtwCoeCVyxQK6eGy+91/lnvpV58bRzIs34z1uhxSBRsHIBoHdEMAZVATjEG7UeEqiDA5/jGAg3pDPcbTXI5EIoHeXZrXrvSXEOUGMkMAZm3ntZcgglcrjOJNeAgZEmoIwksd7eNv0cSe04PklZvgIuSWiAvTMz5qot6qx673T3mrm+TG0X6D6Fbrfxe0/JMnjDolxSuyor9eb8R45lnPiWJAuJDGUD0coPC9LVigSxQppO4E+zHunLh+iPh9mc0vvrAp6HpocHiU8bCQCQUl7bAmPcPbgPGK5IKlggdFgmM7kBDsapj0249pPn69CQxmcwjjKimOYTWTSO/EFlgBVkV4sVjefZBDoL+HV+2gILaGS3MKreYfhkTTySLsLx5g34334Om+WsdU2C2q8WTiqX4gjWbfT6erD6mhLs61atYp9+/Zx4MABPM/j3nvv5cwzz6S9vR2fz8fLL78MwN13382ZZ56JZVmsXbuW++67b0Y7wFlnncXdd98NwH333cfatWuxrNkJkH5baazUseCORVhPKHRvBedb48j5fqyb6jHOjGCdGyXcpJGHGtUlJNaZ2E3T18FuAEaz6HkB1LCDnDM7fkt1Bjh4H5TiAagzsD7SwvgrhwmQIhR2afQLWcxtOaxr6tA/SM5IdqWfyWKOlWdkp680ONSHenjE2EPu3Z8jecMnGbj6g9xDPf/rZ6+R7Jp9+8n5Pv6s6S38tPFTnJ09i3PO/zHLTv0Eay/8ItGmc960mys1fjsxDIl5pDJjk/gdSfQ1h6Z/z/OxF07gP9uvJlspMb5gtj+3XOCHqFGN5z4MYQvUZfWkXpl5XnG/QhUhvHRm/GvD6RKz4lTzSdxQj4yZqIMzY2GNvgLBuTMFSN1JEh5LoveUcb41jrEwiPh6EvIe5nkB7BPhhHHNbcuWAZCihBeY9ts11odxn8oi6sxqHotzo1jvrcdc70fdm8T50ihirzcl0gGoaOx7FNqJ8OTe7041r2o9l5Bs+LnXtsaxJ5AOorcX8ddJshs9vCRYfo0edilEHIyzozOOF50WTsMvypv9xqOUhsVHWEcsDOIoTfxEc0ZMsRECKyE51IlOa9C+AHJ121SbqAsgl3WiPY/oIkndKYKOczQdZ2saTpPETzTQcubzRA87GIv9mFfEMc6KYF2bQPWWkXmBJx0QIJf4MdaFIGqAFLj87uTH+W2g7GaxvADaKgGChFZEy2J2fLqGJUNt9MbHUYd9zxUUE1RQQAyLIAYBJBFMfEKyIzrKvNGqMB8OR5mbHJtxfpwAKbNIhTqs4gAAhtWGU95xxDHriEROVHfEWqKrGEo/T8Sw2VcY/PUvSI0aNX6nOSqL+rXXXstdd901o+2GG27gvvvu49vf/vZRvZHP5+Mzn/kMf/iHf0i5XOass87i4osvBuCzn/0sf/mXf0k+n2fp0qW8853vBOBv/uZv+PjHP84Xv/hFWltb+fznPw/ARz/6UT7+8Y9z2WWXEYlE+OxnP3vUH/i3gUS6HjGSntHm3pXE+oMGvAabsjaQGZeOCySlkoHrCIyQYOhHFXzNgu63SxioYM6xEIVGMkmwL27E9LvIlQHUxiIIME4JU260wNOMPKfpWCERWQ9Vmi1clAv4BeapYdS+MhSPIJj9CvOyIO59BeQcH/GGKE12NxXT5s5nPoDWirmrPsW/bt/De5c1s907yPoTO6ZiJEW3jbE4QOC/JtAjLposstum/eZLSQe8wzfEa9T4lRFCMT74Mge2341WHu3zr8AKLUbp6Z9EKQX2K2W8H6YA8B2EhVsEf/TeU9jZlCGxLIK5pbozJRf70SGBtiTq9DiMuzDggAnGpQn0PD9uxaDhAlkttZbWpF+sZnb38tVNt8aLLYQBvojG2J5Fdtg43x+fKrtorA8jlwVQWyYt3c9maf7jMKXlBqUkBOo11stp6J8WDt6zWeS8AMYSgfuzbTBRPXdhd4wfXLGIA9qPW6coXqYJ3CeQ9Rbe0zm8gxXk20LogwdxfzYKfhPznIW4ORNdnj0TjUFFt93NsqbT6M/uZnXbBZzUfg2o350N1N9GhBRk8TPxeFUkVMY0hX3QdaNNwS4SbDQQH2nFzVQFsMxUyFmvu4gfHxxH4cR82KuCsGHyWbA6RDnsw3Yl45scGi+1cNMaaQmEBeUBF1/PzH50ykN0d2F2NAMa7ZhQsBGuwMyViW5JoSerHYSaTay2BB6H3Z9S4D6cqa6MggZe1quaMy6LI9tMzBvr8V7Mo/aXMU8OIxbYpIMpzPKRSrPUOBZUvDy268cz82gdpNkrE3I0ucOEeks2BgKSgfyM9hweBVyCGFhHsFVZSFKhPPMzDYQLAUaDimWjgwQrZQp21TpiCklI24wKk/ZSP+XoUkyrjXzxNbSqIOTMpAU6JBAFDY4mYCWI+jvoIc2mzF7mhdpnjaFGjRo1XucXCvV3vetdbNq0iVKpxJo100kylFIsWbLkqN7g0Ucfnfr3qaeeyj333DPrmMWLF3PHHXfMam9vb+eb3/zmrPZ4PM6XvvSlWe2/K9janr1HbwsqiQD9j1bwNUi8gibUBZElmvQmRX4HuEmNm9TE28D80TguYL2zDn+r4OA3NUbIJDwvTvC6KP5GgX41QzCQo+t8j+FNEWS9CVmPxBqT4YcO8YcUEAgrjJAftbeCHncRLRZ66JBRmkAhh9rVi33LcrxNFfjXDPXvS7BdPIqe9EnL6ACntgZ5OfkkD5WTnNp+K76OBFprdMHD21xEj0yb5fWBCmwrI042Z2X5rVHjV8Ur7eLFBz/I676Sg/se5MQL/h0zuHzqGF9J4j14WAZsR7MsW4dr5FBrS8hz42QrQSZKBj0RRWVI0feYIL6qgeApCoUguUPTvMJk4qcu5QFFoFMifZqG8yxSL7p4JU3mVQ+zzqPnTIVz2yhyfaQa616evum9p3NY11TLKIrO6gab8BSFYUisN5BP5fA2zFyU4pcQADU2VhXppgTbQB9M09qbwFpUR7pYZGvH/cx7/2IWqNVANZu1t2Ufek81bp2ii3vfVszLVoOavbh1T7TwWRnetvxTVLwiFuFaErk3AZ4wSL420xNDu1DJC0IZgWP56L9DoSuAgMYzbKyUg3McdacQYNZJnNPqsE6uWvgrponh0yA9It2CkQccQgsEXgGKvZrOG0wq7uHu8DbkFTploXMK2W2jci7SZ6P7ilMiHYBhF7WzhDxhpqBSCQuaTBhxIVN9Bor1MZygxErbVL4/POU15j6cxrRixHuCHDn6ucYbjdYa1ytiO35UIIejffRk8xQNA2Uc6mIB80abGYglp/acPDRpHDwgglnNO/BzMISgL5Kka6yBzV0FhkMR5ibH2Nw8Laqj+DloF+moFJFuDmWGMcwGnPJe7MDimR0KgZq0qqtmk874KSQHb+e11EKubj3jDbxCNWrU+F3jFwr1L3zhC6RSKT75yU/y6U9/evok05yRqb3GG4s2BHKeD3VI/Vt5TT3pbQ7tq13k5ny1vMzcCHpngfimAvFOH85FYQYe0ehotS6u91oB564U5nkenTd1kHzBo5LUBBsE6uksbCrACVHEXS/RevNa2AbuUzn8a8M0nx0htV1jBKF+jcQuVXAfymKeHMZ9IYd1XT3uM1l0bwWRMDCvjuDe/xqkS+iRPN6z1bEndtQTWt1Ie8Op9DRcTKxxPj2+V0jmJdSvIF0pkfh+VVzIZYFZGXcB9P4y5uk+HGd2TfcaNX5ZDEMysO8BDg9o7NtxO/PWrsTzqu1aVl3W9WHaN+ZK3K9WXRw1YF+6BC9Rh/dgFrUiDgpSrypSk0dIP5R6Nb6IpvlkF7E1BwkLGiIE32Ez+mOH+MkmiZXg/XgcABE30KOz60ITMcBTqM1FjBNDiAmHuqV+Srs9jIQfw8qAMylgBBinhtEZB7WzgHnjiaD96DEPERcg8ujyBLnhOk6dew1ZOULWyROOGsg5Bt4jE7PeXjtl1GZVrRDx0wwUFfoEH7kTDpDc/zPaFr4bQ4Vr3i9vFoSH9IF3+M6vCbb0MfDkpEgH0DD6pKJzvg+H41dyzLIMyq6LNVZBvJAGAda6GLrJwsQg2Okx51KFei4LYQPjhjBeUOHzCQqF6r0uBAghqdw7PuXt5T0H1k31UBaowdnu6fpghcOrfZZSCrW+AV+ujBiroLoDpJMmsaJCD7ozQrsA3Mcy2OuaIVB7Nh0PHFUEIfA7PlQoS1mZtBfSZK2ZyXXrCiGkFqT9BRSaAh55PPwYhI6yKvF4KEdPph63LBkIx1gyPszmprbqzQaEsRkyspTMOqxiL+XIEkyrg3Jp22yhDuioRIx50GwS8jUR9XeyY+JplH7/byROvUaNGr8d/MJfh3A4TEdHB7fddhv33nsv7e3V3cT/+q//olSanfylxhuDUAK5PID5tjrkaWHMm+rRTSYRXUbcNY7eVUK/mMf7yjCGLdHDDvqlHOaDozScKjCTFYgYyB4fZBUsasUoezQMJ2kup7HvGoZteeSZIQgKsCRy/yhqx+R3+lKO4L40HesVrWeC33JRe4roIadarikgcW4fR7ZYmJfHsd4ex33wNUgf4Z5ISBbE3sby3k9h3XEu7p0x6rMdHNz5Pfa89Ckq3eOUV1f3i1RvpVrv/XAW1UR6jWOP5jCXDeFhXh6b2RY10KWZ9jP7sV3MbfUQYx5WWMzMHQGEFkkqY4qErww/GkfvKqNfyKG/OoTMuNgdAjMG5bzEPTkBJ4XRw87MWuavk/VQO8rofgf3nhQ6rRDPpin0wfgusD7cjHlJDOPcKMatLaQdm/I8E+OsheheE+er47j3pHD+O4n3vMAnfDQcEKRuiyPumEc2U6L4IYGKmIj6I8TaWyZqTxk14WD9URPWJ5oonjfIlq1/i1vJImoS/U2FKz0az7UwE4K6EwXhRQKzDuwY4Ejc7Gw3Ja9wfGPUhQAj48Lto+gDFfT+CvxgFJlz0SEwxh28b4yid5bQr+RxvziMqcE7PBn8qFsV6YKplY370wy6AnLR7OeKXB7AM2ber3YChp/W9G21GSxF6H3aoJIH6Z/Mmnr42APyiO01jg0FJwPCxlcx8chS1AaNxRJZa+b325WsZyScIS88xqhQQRHFxH+UIh1ASc1wOMOCiWYG/TaGUjQWpn/7pRBElJ8REcYuHgStMe12XKcPpQ4vXluNUxcT0/fbvPqzaFADbE1t+hWuRI0aNX5fOKpfrU984hOkUikAotEoQgj+6q/+6liO6/caFZBkgyH2PmXRNxZm76MmRsyAZw5zwy1rtKunwwknPCLtGrEpi/d0FrkygDwjTDFtks+ZqMsa0I5CtFlYb62DfhfnzgLmBauhLYZxZgS50I+c40PUGYiih3owjfPtcYQWWDfU4z6Uxjo/hrwiRjHgQJ2H+/R2SE6K9NYIKj25cglK1IooyTsClLbbaAfK+wycexazpvsjADyx+c947oI97PxIkdItFsUTHMSySYEgQK4L4cyvifQabxyep2jpuZjDa/l0LnrblDXdUhrrkX14r+3AemsQ4xQ/xpURzFtieI/NTBhkXjgPfdcO5DkhcilB48U2/vbqAj602CBxmkF8EeinUjMHUtLIsQqxeYLUzzwGvlOh90eKMR3GC5uY6yOIxkmnJ1tgXpfAfWWmed97KQ8e1C2F2FITR0uUCbLTwvvhGGbeI7/HhqKB+9jM3w8dMsk8GKWwUaIdcHoN3O+3M+QOs2PRNkrXzgVjWrTJFS0QDWFeE8c8JYx6JINz2ziRRzo4c/l3ae44B1fX5uqbCaElIatM16IS0ZdGaRhN0nOJQGbKSENgxg8T5QKk//gKdQDx6uwsz2JTHlEC96GZ+VrwQO0pzQiF8jzQrkZfXk/lulZKV7ei3t5UTfhmA1pjnBiaelbKpQEISKSa+VmFX9Jxo4UZE1RGNeHFkqZzTVxbIVosRGKmKjcvilO0aptTx4uim0Fj4ndNlM5Q1IJYWZH2haaOsVyD+nyEPaEJinhEMAn9D67uP4/BcJp5mXpyStEbibN4bHjG61F8HDQEQjkYTgohLEyzFae0c1ZfOiyQWTXlleG3YuDr4ZG9t/3S46pRo8bvD0eVTG7//v3827/9GwCRSIRPfvKTXHnllcd0YL/PeJ7ByENONenUZIxqbq8iIDnc5vd6SdgpyqMaTqnD3D+EiBioxRFGb3dw01VBX396PYlWF+fLI1PnOAccrPfU49w9jrEqBKdE0FLgfH24apEHvGdz6FEH4/QwGILRugL/1HgXH2u7ijB1RBYl8GJ+dDhOdsTG94dxknmFv6BxZj7b8PIQcedVx1vJ0BI22VC8FzN6El9+4WO85dw/ZeV5Z2AYJrouRz75GqY5B9PXieIIFvcaNX5JjMACTr30yxzYfhdaubTPv3IymVz1dX+yjH6uDwB3zziiPoje71FethSfnF7wibYIuj8De5J46y3Gb9cgKkSWGgQXWJR6PSi6VKwhLENy+AyWtiD1ssLNTLfn92qiV4QwswWst9ZVBciQg7b1zDhbQPgFuJrCQc3wEy7Chs7rw6ivDmKujyIrZZJ9PrxzDIzlAdT+MjpZFdOqM0DpyZnXRZXBSIUYN3vZU15B3a2L0BNZYuEY4Zd9OF9NYr2jHud7E+iBqjuxt6GA6KsQff8ikqVefL7FR6wUUuP4o0yF3l3Ce2xSCCc9nC8OYf9hM65W1J9jkX6pgr9eUslogvNMDHl8hafWQPgINoOQBEej5ZEElpiR9d0wQM0LMXCng5ucHL8UdLy9Hmkp1IgLWQ/zijgAal+5GqN+5kyvESProb42QuvpUfQ6H2xIIx8EeXUM7Smsdzagxh3wQIQk+CbFvlG7348HBSeNh4EPG6WyKCUJlyFbP+3G1JyJMxLIomQ1Fv3XwTE8JgI5Fieb2JHQXHrwAD7XoWxWkxAGhIUrFAVfI3ZhL0X7RExfF+XiJmDtzM6kQIUlMumhGqsbPo3RE5gYf4Ad40+zqH79rzXWGjVq/G5yVBb112uVv04+n68txI4hXl7PUuRjT1bjQmcQkmAfsohZ4Cc/Jhl7WSPOjaJsyehPJ0U6gIbsLoW3IY+xKlgt/XRVAmNVEO+5PHKuH29rkWLZxOlzp0T666hdZeR8PyrpECPIpe1noZBUskXUCwOIFwbRqTLJ5zz6b/fwpIlj5Y94lymzGsM+f84VJDMHuMT/Lrr6l/BH3V/kxdH7uaPwz6TsF3n6ges5eOBBUkPPU0i/hGnUSuHU+PXRWlLfuoaeVR9n7pq/xAgun8r4rmQBVTzEdVGDHitAukzeBeMD6zCuWolx9gLkKe2oA1WLnzdRIrrKoP4sCzenmXjcobBHUXZz4C9hXRqfOYiIpFA2sNpmT5DysAJTUPnWON6zOYQhMJotCM481lgXxmu0SW6pznFdgdRrCrk6BCboUYe2MyXiZznUiFOd9xe97s6vEUfwrrcCFmG6EJ6JP9xEuq2ehyIjiAWTJbR8YkqkT12icRcz6Wd47DXczBasmmX9TYFdlHhPHZbqzKVaVUO6BCMurfVF4htGafYyRBpc5HEW6kKAscyc6UJugrFIojyJWH9Y+IktUM2+GUIdoDgBbvKQBgVjTyl0WSKWhVD7yrj3pHDvSaG2FBGnR9HisAdt2kVf1sh4ys/AM4JsUwyv1Y8sgp7w0GWFkKJqFTUFalMBw61Z1I8XRSeNi8QvbTwvS2PFRSIoW9OCvDkTYyiQJfBLuLn/IgbDaZYlWxiTiqFwmEWHWNWlhIjjZ8CIYBX7QDkYZjPKy+FURmf1pSMSMT6d6KDN38yQbOb+3f+C49XCSWvUqDGbo9puvPrqq3nrW9/KxRdfjBCChx9+mGuvvfZYj+33FjMIwoBD17pWXTWG1nprHWpfCWImss6ANhuxPoKqtyk4Fqmf6WqZnQUBvIMVCvtnPqy0A8biAN7PcngPpuHUKHplBCMh4MEk4sQQY4+7NJ9wBCuGAXrQgQq8FNnLEtFNfb8fw5RoKw17xpEHkjRdto6Bh3z4xl3Gu/vwn7kA9/Hp/gKrS+wpPcLihTfT1nkN9RvCBH6QAQ3topE/fPtnKXQM8MLjf8L8Ve8mPbadXa99BdMKsejEDxFpOQ+tfbPHV6PGL8nrru5QTWZ+sJJmV2qM9dFGGn0GlKcnoW4LY+gA3ndz6IMVCFkY19ajVyl46QBibgKfH1QB7EZBeLFF8lkXs04SGKjH21PEuq46f0Wdiei0GdsM4eWz51qg00BaFtZlcbytRdztBYzl9VhXJ9BZD11SyE4b15Ak+ySVsWnB4WRAnhJA9DvI1WG8rw1Dqvo53H4HuTyAcVEUFpsEw4L8fdPnJi7W2MEWGlU3nilI3u7QEG/gknVNeMMZrGsTiNCRg3Jdw8Qon8/zQ59l3eJ3YgaPrjJIjWOHgOqGbvqwjRNLoEJg3JNCh03U+jii6MGdY5jvO76JYrUGL6SRH2xGZD0QAh2RKLOICrrkhUXg+ibk7jw6YOB1BNG2Cd5M7xIvP9t44GY0Qhike8H/lmbM8RJ4Gq/ZT2qXpGHRzLnnJXwMv+QRWWRit1efwcmkTWNAIppN9PN53Cey1coLTSbWW+twfB5HafOo8WtScDNUtMCWFiVVoKtgkTXN6uYJIFxJrOwn31B+wwoM5nxlKobLvGw9m2Me5wwOsqWpFU8aICDi+RjSWeZYCXyFfZTDCzF93RSyG7CDZ83oS0clcsCFyVxzUTNAWUYJ+QI82/ddzup+zxs06ho1avyucFRC/QMf+ADz58/nueeewzRNPvaxj3HWWWf9zyfW+JWQlqb1QsnQYwpVAiMMrecZsLmIdjUqq6BQQTaGUDmPdDhM2OcS8ioEzjNxQyZ6Ww7yikC3TXH/ISWeihqdU6gDFdTbmhl8FtyNGjMKLZc1YvYX0H2QHZUkFgfQ26cti+b6CN5rBfSEw0knr2LgPxz6ioCM03TGKoLWZjiYwm/lCXQHcIoeL2TKFJv6uOCmdkiBimj6oiU2ZS7mmdEy71eSjjsy0x4EPoEatLDmLWL5qr9kPPM8o33PAuA6Obb87J848cIezMDS4/eF1PidRwjBq9kk//ulrQB8w57gP/9gBfWDoCoSEdQMdxi03D4p0gHyCu9boxgfbMFb2MbYQx7F/QphQeJ0CzeraL3eBiXQwymEz0CnXXRBoXJlzLhJbBFoG/xdgtLBanhK4nQTEZWU8GHXKUSHDRMe3qYSZlyiUy7ethLq5TzO9a1kts+0bocWGpSlxD9X4I16UyL9ddTmItaaIM4/DRO9JIq6yU9hQpFosSg+qkk+oAGHwFxJcL5B8imX/B6PjrdH0f+vD7nSj3FGeIalVq4NsSsl2bqpkTUXfYR9wz9m6cJlOE7N2vibpBQuYV+eQH17bLqxyUS12JgTLu7yKEMvQGUrGEGD5nMbMZIuRI7fGIUAV4SRvWXEM9Ws7/r0GKonjF2BwByTkfs0uhLBS0I4ZhDt0RxWnQ2rQc4KBYusNFA4eHlB/+MaI+gDCd5rEFyg0eZMOVfxBI3rDOR4GVHxUCETtdjG0QJ7ooL7+HQsvR5xq5UPbo6CWfMwPB4UnDRlwGc6FIVFe6lM3pp2C4pmw4wF8ojDPSV+TQbDaVZMtPDDnnFSvgCLxkbY2tQKQEDYKBRZXzPx3E7KoXlY9hwKuUexAqchhDXVz1ScugcY1edOq78ef7CR5/vvYG3r1YTsxBs69ho1avx2c9QBPOeddx7nnXfesRxLjUkqFQg2aTrPnhTqAY2MGOiyxntyeqHgbC9hfaiZeDaHeqDaLgH/hTF0vYFZZ9C00Kb/25Vqdl8BDWeaqNfScGaMgSc03uRa281oBn7o0HWtj7qwZOQ+F3NtjNDSECLtYnZb6Jfz6N4KXBBn6C6XqcSmCkaeMOi8YB7GwZfxShK7SVJcWuK/dj7P362+hve/9DM+tGIhnlLcvbePi7paOa9Ts7gcAm8yWVC9iXtJIyPPKpwvQmj+euaddjKqzaF/4N6pz51P7SVWE+o13kByQvH/Nu2e+tsvDbKZEIXHzGrWaB9Er5GI0ZGZJ2oQCJIbJMX9VUGsHZh43KHxEoviXoXdZCLCsprQ7ZDEWM7OMv5bmslkILbGpPF8DbZBdpNL3zcqoKD+PJNYuYi6J1U9BzDWBJHtNmpLAV9C0XiJRfI5FzyIrTUp9SvcNJg7U1jnxJgVLGJQjVNXYP8kg3W5yz+E9/AX+1bCgWnLYHGvItBlIP2gSlDJgt8AtbGEcVYE610N6HEHr8EmlzDpMCWtYYPiq91Emt6NkbNwfOXD373GcUSWYXirQf3bm5ATFQgaFLEQExBqsxh6waUyqeG9Agw8pOh5jw3HsTwbgDHioH80Pq2x7x7HeEcTdNnktnlYCYGvqerJkdvuEZxjYrQe1odf0/o2m/FHHby8JrbWJNAtwXOIrPCRetHDK0wfn1hnotTM2WFawBMTsKP6cJOAcVUdtIarddWXBlBLwiglMEsu6skUZsUA8wilFGu84aTLYyhMMAs4wkdDsUDWigNQQdGejZHxF35xJ78CY8Ecc1INtBXDvJZwOXtogJ31TbiGgTAg7PkZMiRRI4Sd30slvADTqqdS2okvsGy6I0OgQgIx4aEn49RbfHUcLGdYEF/D073f4qJ5f/iGj79GjRq/vRyVv9bq1atZs2bNrP9qHBssS+PcNozckMPOlBGv5JA5D++Fw2INFZByUc/OzJirHk5jBCTuHUnERIXm62waLrJovMTCCGjEwgBewJwS6VPnlcGteFh1goYLLPLDgolRm9LcCE6+mjQKgPkB3OQRyvr4gtAepuSGSb/o4vdcPnviOlZn9/F/VjWgSlnWBeu4ZcF8vrFtL1/fvpctOgeB6m2oz07Q/4DCSVX7y+9WjD9pMr/hfRjmdNIfX7Dh17m8NWrMoqIUyfK0OHlPyzyMH5tTNaZVGYpPa3hHE2JNaEY8rSNNcltnx2R7JVCOJr/Hg/kxvMMytqOBwQpIGL7bIbNZk3rBwctD2w0+/B0Cy/PwfpKa2e8rBcTJEUpXtpDvhfKAR2iBQXipQepFh9wWDxkU6ICB2lVE9MwMRDfOikJEYpxczYJdt9Hl7KZGAvtn79uWhxR2/eT8NDWVK4KIRhNnuMRwNMvLUR86YhB5OoP/7nGCYyVkSZN+MMjwtxxsx5rVZ43jiDSoJAW9Dwt6t/g48KzJyFMaYQi8MlMifQoFlfzxz/rOhtysJrE5B4Ygu9GjeEDhZjWVMUV5WFHqn+mpoXU178TQXRX8HZLoCSbZzR6lQYU0TIx8hc53WITmSwJzBO3XW9iV2ZsRRtqZEulTfT+YxCh7qHYfqcYYvY9J+p8Q9G220Dc2owK/gev1e0qmMoahfSgzTxmTeNkjY1dzZxSVR2shSjLwxgt1BAxE0qycaKXPZzLhD7JsZLD6mtSEKz7GVJ5ysBt/bjsoB39gHuXCq7O60lEDOSNOvYG9+QGWNpzDa8P3U3h9AVSjRo0aHKVF/d57p62ZjuPw0EMPYRi14qHHjKQHZY3aUkRtKWLeUA9FjYgY6NJhO/e2xLwohh5xqkLao2rli5sQkKhhl5FNmvjJJroicFyJURfA8FyECfrQ7gTIiGTghy6qrAn0GLgZzfCdFVqusPGdFsFa6sdzPKyExDlMrBuuRK5eSXHIAjRmNsh45TEiwzbd3WcyZ2MXpe0mPS1RPn/aWv7i4GtEfDbW1UGc+1I4WnJ4GebCfk3jkjj1TScxMvAkiaYTCEYX1ao11/i1MHFRvVuIjvWjo00Q7+G05jqeHZ4AIFGplhN8nbqzLbyspu9HLlY8QsONEYx7R2FOgOKIxtciKe4/rCZznWDsYZe6M01QJUTUQI/NnL/eIfd8+mWPzpssjJcz8FOHtjUhXNOozulDkEv84BOUkiARxNYa9H69gl0PLWcIjKKLEdewPoL3pWHEnzQzNpyjLhzBdQ2Eq7FGioghB+vaBOnhEi9OJDlvXjvsn/levhZJar9TrfPeXOYj+S9wytVLGPZSzPO3cUP7OfAvo3jO5G/BrhKJy+vIhyycMVBjEg6zfNY4fnhakzjNZPQBBzWZq8puEBhBEGb1/95huuY3Up6tzpxV0UTUVTfKYusMvDwUD3gICQ0XWNXxHzbM8oBH6zUmVgCE1kQWm4w+7hI70cYsVtD/MUzjPH/Vo+TLJYy316MJMGOCHe5PD1DSoDRl1yS9aVrce3kYfQWae0wwa0lOjwc5J4WhfCgji1KCUAUydgANRApBcnYZ9xglQxwKpzlpoJu4Y/NqXYIL+vvYVd9IwfLhc20Umpy0saw6ApnNeI0notQruJUBTLttqh8dk8j+Q+PUJzcatKArtoqXBn7Emd3vOiafoUaNGr99HJVFvb29feq/np4ebrnlFh544IFjPbbfX8Jyqt6raLFAaNwX85jnRWce12RCSeE+kkZNuNi3NsFCHzRbKAXWpTHsBLQvKBPpzRCPl4mFS/jrFYZSNJ01/T4AdWebyJiJVhpVATer8YrVhYtbFqSjEVTGw0DTfKGBfN3ILaHpbInckMG7fYJEvYOwwYxr1nV8gP2NtyKeXkbhVT+qCO4+iN7h44PtC1hhhnHuT2GeGMJomL35U12QabqXXs+a8/6Fxaf+H5SoxXDV+NUxUIR2PkTltnczPLaDpzK72Jnfxg3z4pzSEkcATlAhJrcx/Z0SZ0KRftnFK0BpQNP/gEbd3ErSH2b0px7RVSbykPyGwQUSJ60QNgQ6BWJcYF4SmzHfiBuUbWtKJ5hhgdySg71FhAnejyYwhYIuG9FuIU8JYn2gEQR4XxwicTBJNO4hbUX7zT46zhGY3x9G/HAM9ZUR9MMpzLMjKLtEoCtK3z2awbtdBu71GNhlo1ZEUPvKmGcHuGlBG9n5WazuaaESWCCwGjV15xpY1xe4ZvPHOZAf4fujT/BUchMSCX0lcGaKG/FMmtiS6gfVqha7+xvFKlFJejRealF3pknDBRbh5SZQQQtB3dnWjFVA7CQT5PH/zuTJ4ZkVTHwCsSaEEBojKLDiArtR4m83QIMZkXiH6GshINQjsFMVxDeG0bcNIl/M0HKugTQ1ancJNMhmE9liT8ap52dVVxH1xsxxAGJpABUwcCarp1gNgkC3RJhQ6lXUChwcP4pOCssL4MkskYqHKwWuaVDCozMfI+0v/s+d/Ip4UjEcyrJqoo1BS3AgmuDk/gNVA4cBEfyMe0UqwR6sUi9GeQLLN49S4ZUZ/eiwQObU1O+mEII2fz278/0srl/Pi4N34anaxk+NGjWq/EpFJvfs2cP4+PgbPZYakxQl+M6LwyMpzKvj6O0l9LCDarMwbm3GO1BBxgyk6+F8bxwU6P0VKl8dxXpPI8QM1MEyosmi8t9j1aRXAoga6IVBcDRSa/zbUnSeE8bzJLLDgrKHsbVM1/kGCEmpIsgPShKnSYRfE0poRL/CC0kyBxR1Z1r4wwrDD8bOPKLZQiz2427O0XBJiH29n8PrzzO3828Y2zPzVlNlWKyiWFmFuSaE+2gGI6cILwgjbUG4SYEAo9FAxECGliKZZXCvUeOXJlAcQt33eUbOezf/i0H29W7m9OJizm5eRlZv4ualK9jiPk/b5ScjfxwhtMBg/PHphZOZEERXGCgpcHW1QkNmg0PXjSaVAQ/RaFWtdjlN2zkac2OWyo9TiNUBjD9sRR8soy0JrTbpZzWh+dV+my41sX1+dJuJznqYp4RRUmFfk4CyhqDA+c4Eeqg6Fr2nDMMjmB9oJr1BYe2amGER1HvKiNPCjAT3Yzw0H+1QjTcvg5OEkmkR2J8iOfwE8cYG/r9duzhr7XwuOmMJXcEwAdtF9AN+yYbMXmxp4k6qoyva17G2bj72sDErgReGQCuBEdaYDZXDHQJqHEek45LoLuLlfEjpopVE10ukl0LThJPRNFxgoV2QJhQHPIT569We/pUQYH20GV2ZtJRb1VtKqWrm9ZF7q5u/eCBMaHmrD+MwM4PMuHjfnS6JpZ/IIAICdU4Y2k3sDzThvZBDuwr73Y14Ey7amPlEEXmFdX097pMZ9JCDsTKI7LKh4mHVm/S8XWLmHXRRwakWmQlJLpxFOL+Ba/Z7SNnNYbvduMYoTUWXvFmNES/g0ZGPcyB+bNel/ZEUq4c6eamhj43xGBf39dKVmmC/v56o62PAzNBpxSgH5+Mbew6r7izyuYdRXg5phKudvF5PfcJDNVfvm1Z/AztzBzm1bhkRu5Ed48+wtPHsY/pZatSo8dvBUT1dVq9ejZj0M9Na4zgOf/Znf3ZMB/b7jLAko0kfjR9owXs0idpZTcjk9VagxUK9tQlKHmwpYt/ShE571QVJ2kP3VlDPVZDnhNHDHuSrCxHjpgYyOYuJ73rIsKDp/ADWJQGMByeQGwsY50eR3T6cn6QgVz3H7rTxX1mH6i1iNhj/P3v/HSbHdd5pw/c5FTqHmZ4cgAGRM0ASBMEgEsyZEkWRFJWoZNkKXklrv6/t9X6rtfXZ6yxrbctJsmVFikFMEpOYE0gQRCByjpND51DhnPePGsxgMJBEySJImX1fF3hxqrurqrtPdZ3feZ7n9+DFLPymEKoG+Q0Ks0Ex80pwD3v40kBszKGrGuuWBpzURgZ2P4rWPu3NGxHmqqlp9kDFGKVPCrpKEczr0qgdFdqu0nhPZNHPBXmackUU78YYQg0ijTi+jp6276HOf01EJQ++x86mVg4c3gLAyyN7uGHG+Wwa282msd0APBlZz2dvfy+t4R6MV2z8IsQXG9hNguw6j9I+aD9fIOcqRMbAl2DVXPjeMAwFg936TAvuj7MA+AuSHPyuQkgLrQDl0/FBG5VTdN5uE/Zr+K+U8Z8fr9cVYN3WiPOjYcj7yBVRrBvTqF4XIQXa03iP5lCjPmZEogenR2FqXgXLimE1mUSuEPhFjZkQgeFcWcEcweDw83h9Jb4558tERwx8dwxDVBH/15j4/Tgv1so3PvXbfHnsbq5sms/MeBtdxRSGkMjr0whD4D6WC56/No1fcYhcs5WR/CCJlsvQ9cD6W4JBGDProe/MjhsfguyxMG8M4xiacIfEy0Fpj0+4S5JaYf3KHbN/HloDFYX3VB69LYiIiqURjIuTCGlSOeLR80EDMeKCJfCjFuVRReSk/Yje6caFel0R47w4oiWE83cDEwtKzuYy1qea0eqk6HlNoTyFfG8TygMR0vhP5zDnhomGXbyHx3B3HK8hEKQ/0UxJG6fZeu+didYK1y8TcmJ4kX20VT1KZuC/4XuChBOiaL+5vcgd02MkUmL5SAfrWg6ztbmdNUcP0H9GEjwbTCgph3ioCe1nieW34IS6qZY3EU1cMPleUhIx5MO4UO8KN7NubBtKa+Y2rubVvvvqQr1OnTrAL1GjLoQgmUwSj8fftJOqA/ElFt6Yi7n7pMlHv4s65uKnJNEzwjj/NDgx+ZArIhgXJlAPZjHXJtDHo2smVO0Qw896hDsl8cUG/Q+4qArE5qZouimKMBVqQ2lCpANwxEEerKIez+E5GjosKhdlsDsNjJhHy9U2gxtdCjuDtN/mtS2Et45BWZN6chYrr/xjXtv+B+zd8xUWX/ZvjD4yaWoVmq/4obOOQ9Wj/PXMjyF+lEMujKD3VdE7J2+2alMZa77Jc/13EI62smjN72OE59Un/nV+afxEC0askcIJsV5P+3has7Z9JU/1bSRihPhE5gYWV2bh+xWar4zSf59HuEsy/KiLEYO2BT76n4bxL29g7KBJfpuHsEyaLm0lnNZI10dLgihgxqQ0LEDpQKSPk33ZI7VEUjnsE+kC77hIB9DgPpDFPD+B91gOtamM6rTxnshBVSPSRtBn3ZIUDkBsWQxOMpysNlfRJR8nqyk+N7lSljrLJDpTUOo5Sm7vds5t/XvMrwTXndkcRS4K45dO2FdJ0bbJ4IOzimBaNOeaSf6TiyrVgiyXkMD6UBNKugxHNrBn9z9Q3LKfhtblLGq7DK8eVn9L0CKE95PRKRkP+qAL5SQipakc1BS3eoQ6JaVdPuV9Pq3Xn97osBCgjjkTIh1Av15BL4ggekI0Lwb/H/vQzniqcJtF4kPNQZbYiYSnV/KJtAE2+Bunp7n7zxWRSzNTtuk2g+pRi4F7FF5eE+4StF3WgI5oOOSgdpwgBB2N+1CW6CczOHbd9f3NpuoVEcIk7FloVaS54lG0orgoOsoJcuEK+jTYKxxNjrF8oIuNmV6ORSTN8RQX9u3l0ZYFpKJhBv0ScWnjJeZgjrxG3Owi624hEl890apNJyXGAXfiDhQzw8SNCEcrg8xILmN93w/JVvtJh9ve/DdUp06dtzU/s0Z9/fr1rF+/nt7e3ol/x44dY8eOHaxfv/50neM7DlEVhFokdvtP+XqEwI4J3PvHpkw+1KYKojq5QbQHK7yyyya/K1AHiaUGw4+5E63VSnsUY4MWYqaN6p8ekdNjXtBaCqDXJeT4aCXoXuNR3u1R2KFBB+2bBp5W+CtT6KqPGFQkd84hnp6DUx0l3HOQzttztFyraf2gYP05G7hn6GleLezlk6Gv0f9pA3ltErVn+oq43u0ST/VQyh1g05P/HaFOtiquU+eNU7EbEbf9KXNEGDFeNC4QHB0a4ndC7+aHM/4ndy/8EvMfWEwka5OqxAk9PsSMGyXVo8F11LhcwCOjiLRB0QiT36pAg3Zg6DEPX0hcLfEa7XGfCdCnmEVqH3JbFKPP++jCKQo7SmpKzaw6UgtS6wGd9dFKo4XGGdLUuuOwPBqETWMSPhLnee8+UkMdFF+fqpRzr3mIRkVf4Slmdb0f86HwxGMiYaCHpitra9DGNMO8WBigbUd0ItoOQE3j7ShSa3Qoje6hUgockVOZxWhOw+y5zikxasFv+MnoKqANcq8GvgvlPQp3ROMMaNRb0FFPbZ9eW6x3VJBSox7PgnPCja7fRRydfpKiwUQ0n7DIYIBxQSIYf6caggL0Serdq5gce9DHywfbq0c1fY8F9zxdmn596j4XUdfop4WSm0WIEGFtIrRDY80nb0Wp4NNZTpF/k6Ppx6laLiPREmeOdJDHZW9jM3HHYclYHynCDKtysHgpDKqJhURK+wkRw6numtiHjglETQdGheN0RprYWTyMIS16UivZPPDwaXk/derUeXvzM5fO/+iP/giASqVCb28vc+bMwTRNdu/ezezZs7n//vtPy0m+0zDjBiNPuaQWGNjnxNEnRsnmhCmOCBpb9Ckn9rqkMM6KorI+aI31vgz+wSqhZkFxB5zKo6SwS5FZAMayKL5XwlgWRZcV/mslZKuF/9Lk8YWn0H0uDHkU9p6inVNFEplpQk1j7LFpuHgx6RmXErrrf0BuANuOUrjpvzFqBeLfEJIPJdYS3qpQI3nk3DBq39RJmJwTpjx0BADXKVAr92HF6i3a6vziCCFwRZbhxhbmd5zBX2U6+dtdD/DR1OWsfWgm8mBggS1mCxreF4WdJbx1FTjsIO8eJLSmhSJghICCgnPiFPZNT++o7vVI+yWEDiE+1ox//yjxFsXYSfXc8YUGQw8HF6UfNREZA2NpFO3qoA1bykCPTioB2WHj7Z1sx6gKCiMq6LpRMrZNU21LkX5XCiPvkp1TxjhiIKoG08KJGvQ+xRk73o97aTl4L8f3edjBujY9TTwZC/IsTJ7FM5UBjJHp71kMauz7NN39V9J4ywo2HvgSrbOuwffr6S9vFW5IYK6MoV89oTWgANVggRbThgUwJePjdCFmh9G7ThJaZ4RRnoKhUy0g+0ybvkSDloOy3UZL0AUfHRaocOCD4r9QnGJyYl6UwDvJOM/J6WlGKLVejV8RmM3TWw0ayyL4J+fg13lTKLtZFCYx6eNrk5jrkg/FqODSXk5w8E2uTz+RI8lRVvR3s6mhj4ql2NLaweqjhxjNxTiWMMiqCq3E0UaEWnwejcVdDLIeO7w4KCMVApWSyEEfNSMYx92RVjbmdnNFyypmpc9i3bEf8K4Zd0yUndapU+edyc+MqD/44IM8+OCDLFmyhG9/+9s88MAD3Hvvvdx5553MmDHjdJ3jOw5V1IQykt57XPKpOPrGDHJNHPP9GdwLGslt0VRyEjk7NPWFEkSbiXF5Cn9rGRGR6JKP0RUiPltgxJlwsj4RKyNQr5eRSyMYK6N4LxdRB2pY78+gct7kZE6CSlqYA1UYcbEz028gVovAHxwOInqLDLrnX0bz+ucgNxA8wSkT3/g070q0sjg9g0+1XMMF93SRflihXimDJRBdkxMieUYIoho7POn0blrJkw9b552KgGHK7KwNMqCL6J9RXytkkVJ1K2P5fRwtj7FxaAfhoVf42+UfZG3fAuTBSUGs99Xwt1Vwu2MgNOa1aYwLEhPXUblfwOwQjLiEMtOPZcXBf76I3lLB31ZGnOUSanXp/qhFYplBbIFB+202hS3+hDAoHAHjwiTea2XUrirWtSnMd6fxXwlElpwTCq7F8qSS8FvDuIc9zKdHaI6WSA/l0f/Sj/9UnpiOsaTpSiwUZmrq+YXaBOZAFbkNosU2WD4ZUcfTqGM1zHfHISIgIjCv8lFDPyDUf5CL0rOxl08vfZKLw6j9NXTWJ/pwO+ef/ecYdtfP/w7rvGkoLan0JBBnx8AAMibc2kxVG0H/50VTO22YKYH5FvQFl4ujiO7J0ijRYyPnhxE2iLNPMdZmhaZt00IjWmzcB7K43x6BER8RllAzKHkm5m+2YqyOYZwVxfrNFoq+hTwp1G7Ep793I0rgAWMLrJsbYLx9nZwXQp4XR/lvqHlOnf8kJXcMT5uEpUPcMXAleKaB9iXx01CffiI102Mgnuec4W7y+FQsmy2Zdi4+uJduz6Dfmwxu+HYjbriDxmoezzkysV2nJHJw8p7TFmpkuJaj4JVpisxEacWxwo7T9p7q1Knz9uQN3WEOHDjAmWeeOfH34sWLOXTo0Jt2Uu90hBSMPhsI5LHNmkLVxlucoBoPIRoMUueYDL+skDc2IucHE2zRaGB/qgX32RzOn/Vhrk6gB3y8h7K4d40iX8nRtVYTTmuisye/dmFA6yUG4kDQs917PA8lhR5wcb85jJwVDiYm7RbGx1owmiQ8nUNvLdO0lMCJd5xwl8Y6egQZs6DVIDd/L/t3/gB35aUgJo8ps4OE3ALva27nPdbZGAMn1Ao/lEX2hLE+1oR1YwOi2aR2dATfC27Cs5Z8EDNcb8xcJ4iOb6n08YmX7uSLrz7Ax1+8k6fG9qFO0UdX6lH2v/ZXvP7IZ9j/xBdxN/8dTdqjqWEpB/fdjbFtuh2UOFwluxfMtSm8x3N4D2XhG/3MuEESW2oib8iALUmfoZEn6Fy7GcI1BxyNv7GE9EFWwfvHFzG2HiS2wMAva/yynkill2GIGw7efWOQ99HDHu7dY4DAvDaF9aEM5s0NQQpvWAT16e/PUCoblCsmytGorRWoqaC11RUNlLZYNNdmoO4epuNcTWK+wEwIUiskbZdJ9AtBZN4/VIbro6iV0eCO0GaizxWQfA774xr7YwLRdhjdvxGdH2KVPx9/7xHMG2JBDXDSwLwmhmgVkynKR1ys3mFkvU/DW4phK2oVg/5agtpN7RTObuboCxIjIpEKQp2CzlstOq8RdL7XpOkKa1q7vdOBUD7ylgzyEy0Yn2xF3pxBKIWqgLE4jDw/EYzNiMR8XyMiOlVQaw0i5+N+Ywg94EJJ4T2aQ++oYmqf/Ouafd+Hfp1kIJRi712CsZcVJxe6G0lB6swTpkUCWq+zUCaIfg/vqQLm1Wms2xoRMQP3X4YwT9V7vc6vnKIzioskJB0aKlC0DBypaK8kKIRqp6U+/USOJseYkW8gWrVxUAzH4hyKN/CefQdxvArVE/r2uZEZICNYI49PbFNpAzk8uVBrCEl3pIUdhUMIIZiVXsnrg4+d3jdVp06dtx1vyDUmHA5z7733cuONN6K15q677iKZrEc13yxUNah3lSHovEwgHxuGR12MjIlxexP+LBMzaZDthfi7m7ClQjsKd2cVuTyJ7ojAqId3z+jEPvURF8OowNODNJ+XxLskglJgNwnE5hxyXgT/5eK0c/H6PKo3tGF3GxjFGsauEn6TiR70kPcN0n1ZI17MQhgO5tE+5KERxII52NfEyAwlaAgtJGtuwzz7OoyNj4DnUFxxMVnpkjDbiFQjaE5IsdWgex1YHEZrja4qxHkmXbXrMQybkb7XaK72Iuye0/BN1Hk7k6XCl7f8BEcFEyKF5q+3PcOi81ppE4mJ5wkBhcENDB56avK1/a+RbpxD38GnWLXyrzHmR/BOMm7UMyNEYypogXhcgJYV6mt9RL/YHpSk9NjYjsOM60I42oAxD2usBg9ng2MnDfA1ojGIAIpjI9gLZtK6UqGrHp3vDzO6ziPcBmJzcVomstpehrUpZNWHUYXosrA/2oSuabwn8kRuaaXWp1E3t1LY7lPt1SSukfjDMPqsRzYKnZc3IF7Ok768gVCPxM0q/DIYHTYcdai1jjHQ+wMab/wY1pUJdEhRlf00vtSBv31ncCJSYL7n9ymk9tGWV6h1h/FjfcgzOxGGwN+wF7Nl4eRn3magG5N4uh5tfCsRZYEZ17Sfq9GHq4QTJsmbQlSyikiTIt0l8O4cgiMOpAzCtzah7dP/nak9DurZPMZZMdDgbSjBpSlEl4139xBySQTrt1sRGtwncxha458QgQcCgX4S/oYS8uI44U5BcSuU904uHEW6ZJBlcAK1QUisMInNB1XRmGlBbURhVgQiBHrEw/vh2MTzRYtJPTP59FB0xqhpMGSFTNWnbFrUhOKMcoKC/eb1T/9peFJxODXKBQM9PDFjNxnDZn+kkTgOHzw0zMPzknQb6eDJQlCLLyGSexm3sAWVWAa2QIcEIqvQjcE1NzPayuv5fZzTsJCe1Jk8duDvuXL2Z5GnSoWsU6fOO4I3dPX/yZ/8Cb/zO7/DH/7hHwKwZMkS/uqv/uqXPuiHP/xhRkZGMMf7tf7RH/0Rhw8f5mtf+xqu63LHHXfwgQ98AIAXX3yRP/3TP6VWq3H11VfzhS98AYAdO3bwh3/4hxSLRc4++2z+9//+3xP7+3XHTAuECY1nCowHh9DZQIjoEQ/vXwbQ17Qy8vT4lP5cQWLnGHr/eAs3QN7YyEldZ9AHaxjXpPBfLsGLeUzyEBbYn2jBeaGIuKkBjhowNtVESluSUr/AqWmS2sdwNNa7G3APVhEHHKRbxY5VEU4F3RlG+3FQYZx/GpqIzCTiPYiPnk05+jFCnQ6DCQdPmiypJjD2efgtgfAHMN7XGETz/3koWKxYEWW0uIW9274+cU7ts68j2tjzq//g6/xakfeqFN2p4lqhyTlV2sxGqmWBHYJIxGPk2Lppr88ObafnjPdh3x+FHo1cGJ5wdRZLIpTDNqavphj+AOCDygZeDXp3dSJmHLkiib+7ij44GZ03L01Ci4n33G7M6+ZBJobYlMd/uoAATAM6PteGG7fhsAmHp0b2RcSAgzWc7wWLBaLVwrwkifdcHuPGRvof94jNNui9y8EvgtUkyG/2CbVJwt2S6hGFG7KQ56Y5dpfiePgmB3Rd3Yg40M8x/1H6Dj+BOeNsnhi5n0y0g7XeNejtk4IEpfHXOcQ+cBnSCaMawjBWRb10cPJcjydoRSXWTYJqpqXeneGtJuqREKD+5QQDzhaT6EdbURGJ92+D0DcucHM+3tcHsH6n47SfptpZQWd9vCfyE9v0ngrGhRFYHUdtLOH+KBcYxJ2fQCQM5An15UKASJ7i/p820LYm3inItwBaIAzwK5rUPAI3xxOw0oJj33eIdQjsBk12PVjNktgcgWoJQYcFveOflwB5QyOeVR/kp4OiM0xFgDaKNFUVRSNCFZ+2coLBeP7n7+BNoC+eY2UxSUshhZMsgyHYnmljZf9hzj5wkN7ZyzDGswmFEaIUaiIx+jTV8Ay0lQ6i6oMu/vhCbnekledGXqfi10iGmolZaQ5kNzK7YdVb8v7q1Knz1vOGlO3s2bP54Q9/yMhIYNaRyZyiKPMNorVm//79PP300xPCemBggC984Qvce++92LbNbbfdxurVq+nq6uIP/uAP+Na3vkV7ezuf+tSneOaZZ7jooov43d/9Xb785S+zYsUK/uAP/oAf/OAH3H777b/0eb2d8Co+zVdaWBUHXTjJfbmqsZTP8aqFaEKhh6fazqqHx7D/Wxsn+zY7D2cxP9WKGHZBa0Sziep3sW5sQFU8rCtSOP86NGmm02RCVwh1TON6CqNRIeeGcb4xxM7fqPDIgtf4f+LXovc4eDstRFwgV83Ae748NX2yqNA7HYZ3NqIV9L2nwGtilEWjNt4zWcxr01BSqLBEGxL17AlmWZvKNM9YTSjSRK0STDZNu57NUQcarCiNoSijtfLEtoQZotvr4On7fZxqUHFx9kWSxuQi+nhiyuuTmXnE1SzYrVCmg1wZxZgXOEOpYzXCMY2rZFD6caJYN6GaFdgrE7B7si7SM0zKyxpJXOgjagqRNnA8icxIZNjEe2g35jXL8J8+Ifrjg7fP4dhGTfc1CdhegeOptInAh8L5h8GJp+sBF++FArLNBkfjDCiSSw2EgK6rwDxQAkehWmPUZlhUjyhEoyS7UXOyc1h+QCDO2c/BV+4k2byEp4/cx9aBZwE4v/FCoic8V8xvQc7oJvbVCq5fwbpsOerYYdSWwN1dntWG0+Yz/KFj1BIFmmevwvXC1HlrsUo23sMDUzcOesghB3xzUqQfxwc15EHj6TtHALkwgr/7pBrjeRGU0OgBF7WnNnF+/rMFjHnh8S4Kk2NatdhBDf7I+P1QgrwijZYSBmp0X26iNpbA08iL4/hDtcBn4gT8sqbzAo18bhS9wSW+OEqtKYl2NKUhgTqricgFLlKAi8HAemidJd9gEWGd/ww5ZxitbTyRp7Gm6E9FcLSioRplX2bw5+/gzUDA/oZhzhuYyY/iW4lLE18JtnR0ceaRA3B0PwPdcyafHplF2R0jMvQQ1fb3o9MS45CLvyB4PCQtOsNNbCsc4Oz0AnpSK9ky8GhdqNep8w7mDQn14eFhfv/3f5+XXnoJ3/dZtWoVf/EXf0Fra+svfMD9+/cjhOCTn/wkIyMj3HLLLcRiMc4991zS6TQAV155JY888gjnnHMOM2fOpLu7G4Drr7+eRx55hDlz5lCtVlmxYgUAN910E1/96lf/ywh1tCDSoPASNpUb2rFshbm9CK+XAsdey0BGNJmLLSquRp/fQjStkE+MwKAHjsbzgKsb4LGxIMwek+gLGtj/fUXTpWESu0fxv1OeetxbTaz3NKCFQFkG5YrB4H0+HRcLQvkqZH1Eq8L4aCPtMcWt267DjUr0XZOOwrrKtKg8AHkfGRG4w5q5oyn+Re2l32ykS4H3YBZSBvq2FuSGU6yMb/ZIr1rMQOUZmrvOJxybVa98rUNC23xpxZX8/zY+QtapEDUtvnLmzWz8USDSIXCwXv+U4sprzqGxeSWjQxuD1zbMIRRuxK3kwARjfhj3u6NT9i8OO0Q/mkHcGMO9rwQ1DZaAGzMMv6YJNRhkbm5CvJpHxCX+khj6kCZfM8GA0R+7qLKm8z0u9uULsC7uQWdtYNyd2BZwYRK3PYL/gsfQFkH7BzLoES+Y+Ougv/TJ6MMOYmUUL2aRPhfcvKb1Ogvj4UH0/uD5YkOJ2MdbEREYa85hhqcvbkmlST0znwVLP0ep1eb1nV8BYG3L+0nEO0CM99+WAmN2F+49k9e5e28O68OzEPMb8CMe+8sPUunPs6uwkUJ/L9fPuhOTulB/y1Fiahu9cbSjwZYQlVPMCYFg27Rl3jcXrzuCmF2G4x0/5oZRHWFMV+BunZ7W7B91kMunjq/CEZAXNhEWHngKL2ox+LKgbabASJl4f98/sQit1hUxfqN1WqlJyPDx7xyc6NmuN5QIFX3EgiaQAmumSXnUwC9owt2SUIeH0Cflz9d5U8jXhrH8COhjxFzNiB2mqRalZnp4p/AlOV3kwhUKoSrzh9o42jSE8AReyGBDVxerDx3GCic42hzMlaWMULMShHwXa+xF3IYLgjZtJQ2xIA3yjFgHr2V3Twj1B/b8Ga5fxTLqv6d16rwTeUNC/Y//+I9Zvnw5f/VXf4Xv+3zrW9/iS1/6El/72td+4QPm83nWrFnDl770JarVKh/+8Ie5+uqraW5unnhOS0sLW7ZsYXBwcNr2gYGBadubm5sZGDgpavBrjB3VjDynyW+ZjHZkVidJZF3k0iiDOyFzscXIk+5Ez9sRAV1XN2F8px+5Isrwek1t2Cbz7jakUhhdJkfv9cGHSLtC7RaIZVH0zspk/W2fh7upDEUf84vNhFzBrB7w/3UQvxjcCP3nCli3NFJ7JUQ0Dbw8tae52lHFuj6NOjhVYOiZEdy9wXFqeZ9yyOeleJ6be6KIgw74Gr+qES0hoDTltXJOiI6ei5jRfg6h7gtxdT2iXicwkJprZfin1Tcz6pRJWWES1RSv56c3Ni4NpFlevIrK4nfjtMUZG9jAnk1fp3veB2i+7gL0qQy0Bj1EwUXbZczPNuHnJcqQVEoSq1FR3KcoHTHouLkFw9Ic/baD3SIxk4Jar0/TJRaDP3YxVRW5N4f70G7Mm1cGdbEa9C0t9L4oSLcEE7TSbk11tonxzCgUA58K69bpoU3RZOK1hTl6r4c/bisxBnRclcE62j9xPatns7R+IkZ50CXZpSlsZUoHh0Sbhiddus68lj87+DGU9ri05YOsXfdujFEwr1+K9/RuRMJGHZj++fjrypSuGOWl1z4JQOe8dwOazs6LsET6VJ2/6pxm/IiPvDCBeuqEBVATZKuFimrEjRn094cmx8V5CXTa4HQL9eGXNVZXA7EVetxTQuBvgI55BmKWHSxenYBsM1EnaTMzJem72w8cUoUBCiI9IMMCtTlY5BaLImCA3lFFv5hHLpuaHSiK7tSe7YDeVcX0fEItFr3fcSZ6rAO0vdfGMOuN1E8HJTeL7XaSrpRxDCgaFj3FJIXT6Pb+0ziYHmZl3wx602Mo3w+uJzvMU+1NXHloLzXbZigVdK4x7U4KzmEaitvwo2egGlqQAx7qjKDbzcxIKy+MbiHvlUhaSZqjM9k18jxLWi57C99hnTp13irekFA/ePAgf/u3fzvx92//9m9z7bXX/lIHXLlyJStXrgQgGo1y880386d/+qf85m/+5pTnCSHQpyhw/FnbfxEymektX94ulAadKSIdYGS9InFrBr2nTONyqJT0hEgHAof4nYLWWzKojhC1RxTuGPguWIbC7KvRfZmJmGHhlwRDIomPpuHdKeztOdhaRnbZuI0hhKcRRQf5H88hrjgrEA0n4P0kR+TsZtSp5idVDSkD8/o03jMFhCXQF6UZ3is5XrCa7XYYOFTla5UD6MtncXusEw7VwPGpRmzC3XZgbgSQMdGrI2Tu/AfkzOWYi25AhiaTcpubE6c4iTq/LG/mdfFmfVdNJDhj/P8rJUUs4VMqTP2NiHYnMZ4dgkUr+H42SjJ0EZdefj39O9t5fdY6FjnLp2Wvihk2Ig7et3dCV5rahWcy+GMXv6SIzTNousJi+DEXGRMUXvdputKmvNfHHVUklpkIWzDrYyay5sOhMuZ1S/H7JNaHmvD21hjYIvCymtJOn9Q5JrlXPHqf1DRf3kIk5qNcQcmQhM5LoF8cLwexBOZ1aYo1E7849Tdi+DVF+4oYvDKu3pXGjJZpfEKgs8N0X5mhOCgRQhOfIxE/GgLA7TNYdMZFPHvwuyz3L8LaDhoP9yGJuXoxYqaJ6gOYGtkUKQM1/iMkhEGyfQWh2lGWLLqDpsYGTgen6/p/o9fF2+33qNCfJ7QwDGGJfrkAjSbmNQ14noPpmfg9FnyyDT3kQdJAtpsY1Ghu/tUshr7RzyPc7jH6vCI3sUWTuRiUUBiXpFC7a5APFg/EgjC60yaRiJA4YffVUAW7BZxBQAetSJtWCNAeOmXg3dbG6I6gLL3x5hTWcBlpTD1H51ApcJZfHYO4ge518HdWwBA4A3qKSAcY/olLdIZFc+cbj3a+3cbIL8vpnkPV/DwJdxaZmqRkaRxD01qOUwrVkG+xo59vBsZyK/u6ebXtIMe7hIpYiidaPS7fu4N1S1ZSiMUxzWaKzj685DzCI4+iWj+MOqawlk2OoXnFGWyr7Ofq7tUs6ljDttFHWbv4Pb/Sc347j8O387nVqXO6eUNC3fM8arUaoVBQz1WpVH5hYXycV199Fdd1WbNmDRDUrHd2djI8PBmZHRwcpKWlhdbW1je0fWhoiJaWll/oPEZGiij19oz5hKv29I0KlBIYe6vY/Q7lnumRNt/ReB0h+h5UNF9tYXo+4t5BGPACk7nlUbxYA0fudCciKH1HNW2XpYi2GmQLFsMvaIQFzQmT2IU9kLaCNNwTtLp2NYkzFP5ICfPmGN5DZfThcdFgC3bH+pnVvB7/4/PZrUdoHFuEn89gtwnstfDvpcnWfi8Vx7h5RwL5fAUiEvvmFiqrM9jn+MgwWM0K/1vrMVf+EWKWS2V0lLIZTNiamxMMDU3Ws9cJ+M/c5N6s6+J0flerLg/x4o9cnFpgMrVyjSASdcj/1lpeH3uNHcdmc6RQ4kxnLoWswfbwnbzofJ9P3fYX6Ltz4GlExkTemMHvHQRX4Z85h767JoVxaZePMCGz1sCqOYQ6bQZ/5EwsntX6PNLnmkS35xAdIXRXE94DDjrmoAsgrmig+vfB/iqHFDIkaL7KItyksT0fVRGItElpN7itcdKfiaB7XWSPiauqqNHpwkDVBIQmlxuM8+I4roNwnKBLw3cHSDWZoDSyFkXlFRqoNNic1XYrcTGD+OgJDddLCl30YaCAbIzhn5gmbQvk0jAjag+ZM66ha+615IVi6Yr/RowZp+W7/mXG1C97bbyR6+Lt+HtUDtdocgxoC8EHoqDB3VemcKZLppYAH4a2gN0Qwh1UpCIaMyF/Je/jjX4eDQ0Rkoskuc0+/ngylRmHxDyJrinG9gnMi5uxDR8MQXFYEs8buKUS5fHxKCUkBn3auxXu0jBKg619xJNj6Lkp/DkJjv3HZJZX7xHovC2JVGrKOcZjNtatjXj3jaGzPvKMENYnWvCUOGXWjapptNZv+PN6O46RN/Oa+FVR80poDXENDRUomSY14dNcibMz04/6JV0rTeUTVg6m8vGFwBMmNcNCiV/cdKA/nqO5FKepnGDUKiBNsLXBYDTK5iaTVds38/TildTsEIbVQcEdIWmk0dXn0Pk1FIcrgR8KcIbdwdP9m1gdX0KTOZ9nRr/HvqP7SYaaf85ZvDHejuPwOG+Xc6svFtR5u/CGhPo111zDHXfcwU033QTAvffey5VXXvlLHbBQKPDVr36V73//+7iuyw9/+EP+4i/+gt/93d9ldHSUSCTCY489xh//8R8zf/58Dhw4wKFDh+jq6uKhhx7ive99L52dnYRCITZs2MBZZ53Ffffdx7ve9a5f6nzejlgxMOJMpLUChFoFcksBuSgCCUkMj7GTXtdwrknxoE+kRzLylEfrPBcGxsPeAoyFYXL7/ZM9pRjbDuqcJMP3Bc8NtUkwDQZGexBZQfr9YcxnRyei3OZlSfxHNmAOlPAEGFfMRc9LoR3YumSA/yg/wZf3HUCsmcUzx37AYOkAZ669Gl+7tKRWck3bmewrFbhmVowLRAvy++ORuopCfref6PIYYm0S/cRW/N1DiIYI/qYKRqKJWleSeoF6nZORUiCloOrnGbZeY+6VUcJOG2kkqR8/TtXrZm96L08cvotV3V/GEnGGD9jkRxVz5l7EkcJ6RvptYje0ItHUKpLRHyi6bmzAiNu4dgKkR3KBJNqqqWYFue0+meVyPFHkpAwXILfeI3Wujbp7FPsTTbhXS3J9ktqoILlbkbnUpHpEE+6SmEmwwhrjiVG8bePXQ0TQ+JFWZAbcPxkEDeZvphFDFiHbASmmXAuNqyXGYYVeFMFYE8d/Lk+kMY1YaU2UouhhL3CrbrXwawrz0800CIdkbxhTXQtRAfSPH18iWqv4P9kDYRPrqvloP4qIhaDJ5Eh4F3mrHTNusPvQj1nY8gHMbBIz5eJHp6cm1zn9GMrBjTXQ92094XAenhkmvKqMMgW93/ZxR6BM8Ntf2gU9HzeA05vOLYeH6fxAC9U+hRAQbpfIgX5It1LY5GJEIDXPRNWgeEBjNCoikx5dQYu0rhDc3YedMCAi0f0uxq0ZCFvkt09P5R971adt0VRBZqJwvzU8kfmv9tfQ948hP96M3Rx0Y9EnfDTp1Saynvr+plNwhpEiQky4NFZhzLKxPJOQb1K2pvt4/Dws5dJUyxH1a9SkiS8MJBpT+VjKw5MSR9o40sSRFo40qUkb/bMCVAL2NQyxfLiLZ8K7IRIMorQMsynm0+SlWbNrG88uXo5ptVMtrcOJryUy9gJOw1xkb8dE+ntLKI2FyZ7iEeYnZjAztZLNAw9z4YwP/1KfX506dX59eUNC/TOf+QxtbW0899xzKKW46aabuPnmm3+pA65du5bNmzfz7ne/G6UUt99+O2eddRZf+MIX+PCHP4zrutx8880sW7YMgP/zf/4Pn/vc56jValx00UVcddVVAPzlX/4lf/iHf0ipVGLRokV8+MP/dX7ATO3TeYlgeBtUj2piPYKGmQr9gwKeBuuGBoxXR+m8OsPIdtAONJxjYrketlfDXBrmwHaF8E9oX9NkovpcZHx6tF6GoLT9eK4WJJcbDD50PHqoKe2BrpuasH4yglwVhWwfDJSOP4z/6B7Ex1YzEhV8as/f8X/O/AQ5dTkHts/ghtVf5Nubf4+XBu7h8iV/ztf3S3K17Xx+ZTN37XmY9zZ8HGR2siTSB/1aCXOhgTdQwLxhJeoogRO2YWEpA7+u1OuMIyVo3c/o0XWM9b9Geub5rB9+ls2DzwPQETuDT87/ECU5SrUaLG3Z7joWNqzF8j1qxyRzw+fSFT6DwkOCwkRkJhhjbs0mdP0CpFJ0XSUwN2RhUBMWkLoyhWcZhLaVkd3Tf0qFxWS9eJ9H/54QznDgvl49Cuk1JmZa41c0xR2K1gUeetsJ6eUVjf7JGPp9mcnFNSOEumcAmTHpurqJ0R3glSG9TBI5UED1OUG5yZCLOuAgD9bwd1cxb2/E0wbalFjtBsqsYX6hEf/fRtB9wXttzpSp3N5I7dYmQj8exZhto3bsC45b9fAe3hbU+V7Ujd/SSUdtLu17XHSfi1xwHaMvWYy8rpEhaL7FR3bJt23W0juFpIox+GNNcoWBnZFoBfnNHpHhDF4U3BGQEQi1S9wxjTemccc47a7vblMzR79RQ49rLhmCzjtasU1F83kCa3cR7i9ASBK9rAG/PYQ8KehZqUoin26DHWV03kdek6ZYNYm6BtKaLtSlDciTxueIO608Xx9ykCWF7Xh0vt9m9CUPL6dJLjeINSuEPv3me+808rVhtA4RFjUaqooD0TCd1URQn/4LJnfG3RIttSw5I8KIlZkQ31oH1gZSaCztY2sPU/vEvTKW8rCUT8UMkbXilIzIKY9bDjkMRorMHmvhYDroiGELk6g0eTVtc5HjsHL/bjbMXoBhtuE4BzHjS7FKT6KPvm9CqAshWJiYyXOjm5mfmMGchtU8f/TbnN/9AaSomxfWqfNO4g0J9Y985CN885vf5L3vfe+v5KCf//zn+fznPz9l2/XXX8/1118/7blr1qzhgQcemLZ9wYIF3H333b+S83m74domOg2J5ZrYAoEhFKK/NhE9076GYy7m9/tpf18GtyVM3wMufh6sRpv2jKbpPIFnWpjjaeu64CMikmhMIWwxMSFCQMP5JtlXA2Ee7pKU9k4XwvndmpaL4ohWjft/DwbRvCVt0NEIx0YRxQqqMcqfrfgNhss59uXmMzYkobCb+S2rmdl0AX+zHY4US5zdkuLOg4/xufC12PeUkOcn8E9oySZaTIj4mBctxL2rMjEH8l+uEvq8RbWtfqOqA67IsXvkGV4++iOaw22snHEWB9b/PWctuon9oe0UaqP0lvbT3+mS9iwOH3qMW1f9T7L5GrMSYUiXsA3YV/0x57TcgNMqqPVPnbgrJCKVIGKCt8uhPDdF4Zgg2gLxokukzcAb87B7PMwG8E5Ic2laLRFP59GAbjADAXQC2Zc9MhdbCBOcIYWYcYrIXK872epwZRQK49fmiIfxnX5aFoQhZmA4Nt7LheBYGRNZVJiXJvE3ldEKKl6IwecUqgZ2s0/buSbWcGVCpAPoEZ/YrhJDa1JYC9vA8JAPR+HYCWmIGqQRQu7R+OtyEyZfal2F9OVpypkQ7ggM3WXQ9psKZ2r3qzqnGaNq07DGJLfBJ7/RRZjQeKEFaGRM03ChiTAElcM+8XkGVlpgpE7/4kp+s4/2IDo7UN/lA4riVkXjxZrwaAX/5fExWFbwwAjhz7ZyYhxVa7BReP86AJXg/P1XSoRuaQZlkOjUZE+MhgtomA9CnaT27VOkPMclQgoYcDHuGaTlqjQsMNHPj8FhB/E/2t/gTKrOL0vRGcbTBmFdJOTDkG0zNxenEHrjRnJKQcrJ0+QVGbDSeIYx7ksyOd6P/19Nm9SEiWHoCT0u0ET8Gs21MdKySH84g3+KFPmj6VHO7JvBQG6MYiI4vwYR4ZgqsK21jbOPHOKM/mPsbe2iVt6Al1yAUT2IqbfiFc+BeLDPObFOXs3upL82Smukm5ARZe/oy8zLnPeLf4B16tT5teUN3V4KhQLlcploNPrzn1znP41yNQOP+jgDkzeQ2JwQTUujsHNqD2a/McTR73gTdxh3FPpf0rRfadL7Y4+O21rg2SzkPES7hXw6R/dFKcoFiS8lkR6D3CaPUJNB7GpBfouPPMXkWoQF3tMFrPclEZ0J/PMXMrgxQvVZiHQ10dJmkKr6nHdfG77dAhfDvpZRvrXjL3D8Kq2N13CkGJhXNYRMDjk1Wg5F0cdc6LCDXu5HaogWCzE7BIUK6oiaGqjQ4D1RIPThMDW/3qrknYxhCNb13s9ju78OwFF2sn34Fe5Y+RmS5XZ+o/U6BjnAU2PfwBceqcOCc5Z/maHNSzi6x2AwDEvP92mYt51jg2U29j/O+ed9lMGHnIlFrPgig8phRao96M08OmRT2BVExCsHIJ8xaG+H0UiSTNqg5UqoDWq8gibWI7FeGkWP+YhuC5200P7UBTAx7v6ODgSESljTDe0WRSEukJ9sxUcg1AkXhA7cq2kxoceGqETOCWNemUTvraFGPOSyCG44RP9PJo/tDMHABkHH3OkLXn5rHP285shmhZmGpqvnEe4tTmbQxG2EnQRfTnPi5pkcDde0MvisRlVBFTWE3lqTp3c6whIUd/nUxhdktAcjT7l0fsRGSIVf0uRfC77Hyn6F2SCIzjmFR8qbfqLQcrVFYYePENByjU1t1EdqiftycdrT9d4qzJ96DzBGHbzKSYsMz2YRZzViOj5dl0rKWQPta2IZjVVyUGLqzU4nDYwzo/ivlSfOy7omjR+WyJgBjkY/MLniJmeHwKiP8TebvDNMDUlLNU/FgpJp0VpOMJz4+bXMSoOvBBk3R6NfZDCUwhfGKQPxx7cZUqMJXnf8bxCUjTBlI0zSLTGj3M+xSDOOtKYeTyr2JYdZ2NfJ+vheEAIpJCkZ4qguEeucyfJDe8nGE/RbGZzaXmRiOSHnWYzDC/AXpQEwpcHi5CyeGtrI+7suZV7jeaw79oO6UK9T5x3GGxLqkUiEtWvXMn/+/Cli/R//8R/ftBN7J+M7YopIByjt1TReFiO0NIL342ywscPCU3Ki9vA4zhBoU+CXYfiwQeuHmhGv5PF+nMVYGsWKKMxGGNoh6P1uoEpKKKwmaLtCo2SIwuv+ZP2rhOhsA0ZN0Bpx3RJ6fxjCG3fWrhyBY3cqOi/WSA/EkEL/wzA9n0nj+FVa4jOJh5uImaO8r3Um7zJbCYd6CKd9oIS/voRvlhCtVmB05ShEuzU9LREQLnhODuo9Rd+xxMtFiuVentr77YltnemFLO64hpbSdYzcI8GHhGjhlivnEQ8PYK3ppLA3ydE9wZhyqnBgi8GFF/bQan4AHY3hHK7R+C4bOyMQaCpHFWMv+LScrfEsk8KuqULbHQHlCZrSFYaeDlPaq5BhMKKC8n6frkuTmOdGUbtrePuqWA32lKh6+hyT/Ose6bOCvutjByRNVzWgn8iCqxHzQhgXxBH7angtNvKZAr7ykRclUM+MT1BtgX1rBu/pPOa5cdRRB+/eUWRPGP/5Aur/acYdNDk5NbfWq9HvOukamhti9JBBcWvwPr1RQf/3BM0fWUhsOIc2BVY4hfdQDWPFKcScrzkeYJIhMOIGXr1M5S2lpgTlvdPTsp2CxghL8hun1vd6Yxp3RMNpbooSO8OYuBcBlPc5dHzQBqUQTWbgrXAiialLWkIA/inuF77GBXTYxNqeJ76zFnRv67TwLkhhnGTY4kcM5Ko4Vk8I7WhEXOK3hRCGj2g1MVbF8NePL1pFJOYNDXhhpvm+1PnVUqgNUQFaqhUqJlQQNFajHMgM/czXeQq0FjR6BTJ+kUE7EOlvBMFUwS4EGON27nkrhu8bdFWGOBxpwZNTp9JjsSJt5SStww0MNGcBSMoQvV6NIUOxt62LVXt28MTiRRRqW7HDc/FD3dijL1FRV3N8xXZRoofvH3uCUSdPT+pMNg08TF9xN+3xeb/Ap1enTp1fZ36uUN+9ezeXXnopF1xwAW1tbafjnN7xnFx7N7H9jBC1skJfnglqqZpM5CnuOUZCIEdrdC92Mbst9HMVvOeDqIQvK+iCj9sZpbDlJOExDHqkSqUsg4hGvwIBoVZJbqNLx0Vx1IiHY8gJkX4cL69R0kBd0kh5AGIxj9CuKgu6z2NW4xIO5Yb4ypKzCd1v4fdBjQjiSgGpKuR88ED3u1jXp1BjLt53R7He14gvmDIJqq3K4lPPp32nEqtV4d9+iDhnBqZh46oaq2a9n/0qRaXUzdgPJRNDRkP10QaaPt1MMaI5vHNSsCyY57OofBT++hBRgLO6qS2YjRAedq8Dm4rYzRaxaxIoSwYeCSeNRQA96OIbxkS5iKqCqgZPch2QlRre0wUwoP36DBXfppaD+PygXnjsBc3YOo/Wa22yGzz6jtm0fKwNOwF6RwXvq/2gwP5UC+6IizpQQy4KY324KaipbDbwHs2htlVRJ7RPM86Lk/+tNF4qh+1FgKmi3GwQSEvDuTH8lwPhIS5IU3jgJGGtYHQ4x5E5A6SyYToPNiAzJqLDDlI0T2jdKNYkyO4MavOb3wduRNeNH99iapbCbhc4vVMHrhP3icpT3/7fihLY/KbpiwnF131iC03MS5I4+2sTfg+izUJ2T10o0hpUcyiY0Zyo6c9PYSgL/DF09TDmpa2AQB3pwyhqTi7Gdwc1TsEm3GJCTaGjBrnXNZlzJdJxEDNsrE47KD8zBNR88IKFtjpvHtnaYCDUKx55y6TZiVEzPTxD/dRFEtcPouEpVabZzTFop3+2SFdMtJAN3Hcn/m9CsHu+QBoaCZSMMAJNZ2WII9HWqU7xEvYlh1g+2MlwKo9vK0CQMWL0+0Xi8UaSlRSrDhzkmZnN1CrbEclFhKuPYRwdwp8RuLuHpMWieA8/GdrALZ1rWZi5iGcPf5NbF/3/f+nPsk6dOr9e/Eyhfs899/Bnf/ZnzJw5k8OHD/OXf/mXXHjhhafr3N6xWIYi1C6o9U3egRKLJWbegaMD6J4WXDMEmwsY/S6NF6QZfX68x6wBbZdJ9PcGoazwe2zMNfGJeJpoNNFDHqLzpxzcV4TDNXof0lgNgvRyQbhUI9auoGKgDznos2PTRYsAkfWwlIsZNdAjHuacCNfP/O+M+QfZU3CxDxn4fZMv6X9C03lbC6FCDXIqSK+/K4uOafzPCjzlYn86hXqmhPAEtXOyFJqPETXXUPeoeucgBISUB1pgjI6hTImTtLm16//lWHEfh3Ujjxx6kY/33EDjOR7C0/gxi4ENQSmIVwIR1yQaNCP9AsOEudExeOTg5EHWHyY0owH6Qugn8sG2QQ+jNYTXHMVo0CSXKfKbJ5Wn3SIwR6roRvuUIl4YHjI5nhbpg7hvhGhMEuuwsJalcB7N03VZEk9KZESTWCqJtwuMAxX06y6iycQ4N47/YhHV6yBWx9EHaqjtVdT2oPbR/nwL6sB012O36vOv0QN8IN+HblxG8lyb/LpgIilMaFsDetiBQQ/zujSixURFBGaCab2iPbtKxsnQ+J0wfn/Q6drfVsH6cBP+9jL0ehRWlrEXStJVFzNp4kQ0un6RvuUIWSZ2TRzvW/5EV4LoCkm5sUqTjpA+xyD78qRItpoEdlJR+yn7e7OQximi4QYoXyGqCuvqNFpphBToss/JF5wQUKuBcWsrxrYiFDz08gQFxyTta/SuftSeYfw9k21dRc1BrJkq1IWAoUdcZATMhMAZ8RASMqsljCm8e6YaTci5IcwPZepC/U1mrNpH3DNprMK+pEV7JRkYyf0UXF8ghCauarTVRhmyU3inimpAINDV+ELsiQnxxxdnx0sbBME4VUqgCMR70YhgKZ+W6ij94aYpL3dCHn3RPLN729ndcxQQhIRJWJoM+CXM5jaWHNrPopEEr6cP44dm4VtzsPueozLjpon9LE2dwQ+OPcWwcybzMudx3+4/obewi47E/F/uw6xTp86vFT9TqH/rW9/iwQcfpLW1lY0bN/I3f/M3daF+GpBll9YeRWW+TaUfoi2acL6C9w9Z5JIIRncZGbYYykZILQwTTSqiH7bQJY0V0hhDVbzxG4ZoNiFuIGeHUPtqqAM1zLUJ9LYiycUp8tsmJzuRmWD2D6I70jS+Kwpln+imMdhfDe5lgHdrnM3sZe658xh7aVK0NK4SmIaP/0IJPRAY07k/gaaPpHGWtDJHOBiDckqAzWoUOI6Ji8BoFdi2j3kBeC8VsLwE6xMusZpB980WmsMoBNHI2Sj9i/c4fTMwhSBUkmhDU43ousP1m4ClfKKHj6J/8hJEI/jXXk7h2ptROUlz1sdOtfPdQw/xB623kvm3KowFtaWGgPb3t3DkSYFMgofH6gs1akBhhE3s5wenBWJMNO4z4yI9JFA3t9D7tMZ/yUUY0HGrRbRLUtyniHRLotUqPJBDzI2QXJIi//qJ15LAzij0gMK8LIn3RD7QFY7GPDOGLiisi5MYQmCEBSoqUQddxNYc3pbyxH6Ms2LIBeEgBbfJRswLw+7A6dhYm0CHXIzlYfyXylPeS6FZcG7UYdsz/5vFV3yF2OJBanObmKnasEOgqi7DcWh8zIdDNThYQ+2t0fKeVnof1RMayOxxqDZXEMfCyP4TMllqGvcbQxz+/ACHvd00+N1ktKahrY1a7QDVsT7scCNmeAaKepnKW0XCySNyJslVAtc0kVJjDBRxXdDCIT3PItRiUdyjCHcIYu0aIU9/u7HUSoP8NjWl3Cq1VCKHPNyHc+g+d8rzzYiEWZPjUWsIpSXHnlHIcAKZEVTWKbpuMCAMIhVCX7YQN5IKjOe8AnI0Cye5QtgpTbhTUD2mccbr3TPnSVSsNmnkeALqgFPPGjkN5GuDNDhNJGswYoWYn//pRnLHRXpEu3RUhxm1Erg/JXtkolxCipOHAiCCiiFPgzkp1k9Mh5dSk7ViNNeyJLwSBSs2+WoJR2NjnDkyg4PZCE66AggaRIRev0BKhtnV2c3yg3sZirUzWHoFo+EiwgM/wejvw29rByAsbRYnenhscD23d13GspYreWT/V/nosr9D/Kx2cXXq1Pkvwc9NfW9tbQVg5cqVjI2d3Lm7zpuBnzYRW0dIX5smZXmoXgf/pSA9VW2tYsy1ES8+T8vHzqdyxCAUUuh/74OsH6RnhQTWexpxfziKeV4SvxDU+VlLomitEWkDWVQ0UCZ6SZTymCDUaiBtjeu040fiFDf7dK7RqMem3gzN+0ss/2Q3oc4a8Q9FcI/6mIbG2J5Hzg7hD0ydUHFPHjEzzraRf+PSuV9kbMN4VM+G9DkWgw84E6LAzkD7HLAuTOCEo3Tc5+EMw2gYkpe30ut+ne6F70WGZk9mqL1FRCsGxuMF1EslRFgSf2+ayiIT9xR19XV+eSIDg6jnX6N83WUcCwlSr8UpvTL5eOyCeXxx8XtZcrAVxnKTD2iQL2aZ+YkmXNvDqvqY/5pHHvUgZSDPS+JzUn1jMgKmC55GrEnS9wL4x7sQ+nDsey4z3i9oW+agsgqR99HLo4hum+ZGj1iXSXlAEE5DpMFHeALwUHsdrBsb0OMRGm9dAXltI4cfAq8I4GMmFDOuF/gPTxXc/oYS1q2N0GRhKR//XSnk2hTCVaincuh2kAt9dDmM2lKFqMS8McnB1j4iRozYotupFvrp8wx6zCZC/zIAeYUBNLQY+B9oxDri4Y5HCq2nhum+tAHXl9AGxZYcTxz+Nld4H2Ja4bKClNHFGc8swt1v4hhQW+tzVH2P/t5HADhj6R20zH4/WtfLVd4KfNGI8f0c5HxOtLwKL2iETpvyIc3QOpdQiyT7ik+tRdByiQHx06s+rW15uq+IUBySCDSxZo2xowCro2BNFyPipG1CgOF6dF4mqY1qlK9pWiqwoz7aB7VgJsfuVnjjPxFGLET3LS3IkyzFhqNHaLuoE61k0G3BBDMGI8Zhmhu7p52HnBcCo96a7c3E9Wu4foVZNYeaATkjRHMlzq5M/7TneuPDNoxHd2WQrBWnavwUc8QTRfpP07sG4IspYh0mBbuvBArBqJWguZalYoQnI/cClIQDDUOc0zeTR2PbiFkmUkgaZYSjXo6wnWFfWxerD/Ty6Nwotep2TGse9pHnqLTdMnG8pcnZ3HnsSfqqI8xpWM3e0ZfYPPgIK1qv/sU+zDp16vza8TNDkyev1hlGPb/rdFCrgHlVGvf7I7h3jqL21rDe1wj2eL/PhI1/zUpKu31EGIyBKmRPmCzUNGpfFeO3WvH7atQsCx+Jt7GEK0wcz0BdmMRYGiHmVWmKlYipCnbWwbZCFLYpnAGNN3qKCUhVE5US9Z2XsLMjRA8VMH4wAFvLp051rShalMXs2BzyyS003xS4aSeWGWTXOVPShZ0RcOIh3AEvcL0fz1JUVcg+1ExT7DI2PvkFhBqefpzTiJRgvFxGvVAKoillhf+tUcKDmgqD1OQo9YXu/zymKcmGGnFnXUP4G5JZR5qniHSA2guwqG8G5vApxl7eJ79e0/f34O0OIVPjUiXnI0IpyEQmn5sOQyyCeVESAD9l4WVPLkgHVXFxTQOvaiIWRtBFH+/BLN49o8TCHg3pGpGwR60gye62qYVjyHPjuA+O4T2UxdtQwnhvhnLV4EQDd6+g8Us/RRy1WLhbKoFnZEji76jiP5bFWBVHoMEpYV7qYf12nOpnw3wx9B98euNX+e1X/4EdmYWEUwsYS7bT8HoE8ifUlA/66INV3M4Tftf7XOQPBgnd009F1Bgo91NxChyN7oXU1NuFcbmNer0Bd3+w3qt9GPuJQXvytonn7H/931HVo6d+X3XedGzXhLJCLIrAjRm4PA0JiahKdE0y9JLCSgrsFondJCnu17jl05+xpAdc5HcHSL02RHLDEPK7A4hhFyXBuCo29clhgegUqBMuF63BL2nkkQqxZkW8Q2P7Ht6mMsrQFPYxIdIhWIDLbmFauUrKT2COufDtQfiXfsSPRxFFl4hOQMTBuCEFN2RQ725CrE1iXmijTpG2X+dXR642gCnizKyUKNugVYSQb1K2ppb8KB0Yx9nCp7MySN6MUjZ+ygKhF7i4Y/wMkX4cg2AlyJv+PRtSI6WmikXRiNBSG50YUwIQQjMWKlMMV1nY30FOBYGMqLQJCZMBr8hoIslIIsV5RzWuc5RyVCP1MEb/5O+mLU1WpObwo4GXkEKyuvMWHt//D+Rqg2/sQ6xTp86vLb/QHbmeZnN6CNsC79+H0ONCWfe5eI9mMa9LYd3aiO6VyF6JKSWjL/j4fV4g4k/4etSAy9hOcGMhqmNQ6EyQW97MwH6LqmnTf7/i6OOCUjKGXpTAzJjIx0bQD4wQDpIo8EwTbIHotuHaRrgoBasieC/uC+6KYwpCAvP6dJCi22FNi34YawwKhZcwD2wn3ttB7iWBV1Akz5R4p+isopRAZ0LT+lmjwag24dby1ErHTvm5KaNKmX58ozTtsYLweK08zOOjR9nnFvDF1P3/IkPbdgz0uunHqO0d4+/XfYKvvPABNo8+gJKVU7y6zhtl0NOo1zXGA3nI+ajyKSbEGnROI1LmtAmXPjtJfpdGVWDoYYW3JDXxHPfBMsb1KxAfPgtx+5mIj63G7/NRfQ7WexowEwIzOXWHDeeb5PbZHPquZLTfxHuqgNo7Xs3bGaJUtOjbbtP3moEnTcr9cOROyOsQ/h3tmJ9pxVgZw99SwfJ8MhebJJZPiuSaI6F1apKTmGEzslOizk7R+4Lk4PcUvb1h/JtacR/N4X6niPttE+8Bj4FEjst3/S9eGtsx8fp/3vMQo2aMBbEmODh94c3cVyWnX4HMSYuwnQZFy+X7WxdxSc9n0EkX85ZRjPNA9oQwr3LQPb2Ut0+/hYhcC5adnPjbqWWnf291Tgu+lPDxNspL0ziGSS0dxru5Fb/ZQhH0VI/0GJQP+MiwoOUG+y3J5DbOCrrJ6BFv8r63JM7h72hG+yLoz3fB+Qm4LIV/cyuuFFN+s4UAlQxMDtXOKmpDKRBWS2IIIXDGpv/AV4dAnZSaFcrGcL83Avnxczjk4P0oT7SaxgeKsSiH15kcedagfzSKGwuh/HpE/c0kVxtA6xDd5TIVE1oqDeRClWm/974vMIRPV3WIigxTNCPTd3Y8lV2KX8xX4LhYP1VnAQhS4GUUS3kk/MmsKCFBK8GBhmHOKGRIFKKM+Q6gaZQRCqpGXtXxZVk6AABj/UlEQVQ43NJG2PNYPpbBcXdSsxqxDz8/5TiL4j0M1EbZWzpKJtLFwqaLuHvH/8JXp79UpU6dOqePn5n6vmvXLs4888yJv6vVKmeeeWaQPi0Er7322pt+gu9ERNaB2tQbgh71EU0W7j9PpuvaSYOWm1pwwymc5jhmSGMPVuCZHHppnNwmTX4rtN8mqR7TjDzn0nqjRf+9k+np/Q95NF1p4Y9JUh9rwzhUJjFXktusGHxZ0/npDnKvK3KvKIykTctVcexH9kJzDNEZw/vRGAiBaDLxD1cDp/YNJfSwh3FOBHf+ETZt+AtWzP4WuQdaOL7cPPhDj+TZkspeTWYJSNfHtwysqI8Y9DET5jRneWUHzvWGdVKEBcjrg9y98U84mttFU6ybW5f9IU3WQrTWlITHn7z+Cq+PTUbif2/5Obwr3U64OozZvx1yg9CxgHLjHDzxs9N0lanRbRaMTZ2g+Y0W57R8mt29P+S+rX9F8zkzaQ8tJ+xJrOHAXMtvMqjY9Yndz0MIqGR9ZrxYngh6mSUXMxmaYnZmNQWCWjtg3dGE92gOXVSINQnyaupzS0OQbgrMFOkOMXo0wtgLQVpk02UW8YSL3lJBba0gum3aLs7Q+5jGTAnsVoGZhLEXAhkT6wC9bnwhRoK7MkXfY5MSZ+gRl7abbWJzguuu590S7x8GJrqkSSNH+LZWVKOFEfPxSyBjEuvGRtTmEuqgg5wbQq9IoPZI+h90ccezBtxROHqnS/fFacT9IwCovdA0nOHu+KfwDM3m2BB/0fcArvIoujWObP9z2s/+Y9g99XN2FuTYvOXLrLz5j0i9dgZ6r4+c6yHP2El8IE/VuwZfz+aczhnox7+ArlURnTOh4QJM61J6rvbRIYPCERh6UaMdKGUs3J7fI37oQcoDGwjHOurdq94iPNvFsGySTT4UFSIk8A2BZ9lYMU3liKK8Z7wd35hP9bBP90ft024mpw46WDc14G0oBaL73BT9GwXusGZs2Kfaq0mtSuNlNboGRs7EUlPLrGypcL4zDOOZKf4LRayPN+MaPrGeCPnNU4+ZnC9ATF2W0Dk1reZcH3HQVYnrxxl8dPK3u3pEM/KKQer6X93nUGc6udoAnrboKHscTRi0l1LTjOQ8FUSvO2qj+EjyVnT6jn5mPfobwCD4/T7u+H8CYvzxUeI018YoGeHADXG8e64vFXubBlnbO5t7Z73OkHZpNi2ajBjHvAJhs5FdHTNYdnAvY7EeDtuHyHgK49Bh/JkzgsNLydnpBTzQ/wKfP+MWFjetZah8kIf3fYVr5/z3eiCtTp3/ovxMof7444+frvOocwIieoq7iC2m95LN+1iOx4ETWirFzojQ/EGbXL+BGjfDQSuEIWi5zqJ6bPqUubjVx0wK+p5WtKV8jIEy3beHcPOa/C5NduPxiZym9/se3beswD5SxvthAeu6BtyHxtD9bhBNdzQkJXJ2jNLKPHlVJtO6mtqWqa39nCFN0xUGDXYFdXeQLmYYYN3ciG6XtLYKen+kJ1rEx84pMFh4iM65N2KGuqbMpTyZ55uv/B5jlcBSfrh0hH9d/wV++7xvEqGFQ5XCFJEO8PfbN3HR2TbGxsfw7DMQqhH50F8SveB2SvOuBDS+rxECIk4NWSihI2Eq0SjHdAG91mXWPjHRMsjvMnk0fJS/3X2M23o+yCzrQQ5ltzA7tRL57SxqTzD1lc0m8d/MUDzNNaC/jrhSoRNyoqxD7C7ReUucoac9qkcUkZkGsXkGFa0I/2gYqhpjRRSxPMLQMYP8+qlj3WoQ6PFJvLokzdj9JyyYCCgMCFLXpfF/lEUfcjB+MkzXh9vIbfJxBjXaE6TONsm96uEWIZwxYcRDzgiRPzTd9r2w1Ue7mtZrLfSm3NRW5j4Yu4pUrSTxRSZGBCK4uP88iOi0kDPsIFqvBKF5KXInvRftgieNKXXHsteh7fGgbrOjIUToPTfxr2NP0RJqYOfIFnoXbaLzshXop2pB/eRaQbm1j9Uz/xHfjFK6XhM5shP9yrfxn9xOes1trFoyyouFMsVwlPkrryL10FeQZ30W7+k5eEeCRQLRYZFYHSd0eYhjO31+InbyjT3b+b15N3He4tvBbKv3mX6LsD0DE4XzjWGoBGPfWB3DvsjArRoTIv04fhncnDrtfdQJCdwHsxiLIvjnJDl8v0afcLurHFDE5miGH3cxE4KWGy1OHlT+MWdCpB/HfTiLMTdDJOLRdIFkZJ1Ca2g4UxJrUkEt+gno5CnCrAmJF9fUhqcLoeJuTUoJqHuTvGmMVY7hu4pUDV7LhFk1lOBgw8jE48dT3pu9HCHlMhhKT92BD0JrtPgFo+inwhiPqv8Use4YFmU/RJOTYzgcdBQQIpjHZCNlhqNFLu2bwyNdu+jzarQYNmkZ4pCX5Qwrza7OGZx94AClBYspWzuxhh7E6/gUwgqm6rOjHewoHGJ9dgerGxZxftcHeHT//+XFo9/j/O7b/5Nvrk6dOm9HfqZQ7+z8aT286ryZOLhYFyXwnpnMDTdvaMBff1KuuCUQCUnnFeBWBaObobRfE10YYuy18WiDAKldZMhm+FGX5MrpX7kRg0iHxisK3GUp+p/xUC9By9UG+c1+8HiPgVfQVA8rnFGLUCyEHi7hvVTAvDYNUiB6bLzHcqhuk/2LKvzdsfWsyMzk4hWfQ/b7nDzcLMdH3TtZ04UP7n1j2L9lUYlWGfughcgZZFJR4mYK68jvYacEpidxzMloSt4ZnBDpx6l5ZbLVPiKhFqqnSE1ssCyMaoS+A7dTPSQQJjSdfzYyupN9h79FuTrCjI6LaPE7Mf7tR1AsI2yLxHsu46UWyQ/ym/gfv30RjZUwuUqFww1F+nWOuakmvnNgF//vgmtoDknMPS7+nhPiU0Me4qUyxpUR/LpL/M9AEdMvU73+TMJfczGvSaNrPkapRvsihVpmIBKKsW2a0hFNx/wIenM5WCzq80gsFhRek+jxMkYjBeXuXejPdrGnOMqMSB70ZHp29ahCeYLwWXH0zWGEq6DLpu/bDmo8cF45pEivNrFbBdnXNYkrG+HOQXRVYUZOUb8YhuqIxs1CuHQqvweFkRakVkpEVMNzQTmFPubiHwvGt6gpYhenEDYT72Vi/ydOOgWIiI88qxNcH7YPcvZYB13L7iBDBa0VP9n2P1m58BPMP+dqyrVhUk4n6XsXwICHWBbCWZBkMD0f+4LraLxrO+WZy/nB/r2M1BzYA185cwWzrvkcDWNL0Ecmf4t0rws5H6O/wObz+vjGgZcB+Paxg5x71k1ovx7peaswPAP3vpEJkQ7gv1zCWB5FtJsIg4nF0OMI4y3oqjE/Ci+X8DeX8efG0Z5AhsBMCtwxPeUcvYLGy2msE1q0KUVg/nYSoqbxhMYedom9XCC2KhHcqzYVkDKCv2Bq5LUkfcKr4rC+OL4D4OoMZd9FJDQnK71QO/hWDfyfYlhW5z/NSOUIndUKZQsqRIm7IYrjEfXj7utRXaXRLTAQSqOP58T7GqFBC9BvpBb9jSJFsDrgM034CyBnxmh3RslbcarSQkhQvsAwNYcbRljS38m5w91saD5Gv1+j2bBxpc9hLwfRBg41t3H+nl08vWgZZmEDxZ3/gb3oAwgjhBCCcxsW8cjAyyxNziZqhLlk5id4dP//JW5nWN565a/oTdapU+ftws91fa9z+smHHRoGfaybGiAqETED97k8xrwI3qFxgWqC9b5GvHtHMIc8zLBg5vsyHH3NmJjQmylBx3UGZgwGHvXxy8HER4YDgzYAJDTM0RhZB7EiSu+dkwK4sA3Sa0xQUNrlYzVIWm6wMRwX7yd5jPMS+M8W8F8oQocFiw0G7jD4m10vsHl/L5c1zKF3bJg7Kzk+sypOeWd8Iq1Q2GA6/nR/lppmWIT4xL6HGagUWZ7s4Hd3XcnAuuMTTU1kmUPkqiKQwPZcWottXNf5aZ4Y/A8qbnFiV0nHJFHNMiMZJyQNauPuXZlQmD9f3M3YkwbVQ+MGfR4MPZMicnMTG/b9DgA79t3DZYv+gC53PLTjuKgfPMKiT3+IPx+4iNTDCr+throqzHdGvsfOwmHOa17GhW1nUsVjZno++uD0JFK9q4Z5hYlfv/x+Ko7fy3Mv/0+WnvXHLP/iBegdNYy5kSmRQYDMR5pQpo1cEEfODuM9mUMPelj9UTo/nqJvoEJJOxyNjWJHs+TxeNU7zKVOgTNab8IdCERJcYdP05UWRgSOPK3RvqDpciZE+nFyGzwaL7QYecplaK+k4wvt6ANVUh0Gua3eRBRQmBDuNshv8kFoxKoEvDbV0V0tjhNPCsRfHUWuiiHm2MBU7wPdHUL7mu7bDUQ1iOJoDTLvYRoKdU4MtaeK+d40uuig9jRCCMxrOkmLIrmx/Yh0kM2SiHeiheL7L7+Xy8/4U9L/loHx89WvlbBrGiPZwuiabuR7fp+vl4tcPmMu39/TiyEEXz9UZFV7Cx/eML0mUh1zEGHBmkgn/zr3NvorRXbQh36r2zO8wxG+QB91pm3XBQXtmsZ3mYw8Nfl9RmYG94fTzcArgvjlLcQyGqPBoDUJdiKo/VVKBHJ81KNtrWBsJ/hljXnS0PLPsCbTk8eRlyepxhXmvioMuPCjUSAQeMoScNXUOmbfkIyacVI3RxGuwrcNRrZDcrkglzxIfOk8iq8HB5AhSFzuUBZDWNSDGm8WQ+XDLCg6lCxIV1vIh6rocdGtNAit6KiNMGbF8YURRLs1IATa4Fcn0I8TuMQFB1fT0+i1FIwZMZoroxyJtgaP+6AVIGFnSz/L+roo2jX2pYcZ8B0aZYgyVY56WWS6kYhTY82enayb0UO82svo3u8Rm3Mb0gjTHErTE23j4YF1vLfjImJ2A5f0/AaP7f874nYjsxtW/YrfcJ06dd5K6krhbUg16iOXR1BHHdReB2NRBFHV6ILCvCaFv6WCcU4M96k8DI1Psqoa79vDdH22DTcmaX+fSaxWg94a/h6FkMGEZOQpl8zFFn4VhKGJWh7y0WFkq4WjJmdoZlJgNQrMtGDgfhd8qPX7lPb5dL/HQA96iNUG+j0ZCiWLcp/A2qmoLTLoNpJ8wV5F6481yoS+S6EwQ9LxkQK1Q2lkTROJ+JD1gpYnJ6r1uGQs6jBQKRI1Lb7QcjHq4RMTfKGyxSBylqI6msX6twIM+bwrfTln3baWvx78TQrOCJec8SFUOUJ++BCN8xr4l/OWs6fg8s29B/mHeR30Dx7C3r1g2mcv82mkMFHjimv9ke/gf/b3KecjZI4Vad15mPanBPpFBw3IEY/EXof3fewC/kf2mzzZv4GKV+UTc2/E9FIwX8HzU8WXt7BMsbAfK77yVzBa/mtSrgygtc++bV9l5ZILkTGJOlCbItIBvCfzNN3ehKyBc9fIxCRdv1pG5D3+44JNPDq8B4bAFJIvn3UlvZUS+/VOMpdvIL15Kf6+MEa3Q2iWQJY1bTfb5F71EKf6dZQQ7YTwrRZOTuM+kwuib8k83VdnqFQMiErsNkl2g0uoTaAq0LcNWm5rQbwSWE8bl6ZwUjZCCfxPdqCHHIx2E473SQdISPxlScyKQvxoFL2vFuz7yhTeqyXcIw5yYRjzujQ6p/Cfr6LH2yO6h13Up1OMRNvxDz5BItrB3JlXs2Xnt4NdF1onRPpx9LYy8ZuSjORN/K5ubnZrlCMO57d0sL/cj6LE5d45yLkG6vWpKxiyJ4Su+cQPxyk9rpjhJ5nb00Gy1aQWni4U65welCWQ88KoXVNrekWjgVSa+GCe8OVRKjmJHdeEclXM6unPgEjMhojpgpIoXxBOGxS2+pT7IT5Tk5grUHcOYPiatgtSMCuOd4IhqNYwkjhG22+l8Z7ygxaE51nU5o7h1ZoQsy3YMPWYYr6Nc1L6vBlSVD2Do08KjodLW6+QuIZPizuPnICmK61AeGnQvSZWV2zatVTnV4PWmmy1jwV5TdmC5nImMJIbx/OhzcviCouKEQrmEoI35ub+n0ECCITSaDn9QBUzTNypknCKFEJxpNRBkEKCa/jsaOlj6UAXrulzLJFjVLkkhU1NOxz2sujmNhb0HmFFX5atGU2oYFM6eBfxWbchpMXZDQu5+9hTnJWeT0+0jXS4jXfNuIN7d/4RH172FVpjs9/EN1+nTp3TyVuQ41bn55EejeA/lkeeFYeawnsyj2ixEEui1BI27hUZVHcY+k7qWa6BnEe13ycmPfynC6gDNdRP8jQuCW4mqhoYXWXXu8RbFfK7AzDkoaMSnQxEUGpV4Ead2+iTe8Wn5VobKzMeeXbAGVKBSUqHzcBOi9FXNdVjisJTIH8U5pPRVbR830H3uYgjLh3/7tLUG6Jfh0mlqkSfGED8YBD/mTzWexogMj4ME5JDt0vWecdoDsX4p6YbmHksdsr6VtMJof85B0Pj7rxZn+g3JL87/+u8f9VX2Oyewa17n+TT3l42eA5f3PQfPDbwBF9aOQ+rbycDhU2Y7dPTkVW0OCHShZBkUnOpeVk8aw/HzhBUr70U/dJJju81zcxC48Sf64a3Y8txQ6NZFpxvTEwa9ELBcMdrHNtzP6ZZv/x+GgkzTSzaztUL/h7pSvwB75SOuzgaVVCoEX9qDTggdztcFZ078benFUWnxIHCGK1Na7nzyP/ige7/h973/JiGVSXM742gMXBGNJEeiYxNd37PnC2Q3+vH+l4foaiCXeOTxryPuHOQ6GP9RHMl+u50iPaYNF9lMfqCR+UoHH5CMNzSwHBrA/maTf8DHrVjmrFNmsNPSrL7JO5Fjfg3t+C/p5nKJS1kDwA/yQYiHaCscH84hrkqMFRUO6qQV3iP5RAfakZ/qgPxW22Y16dw9lXJKo/88HYuOv9PSbWfPzG2PfsUdmExie+CFYPkU99i5ne+RHJwF/sGn+Dx3leZOZoh+tcFGPUwVk6mDMulEYgKODNB/yNqIk25dhAKLygMUR/nbxWCoCuHaBtf7DTBvCENSYlR8eG5AuYPBki8MEDoh/3weBbyp1d1+r4m1gZV1+bo0xIw6L3fZfRVRfWoYvgFxeALCuOmxuAafyaHMeZOac8mBKRH9uPfdS0y9sfIWX+PevEWzJf+EdvwOdgzBgsnTULFTItjS0rIk+uMBx0yVpmOS6FtraD7Mk143QhW0cAdkBS2+Aw/6jL8E5eRJ1xGn/GIFZtPzwf1DqTgDCO1wcySx2hI0lFJkwsHv7lKQ1g5pLwyWSOKmHBzf+MiXQM+Gg8f54R/Hj4++mdba8ggei5O0bYNAVkrQbObQ6hgvqS8yZMq2w47Wno589hMuvJpGqVFUftIbeNrzWEvx872TiKOw/zRMAldQ5ZClI48gNaKsLQ4r3Epdx57Amfc9b01dgZnt9/I97b9HiUn+8Y+gDp16rztqUfU34ZYaHhfM05OY12TRpQV3stF3I4ox56WgKL7dhOZMiA3VZ2IsCQSB30gcI32ni2Ap7FeGqXrqgbyxwLxEYt7mP1VPAmizUJc2UCiLHDne0gbxp4bn9CPaarHHJqvshh6eLxuFjCuTOGEbWoDUxcLvIMCc42NXBRBbZ9c+Zav1cgsFMixEr4t4IIG/J4INQTGB0JYhiLX4PDZPd+jtZzgz2dexYyv+oizK4RaY9QGNHaLILnMxExAJJZEXmQgpMB7pYg+5oKjCQ8LvjS2g2s6O/nd+TNAK4ZKI1zecQ7f2v8IKdnI+0M3Y+h/xlizH9k/d6IMILYYjqgnAGhOLubCht8hsb4JvUFRPGeE50b/kpaeT9AemgnVqTdn15ycNSatKK6XBdlELawYO/NFwvNSCG3QV3yavt2PMWvJB1H1GvVTYgowjxi8a+6dRPIG2V5IJwxERAZLiydM0I3z4lQrgogF4oxQYLq4txpEuKKSztAY17V381DfEQASdozPL3kXPbUoF3Z8HcKasJlEftPBWdvE4N0+ftkn3ClJniWD0g9X4/QrYu0ae18xWNgChK8RnTY6f0J0uarREQvlQHGrR/ScyfPVfmA+BRCeAe5wYI7VfKVFea9PcadPx+UCd0jjW5L8dkgvBv3C9DZ/2jkhmlj08W9p4+h3g/IWGYa2S6JEGi2SVo5wvJO8Fvzlrke4Y/atbN/zHQ7yAsvmvwe564QxeGUjpWSOpDGCtfNl0Ir2536Eu7KNa9vfRc+mNLga7+lCEMm/tRGZMdEpgd5dpbCXoFbeDMzuAIrbFPGLDHxzaiZEndODcBXO1wexrhlfEDUE3ot5RMpE9FiQkFBQwb9xZPKki+zNPkchqGYlfQ8H9xy3oLGS0HImyPH08+HNGq/BntBf+lANsSjM8VVcIcAYOgRaow++Mrnv/a9hOnl+WHkB4yKb9116HlILHuY1dmQP8UczPzHlXEKJCPrFQSzyk/toMLAjFu4pIqfSEsg3NXT7zma4fJjmSgSoUpJJop494fju+zDDHSVnxtBaBpHtn7MmOCnMFQqFRiM4/h1Ofo8KjcKbeNxEYiERJ3/XMjCyw9PBjesEPGlSNEI0V8cYjGaCVm1+YAYPUAzV2NHSy8reGdi+waGGUXLKRWgTLTwO+XlkxwyWHTnAfF3k9cQ88sZeKgPPEG1byxmxdg5V+rm/73ne13kxALPSZzFW7eOenf+LDy79a6T4z7rn1alT562mLtTfjqQs9NYq4rFskFEnwLqpATHLwHhFgYBaURC5NgM/GJxIu5OXptCOj+9Z2G0WOi+x3tOAv7GM2lXFONRPY7uFuKoB/WIBPS+M8XsdaCnJ79CU9vhEZkjMBjFVEOkgEi8MMBIQ7jHQB4yfmu6nDYk6J4k5L4R3XzbY1mwS3d+Lt6kXPnoBQy+BdUCQO6HeNdwR4hNr1/DXh57GsgFHo9cVaHtvmOICEy0NKgc90jM1/phCdofx7h7GXJtC+xrhaHSixEca5rD38N+xr3gIgEQow/Ur/jffAq5LXMLGR2Deqps47NxPww2HSHhzCCdbEU1xZqjzKejDrLI/TuRfbPR4o6L41iQXfPy/sz37AN1XfW7ifQHQaVFtLMH4po/MOo/+0dfobJ6L72uSrUvYsOUzeG4QiTftOC0zLq8L9Z+C6XuMWR1YjolyNYmdo8ilUbz1BaxbMvibSuiywrwggd9gYyhNLR5hrMPCr2oabgZrUw5rSZiU63FxcoBH+iVXds0nVrFZ4jeQuGc39AWGaHJVJ951c+j7oZrI3qgeUyA9Qm2CzAIf9fIY+iVnMmofEdRGIXR2CnmkBsXgYhGLIhQKBmiNsEB4PsmVFvnXJhfUjDhTHK2Le3ySyyTpUBX/b8aQBJdfx3sbYX4Ev9mcLHEZR1iTk0LRbdP7Yx9VCUQ6AvoeU3RfIhgxHJpm34jhlVians2s2IV0Np7N0MhmSjeWiObbkEUDGgyqiSqxoWeI3/UP4wWVIHt30XP+GuKynZSKcryGXu2oonZUET02xi1J1EGX2CUSs1nilwOH/ewmH+VrVF2kv3VIARWN+52RqdtXxvCSAusDTbj/Mjgxrs1r0+j06RXqANmtk8czwtDSVEXfFZSJSAEd720CS05EOEWbzYkeoVpDrbGVkztnuzMX4xqSS9pX8tn1X+N7+ung9Qi+cvZv4DlTF7pr2sK+JAVPBsfGFqirM9Qcg3AGzISY0ja08V0mnlEv7XizGCzvZ/aYTy4MyUonY6Fy0LFCQ8ovYWhFSds/teWaYryjGhp/4q/jgtvEgInf25+23KJQ+GjKuAgENhIDY/L5BuCfWqwXrBht1TFCbpWaEUb7AmFMjp9iqMbW1qMsGuwg6tjsbOknrz1cbWALxX6VR3TNZMXhfbjGa2z11lIUz+GEW7HTizi/cSn39T3Hy2PbWd2wCIAVrdfwxMF/4plD/87ano//0p99nTp13h7UhfrbEDMnUI9nJzdocB/MYn2mmZ4bBP6eGjrroedH0B9pQxR8jAYDKh41y0bUVNAybdw52lgVw1gdw3+5hE6YFHol8VlhvKiFk5UUtyqKW4MJS/WIwmoQNJxnMvb8pDgwYtB8kSQ6U6B6awzus0mlNNHZgvK+yRtPbJGBDgn61kHDWTHCC6pwqEZoQQQx2IruyjC4UZFcblE9qgm1C2p9weurvZqrjYXMaoySSEcgXgoE0H3DRD/Sxugmj6ZIGe4Koh2eBOv2DO4jWawb0/hPF9D7JEuvjrFuXKQDFGoj7O1/kqWpHmLVIGV49/omkpmP0rykjB1JoJWFWwORmMPSFf+DyLfLwNS6ztimBmaffxlqXRXrpgb0qAdxAxzN7KrmD2dcyuzm+fhuFcuajyk1rg/C7uHsK/6ZUm4PIIil54LZQd1n69T4lkXfqGD2DInx3CgMuHjtPvLSNIx5yEsTiJSFKyW1fo2d0hz5N2dCW1T2Q8fNGZT0ESNdLGto4zvLi7QctJHfKoBTxrh6If6lEjVWgcODeMgJcXqc6hFFw/k2KqaRK2P4R45byINxaxPOsCSWAXl5CjyNkAIVlXjjQy+x2KKYN2joGCPU0kB+iyLUIQi3SoYem8xEMSKCeLtCPFHEuC4NAvSAi3//KNbsdoxbG3H+8YQFuRWBfwUGmJclcW0TITWdVwjMsRoojZ8JoSKS+SLNkeKzFIuD3KGuJfLoXhA2M866Cf8VQXVOhU2zxvjizif4p87ZLLr/L6Z+F7OWk5RzmH1PO/aSEK4sTdFw5lkxOKQQ70rR/6hP5ejkoG65zsKeBdXT3pW7znE8Q2Bcngq6TTQHxqD+5iKqyUZWFMNHTCLvacPwfLQlGe0VNHmnt1TBcbwpJSaGo1CP5yafoEE/NIJ5Wya4BOaH8TMWJ9a6aA0jjRmaVlxKeFOQFaUbOxg5ay2WsMm6gj9Z+QleHt6G43tc0LKUMUegw1PbKroCsiNhGt4XRroKTxqMvKppWmAiKjXa3x2idFjhFSDaIwjhUqtXdrxp9Bf3cHa+QsGG1kI7Y5HxhUJf0+rmGDXj4+kUU1/njf9T4xJdIDCQGCf0Cjj+ravx5x53JThZtEskErAwUChcNDUcDCTWcck+LtaFp9EniHWNYMyK0+aMciDSBtpAKzixGqhiu2xpP8qCwXbiTpjXOg9RwKOsISJgjy4ius9g5aHdOC3b2DVyNiX9BEa4CTvcwhUtq3io/0ViRpglyTOQQnJ+1+38eO9fMyt9Fj3pFb+6L6ROnTqnnbpQfxsiqnp6XbajoabxvjY48ZiISfRHWvENCzfnUzho4uYVHY1F1LFJIeCvL2F9tAlvToxCr6TaB/GVFn7MxssyIdKP445p7MzkzcZMCYyYoLAfKlloXmHTmq8gd7pEVsUpzzKoDOighVtZM3i/S/pci9IhReiqRkLSw/3+MPqAAyY0Xx0ie0xS2KyIzTNILJH4FY0RE/hZWFLuRD6fxfqNJrw+RdU18WsG6VnuhEgHQBHU654Xh1wB0TqMsWwG7aPTawb7cjv43OIvofQws1fHOLwxTTxiEjucJveSB9rBiELlvT5fGd7J3xo90y4ObQbtXtTOKmpnFeIyMDfzIdkwyDVtq3EesjB7FSNnwuFFY2QaktjaALOTaCZwBtYT/6lzKjyliTeApRXEDCqXtDK2TSPHoGmFSajsk89JRp5yab/CpbTDgJP6IY++5NGx1MOIGJRGLTrdOP5/DAYPLo2Rc2OM3KfQfojorDSZBoOTo4hmUuCMKEp7JcmFESK/baPHPETSpIRJNCrQPxnB2zo1Nb3xjhaiS0O4YwoZEmhXEY+PEXt3Gqco6b/TnUgNFwZEZxsY2sdYHMV7JAseiBk21rsbqezzEQ0Gkc+1oYY9CEkICRhwsFYa+BsPIBbNp/1dBsZdA8HvBEHrNutzbTSGBP+49e/4nda/JfydgxPDzjswhnnDcuwfuCy9Kc2a5m5+UKnwPy/9LYyn/gWUB5kujq25glnDy5A7qnj5PPYnmvFeKgY965dG8LdVEK0WTjxM5ejUz2/4Jy7dv1Uf6G8pUiAXRvA2DqPXFcESmDem0VGBVzPJrnPIapgMR2oSK8Vp7aOutSa+wCS/yUd7IIqnSNWqamTKQH6yjXJBIkZBdE0dW0NGhtfnNDF7zqcwfZ+jRgkr0soSYfJE7z6e6DtAVyyFKQx+dOR5ljS0ccHKuVO8LYZTeWQ8RO8TxyWbhhtqjIZrNBkRzIeHSM4IQ8aEpwroNSk8LUGcov1inf80Q/k9zCq67GyS9FTSHGk4hNLQ5BVwpIkjrCCaPo4PuJwo0AMxLU8Kt5+Y6H78EcWkaJcEk+OTo+wSiQ3ocdFeG0+PNzAwDYmhQHp6Shp+1bCJ+hZNtTzDdjoY4yd183MNn22tx5gz0sIFB+eybsY+TMMhNy7Wd+kSRmcny48ewetKs7u4hNLee4jP+QjpcJwrW87hnt5nyLklzmtcQtRKcW7nrfxw15f5rbP+jbCZ+M9+FXXq1HmL+LUW6g8++CBf+9rXcF2XO+64gw984ANv9Sn9aoibYIuJSTeASBvoPneqwCsp5KEaRzdY4y3ZNOmVEr2pevIecQ+4HNsfBqXputWiMqQZfdgjvcqEqUEFACzl03WLgVMJUln773bQPrRcKPC/PgRjfjC/ebFI/IYU9oIoR+930Q6kV5soF2KzJfL/a+++w6Qo8gaOf7t74u5sTixRkkQBDxEQDwRU0i6KBAkH552goqKioKCgp4gY8RAjesqJAQwLCCIiIO8pICKKIoLksLCJzWFCT3e9fww77LJLEthdsD73+Bw709P9m5rqrqqu6iqbwFhdFGikA/jBWJJL6JB4ckoUCjYbhDQV2KIVjnxZuraVQp0h8aib88kxXRT8YhLXO/C8YgXFJoRrmNn5mD+lYe7LJWz45agZGmaZxXcbxP6VyZtWoQuTB1u05JLoEBJy2lG8qMwNjRJwrrRxaadwDrRTabSxTLooUNQmm8P+X4huVx9lsx4c7kxdC2aTJhgzi9DcHgQQfRDseTYOXFdAfWvUKX9y6RghIKaegS1LoeSScHK/N4i80oLwg9cAS10LYZkeIq4HkW/gddo5vpGtaGD+UoL4tYSQv8UjdhxrTOstXBxZeWz7kr1g+9UksotG3tqjeUYNDGs1fSDcfqy/e/Atzzu6c7APiaXEsCJ2VDzXyPOT/o0CIjAUvc6gSITwkfqeiakbxHQ/uuqCIghprGKWmNhM0Mv0IooDPoxIN6JdJGnLTOp19GPJ0/F/HbhRZekdiv/bXyHfi9ahDmqmHaPM9QIDzP8rILRXKPUiWxC9ueLATnNPOkq9BOxrTCaO70iuopJTEkJU4y4Uu/dSEGLFFdEU20+WQOrqYGwuCZxzNgX9s1zwg+pQMX0VG+SmB/ArYJON9eqiYeJfmodIPXqd0wX+T3KxjrPhd2mVXvsxq/aZa6tVI+snndjrrBhuUKP8GMetBqJEaYgQlb3zTByJJlFJXgyz/GogYRYHG7Vr0O25ILy4RRwNLXHYhEZDVwQAqcXHzrEmYeEoZvkE2G9m83OLw3Rv2QyH10qeq4R38tcz3uyKw+3EUj+SkDADxSPwt48i52dwXVbaHyudS4apE5Z5ALcVFH9diqxedM1A8ZtE+wvJ0CICjfTApRYfpc+W60cb6NYzmi25dAg8BEqT0pqBRoUO+6O97yoaKgKBgcCHgVBNFDXwzLuGcrQ3XiHP6qKWN4cCw4lPcSBMwfFzbJqqYEdsBg3yYui6pxnrLtmFavOSZ+pYga0WP9aEMFoe+g3qtWabvzbu31IIcw0krlY4ybW6sDrrR7YU7qF3fEcuCW/J4aLtLN35AoNa/OvMEl+SpBrjgh20lZGRwYsvvsgHH3zA4sWLWbBgAbt27arusM4JQzWx3hwG4YHiQYnVsA4Ow9hYXGFbxTApO1+IN1egXFpxIVytvo2ozlaiulhRCnSEqeDPExTvMAj/S/liyF5LQdtXgl01KNljkr1KD87k7NAMyC3fe2D86iF/Z2BG+MjOlsBMvV/qZCzSKdpmYPxacTIstdgfzH0lO00s4WWyooDMr3TMZoFGOkDxLgM13lIhxyq1rKiJYP4QmCyMXA94dNo3GIymWFBQaVunD6qzJbk+N0W6l/f3HeC3khU4LRWfLTTSoJk9nGk5O9g1JgTR1QnXOBDjXRyM2cNW/26+u3IVhTd4US61U5zkZNdQE1+mqLB0WOi3PrQCUCuZhEg6OZ+7CMWikbfZIKqLlawVgZmWj3ypk7XSj1AU9Hey8Kfk47QYFZZSi24JYltg3XJlXT5aw6MzPjtVfEUVf4+i30xsUSq1h9tIuNFK7HVWctf5MUoEEfUFZmkjHQJzNizKxhoOXGKvsC/DqgXr/qYH9Fzw48BfIDDdgVUXctfqgdng9wsOfejHf1CvsB9zuwd/UWAfhlBRLrGhtnZgHRCCyDkM+UeHlPsMqKQXUhSbaKoFv+HDqGxtbLs1MA9EiMCd6WDLB5H8b5GNnwqyefL3KfxfxkrsIgoaB76jyPajJtow93gDI0pKh+LHWLBFKSjl2024WijoobKRXq28ILZVvP6a2QZWp0FE+/Inji1ewRZVtb+ZYQhsdQVZX+jkrtMxrQrWm6KOlX9xFix9IxGaQp0+CuHX57PT9n2FOT5qqxpd40NAS8Ct1KNxeDhNbCX4fCpXxTegdsixYQJRNif96jZDP+7mb2JIGEuzt3JXxkfcmvc+D6QuxGm3EmNzQS0fedsEB79WOfCNxuGVAntH4LjeUencSC/exWU5Gvl2iClqQI6zCFNAnJ5HiWrHUAM3mgwCD6kZ+BH4saJhQzurym1gqHuggW4CXgKXu8rOjNLJ5uxoOLBiO9o4NxB48VOCj0JFJ93qpI6ejV8xME+0sIIC+6OySQ/Lp+ueS4l1hxCtWjHREGj84ghheww0O7Sdy9xWhAOK3SuxfO8mbiUM3N2eekcieH/fCl7YOZ88Sx0OFGzll8wVZ5EakiRVpwu2R33dunV06tSJyMhIAHr16sXy5cu5++67qzewc0DzluD//CcsnRqA04ESpmJs3YfWtjb+1DIVegXUSx3YsxTce01sCUqgIW4PR9ntQWQeLQ3ahpB7WCP3Rx3NBaGDLfjTBVqYgnu/SfSVFkIiFErSFRzRAofhg2X5EK1iiw+BrWWCq+zB6iIDa2ig8WMNV8g7dKzy48kCVz0bYmf551SFQwt2girW8pNrAfgLQJRZOqdkl0m2QyX+73EYH2dDkYlSy4q1fyj6e9+Dv8wxVZNl2Qlcf+njaAr4lDDm79sZfH9nQQ43JNTBpAgoPyTMUge2uvPYX1LCeN9WHu+Qj8XiwW/vQrq3NpqtFju8W/G0SGTPJYlEOmFb3gEesXSpmC5WBafVipAPo5+xSFsY4tsiwlqHkbtOL9dhXrLHxNtYpbRdqC47Qr2kOIqLA6M/XAkC7X85x9ZU9wmIsQZGpRQYWEMqHA5bLYWi7QYx11jwpkPueh3TDQU/G0ReIypOreUVmLkmdIzEkpmFyDt6sM5hFKRplK3SCU1FcaiUe6ZW51gnnADDUUnFsq4Nd7pAsYGqmigJVoyOXsRHv5fL70qeQG0binHcsoFax1CO6BkcKtjBkTY6iT8dnYUJwKKi1o3DWOtGvz2E3zaGAoLazYvZkp+CwxJKt0bDMU0VvYGK7a9hGN8UYh72oV0T+DcmaG1CwKlSomRSe2gs2WvAd0QQ3kolspNJkcz71cq0qlDHBqnH3ZQM0zAtfiLb2bDFWXHvN7HFKbgaqVhsFW8anU8lJT6KL/Xg2BWCZzf4saAYOpYrQ8GhIooM9KP9mim2x9l/+Df+0f6FCq0mQ4mhnhVqhaRhmn4s1ihUrQFCQKISzzN/uZbdRUcwhaCxK5Z4JbZCcVZHi+Cp9km8uWM9+4ty6BzfkOENL8emq/hDdeyj/LDLjshRUJv70et6sPoquaBIZ+1g3i90ztHZE6nRIjueTYkHsPh1wg036VokqAo64EMg0APDzyv0fZ+d0l72wGzxgV57tcx/ld2CV1CP9cILwDQRgEfTcJt+EvUj7HKEYzM07JpWab9/eng+uubnqv1N2FhnH4q9kGJFxa3CTmc4plpM04z9hHhiWVcrg8LG3+N0Xo3qhlbF9Wl1sA7pZg676maTaoki//dn+bE4l6TavYm1R5zTNJIk6fy6YBvqmZmZxMUdexY5Pj6eX375pRojOnd0xYPm9mN8szv4mtapLmoTDeuQSPzfFKOEqGhXhOLfWkJslzBEVwtYFXxZBkZ2LtbBIQjdgSdPoeCAQuGPgRpJzF+tqBluwmrbCKln4XCKTt4mgzh/AY4sHfGjASVHZ7CubcXlUin6VcGXFfi832nF6lDKLU+mtnASWleQ66DCneKinYKoflGohzKP7bdNCAV5xxozsT0t5G0s/8GI1grqtmJCGzkp3hPYruhXE3+eSp2hIZDlhkQ7/h3pgfHQRymtEtjqKGB3YQ6vFeZQyxnGTZe04UBxXnCbK2ITycz/nm21mtHoussoWaWCGXgmOecaN8v3pAFwe9Nw9vz+DA2aD0HXNZ7fUkytkA6E26y8t6UIQ6Txj5Zx9K3bEgw7xFsg89j3KOhlxxppOX6OMuk0WIRAfFtA6LhwsldXMqy6bHYpNFE+zCDqtiiUUBXfv7PLV+I7hFN0UBDaMxzFD2pM+UkQVSe4WlpQHT4Kwg5gq1uHuL42ircZeDNNRIQWmM237FDcGAtej0rOOpOYq+NwhpvYamm43Sr5C44Fp1hBrQOqp4TwNjYKfjm2j4grLBRtD+Rdr9AIvdyJ+dPR3k+nitkliqIvBbV6qtiiNQ7p2Wx17af7XxJgYxpoKto19TBxIA57sY2OQ1+RD4bA0i0c6mrYwu0Mbfso63JWc/0dQwjZbWJVrCh1I/GmGZj3heKOtNLSrmIJ0SlxpmP396SP63bCCEx46LGbuIbH4PlrCKYh8Eer6H+1YTc1hB3cqptiuwe7WkCtIeEohoI31KTILzN+ddPD/FhvisaYkxG8ZivtXYg4K0LX0fw64YkQVkdDMf1gATPfD46qHWxncakc7ncIJUfB8DQg61eNmMtCUP0m/iiV7G8EtesJWtXvRs/QMThE5WuXm0oMmiMm2FwrbYgLIYghllhXbPD1yu4hCUPQwhbHE2174zb9RKgOrEfXv7aYViyRkNshDx2DSMWFzS8b6eeLvvd/+FQI8dUj3+bBqxrUc+dQoIZgqBq6Av6jz6JX9hz6uRSYIz6g7LPsCuUb7RUa7gqgBRZ/0wxBvhpKLX8+dX0+0q1Ois3ATTGHomFXtHLLvx1xFuONSKdD6iVsiU3lYGQuNtNBrqqQafFQHKPRvKCIvntgTd3fcaPidHVBCbNALQsJnlokHohB+JqyrfmP7E/7gLGZm7ksshm3af2oReXnkCRJNYsiLtDuvtdffx2328348eMB+Pjjj9myZQtPPPFENUd29vIz0rC+sQMl41gPmdIiFrVpNObmdLSOl6DUj0IUmJgWFbcOHlS8mkJCVgmaqoPuRakVg9etkv87+AoFke00nGEmxr/TUBvbUduHYsTZA5PhhHtRPslH3aWDU8Ec6OTrOjswvdA57HKsuXZ8hkluuJdafoHz/zyYGX60DqGoje2IIj+6y4bu0Tj8fvnem6j2CrHtFcz9PjBMjGYa3hwVX4GKNR5yI3IJyw4lf7kVfz5E/EUjQvXA0hzoF02RsFF8UMHWSBDRSEd7fyPoJlzfiPwGiYRnFqCkFyLiXJTUC+dby1YyPcU0tEVwadZB1lodvJy6A900aOSK5u+XxGGjkFBXG6K1eOwFDqw+FS3SZLt7FxnFhURpxWTv/S/hoXE0bnArmWmX8EzOJrI9x75bi6gwxrW9hJaRcTgsVsx0H95fijDSdIwWVswmVqKi5SQuf4Qn5wh84MfUFHJCIyj8pczjFgrUT1JQ/pt+7DWXiqW7gbH5IJarW2D87Ee4gQ5hlFgs0Nik0MgiIjMUm08hLy6fIrcXl4hDc4ESYrDL2ICqNcBvCBqENcAhbPgLd7N/21u0iXsQZb6AfAMl3oI2NJbD34InVeBqqRLTFpifBW1C8F4SRt5PBlqESmgHFV9iEZ7cQ8T5GmLkaPhyBPYEDcUJelZgRQWrw41mVVELVIRfIGJseIsVLFaBZjcoCvGwQ0sjzBpKlAglpkBHwSQ/wkmYLQQ134fhMNFEKIpfoLkEitOOIh+7+NMrOuzDkmtCth+cKiLaihHvIzQ0BN+XuRjfl0DbUJQDPjAM7LfGo4ZXzz38I+4CQtMdHHhLxywzCMsaq1B/pIatTsVHTaSLj2H6+fH5zthNk4S8Xhx0leC1ZhLnLyDdEolXUzCPPntjLbtUWhUzOTaDvMmxxvrJetwtpkGiL5+D1igKLQ5MxcRUTfyYONTA8Hl8CkKAagGn30rLrERSI3LZHp+GoQgKTC/1fYcpwEVLXxT1cw6yJU5jX70muOJ7oRx9HlIApOn493jY0eoblFArSnQPvk7bTN3QOMY0S+LK2OYoiiwnJKmmumAb6gsXLuSHH35g+vTpALzyyisIIU576Ht2dlGNXcdaUcBenI+2KQd1ZyHGZZGYl0VSbC3EpUQiTAOP5iPTk4mRn4PQaxEeXgclwkKUT8G+Wsf8thitqwvq2FAKDYQuUBJt6LVVzB+L0b4oBkOgXOlE9FLxOIrweHVsBRbseFEjDfZbilFtsURYrKgF+Ry0xrK/uACbxU+L0HBq+8JR8KPmbsFbmI6IrYvFWQs9rQGZS/0YxRDeSiX6KgV9fzqG4oNLNIzYUAp82RTruUTaE/AQh9dvUNtiw+q3kGdJw5XtwLHLgupXoZUDT5SHDHR0AXV0J1ahYo0PY+M+nf/b4eeScBWnU9C0jh+rtZDVGRv5PmsrUyPbkrD7J9Lrt6Ukpj7OEBsqgjCtNoiKw+RM8skr3Ep27m9ERTYhOqIVZnFdflyjE9bOx+cF+/gtP58uiXEMaFCH6OP2oSigKEq15q24uD9+c+B8nRdxcWFkZRWe9vaKAq4CBfFxPqJlGDnpFgp3CCxhCvE9NUJ8HsydHoxtHmigYukTDrsOY/6aA83CKbg8GhFuRVUtKMKCqvugaBe+nL0osY3IdtrJKt6PTbMRG1Ib3VdIsb+AcEcCPiWcYt3AYbPgNHyE+Atx2lw4jLpYikxUi4ZQLJgOgR8Dw1qCTYSh+cBwmngtAodmRVE0CguPtTYsFgVVVTFNE7//xGl8pmlVFS6WmP7ouXE650VNTKNSIU4No0RBWAUmx/Kf1VBwZAJpOkqUhq+2hsd+bkZC/NH0cNhV1FQb6Ut8+LLA2UAhobeVoshKJm68wNTEPHI+z4k/6tDh/3HJgkfZFxFLgyNXszF+H429aWRo4RRarQh8R4e6B3rRjzWYzYqPKVWBY73pgXjKLv9WWePdYejE6YUcsEVTpNoRSqDM82OgK4ERAg7l2PezGCrNsmsBsKn2forsXgyhk+A7SKYWis1fn45FhwkvymFftJPsxK4URF6Cz+oARUEpMlF2uPmlyReER9Xj6pa3883Brfxf9mbCLaGMqt+b9hGX1ogGe005R86mHiVJ59IF21DPyMhg2LBhfPLJJzidToYOHcq0adNo06bNaX2+JjfUIXDRtik+FEVHCBve42a41SwKoOPXs7FoTkwRHhzKZ1UVHCV+DIuKx6Zi9yoIDbyaiRCgaQrWQrCg4A8twlBVhBKKYZjYjWJM1YIPW+AYQsEwjhV9mhYoOMq+BmDDhyJMfGpg1iqb34qmq5gOA69aBEoJiuLE9DvPKA1AOeEz3nFxYRw5UogXK4ZQCFH9CNMMfrZE8WBVLDiEhoJAN09/aJyqlm9sq1jw+1Q0p4lH6DhQETU0/1wMDfVSoYaCUiwQTjDR8GsmqsWDoyQEw64jTD9uu4YiFOwWFU0oGFYLPq+JEKJCPj0XMZ1vNTGuiyWmP2tDvTqcTXqoKrh8NoRHQXEJCqg48eeFqCbmkZrYUD+86FYsGXuIKelGhg1U2x4MIMMWikDHcnRO9cAs7wLB8cutVToQ/aiTxXy6DVVR7t+Cyvda8TUFBQUVcBk+4vUiDtsiKVCdgRsMgSoXpmLgVwJll710cjyhkFgYSf2CKA6F57I76gjFtmIi9VTcisrvtvo0NZ1cWriPqOJ8wr0qiqLiszrwazYMxYLfq5IVmoc3IpSQxGtwxzTkO5vJV3lbiLC6+Hu93lwe0bRaG+w15RyRDXWpprhgn1FPSEhg/PjxjBo1Cl3XGTRo0Gk30i8EQoBX2IiLi6n0omX4BWBBURI4vi2imwLdcbSn1xS4raUP6h39rCEwQo6+IEKOznEVGFrsUY89cxc4Rvmi5kQNHx+2ckv9eDX92JompgNwnPHS4eI0FhwXAmxHF1Ip+yy4EOAUgZsGgQFyZ1bwHF8BMfGj2kAYYEc5WjRL51uxJiC89K+jw99NO16HAaig2YKzj5cYAAJ8VTsZliRJ55ZpQoHFV6XruUs1g5l7gPr7dpPqqo3NH05J2G9EmTqHbeGAHw0Ng0DveaCn+kyfTj8XjVDlBP+uXKBqIoL/M4B8zYKHMOroeYSqbjKs4QihglAwVQ1NaIFl3xQDt+LHisJ+VyZZzjzqFEdz9YEmmIog39EEv5ZPa4rJsVjJUNtR5BTYHNkYqgfFEYVwuhCqQNV1bJklGIX7MQoW0cAfwmUlbobF1OfXhLp8kPsO70dG0y+2NdEqFOu5GMKP0xJGfGgj6oS1wKLKZQ4kqSpdsA11gOTkZJKTk6s7DEmSJEmSJOlsCBNj6cMcCLXTMKc9u8IPE2cUst8eCopAoGICKoG1yi8UJ+rl92sq+9VIov1uLvVk4lE13IoVgYqGiU0Y2ISJxTRQEJhHe7pV7SBHwlVMEYZihKOYIUA0cV6dROFBx4qfBOzCijNHwak7EQoUhvgpDIvDMBqSrubwS8O9uH1uahXvo/6hfTxTYFBsgU0xK1gXG4Y17hJi7FH4DDc/pC2mwJtJ85iudKo7mITQxlWcipL053RBN9QlSZIkSZKkC5wQKGtewleQTm1vV3IcOYQrBzloC0FXrIGZ08/r3O5VTwVQVPKsoeRaQnCYfizCjyoEPlWlBA1d0TBV7eiQ+UBDXSBQhIHFNLCLI9hMPyGmga5AnmrFqyoYiokNA4sw8GPBKhw4dRchBaHE+iK4VI+l5Ego+aENKQgvoTCymI21vIR5immVX0TXbfmYv21mb7iNgtjaOBJb46ldlwPe/czbcj+JrkvpVv8f1A1vWa1pKEkXO9lQlyRJkiRJkqqF8HnwrJiFdc9awnzd8atefPZdHLKFoqtWLBdU//kfoygKXs2Kl8B8RKVP8gUmyis7v3xgrXYUC7pmwc3RlRCEwCL8hJg6oYZOiKnjVTXyNCfFmoZPAYu1EJvIx8FBHIaJ0+fCrkdSKzOMBmY8Dr8DQxV4bD5KbF501U1cST6J+wpx7NpAqLGav1jclDhtFDm2k/7LvWSGxVCnbk8SEq+GiDrgCCudYOi0GKYfn1GCKYxAGvgtCCFqxMR2klQT/Gkb6uoFtGzRhRRrVZNpc26dz/Ssib9VTYwJamZcf+aYTvc4NTGNqpNMj4ouljT5o9/D64esYpO92UWkp24lZu8qemb9RphZB5u/J0WO/RwKzSDXGgmKSsW1Wf4c1HL/r4IQCCEQKsG57UsXLT067y66YqVAtVIAKAgchp8Q00eU343NNCjRLBRpVko0OzkWFcMqsIgsQkQGIaaOxTTRFRemEYYiQrAadqxmIg6jHoppQTNUHD4LmseHQyshRi2CjCLYvR5NrMZqulHwUGKDEqeTfGcIR0IcZDitZNk1cu1QpOmguBFmEYZRjGnqWDQ7mmIBIfALH0KYhFgjCbPFEmWrRYxSl2ijFtGeBCLdMYQVR2Lz2Dj6HATCoUCoigjTEBEqIjzwH3bljG4aSFJNdMHO+i5JkiRJkiTVLNvTDR77ooiDzn0IpQjTuq/c+y9t/Zy2ue0qfM5UChGKnAy0pqi8caCgCjuKcKKczTgH9XewbK70Lb8CD/1FQ9H8f+gId387g3p5TU+6je2fcViuljO7SzWfbKhLkiRJkiRJkiRJUg1yMc3LIUmSJEmSJEmSJEkXPNlQlyRJkiRJkiRJkqQaRDbUJUmSJEmSJEmSJKkGkQ11SZIkSZIkSZIkSapBZENdkiRJkiRJkiRJkmoQ2VCXJEmSJEmSJEmSpBpENtQlSZIkSZIkSZIkqQaRDXVJkiRJkiRJkiRJqkFkQ72GWLJkCX379uW6667j/fffr/D+tm3bGDhwIL169eKRRx7B7/dXQ5TV41Rps3LlSm644Qb69+/PnXfeSX5+fjVEWfVOlS6l1qxZQ48ePaowsvJON87z4eWXX6Zfv37069ePZ599FoB169aRnJzM9ddfz4svvhjctqrPsWeeeYZJkyad9NiHDx9mxIgR9O7dm7Fjx1JcXHxeYlm9ejU33XQTvXv35sknnwRqRjotXrw4+Ps988wzJz3++UiroqIikpKSSE1NBSAlJYW+ffuSnJzMk08+GTx2amoqI0aM4IYbbmDkyJEcOnQIAJ/Px8SJE+nTpw8DBgxg9+7dZx1TdToX51NV5emqciZpcqKy6mJLk/OtqsuU468D5yrPFxQUcNttt9GnTx9GjBhBVlbWGcV1Ps/Hs40NYNasWfTt25d+/frxzjvv1Lj4JOmCIKRql56eLrp37y5yc3NFcXGxSE5OFjt37iy3Tb9+/cRPP/0khBBi8uTJ4v3336+GSKveqdKmsLBQdOnSRaSnpwshhPj3v/8tpk2bVl3hVpnTyTNCCJGVlSV69+4tunfvXg1Rnn6c58PatWvFzTffLLxer/D5fGLUqFFiyZIlolu3buLAgQNC13Xxz3/+U6xZs0YIUbXn2Lp160THjh3FQw89dNJj33bbbWLp0qVCCCFefvll8eyzz57zWA4cOCCuvvpqkZaWJnw+nxg2bJhYs2ZNtadTSUmJ6NChg8jOzha6rotBgwaJtWvXVllabd68WSQlJYlWrVqJgwcPit27d4u//vWvIiMjQwghxGOPPSbefvttIYQQEyZMCMbx7rvvigceeEAIIcRbb70lpk6dKoQQ4vvvvxeDBg06q5iq07k6n6oiT1eVM0mTk5VVF1OanG9VXaYcfx1wu93nLM8//vjj4o033hBCCLFw4UJx7733nnZc5/t8PJvYhBBiw4YNYujQoULXdeF2u0X37t3Ftm3bakx8knShkD3qNcC6devo1KkTkZGRhISE0KtXL5YvXx58/9ChQ3g8Htq1awfATTfdVO79i9mp0kbXdf71r3+RkJAAQLNmzUhLS6uucKvMqdKl1JQpU7j77rurIcKA043zfIiLi2PSpEnYbDasViuNGzdm3759NGjQgHr16mGxWEhOTmb58uVVeo7l5eXx4osvcscddwAnPr91XWfjxo306tXrvMb01Vdf0bdvX2rVqoXVauXFF1/E6XRWezoZhoFpmrjdbvx+P36/H4vFUmVp9dFHH/HYY48RHx8PwO+//067du2Cf3fv3p2VK1cCYJomRUVFALjdbhwOBxAYzdK/f38AOnToQG5uLocPHz6ruKrLuTifqipPV5UzSZMTlVUXW5qcb1Vdphx/Hfjll1/OWZ5fs2YNycnJACQlJfG///0PXddPK67zfT6eTWwAV155Je+++y4Wi4Xs7GwMw6CgoKDGxCdJFwrZUK8BMjMziYuLC/4dHx9PRkbGCd+Pi4sr9/7F7FRpExUVxbXXXguAx+Nhzpw5wb8vZqdKF4B3332Xli1b0rZt26oOL+h04jxfmjZtGiz49+3bx7Jly1AUpdJ4qvIce/TRRxk/fjzh4eHAic/v3NxcXC4XFovlvMa0f/9+DMPg1ltvpX///nzwwQcn/N2qMp1cLhf33nsvffr0oWvXrtSpUwer1VplaTV9+nSuuOKK4N/Nmzfn559/Ji0tDcMwWL58OUeOHAHg3nvvZe7cufz1r3/l7bffZsyYMUDlv216evpZxVVdzsX5VFV5uqqcSZqcqKy62NLkfKvqMuX468CZXhtP9vuW/YzFYsHlcpGTk3NacZ3v8/FsYitltVp56aWX6NevH507d64xaSdJFxLZUK8BhBAVXlMU5bTfv5id7ncvLCxkzJgxNG/enAEDBlRFaNXqVOmyY8cOVqxYwZ133lmVYVVQE/Luzp07+ec//8lDDz1E/fr1K42nquL8+OOPSUxMpHPnzsHXTnTsqorJMAzWr1/Pc889x0cffcSWLVuCz2JWV0wA27dv59NPP+Xrr7/m22+/RVVV1q5dW21xNWzYkAceeICxY8cyYsQImjVrhtVqBeChhx7iiSee4JtvvuHxxx/n7rvvrjQmAFW9sIvdszmfasL14Hw4nTQpdXxZdbGmyflS3el1pnn7TOM90+tDVZ6Pf+Tadc8997B+/XrS0tLYt29fjYtPkmo6matrgISEhGDPDATuFJYOs6rs/aysrHLvX8xOlTalrw0fPpzmzZszffr0qg6xWpwqXZYvX05WVhYDBw7ktttuC6ZRTYvzfNu0aRO33HILDzzwAAMGDDhhPFV1ji1btoy1a9dyww038NJLL7F69Wo+/vjjSo8dHR1NUVERhmGc15hiY2Pp3Lkz0dHROBwOevbsydq1a6s1nQC+/fZbOnfuTExMDDabjZtuuokNGzZUW1p5vV7atGnDokWLmD9/PrVr16ZevXrk5OSwZ8+eYG9pr169yMrKIjc3l/j4+HKTHF3o1+6zPZ+qKk9XpdNNk9J/H19WXYxpcj5Vd5lyLvN8fHx88DN+v5+ioiIiIyNPO5bzeT6ebWy7d+9m27ZtADidTq6//voK1+/qjE+SLhSyoV4DXHXVVaxfv56cnBzcbjcrVqyga9euwffr1KmD3W5n06ZNACxatKjc+xezU6WNYRjccccd9OnTh0ceeeRP0xNxqnS55557+PLLL1m8eDFz5swhPj6eDz74oMbFeT6lpaVx11138fzzz9OvXz8A2rZty969e4PDvZcuXRocVl0V59g777zD0qVLWbx4Mffccw89evRgxowZlR7barVyxRVXsGzZsvMaU/fu3fn2228pKCjAMAy++eYbevfuXa3pBIGh5uvWraOkpAQhBKtXr+bKK6+strQqKSnh73//O0VFRfh8PubNm0ffvn2JiorCbrfzww8/AIHKc2hoKNHR0XTr1o3FixcD8MMPP2C326ldu/Y5jauqnIvzqarydFU5kzQ5UVl1saXJ+VadZQqc2zzfrVs3Fi1aBARu4l5xxRXBUTqncr7Px7OJDQIrYUyZMgWfz4fP52PVqlUMHTq0xsQnSRcKRZxofJ5UpZYsWcIbb7yBrusMGjSIMWPGMGbMGO655x4uu+wytm/fzpQpUyguLqZly5bMmDEDm81W3WFXiZOlTXp6OuPGjaNZs2bB7Vu3bv2n6Fk/VZ4plZqayqhRo1i9enWNibMqPPnkk3z66aflhgMOHTqUSy65hBkzZuD1eunWrRuTJ09GUZQqP8dSUlL4/vvvefrpp0947EOHDjFp0iSys7NJTExk5syZREREnPNYPvnkE+bOnYuu63Tp0oUpU6awYcOGak+nOXPmkJKSgtVq5bLLLuOxxx5j7969VZpWPXr04N1336Vu3bp8/PHHzJ07F7/fT1JSEuPGjQMCE0xNmzYNj8dDaGgojz76KC1btsTr9fLoo4/y66+/YrPZePLJJ2nVqtVZx1QdztX5VFV5uiqcSZqsXLnyhGXVxZQmVaE6ypSy14H169efkzyfl5fHpEmTOHjwIGFhYTz//PPUrVv3tOI53+fj2cRW6qWXXmL58uVomsb111/PuHHjakTaSdKFRDbUJUmSJEmSJEmSJKkGkUPfJUmSJEmSJEmSJKkGkQ11SZIkSZIkSZIkSapBZENdkiRJkiRJkiRJkmoQ2VCXJEmSJEmSJEmSpBpENtQlSZIkSZIkSZIkqQaRDfUqlpqaSosWLbjhhhuC//Xv359PPvnkrPablJTEhg0b/tBnCwsLGTVq1Gltu2bNGm6++Wb69+9Pv379uPfee0lPTwcCy03dfvvtZ3z8Zs2akZOTg2EYjB07ll69evHee+9Vuq0QgmuvvZalS5dWeG/atGk8+eSTzJo1K7i+5onccMMNFBQUnDTmRx55hHXr1pGamsrll18OwIcffsicOXMA+Pjjj3n//ffP4JtKkvRntXnzZkaOHElycjJJSUmMHj2anTt3VndYf8js2bN54oknzugza9asYdasWae17cGDBxk/fjzXXnst/fv3Z9CgQWddRp7MqlWrePLJJ8/5fjdv3kzbtm357bffyr3+wQcf0Lt3b4qLi8/5MS8UF3Nd6EI0adIk/vOf/5zRZ063DlRUVMSUKVNITk6mf//+3HjjjXz88cen/NyGDRtISko6o5gk6WJjqe4A/owcDgeLFy8O/p2RkUFSUhKtW7emefPmVR5Pfn4+W7ZsOeV2GRkZPPTQQ6SkpFCnTh0AXnvtNe677z7mz59/1nFkZGTw7bffsnnzZjRNq3QbRVEYNmwYn376abkLuMfjYcmSJcyfP59GjRqd8lhl0/9EStdiT01NDb42bNiw4L83bdpE06ZNT7kfqWZITU3luuuu49JLLw2+JoRg1KhRDBo06A/vNykpialTp9KxY8cz/mxhYSF33XUX77777km327BhA2PGjKFhw4YoioIQAk3TuPvuu+nRo0eF7T/88EMKCwu57bbbzjgm6dzz+XzcfvvtvP3228F11BcvXsyYMWNYtWrVCa93F5MtW7aQn59/yu1SU1P529/+xn333cfMmTNRFIWMjAwmTJjA/v37eeCBB855bD179qRnz57nfL/t2rXjtttu48EHHyQlJQWbzcbevXuZNWsW//3vfwkNDT3nx7yQyLrQhe1060AvvPACISEhfPbZZ8Hz+eabbyYxMZGrr766CiKVpAuXbKjXAAkJCTRo0IC1a9fyxBNP4Ha7cblczJs3j1deeYXPP/8cTdNo2LAhU6dOJS4ujl27dvHwww/jdrtp1KgRJSUlQKCSk5yczE8//VTp32+88QYLFy7EYrHQoEEDnn76aSZPnozH4+GGG24gJSXlhJXG3NxcdF0PHgvg73//Oy1atAj+nZWVxW233UZaWhqapvHCCy/QuHFjCgsLmT59Ojt27EDXdTp37syDDz6IxRLIgl6vl9GjR+P3+7npppuYPXs29evXrzSOgQMHMnv2bA4dOhQsJL/44gtatWpFo0aNmDRpEk2bNuXWW2/lpZde4quvvsJqtRIVFcWMGTOIj4+nWbNmrF+/PhjzrbfeSmZmJnXq1GHatGnExcUxcuRIRowYQevWrYPHnj17Nrm5uXTu3JnVq1ezdu1aHA4H7777LlOnTg0WOlOmTKFp06b8/e9/P/MMIZ03F2rFEKB+/frlYt++fTvDhg1j1apVREdHl9u27A0lqfq53W4KCwvLXTv79++Py+Vi/fr1PP3008FRQhs2bGDatGksXbqU2bNns3PnTo4cOUJ2djbNmzdn+vTpuFwuMjIyeOKJJ0hLS0PXdfr168cdd9xBamoqI0aMoHHjxhw6dIinn36aCRMm0KlTJzZv3ozf7+fBBx9kwYIF7Nmzh9atWzNz5kxUVeX1119n5cqVeL1e3G43Dz30ENdddx2zZ89m8+bNZGZm0qxZMxo0aBD8HnPnzmXhwoW89dZbxMXF8dprr7FixQpM06ROnTo89thjpKenM3/+fAzDICwsjPHjx58wrebMmUNSUhIDBgwIvpaQkMCLL75Iz549GTlyJN9++y2ff/45pmmSkZFBQkICTz/9NAkJCSctay677DJuu+021q5dS2ZmJqNGjeKWW24hJSWFL7/8kjfeeIORI0fSrl07fvzxR9LS0mjfvj3PPPMMqqqSkpLCnDlzcDgcdOrUiXfffbdCb/nxxo4dy7p163jxxReZMGECDz30EPfffz/Nmzdn9+7dTJ8+nby8PAzDYOTIkQwaNAjTNHnqqaf4+eefKS4uRgjBk08+Sfv27Zk0aRJ5eXkcPHiQa665hokTJ/7RbFnjXCx1obL56fi/J02ahKIo7N69m5ycHLp06cKUKVOwWq0nzA8bNmxg+vTphISEUFJSwsSJE3n55ZeJj49n586dOJ1Oxo0bx7x589i7dy/XX389Dz/88Bnlo7JmzJjB9u3befXVV7FarTz//PNs3LgRwzBo2bIlU6ZMYf369eXqQCNGjDjh75qVlUVMTAy6rmOz2UhISGD27NlERkYC8PXXX/PGG2/g8/nIycnhxhtv5L777iu3D5/PV2kcLpeLDz74gPnz52O1WrHb7TzxxBM0adLk9DKdJNVwcuh7DfDTTz9x4MABPB4Pu3btYt68ecybN49PP/2Ub775hk8++YQlS5bQtGlTJk2aBMCECRMYPHgwS5YsYdSoURw+fPiUx1m1ahUpKSksWLCApUuXUrduXd577z1mzJgRbMCcrGenefPmDBkyhAEDBtC3b1+mTJnC119/Xe6O6MGDB3nkkUdYsmQJV1xxRXAo1VNPPUWrVq1ISUlh0aJF5Obm8s477wQ/Z7fbgxWgxYsXn7CRDhAZGUmfPn1ISUkJvrZgwYIKBUVaWhr//e9/+fTTT0lJSaFLly788ssvFfa3d+9eHn30UZYsWcKll14a7Ek/meuuu44ePXpwyy23MGLECIYNGxYcylVUVMSqVavKVTSlmqlsxXD48OEMGDCAkSNHAvDKK6/Qt29fkpOTueeee8jKygJg165dDBkyhOTkZO69995yFcPSRyQq+/uNN96gd+/eJCUlcdddd1FYWFiuYmgYxhnF3rx5cxwOB4cOHWL27NnceuutJCcnM2HChHJDk/fu3cvIkSPp168fycnJLFu2DAjcpLjrrru46aabSE5O5vXXX//jCSmdVEREBBMnTmT06NH07NmTiRMn8umnn3LVVVdhtVpP+tmff/6Zl156iS+++AKLxcIrr7wCwMSJExk4cCApKSl88sknrFu3Lvjbpqenc+edd/Lll18SFxdHamoqPXr04PPPP6dTp05Mnz6dmTNn8vnnn/PDDz+wefNmDh06xLp163jvvfdYsmQJ48eP56WXXgrGcejQIRYuXMjzzz8ffO3NN99k+fLlzJs3j7i4OBYtWsSOHTv4+OOPWbx4Md26dWPKlCm0bduWoUOH0rdv35M20iFQHnbo0KHC67GxsTRu3Jiff/4ZgB9//JFHH32UZcuW0apVq+B1+2Rljc/nIyoqivnz5/PSSy/xwgsv4PV6KxzrwIEDzJs3j88++4zvvvuO77//nl27dvH8888zd+5cFi1ahMvlOq1zVlVVnnvuORYuXMjDDz9M3bp1ufnmm/H7/dxzzz088MADpKSk8N577/H222+zefNmfv75ZzIzM1mwYAHLli1jwIABvPnmm8F9ejwePv/884uqkQ4XV13oZLZv384777zDsmXL2L17NwsWLDhpfgDYuXMnL7zwAp999hk2m40tW7YwduxYli9fTkxMDHPmzOGNN94gJSWFDz74gIyMjDPOR0IIHn/8cQ4dOsSbb75JaGgoc+bMQdM0UlJS+Oyzz4iPj+f555+vUAc6mbvvvpvvvvuOTp06ceutt/LKK6/gcrmoV68eQgjefvttnn766eBvMmfOHHJycsrt40RxGIbBU089xVtvvcWnn37KkCFD2LRp02n9DpJ0IZA96tWgtGIOYBgGUVFRPPfcc2RnZ9OsWTNcLhcA//vf/7jpppsICQkBYNSoUbz++uscOXKE33//nRtvvBGA9u3bn9bwo/Xr19O7d28iIiIAmDx5MlB+aPepTJo0idtvv53vv/+ejRs38uyzzzJv3rzgc0pt2rQJ9ra0aNGCr776Cgg8z7Vly5bg82cej+e0j1mZYcOGcd9993H33Xeza9cuMjMz6d69e7ltEhISaN68OQMGDKBr16507dqVzp07V9jXVVddFYx50KBBf2gY9E033cQrr7xCTk4Oy5cv55prriE8PPyPfTmpyhxfMVy9ejUul6tcxTAkJITZs2cHn+GbMGECI0aMYPDgwWzatOmUlRQ4VjH86KOPiIiIYMaMGcGKYXJy8mk9inG8FStWoKoqTZo0Yc2aNRw6dIilS5disViYPXt2cLv777+fQYMGMWLECNLS0hg5ciRdu3Zl4sSJ3HLLLfTo0QOv18uYMWOoX78+ffv2PeNYpFP7xz/+weDBg9m4cSMbN27kzTff5M033zxlY6t3797ExsYCgevTU089xbhx49i4cSP5+fnB575LSkrYvn07bdq0wWKx0K5du+A+rFZr8BGJ+vXrc/nllwfLmfj4ePLz8/nLX/7CM888w5IlS9i/f3+wF65Uu3btgiOgIJD/srKyeP3114PXuq+//potW7YwcOBAAEzTxO12n3Fa+f3+Sl/3+XwoigJAly5daNiwIQBDhgwJlqmnKmtKh7i3atUKn89Xrle0VPfu3VFVFZfLRYMGDcjPz2f79u106dKFWrVqAfC3v/2t3Hl2MrVr12bChAnMnDmTFStWALBv3z4OHDjAww8/HNzO4/Hw22+/MXz4cCIiIpg/fz4HDx5kw4YN5YbJt2/f/rSOW9NdzHWhkxkwYEDw97zhhhtYtWoVnTp1OmF+aNy4MYmJicERhAB169alZcuWQOCcDgsLw2azER0dTWhoKPn5+Vx++eVnlI/mzp1LdnY2ixYtwmazAYHzqbCwkHXr1gGg6zoxMTGnnU4QuLGxfPlytm7dysaNG1m7di2vv/46s2bNokePHrz++uusWbOGpUuXsnv3boQQFa4bJ4pD0zR69+7N0KFDueaaa+jSpQvJyclnFJ8k1WSyoV4Njh9+WyolJSVYEEHg7mZZpmmWq8CUfb+0AlX6/GopXdeD/9Y0LVjJASgoKKCgoOC04161ahV5eXkMHDiQXr160atXL8aPH88111wTHP5XtiJXNhbTNJk1axaNGzcOHrtsLGeqTZs2REdHs27dOtasWcPQoUMr3AFXVZX33nuPLVu2sH79ep566ik6duzIlClTym1X9nNCiHLf4XSFh4fTu3dvPvvsM5YsWcJjjz32x76YdF5dyBXDAwcOBGP3+/3UqlWLV199FafTCVRsSAHk5eWxfft2Bg8eDEBiYiIrV66kpKTkhA092VA/9zZt2sRPP/3E6NGj6d69O927d+f+++8nOTmZ7du3n/CaDeWvT6ZpoqoqpmkihGD+/PnB3z8nJwe73U5ubi42m61cXrBareWut5X14m/dupU777yTW265hS5dutChQwcef/zx4PtlyyaABg0aMHXqVB5//HHat29PeHg4pmkyevRohg8fDgQa1qfzXHpZl19+ORs2bODaa68t93pGRgapqalcdtllrF27tkK6lP59qrLGbrcDBF87vpyFQBldquycEGW3PdN5BerVq0d0dHTwGmMYBuHh4eXqAkeOHCEsLIw1a9Ywffp0/vGPf9CzZ08aNWrEZ599Ftzu+N/iQnWx1oVOduzS45eNXVXVk+aHzZs3V/jNSxvSx3/vss40H3Xo0IG//OUvTJ48mQULFmC1WjFNk4cffphu3boBUFxcXOkolBPx+/08/vjjPPDAA7Ru3ZrWrVvzj3/8g1dffZUFCxbQqVMnBgwYwLXXXssVV1zBwIEDWblyZaW/+YnieP7559mxYwfr1q3jzTff5JNPPuG111477RglqSaTQ99rsKuvvpqUlJTgHf958+bRoUMHYmNjadWqVXCo9datW9mxYwcQaDDqus6uXbsAgj3aEOg5/uqrrygqKgICz1vPnTsXi8WCYRiVVljKCg0NZebMmcF9Q6ChYbfbTzpUvfS7zJ07FyEEPp+PsWPHnnBm99M1fPhwFi5cyIoVKyrtBd++fTtJSUk0btyY22+/nVtuuYXff/+9wnYbNmwIDpf78MMP6dq162kdX9O0cpWFESNG8O677yKEoE2bNn/wW0nnU2nFcPHixSxdupR58+YFC/6qrhieSSMdjj2jvnjxYj7//HP+85//0LZt2+D7lVXey8ZWas+ePcHzff78+cF9Lliw4A+t2iCdWnR0NK+99ho//PBD8LWsrCzcbjfXXnsthw8fJjs7GyEEK1euLPfZVatWUVhYiGmafPTRR3Tv3h2Xy0W7du2CQ7oLCgqC8xX8URs3bgxWoq+88kpWrVp10qHdzZo1o1evXnTu3DnYoL/66qv55JNPgmXMrFmzePDBB4GK18sTGTt2LF988QULFy4Mvpaens7999/PsGHDSEhIAOC7774jIyMDgPnz5wdHVJ2PsqZ0v+vXrw8e83RmrT6Zhg0bYrfbgw2ztLQ0kpKS+PXXX1m7di3du3dn+PDhXHbZZaxcufKMH425mFxodaHo6Gh27tyJ1+vF7/fz9ddfl/v8F198gc/nw+v1snDhQrp3737S/PBHnWk+at26NX/7298ICwvj5ZdfBgJp//777+Pz+TBNk6lTpzJz5kzg9M5pi8XCvn37ePXVV4Nlot/v5+DBg7Rs2ZL9+/dTVFTEfffdR48ePfj++++DxyrrRHHk5OTQrVs3IiMjueWWW7jvvvsqredJ0oVK9qjXYIMGDSItLY3BgwdjmiYNGjQIPh84c+ZMJk+ezPz586lfv35wpvOwsDAmTpzImDFjiI6Opnfv3sH9devWjV27dgUnmmrSpAnTpk3D6XTSsmVL+vTpw4cffkhUVFSl8XTq1ImpU6fy0EMPUVhYiKZpxMXF8eqrrwZ7Ck/kkUceYfr06SQnJ6PrOldddRWjR48+q/Tp168fzz77LN26daswmRYEhlv16dOHgQMHEhISgsPhqNCbDnDppZfy8MMPc+TIERo1anTayw517dqVadOmAXD77bfTvHlzIiIiGDp06Fl9L6n6lVYMk5KSCAkJqbRiOGLEiBNWDJs0aVKhYvjss88yevRoXC4Xs2fPRgjB6NGjgxXDsxlhciIul4tWrVqxaNEiBg8eTFpaGsOGDWPp0qXBht6dd94ZbOjddddd9O/f/5zH8WfXsGFDXnnlFV588UXS09Ox2+2EhYXxxBNP0Lx5c4YOHcrAgQOJi4urMLFTbGwsY8aMITc3lw4dOnDHHXcAgV6kadOmkZycjM/nIykpif79+5/xDaBSSUlJrFixgr59+2K1WuncuTP5+fnBxsyJPPzwwyQlJbFs2TIGDx5MRkYGQ4YMQVEUEhMTefrppwHo3Lkz48aNw2q1MnXq1BPuLzExkQULFvDvf/+b119/HYvFgt1uZ8iQIdx8883B7RISEpg4cSJZWVk0adIkeN0+H2UNBH7DyZMnc+utt2Kz2WjRokVwNMMfYbPZePXVV5k+fTpvvfUWfr+fe++9l/bt2xMZGcmECRNITk5G0zSuuOKK4AR9f0YXWl2odERKnz59iIuLo2PHjuUajw6Hg+HDh1NQUECvXr0YOHAgqqqeMD/80eXmhg4desb5SFEUnnrqKW688Ua6devGnXfeyTPPPMOAAQMwDIMWLVoE5wc4vg50IrNmzeK5556jV69eOJ3O4DK7d911F6qqcs0119CnTx/Cw8OpX78+TZo0Yf/+/eVGDZwoDpfLxdixY7nllltwOBxomnZellqUpOqiiFPdOpQk6bQcOHCAkSNHsnz58rOqwEnnx/Gz/pZ1/Cy9pmkye/bsYKWmQYMG/Otf/6JWrVocOHCAyZMnU1BQQP369Tlw4ABTpkyhY8eOzJ07l//+97/BiuGrr74aPN5//vMfFi1aBJSvGI4YMYK8vLyTVgzLzgRemdLVCB599NEKf+/fv5/HH3+cI0eOoCgK48aN49prryU1NZVp06Zx+PDhYENv3LhxZ5vM0jl0/O8qBRx/vlaFgwcPsnjxYu68805UVWXFihW8+eabZ92zLv25lF2VRpIk6VRkQ10qZ/jw4eUmECrr/fffDz5f92eJ43TNmjWLjz76iEceeUQ+4ytJ0jlxMTbUv/vuO2bMmFHpex07diw3mdaJVEdDXdd1pk2bxqZNm9A0jbCwMP71r3+xbdu24Oomx0tOTj4nvflS1TtfdZCLsaH+2WefyXNAks4T2VCXJEmqAS60m1OSJEmSJEnS+SMb6pIkSZIkSZIkSZJUg8hZ3yVJkiRJkiRJkiSpBpENdUmSJEmSJEmSJEmqQWRDXZIkSZIkSZIkSZJqENlQlyRJkiRJkiRJkqQaRDbUJUmSJEmSJEmSJKkG+X+Ct6za9CIfCgAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# pair plot of columns without missing values\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "cat_cols_pair = ['Product_Fat_Content','Product_Type','Supermarket_Location_Type']\n", + "\n", + "cols_2_pair = ['Product_Fat_Content',\n", + " 'Product_Shelf_Visibility',\n", + " 'Product_Type',\n", + " 'Product_Price',\n", + " 'Supermarket_Opening_Year',\n", + " 'Supermarket_Location_Type',\n", + " 'Supermarket_Type',\n", + " 'Product_Supermarket_Sales']\n", + "\n", + "for col in cat_cols_pair:\n", + " sns.set()\n", + " plt.figure()\n", + " sns.pairplot(data=newdata[cols_2_pair], height=3.0, hue=col)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Low Fat', 'Ultra Low fat', 'Normal Fat'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FEATURE ENGINEERING\n", + "# print all unique values\n", + "newdata['Product_Fat_Content'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3217\n", + "1 1773\n", + "Name: is_normal_fat, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fat_content_dict = {'Low Fat': 0, 'Ultra Low fat': 0, 'Normal Fat': 1}\n", + "newdata['is_normal_fat'] = newdata['Product_Fat_Content'].map(fat_content_dict)\n", + "\n", + "# preview the values\n", + "newdata['is_normal_fat'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# assign year 2000 and above as 1, 1996 and below as 0\n", + "def cluster_open_year(year):\n", + " if year <= 1996:\n", + " return 0\n", + " else:\n", + " return 1\n", + " \n", + "newdata['open_in_the_2000s'] = newdata['Supermarket_Opening_Year'].apply(cluster_open_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Supermarket_Opening_Yearopen_in_the_2000s
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" + ], + "text/plain": [ + " Supermarket_Opening_Year open_in_the_2000s\n", + "0 2005 1\n", + "1 1994 0\n", + "2 2014 1\n", + "3 2016 1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# preview feature\n", + "newdata[['Supermarket_Opening_Year', 'open_in_the_2000s']].head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# get the unique categories in the column as a list\n", + "prod_type_cats = list(newdata['Product_Type'].unique())\n", + "\n", + "# remove the class 1 categories\n", + "prod_type_cats.remove('Health and Hygiene')\n", + "prod_type_cats.remove('Household')\n", + "prod_type_cats.remove('Others')\n", + "\n", + "def cluster_prod_type(product):\n", + " if product in prod_type_cats:\n", + " return 0\n", + " else:\n", + " return 1\n", + " \n", + "newdata['Product_type_cluster'] = newdata['Product_Type'].apply(cluster_prod_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Product_TypeProduct_type_cluster
4980Health and Hygiene1
4981Health and Hygiene1
4982Health and Hygiene1
4983Household1
4984Household1
4985Household1
4986Household1
4987Household1
4988Household1
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\n", + "
" + ], + "text/plain": [ + " Product_Type Product_type_cluster\n", + "4980 Health and Hygiene 1\n", + "4981 Health and Hygiene 1\n", + "4982 Health and Hygiene 1\n", + "4983 Household 1\n", + "4984 Household 1\n", + "4985 Household 1\n", + "4986 Household 1\n", + "4987 Household 1\n", + "4988 Household 1\n", + "4989 Household 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newdata[['Product_Type', 'Product_type_cluster']].tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transformation of Product_Supermarket_Sales feature')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# transforming skewed features\n", + "fig, ax = plt.subplots(1,2)\n", + "\n", + "# plot of normal Product_Supermarket_Sales on the first axis\n", + "sns.histplot(data=newdata['Product_Supermarket_Sales'], bins=15, ax=ax[0])\n", + "\n", + "# transform the Product_Supermarket_Sales and plot on the second axis\n", + "newdata['Product_Supermarket_Sales'] = np.log1p(newdata['Product_Supermarket_Sales'])\n", + "sns.histplot(data=newdata['Product_Supermarket_Sales'], bins=15, ax=ax[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.title(\"Transformation of Product_Supermarket_Sales feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transformation of Product_Shelf_Visibility feature')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# next, let's transform Product_Shelf_Visibility\n", + "fig, ax = plt.subplots(1,2)\n", + "\n", + "# plot of normal Product_Supermarket_Sales on the first axis\n", + "sns.histplot(data=newdata['Product_Shelf_Visibility'], bins=15, ax=ax[0])\n", + "\n", + "# transform the Product_Supermarket_Sales and plot on the second axis\n", + "newdata['Product_Shelf_Visibility'] = np.log1p(newdata['Product_Shelf_Visibility'])\n", + "sns.histplot(data=newdata['Product_Shelf_Visibility'], bins=15, ax=ax[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.title(\"Transformation of Product_Shelf_Visibility feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value Count for Product_Fat_Content\n", + "Low Fat 3039\n", + "Normal Fat 1773\n", + "Ultra Low fat 178\n", + "Name: Product_Fat_Content, dtype: int64\n", + "---------------------------\n", + "Value Count for Product_Type\n", + "Snack Foods 758\n", + "Fruits and Vegetables 747\n", + "Household 567\n", + "Frozen Foods 457\n", + "Canned 376\n", + "Dairy 350\n", + "Baking Goods 344\n", + "Health and Hygiene 307\n", + "Meat 264\n", + "Soft Drinks 261\n", + "Breads 137\n", + "Hard Drinks 134\n", + "Others 100\n", + "Starchy Foods 81\n", + "Breakfast 66\n", + "Seafood 41\n", + "Name: Product_Type, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket _Size\n", + "Medium 1582\n", + "Small 1364\n", + "High 594\n", + "Name: Supermarket _Size, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket_Location_Type\n", + "Cluster 3 1940\n", + "Cluster 2 1581\n", + "Cluster 1 1469\n", + "Name: Supermarket_Location_Type, dtype: int64\n", + "---------------------------\n", + "Value Count for Supermarket_Type\n", + "Supermarket Type1 3304\n", + "Grocery Store 724\n", + "Supermarket Type2 500\n", + "Supermarket Type3 462\n", + "Name: Supermarket_Type, dtype: int64\n", + "---------------------------\n" + ] + } + ], + "source": [ + "# feature encoding\n", + "for col in cat_cols:\n", + " print('Value Count for', col)\n", + " print(newdata[col].value_counts())\n", + " print(\"---------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 37 columns

\n", + "
" + ], + "text/plain": [ + " Product_Weight Product_Shelf_Visibility Product_Price \\\n", + "0 11.6 0.066289 357.54 \n", + "1 11.6 0.040097 355.79 \n", + "2 11.6 0.040352 350.79 \n", + "3 11.6 0.040290 355.04 \n", + "4 11.6 0.000000 354.79 \n", + "\n", + " Supermarket_Opening_Year Product_Supermarket_Sales is_normal_fat \\\n", + "0 2005 6.565378 0 \n", + "1 1994 8.761345 0 \n", + "2 2014 8.761345 0 \n", + "3 2016 7.663046 0 \n", + "4 2011 7.817130 0 \n", + "\n", + " open_in_the_2000s Product_type_cluster Product_Fat_Content_Low Fat \\\n", + "0 1 0 1 \n", + "1 0 0 1 \n", + "2 1 0 1 \n", + "3 1 0 1 \n", + "4 1 0 0 \n", + "\n", + " Product_Fat_Content_Normal Fat ... Supermarket _Size_High \\\n", + "0 0 ... 0 \n", + "1 0 ... 1 \n", + "2 0 ... 0 \n", + "3 0 ... 0 \n", + "4 0 ... 0 \n", + "\n", + " Supermarket _Size_Medium Supermarket _Size_Small \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 1 0 \n", + "4 0 1 \n", + "\n", + " Supermarket_Location_Type_Cluster 1 Supermarket_Location_Type_Cluster 2 \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 1 \n", + "3 0 0 \n", + "4 0 1 \n", + "\n", + " Supermarket_Location_Type_Cluster 3 Supermarket_Type_Grocery Store \\\n", + "0 1 1 \n", + "1 1 0 \n", + "2 0 0 \n", + "3 1 0 \n", + "4 0 0 \n", + "\n", + " Supermarket_Type_Supermarket Type1 Supermarket_Type_Supermarket Type2 \\\n", + "0 0 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 0 1 \n", + "4 1 0 \n", + "\n", + " Supermarket_Type_Supermarket Type3 \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 37 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save the target value to a new variable\n", + "import typing\n", + "\n", + "# one hot encode using pandas dummy() function\n", + "dummified_data = pd.get_dummies(newdata)\n", + "dummified_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Product_Weight 802\n", + "Product_Shelf_Visibility 0\n", + "Product_Price 0\n", + "Supermarket_Opening_Year 0\n", + "Product_Supermarket_Sales 0\n", + "is_normal_fat 0\n", + "open_in_the_2000s 0\n", + "Product_type_cluster 0\n", + "Product_Fat_Content_Low Fat 0\n", + "Product_Fat_Content_Normal Fat 0\n", + "Product_Fat_Content_Ultra Low fat 0\n", + "Product_Type_Baking Goods 0\n", + "Product_Type_Breads 0\n", + "Product_Type_Breakfast 0\n", + "Product_Type_Canned 0\n", + "Product_Type_Dairy 0\n", + "Product_Type_Frozen Foods 0\n", + "Product_Type_Fruits and Vegetables 0\n", + "Product_Type_Hard Drinks 0\n", + "Product_Type_Health and Hygiene 0\n", + "Product_Type_Household 0\n", + "Product_Type_Meat 0\n", + "Product_Type_Others 0\n", + "Product_Type_Seafood 0\n", + "Product_Type_Snack Foods 0\n", + "Product_Type_Soft Drinks 0\n", + "Product_Type_Starchy Foods 0\n", + "Supermarket _Size_High 0\n", + "Supermarket _Size_Medium 0\n", + "Supermarket _Size_Small 0\n", + "Supermarket_Location_Type_Cluster 1 0\n", + "Supermarket_Location_Type_Cluster 2 0\n", + "Supermarket_Location_Type_Cluster 3 0\n", + "Supermarket_Type_Grocery Store 0\n", + "Supermarket_Type_Supermarket Type1 0\n", + "Supermarket_Type_Supermarket Type2 0\n", + "Supermarket_Type_Supermarket Type3 0\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fill-in missing values\n", + "# print null columns\n", + "dummified_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the mean\n", + "mean_pw = dummified_data['Product_Weight'].mean()\n", + "\n", + "# fill the missing values with calculated mean\n", + "dummified_data['Product_Weight'].fillna(mean_pw, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Product_Weight 0\n", + "Product_Shelf_Visibility 0\n", + "Product_Price 0\n", + "Supermarket_Opening_Year 0\n", + "Product_Supermarket_Sales 0\n", + "is_normal_fat 0\n", + "open_in_the_2000s 0\n", + "Product_type_cluster 0\n", + "Product_Fat_Content_Low Fat 0\n", + "Product_Fat_Content_Normal Fat 0\n", + "Product_Fat_Content_Ultra Low fat 0\n", + "Product_Type_Baking Goods 0\n", + "Product_Type_Breads 0\n", + "Product_Type_Breakfast 0\n", + "Product_Type_Canned 0\n", + "Product_Type_Dairy 0\n", + "Product_Type_Frozen Foods 0\n", + "Product_Type_Fruits and Vegetables 0\n", + "Product_Type_Hard Drinks 0\n", + "Product_Type_Health and Hygiene 0\n", + "Product_Type_Household 0\n", + "Product_Type_Meat 0\n", + "Product_Type_Others 0\n", + "Product_Type_Seafood 0\n", + "Product_Type_Snack Foods 0\n", + "Product_Type_Soft Drinks 0\n", + "Product_Type_Starchy Foods 0\n", + "Supermarket _Size_High 0\n", + "Supermarket _Size_Medium 0\n", + "Supermarket _Size_Small 0\n", + "Supermarket_Location_Type_Cluster 1 0\n", + "Supermarket_Location_Type_Cluster 2 0\n", + "Supermarket_Location_Type_Cluster 3 0\n", + "Supermarket_Type_Grocery Store 0\n", + "Supermarket_Type_Supermarket Type1 0\n", + "Supermarket_Type_Supermarket Type2 0\n", + "Supermarket_Type_Supermarket Type3 0\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check if filling is successful\n", + "dummified_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "outputs" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "literals {\n", + " key: \"dataset\"\n", + " value {\n", + " scalar {\n", + " primitive {\n", + " string_value: 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+ " }\n", + " }\n", + " }\n", + "}\n", + "literals {\n", + " key: \"dummified_data\"\n", + " value {\n", + " scalar {\n", + " schema {\n", + " uri: \"/tmp/flyte/20210729_181120/mock_remote/33fd2d71e9f04311d48c005bf32a5988/\"\n", + " type {\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from flytekitplugins.papermill import record_outputs\n", + "record_outputs(dummified_data=dummified_data, dataset=dummified_data.to_json())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3.9.9 64-bit ('flytesnacks')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + }, + "vscode": { + "interpreter": { + "hash": "93d1c4f33f306e18e1c08a771c972fe86afbedaedb2338666e30a98a5179caac" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_2.ipynb b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_2.ipynb new file mode 100644 index 000000000..47458fc1f --- /dev/null +++ b/docs/examples/exploratory_data_analysis/exploratory_data_analysis/supermarket_regression_2.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supermarket Regression 2 Notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "dataset = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "dataset = pd.DataFrame.from_dict(json.loads(dataset))\n", + "y_target = dataset['Product_Supermarket_Sales']\n", + "dataset.drop(['Product_Supermarket_Sales'], axis=1, inplace=True)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(dataset, y_target, test_size = 0.3)\n", + "\n", + "print(\"Training data is\", X_train.shape)\n", + "print(\"Training target is\", y_train.shape)\n", + "print(\"test data is\", X_test.shape)\n", + "print(\"test target is\", y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import RobustScaler, StandardScaler\n", + "scaler = RobustScaler()\n", + "\n", + "scaler.fit(X_train)\n", + "\n", + "X_train = scaler.transform(X_train) \n", + "X_test = scaler.transform(X_test)\n", + "\n", + "X_train[:5, :5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.model_selection import KFold, cross_val_score\n", + "\n", + "\n", + "def cross_validate(model, nfolds, feats, targets):\n", + " score = -1 * (cross_val_score(model, feats, targets, cv=nfolds, scoring='neg_mean_absolute_error'))\n", + " return np.mean(score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "n_estimators=150\n", + "max_depth=3\n", + "max_features='sqrt'\n", + "min_samples_split=4\n", + "random_state=2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "gb_model = GradientBoostingRegressor(n_estimators=n_estimators, max_depth=max_depth, max_features=max_features, min_samples_split=min_samples_split, random_state=random_state)\n", + "\n", + "mae_score = cross_validate(gb_model, 10, X_train, y_train)\n", + "print(\"MAE Score: \", mae_score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "outputs" + ] + }, + "outputs": [], + "source": [ + "from flytekitplugins.papermill import record_outputs\n", + "record_outputs(mae_score=float(mae_score))" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3.9.9 64-bit ('flytesnacks')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + }, + "vscode": { + "interpreter": { + "hash": "93d1c4f33f306e18e1c08a771c972fe86afbedaedb2338666e30a98a5179caac" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/exploratory_data_analysis/requirements.in b/docs/examples/exploratory_data_analysis/requirements.in new file mode 100644 index 000000000..352ef880e --- /dev/null +++ b/docs/examples/exploratory_data_analysis/requirements.in @@ -0,0 +1,9 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-papermill +matplotlib +seaborn +scikit-learn +pysocks diff --git a/docs/examples/exploratory_data_analysis/requirements.txt b/docs/examples/exploratory_data_analysis/requirements.txt new file mode 100644 index 000000000..6695eaad8 --- /dev/null +++ b/docs/examples/exploratory_data_analysis/requirements.txt @@ -0,0 +1,360 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +ansiwrap==0.8.4 + # via papermill +arrow==1.2.2 + # via jinja2-time +asttokens==2.0.5 + # via stack-data +attrs==21.4.0 + # via jsonschema +backcall==0.2.0 + # via ipython +beautifulsoup4==4.11.1 + # via nbconvert +binaryornot==0.4.4 + # via cookiecutter +bleach==5.0.0 + # via nbconvert +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit + # papermill +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==37.0.3 + # via secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +debugpy==1.6.0 + # via ipykernel +decorator==5.1.1 + # via + # ipython + # retry +defusedxml==0.7.1 + # via nbconvert +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +entrypoints==0.4 + # via + # jupyter-client + # nbconvert + # papermill +executing==0.8.3 + # via stack-data +fastjsonschema==2.15.3 + # via nbformat +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-papermill +flytekitplugins-papermill==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +ipykernel==6.13.0 + # via flytekitplugins-papermill +ipython==8.2.0 + # via ipykernel +jedi==0.18.1 + # via ipython +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time + # nbconvert +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.1.0 + # via scikit-learn +jsonschema==4.4.0 + # via nbformat +jupyter-client==7.2.2 + # via + # ipykernel + # nbclient +jupyter-core==4.9.2 + # via + # jupyter-client + # nbconvert + # nbformat +jupyterlab-pygments==0.2.1 + # via nbconvert +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via + # jinja2 + # nbconvert +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via + # -r ../../../common/requirements-common.in + # -r requirements.in + # seaborn +matplotlib-inline==0.1.3 + # via + # ipykernel + # ipython +mistune==0.8.4 + # via nbconvert +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +nbclient==0.6.0 + # via + # nbconvert + # papermill +nbconvert==6.5.0 + # via flytekitplugins-papermill +nbformat==5.3.0 + # via + # nbclient + # nbconvert + # papermill +nest-asyncio==1.5.5 + # via + # ipykernel + # jupyter-client + # nbclient +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow + # scikit-learn + # scipy + # seaborn +packaging==21.3 + # via + # ipykernel + # marshmallow + # matplotlib + # nbconvert +pandas==1.4.2 + # via + # flytekit + # seaborn +pandocfilters==1.5.0 + # via nbconvert +papermill==2.3.4 + # via flytekitplugins-papermill +parso==0.8.3 + # via jedi +pexpect==4.8.0 + # via ipython +pickleshare==0.7.5 + # via ipython +pillow==9.1.0 + # via matplotlib +prompt-toolkit==3.0.29 + # via ipython +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.0 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pygments==2.11.2 + # via + # ipython + # nbconvert +pyparsing==3.0.8 + # via + # matplotlib + # packaging +pyrsistent==0.18.1 + # via jsonschema +pysocks==1.7.1 + # via -r requirements.in +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # jupyter-client + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # papermill +pyzmq==22.3.0 + # via jupyter-client +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # papermill + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +scikit-learn==1.0.2 + # via -r requirements.in +scipy==1.8.0 + # via + # scikit-learn + # seaborn +seaborn==0.11.2 + # via -r requirements.in +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # asttokens + # bleach + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +soupsieve==2.3.2 + # via beautifulsoup4 +stack-data==0.2.0 + # via ipython +statsd==3.3.0 + # via flytekit +tenacity==8.0.1 + # via papermill +text-unidecode==1.3 + # via python-slugify +textwrap3==0.9.2 + # via ansiwrap +threadpoolctl==3.1.0 + # via scikit-learn +tinycss2==1.1.1 + # via nbconvert +tornado==6.1 + # via + # ipykernel + # jupyter-client +tqdm==4.64.0 + # via papermill +traitlets==5.1.1 + # via + # ipykernel + # ipython + # jupyter-client + # jupyter-core + # matplotlib-inline + # nbclient + # nbconvert + # nbformat +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wcwidth==0.2.5 + # via prompt-toolkit +webencodings==0.5.1 + # via + # bleach + # tinycss2 +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/extending/README.md b/docs/examples/extending/README.md new file mode 100644 index 000000000..8a5bdc82f --- /dev/null +++ b/docs/examples/extending/README.md @@ -0,0 +1,214 @@ +(plugins_extend)= + +# Extending Flyte + +The core of Flyte is a container execution engine, where you can write one or more tasks and compose them together to +form a data dependency DAG, called a `workflow`. If your work involves writing simple Python or Java tasks that can +either perform operations on their own or call out to {ref}`Supported external services `, +then there's _no need to extend Flyte_. + +## Define a Custom Type + +Flyte, just like a programming language, has a core type-system, which can be extended by adding user-defined data types. +For example, Flyte supports adding support for a dataframe type from a new library, a custom user data structure, or a +grouping of images in a specific encoding. + +Flytekit natively supports structured data like {py:func}`~dataclasses.dataclass` using JSON as the +representation format (see {ref}`Using Custom Python Objects `). + +Flytekit allows users to extend Flyte's type system and implement types in Python that are not representable as JSON documents. The user has to implement a {py:class}`~flytekit.extend.TypeTransformer` +class to enable the translation of type from user type to Flyte-understood type. + +As an example, instead of using {py:class}`pandas.DataFrame` directly, you may want to use +[Pandera](https://pandera.readthedocs.io/en/stable/) to perform validation of an input or output dataframe +(see {ref}`Basic Schema Example `). + +To extend the type system, refer to {ref}`advanced_custom_types`. + +## Add a New Task Plugin + +Often you want to interact with services like: + +- Databases (e.g., Postgres, MySQL, etc.) +- DataWarehouses (e.g., Snowflake, BigQuery, Redshift etc.) +- Computation (e.g., AWS EMR, Databricks etc.) + +You might want this interaction to be available as a template for the open-source community or in your organization. This +can be done by creating a task plugin, which makes it possible to reuse the task's underlying functionality within Flyte +workflows. + +If you want users to write code simply using the {py:func}`~flytekit.task` decorator, but want to provide the +capability of running the function as a spark job or a sagemaker training job, then you can extend Flyte's task system. + +```{code-block} python +@task(task_config=MyContainerExecutionTask( + plugin_specific_config_a=..., + plugin_specific_config_b=..., + ... +)) +def foo(...) -> ...: + ... +``` + +Alternatively, you can provide an interface like this: + +```{code-block} python +query_task = SnowflakeTask( + query="Select * from x where x.time < {{.inputs.time}}", + inputs=kwtypes(time=datetime), + output_schema_type=pandas.DataFrame, +) + +@workflow +def my_wf(t: datetime) -> ...: + df = query_task(time=t) + return process(df=df) +``` + +There are two options when writing a new task plugin: you can write a task plugin as an extension in Flytekit or you can go deeper and write a plugin in the Flyte backend. + +## Flytekit-Only Task Plugin + +Flytekit is designed to be extremely extensible. You can add new task-types that are useful only for your use-case. +Flyte does come with the capability of extending the backend, but that is only required if you want the capability to be +extended to all users of Flyte, or there is a cost/visibility benefit of doing so. + +Writing your own Flytekit plugin is simple and is typically where you want to start when enabling custom task functionality. + +```{list-table} +:widths: 50 50 +:header-rows: 1 + +* - Pros + - Cons +* - Simple to write — implement in Python. Flyte will treat it like a container execution and blindly pass + control to the plugin. + - Limited ways of providing additional visibility in progress, external links, etc. +* - Simple to publish: `flytekitplugins` can be published as independent libraries and they follow a simple API. + - Has to be implemented in every language as these are SDK-side plugins only. +* - Simple to perform testing: test locally in flytekit. + - In case of side-effects, it could lead to resource leaks. For example, if the plugin runs a BigQuery job, + it is possible that the plugin may crash after running the job and Flyte cannot guarantee that the BigQuery job + will be successfully terminated. +* - + - Potentially expensive: in cases where the plugin runs a remote job, running a new pod for every task execution + causes severe strain on Kubernetes and the task itself uses almost no CPUs. Also because of its stateful nature, + using spot-instances is not trivial. +* - + - A bug fix to the runtime needs a new library version of the plugin. +* - + - Not trivial to implement resource controls, like throttling, resource pooling, etc. +``` + +### User Container vs. Pre-built Container Task Plugin + +A Flytekit-only task plugin can be a {ref}`user container ` or {ref}`pre-built container ` task plugin. + +```{list-table} +:widths: 10 50 50 +:header-rows: 1 + +* - + - User Container + - Pre-built Container +* - Serialization + - At serialization time, a Docker container image is required. The assumption is that this Docker image has the task code. + - The Docker container image is hardcoded at serialization time into the task definition by the author of that task plugin. +* - Serialization + - The serialized task contains instructions to the container on how to reconstitute the task. + - Serialized task should contain all the information needed to run that task instance (but not necessarily to reconstitute it). +* - Run-time + - When Flyte runs the task, the container is launched, and the user-given instructions recreate a Python object representing the task. + - When Flyte runs the task, the container is launched. The container should have an executor built into it that knows how to execute the task. +* - Run-time + - The task object that gets serialized at compile-time is recreated using the user's code at run time. + - The task object that gets serialized at compile-time does not exist at run time. +* - Run-time + - At platform-run-time, the user-decorated function is executed. + - At platform-run-time, there is no user function, and the executor is responsible for producing outputs, given the inputs to the task. +``` + +### Backend Plugin + +{ref}`Writing a Backend plugin ` makes it possible for users to write extensions for FlytePropeller - Flyte's scheduling engine. This enables complete control of the visualization and availability +of the plugin. + +```{list-table} +:widths: 50 50 +:header-rows: 1 + +* - Pros + - Cons +* - Service oriented way of deploying new plugins - strong contracts. Maintainers can deploy new versions of the backend plugin, fix bugs, without needing the users to upgrade libraries, etc. + - Need to be implemented in Golang. +* - Drastically cheaper and more efficient to execute. FlytePropeller is written in Golang and uses an event loop model. Each process of FlytePropeller can execute thousands of tasks concurrently. + - Needs a FlytePropeller build (*currently*). +* - Flyte guarantees resource cleanup. + - Need to implement contract in a spec language like protobuf, OpenAPI, etc. +* - Flyteconsole plugins (capability coming soon!) can be added to customize visualization and progress tracking of the execution. + - Development cycle can be much slower than flytekit-only plugins. +* - Resource controls and backpressure management is available. + - +* - Implement once, use in any SDK or language! + - +``` + +#### Flyte Agent Service + +_New in Flyte 1.7.0_ + +{ref}`Flyte Agent Service ` allows you to write backend +plugins in Python. + +### Summary + +```{mermaid} + +flowchart LR + U{Use Case} + F([Python Flytekit Plugin]) + B([Golang
Backend Plugin]) + + subgraph WFTP[Writing Flytekit Task Plugins] + UCP([User Container Plugin]) + PCP([Pre-built Container Plugin]) + end + + subgraph WBE[Writing Backend Extensions] + K8S([K8s Plugin]) + WP([WebAPI Plugin]) + CP([Complex Plugin]) + end + + subgraph WCFT[Writing Custom Flyte Types] + T([Flytekit
Type Transformer]) + end + + U -- Light-weight
Extensions --> F + U -- Performant
Multi-language
Extensions --> B + U -- Specialized
Domain-specific Types --> T + F -- Require
user-defined
container --> UCP + F -- Provide
prebuilt
container --> PCP + B --> K8S + B --> WP + B --> CP + + style WCFT fill:#eee,stroke:#aaa + style WFTP fill:#eee,stroke:#aaa + style WBE fill:#eee,stroke:#aaa + style U fill:#fff2b2,stroke:#333 + style B fill:#EAD1DC,stroke:#333 + style K8S fill:#EAD1DC,stroke:#333 + style WP fill:#EAD1DC,stroke:#333 + style CP fill:#EAD1DC,stroke:#333 +``` + +Use the flow-chart above to point you to one of these examples: + +```{auto-examples-toc} +custom_types +prebuilt_container +user_container +backend_plugins +container_interface +``` diff --git a/docs/examples/extending/extending/__init__.py b/docs/examples/extending/extending/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/extending/extending/backend_plugins.md b/docs/examples/extending/extending/backend_plugins.md new file mode 100644 index 000000000..b2ac1aec0 --- /dev/null +++ b/docs/examples/extending/extending/backend_plugins.md @@ -0,0 +1,74 @@ +%% [markdown] +(extend-plugin-flyte-backend)= + +# Writing Backend Extensions + +```{eval-rst} +.. tags:: Extensibility, Contribute, Intermediate +``` + +This guide will take you through the why and how of writing a backend plugin for +Flyte. + +To recap, here are a few examples of why you would want to implement a backend plugin: + +1. We want to add a new capability to the Flyte Platform, for example we might want to: + - Talk to a new service like AWS Sagemaker, Snowflake, Redshift, Athena, BigQuery, etc. + - Orchestrate a set of containers in a new way like Spark, Flink, Distributed + training on Kubernetes (usually using a Kubernetes operator). + - Use a new container orchestration engine like AWS Batch/ECS, Hashicorp' Nomad + - Use a completely new runtime like AWS Lambda, KNative, etc. +3. You want to retain the capability to update the plugin implementation and roll + out new changes and fixes without affecting the users code or requiring them to updat + versions of their plugins. +4. You want the same plugin to be accessible across multiple language SDK's. + +```{note} +Talking to a new service can be done using flytekit extensions and usually is the better way to get started. But, once matured, most of these extensions are better to be migrated to the backend. For the rest of the cases, it is possible to extend flytekit to achieve these scenarios, but this is less desirable, because of the associated overhead of first launching a container that launches these jobs downstream. +``` + +## Basics + +In this section we'll go through the components of a backend plugin using the {ref}`K8s Spark plugin` as a reference. A Flyte backend extension consists of 3 parts: interface +specification, `flytekit` plugin implementation, and `flytepropeller` plugin implementation. + +### Interface specification + +Usually Flyte extensions need information that is not covered by a {std:ref}`Flyte TaskTemplate `. The TaskTemplate consists of a +the interface, task_type identifier, some metadata and other fields. + +```{note} +An important field to note here is {std:ref}`custom `. The custom field is essentially an unstructured JSON. This makes it possible to extend a task-template beyond the default supported targets {std:ref}`container `. + +The motivation of the `custom`` field is to marshal a JSON structure that specifies information beyond what a regular TaskTemplate can capture. The actual structure of the JSON is known only to the implemented backend-plugin and the SDK components. The core Flyte platform, does not understand of look into the specifics of this structure. +``` + +It is highly recommended to use an interface definition language like Protobuf, OpenAPISpec etc to declare specify the structure of the JSON. From here, on we refer to this as the **Plugin Specification**. + +```{note} +For Spark we decided to use Protobuf to specify the plugin as can be seen [here](https://github.com/flyteorg/flyteidl/blob/master/protos/flyteidl/plugins/spark.proto). Note it isn't necessary to have the Plugin structure specified in `flyteidl`, but we do it for simplicity, ease of maintenance alongside the core platform, and convenience leveraging existing tooling to generate code for protobuf. +``` + +### Flytekit Plugin implementation + +Now that you have a specification, we have to implement a method to generate this new TaskTemplate, with the special custom field. Also, this is where the UX design comes into play. You want to write the best possible interface in the SDK that users are delighted to use. The end goal is to create the TaskTemplate with the Custom field populated with the actual JSON structure. + +We will currently refer to the Python `flytekit` SDK as an example for extending and +implementing the SDK. + +The SDK task should be implemented as an extension of {py:class}`flytekit.core.base_task.PythonTask`, or more commonly {py:class}`flytekit.PythonFunctionTask`. +In the case of Spark, we extend the {py:class}`flytekit.PythonFunctionTask`, as shown [here](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-spark/flytekitplugins/spark/task.py#L77-L123). + +The `SparkTask` is implemented as a regular flytekit plugin, with one exception: the `custom` field is now actually the `SparkJob` protocol buffer. When serializing a task, `flytekit` base classes will automatically invoke the [`get_custom` method](https://github.com/flyteorg/flytekit/blob/c02075d472b5587d199630bcfc7f9937673c6a0e/flytekit/core/base_task.py#L255). + +### FlytePropeller backend Plugin + +The backend plugin is where the actual logic of the execution is implemented. The backend plugin uses the **Flyte PluginMachinery** interface to implement a plugin which can be one of the following supported types: + +1. [Kubernetes operator Plugin](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/k8s#Plugin): The demo in the video below shows two examples of K8s backend plugins: flytekit `Athena` & `Spark`, and Flyte K8s `Pod` & `Spark`. + + ```{youtube} oK2RGQuP94k + ``` + +2. **A Web API plugin:** [Async](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/webapi#AsyncPlugin) or [Sync](https://pkg.go.dev/github.com/lyft/flyteplugins@v0.5.26/go/tasks/pluginmachinery/webapi#SyncPlugin). +3. [Core Plugin](https://pkg.go.dev/github.com/lyft/flyteplugins/go/tasks/pluginmachinery/core#Plugin): if none of the above fits diff --git a/docs/examples/extending/extending/container_interface.py b/docs/examples/extending/extending/container_interface.py new file mode 100644 index 000000000..dd9dc8103 --- /dev/null +++ b/docs/examples/extending/extending/container_interface.py @@ -0,0 +1,66 @@ +# %% [markdown] +# (core-extend-flyte-container-interface)= +# +# # Container Interface +# +# ```{eval-rst} +# .. tags:: Extensibility, Contribute, Intermediate +# ``` +# +# Flyte typically interacts with containers in the course of its task execution (since most tasks are container +# tasks). This is what that process looks like: +# +# 1. At compilation time for a container task, the arguments to that container (and the container image itself) are set. +# +# 1. This is done by flytekit for instance for your run of the mill `@task`. This step is **crucial** - the task needs to specify an image available in the registry configured in the flyte installation. +# +# 2. At runtime, Flyte will execute your task via a plugin. The default container plugin will do the following: +# +# 1. Set a series of environment variables. +# 2. Before running the container, search/replace values in the container arguments. The command templating section below details how this happens. +# +# :::{note} +# This templating process *should* be done by **all** plugins, even plugins that don't run a container but need +# some information from the execution side. For example, a query task that submits a query to an engine that +# writes the output to the raw output location. Or a query that uses the unique retry key as a temp table name, etc. +# ::: +# +# ## Command Templating +# +# The templating of container arguments at run-time is one of the more advanced constructs of Flyte, but one that +# authors of new task types should be aware of. For example, when looking at the hello world task in the UI, +# if you click the Task tab, you'd see JSON that contains something like the following: +# +# ```json +# "container": { +# "command": [], +# "args": [ +# "pyflyte-execute", +# "--inputs", +# "{{.input}}", +# "--output-prefix", +# "{{.outputPrefix}}", +# "--raw-output-data-prefix", +# "{{.rawOutputDataPrefix}}", +# "--resolver", +# "flytekit.core.python_auto_container.default_task_resolver", +# "--", +# "task-module", +# "core.basic.hello_world", +# "task-name", +# "say_hello" +# ], +# ``` +# +# The following table explains what each of the `{{}}` items mean, along with some others. +# +# | Template | Example | Description | +# | ------------------------ | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +# | {{.Input}} | `s3://my-bucket/inputs.pb` | Pb file containing a LiteralMap containing the inputs | +# | {{.InputPrefix}} | `s3://my-bucket` | Just the bucket where the inputs.pb file can be found | +# | {{.Inputs.\}} | `"hello world"` | For primitive inputs, the task can request that Flyte unpack the actual literal value, saving the task from having to download the file. Note that for Blob, Schema and StructuredDataset types, the uri where the data is stored will be filled in as the value. | +# | {{.OutputPrefix}} | `s3://my-bucket/abc/data` | Location where the task should write a LiteralMap of output values in a file called `outputs.pb` | +# | {{.RawOutputDataPrefix}} | `s3://your-data/` | Bucket where off-loaded data types (schemas, files, structureddatasets, etc.) are written. | +# | {{.PerRetryUniqueKey}} | (random characters) | This is a random string that allows the task to differentiate between different executions of a task. Values will be unique per retry as well. | +# | {{.TaskTemplatePath}} | `s3://my-bucket/task.pb` | For tasks that need the full task definition, use this template to access the full TaskTemplate IDL message. To ensure performance, propeller will not upload this file if this template was not requested by the task. | +# diff --git a/docs/examples/extending/extending/custom_types.py b/docs/examples/extending/extending/custom_types.py new file mode 100644 index 000000000..39fc88f0e --- /dev/null +++ b/docs/examples/extending/extending/custom_types.py @@ -0,0 +1,167 @@ +# %% [markdown] +# (advanced_custom_types)= +# +# # Writing Custom Flyte Types +# +# ```{eval-rst} +# .. tags:: Extensibility, Contribute, Intermediate +# ``` +# +# Flyte is a strongly-typed framework for authoring tasks and workflows. But there are situations when the existing +# types do not directly work. This is true with any programming language! +# +# Similar to a programming language enabling higher-level concepts to describe user-specific objects such as classes in Python/Java/C++, struct in C/Golang, etc., +# Flytekit allows modeling user classes. The idea is to make an interface that is more productive for the +# use case, while writing a transformer that converts the user-defined type into one of the generic constructs in Flyte's type system. +# +# This example will try to model an example user-defined dataset and show how it can be seamlessly integrated with Flytekit's type engine. +# +# The example is demonstrated in the video below: +# +# ```{eval-rst} +# .. youtube:: 1xExpRzz8Tw +# +# +# ``` + +# %% [markdown] +# First, we import the dependencies. +# %% +import os +import tempfile +import typing +from typing import Type + +from flytekit import Blob, BlobMetadata, BlobType, FlyteContext, Literal, LiteralType, Scalar, task, workflow +from flytekit.extend import TypeEngine, TypeTransformer + + +# %% [markdown] +# :::{note} +# `FlyteContext` is used to access a random local directory. +# ::: +# +# Defined type here represents a list of files on the disk. We will refer to it as `MyDataset`. +# %% +class MyDataset(object): + """ + ``MyDataset`` is a collection of files. In Flyte, this maps to a multi-part blob or directory. + """ + + def __init__(self, base_dir: str = None): + if base_dir is None: + self._tmp_dir = tempfile.TemporaryDirectory() + self._base_dir = self._tmp_dir.name + self._files = [] + else: + self._base_dir = base_dir + files = os.listdir(base_dir) + self._files = [os.path.join(base_dir, f) for f in files] + + @property + def base_dir(self) -> str: + return self._base_dir + + @property + def files(self) -> typing.List[str]: + return self._files + + def new_file(self, name: str) -> str: + new_file = os.path.join(self._base_dir, name) + self._files.append(new_file) + return new_file + + +# %% [markdown] +# `MyDataset` represents a set of files locally. However, when a workflow consists of multiple steps, we want the data to +# flow between different steps. To achieve this, it is necessary to explain how the data will be transformed to +# Flyte's remote references. To do this, we create a new instance of +# {py:class}`~flytekit:flytekit.extend.TypeTransformer`, for the type `MyDataset` as follows: +# +# :::{note} +# The `TypeTransformer` is a Generic abstract base class. The `Generic` type argument refers to the actual object +# that we want to work with. In this case, it is the `MyDataset` object. +# ::: +# %% +class MyDatasetTransformer(TypeTransformer[MyDataset]): + _TYPE_INFO = BlobType(format="binary", dimensionality=BlobType.BlobDimensionality.MULTIPART) + + def __init__(self): + super(MyDatasetTransformer, self).__init__(name="mydataset-transform", t=MyDataset) + + def get_literal_type(self, t: Type[MyDataset]) -> LiteralType: + """ + This is useful to tell the Flytekit type system that ``MyDataset`` actually refers to what corresponding type. + In this example, we say its of format binary (do not try to introspect) and there is more than one file in it. + """ + return LiteralType(blob=self._TYPE_INFO) + + def to_literal( + self, + ctx: FlyteContext, + python_val: MyDataset, + python_type: Type[MyDataset], + expected: LiteralType, + ) -> Literal: + """ + This method is used to convert from the given python type object ``MyDataset`` to the Literal representation. + """ + # Step 1: Upload all the data into a remote place recommended by Flyte + remote_dir = ctx.file_access.get_random_remote_directory() + ctx.file_access.upload_directory(python_val.base_dir, remote_dir) + # Step 2: Return a pointer to this remote_dir in the form of a Literal + return Literal(scalar=Scalar(blob=Blob(uri=remote_dir, metadata=BlobMetadata(type=self._TYPE_INFO)))) + + def to_python_value(self, ctx: FlyteContext, lv: Literal, expected_python_type: Type[MyDataset]) -> MyDataset: + """ + In this method, we want to be able to re-hydrate the custom object from Flyte Literal value. + """ + # Step 1: Download remote data locally + local_dir = ctx.file_access.get_random_local_directory() + ctx.file_access.download_directory(lv.scalar.blob.uri, local_dir) + # Step 2: Create the ``MyDataset`` object + return MyDataset(base_dir=local_dir) + + +# %% [markdown] +# Before we can use MyDataset in our tasks, we need to let Flytekit know that `MyDataset` should be considered as a valid type. +# This is done using {py:class}`~flytekit:flytekit.extend.TypeEngine`'s `register` method. +# %% +TypeEngine.register(MyDatasetTransformer()) + + +# %% [markdown] +# The new type should be ready to use! Let us write an example generator and consumer for this new datatype. +# %% +@task +def generate() -> MyDataset: + d = MyDataset() + for i in range(3): + fp = d.new_file(f"x{i}") + with open(fp, "w") as f: + f.write(f"Contents of file{i}") + + return d + + +@task +def consume(d: MyDataset) -> str: + s = "" + for f in d.files: + with open(f) as fp: + s += fp.read() + s += "\n" + return s + + +@workflow +def wf() -> str: + return consume(d=generate()) + + +# %% [markdown] +# This workflow can be executed and tested locally. Flytekit will exercise the entire path even if you run it locally. +# +# %% +if __name__ == "__main__": + print(wf()) diff --git a/docs/examples/extending/extending/prebuilt_container.py b/docs/examples/extending/extending/prebuilt_container.py new file mode 100644 index 000000000..b9b4a3902 --- /dev/null +++ b/docs/examples/extending/extending/prebuilt_container.py @@ -0,0 +1,90 @@ +# %% [markdown] +# (prebuilt_container)= +# +# # Pre-built Container Task Plugin +# +# ```{eval-rst} +# .. tags:: Extensibility, Contribute, Intermediate +# ``` +# +# A pre-built container task plugin runs a pre-built container. The following are the advantages of using a pre-built container in comparison to a user-defined container: +# +# - Shifts the burden of writing Dockerfile from the user who uses the task in workflows to the author of the task type. +# - Allows the author to optimize the image that the task runs on. +# - Makes it possible to (largely) extend the Flyte task execution behavior without using the backend GOlang plugin. +# The caveat is that these tasks can't access the K8s cluster, so you'll still need a backend plugin if you want a custom task type that generates CRD. +# +# ## Usage +# +# Take a look at the [example PR](https://github.com/flyteorg/flytekit/pull/470), where we switched the built-in SQLite3 task from the old (user-container) to the new style of writing tasks. +# +# There aren't many changes from the user's standpoint: +# \- Install whichever Python library has the task type definition (in the case of SQLite3, it's bundled in Flytekit, but this isn't always the case (for example, [SQLAlchemy](https://github.com/flyteorg/flytekit/tree/master/plugins/flytekit-sqlalchemy))). +# \- Import and instantiate the task as you would for any other type of non-function-based task. +# +# ## How to Write a Task +# +# Writing a pre-built container task consists of three steps: +# +# 1. Defining a Task class +# 2. Defining an Executor class +# 3. Creating a Dockerfile that is executed when any user runs your task. It'll most likely include Flytekit, Python, and your task extension code. +# +# To follow along, use the [PR (mentioned above)](https://github.com/flyteorg/flytekit/pull/470) where we migrate the SQLite3 task. +# +# ## Python Library +# +# ### Defining a task +# +# New tasks of this type must be created as a subclass of the `PythonCustomizedContainerTask` class. +# +# Specifically, you need to customize the following three arguments which would be sent to the parent class constructor: +# +# - `container_image`: This is the container image that will run on a Flyte platform when the user invokes the job. +# - `executor_type`: This should be the Python class that inherits the `ShimTaskExecutor`. +# - `task_type`: All types have a task type. Flyte engine uses this string to identify which plugin to use when running a task. +# +# The container plugin will be used for everything that doesn't have an explicit match (which is correct in this case). +# So you may call it whatever you want, just not something that's already been claimed (like "spark"). +# +# Referring to the SQLite3 example, +# +# ``` +# container_image="ghcr.io/flyteorg/flytekit:py38-v0.19.0b7", +# executor_type=SQLite3TaskExecutor, +# task_type="sqlite", +# ``` +# +# Note that the container is special in this case since we utilize the Flytekit image. +# +# Furthermore, you need to override the `get_custom` function to include all the information the executor will need to run. +# +# Keep in mind that the task's execution behavior is entirely defined by the task's serialized form (that is, the serialized `TaskTemplate`). +# This function stores and inserts the data into the template's [custom field](https://github.com/flyteorg/flyteidl/blob/7302971c064b6061a148f2bee79f673bc8cf30ee/protos/flyteidl/core/tasks.proto#L114). +# However, keep the task template's overall size to a minimum. +# +# ### Executor +# +# You must subclass and override the `execute_from_model` function for the `ShimTaskExecutor` abstract class. +# This function will be invoked in both local workflow execution and platform-run-time execution, and will include all of the business logic of your task. +# +# The signature of this execute function differs from the `execute` functions of most other tasks since the `TaskTemplate` determines all the business logic, including how the task is run. +# +# ### Image +# +# This is the custom image that you specified in the subclass `PythonCustomizedContainerTask`. Out of the box, when Flyte runs the container, these tasks will run a command that looks like this +# +# ``` +# pyflyte-execute --inputs s3://inputs.pb --output-prefix s3://outputs --raw-output-data-prefix s3://user-data --resolver flytekit.core.python_customized_container_task.default_task_template_resolver -- {{.taskTemplatePath}} path.to.your.executor.subclass +# ``` +# +# This means that your [Docker image](https://github.com/flyteorg/flytekit/blob/master/Dockerfile) will need Python and Flytekit installed. +# The container's Python interpreter should be able to find your custom executor class at the import path `path.to.your.executor.subclass`. +# +# ______________________________________________________________________ +# +# The key takeaways of a pre-built container task plugin are: +# +# - The task object serialized at compile time does not exist at run time. +# - There is no user function at platform run time, and the executor is responsible for producing outputs based on the task's inputs. +# diff --git a/docs/examples/extending/extending/user_container.py b/docs/examples/extending/extending/user_container.py new file mode 100644 index 000000000..bd115fde7 --- /dev/null +++ b/docs/examples/extending/extending/user_container.py @@ -0,0 +1,142 @@ +# %% [markdown] +# (user_container)= +# +# # User Container Task Plugin +# +# ```{eval-rst} +# .. tags:: Extensibility, Contribute, Intermediate +# ``` +# +# A user container task plugin runs a user-defined container that has the user code. +# +# This tutorial will walk you through writing your own sensor-style plugin that allows users to wait for a file to land +# in the object store. Remember that if you follow the flyte/flytekit constructs, you will automatically make your plugin portable +# across all cloud platforms that Flyte supports. +# +# ## Sensor Plugin +# +# A sensor plugin waits for some event to happen before marking the task as success. You need not worry about the +# timeout as that will be handled by the flyte engine itself when running in production. +# +# ### Plugin API +# +# ```python +# sensor = WaitForObjectStoreFile(metadata=metadata(timeout="1H", retries=10)) +# +# @workflow +# def wait_and_run(path: str) -> int: +# # To demonstrate how to create outputs, we will also +# # return the output from the sensor. The output will be the +# # same as the path +# path = sensor(path=path) +# return do_next(path=path) +# ``` +# %% +import typing +from datetime import timedelta +from time import sleep + +from flytekit import TaskMetadata, task, workflow +from flytekit.extend import Interface, PythonTask, context_manager + + +# %% [markdown] +# ### Plugin Structure +# +# As illustrated above, to achieve this structure we need to create a class named `WaitForObjectStoreFile`, which +# derives from {py:class}`flytekit.PythonFunctionTask` as follows. +# %% +class WaitForObjectStoreFile(PythonTask): + """ + Add documentation here for your plugin. + This plugin creates an object store file sensor that waits and exits only when the file exists. + """ + + _VAR_NAME: str = "path" + + def __init__( + self, + name: str, + poll_interval: timedelta = timedelta(seconds=10), + **kwargs, + ): + super(WaitForObjectStoreFile, self).__init__( + task_type="object-store-sensor", + name=name, + task_config=None, + interface=Interface(inputs={self._VAR_NAME: str}, outputs={self._VAR_NAME: str}), + **kwargs, + ) + self._poll_interval = poll_interval + + def execute(self, **kwargs) -> typing.Any: + # No need to check for existence, as that is guaranteed. + path = kwargs[self._VAR_NAME] + ctx = context_manager.FlyteContext.current_context() + user_context = ctx.user_space_params + while True: + user_context.logging.info(f"Sensing file in path {path}...") + if ctx.file_access.exists(path): + user_context.logging.info(f"file in path {path} exists!") + return path + user_context.logging.warning(f"file in path {path} does not exists!") + sleep(self._poll_interval.seconds) + + +# %% [markdown] +# #### Config Objects +# +# Flytekit routes to the right plugin based on the type of `task_config` class if using the `@task` decorator. +# Config is very useful for cases when you want to customize the behavior of the plugin or pass the config information +# to the backend plugin; however, in this case there's no real configuration. The config object can be any class that your +# plugin understands. +# +# :::{note} +# Observe that the base class is Generic; it is parameterized with the desired config class. +# ::: +# +# :::{note} +# To create a task decorator-based plugin, `task_config` is required. +# In this example, we are creating a named class plugin, and hence, this construct does not need a plugin. +# ::: +# +# Refer to the [spark plugin](https://github.com/flyteorg/flytekit/tree/master/plugins/flytekit-spark) for an example of a config object. + +# %% [markdown] +# ### Actual Usage + +# %% +sensor = WaitForObjectStoreFile( + name="my-objectstore-sensor", + metadata=TaskMetadata(retries=10, timeout=timedelta(minutes=20)), + poll_interval=timedelta(seconds=1), +) + + +@task +def print_file(path: str) -> str: + print(path) + return path + + +@workflow +def my_workflow(path: str) -> str: + return print_file(path=sensor(path=path)) + + +# %% [markdown] +# And of course, you can run the workflow locally using your own new shiny plugin! +# %% +if __name__ == "__main__": + f = "/tmp/some-file" + with open(f, "w") as w: + w.write("Hello World!") + + print(my_workflow(path=f)) + +# %% [markdown] +# The key takeaways of a user container task plugin are: +# +# - The task object that gets serialized at compile-time is recreated using the user's code at run time. +# - At platform-run-time, the user-decorated function is executed. +# diff --git a/docs/examples/feast_integration/Dockerfile b/docs/examples/feast_integration/Dockerfile new file mode 100644 index 000000000..e2472d4b8 --- /dev/null +++ b/docs/examples/feast_integration/Dockerfile @@ -0,0 +1,41 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH "$PYTHONPATH:/root/feast_integration" + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy the actual co +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag +ENV DEMO 1 + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv diff --git a/docs/examples/feast_integration/README.md b/docs/examples/feast_integration/README.md new file mode 100644 index 000000000..017b85047 --- /dev/null +++ b/docs/examples/feast_integration/README.md @@ -0,0 +1,76 @@ +# Feast Integration + +```{eval-rst} +.. tags:: Data, MachineLearning, Advanced +``` + +```{image} https://img.shields.io/badge/Blog-Feast%20Integration-blue?style=for-the-badge +:target: https://blog.flyte.org/bring-ml-close-to-data-using-feast-and-flyte +:alt: Feast Integration Blog Post +``` + +[Feast](https://feast.dev/) is an operational data system for managing and serving machine learning features to models in production. + +Flyte provides a way to train models and perform feature engineering as a single pipeline. +But it provides no way to serve these features to production when the model matures and is ready to be served in production. + +This is where Feast can be helpful. + +On leveraging the collective capabilities, Flyte enables engineering the features, and Feast provides the feature registry and online feature serving system. +Moreover, Flyte can help ensure incremental development of features and enables to turn on the sync to online stores only when one is confident about the features. + +In this tutorial, we'll walk through how Feast can be used to store and retrieve features to train and test the model through a pipeline curated using Flyte. + +## Dataset + +We'll use the horse colic dataset to determine if the lesion of the horse is surgical or not. This is a modified version of the original dataset. + +The dataset will have the following columns: + +```{list-table} Horse Colic Features +:widths: 25 25 25 25 25 + +* - surgery + - Age + - Hospital Number + - rectal temperature + - pulse +* - respiratory rate + - temperature of extremities + - peripheral pulse + - mucous membranes + - capillary refill time +* - pain + - peristalsis + - abdominal distension + - nasogastric tube + - nasogastric reflux +* - nasogastric reflux PH + - rectal examination + - abdomen + - packed cell volume + - total protein +* - abdominocentesis appearance + - abdomcentesis total protein + - outcome + - surgical lesion + - timestamp +``` + +The horse colic dataset will be a compressed zip file consisting of the SQLite DB. +For this example, we wanted a dataset available online, but this could be easily plugged into another dataset/data management system +like Snowflake, Athena, Hive, BigQuery, or Spark, all of which are supported by Flyte. + +## Takeaways + +1. Source data is from SQL-like data sources +2. Procreated feature transforms +3. Serve features to production using Feast + +## Examples + +```{auto-examples-toc} +feature_eng_tasks +feast_workflow +feast_flyte_remote +``` diff --git a/docs/examples/feast_integration/feast_integration/__init__.py b/docs/examples/feast_integration/feast_integration/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/feast_integration/feast_integration/feast_flyte_remote.ipynb b/docs/examples/feast_integration/feast_integration/feast_flyte_remote.ipynb new file mode 100644 index 000000000..0b93cb5df --- /dev/null +++ b/docs/examples/feast_integration/feast_integration/feast_flyte_remote.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "8711cbe4", + "metadata": {}, + "source": [ + "# How to Trigger the Feast Workflow using FlyteRemote\n", + "\n", + "The goal of this notebook is to train a simple [Gaussian Naive Bayes model using sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) on a modified [Horse-Colic dataset from UCI](https://archive.ics.uci.edu/ml/datasets/Horse+Colic).\n", + "\n", + "The model aims to classify if the lesion of the horse is surgical or not.\n", + "\n", + "Let's get started!" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d5f891e8", + "metadata": {}, + "source": [ + "Set the AWS environment variables before importing Flytekit." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "75ff01b3", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"FLYTE_AWS_ENDPOINT\"] = os.environ[\"FEAST_S3_ENDPOINT_URL\"] = \"http://localhost:30084/\"\n", + "os.environ[\"FLYTE_AWS_ACCESS_KEY_ID\"] = os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minio\"\n", + "os.environ[\"FLYTE_AWS_SECRET_ACCESS_KEY\"] = os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"miniostorage\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f79f28b4", + "metadata": {}, + "source": [ + "## 01. Register the code\n", + "\n", + "The actual workflow code is auto-documented and rendered using sphinx [here](https://docs.flyte.org/projects/cookbook/en/latest/auto/case_studies/feature_engineering/feast_integration/index.html). We've used [Flytekit](https://docs.flyte.org/projects/flytekit/en/latest/) to express the pipeline in pure Python.\n", + "\n", + "You can use [FlyteConsole](https://github.com/flyteorg/flyteconsole) to launch, monitor, and introspect Flyte executions. However here, let's use [flytekit.remote](https://docs.flyte.org/projects/flytekit/en/latest/design/control_plane.html) to interact with the Flyte backend." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2330891", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/samhitaalla/.pyenv/versions/3.9.9/envs/flytesnacks/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from flytekit.remote import FlyteRemote\n", + "from flytekit.configuration import Config\n", + "\n", + "# The `for_sandbox` method instantiates a connection to the demo cluster.\n", + "remote = FlyteRemote(\n", + " config=Config.for_sandbox(),\n", + " default_project=\"flytesnacks\",\n", + " default_domain=\"development\"\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d3d32822", + "metadata": {}, + "source": [ + "The ``register_script`` method can be used to register the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e5a60057", + "metadata": {}, + "outputs": [], + "source": [ + "from flytekit.configuration import ImageConfig\n", + "\n", + "from feast_workflow import feast_workflow\n", + "\n", + "wf = remote.register_script(\n", + " feast_workflow,\n", + " image_config=ImageConfig.from_images(\n", + " \"ghcr.io/flyteorg/flytecookbook:feast_integration-latest\"\n", + " ),\n", + " version=\"v2\",\n", + " source_path=\"../\",\n", + " module_name=\"feast_workflow\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8fcea449", + "metadata": {}, + "source": [ + "## 02: Launch an execution" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "64d6295a", + "metadata": {}, + "source": [ + "FlyteRemote provides convenient methods to retrieve version of the pipeline from the remote server.\n", + "\n", + "**NOTE**: It is possible to get a specific version of the workflow and trigger a launch for that, but let's just get the latest." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d28014f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'v1'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lp = remote.fetch_launch_plan(name=\"feast_integration.feast_workflow.feast_workflow\")\n", + "lp.id.version" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c71210a7", + "metadata": {}, + "source": [ + "The ``execute`` method can be used to execute a Flyte entity — a launch plan in our case." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c13770fc", + "metadata": {}, + "outputs": [], + "source": [ + "execution = remote.execute(\n", + " lp,\n", + " inputs={\"num_features_univariate\": 5},\n", + " wait=True\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "07bd9e37", + "metadata": {}, + "source": [ + "## 03. Sync an execution\n", + "\n", + "You can sync an execution to retrieve the workflow's outputs. ``sync_nodes`` is set to True to retrieve the intermediary nodes' outputs as well.\n", + "\n", + "**NOTE**: It is possible to fetch an existing execution or simply retrieve an already commenced execution. Also, if you launch an execution with the same name, Flyte will respect that and not restart a new execution!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a8bd9614", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution f218aba055ba34a3fb75 is in SUCCEEDED phase\n" + ] + } + ], + "source": [ + "from flytekit.models.core.execution import WorkflowExecutionPhase\n", + "\n", + "synced_execution = remote.sync(execution, sync_nodes=True)\n", + "print(f\"Execution {synced_execution.id.name} is in {WorkflowExecutionPhase.enum_to_string(synced_execution.closure.phase)} phase\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "65e5b181", + "metadata": {}, + "source": [ + "## 04. Retrieve the output\n", + "\n", + "Fetch the model and the model prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ab24b1c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "/var/folders/6r/9pdkgpkd5nx1t34ndh1f_3q80000gn/T/flyteaqx6tlyu/control_plane_metadata/local_flytekit/e1a690494fe33da04a4dca7737096234/0c81c76dc3a029267a96f275431b5bc5.npy" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = synced_execution.outputs[\"o0\"]\n", + "prediction = synced_execution.outputs[\"o1\"]\n", + "prediction" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "af8277d3", + "metadata": {}, + "source": [ + "**NOTE**: The output model is available locally as a JobLibSerialized file, which can be downloaded and loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8a841e22", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "/var/folders/6r/9pdkgpkd5nx1t34ndh1f_3q80000gn/T/flyteaqx6tlyu/control_plane_metadata/local_flytekit/91246ef2160dde99a7512ab3aa9aa2ce/model.joblib.dat" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "91b3bcbc", + "metadata": {}, + "source": [ + "Fetch the ``repo_config``." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a21feeb6", + "metadata": {}, + "outputs": [], + "source": [ + "repo_config = synced_execution.node_executions[\"n0\"].outputs[\"o0\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7a535b4d", + "metadata": {}, + "source": [ + "## 05. Generate predictions\n", + "\n", + "Re-use the `predict` function from the workflow to generate predictions — Flytekit will automatically manage the IO for you!" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "dff58f63", + "metadata": {}, + "source": [ + "### Load features from the online feature store" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c7a2c3c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'total protein': [70.0],\n", + " 'peripheral pulse': [3.0],\n", + " 'nasogastric reflux PH': [4.718545454545455],\n", + " 'surgical lesion': ['1'],\n", + " 'rectal temperature': [38.17717842323652],\n", + " 'nasogastric tube': ['1.751269035532995'],\n", + " 'Hospital Number': ['533738'],\n", + " 'packed cell volume': [43.0],\n", + " 'outcome': ['1'],\n", + " 'abdominal distension': [4.0]}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "from feast_workflow import predict, FEAST_FEATURES, retrieve_online\n", + "\n", + "inference_point = retrieve_online(\n", + " repo_config=repo_config,\n", + " online_store=synced_execution.node_executions[\"n4\"].outputs[\"o0\"],\n", + " data_point=533738,\n", + ")\n", + "inference_point" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9a49e572", + "metadata": {}, + "source": [ + "### Generate a prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e44c62e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2'], dtype=' RepoConfig: + client = boto3.client( + "s3", + aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"), + use_ssl=False, + endpoint_url=ENDPOINT, + ) + + try: + client.create_bucket(Bucket=bucket_name) + except client.exceptions.BucketAlreadyOwnedByYou: + logger.info(f"Bucket {bucket_name} has already been created by you.") + + return RepoConfig( + registry=f"s3://{bucket_name}/{registry_path}", + project="horsecolic", + provider="local", + offline_store=FileOfflineStoreConfig(), + online_store=SqliteOnlineStoreConfig(path=online_store_path), + entity_key_serialization_version=2, + ) + + +# %% [markdown] +# Define a `SQLite3Task` that fetches data from a data source for feature ingestion. +# %% +load_horse_colic_sql = SQLite3Task( + name="sqlite3.load_horse_colic", + query_template="select * from data", + output_schema_type=StructuredDataset, + task_config=SQLite3Config( + uri=DATABASE_URI, + compressed=True, + ), +) + + +# %% [markdown] +# Set the datatype of the timestamp column in the underlying DataFrane to `datetime`, which would otherwise be a string. +# %% +@task +def convert_timestamp_column(dataframe: pd.DataFrame, timestamp_column: str) -> pd.DataFrame: + dataframe[timestamp_column] = pd.to_datetime(dataframe[timestamp_column]) + return dataframe + + +# %% [markdown] +# Define `store_offline` and `load_historical_features` tasks to store and retrieve the historial features, respectively. +# +# ```{eval-rst} +# .. list-table:: Decoding the ``Feast`` Jargon +# :widths: 25 25 +# +# * - ``FeatureStore`` +# - A FeatureStore object is used to define, create, and retrieve features. +# * - ``Entity`` +# - Represents a collection of entities and associated metadata. It's usually the primary key of your data. +# * - ``FeatureView`` +# - A FeatureView defines a logical grouping of serve-able features. +# * - ``FileSource`` +# - File data sources allow for the retrieval of historical feature values from files on disk for building training datasets, as well as for materializing features into an online store. +# * - ``apply()`` +# - Registers objects to metadata store and updates the related infrastructure. +# * - ``get_historical_features()`` +# - Enriches an entity dataframe with historical feature values for either training or batch scoring. +# ``` +# +# :::{note} +# The Feast feature store is mutable, so be careful, as Flyte workflows can be highly concurrent! +# TODO: use postgres db as the registry to support concurrent writes. +# ::: +# %% +@task(limits=Resources(mem="400Mi")) +def store_offline(repo_config: RepoConfig, dataframe: StructuredDataset) -> FlyteFile: + horse_colic_entity = Entity(name="Hospital Number") + + ctx = flytekit.current_context() + data_dir = os.path.join(ctx.working_directory, "parquet-data") + os.makedirs(data_dir, exist_ok=True) + + FlyteContext.current_context().file_access.get_data( + dataframe._literal_sd.uri + "/00000", + dataframe._literal_sd.uri + ".parquet", + ) + + horse_colic_feature_view = FeatureView( + name="horse_colic_stats", + entities=[horse_colic_entity], + schema=[ + Field(name="rectal temperature", dtype=Float64), + Field(name="total protein", dtype=Float64), + Field(name="peripheral pulse", dtype=Float64), + Field(name="surgical lesion", dtype=String), + Field(name="abdominal distension", dtype=Float64), + Field(name="nasogastric tube", dtype=String), + Field(name="outcome", dtype=String), + Field(name="packed cell volume", dtype=Float64), + Field(name="nasogastric reflux PH", dtype=Float64), + ], + source=FileSource( + path=dataframe._literal_sd.uri + ".parquet", + timestamp_field="timestamp", + s3_endpoint_override=ENDPOINT, + ), + ttl=timedelta(days=1), + ) + + # ingest the data into Feast + FeatureStore(config=repo_config).apply([horse_colic_entity, horse_colic_feature_view]) + + return FlyteFile(path=repo_config.online_store.path) + + +@task(limits=Resources(mem="400Mi")) +def load_historical_features(repo_config: RepoConfig) -> pd.DataFrame: + entity_df = pd.DataFrame.from_dict( + { + "Hospital Number": [ + "530101", + "5290409", + "5291329", + "530051", + "529518", + "530101", + "529340", + "5290409", + "530034", + ], + "event_timestamp": [ + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 7, 5, 11, 36, 1), + datetime(2021, 6, 25, 16, 36, 27), + datetime(2021, 7, 5, 11, 50, 40), + datetime(2021, 6, 25, 16, 36, 27), + ], + } + ) + + historical_features = ( + FeatureStore(config=repo_config).get_historical_features(entity_df=entity_df, features=FEAST_FEATURES).to_df() + ) # noqa + + return historical_features + + +# %% [markdown] +# Train a Naive Bayes model by fetching features from the offline store and the corresponding data from the parquet file. +# %% +@task +def train_model(dataset: pd.DataFrame, data_class: str) -> JoblibSerializedFile: + x_train, _, y_train, _ = train_test_split( + dataset[dataset.columns[~dataset.columns.isin([data_class])]], + dataset[data_class], + test_size=0.33, + random_state=42, + ) + model = GaussianNB() + model.fit(x_train.values, y_train.values) + model.feature_names = list(x_train.columns.values) + fname = "/tmp/model.joblib.dat" + joblib.dump(model, fname) + return fname + + +# %% [markdown] +# To generate predictions, define `store_online` and `retrieve_online` tasks. +# +# ```{eval-rst} +# .. list-table:: Decoding the ``Feast`` Jargon +# :widths: 25 25 +# +# * - ``materialize()`` +# - Materializes data from offline to an online store. +# * - ``get_online_features()`` +# - Retrieves the latest online feature data. +# ``` +# +# :::{note} +# One key difference between an online and offline store is that only the latest feature values are stored per entity +# key in an online store, unlike an offline store where all feature values are stored. +# Our dataset has two such entries with the same `Hospital Number` but different time stamps. +# Only data point with the latest timestamp will be stored in the online store. +# ::: +# %% +@task(limits=Resources(mem="400Mi")) +def store_online(repo_config: RepoConfig, online_store: FlyteFile) -> FlyteFile: + # download the online store file and copy the content to the actual online store path + FlyteContext.current_context().file_access.get_data(online_store.download(), repo_config.online_store.path) + + FeatureStore(config=repo_config).materialize( + start_date=datetime.utcnow() - timedelta(days=2000), + end_date=datetime.utcnow() - timedelta(minutes=10), + ) + + return FlyteFile(path=repo_config.online_store.path) + + +# %% [markdown] +# Retrieve a feature vector from the online store. +# %% +@task +def retrieve_online( + repo_config: RepoConfig, + online_store: FlyteFile, + data_point: int, +) -> dict: + # retrieve a data point + logger.info(f"Hospital Number chosen for inference is: {data_point}") + entity_rows = [{"Hospital Number": data_point}] + + # download the online store file and copy the content to the actual online store path + FlyteContext.current_context().file_access.get_data(online_store.download(), repo_config.online_store.path) + + # get the feature vector + feature_vector = FeatureStore(config=repo_config).get_online_features(FEAST_FEATURES, entity_rows).to_dict() + return feature_vector + + +# %% [markdown] +# Use the inference data point fetched earlier to generate the prediction. +# %% +@task +def predict(model_ser: JoblibSerializedFile, features: dict) -> np.ndarray: + model = joblib.load(model_ser) + f_names = model.feature_names + + test_list = [] + for each_name in f_names: + test_list.append(features[each_name][0]) + + if all(test_list): + prediction = model.predict([[float(x) for x in test_list]]) + else: + prediction = ["The requested data is not found in the online feature store"] + return prediction + + +# %% [markdown] +# Define a workflow that loads the data from SQLite3 database, does feature engineering, and stores the offline features in a feature store. +# %% +@workflow +def featurize(repo_config: RepoConfig, imputation_method: str = "mean") -> (StructuredDataset, FlyteFile): + # load parquet file from sqlite task + df = load_horse_colic_sql() + + # perform mean median imputation + df = mean_median_imputer(dataframe=df, imputation_method=imputation_method) + + # convert timestamp column from string to datetime + converted_df = convert_timestamp_column(dataframe=df, timestamp_column="timestamp") + + online_store = store_offline( + repo_config=repo_config, + dataframe=converted_df, + ) + + return df, online_store + + +# %% [markdown] +# Define a workflow that trains a Naive Bayes model. +# %% +@workflow +def trainer(df: StructuredDataset, num_features_univariate: int = 7) -> JoblibSerializedFile: + # perform univariate feature selection + selected_features_dataset = univariate_selection( + dataframe=df, # noqa + num_features=num_features_univariate, + data_class=DATA_CLASS, + ) + + # train the Naive Bayes model + trained_model = train_model( + dataset=selected_features_dataset, + data_class=DATA_CLASS, + ) + + return trained_model + + +# %% [markdown] +# Lastly, define a workflow to encapsulate and run the previously defined tasks and workflows. +# %% +@workflow +def feast_workflow( + s3_bucket: str = "feast-integration", + registry_path: str = "registry.db", + online_store_path: str = "online.db", + imputation_method: str = "mean", + num_features_univariate: int = 7, +) -> (JoblibSerializedFile, np.ndarray): + repo_config = create_bucket( + bucket_name=s3_bucket, + registry_path=registry_path, + online_store_path=online_store_path, + ) + + # feature engineering and store features in an offline store + df, online_store = featurize( + repo_config=repo_config, + imputation_method=imputation_method, + ) + + # load features from the offline store + historical_features = load_historical_features(repo_config=repo_config) + + # load features after performing feature engineering + df >> historical_features + + # train a Naive Bayes model + model = trainer(df=historical_features, num_features_univariate=num_features_univariate) + + # materialize features into an online store + loaded_online_store = store_online( + repo_config=repo_config, + online_store=online_store, + ) + + # retrieve features from the online store + feature_vector = retrieve_online( + repo_config=repo_config, + online_store=loaded_online_store, + data_point=533738, + ) + + # generate a prediction + prediction = predict(model_ser=model, features=feature_vector) # noqa + + return model, prediction + + +if __name__ == "__main__": + print(f"{feast_workflow()}") + +# %% [markdown] +# You should see prediction against the test input as the workflow output. +# diff --git a/docs/examples/feast_integration/feast_integration/feature_eng_tasks.py b/docs/examples/feast_integration/feast_integration/feature_eng_tasks.py new file mode 100644 index 000000000..411f0f576 --- /dev/null +++ b/docs/examples/feast_integration/feast_integration/feature_eng_tasks.py @@ -0,0 +1,71 @@ +# %% [markdown] +# # Feature Engineering Tasks +# +# Let's define some feature engineering tasks to be used in conjunction with the Flyte workflow. + +# %% [markdown] +# Import the necessary libraries. +# %% +import numpy as np +import pandas as pd +from flytekit import task +from numpy.core.fromnumeric import sort +from sklearn.feature_selection import SelectKBest, f_classif +from sklearn.impute import SimpleImputer + +# %% [markdown] +# There are a specific set of columns for which imputation isn't required. Ignore them. +# %% +NO_IMPUTATION_COLS = [ + "Hospital Number", + "surgery", + "Age", + "outcome", + "surgical lesion", + "timestamp", +] + +# %% [markdown] +# Use the [SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html) class from the `scikit-learn` library +# to fill in the missing values of the dataset. +# %% +@task +def mean_median_imputer( + dataframe: pd.DataFrame, + imputation_method: str, +) -> pd.DataFrame: + dataframe = dataframe.replace("?", np.nan) + if imputation_method not in ["median", "mean"]: + raise ValueError("imputation_method takes only values 'median' or 'mean'") + + imputer = SimpleImputer(missing_values=np.nan, strategy=imputation_method) + + imputer = imputer.fit(dataframe[dataframe.columns[~dataframe.columns.isin(NO_IMPUTATION_COLS)]]) + dataframe[dataframe.columns[~dataframe.columns.isin(NO_IMPUTATION_COLS)]] = imputer.transform( + dataframe[dataframe.columns[~dataframe.columns.isin(NO_IMPUTATION_COLS)]] + ) + return dataframe + + +# %% [markdown] +# The [SelectKBest](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest) method +# removes all but the highest scoring features. +# +# %% +@task +def univariate_selection(dataframe: pd.DataFrame, num_features: int, data_class: str) -> pd.DataFrame: + # remove ``timestamp`` and ``Hospital Number`` columns as they ought to be present in the dataset + dataframe = dataframe.drop(["event_timestamp", "Hospital Number"], axis=1) + + if num_features > 9: + raise ValueError(f"Number of features must be <= 9; you've given {num_features}") + + X = dataframe.iloc[:, dataframe.columns != data_class] + y = dataframe.loc[:, data_class] + test = SelectKBest(score_func=f_classif, k=num_features) + fit = test.fit(X, y) + indices = sort((-fit.scores_).argsort()[:num_features]) + column_names = list(map(X.columns.__getitem__, indices)) + column_names.extend([data_class]) + features = fit.transform(X) + return pd.DataFrame(np.c_[features, y.to_numpy()], columns=column_names) diff --git a/docs/examples/feast_integration/requirements.in b/docs/examples/feast_integration/requirements.in new file mode 100644 index 000000000..8b5c6d71f --- /dev/null +++ b/docs/examples/feast_integration/requirements.in @@ -0,0 +1,7 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +scikit-learn +numpy +feast[aws] diff --git a/docs/examples/feast_integration/requirements.txt b/docs/examples/feast_integration/requirements.txt new file mode 100644 index 000000000..98dc42e49 --- /dev/null +++ b/docs/examples/feast_integration/requirements.txt @@ -0,0 +1,500 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +absl-py==1.2.0 + # via tensorflow-metadata +aiobotocore==2.1.2 + # via s3fs +aiohttp==3.8.1 + # via + # aiobotocore + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.2.0 + # via aiohttp +anyio==3.6.1 + # via + # starlette + # watchfiles +appdirs==1.4.4 + # via fissix +arrow==1.2.3 + # via jinja2-time +async-timeout==4.0.2 + # via aiohttp +attrs==22.1.0 + # via + # aiohttp + # bowler + # jsonschema + # visions +binaryornot==0.4.4 + # via cookiecutter +boto3==1.20.23 + # via feast +botocore==1.23.24 + # via + # aiobotocore + # boto3 + # s3transfer +bowler==0.9.0 + # via feast +cachetools==5.2.0 + # via google-auth +certifi==2022.9.14 + # via requests +cffi==1.15.1 + # via cryptography +chardet==5.0.0 + # via binaryornot +charset-normalizer==2.1.1 + # via + # aiohttp + # requests +click==8.1.3 + # via + # bowler + # cookiecutter + # feast + # flytekit + # moreorless + # uvicorn +cloudpickle==2.2.0 + # via + # dask + # flytekit +colorama==0.4.5 + # via feast +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.7 + # via flytekit +cryptography==38.0.1 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dask==2022.1.1 + # via feast +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +dill==0.3.5.1 + # via feast +diskcache==5.4.0 + # via flytekit +docker==6.0.0 + # via + # feast + # flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +fastapi==0.85.0 + # via feast +fastavro==1.6.1 + # via + # feast + # pandavro +feast[aws]==0.25.0 + # via -r requirements.in +fissix==21.11.13 + # via bowler +flyteidl==1.1.17 + # via flytekit +flytekit==1.1.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.1.3 + # via -r ../../../common/requirements-common.in +fonttools==4.37.3 + # via matplotlib +frozenlist==1.3.1 + # via + # aiohttp + # aiosignal +fsspec==2022.1.0 + # via + # dask + # s3fs +google-api-core==2.10.1 + # via feast +google-auth==2.11.1 + # via google-api-core +googleapis-common-protos==1.56.4 + # via + # feast + # flyteidl + # google-api-core + # grpcio-status + # tensorflow-metadata +greenlet==1.1.3 + # via sqlalchemy +grpcio==1.48.1 + # via + # feast + # flytekit + # grpcio-reflection + # grpcio-status +grpcio-reflection==1.48.1 + # via feast +grpcio-status==1.48.1 + # via flytekit +h11==0.13.0 + # via uvicorn +htmlmin==0.1.12 + # via pandas-profiling +httptools==0.5.0 + # via uvicorn +idna==3.4 + # via + # anyio + # requests + # yarl +imagehash==4.3.0 + # via visions +importlib-metadata==4.12.0 + # via + # flytekit + # keyring + # markdown +jaraco-classes==3.2.2 + # via keyring +jinja2==3.1.2 + # via + # cookiecutter + # feast + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +jmespath==0.10.0 + # via + # boto3 + # botocore +joblib==1.1.0 + # via + # flytekit + # pandas-profiling + # phik + # scikit-learn +jsonschema==4.16.0 + # via feast +keyring==23.9.3 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +locket==1.0.0 + # via partd +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.18.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.3 + # via + # -r ../../../common/requirements-common.in + # missingno + # pandas-profiling + # phik + # seaborn +missingno==0.5.1 + # via pandas-profiling +mmh3==3.0.0 + # via feast +more-itertools==8.14.0 + # via jaraco-classes +moreorless==0.4.0 + # via bowler +multidict==6.0.2 + # via + # aiohttp + # yarl +multimethod==1.8 + # via + # pandas-profiling + # visions +mypy==0.971 + # via sqlalchemy +mypy-extensions==0.4.3 + # via + # mypy + # typing-inspect +natsort==8.2.0 + # via flytekit +networkx==2.8.6 + # via visions +numpy==1.23.3 + # via + # -r requirements.in + # feast + # imagehash + # matplotlib + # missingno + # pandas + # pandas-profiling + # pandavro + # patsy + # phik + # pyarrow + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions +packaging==21.3 + # via + # dask + # docker + # marshmallow + # matplotlib + # statsmodels +pandas==1.4.4 + # via + # feast + # flytekit + # pandas-profiling + # pandavro + # phik + # seaborn + # statsmodels + # visions +pandas-profiling==3.3.0 + # via flytekitplugins-deck-standard +pandavro==1.5.2 + # via feast +partd==1.3.0 + # via dask +patsy==0.5.2 + # via statsmodels +phik==0.12.2 + # via pandas-profiling +pillow==9.2.0 + # via + # imagehash + # matplotlib + # visions +plotly==5.10.0 + # via flytekitplugins-deck-standard +proto-plus==1.22.1 + # via feast +protobuf==3.20.2 + # via + # feast + # flyteidl + # flytekit + # google-api-core + # googleapis-common-protos + # grpcio-reflection + # grpcio-status + # proto-plus + # protoc-gen-swagger + # tensorflow-metadata +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via + # feast + # flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.9.2 + # via + # fastapi + # feast + # pandas-profiling +pygments==2.13.0 + # via feast +pyopenssl==22.0.0 + # via flytekit +pyparsing==3.0.9 + # via + # matplotlib + # packaging +pyrsistent==0.18.1 + # via jsonschema +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # matplotlib + # pandas +python-dotenv==0.21.0 + # via uvicorn +python-json-logger==2.0.4 + # via flytekit +python-slugify==6.1.2 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.2.1 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # dask + # feast + # flytekit + # pandas-profiling + # uvicorn +regex==2022.9.13 + # via docker-image-py +requests==2.28.1 + # via + # cookiecutter + # docker + # flytekit + # google-api-core + # pandas-profiling + # responses +responses==0.21.0 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2022.1.0 + # via feast +s3transfer==0.5.2 + # via boto3 +scikit-learn==1.1.2 + # via -r requirements.in +scipy==1.9.1 + # via + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # seaborn + # statsmodels +seaborn==0.11.2 + # via + # missingno + # pandas-profiling +six==1.16.0 + # via + # google-auth + # grpcio + # pandavro + # patsy + # python-dateutil +sniffio==1.3.0 + # via anyio +sortedcontainers==2.4.0 + # via flytekit +sqlalchemy[mypy]==1.4.41 + # via feast +sqlalchemy2-stubs==0.0.2a27 + # via sqlalchemy +starlette==0.20.4 + # via fastapi +statsd==3.3.0 + # via flytekit +statsmodels==0.13.2 + # via pandas-profiling +tabulate==0.8.10 + # via feast +tangled-up-in-unicode==0.2.0 + # via + # pandas-profiling + # visions +tenacity==8.1.0 + # via + # feast + # plotly +tensorflow-metadata==1.10.0 + # via feast +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +toml==0.10.2 + # via feast +tomli==2.0.1 + # via mypy +toolz==0.12.0 + # via + # dask + # partd +tqdm==4.64.1 + # via + # feast + # pandas-profiling +typeguard==2.13.3 + # via feast +typing-extensions==4.3.0 + # via + # aioitertools + # flytekit + # mypy + # pydantic + # sqlalchemy2-stubs + # starlette + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.12 + # via + # botocore + # docker + # flytekit + # requests + # responses +uvicorn[standard]==0.18.3 + # via feast +uvloop==0.17.0 + # via uvicorn +visions[type_image_path]==0.7.5 + # via pandas-profiling +volatile==2.1.0 + # via bowler +watchfiles==0.17.0 + # via uvicorn +websocket-client==1.4.1 + # via docker +websockets==10.3 + # via uvicorn +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.1 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.8.1 + # via aiohttp +zipp==3.8.1 + # via importlib-metadata diff --git a/docs/examples/forecasting_sales/Dockerfile b/docs/examples/forecasting_sales/Dockerfile new file mode 100644 index 000000000..1b8b76f01 --- /dev/null +++ b/docs/examples/forecasting_sales/Dockerfile @@ -0,0 +1,116 @@ +FROM ubuntu:focal +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python3 and other basics +RUN apt-get update \ + && apt-get install -y software-properties-common \ + && add-apt-repository ppa:ubuntu-toolchain-r/test \ + && apt-get install -y \ + build-essential \ + cmake \ + g++-7 \ + curl \ + git \ + wget \ + python3.8 \ + python3.8-venv \ + python3.8-dev \ + make \ + libssl-dev \ + python3-pip \ + python3-wheel \ + libuv1 + +ENV VENV /opt/venv +# Virtual environment +RUN python3.8 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install AWS CLI to run on AWS (for GCS install GSutil). This will be removed +# in future versions to make it completely portable +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Install wheel after venv is activated +RUN pip3 install wheel + +# MPI +# Install Open MPI +RUN mkdir /tmp/openmpi && \ + cd /tmp/openmpi && \ + wget https://www.open-mpi.org/software/ompi/v4.0/downloads/openmpi-4.0.0.tar.gz && \ + tar zxf openmpi-4.0.0.tar.gz && \ + cd openmpi-4.0.0 && \ + ./configure --enable-orterun-prefix-by-default && \ + make -j $(nproc) all && \ + make install && \ + ldconfig && \ + rm -rf /tmp/openmpi + +# Install OpenSSH for MPI to communicate between containers +RUN apt-get install -y --no-install-recommends openssh-client openssh-server && \ + mkdir -p /var/run/sshd + +# Allow OpenSSH to talk to containers without asking for confirmation +# by disabling StrictHostKeyChecking. +# mpi-operator mounts the .ssh folder from a Secret. For that to work, we need +# to disable UserKnownHostsFile to avoid write permissions. +# Disabling StrictModes avoids directory and files read permission checks. +RUN sed -i 's/[ #]\(.*StrictHostKeyChecking \).*/ \1no/g' /etc/ssh/ssh_config && \ + echo " UserKnownHostsFile /dev/null" >> /etc/ssh/ssh_config && \ + sed -i 's/#\(StrictModes \).*/\1no/g' /etc/ssh/sshd_config + +# Horovod-related installations +RUN pip install tensorflow==2.9.1 +# Enable GPU +# ENV HOROVOD_GPU_OPERATIONS NCCL +RUN HOROVOD_WITH_MPI=1 HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod[spark,tensorflow]==0.24.3 + +# Setup HOROVOD entrypoint +# TODO: Set the entrypoint +# ENV HOROVOD_PROGRAM /opt/venv/bin/flytekit_mpi_runner.py + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# SPARK +RUN flytekit_install_spark3.sh +# Adding Tini support for the spark pods +RUN wget https://github.com/krallin/tini/releases/download/v0.18.0/tini && \ + cp tini /sbin/tini && cp tini /usr/bin/tini && \ + chmod a+x /sbin/tini && chmod a+x /usr/bin/tini + +# Setup Spark environment +ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64 +ENV SPARK_HOME /opt/spark +ENV SPARK_VERSION 3.0.1 +ENV PYSPARK_PYTHON ${VENV}/bin/python3 +ENV PYSPARK_DRIVER_PYTHON ${VENV}/bin/python3 + + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +ENTRYPOINT ["/opt/entrypoint.sh"] diff --git a/docs/examples/forecasting_sales/README.md b/docs/examples/forecasting_sales/README.md new file mode 100644 index 000000000..627ad55f2 --- /dev/null +++ b/docs/examples/forecasting_sales/README.md @@ -0,0 +1,96 @@ +(spark-horovod)= + +# Forecasting Rossman Store Sales with Horovod and Spark + +```{eval-rst} +.. tags:: MachineLearning, Integration, Advanced +``` + +```{image} https://img.shields.io/badge/Blog-Horovod%20and%20Spark-blue?style=for-the-badge +:target: https://blog.flyte.org/data-parallel-distributed-training-with-horovod-and-flyte +:alt: Horovod with Spark Blog Post +``` + +The problem statement we will be looking at is forecasting sales using [rossmann store sales](https://www.kaggle.com/c/rossmann-store-sales) data. +Our example is an adaptation of the [Horovod-Spark example](https://github.com/horovod/horovod/blob/master/examples/spark/keras/keras_spark_rossmann_estimator.py). +Here's how the code is streamlined: + +- Fetch the rossmann sales data +- Perform complicated data pre-processing using Flyte-Spark plugin +- Define a Keras model and perform distributed training using Horovod on Spark +- Generate predictions and store them in a submission file + +## About Horovod + +Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. +The goal of Horovod is to make distributed deep learning fast and easy to use. +It uses the all-reduce algorithm for fast distributed training instead of a parameter server approach. +It builds on top of low-level frameworks like MPI and NCCL and provides optimized algorithms for sharing data between parallel training processes. + +```{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/tutorials/horovod/all_reduce.png +:alt: Parameter server vs. all-reduce + +Parameter server vs. all-reduce +``` + +## About Spark + +Spark is a data processing and analytics engine to deal with large-scale data. + +Here's an interesting fact about Spark integration: + +**Spark integrates with both Horovod and Flyte** + +### Horovod and Spark + +Horovod implicitly supports Spark—it provides the `horovod.spark` package. +It facilitates running distributed jobs on the Spark cluster. +In our example, we use an Estimator API. +An estimator API abstracts the data processing, model training and checkpointing, and distributed training, which makes it easy to integrate and run our example code. + +Since we use the Keras deep learning library, here's how we install the relevant Horovod packages: + +```bash +HOROVOD_WITH_MPI=1 HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod[spark,tensorflow]==0.22.1 +``` + +The installation includes enabling MPI and TensorFlow environments. + +(flyte-and-spark)= + +### Flyte and Spark + +Flyte can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster's lifecycle, spin-up, and tear down. +It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. +This is like running a transient spark cluster—a type of cluster spun up for a specific Spark job and torn down after completion. + +To install the Spark plugin on Flyte, we use the following command: + +```bash +pip install flytekitplugins-spark +``` + +```{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/tutorials/horovod/flyte_spark.png +:alt: Flyte-Spark plugin + +Flyte-Spark plugin +``` + +The plugin requires configuring the Flyte backend as well. Refer to {ref}`Kubernetes Spark Jobs ` for setup instructions. + +In a nutshell, here's how Horovod-Spark-Flyte can be beneficial: + +Horovod provides the distributed framework, Spark enables extracting, preprocessing, and partitioning data, +Flyte can stitch the former two pieces together, e.g., by connecting the data output of a Spark transform to a training system using Horovod while ensuring high utilization of GPUs! + +Run workflows in this directory with the custom-built base image like so: + +```{prompt} bash $ +pyflyte run --remote forecasting_sales/keras_spark_rossmann_estimator.py horovod_spark_wf --image ghcr.io/flyteorg/flytecookbook:spark_horovod-latest +``` + +## Examples + +```{auto-examples-toc} +keras_spark_rossmann_estimator +``` diff --git a/docs/examples/forecasting_sales/forecasting_sales/__init__.py b/docs/examples/forecasting_sales/forecasting_sales/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/forecasting_sales/forecasting_sales/keras_spark_rossmann_estimator.py b/docs/examples/forecasting_sales/forecasting_sales/keras_spark_rossmann_estimator.py new file mode 100644 index 000000000..294be3167 --- /dev/null +++ b/docs/examples/forecasting_sales/forecasting_sales/keras_spark_rossmann_estimator.py @@ -0,0 +1,694 @@ +# %% [markdown] +# (spark_horovod_keras)= +# +# # Data-Parallel Distributed Training Using Horovod on Spark +# +# When time- and compute-intensive deep learning workloads need to be trained efficiently, data-parallel distributed training comes to the rescue. +# This technique parallelizes the data and requires sharing of weights between different worker nodes involved in the distributed training after every epoch, which ensures that all worker nodes train a consistent model. +# Overall, data-parallel distributed training can help speed up the execution time. +# +# In this tutorial, we will understand how data-parallel distributed training works with Flyte, Horovod, and Spark. +# +# We will forecast sales using the Rossmann store sales dataset. As the data preparation step, we will process the data using Spark, a data processing engine. To improve the speed and ease of distributed training, we will use Horovod, a distributed deep learning training framework. +# Lastly, we will build a Keras model and perform distributed training using Horovod's [KerasEstimator API](https://github.com/horovod/horovod/blob/8d34c85ce7ec76e81fb3be99418b0e4d35204dc3/horovod/spark/keras/estimator.py#L88). +# +# Before executing the code, create `work_dir`, an s3 bucket. +# +# Let's get started with the example! +# %% [markdown] +# First, let's import the required packages into the environment. +# %% +import datetime +import os +import pathlib +import subprocess +import sys +from dataclasses import dataclass +from distutils.version import LooseVersion +from typing import Any, Dict, List, Tuple + +import flytekit +import horovod.spark.keras as hvd +import pyspark +import pyspark.sql.functions as F +import pyspark.sql.types as T +import tensorflow as tf +import tensorflow.keras.backend as K +from dataclasses_json import dataclass_json +from flytekit import Resources, task, workflow +from flytekit.types.directory import FlyteDirectory +from flytekitplugins.spark import Spark +from horovod.spark.common.backend import SparkBackend +from horovod.spark.common.store import Store +from horovod.tensorflow.keras.callbacks import BestModelCheckpoint +from pyspark import Row +from tensorflow.keras.layers import BatchNormalization, Concatenate, Dense, Dropout, Embedding, Flatten, Input, Reshape + +# %% [markdown] +# We define two variables to represent categorical and continuous columns in the dataset. +# %% +CATEGORICAL_COLS = [ + "Store", + "State", + "DayOfWeek", + "Year", + "Month", + "Day", + "Week", + "CompetitionMonthsOpen", + "Promo2Weeks", + "StoreType", + "Assortment", + "PromoInterval", + "CompetitionOpenSinceYear", + "Promo2SinceYear", + "Events", + "Promo", + "StateHoliday", + "SchoolHoliday", +] + +CONTINUOUS_COLS = [ + "CompetitionDistance", + "Max_TemperatureC", + "Mean_TemperatureC", + "Min_TemperatureC", + "Max_Humidity", + "Mean_Humidity", + "Min_Humidity", + "Max_Wind_SpeedKm_h", + "Mean_Wind_SpeedKm_h", + "CloudCover", + "trend", + "trend_de", + "BeforePromo", + "AfterPromo", + "AfterStateHoliday", + "BeforeStateHoliday", + "BeforeSchoolHoliday", + "AfterSchoolHoliday", +] + + +# %% [markdown] +# Next, let's initialize a data class to store the hyperparameters that will be used with the model (`epochs`, `learning_rate`, `batch_size`, etc.). + +# %% +@dataclass_json +@dataclass +class Hyperparameters: + batch_size: int = 100 + sample_rate: float = 0.01 + learning_rate: float = 0.0001 + num_proc: int = 2 + epochs: int = 100 + local_checkpoint_file: str = "checkpoint.h5" + local_submission_csv: str = "submission.csv" + + +# %% [markdown] +# ## Downloading the Data +# +# We define a task to download the data into a `FlyteDirectory`. +# %% +@task( + cache=True, + cache_version="0.1", +) +def download_data(dataset: str) -> FlyteDirectory: + # create a directory named 'data' + print("==============") + print("Downloading data") + print("==============") + + working_dir = flytekit.current_context().working_directory + data_dir = pathlib.Path(os.path.join(working_dir, "data")) + data_dir.mkdir(exist_ok=True) + + # download the dataset + download_subprocess = subprocess.run( + [ + "curl", + dataset, + ], + check=True, + capture_output=True, + ) + + # untar the data + subprocess.run( + [ + "tar", + "-xz", + "-C", + data_dir, + ], + input=download_subprocess.stdout, + ) + + # return the directory populated with Rossmann data files + return FlyteDirectory(path=str(data_dir)) + + +# %% [markdown] +# ## Data Preprocessing +# +# 1. Let's start with cleaning and preparing the Google trend data. We create new 'Date' and 'State' columns using PySpark's `withColumn`. These columns, in addition to other features, will contribute to the prediction of sales. +# %% +def prepare_google_trend( + google_trend_csv: pyspark.sql.DataFrame, +) -> pyspark.sql.DataFrame: + google_trend_all = google_trend_csv.withColumn( + "Date", F.regexp_extract(google_trend_csv.week, "(.*?) -", 1) + ).withColumn("State", F.regexp_extract(google_trend_csv.file, "Rossmann_DE_(.*)", 1)) + + # map state NI -> HB,NI to align with other data sources + google_trend_all = google_trend_all.withColumn( + "State", + F.when(google_trend_all.State == "NI", "HB,NI").otherwise(google_trend_all.State), + ) + + # expand dates + return expand_date(google_trend_all) + + +# %% [markdown] +# 2. Next, we set a few date-specific values in the DataFrame to analyze the seasonal effects on sales. +# %% +def expand_date(df: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame: + df = df.withColumn("Date", df.Date.cast(T.DateType())) + return ( + df.withColumn("Year", F.year(df.Date)) + .withColumn("Month", F.month(df.Date)) + .withColumn("Week", F.weekofyear(df.Date)) + .withColumn("Day", F.dayofmonth(df.Date)) + ) + + +# %% [markdown] +# 3. We retrieve the number of days before/after a special event (such as a promo or holiday). This data helps analyze how the sales may vary before/after a special event. +# %% +def add_elapsed(df: pyspark.sql.DataFrame, cols: List[str]) -> pyspark.sql.DataFrame: + def add_elapsed_column(col, asc): + def fn(rows): + last_store, last_date = None, None + for r in rows: + if last_store != r.Store: + last_store = r.Store + last_date = r.Date + if r[col]: + last_date = r.Date + fields = r.asDict().copy() + fields[("After" if asc else "Before") + col] = (r.Date - last_date).days + yield Row(**fields) + + return fn + + # repartition: rearrange the rows in the DataFrame based on the partitioning expression + # sortWithinPartitions: sort every partition in the DataFrame based on specific columns + # mapPartitions: apply the 'add_elapsed_column' method to each partition in the dataset, and convert the partitions into a DataFrame + df = df.repartition(df.Store) + for asc in [False, True]: + sort_col = df.Date.asc() if asc else df.Date.desc() + rdd = df.sortWithinPartitions(df.Store.asc(), sort_col).rdd + for col in cols: + rdd = rdd.mapPartitions(add_elapsed_column(col, asc)) + df = rdd.toDF() + return df + + +# %% [markdown] +# 4. We define a function to merge several Spark DataFrames into a single DataFrame to create training and test data. +# %% +def prepare_df( + df: pyspark.sql.DataFrame, + store_csv: pyspark.sql.DataFrame, + store_states_csv: pyspark.sql.DataFrame, + state_names_csv: pyspark.sql.DataFrame, + google_trend_csv: pyspark.sql.DataFrame, + weather_csv: pyspark.sql.DataFrame, +) -> pyspark.sql.DataFrame: + num_rows = df.count() + + # expand dates + df = expand_date(df) + + # create new columns in the DataFrame by filtering out special events(promo/holiday where sales was zero or store was closed). + df = ( + df.withColumn("Open", df.Open != "0") + .withColumn("Promo", df.Promo != "0") + .withColumn("StateHoliday", df.StateHoliday != "0") + .withColumn("SchoolHoliday", df.SchoolHoliday != "0") + ) + + # merge store information + store = store_csv.join(store_states_csv, "Store") + df = df.join(store, "Store") + + # merge Google Trend information + google_trend_all = prepare_google_trend(google_trend_csv) + df = df.join(google_trend_all, ["State", "Year", "Week"]).select(df["*"], google_trend_all.trend) + + # merge in Google Trend for whole Germany + google_trend_de = google_trend_all[google_trend_all.file == "Rossmann_DE"].withColumnRenamed("trend", "trend_de") + df = df.join(google_trend_de, ["Year", "Week"]).select(df["*"], google_trend_de.trend_de) + + # merge weather + weather = weather_csv.join(state_names_csv, weather_csv.file == state_names_csv.StateName) + df = df.join(weather, ["State", "Date"]) + + # fix null values + df = ( + df.withColumn( + "CompetitionOpenSinceYear", + F.coalesce(df.CompetitionOpenSinceYear, F.lit(1900)), + ) + .withColumn( + "CompetitionOpenSinceMonth", + F.coalesce(df.CompetitionOpenSinceMonth, F.lit(1)), + ) + .withColumn("Promo2SinceYear", F.coalesce(df.Promo2SinceYear, F.lit(1900))) + .withColumn("Promo2SinceWeek", F.coalesce(df.Promo2SinceWeek, F.lit(1))) + ) + + # days and months since the competition has been open, cap it to 2 years + df = df.withColumn( + "CompetitionOpenSince", + F.to_date(F.format_string("%s-%s-15", df.CompetitionOpenSinceYear, df.CompetitionOpenSinceMonth)), + ) + df = df.withColumn( + "CompetitionDaysOpen", + F.when( + df.CompetitionOpenSinceYear > 1900, + F.greatest( + F.lit(0), + F.least(F.lit(360 * 2), F.datediff(df.Date, df.CompetitionOpenSince)), + ), + ).otherwise(0), + ) + df = df.withColumn("CompetitionMonthsOpen", (df.CompetitionDaysOpen / 30).cast(T.IntegerType())) + + # days and weeks of promotion, cap it to 25 weeks + df = df.withColumn( + "Promo2Since", + F.expr('date_add(format_string("%s-01-01", Promo2SinceYear), (cast(Promo2SinceWeek as int) - 1) * 7)'), + ) + df = df.withColumn( + "Promo2Days", + F.when( + df.Promo2SinceYear > 1900, + F.greatest(F.lit(0), F.least(F.lit(25 * 7), F.datediff(df.Date, df.Promo2Since))), + ).otherwise(0), + ) + df = df.withColumn("Promo2Weeks", (df.Promo2Days / 7).cast(T.IntegerType())) + + # ensure that no row was lost through inner joins + assert num_rows == df.count(), "lost rows in joins" + return df + + +# %% [markdown] +# 5. We build a dictionary of sorted, distinct categorical variables to create an embedding layer in our Keras model. +# %% +def build_vocabulary(df: pyspark.sql.DataFrame) -> Dict[str, List[Any]]: + vocab = {} + for col in CATEGORICAL_COLS: + values = [r[0] for r in df.select(col).distinct().collect()] + col_type = type([x for x in values if x is not None][0]) + default_value = col_type() + vocab[col] = sorted(values, key=lambda x: x or default_value) + return vocab + + +# %% [markdown] +# 6. Next, we cast continuous columns to float as part of data preprocessing. +# %% +def cast_columns(df: pyspark.sql.DataFrame, cols: List[str]) -> pyspark.sql.DataFrame: + for col in cols: + df = df.withColumn(col, F.coalesce(df[col].cast(T.FloatType()), F.lit(0.0))) + return df + + +# %% [markdown] +# 7. Lastly, define a function that returns a list of values based on a key. +# %% +def lookup_columns(df: pyspark.sql.DataFrame, vocab: Dict[str, List[Any]]) -> pyspark.sql.DataFrame: + def lookup(mapping): + def fn(v): + return mapping.index(v) + + return F.udf(fn, returnType=T.IntegerType()) + + for col, mapping in vocab.items(): + df = df.withColumn(col, lookup(mapping)(df[col])) + return df + + +# %% [markdown] +# The `data_preparation` function consolidates all the aforementioned data processing functions. +# %% +def data_preparation( + data_dir: FlyteDirectory, hp: Hyperparameters +) -> Tuple[float, Dict[str, List[Any]], pyspark.sql.DataFrame, pyspark.sql.DataFrame]: + print("================") + print("Data preparation") + print("================") + + # 'current_context' gives the handle of specific parameters in ``data_preparation`` task + spark = flytekit.current_context().spark_session + data_dir_path = data_dir.remote_source + # read the CSV data into Spark DataFrame + train_csv = spark.read.csv("%s/train.csv" % data_dir_path, header=True) + test_csv = spark.read.csv("%s/test.csv" % data_dir_path, header=True) + + store_csv = spark.read.csv("%s/store.csv" % data_dir_path, header=True) + store_states_csv = spark.read.csv("%s/store_states.csv" % data_dir_path, header=True) + state_names_csv = spark.read.csv("%s/state_names.csv" % data_dir_path, header=True) + google_trend_csv = spark.read.csv("%s/googletrend.csv" % data_dir_path, header=True) + weather_csv = spark.read.csv("%s/weather.csv" % data_dir_path, header=True) + + # retrieve a sampled subset of the train and test data + if hp.sample_rate: + train_csv = train_csv.sample(withReplacement=False, fraction=hp.sample_rate) + test_csv = test_csv.sample(withReplacement=False, fraction=hp.sample_rate) + + # prepare the DataFrames from the CSV files + train_df = prepare_df( + train_csv, + store_csv, + store_states_csv, + state_names_csv, + google_trend_csv, + weather_csv, + ).cache() + test_df = prepare_df( + test_csv, + store_csv, + store_states_csv, + state_names_csv, + google_trend_csv, + weather_csv, + ).cache() + + # add elapsed times from the data spanning training & test datasets + elapsed_cols = ["Promo", "StateHoliday", "SchoolHoliday"] + elapsed = add_elapsed( + train_df.select("Date", "Store", *elapsed_cols).unionAll(test_df.select("Date", "Store", *elapsed_cols)), + elapsed_cols, + ) + + # join with the elapsed times + train_df = train_df.join(elapsed, ["Date", "Store"]).select( + train_df["*"], + *[prefix + col for prefix in ["Before", "After"] for col in elapsed_cols], + ) + test_df = test_df.join(elapsed, ["Date", "Store"]).select( + test_df["*"], + *[prefix + col for prefix in ["Before", "After"] for col in elapsed_cols], + ) + + # filter out zero sales + train_df = train_df.filter(train_df.Sales > 0) + + print("===================") + print("Prepared data frame") + print("===================") + train_df.show() + + all_cols = CATEGORICAL_COLS + CONTINUOUS_COLS + + # select features + train_df = train_df.select(*(all_cols + ["Sales", "Date"])).cache() + test_df = test_df.select(*(all_cols + ["Id", "Date"])).cache() + + # build a vocabulary of categorical columns + vocab = build_vocabulary( + train_df.select(*CATEGORICAL_COLS).unionAll(test_df.select(*CATEGORICAL_COLS)).cache(), + ) + + # cast continuous columns to float + train_df = cast_columns(train_df, CONTINUOUS_COLS + ["Sales"]) + # search for a key and return a list of values based on a key + train_df = lookup_columns(train_df, vocab) + test_df = cast_columns(test_df, CONTINUOUS_COLS) + test_df = lookup_columns(test_df, vocab) + + # split into training & validation + # test set is in 2015, use the same period in 2014 from the training set as a validation set + test_min_date = test_df.agg(F.min(test_df.Date)).collect()[0][0] + test_max_date = test_df.agg(F.max(test_df.Date)).collect()[0][0] + one_year = datetime.timedelta(365) + train_df = train_df.withColumn( + "Validation", + (train_df.Date > test_min_date - one_year) & (train_df.Date <= test_max_date - one_year), + ) + + # determine max Sales number + max_sales = train_df.agg(F.max(train_df.Sales)).collect()[0][0] + + # convert Sales to log domain + train_df = train_df.withColumn("Sales", F.log(train_df.Sales)) + + print("===================================") + print("Data frame with transformed columns") + print("===================================") + train_df.show() + + print("================") + print("Data frame sizes") + print("================") + + # filter out column validation from the DataFrame, and get the count + train_rows = train_df.filter(~train_df.Validation).count() + val_rows = train_df.filter(train_df.Validation).count() + test_rows = test_df.count() + + # print the number of rows in training, validation and test data + print("Training: %d" % train_rows) + print("Validation: %d" % val_rows) + print("Test: %d" % test_rows) + + return max_sales, vocab, train_df, test_df + + +# %% [markdown] +# ## Training +# +# We use `KerasEstimator` in Horovod to train our Keras model on an existing pre-processed Spark DataFrame. +# The Estimator leverages Horovod's ability to scale across multiple workers, thereby eliminating any specialized code to perform distributed training. +# %% +def train( + max_sales: float, + vocab: Dict[str, List[Any]], + hp: Hyperparameters, + work_dir: FlyteDirectory, + train_df: pyspark.sql.DataFrame, + working_dir: FlyteDirectory, +): + print("==============") + print("Model training") + print("==============") + + # a method to determine root mean square percentage error of exponential of predictions + def exp_rmspe(y_true, y_pred): + """Competition evaluation metric, expects logarmithic inputs.""" + pct = tf.square((tf.exp(y_true) - tf.exp(y_pred)) / tf.exp(y_true)) + + # compute mean excluding stores with zero denominator + x = tf.reduce_sum(tf.where(y_true > 0.001, pct, tf.zeros_like(pct))) + y = tf.reduce_sum(tf.where(y_true > 0.001, tf.ones_like(pct), tf.zeros_like(pct))) + return tf.sqrt(x / y) + + def act_sigmoid_scaled(x): + """Sigmoid scaled to logarithm of maximum sales scaled by 20%.""" + return tf.nn.sigmoid(x) * tf.math.log(max_sales) * 1.2 + + # NOTE: exp_rmse and act_sigmoid_scaled functions are not placed at the module level + # this is because we cannot explicitly send max_sales as an argument to act_sigmoid_scaled since it is an activation function + # two of them are custom objects, and placing one at the module level and the other within the function doesn't really add up + + all_cols = CATEGORICAL_COLS + CONTINUOUS_COLS + CUSTOM_OBJECTS = {"exp_rmspe": exp_rmspe, "act_sigmoid_scaled": act_sigmoid_scaled} + + # disable GPUs when building the model to prevent memory leaks + if LooseVersion(tf.__version__) >= LooseVersion("2.0.0"): + # See https://github.com/tensorflow/tensorflow/issues/33168 + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + else: + K.set_session(tf.Session(config=tf.ConfigProto(device_count={"GPU": 0}))) + + # build the Keras model + inputs = {col: Input(shape=(1,), name=col) for col in all_cols} + embeddings = [ + Embedding(len(vocab[col]), 10, input_length=1, name="emb_" + col)(inputs[col]) for col in CATEGORICAL_COLS + ] + continuous_bn = Concatenate()([Reshape((1, 1), name="reshape_" + col)(inputs[col]) for col in CONTINUOUS_COLS]) + continuous_bn = BatchNormalization()(continuous_bn) + x = Concatenate()(embeddings + [continuous_bn]) + x = Flatten()(x) + x = Dense(1000, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x) + x = Dense(1000, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x) + x = Dense(1000, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x) + x = Dense(500, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x) + x = Dropout(0.5)(x) + # specify element-wise activation + output = Dense(1, activation=act_sigmoid_scaled)(x) + model = tf.keras.Model([inputs[f] for f in all_cols], output) + # display the details of the Keras model + model.summary() + + opt = tf.keras.optimizers.Adam(lr=hp.learning_rate, epsilon=1e-3) + + # checkpoint callback to specify the options for the returned Keras model + ckpt_callback = BestModelCheckpoint(monitor="val_loss", mode="auto", save_freq="epoch") + + # create an object of Store class + store = Store.create(work_dir.remote_source) + # 'SparkBackend' uses `horovod.spark.run` to execute the distributed training function, and + # returns a list of results by running 'train' on every worker in the cluster + backend = SparkBackend( + num_proc=hp.num_proc, + stdout=sys.stdout, + stderr=sys.stderr, + prefix_output_with_timestamp=True, + ) + # define a Spark Estimator that fits Keras models to a DataFrame + keras_estimator = hvd.KerasEstimator( + backend=backend, + store=store, + model=model, + optimizer=opt, + loss="mae", + metrics=[exp_rmspe], + custom_objects=CUSTOM_OBJECTS, + feature_cols=all_cols, + label_cols=["Sales"], + validation="Validation", + batch_size=hp.batch_size, + epochs=hp.epochs, + verbose=2, + checkpoint_callback=ckpt_callback, + ) + + # The Estimator hides the following details: + # 1. Binding Spark DataFrames to a deep learning training script + # 2. Reading data into a format that can be interpreted by the training framework + # 3. Distributed training using Horovod + # the user would provide a Keras model to the `KerasEstimator`` + # this `KerasEstimator`` will fit the data and store it in a Spark DataFrame + keras_model = keras_estimator.fit(train_df).setOutputCols(["Sales_output"]) + # retrieve the model training history + history = keras_model.getHistory() + best_val_rmspe = min(history["val_exp_rmspe"]) + print("Best RMSPE: %f" % best_val_rmspe) + + # save the trained model + keras_model.save(os.path.join(working_dir, hp.local_checkpoint_file)) + print("Written checkpoint to %s" % os.path.join(working_dir, hp.local_checkpoint_file)) + # the Estimator returns a Transformer representation of the trained model once training is complete + return keras_model + + +# %% [markdown] +# ## Evaluation +# +# We use the model transformer to forecast sales. +# %% +def test( + keras_model, + working_dir: FlyteDirectory, + test_df: pyspark.sql.DataFrame, + hp: Hyperparameters, +) -> FlyteDirectory: + + print("================") + print("Final prediction") + print("================") + + pred_df = keras_model.transform(test_df) + pred_df.printSchema() + pred_df.show(5) + # convert from log domain to real Sales numbers + pred_df = pred_df.withColumn("Sales_pred", F.exp(pred_df.Sales_output)) + + submission_df = pred_df.select(pred_df.Id.cast(T.IntegerType()), pred_df.Sales_pred).toPandas() + submission_df.sort_values(by=["Id"]).to_csv(os.path.join(working_dir, hp.local_submission_csv), index=False) + # predictions are saved to a CSV file. + print("Saved predictions to %s" % hp.local_submission_csv) + + return working_dir + + +# %% [markdown] +# ## Defining the Spark Task +# +# Flyte provides an easy-to-use interface to specify Spark-related attributes. +# The Spark attributes need to be attached to a specific task, and just like that, Flyte can run Spark jobs natively on Kubernetes clusters! +# Within the task, let's call the data pre-processing, training, and evaluation functions. +# +# :::{note} +# To set up Spark, refer to {ref}`flyte-and-spark`. +# ::: +# %% +@task( + task_config=Spark( + # the below configuration is applied to the Spark cluster + spark_conf={ + "spark.driver.memory": "2000M", + "spark.executor.memory": "2000M", + "spark.executor.cores": "1", + "spark.executor.instances": "2", + "spark.driver.cores": "1", + "spark.sql.shuffle.partitions": "16", + "spark.worker.timeout": "120", + } + ), + cache=True, + cache_version="0.2", + requests=Resources(mem="1Gi"), + limits=Resources(mem="1Gi"), +) +def horovod_spark_task(data_dir: FlyteDirectory, hp: Hyperparameters, work_dir: FlyteDirectory) -> FlyteDirectory: + + max_sales, vocab, train_df, test_df = data_preparation(data_dir, hp) + + # working directory will have the model and predictions as separate files + working_dir = flytekit.current_context().working_directory + + keras_model = train( + max_sales, + vocab, + hp, + work_dir, + train_df, + working_dir, + ) + + # generate predictions + return test(keras_model, working_dir, test_df, hp) + + +# %% [markdown] +# Lastly, we define a workflow to run the pipeline. +# %% +@workflow +def horovod_spark_wf( + dataset: str = "https://cdn.discordapp.com/attachments/545481172399030272/886952942903627786/rossmann.tgz", + hp: Hyperparameters = Hyperparameters(), + work_dir: FlyteDirectory = "s3://flyte-demo/horovod-tmp/", +) -> FlyteDirectory: + data_dir = download_data(dataset=dataset) + + # work_dir corresponds to the Horovod-Spark store + return horovod_spark_task(data_dir=data_dir, hp=hp, work_dir=work_dir) + + +# %% [markdown] +# ## Running the Model Locally +# +# We can run the code locally too, provided Spark is enabled and the plugin is set up in the environment. +# +# %% +if __name__ == "__main__": + metrics_directory = horovod_spark_wf() + print(f"Find the model and predictions at {metrics_directory}") diff --git a/docs/examples/forecasting_sales/requirements.in b/docs/examples/forecasting_sales/requirements.in new file mode 100644 index 000000000..771c894a4 --- /dev/null +++ b/docs/examples/forecasting_sales/requirements.in @@ -0,0 +1,7 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-spark>=0.16.0 +pyspark==3.0.1 +s3fs diff --git a/docs/examples/forecasting_sales/requirements.txt b/docs/examples/forecasting_sales/requirements.txt new file mode 100644 index 000000000..3f137afd8 --- /dev/null +++ b/docs/examples/forecasting_sales/requirements.txt @@ -0,0 +1,223 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +aiobotocore==2.2.0 + # via s3fs +aiohttp==3.8.1 + # via + # aiobotocore + # s3fs +aioitertools==0.10.0 + # via aiobotocore +aiosignal==1.2.0 + # via aiohttp +arrow==1.2.2 + # via jinja2-time +async-timeout==4.0.2 + # via aiohttp +attrs==21.4.0 + # via aiohttp +binaryornot==0.4.4 + # via cookiecutter +botocore==1.24.21 + # via aiobotocore +certifi==2021.10.8 + # via requests +cffi==1.15.1 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via + # aiohttp + # requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==37.0.4 + # via secretstorage +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via flytekitplugins-spark +flytekitplugins-spark==0.32.3 + # via -r requirements.in +frozenlist==1.3.0 + # via + # aiohttp + # aiosignal +fsspec==2022.3.0 + # via s3fs +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via + # requests + # yarl +importlib-metadata==4.11.3 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.0 + # via botocore +keyring==23.5.0 + # via flytekit +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +multidict==6.0.2 + # via + # aiohttp + # yarl +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # pandas + # pyarrow +packaging==21.3 + # via marshmallow +pandas==1.4.2 + # via flytekit +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +py4j==0.10.9 + # via pyspark +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pyparsing==3.0.8 + # via packaging +pyspark==3.0.1 + # via + # -r requirements.in + # flytekitplugins-spark +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +s3fs==2022.3.0 + # via -r requirements.in +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # botocore + # flytekit + # requests + # responses +wheel==0.37.1 + # via flytekit +wrapt==1.14.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.7.2 + # via aiohttp +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/greatexpectations_plugin/Dockerfile b/docs/examples/greatexpectations_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/greatexpectations_plugin/README.md b/docs/examples/greatexpectations_plugin/README.md new file mode 100644 index 000000000..f9ba3c625 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/README.md @@ -0,0 +1,123 @@ +(great-expectations)= + +# Great Expectations + +```{eval-rst} +.. tags:: Integration, Data, DataFrame, Intermediate +``` + +```{image} https://img.shields.io/badge/Blog-Great%20Expectations-blue?style=for-the-badge +:target: https://blog.flyte.org/data-quality-enforcement-using-great-expectations-and-flyte +:alt: Great Expectations Blog Post +``` + +**Great Expectations** is a Python-based open-source library for validating, documenting, and profiling your data. +It helps maintain data quality and improve communication about data between teams. + +The goodness of data validation in Great Expectations can be integrated with Flyte to validate the data moving in and out of +the pipeline entities you may have defined in Flyte. This helps establish stricter boundaries around your data to +ensure that everything is as you expected and hence, data will not crash your pipelines anymore unexpectedly! + +## How to Define the Integration + +Great Expectations supports native execution of expectations against various [Datasources](https://docs.greatexpectations.io/docs/terms/datasource/), +such as Pandas dataframes, Spark dataframes, and SQL databases via SQLAlchemy. + +We're supporting two Flyte types that should suit Great Expectations' `Datasources`: + +- {py:class}`flytekit.types.file.FlyteFile`: `FlyteFile` represents an automatic persistence object in Flyte. + It can represent files in remote storage and Flyte transparently materializes them in every task execution. +- {py:class}`flytekit.types.structured.StructuredDataset`: `StructuredDataset` supports pandas dataframes, which the plugin will convert into a parquet file and validate the data using Great Expectations. + +:::{note} +Flyte types are added because, in Great Expectations, we have the privilege to give a non-string (Pandas/Spark DataFrame) when using a +`RuntimeDataConnector` +but not when using an +`InferredAssetFilesystemDataConnector` +or a +`ConfiguredAssetFilesystemDataConnector`. +For the latter case, with the integration of Flyte types, we can give a Pandas/Spark DataFrame or a remote URI as the dataset. +::: + +The datasources can be well-integrated with the plugin using the following two modes: + +- **Flyte Task**: A Flyte task defines the task prototype that one could use within a task or a workflow to validate data using Great Expectations. +- **Flyte Type**: A Flyte type helps attach the `GreatExpectationsType` to any dataset. + Under the hood, `GreatExpectationsType` can be assumed as a combination of Great Expectations and Flyte types where every data is validated against the expectations, much like the OpenAPI Spec or the gRPC validator. + +(validation-failure)= + +### Data Validation Failure + +If the data validation fails, the plugin will raise a `GreatExpectationsValidationError`. + +For example, this is how the error message looks on the Flyte UI: + +```bash +Traceback (most recent call last): +... +great_expectations.marshmallow__shade.exceptions.ValidationError: Validation failed! +COLUMN FAILED EXPECTATION +passenger_count -> expect_column_min_to_be_between +passenger_count -> expect_column_mean_to_be_between +passenger_count -> expect_column_quantile_values_to_be_between +passenger_count -> expect_column_values_to_be_in_set +passenger_count -> expect_column_proportion_of_unique_values_to_be_between +trip_distance -> expect_column_max_to_be_between +trip_distance -> expect_column_mean_to_be_between +trip_distance -> expect_column_median_to_be_between +trip_distance -> expect_column_quantile_values_to_be_between +trip_distance -> expect_column_proportion_of_unique_values_to_be_between +rate_code_id -> expect_column_max_to_be_between +rate_code_id -> expect_column_mean_to_be_between +rate_code_id -> expect_column_proportion_of_unique_values_to_be_between +``` + +## Plugin Parameters + +- **datasource_name**: Data source, in general, is the "name" we use in the Great Expectations config file. + A Datasource brings together a way of interacting with data (like a database or Spark cluster) and some specific data + (like a CSV file, or a database table). Moreover, data source assists in building batches out of data (for validation). +- **expectation_suite_name**: Defines the data validation. +- **data_connector_name**: Tells how the data batches have to be identified. + +### Optional Parameters + +- **context_root_dir**: Sets the path of the great expectations config directory. + +- **checkpoint_params**: Optional `SimpleCheckpoint` class parameters. + +- **batch_request_config**: Additional batch request configuration parameters. + + - data_connector_query: Query to request a data batch + - runtime_parameters: Parameters to be sent at run-time + - batch_identifiers: Batch identifiers + - batch_spec_passthrough: Reader method if your file doesn't have an extension + +- **data_asset_name**: Name of the data asset (to be used for `RuntimeBatchRequest`) + +- **local_file_path**: Helpful to download the given dataset to the user-given path + +:::{note} +You may always want to mention the **context_root_dir** parameter, as providing a path means no harm! +Moreover, **local_file_path** is essential when using `FlyteFile` and `FlyteSchema`. +::: + +## Plugin Installation + +To use the Great Expectations Flyte plugin, run the following command: + +```{eval-rst} +.. prompt:: bash $ + + pip install flytekitplugins-great_expectations +``` + +:::{note} +Make sure to run workflows from the "flytekit_plugins" directory. +::: + +```{auto-examples-toc} +task_example +type_example +``` diff --git a/docs/examples/greatexpectations_plugin/data/movies.sqlite b/docs/examples/greatexpectations_plugin/data/movies.sqlite new file mode 100644 index 000000000..2a6afb816 Binary files /dev/null and b/docs/examples/greatexpectations_plugin/data/movies.sqlite differ diff --git a/docs/examples/greatexpectations_plugin/data/yellow_tripdata_sample_2019-01.csv b/docs/examples/greatexpectations_plugin/data/yellow_tripdata_sample_2019-01.csv new file mode 100644 index 000000000..0a31825dc --- /dev/null +++ b/docs/examples/greatexpectations_plugin/data/yellow_tripdata_sample_2019-01.csv @@ -0,0 +1,10001 @@ +vendor_id,pickup_datetime,dropoff_datetime,passenger_count,trip_distance,rate_code_id,store_and_fwd_flag,pickup_location_id,dropoff_location_id,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount,congestion_surcharge +1,2019-01-15 03:36:12,2019-01-15 03:42:19,1,1.0,1,N,230,48,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +1,2019-01-25 18:20:32,2019-01-25 18:26:55,1,0.8,1,N,112,112,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-05 06:47:31,2019-01-05 06:52:19,1,1.1,1,N,107,4,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-09 15:08:02,2019-01-09 15:20:17,1,2.5,1,N,143,158,1,11.0,0.0,0.5,3.0,0.0,0.3,14.8, +1,2019-01-25 18:49:51,2019-01-25 18:56:44,1,0.8,1,N,246,90,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-26 18:24:17,2019-01-26 18:39:27,1,1.3,1,N,144,45,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-18 15:24:25,2019-01-18 15:40:01,1,3.6,1,N,13,68,1,14.5,0.0,0.5,3.8,0.0,0.3,19.1, +1,2019-01-03 14:56:38,2019-01-03 15:10:30,1,1.8,1,N,239,237,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-04 15:50:52,2019-01-04 16:36:47,1,20.8,2,N,132,238,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +1,2019-01-05 17:55:44,2019-01-05 18:10:41,1,1.2,1,N,264,264,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-20 22:28:16,2019-01-20 22:37:15,1,2.6,1,N,90,229,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +1,2019-01-17 09:50:15,2019-01-17 09:55:11,1,1.1,1,N,264,264,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-22 14:43:21,2019-01-22 14:52:39,1,1.1,1,N,237,236,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-29 20:39:15,2019-01-29 20:48:39,1,1.6,1,N,162,246,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +1,2019-01-03 21:09:26,2019-01-03 21:21:00,1,2.7,1,N,164,125,1,10.5,0.5,0.5,2.0,0.0,0.3,13.8, +1,2019-01-04 17:04:37,2019-01-04 17:08:47,1,1.1,1,N,263,140,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-29 07:53:40,2019-01-29 08:08:44,1,2.3,1,N,264,264,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-05 00:21:15,2019-01-05 00:25:42,1,0.6,1,N,100,170,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-30 21:03:49,2019-01-30 21:35:32,1,10.9,1,N,144,244,4,34.0,0.5,0.5,0.0,0.0,0.3,35.3,0.0 +1,2019-01-07 08:23:36,2019-01-07 09:02:53,1,15.4,1,N,132,208,1,44.0,0.0,0.5,3.0,5.76,0.3,53.56, +1,2019-01-10 17:23:12,2019-01-10 17:34:29,1,1.5,1,N,162,141,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-26 07:05:03,2019-01-26 07:12:29,1,2.2,1,N,186,237,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-27 10:45:04,2019-01-27 10:53:03,1,1.4,1,N,162,246,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-29 06:57:05,2019-01-29 06:59:45,1,0.4,1,N,229,161,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95,0.0 +1,2019-01-14 13:20:31,2019-01-14 13:30:50,1,1.7,1,N,48,162,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-26 16:44:14,2019-01-26 16:57:09,1,1.7,1,N,236,43,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-15 10:42:23,2019-01-15 11:09:12,1,7.1,2,N,223,142,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-11 23:14:56,2019-01-11 23:42:20,1,4.8,1,N,100,7,1,21.5,0.5,0.5,6.8,0.0,0.3,29.6, +1,2019-01-24 00:08:41,2019-01-24 00:20:31,1,2.8,1,N,231,186,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-05 23:42:05,2019-01-06 00:11:13,1,12.7,1,N,132,85,2,37.0,0.5,0.5,0.0,0.0,0.3,38.3, +1,2019-01-22 10:15:11,2019-01-22 10:41:28,1,2.7,1,N,162,144,1,16.5,0.0,0.5,3.45,0.0,0.3,20.75,0.0 +1,2019-01-21 16:29:25,2019-01-21 16:35:53,1,1.1,1,N,166,166,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-14 12:10:41,2019-01-14 12:30:09,1,3.8,1,N,68,13,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35, +1,2019-01-14 11:55:40,2019-01-14 12:08:04,1,0.7,1,N,100,234,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-03 21:15:08,2019-01-03 21:23:46,1,1.5,1,N,236,161,1,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-27 22:59:54,2019-01-27 23:22:18,1,11.3,1,N,186,92,1,32.5,0.5,0.5,7.9,5.76,0.3,47.46,0.0 +1,2019-01-31 07:17:18,2019-01-31 07:20:47,1,0.5,1,N,68,246,1,4.5,0.0,0.5,0.53,0.0,0.3,5.83,0.0 +1,2019-01-11 17:03:49,2019-01-11 17:15:17,1,1.7,1,N,140,43,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-04 00:39:36,2019-01-04 00:47:43,1,1.4,1,N,100,162,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-02 07:32:00,2019-01-02 07:52:09,1,3.9,1,N,167,75,1,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-14 09:48:11,2019-01-14 10:24:00,1,2.1,1,N,229,234,1,20.5,0.0,0.5,4.25,0.0,0.3,25.55, +1,2019-01-20 14:43:41,2019-01-20 14:52:24,1,1.5,1,N,79,234,1,7.5,0.0,0.5,2.05,0.0,0.3,10.35, +1,2019-01-30 11:52:06,2019-01-30 12:02:18,1,1.3,1,N,186,113,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-07 06:25:43,2019-01-07 06:27:55,1,0.6,1,N,262,140,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-07 08:45:50,2019-01-07 08:56:34,1,1.1,1,N,65,97,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-22 16:39:03,2019-01-22 16:50:25,1,2.1,1,N,170,211,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-22 19:13:02,2019-01-22 19:36:07,1,7.5,1,N,140,97,1,24.0,1.0,0.5,3.87,0.0,0.3,29.67,0.0 +1,2019-01-02 20:56:12,2019-01-02 21:17:27,1,4.6,1,N,249,236,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +1,2019-01-01 05:07:45,2019-01-01 05:19:02,1,3.1,1,N,264,264,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +1,2019-01-15 11:14:24,2019-01-15 11:25:18,1,1.5,1,N,162,246,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-23 21:45:44,2019-01-23 21:48:26,1,1.1,1,N,141,75,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-14 18:37:13,2019-01-14 18:41:16,1,0.4,1,N,237,237,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-07 11:05:06,2019-01-07 11:16:44,1,2.2,1,N,161,143,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-24 13:19:17,2019-01-24 13:32:02,1,1.2,1,N,161,237,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-23 23:39:37,2019-01-24 00:05:34,1,5.1,1,N,230,7,1,21.0,0.5,0.5,4.45,0.0,0.3,26.75,0.0 +1,2019-01-11 16:15:05,2019-01-11 16:52:55,1,11.6,5,N,170,130,1,75.0,0.0,0.0,8.0,5.76,0.3,89.06, +1,2019-01-25 20:04:00,2019-01-25 20:08:29,1,0.5,1,N,231,231,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-11 10:54:51,2019-01-11 10:55:42,1,2.0,1,N,141,141,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-28 13:46:19,2019-01-28 14:56:22,1,18.0,2,N,132,13,1,52.0,0.0,0.5,10.55,0.0,0.3,63.35,0.0 +1,2019-01-15 08:35:53,2019-01-15 08:38:15,1,0.0,1,N,170,233,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-23 14:17:54,2019-01-23 14:25:08,1,1.0,1,N,140,236,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-17 06:43:42,2019-01-17 06:46:34,1,0.5,1,N,141,162,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +1,2019-01-12 21:03:21,2019-01-12 21:25:00,1,4.9,1,N,79,75,2,18.0,0.5,0.5,0.0,0.0,0.3,19.3, +1,2019-01-21 19:22:31,2019-01-21 19:37:31,1,2.9,1,N,48,224,1,12.5,0.0,0.5,5.0,0.0,0.3,18.3,0.0 +1,2019-01-06 14:48:34,2019-01-06 14:59:15,1,1.8,1,N,264,264,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-17 19:28:15,2019-01-17 19:34:08,1,0.9,1,N,264,264,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-15 23:53:05,2019-01-16 00:01:31,1,1.6,1,N,264,264,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-21 10:59:33,2019-01-21 11:09:35,1,2.6,1,N,75,239,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +1,2019-01-22 09:10:46,2019-01-22 09:27:43,1,1.6,1,N,68,161,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-14 14:06:53,2019-01-14 14:17:56,1,1.0,2,N,170,234,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +1,2019-01-15 15:59:02,2019-01-15 16:10:05,1,1.2,1,N,142,237,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-09 22:56:31,2019-01-09 23:08:11,1,6.6,1,N,263,232,2,19.5,0.5,0.5,0.0,0.0,0.3,20.8, +1,2019-01-30 14:34:07,2019-01-30 14:45:55,1,1.2,1,N,148,114,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-28 14:11:52,2019-01-28 14:31:40,1,2.0,1,N,100,163,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +1,2019-01-31 14:49:55,2019-01-31 15:16:51,1,3.4,1,N,186,264,1,18.0,0.0,0.5,5.6,0.0,0.3,24.4,0.0 +1,2019-01-06 20:33:34,2019-01-06 20:45:10,1,3.2,1,N,138,7,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +1,2019-01-28 21:32:37,2019-01-28 21:44:25,1,2.3,1,N,100,79,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-10 16:26:17,2019-01-10 16:31:38,1,1.0,1,N,170,79,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-26 06:24:43,2019-01-26 07:11:31,1,7.4,1,N,146,224,3,33.5,0.0,0.5,0.0,0.0,0.3,34.3,0.0 +1,2019-01-22 09:00:33,2019-01-22 09:11:39,1,0.8,1,N,237,161,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-12 17:54:18,2019-01-12 18:03:26,1,1.3,1,N,107,186,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-12 13:00:27,2019-01-12 13:06:05,1,0.8,1,N,234,170,1,5.5,0.0,0.5,3.0,0.0,0.3,9.3, +1,2019-01-29 05:19:19,2019-01-29 05:29:53,1,6.4,1,N,132,134,1,19.0,0.5,0.5,4.05,0.0,0.3,24.35,0.0 +1,2019-01-01 17:56:15,2019-01-01 18:32:06,1,11.5,1,N,132,62,2,36.0,0.0,0.5,0.0,0.0,0.3,36.8, +1,2019-01-02 08:30:46,2019-01-02 08:42:51,1,2.0,1,N,162,140,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-14 05:21:23,2019-01-14 05:42:36,1,11.2,1,N,48,138,1,32.0,0.5,0.5,7.8,5.76,0.3,46.86, +1,2019-01-13 10:30:41,2019-01-13 10:38:38,1,2.0,1,N,237,164,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7, +1,2019-01-07 21:29:51,2019-01-07 21:45:36,1,3.9,1,N,100,238,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +1,2019-01-14 09:43:59,2019-01-14 10:01:33,1,3.4,1,N,263,233,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-25 20:05:23,2019-01-25 20:21:13,1,2.1,1,N,264,264,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-04 19:37:56,2019-01-04 19:46:53,1,0.8,1,N,264,264,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-11 12:15:00,2019-01-11 12:26:57,1,1.1,1,N,163,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-09 16:13:12,2019-01-09 16:33:12,1,1.6,1,N,161,236,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-02 10:15:42,2019-01-02 10:22:01,1,1.3,1,N,125,68,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-22 05:50:10,2019-01-22 05:53:42,1,1.0,1,N,162,107,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-10 15:15:33,2019-01-10 15:47:17,1,3.9,1,N,141,166,1,21.0,0.0,0.5,5.45,0.0,0.3,27.25, +1,2019-01-25 17:06:34,2019-01-25 17:25:34,1,5.2,1,N,87,170,1,19.5,1.0,0.5,6.35,0.0,0.3,27.65,0.0 +1,2019-01-11 14:08:12,2019-01-11 14:10:56,1,0.4,1,N,239,239,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-01 00:00:50,2019-01-01 00:31:48,1,18.0,2,N,132,140,1,52.0,0.0,0.5,7.0,0.0,0.3,59.8, +1,2019-01-29 08:17:06,2019-01-29 08:34:34,1,2.5,1,N,239,229,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-25 22:30:30,2019-01-25 22:37:24,1,1.0,1,N,230,142,4,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-15 20:45:23,2019-01-15 20:50:32,1,1.5,1,N,229,264,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +1,2019-01-15 10:32:59,2019-01-15 10:33:01,1,1.3,1,N,162,162,3,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-20 13:42:38,2019-01-20 13:52:11,1,1.5,1,N,230,246,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-07 16:47:51,2019-01-07 16:55:00,1,1.4,1,N,238,41,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-31 19:40:55,2019-01-31 19:46:11,1,0.7,1,N,237,163,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-05 11:42:24,2019-01-05 11:56:38,1,2.5,1,N,164,43,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-30 08:20:17,2019-01-30 08:35:26,1,1.8,1,N,231,79,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-02 09:40:45,2019-01-02 09:49:30,1,0.7,1,N,162,229,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-11 08:57:08,2019-01-11 09:03:28,1,0.7,1,N,162,237,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +1,2019-01-17 13:15:52,2019-01-17 13:22:00,1,1.0,1,N,79,114,3,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-21 18:50:22,2019-01-21 18:56:18,1,1.9,1,N,161,236,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-12 16:02:53,2019-01-12 16:09:51,1,2.3,1,N,137,232,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-29 07:27:18,2019-01-29 07:45:11,1,2.0,1,N,107,230,1,12.5,0.0,0.5,1.0,0.0,0.3,14.3,0.0 +1,2019-01-19 16:14:20,2019-01-19 16:20:01,1,0.8,1,N,100,50,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-15 18:42:50,2019-01-15 18:47:34,1,0.7,1,N,141,263,1,5.0,1.0,0.5,1.02,0.0,0.3,7.82, +1,2019-01-29 11:58:24,2019-01-29 12:24:50,1,1.7,1,N,186,142,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-20 17:44:52,2019-01-20 18:03:46,1,2.3,1,N,236,230,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-06 14:47:43,2019-01-06 14:56:33,1,1.5,1,N,151,41,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-02 08:01:46,2019-01-02 08:08:13,1,0.8,1,N,264,264,1,6.0,0.0,0.5,0.34,0.0,0.3,7.14, +1,2019-01-09 09:30:39,2019-01-09 09:36:10,1,0.8,1,N,48,230,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +1,2019-01-28 17:42:44,2019-01-28 17:59:11,1,2.8,1,N,163,151,1,12.5,1.0,0.5,2.0,0.0,0.3,16.3,0.0 +1,2019-01-27 11:32:49,2019-01-27 11:43:24,1,2.0,1,N,24,142,4,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-28 18:20:31,2019-01-28 18:43:05,1,8.1,1,N,138,116,1,26.0,1.0,0.5,6.7,5.76,0.3,40.26,0.0 +1,2019-01-06 11:42:32,2019-01-06 11:52:01,1,2.1,1,N,161,50,1,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-31 06:19:46,2019-01-31 06:37:52,1,4.4,1,N,264,264,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-31 20:41:25,2019-01-31 20:48:08,1,2.1,1,N,233,262,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-20 21:58:46,2019-01-20 22:08:26,1,2.0,1,N,142,229,1,9.0,0.5,0.5,2.55,0.0,0.3,12.85, +1,2019-01-11 07:56:34,2019-01-11 08:00:38,1,0.8,1,N,100,161,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-14 18:12:31,2019-01-14 18:33:56,1,2.6,1,N,264,264,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-28 07:22:30,2019-01-28 07:30:37,1,1.7,1,N,186,161,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-11 16:20:27,2019-01-11 16:28:41,1,0.6,1,N,164,234,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-07 18:25:46,2019-01-07 18:38:28,1,1.7,1,N,237,262,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-14 09:04:21,2019-01-14 09:06:59,1,0.5,1,N,162,163,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-22 15:05:04,2019-01-22 15:15:49,1,1.1,1,N,261,231,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-14 08:47:03,2019-01-14 09:13:05,1,2.5,1,N,237,107,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-21 13:38:03,2019-01-21 13:42:05,1,0.6,1,N,237,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-25 19:24:39,2019-01-25 19:35:29,1,1.4,1,N,230,143,1,8.5,1.0,0.5,2.55,0.0,0.3,12.85,0.0 +1,2019-01-11 14:31:37,2019-01-11 14:51:51,1,2.0,1,N,264,264,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-05 06:36:51,2019-01-05 06:50:42,1,3.72,1,N,68,236,1,14.0,0.0,0.5,1.0,0.0,0.3,15.8, +1,2019-01-23 15:22:13,2019-01-23 15:32:50,1,3.3,1,N,12,232,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +1,2019-01-05 12:07:08,2019-01-05 12:14:06,1,1.0,1,N,25,49,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-22 18:26:28,2019-01-22 18:35:38,1,1.69,1,N,142,238,1,8.0,1.0,0.5,1.2,0.0,0.3,11.0,0.0 +2,2019-01-20 23:03:56,2019-01-20 23:21:49,1,5.28,1,N,114,239,1,18.0,0.5,0.5,3.86,0.0,0.3,23.16, +2,2019-01-23 00:17:10,2019-01-23 00:32:58,1,2.75,1,N,161,79,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-28 12:58:13,2019-01-28 13:47:02,1,12.1,1,N,236,138,1,43.0,0.0,0.5,10.9,0.0,0.3,54.7,0.0 +2,2019-01-28 21:28:11,2019-01-28 21:46:24,1,4.73,1,N,141,129,1,17.0,0.5,0.5,5.49,0.0,0.3,23.79,0.0 +2,2019-01-19 09:30:43,2019-01-19 09:35:09,1,0.86,1,N,74,74,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-04 23:55:59,2019-01-05 00:06:47,1,3.0,1,N,237,41,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-04 07:29:03,2019-01-04 07:36:41,1,1.7,1,N,264,264,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-11 13:00:03,2019-01-11 13:00:26,1,0.0,1,N,79,79,3,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-17 18:39:19,2019-01-17 18:43:53,1,0.8,1,N,48,50,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-11 20:04:17,2019-01-11 20:26:21,1,11.3,1,N,100,220,2,33.0,0.5,0.5,0.0,2.64,0.3,36.94, +1,2019-01-02 02:42:52,2019-01-02 02:46:26,1,0.7,1,N,230,246,1,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-17 20:05:40,2019-01-17 20:10:23,1,0.6,1,N,234,170,1,5.0,0.5,0.5,0.63,0.0,0.3,6.93, +1,2019-01-17 15:11:15,2019-01-17 15:27:19,1,1.6,1,N,107,186,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-14 14:56:28,2019-01-14 15:05:07,1,1.36,1,N,161,164,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-20 16:49:05,2019-01-20 16:55:32,1,1.3,1,N,164,43,1,6.5,0.0,0.5,2.0,0.0,0.3,9.3, +2,2019-01-16 15:52:42,2019-01-16 15:58:47,1,0.95,1,N,142,239,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-31 07:56:50,2019-01-31 08:00:28,1,0.4,1,N,264,264,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +4,2019-01-22 08:35:33,2019-01-22 09:05:26,1,9.39,1,N,113,138,1,30.5,0.0,0.5,7.41,5.76,0.3,44.47, +2,2019-01-12 05:55:40,2019-01-12 06:00:11,1,0.59,1,N,48,68,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-23 18:33:56,2019-01-23 18:56:21,1,8.71,1,N,138,107,1,26.5,1.0,0.5,6.81,5.76,0.3,40.87,0.0 +2,2019-01-23 20:27:31,2019-01-23 20:50:16,1,3.85,1,N,113,140,1,16.5,0.5,0.5,1.2,0.0,0.3,19.0,0.0 +2,2019-01-18 14:03:51,2019-01-18 14:17:22,1,1.95,1,N,186,114,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-13 12:53:33,2019-01-13 13:20:29,1,19.7,2,N,263,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-09 02:15:11,2019-01-09 02:22:06,1,1.7,1,N,68,230,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-20 08:03:09,2019-01-20 08:08:08,1,0.7,1,N,246,246,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-21 18:10:15,2019-01-21 18:12:47,1,0.45,1,N,231,231,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +1,2019-01-13 20:58:44,2019-01-13 21:19:13,1,3.9,1,N,125,49,1,16.5,0.5,0.5,4.45,0.0,0.3,22.25, +2,2019-01-29 22:40:15,2019-01-29 22:51:08,1,2.45,1,N,161,239,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-15 08:06:12,2019-01-15 08:24:26,1,1.63,1,N,151,236,1,12.0,0.0,0.5,3.0,0.0,0.3,15.8, +2,2019-01-07 18:15:21,2019-01-07 18:24:40,1,1.05,1,N,140,236,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-10 22:32:13,2019-01-10 22:37:35,1,1.04,1,N,114,231,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8, +2,2019-01-08 17:15:39,2019-01-08 17:59:04,1,12.26,1,N,138,77,2,40.0,1.0,0.5,0.0,0.0,0.3,41.8, +2,2019-01-20 19:26:30,2019-01-20 19:31:31,1,0.9,1,N,114,113,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 07:51:32,2019-01-09 08:00:39,1,2.16,1,N,170,211,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-03 18:30:51,2019-01-03 18:38:42,1,1.2,1,N,237,263,1,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-23 21:45:26,2019-01-23 21:52:55,1,1.11,1,N,48,161,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-09 16:02:53,2019-01-09 16:16:10,1,0.8,1,N,264,264,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-25 10:59:27,2019-01-25 11:44:52,1,24.75,1,N,132,265,2,68.5,0.0,0.5,0.0,5.76,0.3,75.06,0.0 +2,2019-01-05 18:38:18,2019-01-05 18:54:57,1,1.88,1,N,48,137,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-07 14:59:14,2019-01-07 15:10:26,1,1.47,1,N,234,68,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-18 11:42:49,2019-01-18 11:57:24,1,1.85,1,N,249,161,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-29 20:19:27,2019-01-29 20:36:52,1,3.7,1,N,234,263,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-21 13:20:49,2019-01-21 13:26:15,1,0.8,1,N,141,141,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-08 20:22:05,2019-01-08 20:26:07,1,0.46,1,N,141,236,1,4.5,0.5,0.5,1.74,0.0,0.3,7.54, +2,2019-01-28 19:51:27,2019-01-28 19:58:56,1,1.29,1,N,107,68,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-30 21:46:51,2019-01-30 22:00:23,1,2.22,1,N,237,68,1,10.5,0.5,0.5,1.18,0.0,0.3,12.98,0.0 +1,2019-01-01 19:13:24,2019-01-01 19:15:30,1,0.4,1,N,238,151,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-25 16:10:51,2019-01-25 16:19:30,1,1.02,1,N,233,161,1,7.0,1.0,0.5,2.0,0.0,0.3,10.8,0.0 +1,2019-01-06 01:09:36,2019-01-06 01:23:56,1,2.5,1,N,87,79,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +2,2019-01-19 09:28:59,2019-01-19 09:38:27,1,2.43,1,N,263,74,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-10 13:10:07,2019-01-10 13:40:02,1,17.69,2,N,132,107,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-23 15:33:24,2019-01-23 15:44:42,1,1.6,1,N,237,239,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-30 18:03:18,2019-01-30 18:21:33,1,1.7,1,N,230,229,1,12.5,1.0,0.5,3.55,0.0,0.3,17.85,0.0 +1,2019-01-23 21:55:51,2019-01-23 22:00:41,1,0.5,1,N,107,234,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-29 17:04:02,2019-01-29 17:11:10,1,0.85,1,N,170,163,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-11 12:53:27,2019-01-11 13:11:20,1,2.3,1,N,229,236,1,12.5,0.0,0.5,2.39,0.0,0.3,15.69, +1,2019-01-24 14:02:50,2019-01-24 14:06:00,1,0.4,1,N,231,231,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-13 13:58:34,2019-01-13 14:00:36,1,0.42,1,N,164,161,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-02 10:26:40,2019-01-02 10:29:25,1,1.07,1,N,170,229,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-23 21:02:56,2019-01-23 21:14:10,1,1.76,1,N,163,234,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3,0.0 +1,2019-01-06 12:04:20,2019-01-06 12:21:36,1,3.5,1,N,261,246,1,15.0,0.0,0.5,3.5,0.0,0.3,19.3, +2,2019-01-20 23:27:41,2019-01-20 23:40:52,1,3.65,1,N,41,243,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-07 17:56:18,2019-01-07 18:24:27,1,9.34,1,N,138,148,2,30.5,1.0,0.5,0.0,0.0,0.3,32.3, +2,2019-01-29 18:37:58,2019-01-29 18:46:46,1,0.9,1,N,170,137,1,7.0,1.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-24 08:20:40,2019-01-24 08:44:02,1,2.04,1,N,141,161,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-29 07:29:12,2019-01-29 07:38:48,1,1.6,1,N,42,41,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-15 17:26:20,2019-01-15 17:32:59,1,0.76,1,N,163,162,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-02 18:22:15,2019-01-02 18:30:50,1,0.8,1,N,161,164,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-22 20:11:19,2019-01-22 20:19:58,1,1.15,1,N,186,246,1,7.0,0.5,0.5,1.5,0.0,0.3,9.8,0.0 +2,2019-01-25 17:57:34,2019-01-25 18:11:48,1,3.17,1,N,50,231,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-05 16:34:55,2019-01-05 16:51:17,1,3.0,1,N,170,263,1,13.5,0.0,0.5,1.43,0.0,0.3,15.73, +1,2019-01-07 16:16:46,2019-01-07 16:20:41,1,0.5,1,N,236,262,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-17 19:39:25,2019-01-17 19:57:48,1,2.74,1,N,230,158,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-01 16:04:58,2019-01-01 16:12:16,1,1.69,1,N,261,211,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-30 20:02:19,2019-01-30 20:15:59,1,2.03,1,N,161,107,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-04 13:38:20,2019-01-04 13:45:10,1,0.8,1,N,249,107,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-01 16:30:13,2019-01-01 16:53:25,1,9.27,1,N,237,138,1,28.5,0.0,0.5,5.86,0.0,0.3,35.16, +1,2019-01-15 07:56:57,2019-01-15 08:08:58,1,3.8,1,N,229,232,1,13.5,0.0,0.5,1.0,0.0,0.3,15.3, +2,2019-01-19 14:38:40,2019-01-19 15:05:19,1,10.56,1,N,138,230,2,32.5,0.0,0.5,0.0,5.76,0.3,39.06, +2,2019-01-12 02:34:41,2019-01-12 02:45:13,1,3.61,1,N,164,263,1,12.5,0.5,0.5,4.14,0.0,0.3,17.94, +2,2019-01-12 18:28:23,2019-01-12 18:36:22,1,1.47,1,N,236,141,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-21 21:10:31,2019-01-21 21:16:24,1,2.5,1,N,236,74,1,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-22 11:10:50,2019-01-22 11:11:17,1,0.1,1,N,236,236,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-07 08:44:05,2019-01-07 09:02:34,1,6.32,1,N,140,87,1,21.0,0.0,0.5,2.0,0.0,0.3,23.8, +2,2019-01-31 09:04:08,2019-01-31 09:07:13,1,0.61,1,N,262,140,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-25 22:51:42,2019-01-25 23:04:06,1,1.71,1,N,142,170,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-05 21:46:43,2019-01-05 21:59:26,1,2.35,1,N,100,143,1,11.0,0.5,0.5,3.08,0.0,0.3,15.38, +2,2019-01-02 14:04:18,2019-01-02 14:23:23,1,2.41,1,N,229,246,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88, +1,2019-01-08 22:14:14,2019-01-08 22:30:51,1,3.5,1,N,230,231,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75, +1,2019-01-17 08:19:22,2019-01-17 08:28:41,1,0.8,1,N,230,161,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-08 15:44:24,2019-01-08 15:53:28,1,1.08,1,N,236,237,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-30 04:54:28,2019-01-30 05:09:55,1,5.4,1,N,236,114,1,17.5,0.5,0.5,3.5,0.0,0.3,22.3,0.0 +2,2019-01-14 07:20:17,2019-01-14 07:34:28,1,2.4,1,N,107,231,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-17 10:53:55,2019-01-17 10:59:04,1,0.6,1,N,239,239,1,5.0,0.0,0.5,2.0,0.0,0.3,7.8, +2,2019-01-13 17:18:33,2019-01-13 17:25:41,1,1.05,1,N,141,229,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-30 09:36:09,2019-01-30 09:48:55,1,1.43,1,N,246,246,1,9.5,0.0,0.5,1.7,0.0,0.3,12.0,0.0 +2,2019-01-06 00:01:52,2019-01-06 00:10:13,1,1.5,1,N,68,48,1,7.5,0.5,0.5,2.64,0.0,0.3,11.44, +2,2019-01-11 20:12:35,2019-01-11 20:20:39,1,1.23,1,N,48,68,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +1,2019-01-10 07:58:29,2019-01-10 08:14:30,1,2.2,1,N,43,140,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-20 20:41:18,2019-01-20 20:53:32,1,4.91,1,N,132,130,2,16.5,0.5,0.5,0.0,0.0,0.3,17.8, +1,2019-01-21 18:21:31,2019-01-21 18:25:05,1,0.4,1,N,231,13,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-19 20:20:14,2019-01-19 20:28:39,1,1.32,1,N,170,163,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-21 18:15:20,2019-01-21 18:21:17,1,1.7,1,N,229,262,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +4,2019-01-19 20:18:58,2019-01-19 20:31:29,1,1.34,1,N,107,246,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-17 14:42:54,2019-01-17 14:50:53,1,0.8,1,N,234,107,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-16 18:25:57,2019-01-16 18:27:27,1,0.34,1,N,141,141,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-20 17:27:32,2019-01-20 18:04:41,1,21.35,2,N,239,132,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-29 21:40:43,2019-01-29 21:45:19,1,0.82,1,N,186,246,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-08 17:38:03,2019-01-08 17:51:45,1,1.35,1,N,162,230,1,9.5,1.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-28 13:41:37,2019-01-28 13:47:48,1,0.94,1,N,164,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-19 08:51:26,2019-01-19 08:59:41,1,2.47,1,N,238,230,1,9.5,0.0,0.5,3.09,0.0,0.3,13.39, +1,2019-01-17 18:01:30,2019-01-17 18:17:25,1,2.4,1,N,142,143,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-05 21:49:42,2019-01-05 22:03:03,1,3.62,1,N,161,7,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +1,2019-01-14 08:07:15,2019-01-14 08:12:40,1,0.5,1,N,48,48,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-28 12:37:11,2019-01-28 12:45:35,1,1.4,1,N,238,236,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-09 07:02:16,2019-01-09 07:05:21,1,0.65,1,N,79,148,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-16 13:48:48,2019-01-16 13:54:47,1,0.93,1,N,68,113,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-01 07:11:52,2019-01-01 07:21:01,1,2.43,1,N,164,148,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-06 15:28:22,2019-01-06 15:34:20,1,1.22,1,N,142,48,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-02 15:36:18,2019-01-02 15:46:02,1,1.8,1,N,264,264,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-21 21:39:51,2019-01-21 22:05:19,1,7.22,1,N,237,160,1,25.0,0.5,0.5,5.26,0.0,0.3,31.56,0.0 +2,2019-01-05 16:05:56,2019-01-05 16:31:07,1,3.74,1,N,211,163,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-06 10:27:22,2019-01-06 10:29:38,1,0.32,1,N,90,90,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-14 20:18:32,2019-01-14 20:23:08,1,0.6,1,N,162,237,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +2,2019-01-25 13:42:25,2019-01-25 13:50:27,1,0.66,1,N,237,140,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +4,2019-01-15 09:51:33,2019-01-15 10:17:08,1,2.43,1,N,236,162,1,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-19 09:54:39,2019-01-19 09:57:39,1,0.7,1,N,90,186,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-04 23:58:28,2019-01-05 00:00:10,1,0.6,1,N,237,263,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-14 13:17:06,2019-01-14 13:23:51,1,1.2,1,N,142,238,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-10 16:47:58,2019-01-10 16:58:46,1,1.1,1,N,161,48,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-15 23:48:01,2019-01-16 00:01:23,1,2.65,1,N,79,87,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +1,2019-01-30 15:32:09,2019-01-30 15:46:12,1,1.1,1,N,230,162,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +2,2019-01-19 10:37:50,2019-01-19 10:45:55,1,1.3,1,N,151,239,1,7.5,0.0,0.5,1.24,0.0,0.3,9.54, +1,2019-01-07 12:15:34,2019-01-07 12:24:47,1,1.3,1,N,79,211,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-27 13:17:55,2019-01-27 13:48:27,1,10.2,1,N,138,68,1,32.0,0.0,0.5,7.7,5.76,0.3,46.26,0.0 +1,2019-01-24 14:39:34,2019-01-24 14:43:07,1,0.4,1,N,236,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +1,2019-01-01 12:04:25,2019-01-01 12:10:44,1,1.7,1,N,230,239,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-29 06:35:02,2019-01-29 06:54:01,1,6.12,1,N,140,223,1,20.5,0.0,0.5,5.41,5.76,0.3,32.47,0.0 +2,2019-01-21 20:37:21,2019-01-21 20:42:13,1,0.97,1,N,162,237,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-20 14:41:45,2019-01-20 14:44:57,1,0.67,1,N,100,90,1,4.5,0.0,0.5,0.26,0.0,0.3,5.56, +2,2019-01-26 18:01:48,2019-01-26 18:23:16,1,3.7,1,N,164,263,1,16.0,0.0,0.5,2.45,0.0,0.3,19.25,0.0 +2,2019-01-26 08:50:18,2019-01-26 09:08:30,1,4.07,1,N,231,230,1,16.0,0.0,0.5,4.2,0.0,0.3,21.0,0.0 +1,2019-01-31 18:59:12,2019-01-31 19:03:53,1,0.3,1,N,233,229,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3,0.0 +1,2019-01-07 18:57:34,2019-01-07 19:04:34,1,1.1,1,N,230,170,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-26 19:45:57,2019-01-26 19:50:06,1,0.4,1,N,237,141,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-29 14:30:18,2019-01-29 14:46:02,1,1.74,1,N,107,163,1,11.0,0.0,0.5,1.18,0.0,0.3,12.98,0.0 +2,2019-01-04 14:29:05,2019-01-04 14:57:51,1,7.43,1,N,87,236,1,26.0,0.0,0.5,6.7,0.0,0.3,33.5, +2,2019-01-26 17:22:48,2019-01-26 17:38:42,1,2.96,1,N,229,90,1,12.5,0.0,0.5,3.32,0.0,0.3,16.62,0.0 +1,2019-01-25 05:54:50,2019-01-25 06:02:30,1,1.4,1,N,161,137,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-16 15:51:59,2019-01-16 15:56:34,1,0.5,1,N,237,141,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-12 11:40:24,2019-01-12 12:01:50,1,4.85,1,N,239,145,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +2,2019-01-16 18:11:59,2019-01-16 18:31:33,1,1.64,1,N,236,237,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-30 12:54:16,2019-01-30 13:15:43,1,2.8,1,N,186,237,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55,0.0 +1,2019-01-31 19:04:40,2019-01-31 19:16:10,1,0.6,1,N,230,100,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-01 22:38:05,2019-01-01 23:00:30,1,1.34,1,N,162,48,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +1,2019-01-27 00:36:04,2019-01-27 01:06:54,1,4.7,1,N,107,17,2,23.0,0.5,0.5,0.0,0.0,0.3,24.3,0.0 +2,2019-01-16 12:50:20,2019-01-16 12:57:17,1,1.42,1,N,141,236,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-25 13:06:54,2019-01-25 13:12:11,1,0.63,1,N,50,100,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-09 08:01:01,2019-01-09 08:10:49,1,1.6,1,N,236,74,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-10 14:08:03,2019-01-10 14:24:13,1,6.6,1,N,138,134,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +1,2019-01-01 22:50:39,2019-01-01 23:15:29,1,17.4,1,N,132,21,1,46.5,0.5,0.5,4.78,0.0,0.3,52.58, +2,2019-01-10 12:40:55,2019-01-10 12:51:41,1,1.69,1,N,234,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-12 15:06:06,2019-01-12 15:16:59,1,1.58,1,N,125,209,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-29 09:03:55,2019-01-29 09:20:41,1,2.48,1,N,79,229,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8,0.0 +2,2019-01-27 14:16:55,2019-01-27 14:26:35,1,2.18,1,N,246,231,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8,0.0 +2,2019-01-19 20:50:05,2019-01-19 21:01:26,1,1.19,1,N,249,144,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +1,2019-01-06 15:19:19,2019-01-06 15:23:13,1,0.7,1,N,186,90,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-24 18:57:52,2019-01-24 19:04:22,1,0.5,1,N,79,114,1,5.5,1.0,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-09 22:20:50,2019-01-09 22:30:15,1,1.97,1,N,100,50,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-03 16:33:16,2019-01-03 16:46:26,1,2.1,1,N,140,162,1,11.0,1.0,0.5,1.0,0.0,0.3,13.8, +2,2019-01-09 21:46:46,2019-01-09 21:54:09,1,1.59,1,N,186,113,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-18 21:54:12,2019-01-18 21:56:58,1,0.84,1,N,141,162,1,4.5,0.5,0.5,1.45,0.0,0.3,7.25, +2,2019-01-13 12:51:08,2019-01-13 12:51:25,1,0.0,1,N,140,140,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +2,2019-01-18 21:03:01,2019-01-18 21:09:55,1,1.24,1,N,141,236,1,7.0,0.5,0.5,2.49,0.0,0.3,10.79, +2,2019-01-06 08:50:13,2019-01-06 09:02:54,1,4.84,5,N,22,227,1,15.61,0.0,0.0,0.0,0.0,0.3,15.91, +2,2019-01-18 20:22:42,2019-01-18 20:37:08,1,1.65,1,N,164,79,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +1,2019-01-23 19:17:40,2019-01-23 19:43:45,1,3.2,1,N,161,231,1,17.0,1.0,0.5,1.88,0.0,0.3,20.68,0.0 +1,2019-01-01 05:13:50,2019-01-01 05:32:44,1,9.3,4,N,132,265,1,33.5,0.5,0.5,3.0,0.0,0.3,37.8, +2,2019-01-10 20:18:09,2019-01-10 20:20:54,1,0.52,1,N,48,142,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +2,2019-01-27 03:11:35,2019-01-27 03:29:10,1,7.71,1,N,48,42,1,24.0,0.5,0.5,5.06,0.0,0.3,30.36,0.0 +2,2019-01-14 12:21:55,2019-01-14 12:48:51,1,3.14,1,N,113,237,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +1,2019-01-27 14:56:55,2019-01-27 15:03:02,1,0.6,1,N,144,211,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-18 11:43:46,2019-01-18 12:06:31,1,1.82,1,N,233,107,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-06 16:42:13,2019-01-06 17:18:29,1,7.1,1,N,239,232,1,28.5,0.0,0.5,2.0,0.0,0.3,31.3, +2,2019-01-29 03:15:48,2019-01-29 03:24:07,1,1.8,1,N,211,234,1,8.5,0.5,0.5,1.0,0.0,0.3,10.8,0.0 +2,2019-01-11 12:30:02,2019-01-11 12:34:03,1,0.37,1,N,260,260,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-24 09:08:00,2019-01-24 09:22:20,1,1.41,1,N,233,234,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84,0.0 +2,2019-01-26 06:18:27,2019-01-26 06:30:46,1,5.91,1,N,74,234,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-09 21:00:23,2019-01-09 21:01:45,1,0.0,1,N,230,230,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3, +2,2019-01-11 12:14:17,2019-01-11 12:19:19,1,0.82,1,N,143,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-31 16:02:14,2019-01-31 16:10:01,1,1.1,1,N,100,137,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-21 18:04:21,2019-01-21 18:13:55,1,1.7,1,N,170,237,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-12 07:57:13,2019-01-12 08:05:11,1,1.7,1,N,74,236,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +2,2019-01-05 19:58:53,2019-01-05 20:07:08,1,0.8,1,N,234,107,1,6.5,0.5,0.5,0.78,0.0,0.3,8.58, +2,2019-01-18 07:11:45,2019-01-18 07:18:29,1,1.17,1,N,48,161,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +1,2019-01-14 11:48:31,2019-01-14 11:52:14,1,0.6,1,N,163,161,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6, +2,2019-01-27 16:37:18,2019-01-27 16:56:34,1,2.11,1,N,186,143,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +4,2019-01-01 21:45:15,2019-01-01 21:52:54,1,0.91,1,N,48,230,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-24 23:07:38,2019-01-24 23:13:20,1,0.99,1,N,164,162,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-18 10:05:46,2019-01-18 10:18:17,1,1.11,1,N,233,107,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-19 17:31:27,2019-01-19 17:52:39,1,4.94,1,N,164,151,1,19.0,0.0,0.5,4.95,0.0,0.3,24.75, +2,2019-01-16 20:53:26,2019-01-16 20:57:36,1,0.88,1,N,263,140,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88, +1,2019-01-22 12:59:38,2019-01-22 13:25:39,1,3.0,1,N,100,236,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25,0.0 +2,2019-01-21 20:08:32,2019-01-21 20:32:14,1,2.97,1,N,237,68,1,16.0,0.5,0.5,2.0,0.0,0.3,19.3,0.0 +2,2019-01-28 19:06:06,2019-01-28 19:13:40,1,1.4,1,N,186,48,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-15 12:41:52,2019-01-15 12:51:22,1,1.64,1,N,163,170,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-28 14:23:13,2019-01-28 14:30:11,1,1.15,1,N,237,162,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-15 09:33:15,2019-01-15 09:48:46,1,1.49,1,N,163,233,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +1,2019-01-06 11:02:25,2019-01-06 11:05:45,1,0.9,1,N,137,229,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-06 20:36:38,2019-01-06 20:42:54,1,0.95,1,N,170,186,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +1,2019-01-16 12:33:45,2019-01-16 12:48:45,1,1.6,1,N,230,234,1,10.5,0.0,0.5,2.8,0.0,0.3,14.1, +1,2019-01-07 19:23:11,2019-01-07 19:26:13,1,0.6,1,N,114,249,1,4.0,1.0,0.5,1.7,0.0,0.3,7.5, +2,2019-01-20 18:15:11,2019-01-20 18:37:59,1,10.66,1,N,138,113,1,31.5,0.0,0.5,6.46,0.0,0.3,38.76, +1,2019-01-26 00:29:13,2019-01-26 00:33:03,1,0.7,1,N,161,161,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-10 18:22:39,2019-01-10 18:36:22,1,1.83,1,N,161,143,1,10.0,1.0,0.5,1.2,0.0,0.3,13.0, +2,2019-01-10 09:15:10,2019-01-10 09:21:34,1,1.72,1,N,140,229,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-31 09:50:13,2019-01-31 09:54:45,1,0.57,1,N,236,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-11 17:31:54,2019-01-11 17:48:40,1,3.75,1,N,13,48,1,15.0,1.0,0.5,3.36,0.0,0.3,20.16, +1,2019-01-16 15:34:17,2019-01-16 16:10:40,1,11.2,1,N,138,164,1,35.0,0.0,0.5,4.0,5.76,0.3,45.56, +1,2019-01-29 15:38:47,2019-01-29 15:51:45,1,2.6,1,N,143,68,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-24 14:14:51,2019-01-24 14:25:40,1,2.2,1,N,41,236,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8,0.0 +1,2019-01-29 19:14:22,2019-01-29 19:32:23,1,2.5,1,N,262,151,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-05 18:57:15,2019-01-05 19:06:33,1,2.6,1,N,238,74,1,9.5,1.0,0.5,2.83,0.0,0.3,14.13, +2,2019-01-30 20:57:34,2019-01-30 21:00:43,1,0.36,1,N,161,162,1,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-08 01:56:02,2019-01-08 01:58:00,1,0.5,1,N,148,79,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-06 14:11:51,2019-01-06 14:32:00,1,2.6,1,N,262,161,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-14 18:15:54,2019-01-14 18:21:30,1,0.7,1,N,137,170,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-27 21:39:59,2019-01-27 21:51:49,1,2.33,1,N,114,164,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-22 10:56:43,2019-01-22 11:19:14,1,3.07,1,N,163,43,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-10 15:30:18,2019-01-10 16:03:43,1,13.12,1,N,138,79,1,37.5,0.0,0.5,8.81,5.76,0.3,52.87, +2,2019-01-12 01:12:03,2019-01-12 01:18:18,1,1.04,1,N,186,48,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +2,2019-01-16 22:21:59,2019-01-16 22:34:54,1,2.75,1,N,263,238,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +2,2019-01-18 11:50:44,2019-01-18 12:05:37,1,1.13,1,N,170,186,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-04 13:50:38,2019-01-04 13:56:05,1,1.04,1,N,239,50,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-06 07:45:32,2019-01-06 07:54:20,1,2.74,1,N,236,170,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-25 09:53:22,2019-01-25 10:05:40,1,1.11,1,N,186,170,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-06 12:36:01,2019-01-06 12:45:01,1,0.55,1,N,48,230,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-31 22:07:52,2019-01-31 22:17:15,1,1.9,1,N,163,246,1,8.5,0.5,0.5,2.0,0.0,0.3,11.8,0.0 +1,2019-01-17 10:02:09,2019-01-17 10:18:59,1,2.2,1,N,162,142,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-24 08:03:44,2019-01-24 08:13:09,1,0.82,1,N,230,161,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-14 14:17:02,2019-01-14 14:27:18,1,1.6,1,N,142,236,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-05 03:59:22,2019-01-05 04:01:50,1,0.0,1,N,162,170,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-08 22:33:06,2019-01-08 22:40:52,1,1.1,1,N,230,233,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +1,2019-01-16 10:28:07,2019-01-16 10:39:41,1,2.1,1,N,236,140,1,9.5,0.0,0.5,1.24,0.0,0.3,11.54, +1,2019-01-05 20:02:48,2019-01-05 20:23:21,1,2.1,1,N,237,90,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75, +2,2019-01-24 00:22:40,2019-01-24 00:28:21,1,1.11,1,N,88,231,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-18 18:15:39,2019-01-18 18:20:49,1,0.5,1,N,238,151,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-05 17:59:18,2019-01-05 18:42:14,1,10.8,1,N,138,50,1,38.0,0.0,0.5,5.82,0.0,0.3,44.62, +2,2019-01-10 23:28:40,2019-01-10 23:39:13,1,1.15,1,N,161,233,1,8.0,0.5,0.5,1.4,0.0,0.3,10.7, +2,2019-01-14 17:35:38,2019-01-14 17:42:59,1,1.02,1,N,162,170,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-12 12:07:04,2019-01-12 12:20:39,1,2.17,1,N,229,186,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-07 20:16:02,2019-01-07 20:29:52,1,2.02,1,N,249,107,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-18 06:18:19,2019-01-18 06:21:17,1,0.8,1,N,48,163,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-04 09:08:34,2019-01-04 09:18:30,1,0.86,1,N,186,170,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-19 19:47:42,2019-01-19 19:52:08,1,0.58,1,N,113,234,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-08 13:45:12,2019-01-08 14:02:07,1,3.25,1,N,43,233,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-06 02:08:51,2019-01-06 02:14:35,1,1.17,1,N,68,186,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-12 06:25:05,2019-01-12 06:32:26,1,0.9,1,N,45,231,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-28 08:59:11,2019-01-28 09:08:15,1,0.7,1,N,162,100,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-29 19:53:55,2019-01-29 20:02:25,1,3.4,1,Y,238,50,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-26 19:12:43,2019-01-26 19:24:36,1,2.4,1,N,142,41,1,11.0,0.0,0.5,0.83,0.0,0.3,12.63,0.0 +2,2019-01-26 07:55:41,2019-01-26 07:58:42,1,0.57,1,N,239,142,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-13 11:05:34,2019-01-13 11:13:27,1,1.1,1,N,107,90,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-25 19:06:05,2019-01-25 19:13:35,1,1.13,1,N,161,170,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-27 22:25:27,2019-01-27 22:34:15,1,1.5,1,N,114,231,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-26 19:32:45,2019-01-26 19:49:12,1,7.62,1,N,142,244,1,23.0,0.0,0.5,4.76,0.0,0.3,28.56,0.0 +2,2019-01-05 13:28:50,2019-01-05 13:32:53,1,0.69,1,N,161,43,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-29 16:45:34,2019-01-29 16:45:34,1,0.0,1,Y,186,264,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-07 11:12:43,2019-01-07 11:26:22,1,2.09,1,N,164,246,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-29 17:55:46,2019-01-29 18:12:01,1,3.07,1,N,237,151,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-15 11:01:19,2019-01-15 11:37:51,1,17.27,2,N,132,170,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-16 17:21:32,2019-01-16 17:36:21,1,4.3,1,N,231,143,1,15.0,1.0,0.5,3.35,0.0,0.3,20.15, +2,2019-01-28 18:48:24,2019-01-28 19:03:37,1,2.12,1,N,170,236,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-25 21:26:00,2019-01-25 21:55:26,1,3.8,1,N,142,79,1,19.5,0.5,0.5,2.0,0.0,0.3,22.8,0.0 +1,2019-01-25 11:29:32,2019-01-25 11:39:37,1,0.8,1,N,114,158,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-15 17:14:46,2019-01-15 17:24:02,1,1.88,1,N,261,249,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-18 19:21:26,2019-01-18 19:28:39,1,1.8,1,N,263,74,1,7.5,1.0,0.5,2.0,0.0,0.3,11.3, +2,2019-01-24 14:43:31,2019-01-24 14:50:05,1,0.81,1,N,142,239,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-19 19:15:54,2019-01-19 19:24:44,1,1.38,1,N,161,234,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-25 00:46:48,2019-01-25 01:04:41,1,5.81,1,N,229,116,1,18.0,0.5,0.5,3.0,0.0,0.3,22.3,0.0 +2,2019-01-29 09:29:55,2019-01-29 10:33:45,1,11.39,1,N,168,261,1,45.0,0.0,0.5,0.0,5.76,0.3,51.56,0.0 +2,2019-01-03 20:33:21,2019-01-03 20:40:19,1,1.42,1,N,233,107,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +1,2019-01-13 07:48:38,2019-01-13 07:56:58,1,0.6,1,N,100,246,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-26 00:42:03,2019-01-26 01:09:28,1,9.04,1,N,249,89,1,29.0,0.5,0.5,7.58,0.0,0.3,37.88,0.0 +1,2019-01-14 12:43:59,2019-01-14 12:57:22,1,1.6,1,N,142,161,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-31 18:22:40,2019-01-31 18:35:15,1,2.59,1,N,142,151,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-24 07:23:27,2019-01-24 07:39:03,1,4.95,1,N,162,209,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +2,2019-01-31 11:44:48,2019-01-31 11:52:10,1,0.77,1,N,164,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-16 08:27:45,2019-01-16 08:33:05,1,0.7,1,N,162,229,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +1,2019-01-01 00:50:20,2019-01-01 01:07:42,1,2.3,1,N,68,79,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-13 10:44:34,2019-01-13 10:53:54,1,2.71,1,N,170,263,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-23 08:45:19,2019-01-23 08:54:16,1,0.62,1,N,161,230,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49,0.0 +2,2019-01-03 21:41:39,2019-01-03 21:47:55,1,1.27,1,N,141,140,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-17 17:02:43,2019-01-17 17:08:00,1,0.9,1,N,236,237,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +1,2019-01-12 23:31:00,2019-01-12 23:39:41,1,1.0,1,N,264,264,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-08 19:08:28,2019-01-08 19:14:23,1,1.2,1,N,239,236,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-17 16:44:33,2019-01-17 17:02:12,1,5.3,1,N,98,56,1,18.0,1.0,0.5,0.0,0.0,0.3,19.8, +1,2019-01-11 14:44:01,2019-01-11 15:05:26,1,1.0,1,N,163,161,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15, +1,2019-01-07 13:34:49,2019-01-07 13:57:52,1,4.6,1,N,141,42,1,18.0,0.0,0.5,3.75,0.0,0.3,22.55, +2,2019-01-11 06:39:07,2019-01-11 06:49:53,1,2.22,1,N,68,161,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-22 11:11:44,2019-01-22 11:27:55,1,1.98,1,N,164,142,1,11.5,0.0,0.5,1.0,0.0,0.3,13.3,0.0 +2,2019-01-01 17:46:23,2019-01-01 17:57:41,1,1.97,1,N,237,142,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-30 22:32:15,2019-01-30 22:43:59,1,2.0,1,N,161,239,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-19 13:32:37,2019-01-19 13:47:28,1,3.1,1,N,236,164,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-28 06:28:21,2019-01-28 06:29:46,1,0.4,1,N,68,68,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15,0.0 +2,2019-01-29 20:33:54,2019-01-29 21:00:31,1,16.53,2,N,132,170,1,52.0,0.0,0.5,3.0,5.76,0.3,61.56,0.0 +2,2019-01-14 16:48:34,2019-01-14 17:29:57,1,10.41,1,N,138,161,1,36.5,1.0,0.5,8.81,5.76,0.3,52.87, +2,2019-01-10 20:15:19,2019-01-10 20:29:54,1,2.81,1,N,170,236,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-10 11:34:15,2019-01-10 11:38:39,1,0.69,1,N,164,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-20 08:00:34,2019-01-20 08:02:50,1,0.73,1,N,239,142,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +2,2019-01-02 21:53:32,2019-01-02 21:58:00,1,1.05,1,N,234,158,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-13 11:45:38,2019-01-13 11:52:16,1,1.5,1,N,48,68,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-08 19:57:11,2019-01-08 20:01:11,1,0.6,1,N,262,262,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-03 16:48:29,2019-01-03 16:53:31,1,0.9,1,N,234,107,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-16 19:29:05,2019-01-16 19:41:44,1,1.98,1,N,48,143,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-27 10:56:44,2019-01-27 11:09:53,1,2.04,1,N,107,45,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-02 21:49:34,2019-01-02 22:00:23,1,2.12,1,N,238,143,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-21 18:34:12,2019-01-21 18:43:02,1,1.42,1,N,141,142,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-02 08:30:35,2019-01-02 09:15:48,1,17.19,2,N,132,234,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-28 12:10:43,2019-01-28 12:19:19,1,0.5,1,N,140,141,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-14 20:28:03,2019-01-14 20:32:57,1,0.58,1,N,142,142,1,5.0,0.5,0.5,1.89,0.0,0.3,8.19, +1,2019-01-24 20:46:11,2019-01-24 20:57:20,1,2.2,1,N,170,142,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5,0.0 +1,2019-01-25 09:01:00,2019-01-25 09:11:06,1,0.8,1,N,137,164,1,7.5,0.0,0.5,2.0,0.0,0.3,10.3,0.0 +1,2019-01-23 23:06:03,2019-01-23 23:13:32,1,2.5,1,N,142,236,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-31 15:04:50,2019-01-31 15:23:08,1,2.49,1,N,246,161,1,13.0,0.0,0.5,1.25,0.0,0.3,15.05,0.0 +2,2019-01-14 19:14:50,2019-01-14 19:40:21,1,7.51,1,N,261,236,1,25.0,1.0,0.5,5.36,0.0,0.3,32.16, +2,2019-01-23 13:55:43,2019-01-23 14:30:06,1,3.45,1,N,164,231,1,22.0,0.0,0.5,2.5,0.0,0.3,25.3,0.0 +2,2019-01-25 22:13:13,2019-01-25 22:25:29,1,6.56,1,N,138,80,1,19.5,0.5,0.5,6.24,0.0,0.3,27.04,0.0 +2,2019-01-23 00:03:38,2019-01-23 00:17:45,1,2.55,1,N,151,42,1,12.0,0.5,0.5,2.0,0.0,0.3,15.3,0.0 +1,2019-01-29 16:12:25,2019-01-29 16:17:29,1,0.7,1,N,141,262,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-13 08:58:36,2019-01-13 09:06:19,1,2.1,1,N,48,158,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-21 08:31:33,2019-01-21 08:45:25,1,2.81,1,N,246,229,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8,0.0 +2,2019-01-19 18:15:28,2019-01-19 18:27:07,1,1.29,1,N,100,90,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-03 11:17:14,2019-01-03 11:45:04,1,8.5,5,N,95,140,1,25.74,0.0,0.5,0.0,0.0,0.3,26.54, +2,2019-01-16 13:05:03,2019-01-16 13:30:58,1,9.0,1,N,186,138,2,29.0,0.0,0.5,0.0,5.76,0.3,35.56, +1,2019-01-08 08:58:41,2019-01-08 09:02:22,1,0.5,1,N,161,230,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-21 13:38:08,2019-01-21 13:41:49,1,0.9,1,N,140,263,1,5.0,0.0,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-14 18:39:00,2019-01-14 18:41:10,1,0.52,1,N,238,238,1,3.5,1.0,0.5,1.0,0.0,0.3,6.3, +2,2019-01-28 16:47:23,2019-01-28 17:01:19,1,1.63,1,N,236,151,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-11 18:24:22,2019-01-11 18:27:48,1,0.4,1,N,229,162,1,4.0,1.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-19 09:32:50,2019-01-19 09:43:38,1,1.83,1,N,262,162,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +2,2019-01-20 18:03:48,2019-01-20 18:13:00,1,1.24,1,N,48,164,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-06 20:41:48,2019-01-06 20:47:18,1,1.4,1,N,170,79,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +2,2019-01-17 13:29:18,2019-01-17 13:29:21,1,0.0,5,N,264,11,3,-0.01,0.0,0.0,0.0,0.0,-0.3,-0.31, +1,2019-01-14 15:48:29,2019-01-14 15:48:29,1,0.0,1,Y,141,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-05 15:25:18,2019-01-05 15:54:06,1,4.3,1,N,114,236,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +2,2019-01-17 19:56:29,2019-01-17 20:03:24,1,1.16,1,N,68,230,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38, +1,2019-01-29 09:54:31,2019-01-29 10:05:26,1,1.0,1,N,100,164,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-10 08:22:12,2019-01-10 08:26:16,1,0.58,1,N,143,50,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-04 20:54:36,2019-01-04 20:59:08,1,0.7,1,N,234,170,1,5.0,0.5,0.5,1.55,0.0,0.3,7.85, +2,2019-01-18 21:01:08,2019-01-18 21:48:53,1,5.46,1,N,140,50,2,29.5,0.5,0.5,0.0,0.0,0.3,30.8, +1,2019-01-17 08:51:24,2019-01-17 09:03:22,1,2.7,1,N,13,68,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95, +1,2019-01-30 20:44:53,2019-01-30 21:01:07,1,2.9,1,N,263,143,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-02 08:26:05,2019-01-02 08:34:16,1,1.37,1,N,162,107,1,7.5,0.0,0.5,1.2,0.0,0.3,9.5, +1,2019-01-09 19:29:21,2019-01-09 19:32:52,1,0.9,1,N,143,239,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-23 11:49:26,2019-01-23 11:57:04,1,0.7,1,N,263,141,1,6.5,0.0,0.5,1.1,0.0,0.3,8.4,0.0 +1,2019-01-17 12:39:29,2019-01-17 12:56:40,1,5.6,1,N,236,244,1,19.5,0.0,0.5,4.05,0.0,0.3,24.35, +1,2019-01-30 09:56:19,2019-01-30 10:01:28,1,0.7,1,N,164,234,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-15 16:58:56,2019-01-15 17:09:34,1,1.71,1,N,239,43,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-17 14:31:40,2019-01-17 15:11:10,1,10.77,1,N,211,138,1,35.5,0.0,0.5,7.26,0.0,0.3,43.56, +1,2019-01-26 20:49:28,2019-01-26 20:52:43,1,0.6,1,N,141,237,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8,0.0 +1,2019-01-15 12:36:35,2019-01-15 13:14:27,1,18.1,3,N,170,1,1,70.5,0.0,0.0,5.0,10.5,0.3,86.3, +2,2019-01-11 08:35:36,2019-01-11 08:39:28,1,0.32,1,N,162,161,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-21 22:54:52,2019-01-21 23:06:51,1,2.3,1,N,234,50,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-12 21:09:53,2019-01-12 21:20:47,1,5.5,1,N,263,79,1,17.0,0.5,0.5,3.0,0.0,0.3,21.3, +1,2019-01-15 14:55:42,2019-01-15 15:01:08,1,0.3,1,N,163,161,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-12 18:59:48,2019-01-12 19:12:50,1,0.9,1,N,79,90,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +2,2019-01-30 07:51:58,2019-01-30 07:57:30,1,1.08,1,N,234,113,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-22 11:55:21,2019-01-22 12:25:07,1,2.86,1,N,161,43,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36,0.0 +2,2019-01-14 07:31:05,2019-01-14 07:33:10,1,0.74,1,N,229,170,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-18 18:24:02,2019-01-18 18:29:58,1,0.93,1,N,74,74,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-11 15:49:16,2019-01-11 16:35:52,1,13.8,1,N,132,260,1,42.5,1.0,0.5,8.85,0.0,0.3,53.15, +1,2019-01-13 13:17:54,2019-01-13 13:26:42,1,1.1,1,N,107,114,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-10 12:40:48,2019-01-10 12:54:32,1,1.67,1,N,262,237,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-22 11:52:25,2019-01-22 12:24:12,1,0.0,99,N,193,193,2,2.5,0.8,0.5,0.0,0.0,0.0,3.8,0.0 +1,2019-01-08 15:40:18,2019-01-08 15:54:06,1,2.0,1,N,161,107,1,10.5,0.0,0.5,1.13,0.0,0.3,12.43, +1,2019-01-16 08:45:16,2019-01-16 08:52:44,1,0.7,1,N,237,237,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-01 21:32:32,2019-01-01 21:42:45,1,2.24,1,N,230,90,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +2,2019-01-19 01:04:30,2019-01-19 01:12:31,1,0.81,1,N,189,49,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-20 15:37:31,2019-01-20 15:43:59,1,0.8,1,N,211,79,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-08 05:12:57,2019-01-08 05:27:40,1,5.07,1,N,107,112,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-22 18:09:49,2019-01-22 18:16:48,1,0.48,1,N,162,170,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-24 15:52:51,2019-01-24 17:09:39,1,17.15,2,N,237,132,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56,0.0 +1,2019-01-31 02:19:52,2019-01-31 02:26:05,1,1.0,1,N,230,230,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-12 17:20:16,2019-01-12 17:27:02,1,1.1,1,N,249,68,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-13 10:54:07,2019-01-13 11:00:46,1,1.18,1,N,261,144,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-22 18:46:46,2019-01-22 19:03:12,1,2.34,1,N,143,186,1,12.0,1.0,0.5,3.45,0.0,0.3,17.25,0.0 +2,2019-01-16 11:33:13,2019-01-16 11:49:36,1,2.86,1,N,186,239,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-07 07:33:29,2019-01-07 07:44:15,1,1.2,1,N,74,41,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-05 14:51:00,2019-01-05 14:53:08,1,0.3,1,N,141,141,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +2,2019-01-01 02:01:04,2019-01-01 02:03:47,1,0.05,1,N,79,79,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-04 14:12:28,2019-01-04 14:18:24,1,0.8,1,N,140,237,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-12 14:02:47,2019-01-12 14:20:28,1,3.7,1,N,237,4,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55, +2,2019-01-14 06:23:07,2019-01-14 06:27:53,1,0.77,1,N,137,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-26 11:56:31,2019-01-26 12:05:59,1,1.0,1,N,161,50,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-31 08:05:46,2019-01-31 08:11:11,1,0.8,1,N,100,230,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-22 01:08:51,2019-01-22 01:35:54,1,19.65,2,N,132,79,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +1,2019-01-24 23:33:05,2019-01-24 23:48:25,1,3.6,1,N,141,68,1,14.0,0.5,0.5,3.05,0.0,0.3,18.35,0.0 +2,2019-01-04 12:43:38,2019-01-04 12:50:06,1,0.89,1,N,162,140,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-20 14:09:27,2019-01-20 14:18:05,1,1.59,1,N,142,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-25 13:20:02,2019-01-25 13:46:32,1,4.6,1,N,87,100,1,20.0,0.0,0.5,1.0,0.0,0.3,21.8,0.0 +2,2019-01-24 13:51:35,2019-01-24 14:05:52,1,0.98,1,N,186,161,1,9.5,0.0,0.5,3.09,0.0,0.3,13.39,0.0 +1,2019-01-22 11:26:18,2019-01-22 11:49:18,1,7.5,1,N,244,161,1,25.5,0.0,0.5,5.0,0.0,0.3,31.3,0.0 +1,2019-01-05 16:36:50,2019-01-05 16:53:15,1,2.1,1,N,162,143,1,12.0,0.0,0.5,3.8,0.0,0.3,16.6, +1,2019-01-06 18:58:36,2019-01-06 19:40:16,1,23.5,1,N,132,220,1,64.0,0.0,0.5,14.0,5.76,0.3,84.56, +1,2019-01-11 15:01:07,2019-01-11 15:19:23,1,2.2,1,N,162,263,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-04 18:35:19,2019-01-04 18:54:02,1,3.01,1,N,162,79,1,14.5,1.0,0.5,3.26,0.0,0.3,19.56, +2,2019-01-26 08:10:34,2019-01-26 08:20:40,1,2.46,1,N,41,239,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-23 11:48:33,2019-01-23 11:55:04,1,0.71,1,N,234,90,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-27 17:39:30,2019-01-27 17:55:58,1,1.75,1,N,107,186,1,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-31 11:15:23,2019-01-31 11:35:07,1,2.9,1,N,143,186,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-06 04:42:26,2019-01-06 04:46:25,1,0.6,1,N,236,262,1,5.0,0.5,0.5,0.63,0.0,0.3,6.93, +4,2019-01-29 11:09:39,2019-01-29 11:21:39,1,2.35,1,N,13,45,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-08 19:47:12,2019-01-08 19:58:22,1,1.63,1,N,164,229,1,9.0,0.5,0.5,3.09,0.0,0.3,13.39, +2,2019-01-23 08:16:51,2019-01-23 08:27:16,1,1.16,1,N,238,75,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +1,2019-01-12 01:25:55,2019-01-12 01:30:37,1,0.4,1,N,79,79,3,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-14 15:10:59,2019-01-14 15:30:59,1,2.54,1,N,236,143,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-21 02:15:54,2019-01-21 02:21:24,1,1.25,1,N,186,48,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14,0.0 +1,2019-01-11 01:29:57,2019-01-11 01:32:34,1,0.7,1,N,137,170,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67, +1,2019-01-08 13:11:04,2019-01-08 13:22:07,1,1.2,1,N,43,236,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-09 15:02:48,2019-01-09 15:12:06,1,1.1,1,N,170,162,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-05 09:50:48,2019-01-05 09:53:36,1,0.67,1,N,236,263,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3, +2,2019-01-03 13:15:15,2019-01-03 13:19:59,1,0.93,1,N,238,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-28 00:12:15,2019-01-28 00:16:49,1,0.8,1,N,226,146,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +1,2019-01-28 19:58:58,2019-01-28 20:04:58,1,0.8,1,N,230,142,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-01 01:24:54,2019-01-01 01:41:10,1,2.94,1,N,141,239,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +1,2019-01-14 22:44:47,2019-01-14 23:07:35,1,5.1,1,N,48,112,2,20.0,0.5,0.5,0.0,0.0,0.3,21.3, +2,2019-01-10 11:15:02,2019-01-10 11:26:15,1,1.25,1,N,170,161,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-02 23:17:05,2019-01-02 23:36:37,1,6.1,1,N,114,238,1,21.5,0.5,0.5,2.28,0.0,0.3,25.08, +2,2019-01-15 17:17:33,2019-01-15 17:22:22,1,0.61,1,N,238,238,1,5.0,1.0,0.5,1.02,0.0,0.3,7.82, +1,2019-01-21 18:13:02,2019-01-21 18:20:34,1,1.3,1,N,162,141,1,7.5,0.0,0.5,2.05,0.0,0.3,10.35,0.0 +2,2019-01-27 13:18:02,2019-01-27 14:06:18,1,21.93,2,N,132,68,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-24 11:54:37,2019-01-24 11:59:37,1,0.87,1,N,52,33,1,5.0,0.0,0.5,1.5,0.0,0.3,7.3,0.0 +2,2019-01-16 15:48:31,2019-01-16 16:38:34,1,17.9,5,N,161,1,1,90.0,0.0,0.5,15.0,19.5,0.3,125.3, +2,2019-01-15 14:13:54,2019-01-15 14:51:45,1,10.03,1,N,246,138,1,35.5,0.0,0.5,5.0,5.76,0.3,47.06, +1,2019-01-12 05:28:02,2019-01-12 05:31:07,1,0.6,1,N,148,232,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +2,2019-01-28 06:12:10,2019-01-28 06:22:35,1,3.27,1,N,186,140,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-17 07:46:37,2019-01-17 07:58:38,1,1.5,1,N,4,79,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +1,2019-01-03 20:29:48,2019-01-03 20:37:50,1,1.0,1,N,186,48,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-10 18:31:47,2019-01-10 18:42:35,1,1.65,1,N,163,141,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-01 17:10:13,2019-01-01 17:20:40,1,2.15,1,N,262,229,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-26 22:26:30,2019-01-26 22:45:11,1,2.68,1,N,249,163,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-18 13:33:55,2019-01-18 13:39:14,1,1.06,1,N,142,237,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-03 20:33:54,2019-01-03 20:53:02,1,2.9,1,N,237,113,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +2,2019-01-07 23:28:11,2019-01-07 23:35:14,1,1.04,1,N,79,148,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-14 09:52:25,2019-01-14 10:00:53,1,1.88,1,N,140,229,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-02 21:44:54,2019-01-02 21:58:30,1,2.0,1,N,181,228,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +2,2019-01-10 07:19:03,2019-01-10 07:38:09,1,6.43,1,N,13,162,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36, +2,2019-01-24 16:44:22,2019-01-24 16:47:16,1,0.37,1,N,141,140,1,4.0,1.0,0.5,0.87,0.0,0.3,6.67,0.0 +1,2019-01-18 11:32:23,2019-01-18 11:44:07,1,0.8,1,N,158,249,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-14 13:32:07,2019-01-14 13:42:06,1,1.4,1,N,229,164,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-30 14:26:17,2019-01-30 14:34:06,1,0.6,1,N,100,170,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-26 20:08:46,2019-01-26 20:18:18,1,0.67,1,N,125,144,1,7.5,0.5,0.5,2.64,0.0,0.3,11.44,0.0 +1,2019-01-06 16:14:57,2019-01-06 16:28:55,1,6.6,2,N,134,132,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +1,2019-01-18 07:46:33,2019-01-18 07:54:51,1,1.2,1,N,234,161,1,7.0,0.0,0.5,0.78,0.0,0.3,8.58, +2,2019-01-22 12:33:46,2019-01-22 13:18:58,1,5.8,1,N,161,231,1,30.0,0.0,0.5,4.62,0.0,0.3,35.42,0.0 +1,2019-01-11 16:53:38,2019-01-11 17:09:01,1,2.5,1,N,143,236,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15, +2,2019-01-30 14:56:45,2019-01-30 15:10:59,1,1.65,1,N,113,137,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-02 14:18:06,2019-01-02 14:34:22,1,9.12,1,N,138,140,2,26.0,0.0,0.5,0.0,5.76,0.3,32.56, +1,2019-01-30 01:13:42,2019-01-30 01:33:07,1,6.9,1,N,231,238,1,22.0,0.5,0.5,5.0,0.0,0.3,28.3,0.0 +1,2019-01-17 14:22:06,2019-01-17 14:27:34,1,0.9,1,N,236,236,1,6.0,0.0,0.5,0.68,0.0,0.3,7.48, +1,2019-01-15 10:57:00,2019-01-15 11:06:38,1,1.4,1,N,100,249,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-13 21:49:26,2019-01-13 21:59:30,1,1.6,1,N,234,230,1,8.5,0.5,0.5,3.0,0.0,0.3,12.8, +2,2019-01-24 13:30:48,2019-01-24 13:44:13,1,0.97,1,N,237,161,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-16 09:07:33,2019-01-16 09:15:28,1,1.1,1,N,262,140,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-17 18:31:57,2019-01-17 19:12:01,1,9.6,1,N,246,255,1,33.0,1.0,0.5,6.0,0.0,0.3,40.8, +2,2019-01-28 09:53:05,2019-01-28 09:55:17,1,0.45,1,N,142,142,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-14 10:58:02,2019-01-14 11:22:04,1,9.12,1,N,138,230,1,29.0,0.0,0.5,7.11,5.76,0.3,42.67, +1,2019-01-25 22:04:13,2019-01-25 22:31:59,1,4.0,1,N,239,107,1,19.5,0.5,0.5,3.12,0.0,0.3,23.92,0.0 +2,2019-01-21 12:02:53,2019-01-21 12:29:11,1,4.16,1,N,166,100,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76,0.0 +1,2019-01-22 21:58:44,2019-01-22 22:13:34,1,3.2,1,N,48,141,1,12.5,0.5,0.5,2.07,0.0,0.3,15.87,0.0 +2,2019-01-28 07:37:49,2019-01-28 07:43:01,1,0.84,1,N,79,137,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-16 09:21:49,2019-01-16 09:29:10,1,1.09,1,N,170,234,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-16 17:55:23,2019-01-16 18:07:43,1,2.4,1,N,43,237,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-28 09:20:00,2019-01-28 09:38:27,1,1.0,1,N,74,75,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-28 18:33:29,2019-01-28 18:37:49,1,0.5,1,N,239,239,1,4.5,1.0,0.5,1.55,0.0,0.3,7.85,0.0 +2,2019-01-10 05:43:24,2019-01-10 05:45:16,1,0.43,1,N,140,140,1,3.5,0.5,0.5,0.72,0.0,0.3,5.52, +2,2019-01-22 08:59:11,2019-01-22 09:06:38,1,1.83,1,N,48,239,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-31 17:16:21,2019-01-31 17:24:49,1,1.2,1,N,100,163,1,7.5,1.0,0.5,2.75,0.0,0.3,12.05,0.0 +2,2019-01-07 23:55:04,2019-01-07 23:59:31,1,1.73,1,N,107,229,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14, +2,2019-01-15 18:04:16,2019-01-15 18:55:23,1,18.28,2,N,132,164,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67, +2,2019-01-05 16:10:47,2019-01-05 16:21:45,1,2.19,1,N,161,236,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-03 13:22:12,2019-01-03 13:27:57,1,0.8,1,N,264,264,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-28 15:09:26,2019-01-28 15:20:23,1,1.4,1,N,68,234,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-05 15:27:26,2019-01-05 15:31:48,1,0.58,1,N,231,114,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-30 23:08:17,2019-01-30 23:13:42,1,1.17,1,N,230,162,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-20 18:30:36,2019-01-20 18:35:40,1,0.7,1,N,229,162,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-28 10:49:33,2019-01-28 11:03:56,1,1.1,1,N,236,162,1,10.0,0.0,0.5,1.2,0.0,0.3,12.0,0.0 +1,2019-01-16 18:33:04,2019-01-16 18:44:52,1,1.7,1,N,141,142,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-15 08:43:05,2019-01-15 08:53:33,1,1.41,1,N,162,48,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-09 21:52:46,2019-01-09 22:06:02,1,2.6,1,N,162,146,1,12.0,0.5,0.5,3.99,0.0,0.3,17.29, +2,2019-01-26 13:20:51,2019-01-26 13:22:53,1,0.4,1,N,151,24,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-12 19:34:41,2019-01-12 19:40:43,1,1.0,1,N,236,236,1,6.5,0.0,0.5,0.75,0.0,0.3,8.05, +2,2019-01-17 11:39:54,2019-01-17 11:46:58,1,0.5,1,N,140,229,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-12 21:37:38,2019-01-12 21:46:18,1,1.2,1,N,158,68,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-20 18:39:13,2019-01-20 18:44:22,1,0.83,1,N,234,100,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-23 13:58:51,2019-01-23 14:06:05,1,0.7,1,N,144,125,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-18 08:24:53,2019-01-18 08:50:35,1,8.1,1,N,140,127,1,27.0,0.0,0.5,5.56,0.0,0.3,33.36, +2,2019-01-17 01:08:57,2019-01-17 01:13:49,1,0.73,1,N,79,79,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +2,2019-01-27 13:08:52,2019-01-27 13:13:32,1,0.77,1,N,113,90,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +1,2019-01-12 23:26:34,2019-01-12 23:28:51,1,1.1,1,N,50,143,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +1,2019-01-05 19:26:06,2019-01-05 19:35:24,1,2.0,1,N,79,233,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-09 18:30:53,2019-01-09 18:51:15,1,5.38,1,N,114,239,1,19.5,1.0,0.5,4.26,0.0,0.3,27.51, +2,2019-01-11 11:04:27,2019-01-11 11:15:44,1,1.6,1,N,231,65,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-20 00:46:05,2019-01-20 00:57:52,1,1.94,1,N,164,233,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-27 12:04:55,2019-01-27 12:21:16,1,3.19,1,N,236,234,1,14.0,0.0,0.5,1.44,0.0,0.3,16.24,0.0 +1,2019-01-06 09:48:29,2019-01-06 09:52:28,1,0.5,1,N,100,186,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-30 23:02:02,2019-01-30 23:15:49,1,3.91,1,N,233,75,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-23 07:54:39,2019-01-23 08:00:39,1,0.9,1,N,100,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-30 10:14:31,2019-01-30 10:39:56,1,3.51,1,N,186,229,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-31 15:36:01,2019-01-31 15:58:36,1,2.74,1,N,263,161,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-05 20:39:49,2019-01-05 20:52:12,1,1.76,1,N,186,125,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +1,2019-01-09 09:17:42,2019-01-09 09:37:10,1,2.3,1,N,224,161,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-04 02:53:57,2019-01-04 03:11:24,1,6.1,1,N,249,226,2,19.5,0.5,0.5,0.0,0.0,0.3,20.8, +2,2019-01-14 13:17:03,2019-01-14 13:24:07,1,1.24,1,N,163,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-17 15:59:56,2019-01-17 16:35:42,1,4.5,1,N,141,231,1,23.0,1.0,0.5,6.2,0.0,0.3,31.0, +2,2019-01-29 09:46:38,2019-01-29 10:06:29,1,1.44,1,N,170,230,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-20 06:50:43,2019-01-20 06:50:47,1,0.0,5,N,1,1,1,102.5,0.0,0.0,0.0,0.0,0.3,102.8, +1,2019-01-20 09:57:10,2019-01-20 10:03:33,1,1.9,1,N,48,68,1,8.0,0.0,0.5,0.44,0.0,0.3,9.24, +2,2019-01-30 12:51:55,2019-01-30 13:20:23,1,3.29,1,N,211,161,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16,0.0 +2,2019-01-16 10:40:08,2019-01-16 10:48:49,1,1.17,1,N,236,237,1,7.0,0.0,0.5,0.39,0.0,0.3,8.19, +2,2019-01-02 09:01:11,2019-01-02 09:08:11,1,1.68,1,N,107,233,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-25 14:22:37,2019-01-25 14:52:51,1,2.2,1,N,186,233,1,18.0,0.0,0.5,5.0,0.0,0.3,23.8,0.0 +2,2019-01-18 12:08:04,2019-01-18 12:33:14,1,3.87,5,N,68,236,1,17.18,0.0,0.5,0.0,0.0,0.3,17.98, +1,2019-01-28 00:13:48,2019-01-28 00:19:44,1,2.2,1,N,141,137,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-14 17:32:30,2019-01-14 18:26:36,1,16.12,2,N,107,132,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67, +2,2019-01-14 15:49:11,2019-01-14 15:56:47,1,1.23,1,N,237,170,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-28 14:18:35,2019-01-28 14:23:21,1,0.5,1,N,125,249,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-23 22:59:09,2019-01-23 23:11:21,1,3.3,1,N,145,233,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55,0.0 +2,2019-01-17 07:29:54,2019-01-17 07:37:49,1,2.21,1,N,263,74,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-06 00:42:56,2019-01-06 00:52:47,1,1.66,1,N,163,141,1,8.5,0.5,0.5,2.08,0.0,0.3,11.88, +2,2019-01-15 21:07:57,2019-01-15 21:19:54,1,2.21,1,N,161,140,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-11 19:18:37,2019-01-11 19:32:08,1,2.1,1,N,163,246,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-13 15:15:52,2019-01-13 15:36:43,1,3.38,1,N,114,163,1,15.5,0.0,0.5,4.89,0.0,0.3,21.19, +1,2019-01-31 11:02:59,2019-01-31 11:06:53,1,0.4,1,N,142,230,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-27 16:11:32,2019-01-27 16:26:49,1,2.6,1,N,142,263,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-30 10:37:07,2019-01-30 10:42:59,1,0.99,1,N,148,107,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-16 08:45:57,2019-01-16 08:55:20,1,1.6,1,N,17,62,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-09 10:17:38,2019-01-09 10:21:22,1,0.43,1,N,262,262,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-16 18:05:42,2019-01-16 18:12:02,1,1.09,1,N,263,236,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-01 08:51:19,2019-01-01 09:00:56,1,4.7,1,N,238,244,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-07 15:20:25,2019-01-07 15:27:19,1,0.3,1,N,140,140,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-08 15:14:19,2019-01-08 15:27:20,1,1.2,1,N,237,236,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85, +1,2019-01-23 00:01:49,2019-01-23 00:15:48,1,3.4,1,N,48,262,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-17 06:05:14,2019-01-17 06:16:58,1,1.99,1,N,107,43,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-24 19:16:15,2019-01-24 19:27:17,1,1.51,1,N,137,234,1,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-02 23:53:02,2019-01-02 23:57:12,1,0.92,1,N,125,45,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-10 22:38:37,2019-01-10 22:53:32,1,2.88,1,N,162,238,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +1,2019-01-27 09:12:59,2019-01-27 09:33:55,1,9.7,1,Y,230,138,2,29.0,0.0,0.5,0.0,0.0,0.3,29.8,0.0 +2,2019-01-25 18:26:44,2019-01-25 18:37:38,1,2.09,1,N,170,263,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-22 08:32:55,2019-01-22 08:42:59,1,1.43,1,N,162,137,1,8.0,0.0,0.5,0.88,0.0,0.3,9.68,0.0 +2,2019-01-03 15:52:40,2019-01-03 16:15:51,1,3.0,1,N,236,107,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16, +2,2019-01-16 04:52:56,2019-01-16 04:57:00,1,1.28,1,N,170,161,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +1,2019-01-01 18:58:58,2019-01-01 19:05:19,1,0.9,1,N,162,137,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-11 13:15:11,2019-01-11 13:17:06,1,0.36,1,N,142,43,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-08 20:51:58,2019-01-08 21:13:07,1,4.29,1,N,166,229,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-10 09:47:33,2019-01-10 09:53:58,1,1.56,1,N,116,152,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-19 21:17:16,2019-01-19 21:19:40,1,0.74,1,N,231,13,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +1,2019-01-29 15:02:57,2019-01-29 15:14:55,1,0.8,1,N,261,88,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6,0.0 +1,2019-01-15 15:17:18,2019-01-15 15:50:05,1,9.5,1,N,107,244,1,30.5,0.0,0.5,6.25,0.0,0.3,37.55, +1,2019-01-25 08:43:25,2019-01-25 08:46:53,1,0.6,1,N,87,88,1,4.5,0.0,0.5,3.0,0.0,0.3,8.3,0.0 +2,2019-01-31 21:57:20,2019-01-31 22:01:16,1,0.67,1,N,79,79,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-17 17:30:47,2019-01-17 18:16:42,1,8.51,1,N,233,138,1,34.0,1.0,0.5,7.5,5.76,0.3,49.06, +2,2019-01-17 14:35:56,2019-01-17 14:45:08,1,1.38,1,N,233,140,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-07 07:59:54,2019-01-07 08:14:06,1,2.03,1,N,50,163,1,10.5,0.0,0.5,1.5,0.0,0.3,12.8, +2,2019-01-01 11:40:37,2019-01-01 11:52:49,1,7.13,1,N,261,140,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +2,2019-01-20 21:41:02,2019-01-20 21:42:34,1,0.61,1,N,141,263,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-23 07:33:49,2019-01-23 07:41:45,1,1.01,1,N,100,161,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-17 07:29:57,2019-01-17 07:38:05,1,1.31,1,N,236,262,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97, +2,2019-01-13 09:26:19,2019-01-13 09:29:30,1,0.77,1,N,141,263,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-19 20:51:33,2019-01-19 20:54:42,1,0.32,1,N,233,233,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-08 00:38:49,2019-01-08 00:52:30,1,7.07,1,N,132,197,1,21.0,0.5,0.5,4.46,0.0,0.3,26.76, +2,2019-01-31 13:39:28,2019-01-31 13:50:35,1,1.04,1,N,144,231,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7,0.0 +1,2019-01-14 08:13:19,2019-01-14 08:38:58,1,1.9,1,N,162,246,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-04 11:40:10,2019-01-04 11:47:04,1,0.8,1,N,239,143,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-02 14:50:06,2019-01-02 15:31:10,1,17.13,2,N,132,233,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +1,2019-01-31 19:06:46,2019-01-31 19:17:23,1,1.3,1,N,148,231,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-18 11:37:08,2019-01-18 11:52:34,1,2.07,1,N,237,238,1,11.5,0.0,0.5,1.25,0.0,0.3,13.55, +2,2019-01-19 10:22:40,2019-01-19 10:43:02,1,1.28,1,N,230,161,1,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-31 07:37:49,2019-01-31 07:50:29,1,1.9,1,N,186,50,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-31 18:36:25,2019-01-31 18:41:32,1,0.76,1,N,236,236,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-09 17:59:57,2019-01-09 18:08:23,1,0.8,1,N,249,79,1,7.0,1.0,0.5,0.88,0.0,0.3,9.68, +2,2019-01-25 16:32:00,2019-01-25 16:43:28,1,2.07,1,N,142,24,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-11 02:10:04,2019-01-11 02:24:41,1,4.1,1,N,142,224,1,14.5,0.5,0.5,3.15,0.0,0.3,18.95, +1,2019-01-16 05:36:54,2019-01-16 05:41:35,1,1.6,1,N,141,170,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +2,2019-01-13 01:41:50,2019-01-13 01:50:12,1,1.92,1,N,161,237,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-06 11:47:16,2019-01-06 11:53:02,1,1.24,1,N,162,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-29 19:15:48,2019-01-29 19:23:21,1,0.42,1,N,164,186,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-17 12:54:55,2019-01-17 13:25:45,1,6.04,1,N,43,113,1,24.5,0.0,0.5,6.32,0.0,0.3,31.62, +1,2019-01-08 13:18:21,2019-01-08 13:36:14,1,2.0,1,N,249,170,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-04 20:50:47,2019-01-04 21:04:56,1,1.5,1,N,164,186,1,10.5,0.5,0.5,2.95,0.0,0.3,14.75, +2,2019-01-25 21:26:33,2019-01-25 21:35:58,1,1.93,1,N,161,237,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-28 11:34:28,2019-01-28 11:35:58,1,0.41,1,N,164,234,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-28 15:40:07,2019-01-28 15:41:42,1,0.39,1,N,113,90,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-16 06:12:36,2019-01-16 06:20:25,1,1.65,1,N,186,229,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-24 09:52:47,2019-01-24 10:18:40,1,1.33,1,N,237,161,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-08 07:43:22,2019-01-08 07:54:38,1,1.8,1,N,50,234,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-23 21:15:21,2019-01-23 21:36:33,1,10.42,1,N,45,14,1,30.0,0.5,0.5,6.26,0.0,0.3,37.56,0.0 +1,2019-01-19 18:47:52,2019-01-19 19:02:32,1,2.2,1,N,249,161,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-25 00:04:30,2019-01-25 00:16:27,1,1.5,1,N,163,164,1,9.0,0.5,0.5,1.55,0.0,0.3,11.85,0.0 +1,2019-01-05 18:46:59,2019-01-05 19:03:06,1,4.0,1,N,79,140,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-11 14:40:23,2019-01-11 15:36:05,1,19.96,2,N,132,141,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56, +1,2019-01-30 15:57:56,2019-01-30 16:09:35,1,1.3,1,N,141,262,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-02 15:12:32,2019-01-02 15:24:28,1,2.2,1,N,140,230,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-04 20:10:07,2019-01-04 20:15:23,1,0.61,1,N,161,230,1,5.0,0.5,0.5,5.0,0.0,0.3,11.3, +2,2019-01-26 20:46:36,2019-01-26 20:53:43,1,0.96,1,N,13,261,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-18 09:28:10,2019-01-18 09:48:20,1,1.61,1,N,234,68,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-21 22:44:03,2019-01-21 22:51:55,1,1.86,1,N,141,236,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-28 10:13:29,2019-01-28 10:21:01,1,1.23,1,N,162,137,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-27 18:56:31,2019-01-27 19:14:01,1,2.41,1,N,234,141,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-03 21:33:07,2019-01-03 21:50:24,1,3.1,1,N,239,233,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75, +1,2019-01-30 08:37:38,2019-01-30 08:47:33,1,1.4,1,N,236,43,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-26 16:27:04,2019-01-26 16:35:36,1,1.36,1,N,237,233,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-24 21:07:02,2019-01-24 21:18:44,1,3.67,1,N,50,151,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-23 18:10:26,2019-01-23 18:20:47,1,2.1,1,N,48,238,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-14 13:20:39,2019-01-14 13:40:41,1,2.9,1,N,107,162,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +4,2019-01-26 23:27:22,2019-01-26 23:57:54,1,19.64,1,N,132,33,1,53.0,0.5,0.5,10.0,0.0,0.3,64.3, +2,2019-01-21 10:36:07,2019-01-21 10:56:35,1,2.97,1,N,163,113,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88,0.0 +2,2019-01-09 12:10:56,2019-01-09 12:17:53,1,0.5,1,N,246,50,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-03 21:20:13,2019-01-03 21:28:54,1,1.5,1,N,161,141,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15, +2,2019-01-31 17:29:06,2019-01-31 18:32:20,1,21.96,5,N,261,39,1,64.77,0.0,0.5,0.0,5.76,0.3,71.33,0.0 +2,2019-01-15 17:41:33,2019-01-15 17:55:48,1,2.58,1,N,236,230,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +4,2019-01-15 11:50:23,2019-01-15 12:05:40,1,1.43,1,N,90,230,1,10.5,0.0,0.5,2.83,0.0,0.3,14.13, +2,2019-01-06 20:54:18,2019-01-06 21:04:06,1,1.7,1,N,230,162,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-17 20:35:13,2019-01-17 20:50:22,1,2.07,1,N,141,137,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-28 12:52:07,2019-01-28 13:02:09,1,2.65,1,N,158,12,1,10.5,0.0,0.5,2.08,0.0,0.3,13.38,0.0 +1,2019-01-14 15:11:18,2019-01-14 15:18:40,1,0.8,1,N,239,142,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-29 18:05:13,2019-01-29 18:23:32,1,2.5,1,N,170,249,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-27 12:18:08,2019-01-27 12:21:17,1,0.38,1,N,237,237,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-31 19:50:08,2019-01-31 20:16:25,1,5.6,1,N,230,261,1,22.5,1.0,0.5,6.08,0.0,0.3,30.38,0.0 +2,2019-01-19 21:53:35,2019-01-19 22:01:01,1,1.74,1,N,233,79,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-18 21:58:33,2019-01-18 22:03:08,1,0.95,1,N,229,141,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-19 23:52:06,2019-01-19 23:57:56,1,1.4,1,N,234,125,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-09 22:05:25,2019-01-09 22:11:47,1,0.69,1,N,79,249,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-31 05:23:15,2019-01-31 05:31:10,1,1.65,1,N,246,90,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-26 20:04:22,2019-01-26 20:07:28,1,0.76,1,N,151,238,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-18 00:12:26,2019-01-18 00:18:55,1,0.96,1,N,132,132,1,6.5,0.5,0.5,0.78,0.0,0.3,8.58, +2,2019-01-31 12:25:06,2019-01-31 12:31:59,1,0.43,1,N,48,163,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-22 22:14:07,2019-01-22 22:19:41,1,0.98,1,N,75,262,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-19 16:15:06,2019-01-19 16:26:11,1,1.9,1,N,236,229,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-11 12:59:26,2019-01-11 13:10:57,1,1.33,1,N,237,140,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-17 05:42:39,2019-01-17 06:06:37,1,13.85,1,N,13,138,1,38.5,0.5,0.5,7.96,0.0,0.3,47.76, +2,2019-01-17 07:10:33,2019-01-17 07:15:52,1,1.41,1,N,231,113,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-21 17:12:40,2019-01-21 17:51:17,1,12.49,1,N,75,53,1,40.5,0.0,0.5,0.0,5.76,0.3,49.01,0.0 +2,2019-01-10 19:32:58,2019-01-10 19:41:25,1,2.65,1,N,151,116,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-19 16:21:36,2019-01-19 16:41:49,1,2.82,1,N,239,100,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-10 01:07:21,2019-01-10 01:15:20,1,2.9,1,N,263,137,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-19 19:13:59,2019-01-19 19:29:59,1,5.79,1,N,261,161,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-13 02:52:52,2019-01-13 03:09:14,1,3.35,1,N,68,87,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-04 00:43:01,2019-01-04 00:47:56,1,1.69,1,N,162,236,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-29 02:38:58,2019-01-29 03:07:53,1,14.0,1,N,249,212,1,39.5,0.5,0.5,2.0,0.0,0.3,42.8,0.0 +1,2019-01-16 13:40:56,2019-01-16 14:08:22,1,12.0,1,N,138,231,1,35.0,0.0,0.5,7.15,0.0,0.3,42.95, +1,2019-01-09 10:25:56,2019-01-09 10:35:57,1,0.5,1,N,263,237,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-24 17:20:55,2019-01-24 17:40:42,1,2.4,1,N,237,107,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-01 09:33:54,2019-01-01 09:50:34,1,4.38,1,N,161,231,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-25 18:57:16,2019-01-25 19:03:39,1,0.83,1,N,162,237,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97,0.0 +4,2019-01-03 22:23:06,2019-01-03 22:41:22,1,2.46,1,N,211,230,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +1,2019-01-06 10:48:34,2019-01-06 10:51:56,1,0.5,1,N,141,237,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3, +1,2019-01-28 22:04:15,2019-01-28 22:06:03,1,0.5,1,N,48,50,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-25 18:32:49,2019-01-25 19:05:48,1,4.92,1,N,249,181,2,22.0,1.0,0.5,0.0,0.0,0.3,23.8,0.0 +2,2019-01-28 15:55:53,2019-01-28 16:02:06,1,0.8,1,N,237,237,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-27 20:23:39,2019-01-27 20:30:10,1,1.4,1,N,164,162,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-18 15:53:02,2019-01-18 16:03:08,1,1.75,1,N,236,239,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-06 13:25:48,2019-01-06 13:46:00,1,3.0,1,N,43,234,1,15.0,0.0,0.5,4.7,0.0,0.3,20.5, +2,2019-01-15 14:28:54,2019-01-15 14:48:42,1,6.71,1,N,137,97,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +2,2019-01-29 21:35:08,2019-01-29 21:39:10,1,0.62,1,N,142,142,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-22 12:05:22,2019-01-22 12:14:55,1,0.74,1,N,141,141,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-14 11:30:51,2019-01-14 11:43:19,1,1.21,1,N,237,140,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-23 07:48:43,2019-01-23 07:58:37,1,1.78,1,N,113,100,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-10 13:40:16,2019-01-10 14:13:46,1,16.75,2,N,162,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-29 07:26:54,2019-01-29 07:33:10,1,1.19,1,N,166,41,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-15 07:45:17,2019-01-15 07:49:53,1,0.57,1,N,43,143,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-29 15:10:20,2019-01-29 15:11:49,1,0.29,1,N,113,113,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +1,2019-01-13 03:47:04,2019-01-13 04:07:56,1,7.4,1,N,68,42,1,23.5,0.5,0.5,4.95,0.0,0.3,29.75, +1,2019-01-13 01:52:47,2019-01-13 01:57:34,1,1.4,1,N,68,50,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-31 16:46:40,2019-01-31 16:52:19,1,1.1,1,N,264,264,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-27 23:58:13,2019-01-28 00:01:44,1,0.94,1,N,79,148,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-26 09:39:46,2019-01-26 09:46:41,1,0.9,1,N,264,264,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-09 16:36:40,2019-01-09 17:19:28,1,14.88,2,N,10,43,1,52.0,4.5,0.5,7.0,5.76,0.3,70.06, +1,2019-01-20 14:46:36,2019-01-20 15:23:15,1,18.9,2,N,132,48,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +2,2019-01-10 08:30:04,2019-01-10 08:41:39,1,1.7,1,N,164,229,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-24 15:05:15,2019-01-24 15:16:37,1,1.81,1,N,164,79,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-26 00:12:48,2019-01-26 00:17:57,1,1.52,1,N,229,107,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-30 08:41:43,2019-01-30 08:57:12,1,0.9,1,N,140,229,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-29 12:12:28,2019-01-29 12:30:34,1,2.2,1,N,246,161,1,13.0,0.0,0.5,3.45,0.0,0.3,17.25,0.0 +2,2019-01-21 08:34:03,2019-01-21 08:54:09,1,7.85,1,N,87,145,1,24.5,0.0,0.5,3.25,0.0,0.3,28.55,0.0 +2,2019-01-12 16:27:04,2019-01-12 16:36:44,1,1.0,1,N,43,161,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-24 14:03:26,2019-01-24 14:08:11,1,0.68,1,N,143,142,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25,0.0 +2,2019-01-11 19:05:51,2019-01-11 19:27:33,1,7.76,1,N,138,255,1,24.0,1.0,0.5,3.0,0.0,0.3,28.8, +1,2019-01-31 08:03:55,2019-01-31 08:15:45,1,1.5,1,N,141,163,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-12 10:47:21,2019-01-12 10:53:26,1,2.0,1,N,142,166,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-22 12:33:03,2019-01-22 12:43:50,1,1.6,1,N,141,238,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-09 12:59:14,2019-01-09 13:07:22,1,0.5,1,N,142,163,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-05 14:10:54,2019-01-05 14:22:08,1,2.18,1,N,238,50,1,10.0,0.0,0.5,1.5,0.0,0.3,12.3, +1,2019-01-06 00:04:01,2019-01-06 00:04:12,1,6.5,1,N,97,97,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +1,2019-01-06 00:12:27,2019-01-06 00:25:37,1,2.4,1,N,263,142,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-16 17:36:23,2019-01-16 17:55:06,1,1.58,1,N,68,161,1,12.0,1.0,0.5,3.45,0.0,0.3,17.25, +2,2019-01-05 14:03:13,2019-01-05 14:06:06,1,0.93,1,N,162,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 15:18:42,2019-01-31 15:27:51,1,1.27,1,N,238,236,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-02 19:55:42,2019-01-02 20:01:37,1,1.04,1,N,163,237,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-31 21:55:00,2019-01-31 22:07:21,1,2.1,1,N,264,264,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-05 17:14:25,2019-01-05 17:17:56,1,0.95,1,N,141,141,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-23 18:30:42,2019-01-23 18:41:43,1,1.6,1,N,163,263,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +4,2019-01-25 23:14:51,2019-01-25 23:52:49,1,12.17,1,N,170,14,1,39.0,0.5,0.5,8.06,0.0,0.3,48.36, +1,2019-01-19 12:56:05,2019-01-19 13:27:44,1,2.4,1,N,186,48,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +1,2019-01-22 14:10:02,2019-01-22 14:17:05,1,0.8,1,N,261,211,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-18 09:15:38,2019-01-18 09:18:24,1,0.65,1,N,90,114,1,4.0,0.0,0.5,0.5,0.0,0.3,5.3, +2,2019-01-07 08:32:22,2019-01-07 08:44:47,1,1.58,1,N,140,163,1,9.5,0.0,0.5,1.5,0.0,0.3,11.8, +1,2019-01-03 04:22:44,2019-01-03 04:40:39,1,5.1,1,N,79,143,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +2,2019-01-18 12:00:54,2019-01-18 12:27:21,1,4.63,1,N,125,49,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96, +2,2019-01-08 17:07:46,2019-01-08 17:16:40,1,1.72,1,N,236,74,1,8.5,1.0,0.5,3.09,0.0,0.3,13.39, +2,2019-01-12 20:45:01,2019-01-12 20:48:02,1,1.07,1,N,263,263,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-08 12:08:02,2019-01-08 12:19:10,1,0.98,1,N,186,230,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-28 09:08:15,2019-01-28 09:25:24,1,1.44,1,N,236,237,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-22 11:09:20,2019-01-22 11:22:27,1,1.58,1,N,262,237,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-01 23:43:24,2019-01-01 23:51:20,1,2.72,1,N,75,162,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-17 11:05:27,2019-01-17 11:43:05,1,3.7,1,N,148,48,1,23.5,0.0,0.5,4.0,0.0,0.3,28.3, +2,2019-01-15 20:34:59,2019-01-15 20:49:01,1,2.98,1,N,230,236,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +1,2019-01-28 13:10:00,2019-01-28 13:15:11,1,0.7,1,N,140,237,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2,0.0 +2,2019-01-30 16:45:27,2019-01-30 16:56:49,1,1.38,1,N,186,48,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-28 22:31:32,2019-01-28 22:39:53,1,2.1,1,N,48,113,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-24 11:30:40,2019-01-24 11:35:38,1,0.4,1,N,48,68,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-03 23:35:35,2019-01-03 23:45:59,1,1.56,1,N,107,158,1,8.5,0.5,0.5,1.47,0.0,0.3,11.27, +2,2019-01-13 14:18:57,2019-01-13 14:25:32,1,0.86,1,N,161,229,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0, +1,2019-01-18 00:51:24,2019-01-18 00:54:02,1,1.0,1,N,164,90,1,5.0,0.5,0.5,0.63,0.0,0.3,6.93, +2,2019-01-05 21:15:44,2019-01-05 21:21:04,1,1.64,1,N,74,75,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-28 16:55:29,2019-01-28 17:03:18,1,1.2,1,N,238,236,1,7.0,1.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-31 07:42:46,2019-01-31 07:52:27,1,0.69,1,N,237,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-23 19:43:54,2019-01-23 19:52:14,1,1.09,1,N,249,211,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-06 23:26:55,2019-01-06 23:38:06,1,5.8,1,N,132,124,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3, +1,2019-01-18 22:56:41,2019-01-18 23:08:11,1,2.3,1,N,229,263,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-05 02:12:21,2019-01-05 02:51:44,1,25.92,1,N,68,5,1,68.5,0.5,0.5,10.0,17.28,0.3,97.08, +2,2019-01-13 00:58:49,2019-01-13 01:07:56,1,0.93,1,N,144,232,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-14 08:53:18,2019-01-14 08:59:47,1,0.4,1,N,237,237,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-26 15:26:42,2019-01-26 15:32:33,1,0.72,1,N,237,263,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-16 19:44:52,2019-01-16 20:01:34,1,2.03,1,N,236,237,1,12.5,1.0,0.5,2.0,0.0,0.3,16.3, +1,2019-01-21 00:24:59,2019-01-21 00:37:03,1,6.7,1,N,238,243,2,20.0,0.5,0.5,0.0,0.0,0.3,21.3,0.0 +1,2019-01-26 18:34:52,2019-01-26 18:50:39,1,3.6,1,N,229,226,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-24 17:28:21,2019-01-24 17:40:43,1,1.8,1,N,237,230,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-25 21:29:45,2019-01-25 21:44:21,1,2.4,1,N,264,264,1,11.5,0.5,0.5,1.5,0.0,0.3,14.3,0.0 +2,2019-01-17 19:01:53,2019-01-17 19:08:43,1,0.58,1,N,161,163,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-15 19:12:30,2019-01-15 19:15:44,1,0.7,1,N,166,166,1,5.0,1.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-31 23:29:15,2019-01-31 23:43:43,1,2.85,1,N,234,87,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-14 18:14:45,2019-01-14 18:19:55,1,0.8,1,N,239,142,1,5.5,1.0,0.5,1.25,0.0,0.3,8.55, +2,2019-01-03 17:27:08,2019-01-03 17:31:26,1,1.02,1,N,211,90,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-15 20:45:44,2019-01-15 20:52:50,1,1.39,1,N,137,100,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-18 18:45:04,2019-01-18 18:54:44,1,0.95,1,N,164,162,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-08 09:56:43,2019-01-08 10:07:43,1,1.2,1,N,249,90,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-30 08:10:30,2019-01-30 08:25:18,1,4.34,1,N,186,238,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-30 11:11:22,2019-01-30 11:40:22,1,2.72,1,N,238,163,1,19.0,0.0,0.5,3.5,0.0,0.3,23.3,0.0 +2,2019-01-31 16:32:31,2019-01-31 16:47:41,1,1.29,1,N,161,137,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-11 23:22:02,2019-01-11 23:32:51,1,2.2,1,N,231,68,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +1,2019-01-20 12:49:12,2019-01-20 12:59:48,1,2.5,1,N,146,179,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-15 07:28:27,2019-01-15 07:42:28,1,2.27,1,N,238,163,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-01 03:11:36,2019-01-01 03:19:32,1,1.46,1,N,125,13,1,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-04 12:24:49,2019-01-04 12:27:51,1,0.5,1,N,239,238,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-27 12:24:30,2019-01-27 12:30:54,1,0.9,1,N,162,43,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-06 02:57:35,2019-01-06 03:01:28,1,1.14,1,N,100,68,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8, +2,2019-01-23 16:07:59,2019-01-23 16:21:46,1,0.77,1,N,107,186,1,9.5,1.0,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-24 21:49:39,2019-01-24 21:53:44,1,0.8,1,N,170,186,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-14 15:06:42,2019-01-14 15:22:25,1,2.1,1,N,143,263,1,11.5,0.0,0.5,1.84,0.0,0.3,14.14, +1,2019-01-18 12:06:11,2019-01-18 12:15:35,1,0.8,1,N,238,239,1,7.5,0.0,0.5,1.25,0.0,0.3,9.55, +2,2019-01-25 17:29:53,2019-01-25 17:51:57,1,1.54,1,N,162,246,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-02 16:11:34,2019-01-02 16:18:30,1,1.58,1,N,143,238,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-27 03:56:50,2019-01-27 04:00:27,1,0.93,1,N,163,48,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-20 11:00:23,2019-01-20 11:05:23,1,1.0,1,N,116,166,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-28 09:14:48,2019-01-28 09:32:17,1,2.74,1,N,143,246,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-30 08:48:37,2019-01-30 09:03:56,1,1.94,1,N,48,246,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-18 07:34:31,2019-01-18 07:42:27,1,1.98,1,N,234,249,1,8.5,0.0,0.5,2.79,0.0,0.3,12.09, +2,2019-01-17 15:47:27,2019-01-17 16:02:24,1,1.68,1,N,238,237,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-20 07:04:59,2019-01-20 07:12:36,1,2.1,1,N,246,161,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6, +2,2019-01-01 12:42:07,2019-01-01 12:58:21,1,3.13,1,N,48,262,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-12 13:18:38,2019-01-12 13:39:12,1,1.39,1,N,186,230,1,13.0,0.0,0.5,4.14,0.0,0.3,17.94, +4,2019-01-06 17:30:58,2019-01-06 17:44:54,1,1.95,1,N,74,151,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8, +1,2019-01-21 09:43:51,2019-01-21 09:51:03,1,0.8,1,N,264,264,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-09 08:01:07,2019-01-09 08:11:08,1,3.8,1,N,224,88,1,13.5,0.0,0.5,1.43,0.0,0.3,15.73, +1,2019-01-08 05:49:03,2019-01-08 05:56:28,1,1.2,1,N,229,230,1,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-15 09:59:13,2019-01-15 10:08:59,1,0.8,1,N,170,170,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-11 14:06:29,2019-01-11 14:17:06,1,2.31,1,N,161,75,1,9.5,0.0,0.5,1.03,0.0,0.3,11.33, +1,2019-01-28 06:46:44,2019-01-28 06:52:00,1,0.8,1,N,229,140,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-12 02:53:34,2019-01-12 03:02:17,1,3.3,1,Y,229,74,1,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +1,2019-01-16 08:44:30,2019-01-16 09:00:40,1,1.8,1,N,229,164,1,11.5,0.0,0.5,1.25,0.0,0.3,13.55, +4,2019-01-24 12:28:00,2019-01-24 12:37:17,1,0.55,1,N,170,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-15 08:26:56,2019-01-15 08:38:06,1,0.95,1,N,186,246,1,8.0,0.0,0.5,2.64,0.0,0.3,11.44, +2,2019-01-14 15:15:34,2019-01-14 15:31:13,1,1.84,1,N,237,143,1,11.5,0.0,0.5,1.2,0.0,0.3,13.5, +2,2019-01-28 09:33:18,2019-01-28 09:47:35,1,1.68,1,N,164,230,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-22 11:39:09,2019-01-22 11:41:44,1,0.4,1,N,237,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-25 18:43:20,2019-01-25 18:54:29,1,2.38,1,N,163,145,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3,0.0 +1,2019-01-10 09:59:20,2019-01-10 10:19:59,1,1.6,1,N,163,143,1,13.5,0.0,0.5,3.0,0.0,0.3,17.3, +2,2019-01-28 18:05:45,2019-01-28 18:19:46,1,1.93,1,N,90,161,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3,0.0 +2,2019-01-14 05:31:36,2019-01-14 06:00:32,1,20.45,3,N,163,1,2,72.5,0.5,0.0,0.0,10.5,0.3,83.8, +1,2019-01-10 19:12:01,2019-01-10 19:22:43,1,2.1,1,N,65,49,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-25 12:54:58,2019-01-25 13:08:44,1,1.51,1,N,162,50,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-08 16:13:07,2019-01-08 16:13:18,1,0.0,2,N,164,100,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67, +2,2019-01-03 13:47:01,2019-01-03 13:56:27,1,1.39,1,N,74,236,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-06 13:54:13,2019-01-06 14:16:13,1,11.3,1,N,132,138,1,31.5,0.0,0.5,6.46,0.0,0.3,38.76, +2,2019-01-09 21:01:08,2019-01-09 21:36:44,1,10.13,1,N,163,181,1,33.5,0.5,0.5,5.22,0.0,0.3,40.02, +2,2019-01-16 08:09:44,2019-01-16 08:12:12,1,0.51,1,N,100,186,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-15 10:12:09,2019-01-15 10:37:44,1,3.98,1,N,236,100,1,18.0,0.0,0.5,4.7,0.0,0.3,23.5, +2,2019-01-12 18:09:23,2019-01-12 18:31:08,1,2.97,1,N,113,43,1,15.5,0.0,0.5,4.08,0.0,0.3,20.38, +2,2019-01-25 14:42:58,2019-01-25 14:55:13,1,1.8,1,N,100,163,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-09 16:00:51,2019-01-09 16:03:27,1,0.33,1,N,162,164,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-02 16:42:44,2019-01-02 16:46:37,1,0.4,1,N,113,144,1,4.5,1.0,0.5,1.55,0.0,0.3,7.85, +2,2019-01-02 14:23:42,2019-01-02 15:10:39,1,19.78,2,N,236,132,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-08 17:07:17,2019-01-08 17:11:00,1,0.67,1,N,100,68,1,4.5,1.0,0.5,1.89,0.0,0.3,8.19, +2,2019-01-17 18:44:09,2019-01-17 19:22:44,1,8.73,1,N,138,49,1,32.5,1.0,0.5,5.5,0.0,0.3,39.8, +2,2019-01-20 11:03:56,2019-01-20 11:07:28,1,1.09,1,N,263,75,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-04 07:20:00,2019-01-04 07:29:48,1,2.31,1,N,48,237,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-02 07:52:51,2019-01-02 08:06:06,1,3.3,1,N,50,231,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-24 12:48:30,2019-01-24 13:04:07,1,1.28,1,N,114,234,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-19 16:54:38,2019-01-19 17:06:14,1,1.26,1,N,230,186,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-16 21:37:15,2019-01-16 22:16:30,1,17.2,2,N,132,237,1,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +1,2019-01-04 17:06:00,2019-01-04 17:20:24,1,1.2,1,N,230,186,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-15 15:53:49,2019-01-15 16:08:10,1,1.5,1,N,246,161,1,10.0,1.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-29 14:16:11,2019-01-29 14:23:54,1,1.16,1,N,211,79,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-22 11:02:31,2019-01-22 11:34:39,1,1.92,1,N,163,68,1,18.5,0.0,0.5,4.82,0.0,0.3,24.12,0.0 +2,2019-01-15 16:05:10,2019-01-15 16:15:27,1,1.29,1,N,230,142,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88, +2,2019-01-24 15:48:37,2019-01-24 15:52:49,1,0.48,1,N,234,90,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-22 13:25:57,2019-01-22 13:44:08,1,3.07,1,N,140,230,1,14.0,0.0,0.5,2.11,0.0,0.3,16.91,0.0 +2,2019-01-10 19:45:15,2019-01-10 19:53:18,1,1.12,1,N,161,142,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-03 13:50:26,2019-01-03 13:59:16,1,1.1,1,N,236,237,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-17 05:04:49,2019-01-17 05:16:38,1,3.4,1,N,262,42,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +1,2019-01-01 02:01:12,2019-01-01 02:15:41,1,1.9,1,N,48,229,1,10.5,0.5,0.5,1.77,0.0,0.3,13.57, +1,2019-01-19 14:53:01,2019-01-19 14:57:59,1,0.6,1,N,79,114,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-15 11:04:12,2019-01-15 11:20:32,1,1.93,1,N,140,161,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-26 14:56:50,2019-01-26 15:01:49,1,0.77,1,N,79,113,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-06 01:17:45,2019-01-06 01:22:37,1,0.8,1,N,79,148,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +2,2019-01-12 09:39:31,2019-01-12 09:44:08,1,0.92,1,N,263,263,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-17 19:05:28,2019-01-17 19:15:18,1,1.02,1,N,125,45,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-28 11:39:46,2019-01-28 11:44:06,1,0.9,1,N,239,238,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-20 13:12:35,2019-01-20 13:21:36,1,1.4,1,N,100,234,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-18 00:22:33,2019-01-18 00:25:46,1,0.5,1,N,249,211,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +1,2019-01-19 20:17:39,2019-01-19 20:31:46,1,2.7,1,N,237,238,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +2,2019-01-18 06:20:27,2019-01-18 06:20:36,1,0.0,1,N,224,224,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +2,2019-01-01 20:44:48,2019-01-01 21:03:20,1,4.5,1,N,170,239,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56, +2,2019-01-04 09:02:34,2019-01-04 09:09:50,1,1.1,1,N,141,237,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-30 09:44:48,2019-01-30 09:59:07,1,1.58,1,N,142,237,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-30 21:41:32,2019-01-30 21:45:46,1,0.9,1,N,236,141,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-01 10:59:58,2019-01-01 11:04:52,1,1.03,1,N,141,237,1,5.5,0.0,0.5,1.58,0.0,0.3,7.88, +1,2019-01-22 15:53:33,2019-01-22 16:03:33,1,0.7,1,N,164,100,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-11 18:00:42,2019-01-11 18:25:37,1,2.9,1,N,236,164,1,17.0,1.0,0.5,4.7,0.0,0.3,23.5, +2,2019-01-29 15:28:37,2019-01-29 15:48:13,1,1.87,1,N,234,79,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-19 02:18:18,2019-01-19 02:26:24,1,1.31,1,N,129,82,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-31 14:34:55,2019-01-31 14:39:34,1,0.65,1,N,158,249,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-17 13:38:00,2019-01-17 13:44:05,1,1.05,1,N,138,138,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84, +1,2019-01-16 05:53:48,2019-01-16 05:57:57,1,0.8,1,N,141,163,1,5.0,0.5,0.5,1.85,0.0,0.3,8.15, +2,2019-01-08 22:17:27,2019-01-08 22:45:12,1,11.77,1,N,238,257,2,35.5,0.5,0.5,0.0,5.76,0.3,42.56, +2,2019-01-17 21:49:28,2019-01-17 21:55:06,1,0.69,1,N,43,230,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5, +2,2019-01-27 18:13:40,2019-01-27 18:27:26,1,1.57,1,N,161,48,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-31 06:54:14,2019-01-31 06:57:27,1,0.4,1,N,230,161,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-05 17:31:35,2019-01-05 17:45:09,1,2.24,1,N,141,100,1,10.5,0.0,0.5,1.5,0.0,0.3,12.8, +2,2019-01-23 21:39:01,2019-01-23 21:50:57,1,3.7,1,N,88,79,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-18 07:57:30,2019-01-18 08:45:58,1,17.8,2,N,230,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-07 17:10:09,2019-01-07 17:15:41,1,0.67,1,N,143,50,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-11 13:40:52,2019-01-11 13:50:55,1,1.54,1,N,236,239,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-05 19:46:02,2019-01-05 19:51:57,1,2.1,1,N,263,74,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-22 15:27:38,2019-01-22 15:42:02,1,2.2,1,N,144,170,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8,0.0 +2,2019-01-11 13:19:05,2019-01-11 13:23:34,1,0.61,1,N,230,161,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-27 01:00:35,2019-01-27 01:04:31,1,0.9,1,N,230,48,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-14 12:03:44,2019-01-14 12:11:41,1,1.4,1,N,142,238,1,7.5,0.0,0.5,2.45,0.0,0.3,10.75, +1,2019-01-30 09:51:28,2019-01-30 09:54:51,1,0.3,1,N,238,151,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +4,2019-01-07 09:01:00,2019-01-07 09:08:20,1,0.75,1,N,107,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-19 12:22:32,2019-01-19 12:31:59,1,1.62,1,N,75,262,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-04 17:59:31,2019-01-04 18:21:11,1,1.3,1,N,161,48,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-30 14:19:28,2019-01-30 14:24:59,1,0.68,1,N,161,170,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-10 15:33:18,2019-01-10 15:43:42,1,2.09,1,N,239,230,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-11 09:22:48,2019-01-11 09:38:45,1,2.15,1,N,239,262,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-11 14:40:54,2019-01-11 15:00:49,1,2.94,1,N,261,114,1,14.5,0.0,0.5,3.82,0.0,0.3,19.12, +2,2019-01-23 22:09:44,2019-01-23 22:25:42,1,3.42,1,N,230,151,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-13 00:46:32,2019-01-13 01:22:50,1,7.6,1,N,114,89,1,29.5,0.5,0.5,7.7,0.0,0.3,38.5, +1,2019-01-02 22:35:41,2019-01-02 22:47:33,1,2.8,1,N,100,249,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-22 12:20:32,2019-01-22 12:38:12,1,1.39,1,N,170,162,1,12.0,0.0,0.5,2.7,0.0,0.3,15.5,0.0 +2,2019-01-22 15:12:55,2019-01-22 15:30:02,1,2.21,1,N,140,142,1,12.5,0.0,0.5,1.0,0.0,0.3,14.3,0.0 +2,2019-01-01 18:57:09,2019-01-01 19:10:21,1,1.59,1,N,186,170,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-07 10:12:47,2019-01-07 10:19:55,1,0.7,1,N,48,230,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-31 10:24:00,2019-01-31 10:43:34,1,2.52,1,N,48,141,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-22 15:13:17,2019-01-22 15:28:17,1,2.25,1,N,100,224,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-11 19:25:29,2019-01-11 19:35:18,1,1.1,1,N,230,162,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-11 09:02:16,2019-01-11 09:13:31,1,2.0,1,N,264,264,1,10.0,0.0,0.5,3.5,0.0,0.3,14.3, +1,2019-01-23 20:34:05,2019-01-23 20:43:51,1,2.1,1,N,48,90,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-25 12:27:43,2019-01-25 12:42:22,1,1.76,1,N,239,236,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-12 21:52:53,2019-01-12 21:55:51,1,0.46,1,N,68,68,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3, +2,2019-01-29 00:01:32,2019-01-29 00:14:44,1,3.19,1,N,161,151,1,13.0,0.5,0.5,2.0,0.0,0.3,16.3,0.0 +2,2019-01-12 15:59:44,2019-01-12 16:04:01,1,0.73,1,N,263,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-08 18:45:56,2019-01-08 18:47:53,1,0.37,1,N,161,161,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 14:24:13,2019-01-31 14:56:02,1,8.68,1,N,43,52,1,28.5,0.0,0.5,5.86,0.0,0.3,35.16,0.0 +2,2019-01-18 16:59:19,2019-01-18 17:12:53,1,1.4,1,N,230,164,1,9.5,1.0,0.5,1.51,0.0,0.3,12.81, +1,2019-01-15 09:01:57,2019-01-15 09:09:17,1,0.7,1,N,13,231,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-10 06:34:56,2019-01-10 07:14:28,1,14.48,1,N,151,165,1,44.5,0.0,0.5,3.0,0.0,0.3,48.3, +2,2019-01-04 06:47:43,2019-01-04 06:59:12,1,3.41,1,N,186,261,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-28 21:25:27,2019-01-28 21:34:47,1,1.6,1,N,163,48,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-03 12:23:52,2019-01-03 12:33:46,1,1.63,1,N,116,41,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-07 12:05:43,2019-01-07 12:13:35,1,1.1,1,N,237,142,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 21:08:33,2019-01-26 21:14:53,1,0.69,1,N,79,114,1,6.0,0.5,0.5,1.1,0.0,0.3,8.4,0.0 +1,2019-01-23 00:39:15,2019-01-23 01:10:45,1,7.7,1,N,264,264,1,28.5,0.5,0.5,3.0,0.0,0.3,32.8,0.0 +1,2019-01-05 11:03:28,2019-01-05 11:15:54,1,2.1,1,N,229,236,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-30 12:55:45,2019-01-30 13:21:04,1,7.69,1,N,261,236,1,26.0,0.0,0.5,5.36,0.0,0.3,32.16,0.0 +1,2019-01-23 20:48:26,2019-01-23 20:56:14,1,1.3,1,N,234,144,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-13 11:34:15,2019-01-13 11:42:32,1,1.93,1,N,246,230,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-05 07:13:20,2019-01-05 07:54:34,1,22.07,5,N,210,170,1,57.03,0.0,0.5,0.0,0.0,0.3,57.83, +1,2019-01-27 15:38:01,2019-01-27 15:54:35,1,2.7,1,N,24,163,1,13.5,0.0,0.5,1.5,0.0,0.3,15.8,0.0 +2,2019-01-17 03:44:04,2019-01-18 00:00:00,1,0.12,1,N,234,107,1,2.5,0.5,0.5,1.33,0.0,0.3,5.13, +2,2019-01-17 21:46:05,2019-01-17 21:53:24,1,1.24,1,N,48,50,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-19 17:12:32,2019-01-19 17:25:39,1,2.44,1,N,140,75,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-24 20:08:12,2019-01-24 20:33:32,1,3.2,1,N,229,249,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-16 20:15:33,2019-01-16 20:24:02,1,1.44,1,N,48,239,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-28 17:25:30,2019-01-28 17:38:44,1,1.67,1,N,161,234,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-04 16:02:21,2019-01-04 16:10:54,1,1.22,1,N,237,142,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-16 19:45:42,2019-01-16 19:57:47,1,2.41,1,N,237,151,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-25 10:35:47,2019-01-25 10:48:44,1,2.0,1,N,229,234,1,10.0,0.0,0.5,1.25,0.0,0.3,12.05,0.0 +2,2019-01-31 19:19:24,2019-01-31 19:44:07,1,9.48,1,N,138,141,1,29.5,1.0,0.5,7.41,5.76,0.3,44.47,0.0 +2,2019-01-11 18:14:22,2019-01-11 18:24:38,1,1.37,1,N,140,162,1,8.0,1.0,0.5,1.47,0.0,0.3,11.27, +1,2019-01-10 12:57:10,2019-01-10 13:09:18,1,1.7,1,N,161,246,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +2,2019-01-03 16:43:04,2019-01-03 16:47:02,1,0.55,1,N,239,239,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-06 15:49:35,2019-01-06 15:58:58,1,2.4,1,N,140,161,1,9.5,0.0,0.5,2.7,0.0,0.3,13.0, +2,2019-01-23 17:48:22,2019-01-23 18:08:53,1,2.02,1,N,163,107,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-07 08:36:13,2019-01-07 08:48:34,1,1.4,1,N,43,161,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-15 18:44:31,2019-01-15 18:52:08,1,1.12,1,N,249,186,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-22 16:31:11,2019-01-22 16:49:00,1,3.31,1,N,246,237,1,14.5,1.0,0.5,4.89,0.0,0.3,21.19,0.0 +1,2019-01-30 22:13:35,2019-01-30 22:18:17,1,0.8,1,N,234,107,1,5.0,0.5,0.5,1.85,0.0,0.3,8.15,0.0 +2,2019-01-29 21:56:58,2019-01-29 22:06:07,1,1.47,1,N,13,88,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-06 19:35:31,2019-01-06 19:39:58,1,1.03,1,N,211,231,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-17 18:48:08,2019-01-17 19:00:04,1,1.8,1,N,107,211,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-13 14:09:57,2019-01-13 14:24:25,1,2.51,1,N,237,166,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-28 07:38:10,2019-01-28 07:51:13,1,1.81,1,N,140,75,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-10 09:41:26,2019-01-10 10:51:09,1,9.41,5,N,89,164,1,30.16,0.0,0.5,0.0,0.0,0.3,30.96, +2,2019-01-17 21:46:56,2019-01-17 22:14:01,1,4.29,1,N,107,238,1,19.5,0.5,0.5,3.74,0.0,0.3,24.54, +1,2019-01-27 01:29:16,2019-01-27 01:34:53,1,1.0,1,N,230,50,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-10 13:24:55,2019-01-10 13:30:16,1,1.1,1,N,238,236,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-13 04:05:31,2019-01-13 04:25:05,1,5.2,1,N,107,181,1,19.0,0.5,0.5,4.05,0.0,0.3,24.35, +2,2019-01-03 01:41:18,2019-01-03 01:47:21,1,1.43,1,N,114,45,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-25 10:50:24,2019-01-25 11:38:58,1,12.07,1,N,138,186,1,43.5,0.0,0.5,10.01,5.76,0.3,60.07,0.0 +2,2019-01-02 15:58:41,2019-01-02 16:11:07,1,1.03,1,N,161,43,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-10 13:49:52,2019-01-10 14:02:04,1,1.2,1,N,50,230,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-02 16:06:23,2019-01-02 16:20:21,1,1.95,1,N,234,4,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-02 08:42:33,2019-01-02 08:52:57,1,1.47,1,N,48,163,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-06 23:47:55,2019-01-07 00:11:06,1,5.12,1,N,186,7,1,19.5,0.5,0.5,5.2,0.0,0.3,26.0, +2,2019-01-30 15:38:28,2019-01-30 15:44:42,1,0.66,1,N,238,239,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-08 19:02:05,2019-01-08 19:11:43,1,1.88,1,N,90,125,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-02 21:40:35,2019-01-02 22:00:17,1,1.6,1,N,68,48,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75, +2,2019-01-23 18:55:13,2019-01-23 19:13:26,1,2.58,1,N,170,143,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76,0.0 +1,2019-01-30 21:44:52,2019-01-30 22:01:55,1,2.5,1,N,170,143,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-02 12:28:51,2019-01-02 12:51:32,1,4.48,1,N,246,236,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56, +2,2019-01-26 03:54:00,2019-01-26 03:56:35,1,0.52,1,N,237,141,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-31 17:14:42,2019-01-31 17:18:46,1,0.7,1,N,141,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-31 19:59:05,2019-01-31 20:08:10,1,1.35,1,N,24,239,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-12 07:34:55,2019-01-12 07:41:57,1,2.1,1,N,233,141,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-29 12:27:25,2019-01-29 12:52:04,1,2.69,1,N,239,164,1,16.5,0.0,0.5,4.33,0.0,0.3,21.63,0.0 +2,2019-01-25 17:02:17,2019-01-25 17:40:44,1,5.79,1,N,186,65,1,26.0,1.0,0.5,6.95,0.0,0.3,34.75,0.0 +1,2019-01-03 22:04:53,2019-01-03 22:19:30,1,2.6,1,N,264,264,1,12.5,0.5,0.5,2.0,0.0,0.3,15.8, +1,2019-01-10 00:53:04,2019-01-10 00:56:21,1,1.2,1,N,229,141,1,5.5,0.5,0.5,1.2,0.0,0.3,8.0, +1,2019-01-04 15:15:47,2019-01-04 15:44:00,1,3.5,1,N,143,137,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +1,2019-01-29 19:26:21,2019-01-29 19:32:20,1,0.6,1,N,100,50,1,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-25 12:10:23,2019-01-25 12:26:46,1,1.67,1,N,161,140,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-16 17:06:16,2019-01-16 17:11:17,1,1.7,1,N,239,24,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-18 13:51:08,2019-01-18 14:02:44,1,0.76,1,N,107,234,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-15 17:00:52,2019-01-15 17:13:49,1,1.99,1,N,161,107,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-24 09:33:23,2019-01-24 09:43:47,1,1.6,1,N,238,143,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-20 19:50:05,2019-01-20 19:55:46,1,0.52,1,N,230,100,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-26 18:09:21,2019-01-26 18:16:55,1,1.24,1,N,263,237,1,7.0,0.0,0.5,0.7,0.0,0.3,8.5,0.0 +2,2019-01-29 15:27:10,2019-01-29 16:04:42,1,9.98,1,N,138,230,1,33.0,0.0,0.5,7.91,5.76,0.3,47.47,0.0 +2,2019-01-03 15:50:32,2019-01-03 16:06:56,1,5.13,1,N,25,4,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-26 21:50:57,2019-01-26 22:15:34,1,2.5,1,N,249,148,1,16.0,1.0,0.5,1.0,0.0,0.3,18.8,0.0 +2,2019-01-05 06:54:31,2019-01-05 06:58:26,1,0.89,1,N,68,246,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-05 22:48:18,2019-01-05 22:55:09,1,0.91,1,N,264,264,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-02 08:38:29,2019-01-02 09:17:54,1,19.25,3,N,230,1,2,69.5,0.0,0.0,0.0,17.5,0.3,87.3, +2,2019-01-09 14:20:43,2019-01-09 14:33:20,1,1.16,1,N,170,163,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-05 14:21:19,2019-01-05 14:24:08,1,0.6,1,N,262,263,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-04 17:13:30,2019-01-04 17:21:53,1,0.81,1,N,230,237,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-22 17:31:22,2019-01-22 17:49:16,1,3.4,1,N,88,68,1,15.0,1.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-06 08:42:51,2019-01-06 08:46:21,1,1.21,1,N,170,113,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-21 22:00:08,2019-01-21 22:25:19,1,15.21,1,N,132,226,2,42.0,0.5,0.5,0.0,0.0,0.3,43.3,0.0 +2,2019-01-09 07:55:50,2019-01-09 08:14:19,1,6.41,1,N,13,162,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +2,2019-01-30 11:12:31,2019-01-30 11:24:48,1,0.85,1,N,170,162,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7,0.0 +2,2019-01-09 08:06:53,2019-01-09 08:12:15,1,1.05,1,N,237,162,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-19 22:44:26,2019-01-19 22:59:29,1,3.6,1,N,107,262,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75, +2,2019-01-25 22:49:44,2019-01-25 22:54:38,1,0.59,1,N,164,137,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-03 05:42:26,2019-01-03 06:03:14,1,6.0,1,N,7,74,1,19.5,0.5,0.5,1.0,0.0,0.3,21.8, +2,2019-01-04 13:19:16,2019-01-04 13:26:00,1,1.05,1,N,48,142,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 18:41:48,2019-01-24 18:46:49,1,0.79,1,N,90,246,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-19 19:13:57,2019-01-19 19:16:17,1,0.53,1,N,233,162,1,3.5,0.0,0.5,0.77,0.0,0.3,5.07, +2,2019-01-28 06:49:00,2019-01-28 07:24:27,1,5.84,5,N,102,65,1,19.3,0.0,0.5,0.0,0.0,0.3,20.1,0.0 +1,2019-01-06 02:17:35,2019-01-06 02:28:01,1,2.0,1,N,148,234,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-26 17:23:15,2019-01-26 17:23:28,1,0.0,2,N,107,107,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +1,2019-01-22 17:13:24,2019-01-22 17:22:41,1,1.3,1,N,229,237,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-13 12:40:16,2019-01-13 12:48:16,1,1.48,1,N,239,163,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-15 20:11:55,2019-01-15 20:21:35,1,2.28,1,N,249,88,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-15 20:05:59,2019-01-15 20:22:11,1,3.96,1,N,233,41,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +1,2019-01-18 15:12:12,2019-01-18 15:17:21,1,0.6,1,N,238,151,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-06 15:42:06,2019-01-06 15:55:30,1,1.9,1,N,170,246,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-16 08:05:06,2019-01-16 08:18:36,1,1.56,1,N,48,237,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-12 20:00:03,2019-01-12 20:03:07,1,0.62,1,N,237,43,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-06 11:55:42,2019-01-06 11:57:23,1,0.4,1,N,140,140,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +1,2019-01-18 14:14:06,2019-01-18 14:30:10,1,2.5,1,N,87,4,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-31 11:23:16,2019-01-31 11:29:39,1,0.91,1,N,90,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-18 10:45:45,2019-01-18 10:51:22,1,1.21,1,N,238,151,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-26 22:14:34,2019-01-26 22:54:07,1,20.3,2,N,132,87,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8,0.0 +2,2019-01-18 07:27:29,2019-01-18 07:28:27,1,0.2,1,N,237,237,1,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +1,2019-01-21 11:45:21,2019-01-21 11:46:55,1,0.4,1,N,237,236,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15,0.0 +2,2019-01-09 08:49:58,2019-01-09 08:53:59,1,1.19,1,N,151,239,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-03 07:00:00,2019-01-03 07:06:38,1,2.31,1,N,236,74,1,8.5,0.0,0.5,1.12,0.0,0.3,10.42, +2,2019-01-08 23:47:46,2019-01-09 00:14:31,1,16.36,1,N,132,212,2,43.5,0.5,0.5,0.0,5.76,0.3,50.56, +1,2019-01-31 11:16:51,2019-01-31 11:21:03,1,0.6,1,N,236,236,1,5.0,0.0,0.5,1.7,0.0,0.3,7.5,0.0 +1,2019-01-21 23:25:03,2019-01-22 00:03:27,1,18.6,2,N,132,114,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +2,2019-01-12 23:19:51,2019-01-12 23:25:09,1,1.08,1,N,164,162,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-14 16:33:48,2019-01-14 16:51:57,1,2.39,1,N,211,164,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-27 16:19:58,2019-01-27 16:33:37,1,1.97,1,N,161,68,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-02 12:57:52,2019-01-02 13:38:11,1,20.39,2,N,132,244,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-25 20:10:06,2019-01-25 20:30:43,1,3.41,1,N,100,232,1,15.5,0.5,0.5,2.0,0.0,0.3,18.8,0.0 +2,2019-01-22 18:21:25,2019-01-22 18:25:54,1,0.94,1,N,114,107,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-02 15:18:23,2019-01-02 15:30:18,1,2.0,1,N,249,107,1,10.0,0.0,0.5,1.5,0.0,0.3,12.3, +1,2019-01-30 14:48:12,2019-01-30 14:56:07,1,0.6,1,N,234,186,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-23 19:01:09,2019-01-23 19:20:22,1,2.37,1,N,170,43,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-07 12:51:37,2019-01-07 12:57:57,1,0.9,1,N,249,79,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-16 18:13:57,2019-01-16 18:47:40,1,12.27,5,N,162,128,1,41.3,0.0,0.0,0.0,2.64,0.3,44.24, +2,2019-01-02 20:57:15,2019-01-02 21:06:21,1,1.57,1,N,142,48,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-07 22:32:15,2019-01-07 22:37:43,1,1.66,1,N,230,239,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-18 23:42:12,2019-01-18 23:53:21,1,1.4,1,N,230,234,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-05 08:40:57,2019-01-05 08:45:22,1,1.5,1,N,114,164,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-24 22:08:03,2019-01-24 22:41:14,1,6.38,1,N,239,260,1,25.5,0.5,0.5,1.0,0.0,0.3,27.8,0.0 +1,2019-01-02 22:09:28,2019-01-02 22:45:42,1,11.0,1,N,100,61,1,35.5,0.5,0.5,4.0,0.0,0.3,40.8, +1,2019-01-22 15:22:39,2019-01-22 15:36:08,1,1.3,1,N,170,163,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-24 22:57:24,2019-01-24 23:04:00,1,1.24,1,N,90,170,1,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-24 04:12:31,2019-01-24 04:30:36,1,10.71,1,N,161,138,1,30.5,0.5,0.5,7.51,5.76,0.3,45.07,0.0 +1,2019-01-28 06:57:45,2019-01-28 07:06:55,1,2.1,1,N,158,230,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-22 17:43:23,2019-01-22 17:50:12,1,0.7,1,N,170,234,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-07 13:48:36,2019-01-07 13:58:40,1,1.7,1,N,48,162,1,9.0,0.0,0.5,1.47,0.0,0.3,11.27, +2,2019-01-17 18:49:45,2019-01-17 18:59:00,1,1.17,1,N,162,186,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +1,2019-01-17 08:40:14,2019-01-17 08:49:26,1,0.6,1,N,161,162,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-18 00:04:25,2019-01-18 00:19:34,1,2.0,1,N,170,100,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +1,2019-01-20 11:58:09,2019-01-20 12:01:26,1,0.7,1,N,90,100,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6, +1,2019-01-14 08:08:11,2019-01-14 08:45:07,1,7.7,1,N,166,13,1,29.5,0.0,0.5,4.77,0.0,0.3,35.07, +2,2019-01-16 13:02:00,2019-01-16 13:16:51,1,1.87,1,N,236,140,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-18 11:16:32,2019-01-18 11:42:24,1,4.34,1,N,13,230,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36, +2,2019-01-13 12:03:16,2019-01-13 12:05:57,1,0.47,1,N,237,237,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-25 14:08:06,2019-01-25 14:13:40,1,0.81,1,N,140,262,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-17 19:47:29,2019-01-17 19:57:37,1,0.9,1,N,246,234,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +2,2019-01-18 22:27:38,2019-01-18 22:34:29,1,0.89,1,N,48,68,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12, +1,2019-01-10 14:43:39,2019-01-10 14:55:07,1,1.8,1,N,142,236,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-01 15:07:51,2019-01-01 15:17:15,1,2.0,1,N,162,186,1,9.0,0.0,0.5,1.5,0.0,0.3,11.3, +1,2019-01-11 23:39:26,2019-01-12 00:04:48,1,6.4,1,N,79,238,1,22.0,0.5,0.5,4.65,0.0,0.3,27.95, +2,2019-01-09 07:15:59,2019-01-09 07:49:34,1,10.78,1,N,142,138,1,35.5,0.0,0.5,8.41,5.76,0.3,50.47, +2,2019-01-21 15:08:23,2019-01-21 15:21:58,1,2.01,1,N,140,142,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-28 18:15:19,2019-01-28 18:21:11,1,0.8,1,N,140,229,1,5.5,1.0,0.5,3.0,0.0,0.3,10.3,0.0 +2,2019-01-22 15:03:43,2019-01-22 15:13:11,1,0.76,1,N,236,237,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-16 19:09:56,2019-01-16 19:15:31,1,1.28,1,N,141,236,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-16 16:09:57,2019-01-16 16:13:00,1,0.6,1,N,170,164,1,4.5,1.0,0.5,1.85,0.0,0.3,8.15, +2,2019-01-04 22:14:57,2019-01-04 22:32:22,1,2.35,1,N,79,186,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +1,2019-01-29 11:45:15,2019-01-29 11:57:48,1,1.3,1,N,264,264,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-08 19:22:46,2019-01-08 19:27:00,1,0.79,1,N,68,90,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-14 16:23:05,2019-01-14 16:29:00,1,1.09,1,N,113,234,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 11:00:37,2019-01-26 11:02:42,1,0.42,1,N,262,140,1,3.5,0.0,0.5,1.0,0.0,0.3,5.3,0.0 +1,2019-01-24 05:56:36,2019-01-24 06:08:26,1,3.0,1,N,224,161,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3,0.0 +1,2019-01-01 16:18:15,2019-01-01 16:30:09,1,2.3,1,Y,230,224,1,10.5,0.0,0.5,2.8,0.0,0.3,14.1, +1,2019-01-23 20:33:35,2019-01-23 20:37:49,1,0.7,1,N,125,79,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-13 23:53:32,2019-01-13 23:57:47,1,1.8,1,N,263,74,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-28 12:48:50,2019-01-28 13:04:43,1,1.9,1,N,264,264,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95,0.0 +2,2019-01-31 10:10:01,2019-01-31 10:24:21,1,2.16,1,N,238,163,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +1,2019-01-28 16:43:58,2019-01-28 16:53:37,1,1.3,1,N,264,264,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-14 19:22:07,2019-01-14 19:28:52,1,1.18,1,N,186,107,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-26 11:42:54,2019-01-26 11:45:08,1,0.43,1,N,238,151,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-30 17:47:12,2019-01-30 18:05:34,1,3.56,1,N,143,234,1,14.5,1.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-29 14:37:23,2019-01-29 14:45:53,1,1.61,1,N,140,263,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-03 12:57:29,2019-01-03 13:03:11,1,1.11,1,N,238,142,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-15 16:35:32,2019-01-15 16:41:20,1,0.9,1,N,236,237,1,6.0,1.0,0.5,2.0,0.0,0.3,9.8, +2,2019-01-18 12:53:13,2019-01-18 13:04:42,1,1.4,1,N,142,141,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-16 22:26:21,2019-01-16 22:51:43,1,4.2,1,N,231,49,1,19.0,0.5,0.5,4.05,0.0,0.3,24.35, +2,2019-01-02 07:52:03,2019-01-02 07:55:32,1,0.5,1,N,229,233,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3, +1,2019-01-31 07:01:11,2019-01-31 07:21:28,1,3.3,1,N,222,72,1,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-17 19:35:34,2019-01-17 19:43:05,1,2.15,1,N,236,74,1,8.5,1.0,0.5,3.09,0.0,0.3,13.39, +2,2019-01-17 08:36:34,2019-01-17 09:02:06,1,2.14,1,N,107,163,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-21 04:00:52,2019-01-21 04:05:51,1,1.18,1,N,75,237,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-21 07:03:29,2019-01-21 07:10:14,1,1.27,1,N,262,140,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-29 15:37:49,2019-01-29 15:54:31,1,2.3,1,N,161,236,1,12.5,0.0,0.5,1.33,0.0,0.3,14.63,0.0 +1,2019-01-26 13:36:38,2019-01-26 13:41:55,1,0.8,1,N,239,238,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-02 08:15:57,2019-01-02 08:18:29,1,0.35,1,N,249,249,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +2,2019-01-16 20:11:52,2019-01-16 20:19:14,1,0.98,1,N,144,4,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-09 21:52:12,2019-01-09 21:58:05,1,1.0,1,N,238,143,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-07 09:52:06,2019-01-07 09:57:56,1,0.72,1,N,90,100,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-13 00:43:34,2019-01-13 00:56:13,1,2.8,1,N,230,224,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +1,2019-01-22 03:58:53,2019-01-22 04:01:45,1,0.9,1,N,113,107,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-21 14:49:40,2019-01-21 14:57:22,1,1.0,1,N,234,158,1,7.0,0.0,0.5,2.2,0.0,0.3,10.0,0.0 +1,2019-01-05 10:16:48,2019-01-05 10:20:41,1,0.7,1,N,237,237,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-25 07:49:24,2019-01-25 08:37:21,1,15.7,1,N,138,13,1,49.0,0.0,0.5,13.85,5.76,0.3,69.41,0.0 +2,2019-01-15 09:01:46,2019-01-15 09:08:15,1,0.75,1,N,141,141,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-24 20:00:38,2019-01-24 20:08:09,1,1.7,1,N,163,239,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-31 12:18:20,2019-01-31 12:19:20,1,0.18,1,N,237,43,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-11 14:57:22,2019-01-11 15:08:27,1,3.01,1,N,140,107,1,11.5,0.0,0.5,1.84,0.0,0.3,14.14, +2,2019-01-28 18:52:10,2019-01-28 19:05:57,1,2.44,1,N,170,4,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-19 18:30:06,2019-01-19 18:53:44,1,3.61,1,N,161,125,1,16.5,0.0,0.5,4.32,0.0,0.3,21.62, +2,2019-01-01 22:30:28,2019-01-01 22:56:16,1,19.22,1,N,132,33,1,51.0,0.5,0.5,13.08,0.0,0.3,65.38, +1,2019-01-22 10:40:11,2019-01-22 10:44:22,1,0.5,1,N,236,236,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-23 19:51:07,2019-01-23 19:57:09,1,0.82,1,N,170,229,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-01 22:00:02,2019-01-01 22:11:40,1,2.16,1,N,161,90,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +1,2019-01-31 06:56:34,2019-01-31 07:06:13,1,5.0,1,N,233,88,1,15.5,0.0,0.5,2.5,0.0,0.3,18.8,0.0 +2,2019-01-27 21:58:10,2019-01-27 22:23:01,1,9.64,1,N,138,234,1,28.5,0.5,0.5,4.0,5.76,0.3,39.56,0.0 +1,2019-01-13 11:16:55,2019-01-13 11:33:43,1,3.0,1,N,151,48,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-20 14:32:35,2019-01-20 14:41:55,1,1.43,1,N,186,246,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-14 18:44:39,2019-01-14 19:07:48,1,4.39,1,N,87,49,2,17.5,1.0,0.5,0.0,0.0,0.3,19.3, +1,2019-01-31 21:37:12,2019-01-31 22:09:57,1,10.5,1,N,100,250,1,33.5,0.5,0.5,6.0,0.0,0.3,40.8,0.0 +1,2019-01-30 17:11:18,2019-01-30 17:22:52,1,1.3,1,N,162,142,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88,0.0 +1,2019-01-26 20:08:54,2019-01-26 20:24:41,1,3.2,1,N,114,246,1,13.5,0.5,0.5,1.5,0.0,0.3,16.3,0.0 +2,2019-01-25 09:33:05,2019-01-25 09:58:12,1,4.81,1,N,87,170,1,21.5,0.0,0.5,6.69,0.0,0.3,28.99,0.0 +2,2019-01-04 17:23:00,2019-01-04 17:35:28,1,3.96,1,N,263,69,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-25 07:36:16,2019-01-25 07:54:53,1,4.1,1,N,151,48,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +1,2019-01-01 12:54:16,2019-01-01 13:02:09,1,1.4,1,N,48,142,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-31 18:49:53,2019-01-31 19:11:18,1,2.8,1,N,229,113,1,14.5,1.0,0.5,1.5,0.0,0.3,17.8,0.0 +2,2019-01-24 13:53:24,2019-01-24 14:12:22,1,1.79,1,N,186,234,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-06 15:43:57,2019-01-06 16:00:03,1,3.82,1,N,142,74,1,14.5,0.0,0.5,1.0,0.0,0.3,16.3, +2,2019-01-02 05:18:55,2019-01-02 05:44:48,1,16.86,2,N,132,233,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56, +2,2019-01-31 15:33:46,2019-01-31 15:48:51,1,2.28,1,N,229,142,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-21 13:13:00,2019-01-21 13:19:55,1,0.6,1,N,238,238,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-17 11:07:16,2019-01-17 11:17:15,1,1.02,1,N,163,48,1,8.0,0.0,0.5,1.5,0.0,0.3,10.3, +2,2019-01-21 23:54:52,2019-01-22 00:01:29,1,1.64,1,N,186,158,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-20 12:09:20,2019-01-20 12:18:23,1,1.8,1,N,186,107,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-20 15:33:56,2019-01-20 15:43:55,1,1.45,1,N,230,143,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-25 19:24:24,2019-01-25 19:37:22,1,1.7,1,N,236,142,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-07 03:13:55,2019-01-07 03:17:01,1,1.0,1,N,246,50,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-31 07:26:02,2019-01-31 07:39:51,1,2.6,1,N,186,144,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-28 16:56:59,2019-01-28 17:03:14,1,0.87,1,N,100,163,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14,0.0 +2,2019-01-19 21:04:56,2019-01-19 21:20:39,1,0.95,1,N,68,186,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +4,2019-01-07 18:23:20,2019-01-07 18:54:44,1,7.83,1,N,114,75,1,28.0,1.0,0.5,2.0,0.0,0.3,31.8, +2,2019-01-25 22:43:22,2019-01-25 22:50:23,1,0.91,1,N,113,249,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-31 00:02:20,2019-01-31 00:22:42,1,4.39,1,N,163,42,1,18.0,0.5,0.5,4.82,0.0,0.3,24.12,0.0 +1,2019-01-24 14:05:58,2019-01-24 15:08:00,1,18.4,2,N,231,132,1,52.0,0.0,0.5,13.15,0.0,0.3,65.95,0.0 +2,2019-01-18 10:59:36,2019-01-18 11:02:49,1,0.4,1,N,141,237,1,4.0,0.0,0.5,0.76,0.0,0.3,5.56, +1,2019-01-24 07:16:55,2019-01-24 07:43:55,1,7.0,1,N,166,249,1,24.0,0.0,0.5,3.0,0.0,0.3,27.8,0.0 +2,2019-01-29 09:57:25,2019-01-29 10:24:53,1,11.41,1,N,163,121,2,35.0,0.0,0.5,0.0,0.0,0.3,35.8,0.0 +1,2019-01-28 16:13:54,2019-01-28 16:51:38,1,9.4,1,N,138,68,1,32.5,1.0,0.5,10.0,5.76,0.3,50.06,0.0 +1,2019-01-14 16:02:18,2019-01-14 16:27:18,1,8.5,1,N,138,161,1,26.5,1.0,0.5,6.8,5.76,0.3,40.86, +2,2019-01-27 20:35:28,2019-01-27 21:01:04,1,6.7,1,N,113,166,1,24.5,0.5,0.5,4.0,0.0,0.3,29.8,0.0 +1,2019-01-09 11:41:09,2019-01-09 12:01:28,1,5.0,1,N,163,13,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-27 08:17:15,2019-01-27 08:23:26,1,1.08,1,N,238,239,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-09 12:36:34,2019-01-09 12:49:46,1,1.6,1,N,162,234,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-26 10:29:27,2019-01-26 10:36:14,1,1.2,1,N,90,114,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-16 16:49:51,2019-01-16 16:56:49,1,1.0,1,N,264,264,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-12 10:54:30,2019-01-12 10:57:49,1,0.64,1,N,239,239,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-10 17:53:01,2019-01-10 18:04:01,1,1.4,1,N,141,162,1,8.5,1.0,0.5,3.05,0.0,0.3,13.35, +2,2019-01-05 14:35:12,2019-01-05 15:24:45,1,20.35,2,N,132,263,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-15 18:32:25,2019-01-15 18:38:06,1,1.04,1,N,170,107,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-10 16:21:32,2019-01-10 16:33:38,1,1.05,1,N,161,237,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-25 03:07:49,2019-01-25 03:18:26,1,4.0,1,N,249,143,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-11 08:03:18,2019-01-11 08:36:35,1,3.2,1,N,151,161,1,20.0,0.0,0.5,2.0,0.0,0.3,22.8, +2,2019-01-08 17:48:09,2019-01-08 18:07:28,1,2.9,1,N,163,238,1,14.0,1.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-27 20:12:51,2019-01-27 20:19:16,1,0.95,1,N,231,87,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-11 11:42:04,2019-01-11 11:48:21,1,0.9,1,N,24,238,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-03 22:15:38,2019-01-03 22:18:21,1,0.5,1,N,186,100,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +1,2019-01-12 16:07:20,2019-01-12 16:17:44,1,1.5,1,N,161,48,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-15 18:19:27,2019-01-15 18:27:17,1,0.9,1,Y,239,238,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-15 09:11:18,2019-01-15 09:20:25,1,1.53,1,N,249,246,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-01 03:45:58,2019-01-01 04:01:51,1,3.5,1,N,158,233,3,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +4,2019-01-15 06:43:06,2019-01-15 06:51:51,1,1.0,1,N,68,170,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-17 21:27:26,2019-01-17 21:45:56,1,3.23,1,N,143,141,1,14.5,0.5,0.5,6.0,0.0,0.3,21.8, +2,2019-01-22 19:21:39,2019-01-22 19:30:43,1,1.27,1,N,163,164,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-15 21:56:14,2019-01-15 22:00:31,1,0.89,1,N,163,237,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-26 22:17:28,2019-01-26 22:51:48,1,4.3,1,N,79,181,2,22.0,0.5,0.5,0.0,0.0,0.3,23.3,0.0 +1,2019-01-23 11:09:31,2019-01-23 11:30:43,1,2.3,1,N,161,236,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +4,2019-01-04 03:31:50,2019-01-04 03:53:10,1,6.67,1,N,158,236,1,23.0,0.5,0.5,1.0,0.0,0.3,25.3, +1,2019-01-17 11:40:22,2019-01-17 11:48:42,1,1.3,1,N,161,234,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-26 19:40:14,2019-01-26 19:44:01,1,0.5,1,N,232,232,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-24 10:19:59,2019-01-24 11:05:05,1,21.33,2,N,132,243,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-29 18:23:11,2019-01-29 19:00:46,1,17.7,2,N,215,261,1,52.0,4.5,0.5,12.6,5.76,0.3,75.66,0.0 +2,2019-01-11 08:07:38,2019-01-11 08:18:23,1,1.47,1,N,264,264,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-14 20:47:32,2019-01-14 20:59:27,1,2.9,1,N,163,263,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-11 18:35:02,2019-01-11 19:03:54,1,8.21,1,N,50,228,1,27.5,1.0,0.5,7.01,5.76,0.3,42.07, +1,2019-01-24 08:16:15,2019-01-24 08:22:11,1,0.9,1,N,48,50,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-02 09:38:47,2019-01-02 09:44:22,1,0.55,1,N,48,230,1,5.5,0.0,0.5,1.58,0.0,0.3,7.88, +1,2019-01-07 17:55:07,2019-01-07 18:02:09,1,1.1,1,N,239,163,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-22 07:12:06,2019-01-22 07:17:14,1,1.0,1,N,233,107,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-13 00:11:31,2019-01-13 00:16:14,1,0.91,1,N,237,141,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-30 17:59:44,2019-01-30 18:02:07,1,0.2,1,N,162,233,1,3.5,1.0,0.5,0.8,0.0,0.3,6.1,0.0 +1,2019-01-10 12:02:09,2019-01-10 12:17:41,1,1.1,1,N,161,163,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-30 07:05:32,2019-01-30 07:16:10,1,2.82,1,N,239,75,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-13 13:13:37,2019-01-13 13:17:19,1,0.75,1,N,141,141,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-11 15:45:54,2019-01-11 15:48:59,1,0.38,1,N,239,239,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-25 06:35:32,2019-01-25 06:48:30,1,2.1,1,N,100,107,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3,0.0 +2,2019-01-18 12:02:42,2019-01-18 12:25:08,1,5.49,1,N,113,33,1,20.5,0.0,0.5,5.32,0.0,0.3,26.62, +4,2019-01-23 21:04:34,2019-01-23 21:19:38,1,3.32,1,N,148,112,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +1,2019-01-24 20:57:53,2019-01-24 21:04:00,1,1.0,1,N,170,90,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-31 08:35:27,2019-01-31 08:44:38,1,0.56,1,N,142,163,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8,0.0 +2,2019-01-08 07:47:18,2019-01-08 08:00:55,1,1.76,1,N,209,114,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +1,2019-01-21 08:03:02,2019-01-21 08:15:29,1,3.7,1,N,186,261,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-18 20:49:53,2019-01-18 21:15:40,1,14.6,1,N,262,227,2,40.5,0.5,0.5,0.0,5.76,0.3,47.56, +1,2019-01-16 08:10:19,2019-01-16 08:22:53,1,1.4,1,N,239,43,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-14 20:32:18,2019-01-14 20:32:25,1,0.0,5,N,264,141,1,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +4,2019-01-12 16:09:39,2019-01-12 16:15:47,1,1.02,1,N,237,142,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-22 08:27:51,2019-01-22 08:39:38,1,1.1,1,N,229,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-25 15:05:46,2019-01-25 15:11:25,1,0.67,1,N,262,262,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-03 11:38:01,2019-01-03 11:48:53,1,0.9,1,N,234,186,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-31 17:04:43,2019-01-31 17:24:49,1,1.96,1,N,186,237,1,13.5,1.0,0.5,3.82,0.0,0.3,19.12,0.0 +1,2019-01-31 12:16:29,2019-01-31 12:36:09,1,2.7,1,N,142,151,1,14.5,0.0,0.5,3.05,0.0,0.3,18.35,0.0 +2,2019-01-29 14:21:36,2019-01-29 14:45:01,1,2.93,1,N,211,100,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +1,2019-01-19 03:24:46,2019-01-19 03:45:26,1,5.1,1,N,79,112,1,19.5,0.5,0.5,5.2,0.0,0.3,26.0, +1,2019-01-14 15:48:49,2019-01-14 16:09:11,1,2.9,1,N,211,68,1,14.5,0.0,0.5,3.05,0.0,0.3,18.35, +2,2019-01-09 18:11:46,2019-01-09 18:22:55,1,1.88,1,N,140,162,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-13 21:43:11,2019-01-13 21:58:38,1,4.56,1,N,113,25,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-23 08:38:35,2019-01-23 08:58:31,1,2.82,1,N,186,237,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-26 01:30:57,2019-01-26 01:39:43,1,1.4,1,N,148,249,1,7.5,0.5,0.5,1.32,0.0,0.3,10.12,0.0 +1,2019-01-19 20:39:35,2019-01-19 20:51:52,1,2.0,1,Y,100,162,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-11 18:36:36,2019-01-11 18:48:32,1,1.23,1,N,234,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-18 15:02:53,2019-01-18 15:35:43,1,15.11,1,N,87,138,1,41.5,0.0,0.5,9.61,5.76,0.3,57.67, +2,2019-01-30 12:51:26,2019-01-30 12:59:23,1,1.45,1,N,246,68,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-02 08:23:30,2019-01-02 08:30:37,1,1.39,1,N,170,141,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-07 20:24:28,2019-01-07 20:37:57,1,3.7,1,N,234,263,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15, +2,2019-01-12 18:29:59,2019-01-12 18:40:54,1,1.54,1,N,141,239,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-11 20:52:26,2019-01-11 20:59:46,1,0.8,1,N,186,234,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-30 09:32:05,2019-01-30 10:06:45,1,10.87,1,N,138,162,1,33.5,0.0,0.5,8.0,5.76,0.3,48.06,0.0 +2,2019-01-19 00:10:06,2019-01-19 00:34:00,1,7.38,1,N,238,79,1,25.0,0.5,0.5,5.26,0.0,0.3,31.56, +2,2019-01-16 07:21:59,2019-01-16 07:49:37,1,4.69,1,N,68,263,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96, +2,2019-01-26 22:50:57,2019-01-26 22:53:21,1,0.39,1,N,229,229,1,3.5,0.5,0.5,1.2,0.0,0.3,6.0,0.0 +1,2019-01-15 23:53:00,2019-01-16 00:03:39,1,2.1,1,N,148,249,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +1,2019-01-04 20:13:43,2019-01-04 20:19:20,1,0.7,1,N,144,125,1,5.5,0.5,0.5,0.75,0.0,0.3,7.55, +1,2019-01-19 16:25:57,2019-01-19 16:32:22,1,0.9,1,N,239,151,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-22 01:08:14,2019-01-22 01:19:06,1,3.39,1,N,48,141,1,12.0,0.5,0.5,3.32,0.0,0.3,16.62,0.0 +2,2019-01-12 14:11:43,2019-01-12 14:29:41,1,3.41,1,N,230,75,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-09 08:55:35,2019-01-09 08:59:52,1,0.2,1,N,87,261,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-05 12:48:38,2019-01-05 12:58:18,1,0.8,1,N,230,48,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-25 19:25:01,2019-01-25 19:37:45,1,1.6,1,N,163,90,1,9.5,1.0,0.5,1.7,0.0,0.3,13.0,0.0 +2,2019-01-17 19:00:46,2019-01-17 19:18:56,1,2.3,1,N,142,234,1,12.5,1.0,0.5,2.57,0.0,0.3,16.87, +2,2019-01-21 20:59:35,2019-01-21 21:12:31,1,2.42,1,N,162,50,1,11.0,0.5,0.5,2.0,0.0,0.3,14.3,0.0 +1,2019-01-15 20:01:51,2019-01-15 20:10:33,1,1.5,1,N,239,141,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-22 10:07:54,2019-01-22 10:20:30,1,2.5,1,N,158,13,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-07 09:55:16,2019-01-07 10:16:02,1,6.92,1,N,229,231,1,23.0,0.0,0.5,3.0,0.0,0.3,26.8, +2,2019-01-15 22:01:38,2019-01-15 22:30:00,1,15.4,1,N,132,179,1,43.0,0.5,0.5,8.86,0.0,0.3,53.16, +2,2019-01-17 17:44:27,2019-01-17 17:57:26,1,1.77,1,N,186,163,1,10.5,1.0,0.5,3.69,0.0,0.3,15.99, +2,2019-01-27 12:46:46,2019-01-27 12:56:24,1,1.36,1,N,113,68,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-15 07:08:20,2019-01-15 07:59:52,1,15.94,5,N,91,162,1,45.0,0.0,0.5,0.0,5.76,0.3,51.56, +1,2019-01-26 13:59:43,2019-01-26 14:38:30,1,17.7,2,N,132,161,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-10 19:49:20,2019-01-10 20:02:10,1,1.7,1,N,163,170,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-27 02:25:58,2019-01-27 02:34:25,1,1.8,1,N,68,137,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-11 00:08:11,2019-01-11 00:21:47,1,3.37,1,N,231,33,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-24 01:28:46,2019-01-24 01:37:24,1,1.51,1,N,233,107,1,7.0,0.5,0.5,1.24,0.0,0.3,9.54,0.0 +2,2019-01-25 08:53:33,2019-01-25 08:57:41,1,0.56,1,N,233,233,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-30 17:21:44,2019-01-30 17:50:55,1,3.4,1,N,145,157,2,18.5,1.0,0.5,0.0,0.0,0.3,20.3,0.0 +2,2019-01-17 07:28:08,2019-01-17 07:32:14,1,0.62,1,N,236,263,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-24 18:52:55,2019-01-24 19:09:48,1,2.2,1,N,161,113,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-11 21:58:32,2019-01-11 22:01:19,1,0.44,1,N,161,162,1,4.0,0.5,0.5,0.5,0.0,0.3,5.8, +1,2019-01-04 22:44:35,2019-01-04 23:18:52,1,19.5,2,N,132,237,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-01 20:27:24,2019-01-01 20:32:27,1,0.78,1,N,142,230,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-09 21:13:38,2019-01-09 21:26:23,1,3.5,1,N,50,151,1,13.0,0.5,0.5,2.57,0.0,0.3,16.87, +1,2019-01-18 23:40:42,2019-01-18 23:43:06,1,0.5,1,N,164,107,1,3.5,0.5,0.5,1.15,0.0,0.3,5.95, +2,2019-01-19 22:17:56,2019-01-19 22:37:30,1,2.53,1,N,163,140,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +1,2019-01-02 17:06:44,2019-01-02 17:21:12,1,0.7,1,N,161,48,1,9.5,1.0,0.5,1.7,0.0,0.3,13.0, +1,2019-01-12 12:36:08,2019-01-12 12:38:26,1,0.8,1,N,236,238,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-03 14:38:34,2019-01-03 14:43:02,1,0.8,1,N,140,262,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-08 21:44:59,2019-01-08 21:52:43,1,1.3,1,N,114,90,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-30 19:49:02,2019-01-30 20:19:33,1,19.31,2,N,132,236,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67,0.0 +2,2019-01-11 07:59:49,2019-01-11 08:18:55,1,2.57,1,N,224,100,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-18 01:18:37,2019-01-18 01:22:43,1,1.19,1,N,230,186,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-10 21:08:36,2019-01-10 21:18:58,1,1.39,1,N,234,79,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-07 05:32:24,2019-01-07 05:35:17,1,0.5,1,N,48,246,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-18 14:47:29,2019-01-18 15:52:40,1,15.1,2,N,231,132,1,52.0,0.0,0.5,5.2,0.0,0.3,58.0, +2,2019-01-19 10:19:21,2019-01-19 10:33:27,1,3.14,1,N,211,246,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-04 21:07:38,2019-01-04 21:25:04,1,3.17,1,N,234,236,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +1,2019-01-11 17:06:54,2019-01-11 17:25:47,1,3.2,1,N,186,144,1,14.0,1.0,0.5,2.37,0.0,0.3,18.17, +1,2019-01-07 04:17:41,2019-01-07 04:25:02,1,1.0,1,N,137,164,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-27 18:18:25,2019-01-27 18:34:18,1,4.25,1,N,87,107,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +1,2019-01-08 10:54:54,2019-01-08 11:17:55,1,2.6,1,N,246,162,1,15.5,0.0,0.5,2.0,0.0,0.3,18.3, +1,2019-01-26 00:00:38,2019-01-26 00:02:42,1,0.4,1,N,231,231,1,3.5,0.5,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-24 08:14:17,2019-01-24 08:20:41,1,0.93,1,N,75,74,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-26 22:20:03,2019-01-26 22:26:40,1,1.9,1,N,263,74,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-07 08:13:36,2019-01-07 08:29:12,1,2.5,1,N,236,162,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-26 12:47:11,2019-01-26 12:50:09,1,0.7,1,N,163,43,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-17 21:09:35,2019-01-17 21:25:08,1,2.9,1,N,140,239,1,13.0,0.5,0.5,1.75,0.0,0.3,16.05, +1,2019-01-18 12:37:48,2019-01-18 12:42:58,1,1.2,1,N,239,238,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-01 11:42:14,2019-01-01 11:43:28,1,0.07,1,N,236,263,1,2.5,0.0,0.5,0.66,0.0,0.3,3.96, +2,2019-01-28 16:02:18,2019-01-28 16:21:57,1,2.99,1,N,236,170,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-27 10:20:58,2019-01-27 10:30:47,1,1.89,1,N,239,50,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +4,2019-01-20 17:06:32,2019-01-20 17:19:18,1,2.39,1,N,142,170,1,10.5,0.0,0.5,2.83,0.0,0.3,14.13, +2,2019-01-11 21:47:55,2019-01-11 22:02:22,1,2.11,1,N,186,249,1,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-11 17:31:00,2019-01-11 17:35:12,1,0.51,1,N,186,230,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 10:56:32,2019-01-09 10:59:49,1,0.46,1,N,239,239,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +2,2019-01-12 15:30:37,2019-01-12 15:32:59,1,0.53,1,N,170,137,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0, +2,2019-01-26 17:00:29,2019-01-26 17:35:32,1,9.31,1,N,166,261,1,31.5,0.0,0.5,1.0,0.0,0.3,33.3,0.0 +2,2019-01-15 15:32:35,2019-01-15 15:46:15,1,1.99,1,N,161,246,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +1,2019-01-04 11:04:37,2019-01-04 11:14:09,1,1.5,1,N,79,170,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7, +2,2019-01-08 17:45:21,2019-01-08 17:48:37,1,0.55,1,N,239,239,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-20 09:53:15,2019-01-20 10:13:37,1,2.8,1,N,209,211,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-29 10:23:46,2019-01-29 10:33:41,1,1.67,1,N,233,79,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-15 20:43:15,2019-01-15 20:54:42,1,1.42,1,N,107,90,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +2,2019-01-03 02:11:57,2019-01-03 02:16:59,1,0.58,1,N,236,236,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-25 18:56:41,2019-01-25 19:06:00,1,1.2,1,N,100,68,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-07 01:25:33,2019-01-07 01:35:53,1,3.3,1,N,237,226,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95, +2,2019-01-01 01:51:56,2019-01-01 02:14:42,1,4.06,1,N,113,143,1,17.5,0.5,0.5,5.64,0.0,0.3,24.44, +1,2019-01-20 23:25:59,2019-01-20 23:39:38,1,4.2,1,N,230,7,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +2,2019-01-24 06:24:32,2019-01-24 06:29:17,1,1.41,1,N,249,234,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-16 10:08:05,2019-01-16 10:15:54,1,1.2,1,N,113,170,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-21 01:42:41,2019-01-21 01:46:01,1,0.7,1,N,137,170,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-30 10:27:11,2019-01-30 10:34:03,1,0.83,1,N,141,229,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-05 12:37:46,2019-01-05 12:42:40,1,1.22,1,N,263,75,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-30 20:27:19,2019-01-30 21:02:46,1,8.64,1,N,261,41,2,31.5,0.5,0.5,0.0,0.0,0.3,32.8,0.0 +2,2019-01-18 09:43:50,2019-01-18 10:18:04,1,3.7,1,N,68,140,1,22.0,0.0,0.5,3.2,0.0,0.3,26.0, +2,2019-01-03 08:54:05,2019-01-03 09:37:38,1,8.63,1,N,138,141,1,33.5,0.0,0.5,5.0,0.0,0.3,39.3, +1,2019-01-16 12:13:54,2019-01-16 12:22:25,1,0.8,1,N,45,87,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-13 16:09:37,2019-01-13 16:17:04,1,1.3,1,N,163,236,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-17 09:54:49,2019-01-17 10:01:05,1,0.6,1,N,162,170,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-28 12:40:26,2019-01-28 12:55:29,1,2.1,1,N,236,161,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-11 18:23:30,2019-01-11 18:38:48,1,2.64,1,N,236,233,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-25 11:40:27,2019-01-25 11:45:56,1,0.69,1,N,233,229,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-02 02:23:48,2019-01-02 02:24:04,1,0.0,6,N,174,174,3,655.35,0.0,0.5,0.0,0.0,0.3,656.15, +1,2019-01-15 16:08:57,2019-01-15 16:31:55,1,6.1,1,N,132,28,2,22.0,1.0,0.5,0.0,0.0,0.3,23.8, +2,2019-01-20 16:43:51,2019-01-20 16:57:54,1,2.28,1,N,264,264,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-19 04:07:32,2019-01-19 04:23:25,1,3.6,1,N,264,264,1,13.5,0.5,0.5,1.0,0.0,0.3,15.8, +2,2019-01-30 18:41:11,2019-01-30 18:50:47,1,1.71,1,N,233,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-30 19:46:03,2019-01-30 19:51:36,1,0.78,1,N,237,141,1,5.5,1.0,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-26 01:12:28,2019-01-26 01:22:48,1,0.91,1,N,158,114,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-14 18:15:04,2019-01-14 18:23:42,1,1.12,1,N,236,237,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-02 09:31:55,2019-01-02 09:43:07,1,2.02,1,N,125,186,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-22 18:00:20,2019-01-22 18:05:37,1,0.42,1,N,114,144,1,5.0,1.0,0.5,1.02,0.0,0.3,7.82,0.0 +2,2019-01-11 21:17:03,2019-01-11 21:24:14,1,1.01,1,N,137,79,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-23 09:23:17,2019-01-23 09:27:33,1,0.64,1,N,263,263,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-17 11:55:50,2019-01-17 12:09:14,1,0.8,1,N,48,161,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-17 17:59:55,2019-01-17 18:19:58,1,1.96,1,N,79,231,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-23 18:05:02,2019-01-23 18:32:21,1,4.52,1,N,234,75,1,20.5,1.0,0.5,4.46,0.0,0.3,26.76,0.0 +2,2019-01-27 09:54:15,2019-01-27 09:57:56,1,0.95,1,N,237,162,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-18 04:46:48,2019-01-18 04:54:36,1,2.01,1,N,114,88,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-23 15:08:27,2019-01-23 15:16:24,1,2.45,1,N,233,145,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-10 16:29:05,2019-01-10 16:33:33,1,0.7,1,N,107,137,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-26 08:37:25,2019-01-26 08:55:56,1,4.37,1,N,211,237,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3,0.0 +1,2019-01-16 07:15:22,2019-01-16 07:53:42,1,15.6,1,N,138,88,1,45.5,0.0,0.5,10.4,5.76,0.3,62.46, +2,2019-01-25 06:33:36,2019-01-25 06:39:12,1,1.44,1,N,100,50,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +1,2019-01-12 16:17:47,2019-01-12 16:59:20,1,18.4,1,N,264,97,1,51.5,0.0,0.5,6.0,0.0,0.3,58.3, +4,2019-01-09 09:42:50,2019-01-09 10:04:29,1,1.31,1,N,162,237,1,13.5,0.0,0.5,1.7,0.0,0.3,16.0, +2,2019-01-19 14:58:38,2019-01-19 15:04:41,1,0.62,1,N,125,211,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-05 08:54:36,2019-01-05 09:03:40,1,1.3,1,N,41,166,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-31 08:46:50,2019-01-31 09:01:45,1,1.73,1,N,148,231,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-04 07:38:44,2019-01-04 07:41:05,1,0.88,1,N,107,170,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-04 22:39:32,2019-01-04 22:57:42,1,3.11,1,N,263,164,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +2,2019-01-24 16:53:12,2019-01-24 17:23:52,1,3.71,1,N,264,264,2,19.5,1.0,0.5,0.0,0.0,0.3,21.3,0.0 +1,2019-01-11 17:56:26,2019-01-11 18:07:40,1,1.8,1,N,230,236,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-11 13:36:21,2019-01-11 13:38:56,1,0.5,1,N,170,161,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-09 15:17:42,2019-01-09 15:41:16,1,8.73,1,N,138,161,1,27.5,0.0,0.5,10.22,5.76,0.3,44.28, +2,2019-01-31 18:49:42,2019-01-31 18:59:36,1,1.02,1,N,43,43,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-08 16:57:24,2019-01-08 17:08:06,1,0.6,1,N,170,100,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-22 11:20:15,2019-01-22 11:31:33,1,1.6,1,N,41,24,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-06 17:53:31,2019-01-06 17:59:28,1,1.12,1,N,211,209,1,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-05 12:40:39,2019-01-05 12:56:16,1,2.53,1,N,142,263,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-06 21:44:27,2019-01-06 21:47:20,1,1.2,1,N,75,236,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +1,2019-01-24 06:35:29,2019-01-24 06:41:55,1,1.3,1,N,100,161,1,6.5,0.0,0.5,0.88,0.0,0.3,8.18,0.0 +2,2019-01-06 12:27:12,2019-01-06 12:46:04,1,6.2,1,N,163,87,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +1,2019-01-25 14:19:49,2019-01-25 14:39:39,1,3.2,1,N,262,142,1,14.5,0.0,0.5,3.0,0.0,0.3,18.3,0.0 +2,2019-01-31 08:32:04,2019-01-31 08:39:48,1,0.86,1,N,209,231,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-20 17:14:08,2019-01-20 17:34:45,1,10.2,1,N,264,264,1,29.5,0.0,0.5,5.0,0.0,0.3,35.3, +2,2019-01-09 22:11:31,2019-01-09 22:27:44,1,4.14,1,N,141,41,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-19 19:59:46,2019-01-19 20:07:47,1,0.38,1,N,230,230,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-03 08:14:33,2019-01-03 08:36:06,1,10.51,1,N,229,138,1,31.0,0.0,0.5,7.51,5.76,0.3,45.07, +1,2019-01-28 12:37:43,2019-01-28 12:45:28,1,0.8,1,N,43,140,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-08 16:22:43,2019-01-08 16:35:49,1,2.6,1,N,68,239,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-23 17:20:01,2019-01-23 17:27:16,1,1.39,1,N,97,66,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-29 01:14:08,2019-01-29 01:36:02,1,19.86,2,N,132,87,1,52.0,0.0,0.5,13.2,0.0,0.3,66.0,0.0 +2,2019-01-04 11:07:22,2019-01-04 11:13:09,1,0.73,1,N,43,263,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-14 23:18:17,2019-01-14 23:29:54,1,4.11,1,N,246,166,1,14.0,0.5,0.5,2.0,0.0,0.3,17.3, +1,2019-01-23 00:54:49,2019-01-23 01:05:46,1,1.5,1,N,68,114,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-17 13:48:45,2019-01-17 14:05:33,1,2.0,1,N,137,148,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-25 23:23:30,2019-01-25 23:38:20,1,4.9,1,N,88,170,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95,0.0 +1,2019-01-24 16:33:32,2019-01-24 16:48:25,1,2.6,1,N,87,65,1,12.0,1.0,0.5,1.0,0.0,0.3,14.8,0.0 +1,2019-01-23 05:22:21,2019-01-23 05:33:14,1,6.2,1,N,132,197,1,19.0,0.5,0.5,3.0,0.0,0.3,23.3,0.0 +2,2019-01-31 15:49:06,2019-01-31 16:10:22,1,2.41,1,N,161,113,1,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-11 21:01:03,2019-01-11 21:08:39,1,1.6,1,N,170,229,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-04 21:44:13,2019-01-04 21:48:17,1,0.8,1,N,239,142,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-14 22:08:12,2019-01-14 22:19:20,1,1.3,1,N,164,100,1,8.5,0.5,0.5,1.0,0.0,0.3,10.8, +2,2019-01-03 13:44:48,2019-01-03 13:49:04,1,0.66,1,N,143,142,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-29 17:58:08,2019-01-29 18:11:19,1,0.72,1,N,13,13,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-24 13:25:35,2019-01-24 13:29:53,1,0.7,1,N,143,239,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-23 09:34:13,2019-01-23 09:41:28,1,0.57,1,N,68,90,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-22 06:39:56,2019-01-22 07:09:45,1,8.81,1,N,236,138,1,30.5,0.0,0.5,7.41,5.76,0.3,44.47,0.0 +2,2019-01-21 13:58:54,2019-01-21 14:03:35,1,0.71,1,N,186,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-21 14:19:58,2019-01-21 14:25:02,1,0.94,1,N,262,236,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-11 09:56:21,2019-01-11 10:01:20,1,0.52,1,N,231,13,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-20 13:51:52,2019-01-20 14:00:54,1,0.8,1,N,162,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-22 10:49:13,2019-01-22 11:09:50,1,2.35,1,N,262,161,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-29 07:30:00,2019-01-29 07:54:40,1,2.8,1,N,107,48,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35,0.0 +1,2019-01-15 18:01:15,2019-01-15 18:17:56,1,1.1,1,N,170,100,1,11.0,1.0,0.5,1.5,0.0,0.3,14.3, +2,2019-01-09 09:29:57,2019-01-09 09:51:39,1,2.3,1,N,43,75,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-15 22:18:44,2019-01-15 22:28:08,1,2.08,1,N,41,116,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-03 19:07:39,2019-01-03 19:19:02,1,2.51,1,N,144,68,1,10.0,1.0,0.5,1.5,0.0,0.3,13.3, +1,2019-01-28 10:05:37,2019-01-28 10:50:14,1,3.5,1,N,151,164,1,27.0,0.0,0.5,3.0,0.0,0.3,30.8,0.0 +2,2019-01-14 07:47:28,2019-01-14 08:12:34,1,1.5,1,N,163,50,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56, +1,2019-01-06 20:01:05,2019-01-06 20:15:11,1,2.3,1,N,161,163,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-20 23:33:30,2019-01-20 23:40:40,1,2.72,1,N,143,151,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-08 20:11:14,2019-01-08 20:23:37,1,1.77,1,N,234,68,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-20 19:36:09,2019-01-20 20:10:42,1,10.78,1,N,186,138,1,35.0,0.0,0.5,7.16,0.0,0.3,42.96, +2,2019-01-20 11:23:36,2019-01-20 11:34:58,1,2.36,1,N,13,158,1,11.0,0.0,0.5,2.2,0.0,0.3,14.0, +2,2019-01-11 09:54:45,2019-01-11 10:11:11,1,1.6,1,N,114,158,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-25 16:44:27,2019-01-25 17:00:09,1,2.38,1,N,237,233,1,12.5,1.0,0.5,2.14,0.0,0.3,16.44,0.0 +2,2019-01-21 09:06:43,2019-01-21 09:09:37,1,0.66,1,N,50,48,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-10 03:00:42,2019-01-10 03:05:32,1,1.79,1,N,79,137,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-10 19:03:45,2019-01-10 19:16:57,1,1.65,1,N,137,48,1,10.0,1.0,0.5,2.0,0.0,0.3,13.8, +1,2019-01-27 14:06:31,2019-01-27 14:11:52,1,0.9,1,N,238,239,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0,0.0 +2,2019-01-09 04:33:31,2019-01-09 04:40:36,1,1.89,1,N,43,238,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-16 06:48:16,2019-01-16 06:52:45,1,1.41,1,N,68,48,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-31 09:23:02,2019-01-31 09:29:02,1,0.92,1,N,170,229,1,6.0,0.0,0.5,1.5,0.0,0.3,8.3,0.0 +1,2019-01-15 11:32:34,2019-01-15 11:44:20,1,3.1,1,N,146,137,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8, +2,2019-01-19 20:15:17,2019-01-19 20:34:35,1,3.19,1,N,48,79,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +1,2019-01-18 19:12:29,2019-01-18 19:13:18,1,0.2,1,N,164,234,2,3.0,1.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-24 14:30:56,2019-01-24 14:59:08,1,2.0,1,N,163,236,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35,0.0 +1,2019-01-17 12:16:25,2019-01-17 12:19:35,1,0.4,1,N,66,66,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-19 13:09:39,2019-01-19 13:35:09,1,3.0,1,N,166,50,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +4,2019-01-17 10:42:41,2019-01-17 11:16:57,1,3.0,1,N,233,125,1,21.0,0.0,0.5,4.0,0.0,0.3,25.8, +2,2019-01-05 16:28:01,2019-01-05 16:43:09,1,2.23,1,N,249,261,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-28 15:43:21,2019-01-28 15:49:43,1,0.8,1,N,237,237,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-14 19:00:59,2019-01-14 19:18:08,1,3.8,1,N,164,238,1,14.5,1.0,0.5,2.45,0.0,0.3,18.75, +1,2019-01-02 01:08:16,2019-01-02 01:28:12,1,9.5,1,N,142,213,2,27.0,0.5,0.5,0.0,0.0,0.3,28.3, +2,2019-01-28 08:21:17,2019-01-28 08:27:22,1,0.92,1,N,100,234,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-20 13:05:46,2019-01-20 13:05:53,1,0.04,2,N,142,142,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +2,2019-01-21 12:43:52,2019-01-21 12:52:46,1,1.48,1,N,234,211,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-10 17:32:15,2019-01-10 17:42:33,1,1.4,1,N,107,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-12 11:14:42,2019-01-12 11:37:02,1,6.6,1,N,146,211,1,22.5,0.0,0.5,4.65,0.0,0.3,27.95, +2,2019-01-22 21:08:14,2019-01-22 21:20:39,1,2.06,1,N,142,140,1,10.0,0.5,0.5,1.7,0.0,0.3,13.0,0.0 +2,2019-01-20 01:55:46,2019-01-20 02:04:58,1,2.07,1,N,239,141,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +2,2019-01-14 01:52:09,2019-01-14 01:56:34,1,1.0,1,N,68,186,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-24 19:18:30,2019-01-24 19:20:08,1,0.39,1,N,223,223,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-11 21:44:10,2019-01-11 21:48:00,1,0.6,1,N,161,48,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-20 17:32:57,2019-01-20 17:35:10,1,0.61,1,N,237,236,1,4.0,0.0,0.5,0.8,0.0,0.3,5.6, +2,2019-01-10 19:26:35,2019-01-10 19:42:32,1,2.36,1,N,87,79,1,12.0,1.0,0.5,2.07,0.0,0.3,15.87, +2,2019-01-10 17:47:50,2019-01-10 17:54:15,1,0.8,1,N,100,161,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-11 17:58:21,2019-01-11 18:00:12,1,0.31,1,N,163,230,1,3.5,1.0,0.5,0.5,0.0,0.3,5.8, +2,2019-01-07 08:29:21,2019-01-07 08:38:59,1,1.31,1,N,186,246,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-22 08:59:24,2019-01-22 09:11:14,1,1.9,1,N,140,162,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-26 16:06:00,2019-01-26 16:16:16,1,1.76,1,N,238,142,1,9.0,0.0,0.5,1.5,0.0,0.3,11.3,0.0 +1,2019-01-10 19:03:30,2019-01-10 19:27:53,1,2.9,1,N,264,264,1,16.0,1.0,0.5,3.55,0.0,0.3,21.35, +1,2019-01-22 17:53:57,2019-01-22 17:57:20,1,0.6,1,N,48,48,1,4.5,1.0,0.5,1.85,0.0,0.3,8.15,0.0 +1,2019-01-05 13:42:06,2019-01-05 13:51:20,1,3.0,1,N,151,50,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-10 17:17:30,2019-01-10 17:28:44,1,1.21,1,N,236,141,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-17 15:18:28,2019-01-17 15:29:02,1,1.8,1,N,140,162,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-26 11:27:23,2019-01-26 12:02:09,1,11.54,1,N,138,181,1,36.0,0.0,0.5,7.36,0.0,0.3,44.16,0.0 +2,2019-01-19 20:02:05,2019-01-19 20:16:10,1,1.9,1,N,87,148,1,10.5,0.5,0.5,1.0,0.0,0.3,12.8, +2,2019-01-19 04:25:10,2019-01-19 04:30:06,1,1.36,1,N,231,114,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-02 12:42:58,2019-01-02 12:49:05,1,0.77,1,N,170,186,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-10 17:20:43,2019-01-10 17:34:31,1,2.36,1,N,263,151,1,11.0,1.0,0.5,0.5,0.0,0.3,13.3, +2,2019-01-10 17:46:57,2019-01-10 18:15:38,1,3.91,1,N,231,181,1,20.0,1.0,0.5,3.27,0.0,0.3,25.07, +1,2019-01-03 10:14:46,2019-01-03 10:41:10,1,3.5,1,N,162,129,2,19.5,0.0,0.5,0.0,0.0,0.3,20.3, +2,2019-01-23 21:11:02,2019-01-23 21:32:43,1,3.19,1,N,48,113,1,15.5,0.5,0.5,4.2,0.0,0.3,21.0,0.0 +2,2019-01-05 09:10:46,2019-01-05 09:14:48,1,1.01,1,N,231,249,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-25 10:52:39,2019-01-25 11:00:20,1,0.66,1,N,162,164,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-21 10:47:25,2019-01-21 11:02:44,1,3.5,1,N,68,236,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15,0.0 +1,2019-01-20 14:35:59,2019-01-20 14:41:31,1,1.0,1,N,229,233,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-17 08:37:20,2019-01-17 08:42:41,1,0.6,1,N,162,170,1,5.5,0.0,0.5,3.0,0.0,0.3,9.3, +1,2019-01-30 14:49:34,2019-01-30 15:01:20,1,2.4,1,N,142,246,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-29 19:38:01,2019-01-29 19:47:23,1,1.67,1,N,90,231,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-08 17:42:52,2019-01-08 18:00:17,1,1.84,1,N,162,107,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-04 22:33:16,2019-01-04 22:53:39,1,10.3,1,N,138,205,2,30.0,0.5,0.5,0.0,0.0,0.3,31.3, +2,2019-01-22 06:17:54,2019-01-22 07:02:56,1,18.83,2,N,132,113,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-24 09:33:10,2019-01-24 09:42:57,1,0.7,1,N,170,164,1,7.5,0.0,0.5,1.5,0.0,0.3,9.8,0.0 +2,2019-01-02 15:37:03,2019-01-02 16:03:15,1,3.91,1,N,79,50,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +1,2019-01-24 11:09:23,2019-01-24 11:14:46,1,1.1,1,N,236,239,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-31 07:30:43,2019-01-31 07:59:53,1,3.5,1,N,68,140,1,18.5,0.0,0.5,3.85,0.0,0.3,23.15,0.0 +1,2019-01-25 19:54:18,2019-01-25 20:07:31,1,6.4,1,N,138,74,1,19.0,0.5,0.5,6.5,5.76,0.3,32.56,0.0 +1,2019-01-11 08:02:19,2019-01-11 08:08:26,1,0.4,1,N,237,237,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 10:10:10,2019-01-09 10:44:55,1,4.82,1,N,170,146,2,23.0,0.0,0.5,0.0,0.0,0.3,23.8, +2,2019-01-01 03:28:55,2019-01-01 03:44:31,1,6.45,1,N,193,95,2,20.5,0.5,0.5,0.0,0.0,0.3,21.8, +2,2019-01-18 18:30:10,2019-01-18 18:37:12,1,1.12,1,N,234,114,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-24 19:48:27,2019-01-24 19:57:01,1,1.1,1,N,237,162,3,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-26 18:28:53,2019-01-26 18:34:37,1,0.95,1,N,141,236,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-09 09:54:39,2019-01-09 10:08:51,1,2.08,1,N,236,142,1,11.0,0.0,0.5,3.54,0.0,0.3,15.34, +2,2019-01-08 12:46:25,2019-01-08 13:00:40,1,2.05,1,N,107,233,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-26 08:39:31,2019-01-26 08:39:36,1,0.0,1,N,48,48,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-31 10:01:45,2019-01-31 10:15:45,1,1.27,1,N,142,237,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-15 23:20:02,2019-01-15 23:30:14,1,1.66,1,N,148,107,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-17 14:29:02,2019-01-17 14:46:53,1,1.4,1,N,229,163,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-27 16:28:25,2019-01-27 16:51:26,1,5.9,1,N,237,144,2,22.0,0.0,0.5,0.0,0.0,0.3,22.8,0.0 +1,2019-01-02 09:10:27,2019-01-02 09:42:41,1,20.3,1,N,138,249,1,56.0,0.0,0.5,12.5,5.76,0.3,75.06, +1,2019-01-07 22:43:22,2019-01-07 22:59:38,1,4.4,1,N,125,142,1,16.5,0.5,0.5,4.45,0.0,0.3,22.25, +1,2019-01-15 06:54:39,2019-01-15 06:59:17,1,0.8,1,N,107,79,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-07 19:50:27,2019-01-07 19:54:32,1,0.73,1,N,68,234,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-19 02:08:12,2019-01-19 02:15:56,1,1.98,1,N,238,152,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-08 12:14:44,2019-01-08 12:30:32,1,1.95,1,N,43,140,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-29 11:16:25,2019-01-29 11:34:22,1,2.27,1,N,186,144,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-30 11:20:52,2019-01-30 11:28:37,1,0.7,1,N,230,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-16 06:38:12,2019-01-16 06:47:07,1,4.3,1,N,137,209,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-01 11:47:25,2019-01-01 11:59:12,1,4.64,1,N,151,246,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56, +2,2019-01-13 12:40:37,2019-01-13 12:48:34,1,1.69,1,N,237,75,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-25 16:37:40,2019-01-25 16:40:24,1,0.4,1,N,161,229,1,4.0,1.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-11 15:52:04,2019-01-11 16:12:58,1,3.06,1,N,186,144,1,15.0,0.0,0.5,1.0,0.0,0.3,16.8, +1,2019-01-18 04:02:49,2019-01-18 04:11:08,1,2.5,1,N,113,12,1,9.5,0.5,0.5,5.0,0.0,0.3,15.8, +2,2019-01-18 00:16:42,2019-01-18 00:25:30,1,1.9,1,N,170,141,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-07 10:22:51,2019-01-07 10:28:43,1,1.34,1,N,237,239,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-10 02:31:50,2019-01-10 02:37:20,1,1.0,1,N,37,225,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-19 11:57:06,2019-01-19 11:59:42,1,0.99,1,N,68,90,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-18 13:47:32,2019-01-18 13:51:07,1,0.88,1,N,233,229,1,5.0,0.0,0.5,1.74,0.0,0.3,7.54, +2,2019-01-20 17:14:54,2019-01-20 17:17:27,1,0.48,1,N,141,141,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-07 07:09:47,2019-01-07 07:15:02,1,0.9,1,N,100,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-30 19:14:52,2019-01-30 19:21:56,1,0.56,1,N,141,141,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-30 22:19:07,2019-01-30 22:27:46,1,1.5,1,N,236,238,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-13 07:43:27,2019-01-13 08:01:40,1,1.38,1,N,246,246,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-09 10:48:10,2019-01-09 10:53:09,1,0.77,1,N,262,236,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-16 17:25:56,2019-01-16 17:35:55,1,1.02,1,N,97,181,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-29 20:51:20,2019-01-29 21:12:58,1,6.28,1,N,164,255,1,21.5,0.5,0.5,2.5,0.0,0.3,25.3,0.0 +2,2019-01-12 08:47:44,2019-01-12 08:50:36,1,0.61,1,N,262,263,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-31 19:24:25,2019-01-31 19:36:25,1,1.67,1,N,161,142,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-04 23:18:44,2019-01-04 23:37:19,1,5.84,1,N,162,231,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-07 15:05:49,2019-01-07 15:13:01,1,1.91,1,N,90,231,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-01 02:11:55,2019-01-01 02:43:52,1,8.4,1,N,246,106,1,29.0,0.5,0.5,6.05,0.0,0.3,36.35, +2,2019-01-06 18:57:40,2019-01-06 19:03:28,1,0.89,1,N,142,48,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-08 17:55:13,2019-01-08 18:11:03,1,1.43,1,N,161,234,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-10 18:18:50,2019-01-10 18:32:12,1,1.71,1,N,209,114,1,10.0,1.0,0.5,1.2,0.0,0.3,13.0, +2,2019-01-05 01:24:15,2019-01-05 01:38:29,1,2.96,1,N,166,48,1,12.5,0.5,0.5,1.38,0.0,0.3,15.18, +1,2019-01-03 21:51:44,2019-01-03 21:59:15,1,1.9,1,N,43,263,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +2,2019-01-14 20:55:26,2019-01-14 21:13:17,1,4.66,1,N,68,66,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-22 14:11:42,2019-01-22 14:17:30,1,0.79,1,N,166,41,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-31 08:44:18,2019-01-31 08:49:10,1,0.94,1,N,229,141,1,5.5,0.0,0.5,2.0,0.0,0.3,8.3,0.0 +2,2019-01-27 10:39:52,2019-01-27 10:46:13,1,0.84,1,N,141,236,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-26 20:16:22,2019-01-26 20:30:28,1,7.6,1,N,143,128,1,22.5,0.5,0.5,7.1,0.0,0.3,30.9,0.0 +1,2019-01-13 15:45:45,2019-01-13 16:13:06,1,2.2,1,N,90,249,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-31 06:56:06,2019-01-31 07:11:57,1,3.08,1,N,249,162,1,13.0,0.0,0.5,1.55,0.0,0.3,15.35,0.0 +2,2019-01-05 00:20:23,2019-01-05 00:32:01,1,1.69,1,N,246,249,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-17 11:24:10,2019-01-17 11:47:35,1,2.06,1,N,107,231,1,15.5,0.0,0.5,2.44,0.0,0.3,18.74, +1,2019-01-03 19:43:38,2019-01-03 19:47:08,1,0.7,1,N,239,142,1,4.5,1.0,0.5,0.7,0.0,0.3,7.0, +2,2019-01-24 11:10:40,2019-01-24 11:41:54,1,2.04,1,N,186,162,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16,0.0 +4,2019-01-12 14:06:51,2019-01-12 14:21:00,1,1.17,1,N,186,230,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-23 11:51:46,2019-01-23 12:00:19,1,0.65,1,N,43,263,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-03 20:14:30,2019-01-03 20:23:04,1,1.18,1,N,163,233,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-30 07:09:46,2019-01-30 07:19:17,1,2.1,1,N,263,142,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-13 01:08:21,2019-01-13 01:17:10,1,1.8,1,N,144,234,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +1,2019-01-25 12:31:37,2019-01-25 12:46:20,1,2.9,1,N,79,162,1,12.5,0.0,0.5,2.7,0.0,0.3,16.0,0.0 +2,2019-01-25 22:18:50,2019-01-25 22:33:57,1,3.56,1,N,170,145,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-15 09:15:46,2019-01-15 09:31:17,1,0.85,1,N,230,162,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-18 19:02:44,2019-01-18 19:10:09,1,1.13,1,N,211,209,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-15 14:20:28,2019-01-15 14:24:41,1,0.84,1,N,237,229,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-07 07:55:14,2019-01-07 08:14:24,1,9.3,1,N,246,127,2,28.0,0.0,0.5,0.0,0.0,0.3,28.8, +2,2019-01-05 08:40:13,2019-01-05 09:00:00,1,7.03,1,N,41,224,1,23.0,0.0,0.5,5.95,0.0,0.3,29.75, +2,2019-01-27 17:23:32,2019-01-27 17:31:31,1,0.26,1,N,100,48,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-27 21:23:59,2019-01-27 21:29:06,1,0.56,1,N,237,229,1,5.0,0.5,0.5,2.0,0.0,0.3,8.3,0.0 +1,2019-01-02 22:08:07,2019-01-02 22:24:02,1,3.8,1,N,234,143,1,14.5,0.5,0.5,2.0,0.0,0.3,17.8, +1,2019-01-25 11:06:23,2019-01-25 11:12:19,1,1.3,1,N,236,75,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-15 10:24:04,2019-01-15 10:52:30,1,9.3,1,N,138,161,1,30.5,0.0,0.5,7.4,5.76,0.3,44.46, +2,2019-01-29 13:40:09,2019-01-29 14:21:25,1,28.46,2,N,132,261,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-14 16:15:21,2019-01-14 16:37:12,1,7.5,1,N,239,127,2,22.5,1.0,0.5,0.0,0.0,0.3,24.3, +1,2019-01-29 16:48:27,2019-01-29 16:51:46,1,0.5,1,N,79,107,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-31 18:29:24,2019-01-31 18:43:44,1,3.12,1,N,13,68,1,12.5,1.0,0.5,1.7,0.0,0.3,16.0,0.0 +2,2019-01-22 10:54:22,2019-01-22 11:22:59,1,4.28,1,N,186,261,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16,0.0 +2,2019-01-08 10:42:28,2019-01-08 10:45:34,1,0.52,1,N,263,141,1,4.0,0.0,0.5,0.48,0.0,0.3,5.28, +2,2019-01-17 21:49:55,2019-01-17 22:03:41,1,1.88,1,N,229,107,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-24 18:16:23,2019-01-24 18:32:30,1,2.66,1,N,237,238,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-16 07:36:28,2019-01-16 07:40:37,1,0.9,1,N,237,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-24 22:05:45,2019-01-24 22:14:38,1,2.3,1,N,162,262,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-25 09:00:39,2019-01-25 09:09:19,1,1.06,1,N,142,163,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-20 20:10:51,2019-01-20 20:23:00,1,2.22,1,N,151,75,1,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-28 14:50:48,2019-01-28 14:55:41,1,0.67,1,N,236,236,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25,0.0 +2,2019-01-08 20:01:20,2019-01-08 20:06:43,1,1.3,1,N,239,151,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-10 07:50:35,2019-01-10 07:59:01,1,1.9,1,N,236,163,3,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-01 11:30:45,2019-01-01 11:36:38,1,0.9,1,N,170,233,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-17 09:57:48,2019-01-17 10:27:09,1,4.3,1,N,231,17,2,22.0,0.0,0.5,0.0,0.0,0.3,22.8, +1,2019-01-30 14:39:15,2019-01-30 15:30:31,1,16.5,2,N,142,132,1,52.0,0.0,0.5,0.0,0.0,0.3,52.8,0.0 +2,2019-01-01 00:44:01,2019-01-01 00:49:52,1,0.81,1,N,68,186,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-18 09:24:40,2019-01-18 09:39:19,1,1.3,1,N,107,234,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-17 19:38:28,2019-01-17 19:43:46,1,1.21,1,N,142,239,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +2,2019-01-17 09:41:51,2019-01-17 09:50:27,1,1.46,1,N,170,229,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-14 18:42:59,2019-01-14 19:02:34,1,9.3,1,N,233,138,1,27.0,1.0,0.5,6.9,5.76,0.3,41.46, +1,2019-01-30 11:29:38,2019-01-30 11:39:15,1,1.3,1,N,4,107,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-13 19:05:12,2019-01-13 19:12:37,1,0.8,1,N,143,142,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-31 20:30:51,2019-01-31 20:49:14,1,3.0,1,N,170,239,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-09 14:19:29,2019-01-09 14:26:07,1,1.02,1,N,107,170,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-06 23:12:10,2019-01-06 23:40:23,1,16.42,2,N,132,237,1,52.0,0.0,0.5,6.0,0.0,0.3,58.8, +1,2019-01-18 19:46:07,2019-01-18 19:47:21,1,0.3,1,N,262,263,1,3.0,1.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-16 15:43:28,2019-01-16 15:56:59,1,2.5,1,N,141,100,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +4,2019-01-07 07:07:15,2019-01-07 07:30:02,1,5.43,1,N,87,162,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96, +2,2019-01-29 20:31:26,2019-01-29 20:37:18,1,1.51,1,N,237,263,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-28 18:52:47,2019-01-28 19:04:48,1,1.4,1,N,230,233,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95,0.0 +1,2019-01-06 05:22:11,2019-01-06 05:25:22,1,0.7,1,N,142,237,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-30 22:48:30,2019-01-30 22:58:24,1,1.5,1,N,162,48,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-31 06:51:25,2019-01-31 06:51:30,1,0.0,1,N,193,193,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-16 09:56:44,2019-01-16 10:06:49,1,1.11,1,N,170,161,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-02 11:03:18,2019-01-02 11:37:20,1,9.9,1,N,138,237,2,33.5,0.0,0.5,0.0,5.76,0.3,40.06, +1,2019-01-19 12:46:22,2019-01-19 12:51:13,1,0.5,1,N,143,143,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-21 08:48:56,2019-01-21 08:55:33,1,1.4,1,N,141,161,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-03 22:53:10,2019-01-03 22:58:09,1,1.2,1,N,163,229,1,6.0,0.5,0.5,1.8,0.0,0.3,9.1, +2,2019-01-15 13:22:32,2019-01-15 13:40:40,1,1.35,1,N,162,230,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-15 07:02:40,2019-01-15 07:06:06,1,1.21,1,N,236,237,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-16 15:09:15,2019-01-16 15:22:20,1,1.97,1,N,162,236,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-04 18:48:21,2019-01-04 18:54:51,1,1.36,1,N,237,236,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-14 21:43:49,2019-01-14 21:50:53,1,1.03,1,N,230,68,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-18 00:07:48,2019-01-18 00:18:06,1,2.21,1,N,144,256,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-09 10:43:43,2019-01-10 10:23:58,1,10.38,1,N,230,138,1,35.5,0.0,0.5,7.26,0.0,0.3,43.56, +2,2019-01-18 19:48:55,2019-01-18 20:07:03,1,2.91,1,N,162,79,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-21 12:04:25,2019-01-21 12:33:23,1,5.32,1,N,236,246,1,22.5,0.0,0.5,3.5,0.0,0.3,26.8,0.0 +2,2019-01-26 08:27:50,2019-01-26 09:02:50,1,26.63,1,N,132,40,1,70.0,0.0,0.5,17.7,0.0,0.3,88.5,0.0 +2,2019-01-03 22:49:20,2019-01-03 22:54:33,1,0.86,1,N,164,233,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-15 08:21:28,2019-01-15 08:27:03,1,1.06,1,N,236,238,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-12 00:17:04,2019-01-12 00:38:11,1,9.9,1,N,138,208,1,29.0,0.5,0.5,7.0,5.76,0.3,43.06, +1,2019-01-08 12:17:54,2019-01-08 12:28:21,1,1.3,1,N,125,234,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6, +2,2019-01-27 11:55:36,2019-01-27 12:06:24,1,2.29,1,N,263,142,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-30 17:58:58,2019-01-30 18:06:58,1,1.01,1,N,68,234,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0,0.0 +1,2019-01-31 16:12:50,2019-01-31 16:32:13,1,6.8,1,N,13,230,1,22.5,1.0,0.5,4.85,0.0,0.3,29.15,0.0 +2,2019-01-25 05:49:31,2019-01-25 05:58:36,1,1.01,1,N,164,137,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-06 00:01:44,2019-01-06 00:15:53,1,3.52,1,N,43,236,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-28 18:18:38,2019-01-28 18:22:25,1,0.54,1,N,140,141,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-29 11:59:53,2019-01-29 12:14:44,1,0.91,1,N,161,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-14 19:49:34,2019-01-14 20:01:45,1,1.2,1,N,161,186,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-02 09:56:57,2019-01-02 10:11:28,1,2.12,1,N,161,263,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8, +1,2019-01-05 11:26:54,2019-01-05 11:27:39,1,0.1,5,N,87,87,3,0.0,0.0,0.0,0.0,0.0,0.3,0.3, +2,2019-01-28 07:37:02,2019-01-28 07:47:28,1,1.73,1,N,48,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-30 12:53:30,2019-01-30 13:17:16,1,1.72,1,N,50,163,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-15 22:33:47,2019-01-15 22:40:28,1,1.1,1,N,263,237,1,6.5,0.5,0.5,2.2,0.0,0.3,10.0, +2,2019-01-29 00:13:40,2019-01-29 00:26:40,1,3.57,1,N,263,168,1,13.5,0.5,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-31 19:55:26,2019-01-31 20:08:41,1,3.6,1,N,138,7,1,14.5,1.0,0.5,3.25,0.0,0.3,19.55,0.0 +1,2019-01-06 14:46:55,2019-01-06 14:59:46,1,2.4,1,N,170,249,1,10.5,0.0,0.5,1.13,0.0,0.3,12.43, +2,2019-01-21 23:11:05,2019-01-21 23:20:03,1,2.24,1,N,163,239,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-15 17:58:07,2019-01-15 18:10:32,1,1.5,1,N,264,264,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-08 11:24:32,2019-01-08 11:30:39,1,0.6,1,N,230,163,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-15 18:39:29,2019-01-15 18:42:57,1,0.6,1,N,141,237,1,4.5,1.0,0.5,0.95,0.0,0.3,7.25, +1,2019-01-29 16:46:27,2019-01-29 16:54:07,1,1.5,1,N,233,224,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-30 01:07:16,2019-01-30 01:12:52,1,0.71,1,N,100,48,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-21 20:19:31,2019-01-21 20:22:25,1,0.98,1,N,238,238,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-21 22:59:42,2019-01-21 23:35:49,1,20.24,2,N,132,24,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-05 23:09:11,2019-01-05 23:30:10,1,5.12,1,N,249,239,1,19.0,0.5,0.5,4.06,0.0,0.3,24.36, +2,2019-01-04 03:08:59,2019-01-04 03:27:13,1,8.72,1,N,148,227,1,25.5,0.5,0.5,5.36,0.0,0.3,32.16, +2,2019-01-26 06:31:06,2019-01-26 06:46:18,1,8.73,1,N,13,262,1,25.5,0.0,0.5,5.26,0.0,0.3,31.56,0.0 +2,2019-01-03 15:09:12,2019-01-03 15:30:02,1,8.89,1,N,138,164,1,27.0,0.0,0.5,6.71,5.76,0.3,40.27, +2,2019-01-18 12:14:51,2019-01-18 12:22:35,1,0.98,1,N,230,186,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-31 16:27:21,2019-01-31 16:49:09,1,1.62,1,N,186,162,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-03 06:41:32,2019-01-03 06:49:01,1,1.13,1,N,137,186,1,7.0,0.0,0.5,2.2,0.0,0.3,10.0, +2,2019-01-21 19:54:27,2019-01-21 19:57:10,1,0.81,1,N,239,142,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-15 12:18:10,2019-01-15 12:23:54,1,1.2,1,N,238,236,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-18 09:05:59,2019-01-18 09:20:55,1,3.66,1,N,261,186,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88, +1,2019-01-17 19:52:25,2019-01-17 20:08:30,1,2.5,1,N,48,249,1,12.0,1.0,0.5,2.0,0.0,0.3,15.8, +2,2019-01-17 20:17:09,2019-01-17 20:24:24,1,0.88,1,N,161,163,1,6.5,0.5,0.5,2.22,0.0,0.3,10.02, +2,2019-01-18 19:39:06,2019-01-18 19:46:19,1,1.56,1,N,161,236,1,7.5,1.0,0.5,1.2,0.0,0.3,10.5, +1,2019-01-25 12:06:53,2019-01-25 12:30:18,1,2.5,1,N,158,161,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55,0.0 +1,2019-01-05 18:53:10,2019-01-05 18:58:21,1,1.1,1,N,145,145,1,6.0,0.0,0.5,2.0,0.0,0.3,8.8, +2,2019-01-14 21:18:39,2019-01-14 21:27:13,1,1.85,1,N,48,163,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-03 17:27:14,2019-01-03 17:33:54,1,1.07,1,N,141,236,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-27 09:26:29,2019-01-27 09:35:38,1,3.05,1,N,263,137,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-19 03:13:24,2019-01-19 03:15:23,1,0.6,1,N,107,137,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +2,2019-01-02 23:19:57,2019-01-02 23:44:56,1,11.41,1,N,230,167,2,34.0,0.5,0.5,0.0,0.0,0.3,35.3, +2,2019-01-05 20:34:46,2019-01-05 20:38:56,1,1.22,1,N,236,239,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-07 17:40:42,2019-01-07 17:54:24,1,1.67,1,N,161,186,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-19 14:27:27,2019-01-19 14:30:40,1,0.7,1,N,229,141,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6, +2,2019-01-17 08:29:27,2019-01-17 08:37:35,1,0.79,1,N,90,234,1,6.5,0.0,0.5,5.0,0.0,0.3,12.3, +2,2019-01-26 22:36:52,2019-01-26 22:57:31,1,3.76,1,N,238,164,1,16.0,0.5,0.5,2.0,0.0,0.3,19.3,0.0 +2,2019-01-17 19:11:52,2019-01-17 19:13:56,1,0.2,1,N,230,230,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-31 10:27:48,2019-01-31 10:38:05,1,4.1,1,N,137,87,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-25 10:39:20,2019-01-25 10:49:06,1,1.0,1,N,107,90,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-04 12:03:39,2019-01-04 12:21:29,1,2.6,1,N,164,141,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-31 21:58:08,2019-01-31 22:07:13,1,0.65,1,N,48,143,1,7.5,0.5,0.5,1.32,0.0,0.3,10.12,0.0 +1,2019-01-18 10:12:09,2019-01-18 10:15:44,1,0.5,1,Y,90,68,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-17 13:21:02,2019-01-17 13:39:56,1,3.3,1,N,234,13,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-27 11:01:12,2019-01-27 11:12:51,1,2.17,1,N,239,262,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-18 00:10:24,2019-01-18 00:20:19,1,2.37,1,N,249,231,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-17 06:46:18,2019-01-17 06:50:46,1,0.89,1,N,263,75,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-18 23:08:00,2019-01-18 23:40:06,1,6.2,1,N,164,49,1,24.0,0.5,0.5,6.32,0.0,0.3,31.62, +2,2019-01-24 10:52:37,2019-01-24 11:31:50,1,3.02,1,N,142,170,1,23.5,0.0,0.5,4.86,0.0,0.3,29.16,0.0 +1,2019-01-07 11:13:02,2019-01-07 11:22:09,1,2.1,1,N,141,143,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-30 15:36:03,2019-01-30 15:40:25,1,0.59,1,N,100,186,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-31 14:36:17,2019-01-31 14:50:28,1,1.56,1,N,229,48,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-16 14:26:22,2019-01-16 14:29:01,1,0.4,1,N,236,236,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95, +1,2019-01-26 15:28:35,2019-01-26 15:35:50,1,0.5,1,N,246,246,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-15 18:02:09,2019-01-15 18:16:46,1,2.5,1,N,4,233,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-06 20:06:05,2019-01-06 20:22:24,1,3.16,1,N,249,233,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-22 07:04:10,2019-01-22 07:29:20,1,9.85,5,N,36,133,1,26.29,0.0,0.5,0.0,0.0,0.3,27.09,0.0 +1,2019-01-25 21:14:52,2019-01-25 21:52:36,1,19.9,2,N,140,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +2,2019-01-08 17:31:42,2019-01-08 18:10:40,1,19.23,2,N,132,236,1,52.0,4.5,0.5,18.92,5.76,0.3,81.98, +1,2019-01-29 19:14:48,2019-01-29 19:27:42,1,1.1,1,N,233,164,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-19 19:02:51,2019-01-19 19:12:45,1,0.71,1,N,161,100,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-09 16:45:59,2019-01-09 16:51:11,1,0.85,1,N,140,262,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-19 00:06:47,2019-01-19 00:17:45,1,0.8,1,N,114,148,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-18 23:25:07,2019-01-18 23:31:21,1,1.55,1,N,236,237,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-31 19:09:55,2019-01-31 19:26:50,1,2.6,1,Y,90,148,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-09 17:57:47,2019-01-09 18:09:39,1,1.32,1,N,143,161,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42, +2,2019-01-13 17:11:13,2019-01-13 17:18:04,1,2.86,1,N,132,10,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-08 23:06:22,2019-01-08 23:43:13,1,21.42,1,N,132,40,1,57.5,0.5,0.5,11.76,0.0,0.3,70.56, +1,2019-01-02 09:23:51,2019-01-02 09:35:31,1,2.2,1,N,142,236,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-20 10:54:49,2019-01-20 11:03:00,1,1.3,1,N,148,113,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-29 06:12:25,2019-01-29 06:23:19,1,2.7,1,N,79,230,1,11.0,0.0,0.5,1.25,0.0,0.3,13.05,0.0 +4,2019-01-25 19:37:57,2019-01-25 19:42:02,1,0.61,1,N,142,142,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-15 08:50:01,2019-01-15 08:58:44,1,0.7,1,N,164,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-19 02:03:58,2019-01-19 02:11:14,1,1.5,1,N,170,107,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +2,2019-01-06 19:56:16,2019-01-06 20:04:20,1,1.02,1,N,233,163,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 20:06:14,2019-01-24 20:24:37,1,0.0,1,N,88,48,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-30 14:01:06,2019-01-30 14:08:09,1,0.75,1,N,263,237,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-26 19:11:58,2019-01-26 19:15:38,1,0.5,1,N,249,158,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3,0.0 +2,2019-01-21 14:17:18,2019-01-21 14:22:02,1,0.6,1,N,88,87,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-20 02:09:01,2019-01-20 02:25:18,1,5.07,1,N,148,36,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +1,2019-01-01 12:51:03,2019-01-01 12:53:39,1,0.8,1,N,262,141,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-09 08:12:43,2019-01-09 08:20:03,1,1.3,1,N,114,231,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-21 23:11:18,2019-01-21 23:32:18,1,11.45,1,N,138,231,1,33.5,0.5,0.5,8.7,0.0,0.3,43.5,0.0 +2,2019-01-25 15:13:22,2019-01-25 15:18:53,1,0.73,1,N,113,79,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-06 09:47:18,2019-01-06 09:54:08,1,1.06,1,N,237,161,1,6.5,0.0,0.5,0.7,0.0,0.3,8.0, +2,2019-01-23 17:01:29,2019-01-23 18:25:32,1,15.52,5,N,166,232,1,61.68,0.0,0.5,0.0,0.0,0.3,62.48,0.0 +1,2019-01-10 06:24:28,2019-01-10 06:30:38,1,0.7,1,N,79,114,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-19 19:04:46,2019-01-19 19:09:11,1,0.62,1,N,24,151,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-18 11:44:03,2019-01-18 12:05:31,1,2.68,1,N,237,90,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-11 16:42:29,2019-01-11 16:48:54,1,0.8,1,N,263,237,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-22 20:17:05,2019-01-22 20:27:37,1,1.8,1,N,142,229,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-22 15:12:08,2019-01-22 15:24:08,1,2.07,1,N,144,164,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-20 17:59:05,2019-01-20 18:05:00,1,0.68,1,N,162,164,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-28 08:56:46,2019-01-28 09:02:23,1,0.9,1,N,113,234,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-04 21:16:24,2019-01-04 21:33:03,1,4.3,1,N,140,68,1,16.5,0.5,0.5,2.67,0.0,0.3,20.47, +1,2019-01-28 12:52:49,2019-01-28 13:15:17,1,5.3,1,N,100,74,1,20.0,0.0,0.5,0.0,0.0,0.3,20.8,0.0 +1,2019-01-11 20:26:14,2019-01-11 20:27:40,1,0.4,1,N,239,239,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-16 06:59:25,2019-01-16 07:08:39,1,1.37,1,N,230,186,1,8.0,0.0,0.5,2.64,0.0,0.3,11.44, +1,2019-01-18 16:17:26,2019-01-18 16:21:15,1,0.6,1,N,249,234,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-31 17:41:52,2019-01-31 17:48:28,1,1.4,1,N,236,142,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-16 21:24:26,2019-01-16 21:33:59,1,1.1,1,N,234,79,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15, +2,2019-01-19 23:45:12,2019-01-19 23:58:13,1,2.96,1,N,90,143,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +2,2019-01-31 00:10:34,2019-01-31 00:17:14,1,2.64,1,N,162,75,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-02 05:34:57,2019-01-02 05:45:21,1,2.6,1,N,238,237,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +1,2019-01-26 09:24:49,2019-01-26 09:26:32,1,0.6,1,N,75,263,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-27 19:40:23,2019-01-27 19:46:50,1,1.28,1,N,239,237,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-15 14:47:22,2019-01-15 15:32:48,1,18.9,3,N,162,1,1,73.0,0.0,0.0,17.75,15.5,0.3,106.55, +2,2019-01-21 16:06:35,2019-01-21 16:25:28,1,3.95,1,N,68,263,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-12 20:16:18,2019-01-12 20:23:03,1,0.73,1,N,170,234,1,6.0,0.5,0.5,1.1,0.0,0.3,8.4, +1,2019-01-28 15:46:55,2019-01-28 16:02:44,1,3.1,1,N,161,151,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-22 15:15:48,2019-01-22 15:20:05,1,0.65,1,N,186,90,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-29 21:59:57,2019-01-29 22:07:52,1,1.18,1,N,229,141,1,7.0,0.5,0.5,0.83,0.0,0.3,9.13,0.0 +1,2019-01-30 09:51:09,2019-01-30 10:08:34,1,1.2,1,N,48,50,1,11.5,0.0,0.5,3.05,0.0,0.3,15.35,0.0 +2,2019-01-23 07:24:11,2019-01-23 07:47:58,1,3.32,1,N,100,231,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96,0.0 +1,2019-01-13 15:24:37,2019-01-13 15:30:51,1,1.3,1,N,246,90,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-01 05:55:40,2019-01-01 06:05:41,1,3.18,1,N,90,88,1,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +1,2019-01-31 20:15:38,2019-01-31 20:24:55,1,1.6,1,N,163,239,1,8.5,0.5,0.5,2.5,0.0,0.3,12.3,0.0 +2,2019-01-14 17:43:59,2019-01-14 17:56:01,1,2.24,1,N,230,43,1,10.0,1.0,0.5,1.77,0.0,0.3,13.57, +2,2019-01-07 09:55:17,2019-01-07 10:01:56,1,1.52,1,N,43,142,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97, +1,2019-01-11 16:05:11,2019-01-11 16:07:05,1,0.3,1,N,236,236,1,3.5,1.0,0.5,1.0,0.0,0.3,6.3, +1,2019-01-26 05:49:51,2019-01-26 05:56:06,1,1.2,1,N,41,75,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-15 15:46:09,2019-01-15 16:26:43,1,6.32,1,N,138,162,1,29.0,0.0,0.5,7.45,0.0,0.3,37.25, +2,2019-01-01 14:01:02,2019-01-01 14:02:20,1,0.6,5,N,132,132,1,57.6,0.0,0.5,11.68,0.0,0.3,70.08, +1,2019-01-17 22:01:34,2019-01-17 22:04:27,1,0.7,1,N,237,237,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-29 11:24:42,2019-01-29 11:48:18,1,3.4,1,N,143,234,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-20 10:08:10,2019-01-20 10:24:01,1,3.1,1,N,166,141,1,14.0,0.0,0.5,2.0,0.0,0.3,16.8, +1,2019-01-23 10:25:32,2019-01-23 10:35:22,1,1.3,1,N,263,237,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-30 17:47:05,2019-01-30 17:52:59,1,1.13,1,N,48,237,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-25 12:56:51,2019-01-25 13:27:47,1,3.6,1,N,90,141,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8,0.0 +2,2019-01-25 10:09:32,2019-01-25 10:25:01,1,2.52,1,N,163,246,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-25 09:40:00,2019-01-25 09:49:18,1,0.7,1,N,170,230,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-20 03:23:01,2019-01-20 03:37:34,1,3.4,1,N,151,161,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-01 18:43:15,2019-01-01 18:58:24,1,2.83,1,N,186,4,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-19 20:05:20,2019-01-19 20:11:26,1,0.7,1,N,125,144,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +1,2019-01-31 17:20:46,2019-01-31 18:16:10,1,9.9,1,N,22,177,1,40.5,1.0,0.5,0.0,5.0,0.3,47.3,0.0 +2,2019-01-23 11:38:41,2019-01-23 11:58:52,1,2.0,1,N,229,234,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-14 06:28:31,2019-01-14 06:32:00,1,0.97,1,N,164,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-17 11:14:22,2019-01-17 11:34:43,1,2.55,1,N,230,140,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-08 22:41:26,2019-01-08 23:00:06,1,8.65,1,N,144,14,2,25.5,0.5,0.5,0.0,0.0,0.3,26.8, +1,2019-01-14 13:04:01,2019-01-14 13:43:08,1,4.2,1,N,143,107,2,25.0,0.0,0.5,0.0,0.0,0.3,25.8, +2,2019-01-10 15:45:55,2019-01-10 15:55:37,1,1.6,1,N,234,230,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-27 04:05:51,2019-01-27 04:07:36,1,0.7,1,N,68,186,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-20 12:11:50,2019-01-20 12:24:36,1,2.37,1,N,170,144,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-21 18:20:26,2019-01-21 18:24:26,1,1.07,1,N,233,137,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-17 15:35:36,2019-01-17 15:41:08,1,0.7,1,N,48,230,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-31 17:54:00,2019-01-31 18:07:39,1,1.99,1,N,239,141,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-11 11:43:47,2019-01-11 11:47:17,1,0.4,1,N,166,24,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-17 22:16:11,2019-01-17 22:31:55,1,2.38,1,N,234,229,1,12.0,0.5,0.5,2.0,0.0,0.3,15.3, +2,2019-01-16 21:01:44,2019-01-16 21:18:16,1,3.06,1,N,162,68,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +1,2019-01-23 13:53:06,2019-01-23 13:59:22,1,0.7,1,N,170,162,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-12 15:51:47,2019-01-12 15:54:47,1,0.74,1,N,140,262,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +1,2019-01-30 09:37:08,2019-01-30 09:54:58,1,2.5,1,N,230,246,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-17 23:20:47,2019-01-17 23:32:06,1,1.96,1,N,163,238,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-02 14:27:44,2019-01-02 14:33:32,1,0.85,1,N,234,234,1,5.5,0.0,0.5,1.58,0.0,0.3,7.88, +1,2019-01-18 18:56:27,2019-01-18 19:21:00,1,9.2,1,N,138,151,1,28.5,1.0,0.5,7.2,5.76,0.3,43.26, +2,2019-01-31 22:33:51,2019-01-31 22:39:08,1,1.04,1,N,238,166,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-31 08:21:43,2019-01-31 08:57:39,1,9.6,1,N,141,242,1,33.0,0.0,0.5,6.76,0.0,0.3,40.56,0.0 +1,2019-01-10 07:32:46,2019-01-10 07:43:32,1,1.3,1,N,186,137,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-14 20:05:05,2019-01-14 20:15:24,1,2.04,1,N,237,236,1,9.5,0.5,0.5,0.54,0.0,0.3,11.34, +2,2019-01-17 00:21:41,2019-01-17 00:39:37,1,4.27,1,N,249,25,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +2,2019-01-22 08:25:02,2019-01-22 08:47:04,1,5.17,1,N,13,137,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16,0.0 +1,2019-01-17 21:26:26,2019-01-17 21:39:34,1,2.8,1,N,230,238,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-22 00:02:49,2019-01-22 00:24:19,1,11.5,1,N,138,48,1,33.0,0.5,0.5,10.0,5.76,0.3,50.06,0.0 +2,2019-01-02 22:08:27,2019-01-02 22:18:53,1,1.25,1,N,249,68,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-02 19:30:08,2019-01-02 19:43:41,1,2.8,1,N,249,48,1,12.0,1.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-06 03:38:55,2019-01-06 03:41:51,1,0.59,1,N,246,186,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-20 20:26:12,2019-01-20 20:33:04,1,1.2,1,N,264,264,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +2,2019-01-30 09:38:33,2019-01-30 09:58:34,1,1.6,1,N,142,237,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-02 11:55:09,2019-01-02 12:01:18,1,1.0,1,N,236,237,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-14 08:45:44,2019-01-14 09:01:30,1,2.34,1,N,143,140,1,11.5,0.0,0.5,1.84,0.0,0.3,16.09, +1,2019-01-03 08:11:42,2019-01-03 08:27:45,1,2.3,1,N,236,161,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-01 01:46:26,2019-01-01 02:00:15,1,2.9,1,N,263,41,1,12.0,0.5,0.5,3.95,0.0,0.3,17.25, +1,2019-01-11 10:48:54,2019-01-11 11:14:51,1,5.7,1,N,100,87,1,22.5,0.0,0.5,4.65,0.0,0.3,27.95, +1,2019-01-07 18:03:22,2019-01-07 18:07:44,1,0.8,1,N,234,170,1,5.0,1.0,0.5,1.5,0.0,0.3,8.3, +1,2019-01-11 08:12:37,2019-01-11 08:41:31,1,2.9,1,Y,151,237,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56, +2,2019-01-23 10:24:21,2019-01-23 10:33:14,1,0.7,1,N,237,163,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-18 08:13:15,2019-01-18 08:19:25,1,0.7,1,N,162,237,1,6.0,0.0,0.5,0.5,0.0,0.3,7.3, +1,2019-01-29 09:16:03,2019-01-29 09:28:05,1,1.4,1,N,162,234,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-21 13:31:52,2019-01-21 13:35:34,1,0.9,1,N,41,41,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-07 16:38:51,2019-01-07 16:58:27,1,1.97,1,N,163,234,1,13.0,1.0,0.5,1.0,0.0,0.3,15.8, +2,2019-01-14 18:00:42,2019-01-14 18:04:07,1,0.98,1,N,142,43,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-16 14:11:52,2019-01-16 14:18:16,1,0.9,1,N,186,90,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-24 16:58:30,2019-01-24 17:14:51,1,1.1,1,N,161,186,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-02 10:26:14,2019-01-02 10:40:14,1,2.57,1,N,141,48,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-14 10:46:09,2019-01-14 10:59:38,1,1.1,1,N,237,161,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-09 19:17:47,2019-01-09 19:19:43,1,0.46,1,N,143,143,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-01 07:19:17,2019-01-01 07:45:49,1,20.09,2,N,132,237,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-16 14:55:03,2019-01-16 15:01:37,1,1.16,1,N,142,68,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-02 21:14:42,2019-01-02 21:19:53,1,0.87,1,N,230,163,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-10 05:50:36,2019-01-10 05:54:41,1,0.7,1,N,141,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-01 12:44:19,2019-01-01 12:54:24,1,1.85,1,N,162,142,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-07 10:58:32,2019-01-07 11:10:57,1,1.6,1,N,164,163,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-21 12:40:39,2019-01-21 12:56:55,1,3.53,1,N,87,234,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-18 06:35:33,2019-01-18 06:43:29,1,2.3,1,N,113,230,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-13 21:59:03,2019-01-13 22:00:47,1,0.3,1,N,75,75,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3, +1,2019-01-20 19:09:44,2019-01-20 19:25:13,1,2.3,1,N,234,232,1,12.0,0.0,0.5,1.92,0.0,0.3,14.72, +2,2019-01-01 00:41:22,2019-01-01 00:47:55,1,0.84,1,N,65,25,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-17 22:16:18,2019-01-17 22:40:54,1,5.28,1,N,234,40,2,20.5,0.5,0.5,0.0,0.0,0.3,21.8, +2,2019-01-30 08:30:10,2019-01-30 08:33:55,1,0.39,1,N,231,231,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-18 15:50:30,2019-01-18 16:03:48,1,1.9,1,N,161,236,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-01 21:18:05,2019-01-01 21:25:33,1,0.8,1,N,100,161,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-03 17:02:27,2019-01-03 17:47:44,1,12.9,5,N,237,51,1,43.6,0.0,0.0,0.0,0.0,0.3,43.9, +2,2019-01-21 15:36:32,2019-01-21 16:00:18,1,6.81,1,N,236,13,1,23.0,0.0,0.5,2.2,0.0,0.3,26.0,0.0 +1,2019-01-30 01:05:36,2019-01-30 01:23:52,1,5.7,1,N,163,129,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3,0.0 +1,2019-01-24 21:51:44,2019-01-24 21:59:53,1,0.5,1,N,48,48,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-13 18:22:06,2019-01-13 18:42:57,1,6.36,1,N,144,262,1,21.0,0.0,0.5,1.75,0.0,0.3,23.55, +1,2019-01-24 12:30:33,2019-01-24 12:41:44,1,0.4,1,N,161,230,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-19 01:07:05,2019-01-19 01:19:05,1,4.49,1,N,79,140,1,15.0,0.5,0.5,2.0,0.0,0.3,18.3, +1,2019-01-03 15:50:25,2019-01-03 15:53:46,1,0.8,1,N,125,158,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-10 11:34:30,2019-01-10 11:42:51,1,1.1,1,N,229,140,1,7.0,0.0,0.5,2.3,0.0,0.3,10.1, +1,2019-01-02 09:10:50,2019-01-02 09:25:32,1,3.0,1,N,50,166,1,12.5,0.0,0.5,1.5,0.0,0.3,14.8, +1,2019-01-30 17:22:32,2019-01-30 17:37:40,1,2.3,1,N,186,144,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-27 03:28:51,2019-01-27 03:36:23,1,1.8,1,N,79,170,1,8.0,0.5,0.5,2.75,0.0,0.3,12.05,0.0 +2,2019-01-12 21:38:30,2019-01-12 21:56:20,1,4.36,1,N,24,243,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +1,2019-01-12 13:27:41,2019-01-12 13:30:50,1,0.5,1,N,237,161,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-22 12:07:41,2019-01-22 12:17:00,1,1.73,1,N,229,236,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-27 23:04:12,2019-01-27 23:11:48,1,3.4,1,N,148,88,1,11.5,0.5,0.5,1.92,0.0,0.3,14.72,0.0 +2,2019-01-20 17:19:26,2019-01-20 18:02:27,1,7.91,1,N,230,33,1,33.0,0.0,0.5,8.45,0.0,0.3,42.25, +2,2019-01-15 22:22:01,2019-01-15 22:24:25,1,0.27,1,N,113,249,1,3.5,0.5,0.5,0.96,0.0,0.3,5.76, +2,2019-01-25 17:37:36,2019-01-25 17:45:37,1,1.02,1,N,125,113,1,7.0,1.0,0.5,1.0,0.0,0.3,9.8,0.0 +4,2019-01-24 08:24:53,2019-01-24 08:37:55,1,1.31,1,N,236,75,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-04 11:08:15,2019-01-04 12:14:19,1,57.95,5,N,223,265,1,140.0,0.0,0.5,10.0,0.0,0.3,150.8, +2,2019-01-26 01:55:21,2019-01-26 02:00:11,1,0.8,1,N,249,90,1,5.0,0.5,0.5,0.94,0.0,0.3,7.24,0.0 +1,2019-01-30 22:57:49,2019-01-30 23:16:51,1,3.7,1,N,239,249,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-11 16:55:45,2019-01-11 17:09:24,1,2.63,1,N,100,239,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-19 20:23:29,2019-01-19 20:50:08,1,16.3,2,N,132,234,1,52.0,0.0,0.5,8.0,5.76,0.3,66.56, +2,2019-01-30 21:19:20,2019-01-30 21:22:04,1,0.48,1,N,170,170,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-16 10:20:04,2019-01-16 10:46:35,1,6.29,1,N,13,170,1,23.5,0.0,0.5,4.86,0.0,0.3,29.16, +1,2019-01-16 10:01:59,2019-01-16 10:04:01,1,0.3,1,N,142,142,1,3.5,0.0,0.5,0.64,0.0,0.3,4.94, +1,2019-01-24 10:41:54,2019-01-24 11:28:06,1,17.8,3,N,162,1,1,72.0,0.0,0.0,16.55,10.5,0.3,99.35,0.0 +1,2019-01-29 17:59:01,2019-01-29 18:10:05,1,1.5,1,N,68,230,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-12 17:52:04,2019-01-12 17:58:33,1,0.79,1,N,246,246,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-21 13:21:11,2019-01-21 13:31:45,1,2.31,1,N,50,90,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-07 09:35:34,2019-01-07 09:58:56,1,2.4,1,N,237,230,1,15.5,0.0,0.5,1.0,0.0,0.3,17.3, +2,2019-01-22 20:31:38,2019-01-22 20:51:05,1,2.7,1,N,234,141,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-06 05:07:25,2019-01-06 05:13:19,1,1.4,1,N,142,246,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +1,2019-01-26 09:16:08,2019-01-26 09:17:16,1,0.5,1,N,151,238,1,3.5,0.0,0.5,1.25,0.0,0.3,5.55,0.0 +2,2019-01-15 17:45:14,2019-01-15 17:48:00,1,0.41,1,N,90,249,1,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-25 16:02:26,2019-01-25 16:13:46,1,0.6,1,N,186,246,1,8.0,1.0,0.5,5.0,0.0,0.3,14.8,0.0 +2,2019-01-31 06:28:44,2019-01-31 06:35:52,1,1.87,1,N,237,170,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-09 12:50:36,2019-01-09 13:15:52,1,1.68,1,N,234,161,1,16.0,0.0,0.5,2.0,0.0,0.3,18.8, +2,2019-01-10 17:20:40,2019-01-10 17:27:19,1,0.73,1,N,164,230,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-04 02:03:45,2019-01-04 02:10:49,1,1.74,1,N,230,68,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-03 16:38:43,2019-01-03 16:53:36,1,2.7,1,N,161,263,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-29 21:04:00,2019-01-29 21:17:24,1,2.4,1,N,48,249,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15,0.0 +1,2019-01-21 18:52:27,2019-01-21 18:57:41,1,0.6,1,N,144,79,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-13 18:25:55,2019-01-13 18:48:50,1,4.31,1,N,158,33,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-08 23:57:54,2019-01-09 00:37:30,1,13.71,1,N,132,181,1,40.5,0.5,0.5,3.0,0.0,0.3,44.8, +1,2019-01-22 16:51:28,2019-01-22 16:59:20,1,0.8,1,N,230,237,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-28 08:12:44,2019-01-28 08:29:58,1,1.8,1,N,43,236,1,12.0,0.0,0.5,1.0,0.0,0.3,13.8,0.0 +2,2019-01-17 05:49:42,2019-01-17 05:58:24,1,2.27,1,N,226,7,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-26 13:53:10,2019-01-26 14:13:37,1,3.03,1,N,163,249,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-11 16:18:36,2019-01-11 16:34:03,1,3.4,1,N,25,228,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-12 18:21:13,2019-01-12 18:35:57,1,1.2,1,N,90,224,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-10 23:49:43,2019-01-11 00:22:04,1,2.4,1,N,162,145,1,21.0,0.5,0.5,3.35,0.0,0.3,25.65, +1,2019-01-07 23:04:25,2019-01-07 23:16:17,1,2.7,1,N,13,90,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-29 20:40:21,2019-01-29 20:43:59,1,0.5,1,N,230,163,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-26 08:21:02,2019-01-26 08:26:39,1,1.24,1,N,238,142,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-19 12:34:02,2019-01-19 12:53:45,1,4.24,1,N,249,181,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +2,2019-01-15 08:22:36,2019-01-15 08:30:57,1,1.37,1,N,234,170,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-24 13:05:03,2019-01-24 13:16:52,1,1.35,1,N,263,262,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-15 12:33:08,2019-01-15 12:43:41,1,0.72,1,N,229,161,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-03 19:20:26,2019-01-03 19:35:49,1,2.7,1,N,238,48,1,12.0,1.0,0.5,1.0,0.0,0.3,14.8, +1,2019-01-31 22:22:38,2019-01-31 22:29:26,1,1.5,1,N,239,163,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-15 14:50:05,2019-01-15 14:54:56,1,0.3,1,N,151,151,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-13 16:48:39,2019-01-13 17:01:44,1,2.87,1,N,164,236,1,11.5,0.0,0.5,0.98,0.0,0.3,13.28, +2,2019-01-09 09:47:21,2019-01-09 09:48:36,1,0.35,1,N,79,107,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +1,2019-01-24 09:44:34,2019-01-24 09:53:49,1,1.0,1,N,264,107,1,7.5,0.0,0.5,0.83,0.0,0.3,9.13,0.0 +2,2019-01-30 11:46:46,2019-01-30 12:21:25,1,4.44,1,N,263,68,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36,0.0 +1,2019-01-12 19:55:57,2019-01-12 20:05:43,1,0.8,1,N,264,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +4,2019-01-05 14:35:17,2019-01-05 14:47:55,1,1.06,1,N,158,113,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-26 21:48:53,2019-01-26 22:01:02,1,1.38,1,N,79,144,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-02 06:42:15,2019-01-02 06:47:18,1,0.7,1,N,186,164,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-31 16:22:05,2019-01-31 16:33:30,1,0.6,1,N,230,161,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-03 06:27:20,2019-01-03 06:56:29,1,12.8,1,N,230,138,1,37.0,0.0,0.5,8.71,5.76,0.3,52.27, +2,2019-01-25 15:07:24,2019-01-25 15:25:24,1,0.65,1,N,239,238,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-31 21:24:49,2019-01-31 21:38:40,1,6.04,1,N,263,244,1,19.0,0.5,0.5,4.06,0.0,0.3,24.36,0.0 +1,2019-01-02 08:36:21,2019-01-02 08:43:47,1,0.6,1,N,237,162,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-14 08:42:08,2019-01-14 08:52:26,1,1.71,1,N,231,114,1,9.0,0.0,0.5,1.5,0.0,0.3,11.3, +2,2019-01-27 15:28:26,2019-01-27 15:33:17,1,0.69,1,N,230,186,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-08 18:08:52,2019-01-08 18:29:03,1,3.4,1,N,246,237,1,15.5,1.0,0.5,3.46,0.0,0.3,20.76, +1,2019-01-06 13:52:37,2019-01-06 13:58:34,1,1.0,1,N,141,237,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-11 10:45:23,2019-01-11 11:04:33,1,2.1,1,N,75,43,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-23 22:06:33,2019-01-23 22:35:48,1,19.02,2,N,132,42,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-23 13:22:38,2019-01-23 13:39:05,1,2.5,1,N,143,140,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-23 17:35:05,2019-01-23 17:40:40,1,0.7,1,N,237,140,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-03 20:21:32,2019-01-03 20:33:19,1,1.34,1,N,170,163,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-23 14:56:37,2019-01-23 15:26:26,1,9.7,1,N,264,264,1,31.5,0.0,0.5,8.05,0.0,0.3,40.35,0.0 +2,2019-01-05 18:58:22,2019-01-05 19:25:39,1,5.79,1,N,237,231,2,22.5,0.0,0.5,0.0,0.0,0.3,23.3, +2,2019-01-19 05:29:10,2019-01-19 05:31:24,1,0.97,1,N,75,74,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-20 02:29:22,2019-01-20 02:32:31,1,0.5,1,N,234,164,1,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-07 15:23:22,2019-01-07 15:28:12,1,1.0,1,N,239,48,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-22 16:43:07,2019-01-22 16:53:31,1,2.08,1,N,137,144,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-11 18:02:05,2019-01-11 18:17:37,1,1.8,1,N,170,90,1,11.5,1.0,0.5,1.7,0.0,0.3,15.0, +2,2019-01-24 19:03:43,2019-01-24 19:15:14,1,1.19,1,N,140,43,1,8.5,1.0,0.5,2.0,0.0,0.3,12.3,0.0 +1,2019-01-18 23:14:16,2019-01-18 23:20:56,1,1.0,1,N,161,137,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +1,2019-01-01 22:11:17,2019-01-01 22:18:57,1,2.4,1,N,233,262,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-15 08:44:40,2019-01-15 08:50:30,1,0.66,1,N,234,68,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24, +1,2019-01-17 18:49:22,2019-01-17 18:57:24,1,1.0,1,N,234,68,1,7.0,1.0,0.5,2.0,0.0,0.3,10.8, +2,2019-01-23 10:29:33,2019-01-23 10:54:10,1,1.98,1,N,68,237,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-08 13:01:39,2019-01-08 13:16:39,1,2.4,1,N,246,162,1,12.0,0.0,0.5,1.5,0.0,0.3,14.3, +1,2019-01-27 03:21:44,2019-01-27 03:25:27,1,0.7,1,N,90,107,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-29 16:47:08,2019-01-29 16:51:42,1,0.61,1,N,236,140,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-02 18:58:01,2019-01-02 19:18:35,1,4.12,1,N,264,264,1,16.5,1.0,0.5,5.49,0.0,0.3,23.79, +2,2019-01-25 11:25:27,2019-01-25 11:35:34,1,0.91,1,N,162,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-14 11:13:23,2019-01-14 11:43:24,1,1.82,1,N,170,48,1,18.0,0.0,0.5,5.64,0.0,0.3,24.44, +2,2019-01-12 22:02:50,2019-01-12 22:21:00,1,2.62,1,N,162,68,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-13 11:15:46,2019-01-13 11:16:52,1,0.08,1,N,230,230,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-04 19:48:18,2019-01-04 19:56:35,1,1.62,1,N,262,229,1,8.0,1.0,0.5,3.0,0.0,0.3,12.8, +2,2019-01-23 17:22:43,2019-01-23 17:30:31,1,0.97,1,N,162,237,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-03 07:17:14,2019-01-03 07:23:42,1,0.92,1,N,162,230,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-20 22:19:15,2019-01-20 22:22:52,1,0.58,1,N,230,186,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-14 21:09:29,2019-01-14 21:17:36,1,1.6,1,N,230,249,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-07 17:01:31,2019-01-07 17:08:13,1,0.76,1,N,237,141,1,6.0,1.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-23 15:44:53,2019-01-23 15:47:51,1,0.5,1,N,151,238,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-21 16:24:58,2019-01-21 17:10:35,1,18.12,2,N,132,163,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-22 13:56:47,2019-01-22 14:26:10,1,2.95,1,N,237,79,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8,0.0 +1,2019-01-10 20:04:00,2019-01-10 20:22:42,1,2.8,1,N,161,236,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-28 17:06:49,2019-01-28 17:26:56,1,4.61,1,N,50,145,1,17.5,1.0,0.5,3.86,0.0,0.3,23.16,0.0 +1,2019-01-03 07:30:44,2019-01-03 07:39:18,1,1.2,1,N,140,161,1,8.0,0.0,0.5,1.4,0.0,0.3,10.2, +2,2019-01-05 10:56:42,2019-01-05 10:59:39,1,0.53,1,N,113,249,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-25 02:05:25,2019-01-25 02:10:38,1,1.22,1,N,107,186,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-31 12:58:27,2019-01-31 13:16:08,1,1.09,1,N,161,186,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +2,2019-01-15 12:37:48,2019-01-15 12:44:06,1,0.79,1,N,142,163,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-11 19:34:59,2019-01-11 19:51:18,1,2.6,1,N,162,114,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15, +1,2019-01-09 16:48:45,2019-01-09 16:59:59,1,1.9,1,Y,79,170,1,9.5,1.0,0.5,1.0,0.0,0.3,12.3, +1,2019-01-14 10:10:01,2019-01-14 10:21:03,1,3.5,1,N,79,88,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-01 18:16:25,2019-01-01 18:26:16,1,4.73,1,N,87,233,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +1,2019-01-12 03:07:58,2019-01-12 03:12:41,1,1.0,1,N,79,249,1,5.5,0.5,0.5,0.6,0.0,0.3,7.4, +1,2019-01-11 14:00:05,2019-01-11 14:05:03,1,0.8,1,N,48,68,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-30 12:49:57,2019-01-30 13:10:57,1,8.3,1,N,48,244,1,26.0,0.0,0.5,5.35,0.0,0.3,32.15,0.0 +2,2019-01-07 17:48:06,2019-01-07 17:53:56,1,0.66,1,N,41,166,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-27 03:21:09,2019-01-27 03:33:32,1,3.58,1,N,125,162,1,12.5,0.5,0.5,1.0,0.0,0.3,14.8,0.0 +1,2019-01-28 16:38:02,2019-01-28 16:50:27,1,1.0,1,N,162,161,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95,0.0 +1,2019-01-07 12:19:09,2019-01-07 12:24:09,1,0.8,1,N,239,142,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +1,2019-01-25 12:21:57,2019-01-25 12:24:02,1,0.4,1,N,264,264,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-12 19:44:47,2019-01-12 20:08:02,1,4.1,1,N,125,142,1,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-07 21:25:36,2019-01-07 21:30:40,1,2.64,1,N,132,10,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-08 21:36:08,2019-01-08 21:47:47,1,2.24,1,N,68,114,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-08 21:25:02,2019-01-08 21:38:42,1,2.9,1,N,48,79,1,12.0,0.5,0.5,3.3,0.0,0.3,16.6, +1,2019-01-26 12:09:13,2019-01-26 12:18:54,1,1.9,1,N,90,162,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8,0.0 +2,2019-01-16 09:03:58,2019-01-16 09:05:13,1,0.3,1,N,234,234,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-12 16:28:15,2019-01-12 16:31:01,1,0.24,5,N,142,143,1,30.34,0.0,0.5,0.0,0.0,0.3,31.14, +2,2019-01-17 11:39:55,2019-01-17 11:43:05,1,0.52,1,N,239,239,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-15 12:05:28,2019-01-15 12:18:31,1,1.23,1,N,249,107,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-13 12:47:05,2019-01-13 13:06:27,1,12.29,1,N,138,20,2,33.5,0.0,0.5,0.0,5.76,0.3,40.06, +1,2019-01-14 14:49:33,2019-01-14 14:50:57,1,0.3,1,N,229,229,1,3.0,0.0,0.5,1.0,0.0,0.3,4.8, +1,2019-01-27 02:25:28,2019-01-27 02:36:02,1,1.8,1,N,163,50,1,9.0,0.5,0.5,0.3,0.0,0.3,10.6,0.0 +2,2019-01-15 14:21:39,2019-01-15 14:27:37,1,0.81,1,N,162,162,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-18 07:30:39,2019-01-18 08:22:28,1,9.45,5,N,198,100,1,25.69,0.0,0.5,0.0,5.76,0.3,32.25, +1,2019-01-31 16:30:56,2019-01-31 16:37:01,1,1.0,1,N,264,264,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-24 04:31:13,2019-01-24 04:39:29,1,2.23,1,N,48,158,1,8.5,0.5,0.5,75.0,0.0,0.3,84.8,0.0 +1,2019-01-18 17:05:23,2019-01-18 17:42:28,1,10.0,1,N,209,129,1,34.5,1.0,0.5,0.0,0.0,0.3,36.3, +2,2019-01-26 19:11:56,2019-01-26 19:27:42,1,1.03,1,N,158,113,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-31 18:40:46,2019-01-31 18:47:38,1,0.73,1,N,158,113,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-13 12:44:01,2019-01-13 13:04:23,1,3.6,1,N,234,13,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16, +1,2019-01-17 13:31:22,2019-01-17 13:39:10,1,0.7,1,N,230,161,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-09 19:57:38,2019-01-09 20:08:27,1,1.98,1,N,100,237,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-17 01:15:51,2019-01-17 01:30:47,1,7.3,1,N,148,74,2,21.5,0.5,0.5,0.0,0.0,0.3,22.8, +2,2019-01-09 19:08:15,2019-01-09 19:33:49,1,5.71,1,N,229,82,1,21.0,1.0,0.5,6.84,0.0,0.3,29.64, +1,2019-01-16 21:39:42,2019-01-16 21:44:34,1,1.0,1,N,90,68,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5, +1,2019-01-23 13:49:30,2019-01-23 13:52:02,1,0.2,1,N,262,262,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +4,2019-01-22 00:36:16,2019-01-22 00:39:43,1,0.89,1,N,263,236,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-15 21:37:30,2019-01-15 21:46:31,1,1.7,1,N,181,40,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-12 11:38:02,2019-01-12 11:44:59,1,1.1,1,N,142,43,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-13 00:50:08,2019-01-13 00:50:27,1,0.04,1,N,148,148,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +1,2019-01-13 01:06:36,2019-01-13 01:24:05,1,6.4,1,N,164,129,2,21.0,0.5,0.5,0.0,5.76,0.3,28.06, +2,2019-01-27 16:25:13,2019-01-27 16:32:01,1,1.32,1,N,170,90,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +1,2019-01-24 23:14:56,2019-01-24 23:24:32,1,2.0,1,N,144,90,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-27 16:00:24,2019-01-27 16:09:41,1,1.3,1,N,141,229,1,8.0,0.0,0.5,0.88,0.0,0.3,9.68,0.0 +1,2019-01-11 02:52:49,2019-01-11 03:14:24,1,5.6,1,N,158,145,2,20.5,0.5,0.5,0.0,0.0,0.3,21.8, +2,2019-01-31 16:34:21,2019-01-31 16:54:19,1,2.21,1,N,164,142,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-12 22:51:35,2019-01-12 23:07:43,1,3.83,1,N,79,112,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +2,2019-01-16 19:35:35,2019-01-16 19:43:12,1,1.27,1,N,234,68,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-11 23:28:58,2019-01-11 23:37:10,1,1.2,1,N,163,170,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-25 12:16:49,2019-01-25 12:26:00,1,1.42,1,N,113,137,1,7.5,0.0,0.5,2.0,0.0,0.3,10.3,0.0 +1,2019-01-06 10:52:22,2019-01-06 11:05:32,1,2.8,1,N,79,161,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-15 11:12:05,2019-01-15 11:26:09,1,1.0,1,N,237,140,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-11 17:19:48,2019-01-11 17:35:13,1,1.41,1,N,162,163,1,10.5,1.0,0.5,1.0,0.0,0.3,13.3, +2,2019-01-18 16:41:01,2019-01-18 16:49:40,1,1.35,1,N,163,170,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-16 14:08:49,2019-01-16 14:57:42,1,13.53,1,N,234,242,1,44.5,0.0,0.5,0.0,0.0,0.3,45.3, +2,2019-01-19 06:07:42,2019-01-19 06:11:47,1,1.34,1,N,233,237,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-15 07:10:28,2019-01-15 07:23:41,1,1.9,1,N,100,163,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-17 15:27:09,2019-01-17 15:38:05,1,1.31,1,N,107,249,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-30 19:37:39,2019-01-30 19:40:40,1,0.41,1,N,263,236,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-05 13:42:09,2019-01-05 13:52:46,1,1.1,1,N,186,164,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-14 15:24:23,2019-01-14 15:39:35,1,1.98,1,N,229,262,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +4,2019-01-25 06:37:23,2019-01-25 06:57:27,1,5.04,1,N,33,170,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56, +2,2019-01-24 20:47:14,2019-01-24 20:50:53,1,0.72,1,N,141,140,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67,0.0 +1,2019-01-07 06:02:01,2019-01-07 06:05:40,1,0.7,1,N,186,100,1,4.5,0.0,0.5,0.79,0.0,0.3,6.09, +2,2019-01-14 18:36:55,2019-01-14 19:35:20,1,20.6,1,N,87,191,2,59.0,1.0,0.5,0.0,0.0,0.3,60.8, +2,2019-01-01 13:54:17,2019-01-01 13:56:57,1,1.04,1,N,263,75,1,5.0,0.0,0.5,2.0,0.0,0.3,7.8, +1,2019-01-14 18:31:41,2019-01-14 18:48:02,1,2.3,1,N,170,236,1,12.0,1.0,0.5,1.0,0.0,0.3,14.8, +1,2019-01-20 21:00:55,2019-01-20 21:08:29,1,2.8,1,N,264,264,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +1,2019-01-26 11:28:35,2019-01-26 11:35:47,1,0.7,1,N,246,246,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-26 08:52:42,2019-01-26 09:07:21,1,2.1,1,N,137,163,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-31 12:27:05,2019-01-31 12:43:06,1,4.34,1,N,246,151,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76,0.0 +1,2019-01-14 19:21:03,2019-01-14 19:27:04,1,1.4,1,N,43,142,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-31 18:38:03,2019-01-31 18:44:27,1,0.7,1,N,164,170,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-11 08:35:13,2019-01-11 08:53:08,1,1.96,1,N,239,237,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8, +1,2019-01-05 12:08:08,2019-01-05 12:15:05,1,1.2,1,N,239,238,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-31 07:27:22,2019-01-31 07:37:10,1,1.4,1,N,264,264,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-11 15:07:13,2019-01-11 15:23:09,1,2.3,1,N,236,161,1,12.0,0.0,0.5,1.92,0.0,0.3,14.72, +1,2019-01-19 23:49:12,2019-01-20 00:03:44,1,2.5,1,N,170,4,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +2,2019-01-27 16:25:10,2019-01-27 16:43:17,1,3.07,1,N,236,230,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-16 08:45:41,2019-01-16 08:58:33,1,1.3,1,N,50,162,1,9.5,0.0,0.5,1.5,0.0,0.3,11.8, +2,2019-01-24 21:14:49,2019-01-24 21:23:35,1,1.27,1,N,141,229,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-04 03:26:15,2019-01-04 03:28:37,1,0.3,1,N,263,262,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +2,2019-01-09 19:32:43,2019-01-09 19:35:30,1,0.53,1,N,152,152,1,4.0,1.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-10 22:43:29,2019-01-10 22:49:10,1,1.82,1,N,90,170,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +2,2019-01-28 13:27:44,2019-01-28 13:40:19,1,0.92,1,N,68,100,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-07 15:32:09,2019-01-07 15:38:19,1,0.6,1,N,163,161,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-01 19:17:45,2019-01-01 19:30:53,1,3.74,1,N,186,140,1,13.0,0.0,0.5,1.0,0.0,0.3,14.8, +2,2019-01-23 21:52:41,2019-01-23 22:00:56,1,1.48,1,N,234,170,1,7.5,0.5,0.5,2.0,0.0,0.3,10.8,0.0 +2,2019-01-17 13:47:02,2019-01-17 13:48:20,1,0.21,1,N,107,107,1,3.0,0.0,0.5,0.76,0.0,0.3,4.56, +2,2019-01-17 19:54:38,2019-01-17 20:12:25,1,2.45,1,N,142,142,1,12.5,1.0,0.5,4.29,0.0,0.3,18.59, +2,2019-01-05 12:34:55,2019-01-05 12:47:55,1,2.19,1,N,141,164,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-17 14:24:09,2019-01-17 14:31:34,1,1.2,1,N,166,42,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-13 08:13:52,2019-01-13 08:39:58,1,16.46,1,N,132,149,2,45.0,0.0,0.5,0.0,0.0,0.3,45.8, +4,2019-01-03 08:09:12,2019-01-03 08:25:49,1,3.58,1,N,237,158,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-20 22:40:34,2019-01-20 22:47:45,1,0.48,1,N,249,114,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-17 18:05:59,2019-01-17 18:12:58,1,1.26,1,N,143,48,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-13 05:32:14,2019-01-13 05:53:20,1,8.21,1,N,186,116,2,25.5,0.5,0.5,0.0,0.0,0.3,26.8, +2,2019-01-31 19:53:11,2019-01-31 20:28:02,1,4.9,1,N,107,239,1,23.5,1.0,0.5,5.06,0.0,0.3,30.36,0.0 +1,2019-01-21 15:38:43,2019-01-21 15:48:28,1,1.2,1,N,186,246,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-20 09:00:01,2019-01-20 09:04:46,1,0.66,1,N,148,4,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-11 23:12:55,2019-01-11 23:25:39,1,3.67,1,N,234,236,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +2,2019-01-10 14:25:49,2019-01-10 14:32:15,1,0.78,1,N,239,239,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-16 19:01:08,2019-01-16 19:24:38,1,2.97,1,N,161,262,1,16.0,1.0,0.5,3.56,0.0,0.3,21.36, +2,2019-01-30 16:15:29,2019-01-30 16:27:45,1,1.47,1,N,164,246,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-31 22:03:40,2019-01-31 22:07:58,1,0.49,1,N,142,143,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-27 17:01:03,2019-01-27 17:07:29,1,0.72,1,N,148,114,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-03 20:26:15,2019-01-03 20:32:18,1,1.2,1,N,239,236,1,6.5,0.5,0.5,2.0,0.0,0.3,9.8, +1,2019-01-17 16:18:13,2019-01-17 16:22:11,1,0.5,1,N,264,264,1,4.5,1.0,0.5,2.0,0.0,0.3,8.3, +1,2019-01-29 08:08:24,2019-01-29 08:20:57,1,1.3,1,N,186,170,1,9.0,0.0,0.5,2.0,0.0,0.3,11.8,0.0 +2,2019-01-13 14:36:57,2019-01-13 14:51:58,1,3.57,1,N,79,87,1,14.5,0.0,0.5,3.82,0.0,0.3,19.12, +2,2019-01-04 11:15:12,2019-01-04 11:34:00,1,2.25,1,N,162,100,1,13.0,0.0,0.5,2.0,0.0,0.3,15.8, +1,2019-01-11 12:25:24,2019-01-11 12:33:32,1,1.0,1,N,75,236,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-26 15:38:12,2019-01-26 16:20:18,1,5.96,1,N,170,232,1,28.5,0.0,0.5,2.7,0.0,0.3,32.0,0.0 +2,2019-01-22 02:22:18,2019-01-22 02:25:20,1,0.4,1,N,170,164,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-11 09:22:06,2019-01-11 09:25:25,1,0.01,1,N,193,193,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-09 06:35:29,2019-01-09 06:42:49,1,1.1,1,N,100,162,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-23 06:41:43,2019-01-23 06:47:38,1,0.89,1,N,186,230,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-29 20:50:51,2019-01-29 20:53:53,1,0.8,1,N,68,68,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-25 16:31:06,2019-01-25 16:32:38,1,0.4,1,N,100,230,1,3.5,1.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-29 14:40:57,2019-01-29 14:43:52,1,0.41,1,N,230,230,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-31 16:37:13,2019-01-31 16:43:58,1,0.69,1,N,237,237,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-19 12:23:35,2019-01-19 12:32:49,1,2.62,1,N,132,10,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-28 15:41:16,2019-01-28 15:45:57,1,0.4,1,N,163,229,1,5.0,0.0,0.5,0.58,0.0,0.3,6.38,0.0 +1,2019-01-31 07:05:27,2019-01-31 07:10:47,1,0.7,1,N,162,230,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-03 14:44:15,2019-01-03 14:48:46,1,1.21,1,N,186,246,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-12 00:46:11,2019-01-12 01:11:54,1,6.0,1,N,249,61,1,22.0,0.5,0.5,4.65,0.0,0.3,27.95, +2,2019-01-30 14:41:58,2019-01-30 14:47:00,1,0.42,1,N,161,43,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-11 16:49:31,2019-01-11 16:55:02,1,0.3,1,N,141,237,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-14 20:33:12,2019-01-14 20:38:36,1,1.24,1,N,137,107,1,6.0,0.5,0.5,2.19,0.0,0.3,9.49, +2,2019-01-15 18:40:03,2019-01-15 18:46:36,1,1.28,1,N,246,68,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-21 15:23:08,2019-01-21 15:30:34,1,1.4,1,Y,68,100,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-05 13:46:22,2019-01-05 13:59:40,1,2.5,1,N,125,137,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-13 03:24:21,2019-01-13 03:29:16,1,0.96,1,N,238,239,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +1,2019-01-19 10:56:14,2019-01-19 11:16:20,1,4.3,1,N,161,261,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-22 07:51:04,2019-01-22 08:12:36,1,4.0,1,N,13,230,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25,0.0 +1,2019-01-30 20:08:23,2019-01-30 20:10:55,1,0.4,1,N,79,79,1,4.0,0.5,0.5,0.53,0.0,0.3,5.83,0.0 +1,2019-01-04 13:50:18,2019-01-04 13:58:58,1,1.1,1,N,161,164,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 12:11:25,2019-01-26 12:22:11,1,1.68,1,N,141,142,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-16 10:56:57,2019-01-16 11:12:40,1,2.9,1,N,43,24,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-31 07:36:03,2019-01-31 07:40:04,1,1.04,1,N,236,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-15 10:12:24,2019-01-15 10:28:36,1,1.22,1,N,164,246,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-27 13:00:40,2019-01-27 13:14:59,1,1.57,1,N,79,90,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-10 09:32:20,2019-01-10 09:42:44,1,1.0,1,N,114,231,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-08 08:35:15,2019-01-08 08:42:25,1,2.21,1,N,263,74,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-17 18:06:54,2019-01-17 18:13:17,1,0.81,1,N,141,162,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 22:12:27,2019-01-10 22:15:44,1,1.14,1,N,70,129,1,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-20 14:05:09,2019-01-20 14:16:16,1,2.63,1,N,142,236,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-23 08:50:31,2019-01-23 09:15:35,1,2.0,1,N,249,170,1,15.5,0.0,0.5,2.0,0.0,0.3,18.3,0.0 +2,2019-01-17 20:31:18,2019-01-17 20:36:49,1,0.98,1,N,263,262,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-05 23:43:54,2019-01-05 23:55:23,1,2.49,1,N,263,162,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-13 11:59:07,2019-01-13 12:19:31,1,2.7,1,N,237,48,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-23 20:57:53,2019-01-23 21:03:00,1,1.15,1,N,114,90,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-23 13:04:06,2019-01-23 13:19:21,1,0.89,1,N,161,230,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-10 15:47:58,2019-01-10 16:50:57,1,21.6,1,N,132,235,1,64.5,0.0,0.5,0.0,5.76,0.3,71.06, +2,2019-01-18 07:35:22,2019-01-18 07:39:09,1,0.61,1,N,186,100,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-17 08:58:39,2019-01-17 09:20:05,1,3.56,1,N,168,141,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-27 19:31:33,2019-01-27 19:41:36,1,1.45,1,N,4,249,1,8.0,0.0,0.5,1.2,0.0,0.3,10.0,0.0 +4,2019-01-09 10:55:36,2019-01-09 11:13:38,1,1.81,1,N,50,141,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-04 18:28:40,2019-01-04 18:35:35,1,1.1,1,N,142,239,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-01 18:52:28,2019-01-01 19:10:43,1,9.0,1,N,106,22,1,27.0,0.0,0.5,0.0,0.0,0.3,27.8, +2,2019-01-11 07:36:34,2019-01-11 07:42:43,1,1.12,1,N,170,107,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-22 09:03:00,2019-01-22 09:15:23,1,0.7,1,N,237,161,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6,0.0 +2,2019-01-17 13:51:05,2019-01-17 14:30:32,1,16.32,1,N,231,138,1,46.5,0.0,0.5,10.61,5.76,0.3,63.67, +1,2019-01-14 17:26:04,2019-01-14 17:46:39,1,2.4,1,N,140,238,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-31 13:28:21,2019-01-31 13:52:07,1,1.9,1,N,186,162,1,14.5,0.0,0.5,4.0,0.0,0.3,19.3,0.0 +2,2019-01-25 20:19:02,2019-01-25 20:32:57,1,1.44,1,N,79,144,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-09 08:12:59,2019-01-09 08:19:59,1,0.63,1,N,162,161,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-02 18:08:46,2019-01-02 18:18:52,1,2.94,1,N,138,179,1,11.5,1.0,0.5,3.99,0.0,0.3,17.29, +1,2019-01-05 11:30:05,2019-01-05 11:56:54,1,5.1,1,N,163,231,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +1,2019-01-21 09:54:58,2019-01-21 10:01:38,1,0.5,1,N,50,48,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-18 13:09:28,2019-01-18 13:11:30,1,0.2,1,N,238,238,1,3.5,0.0,0.5,0.43,0.0,0.3,4.73, +1,2019-01-30 11:35:46,2019-01-30 11:52:14,1,2.2,1,N,24,42,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8,0.0 +2,2019-01-03 10:18:04,2019-01-03 10:23:00,1,1.21,1,N,13,125,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-22 20:53:08,2019-01-22 20:57:34,1,1.23,1,N,141,263,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-09 07:59:52,2019-01-09 08:10:05,1,1.7,1,N,90,161,1,9.0,0.0,0.5,5.0,0.0,0.3,14.8, +2,2019-01-01 00:56:29,2019-01-01 01:15:51,1,3.33,1,N,45,68,2,15.0,0.5,0.5,0.0,0.0,0.3,16.3, +2,2019-01-10 23:12:46,2019-01-10 23:24:24,1,3.64,1,N,234,75,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-02 22:41:52,2019-01-02 22:59:15,1,5.52,1,N,231,225,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +1,2019-01-20 15:05:33,2019-01-20 15:39:09,1,18.5,2,N,132,166,1,52.0,0.0,0.5,6.0,5.76,0.3,64.56, +2,2019-01-21 12:59:58,2019-01-21 13:07:47,1,1.09,1,N,141,236,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79,0.0 +2,2019-01-16 22:29:07,2019-01-16 22:45:59,1,8.4,1,N,13,263,1,24.5,0.5,0.5,4.0,0.0,0.3,29.8, +2,2019-01-04 09:26:46,2019-01-04 09:35:53,1,1.9,1,N,107,45,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +1,2019-01-21 21:06:39,2019-01-21 21:20:52,1,2.3,1,N,186,107,1,11.5,0.5,0.5,1.28,0.0,0.3,14.08,0.0 +2,2019-01-19 12:54:15,2019-01-19 13:06:39,1,3.24,1,N,137,232,1,12.5,0.0,0.5,3.32,0.0,0.3,16.62, +2,2019-01-07 01:10:18,2019-01-07 01:48:38,1,19.14,1,N,161,21,1,53.0,0.5,0.5,10.86,0.0,0.3,65.16, +2,2019-01-30 21:48:49,2019-01-30 22:06:02,1,4.47,1,N,164,87,1,16.5,0.5,0.5,4.45,0.0,0.3,22.25,0.0 +2,2019-01-02 18:11:45,2019-01-02 18:16:25,1,0.88,1,N,42,42,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-08 12:42:54,2019-01-08 12:47:50,1,0.84,1,N,68,68,1,5.5,0.0,0.5,1.26,0.0,0.3,9.51, +2,2019-01-27 21:51:31,2019-01-27 22:00:13,1,3.01,1,N,263,233,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-05 06:31:30,2019-01-05 06:35:37,1,0.7,1,N,262,236,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-17 23:25:40,2019-01-17 23:35:55,1,0.7,1,N,79,113,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-04 22:18:36,2019-01-04 22:27:23,1,1.4,1,N,107,229,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-14 15:06:21,2019-01-14 15:19:37,1,1.83,1,N,237,236,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-31 18:05:19,2019-01-31 18:29:48,1,2.8,1,N,230,114,1,16.5,1.0,0.5,1.0,0.0,0.3,19.3,0.0 +1,2019-01-28 06:40:15,2019-01-28 06:46:33,1,1.1,1,N,164,48,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-30 12:59:41,2019-01-30 13:05:59,1,0.4,1,N,161,161,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-09 17:45:10,2019-01-09 18:02:59,1,2.37,1,N,48,107,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-30 22:21:41,2019-01-30 22:33:42,1,2.0,1,N,48,170,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5,0.0 +1,2019-01-08 22:55:37,2019-01-08 23:00:46,1,0.8,1,N,144,79,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-01 02:38:59,2019-01-02 02:02:54,1,3.73,1,N,148,230,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +1,2019-01-29 09:35:31,2019-01-29 09:41:23,1,0.8,1,N,162,163,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-17 19:26:58,2019-01-17 19:34:09,1,1.24,1,N,151,239,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-09 19:27:16,2019-01-09 19:51:32,1,4.7,1,N,148,61,3,19.5,1.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-20 00:16:34,2019-01-20 00:19:07,1,0.6,1,N,164,170,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-30 15:34:20,2019-01-30 15:45:34,1,1.6,1,N,186,186,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8,0.0 +1,2019-01-25 09:56:31,2019-01-25 10:18:26,1,5.9,1,N,233,65,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8,0.0 +2,2019-01-24 08:52:14,2019-01-24 09:54:54,1,13.24,1,N,138,246,1,47.5,0.0,0.5,10.81,5.76,0.3,64.87,0.0 +2,2019-01-27 11:15:31,2019-01-27 11:42:19,1,3.72,1,N,239,186,1,20.0,0.0,0.5,5.2,0.0,0.3,26.0,0.0 +2,2019-01-18 16:05:11,2019-01-18 16:13:59,1,1.67,1,N,237,43,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-12 21:24:16,2019-01-12 21:27:18,1,0.5,1,N,79,4,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-04 06:44:13,2019-01-04 06:52:16,1,2.0,1,N,234,232,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-25 06:50:10,2019-01-25 06:57:51,1,1.7,1,N,148,113,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-23 22:15:50,2019-01-23 22:24:38,1,1.5,1,N,229,163,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-07 15:58:19,2019-01-07 16:07:57,1,1.6,1,N,264,264,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-02 11:09:06,2019-01-02 11:26:14,1,2.89,1,N,262,143,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-07 04:49:19,2019-01-07 04:59:49,1,3.7,1,N,74,162,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-16 14:12:49,2019-01-16 14:20:01,1,1.16,1,N,142,239,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 08:41:31,2019-01-10 08:53:22,1,1.97,1,N,262,229,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +1,2019-01-22 02:29:00,2019-01-22 02:37:49,1,1.8,1,N,48,170,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-01 16:30:42,2019-01-01 16:39:57,1,1.6,1,N,186,162,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-08 13:19:43,2019-01-08 13:28:32,1,1.5,1,N,237,143,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-10 11:22:06,2019-01-10 11:45:34,1,9.1,1,N,100,138,1,28.5,0.0,0.5,10.5,5.76,0.3,45.56, +2,2019-01-28 10:00:27,2019-01-28 10:20:09,1,2.53,1,N,239,140,1,14.0,0.0,0.5,3.7,0.0,0.3,18.5,0.0 +1,2019-01-02 07:08:44,2019-01-02 07:21:07,1,7.4,1,N,263,88,1,21.5,0.0,0.5,5.55,0.0,0.3,27.85, +2,2019-01-24 22:36:14,2019-01-24 22:57:05,1,4.25,1,N,100,145,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8,0.0 +1,2019-01-11 13:55:01,2019-01-11 14:03:28,1,1.1,1,N,234,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-03 14:30:35,2019-01-03 15:22:19,1,20.9,2,N,132,239,1,52.0,0.0,0.5,9.0,5.76,0.3,67.56, +2,2019-01-28 07:23:52,2019-01-28 07:27:23,1,0.91,1,N,68,246,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-24 07:10:58,2019-01-24 07:15:13,1,0.57,1,N,42,152,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-16 11:56:46,2019-01-16 12:05:06,1,0.6,1,N,236,141,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-05 00:18:53,2019-01-05 00:31:03,1,2.14,1,N,144,68,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +1,2019-01-16 18:40:12,2019-01-16 18:50:08,1,3.0,1,N,229,7,1,11.5,1.0,0.5,5.7,0.0,0.3,19.0, +2,2019-01-30 14:59:17,2019-01-30 15:17:44,1,3.32,1,N,236,48,1,14.5,0.0,0.5,2.0,0.0,0.3,17.3,0.0 +1,2019-01-22 13:08:56,2019-01-22 13:34:00,1,1.5,1,N,163,100,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-15 23:47:44,2019-01-15 23:54:12,1,1.14,1,N,48,162,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-05 20:26:46,2019-01-05 20:37:56,1,3.4,1,N,88,4,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +2,2019-01-24 10:53:56,2019-01-24 10:56:14,1,0.29,1,N,237,237,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-13 17:25:09,2019-01-13 17:56:33,1,15.96,1,N,138,87,1,45.0,0.0,0.5,15.47,5.76,0.3,67.03, +2,2019-01-30 18:26:10,2019-01-30 18:51:46,1,8.29,1,N,138,100,1,26.5,1.0,0.5,6.81,5.76,0.3,40.87,0.0 +1,2019-01-10 21:26:49,2019-01-10 21:49:44,1,6.4,1,N,142,129,1,21.5,0.5,0.5,3.5,0.0,0.3,26.3, +2,2019-01-17 02:10:19,2019-01-17 02:14:30,1,1.9,1,N,48,68,1,7.0,0.5,0.5,1.24,0.0,0.3,9.54, +2,2019-01-07 15:11:13,2019-01-07 15:25:28,1,2.41,1,N,246,211,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-04 10:42:13,2019-01-04 10:48:10,1,0.8,1,N,239,238,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-15 22:50:06,2019-01-15 22:58:21,1,2.52,1,N,13,45,1,10.0,0.5,0.5,2.82,0.0,0.3,14.12, +2,2019-01-18 18:14:50,2019-01-18 18:21:22,1,1.34,1,N,90,68,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-26 12:57:10,2019-01-26 13:00:41,1,0.91,1,N,90,158,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-04 15:01:20,2019-01-04 16:10:54,1,14.51,1,N,68,188,1,52.5,0.0,0.5,0.0,5.76,0.3,59.06, +2,2019-01-31 12:06:03,2019-01-31 12:11:48,1,0.94,1,N,239,143,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24,0.0 +2,2019-01-25 13:06:04,2019-01-25 13:32:58,1,3.42,1,N,186,13,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76,0.0 +1,2019-01-18 00:48:52,2019-01-18 00:53:14,1,0.8,1,N,116,152,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-28 07:03:00,2019-01-28 07:11:43,1,1.56,1,N,100,237,1,8.0,0.0,0.5,2.0,0.0,0.3,10.8,0.0 +1,2019-01-23 16:58:37,2019-01-23 17:33:30,1,6.8,1,N,161,13,1,27.5,1.0,0.5,5.0,0.0,0.3,34.3,0.0 +1,2019-01-22 16:20:55,2019-01-22 16:20:59,1,0.0,1,N,237,237,3,2.5,1.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-23 23:01:33,2019-01-23 23:12:02,1,1.9,1,N,68,79,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-10 22:27:30,2019-01-10 22:42:50,1,6.9,1,N,231,229,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3, +1,2019-01-16 21:22:41,2019-01-16 21:27:03,1,1.3,1,N,246,50,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +1,2019-01-20 18:55:04,2019-01-20 18:57:12,1,0.6,1,N,238,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-24 11:51:52,2019-01-24 12:47:25,1,21.2,2,N,132,237,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +2,2019-01-31 09:04:16,2019-01-31 09:13:28,1,1.35,1,N,229,140,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-23 13:04:16,2019-01-23 13:24:05,1,7.1,1,N,65,129,1,22.5,0.0,0.5,2.0,0.0,0.3,25.3,0.0 +2,2019-01-22 20:56:18,2019-01-22 21:12:31,1,3.53,1,N,234,43,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +1,2019-01-31 03:58:51,2019-01-31 05:10:20,1,17.1,2,N,186,132,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +2,2019-01-04 14:32:19,2019-01-04 14:36:40,1,0.7,1,N,237,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-31 15:52:49,2019-01-31 16:02:19,1,1.1,1,N,237,236,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-24 10:06:30,2019-01-24 10:08:35,1,0.4,1,N,113,113,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +2,2019-01-11 17:59:47,2019-01-11 18:34:42,1,10.33,1,N,138,142,1,33.5,1.0,0.5,8.21,5.76,0.3,49.27, +1,2019-01-07 11:17:29,2019-01-07 11:22:58,1,0.9,1,N,7,7,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-08 10:01:11,2019-01-08 10:09:55,1,0.6,1,N,170,233,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +1,2019-01-22 10:37:16,2019-01-22 10:51:10,1,1.4,1,N,234,144,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +1,2019-01-14 20:08:18,2019-01-14 20:19:02,1,1.5,1,N,142,238,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-21 23:32:22,2019-01-21 23:37:13,1,1.15,1,N,7,179,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-26 19:25:35,2019-01-26 19:28:28,1,0.6,1,N,13,231,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6,0.0 +2,2019-01-13 19:40:14,2019-01-13 19:44:10,1,0.68,1,N,114,113,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-30 19:51:21,2019-01-30 19:56:48,1,1.1,1,N,263,141,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-30 19:01:04,2019-01-30 19:13:39,1,1.56,1,N,43,68,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-27 20:58:09,2019-01-27 21:18:10,1,2.85,1,N,186,148,1,14.5,0.5,0.5,4.0,0.0,0.3,19.8,0.0 +2,2019-01-18 15:39:43,2019-01-18 15:48:19,1,1.21,1,N,236,237,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-28 14:51:31,2019-01-28 15:37:31,1,18.08,2,N,132,48,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-26 09:25:04,2019-01-26 09:26:36,1,0.3,1,N,113,234,1,3.5,0.0,0.5,0.64,0.0,0.3,4.94,0.0 +2,2019-01-09 20:04:05,2019-01-09 20:41:00,1,8.59,1,N,161,49,2,30.5,0.5,0.5,0.0,0.0,0.3,31.8, +1,2019-01-13 14:36:20,2019-01-13 14:46:07,1,1.5,1,N,142,50,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-01 14:58:06,2019-01-01 15:06:51,1,1.08,1,N,164,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-24 10:43:26,2019-01-24 10:56:50,1,0.8,1,N,234,113,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-03 09:33:23,2019-01-03 09:38:11,1,0.68,1,N,231,125,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-02 09:33:09,2019-01-02 09:48:20,1,1.79,1,N,68,50,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-10 20:29:55,2019-01-10 21:00:53,1,6.2,1,N,264,264,1,24.0,0.5,0.5,6.32,0.0,0.3,31.62, +2,2019-01-10 13:14:44,2019-01-10 13:21:03,1,1.17,1,N,48,43,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-19 00:37:13,2019-01-20 00:31:42,1,3.83,1,N,186,151,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +1,2019-01-07 14:58:37,2019-01-07 15:01:48,1,0.6,1,N,236,236,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-11 10:58:04,2019-01-11 11:03:31,1,0.64,1,N,234,68,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-11 16:53:54,2019-01-11 16:59:59,1,1.2,1,N,237,236,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-22 18:56:42,2019-01-22 19:10:28,1,1.41,1,N,229,142,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-30 12:23:05,2019-01-30 12:36:29,1,1.05,1,N,237,163,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +4,2019-01-13 00:57:30,2019-01-13 01:05:35,1,1.34,1,N,234,79,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-09 03:57:16,2019-01-09 04:04:30,1,2.2,1,N,161,68,1,9.0,0.5,0.5,4.0,0.0,0.3,14.3, +1,2019-01-30 18:22:16,2019-01-30 18:43:11,1,2.6,1,N,114,68,1,14.5,1.0,0.5,4.08,0.0,0.3,20.38,0.0 +1,2019-01-07 12:10:42,2019-01-07 12:14:14,1,0.6,1,N,238,239,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-10 13:13:47,2019-01-10 13:53:20,1,11.6,1,N,138,142,1,38.5,0.0,0.5,8.0,5.76,0.3,53.06, +2,2019-01-10 07:57:57,2019-01-10 08:06:01,1,1.37,1,N,209,114,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-29 13:41:12,2019-01-29 13:51:38,1,1.85,1,N,237,239,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-29 01:52:48,2019-01-29 01:59:03,1,1.34,1,N,79,232,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-06 16:01:18,2019-01-06 16:18:37,1,2.6,1,N,249,87,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-28 15:51:49,2019-01-28 16:00:50,1,2.31,1,N,246,239,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +1,2019-01-30 15:05:01,2019-01-30 15:08:46,1,0.5,1,N,162,230,1,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-21 12:26:09,2019-01-21 12:35:38,1,1.3,1,N,230,233,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-27 17:22:17,2019-01-27 17:32:29,1,1.87,1,N,186,79,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-31 14:22:20,2019-01-31 14:38:13,1,3.0,1,N,43,166,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-18 09:43:47,2019-01-18 10:03:58,1,2.84,1,N,170,211,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-20 07:21:24,2019-01-20 07:24:14,1,0.81,1,N,170,137,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +2,2019-01-02 17:12:48,2019-01-02 17:20:07,1,1.78,1,N,75,238,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-25 16:06:04,2019-01-25 16:28:56,1,2.4,1,N,88,113,1,15.5,1.0,0.5,2.6,0.0,0.3,19.9,0.0 +2,2019-01-18 20:13:53,2019-01-18 20:34:15,1,8.76,1,N,138,263,1,26.5,0.5,0.5,5.03,5.76,0.3,38.59, +2,2019-01-31 10:28:20,2019-01-31 10:37:22,1,1.01,1,N,170,161,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-11 16:37:24,2019-01-11 16:43:45,1,0.98,1,N,163,164,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-26 19:36:34,2019-01-26 19:43:03,1,1.21,1,N,144,113,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49,0.0 +2,2019-01-24 12:35:18,2019-01-24 12:41:52,1,0.74,1,N,148,79,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-19 08:34:20,2019-01-19 08:41:35,1,1.2,1,N,42,166,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-08 19:32:05,2019-01-08 19:39:58,1,1.13,1,N,65,49,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-02 20:49:59,2019-01-02 20:52:46,1,0.6,1,N,264,163,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +1,2019-01-27 12:48:34,2019-01-27 13:01:29,1,2.2,1,N,236,229,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +1,2019-01-22 11:12:36,2019-01-22 11:30:54,1,1.0,1,N,100,170,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-30 19:08:29,2019-01-30 19:43:27,1,12.11,1,N,138,181,1,38.0,1.0,0.5,7.96,0.0,0.3,47.76,0.0 +1,2019-01-08 17:51:27,2019-01-08 18:01:21,1,1.0,1,N,107,249,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-25 20:44:29,2019-01-25 21:03:23,1,4.04,1,N,170,238,1,16.5,0.5,0.5,4.45,0.0,0.3,22.25,0.0 +1,2019-01-10 11:55:52,2019-01-10 12:00:08,1,0.3,1,N,239,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-10 14:05:15,2019-01-10 14:25:31,1,7.61,1,N,138,112,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3, +1,2019-01-02 11:35:49,2019-01-02 11:40:24,1,0.5,1,N,186,90,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-13 15:54:42,2019-01-13 15:59:00,1,0.96,1,N,140,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-18 23:20:20,2019-01-18 23:44:56,1,5.42,1,N,186,87,1,21.0,0.5,0.5,4.46,0.0,0.3,26.76, +1,2019-01-27 00:11:34,2019-01-27 00:32:53,1,10.2,1,N,233,250,1,29.0,0.5,0.5,0.0,0.0,0.3,30.3,0.0 +1,2019-01-30 18:10:23,2019-01-30 18:26:28,1,1.6,1,N,113,144,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-01 16:28:54,2019-01-01 16:32:19,1,0.7,1,N,186,90,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-19 14:55:14,2019-01-19 15:15:51,1,2.38,1,N,80,225,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-19 23:53:24,2019-01-20 00:00:12,1,1.14,1,N,249,114,1,6.5,0.5,0.5,0.78,0.0,0.3,8.58, +2,2019-01-04 12:33:40,2019-01-04 12:43:40,1,1.39,1,N,249,114,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-13 09:06:47,2019-01-13 09:09:33,1,0.51,1,N,142,142,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-10 19:51:14,2019-01-10 20:00:06,1,1.1,1,N,237,163,1,7.5,1.0,0.5,2.3,0.0,0.3,11.6, +1,2019-01-11 18:20:21,2019-01-11 18:24:04,1,0.5,1,N,170,170,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-04 13:35:41,2019-01-04 13:45:38,1,1.43,1,N,161,43,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-10 11:49:33,2019-01-10 12:04:08,1,1.6,1,N,263,237,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0, +1,2019-01-10 14:36:34,2019-01-10 14:43:03,1,1.6,1,N,125,90,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-22 08:13:57,2019-01-22 08:25:56,1,2.35,1,N,68,163,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-30 19:36:38,2019-01-30 19:41:44,1,0.73,1,N,50,48,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-10 00:04:23,2019-01-10 00:16:41,1,4.13,1,N,48,74,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +1,2019-01-11 20:02:11,2019-01-11 20:13:01,1,1.6,1,N,233,79,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-17 20:21:23,2019-01-17 20:28:59,1,1.78,1,N,75,41,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-05 05:36:39,2019-01-05 05:54:39,1,4.72,1,N,144,142,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56, +2,2019-01-24 18:57:17,2019-01-24 19:24:49,1,4.12,1,N,186,263,1,19.5,1.0,0.5,4.26,0.0,0.3,25.56,0.0 +2,2019-01-05 12:30:20,2019-01-05 12:34:57,1,0.98,1,N,249,68,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-29 10:54:45,2019-01-29 11:03:02,1,2.24,1,N,262,233,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-07 07:44:06,2019-01-07 07:47:48,1,0.6,1,N,231,249,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-26 12:49:46,2019-01-26 12:56:18,1,1.53,1,N,166,238,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-15 10:02:53,2019-01-15 10:06:56,1,0.56,1,N,107,137,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +2,2019-01-20 15:08:19,2019-01-20 15:18:23,1,2.68,1,N,151,141,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +1,2019-01-13 15:56:08,2019-01-13 16:29:00,1,10.7,1,N,142,138,1,35.0,0.0,0.5,10.7,0.0,0.3,46.5, +2,2019-01-14 10:40:29,2019-01-14 10:47:29,1,0.65,1,N,141,229,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-11 18:13:18,2019-01-11 18:25:44,1,1.38,1,N,140,161,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-22 13:52:25,2019-01-22 14:19:09,1,6.2,1,N,87,141,1,23.5,0.0,0.5,3.7,0.0,0.3,28.0,0.0 +2,2019-01-04 16:30:31,2019-01-04 16:42:03,1,1.7,1,N,125,4,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-26 00:51:31,2019-01-27 00:26:24,1,0.0,5,N,265,265,1,60.0,0.0,0.5,0.0,0.0,0.3,60.8,0.0 +2,2019-01-28 22:25:03,2019-01-28 22:27:56,1,0.43,1,N,68,68,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-22 13:25:03,2019-01-22 13:59:22,1,5.7,1,N,239,113,1,25.5,0.0,0.5,1.0,0.0,0.3,27.3,0.0 +2,2019-01-08 18:43:36,2019-01-08 18:56:33,1,1.75,1,N,181,49,1,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-23 10:53:08,2019-01-23 11:04:12,1,0.9,1,N,162,164,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-22 09:28:21,2019-01-22 10:10:20,1,11.8,1,N,138,170,1,38.5,0.0,0.5,9.01,5.76,0.3,54.07,0.0 +2,2019-01-09 23:34:04,2019-01-10 00:01:06,1,7.38,1,N,88,238,1,25.5,0.5,0.5,6.7,0.0,0.3,33.5, +2,2019-01-22 16:16:49,2019-01-22 16:24:03,1,0.6,1,N,161,161,1,6.0,1.0,0.5,1.25,0.0,0.3,9.05,0.0 +2,2019-01-25 20:55:23,2019-01-25 21:01:40,1,0.75,1,N,114,125,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-30 18:32:51,2019-01-30 18:46:53,1,2.11,1,N,43,239,1,11.0,1.0,0.5,1.92,0.0,0.3,14.72,0.0 +2,2019-01-31 14:53:38,2019-01-31 14:59:48,1,1.14,1,N,236,238,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-03 16:01:58,2019-01-03 16:14:07,1,1.2,1,N,162,163,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-06 22:04:37,2019-01-06 22:18:28,1,2.4,1,N,140,48,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-18 16:53:49,2019-01-18 16:59:40,1,0.6,1,N,142,143,3,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-27 23:11:44,2019-01-27 23:18:34,1,1.4,1,N,230,142,1,7.0,0.5,0.5,2.45,0.0,0.3,10.75,0.0 +2,2019-01-29 16:30:53,2019-01-29 17:26:34,1,11.74,1,N,100,241,2,42.0,1.0,0.5,0.0,2.64,0.3,46.44,0.0 +1,2019-01-17 12:57:39,2019-01-17 13:28:28,1,13.6,1,N,13,138,1,39.5,0.0,0.5,9.2,5.76,0.3,55.26, +2,2019-01-26 22:28:58,2019-01-26 22:36:33,1,0.35,1,N,230,48,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-16 17:13:52,2019-01-16 17:34:38,1,2.98,1,N,161,211,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-31 18:44:27,2019-02-01 18:37:29,1,15.63,2,N,107,132,1,52.0,4.5,0.5,9.46,5.76,0.3,72.52,0.0 +2,2019-01-22 18:35:01,2019-01-22 18:51:58,1,2.81,1,N,68,231,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-13 00:02:56,2019-01-13 00:12:58,1,1.33,1,N,234,246,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-16 10:14:16,2019-01-16 10:20:10,1,0.8,1,N,232,45,1,6.0,0.0,0.5,0.7,0.0,0.3,7.5, +2,2019-01-26 19:48:21,2019-01-26 19:53:56,1,0.64,1,N,233,137,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19,0.0 +1,2019-01-28 19:48:32,2019-01-28 19:54:50,1,0.8,1,N,100,162,1,5.5,1.0,0.5,2.15,0.0,0.3,9.45,0.0 +2,2019-01-11 18:41:13,2019-01-11 19:02:14,1,4.87,1,N,234,238,1,18.0,1.0,0.5,1.0,0.0,0.3,20.8, +2,2019-01-01 04:17:25,2019-01-01 04:21:14,1,0.94,1,N,141,237,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-28 02:12:14,2019-01-28 02:31:51,1,8.31,1,N,264,264,1,25.5,0.5,0.5,5.36,0.0,0.3,32.16,0.0 +2,2019-01-20 17:56:28,2019-01-20 18:08:16,1,1.43,1,N,142,48,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-24 22:14:39,2019-01-24 22:30:11,1,2.69,1,N,90,45,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-09 19:02:21,2019-01-09 19:19:17,1,4.35,1,N,186,24,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-07 02:18:03,2019-01-07 02:42:20,1,11.3,1,N,138,249,1,34.0,0.5,0.5,5.0,0.0,0.3,40.3, +2,2019-01-01 19:27:38,2019-01-01 19:40:14,1,2.13,1,N,107,68,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-21 17:10:28,2019-01-21 17:16:59,1,1.01,1,N,142,163,1,6.5,0.0,0.5,1.1,0.0,0.3,8.4,0.0 +1,2019-01-07 19:40:02,2019-01-07 19:50:40,1,1.8,1,N,161,113,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-03 18:38:23,2019-01-03 18:39:13,1,0.15,1,N,236,236,1,2.5,1.0,0.5,0.55,0.0,0.3,4.85, +2,2019-01-10 17:45:56,2019-01-10 17:53:18,1,1.35,1,N,239,151,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-10 20:42:41,2019-01-10 20:46:26,1,0.85,1,N,262,75,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-30 00:27:32,2019-01-30 00:50:49,1,5.8,1,N,264,264,1,20.5,0.5,0.5,4.35,0.0,0.3,26.15,0.0 +1,2019-01-16 23:10:20,2019-01-16 23:18:09,1,1.3,1,N,249,107,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +1,2019-01-30 08:13:21,2019-01-30 08:39:28,1,4.1,1,N,90,236,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76,0.0 +1,2019-01-16 07:47:20,2019-01-16 07:53:51,1,0.8,1,N,230,230,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-31 19:02:04,2019-01-31 19:07:37,1,0.87,1,N,13,231,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-23 17:49:38,2019-01-23 18:04:36,1,1.1,1,N,42,74,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-11 10:07:24,2019-01-11 10:24:13,1,1.2,1,N,264,264,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-15 21:13:22,2019-01-15 21:33:30,1,4.58,1,N,162,7,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56, +2,2019-01-12 18:49:20,2019-01-12 19:08:20,1,2.59,1,N,236,161,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-01 13:51:25,2019-01-01 14:12:56,1,4.22,1,N,229,151,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-26 12:39:48,2019-01-26 12:45:36,1,0.86,1,N,236,263,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-20 03:23:22,2019-01-20 03:29:02,1,0.5,1,N,79,79,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-15 14:52:40,2019-01-15 15:07:40,1,1.9,1,N,237,262,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8, +1,2019-01-14 15:22:12,2019-01-14 15:26:53,1,0.8,1,N,233,137,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-24 20:06:02,2019-01-24 20:31:20,1,3.4,1,N,230,231,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95,0.0 +1,2019-01-23 15:58:53,2019-01-23 16:21:22,1,8.1,1,N,87,236,2,25.0,0.0,0.5,0.0,0.0,0.3,25.8,0.0 +2,2019-01-11 06:42:14,2019-01-11 06:43:18,1,0.34,1,N,226,226,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-11 18:06:53,2019-01-11 18:14:14,1,1.01,1,N,233,237,1,6.5,1.0,0.5,1.24,0.0,0.3,9.54, +2,2019-01-24 09:28:16,2019-01-24 09:51:30,1,1.55,1,N,237,229,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-18 21:47:14,2019-01-18 21:50:08,1,0.63,1,N,239,151,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-15 08:19:35,2019-01-15 08:24:26,1,0.73,1,N,237,263,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-07 19:48:56,2019-01-07 19:59:57,1,2.1,1,N,141,48,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-23 12:42:27,2019-01-23 12:55:48,1,1.67,1,N,229,263,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-06 11:46:35,2019-01-06 12:01:42,1,1.5,1,N,50,161,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +1,2019-01-24 14:42:39,2019-01-24 14:53:22,1,1.0,1,N,161,229,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-04 17:34:22,2019-01-04 17:38:32,1,0.4,1,N,164,170,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-03 00:17:24,2019-01-03 00:29:07,1,3.1,1,N,230,146,1,12.0,0.5,0.5,3.3,0.0,0.3,16.6, +1,2019-01-04 07:29:59,2019-01-04 07:43:29,1,1.9,1,N,50,161,1,10.5,0.0,0.5,1.5,0.0,0.3,12.8, +1,2019-01-05 15:11:26,2019-01-05 15:18:45,1,1.2,1,N,166,238,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-26 05:42:51,2019-01-26 05:48:18,1,1.7,1,N,234,246,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-22 00:27:59,2019-01-22 00:33:42,1,0.66,1,N,230,163,1,5.5,0.5,0.5,0.08,0.0,0.3,6.88,0.0 +1,2019-01-25 12:50:03,2019-01-25 13:28:32,1,5.2,1,N,33,186,1,26.5,0.0,0.5,3.0,0.0,0.3,30.3,0.0 +2,2019-01-31 19:58:16,2019-01-31 20:06:14,1,2.34,1,N,138,223,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-31 02:01:29,2019-01-31 02:32:35,1,7.88,1,N,107,36,1,27.5,0.5,0.5,5.76,0.0,0.3,34.56,0.0 +2,2019-01-26 10:10:59,2019-01-26 10:15:43,1,0.88,1,N,79,234,1,5.5,0.0,0.5,0.76,0.0,0.3,7.06,0.0 +1,2019-01-10 20:45:56,2019-01-10 20:52:05,1,1.2,1,N,238,166,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-23 19:52:23,2019-01-23 19:55:29,1,0.6,1,N,142,239,1,4.5,1.0,0.5,1.55,0.0,0.3,7.85,0.0 +1,2019-01-30 04:57:21,2019-01-30 05:21:23,1,16.9,2,N,132,164,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56,0.0 +2,2019-01-29 12:49:44,2019-01-29 12:56:39,1,0.48,1,N,237,162,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-09 20:58:24,2019-01-09 21:27:53,1,3.6,1,N,234,236,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-10 20:06:46,2019-01-10 20:26:10,1,9.42,1,N,138,10,2,27.0,0.5,0.5,0.0,0.0,0.3,28.3, +1,2019-01-12 05:58:00,2019-01-12 06:33:08,1,9.0,1,N,90,71,1,31.0,0.0,0.5,6.35,0.0,0.3,38.15, +1,2019-01-17 21:03:07,2019-01-17 21:16:55,1,1.4,1,N,48,229,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3, +1,2019-01-27 22:51:00,2019-01-27 23:16:32,1,17.6,2,N,132,161,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +2,2019-01-27 15:18:08,2019-01-27 15:21:59,1,0.63,1,N,263,237,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-31 00:55:38,2019-01-31 00:55:41,1,0.0,5,N,186,186,1,67.0,0.0,0.0,13.46,0.0,0.3,80.76,0.0 +1,2019-01-09 18:46:21,2019-01-09 18:52:25,1,1.0,1,N,158,249,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 10:52:27,2019-01-10 10:57:31,1,0.58,1,N,261,231,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-19 16:42:17,2019-01-19 16:52:37,1,1.15,1,N,162,230,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-15 03:37:07,2019-01-15 03:43:12,1,0.9,1,N,48,230,1,6.0,0.5,0.5,1.8,0.0,0.3,9.1, +2,2019-01-27 13:38:52,2019-01-27 13:42:55,1,0.85,1,N,48,48,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-17 14:24:08,2019-01-17 14:39:13,1,1.98,1,N,230,141,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-11 10:15:06,2019-01-11 10:18:37,1,0.4,1,N,236,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-25 17:55:28,2019-01-25 18:01:27,1,0.84,1,N,140,236,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-31 20:33:18,2019-01-31 20:50:03,1,2.4,1,N,170,158,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-23 12:43:05,2019-01-23 12:53:29,1,0.76,1,N,164,100,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-04 17:05:32,2019-01-04 17:09:46,1,0.8,1,N,13,88,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-17 13:40:23,2019-01-17 13:52:01,1,1.52,1,N,138,129,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-29 20:06:04,2019-01-29 20:18:10,1,1.6,1,N,186,162,1,9.0,0.5,0.5,2.55,0.0,0.3,12.85,0.0 +2,2019-01-08 16:07:14,2019-01-08 17:01:52,1,22.81,1,N,231,72,1,65.5,1.0,0.5,0.0,5.76,0.3,73.06, +1,2019-01-16 11:45:34,2019-01-16 11:51:32,1,0.8,1,N,237,140,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93, +1,2019-01-26 01:26:20,2019-01-26 01:36:22,1,1.6,1,N,148,107,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-03 14:05:14,2019-01-03 14:18:22,1,2.57,1,N,164,125,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-04 16:28:49,2019-01-04 16:44:33,1,1.4,1,N,163,164,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-23 19:47:29,2019-01-23 19:51:14,1,0.56,1,N,142,48,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-24 00:34:36,2019-01-24 00:59:11,1,8.32,1,N,230,160,2,26.5,0.5,0.5,0.0,0.0,0.3,27.8,0.0 +1,2019-01-17 20:39:16,2019-01-17 20:59:20,1,3.2,1,N,238,74,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95, +1,2019-01-16 16:59:43,2019-01-16 17:06:02,1,0.8,1,N,164,107,1,6.0,1.0,0.5,2.3,0.0,0.3,10.1, +2,2019-01-15 20:32:11,2019-01-15 21:01:02,1,16.95,2,N,132,164,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56, +2,2019-01-24 11:00:13,2019-01-24 11:07:08,1,0.49,1,N,237,141,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-05 11:54:26,2019-01-05 12:21:37,1,5.5,1,N,142,148,1,22.0,0.0,0.5,4.55,0.0,0.3,27.35, +2,2019-01-27 02:54:39,2019-01-27 02:57:26,1,0.3,1,N,148,148,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-26 10:21:17,2019-01-26 10:31:57,1,1.26,1,N,75,262,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-03 17:57:47,2019-01-03 18:03:01,1,1.0,1,N,237,236,1,5.5,1.0,0.5,2.19,0.0,0.3,9.49, +1,2019-01-02 07:15:41,2019-01-02 07:37:41,1,10.1,1,N,138,237,1,29.0,0.0,0.5,7.1,5.76,0.3,42.66, +2,2019-01-20 13:05:02,2019-01-20 13:09:33,1,0.5,1,N,209,87,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-23 06:29:25,2019-01-23 06:37:54,1,1.93,1,N,249,230,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3,0.0 +1,2019-01-25 18:43:21,2019-01-25 18:48:49,1,0.9,1,N,264,264,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-15 10:14:37,2019-01-15 10:17:38,1,0.77,1,N,238,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-22 15:34:13,2019-01-22 15:58:56,1,3.21,1,N,239,234,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +1,2019-01-13 11:24:34,2019-01-13 11:34:55,1,2.4,1,N,261,68,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-20 00:19:43,2019-01-20 00:27:49,1,0.69,1,N,114,158,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-16 19:02:51,2019-01-16 19:09:44,1,0.9,1,N,186,161,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-25 12:33:27,2019-01-25 12:52:36,1,2.7,1,N,162,143,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-14 04:49:54,2019-01-14 05:00:09,1,2.03,1,N,90,148,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +2,2019-01-02 17:24:48,2019-01-02 17:48:46,1,3.66,1,N,239,170,1,17.5,1.0,0.5,5.79,0.0,0.3,25.09, +1,2019-01-04 17:49:37,2019-01-04 18:00:26,1,1.4,1,N,68,164,1,9.0,1.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-21 13:55:25,2019-01-21 14:03:05,1,1.0,1,N,230,161,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-16 18:34:29,2019-01-16 18:44:27,1,1.79,1,N,141,236,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-21 04:26:34,2019-01-21 04:27:37,1,0.27,1,N,50,48,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-09 17:32:14,2019-01-09 17:39:10,1,1.0,1,N,262,237,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-28 19:37:44,2019-01-28 19:43:05,1,0.93,1,N,161,162,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-11 17:47:50,2019-01-11 17:57:24,1,1.66,1,N,263,141,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88, +1,2019-01-11 13:17:52,2019-01-11 13:24:07,1,0.8,1,N,238,239,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-28 14:55:18,2019-01-28 15:09:51,1,1.61,1,N,48,142,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-29 19:30:54,2019-01-29 19:36:42,1,1.2,1,N,161,43,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-17 22:40:50,2019-01-17 22:52:56,1,3.2,1,N,246,237,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-31 16:27:54,2019-01-31 16:35:50,1,1.4,1,N,239,151,1,7.5,1.0,0.5,2.3,0.0,0.3,11.6,0.0 +1,2019-01-11 03:04:39,2019-01-11 03:10:13,1,0.6,1,N,163,163,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-08 14:35:54,2019-01-08 14:56:36,1,2.0,1,N,162,186,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +1,2019-01-28 14:05:50,2019-01-28 14:06:52,1,0.0,1,N,236,43,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +1,2019-01-11 12:48:06,2019-01-11 13:04:05,1,1.5,1,N,68,137,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-05 16:54:39,2019-01-05 17:25:08,1,9.78,1,N,230,138,1,31.5,0.0,0.5,4.0,5.76,0.3,42.06, +2,2019-01-29 23:34:48,2019-01-29 23:38:50,1,1.76,1,N,233,141,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-28 18:08:00,2019-01-28 18:25:19,1,2.5,1,N,161,263,1,13.0,1.0,0.5,1.0,0.0,0.3,15.8,0.0 +1,2019-01-10 09:18:05,2019-01-10 09:41:02,1,2.2,1,N,237,164,1,15.0,0.0,0.5,1.2,0.0,0.3,17.0, +2,2019-01-23 11:42:04,2019-01-23 12:33:45,1,21.99,1,N,162,23,1,65.0,0.0,0.5,0.0,17.28,0.3,83.08,0.0 +1,2019-01-22 08:32:43,2019-01-22 08:33:25,1,0.0,1,N,90,90,3,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +1,2019-01-02 11:14:19,2019-01-02 11:16:54,1,0.4,1,N,142,48,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-26 11:56:16,2019-01-26 12:04:52,1,1.57,1,N,239,166,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62,0.0 +1,2019-01-31 08:17:10,2019-01-31 08:56:18,1,11.2,1,N,170,26,1,38.5,0.0,0.5,5.0,5.76,0.3,50.06,0.0 +1,2019-01-16 19:33:12,2019-01-16 19:51:17,1,3.3,1,N,162,238,1,15.0,1.0,0.5,3.35,0.0,0.3,20.15, +1,2019-01-01 23:19:53,2019-01-01 23:47:11,1,19.1,1,N,132,119,1,51.0,0.5,0.5,6.94,5.76,0.3,65.0, +4,2019-01-31 09:31:52,2019-01-31 09:45:53,1,2.39,1,N,262,233,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-31 07:03:58,2019-01-31 07:09:25,1,1.2,1,N,90,230,1,6.0,0.0,0.5,0.68,0.0,0.3,7.48,0.0 +2,2019-01-09 13:49:34,2019-01-09 13:57:13,1,1.64,1,N,140,229,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-17 09:39:14,2019-01-17 10:03:21,1,1.96,1,N,233,237,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-13 23:08:11,2019-01-13 23:13:31,1,1.3,1,N,107,249,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +2,2019-01-20 11:55:40,2019-01-20 11:58:23,1,0.55,1,N,237,237,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +1,2019-01-31 15:56:30,2019-01-31 16:09:02,1,1.7,1,N,162,224,1,10.0,1.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-15 19:25:04,2019-01-15 20:11:38,1,18.91,2,N,132,50,1,52.0,4.5,0.5,10.0,5.76,0.3,73.06, +1,2019-01-30 21:21:18,2019-01-30 21:26:46,1,1.8,1,N,50,239,1,7.0,0.5,0.5,1.3,0.0,0.3,9.6,0.0 +1,2019-01-10 05:08:44,2019-01-10 05:14:44,1,2.0,1,N,246,161,1,8.0,0.5,0.5,1.5,0.0,0.3,10.8, +1,2019-01-02 16:14:35,2019-01-02 16:24:39,1,1.1,1,N,263,237,1,8.0,1.0,0.5,1.0,0.0,0.3,10.8, +1,2019-01-28 21:46:14,2019-01-28 21:53:33,1,1.0,1,N,161,229,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-31 12:32:18,2019-01-31 12:34:37,1,0.4,1,N,239,239,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-01 09:38:53,2019-01-01 09:53:02,1,10.28,1,N,138,229,1,28.0,0.0,0.5,3.46,5.76,0.3,38.02, +1,2019-01-20 20:44:51,2019-01-20 20:51:26,1,1.5,1,N,236,238,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-17 19:54:26,2019-01-17 20:03:34,1,1.2,1,N,43,143,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3, +1,2019-01-13 20:59:50,2019-01-13 21:08:29,1,1.2,1,N,162,234,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +1,2019-01-29 16:46:22,2019-01-29 17:00:10,1,0.8,1,N,170,186,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-21 22:00:09,2019-01-21 22:04:16,1,0.71,1,N,107,164,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-21 17:24:22,2019-01-21 17:30:03,1,1.05,1,N,237,162,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-12 15:06:50,2019-01-12 15:12:33,1,1.19,1,N,142,238,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-01 04:43:04,2019-01-01 04:48:55,1,1.4,1,N,164,68,1,6.5,0.5,0.5,1.4,0.0,0.3,9.2, +1,2019-01-23 18:35:32,2019-01-23 18:45:24,1,1.0,1,N,162,234,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-10 14:07:56,2019-01-10 14:20:58,1,1.26,1,N,163,170,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-13 23:23:57,2019-01-13 23:35:33,1,3.43,1,N,48,263,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3, +1,2019-01-19 02:20:51,2019-01-19 02:31:10,1,4.8,1,N,148,141,1,15.5,0.5,0.5,1.0,0.0,0.3,17.8, +2,2019-01-30 15:28:34,2019-01-30 15:35:57,1,0.76,1,N,238,239,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-18 14:12:32,2019-01-18 14:22:58,1,1.4,1,N,239,236,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-26 16:13:48,2019-01-26 16:32:53,1,3.2,1,N,234,239,1,14.5,0.0,0.5,3.05,0.0,0.3,18.35,0.0 +1,2019-01-23 13:41:42,2019-01-23 14:01:11,1,1.7,1,N,236,161,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +1,2019-01-11 16:16:37,2019-01-11 16:24:48,1,1.5,1,N,229,233,1,7.5,1.0,0.5,1.5,0.0,0.3,10.8, +2,2019-01-27 16:26:09,2019-01-27 16:48:54,1,5.12,1,N,24,186,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56,0.0 +2,2019-01-24 06:55:06,2019-01-24 07:15:16,1,2.6,1,N,186,79,1,14.0,0.0,0.5,1.0,0.0,0.3,15.8,0.0 +1,2019-01-11 19:20:35,2019-01-11 20:02:04,1,17.6,2,N,132,237,1,52.0,4.5,0.5,11.45,0.0,0.3,68.75, +2,2019-01-10 12:24:28,2019-01-10 12:49:56,1,5.2,1,N,61,148,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +2,2019-01-08 19:05:42,2019-01-08 19:19:06,1,1.63,1,N,161,236,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-17 11:34:49,2019-01-17 11:40:56,1,1.19,1,N,48,68,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-19 08:38:44,2019-01-19 08:42:55,1,0.9,1,N,100,164,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-21 18:48:28,2019-01-21 19:08:14,1,2.89,1,N,232,186,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-18 01:19:06,2019-01-18 01:40:05,1,5.49,1,N,144,141,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-26 21:33:11,2019-01-26 21:37:28,1,0.48,1,N,229,162,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-02 08:01:28,2019-01-02 08:33:23,1,10.77,1,N,230,138,1,34.0,0.0,0.5,8.11,5.76,0.3,48.67, +1,2019-01-18 19:30:29,2019-01-18 19:37:01,1,1.0,1,N,255,80,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-04 13:26:06,2019-01-04 13:34:49,1,1.5,1,N,141,262,1,7.5,0.0,0.5,1.25,0.0,0.3,9.55, +2,2019-01-02 23:07:36,2019-01-02 23:09:16,1,0.45,1,N,161,162,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-05 15:27:40,2019-01-05 15:31:52,1,0.8,1,N,237,236,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-29 19:01:13,2019-01-29 19:09:37,1,0.83,1,N,113,107,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-24 08:36:49,2019-01-24 08:52:05,1,0.5,1,N,142,163,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-31 19:19:29,2019-01-31 19:28:03,1,1.01,1,N,234,186,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-03 22:03:17,2019-01-03 22:15:42,1,2.99,1,N,234,141,1,11.5,0.5,0.5,1.2,0.0,0.3,14.0, +2,2019-01-03 05:58:02,2019-01-03 06:01:46,1,0.86,1,N,249,249,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-02 22:59:25,2019-01-02 23:08:42,1,2.36,1,N,230,43,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +1,2019-01-26 08:14:57,2019-01-26 08:34:01,1,13.4,1,N,83,132,2,36.5,0.0,0.5,0.0,0.0,0.3,37.3,0.0 +2,2019-01-18 17:59:36,2019-01-18 18:16:12,1,3.9,1,N,162,74,1,14.5,1.0,0.5,2.0,0.0,0.3,18.3, +2,2019-01-27 07:44:02,2019-01-27 07:52:36,1,2.77,1,N,75,48,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0,0.0 +2,2019-01-21 14:25:19,2019-01-21 14:32:26,1,1.34,1,N,163,100,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-06 16:41:36,2019-01-06 17:07:54,1,4.3,1,N,48,41,1,17.5,0.0,0.5,3.65,0.0,0.3,21.95, +2,2019-01-16 17:44:33,2019-01-16 17:54:46,1,2.09,1,N,262,237,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-28 14:19:39,2019-01-28 14:37:21,1,2.11,1,N,163,236,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +1,2019-01-06 03:18:44,2019-01-06 03:23:15,1,0.7,1,N,148,79,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +1,2019-01-28 19:10:49,2019-01-28 19:15:26,1,1.0,1,N,74,75,1,5.0,1.0,0.5,3.0,0.0,0.3,9.8,0.0 +1,2019-01-17 10:04:52,2019-01-17 10:34:32,1,2.3,1,N,141,48,1,18.5,0.0,0.5,4.0,0.0,0.3,23.3, +2,2019-01-17 18:43:17,2019-01-17 19:11:52,1,3.12,1,N,90,141,1,18.0,1.0,0.5,3.96,0.0,0.3,23.76, +2,2019-01-31 18:55:05,2019-01-31 19:07:55,1,1.77,1,N,79,148,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-15 14:30:41,2019-01-15 14:41:00,1,1.1,1,N,141,163,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-09 18:52:16,2019-01-09 19:01:44,1,1.76,1,N,231,113,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-06 15:21:09,2019-01-06 15:26:50,1,1.1,1,N,264,264,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-09 17:36:52,2019-01-09 17:41:29,1,0.67,1,N,141,141,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-04 07:31:15,2019-01-04 07:35:46,1,0.4,1,N,233,162,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 16:35:15,2019-01-31 16:44:08,1,1.48,1,N,151,142,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-18 20:14:30,2019-01-18 20:27:49,1,1.2,1,N,229,163,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +2,2019-01-19 23:03:47,2019-01-19 23:11:57,1,1.04,1,N,142,230,1,7.0,0.5,0.5,2.49,0.0,0.3,10.79, +2,2019-01-18 12:30:12,2019-01-18 12:34:40,1,0.81,1,N,143,239,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-02 12:33:51,2019-01-02 12:43:38,1,1.94,1,N,151,143,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +2,2019-01-29 16:02:24,2019-01-29 16:08:28,1,0.82,1,N,164,230,1,6.0,1.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-09 21:11:19,2019-01-09 21:21:20,1,2.01,1,N,230,137,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +1,2019-01-23 17:43:39,2019-01-23 17:52:35,1,2.4,1,N,137,140,1,9.5,1.0,0.5,1.0,0.0,0.3,12.3,0.0 +2,2019-01-03 19:48:07,2019-01-03 19:57:27,1,2.02,1,N,234,141,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-08 23:54:05,2019-01-09 00:20:06,1,19.28,2,N,132,262,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-14 11:14:31,2019-01-14 11:23:09,1,0.8,1,N,249,186,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-25 16:59:36,2019-01-25 17:11:05,1,2.41,1,N,87,79,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3,0.0 +2,2019-01-28 08:12:33,2019-01-28 09:16:42,1,18.87,1,N,132,65,1,57.5,0.0,0.5,6.0,0.0,0.3,64.3,0.0 +1,2019-01-06 16:39:40,2019-01-06 16:41:52,1,0.8,1,N,264,264,1,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-23 09:49:40,2019-01-23 09:53:56,1,0.5,1,N,249,125,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-10 07:46:28,2019-01-10 08:01:03,1,5.02,1,N,233,87,1,17.0,0.0,0.5,3.0,0.0,0.3,20.8, +1,2019-01-11 22:27:30,2019-01-11 22:54:45,1,3.9,1,N,142,113,1,19.0,0.5,0.5,4.05,0.0,0.3,24.35, +2,2019-01-28 04:59:56,2019-01-28 05:11:04,1,2.76,1,N,177,61,1,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-22 08:08:40,2019-01-22 08:15:08,1,0.8,1,N,229,141,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-16 23:26:33,2019-01-16 23:32:08,1,1.03,1,N,114,4,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-31 07:11:45,2019-01-31 07:32:57,1,4.89,1,N,41,48,1,17.5,0.0,0.5,4.58,0.0,0.3,22.88,0.0 +2,2019-01-09 11:25:11,2019-01-09 11:27:53,1,0.69,1,N,75,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-25 05:54:32,2019-01-25 06:12:01,1,7.96,1,N,236,138,1,23.5,0.5,0.5,6.11,5.76,0.3,36.67,0.0 +1,2019-01-05 08:17:38,2019-01-05 08:24:11,1,1.1,1,N,48,170,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-02 11:27:44,2019-01-02 11:41:44,1,2.5,1,N,186,144,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-08 21:14:45,2019-01-08 21:24:38,1,2.7,1,N,246,238,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +2,2019-01-03 08:21:21,2019-01-03 08:22:47,1,0.04,3,N,233,233,2,20.5,0.0,0.0,0.0,0.0,0.3,20.8, +2,2019-01-18 10:40:57,2019-01-18 10:47:26,1,1.13,1,N,144,125,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-06 15:22:20,2019-01-06 15:31:04,1,1.5,1,N,238,142,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-24 10:35:57,2019-01-24 10:59:09,1,2.55,1,N,186,79,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-30 18:08:06,2019-01-30 18:14:45,1,1.12,1,N,151,238,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-30 16:56:09,2019-01-30 17:02:16,1,0.9,1,Y,239,238,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-07 02:13:33,2019-01-07 02:18:57,1,1.22,1,N,234,233,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-04 18:33:12,2019-01-04 18:49:34,1,3.77,1,N,143,74,1,14.0,1.0,0.5,2.2,0.0,0.3,18.0, +1,2019-01-03 14:19:24,2019-01-03 14:40:58,1,11.2,1,N,138,137,1,32.0,0.0,0.5,7.7,5.76,0.3,46.26, +2,2019-01-04 04:41:35,2019-01-04 04:55:16,1,2.36,1,N,74,116,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-20 19:50:40,2019-01-20 19:56:26,1,0.8,1,N,162,230,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-07 16:47:44,2019-01-07 16:49:59,1,0.44,1,N,239,238,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-22 15:15:51,2019-01-22 15:29:03,1,1.6,1,N,238,263,3,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-14 18:02:57,2019-01-14 18:08:16,1,0.57,1,N,137,107,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-11 22:48:23,2019-01-11 23:04:57,1,1.6,1,N,125,45,1,11.5,0.5,0.5,3.2,0.0,0.3,16.0, +1,2019-01-07 20:47:38,2019-01-07 20:51:48,1,0.9,1,N,186,68,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 02:21:03,2019-01-01 02:26:27,1,1.23,1,N,90,79,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 22:46:55,2019-01-24 23:18:22,1,13.37,1,N,264,264,1,40.0,0.5,0.5,8.26,0.0,0.3,49.56,0.0 +2,2019-01-03 14:20:17,2019-01-03 14:22:57,1,0.74,1,N,75,262,1,4.5,0.0,0.5,1.2,0.0,0.3,6.5, +1,2019-01-15 22:54:58,2019-01-15 23:14:47,1,3.7,1,N,113,49,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75, +2,2019-01-15 18:50:38,2019-01-15 19:04:17,1,1.92,1,N,237,229,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-25 19:15:55,2019-01-25 19:22:02,1,0.9,1,N,230,170,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-05 14:51:51,2019-01-05 14:59:49,1,1.21,1,N,107,114,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-17 16:46:58,2019-01-17 16:52:14,1,1.2,1,N,41,42,1,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-24 07:42:52,2019-01-24 08:06:08,1,3.12,1,N,231,170,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-31 12:34:44,2019-01-31 12:47:21,1,1.53,1,N,162,141,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-07 09:49:18,2019-01-07 10:07:01,1,1.67,1,N,186,137,1,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-18 15:48:27,2019-01-18 15:57:28,1,1.0,1,N,161,170,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-29 19:43:22,2019-01-29 19:50:45,1,0.96,1,N,68,234,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-17 21:54:23,2019-01-17 22:03:08,1,2.2,1,N,255,49,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +2,2019-01-03 15:37:18,2019-01-03 15:54:52,1,2.44,1,N,236,143,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-11 19:43:13,2019-01-11 20:04:19,1,2.4,1,N,246,233,1,14.0,1.0,0.5,3.0,0.0,0.3,18.8, +1,2019-01-01 23:51:02,2019-01-02 00:11:20,1,5.7,1,N,162,87,1,20.0,0.5,0.5,2.13,0.0,0.3,23.43, +1,2019-01-25 23:43:29,2019-01-25 23:45:21,1,0.4,1,N,114,113,1,3.5,0.5,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-18 22:54:08,2019-01-18 23:08:05,1,2.53,1,N,163,262,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +1,2019-01-23 12:31:17,2019-01-23 12:49:28,1,1.3,1,N,164,68,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-27 10:43:30,2019-01-27 10:49:18,1,1.5,1,N,238,143,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-14 01:53:05,2019-01-14 02:01:04,1,2.3,1,N,230,239,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-29 09:29:34,2019-01-29 09:59:42,1,7.28,1,N,263,87,2,25.5,0.0,0.5,0.0,0.0,0.3,26.3,0.0 +2,2019-01-22 23:49:09,2019-01-23 00:02:41,1,3.62,1,N,142,107,1,13.5,0.5,0.5,3.7,0.0,0.3,18.5,0.0 +2,2019-01-27 11:08:29,2019-01-27 11:19:01,1,3.32,1,N,113,66,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0,0.0 +1,2019-01-10 23:15:04,2019-01-10 23:40:25,1,16.6,2,N,132,170,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-11 18:30:54,2019-01-11 18:44:56,1,2.24,1,N,48,113,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-17 09:23:24,2019-01-17 09:35:09,1,1.5,1,N,231,231,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +1,2019-01-30 08:09:04,2019-01-30 08:20:59,1,1.5,1,N,113,170,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85,0.0 +2,2019-01-25 13:23:31,2019-01-25 13:50:39,1,4.26,1,N,234,75,1,20.0,0.0,0.5,2.5,0.0,0.3,23.3,0.0 +1,2019-01-27 12:40:10,2019-01-27 12:47:07,1,1.4,1,N,170,237,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-02 19:51:47,2019-01-02 19:58:10,1,0.92,1,N,164,162,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-23 12:44:51,2019-01-23 13:03:38,1,2.7,1,N,263,48,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-24 08:08:44,2019-01-24 08:31:59,1,4.22,1,N,234,239,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16,0.0 +2,2019-01-08 19:40:45,2019-01-08 19:49:12,1,1.53,1,N,161,143,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-17 20:50:16,2019-01-17 20:57:47,1,0.7,1,N,113,249,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-29 08:59:28,2019-01-29 09:19:45,1,4.9,1,N,170,45,1,19.5,0.0,0.5,5.08,0.0,0.3,25.38,0.0 +2,2019-01-02 19:18:27,2019-01-02 19:24:01,1,1.02,1,N,162,142,1,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-21 12:05:38,2019-01-21 12:05:40,1,0.0,5,N,264,246,1,6.3,0.0,0.0,1.32,0.0,0.3,7.92,0.0 +2,2019-01-28 23:12:46,2019-01-28 23:27:52,1,2.73,1,N,257,97,1,13.0,0.5,0.5,2.14,0.0,0.3,18.39,0.0 +1,2019-01-15 15:29:41,2019-01-15 15:33:14,1,0.4,1,N,236,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-27 00:38:46,2019-01-27 00:50:10,1,1.86,1,N,68,144,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-27 01:16:26,2019-01-27 01:21:22,1,1.1,1,N,229,140,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-01 04:06:05,2019-01-01 04:44:40,1,16.27,1,N,189,115,1,47.0,0.5,0.5,0.0,11.52,0.3,59.82, +1,2019-01-11 12:51:31,2019-01-11 13:07:01,1,5.3,1,N,13,107,1,18.5,0.0,0.5,3.85,0.0,0.3,23.15, +2,2019-01-25 23:03:28,2019-01-25 23:24:42,1,3.34,1,N,229,112,1,16.0,0.5,0.5,4.61,5.76,0.3,27.67,0.0 +1,2019-01-05 19:56:37,2019-01-05 20:12:35,1,4.5,1,N,264,264,1,16.0,0.0,0.5,2.0,0.0,0.3,18.8, +2,2019-01-14 15:11:04,2019-01-14 15:21:39,1,0.74,1,N,141,229,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-10 16:02:45,2019-01-10 16:22:07,1,2.0,1,N,238,237,1,13.5,1.0,0.5,3.05,0.0,0.3,18.35, +2,2019-01-30 21:36:35,2019-01-30 21:47:29,1,1.26,1,N,186,170,1,8.5,0.5,0.5,2.94,0.0,0.3,12.74,0.0 +2,2019-01-26 18:38:55,2019-01-26 18:48:47,1,1.27,1,N,234,114,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-22 11:54:52,2019-01-22 12:07:25,1,0.9,1,N,162,234,1,9.0,0.0,0.5,1.2,0.0,0.3,11.0,0.0 +1,2019-01-17 19:16:05,2019-01-17 19:40:26,1,3.4,1,N,143,234,1,16.5,1.0,0.5,2.0,0.0,0.3,20.3, +1,2019-01-07 20:16:12,2019-01-07 20:28:29,1,2.5,1,N,164,231,1,10.5,0.5,0.5,2.0,0.0,0.3,13.8, +2,2019-01-04 21:42:25,2019-01-04 21:49:32,1,1.13,1,N,170,162,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-02 08:57:28,2019-01-02 09:09:36,1,1.12,1,N,162,230,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-01 01:11:55,2019-01-01 01:30:15,1,2.98,1,N,43,113,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-31 19:28:34,2019-01-31 19:34:12,1,0.75,1,N,68,48,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-26 00:27:25,2019-01-26 00:38:01,1,1.8,1,N,164,79,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-12 09:48:51,2019-01-12 09:58:36,1,2.1,1,N,113,261,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-06 00:15:48,2019-01-06 00:46:58,1,5.88,1,N,238,79,2,24.0,0.5,0.5,0.0,0.0,0.3,25.3, +2,2019-01-02 21:45:42,2019-01-02 21:55:13,1,2.05,1,N,170,4,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-30 22:51:59,2019-01-30 23:06:19,1,2.7,1,N,234,87,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-09 16:40:31,2019-01-09 16:46:49,1,0.7,1,N,230,229,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-29 20:28:49,2019-01-29 20:42:31,1,2.1,1,N,263,162,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-28 23:01:36,2019-01-28 23:17:58,1,3.5,1,N,142,107,1,14.0,0.5,0.5,3.05,0.0,0.3,18.35,0.0 +2,2019-01-29 17:09:30,2019-01-29 17:22:47,1,1.16,1,N,140,229,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-12 07:31:34,2019-01-12 07:36:25,1,0.58,1,N,186,164,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-04 15:45:45,2019-01-04 16:00:00,1,2.68,1,N,158,230,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-11 15:22:45,2019-01-11 15:31:23,1,0.9,1,N,238,236,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-31 17:16:24,2019-01-31 17:31:37,1,1.48,1,N,234,161,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-27 22:28:31,2019-01-27 22:51:47,1,12.3,1,N,138,87,1,34.5,0.5,0.5,10.7,0.0,0.3,46.5,0.0 +1,2019-01-20 14:58:37,2019-01-20 15:17:34,1,8.6,1,N,138,170,1,26.0,0.0,0.5,6.5,5.76,0.3,39.06, +2,2019-01-24 18:12:09,2019-01-24 18:23:45,1,1.33,1,N,161,236,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-03 07:15:34,2019-01-03 07:28:17,1,5.2,1,N,87,161,1,16.5,0.0,0.5,1.7,0.0,0.3,19.0, +1,2019-01-06 12:16:01,2019-01-06 12:26:23,1,1.5,1,N,211,68,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-28 15:03:07,2019-01-28 15:18:44,1,1.8,1,N,141,143,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95,0.0 +2,2019-01-14 17:43:42,2019-01-14 17:54:05,1,2.02,1,N,144,158,1,9.5,1.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-20 20:19:47,2019-01-20 20:26:15,1,1.3,1,N,186,50,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-15 11:37:20,2019-01-15 12:05:58,1,8.5,1,N,138,100,1,28.5,0.0,0.5,7.0,5.76,0.3,42.06, +2,2019-01-03 20:27:02,2019-01-03 20:48:38,1,3.31,1,N,161,144,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8, +1,2019-01-12 23:33:52,2019-01-12 23:45:09,1,1.4,1,N,162,48,1,9.0,0.5,0.5,1.54,0.0,0.3,11.84, +1,2019-01-20 16:39:27,2019-01-20 16:47:18,1,1.6,1,N,239,262,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-23 08:14:26,2019-01-23 08:27:36,1,1.2,1,N,127,243,1,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-08 13:17:34,2019-01-08 13:30:08,1,1.19,1,N,170,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-30 22:20:51,2019-01-30 22:30:38,1,2.52,1,N,161,75,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-29 18:50:16,2019-01-29 19:04:52,1,1.44,1,N,162,186,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-15 22:52:39,2019-01-15 23:01:20,1,2.53,1,N,229,148,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-31 20:44:55,2019-01-31 20:51:30,1,0.94,1,N,151,238,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-24 17:33:00,2019-01-24 17:43:10,1,1.7,1,N,146,7,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-24 22:43:02,2019-01-24 22:46:44,1,0.5,1,N,249,79,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-22 19:10:50,2019-01-22 19:29:19,1,2.9,1,N,237,246,1,14.0,1.0,0.5,1.5,0.0,0.3,17.3,0.0 +1,2019-01-04 09:43:53,2019-01-04 09:47:22,1,0.4,1,N,239,239,1,4.5,0.0,0.5,1.55,0.0,0.3,6.85, +1,2019-01-01 12:05:32,2019-01-01 12:09:30,1,1.1,1,N,236,237,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-21 19:32:13,2019-01-21 19:46:22,1,4.92,1,N,48,232,1,16.5,0.0,0.5,4.32,0.0,0.3,21.62,0.0 +2,2019-01-10 12:29:51,2019-01-10 12:47:05,1,2.06,1,N,236,162,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +4,2019-01-15 18:10:52,2019-01-15 18:15:26,1,0.7,1,N,13,88,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-26 12:28:29,2019-01-26 12:44:38,1,2.2,1,N,90,143,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-26 14:45:27,2019-01-26 15:00:27,1,2.36,1,N,239,236,1,12.0,0.0,0.5,1.5,0.0,0.3,14.3,0.0 +1,2019-01-13 12:43:12,2019-01-13 12:48:00,1,1.0,1,N,262,140,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-02 10:42:47,2019-01-02 10:47:16,1,1.04,1,N,238,236,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-15 12:14:40,2019-01-15 12:28:26,1,1.87,1,N,236,163,1,10.5,0.0,0.5,3.39,0.0,0.3,14.69, +2,2019-01-15 08:08:43,2019-01-15 08:19:16,1,1.16,1,N,249,246,1,8.5,0.0,0.5,1.0,0.0,0.3,12.25, +2,2019-01-28 15:56:59,2019-01-28 16:17:46,1,3.59,1,N,88,90,1,15.5,0.0,0.5,1.5,0.0,0.3,17.8,0.0 +1,2019-01-06 16:25:16,2019-01-06 16:38:56,1,3.8,1,N,116,239,1,14.5,0.0,0.5,3.05,0.0,0.3,18.35, +1,2019-01-11 10:41:22,2019-01-11 10:49:05,1,1.2,1,N,100,163,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-05 14:45:12,2019-01-05 14:55:05,1,1.3,1,N,236,262,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-15 09:12:41,2019-01-15 09:14:41,1,0.3,1,N,230,230,1,3.5,0.0,0.5,0.43,0.0,0.3,4.73, +1,2019-01-26 07:31:16,2019-01-26 07:43:08,1,4.2,1,N,224,226,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-28 18:59:13,2019-01-28 19:07:49,1,1.9,1,N,229,263,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-08 06:29:03,2019-01-08 06:34:22,1,2.0,1,N,107,230,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-17 16:19:46,2019-01-17 16:26:37,1,1.3,1,N,141,236,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-11 13:27:41,2019-01-11 13:39:14,1,1.46,1,N,237,170,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-16 22:19:48,2019-01-16 22:34:06,1,2.0,1,N,249,261,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +2,2019-01-01 01:33:01,2019-01-01 01:47:38,1,3.89,1,N,48,166,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +2,2019-01-09 06:02:35,2019-01-09 06:10:29,1,2.86,1,N,137,236,1,9.5,0.0,0.5,1.11,0.0,0.3,11.41, +1,2019-01-31 10:58:15,2019-01-31 11:04:55,1,0.9,1,N,142,48,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-31 12:08:52,2019-01-31 12:16:31,1,0.61,1,N,237,237,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-13 02:09:30,2019-01-13 02:20:53,1,3.0,1,N,141,186,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-12 16:11:04,2019-01-12 16:15:46,1,0.79,1,N,237,263,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-22 11:01:10,2019-01-22 11:05:25,1,0.79,1,N,238,239,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-31 16:22:46,2019-01-31 16:32:40,1,1.4,1,N,166,238,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-23 10:41:50,2019-01-23 10:49:21,1,1.62,1,N,151,236,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79,0.0 +1,2019-01-11 06:16:03,2019-01-11 06:19:53,1,0.9,1,N,162,107,1,5.5,0.0,0.5,0.7,0.0,0.3,7.0, +1,2019-01-15 20:17:23,2019-01-15 20:22:45,1,0.8,1,N,125,113,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +2,2019-01-20 18:42:18,2019-01-20 18:48:35,1,0.66,1,N,162,229,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-04 07:36:49,2019-01-04 07:50:39,1,5.68,1,N,107,231,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +1,2019-01-12 00:55:57,2019-01-12 01:05:51,1,2.3,1,N,234,142,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-27 20:19:43,2019-01-27 20:28:33,1,2.49,1,N,231,68,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3,0.0 +1,2019-01-08 12:21:01,2019-01-08 12:46:41,1,6.0,1,N,264,264,2,23.0,0.0,0.5,0.0,0.0,0.3,23.8, +2,2019-01-17 21:29:18,2019-01-17 21:45:16,1,3.71,1,N,186,238,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-19 21:28:09,2019-01-19 21:36:55,1,0.82,1,N,162,164,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-08 09:46:33,2019-01-08 09:53:52,1,1.1,1,N,100,234,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-17 17:41:17,2019-01-17 17:57:03,1,1.0,1,N,162,230,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3, +1,2019-01-01 01:48:56,2019-01-01 02:04:31,1,4.6,1,N,263,166,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +2,2019-01-31 13:22:54,2019-01-31 13:34:48,1,1.18,1,N,262,140,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7,0.0 +2,2019-01-27 14:40:06,2019-01-27 14:43:54,1,0.51,1,N,75,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-23 13:56:58,2019-01-23 14:12:41,1,1.2,1,N,161,100,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-08 21:58:34,2019-01-08 22:10:13,1,1.8,1,N,230,113,1,9.5,0.5,0.5,1.2,0.0,0.3,12.0, +2,2019-01-22 17:58:32,2019-01-22 18:09:21,1,1.13,1,N,164,233,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-16 07:53:30,2019-01-16 08:03:55,1,1.3,1,N,239,43,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-11 22:39:25,2019-01-11 23:09:02,1,3.7,1,N,231,230,2,20.0,0.5,0.5,0.0,0.0,0.3,21.3, +2,2019-01-21 00:34:54,2019-01-21 01:07:39,1,19.6,5,N,132,151,2,69.0,0.0,0.5,0.0,0.0,0.3,69.8,0.0 +2,2019-01-27 18:37:42,2019-01-27 18:40:04,1,0.84,1,N,237,237,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-29 11:47:24,2019-01-29 12:03:53,1,5.85,1,N,13,170,1,19.5,0.0,0.5,3.04,0.0,0.3,23.34,0.0 +1,2019-01-26 11:03:25,2019-01-26 11:10:25,1,0.7,1,N,113,107,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-06 21:57:49,2019-01-06 22:21:46,1,12.3,5,N,76,11,1,33.06,0.0,0.5,0.0,0.0,0.3,33.86, +2,2019-01-26 23:32:24,2019-01-26 23:47:44,1,2.13,1,N,142,236,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-30 09:37:30,2019-01-30 10:15:14,1,14.36,1,N,138,87,1,41.0,0.0,0.5,9.51,5.76,0.3,57.07,0.0 +1,2019-01-19 01:59:30,2019-01-19 02:19:06,1,6.8,1,N,114,74,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3, +2,2019-01-31 13:22:54,2019-01-31 13:31:08,1,1.05,1,N,237,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-16 13:28:58,2019-01-16 13:36:42,1,1.4,1,N,162,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-25 09:25:28,2019-01-25 09:35:30,1,1.33,1,N,163,43,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-28 11:24:25,2019-01-28 11:43:15,1,2.89,1,N,142,234,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-25 16:55:17,2019-01-25 17:06:41,1,1.36,1,N,234,170,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-07 14:28:04,2019-01-07 14:41:18,1,2.52,1,N,262,239,1,11.5,0.0,0.5,1.5,0.0,0.3,13.8, +1,2019-01-26 07:59:08,2019-01-26 08:03:31,1,1.0,1,N,100,246,1,5.5,0.0,0.5,0.25,0.0,0.3,6.55,0.0 +1,2019-01-08 08:16:47,2019-01-08 08:25:29,1,1.3,1,N,263,75,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-09 10:12:25,2019-01-09 10:25:19,1,1.3,1,N,229,137,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-12 14:06:41,2019-01-12 14:23:30,1,6.8,1,N,230,244,1,22.0,0.0,0.5,4.55,0.0,0.3,27.35, +2,2019-01-30 12:48:19,2019-01-30 12:54:38,1,0.88,1,N,262,141,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84,0.0 +2,2019-01-16 15:15:04,2019-01-16 15:24:31,1,0.0,1,N,142,48,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-12 06:18:29,2019-01-12 06:22:38,1,0.6,1,N,161,162,1,5.0,0.0,0.5,2.0,0.0,0.3,7.8, +1,2019-01-02 15:06:18,2019-01-02 15:12:19,1,0.4,1,N,141,140,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +1,2019-01-06 15:10:37,2019-01-06 15:17:24,1,0.9,1,N,239,238,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-29 11:13:32,2019-01-29 11:21:37,1,1.74,1,N,239,166,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-14 10:24:24,2019-01-14 10:32:21,1,0.95,1,N,186,249,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-30 13:05:19,2019-01-30 13:28:15,1,2.19,1,N,162,234,1,15.0,0.0,0.5,4.74,0.0,0.3,20.54,0.0 +1,2019-01-04 08:50:11,2019-01-04 09:09:21,1,3.2,1,N,4,68,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-30 14:09:42,2019-01-30 14:25:15,1,3.1,1,N,13,186,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15,0.0 +1,2019-01-09 10:27:38,2019-01-09 10:36:47,1,0.8,1,N,170,164,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 11:30:35,2019-01-26 11:39:45,1,1.09,1,N,50,100,1,7.5,0.0,0.5,2.0,0.0,0.3,10.3,0.0 +1,2019-01-10 16:20:11,2019-01-10 16:32:33,1,1.6,1,N,246,170,1,9.5,1.0,0.5,2.8,0.0,0.3,14.1, +2,2019-01-06 00:34:37,2019-01-06 00:42:34,1,1.89,1,N,164,50,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-06 02:36:46,2019-01-06 02:41:25,1,1.1,1,N,107,137,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-22 13:29:39,2019-01-22 13:32:49,1,0.6,1,N,41,74,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-03 21:36:57,2019-01-03 21:40:29,1,1.06,1,N,107,162,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-19 08:44:28,2019-01-19 08:48:20,1,1.0,1,N,163,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-10 18:27:52,2019-01-10 18:41:14,1,2.0,1,N,262,229,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-12 00:52:00,2019-01-12 01:06:18,1,2.86,1,N,65,181,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +1,2019-01-26 15:18:28,2019-01-26 15:46:24,1,4.0,1,N,261,162,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8,0.0 +2,2019-01-31 08:21:19,2019-01-31 08:53:08,1,2.4,1,N,143,140,1,19.5,0.0,0.5,2.03,0.0,0.3,22.33,0.0 +1,2019-01-05 02:54:11,2019-01-05 03:02:42,1,1.9,1,N,137,114,1,8.5,0.5,0.5,1.76,0.0,0.3,11.56, +2,2019-01-19 20:57:43,2019-01-19 21:10:14,1,1.97,1,N,113,170,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-14 07:20:25,2019-01-14 07:23:04,1,0.5,1,N,100,230,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +2,2019-01-29 07:59:06,2019-01-29 08:07:43,1,1.39,1,N,196,82,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-31 12:03:29,2019-01-31 12:22:42,1,1.7,1,N,161,141,1,12.5,0.0,0.5,3.95,0.0,0.3,17.25,0.0 +1,2019-01-28 18:40:10,2019-01-28 18:59:59,1,4.7,1,N,264,264,1,18.0,1.0,0.5,5.9,0.0,0.3,25.7,0.0 +2,2019-01-17 18:03:47,2019-01-17 18:10:06,1,1.12,1,N,239,238,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-28 17:54:05,2019-01-28 18:12:15,1,2.56,1,N,161,238,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-10 11:41:05,2019-01-10 11:59:22,1,1.57,1,N,233,43,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-31 20:18:19,2019-01-31 20:23:42,1,0.9,1,N,107,137,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-29 06:15:39,2019-01-29 06:30:54,1,6.13,1,N,87,230,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36,0.0 +1,2019-01-17 08:47:57,2019-01-17 09:03:04,1,0.9,1,N,161,163,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-10 16:09:11,2019-01-10 16:16:36,1,0.9,1,N,264,264,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-08 01:08:00,2019-01-08 01:11:14,1,1.1,1,N,263,141,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-21 10:19:17,2019-01-21 10:25:37,1,1.63,1,N,48,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-28 11:58:42,2019-01-28 12:06:12,1,0.74,1,N,170,100,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-27 22:13:32,2019-01-27 22:20:48,1,1.3,1,N,125,79,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-19 08:27:28,2019-01-19 08:37:17,1,3.82,1,N,133,25,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-26 10:07:21,2019-01-26 10:12:13,1,1.2,1,N,230,186,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-04 17:48:41,2019-01-04 17:49:53,1,0.3,1,N,264,264,2,3.0,1.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-18 05:30:54,2019-01-18 05:42:00,1,3.6,1,N,65,255,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-27 13:18:02,2019-01-27 13:24:04,1,1.23,1,N,164,229,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-03 08:12:42,2019-01-03 08:22:53,1,4.3,1,N,88,79,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-05 18:40:41,2019-01-05 18:53:32,1,2.23,1,N,229,262,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +4,2019-01-11 19:12:39,2019-01-11 19:33:16,1,1.42,1,N,162,100,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-17 12:18:53,2019-01-17 12:21:55,1,0.48,1,N,170,137,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-21 00:23:13,2019-01-21 00:26:15,1,0.59,1,N,229,233,1,4.0,0.5,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-26 14:10:53,2019-01-26 14:29:03,1,2.33,1,N,158,170,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-24 17:07:35,2019-01-24 17:17:43,1,1.02,1,N,230,142,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-30 13:22:45,2019-01-30 13:34:29,1,1.4,1,N,113,137,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-22 16:17:59,2019-01-22 16:51:26,1,4.95,1,N,90,236,2,23.0,1.0,0.5,0.0,0.0,0.3,24.8,0.0 +2,2019-01-16 20:58:21,2019-01-16 21:03:11,1,0.99,1,N,158,90,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5, +1,2019-01-22 19:32:11,2019-01-22 19:38:31,1,1.0,1,N,164,234,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-14 10:50:55,2019-01-14 11:00:46,1,1.5,1,N,224,170,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-10 11:43:14,2019-01-10 11:46:13,1,0.31,1,N,237,237,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-28 07:31:24,2019-01-28 07:53:10,1,3.97,1,N,237,144,1,17.5,0.0,0.5,3.0,0.0,0.3,21.3,0.0 +1,2019-01-08 19:33:04,2019-01-08 20:10:59,1,16.4,1,N,254,216,1,50.0,0.5,0.5,0.0,5.76,0.3,57.06, +2,2019-01-27 02:32:01,2019-01-27 02:38:55,1,2.0,1,N,158,48,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-01 04:12:24,2019-01-01 04:40:26,1,7.99,1,N,164,168,2,28.0,0.5,0.5,0.0,0.0,0.3,29.3, +1,2019-01-23 06:37:55,2019-01-23 06:42:50,1,1.0,1,N,161,161,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-11 15:25:48,2019-01-11 15:42:38,1,2.3,1,N,162,107,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-29 08:23:04,2019-01-29 08:27:22,1,0.5,1,N,234,100,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-17 23:25:19,2019-01-17 23:29:28,1,0.7,1,N,230,170,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-27 22:33:54,2019-01-27 22:41:15,1,1.5,1,N,144,107,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-29 06:48:46,2019-01-29 06:57:32,1,1.4,1,N,141,162,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-27 12:23:08,2019-01-27 12:37:43,1,2.4,1,N,237,48,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-17 07:10:16,2019-01-17 07:11:57,1,0.2,1,N,140,141,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-25 16:15:41,2019-01-25 16:26:23,1,1.6,1,N,234,68,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-06 10:29:00,2019-01-06 10:33:45,1,0.7,1,N,48,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-21 07:01:59,2019-01-21 07:05:18,1,0.66,1,N,13,231,1,4.5,0.0,0.5,1.2,0.0,0.3,6.5,0.0 +2,2019-01-25 17:37:37,2019-01-25 17:54:02,1,2.41,1,N,162,68,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-12 01:07:39,2019-01-12 01:35:25,1,15.5,1,N,132,112,1,44.0,0.5,0.5,6.8,0.0,0.3,52.1, +2,2019-01-08 17:53:43,2019-01-08 17:56:51,1,0.73,1,N,233,229,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-15 12:58:22,2019-01-15 13:04:52,1,0.91,1,N,161,100,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-21 18:52:11,2019-01-21 19:00:43,1,1.3,1,N,161,68,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-05 20:59:32,2019-01-05 21:04:15,1,0.9,1,N,186,249,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-21 16:41:44,2019-01-21 16:52:39,1,1.25,1,N,162,48,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-13 16:44:43,2019-01-13 16:48:59,1,0.87,1,N,237,237,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-20 10:42:07,2019-01-20 10:48:10,1,1.23,1,N,230,43,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-28 17:33:56,2019-01-28 18:07:36,1,8.94,1,N,138,142,1,31.5,1.0,0.5,7.81,5.76,0.3,46.87,0.0 +2,2019-01-31 20:41:12,2019-01-31 21:02:13,1,3.65,1,N,68,87,1,16.5,0.5,0.5,4.45,0.0,0.3,22.25,0.0 +2,2019-01-17 17:14:06,2019-01-17 18:22:17,1,11.33,1,N,237,216,2,46.0,1.0,0.5,0.0,0.0,0.3,47.8, +2,2019-01-18 14:32:57,2019-01-18 14:45:20,1,1.92,1,N,114,90,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-25 23:41:36,2019-01-26 00:03:02,1,10.6,1,N,138,162,1,30.5,0.5,0.5,5.0,5.76,0.3,42.56,0.0 +1,2019-01-22 05:58:50,2019-01-22 06:02:13,1,1.2,1,N,141,229,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-17 14:05:49,2019-01-17 14:24:08,1,2.95,1,N,162,238,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-28 21:44:21,2019-01-28 21:47:14,1,0.7,1,N,246,186,1,4.5,0.5,0.5,1.7,0.0,0.3,7.5,0.0 +2,2019-01-09 19:55:37,2019-01-09 20:09:15,1,6.99,1,N,137,74,1,20.5,1.0,0.5,4.46,0.0,0.3,26.76, +1,2019-01-06 20:42:58,2019-01-06 20:48:12,1,1.0,1,N,170,224,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +1,2019-01-01 14:27:06,2019-01-01 14:27:12,1,0.0,1,N,146,146,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-28 18:34:44,2019-01-28 18:43:38,1,1.0,1,N,161,186,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-15 10:47:30,2019-01-15 10:58:31,1,0.89,1,N,113,107,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-06 14:44:35,2019-01-06 14:54:51,1,2.16,1,N,164,79,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-30 16:58:52,2019-01-30 17:25:54,1,4.4,1,N,249,236,1,19.5,1.0,0.5,2.0,0.0,0.3,23.3,0.0 +1,2019-01-17 18:32:07,2019-01-17 18:57:30,1,2.4,1,N,142,100,1,16.5,1.0,0.5,2.0,0.0,0.3,20.3, +2,2019-01-07 17:19:54,2019-01-07 17:28:53,1,2.07,1,N,48,239,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-10 07:51:32,2019-01-10 08:01:29,1,1.0,1,N,107,234,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-11 08:18:13,2019-01-11 08:25:09,1,1.03,1,N,163,142,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-30 19:28:56,2019-01-30 19:35:57,1,1.03,1,N,114,148,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-25 00:09:40,2019-01-25 00:14:57,1,0.95,1,N,79,79,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-07 14:04:25,2019-01-07 15:04:05,1,4.54,1,N,114,233,1,35.5,0.0,0.5,6.53,0.0,0.3,44.78, +2,2019-01-14 22:04:41,2019-01-14 22:26:22,1,7.68,1,N,234,181,1,24.0,0.5,0.5,6.21,5.76,0.3,37.27, +2,2019-01-19 13:16:00,2019-01-19 13:30:25,1,2.08,1,N,113,68,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-09 13:55:47,2019-01-09 14:16:08,1,4.2,1,N,7,137,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-05 00:51:14,2019-01-05 01:11:40,1,7.2,1,Y,107,159,1,23.5,0.5,0.5,6.2,0.0,0.3,31.0, +2,2019-01-23 13:09:10,2019-01-23 13:22:29,1,1.07,1,N,230,170,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-06 21:13:01,2019-01-06 21:18:50,1,0.74,1,N,140,237,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +1,2019-01-30 22:00:30,2019-01-30 22:06:31,1,0.8,1,N,170,161,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-09 15:30:40,2019-01-09 16:01:45,1,6.37,1,N,209,140,1,25.5,0.0,0.5,0.0,0.0,0.3,26.3, +2,2019-01-19 22:06:47,2019-01-19 22:13:09,1,0.7,1,N,238,151,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-04 17:25:14,2019-01-04 17:38:23,1,4.92,1,N,87,170,1,16.0,1.0,0.5,1.0,0.0,0.3,18.8, +2,2019-01-01 16:39:55,2019-01-01 17:01:47,1,4.41,1,N,230,179,1,18.5,0.0,0.5,3.0,0.0,0.3,22.3, +1,2019-01-14 01:33:01,2019-01-14 02:05:40,1,8.4,1,N,48,37,1,28.0,0.5,0.5,5.85,0.0,0.3,35.15, +1,2019-01-31 09:42:27,2019-01-31 10:04:00,1,2.1,1,N,79,162,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-02 08:05:41,2019-01-02 08:43:41,1,11.68,1,N,138,181,1,37.5,0.0,0.5,7.66,0.0,0.3,45.96, +2,2019-01-17 18:04:23,2019-01-17 18:28:25,1,4.73,1,N,162,255,1,18.0,1.0,0.5,5.11,5.76,0.3,30.67, +2,2019-01-14 14:26:23,2019-01-14 14:30:37,1,0.55,1,N,107,234,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-18 16:10:39,2019-01-18 16:19:24,1,1.6,1,N,68,48,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-01 09:35:38,2019-01-01 09:48:53,1,3.0,1,N,164,231,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-16 18:42:26,2019-01-16 18:45:58,1,1.2,1,N,140,262,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-03 17:57:04,2019-01-03 18:12:15,1,1.08,1,N,142,230,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-28 20:47:55,2019-01-28 20:54:41,1,1.5,1,N,48,143,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-02 17:00:41,2019-01-02 17:49:38,1,17.06,2,N,132,141,1,52.0,4.5,0.5,11.46,0.0,0.3,68.76, +2,2019-01-14 19:38:43,2019-01-14 20:01:23,1,3.15,1,N,125,230,1,15.5,1.0,0.5,3.46,0.0,0.3,20.76, +1,2019-01-18 00:36:23,2019-01-18 00:42:24,1,1.2,1,N,48,143,1,6.5,0.5,0.5,2.3,0.0,0.3,10.1, +1,2019-01-26 19:11:22,2019-01-26 19:30:30,1,3.0,1,N,143,186,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-09 18:11:09,2019-01-09 18:32:08,1,2.77,1,N,163,238,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-15 13:35:31,2019-01-15 13:55:18,1,1.9,1,N,234,163,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +1,2019-01-10 23:27:08,2019-01-10 23:29:22,1,0.5,1,N,43,142,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +2,2019-01-19 18:51:47,2019-01-19 18:59:35,1,1.42,1,N,229,137,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +1,2019-01-22 05:04:58,2019-01-22 05:05:56,1,0.3,1,N,140,140,1,3.0,0.5,0.5,0.64,0.0,0.3,4.94,0.0 +1,2019-01-17 10:14:36,2019-01-17 10:43:57,1,2.2,1,N,246,161,1,17.5,0.0,0.5,4.55,0.0,0.3,22.85, +1,2019-01-26 17:28:02,2019-01-26 17:34:59,1,1.4,1,N,229,170,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-06 08:23:26,2019-01-06 08:51:03,1,17.92,2,N,132,107,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-18 13:11:34,2019-01-18 13:21:30,1,1.8,1,N,239,262,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-08 18:25:49,2019-01-08 18:45:09,1,2.78,1,N,186,236,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-02 07:18:27,2019-01-02 07:24:09,1,1.33,1,N,202,263,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-23 12:22:40,2019-01-23 12:30:31,1,1.79,1,N,237,43,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-15 19:44:52,2019-01-15 19:55:53,1,1.8,1,N,100,237,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-23 14:44:00,2019-01-23 15:05:04,1,1.63,1,N,162,186,1,13.5,0.0,0.5,4.29,0.0,0.3,18.59,0.0 +2,2019-01-07 01:10:47,2019-01-07 01:22:17,1,4.3,1,N,79,263,1,13.5,0.5,0.5,1.48,0.0,0.3,16.28, +1,2019-01-28 08:38:45,2019-01-28 08:57:11,1,3.2,1,N,262,162,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-26 01:16:11,2019-01-26 01:37:47,1,6.3,1,N,107,7,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3,0.0 +2,2019-01-14 12:27:54,2019-01-14 12:40:29,1,1.91,1,N,234,68,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +1,2019-01-15 12:47:45,2019-01-15 12:58:22,1,1.1,1,N,229,230,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-18 19:39:22,2019-01-18 19:44:43,1,2.0,1,N,236,74,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-10 11:52:11,2019-01-10 12:32:44,1,7.2,1,N,211,75,1,31.5,0.0,0.5,0.0,0.0,0.3,32.3, +1,2019-01-28 21:06:52,2019-01-28 21:14:36,1,1.8,1,N,238,41,1,8.0,0.5,0.5,1.5,0.0,0.3,10.8,0.0 +1,2019-01-25 00:03:14,2019-01-25 00:07:42,1,1.1,1,N,114,234,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-01 12:03:57,2019-01-01 14:02:53,1,25.2,1,N,43,43,2,88.0,0.0,0.5,0.0,0.0,0.3,88.8, +2,2019-01-20 11:29:37,2019-01-20 11:35:16,1,1.02,1,N,107,114,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-18 20:55:35,2019-01-18 21:08:42,1,1.71,1,N,234,158,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-12 13:36:49,2019-01-12 13:52:55,1,2.33,1,N,211,186,1,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-04 13:12:12,2019-01-04 13:18:01,1,0.35,1,N,236,263,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-21 21:06:17,2019-01-21 21:38:04,1,16.78,2,N,132,140,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +2,2019-01-18 19:25:16,2019-01-18 19:36:35,1,1.49,1,N,140,229,1,9.0,1.0,0.5,2.7,0.0,0.3,15.45, +2,2019-01-15 11:50:33,2019-01-15 12:18:40,1,1.02,1,N,246,164,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-07 21:47:14,2019-01-07 22:02:18,1,6.43,1,N,231,141,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-27 11:08:11,2019-01-27 11:31:18,1,5.53,1,N,107,179,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3,0.0 +1,2019-01-07 07:43:23,2019-01-07 08:16:16,1,9.1,1,N,14,90,1,30.0,0.0,0.5,0.0,5.76,0.3,36.56, +2,2019-01-24 20:59:47,2019-01-24 21:20:57,1,2.78,1,N,79,163,1,15.0,0.5,0.5,2.5,0.0,0.3,18.8,0.0 +1,2019-01-09 20:08:57,2019-01-09 20:20:40,1,1.7,1,N,107,114,1,9.5,0.5,0.5,1.2,0.0,0.3,12.0, +2,2019-01-23 06:22:13,2019-01-23 06:28:36,1,1.16,1,N,186,161,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-07 10:18:05,2019-01-07 10:29:18,1,1.3,1,N,161,233,1,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-01 11:49:22,2019-01-01 11:57:55,1,1.6,1,N,142,141,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-10 20:57:05,2019-01-10 21:13:29,1,2.98,1,N,163,114,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +4,2019-01-28 19:34:17,2019-01-28 19:45:15,1,1.43,1,N,249,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-07 11:06:03,2019-01-07 11:08:09,1,0.5,1,N,48,142,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +1,2019-01-30 16:08:03,2019-01-30 16:12:00,1,0.8,1,N,74,75,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-14 06:47:30,2019-01-14 06:55:37,1,1.9,1,N,68,143,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-06 22:07:16,2019-01-06 22:10:57,1,0.79,1,N,186,68,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-03 19:43:49,2019-01-03 19:48:02,1,1.14,1,N,237,142,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-14 15:31:32,2019-01-14 16:03:12,1,13.0,2,N,10,170,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +1,2019-01-04 15:00:27,2019-01-04 15:04:31,1,0.9,1,N,233,164,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-14 12:41:10,2019-01-14 12:46:08,1,0.5,1,N,237,237,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-11 22:19:47,2019-01-11 22:53:13,1,18.15,2,N,132,166,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-25 14:24:46,2019-01-25 14:29:40,1,0.79,1,N,237,236,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24,0.0 +2,2019-01-06 20:03:09,2019-01-06 20:21:23,1,3.73,1,N,142,249,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +2,2019-01-19 13:08:19,2019-01-19 13:30:45,1,2.07,1,N,143,48,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +1,2019-01-02 14:40:09,2019-01-02 14:41:46,1,0.4,1,N,140,141,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-07 20:25:26,2019-01-07 20:28:46,1,0.5,1,N,79,114,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +2,2019-01-29 15:50:09,2019-01-29 16:05:19,1,1.52,1,N,236,237,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-29 17:15:44,2019-01-29 17:22:58,1,1.0,1,N,237,236,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-10 11:34:50,2019-01-10 11:45:03,1,1.53,1,N,238,263,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-13 18:21:03,2019-01-13 18:32:56,1,1.26,1,N,100,234,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-27 23:59:03,2019-01-28 00:01:57,1,0.5,1,N,107,107,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-31 16:48:01,2019-01-31 16:56:46,1,1.13,1,N,142,48,1,7.5,1.0,0.5,1.4,0.0,0.3,10.7,0.0 +1,2019-01-25 15:45:41,2019-01-25 15:55:23,1,1.6,1,N,48,90,3,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-30 21:58:28,2019-01-30 22:07:08,1,1.5,1,N,249,107,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-29 07:39:31,2019-01-29 08:20:10,1,4.2,1,N,239,161,1,25.0,0.0,0.5,2.58,0.0,0.3,28.38,0.0 +2,2019-01-15 12:37:42,2019-01-15 12:43:13,1,0.83,1,N,164,162,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-21 10:13:10,2019-01-21 10:16:52,1,0.87,1,N,230,163,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-22 07:46:58,2019-01-22 08:10:53,1,2.44,1,N,143,141,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +1,2019-01-31 07:05:30,2019-01-31 07:13:45,1,1.1,1,N,186,161,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-23 16:15:21,2019-01-23 16:19:02,1,0.5,1,N,246,246,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-20 17:52:59,2019-01-20 18:06:29,1,1.09,1,N,230,100,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-17 15:20:58,2019-01-17 15:35:23,1,1.96,1,N,161,234,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-02 09:40:10,2019-01-02 09:58:35,1,3.01,1,N,164,231,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-28 22:58:24,2019-01-28 23:14:59,1,9.92,1,N,138,79,1,28.0,0.5,0.5,5.86,0.0,0.3,35.16,0.0 +2,2019-01-26 12:50:37,2019-01-26 12:56:57,1,0.83,1,N,249,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-30 10:29:10,2019-01-30 10:43:27,1,1.85,1,N,170,141,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-15 13:01:27,2019-01-15 13:14:31,1,1.2,1,N,163,170,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-06 21:47:45,2019-01-06 21:52:34,1,0.82,1,N,114,113,1,5.0,0.5,0.5,0.63,0.0,0.3,6.93, +2,2019-01-17 10:43:46,2019-01-17 10:59:26,1,1.29,1,N,229,237,1,10.5,0.0,0.5,0.5,0.0,0.3,11.8, +1,2019-01-15 15:09:15,2019-01-15 15:23:10,1,1.1,1,N,234,100,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-10 17:31:50,2019-01-10 17:41:51,1,0.7,1,N,163,161,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-02 07:53:10,2019-01-02 08:16:46,1,5.79,1,N,74,100,1,21.5,0.0,0.5,1.0,0.0,0.3,23.3, +2,2019-01-27 12:15:25,2019-01-27 12:19:58,1,0.93,1,N,230,43,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-30 16:56:35,2019-01-30 17:03:26,1,0.47,1,N,236,262,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-04 19:10:22,2019-01-04 19:18:33,1,3.39,1,N,246,151,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-30 12:55:47,2019-01-30 12:58:44,1,0.29,1,N,263,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-27 17:04:02,2019-01-27 17:31:28,1,3.26,1,N,186,79,1,18.0,0.0,0.5,4.0,0.0,0.3,22.8,0.0 +1,2019-01-19 07:52:05,2019-01-19 08:01:13,1,2.3,1,N,262,74,1,9.5,0.0,0.5,3.05,0.0,0.3,13.35, +2,2019-01-22 21:37:17,2019-01-22 21:50:11,1,2.71,1,N,142,236,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-06 18:18:56,2019-01-06 18:26:41,1,1.32,1,N,75,74,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-24 14:41:48,2019-01-24 15:07:47,1,8.2,1,N,138,262,1,26.0,0.0,0.5,6.5,5.76,0.3,39.06,0.0 +2,2019-01-06 12:53:00,2019-01-06 13:02:23,1,4.1,1,N,224,261,1,13.5,0.0,0.5,2.0,0.0,0.3,16.3, +2,2019-01-17 08:38:21,2019-01-17 08:45:13,1,1.23,1,N,151,236,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-26 14:56:02,2019-01-26 14:59:23,1,0.79,1,N,229,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-31 23:02:45,2019-01-31 23:09:35,1,0.63,1,N,170,100,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-27 13:40:07,2019-01-27 14:01:18,1,2.96,1,N,140,234,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +1,2019-01-18 23:43:44,2019-01-18 23:47:03,1,0.7,1,N,264,264,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-17 23:07:24,2019-01-17 23:15:51,1,1.49,1,N,161,141,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-17 23:54:49,2019-01-18 00:05:05,1,1.7,1,N,261,148,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-24 15:07:16,2019-01-24 15:24:46,1,2.2,1,N,170,237,1,12.5,0.0,0.5,1.0,0.0,0.3,14.3,0.0 +1,2019-01-15 18:47:31,2019-01-15 18:57:28,1,1.3,1,N,231,87,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-10 14:28:31,2019-01-10 14:48:02,1,6.23,1,N,138,226,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +1,2019-01-08 18:36:03,2019-01-08 19:05:51,1,3.7,1,N,166,262,1,20.5,1.0,0.5,3.35,0.0,0.3,25.65, +1,2019-01-06 22:34:06,2019-01-06 22:38:35,1,1.0,1,N,48,163,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-25 21:13:24,2019-01-25 21:23:24,1,2.6,1,N,237,238,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-19 01:51:03,2019-01-19 02:16:25,1,12.66,1,N,163,242,2,35.5,0.5,0.5,0.0,0.0,0.3,36.8, +2,2019-01-27 11:33:53,2019-01-28 10:47:49,1,4.09,1,N,234,87,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-27 16:03:18,2019-01-27 16:19:31,1,3.51,1,N,100,236,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-29 19:00:14,2019-01-29 19:03:38,1,0.41,1,N,170,170,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-29 21:36:10,2019-01-29 21:45:45,1,1.48,1,N,90,229,1,8.0,0.5,0.5,2.79,0.0,0.3,12.09,0.0 +2,2019-01-29 13:14:07,2019-01-29 13:20:44,1,0.29,1,N,107,234,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-27 00:55:55,2019-01-27 01:16:16,1,7.48,1,N,87,263,1,24.0,0.5,0.5,6.32,0.0,0.3,31.62,0.0 +2,2019-01-06 20:12:02,2019-01-06 20:21:54,1,2.6,1,N,75,143,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3, +2,2019-01-26 23:01:10,2019-01-26 23:13:21,1,2.14,1,N,239,161,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-16 20:25:18,2019-01-16 20:34:41,1,1.78,1,N,161,186,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-29 21:54:37,2019-01-29 21:59:31,1,1.07,1,N,237,236,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82,0.0 +1,2019-01-03 09:59:55,2019-01-03 10:19:46,1,8.1,1,N,75,88,1,25.0,0.0,0.5,5.0,0.0,0.3,30.8, +2,2019-01-22 13:08:12,2019-01-22 13:23:04,1,2.52,1,N,239,263,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-07 18:35:53,2019-01-07 18:51:43,1,3.0,1,N,151,74,1,13.0,1.0,0.5,1.0,0.0,0.3,15.8, +2,2019-01-24 18:17:56,2019-01-24 18:22:39,1,0.56,1,N,144,114,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-29 13:55:12,2019-01-29 13:55:14,1,0.0,5,N,264,237,1,17.5,0.0,0.0,3.56,0.0,0.3,21.36,0.0 +2,2019-01-22 03:18:06,2019-01-22 03:44:26,1,18.62,2,N,132,230,1,52.0,0.0,0.5,8.78,5.76,0.3,67.34,0.0 +1,2019-01-15 15:16:23,2019-01-15 15:29:22,1,1.5,1,N,236,237,1,10.0,0.0,0.5,1.5,0.0,0.3,12.3, +1,2019-01-23 12:56:07,2019-01-23 13:10:00,1,3.0,1,N,239,116,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-21 13:28:01,2019-01-21 13:32:01,1,0.92,1,N,140,140,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-09 09:40:34,2019-01-09 09:56:05,1,1.35,1,N,237,230,1,10.5,0.0,0.5,2.03,0.0,0.3,13.33, +2,2019-01-20 15:45:02,2019-01-20 15:51:04,1,1.05,1,N,144,107,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-12 12:47:19,2019-01-12 12:58:53,1,2.12,1,N,238,41,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-25 09:27:22,2019-01-25 09:38:38,1,0.0,1,N,210,210,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-16 16:37:41,2019-01-16 16:41:52,1,0.59,1,N,161,237,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-23 11:55:24,2019-01-23 11:59:47,1,1.11,1,N,238,151,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-15 20:44:07,2019-01-15 20:54:43,1,1.9,1,N,140,236,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-19 13:25:48,2019-01-19 13:30:13,1,0.59,1,N,263,141,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-11 05:46:27,2019-01-11 06:12:36,1,16.98,2,N,164,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +1,2019-01-08 22:14:10,2019-01-08 22:27:48,1,4.2,1,N,68,151,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +2,2019-01-28 09:30:06,2019-01-28 09:49:41,1,2.82,1,N,230,237,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-30 11:14:47,2019-01-30 11:41:30,1,2.7,1,N,48,79,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +2,2019-01-15 22:02:40,2019-01-15 22:17:55,1,1.97,1,N,137,163,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +2,2019-01-16 22:47:12,2019-01-16 22:49:42,1,0.08,1,N,162,161,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-12 12:25:43,2019-01-12 12:50:55,1,5.25,1,N,179,234,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +2,2019-01-12 15:22:58,2019-01-12 15:39:26,1,1.48,1,N,90,114,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-23 21:51:02,2019-01-23 22:05:56,1,2.58,1,N,246,211,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-01 15:55:19,2019-01-01 16:39:59,1,18.36,5,N,161,55,1,60.94,0.0,0.5,0.0,5.76,0.3,67.5, +1,2019-01-29 08:54:32,2019-01-29 08:57:14,1,0.0,1,N,114,114,4,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +4,2019-01-12 22:28:41,2019-01-12 22:39:52,1,1.97,1,N,144,13,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-06 09:57:28,2019-01-06 10:37:51,1,14.1,1,N,48,165,2,44.5,0.0,0.5,0.0,5.76,0.3,51.06, +1,2019-01-24 00:21:46,2019-01-24 00:28:49,1,1.2,1,N,239,142,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-29 20:41:56,2019-01-29 20:48:44,1,1.43,1,N,230,143,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-05 09:45:07,2019-01-05 09:48:52,1,0.57,1,N,236,263,1,4.5,0.0,0.5,3.0,0.0,0.3,8.3, +1,2019-01-15 19:57:58,2019-01-15 20:02:56,1,0.8,1,N,239,239,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-28 07:51:40,2019-01-28 07:55:26,1,0.61,1,N,162,170,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-18 22:58:27,2019-01-18 23:13:56,1,2.7,1,N,261,107,1,12.0,0.5,0.5,0.0,0.0,0.3,13.3, +2,2019-01-11 11:54:22,2019-01-11 12:10:59,1,0.83,1,N,233,161,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-25 09:24:53,2019-01-25 09:37:06,1,1.3,1,N,68,114,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +1,2019-01-24 10:19:13,2019-01-24 10:25:11,1,0.5,1,N,230,230,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-21 11:32:18,2019-01-21 11:36:44,1,0.94,1,N,238,24,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-14 22:38:56,2019-01-14 22:43:05,1,1.12,1,N,79,224,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-21 15:28:54,2019-01-21 15:34:04,1,0.8,1,N,170,161,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2,0.0 +1,2019-01-09 20:47:10,2019-01-09 20:51:16,1,0.7,1,N,236,236,1,5.0,0.5,0.5,0.95,0.0,0.3,7.25, +2,2019-01-26 17:36:10,2019-01-26 17:46:46,1,0.97,1,N,43,230,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-16 08:33:13,2019-01-16 08:38:59,1,0.94,1,N,143,48,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0, +1,2019-01-30 23:17:00,2019-01-30 23:28:50,1,4.3,1,N,132,216,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-10 17:53:05,2019-01-10 17:58:39,1,1.0,1,N,141,141,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-15 13:52:25,2019-01-15 14:10:12,1,2.04,1,N,246,162,1,12.5,0.0,0.5,3.99,0.0,0.3,17.29, +2,2019-01-15 12:12:41,2019-01-15 12:20:11,1,0.52,1,N,144,144,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-24 05:38:37,2019-01-24 06:01:00,1,13.7,1,N,132,155,1,37.5,0.5,0.5,7.76,0.0,0.3,46.56,0.0 +2,2019-01-07 11:35:27,2019-01-07 11:48:00,1,1.72,1,N,239,141,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-07 08:41:23,2019-01-07 08:50:13,1,1.62,1,N,7,223,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-31 23:59:11,2019-02-01 00:05:04,1,0.7,1,N,234,137,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-10 17:26:46,2019-01-10 17:32:34,1,0.81,1,N,113,144,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-03 09:48:19,2019-01-03 10:06:35,1,9.76,1,N,138,198,1,28.0,0.0,0.5,8.64,0.0,0.3,37.44, +2,2019-01-07 17:59:31,2019-01-07 18:02:14,1,0.46,1,N,164,161,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-08 22:11:19,2019-01-08 22:17:29,1,0.7,1,N,186,170,1,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-22 15:31:24,2019-01-22 15:39:32,1,0.87,1,N,234,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-15 21:19:06,2019-01-15 21:33:30,1,2.0,1,N,161,246,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +2,2019-01-08 00:10:01,2019-01-08 00:23:20,1,3.74,1,N,79,237,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +1,2019-01-15 20:00:52,2019-01-15 20:15:09,1,2.6,1,N,162,114,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-01 02:01:30,2019-01-01 02:05:19,1,0.9,1,N,162,107,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +2,2019-01-20 08:15:25,2019-01-20 08:23:10,1,2.76,1,N,264,75,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +2,2019-01-18 21:21:23,2019-01-18 21:37:59,1,1.74,1,N,114,137,1,11.5,0.5,0.5,1.25,0.0,0.3,14.05, +1,2019-01-22 21:41:48,2019-01-22 21:45:44,1,0.5,1,N,234,249,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-21 20:13:49,2019-01-21 20:19:45,1,0.7,1,N,87,231,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-23 20:00:06,2019-01-23 20:10:46,1,2.5,1,N,144,33,1,10.0,1.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-17 22:09:44,2019-01-17 22:27:44,1,4.92,1,N,43,231,1,17.5,0.5,0.5,4.7,0.0,0.3,23.5, +1,2019-01-01 20:18:10,2019-01-01 20:36:16,1,0.8,1,N,230,100,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-11 18:52:28,2019-01-11 18:56:04,1,0.5,1,N,141,141,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-11 12:44:44,2019-01-11 13:00:44,1,1.99,1,N,234,161,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-07 08:15:41,2019-01-07 08:26:54,1,1.45,1,N,262,140,1,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-03 16:24:16,2019-01-03 16:26:02,1,0.54,1,N,141,263,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-31 12:06:45,2019-01-31 12:18:30,1,0.9,1,N,229,237,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-27 17:38:28,2019-01-27 17:49:48,1,1.83,1,N,151,143,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3,0.0 +2,2019-01-31 14:28:05,2019-01-31 14:41:54,1,2.23,1,N,163,246,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-22 17:36:45,2019-01-22 17:51:20,1,1.75,1,N,107,164,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-05 19:58:22,2019-01-05 20:16:23,1,4.76,1,N,226,36,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +1,2019-01-25 09:43:27,2019-01-25 09:51:33,1,0.9,1,N,163,229,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-02 11:18:48,2019-01-02 11:31:45,1,1.92,1,N,230,50,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-14 13:47:17,2019-01-14 13:48:56,1,0.4,1,N,264,264,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-15 07:42:12,2019-01-15 07:47:40,1,1.07,1,N,79,137,1,6.0,0.0,0.5,0.58,0.0,0.3,7.38, +1,2019-01-29 18:46:39,2019-01-29 18:58:56,1,1.6,1,N,151,41,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-03 14:28:57,2019-01-03 14:51:17,1,2.63,1,N,162,90,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-22 16:51:21,2019-01-22 16:56:25,1,1.03,1,N,229,237,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-30 15:19:22,2019-01-30 15:32:01,1,2.0,1,N,163,246,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-21 07:44:16,2019-01-21 07:49:23,1,0.65,1,N,68,246,1,5.0,0.0,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-23 10:56:59,2019-01-23 11:00:02,1,1.1,1,N,238,142,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-11 17:58:50,2019-01-11 18:13:17,1,2.0,1,N,142,186,1,11.0,1.0,0.5,1.6,0.0,0.3,14.4, +1,2019-01-23 15:39:26,2019-01-23 16:03:10,1,1.7,1,N,90,48,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-24 15:45:55,2019-01-24 16:20:24,1,4.91,1,N,170,7,1,23.0,0.0,0.5,5.2,0.0,0.3,29.0,0.0 +1,2019-01-10 22:33:14,2019-01-10 23:10:09,1,6.1,1,N,264,264,1,26.5,0.5,0.5,6.95,0.0,0.3,34.75, +2,2019-01-16 01:03:25,2019-01-16 01:13:33,1,3.94,1,N,161,232,1,13.5,0.5,0.5,3.7,0.0,0.3,18.5, +1,2019-01-09 09:05:52,2019-01-09 09:43:58,1,15.6,1,N,186,259,2,46.5,0.0,0.5,0.0,0.0,0.3,47.3, +2,2019-01-17 12:20:12,2019-01-17 12:33:15,1,1.39,1,N,239,142,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-02 15:31:23,2019-01-02 15:37:24,1,1.31,1,N,237,75,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-11 14:00:03,2019-01-11 14:25:10,1,3.5,1,N,162,151,1,18.0,0.0,0.5,3.75,0.0,0.3,22.55, +2,2019-01-09 17:44:16,2019-01-09 18:01:38,1,2.46,1,N,161,236,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-31 20:40:11,2019-01-31 20:51:10,1,2.04,1,N,186,249,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-16 20:57:10,2019-01-16 21:01:28,1,0.84,1,N,238,151,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-22 07:43:35,2019-01-22 07:48:43,1,0.76,1,N,48,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-01 00:24:04,2019-01-01 23:43:29,1,1.37,1,N,249,79,1,7.5,0.5,0.5,0.88,0.0,0.3,9.68, +2,2019-01-01 21:09:38,2019-01-01 21:19:37,1,0.99,1,N,48,170,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-29 18:47:09,2019-01-29 18:55:07,1,1.49,1,N,237,263,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-02 17:41:57,2019-01-02 17:49:35,1,0.97,1,N,164,234,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-11 05:47:11,2019-01-11 05:54:57,1,2.5,1,N,264,264,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +1,2019-01-25 16:21:40,2019-01-25 16:31:54,1,1.3,1,N,161,236,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-16 11:07:53,2019-01-16 11:11:48,1,0.96,1,N,143,238,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-10 23:36:15,2019-01-10 23:40:24,1,1.19,1,N,107,229,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-23 20:40:04,2019-01-23 20:44:20,1,0.88,1,N,142,142,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-03 13:58:52,2019-01-03 14:11:07,1,1.99,1,N,237,239,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-09 17:48:02,2019-01-09 17:52:50,1,0.7,1,N,170,161,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-19 17:53:20,2019-01-19 18:13:19,1,2.3,1,N,107,50,1,13.5,0.0,0.5,1.8,0.0,0.3,16.1, +1,2019-01-18 16:13:21,2019-01-18 16:20:00,1,0.9,1,N,239,238,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-08 08:09:18,2019-01-08 08:13:33,1,0.9,1,N,234,249,1,5.0,0.0,0.5,1.74,0.0,0.3,7.54, +2,2019-01-17 14:09:08,2019-01-17 14:22:52,1,1.73,1,N,231,45,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-18 20:19:53,2019-01-18 20:29:46,1,1.4,1,N,113,231,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +1,2019-01-11 16:17:53,2019-01-11 17:10:04,1,16.0,2,N,132,233,2,52.0,4.5,0.5,0.0,5.76,0.3,63.06, +4,2019-01-25 18:26:51,2019-01-25 18:37:42,1,2.48,1,N,140,74,1,10.0,1.0,0.5,1.0,0.0,0.3,12.8, +2,2019-01-31 18:50:31,2019-01-31 18:57:53,1,0.94,1,N,230,142,1,6.5,1.0,0.5,2.0,0.0,0.3,10.3,0.0 +2,2019-01-11 11:24:23,2019-01-11 11:29:15,1,0.67,1,N,236,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-04 13:11:40,2019-01-04 13:20:28,1,0.81,1,N,229,237,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14, +2,2019-01-03 19:19:07,2019-01-03 19:27:08,1,2.09,1,N,236,74,1,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-11 22:49:27,2019-01-11 22:58:02,1,1.4,1,N,114,114,1,7.5,0.5,0.5,0.2,0.0,0.3,9.0, +2,2019-01-06 12:25:41,2019-01-06 12:50:19,1,9.39,1,N,138,50,1,30.0,0.0,0.5,5.0,5.76,0.3,41.56, +1,2019-01-17 07:59:40,2019-01-17 08:12:34,1,2.3,1,N,236,161,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-02 11:17:28,2019-01-02 11:27:43,1,0.99,1,N,230,100,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-12 00:57:00,2019-01-12 01:00:02,1,0.46,1,N,181,181,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-22 07:22:29,2019-01-22 07:26:25,1,1.0,1,N,233,229,1,5.5,0.0,0.5,1.7,0.0,0.3,8.0,0.0 +1,2019-01-11 20:02:16,2019-01-11 20:11:37,1,1.1,1,N,163,100,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-17 17:09:16,2019-01-17 18:05:16,1,10.4,1,N,163,138,1,41.0,1.0,0.5,12.1,5.76,0.3,60.66, +2,2019-01-16 17:54:48,2019-01-16 18:30:10,1,13.44,1,N,264,264,1,39.0,1.0,0.5,9.31,5.76,0.3,55.87, +1,2019-01-12 09:09:17,2019-01-12 09:17:22,1,1.2,1,N,236,237,1,7.5,0.0,0.5,0.83,0.0,0.3,9.13, +1,2019-01-09 11:42:45,2019-01-09 11:58:32,1,1.6,1,N,48,170,1,11.0,0.0,0.5,1.18,0.0,0.3,12.98, +2,2019-01-31 06:49:21,2019-01-31 06:57:24,1,0.92,1,N,244,244,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-30 17:35:33,2019-01-30 17:45:40,1,1.5,1,N,162,137,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-21 15:54:17,2019-01-21 16:03:32,1,1.74,1,N,43,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-19 09:25:23,2019-01-19 09:32:53,1,1.0,1,N,43,239,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-07 11:31:24,2019-01-07 11:40:37,1,1.11,1,N,186,246,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +1,2019-01-27 07:51:43,2019-01-27 08:14:10,1,16.5,1,N,132,256,1,44.5,0.0,0.5,9.05,0.0,0.3,54.35,0.0 +2,2019-01-17 07:41:41,2019-01-18 06:58:41,1,2.09,1,N,43,161,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-14 10:39:04,2019-01-14 10:54:02,1,1.2,1,N,233,230,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-05 16:36:21,2019-01-05 17:15:11,1,5.85,1,N,90,49,1,27.0,0.0,0.5,0.0,0.0,0.3,27.8, +2,2019-01-10 13:24:50,2019-01-10 13:32:59,1,0.91,1,N,164,170,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-19 00:33:22,2019-01-19 00:48:41,1,3.4,1,N,239,137,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +1,2019-01-30 12:02:00,2019-01-30 12:12:24,1,1.5,1,N,237,236,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7,0.0 +1,2019-01-18 21:51:05,2019-01-18 21:55:18,1,1.1,1,N,237,75,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-29 04:41:12,2019-01-29 04:49:51,1,2.4,1,N,140,75,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-18 06:32:23,2019-01-18 06:39:29,1,2.15,1,N,226,229,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-31 21:43:55,2019-01-31 21:51:09,1,2.1,1,N,48,239,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-26 14:07:57,2019-01-26 14:31:48,1,5.53,1,N,79,40,1,21.0,0.0,0.5,3.92,0.0,0.3,25.72,0.0 +1,2019-01-28 13:43:30,2019-01-28 14:13:32,1,4.7,1,N,144,236,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3,0.0 +1,2019-01-03 08:35:14,2019-01-03 08:46:23,1,1.8,1,N,236,143,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-31 19:50:57,2019-01-31 20:03:31,1,2.1,1,N,48,249,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-27 06:43:00,2019-01-27 06:46:02,1,0.9,1,N,234,186,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-28 06:35:04,2019-01-28 06:53:32,1,4.18,1,N,25,158,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-28 23:35:27,2019-01-28 23:55:17,1,4.53,1,N,79,37,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3,0.0 +1,2019-01-16 09:16:18,2019-01-16 09:27:58,1,1.3,1,N,141,161,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-10 23:10:46,2019-01-10 23:30:30,1,4.42,1,N,234,43,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-10 11:40:07,2019-01-10 11:44:07,1,0.63,1,N,13,231,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-02 14:08:37,2019-01-02 14:17:04,1,1.2,1,N,107,164,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-18 08:09:18,2019-01-18 08:16:22,1,1.06,1,N,144,158,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-21 06:49:15,2019-01-21 06:50:42,1,0.3,1,N,100,100,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +2,2019-01-23 22:46:31,2019-01-23 22:54:11,1,1.86,1,N,246,249,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-26 02:58:31,2019-01-26 03:06:22,1,1.76,1,N,114,90,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-13 15:05:24,2019-01-13 15:14:30,1,1.3,1,N,239,238,1,8.0,0.0,0.5,2.6,0.0,0.3,11.4, +1,2019-01-29 21:25:44,2019-01-29 21:28:10,1,0.5,1,N,90,68,1,3.5,0.5,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-21 14:15:08,2019-01-21 14:24:22,1,1.69,1,N,114,107,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-01 18:01:43,2019-01-01 18:13:11,1,1.22,1,N,142,162,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-29 14:20:56,2019-01-29 14:20:58,1,4.3,3,N,132,132,3,20.0,0.0,0.0,0.0,0.0,0.3,20.3,0.0 +1,2019-01-08 13:28:57,2019-01-08 13:40:49,1,1.8,1,N,170,141,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-29 15:54:27,2019-01-29 16:25:24,1,16.32,2,N,132,170,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-24 21:06:16,2019-01-24 21:18:11,1,1.98,1,N,229,263,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-31 00:23:00,2019-01-31 00:24:52,1,0.23,1,N,246,246,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-21 05:08:32,2019-01-21 05:37:51,1,16.9,3,N,48,1,1,64.0,0.5,0.0,15.0,10.5,0.3,90.3,0.0 +2,2019-01-05 23:01:29,2019-01-05 23:11:54,1,2.25,1,N,237,238,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-17 10:44:00,2019-01-17 11:05:50,1,1.18,1,N,234,230,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-15 21:02:42,2019-01-15 21:14:29,1,1.71,1,N,166,74,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-26 16:45:18,2019-01-26 16:48:03,1,1.06,1,N,239,151,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-03 20:45:04,2019-01-03 20:46:31,1,0.44,1,N,170,233,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-14 00:01:05,2019-01-14 00:05:07,1,0.72,1,N,137,164,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-15 13:21:35,2019-01-15 13:42:04,1,2.3,1,N,142,162,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-10 18:36:40,2019-01-10 18:47:06,1,0.82,1,N,141,161,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-01 11:28:10,2019-01-01 11:31:41,1,1.1,1,N,143,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 08:53:57,2019-01-09 09:06:21,1,1.3,1,N,186,107,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-28 19:18:40,2019-01-28 19:54:03,1,19.14,2,N,132,125,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67,0.0 +2,2019-01-26 17:53:37,2019-01-26 18:16:12,1,3.59,1,N,239,164,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-27 18:39:13,2019-01-27 18:44:54,1,0.63,1,N,13,231,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-06 15:32:23,2019-01-06 15:46:27,1,3.19,1,N,107,141,1,12.0,0.0,0.5,1.0,0.0,0.3,13.8, +1,2019-01-11 19:25:50,2019-01-11 19:40:00,1,0.9,1,N,68,164,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-22 11:49:44,2019-01-22 11:56:10,1,0.55,1,N,113,107,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-04 11:55:21,2019-01-04 12:04:27,1,1.97,1,N,163,246,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-18 22:11:37,2019-01-18 22:27:48,1,3.81,1,N,161,7,1,15.0,0.5,0.5,2.44,0.0,0.3,18.74, +2,2019-01-12 00:42:36,2019-01-12 00:51:04,1,1.44,1,N,114,164,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-27 12:10:50,2019-01-27 12:27:14,1,3.81,1,N,238,116,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-24 20:48:29,2019-01-24 21:07:05,1,1.94,1,N,170,140,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8,0.0 +2,2019-01-17 06:13:32,2019-01-17 06:58:50,1,24.74,1,N,132,181,1,67.5,0.0,0.5,13.66,0.0,0.3,81.96, +1,2019-01-16 07:40:17,2019-01-16 07:43:40,1,0.5,1,N,249,249,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-22 19:06:24,2019-01-22 19:12:55,1,0.8,1,N,264,264,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-20 09:54:06,2019-01-20 10:08:40,1,3.34,1,N,237,68,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-31 20:34:07,2019-01-31 20:38:52,1,0.5,1,N,234,186,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-13 15:41:51,2019-01-13 15:45:35,1,0.7,1,N,236,238,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2, +1,2019-01-29 23:20:19,2019-01-29 23:28:15,1,1.5,1,N,234,211,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +1,2019-01-15 15:28:49,2019-01-15 15:40:31,1,1.4,1,N,236,238,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-10 13:18:58,2019-01-10 13:21:43,1,0.37,1,N,237,141,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-07 11:25:29,2019-01-07 11:38:20,1,1.94,1,N,231,148,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-24 14:19:18,2019-01-24 14:35:29,1,1.7,1,N,237,238,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-25 00:59:23,2019-01-25 01:17:25,1,4.83,1,N,249,262,1,17.0,0.5,0.5,1.0,0.0,0.3,19.3,0.0 +2,2019-01-10 20:04:03,2019-01-10 20:15:59,1,2.31,1,N,162,90,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-31 10:45:43,2019-01-31 10:59:25,1,1.0,1,N,161,50,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-27 07:44:32,2019-01-27 07:53:05,1,1.7,1,N,107,107,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7,0.0 +1,2019-01-16 06:48:16,2019-01-16 06:53:18,1,1.5,1,N,141,236,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-21 10:36:14,2019-01-21 10:50:50,1,2.5,1,N,43,100,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95,0.0 +1,2019-01-05 01:36:14,2019-01-05 01:45:21,1,2.1,1,N,148,137,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-05 06:18:46,2019-01-05 06:34:07,1,5.7,1,N,234,42,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-20 10:25:47,2019-01-20 10:30:00,1,0.82,1,N,100,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-21 07:49:41,2019-01-21 07:53:35,1,0.6,1,N,231,13,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-21 17:09:46,2019-01-21 17:14:50,1,0.8,1,N,48,163,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-09 16:03:00,2019-01-09 16:14:40,1,1.67,1,N,68,224,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-05 19:21:46,2019-01-05 19:48:51,1,2.7,1,N,90,148,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95, +2,2019-01-25 18:27:53,2019-01-25 18:48:34,1,5.37,1,N,162,87,1,19.5,1.0,0.5,5.32,0.0,0.3,26.62,0.0 +1,2019-01-20 20:00:44,2019-01-20 20:03:46,1,0.9,1,N,74,75,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +2,2019-01-09 13:22:28,2019-01-09 13:26:46,1,0.65,1,N,263,262,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-12 19:16:03,2019-01-12 19:22:33,1,0.38,1,N,79,79,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-22 06:14:08,2019-01-22 06:59:31,1,21.83,1,N,132,26,1,62.0,0.0,0.5,5.0,0.0,0.3,67.8,0.0 +2,2019-01-19 23:26:41,2019-01-19 23:36:14,1,1.43,1,N,148,114,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-05 11:10:29,2019-01-05 11:24:17,1,2.7,1,N,41,143,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-23 22:09:45,2019-01-23 22:17:16,1,1.11,1,N,79,148,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-18 14:40:07,2019-01-18 15:01:42,1,2.58,1,N,237,239,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-23 20:03:40,2019-01-23 20:10:23,1,0.8,1,N,236,262,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-02 09:39:34,2019-01-02 09:43:23,1,0.49,1,N,141,141,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3, +1,2019-01-08 18:57:47,2019-01-08 19:10:20,1,2.0,1,N,186,48,1,10.0,1.0,0.5,2.0,0.0,0.3,13.8, +2,2019-01-26 18:37:07,2019-01-26 18:40:14,1,0.58,1,N,236,236,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-16 13:01:33,2019-01-16 13:13:06,1,1.82,1,N,237,239,1,9.5,0.0,0.5,1.5,0.0,0.3,11.8, +1,2019-01-27 10:39:08,2019-01-27 11:08:59,1,12.2,1,N,56,42,1,36.0,0.0,0.5,0.0,5.76,0.3,42.56,0.0 +2,2019-01-06 16:45:38,2019-01-06 17:33:23,1,18.04,2,N,230,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-28 09:52:46,2019-01-28 10:12:21,1,2.6,1,N,263,161,1,14.0,0.0,0.5,2.0,0.0,0.3,16.8,0.0 +2,2019-01-08 15:32:58,2019-01-08 15:54:33,1,3.06,1,N,143,262,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-28 21:34:47,2019-01-28 21:59:00,1,6.79,1,N,13,238,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16,0.0 +2,2019-01-25 13:30:25,2019-01-25 13:30:45,1,0.0,5,N,265,1,1,85.0,0.0,0.0,10.0,20.0,0.3,115.3,0.0 +2,2019-01-28 21:42:56,2019-01-28 21:48:34,1,1.39,1,N,50,143,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14,0.0 +2,2019-01-08 18:47:09,2019-01-08 18:52:06,1,0.68,1,N,234,170,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-20 14:21:29,2019-01-20 14:30:09,1,0.9,1,N,264,264,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-07 10:46:42,2019-01-07 11:07:04,1,5.04,1,N,7,75,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-09 16:30:38,2019-01-09 16:34:06,1,0.6,1,N,263,141,1,4.5,1.0,0.5,1.58,0.0,0.3,7.88, +2,2019-01-09 15:28:01,2019-01-09 15:35:51,1,0.97,1,N,230,186,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-30 23:30:53,2019-01-30 23:38:21,1,1.7,1,N,79,231,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-31 23:02:24,2019-01-31 23:15:59,1,3.9,1,N,161,232,1,14.5,0.5,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-08 12:47:51,2019-01-08 12:54:44,1,0.95,1,N,186,68,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-20 06:50:56,2019-01-20 07:11:18,1,4.28,1,N,226,50,1,17.5,0.0,0.5,3.0,0.0,0.3,21.3, +1,2019-01-26 18:30:05,2019-01-26 18:34:33,1,2.3,1,N,132,132,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-11 23:36:34,2019-01-11 23:55:34,1,4.87,1,N,158,239,1,18.0,0.5,0.5,4.82,0.0,0.3,24.12, +2,2019-01-28 10:47:39,2019-01-28 10:55:07,1,1.04,1,N,236,237,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-18 21:26:10,2019-01-18 21:45:22,1,6.65,1,N,161,168,1,21.5,0.5,0.5,6.84,0.0,0.3,29.64, +1,2019-01-26 02:15:29,2019-01-26 02:29:36,1,2.1,1,N,158,137,1,11.0,0.5,0.5,1.5,0.0,0.3,13.8,0.0 +2,2019-01-23 13:28:06,2019-01-23 13:35:56,1,1.84,1,N,43,142,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-24 18:34:13,2019-01-24 19:14:25,1,7.85,1,N,90,223,1,31.0,1.0,0.5,8.2,0.0,0.3,41.0,0.0 +2,2019-01-21 20:00:38,2019-01-21 20:10:27,1,2.52,1,N,229,113,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-17 17:47:29,2019-01-17 17:58:45,1,1.9,1,N,100,143,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-22 19:55:50,2019-01-22 20:03:19,1,1.76,1,N,238,166,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-18 19:09:40,2019-01-18 19:12:30,1,0.56,1,N,151,166,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-08 16:35:56,2019-01-08 16:51:22,1,2.3,1,N,230,140,1,12.0,1.0,0.5,2.07,0.0,0.3,15.87, +2,2019-01-16 22:00:10,2019-01-16 22:07:46,1,1.22,1,N,107,68,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-12 13:35:59,2019-01-12 13:43:52,1,0.91,1,N,236,236,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-14 19:43:28,2019-01-14 19:47:03,1,0.75,1,N,238,143,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-05 15:04:13,2019-01-05 15:09:34,1,0.7,1,N,151,238,1,5.0,0.0,0.5,2.0,0.0,0.3,7.8, +2,2019-01-28 18:17:22,2019-01-28 18:29:49,1,1.49,1,N,234,148,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-21 10:52:22,2019-01-21 10:57:28,1,0.72,1,N,239,142,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-11 11:24:38,2019-01-11 11:43:50,1,2.72,1,N,263,142,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-11 23:25:00,2019-01-11 23:29:36,1,0.4,1,N,144,148,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-14 15:52:01,2019-01-14 16:15:35,1,3.1,1,N,163,144,2,16.5,1.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-09 19:21:55,2019-01-09 19:27:08,1,0.8,1,N,161,142,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-11 23:31:59,2019-01-11 23:58:48,1,7.32,1,N,48,33,1,25.0,0.5,0.5,6.58,0.0,0.3,32.88, +2,2019-01-30 20:17:15,2019-01-30 20:31:30,1,2.98,1,N,230,236,1,12.5,0.5,0.5,2.2,0.0,0.3,16.0,0.0 +2,2019-01-07 06:06:41,2019-01-07 06:26:01,1,4.32,1,N,65,234,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25, +2,2019-01-29 17:11:22,2019-01-29 17:23:37,1,1.84,1,N,239,237,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-16 09:26:21,2019-01-16 09:29:34,1,0.6,1,N,186,68,1,4.0,0.0,0.5,1.4,0.0,0.3,6.2, +1,2019-01-18 23:19:43,2019-01-18 23:39:04,1,3.0,1,N,158,233,1,14.0,0.5,0.5,3.05,0.0,0.3,18.35, +2,2019-01-26 14:38:02,2019-01-26 14:57:06,1,11.44,1,N,132,175,1,31.5,0.0,0.5,6.46,0.0,0.3,38.76,0.0 +2,2019-01-19 09:24:30,2019-01-19 09:34:10,1,2.59,1,N,263,170,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-17 10:48:38,2019-01-17 10:54:30,1,0.5,1,N,237,263,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-08 14:10:50,2019-01-08 14:23:51,1,2.8,1,N,236,41,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-11 08:29:33,2019-01-11 09:25:20,1,11.5,1,N,132,61,1,43.0,0.0,0.5,8.75,0.0,0.3,52.55, +1,2019-01-23 17:49:17,2019-01-23 18:04:57,1,1.7,1,N,113,161,1,11.0,1.0,0.5,2.0,0.0,0.3,14.8,0.0 +2,2019-01-07 18:59:55,2019-01-07 19:15:54,1,2.32,1,N,263,170,1,11.5,1.0,0.5,3.32,0.0,0.3,16.62, +2,2019-01-26 10:41:27,2019-01-26 10:47:09,1,1.42,1,N,237,238,1,6.5,0.0,0.5,1.02,0.0,0.3,8.32,0.0 +1,2019-01-10 23:30:54,2019-01-10 23:39:49,1,1.9,1,N,234,162,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-23 11:13:04,2019-01-23 11:42:51,1,10.36,1,N,113,14,1,32.0,0.0,0.5,0.0,5.76,0.3,38.56,0.0 +2,2019-01-04 18:25:43,2019-01-04 18:32:24,1,1.6,1,N,166,238,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-29 07:46:06,2019-01-29 08:01:48,1,2.75,1,N,142,234,1,12.5,0.0,0.5,1.11,0.0,0.3,14.41,0.0 +1,2019-01-18 19:32:26,2019-01-18 19:34:09,1,0.1,1,N,100,100,1,3.0,1.0,0.5,1.0,0.0,0.3,5.8, +2,2019-01-16 06:54:19,2019-01-16 06:57:28,1,0.56,1,N,170,137,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-21 18:05:15,2019-01-21 18:10:00,1,0.56,1,N,79,114,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-05 02:10:27,2019-01-05 02:18:52,1,1.19,1,N,79,79,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-27 11:06:40,2019-01-27 11:25:10,1,4.87,1,N,48,13,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-24 15:39:38,2019-01-24 15:50:53,1,1.47,1,N,170,246,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-10 17:12:08,2019-01-10 17:15:42,1,0.4,1,N,264,264,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-10 19:45:48,2019-01-10 19:55:57,1,1.42,1,N,162,141,1,8.5,1.0,0.5,3.09,0.0,0.3,13.39, +2,2019-01-10 19:38:23,2019-01-10 19:52:17,1,6.51,1,N,138,112,1,20.0,1.0,0.5,5.45,0.0,0.3,27.25, +2,2019-01-08 19:30:09,2019-01-08 19:39:01,1,1.31,1,N,170,237,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-17 12:50:55,2019-01-17 13:05:42,1,2.2,1,N,43,143,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-24 18:46:23,2019-01-24 19:03:37,1,3.7,1,N,246,88,2,15.5,1.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-14 10:18:17,2019-01-14 10:35:12,1,1.0,1,N,186,161,1,11.5,0.0,0.5,1.85,0.0,0.3,14.15, +2,2019-01-23 09:42:12,2019-01-23 09:47:56,1,0.96,1,N,151,236,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-16 23:17:50,2019-01-16 23:18:53,1,0.3,1,N,263,141,1,3.0,0.5,0.5,0.85,0.0,0.3,5.15, +2,2019-01-03 11:48:15,2019-01-03 12:01:36,1,0.92,1,N,236,237,1,8.5,0.0,0.5,2.0,0.0,0.3,11.3, +2,2019-01-30 14:28:23,2019-01-30 14:36:31,1,2.36,1,N,140,170,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-28 11:09:22,2019-01-28 11:15:26,1,1.06,1,N,246,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-07 20:57:34,2019-01-07 21:37:52,1,8.4,1,N,186,70,2,31.5,0.5,0.5,0.0,0.0,0.3,32.8, +1,2019-01-14 08:49:39,2019-01-14 09:04:44,1,4.0,1,N,48,41,1,15.0,0.0,0.5,2.37,0.0,0.3,18.17, +2,2019-01-16 10:26:04,2019-01-16 10:49:31,1,4.29,1,N,100,261,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-26 10:51:14,2019-01-26 11:02:44,1,1.15,1,N,249,234,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-03 10:44:44,2019-01-03 11:06:01,1,6.0,1,N,50,87,1,20.5,0.0,0.5,5.3,0.0,0.3,26.6, +2,2019-01-11 13:08:33,2019-01-11 13:19:57,1,0.97,1,N,161,162,1,8.5,0.0,0.5,2.79,0.0,0.3,12.09, +1,2019-01-28 23:48:12,2019-01-28 23:55:01,1,1.4,1,N,264,264,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-01 00:17:00,2019-01-01 00:26:47,1,1.5,1,N,114,232,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-01 10:37:10,2019-01-01 10:56:30,1,6.06,1,N,48,157,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-10 08:35:49,2019-01-10 08:49:37,1,1.2,1,N,186,170,1,9.5,0.0,0.5,3.05,0.0,0.3,13.35, +2,2019-01-08 11:29:52,2019-01-08 11:35:52,1,0.74,1,N,239,239,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-14 22:51:36,2019-01-14 23:21:53,1,5.3,1,N,162,129,1,23.0,0.5,0.5,4.85,0.0,0.3,29.15, +2,2019-01-20 17:01:58,2019-01-20 17:10:29,1,2.04,1,N,230,236,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-17 09:11:17,2019-01-17 09:21:11,1,1.1,1,N,236,75,1,8.0,0.0,0.5,2.0,0.0,0.3,10.8, +1,2019-01-01 04:42:28,2019-01-01 04:45:23,1,0.7,1,N,82,173,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-28 17:46:00,2019-01-28 17:49:51,1,1.0,1,N,238,24,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-08 19:45:37,2019-01-08 20:01:02,1,3.1,1,N,74,229,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-18 08:19:10,2019-01-18 08:22:07,1,0.6,1,N,229,237,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-22 12:47:24,2019-01-22 12:54:14,1,1.07,1,N,137,79,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-19 23:10:40,2019-01-19 23:16:34,1,0.85,1,N,48,161,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-10 22:10:03,2019-01-10 22:18:08,1,2.29,1,N,249,13,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-01 16:34:24,2019-01-01 16:54:25,1,2.6,1,N,113,163,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-04 20:35:03,2019-01-04 20:39:41,1,0.93,1,N,230,142,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-19 12:32:35,2019-01-19 13:14:39,1,5.73,1,N,48,42,1,28.0,0.0,0.5,7.2,0.0,0.3,36.0, +2,2019-01-29 19:23:58,2019-01-29 19:29:00,1,1.12,1,N,239,24,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-07 10:05:41,2019-01-07 10:12:40,1,1.5,1,N,107,233,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-25 18:03:15,2019-01-25 18:09:15,1,0.9,1,N,68,249,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-18 17:37:55,2019-01-18 17:58:36,1,5.64,1,N,87,162,1,20.5,1.0,0.5,4.46,0.0,0.3,26.76, +2,2019-01-22 12:11:07,2019-01-22 12:23:32,1,0.98,1,N,107,164,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-26 15:20:43,2019-01-26 15:24:37,1,0.74,1,N,142,239,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-04 11:51:44,2019-01-04 12:01:36,1,1.1,1,N,144,125,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-16 15:15:32,2019-01-16 15:22:55,1,1.36,1,N,236,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-19 18:22:31,2019-01-19 18:46:24,1,3.18,1,N,79,48,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-30 18:40:24,2019-01-30 18:48:42,1,0.52,1,N,164,186,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-31 19:35:10,2019-01-31 19:48:49,1,1.74,1,N,141,161,1,10.5,1.0,0.5,1.0,0.0,0.3,13.3,0.0 +1,2019-01-02 21:41:19,2019-01-02 21:49:55,1,1.1,1,N,114,231,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +2,2019-01-19 11:20:56,2019-01-19 11:26:55,1,1.01,1,N,239,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-23 22:25:49,2019-01-23 22:40:13,1,1.35,1,N,161,48,1,10.0,0.5,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-19 11:01:59,2019-01-19 11:31:17,1,9.15,1,N,138,230,1,30.5,0.0,0.5,5.56,5.76,0.3,42.62, +2,2019-01-18 18:10:00,2019-01-18 18:21:23,1,1.91,1,N,75,238,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-03 08:49:57,2019-01-03 09:09:10,1,7.0,1,N,236,87,1,22.0,0.0,0.5,2.0,0.0,0.3,24.8, +2,2019-01-15 15:04:37,2019-01-15 15:18:30,1,2.18,1,N,161,114,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-13 11:31:18,2019-01-13 11:32:36,1,0.36,1,N,237,237,1,3.0,0.0,0.5,0.76,0.0,0.3,4.56, +2,2019-01-22 13:52:02,2019-01-22 14:00:56,1,0.94,1,N,90,79,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-30 09:37:21,2019-01-30 09:48:14,1,2.07,1,N,170,114,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-19 14:59:32,2019-01-19 15:11:22,1,1.46,1,N,113,158,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-17 05:51:07,2019-01-17 06:01:16,1,5.69,1,N,70,168,2,16.5,0.5,0.5,0.0,5.76,0.3,23.56, +2,2019-01-09 13:50:48,2019-01-09 14:00:48,1,0.72,1,N,162,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +4,2019-01-08 12:52:16,2019-01-08 12:54:06,1,0.31,1,N,48,163,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-22 19:06:44,2019-01-22 19:19:52,1,1.3,1,N,264,264,1,9.5,1.0,0.5,0.56,0.0,0.3,11.86,0.0 +2,2019-01-21 09:57:28,2019-01-21 10:13:01,1,1.45,1,N,162,186,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-16 11:56:03,2019-01-16 12:04:54,1,2.2,1,N,263,229,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-31 21:11:45,2019-01-31 21:19:00,1,0.86,1,N,230,233,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-04 01:16:52,2019-01-04 01:31:10,1,2.93,1,N,114,163,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +2,2019-01-09 05:29:49,2019-01-09 05:45:57,1,1.99,1,N,170,79,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-02 09:02:16,2019-01-02 09:26:21,1,4.02,1,N,151,164,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96, +2,2019-01-27 01:17:53,2019-01-27 01:24:10,1,1.04,1,N,107,90,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-13 17:05:26,2019-01-13 17:12:35,1,0.9,1,N,163,237,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-20 11:30:06,2019-01-20 11:36:00,1,1.04,1,N,186,90,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-11 11:52:01,2019-01-11 12:10:37,1,2.8,1,N,234,142,1,14.0,0.0,0.5,1.78,0.0,0.3,16.58, +1,2019-01-28 22:55:25,2019-01-28 23:04:29,1,1.3,1,N,230,229,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-17 14:44:13,2019-01-17 15:06:22,1,2.49,1,N,158,170,1,14.5,0.0,0.5,4.59,0.0,0.3,19.89, +1,2019-01-31 17:56:13,2019-01-31 18:21:01,1,4.1,1,N,166,141,1,18.5,1.0,0.5,4.05,0.0,0.3,24.35,0.0 +2,2019-01-08 18:47:35,2019-01-08 18:56:29,1,1.78,1,N,163,263,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-05 16:24:41,2019-01-05 16:31:05,1,0.89,1,N,234,158,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-17 12:50:44,2019-01-17 13:01:35,1,1.6,1,N,142,236,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-30 16:46:54,2019-01-30 16:59:04,1,1.58,1,N,100,161,1,9.5,1.0,0.5,2.82,0.0,0.3,14.12,0.0 +1,2019-01-30 16:19:15,2019-01-30 16:23:58,1,0.8,1,N,263,75,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-05 17:28:58,2019-01-05 17:48:00,1,2.7,1,N,90,87,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-04 08:43:46,2019-01-04 09:00:44,1,0.77,1,N,233,164,1,11.0,0.0,0.5,1.5,0.0,0.3,13.3, +2,2019-01-10 05:27:41,2019-01-10 05:32:40,1,1.16,1,N,249,231,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-07 05:13:14,2019-01-07 05:19:54,1,2.45,1,N,236,164,1,9.0,0.5,0.5,0.7,0.0,0.3,11.0, +2,2019-01-17 14:32:37,2019-01-17 14:36:57,1,0.4,1,N,234,234,1,4.5,0.0,0.5,0.8,0.0,0.3,6.1, +2,2019-01-27 02:08:50,2019-01-27 02:11:16,1,0.61,1,N,142,239,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-11 14:47:35,2019-01-11 14:58:30,1,1.12,1,N,237,163,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-23 16:28:24,2019-01-23 16:38:09,1,1.4,1,N,170,163,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-17 20:32:58,2019-01-17 20:39:42,1,1.3,1,N,68,249,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +4,2019-01-22 15:59:17,2019-01-22 16:11:26,1,0.73,1,N,237,237,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-09 18:37:28,2019-01-09 18:39:59,1,0.8,1,N,264,264,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-06 20:25:08,2019-01-06 20:50:18,1,5.86,1,N,262,7,1,21.0,0.5,0.5,0.0,0.0,0.3,22.3, +1,2019-01-13 22:23:13,2019-01-13 22:33:46,1,5.3,1,N,138,168,2,16.5,0.5,0.5,0.0,5.76,0.3,23.56, +2,2019-01-17 15:37:47,2019-01-17 16:30:11,1,14.95,1,N,231,138,1,48.5,0.0,0.5,13.76,5.76,0.3,68.82, +1,2019-01-22 17:44:53,2019-01-22 18:02:08,1,1.2,1,N,48,237,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-02 22:12:44,2019-01-02 22:32:20,1,5.34,1,N,237,255,1,18.5,0.5,0.5,4.95,0.0,0.3,24.75, +1,2019-01-07 22:47:12,2019-01-07 23:09:49,1,10.5,1,N,138,163,1,31.5,0.5,0.5,7.7,5.76,0.3,46.26, +2,2019-01-01 15:36:22,2019-01-01 15:54:16,1,2.24,1,N,68,164,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-05 09:21:21,2019-01-05 09:38:27,1,6.08,1,N,230,88,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8, +2,2019-01-26 16:56:57,2019-01-26 17:07:10,1,1.67,1,N,230,237,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-05 21:35:46,2019-01-05 22:02:09,1,4.5,1,N,114,263,1,19.5,0.5,0.5,3.2,0.0,0.3,24.0, +2,2019-01-09 20:20:18,2019-01-09 20:41:10,1,7.83,1,N,25,140,1,25.0,0.5,0.5,3.94,0.0,0.3,30.24, +2,2019-01-24 20:33:04,2019-01-24 20:59:58,1,15.29,1,N,132,149,2,42.5,0.5,0.5,0.0,0.0,0.3,43.8,0.0 +2,2019-01-09 01:46:17,2019-01-09 02:00:16,1,3.46,1,N,237,68,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +1,2019-01-27 22:26:00,2019-01-27 22:35:43,1,2.4,1,N,48,142,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-31 22:17:36,2019-01-31 22:32:46,1,2.5,1,N,48,90,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-12 15:04:19,2019-01-12 15:08:10,1,0.4,1,N,229,162,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-01 01:19:33,2019-01-01 01:25:39,1,0.0,1,N,68,68,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +4,2019-01-01 12:16:02,2019-01-01 12:19:58,1,0.73,1,N,249,158,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-18 21:16:24,2019-01-18 21:21:29,1,0.7,1,N,237,163,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +2,2019-01-16 08:39:28,2019-01-16 08:45:07,1,1.05,1,N,68,68,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-16 05:38:31,2019-01-16 05:48:03,1,3.7,1,N,79,80,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +1,2019-01-23 14:49:23,2019-01-23 15:04:06,1,4.2,1,N,152,142,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +1,2019-01-26 18:38:54,2019-01-26 18:48:47,1,1.5,1,N,170,107,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-31 14:13:04,2019-01-31 14:20:54,1,0.7,1,N,264,264,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-16 13:39:05,2019-01-16 13:58:28,1,5.55,1,N,170,88,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +4,2019-01-01 16:51:07,2019-01-01 17:00:48,1,3.11,1,N,138,7,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-18 18:22:40,2019-01-18 18:48:07,1,3.11,1,N,230,79,1,17.0,1.0,0.5,4.7,0.0,0.3,23.5, +1,2019-01-01 03:31:08,2019-01-01 04:14:08,1,25.4,3,N,192,1,1,85.5,0.5,0.0,10.0,15.6,0.3,111.9, +2,2019-01-25 18:06:25,2019-01-25 18:17:27,1,1.13,1,N,186,161,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-26 14:09:17,2019-01-26 14:12:34,1,0.4,1,N,238,238,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-30 14:44:34,2019-01-30 15:02:40,1,2.73,1,N,163,68,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-16 17:36:32,2019-01-16 17:55:48,1,3.3,1,N,88,90,1,15.0,1.0,0.5,1.68,0.0,0.3,18.48, +1,2019-01-08 23:06:18,2019-01-08 23:34:07,1,7.2,1,Y,79,42,1,24.0,0.5,0.5,1.5,0.0,0.3,26.8, +1,2019-01-30 08:18:50,2019-01-30 08:25:38,1,3.6,1,N,224,88,1,12.0,0.0,0.5,1.28,0.0,0.3,14.08,0.0 +2,2019-01-16 19:21:29,2019-01-16 19:30:36,1,2.64,1,N,141,41,1,10.0,1.0,0.5,2.0,0.0,0.3,13.8, +1,2019-01-15 08:55:45,2019-01-15 08:57:06,1,0.2,1,N,148,148,4,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-24 22:25:15,2019-01-24 23:17:49,1,11.94,1,N,68,87,1,43.0,0.5,0.5,6.0,0.0,0.3,50.3,0.0 +2,2019-01-07 16:55:14,2019-01-07 17:48:39,1,15.44,1,N,22,83,1,48.0,1.0,0.5,0.0,0.0,0.3,49.8, +2,2019-01-01 23:39:10,2019-01-02 00:02:28,1,15.86,1,N,132,255,2,42.5,0.5,0.5,0.0,0.0,0.3,43.8, +2,2019-01-11 21:26:19,2019-01-11 21:30:25,1,0.9,1,N,142,239,1,5.0,0.5,0.5,3.0,0.0,0.3,9.3, +1,2019-01-08 08:31:17,2019-01-08 08:42:42,1,1.3,1,N,48,233,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-03 10:13:53,2019-01-03 10:15:31,1,0.4,1,N,237,141,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-17 22:24:37,2019-01-17 22:31:50,1,1.26,1,N,50,68,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-18 16:45:13,2019-01-18 16:50:22,1,0.76,1,N,141,263,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-23 21:08:13,2019-01-23 21:24:07,1,5.6,1,N,13,137,1,19.0,0.5,0.5,4.06,0.0,0.3,24.36,0.0 +2,2019-01-19 01:21:03,2019-01-19 01:24:55,1,0.75,1,N,230,48,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-03 10:04:08,2019-01-03 10:10:53,1,1.12,1,N,50,230,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-08 18:09:54,2019-01-08 18:37:45,1,3.3,1,N,163,144,1,18.5,1.0,0.5,1.0,0.0,0.3,21.3, +1,2019-01-12 18:22:54,2019-01-12 18:41:45,1,2.0,1,N,79,158,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-14 11:31:32,2019-01-14 11:36:08,1,0.32,1,N,237,163,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +1,2019-01-16 14:18:09,2019-01-16 14:28:40,1,1.1,1,N,237,162,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-15 16:23:21,2019-01-15 16:34:46,1,1.46,1,N,162,140,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-24 18:24:19,2019-01-24 18:38:10,1,2.93,1,N,236,166,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-09 06:04:23,2019-01-09 06:18:43,1,4.64,1,N,92,138,1,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-06 16:12:20,2019-01-06 16:12:27,1,0.0,5,N,265,265,1,80.0,0.0,0.0,11.0,0.0,0.3,91.3, +1,2019-01-05 16:08:01,2019-01-05 16:28:17,1,3.2,1,N,230,114,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-25 05:56:46,2019-01-25 06:01:50,1,1.13,1,N,48,161,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-06 11:54:55,2019-01-06 12:03:35,1,1.78,1,N,107,211,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-22 18:24:56,2019-01-22 18:45:13,1,4.5,1,N,230,166,1,18.5,1.0,0.5,5.05,0.0,0.3,25.35,0.0 +2,2019-01-19 16:24:02,2019-01-19 16:39:32,1,2.04,1,N,113,230,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-14 10:57:01,2019-01-14 11:12:37,1,1.14,1,N,162,100,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-07 13:10:51,2019-01-07 13:20:18,1,1.1,1,N,261,231,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-23 12:26:21,2019-01-23 12:33:30,1,1.0,1,N,239,238,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-31 06:44:37,2019-01-31 06:55:35,1,2.62,1,N,52,209,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-20 12:06:50,2019-01-20 12:14:16,1,1.18,1,N,75,238,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-26 16:12:31,2019-01-26 16:18:57,1,1.1,1,N,264,264,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-10 18:16:38,2019-01-10 18:32:15,1,1.7,1,N,75,238,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-10 12:51:37,2019-01-10 13:05:34,1,3.22,1,N,166,244,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-14 06:11:12,2019-01-14 06:15:50,1,0.6,1,N,100,161,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +2,2019-01-25 11:10:06,2019-01-25 11:24:17,1,5.11,1,N,87,162,1,17.0,0.0,0.5,1.0,0.0,0.3,18.8,0.0 +2,2019-01-04 11:57:15,2019-01-04 12:05:12,1,1.49,1,N,249,100,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-16 18:25:04,2019-01-16 18:33:37,1,0.75,1,N,263,236,1,7.0,1.0,0.5,2.64,0.0,0.3,11.44, +1,2019-01-15 14:35:23,2019-01-15 14:48:10,1,2.2,1,N,140,75,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +1,2019-01-13 05:44:29,2019-01-13 06:11:02,1,17.5,2,N,161,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-07 23:29:38,2019-01-07 23:34:59,1,0.93,1,N,249,186,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-29 18:59:43,2019-01-29 19:08:47,1,1.04,1,N,236,236,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-04 21:26:40,2019-01-04 21:32:35,1,1.34,1,N,48,239,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-12 20:48:05,2019-01-12 20:51:09,1,0.6,1,N,141,141,1,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-18 21:21:04,2019-01-18 21:30:32,1,0.81,1,N,113,249,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-24 19:40:04,2019-01-24 19:44:48,1,0.4,1,N,236,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-25 14:57:22,2019-01-25 15:03:19,1,1.4,1,N,152,24,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-28 08:08:52,2019-01-28 08:23:31,1,2.0,1,N,79,87,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-09 08:04:42,2019-01-09 08:12:02,1,1.2,1,N,142,230,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-03 10:30:45,2019-01-03 10:44:52,1,1.7,1,N,107,230,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-16 21:05:43,2019-01-16 21:10:16,1,0.76,1,N,161,100,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-04 07:01:08,2019-01-04 07:14:39,1,2.6,1,N,186,143,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-29 20:06:48,2019-01-29 20:32:29,1,6.82,1,N,162,13,1,23.5,0.5,0.5,3.72,0.0,0.3,28.52,0.0 +2,2019-01-07 11:07:12,2019-01-07 11:16:47,1,1.11,1,N,141,237,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79, +2,2019-01-26 01:53:40,2019-01-26 02:13:53,1,6.55,1,N,68,181,2,21.0,0.5,0.5,0.0,5.76,0.3,28.06,0.0 +1,2019-01-13 16:26:51,2019-01-13 16:29:55,1,0.8,1,N,107,79,1,4.5,0.0,0.5,0.85,0.0,0.3,6.15, +1,2019-01-21 10:44:37,2019-01-21 10:55:41,1,2.4,1,N,262,142,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-03 09:30:35,2019-01-03 09:37:17,1,1.34,1,N,143,163,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-25 15:29:25,2019-01-25 15:49:07,1,1.89,1,N,113,231,1,13.0,0.0,0.5,4.14,0.0,0.3,17.94,0.0 +1,2019-01-10 16:57:34,2019-01-10 17:12:23,1,2.9,1,N,262,170,1,13.5,1.0,0.5,2.0,0.0,0.3,17.3, +2,2019-01-28 19:13:47,2019-01-28 19:38:11,1,6.04,1,N,162,88,1,21.5,1.0,0.5,5.82,0.0,0.3,29.12,0.0 +2,2019-01-25 17:54:04,2019-01-25 18:05:39,1,2.41,1,N,163,151,1,10.5,1.0,0.5,3.08,0.0,0.3,15.38,0.0 +2,2019-01-16 15:08:08,2019-01-16 15:11:08,1,0.6,1,N,142,163,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-10 10:12:30,2019-01-10 11:02:59,1,14.69,1,N,132,25,1,47.5,0.0,0.5,9.66,0.0,0.3,57.96, +1,2019-01-16 11:52:06,2019-01-16 12:02:36,1,0.7,1,N,186,230,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-07 10:40:46,2019-01-07 10:44:38,1,0.8,1,N,262,140,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-09 12:37:39,2019-01-09 12:50:16,1,1.5,1,N,43,237,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +2,2019-01-12 20:11:40,2019-01-12 20:14:23,1,0.53,1,N,68,48,1,4.0,0.5,0.5,1.59,0.0,0.3,6.89, +1,2019-01-26 13:10:01,2019-01-26 13:15:08,1,0.7,1,N,229,162,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-13 19:53:00,2019-01-13 20:13:39,1,3.95,1,N,50,144,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +1,2019-01-11 21:50:16,2019-01-11 21:57:10,1,1.3,1,N,75,238,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +2,2019-01-31 10:20:11,2019-01-31 10:24:29,1,0.48,1,N,263,236,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-15 05:04:07,2019-01-15 05:32:00,1,10.25,1,N,177,137,1,32.0,0.5,0.5,0.0,0.0,0.3,33.3, +2,2019-01-15 11:21:34,2019-01-15 11:43:23,1,1.67,1,N,246,163,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-14 12:11:26,2019-01-14 12:24:48,1,0.8,1,N,142,163,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85, +2,2019-01-07 09:21:14,2019-01-07 09:27:19,1,0.93,1,N,68,48,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-04 13:01:51,2019-01-04 13:14:41,1,1.69,1,N,249,148,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-24 16:35:54,2019-01-24 16:53:47,1,2.3,1,N,75,237,1,12.5,1.0,0.5,1.5,0.0,0.3,15.8,0.0 +2,2019-01-12 19:44:25,2019-01-12 19:51:28,1,0.51,1,N,158,249,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-29 12:31:40,2019-01-29 12:56:54,1,5.0,1,N,261,107,1,21.0,0.0,0.5,4.35,0.0,0.3,26.15,0.0 +2,2019-01-12 14:53:01,2019-01-12 15:15:47,1,2.55,1,N,141,142,1,15.5,0.0,0.5,2.0,0.0,0.3,18.3, +2,2019-01-25 02:49:09,2019-01-25 02:54:11,1,1.69,1,N,229,263,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38,0.0 +1,2019-01-26 11:14:39,2019-01-26 11:18:10,1,0.9,1,N,263,141,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-11 15:02:01,2019-01-11 15:16:10,1,2.16,1,N,261,148,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-14 23:06:44,2019-01-14 23:19:53,1,2.8,1,N,230,262,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-12 16:35:08,2019-01-12 17:21:43,1,10.8,1,N,43,181,1,37.5,0.0,0.5,1.7,0.0,0.3,40.0, +1,2019-01-07 07:12:19,2019-01-07 07:16:40,1,1.5,1,N,90,161,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-10 15:41:37,2019-01-10 15:51:58,1,0.86,1,N,68,234,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-29 18:34:34,2019-01-29 18:56:35,1,4.28,1,N,48,231,1,18.0,1.0,0.5,3.96,0.0,0.3,23.76,0.0 +2,2019-01-11 11:05:42,2019-01-11 11:18:08,1,2.85,1,N,143,68,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-01 13:45:06,2019-01-01 13:47:08,1,0.41,1,N,237,43,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-31 16:04:40,2019-01-31 16:09:49,1,1.26,1,N,158,246,1,6.0,1.0,0.5,1.4,0.0,0.3,9.2,0.0 +1,2019-01-18 06:33:53,2019-01-18 06:56:41,1,9.4,1,N,249,243,1,29.0,0.0,0.5,7.45,0.0,0.3,37.25, +2,2019-01-13 00:19:13,2019-01-13 00:32:26,1,2.03,1,N,231,79,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-15 04:58:55,2019-01-15 05:03:26,1,1.1,1,N,88,231,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-09 12:47:06,2019-01-09 12:58:17,1,1.03,1,N,90,107,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-22 17:14:18,2019-01-22 17:30:10,1,2.1,1,N,186,79,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-01 13:45:23,2019-01-01 13:55:58,1,2.09,1,N,68,164,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-15 17:15:39,2019-01-15 17:19:58,1,0.8,1,N,163,237,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-18 04:45:45,2019-01-18 05:04:44,1,8.8,1,N,237,138,1,26.5,0.5,0.5,5.0,5.76,0.3,38.56, +1,2019-01-30 22:48:53,2019-01-30 22:53:52,1,0.4,1,N,264,264,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-26 23:29:36,2019-01-26 23:54:51,1,3.8,1,N,249,162,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-15 21:39:15,2019-01-15 21:42:02,1,0.52,1,N,141,43,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-06 03:12:44,2019-01-06 03:26:23,1,4.65,1,N,170,151,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +2,2019-01-23 09:55:15,2019-01-23 10:08:54,1,1.72,1,N,50,163,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-30 09:21:26,2019-01-30 09:24:53,1,0.55,1,N,162,170,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-04 21:50:12,2019-01-04 22:06:29,1,3.23,1,N,144,229,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +1,2019-01-29 22:37:42,2019-01-29 22:56:19,1,7.0,1,N,162,65,1,21.5,0.5,0.5,4.55,0.0,0.3,27.35,0.0 +2,2019-01-11 18:28:09,2019-01-11 18:40:41,1,2.47,1,N,65,79,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-06 00:45:39,2019-01-06 00:46:11,1,2.5,1,N,137,137,3,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +1,2019-01-31 20:13:26,2019-01-31 20:19:50,1,1.4,1,N,211,79,1,6.5,0.5,0.5,1.7,0.0,0.3,9.5,0.0 +2,2019-01-11 11:48:00,2019-01-11 12:00:34,1,2.05,1,N,125,224,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-07 10:23:05,2019-01-07 10:26:27,1,0.62,1,N,43,50,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-31 18:56:57,2019-01-31 19:13:00,1,1.6,1,N,107,211,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-14 13:42:34,2019-01-14 13:45:25,1,0.7,1,N,238,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-15 08:42:40,2019-01-15 08:57:14,1,1.5,1,N,68,137,1,10.0,0.0,0.5,2.5,0.0,0.3,13.3, +2,2019-01-16 22:19:16,2019-01-16 22:34:25,1,1.78,1,N,161,162,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-15 08:58:38,2019-01-15 09:04:03,1,1.14,1,N,100,164,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-13 06:07:12,2019-01-13 06:35:39,1,7.7,1,N,80,48,1,25.5,0.0,0.5,5.25,0.0,0.3,31.55, +1,2019-01-18 13:49:40,2019-01-18 14:14:52,1,5.6,1,N,261,163,1,22.0,0.0,0.5,4.55,0.0,0.3,27.35, +2,2019-01-04 05:27:30,2019-01-04 05:50:50,1,10.82,1,N,249,95,2,32.0,0.5,0.5,0.0,0.0,0.3,33.3, +1,2019-01-24 14:25:39,2019-01-24 14:33:32,1,1.1,1,N,170,163,1,6.5,0.0,0.5,3.0,0.0,0.3,10.3,0.0 +1,2019-01-24 15:42:22,2019-01-24 16:15:32,1,8.4,1,N,141,33,2,27.0,0.0,0.5,0.0,0.0,0.3,27.8,0.0 +2,2019-01-04 13:01:23,2019-01-04 13:11:12,1,1.41,1,N,79,232,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-26 22:34:16,2019-01-26 22:41:04,1,1.1,1,N,79,137,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-02 08:12:44,2019-01-02 08:15:47,1,0.57,1,N,137,164,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-15 17:16:13,2019-01-15 17:22:43,1,1.11,1,N,142,230,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-02 11:34:04,2019-01-02 11:59:01,1,2.85,1,N,164,50,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-27 18:16:47,2019-01-27 18:38:02,1,6.2,1,N,263,244,1,22.0,0.0,0.5,4.0,0.0,0.3,26.8,0.0 +2,2019-01-02 01:18:31,2019-01-02 01:22:24,1,0.96,1,N,231,87,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-29 07:54:40,2019-01-29 08:20:50,1,2.95,1,N,229,75,1,17.5,0.0,0.5,4.58,0.0,0.3,22.88,0.0 +1,2019-01-23 22:26:11,2019-01-23 22:36:31,1,2.0,1,N,141,142,1,9.5,0.5,0.5,1.62,0.0,0.3,12.42,0.0 +1,2019-01-24 08:19:50,2019-01-24 08:30:59,1,1.1,1,N,237,163,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-24 22:31:21,2019-01-24 23:14:09,1,5.17,1,N,186,25,1,28.0,0.5,0.5,5.86,0.0,0.3,35.16,0.0 +1,2019-01-21 20:28:30,2019-01-21 20:35:04,1,1.1,1,N,246,158,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-24 10:54:44,2019-01-24 11:02:55,1,1.63,1,N,238,75,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +4,2019-01-24 07:01:39,2019-01-24 07:23:56,1,4.05,1,N,230,87,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25, +1,2019-01-16 18:57:37,2019-01-16 19:10:47,1,1.4,1,N,90,107,1,9.5,1.0,0.5,2.0,0.0,0.3,13.3, +2,2019-01-21 17:12:54,2019-01-21 17:20:51,1,1.39,1,N,163,186,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-18 15:47:54,2019-01-18 16:03:02,1,2.76,1,N,231,90,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-28 20:50:23,2019-01-28 21:00:25,1,3.3,1,N,246,238,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-08 16:36:11,2019-01-08 16:44:48,1,1.06,1,N,236,263,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-22 15:11:27,2019-01-22 15:26:26,1,2.19,1,N,161,236,1,11.5,0.0,0.5,1.7,0.0,0.3,14.0,0.0 +2,2019-01-07 19:23:59,2019-01-07 19:36:45,1,2.09,1,N,230,107,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-27 15:37:28,2019-01-27 15:42:06,1,0.82,1,N,236,263,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-26 20:43:57,2019-01-26 21:03:51,1,4.4,1,N,13,230,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3,0.0 +2,2019-01-11 07:52:52,2019-01-11 07:58:25,1,0.49,1,N,140,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-17 13:31:37,2019-01-17 13:41:03,1,0.96,1,N,163,142,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79, +1,2019-01-15 07:52:30,2019-01-15 07:59:22,1,0.6,1,N,264,264,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-10 07:13:46,2019-01-10 07:21:06,1,1.72,1,N,263,229,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-30 06:01:45,2019-01-30 06:26:18,1,19.19,2,N,263,132,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2,0.0 +2,2019-01-27 10:56:42,2019-01-27 11:02:52,1,1.58,1,N,137,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-25 06:43:24,2019-01-25 06:47:44,1,0.7,1,N,48,230,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-31 17:11:06,2019-01-31 17:15:53,1,0.4,1,N,162,229,1,5.0,1.0,0.5,1.2,0.0,0.3,8.0,0.0 +1,2019-01-16 10:10:50,2019-01-16 10:17:41,1,0.5,1,N,163,163,1,6.0,0.0,0.5,2.0,0.0,0.3,8.8, +1,2019-01-24 06:39:44,2019-01-24 06:49:44,1,2.2,1,N,237,238,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-13 19:46:24,2019-01-13 20:09:32,1,10.31,1,N,138,97,1,31.0,0.0,0.5,7.95,0.0,0.3,39.75, +1,2019-01-23 00:01:45,2019-01-23 00:13:47,1,3.6,1,N,158,237,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55,0.0 +1,2019-01-18 19:19:14,2019-01-18 19:27:20,1,1.4,1,N,181,25,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25, +1,2019-01-30 12:14:06,2019-01-30 12:25:52,1,1.0,1,N,113,107,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-04 23:03:06,2019-01-04 23:06:39,1,0.5,1,N,264,264,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-15 23:48:27,2019-01-15 23:59:40,1,2.37,1,N,48,141,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-12 11:47:32,2019-01-12 12:06:06,1,2.2,1,N,186,114,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +1,2019-01-12 04:01:02,2019-01-12 04:12:28,1,2.8,1,N,148,170,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +1,2019-01-24 12:20:12,2019-01-24 12:28:07,1,1.7,1,Y,13,249,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-16 17:49:32,2019-01-16 17:59:01,1,1.6,1,N,230,143,1,8.5,1.0,0.5,1.55,0.0,0.3,11.85, +2,2019-01-16 12:32:47,2019-01-16 12:42:41,1,0.68,1,N,113,107,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-23 16:17:29,2019-01-23 16:20:57,1,0.67,1,N,238,239,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-20 12:51:57,2019-01-20 13:07:03,1,2.25,1,N,137,230,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-09 13:14:05,2019-01-09 13:24:47,1,2.2,1,N,107,4,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-18 22:46:50,2019-01-18 22:55:25,1,0.89,1,N,170,164,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +2,2019-01-10 11:46:08,2019-01-10 12:00:16,1,1.2,1,N,186,158,1,9.5,0.0,0.5,3.09,0.0,0.3,13.39, +2,2019-01-13 07:28:36,2019-01-13 07:32:52,1,1.84,1,N,263,74,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-25 19:21:40,2019-01-25 19:47:17,1,6.33,1,N,234,52,1,23.0,1.0,0.5,4.96,0.0,0.3,29.76,0.0 +1,2019-01-28 17:50:35,2019-01-28 18:01:27,1,0.9,1,N,264,264,1,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-26 10:55:43,2019-01-26 10:59:15,1,0.8,1,N,246,246,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-18 16:56:24,2019-01-18 17:35:55,1,10.52,1,N,138,142,1,36.0,1.0,0.5,8.71,5.76,0.3,52.27, +1,2019-01-15 18:10:03,2019-01-15 18:28:15,1,5.5,1,N,246,152,1,17.5,1.0,0.5,3.85,0.0,0.3,23.15, +1,2019-01-10 19:32:13,2019-01-10 19:50:43,1,2.6,1,Y,186,233,1,13.0,1.0,0.5,2.95,0.0,0.3,17.75, +1,2019-01-24 11:55:22,2019-01-24 12:13:24,1,1.5,1,N,229,140,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-10 10:55:32,2019-01-10 11:05:54,1,1.91,1,N,141,229,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-19 23:13:24,2019-01-19 23:16:30,1,0.49,1,N,236,236,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +2,2019-01-10 16:37:56,2019-01-10 16:58:19,1,2.62,1,N,224,230,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-15 18:34:45,2019-01-15 18:42:18,1,1.51,1,N,143,238,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-06 05:53:53,2019-01-06 06:29:03,1,21.19,2,N,142,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-29 18:14:49,2019-01-29 18:22:06,1,0.8,1,N,140,262,1,6.5,1.0,0.5,1.25,0.0,0.3,9.55,0.0 +2,2019-01-22 00:39:31,2019-01-22 00:43:23,1,0.96,1,N,79,224,1,5.0,0.5,0.5,0.63,0.0,0.3,6.93,0.0 +1,2019-01-24 19:22:40,2019-01-24 19:30:38,1,0.8,1,N,234,249,1,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-08 22:18:33,2019-01-08 22:23:34,1,0.8,1,N,48,100,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-09 20:01:46,2019-01-09 20:11:55,1,1.88,1,N,4,144,1,9.0,1.0,0.5,3.24,0.0,0.3,14.04, +2,2019-01-05 23:32:24,2019-01-05 23:40:34,1,1.89,1,N,238,142,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-14 00:00:25,2019-01-14 00:11:56,1,2.99,1,N,114,163,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +1,2019-01-31 12:54:57,2019-01-31 13:04:15,1,1.0,1,N,230,163,1,7.5,0.0,0.5,2.45,0.0,0.3,10.75,0.0 +2,2019-01-06 18:29:50,2019-01-06 18:35:11,1,0.9,1,N,100,234,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24, +1,2019-01-30 22:09:41,2019-01-30 22:32:50,1,9.2,1,N,138,61,1,28.0,0.5,0.5,5.85,0.0,0.3,35.15,0.0 +1,2019-01-22 22:00:13,2019-01-22 22:13:49,1,2.4,1,N,230,114,1,11.5,0.5,0.5,3.8,0.0,0.3,16.6,0.0 +1,2019-01-30 19:37:15,2019-01-30 19:49:11,1,1.7,1,N,170,43,1,9.5,1.0,0.5,2.8,0.0,0.3,14.1,0.0 +1,2019-01-18 11:21:19,2019-01-18 11:26:06,1,0.5,1,N,186,234,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-25 18:16:36,2019-01-25 18:29:28,1,1.4,1,N,43,237,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-15 06:15:01,2019-01-15 06:23:19,1,1.9,1,N,186,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-31 19:24:59,2019-01-31 19:36:11,1,1.92,1,N,230,142,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-24 14:57:47,2019-01-24 15:25:25,1,3.5,1,N,237,166,1,18.5,0.0,0.5,4.8,0.0,0.3,24.1,0.0 +2,2019-01-25 16:03:49,2019-01-25 16:22:14,1,2.78,1,N,25,195,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-23 07:33:58,2019-01-23 07:48:04,1,2.9,1,N,140,143,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-31 11:33:23,2019-01-31 11:53:25,1,1.23,1,N,237,229,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-27 09:12:08,2019-01-27 09:33:56,1,10.19,1,N,234,234,1,30.0,0.0,0.5,7.31,5.76,0.3,43.87,0.0 +2,2019-01-07 12:46:37,2019-01-07 12:54:07,1,0.73,1,N,142,163,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-07 08:58:38,2019-01-07 09:06:30,1,1.0,1,N,186,68,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-20 07:57:14,2019-01-20 08:12:08,1,3.1,1,N,140,48,1,13.5,0.0,0.5,2.0,0.0,0.3,16.3, +2,2019-01-06 08:28:10,2019-01-06 08:30:29,1,1.0,1,N,141,236,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-20 17:34:50,2019-01-20 17:54:16,1,3.0,1,N,163,249,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-15 09:54:57,2019-01-15 10:11:03,1,2.26,1,N,231,234,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-12 12:51:36,2019-01-12 12:56:17,1,0.92,1,N,238,151,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19, +1,2019-01-02 00:14:45,2019-01-02 00:26:18,1,3.1,1,N,226,196,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-25 08:31:32,2019-01-25 08:50:41,1,8.24,1,N,236,138,1,25.0,0.0,0.5,6.31,5.76,0.3,37.87,0.0 +1,2019-01-14 12:04:25,2019-01-14 12:23:27,1,2.5,1,N,170,211,1,14.0,0.0,0.5,2.0,0.0,0.3,16.8, +2,2019-01-08 12:27:52,2019-01-08 12:34:35,1,0.98,1,N,43,239,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-10 20:48:13,2019-01-10 20:55:02,1,1.45,1,N,140,236,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-05 19:43:40,2019-01-05 19:55:54,1,2.3,1,N,142,263,1,11.0,0.0,0.5,2.5,0.0,0.3,14.3, +2,2019-01-29 19:31:12,2019-01-29 19:33:11,1,0.26,1,N,114,114,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-05 09:20:12,2019-01-05 09:25:33,1,0.67,1,N,13,231,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +1,2019-01-26 20:09:01,2019-01-26 20:45:09,1,17.2,1,N,48,132,1,49.0,0.5,0.5,11.2,5.75,0.3,67.25,0.0 +1,2019-01-20 22:25:42,2019-01-20 22:30:35,1,0.8,1,N,186,170,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-15 07:39:44,2019-01-15 07:45:10,1,0.81,1,N,263,236,1,5.5,0.0,0.5,1.58,0.0,0.3,7.88, +1,2019-01-08 07:38:07,2019-01-08 08:22:24,1,8.6,1,N,260,69,1,34.0,0.0,0.5,12.15,5.76,0.3,52.71, +4,2019-01-02 11:33:31,2019-01-02 11:42:14,1,1.02,1,N,148,231,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-15 13:11:31,2019-01-15 13:51:54,1,23.23,1,N,88,77,1,64.5,0.0,0.5,0.0,5.76,0.3,71.06, +2,2019-01-09 11:41:58,2019-01-09 11:46:04,1,0.46,1,N,43,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-14 14:12:48,2019-01-14 14:28:37,1,3.1,1,N,231,232,1,14.0,0.0,0.5,2.0,0.0,0.3,16.8, +1,2019-01-29 20:49:11,2019-01-29 20:52:13,1,0.7,1,N,142,238,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-18 13:26:18,2019-01-18 14:04:40,1,7.2,1,N,25,76,1,29.5,0.0,0.5,0.0,0.0,0.3,30.3, +1,2019-01-07 09:52:06,2019-01-07 09:57:25,1,0.8,1,N,249,113,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-06 08:17:29,2019-01-06 08:38:57,1,6.9,1,N,137,129,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3, +1,2019-01-21 11:23:55,2019-01-21 11:33:48,1,1.1,1,N,246,100,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-01 02:25:35,2019-01-01 02:38:11,1,1.9,1,N,68,170,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-12 00:26:49,2019-01-12 00:29:31,1,0.61,1,N,170,48,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-31 23:02:19,2019-01-31 23:11:02,1,1.3,1,N,211,232,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-12 10:05:45,2019-01-12 10:20:11,1,2.86,1,N,230,43,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-05 00:56:10,2019-01-05 01:07:35,1,1.67,1,N,4,90,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-11 18:54:20,2019-01-11 19:00:47,1,0.83,1,N,230,229,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-04 20:30:19,2019-01-04 21:04:59,1,14.97,5,N,247,25,1,42.45,0.0,0.5,0.0,0.0,0.3,43.25, +2,2019-01-05 22:36:16,2019-01-05 22:44:45,1,0.95,1,N,100,170,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-11 16:42:25,2019-01-11 17:04:11,1,2.6,1,N,144,161,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-26 00:16:58,2019-01-26 00:22:58,1,0.81,1,N,163,161,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-31 13:12:28,2019-01-31 13:19:59,1,0.92,1,N,237,162,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-21 15:43:47,2019-01-21 15:48:47,1,0.64,1,N,148,211,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25,0.0 +2,2019-01-15 14:51:10,2019-01-15 15:00:44,1,1.05,1,N,239,142,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-27 00:52:44,2019-01-27 01:05:14,1,2.4,1,N,141,164,1,11.0,0.5,0.5,3.65,0.0,0.3,15.95,0.0 +1,2019-01-15 00:44:57,2019-01-15 01:01:52,1,4.4,1,N,163,179,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75, +1,2019-01-03 18:22:42,2019-01-03 18:33:33,1,0.9,1,N,264,264,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-17 05:18:17,2019-01-17 05:24:33,1,1.48,1,N,41,238,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-10 16:06:03,2019-01-10 16:13:43,1,0.69,1,N,161,233,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-11 07:47:54,2019-01-11 08:04:14,1,1.65,1,N,264,264,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-23 01:19:12,2019-01-23 01:23:37,1,1.6,1,N,68,50,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-22 09:11:12,2019-01-22 09:27:37,1,1.6,1,N,140,161,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-11 20:17:15,2019-01-11 20:26:09,1,1.38,1,N,230,170,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-06 17:35:47,2019-01-06 17:45:41,1,1.9,1,N,158,230,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-13 12:53:34,2019-01-13 13:11:04,1,2.7,1,N,236,163,1,13.5,0.0,0.5,1.72,0.0,0.3,16.02, +2,2019-01-11 09:50:53,2019-01-11 10:11:20,1,2.72,1,N,233,236,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-10 19:14:07,2019-01-10 19:31:52,1,2.41,1,N,230,158,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-15 14:54:58,2019-01-15 15:09:41,1,2.7,1,N,237,74,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-24 00:57:50,2019-01-24 01:01:05,1,0.6,1,N,79,148,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-09 17:56:28,2019-01-09 18:19:48,1,9.33,1,N,132,39,2,28.5,1.0,0.5,0.0,0.0,0.3,30.3, +2,2019-01-30 18:27:37,2019-01-30 18:47:01,1,3.9,1,N,13,66,1,15.0,1.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-11 08:13:47,2019-01-11 08:29:12,1,2.32,1,N,163,234,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38, +1,2019-01-06 09:21:06,2019-01-06 09:29:16,1,1.7,1,N,90,114,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-31 14:25:50,2019-01-31 14:39:36,1,1.5,1,N,186,163,1,10.0,0.0,0.5,3.24,0.0,0.3,14.04,0.0 +2,2019-01-17 23:10:55,2019-01-17 23:20:43,1,2.05,1,N,239,43,1,9.5,0.5,0.5,2.0,0.0,0.3,12.8, +2,2019-01-27 15:47:02,2019-01-27 15:55:32,1,1.42,1,N,236,239,1,8.0,0.0,0.5,1.4,0.0,0.3,10.2,0.0 +2,2019-01-13 13:34:10,2019-01-13 13:38:33,1,0.97,1,N,263,75,1,5.5,0.0,0.5,1.7,0.0,0.3,8.0, +1,2019-01-28 14:33:47,2019-01-28 14:41:05,1,1.3,1,N,238,236,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-14 19:18:39,2019-01-14 19:26:43,1,1.0,1,N,230,233,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-12 13:41:49,2019-01-12 13:43:28,1,0.3,1,N,166,166,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-18 03:19:30,2019-01-18 03:21:38,1,0.6,1,N,79,107,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3, +1,2019-01-28 10:10:19,2019-01-28 10:32:55,1,2.3,1,N,113,162,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +1,2019-01-25 07:28:12,2019-01-25 07:33:25,1,0.7,1,N,162,230,1,5.5,0.0,0.5,0.95,0.0,0.3,7.25,0.0 +1,2019-01-23 20:12:33,2019-01-23 20:36:28,1,12.1,1,N,116,92,1,34.0,0.5,0.5,0.0,5.76,0.3,41.06,0.0 +4,2019-01-04 15:03:16,2019-01-04 15:07:26,1,0.55,1,N,142,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-25 11:48:02,2019-01-26 11:44:46,1,1.94,1,N,141,236,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42,0.0 +1,2019-01-29 13:16:01,2019-01-29 13:19:52,1,0.5,1,N,236,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-25 09:53:59,2019-01-25 10:10:05,1,1.3,1,N,209,231,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-28 13:27:13,2019-01-28 13:48:22,1,3.0,1,N,140,100,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-20 22:36:28,2019-01-20 22:45:31,1,0.92,1,N,230,164,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-27 20:30:31,2019-01-27 20:39:08,1,1.51,1,N,226,7,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-17 20:31:12,2019-01-17 20:41:52,1,1.8,1,N,68,249,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-27 18:28:59,2019-01-27 18:42:07,1,1.41,1,N,233,186,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-04 10:17:01,2019-01-04 10:24:00,1,1.2,1,N,229,170,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-14 13:44:29,2019-01-14 13:54:21,1,2.35,1,N,140,75,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-23 19:07:13,2019-01-23 19:17:50,1,1.6,1,N,163,142,1,8.5,1.0,0.5,1.5,0.0,0.3,11.8,0.0 +2,2019-01-06 11:57:33,2019-01-06 12:16:05,1,3.65,1,N,75,137,1,14.5,0.0,0.5,1.99,0.0,0.3,17.29, +1,2019-01-06 19:50:34,2019-01-06 20:05:03,1,2.9,1,N,236,42,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-07 15:33:43,2019-01-07 15:43:13,1,1.07,1,N,161,100,1,8.0,0.0,0.5,2.64,0.0,0.3,11.44, +1,2019-01-02 13:14:00,2019-01-02 13:23:04,1,1.5,1,N,237,161,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-31 17:08:38,2019-01-31 17:14:27,1,0.5,1,N,237,141,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-19 20:40:14,2019-01-19 20:54:48,1,1.76,1,N,107,232,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +1,2019-01-15 19:48:20,2019-01-15 20:11:36,1,3.4,1,N,237,249,1,17.0,1.0,0.5,4.7,0.0,0.3,23.5, +1,2019-01-19 14:22:13,2019-01-19 14:31:13,1,1.1,1,N,234,79,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-27 14:41:53,2019-01-27 14:49:04,1,0.94,1,N,260,260,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-15 19:19:53,2019-01-15 19:59:37,1,13.6,1,N,10,80,1,42.0,1.0,0.5,10.9,0.0,0.3,54.7, +2,2019-01-24 19:20:02,2019-01-24 19:26:30,1,1.04,1,N,181,40,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-29 07:57:13,2019-01-29 08:05:00,1,1.2,1,N,68,158,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-24 12:03:18,2019-01-24 12:34:05,1,4.08,1,N,137,239,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3,0.0 +1,2019-01-30 19:27:38,2019-01-30 19:42:40,1,2.3,1,N,162,239,1,11.5,1.0,0.5,2.0,0.0,0.3,15.3,0.0 +2,2019-01-05 00:38:52,2019-01-05 01:00:59,1,4.81,1,N,114,158,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +1,2019-01-06 02:42:35,2019-01-06 02:53:58,1,1.7,1,N,148,234,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-05 02:51:41,2019-01-05 02:57:28,1,1.41,1,N,164,229,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-28 09:00:22,2019-01-28 09:30:08,1,2.7,1,N,142,170,1,18.0,0.0,0.5,3.75,0.0,0.3,22.55,0.0 +1,2019-01-05 18:45:16,2019-01-05 18:54:12,1,1.2,1,N,264,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-09 20:50:36,2019-01-09 20:58:50,1,1.56,1,N,170,234,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-15 23:11:45,2019-01-15 23:24:12,1,2.83,1,N,230,262,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-29 19:14:49,2019-01-29 19:18:37,1,0.92,1,N,43,151,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-21 13:50:11,2019-01-21 14:01:37,1,1.4,1,N,164,163,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-03 19:58:26,2019-01-03 20:14:18,1,3.43,1,N,234,261,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-10 09:41:11,2019-01-10 09:45:48,1,0.51,1,N,236,236,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +1,2019-01-12 23:37:02,2019-01-12 23:50:25,1,1.3,1,N,125,4,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-16 18:19:23,2019-01-16 18:43:25,1,4.68,1,N,137,256,1,19.5,1.0,0.5,4.26,0.0,0.3,27.51, +1,2019-01-19 13:44:29,2019-01-19 14:07:36,1,3.0,1,N,161,238,1,16.0,0.0,0.5,3.35,0.0,0.3,20.15, +2,2019-01-06 20:12:43,2019-01-06 20:19:49,1,1.83,1,N,79,137,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-21 10:58:16,2019-01-21 11:00:25,1,0.33,1,N,239,238,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +2,2019-01-06 13:39:10,2019-01-06 13:54:59,1,3.13,1,N,262,166,1,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-29 15:04:54,2019-01-29 15:11:37,1,0.75,1,N,234,186,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-10 20:01:00,2019-01-10 20:44:48,1,9.0,1,N,224,85,2,33.5,1.0,0.5,0.0,0.0,0.3,35.3, +2,2019-01-16 18:51:22,2019-01-16 18:55:34,1,0.63,1,N,50,48,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 00:04:26,2019-01-09 00:07:32,1,0.53,1,N,113,148,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-19 18:39:36,2019-01-19 18:52:22,1,1.4,1,N,79,234,1,9.5,0.0,0.5,1.5,0.0,0.3,11.8, +2,2019-01-24 21:44:28,2019-01-24 21:44:33,1,0.0,2,N,219,219,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +1,2019-01-01 05:07:47,2019-01-01 05:18:33,1,2.8,1,Y,144,162,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +2,2019-01-03 13:28:07,2019-01-03 13:33:15,1,1.15,1,N,262,262,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-02 09:31:33,2019-01-02 09:40:41,1,1.54,1,N,234,125,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-06 21:20:56,2019-01-06 21:32:16,1,2.79,1,N,246,13,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +2,2019-01-10 12:37:50,2019-01-10 13:01:31,1,1.93,1,N,236,140,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-01 17:26:11,2019-01-01 17:48:16,1,8.68,1,N,138,100,1,27.0,0.0,0.5,6.71,5.76,0.3,40.27, +1,2019-01-15 07:40:09,2019-01-15 07:49:52,1,1.4,1,Y,230,162,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-07 21:16:58,2019-01-07 21:22:02,1,1.01,1,N,170,161,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-12 22:56:16,2019-01-12 23:06:15,1,1.74,1,N,24,42,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +1,2019-01-04 03:46:31,2019-01-04 03:52:43,1,1.4,1,N,237,141,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-30 19:08:31,2019-01-30 19:21:22,1,1.48,1,N,170,107,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-31 07:30:01,2019-01-31 07:38:59,1,0.58,1,N,186,164,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14,0.0 +2,2019-01-02 16:00:00,2019-01-02 16:02:40,1,0.64,1,N,43,43,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-28 13:19:09,2019-01-28 13:28:27,1,1.14,1,N,137,234,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-25 23:59:01,2019-01-26 00:01:56,1,1.2,1,N,239,151,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-14 23:36:15,2019-01-14 23:39:16,1,0.7,1,N,262,263,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +2,2019-01-25 21:33:21,2019-01-25 21:43:54,1,1.15,1,N,162,163,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-28 17:43:21,2019-01-28 17:49:11,1,0.6,1,N,163,48,1,5.5,1.0,0.5,1.8,0.0,0.3,9.1,0.0 +1,2019-01-23 06:35:02,2019-01-23 06:43:49,1,2.6,1,N,43,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-24 11:25:12,2019-01-24 11:34:49,1,0.7,1,N,162,161,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-26 02:14:23,2019-01-26 02:21:47,1,1.05,1,N,114,114,1,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-03 15:07:06,2019-01-03 15:26:13,1,1.7,1,N,48,233,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-26 18:13:59,2019-01-26 18:21:42,1,1.1,1,N,142,239,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-25 10:16:02,2019-01-25 10:17:44,1,0.44,1,N,262,263,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-17 16:05:31,2019-01-17 16:12:22,1,0.97,1,N,230,186,1,6.5,1.0,0.5,2.49,0.0,0.3,10.79, +1,2019-01-20 16:36:53,2019-01-20 16:42:06,1,0.7,1,N,236,75,1,5.5,0.0,0.5,2.0,0.0,0.3,8.3, +2,2019-01-13 15:15:07,2019-01-13 15:23:11,1,1.63,1,N,239,237,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-26 13:57:13,2019-01-26 14:09:47,1,2.4,1,N,237,142,1,10.5,0.0,0.5,1.5,0.0,0.3,12.8,0.0 +1,2019-01-13 11:29:48,2019-01-13 11:35:34,1,1.1,1,N,264,264,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-02 20:41:07,2019-01-02 20:50:35,1,1.5,1,N,164,48,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-12 15:17:16,2019-01-12 15:38:26,1,3.14,1,N,246,211,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-22 18:47:48,2019-01-22 19:01:20,1,3.3,1,N,263,170,3,13.0,1.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-07 14:26:41,2019-01-07 14:32:23,1,1.08,1,N,142,163,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-24 15:18:08,2019-01-24 15:31:22,1,1.3,1,N,90,249,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-30 01:50:56,2019-01-30 02:14:28,1,7.01,1,N,144,7,1,22.5,0.5,0.5,4.76,0.0,0.3,28.56,0.0 +2,2019-01-27 19:32:08,2019-01-27 19:38:52,1,1.4,1,N,137,224,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +1,2019-01-30 18:00:33,2019-01-30 18:11:21,1,1.1,1,N,170,230,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-25 06:52:41,2019-01-25 07:09:59,1,1.3,1,N,186,137,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-25 01:45:20,2019-01-25 02:00:37,1,3.23,1,N,209,107,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-13 12:08:58,2019-01-13 12:16:54,1,1.1,1,N,100,48,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-15 12:27:31,2019-01-15 12:54:47,1,4.47,1,N,261,161,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36, +1,2019-01-20 00:49:01,2019-01-20 00:57:58,1,1.1,1,N,79,79,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-21 09:45:35,2019-01-21 09:50:03,1,0.79,1,N,143,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-24 11:19:59,2019-01-24 11:37:37,1,1.7,1,N,234,45,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-07 07:47:40,2019-01-07 08:03:35,1,2.23,1,N,125,211,1,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-05 12:23:33,2019-01-05 12:45:47,1,2.8,1,N,186,140,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-16 12:39:08,2019-01-16 12:45:22,1,1.39,1,N,138,70,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-26 22:56:32,2019-01-26 22:59:40,1,0.4,1,N,143,143,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-31 11:27:25,2019-01-31 11:43:22,1,3.29,1,N,13,186,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-02 06:40:55,2019-01-02 06:44:16,1,0.63,1,N,13,231,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-29 08:58:49,2019-01-29 09:07:10,1,2.91,1,N,237,74,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-15 18:33:26,2019-01-15 18:46:06,1,1.7,1,N,162,142,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-14 15:32:04,2019-01-14 15:50:07,1,1.74,1,N,163,236,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-22 19:53:47,2019-01-22 19:59:11,1,0.8,1,N,264,264,1,5.5,1.0,0.5,1.8,0.0,0.3,9.1,0.0 +1,2019-01-13 00:45:06,2019-01-13 00:55:14,1,2.3,1,N,234,162,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-03 19:05:15,2019-01-03 19:09:46,1,0.6,1,N,236,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-05 18:23:12,2019-01-05 18:35:24,1,1.8,1,N,43,163,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-25 23:04:49,2019-01-25 23:05:32,1,0.0,1,N,114,114,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-05 19:54:08,2019-01-05 19:56:30,1,0.59,1,N,140,262,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-31 18:42:22,2019-01-31 19:01:50,1,5.54,1,N,233,255,1,18.5,1.0,0.5,4.06,0.0,0.3,24.36,0.0 +2,2019-01-24 16:27:02,2019-01-24 16:41:20,1,1.32,1,N,13,144,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-08 17:48:15,2019-01-08 17:59:52,1,1.99,1,N,50,237,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-15 18:54:36,2019-01-15 19:06:30,1,1.7,1,N,234,114,1,9.5,1.0,0.5,2.0,0.0,0.3,13.3, +2,2019-01-10 17:03:29,2019-01-10 17:08:11,1,0.47,1,N,48,48,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-25 08:56:37,2019-01-25 09:06:58,1,1.5,1,N,114,246,1,8.0,0.0,0.5,1.32,0.0,0.3,10.12,0.0 +1,2019-01-13 10:11:08,2019-01-13 10:26:38,1,4.8,1,N,161,74,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-31 09:36:11,2019-01-31 09:51:48,1,1.2,1,N,142,141,1,10.5,0.0,0.5,0.02,0.0,0.3,11.32,0.0 +1,2019-01-03 11:25:27,2019-01-03 11:28:05,1,0.4,1,N,137,170,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-04 07:52:16,2019-01-04 08:00:21,1,1.2,1,N,236,262,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-02 17:34:38,2019-01-02 17:48:47,1,1.9,1,N,186,48,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-22 20:38:26,2019-01-22 20:45:55,1,1.4,1,N,114,107,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-30 17:00:49,2019-01-30 17:08:08,1,1.8,1,N,263,75,1,7.5,1.0,0.5,2.75,0.0,0.3,12.05,0.0 +1,2019-01-27 17:07:57,2019-01-27 17:23:06,1,1.8,1,N,113,186,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-13 13:19:11,2019-01-13 13:24:20,1,1.6,1,N,237,263,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-28 08:51:00,2019-01-28 08:53:42,1,0.35,1,N,75,75,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-12 22:01:56,2019-01-12 22:12:21,1,1.8,1,N,163,234,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +1,2019-01-06 16:03:44,2019-01-06 16:12:10,1,1.4,1,N,264,264,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +4,2019-01-24 19:26:28,2019-01-24 19:42:40,1,2.45,1,N,74,42,1,12.5,1.0,0.5,1.0,0.0,0.3,15.3, +1,2019-01-16 19:36:19,2019-01-16 19:39:03,1,0.7,1,N,141,237,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-31 22:31:05,2019-01-31 22:37:37,1,1.1,1,N,249,234,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-17 11:14:33,2019-01-17 11:31:24,1,2.1,1,N,137,231,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-26 16:38:45,2019-01-26 16:49:19,1,1.32,1,N,209,13,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-15 21:25:37,2019-01-15 21:28:02,1,0.3,1,N,163,163,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +2,2019-01-13 22:01:20,2019-01-13 22:06:54,1,1.43,1,N,68,231,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-05 11:28:18,2019-01-05 11:44:29,1,2.56,1,N,234,141,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-11 06:09:19,2019-01-11 06:21:10,1,2.7,1,N,224,161,1,11.0,0.0,0.5,1.25,0.0,0.3,13.05, +2,2019-01-06 10:54:08,2019-01-06 11:05:26,1,1.7,1,N,170,48,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-15 18:36:05,2019-01-15 19:01:24,1,4.7,1,N,161,260,1,20.5,1.0,0.5,4.46,0.0,0.3,26.76, +1,2019-01-23 16:55:09,2019-01-23 17:12:07,1,2.6,1,N,231,100,1,13.0,1.0,0.5,3.7,0.0,0.3,18.5,0.0 +2,2019-01-05 23:38:18,2019-01-05 23:49:07,1,1.98,1,N,75,151,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-11 10:24:20,2019-01-11 10:37:01,1,1.59,1,N,236,141,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +2,2019-01-13 13:13:25,2019-01-13 13:42:14,1,5.13,1,N,125,140,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36, +1,2019-01-09 16:21:10,2019-01-09 16:37:56,1,3.0,1,N,148,233,1,13.5,1.0,0.5,3.05,0.0,0.3,18.35, +2,2019-01-29 06:14:25,2019-01-29 06:30:14,1,3.66,1,N,143,137,1,13.5,0.0,0.5,1.0,0.0,0.3,15.3,0.0 +1,2019-01-10 11:31:29,2019-01-10 11:36:00,1,0.7,1,N,142,239,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-17 23:46:06,2019-01-17 23:54:36,1,1.3,1,N,114,148,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-24 17:42:20,2019-01-24 18:32:14,1,15.76,1,N,261,138,1,46.0,1.0,0.5,10.71,5.76,0.3,64.27,0.0 +2,2019-01-14 23:10:18,2019-01-14 23:27:07,1,3.9,1,N,230,148,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +1,2019-01-23 19:28:13,2019-01-23 19:44:45,1,1.4,1,N,79,148,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-11 11:29:38,2019-01-11 11:33:10,1,0.54,1,N,230,48,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +2,2019-01-14 11:44:04,2019-01-14 11:56:49,1,1.95,1,N,262,236,1,10.0,1.0,0.5,1.0,0.0,0.3,12.8, +1,2019-01-16 19:08:52,2019-01-16 19:24:54,1,5.2,1,N,138,252,1,18.0,1.0,0.5,4.95,0.0,0.3,24.75, +1,2019-01-17 22:10:08,2019-01-17 22:13:19,1,0.8,1,N,75,236,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-08 19:54:22,2019-01-08 20:05:12,1,1.3,1,N,230,186,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-04 19:40:33,2019-01-04 19:48:59,1,1.17,1,N,144,125,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-17 15:28:35,2019-01-17 16:03:53,1,6.31,1,N,263,152,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3, +2,2019-01-29 14:06:36,2019-01-29 14:22:59,1,1.55,1,N,234,161,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-24 12:09:33,2019-01-24 12:38:39,1,5.79,1,N,88,164,1,23.5,0.0,0.5,4.86,0.0,0.3,29.16,0.0 +1,2019-01-31 22:09:18,2019-01-31 22:19:13,1,1.9,1,N,161,141,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-03 14:32:10,2019-01-03 14:56:11,1,3.9,1,N,231,161,1,17.5,0.0,0.5,3.5,0.0,0.3,21.8, +2,2019-01-28 08:45:10,2019-01-28 09:02:12,1,1.74,1,N,162,90,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-28 11:47:32,2019-01-28 11:54:49,1,0.9,1,N,237,141,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-23 16:07:18,2019-01-23 16:13:52,1,1.0,1,N,158,158,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-27 18:27:13,2019-01-27 18:50:38,1,3.86,1,N,4,246,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +2,2019-01-09 11:24:08,2019-01-09 11:29:17,1,0.54,1,N,237,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-18 22:16:01,2019-01-18 22:23:08,1,1.24,1,N,137,107,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-28 21:31:15,2019-01-28 21:34:03,1,1.0,1,N,264,264,2,3004.0,-36.71,37.51,0.0,0.0,0.0,3004.8,0.0 +2,2019-01-26 02:55:10,2019-01-26 03:03:32,1,3.0,1,N,79,141,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-23 21:03:48,2019-01-23 21:15:03,1,2.6,1,N,162,146,1,11.0,0.5,0.5,1.85,0.0,0.3,14.15,0.0 +1,2019-01-27 03:29:29,2019-01-27 03:41:15,1,2.7,1,N,79,100,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-07 09:03:45,2019-01-07 09:19:51,1,2.2,1,N,262,163,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-11 23:59:18,2019-01-12 00:11:45,1,2.8,1,N,186,141,1,12.0,0.5,0.5,1.0,0.0,0.3,14.3, +1,2019-01-06 08:23:48,2019-01-06 08:26:33,1,0.9,1,Y,100,48,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-29 10:57:22,2019-01-29 11:28:47,1,5.0,1,N,186,87,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3,0.0 +1,2019-01-03 17:16:39,2019-01-03 17:29:47,1,1.6,1,N,43,161,1,10.0,1.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-17 11:42:50,2019-01-17 12:03:57,1,3.06,1,N,166,263,1,15.5,0.0,0.5,1.0,0.0,0.3,17.3, +2,2019-01-04 15:01:00,2019-01-04 15:14:40,1,2.02,1,N,170,249,1,10.5,0.0,0.5,1.2,0.0,0.3,12.5, +2,2019-01-17 10:15:46,2019-01-17 10:20:06,1,0.69,1,N,158,90,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-28 16:03:38,2019-01-28 16:09:38,1,1.03,1,N,239,238,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-24 12:37:42,2019-01-24 13:05:39,1,9.4,1,N,161,138,1,30.0,0.0,0.5,5.0,5.76,0.3,41.56,0.0 +1,2019-01-06 02:26:58,2019-01-06 02:35:44,1,1.2,1,N,164,246,1,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-25 16:46:53,2019-01-25 17:17:04,1,6.9,1,N,53,179,1,26.0,1.0,0.5,0.0,0.0,0.3,27.8,0.0 +2,2019-01-19 18:07:01,2019-01-19 18:09:41,1,0.55,1,N,141,229,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-07 12:31:43,2019-01-07 13:05:15,1,10.69,1,N,138,106,1,35.0,0.0,0.5,10.74,0.0,0.3,46.54, +2,2019-01-25 15:27:58,2019-01-25 15:29:30,1,0.54,1,N,114,79,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-27 23:39:15,2019-01-27 23:53:54,1,7.59,4,N,132,265,2,23.5,0.5,0.5,0.0,0.0,0.3,24.8,0.0 +2,2019-01-07 18:09:17,2019-01-07 18:35:20,1,5.16,1,N,107,66,1,20.0,1.0,0.5,4.36,0.0,0.3,26.16, +1,2019-01-28 18:14:28,2019-01-28 18:18:33,1,0.6,1,N,263,236,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-16 08:24:21,2019-01-16 08:31:49,1,1.1,1,N,74,75,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-09 10:00:34,2019-01-09 10:11:25,1,0.8,1,N,164,161,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-13 02:32:26,2019-01-13 02:38:51,1,1.49,1,N,68,234,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-12 02:18:59,2019-01-12 02:26:36,1,2.07,1,N,234,161,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-08 14:20:38,2019-01-08 14:26:40,1,1.6,1,N,50,239,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-31 18:20:34,2019-01-31 18:30:12,1,1.3,1,N,230,142,3,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-01 03:41:21,2019-01-01 04:02:08,1,4.9,1,N,249,106,1,18.0,0.5,0.5,5.79,0.0,0.3,25.09, +2,2019-01-10 20:02:14,2019-01-10 20:14:43,1,2.32,1,N,68,231,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-25 23:41:24,2019-01-25 23:57:55,1,2.9,1,N,79,209,1,14.5,0.5,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-06 19:41:49,2019-01-06 19:45:07,1,0.69,1,N,161,230,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-28 08:29:45,2019-01-28 08:54:38,1,2.58,1,N,140,170,1,16.0,0.0,0.5,1.0,0.0,0.3,17.8,0.0 +1,2019-01-30 21:46:10,2019-01-30 21:52:52,1,1.4,1,N,142,239,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-14 06:18:49,2019-01-14 06:22:58,1,0.6,1,N,100,164,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-04 13:29:02,2019-01-04 14:03:44,1,17.29,2,N,164,132,1,52.0,0.0,0.5,5.0,12.5,0.3,70.3, +2,2019-01-15 20:03:40,2019-01-15 20:41:24,1,16.63,1,N,88,130,1,48.0,0.5,0.5,5.0,5.76,0.3,60.06, +1,2019-01-15 11:21:28,2019-01-15 11:29:05,1,1.1,1,N,90,158,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-15 21:58:40,2019-01-15 22:02:39,1,1.06,1,N,43,43,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8, +1,2019-01-10 11:28:55,2019-01-10 11:32:45,1,0.8,1,N,262,263,1,5.0,0.0,0.5,1.04,0.0,0.3,6.84, +1,2019-01-14 13:42:42,2019-01-14 13:50:30,1,1.4,1,N,237,236,1,7.0,0.0,0.5,2.3,0.0,0.3,10.1, +2,2019-01-17 16:46:02,2019-01-17 16:51:38,1,0.93,1,N,236,141,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-12 12:39:06,2019-01-12 12:44:57,1,0.9,1,N,90,234,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-11 08:09:08,2019-01-11 08:23:12,1,1.62,1,N,236,237,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-28 09:21:00,2019-01-28 09:31:23,1,1.4,1,N,237,236,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-16 09:00:10,2019-01-16 09:05:14,1,0.5,1,N,237,237,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-15 08:37:27,2019-01-15 08:48:37,1,0.86,1,N,170,100,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-11 12:08:13,2019-01-11 12:22:58,1,1.64,1,N,239,43,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-23 10:02:06,2019-01-23 10:23:53,1,1.88,1,N,236,161,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-18 21:05:15,2019-01-18 21:24:49,1,7.06,1,N,138,256,1,22.5,0.5,0.5,4.76,0.0,0.3,28.56, +1,2019-01-15 11:32:54,2019-01-15 11:41:46,1,1.0,1,N,234,113,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-19 09:10:58,2019-01-19 09:17:15,1,1.3,1,N,239,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-29 05:12:03,2019-01-29 05:22:15,1,4.1,1,N,262,186,1,13.5,0.5,0.5,2.0,0.0,0.3,16.8,0.0 +1,2019-01-08 08:36:20,2019-01-08 08:38:52,1,0.4,1,N,238,238,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +2,2019-01-23 16:37:21,2019-01-23 16:46:01,1,1.45,1,N,107,113,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-10 08:15:28,2019-01-10 08:59:35,1,7.36,1,N,68,225,1,32.0,0.0,0.5,0.0,0.0,0.3,32.8, +1,2019-01-15 14:34:17,2019-01-15 14:48:14,1,1.2,1,N,90,170,1,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-18 23:49:44,2019-01-18 23:59:16,1,1.3,1,N,107,186,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +1,2019-01-22 18:21:09,2019-01-22 18:23:48,1,0.5,1,N,100,90,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-04 12:57:11,2019-01-04 13:11:10,1,2.0,1,N,142,161,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-23 23:27:07,2019-01-23 23:33:06,1,1.21,1,N,186,48,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-07 17:58:10,2019-01-07 18:07:17,1,1.5,1,N,170,237,1,8.0,1.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-24 19:04:33,2019-01-24 19:16:38,1,1.05,1,N,163,142,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-17 21:43:33,2019-01-17 21:49:51,1,1.04,1,N,186,246,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-28 21:07:02,2019-01-28 21:39:58,1,6.84,1,N,48,181,1,26.0,0.5,0.5,5.46,0.0,0.3,32.76,0.0 +2,2019-01-09 13:50:09,2019-01-09 14:07:17,1,1.77,1,N,162,230,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-04 17:37:01,2019-01-04 17:42:51,1,1.15,1,N,100,90,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-29 18:05:06,2019-01-29 18:45:28,1,5.3,5,N,25,198,1,21.77,0.0,0.5,0.0,0.0,0.3,22.57,0.0 +1,2019-01-14 09:41:13,2019-01-14 09:56:01,1,1.6,1,N,263,237,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-04 09:31:25,2019-01-04 09:39:55,1,1.2,1,N,138,129,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-11 15:37:11,2019-01-11 15:49:38,1,1.51,1,N,100,142,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-23 07:27:26,2019-01-23 07:33:19,1,1.0,1,N,230,237,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-08 07:25:27,2019-01-08 08:09:24,1,9.38,5,N,61,170,1,29.42,0.0,0.5,0.0,0.0,0.3,30.22, +2,2019-01-03 09:01:42,2019-01-03 09:20:49,1,2.54,1,N,238,163,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88, +2,2019-01-21 17:57:13,2019-01-21 18:03:52,1,0.45,1,N,100,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-31 06:52:53,2019-01-31 06:57:10,1,0.57,1,N,229,162,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67,0.0 +1,2019-01-17 15:11:46,2019-01-17 15:16:15,1,0.5,1,N,90,234,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-10 12:32:33,2019-01-10 12:39:56,1,1.3,1,N,87,232,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-15 11:12:57,2019-01-15 11:55:10,1,11.1,1,N,246,138,1,39.5,0.0,0.5,9.21,5.76,0.3,55.27, +1,2019-01-12 16:46:34,2019-01-12 17:12:53,1,4.5,1,N,142,41,1,19.0,0.0,0.5,2.0,0.0,0.3,21.8, +1,2019-01-10 20:03:46,2019-01-10 20:09:34,1,1.7,1,N,239,141,1,7.0,0.5,0.5,2.0,0.0,0.3,10.3, +1,2019-01-12 20:35:50,2019-01-12 20:50:27,1,1.5,1,N,164,249,1,10.5,0.5,0.5,1.77,0.0,0.3,13.57, +1,2019-01-21 05:41:57,2019-01-21 05:44:17,1,0.6,1,N,48,246,3,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-26 16:59:01,2019-01-26 17:08:56,1,1.0,1,N,100,48,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-15 08:01:20,2019-01-15 08:04:19,1,0.5,1,N,236,236,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-08 16:56:12,2019-01-08 17:02:48,1,0.8,1,N,246,158,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-12 02:58:37,2019-01-12 03:09:16,1,3.1,1,N,79,140,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +2,2019-01-25 20:07:09,2019-01-25 20:19:31,1,1.18,1,N,162,48,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-13 17:30:09,2019-01-13 17:46:53,1,1.01,1,N,264,264,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-30 19:27:54,2019-01-30 19:34:29,1,1.35,1,N,148,87,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-28 16:00:56,2019-01-28 16:14:19,1,2.11,1,N,249,231,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-24 15:04:20,2019-01-24 15:12:30,1,0.88,1,N,107,90,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-02 07:53:14,2019-01-02 08:06:17,1,2.0,1,N,48,234,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-24 16:16:22,2019-01-24 16:27:18,1,0.8,1,N,162,162,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-27 14:57:38,2019-01-27 15:22:13,1,2.9,1,N,107,48,1,17.0,0.0,0.5,2.6,0.0,0.3,20.4,0.0 +1,2019-01-25 20:52:05,2019-01-25 21:04:37,1,2.2,1,N,237,164,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-30 09:56:04,2019-01-30 09:56:08,1,5.8,1,N,237,237,1,2.5,0.0,0.5,5.0,0.0,0.3,8.3,0.0 +2,2019-01-29 19:41:11,2019-01-29 19:43:45,1,0.55,1,N,229,229,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-19 22:03:09,2019-01-19 22:21:50,1,6.7,1,N,100,116,1,21.5,0.5,0.5,1.0,0.0,0.3,23.8, +1,2019-01-15 15:56:25,2019-01-15 16:00:36,1,0.3,1,N,236,236,1,4.5,0.0,0.5,0.79,0.0,0.3,6.09, +1,2019-01-07 22:05:02,2019-01-07 22:15:19,1,2.5,1,N,161,239,1,10.0,0.5,0.5,1.5,0.0,0.3,12.8, +2,2019-01-29 23:38:47,2019-01-29 23:38:49,1,0.0,1,N,264,100,1,2.5,0.5,0.5,0.76,0.0,0.3,4.56,0.0 +2,2019-01-19 08:22:00,2019-01-19 08:26:48,1,1.31,1,N,249,186,1,6.5,0.0,0.5,1.5,0.0,0.3,8.8, +1,2019-01-23 23:01:10,2019-01-23 23:03:05,1,0.5,1,N,230,100,1,3.5,0.5,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-14 13:14:02,2019-01-14 13:26:13,1,2.36,1,N,162,141,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-31 11:52:44,2019-01-31 12:25:23,1,11.97,1,N,138,164,1,37.5,0.0,0.5,6.61,5.76,0.3,50.67,0.0 +1,2019-01-14 04:14:13,2019-01-14 04:38:04,1,5.1,1,N,163,148,2,20.0,0.5,0.5,0.0,0.0,0.3,21.3, +1,2019-01-12 10:52:34,2019-01-12 11:03:51,1,1.8,1,N,68,79,1,9.0,0.0,0.5,1.47,0.0,0.3,11.27, +1,2019-01-12 13:48:55,2019-01-12 14:30:39,1,4.9,1,N,236,246,1,27.0,0.0,0.5,5.55,0.0,0.3,33.35, +2,2019-01-17 08:16:52,2019-01-17 08:23:56,1,0.73,1,N,229,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-15 07:03:03,2019-01-15 07:18:45,1,2.61,1,N,113,162,1,12.5,0.0,0.5,1.0,0.0,0.3,14.3, +1,2019-01-29 18:11:16,2019-01-29 18:30:03,1,1.7,1,N,237,186,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-30 20:42:43,2019-01-30 20:52:39,1,1.7,1,N,263,238,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-09 16:25:58,2019-01-09 16:36:13,1,1.53,1,N,164,237,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-11 14:19:28,2019-01-11 14:37:07,1,1.64,1,N,161,186,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +1,2019-01-27 02:02:09,2019-01-27 02:19:47,1,5.0,1,N,249,263,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95,0.0 +2,2019-01-18 08:16:27,2019-01-18 08:22:05,1,1.28,1,N,13,88,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-22 14:31:05,2019-01-22 14:43:31,1,1.3,1,N,100,50,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-30 22:08:53,2019-01-30 22:14:19,1,0.7,1,Y,170,48,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-04 00:18:19,2019-01-04 00:25:55,1,1.6,1,N,264,264,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +2,2019-01-19 21:07:04,2019-01-19 21:26:13,1,4.46,1,N,144,141,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +1,2019-01-11 14:20:58,2019-01-11 14:36:38,1,2.0,1,N,79,158,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-02 11:47:23,2019-01-02 12:04:32,1,1.65,1,N,239,237,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-23 09:39:01,2019-01-23 10:45:29,1,16.0,2,N,132,87,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8,0.0 +2,2019-01-09 07:33:41,2019-01-09 07:45:18,1,1.95,1,N,140,161,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84, +2,2019-01-10 09:39:32,2019-01-10 09:48:44,1,1.36,1,N,170,234,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-24 17:26:10,2019-01-24 17:30:16,1,0.5,1,N,142,142,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-20 21:53:33,2019-01-20 21:59:20,1,1.3,1,N,50,142,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-29 19:11:37,2019-01-29 19:31:09,1,2.92,1,N,90,229,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-24 21:19:30,2019-01-24 21:47:04,1,11.78,1,N,138,13,2,35.5,0.5,0.5,0.0,0.0,0.3,36.8,0.0 +2,2019-01-23 19:57:31,2019-01-23 20:11:43,1,1.63,1,N,90,79,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-23 13:19:44,2019-01-23 13:25:04,1,0.68,1,N,90,100,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-28 10:15:04,2019-01-28 10:28:12,1,1.5,1,N,233,234,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +2,2019-01-24 20:49:15,2019-01-24 20:52:20,1,0.9,1,N,48,68,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-05 08:05:34,2019-01-05 08:08:35,1,0.61,1,N,263,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-25 18:20:08,2019-01-25 18:26:43,1,0.8,1,N,79,79,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-17 16:17:30,2019-01-17 16:28:22,1,1.3,1,N,234,170,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-19 14:41:24,2019-01-19 14:50:31,1,2.22,1,N,140,50,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-08 20:21:16,2019-01-08 20:40:52,1,6.1,1,N,264,264,1,21.0,0.5,0.5,5.55,0.0,0.3,27.85, +2,2019-01-24 07:20:30,2019-01-24 07:38:20,1,2.63,1,N,148,186,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-09 23:48:01,2019-01-10 00:23:30,1,12.4,1,N,138,254,2,38.0,0.5,0.5,0.0,5.76,0.3,45.06, +2,2019-01-26 16:13:04,2019-01-26 16:24:18,1,1.79,1,N,151,142,1,9.5,0.0,0.5,3.09,0.0,0.3,13.39,0.0 +1,2019-01-14 08:21:37,2019-01-14 08:23:55,1,0.0,1,N,234,107,3,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-18 13:34:56,2019-01-18 13:53:49,1,3.8,1,N,138,146,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-03 12:04:06,2019-01-03 12:23:37,1,2.5,1,N,113,163,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-09 23:40:39,2019-01-09 23:47:00,1,2.03,1,N,161,236,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-11 07:16:01,2019-01-11 07:26:16,1,2.45,1,N,43,162,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-27 09:00:29,2019-01-27 09:25:56,1,8.8,1,N,205,76,1,28.0,0.0,0.5,0.0,0.0,0.3,28.8,0.0 +4,2019-01-13 16:16:15,2019-01-13 16:23:31,1,1.21,1,N,236,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-19 08:29:23,2019-01-19 08:33:54,1,1.03,1,N,162,237,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-02 21:28:21,2019-01-02 21:40:27,1,1.9,1,N,249,107,1,9.5,0.5,0.5,1.62,0.0,0.3,12.42, +1,2019-01-23 20:05:21,2019-01-23 20:15:29,1,1.9,1,N,151,239,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-26 09:57:33,2019-01-26 10:06:53,1,1.83,1,N,238,142,1,9.0,0.0,0.5,2.94,0.0,0.3,12.74,0.0 +1,2019-01-17 07:09:36,2019-01-17 07:13:50,1,1.0,1,N,260,226,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-02 17:36:51,2019-01-02 17:45:15,1,1.06,1,N,263,238,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-19 23:07:12,2019-01-19 23:12:15,1,0.91,1,N,161,161,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-22 19:10:31,2019-01-22 19:17:55,1,1.08,1,N,162,48,1,6.5,1.0,0.5,2.49,0.0,0.3,10.79,0.0 +2,2019-01-19 17:14:29,2019-01-19 17:22:21,1,1.35,1,N,236,163,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-10 17:01:49,2019-01-10 17:06:30,1,0.68,1,N,186,90,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-06 23:19:41,2019-01-06 23:24:32,1,0.87,1,N,50,48,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +2,2019-01-27 02:08:40,2019-01-27 02:15:06,1,0.96,1,N,148,211,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-14 06:06:17,2019-01-14 06:48:48,1,18.48,2,N,132,113,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +1,2019-01-06 14:24:55,2019-01-06 14:35:53,1,1.4,1,N,152,41,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-01 21:45:42,2019-01-01 21:45:50,1,0.0,2,N,107,107,1,52.0,0.0,0.5,7.5,5.76,0.3,66.06, +1,2019-01-30 16:29:59,2019-01-30 16:33:38,1,0.3,1,N,237,237,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-25 18:50:01,2019-01-25 18:57:50,1,2.04,1,N,163,43,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-31 18:29:33,2019-01-31 18:45:26,1,3.16,1,N,246,48,1,13.0,1.0,0.5,3.7,0.0,0.3,18.5,0.0 +4,2019-01-31 12:40:33,2019-01-31 12:44:11,1,0.82,1,N,238,239,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-26 15:32:27,2019-01-26 15:37:42,1,0.8,1,N,246,90,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-26 14:49:32,2019-01-26 15:11:03,1,3.5,1,N,264,264,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25,0.0 +1,2019-01-10 18:09:16,2019-01-10 18:38:51,1,6.2,1,N,234,33,1,23.5,1.0,0.5,5.05,0.0,0.3,30.35, +2,2019-01-24 23:05:57,2019-01-24 23:36:56,1,20.73,2,N,132,261,1,52.0,0.0,0.5,2.0,0.0,0.3,54.8,0.0 +2,2019-01-11 14:11:16,2019-01-11 14:15:16,1,0.9,1,N,79,137,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-19 13:33:10,2019-01-19 13:41:16,1,2.28,1,N,7,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-08 20:53:53,2019-01-08 20:57:10,1,0.64,1,N,140,236,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-23 07:52:38,2019-01-23 07:58:38,1,1.39,1,N,43,186,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49,0.0 +2,2019-01-20 10:15:07,2019-01-20 10:29:17,1,2.94,1,N,264,264,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-14 14:40:47,2019-01-14 14:57:41,1,4.2,1,N,238,244,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-06 14:08:58,2019-01-06 14:27:46,1,3.3,1,N,262,48,1,14.5,0.0,0.5,1.0,0.0,0.3,16.3, +2,2019-01-08 20:17:59,2019-01-08 20:30:00,1,1.77,1,N,162,50,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-18 13:18:41,2019-01-18 13:35:07,1,1.9,1,N,230,107,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95, +2,2019-01-24 16:06:33,2019-01-24 16:12:15,1,0.81,1,N,262,262,1,5.5,1.0,0.5,1.5,0.0,0.3,8.8,0.0 +2,2019-01-16 10:02:25,2019-01-16 10:16:56,1,3.16,1,N,246,231,1,13.0,0.0,0.5,2.6,0.0,0.3,16.4, +1,2019-01-25 08:28:55,2019-01-25 08:33:32,1,0.6,1,N,236,237,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67,0.0 +1,2019-01-16 22:25:34,2019-01-16 22:47:14,1,5.5,1,N,161,42,1,19.5,0.5,0.5,4.15,0.0,0.3,24.95, +2,2019-01-30 22:22:35,2019-01-30 22:56:29,1,6.31,1,N,229,229,1,25.0,0.5,0.5,5.26,0.0,0.3,31.56,0.0 +1,2019-01-09 16:36:15,2019-01-09 16:50:43,1,1.3,1,N,264,264,1,10.0,1.0,0.5,2.35,0.0,0.3,14.15, +1,2019-01-02 19:53:01,2019-01-02 19:59:30,1,1.0,1,N,163,142,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97, +2,2019-01-30 17:24:00,2019-01-30 17:53:35,1,8.96,1,N,244,174,2,30.5,1.0,0.5,0.0,2.64,0.3,34.94,0.0 +1,2019-01-19 14:38:29,2019-01-19 14:42:57,1,0.5,1,N,79,79,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-06 14:19:26,2019-01-06 14:26:39,1,1.6,1,N,163,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-25 15:55:27,2019-01-25 15:59:35,1,0.4,1,N,162,170,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6,0.0 +1,2019-01-21 11:16:32,2019-01-21 11:20:56,1,0.8,1,N,90,113,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-19 16:27:18,2019-01-19 16:39:40,1,2.47,1,N,151,116,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +1,2019-01-22 09:03:26,2019-01-22 09:10:24,1,1.1,1,N,113,125,1,6.5,0.0,0.5,1.31,0.0,0.3,8.61,0.0 +2,2019-01-04 13:43:24,2019-01-04 13:51:50,1,1.13,1,N,90,224,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8, +2,2019-01-14 10:13:26,2019-01-14 10:26:16,1,0.48,1,N,186,170,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-23 14:03:01,2019-01-23 14:07:48,1,0.4,1,N,186,100,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-09 00:33:43,2019-01-09 00:47:15,1,3.37,1,N,230,79,1,12.5,0.5,0.5,2.0,0.0,0.3,15.8, +1,2019-01-20 00:41:51,2019-01-20 00:43:37,1,0.4,1,N,141,141,1,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-06 10:46:01,2019-01-06 10:56:57,1,1.8,1,N,151,74,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-05 12:40:22,2019-01-05 12:49:53,1,1.64,1,N,262,237,1,8.5,0.0,0.5,2.79,0.0,0.3,12.09, +1,2019-01-02 17:38:38,2019-01-02 17:44:41,1,0.8,1,N,107,170,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97, +2,2019-01-02 10:34:28,2019-01-02 10:37:52,1,0.49,1,N,230,100,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-23 16:26:34,2019-01-23 16:40:30,1,2.87,1,N,142,43,1,12.0,1.0,0.5,2.07,0.0,0.3,15.87,0.0 +2,2019-01-22 14:20:38,2019-01-22 14:37:36,1,1.55,1,N,107,90,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-21 12:42:32,2019-01-21 12:52:46,1,1.2,1,N,161,107,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-24 17:46:43,2019-01-24 18:15:05,1,9.9,1,N,261,74,1,29.0,1.0,0.5,0.0,0.0,0.3,30.8,0.0 +2,2019-01-05 19:52:55,2019-01-05 19:57:45,1,0.59,1,N,43,236,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-24 12:11:13,2019-01-24 12:20:40,1,0.43,1,N,162,162,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-04 10:46:33,2019-01-04 10:56:03,1,1.2,1,N,246,234,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-05 01:24:24,2019-01-05 01:31:33,1,1.2,1,N,249,231,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +1,2019-01-09 14:10:47,2019-01-09 14:20:42,1,0.9,1,N,262,236,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-25 08:31:24,2019-01-25 08:48:50,1,6.3,1,N,155,123,1,21.0,0.0,0.5,0.0,0.0,0.3,21.8,0.0 +1,2019-01-13 01:26:30,2019-01-13 01:42:21,1,3.8,1,N,239,249,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +1,2019-01-02 06:59:28,2019-01-02 07:04:07,1,0.8,1,N,162,170,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-28 22:32:26,2019-01-28 22:38:16,1,1.38,1,N,142,239,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-25 07:52:49,2019-01-25 07:56:44,1,0.8,1,N,41,151,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-16 07:02:53,2019-01-16 07:19:56,1,3.93,1,N,238,158,1,15.0,0.0,0.5,2.37,0.0,0.3,18.17, +2,2019-01-04 15:53:50,2019-01-04 16:09:54,1,1.87,1,N,100,162,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-19 23:25:24,2019-01-19 23:31:02,1,0.9,1,N,249,90,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-04 06:48:30,2019-01-04 07:24:43,1,10.5,1,Y,132,160,2,33.0,0.0,0.5,0.0,0.0,0.3,33.8, +2,2019-01-30 23:45:28,2019-01-30 23:58:53,1,4.36,1,N,45,195,1,15.0,0.5,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-03 18:52:50,2019-01-03 18:57:27,1,0.59,1,N,230,162,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-15 07:41:43,2019-01-15 07:56:03,1,1.8,1,N,236,239,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +2,2019-01-28 21:55:58,2019-01-28 21:57:37,1,0.56,1,N,141,263,1,3.5,0.5,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-25 08:42:28,2019-01-25 09:34:59,1,18.61,1,N,132,66,1,51.5,0.0,0.5,10.46,0.0,0.3,62.76,0.0 +2,2019-01-24 15:45:09,2019-01-24 16:05:21,1,0.87,1,N,186,170,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-04 18:59:07,2019-01-04 19:15:07,1,6.05,1,N,170,129,1,19.0,1.0,0.5,3.44,5.76,0.3,30.0, +1,2019-01-30 21:55:06,2019-01-30 21:59:37,1,1.0,1,N,48,246,1,5.5,0.5,0.5,1.2,0.0,0.3,8.0,0.0 +2,2019-01-24 15:04:49,2019-01-24 15:22:15,1,1.79,1,N,161,186,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-03 10:18:03,2019-01-03 10:29:37,1,1.5,1,N,13,87,1,9.5,0.0,0.5,1.03,0.0,0.3,11.33, +2,2019-01-23 21:01:28,2019-01-23 21:13:11,1,2.25,1,N,162,234,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-31 20:13:28,2019-01-31 20:34:11,1,5.8,1,N,158,236,1,21.0,0.5,0.5,4.45,0.0,0.3,26.75,0.0 +1,2019-01-15 15:58:12,2019-01-15 16:02:55,1,0.6,1,N,74,75,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-19 12:43:07,2019-01-19 12:53:25,1,1.6,1,N,238,143,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-11 20:17:43,2019-01-11 20:34:54,1,1.9,1,N,186,79,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +2,2019-01-11 10:49:44,2019-01-11 10:51:45,1,0.22,2,N,162,161,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +1,2019-01-03 09:23:23,2019-01-03 09:29:31,1,1.0,1,N,239,143,1,6.0,0.0,0.5,2.2,0.0,0.3,9.0, +2,2019-01-01 15:03:42,2019-01-01 15:32:01,1,4.97,1,N,186,151,1,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +2,2019-01-13 16:47:50,2019-01-13 16:58:03,1,1.12,1,N,161,48,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-21 14:13:38,2019-01-21 14:33:28,1,3.6,1,N,113,237,1,15.5,0.0,0.5,2.0,0.0,0.3,18.3,0.0 +2,2019-01-30 14:57:58,2019-01-30 15:01:03,1,0.55,1,N,239,143,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-24 09:19:30,2019-01-24 09:42:54,1,2.06,1,N,238,230,1,15.5,0.0,0.5,4.89,0.0,0.3,21.19,0.0 +1,2019-01-31 00:36:30,2019-01-31 00:42:24,1,1.6,1,N,142,230,1,7.0,0.5,0.5,2.45,0.0,0.3,10.75,0.0 +1,2019-01-14 18:30:17,2019-01-14 18:46:30,1,3.7,1,N,100,236,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96, +1,2019-01-05 22:37:35,2019-01-05 22:45:43,1,1.3,1,N,114,87,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +1,2019-01-23 15:18:19,2019-01-23 15:22:01,1,0.8,1,N,125,231,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2,0.0 +1,2019-01-01 14:21:03,2019-01-01 14:33:54,1,3.3,1,N,141,41,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-18 18:53:44,2019-01-18 19:07:20,1,1.75,1,N,230,142,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3, +4,2019-01-27 10:28:38,2019-01-27 10:31:28,1,1.04,1,N,236,75,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-25 18:45:31,2019-01-25 18:51:11,1,0.61,1,N,236,263,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-13 01:21:37,2019-01-13 01:27:00,1,0.5,1,N,211,144,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8, +1,2019-01-28 15:45:48,2019-01-28 15:51:57,1,0.9,1,N,163,170,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-31 11:20:52,2019-01-31 11:46:07,1,3.6,1,N,186,238,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-11 08:48:53,2019-01-11 09:06:03,1,2.74,1,N,262,162,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-14 15:18:46,2019-01-14 15:28:07,1,0.64,1,N,236,236,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +1,2019-01-15 19:35:16,2019-01-15 19:58:40,1,1.8,1,N,163,186,1,14.5,1.0,0.5,3.25,0.0,0.3,19.55, +2,2019-01-07 16:55:56,2019-01-07 17:01:41,1,0.73,1,N,249,114,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-13 13:21:38,2019-01-13 13:28:03,1,1.5,1,N,229,141,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-30 21:34:51,2019-01-30 21:49:41,1,6.15,1,N,233,261,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3,0.0 +2,2019-01-06 00:37:20,2019-01-06 00:42:55,1,0.92,1,N,79,249,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-26 16:50:18,2019-01-26 17:05:27,1,1.8,1,N,142,246,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-25 07:29:51,2019-01-26 06:48:30,1,2.21,1,N,163,140,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-09 23:36:48,2019-01-10 00:01:51,1,17.6,2,N,132,107,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-31 10:50:40,2019-01-31 11:01:46,1,0.95,1,N,161,161,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62,0.0 +2,2019-01-15 21:45:26,2019-01-15 21:49:38,1,0.67,1,N,163,142,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-20 18:34:16,2019-01-20 18:51:37,1,2.97,1,N,230,79,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-10 13:23:21,2019-01-10 13:30:49,1,0.88,1,N,237,237,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-12 08:22:51,2019-01-12 08:25:23,1,0.39,1,N,141,237,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-03 07:19:40,2019-01-03 07:34:24,1,5.83,1,N,137,13,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +2,2019-01-10 22:06:53,2019-01-10 22:11:58,1,0.61,1,N,161,170,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-25 19:11:45,2019-01-25 19:29:39,1,1.64,1,N,107,158,1,12.0,1.0,0.5,3.45,0.0,0.3,17.25,0.0 +1,2019-01-15 18:29:26,2019-01-15 18:32:03,1,0.4,1,N,238,239,1,4.0,1.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-18 20:21:48,2019-01-18 20:35:53,1,3.69,1,N,162,7,1,13.5,0.5,0.5,3.7,0.0,0.3,18.5, +2,2019-01-06 19:46:31,2019-01-06 19:57:52,1,1.5,1,N,107,100,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-16 14:22:21,2019-01-16 14:34:20,1,1.98,1,N,239,263,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-22 12:12:53,2019-01-22 12:49:46,1,8.78,1,N,138,230,1,33.0,0.0,0.5,9.89,5.76,0.3,49.45,0.0 +2,2019-01-08 06:47:57,2019-01-08 06:56:09,1,2.32,1,N,262,161,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-16 12:39:38,2019-01-16 12:49:10,1,3.66,1,N,142,166,1,13.0,0.0,0.5,1.0,0.0,0.3,14.8, +2,2019-01-02 10:27:59,2019-01-02 10:36:40,1,1.19,1,N,140,237,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-12 20:58:48,2019-01-12 21:10:23,1,2.1,1,N,163,239,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-09 16:48:45,2019-01-09 17:55:38,1,17.97,5,N,50,131,1,48.4,0.0,0.5,0.0,0.0,0.3,49.2, +1,2019-01-14 09:40:59,2019-01-14 09:40:59,1,0.0,1,N,140,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +2,2019-01-19 17:25:57,2019-01-19 17:36:07,1,2.09,1,N,107,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-06 02:58:26,2019-01-06 03:09:06,1,2.07,1,N,79,186,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-09 17:14:25,2019-01-09 17:22:13,1,0.7,1,N,264,264,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-18 08:44:40,2019-01-18 09:02:43,1,4.9,1,N,233,87,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-20 22:17:04,2019-01-20 22:36:03,1,4.75,1,N,114,239,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +2,2019-01-29 19:34:59,2019-01-29 19:45:11,1,1.6,1,N,234,125,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-30 17:39:34,2019-01-30 17:44:12,1,0.56,1,N,237,141,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-28 12:01:20,2019-01-28 12:05:19,1,0.4,1,N,161,161,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-18 17:30:17,2019-01-18 17:32:33,1,0.33,1,N,238,238,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-11 19:36:21,2019-01-11 19:50:07,1,1.5,1,N,234,50,1,10.0,1.0,0.5,1.77,0.0,0.3,13.57, +2,2019-01-11 10:17:18,2019-01-11 10:47:47,1,11.65,1,N,138,261,2,34.0,0.0,0.5,0.0,0.0,0.3,34.8, +2,2019-01-27 09:45:58,2019-01-27 09:58:49,1,1.49,1,N,162,186,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88,0.0 +4,2019-01-14 18:10:30,2019-01-14 18:26:44,1,1.73,1,N,161,141,1,11.5,1.0,0.5,4.0,0.0,0.3,17.3, +4,2019-01-12 22:18:41,2019-01-12 22:22:39,1,0.45,1,N,43,142,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-28 16:52:09,2019-01-28 16:55:36,1,0.53,1,N,239,143,1,4.5,1.0,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-04 07:49:35,2019-01-04 08:02:18,1,1.69,1,N,143,161,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-09 19:41:37,2019-01-09 19:53:03,1,1.17,1,N,107,164,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-21 20:38:36,2019-01-21 20:48:36,1,1.5,1,N,114,249,1,8.0,0.5,0.5,1.4,0.0,0.3,10.7,0.0 +2,2019-01-03 21:58:38,2019-01-03 22:04:42,1,1.21,1,N,79,137,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-22 08:48:32,2019-01-22 08:51:27,1,0.41,1,N,236,236,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-18 21:56:15,2019-01-18 22:14:11,1,2.4,1,N,148,68,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-05 17:25:06,2019-01-05 17:47:45,1,5.33,1,N,164,256,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +1,2019-01-06 10:38:26,2019-01-06 10:47:02,1,1.6,1,N,140,236,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-02 23:20:43,2019-01-02 23:24:41,1,0.95,1,N,97,49,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-24 19:44:59,2019-01-24 19:55:14,1,1.32,1,N,186,246,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-26 22:42:23,2019-01-26 22:52:54,1,0.91,1,N,107,137,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-17 16:34:15,2019-01-17 16:41:22,1,1.14,1,N,142,48,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-02 23:13:01,2019-01-02 23:23:35,1,2.7,1,N,231,256,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +1,2019-01-06 19:57:06,2019-01-06 20:03:55,1,0.9,1,N,113,158,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-02 19:19:04,2019-01-02 19:28:18,1,1.99,1,N,142,151,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-18 00:21:09,2019-01-18 00:28:44,1,2.6,1,N,246,143,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +2,2019-01-13 01:24:04,2019-01-13 01:54:19,1,13.14,1,N,48,242,1,38.0,0.5,0.5,7.86,0.0,0.3,47.16, +2,2019-01-23 19:31:56,2019-01-23 19:46:04,1,1.83,1,N,170,249,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-07 13:44:35,2019-01-07 14:00:56,1,3.57,1,N,246,237,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-17 07:59:19,2019-01-17 08:10:44,1,1.1,1,Y,186,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-23 23:52:45,2019-01-24 00:04:55,1,3.57,1,N,79,263,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-22 10:17:17,2019-01-22 10:54:27,1,11.2,1,N,132,17,1,35.0,0.0,0.5,7.16,0.0,0.3,42.96,0.0 +1,2019-01-24 18:31:47,2019-01-24 18:37:57,1,0.6,1,N,230,68,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-16 14:35:08,2019-01-16 14:43:01,1,0.5,1,N,162,170,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-15 20:50:35,2019-01-15 21:09:55,1,4.75,1,N,232,190,1,17.5,0.5,0.5,2.0,0.0,0.3,20.8, +2,2019-01-29 18:11:17,2019-01-29 18:18:14,1,1.0,1,N,262,237,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-27 21:05:17,2019-01-27 21:12:54,1,2.12,1,N,75,166,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-01 22:05:17,2019-01-01 22:14:37,1,2.13,1,N,170,4,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +1,2019-01-03 17:54:41,2019-01-03 18:03:37,1,1.9,1,N,144,158,1,8.0,1.0,0.5,1.0,0.0,0.3,10.8, +1,2019-01-13 03:46:48,2019-01-13 03:51:16,1,0.5,1,N,186,164,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +1,2019-01-22 09:09:42,2019-01-22 09:19:12,1,0.7,1,N,68,234,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-17 11:57:00,2019-01-17 12:06:56,1,1.0,1,N,113,234,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-10 20:49:09,2019-01-10 20:58:16,1,1.25,1,N,230,164,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-12 16:36:00,2019-01-12 16:44:45,1,1.6,1,N,162,186,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-17 01:33:10,2019-01-17 01:40:10,1,1.0,1,N,79,45,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +2,2019-01-23 14:11:59,2019-01-23 14:49:38,1,17.23,2,N,100,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-05 18:38:55,2019-01-05 18:43:33,1,0.7,1,N,162,141,1,5.0,1.0,0.5,1.2,0.0,0.3,8.0, +2,2019-01-19 03:12:34,2019-01-19 03:19:23,1,0.83,1,N,249,246,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +4,2019-01-02 06:49:14,2019-01-02 07:01:12,1,3.26,1,N,211,163,1,11.5,0.0,0.5,1.2,0.0,0.3,13.5, +2,2019-01-28 21:27:33,2019-01-29 21:15:03,1,2.31,1,N,162,113,1,9.5,0.5,0.5,1.08,0.0,0.3,11.88,0.0 +2,2019-01-30 19:02:24,2019-01-30 19:16:44,1,1.42,1,N,107,186,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-30 09:10:53,2019-01-30 09:14:11,1,0.4,1,N,137,233,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +1,2019-01-22 08:49:37,2019-01-22 09:07:09,1,2.9,1,N,236,142,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-27 13:30:23,2019-01-27 13:40:47,1,1.55,1,N,237,161,1,9.0,0.0,0.5,2.0,0.0,0.3,11.8,0.0 +2,2019-01-31 08:23:59,2019-01-31 08:35:31,1,0.96,1,N,140,237,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-31 22:32:09,2019-01-31 22:42:41,1,2.01,1,N,146,7,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8,0.0 +1,2019-01-04 11:06:28,2019-01-04 11:06:28,1,0.0,1,Y,236,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-02 06:41:07,2019-01-02 07:07:42,1,13.9,1,N,138,257,1,38.5,0.0,0.5,7.85,0.0,0.3,47.15, +2,2019-01-28 13:09:20,2019-01-28 13:16:44,1,0.86,1,N,230,161,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-04 21:19:22,2019-01-04 21:32:41,1,1.74,1,N,234,79,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3, +2,2019-01-03 22:02:21,2019-01-03 22:14:12,1,2.74,1,N,114,13,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-17 08:32:04,2019-01-17 08:40:46,1,0.86,1,N,43,163,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-07 12:15:09,2019-01-07 12:18:30,1,0.5,1,N,79,79,1,4.5,0.0,0.5,0.5,0.0,0.3,5.8, +2,2019-01-28 08:02:19,2019-01-28 08:25:37,1,2.31,1,N,237,50,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-09 19:14:59,2019-01-09 19:25:36,1,1.87,1,N,239,262,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-12 02:38:03,2019-01-12 02:47:50,1,2.36,1,N,158,186,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-24 09:25:28,2019-01-24 09:27:20,1,0.3,1,N,236,236,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-23 10:41:26,2019-01-23 10:44:48,1,0.6,1,N,170,107,1,4.5,0.0,0.5,0.64,0.0,0.3,5.94,0.0 +1,2019-01-31 21:51:37,2019-01-31 22:00:14,1,2.1,1,N,68,13,1,9.0,0.5,0.5,2.55,0.0,0.3,12.85,0.0 +1,2019-01-07 11:53:20,2019-01-07 12:04:31,1,1.4,1,N,125,87,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-26 12:41:51,2019-01-26 12:48:14,1,0.8,1,N,163,142,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-30 14:21:54,2019-01-30 14:35:12,1,1.8,1,N,141,74,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8,0.0 +1,2019-01-08 12:16:34,2019-01-08 12:35:53,1,2.4,1,N,238,237,1,14.0,0.0,0.5,2.0,0.0,0.3,16.8, +1,2019-01-15 02:43:49,2019-01-15 02:54:45,1,1.7,1,Y,237,162,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-16 13:04:13,2019-01-16 13:07:09,1,0.57,1,N,90,246,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +2,2019-01-11 18:54:23,2019-01-11 18:57:07,1,0.53,1,N,141,141,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-09 19:57:48,2019-01-09 20:11:57,1,4.01,1,N,87,107,1,15.0,1.0,0.5,2.0,0.0,0.3,18.8, +2,2019-01-08 15:54:12,2019-01-08 16:02:22,1,0.98,1,N,238,142,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-14 08:48:12,2019-01-14 08:51:30,1,0.43,1,N,141,141,1,4.0,0.0,0.5,0.5,0.0,0.3,5.3, +2,2019-01-18 18:39:32,2019-01-18 18:58:37,1,2.49,1,N,50,234,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-10 09:55:24,2019-01-10 10:24:49,1,6.9,1,N,236,144,1,25.0,0.0,0.5,5.16,0.0,0.3,30.96, +2,2019-01-06 13:38:17,2019-01-06 13:41:35,1,0.72,1,N,263,263,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-29 23:56:14,2019-01-30 00:08:10,1,1.86,1,N,249,4,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-10 07:42:06,2019-01-10 08:03:05,1,4.2,1,N,161,87,1,16.5,0.0,0.5,2.0,0.0,0.3,19.3, +1,2019-01-07 12:28:08,2019-01-07 12:33:41,1,0.8,1,N,90,234,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-05 18:57:20,2019-01-05 19:08:23,1,1.4,1,N,170,113,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-24 18:57:03,2019-01-24 19:01:22,1,0.79,1,N,186,68,1,5.0,0.5,0.5,0.9,0.0,0.3,7.2,0.0 +2,2019-01-03 21:51:35,2019-01-03 21:54:02,1,0.7,1,N,142,48,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-01 21:48:09,2019-01-01 22:19:15,1,11.75,1,N,79,197,2,35.5,0.5,0.5,0.0,5.76,0.3,42.56, +1,2019-01-06 16:05:14,2019-01-06 16:13:50,1,1.3,1,N,161,236,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-16 10:11:59,2019-01-16 10:32:24,1,1.3,1,N,142,161,1,13.0,0.0,0.5,1.0,0.0,0.3,14.8, +1,2019-01-12 08:18:07,2019-01-12 08:23:12,1,0.9,1,N,236,239,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93, +1,2019-01-19 04:16:35,2019-01-19 04:21:14,1,1.0,1,N,231,249,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +2,2019-01-21 19:32:56,2019-01-21 19:53:13,1,3.8,1,N,263,234,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-16 14:35:01,2019-01-16 14:56:58,1,3.59,1,N,90,142,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +1,2019-01-13 02:44:21,2019-01-13 02:54:18,1,2.5,1,N,163,141,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-05 20:23:54,2019-01-05 20:31:53,1,0.9,1,N,68,249,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +2,2019-01-08 20:03:26,2019-01-08 20:21:05,1,2.85,1,N,48,137,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-25 15:52:32,2019-01-25 16:14:38,1,2.36,1,N,170,140,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +1,2019-01-29 20:54:43,2019-01-29 20:58:24,1,0.5,1,N,231,261,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-08 22:21:00,2019-01-08 22:45:19,1,5.43,1,N,114,75,1,20.5,0.5,0.5,4.0,0.0,0.3,25.8, +2,2019-01-17 19:05:08,2019-01-17 19:37:48,1,4.49,1,N,234,238,1,22.0,1.0,0.5,5.95,0.0,0.3,29.75, +2,2019-01-04 18:45:09,2019-01-04 19:07:36,1,2.64,1,N,90,230,1,15.0,1.0,0.5,2.0,0.0,0.3,18.8, +1,2019-01-06 09:12:55,2019-01-06 09:22:27,1,2.1,1,N,186,50,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-25 07:12:20,2019-01-25 07:17:38,1,1.21,1,N,137,162,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-14 07:45:16,2019-01-14 08:06:02,1,6.2,1,N,13,170,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +1,2019-01-17 14:04:38,2019-01-17 14:10:25,1,0.6,1,N,236,43,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +1,2019-01-17 11:21:51,2019-01-17 11:34:24,1,0.5,1,N,161,163,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-20 08:12:50,2019-01-20 08:20:06,1,3.44,1,N,261,4,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-09 22:20:29,2019-01-09 22:33:20,1,3.72,1,N,229,140,1,13.5,0.5,0.5,1.0,0.0,0.3,15.8, +1,2019-01-06 10:49:20,2019-01-06 10:51:36,1,0.3,1,N,161,161,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +2,2019-01-15 10:56:27,2019-01-15 11:15:25,1,7.52,5,N,170,196,1,20.69,0.0,0.5,0.0,5.76,0.3,27.25, +1,2019-01-06 11:48:42,2019-01-06 11:57:53,1,3.6,1,N,138,179,1,12.0,0.0,0.5,2.3,0.0,0.3,15.1, +1,2019-01-30 21:56:47,2019-01-30 22:08:30,1,2.1,1,N,249,148,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-04 16:35:20,2019-01-04 16:45:29,1,1.95,1,N,234,50,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-08 09:02:25,2019-01-08 09:27:01,1,3.6,1,N,161,231,1,17.5,0.0,0.5,3.65,0.0,0.3,21.95, +2,2019-01-27 01:45:08,2019-01-27 01:52:30,1,0.94,1,N,79,148,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-22 17:18:00,2019-01-22 17:35:57,1,3.5,1,N,236,166,1,14.5,1.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-17 23:17:04,2019-01-17 23:30:22,1,4.13,1,N,75,247,1,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +2,2019-01-08 22:21:59,2019-01-08 22:33:52,1,2.69,1,N,234,231,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-17 17:17:13,2019-01-17 17:27:52,1,0.6,1,N,161,100,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-05 19:54:05,2019-01-05 20:09:58,1,4.4,1,N,141,79,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +1,2019-01-31 22:49:36,2019-01-31 23:03:38,1,2.6,1,N,246,163,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-25 17:49:24,2019-01-25 18:18:24,1,3.3,1,N,142,107,1,18.5,1.0,0.5,5.05,0.0,0.3,25.35,0.0 +2,2019-01-26 11:11:00,2019-01-26 11:22:37,1,1.86,1,N,113,231,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-22 08:24:15,2019-01-22 08:39:47,1,2.1,1,N,161,236,1,11.5,0.0,0.5,3.65,0.0,0.3,15.95,0.0 +2,2019-01-17 15:31:44,2019-01-17 16:40:55,1,22.26,2,N,132,48,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-04 16:54:02,2019-01-04 17:13:08,1,1.16,1,N,164,161,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-18 18:25:30,2019-01-18 18:36:20,1,1.23,1,N,237,233,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-16 21:50:10,2019-01-16 22:00:57,1,1.8,1,N,236,239,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-23 10:25:04,2019-01-23 10:30:22,1,0.3,1,N,230,161,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-13 06:07:17,2019-01-13 06:10:27,1,0.97,1,N,162,141,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-07 20:30:23,2019-01-07 20:41:02,1,2.4,1,N,161,236,1,10.0,0.5,0.5,2.8,0.0,0.3,14.1, +2,2019-01-22 16:34:16,2019-01-22 16:47:07,1,2.03,1,N,129,82,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-13 08:52:18,2019-01-13 08:58:42,1,0.92,1,N,114,148,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-14 13:44:10,2019-01-14 14:06:58,1,4.22,1,N,161,231,1,18.5,0.0,0.5,1.93,0.0,0.3,21.23, +1,2019-01-11 22:56:12,2019-01-11 23:10:15,1,2.7,1,N,48,151,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95, +1,2019-01-31 16:09:45,2019-01-31 16:21:34,1,2.2,1,N,246,43,1,10.0,1.0,0.5,3.5,0.0,0.3,15.3,0.0 +1,2019-01-27 09:03:51,2019-01-27 09:18:26,1,4.1,1,N,246,236,1,15.5,0.0,0.5,4.05,0.0,0.3,20.35,0.0 +2,2019-01-02 10:25:03,2019-01-02 10:51:12,1,10.07,1,N,140,32,1,31.5,0.0,0.5,0.0,0.0,0.3,32.3, +1,2019-01-07 16:42:38,2019-01-07 17:03:08,1,3.3,1,N,146,163,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-24 11:41:52,2019-01-24 12:01:13,1,1.9,1,N,236,161,1,13.0,0.0,0.5,1.5,0.0,0.3,15.3,0.0 +2,2019-01-07 14:04:07,2019-01-07 14:09:56,1,0.98,1,N,230,186,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-10 06:20:00,2019-01-10 06:26:11,1,0.85,1,N,186,246,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-20 00:32:05,2019-01-20 00:39:46,1,1.58,1,N,68,79,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +2,2019-01-04 17:58:01,2019-01-04 18:05:48,1,1.48,1,N,148,107,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-15 08:52:24,2019-01-15 09:28:26,1,9.31,1,N,138,140,1,31.0,0.0,0.5,6.0,5.76,0.3,43.56, +1,2019-01-31 19:01:54,2019-01-31 19:18:59,1,3.2,1,N,234,142,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8,0.0 +4,2019-01-09 13:26:08,2019-01-09 13:32:12,1,1.09,1,N,230,143,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-16 20:34:54,2019-01-16 21:00:04,1,7.9,1,N,107,152,1,26.5,0.5,0.5,4.17,0.0,0.3,31.97, +2,2019-01-10 14:36:00,2019-01-10 14:51:48,1,1.69,1,N,236,161,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-27 16:57:53,2019-01-27 17:01:52,1,1.62,2,N,170,79,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-31 05:21:12,2019-01-31 05:31:06,1,3.5,1,N,237,113,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-10 12:30:44,2019-01-10 12:56:22,1,5.94,1,N,138,145,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +1,2019-01-10 16:23:33,2019-01-10 16:28:10,1,0.7,1,N,249,158,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5, +1,2019-01-22 07:30:26,2019-01-22 07:37:58,1,0.0,1,N,68,163,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-22 19:04:41,2019-01-22 19:13:48,1,1.31,1,N,230,233,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-31 08:25:14,2019-01-31 08:46:54,1,1.4,1,N,239,237,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-19 10:41:05,2019-01-19 10:46:51,1,1.41,1,N,236,141,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-12 17:01:05,2019-01-12 17:09:51,1,0.9,1,N,234,79,1,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-15 19:44:54,2019-01-15 19:53:51,1,1.3,1,N,262,236,1,7.5,1.0,0.5,1.4,0.0,0.3,10.7, +2,2019-01-11 12:13:53,2019-01-11 12:23:05,1,1.29,1,N,142,239,1,8.0,0.0,0.5,1.32,0.0,0.3,10.12, +2,2019-01-09 19:52:15,2019-01-09 19:58:14,1,0.85,1,N,48,246,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-03 18:26:53,2019-01-03 18:29:25,1,0.3,1,N,239,239,1,3.5,1.0,0.5,1.3,0.0,0.3,6.6, +2,2019-01-09 10:04:34,2019-01-09 10:04:41,1,0.02,2,N,138,138,2,-52.0,0.0,-0.5,0.0,0.0,-0.3,-52.8, +2,2019-01-25 10:07:50,2019-01-25 10:26:25,1,2.63,1,N,50,90,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-22 08:51:04,2019-01-22 09:05:33,1,2.26,1,N,74,141,1,11.0,0.0,0.5,1.77,0.0,0.3,13.57,0.0 +1,2019-01-18 22:33:48,2019-01-18 22:47:15,1,2.5,1,N,236,41,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +2,2019-01-31 20:46:10,2019-01-31 20:55:54,1,1.03,1,N,234,79,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-19 21:53:54,2019-01-19 22:13:52,1,2.0,1,N,107,249,1,13.0,0.5,0.5,1.0,0.0,0.3,15.3, +1,2019-01-16 13:42:08,2019-01-16 14:05:14,1,2.4,1,N,249,233,1,15.0,0.0,0.5,3.95,0.0,0.3,19.75, +2,2019-01-03 10:52:37,2019-01-03 10:58:15,1,0.8,1,N,164,170,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-23 11:35:09,2019-01-23 12:02:45,1,0.9,1,N,234,68,1,16.0,0.0,0.5,2.0,0.0,0.3,18.8,0.0 +2,2019-01-30 13:31:56,2019-01-30 13:49:39,1,1.56,1,N,233,142,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-27 03:32:41,2019-01-27 03:37:01,1,0.95,1,N,68,249,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-22 18:28:43,2019-01-22 18:51:07,1,3.37,1,N,246,79,1,16.0,1.0,0.5,3.56,0.0,0.3,21.36,0.0 +1,2019-01-10 20:06:02,2019-01-10 20:10:17,1,0.6,1,N,164,48,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-31 01:45:12,2019-01-31 01:52:35,1,1.9,1,N,264,264,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-23 11:32:23,2019-01-23 11:44:23,1,2.49,1,N,43,163,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-31 09:19:11,2019-01-31 09:30:00,1,0.8,1,N,229,162,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-11 15:21:47,2019-01-11 15:25:19,1,0.4,1,N,75,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-19 20:17:56,2019-01-19 20:25:18,1,1.0,1,N,233,163,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-05 14:52:29,2019-01-05 15:07:48,1,2.96,1,N,41,142,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-10 14:16:28,2019-01-10 14:20:26,1,0.92,1,N,229,140,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-02 09:03:02,2019-01-02 09:25:35,1,9.36,1,N,138,237,1,28.5,0.0,0.5,7.01,5.76,0.3,42.07, +2,2019-01-16 14:04:07,2019-01-16 14:13:25,1,0.78,1,N,161,100,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-14 14:15:34,2019-01-14 14:19:05,1,0.44,1,N,234,186,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-28 11:22:52,2019-01-28 11:28:11,1,0.74,1,N,162,170,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-07 19:11:10,2019-01-07 19:15:32,1,0.71,1,N,237,236,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-31 09:12:55,2019-01-31 09:33:34,1,2.39,1,N,114,170,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-04 14:35:44,2019-01-04 14:48:17,1,1.96,1,N,211,261,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-18 06:37:19,2019-01-18 06:44:38,1,1.4,1,N,186,161,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-11 21:25:23,2019-01-11 21:34:36,1,0.9,1,N,68,230,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +2,2019-01-25 14:43:42,2019-01-25 14:53:21,1,1.63,1,N,162,234,1,8.5,0.0,0.5,1.7,0.0,0.3,11.0,0.0 +2,2019-01-05 15:41:35,2019-01-05 16:14:44,1,3.98,1,N,114,236,1,21.5,0.0,0.5,4.46,0.0,0.3,26.76, +2,2019-01-13 21:55:16,2019-01-13 22:00:41,1,1.24,1,N,234,230,1,6.0,0.5,0.5,2.19,0.0,0.3,9.49, +1,2019-01-30 09:00:26,2019-01-30 09:11:09,1,1.4,1,N,229,237,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-18 08:21:16,2019-01-18 08:28:30,1,0.8,1,N,163,161,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-14 07:00:35,2019-01-14 07:08:53,1,1.2,1,N,186,137,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +1,2019-01-15 08:54:01,2019-01-15 09:06:03,1,1.6,1,N,113,231,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-22 19:09:48,2019-01-22 19:30:57,1,3.3,1,N,249,163,1,16.0,1.0,0.5,3.55,0.0,0.3,21.35,0.0 +1,2019-01-16 08:45:59,2019-01-16 08:50:53,1,0.6,1,N,68,246,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-27 09:46:15,2019-01-27 09:49:07,1,0.3,1,N,249,113,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-01 18:21:56,2019-01-01 18:36:25,1,2.78,1,N,144,186,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-05 16:38:20,2019-01-05 16:45:13,1,0.96,1,N,107,114,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-28 21:12:23,2019-01-28 21:20:08,1,1.8,1,N,142,246,1,8.0,0.5,0.5,1.53,0.0,0.3,10.83,0.0 +1,2019-01-31 09:55:27,2019-01-31 10:11:23,1,0.9,1,N,170,161,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3,0.0 +2,2019-01-20 17:50:13,2019-01-20 17:56:41,1,1.26,1,N,239,151,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-17 20:33:18,2019-01-17 20:42:56,1,2.39,1,N,151,116,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +1,2019-01-24 17:46:19,2019-01-24 17:52:17,1,0.9,1,N,52,25,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-16 13:37:44,2019-01-16 13:46:59,1,1.6,1,N,264,264,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-02 18:09:25,2019-01-02 18:12:54,1,0.78,1,N,113,211,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-25 11:11:46,2019-01-25 11:38:03,1,7.46,1,N,138,112,1,26.0,0.0,0.5,5.36,0.0,0.3,32.16,0.0 +1,2019-01-15 15:32:05,2019-01-15 15:44:18,1,2.2,1,N,50,249,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-04 09:28:30,2019-01-04 09:55:23,1,1.61,1,N,48,229,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-15 07:10:08,2019-01-15 07:15:43,1,1.55,1,N,236,141,1,6.5,0.0,0.5,0.58,0.0,0.3,7.88, +1,2019-01-29 06:46:43,2019-01-29 06:51:59,1,0.8,1,N,48,161,1,5.5,0.0,0.5,0.95,0.0,0.3,7.25,0.0 +2,2019-01-11 21:24:24,2019-01-11 21:42:05,1,11.18,1,N,48,182,1,31.5,0.5,0.5,9.84,0.0,0.3,42.64, +1,2019-01-30 07:58:03,2019-01-30 08:27:35,1,2.7,1,N,236,164,1,18.5,0.0,0.5,2.9,0.0,0.3,22.2,0.0 +2,2019-01-12 11:27:56,2019-01-12 11:38:38,1,1.56,1,N,249,170,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-29 20:34:38,2019-01-29 20:38:39,1,0.59,1,N,237,237,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-04 06:24:26,2019-01-04 06:31:13,1,1.1,1,N,168,168,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-07 17:17:13,2019-01-07 17:23:26,1,0.9,1,N,262,141,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97, +1,2019-01-23 18:53:41,2019-01-23 19:24:19,1,6.9,1,N,231,236,1,25.5,1.0,0.5,5.45,0.0,0.3,32.75,0.0 +2,2019-01-15 07:28:25,2019-01-15 07:58:08,1,10.28,1,N,230,138,1,32.5,0.0,0.5,7.81,5.76,0.3,46.87, +1,2019-01-28 08:54:42,2019-01-28 09:09:55,1,0.8,1,N,48,170,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8,0.0 +1,2019-01-16 20:53:27,2019-01-16 21:15:11,1,10.9,1,N,163,200,1,32.0,0.5,0.5,1.0,2.64,0.3,36.94, +2,2019-01-06 16:55:42,2019-01-06 17:13:04,1,2.44,1,N,237,75,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-25 05:14:37,2019-01-25 05:35:05,1,15.9,2,N,170,132,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +1,2019-01-25 08:28:50,2019-01-25 08:44:43,1,2.0,1,N,186,162,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-22 15:53:37,2019-01-22 16:17:15,1,5.1,1,N,140,158,1,20.0,0.0,0.5,6.2,0.0,0.3,27.0,0.0 +1,2019-01-02 20:10:58,2019-01-02 20:19:15,1,1.2,1,N,142,239,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-28 16:59:07,2019-01-28 17:05:06,1,1.12,1,N,238,24,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-02 11:17:30,2019-01-02 11:42:05,1,7.62,1,N,138,233,1,25.0,0.0,0.5,5.16,0.0,0.3,30.96, +2,2019-01-27 14:08:38,2019-01-27 14:12:42,1,0.61,1,N,239,143,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-10 17:32:01,2019-01-10 17:37:57,1,0.89,1,N,107,249,1,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-11 00:00:04,2019-01-11 00:03:37,1,1.0,1,N,236,75,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-24 13:25:40,2019-01-24 13:36:06,1,1.01,1,N,170,161,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-05 12:56:14,2019-01-05 13:41:47,1,18.63,2,N,249,132,1,52.0,0.0,0.5,6.0,5.76,0.3,64.56, +2,2019-01-20 11:47:35,2019-01-20 12:02:33,1,3.9,1,N,170,145,1,14.5,0.0,0.5,3.0,0.0,0.3,18.3, +2,2019-01-09 12:34:47,2019-01-09 12:37:23,1,0.66,1,N,90,158,1,4.0,0.0,0.5,0.72,0.0,0.3,5.52, +2,2019-01-31 22:37:58,2019-01-31 22:47:09,1,2.14,1,N,79,162,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3,0.0 +2,2019-01-15 12:42:17,2019-01-15 12:48:59,1,1.04,1,N,237,236,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-24 08:31:02,2019-01-24 08:34:30,1,0.4,1,N,87,209,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +1,2019-01-30 22:04:59,2019-01-30 22:32:31,1,5.4,1,N,261,141,1,21.0,0.5,0.5,1.0,0.0,0.3,23.3,0.0 +1,2019-01-17 15:37:14,2019-01-17 15:48:50,1,1.2,1,N,113,4,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-24 08:46:26,2019-01-24 09:10:27,1,4.3,1,N,74,143,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8,0.0 +2,2019-01-02 08:35:38,2019-01-02 08:54:58,1,8.41,1,N,263,13,1,25.5,0.0,0.5,5.26,0.0,0.3,31.56, +2,2019-01-04 08:06:45,2019-01-04 08:39:09,1,7.21,1,N,48,33,1,27.5,0.0,0.5,0.0,0.0,0.3,28.3, +2,2019-01-19 00:52:43,2019-01-19 01:04:41,1,2.07,1,N,170,148,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-17 15:27:46,2019-01-17 15:38:39,1,0.7,1,N,161,233,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-17 12:43:27,2019-01-17 13:02:55,1,2.1,1,N,237,170,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-18 00:21:10,2019-01-18 00:27:21,1,1.1,1,N,90,137,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +2,2019-01-12 02:11:54,2019-01-12 02:20:22,1,1.81,1,N,158,100,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-27 08:22:34,2019-01-27 08:24:49,1,0.6,1,Y,263,236,1,4.0,0.0,0.5,1.4,0.0,0.3,6.2,0.0 +1,2019-01-31 08:38:55,2019-01-31 08:50:29,1,0.8,1,N,170,161,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-23 10:21:14,2019-01-23 10:41:58,1,2.54,1,N,48,236,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +1,2019-01-04 08:19:21,2019-01-04 08:22:22,1,0.4,1,N,7,7,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-27 20:55:39,2019-01-27 21:06:53,1,1.38,1,N,186,170,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-02 20:39:40,2019-01-02 20:42:54,1,0.7,1,N,141,263,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-20 01:10:20,2019-01-20 01:18:16,1,0.6,1,N,164,186,1,6.5,0.5,0.5,3.0,0.0,0.3,10.8, +2,2019-01-10 18:04:52,2019-01-10 18:36:25,1,4.71,1,N,113,181,2,21.5,1.0,0.5,0.0,0.0,0.3,23.3, +2,2019-01-08 12:07:57,2019-01-08 12:17:30,1,1.56,1,N,162,50,1,8.0,0.0,0.5,2.0,0.0,0.3,10.8, +2,2019-01-19 23:22:16,2019-01-19 23:25:06,1,0.64,1,N,239,238,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3, +4,2019-01-26 17:31:18,2019-01-26 17:41:53,1,0.7,1,N,230,186,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-11 19:07:45,2019-01-11 19:16:35,1,0.8,1,N,230,142,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-16 13:47:03,2019-01-16 13:54:53,1,0.6,1,N,68,230,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-22 18:17:09,2019-01-22 18:26:51,1,2.3,1,N,166,239,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-25 23:21:23,2019-01-25 23:31:46,1,1.66,1,N,237,262,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-24 16:48:57,2019-01-24 16:57:10,1,0.6,1,N,237,163,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-29 08:39:54,2019-01-29 08:57:00,1,2.26,1,N,43,161,1,12.5,0.0,0.5,1.5,0.0,0.3,14.8,0.0 +2,2019-01-24 09:15:23,2019-01-24 09:20:00,1,0.95,1,N,238,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-12 02:50:48,2019-01-12 02:54:15,1,0.59,1,N,7,223,1,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-16 16:57:32,2019-01-16 17:05:10,1,1.0,1,N,230,161,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-01 01:13:49,2019-01-01 01:17:21,1,0.58,1,N,262,263,1,4.5,0.5,0.5,1.45,0.0,0.3,7.25, +2,2019-01-30 21:12:31,2019-01-30 21:16:08,1,0.73,1,N,162,170,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-05 11:09:25,2019-01-05 11:14:21,1,0.9,1,N,238,239,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-24 07:47:51,2019-01-24 07:58:19,1,1.3,1,N,48,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-25 07:47:07,2019-01-25 07:48:09,1,0.23,5,N,101,101,3,-0.1,0.0,0.0,0.0,0.0,-0.3,-0.4,0.0 +2,2019-01-05 22:24:10,2019-01-05 22:26:29,1,0.3,1,N,164,186,1,3.5,0.5,0.5,1.44,0.0,0.3,6.24, +1,2019-01-22 15:22:56,2019-01-22 15:29:36,1,0.6,1,N,164,161,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-12 19:11:43,2019-01-12 19:16:02,1,0.55,1,N,236,236,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-20 04:19:58,2019-01-20 04:37:21,1,4.4,1,N,79,48,1,15.5,0.5,0.5,2.5,0.0,0.3,19.3, +2,2019-01-23 20:01:13,2019-01-23 20:23:10,1,8.95,1,N,68,244,2,27.0,1.0,0.5,0.0,0.0,0.3,28.8,0.0 +2,2019-01-25 15:42:39,2019-01-25 15:50:54,1,1.26,1,N,170,229,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-24 00:21:05,2019-01-24 00:43:38,1,5.16,1,N,234,25,1,19.5,0.5,0.5,4.16,0.0,0.3,24.96,0.0 +2,2019-01-07 23:32:53,2019-01-07 23:43:09,1,3.1,1,N,230,151,1,11.0,0.5,0.5,2.0,0.0,0.3,14.3, +2,2019-01-13 14:32:25,2019-01-13 14:45:33,1,2.37,1,N,140,137,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-05 20:43:02,2019-01-05 20:57:57,1,3.14,1,N,144,209,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-10 15:49:59,2019-01-10 15:51:38,1,0.49,1,N,114,113,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38, +1,2019-01-27 13:30:48,2019-01-27 13:39:22,1,1.2,1,N,68,249,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-23 17:14:43,2019-01-23 17:39:09,1,2.3,1,N,140,239,1,16.5,1.0,0.5,3.65,0.0,0.3,21.95,0.0 +2,2019-01-12 00:17:55,2019-01-12 00:39:53,1,2.41,1,N,158,79,1,15.0,0.5,0.5,4.08,0.0,0.3,20.38, +2,2019-01-22 14:11:16,2019-01-22 15:00:54,1,18.82,2,N,132,163,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2,0.0 +1,2019-01-09 07:47:44,2019-01-09 07:55:40,1,1.4,1,N,237,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-23 16:20:21,2019-01-23 16:34:23,1,1.34,1,N,100,230,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-08 12:18:08,2019-01-08 12:30:32,1,1.47,1,N,113,100,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-20 00:28:52,2019-01-20 00:34:28,1,1.4,1,N,48,90,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +2,2019-01-16 20:43:52,2019-01-16 20:57:35,1,1.53,1,N,230,137,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-02 21:46:55,2019-01-02 22:00:49,1,2.83,1,N,186,125,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +1,2019-01-19 18:33:01,2019-01-19 18:53:42,1,11.3,1,N,138,241,2,33.0,0.0,0.5,0.0,5.76,0.3,39.56, +2,2019-01-12 15:14:50,2019-01-12 15:42:18,1,4.59,1,N,100,7,1,20.0,0.0,0.5,5.2,0.0,0.3,26.0, +2,2019-01-29 07:51:11,2019-01-29 07:57:40,1,0.82,1,N,249,234,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-13 16:09:34,2019-01-13 16:34:57,1,3.13,1,N,237,234,1,16.5,0.0,0.5,0.18,0.0,0.3,17.48, +2,2019-01-04 08:23:01,2019-01-04 08:33:04,1,1.93,1,N,236,140,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-19 23:28:39,2019-01-19 23:33:08,1,0.81,1,N,234,107,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-27 02:45:42,2019-01-27 02:57:06,1,2.64,1,N,148,164,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-30 19:45:47,2019-01-30 19:54:22,1,1.2,1,N,68,170,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-14 11:18:25,2019-01-14 11:23:41,1,0.91,1,N,143,239,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-17 18:30:58,2019-01-17 19:24:32,1,18.0,2,N,132,79,1,52.0,4.5,0.5,11.45,0.0,0.3,68.75, +2,2019-01-26 22:24:36,2019-01-26 22:33:35,1,1.0,1,N,48,186,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +1,2019-01-07 16:13:00,2019-01-07 16:27:25,1,3.3,1,N,125,162,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15, +1,2019-01-01 16:16:34,2019-01-01 16:18:34,1,0.3,1,N,163,237,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +1,2019-01-30 14:36:14,2019-01-30 14:48:53,1,2.1,1,N,161,246,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-27 22:20:45,2019-01-27 22:27:19,1,1.3,1,N,170,224,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-01 08:44:48,2019-01-01 08:47:22,1,0.39,1,N,249,158,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-04 19:24:40,2019-01-04 19:33:46,1,1.4,1,N,79,107,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-09 21:26:05,2019-01-09 21:35:43,1,2.74,1,N,164,141,1,10.0,0.5,0.5,2.82,0.0,0.3,14.12, +4,2019-01-19 12:54:23,2019-01-19 13:27:08,1,1.99,1,N,162,143,2,19.5,0.0,0.5,0.0,0.0,0.3,20.3, +2,2019-01-20 04:25:00,2019-01-20 04:33:14,1,2.61,1,N,223,260,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-09 09:14:38,2019-01-09 09:37:03,1,2.62,1,N,113,161,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56, +2,2019-01-27 12:10:32,2019-01-27 12:13:27,1,0.44,1,N,231,231,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-12 16:37:22,2019-01-12 16:45:48,1,1.27,1,N,161,164,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-27 01:51:55,2019-01-27 02:03:24,1,1.99,1,N,50,164,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-17 15:04:33,2019-01-17 15:16:22,1,0.97,1,N,238,236,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-22 07:05:41,2019-01-22 07:14:23,1,1.7,1,N,233,140,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +1,2019-01-29 08:04:44,2019-01-29 08:09:19,1,0.9,1,N,48,68,1,5.5,0.0,0.5,0.95,0.0,0.3,7.25,0.0 +2,2019-01-15 02:04:57,2019-01-15 02:17:53,1,4.92,1,N,255,226,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +2,2019-01-07 18:33:06,2019-01-07 18:44:17,1,6.29,1,N,140,13,1,18.5,1.0,0.5,2.0,0.0,0.3,22.3, +2,2019-01-19 02:19:49,2019-01-19 02:39:01,1,7.59,1,N,264,264,2,23.5,0.5,0.5,0.0,0.0,0.3,24.8, +1,2019-01-30 08:59:01,2019-01-30 09:01:52,1,0.4,1,N,229,233,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-29 18:30:21,2019-01-29 18:58:33,1,3.05,1,N,236,100,2,18.0,1.0,0.5,0.0,0.0,0.3,19.8,0.0 +2,2019-01-11 21:21:09,2019-01-11 21:23:49,1,0.7,1,N,48,142,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-05 03:52:10,2019-01-05 03:55:28,1,0.8,1,N,186,230,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-27 19:03:46,2019-01-27 19:06:33,1,0.4,1,N,236,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-03 09:14:10,2019-01-03 09:25:38,1,0.63,1,N,164,186,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-30 09:53:42,2019-01-30 09:57:06,1,0.6,1,N,264,264,1,4.5,0.0,0.5,0.64,0.0,0.3,5.94,0.0 +2,2019-01-03 18:34:46,2019-01-03 18:52:52,1,2.04,1,N,141,162,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-16 18:37:00,2019-01-16 18:47:03,1,0.97,1,N,234,90,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-05 15:57:04,2019-01-05 15:59:34,1,0.52,1,N,229,141,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-29 11:51:37,2019-01-29 11:58:04,1,0.8,1,N,233,140,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-15 08:11:24,2019-01-15 08:21:31,1,1.61,1,N,142,162,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +1,2019-01-22 22:02:23,2019-01-22 22:08:36,1,0.8,1,N,161,230,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-08 09:14:41,2019-01-08 09:38:19,1,2.5,1,N,142,68,1,16.0,0.0,0.5,4.2,0.0,0.3,21.0, +2,2019-01-22 08:59:58,2019-01-22 09:27:48,1,1.3,1,N,161,50,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-29 15:26:24,2019-01-29 15:32:22,1,0.86,1,N,151,238,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-23 22:41:17,2019-01-23 22:52:36,1,2.04,1,N,230,237,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3,0.0 +2,2019-01-30 14:13:26,2019-01-30 14:20:41,1,1.5,1,N,239,166,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-25 01:22:47,2019-01-25 01:23:09,1,0.0,1,N,107,107,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-21 18:49:49,2019-01-21 18:59:25,1,3.35,1,N,163,151,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-16 07:37:42,2019-01-16 07:47:56,1,1.53,1,N,186,48,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-25 06:59:18,2019-01-25 07:04:43,1,0.65,1,N,264,264,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-12 17:38:36,2019-01-12 18:15:24,1,17.2,2,N,132,164,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +1,2019-01-21 12:13:00,2019-01-21 12:19:03,1,0.6,1,N,107,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-26 10:33:18,2019-01-26 10:38:40,1,0.98,1,N,236,237,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-08 10:09:25,2019-01-08 10:20:05,1,2.19,1,N,239,141,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-30 06:44:27,2019-01-30 06:50:55,1,0.86,1,N,162,164,1,6.0,0.0,0.5,2.2,0.0,0.3,9.0,0.0 +2,2019-01-12 16:28:30,2019-01-12 16:37:31,1,1.83,1,N,161,141,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +1,2019-01-10 09:56:06,2019-01-10 10:00:56,1,0.6,1,N,161,161,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +1,2019-01-07 18:04:55,2019-01-07 18:12:36,1,1.5,1,N,237,142,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-05 01:56:47,2019-01-05 02:19:52,1,3.8,1,N,79,100,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-01 20:53:49,2019-01-01 21:01:03,1,1.6,1,N,231,114,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-09 13:15:12,2019-01-09 13:42:59,1,6.28,1,N,88,161,1,25.0,0.0,0.5,1.5,0.0,0.3,27.3, +1,2019-01-29 10:10:56,2019-01-29 10:16:40,1,0.7,1,N,164,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-31 22:45:16,2019-01-31 22:56:31,1,1.8,1,N,264,234,1,9.0,0.5,0.5,1.7,0.0,0.3,12.0,0.0 +1,2019-01-18 22:06:09,2019-01-18 22:19:53,1,1.7,1,N,107,68,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-26 09:56:49,2019-01-26 10:05:20,1,1.69,1,N,263,162,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-05 00:45:38,2019-01-05 01:15:08,1,11.09,1,N,230,213,2,33.5,0.5,0.5,0.0,0.0,0.3,34.8, +2,2019-01-16 15:53:26,2019-01-16 16:20:30,1,7.05,1,N,13,161,1,24.5,0.0,0.5,5.06,0.0,0.3,30.36, +2,2019-01-23 14:28:38,2019-01-23 14:41:13,1,1.32,1,N,262,162,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-05 10:20:05,2019-01-05 10:27:21,1,1.31,1,N,230,161,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-15 16:38:30,2019-01-15 17:06:47,1,3.8,1,N,68,144,2,18.0,1.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-15 14:40:57,2019-01-15 14:44:35,1,0.43,1,N,230,48,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-08 15:44:38,2019-01-08 15:50:03,1,0.76,1,N,246,186,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-22 20:59:41,2019-01-22 21:01:08,1,0.2,1,N,162,161,1,3.0,0.5,0.5,1.2,0.0,0.3,5.5,0.0 +2,2019-01-09 18:28:57,2019-01-09 18:38:27,1,1.63,1,N,74,236,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-10 18:09:44,2019-01-10 18:18:33,1,0.99,1,N,125,90,1,7.0,1.0,0.5,2.64,0.0,0.3,11.44, +2,2019-01-10 07:48:20,2019-01-10 07:48:31,1,0.0,2,N,90,90,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-09 09:31:06,2019-01-09 09:41:13,1,1.27,1,N,237,236,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7, +2,2019-01-30 10:45:35,2019-01-30 10:56:43,1,1.11,1,N,162,161,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-05 19:39:15,2019-01-05 19:55:53,1,4.53,1,N,239,158,1,16.5,0.0,0.5,5.19,0.0,0.3,22.49, +1,2019-01-05 16:14:48,2019-01-05 16:19:28,1,1.0,1,N,238,263,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-24 01:20:10,2019-01-24 01:30:17,1,2.2,1,N,114,246,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-23 21:57:01,2019-01-23 22:02:26,1,1.48,1,N,237,239,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-13 15:30:20,2019-01-13 15:35:23,1,1.19,1,N,141,140,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-12 13:31:51,2019-01-12 13:41:40,1,2.0,1,N,162,262,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-31 14:59:28,2019-01-31 15:24:01,1,3.35,1,N,163,211,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-09 10:35:06,2019-01-09 11:02:03,1,11.6,1,N,230,138,1,34.5,0.0,0.5,8.2,5.76,0.3,49.26, +1,2019-01-15 14:33:06,2019-01-15 14:49:32,1,5.1,2,N,260,141,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56, +2,2019-01-11 22:09:06,2019-01-11 22:41:05,1,12.37,1,N,164,14,1,37.5,0.5,0.5,10.0,0.0,0.3,48.8, +2,2019-01-24 18:02:17,2019-01-24 18:24:59,1,1.64,1,N,233,107,1,14.5,1.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-19 15:52:28,2019-01-19 15:58:52,1,1.52,1,N,186,163,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-23 17:47:12,2019-01-23 17:55:34,1,0.98,1,N,42,152,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-31 10:21:06,2019-01-31 10:34:43,1,5.65,1,N,141,45,1,16.5,0.0,0.5,4.33,0.0,0.3,21.63,0.0 +2,2019-01-28 00:14:19,2019-01-28 00:19:23,1,1.61,1,N,90,170,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-20 17:50:26,2019-01-20 18:06:50,1,2.58,1,N,79,68,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-12 11:15:12,2019-01-12 11:21:17,1,1.52,1,N,141,263,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-07 17:08:25,2019-01-07 17:16:08,1,1.63,1,N,142,237,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-03 18:48:47,2019-01-03 18:52:17,1,0.55,1,N,236,236,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-09 22:09:36,2019-01-09 22:14:42,1,1.5,1,N,162,107,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +1,2019-01-22 11:13:19,2019-01-22 11:17:35,1,0.8,1,N,143,50,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-08 11:35:42,2019-01-08 11:46:16,1,2.5,1,N,232,256,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-11 06:32:23,2019-01-11 06:45:30,1,1.59,1,N,186,161,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-20 18:37:38,2019-01-20 18:43:48,1,1.5,1,N,236,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-27 02:06:15,2019-01-27 02:13:33,1,1.2,1,N,48,162,1,7.0,0.5,0.5,2.05,0.0,0.3,10.35,0.0 +1,2019-01-11 12:31:12,2019-01-11 12:46:12,1,8.3,1,N,138,233,1,24.0,0.0,0.5,5.0,5.76,0.3,35.56, +1,2019-01-01 14:32:38,2019-01-01 14:41:34,1,1.2,1,N,79,68,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-22 17:18:15,2019-01-22 17:29:39,1,1.2,1,N,161,237,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-06 20:04:58,2019-01-06 20:08:20,1,1.12,1,N,170,162,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88, +2,2019-01-13 11:30:33,2019-01-13 11:54:39,1,4.09,1,N,163,144,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +1,2019-01-30 19:59:30,2019-01-30 20:10:34,1,1.9,1,N,186,79,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-07 02:24:22,2019-01-07 03:01:08,1,17.2,4,N,161,265,2,56.5,0.5,0.5,0.0,0.0,0.3,57.8, +1,2019-01-11 21:25:55,2019-01-11 21:40:43,1,1.7,1,N,186,163,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75, +2,2019-01-28 21:28:39,2019-01-28 21:32:23,1,0.8,1,N,161,163,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-19 00:33:28,2019-01-19 00:33:31,1,0.04,1,N,264,100,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-27 10:42:32,2019-01-27 11:00:29,1,3.1,1,N,186,237,1,14.0,0.0,0.5,2.22,0.0,0.3,17.02,0.0 +2,2019-01-24 16:10:59,2019-01-24 16:22:05,1,1.49,1,N,114,158,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-14 08:49:45,2019-01-14 08:55:09,1,0.91,1,N,234,79,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24, +1,2019-01-12 10:14:57,2019-01-12 10:22:10,1,1.3,1,N,186,48,1,7.0,0.0,0.5,0.39,0.0,0.3,8.19, +2,2019-01-11 07:21:43,2019-01-11 07:46:23,1,2.9,1,N,230,246,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-18 11:39:59,2019-01-18 11:44:56,1,0.81,1,N,143,239,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-03 16:03:44,2019-01-03 16:23:47,1,1.86,1,N,43,161,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-23 10:55:24,2019-01-23 10:57:42,1,0.1,1,N,50,50,1,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-28 11:32:40,2019-01-28 12:07:33,1,1.5,1,N,137,141,1,20.0,0.0,0.5,4.15,0.0,0.3,24.95,0.0 +1,2019-01-15 10:26:13,2019-01-15 11:04:47,1,10.8,1,N,138,162,2,33.5,0.0,0.5,0.0,5.76,0.3,40.06, +2,2019-01-09 18:32:57,2019-01-09 18:39:53,1,0.8,1,N,234,113,1,6.0,1.0,0.5,3.0,0.0,0.3,10.8, +1,2019-01-27 13:51:02,2019-01-27 13:54:38,1,0.8,1,N,236,75,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +1,2019-01-07 19:45:34,2019-01-07 20:09:55,1,4.0,1,N,230,231,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +1,2019-01-25 07:54:43,2019-01-25 08:02:27,1,1.6,1,N,239,166,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-16 13:39:26,2019-01-16 14:00:02,1,1.76,1,N,107,230,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-08 08:24:44,2019-01-08 08:30:44,1,0.85,1,N,100,161,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-18 15:26:32,2019-01-18 16:16:55,1,18.16,2,N,132,41,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-28 09:38:32,2019-01-28 09:50:50,1,1.0,1,N,162,161,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-07 09:07:42,2019-01-07 09:28:37,1,4.59,1,N,87,170,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +2,2019-01-03 13:51:38,2019-01-03 14:05:16,1,1.16,1,N,237,162,1,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-06 19:47:07,2019-01-06 19:58:59,1,1.73,1,N,68,137,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-18 09:24:46,2019-01-18 09:32:00,1,1.8,1,N,264,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-23 23:41:23,2019-01-23 23:49:11,1,1.95,1,N,137,237,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-17 06:24:24,2019-01-17 06:37:21,1,4.3,1,N,142,231,1,14.0,0.0,0.5,1.48,0.0,0.3,16.28, +2,2019-01-03 20:25:03,2019-01-03 20:32:34,1,1.12,1,N,170,161,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-09 09:44:27,2019-01-09 09:47:11,1,0.45,1,N,237,236,1,3.5,0.0,0.5,0.65,0.0,0.3,4.95, +1,2019-01-14 16:09:02,2019-01-14 16:14:42,1,1.2,1,Y,90,158,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +1,2019-01-11 11:08:24,2019-01-11 11:22:23,1,1.3,1,N,236,141,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +2,2019-01-19 01:44:59,2019-01-19 01:45:20,1,0.13,1,N,41,74,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +1,2019-01-30 15:18:27,2019-01-30 15:21:04,1,0.3,1,N,186,100,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +2,2019-01-21 21:37:52,2019-01-21 21:45:23,1,1.21,1,N,186,170,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3,0.0 +1,2019-01-01 22:16:51,2019-01-01 22:28:34,1,2.8,1,N,238,116,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +1,2019-01-21 13:34:31,2019-01-21 13:45:52,1,1.8,1,N,229,142,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85,0.0 +2,2019-01-01 07:35:54,2019-01-01 07:45:58,1,3.99,1,N,129,141,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-19 02:40:16,2019-01-19 03:11:35,1,11.7,1,N,148,178,1,36.0,0.5,0.5,0.0,0.0,0.3,37.3, +2,2019-01-15 08:15:05,2019-01-15 08:31:58,1,1.67,1,N,143,230,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-06 23:13:51,2019-01-06 23:22:14,1,1.84,1,N,48,234,1,8.0,0.5,0.5,2.79,0.0,0.3,12.09, +2,2019-01-24 16:28:50,2019-01-24 16:35:28,1,0.93,1,N,113,158,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-19 04:58:45,2019-01-19 05:03:20,1,0.83,1,N,249,90,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-30 19:50:57,2019-01-30 19:59:07,1,1.15,1,N,237,43,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-24 20:44:35,2019-01-24 20:51:28,1,1.7,1,N,229,263,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +1,2019-01-24 17:32:37,2019-01-24 17:40:34,1,0.7,1,N,249,113,1,6.5,1.0,0.5,2.05,0.0,0.3,10.35,0.0 +2,2019-01-05 16:30:03,2019-01-05 16:34:13,1,0.69,1,N,114,125,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-06 12:16:23,2019-01-06 12:20:58,1,1.3,1,N,237,170,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-15 14:42:01,2019-01-15 14:54:54,1,1.9,1,N,100,114,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-16 01:03:41,2019-01-16 01:19:26,1,4.62,1,N,236,7,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +2,2019-01-03 07:19:21,2019-01-03 07:26:18,1,1.16,1,N,263,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-16 19:31:42,2019-01-16 19:36:36,1,1.27,1,N,237,236,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-19 13:15:55,2019-01-19 13:28:20,1,1.8,1,N,113,170,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-24 23:41:46,2019-01-24 23:50:42,1,1.7,1,N,170,140,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +1,2019-01-03 06:45:04,2019-01-03 06:49:58,1,0.9,1,N,107,170,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-08 07:01:58,2019-01-08 07:10:57,1,0.93,1,N,230,50,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-20 19:23:08,2019-01-20 19:54:38,1,6.25,1,N,145,198,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3, +1,2019-01-23 09:08:34,2019-01-23 09:14:28,1,1.1,1,N,264,264,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-28 05:37:49,2019-01-28 05:41:58,1,1.0,1,N,186,68,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-05 22:51:20,2019-01-05 23:01:55,1,2.2,1,N,79,229,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-28 12:46:12,2019-01-28 12:57:02,1,1.8,1,N,162,236,1,9.0,0.0,0.5,1.25,0.0,0.3,11.05,0.0 +2,2019-01-10 16:10:54,2019-01-10 16:17:24,1,1.05,1,N,263,141,1,6.0,1.0,0.5,1.5,0.0,0.3,9.3, +2,2019-01-25 11:10:35,2019-01-25 11:47:14,1,3.63,1,N,45,161,1,23.0,0.0,0.5,5.95,0.0,0.3,29.75,0.0 +2,2019-01-30 18:31:33,2019-01-30 18:45:11,1,2.1,1,N,140,142,1,11.0,1.0,0.5,2.0,0.0,0.3,14.8,0.0 +2,2019-01-22 18:21:52,2019-01-22 18:25:57,1,0.61,1,N,263,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-07 08:17:28,2019-01-07 08:20:33,1,0.56,1,N,239,142,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-10 13:27:18,2019-01-10 13:46:04,1,3.1,1,N,231,137,1,14.5,0.0,0.5,3.05,0.0,0.3,18.35, +1,2019-01-08 16:28:40,2019-01-08 16:37:17,1,1.2,1,N,142,239,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-12 12:15:33,2019-01-12 12:31:20,1,2.46,1,N,237,137,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-29 06:47:03,2019-01-29 07:04:41,1,3.53,1,N,43,137,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-16 18:02:58,2019-01-16 18:45:24,1,8.27,1,N,236,225,1,31.5,1.0,0.5,0.0,0.0,0.3,33.3, +2,2019-01-15 17:10:48,2019-01-15 17:13:25,1,0.79,1,N,229,170,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-14 12:48:46,2019-01-14 13:01:24,1,1.2,1,N,236,163,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-24 08:14:57,2019-01-24 08:23:57,1,1.2,1,N,107,164,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-21 22:40:51,2019-01-21 23:08:50,1,18.8,1,N,132,49,1,50.0,0.5,0.5,10.26,0.0,0.3,61.56,0.0 +2,2019-01-03 22:34:18,2019-01-03 22:48:30,1,3.64,1,N,231,246,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-30 08:46:17,2019-01-30 09:01:35,1,3.41,1,N,13,68,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-11 15:56:23,2019-01-11 16:44:41,1,11.47,1,N,164,138,1,38.0,0.0,0.5,11.14,5.76,0.3,55.7, +2,2019-01-11 14:57:54,2019-01-11 15:02:43,1,0.63,1,N,237,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-20 12:44:38,2019-01-20 12:50:42,1,1.28,1,N,143,50,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-13 22:04:11,2019-01-13 22:15:48,1,3.1,1,N,236,137,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-23 08:10:16,2019-01-23 08:18:01,1,1.1,1,N,263,140,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-11 19:23:12,2019-01-11 19:30:05,1,0.8,1,N,234,249,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-18 09:36:17,2019-01-18 09:46:53,1,1.4,1,N,141,237,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-30 18:16:57,2019-01-30 18:20:17,1,0.26,1,N,113,113,1,4.0,1.0,0.5,1.45,0.0,0.3,7.25,0.0 +4,2019-01-06 16:22:59,2019-01-06 16:27:24,1,0.51,1,N,236,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-13 00:24:32,2019-01-13 00:37:22,1,1.6,1,N,107,249,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-24 21:27:20,2019-01-24 21:35:58,1,2.1,1,N,162,79,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25,0.0 +4,2019-01-01 19:40:16,2019-01-01 19:44:22,1,0.59,1,N,151,238,1,4.5,1.0,0.5,1.5,0.0,0.3,7.8, +1,2019-01-08 06:42:32,2019-01-08 06:59:02,1,3.7,1,N,162,13,1,14.5,0.0,0.5,1.53,0.0,0.3,16.83, +2,2019-01-15 21:54:15,2019-01-15 22:03:19,1,1.44,1,N,230,90,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +2,2019-01-30 15:59:07,2019-01-30 16:15:34,1,2.09,1,N,68,113,1,12.0,0.0,0.5,2.4,0.0,0.3,15.2,0.0 +2,2019-01-23 20:33:01,2019-01-23 20:34:39,1,0.32,1,N,237,237,1,3.5,0.5,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-09 15:15:03,2019-01-09 15:20:36,1,0.74,1,N,238,238,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-07 07:59:50,2019-01-07 08:05:49,1,0.62,1,N,68,68,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-29 14:09:34,2019-01-29 14:22:52,1,1.85,1,N,161,236,1,10.5,0.0,0.5,3.39,0.0,0.3,14.69,0.0 +2,2019-01-12 06:37:23,2019-01-12 06:41:27,1,1.76,1,N,236,140,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-26 02:56:54,2019-01-26 03:13:23,1,3.0,1,N,164,232,1,13.5,0.5,0.5,2.95,0.0,0.3,17.75,0.0 +2,2019-01-03 20:59:14,2019-01-03 21:19:36,1,4.37,1,N,229,231,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +1,2019-01-01 01:33:08,2019-01-01 01:47:32,1,2.6,1,N,49,256,1,12.0,0.5,0.5,2.0,0.0,0.3,15.3, +1,2019-01-26 21:21:24,2019-01-26 21:26:29,1,0.4,1,N,230,230,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-31 17:12:47,2019-01-31 17:20:51,1,1.5,1,N,238,236,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-16 09:46:45,2019-01-16 09:51:19,1,0.92,1,N,68,68,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19, +2,2019-01-22 13:19:00,2019-01-22 13:27:01,1,1.07,1,N,232,79,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14,0.0 +1,2019-01-25 01:08:10,2019-01-25 01:13:35,1,0.9,1,N,264,264,1,6.0,0.5,0.5,1.1,0.0,0.3,8.4,0.0 +1,2019-01-02 09:02:44,2019-01-02 09:11:46,1,2.0,1,N,24,142,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-21 18:10:39,2019-01-21 18:20:11,1,1.47,1,N,162,50,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-21 15:44:56,2019-01-21 16:04:50,1,3.62,1,N,48,262,1,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-11 19:46:51,2019-01-11 20:08:48,1,7.09,1,N,87,36,1,23.5,0.5,0.5,4.96,0.0,0.3,29.76, +1,2019-01-25 23:23:27,2019-01-25 23:35:58,1,1.6,1,N,164,249,1,9.5,0.5,0.5,3.0,0.0,0.3,13.8,0.0 +1,2019-01-15 12:25:25,2019-01-15 12:29:25,1,1.1,1,N,142,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-05 01:28:49,2019-01-05 01:41:22,1,3.4,1,N,164,263,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +1,2019-01-10 09:20:22,2019-01-10 09:25:37,1,0.6,1,N,164,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-23 09:42:35,2019-01-23 09:46:20,1,0.89,1,N,48,143,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-28 22:49:30,2019-01-28 23:00:16,1,2.1,1,N,163,107,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95,0.0 +1,2019-01-31 12:44:39,2019-01-31 12:59:59,1,4.3,1,N,141,148,1,16.0,0.0,0.5,1.2,0.0,0.3,18.0,0.0 +2,2019-01-11 13:24:08,2019-01-11 13:44:32,1,2.34,1,N,229,143,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-19 09:41:46,2019-01-19 09:49:35,1,2.12,1,N,79,170,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-20 02:01:29,2019-01-20 02:09:54,1,1.97,1,N,230,107,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-26 16:03:04,2019-01-26 16:25:58,1,3.09,1,N,142,107,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-30 16:42:29,2019-01-30 16:47:57,1,0.86,1,N,163,230,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-02 19:33:07,2019-01-02 19:45:01,1,2.7,1,N,263,137,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +1,2019-01-24 10:44:19,2019-01-24 11:09:58,1,1.8,1,N,100,161,1,15.5,0.0,0.5,4.85,0.0,0.3,21.15,0.0 +2,2019-01-10 14:59:05,2019-01-10 15:13:17,1,1.85,1,N,238,43,1,11.0,0.0,0.5,1.5,0.0,0.3,13.3, +2,2019-01-20 05:17:29,2019-01-20 05:38:31,1,7.88,1,N,163,17,1,24.5,0.5,0.5,1.0,0.0,0.3,26.8, +1,2019-01-29 07:01:46,2019-01-29 07:06:13,1,0.6,1,N,100,230,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-24 10:24:50,2019-01-24 10:25:06,1,0.0,2,N,164,164,1,52.0,0.0,0.5,7.0,5.76,0.3,65.56,0.0 +1,2019-01-07 16:46:03,2019-01-07 16:46:12,1,0.0,1,N,132,132,2,2.5,1.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-08 19:08:38,2019-01-08 19:20:04,1,1.6,1,N,229,107,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-30 08:47:22,2019-01-30 09:08:00,1,1.48,1,N,229,161,1,13.5,0.0,0.5,1.5,0.0,0.3,15.8,0.0 +2,2019-01-20 03:36:35,2019-01-20 03:48:28,1,4.05,1,N,79,112,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +2,2019-01-02 14:44:42,2019-01-02 14:52:39,1,1.48,1,N,246,48,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-07 15:59:57,2019-01-07 16:10:45,1,1.03,1,N,163,230,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-18 10:48:32,2019-01-18 10:58:19,1,0.74,1,N,234,249,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-17 10:27:07,2019-01-17 10:54:51,1,2.47,1,N,229,107,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-23 23:57:40,2019-01-24 00:02:20,1,0.8,1,N,90,100,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +4,2019-01-22 08:05:10,2019-01-22 08:14:05,1,0.83,1,N,162,237,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-07 11:21:42,2019-01-07 11:45:43,1,10.46,1,N,161,138,1,30.5,0.0,0.5,7.41,5.76,0.3,44.47, +1,2019-01-08 17:36:09,2019-01-08 17:44:46,1,1.1,1,N,141,237,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-12 21:10:57,2019-01-12 21:29:54,1,2.7,1,N,161,79,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-18 17:07:39,2019-01-18 17:20:44,1,1.22,1,N,90,161,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-24 23:16:38,2019-01-24 23:28:30,1,2.1,1,N,230,141,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3,0.0 +1,2019-01-29 14:58:39,2019-01-29 15:07:15,1,0.9,1,N,142,143,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +1,2019-01-15 11:57:55,2019-01-15 12:08:11,1,1.0,1,N,237,230,1,8.0,0.0,0.5,2.11,0.0,0.3,10.91, +1,2019-01-14 18:12:23,2019-01-14 18:21:23,1,1.1,1,Y,170,161,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3, +1,2019-01-04 21:04:33,2019-01-04 21:14:01,1,0.8,1,N,231,148,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-25 20:02:33,2019-01-25 20:34:54,1,10.1,1,N,138,142,1,32.5,0.5,0.5,5.0,0.0,0.3,38.8,0.0 +2,2019-01-09 08:42:00,2019-01-09 08:52:56,1,1.59,1,N,140,162,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-06 16:34:23,2019-01-06 16:36:12,1,0.63,1,N,236,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-23 19:23:23,2019-01-23 19:32:19,1,1.8,1,N,163,236,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-07 09:21:00,2019-01-07 09:36:58,1,2.55,1,N,90,231,1,12.5,0.0,0.5,1.25,0.0,0.3,14.55, +1,2019-01-14 04:02:29,2019-01-14 04:18:29,1,4.5,1,N,249,255,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75, +2,2019-01-08 08:45:16,2019-01-08 08:51:52,1,1.11,1,N,237,236,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82, +1,2019-01-31 15:09:02,2019-01-31 15:23:13,1,1.9,1,N,161,43,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0,0.0 +2,2019-01-20 03:32:36,2019-01-20 03:42:55,1,3.75,1,N,90,141,2,12.0,0.5,0.5,0.0,0.0,0.3,13.3, +2,2019-01-20 13:57:40,2019-01-20 14:31:32,1,10.23,1,N,161,138,1,34.0,0.0,0.5,8.11,5.76,0.3,48.67, +1,2019-01-25 09:21:53,2019-01-25 09:32:08,1,0.7,1,N,229,170,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-13 15:50:17,2019-01-13 15:54:17,1,0.7,1,N,238,238,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-15 07:50:01,2019-01-15 07:58:45,1,1.34,1,N,163,100,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-23 14:30:01,2019-01-23 14:47:24,1,1.76,1,N,68,164,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-21 05:17:13,2019-01-21 05:24:23,1,1.9,1,N,7,223,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-04 20:41:41,2019-01-04 20:56:16,1,1.8,1,N,114,90,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +1,2019-01-08 07:38:26,2019-01-08 07:48:39,1,1.4,1,N,107,161,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-12 18:12:53,2019-01-12 18:39:23,1,5.3,1,N,24,164,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +1,2019-01-28 20:44:07,2019-01-28 20:52:37,1,1.4,1,N,164,48,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +4,2019-01-04 13:51:52,2019-01-04 13:55:55,1,0.73,1,N,79,4,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-13 04:01:47,2019-01-13 04:10:22,1,2.8,1,N,113,48,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-04 07:54:10,2019-01-04 07:56:33,1,0.91,1,N,48,68,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-03 12:08:48,2019-01-03 12:37:25,1,2.9,1,N,170,239,1,18.5,0.0,0.5,4.8,0.0,0.3,24.1, +2,2019-01-10 13:09:07,2019-01-10 13:17:33,1,0.94,1,N,162,161,1,7.0,0.0,0.5,1.5,0.0,0.3,9.3, +2,2019-01-12 06:07:41,2019-01-12 06:14:56,1,1.35,1,N,238,143,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-04 19:40:57,2019-01-04 20:10:42,1,16.06,1,N,132,112,1,44.0,1.0,0.5,8.0,0.0,0.3,53.8, +2,2019-01-24 08:28:37,2019-01-24 08:41:02,1,1.42,1,N,236,161,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-16 22:45:54,2019-01-16 22:50:35,1,0.78,1,N,158,249,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-15 08:53:02,2019-01-15 09:24:33,1,3.9,1,N,43,230,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +2,2019-01-02 16:34:37,2019-01-02 16:37:24,1,0.66,1,N,186,186,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-14 20:08:56,2019-01-14 20:18:50,1,2.13,1,N,170,236,1,9.5,0.5,0.5,1.5,0.0,0.3,12.3, +2,2019-01-16 12:10:25,2019-01-16 12:27:32,1,1.9,1,N,231,234,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-23 12:58:22,2019-01-23 13:36:35,1,9.8,1,N,138,161,1,36.5,0.0,0.5,8.6,5.76,0.3,51.66,0.0 +2,2019-01-05 14:04:01,2019-01-05 14:15:55,1,1.4,1,N,161,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-09 11:42:03,2019-01-09 12:02:26,1,2.21,1,N,230,107,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-02 08:30:54,2019-01-02 08:39:07,1,0.9,1,N,100,161,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-16 18:23:57,2019-01-16 18:28:58,1,0.8,1,N,107,113,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-30 06:31:47,2019-01-30 06:44:23,1,4.29,1,N,88,246,1,14.5,0.0,0.5,3.06,0.0,0.3,20.31,0.0 +2,2019-01-06 18:12:18,2019-01-06 18:17:51,1,1.1,1,N,239,239,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-02 19:16:12,2019-01-02 19:23:53,1,0.9,1,N,186,170,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-29 12:32:35,2019-01-29 12:40:27,1,0.6,1,N,90,158,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-05 22:12:22,2019-01-05 22:21:25,1,1.34,1,N,234,144,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-12 14:14:58,2019-01-12 14:27:30,1,1.2,1,N,161,186,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-23 08:31:23,2019-01-23 09:02:13,1,3.23,1,N,239,164,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96,0.0 +2,2019-01-08 10:22:28,2019-01-08 10:36:57,1,2.4,1,N,43,246,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-31 05:29:50,2019-01-31 05:35:08,1,1.47,1,N,249,186,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-29 13:45:21,2019-01-29 13:57:58,1,1.28,1,N,114,144,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-11 07:08:56,2019-01-11 07:14:03,1,0.66,1,N,237,162,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19, +2,2019-01-26 09:00:25,2019-01-26 09:13:36,1,3.47,1,N,87,97,1,13.0,0.0,0.5,4.14,0.0,0.3,17.94,0.0 +1,2019-01-05 19:40:05,2019-01-05 19:53:06,1,1.3,1,N,114,234,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-12 09:55:43,2019-01-12 10:02:45,1,0.88,1,N,143,239,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-15 16:29:38,2019-01-15 16:34:24,1,0.8,1,N,25,189,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-04 15:10:14,2019-01-04 16:14:19,1,11.3,1,N,138,230,1,47.0,0.0,0.5,13.35,5.76,0.3,66.91, +2,2019-01-09 13:37:12,2019-01-09 13:45:22,1,1.15,1,N,163,142,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +1,2019-01-09 18:08:12,2019-01-09 18:15:42,1,1.3,1,N,144,249,1,7.0,1.0,0.5,1.32,0.0,0.3,10.12, +2,2019-01-07 11:22:18,2019-01-07 11:30:20,1,1.09,1,N,233,161,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 02:18:13,2019-01-26 02:27:50,1,2.05,1,N,148,170,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-31 03:22:31,2019-01-31 03:34:58,1,6.22,1,N,186,166,2,18.5,0.5,0.5,0.0,0.0,0.3,19.8,0.0 +2,2019-01-14 13:43:36,2019-01-14 13:56:30,1,2.08,1,N,238,141,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0, +1,2019-01-14 10:26:49,2019-01-14 10:44:49,1,2.0,1,N,236,162,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-15 21:41:41,2019-01-15 21:56:28,1,2.64,1,N,234,141,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-09 19:20:54,2019-01-09 19:33:05,1,1.64,1,N,161,142,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-27 22:26:22,2019-01-27 22:37:43,1,2.75,1,N,50,140,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-11 17:42:53,2019-01-11 17:48:42,1,1.1,1,N,237,237,1,6.0,1.0,0.5,2.3,0.0,0.3,10.1, +1,2019-01-21 16:19:49,2019-01-21 16:22:58,1,0.6,1,N,186,230,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +1,2019-01-28 18:23:30,2019-01-28 18:37:05,1,2.1,1,N,236,24,1,11.0,1.0,0.5,1.92,0.0,0.3,14.72,0.0 +2,2019-01-17 07:52:18,2019-01-17 08:11:17,1,3.0,1,N,68,239,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-19 08:25:20,2019-01-19 08:43:06,1,10.15,5,N,45,42,1,28.11,0.0,0.5,0.0,0.0,0.3,28.91, +2,2019-01-12 17:42:36,2019-01-12 17:48:08,1,1.04,1,N,143,48,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-04 19:16:32,2019-01-04 19:21:23,1,0.54,1,N,163,162,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-01 08:53:08,2019-01-01 09:02:20,1,3.0,1,N,79,163,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-11 20:11:59,2019-01-11 20:33:46,1,4.31,1,N,170,41,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56, +1,2019-01-08 10:59:30,2019-01-08 11:09:09,1,1.2,1,N,237,142,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-03 10:26:12,2019-01-03 10:59:32,1,10.85,1,N,138,142,1,35.0,0.0,0.5,8.31,5.76,0.3,49.87, +1,2019-01-22 11:35:06,2019-01-22 11:37:29,1,0.2,1,N,236,236,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +2,2019-01-01 06:37:10,2019-01-01 06:47:29,1,2.84,1,N,48,238,1,11.0,0.0,0.5,2.2,0.0,0.3,14.0, +2,2019-01-04 07:32:28,2019-01-04 07:45:16,1,2.52,1,N,43,229,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 23:01:05,2019-01-11 23:12:04,1,2.28,1,N,142,164,1,10.0,0.5,0.5,2.82,0.0,0.3,14.12, +2,2019-01-22 21:31:35,2019-01-22 21:56:41,1,14.21,1,N,162,51,1,39.0,0.5,0.5,0.0,0.0,0.3,40.3,0.0 +2,2019-01-31 11:46:01,2019-01-31 12:04:54,1,0.66,1,N,230,237,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-17 13:30:50,2019-01-17 13:32:21,1,0.2,1,N,255,255,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-28 06:25:44,2019-01-28 06:30:52,1,1.5,1,N,68,230,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-05 11:05:14,2019-01-05 11:17:20,1,1.59,1,N,181,33,1,9.5,0.0,0.5,1.7,0.0,0.3,12.0, +2,2019-01-14 19:25:26,2019-01-14 19:43:46,1,2.59,1,N,142,137,1,13.0,1.0,0.5,1.5,0.0,0.3,16.3, +1,2019-01-21 20:57:26,2019-01-21 21:03:16,1,0.8,1,N,230,48,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-10 18:26:27,2019-01-10 18:33:35,1,1.0,1,N,263,237,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-11 16:10:00,2019-01-11 16:18:06,1,1.37,1,N,230,143,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-11 17:26:51,2019-01-11 17:34:26,1,0.98,1,N,144,125,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-24 01:12:41,2019-01-24 01:23:30,1,3.0,1,N,162,262,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-22 14:28:24,2019-01-22 14:40:21,1,2.0,1,N,68,237,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +1,2019-01-12 01:39:59,2019-01-12 01:52:50,1,5.0,1,N,48,152,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95, +2,2019-01-20 22:28:18,2019-01-20 22:33:15,1,0.69,1,N,148,211,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +2,2019-01-25 06:40:59,2019-01-25 06:55:30,1,3.49,1,N,50,107,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +2,2019-01-02 23:25:02,2019-01-02 23:30:46,1,1.02,1,N,158,114,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-25 08:48:07,2019-01-25 09:00:49,1,1.43,1,N,143,237,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-10 07:43:57,2019-01-10 07:55:18,1,1.62,1,N,48,237,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-19 17:31:11,2019-01-19 17:37:21,1,0.96,1,N,161,100,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-04 10:44:17,2019-01-04 10:57:09,1,1.76,1,N,162,100,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-07 18:37:42,2019-01-07 18:40:24,1,0.56,1,N,107,137,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-14 13:01:23,2019-01-14 13:23:28,1,3.05,1,N,186,148,1,16.0,0.0,0.5,4.2,0.0,0.3,21.0, +2,2019-01-09 11:54:10,2019-01-09 12:25:46,1,4.05,1,N,79,143,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +1,2019-01-20 18:45:32,2019-01-20 19:00:11,1,3.2,1,N,238,233,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +1,2019-01-24 23:44:08,2019-01-24 23:47:23,1,1.0,1,N,263,75,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-17 19:13:03,2019-01-17 19:17:15,1,0.72,1,N,143,239,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-16 21:37:32,2019-01-16 21:56:24,1,3.81,1,N,113,142,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +1,2019-01-24 22:13:07,2019-01-24 22:29:56,1,2.8,1,N,140,186,3,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-16 12:43:09,2019-01-16 12:51:44,1,1.1,1,N,158,90,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-22 14:47:56,2019-01-22 14:53:07,1,0.5,1,N,164,170,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +1,2019-01-26 10:45:30,2019-01-26 10:48:16,1,0.6,1,N,234,113,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +1,2019-01-15 06:37:09,2019-01-15 06:45:17,1,1.3,1,N,48,246,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-17 12:46:24,2019-01-17 12:58:45,1,1.43,1,N,237,161,1,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-04 15:54:36,2019-01-04 15:59:39,1,1.09,1,N,239,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-08 07:19:17,2019-01-08 07:24:26,1,0.8,1,N,229,140,1,5.5,0.0,0.5,0.95,0.0,0.3,7.25, +1,2019-01-05 15:51:09,2019-01-05 15:59:09,1,0.9,1,N,164,161,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-19 17:45:07,2019-01-19 17:55:26,1,2.3,1,N,263,170,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-22 12:23:24,2019-01-22 12:48:11,1,9.69,5,N,140,95,1,27.78,0.0,0.5,0.0,5.76,0.3,34.34,0.0 +1,2019-01-09 13:36:24,2019-01-09 13:40:04,1,0.5,1,N,236,236,1,4.5,0.0,0.5,1.55,0.0,0.3,6.85, +1,2019-01-18 20:22:52,2019-01-18 20:29:13,1,1.0,1,N,234,114,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-27 23:04:47,2019-01-27 23:06:53,1,0.94,1,N,33,33,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-14 09:58:15,2019-01-14 10:07:15,1,0.5,1,N,142,142,1,7.0,0.0,0.5,2.3,0.0,0.3,10.1, +2,2019-01-26 15:59:09,2019-01-26 16:15:12,1,1.88,1,N,164,246,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-31 21:32:17,2019-01-31 21:44:15,1,3.1,1,N,170,239,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-24 12:19:41,2019-01-24 12:24:48,1,0.92,1,N,158,249,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-04 08:07:21,2019-01-04 08:21:35,1,1.9,1,N,186,162,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-27 21:34:02,2019-01-27 21:39:16,1,0.83,1,N,237,263,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-06 11:57:59,2019-01-06 12:03:05,1,1.02,1,N,162,164,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-27 14:48:18,2019-01-27 14:58:27,1,1.84,1,N,162,234,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62,0.0 +2,2019-01-16 11:52:14,2019-01-16 12:16:38,1,2.12,1,N,107,161,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-07 13:07:03,2019-01-07 13:09:20,1,0.2,1,N,141,141,1,3.5,0.0,0.5,0.64,0.0,0.3,4.94, +2,2019-01-09 14:24:14,2019-01-09 14:39:33,1,1.55,1,N,163,186,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-18 17:13:21,2019-01-18 17:19:39,1,1.4,1,N,234,125,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-29 22:40:51,2019-01-29 22:56:08,1,2.7,1,N,79,246,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55,0.0 +1,2019-01-17 15:59:17,2019-01-17 16:03:14,1,0.7,1,N,230,100,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-03 13:29:41,2019-01-03 13:42:36,1,1.9,1,N,229,236,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-29 21:40:48,2019-01-29 21:48:10,1,0.96,1,N,114,148,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-16 22:12:04,2019-01-16 22:15:14,1,1.3,1,N,141,75,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-31 13:47:16,2019-01-31 14:01:16,1,3.2,1,N,164,232,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55,0.0 +1,2019-01-29 18:34:56,2019-01-29 18:54:03,1,4.6,1,N,264,264,1,17.0,1.0,0.5,2.2,0.0,0.3,21.0,0.0 +2,2019-01-28 14:56:56,2019-01-28 15:07:58,1,0.83,1,N,236,263,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-13 04:29:11,2019-01-13 04:48:12,1,4.7,1,N,17,91,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3, +2,2019-01-12 06:40:07,2019-01-12 06:45:18,1,1.55,1,N,186,233,1,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-21 13:14:58,2019-01-21 13:25:33,1,2.37,1,N,237,145,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-28 22:45:36,2019-01-28 22:56:58,1,2.62,1,N,236,166,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-17 12:15:04,2019-01-17 12:17:51,1,0.42,1,N,13,13,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0, +1,2019-01-09 18:42:11,2019-01-09 18:44:59,1,0.4,1,N,170,170,1,4.0,1.0,0.5,0.87,0.0,0.3,6.67, +1,2019-01-13 18:37:16,2019-01-13 18:48:08,1,1.9,1,N,239,162,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-29 13:09:55,2019-01-29 13:12:21,1,0.44,1,N,61,61,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-07 22:56:21,2019-01-07 23:01:44,1,1.21,1,N,161,170,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-26 03:37:02,2019-01-26 03:48:30,1,2.6,1,N,107,50,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15,0.0 +1,2019-01-04 22:51:07,2019-01-04 23:00:07,1,1.1,1,N,249,107,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +1,2019-01-30 20:50:57,2019-01-30 21:03:05,1,2.8,1,N,107,236,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-18 10:33:57,2019-01-18 10:51:24,1,1.8,1,N,230,237,1,12.0,0.0,0.5,3.8,0.0,0.3,16.6, +2,2019-01-09 21:02:24,2019-01-09 21:15:22,1,2.31,1,N,229,263,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-17 14:38:02,2019-01-17 14:45:58,1,0.67,1,N,236,140,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-03 20:14:40,2019-01-03 20:27:46,1,6.4,1,N,162,261,1,19.5,0.5,0.5,2.0,0.0,0.3,22.8, +2,2019-01-27 01:07:30,2019-01-27 01:21:28,1,2.64,1,N,163,145,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-15 12:38:36,2019-01-15 12:45:00,1,1.0,1,N,263,75,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-05 12:04:14,2019-01-05 12:13:47,1,1.2,1,N,161,170,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-25 09:29:20,2019-01-25 09:51:52,1,1.56,1,N,161,100,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-18 22:53:01,2019-01-18 23:15:03,1,4.2,1,N,75,90,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +1,2019-01-29 16:43:15,2019-01-29 16:50:15,1,1.0,1,N,263,236,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-31 10:16:40,2019-01-31 10:48:49,1,11.09,1,N,162,138,1,34.5,0.0,0.5,8.21,5.76,0.3,49.27,0.0 +2,2019-01-13 12:24:39,2019-01-13 12:43:33,1,3.7,1,N,68,141,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56, +2,2019-01-29 22:11:13,2019-01-29 22:21:26,1,1.69,1,N,68,107,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-14 10:09:44,2019-01-14 10:15:08,1,0.57,1,N,162,161,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-23 17:24:57,2019-01-23 17:52:57,1,4.82,1,N,262,234,1,21.0,1.0,0.5,5.7,0.0,0.3,28.5,0.0 +1,2019-01-08 21:20:28,2019-01-08 21:44:03,1,5.3,1,N,43,231,1,19.5,0.5,0.5,4.15,0.0,0.3,24.95, +1,2019-01-05 18:48:56,2019-01-05 19:01:49,1,2.5,1,N,151,42,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-20 04:48:59,2019-01-20 05:03:00,1,4.6,1,N,230,226,1,16.0,0.5,0.5,5.15,0.0,0.3,22.45, +2,2019-01-10 06:47:05,2019-01-10 06:59:07,1,3.13,1,N,141,234,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-24 08:58:22,2019-01-24 09:05:00,1,0.61,1,N,161,233,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-31 20:43:30,2019-01-31 20:56:26,1,3.49,1,N,233,238,1,12.5,0.5,0.5,1.38,0.0,0.3,15.18,0.0 +2,2019-01-12 09:44:03,2019-01-12 10:01:02,1,10.21,1,N,138,162,1,28.5,0.0,0.5,7.01,5.76,0.3,42.07, +1,2019-01-04 15:06:53,2019-01-04 15:15:33,1,1.0,1,N,264,264,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-17 22:05:14,2019-01-17 22:35:07,1,5.9,1,N,162,25,1,23.5,0.5,0.5,2.0,0.0,0.3,26.8, +2,2019-01-07 19:48:08,2019-01-07 19:56:53,1,1.84,1,N,264,264,1,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-04 20:47:16,2019-01-04 20:56:06,1,1.15,1,N,249,90,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-23 20:45:37,2019-01-23 20:56:39,1,2.45,1,N,236,142,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-27 12:22:11,2019-01-27 12:29:19,1,1.04,1,N,234,68,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-26 15:52:03,2019-01-26 15:55:27,1,0.5,1,N,140,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-27 15:13:42,2019-01-27 15:21:31,1,1.4,1,N,211,249,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8,0.0 +2,2019-01-05 10:50:31,2019-01-05 11:02:47,1,2.26,1,N,170,140,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-21 08:29:07,2019-01-21 08:47:52,1,6.53,1,N,74,164,2,22.0,0.0,0.5,0.0,0.0,0.3,22.8,0.0 +1,2019-01-31 18:43:25,2019-01-31 18:48:31,1,0.7,1,N,141,140,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-06 11:35:26,2019-01-06 11:48:43,1,3.1,1,N,162,144,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-28 14:20:09,2019-01-28 14:26:59,1,0.9,1,N,234,90,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-14 18:08:51,2019-01-14 18:26:04,1,8.2,1,N,138,233,1,24.5,1.0,0.5,5.0,5.76,0.3,37.06, +1,2019-01-15 08:31:52,2019-01-15 08:36:30,1,0.5,1,N,229,162,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-08 18:28:46,2019-01-08 18:33:54,1,0.9,1,N,68,113,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-24 17:36:54,2019-01-24 17:48:54,1,0.7,1,N,163,161,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-23 10:14:47,2019-01-23 10:19:01,1,0.2,1,N,90,90,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-25 21:58:30,2019-01-25 22:11:55,1,1.5,1,N,264,79,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95,0.0 +1,2019-01-04 07:26:01,2019-01-04 07:31:26,1,1.3,1,N,50,43,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-21 09:11:29,2019-01-21 09:22:42,1,4.5,1,N,238,48,1,15.0,0.0,0.5,4.7,0.0,0.3,20.5,0.0 +1,2019-01-26 21:06:43,2019-01-26 21:11:36,1,1.1,1,N,79,137,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-23 17:41:34,2019-01-23 18:02:41,1,4.1,1,N,236,224,1,16.5,1.0,0.5,2.75,0.0,0.3,21.05,0.0 +1,2019-01-11 03:12:15,2019-01-11 03:14:56,1,0.7,1,N,7,7,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-20 18:00:46,2019-01-20 18:18:20,1,7.7,1,N,138,255,1,24.0,0.0,0.5,4.95,0.0,0.3,29.75, +2,2019-01-30 10:51:52,2019-01-30 10:58:18,1,0.45,1,N,237,162,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-11 13:01:00,2019-01-11 13:04:44,1,0.98,1,N,100,163,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-25 13:11:58,2019-01-25 13:28:10,1,2.8,1,N,264,264,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +4,2019-01-22 14:56:17,2019-01-22 15:04:25,1,1.28,1,N,236,238,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-07 20:30:06,2019-01-07 20:33:47,1,0.7,1,N,237,263,1,5.0,0.5,0.5,1.55,0.0,0.3,7.85, +2,2019-01-23 10:34:09,2019-01-23 10:43:12,1,0.63,1,N,211,148,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +4,2019-01-14 16:19:22,2019-01-14 16:58:04,1,4.23,1,N,161,261,1,24.0,1.0,0.5,5.16,0.0,0.3,30.96, +2,2019-01-18 19:12:05,2019-01-18 19:21:01,1,1.04,1,N,162,163,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +4,2019-01-09 19:05:38,2019-01-09 19:11:46,1,0.78,1,N,107,79,1,6.0,1.0,0.5,1.2,0.0,0.3,9.0, +1,2019-01-26 15:58:04,2019-01-26 16:02:59,1,0.8,1,N,143,238,1,5.0,0.0,0.5,1.04,0.0,0.3,6.84,0.0 +1,2019-01-16 16:39:05,2019-01-16 16:42:09,1,0.7,1,N,68,158,1,4.5,1.0,0.5,1.85,0.0,0.3,8.15, +2,2019-01-15 13:34:33,2019-01-15 13:43:56,1,1.36,1,N,170,68,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-19 09:16:58,2019-01-19 09:35:50,1,2.51,1,N,113,43,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-05 18:50:03,2019-01-05 19:01:14,1,1.1,1,N,186,107,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-10 08:40:35,2019-01-10 08:46:40,1,0.8,1,N,158,249,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-31 16:57:52,2019-01-31 17:08:31,1,1.82,1,N,237,239,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-12 16:06:09,2019-01-12 16:13:03,1,2.7,1,N,231,232,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-03 19:53:41,2019-01-03 20:13:45,1,7.58,1,N,141,243,1,23.5,1.0,0.5,7.59,0.0,0.3,32.89, +2,2019-01-24 22:01:02,2019-01-24 22:06:40,1,0.9,1,N,148,79,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-25 22:31:18,2019-01-25 22:44:19,1,1.85,1,N,142,161,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-30 10:51:07,2019-01-30 10:55:25,1,0.7,1,N,239,143,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-19 01:52:49,2019-01-19 01:57:53,1,1.0,1,N,246,48,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +1,2019-01-30 12:42:43,2019-01-30 12:50:24,1,1.0,1,N,238,142,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-05 09:42:26,2019-01-05 10:14:42,1,18.3,2,N,132,230,1,52.0,0.0,0.5,8.78,5.76,0.3,67.34, +2,2019-01-24 11:02:39,2019-01-24 11:32:18,1,5.3,1,N,142,231,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36,0.0 +1,2019-01-17 20:25:32,2019-01-17 20:52:26,1,9.8,1,N,138,234,1,31.0,0.5,0.5,7.6,5.76,0.3,45.66, +1,2019-01-07 20:20:10,2019-01-07 20:28:14,1,2.0,1,N,262,229,1,9.0,0.5,0.5,1.5,0.0,0.3,11.8, +1,2019-01-28 15:02:12,2019-01-28 15:08:12,1,0.6,1,N,161,230,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-10 19:08:32,2019-01-10 19:15:21,1,1.91,1,N,148,256,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-27 14:42:10,2019-01-27 14:47:45,1,1.13,1,N,50,142,1,6.0,0.0,0.5,1.22,0.0,0.3,8.02,0.0 +2,2019-01-23 10:56:51,2019-01-23 11:01:36,1,1.15,1,N,151,239,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-09 16:30:54,2019-01-09 16:43:39,1,1.57,1,N,230,43,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-15 09:25:39,2019-01-15 09:38:44,1,1.13,1,N,237,48,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-14 11:53:59,2019-01-14 12:03:34,1,0.98,1,N,100,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-22 19:02:36,2019-01-22 19:19:44,1,2.07,1,N,161,140,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-28 06:39:36,2019-01-28 06:57:10,1,6.09,1,N,229,255,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8,0.0 +1,2019-01-12 00:02:44,2019-01-12 00:05:19,1,0.8,1,N,238,239,1,4.5,0.5,0.5,0.75,0.0,0.3,6.55, +1,2019-01-31 22:04:21,2019-01-31 22:21:33,1,3.8,1,N,264,264,1,15.5,0.5,0.5,3.35,0.0,0.3,20.15,0.0 +1,2019-01-01 15:34:37,2019-01-01 15:38:42,1,0.7,1,N,90,68,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-03 13:56:15,2019-01-03 14:13:24,1,0.9,1,N,141,140,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-14 16:11:04,2019-01-14 16:36:19,1,3.5,1,N,144,161,2,17.5,1.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-23 15:45:16,2019-01-23 16:00:12,1,3.22,1,N,248,32,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-16 18:55:58,2019-01-16 19:09:26,1,2.35,1,N,166,41,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-07 14:38:03,2019-01-07 14:55:55,1,3.1,1,N,68,239,1,14.0,0.0,0.5,6.0,0.0,0.3,20.8, +2,2019-01-14 14:08:02,2019-01-14 14:17:28,1,1.65,1,N,143,238,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-31 16:01:44,2019-01-31 16:23:52,1,4.12,1,N,152,237,2,17.5,1.0,0.5,0.0,0.0,0.3,19.3,0.0 +1,2019-01-18 13:53:26,2019-01-18 14:10:37,1,2.8,1,N,164,236,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-03 18:41:19,2019-01-03 19:02:07,1,3.64,1,N,170,239,2,16.0,1.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-22 11:55:52,2019-01-22 12:13:30,1,3.19,1,N,229,238,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-27 06:22:30,2019-01-27 06:31:38,1,1.45,1,N,164,246,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-06 19:35:34,2019-01-06 19:41:39,1,1.26,1,N,113,231,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-19 14:04:10,2019-01-19 14:16:39,1,2.22,1,N,140,162,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-08 10:49:30,2019-01-08 10:56:30,1,1.1,1,N,113,164,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-03 18:54:37,2019-01-03 19:02:27,1,1.33,1,N,186,158,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-20 18:20:14,2019-01-20 18:23:32,1,0.99,1,N,263,140,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-11 09:20:49,2019-01-11 09:25:36,1,0.5,1,N,234,164,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +2,2019-01-13 19:31:30,2019-01-13 19:41:39,1,1.59,1,N,230,170,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +4,2019-01-29 22:15:46,2019-01-29 22:19:44,1,0.72,1,N,170,234,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-27 19:30:03,2019-01-27 19:36:11,1,1.2,1,N,234,100,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-14 21:04:51,2019-01-14 21:15:08,1,2.3,1,N,68,211,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +2,2019-01-18 06:34:59,2019-01-18 06:40:45,1,1.28,1,N,100,162,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-19 01:44:55,2019-01-19 01:47:38,1,0.8,1,N,90,113,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-13 13:53:52,2019-01-13 14:02:45,1,1.47,1,N,233,234,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +4,2019-01-23 01:18:00,2019-01-23 01:27:27,1,2.16,1,N,163,263,1,9.5,0.5,0.5,3.24,0.0,0.3,14.04, +2,2019-01-06 12:15:33,2019-01-06 12:28:29,1,2.56,1,N,261,68,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-31 17:01:45,2019-01-31 17:17:06,1,1.04,1,N,186,170,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-24 12:49:42,2019-01-24 12:58:33,1,0.6,1,N,186,90,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-14 08:41:54,2019-01-14 08:44:13,1,0.71,1,N,238,24,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-15 07:08:40,2019-01-15 07:28:54,1,6.8,1,N,13,229,1,22.0,0.0,0.5,4.2,0.0,0.3,27.0, +1,2019-01-18 19:00:14,2019-01-18 19:09:18,1,1.5,1,Y,100,229,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-21 14:22:46,2019-01-21 14:24:27,1,0.2,1,N,261,231,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-19 13:21:05,2019-01-19 13:29:06,1,1.0,1,N,237,161,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-21 09:55:41,2019-01-21 10:07:03,1,1.85,1,N,186,246,1,8.0,0.0,0.5,1.5,0.0,0.3,10.3,0.0 +2,2019-01-30 05:31:37,2019-01-30 05:35:28,1,1.15,1,N,107,100,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-25 01:44:46,2019-01-25 01:56:05,1,2.48,1,N,164,143,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-26 17:54:38,2019-01-26 18:03:59,1,2.6,1,N,264,264,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-15 08:13:50,2019-01-15 08:53:56,1,13.61,1,N,138,113,1,41.0,0.0,0.5,9.51,5.76,0.3,57.07, +2,2019-01-05 12:05:49,2019-01-05 12:26:01,1,6.62,1,N,65,68,2,22.0,0.0,0.5,0.0,0.0,0.3,22.8, +2,2019-01-21 11:59:30,2019-01-21 12:01:06,1,0.35,1,N,74,75,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-07 17:23:49,2019-01-07 18:01:01,1,17.95,2,N,132,75,2,52.0,4.5,0.5,0.0,5.76,0.3,63.06, +2,2019-01-08 15:07:15,2019-01-08 15:16:03,1,1.24,1,N,237,163,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-28 22:34:00,2019-01-28 22:44:01,1,2.1,1,N,107,237,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-12 10:58:46,2019-01-12 11:17:42,1,4.01,1,N,68,263,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-30 16:53:11,2019-01-30 17:03:05,1,1.5,1,N,163,90,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-21 15:56:48,2019-01-21 16:07:03,1,1.53,1,N,113,224,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-16 14:28:46,2019-01-16 14:35:05,1,0.6,1,N,239,239,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-30 08:46:09,2019-01-30 08:53:53,1,0.97,1,N,226,146,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-26 01:10:20,2019-01-26 01:21:14,1,2.7,1,N,249,230,1,10.5,0.5,0.5,1.0,0.0,0.3,12.8,0.0 +2,2019-01-25 21:12:42,2019-01-25 21:21:33,1,2.62,1,N,140,74,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-14 11:31:43,2019-01-14 11:42:31,1,2.3,1,N,75,143,1,10.0,0.0,0.5,3.2,0.0,0.3,14.0, +2,2019-01-19 09:01:08,2019-01-19 09:06:02,1,1.3,1,N,237,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-31 06:49:47,2019-01-31 07:11:30,1,4.27,1,N,148,140,1,18.0,0.0,0.5,1.88,0.0,0.3,20.68,0.0 +1,2019-01-12 13:38:41,2019-01-12 13:58:23,1,1.9,1,N,90,4,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-31 00:44:23,2019-01-31 00:50:33,1,1.62,1,N,211,158,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-30 10:13:51,2019-01-30 10:33:40,1,3.4,1,N,239,74,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +1,2019-01-19 13:30:35,2019-01-19 13:39:16,1,1.1,1,N,237,262,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +1,2019-01-09 08:49:49,2019-01-09 09:02:26,1,2.1,1,N,163,246,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-30 16:32:18,2019-01-30 16:35:47,1,0.54,1,N,236,236,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-13 01:50:30,2019-01-13 01:56:15,1,1.09,1,N,264,264,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-04 02:35:07,2019-01-04 02:45:21,1,3.2,1,N,249,43,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +1,2019-01-19 07:51:11,2019-01-19 07:56:04,1,1.0,1,N,48,246,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-30 12:28:32,2019-01-30 12:52:05,1,2.19,1,N,79,100,1,14.5,0.0,0.5,2.75,0.0,0.3,18.05,0.0 +1,2019-01-26 07:28:48,2019-01-26 07:30:53,1,0.9,1,N,237,263,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-20 04:01:35,2019-01-20 04:07:39,1,2.18,1,N,79,162,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-07 11:47:54,2019-01-07 11:52:18,1,0.62,1,N,234,164,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-27 18:20:20,2019-01-27 18:33:25,1,1.49,1,N,90,137,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-07 01:25:27,2019-01-07 01:33:48,1,1.59,1,N,233,50,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-18 18:20:30,2019-01-18 18:36:13,1,1.64,1,N,158,79,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-09 12:29:41,2019-01-09 13:01:11,1,9.24,1,N,43,209,1,30.5,0.0,0.5,6.26,0.0,0.3,37.56, +2,2019-01-28 22:35:19,2019-01-28 22:46:59,1,2.31,1,N,90,163,1,10.5,0.5,0.5,1.77,0.0,0.3,13.57,0.0 +1,2019-01-04 18:00:44,2019-01-04 18:08:55,1,1.3,1,N,264,264,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-02 14:10:02,2019-01-02 14:26:39,1,2.8,1,N,114,161,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +1,2019-01-12 22:18:04,2019-01-12 22:45:06,1,15.2,1,N,132,129,2,42.5,0.5,0.5,0.0,0.0,0.3,43.8, +1,2019-01-05 13:08:56,2019-01-05 13:15:39,1,0.9,1,N,236,238,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-23 08:22:59,2019-01-23 08:34:54,1,1.8,1,N,142,100,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-19 22:25:47,2019-01-19 22:32:57,1,1.6,1,N,170,79,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-25 08:07:35,2019-01-25 08:15:11,1,0.62,1,N,48,50,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-12 17:36:37,2019-01-12 17:43:50,1,1.2,1,N,262,236,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-07 17:57:04,2019-01-07 18:01:31,1,0.69,1,N,161,161,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-28 21:16:20,2019-01-28 21:23:47,1,1.32,1,N,162,186,1,7.0,0.5,0.5,2.49,0.0,0.3,10.79,0.0 +2,2019-01-10 13:16:56,2019-01-10 13:19:33,1,0.33,1,N,142,239,1,3.5,1.0,0.5,1.59,0.0,0.3,6.89, +1,2019-01-07 19:19:52,2019-01-07 19:42:32,1,12.0,1,N,132,138,1,34.0,1.0,0.5,7.15,0.0,0.3,42.95, +1,2019-01-15 15:23:33,2019-01-15 15:40:21,1,2.3,1,N,263,229,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95, +2,2019-01-26 21:25:34,2019-01-26 21:28:45,1,1.0,1,N,141,262,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88,0.0 +2,2019-01-06 15:43:27,2019-01-06 16:44:31,1,27.75,4,N,161,23,2,139.5,0.0,0.5,0.0,0.0,0.3,140.3, +1,2019-01-12 02:48:59,2019-01-12 02:51:09,1,0.6,1,N,87,88,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-21 21:20:46,2019-01-21 21:25:01,1,0.81,1,N,164,170,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-07 06:44:01,2019-01-07 06:51:18,1,1.14,1,N,162,163,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-03 18:24:25,2019-01-03 18:31:17,1,1.29,1,N,234,186,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-01 04:00:12,2019-01-01 04:22:03,1,6.5,1,N,158,74,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3, +2,2019-01-11 10:19:09,2019-01-11 10:31:55,1,1.46,1,N,68,107,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-18 11:48:23,2019-01-18 11:56:04,1,1.1,1,N,140,263,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-25 19:12:43,2019-01-25 19:21:49,1,0.75,1,N,79,113,1,7.0,1.0,0.5,1.8,0.0,0.3,10.6,0.0 +2,2019-01-20 17:32:43,2019-01-20 17:46:46,1,2.34,1,N,239,236,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-07 00:35:13,2019-01-07 00:56:41,1,8.5,1,N,230,228,1,25.0,0.5,0.5,5.25,0.0,0.3,31.55, +1,2019-01-09 12:13:48,2019-01-09 12:17:48,1,0.4,1,N,237,141,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-18 10:03:29,2019-01-18 10:12:49,1,1.2,1,N,140,237,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-25 20:03:04,2019-01-25 20:19:23,1,3.45,1,N,140,48,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-01 01:49:46,2019-01-01 02:06:47,1,5.34,1,N,68,87,1,18.0,0.5,0.5,3.86,0.0,0.3,23.16, +4,2019-01-10 21:55:55,2019-01-10 22:03:31,1,1.56,1,N,244,120,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-11 01:19:25,2019-01-11 01:40:42,1,8.96,1,N,230,167,2,27.0,0.5,0.5,0.0,0.0,0.3,28.3, +2,2019-01-16 20:47:02,2019-01-16 20:53:11,1,0.9,1,N,140,236,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +2,2019-01-07 18:33:15,2019-01-07 18:53:09,1,1.56,1,N,229,229,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-15 18:07:04,2019-01-15 18:10:31,1,0.4,1,N,68,249,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-11 10:36:23,2019-01-11 10:45:27,1,0.8,1,N,170,100,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-30 06:59:09,2019-01-30 07:19:29,1,3.0,1,N,68,231,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-29 09:46:21,2019-01-29 10:10:28,1,1.66,1,N,113,170,1,15.0,0.0,0.5,1.58,0.0,0.3,17.38,0.0 +2,2019-01-21 14:48:35,2019-01-21 14:54:59,1,1.99,1,N,83,196,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-06 20:23:09,2019-01-06 20:26:50,1,1.04,1,N,79,137,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +1,2019-01-26 15:38:32,2019-01-26 15:56:27,1,0.0,1,N,68,237,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-11 12:30:54,2019-01-11 12:37:41,1,0.47,1,N,170,164,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-30 21:43:43,2019-01-30 21:51:01,1,0.93,1,N,161,233,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-31 15:46:51,2019-01-31 16:06:48,1,2.65,1,N,231,186,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-22 14:18:25,2019-01-22 14:26:23,1,0.47,1,N,263,236,1,6.0,0.0,0.5,2.0,0.0,0.3,8.8,0.0 +1,2019-01-09 06:34:15,2019-01-09 07:22:15,1,9.8,1,N,88,211,1,36.0,0.0,0.5,7.36,0.0,0.3,44.16, +1,2019-01-29 13:42:54,2019-01-29 14:04:08,1,10.4,1,N,138,162,2,30.0,0.0,0.5,0.0,5.76,0.3,36.56,0.0 +1,2019-01-26 10:33:42,2019-01-26 10:49:05,1,4.0,1,N,90,236,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +2,2019-01-22 01:29:56,2019-01-22 01:38:09,1,0.58,1,N,79,137,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-10 17:33:58,2019-01-10 17:41:55,1,1.42,1,N,238,166,1,7.5,1.0,0.5,2.33,0.0,0.3,11.63, +2,2019-01-24 09:16:19,2019-01-24 09:48:25,1,3.02,1,N,74,237,1,20.0,0.0,0.5,1.04,0.0,0.3,21.84,0.0 +2,2019-01-13 01:54:16,2019-01-13 02:01:00,1,1.55,1,N,50,50,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-14 07:17:30,2019-01-14 07:22:10,1,0.6,1,N,170,162,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-08 09:39:51,2019-01-08 10:14:28,1,3.08,1,N,186,140,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16, +1,2019-01-06 14:00:12,2019-01-06 14:01:02,1,0.3,1,N,50,142,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8, +2,2019-01-20 18:19:36,2019-01-20 18:35:59,1,3.28,1,N,249,229,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-30 16:52:05,2019-01-30 17:00:30,1,0.75,1,N,162,237,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3,0.0 +1,2019-01-14 16:03:29,2019-01-14 16:11:24,1,1.1,1,N,164,246,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-04 09:04:55,2019-01-04 09:45:13,1,5.4,1,N,143,231,2,26.0,0.0,0.5,0.0,0.0,0.3,26.8, +2,2019-01-12 15:53:37,2019-01-12 16:09:18,1,1.89,1,N,186,114,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 12:01:43,2019-01-11 12:06:20,1,0.57,1,N,141,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-12 00:17:32,2019-01-12 00:22:15,1,0.5,1,N,231,231,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +1,2019-01-16 08:01:18,2019-01-16 08:13:33,1,1.0,1,N,74,75,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +2,2019-01-04 22:24:00,2019-01-04 22:31:43,1,1.0,1,N,68,230,1,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-16 11:00:14,2019-01-16 11:16:02,1,1.13,1,N,162,141,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-27 14:46:24,2019-01-27 15:07:20,1,6.21,1,N,263,114,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3,0.0 +1,2019-01-24 07:08:02,2019-01-24 07:21:00,1,1.0,1,N,170,230,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-15 20:19:08,2019-01-15 20:29:51,1,2.1,1,N,163,239,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +1,2019-01-02 18:42:29,2019-01-02 18:52:02,1,1.8,1,N,137,141,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-17 05:33:09,2019-01-17 05:37:38,1,1.0,1,N,186,68,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +1,2019-01-18 13:03:25,2019-01-18 13:12:25,1,2.4,1,N,246,158,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-20 00:33:11,2019-01-20 00:45:17,1,3.49,1,N,170,75,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +2,2019-01-28 21:47:26,2019-01-28 21:54:35,1,0.98,1,N,114,158,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-18 18:11:20,2019-01-18 18:22:19,1,1.28,1,N,162,230,1,8.5,1.0,0.5,5.0,0.0,0.3,15.3, +2,2019-01-28 22:01:44,2019-01-28 22:10:13,1,1.58,1,N,234,233,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3,0.0 +1,2019-01-14 15:30:11,2019-01-14 15:52:06,1,3.9,1,N,7,233,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-26 00:20:18,2019-01-26 00:42:15,1,3.5,1,N,50,107,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75,0.0 +1,2019-01-12 18:03:12,2019-01-12 18:12:01,1,0.6,1,N,249,113,1,7.0,0.0,0.5,1.5,0.0,0.3,9.3, +1,2019-01-29 13:37:54,2019-01-29 13:40:49,1,0.2,1,N,264,264,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-30 16:52:28,2019-01-30 16:54:07,1,0.6,1,N,125,158,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-09 22:22:08,2019-01-09 22:39:30,1,8.29,1,N,144,262,2,24.5,0.5,0.5,0.0,0.0,0.3,25.8, +2,2019-01-08 02:32:01,2019-01-08 02:43:59,1,3.54,1,N,144,106,1,13.5,0.5,0.5,5.2,0.0,0.3,20.0, +2,2019-01-14 20:21:03,2019-01-14 20:25:40,1,0.85,1,N,239,238,1,5.5,0.5,0.5,2.04,0.0,0.3,8.84, +2,2019-01-03 13:00:15,2019-01-03 13:17:11,1,3.92,1,N,48,75,1,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-09 10:58:29,2019-01-09 11:16:07,1,2.24,1,N,140,162,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-09 20:54:27,2019-01-09 21:02:22,1,2.34,1,N,229,145,1,9.0,0.5,0.5,0.7,0.0,0.3,11.0, +1,2019-01-29 01:43:41,2019-01-29 01:54:50,1,4.8,1,N,142,116,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-22 19:51:02,2019-01-22 19:53:08,1,0.33,1,N,141,237,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-25 19:50:59,2019-01-25 19:56:08,1,1.14,1,N,263,75,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-09 15:19:37,2019-01-09 15:27:46,1,0.7,1,N,140,237,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-31 09:48:26,2019-01-31 09:56:07,1,1.3,1,N,239,43,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-30 19:18:45,2019-01-30 19:20:46,1,0.32,1,N,234,164,1,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-17 15:02:38,2019-01-17 15:18:25,1,2.07,1,N,163,262,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-17 22:50:42,2019-01-17 23:00:08,1,1.85,1,N,230,170,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-11 08:35:29,2019-01-11 08:58:50,1,6.53,1,N,88,140,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36, +2,2019-01-18 15:06:44,2019-01-18 15:15:46,1,1.01,1,N,261,87,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-16 07:46:37,2019-01-16 08:07:09,1,3.4,1,N,179,264,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-28 15:40:24,2019-01-28 16:00:05,1,4.05,1,N,48,226,1,16.5,0.0,0.5,3.0,5.76,0.3,26.06,0.0 +2,2019-01-16 12:40:13,2019-01-16 12:48:13,1,1.05,1,N,113,107,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 08:42:30,2019-01-24 09:44:37,1,12.69,1,N,230,138,1,51.0,0.0,0.5,14.39,5.76,0.3,71.95,0.0 +2,2019-01-07 09:01:17,2019-01-07 09:06:45,1,0.69,1,N,236,263,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-05 22:45:24,2019-01-05 22:57:36,1,1.2,1,N,230,142,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-12 14:26:03,2019-01-12 14:34:05,1,1.22,1,N,161,142,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-04 04:19:18,2019-01-04 04:22:15,1,1.23,1,N,74,263,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-21 20:44:50,2019-01-21 20:52:59,1,1.75,1,N,90,163,1,8.0,0.5,0.5,2.79,0.0,0.3,12.09,0.0 +2,2019-01-01 18:21:22,2019-01-01 18:29:48,1,2.03,1,N,142,68,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-09 04:44:22,2019-01-09 04:48:48,1,1.23,1,N,143,163,1,6.0,0.5,0.5,2.19,0.0,0.3,9.49, +4,2019-01-08 08:32:36,2019-01-08 08:44:44,1,1.25,1,N,237,161,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +1,2019-01-31 12:15:16,2019-01-31 12:24:57,1,1.0,1,N,236,237,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-20 19:46:31,2019-01-20 20:02:42,1,2.72,1,N,68,79,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-29 20:35:37,2019-01-29 21:06:42,1,16.2,1,N,132,112,1,45.5,0.5,0.5,7.0,0.0,0.3,53.8,0.0 +1,2019-01-10 21:37:33,2019-01-10 21:40:45,1,0.5,1,N,151,238,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67, +2,2019-01-17 22:46:33,2019-01-17 22:51:17,1,0.8,1,N,13,231,1,5.5,0.5,0.5,2.04,0.0,0.3,8.84, +2,2019-01-04 21:12:59,2019-01-04 21:20:45,1,1.29,1,N,163,233,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-21 21:33:16,2019-01-21 21:34:46,1,0.47,1,N,237,43,1,3.5,0.5,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-17 07:39:07,2019-01-17 07:45:00,1,0.49,1,N,162,161,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +1,2019-01-26 09:37:21,2019-01-26 09:47:40,1,1.6,1,N,224,211,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-15 18:41:50,2019-01-15 19:09:45,1,9.32,1,N,125,262,2,30.0,1.0,0.5,0.0,0.0,0.3,31.8, +2,2019-01-26 03:12:41,2019-01-26 03:20:32,1,1.56,1,N,246,107,1,7.5,0.5,0.5,1.58,0.0,0.3,10.38,0.0 +2,2019-01-12 02:59:20,2019-01-12 03:13:34,1,2.61,1,N,79,88,1,12.0,0.5,0.5,3.32,0.0,0.3,16.62, +2,2019-01-12 18:57:54,2019-01-12 19:19:33,1,3.29,1,N,140,186,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-19 00:28:23,2019-01-19 00:43:40,1,2.6,1,N,161,68,1,12.0,0.5,0.5,2.0,0.0,0.3,15.3, +1,2019-01-28 19:52:10,2019-01-28 20:01:53,1,1.4,1,N,186,137,1,8.0,1.0,0.5,0.98,0.0,0.3,10.78,0.0 +1,2019-01-04 11:25:19,2019-01-04 11:36:42,1,1.1,1,N,229,233,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +1,2019-01-21 16:06:41,2019-01-21 16:11:55,1,0.7,1,N,162,229,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-26 23:46:40,2019-01-27 22:59:59,1,2.6,1,N,211,170,1,11.0,0.5,0.5,1.84,0.0,0.3,14.14,0.0 +2,2019-01-21 14:56:16,2019-01-21 15:02:26,1,0.57,1,N,90,249,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-09 08:10:21,2019-01-09 08:15:11,1,0.59,1,N,230,163,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-25 21:42:19,2019-01-25 21:52:38,1,1.77,1,N,162,68,1,8.5,0.5,0.5,2.0,0.0,0.3,11.8,0.0 +1,2019-01-02 06:39:38,2019-01-02 06:42:15,1,0.6,1,N,141,140,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-11 05:29:21,2019-01-11 05:32:16,1,0.6,1,N,236,236,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-30 07:23:17,2019-01-30 07:27:27,1,0.9,1,N,48,163,1,5.0,0.0,0.5,0.58,0.0,0.3,6.38,0.0 +2,2019-01-21 11:39:13,2019-01-21 11:46:11,1,1.38,1,N,162,141,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-25 18:40:27,2019-01-25 18:47:05,1,0.96,1,N,141,236,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-11 07:13:41,2019-01-11 07:22:09,1,2.8,1,N,262,233,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +4,2019-01-30 17:48:22,2019-01-30 18:04:58,1,2.12,1,N,68,224,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-15 12:22:29,2019-01-15 12:30:37,1,1.5,1,N,143,238,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-18 22:12:50,2019-01-18 22:27:10,1,2.3,1,N,186,143,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +2,2019-01-11 12:56:02,2019-01-11 13:03:46,1,0.58,1,N,186,48,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-10 15:30:50,2019-01-10 15:40:07,1,1.12,1,N,234,249,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +4,2019-01-12 20:21:39,2019-01-12 20:30:58,1,1.65,1,N,236,237,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-24 14:21:29,2019-01-24 14:24:06,1,0.3,1,N,236,236,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +2,2019-01-05 18:53:12,2019-01-05 19:03:33,1,1.33,1,N,163,48,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +2,2019-01-26 19:48:59,2019-01-26 19:49:04,1,0.01,1,N,264,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-09 14:35:06,2019-01-09 14:46:40,1,1.91,1,N,113,170,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-16 08:57:23,2019-01-16 08:59:25,1,0.67,1,N,262,140,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-03 23:43:34,2019-01-03 23:49:02,1,1.1,1,N,114,249,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-21 11:44:57,2019-01-21 11:59:01,1,2.17,1,N,186,107,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-16 13:32:04,2019-01-16 13:40:12,1,1.22,1,N,262,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-21 13:54:06,2019-01-21 14:10:01,1,3.1,1,N,236,100,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15,0.0 +1,2019-01-11 22:29:02,2019-01-11 22:35:39,1,0.9,1,N,161,164,1,6.0,0.5,0.5,1.1,0.0,0.3,8.4, +1,2019-01-10 02:34:19,2019-01-10 02:41:33,1,1.1,1,N,146,193,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-09 07:56:23,2019-01-09 08:04:35,1,3.1,1,N,140,137,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-29 15:51:47,2019-01-29 15:56:50,1,0.84,1,N,239,239,1,5.5,0.0,0.5,1.57,0.0,0.3,7.87,0.0 +2,2019-01-14 12:32:55,2019-01-14 12:39:28,1,1.09,1,N,170,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-10 09:42:43,2019-01-10 09:55:41,1,1.93,1,N,75,140,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-19 21:38:41,2019-01-19 22:00:58,1,3.6,1,N,114,141,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +2,2019-01-21 19:40:28,2019-01-21 19:47:32,1,1.12,1,N,181,189,1,6.5,0.0,0.5,1.46,0.0,0.3,10.71,0.0 +1,2019-01-02 06:27:52,2019-01-02 06:35:26,1,1.4,1,N,48,234,1,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-13 17:36:12,2019-01-13 17:42:11,1,1.15,1,N,48,142,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-16 14:30:30,2019-01-16 14:33:18,1,0.4,1,N,238,151,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-29 17:07:21,2019-01-29 17:13:47,1,0.6,1,N,236,236,1,6.0,1.0,0.5,0.78,0.0,0.3,8.58,0.0 +2,2019-01-19 19:54:23,2019-01-19 19:56:47,1,0.47,1,N,262,140,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-25 14:16:29,2019-01-25 14:23:58,1,1.0,1,N,164,161,1,6.5,0.0,0.5,2.15,0.0,0.3,9.45,0.0 +1,2019-01-15 07:22:13,2019-01-15 07:25:40,1,0.7,1,N,100,163,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-11 11:51:26,2019-01-11 11:58:50,1,0.7,1,N,237,263,1,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-03 23:18:34,2019-01-03 23:27:35,1,2.44,1,N,162,262,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-15 09:03:06,2019-01-15 09:11:49,1,1.0,1,N,264,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-24 14:17:22,2019-01-24 14:27:18,1,1.1,1,N,262,236,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79,0.0 +2,2019-01-29 02:30:28,2019-01-29 03:01:57,1,20.91,2,N,132,244,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-16 12:20:29,2019-01-16 12:43:42,1,2.56,1,N,237,239,1,15.5,0.0,0.5,3.0,0.0,0.3,19.3, +2,2019-01-24 07:56:33,2019-01-24 07:59:39,1,0.49,1,N,74,74,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-26 07:50:31,2019-01-26 07:54:03,1,0.82,1,N,230,162,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-10 07:19:52,2019-01-10 07:43:24,1,7.4,1,N,186,116,2,25.5,0.0,0.5,0.0,0.0,0.3,26.3, +2,2019-01-10 17:54:27,2019-01-10 18:20:05,1,9.28,1,N,164,138,1,28.0,1.0,0.5,7.11,5.76,0.3,42.67, +2,2019-01-21 09:53:06,2019-01-21 10:12:56,1,4.07,1,N,158,52,1,16.5,0.0,0.5,2.6,0.0,0.3,21.85,0.0 +2,2019-01-07 17:59:58,2019-01-07 18:15:40,1,3.51,1,N,246,88,1,14.5,1.0,0.5,3.26,0.0,0.3,19.56, +1,2019-01-25 06:37:13,2019-01-25 06:46:25,1,3.7,1,N,132,10,1,12.5,0.0,0.5,5.0,0.0,0.3,18.3,0.0 +2,2019-01-14 14:07:46,2019-01-14 14:13:20,1,0.69,1,N,237,237,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-27 06:22:51,2019-01-27 06:26:00,1,0.93,1,N,140,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-23 08:12:20,2019-01-23 08:44:51,1,3.7,1,N,186,13,1,22.0,0.5,0.5,4.65,0.0,0.3,27.95,0.0 +2,2019-01-15 06:02:06,2019-01-15 06:56:18,1,18.59,5,N,205,231,1,50.41,0.0,0.5,0.0,0.0,0.3,51.21, +1,2019-01-28 15:35:47,2019-01-28 15:49:08,1,2.6,1,N,137,145,2,11.5,0.0,0.5,0.0,5.76,0.3,18.06,0.0 +1,2019-01-10 18:27:35,2019-01-10 18:40:03,1,2.4,1,N,170,114,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-04 12:21:16,2019-01-04 12:27:51,1,1.16,1,N,79,144,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-03 19:30:43,2019-01-03 19:37:56,1,1.23,1,N,90,100,1,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-15 09:37:20,2019-01-15 10:08:18,1,5.8,1,N,237,87,1,25.0,0.0,0.5,5.15,0.0,0.3,30.95, +2,2019-01-08 07:11:23,2019-01-08 07:18:13,1,0.8,1,N,164,161,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-13 17:02:47,2019-01-13 17:30:07,1,10.77,1,N,138,230,2,32.5,0.0,0.5,0.0,5.76,0.3,39.06, +2,2019-01-10 17:36:30,2019-01-10 18:04:00,1,9.45,1,N,138,90,1,29.0,1.0,0.5,7.31,5.76,0.3,43.87, +2,2019-01-12 19:40:32,2019-01-12 19:43:27,1,0.58,1,N,50,48,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-12 11:39:13,2019-01-12 11:48:33,1,1.93,1,N,166,239,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-28 10:23:07,2019-01-28 10:39:33,1,3.35,1,N,166,142,1,14.5,0.0,0.5,2.75,0.0,0.3,18.05,0.0 +1,2019-01-05 15:55:06,2019-01-05 16:18:21,1,7.2,1,N,75,144,1,23.5,0.0,0.5,4.85,0.0,0.3,29.15, +1,2019-01-15 10:15:25,2019-01-15 10:31:42,1,3.7,1,N,140,107,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56, +2,2019-01-02 10:26:50,2019-01-02 10:33:04,1,1.12,1,N,143,142,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-22 12:11:22,2019-01-22 12:24:43,1,1.9,1,N,246,211,1,10.5,0.0,0.5,2.8,0.0,0.3,14.1,0.0 +1,2019-01-01 21:51:38,2019-01-01 22:08:03,1,8.4,1,N,79,82,2,25.0,0.5,0.5,0.0,0.0,0.3,26.3, +1,2019-01-23 16:26:22,2019-01-23 16:30:36,1,0.4,1,N,263,236,3,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-07 09:32:53,2019-01-07 09:52:07,1,4.1,1,N,140,234,1,16.5,0.0,0.5,3.45,0.0,0.3,20.75, +1,2019-01-14 21:16:40,2019-01-14 21:19:37,1,0.6,1,N,79,107,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-12 19:42:24,2019-01-12 20:10:00,1,6.68,1,N,231,107,1,24.5,0.0,0.5,3.0,0.0,0.3,28.3, +2,2019-01-04 19:46:36,2019-01-04 19:57:52,1,1.8,1,N,229,234,1,9.0,1.0,0.5,1.94,0.0,0.3,12.74, +2,2019-01-21 17:35:52,2019-01-21 17:45:05,1,1.05,1,N,246,249,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +1,2019-01-20 18:45:06,2019-01-20 18:48:50,1,0.7,1,N,239,238,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-03 07:50:55,2019-01-03 07:55:31,1,0.58,1,N,163,140,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-03 11:49:38,2019-01-03 12:02:49,1,3.1,1,N,262,170,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-22 19:41:10,2019-01-22 19:49:33,1,1.5,1,N,166,239,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-17 20:48:08,2019-01-17 21:00:00,1,1.6,1,N,144,107,1,9.0,0.5,0.5,2.0,0.0,0.3,12.3, +1,2019-01-16 20:01:38,2019-01-16 20:09:09,1,1.4,1,N,162,107,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +2,2019-01-26 22:45:29,2019-01-26 22:45:33,1,0.0,5,N,142,142,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96,0.0 +1,2019-01-26 17:14:04,2019-01-26 17:22:45,1,0.8,1,N,137,234,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-17 08:07:58,2019-01-17 08:14:26,1,1.0,1,N,236,237,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-02 16:42:24,2019-01-02 17:17:41,1,10.5,1,N,138,66,1,33.0,1.0,0.5,5.22,0.0,0.3,40.02, +1,2019-01-22 05:55:33,2019-01-22 05:59:07,1,1.1,1,N,263,237,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-13 09:05:31,2019-01-13 09:22:28,1,5.1,1,N,143,231,1,18.0,0.0,0.5,4.7,0.0,0.3,23.5, +1,2019-01-14 21:55:00,2019-01-14 22:08:40,1,3.1,1,N,170,145,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +1,2019-01-10 20:32:52,2019-01-10 20:35:20,1,0.5,1,N,237,237,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +2,2019-01-21 10:35:01,2019-01-21 10:47:46,1,2.54,1,N,148,164,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8,0.0 +2,2019-01-17 16:19:51,2019-01-17 17:11:31,1,11.99,1,N,261,138,1,40.5,1.0,0.5,8.46,0.0,0.3,50.76, +1,2019-01-01 05:33:05,2019-01-01 06:07:16,1,17.0,1,N,132,178,3,46.5,0.5,0.5,0.0,0.0,0.3,47.8, +2,2019-01-03 22:24:18,2019-01-03 22:48:04,1,6.35,1,N,79,226,1,22.0,0.5,0.5,3.0,0.0,0.3,26.3, +1,2019-01-06 17:44:11,2019-01-06 17:52:38,1,1.5,1,N,237,263,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-25 08:26:17,2019-01-25 08:32:37,1,0.72,1,N,68,246,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-16 20:17:28,2019-01-16 20:32:14,1,3.34,1,N,164,262,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-23 21:11:27,2019-01-23 21:14:37,1,0.63,1,N,151,166,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-31 23:45:03,2019-02-01 00:03:45,1,2.66,1,N,229,145,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-01 19:06:13,2019-01-01 19:20:23,1,2.55,1,N,107,209,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-30 14:49:34,2019-01-30 15:00:21,1,1.25,1,N,237,142,1,8.5,0.0,0.5,0.93,0.0,0.3,10.23,0.0 +2,2019-01-20 19:55:54,2019-01-20 20:11:11,1,3.16,1,N,170,236,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-14 09:55:21,2019-01-14 10:25:46,1,11.42,1,N,143,138,1,34.5,0.0,0.5,20.0,5.76,0.3,61.06, +2,2019-01-15 19:53:21,2019-01-15 19:55:56,1,0.41,1,N,236,236,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-04 20:55:50,2019-01-04 20:59:17,1,0.91,1,N,263,263,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-25 16:23:05,2019-01-25 16:41:48,1,2.22,1,N,164,237,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76,0.0 +1,2019-01-24 19:26:18,2019-01-24 19:48:56,1,5.6,1,N,211,141,1,20.5,1.0,0.5,4.45,0.0,0.3,26.75,0.0 +2,2019-01-15 12:55:46,2019-01-15 13:07:10,1,1.61,1,N,79,164,1,9.0,0.0,0.5,1.5,0.0,0.3,11.3, +2,2019-01-29 07:28:16,2019-01-29 07:47:59,1,2.79,1,N,238,152,1,15.0,0.0,0.5,1.58,0.0,0.3,17.38,0.0 +2,2019-01-12 10:33:34,2019-01-12 10:48:28,1,2.73,1,N,231,170,1,12.5,0.0,0.5,3.32,0.0,0.3,16.62, +2,2019-01-31 12:31:50,2019-01-31 12:36:07,1,0.71,1,N,158,125,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-22 09:23:52,2019-01-22 09:51:40,1,2.46,1,N,170,50,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96,0.0 +1,2019-01-01 14:03:52,2019-01-01 14:09:43,1,1.2,1,N,186,246,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-07 22:03:34,2019-01-07 22:15:12,1,2.1,1,N,48,113,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +2,2019-01-12 15:38:45,2019-01-12 15:41:46,1,0.37,1,N,236,236,1,4.0,0.0,0.5,0.72,0.0,0.3,5.52, +1,2019-01-03 12:06:22,2019-01-03 12:13:02,1,1.3,1,N,170,107,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-15 20:33:56,2019-01-15 20:44:15,1,1.7,1,N,162,43,1,9.5,0.5,0.5,1.62,0.0,0.3,12.42, +2,2019-01-25 11:11:44,2019-01-25 11:23:44,1,2.94,1,N,236,137,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-28 15:27:34,2019-01-28 15:36:58,1,1.5,1,N,114,231,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-24 02:59:11,2019-01-24 03:09:47,1,2.88,1,N,162,68,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-07 16:29:50,2019-01-07 16:33:35,1,0.5,1,N,236,237,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-27 16:56:36,2019-01-27 17:07:20,1,1.7,1,N,161,236,1,9.0,0.0,0.5,1.37,0.0,0.3,11.17,0.0 +2,2019-01-24 15:55:26,2019-01-24 16:19:11,1,2.92,1,N,43,229,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-30 12:04:12,2019-01-30 12:10:32,1,0.86,1,N,186,249,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-17 14:53:02,2019-01-17 15:02:10,1,1.5,1,N,107,4,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-06 19:54:57,2019-01-06 20:05:14,1,1.6,1,N,107,164,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +1,2019-01-15 08:14:40,2019-01-15 08:24:34,1,1.6,1,Y,237,236,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-29 13:39:52,2019-01-29 13:48:19,1,3.78,1,N,132,10,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +1,2019-01-16 15:30:27,2019-01-16 15:36:38,1,0.8,1,N,237,161,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-18 13:16:31,2019-01-18 13:24:33,1,1.91,1,N,74,141,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-28 12:34:05,2019-01-28 12:44:52,1,1.0,1,N,100,170,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-17 12:52:43,2019-01-17 12:57:33,1,0.3,1,N,161,161,1,4.5,0.0,0.5,3.0,0.0,0.3,8.3, +2,2019-01-10 17:24:30,2019-01-10 17:32:30,1,1.35,1,N,163,186,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-05 00:15:51,2019-01-05 00:28:17,1,1.4,1,N,144,148,1,9.5,0.5,0.5,3.2,0.0,0.3,14.0, +1,2019-01-08 09:18:44,2019-01-08 09:24:01,1,0.5,1,N,161,230,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-26 23:04:01,2019-01-26 23:12:50,1,0.8,1,N,230,164,1,7.0,0.5,0.5,2.0,0.0,0.3,10.3,0.0 +2,2019-01-13 12:01:51,2019-01-13 12:25:51,1,3.04,1,N,236,186,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +1,2019-01-31 16:07:29,2019-01-31 16:19:06,1,1.2,1,N,163,164,1,8.5,1.0,0.5,1.5,0.0,0.3,11.8,0.0 +1,2019-01-17 21:29:37,2019-01-17 21:52:24,1,4.8,1,N,100,33,2,18.5,0.5,0.5,0.0,0.0,0.3,19.8, +2,2019-01-22 17:11:38,2019-01-22 17:14:33,1,0.52,1,N,151,238,1,4.0,1.0,0.5,1.74,0.0,0.3,7.54,0.0 +1,2019-01-28 19:29:20,2019-01-28 19:37:19,1,1.3,1,N,233,140,1,7.5,1.0,0.5,2.0,0.0,0.3,11.3,0.0 +1,2019-01-13 20:28:29,2019-01-13 20:42:38,1,3.1,1,N,262,170,1,12.5,0.5,0.5,1.0,0.0,0.3,14.8, +1,2019-01-30 18:29:02,2019-01-30 18:34:01,1,0.7,1,N,230,161,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-29 18:16:08,2019-01-29 18:32:24,1,1.8,1,N,79,90,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-14 07:54:44,2019-01-14 08:07:40,1,0.91,1,N,263,75,1,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-28 11:39:00,2019-01-28 11:53:11,1,1.4,1,N,163,48,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-30 18:35:22,2019-01-30 18:50:43,1,2.15,1,N,107,141,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-31 21:19:59,2019-01-31 21:38:45,1,4.14,1,N,211,143,1,15.0,0.5,0.5,2.0,0.0,0.3,18.3,0.0 +2,2019-01-23 19:12:50,2019-01-23 19:27:14,1,1.57,1,N,161,234,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-27 15:43:02,2019-01-27 15:46:18,1,0.5,1,N,141,236,1,4.5,0.0,0.5,0.65,0.0,0.3,5.95,0.0 +1,2019-01-26 06:10:47,2019-01-26 06:34:31,1,17.0,2,N,162,132,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56,0.0 +1,2019-01-18 18:17:02,2019-01-18 18:23:26,1,0.8,1,N,234,100,1,6.5,1.0,0.5,2.05,0.0,0.3,10.35, +1,2019-01-10 05:59:01,2019-01-10 06:10:44,1,3.0,1,N,79,161,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-26 16:45:52,2019-01-26 16:54:28,1,1.0,1,N,229,170,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-29 12:26:43,2019-01-29 12:31:19,1,0.7,1,N,263,236,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-03 20:12:50,2019-01-03 20:21:02,1,1.52,1,N,170,234,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-11 17:02:34,2019-01-11 17:02:38,1,0.02,1,N,230,264,2,2.5,1.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-04 13:51:17,2019-01-04 13:57:23,1,0.9,1,N,163,161,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-25 07:41:32,2019-01-25 08:15:51,1,4.53,1,N,41,229,1,22.5,0.0,0.5,2.33,0.0,0.3,25.63,0.0 +2,2019-01-07 18:45:30,2019-01-07 18:58:55,1,5.09,1,N,138,82,2,16.0,1.0,0.5,0.0,0.0,0.3,17.8, +1,2019-01-15 15:55:04,2019-01-15 16:04:17,1,1.6,1,N,237,236,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-30 08:08:46,2019-01-30 08:18:05,1,1.52,1,N,158,13,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-17 22:21:12,2019-01-17 22:30:09,1,2.8,1,N,141,74,1,10.0,0.5,0.5,2.0,0.0,0.3,13.3, +2,2019-01-01 18:11:53,2019-01-01 18:28:16,1,7.64,1,N,138,112,1,22.0,0.0,0.5,3.42,0.0,0.3,26.22, +1,2019-01-17 19:58:40,2019-01-17 20:01:14,1,0.4,1,Y,141,141,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +1,2019-01-10 21:05:05,2019-01-10 21:21:57,1,3.2,1,N,264,264,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +2,2019-01-13 00:08:20,2019-01-13 00:28:50,1,5.2,1,N,231,239,1,19.0,0.5,0.5,6.09,0.0,0.3,26.39, +1,2019-01-23 08:31:58,2019-01-23 08:55:43,1,2.6,1,N,246,163,1,16.0,0.0,0.5,5.0,0.0,0.3,21.8,0.0 +1,2019-01-12 17:12:36,2019-01-12 17:18:04,1,0.5,1,N,170,164,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-10 10:00:08,2019-01-10 10:15:05,1,1.5,1,N,186,50,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +1,2019-01-22 19:56:53,2019-01-22 20:13:59,1,4.1,1,N,230,41,1,16.0,1.0,0.5,3.55,0.0,0.3,21.35,0.0 +2,2019-01-11 09:57:45,2019-01-11 10:05:03,1,0.79,1,N,234,137,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-17 21:16:54,2019-01-17 21:29:26,1,1.92,1,N,48,239,1,10.5,0.5,0.5,3.54,0.0,0.3,15.34, +2,2019-01-12 13:56:06,2019-01-12 14:02:17,1,0.91,1,N,263,237,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-16 08:03:13,2019-01-16 08:16:17,1,2.19,1,N,239,230,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-02 17:32:11,2019-01-02 17:38:58,1,1.0,1,N,140,262,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-28 18:18:44,2019-01-28 18:25:58,1,0.79,1,N,141,229,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-14 14:19:17,2019-01-14 14:31:26,1,1.9,1,N,151,41,1,10.0,0.0,0.5,3.2,0.0,0.3,14.0, +1,2019-01-23 07:20:55,2019-01-23 07:25:48,1,0.8,1,N,143,239,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-15 12:40:20,2019-01-15 12:55:27,1,2.41,1,N,137,141,1,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-16 06:36:54,2019-01-16 06:56:25,1,5.48,1,N,226,79,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-18 19:18:43,2019-01-18 19:23:51,1,1.52,1,N,74,263,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-24 15:46:30,2019-01-24 15:57:55,1,0.8,1,N,264,264,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-29 08:27:39,2019-01-29 08:57:01,1,6.69,1,N,140,45,1,25.0,0.0,0.5,5.16,0.0,0.3,32.91,0.0 +2,2019-01-09 18:48:56,2019-01-09 19:02:49,1,2.98,1,N,148,229,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-13 14:22:23,2019-01-13 14:28:28,1,0.89,1,N,164,234,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-29 17:36:29,2019-01-29 17:41:17,1,0.84,1,N,236,262,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-06 20:06:23,2019-01-06 20:10:05,1,1.4,1,N,142,238,1,6.0,0.5,0.5,1.8,0.0,0.3,9.1, +2,2019-01-17 21:50:40,2019-01-17 21:59:59,1,1.64,1,N,144,90,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-02 23:04:20,2019-01-02 23:08:28,1,0.93,1,N,88,13,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-27 11:02:31,2019-01-27 11:08:54,1,1.5,1,N,229,237,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-20 18:27:09,2019-01-20 18:28:41,1,0.4,1,N,114,79,1,3.5,0.0,0.5,1.0,0.0,0.3,5.3, +2,2019-01-22 09:44:10,2019-01-22 09:53:34,1,1.01,1,N,236,140,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-26 17:10:28,2019-01-26 17:13:50,1,0.4,1,N,236,262,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95,0.0 +1,2019-01-18 17:27:01,2019-01-18 17:38:41,1,1.5,1,N,79,232,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-27 13:17:37,2019-01-27 13:26:53,1,1.2,1,N,237,142,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-13 00:52:23,2019-01-13 01:03:28,1,2.07,1,N,148,256,1,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-09 18:49:39,2019-01-09 18:57:01,1,0.75,1,N,237,141,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-11 07:27:15,2019-01-11 07:32:20,1,0.7,1,N,50,163,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-23 17:24:06,2019-01-23 17:36:52,1,1.69,1,N,246,230,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-08 13:47:22,2019-01-08 14:39:19,1,11.8,1,N,43,62,1,43.5,0.0,0.5,0.0,0.0,0.3,44.3, +2,2019-01-18 18:15:51,2019-01-18 18:20:44,1,0.58,1,N,142,142,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-21 14:47:11,2019-01-21 14:55:33,1,2.36,1,N,229,75,1,9.0,0.0,0.5,1.7,0.0,0.3,11.5,0.0 +2,2019-01-31 11:13:46,2019-01-31 11:34:05,1,1.55,1,N,163,236,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-10 15:39:59,2019-01-10 15:56:31,1,2.4,1,N,87,65,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-30 12:13:45,2019-01-30 12:27:41,1,3.26,1,N,79,140,1,13.0,0.0,0.5,1.38,0.0,0.3,15.18,0.0 +1,2019-01-20 23:35:46,2019-01-20 23:39:47,1,1.2,1,N,186,113,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-28 13:12:10,2019-01-28 13:22:36,1,0.8,1,N,234,186,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-13 18:56:38,2019-01-13 19:11:10,1,2.6,1,N,114,230,1,11.5,0.0,0.5,5.0,0.0,0.3,17.3, +2,2019-01-12 17:08:19,2019-01-12 17:43:20,1,5.99,1,N,249,189,1,24.5,0.0,0.5,3.8,0.0,0.3,29.1, +1,2019-01-23 07:17:24,2019-01-23 07:30:42,1,0.8,1,N,186,234,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-25 15:52:48,2019-01-25 15:58:38,1,1.04,1,N,237,43,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-25 06:28:24,2019-01-25 06:34:06,1,1.1,1,N,186,186,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84,0.0 +2,2019-01-08 10:38:35,2019-01-08 10:53:22,1,1.62,1,N,43,162,1,10.0,0.0,0.5,3.24,0.0,0.3,14.04, +2,2019-01-30 22:25:05,2019-01-30 22:48:16,1,7.74,1,N,229,40,1,25.0,0.5,0.5,5.26,0.0,0.3,31.56,0.0 +2,2019-01-17 15:16:42,2019-01-17 15:37:06,1,1.84,1,N,162,236,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +1,2019-01-14 09:30:12,2019-01-14 09:34:46,1,0.3,1,N,100,48,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-16 17:45:38,2019-01-16 18:21:17,1,8.19,1,N,137,95,1,28.0,1.0,0.5,0.0,5.76,0.3,35.56, +1,2019-01-02 20:15:15,2019-01-02 20:25:49,1,4.4,1,N,87,170,1,14.5,0.5,0.5,1.0,0.0,0.3,16.8, +2,2019-01-31 12:26:21,2019-01-31 12:47:59,1,3.57,1,N,161,238,1,16.5,0.0,0.5,2.75,0.0,0.3,20.05,0.0 +2,2019-01-08 21:04:41,2019-01-08 21:19:25,1,3.0,1,N,90,236,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-16 17:31:08,2019-01-16 17:37:51,1,1.49,1,N,239,24,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-02 22:07:30,2019-01-02 22:14:47,1,1.6,1,N,211,13,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-26 21:11:21,2019-01-26 21:38:07,1,3.32,1,N,237,90,1,18.5,0.5,0.5,3.33,0.0,0.3,23.13,0.0 +1,2019-01-17 18:31:53,2019-01-17 18:40:53,1,0.9,1,N,249,68,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-18 12:39:47,2019-01-18 12:58:55,1,1.67,1,N,186,137,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-21 22:47:41,2019-01-21 23:18:42,1,19.0,2,N,132,249,1,52.0,0.0,0.5,17.57,5.76,0.3,76.13,0.0 +2,2019-01-27 14:11:04,2019-01-27 14:25:24,1,2.43,1,N,170,144,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-02 13:50:27,2019-01-02 13:54:00,1,0.5,1,N,158,249,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-31 10:08:33,2019-01-31 10:14:41,1,0.9,1,N,236,43,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-12 20:54:29,2019-01-12 21:00:17,1,1.24,1,N,141,263,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-22 20:40:50,2019-01-22 20:50:11,1,1.59,1,N,234,79,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-11 22:34:29,2019-01-11 22:48:10,1,3.4,1,N,62,72,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-02 13:26:47,2019-01-02 13:37:25,1,0.42,1,N,229,163,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-18 18:35:00,2019-01-18 18:44:10,1,1.97,1,N,140,236,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-28 12:49:58,2019-01-28 12:56:56,1,1.12,1,N,239,142,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-04 06:18:15,2019-01-04 06:25:23,1,0.87,1,N,162,233,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-13 01:41:42,2019-01-13 01:50:37,1,0.58,1,N,107,234,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-15 12:26:26,2019-01-15 12:46:13,1,2.0,1,N,186,79,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-26 14:16:48,2019-01-26 14:26:58,1,0.97,1,N,246,186,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-29 14:27:59,2019-01-29 14:40:40,1,1.01,1,N,162,170,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-21 21:53:47,2019-01-21 22:03:04,1,1.1,1,N,234,224,1,8.0,0.5,0.5,2.3,0.0,0.3,11.6,0.0 +2,2019-01-24 13:49:06,2019-01-24 14:02:18,1,2.36,1,N,239,163,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +2,2019-01-22 19:26:26,2019-01-22 19:39:36,1,1.31,1,N,164,48,1,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-07 13:09:31,2019-01-07 13:24:41,1,3.5,1,N,163,238,1,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-15 23:58:50,2019-01-16 00:04:37,1,0.9,1,N,100,68,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-22 06:36:16,2019-01-22 06:42:50,1,1.16,1,N,48,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-30 12:46:21,2019-01-30 12:48:21,1,0.32,1,N,48,50,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38,0.0 +1,2019-01-15 07:04:54,2019-01-15 07:11:08,1,1.2,1,N,68,158,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-03 06:09:20,2019-01-03 06:20:04,1,4.6,1,N,224,13,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55, +1,2019-01-19 19:43:55,2019-01-19 19:48:49,1,0.8,1,N,211,79,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-15 20:06:21,2019-01-15 20:19:08,1,3.04,1,N,229,43,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-09 20:15:29,2019-01-09 20:24:14,1,1.39,1,N,161,141,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-14 21:01:44,2019-01-14 21:21:53,1,3.6,1,N,230,148,1,15.0,0.5,0.5,3.25,0.0,0.3,19.55, +1,2019-01-17 18:35:55,2019-01-17 18:41:06,1,1.2,1,N,50,239,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-13 00:14:02,2019-01-13 00:29:40,1,1.8,1,N,79,232,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +1,2019-01-27 02:02:10,2019-01-27 02:15:25,1,1.5,1,N,114,4,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-04 19:44:13,2019-01-04 19:52:12,1,1.46,1,N,237,142,1,7.5,1.0,0.5,1.4,0.0,0.3,10.7, +2,2019-01-15 23:31:23,2019-01-15 23:48:48,1,9.51,1,N,138,262,1,27.5,0.5,0.5,8.64,5.76,0.3,43.2, +1,2019-01-05 02:13:23,2019-01-05 02:21:30,1,1.2,1,N,48,233,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-30 15:48:35,2019-01-30 16:02:58,1,1.32,1,N,107,249,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-04 22:06:08,2019-01-04 22:09:13,1,0.6,1,N,234,113,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-28 19:44:18,2019-01-28 19:48:27,1,0.6,1,N,142,143,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-06 08:40:08,2019-01-06 08:47:21,1,2.2,1,N,233,237,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-09 20:40:07,2019-01-09 20:58:10,1,5.4,1,N,132,124,2,18.5,0.5,0.5,0.0,0.0,0.3,19.8, +2,2019-01-04 11:25:28,2019-01-04 11:31:41,1,1.11,1,N,236,141,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-08 12:34:34,2019-01-08 12:53:50,1,2.51,1,N,107,237,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88, +2,2019-01-23 09:51:42,2019-01-23 10:07:41,1,1.87,1,N,237,170,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-31 14:33:30,2019-01-31 14:37:35,1,0.7,1,N,141,262,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2,0.0 +2,2019-01-14 11:04:53,2019-01-14 11:33:38,1,18.17,5,N,138,265,1,102.0,0.0,0.5,21.71,5.76,0.3,130.27, +2,2019-01-14 21:39:57,2019-01-14 21:42:38,1,0.3,1,N,236,236,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3, +1,2019-01-18 21:21:42,2019-01-18 21:29:44,1,1.3,1,N,100,48,1,7.0,0.5,0.5,1.25,0.0,0.3,9.55, +1,2019-01-18 18:48:20,2019-01-18 18:55:16,1,0.6,1,N,164,48,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-12 22:42:50,2019-01-12 22:47:10,1,0.67,1,N,233,229,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +2,2019-01-15 07:20:44,2019-01-15 07:34:58,1,4.09,1,N,107,209,1,15.0,0.0,0.5,2.5,0.0,0.3,18.3, +2,2019-01-18 23:01:59,2019-01-18 23:11:49,1,1.76,1,N,113,170,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-14 20:43:29,2019-01-14 20:50:09,1,1.41,1,N,125,79,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-07 23:37:06,2019-01-07 23:47:11,1,2.68,1,N,142,263,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-08 21:14:14,2019-01-08 21:23:37,1,1.45,1,N,230,229,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-13 08:29:33,2019-01-13 08:33:42,1,1.0,1,N,162,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-24 12:10:23,2019-01-24 12:20:04,1,1.2,1,N,264,48,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-12 16:46:58,2019-01-12 16:53:06,1,1.09,1,N,229,140,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-17 20:00:00,2019-01-17 20:06:25,1,1.16,1,N,249,125,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-11 21:01:25,2019-01-11 21:13:51,1,2.2,1,N,249,230,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +1,2019-01-12 05:01:30,2019-01-12 05:26:14,1,15.3,3,N,170,1,1,60.0,0.5,0.0,8.0,10.5,0.3,79.3, +2,2019-01-11 10:55:11,2019-01-11 11:30:30,1,4.37,1,N,114,236,2,23.5,0.0,0.5,0.0,0.0,0.3,24.3, +2,2019-01-31 14:16:56,2019-01-31 14:37:46,1,3.72,1,N,236,170,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-04 18:37:45,2019-01-04 18:51:50,1,1.8,1,N,107,125,1,10.5,1.0,0.5,3.0,0.0,0.3,15.3, +2,2019-01-15 20:58:27,2019-01-15 21:07:22,1,1.4,1,N,249,234,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-17 18:19:15,2019-01-17 18:27:11,1,1.14,1,N,237,140,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-23 23:24:13,2019-01-23 23:32:11,1,1.4,1,N,88,231,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-05 07:16:01,2019-01-05 07:20:48,1,0.93,1,N,236,236,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-09 22:37:38,2019-01-09 22:45:15,1,1.09,1,N,230,161,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-18 07:41:22,2019-01-18 07:54:56,1,2.21,1,N,142,43,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-31 08:04:22,2019-01-31 08:16:54,1,1.64,1,N,140,236,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-21 00:14:38,2019-01-21 00:21:37,1,1.45,1,N,186,163,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-28 06:46:31,2019-01-28 06:55:38,1,2.0,1,N,113,170,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-02 07:38:09,2019-01-02 07:44:28,1,0.8,1,N,164,186,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-14 06:41:36,2019-01-14 06:59:39,1,3.8,1,N,75,186,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95, +1,2019-01-10 06:49:13,2019-01-10 07:03:02,1,3.5,1,N,231,161,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-19 17:50:03,2019-01-19 18:03:11,1,2.09,1,N,239,141,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0, +1,2019-01-28 20:23:02,2019-01-28 20:42:39,1,6.0,1,N,211,141,1,20.5,0.5,0.5,3.27,0.0,0.3,25.07,0.0 +2,2019-01-10 09:43:48,2019-01-10 09:50:41,1,0.7,1,N,140,141,1,6.0,0.0,0.5,3.0,0.0,0.3,9.8, +1,2019-01-05 11:53:55,2019-01-05 11:56:44,1,0.6,1,N,113,90,1,4.5,0.0,0.5,1.3,0.0,0.3,6.6, +1,2019-01-18 09:10:21,2019-01-18 09:21:59,1,1.3,1,N,237,142,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-26 15:22:00,2019-01-26 15:44:37,1,3.1,1,N,75,161,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3,0.0 +4,2019-01-13 16:39:25,2019-01-13 16:56:43,1,1.16,1,N,163,233,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-12 12:57:22,2019-01-12 12:59:54,1,0.4,1,N,162,237,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-01 08:29:35,2019-01-01 08:34:44,1,1.21,1,N,238,75,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-18 15:23:32,2019-01-18 15:28:42,1,0.65,1,N,170,170,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-14 13:16:56,2019-01-14 13:23:21,1,0.8,1,N,163,141,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-17 22:56:37,2019-01-17 23:14:40,1,3.63,1,N,249,65,1,14.5,0.5,0.5,3.95,0.0,0.3,19.75, +2,2019-01-13 12:51:56,2019-01-13 13:02:09,1,1.55,1,N,161,246,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-10 11:50:00,2019-01-10 12:04:55,1,2.82,1,N,246,211,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-09 12:46:50,2019-01-09 13:14:58,1,12.2,1,N,138,234,1,36.0,0.0,0.5,8.5,5.76,0.3,51.06, +2,2019-01-22 12:09:20,2019-01-22 12:27:39,1,2.01,1,N,236,163,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-12 12:44:47,2019-01-12 12:58:54,1,4.25,1,N,79,263,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-25 08:04:06,2019-01-25 08:23:48,1,2.31,1,N,236,170,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +2,2019-01-28 12:23:18,2019-01-28 12:41:54,1,2.65,1,N,186,231,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-05 21:56:50,2019-01-05 22:06:32,1,2.78,1,N,229,146,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-05 23:14:14,2019-01-05 23:21:48,1,1.64,1,N,48,90,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-06 07:18:54,2019-01-06 07:25:16,1,2.0,1,N,264,264,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-02 18:15:39,2019-01-02 18:20:26,1,0.47,1,N,264,264,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-04 15:43:05,2019-01-04 15:53:34,1,2.03,1,N,236,142,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-09 11:58:36,2019-01-09 12:24:42,1,5.46,1,N,161,87,1,22.5,0.0,0.5,3.0,0.0,0.3,26.3, +2,2019-01-07 06:28:52,2019-01-07 06:32:24,1,0.8,1,N,107,113,1,5.0,0.0,0.5,0.58,0.0,0.3,6.38, +2,2019-01-10 19:25:21,2019-01-10 19:31:52,1,1.13,1,N,137,162,1,6.0,1.0,0.5,0.78,0.0,0.3,8.58, +1,2019-01-21 19:32:04,2019-01-21 19:41:59,1,1.9,1,N,263,238,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8,0.0 +1,2019-01-25 23:51:39,2019-01-25 23:59:10,1,1.1,1,N,142,230,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-12 23:25:52,2019-01-12 23:43:02,1,2.41,1,N,158,170,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +2,2019-01-21 10:25:51,2019-01-21 10:36:32,1,2.93,1,N,79,80,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-21 19:03:06,2019-01-21 19:12:23,1,2.54,1,N,88,65,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-30 16:11:55,2019-01-30 16:29:44,1,2.5,1,N,137,263,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-11 10:15:34,2019-01-11 10:34:12,1,5.24,1,N,162,88,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76, +2,2019-01-13 16:27:23,2019-01-13 16:44:35,1,3.83,1,N,161,202,1,15.5,0.0,0.5,4.08,0.0,0.3,20.38, +2,2019-01-19 16:07:21,2019-01-19 16:28:19,1,3.49,1,N,211,163,1,15.5,0.0,0.5,3.0,0.0,0.3,19.3, +1,2019-01-19 22:55:04,2019-01-19 23:10:39,1,2.8,1,N,48,263,1,12.5,0.5,0.5,2.75,0.0,0.3,16.55, +2,2019-01-25 21:11:17,2019-01-25 21:16:06,1,0.93,1,N,162,137,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-05 14:19:29,2019-01-05 14:22:44,1,0.74,1,N,90,249,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-04 17:55:06,2019-01-04 18:08:02,1,0.7,1,N,230,161,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +1,2019-01-02 16:44:06,2019-01-02 16:49:16,1,0.8,1,N,161,237,1,5.5,1.0,0.5,2.0,0.0,0.3,9.3, +4,2019-01-07 09:20:15,2019-01-07 09:36:31,1,1.78,1,N,263,161,1,11.5,0.0,0.5,1.0,0.0,0.3,13.3, +2,2019-01-16 17:23:29,2019-01-16 17:37:10,1,1.36,1,N,162,186,1,10.5,1.0,0.5,3.08,0.0,0.3,15.38, +2,2019-01-10 14:23:54,2019-01-10 14:55:04,1,8.01,1,N,186,33,1,29.0,0.0,0.5,5.96,0.0,0.3,35.76, +2,2019-01-29 18:57:26,2019-01-29 19:08:58,1,2.23,1,N,13,87,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-19 14:42:48,2019-01-19 14:50:49,1,0.5,1,N,90,107,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-04 12:29:31,2019-01-04 12:31:50,1,0.4,1,N,237,237,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-08 12:12:10,2019-01-08 12:21:43,1,1.26,1,N,100,43,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-01 15:28:54,2019-01-01 15:33:15,1,1.2,1,Y,237,263,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-20 16:28:18,2019-01-20 16:37:38,1,1.9,1,N,230,249,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-28 12:12:46,2019-01-28 12:16:48,1,0.6,1,N,140,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-23 15:26:32,2019-01-23 15:46:47,1,4.2,1,N,97,234,1,17.5,0.0,0.5,3.65,0.0,0.3,21.95,0.0 +1,2019-01-31 21:50:11,2019-01-31 22:01:40,1,1.5,1,N,233,137,1,9.5,0.5,0.5,1.2,0.0,0.3,12.0,0.0 +2,2019-01-05 03:44:48,2019-01-05 04:07:10,1,6.45,1,N,231,227,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16, +2,2019-01-19 14:31:35,2019-01-20 13:25:28,1,2.52,1,N,43,234,1,13.5,0.0,0.5,1.43,0.0,0.3,15.73, +1,2019-01-25 21:22:17,2019-01-25 21:32:11,1,1.3,1,N,234,79,1,8.0,0.5,0.5,0.93,0.0,0.3,10.23,0.0 +2,2019-01-26 02:45:24,2019-01-26 02:48:47,1,1.2,1,N,236,74,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-11 10:04:41,2019-01-11 10:13:04,1,1.2,1,N,68,68,1,7.0,0.0,0.5,3.0,0.0,0.3,10.8, +2,2019-01-07 17:55:28,2019-01-07 18:02:21,1,0.94,1,N,161,237,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-16 20:04:52,2019-01-16 20:09:49,1,0.9,1,N,234,113,1,5.5,0.5,0.5,1.1,0.0,0.3,7.9, +1,2019-01-28 12:59:53,2019-01-28 13:34:46,1,12.1,1,N,163,138,2,39.0,0.0,0.5,0.0,0.0,0.3,39.8,0.0 +1,2019-01-14 20:23:27,2019-01-14 20:26:24,1,0.8,1,N,48,68,1,4.5,0.5,0.5,1.7,0.0,0.3,7.5, +2,2019-01-16 12:08:47,2019-01-16 12:22:17,1,1.06,1,N,234,164,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-18 14:25:11,2019-01-18 14:33:52,1,1.58,1,N,239,163,1,8.0,0.0,0.5,5.0,0.0,0.3,13.8, +2,2019-01-13 07:29:30,2019-01-13 07:35:15,1,1.04,1,N,230,246,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-18 23:56:37,2019-01-19 00:03:50,1,1.57,1,N,142,68,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-09 06:22:08,2019-01-09 06:23:43,1,0.5,1,N,113,90,1,3.5,0.0,0.5,1.05,0.0,0.3,5.35, +2,2019-01-21 21:11:02,2019-01-21 21:44:42,1,19.74,2,N,132,236,1,52.0,0.0,0.5,10.54,5.76,0.3,69.1,0.0 +2,2019-01-23 22:14:24,2019-01-23 22:20:47,1,1.41,1,N,186,249,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-03 12:25:18,2019-01-03 12:31:58,1,0.14,1,N,209,209,1,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-13 13:42:46,2019-01-13 13:45:23,1,0.5,1,N,236,237,1,4.0,0.0,0.5,0.5,0.0,0.3,5.3, +2,2019-01-10 23:44:12,2019-01-10 23:49:36,1,0.93,1,N,158,249,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-12 12:30:36,2019-01-12 12:53:34,1,3.9,1,N,164,239,1,18.0,0.0,0.5,3.75,0.0,0.3,22.55, +2,2019-01-11 17:06:04,2019-01-11 17:10:47,1,0.94,1,N,239,151,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-18 21:01:29,2019-01-18 21:09:56,1,2.4,1,N,148,97,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +1,2019-01-07 18:35:20,2019-01-07 18:37:39,1,0.9,1,N,48,142,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-05 17:21:33,2019-01-05 17:33:19,1,1.16,1,N,186,170,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-28 23:09:59,2019-01-28 23:19:12,1,2.19,1,N,145,260,1,9.0,0.5,0.5,3.09,0.0,0.3,13.39,0.0 +2,2019-01-01 11:40:56,2019-01-01 11:43:30,1,0.63,1,N,43,237,1,4.0,0.0,0.5,0.75,0.0,0.3,5.55, +2,2019-01-30 12:50:20,2019-01-30 12:55:18,1,0.61,1,N,162,229,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +1,2019-01-13 02:26:30,2019-01-13 02:35:40,1,2.5,1,N,114,68,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +1,2019-01-30 08:29:41,2019-01-30 08:59:37,1,12.8,1,N,264,264,1,38.5,0.0,0.5,10.15,11.52,0.3,60.97,0.0 +2,2019-01-11 07:04:53,2019-01-11 07:11:55,1,1.83,1,N,142,151,1,8.0,0.0,0.5,0.7,0.0,0.3,9.5, +1,2019-01-12 11:35:39,2019-01-12 11:51:11,1,2.6,1,N,234,237,1,12.5,0.0,0.5,2.0,0.0,0.3,15.3, +1,2019-01-09 10:26:12,2019-01-09 10:33:14,1,0.6,1,N,236,237,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-24 18:57:17,2019-01-24 19:01:53,1,0.77,1,N,236,237,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-16 16:42:01,2019-01-16 17:57:39,1,18.64,2,N,142,132,1,52.0,4.5,0.5,5.0,0.0,0.3,62.3, +2,2019-01-20 11:23:21,2019-01-20 11:28:43,1,0.94,1,N,239,142,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-14 14:47:18,2019-01-14 14:54:44,1,0.5,1,N,140,237,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +4,2019-01-22 08:37:12,2019-01-22 08:58:58,1,2.71,1,N,141,100,1,15.5,0.0,0.5,1.2,0.0,0.3,17.5, +1,2019-01-20 13:07:43,2019-01-20 13:15:08,1,1.4,1,N,237,161,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +4,2019-01-18 16:23:30,2019-01-18 16:35:16,1,2.2,1,N,161,239,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-29 12:09:42,2019-01-29 12:27:20,1,1.69,1,N,230,237,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-09 16:04:56,2019-01-09 16:14:14,1,1.14,1,N,75,236,1,7.5,1.0,0.5,1.4,0.0,0.3,10.7, +1,2019-01-09 12:39:30,2019-01-09 12:52:34,1,1.1,1,N,237,140,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-19 11:46:29,2019-01-19 11:50:23,1,0.81,1,N,140,229,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-29 13:59:22,2019-01-29 14:12:49,1,2.6,1,N,143,236,1,11.0,0.0,0.5,2.7,0.0,0.3,14.5,0.0 +1,2019-01-09 22:24:20,2019-01-09 22:33:05,1,1.4,1,N,264,264,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-20 17:24:41,2019-01-20 17:32:15,1,1.16,1,N,249,79,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-25 19:33:38,2019-01-25 19:39:30,1,1.44,1,N,239,24,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-07 18:31:59,2019-01-07 18:49:09,1,3.4,1,N,87,54,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-11 16:33:52,2019-01-11 16:58:51,1,3.11,1,N,161,263,1,17.0,1.0,0.5,3.0,0.0,0.3,21.8, +1,2019-01-25 12:22:27,2019-01-25 12:38:58,1,2.7,1,N,43,161,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-05 23:16:17,2019-01-05 23:32:36,1,4.14,1,N,114,112,1,16.0,0.5,0.5,4.32,0.0,0.3,21.62, +1,2019-01-29 12:38:16,2019-01-29 13:08:55,1,13.5,1,N,12,138,1,40.5,0.0,0.5,11.75,5.76,0.3,58.81,0.0 +1,2019-01-23 09:29:56,2019-01-23 09:35:03,1,0.7,1,N,264,264,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-08 04:57:33,2019-01-08 05:07:37,1,2.5,1,N,249,88,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-30 04:41:53,2019-01-30 04:54:00,1,5.04,1,N,263,226,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-21 19:12:49,2019-01-21 19:23:46,1,1.68,1,N,170,90,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +1,2019-01-24 12:47:33,2019-01-24 13:22:43,1,2.3,1,N,142,233,1,21.0,0.0,0.5,4.35,0.0,0.3,26.15,0.0 +1,2019-01-25 10:00:39,2019-01-25 10:04:18,1,0.5,1,N,170,164,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-15 01:51:26,2019-01-15 01:59:25,1,2.8,1,N,141,170,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-27 19:50:41,2019-01-27 19:53:10,1,0.44,1,N,231,231,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-03 18:06:38,2019-01-03 18:31:52,1,7.54,1,N,237,231,1,25.0,1.0,0.5,5.36,0.0,0.3,32.16, +1,2019-01-03 12:25:15,2019-01-03 12:32:02,1,0.9,1,N,238,143,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-09 08:29:20,2019-01-09 08:40:47,1,1.1,1,N,234,170,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +1,2019-01-30 20:38:36,2019-01-30 20:49:06,1,0.9,1,N,161,161,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-10 19:14:11,2019-01-10 19:22:47,1,1.01,1,N,209,231,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-25 20:59:38,2019-01-25 21:05:17,1,0.8,1,N,255,255,4,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-12 10:30:25,2019-01-12 10:37:06,1,0.99,1,N,230,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-17 10:50:01,2019-01-17 11:00:53,1,1.03,1,N,234,68,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-17 19:28:12,2019-01-17 19:32:56,1,1.14,1,N,238,236,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-22 21:42:58,2019-01-22 21:47:45,1,0.76,1,N,233,229,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-23 19:31:02,2019-01-23 19:44:40,1,1.6,1,N,234,114,1,9.5,1.0,0.5,3.35,0.0,0.3,14.65,0.0 +1,2019-01-10 05:02:39,2019-01-10 05:14:09,1,4.5,1,N,90,236,1,14.5,0.5,0.5,4.7,0.0,0.3,20.5, +1,2019-01-30 18:52:57,2019-01-30 19:00:54,1,0.7,1,N,100,164,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-05 13:57:30,2019-01-05 14:40:52,1,18.29,2,N,48,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +1,2019-01-03 13:40:18,2019-01-03 13:53:01,1,2.0,1,N,88,148,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-05 11:27:48,2019-01-05 11:35:40,1,1.1,1,N,161,237,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-31 16:39:01,2019-01-31 17:19:05,1,18.41,2,N,132,41,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67,0.0 +1,2019-01-11 21:43:02,2019-01-11 21:53:27,1,3.4,1,N,4,233,1,12.0,0.5,0.5,3.95,0.0,0.3,17.25, +1,2019-01-21 15:11:30,2019-01-21 15:14:24,1,0.7,1,N,239,142,1,4.5,0.0,0.5,0.53,0.0,0.3,5.83,0.0 +2,2019-01-22 19:46:37,2019-01-22 19:54:26,1,1.48,1,N,90,100,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-28 15:49:31,2019-01-28 16:05:37,1,2.09,1,N,236,229,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-22 21:05:12,2019-01-22 21:26:45,1,8.7,1,N,132,196,1,27.0,0.5,0.5,6.0,0.0,0.3,34.3,0.0 +2,2019-01-14 14:46:05,2019-01-14 14:53:35,1,0.86,1,N,162,233,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-10 14:19:50,2019-01-10 14:25:03,1,0.8,1,N,264,264,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93, +2,2019-01-02 07:27:46,2019-01-02 07:33:18,1,1.37,1,N,234,161,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-01 21:39:40,2019-01-01 21:44:07,1,1.75,1,N,142,151,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-02 22:31:54,2019-01-02 22:40:05,1,1.03,1,N,237,141,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-25 16:35:21,2019-01-25 16:53:12,1,4.71,1,N,231,195,1,17.5,1.0,0.5,0.0,5.76,0.3,25.06,0.0 +1,2019-01-08 07:29:09,2019-01-08 07:44:10,1,5.4,1,N,238,243,1,17.5,0.0,0.5,2.0,0.0,0.3,20.3, +2,2019-01-05 23:03:22,2019-01-05 23:28:35,1,13.47,1,N,132,129,2,38.5,0.5,0.5,0.0,0.0,0.3,39.8, +2,2019-01-26 10:17:49,2019-01-26 10:24:18,1,0.78,1,N,186,230,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-01 02:59:50,2019-01-01 03:15:22,1,6.5,1,N,229,261,1,20.0,0.5,0.5,4.25,0.0,0.3,25.55, +2,2019-01-11 12:25:10,2019-01-11 12:37:33,1,0.82,1,N,170,186,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-26 22:24:42,2019-01-26 22:39:11,1,1.4,1,N,114,224,1,10.0,0.5,0.5,3.35,0.0,0.3,14.65,0.0 +1,2019-01-15 17:51:51,2019-01-15 18:07:26,1,1.8,1,N,140,229,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-23 08:10:20,2019-01-23 08:27:21,1,2.9,1,N,211,162,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-04 15:28:38,2019-01-04 15:32:13,1,0.58,1,N,113,114,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-12 17:04:29,2019-01-12 17:22:12,1,1.51,1,N,79,231,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-11 00:35:04,2019-01-11 00:42:28,1,1.66,1,N,114,114,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-19 15:21:55,2019-01-19 15:25:43,1,0.6,1,N,231,209,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-10 13:49:48,2019-01-10 13:53:12,1,0.79,1,N,48,68,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-22 13:05:57,2019-01-22 13:16:07,1,1.0,1,N,68,48,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-26 10:18:30,2019-01-26 10:26:49,1,2.6,1,N,141,170,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35,0.0 +1,2019-01-23 18:31:07,2019-01-23 18:56:28,1,7.3,1,N,13,238,1,24.5,1.0,0.5,1.7,0.0,0.3,28.0,0.0 +1,2019-01-03 23:56:31,2019-01-04 00:05:40,1,1.6,1,N,211,261,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15, +1,2019-01-05 20:54:10,2019-01-05 21:16:13,1,4.3,1,N,234,263,1,18.0,0.5,0.5,3.85,0.0,0.3,23.15, +1,2019-01-29 12:20:43,2019-01-29 12:27:32,1,1.1,1,N,140,263,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-09 23:08:57,2019-01-09 23:13:10,1,0.6,1,N,68,68,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +2,2019-01-16 15:55:34,2019-01-16 16:03:26,1,1.6,1,N,230,246,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-17 18:04:15,2019-01-17 18:19:09,1,2.0,1,N,170,141,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-01 13:07:29,2019-01-01 13:11:10,1,0.9,1,N,234,114,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-27 21:41:10,2019-01-27 21:49:36,1,1.52,1,N,107,79,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-25 13:59:30,2019-01-25 14:35:45,1,9.97,1,N,138,239,1,34.0,0.0,0.5,7.5,5.76,0.3,48.06,0.0 +2,2019-01-29 00:07:13,2019-01-29 00:14:03,1,0.9,1,N,234,164,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-11 19:04:43,2019-01-11 19:19:37,1,2.07,1,N,229,234,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-09 22:32:52,2019-01-09 22:38:45,1,1.44,1,N,48,246,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-17 12:30:57,2019-01-17 12:42:36,1,1.1,1,N,186,234,1,8.5,0.0,0.5,5.0,0.0,0.3,14.3, +2,2019-01-08 05:55:25,2019-01-08 06:20:06,1,10.72,1,N,230,138,1,32.0,0.5,0.5,9.76,5.76,0.3,48.82, +1,2019-01-26 23:47:22,2019-01-26 23:56:26,1,1.3,1,N,162,246,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-02 11:43:43,2019-01-02 11:49:32,1,0.93,1,N,142,163,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-05 06:10:37,2019-01-05 06:18:19,1,1.4,1,N,48,234,1,7.5,0.5,0.5,2.0,0.0,0.3,10.8, +1,2019-01-29 09:35:59,2019-01-29 09:52:37,1,1.7,1,N,144,164,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-25 22:09:35,2019-01-25 22:18:39,1,1.02,1,N,233,164,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-04 13:10:00,2019-01-04 13:18:37,1,2.93,1,N,141,233,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-07 18:15:42,2019-01-07 18:27:59,1,1.73,1,N,238,263,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-17 19:49:34,2019-01-17 19:54:35,1,0.79,1,N,236,237,1,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-22 06:53:24,2019-01-22 07:11:40,1,4.59,1,N,246,75,1,16.5,0.0,0.5,0.7,0.0,0.3,18.0,0.0 +2,2019-01-10 21:00:09,2019-01-10 21:04:32,1,0.67,1,N,261,231,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-11 21:10:16,2019-01-11 21:13:38,1,0.5,1,N,140,237,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8, +2,2019-01-09 22:25:14,2019-01-09 22:31:23,1,1.19,1,N,48,246,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +1,2019-01-21 00:15:39,2019-01-21 00:20:39,1,2.2,1,N,137,232,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-08 15:32:50,2019-01-08 15:45:13,1,1.31,1,N,162,100,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-24 15:22:43,2019-01-24 15:26:38,1,0.46,1,N,163,237,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-17 17:27:39,2019-01-17 17:40:39,1,2.02,1,N,162,79,1,10.0,1.0,0.5,2.0,0.0,0.3,13.8, +2,2019-01-11 23:31:24,2019-01-11 23:35:45,1,0.93,1,N,264,264,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-15 12:43:05,2019-01-15 12:55:17,1,1.91,1,N,142,238,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-26 09:14:43,2019-01-26 09:20:10,1,1.34,1,N,48,246,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-16 18:14:09,2019-01-16 18:22:27,1,1.2,1,N,68,186,1,7.5,1.0,0.5,0.93,0.0,0.3,10.23, +2,2019-01-05 20:18:06,2019-01-05 20:23:42,1,1.18,1,N,237,170,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-09 13:01:40,2019-01-09 13:09:58,1,1.2,1,N,143,238,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-15 14:31:19,2019-01-15 14:44:31,1,1.36,1,N,170,237,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-31 13:51:05,2019-01-31 14:10:21,1,2.77,1,N,142,236,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +1,2019-01-13 18:10:00,2019-01-13 18:19:58,1,1.9,1,N,151,263,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-05 22:10:36,2019-01-05 23:25:43,1,2.14,1,N,158,170,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +1,2019-01-09 23:28:14,2019-01-09 23:41:24,1,3.1,1,N,231,246,1,12.5,0.5,0.5,2.0,0.0,0.3,15.8, +2,2019-01-27 13:42:58,2019-01-27 13:47:36,1,0.82,1,N,236,239,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-10 23:24:41,2019-01-10 23:34:09,1,1.3,1,N,170,229,1,8.0,0.5,0.5,2.75,0.0,0.3,12.05, +1,2019-01-25 15:50:44,2019-01-25 16:02:37,1,1.7,1,N,246,162,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85,0.0 +1,2019-01-04 18:36:56,2019-01-04 18:42:25,1,0.5,1,N,163,142,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-09 12:13:57,2019-01-09 12:25:14,1,1.3,1,N,88,13,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-31 04:52:54,2019-01-31 04:58:12,1,1.45,1,N,234,100,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-07 07:59:21,2019-01-07 08:07:26,1,0.9,1,N,25,33,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 23:34:54,2019-01-26 23:47:30,1,1.6,1,N,142,237,1,10.0,0.5,0.5,2.0,0.0,0.3,13.3,0.0 +1,2019-01-23 10:45:39,2019-01-23 10:50:34,1,0.7,1,N,264,264,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-09 13:07:36,2019-01-09 13:23:29,1,6.73,1,N,13,164,1,21.5,0.0,0.5,3.34,0.0,0.3,25.64, +1,2019-01-23 22:41:03,2019-01-23 22:51:05,1,5.2,1,N,263,79,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75,0.0 +1,2019-01-10 14:16:03,2019-01-10 14:41:44,1,3.0,1,N,43,24,1,18.0,0.0,0.5,4.7,0.0,0.3,23.5, +2,2019-01-22 17:08:45,2019-01-22 17:11:09,1,0.74,1,N,238,151,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-18 22:28:43,2019-01-18 22:45:05,1,2.7,1,N,79,170,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +1,2019-01-20 18:29:36,2019-01-20 18:40:33,1,2.0,1,N,142,141,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-31 21:11:24,2019-01-31 21:25:17,1,2.73,1,N,161,249,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-01 14:30:10,2019-01-01 14:36:28,1,0.75,1,N,114,144,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-17 22:33:47,2019-01-17 22:43:42,1,1.4,1,N,230,246,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-24 06:46:57,2019-01-24 06:51:11,1,0.7,1,N,231,231,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67,0.0 +2,2019-01-23 14:16:38,2019-01-23 14:51:44,1,11.15,1,N,161,138,1,37.5,0.0,0.5,13.22,5.76,0.3,57.28,0.0 +1,2019-01-17 17:24:11,2019-01-17 17:29:52,1,0.9,1,N,137,79,1,5.5,1.0,0.5,1.8,0.0,0.3,9.1, +2,2019-01-14 14:39:23,2019-01-14 14:48:44,1,0.86,1,N,140,236,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-21 12:31:55,2019-01-21 12:38:40,1,0.8,1,N,114,211,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-07 22:28:59,2019-01-07 22:38:02,1,1.77,1,N,141,142,1,9.0,0.5,0.5,2.58,0.0,0.3,12.88, +2,2019-01-07 22:49:21,2019-01-07 23:01:06,1,1.87,1,N,141,164,1,9.5,0.5,0.5,0.08,0.0,0.3,10.88, +2,2019-01-17 12:26:40,2019-01-17 12:43:59,1,1.28,1,N,229,161,1,11.5,0.0,0.5,1.0,0.0,0.3,13.3, +4,2019-01-24 09:02:57,2019-01-24 09:24:00,1,1.14,1,N,162,142,1,13.5,0.0,0.5,2.0,0.0,0.3,16.3, +2,2019-01-23 08:58:36,2019-01-23 09:28:49,1,3.37,1,N,125,161,1,19.5,0.0,0.5,1.0,0.0,0.3,21.3,0.0 +2,2019-01-16 21:21:56,2019-01-16 21:28:23,1,1.23,1,N,4,144,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +1,2019-01-12 16:42:23,2019-01-12 16:53:11,1,1.6,1,N,79,233,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-31 07:31:07,2019-01-31 07:49:04,1,3.63,1,N,233,231,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-30 15:35:27,2019-01-30 16:25:05,1,13.06,3,N,114,1,1,61.0,0.0,0.0,14.76,12.5,0.3,88.56,0.0 +1,2019-01-15 23:33:52,2019-01-15 23:50:14,1,4.8,1,N,144,143,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +1,2019-01-08 07:31:55,2019-01-08 07:45:33,1,1.6,1,N,229,236,1,10.0,0.0,0.5,3.2,0.0,0.3,14.0, +2,2019-01-16 12:11:10,2019-01-16 12:34:10,1,8.7,1,N,170,138,1,28.0,0.0,0.5,6.87,5.54,0.3,41.21, +1,2019-01-11 18:20:22,2019-01-11 18:32:18,1,1.5,1,N,13,249,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-15 18:48:46,2019-01-15 19:09:06,1,9.02,1,N,148,223,2,26.5,1.0,0.5,0.0,0.0,0.3,28.3, +1,2019-01-17 19:53:55,2019-01-17 19:58:51,1,0.7,1,N,48,142,1,5.0,0.5,0.5,2.0,0.0,0.3,8.3, +2,2019-01-11 11:14:54,2019-01-11 11:33:53,1,5.82,1,N,152,161,1,21.0,0.0,0.5,0.0,0.0,0.3,21.8, +1,2019-01-05 07:13:57,2019-01-05 07:21:10,1,1.5,1,N,50,246,1,7.5,0.0,0.5,1.7,0.0,0.3,10.0, +1,2019-01-08 07:52:28,2019-01-08 08:01:40,1,1.6,1,N,236,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-19 13:01:33,2019-01-19 13:08:06,1,1.08,1,N,164,113,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-18 22:53:34,2019-01-18 23:00:25,1,1.0,1,N,48,142,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-08 19:44:50,2019-01-08 19:56:22,1,1.6,1,N,162,107,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-29 21:44:32,2019-01-29 21:52:01,1,3.4,1,N,262,233,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8,0.0 +1,2019-01-01 00:24:33,2019-01-01 00:28:57,1,1.3,1,N,141,263,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +2,2019-01-18 08:10:53,2019-01-18 08:20:16,1,1.34,1,N,164,164,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-22 13:44:16,2019-01-22 13:57:26,1,1.4,1,N,140,262,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-12 10:57:59,2019-01-12 11:08:43,1,1.54,1,N,246,186,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-19 02:40:15,2019-01-19 02:55:33,1,3.2,1,N,114,162,1,13.5,0.5,0.5,3.7,0.0,0.3,18.5, +1,2019-01-12 20:02:22,2019-01-12 20:23:10,1,2.5,1,N,239,164,1,14.5,0.5,0.5,1.0,0.0,0.3,16.8, +1,2019-01-12 20:56:38,2019-01-12 21:11:32,1,3.6,1,N,141,24,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +2,2019-01-21 18:20:55,2019-01-21 18:29:26,1,1.28,1,N,161,233,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14,0.0 +1,2019-01-21 06:45:51,2019-01-21 06:51:21,1,1.6,1,N,239,151,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-07 20:11:58,2019-01-07 20:39:37,1,7.88,1,N,163,65,1,26.5,0.5,0.5,6.95,0.0,0.3,34.75, +2,2019-01-29 21:36:24,2019-01-29 21:48:14,1,1.69,1,N,162,107,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-24 16:40:43,2019-01-24 16:55:48,1,1.7,1,N,164,237,1,10.5,1.0,0.5,3.05,0.0,0.3,15.35,0.0 +1,2019-01-11 16:15:19,2019-01-11 16:21:06,1,0.4,1,N,163,162,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-22 09:51:23,2019-01-22 10:40:52,1,12.6,1,N,138,90,1,40.5,0.0,0.5,4.71,5.76,0.3,51.77,0.0 +2,2019-01-10 18:58:29,2019-01-10 19:09:18,1,1.02,1,N,237,142,1,8.0,1.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-11 13:47:37,2019-01-11 13:51:19,1,0.83,1,N,162,229,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-07 10:34:20,2019-01-07 11:09:30,1,13.5,1,N,236,185,1,41.0,0.0,0.5,0.0,0.0,0.3,41.8, +2,2019-01-24 18:54:46,2019-01-24 19:06:46,1,1.34,1,N,107,90,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-17 15:06:59,2019-01-17 15:22:38,1,1.83,1,N,262,238,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-23 12:00:42,2019-01-23 12:22:08,1,1.53,1,N,162,186,1,13.5,0.0,0.5,4.29,0.0,0.3,18.59,0.0 +2,2019-01-19 19:04:42,2019-01-19 19:06:59,1,0.23,1,N,107,107,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-28 20:26:14,2019-01-28 20:40:18,1,3.21,1,N,48,236,1,13.0,0.5,0.5,2.0,0.0,0.3,16.3,0.0 +2,2019-01-14 11:34:51,2019-01-14 11:43:04,1,0.81,1,N,141,140,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-03 22:14:43,2019-01-03 22:20:43,1,1.31,1,N,234,90,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +2,2019-01-01 12:35:31,2019-01-01 12:37:52,1,1.07,1,N,263,141,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-16 17:18:27,2019-01-16 17:27:53,1,1.18,1,N,163,164,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +4,2019-01-02 14:37:55,2019-01-02 14:48:34,1,1.43,1,N,163,236,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-16 19:31:32,2019-01-16 19:36:13,1,0.6,1,N,48,100,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-25 09:05:10,2019-01-25 09:15:56,1,1.1,1,N,163,162,1,8.0,0.0,0.5,1.32,0.0,0.3,10.12,0.0 +2,2019-01-15 20:03:33,2019-01-15 20:15:12,1,1.93,1,N,166,239,1,10.0,0.5,0.5,3.39,0.0,0.3,14.69, +1,2019-01-08 20:58:03,2019-01-08 21:02:16,1,0.8,1,N,170,170,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 11:24:45,2019-01-09 11:27:41,1,0.42,1,N,143,239,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-15 09:40:42,2019-01-15 09:47:31,1,0.9,1,N,186,230,1,6.5,0.0,0.5,2.2,0.0,0.3,9.5, +1,2019-01-12 16:41:39,2019-01-12 16:54:16,1,1.5,1,N,186,161,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +1,2019-01-06 08:57:46,2019-01-06 09:03:21,1,0.9,1,N,263,75,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-22 18:16:35,2019-01-22 18:21:12,1,1.29,1,N,238,142,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-11 18:15:39,2019-01-11 18:23:34,1,1.9,1,N,107,232,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-14 10:29:32,2019-01-14 11:01:40,1,8.91,1,N,138,229,1,31.5,0.0,0.5,6.46,0.0,0.3,38.76, +1,2019-01-15 08:18:29,2019-01-15 08:30:31,1,1.2,1,N,237,162,1,9.0,0.0,0.5,1.0,0.0,0.3,10.8, +2,2019-01-17 22:15:23,2019-01-17 22:32:25,1,9.17,1,N,138,140,1,27.0,0.5,0.5,6.81,5.76,0.3,40.87, +2,2019-01-31 20:14:08,2019-01-31 20:24:20,1,1.88,1,N,162,141,1,8.5,0.5,0.5,0.98,0.0,0.3,10.78,0.0 +1,2019-01-09 20:19:45,2019-01-09 20:36:30,1,3.7,1,N,237,202,1,14.5,0.5,0.5,3.95,0.0,0.3,19.75, +1,2019-01-24 04:20:37,2019-01-24 04:33:14,1,4.5,1,N,164,75,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-31 10:56:53,2019-01-31 11:04:08,1,0.82,1,N,142,143,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-12 22:18:50,2019-01-12 22:26:33,1,0.83,1,N,113,234,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +1,2019-01-12 16:50:49,2019-01-12 16:57:30,1,0.7,1,N,164,234,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-20 11:04:46,2019-01-20 11:07:06,1,0.6,1,N,230,50,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-22 18:39:05,2019-01-22 18:51:16,1,3.3,1,N,239,246,1,12.5,1.0,0.5,1.5,0.0,0.3,15.8,0.0 +1,2019-01-10 07:45:21,2019-01-10 08:01:31,1,5.9,1,N,87,140,1,18.5,0.0,0.5,2.9,0.0,0.3,22.2, +2,2019-01-05 15:36:36,2019-01-05 15:51:56,1,3.49,1,N,114,181,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-16 19:14:40,2019-01-16 19:24:37,1,1.69,1,N,162,107,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-28 17:08:57,2019-01-28 17:15:52,1,0.68,1,N,164,170,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14,0.0 +1,2019-01-05 15:41:08,2019-01-05 15:53:02,1,1.2,1,N,246,164,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +1,2019-01-21 10:57:50,2019-01-21 11:18:49,1,6.6,1,N,186,166,1,22.0,0.0,0.5,2.0,0.0,0.3,24.8,0.0 +1,2019-01-13 14:17:47,2019-01-13 14:27:41,1,0.8,1,N,100,164,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-31 18:58:44,2019-01-31 19:06:01,1,0.6,1,N,237,140,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-19 02:40:51,2019-01-19 02:51:21,1,1.5,1,N,148,158,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +2,2019-01-17 05:30:54,2019-01-17 05:35:55,1,1.06,1,N,230,186,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-30 22:41:39,2019-01-30 22:58:48,1,6.8,1,N,41,107,1,21.0,0.5,0.5,4.45,0.0,0.3,26.75,0.0 +2,2019-01-21 22:34:02,2019-01-21 22:42:26,1,2.73,1,N,230,263,1,10.0,0.5,0.5,2.0,0.0,0.3,13.3,0.0 +1,2019-01-04 16:17:52,2019-01-04 16:23:11,1,0.5,1,N,237,237,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-27 00:28:45,2019-01-27 00:44:16,1,2.07,1,N,79,186,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-22 14:05:34,2019-01-22 14:26:47,1,4.3,1,N,162,236,1,17.5,0.0,0.5,1.0,0.0,0.3,19.3,0.0 +1,2019-01-22 06:37:07,2019-01-22 06:47:16,1,2.1,1,N,262,161,1,9.5,0.0,0.5,3.05,0.0,0.3,13.35,0.0 +1,2019-01-01 00:32:12,2019-01-01 00:33:41,1,0.2,1,N,79,79,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3, +4,2019-01-27 13:46:59,2019-01-27 14:01:46,1,2.09,1,N,114,68,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-26 23:22:40,2019-01-26 23:33:19,1,1.7,1,N,113,68,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-07 15:47:10,2019-01-07 15:58:18,1,1.5,1,N,236,43,1,9.5,1.0,0.5,1.13,0.0,0.3,12.43, +2,2019-01-09 19:13:13,2019-01-09 19:16:35,1,0.56,1,N,161,161,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-12 12:13:35,2019-01-12 12:25:21,1,2.5,1,N,107,237,1,10.5,0.0,0.5,1.7,0.0,0.3,13.0, +2,2019-01-26 22:15:25,2019-01-26 22:26:41,1,1.69,1,N,161,107,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +4,2019-01-28 13:50:17,2019-01-28 14:13:24,1,1.93,1,N,141,141,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +1,2019-01-04 05:19:34,2019-01-04 05:26:56,1,1.6,1,N,162,234,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-20 22:35:13,2019-01-20 22:46:11,1,2.46,1,N,48,43,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-25 18:36:03,2019-01-25 19:05:51,1,4.01,1,N,68,236,1,21.0,1.0,0.5,3.42,0.0,0.3,26.22,0.0 +1,2019-01-26 15:30:05,2019-01-26 15:44:38,1,1.1,1,N,107,164,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-09 17:55:44,2019-01-09 18:04:28,1,1.86,1,N,140,233,1,9.0,1.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-15 15:15:31,2019-01-15 15:42:10,1,3.88,1,N,237,125,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +1,2019-01-07 13:07:19,2019-01-07 13:22:25,1,2.3,1,N,264,238,1,11.5,0.0,0.5,1.85,0.0,0.3,14.15, +2,2019-01-15 19:12:26,2019-01-15 19:18:55,1,1.38,1,N,48,143,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-05 19:56:43,2019-01-05 20:02:43,1,0.7,1,N,230,48,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-03 12:57:31,2019-01-03 13:14:31,1,2.9,1,N,107,163,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-09 18:15:03,2019-01-09 18:38:30,1,6.82,1,N,261,237,2,22.5,1.0,0.5,0.0,0.0,0.3,24.3, +2,2019-01-15 12:05:12,2019-01-15 12:13:15,1,0.97,1,N,125,231,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-12 22:45:39,2019-01-12 22:57:52,1,1.85,1,N,87,114,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-30 12:49:23,2019-01-30 12:57:07,1,0.88,1,N,263,263,1,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-09 19:52:21,2019-01-09 20:02:55,1,1.46,1,N,234,170,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-01 01:24:19,2019-01-01 01:31:55,1,3.04,1,N,237,41,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-26 18:35:27,2019-01-26 18:40:11,1,1.25,1,N,231,158,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-19 18:03:39,2019-01-19 18:14:55,1,1.7,1,N,164,43,1,9.5,0.0,0.5,3.05,0.0,0.3,13.35, +2,2019-01-16 16:46:54,2019-01-16 17:01:30,1,1.87,1,N,237,170,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-17 08:09:48,2019-01-17 08:26:51,1,2.9,1,N,75,141,1,13.0,0.0,0.5,4.1,0.0,0.3,17.9, +1,2019-01-25 07:19:00,2019-01-25 07:33:42,1,2.3,1,N,143,141,1,11.5,0.0,0.5,1.85,0.0,0.3,14.15,0.0 +1,2019-01-02 11:25:31,2019-01-02 11:35:19,1,1.3,1,N,164,161,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-27 19:11:17,2019-01-27 19:23:11,1,2.1,1,N,239,262,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8,0.0 +1,2019-01-16 18:22:17,2019-01-16 18:25:29,1,0.5,1,N,264,264,1,4.0,1.0,0.5,1.7,0.0,0.3,7.5, +2,2019-01-10 19:00:27,2019-01-10 19:03:08,1,0.24,1,N,231,87,1,3.5,1.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-29 19:36:35,2019-01-29 19:43:38,1,1.45,1,N,186,161,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-06 19:29:37,2019-01-06 19:46:52,1,3.1,1,N,79,48,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-30 16:13:18,2019-01-30 16:28:56,1,2.3,1,N,114,186,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-22 05:55:03,2019-01-22 06:06:23,1,4.52,1,N,151,246,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-11 20:02:07,2019-01-11 20:24:01,1,1.93,1,N,158,107,1,14.0,0.5,0.5,3.06,0.0,0.3,18.36, +1,2019-01-13 01:17:57,2019-01-13 01:29:12,1,1.2,1,N,249,148,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-14 09:02:27,2019-01-14 09:29:16,1,12.32,5,N,11,76,1,33.29,0.0,0.5,0.0,0.0,0.3,34.09, +1,2019-01-30 20:13:02,2019-01-30 20:18:56,1,0.9,1,N,236,263,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-10 16:21:08,2019-01-10 16:37:57,1,1.6,1,N,162,43,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-17 17:51:51,2019-01-17 17:59:48,1,0.7,1,N,142,142,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +4,2019-01-06 12:20:41,2019-01-06 12:33:49,1,3.09,1,N,237,24,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-07 16:07:24,2019-01-07 16:18:35,1,1.2,1,N,237,230,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-03 13:40:56,2019-01-03 13:50:30,1,1.0,1,N,164,68,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-17 21:54:15,2019-01-17 22:00:29,1,0.5,1,N,230,161,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-14 09:40:23,2019-01-14 10:01:20,1,9.31,1,N,100,138,1,27.5,0.0,0.5,6.81,5.76,0.3,40.87, +2,2019-01-28 10:35:14,2019-01-28 10:35:38,1,0.08,1,N,262,140,1,2.5,0.0,0.5,0.99,0.0,0.3,4.29,0.0 +1,2019-01-24 08:26:12,2019-01-24 09:07:10,1,5.2,1,N,236,158,1,26.5,0.0,0.5,5.45,0.0,0.3,32.75,0.0 +1,2019-01-09 11:29:46,2019-01-09 11:53:48,1,2.6,1,N,140,230,1,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-12 13:21:32,2019-01-12 13:29:45,1,1.0,1,N,234,186,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-23 18:14:18,2019-01-23 18:37:15,1,2.66,1,N,144,186,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-23 12:47:21,2019-01-23 12:54:28,1,0.69,1,N,237,236,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-18 13:02:04,2019-01-18 13:12:36,1,4.8,1,N,233,87,1,15.5,0.0,0.5,1.5,0.0,0.3,17.8, +2,2019-01-14 20:11:27,2019-01-14 20:19:39,1,1.5,1,N,233,137,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-15 23:53:58,2019-01-16 00:11:31,1,4.9,1,N,48,13,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +1,2019-01-18 15:42:31,2019-01-18 16:24:33,1,10.6,1,N,234,138,1,35.5,0.0,0.5,8.4,5.76,0.3,50.46, +2,2019-01-29 20:41:14,2019-01-29 20:52:33,1,2.17,1,N,229,236,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-31 21:53:35,2019-01-31 22:00:37,1,0.7,1,N,48,50,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-24 05:56:58,2019-01-24 06:03:11,1,1.7,1,N,142,161,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-09 17:24:36,2019-01-09 17:47:26,1,3.4,1,N,234,236,1,16.5,1.0,0.5,3.65,0.0,0.3,21.95, +1,2019-01-29 07:36:30,2019-01-29 07:40:16,1,0.9,1,N,90,186,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-09 13:08:27,2019-01-09 13:13:20,1,0.7,1,N,237,141,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-06 09:54:54,2019-01-06 10:11:44,1,2.91,1,N,143,229,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-11 03:22:41,2019-01-11 03:36:07,1,5.0,1,N,229,83,2,16.5,0.5,0.5,0.0,0.0,0.3,17.8, +1,2019-01-27 17:50:13,2019-01-27 17:56:58,1,1.1,1,N,164,229,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-27 06:06:58,2019-01-27 06:19:31,1,2.9,1,N,100,140,1,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-25 23:59:17,2019-01-26 00:13:29,1,1.8,1,N,234,100,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-15 23:42:52,2019-01-16 00:07:23,1,8.5,1,N,249,116,1,27.5,0.5,0.5,7.2,0.0,0.3,36.0, +2,2019-01-29 18:22:02,2019-01-29 18:46:33,1,2.86,1,N,237,234,1,16.0,1.0,0.5,3.56,0.0,0.3,21.36,0.0 +1,2019-01-22 20:01:47,2019-01-22 20:08:28,1,0.6,1,N,164,137,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-09 12:21:49,2019-01-09 12:39:53,1,2.4,1,N,114,161,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-05 16:30:09,2019-01-05 16:37:20,1,0.9,1,N,162,163,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-03 17:14:08,2019-01-03 17:17:20,1,0.9,1,N,236,237,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-17 16:55:39,2019-01-17 17:10:42,1,1.84,1,N,68,48,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0, +1,2019-01-02 19:29:30,2019-01-02 19:38:37,1,1.3,1,N,164,230,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-29 16:00:26,2019-01-29 16:07:30,1,0.8,1,N,163,142,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-25 11:19:36,2019-01-25 11:31:22,1,1.25,1,N,113,234,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-03 12:56:25,2019-01-03 13:16:42,1,4.2,1,N,226,239,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-02 12:31:40,2019-01-02 12:48:44,1,2.37,1,N,158,170,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-04 09:33:29,2019-01-04 09:39:01,1,0.88,1,N,237,237,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +1,2019-01-16 06:41:28,2019-01-16 06:55:17,1,2.4,1,N,264,264,1,11.0,0.0,0.5,0.01,0.0,0.3,11.81, +2,2019-01-23 15:59:21,2019-01-23 16:12:45,1,1.78,1,N,234,79,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-21 10:23:00,2019-01-21 10:38:22,1,3.25,1,N,144,162,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-02 12:32:29,2019-01-02 12:38:00,1,1.2,1,N,162,107,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-31 20:01:31,2019-01-31 20:26:25,1,6.74,1,N,162,33,1,22.0,0.5,0.5,4.66,0.0,0.3,27.96,0.0 +2,2019-01-18 08:50:50,2019-01-18 09:01:57,1,1.8,1,N,158,68,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-29 12:45:54,2019-01-29 12:57:52,1,1.6,1,N,142,141,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84,0.0 +1,2019-01-19 03:28:06,2019-01-19 03:29:44,1,0.5,1,N,79,79,1,3.5,0.5,0.5,0.95,0.0,0.3,5.75, +2,2019-01-10 15:58:11,2019-01-10 16:42:36,1,12.13,1,N,48,212,1,38.0,0.0,0.5,0.0,0.0,0.3,38.8, +2,2019-01-15 22:16:11,2019-01-15 22:43:15,1,7.84,1,N,231,41,1,27.0,0.5,0.5,3.0,0.0,0.3,31.3, +2,2019-01-05 22:05:06,2019-01-05 22:11:03,1,0.89,1,N,142,237,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12, +2,2019-01-29 10:46:59,2019-01-29 11:03:33,1,2.18,1,N,229,236,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-29 17:24:54,2019-01-29 18:08:38,1,10.61,1,N,138,142,1,37.0,1.0,0.5,11.14,5.76,0.3,55.7,0.0 +1,2019-01-19 01:27:00,2019-01-19 01:30:05,1,0.4,1,N,152,166,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-11 23:03:20,2019-01-11 23:15:48,1,2.4,1,N,164,48,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +2,2019-01-21 17:54:29,2019-01-21 17:59:03,1,1.23,1,N,230,43,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-31 17:06:55,2019-01-31 17:17:01,1,1.2,1,N,100,163,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-18 17:23:05,2019-01-18 17:35:04,1,1.6,1,N,161,234,1,9.5,1.0,0.5,2.8,0.0,0.3,14.1, +2,2019-01-08 06:13:32,2019-01-08 06:19:06,1,2.08,1,N,79,229,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-15 19:46:02,2019-01-15 19:54:55,1,4.3,1,N,231,4,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-29 16:44:05,2019-01-29 16:57:47,1,2.29,1,N,238,141,1,11.0,1.0,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-31 20:19:42,2019-01-31 20:26:10,1,1.3,1,N,61,17,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-12 20:27:46,2019-01-12 20:36:00,1,1.45,1,N,142,239,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-03 14:54:44,2019-01-03 15:00:10,1,0.39,1,N,239,143,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-21 18:12:06,2019-01-21 18:19:25,1,0.95,1,N,234,224,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-28 19:44:12,2019-01-28 19:48:53,1,1.4,1,N,263,74,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-31 15:30:39,2019-01-31 15:45:46,1,2.5,1,N,158,230,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-26 18:08:58,2019-01-26 18:16:50,1,1.43,1,N,237,236,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-11 15:28:32,2019-01-11 15:37:34,1,1.65,1,N,161,234,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-08 17:28:36,2019-01-08 18:07:32,1,7.4,1,N,230,223,1,28.0,1.0,0.5,5.2,0.0,0.3,35.0, +4,2019-01-19 15:04:47,2019-01-19 15:12:51,1,2.05,1,N,263,229,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-10 18:16:33,2019-01-10 18:43:42,1,3.6,1,N,143,262,1,18.5,1.0,0.5,1.0,0.0,0.3,21.3, +2,2019-01-07 17:51:57,2019-01-07 17:56:15,1,0.74,1,N,263,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-13 09:16:16,2019-01-13 09:20:35,1,0.83,1,N,264,264,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-25 17:35:18,2019-01-25 17:45:55,1,0.8,1,N,164,230,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-04 19:50:17,2019-01-04 20:24:17,1,6.9,1,N,231,36,2,26.0,1.0,0.5,0.0,0.0,0.3,27.8, +1,2019-01-29 11:27:37,2019-01-29 11:46:23,1,6.2,1,N,170,13,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16,0.0 +1,2019-01-09 23:01:12,2019-01-09 23:02:25,1,0.5,1,N,170,170,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-27 11:30:16,2019-01-27 11:35:51,1,0.88,1,N,263,140,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-28 15:26:20,2019-01-28 15:29:08,1,0.3,1,N,116,116,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-07 16:41:16,2019-01-07 16:59:02,1,2.3,1,N,237,143,1,12.5,1.0,0.5,2.15,0.0,0.3,16.45, +1,2019-01-24 16:15:24,2019-01-24 16:18:18,1,0.6,1,N,237,236,1,4.0,1.0,0.5,1.5,0.0,0.3,7.3,0.0 +2,2019-01-08 17:19:03,2019-01-08 17:39:45,1,3.85,1,N,164,238,1,16.5,1.0,0.5,3.66,0.0,0.3,21.96, +1,2019-01-19 12:42:21,2019-01-19 13:14:38,1,3.3,1,N,163,113,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-02 13:28:29,2019-01-02 13:39:15,1,2.4,1,N,264,264,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-08 07:18:25,2019-01-08 07:36:57,1,3.5,1,N,162,231,1,14.5,0.0,0.5,3.83,0.0,0.3,19.13, +2,2019-01-06 08:39:26,2019-01-06 08:42:36,1,0.7,1,N,237,161,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-29 16:38:47,2019-01-29 16:48:19,1,1.06,1,N,170,100,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62,0.0 +1,2019-01-30 19:50:45,2019-01-30 20:01:56,1,1.5,1,N,230,90,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-15 15:10:30,2019-01-15 15:20:27,1,0.76,1,N,237,141,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-31 17:12:15,2019-01-31 17:20:52,1,0.82,1,N,162,229,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-18 10:14:01,2019-01-18 10:19:41,1,0.6,1,N,170,137,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-09 13:39:25,2019-01-09 14:06:28,1,3.85,1,N,186,263,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76, +1,2019-01-31 09:32:37,2019-01-31 09:59:09,1,2.9,1,N,231,170,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35,0.0 +1,2019-01-31 16:11:39,2019-01-31 16:22:25,1,1.6,1,N,163,164,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-05 20:11:06,2019-01-05 20:27:50,1,2.12,1,N,211,170,1,12.0,0.5,0.5,1.5,0.0,0.3,14.8, +1,2019-01-19 20:30:23,2019-01-19 20:30:37,1,0.0,1,N,68,68,4,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-14 07:08:01,2019-01-14 07:17:05,1,1.77,1,N,107,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-27 14:55:09,2019-01-27 15:16:36,1,3.29,1,N,113,141,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +4,2019-01-22 13:02:10,2019-01-22 13:33:17,1,7.19,1,N,263,209,1,24.5,0.0,0.5,5.06,0.0,0.3,30.36, +2,2019-01-16 20:29:10,2019-01-16 20:41:57,1,2.93,1,N,229,75,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-07 16:31:21,2019-01-07 16:34:06,1,0.53,1,N,113,90,1,4.0,1.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-17 09:45:56,2019-01-17 10:07:02,1,1.63,1,N,209,144,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +1,2019-01-07 05:57:43,2019-01-07 06:11:04,1,4.8,1,N,234,74,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-22 10:22:13,2019-01-22 11:13:32,1,11.67,1,N,138,161,1,41.5,0.0,0.5,9.61,5.76,0.3,57.67,0.0 +2,2019-01-04 19:23:49,2019-01-04 19:29:10,1,0.38,1,N,264,264,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-10 02:27:58,2019-01-10 02:36:57,1,3.3,1,N,230,231,1,11.0,0.5,0.5,1.85,0.0,0.3,14.15, +1,2019-01-26 15:11:56,2019-01-26 15:25:28,1,1.8,1,N,234,229,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-17 10:25:09,2019-01-17 10:35:25,1,2.1,1,N,166,238,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-25 19:02:31,2019-01-25 19:31:55,1,4.9,1,N,186,87,1,21.5,1.0,0.5,4.65,0.0,0.3,27.95,0.0 +2,2019-01-10 11:30:32,2019-01-10 11:51:54,1,5.87,1,N,43,79,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16, +1,2019-01-09 00:11:09,2019-01-09 00:32:35,1,9.7,1,N,231,236,1,28.5,0.5,0.5,4.47,0.0,0.3,34.27, +2,2019-01-04 21:26:31,2019-01-04 21:45:34,1,4.27,1,N,75,230,2,16.5,0.5,0.5,0.0,0.0,0.3,17.8, +2,2019-01-19 19:58:25,2019-01-19 20:09:34,1,3.28,1,N,231,148,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +1,2019-01-30 15:38:11,2019-01-30 16:12:08,1,3.7,1,N,236,186,1,21.5,0.0,0.5,4.45,0.0,0.3,26.75,0.0 +2,2019-01-26 23:54:30,2019-01-27 00:01:13,1,1.42,1,N,249,234,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-11 22:11:03,2019-01-11 22:17:41,1,1.0,1,N,142,163,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-27 00:22:30,2019-01-27 00:25:42,1,0.68,1,N,233,137,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-25 17:23:42,2019-01-25 17:52:12,1,4.43,1,N,13,161,2,20.0,1.0,0.5,0.0,0.0,0.3,21.8,0.0 +1,2019-01-20 13:41:03,2019-01-20 13:50:03,1,2.0,1,N,68,100,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-08 09:16:36,2019-01-08 09:23:57,1,0.69,1,N,140,237,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82, +1,2019-01-05 16:58:05,2019-01-05 17:04:58,1,1.1,1,N,239,24,1,7.0,0.0,0.5,2.3,0.0,0.3,10.1, +2,2019-01-01 17:00:18,2019-01-01 17:09:04,1,1.31,1,N,230,107,1,7.5,0.0,0.5,1.5,0.0,0.3,9.8, +2,2019-01-25 07:55:04,2019-01-25 08:05:52,1,1.22,1,N,43,239,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-17 19:11:53,2019-01-17 19:27:53,1,1.9,1,N,113,170,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-30 12:25:01,2019-01-30 12:46:04,1,7.7,1,N,138,236,1,24.5,0.0,0.5,3.0,5.76,0.3,34.06,0.0 +1,2019-01-05 20:19:51,2019-01-05 20:28:29,1,1.8,1,N,262,229,1,8.5,0.5,0.5,1.47,0.0,0.3,11.27, +1,2019-01-29 18:57:25,2019-01-29 19:01:43,1,0.8,1,N,236,237,1,5.5,1.0,0.5,1.2,0.0,0.3,8.5,0.0 +2,2019-01-19 15:34:45,2019-01-19 16:03:02,1,4.74,1,N,79,106,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16, +2,2019-01-04 14:32:10,2019-01-04 14:37:00,1,0.76,1,N,234,100,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +1,2019-01-24 13:53:23,2019-01-24 14:01:39,1,1.4,1,N,140,162,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-25 07:24:48,2019-01-25 07:30:19,1,0.69,1,N,261,265,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-30 22:39:02,2019-01-30 22:54:23,1,2.84,1,N,232,90,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-01 01:40:19,2019-01-01 02:01:26,1,6.48,1,N,7,74,1,21.0,0.5,0.5,4.46,0.0,0.3,26.76, +1,2019-01-30 11:32:35,2019-01-30 12:26:42,1,9.6,1,N,137,63,1,39.0,0.0,0.5,0.0,0.0,0.3,39.8,0.0 +2,2019-01-12 11:26:59,2019-01-12 11:33:41,1,1.33,1,N,233,234,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-07 17:03:00,2019-01-07 17:15:42,1,2.15,1,N,237,238,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-19 11:25:33,2019-01-19 11:28:42,1,0.35,1,N,143,143,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-19 18:49:17,2019-01-19 19:00:10,1,1.64,1,N,68,249,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-11 14:46:26,2019-01-11 15:02:28,1,1.93,1,N,140,43,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-10 18:50:45,2019-01-10 18:56:31,1,0.65,1,N,24,41,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-18 20:55:51,2019-01-18 21:08:39,1,1.6,1,N,186,161,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-26 20:23:50,2019-01-26 20:40:51,1,3.42,1,N,249,143,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-22 09:23:36,2019-01-22 09:27:20,1,0.47,1,N,141,141,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-21 15:06:11,2019-01-21 15:08:47,1,0.65,1,N,48,68,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +1,2019-01-30 19:20:48,2019-01-30 19:25:00,1,0.9,1,N,237,236,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-04 13:06:44,2019-01-04 13:15:58,1,1.1,1,N,238,43,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-22 17:13:18,2019-01-22 17:26:40,1,2.44,1,N,166,236,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-25 18:10:07,2019-01-25 18:18:28,1,0.87,1,N,162,230,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-06 01:43:49,2019-01-06 01:55:30,1,3.68,1,N,186,13,1,13.0,0.5,0.5,3.58,0.0,0.3,17.88, +2,2019-01-21 10:25:36,2019-01-21 10:36:21,1,2.36,1,N,107,4,1,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-20 12:38:57,2019-01-20 12:42:22,1,0.98,1,N,264,264,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-13 15:30:24,2019-01-13 15:32:18,1,0.5,1,N,48,50,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +2,2019-01-14 09:06:23,2019-01-14 09:24:29,1,2.95,1,N,186,238,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-05 18:48:18,2019-01-05 18:48:18,1,0.0,1,Y,186,264,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-08 14:07:27,2019-01-08 14:33:33,1,10.0,1,N,138,144,1,31.0,0.0,0.5,6.35,0.0,0.3,38.15, +4,2019-01-10 22:44:31,2019-01-10 23:17:01,1,6.28,1,N,249,181,1,24.0,0.5,0.5,2.0,0.0,0.3,27.3, +2,2019-01-04 14:38:38,2019-01-04 14:45:55,1,0.73,1,N,229,141,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-25 19:49:22,2019-01-25 19:53:48,1,0.8,1,N,48,246,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-15 15:44:23,2019-01-15 15:58:04,1,1.92,1,N,238,142,1,11.0,0.0,0.5,1.5,0.0,0.3,13.3, +2,2019-01-18 22:26:01,2019-01-18 22:34:47,1,1.55,1,N,107,233,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-17 10:41:37,2019-01-17 10:56:46,1,4.83,1,N,79,231,1,16.5,0.0,0.5,0.5,0.0,0.3,17.8, +2,2019-01-31 07:02:34,2019-01-31 07:15:36,1,2.27,1,N,107,246,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-06 19:57:14,2019-01-06 20:07:33,1,2.2,1,Y,107,162,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-17 07:27:28,2019-01-17 07:32:18,1,0.78,1,N,186,234,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +1,2019-01-23 20:08:36,2019-01-23 20:28:37,1,2.8,1,N,100,144,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-16 09:34:27,2019-01-16 09:57:50,1,3.98,1,N,48,231,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +2,2019-01-11 04:42:34,2019-01-11 04:59:28,1,5.33,1,N,48,7,1,18.0,0.5,0.5,3.86,0.0,0.3,23.16, +1,2019-01-07 11:13:44,2019-01-07 11:36:30,1,2.0,1,N,169,18,1,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-16 12:50:02,2019-01-16 12:55:40,1,1.0,1,N,236,237,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-02 11:05:15,2019-01-02 11:30:49,1,11.8,1,N,132,138,1,35.5,0.0,0.5,3.0,0.0,0.3,39.3, +1,2019-01-27 12:38:07,2019-01-27 12:45:27,1,0.9,1,N,229,163,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-29 14:30:18,2019-01-29 14:49:12,1,1.5,1,N,263,140,1,12.5,0.0,0.5,3.3,0.0,0.3,16.6,0.0 +2,2019-01-15 15:45:23,2019-01-15 16:38:01,1,13.65,1,N,223,76,1,46.5,0.0,0.5,0.0,0.0,0.3,47.3, +2,2019-01-03 08:09:33,2019-01-03 08:13:45,1,0.51,1,N,41,41,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-17 10:51:42,2019-01-17 10:57:56,1,1.2,1,N,137,229,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-14 18:14:32,2019-01-14 18:24:33,1,1.61,1,N,48,158,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-31 07:29:09,2019-01-31 07:35:24,1,1.2,1,N,162,141,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-07 10:51:44,2019-01-07 11:07:11,1,4.57,1,N,138,226,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-26 17:45:23,2019-01-26 18:03:16,1,2.1,1,N,186,211,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-30 23:03:48,2019-01-30 23:12:35,1,1.91,1,N,143,151,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +4,2019-01-29 22:09:46,2019-01-29 22:16:29,1,1.15,1,N,234,107,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-04 00:20:57,2019-01-04 00:52:59,1,7.04,1,N,163,244,1,25.5,0.5,0.5,5.0,0.0,0.3,31.8, +2,2019-01-23 21:36:40,2019-01-23 21:53:52,1,3.07,1,N,163,224,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-03 06:50:12,2019-01-03 06:55:54,1,0.8,1,N,100,161,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-14 06:53:50,2019-01-14 06:56:26,1,0.5,1,N,68,100,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-31 10:50:40,2019-01-31 11:02:30,1,2.3,1,N,140,170,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-08 13:19:40,2019-01-08 13:42:45,1,9.9,1,N,138,162,1,30.0,0.0,0.5,7.3,5.76,0.3,43.86, +2,2019-01-05 05:10:29,2019-01-05 05:16:07,1,1.27,1,N,186,161,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14, +2,2019-01-04 17:12:32,2019-01-04 17:39:47,1,4.78,1,N,261,229,2,20.0,1.0,0.5,0.0,0.0,0.3,21.8, +2,2019-01-11 10:50:00,2019-01-11 10:55:44,1,0.58,1,N,144,125,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19, +1,2019-01-29 14:22:29,2019-01-29 14:29:42,1,0.8,1,N,161,229,1,6.5,0.0,0.5,1.83,0.0,0.3,9.13,0.0 +1,2019-01-11 08:25:00,2019-01-11 08:29:11,1,0.7,1,N,114,113,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-14 15:14:30,2019-01-14 15:23:46,1,1.2,1,N,237,263,1,7.5,0.0,0.5,2.05,0.0,0.3,10.35, +2,2019-01-10 13:40:17,2019-01-10 13:49:56,1,1.75,1,N,158,100,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-17 19:41:30,2019-01-17 19:45:20,1,1.01,1,N,162,141,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-14 18:19:17,2019-01-14 18:34:29,1,2.3,1,N,162,164,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-14 16:12:51,2019-01-14 16:19:32,1,0.82,1,N,166,41,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-13 20:29:48,2019-01-13 20:37:23,1,1.4,1,N,79,4,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-03 05:46:06,2019-01-03 05:48:36,1,1.0,1,N,237,263,1,4.5,0.5,0.5,1.04,0.0,0.3,6.84, +2,2019-01-02 09:18:13,2019-01-02 09:28:57,1,1.5,1,N,249,68,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-08 08:06:43,2019-01-08 08:20:48,1,0.0,1,N,161,141,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-17 18:50:33,2019-01-17 19:03:46,1,1.7,1,N,100,107,1,10.0,1.0,0.5,1.0,0.0,0.3,12.8, +2,2019-01-06 17:22:31,2019-01-06 17:37:34,1,1.62,1,N,48,161,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-21 18:00:54,2019-01-21 18:04:15,1,0.5,1,N,79,148,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-14 20:13:30,2019-01-14 20:19:24,1,1.2,1,N,234,79,1,6.0,0.5,0.5,2.19,0.0,0.3,9.49, +1,2019-01-15 21:17:40,2019-01-15 21:33:16,1,3.1,1,N,164,140,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15, +2,2019-01-28 20:23:24,2019-01-28 20:28:26,1,0.48,1,N,161,162,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-22 14:55:12,2019-01-22 14:57:45,1,0.53,1,N,238,239,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24,0.0 +1,2019-01-20 17:15:49,2019-01-20 17:18:29,1,0.5,1,N,151,238,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-13 17:39:33,2019-01-13 17:50:38,1,2.0,1,N,68,158,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-01 03:44:26,2019-01-01 03:48:38,1,1.61,1,N,79,233,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +2,2019-01-19 08:48:37,2019-01-19 08:52:47,1,1.09,1,N,238,236,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-30 22:25:27,2019-01-30 22:40:37,1,2.27,1,N,233,246,1,11.0,0.5,0.5,1.0,0.0,0.3,13.3,0.0 +2,2019-01-12 18:26:29,2019-01-12 18:43:08,1,2.17,1,N,186,144,1,11.5,0.0,0.5,1.2,0.0,0.3,13.5, +2,2019-01-14 18:50:10,2019-01-14 18:55:43,1,0.98,1,N,43,238,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-24 21:44:16,2019-01-24 21:51:24,1,2.4,1,N,237,75,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-28 17:18:19,2019-01-28 17:28:42,1,1.43,1,N,161,107,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +4,2019-01-31 22:51:50,2019-01-31 23:06:58,1,2.4,1,N,163,262,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-15 16:20:29,2019-01-15 16:45:14,1,2.1,1,N,186,162,1,15.5,1.0,0.5,3.45,0.0,0.3,20.75, +2,2019-01-05 23:53:27,2019-01-06 00:11:33,1,8.4,1,N,138,166,1,24.5,0.5,0.5,6.31,5.76,0.3,37.87, +2,2019-01-09 15:11:49,2019-01-09 15:27:06,1,2.09,1,N,164,142,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38, +1,2019-01-04 02:51:06,2019-01-04 02:55:39,1,1.2,1,N,186,48,1,6.0,0.5,0.5,1.8,0.0,0.3,9.1, +1,2019-01-23 13:16:13,2019-01-23 13:45:23,1,6.8,1,N,264,114,1,25.5,0.0,0.5,6.55,0.0,0.3,32.85,0.0 +2,2019-01-16 14:37:16,2019-01-16 15:31:29,1,15.68,2,N,10,113,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36, +2,2019-01-25 20:00:10,2019-01-25 20:32:34,1,10.27,5,N,79,116,1,31.97,0.0,0.5,0.0,0.0,0.3,32.77,0.0 +2,2019-01-26 01:09:14,2019-01-26 01:31:23,1,5.22,1,N,230,88,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3,0.0 +1,2019-01-30 01:12:22,2019-01-30 01:17:15,1,0.9,1,N,164,68,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-19 18:36:19,2019-01-19 18:53:11,1,2.92,1,N,230,68,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-16 09:12:07,2019-01-16 09:20:30,1,0.98,1,N,249,186,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-30 00:06:08,2019-01-30 00:16:44,1,1.8,1,N,186,163,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-14 11:04:16,2019-01-14 11:11:56,1,1.3,1,N,237,140,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-13 17:25:19,2019-01-13 17:33:34,1,0.8,1,N,158,211,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-06 15:40:16,2019-01-06 15:45:37,1,0.9,1,N,79,4,1,5.5,0.0,0.5,2.5,0.0,0.3,8.8, +1,2019-01-14 19:45:00,2019-01-14 19:55:25,1,1.4,1,N,141,163,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-19 02:21:27,2019-01-19 02:41:55,1,6.4,1,N,170,166,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-25 17:11:52,2019-01-25 17:21:18,1,0.93,1,N,100,170,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62,0.0 +1,2019-01-25 08:04:33,2019-01-25 08:15:37,1,2.5,1,N,262,161,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +2,2019-01-23 07:26:10,2019-01-23 07:40:45,1,2.16,1,N,107,125,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-28 14:17:25,2019-01-28 14:25:55,1,1.17,1,N,233,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-06 22:45:51,2019-01-06 22:52:41,1,1.15,1,N,186,170,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +1,2019-01-04 16:14:31,2019-01-04 16:20:16,1,1.2,1,N,237,141,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-27 13:48:48,2019-01-27 13:56:23,1,1.04,1,N,140,263,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +1,2019-01-12 14:46:11,2019-01-12 14:58:44,1,2.1,1,N,143,151,1,10.5,0.0,0.5,3.35,0.0,0.3,14.65, +2,2019-01-06 08:38:44,2019-01-06 08:42:42,1,0.72,1,N,113,249,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-24 06:07:52,2019-01-24 06:10:55,1,1.0,1,N,238,166,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-18 09:00:17,2019-01-18 09:06:38,1,0.7,1,N,236,236,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-30 10:13:43,2019-01-30 10:38:21,1,2.79,1,N,238,48,1,17.0,0.0,0.5,1.2,0.0,0.3,19.0,0.0 +2,2019-01-05 07:18:12,2019-01-05 08:01:05,1,18.69,2,N,132,24,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-05 23:20:13,2019-01-05 23:25:16,1,1.9,1,N,144,65,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-10 08:04:51,2019-01-10 08:12:55,1,1.43,1,N,100,163,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-05 02:07:33,2019-01-05 02:17:43,1,2.91,1,N,148,100,1,10.5,0.5,0.5,1.0,0.0,0.3,12.8, +1,2019-01-18 06:31:29,2019-01-18 06:34:42,1,0.7,1,N,100,230,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-11 17:47:28,2019-01-11 18:02:54,1,1.8,1,N,90,48,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-28 20:53:48,2019-01-28 20:56:43,1,0.85,1,N,186,90,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67,0.0 +2,2019-01-14 22:24:44,2019-01-14 22:27:31,1,0.49,1,N,143,239,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +2,2019-01-30 01:17:27,2019-01-30 01:30:31,1,3.81,1,N,162,231,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-11 21:26:02,2019-01-11 21:33:08,1,0.79,1,N,137,170,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-17 11:37:59,2019-01-17 11:49:00,1,2.16,1,N,237,238,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-25 11:13:49,2019-01-25 11:23:39,1,1.1,1,N,163,239,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +1,2019-01-12 20:03:27,2019-01-12 20:18:36,1,1.4,1,N,230,137,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +1,2019-01-11 08:51:31,2019-01-11 09:00:33,1,0.7,1,N,162,141,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-03 08:56:10,2019-01-03 09:01:25,1,0.6,1,N,68,246,1,5.5,0.0,0.5,2.5,0.0,0.3,8.8, +1,2019-01-04 09:29:49,2019-01-04 09:43:55,1,1.0,1,N,229,161,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-15 14:44:37,2019-01-15 14:55:37,1,1.6,1,N,236,140,1,9.0,0.0,0.5,1.75,0.0,0.3,11.55, +1,2019-01-23 22:36:01,2019-01-23 22:43:44,1,2.0,1,N,229,236,1,8.0,0.5,0.5,2.3,0.0,0.3,11.6,0.0 +2,2019-01-24 15:43:30,2019-01-24 16:03:52,1,1.56,1,N,236,140,1,13.0,0.0,0.5,1.0,0.0,0.3,14.8,0.0 +2,2019-01-11 19:06:06,2019-01-11 19:10:04,1,0.65,1,N,234,170,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-02 07:23:14,2019-01-02 07:43:19,1,5.42,1,N,231,236,1,18.5,0.0,0.5,3.0,0.0,0.3,22.3, +2,2019-01-07 23:13:48,2019-01-07 23:34:05,1,7.14,1,N,79,151,1,23.5,0.5,0.5,4.96,0.0,0.3,29.76, +2,2019-01-30 22:47:40,2019-01-30 23:14:10,1,7.64,1,N,161,25,1,26.5,0.5,0.5,3.0,0.0,0.3,30.8,0.0 +2,2019-01-16 21:08:09,2019-01-16 21:27:13,1,3.92,1,N,164,75,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +1,2019-01-13 03:21:41,2019-01-13 03:45:14,1,9.7,1,N,226,181,1,29.5,0.5,0.5,5.0,0.0,0.3,35.8, +2,2019-01-25 17:04:01,2019-01-25 17:29:05,1,3.49,1,N,146,129,2,17.0,1.0,0.5,0.0,0.0,0.3,18.8,0.0 +1,2019-01-17 18:33:21,2019-01-17 18:45:18,1,3.0,1,N,143,75,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95, +1,2019-01-31 17:10:56,2019-01-31 17:42:33,1,6.1,1,Y,68,166,1,25.0,1.0,0.5,6.7,0.0,0.3,33.5,0.0 +1,2019-01-25 12:42:55,2019-01-25 12:49:05,1,1.3,1,N,142,163,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-24 09:29:58,2019-01-24 10:09:53,1,12.4,1,N,138,88,1,37.5,0.0,0.5,8.8,5.76,0.3,52.86,0.0 +1,2019-01-25 15:03:38,2019-01-25 15:26:33,1,4.9,1,N,162,209,1,19.5,0.0,0.5,1.5,0.0,0.3,21.8,0.0 +2,2019-01-04 09:33:27,2019-01-04 09:59:26,1,9.93,1,N,138,238,1,30.0,0.0,0.5,7.31,5.76,0.3,43.87, +2,2019-01-23 22:29:29,2019-01-23 22:57:11,1,11.7,1,N,170,220,1,33.5,0.5,0.5,9.36,2.64,0.3,46.8,0.0 +1,2019-01-21 01:38:56,2019-01-21 01:40:59,1,0.7,1,N,239,143,1,4.0,0.5,0.5,2.0,0.0,0.3,7.3,0.0 +1,2019-01-16 12:03:28,2019-01-16 12:04:22,1,1.1,1,N,238,238,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-17 19:38:27,2019-01-17 19:40:53,1,0.2,1,N,237,237,1,3.5,1.0,0.5,0.79,0.0,0.3,6.09, +1,2019-01-04 09:25:27,2019-01-04 10:04:11,1,2.4,1,N,161,186,2,23.0,0.0,0.5,0.0,0.0,0.3,23.8, +2,2019-01-21 21:46:00,2019-01-21 21:49:03,1,0.41,1,N,142,43,1,4.0,0.5,0.5,0.8,0.0,0.3,6.1,0.0 +2,2019-01-05 18:28:41,2019-01-05 18:53:36,1,5.6,1,N,230,79,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96, +1,2019-01-28 17:59:04,2019-01-28 18:06:30,1,0.8,1,N,161,164,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-26 22:51:04,2019-01-26 22:53:59,1,0.42,1,N,144,211,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-01 14:40:59,2019-01-01 14:50:36,1,2.11,1,N,143,68,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-29 11:05:04,2019-01-29 11:16:28,1,1.32,1,N,161,186,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-27 17:17:48,2019-01-27 17:22:41,1,0.9,1,N,90,158,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-26 03:15:25,2019-01-26 03:22:15,1,0.8,1,N,264,264,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-17 18:16:25,2019-01-17 18:21:19,1,0.9,1,N,140,262,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-15 06:43:25,2019-01-15 06:55:26,1,3.83,1,N,162,261,1,13.0,0.0,0.5,3.0,0.0,0.3,16.8, +2,2019-01-16 09:44:58,2019-01-16 10:05:53,1,6.66,1,N,237,88,1,23.0,0.0,0.5,4.76,0.0,0.3,28.56, +1,2019-01-07 08:09:57,2019-01-07 08:13:58,1,0.4,1,N,41,42,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3, +2,2019-01-19 19:56:30,2019-01-19 20:11:26,1,1.53,1,N,186,107,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-11 07:55:33,2019-01-11 08:13:08,1,5.1,1,N,87,162,1,18.0,0.0,0.5,4.7,0.0,0.3,23.5, +1,2019-01-01 01:47:06,2019-01-01 01:49:17,1,0.8,1,N,142,238,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-07 14:36:49,2019-01-07 14:55:44,1,3.14,1,N,234,236,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-08 05:39:26,2019-01-08 05:55:05,1,5.21,1,N,25,162,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +2,2019-01-30 15:09:56,2019-01-30 15:40:24,1,3.85,1,N,48,262,1,20.0,0.0,0.5,3.0,0.0,0.3,23.8,0.0 +1,2019-01-01 01:55:42,2019-01-01 02:24:07,1,5.2,1,N,79,61,1,22.0,0.5,0.5,0.0,0.0,0.3,23.3, +2,2019-01-07 12:13:02,2019-01-07 12:22:44,1,1.02,1,N,163,161,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-09 10:55:48,2019-01-09 11:27:35,1,12.34,1,N,261,221,1,38.5,0.0,0.5,10.16,11.52,0.3,60.98, +2,2019-01-27 01:13:37,2019-01-27 01:19:33,1,0.83,1,N,79,232,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-30 15:16:14,2019-01-30 15:18:52,1,0.37,1,N,239,239,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-18 15:35:39,2019-01-18 15:47:04,1,1.4,1,N,107,162,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-06 23:06:54,2019-01-06 23:30:50,1,17.3,2,N,132,233,1,52.0,0.0,0.5,5.86,5.76,0.3,64.42, +1,2019-01-05 00:39:50,2019-01-05 00:46:13,1,1.4,1,N,90,48,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-19 06:00:46,2019-01-19 06:03:43,1,1.14,1,N,249,246,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-23 15:42:11,2019-01-23 16:03:08,1,8.79,1,N,138,236,1,26.0,0.0,0.5,6.51,5.76,0.3,39.07,0.0 +1,2019-01-03 20:50:10,2019-01-03 20:56:54,1,1.2,1,N,234,79,1,6.5,0.5,0.5,1.17,0.0,0.3,8.97, +1,2019-01-10 17:04:38,2019-01-10 17:10:35,1,0.8,1,N,234,170,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-27 22:44:46,2019-01-27 22:58:27,1,2.4,1,N,68,144,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-13 14:18:06,2019-01-13 14:56:04,1,16.3,2,N,132,48,1,52.0,0.0,0.5,14.6,5.76,0.3,73.16, +2,2019-01-07 09:01:21,2019-01-07 09:18:19,1,1.84,1,N,237,170,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-17 18:49:55,2019-01-17 18:57:08,1,1.1,1,N,249,148,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-29 17:34:17,2019-01-29 17:49:33,1,1.29,1,N,186,107,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-06 09:11:36,2019-01-06 09:15:50,1,0.9,1,N,237,161,1,5.5,0.0,0.5,1.2,0.0,0.3,7.5, +2,2019-01-16 16:11:47,2019-01-16 16:42:55,1,2.33,1,N,236,141,1,19.0,1.0,0.5,4.16,0.0,0.3,24.96, +1,2019-01-18 21:53:12,2019-01-18 22:37:17,1,18.7,2,N,132,143,1,52.0,0.0,0.5,10.55,0.0,0.3,63.35, +2,2019-01-25 22:14:15,2019-01-25 22:19:56,1,0.78,1,N,141,141,1,5.5,0.5,0.5,1.22,0.0,0.3,8.02,0.0 +2,2019-01-18 17:38:20,2019-01-18 17:47:43,1,3.39,1,N,152,239,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-12 17:21:37,2019-01-12 17:25:04,1,0.46,1,N,239,239,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-29 09:00:46,2019-01-29 09:11:11,1,1.1,1,N,236,141,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-23 15:14:20,2019-01-23 15:14:37,1,0.0,1,N,237,237,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-28 08:31:24,2019-01-28 09:26:45,1,16.89,1,N,138,13,1,53.0,0.0,0.5,8.93,5.76,0.3,68.49,0.0 +2,2019-01-18 08:39:38,2019-01-18 09:09:31,1,3.16,1,N,24,163,2,19.5,0.0,0.5,0.0,0.0,0.3,20.3, +4,2019-01-07 20:00:09,2019-01-07 20:03:00,1,0.84,1,N,143,239,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8, +2,2019-01-13 13:19:10,2019-01-13 13:29:51,1,0.61,1,N,186,234,1,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-07 19:26:34,2019-01-07 19:34:43,1,1.4,1,N,233,141,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-24 22:01:38,2019-01-24 22:01:38,1,0.0,1,Y,186,264,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3,0.0 +1,2019-01-16 15:25:08,2019-01-16 15:40:44,1,1.3,1,N,264,264,1,10.5,0.0,0.5,1.36,0.0,0.3,12.66, +1,2019-01-18 11:24:21,2019-01-18 11:26:59,1,0.6,1,N,236,263,1,4.0,0.0,0.5,0.72,0.0,0.3,5.52, +1,2019-01-15 08:28:55,2019-01-15 08:41:06,1,1.6,1,N,142,237,1,9.5,0.0,0.5,1.55,0.0,0.3,11.85, +2,2019-01-23 15:05:57,2019-01-23 15:06:13,1,0.0,1,N,264,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-06 16:52:30,2019-01-06 17:42:09,1,19.75,2,N,132,116,1,52.0,0.0,0.5,7.0,5.76,0.3,65.56, +1,2019-01-27 14:39:55,2019-01-27 14:46:27,1,1.6,1,N,161,234,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-15 23:47:53,2019-01-15 23:57:19,1,1.5,1,N,48,233,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +1,2019-01-21 13:19:03,2019-01-21 13:20:19,1,0.1,1,N,236,236,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-29 07:54:08,2019-01-29 08:01:05,1,0.82,1,N,162,170,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-27 12:51:20,2019-01-27 12:53:33,1,0.5,1,N,151,166,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-31 18:41:36,2019-01-31 18:47:20,1,0.54,1,N,144,148,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-07 08:24:04,2019-01-07 08:51:15,1,8.74,1,N,170,138,1,29.5,0.0,0.5,7.21,5.76,0.3,43.27, +2,2019-01-05 20:40:54,2019-01-05 21:00:05,1,2.95,1,N,25,61,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +1,2019-01-14 08:28:59,2019-01-14 08:38:01,1,1.4,1,N,229,137,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-29 12:29:09,2019-01-29 12:43:41,1,2.61,1,N,141,239,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +1,2019-01-25 19:31:18,2019-01-25 19:38:01,1,0.7,1,N,231,87,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-16 13:46:00,2019-01-16 13:59:05,1,2.38,1,N,249,209,1,10.5,0.0,0.5,2.26,0.0,0.3,15.51, +1,2019-01-09 09:49:45,2019-01-09 10:10:01,1,1.7,1,N,142,140,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15, +2,2019-01-30 14:11:32,2019-01-30 14:40:48,1,3.74,1,N,229,125,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96,0.0 +2,2019-01-29 22:26:37,2019-01-29 22:35:13,1,1.8,1,N,239,263,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-24 21:32:50,2019-01-24 21:41:55,1,1.5,1,N,162,164,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +1,2019-01-15 17:27:30,2019-01-15 17:38:21,1,1.4,1,N,233,79,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-14 11:10:16,2019-01-14 11:32:14,1,2.58,1,N,230,113,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-16 16:32:12,2019-01-16 16:44:15,1,0.13,1,N,144,144,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-24 18:28:54,2019-01-24 18:34:09,1,0.56,1,N,162,170,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-29 15:06:28,2019-01-29 15:16:07,1,1.2,1,N,125,68,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-11 07:42:14,2019-01-11 07:50:26,1,0.53,1,N,161,230,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-17 16:39:59,2019-01-17 16:47:19,1,1.14,1,N,246,234,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-07 20:01:41,2019-01-07 20:13:42,1,2.38,1,N,161,79,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 13:05:13,2019-01-11 13:15:33,1,1.8,1,N,142,237,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-31 22:21:36,2019-01-31 22:42:10,1,8.9,1,N,138,37,1,26.0,0.5,0.5,4.5,0.0,0.3,31.8,0.0 +1,2019-01-03 08:29:58,2019-01-03 08:38:12,1,1.3,1,N,87,13,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-16 14:58:57,2019-01-16 15:10:21,1,0.6,1,N,170,186,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-26 16:17:35,2019-01-26 16:33:46,1,1.9,1,N,107,229,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-09 12:31:58,2019-01-09 12:55:11,1,2.48,1,N,47,235,1,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-19 14:48:17,2019-01-19 14:55:42,1,0.8,1,N,170,229,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-09 11:43:46,2019-01-09 11:55:29,1,2.64,1,N,166,239,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-09 07:31:24,2019-01-09 07:37:46,1,1.48,1,N,249,231,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-20 20:26:19,2019-01-20 20:35:08,1,1.4,1,N,237,262,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-21 12:24:01,2019-01-21 12:29:14,1,1.3,1,N,239,151,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-17 10:41:18,2019-01-17 10:45:12,1,0.48,1,N,143,239,1,4.5,0.0,0.5,0.53,0.0,0.3,5.83, +2,2019-01-25 06:43:05,2019-01-25 06:53:16,1,2.31,1,N,142,164,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-16 10:01:47,2019-01-16 10:07:04,1,0.6,1,N,264,264,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-11 21:05:26,2019-01-11 21:40:24,1,4.9,1,N,142,232,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16, +2,2019-01-22 18:20:09,2019-01-22 18:33:51,1,2.2,1,N,211,164,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-24 14:18:48,2019-01-24 14:21:50,1,0.5,1,N,113,79,3,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-24 11:19:07,2019-01-24 11:37:16,1,1.03,1,N,230,161,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-06 08:46:37,2019-01-06 08:56:42,1,3.4,1,N,238,68,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-10 10:53:53,2019-01-10 11:00:30,1,0.7,1,N,237,162,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-21 19:00:06,2019-01-21 19:06:34,1,1.24,1,N,142,238,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-25 11:48:27,2019-01-25 12:03:03,1,2.2,1,N,141,238,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-06 11:07:22,2019-01-06 11:12:02,1,1.54,1,N,158,13,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +2,2019-01-09 16:50:17,2019-01-09 17:14:02,1,4.54,1,N,13,161,1,19.0,1.0,0.5,4.16,0.0,0.3,24.96, +1,2019-01-28 21:07:10,2019-01-28 21:10:54,1,0.7,1,N,161,229,1,4.5,0.5,0.5,0.58,0.0,0.3,6.38,0.0 +4,2019-01-30 22:39:42,2019-01-30 22:56:49,1,2.28,1,N,140,230,1,12.5,0.5,0.5,3.45,0.0,0.3,17.25,0.0 +2,2019-01-27 10:57:23,2019-01-27 11:02:08,1,0.86,1,N,144,231,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-12 00:16:22,2019-01-12 00:29:33,1,3.8,1,N,170,74,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +1,2019-01-11 16:50:03,2019-01-11 16:57:02,1,0.7,1,N,211,113,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 11:38:23,2019-01-10 11:54:03,1,1.91,1,N,142,236,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-08 09:40:47,2019-01-08 10:04:09,1,3.73,1,N,236,246,1,17.0,0.0,0.5,2.67,0.0,0.3,20.47, +1,2019-01-18 23:54:25,2019-01-19 00:08:46,1,7.5,1,N,138,140,2,22.0,0.5,0.5,0.0,5.76,0.3,29.06, +2,2019-01-10 14:16:50,2019-01-10 14:34:52,1,4.18,1,N,144,162,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +2,2019-01-26 12:59:53,2019-01-26 13:30:45,1,3.3,1,N,163,158,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96,0.0 +1,2019-01-20 23:15:15,2019-01-20 23:35:22,1,5.7,1,N,79,181,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +2,2019-01-19 17:47:38,2019-01-19 18:07:40,1,2.98,1,N,161,158,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-22 06:52:28,2019-01-22 07:08:19,1,4.33,1,N,262,234,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +1,2019-01-05 12:21:59,2019-01-05 12:34:03,1,1.9,1,N,239,163,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-15 06:44:36,2019-01-15 06:55:40,1,2.0,1,N,151,74,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-18 21:32:46,2019-01-18 22:04:05,1,17.57,2,N,132,107,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-11 11:27:02,2019-01-11 12:04:36,1,8.84,1,N,162,138,1,33.5,0.0,0.5,5.14,0.0,0.3,41.39, +1,2019-01-24 10:12:20,2019-01-24 10:39:45,1,2.7,1,N,163,246,1,17.5,0.0,0.5,3.65,0.0,0.3,21.95,0.0 +1,2019-01-04 06:51:44,2019-01-04 06:58:50,1,1.6,1,N,141,161,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-12 09:20:26,2019-01-12 09:26:29,1,1.23,1,N,162,140,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-26 15:26:01,2019-01-26 15:40:33,1,1.6,1,N,79,164,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15,0.0 +1,2019-01-10 11:49:49,2019-01-10 12:18:41,1,4.1,1,N,238,186,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8, +1,2019-01-24 19:51:03,2019-01-24 20:02:16,1,4.3,1,N,231,52,2,13.5,1.0,0.5,0.0,5.76,0.3,21.06,0.0 +1,2019-01-15 15:27:32,2019-01-15 15:38:48,1,2.6,1,N,246,231,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8, +1,2019-01-25 21:15:14,2019-01-25 21:19:00,1,0.5,1,N,236,262,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67,0.0 +2,2019-01-04 23:47:41,2019-01-04 23:52:19,1,0.76,1,N,264,264,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-17 10:38:55,2019-01-17 10:55:17,1,2.0,1,N,161,68,1,11.5,0.0,0.5,3.05,0.0,0.3,15.35, +1,2019-01-12 18:58:39,2019-01-12 19:02:00,1,0.6,1,N,263,236,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 20:42:07,2019-01-31 21:04:16,1,3.63,1,N,186,239,1,16.5,0.5,0.5,3.56,0.0,0.3,23.31,0.0 +1,2019-01-08 19:11:50,2019-01-08 19:18:10,1,1.0,1,N,170,107,1,6.5,1.0,0.5,1.5,0.0,0.3,9.8, +4,2019-01-26 20:49:52,2019-01-26 20:55:58,1,0.77,1,N,4,79,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5, +1,2019-01-04 18:55:43,2019-01-04 19:07:57,1,2.8,1,N,68,239,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-31 16:49:36,2019-01-31 16:53:57,1,1.19,1,N,239,24,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-11 15:14:56,2019-01-11 15:56:09,1,5.29,1,N,236,112,1,28.0,0.0,0.5,2.0,0.0,0.3,30.8, +2,2019-01-09 21:52:21,2019-01-09 21:55:54,1,0.56,1,N,90,246,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-11 18:42:59,2019-01-11 19:08:57,1,3.5,1,N,144,161,1,17.5,1.0,0.5,3.0,0.0,0.3,22.3, +2,2019-01-30 19:33:39,2019-01-30 20:00:12,1,5.0,1,N,79,75,1,20.0,1.0,0.5,2.0,0.0,0.3,23.8,0.0 +2,2019-01-13 00:51:07,2019-01-13 00:52:55,1,0.29,1,N,68,68,1,3.5,0.5,0.5,0.96,0.0,0.3,5.76, +2,2019-01-28 07:18:51,2019-01-28 07:24:05,1,1.25,1,N,231,113,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-24 19:37:38,2019-01-24 19:47:56,1,1.97,1,N,230,238,1,9.0,1.0,0.5,2.0,0.0,0.3,12.8,0.0 +1,2019-01-09 15:39:07,2019-01-09 15:44:21,1,1.0,1,N,249,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-26 16:32:49,2019-01-26 16:50:12,1,1.95,1,N,48,229,1,12.0,0.0,0.5,1.4,0.0,0.3,14.2,0.0 +1,2019-01-07 10:57:24,2019-01-07 11:05:46,1,1.1,1,N,140,263,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-29 20:24:10,2019-01-29 20:32:01,1,1.24,1,N,234,246,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-18 14:27:40,2019-01-18 14:40:21,1,2.0,1,N,140,239,3,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-10 13:17:34,2019-01-10 13:24:59,1,1.2,1,N,142,239,1,7.0,0.0,0.5,0.78,0.0,0.3,8.58, +2,2019-01-15 13:11:26,2019-01-15 13:19:17,1,0.94,1,N,229,137,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-26 07:38:32,2019-01-26 07:47:19,1,2.64,1,N,237,238,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-18 18:45:53,2019-01-18 18:52:38,1,1.0,1,N,48,68,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-22 12:11:47,2019-01-22 12:26:20,1,1.88,1,N,186,79,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-23 15:20:53,2019-01-23 16:25:36,1,21.0,2,N,132,239,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-15 05:42:16,2019-01-15 05:55:59,1,3.9,1,N,260,161,1,14.0,0.5,0.5,4.7,0.0,0.3,20.0, +2,2019-01-31 07:15:05,2019-01-31 07:19:46,1,0.81,1,N,7,179,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-10 17:45:53,2019-01-10 17:59:51,1,1.13,1,N,48,161,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-18 17:18:35,2019-01-18 17:22:04,1,0.55,1,N,236,236,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-16 13:45:06,2019-01-16 13:47:06,1,0.3,1,N,163,163,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-25 19:26:53,2019-01-25 19:43:13,1,2.3,1,N,236,164,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8,0.0 +2,2019-01-06 04:07:43,2019-01-06 04:24:49,1,4.3,1,N,164,145,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-01 14:32:17,2019-01-01 14:37:58,1,1.14,1,N,262,75,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-28 15:54:21,2019-01-28 15:56:23,1,0.4,1,N,161,230,1,3.5,0.0,0.5,2.0,0.0,0.3,6.3,0.0 +4,2019-01-09 13:23:05,2019-01-09 13:27:31,1,0.71,1,N,231,125,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-11 07:12:24,2019-01-11 07:33:38,1,3.2,1,N,68,237,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-18 15:28:27,2019-01-18 15:38:25,1,1.8,1,N,239,50,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +1,2019-01-17 14:38:02,2019-01-17 15:07:05,1,9.7,1,N,162,138,1,30.0,0.0,0.5,10.95,5.76,0.3,47.51, +2,2019-01-11 23:32:37,2019-01-11 23:44:23,1,1.23,1,N,161,186,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-27 15:03:29,2019-01-27 15:18:53,1,1.78,1,N,249,107,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-18 02:12:29,2019-01-18 02:21:26,1,1.7,1,N,164,233,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +1,2019-01-13 21:12:47,2019-01-13 21:15:55,1,0.3,1,N,231,13,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +1,2019-01-10 14:52:43,2019-01-10 15:04:55,1,2.0,1,N,48,246,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-18 18:53:09,2019-01-18 19:06:32,1,2.82,1,N,186,143,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-09 12:18:23,2019-01-09 12:31:58,1,0.3,1,N,233,170,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-24 23:13:56,2019-01-24 23:17:57,1,1.2,1,N,50,246,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-02 20:48:01,2019-01-02 20:55:13,1,1.0,1,N,113,4,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 16:12:47,2019-01-10 17:13:32,1,12.79,5,N,161,26,1,36.79,0.0,0.5,0.0,5.76,0.3,43.35, +2,2019-01-24 06:10:43,2019-01-24 06:20:09,1,1.84,1,N,186,143,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-31 13:35:21,2019-01-31 13:44:09,1,0.64,1,N,237,140,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-24 15:19:17,2019-01-24 15:36:38,1,1.24,1,N,142,161,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-31 18:48:07,2019-01-31 19:10:56,1,4.63,1,N,239,158,2,18.5,1.0,0.5,0.0,0.0,0.3,20.3,0.0 +2,2019-01-24 07:27:12,2019-01-24 07:42:07,1,2.15,1,N,142,43,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-10 20:10:36,2019-01-10 20:34:46,1,3.2,1,N,264,264,1,16.5,1.0,0.5,2.55,0.0,0.3,20.85, +2,2019-01-14 12:46:34,2019-01-14 13:01:59,1,2.66,1,N,140,170,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-19 04:13:22,2019-01-19 04:20:14,1,1.01,1,N,230,162,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14, +2,2019-01-08 19:14:39,2019-01-08 19:28:00,1,3.35,1,N,13,90,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16, +1,2019-01-01 05:20:58,2019-01-01 05:24:09,1,0.5,1,N,164,164,1,4.0,0.5,0.5,1.3,0.0,0.3,6.6, +1,2019-01-31 18:43:49,2019-01-31 18:50:16,1,0.9,1,N,237,229,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-08 19:50:51,2019-01-08 19:58:23,1,0.8,1,N,161,233,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-04 19:24:12,2019-01-04 19:41:58,1,1.8,1,N,141,170,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-04 20:43:00,2019-01-04 21:17:45,1,18.01,2,N,132,68,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-14 11:32:24,2019-01-14 11:37:00,1,0.57,1,N,236,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-28 10:55:42,2019-01-28 11:05:44,1,1.06,1,N,236,263,1,8.0,0.0,0.5,1.32,0.0,0.3,10.12,0.0 +2,2019-01-07 22:29:02,2019-01-07 22:36:56,1,2.05,1,N,230,263,1,8.5,0.5,0.5,2.2,0.0,0.3,12.0, +1,2019-01-24 11:32:05,2019-01-24 11:57:31,1,2.7,1,N,170,236,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35,0.0 +2,2019-01-30 20:31:45,2019-01-30 20:39:59,1,1.0,1,N,234,68,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-05 13:47:00,2019-01-05 13:58:27,1,1.6,1,N,231,232,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-21 14:02:20,2019-01-21 14:08:27,1,1.1,1,N,161,162,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-28 21:18:38,2019-01-28 21:32:21,1,2.71,1,N,151,237,1,12.5,0.5,0.5,2.07,0.0,0.3,15.87,0.0 +1,2019-01-01 20:54:00,2019-01-01 21:01:27,1,1.3,1,N,75,262,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-27 16:15:29,2019-01-27 17:01:20,1,11.39,1,N,62,50,1,41.0,0.0,0.5,0.0,5.76,0.3,47.56,0.0 +1,2019-01-29 21:36:41,2019-01-29 21:44:18,1,1.2,1,N,170,48,1,7.0,0.5,0.5,2.05,0.0,0.3,10.35,0.0 +2,2019-01-03 14:29:52,2019-01-03 14:35:13,1,0.81,1,N,238,239,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-26 13:27:27,2019-01-26 14:05:37,1,18.86,2,N,132,48,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-03 22:29:52,2019-01-03 22:33:17,1,0.89,1,N,68,249,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-20 22:35:30,2019-01-20 22:39:56,1,0.93,1,N,236,237,1,5.5,0.5,0.5,2.04,0.0,0.3,8.84, +1,2019-01-31 10:26:11,2019-01-31 10:27:30,1,0.2,1,N,239,239,1,3.0,0.0,0.5,1.0,0.0,0.3,4.8,0.0 +2,2019-01-20 22:27:20,2019-01-20 22:33:33,1,1.37,1,N,142,237,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-11 12:10:20,2019-01-11 12:24:43,1,1.88,1,N,100,113,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-07 12:41:05,2019-01-07 12:47:49,1,0.67,1,N,263,75,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-27 01:45:12,2019-01-27 01:48:29,1,0.07,1,N,148,148,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-27 18:22:29,2019-01-27 18:32:48,1,1.51,1,N,230,233,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-21 14:45:41,2019-01-21 14:49:57,1,0.5,1,N,236,236,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-25 22:17:27,2019-01-25 22:26:09,1,1.2,1,N,234,144,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-02 15:25:23,2019-01-02 15:49:21,1,5.59,5,N,142,42,1,21.48,0.0,0.0,0.0,0.0,0.3,21.78, +2,2019-01-11 16:23:33,2019-01-11 16:35:33,1,0.63,1,N,161,230,1,8.5,1.0,0.5,1.54,0.0,0.3,11.84, +2,2019-01-13 12:07:20,2019-01-13 12:17:44,1,1.31,1,N,186,230,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-21 19:25:10,2019-01-21 19:27:10,1,0.51,1,N,170,170,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-31 22:52:07,2019-01-31 22:55:35,1,0.79,1,N,236,236,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +1,2019-01-09 19:53:26,2019-01-09 20:01:34,1,2.0,1,N,50,238,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-18 09:35:54,2019-01-18 09:44:29,1,0.98,1,N,162,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-25 07:35:59,2019-01-25 07:57:44,1,5.12,1,N,143,231,1,18.5,0.0,0.5,2.5,0.0,0.3,21.8,0.0 +1,2019-01-18 07:42:56,2019-01-18 08:00:50,1,3.1,1,N,141,238,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-13 09:28:06,2019-01-13 09:35:37,1,1.74,1,N,237,262,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-20 12:24:41,2019-01-20 12:32:10,1,1.63,1,N,163,239,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-31 12:48:30,2019-01-31 13:01:30,1,1.9,1,N,239,230,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-01 02:57:49,2019-01-01 03:07:54,1,1.8,1,N,264,264,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-19 16:43:42,2019-01-19 17:23:35,1,27.16,1,N,132,181,2,71.5,0.0,0.5,0.0,0.0,0.3,72.3, +1,2019-01-23 10:01:56,2019-01-23 10:04:56,1,0.6,1,N,236,263,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +1,2019-01-29 13:34:36,2019-01-29 13:43:21,1,1.1,1,N,234,211,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-24 21:33:18,2019-01-24 22:08:39,1,7.7,1,N,100,223,1,27.5,0.5,0.5,3.0,0.0,0.3,31.8,0.0 +2,2019-01-26 09:49:13,2019-01-26 10:04:50,1,4.11,1,N,141,179,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-28 14:19:45,2019-01-28 14:34:44,1,1.97,1,N,162,107,1,11.0,0.0,0.5,2.0,0.0,0.3,13.8,0.0 +2,2019-01-20 09:08:21,2019-01-20 09:10:38,1,0.63,1,N,262,236,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-03 19:55:12,2019-01-03 20:09:49,1,2.5,1,N,143,263,1,12.0,1.0,0.5,1.0,0.0,0.3,14.8, +2,2019-01-10 08:27:28,2019-01-10 08:38:54,1,2.02,1,N,107,162,1,9.5,0.0,0.5,3.0,0.0,0.3,13.3, +1,2019-01-03 09:36:15,2019-01-03 09:44:46,1,1.3,1,N,141,162,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-07 12:29:11,2019-01-07 12:31:56,1,0.5,1,N,236,263,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95, +1,2019-01-12 13:14:25,2019-01-12 13:21:10,1,0.9,1,N,237,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-23 16:50:14,2019-01-23 17:24:59,1,7.1,1,N,43,148,1,27.5,1.0,0.5,0.0,0.0,0.3,29.3,0.0 +2,2019-01-11 10:05:47,2019-01-11 10:12:48,1,1.73,1,N,239,166,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-26 09:03:03,2019-01-26 09:51:02,1,6.6,1,N,7,151,2,32.0,0.0,0.5,0.0,5.76,0.3,38.56,0.0 +2,2019-01-14 09:13:31,2019-01-14 09:28:16,1,1.26,1,N,233,161,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-13 16:58:08,2019-01-13 17:01:16,1,0.8,1,N,141,263,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-17 21:42:58,2019-01-17 22:14:27,1,13.05,1,N,230,67,1,39.0,0.5,0.5,2.0,5.76,0.3,48.06, +1,2019-01-13 00:33:42,2019-01-13 00:44:26,1,1.5,1,N,234,48,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-06 22:27:16,2019-01-06 22:33:58,1,1.52,1,N,161,234,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-29 06:37:39,2019-01-29 06:40:49,1,0.8,1,N,186,246,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-21 14:20:05,2019-01-21 14:38:03,1,2.7,1,N,236,100,1,13.0,0.0,0.5,3.0,0.0,0.3,16.8,0.0 +1,2019-01-09 23:32:48,2019-01-10 00:02:42,1,6.9,1,N,161,181,1,25.0,0.5,0.5,5.25,0.0,0.3,31.55, +1,2019-01-26 08:02:11,2019-01-26 08:06:16,1,1.5,1,N,262,74,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +1,2019-01-21 19:30:02,2019-01-21 19:48:53,1,6.0,1,N,138,145,1,20.0,0.0,0.5,4.15,0.0,0.3,24.95,0.0 +2,2019-01-11 22:04:22,2019-01-11 22:12:06,1,1.66,1,N,161,236,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-17 13:46:53,2019-01-17 13:55:44,1,1.53,1,N,141,239,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-05 23:48:00,2019-01-05 23:52:47,1,0.7,1,N,113,234,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-04 11:44:43,2019-01-04 12:01:47,1,7.4,1,N,125,233,1,23.0,0.0,0.5,4.75,0.0,0.3,28.55, +1,2019-01-21 12:53:18,2019-01-21 13:12:25,1,5.8,1,N,161,261,1,20.5,0.0,0.5,5.3,0.0,0.3,26.6,0.0 +2,2019-01-20 00:43:28,2019-01-20 00:50:23,1,1.44,1,N,42,41,1,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-17 19:19:39,2019-01-17 19:32:03,1,1.43,1,N,229,230,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-10 09:32:07,2019-01-10 10:03:02,1,2.3,1,N,229,68,1,19.0,0.0,0.5,2.97,0.0,0.3,22.77, +1,2019-01-13 19:17:24,2019-01-13 19:22:37,1,0.8,1,N,236,141,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-12 13:17:23,2019-01-12 13:30:59,1,1.12,1,N,143,163,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-11 10:20:56,2019-01-11 10:26:06,1,0.78,1,N,90,246,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-13 19:46:59,2019-01-13 19:57:06,1,1.1,1,N,68,48,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-31 16:02:43,2019-01-31 16:11:54,1,1.16,1,N,163,186,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-21 05:44:03,2019-01-21 05:56:01,1,2.93,1,N,238,162,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-28 20:07:58,2019-01-28 20:24:24,1,6.2,1,N,246,74,1,20.0,0.5,0.5,4.25,0.0,0.3,25.55,0.0 +2,2019-01-31 23:08:05,2019-01-31 23:16:53,1,2.78,1,N,143,166,1,10.5,0.5,0.5,1.0,0.0,0.3,12.8,0.0 +1,2019-01-09 21:38:57,2019-01-09 21:46:36,1,1.6,1,N,161,50,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +4,2019-01-03 22:21:32,2019-01-03 22:37:30,1,2.38,1,N,114,161,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-24 14:55:41,2019-01-24 15:28:26,1,4.42,1,N,79,239,1,21.5,0.0,0.5,3.0,0.0,0.3,25.3,0.0 +1,2019-01-20 19:32:50,2019-01-20 19:47:07,1,4.4,1,N,68,87,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-11 13:51:59,2019-01-11 13:54:28,1,0.29,1,N,229,229,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +1,2019-01-21 08:27:08,2019-01-21 08:42:32,1,2.5,1,N,170,50,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-24 23:06:54,2019-01-24 23:18:36,1,2.21,1,N,68,231,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-16 10:06:59,2019-01-16 10:36:23,1,2.86,1,N,239,170,1,18.5,0.0,0.5,4.0,0.0,0.3,23.3, +2,2019-01-29 08:09:59,2019-01-29 08:33:55,1,3.01,1,N,234,43,1,17.0,0.0,0.5,4.45,0.0,0.3,22.25,0.0 +2,2019-01-23 08:36:04,2019-01-23 09:00:52,1,1.77,1,N,142,162,1,15.0,0.0,0.5,2.0,0.0,0.3,17.8,0.0 +2,2019-01-25 19:27:04,2019-01-25 19:40:28,1,1.84,1,N,161,113,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-27 02:06:08,2019-01-27 02:09:22,1,0.78,1,N,141,141,1,4.5,0.5,0.5,0.87,0.0,0.3,6.67,0.0 +2,2019-01-11 08:53:36,2019-01-11 09:06:06,1,1.39,1,N,237,262,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84, +1,2019-01-16 01:31:52,2019-01-16 01:41:53,1,2.6,1,N,148,170,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +1,2019-01-10 09:23:34,2019-01-10 09:40:03,1,2.1,1,N,236,142,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-28 06:26:10,2019-01-28 06:34:46,1,1.7,1,N,238,75,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6,0.0 +2,2019-01-03 20:54:47,2019-01-03 20:58:34,1,0.82,1,N,238,151,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-17 18:42:32,2019-01-17 19:02:41,1,4.0,1,N,137,146,2,16.5,1.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-07 09:38:46,2019-01-07 10:05:51,1,4.94,1,N,100,261,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36, +2,2019-01-04 09:34:54,2019-01-04 09:51:32,1,2.04,1,N,170,142,1,11.5,0.0,0.5,2.2,0.0,0.3,14.5, +1,2019-01-13 04:44:32,2019-01-13 05:02:27,1,5.3,1,N,249,262,1,18.5,0.5,0.5,3.95,0.0,0.3,23.75, +2,2019-01-26 11:47:30,2019-01-26 12:02:47,1,2.31,1,N,162,43,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0,0.0 +1,2019-01-16 23:25:34,2019-01-16 23:34:07,1,1.7,1,N,186,113,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15, +1,2019-01-13 11:29:13,2019-01-13 11:44:41,1,2.0,1,N,163,170,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +1,2019-01-06 12:52:03,2019-01-06 12:54:54,1,0.8,1,N,237,140,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-05 00:06:31,2019-01-05 00:26:46,1,4.6,1,N,234,25,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +2,2019-01-25 18:10:31,2019-01-25 18:32:08,1,2.01,1,N,186,114,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3,0.0 +2,2019-01-31 21:50:33,2019-01-31 22:00:58,1,1.28,1,N,48,162,1,8.0,0.5,0.5,0.93,0.0,0.3,10.23,0.0 +2,2019-01-08 08:57:52,2019-01-08 09:08:44,1,1.36,1,N,246,230,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-25 11:05:27,2019-01-25 11:12:47,1,1.02,1,N,238,238,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-07 21:52:29,2019-01-07 22:09:11,1,6.11,1,N,249,151,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +1,2019-01-08 16:11:12,2019-01-08 16:31:33,1,2.4,1,N,143,236,1,14.5,1.0,0.5,1.0,0.0,0.3,17.3, +1,2019-01-07 20:17:48,2019-01-07 20:24:23,1,0.7,1,N,79,79,1,6.0,0.5,0.5,2.15,0.0,0.3,9.45, +2,2019-01-25 16:29:25,2019-01-25 17:39:18,1,19.7,1,N,132,25,1,62.5,1.0,0.5,9.64,0.0,0.3,73.94,0.0 +1,2019-01-17 12:15:52,2019-01-17 12:19:42,1,0.7,1,N,143,143,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-25 13:40:23,2019-01-25 13:47:33,1,1.4,1,N,262,140,1,7.5,0.0,0.5,2.0,0.0,0.3,10.3,0.0 +2,2019-01-22 22:50:46,2019-01-22 22:59:08,1,3.68,1,N,261,224,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-24 20:51:39,2019-01-24 21:01:13,1,2.45,1,N,162,140,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-24 09:42:24,2019-01-24 09:57:31,1,2.6,1,N,263,162,1,11.5,0.0,0.5,3.05,0.0,0.3,15.35,0.0 +2,2019-01-07 06:43:37,2019-01-07 06:51:58,1,1.48,1,N,113,234,1,8.0,0.0,0.5,1.08,0.0,0.3,9.88, +1,2019-01-06 16:44:17,2019-01-06 16:48:46,1,1.1,1,N,233,107,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-06 11:15:03,2019-01-06 11:25:10,1,2.17,1,N,68,229,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-08 17:57:04,2019-01-08 18:20:48,1,3.8,1,N,164,238,1,17.5,1.0,0.5,3.0,0.0,0.3,22.3, +2,2019-01-21 15:52:01,2019-01-21 15:53:50,1,0.26,1,N,236,236,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38,0.0 +2,2019-01-21 15:02:54,2019-01-21 15:14:47,1,2.43,1,N,229,239,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-14 02:37:53,2019-01-14 02:42:16,1,0.75,1,N,170,164,1,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-17 11:27:44,2019-01-17 11:40:43,1,1.3,1,N,48,43,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-23 10:10:38,2019-01-23 10:15:01,1,0.53,1,N,161,230,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-10 13:57:41,2019-01-10 14:07:54,1,1.16,1,N,43,170,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-25 20:47:25,2019-01-25 21:01:16,1,1.1,1,N,113,148,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95,0.0 +1,2019-01-09 12:44:19,2019-01-09 12:54:34,1,1.4,1,N,161,234,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-06 22:55:53,2019-01-06 23:02:08,1,1.2,1,N,186,50,1,6.5,0.5,0.5,2.3,0.0,0.3,10.1, +2,2019-01-06 21:45:22,2019-01-06 21:55:38,1,2.15,1,N,161,239,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-30 20:08:05,2019-01-30 20:32:04,1,4.6,1,N,90,140,1,19.0,0.5,0.5,3.05,0.0,0.3,23.35,0.0 +4,2019-01-04 17:50:24,2019-01-04 18:13:44,1,3.06,1,N,164,140,1,17.0,1.0,0.5,5.64,0.0,0.3,24.44, +2,2019-01-25 06:36:45,2019-01-25 07:07:31,1,19.22,2,N,236,132,1,52.0,0.0,0.5,9.0,5.76,0.3,67.56,0.0 +2,2019-01-12 16:32:02,2019-01-12 16:39:41,1,1.1,1,N,237,161,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-22 14:52:32,2019-01-22 15:03:18,1,1.1,1,N,263,141,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +4,2019-01-26 14:27:41,2019-01-26 14:39:27,1,1.17,1,N,100,170,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-06 01:07:49,2019-01-06 01:12:49,1,1.31,1,N,142,237,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-18 14:47:12,2019-01-18 15:04:35,1,2.66,1,N,68,170,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-15 14:37:10,2019-01-15 14:46:32,1,1.37,1,N,141,170,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-03 19:45:20,2019-01-03 19:50:00,1,0.9,1,N,114,234,1,5.5,0.5,0.5,1.5,0.0,0.3,8.3, +2,2019-01-25 21:52:29,2019-01-25 22:04:06,1,2.39,1,N,238,74,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-23 10:57:33,2019-01-23 11:12:33,1,1.64,1,N,234,161,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-19 18:53:21,2019-01-19 18:59:06,1,0.72,1,N,142,239,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-07 12:14:42,2019-01-07 12:25:04,1,4.81,1,N,138,95,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-15 13:57:31,2019-01-15 13:59:14,1,0.42,1,N,161,170,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +1,2019-01-30 10:33:23,2019-01-30 11:08:00,1,9.4,1,N,42,211,1,31.0,0.0,0.5,6.35,0.0,0.3,38.15,0.0 +1,2019-01-26 08:29:02,2019-01-26 08:33:21,1,0.7,1,N,79,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-29 12:22:57,2019-01-29 12:26:13,1,0.4,1,N,137,170,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75,0.0 +1,2019-01-22 08:11:55,2019-01-22 08:21:05,1,1.6,1,N,237,143,1,8.5,0.0,0.5,2.0,0.0,0.3,11.3,0.0 +2,2019-01-01 23:30:07,2019-01-01 23:37:04,1,1.91,1,N,229,263,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-20 10:39:46,2019-01-20 10:43:00,1,0.62,1,N,48,50,1,4.5,0.0,0.5,2.7,0.0,0.3,8.0, +1,2019-01-20 23:53:50,2019-01-20 23:55:59,1,0.8,1,N,237,162,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-17 12:01:17,2019-01-17 12:21:28,1,8.5,1,N,138,234,1,26.0,0.0,0.5,6.51,5.76,0.3,39.07, +1,2019-01-19 13:43:17,2019-01-19 13:50:41,1,1.2,1,N,43,237,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-17 14:49:02,2019-01-17 15:01:44,1,1.7,1,N,237,48,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-30 08:16:09,2019-01-30 08:24:47,1,1.6,1,N,158,68,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-09 14:53:01,2019-01-09 15:02:49,1,1.6,1,N,48,249,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-18 22:56:53,2019-01-18 23:28:11,1,7.85,1,N,65,239,1,26.5,0.5,0.5,5.56,0.0,0.3,33.36, +1,2019-01-07 18:48:15,2019-01-07 18:50:51,1,0.4,1,N,231,231,1,4.0,1.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-04 15:02:06,2019-01-04 15:14:46,1,2.3,1,N,43,236,1,11.0,0.0,0.5,1.25,0.0,0.3,13.05, +2,2019-01-17 07:32:56,2019-01-17 07:43:35,1,2.41,1,N,231,230,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-31 16:17:41,2019-01-31 16:29:49,1,1.48,1,N,239,237,1,9.0,1.0,0.5,3.24,0.0,0.3,14.04,0.0 +1,2019-01-04 06:44:19,2019-01-04 06:52:16,1,1.5,1,N,233,224,1,8.0,0.0,0.5,1.05,0.0,0.3,9.85, +2,2019-01-31 11:51:05,2019-01-31 11:57:29,1,0.9,1,N,238,24,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-10 13:47:20,2019-01-10 13:52:54,1,1.38,1,N,166,238,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-23 14:52:45,2019-01-23 15:26:17,1,1.41,1,N,234,246,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36,0.0 +2,2019-01-04 19:27:43,2019-01-04 19:36:12,1,1.18,1,N,68,249,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-02 08:32:59,2019-01-02 08:37:37,1,0.79,1,N,68,68,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-02 19:33:56,2019-01-02 19:37:58,1,0.68,1,N,125,114,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-17 17:01:49,2019-01-17 17:16:56,1,5.31,1,N,138,226,2,17.5,1.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-23 07:57:30,2019-01-23 07:59:29,1,0.43,1,N,233,229,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16,0.0 +1,2019-01-26 13:08:37,2019-01-26 13:13:01,1,1.0,1,N,246,48,1,5.5,0.0,0.5,1.85,0.0,0.3,8.15,0.0 +2,2019-01-20 10:27:15,2019-01-20 10:32:43,1,0.69,1,N,162,163,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-13 15:54:10,2019-01-13 16:19:16,1,8.51,1,N,138,137,1,28.0,0.0,0.5,8.64,0.0,0.3,37.44, +2,2019-01-26 19:33:05,2019-01-26 19:45:53,1,1.66,1,N,233,79,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-02 05:46:02,2019-01-02 05:51:11,1,1.5,1,N,48,143,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-18 12:51:31,2019-01-18 12:57:20,1,1.0,1,N,166,238,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-17 16:32:31,2019-01-17 16:42:45,1,0.53,1,N,161,163,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-09 00:37:36,2019-01-09 00:52:03,1,4.5,1,N,234,238,1,15.0,0.5,0.5,4.0,0.0,0.3,20.3, +2,2019-01-28 06:47:42,2019-01-28 07:15:36,1,8.88,1,N,238,138,1,30.0,0.0,0.5,7.31,5.76,0.3,45.82,0.0 +2,2019-01-05 15:03:23,2019-01-05 15:10:13,1,0.6,1,N,13,231,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-06 01:04:13,2019-01-06 01:21:08,1,4.1,1,N,148,141,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +1,2019-01-22 20:21:04,2019-01-22 20:38:07,1,2.3,1,N,161,143,1,12.5,0.5,0.5,1.5,0.0,0.3,15.3,0.0 +1,2019-01-30 13:36:51,2019-01-30 13:54:27,1,2.3,1,N,234,231,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-30 05:25:11,2019-01-30 05:47:07,1,11.33,1,N,161,138,1,32.5,0.5,0.5,7.91,5.76,0.3,47.47,0.0 +2,2019-01-22 16:00:23,2019-01-22 16:14:46,1,1.65,1,N,142,161,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-22 09:33:53,2019-01-22 09:47:59,1,2.53,1,N,151,50,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +1,2019-01-23 15:10:44,2019-01-23 15:21:45,1,1.0,1,N,234,114,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-16 20:41:06,2019-01-16 20:49:50,1,1.74,1,N,229,107,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-31 22:52:32,2019-01-31 23:00:41,1,1.7,1,N,234,233,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-01 16:18:45,2019-01-01 16:22:38,1,0.61,1,N,137,234,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +2,2019-01-07 10:33:47,2019-01-07 10:52:25,1,4.28,1,N,79,237,1,16.5,0.0,0.5,4.32,0.0,0.3,21.62, +1,2019-01-20 16:55:57,2019-01-20 17:06:09,1,1.8,1,N,234,144,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-31 08:01:29,2019-01-31 08:13:16,1,2.67,1,N,141,74,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-20 13:55:06,2019-01-20 14:03:21,1,1.02,1,N,170,186,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-13 11:18:44,2019-01-13 11:49:36,1,18.2,2,N,132,230,1,52.0,0.0,0.5,6.0,5.76,0.3,64.56, +2,2019-01-31 17:26:45,2019-01-31 17:32:14,1,0.78,1,N,68,100,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-06 03:20:40,2019-01-06 03:22:54,1,0.1,1,N,230,230,4,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-18 23:59:11,2019-01-19 00:03:54,1,0.9,1,N,114,158,1,5.5,0.5,0.5,1.2,0.0,0.3,8.0, +2,2019-01-05 08:14:49,2019-01-05 08:40:10,1,20.08,3,N,142,1,1,70.5,0.0,0.0,16.26,10.5,0.3,97.56, +2,2019-01-27 20:21:11,2019-01-27 20:29:03,1,0.96,1,N,186,107,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-09 23:12:30,2019-01-09 23:24:50,1,4.31,1,N,166,246,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +1,2019-01-25 19:12:57,2019-01-25 19:24:39,1,1.3,1,N,90,68,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-17 06:05:41,2019-01-17 06:09:20,1,0.8,1,N,50,48,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-05 23:43:26,2019-01-05 23:57:01,1,3.04,1,N,143,75,1,12.5,0.5,0.5,1.5,0.0,0.3,15.3, +2,2019-01-16 20:49:16,2019-01-16 20:55:17,1,0.55,1,N,234,224,4,-5.5,-0.5,-0.5,0.0,0.0,-0.3,-6.8, +2,2019-01-03 01:36:30,2019-01-03 01:44:59,1,3.09,1,N,229,226,1,11.0,0.5,0.5,3.08,0.0,0.3,15.38, +2,2019-01-08 22:47:23,2019-01-08 22:52:00,1,1.14,1,N,143,239,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-31 19:54:22,2019-01-31 19:58:51,1,0.95,1,N,163,142,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-18 12:30:02,2019-01-18 12:45:50,1,1.95,1,N,230,249,1,11.0,0.0,0.5,1.18,0.0,0.3,12.98, +1,2019-01-22 09:09:05,2019-01-22 09:15:43,1,0.7,1,N,143,50,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-21 20:30:35,2019-01-21 20:40:30,1,1.6,1,N,100,107,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15,0.0 +1,2019-01-26 13:43:27,2019-01-26 13:49:50,1,1.1,1,N,264,264,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +1,2019-01-10 12:38:36,2019-01-10 12:56:09,1,1.4,1,N,236,161,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-28 10:44:27,2019-01-28 10:56:15,1,1.2,1,N,161,236,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-14 13:55:07,2019-01-14 14:19:18,1,3.0,1,N,140,186,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-13 04:16:18,2019-01-13 04:30:06,1,3.93,1,N,68,141,1,14.0,0.5,0.5,4.7,0.0,0.3,20.0, +2,2019-01-13 19:51:40,2019-01-13 20:04:58,1,2.52,1,N,148,256,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-22 19:57:27,2019-01-22 20:04:18,1,0.87,1,N,107,90,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-11 11:10:29,2019-01-11 11:15:46,1,0.97,1,N,161,234,1,5.5,0.0,0.5,0.7,0.0,0.3,7.0, +1,2019-01-22 17:14:01,2019-01-22 17:19:26,1,0.4,1,N,170,170,1,5.0,1.0,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-17 23:17:59,2019-01-17 23:35:41,1,2.2,1,N,143,68,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-04 14:23:00,2019-01-04 15:07:09,1,19.35,2,N,132,231,1,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +2,2019-01-31 13:20:52,2019-01-31 13:22:55,1,0.28,1,N,229,229,1,3.5,0.0,0.5,0.7,0.0,0.3,5.0,0.0 +2,2019-01-10 10:39:15,2019-01-10 10:49:05,1,1.32,1,N,74,75,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-06 10:52:09,2019-01-06 10:54:39,1,0.5,1,N,186,90,1,4.0,0.0,0.5,3.0,0.0,0.3,7.8, +2,2019-01-27 22:43:13,2019-01-27 22:50:29,1,1.45,1,N,230,234,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-31 07:27:19,2019-01-31 07:29:46,1,0.45,1,N,226,226,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-18 08:18:22,2019-01-18 08:27:39,1,1.26,1,N,164,162,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-20 17:35:31,2019-01-20 17:45:32,1,1.93,1,N,143,24,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-10 11:58:21,2019-01-10 12:01:20,1,0.6,1,N,236,236,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-09 21:16:58,2019-01-09 22:00:41,1,20.8,5,N,142,38,1,66.17,0.0,0.0,0.0,0.0,0.3,66.47, +2,2019-01-03 09:39:14,2019-01-03 10:12:26,1,10.57,1,N,138,249,1,33.5,0.0,0.5,8.01,5.76,0.3,48.07, +1,2019-01-12 10:54:25,2019-01-12 11:06:25,1,3.8,1,N,246,13,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15, +2,2019-01-06 11:22:54,2019-01-06 11:27:07,1,0.81,1,N,140,141,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-08 17:27:03,2019-01-08 17:32:10,1,0.72,1,N,162,237,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-06 01:33:56,2019-01-06 01:36:28,1,0.52,1,N,170,233,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +1,2019-01-24 17:50:37,2019-01-24 18:13:38,1,7.6,1,N,138,255,1,24.5,1.0,0.5,4.0,0.0,0.3,30.3,0.0 +1,2019-01-04 04:42:09,2019-01-04 05:01:05,1,5.9,1,N,260,79,2,20.0,0.5,0.5,0.0,0.0,0.3,21.3, +1,2019-01-22 21:54:50,2019-01-22 21:57:22,1,0.9,1,N,236,75,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95,0.0 +2,2019-01-16 17:29:10,2019-01-16 18:01:44,1,3.62,1,N,164,239,2,20.5,1.0,0.5,0.0,0.0,0.3,22.3, +1,2019-01-30 18:29:49,2019-01-30 18:33:00,1,0.4,1,N,114,249,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-28 06:57:59,2019-01-28 07:07:21,1,1.3,1,N,100,162,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-26 22:57:33,2019-01-26 23:00:19,1,0.26,1,N,68,158,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-26 20:18:36,2019-01-26 20:23:27,1,0.68,1,N,141,229,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-25 07:23:47,2019-01-25 07:35:28,1,1.82,1,N,141,137,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-22 15:36:03,2019-01-22 15:45:54,1,1.3,1,N,163,186,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-09 17:43:17,2019-01-09 17:48:12,1,0.4,1,N,164,186,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-18 08:59:35,2019-01-18 09:16:01,1,1.11,1,N,161,163,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-17 20:38:28,2019-01-17 20:50:53,1,2.9,1,N,161,238,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +2,2019-01-23 08:09:18,2019-01-23 08:24:24,1,2.09,1,N,140,161,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-13 16:36:58,2019-01-13 16:43:53,1,0.77,1,N,262,141,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-16 07:54:53,2019-01-16 08:14:54,1,1.75,1,N,246,161,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-14 08:56:55,2019-01-14 09:34:13,1,1.8,1,N,246,48,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +2,2019-01-17 19:23:42,2019-01-17 19:30:22,1,1.12,1,N,246,249,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-31 21:35:30,2019-01-31 21:46:01,1,2.2,1,N,230,239,1,10.0,0.5,0.5,3.35,0.0,0.3,14.65,0.0 +2,2019-01-27 00:47:27,2019-01-27 00:59:08,1,4.92,1,N,148,141,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-31 12:29:44,2019-01-31 12:39:48,1,2.58,1,N,75,140,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-08 11:03:20,2019-01-08 11:38:41,1,6.55,1,N,141,141,2,26.0,0.0,0.5,0.0,0.0,0.3,26.8, +2,2019-01-05 21:07:04,2019-01-05 21:33:15,1,3.98,1,N,107,255,1,19.5,0.5,0.5,5.2,0.0,0.3,26.0, +1,2019-01-23 08:14:48,2019-01-23 08:29:11,1,2.2,1,N,249,170,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-28 18:10:30,2019-01-28 18:25:48,1,2.2,1,N,234,141,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-26 10:21:30,2019-01-26 10:28:01,1,1.2,1,N,48,43,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-01 17:49:24,2019-01-01 18:17:09,1,16.79,2,N,132,233,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56, +1,2019-01-29 17:25:38,2019-01-29 17:59:07,1,4.3,1,N,231,230,1,23.0,1.0,0.5,4.95,0.0,0.3,29.75,0.0 +1,2019-01-25 19:04:31,2019-01-25 19:11:47,1,0.9,1,N,170,137,1,6.5,1.0,0.5,2.05,0.0,0.3,10.35,0.0 +2,2019-01-22 20:43:54,2019-01-22 20:55:03,1,1.56,1,N,79,137,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-26 18:41:40,2019-01-26 18:46:21,1,0.97,1,N,142,238,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-30 18:39:45,2019-01-30 18:45:26,1,1.24,1,N,158,186,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-01 22:44:38,2019-01-01 22:44:45,1,0.0,1,N,216,216,3,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-07 12:36:46,2019-01-07 12:41:19,1,0.65,1,N,161,164,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-06 03:35:40,2019-01-06 03:40:45,1,0.3,1,N,79,79,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +1,2019-01-16 10:35:23,2019-01-16 10:55:03,1,1.4,1,N,186,170,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-29 04:51:44,2019-01-29 05:03:06,1,3.67,1,N,75,142,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-16 08:35:35,2019-01-16 08:54:31,1,1.8,1,N,246,170,1,12.0,0.0,0.5,1.92,0.0,0.3,14.72, +1,2019-01-05 11:51:49,2019-01-05 12:06:36,1,2.5,1,N,143,229,1,12.0,0.0,0.5,1.65,0.0,0.3,14.45, +2,2019-01-18 00:12:38,2019-01-18 00:22:33,1,2.45,1,N,163,113,1,9.5,0.5,0.5,2.0,0.0,0.3,12.8, +2,2019-01-08 09:05:21,2019-01-08 09:12:42,1,0.93,1,N,237,236,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +2,2019-01-06 02:16:24,2019-01-06 02:34:50,1,4.9,1,N,164,256,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +2,2019-01-25 16:11:26,2019-01-25 16:24:30,1,1.52,1,N,163,246,1,10.0,1.0,0.5,2.95,0.0,0.3,14.75,0.0 +1,2019-01-04 05:55:54,2019-01-04 05:58:42,1,1.4,1,N,236,237,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-01 01:32:48,2019-01-01 01:46:50,1,4.1,1,N,79,261,1,15.5,0.5,0.5,7.0,0.0,0.3,23.8, +1,2019-01-26 11:15:38,2019-01-26 11:19:53,1,1.0,1,N,230,186,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-20 11:11:43,2019-01-20 11:32:46,1,9.37,1,N,138,90,1,28.0,0.0,0.5,5.0,5.76,0.3,39.56, +2,2019-01-04 05:29:27,2019-01-04 05:32:18,1,0.76,1,N,100,230,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-29 18:44:37,2019-01-29 19:28:35,1,7.8,1,N,140,234,1,31.5,1.0,0.5,3.0,0.0,0.3,36.3,0.0 +1,2019-01-27 21:37:29,2019-01-27 21:59:16,1,4.9,1,N,186,66,1,18.5,0.5,0.5,3.95,0.0,0.3,23.75,0.0 +2,2019-01-17 21:50:53,2019-01-17 22:01:35,1,2.26,1,N,162,79,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-17 17:35:29,2019-01-17 17:44:52,1,1.4,1,N,237,236,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-27 15:14:48,2019-01-27 15:28:38,1,2.4,1,N,75,230,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-09 07:49:24,2019-01-09 08:05:34,1,2.2,1,N,236,151,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-14 19:33:54,2019-01-14 19:41:11,1,1.06,1,N,100,50,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-16 09:14:04,2019-01-16 09:46:53,1,3.7,1,N,264,264,1,21.0,0.0,0.5,4.0,0.0,0.3,25.8, +2,2019-01-21 20:59:31,2019-01-21 21:05:19,1,0.83,1,N,236,262,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-13 18:47:37,2019-01-13 18:55:36,1,1.3,1,N,249,231,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-06 17:44:49,2019-01-06 17:59:42,1,3.17,1,N,186,141,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-15 14:39:53,2019-01-15 15:30:05,1,19.95,2,N,132,166,1,52.0,0.0,0.5,12.0,5.76,0.3,70.56, +2,2019-01-19 14:55:50,2019-01-19 15:35:30,1,15.39,5,N,144,241,1,47.22,0.0,0.5,0.0,0.0,0.3,48.02, +2,2019-01-23 09:02:35,2019-01-23 09:42:06,1,12.65,1,N,138,162,1,40.0,0.0,0.5,9.31,5.76,0.3,55.87,0.0 +1,2019-01-12 21:36:52,2019-01-12 21:56:59,1,8.1,1,Y,238,220,2,25.5,0.5,0.5,0.0,0.0,0.3,26.8, +1,2019-01-18 08:47:28,2019-01-18 09:04:56,1,1.8,1,N,234,161,1,12.0,0.0,0.5,1.2,0.0,0.3,14.0, +1,2019-01-09 18:04:20,2019-01-09 18:20:48,1,4.1,1,N,209,79,1,16.0,1.0,0.5,3.55,0.0,0.3,21.35, +2,2019-01-27 10:04:51,2019-01-27 10:10:12,1,1.37,1,N,233,140,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-04 17:43:47,2019-01-04 18:00:50,1,1.47,1,N,186,233,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3, +1,2019-01-12 15:51:26,2019-01-12 16:00:39,1,0.7,1,N,230,229,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-21 10:31:49,2019-01-21 10:34:21,1,0.57,1,N,249,234,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-15 17:12:55,2019-01-15 17:34:15,1,5.17,1,N,152,141,2,19.5,1.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-18 00:47:48,2019-01-18 01:02:38,1,4.7,1,N,237,224,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +1,2019-01-06 23:24:56,2019-01-06 23:28:01,1,0.6,1,N,106,181,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-14 14:51:55,2019-01-14 14:56:11,1,0.74,1,N,142,48,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-20 16:54:16,2019-01-20 16:58:58,1,1.82,1,N,239,166,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-14 10:21:31,2019-01-14 10:25:19,1,0.5,1,N,161,163,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-20 12:12:48,2019-01-20 12:19:56,1,1.23,1,N,142,161,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +1,2019-01-13 20:14:37,2019-01-13 20:20:06,1,1.1,1,N,263,141,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-18 22:38:19,2019-01-18 22:53:48,1,2.5,1,N,148,256,1,13.5,0.5,0.5,2.0,0.0,0.3,16.8, +2,2019-01-10 22:19:32,2019-01-10 22:35:35,1,5.87,1,N,132,10,1,19.5,0.5,0.5,4.16,0.0,0.3,24.96, +2,2019-01-05 18:56:53,2019-01-05 19:22:44,1,5.21,1,N,158,263,1,20.0,0.0,0.5,2.2,0.0,0.3,23.0, +2,2019-01-03 21:14:28,2019-01-03 21:21:33,1,0.85,1,N,162,48,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-23 12:31:27,2019-01-23 13:01:02,1,8.9,1,N,138,164,1,31.0,0.0,0.5,7.45,5.5,0.3,44.75,0.0 +2,2019-01-31 07:08:58,2019-01-31 07:14:21,1,1.6,1,N,163,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-27 10:48:32,2019-01-27 10:59:07,1,2.71,1,N,13,246,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-05 00:22:59,2019-01-05 00:29:00,1,1.3,1,N,137,229,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35, +2,2019-01-09 09:08:42,2019-01-09 09:24:00,1,3.64,1,N,237,166,1,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-26 20:06:07,2019-01-26 20:22:52,1,1.74,1,N,233,90,1,11.5,0.5,0.5,3.2,0.0,0.3,16.0,0.0 +2,2019-01-07 14:22:02,2019-01-07 14:27:01,1,1.28,1,N,75,74,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-22 11:42:43,2019-01-22 11:54:47,1,1.3,1,N,100,230,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-07 20:05:58,2019-01-07 20:11:26,1,0.8,1,N,144,113,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-22 12:22:06,2019-01-22 12:34:58,1,0.8,1,N,140,229,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-14 19:52:47,2019-01-14 20:05:16,1,6.0,1,N,231,137,2,18.5,0.5,0.5,0.0,0.0,0.3,19.8, +2,2019-01-09 15:00:23,2019-01-09 15:19:21,1,3.68,1,N,13,164,1,15.5,0.0,0.5,2.7,0.0,0.3,19.0, +1,2019-01-26 12:46:18,2019-01-26 12:59:40,1,0.7,1,N,186,90,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-30 23:05:00,2019-01-30 23:23:51,1,4.24,1,N,237,262,1,16.0,0.5,0.5,3.0,0.0,0.3,20.3,0.0 +2,2019-01-28 08:54:32,2019-01-28 09:07:07,1,1.42,1,N,233,230,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +2,2019-01-03 00:07:12,2019-01-03 00:27:08,1,10.17,1,N,48,243,2,29.0,0.5,0.5,0.0,0.0,0.3,30.3, +2,2019-01-22 23:39:34,2019-01-23 00:10:08,1,16.4,1,N,138,231,1,46.0,0.5,0.5,9.46,0.0,0.3,56.76,0.0 +1,2019-01-18 16:25:06,2019-01-18 16:54:46,1,8.0,1,N,263,138,1,27.0,1.0,0.5,6.9,5.76,0.3,41.46, +2,2019-01-20 18:26:21,2019-01-20 18:39:14,1,0.56,1,N,48,186,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-31 15:40:58,2019-01-31 15:49:01,1,1.47,1,N,50,164,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79,0.0 +1,2019-01-21 13:11:21,2019-01-21 13:40:34,1,17.7,2,N,132,170,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56,0.0 +2,2019-01-28 10:07:05,2019-01-28 10:21:00,1,2.5,1,N,35,71,1,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +4,2019-01-23 09:12:07,2019-01-23 09:18:18,1,0.71,1,N,90,100,1,6.0,0.0,0.5,2.0,0.0,0.3,8.8, +2,2019-01-10 20:07:47,2019-01-10 20:17:32,1,1.6,1,N,107,229,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +1,2019-01-07 10:41:01,2019-01-07 10:45:50,1,0.7,1,N,140,141,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-13 10:30:51,2019-01-13 10:40:18,1,1.1,1,N,186,170,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-26 15:22:34,2019-01-26 15:54:00,1,19.3,2,N,132,116,4,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +2,2019-01-05 15:18:26,2019-01-05 15:25:44,1,1.38,1,N,43,142,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-12 20:49:23,2019-01-12 20:53:45,1,0.48,1,N,79,232,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-25 14:04:35,2019-01-25 14:31:09,1,3.4,1,N,186,141,1,18.5,0.0,0.5,5.75,0.0,0.3,25.05,0.0 +2,2019-01-12 09:06:44,2019-01-12 09:21:48,1,1.95,1,N,236,43,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-25 06:52:07,2019-01-25 06:56:03,1,0.6,1,N,237,140,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35,0.0 +2,2019-01-05 14:26:01,2019-01-05 14:38:59,1,2.48,1,N,143,24,1,11.5,0.0,0.5,1.5,0.0,0.3,13.8, +1,2019-01-07 12:01:55,2019-01-07 12:14:28,1,0.9,1,N,142,161,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-02 19:38:09,2019-01-02 19:43:34,1,0.8,1,N,249,249,1,5.5,1.0,0.5,0.8,0.0,0.3,8.1, +1,2019-01-23 16:30:52,2019-01-23 17:09:34,1,4.3,1,N,48,261,1,25.0,1.0,0.5,2.0,0.0,0.3,28.8,0.0 +2,2019-01-17 07:37:23,2019-01-17 07:49:21,1,2.13,1,N,229,107,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42, +2,2019-01-24 12:16:41,2019-01-24 12:29:18,1,1.16,1,N,236,237,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-08 13:08:05,2019-01-08 13:17:20,1,1.14,1,N,142,50,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-15 16:55:55,2019-01-15 17:05:50,1,1.19,1,N,48,90,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-11 12:18:03,2019-01-11 12:24:04,1,1.02,1,N,107,137,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-13 05:29:22,2019-01-13 05:33:24,1,1.51,1,N,164,163,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-29 08:49:45,2019-01-29 09:05:36,1,1.77,1,N,262,141,1,11.0,0.0,0.5,2.12,0.0,0.3,13.92,0.0 +2,2019-01-22 14:04:44,2019-01-22 14:28:18,1,2.55,1,N,237,107,1,15.5,0.0,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-25 10:24:13,2019-01-25 10:33:02,1,1.09,1,N,161,43,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-31 15:21:04,2019-01-31 15:29:21,1,1.28,1,N,237,140,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-08 17:57:59,2019-01-08 18:10:59,1,1.6,1,N,142,161,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-11 13:09:45,2019-01-11 13:44:22,1,11.3,1,N,161,138,1,37.0,0.0,0.5,8.7,5.76,0.3,52.26, +2,2019-01-14 15:02:16,2019-01-14 15:09:53,1,0.68,1,N,231,13,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-16 19:21:42,2019-01-16 19:28:25,1,0.7,1,N,163,48,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-09 19:39:52,2019-01-09 20:08:29,1,3.99,1,N,162,114,1,19.0,1.0,0.5,1.0,0.0,0.3,21.8, +1,2019-01-02 10:04:48,2019-01-02 10:07:34,1,0.5,1,N,236,237,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-28 06:54:31,2019-01-28 06:55:24,1,0.3,1,N,236,236,1,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-23 12:51:16,2019-01-23 12:55:08,1,0.89,1,N,75,262,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-06 22:20:06,2019-01-06 22:34:21,1,5.68,1,N,138,202,1,17.5,0.5,0.5,5.64,0.0,0.3,24.44, +1,2019-01-27 11:13:29,2019-01-27 11:26:35,1,2.0,1,N,164,137,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-18 06:48:48,2019-01-18 06:58:12,1,2.0,1,N,186,163,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +1,2019-01-25 08:28:21,2019-01-25 08:31:37,1,0.7,1,N,186,246,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-22 09:07:59,2019-01-22 09:16:16,1,2.46,1,N,262,229,1,9.0,0.0,0.5,1.76,0.0,0.3,11.56,0.0 +1,2019-01-21 15:49:51,2019-01-21 16:05:34,1,2.0,1,N,186,224,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-08 05:08:24,2019-01-08 05:21:30,1,4.7,1,N,238,244,1,15.5,0.5,0.5,5.0,0.0,0.3,21.8, +2,2019-01-14 19:49:36,2019-01-14 20:06:08,1,3.17,1,N,144,162,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-12 19:55:34,2019-01-12 20:11:24,1,1.4,1,N,164,68,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-13 05:08:49,2019-01-13 05:12:44,1,1.16,1,N,48,90,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-13 18:23:08,2019-01-13 18:43:27,1,8.3,1,N,236,138,1,25.5,0.0,0.5,6.4,5.76,0.3,38.46, +2,2019-01-30 10:19:16,2019-01-30 10:38:40,1,1.09,1,N,48,48,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-09 01:15:37,2019-01-09 01:25:56,1,2.6,1,N,161,238,1,10.5,0.5,0.5,2.95,0.0,0.3,14.75, +2,2019-01-06 01:42:34,2019-01-06 01:48:43,1,1.32,1,N,48,164,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-17 18:01:23,2019-01-17 18:14:26,1,1.19,1,N,170,230,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-29 01:13:42,2019-01-29 01:33:26,1,9.1,1,N,138,237,1,27.0,0.5,0.5,6.8,5.76,0.3,40.86,0.0 +2,2019-01-13 09:17:47,2019-01-13 09:23:58,1,1.23,1,N,114,79,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-17 17:02:08,2019-01-17 17:13:18,1,1.4,1,N,170,246,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-28 10:39:29,2019-01-28 10:49:18,1,0.9,1,N,237,162,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-11 15:57:56,2019-01-11 15:58:00,1,0.0,1,N,100,100,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-16 23:19:30,2019-01-16 23:30:22,1,1.4,1,N,231,148,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-06 16:39:13,2019-01-06 16:50:32,1,2.23,1,N,141,238,1,10.0,0.0,0.5,1.2,0.0,0.3,12.0, +1,2019-01-11 16:11:27,2019-01-11 16:13:00,1,0.2,1,N,230,100,2,3.0,1.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-25 18:56:05,2019-01-25 19:07:56,1,2.4,1,N,264,75,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-03 17:59:41,2019-01-03 18:13:37,1,2.0,1,N,170,236,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-19 12:11:19,2019-01-19 12:18:43,1,1.55,1,N,230,143,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +1,2019-01-28 15:27:54,2019-01-28 15:33:43,1,0.8,1,N,236,237,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-03 03:43:06,2019-01-03 03:46:35,1,0.7,1,N,68,164,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-17 13:39:26,2019-01-17 13:59:20,1,2.52,1,N,237,164,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +1,2019-01-04 17:16:25,2019-01-04 17:28:24,1,1.4,1,N,237,170,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-24 17:27:37,2019-01-24 17:44:43,1,1.82,1,N,234,161,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-20 10:09:38,2019-01-20 10:40:16,1,12.8,1,N,138,62,2,38.5,0.0,0.5,0.0,0.0,0.3,39.3, +2,2019-01-31 17:55:37,2019-01-31 17:58:53,1,0.47,1,N,79,107,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-12 20:44:29,2019-01-12 21:01:28,1,1.1,1,N,186,137,1,11.0,0.5,0.5,1.0,0.0,0.3,13.3, +1,2019-01-20 12:31:20,2019-01-20 12:38:50,1,1.7,1,N,246,142,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-03 00:54:19,2019-01-03 01:12:51,1,7.92,1,N,138,42,1,24.0,0.5,0.5,0.0,5.76,0.3,31.06, +2,2019-01-17 10:29:01,2019-01-17 10:53:08,1,2.9,1,N,141,238,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-08 06:37:49,2019-01-08 06:53:17,1,2.68,1,N,158,161,1,12.0,0.0,0.5,1.5,0.0,0.3,14.3, +2,2019-01-16 16:49:26,2019-01-16 16:58:35,1,1.12,1,N,107,170,1,7.5,1.0,0.5,0.93,0.0,0.3,10.23, +1,2019-01-28 09:05:48,2019-01-28 09:19:23,1,1.2,1,N,143,230,1,9.5,0.0,0.5,2.55,0.0,0.3,12.85,0.0 +2,2019-01-12 00:39:58,2019-01-12 00:47:21,1,1.2,1,N,161,237,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38, +2,2019-01-25 21:57:51,2019-01-25 22:31:16,1,6.9,1,N,186,223,1,26.0,0.5,0.5,5.46,0.0,0.3,32.76,0.0 +2,2019-01-26 18:50:07,2019-01-26 19:11:38,1,5.07,1,N,263,145,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-25 23:43:41,2019-01-25 23:50:50,1,1.03,1,N,249,125,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-22 17:00:51,2019-01-22 17:22:44,1,10.16,1,N,138,138,2,30.0,1.0,0.5,0.0,0.0,0.3,31.8,0.0 +1,2019-01-25 21:47:41,2019-01-25 21:57:01,1,1.1,1,N,249,107,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-21 20:43:44,2019-01-21 20:49:01,1,1.17,1,N,144,13,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-27 23:10:05,2019-01-27 23:20:26,1,2.04,1,N,48,164,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-04 10:40:06,2019-01-04 10:54:28,1,2.22,1,N,137,158,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-12 09:45:52,2019-01-12 09:57:14,1,1.59,1,N,170,246,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-11 16:27:49,2019-01-11 16:52:08,1,3.91,1,N,143,186,2,17.5,1.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-31 13:30:58,2019-01-31 13:35:35,1,0.64,1,N,140,141,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-14 17:33:11,2019-01-14 18:06:40,1,4.2,1,N,100,4,1,22.0,1.0,0.5,7.1,0.0,0.3,30.9, +2,2019-01-13 09:53:25,2019-01-13 10:01:59,1,1.87,1,N,151,116,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-26 19:22:16,2019-01-26 19:44:46,1,6.92,1,N,48,244,1,24.0,0.0,0.5,4.96,0.0,0.3,29.76,0.0 +2,2019-01-08 22:58:52,2019-01-08 23:03:35,1,0.86,1,N,79,107,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-18 18:36:24,2019-01-18 18:37:06,1,0.17,1,N,132,132,3,-3.0,-1.0,-0.5,0.0,0.0,-0.3,-4.8, +2,2019-01-11 16:32:22,2019-01-11 16:57:40,1,4.36,1,N,142,74,2,19.0,1.0,0.5,0.0,0.0,0.3,20.8, +1,2019-01-21 11:45:05,2019-01-21 11:49:38,1,0.6,1,N,236,237,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95,0.0 +1,2019-01-07 19:29:21,2019-01-07 19:44:38,1,2.8,1,N,161,238,1,12.5,1.0,0.5,2.85,0.0,0.3,17.15, +1,2019-01-11 06:51:43,2019-01-11 07:07:21,1,7.0,1,N,48,244,1,22.5,0.0,0.5,2.0,0.0,0.3,25.3, +2,2019-01-26 13:43:43,2019-01-26 13:54:27,1,2.58,1,N,238,74,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-03 12:10:34,2019-01-03 12:34:09,1,10.04,1,N,138,249,1,29.5,0.0,0.5,7.75,5.76,0.3,43.81, +2,2019-01-13 01:48:01,2019-01-13 02:12:58,1,5.41,1,N,158,237,1,21.5,0.5,0.5,4.56,0.0,0.3,27.36, +2,2019-01-26 11:24:38,2019-01-26 11:32:22,1,1.91,1,N,170,141,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-06 07:28:01,2019-01-06 07:38:26,1,4.41,1,N,263,69,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-21 22:16:19,2019-01-21 22:20:19,1,0.94,1,N,137,224,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-31 08:56:28,2019-01-31 09:01:40,1,0.62,1,N,229,162,1,5.0,0.0,0.5,0.58,0.0,0.3,6.38,0.0 +1,2019-01-17 17:31:14,2019-01-17 17:40:19,1,1.2,1,N,264,264,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-09 18:40:53,2019-01-09 18:52:15,1,2.7,1,N,48,24,1,10.5,1.0,0.5,1.85,0.0,0.3,14.15, +2,2019-01-11 17:35:33,2019-01-11 17:50:28,1,2.39,1,N,138,223,1,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-03 17:54:50,2019-01-03 18:14:47,1,5.17,1,N,107,255,1,18.5,1.0,0.5,3.0,0.0,0.3,23.3, +1,2019-01-28 20:14:49,2019-01-28 20:20:08,1,0.8,1,N,234,170,1,5.5,0.5,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-13 16:34:40,2019-01-13 16:37:20,1,0.64,1,N,234,186,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-04 17:13:31,2019-01-04 17:24:06,1,3.6,1,N,239,152,1,12.0,1.0,0.5,3.45,0.0,0.3,17.25, +1,2019-01-11 12:29:37,2019-01-11 12:41:57,1,1.6,1,N,237,142,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-30 09:00:45,2019-01-30 09:14:21,1,1.14,1,N,170,234,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84,0.0 +4,2019-01-05 18:54:24,2019-01-05 19:02:08,1,1.43,1,N,163,239,1,7.5,0.0,0.5,1.2,0.0,0.3,9.5, +4,2019-01-08 17:01:16,2019-01-08 17:29:42,1,4.28,1,N,263,68,1,20.5,1.0,0.5,4.46,0.0,0.3,26.76, +2,2019-01-02 12:35:02,2019-01-02 12:41:19,1,0.97,1,N,90,137,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-03 00:52:17,2019-01-03 01:02:48,1,2.42,1,N,162,90,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-05 15:09:49,2019-01-05 15:24:06,1,1.4,1,N,249,107,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-25 12:29:28,2019-01-25 12:56:14,1,9.69,5,N,170,134,1,29.49,0.0,0.5,0.0,5.76,0.3,36.05,0.0 +2,2019-01-04 07:31:14,2019-01-04 07:33:04,1,0.26,1,N,170,170,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-29 11:31:48,2019-01-29 11:46:49,1,1.45,1,N,161,100,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +1,2019-01-26 20:29:41,2019-01-26 20:35:42,1,0.6,1,N,90,90,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-24 02:07:24,2019-01-24 02:21:09,1,4.5,1,N,170,87,2,16.5,0.5,0.5,0.0,0.0,0.3,17.8,0.0 +1,2019-01-25 19:08:16,2019-01-25 19:12:50,1,0.7,1,N,113,114,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-31 13:39:46,2019-01-31 13:43:18,1,0.3,1,N,140,141,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +1,2019-01-23 06:07:26,2019-01-23 06:11:12,1,0.9,1,N,48,142,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-10 23:20:57,2019-01-10 23:29:03,1,1.23,1,N,163,141,1,7.5,0.5,0.5,2.64,0.0,0.3,11.44, +1,2019-01-17 18:48:10,2019-01-17 18:52:22,1,0.5,1,N,142,230,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-30 08:45:07,2019-01-30 08:57:54,1,1.02,1,N,186,170,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-02 05:26:44,2019-01-02 05:31:13,1,0.6,1,N,88,261,1,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-05 12:10:55,2019-01-05 12:16:58,1,1.03,1,N,68,246,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +1,2019-01-04 18:33:50,2019-01-04 18:41:27,1,1.8,1,N,237,236,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-29 18:52:56,2019-01-29 19:04:00,1,1.23,1,N,211,79,1,8.5,1.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-22 11:16:58,2019-01-22 11:22:52,1,0.41,1,N,237,162,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-07 05:24:12,2019-01-07 05:29:16,1,1.3,1,N,170,161,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-24 09:53:20,2019-01-24 10:39:49,1,16.7,1,N,132,80,1,47.5,0.0,0.5,9.66,0.0,0.3,57.96,0.0 +2,2019-01-23 07:27:31,2019-01-23 07:35:47,1,2.02,1,N,140,233,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-20 22:07:13,2019-01-20 22:11:54,1,1.39,1,N,226,7,1,6.0,0.5,0.5,1.5,0.0,0.3,8.8, +2,2019-01-21 11:14:06,2019-01-21 11:14:43,1,0.11,1,N,146,146,1,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +1,2019-01-10 14:06:48,2019-01-10 14:30:24,1,3.6,1,N,238,229,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +1,2019-01-11 17:46:29,2019-01-11 17:51:11,1,0.5,1,N,142,48,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-12 22:03:13,2019-01-12 22:13:22,1,1.4,1,N,264,264,1,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-20 13:41:57,2019-01-20 14:07:34,1,6.6,1,N,163,65,1,24.0,0.0,0.5,6.2,0.0,0.3,31.0, +2,2019-01-18 00:09:27,2019-01-18 00:34:17,1,7.53,1,N,231,166,1,25.0,0.5,0.5,5.26,0.0,0.3,31.56, +2,2019-01-28 17:45:34,2019-01-28 17:53:57,1,1.81,1,N,140,263,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-28 12:25:27,2019-01-28 12:47:45,1,3.15,1,N,249,163,1,15.5,0.0,0.5,4.08,0.0,0.3,20.38,0.0 +1,2019-01-31 17:35:41,2019-01-31 17:53:32,1,1.9,1,N,229,142,1,12.0,1.0,0.5,2.48,0.0,0.3,16.28,0.0 +2,2019-01-01 00:44:56,2019-01-01 00:51:02,1,1.19,1,N,262,237,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-07 13:20:29,2019-01-07 13:30:41,1,2.0,1,N,107,211,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25, +2,2019-01-15 10:27:13,2019-01-15 10:46:52,1,6.36,1,N,141,209,1,21.5,0.0,0.5,3.5,0.0,0.3,25.8, +1,2019-01-21 20:07:34,2019-01-21 20:35:39,1,10.5,1,N,132,71,1,32.5,0.5,0.5,5.0,0.0,0.3,38.8,0.0 +1,2019-01-30 07:40:14,2019-01-30 07:55:12,1,1.6,1,N,229,236,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8,0.0 +2,2019-01-17 23:03:14,2019-01-17 23:11:58,1,1.65,1,N,234,170,1,8.0,0.5,0.5,2.0,0.0,0.3,11.3, +2,2019-01-21 16:00:33,2019-01-21 16:24:02,1,8.5,1,N,138,152,1,26.5,0.0,0.5,6.61,5.76,0.3,39.67,0.0 +2,2019-01-26 18:07:57,2019-01-26 18:39:36,1,8.31,1,N,148,238,1,29.0,0.0,0.5,5.96,0.0,0.3,35.76,0.0 +2,2019-01-31 07:15:33,2019-01-31 07:25:17,1,1.37,1,N,68,161,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-07 04:19:09,2019-01-07 04:24:44,1,1.65,1,N,142,186,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-23 10:52:48,2019-01-23 11:07:27,1,1.03,1,N,48,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-24 07:41:39,2019-01-24 07:51:56,1,1.42,1,N,143,163,1,8.5,0.0,0.5,0.93,0.0,0.3,12.18,0.0 +1,2019-01-26 00:56:44,2019-01-26 01:09:38,1,2.8,1,N,68,261,3,11.5,0.5,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-17 15:21:15,2019-01-17 15:34:15,1,0.7,1,N,234,186,1,9.0,0.0,0.5,3.0,0.0,0.3,12.8, +1,2019-01-15 08:14:54,2019-01-15 08:39:29,1,5.1,1,N,234,87,1,20.5,0.0,0.5,4.25,0.0,0.3,25.55, +2,2019-01-09 20:02:47,2019-01-09 20:13:35,1,3.28,1,N,79,141,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36, +1,2019-01-22 22:52:36,2019-01-22 22:59:52,1,0.7,1,N,230,163,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-27 11:36:06,2019-01-27 11:46:41,1,2.06,1,N,163,236,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42,0.0 +1,2019-01-12 10:43:55,2019-01-12 10:54:45,1,1.9,1,N,170,224,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-19 10:42:04,2019-01-19 10:45:25,1,0.55,1,N,162,170,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0, +1,2019-01-03 14:38:35,2019-01-03 15:38:30,1,28.2,2,N,13,132,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +1,2019-01-08 07:26:34,2019-01-08 08:06:34,1,12.0,1,N,161,138,1,38.0,0.0,0.5,11.1,5.76,0.3,55.66, +1,2019-01-17 07:53:51,2019-01-17 08:04:01,1,1.1,1,N,246,230,1,8.0,0.0,0.5,2.6,0.0,0.3,11.4, +1,2019-01-15 15:10:44,2019-01-15 15:16:31,1,0.9,1,N,142,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-09 12:20:42,2019-01-09 12:41:02,1,1.2,1,N,162,100,1,12.5,0.0,0.5,3.3,0.0,0.3,16.6, +1,2019-01-06 22:00:49,2019-01-06 22:06:31,1,1.1,1,N,142,48,1,6.0,0.5,0.5,0.7,0.0,0.3,8.0, +2,2019-01-26 17:25:05,2019-01-26 17:28:36,1,0.57,1,N,142,239,1,4.5,0.0,0.5,0.8,0.0,0.3,6.1,0.0 +2,2019-01-14 21:56:35,2019-01-14 22:06:31,1,2.61,1,N,249,50,1,10.5,0.5,0.5,0.8,0.0,0.3,12.6, +2,2019-01-30 21:33:11,2019-01-30 21:37:45,1,0.67,1,N,162,229,1,5.0,0.5,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-25 12:23:09,2019-01-25 12:31:25,1,1.04,1,N,141,75,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-15 21:41:17,2019-01-15 21:52:28,1,2.6,1,N,143,68,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-04 09:45:04,2019-01-04 09:55:33,1,2.06,1,N,236,142,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +2,2019-01-14 14:48:02,2019-01-14 14:55:38,1,1.01,1,N,230,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-18 07:39:09,2019-01-18 07:46:39,1,1.0,1,N,230,161,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-27 11:52:43,2019-01-27 11:56:32,1,0.8,1,N,234,114,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-20 23:15:00,2019-01-20 23:20:46,1,1.42,1,N,255,112,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-31 12:33:29,2019-01-31 12:55:21,1,1.63,1,N,163,170,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-14 10:34:10,2019-01-14 11:00:05,1,2.66,1,N,107,231,1,17.0,0.0,0.5,3.56,0.0,0.3,23.31, +1,2019-01-12 22:52:18,2019-01-12 22:59:25,1,1.2,1,N,236,263,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-06 18:42:03,2019-01-06 18:48:26,1,0.94,1,N,239,142,1,6.0,0.0,0.5,1.5,0.0,0.3,8.3, +2,2019-01-18 19:46:31,2019-01-18 20:19:06,1,9.67,1,N,138,189,1,32.0,1.0,0.5,8.45,0.0,0.3,42.25, +2,2019-01-12 19:44:03,2019-01-12 19:54:03,1,1.71,1,N,107,229,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-25 22:10:12,2019-01-25 22:38:30,1,8.6,1,N,138,230,1,28.5,0.5,0.5,7.1,5.76,0.3,42.66,0.0 +2,2019-01-19 13:40:54,2019-01-19 13:42:42,1,0.39,1,N,238,238,1,3.5,0.0,0.5,0.43,0.0,0.3,4.73, +2,2019-01-10 17:28:28,2019-01-10 17:41:56,1,1.57,1,N,113,68,1,9.5,1.0,0.5,1.5,0.0,0.3,12.8, +2,2019-01-15 20:14:09,2019-01-15 20:26:51,1,4.25,1,N,138,223,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +1,2019-01-30 19:46:34,2019-01-30 19:52:05,1,1.3,1,N,75,41,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-17 16:46:20,2019-01-17 17:01:48,1,1.6,1,N,233,186,1,11.0,1.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-15 16:10:48,2019-01-15 16:41:35,1,9.04,1,N,264,264,1,30.0,1.0,0.5,7.51,5.76,0.3,45.07, +1,2019-01-02 17:09:42,2019-01-02 17:13:51,1,0.5,1,N,234,90,1,4.5,1.0,0.5,0.95,0.0,0.3,7.25, +2,2019-01-05 13:05:32,2019-01-05 13:38:05,1,17.61,2,N,132,164,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-23 17:16:30,2019-01-23 17:34:45,1,2.52,1,N,238,237,1,13.5,1.0,0.5,1.0,0.0,0.3,16.3,0.0 +2,2019-01-18 15:01:48,2019-01-18 15:21:24,1,3.16,1,N,141,238,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-30 21:05:26,2019-01-30 21:19:14,1,2.4,1,N,162,236,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35,0.0 +2,2019-01-03 21:41:15,2019-01-03 22:02:35,1,4.25,1,N,230,75,1,17.0,0.5,0.5,2.0,0.0,0.3,20.3, +1,2019-01-08 16:15:21,2019-01-08 16:21:19,1,1.1,1,N,249,231,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-07 19:46:53,2019-01-07 20:03:50,1,2.53,1,N,237,137,1,12.5,1.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-20 10:52:41,2019-01-20 10:59:00,1,1.17,1,N,237,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-14 15:18:15,2019-01-14 15:27:58,1,1.32,1,N,246,161,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-05 20:56:48,2019-01-05 21:08:35,1,1.3,1,N,79,137,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-14 20:55:23,2019-01-14 21:05:47,1,1.62,1,N,162,141,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +1,2019-01-23 20:24:51,2019-01-23 20:55:53,1,11.3,1,N,138,50,1,35.0,0.5,0.5,12.6,5.76,0.3,54.66,0.0 +2,2019-01-24 14:59:06,2019-01-24 15:03:30,1,0.44,1,N,239,238,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-11 12:38:28,2019-01-11 12:54:05,1,1.6,1,N,142,141,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-18 18:20:08,2019-01-18 18:31:05,1,1.52,1,N,125,79,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-29 18:41:13,2019-01-29 18:48:27,1,0.67,1,N,142,142,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-31 17:36:57,2019-01-31 17:41:21,1,0.8,1,Y,264,264,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-22 14:13:05,2019-01-22 14:24:14,1,1.62,1,N,140,237,1,9.0,0.0,0.5,1.96,0.0,0.3,13.71,0.0 +1,2019-01-10 21:43:28,2019-01-10 21:51:17,1,1.3,1,N,107,249,1,7.0,0.5,0.5,1.2,0.0,0.3,9.5, +2,2019-01-04 20:24:46,2019-01-04 20:31:27,1,0.88,1,N,163,233,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12, +2,2019-01-27 22:26:29,2019-01-27 22:31:58,1,1.72,1,N,231,231,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-08 16:12:33,2019-01-08 16:15:09,1,0.4,1,N,90,249,1,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-22 19:14:00,2019-01-22 19:14:00,1,0.0,1,N,234,264,2,2.5,1.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-12 18:42:12,2019-01-12 18:47:56,1,0.7,1,N,236,237,1,6.0,0.0,0.5,5.0,0.0,0.3,11.8, +2,2019-01-22 19:17:48,2019-01-22 19:27:00,1,1.29,1,N,236,140,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-20 12:04:42,2019-01-20 12:16:52,1,2.04,1,N,186,234,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-10 07:15:01,2019-01-10 07:23:14,1,1.5,1,N,48,163,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-04 23:32:57,2019-01-04 23:42:53,1,1.2,1,N,50,161,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-01 02:18:36,2019-01-01 02:39:10,1,7.86,1,N,163,243,1,25.0,0.5,0.5,6.58,0.0,0.3,32.88, +2,2019-01-30 11:35:19,2019-01-30 12:04:00,1,11.13,1,N,138,162,1,33.0,0.0,0.5,5.5,5.76,0.3,45.06,0.0 +2,2019-01-04 16:48:40,2019-01-04 17:18:48,1,14.0,1,N,132,7,1,39.0,1.0,0.5,10.2,0.0,0.3,51.0, +1,2019-01-23 18:10:16,2019-01-23 18:16:14,1,0.8,1,N,107,170,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +4,2019-01-28 20:15:24,2019-01-28 20:25:46,1,2.06,1,N,141,170,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-31 12:27:03,2019-01-31 12:37:49,1,1.48,1,N,87,211,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-17 18:00:43,2019-01-18 17:38:04,1,0.53,1,N,230,163,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-04 21:01:31,2019-01-04 21:16:44,1,2.3,1,N,161,68,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +1,2019-01-22 17:42:34,2019-01-22 17:56:49,1,1.7,1,N,264,264,1,10.5,1.0,0.5,1.0,0.0,0.3,13.3,0.0 +1,2019-01-09 19:50:16,2019-01-09 20:01:06,1,2.1,1,N,229,79,1,9.5,1.0,0.5,2.8,0.0,0.3,14.1, +2,2019-01-03 20:34:09,2019-01-03 21:16:38,1,17.8,2,N,132,162,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +2,2019-01-22 22:17:05,2019-01-22 22:21:33,1,0.94,1,N,229,170,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-10 19:27:03,2019-01-10 19:36:45,1,1.35,1,N,162,48,1,8.0,1.0,0.5,0.98,0.0,0.3,10.78, +1,2019-01-02 10:08:47,2019-01-02 10:20:57,1,1.2,1,N,161,48,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-05 21:43:29,2019-01-05 21:53:36,1,1.8,1,N,211,246,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +1,2019-01-25 20:43:10,2019-01-25 20:51:03,1,1.2,1,N,234,170,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-27 01:56:35,2019-01-27 02:16:39,1,4.18,1,N,249,141,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36,0.0 +1,2019-01-29 09:31:08,2019-01-29 09:36:43,1,0.7,1,N,237,264,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-10 21:44:42,2019-01-10 22:05:53,1,3.95,1,N,114,97,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3, +1,2019-01-15 07:29:32,2019-01-15 07:41:12,1,1.8,1,N,116,244,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-15 14:10:08,2019-01-15 14:18:46,1,1.01,1,N,262,236,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-31 07:37:29,2019-01-31 07:51:53,1,2.52,1,N,125,107,1,11.5,0.0,0.5,1.0,0.0,0.3,13.3,0.0 +2,2019-01-05 21:26:51,2019-01-05 21:40:33,1,2.32,1,N,48,249,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-08 18:03:01,2019-01-08 18:06:56,1,0.09,1,N,263,236,1,4.0,1.0,0.5,0.55,0.0,0.3,6.35, +2,2019-01-13 10:24:17,2019-01-13 10:38:51,1,3.49,1,N,140,41,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-01 00:30:24,2019-01-01 00:34:22,1,0.93,1,N,161,162,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-15 20:22:35,2019-01-15 20:31:17,1,0.9,1,N,229,230,1,7.0,0.5,0.5,2.45,0.0,0.3,10.75, +2,2019-01-07 09:29:10,2019-01-07 09:40:35,1,2.25,1,N,76,76,1,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-25 11:17:29,2019-01-25 11:43:40,1,7.52,1,N,141,33,2,26.0,0.0,0.5,0.0,0.0,0.3,26.8,0.0 +2,2019-01-16 09:39:17,2019-01-16 09:44:32,1,0.62,1,N,249,125,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-29 21:13:45,2019-01-29 21:25:27,1,2.53,1,N,162,114,1,10.5,0.5,0.5,1.5,0.0,0.3,13.3,0.0 +1,2019-01-01 11:42:18,2019-01-01 11:46:14,1,1.8,1,N,263,74,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-02 07:59:54,2019-01-02 08:13:42,1,5.92,1,N,233,261,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +1,2019-01-26 20:07:29,2019-01-26 20:13:40,1,1.1,1,N,142,239,1,6.5,0.5,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-07 06:26:00,2019-01-07 06:29:02,1,1.25,1,N,107,162,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-09 12:07:36,2019-01-09 12:25:33,1,1.4,1,N,142,161,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-04 12:53:51,2019-01-04 13:12:55,1,1.6,1,N,142,68,1,12.5,0.0,0.5,1.0,0.0,0.3,14.3, +2,2019-01-10 11:09:44,2019-01-10 11:23:10,1,1.59,1,N,161,239,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-19 00:00:51,2019-01-19 00:17:45,1,3.5,1,N,48,79,1,14.0,0.5,0.5,2.0,0.0,0.3,17.3, +2,2019-01-23 08:38:41,2019-01-23 08:52:04,1,1.42,1,N,13,88,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-07 17:16:52,2019-01-07 17:23:31,1,0.93,1,N,231,13,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-29 14:49:10,2019-01-29 15:08:28,1,2.4,1,N,229,234,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15,0.0 +1,2019-01-29 18:24:08,2019-01-29 18:30:45,1,1.0,1,N,162,137,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-10 07:30:30,2019-01-10 07:34:21,1,0.71,1,N,236,236,1,4.5,0.0,0.5,1.2,0.0,0.3,6.5, +2,2019-01-25 19:28:06,2019-01-25 19:37:18,1,1.12,1,N,164,246,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-31 09:02:44,2019-01-31 09:06:23,1,0.53,1,N,262,262,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-11 07:30:33,2019-01-11 07:40:04,1,2.0,1,N,114,164,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-24 20:03:36,2019-01-24 20:06:59,1,0.6,1,N,263,262,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +4,2019-01-28 06:54:30,2019-01-28 06:55:41,1,0.45,1,N,141,141,1,3.5,0.0,0.5,1.07,0.0,0.3,5.37,0.0 +2,2019-01-05 17:06:03,2019-01-05 17:22:36,1,2.4,1,N,24,74,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-13 18:34:08,2019-01-13 18:39:06,1,1.1,1,N,161,229,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-28 17:28:01,2019-01-28 17:33:08,1,0.6,1,N,162,162,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-17 17:45:15,2019-01-17 18:05:56,1,2.92,1,N,234,43,1,14.5,1.0,0.5,2.0,0.0,0.3,18.3, +1,2019-01-13 18:57:58,2019-01-13 19:05:44,1,1.7,1,N,137,4,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +2,2019-01-22 13:07:15,2019-01-22 13:30:31,1,2.97,1,N,237,113,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-30 21:27:05,2019-01-30 21:34:52,1,1.27,1,N,90,224,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-25 18:21:20,2019-01-25 18:29:02,1,1.31,1,N,143,48,1,7.0,1.0,0.5,2.0,0.0,0.3,10.8,0.0 +1,2019-01-23 15:47:24,2019-01-23 16:01:02,1,1.7,1,N,262,237,1,10.5,1.0,0.5,1.0,0.0,0.3,13.3,0.0 +1,2019-01-06 21:18:25,2019-01-06 21:25:43,1,1.7,1,N,162,137,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-29 18:57:29,2019-01-29 19:16:26,1,7.13,5,N,138,28,2,21.0,0.0,0.5,0.0,0.0,0.3,21.8,0.0 +2,2019-01-30 12:02:37,2019-01-30 12:46:38,1,17.87,2,N,132,68,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2,0.0 +2,2019-01-12 04:17:11,2019-01-12 04:26:23,1,1.63,1,N,232,209,1,8.5,0.5,0.5,1.0,0.0,0.3,10.8, +1,2019-01-10 21:42:38,2019-01-10 22:22:43,1,28.4,1,N,132,265,2,74.5,0.5,0.5,0.0,15.76,0.3,91.56, +1,2019-01-18 06:44:26,2019-01-18 06:50:32,1,1.1,1,N,68,161,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-19 08:19:52,2019-01-19 08:31:10,1,2.7,1,N,68,125,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-21 19:56:36,2019-01-21 20:05:50,1,1.24,1,N,249,114,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-09 02:32:51,2019-01-09 02:51:48,1,5.5,1,N,7,230,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3, +2,2019-01-10 18:05:10,2019-01-10 18:15:26,1,1.33,1,N,141,162,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-20 11:27:59,2019-01-20 11:30:11,1,0.5,1,N,263,262,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-16 17:43:26,2019-01-16 17:56:34,1,2.33,1,N,113,246,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-20 16:47:20,2019-01-20 16:56:25,1,1.01,1,N,170,68,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-25 13:30:30,2019-01-25 13:35:13,1,0.7,1,N,140,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-16 15:29:31,2019-01-16 15:38:41,1,1.72,1,N,142,151,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-31 11:57:16,2019-01-31 12:11:59,1,0.78,1,N,162,237,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8,0.0 +1,2019-01-29 12:03:43,2019-01-29 12:24:37,1,3.4,1,N,50,211,1,15.0,0.0,0.5,2.0,0.0,0.3,17.8,0.0 +1,2019-01-08 15:43:21,2019-01-08 15:56:51,1,1.2,1,N,231,148,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +1,2019-01-11 17:14:23,2019-01-11 17:24:28,1,1.9,1,N,50,239,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-14 18:52:17,2019-01-14 18:57:24,1,0.6,1,N,162,161,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-25 15:42:56,2019-01-25 15:53:58,1,1.6,1,N,238,142,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-30 19:15:01,2019-01-30 19:27:55,1,1.76,1,N,236,229,1,10.0,1.0,0.5,3.0,0.0,0.3,14.8,0.0 +2,2019-01-19 03:19:05,2019-01-19 03:32:11,1,3.58,1,N,164,263,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-07 11:53:02,2019-01-07 11:59:58,1,0.78,1,N,163,142,1,6.0,0.0,0.5,0.9,0.0,0.3,7.7, +1,2019-01-27 16:13:00,2019-01-27 16:19:12,1,1.1,1,N,236,75,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-01 16:05:20,2019-01-01 16:15:57,1,1.7,1,N,249,148,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-26 10:17:48,2019-01-26 10:33:33,1,1.73,1,N,161,246,1,11.0,0.0,0.5,3.54,0.0,0.3,15.34,0.0 +1,2019-01-14 13:19:14,2019-01-14 13:40:12,1,1.8,1,N,263,237,1,13.5,0.0,0.5,2.85,0.0,0.3,17.15, +2,2019-01-15 16:12:16,2019-01-15 16:22:38,1,1.76,1,N,141,142,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-04 22:16:32,2019-01-04 22:45:50,1,5.26,1,N,234,263,1,22.5,0.5,0.5,4.76,0.0,0.3,28.56, +2,2019-01-29 21:25:34,2019-01-29 21:46:04,1,4.8,1,N,100,179,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-03 21:53:28,2019-01-03 21:54:41,1,0.4,1,N,42,116,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-15 18:01:51,2019-01-15 18:15:56,1,2.3,1,N,107,163,1,11.5,1.0,0.5,2.75,0.0,0.3,16.05, +2,2019-01-13 18:24:16,2019-01-13 18:29:14,1,0.66,1,N,237,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-11 00:50:03,2019-01-11 00:58:53,1,1.8,1,N,239,41,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +1,2019-01-30 22:17:04,2019-01-30 22:20:40,1,1.0,1,N,264,246,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55,0.0 +1,2019-01-29 19:34:10,2019-01-29 19:47:55,1,2.4,1,N,161,263,1,11.5,1.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-22 11:44:50,2019-01-22 11:54:52,1,2.47,1,N,75,143,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-23 15:20:53,2019-01-23 15:56:00,1,11.79,1,N,226,252,1,36.5,0.0,0.5,0.0,0.0,0.3,37.3,0.0 +2,2019-01-19 20:44:35,2019-01-19 21:05:13,1,3.16,1,N,142,107,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-26 21:25:09,2019-01-26 21:42:27,1,1.22,1,N,144,79,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-02 19:34:02,2019-01-02 19:37:16,1,0.7,1,N,144,79,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-30 17:57:57,2019-01-30 18:20:00,1,1.99,1,N,237,100,1,14.0,1.0,0.5,2.2,0.0,0.3,18.0,0.0 +1,2019-01-23 18:08:50,2019-01-23 18:12:39,1,0.5,1,N,144,79,1,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-05 12:01:35,2019-01-05 12:10:44,1,1.4,1,N,237,239,1,8.0,0.0,0.5,2.0,0.0,0.3,10.8, +1,2019-01-25 10:21:54,2019-01-25 10:34:08,1,1.3,1,N,229,162,1,9.0,0.0,0.5,2.0,0.0,0.3,11.8,0.0 +2,2019-01-31 17:21:19,2019-01-31 17:53:16,1,3.87,1,N,162,231,1,21.0,1.0,0.5,2.5,0.0,0.3,25.3,0.0 +2,2019-01-12 03:05:57,2019-01-12 03:17:56,1,3.51,1,N,170,143,1,12.5,0.5,0.5,2.5,0.0,0.3,16.3, +2,2019-01-28 13:20:08,2019-01-28 13:34:57,1,2.55,1,N,88,234,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-21 22:31:23,2019-01-21 22:59:36,1,15.82,1,N,132,255,1,43.5,0.5,0.5,8.96,0.0,0.3,53.76,0.0 +2,2019-01-30 07:30:50,2019-01-30 07:37:37,1,1.3,1,N,151,166,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-02 19:20:41,2019-01-02 19:34:54,1,2.3,1,N,170,144,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-12 23:32:31,2019-01-12 23:52:54,1,3.13,1,N,79,48,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-14 19:35:01,2019-01-14 19:50:26,1,2.83,1,N,164,236,1,12.5,1.0,0.5,3.0,0.0,0.3,17.3, +2,2019-01-15 07:30:42,2019-01-15 07:37:48,1,0.8,1,N,137,164,1,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-24 01:19:32,2019-01-24 01:33:03,1,4.28,1,N,68,151,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96,0.0 +1,2019-01-15 16:26:18,2019-01-15 16:43:01,1,0.9,1,N,186,170,1,11.0,1.0,0.5,3.8,0.0,0.3,16.6, +2,2019-01-10 20:01:53,2019-01-10 20:04:54,1,0.54,1,N,164,68,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3, +1,2019-01-09 19:53:12,2019-01-09 20:11:06,1,3.1,1,N,65,62,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-08 06:07:28,2019-01-08 06:14:06,1,1.2,1,N,186,137,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +2,2019-01-08 14:06:21,2019-01-08 14:06:26,1,0.0,2,N,234,234,2,-52.0,0.0,-0.5,0.0,0.0,-0.3,-52.8, +2,2019-01-26 22:57:38,2019-01-26 23:13:22,1,2.01,1,N,143,141,1,11.5,0.5,0.5,1.92,0.0,0.3,14.72,0.0 +1,2019-01-24 16:24:53,2019-01-24 16:37:05,1,1.3,1,N,162,186,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-21 08:26:44,2019-01-21 08:57:19,1,17.78,2,N,238,216,1,52.0,0.0,0.5,7.92,0.0,0.3,60.72,0.0 +2,2019-01-27 15:47:14,2019-01-27 16:14:38,1,3.31,1,N,233,75,1,18.5,0.0,0.5,4.82,0.0,0.3,24.12,0.0 +2,2019-01-14 16:51:49,2019-01-14 16:55:57,1,0.68,1,N,162,170,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-05 19:36:07,2019-01-05 19:55:54,1,2.7,1,N,151,230,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-20 14:30:56,2019-01-20 14:38:17,1,2.68,1,N,79,229,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-10 12:42:40,2019-01-10 12:48:02,1,0.9,1,N,236,236,1,5.5,0.0,0.5,1.55,0.0,0.3,7.85, +4,2019-01-15 12:22:38,2019-01-15 12:29:18,1,0.78,1,N,114,79,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-01 22:30:53,2019-01-01 22:31:28,1,0.27,1,N,237,141,1,3.0,0.5,0.5,0.86,0.0,0.3,5.16, +1,2019-01-21 19:01:03,2019-01-21 19:21:55,1,4.3,1,N,249,140,1,16.5,0.0,0.5,3.45,0.0,0.3,20.75,0.0 +1,2019-01-15 12:50:22,2019-01-15 13:00:32,1,1.3,1,N,68,246,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-15 05:20:27,2019-01-15 05:26:12,1,1.4,1,N,264,264,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-31 21:12:07,2019-01-31 21:15:11,1,0.99,1,N,238,24,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-23 09:34:47,2019-01-23 09:36:42,1,0.63,1,N,68,246,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-26 09:47:30,2019-01-26 09:50:45,1,0.31,1,N,161,161,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38,0.0 +2,2019-01-07 17:48:04,2019-01-07 18:00:43,1,1.86,1,N,68,107,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 15:38:54,2019-01-11 15:49:17,1,1.37,1,N,144,4,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-09 18:54:00,2019-01-09 19:06:18,1,1.81,1,N,161,107,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-22 16:21:14,2019-01-22 16:27:52,1,0.8,1,N,163,141,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-14 16:09:43,2019-01-14 16:48:04,1,17.2,2,N,132,233,1,52.0,4.5,0.5,12.6,5.76,0.3,75.66, +2,2019-01-24 19:22:36,2019-01-24 19:27:11,1,0.93,1,N,237,236,1,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-10 12:59:33,2019-01-10 13:19:13,1,1.8,1,N,237,50,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +1,2019-01-14 18:49:00,2019-01-14 19:02:42,1,1.3,1,N,170,237,1,10.0,1.0,0.5,1.77,0.0,0.3,13.57, +2,2019-01-01 21:07:33,2019-01-01 21:14:11,1,1.9,1,N,48,143,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-08 08:20:04,2019-01-08 08:28:25,1,0.8,1,N,137,170,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-11 04:48:52,2019-01-11 05:01:58,1,4.3,1,N,144,225,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56, +2,2019-01-17 16:29:15,2019-01-17 16:45:38,1,2.35,1,N,246,13,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +4,2019-01-18 14:14:03,2019-01-18 14:25:45,1,1.35,1,N,162,234,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-18 16:22:04,2019-01-18 16:29:03,1,0.86,1,N,237,141,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-22 13:15:58,2019-01-22 13:33:52,1,2.9,1,N,142,246,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75,0.0 +1,2019-01-18 19:56:53,2019-01-18 20:09:49,1,1.7,1,N,113,158,1,9.5,1.0,0.5,2.5,0.0,0.3,13.8, +2,2019-01-17 08:56:16,2019-01-17 09:31:32,1,8.47,1,N,233,181,1,32.0,0.0,0.5,0.0,0.0,0.3,32.8, +1,2019-01-17 20:29:46,2019-01-17 20:38:01,1,1.4,1,N,170,107,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-18 07:36:19,2019-01-18 07:49:09,1,2.3,1,N,249,209,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-05 14:49:07,2019-01-05 14:53:46,1,0.77,1,N,186,164,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-11 21:44:44,2019-01-11 21:55:01,1,2.19,1,N,151,263,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-07 13:39:04,2019-01-07 13:52:41,1,3.9,1,N,264,264,1,14.0,0.0,0.5,2.07,0.0,0.3,16.87, +2,2019-01-17 09:53:04,2019-01-17 10:07:56,1,1.61,1,N,233,246,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-23 14:42:46,2019-01-23 15:06:04,1,5.67,1,N,140,211,1,21.5,0.0,0.5,1.5,0.0,0.3,23.8,0.0 +2,2019-01-21 21:38:37,2019-01-21 21:40:51,1,0.4,1,N,142,142,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-10 15:24:57,2019-01-10 15:37:05,1,2.46,1,N,262,74,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-16 18:27:37,2019-01-16 18:44:56,1,2.55,1,N,87,65,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-19 23:10:48,2019-01-19 23:31:13,1,4.73,1,N,249,40,1,17.5,0.5,0.5,1.0,0.0,0.3,19.8, +2,2019-01-28 12:21:10,2019-01-28 12:32:24,1,1.89,1,N,68,113,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-03 20:38:10,2019-01-03 20:59:50,1,3.21,1,N,249,142,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8, +2,2019-01-26 10:38:09,2019-01-26 10:51:06,1,2.33,1,N,229,234,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-08 19:17:28,2019-01-08 19:23:26,1,0.7,1,N,43,142,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-10 14:51:54,2019-01-10 15:15:28,1,6.2,1,N,87,161,1,22.5,0.0,0.5,6.0,0.0,0.3,29.3, +2,2019-01-04 16:09:29,2019-01-04 16:18:17,1,1.15,1,N,68,234,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +1,2019-01-20 20:39:22,2019-01-20 20:48:30,1,2.7,1,N,90,87,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +2,2019-01-10 01:21:39,2019-01-10 01:21:43,1,0.03,1,N,140,140,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-27 09:28:31,2019-01-27 09:32:33,1,0.58,1,N,143,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-25 22:48:45,2019-01-25 22:50:08,1,0.01,1,N,230,230,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3,0.0 +2,2019-01-02 20:04:57,2019-01-02 20:11:53,1,1.42,1,N,113,164,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-14 18:24:50,2019-01-14 18:42:22,1,3.3,1,N,236,230,1,14.0,1.0,0.5,3.0,0.0,0.3,18.8, +2,2019-01-17 23:39:45,2019-01-17 23:45:48,1,0.99,1,N,97,49,1,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-04 09:28:46,2019-01-04 09:39:13,1,1.38,1,N,87,125,1,8.5,0.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-31 21:52:40,2019-01-31 21:59:41,1,1.42,1,N,233,237,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-04 15:13:36,2019-01-04 15:55:45,1,12.73,2,N,202,132,1,52.0,0.0,0.5,15.84,0.0,0.3,68.64, +2,2019-01-11 09:41:53,2019-01-11 09:52:09,1,1.23,1,N,234,170,1,8.0,0.0,0.5,1.32,0.0,0.3,10.12, +1,2019-01-08 16:40:10,2019-01-08 16:51:22,1,1.9,1,N,264,264,1,9.5,1.0,0.5,2.8,0.0,0.3,14.1, +2,2019-01-26 23:59:53,2019-01-27 00:16:10,1,1.96,1,N,232,137,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-23 10:28:13,2019-01-23 10:42:59,1,1.72,1,N,237,236,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-21 20:48:14,2019-01-21 21:04:50,1,4.99,1,N,90,24,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-04 05:28:04,2019-01-04 05:34:54,1,2.03,1,N,68,142,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-27 23:07:42,2019-01-27 23:19:29,1,4.0,1,N,48,166,1,13.0,0.5,0.5,4.25,0.0,0.3,18.55,0.0 +1,2019-01-12 19:51:09,2019-01-12 20:07:19,1,1.8,1,N,48,186,1,11.0,0.0,0.5,2.0,0.0,0.3,13.8, +1,2019-01-13 18:14:16,2019-01-13 18:36:28,1,8.3,1,N,141,138,1,25.5,0.0,0.5,6.4,5.76,0.3,38.46, +2,2019-01-22 12:09:35,2019-01-22 12:37:08,1,3.91,1,N,231,161,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76,0.0 +2,2019-01-17 18:03:56,2019-01-17 18:49:21,1,9.18,1,N,138,246,1,35.5,1.0,0.5,8.61,5.76,0.3,51.67, +1,2019-01-27 00:26:04,2019-01-27 00:37:32,1,3.3,1,N,164,239,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-05 12:32:15,2019-01-05 12:39:35,1,1.2,1,N,113,224,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-05 16:58:09,2019-01-05 17:04:52,1,1.33,1,N,142,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-13 16:02:03,2019-01-13 16:10:27,1,2.0,1,N,186,125,1,8.5,0.0,0.5,2.0,0.0,0.3,11.3, +2,2019-01-09 14:08:55,2019-01-09 14:28:03,1,1.47,1,N,107,186,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-15 09:17:39,2019-01-15 09:28:13,1,0.83,1,N,40,40,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-07 21:51:06,2019-01-07 22:11:13,1,4.6,1,N,68,236,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +2,2019-01-15 08:12:43,2019-01-15 08:16:28,1,0.46,1,N,234,164,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-12 04:33:50,2019-01-12 04:59:08,1,7.24,1,N,100,17,2,25.5,0.5,0.5,0.0,0.0,0.3,26.8, +1,2019-01-13 03:13:50,2019-01-13 03:19:35,1,1.5,1,N,163,68,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-18 16:53:43,2019-01-18 16:58:32,1,1.3,1,N,68,114,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-17 00:59:54,2019-01-17 01:13:21,1,3.8,1,N,230,209,1,13.0,0.5,0.5,2.0,0.0,0.3,16.3, +1,2019-01-11 19:10:36,2019-01-11 19:27:05,1,4.5,1,N,79,13,1,17.0,1.0,0.5,3.0,0.0,0.3,21.8, +1,2019-01-24 04:37:07,2019-01-24 04:41:31,1,1.4,1,N,162,137,1,6.0,0.5,0.5,2.15,0.0,0.3,9.45,0.0 +1,2019-01-02 10:41:57,2019-01-02 10:50:37,1,1.2,1,N,161,229,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-21 16:56:03,2019-01-21 17:01:06,1,0.96,1,N,142,239,1,5.5,0.0,0.5,0.6,0.0,0.3,6.9,0.0 +2,2019-01-21 20:36:19,2019-01-21 21:08:15,1,20.63,2,N,132,231,1,52.0,0.0,0.5,13.2,0.0,0.3,66.0,0.0 +1,2019-01-09 18:16:56,2019-01-09 18:25:15,1,1.5,1,N,236,239,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-15 08:22:14,2019-01-15 08:39:48,1,1.21,1,N,141,162,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38, +2,2019-01-07 08:44:52,2019-01-07 09:08:13,1,1.77,1,N,90,170,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-01 16:08:58,2019-01-01 16:18:57,1,1.12,1,N,237,43,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-22 19:09:31,2019-01-22 19:17:46,1,1.95,1,N,74,168,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-24 07:18:38,2019-01-24 07:27:55,1,1.12,1,N,264,264,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-13 14:50:08,2019-01-13 14:52:43,1,0.44,1,N,144,231,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-05 00:12:24,2019-01-05 00:37:41,1,7.9,1,N,138,36,1,26.0,0.5,0.5,5.45,0.0,0.3,32.75, +1,2019-01-20 19:23:08,2019-01-20 19:30:50,1,1.3,1,N,229,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-10 09:07:30,2019-01-10 09:22:51,1,1.43,1,N,230,233,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-13 11:26:01,2019-01-13 11:43:28,1,4.9,1,N,107,181,1,17.5,0.0,0.5,4.55,0.0,0.3,22.85, +2,2019-01-21 11:40:48,2019-01-21 12:07:15,1,18.11,1,N,132,22,2,49.0,0.0,0.5,0.0,0.0,0.3,49.8,0.0 +2,2019-01-22 22:40:24,2019-01-22 22:46:14,1,1.03,1,N,4,79,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-22 18:47:44,2019-01-22 18:54:18,1,0.9,1,N,144,231,1,6.0,1.0,0.5,1.5,0.0,0.3,9.3,0.0 +2,2019-01-20 15:51:17,2019-01-20 15:53:59,1,1.08,1,N,141,263,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-04 17:14:32,2019-01-04 17:28:10,1,3.0,1,N,143,75,1,12.0,1.0,0.5,2.2,0.0,0.3,16.0, +1,2019-01-06 15:33:54,2019-01-06 15:39:44,1,1.2,1,N,114,90,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-21 13:40:26,2019-01-21 13:53:51,1,2.5,1,N,48,140,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-28 20:06:04,2019-01-28 20:14:52,1,2.3,1,N,264,264,1,9.0,0.5,0.5,1.59,0.0,0.3,11.89,0.0 +2,2019-01-01 21:16:27,2019-01-01 21:30:44,1,3.98,1,N,79,48,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3, +1,2019-01-05 13:05:23,2019-01-05 13:10:56,1,0.8,1,N,264,264,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +1,2019-01-14 06:27:26,2019-01-14 06:32:52,1,1.8,1,N,140,236,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-21 13:33:33,2019-01-22 12:38:05,1,0.58,1,N,170,161,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-02 17:27:43,2019-01-02 17:34:47,1,1.68,1,N,24,239,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-16 20:09:36,2019-01-16 20:15:36,1,1.36,1,N,68,249,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14, +1,2019-01-24 22:19:15,2019-01-24 22:44:48,1,5.3,1,Y,170,7,1,20.5,0.5,0.5,5.45,0.0,0.3,27.25,0.0 +2,2019-01-20 14:19:30,2019-01-20 14:33:33,1,2.22,1,N,140,230,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-17 19:34:59,2019-01-17 19:38:35,1,0.5,1,N,141,229,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-28 12:51:14,2019-01-28 13:13:27,1,9.9,1,N,163,138,1,30.0,0.0,0.5,6.0,5.76,0.3,42.56,0.0 +1,2019-01-10 10:33:04,2019-01-10 10:49:46,1,1.5,1,N,140,162,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-21 11:30:03,2019-01-21 11:45:12,1,1.6,1,N,231,87,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-12 20:33:04,2019-01-12 21:14:31,1,19.0,2,N,132,237,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-26 10:35:22,2019-01-26 10:41:51,1,1.13,1,N,263,236,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-29 14:33:41,2019-01-29 14:39:29,1,0.7,1,N,264,264,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-10 13:55:22,2019-01-10 14:02:53,1,0.95,1,N,90,158,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-14 20:38:01,2019-01-14 20:43:43,1,2.3,1,N,137,232,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-03 18:43:47,2019-01-03 18:48:19,1,0.69,1,N,234,170,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-29 20:22:10,2019-01-29 20:45:56,1,7.5,1,N,79,83,2,24.5,0.5,0.5,0.0,0.0,0.3,25.8,0.0 +2,2019-01-27 19:13:01,2019-01-27 19:23:59,1,1.44,1,N,48,90,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-15 23:03:37,2019-01-15 23:29:34,1,19.2,2,N,132,262,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +1,2019-01-25 07:29:47,2019-01-25 07:34:49,1,1.0,1,N,7,179,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-03 02:57:48,2019-01-03 03:05:55,1,2.02,1,N,246,161,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-11 12:54:33,2019-01-11 12:58:13,1,0.8,1,N,186,230,1,5.0,0.0,0.5,1.4,0.0,0.3,7.2, +1,2019-01-27 15:55:58,2019-01-27 16:00:48,1,0.8,1,N,229,141,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-17 09:34:01,2019-01-17 09:42:15,1,1.12,1,N,236,75,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-02 19:06:56,2019-01-02 19:19:44,1,6.0,1,N,13,233,1,19.0,1.0,0.5,4.15,0.0,0.3,24.95, +2,2019-01-12 23:39:08,2019-01-12 23:43:28,1,0.66,1,N,48,163,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-07 10:57:06,2019-01-07 11:11:21,1,2.5,1,N,170,144,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +1,2019-01-10 20:52:16,2019-01-10 21:15:27,1,4.6,1,N,170,13,1,19.0,0.5,0.5,5.05,0.0,0.3,25.35, +2,2019-01-21 17:56:35,2019-01-21 18:05:09,1,1.48,1,N,186,107,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-24 14:50:40,2019-01-24 14:55:30,1,0.4,1,N,246,246,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-13 09:48:14,2019-01-13 10:14:03,1,15.2,5,N,131,25,1,40.04,0.0,0.5,0.0,0.0,0.3,40.84, +2,2019-01-27 10:23:21,2019-01-27 10:26:52,1,0.8,1,N,50,163,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-10 18:51:20,2019-01-10 18:56:05,1,0.8,1,N,143,143,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-06 19:25:28,2019-01-06 19:35:17,1,1.99,1,N,50,68,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-21 12:51:22,2019-01-21 12:56:08,1,1.35,1,N,141,263,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82,0.0 +2,2019-01-09 12:18:45,2019-01-09 12:24:12,1,1.2,1,N,230,50,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-15 10:49:52,2019-01-15 11:10:04,1,0.99,1,N,161,163,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +4,2019-01-09 13:38:38,2019-01-09 13:48:02,1,1.11,1,N,236,237,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-18 12:46:02,2019-01-18 12:55:50,1,1.89,1,N,246,158,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-04 08:41:17,2019-01-04 08:54:55,1,1.1,1,N,233,230,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-30 09:57:56,2019-01-30 10:16:52,1,1.1,1,N,233,230,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35,0.0 +1,2019-01-30 17:41:27,2019-01-30 17:45:47,1,0.6,1,N,164,234,1,5.0,1.0,0.5,2.0,0.0,0.3,8.8,0.0 +1,2019-01-06 07:04:36,2019-01-06 07:08:02,1,0.8,1,N,186,170,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-18 12:07:19,2019-01-18 12:13:42,1,1.2,1,N,138,138,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8, +1,2019-01-15 00:12:59,2019-01-15 00:34:32,1,8.3,1,N,113,133,1,25.0,0.5,0.5,7.0,0.0,0.3,33.3, +2,2019-01-21 20:09:31,2019-01-21 20:17:44,1,1.1,1,N,79,249,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-05 13:37:42,2019-01-05 13:47:39,1,1.52,1,N,239,151,1,9.0,0.0,0.5,2.0,0.0,0.3,11.8, +1,2019-01-04 19:03:04,2019-01-04 19:16:46,1,1.4,1,N,163,233,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-15 01:03:38,2019-01-15 01:18:23,1,5.3,1,N,162,166,2,16.5,0.5,0.5,0.0,0.0,0.3,17.8, +1,2019-01-25 14:48:34,2019-01-25 14:50:49,1,0.4,1,N,230,230,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3,0.0 +1,2019-01-13 16:19:39,2019-01-13 16:22:56,1,0.5,1,N,100,48,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-27 10:04:02,2019-01-27 10:07:40,1,0.71,1,N,143,239,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3,0.0 +1,2019-01-30 08:55:29,2019-01-30 09:00:33,1,0.4,1,N,113,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-08 21:52:43,2019-01-08 22:03:29,1,2.1,1,N,239,48,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-14 18:38:04,2019-01-14 19:34:05,1,18.86,2,N,113,132,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-11 19:41:58,2019-01-11 19:56:58,1,3.28,1,N,140,41,1,12.5,1.0,0.5,1.5,0.0,0.3,15.8, +1,2019-01-31 15:09:56,2019-01-31 15:17:29,1,0.5,1,N,236,236,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-30 11:32:50,2019-01-30 11:40:23,1,0.6,1,N,107,234,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-11 14:48:27,2019-01-11 15:09:28,1,3.96,1,N,230,232,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +1,2019-01-02 11:55:28,2019-01-02 12:04:50,1,1.6,1,N,90,79,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3, +1,2019-01-16 17:40:30,2019-01-16 17:47:34,1,1.0,1,N,230,186,1,6.5,1.0,0.5,1.75,0.0,0.3,10.05, +1,2019-01-22 07:09:11,2019-01-22 07:24:58,1,2.9,1,N,231,161,1,12.5,0.0,0.5,2.0,0.0,0.3,15.3,0.0 +2,2019-01-20 15:40:34,2019-01-20 15:44:55,1,1.4,1,N,262,74,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-09 09:31:51,2019-01-09 09:36:49,1,0.4,1,N,142,142,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-24 14:58:01,2019-01-24 15:04:36,1,0.5,1,N,249,113,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-12 14:20:57,2019-01-12 14:36:38,1,1.69,1,N,113,231,1,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-20 14:55:43,2019-01-20 15:09:16,1,3.66,1,N,148,112,1,14.5,0.0,0.5,2.0,0.0,0.3,17.3, +2,2019-01-23 21:45:32,2019-01-23 21:53:31,1,1.43,1,N,48,170,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-16 19:44:48,2019-01-16 19:49:24,1,0.66,1,N,48,163,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-19 11:59:36,2019-01-19 12:22:42,1,5.5,1,N,179,137,1,20.5,0.0,0.5,3.2,0.0,0.3,24.5, +2,2019-01-30 12:17:56,2019-01-30 12:25:17,1,1.1,1,N,140,263,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-20 15:03:47,2019-01-20 15:17:17,1,2.03,1,N,186,234,1,10.0,0.0,0.5,3.24,0.0,0.3,14.04, +1,2019-01-07 21:51:16,2019-01-07 22:08:34,1,9.2,1,N,229,243,1,27.0,0.5,0.5,3.0,0.0,0.3,31.3, +2,2019-01-27 10:53:13,2019-01-27 11:04:20,1,1.5,1,N,68,163,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-29 15:31:17,2019-01-29 15:44:55,1,1.79,1,N,236,239,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-13 17:00:22,2019-01-13 17:07:46,1,1.0,1,N,90,114,1,6.5,0.0,0.5,1.2,0.0,0.3,8.5, +2,2019-01-10 15:40:03,2019-01-10 15:53:28,1,1.94,1,N,264,264,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-24 14:46:51,2019-01-24 14:54:52,1,1.77,1,N,83,196,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-25 00:38:40,2019-01-25 00:44:49,1,1.48,1,N,148,137,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-30 18:33:32,2019-01-30 18:35:59,1,0.28,1,N,209,87,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-09 10:44:18,2019-01-09 10:55:05,1,1.29,1,N,163,233,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-14 07:02:27,2019-01-14 07:19:47,1,6.1,1,N,90,152,1,20.5,0.0,0.5,2.0,0.0,0.3,23.3, +2,2019-01-27 18:45:35,2019-01-27 19:04:22,1,1.71,1,N,170,48,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-12 22:14:10,2019-01-12 22:36:29,1,8.73,1,N,142,66,1,27.0,0.5,0.5,5.66,0.0,0.3,33.96, +2,2019-01-16 20:27:45,2019-01-16 20:36:40,1,0.91,1,N,170,164,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-04 11:27:02,2019-01-04 11:47:17,1,4.83,1,N,13,186,1,18.5,0.0,0.5,5.79,0.0,0.3,25.09, +1,2019-01-25 14:57:29,2019-01-25 15:04:48,1,0.6,1,N,234,186,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-11 17:12:26,2019-01-11 17:23:47,1,1.54,1,N,163,143,1,9.0,1.0,0.5,2.0,0.0,0.3,12.8, +2,2019-01-18 08:58:38,2019-01-18 09:29:22,1,7.47,1,N,231,262,1,27.5,0.0,0.5,3.0,0.0,0.3,31.3, +2,2019-01-18 23:18:30,2019-01-18 23:42:58,1,4.07,1,N,79,49,1,18.5,0.5,0.5,3.96,0.0,0.3,23.76, +1,2019-01-14 11:26:06,2019-01-14 11:34:07,1,0.7,1,N,161,163,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-20 16:12:50,2019-01-20 16:17:09,1,1.0,1,N,170,234,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-20 18:12:37,2019-01-20 18:15:43,1,0.95,1,N,239,151,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-15 20:21:17,2019-01-15 20:53:25,1,15.2,1,N,234,11,1,43.5,0.5,0.5,8.95,0.0,0.3,53.75, +1,2019-01-12 12:39:06,2019-01-12 12:41:46,1,0.3,1,N,144,114,1,4.0,0.0,0.5,1.4,0.0,0.3,6.2, +2,2019-01-07 14:43:43,2019-01-07 14:59:28,1,2.08,1,N,234,237,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-30 22:45:43,2019-01-30 22:50:34,1,0.9,1,N,164,90,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15,0.0 +1,2019-01-29 18:29:40,2019-01-29 18:35:59,1,0.8,1,N,239,142,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-03 22:34:43,2019-01-03 22:41:40,1,1.21,1,N,164,230,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-17 11:35:33,2019-01-17 11:40:38,1,0.93,1,N,114,107,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-22 07:55:18,2019-01-22 07:59:51,1,0.59,1,N,236,236,1,4.5,0.0,0.5,0.8,0.0,0.3,6.1,0.0 +2,2019-01-29 15:15:03,2019-01-29 16:07:50,1,17.03,2,N,234,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +1,2019-01-12 19:15:12,2019-01-12 19:28:03,1,2.5,1,N,161,263,1,11.0,0.0,0.5,1.77,0.0,0.3,13.57, +2,2019-01-26 01:23:01,2019-01-26 01:28:47,1,0.74,1,N,144,114,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-17 18:55:09,2019-01-17 19:23:12,1,3.09,1,N,230,114,1,18.0,1.0,0.5,4.95,0.0,0.3,24.75, +2,2019-01-30 07:59:10,2019-01-30 08:13:46,1,1.62,1,N,48,229,1,10.5,0.0,0.5,1.13,0.0,0.3,12.43,0.0 +1,2019-01-16 22:35:15,2019-01-16 22:46:40,1,1.6,1,N,161,68,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-01 08:42:44,2019-01-01 08:49:19,1,2.14,1,N,75,237,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-30 16:59:19,2019-01-30 17:14:52,1,1.79,1,N,100,237,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0,0.0 +2,2019-01-31 14:28:29,2019-01-31 14:43:57,1,2.55,1,N,161,238,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-02 18:31:03,2019-01-02 18:34:55,1,0.72,1,N,140,141,1,5.0,1.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-09 14:39:42,2019-01-09 14:50:50,1,1.4,1,N,161,141,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-30 16:11:26,2019-01-30 16:25:43,1,2.08,1,N,236,142,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-02 11:46:10,2019-01-02 11:53:38,1,1.5,1,N,158,234,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-25 19:28:26,2019-01-25 19:38:18,1,1.08,1,N,231,148,1,7.5,1.0,0.5,1.0,0.0,0.3,10.3,0.0 +2,2019-01-30 12:06:37,2019-01-30 12:06:42,1,0.01,1,N,152,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +1,2019-01-26 16:09:57,2019-01-26 16:20:27,1,1.6,1,N,238,263,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +1,2019-01-24 21:08:17,2019-01-24 21:19:07,1,1.6,1,N,162,234,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-06 17:30:27,2019-01-06 17:41:21,1,1.51,1,N,144,249,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-05 18:17:34,2019-01-05 18:32:54,1,3.86,1,N,209,158,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +1,2019-01-15 22:05:54,2019-01-15 22:08:06,1,0.6,1,N,114,113,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35, +2,2019-01-15 15:37:19,2019-01-15 15:46:34,1,1.05,1,N,161,186,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-14 09:17:24,2019-01-14 09:22:45,1,0.48,1,N,236,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-17 11:36:57,2019-01-17 11:43:32,1,0.61,1,N,170,162,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-20 16:12:05,2019-01-20 16:15:26,1,0.7,1,N,90,234,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-25 19:35:36,2019-01-25 19:49:12,1,1.9,1,N,151,142,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-20 18:03:28,2019-01-20 18:15:55,1,2.5,1,N,161,238,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-02 12:09:08,2019-01-02 12:20:11,1,1.68,1,N,161,234,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-10 19:56:30,2019-01-10 20:12:31,1,2.83,1,N,230,140,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-11 23:30:29,2019-01-11 23:40:20,1,1.73,1,N,161,186,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-14 12:14:54,2019-01-14 12:24:09,1,1.88,1,N,79,233,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-05 20:52:03,2019-01-05 21:06:12,1,2.4,1,N,79,13,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95, +2,2019-01-26 13:11:10,2019-01-26 13:17:22,1,1.25,1,N,239,151,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-04 17:50:46,2019-01-04 18:03:31,1,0.9,1,N,237,161,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-25 14:23:59,2019-01-25 14:46:58,1,3.22,1,N,163,114,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8,0.0 +1,2019-01-18 18:08:37,2019-01-18 18:51:42,1,15.8,1,N,170,108,1,47.0,1.0,0.5,10.9,5.76,0.3,65.46, +1,2019-01-16 19:26:22,2019-01-16 19:32:50,1,0.8,1,N,264,264,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-12 18:10:07,2019-01-12 18:18:05,1,0.98,1,N,48,142,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-13 16:10:41,2019-01-13 16:20:08,1,1.38,1,N,68,249,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-17 19:36:45,2019-01-17 19:42:35,1,1.29,1,N,264,264,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-26 07:25:07,2019-01-26 07:28:07,1,0.69,1,N,141,229,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-10 19:08:58,2019-01-10 19:21:21,1,0.95,1,N,48,161,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-30 18:51:31,2019-01-30 19:02:11,1,2.4,1,N,170,4,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-14 16:23:23,2019-01-14 16:29:01,1,1.1,1,N,144,4,1,6.5,1.0,0.5,1.7,0.0,0.3,10.0, +1,2019-01-06 17:18:38,2019-01-06 17:20:43,1,0.2,1,N,162,141,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +2,2019-01-14 16:35:52,2019-01-14 16:54:16,1,4.85,1,N,107,263,2,16.5,1.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-05 12:05:56,2019-01-05 12:11:03,1,0.99,1,N,141,162,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-26 11:28:29,2019-01-26 11:38:37,1,1.99,1,N,142,246,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-29 13:46:47,2019-01-29 14:07:21,1,2.61,1,N,125,170,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +1,2019-01-07 08:31:24,2019-01-07 08:47:34,1,1.6,1,N,141,137,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 21:57:37,2019-01-11 22:15:58,1,2.36,1,N,229,234,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +1,2019-01-31 12:36:19,2019-01-31 12:48:24,1,1.7,1,N,237,239,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-01 05:39:35,2019-01-01 06:04:19,1,16.5,2,N,162,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-31 14:23:02,2019-01-31 14:35:24,1,1.17,1,N,170,230,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +1,2019-01-24 13:08:20,2019-01-24 13:30:16,1,2.1,1,N,170,249,1,14.0,0.0,0.5,1.0,0.0,0.3,15.8,0.0 +2,2019-01-24 07:29:50,2019-01-25 06:39:33,1,1.38,1,N,249,186,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-04 10:54:47,2019-01-04 11:18:31,2,3.09,1,N,234,236,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +1,2019-01-06 10:52:46,2019-01-06 11:08:39,2,3.3,5,N,144,161,1,15.0,0.0,0.0,0.0,0.0,0.3,15.3, +1,2019-01-19 18:28:59,2019-01-19 18:40:48,2,1.6,1,N,164,211,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-19 21:20:17,2019-01-19 21:21:50,2,0.56,1,N,75,74,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-11 21:33:53,2019-01-11 22:18:31,2,19.73,1,N,132,25,2,56.5,0.5,0.5,0.0,0.0,0.3,57.8, +2,2019-01-08 18:49:10,2019-01-08 19:32:40,2,18.03,2,N,132,142,1,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-28 14:03:47,2019-01-28 14:17:39,2,1.39,1,N,233,68,1,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-25 08:45:55,2019-01-25 08:59:44,2,2.15,1,N,142,263,1,11.0,0.0,0.5,1.0,0.0,0.3,12.8,0.0 +1,2019-01-01 00:16:10,2019-01-01 00:26:46,2,1.2,1,N,170,170,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-28 10:02:43,2019-01-28 10:23:04,2,1.59,1,N,230,170,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-06 19:07:25,2019-01-06 19:12:21,2,0.41,1,N,161,163,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-09 23:05:15,2019-01-09 23:20:05,2,5.29,2,N,260,230,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-31 08:59:42,2019-01-31 09:11:01,2,1.46,1,N,33,34,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-25 21:59:57,2019-01-25 22:30:01,2,5.49,1,N,79,112,1,24.0,0.5,0.5,5.06,0.0,0.3,30.36,0.0 +2,2019-01-19 20:38:18,2019-01-19 20:59:17,2,8.64,1,N,236,127,1,26.0,0.5,0.5,5.46,0.0,0.3,32.76, +1,2019-01-25 14:38:32,2019-01-25 14:48:23,2,0.7,1,N,231,125,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-28 16:37:47,2019-01-28 16:47:44,2,1.9,1,N,229,236,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-07 22:38:10,2019-01-07 22:44:31,2,2.4,1,N,233,263,1,8.5,0.5,0.5,1.47,0.0,0.3,11.27, +2,2019-01-26 10:54:09,2019-01-26 11:06:14,2,1.65,1,N,246,161,1,9.5,0.0,0.5,3.09,0.0,0.3,13.39,0.0 +2,2019-01-26 02:47:53,2019-01-26 02:54:09,2,1.13,1,N,144,107,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14,0.0 +2,2019-01-28 09:06:14,2019-01-28 09:19:33,2,2.22,1,N,239,48,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-22 14:35:35,2019-01-22 14:49:38,2,1.23,1,N,161,164,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-12 09:20:23,2019-01-12 09:27:59,2,1.38,1,N,229,161,1,7.0,0.0,0.5,2.34,0.0,0.3,10.14, +1,2019-01-02 07:46:17,2019-01-02 07:53:33,2,0.8,1,N,170,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-10 23:30:45,2019-01-10 23:36:58,2,1.0,1,N,231,231,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-17 21:17:14,2019-01-17 21:38:34,2,3.57,1,N,107,142,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76, +2,2019-01-23 13:02:54,2019-01-23 13:11:13,2,1.47,1,N,142,48,1,7.5,0.0,0.5,2.0,0.0,0.3,10.3,0.0 +2,2019-01-28 11:46:57,2019-01-28 11:52:47,2,1.07,1,N,141,236,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82,0.0 +2,2019-01-20 20:37:56,2019-01-20 20:46:05,2,0.66,1,N,230,100,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-28 19:25:30,2019-01-28 19:34:36,2,1.84,1,N,164,158,1,8.5,1.0,0.5,2.0,0.0,0.3,12.3,0.0 +2,2019-01-14 13:44:40,2019-01-14 14:02:29,2,2.2,1,N,143,141,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-16 18:28:42,2019-01-16 18:50:24,2,2.51,1,N,237,170,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-13 13:04:58,2019-01-13 13:36:40,2,9.4,1,N,68,138,1,33.0,0.0,0.5,7.9,5.76,0.3,47.46, +1,2019-01-10 21:50:26,2019-01-10 22:06:29,2,4.2,1,N,162,41,1,15.0,0.5,0.5,4.05,0.0,0.3,20.35, +1,2019-01-24 15:36:09,2019-01-24 15:50:58,2,2.9,1,N,113,87,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +1,2019-01-05 14:11:13,2019-01-05 14:17:26,2,1.7,1,N,74,263,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8, +2,2019-01-24 10:33:19,2019-01-24 10:48:04,2,1.42,1,N,249,164,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-07 20:53:06,2019-01-07 20:56:52,2,1.07,1,N,48,143,1,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-03 12:10:03,2019-01-03 12:17:40,2,0.82,1,N,162,140,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-23 15:36:57,2019-01-23 16:08:48,2,11.68,1,N,230,138,1,36.0,0.0,0.5,8.51,5.76,0.3,51.07,0.0 +1,2019-01-14 15:58:14,2019-01-14 16:15:38,2,1.8,1,N,43,162,1,12.0,1.0,0.5,4.1,0.0,0.3,17.9, +2,2019-01-18 11:33:05,2019-01-18 11:40:46,2,0.57,1,N,224,107,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-06 12:54:50,2019-01-06 12:59:15,2,0.68,1,N,237,263,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-22 10:58:31,2019-01-22 11:08:16,2,1.7,1,N,230,43,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-04 13:17:09,2019-01-04 13:32:51,2,5.0,1,N,141,144,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35, +1,2019-01-19 23:43:01,2019-01-19 23:49:57,2,1.7,1,N,161,140,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +1,2019-01-17 21:03:26,2019-01-17 21:16:43,2,1.7,1,N,229,164,1,10.0,0.5,0.5,2.0,0.0,0.3,13.3, +1,2019-01-20 18:04:59,2019-01-20 18:14:27,2,0.8,1,N,230,161,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-23 11:46:35,2019-01-23 11:50:48,2,1.0,1,N,264,264,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-21 08:01:09,2019-01-21 08:26:16,2,9.67,1,N,237,138,2,29.5,0.0,0.5,0.0,5.76,0.3,36.06,0.0 +2,2019-01-05 16:29:52,2019-01-05 17:10:29,2,5.92,1,N,43,234,1,26.5,0.0,0.5,6.83,0.0,0.3,34.13, +2,2019-01-04 14:39:25,2019-01-04 14:53:22,2,1.82,1,N,186,234,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-18 12:09:29,2019-01-18 12:18:01,2,1.19,1,N,163,140,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-24 23:17:46,2019-01-24 23:31:34,2,3.49,1,N,148,97,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-08 07:57:07,2019-01-08 08:11:11,2,2.4,1,N,13,234,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-26 12:35:38,2019-01-26 12:42:26,2,1.17,1,N,229,137,1,6.5,0.0,0.5,0.88,0.0,0.3,8.18,0.0 +1,2019-01-18 22:50:15,2019-01-18 22:57:35,2,0.8,1,N,48,43,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-10 19:51:47,2019-01-10 19:56:39,2,1.0,1,N,264,264,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-13 09:47:36,2019-01-13 10:15:43,2,23.24,1,N,132,20,2,61.0,0.0,0.5,0.0,5.76,0.3,67.56, +2,2019-01-03 14:34:23,2019-01-03 15:00:49,2,2.89,1,N,186,144,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +2,2019-01-24 18:10:06,2019-01-24 18:17:05,2,0.75,1,N,237,141,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-15 17:12:52,2019-01-15 17:25:25,2,1.94,1,N,246,162,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-31 14:54:00,2019-01-31 15:33:38,2,17.49,2,N,132,90,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56,0.0 +2,2019-01-06 13:55:09,2019-01-06 14:07:06,2,2.15,1,N,233,79,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-02 21:19:48,2019-01-02 21:26:42,2,1.3,1,N,234,161,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-16 21:55:45,2019-01-16 22:03:14,2,1.2,1,N,233,170,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-08 10:51:27,2019-01-08 11:29:00,2,8.73,1,N,138,186,2,33.5,0.0,0.5,0.0,5.76,0.3,40.06, +2,2019-01-22 20:51:06,2019-01-22 21:00:30,2,1.9,1,N,230,234,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-11 12:43:13,2019-01-11 12:56:35,2,1.58,1,N,186,90,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-09 16:35:25,2019-01-09 16:39:26,2,0.5,1,N,90,234,1,4.5,1.0,0.5,1.5,0.0,0.3,7.8, +2,2019-01-31 18:16:49,2019-01-31 18:27:35,2,1.29,1,N,68,50,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-25 23:24:12,2019-01-25 23:33:13,2,1.0,1,N,144,113,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-09 19:03:01,2019-01-09 19:10:26,2,0.92,1,N,90,234,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-25 20:29:12,2019-01-25 20:35:30,2,1.02,1,N,100,170,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-18 08:17:51,2019-01-18 08:31:35,2,1.93,1,N,141,100,1,10.5,0.0,0.5,2.0,0.0,0.3,13.3, +2,2019-01-02 21:33:40,2019-01-02 21:36:38,2,2.17,1,N,61,61,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-09 09:26:49,2019-01-09 09:42:16,2,2.36,1,N,239,75,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +1,2019-01-11 10:15:39,2019-01-11 10:38:34,2,7.0,1,N,140,244,1,24.0,0.0,0.5,4.95,0.0,0.3,29.75, +1,2019-01-14 10:27:26,2019-01-14 10:50:28,2,0.6,1,N,48,50,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +1,2019-01-06 12:57:40,2019-01-06 13:06:05,2,2.2,1,N,264,237,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-20 16:44:31,2019-01-20 16:50:56,2,0.6,1,N,162,141,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-10 21:58:45,2019-01-10 22:30:46,2,5.25,1,N,246,262,1,24.0,0.5,0.5,4.0,0.0,0.3,29.3, +1,2019-01-15 21:32:38,2019-01-15 21:52:34,2,4.6,1,N,229,80,1,17.5,0.5,0.5,4.7,0.0,0.3,23.5, +2,2019-01-07 06:48:20,2019-01-07 06:51:01,2,0.71,1,N,100,230,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-03 13:52:56,2019-01-03 14:32:36,2,17.74,2,N,132,230,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-25 16:35:12,2019-01-25 17:23:25,2,8.46,1,N,161,181,1,32.5,1.0,0.5,3.0,0.0,0.3,37.3,0.0 +2,2019-01-15 20:11:39,2019-01-15 20:18:00,2,1.3,1,N,263,140,1,6.5,0.5,0.5,1.0,0.0,0.3,8.8, +2,2019-01-30 17:54:41,2019-01-30 18:21:32,2,4.46,1,N,48,144,1,18.5,1.0,0.5,3.04,0.0,0.3,23.34,0.0 +1,2019-01-22 08:24:10,2019-01-22 08:41:36,2,2.7,1,N,48,158,1,12.5,0.0,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-05 02:48:34,2019-01-05 03:05:16,2,3.45,1,N,79,66,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-21 20:30:58,2019-01-21 20:40:10,2,1.98,1,N,141,170,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-17 20:02:22,2019-01-17 20:27:28,2,3.51,1,N,125,237,1,17.5,0.5,0.5,5.64,0.0,0.3,24.44, +2,2019-01-20 18:29:53,2019-01-20 18:39:17,2,2.54,1,N,137,229,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-11 20:05:57,2019-01-11 20:30:06,2,13.07,1,N,132,91,1,36.5,0.5,0.5,2.2,0.0,0.3,40.0, +1,2019-01-18 20:14:25,2019-01-18 20:46:26,2,4.9,1,N,114,238,1,23.0,0.5,0.5,4.85,0.0,0.3,29.15, +1,2019-01-12 00:23:22,2019-01-12 00:54:54,2,6.1,1,N,264,264,1,25.0,0.5,0.5,3.0,0.0,0.3,29.3, +2,2019-01-02 12:47:01,2019-01-02 12:52:10,2,0.38,2,N,163,162,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-31 22:57:32,2019-01-31 23:21:46,2,10.69,1,N,264,264,1,31.0,0.5,0.5,6.46,0.0,0.3,38.76,0.0 +2,2019-01-25 23:53:38,2019-01-25 23:59:18,2,0.63,1,N,90,249,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-20 12:47:29,2019-01-20 12:51:54,2,0.97,1,N,186,234,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-04 23:13:34,2019-01-04 23:33:56,2,4.7,1,N,264,264,1,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +1,2019-01-12 18:18:32,2019-01-12 18:22:41,2,0.6,1,N,237,237,1,5.0,0.0,0.5,0.75,0.0,0.3,6.55, +1,2019-01-12 09:07:34,2019-01-12 09:14:19,2,1.2,1,N,140,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-05 16:31:50,2019-01-05 17:06:17,2,7.8,1,N,107,198,1,28.0,0.0,0.5,10.35,5.76,0.3,44.91, +2,2019-01-10 18:28:36,2019-01-10 18:35:49,2,0.47,1,N,170,170,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-10 20:59:56,2019-01-10 21:19:50,2,3.05,1,N,43,246,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-20 08:45:43,2019-01-20 08:49:51,2,1.4,1,N,48,68,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-13 19:04:14,2019-01-13 19:17:13,2,2.27,1,N,41,247,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-26 21:50:32,2019-01-26 22:03:16,2,1.5,1,N,114,231,3,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-25 18:49:35,2019-01-25 19:01:53,2,2.86,1,N,239,161,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-25 18:16:40,2019-01-25 18:35:02,2,2.89,1,N,140,164,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-16 08:58:42,2019-01-16 09:14:43,2,1.33,1,N,107,170,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-31 00:21:46,2019-01-31 00:25:47,2,0.32,1,N,161,162,1,4.5,0.5,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-29 06:31:29,2019-01-29 07:27:03,2,23.27,1,N,132,147,2,66.0,0.0,0.5,0.0,16.0,0.3,82.8,0.0 +1,2019-01-01 01:36:23,2019-01-01 01:48:47,2,1.7,1,N,224,90,1,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-12 22:17:24,2019-01-12 22:36:48,2,2.0,1,N,224,158,1,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-23 07:55:25,2019-01-23 09:23:08,2,18.19,2,N,132,234,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56,0.0 +1,2019-01-19 22:26:41,2019-01-19 22:30:15,2,0.5,1,N,164,170,1,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +1,2019-01-11 12:33:28,2019-01-11 13:07:09,2,9.6,1,N,138,43,1,32.5,0.5,0.5,7.9,5.76,0.3,47.46, +1,2019-01-09 11:50:03,2019-01-09 11:57:49,2,0.7,1,N,237,162,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +2,2019-01-20 22:30:48,2019-01-20 22:54:07,2,9.37,1,N,125,89,1,28.5,0.5,0.5,5.96,0.0,0.3,35.76, +2,2019-01-13 14:22:14,2019-01-13 14:41:20,2,6.34,1,N,244,48,1,22.0,0.0,0.5,4.56,0.0,0.3,27.36, +1,2019-01-08 10:37:28,2019-01-08 10:47:50,2,1.6,1,N,239,143,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +1,2019-01-13 09:47:11,2019-01-13 09:50:47,2,0.4,1,N,48,230,1,4.0,0.0,0.5,2.0,0.0,0.3,6.8, +2,2019-01-13 10:31:30,2019-01-13 10:48:12,2,3.79,1,N,223,138,1,14.5,0.0,0.5,3.06,0.0,0.3,20.31, +2,2019-01-11 09:37:02,2019-01-11 09:50:27,2,1.7,1,N,144,90,1,10.0,0.0,0.5,1.73,0.0,0.3,12.53, +2,2019-01-02 19:22:37,2019-01-02 19:37:53,2,4.16,1,N,161,79,1,15.0,1.0,0.5,4.0,0.0,0.3,20.8, +2,2019-01-14 13:49:44,2019-01-14 14:04:01,2,1.86,1,N,234,230,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-27 00:12:09,2019-01-27 00:18:57,2,0.89,1,N,164,48,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-12 19:34:04,2019-01-12 19:55:35,2,4.0,1,N,164,75,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +1,2019-01-02 00:11:45,2019-01-02 00:29:24,2,3.7,1,N,114,49,1,15.5,0.5,0.5,4.2,0.0,0.3,21.0, +2,2019-01-25 16:36:58,2019-01-25 17:11:00,2,4.04,1,N,261,48,2,21.5,1.0,0.5,0.0,0.0,0.3,23.3,0.0 +2,2019-01-18 21:05:54,2019-01-18 21:17:45,2,1.86,1,N,234,231,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-06 18:51:18,2019-01-06 19:01:54,2,1.59,1,N,234,4,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-18 16:46:55,2019-01-18 17:03:24,2,1.74,1,N,161,140,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-26 17:59:37,2019-01-26 18:03:27,2,0.69,1,N,142,239,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-25 16:25:24,2019-01-25 16:34:42,2,0.81,1,N,249,79,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-18 19:03:42,2019-01-18 19:14:13,2,1.7,1,N,238,263,1,8.5,1.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-09 22:03:26,2019-01-09 22:06:48,2,0.7,1,N,33,33,1,4.5,0.5,0.5,2.0,0.0,0.3,7.8, +1,2019-01-07 09:10:25,2019-01-07 09:28:59,2,2.2,1,N,236,162,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-26 00:57:30,2019-01-26 01:04:46,2,2.7,1,N,263,137,1,9.0,0.5,0.5,2.0,0.0,0.3,12.3,0.0 +2,2019-01-30 10:26:52,2019-01-30 10:48:32,2,6.3,1,N,170,231,1,22.0,0.0,0.5,4.0,0.0,0.3,26.8,0.0 +1,2019-01-21 17:19:00,2019-01-21 17:25:25,2,1.2,1,N,263,140,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-26 22:23:01,2019-01-26 22:39:19,2,3.0,1,N,209,97,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-28 11:39:27,2019-01-28 11:47:34,2,1.06,1,N,236,140,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-10 18:32:48,2019-01-10 18:48:52,2,2.37,1,N,158,48,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-24 19:28:52,2019-01-24 19:34:46,2,0.7,1,N,113,114,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-11 13:12:16,2019-01-11 13:46:23,2,10.4,1,N,138,244,1,34.0,0.0,0.5,8.1,5.76,0.3,48.66, +2,2019-01-22 19:19:34,2019-01-22 19:25:17,2,0.89,1,N,164,234,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-19 23:29:28,2019-01-19 23:34:22,2,0.52,1,N,148,45,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-06 21:02:46,2019-01-06 21:13:29,2,2.19,1,N,230,237,1,9.5,0.5,0.5,1.5,0.0,0.3,12.3, +2,2019-01-26 22:29:58,2019-01-26 22:39:16,2,2.02,1,N,116,24,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-31 16:55:38,2019-01-31 17:02:22,2,0.69,1,N,249,158,1,6.0,1.0,0.5,1.2,0.0,0.3,9.0,0.0 +1,2019-01-16 08:06:17,2019-01-16 08:09:33,2,0.4,1,N,237,263,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-29 08:02:06,2019-01-29 08:06:06,2,0.78,1,N,186,161,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-30 18:33:00,2019-01-30 18:40:10,2,1.2,1,N,114,231,1,7.0,1.0,0.5,2.64,0.0,0.3,11.44,0.0 +2,2019-01-13 20:26:16,2019-01-13 20:31:22,2,0.96,1,N,234,186,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-19 09:38:42,2019-01-19 09:43:33,2,1.23,1,N,163,186,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-23 14:00:55,2019-01-23 14:10:37,2,5.63,1,N,132,219,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3,0.0 +2,2019-01-01 21:18:12,2019-01-01 21:32:21,2,4.09,2,N,145,246,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-21 19:28:18,2019-01-21 19:38:54,2,1.8,1,N,97,49,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-20 01:35:28,2019-01-20 01:59:28,2,7.42,1,N,164,37,1,24.5,0.5,0.5,7.89,5.76,0.3,39.45, +2,2019-01-06 20:09:49,2019-01-06 20:14:30,2,0.7,1,N,161,233,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-19 17:12:00,2019-01-19 17:35:59,2,2.9,1,N,230,249,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-19 14:30:31,2019-01-19 14:33:58,2,0.88,1,N,237,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-27 03:50:19,2019-01-27 04:05:43,2,4.63,1,N,148,236,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56,0.0 +1,2019-01-27 10:02:48,2019-01-27 10:29:11,2,17.7,3,N,264,264,1,64.5,0.0,0.0,5.0,10.5,0.3,80.3,0.0 +1,2019-01-15 07:49:13,2019-01-15 07:59:52,2,0.6,1,N,246,186,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-22 17:22:44,2019-01-22 17:39:55,2,2.06,1,N,234,170,1,12.0,1.0,0.5,1.0,0.0,0.3,14.8,0.0 +2,2019-01-12 00:57:17,2019-01-12 01:02:20,2,0.52,1,N,148,148,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-29 19:23:24,2019-01-29 19:40:25,2,1.4,1,N,25,189,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-19 14:52:56,2019-01-19 15:03:13,2,2.3,1,N,68,13,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42, +1,2019-01-25 12:26:50,2019-01-25 12:50:51,2,8.5,1,N,138,230,1,27.5,0.0,0.5,6.0,5.76,0.3,40.06,0.0 +2,2019-01-08 18:30:29,2019-01-08 18:33:37,2,0.65,1,N,40,25,1,4.0,1.0,0.5,0.5,0.0,0.3,6.3, +2,2019-01-11 19:36:31,2019-01-11 19:55:07,2,2.57,1,N,164,142,1,13.5,1.0,0.5,4.59,0.0,0.3,19.89, +2,2019-01-17 08:16:09,2019-01-17 08:51:36,2,9.25,1,N,236,231,1,32.0,0.0,0.5,6.56,0.0,0.3,39.36, +1,2019-01-07 19:51:08,2019-01-07 20:22:11,2,5.9,1,N,164,142,1,23.5,1.0,0.5,5.05,0.0,0.3,30.35, +2,2019-01-25 12:31:18,2019-01-25 13:06:26,2,3.94,1,N,85,39,1,23.5,0.0,0.5,0.0,0.0,0.3,24.3,0.0 +1,2019-01-01 22:52:16,2019-01-01 22:59:23,2,1.3,1,N,79,186,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +1,2019-01-23 07:46:57,2019-01-23 07:52:27,2,0.7,1,N,263,262,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55,0.0 +2,2019-01-30 17:42:43,2019-01-30 17:53:43,2,1.89,1,N,236,74,1,9.5,1.0,0.5,1.0,0.0,0.3,12.3,0.0 +1,2019-01-24 10:13:05,2019-01-24 10:35:42,2,7.2,1,N,107,42,2,24.0,0.0,0.5,0.0,0.0,0.3,24.8,0.0 +2,2019-01-17 18:43:38,2019-01-17 18:53:57,2,1.22,1,N,229,230,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-19 11:51:44,2019-01-19 12:00:27,2,0.45,1,N,239,142,1,6.5,0.0,0.5,0.73,0.0,0.3,8.03, +1,2019-01-15 12:54:13,2019-01-15 13:15:26,2,1.8,1,N,237,142,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-08 14:54:58,2019-01-08 15:11:59,2,1.94,1,N,238,236,1,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-21 14:52:34,2019-01-21 15:00:24,2,1.3,1,N,239,163,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-18 11:57:09,2019-01-18 12:08:32,2,0.83,1,N,161,162,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-04 12:35:13,2019-01-04 12:41:09,2,1.04,1,N,74,75,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-16 20:15:34,2019-01-16 20:22:34,2,1.14,1,N,148,231,1,6.5,0.5,0.5,1.17,0.0,0.3,8.97, +1,2019-01-27 16:37:26,2019-01-27 16:46:44,2,1.0,1,N,163,186,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-10 19:19:14,2019-01-10 19:35:49,2,1.85,1,N,264,264,1,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-30 08:27:23,2019-01-30 08:32:51,2,0.65,1,N,186,234,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-05 19:28:49,2019-01-05 19:38:21,2,1.93,1,N,48,68,1,8.5,0.0,0.5,1.67,0.0,0.3,10.97, +1,2019-01-16 13:03:23,2019-01-16 13:36:26,2,18.8,2,N,132,141,1,52.0,0.0,0.5,6.0,5.76,0.3,64.56, +2,2019-01-31 09:27:54,2019-01-31 10:05:20,2,3.93,1,N,186,236,2,24.0,0.0,0.5,0.0,0.0,0.3,24.8,0.0 +2,2019-01-12 13:36:01,2019-01-12 13:46:05,2,1.35,1,N,48,90,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-25 17:47:42,2019-01-25 17:52:37,2,0.6,1,N,68,230,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-20 19:50:38,2019-01-20 19:57:43,2,1.6,1,N,68,100,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-14 17:44:49,2019-01-14 17:58:06,2,1.03,1,N,230,162,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-13 19:26:13,2019-01-13 19:43:29,2,3.3,1,N,144,230,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-21 09:43:26,2019-01-21 09:57:05,2,1.9,1,N,48,142,3,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-01 17:26:33,2019-01-01 18:02:29,2,18.3,2,N,132,79,1,52.0,4.5,0.5,11.46,0.0,0.3,68.76, +2,2019-01-05 15:06:17,2019-01-05 15:34:16,2,3.55,1,N,186,87,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +1,2019-01-12 12:42:57,2019-01-12 12:55:57,2,1.8,1,N,161,239,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-05 19:55:42,2019-01-05 20:03:06,2,1.5,1,N,186,50,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +1,2019-01-05 12:43:02,2019-01-05 12:48:43,2,0.7,1,N,263,263,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-27 12:07:27,2019-01-27 12:13:51,2,0.91,1,N,90,113,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-26 20:18:58,2019-01-26 20:28:26,2,1.13,1,N,162,230,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-29 20:58:17,2019-01-29 21:09:02,2,2.2,1,N,161,236,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-23 14:01:35,2019-01-23 14:09:18,2,0.67,1,N,261,13,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-02 18:22:43,2019-01-02 18:56:23,2,15.84,1,N,132,145,1,44.0,1.0,0.5,9.16,0.0,0.3,54.96, +1,2019-01-26 14:32:36,2019-01-26 14:52:40,2,2.3,1,N,162,90,1,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-20 12:55:46,2019-01-20 13:10:11,2,2.91,1,N,142,262,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-22 08:11:07,2019-01-22 08:21:40,2,1.9,1,N,68,158,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-11 18:39:25,2019-01-11 18:52:46,2,2.01,1,N,24,75,1,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-27 08:00:14,2019-01-27 08:03:36,2,0.69,1,N,79,232,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-22 18:07:41,2019-01-22 18:16:56,2,1.4,1,N,237,140,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-31 16:23:30,2019-01-31 16:46:58,2,1.9,1,N,90,163,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8,0.0 +1,2019-01-06 19:59:40,2019-01-06 20:11:38,2,2.7,1,N,100,141,1,11.0,0.5,0.5,1.23,0.0,0.3,13.53, +1,2019-01-17 14:03:15,2019-01-17 14:08:44,2,0.8,1,N,237,161,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 12:53:40,2019-01-01 12:59:55,2,1.5,1,N,236,161,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-16 08:48:14,2019-01-16 08:50:12,2,0.39,1,N,68,246,1,3.5,0.0,0.5,0.5,0.0,0.3,4.8, +1,2019-01-03 12:43:26,2019-01-03 12:46:34,2,1.0,1,N,170,234,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-07 02:33:50,2019-01-07 02:37:12,2,0.82,1,N,161,162,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-25 01:57:19,2019-01-25 02:04:02,2,3.4,1,N,87,224,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-12 21:19:11,2019-01-12 21:39:18,2,2.4,1,N,142,186,1,14.0,0.5,0.5,3.05,0.0,0.3,18.35, +1,2019-01-27 11:51:49,2019-01-27 11:55:29,2,0.6,1,N,246,246,1,4.5,0.0,0.5,1.5,0.0,0.3,6.8,0.0 +1,2019-01-12 13:00:26,2019-01-12 13:09:54,2,1.6,1,N,236,239,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6, +2,2019-01-25 22:42:44,2019-01-25 23:05:03,2,3.33,1,N,148,112,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-24 06:55:44,2019-01-24 07:00:54,2,1.26,1,N,237,161,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-07 10:20:13,2019-01-07 10:26:59,2,0.85,1,N,224,234,1,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-12 19:37:30,2019-01-12 19:45:51,2,0.8,1,N,249,114,1,6.5,0.0,0.5,1.8,0.0,0.3,9.1, +1,2019-01-05 15:13:15,2019-01-05 15:16:38,2,0.5,1,N,263,263,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-11 19:34:45,2019-01-11 19:43:35,2,0.85,1,N,230,48,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-27 10:03:44,2019-01-27 10:13:50,2,1.86,1,N,263,239,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-22 20:30:41,2019-01-22 20:35:10,2,0.69,1,N,236,237,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-06 17:56:17,2019-01-06 18:01:38,2,0.99,1,N,79,234,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-24 19:32:49,2019-01-24 19:51:50,2,2.04,1,N,162,234,1,12.5,1.0,0.5,1.0,0.0,0.3,15.3,0.0 +2,2019-01-24 07:50:27,2019-01-24 07:58:33,2,0.8,1,N,230,161,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-02 09:13:34,2019-01-02 09:19:43,2,1.24,1,N,140,229,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-02 14:33:17,2019-01-02 14:55:11,2,3.09,1,N,230,144,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-21 21:22:59,2019-01-21 21:31:13,2,2.41,1,N,230,239,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-12 00:09:01,2019-01-12 00:13:40,2,0.81,1,N,186,230,1,5.0,0.5,0.5,1.89,0.0,0.3,8.19, +2,2019-01-19 15:59:48,2019-01-19 16:05:44,2,1.14,1,N,170,237,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 10:47:56,2019-01-24 11:00:15,2,0.94,1,N,170,48,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-22 17:53:10,2019-01-22 17:59:58,2,0.8,1,N,107,79,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-25 12:10:52,2019-01-25 12:20:41,2,1.04,1,N,140,237,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-05 13:08:04,2019-01-05 13:16:48,2,1.7,1,N,236,238,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +1,2019-01-31 10:03:28,2019-01-31 10:17:43,2,1.9,1,N,264,264,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-18 13:30:38,2019-01-18 13:33:40,2,0.49,1,N,143,142,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-06 12:21:07,2019-01-06 12:39:57,2,8.6,1,N,237,138,2,25.5,0.0,0.5,0.0,5.76,0.3,32.06, +2,2019-01-22 09:42:58,2019-01-22 10:25:18,2,9.71,1,N,138,234,1,34.5,0.0,0.5,8.21,5.76,0.3,49.27,0.0 +1,2019-01-21 15:18:08,2019-01-21 15:38:06,2,3.8,1,N,249,237,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55,0.0 +2,2019-01-26 11:33:08,2019-01-26 11:37:30,2,1.04,1,N,24,166,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-29 18:21:57,2019-01-29 18:29:49,2,1.08,1,N,75,263,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0,0.0 +1,2019-01-02 10:02:28,2019-01-02 10:18:11,2,3.6,1,N,45,48,1,14.0,0.0,0.5,4.4,0.0,0.3,19.2, +1,2019-01-17 15:55:51,2019-01-17 17:06:39,2,22.0,2,N,132,163,1,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-24 14:02:19,2019-01-24 14:36:18,2,8.93,1,N,138,229,2,30.5,0.0,0.5,0.0,0.0,0.3,31.3,0.0 +2,2019-01-10 16:30:24,2019-01-10 16:40:04,2,0.52,1,N,163,161,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-29 07:38:58,2019-01-29 07:46:25,2,1.13,1,N,140,236,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-11 15:39:03,2019-01-11 15:49:10,2,0.96,1,N,142,163,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +1,2019-01-30 18:02:42,2019-01-30 18:07:36,2,0.7,1,N,262,236,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-14 11:17:44,2019-01-14 11:25:32,2,1.3,1,N,237,141,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-08 08:58:12,2019-01-08 09:10:53,2,1.83,1,N,236,239,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +1,2019-01-15 21:01:04,2019-01-15 21:04:02,2,0.5,1,N,236,263,1,4.5,0.5,0.5,1.7,0.0,0.3,7.5, +2,2019-01-01 19:50:07,2019-01-01 19:55:31,2,0.76,1,N,144,125,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-08 19:13:25,2019-01-08 19:27:40,2,1.95,1,N,107,231,1,10.5,1.0,0.5,1.0,0.0,0.3,13.3, +1,2019-01-12 19:57:01,2019-01-12 20:07:31,2,1.9,1,N,161,236,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-08 00:30:59,2019-01-08 00:59:52,2,19.12,2,N,132,75,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-11 19:09:17,2019-01-11 19:26:41,2,2.83,1,N,236,142,1,13.5,1.0,0.5,3.0,0.0,0.3,18.3, +2,2019-01-17 23:35:20,2019-01-17 23:38:55,2,0.67,1,N,107,170,1,4.5,0.5,0.5,1.74,0.0,0.3,7.54, +1,2019-01-25 20:08:18,2019-01-25 20:50:43,2,18.3,2,N,132,48,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-12 14:07:30,2019-01-12 14:18:11,2,1.6,1,N,236,141,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-09 06:20:44,2019-01-09 06:28:39,2,1.76,1,N,100,142,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-21 16:13:27,2019-01-21 16:18:53,2,0.94,1,N,230,163,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-09 08:33:19,2019-01-09 08:59:05,2,2.5,1,N,48,237,1,17.0,0.0,0.5,1.5,0.0,0.3,19.3, +1,2019-01-14 09:48:34,2019-01-14 10:20:39,2,4.2,1,N,186,261,1,23.0,0.0,0.5,2.0,0.0,0.3,25.8, +2,2019-01-13 06:37:16,2019-01-13 06:42:01,2,0.76,1,N,48,246,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-15 06:55:24,2019-01-16 06:35:41,2,3.1,1,N,262,100,1,13.0,0.0,0.5,4.14,0.0,0.3,17.94, +2,2019-01-14 12:30:47,2019-01-14 12:39:47,2,1.16,1,N,107,164,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-02 21:20:40,2019-01-02 21:29:31,2,1.51,1,N,249,107,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +1,2019-01-07 18:42:59,2019-01-07 18:51:27,2,0.9,1,N,164,163,1,7.0,1.0,0.5,1.2,0.0,0.3,10.0, +2,2019-01-01 02:48:20,2019-01-01 03:12:33,2,3.22,1,N,255,225,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96, +2,2019-01-02 08:22:14,2019-01-02 08:55:18,2,9.05,1,N,238,20,2,29.5,0.0,0.5,0.0,0.0,0.3,30.3, +2,2019-01-05 01:01:26,2019-01-05 01:14:14,2,2.16,1,N,158,170,1,10.0,0.5,0.5,1.7,0.0,0.3,13.0, +1,2019-01-16 10:08:16,2019-01-16 11:13:21,2,18.9,2,N,132,48,4,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +1,2019-01-08 15:39:02,2019-01-08 15:48:12,2,1.1,1,N,233,230,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-22 16:54:06,2019-01-22 16:59:43,2,0.5,1,N,239,143,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-27 17:13:56,2019-01-27 17:19:20,2,1.08,1,N,239,151,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-01 15:28:13,2019-01-01 15:32:37,2,0.9,1,N,140,263,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +1,2019-01-11 18:02:42,2019-01-11 18:12:45,2,1.3,1,N,114,45,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +1,2019-01-09 11:31:39,2019-01-09 11:34:48,2,0.4,1,N,107,107,1,4.0,0.0,0.5,1.4,0.0,0.3,6.2, +1,2019-01-09 22:11:56,2019-01-09 22:24:46,2,2.5,1,N,114,161,1,11.0,0.5,0.5,2.0,0.0,0.3,14.3, +2,2019-01-10 18:12:33,2019-01-10 18:17:45,2,0.75,1,N,249,113,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-25 14:24:26,2019-01-25 14:42:43,2,1.3,1,N,107,107,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-22 13:47:57,2019-01-22 13:54:58,2,0.93,1,N,74,75,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-24 18:06:22,2019-01-24 18:21:36,2,1.76,1,N,148,113,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-13 17:35:06,2019-01-13 17:55:46,2,10.1,1,N,138,162,1,29.0,0.0,0.5,7.1,5.76,0.3,42.66, +2,2019-01-31 11:41:20,2019-01-31 11:47:13,2,0.44,1,N,164,170,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-21 14:33:40,2019-01-21 14:37:16,2,0.64,1,N,68,68,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-19 12:30:33,2019-01-19 12:48:50,2,2.86,1,N,233,113,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-23 09:43:52,2019-01-23 09:58:57,2,0.9,1,N,107,90,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95,0.0 +2,2019-01-30 05:53:57,2019-01-30 05:59:21,2,1.44,1,N,48,164,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-22 13:26:24,2019-01-22 13:45:04,2,1.49,1,N,170,107,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-06 21:14:08,2019-01-06 21:18:07,2,0.56,1,N,231,13,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-29 13:56:41,2019-01-29 14:11:57,2,1.42,1,N,170,48,1,10.5,0.0,0.5,2.5,0.0,0.3,13.8,0.0 +1,2019-01-31 12:36:25,2019-01-31 12:44:54,2,1.4,1,N,238,143,1,7.5,0.0,0.5,2.45,0.0,0.3,10.75,0.0 +2,2019-01-05 03:07:55,2019-01-05 03:15:39,2,3.1,1,N,231,164,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-07 18:29:32,2019-01-07 18:42:27,2,3.62,1,N,262,170,2,14.0,1.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-17 22:27:33,2019-01-17 22:33:08,2,0.7,1,N,189,181,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +1,2019-01-26 13:56:47,2019-01-26 14:07:17,2,0.9,1,N,87,87,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-24 20:03:51,2019-01-24 20:13:59,2,1.55,1,N,114,231,1,8.0,0.5,0.5,2.32,0.0,0.3,11.62,0.0 +1,2019-01-23 20:05:49,2019-01-23 20:15:28,2,1.6,1,N,97,52,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-13 10:29:59,2019-01-13 10:33:31,2,0.7,1,N,162,162,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-11 08:51:17,2019-01-11 08:56:08,2,0.98,1,N,238,236,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-22 21:50:42,2019-01-22 21:57:30,2,1.74,1,N,237,239,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-05 18:47:53,2019-01-05 18:55:48,2,1.65,1,N,236,239,1,8.0,0.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-11 10:19:51,2019-01-11 10:29:46,2,1.61,1,N,239,151,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-31 02:38:16,2019-01-31 02:46:48,2,1.73,1,N,144,249,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-01 20:05:18,2019-01-01 20:11:27,2,1.3,1,N,161,237,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-11 07:34:37,2019-01-11 07:54:22,2,3.3,1,N,162,231,1,14.5,0.0,0.5,2.75,0.0,0.3,18.05, +1,2019-01-13 12:17:12,2019-01-13 12:20:45,2,0.5,1,N,264,264,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-07 12:35:19,2019-01-07 12:40:02,2,0.97,1,N,7,179,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-04 18:17:03,2019-01-04 18:22:56,2,1.27,1,N,142,163,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-26 20:34:34,2019-01-26 20:41:39,2,1.01,1,N,45,209,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-14 17:48:51,2019-01-14 18:02:31,2,2.26,1,N,263,238,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-24 18:27:55,2019-01-24 18:30:56,2,0.39,1,N,161,163,1,4.0,1.0,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-07 18:27:40,2019-01-07 18:44:15,2,2.77,1,N,264,264,1,12.5,1.0,0.5,2.86,0.0,0.3,19.11, +1,2019-01-22 22:33:39,2019-01-22 22:44:09,2,1.7,1,N,164,163,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-17 06:45:42,2019-01-17 07:07:42,2,5.69,1,N,142,87,1,20.5,0.0,0.5,3.0,0.0,0.3,24.3, +2,2019-01-10 22:20:58,2019-01-10 22:31:31,2,2.71,1,N,233,263,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-17 20:39:56,2019-01-17 20:57:10,2,6.59,1,N,162,65,1,20.0,0.5,0.5,5.32,0.0,0.3,26.62, +2,2019-01-21 09:49:11,2019-01-21 09:54:18,2,2.01,1,N,233,145,2,7.5,0.0,0.5,0.0,5.76,0.3,14.06,0.0 +1,2019-01-09 10:51:01,2019-01-09 11:11:09,2,4.9,1,N,75,50,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-20 13:11:03,2019-01-20 13:19:34,2,1.46,1,N,143,143,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-20 23:30:19,2019-01-20 23:32:00,2,0.4,1,N,236,75,1,3.5,0.5,0.5,1.15,0.0,0.3,5.95, +2,2019-01-18 23:40:09,2019-01-19 00:09:07,2,10.88,1,N,236,97,1,33.5,0.5,0.5,0.0,0.0,0.3,34.8, +2,2019-01-01 05:49:29,2019-01-01 05:51:22,2,0.37,1,N,163,163,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-21 02:09:46,2019-01-21 02:11:40,2,0.72,1,N,100,161,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-14 21:23:41,2019-01-14 21:31:43,2,1.78,1,N,237,230,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-20 21:10:41,2019-01-20 21:19:03,2,1.4,1,N,125,79,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-31 19:05:15,2019-01-31 19:12:48,2,2.21,1,N,158,13,1,8.5,1.0,0.5,3.09,0.0,0.3,13.39,0.0 +2,2019-01-08 16:05:38,2019-01-08 16:11:11,2,0.87,1,N,237,236,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-06 10:22:12,2019-01-06 10:28:40,2,1.82,1,N,238,142,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-05 22:41:57,2019-01-05 22:45:40,2,0.9,1,N,141,263,1,5.0,0.5,0.5,1.55,0.0,0.3,7.85, +1,2019-01-25 20:09:35,2019-01-25 20:15:28,2,0.9,1,N,263,236,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +1,2019-01-08 20:05:28,2019-01-08 20:06:30,2,0.2,1,N,140,140,2,3.0,0.5,0.5,0.0,0.0,0.3,4.3, +2,2019-01-31 20:22:13,2019-01-31 20:34:05,2,1.13,1,N,100,170,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-15 20:55:07,2019-01-15 20:58:37,2,1.18,1,N,137,79,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-17 21:45:54,2019-01-17 22:02:44,2,6.29,1,N,116,100,1,20.5,0.5,0.5,3.5,0.0,0.3,25.3, +2,2019-01-31 16:07:25,2019-01-31 16:56:53,2,10.28,1,N,138,163,1,39.0,1.0,0.5,4.5,5.76,0.3,51.06,0.0 +2,2019-01-03 17:36:22,2019-01-03 17:38:02,2,0.0,5,N,132,132,1,68.0,0.0,0.5,17.2,0.0,0.3,86.0, +1,2019-01-15 16:38:47,2019-01-15 16:46:55,2,1.6,1,N,143,151,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-18 16:19:30,2019-01-18 16:29:10,2,0.6,1,N,237,163,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-16 13:21:45,2019-01-16 13:42:27,2,1.5,1,N,230,233,1,13.0,0.0,0.5,2.0,0.0,0.3,15.8, +1,2019-01-30 10:50:15,2019-01-30 11:14:24,2,3.2,1,N,125,230,1,16.5,0.0,0.5,3.45,0.0,0.3,20.75,0.0 +2,2019-01-26 22:34:59,2019-01-26 22:55:31,2,3.08,1,N,68,162,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-18 23:43:21,2019-01-18 23:50:42,2,1.13,1,N,75,24,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-05 09:57:10,2019-01-05 10:10:42,2,2.7,1,N,231,164,1,12.0,0.0,0.5,1.92,0.0,0.3,14.72, +1,2019-01-20 11:55:36,2019-01-20 12:07:59,2,2.8,1,N,230,125,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-24 22:52:52,2019-01-24 22:58:11,2,0.8,1,N,249,90,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-11 17:28:36,2019-01-11 17:46:08,2,1.27,1,N,141,233,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-24 08:19:27,2019-01-24 08:42:58,2,2.3,1,N,68,163,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95,0.0 +1,2019-01-27 15:17:10,2019-01-27 15:26:01,2,1.7,1,N,163,186,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +1,2019-01-21 21:23:25,2019-01-21 21:28:12,2,1.2,1,N,137,224,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-03 10:03:01,2019-01-03 10:17:07,2,2.22,1,N,238,230,1,10.5,0.0,0.5,2.7,0.0,0.3,14.0, +2,2019-01-15 08:21:12,2019-01-15 08:24:35,2,0.76,1,N,186,234,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-14 14:31:54,2019-01-14 14:49:31,2,2.5,1,N,229,234,1,13.0,0.0,0.5,3.45,0.0,0.3,17.25, +1,2019-01-25 20:52:43,2019-01-25 21:01:18,2,2.1,1,N,50,237,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-22 20:50:01,2019-01-22 20:59:05,2,1.72,1,N,164,158,1,8.5,0.5,0.5,1.0,0.0,0.3,10.8,0.0 +1,2019-01-27 21:16:43,2019-01-27 21:31:26,2,3.0,1,N,230,231,1,12.0,0.5,0.5,2.65,0.0,0.3,15.95,0.0 +2,2019-01-04 21:33:03,2019-01-04 21:36:16,2,0.68,1,N,170,107,1,4.5,0.5,0.5,1.45,0.0,0.3,7.25, +2,2019-01-17 14:33:22,2019-01-17 14:51:32,2,3.35,5,N,161,265,1,40.25,0.0,0.5,10.31,10.5,0.3,61.86, +2,2019-01-22 09:08:39,2019-01-22 09:16:47,2,2.1,1,N,231,246,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78,0.0 +1,2019-01-15 17:56:50,2019-01-15 18:08:33,2,1.4,1,N,164,113,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +1,2019-01-09 18:39:43,2019-01-09 18:51:15,2,1.6,1,N,100,113,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-07 11:45:28,2019-01-07 12:06:29,2,3.56,1,N,123,91,1,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-03 18:02:31,2019-01-03 18:28:55,2,4.64,1,N,43,158,2,19.0,1.0,0.5,0.0,0.0,0.3,20.8, +2,2019-01-03 18:07:32,2019-01-03 18:27:28,2,2.03,1,N,239,230,1,13.5,1.0,0.5,3.3,0.0,0.3,18.6, +2,2019-01-26 12:17:16,2019-01-26 12:18:35,2,0.37,1,N,141,141,1,3.0,0.0,0.5,1.14,0.0,0.3,4.94,0.0 +2,2019-01-06 02:26:34,2019-01-06 02:36:49,2,2.01,1,N,50,164,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-10 12:51:22,2019-01-10 12:59:29,2,0.93,1,N,236,237,1,7.0,0.0,0.5,0.78,0.0,0.3,8.58, +2,2019-01-26 09:14:55,2019-01-26 09:27:09,2,2.46,1,N,170,45,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-14 07:25:35,2019-01-14 07:34:12,2,1.87,1,N,236,229,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-11 13:12:47,2019-01-11 13:18:36,2,0.87,1,N,237,141,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-18 14:40:40,2019-01-18 14:54:13,2,2.0,1,N,170,114,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +2,2019-01-23 19:48:06,2019-01-23 20:03:43,2,2.46,1,N,249,161,2,11.5,1.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-14 15:45:21,2019-01-14 15:56:16,2,1.06,1,N,161,237,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-09 23:20:53,2019-01-09 23:29:27,2,1.48,1,N,114,4,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +1,2019-01-24 15:50:08,2019-01-24 16:07:15,2,1.1,1,N,186,230,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-24 16:59:37,2019-01-24 17:24:55,2,2.13,1,N,237,186,1,16.0,1.0,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-20 13:01:38,2019-01-20 13:05:02,2,0.65,1,N,264,264,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-20 14:51:50,2019-01-20 14:57:25,2,0.7,1,N,142,142,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-25 18:45:07,2019-01-25 19:18:45,2,16.66,2,N,132,162,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67,0.0 +2,2019-01-19 13:50:06,2019-01-19 14:05:01,2,10.06,1,N,132,196,2,28.0,0.0,0.5,0.0,0.0,0.3,28.8, +2,2019-01-04 17:27:13,2019-01-04 17:51:08,2,3.6,1,N,107,142,2,17.0,1.0,0.5,0.0,0.0,0.3,18.8, +1,2019-01-23 22:32:19,2019-01-23 23:01:40,2,10.4,1,N,230,133,1,33.0,0.5,0.5,8.0,5.76,0.3,48.06,0.0 +2,2019-01-24 17:26:13,2019-01-24 18:20:22,2,6.84,1,N,140,138,1,35.5,1.0,0.5,7.46,0.0,0.3,44.76,0.0 +1,2019-01-25 01:31:27,2019-01-25 01:50:40,2,4.7,1,N,107,37,2,18.0,0.5,0.5,0.0,0.0,0.3,19.3,0.0 +1,2019-01-11 18:04:09,2019-01-11 18:24:26,2,3.5,1,N,79,50,2,15.5,1.0,0.5,0.0,0.0,0.3,17.3, +2,2019-01-08 22:18:08,2019-01-08 22:23:35,2,0.92,1,N,231,87,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-13 14:22:55,2019-01-13 14:41:54,2,2.95,1,N,163,114,1,14.0,0.0,0.5,3.7,0.0,0.3,18.5, +2,2019-01-18 09:45:41,2019-01-18 09:50:27,2,0.91,1,N,68,158,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-10 18:32:38,2019-01-10 18:49:06,2,1.96,1,N,211,186,1,11.5,1.0,0.5,3.0,0.0,0.3,16.3, +2,2019-01-29 14:42:24,2019-01-29 14:57:09,2,1.56,1,N,161,68,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-16 13:16:19,2019-01-16 13:25:33,2,1.85,1,N,236,143,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-31 16:05:02,2019-02-01 15:11:52,2,0.79,1,N,161,100,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-06 11:10:45,2019-01-06 11:15:21,2,0.95,1,N,162,170,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-10 19:51:53,2019-01-10 20:25:35,2,17.51,2,N,132,100,2,52.0,4.5,0.5,0.0,5.76,0.3,63.06, +1,2019-01-26 22:56:02,2019-01-26 23:06:17,2,1.0,1,N,48,186,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-18 19:49:57,2019-01-18 19:58:39,2,1.09,1,N,161,229,1,7.0,1.0,0.5,1.32,0.0,0.3,10.12, +2,2019-01-03 10:16:30,2019-01-03 10:46:39,2,11.76,1,N,125,247,2,36.5,0.0,0.5,0.0,0.0,0.3,37.3, +2,2019-01-06 17:42:56,2019-01-06 18:01:56,2,2.2,1,N,262,162,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-18 22:00:39,2019-01-18 22:14:17,2,2.5,1,N,166,239,1,12.0,0.5,0.5,1.5,0.0,0.3,14.8, +2,2019-01-09 12:51:51,2019-01-09 13:02:33,2,0.44,1,N,48,163,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-15 15:18:42,2019-01-15 15:32:45,2,1.79,1,N,114,107,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +1,2019-01-02 18:28:21,2019-01-02 19:33:05,2,19.0,2,N,246,132,2,52.0,4.5,0.5,0.0,5.76,0.3,63.06, +2,2019-01-11 10:42:09,2019-01-11 11:34:08,2,10.34,1,N,229,262,1,42.0,0.0,0.5,9.71,5.76,0.3,58.27, +2,2019-01-30 20:25:28,2019-01-30 20:29:04,2,0.57,1,N,186,100,1,4.5,0.5,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-26 14:45:02,2019-01-26 15:21:41,2,26.88,2,N,132,13,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-22 10:36:01,2019-01-22 10:50:51,2,1.66,1,N,237,170,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-11 16:25:18,2019-01-11 16:36:00,2,0.7,1,N,233,230,2,7.5,1.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-11 12:54:41,2019-01-11 13:11:47,2,2.0,1,N,142,162,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-28 18:16:01,2019-01-28 18:23:44,2,0.65,1,N,234,234,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-13 14:56:35,2019-01-13 15:19:26,2,9.4,1,N,138,83,2,31.5,0.0,0.5,0.0,0.0,0.3,32.3, +1,2019-01-05 22:04:18,2019-01-05 22:32:46,2,10.4,1,N,138,141,1,32.0,0.5,0.5,7.81,5.76,0.3,46.87, +1,2019-01-28 19:39:18,2019-01-28 19:41:16,2,0.5,1,N,137,107,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-23 06:56:17,2019-01-23 07:11:30,2,2.91,1,N,236,234,1,12.5,0.0,0.5,1.33,0.0,0.3,14.63,0.0 +2,2019-01-20 09:47:18,2019-01-20 09:58:29,2,2.23,1,N,141,143,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +1,2019-01-06 11:36:32,2019-01-06 11:38:25,2,0.4,1,N,246,246,1,3.5,0.0,0.5,0.85,0.0,0.3,5.15, +1,2019-01-14 17:07:07,2019-01-14 17:17:36,2,1.6,1,N,236,238,1,9.0,1.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-16 08:40:00,2019-01-16 08:45:44,2,1.57,1,N,140,233,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-27 11:17:42,2019-01-27 11:21:16,2,0.6,1,N,263,75,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3,0.0 +1,2019-01-10 16:04:43,2019-01-10 16:09:15,2,0.7,1,N,48,142,1,5.0,1.0,0.5,1.7,0.0,0.3,8.5, +1,2019-01-28 13:21:41,2019-01-28 13:28:36,2,0.8,1,N,164,186,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-01 17:21:19,2019-01-01 17:38:05,2,3.85,1,N,151,48,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-22 08:09:02,2019-01-22 08:23:55,2,2.0,1,N,162,140,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-24 19:35:16,2019-01-24 19:45:15,2,1.13,1,N,237,142,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-01 04:39:44,2019-01-01 04:42:46,2,0.82,1,N,186,246,1,4.5,0.5,0.5,1.45,0.0,0.3,7.25, +2,2019-01-19 17:35:53,2019-01-19 18:00:51,2,7.56,1,N,239,87,1,25.5,0.0,0.5,0.1,0.0,0.3,26.4, +2,2019-01-24 07:52:12,2019-01-24 07:57:17,2,0.91,1,N,237,236,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-21 09:28:00,2019-01-21 09:33:05,2,1.03,1,N,79,234,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-30 08:26:09,2019-01-30 08:30:05,2,0.58,1,N,113,90,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-28 18:26:44,2019-01-28 18:58:09,2,4.58,1,N,43,211,1,21.0,1.0,0.5,4.56,0.0,0.3,27.36,0.0 +1,2019-01-27 13:24:41,2019-01-27 13:41:05,2,2.3,1,N,74,166,1,12.5,0.0,0.5,2.0,0.0,0.3,15.3,0.0 +2,2019-01-29 14:34:23,2019-01-29 15:04:13,2,4.22,1,N,142,164,1,20.5,0.0,0.5,3.2,0.0,0.3,24.5,0.0 +2,2019-01-15 22:42:46,2019-01-15 22:55:11,2,1.52,1,N,162,50,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +1,2019-01-06 12:27:51,2019-01-06 12:37:28,2,1.7,1,N,236,162,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-06 18:33:06,2019-01-06 19:10:03,2,18.36,2,N,132,68,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56, +1,2019-01-19 11:35:06,2019-01-19 11:38:31,2,0.4,1,N,262,263,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-16 08:00:51,2019-01-16 08:05:41,2,1.05,1,N,40,195,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-03 18:55:47,2019-01-03 18:57:55,2,0.69,1,N,162,141,1,4.0,1.0,0.5,1.74,0.0,0.3,7.54, +2,2019-01-10 13:30:46,2019-01-10 14:08:06,2,17.06,1,N,132,255,1,48.0,0.0,0.5,9.76,0.0,0.3,58.56, +2,2019-01-16 15:33:00,2019-01-16 15:53:38,2,3.0,1,N,186,148,1,14.5,0.0,0.5,3.82,0.0,0.3,19.12, +1,2019-01-17 18:13:24,2019-01-17 18:51:41,2,6.5,1,N,88,230,1,27.0,1.0,0.5,5.75,0.0,0.3,34.55, +2,2019-01-09 17:37:51,2019-01-09 17:49:26,2,1.63,1,N,237,142,1,9.5,1.0,0.5,1.5,0.0,0.3,12.8, +2,2019-01-24 14:07:26,2019-01-24 14:16:45,2,0.84,1,N,79,4,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-17 20:14:28,2019-01-17 20:26:32,2,1.6,1,N,79,164,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +2,2019-01-26 00:29:37,2019-01-26 00:37:55,2,1.53,1,N,90,48,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-19 10:53:56,2019-01-19 11:00:16,2,0.92,1,N,237,140,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-16 07:32:17,2019-01-16 07:39:45,2,0.89,1,N,263,262,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-28 20:20:41,2019-01-28 20:33:59,2,2.7,1,N,142,236,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-13 11:58:35,2019-01-13 12:14:12,2,3.34,1,N,140,100,1,14.0,0.0,0.5,1.85,0.0,0.3,16.65, +2,2019-01-25 19:40:08,2019-01-25 19:46:15,2,0.77,1,N,237,163,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-09 19:16:44,2019-01-09 19:24:31,2,1.0,1,N,48,143,1,7.0,1.0,0.5,1.32,0.0,0.3,10.12, +2,2019-01-09 13:07:54,2019-01-09 13:39:42,2,19.14,5,N,132,263,1,52.0,0.0,0.5,5.0,5.76,0.3,63.56, +1,2019-01-31 18:49:51,2019-01-31 19:11:59,2,2.7,1,N,186,140,1,15.0,1.0,0.5,2.0,0.0,0.3,18.8,0.0 +2,2019-01-20 22:20:23,2019-01-20 22:24:43,2,0.98,1,N,229,137,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-11 23:26:38,2019-01-12 00:16:09,2,7.1,1,N,142,145,1,31.5,0.5,0.5,6.55,0.0,0.3,39.35, +2,2019-01-23 12:33:52,2019-01-23 12:41:49,2,1.28,1,N,239,43,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-12 07:22:22,2019-01-12 07:33:09,2,4.34,1,N,79,66,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-29 18:11:32,2019-01-29 18:32:19,2,6.52,1,N,65,229,1,21.5,1.0,0.5,4.66,0.0,0.3,27.96,0.0 +1,2019-01-24 10:51:19,2019-01-24 11:00:49,2,1.6,1,N,238,142,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15,0.0 +2,2019-01-10 18:56:59,2019-01-10 19:06:09,2,1.11,1,N,100,162,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-12 12:20:18,2019-01-12 12:41:54,2,3.16,1,N,232,68,1,16.0,0.0,0.5,3.0,0.0,0.3,19.8, +1,2019-01-17 17:15:09,2019-01-17 17:21:04,2,0.9,1,N,263,237,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-29 13:46:23,2019-01-29 13:55:03,2,1.29,1,N,151,166,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-13 12:38:19,2019-01-13 12:46:15,2,0.88,1,N,233,164,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-25 20:04:25,2019-01-25 20:27:42,2,5.4,1,N,234,33,1,20.5,0.5,0.5,20.5,0.0,0.3,42.3,0.0 +2,2019-01-03 08:18:27,2019-01-03 08:24:23,2,1.15,1,N,262,237,1,6.0,0.0,0.5,0.68,0.0,0.3,7.48, +2,2019-01-24 06:08:59,2019-01-24 06:16:55,2,1.56,1,N,186,233,1,7.5,0.0,0.5,3.0,0.0,0.3,11.3,0.0 +1,2019-01-06 15:28:56,2019-01-06 16:10:03,2,20.2,2,N,132,140,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +1,2019-01-28 06:49:43,2019-01-28 06:53:36,2,0.5,1,N,48,100,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-28 22:56:55,2019-01-28 23:09:27,2,3.25,1,N,170,239,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-31 08:21:59,2019-01-31 08:42:51,2,2.0,1,N,79,125,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75,0.0 +2,2019-01-21 10:52:16,2019-01-21 10:55:16,2,0.65,1,N,161,142,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-03 21:44:03,2019-01-03 22:02:54,2,3.5,1,N,100,151,1,15.5,0.5,0.5,3.35,0.0,0.3,20.15, +2,2019-01-13 17:22:26,2019-01-13 17:25:44,2,0.95,1,N,237,263,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-17 19:55:41,2019-01-17 20:00:13,2,0.67,1,N,142,48,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-26 21:51:18,2019-01-26 22:00:17,2,0.8,1,N,148,4,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-04 19:11:53,2019-01-04 19:21:41,2,0.8,1,N,100,230,1,7.5,1.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-24 14:39:26,2019-01-24 14:59:41,2,2.05,1,N,234,163,1,13.0,0.0,0.5,2.0,0.0,0.3,15.8,0.0 +2,2019-01-02 21:05:58,2019-01-02 21:10:35,2,1.06,1,N,229,170,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-16 09:38:32,2019-01-16 09:38:36,2,0.03,1,N,264,264,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +1,2019-01-16 08:24:58,2019-01-16 08:58:06,2,4.6,1,N,113,239,1,22.5,0.0,0.5,4.65,0.0,0.3,27.95, +1,2019-01-27 02:03:09,2019-01-27 02:16:15,2,5.1,1,N,148,263,1,17.0,0.5,0.5,3.65,0.0,0.3,21.95,0.0 +2,2019-01-01 15:27:40,2019-01-01 15:33:26,2,1.6,1,N,68,48,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-08 23:04:08,2019-01-08 23:10:37,2,1.25,1,N,230,142,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-16 22:35:58,2019-01-16 23:09:30,2,5.22,1,N,68,145,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16, +1,2019-01-23 21:11:36,2019-01-23 21:23:03,2,1.9,1,N,246,234,1,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +1,2019-01-09 15:10:11,2019-01-09 15:14:59,2,0.8,1,N,107,233,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-13 17:40:43,2019-01-13 17:45:50,2,1.1,1,N,142,236,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +1,2019-01-10 23:07:51,2019-01-10 23:16:25,2,1.6,1,N,234,161,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-25 06:44:06,2019-01-25 06:49:45,2,0.69,1,N,142,143,1,5.5,0.0,0.5,1.08,0.0,0.3,7.38,0.0 +2,2019-01-08 23:44:33,2019-01-08 23:53:52,2,2.0,1,N,161,262,1,9.0,0.5,0.5,1.03,0.0,0.3,11.33, +2,2019-01-04 06:54:24,2019-01-04 07:08:17,2,3.7,1,N,234,75,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-10 13:37:53,2019-01-10 13:49:37,2,1.3,1,N,161,234,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +1,2019-01-18 13:57:16,2019-01-18 14:27:53,2,13.2,3,N,261,1,1,55.5,0.0,0.0,13.25,10.5,0.3,79.55, +2,2019-01-27 19:33:11,2019-01-27 19:41:43,2,1.26,1,N,43,50,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-01 00:29:11,2019-01-01 00:49:11,2,7.94,1,N,255,181,1,25.0,0.5,0.5,7.89,0.0,0.3,34.19, +1,2019-01-06 13:21:23,2019-01-06 13:36:14,2,1.9,1,N,68,249,1,11.0,0.0,0.5,0.02,0.0,0.3,11.82, +2,2019-01-01 18:47:47,2019-01-01 18:57:28,2,2.26,1,N,142,263,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-19 23:09:29,2019-01-19 23:09:34,2,0.0,1,N,161,161,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-13 00:58:10,2019-01-13 01:04:20,2,1.21,1,N,246,163,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-25 23:59:47,2019-01-26 00:05:04,2,0.75,1,N,141,141,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-25 21:06:53,2019-01-25 21:29:17,2,4.8,1,N,48,148,1,18.5,0.5,0.5,3.96,0.0,0.3,23.76,0.0 +2,2019-01-09 01:16:53,2019-01-09 01:32:55,2,5.41,1,N,48,87,1,17.5,0.5,0.5,3.76,0.0,0.3,22.56, +2,2019-01-17 07:54:56,2019-01-17 08:24:04,2,4.0,1,N,48,75,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-12 11:42:07,2019-01-12 11:55:19,2,3.3,1,N,238,116,1,13.0,0.0,0.5,4.1,0.0,0.3,17.9, +1,2019-01-26 20:30:54,2019-01-26 20:35:13,2,1.1,1,N,43,239,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-22 06:43:02,2019-01-22 06:52:21,2,2.58,1,N,142,166,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-11 22:25:20,2019-01-11 22:44:39,2,4.73,5,N,234,265,1,50.0,0.0,0.5,15.24,0.0,0.3,66.04, +1,2019-01-16 08:57:12,2019-01-16 09:03:41,2,0.7,1,N,143,48,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-14 16:33:27,2019-01-14 16:40:56,2,0.66,1,N,186,100,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-26 21:05:34,2019-01-26 21:12:57,2,0.7,1,N,90,249,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75,0.0 +2,2019-01-23 07:20:12,2019-01-23 07:24:59,2,0.82,1,N,186,68,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19,0.0 +2,2019-01-21 21:10:18,2019-01-21 21:24:38,2,3.4,1,N,161,238,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-01 15:01:57,2019-01-01 15:13:02,2,1.79,1,N,246,164,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-06 12:24:38,2019-01-06 12:26:13,2,3.7,5,N,228,228,2,300.0,0.0,0.0,0.0,0.0,0.3,300.3, +1,2019-01-30 18:40:01,2019-01-30 18:52:00,2,1.4,1,N,236,237,1,9.5,1.0,0.5,1.0,0.0,0.3,12.3,0.0 +1,2019-01-06 16:14:54,2019-01-06 16:23:47,2,1.5,1,N,262,141,1,8.0,0.0,0.5,1.2,0.0,0.3,10.0, +2,2019-01-24 11:24:56,2019-01-24 11:46:00,2,2.64,1,N,142,263,1,15.0,0.0,0.5,3.95,0.0,0.3,19.75,0.0 +2,2019-01-19 22:11:04,2019-01-19 22:16:31,2,0.74,1,N,230,164,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-06 15:08:40,2019-01-06 15:20:04,2,1.1,1,N,100,170,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +1,2019-01-05 18:17:17,2019-01-05 18:37:30,2,4.6,1,N,211,48,4,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +2,2019-01-01 01:37:18,2019-01-01 01:57:19,2,2.49,1,N,90,232,1,13.5,0.5,0.5,2.0,0.0,0.3,16.8, +2,2019-01-09 20:39:19,2019-01-09 20:49:22,2,1.93,1,N,43,48,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-01 00:12:28,2019-01-01 00:30:35,2,3.04,1,N,239,141,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-01 01:53:48,2019-01-01 01:59:13,2,1.05,1,N,48,48,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +1,2019-01-12 19:04:15,2019-01-12 19:11:21,2,1.3,1,N,107,162,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-17 09:28:06,2019-01-17 09:42:56,2,2.67,1,N,48,142,1,12.5,0.0,0.5,2.0,0.0,0.3,15.3, +2,2019-01-24 08:01:12,2019-01-24 08:04:22,2,0.49,1,N,113,234,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-20 17:59:24,2019-01-20 18:07:47,2,1.49,1,N,249,90,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-15 09:43:54,2019-01-15 10:08:54,2,2.38,1,N,237,234,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16, +2,2019-01-17 22:49:28,2019-01-17 22:56:30,2,1.34,1,N,100,162,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38, +1,2019-01-25 20:12:43,2019-01-25 20:30:22,2,2.4,1,N,68,239,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-25 11:58:43,2019-01-25 12:34:46,2,10.87,5,N,213,170,1,24.2,0.0,0.5,0.0,0.0,0.3,25.0,0.0 +1,2019-01-19 14:26:16,2019-01-19 14:29:49,2,0.5,1,N,230,161,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-18 20:59:36,2019-01-18 21:08:20,2,1.24,1,N,68,234,1,7.5,0.5,0.5,1.32,0.0,0.3,10.12, +2,2019-01-15 15:19:37,2019-01-15 15:46:44,2,4.67,5,N,188,65,1,18.05,0.0,0.0,0.0,0.0,0.3,18.35, +2,2019-01-26 19:36:26,2019-01-26 19:44:18,2,0.9,1,N,137,113,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-26 19:13:26,2019-01-26 19:32:53,2,2.6,1,N,140,170,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-20 22:33:37,2019-01-20 22:36:36,2,0.45,1,N,68,249,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-12 17:45:15,2019-01-12 17:53:56,2,1.23,1,N,43,163,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-22 08:31:33,2019-01-22 09:31:29,2,14.5,1,N,138,90,1,50.0,0.0,0.5,14.1,5.76,0.3,70.66,0.0 +1,2019-01-26 18:44:33,2019-01-26 18:47:42,2,0.4,1,N,141,140,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-14 08:40:59,2019-01-14 09:35:48,2,9.85,1,N,42,231,1,38.0,0.0,0.5,0.0,0.0,0.3,38.8, +1,2019-01-20 02:04:23,2019-01-20 02:12:10,2,1.4,1,N,148,87,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-25 16:36:04,2019-01-25 16:43:27,2,1.21,1,N,43,142,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-28 10:50:57,2019-01-28 11:06:00,2,2.1,1,N,143,164,1,11.5,0.0,0.5,1.6,0.0,0.3,13.9,0.0 +2,2019-01-17 14:38:10,2019-01-17 14:43:39,2,0.56,1,N,107,137,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-24 14:29:14,2019-01-24 14:50:10,2,1.3,1,N,229,161,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55,0.0 +2,2019-01-25 16:40:46,2019-01-25 16:47:52,2,0.72,1,N,100,100,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-23 11:18:48,2019-01-23 11:25:33,2,0.87,1,N,263,262,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-01 04:19:19,2019-01-01 04:24:52,2,1.6,1,N,48,68,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +1,2019-01-19 12:24:11,2019-01-19 12:38:18,2,5.3,1,N,140,42,1,17.5,0.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-11 21:58:03,2019-01-11 22:12:04,2,8.1,2,N,132,192,3,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +2,2019-01-02 12:49:19,2019-01-02 13:13:57,2,3.38,1,N,162,231,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-08 00:09:53,2019-01-08 00:13:30,2,0.8,1,N,161,229,1,5.0,0.5,0.5,1.25,0.0,0.3,7.55, +2,2019-01-17 18:55:38,2019-01-17 19:03:14,2,1.12,1,N,237,141,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-29 19:26:31,2019-01-29 19:47:13,2,4.18,1,N,229,7,1,16.5,1.0,0.5,4.58,0.0,0.3,22.88,0.0 +1,2019-01-18 06:22:54,2019-01-18 06:31:20,2,2.9,1,N,79,140,1,10.0,0.0,0.5,0.8,0.0,0.3,11.6, +2,2019-01-17 21:50:16,2019-01-17 22:06:59,2,5.18,1,N,68,238,1,18.5,0.5,0.5,2.0,0.0,0.3,21.8, +1,2019-01-31 19:58:22,2019-01-31 20:06:31,2,0.5,1,N,161,164,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +1,2019-01-19 18:41:43,2019-01-19 18:46:49,2,1.7,1,N,50,142,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-26 02:57:20,2019-01-26 03:07:40,2,1.58,1,N,148,113,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-07 08:08:52,2019-01-07 08:20:36,2,2.32,1,N,140,162,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-19 09:03:48,2019-01-19 09:17:36,2,1.77,1,N,170,230,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-06 13:39:32,2019-01-06 13:53:53,2,2.4,1,N,158,48,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-23 16:51:09,2019-01-23 16:56:23,2,1.0,1,N,143,163,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-06 17:47:20,2019-01-06 17:51:00,2,0.5,1,N,114,114,1,4.5,1.0,0.5,1.25,0.0,0.3,7.55, +2,2019-01-24 17:29:12,2019-01-24 17:39:50,2,1.38,1,N,142,48,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-11 14:49:09,2019-01-11 15:01:56,2,1.39,1,N,164,237,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-12 16:11:43,2019-01-12 16:15:21,2,0.63,1,N,142,143,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +2,2019-01-15 01:05:08,2019-01-15 01:43:23,2,5.0,1,N,161,129,2,25.5,0.5,0.5,0.0,0.0,0.3,26.8, +2,2019-01-29 09:05:38,2019-01-29 09:21:39,2,0.75,1,N,246,68,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +1,2019-01-19 02:57:43,2019-01-19 03:03:45,2,1.4,1,N,164,90,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-16 21:52:47,2019-01-16 21:54:20,2,1.36,1,N,55,150,1,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-09 05:10:24,2019-01-09 05:33:21,2,17.3,2,N,161,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-13 08:52:14,2019-01-13 08:56:40,2,0.6,1,N,237,229,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-31 21:39:43,2019-01-31 21:45:32,2,1.09,1,N,246,68,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-28 06:51:35,2019-01-28 07:00:35,2,1.69,1,N,234,162,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3,0.0 +1,2019-01-08 12:23:37,2019-01-08 12:46:20,2,6.5,1,N,236,114,1,23.0,0.0,0.5,1.2,0.0,0.3,25.0, +2,2019-01-26 13:57:15,2019-01-26 14:05:05,2,1.58,1,N,236,43,1,8.0,0.0,0.5,2.0,0.0,0.3,10.8,0.0 +2,2019-01-04 09:23:15,2019-01-04 09:37:11,2,2.98,1,N,137,237,1,13.0,0.0,0.5,1.2,0.0,0.3,15.0, +1,2019-01-28 08:09:40,2019-01-28 08:17:46,2,1.0,1,N,75,236,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +1,2019-01-11 18:10:40,2019-01-11 18:15:47,2,0.5,1,N,239,143,1,5.0,1.0,0.5,1.02,0.0,0.3,7.82, +2,2019-01-17 14:25:03,2019-01-17 15:21:18,2,18.86,2,N,233,132,1,52.0,0.0,0.5,8.78,5.76,0.3,67.34, +2,2019-01-05 10:57:35,2019-01-05 11:07:21,2,1.9,1,N,186,125,1,8.5,0.0,0.5,2.33,0.0,0.3,11.63, +1,2019-01-12 14:27:52,2019-01-12 14:35:04,2,0.9,1,N,140,263,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-05 18:01:04,2019-01-05 18:17:12,2,1.8,1,N,186,162,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-12 13:19:11,2019-01-12 13:36:05,2,1.3,1,N,264,48,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-09 17:53:57,2019-01-09 17:58:00,2,0.75,1,N,263,141,1,4.5,1.0,0.5,1.2,0.0,0.3,7.5, +2,2019-01-08 22:18:27,2019-01-08 22:23:39,2,0.5,1,N,113,79,1,5.0,0.5,0.5,0.94,0.0,0.3,7.24, +2,2019-01-29 12:40:39,2019-01-29 12:54:50,2,1.63,1,N,141,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-17 18:09:51,2019-01-17 18:24:03,2,3.26,1,N,68,13,1,13.0,1.0,0.5,3.7,0.0,0.3,18.5, +2,2019-01-16 10:43:53,2019-01-16 10:47:23,2,0.62,1,N,236,141,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-10 10:23:17,2019-01-10 10:38:10,2,1.4,1,N,100,161,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-16 17:10:39,2019-01-16 17:27:09,2,2.6,1,N,144,256,1,14.0,1.0,0.5,4.7,0.0,0.3,20.5, +1,2019-01-22 21:52:12,2019-01-22 22:04:44,2,2.2,1,N,142,236,1,10.5,0.5,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-30 02:18:17,2019-01-30 02:28:22,2,1.9,1,N,82,226,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-11 17:29:53,2019-01-11 17:31:32,2,0.31,1,N,230,48,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-07 19:39:06,2019-01-07 19:42:12,2,0.4,1,N,263,262,1,4.0,1.0,0.5,0.29,0.0,0.3,6.09, +1,2019-01-20 21:06:56,2019-01-20 21:17:14,2,1.3,1,N,114,158,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-10 15:57:20,2019-01-10 16:12:00,2,1.9,1,N,48,170,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-16 07:57:39,2019-01-16 08:09:21,2,1.71,1,N,237,75,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-20 19:54:34,2019-01-20 20:14:01,2,5.92,1,N,161,166,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +2,2019-01-20 17:13:05,2019-01-20 17:22:52,2,1.49,1,N,230,229,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-27 00:48:05,2019-01-27 01:04:05,2,4.21,1,N,68,238,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-17 14:57:36,2019-01-17 15:05:30,2,0.86,1,N,186,48,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-19 02:36:22,2019-01-19 02:40:20,2,0.3,1,N,158,249,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-10 23:45:04,2019-01-10 23:53:22,2,1.35,1,N,234,137,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-30 08:04:30,2019-01-30 08:35:57,2,2.6,1,N,238,236,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8,0.0 +2,2019-01-24 08:28:02,2019-01-24 08:41:04,2,2.07,1,N,170,144,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-07 11:53:44,2019-01-07 11:59:41,2,0.83,1,N,186,68,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-11 21:17:18,2019-01-11 21:29:03,2,1.59,1,N,186,48,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +2,2019-01-05 04:02:36,2019-01-05 04:07:08,2,1.08,1,N,74,75,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-06 18:53:57,2019-01-06 18:58:53,2,0.9,1,N,158,125,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-23 12:46:53,2019-01-23 12:58:37,2,0.81,1,N,170,161,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-05 21:07:58,2019-01-05 21:10:49,2,0.54,1,N,164,234,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-30 10:54:38,2019-01-30 11:06:00,2,1.59,1,N,164,246,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-24 14:05:21,2019-01-24 14:55:50,2,5.88,1,N,231,164,2,32.0,0.0,0.5,0.0,0.0,0.3,32.8,0.0 +2,2019-01-25 12:29:36,2019-01-25 12:31:02,2,0.29,1,N,100,100,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-25 10:26:05,2019-01-25 10:48:20,2,1.77,1,N,186,162,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-19 08:33:35,2019-01-19 08:45:20,2,3.1,1,N,233,43,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-13 09:33:34,2019-01-13 09:39:43,2,1.51,1,N,68,246,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-24 20:30:38,2019-01-24 20:41:41,2,1.17,1,N,234,186,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-01 05:11:48,2019-01-01 05:43:46,2,9.16,1,N,48,61,1,30.0,0.5,0.5,6.26,0.0,0.3,37.56, +2,2019-01-12 11:38:40,2019-01-12 11:46:32,2,0.94,1,N,262,236,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-26 19:22:16,2019-01-26 19:39:12,2,2.63,1,N,236,229,1,12.5,0.0,0.5,2.25,0.0,0.3,15.55,0.0 +2,2019-01-16 19:02:13,2019-01-16 19:11:04,2,1.64,1,N,163,236,1,8.5,1.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-10 23:03:23,2019-01-10 23:10:04,2,1.71,1,N,68,48,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-14 14:08:58,2019-01-14 15:00:51,2,18.38,2,N,132,148,1,52.0,0.0,0.5,15.84,0.0,0.3,68.64, +1,2019-01-25 18:03:35,2019-01-25 18:16:18,2,1.2,1,N,140,229,2,9.0,1.0,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-03 21:00:53,2019-01-03 21:08:02,2,1.38,1,N,68,170,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +1,2019-01-04 13:16:22,2019-01-04 13:43:51,2,3.4,1,N,113,66,1,18.5,0.0,0.5,4.8,0.0,0.3,24.1, +1,2019-01-31 11:24:53,2019-01-31 11:30:32,2,1.0,1,N,237,239,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +2,2019-01-09 20:02:36,2019-01-09 20:04:01,2,0.5,1,N,48,50,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-17 12:00:16,2019-01-17 12:16:56,2,1.82,1,N,239,237,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-21 14:01:51,2019-01-21 14:34:38,2,11.18,1,N,132,61,1,34.5,0.0,0.5,8.82,0.0,0.3,44.12,0.0 +2,2019-01-12 12:26:56,2019-01-12 12:35:38,2,1.89,1,N,238,43,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-15 22:03:15,2019-01-15 22:11:18,2,1.11,1,N,161,142,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-09 14:16:24,2019-01-09 14:36:30,2,2.8,1,N,113,50,1,15.0,0.0,0.5,3.15,0.0,0.3,18.95, +2,2019-01-31 20:57:30,2019-01-31 21:18:28,2,5.96,1,N,68,141,1,20.0,0.5,0.5,2.13,0.0,0.3,23.43,0.0 +2,2019-01-31 19:35:29,2019-01-31 19:41:32,2,0.77,1,N,100,233,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-27 22:26:17,2019-01-27 22:38:29,2,0.9,1,N,100,90,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-12 20:29:24,2019-01-12 20:34:00,2,0.75,1,N,224,4,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-16 22:00:09,2019-01-16 22:18:00,2,4.3,1,N,261,162,2,15.5,0.5,0.5,0.0,0.0,0.3,16.8, +2,2019-01-06 19:24:40,2019-01-06 19:29:34,2,1.27,1,N,43,142,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-15 16:52:07,2019-01-15 16:57:38,2,0.9,1,N,143,142,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-28 08:04:25,2019-01-28 08:59:00,2,12.57,1,N,132,97,1,44.5,0.0,0.5,13.59,0.0,0.3,58.89,0.0 +1,2019-01-26 10:42:21,2019-01-26 10:55:32,2,2.6,1,N,142,246,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-07 06:03:10,2019-01-07 06:09:50,2,2.98,1,N,262,137,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-24 20:44:01,2019-01-24 21:06:41,2,2.42,1,N,234,163,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-25 21:11:14,2019-01-25 21:22:57,2,1.48,1,N,237,229,1,9.0,0.5,0.5,2.0,0.0,0.3,12.3,0.0 +2,2019-01-06 18:03:56,2019-01-06 18:14:03,2,1.14,1,N,211,79,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-04 23:21:11,2019-01-04 23:24:43,2,0.58,1,N,107,113,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-09 00:00:09,2019-01-09 00:04:55,2,0.78,1,N,230,164,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-15 19:03:41,2019-01-15 19:37:59,2,7.31,1,N,230,66,1,27.5,1.0,0.5,5.86,0.0,0.3,35.16, +1,2019-01-07 15:41:19,2019-01-07 16:27:12,2,10.0,1,N,162,181,1,36.5,0.0,0.5,8.6,5.76,0.3,51.66, +2,2019-01-27 00:25:29,2019-01-27 00:32:02,2,1.12,1,N,234,249,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-07 16:23:33,2019-01-07 16:29:59,2,1.87,1,N,229,236,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-29 21:30:20,2019-01-29 21:44:49,2,4.49,1,N,87,181,1,15.5,0.5,0.5,2.0,5.76,0.3,24.56,0.0 +2,2019-01-14 21:32:56,2019-01-14 21:42:02,2,1.19,1,N,170,229,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-24 17:29:23,2019-01-24 17:39:30,2,1.44,1,N,263,239,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-01 16:36:24,2019-01-01 16:46:41,2,2.11,1,N,249,164,1,9.0,0.0,0.5,1.47,0.0,0.3,11.27, +2,2019-01-26 14:48:27,2019-01-26 14:57:20,2,1.26,1,N,246,68,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-12 21:10:47,2019-01-12 21:18:51,2,1.1,1,N,234,170,1,7.0,0.5,0.5,1.7,0.0,0.3,10.0, +1,2019-01-22 17:33:51,2019-01-22 17:56:22,2,3.5,1,N,79,263,1,16.5,1.0,0.5,5.45,0.0,0.3,23.75,0.0 +1,2019-01-13 13:01:35,2019-01-13 13:04:32,2,0.3,1,N,239,142,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-21 14:00:03,2019-01-21 14:06:45,2,0.7,1,N,239,142,1,6.0,0.0,0.5,0.5,0.0,0.3,7.3,0.0 +2,2019-01-16 12:31:48,2019-01-16 12:39:24,2,0.82,1,N,230,48,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +1,2019-01-19 01:00:09,2019-01-19 01:04:03,2,0.3,1,N,264,144,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-15 09:20:26,2019-01-15 09:33:02,2,1.6,1,N,264,68,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-06 14:50:13,2019-01-06 15:30:43,2,30.2,5,N,132,265,2,134.0,0.0,0.0,0.0,24.02,0.3,158.32, +1,2019-01-02 16:14:05,2019-01-02 16:17:21,2,0.7,1,N,143,48,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-23 12:55:44,2019-01-23 12:58:32,2,0.42,1,N,43,142,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-31 19:27:49,2019-01-31 19:37:58,2,1.52,1,N,211,158,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-30 21:20:46,2019-01-30 21:36:50,2,4.2,1,N,107,231,1,16.0,0.5,0.5,3.0,0.0,0.3,20.3,0.0 +2,2019-01-30 15:19:28,2019-01-30 15:19:49,2,0.03,1,N,164,170,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +1,2019-01-05 10:59:07,2019-01-05 11:06:11,2,1.3,1,N,264,264,1,7.5,0.0,0.5,2.05,0.0,0.3,10.35, +1,2019-01-20 12:45:43,2019-01-20 12:51:43,2,0.9,1,N,234,113,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-30 17:14:55,2019-01-30 17:37:34,2,2.46,1,N,239,161,2,15.5,1.0,0.5,0.0,0.0,0.3,17.3,0.0 +2,2019-01-23 09:14:41,2019-01-23 09:18:42,2,0.37,1,N,142,142,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-23 12:05:16,2019-01-23 12:16:47,2,2.55,1,N,146,162,1,11.0,0.0,0.5,1.77,0.0,0.3,13.57,0.0 +1,2019-01-30 03:05:16,2019-01-30 03:43:55,2,20.0,2,N,238,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-26 22:18:48,2019-01-26 22:32:50,2,1.8,1,N,114,158,1,10.5,0.5,0.5,1.0,0.0,0.3,12.8,0.0 +2,2019-01-27 14:28:34,2019-01-27 15:04:08,2,7.16,1,N,186,226,2,27.0,0.0,0.5,0.0,0.0,0.3,27.8,0.0 +2,2019-01-05 21:51:40,2019-01-05 21:59:55,2,1.48,1,N,79,231,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-12 15:48:27,2019-01-12 15:53:33,2,0.5,1,N,237,236,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-10 19:25:02,2019-01-10 19:54:03,2,5.3,1,N,88,230,1,22.5,1.0,0.5,4.86,0.0,0.3,29.16, +2,2019-01-14 12:53:08,2019-01-14 13:09:56,2,1.73,1,N,237,143,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +1,2019-01-22 15:13:59,2019-01-22 15:22:29,2,1.0,1,N,229,162,1,7.0,0.0,0.5,2.0,0.0,0.3,9.8,0.0 +2,2019-01-25 17:22:44,2019-01-25 17:29:15,2,0.95,1,N,211,231,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-08 20:16:07,2019-01-08 20:20:16,2,0.58,1,N,79,4,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-15 07:21:56,2019-01-15 07:24:21,2,0.53,1,N,162,162,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0, +1,2019-01-27 11:04:58,2019-01-27 11:20:22,2,3.3,1,N,161,211,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +2,2019-01-19 13:37:36,2019-01-19 13:55:09,2,2.57,1,N,142,141,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-16 10:18:37,2019-01-16 10:21:19,2,0.28,1,N,43,236,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38, +2,2019-01-23 15:15:37,2019-01-23 15:35:46,2,2.08,1,N,238,140,1,13.5,0.0,0.5,1.0,0.0,0.3,15.3,0.0 +2,2019-01-07 20:12:38,2019-01-07 20:23:49,2,2.02,1,N,151,236,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-23 05:38:46,2019-01-23 05:44:46,2,1.31,1,N,151,239,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-27 00:10:04,2019-01-27 00:17:52,2,1.33,1,N,144,79,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-22 21:59:50,2019-01-22 22:09:16,2,1.3,1,N,142,100,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-20 16:07:42,2019-01-20 16:12:02,2,0.7,1,N,79,113,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +2,2019-01-12 18:43:50,2019-01-12 19:01:41,2,2.69,1,N,113,163,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-01 01:08:25,2019-01-01 01:14:57,2,1.57,1,N,43,229,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +1,2019-01-06 15:28:13,2019-01-06 15:37:58,2,1.4,1,N,233,237,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-22 11:19:33,2019-01-22 11:32:47,2,1.76,1,N,142,141,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-10 17:45:28,2019-01-10 17:55:01,2,1.51,1,N,43,237,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-01 11:28:50,2019-01-01 11:34:02,2,0.95,1,N,237,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-17 17:14:52,2019-01-17 17:29:31,2,1.7,1,N,237,229,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-27 02:43:39,2019-01-27 02:52:34,2,1.7,1,N,114,231,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +1,2019-01-19 19:24:27,2019-01-19 19:28:11,2,0.6,1,N,234,107,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-03 09:22:57,2019-01-03 09:31:17,2,1.04,1,N,144,79,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-08 18:33:53,2019-01-08 18:53:57,2,2.76,1,N,239,140,1,14.5,1.0,0.5,3.26,0.0,0.3,19.56, +1,2019-01-19 09:32:00,2019-01-19 09:56:57,2,10.2,2,Y,93,48,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-18 19:54:16,2019-01-18 20:12:37,2,4.82,1,N,13,48,1,17.5,1.0,0.5,4.82,0.0,0.3,24.12, +2,2019-01-05 16:15:23,2019-01-05 16:20:51,2,0.85,1,N,142,48,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-29 12:42:48,2019-01-29 12:50:27,2,0.89,1,N,137,233,1,6.5,0.0,0.5,0.73,0.0,0.3,8.03,0.0 +2,2019-01-15 05:20:27,2019-01-15 05:23:03,2,0.78,1,N,113,107,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-25 22:28:21,2019-01-25 22:46:18,2,2.6,1,N,4,186,1,13.0,0.5,0.5,2.85,0.0,0.3,17.15,0.0 +2,2019-01-26 22:36:47,2019-01-26 23:02:58,2,4.35,1,N,249,141,1,18.5,0.5,0.5,3.96,0.0,0.3,23.76,0.0 +1,2019-01-17 21:16:18,2019-01-17 21:33:09,2,3.9,1,N,264,264,1,14.5,0.5,0.5,3.16,0.0,0.3,18.96, +2,2019-01-02 17:09:37,2019-01-02 17:28:12,2,1.93,1,N,239,237,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-18 21:18:21,2019-01-18 21:27:19,2,1.3,1,N,48,162,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-27 16:27:45,2019-01-27 16:36:45,2,1.93,1,N,161,234,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-07 08:06:32,2019-01-07 08:25:33,2,3.71,1,N,13,68,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-17 23:12:25,2019-01-17 23:18:47,2,1.5,1,N,48,143,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-13 18:34:41,2019-01-13 18:37:44,2,0.49,1,N,170,170,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-29 14:48:37,2019-01-29 14:53:27,2,0.79,1,N,142,143,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-09 20:32:09,2019-01-09 20:39:02,2,1.34,1,N,90,246,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-25 18:13:11,2019-01-25 18:18:50,2,1.13,1,N,163,236,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-22 20:33:08,2019-01-22 21:05:32,2,9.0,1,N,163,33,1,30.5,0.5,0.5,9.5,0.0,0.3,41.3,0.0 +2,2019-01-23 22:04:43,2019-01-23 22:14:00,2,1.49,1,N,161,107,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-13 00:35:58,2019-01-13 00:44:34,2,1.88,1,N,164,141,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-01 21:52:43,2019-01-01 22:01:07,2,1.56,1,N,236,140,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-23 00:48:49,2019-01-23 01:13:04,2,5.67,1,N,263,82,2,20.5,0.5,0.5,0.0,0.0,0.3,21.8,0.0 +1,2019-01-08 18:02:19,2019-01-08 18:06:52,2,0.7,1,N,142,239,1,5.0,1.0,0.5,1.02,0.0,0.3,7.82, +1,2019-01-26 16:57:47,2019-01-26 17:06:43,2,1.0,1,N,68,170,1,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-18 11:46:20,2019-01-18 11:56:38,2,1.03,1,N,163,48,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-09 23:28:10,2019-01-09 23:49:38,2,4.4,1,N,100,211,2,17.5,0.5,0.5,0.0,0.0,0.3,18.8, +2,2019-01-19 14:09:58,2019-01-19 14:16:18,2,0.78,1,N,113,90,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-21 07:08:21,2019-01-21 07:11:31,2,0.72,1,N,48,230,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-17 13:08:20,2019-01-17 13:15:03,2,0.9,1,N,229,141,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-10 14:44:41,2019-01-10 14:50:54,2,0.94,1,N,151,24,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-21 17:28:20,2019-01-21 17:37:06,2,2.74,1,N,13,68,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-03 17:46:16,2019-01-03 17:53:34,2,0.93,1,N,161,229,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-02 15:06:28,2019-01-02 15:15:59,2,1.7,1,N,164,144,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-05 09:26:13,2019-01-05 09:28:55,2,0.58,1,N,79,148,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +2,2019-01-16 16:53:14,2019-01-16 16:55:02,2,0.68,1,N,263,263,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-03 14:37:36,2019-01-03 15:18:45,2,17.96,2,N,132,224,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-29 08:37:24,2019-01-29 08:47:41,2,0.83,1,N,170,161,1,8.0,0.0,0.5,1.2,0.0,0.3,10.0,0.0 +2,2019-01-25 15:02:51,2019-01-25 15:12:58,2,0.63,1,N,161,100,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-30 20:38:03,2019-01-30 20:59:46,2,3.9,1,N,234,236,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-09 16:20:52,2019-01-09 16:33:22,2,0.92,1,N,163,161,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-27 20:33:35,2019-01-27 20:38:17,2,0.89,1,N,161,170,1,5.5,0.5,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-21 22:40:43,2019-01-21 22:45:43,2,1.27,1,N,231,211,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-04 07:33:37,2019-01-04 07:50:26,2,4.5,1,N,116,142,1,16.5,0.0,0.5,3.45,0.0,0.3,20.75, +1,2019-01-08 14:32:29,2019-01-08 14:42:49,2,1.4,1,N,161,43,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-23 12:01:28,2019-01-23 12:06:39,2,0.74,1,N,161,164,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-21 10:32:05,2019-01-21 11:00:51,2,10.99,1,N,132,36,2,33.5,0.0,0.5,0.0,0.0,0.3,34.3,0.0 +1,2019-01-03 09:06:11,2019-01-03 09:25:12,2,8.0,1,N,263,138,1,25.0,0.0,0.5,4.0,5.76,0.3,35.56, +2,2019-01-31 17:56:22,2019-01-31 17:59:11,2,0.9,1,N,68,48,1,4.5,1.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-09 16:54:49,2019-01-09 17:05:28,2,2.71,1,N,140,137,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-17 10:24:27,2019-01-17 10:40:15,2,1.85,1,N,230,43,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-04 09:36:27,2019-01-04 10:11:09,2,13.29,1,N,81,238,1,39.0,0.0,0.5,0.0,0.0,0.3,39.8, +2,2019-01-14 20:09:32,2019-01-14 20:20:08,2,1.57,1,N,246,230,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-04 19:28:19,2019-01-04 19:34:35,2,0.85,1,N,163,43,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-03 22:14:05,2019-01-03 22:36:02,2,1.8,1,N,100,90,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +2,2019-01-23 18:27:06,2019-01-23 18:49:42,2,4.43,1,N,233,179,1,18.5,1.0,0.5,4.06,0.0,0.3,24.36,0.0 +1,2019-01-17 11:10:11,2019-01-17 11:21:26,2,1.6,1,N,186,158,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-02 18:52:44,2019-01-02 19:23:22,2,11.81,1,N,138,181,1,36.5,1.0,0.5,7.66,0.0,0.3,45.96, +1,2019-01-01 00:19:42,2019-01-01 00:30:54,2,1.3,1,N,148,107,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-20 22:07:56,2019-01-20 22:14:44,2,0.95,1,N,230,162,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-07 21:10:22,2019-01-07 21:51:20,2,22.76,2,N,132,143,1,52.0,0.0,0.5,8.78,5.76,0.3,67.34, +1,2019-01-12 10:49:14,2019-01-12 11:09:01,2,7.3,1,N,138,145,1,23.5,0.0,0.5,4.85,0.0,0.3,29.15, +2,2019-01-22 08:51:06,2019-01-22 08:53:50,2,0.42,1,N,75,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-28 11:43:47,2019-01-28 11:49:15,2,0.38,1,N,164,164,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-14 20:16:41,2019-01-14 20:28:00,2,2.57,1,N,79,229,1,10.5,0.5,0.5,1.05,0.0,0.3,12.85, +2,2019-01-10 09:10:21,2019-01-10 09:24:15,2,3.87,1,N,262,107,1,13.5,0.0,0.5,2.14,0.0,0.3,16.44, +2,2019-01-26 22:47:18,2019-01-26 23:09:40,2,4.7,1,N,88,255,1,18.5,0.5,0.5,2.97,0.0,0.3,22.77,0.0 +1,2019-01-14 22:28:30,2019-01-14 22:35:43,2,1.2,1,N,68,186,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-19 15:15:49,2019-01-19 15:29:57,2,2.9,1,N,166,263,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-19 22:18:20,2019-01-19 22:24:33,2,1.0,1,N,230,50,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-07 15:42:51,2019-01-07 15:43:01,2,0.03,1,N,132,132,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +2,2019-01-30 10:49:55,2019-01-30 10:57:06,2,1.12,1,N,263,237,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-21 08:36:07,2019-01-21 09:13:32,2,28.78,5,N,233,265,1,178.0,0.0,0.0,10.0,12.5,0.3,200.8,0.0 +2,2019-01-02 20:28:11,2019-01-02 20:37:04,2,2.09,1,N,230,238,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-24 22:24:34,2019-01-24 22:33:53,2,1.34,1,N,161,50,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-25 11:15:24,2019-01-25 11:26:01,2,1.75,1,N,144,158,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-13 21:07:49,2019-01-13 21:14:49,2,0.98,1,N,107,79,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-03 12:21:29,2019-01-03 12:50:31,2,6.4,1,N,236,211,1,24.5,0.0,0.5,5.05,0.0,0.3,30.35, +2,2019-01-24 13:40:21,2019-01-24 13:45:14,2,1.02,1,N,231,125,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-02 07:56:37,2019-01-02 07:58:33,2,0.32,1,N,166,24,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-07 21:03:18,2019-01-07 21:14:23,2,4.93,1,N,231,143,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76, +2,2019-01-27 14:17:14,2019-01-27 14:27:21,2,1.01,1,N,162,186,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +1,2019-01-26 01:11:56,2019-01-26 01:23:13,2,2.9,1,N,163,114,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75,0.0 +1,2019-01-12 00:45:14,2019-01-12 00:51:52,2,1.5,1,N,234,162,1,7.0,0.5,0.5,1.65,0.0,0.3,9.95, +2,2019-01-09 10:49:56,2019-01-09 11:00:17,2,0.97,1,N,229,140,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-28 18:12:08,2019-01-28 18:28:35,2,3.22,1,N,209,186,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-03 18:10:45,2019-01-03 18:18:38,2,1.0,1,N,229,141,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-22 03:21:34,2019-01-22 03:28:01,2,2.34,1,N,237,230,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-11 08:36:28,2019-01-11 08:50:50,2,1.33,1,N,48,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-27 11:35:17,2019-01-27 11:58:37,2,4.42,1,N,237,223,2,19.5,0.0,0.5,0.0,0.0,0.3,20.3,0.0 +2,2019-01-18 19:37:00,2019-01-18 19:58:49,2,3.46,1,N,88,234,1,15.5,1.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-18 14:39:44,2019-01-18 14:47:12,2,0.38,1,N,170,164,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-16 13:58:01,2019-01-16 14:24:37,2,2.0,1,N,186,163,2,16.5,0.0,0.5,0.0,0.0,0.3,17.3, +1,2019-01-15 15:39:42,2019-01-15 15:46:55,2,1.1,1,N,237,142,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-04 13:04:45,2019-01-04 13:11:47,2,0.9,1,N,264,264,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-25 19:20:16,2019-01-25 19:25:55,2,1.0,1,N,186,170,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-27 01:17:34,2019-01-27 01:25:33,2,1.61,1,N,164,48,1,8.0,0.5,0.5,2.32,0.0,0.3,11.62,0.0 +1,2019-01-15 13:28:27,2019-01-15 13:31:32,2,0.7,1,N,79,148,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-01 15:28:23,2019-01-01 15:39:09,2,2.45,1,N,230,113,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-07 11:40:48,2019-01-07 11:49:22,2,1.3,1,N,137,4,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-23 15:17:18,2019-01-23 15:30:10,2,1.8,1,N,263,151,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +1,2019-01-14 14:44:24,2019-01-14 14:48:46,2,0.4,1,N,264,264,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +1,2019-01-25 12:26:13,2019-01-25 12:42:39,2,2.9,1,N,43,166,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8,0.0 +1,2019-01-08 21:56:31,2019-01-08 22:13:11,2,3.2,1,N,264,142,1,14.0,0.5,0.5,2.75,0.0,0.3,18.05, +2,2019-01-08 05:39:39,2019-01-08 05:46:39,2,2.25,1,N,263,74,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-22 18:08:49,2019-01-22 18:21:45,2,2.38,1,N,75,166,1,11.0,1.0,0.5,1.92,0.0,0.3,14.72,0.0 +1,2019-01-13 16:22:03,2019-01-13 16:33:06,2,1.4,1,N,161,170,1,8.5,0.0,0.5,3.0,0.0,0.3,12.3, +2,2019-01-26 23:34:47,2019-01-26 23:37:56,2,0.77,1,N,100,230,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-08 14:51:40,2019-01-08 15:11:58,2,2.6,1,N,144,164,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-19 11:10:35,2019-01-19 11:18:49,2,1.2,1,N,237,140,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-14 19:29:59,2019-01-14 19:37:46,2,1.06,1,N,100,170,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +1,2019-01-07 07:17:14,2019-01-07 07:19:32,2,0.6,1,N,229,162,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +1,2019-01-10 17:23:47,2019-01-10 17:47:09,2,2.4,1,N,161,142,1,15.0,1.0,0.5,4.2,0.0,0.3,21.0, +1,2019-01-06 07:10:56,2019-01-06 07:38:30,2,19.7,1,N,132,33,1,52.5,0.0,0.5,10.0,0.0,0.3,63.3, +2,2019-01-28 20:23:19,2019-01-28 20:32:48,2,2.97,1,N,162,75,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-10 07:56:03,2019-01-10 08:06:28,2,1.3,1,N,170,79,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-11 22:26:48,2019-01-11 22:46:04,2,5.7,1,N,162,157,1,19.0,0.5,0.5,4.06,0.0,0.3,24.36, +2,2019-01-14 19:16:20,2019-01-14 19:38:57,2,8.37,1,N,236,13,1,26.5,1.0,0.5,3.4,0.0,0.3,31.7, +1,2019-01-10 17:24:22,2019-01-10 17:57:40,2,3.6,1,N,144,163,2,21.5,1.0,0.5,0.0,0.0,0.3,23.3, +1,2019-01-10 15:40:38,2019-01-10 15:51:27,2,1.2,1,N,230,164,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-18 10:22:22,2019-01-18 10:53:17,2,3.74,1,N,186,236,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +2,2019-01-07 06:32:41,2019-01-07 06:51:03,2,7.51,1,N,236,87,1,23.5,0.0,0.5,2.43,0.0,0.3,26.73, +2,2019-01-12 23:31:29,2019-01-12 23:38:50,2,1.02,1,N,107,170,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-21 13:49:13,2019-01-21 13:53:08,2,1.0,1,N,43,43,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-20 21:45:05,2019-01-20 21:57:56,2,0.85,1,N,164,100,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-12 06:36:58,2019-01-12 06:45:13,2,3.48,1,N,263,107,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-29 16:01:36,2019-01-29 16:08:57,2,0.96,1,N,162,164,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3,0.0 +1,2019-01-12 09:26:49,2019-01-12 09:43:17,2,3.7,1,N,262,50,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-01 00:42:05,2019-01-01 00:48:01,2,1.06,1,N,4,148,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +1,2019-01-27 00:53:39,2019-01-27 00:58:52,2,0.9,1,N,142,143,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +1,2019-01-29 21:41:07,2019-01-29 22:05:28,2,7.8,1,N,161,40,1,25.0,0.5,0.5,5.25,0.0,0.3,31.55,0.0 +2,2019-01-31 14:36:53,2019-01-31 15:42:46,2,11.27,1,N,90,138,1,47.5,0.0,0.5,10.81,5.76,0.3,64.87,0.0 +1,2019-01-20 10:42:03,2019-01-20 10:46:10,2,0.9,1,N,264,264,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-10 11:27:39,2019-01-10 11:31:31,2,0.78,1,N,142,142,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-20 11:32:40,2019-01-20 11:41:40,2,1.7,1,N,113,186,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-31 07:10:17,2019-01-31 07:17:29,2,1.14,1,N,186,230,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-28 05:33:04,2019-01-28 05:36:35,2,0.74,1,N,100,161,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-19 18:53:58,2019-01-19 19:01:25,2,1.28,1,N,142,48,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-24 15:44:38,2019-01-24 15:57:15,2,0.8,1,N,236,262,1,8.5,0.0,0.5,1.0,0.0,0.3,10.3,0.0 +1,2019-01-20 15:02:30,2019-01-20 15:07:44,2,0.8,1,N,246,90,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-01 00:42:48,2019-01-01 00:53:38,2,1.8,1,N,231,87,1,9.0,0.5,0.5,2.55,0.0,0.3,12.85, +2,2019-01-13 18:31:55,2019-01-13 18:44:58,2,2.79,1,N,230,238,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-15 10:39:15,2019-01-15 10:58:37,2,2.29,1,N,143,162,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-29 21:51:22,2019-01-29 21:57:02,2,1.28,1,N,237,142,1,6.5,0.5,0.5,2.34,0.0,0.3,10.14,0.0 +1,2019-01-17 20:46:20,2019-01-17 20:59:54,2,2.5,1,N,48,249,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-11 20:35:12,2019-01-11 20:44:29,2,1.1,1,N,170,234,1,7.5,0.5,0.5,2.0,0.0,0.3,10.8, +2,2019-01-13 04:07:23,2019-01-13 04:47:52,2,13.8,1,N,68,60,2,39.0,0.5,0.5,0.0,0.0,0.3,40.3, +2,2019-01-30 18:48:46,2019-01-30 19:03:38,2,4.88,1,N,132,10,2,16.5,1.0,0.5,0.0,0.0,0.3,18.3,0.0 +2,2019-01-03 06:52:58,2019-01-03 07:00:58,2,0.84,1,N,164,186,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-07 09:54:46,2019-01-07 10:01:06,2,1.1,1,N,170,233,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +2,2019-01-13 17:18:32,2019-01-13 17:25:30,2,1.16,1,N,263,140,1,7.0,0.0,0.5,1.5,0.0,0.3,9.3, +1,2019-01-16 20:47:18,2019-01-16 20:50:59,2,0.5,1,N,186,170,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +1,2019-01-15 10:29:05,2019-01-15 11:08:19,2,12.1,1,N,233,124,1,39.5,0.0,0.5,0.0,5.76,0.3,46.06, +1,2019-01-14 08:31:50,2019-01-14 08:39:02,2,0.6,1,N,68,246,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0, +1,2019-01-07 19:24:37,2019-01-07 19:31:01,2,0.8,1,N,233,230,1,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-05 23:12:46,2019-01-05 23:23:22,2,1.8,1,N,107,45,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +1,2019-01-07 17:06:07,2019-01-07 17:14:02,2,1.4,1,N,233,236,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-19 16:23:09,2019-01-19 16:31:01,2,1.19,1,N,144,4,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-12 12:08:25,2019-01-12 12:23:25,2,1.95,1,N,186,162,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-19 14:57:08,2019-01-19 15:22:07,2,3.81,1,N,100,231,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96, +2,2019-01-24 06:30:37,2019-01-24 06:46:50,2,6.1,1,N,116,68,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8,0.0 +1,2019-01-13 16:57:51,2019-01-13 17:37:11,2,10.1,1,N,100,173,2,34.5,1.0,0.5,0.0,0.0,0.3,36.3, +2,2019-01-14 21:27:09,2019-01-14 21:30:56,2,0.89,1,N,141,262,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-02 08:07:40,2019-01-02 08:50:36,2,18.1,1,N,177,168,1,52.5,0.0,0.5,0.0,5.76,0.3,59.06, +1,2019-01-17 17:36:23,2019-01-17 17:49:36,2,2.1,1,N,125,186,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0, +1,2019-01-17 15:36:30,2019-01-17 15:49:04,2,1.2,1,N,262,141,1,9.0,0.0,0.5,1.95,0.0,0.3,11.75, +2,2019-01-14 07:38:22,2019-01-14 07:43:24,2,1.02,1,N,162,234,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24, +1,2019-01-13 16:33:19,2019-01-13 16:49:27,2,2.9,1,N,230,236,1,13.0,0.0,0.5,2.75,0.0,0.3,16.55, +2,2019-01-22 15:41:19,2019-01-22 15:47:45,2,0.93,1,N,68,230,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-21 15:01:18,2019-01-21 15:20:23,2,2.77,1,N,236,100,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-17 09:26:58,2019-01-17 09:39:36,2,2.01,1,N,236,162,1,10.0,0.0,0.5,1.08,0.0,0.3,11.88, +2,2019-01-28 06:35:25,2019-01-28 06:43:53,2,1.54,1,N,186,233,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-23 17:40:28,2019-01-23 17:55:38,2,1.38,1,N,236,162,1,10.5,1.0,0.5,2.0,0.0,0.3,14.3,0.0 +2,2019-01-25 00:58:45,2019-01-25 01:01:24,2,0.88,1,N,263,236,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-08 15:49:12,2019-01-08 15:58:17,2,1.15,1,N,43,141,1,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-07 09:41:47,2019-01-07 10:00:19,2,1.1,1,N,68,234,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-25 19:00:32,2019-01-25 19:17:18,2,1.26,1,N,107,68,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0,0.0 +1,2019-01-18 16:42:01,2019-01-18 16:49:56,2,1.0,1,N,236,237,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-15 10:41:03,2019-01-15 10:47:08,2,0.61,1,N,170,164,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-06 14:27:40,2019-01-06 14:45:16,2,2.93,1,N,161,211,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-21 18:07:33,2019-01-21 18:21:13,2,2.43,1,N,161,90,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-29 15:16:35,2019-01-29 15:24:22,2,1.75,1,N,140,75,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +1,2019-01-02 18:17:16,2019-01-02 18:28:18,2,2.4,1,N,43,151,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-15 22:04:56,2019-01-15 22:13:23,2,2.1,1,N,148,170,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-28 08:35:52,2019-01-28 08:50:03,2,1.16,1,N,143,48,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-29 13:40:47,2019-01-29 14:01:49,2,2.36,1,N,239,140,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8,0.0 +2,2019-01-06 19:44:44,2019-01-06 19:46:46,2,0.74,1,N,263,141,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +1,2019-01-14 11:44:18,2019-01-14 12:02:01,2,1.1,1,N,237,230,1,11.5,0.0,0.5,2.0,0.0,0.3,14.3, +2,2019-01-29 06:05:13,2019-01-29 06:11:25,2,1.34,1,N,142,230,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-22 19:25:05,2019-01-22 19:30:14,2,0.8,1,N,234,161,1,5.5,1.0,0.5,1.1,0.0,0.3,8.4,0.0 +1,2019-01-04 11:41:22,2019-01-04 11:57:56,2,2.8,1,N,236,230,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +1,2019-01-01 00:59:57,2019-01-01 01:09:44,2,1.3,1,N,141,162,1,8.0,0.5,0.5,1.85,0.0,0.3,11.15, +1,2019-01-30 19:54:18,2019-01-30 20:06:43,2,2.4,1,N,264,264,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-07 10:50:43,2019-01-07 10:58:49,2,1.38,1,N,238,236,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-11 16:45:45,2019-01-11 16:50:10,2,1.39,1,N,246,50,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14, +2,2019-01-30 08:33:11,2019-01-30 09:23:30,2,17.26,2,N,230,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56,0.0 +1,2019-01-29 15:23:19,2019-01-29 15:36:25,2,1.3,1,N,140,262,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-13 17:08:43,2019-01-13 17:12:31,2,0.4,1,N,143,142,1,4.5,0.0,0.5,1.55,0.0,0.3,6.85, +2,2019-01-05 15:39:36,2019-01-05 15:43:16,2,0.45,1,N,236,236,1,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-30 14:33:16,2019-01-30 14:53:59,2,2.1,1,N,143,233,1,14.0,0.0,0.5,3.7,0.0,0.3,18.5,0.0 +1,2019-01-12 20:19:17,2019-01-12 20:31:45,2,1.1,1,N,234,114,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35, +2,2019-01-26 17:17:29,2019-01-26 17:31:55,2,2.65,1,N,231,68,1,11.5,0.0,0.5,3.08,0.0,0.3,15.38,0.0 +2,2019-01-06 12:27:52,2019-01-06 12:36:00,2,1.4,1,N,74,41,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79, +2,2019-01-13 00:28:47,2019-01-13 01:12:07,2,7.71,1,N,142,189,2,31.5,0.5,0.5,0.0,0.0,0.3,32.8, +2,2019-01-10 05:04:06,2019-01-10 05:25:37,2,11.22,1,N,161,138,1,32.5,0.5,0.5,7.91,5.76,0.3,47.47, +1,2019-01-10 19:10:36,2019-01-10 20:03:22,2,14.5,1,N,237,35,2,47.0,1.0,0.5,0.0,0.0,0.3,48.8, +2,2019-01-10 17:00:27,2019-01-10 17:27:42,2,3.19,1,N,236,137,1,18.0,1.0,0.5,3.96,0.0,0.3,23.76, +2,2019-01-06 14:30:55,2019-01-06 14:36:57,2,0.67,1,N,164,90,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-14 21:41:10,2019-01-14 21:55:29,2,2.4,1,N,236,229,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +1,2019-01-12 23:47:30,2019-01-12 23:54:03,2,0.5,1,N,79,79,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-05 20:43:04,2019-01-05 20:54:22,2,1.24,1,N,148,79,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-17 21:35:08,2019-01-17 21:50:54,2,3.84,1,N,107,75,1,14.0,0.5,0.5,2.5,0.0,0.3,17.8, +2,2019-01-15 17:02:34,2019-01-15 17:23:27,2,3.88,1,N,230,43,2,16.0,1.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-19 16:26:01,2019-01-19 16:29:10,2,0.53,1,N,48,142,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-09 16:40:12,2019-01-09 17:02:09,2,4.3,1,N,87,80,1,17.0,1.0,0.5,3.75,0.0,0.3,22.55, +2,2019-01-08 14:18:24,2019-01-08 14:30:55,2,2.18,1,N,43,230,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-08 09:11:36,2019-01-08 09:29:47,2,8.48,1,N,263,138,1,25.0,0.0,0.5,6.31,5.76,0.3,37.87, +2,2019-01-27 09:09:57,2019-01-27 09:21:43,2,3.15,1,N,229,113,1,11.5,0.0,0.5,1.23,0.0,0.3,13.53,0.0 +2,2019-01-23 16:24:58,2019-01-23 16:33:36,2,1.77,1,N,166,238,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3,0.0 +1,2019-01-23 23:33:17,2019-01-23 23:37:53,2,0.8,1,N,230,163,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-15 19:30:36,2019-01-15 19:36:14,2,0.79,1,N,170,233,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-29 16:10:47,2019-01-29 16:17:04,2,0.91,1,N,170,233,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-09 22:51:40,2019-01-09 23:03:18,2,3.2,1,N,162,151,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +1,2019-01-17 22:45:22,2019-01-17 22:51:45,2,0.6,1,N,234,107,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-31 21:38:58,2019-01-31 22:02:06,2,3.76,1,N,114,237,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76,0.0 +1,2019-01-18 23:16:35,2019-01-18 23:39:32,2,4.4,1,N,114,255,1,19.5,0.5,0.5,4.15,0.0,0.3,24.95, +1,2019-01-12 21:10:12,2019-01-12 21:21:17,2,0.9,1,N,107,170,1,8.0,0.5,0.5,2.3,0.0,0.3,11.6, +1,2019-01-18 14:26:03,2019-01-18 14:34:38,2,0.6,1,N,114,113,1,7.0,0.0,0.5,2.3,0.0,0.3,10.1, +2,2019-01-24 18:35:23,2019-01-24 18:42:20,2,0.9,1,N,236,237,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-01 20:28:01,2019-01-01 20:59:13,2,11.7,1,N,138,116,1,35.0,0.5,0.5,8.4,5.76,0.3,50.46, +2,2019-01-29 11:54:06,2019-01-29 12:17:55,2,5.8,1,N,234,75,1,22.0,0.0,0.5,0.0,0.0,0.3,22.8,0.0 +1,2019-01-13 05:34:40,2019-01-13 05:42:56,2,1.6,1,N,186,233,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +1,2019-01-04 10:41:59,2019-01-04 10:57:02,2,1.8,1,N,164,158,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-03 09:16:29,2019-01-03 09:18:39,2,0.46,1,N,237,236,1,3.5,0.0,0.5,0.43,0.0,0.3,4.73, +2,2019-01-11 09:30:36,2019-01-11 09:39:41,2,0.98,1,N,249,231,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-22 14:58:20,2019-01-22 15:03:23,2,0.5,1,N,163,230,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-19 22:22:48,2019-01-19 22:45:49,2,7.4,1,N,246,116,2,24.0,0.5,0.5,0.0,0.0,0.3,25.3, +1,2019-01-17 17:10:03,2019-01-17 17:11:33,2,0.2,5,N,164,170,2,0.0,0.0,0.0,0.0,0.0,0.3,0.3, +2,2019-01-11 14:47:39,2019-01-11 14:53:16,2,1.06,1,N,41,238,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-31 08:47:02,2019-01-31 09:11:18,2,5.4,1,N,262,114,1,19.5,0.0,0.5,3.0,0.0,0.3,23.3,0.0 +2,2019-01-21 05:50:28,2019-01-21 06:23:53,2,16.7,3,N,181,1,1,64.0,0.5,0.0,16.46,17.5,0.3,103.71,0.0 +1,2019-01-13 14:15:05,2019-01-13 14:28:07,2,1.7,1,N,140,161,1,10.0,0.0,0.5,3.2,0.0,0.3,14.0, +1,2019-01-12 23:02:01,2019-01-12 23:09:13,2,0.6,1,N,230,186,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-08 08:30:26,2019-01-08 08:49:03,2,4.15,1,N,107,87,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36, +1,2019-01-08 10:44:42,2019-01-08 10:54:51,2,0.2,1,N,140,141,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-16 20:42:56,2019-01-16 20:52:07,2,1.39,1,N,170,90,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-09 06:40:29,2019-01-10 05:49:19,2,1.12,1,N,262,236,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-22 09:34:02,2019-01-22 09:54:02,2,2.24,1,N,236,163,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88,0.0 +2,2019-01-02 15:14:49,2019-01-02 15:25:41,2,1.32,1,N,75,74,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-31 18:43:01,2019-01-31 19:07:54,2,2.2,1,N,234,163,1,16.0,1.0,0.5,3.55,0.0,0.3,21.35,0.0 +1,2019-01-14 11:30:23,2019-01-14 11:49:46,2,2.5,1,N,237,164,1,14.5,0.0,0.5,3.83,0.0,0.3,19.13, +2,2019-01-26 11:24:12,2019-01-26 11:55:08,2,6.63,1,N,231,181,1,25.5,0.0,0.5,6.41,5.76,0.3,38.47,0.0 +1,2019-01-21 19:02:51,2019-01-21 19:10:58,2,1.4,1,N,186,90,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-30 10:03:07,2019-01-30 10:13:29,2,1.57,1,N,48,142,1,9.0,0.0,0.5,2.94,0.0,0.3,12.74,0.0 +2,2019-01-30 07:04:07,2019-01-30 07:07:20,2,0.77,1,N,249,234,1,4.5,0.0,0.5,0.8,0.0,0.3,6.1,0.0 +1,2019-01-30 03:51:15,2019-01-30 04:01:14,2,1.8,1,N,186,114,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-04 00:12:12,2019-01-04 00:26:52,2,3.54,1,N,231,170,1,13.5,0.5,0.5,2.0,0.0,0.3,16.8, +2,2019-01-16 05:31:45,2019-01-16 05:43:15,2,2.57,1,N,186,141,1,11.0,0.5,0.5,3.08,0.0,0.3,15.38, +2,2019-01-30 20:29:51,2019-01-30 20:42:07,2,2.15,1,N,239,50,1,10.0,0.5,0.5,1.0,0.0,0.3,12.3,0.0 +2,2019-01-31 00:25:55,2019-01-31 00:40:11,2,2.25,1,N,237,163,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8,0.0 +1,2019-01-27 02:59:38,2019-01-27 02:59:59,2,0.0,5,N,265,265,1,50.0,0.0,0.0,5.0,0.0,0.3,55.3,0.0 +1,2019-01-19 15:36:46,2019-01-19 15:43:33,2,0.7,1,N,48,100,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-19 14:13:07,2019-01-19 14:33:35,2,2.4,1,N,100,143,1,14.5,0.0,0.5,5.0,0.0,0.3,20.3, +2,2019-01-01 04:11:04,2019-01-01 04:15:16,2,0.76,1,N,233,161,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-30 20:39:08,2019-01-30 20:42:15,2,0.57,1,N,263,141,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-29 12:39:09,2019-01-29 13:08:12,2,3.9,1,N,239,107,1,19.5,0.0,0.5,3.05,0.0,0.3,23.35,0.0 +1,2019-01-23 02:15:53,2019-01-23 02:22:25,2,1.3,1,N,164,48,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-17 14:17:57,2019-01-17 14:24:34,2,1.27,1,N,158,68,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-25 20:26:51,2019-01-25 20:29:06,2,0.62,1,N,140,263,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-09 14:26:41,2019-01-09 14:36:25,2,1.9,1,N,239,166,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-12 00:02:55,2019-01-12 00:07:01,2,0.89,1,N,164,100,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-26 15:24:02,2019-01-26 15:30:55,2,1.9,1,N,237,263,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-29 19:55:35,2019-01-29 20:11:53,2,1.93,1,N,234,142,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96,0.0 +1,2019-01-09 09:04:07,2019-01-09 09:09:32,2,0.6,1,N,186,48,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-18 16:03:36,2019-01-18 16:14:57,2,2.36,1,N,68,237,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +1,2019-01-25 22:43:16,2019-01-25 22:56:24,2,1.6,1,N,249,246,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-21 21:09:48,2019-01-21 21:46:28,2,18.39,2,N,132,68,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-05 13:07:31,2019-01-05 13:10:07,2,0.89,1,N,141,263,1,4.5,0.0,0.5,0.7,0.0,0.3,6.0, +2,2019-01-02 17:03:25,2019-01-02 17:18:35,2,2.3,1,N,163,238,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-03 16:56:22,2019-01-03 17:09:15,2,1.7,1,N,236,161,3,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-26 22:28:38,2019-01-26 22:44:12,2,3.08,1,N,68,263,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3,0.0 +1,2019-01-04 19:23:36,2019-01-04 19:37:55,2,3.2,1,N,166,141,1,13.0,1.0,0.5,2.95,0.0,0.3,17.75, +2,2019-01-25 11:56:54,2019-01-25 12:02:32,2,1.11,1,N,236,75,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49,0.0 +2,2019-01-30 13:40:40,2019-01-30 13:46:56,2,0.78,1,N,170,100,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5,0.0 +1,2019-01-10 09:42:36,2019-01-10 10:06:22,2,7.2,1,N,262,87,1,24.5,0.0,0.5,3.8,0.0,0.3,29.1, +2,2019-01-07 10:35:31,2019-01-07 10:40:10,2,0.98,1,N,234,158,1,5.5,0.0,0.5,0.94,0.0,0.3,7.24, +2,2019-01-27 01:55:10,2019-01-27 02:00:53,2,0.67,1,N,249,90,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-05 19:20:24,2019-01-05 19:30:28,2,0.92,1,N,234,79,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-09 14:21:22,2019-01-09 14:35:45,2,2.39,1,N,142,233,1,11.5,0.0,0.5,1.84,0.0,0.3,14.14, +1,2019-01-15 12:04:38,2019-01-15 12:20:19,2,2.7,1,N,239,236,1,11.5,0.0,0.5,1.85,0.0,0.3,14.15, +2,2019-01-18 06:29:13,2019-01-18 06:39:58,2,1.92,1,N,137,230,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +1,2019-01-10 21:26:03,2019-01-10 21:46:18,2,3.6,1,N,166,141,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +1,2019-01-29 22:05:22,2019-01-29 22:12:52,2,0.8,1,N,234,68,1,6.5,0.5,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-28 21:20:00,2019-01-28 21:21:09,2,0.33,1,N,236,236,1,3.0,0.5,0.5,0.43,0.0,0.3,4.73,0.0 +1,2019-01-08 16:55:42,2019-01-08 17:04:15,2,1.7,1,N,142,246,1,8.5,1.0,0.5,1.03,0.0,0.3,11.33, +2,2019-01-09 13:49:44,2019-01-09 13:54:08,2,0.66,1,N,90,186,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-19 21:02:48,2019-01-19 21:46:34,2,15.2,5,N,138,265,1,90.0,0.0,0.5,21.41,16.26,0.3,128.47, +1,2019-01-11 19:23:23,2019-01-11 19:36:16,2,1.4,1,N,186,230,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-13 01:30:08,2019-01-13 01:39:25,2,2.55,1,N,170,142,1,9.5,0.5,0.5,2.0,0.0,0.3,12.8, +2,2019-01-05 19:05:09,2019-01-05 19:15:39,2,2.18,1,N,237,263,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +2,2019-01-01 07:27:47,2019-01-01 07:32:40,2,0.83,1,N,48,162,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-10 12:07:51,2019-01-10 12:17:07,2,0.6,1,N,141,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-31 22:41:13,2019-01-31 22:52:25,2,1.42,1,N,114,79,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-22 06:59:57,2019-01-22 07:05:09,2,0.86,1,N,107,164,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93,0.0 +1,2019-01-13 14:33:26,2019-01-13 14:44:22,2,1.3,1,N,43,48,1,8.5,0.0,0.5,2.3,0.0,0.3,11.6, +2,2019-01-03 22:05:57,2019-01-03 22:11:47,2,1.17,1,N,211,87,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-19 15:06:43,2019-01-19 15:10:42,2,0.65,1,N,263,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-13 21:09:33,2019-01-13 21:17:06,2,1.5,1,N,186,50,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8, +1,2019-01-11 10:42:55,2019-01-11 10:52:45,2,2.2,1,N,237,74,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-14 11:36:16,2019-01-14 11:45:28,2,1.2,1,N,162,140,1,7.5,0.0,0.5,1.7,0.0,0.3,10.0, +1,2019-01-03 11:22:53,2019-01-03 11:27:25,2,0.6,1,N,143,142,1,5.0,0.0,0.5,2.0,0.0,0.3,7.8, +2,2019-01-17 08:18:37,2019-01-17 08:24:53,2,0.67,1,N,107,137,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-10 12:15:07,2019-01-10 12:20:01,2,1.22,1,N,162,236,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-26 22:06:45,2019-01-26 22:20:40,2,1.35,1,N,144,107,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-31 10:52:37,2019-01-31 11:09:39,2,1.95,1,N,170,237,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-18 12:35:57,2019-01-18 12:51:59,2,4.0,1,N,7,262,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +1,2019-01-13 21:40:22,2019-01-13 21:49:47,2,1.6,1,N,113,90,1,8.5,0.5,0.5,1.2,0.0,0.3,11.0, +2,2019-01-16 19:32:55,2019-01-16 19:42:18,2,1.26,1,N,100,163,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-10 12:26:43,2019-01-10 12:51:00,2,3.2,1,N,113,163,1,16.0,0.0,0.5,2.0,0.0,0.3,18.8, +2,2019-01-15 19:58:28,2019-01-15 20:14:24,2,1.62,1,N,113,68,1,11.0,0.5,0.5,1.0,0.0,0.3,13.3, +2,2019-01-02 16:17:03,2019-01-02 16:35:55,2,2.14,1,N,246,233,2,13.0,1.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-29 18:36:11,2019-01-29 18:45:41,2,0.93,1,N,233,229,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62,0.0 +2,2019-01-03 08:35:22,2019-01-03 08:51:54,2,2.41,1,N,79,229,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +1,2019-01-14 13:53:48,2019-01-14 14:10:39,2,2.2,1,N,249,163,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +1,2019-01-16 21:15:18,2019-01-16 21:22:00,2,1.3,1,N,90,230,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-08 07:55:47,2019-01-08 08:07:28,2,2.84,1,N,43,164,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-25 15:50:09,2019-01-25 15:57:56,2,0.8,1,N,264,264,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-18 08:35:29,2019-01-18 08:41:55,2,0.79,1,N,237,140,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-15 09:43:38,2019-01-15 10:05:04,2,1.8,1,N,236,162,1,14.0,0.0,0.5,1.5,0.0,0.3,16.3, +1,2019-01-18 09:56:24,2019-01-18 10:09:18,2,1.8,1,N,262,238,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-11 21:55:06,2019-01-11 22:09:23,2,1.9,1,N,234,246,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +1,2019-01-25 20:21:32,2019-01-25 20:35:53,2,1.9,1,N,90,48,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-08 12:41:15,2019-01-08 12:49:54,2,1.03,1,N,140,236,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-26 20:59:13,2019-01-26 21:10:06,2,5.14,1,N,151,244,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36,0.0 +1,2019-01-24 23:01:11,2019-01-24 23:07:29,2,1.0,1,N,144,125,1,6.5,0.5,0.5,2.3,0.0,0.3,10.1,0.0 +2,2019-01-27 13:58:44,2019-01-27 14:40:02,2,18.14,2,N,161,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-12 15:59:48,2019-01-12 16:15:31,2,3.72,1,N,237,41,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-05 01:38:03,2019-01-05 01:48:46,2,2.4,1,N,114,233,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-22 08:35:47,2019-01-22 08:44:47,2,1.94,1,N,236,74,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-12 00:54:06,2019-01-12 00:56:18,2,0.62,1,N,229,141,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +2,2019-01-10 20:25:35,2019-01-10 21:01:24,2,11.21,1,N,230,171,2,36.0,0.5,0.5,0.0,0.0,0.3,37.3, +1,2019-01-18 11:42:50,2019-01-18 12:00:58,2,2.3,1,N,68,239,1,13.5,0.0,0.5,2.0,0.0,0.3,16.3, +2,2019-01-28 15:40:22,2019-01-28 15:47:42,2,1.18,1,N,158,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-09 20:06:46,2019-01-09 20:20:54,2,2.25,1,N,162,236,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-10 20:43:36,2019-01-10 20:56:46,2,6.66,1,N,138,74,1,20.0,0.5,0.5,2.0,5.76,0.3,29.06, +2,2019-01-24 12:59:08,2019-01-24 13:04:48,2,0.9,1,N,151,239,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-29 20:36:16,2019-01-29 20:38:39,2,0.3,1,N,264,264,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-08 09:38:10,2019-01-08 09:41:02,2,0.4,1,N,43,142,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +2,2019-01-14 11:17:31,2019-01-14 11:19:12,2,0.38,1,N,237,237,1,3.5,0.0,0.5,1.08,0.0,0.3,5.38, +2,2019-01-25 16:39:47,2019-01-25 16:54:44,2,2.3,1,N,246,239,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8,0.0 +2,2019-01-04 16:12:01,2019-01-04 16:19:58,2,2.29,1,N,162,236,1,9.5,1.0,0.5,0.95,0.0,0.3,12.25, +2,2019-01-14 10:29:28,2019-01-14 10:37:15,2,0.7,1,N,143,143,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-13 10:31:43,2019-01-13 10:34:39,2,1.0,1,N,239,151,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-23 18:42:23,2019-01-23 18:57:40,2,1.98,1,N,107,161,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-01 03:26:32,2019-01-01 03:39:31,2,1.3,1,N,225,37,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-13 13:13:27,2019-01-13 13:34:32,2,2.53,1,N,100,43,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-30 13:47:39,2019-01-30 14:01:10,2,1.78,1,N,162,141,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-01 17:21:13,2019-01-01 17:44:45,2,6.5,1,N,237,88,1,23.0,0.0,0.5,4.75,0.0,0.3,28.55, +2,2019-01-04 08:53:10,2019-01-04 09:10:17,2,0.94,1,N,90,90,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-18 01:47:54,2019-01-18 01:52:20,2,0.85,1,N,249,90,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3, +2,2019-01-25 19:04:18,2019-01-25 19:11:11,2,1.33,1,N,151,239,1,7.0,1.0,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-31 18:28:25,2019-01-31 18:35:22,2,0.81,1,N,114,249,1,6.0,1.0,0.5,2.34,0.0,0.3,10.14,0.0 +2,2019-01-26 08:06:44,2019-01-26 08:13:30,2,1.06,1,N,233,164,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-13 01:00:11,2019-01-13 01:02:52,2,0.44,1,N,170,234,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36, +1,2019-01-09 10:02:29,2019-01-09 10:15:45,2,1.3,1,N,230,164,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-03 13:10:18,2019-01-03 13:35:52,2,8.6,1,N,138,151,1,28.0,0.0,0.5,8.0,5.76,0.3,42.56, +2,2019-01-18 09:04:21,2019-01-18 09:13:24,2,1.45,1,N,75,263,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-18 11:19:35,2019-01-18 11:27:06,2,0.85,1,N,237,161,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-20 00:27:16,2019-01-20 00:33:02,2,0.89,1,N,246,68,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-15 11:36:27,2019-01-15 11:44:26,2,1.22,1,N,143,230,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-25 20:53:20,2019-01-25 21:02:14,2,1.49,1,N,263,237,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-15 19:37:42,2019-01-15 19:45:40,2,1.48,1,N,162,234,1,7.5,1.0,0.5,2.79,0.0,0.3,12.09, +2,2019-01-23 15:48:49,2019-01-23 15:53:36,2,0.69,1,N,230,163,1,5.0,0.0,0.5,1.74,0.0,0.3,7.54,0.0 +2,2019-01-21 16:34:37,2019-01-21 16:39:31,2,1.15,1,N,162,141,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-13 20:06:11,2019-01-13 20:19:59,2,2.1,1,N,234,230,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +2,2019-01-23 13:15:14,2019-01-23 13:22:31,2,0.76,1,N,231,45,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-14 22:54:36,2019-01-14 23:10:26,2,4.06,1,N,142,224,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76, +2,2019-01-17 18:03:58,2019-01-17 18:21:04,2,2.66,1,N,246,163,1,13.0,1.0,0.5,2.96,0.0,0.3,19.71, +2,2019-01-11 08:01:03,2019-01-11 08:04:09,2,0.6,1,N,161,230,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3, +1,2019-01-09 15:07:44,2019-01-09 15:20:50,2,1.5,1,N,237,262,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-26 15:52:57,2019-01-26 16:36:46,2,10.17,1,N,138,114,1,38.0,0.0,0.5,7.76,0.0,0.3,46.56,0.0 +2,2019-01-23 11:26:44,2019-01-23 11:38:16,2,0.81,1,N,233,170,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-23 09:13:34,2019-01-23 09:42:46,2,2.29,1,N,239,237,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-18 14:51:47,2019-01-18 15:01:23,2,1.43,1,N,237,263,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-13 20:36:12,2019-01-13 20:45:01,2,1.47,1,N,249,164,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-01 01:12:37,2019-01-01 01:26:24,2,2.8,1,N,113,141,1,12.0,0.5,0.5,3.3,0.0,0.3,16.6, +1,2019-01-28 15:43:12,2019-01-28 16:03:45,2,3.4,1,N,211,80,1,16.5,0.0,0.5,0.2,0.0,0.3,17.5,0.0 +1,2019-01-20 20:50:10,2019-01-20 20:54:02,2,0.7,1,N,230,163,1,4.5,0.5,0.5,1.1,0.0,0.3,6.9, +1,2019-01-01 04:01:19,2019-01-01 04:04:31,2,0.8,1,N,263,75,1,4.5,0.5,0.5,0.5,0.0,0.3,6.3, +2,2019-01-14 17:11:35,2019-01-14 17:22:14,2,1.81,1,N,239,141,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42, +1,2019-01-24 23:11:51,2019-01-24 23:22:58,2,1.8,1,N,170,43,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +1,2019-01-19 12:37:07,2019-01-19 13:01:44,2,2.0,1,N,162,143,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-13 02:21:40,2019-01-13 02:26:09,2,0.61,1,N,234,158,1,4.5,0.5,0.5,0.5,0.0,0.3,6.3, +2,2019-01-21 15:07:32,2019-01-21 15:14:37,2,1.1,1,N,163,161,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-09 10:26:02,2019-01-09 10:34:09,2,1.11,1,N,141,229,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-02 12:26:33,2019-01-02 12:37:08,2,0.99,1,N,163,163,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-06 23:29:57,2019-01-06 23:32:08,2,0.4,1,N,68,68,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-08 06:50:19,2019-01-08 06:53:22,2,0.62,1,N,100,230,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-03 21:46:56,2019-01-03 22:04:25,2,3.35,1,N,263,48,1,14.0,0.5,0.5,2.0,0.0,0.3,17.3, +2,2019-01-21 21:28:49,2019-01-21 21:47:33,2,4.84,1,N,79,236,1,17.0,0.5,0.5,3.66,0.0,0.3,21.96,0.0 +2,2019-01-08 10:07:15,2019-01-08 10:13:39,2,0.68,1,N,137,233,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 11:39:09,2019-01-01 11:52:32,2,3.43,1,N,236,166,1,13.0,0.0,0.5,1.5,0.0,0.3,15.3, +1,2019-01-30 18:55:22,2019-01-30 19:18:30,2,6.0,1,N,116,141,2,21.5,1.0,0.5,0.0,0.0,0.3,23.3,0.0 +1,2019-01-06 13:11:51,2019-01-06 13:18:09,2,1.7,1,N,13,158,1,7.5,0.0,0.5,2.05,0.0,0.3,10.35, +2,2019-01-09 23:32:50,2019-01-09 23:43:49,2,1.91,1,N,79,164,1,9.5,0.5,0.5,1.0,0.0,0.3,11.8, +2,2019-01-12 07:31:23,2019-01-12 07:37:29,2,0.86,1,N,68,234,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-30 13:41:09,2019-01-30 14:04:50,2,1.8,1,N,229,100,1,14.5,0.0,0.5,1.0,0.0,0.3,16.3,0.0 +2,2019-01-11 15:05:40,2019-01-11 15:14:19,2,0.95,1,N,236,141,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +1,2019-01-15 15:09:24,2019-01-15 15:25:53,2,2.5,1,N,68,142,1,12.0,0.0,0.5,2.55,0.0,0.3,15.35, +2,2019-01-14 09:02:54,2019-01-14 09:15:09,2,1.14,1,N,170,48,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-20 22:54:48,2019-01-20 23:28:26,2,20.55,2,N,132,87,2,52.0,0.0,0.5,0.0,0.0,0.3,52.8, +1,2019-01-01 16:45:24,2019-01-01 17:02:38,2,3.2,1,N,142,262,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-22 10:14:27,2019-01-22 10:40:48,2,2.7,1,N,232,170,1,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +1,2019-01-18 19:21:12,2019-01-18 19:28:12,2,1.3,1,N,90,161,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-19 12:10:53,2019-01-19 12:16:44,2,0.95,1,N,162,164,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-07 17:26:30,2019-01-07 17:43:32,2,2.97,1,N,113,13,1,13.5,1.0,0.5,2.0,0.0,0.3,17.3, +2,2019-01-06 08:48:27,2019-01-06 09:13:41,2,4.63,1,N,186,148,2,19.5,0.0,0.5,0.0,0.0,0.3,20.3, +2,2019-01-07 19:53:47,2019-01-07 20:03:56,2,5.36,1,N,132,124,2,16.5,1.0,0.5,0.0,0.0,0.3,18.3, +1,2019-01-17 11:41:32,2019-01-17 12:08:58,2,2.4,1,N,90,162,1,17.0,0.0,0.5,1.0,0.0,0.3,18.8, +2,2019-01-07 10:12:48,2019-01-07 10:18:40,2,0.66,1,N,161,230,1,5.5,0.0,0.5,1.58,0.0,0.3,7.88, +2,2019-01-07 09:26:03,2019-01-07 09:31:22,2,1.04,1,N,13,88,1,6.0,0.0,0.5,0.68,0.0,0.3,7.48, +2,2019-01-17 20:13:42,2019-01-17 20:40:26,2,11.64,1,N,125,243,1,35.0,0.5,0.5,7.26,0.0,0.3,43.56, +2,2019-01-30 07:17:29,2019-01-30 07:32:53,2,1.98,1,N,162,68,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75,0.0 +2,2019-01-30 15:42:03,2019-01-30 16:09:10,2,2.58,1,N,164,144,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-04 23:13:40,2019-01-04 23:33:17,2,8.35,1,N,161,243,1,25.5,0.5,0.5,5.0,0.0,0.3,31.8, +2,2019-01-11 12:16:58,2019-01-11 12:28:52,2,1.15,1,N,162,141,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-21 17:28:25,2019-01-21 17:34:14,2,0.9,1,N,48,142,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-20 12:36:33,2019-01-20 12:51:23,2,3.6,1,N,186,148,1,14.0,0.0,0.5,2.95,0.0,0.3,17.75, +1,2019-01-01 04:36:52,2019-01-01 04:39:37,2,0.9,1,N,41,166,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-12 22:08:55,2019-01-12 22:22:52,2,5.4,1,N,263,148,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +2,2019-01-09 18:13:20,2019-01-09 18:23:22,2,1.3,1,N,151,239,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-18 10:26:45,2019-01-18 10:43:03,2,1.1,1,N,164,113,1,11.0,0.0,0.5,2.95,0.0,0.3,14.75, +2,2019-01-16 20:38:46,2019-01-16 20:47:44,2,0.97,1,N,186,137,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +1,2019-01-30 22:43:00,2019-01-30 22:48:50,2,0.7,1,N,161,164,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-23 16:13:42,2019-01-23 16:24:25,2,1.47,1,N,229,237,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +1,2019-01-08 09:19:36,2019-01-08 09:31:13,2,1.7,1,N,211,186,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-07 17:36:29,2019-01-07 18:03:24,2,4.72,1,N,142,42,1,20.0,1.0,0.5,2.18,0.0,0.3,23.98, +2,2019-01-23 00:00:54,2019-01-23 00:16:15,2,3.61,1,N,48,145,1,14.0,0.5,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-20 09:09:43,2019-01-20 09:16:25,2,0.61,1,N,163,230,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-26 13:01:45,2019-01-26 13:03:56,2,0.98,1,N,75,74,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-08 18:53:46,2019-01-08 19:05:50,2,0.98,1,N,141,140,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-07 17:28:38,2019-01-07 18:04:06,2,12.37,1,N,138,132,1,35.5,1.0,0.5,9.32,0.0,0.3,46.62, +1,2019-01-19 16:09:00,2019-01-19 16:21:44,2,2.9,1,N,161,43,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-14 11:50:00,2019-01-14 12:17:22,2,6.83,1,N,87,145,1,24.5,0.0,0.5,6.21,5.76,0.3,37.27, +2,2019-01-27 19:10:51,2019-01-27 19:18:19,2,2.1,1,N,236,239,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-15 18:23:04,2019-01-15 18:30:41,2,0.68,1,N,236,263,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-09 18:11:35,2019-01-09 18:30:54,2,2.02,1,N,229,142,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-31 19:54:31,2019-01-31 20:12:21,2,3.68,1,N,170,239,1,14.5,1.0,0.5,4.0,0.0,0.3,20.3,0.0 +2,2019-01-06 13:18:28,2019-01-06 13:25:28,2,1.09,1,N,68,246,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-09 18:33:23,2019-01-09 18:49:49,2,2.07,1,N,237,234,1,11.5,1.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-01 00:50:28,2019-01-01 01:15:19,2,5.82,1,N,148,239,1,20.0,0.5,0.5,4.26,0.0,0.3,25.56, +1,2019-01-31 08:29:57,2019-01-31 08:51:57,2,3.4,1,N,45,68,1,16.5,0.0,0.5,2.5,0.0,0.3,19.8,0.0 +2,2019-01-26 17:40:37,2019-01-26 17:44:03,2,0.51,1,N,163,48,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-21 12:50:33,2019-01-21 12:57:38,2,1.3,1,N,161,163,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-08 10:51:42,2019-01-08 11:11:53,2,2.14,1,N,229,68,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +1,2019-01-27 19:21:54,2019-01-27 19:26:13,2,0.6,1,N,113,249,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3,0.0 +1,2019-01-28 01:10:48,2019-01-28 01:15:36,2,0.9,1,N,229,230,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-03 09:16:53,2019-01-03 09:32:24,2,1.84,1,N,230,140,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +1,2019-01-09 13:48:22,2019-01-09 14:22:13,2,12.3,1,Y,138,48,1,37.5,0.0,0.5,8.8,5.76,0.3,52.86, +1,2019-01-20 18:39:12,2019-01-20 18:48:50,2,1.3,1,N,246,161,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-28 22:13:23,2019-01-28 22:21:42,2,2.0,1,N,237,262,1,9.0,0.5,0.5,2.05,0.0,0.3,12.35,0.0 +2,2019-01-26 19:43:13,2019-01-26 19:44:43,2,0.14,1,N,238,238,2,3.0,0.0,0.5,0.0,0.0,0.3,3.8,0.0 +1,2019-01-02 06:20:06,2019-01-02 06:29:53,2,2.4,1,N,143,186,1,9.5,0.0,0.5,2.0,0.0,0.3,12.3, +1,2019-01-10 00:45:56,2019-01-10 01:20:09,2,9.8,1,N,186,72,1,32.5,0.5,0.5,0.0,0.0,0.3,33.8, +1,2019-01-05 00:18:25,2019-01-05 00:22:53,2,1.1,1,N,233,137,1,5.5,0.5,0.5,1.35,0.0,0.3,8.15, +2,2019-01-15 15:14:41,2019-01-15 15:35:15,2,2.5,1,N,238,48,1,14.5,0.0,0.5,1.7,0.0,0.3,17.0, +2,2019-01-02 18:11:47,2019-01-02 18:16:25,2,0.85,1,N,79,211,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-13 13:12:30,2019-01-13 13:19:57,2,1.1,1,N,230,246,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +1,2019-01-19 17:32:31,2019-01-19 17:41:03,2,0.8,1,N,90,234,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +1,2019-01-05 15:24:48,2019-01-05 15:31:41,2,1.1,1,N,163,100,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-27 14:13:43,2019-01-27 14:21:52,2,1.36,1,N,230,50,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-23 19:28:22,2019-01-23 19:36:37,2,0.5,1,N,230,186,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-10 17:55:54,2019-01-10 18:12:42,2,2.19,1,N,114,246,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-29 19:40:24,2019-01-29 19:44:11,2,0.98,1,N,141,263,1,5.0,1.0,0.5,2.04,0.0,0.3,8.84,0.0 +2,2019-01-18 18:21:54,2019-01-18 18:33:59,2,1.46,1,N,234,170,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-23 03:34:25,2019-01-23 03:39:03,2,0.97,1,N,230,186,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-01 15:08:40,2019-01-01 15:19:47,2,2.14,1,N,45,90,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +1,2019-01-16 11:57:54,2019-01-16 12:09:26,2,0.9,1,N,90,100,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-20 15:57:38,2019-01-20 16:05:12,2,1.01,1,N,114,232,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-12 17:57:10,2019-01-12 18:00:45,2,0.96,1,N,141,237,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-07 17:31:41,2019-01-07 17:50:50,2,2.18,1,N,141,143,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-05 20:58:20,2019-01-05 21:38:39,2,20.61,2,N,132,238,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +1,2019-01-24 19:05:15,2019-01-24 19:20:13,2,1.9,1,N,186,237,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0,0.0 +1,2019-01-22 22:05:19,2019-01-22 22:08:38,2,0.4,1,N,211,144,1,4.0,0.5,0.5,1.05,0.0,0.3,6.35,0.0 +1,2019-01-17 02:22:26,2019-01-17 02:43:58,2,5.9,1,N,239,226,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3, +1,2019-01-30 16:37:58,2019-01-30 17:03:35,2,2.3,1,N,79,161,1,16.0,1.0,0.5,0.0,0.0,0.3,17.8,0.0 +2,2019-01-11 20:45:48,2019-01-11 21:05:18,2,3.26,1,N,161,79,1,15.0,0.5,0.5,1.0,0.0,0.3,17.3, +2,2019-01-13 01:14:06,2019-01-13 01:28:13,2,0.5,1,N,148,231,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-16 16:46:26,2019-01-16 16:52:20,2,0.61,1,N,230,186,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-28 23:57:49,2019-01-29 00:08:08,2,2.34,1,N,116,151,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8,0.0 +1,2019-01-16 16:26:17,2019-01-16 16:41:36,2,1.4,1,N,233,163,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-09 21:11:34,2019-01-09 21:14:55,2,0.74,1,N,164,186,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-07 19:06:10,2019-01-07 19:09:39,2,0.4,1,N,161,237,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-06 11:00:29,2019-01-06 11:16:18,2,1.8,1,N,161,50,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-25 23:01:45,2019-01-25 23:29:45,2,5.8,1,N,249,61,1,22.0,0.5,0.5,6.95,0.0,0.3,30.25,0.0 +2,2019-01-08 18:44:11,2019-01-08 18:52:39,2,1.84,1,N,237,236,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-13 01:03:11,2019-01-13 01:35:04,2,7.6,1,N,234,129,1,27.5,0.5,0.5,0.0,0.0,0.3,28.8, +2,2019-01-13 21:04:15,2019-01-13 21:13:23,2,1.8,1,N,246,163,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-07 17:57:55,2019-01-07 18:04:45,2,0.98,1,N,229,233,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-08 15:27:08,2019-01-08 15:46:16,2,2.3,1,N,239,263,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-20 10:45:30,2019-01-20 11:08:25,2,12.11,1,N,50,200,2,34.0,0.0,0.5,0.0,2.64,0.3,37.44, +2,2019-01-10 10:34:30,2019-01-10 10:43:43,2,0.63,1,N,161,163,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97, +2,2019-01-13 21:46:45,2019-01-13 22:02:41,2,2.99,1,N,230,236,1,13.0,0.5,0.5,3.58,0.0,0.3,17.88, +2,2019-01-21 14:45:54,2019-01-21 14:55:39,2,1.75,1,N,237,262,1,9.0,0.0,0.5,5.0,0.0,0.3,14.8,0.0 +2,2019-01-22 18:20:58,2019-01-22 18:31:07,2,1.02,1,N,107,90,1,8.0,1.0,0.5,1.5,0.0,0.3,11.3,0.0 +2,2019-01-23 12:03:10,2019-01-23 12:17:51,2,1.27,1,N,237,161,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-23 18:53:39,2019-01-23 19:00:38,2,1.2,1,N,161,237,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-03 10:31:44,2019-01-03 10:54:16,2,5.1,1,N,246,87,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-09 07:14:48,2019-01-09 07:37:14,2,8.57,1,N,141,125,1,27.0,0.0,0.5,8.34,0.0,0.3,36.14, +1,2019-01-26 22:57:27,2019-01-26 23:21:45,2,6.0,1,N,142,223,1,21.0,0.5,0.5,3.5,0.0,0.3,25.8,0.0 +2,2019-01-11 15:31:17,2019-01-11 15:33:44,2,0.43,1,N,162,161,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-06 02:17:56,2019-01-06 02:24:29,2,1.51,1,N,148,249,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-08 15:00:17,2019-01-08 15:12:09,2,4.82,1,N,209,162,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-11 19:09:02,2019-01-11 19:13:11,2,0.73,1,N,90,164,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-17 14:17:20,2019-01-17 14:29:46,2,1.1,1,N,142,161,1,9.0,0.0,0.5,0.49,0.0,0.3,10.29, +2,2019-01-17 08:58:53,2019-01-17 09:02:30,2,0.96,1,N,237,236,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-03 17:59:54,2019-01-03 18:19:03,2,3.1,1,N,107,236,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-13 12:02:09,2019-01-13 12:11:45,2,1.51,1,N,236,237,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-01 18:26:44,2019-01-01 18:35:54,2,1.79,1,N,79,186,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-12 13:43:00,2019-01-12 14:03:06,2,2.76,1,N,162,158,1,14.5,0.0,0.5,2.0,0.0,0.3,17.3, +2,2019-01-28 21:18:35,2019-01-28 21:21:03,2,0.46,1,N,229,162,1,3.5,0.5,0.5,0.96,0.0,0.3,5.76,0.0 +1,2019-01-13 13:22:56,2019-01-13 13:25:48,2,0.4,1,N,264,264,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95, +1,2019-01-19 18:30:34,2019-01-19 19:02:36,2,4.1,1,N,163,211,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3, +2,2019-01-19 14:01:56,2019-01-19 14:19:38,2,1.72,1,N,141,48,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-04 22:30:36,2019-01-04 22:34:40,2,1.0,1,N,75,236,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-30 10:35:34,2019-01-30 10:56:50,2,1.68,1,N,142,161,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +1,2019-01-13 14:55:22,2019-01-13 15:00:22,2,0.9,1,N,211,231,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-26 02:37:09,2019-01-26 02:47:23,2,2.44,1,N,148,186,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-17 18:47:36,2019-01-17 19:02:30,2,1.83,1,N,230,170,1,10.5,1.0,0.5,3.08,0.0,0.3,15.38, +2,2019-01-26 21:44:13,2019-01-26 21:48:31,2,0.8,1,N,236,237,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-30 20:36:44,2019-01-30 20:49:51,2,4.7,1,N,48,166,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3,0.0 +2,2019-01-30 09:28:56,2019-01-30 09:32:21,2,0.7,1,N,263,236,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-08 07:14:05,2019-01-08 07:26:14,2,1.8,1,N,238,143,1,10.0,0.0,0.5,2.15,0.0,0.3,12.95, +2,2019-01-09 05:12:41,2019-01-09 05:40:53,2,23.36,3,N,170,1,2,79.0,0.5,0.0,0.0,20.5,0.3,100.3, +2,2019-01-15 19:28:33,2019-01-15 19:40:29,2,2.19,1,N,161,263,1,10.5,1.0,0.5,1.2,0.0,0.3,13.5, +2,2019-01-23 09:49:25,2019-01-23 10:05:28,2,2.01,1,N,141,236,1,11.5,0.0,0.5,1.75,0.0,0.3,14.05,0.0 +1,2019-01-27 19:54:15,2019-01-27 20:12:02,2,4.0,1,N,264,264,1,16.0,0.5,0.5,3.45,0.0,0.3,20.75,0.0 +1,2019-01-06 20:59:30,2019-01-06 21:07:51,2,2.2,1,N,162,113,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +1,2019-01-31 17:26:13,2019-01-31 17:33:18,2,0.7,1,N,142,50,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35,0.0 +2,2019-01-30 16:23:38,2019-01-30 16:43:39,2,2.74,1,N,143,246,1,15.0,1.0,0.5,0.0,0.0,0.3,16.8,0.0 +1,2019-01-09 09:11:48,2019-01-09 09:22:00,2,1.1,1,N,164,161,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +2,2019-01-10 11:19:13,2019-01-10 11:26:25,2,0.62,1,N,50,100,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-08 01:05:20,2019-01-08 01:11:44,2,1.31,1,N,48,186,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-14 16:14:59,2019-01-14 16:20:47,2,0.92,1,N,107,170,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-28 19:46:35,2019-01-28 20:10:42,2,5.57,1,N,143,211,1,21.0,1.0,0.5,4.56,0.0,0.3,27.36,0.0 +2,2019-01-13 13:41:08,2019-01-13 13:54:02,2,1.83,1,N,186,142,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-08 06:26:33,2019-01-08 06:28:34,2,0.51,1,N,48,48,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-30 14:23:04,2019-01-30 14:30:47,2,0.77,1,N,140,141,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-05 16:51:50,2019-01-05 17:03:19,2,2.1,1,N,45,158,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +4,2019-01-01 18:22:48,2019-01-01 18:28:52,2,1.02,1,N,43,142,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-25 11:13:49,2019-01-25 11:30:58,2,3.51,1,N,50,211,1,14.5,0.0,0.5,2.3,0.0,0.3,17.6,0.0 +1,2019-01-04 11:59:24,2019-01-04 12:24:49,2,7.2,1,N,162,145,1,24.0,0.0,0.5,6.1,5.76,0.3,36.66, +2,2019-01-03 17:44:13,2019-01-03 17:50:00,2,0.9,1,N,113,79,1,5.5,1.0,0.5,3.33,0.0,0.3,10.63, +2,2019-01-12 00:38:13,2019-01-12 00:41:46,2,0.66,1,N,239,239,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-04 14:59:45,2019-01-04 15:18:03,2,1.6,1,N,186,161,2,12.0,0.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-30 09:20:22,2019-01-30 09:41:57,2,2.94,1,N,151,161,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3,0.0 +1,2019-01-17 13:53:20,2019-01-17 14:09:12,2,1.7,1,N,107,231,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +1,2019-01-24 20:22:28,2019-01-24 20:25:24,2,0.3,1,N,161,162,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-20 00:27:24,2019-01-20 00:34:58,2,1.07,1,N,114,148,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-13 14:23:59,2019-01-13 14:34:20,2,2.45,1,N,170,236,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +1,2019-01-25 23:07:19,2019-01-25 23:10:42,2,0.4,1,N,237,237,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-05 11:11:50,2019-01-05 11:28:03,2,1.9,1,N,224,186,1,11.5,0.0,0.5,2.45,0.0,0.3,14.75, +2,2019-01-05 14:34:58,2019-01-05 14:41:44,2,1.18,1,N,211,158,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-10 12:10:16,2019-01-10 12:18:00,2,1.06,1,N,43,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-17 13:15:15,2019-01-17 13:18:34,2,0.35,1,N,140,141,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-23 06:50:42,2019-01-23 06:57:40,2,1.4,1,N,170,90,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-12 03:20:38,2019-01-12 03:31:35,2,2.24,1,N,158,224,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-25 01:29:23,2019-01-25 01:45:02,2,1.15,1,N,48,162,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-13 12:29:42,2019-01-13 12:46:02,2,2.2,1,N,68,79,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +1,2019-01-14 20:29:47,2019-01-14 20:42:14,2,2.4,1,N,163,137,1,10.5,0.5,0.5,2.35,0.0,0.3,14.15, +2,2019-01-13 15:42:19,2019-01-13 15:47:30,2,1.03,1,N,170,234,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-15 23:04:39,2019-01-15 23:14:44,2,1.3,1,N,45,232,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +2,2019-01-09 11:46:00,2019-01-09 12:02:29,2,1.94,1,N,170,249,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-18 21:09:48,2019-01-18 21:25:05,2,2.13,1,N,164,113,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +1,2019-01-13 13:17:12,2019-01-13 13:20:08,2,0.7,1,N,236,263,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-05 23:02:05,2019-01-05 23:13:22,2,3.48,1,N,148,233,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-19 23:48:26,2019-01-20 00:16:49,2,7.89,1,N,164,181,2,26.0,0.5,0.5,0.0,0.0,0.3,27.3, +2,2019-01-17 17:53:16,2019-01-17 18:06:22,2,1.63,1,N,236,238,1,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-25 00:40:36,2019-01-25 01:21:08,2,27.91,4,N,219,265,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +2,2019-01-31 12:57:40,2019-01-31 13:04:56,2,1.04,1,N,186,68,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-16 10:16:44,2019-01-16 10:20:46,2,0.47,1,N,170,170,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-06 00:46:31,2019-01-06 00:49:16,2,0.6,1,N,50,48,1,4.5,0.5,0.5,0.58,0.0,0.3,6.38, +2,2019-01-24 17:47:30,2019-01-24 18:01:35,2,3.1,1,N,25,232,1,13.0,1.0,0.5,2.0,0.0,0.3,16.8,0.0 +2,2019-01-25 14:15:09,2019-01-25 14:23:10,2,1.07,1,N,164,234,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-28 17:21:56,2019-01-28 18:04:43,2,4.97,1,N,33,89,1,27.0,1.0,0.5,6.0,0.0,0.3,34.8,0.0 +1,2019-01-24 07:53:01,2019-01-24 08:05:28,2,1.5,1,N,237,161,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-29 16:32:38,2019-01-29 16:51:46,2,2.14,1,N,163,68,1,13.0,1.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-30 20:16:20,2019-01-30 20:24:33,2,1.26,1,N,164,233,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-28 07:04:30,2019-01-28 07:12:03,2,1.37,1,N,74,74,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-27 11:39:59,2019-01-27 11:43:34,2,0.99,1,N,75,236,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-18 15:32:49,2019-01-18 15:46:20,2,2.18,1,N,43,238,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-16 18:53:56,2019-01-16 19:17:15,2,2.75,1,Y,237,107,1,15.5,1.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-22 16:03:43,2019-01-22 16:10:26,2,0.7,1,N,263,140,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-28 19:09:38,2019-01-28 19:19:26,2,1.68,1,N,249,230,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-06 13:19:11,2019-01-06 13:43:31,2,5.61,1,N,231,262,1,20.5,0.0,0.5,4.26,0.0,0.3,25.56, +2,2019-01-16 08:54:22,2019-01-16 08:59:23,2,1.09,1,N,41,238,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0, +2,2019-01-01 06:31:58,2019-01-01 06:47:49,2,4.78,1,N,107,239,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +1,2019-01-18 18:29:27,2019-01-18 18:33:42,2,0.7,1,N,224,79,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-13 16:08:27,2019-01-13 16:36:57,2,10.83,1,N,138,230,1,33.5,0.0,0.5,12.02,5.76,0.3,52.08, +2,2019-01-06 10:12:55,2019-01-06 10:17:19,2,0.74,1,N,238,239,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-31 22:37:21,2019-01-31 22:53:18,2,2.9,1,N,162,249,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-24 22:29:34,2019-01-24 22:32:09,2,0.43,1,N,48,230,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +1,2019-01-04 19:39:08,2019-01-04 19:49:32,2,2.1,1,N,68,142,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +1,2019-01-22 08:11:12,2019-01-22 08:38:00,2,2.4,1,N,163,140,1,17.0,0.0,0.5,3.55,0.0,0.3,21.35,0.0 +2,2019-01-15 11:26:25,2019-01-15 11:32:53,2,0.8,1,N,237,236,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-29 14:24:03,2019-01-29 14:28:52,2,0.82,1,N,239,238,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-10 16:00:34,2019-01-10 16:16:53,2,1.8,1,N,262,239,1,12.0,1.0,0.5,3.45,0.0,0.3,17.25, +1,2019-01-01 15:37:43,2019-01-01 15:47:54,2,1.6,1,N,237,142,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-02 21:51:43,2019-01-02 22:03:58,2,2.57,1,N,186,148,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-01 06:56:39,2019-01-01 07:07:32,2,6.61,1,N,217,129,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8, +2,2019-01-07 08:49:05,2019-01-07 09:01:23,2,1.18,1,N,142,163,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-31 21:28:07,2019-01-31 21:37:32,2,1.21,1,N,211,79,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-24 15:08:33,2019-01-24 15:13:25,2,0.66,1,N,43,239,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-12 12:14:19,2019-01-12 12:16:53,2,0.6,1,N,107,170,1,4.0,0.0,0.5,2.5,0.0,0.3,7.3, +2,2019-01-18 19:06:43,2019-01-18 19:16:38,2,1.98,1,N,229,79,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-01 07:39:31,2019-01-01 07:50:32,2,2.5,1,N,79,68,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-07 14:34:15,2019-01-07 14:55:11,2,6.41,1,N,261,161,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16, +2,2019-01-30 23:02:36,2019-01-30 23:11:06,2,1.81,1,N,142,246,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8,0.0 +1,2019-01-26 03:25:32,2019-01-26 03:30:20,2,0.6,1,N,87,87,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-11 00:18:23,2019-01-11 00:20:35,2,0.51,1,N,162,161,1,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +1,2019-01-13 15:35:11,2019-01-13 15:41:10,2,0.8,1,N,164,233,1,5.5,0.0,0.5,1.25,0.0,0.3,7.55, +1,2019-01-24 08:36:07,2019-01-24 08:39:36,2,0.5,1,N,239,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +1,2019-01-29 18:00:27,2019-01-29 18:20:41,2,2.5,1,N,230,158,1,14.0,1.0,0.5,3.15,0.0,0.3,18.95,0.0 +1,2019-01-29 20:14:09,2019-01-29 20:23:55,2,1.4,1,N,141,162,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3,0.0 +1,2019-01-15 10:21:38,2019-01-15 10:30:25,2,1.0,1,N,164,170,1,7.0,0.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-22 12:28:10,2019-01-22 12:35:37,2,0.52,1,N,141,237,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-12 07:02:15,2019-01-12 07:29:57,2,18.66,2,N,132,236,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-09 07:25:38,2019-01-09 07:44:33,2,7.1,1,N,263,138,1,23.0,0.0,0.5,5.91,5.76,0.3,35.47, +1,2019-01-11 02:24:28,2019-01-11 02:34:46,2,4.2,1,N,41,161,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-30 08:16:20,2019-01-30 08:44:27,2,2.97,1,N,236,170,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-22 22:11:36,2019-01-22 22:20:57,2,2.25,1,N,142,151,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-15 16:13:42,2019-01-15 16:28:25,2,1.76,1,N,230,234,1,10.5,1.0,0.5,1.84,0.0,0.3,14.14, +2,2019-01-10 07:42:17,2019-01-10 07:51:07,2,0.73,1,N,238,238,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-03 22:07:56,2019-01-03 22:22:47,2,4.67,1,N,13,79,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +1,2019-01-12 09:07:40,2019-01-12 09:10:24,2,0.5,1,N,239,143,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-30 16:46:50,2019-01-30 16:51:05,2,0.55,1,N,113,144,1,5.0,1.0,0.5,2.04,0.0,0.3,8.84,0.0 +2,2019-01-22 11:29:07,2019-01-22 11:39:17,2,1.11,1,N,236,237,1,7.5,0.0,0.5,1.49,0.0,0.3,9.79,0.0 +1,2019-01-10 21:28:32,2019-01-10 21:38:32,2,1.4,1,N,114,90,1,8.0,0.5,0.5,1.67,0.0,0.3,10.97, +1,2019-01-11 16:48:32,2019-01-11 16:55:27,2,0.7,1,N,233,161,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15, +1,2019-01-13 09:06:17,2019-01-13 09:14:23,2,1.8,1,N,48,186,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-14 09:27:21,2019-01-14 09:29:45,2,0.7,1,N,239,238,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-15 14:18:11,2019-01-15 14:28:02,2,1.46,1,N,234,211,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-26 02:03:04,2019-01-26 02:07:13,2,0.63,1,N,211,144,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-03 21:22:38,2019-01-03 21:26:06,2,0.54,1,N,141,141,1,4.5,0.5,0.5,1.2,0.0,0.3,7.0, +2,2019-01-30 18:46:36,2019-01-30 18:51:55,2,1.32,1,N,163,239,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-06 00:49:03,2019-01-06 01:10:25,2,4.6,1,N,158,143,1,17.5,0.5,0.5,3.75,0.0,0.3,22.55, +2,2019-01-13 21:03:46,2019-01-13 21:11:16,2,1.39,1,N,238,143,1,7.0,0.5,0.5,2.49,0.0,0.3,10.79, +2,2019-01-06 09:49:10,2019-01-06 09:50:22,2,0.36,1,N,238,238,1,3.0,0.0,0.5,0.57,0.0,0.3,4.37, +2,2019-01-26 10:25:08,2019-01-26 10:34:17,2,1.22,1,N,186,170,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-10 18:32:43,2019-01-10 18:54:47,2,4.81,1,N,13,97,1,18.0,1.0,0.5,4.95,0.0,0.3,24.75, +2,2019-01-28 07:32:42,2019-01-28 07:39:41,2,0.77,1,N,229,233,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-14 16:19:30,2019-01-14 16:24:55,2,1.0,1,N,143,238,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-18 22:14:57,2019-01-18 22:31:19,2,1.6,1,N,249,224,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-31 17:18:41,2019-01-31 17:23:46,2,0.83,1,N,237,236,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-01 10:22:49,2019-01-01 10:46:56,2,19.67,1,N,132,33,1,52.0,0.0,0.5,7.0,0.0,0.3,59.8, +2,2019-01-26 15:55:11,2019-01-26 16:01:25,2,0.96,1,N,43,43,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-11 17:12:53,2019-01-11 17:29:46,2,1.49,1,N,164,48,1,11.0,1.0,0.5,3.84,0.0,0.3,16.64, +1,2019-01-05 13:15:52,2019-01-05 13:26:07,2,1.3,1,N,263,237,1,8.5,0.0,0.5,1.85,0.0,0.3,11.15, +2,2019-01-17 10:42:53,2019-01-17 11:19:43,2,8.63,1,N,138,161,1,33.0,0.0,0.5,4.0,5.76,0.3,43.56, +2,2019-01-20 02:08:12,2019-01-20 02:09:36,2,0.3,1,N,231,231,1,3.0,0.5,0.5,1.0,0.0,0.3,5.3, +1,2019-01-20 18:49:28,2019-01-20 18:55:56,2,0.9,1,N,164,230,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-11 22:27:59,2019-01-11 22:49:46,2,3.0,1,N,90,143,1,15.5,0.5,0.5,3.35,0.0,0.3,20.15, +2,2019-01-28 13:16:51,2019-01-28 13:30:45,2,1.51,1,N,237,164,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8,0.0 +2,2019-01-26 19:37:55,2019-01-26 19:42:40,2,0.69,1,N,239,142,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-14 17:34:47,2019-01-14 18:02:48,2,7.6,1,N,132,95,1,25.5,1.0,0.5,4.5,0.0,0.3,31.8, +2,2019-01-03 19:36:43,2019-01-03 19:51:50,2,5.7,1,N,263,79,1,18.0,1.0,0.5,3.96,0.0,0.3,23.76, +1,2019-01-18 21:32:26,2019-01-18 21:38:17,2,1.4,1,N,142,237,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-24 09:46:30,2019-01-24 10:23:29,2,2.29,1,N,186,162,2,21.5,0.0,0.5,0.0,0.0,0.3,22.3,0.0 +1,2019-01-30 16:50:51,2019-01-30 17:03:04,2,1.8,1,N,237,262,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-29 17:25:29,2019-01-29 17:54:37,2,3.12,1,N,238,162,1,18.5,1.0,0.5,5.08,0.0,0.3,25.38,0.0 +1,2019-01-20 21:38:12,2019-01-20 21:41:50,2,0.6,1,N,237,237,1,4.5,0.5,0.5,0.5,0.0,0.3,6.3, +2,2019-01-08 09:44:30,2019-01-08 10:04:07,2,4.12,1,N,151,230,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96, +1,2019-01-22 18:16:17,2019-01-22 18:25:14,2,1.4,1,N,236,238,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25,0.0 +1,2019-01-22 16:32:20,2019-01-22 16:43:23,2,1.2,1,N,162,48,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-14 20:12:40,2019-01-14 20:16:51,2,0.8,1,N,246,68,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-08 14:29:44,2019-01-08 14:43:23,2,0.83,1,N,186,230,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +2,2019-01-07 10:00:11,2019-01-07 10:08:53,2,0.55,1,N,237,229,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-02 15:31:19,2019-01-02 15:43:43,2,1.17,1,N,158,114,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-05 21:26:14,2019-01-05 21:36:42,2,1.4,1,N,107,79,1,8.5,0.5,0.5,3.0,0.0,0.3,12.8, +2,2019-01-24 22:12:08,2019-01-24 22:18:33,2,0.98,1,N,100,161,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-12 12:19:24,2019-01-12 12:32:54,2,2.2,1,N,143,100,1,10.5,0.0,0.5,1.5,0.0,0.3,12.8, +2,2019-01-16 17:22:46,2019-01-16 17:46:11,2,6.61,1,N,13,162,1,22.0,1.0,0.5,5.95,0.0,0.3,29.75, +2,2019-01-03 21:27:04,2019-01-03 21:40:16,2,2.72,1,N,158,230,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-02 19:27:38,2019-01-02 19:33:03,2,1.17,1,N,229,141,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-10 15:44:44,2019-01-10 15:59:05,2,1.8,1,N,237,239,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-08 17:54:43,2019-01-08 18:03:00,2,1.41,1,N,113,170,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-31 09:46:30,2019-01-31 09:51:46,2,0.5,1,N,236,141,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-18 13:52:15,2019-01-18 14:02:31,2,0.7,1,N,90,107,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-27 11:35:43,2019-01-27 11:40:44,2,0.73,1,N,237,237,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25,0.0 +2,2019-01-15 06:49:15,2019-01-15 06:53:27,2,0.73,1,N,234,107,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-23 02:02:05,2019-01-23 02:07:14,2,1.45,1,N,162,164,1,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-04 17:12:44,2019-01-04 17:20:09,2,0.7,1,N,264,264,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-29 21:29:15,2019-01-29 21:35:56,2,1.1,1,N,161,186,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-29 09:27:53,2019-01-29 09:31:05,2,0.85,1,N,107,79,1,4.5,0.0,0.5,0.7,0.0,0.3,6.0,0.0 +2,2019-01-16 22:34:33,2019-01-16 22:46:57,2,2.47,1,N,161,239,1,11.5,0.5,0.5,2.0,0.0,0.3,14.8, +2,2019-01-11 14:09:33,2019-01-11 14:25:02,2,1.89,1,N,186,142,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-13 20:56:49,2019-01-13 21:08:03,2,2.01,1,N,114,186,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +1,2019-01-11 21:32:40,2019-01-11 21:37:44,2,0.3,1,N,148,148,3,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-11 17:44:05,2019-01-11 17:52:06,2,1.08,1,N,90,137,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-20 18:44:03,2019-01-20 18:53:55,2,1.78,1,N,186,50,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +1,2019-01-26 03:52:16,2019-01-26 04:27:15,2,8.4,1,N,48,225,2,30.5,0.5,0.5,0.0,0.0,0.3,31.8,0.0 +1,2019-01-17 18:37:19,2019-01-17 18:45:09,2,1.1,1,N,237,262,1,7.0,1.0,0.5,1.32,0.0,0.3,10.12, +1,2019-01-27 04:31:26,2019-01-27 04:40:52,2,2.6,1,N,264,264,1,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-13 12:44:30,2019-01-13 12:48:54,2,0.78,1,N,162,48,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-01 17:52:50,2019-01-01 17:56:44,2,0.78,1,N,148,113,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-04 17:47:30,2019-01-04 18:17:18,2,6.5,1,N,137,49,2,24.5,1.0,0.5,0.0,0.0,0.3,26.3, +2,2019-01-18 10:44:12,2019-01-18 11:15:33,2,14.02,1,N,138,13,1,41.5,0.0,0.5,5.0,5.76,0.3,53.06, +1,2019-01-30 15:44:10,2019-01-30 15:51:19,2,1.1,1,N,249,68,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-16 08:06:24,2019-01-16 08:17:19,2,1.58,1,N,48,90,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-24 23:05:42,2019-01-24 23:12:12,2,1.07,1,N,129,129,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-27 03:23:15,2019-01-27 03:40:37,2,9.1,1,N,264,264,1,27.0,0.5,0.5,4.0,0.0,0.3,32.3,0.0 +2,2019-01-05 00:00:12,2019-01-05 00:11:00,2,2.66,1,N,211,13,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-19 15:19:41,2019-01-19 15:32:18,2,2.31,1,N,148,233,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-05 19:19:38,2019-01-05 19:33:01,2,1.6,1,N,164,230,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-09 10:10:12,2019-01-09 10:29:25,2,2.63,1,N,237,234,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-09 15:53:28,2019-01-09 15:56:23,2,0.32,1,N,43,236,3,-4.0,0.0,-0.5,0.0,0.0,-0.3,-4.8, +2,2019-01-13 03:33:51,2019-01-13 03:44:11,2,2.06,1,N,90,144,1,9.5,0.5,0.5,1.62,0.0,0.3,12.42, +1,2019-01-04 19:52:58,2019-01-04 19:55:23,2,0.8,1,N,229,141,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +2,2019-01-06 12:05:15,2019-01-06 12:08:30,2,0.64,1,N,233,141,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3, +2,2019-01-22 19:36:27,2019-01-22 19:40:33,2,0.87,1,N,261,211,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-12 15:46:32,2019-01-12 16:04:29,2,2.81,1,N,262,142,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-28 15:48:16,2019-01-28 15:53:30,2,0.97,1,N,239,143,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +1,2019-01-19 22:49:37,2019-01-19 22:55:22,2,1.2,1,N,186,68,1,6.0,0.5,0.5,2.0,0.0,0.3,9.3, +1,2019-01-26 18:25:37,2019-01-26 18:31:30,2,0.9,1,N,264,264,1,6.0,0.0,0.5,1.35,0.0,0.3,8.15,0.0 +2,2019-01-01 15:03:26,2019-01-01 15:07:57,2,0.89,1,N,162,164,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-22 21:53:26,2019-01-22 22:01:45,2,1.52,1,N,230,170,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-25 08:16:00,2019-01-25 08:21:47,2,0.79,1,N,143,239,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-17 15:11:20,2019-01-17 15:41:11,2,8.62,1,N,166,261,2,29.5,0.0,0.5,0.0,0.0,0.3,30.3, +1,2019-01-29 18:30:38,2019-01-29 18:38:07,2,1.4,1,N,238,166,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-07 23:49:22,2019-01-08 00:14:26,2,5.24,1,N,231,37,2,20.5,0.5,0.5,0.0,0.0,0.3,21.8, +2,2019-01-05 19:09:56,2019-01-05 19:12:57,2,0.59,1,N,236,236,1,4.5,0.0,0.5,0.53,0.0,0.3,5.83, +1,2019-01-17 22:04:50,2019-01-17 22:19:10,2,2.4,1,N,142,140,1,11.5,0.5,0.5,2.55,0.0,0.3,15.35, +2,2019-01-21 21:24:44,2019-01-21 21:36:31,3,2.74,1,N,170,262,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +1,2019-01-10 18:14:05,2019-01-10 18:17:33,3,0.4,1,N,143,142,1,4.0,1.0,0.5,1.4,0.0,0.3,7.2, +2,2019-01-22 10:37:43,2019-01-22 10:43:14,3,0.58,1,N,234,79,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-17 07:27:42,2019-01-17 07:33:56,3,1.0,1,N,237,140,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +1,2019-01-03 17:14:01,2019-01-03 17:26:48,3,1.7,1,N,45,261,1,9.5,1.0,0.5,2.25,0.0,0.3,13.55, +2,2019-01-14 19:17:35,2019-01-14 19:25:35,3,1.4,1,N,100,163,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-14 18:26:27,2019-01-14 18:36:40,3,1.55,1,N,238,143,1,9.0,1.0,0.5,2.0,0.0,0.3,12.8, +2,2019-01-19 10:20:54,2019-01-19 10:26:26,3,0.98,1,N,140,236,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-24 22:37:58,2019-01-24 22:46:09,3,1.58,1,N,163,164,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-22 18:25:07,2019-01-22 18:31:30,3,1.08,1,N,249,90,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-28 07:55:58,2019-01-28 07:59:01,3,0.43,1,N,231,125,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-21 23:14:15,2019-01-21 23:18:08,3,0.48,1,N,170,234,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8,0.0 +1,2019-01-26 16:20:30,2019-01-26 16:36:02,3,2.5,1,N,43,230,1,12.0,0.0,0.5,3.8,0.0,0.3,16.6,0.0 +1,2019-01-02 12:51:46,2019-01-02 12:56:53,3,0.7,1,N,239,239,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-01 02:12:14,2019-01-01 02:44:14,3,4.9,1,N,163,223,1,22.5,0.5,0.5,0.0,0.0,0.3,23.8, +2,2019-01-05 13:11:49,2019-01-05 13:18:08,3,0.98,1,N,237,161,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-31 10:36:25,2019-01-31 10:52:08,3,2.14,1,N,161,79,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-28 12:40:52,2019-01-28 12:56:24,3,2.6,1,N,231,246,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-02 21:39:28,2019-01-02 21:49:28,3,2.07,1,N,230,236,1,9.5,0.5,0.5,1.73,0.0,0.3,12.53, +1,2019-01-17 16:25:53,2019-01-17 17:22:48,3,17.0,2,N,132,186,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-28 21:05:56,2019-01-28 21:12:18,3,1.35,1,N,164,107,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-19 21:06:04,2019-01-19 21:29:27,3,7.25,1,N,164,33,2,23.5,0.5,0.5,0.0,0.0,0.3,24.8, +2,2019-01-18 18:10:36,2019-01-18 18:28:32,3,2.57,1,N,237,107,2,13.5,1.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-23 20:25:16,2019-01-23 20:33:24,3,1.4,1,N,211,87,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-24 09:27:42,2019-01-24 09:30:53,3,0.51,1,N,237,141,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-07 18:21:22,2019-01-07 18:26:04,3,0.98,1,N,249,90,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-25 11:04:34,2019-01-25 11:14:10,3,0.83,1,N,170,161,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-30 22:11:52,2019-01-30 22:22:12,3,2.31,1,N,163,239,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-14 07:56:15,2019-01-14 08:00:55,3,0.78,1,N,113,107,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-17 21:47:35,2019-01-17 21:55:14,3,1.23,1,N,263,141,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-16 18:11:20,2019-01-16 18:21:52,3,1.52,1,N,231,13,1,9.0,1.0,0.5,2.7,0.0,0.3,13.5, +2,2019-01-31 00:21:06,2019-01-31 00:44:56,3,6.23,1,N,113,7,1,21.5,0.5,0.5,5.7,0.0,0.3,28.5,0.0 +1,2019-01-12 13:59:19,2019-01-12 14:06:02,3,0.8,1,N,238,24,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-11 16:24:18,2019-01-11 16:31:59,3,1.16,1,N,161,141,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-12 22:54:45,2019-01-12 23:16:01,3,7.4,1,N,237,87,1,24.0,0.5,0.5,5.06,0.0,0.3,30.36, +2,2019-01-19 19:48:34,2019-01-19 19:54:31,3,1.01,1,N,151,239,1,6.0,0.0,0.5,1.25,0.0,0.3,8.05, +1,2019-01-08 19:10:47,2019-01-08 19:30:25,3,4.3,1,N,229,41,1,16.0,1.0,0.5,2.0,0.0,0.3,19.8, +2,2019-01-09 14:30:42,2019-01-09 14:38:38,3,1.0,1,N,238,239,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-09 12:54:06,2019-01-09 12:59:03,3,0.94,1,N,238,143,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-23 07:37:27,2019-01-23 07:49:38,3,2.1,1,N,113,161,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-05 13:07:44,2019-01-05 13:11:01,3,0.76,1,N,229,237,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +1,2019-01-10 11:16:47,2019-01-10 11:20:08,3,0.3,1,N,107,107,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-24 18:26:08,2019-01-24 18:56:50,3,6.64,1,N,141,243,1,24.0,1.0,0.5,5.0,0.0,0.3,30.8,0.0 +2,2019-01-01 11:15:26,2019-01-01 11:36:37,3,7.29,1,N,163,45,2,23.0,0.0,0.5,0.0,0.0,0.3,23.8, +2,2019-01-13 10:50:49,2019-01-13 11:01:48,3,1.86,1,N,170,68,1,9.5,0.0,0.5,0.7,0.0,0.3,11.0, +2,2019-01-06 20:54:13,2019-01-06 20:54:55,3,0.02,2,N,231,231,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36, +2,2019-01-02 14:56:57,2019-01-02 15:04:03,3,0.81,1,N,90,249,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-09 19:01:33,2019-01-09 19:02:31,3,0.23,1,N,52,52,1,3.0,1.0,0.5,1.2,0.0,0.3,6.0, +2,2019-01-21 21:34:59,2019-01-21 21:54:46,3,5.88,1,N,13,189,1,20.5,0.5,0.5,5.45,0.0,0.3,27.25,0.0 +2,2019-01-30 12:13:34,2019-01-30 12:37:28,3,3.15,1,N,75,229,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-02 17:50:16,2019-01-02 17:56:37,3,1.15,1,N,234,137,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-27 21:29:35,2019-01-27 21:48:03,3,6.13,1,N,161,244,1,20.5,0.5,0.5,4.36,0.0,0.3,26.16,0.0 +1,2019-01-25 14:49:18,2019-01-25 15:01:53,3,1.4,1,N,237,236,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3,0.0 +1,2019-01-18 19:30:49,2019-01-18 19:34:26,3,0.4,1,N,79,79,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-12 18:44:43,2019-01-12 19:03:13,3,3.27,1,N,246,231,1,14.5,0.0,0.5,2.5,0.0,0.3,17.8, +2,2019-01-14 18:37:46,2019-01-14 18:51:42,3,1.3,1,N,229,48,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-02 21:28:42,2019-01-02 21:37:47,3,1.5,1,N,113,137,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-04 20:06:59,2019-01-04 20:11:56,3,1.1,1,N,237,236,1,6.0,0.5,0.5,1.8,0.0,0.3,9.1, +2,2019-01-29 07:19:25,2019-01-29 07:44:16,3,4.36,1,N,13,230,1,18.5,0.0,0.5,1.0,0.0,0.3,20.3,0.0 +2,2019-01-05 09:03:19,2019-01-05 09:07:38,3,0.9,1,N,142,239,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-12 18:12:37,2019-01-12 18:27:20,3,1.7,1,N,43,141,1,11.0,0.0,0.5,2.35,0.0,0.3,14.15, +1,2019-01-24 06:34:57,2019-01-24 06:43:54,3,1.4,1,N,186,230,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-18 10:58:34,2019-01-18 11:17:32,3,3.37,1,N,4,164,1,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-19 17:36:14,2019-01-19 17:38:51,3,0.54,1,N,263,263,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-12 21:47:03,2019-01-12 22:01:55,3,2.7,1,N,186,43,1,12.0,0.5,0.5,3.32,0.0,0.3,16.62, +2,2019-01-11 20:37:43,2019-01-11 20:46:39,3,1.94,1,N,230,239,1,8.5,0.5,0.5,2.45,0.0,0.3,12.25, +1,2019-01-30 23:33:11,2019-01-30 23:39:32,3,0.8,1,N,68,246,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3,0.0 +2,2019-01-04 11:16:23,2019-01-04 11:22:37,3,0.67,1,N,234,137,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 13:30:28,2019-01-01 13:37:25,3,1.01,1,N,261,88,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-30 13:46:35,2019-01-30 13:53:18,3,1.71,1,N,238,166,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-19 18:57:46,2019-01-19 19:02:38,3,1.04,1,N,237,236,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-22 22:19:10,2019-01-22 22:36:52,3,3.53,1,N,230,231,1,14.5,0.5,0.5,3.06,0.0,0.3,18.86,0.0 +2,2019-01-25 14:21:09,2019-01-25 14:30:14,3,1.5,1,N,170,237,1,8.0,0.0,0.5,2.64,0.0,0.3,11.44,0.0 +2,2019-01-24 21:55:46,2019-01-24 22:05:40,3,1.88,1,N,161,141,1,8.5,0.5,0.5,2.94,0.0,0.3,12.74,0.0 +2,2019-01-23 16:08:05,2019-01-23 16:47:35,3,16.6,1,N,132,132,1,47.5,1.0,0.5,9.86,0.0,0.3,59.16,0.0 +2,2019-01-23 15:54:30,2019-01-23 16:01:44,3,1.42,1,N,163,50,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-22 18:01:40,2019-01-22 18:08:21,3,0.56,1,N,163,43,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8,0.0 +1,2019-01-20 16:05:49,2019-01-20 16:29:53,3,4.8,1,N,79,239,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +1,2019-01-08 12:54:51,2019-01-08 13:14:01,3,5.7,1,N,161,88,2,20.0,0.0,0.5,0.0,0.0,0.3,20.8, +2,2019-01-27 19:55:28,2019-01-27 20:04:05,3,1.63,1,N,162,224,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-09 21:51:20,2019-01-09 21:57:04,3,1.08,1,N,229,163,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12, +2,2019-01-22 18:40:40,2019-01-22 18:53:21,3,1.47,1,N,170,229,1,9.5,1.0,0.5,1.7,0.0,0.3,13.0,0.0 +2,2019-01-13 14:16:01,2019-01-13 14:29:33,3,2.37,1,N,211,186,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-07 20:34:23,2019-01-07 20:47:05,3,2.2,1,N,90,246,1,10.5,0.5,0.5,2.95,0.0,0.3,14.75, +2,2019-01-20 05:19:00,2019-01-20 05:30:00,3,3.75,1,N,107,256,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-29 15:26:58,2019-01-29 15:36:26,3,1.31,1,N,170,163,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8,0.0 +1,2019-01-12 19:58:06,2019-01-12 20:19:40,3,3.5,1,N,262,48,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3, +1,2019-01-31 09:40:12,2019-01-31 09:45:53,3,0.6,1,N,186,48,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-06 16:09:14,2019-01-06 16:22:41,3,2.6,1,N,164,125,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-24 01:24:03,2019-01-24 01:28:12,3,1.27,1,N,239,142,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-11 12:13:51,2019-01-11 12:18:18,3,0.93,1,N,186,90,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-31 19:55:29,2019-01-31 20:01:47,3,1.76,1,N,166,238,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-19 21:26:00,2019-01-19 21:39:14,3,1.47,1,N,142,48,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-23 08:07:36,2019-01-23 09:04:40,3,16.31,2,N,132,144,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2,0.0 +2,2019-01-01 22:19:59,2019-01-01 22:29:42,3,1.91,1,N,230,239,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-08 16:59:14,2019-01-08 17:08:46,3,1.78,1,N,234,68,1,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +1,2019-01-01 18:50:03,2019-01-01 19:01:47,3,2.3,1,N,186,229,1,10.0,0.0,0.5,3.24,0.0,0.3,14.04, +2,2019-01-26 13:53:45,2019-01-26 14:13:11,3,1.57,1,N,100,163,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-19 11:12:15,2019-01-19 11:40:43,3,5.4,1,N,237,112,1,22.0,0.0,0.5,6.84,0.0,0.3,29.64, +2,2019-01-16 11:12:02,2019-01-16 11:59:47,3,10.12,1,N,138,231,1,38.0,0.0,0.5,7.76,0.0,0.3,46.56, +2,2019-01-30 06:34:08,2019-01-30 06:43:25,3,3.6,1,N,148,141,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-04 11:05:58,2019-01-04 11:13:15,3,1.48,1,N,158,246,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +1,2019-01-21 07:17:24,2019-01-21 07:22:32,3,1.5,1,N,151,239,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-29 20:58:03,2019-01-29 21:04:27,3,0.91,1,N,230,162,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-19 15:38:13,2019-01-19 15:47:14,3,0.89,1,N,262,263,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-06 11:11:25,2019-01-06 11:15:16,3,0.54,1,N,90,90,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-01 04:13:58,2019-01-01 04:20:00,3,2.22,1,N,230,238,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-20 20:54:25,2019-01-20 21:01:05,3,1.26,1,N,142,48,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +1,2019-01-01 02:38:32,2019-01-01 02:42:25,3,0.5,1,N,79,79,1,4.5,0.5,0.5,1.15,0.0,0.3,6.95, +2,2019-01-02 16:02:31,2019-01-02 16:10:42,3,1.45,1,N,261,148,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-18 21:12:37,2019-01-18 21:16:51,3,0.66,1,N,75,75,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88, +2,2019-01-15 18:52:14,2019-01-15 18:59:27,3,1.93,1,N,226,260,1,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-18 09:44:49,2019-01-18 10:11:01,3,3.56,1,N,163,158,1,18.5,0.0,0.5,3.86,0.0,0.3,23.16, +1,2019-01-12 16:19:28,2019-01-12 16:27:37,3,4.6,1,N,132,219,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +2,2019-01-11 22:59:25,2019-01-11 23:29:04,3,5.23,1,N,141,83,2,22.5,0.5,0.5,0.0,0.0,0.3,23.8, +2,2019-01-21 05:20:03,2019-01-21 05:22:46,3,0.6,1,N,141,263,1,4.5,0.5,0.5,2.0,0.0,0.3,7.8,0.0 +2,2019-01-13 16:50:18,2019-01-13 17:06:02,3,2.64,1,N,144,186,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-31 16:48:35,2019-01-31 17:15:41,3,2.4,1,N,236,233,1,17.0,1.0,0.5,3.75,0.0,0.3,22.55,0.0 +2,2019-01-23 15:38:45,2019-01-23 15:45:23,3,1.15,1,N,230,142,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-15 08:29:04,2019-01-15 08:40:39,3,1.32,1,N,246,246,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-15 00:37:24,2019-01-15 00:45:45,3,1.25,1,N,230,233,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-26 18:14:43,2019-01-26 18:30:23,3,1.65,1,N,161,234,1,11.0,0.0,0.5,2.36,0.0,0.3,14.16,0.0 +1,2019-01-01 18:06:21,2019-01-01 18:21:44,3,2.9,1,N,236,142,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-03 10:27:18,2019-01-03 10:29:56,3,0.74,1,N,236,75,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 13:24:12,2019-01-31 13:36:39,3,1.7,1,N,234,148,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-24 08:00:28,2019-01-24 08:07:57,3,1.01,1,N,24,238,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +1,2019-01-23 20:15:28,2019-01-23 20:50:03,3,12.0,1,N,137,220,1,37.0,0.5,0.5,4.0,2.64,0.3,44.94,0.0 +2,2019-01-01 12:40:50,2019-01-01 12:43:22,3,0.46,1,N,264,264,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-13 22:22:06,2019-01-13 22:28:40,3,1.36,1,N,158,234,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38, +1,2019-01-12 18:34:06,2019-01-12 19:18:24,3,20.1,2,N,132,239,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-06 10:00:24,2019-01-06 10:22:47,3,11.66,1,N,138,230,1,33.0,0.0,0.5,7.91,5.76,0.3,47.47, +1,2019-01-19 21:45:38,2019-01-19 21:55:29,3,2.9,1,N,50,151,1,11.5,0.5,0.5,3.2,0.0,0.3,16.0, +2,2019-01-21 11:49:09,2019-01-21 12:17:27,3,10.77,1,N,239,138,1,33.0,0.0,0.5,9.89,5.76,0.3,49.45,0.0 +2,2019-01-23 20:39:12,2019-01-23 20:49:05,3,1.16,1,N,234,158,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-28 13:23:58,2019-01-28 13:30:40,3,1.45,1,N,13,125,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-10 21:36:09,2019-01-10 21:41:30,3,0.9,1,N,164,234,1,5.5,0.5,0.5,0.68,0.0,0.3,7.48, +1,2019-01-02 20:27:08,2019-01-02 20:39:51,3,2.4,1,N,142,141,1,11.0,0.5,0.5,3.05,0.0,0.3,15.35, +2,2019-01-21 13:47:42,2019-01-21 13:52:39,3,1.21,1,N,107,170,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8,0.0 +2,2019-01-17 19:05:30,2019-01-17 19:10:56,3,1.06,1,N,170,229,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-26 01:39:20,2019-01-26 01:47:36,3,1.3,1,N,249,68,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-25 06:55:52,2019-01-25 07:01:33,3,1.16,1,N,263,237,1,6.5,0.0,0.5,0.73,0.0,0.3,8.03,0.0 +2,2019-01-18 21:52:15,2019-01-18 21:56:35,3,0.96,1,N,262,140,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-19 16:59:09,2019-01-19 17:04:45,3,1.98,1,N,236,43,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-20 18:47:09,2019-01-20 18:54:04,3,1.34,1,N,264,264,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-28 17:44:04,2019-01-28 17:54:16,3,1.54,1,N,141,233,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-29 10:58:28,2019-01-29 11:33:38,3,7.49,1,N,48,33,2,29.0,0.0,0.5,0.0,0.0,0.3,29.8,0.0 +2,2019-01-29 14:04:28,2019-01-29 14:24:48,3,6.66,1,N,13,161,2,22.5,0.0,0.5,0.0,0.0,0.3,23.3,0.0 +2,2019-01-05 10:36:38,2019-01-05 10:42:06,3,0.91,1,N,90,114,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-03 13:32:41,2019-01-03 13:42:20,3,1.6,1,N,164,113,1,8.0,0.0,0.5,1.0,0.0,0.3,9.8, +1,2019-01-21 02:04:02,2019-01-21 02:25:35,3,6.5,1,N,170,181,1,21.5,0.5,0.5,4.55,0.0,0.3,27.35,0.0 +1,2019-01-09 15:22:59,2019-01-09 15:34:12,3,1.6,1,N,236,142,1,9.0,0.0,0.5,1.7,0.0,0.3,11.5, +2,2019-01-27 14:37:24,2019-01-27 14:45:57,3,1.47,1,N,239,237,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +1,2019-01-04 05:49:17,2019-01-04 05:52:47,3,0.8,1,N,143,142,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-16 15:09:39,2019-01-16 15:13:32,3,0.39,1,N,239,239,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +1,2019-01-05 02:07:24,2019-01-05 02:09:29,3,0.3,1,N,107,234,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +1,2019-01-04 21:20:17,2019-01-04 21:36:34,3,5.1,1,N,246,151,1,17.5,0.5,0.5,4.7,0.0,0.3,23.5, +1,2019-01-02 09:21:17,2019-01-02 09:59:04,3,11.6,1,N,230,138,2,40.0,0.0,0.5,0.0,5.76,0.3,46.56, +2,2019-01-09 19:28:23,2019-01-09 19:32:59,3,0.73,1,N,107,113,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-21 23:20:24,2019-01-21 23:29:03,3,1.8,1,N,142,100,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-05 00:39:29,2019-01-05 00:45:28,3,1.98,1,N,162,263,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-23 11:34:40,2019-01-23 11:41:40,3,0.76,1,N,161,164,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82,0.0 +2,2019-01-11 18:49:53,2019-01-11 18:55:51,3,0.89,1,N,170,162,1,6.0,1.0,0.5,1.5,0.0,0.3,9.3, +2,2019-01-27 22:00:54,2019-01-27 22:04:01,3,0.92,1,N,141,262,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-07 20:54:55,2019-01-07 21:02:46,3,1.14,1,N,170,186,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38, +1,2019-01-04 16:58:12,2019-01-04 17:04:02,3,0.8,1,N,100,90,1,5.5,1.0,0.5,1.45,0.0,0.3,8.75, +2,2019-01-20 02:53:56,2019-01-20 02:58:04,3,1.15,1,N,141,263,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +1,2019-01-18 18:06:18,2019-01-18 18:34:46,3,4.0,1,N,163,166,1,20.0,1.0,0.5,3.27,0.0,0.3,25.07, +2,2019-01-13 22:00:54,2019-01-13 22:16:23,3,2.88,1,N,125,230,2,13.0,0.5,0.5,0.0,0.0,0.3,14.3, +2,2019-01-14 19:55:57,2019-01-14 20:13:55,3,9.59,1,N,138,79,2,27.0,1.0,0.5,0.0,5.76,0.3,34.56, +2,2019-01-14 12:50:32,2019-01-14 12:57:23,3,0.85,1,N,164,170,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-08 19:00:38,2019-01-08 19:04:16,3,0.74,1,N,229,170,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56, +1,2019-01-28 14:30:10,2019-01-28 14:43:48,3,2.0,1,N,114,186,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +2,2019-01-18 08:40:18,2019-01-18 08:47:16,3,0.46,1,N,162,230,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8, +1,2019-01-12 21:03:53,2019-01-12 21:32:31,3,3.4,1,N,234,13,1,19.0,0.5,0.5,3.7,0.0,0.3,24.0, +2,2019-01-25 18:51:12,2019-01-25 19:21:19,3,3.33,1,N,141,246,1,19.5,1.0,0.5,1.0,0.0,0.3,22.3,0.0 +2,2019-01-26 14:43:17,2019-01-26 14:53:37,3,1.82,1,N,238,143,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-01 11:45:09,2019-01-01 11:50:45,3,1.75,1,N,137,144,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-24 01:00:44,2019-01-24 01:24:37,3,11.24,1,N,249,127,1,33.0,0.5,0.5,6.86,0.0,0.3,41.16,0.0 +2,2019-01-07 15:18:42,2019-01-07 15:28:15,3,1.76,1,N,24,239,1,9.0,0.0,0.5,2.0,0.0,0.3,11.8, +2,2019-01-22 21:37:21,2019-01-22 21:41:53,3,0.67,1,N,237,237,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-28 17:06:13,2019-01-28 17:12:38,3,0.8,1,N,230,142,1,6.0,1.0,0.5,1.95,0.0,0.3,9.75,0.0 +1,2019-01-09 13:28:07,2019-01-09 13:42:44,3,2.0,1,N,237,234,1,11.0,0.0,0.5,1.77,0.0,0.3,13.57, +1,2019-01-10 18:22:21,2019-01-10 18:35:40,3,1.8,1,N,238,263,1,10.5,1.0,0.5,2.45,0.0,0.3,14.75, +1,2019-01-28 14:50:06,2019-01-28 15:07:57,3,2.9,1,N,238,163,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-13 17:46:35,2019-01-13 17:58:28,3,1.95,1,N,161,68,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88, +1,2019-01-26 19:48:55,2019-01-26 19:55:40,3,1.4,1,N,141,162,1,7.0,0.0,0.5,1.17,0.0,0.3,8.97,0.0 +2,2019-01-25 18:33:37,2019-01-25 18:43:13,3,0.93,1,N,211,148,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-28 22:27:10,2019-01-28 22:35:05,3,1.87,1,N,264,264,1,8.5,0.5,0.5,1.0,0.0,0.3,10.8,0.0 +2,2019-01-23 22:56:29,2019-01-23 23:00:02,3,0.86,1,N,90,125,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-01 18:51:05,2019-01-01 18:55:10,3,1.54,1,N,239,151,1,6.5,0.0,0.5,1.25,0.0,0.3,8.55, +2,2019-01-09 22:36:58,2019-01-09 22:39:53,3,0.76,1,N,65,49,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-05 16:54:22,2019-01-05 16:58:15,3,0.33,1,N,234,234,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 12:44:28,2019-01-31 12:56:03,3,1.35,1,N,186,162,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +1,2019-01-24 15:40:15,2019-01-24 15:47:26,3,1.1,1,N,263,141,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-04 18:34:33,2019-01-04 18:48:32,3,1.1,1,N,186,161,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +1,2019-01-12 12:17:59,2019-01-12 12:22:36,3,0.6,1,N,237,236,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +2,2019-01-14 12:22:57,2019-01-14 12:28:57,3,0.38,1,N,170,137,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-18 04:30:45,2019-01-18 04:40:24,3,2.94,1,N,48,79,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +1,2019-01-05 02:05:14,2019-01-05 02:15:08,3,1.2,1,N,79,148,1,8.0,0.5,0.5,1.0,0.0,0.3,10.3, +2,2019-01-10 23:00:49,2019-01-10 23:06:36,3,1.59,1,N,114,107,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38, +1,2019-01-01 19:57:52,2019-01-01 20:12:48,3,1.1,1,N,170,230,1,10.0,0.5,0.5,2.25,0.0,0.3,13.55, +2,2019-01-16 00:27:42,2019-01-16 00:41:43,3,3.26,1,N,100,145,1,12.5,0.5,0.5,2.0,5.76,0.3,21.56, +1,2019-01-09 15:09:22,2019-01-09 15:22:26,3,1.2,1,N,236,141,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-06 18:44:55,2019-01-06 19:10:11,3,3.5,1,N,229,238,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +2,2019-01-29 22:15:08,2019-01-29 22:27:54,3,2.17,1,N,114,87,1,11.0,0.5,0.5,2.0,0.0,0.3,14.3,0.0 +2,2019-01-09 18:47:44,2019-01-09 19:14:40,3,6.52,1,N,230,88,1,23.5,1.0,0.5,5.06,0.0,0.3,30.36, +2,2019-01-30 00:22:15,2019-01-30 00:54:53,3,19.12,1,N,132,33,1,52.0,0.5,0.5,10.66,0.0,0.3,63.96,0.0 +1,2019-01-12 23:50:31,2019-01-13 00:01:18,3,1.7,1,N,164,229,1,8.5,0.5,0.5,1.95,0.0,0.3,11.75, +2,2019-01-26 19:01:01,2019-01-26 19:09:11,3,0.8,1,N,230,163,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-02 18:47:39,2019-01-02 18:53:37,3,1.0,1,N,236,237,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +2,2019-01-01 04:23:05,2019-01-01 04:37:42,3,3.27,1,N,230,79,1,13.0,0.5,0.5,4.29,0.0,0.3,18.59, +2,2019-01-18 21:49:20,2019-01-18 21:57:51,3,1.1,1,N,113,90,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-15 19:22:46,2019-01-15 19:34:42,3,2.24,1,N,164,79,1,10.0,1.0,0.5,1.18,0.0,0.3,12.98, +1,2019-01-29 13:04:40,2019-01-29 13:15:04,3,1.6,1,N,263,237,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-31 09:10:36,2019-01-31 09:30:06,3,2.37,1,N,49,62,1,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-29 12:25:23,2019-01-29 12:30:23,3,0.64,1,N,113,249,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-25 18:13:06,2019-01-25 19:02:15,3,18.19,2,N,132,230,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67,0.0 +1,2019-01-09 16:29:38,2019-01-09 16:33:12,3,0.4,1,N,239,143,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-16 14:12:49,2019-01-16 14:58:41,3,17.5,2,N,132,230,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-03 17:51:30,2019-01-03 17:56:46,3,0.88,1,N,142,239,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-17 14:32:38,2019-01-17 14:57:19,3,5.5,1,N,87,161,1,21.0,0.0,0.5,3.27,0.0,0.3,25.07, +1,2019-01-23 16:35:49,2019-01-23 16:42:17,3,0.5,1,N,249,113,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-18 13:39:59,2019-01-18 13:46:55,3,0.88,1,N,264,264,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-13 18:45:25,2019-01-13 19:03:13,3,7.37,1,N,132,95,2,22.0,0.0,0.5,0.0,0.0,0.3,22.8, +2,2019-01-29 19:16:29,2019-01-29 19:23:11,3,0.51,1,N,113,114,1,5.5,1.0,0.5,0.73,0.0,0.3,8.03,0.0 +1,2019-01-14 13:22:19,2019-01-14 13:31:54,3,0.9,1,N,264,264,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +1,2019-01-03 19:59:42,2019-01-03 20:09:03,3,1.1,1,N,249,234,1,7.5,0.5,0.5,1.75,0.0,0.3,10.55, +2,2019-01-25 22:13:13,2019-01-25 22:15:26,3,0.39,1,N,263,140,1,3.5,0.5,0.5,0.45,0.0,0.3,5.25,0.0 +2,2019-01-04 10:55:48,2019-01-04 11:04:15,3,1.6,1,N,236,239,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-11 09:10:33,2019-01-11 09:20:21,3,1.4,1,N,264,264,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-15 22:53:55,2019-01-15 23:07:14,3,3.69,1,N,141,226,1,13.0,0.5,0.5,4.29,0.0,0.3,18.59, +2,2019-01-15 10:43:41,2019-01-15 10:52:13,3,1.26,1,N,151,166,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +1,2019-01-17 04:35:33,2019-01-17 04:38:39,3,1.2,1,N,137,224,1,5.5,0.5,0.5,0.02,0.0,0.3,6.82, +1,2019-01-20 19:57:59,2019-01-20 20:06:34,3,1.2,1,N,234,114,1,7.5,0.0,0.5,1.65,0.0,0.3,9.95, +2,2019-01-20 23:11:11,2019-01-20 23:15:03,3,0.8,1,N,151,24,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88, +1,2019-01-19 10:54:50,2019-01-19 11:01:50,3,1.4,1,N,79,107,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-30 19:18:09,2019-01-30 19:19:51,3,0.66,1,N,238,24,1,4.0,1.0,0.5,1.45,0.0,0.3,7.25,0.0 +2,2019-01-10 22:54:02,2019-01-10 23:00:19,3,1.19,1,N,234,144,1,6.5,0.5,0.5,1.17,0.0,0.3,8.97, +2,2019-01-08 07:56:09,2019-01-08 08:03:33,3,1.02,1,N,43,238,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-17 04:47:29,2019-01-17 05:00:27,3,5.04,1,N,68,24,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-25 15:22:45,2019-01-25 15:35:08,3,1.58,1,N,158,186,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36,0.0 +1,2019-01-24 09:28:10,2019-01-24 09:52:02,3,1.4,1,N,237,161,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +1,2019-01-03 15:35:08,2019-01-03 16:02:13,3,2.7,1,N,163,68,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-27 11:55:02,2019-01-27 11:58:43,3,0.62,1,N,239,238,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +1,2019-01-18 15:09:45,2019-01-18 15:12:26,3,0.4,1,N,43,236,1,4.0,0.0,0.5,1.15,0.0,0.3,5.95, +2,2019-01-20 23:48:37,2019-01-21 00:02:09,3,5.55,1,N,50,116,1,18.0,0.5,0.5,3.86,0.0,0.3,23.16, +2,2019-01-16 21:01:26,2019-01-16 21:05:03,3,0.61,1,N,263,236,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-20 16:43:44,2019-01-20 17:00:56,3,4.01,1,N,161,166,1,15.0,0.0,0.5,3.95,0.0,0.3,19.75, +2,2019-01-21 11:32:51,2019-01-21 11:48:15,3,3.96,1,N,87,158,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-26 23:13:49,2019-01-26 23:29:35,3,2.83,1,N,246,163,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8,0.0 +1,2019-01-12 11:30:54,2019-01-12 11:36:47,3,1.3,1,N,236,238,1,6.5,0.0,0.5,1.45,0.0,0.3,8.75, +1,2019-01-13 19:40:27,2019-01-13 19:50:54,3,2.1,1,N,239,263,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +1,2019-01-23 17:20:54,2019-01-23 17:31:43,3,1.1,1,N,163,233,1,8.0,1.0,0.5,1.95,0.0,0.3,11.75,0.0 +2,2019-01-05 17:10:24,2019-01-05 17:27:01,3,4.37,1,N,140,79,1,16.5,0.0,0.5,1.2,0.0,0.3,18.5, +2,2019-01-20 18:51:24,2019-01-20 18:57:48,3,1.12,1,N,262,141,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-14 16:02:58,2019-01-14 16:08:00,3,1.06,1,N,142,43,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76, +1,2019-01-04 13:47:48,2019-01-04 13:59:07,3,1.9,1,N,152,151,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-21 09:57:36,2019-01-21 10:08:33,3,1.65,1,N,161,48,1,9.0,0.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-07 19:13:23,2019-01-07 19:18:42,3,1.17,1,N,170,234,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-18 10:57:59,2019-01-18 11:52:38,3,4.6,1,N,236,114,2,32.0,0.0,0.5,0.0,0.0,0.3,32.8, +2,2019-01-15 17:43:02,2019-01-15 17:51:01,3,2.0,1,N,140,74,1,8.5,1.0,0.5,3.09,0.0,0.3,13.39, +2,2019-01-31 08:40:33,2019-01-31 08:46:24,3,0.73,1,N,236,236,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-13 01:25:25,2019-01-13 01:33:59,3,2.39,1,N,229,79,1,9.0,0.5,0.5,1.0,0.0,0.3,11.3, +1,2019-01-13 10:55:23,2019-01-13 10:58:54,3,0.6,1,N,263,236,1,4.5,0.0,0.5,1.05,0.0,0.3,6.35, +2,2019-01-24 08:33:34,2019-01-24 09:00:19,3,2.78,1,N,229,186,1,17.5,0.0,0.5,5.49,0.0,0.3,23.79,0.0 +1,2019-01-05 14:16:30,2019-01-05 14:19:16,3,0.4,1,N,237,263,1,4.0,0.0,0.5,0.95,0.0,0.3,5.75, +2,2019-01-01 17:19:45,2019-01-01 17:27:09,3,0.6,1,N,163,141,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-20 12:08:45,2019-01-20 12:13:04,3,0.83,1,N,229,141,1,5.0,0.0,0.5,0.87,0.0,0.3,6.67, +1,2019-01-17 13:47:16,2019-01-17 13:57:00,3,1.3,1,N,237,263,1,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-03 18:43:01,2019-01-03 18:53:11,3,1.85,1,N,142,151,1,9.0,1.0,0.5,1.62,0.0,0.3,12.42, +2,2019-01-14 08:05:56,2019-01-14 08:13:32,3,0.71,1,N,186,48,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-02 07:49:33,2019-01-02 07:53:54,3,0.87,1,N,41,74,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-20 09:20:41,2019-01-20 09:27:36,3,1.71,1,N,113,186,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-06 14:03:21,2019-01-06 14:09:06,3,1.01,1,N,148,107,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-01 03:15:05,2019-01-01 03:19:57,3,1.75,1,N,238,166,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +1,2019-01-11 15:38:21,2019-01-11 15:49:16,3,1.4,1,N,143,238,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-11 14:27:02,2019-01-11 14:45:41,3,6.6,1,N,161,261,1,21.0,0.0,0.5,5.45,0.0,0.3,27.25, +2,2019-01-20 09:03:51,2019-01-20 09:14:07,3,2.07,1,N,161,246,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-01 19:26:59,2019-01-01 19:35:07,3,1.2,1,N,170,161,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-04 08:15:34,2019-01-04 08:23:17,3,1.56,1,N,239,163,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-18 22:27:14,2019-01-18 22:34:55,3,1.69,1,N,142,151,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-29 20:37:28,2019-01-29 20:52:45,3,2.19,1,N,161,249,1,11.5,0.5,0.5,3.2,0.0,0.3,16.0,0.0 +2,2019-01-19 01:08:16,2019-01-19 01:11:34,3,0.54,1,N,90,158,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-12 23:33:52,2019-01-12 23:50:09,3,2.2,1,N,79,170,1,12.0,0.5,0.5,1.0,0.0,0.3,14.3, +1,2019-01-18 16:28:05,2019-01-18 16:34:49,3,0.9,1,N,143,142,1,6.0,1.0,0.5,1.55,0.0,0.3,9.35, +1,2019-01-10 07:45:12,2019-01-10 07:49:58,3,1.0,1,N,238,142,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-06 08:33:44,2019-01-06 08:43:05,3,2.15,1,N,151,142,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +1,2019-01-09 13:35:16,2019-01-09 14:09:55,3,6.6,1,N,143,129,1,26.0,0.0,0.5,3.0,0.0,0.3,29.8, +2,2019-01-27 21:25:09,2019-01-27 21:30:17,3,0.84,1,N,68,249,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-21 16:52:02,2019-01-21 16:55:17,3,0.68,1,N,234,186,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-13 15:54:52,2019-01-13 16:02:13,3,1.1,1,N,237,163,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-29 21:46:22,2019-01-29 22:08:39,3,3.73,1,N,186,261,1,16.0,0.5,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-30 08:15:38,2019-01-30 08:22:34,3,0.55,1,N,141,140,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-28 22:07:04,2019-01-28 22:16:33,3,2.26,1,N,100,125,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-16 20:14:06,2019-01-16 20:20:07,3,1.16,1,N,239,236,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-07 19:26:13,2019-01-07 19:33:59,3,1.14,1,N,263,140,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-14 23:09:24,2019-01-14 23:13:31,3,0.77,1,N,74,42,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-19 03:23:36,2019-01-19 03:29:25,3,1.49,1,N,144,79,1,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-16 20:43:54,2019-01-16 20:50:11,3,0.76,1,N,186,48,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +1,2019-01-26 18:06:32,2019-01-26 18:44:03,3,15.4,2,N,132,170,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26,0.0 +2,2019-01-24 19:29:58,2019-01-24 19:42:09,3,2.35,1,N,237,107,1,10.0,1.0,0.5,1.77,0.0,0.3,13.57,0.0 +2,2019-01-04 16:52:47,2019-01-04 16:59:57,3,1.82,1,N,140,170,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-23 20:54:59,2019-01-23 21:07:00,3,2.07,1,N,161,246,1,10.0,0.5,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-02 22:22:30,2019-01-02 22:26:24,3,0.96,1,N,162,107,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-29 11:47:12,2019-01-29 12:02:10,3,1.9,1,N,142,246,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-26 13:08:53,2019-01-26 13:34:09,3,8.98,1,N,7,68,1,28.0,0.0,0.5,2.0,5.76,0.3,38.51,0.0 +2,2019-01-04 20:46:06,2019-01-04 21:00:46,3,2.02,1,N,234,230,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3, +2,2019-01-22 06:46:38,2019-01-22 06:53:37,3,1.0,1,N,229,161,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-06 17:17:15,2019-01-06 17:40:29,3,2.2,1,N,233,246,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +1,2019-01-18 20:48:04,2019-01-18 20:54:34,3,0.7,1,N,234,164,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +1,2019-01-25 19:23:57,2019-01-25 19:26:58,3,0.3,1,N,263,236,1,4.0,1.0,0.5,0.87,0.0,0.3,6.67,0.0 +2,2019-01-11 19:23:50,2019-01-11 19:37:26,3,1.82,1,N,162,234,1,10.0,1.0,0.5,1.77,0.0,0.3,13.57, +2,2019-01-13 20:56:06,2019-01-13 20:59:22,3,0.54,1,N,237,161,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +1,2019-01-01 17:21:49,2019-01-01 17:58:56,3,17.0,2,N,132,170,1,52.0,4.5,0.5,12.6,5.76,0.3,75.66, +2,2019-01-06 22:46:20,2019-01-06 22:47:19,3,0.41,1,N,48,48,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-31 19:41:23,2019-01-31 19:57:43,3,1.65,1,N,68,233,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36,0.0 +1,2019-01-08 15:06:59,2019-01-08 15:14:32,3,1.0,1,N,239,143,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-20 04:31:27,2019-01-20 04:55:18,3,5.73,1,N,230,83,2,21.5,0.5,0.5,0.0,0.0,0.3,22.8, +2,2019-01-14 21:11:59,2019-01-14 21:15:36,3,1.07,1,N,141,140,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +1,2019-01-01 12:34:17,2019-01-01 12:45:22,3,1.1,1,N,237,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-28 21:34:19,2019-01-28 21:37:21,3,0.33,1,N,164,186,1,4.0,0.5,0.5,1.59,0.0,0.3,6.89,0.0 +1,2019-01-22 09:05:40,2019-01-22 09:20:22,3,1.8,1,N,163,234,1,11.0,0.0,0.5,1.5,0.0,0.3,13.3,0.0 +2,2019-01-28 08:32:59,2019-01-28 08:39:58,3,0.95,1,N,162,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-28 19:58:30,2019-01-28 20:07:13,3,1.78,1,N,137,79,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-06 17:48:54,2019-01-06 17:56:58,3,1.4,1,N,170,237,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-26 16:03:32,2019-01-26 16:11:07,3,1.45,1,N,238,166,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-27 00:24:42,2019-01-27 00:35:40,3,1.8,1,N,79,90,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-11 17:51:37,2019-01-11 18:11:20,3,3.0,1,N,236,107,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96, +1,2019-01-12 13:13:49,2019-01-12 13:49:33,3,16.8,2,N,132,163,1,52.0,0.0,0.5,10.55,0.0,0.3,63.35, +2,2019-01-15 09:15:37,2019-01-15 09:22:05,3,0.74,1,N,50,230,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-15 21:50:23,2019-01-15 22:00:22,3,1.6,1,Y,186,114,1,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-02 10:48:16,2019-01-02 10:57:05,3,1.11,1,N,231,12,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-15 06:12:29,2019-01-15 06:20:09,3,1.31,1,N,186,230,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-30 15:41:17,2019-01-30 15:49:35,3,1.23,1,N,35,35,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-01 11:42:46,2019-01-01 11:44:17,3,0.44,1,N,263,262,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-09 18:20:00,2019-01-09 18:23:54,3,0.64,1,N,233,233,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +1,2019-01-20 09:20:46,2019-01-20 09:23:55,3,0.9,1,N,261,144,1,5.0,0.0,0.5,1.15,0.0,0.3,6.95, +2,2019-01-27 04:23:33,2019-01-27 04:34:44,3,2.09,1,N,79,186,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-09 11:24:10,2019-01-09 11:42:19,3,4.0,1,N,114,141,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-05 15:39:59,2019-01-05 15:43:04,3,0.65,1,N,236,239,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-15 20:16:16,2019-01-15 20:25:28,3,1.67,1,N,263,162,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-21 13:40:25,2019-01-21 13:49:45,3,1.32,1,N,236,162,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-04 21:57:47,2019-01-04 22:14:15,3,2.96,1,N,100,231,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-07 17:50:56,2019-01-07 17:56:58,3,0.85,1,N,230,50,1,5.5,1.0,0.5,1.1,0.0,0.3,8.4, +1,2019-01-20 18:36:24,2019-01-20 18:43:26,3,1.3,1,N,263,237,1,7.0,0.0,0.5,1.33,0.0,0.3,9.13, +2,2019-01-15 14:16:03,2019-01-15 14:27:18,3,2.16,1,N,239,237,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-14 08:58:22,2019-01-14 09:11:48,3,1.48,1,N,170,230,1,10.0,0.0,0.5,2.0,0.0,0.3,12.8, +2,2019-01-08 07:26:14,2019-01-08 07:29:16,3,0.62,1,N,100,230,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8, +2,2019-01-11 18:09:49,2019-01-11 18:19:14,3,1.18,1,N,90,114,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-13 15:57:19,2019-01-13 16:06:35,3,1.56,1,N,164,114,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-11 16:25:28,2019-01-11 16:28:48,3,0.86,1,N,107,79,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-27 19:10:43,2019-01-27 19:12:04,3,0.43,1,N,4,4,1,3.5,0.0,0.5,1.7,0.0,0.3,6.0,0.0 +2,2019-01-17 16:38:35,2019-01-17 16:55:51,3,1.8,1,N,236,237,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +1,2019-01-05 18:34:48,2019-01-05 18:59:50,3,1.8,1,N,162,48,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-25 09:12:52,2019-01-25 09:16:45,3,0.75,1,N,226,226,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-13 13:29:59,2019-01-13 13:46:20,3,3.15,1,N,164,236,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76, +2,2019-01-08 15:25:37,2019-01-08 15:30:38,3,0.7,1,N,48,142,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +1,2019-01-26 22:11:49,2019-01-26 22:27:51,3,1.8,1,N,230,107,1,11.0,0.5,0.5,2.45,0.0,0.3,14.75,0.0 +2,2019-01-25 19:34:49,2019-01-25 19:41:08,3,1.21,1,N,42,152,1,6.5,1.0,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-19 09:13:18,2019-01-19 09:14:42,3,0.73,1,N,229,233,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-05 00:46:41,2019-01-05 01:00:47,3,3.0,1,N,163,68,2,12.5,0.5,0.5,0.0,0.0,0.3,13.8, +2,2019-01-14 09:10:35,2019-01-14 09:32:51,3,3.38,1,N,231,162,1,16.0,0.0,0.5,5.04,0.0,0.3,21.84, +1,2019-01-06 11:00:49,2019-01-06 11:07:12,3,0.8,1,N,238,239,1,6.0,0.0,0.5,2.0,0.0,0.3,8.8, +2,2019-01-04 21:28:22,2019-01-04 21:36:30,3,1.64,1,N,231,249,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-10 20:02:20,2019-01-10 20:08:23,3,1.65,1,N,68,48,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-22 01:39:43,2019-01-22 01:42:12,3,0.38,1,N,260,82,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-22 00:24:33,2019-01-22 00:27:18,3,1.12,1,N,236,75,1,5.0,0.5,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-31 16:58:20,2019-01-31 17:10:04,3,1.52,1,N,164,48,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-12 14:00:50,2019-01-12 14:05:57,3,0.81,1,N,151,166,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-05 23:07:04,2019-01-05 23:41:30,3,7.46,1,N,143,129,1,27.0,0.5,0.5,1.2,0.0,0.3,29.5, +1,2019-01-02 18:06:33,2019-01-02 18:12:34,3,0.9,1,N,170,107,1,6.0,1.0,0.5,1.0,0.0,0.3,8.8, +2,2019-01-08 22:24:50,2019-01-08 22:30:01,3,1.5,1,N,114,170,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-09 21:00:39,2019-01-09 21:21:25,3,9.69,1,N,138,230,1,28.0,0.5,0.5,7.01,5.76,0.3,42.07, +1,2019-01-27 14:22:25,2019-01-27 14:51:08,3,5.8,1,N,151,114,1,23.0,0.0,0.5,5.95,0.0,0.3,29.75,0.0 +2,2019-01-23 15:14:37,2019-01-23 15:35:35,3,2.49,1,N,238,75,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-10 22:34:51,2019-01-10 22:37:56,3,0.39,1,N,87,231,1,4.0,0.5,0.5,0.53,0.0,0.3,5.83, +1,2019-01-22 13:43:55,2019-01-22 13:49:31,3,0.5,1,N,170,161,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-06 19:41:43,2019-01-06 19:44:07,3,0.62,1,N,263,236,1,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +1,2019-01-20 13:15:24,2019-01-20 13:19:22,3,0.5,1,N,229,162,1,4.5,0.0,0.5,1.0,0.0,0.3,6.3, +1,2019-01-05 12:12:44,2019-01-05 12:29:44,3,8.5,1,N,138,164,1,24.0,0.0,0.5,4.5,5.76,0.3,35.06, +2,2019-01-17 09:06:00,2019-01-17 09:25:42,3,3.95,1,N,114,34,1,16.5,0.0,0.5,2.0,0.0,0.3,19.3, +1,2019-01-12 18:50:44,2019-01-12 18:58:08,3,1.8,1,Y,141,262,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55, +2,2019-01-12 19:30:16,2019-01-12 19:42:27,3,1.4,1,N,170,107,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-24 23:54:59,2019-01-24 23:57:24,3,0.67,1,N,262,140,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-02 07:09:40,2019-01-02 07:14:59,3,0.75,1,N,48,161,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-30 19:32:31,2019-01-30 19:35:57,3,0.75,1,N,151,238,1,4.5,1.0,0.5,1.26,0.0,0.3,7.56,0.0 +1,2019-01-13 11:18:32,2019-01-13 11:40:32,3,8.2,1,N,151,138,1,26.0,0.0,0.5,7.0,11.52,0.3,45.32, +2,2019-01-12 21:59:06,2019-01-12 22:04:55,3,1.01,1,N,68,90,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-11 14:10:04,2019-01-11 14:33:23,3,3.72,1,N,142,234,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76, +2,2019-01-28 23:32:57,2019-01-28 23:44:35,3,2.79,1,N,186,43,1,11.5,0.5,0.5,1.2,0.0,0.3,14.0,0.0 +2,2019-01-05 15:27:56,2019-01-05 15:38:46,4,1.57,1,N,142,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +1,2019-01-01 03:59:52,2019-01-01 04:06:07,4,0.6,1,N,148,232,1,6.0,0.5,0.5,1.45,0.0,0.3,8.75, +2,2019-01-22 11:28:59,2019-01-22 11:40:15,4,1.49,1,N,162,107,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-30 13:56:36,2019-01-30 14:13:08,4,4.85,1,N,234,88,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3,0.0 +2,2019-01-12 07:36:53,2019-01-12 08:02:18,4,18.38,3,N,239,1,1,66.0,0.0,0.0,3.96,12.95,0.3,83.21, +1,2019-01-19 09:24:13,2019-01-19 09:37:24,4,3.6,1,N,141,79,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +1,2019-01-18 20:57:05,2019-01-18 21:06:31,4,1.4,1,N,162,164,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-20 16:57:29,2019-01-20 17:11:49,4,3.5,1,N,238,74,1,13.5,0.0,0.5,2.66,0.0,0.3,16.96, +2,2019-01-29 20:08:37,2019-01-29 20:17:05,4,0.84,1,N,162,170,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-17 23:16:38,2019-01-17 23:30:56,4,3.67,1,N,113,97,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76, +2,2019-01-03 14:01:35,2019-01-03 14:04:40,4,0.33,1,N,263,236,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-13 17:07:37,2019-01-13 17:13:02,4,0.6,1,N,164,137,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-12 15:12:53,2019-01-12 15:43:25,4,4.68,1,N,158,141,1,22.0,0.0,0.5,3.42,0.0,0.3,26.22, +2,2019-01-05 04:36:28,2019-01-05 04:42:23,4,1.14,1,N,249,144,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8, +2,2019-01-21 22:05:49,2019-01-21 22:07:55,4,0.43,1,N,68,90,1,3.5,0.5,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-18 23:53:33,2019-01-19 00:01:39,4,1.53,1,N,114,186,1,7.5,0.5,0.5,2.64,0.0,0.3,11.44, +2,2019-01-13 15:28:31,2019-01-13 15:38:07,4,1.85,1,N,170,237,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +2,2019-01-16 12:43:36,2019-01-16 12:57:43,4,2.36,1,N,239,263,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 16:11:20,2019-01-11 16:16:42,4,0.41,1,N,164,170,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +1,2019-01-20 16:36:18,2019-01-20 16:59:50,4,6.8,1,N,161,17,1,23.0,0.0,0.5,2.0,0.0,0.3,25.8, +2,2019-01-26 10:07:03,2019-01-26 10:15:13,4,1.51,1,N,170,246,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-15 20:02:10,2019-01-15 20:04:44,4,0.32,1,N,263,236,1,3.5,1.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-11 19:57:44,2019-01-11 20:15:44,4,3.35,1,N,234,239,1,14.0,1.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-30 15:16:32,2019-01-30 15:24:26,4,0.81,1,N,263,263,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-20 17:15:06,2019-01-20 17:34:57,4,3.6,1,N,79,230,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55, +2,2019-01-17 15:42:28,2019-01-17 15:57:15,4,1.43,1,N,170,186,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +1,2019-01-20 16:59:04,2019-01-20 17:04:29,4,0.6,1,N,48,68,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-10 14:54:57,2019-01-10 15:30:45,4,8.94,1,N,161,244,1,31.5,0.0,0.5,2.0,0.0,0.3,34.3, +2,2019-01-24 17:32:02,2019-01-24 17:38:45,4,0.93,1,N,141,140,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-13 17:30:46,2019-01-13 17:33:30,4,0.44,1,N,90,90,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +1,2019-01-13 12:59:15,2019-01-13 13:40:55,4,19.0,2,N,68,132,1,52.0,0.0,0.5,11.7,5.76,0.3,70.26, +2,2019-01-12 16:53:12,2019-01-12 16:59:27,4,0.66,1,N,237,161,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-31 16:37:41,2019-01-31 16:54:34,4,1.73,1,N,68,237,1,11.5,1.0,0.5,2.7,0.0,0.3,16.0,0.0 +2,2019-01-14 13:19:31,2019-01-14 13:34:33,4,2.93,1,N,239,41,1,12.5,0.0,0.5,2.66,0.0,0.3,15.96, +2,2019-01-26 23:28:40,2019-01-26 23:40:39,4,2.47,1,N,43,262,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-25 17:33:11,2019-01-25 17:51:18,4,1.49,1,N,90,114,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56,0.0 +1,2019-01-04 15:23:22,2019-01-04 15:39:09,4,3.0,1,N,163,24,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-11 10:53:32,2019-01-11 11:36:05,4,17.6,2,N,161,132,2,52.0,0.0,0.5,0.0,5.76,0.3,58.56, +2,2019-01-19 01:27:43,2019-01-19 01:29:35,4,0.67,1,N,141,263,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-29 21:16:25,2019-01-29 21:24:49,4,1.28,1,N,170,229,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-21 22:14:18,2019-01-21 22:26:02,4,3.05,1,N,230,238,1,12.0,0.5,0.5,3.32,0.0,0.3,16.62,0.0 +1,2019-01-27 03:48:22,2019-01-27 03:55:37,4,0.7,1,N,79,144,1,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-24 20:47:47,2019-01-24 21:11:34,4,3.7,1,N,186,263,1,17.0,0.5,0.5,4.55,0.0,0.3,22.85,0.0 +1,2019-01-27 12:45:26,2019-01-27 12:53:00,4,1.2,1,N,230,162,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +1,2019-01-09 19:37:43,2019-01-09 19:39:16,4,0.0,2,N,87,87,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-26 21:15:38,2019-01-26 21:21:21,4,1.62,1,N,142,151,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-13 19:57:23,2019-01-13 20:02:44,4,0.97,1,N,170,162,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-03 22:10:50,2019-01-03 22:23:32,4,2.2,1,N,249,148,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-11 07:31:17,2019-01-11 07:53:46,4,1.5,1,N,43,140,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8, +1,2019-01-20 01:48:45,2019-01-20 02:15:26,4,7.5,1,N,142,37,1,25.0,0.5,0.5,5.25,0.0,0.3,31.55, +2,2019-01-24 23:11:04,2019-01-24 23:26:57,4,4.29,1,N,148,262,1,15.0,0.5,0.5,4.89,0.0,0.3,21.19,0.0 +1,2019-01-03 21:56:53,2019-01-03 22:06:15,4,1.6,1,N,164,68,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +1,2019-01-12 18:59:16,2019-01-12 19:07:28,4,1.5,1,N,163,170,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-18 00:26:51,2019-01-18 00:42:48,4,7.7,1,N,138,170,1,23.0,0.5,0.5,6.01,5.76,0.3,36.07, +2,2019-01-12 11:13:04,2019-01-12 11:28:35,4,2.41,1,N,142,234,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +1,2019-01-03 17:30:31,2019-01-03 17:45:29,4,1.8,1,N,239,230,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +1,2019-01-20 18:56:46,2019-01-20 19:09:09,4,2.1,1,N,163,141,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-11 14:58:15,2019-01-11 15:02:06,4,0.58,1,N,162,162,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-11 08:14:48,2019-01-11 08:22:39,4,0.97,1,N,263,140,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49, +1,2019-01-01 10:50:25,2019-01-01 10:57:55,4,1.9,1,N,50,186,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-24 15:09:38,2019-01-24 15:24:10,4,1.62,1,N,236,237,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +1,2019-01-18 19:10:08,2019-01-18 19:24:00,4,1.4,1,N,186,161,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +1,2019-01-19 14:37:31,2019-01-19 14:41:54,4,0.9,1,N,238,239,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93, +2,2019-01-17 19:00:29,2019-01-17 19:19:26,4,3.05,1,N,186,125,1,14.5,1.0,0.5,1.63,0.0,0.3,17.93, +2,2019-01-31 20:15:59,2019-01-31 20:36:08,4,2.84,1,N,186,231,2,14.0,0.5,0.5,0.0,0.0,0.3,15.3,0.0 +2,2019-01-19 13:58:35,2019-01-19 14:13:47,4,2.47,1,N,263,142,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-22 19:06:38,2019-01-22 19:26:13,4,3.51,1,N,161,151,1,15.5,1.0,0.5,3.46,0.0,0.3,20.76,0.0 +1,2019-01-25 19:57:41,2019-01-25 20:21:59,4,2.9,1,N,43,164,2,16.0,0.5,0.5,0.0,0.0,0.3,17.3,0.0 +1,2019-01-19 22:53:45,2019-01-19 22:58:48,4,1.0,1,N,50,246,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-07 14:43:54,2019-01-07 14:49:06,4,0.21,1,N,141,237,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +1,2019-01-13 13:47:49,2019-01-13 13:59:20,4,1.8,1,N,236,239,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-16 17:57:29,2019-01-16 18:09:59,4,1.42,1,N,230,164,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +1,2019-01-01 13:19:04,2019-01-01 13:27:11,4,1.2,1,N,79,45,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-18 01:07:31,2019-01-18 01:24:00,4,5.99,1,N,41,60,2,19.5,0.5,0.5,0.0,0.0,0.3,20.8, +2,2019-01-28 09:16:44,2019-01-28 09:38:26,4,7.56,1,N,148,82,2,25.0,0.0,0.5,0.0,0.0,0.3,25.8,0.0 +2,2019-01-31 00:32:06,2019-01-31 00:43:29,4,2.55,1,N,107,141,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16,0.0 +2,2019-01-21 21:24:05,2019-01-21 21:27:33,4,0.77,1,N,113,144,1,5.0,0.5,0.5,0.5,0.0,0.3,6.8,0.0 +2,2019-01-16 00:13:38,2019-01-16 00:30:14,4,4.3,1,N,249,141,1,15.5,0.5,0.5,3.36,0.0,0.3,20.16, +2,2019-01-31 21:43:08,2019-01-31 21:59:13,4,5.59,1,N,142,116,1,18.5,0.5,0.5,3.96,0.0,0.3,23.76,0.0 +1,2019-01-25 14:55:36,2019-01-25 15:02:56,4,0.6,1,N,230,48,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +1,2019-01-25 21:01:09,2019-01-25 21:08:45,4,1.5,1,N,142,236,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +1,2019-01-05 21:34:02,2019-01-05 21:40:42,4,0.9,1,N,230,142,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +1,2019-01-05 13:21:35,2019-01-05 13:31:02,4,0.9,1,N,161,100,1,7.5,0.0,0.5,3.0,0.0,0.3,11.3, +1,2019-01-18 14:29:06,2019-01-18 14:48:38,4,3.6,1,N,48,146,1,15.5,0.0,0.5,3.25,0.0,0.3,19.55, +1,2019-01-16 22:16:20,2019-01-16 22:28:38,4,2.5,1,N,233,249,1,10.5,0.5,0.5,2.5,0.0,0.3,14.3, +1,2019-01-23 09:16:23,2019-01-23 10:04:28,4,8.4,2,N,63,87,1,52.0,0.0,0.5,10.55,0.0,0.3,63.35,0.0 +2,2019-01-19 23:51:30,2019-01-20 00:02:44,4,2.31,1,N,236,238,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +1,2019-01-28 16:51:11,2019-01-28 16:57:21,4,0.6,1,N,164,161,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +1,2019-01-16 19:28:35,2019-01-16 20:04:10,4,18.6,2,N,132,229,1,52.0,4.5,0.5,0.0,5.76,0.3,63.06, +1,2019-01-13 02:19:44,2019-01-13 02:41:30,4,3.9,1,N,114,255,3,18.5,0.5,0.5,0.0,0.0,0.3,19.8, +2,2019-01-08 19:35:23,2019-01-08 19:39:27,4,1.09,1,N,79,170,1,5.5,1.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-06 14:55:29,2019-01-06 15:32:35,4,16.96,2,N,132,164,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-22 00:41:10,2019-01-22 00:47:20,4,1.63,1,N,264,264,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-22 00:58:46,2019-01-22 01:07:54,4,2.0,1,N,79,164,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-23 08:56:55,2019-01-23 09:35:38,4,6.66,1,N,13,161,1,29.0,0.0,0.5,7.45,0.0,0.3,37.25,0.0 +2,2019-01-08 10:47:16,2019-01-08 10:56:22,4,0.56,1,N,170,100,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-16 09:10:25,2019-01-16 09:33:11,4,2.47,1,N,113,162,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +1,2019-01-02 21:35:28,2019-01-02 21:49:17,4,3.9,1,N,141,249,1,13.5,0.5,0.5,1.78,0.0,0.3,16.58, +2,2019-01-01 01:31:09,2019-01-01 01:55:58,4,5.44,1,N,148,142,1,20.5,0.5,0.5,3.0,0.0,0.3,24.8, +2,2019-01-23 16:43:59,2019-01-23 17:11:48,4,3.66,1,N,125,170,1,19.0,1.0,0.5,4.16,0.0,0.3,24.96,0.0 +1,2019-01-28 15:20:57,2019-01-28 15:31:37,4,1.2,1,N,107,186,1,8.0,0.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-28 09:25:10,2019-01-28 09:29:53,4,1.01,1,N,263,141,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-23 12:50:06,2019-01-23 12:56:33,4,1.19,1,N,114,107,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +1,2019-01-25 18:21:05,2019-01-25 18:33:19,4,1.3,1,N,234,100,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-27 18:48:02,2019-01-27 19:27:01,4,20.94,2,N,132,237,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27,0.0 +2,2019-01-29 23:42:47,2019-01-29 23:46:06,4,0.74,1,N,238,151,1,5.0,0.5,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-01 18:00:40,2019-01-01 18:32:09,4,14.0,2,N,145,132,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-23 21:09:03,2019-01-23 21:22:28,4,2.42,1,N,211,170,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-05 20:08:40,2019-01-05 20:14:40,4,0.86,1,N,234,186,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-23 17:28:51,2019-01-23 17:49:08,4,1.43,1,N,163,162,2,12.5,1.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-29 10:55:24,2019-01-29 11:35:01,4,11.59,1,N,138,230,1,37.5,0.0,0.5,5.0,5.76,0.3,49.06,0.0 +2,2019-01-28 17:43:57,2019-01-28 17:47:16,4,0.42,1,N,239,143,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96,0.0 +1,2019-01-12 00:25:36,2019-01-12 00:36:59,4,1.8,1,N,79,158,1,9.5,0.5,0.5,2.15,0.0,0.3,12.95, +1,2019-01-22 17:34:15,2019-01-22 17:41:54,4,1.3,1,N,162,234,1,7.0,1.0,0.5,1.75,0.0,0.3,10.55,0.0 +2,2019-01-10 10:50:32,2019-01-10 10:57:47,4,0.94,1,N,231,211,1,6.5,0.0,0.5,0.7,0.0,0.3,8.0, +2,2019-01-29 21:04:15,2019-01-29 21:10:29,4,1.23,1,N,85,71,1,6.5,0.5,0.5,1.56,0.0,0.3,11.31,0.0 +2,2019-01-26 13:18:21,2019-01-26 13:38:24,4,2.24,1,N,43,230,1,14.0,0.0,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-21 09:39:04,2019-01-21 09:47:19,4,1.2,1,N,162,161,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +1,2019-01-04 23:55:55,2019-01-05 00:07:24,4,1.6,1,N,230,186,1,9.0,0.5,0.5,1.55,0.0,0.3,11.85, +1,2019-01-25 17:55:22,2019-01-25 18:02:40,4,1.0,1,N,141,141,1,6.5,1.0,0.5,1.65,0.0,0.3,9.95,0.0 +2,2019-01-31 20:01:30,2019-01-31 20:19:16,4,2.42,1,N,162,90,1,12.5,1.0,0.5,1.0,0.0,0.3,15.3,0.0 +2,2019-01-18 15:52:38,2019-01-18 16:04:08,4,1.32,1,N,238,236,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-04 20:02:22,2019-01-04 20:07:27,4,0.67,1,N,237,141,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +1,2019-01-18 15:37:06,2019-01-18 16:06:07,4,4.6,1,N,45,230,2,20.5,0.0,0.5,0.0,0.0,0.3,21.3, +1,2019-01-11 15:02:54,2019-01-11 15:18:45,4,1.8,1,Y,236,140,1,11.5,0.0,0.5,1.85,0.0,0.3,14.15, +2,2019-01-14 07:45:55,2019-01-14 07:51:35,4,0.73,1,N,100,246,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-26 00:19:55,2019-01-26 00:35:29,4,2.47,1,N,114,68,1,11.5,0.5,0.5,3.2,0.0,0.3,16.0,0.0 +2,2019-01-14 12:13:35,2019-01-14 12:46:51,4,10.28,1,N,138,125,1,35.0,0.0,0.5,7.16,0.0,0.3,42.96, +1,2019-01-01 11:40:02,2019-01-01 11:54:32,4,1.7,1,N,161,246,3,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-27 15:50:08,2019-01-27 16:14:08,4,8.62,1,N,138,217,1,26.5,0.0,0.5,3.0,0.0,0.3,30.3,0.0 +1,2019-01-24 23:13:23,2019-01-24 23:25:47,4,2.5,1,N,143,141,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +1,2019-01-20 18:42:55,2019-01-20 18:46:42,4,0.8,1,N,148,79,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +1,2019-01-05 17:44:37,2019-01-05 18:00:46,4,2.5,1,N,48,229,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-29 14:16:11,2019-01-29 14:23:31,4,1.0,1,N,239,142,1,6.5,0.0,0.5,2.19,0.0,0.3,9.49,0.0 +2,2019-01-06 01:22:30,2019-01-06 01:31:35,4,2.66,1,N,166,239,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-05 10:50:07,2019-01-05 10:55:13,4,0.61,1,N,100,50,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-08 19:41:26,2019-01-08 19:46:01,4,1.33,1,N,143,239,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +1,2019-01-21 18:07:12,2019-01-21 18:20:29,4,2.1,1,N,234,231,1,10.5,0.0,0.5,2.25,0.0,0.3,13.55,0.0 +1,2019-01-10 17:16:47,2019-01-10 17:32:54,4,1.4,1,N,229,163,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-28 11:15:42,2019-01-28 11:35:37,4,2.35,1,N,229,236,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-31 20:19:25,2019-01-31 20:44:31,4,8.86,1,N,138,164,1,29.0,0.5,0.5,7.21,5.76,0.3,43.27,0.0 +2,2019-01-16 15:40:06,2019-01-16 15:43:21,4,0.69,1,N,79,107,1,4.5,0.0,0.5,1.59,0.0,0.3,6.89, +2,2019-01-24 10:34:03,2019-01-24 11:09:26,4,5.19,1,N,239,249,1,24.0,0.0,0.5,4.96,0.0,0.3,29.76,0.0 +1,2019-01-01 15:46:58,2019-01-01 16:01:35,4,2.5,1,N,239,263,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8, +2,2019-01-19 19:57:58,2019-01-19 20:08:49,4,1.43,1,N,211,90,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-26 23:33:27,2019-01-26 23:38:23,4,0.94,1,N,100,162,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-10 16:48:02,2019-01-10 17:02:13,4,1.52,1,N,79,249,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-01 03:13:38,2019-01-01 03:25:36,4,6.61,4,N,132,265,2,23.0,0.5,0.5,0.0,0.0,0.3,24.3, +1,2019-01-26 19:07:57,2019-01-26 19:25:10,4,2.7,1,N,239,237,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16,0.0 +1,2019-01-02 11:03:52,2019-01-02 11:20:15,4,4.2,1,N,186,13,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-20 10:55:54,2019-01-20 11:01:43,4,1.09,1,N,237,236,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-03 20:56:16,2019-01-03 21:06:40,4,1.8,1,N,230,141,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-03 17:53:02,2019-01-03 18:02:21,4,1.28,1,N,239,236,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-30 07:24:06,2019-01-30 07:30:21,4,1.07,1,N,237,162,1,6.0,0.0,0.5,1.11,0.0,0.3,7.91,0.0 +2,2019-01-15 03:42:39,2019-01-15 03:47:09,4,1.41,1,N,264,264,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-31 14:56:21,2019-01-31 15:39:22,4,14.31,5,N,10,48,1,60.0,0.0,0.5,1.0,5.76,0.3,67.56,0.0 +1,2019-01-06 02:12:13,2019-01-06 02:40:12,4,8.2,1,N,249,41,2,27.5,0.5,0.5,0.0,0.0,0.3,28.8, +2,2019-01-22 11:30:30,2019-01-22 11:49:18,4,2.23,1,N,170,90,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +1,2019-01-05 23:10:13,2019-01-05 23:14:47,4,0.9,1,N,4,107,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-27 11:50:59,2019-01-27 11:55:26,4,0.76,1,N,239,142,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-11 22:13:48,2019-01-11 22:22:12,4,1.25,1,N,229,237,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-22 11:08:22,2019-01-22 11:29:03,4,1.9,1,N,230,233,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3,0.0 +2,2019-01-28 16:31:20,2019-01-28 16:34:23,4,0.64,1,N,238,24,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3,0.0 +1,2019-01-20 01:06:28,2019-01-20 01:11:00,4,1.2,1,N,239,238,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +2,2019-01-26 21:04:22,2019-01-26 21:12:19,4,1.5,1,N,236,237,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-29 18:41:13,2019-01-29 18:46:02,4,0.69,1,N,161,164,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-24 21:45:53,2019-01-24 22:17:26,4,15.04,1,N,138,261,1,44.5,0.5,0.5,10.31,5.76,0.3,61.87,0.0 +1,2019-01-01 18:54:14,2019-01-01 19:15:54,4,4.8,1,N,49,4,1,18.5,0.0,0.5,3.85,0.0,0.3,23.15, +2,2019-01-03 18:15:05,2019-01-03 18:42:32,4,3.62,1,N,231,230,1,18.5,1.0,0.5,4.06,0.0,0.3,24.36, +2,2019-01-09 11:27:47,2019-01-09 11:29:51,4,0.6,1,N,33,52,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-20 14:40:35,2019-01-20 14:50:51,4,2.44,1,N,237,233,1,10.5,0.0,0.5,1.25,0.0,0.3,12.55, +1,2019-01-01 16:24:12,2019-01-01 16:58:33,4,18.5,2,N,113,132,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +1,2019-01-07 11:46:57,2019-01-07 12:15:09,4,6.3,1,N,264,264,2,26.0,0.0,0.5,0.0,0.0,0.3,26.8, +2,2019-01-15 09:50:16,2019-01-15 09:55:55,4,0.7,1,N,186,170,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3, +2,2019-01-18 17:31:15,2019-01-18 17:45:48,4,2.32,1,N,238,237,1,11.5,1.0,0.5,1.0,0.0,0.3,14.3, +2,2019-01-13 16:11:08,2019-01-13 16:21:48,4,2.25,1,N,231,90,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-22 07:54:50,2019-01-22 08:35:29,4,10.28,1,N,132,37,1,35.0,0.0,0.5,7.16,0.0,0.3,42.96,0.0 +1,2019-01-24 15:06:51,2019-01-24 15:31:46,4,2.7,1,N,142,234,2,16.0,0.0,0.5,0.0,0.0,0.3,16.8,0.0 +2,2019-01-09 18:43:01,2019-01-09 18:51:47,4,1.72,1,N,90,48,1,8.0,1.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-25 19:53:57,2019-01-25 20:00:43,4,1.13,1,N,233,170,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-21 05:35:14,2019-01-21 05:42:23,4,1.1,1,N,68,161,1,7.0,0.5,0.5,0.0,0.0,0.3,8.3,0.0 +1,2019-01-11 19:06:59,2019-01-11 19:13:30,4,0.6,1,N,262,236,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +1,2019-01-01 21:57:05,2019-01-01 21:57:24,4,1.5,2,N,244,244,1,52.0,0.0,0.5,10.0,5.76,0.3,68.56, +2,2019-01-04 20:21:56,2019-01-04 20:25:40,4,1.35,1,N,263,74,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-23 13:38:05,2019-01-23 13:55:39,4,1.55,1,N,234,100,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-23 17:00:19,2019-01-23 17:03:15,4,0.48,1,N,141,236,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-04 18:23:00,2019-01-04 18:25:22,5,0.41,1,N,151,151,1,3.5,1.0,0.5,0.8,0.0,0.3,6.1, +2,2019-01-21 18:50:33,2019-01-21 18:56:27,5,1.51,1,N,4,233,1,7.0,0.0,0.5,1.0,0.0,0.3,8.8,0.0 +2,2019-01-07 15:27:22,2019-01-07 15:44:54,5,6.38,1,N,231,141,1,20.5,0.0,0.5,1.0,0.0,0.3,22.3, +2,2019-01-15 07:19:58,2019-01-15 07:23:53,5,0.83,1,N,236,43,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-14 15:42:20,2019-01-14 16:04:54,5,5.46,1,N,238,243,2,18.5,0.0,0.5,0.0,0.0,0.3,19.3, +2,2019-01-01 13:57:32,2019-01-01 14:13:58,5,2.77,1,N,113,162,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3, +2,2019-01-29 20:26:07,2019-01-29 20:36:35,5,1.9,1,N,144,4,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-28 09:47:00,2019-01-28 09:57:30,5,1.78,1,N,236,239,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-12 22:43:56,2019-01-12 23:03:13,5,8.17,1,N,233,119,1,24.5,0.5,0.5,5.16,0.0,0.3,30.96, +2,2019-01-01 03:56:38,2019-01-01 04:20:21,5,4.89,1,N,249,37,1,19.5,0.5,0.5,4.16,0.0,0.3,24.96, +2,2019-01-05 01:43:06,2019-01-05 01:49:30,5,1.28,1,N,79,137,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-14 18:28:18,2019-01-14 18:28:24,5,0.0,2,N,132,132,1,52.0,4.5,0.5,11.46,0.0,0.3,68.76, +2,2019-01-04 18:02:48,2019-01-04 18:06:29,5,0.19,1,N,161,161,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-04 22:46:16,2019-01-04 22:55:45,5,1.66,1,N,186,50,1,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-06 20:41:41,2019-01-06 20:46:46,5,0.91,1,N,234,161,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-04 17:09:17,2019-01-04 17:19:11,5,1.44,1,N,79,114,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-19 22:30:51,2019-01-19 22:34:54,5,0.99,1,N,229,141,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-06 04:34:12,2019-01-06 05:12:30,5,12.06,1,N,264,264,2,39.0,0.5,0.5,0.0,0.0,0.3,40.3, +2,2019-01-18 12:01:03,2019-01-18 12:28:43,5,5.06,1,N,239,244,1,22.5,0.0,0.5,4.66,0.0,0.3,27.96, +2,2019-01-08 18:25:22,2019-01-08 18:36:42,5,0.84,1,N,142,230,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-04 20:45:31,2019-01-04 20:53:33,5,1.3,1,N,143,48,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-31 00:15:45,2019-01-31 00:15:49,5,0.02,1,N,238,238,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8,0.0 +2,2019-01-28 07:21:05,2019-01-28 07:23:54,5,0.75,1,N,229,162,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-24 08:57:30,2019-01-24 09:10:44,5,3.06,1,N,143,166,1,12.5,0.0,0.5,1.33,0.0,0.3,16.58,0.0 +2,2019-01-02 15:43:02,2019-01-02 15:59:16,5,3.46,1,N,41,143,1,13.5,0.0,0.5,2.86,0.0,0.3,17.16, +2,2019-01-28 11:07:12,2019-01-28 11:16:00,5,1.34,1,N,170,234,1,8.0,0.0,0.5,1.25,0.0,0.3,10.05,0.0 +2,2019-01-01 22:58:37,2019-01-01 23:08:41,5,2.02,1,N,249,87,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-06 09:05:55,2019-01-06 09:28:09,5,10.77,1,N,138,208,1,31.5,0.0,0.5,11.42,5.76,0.3,49.48, +2,2019-01-19 08:20:49,2019-01-19 08:49:01,5,19.23,1,N,132,3,2,51.5,0.0,0.5,0.0,5.76,0.3,58.06, +2,2019-01-27 09:59:24,2019-01-27 10:03:26,5,0.7,1,N,151,238,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-01 01:15:47,2019-01-01 01:21:14,5,0.66,1,N,229,162,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-02 03:05:22,2019-01-02 03:16:57,5,1.75,1,N,246,170,1,9.5,0.5,0.5,2.7,0.0,0.3,13.5, +1,2019-01-21 16:57:49,2019-01-21 17:17:19,5,3.8,1,N,144,141,1,15.5,0.0,0.5,5.0,0.0,0.3,21.3,0.0 +2,2019-01-30 04:45:55,2019-01-30 05:10:59,5,19.28,2,N,261,132,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +2,2019-01-08 08:45:34,2019-01-08 08:51:39,5,0.91,1,N,43,41,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-18 21:06:59,2019-01-18 21:15:35,5,1.58,1,N,263,238,1,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-30 12:21:25,2019-01-30 12:34:06,5,3.03,1,N,226,141,2,12.5,0.0,0.5,0.0,0.0,0.3,13.3,0.0 +2,2019-01-13 18:44:37,2019-01-13 18:50:03,5,1.1,1,N,234,137,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-28 14:30:24,2019-01-28 14:38:03,5,1.3,1,N,236,238,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-29 16:55:37,2019-01-29 17:01:13,5,0.8,1,N,48,163,2,5.5,1.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-07 01:18:21,2019-01-07 01:19:34,5,0.49,1,N,114,144,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-24 11:31:34,2019-01-24 12:06:43,5,3.65,1,N,90,236,1,21.5,0.0,0.5,2.88,0.0,0.3,25.18,0.0 +2,2019-01-10 16:50:15,2019-01-10 17:12:24,5,2.93,1,N,161,262,1,15.0,1.0,0.5,2.52,0.0,0.3,19.32, +2,2019-01-12 22:50:44,2019-01-12 22:58:55,5,1.59,1,N,234,211,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-03 01:37:06,2019-01-03 01:42:18,5,1.3,1,N,90,79,2,6.0,0.5,0.5,0.0,0.0,0.3,7.3, +2,2019-01-29 22:07:44,2019-01-29 22:24:49,5,3.0,1,N,186,237,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-05 18:32:08,2019-01-05 18:37:01,5,0.83,1,N,237,237,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-04 14:38:10,2019-01-04 14:58:55,5,8.72,1,N,75,261,2,26.0,0.0,0.5,0.0,0.0,0.3,26.8, +2,2019-01-12 11:23:23,2019-01-12 12:00:49,5,4.62,1,N,79,142,1,24.5,0.0,0.5,1.0,0.0,0.3,26.3, +2,2019-01-07 11:31:43,2019-01-07 11:50:26,5,2.19,1,N,236,161,1,13.5,0.0,0.5,2.14,0.0,0.3,16.44, +2,2019-01-14 21:57:41,2019-01-14 22:08:58,5,1.41,1,N,48,233,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-11 20:49:09,2019-01-11 20:55:07,5,1.46,1,N,48,143,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-14 20:57:25,2019-01-14 21:28:41,5,6.02,1,N,264,264,1,23.5,0.5,0.5,0.0,0.0,0.3,24.8, +2,2019-01-20 15:16:20,2019-01-20 15:36:51,5,5.15,1,N,113,262,2,19.0,0.0,0.5,0.0,0.0,0.3,19.8, +2,2019-01-15 17:24:31,2019-01-15 17:33:02,5,0.98,1,N,163,161,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-05 06:52:08,2019-01-05 06:59:11,5,1.88,1,N,234,246,1,7.5,0.0,0.5,2.49,0.0,0.3,12.74, +2,2019-01-19 14:18:20,2019-01-19 14:23:26,5,0.59,1,N,237,237,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +1,2019-01-05 13:45:24,2019-01-05 13:58:44,5,1.8,1,N,170,163,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-10 13:11:14,2019-01-10 13:21:42,5,0.88,1,N,161,170,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-18 10:38:38,2019-01-18 10:40:15,5,0.32,1,N,264,264,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-25 09:14:02,2019-01-25 09:36:25,5,1.83,1,N,141,170,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-03 15:14:27,2019-01-03 15:39:16,5,3.06,1,N,79,148,1,17.0,0.0,0.5,1.0,0.0,0.3,18.8, +2,2019-01-31 15:40:53,2019-01-31 15:46:00,5,0.88,1,N,233,107,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-02 09:13:39,2019-01-02 09:24:47,5,1.88,1,N,43,162,1,9.5,0.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-25 23:31:26,2019-01-25 23:38:31,5,1.47,1,N,236,238,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0,0.0 +2,2019-01-13 08:52:03,2019-01-13 09:05:37,5,3.31,1,N,239,42,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-05 22:08:37,2019-01-05 22:47:40,5,9.8,1,N,100,56,2,36.0,0.5,0.5,0.0,0.0,0.3,37.3, +2,2019-01-02 14:38:28,2019-01-02 14:43:37,5,0.72,1,N,48,163,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-08 10:36:10,2019-01-08 10:58:44,5,2.46,1,N,239,140,2,15.0,0.0,0.5,0.0,0.0,0.3,15.8, +2,2019-01-10 07:13:47,2019-01-10 07:23:43,5,1.29,1,N,68,161,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-23 17:47:52,2019-01-23 17:58:32,5,0.86,1,N,230,162,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-05 12:24:57,2019-01-05 12:33:18,5,0.71,1,N,161,163,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-19 00:17:39,2019-01-19 00:35:44,5,1.94,1,N,249,148,1,13.0,0.5,0.5,1.0,0.0,0.3,15.3, +2,2019-01-11 15:27:23,2019-01-11 15:31:24,5,0.43,1,N,141,140,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-01 22:22:40,2019-01-01 22:40:39,5,9.32,1,N,43,13,1,27.5,0.5,0.5,5.76,0.0,0.3,34.56, +2,2019-01-30 13:20:03,2019-01-30 13:29:57,5,1.8,1,N,166,238,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-04 23:41:10,2019-01-04 23:49:56,5,1.61,1,N,170,79,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16, +2,2019-01-04 07:19:21,2019-01-04 07:22:43,5,0.72,1,N,170,234,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +2,2019-01-13 17:52:55,2019-01-13 17:55:06,5,0.8,1,N,236,75,1,4.5,0.0,0.5,1.32,0.0,0.3,6.62, +2,2019-01-18 07:21:33,2019-01-18 07:32:56,5,2.65,1,N,100,158,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-25 21:20:58,2019-01-25 21:25:54,5,0.54,1,N,79,144,1,5.0,0.5,0.5,0.94,0.0,0.3,7.24,0.0 +2,2019-01-11 09:35:34,2019-01-11 10:03:50,5,2.71,1,N,113,162,2,18.0,0.0,0.5,0.0,0.0,0.3,18.8, +2,2019-01-25 23:27:25,2019-01-26 00:08:21,5,8.09,1,N,125,223,1,30.5,0.5,0.5,4.77,0.0,0.3,36.57,0.0 +2,2019-01-08 08:41:41,2019-01-08 08:44:33,5,0.52,1,N,113,211,1,4.0,0.0,0.5,0.48,0.0,0.3,5.28, +2,2019-01-07 17:24:23,2019-01-07 17:34:19,5,1.58,1,N,163,236,1,8.5,1.0,0.5,1.0,0.0,0.3,11.3, +2,2019-01-11 07:30:39,2019-01-11 07:45:57,5,2.31,1,N,230,246,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-18 06:26:57,2019-01-18 07:15:06,5,21.84,2,N,230,132,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-31 17:37:33,2019-01-31 17:47:07,5,1.14,1,N,262,237,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-19 14:38:17,2019-01-19 14:41:31,5,0.55,1,N,237,141,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-26 02:28:32,2019-01-26 02:36:33,5,1.89,1,N,148,170,1,8.0,0.5,0.5,1.4,0.0,0.3,10.7,0.0 +2,2019-01-15 07:56:06,2019-01-15 08:04:50,5,1.47,1,N,249,246,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-12 11:01:11,2019-01-12 11:12:06,5,1.9,1,N,79,170,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-16 11:24:45,2019-01-16 11:29:08,5,0.56,1,N,237,162,1,4.5,0.0,0.5,0.95,0.0,0.3,6.25, +2,2019-01-17 13:47:12,2019-01-17 14:00:29,5,0.8,1,N,48,170,1,9.0,0.0,0.5,0.98,0.0,0.3,10.78, +2,2019-01-06 15:26:06,2019-01-06 15:34:35,5,1.98,1,N,263,75,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-23 22:10:32,2019-01-23 22:34:59,5,11.21,1,N,230,220,1,33.0,0.5,0.5,7.39,2.64,0.3,44.33,0.0 +2,2019-01-07 13:09:34,2019-01-07 13:21:25,5,2.8,1,N,125,48,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-18 09:37:43,2019-01-18 09:44:36,5,1.09,1,N,162,164,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-03 17:45:02,2019-01-03 17:52:23,5,2.92,1,N,33,261,1,10.0,1.0,0.5,2.36,0.0,0.3,14.16, +2,2019-01-13 10:20:42,2019-01-13 10:31:49,5,1.95,1,N,234,170,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-05 21:43:24,2019-01-05 21:58:56,5,2.3,1,N,48,137,1,11.5,0.5,0.5,1.0,0.0,0.3,13.8, +2,2019-01-16 22:51:14,2019-01-16 22:54:26,5,0.89,1,N,137,170,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-28 19:24:54,2019-01-28 19:30:49,5,1.4,1,N,264,264,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-19 01:29:37,2019-01-19 01:37:46,5,1.17,1,N,90,246,1,7.0,0.5,0.5,1.0,0.0,0.3,9.3, +2,2019-01-22 19:21:34,2019-01-22 19:23:22,5,0.27,1,N,236,236,1,3.5,1.0,0.5,1.32,0.0,0.3,6.62,0.0 +2,2019-01-24 18:17:26,2019-01-24 18:37:38,5,2.69,1,N,164,142,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-12 14:35:29,2019-01-12 14:38:39,5,0.51,1,N,239,142,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-22 20:45:11,2019-01-22 20:57:29,5,1.86,1,N,113,170,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-16 08:31:52,2019-01-16 08:59:40,5,2.98,1,N,13,186,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56, +2,2019-01-26 00:09:37,2019-01-26 00:11:27,5,0.51,1,N,239,143,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-24 21:03:14,2019-01-24 21:27:49,5,7.78,1,N,87,89,1,25.5,0.5,0.5,6.51,5.76,0.3,39.07,0.0 +2,2019-01-22 17:41:06,2019-01-22 17:48:51,5,1.16,1,N,107,144,2,7.0,1.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-03 06:37:25,2019-01-03 07:21:12,5,17.98,3,N,236,1,1,70.0,0.0,0.0,16.56,12.5,0.3,99.36, +2,2019-01-23 19:12:23,2019-01-23 19:33:15,5,5.56,1,N,229,87,1,19.0,1.0,0.5,4.16,0.0,0.3,24.96,0.0 +2,2019-01-23 08:38:55,2019-01-23 09:00:55,5,1.6,1,N,233,100,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-12 22:08:55,2019-01-12 22:13:05,5,1.01,1,N,158,246,1,5.5,0.5,0.5,1.02,0.0,0.3,7.82, +2,2019-01-29 07:41:21,2019-01-29 07:51:30,5,1.24,1,N,74,75,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-28 23:35:46,2019-01-28 23:37:34,5,0.74,1,N,229,170,1,4.0,0.5,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-24 06:13:24,2019-01-24 06:15:44,5,0.41,1,N,237,237,1,3.5,0.0,0.5,0.39,0.0,0.3,4.69,0.0 +2,2019-01-26 17:07:20,2019-01-26 17:23:28,5,1.59,1,N,186,142,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-09 16:06:21,2019-01-09 16:17:17,5,1.42,1,N,234,233,1,8.5,1.0,0.5,2.0,0.0,0.3,12.3, +2,2019-01-31 07:29:03,2019-01-31 07:33:20,5,0.84,1,N,48,48,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-05 14:08:51,2019-01-05 14:11:29,5,0.28,1,N,236,236,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-17 09:10:33,2019-01-17 09:24:00,5,1.79,1,N,113,246,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-12 11:22:06,2019-01-12 11:30:52,5,1.77,1,N,141,162,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-02 15:20:23,2019-01-02 15:49:42,5,15.26,1,N,132,150,1,42.5,0.0,0.5,8.66,0.0,0.3,51.96, +2,2019-01-16 13:07:00,2019-01-16 13:17:02,5,0.65,1,N,237,237,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-02 06:53:06,2019-01-02 07:03:30,5,2.07,1,N,146,140,1,9.5,0.0,0.5,1.03,0.0,0.3,11.33, +2,2019-01-19 00:57:53,2019-01-19 01:02:04,5,0.68,1,N,237,140,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56, +2,2019-01-28 07:07:40,2019-01-28 07:17:31,5,1.74,1,N,237,239,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8,0.0 +2,2019-01-05 04:04:45,2019-01-05 04:10:28,5,1.32,1,N,211,79,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-17 06:58:36,2019-01-17 07:05:51,5,0.86,1,N,186,170,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-08 13:23:55,2019-01-08 13:46:27,5,3.46,1,N,140,234,1,17.5,0.0,0.5,3.66,0.0,0.3,21.96, +2,2019-01-22 20:50:11,2019-01-22 20:51:44,5,0.47,1,N,263,263,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-05 23:01:06,2019-01-05 23:21:22,5,3.19,1,N,148,100,2,14.5,0.5,0.5,0.0,0.0,0.3,15.8, +2,2019-01-30 21:56:46,2019-01-30 22:06:21,5,2.14,1,N,230,237,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-05 18:31:32,2019-01-05 18:42:09,5,1.49,1,N,79,148,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-30 18:50:23,2019-01-30 18:58:51,5,0.69,1,N,163,161,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-26 03:14:19,2019-01-26 03:31:11,5,6.37,1,N,158,166,1,20.5,0.5,0.5,3.75,0.0,0.3,25.55,0.0 +2,2019-01-14 18:02:06,2019-01-14 18:06:05,5,0.69,1,N,246,186,1,5.0,1.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-08 19:15:00,2019-01-08 20:01:21,5,15.33,1,N,75,89,1,48.0,1.0,0.5,8.0,0.0,0.3,57.8, +2,2019-01-21 14:01:31,2019-01-21 14:47:53,5,17.63,2,N,132,163,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +2,2019-01-17 06:56:11,2019-01-17 07:03:31,5,0.92,1,N,140,162,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-19 14:09:01,2019-01-19 14:16:30,5,1.39,1,N,141,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-09 12:58:05,2019-01-09 13:11:56,5,1.55,1,N,239,236,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-31 08:10:54,2019-01-31 08:14:24,5,0.37,1,N,229,233,1,4.0,0.0,0.5,1.2,0.0,0.3,6.0,0.0 +2,2019-01-22 11:30:00,2019-01-22 11:59:22,5,6.62,1,N,163,88,2,25.0,0.0,0.5,0.0,0.0,0.3,25.8,0.0 +2,2019-01-29 19:49:50,2019-01-29 19:52:33,5,0.55,1,N,229,163,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-10 18:39:39,2019-01-10 18:51:49,5,1.47,1,N,246,90,1,9.5,1.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-06 10:52:54,2019-01-06 11:03:22,5,2.34,1,N,113,230,1,9.5,0.0,0.5,1.58,0.0,0.3,11.88, +2,2019-01-17 06:42:22,2019-01-17 06:46:50,5,0.42,1,N,100,230,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-18 23:04:16,2019-01-18 23:07:03,5,0.69,1,N,52,25,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-13 03:47:15,2019-01-13 03:56:00,5,2.29,1,N,79,233,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-06 15:15:01,2019-01-06 15:21:44,5,1.01,1,N,236,141,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-24 04:40:22,2019-01-24 05:10:18,5,13.11,1,N,186,20,1,38.0,0.5,0.5,7.86,0.0,0.3,47.16,0.0 +2,2019-01-19 01:51:07,2019-01-19 01:55:12,5,0.87,1,N,234,113,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-06 02:43:27,2019-01-06 02:53:53,5,2.71,1,N,45,45,1,10.5,0.5,0.5,2.36,0.0,0.3,14.16, +2,2019-01-14 14:09:55,2019-01-14 14:20:25,5,1.43,1,N,239,230,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-07 08:50:26,2019-01-07 08:52:10,5,0.36,1,N,162,229,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-26 04:11:57,2019-01-26 04:36:39,5,6.56,1,N,48,256,1,23.5,0.5,0.5,0.0,0.0,0.3,24.8,0.0 +2,2019-01-23 22:59:09,2019-01-23 23:19:57,5,5.96,1,N,230,87,1,21.5,0.5,0.5,4.56,0.0,0.3,27.36,0.0 +2,2019-01-08 10:33:25,2019-01-08 10:36:46,5,0.41,1,N,68,158,1,4.0,0.0,0.5,1.44,0.0,0.3,6.24, +2,2019-01-03 22:27:45,2019-01-03 22:36:20,5,1.75,1,N,263,238,1,8.5,0.5,0.5,1.2,0.0,0.3,11.0, +2,2019-01-22 10:56:58,2019-01-22 11:26:11,5,10.83,1,N,230,18,1,34.0,0.0,0.5,0.0,0.0,0.3,34.8,0.0 +2,2019-01-04 15:42:15,2019-01-04 15:49:20,5,0.71,1,N,234,170,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82, +2,2019-01-07 18:16:47,2019-01-07 18:32:01,5,2.45,1,N,143,236,2,12.0,1.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-18 19:13:41,2019-01-18 19:40:51,5,3.5,1,N,79,66,1,18.5,1.0,0.5,4.06,0.0,0.3,24.36, +2,2019-01-24 09:10:44,2019-01-24 09:27:15,5,0.95,1,N,170,100,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-13 16:26:07,2019-01-13 16:34:40,5,0.88,1,N,162,230,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-10 21:16:30,2019-01-10 21:18:57,5,0.45,1,N,100,162,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-04 08:40:00,2019-01-04 08:58:22,5,1.27,1,N,137,100,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76, +2,2019-01-20 01:13:38,2019-01-20 01:16:15,5,0.28,1,N,114,148,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-11 09:36:08,2019-01-11 10:03:01,5,11.73,1,N,100,138,1,35.0,0.0,0.5,8.31,5.76,0.3,49.87, +2,2019-01-31 21:26:40,2019-01-31 21:44:53,5,2.76,1,N,137,50,1,13.5,0.5,0.5,2.96,0.0,0.3,17.76,0.0 +2,2019-01-12 00:52:48,2019-01-12 00:56:17,5,0.54,1,N,107,234,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-24 20:58:27,2019-01-24 21:04:07,5,0.84,1,N,144,158,1,5.5,0.5,0.5,0.1,0.0,0.3,6.9,0.0 +2,2019-01-13 13:14:01,2019-01-13 13:26:00,5,2.53,1,N,262,142,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-08 09:21:01,2019-01-08 09:33:44,5,1.62,1,N,142,162,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-15 18:49:19,2019-01-15 19:06:09,5,0.97,1,N,170,100,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8, +2,2019-01-21 15:00:12,2019-01-21 15:06:04,5,0.65,1,N,144,231,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-11 01:01:09,2019-01-11 01:10:51,5,3.07,1,N,211,48,2,10.5,0.5,0.5,0.0,0.0,0.3,11.8, +2,2019-01-20 20:10:29,2019-01-20 20:15:29,5,0.97,1,N,237,263,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-25 23:11:00,2019-01-25 23:13:48,5,0.75,1,N,90,246,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-03 15:35:51,2019-01-03 15:50:15,5,1.82,1,N,43,161,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-27 04:13:19,2019-01-27 04:16:18,5,0.53,1,N,246,246,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-02 13:04:08,2019-01-02 13:11:17,5,1.15,1,N,163,237,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-12 19:58:21,2019-01-12 20:06:11,5,0.96,1,N,107,170,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-12 12:48:28,2019-01-12 13:02:10,5,4.4,1,N,186,238,2,15.5,0.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-13 18:34:47,2019-01-13 18:52:13,5,3.15,1,N,107,236,1,14.5,0.0,0.5,2.3,0.0,0.3,17.6, +2,2019-01-18 16:03:05,2019-01-18 16:07:32,5,0.84,1,N,195,40,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8, +2,2019-01-02 17:17:50,2019-01-02 17:23:43,5,0.75,1,N,234,170,1,5.5,1.0,0.5,1.1,0.0,0.3,8.4, +2,2019-01-24 19:20:44,2019-01-24 19:28:23,5,1.49,1,N,142,238,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-17 14:39:28,2019-01-17 14:50:49,5,0.94,1,N,230,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-25 22:47:07,2019-01-25 22:57:48,5,1.89,1,N,170,237,1,9.5,0.5,0.5,1.62,0.0,0.3,12.42,0.0 +2,2019-01-22 09:34:27,2019-01-22 09:51:07,5,1.9,1,N,143,162,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-22 17:45:49,2019-01-22 17:57:28,5,2.58,1,N,231,246,2,11.0,1.0,0.5,0.0,0.0,0.3,12.8,0.0 +2,2019-01-18 22:55:37,2019-01-18 23:06:41,5,2.1,1,N,163,107,1,10.0,0.5,0.5,2.26,0.0,0.3,13.56, +2,2019-01-27 01:34:10,2019-01-27 01:48:20,5,3.64,1,N,158,163,1,13.5,0.5,0.5,1.0,0.0,0.3,15.8,0.0 +2,2019-01-25 00:26:36,2019-01-25 00:47:12,5,5.98,1,N,137,7,1,19.5,0.5,0.5,4.16,0.0,0.3,24.96,0.0 +2,2019-01-28 09:51:39,2019-01-28 10:15:13,5,2.65,1,N,238,163,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16,0.0 +2,2019-01-11 12:09:27,2019-01-11 12:19:00,5,1.06,1,N,237,236,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-05 13:18:32,2019-01-05 13:22:58,5,1.05,1,N,263,141,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-02 23:01:28,2019-01-02 23:09:22,5,2.05,1,N,205,10,1,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-16 17:14:04,2019-01-16 17:18:22,5,0.63,1,N,249,113,1,5.0,1.0,0.5,1.2,0.0,0.3,8.0, +2,2019-01-27 16:58:46,2019-01-27 17:04:48,5,0.87,1,N,137,224,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-23 08:56:58,2019-01-23 09:10:22,5,1.9,1,N,151,236,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12,0.0 +2,2019-01-31 09:36:30,2019-01-31 09:57:53,5,3.66,1,N,43,166,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-17 23:05:51,2019-01-17 23:19:21,5,2.51,1,N,211,68,1,11.5,0.5,0.5,0.1,0.0,0.3,12.9, +2,2019-01-26 19:46:47,2019-01-26 20:07:35,5,2.59,1,N,13,79,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96,0.0 +2,2019-01-13 21:17:14,2019-01-13 21:23:52,5,1.15,1,N,48,170,1,6.0,0.5,0.5,1.0,0.0,0.3,8.3, +2,2019-01-27 16:58:13,2019-01-27 17:05:47,5,1.17,1,N,137,186,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-31 17:29:59,2019-01-31 17:50:41,5,2.48,1,N,163,113,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36,0.0 +2,2019-01-16 04:51:27,2019-01-16 05:00:42,5,3.06,1,N,75,170,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-15 21:09:30,2019-01-15 21:31:05,5,4.32,1,N,163,231,1,16.5,0.5,0.5,3.56,0.0,0.3,21.36, +2,2019-01-23 15:13:23,2019-01-23 15:18:41,5,0.96,1,N,229,170,1,5.5,0.0,0.5,1.13,0.0,0.3,7.43,0.0 +2,2019-01-21 08:47:22,2019-01-21 08:58:17,5,3.69,1,N,152,143,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88,0.0 +2,2019-01-23 11:15:08,2019-01-23 11:29:23,5,2.14,1,N,141,142,1,11.5,0.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-04 09:34:20,2019-01-04 09:46:21,5,1.2,1,N,48,230,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-17 22:16:37,2019-01-17 22:20:55,5,0.66,1,N,170,100,1,5.0,0.5,0.5,1.89,0.0,0.3,8.19, +2,2019-01-12 22:50:35,2019-01-12 22:56:37,5,1.92,1,N,236,74,2,7.5,0.5,0.5,0.0,0.0,0.3,8.8, +2,2019-01-19 15:18:13,2019-01-19 15:24:25,5,0.81,1,N,113,234,1,6.0,0.0,0.5,1.0,0.0,0.3,7.8, +2,2019-01-17 20:32:02,2019-01-17 20:43:14,5,1.67,1,N,141,229,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-26 15:06:41,2019-01-26 15:32:12,5,2.59,1,N,264,264,1,16.5,0.0,0.5,0.86,0.0,0.3,18.16,0.0 +2,2019-01-30 22:36:11,2019-01-30 22:43:25,5,0.94,1,N,100,186,2,6.5,0.5,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-24 09:55:52,2019-01-24 10:21:22,5,3.01,1,N,161,43,2,17.5,0.0,0.5,0.0,0.0,0.3,18.3,0.0 +2,2019-01-17 13:53:40,2019-01-17 14:09:38,5,0.93,1,N,137,164,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-05 01:30:09,2019-01-05 01:33:12,5,0.59,1,N,164,90,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-14 12:16:05,2019-01-14 12:17:23,5,0.45,1,N,237,237,1,3.5,0.0,0.5,0.7,0.0,0.3,5.0, +2,2019-01-08 17:11:13,2019-01-08 18:11:48,5,15.48,2,N,233,132,1,52.0,4.5,0.5,12.61,5.76,0.3,75.67, +2,2019-01-26 00:18:50,2019-01-26 00:32:26,5,3.19,1,N,148,161,1,12.0,0.5,0.5,1.0,0.0,0.3,14.3,0.0 +2,2019-01-04 01:42:36,2019-01-04 01:46:23,5,1.0,1,N,141,141,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-22 08:15:11,2019-01-22 08:45:36,5,3.9,1,N,236,107,1,19.5,0.0,0.5,3.25,0.0,0.3,23.55,0.0 +2,2019-01-21 08:44:45,2019-01-21 08:53:31,5,2.35,1,N,263,161,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-17 00:44:39,2019-01-17 00:45:09,5,0.05,1,N,79,113,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8, +2,2019-01-22 07:28:47,2019-01-22 07:35:02,5,1.13,1,N,48,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-07 21:22:28,2019-01-07 21:29:13,5,2.2,1,N,48,239,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-02 11:23:23,2019-01-02 11:23:23,5,0.0,2,N,142,142,1,52.0,0.0,0.5,7.0,0.0,0.3,59.8, +2,2019-01-09 20:15:36,2019-01-09 20:29:35,5,1.84,1,N,229,68,1,11.0,0.5,0.5,1.23,0.0,0.3,13.53, +2,2019-01-15 19:09:30,2019-01-15 19:33:35,5,5.97,1,N,43,249,1,22.0,1.0,0.5,4.76,0.0,0.3,28.56, +2,2019-01-11 16:28:44,2019-01-11 16:42:30,5,2.25,1,N,230,236,1,11.0,1.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-05 11:47:45,2019-01-05 11:50:35,5,0.68,1,N,68,48,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-18 06:35:55,2019-01-18 06:41:40,5,1.12,1,N,234,68,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-12 12:52:36,2019-01-12 12:57:12,5,0.75,1,N,239,239,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-22 07:27:11,2019-01-22 07:39:16,5,1.77,1,N,144,90,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-18 14:48:57,2019-01-18 15:02:52,5,1.45,1,N,162,186,1,10.0,0.0,0.5,1.0,0.0,0.3,11.8, +2,2019-01-23 23:46:44,2019-01-23 23:51:14,5,1.18,1,N,158,90,1,6.0,0.5,0.5,1.82,0.0,0.3,9.12,0.0 +2,2019-01-05 07:32:07,2019-01-05 07:41:44,5,1.98,1,N,137,230,1,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-17 13:46:56,2019-01-17 13:56:22,5,1.11,1,N,238,142,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-05 12:59:08,2019-01-05 13:07:38,5,1.81,1,N,43,262,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8, +2,2019-01-17 17:53:46,2019-01-17 18:16:28,5,2.24,1,N,146,226,2,15.0,1.0,0.5,0.0,0.0,0.3,16.8, +2,2019-01-03 12:32:54,2019-01-03 12:43:57,5,1.31,1,N,234,137,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-30 19:08:48,2019-01-30 19:19:30,5,1.41,1,N,166,238,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-26 19:58:25,2019-01-26 20:29:54,5,2.53,1,N,170,158,1,19.0,0.0,0.5,3.96,0.0,0.3,23.76,0.0 +2,2019-01-10 17:13:50,2019-01-10 17:17:55,5,0.72,1,N,142,48,1,5.0,1.0,0.5,2.04,0.0,0.3,8.84, +2,2019-01-11 07:43:11,2019-01-11 07:47:17,5,0.48,1,N,231,13,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-14 19:01:01,2019-01-14 19:09:22,5,1.19,1,N,137,100,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62, +2,2019-01-17 08:03:46,2019-01-17 08:07:00,5,0.63,1,N,249,249,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-02 16:40:48,2019-01-02 16:51:25,5,2.49,1,N,246,142,2,10.0,1.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-01 17:38:35,2019-01-01 17:44:19,5,1.3,1,N,237,142,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-17 08:03:55,2019-01-17 08:11:35,5,1.62,1,N,231,249,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-10 04:35:49,2019-01-10 04:37:49,5,0.4,1,N,226,226,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8, +2,2019-01-29 10:33:22,2019-01-29 10:43:28,5,0.84,1,N,107,234,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-18 22:02:08,2019-01-18 22:29:26,5,6.34,1,N,79,41,1,22.5,0.5,0.5,4.76,0.0,0.3,28.56, +2,2019-01-29 19:41:32,2019-01-29 19:47:43,5,0.95,1,N,239,142,1,6.5,1.0,0.5,1.0,0.0,0.3,9.3,0.0 +2,2019-01-15 07:43:10,2019-01-15 07:50:35,5,1.37,1,N,151,239,1,7.5,0.0,0.5,1.7,0.0,0.3,10.0, +2,2019-01-18 16:14:49,2019-01-18 16:22:02,5,1.77,1,N,50,43,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-12 20:42:03,2019-01-12 20:47:53,5,0.67,1,N,90,249,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16, +2,2019-01-15 07:36:24,2019-01-15 08:15:10,5,12.18,1,N,138,164,1,38.0,0.0,0.5,8.91,5.76,0.3,53.47, +2,2019-01-23 12:07:55,2019-01-23 12:11:19,5,0.57,1,N,239,239,1,4.0,0.0,0.5,1.0,0.0,0.3,5.8,0.0 +2,2019-01-05 02:22:28,2019-01-05 02:26:22,5,0.61,1,N,264,264,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-24 11:23:38,2019-01-24 11:44:35,5,1.62,1,N,50,237,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88,0.0 +2,2019-01-05 18:30:37,2019-01-05 18:43:23,5,1.39,1,N,48,186,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-01 18:21:16,2019-01-01 18:34:24,5,4.96,1,N,261,170,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16, +2,2019-01-07 13:16:37,2019-01-07 13:22:21,5,0.87,1,N,142,237,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-13 17:38:25,2019-01-13 17:48:20,5,1.46,1,N,170,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-05 16:01:16,2019-01-05 16:03:56,5,0.74,1,N,142,48,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-14 20:34:39,2019-01-14 20:39:39,5,0.81,1,N,231,13,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-03 14:00:59,2019-01-03 14:06:21,5,0.4,1,N,90,186,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-14 16:23:11,2019-01-14 18:25:44,5,0.03,2,N,163,163,2,52.0,4.5,0.5,0.0,0.0,0.3,57.3, +2,2019-01-18 15:50:29,2019-01-18 15:53:00,5,0.36,1,N,166,166,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-18 20:50:50,2019-01-18 20:54:54,5,0.84,1,N,246,68,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 18:52:18,2019-01-01 19:06:12,5,1.82,1,N,48,68,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-10 23:12:01,2019-01-10 23:15:13,5,0.56,1,N,42,42,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-11 17:44:47,2019-01-11 18:27:21,5,14.23,1,N,138,231,2,43.0,1.0,0.5,0.0,5.76,0.3,50.56, +2,2019-01-27 10:34:47,2019-01-27 10:38:18,5,0.58,1,N,239,143,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3,0.0 +2,2019-01-19 17:11:11,2019-01-19 17:23:36,5,1.15,1,N,90,170,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-31 15:23:03,2019-01-31 15:32:33,5,0.58,1,N,261,261,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-22 18:07:09,2019-01-22 18:22:31,5,1.72,1,N,90,137,1,11.0,1.0,0.5,3.2,0.0,0.3,16.0,0.0 +2,2019-01-14 16:52:29,2019-01-14 16:54:15,5,0.49,1,N,125,144,2,3.5,1.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-31 20:30:22,2019-01-31 20:39:32,5,1.28,1,N,230,246,1,8.0,0.5,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-28 08:18:04,2019-01-28 08:35:13,5,2.61,1,N,148,170,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56,0.0 +2,2019-01-19 12:55:17,2019-01-19 13:06:33,5,1.69,1,N,249,144,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-14 11:47:50,2019-01-14 11:53:52,5,0.63,1,N,161,162,1,5.5,0.0,0.5,0.63,0.0,0.3,6.93, +2,2019-01-25 11:15:27,2019-01-25 11:33:44,5,1.77,1,N,233,140,1,12.0,0.0,0.5,1.0,0.0,0.3,13.8,0.0 +2,2019-01-28 13:47:38,2019-01-28 14:03:31,5,1.14,1,N,48,162,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56,0.0 +2,2019-01-30 22:37:55,2019-01-30 22:46:46,5,2.16,1,N,161,236,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36,0.0 +2,2019-01-18 13:47:06,2019-01-18 14:13:53,5,5.84,1,N,87,170,1,24.0,0.0,0.5,1.0,0.0,0.3,25.8, +2,2019-01-08 22:02:30,2019-01-08 22:10:11,5,2.51,1,N,162,263,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-16 23:35:35,2019-01-16 23:46:22,5,2.44,1,N,237,145,1,11.0,0.5,0.5,2.0,0.0,0.3,14.3, +2,2019-01-26 17:35:28,2019-01-26 17:44:35,5,1.21,1,N,237,236,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-04 01:23:33,2019-01-04 01:26:15,5,0.58,1,N,50,48,2,4.0,0.5,0.5,0.0,0.0,0.3,5.3, +2,2019-01-17 15:48:26,2019-01-17 15:55:55,5,0.83,1,N,236,262,1,6.5,0.0,0.5,1.0,0.0,0.3,8.3, +2,2019-01-18 20:56:53,2019-01-18 21:24:18,5,9.89,1,N,234,243,2,31.5,0.5,0.5,0.0,0.0,0.3,32.8, +2,2019-01-19 10:14:40,2019-01-19 10:22:47,5,0.94,1,N,239,239,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-17 08:30:17,2019-01-17 08:49:43,5,3.04,1,N,143,263,1,14.5,0.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-10 13:47:18,2019-01-10 14:05:42,5,1.73,1,N,162,140,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-09 07:08:15,2019-01-09 07:26:48,5,9.13,1,N,140,138,1,26.5,0.0,0.5,8.26,5.76,0.3,41.32, +2,2019-01-19 16:38:21,2019-01-19 16:50:53,5,2.43,1,N,234,232,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-23 18:51:36,2019-01-23 18:59:16,5,1.15,1,N,164,162,1,7.0,1.0,0.5,1.2,0.0,0.3,10.0,0.0 +2,2019-01-02 21:38:29,2019-01-02 21:44:56,5,0.98,1,N,79,224,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-27 00:27:38,2019-01-27 00:32:29,5,1.03,1,N,68,234,1,5.5,0.5,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-19 19:55:14,2019-01-19 20:07:48,5,1.56,1,N,161,50,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-07 12:58:01,2019-01-07 13:00:23,5,0.32,1,N,68,48,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-15 08:34:26,2019-01-15 08:43:42,5,1.05,1,N,162,137,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-01 23:25:58,2019-01-01 23:31:52,5,1.47,1,N,239,151,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-12 11:36:33,2019-01-12 11:51:53,5,3.02,1,N,24,48,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-10 11:29:22,2019-01-10 11:53:21,5,1.17,1,N,48,68,1,15.0,0.0,0.5,3.16,0.0,0.3,18.96, +2,2019-01-08 14:24:55,2019-01-08 14:36:17,5,1.7,1,N,140,137,2,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-18 11:42:44,2019-01-18 12:00:16,5,1.56,1,N,186,163,1,12.0,0.0,0.5,2.56,0.0,0.3,15.36, +2,2019-01-07 16:27:17,2019-01-07 16:27:30,5,0.0,1,N,264,137,1,2.5,1.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-23 08:12:56,2019-01-23 08:18:29,5,0.99,1,N,237,237,1,5.5,0.0,0.5,1.0,0.0,0.3,7.3,0.0 +2,2019-01-21 00:12:59,2019-01-21 00:35:04,5,6.88,1,N,211,166,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16,0.0 +2,2019-01-20 22:34:12,2019-01-20 22:55:02,5,9.59,1,N,148,166,2,28.5,0.5,0.5,0.0,0.0,0.3,29.8, +2,2019-01-27 18:09:14,2019-01-27 18:15:12,5,0.88,1,N,233,233,2,6.0,0.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-31 22:14:48,2019-01-31 22:29:16,5,2.45,1,N,236,162,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96,0.0 +2,2019-01-31 21:46:25,2019-01-31 21:57:08,5,2.53,1,N,161,236,1,10.0,0.5,0.5,1.5,0.0,0.3,12.8,0.0 +2,2019-01-29 17:58:04,2019-01-29 18:00:43,5,0.48,1,N,237,237,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-31 21:37:09,2019-01-31 21:50:04,5,2.82,1,N,45,49,1,11.5,0.5,0.5,2.56,0.0,0.3,15.36,0.0 +2,2019-01-22 14:17:58,2019-01-22 14:22:05,5,0.93,1,N,236,43,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-10 17:05:42,2019-01-10 17:12:28,5,0.68,1,N,236,262,1,6.0,1.0,0.5,1.17,0.0,0.3,8.97, +2,2019-01-24 07:51:36,2019-01-24 07:59:13,5,1.21,1,N,25,97,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-05 00:59:53,2019-01-05 01:10:34,5,2.08,1,N,148,90,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-06 22:21:34,2019-01-06 22:26:01,5,0.71,1,N,261,87,1,5.0,0.5,0.5,1.58,0.0,0.3,7.88, +2,2019-01-11 14:00:02,2019-01-11 14:36:25,5,10.77,1,N,163,138,1,36.5,0.0,0.5,10.76,5.76,0.3,53.82, +2,2019-01-24 15:21:42,2019-01-24 15:28:25,5,0.8,1,N,151,238,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-08 14:48:09,2019-01-08 14:58:55,5,1.03,1,N,236,43,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-12 03:43:46,2019-01-12 03:46:59,5,0.62,1,N,137,170,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-18 12:33:51,2019-01-18 12:44:30,5,1.54,1,N,90,50,1,8.5,0.0,0.5,2.0,0.0,0.3,11.3, +2,2019-01-11 13:36:55,2019-01-11 14:12:08,5,10.5,1,N,138,211,1,34.0,0.0,0.5,6.96,0.0,0.3,41.76, +2,2019-01-10 08:00:33,2019-01-10 08:07:20,5,1.12,1,N,186,234,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76, +2,2019-01-04 15:55:11,2019-01-04 15:59:17,5,0.44,1,N,142,143,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-13 23:41:13,2019-01-13 23:47:39,5,1.29,1,N,142,237,1,7.0,0.5,0.5,0.83,0.0,0.3,9.13, +2,2019-01-10 15:19:36,2019-01-10 15:26:18,5,0.76,1,N,113,114,1,6.0,0.0,0.5,3.0,0.0,0.3,9.8, +2,2019-01-28 13:54:18,2019-01-28 14:06:23,5,2.14,1,N,68,211,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96,0.0 +2,2019-01-04 14:44:13,2019-01-04 15:00:18,5,2.45,1,N,237,50,1,12.5,0.0,0.5,3.32,0.0,0.3,16.62, +2,2019-01-19 20:42:23,2019-01-19 20:49:44,5,1.53,1,N,161,142,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-30 11:07:42,2019-01-30 11:35:47,5,9.03,1,N,151,138,1,29.5,0.0,0.5,7.21,5.76,0.3,43.27,0.0 +2,2019-01-23 08:13:50,2019-01-23 08:20:08,5,0.85,1,N,249,113,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16,0.0 +2,2019-01-04 08:25:29,2019-01-04 08:48:29,5,3.83,1,N,151,162,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8, +2,2019-01-21 17:15:34,2019-01-21 17:32:16,5,4.84,1,N,186,24,1,17.0,0.0,0.5,3.56,0.0,0.3,21.36,0.0 +2,2019-01-26 16:06:38,2019-01-26 16:12:08,5,0.59,1,N,236,262,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-05 16:53:08,2019-01-05 17:02:37,5,1.01,1,N,137,164,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-13 14:24:49,2019-01-13 14:32:59,5,1.68,1,N,48,239,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-01 22:47:55,2019-01-01 22:58:43,5,2.17,1,N,129,7,2,10.0,0.5,0.5,0.0,0.0,0.3,11.3, +2,2019-01-20 18:13:30,2019-01-20 18:16:11,5,0.41,1,N,142,239,1,4.0,0.0,0.5,0.48,0.0,0.3,5.28, +2,2019-01-12 15:36:34,2019-01-12 15:42:26,5,1.06,1,N,142,161,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-30 14:04:31,2019-01-30 14:21:38,5,5.06,1,N,246,87,1,18.0,0.0,0.5,3.76,0.0,0.3,22.56,0.0 +2,2019-01-24 20:47:08,2019-01-24 21:09:01,5,10.48,1,N,138,65,1,31.5,0.5,0.5,6.56,0.0,0.3,39.36,0.0 +2,2019-01-09 14:36:31,2019-01-09 14:41:25,5,0.81,1,N,125,231,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-25 20:35:29,2019-01-25 20:39:35,5,0.84,1,N,141,229,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-11 12:50:11,2019-01-11 13:00:06,5,1.72,1,N,161,137,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-03 11:34:44,2019-01-03 11:52:07,5,1.72,1,N,50,161,1,12.0,0.0,0.5,3.84,0.0,0.3,16.64, +2,2019-01-03 17:14:07,2019-01-03 17:30:47,5,2.15,1,N,141,170,1,11.5,1.0,0.5,3.99,0.0,0.3,17.29, +2,2019-01-11 22:29:50,2019-01-11 22:33:05,5,0.49,1,N,48,48,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-11 22:52:39,2019-01-11 23:00:58,5,0.93,1,N,161,233,1,7.0,0.5,0.5,2.49,0.0,0.3,10.79, +2,2019-01-13 05:22:05,2019-01-13 05:38:32,5,6.01,1,N,79,238,1,19.0,0.5,0.5,3.0,0.0,0.3,23.3, +2,2019-01-31 10:07:47,2019-01-31 10:43:44,5,4.51,1,N,75,186,1,23.5,0.0,0.5,4.86,0.0,0.3,29.16,0.0 +2,2019-01-08 18:26:34,2019-01-08 18:39:27,5,1.37,1,N,236,236,1,9.5,1.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-29 12:34:39,2019-01-29 12:43:33,5,0.93,1,N,236,75,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-23 22:17:34,2019-01-23 23:07:57,5,18.28,1,N,163,221,2,53.5,0.5,0.5,0.0,11.52,0.3,66.32,0.0 +2,2019-01-21 14:31:09,2019-01-21 14:44:54,5,1.57,1,N,186,163,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-30 21:36:40,2019-01-30 21:56:40,5,6.38,1,N,97,198,2,21.0,0.5,0.5,0.0,0.0,0.3,22.3,0.0 +2,2019-01-14 09:59:14,2019-01-14 10:22:20,5,7.76,1,N,138,41,2,25.0,0.0,0.5,0.0,5.76,0.3,31.56, +2,2019-01-29 14:09:22,2019-01-29 14:16:03,5,1.19,1,N,45,88,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-22 11:51:51,2019-01-22 11:57:36,5,1.21,1,N,237,140,1,6.0,0.0,0.5,2.04,0.0,0.3,8.84,0.0 +2,2019-01-17 07:19:29,2019-01-17 07:24:34,5,0.77,1,N,50,143,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-06 10:47:56,2019-01-06 10:56:52,5,3.63,1,N,137,75,2,13.0,0.0,0.5,0.0,0.0,0.3,13.8, +2,2019-01-06 17:42:25,2019-01-06 17:45:16,5,0.52,1,N,141,237,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-31 15:24:25,2019-01-31 15:37:47,5,1.41,1,N,90,79,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-23 19:03:10,2019-01-23 19:13:06,5,1.01,1,N,163,237,1,8.0,1.0,0.5,1.0,0.0,0.3,10.8,0.0 +2,2019-01-24 22:39:46,2019-01-24 22:44:58,5,1.2,1,N,79,170,1,6.0,0.5,0.5,0.73,0.0,0.3,8.03,0.0 +2,2019-01-12 23:29:01,2019-01-12 23:40:16,5,1.07,1,N,79,113,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-11 22:08:36,2019-01-11 22:20:13,5,1.17,1,N,164,186,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-26 21:28:28,2019-01-26 21:40:56,5,1.75,1,N,246,170,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8,0.0 +2,2019-01-15 22:15:28,2019-01-15 22:29:01,5,5.58,1,N,263,79,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3, +2,2019-01-23 10:40:49,2019-01-23 10:58:53,5,1.28,1,N,107,90,1,11.5,0.0,0.5,1.0,0.0,0.3,13.3,0.0 +2,2019-01-26 01:17:22,2019-01-26 01:21:24,5,0.72,1,N,166,24,1,5.0,0.5,0.5,1.26,0.0,0.3,7.56,0.0 +2,2019-01-17 09:55:52,2019-01-17 10:23:20,5,0.95,1,N,142,237,1,16.0,0.0,0.5,3.36,0.0,0.3,20.16, +2,2019-01-15 17:19:37,2019-01-15 17:38:15,5,2.75,1,N,237,234,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-19 19:53:42,2019-01-19 20:14:35,5,3.71,1,N,236,164,1,17.0,0.0,0.5,1.78,0.0,0.3,19.58, +2,2019-01-25 23:18:12,2019-01-25 23:29:10,5,1.21,1,N,107,224,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-25 22:26:14,2019-01-25 22:30:48,5,0.82,1,N,239,238,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-27 01:17:57,2019-01-27 01:21:22,5,0.73,1,N,263,236,1,4.5,0.5,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-29 15:55:06,2019-01-29 15:59:47,5,0.7,1,N,161,170,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-22 10:01:55,2019-01-22 10:08:05,5,1.24,1,N,129,70,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-25 16:01:24,2019-01-25 16:06:44,5,1.06,1,N,170,234,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-30 09:09:56,2019-01-30 09:14:40,5,0.7,1,N,41,41,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3,0.0 +2,2019-01-31 19:16:32,2019-01-31 19:24:31,5,1.01,1,N,100,161,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-11 12:01:28,2019-01-11 12:06:23,5,0.84,1,N,162,107,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-18 07:52:39,2019-01-18 08:04:03,5,2.2,1,N,236,142,1,10.0,0.0,0.5,1.62,0.0,0.3,12.42, +2,2019-01-14 21:25:20,2019-01-14 21:44:27,5,6.02,1,N,161,87,1,20.5,0.5,0.5,4.36,0.0,0.3,26.16, +2,2019-01-10 08:25:36,2019-01-10 08:37:41,5,0.83,1,N,48,68,1,8.5,0.0,0.5,1.4,0.0,0.3,10.7, +2,2019-01-31 10:35:58,2019-01-31 10:45:37,5,1.03,1,N,237,162,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-01 18:35:33,2019-01-01 19:06:57,5,15.38,1,N,132,29,1,44.0,0.0,0.5,8.96,0.0,0.3,53.76, +2,2019-01-05 16:26:18,2019-01-05 16:30:39,5,0.6,1,N,263,236,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-30 15:36:35,2019-01-30 15:52:31,5,1.89,1,N,163,50,1,11.0,0.0,0.5,2.0,0.0,0.3,13.8,0.0 +2,2019-01-04 20:05:25,2019-01-04 20:19:56,5,2.27,1,N,43,162,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-06 15:18:40,2019-01-06 15:43:51,5,4.88,1,N,143,79,1,19.5,0.0,0.5,3.0,0.0,0.3,23.3, +2,2019-01-25 06:08:35,2019-01-25 06:16:54,5,3.02,1,N,107,141,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-18 18:22:10,2019-01-18 18:34:59,5,1.22,1,N,90,170,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-10 12:32:09,2019-01-10 12:55:21,5,1.28,1,N,68,113,2,13.5,0.0,0.5,0.0,0.0,0.3,14.3, +2,2019-01-11 19:29:59,2019-01-11 19:34:24,5,0.4,1,N,239,239,2,4.5,1.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-09 07:56:23,2019-01-09 08:16:35,5,3.44,1,N,263,161,1,16.0,0.0,0.5,2.5,0.0,0.3,19.3, +2,2019-01-24 01:43:43,2019-01-24 02:00:58,5,5.33,1,N,161,13,1,18.0,0.5,0.5,2.9,0.0,0.3,22.2,0.0 +2,2019-01-12 01:22:17,2019-01-12 01:33:08,5,2.08,1,N,113,246,1,9.5,0.5,0.5,2.16,0.0,0.3,12.96, +2,2019-01-17 06:31:24,2019-01-17 06:37:50,6,1.46,1,N,238,236,1,7.5,0.0,0.5,2.08,0.0,0.3,10.38, +2,2019-01-16 21:21:15,2019-01-16 21:32:34,6,1.62,1,N,249,234,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-13 13:01:21,2019-01-13 13:07:08,6,0.81,1,N,100,48,1,5.5,0.0,0.5,1.89,0.0,0.3,8.19, +2,2019-01-04 08:20:44,2019-01-04 08:25:46,6,0.57,1,N,161,230,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-16 17:45:58,2019-01-16 17:54:23,6,1.83,1,N,161,113,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-24 17:49:14,2019-01-24 17:53:26,6,0.5,1,N,237,140,1,4.5,1.0,0.5,1.58,0.0,0.3,7.88,0.0 +2,2019-01-02 15:33:17,2019-01-02 15:38:25,6,0.76,3,N,233,230,2,23.0,0.0,0.0,0.0,0.0,0.3,23.3, +2,2019-01-30 10:05:47,2019-01-30 10:18:46,6,1.39,1,N,162,234,1,9.5,0.0,0.5,1.54,0.0,0.3,11.84,0.0 +2,2019-01-20 14:17:26,2019-01-20 14:17:30,6,0.0,1,N,170,170,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3, +2,2019-01-17 18:43:16,2019-01-17 19:06:48,6,3.19,1,N,230,114,1,16.5,1.0,0.5,3.66,0.0,0.3,21.96, +2,2019-01-11 08:11:58,2019-01-11 08:19:44,6,1.33,1,N,13,87,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-03 15:31:40,2019-01-03 15:43:39,6,1.52,1,N,163,100,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-10 07:25:48,2019-01-10 07:28:12,6,0.52,1,N,100,230,2,4.0,0.0,0.5,0.0,0.0,0.3,4.8, +2,2019-01-01 21:28:27,2019-01-01 21:35:02,6,1.51,1,N,186,107,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-12 18:08:32,2019-01-12 18:19:37,6,1.54,1,N,161,107,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-29 15:26:42,2019-01-29 15:40:00,6,1.47,1,N,237,100,1,10.0,0.0,0.5,2.7,0.0,0.3,13.5,0.0 +2,2019-01-10 21:16:24,2019-01-10 21:25:17,6,1.46,1,N,107,148,1,7.5,0.5,0.5,2.2,0.0,0.3,11.0, +2,2019-01-07 17:02:16,2019-01-07 17:09:48,6,1.33,1,N,161,137,1,7.0,1.0,0.5,2.2,0.0,0.3,11.0, +2,2019-01-09 19:17:50,2019-01-09 20:09:19,6,9.48,1,N,186,61,2,36.0,1.0,0.5,0.0,0.0,0.3,37.8, +2,2019-01-10 09:24:56,2019-01-10 09:29:23,6,0.6,1,N,33,25,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-24 08:56:10,2019-01-24 09:25:51,6,5.73,1,N,239,13,1,22.0,0.0,0.5,3.42,0.0,0.3,26.22,0.0 +2,2019-01-12 00:26:57,2019-01-12 00:38:24,6,1.93,1,N,48,170,2,9.5,0.5,0.5,0.0,0.0,0.3,10.8, +2,2019-01-08 18:28:23,2019-01-08 18:33:51,6,1.09,1,N,48,142,2,6.0,1.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-04 07:44:05,2019-01-04 07:51:06,6,0.93,1,N,48,163,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12, +2,2019-01-04 14:05:20,2019-01-04 14:13:12,6,1.17,1,N,234,186,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-08 18:23:44,2019-01-08 18:31:53,6,2.32,1,N,236,24,1,9.0,1.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-02 07:53:59,2019-01-02 08:01:16,6,1.22,1,N,24,24,1,7.0,0.0,0.5,3.0,0.0,0.3,10.8, +2,2019-01-01 03:26:18,2019-01-01 03:41:01,6,2.95,1,N,256,79,1,12.5,0.5,0.5,2.76,0.0,0.3,16.56, +2,2019-01-11 05:33:31,2019-01-11 05:50:53,6,10.1,1,N,140,138,1,28.5,0.5,0.5,10.67,5.76,0.3,46.23, +2,2019-01-31 10:53:04,2019-01-31 11:07:20,6,2.77,1,N,226,112,1,13.0,0.0,0.5,3.0,0.0,0.3,16.8,0.0 +2,2019-01-28 14:57:37,2019-01-28 15:06:00,6,1.34,1,N,164,113,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +1,2019-01-09 10:05:56,2019-01-09 10:14:50,6,2.4,1,N,262,229,1,9.5,0.0,0.5,2.05,0.0,0.3,12.35, +2,2019-01-29 13:40:24,2019-01-29 13:47:55,6,0.99,1,N,48,162,1,6.5,0.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-22 07:22:55,2019-01-22 07:23:08,6,0.05,1,N,242,242,2,2.5,0.0,0.5,0.0,0.0,0.3,3.3,0.0 +2,2019-01-29 07:27:49,2019-01-29 07:47:10,6,4.67,1,N,244,142,2,17.0,0.0,0.5,0.0,0.0,0.3,17.8,0.0 +2,2019-01-18 17:09:04,2019-01-18 17:30:51,6,4.19,1,N,161,41,1,17.0,1.0,0.5,2.0,0.0,0.3,20.8, +2,2019-01-29 16:10:04,2019-01-29 16:50:54,6,17.3,2,N,132,79,1,52.0,4.5,0.5,10.0,5.76,0.3,73.06,0.0 +2,2019-01-01 17:16:09,2019-01-01 17:27:34,6,2.03,1,N,43,238,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-11 06:50:12,2019-01-11 07:16:23,6,6.93,1,N,163,138,1,24.5,0.0,0.5,5.06,0.0,0.3,30.36, +2,2019-01-20 18:59:58,2019-01-20 19:03:32,6,0.94,1,N,238,166,1,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-04 18:04:02,2019-01-04 18:30:37,6,6.54,1,N,88,48,2,23.5,1.0,0.5,0.0,0.0,0.3,25.3, +2,2019-01-04 10:57:12,2019-01-04 11:15:05,6,6.92,1,N,162,231,1,22.0,0.0,0.5,4.0,0.0,0.3,26.8, +2,2019-01-12 07:49:35,2019-01-12 08:15:24,6,16.9,2,N,230,132,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-11 07:31:50,2019-01-11 07:41:51,6,1.17,1,N,170,161,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-25 15:24:47,2019-01-25 15:38:27,6,2.74,1,N,246,13,1,12.0,0.0,0.5,1.5,0.0,0.3,14.3,0.0 +2,2019-01-25 19:33:48,2019-01-25 19:45:44,6,2.15,1,N,261,211,1,10.5,1.0,0.5,2.46,0.0,0.3,14.76,0.0 +2,2019-01-10 13:55:17,2019-01-10 14:04:18,6,0.95,1,N,170,163,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-25 12:33:11,2019-01-25 12:40:30,6,1.05,1,N,164,107,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3,0.0 +2,2019-01-14 13:12:33,2019-01-14 13:19:57,6,1.46,1,N,237,262,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-01 15:22:05,2019-01-01 15:39:34,6,2.7,1,N,234,48,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-27 20:44:28,2019-01-27 20:58:25,6,4.86,1,N,162,45,1,16.5,0.5,0.5,2.0,0.0,0.3,19.8,0.0 +2,2019-01-10 08:56:45,2019-01-10 09:02:31,6,0.9,1,N,234,249,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-12 08:02:52,2019-01-12 08:15:25,6,1.97,1,N,239,236,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-18 12:09:26,2019-01-18 12:12:25,6,0.54,1,N,100,161,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-17 12:28:20,2019-01-17 12:51:41,6,2.75,1,N,237,234,1,15.5,0.0,0.5,2.0,0.0,0.3,18.3, +2,2019-01-22 20:58:08,2019-01-22 21:22:57,6,9.5,1,N,138,68,2,29.0,0.5,0.5,0.0,5.76,0.3,36.06,0.0 +2,2019-01-04 11:21:52,2019-01-04 11:29:03,6,1.1,1,N,229,170,2,6.5,0.0,0.5,0.0,0.0,0.3,7.3, +2,2019-01-27 16:11:30,2019-01-27 16:18:53,6,0.85,1,N,249,114,1,6.5,0.0,0.5,1.82,0.0,0.3,9.12,0.0 +1,2019-01-05 10:51:12,2019-01-05 10:53:33,6,0.4,1,N,162,161,2,3.5,0.0,0.5,0.0,0.0,0.3,4.3, +2,2019-01-19 17:52:40,2019-01-19 18:02:43,6,2.35,1,N,142,41,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-11 08:09:53,2019-01-11 08:21:35,6,1.31,1,N,48,162,2,9.5,0.0,0.5,0.0,0.0,0.3,10.3, +2,2019-01-01 02:15:41,2019-01-01 02:36:14,6,5.2,1,N,107,181,1,18.5,0.5,0.5,3.96,0.0,0.3,23.76, +2,2019-01-11 22:55:52,2019-01-11 23:11:07,6,2.58,1,N,148,170,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-24 08:01:39,2019-01-24 08:10:37,6,1.04,1,N,238,239,1,7.5,0.0,0.5,0.83,0.0,0.3,9.13,0.0 +2,2019-01-23 16:56:34,2019-01-23 17:07:18,6,1.13,1,N,140,237,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-24 14:17:39,2019-01-24 14:40:19,6,2.9,1,N,43,137,1,16.5,0.0,0.5,3.46,0.0,0.3,20.76,0.0 +2,2019-01-20 15:55:48,2019-01-20 16:01:51,6,0.54,1,N,249,158,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-06 06:26:43,2019-01-06 06:31:02,6,0.86,1,N,48,230,2,5.0,0.5,0.5,0.0,0.0,0.3,6.3, +2,2019-01-07 22:50:26,2019-01-07 23:01:52,6,2.85,1,N,239,41,1,11.5,0.5,0.5,3.84,0.0,0.3,16.64, +2,2019-01-31 17:46:12,2019-01-31 17:54:06,6,1.95,1,N,142,238,2,8.5,1.0,0.5,0.0,0.0,0.3,10.3,0.0 +2,2019-01-11 07:25:04,2019-01-11 07:40:27,6,2.49,1,N,262,142,1,12.0,0.0,0.5,3.2,0.0,0.3,16.0, +2,2019-01-11 15:18:20,2019-01-11 15:26:47,6,0.68,1,N,148,114,1,7.0,0.0,0.5,1.17,0.0,0.3,10.92, +2,2019-01-07 18:31:13,2019-01-07 18:56:12,6,6.51,1,N,162,33,1,22.0,1.0,0.5,5.95,0.0,0.3,29.75, +2,2019-01-18 12:38:34,2019-01-18 12:49:06,6,2.51,1,N,262,161,1,10.5,0.0,0.5,2.82,0.0,0.3,14.12, +2,2019-01-07 20:05:17,2019-01-07 20:20:10,6,3.17,1,N,236,48,1,13.0,0.5,0.5,2.86,0.0,0.3,17.16, +2,2019-01-16 21:33:21,2019-01-16 21:45:39,6,2.6,1,N,249,50,1,11.5,0.5,0.5,1.92,0.0,0.3,14.72, +2,2019-01-29 17:23:47,2019-01-29 17:28:20,6,0.82,1,N,170,107,2,5.0,1.0,0.5,0.0,0.0,0.3,6.8,0.0 +2,2019-01-13 16:51:58,2019-01-13 16:57:19,6,0.72,1,N,114,79,1,5.5,0.0,0.5,1.26,0.0,0.3,7.56, +2,2019-01-29 06:38:33,2019-01-29 06:42:18,6,0.77,1,N,262,236,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-25 16:55:42,2019-01-25 17:06:55,6,0.98,1,N,170,161,1,8.5,1.0,0.5,3.0,0.0,0.3,13.3,0.0 +2,2019-01-28 20:02:05,2019-01-28 20:03:35,6,0.5,1,N,263,236,1,3.5,0.5,0.5,0.96,0.0,0.3,5.76,0.0 +2,2019-01-31 09:10:35,2019-01-31 09:20:34,6,1.46,1,N,114,164,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-25 09:52:18,2019-01-25 10:02:49,6,1.1,1,N,140,229,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56,0.0 +2,2019-01-16 23:13:10,2019-01-16 23:18:07,6,1.6,1,N,211,234,1,6.5,0.5,0.5,1.95,0.0,0.3,9.75, +2,2019-01-26 02:16:03,2019-01-26 02:31:01,6,1.98,1,N,148,234,2,11.0,0.5,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-07 06:21:56,2019-01-07 06:24:29,6,0.53,1,N,161,163,1,4.0,0.0,0.5,0.96,0.0,0.3,5.76, +2,2019-01-06 01:27:59,2019-01-06 01:53:51,6,8.37,1,N,230,56,1,26.5,0.5,0.5,0.8,0.0,0.3,28.6, +2,2019-01-01 09:34:09,2019-01-01 10:01:48,6,3.95,1,N,158,158,1,20.0,0.0,0.5,5.0,0.0,0.3,25.8, +2,2019-01-11 21:38:09,2019-01-11 21:45:26,6,0.61,1,N,79,79,1,6.0,0.5,0.5,1.46,0.0,0.3,8.76, +2,2019-01-21 11:11:23,2019-01-21 11:16:06,6,0.51,1,N,24,166,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8,0.0 +2,2019-01-02 13:26:57,2019-01-02 13:37:03,6,2.11,1,N,236,142,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-16 15:22:07,2019-01-16 15:34:56,6,1.26,1,N,163,164,2,9.0,0.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-22 12:05:57,2019-01-22 12:17:58,6,1.36,1,N,164,163,1,9.0,0.0,0.5,2.94,0.0,0.3,12.74,0.0 +2,2019-01-13 18:45:38,2019-01-13 19:11:40,6,3.64,1,N,114,255,1,19.5,0.0,0.5,4.06,0.0,0.3,24.36, +2,2019-01-01 20:13:11,2019-01-01 20:19:44,6,1.45,1,N,162,90,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-28 18:24:03,2019-01-28 18:26:30,6,0.65,1,N,229,140,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-27 16:17:35,2019-01-27 16:25:48,6,1.83,1,N,236,163,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-26 09:07:14,2019-01-26 09:11:35,6,0.82,1,N,50,230,1,5.0,0.0,0.5,1.16,0.0,0.3,6.96,0.0 +2,2019-01-04 06:36:53,2019-01-04 06:49:53,6,2.23,1,N,100,137,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-23 19:15:02,2019-01-23 19:20:20,6,0.88,1,N,234,170,1,5.5,1.0,0.5,1.1,0.0,0.3,8.4,0.0 +2,2019-01-07 17:06:07,2019-01-07 17:16:18,6,2.17,1,N,239,166,2,9.5,1.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-05 01:36:10,2019-01-05 01:46:29,6,1.66,1,N,234,148,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-20 00:22:43,2019-01-20 00:28:06,6,1.83,1,N,230,143,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-10 08:27:07,2019-01-10 08:47:20,6,2.92,1,N,162,246,1,14.0,0.0,0.5,2.5,0.0,0.3,17.3, +2,2019-01-08 22:30:06,2019-01-08 22:35:29,6,1.12,1,N,164,107,1,6.0,0.5,0.5,0.7,0.0,0.3,8.0, +2,2019-01-28 10:14:05,2019-01-28 10:52:02,6,13.72,1,N,138,13,1,41.0,0.0,0.5,9.51,5.76,0.3,57.07,0.0 +2,2019-01-18 10:43:40,2019-01-18 10:57:29,6,1.6,1,N,162,164,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-22 15:52:07,2019-01-22 15:59:34,6,1.03,1,N,186,249,1,6.5,1.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-29 02:50:56,2019-01-29 03:11:44,6,10.42,1,N,230,95,1,30.0,0.5,0.5,6.26,0.0,0.3,37.56,0.0 +2,2019-01-31 17:47:21,2019-01-31 18:10:38,6,7.62,1,N,88,263,1,23.5,1.0,0.5,5.06,0.0,0.3,30.36,0.0 +2,2019-01-20 16:12:39,2019-01-20 16:23:51,6,3.23,1,N,140,145,1,12.0,0.0,0.5,2.0,0.0,0.3,14.8, +2,2019-01-28 23:54:13,2019-01-29 00:15:52,6,11.25,1,N,132,122,2,32.0,0.5,0.5,0.0,0.0,0.3,33.3,0.0 +2,2019-01-28 09:54:25,2019-01-28 09:58:51,6,0.37,1,N,162,229,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36,0.0 +2,2019-01-18 17:36:48,2019-01-18 17:42:20,6,1.34,1,N,142,236,2,6.5,1.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-15 18:34:00,2019-01-15 18:45:37,6,2.06,1,N,238,230,2,10.5,1.0,0.5,0.0,0.0,0.3,12.3, +2,2019-01-16 13:57:39,2019-01-16 14:20:58,6,2.35,1,N,161,236,1,15.0,0.0,0.5,2.37,0.0,0.3,18.17, +2,2019-01-10 11:45:19,2019-01-10 11:54:27,6,0.9,1,N,234,164,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75, +2,2019-01-17 15:44:13,2019-01-17 16:29:07,6,14.69,2,N,10,43,1,52.0,0.0,0.5,11.71,5.76,0.3,70.27, +2,2019-01-04 09:52:52,2019-01-04 10:14:28,6,2.08,1,N,161,43,2,14.5,0.0,0.5,0.0,0.0,0.3,15.3, +2,2019-01-20 16:23:47,2019-01-20 16:27:19,6,0.6,1,N,79,79,1,4.5,0.0,0.5,1.06,0.0,0.3,6.36, +2,2019-01-13 00:41:10,2019-01-13 00:53:51,6,2.63,1,N,114,13,2,11.5,0.5,0.5,0.0,0.0,0.3,12.8, +2,2019-01-25 19:38:46,2019-01-25 19:47:57,6,1.33,1,N,163,230,1,7.5,1.0,0.5,2.32,0.0,0.3,11.62,0.0 +2,2019-01-09 19:59:37,2019-01-09 20:08:10,6,1.3,1,N,211,209,2,7.0,0.5,0.5,0.0,0.0,0.3,8.3, +2,2019-01-21 21:58:48,2019-01-21 22:12:14,6,5.07,1,N,162,87,1,17.0,0.5,0.5,2.0,0.0,0.3,20.3,0.0 +2,2019-01-08 21:50:21,2019-01-08 21:59:27,6,1.54,1,N,143,230,2,8.5,0.5,0.5,0.0,0.0,0.3,9.8, +2,2019-01-14 07:28:49,2019-01-14 07:36:10,6,1.69,1,N,166,238,1,7.5,0.0,0.5,2.49,0.0,0.3,10.79, +2,2019-01-13 17:39:32,2019-01-13 17:43:58,6,0.87,1,N,41,166,1,5.0,0.0,0.5,1.45,0.0,0.3,7.25, +2,2019-01-24 07:36:33,2019-01-24 08:13:17,6,6.62,1,N,43,87,1,27.5,0.0,0.5,2.83,0.0,0.3,31.13,0.0 +2,2019-01-10 20:33:39,2019-01-10 20:48:23,6,1.81,1,N,170,249,1,10.5,0.5,0.5,1.77,0.0,0.3,13.57, +2,2019-01-22 07:51:36,2019-01-22 08:00:32,6,1.18,1,N,90,164,1,7.5,0.0,0.5,1.24,0.0,0.3,9.54,0.0 +2,2019-01-21 23:21:05,2019-01-21 23:40:07,6,7.08,1,N,132,258,1,23.5,0.5,0.5,3.72,0.0,0.3,28.52,0.0 +2,2019-01-01 10:47:39,2019-01-01 10:50:34,6,0.79,1,N,229,141,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-21 00:57:53,2019-01-21 00:59:23,6,0.47,1,N,48,163,2,3.5,0.5,0.5,0.0,0.0,0.3,4.8,0.0 +2,2019-01-06 15:34:52,2019-01-06 15:43:10,6,1.4,1,N,239,43,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96, +2,2019-01-10 10:56:11,2019-01-10 11:07:21,6,0.9,1,N,246,100,1,8.5,0.0,0.5,1.5,0.0,0.3,10.8, +2,2019-01-15 17:52:51,2019-01-15 18:04:42,6,2.08,1,N,158,79,1,10.0,0.5,0.5,3.39,0.0,0.3,14.69, +2,2019-01-08 18:39:11,2019-01-08 19:06:23,6,3.86,1,N,113,141,1,18.5,1.0,0.5,4.06,0.0,0.3,24.36, +2,2019-01-20 18:46:53,2019-01-20 18:56:51,6,2.14,1,N,263,43,1,9.5,0.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-25 09:03:28,2019-01-25 09:14:18,6,1.39,1,N,236,237,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-15 14:22:27,2019-01-15 14:33:45,6,1.1,1,N,161,237,1,8.5,0.0,0.5,2.79,0.0,0.3,12.09, +2,2019-01-12 22:08:25,2019-01-12 22:26:52,6,3.01,1,N,79,209,1,14.5,0.5,0.5,2.5,0.0,0.3,18.3, +2,2019-01-30 19:11:17,2019-01-30 19:20:37,6,1.1,1,N,246,48,1,7.5,1.0,0.5,1.86,0.0,0.3,11.16,0.0 +2,2019-01-30 09:00:07,2019-01-30 09:10:06,6,1.21,1,N,48,161,2,8.0,0.0,0.5,0.0,0.0,0.3,8.8,0.0 +2,2019-01-10 19:05:16,2019-01-10 19:36:34,6,6.31,1,N,233,116,1,23.5,1.0,0.5,6.32,0.0,0.3,31.62, +2,2019-01-25 00:51:11,2019-01-25 01:16:56,6,18.73,2,N,132,232,1,52.0,0.0,0.5,10.56,0.0,0.3,63.36,0.0 +2,2019-01-30 12:55:23,2019-01-30 13:00:26,6,0.33,1,N,163,237,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-28 15:15:07,2019-01-28 15:37:08,6,3.62,1,N,249,229,1,16.5,0.0,0.5,4.32,0.0,0.3,21.62,0.0 +2,2019-01-08 13:32:22,2019-01-08 13:39:08,6,0.74,1,N,263,75,1,6.0,0.0,0.5,1.7,0.0,0.3,8.5, +2,2019-01-17 18:33:20,2019-01-17 18:51:41,6,1.31,1,N,170,100,1,12.0,1.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-05 02:21:52,2019-01-05 02:37:48,6,2.7,1,N,158,170,1,12.0,0.5,0.5,2.66,0.0,0.3,15.96, +2,2019-01-13 16:11:19,2019-01-13 16:17:05,6,0.92,1,N,143,142,1,6.0,0.0,0.5,1.2,0.0,0.3,8.0, +2,2019-01-06 15:14:36,2019-01-06 15:20:36,6,1.01,1,N,137,224,1,6.0,0.0,0.5,1.02,0.0,0.3,7.82, +2,2019-01-08 12:13:43,2019-01-08 12:27:50,6,2.18,1,N,162,236,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8, +2,2019-01-10 20:41:17,2019-01-10 20:43:24,6,0.46,1,N,68,90,1,3.5,0.5,0.5,0.5,0.0,0.3,5.3, +2,2019-01-22 19:25:33,2019-01-22 19:34:41,6,1.34,1,N,231,144,1,8.0,1.0,0.5,2.45,0.0,0.3,12.25,0.0 +2,2019-01-25 13:43:11,2019-01-25 13:55:30,6,1.28,1,N,107,186,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-19 20:11:12,2019-01-19 20:18:56,6,1.96,1,N,236,151,1,8.0,0.5,0.5,2.79,0.0,0.3,12.09, +2,2019-01-02 18:05:33,2019-01-02 18:47:30,6,14.79,1,N,132,89,2,45.5,1.0,0.5,0.0,0.0,0.3,47.3, +2,2019-01-09 14:33:53,2019-01-09 14:42:46,6,1.42,1,N,140,236,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3, +2,2019-01-15 15:31:11,2019-01-15 15:39:24,6,1.22,1,N,246,249,1,7.5,0.0,0.5,1.0,0.0,0.3,9.3, +2,2019-01-27 20:26:25,2019-01-27 20:29:41,6,0.42,1,N,113,113,1,4.0,0.5,0.5,1.0,0.0,0.3,6.3,0.0 +2,2019-01-08 21:20:59,2019-01-08 21:26:27,6,0.91,1,N,164,162,2,5.5,0.5,0.5,0.0,0.0,0.3,6.8, +2,2019-01-10 12:47:39,2019-01-10 12:58:24,6,2.64,1,N,197,63,1,10.0,0.0,0.5,0.0,0.0,0.3,10.8, +2,2019-01-05 14:20:33,2019-01-05 14:31:27,6,1.98,1,N,148,90,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76, +2,2019-01-09 08:58:59,2019-01-09 09:18:18,6,1.91,1,N,186,162,1,13.0,0.0,0.5,2.76,0.0,0.3,16.56, +2,2019-01-26 14:55:10,2019-01-26 15:06:05,6,1.65,1,N,238,236,1,9.5,0.0,0.5,2.58,0.0,0.3,12.88,0.0 +2,2019-01-11 14:05:15,2019-01-11 14:20:03,6,1.52,1,N,263,141,1,10.5,0.0,0.5,1.0,0.0,0.3,12.3, +2,2019-01-27 03:58:49,2019-01-27 04:05:27,6,1.34,1,N,260,82,1,7.0,0.5,0.5,2.08,0.0,0.3,10.38,0.0 +2,2019-01-24 22:59:58,2019-01-24 23:25:07,6,13.04,1,N,138,25,1,37.0,0.5,0.5,7.66,0.0,0.3,45.96,0.0 +2,2019-01-11 21:54:14,2019-01-11 22:12:35,6,2.72,1,N,233,143,1,13.5,0.5,0.5,2.22,0.0,0.3,17.02, +2,2019-01-20 17:17:31,2019-01-20 17:22:03,6,0.49,1,N,263,262,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-01 05:26:57,2019-01-01 05:48:03,6,15.37,1,N,132,112,1,41.5,0.5,0.5,10.7,0.0,0.3,53.5, +2,2019-01-17 08:11:38,2019-01-17 08:45:35,6,11.91,1,N,236,265,1,39.0,0.0,0.5,5.0,2.64,0.3,47.44, +2,2019-01-17 07:23:24,2019-01-17 07:27:42,6,0.91,1,N,143,48,1,5.0,0.0,0.5,1.0,0.0,0.3,6.8, +2,2019-01-20 19:06:02,2019-01-20 19:09:11,6,1.17,1,N,140,262,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8, +2,2019-01-23 16:48:40,2019-01-23 16:54:20,6,0.77,1,N,231,114,1,5.5,1.0,0.5,1.46,0.0,0.3,8.76,0.0 +2,2019-01-09 03:46:51,2019-01-09 04:11:50,6,8.59,1,N,161,168,1,27.5,0.5,0.5,4.2,0.0,0.3,33.0, +2,2019-01-04 22:13:52,2019-01-04 22:23:11,6,2.21,1,N,68,142,2,9.0,0.5,0.5,0.0,0.0,0.3,10.3, +2,2019-01-23 15:06:18,2019-01-23 15:11:29,6,0.53,1,N,162,163,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-22 14:03:27,2019-01-22 14:07:14,6,0.3,1,N,236,237,1,4.5,0.0,0.5,2.0,0.0,0.3,7.3,0.0 +2,2019-01-22 12:02:04,2019-01-22 12:18:32,6,1.09,1,N,264,264,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 +2,2019-01-08 17:45:38,2019-01-08 17:55:23,6,1.39,1,N,161,141,2,8.0,1.0,0.5,0.0,0.0,0.3,9.8, +2,2019-01-24 14:58:18,2019-01-24 15:15:22,6,1.98,1,N,234,231,2,11.5,0.0,0.5,0.0,0.0,0.3,12.3,0.0 +2,2019-01-27 16:05:00,2019-01-27 16:48:09,6,11.83,1,N,163,138,1,39.5,0.0,0.5,8.0,5.76,0.3,54.06,0.0 +2,2019-01-11 23:49:37,2019-01-11 23:53:45,6,0.64,1,N,100,100,2,4.5,0.5,0.5,0.0,0.0,0.3,5.8, +2,2019-01-18 10:49:09,2019-01-18 10:58:24,6,0.62,1,N,161,164,2,7.0,0.0,0.5,0.0,0.0,0.3,7.8, +2,2019-01-08 19:50:10,2019-01-08 20:00:11,6,1.88,1,N,114,224,1,8.5,1.0,0.5,2.06,0.0,0.3,12.36, +2,2019-01-06 02:03:32,2019-01-06 02:07:04,6,0.73,1,N,249,158,1,4.5,0.5,0.5,1.16,0.0,0.3,6.96, +2,2019-01-18 19:21:43,2019-01-18 19:38:23,6,3.4,1,N,229,238,1,13.5,1.0,0.5,3.06,0.0,0.3,18.36, +2,2019-01-10 19:03:58,2019-01-10 19:22:39,6,2.11,1,N,236,142,1,12.5,1.0,0.5,3.58,0.0,0.3,17.88, +2,2019-01-20 10:32:32,2019-01-20 10:36:05,6,1.26,1,N,140,236,2,5.5,0.0,0.5,0.0,0.0,0.3,6.3, +2,2019-01-01 21:09:11,2019-01-01 21:16:13,6,1.53,1,N,142,237,1,7.5,0.5,0.5,1.76,0.0,0.3,10.56, +2,2019-01-15 08:42:23,2019-01-15 09:14:30,6,7.08,1,N,237,45,2,27.5,0.0,0.5,0.0,0.0,0.3,28.3, +2,2019-01-12 20:17:39,2019-01-12 20:25:03,6,1.12,1,N,100,164,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-28 21:13:27,2019-01-28 21:20:37,6,1.36,1,N,163,100,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-03 10:01:21,2019-01-03 10:07:30,6,0.93,1,N,100,234,1,6.0,0.0,0.5,1.36,0.0,0.3,8.16, +2,2019-01-25 07:39:55,2019-01-25 07:46:24,6,1.37,1,N,237,161,1,7.0,0.0,0.5,1.1,0.0,0.3,8.9,0.0 +2,2019-01-29 18:29:30,2019-01-29 18:32:22,6,0.35,1,N,237,237,2,4.0,1.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-05 18:21:02,2019-01-05 18:31:02,6,2.46,1,N,233,262,1,10.0,0.0,0.5,2.16,0.0,0.3,12.96, +2,2019-01-24 15:51:36,2019-01-24 16:11:28,6,2.61,1,N,140,238,1,13.5,0.0,0.5,3.58,0.0,0.3,17.88,0.0 +2,2019-01-30 01:46:50,2019-01-30 02:01:14,6,4.3,1,N,164,238,1,15.0,0.5,0.5,3.26,0.0,0.3,19.56,0.0 +2,2019-01-22 15:13:04,2019-01-22 15:17:38,6,0.59,1,N,79,79,2,5.0,0.0,0.5,0.0,0.0,0.3,5.8,0.0 +2,2019-01-26 04:39:23,2019-01-26 04:57:14,6,7.43,1,N,246,42,1,23.0,0.5,0.5,4.86,0.0,0.3,29.16,0.0 +2,2019-01-10 18:01:38,2019-01-10 18:07:45,6,0.71,1,N,246,158,1,6.0,1.0,0.5,1.56,0.0,0.3,9.36, +2,2019-01-25 13:28:50,2019-01-25 13:40:05,6,1.17,1,N,209,148,1,9.0,0.0,0.5,1.96,0.0,0.3,11.76,0.0 +2,2019-01-12 22:02:28,2019-01-12 22:26:32,6,15.2,1,N,138,261,2,41.0,0.5,0.5,0.0,5.76,0.3,48.06, +2,2019-01-25 12:38:43,2019-01-25 12:46:36,6,0.98,1,N,161,164,1,7.0,0.0,0.5,1.56,0.0,0.3,9.36,0.0 +2,2019-01-22 11:50:53,2019-01-22 11:59:02,6,1.44,1,N,162,263,2,7.5,0.0,0.5,0.0,0.0,0.3,8.3,0.0 +2,2019-01-28 22:11:02,2019-01-28 22:25:00,6,3.34,1,N,144,161,1,13.0,0.5,0.5,2.0,0.0,0.3,16.3,0.0 +2,2019-01-20 12:33:28,2019-01-20 12:49:26,6,5.18,2,N,194,239,1,52.0,0.0,0.5,14.64,5.76,0.3,73.2, +2,2019-01-12 12:58:14,2019-01-12 13:00:35,6,0.46,1,N,68,90,1,3.5,0.0,0.5,0.86,0.0,0.3,5.16, +2,2019-01-06 10:47:27,2019-01-06 10:48:48,6,0.47,1,N,75,263,1,3.5,0.0,0.5,0.7,0.0,0.3,5.0, +2,2019-01-25 07:17:05,2019-01-25 07:24:43,6,1.32,1,N,48,143,1,7.0,0.0,0.5,0.0,0.0,0.3,7.8,0.0 +2,2019-01-21 17:11:15,2019-01-21 17:31:17,6,2.68,1,N,230,237,2,14.0,0.0,0.5,0.0,0.0,0.3,14.8,0.0 +2,2019-01-03 00:52:04,2019-01-03 01:08:21,6,6.69,1,N,79,195,1,21.0,0.5,0.5,4.46,0.0,0.3,26.76, +2,2019-01-16 22:35:05,2019-01-16 22:42:13,6,1.08,1,N,163,229,1,6.5,0.5,0.5,1.56,0.0,0.3,9.36, +2,2019-01-23 15:23:07,2019-01-23 15:38:24,6,1.38,1,N,237,262,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3,0.0 +2,2019-01-12 05:02:54,2019-01-12 05:30:28,6,13.29,1,N,132,181,1,38.0,0.5,0.5,7.86,0.0,0.3,47.16, +2,2019-01-04 15:47:23,2019-01-04 16:11:55,6,5.13,1,N,68,262,1,21.0,0.0,0.5,4.36,0.0,0.3,26.16, +2,2019-01-05 09:43:12,2019-01-05 09:54:31,6,2.26,1,N,161,239,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-16 05:59:50,2019-01-16 06:08:24,6,1.68,1,N,141,239,1,8.5,0.0,0.5,1.86,0.0,0.3,11.16, +2,2019-01-29 18:11:17,2019-01-29 18:19:11,6,1.89,1,N,158,13,1,8.5,1.0,0.5,2.0,0.0,0.3,12.3,0.0 +2,2019-01-01 06:04:41,2019-01-01 06:18:51,6,3.42,1,N,75,159,2,13.5,0.5,0.5,0.0,0.0,0.3,14.8, +2,2019-01-18 21:41:39,2019-01-18 21:54:52,6,1.29,1,N,48,234,1,9.0,0.5,0.5,2.06,0.0,0.3,12.36, +2,2019-01-19 21:18:10,2019-01-19 21:28:27,6,1.3,1,N,162,137,2,8.0,0.5,0.5,0.0,0.0,0.3,9.3, +2,2019-01-27 12:26:10,2019-01-27 12:39:28,6,1.68,1,N,162,186,1,10.0,0.0,0.5,1.08,0.0,0.3,11.88,0.0 +2,2019-01-15 23:31:52,2019-01-15 23:42:38,6,3.15,1,N,237,107,1,11.0,0.5,0.5,2.46,0.0,0.3,14.76, +2,2019-01-01 15:30:19,2019-01-01 15:33:06,6,0.9,1,N,90,114,2,4.5,0.0,0.5,0.0,0.0,0.3,5.3, +2,2019-01-07 18:42:03,2019-01-07 18:49:31,6,1.34,1,N,161,237,1,7.0,1.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-29 09:29:11,2019-01-29 09:37:36,6,1.35,1,N,236,141,1,7.5,0.0,0.5,1.66,0.0,0.3,9.96,0.0 +2,2019-01-14 07:05:29,2019-01-14 07:13:58,6,1.84,1,N,107,68,1,8.0,0.0,0.5,1.76,0.0,0.3,10.56, +2,2019-01-26 16:25:18,2019-01-26 16:35:17,6,1.6,1,N,142,48,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3,0.0 +2,2019-01-09 21:16:22,2019-01-09 21:18:43,6,0.41,1,N,237,237,1,3.5,0.5,0.5,1.2,0.0,0.3,6.0, +2,2019-01-27 23:08:59,2019-01-27 23:13:47,6,0.9,1,N,107,170,1,5.5,0.5,0.5,0.8,0.0,0.3,7.6,0.0 +2,2019-01-10 19:15:45,2019-01-10 19:36:40,6,2.84,1,N,65,181,2,14.5,1.0,0.5,0.0,0.0,0.3,16.3, +2,2019-01-11 15:56:23,2019-01-11 16:10:51,6,1.87,1,N,163,170,1,10.5,0.0,0.5,2.26,0.0,0.3,13.56, +2,2019-01-17 17:31:25,2019-01-17 17:35:41,6,0.85,1,N,238,151,1,5.5,1.0,0.5,0.95,0.0,0.3,8.25, +2,2019-01-25 11:05:14,2019-01-25 11:12:56,6,1.03,1,N,88,13,1,7.0,0.0,0.5,1.95,0.0,0.3,9.75,0.0 +2,2019-01-03 10:53:07,2019-01-03 11:13:25,6,8.72,1,N,138,162,1,26.0,0.0,0.5,6.51,5.76,0.3,39.07, +2,2019-01-22 22:27:37,2019-01-22 22:38:24,6,1.85,1,N,142,229,1,9.0,0.5,0.5,5.15,0.0,0.3,15.45,0.0 +2,2019-01-23 14:29:43,2019-01-23 15:19:03,6,11.67,1,N,161,138,1,41.5,0.0,0.5,9.61,5.76,0.3,57.67,0.0 +2,2019-01-19 11:07:24,2019-01-19 11:34:09,6,4.51,1,N,262,262,1,20.0,0.0,0.5,4.16,0.0,0.3,24.96, +2,2019-01-29 23:44:10,2019-01-29 23:50:41,6,1.77,1,N,50,186,1,7.5,0.5,0.5,1.0,0.0,0.3,9.8,0.0 +2,2019-01-16 21:58:29,2019-01-16 22:05:07,6,1.56,1,N,186,113,1,7.0,0.5,0.5,1.66,0.0,0.3,9.96, +2,2019-01-19 01:10:06,2019-01-19 01:19:10,6,1.61,1,N,114,137,1,8.5,0.5,0.5,1.96,0.0,0.3,11.76, +2,2019-01-11 16:09:01,2019-01-11 16:24:56,6,1.92,1,N,230,141,1,11.5,1.0,0.5,3.99,0.0,0.3,17.29, +2,2019-01-02 11:15:25,2019-01-02 11:28:20,6,2.4,1,N,164,211,2,10.5,0.0,0.5,0.0,0.0,0.3,11.3, +2,2019-01-02 07:48:44,2019-01-02 08:00:13,6,1.07,1,N,50,161,2,8.5,0.0,0.5,0.0,0.0,0.3,9.3, +2,2019-01-16 19:06:45,2019-01-16 19:10:05,6,0.35,1,N,234,234,1,4.0,1.0,0.5,1.16,0.0,0.3,6.96, +2,2019-01-02 09:10:44,2019-01-02 09:36:46,6,4.12,1,N,50,236,1,20.0,0.0,0.5,6.24,0.0,0.3,27.04, +2,2019-01-03 13:28:36,2019-01-03 13:36:42,6,1.17,1,N,137,234,1,7.0,0.0,0.5,0.9,0.0,0.3,8.7, +2,2019-01-26 17:48:59,2019-01-26 18:03:01,6,2.44,1,N,161,236,2,11.0,0.0,0.5,0.0,0.0,0.3,11.8,0.0 diff --git a/docs/examples/greatexpectations_plugin/great_expectations/expectations/sqlite/movies.json b/docs/examples/greatexpectations_plugin/great_expectations/expectations/sqlite/movies.json new file mode 100644 index 000000000..db2dc48b8 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/great_expectations/expectations/sqlite/movies.json @@ -0,0 +1,122 @@ +{ + "data_asset_type": "Dataset", + "expectation_suite_name": "movies", + "expectations": [ + { + "expectation_type": "expect_table_row_count_to_be_between", + "kwargs": { + "min_value": 2000 + }, + "meta": {} + }, + { + "expectation_type": "expect_table_column_count_to_equal", + "kwargs": { + "value": 6 + }, + "meta": {} + }, + { + "expectation_type": "expect_table_columns_to_match_ordered_list", + "kwargs": { + "column_list": [ + "id", + "name", + "release_date", + "country", + "publisher", + "agent" + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_not_be_null", + "kwargs": { + "column": "release_date" + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_between", + "kwargs": { + "column": "release_date", + "min_value": "2015-01-01", + "mostly": 0.95, + "parse_strings_as_datetimes": true + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_not_be_null", + "kwargs": { + "column": "country" + }, + "meta": {} + }, + { + "expectation_type": "expect_column_value_lengths_to_be_between", + "kwargs": { + "column": "country", + "max_value": 10, + "min_value": 2 + }, + "meta": {} + } + ], + "meta": { + "BasicSuiteBuilderProfiler": { + "batch_kwargs": { + "data_asset_name": "main.movies", + "datasource": "my_database", + "schema": "main", + "table": "movies" + }, + "created_at": 1601566174.753483, + "created_by": "BasicSuiteBuilderProfiler" + }, + "citations": [ + { + "batch_kwargs": { + "data_asset_name": "main.movies", + "datasource": "my_database", + "schema": "main", + "table": "movies" + }, + "batch_markers": { + "ge_load_time": "20201001T152934.728018Z" + }, + "batch_parameters": null, + "citation_date": "20201001T152934.776045Z", + "comment": "BasicSuiteBuilderProfiler added a citation based on the current batch." + } + ], + "columns": { + "agent": { + "description": "" + }, + "country": { + "description": "" + }, + "id": { + "description": "" + }, + "name": { + "description": "" + }, + "publisher": { + "description": "" + }, + "release_date": { + "description": "" + } + }, + "great_expectations_version": "0.12.3", + "notes": { + "content": [ + "_To add additional notes, edit the meta.notes.content field in the appropriate Expectation json file._" + ], + "format": "markdown" + } + } + } diff --git a/docs/examples/greatexpectations_plugin/great_expectations/expectations/test/demo.json b/docs/examples/greatexpectations_plugin/great_expectations/expectations/test/demo.json new file mode 100644 index 000000000..ec60d45d2 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/great_expectations/expectations/test/demo.json @@ -0,0 +1,445 @@ +{ + "data_asset_type": null, + "expectation_suite_name": "taxi.demo", + "expectations": [ + { + "expectation_type": "expect_table_columns_to_match_ordered_list", + "kwargs": { + "column_list": [ + "vendor_id", + "pickup_datetime", + "dropoff_datetime", + "passenger_count", + "trip_distance", + "rate_code_id", + "store_and_fwd_flag", + "pickup_location_id", + "dropoff_location_id", + "payment_type", + "fare_amount", + "extra", + "mta_tax", + "tip_amount", + "tolls_amount", + "improvement_surcharge", + "total_amount", + "congestion_surcharge" + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_table_row_count_to_be_between", + "kwargs": { + "max_value": 10000, + "min_value": 10000 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_min_to_be_between", + "kwargs": { + "column": "passenger_count", + "max_value": 1, + "min_value": 1 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_max_to_be_between", + "kwargs": { + "column": "passenger_count", + "max_value": 6, + "min_value": 6 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_mean_to_be_between", + "kwargs": { + "column": "passenger_count", + "max_value": 1.5716, + "min_value": 1.5716 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_median_to_be_between", + "kwargs": { + "column": "passenger_count", + "max_value": 1.0, + "min_value": 1.0 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_quantile_values_to_be_between", + "kwargs": { + "allow_relative_error": "lower", + "column": "passenger_count", + "quantile_ranges": { + "quantiles": [ + 0.05, + 0.25, + 0.5, + 0.75, + 0.95 + ], + "value_ranges": [ + [ + 1, + 1 + ], + [ + 1, + 1 + ], + [ + 1, + 1 + ], + [ + 2, + 2 + ], + [ + 5, + 5 + ] + ] + } + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_in_set", + "kwargs": { + "column": "passenger_count", + "value_set": [ + 1, + 2, + 3, + 4, + 5, + 6 + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_not_be_null", + "kwargs": { + "column": "passenger_count" + }, + "meta": {} + }, + { + "expectation_type": "expect_column_proportion_of_unique_values_to_be_between", + "kwargs": { + "column": "passenger_count", + "max_value": 0.0006, + "min_value": 0.0006 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_in_type_list", + "kwargs": { + "column": "passenger_count", + "type_list": [ + "INTEGER", + "integer", + "int", + "int_", + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "INT", + "TINYINT", + "BYTEINT", + "SMALLINT", + "BIGINT", + "IntegerType", + "LongType", + "DECIMAL" + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_column_min_to_be_between", + "kwargs": { + "column": "trip_distance", + "max_value": 0.0, + "min_value": 0.0 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_max_to_be_between", + "kwargs": { + "column": "trip_distance", + "max_value": 57.95, + "min_value": 57.95 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_mean_to_be_between", + "kwargs": { + "column": "trip_distance", + "max_value": 2.7589909999999995, + "min_value": 2.7589909999999995 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_median_to_be_between", + "kwargs": { + "column": "trip_distance", + "max_value": 1.53, + "min_value": 1.53 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_quantile_values_to_be_between", + "kwargs": { + "allow_relative_error": "lower", + "column": "trip_distance", + "quantile_ranges": { + "quantiles": [ + 0.05, + 0.25, + 0.5, + 0.75, + 0.95 + ], + "value_ranges": [ + [ + 0.45, + 0.45 + ], + [ + 0.9, + 0.9 + ], + [ + 1.53, + 1.53 + ], + [ + 2.79, + 2.79 + ], + [ + 10.31, + 10.31 + ] + ] + } + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_not_be_null", + "kwargs": { + "column": "trip_distance" + }, + "meta": {} + }, + { + "expectation_type": "expect_column_proportion_of_unique_values_to_be_between", + "kwargs": { + "column": "trip_distance", + "max_value": 0.1184, + "min_value": 0.1184 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_in_type_list", + "kwargs": { + "column": "trip_distance", + "type_list": [ + "FLOAT", + "DOUBLE", + "FLOAT4", + "FLOAT8", + "DOUBLE_PRECISION", + "NUMERIC", + "FloatType", + "DoubleType", + "float_", + "float16", + "float32", + "float64", + "number", + "DECIMAL" + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_column_min_to_be_between", + "kwargs": { + "column": "rate_code_id", + "max_value": 1, + "min_value": 1 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_max_to_be_between", + "kwargs": { + "column": "rate_code_id", + "max_value": 99, + "min_value": 99 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_mean_to_be_between", + "kwargs": { + "column": "rate_code_id", + "max_value": 1.0623, + "min_value": 1.0623 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_median_to_be_between", + "kwargs": { + "column": "rate_code_id", + "max_value": 1.0, + "min_value": 1.0 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_quantile_values_to_be_between", + "kwargs": { + "allow_relative_error": "lower", + "column": "rate_code_id", + "quantile_ranges": { + "quantiles": [ + 0.05, + 0.25, + 0.5, + 0.75, + 0.95 + ], + "value_ranges": [ + [ + 1, + 1 + ], + [ + 1, + 1 + ], + [ + 1, + 1 + ], + [ + 1, + 1 + ], + [ + 1, + 1 + ] + ] + } + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_in_set", + "kwargs": { + "column": "rate_code_id", + "value_set": [ + 1, + 2, + 3, + 4, + 5, + 6, + 99 + ] + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_not_be_null", + "kwargs": { + "column": "rate_code_id" + }, + "meta": {} + }, + { + "expectation_type": "expect_column_proportion_of_unique_values_to_be_between", + "kwargs": { + "column": "rate_code_id", + "max_value": 0.0007, + "min_value": 0.0007 + }, + "meta": {} + }, + { + "expectation_type": "expect_column_values_to_be_in_type_list", + "kwargs": { + "column": "rate_code_id", + "type_list": [ + "INTEGER", + "integer", + "int", + "int_", + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "INT", + "TINYINT", + "BYTEINT", + "SMALLINT", + "BIGINT", + "IntegerType", + "LongType", + "DECIMAL" + ] + }, + "meta": {} + } + ], + "meta": { + "citations": [ + { + "batch_definition": null, + "batch_kwargs": null, + "batch_markers": null, + "batch_parameters": null, + "batch_request": { + "data_asset_name": "yellow_tripdata_sample_2019-01.csv", + "data_connector_name": "data_example_data_connector", + "datasource_name": "data", + "limit": 1000 + }, + "batch_spec": null, + "citation_date": "2021-05-28T06:21:41.943214Z", + "comment": "Created suite added via CLI" + } + ], + "great_expectations_version": "0.13.19" + } +} diff --git a/docs/examples/greatexpectations_plugin/great_expectations/great_expectations.yml b/docs/examples/greatexpectations_plugin/great_expectations/great_expectations.yml new file mode 100644 index 000000000..21f432a02 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/great_expectations/great_expectations.yml @@ -0,0 +1,143 @@ +# Welcome to Great Expectations! Always know what to expect from your data. +# +# Here you can define datasources, batch kwargs generators, integrations and +# more. This file is intended to be committed to your repo. For help with +# configuration please: +# - Read our docs: https://docs.greatexpectations.io/en/latest/reference/spare_parts/data_context_reference.html#configuration +# - Join our slack channel: http://greatexpectations.io/slack + +# config_version refers to the syntactic version of this config file, and is used in maintaining backwards compatibility +# It is auto-generated and usually does not need to be changed. +config_version: 3.0 + +# Datasources tell Great Expectations where your data lives and how to get it. +# You can use the CLI command `great_expectations datasource new` to help you +# add a new datasource. Read more at https://docs.greatexpectations.io/en/latest/reference/core_concepts/datasource.html +datasources: + data: + module_name: great_expectations.datasource + execution_engine: + module_name: great_expectations.execution_engine + class_name: PandasExecutionEngine + class_name: Datasource + data_connectors: + data_example_data_connector: + base_directory: ../data + class_name: InferredAssetFilesystemDataConnector + default_regex: + group_names: + - data_asset_name + pattern: (.*) + module_name: great_expectations.datasource.data_connector + data_flytetype_data_connector: + base_directory: /tmp + class_name: InferredAssetFilesystemDataConnector + default_regex: + group_names: + - data_asset_name + pattern: (.*) + module_name: great_expectations.datasource.data_connector + my_data_connector: + base_directory: ../data + class_name: ConfiguredAssetFilesystemDataConnector + module_name: great_expectations.datasource.data_connector + assets: + my_assets: + module_name: great_expectations.datasource.data_connector.asset + pattern: ^([a-z_]+)(\d+)-(\d+)\.csv + class_name: Asset + group_names: + - name + - year + - month + glob_directive: '*.csv' + my_pandas_datasource: + execution_engine: + module_name: great_expectations.execution_engine + class_name: PandasExecutionEngine + module_name: great_expectations.datasource + class_name: Datasource + data_connectors: + my_runtime_data_connector: + class_name: RuntimeDataConnector + batch_identifiers: + - pipeline_stage +# This config file supports variable substitution which enables: 1) keeping +# secrets out of source control & 2) environment-based configuration changes +# such as staging vs prod. +# +# When GE encounters substitution syntax (like `my_key: ${my_value}` or +# `my_key: $my_value`) in the great_expectations.yml file, it will attempt +# to replace the value of `my_key` with the value from an environment +# variable `my_value` or a corresponding key read from this config file, +# which is defined through the `config_variables_file_path`. +# Environment variables take precedence over variables defined here. +# +# Substitution values defined here can be a simple (non-nested) value, +# nested value such as a dictionary, or an environment variable (i.e. ${ENV_VAR}) +# +# +# https://docs.greatexpectations.io/en/latest/guides/how_to_guides/configuring_data_contexts/how_to_use_a_yaml_file_or_environment_variables_to_populate_credentials.html + +config_variables_file_path: uncommitted/config_variables.yml + +# The plugins_directory will be added to your python path for custom modules +# used to override and extend Great Expectations. +plugins_directory: plugins/ + +stores: +# Stores are configurable places to store things like Expectations, Validations +# Data Docs, and more. These are for advanced users only - most users can simply +# leave this section alone. +# +# Three stores are required: expectations, validations, and +# evaluation_parameters, and must exist with a valid store entry. Additional +# stores can be configured for uses such as data_docs, etc. + expectations_store: + class_name: ExpectationsStore + store_backend: + class_name: TupleFilesystemStoreBackend + base_directory: expectations/ + + validations_store: + class_name: ValidationsStore + store_backend: + class_name: TupleFilesystemStoreBackend + base_directory: uncommitted/validations/ + + evaluation_parameter_store: + # Evaluation Parameters enable dynamic expectations. Read more here: + # https://docs.greatexpectations.io/en/latest/reference/core_concepts/evaluation_parameters.html + class_name: EvaluationParameterStore + + checkpoint_store: + class_name: CheckpointStore + store_backend: + class_name: TupleFilesystemStoreBackend + suppress_store_backend_id: true + base_directory: checkpoints/ + +expectations_store_name: expectations_store +validations_store_name: validations_store +evaluation_parameter_store_name: evaluation_parameter_store +checkpoint_store_name: checkpoint_store + +data_docs_sites: + # Data Docs make it simple to visualize data quality in your project. These + # include Expectations, Validations & Profiles. The are built for all + # Datasources from JSON artifacts in the local repo including validations & + # profiles from the uncommitted directory. Read more at https://docs.greatexpectations.io/en/latest/reference/core_concepts/data_docs.html + local_site: + class_name: SiteBuilder + # set to false to hide how-to buttons in Data Docs + show_how_to_buttons: true + store_backend: + class_name: TupleFilesystemStoreBackend + base_directory: uncommitted/data_docs/local_site/ + site_index_builder: + class_name: DefaultSiteIndexBuilder + +anonymous_usage_statistics: + enabled: true + data_context_id: 2f8dcd42-bb6b-4b9f-9ee6-1a7dd1cdb869 +notebooks: diff --git a/docs/examples/greatexpectations_plugin/great_expectations/plugins/custom_data_docs/styles/data_docs_custom_styles.css b/docs/examples/greatexpectations_plugin/great_expectations/plugins/custom_data_docs/styles/data_docs_custom_styles.css new file mode 100644 index 000000000..8bf5a1521 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/great_expectations/plugins/custom_data_docs/styles/data_docs_custom_styles.css @@ -0,0 +1,22 @@ +/*index page*/ +.ge-index-page-site-name-title {} +.ge-index-page-table-container {} +.ge-index-page-table {} +.ge-index-page-table-profiling-links-header {} +.ge-index-page-table-expectations-links-header {} +.ge-index-page-table-validations-links-header {} +.ge-index-page-table-profiling-links-list {} +.ge-index-page-table-profiling-links-item {} +.ge-index-page-table-expectation-suite-link {} +.ge-index-page-table-validation-links-list {} +.ge-index-page-table-validation-links-item {} + +/*breadcrumbs*/ +.ge-breadcrumbs {} +.ge-breadcrumbs-item {} + +/*navigation sidebar*/ +.ge-navigation-sidebar-container {} +.ge-navigation-sidebar-content {} +.ge-navigation-sidebar-title {} +.ge-navigation-sidebar-link {} diff --git a/docs/examples/greatexpectations_plugin/greatexpectations_plugin/__init__.py b/docs/examples/greatexpectations_plugin/greatexpectations_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/greatexpectations_plugin/greatexpectations_plugin/task_example.py b/docs/examples/greatexpectations_plugin/greatexpectations_plugin/task_example.py new file mode 100644 index 000000000..8d0dc5a16 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/greatexpectations_plugin/task_example.py @@ -0,0 +1,231 @@ +# %% [markdown] +# # Task Example +# +# `GreatExpectationsTask` can be used to define data validation within the task. +# In this example, we'll implement a simple task, followed by Great Expectations data validation on `FlyteFile`, `FlyteSchema`, and finally, +# the `RuntimeBatchRequest`. +# +# The following video shows the inner workings of the Great Expectations plugin, plus a demo of the task example. +# +# ```{eval-rst} +# .. youtube:: wjiO7jassrw +# +# ``` + +# %% [markdown] +# First, let's import the required libraries. +# %% +import os +import typing + +import pandas as pd +from flytekit import Resources, kwtypes, task, workflow +from flytekit.extras.sqlite3.task import SQLite3Config, SQLite3Task +from flytekit.types.file import CSVFile +from flytekit.types.schema import FlyteSchema +from flytekitplugins.great_expectations import BatchRequestConfig, GreatExpectationsTask + +# %% [markdown] +# :::{note} +# `BatchRequestConfig` is useful in giving additional batch request parameters to construct +# both Great Expectations' `RuntimeBatchRequest` and `BatchRequest`. +# ::: + +# %% [markdown] +# Next, we define variables that we use throughout the code. +# %% +CONTEXT_ROOT_DIR = "greatexpectations/great_expectations" +DATASET_LOCAL = "yellow_tripdata_sample_2019-01.csv" +DATASET_REMOTE = f"https://raw.githubusercontent.com/superconductive/ge_tutorials/main/data/{DATASET_LOCAL}" +SQLITE_DATASET = "https://cdn.discordapp.com/attachments/545481172399030272/867254085426085909/movies.sqlite" + + +# %% [markdown] +# ## Simple Task +# +# We define a `GreatExpectationsTask` that validates a CSV file. This does pandas data validation. +# %% +simple_task_object = GreatExpectationsTask( + name="great_expectations_task_simple", + datasource_name="data", + inputs=kwtypes(dataset=str), + expectation_suite_name="test.demo", + data_connector_name="data_example_data_connector", + context_root_dir=CONTEXT_ROOT_DIR, +) + +# %% [markdown] +# Next, we define a task that validates the data before returning the shape of the DataFrame. +# %% +@task(limits=Resources(mem="500Mi")) +def simple_task(csv_file: str) -> int: + # GreatExpectationsTask returns Great Expectations' checkpoint result. + # You can print the result to know more about the data within it. + # If the data validation fails, this will return a ValidationError. + result = simple_task_object(dataset=csv_file) + print(result) + df = pd.read_csv(os.path.join("greatexpectations", "data", csv_file)) + return df.shape[0] + + +# %% [markdown] +# Finally, we define a workflow. +# %% +@workflow +def simple_wf(dataset: str = DATASET_LOCAL) -> int: + return simple_task(csv_file=dataset) + + +# %% [markdown] +# ## FlyteFile +# +# We define a `GreatExpectationsTask` that validates a `FlyteFile`. +# Here, we're using a different data connector owing to the different `base_directory` we're using within the Great Expectations config file. +# The `local_file_path` argument helps in copying the remote file to the user-given path. +# +# :::{note} +# `local_file_path`'s directory and `base_directory` in Great Expectations config ought to be the same. +# ::: +# %% +file_task_object = GreatExpectationsTask( + name="great_expectations_task_flytefile", + datasource_name="data", + inputs=kwtypes(dataset=CSVFile), + expectation_suite_name="test.demo", + data_connector_name="data_flytetype_data_connector", + local_file_path="/tmp", + context_root_dir=CONTEXT_ROOT_DIR, +) + +# %% [markdown] +# Next, we define a task that calls the validation logic. +# %% +@task(limits=Resources(mem="500Mi")) +def file_task( + dataset: CSVFile, +) -> int: + file_task_object(dataset=dataset) + return len(pd.read_csv(dataset)) + + +# %% [markdown] +# Finally, we define a workflow to run our task. +# %% +@workflow +def file_wf( + dataset: CSVFile = DATASET_REMOTE, +) -> int: + return file_task(dataset=dataset) + + +# %% [markdown] +# ## FlyteSchema +# +# We define a `GreatExpectationsTask` that validates FlyteSchema. +# The `local_file_path` here refers to the parquet file in which we want to store our DataFrame. +# %% +schema_task_object = GreatExpectationsTask( + name="great_expectations_task_schema", + datasource_name="data", + inputs=kwtypes(dataset=FlyteSchema), + expectation_suite_name="sqlite.movies", + data_connector_name="data_flytetype_data_connector", + local_file_path="/tmp/test.parquet", + context_root_dir=CONTEXT_ROOT_DIR, +) + +# %% [markdown] +# Let's fetch the DataFrame from the SQL Database we've with us. To do so, we use the `SQLite3Task` available within Flyte. +# %% +sql_to_df = SQLite3Task( + name="greatexpectations.task.sqlite3", + query_template="select * from movies", + output_schema_type=FlyteSchema, + task_config=SQLite3Config(uri=SQLITE_DATASET), +) + +# %% [markdown] +# Next, we define a task that validates the data and returns the columns in it. +# %% +@task(limits=Resources(mem="500Mi")) +def schema_task(dataset: pd.DataFrame) -> typing.List[str]: + schema_task_object(dataset=dataset) + return list(dataset.columns) + + +# %% [markdown] +# Finally, we define a workflow to fetch the DataFrame and validate it. +# %% +@workflow +def schema_wf() -> typing.List[str]: + df = sql_to_df() + return schema_task(dataset=df) + + +# %% [markdown] +# ## RuntimeBatchRequest +# +# The `RuntimeBatchRequest` can wrap either an in-memory DataFrame, +# filepath, or SQL query, and must include batch identifiers that uniquely identify the data. +# +# Let's instantiate a `RuntimeBatchRequest` that accepts a DataFrame and thereby validates it. +# +# :::{note} +# The plugin determines the type of request as `RuntimeBatchRequest` by analyzing the user-given data connector. +# ::: +# +# We give `data_asset_name` to associate it with the `RuntimeBatchRequest`. +# The typical Great Expectations' `batch_data` (or) `query` is automatically populated with the dataset. +# +# :::{note} +# If you want to load a database table as a batch, your dataset has to be a SQL query. +# ::: +# %% +runtime_task_obj = GreatExpectationsTask( + name="greatexpectations.task.runtime", + datasource_name="my_pandas_datasource", + inputs=kwtypes(dataframe=FlyteSchema), + expectation_suite_name="test.demo", + data_connector_name="my_runtime_data_connector", + data_asset_name="validate_pandas_data", + task_config=BatchRequestConfig( + batch_identifiers={ + "pipeline_stage": "validation", + }, + ), + context_root_dir=CONTEXT_ROOT_DIR, +) + + +# %% [markdown] +# We define a task to generate DataFrame from the CSV file. +# %% +@task +def runtime_to_df_task(csv_file: str) -> pd.DataFrame: + df = pd.read_csv(os.path.join("greatexpectations", "data", csv_file)) + return df + + +# %% [markdown] +# Finally, we define a workflow to run our task. +# %% +@workflow +def runtime_wf(dataset: str = DATASET_LOCAL) -> None: + dataframe = runtime_to_df_task(csv_file=dataset) + runtime_task_obj(dataframe=dataframe) + + +# %% [markdown] +# Lastly, this particular block of code helps us in running the code locally. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print("Simple Great Expectations Task...") + print(simple_wf()) + print("Great Expectations Task with FlyteFile...") + print(file_wf()) + print("Great Expectations Task with FlyteSchema...") + print(schema_wf()) + print("Great Expectations Task with RuntimeBatchRequest...") + runtime_wf() diff --git a/docs/examples/greatexpectations_plugin/greatexpectations_plugin/type_example.py b/docs/examples/greatexpectations_plugin/greatexpectations_plugin/type_example.py new file mode 100644 index 000000000..774d3c34b --- /dev/null +++ b/docs/examples/greatexpectations_plugin/greatexpectations_plugin/type_example.py @@ -0,0 +1,226 @@ +# %% [markdown] +# # Type Example +# +# `GreatExpectationsType` when accompanied with data can be used for data validation. +# It essentially is the type we attach to the data we want to validate. +# In this example, we'll implement a simple task, followed by Great Expectations data validation on `FlyteFile`, `FlyteSchema`, and finally, +# the `RuntimeBatchRequest`. The following video is a demo of the Great Expectations type example. +# +# ```{eval-rst} +# .. youtube:: pDFTt_g76Wc +# +# ``` + +# %% [markdown] +# First, let's import the required libraries. +# %% +import os + +import pandas as pd +from flytekit import Resources, task, workflow +from flytekit.types.file import CSVFile +from flytekit.types.schema import FlyteSchema +from flytekitplugins.great_expectations import BatchRequestConfig, GreatExpectationsFlyteConfig, GreatExpectationsType + +# %% [markdown] +# :::{note} +# `BatchRequestConfig` is useful in giving additional batch request parameters to construct both Great Expectations' +# `RuntimeBatchRequest` and `BatchRequest`. +# Moreover, there's `GreatExpectationsFlyteConfig` that encapsulates the essential initialization parameters of the plugin. +# ::: + +# %% [markdown] +# Next, we define variables that we use throughout the code. +# %% +DATASET_LOCAL = "yellow_tripdata_sample_2019-01.csv" +DATASET_REMOTE = ( + "https://raw.githubusercontent.com/superconductive/ge_tutorials/main/data/yellow_tripdata_sample_2019-01.csv" +) +CONTEXT_ROOT_DIR = "greatexpectations/great_expectations" + + +# %% [markdown] +# ## Simple Type +# +# We define a `GreatExpectationsType` that checks if the requested `batch_filter_parameters` can be used to fetch files from a directory. +# The directory that's being used is defined in `my_assets`. You can find `my_assets` in the Great Expectations config file. +# +# The parameters within the `data_connector_query` convey that we're fetching all those files that have "2019" and "01" in the file names. + +# %% +@task(limits=Resources(mem="500Mi")) +def simple_task( + directory: GreatExpectationsType[ + str, + GreatExpectationsFlyteConfig( + datasource_name="data", # noqa: F821 + expectation_suite_name="test.demo", # noqa: F821 + data_connector_name="my_data_connector", # noqa: F821 + batch_request_config=BatchRequestConfig( + data_connector_query={ + "batch_filter_parameters": { # noqa: F821 + "year": "2019", # noqa: F821 + "month": "01", # noqa: F821, F722 + }, + "limit": 10, # noqa: F821 + }, + ), + context_root_dir=CONTEXT_ROOT_DIR, + ), + ] +) -> str: + return f"Validation works for {directory}!" + + +# %% [markdown] +# Finally, we define a workflow to call our task. +# %% +@workflow +def simple_wf(directory: str = "my_assets") -> str: + return simple_task(directory=directory) + + +# %% [markdown] +# ## FlyteFile +# +# First, we define `GreatExpectationsFlyteConfig` to initialize all our parameters. Here, we're validating a `FlyteFile`. +# %% +great_expectations_config = GreatExpectationsFlyteConfig( + datasource_name="data", + expectation_suite_name="test.demo", + data_connector_name="data_flytetype_data_connector", + local_file_path="/tmp", + context_root_dir=CONTEXT_ROOT_DIR, +) + + +# %% [markdown] +# Next, we map `dataset` parameter to `GreatExpectationsType`. +# Under the hood, `GreatExpectationsType` validates data in accordance with the `GreatExpectationsFlyteConfig` defined previously. +# This `GreatExpectationsFlyteConfig` is being fetched under the name `great_expectations_config`. +# +# The first value that's being sent within `GreatExpectationsType` is `CSVFile` (this is a pre-formatted FlyteFile type). +# This means that we want to validate the `FlyteFile` data. + +# %% +@task(limits=Resources(mem="500Mi")) +def file_task(dataset: GreatExpectationsType[CSVFile, great_expectations_config]) -> pd.DataFrame: + return pd.read_csv(dataset) + + +# %% [markdown] +# Next, we define a workflow to call our task. +# %% +@workflow +def file_wf() -> pd.DataFrame: + return file_task(dataset=DATASET_REMOTE) + + +# %% [markdown] +# ## FlyteSchema +# +# We define a `GreatExpectationsType` to validate `FlyteSchema`. The `local_file_path` is where we would have our parquet file. +# +# :::{note} +# `local_file_path`'s directory and `base_directory` in Great Expectations config ought to be the same. +# ::: +# %% +@task(limits=Resources(mem="500Mi")) +def schema_task( + dataframe: GreatExpectationsType[ + FlyteSchema, # noqa: F821 + GreatExpectationsFlyteConfig( # noqa: F821 + datasource_name="data", # noqa: F821 + expectation_suite_name="test.demo", # noqa: F821 + data_connector_name="data_flytetype_data_connector", # noqa: F821 + batch_request_config=BatchRequestConfig(data_connector_query={"limit": 10}), # noqa : F841 # noqa: F821 + local_file_path="/tmp/test.parquet", # noqa: F722 + context_root_dir=CONTEXT_ROOT_DIR, + ), + ] +) -> int: + return dataframe.shape[0] + + +# %% [markdown] +# Finally, we define a workflow to call our task. +# We're using DataFrame returned by the `file_task` that we defined in the `FlyteFile` section. +# %% +@workflow +def schema_wf() -> int: + return schema_task(dataframe=file_wf()) + + +# %% [markdown] +# ## RuntimeBatchRequest +# +# The `RuntimeBatchRequest` can wrap either an in-memory DataFrame, +# filepath, or SQL query, and must include batch identifiers that uniquely identify the data. +# +# Let's instantiate a `RuntimeBatchRequest` that accepts a DataFrame and thereby validates it. +# +# :::{note} +# The plugin determines the type of request as `RuntimeBatchRequest` by analyzing the user-given data connector. +# ::: +# +# We instantiate `data_asset_name` to associate it with the `RuntimeBatchRequest`. +# The typical Great Expectations' batch_data (or) query is automatically populated with the dataset. +# +# :::{note} +# If you want to load a database table as a batch, your dataset has to be a SQL query. +# ::: +# %% +runtime_ge_config = GreatExpectationsFlyteConfig( + datasource_name="my_pandas_datasource", + expectation_suite_name="test.demo", + data_connector_name="my_runtime_data_connector", + data_asset_name="validate_pandas_data", + batch_request_config=BatchRequestConfig( + batch_identifiers={ + "pipeline_stage": "validation", + }, + ), + context_root_dir=CONTEXT_ROOT_DIR, +) + + +# %% [markdown] +# We define a task to generate DataFrame from the CSV file. +# %% +@task +def runtime_to_df_task(csv_file: str) -> pd.DataFrame: + df = pd.read_csv(os.path.join("greatexpectations", "data", csv_file)) + return df + + +# %% [markdown] +# We define a task to validate the DataFrame. +# %% +@task +def runtime_validation(dataframe: GreatExpectationsType[FlyteSchema, runtime_ge_config]) -> int: + return len(dataframe) + + +# %% [markdown] +# Finally, we define a workflow to run our tasks. +# %% +@workflow +def runtime_wf(dataset: str = DATASET_LOCAL) -> int: + dataframe = runtime_to_df_task(csv_file=dataset) + return runtime_validation(dataframe=dataframe) + + +# %% [markdown] +# Lastly, this particular block of code helps us in running the code locally. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print("Simple Great Expectations Type...") + print(simple_wf()) + print("Great Expectations Type with FlyteFile...") + print(file_wf()) + print("Great Expectations Type with FlyteSchema...") + print(schema_wf()) + print("Great Expectations Type with RuntimeBatchRequest...") + print(runtime_wf()) diff --git a/docs/examples/greatexpectations_plugin/requirements.in b/docs/examples/greatexpectations_plugin/requirements.in new file mode 100644 index 000000000..5c766bde0 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-great_expectations>=0.22.0 diff --git a/docs/examples/greatexpectations_plugin/requirements.txt b/docs/examples/greatexpectations_plugin/requirements.txt new file mode 100644 index 000000000..d4a6987f8 --- /dev/null +++ b/docs/examples/greatexpectations_plugin/requirements.txt @@ -0,0 +1,308 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +altair==4.2.0 + # via great-expectations +arrow==1.2.2 + # via jinja2-time +asttokens==2.0.5 + # via stack-data +attrs==21.4.0 + # via jsonschema +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit + # great-expectations +cloudpickle==2.0.0 + # via flytekit +colorama==0.4.4 + # via great-expectations +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==36.0.2 + # via + # great-expectations + # secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +entrypoints==0.4 + # via altair +executing==0.8.3 + # via stack-data +fastjsonschema==2.15.3 + # via nbformat +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-great-expectations +flytekitplugins-great-expectations==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +great-expectations==0.15.0 + # via flytekitplugins-great-expectations +greenlet==1.1.2 + # via sqlalchemy +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via + # great-expectations + # keyring +ipython==8.2.0 + # via great-expectations +jedi==0.18.1 + # via ipython +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.0.3 + # via + # altair + # cookiecutter + # great-expectations + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +jsonpatch==1.32 + # via great-expectations +jsonpointer==2.3 + # via jsonpatch +jsonschema==4.4.0 + # via + # altair + # great-expectations + # nbformat +jupyter-core==4.9.2 + # via nbformat +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +matplotlib-inline==0.1.3 + # via ipython +mistune==2.0.2 + # via great-expectations +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +nbformat==5.3.0 + # via great-expectations +numpy==1.22.3 + # via + # altair + # great-expectations + # matplotlib + # pandas + # pyarrow + # scipy +packaging==21.3 + # via + # great-expectations + # marshmallow + # matplotlib +pandas==1.4.2 + # via + # altair + # flytekit + # great-expectations +parso==0.8.3 + # via jedi +pexpect==4.8.0 + # via ipython +pickleshare==0.7.5 + # via ipython +pillow==9.1.0 + # via matplotlib +prompt-toolkit==3.0.29 + # via ipython +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pygments==2.11.2 + # via ipython +pyparsing==2.4.7 + # via + # great-expectations + # matplotlib + # packaging +pyrsistent==0.18.1 + # via jsonschema +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # great-expectations + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # great-expectations + # pandas +pytz-deprecation-shim==0.1.0.post0 + # via tzlocal +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # great-expectations + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +ruamel-yaml==0.17.17 + # via great-expectations +scipy==1.8.0 + # via great-expectations +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +sqlalchemy==1.4.35 + # via flytekitplugins-great-expectations +stack-data==0.2.0 + # via ipython +statsd==3.3.0 + # via flytekit +termcolor==1.1.0 + # via great-expectations +text-unidecode==1.3 + # via python-slugify +toolz==0.11.2 + # via altair +tqdm==4.64.0 + # via great-expectations +traitlets==5.1.1 + # via + # ipython + # jupyter-core + # matplotlib-inline + # nbformat +typing-extensions==4.1.1 + # via + # flytekit + # great-expectations + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +tzdata==2022.1 + # via pytz-deprecation-shim +tzlocal==4.2 + # via great-expectations +urllib3==1.26.9 + # via + # flytekit + # great-expectations + # requests + # responses +wcwidth==0.2.5 + # via prompt-toolkit +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/hive_plugin/README.md b/docs/examples/hive_plugin/README.md new file mode 100644 index 000000000..420a3a09c --- /dev/null +++ b/docs/examples/hive_plugin/README.md @@ -0,0 +1,28 @@ +# Hive + +```{eval-rst} +.. tags:: Integration, Data, Advanced +``` + +Flyte backend can be connected with various hive services. Once enabled it can allow you to query a hive service (e.g. Qubole) and retrieve typed schema (optionally). +This section will provide how to use the Hive Query Plugin using flytekit python + +## Installation + +To use the flytekit hive plugin simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-hive +``` + +## No Need of a dockerfile + +This plugin is purely a spec. Since SQL is completely portable there is no need to build a Docker container. + +% TODO: write a subsection for "Configuring the backend to get hive working" + +```{auto-examples-toc} +hive +``` diff --git a/docs/examples/hive_plugin/hive_plugin/__init__.py b/docs/examples/hive_plugin/hive_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/hive_plugin/hive_plugin/hive.py b/docs/examples/hive_plugin/hive_plugin/hive.py new file mode 100644 index 000000000..2a3ebb21f --- /dev/null +++ b/docs/examples/hive_plugin/hive_plugin/hive.py @@ -0,0 +1,109 @@ +# %% [markdown] +# # Hive Tasks +# +# Tasks often start with a data gathering step, and often that data is gathered through Hive. Flytekit allows users to run +# any kind of Hive query (including queries with multiple statements and staging query commands). +# +# The principal concept to understand with respect to Hive or any other query-engine based task is how Flyte interacts +# with the system. That is, when I write a query, how does Flyte become aware of the result? From the user's perspective, +# this is done by carefully constructing your query. +# +# When a Hive (or other querying) task runs, two things need to happen: a) The output data needs to be written to a place +# accessible to Flyte, and b) Flyte needs to know exactly what that location is. +# +# You get a couple templating args to help make that happen, along with the usual input interpolation that Flyte provides. +# +# - `.PerRetryUniqueKey` - This is a string that will be `[a-zA-Z0-9_]` and start with a character. It will be unique +# per retry. Feel free to use it to name temp tables. +# - `RawOutputDataPrefix` - This is the "directory" (S3/GCS output prefix) where Flyte will expect the outputs. You +# should write the outputs to this location. +# %% +from flytekit import kwtypes, task, workflow +from flytekit.types.schema import FlyteSchema +from flytekitplugins.hive import HiveConfig, HiveSelectTask, HiveTask + +# %% [markdown] +# This is the world's simplest query. Note that in order for registration to work properly, you'll need to give your +# Hive task a name that's unique across your project/domain for your Flyte installation. +# %% +hive_task_no_io = HiveTask( + name="sql.hive.no_io", + inputs={}, + task_config=HiveConfig(cluster_label="flyte"), + query_template=""" + select 1 + """, + output_schema_type=None, +) + + +@workflow +def no_io_wf(): + return hive_task_no_io() + + +# %% [markdown] +# This is a hive task that demonstrates how you would construct your typical read query. Note where the `select 1` is. +# %% +hive_task_w_out = HiveTask( + name="sql.hive.w_out", + inputs={}, + task_config=HiveConfig(cluster_label="flyte"), + query_template=""" + CREATE TEMPORARY TABLE {{ .PerRetryUniqueKey }}_tmp AS select 1; + CREATE EXTERNAL TABLE {{ .PerRetryUniqueKey }} LIKE {{ .PerRetryUniqueKey }}_tmp STORED AS PARQUET; + ALTER TABLE {{ .PerRetryUniqueKey }} SET LOCATION '{{ .RawOutputDataPrefix }}'; + INSERT OVERWRITE TABLE {{ .PerRetryUniqueKey }} + SELECT * + FROM {{ .PerRetryUniqueKey }}_tmp; + DROP TABLE {{ .PerRetryUniqueKey }}; + """, + output_schema_type=FlyteSchema, +) + + +# %% [markdown] +# :::{note} +# There is a helper task that will automatically do the wrapping above. Please be patient as we fill out these docs. +# ::: + +# %% +@workflow +def with_output_wf() -> FlyteSchema: + return hive_task_w_out() + + +# %% [markdown] +# This just demonstrates the things you can do. Note that when an input is a FlyteSchema, the value filled in will +# be the uri, i.e. where the data is stored. +# +# %% +demo_all = HiveSelectTask( + name="sql.hive.demo_all", + inputs=kwtypes(ds=str, earlier_schema=FlyteSchema), + task_config=HiveConfig(cluster_label="flyte"), + select_query=""" + SELECT '.PerRetryUniqueKey' as template_key, '{{ .PerRetryUniqueKey }}' as template_value + UNION + SELECT '.RawOutputDataPrefix' as template_key, '{{ .RawOutputDataPrefix }}' as template_value + UNION + SELECT '.inputs.earlier_schema' as template_key, '{{ .inputs.earlier_schema }}' as template_value + UNION + SELECT '.inputs.ds' as template_key, '{{ .inputs.ds }}' as template_value + """, + output_schema_type=FlyteSchema, +) + + +@task +def print_schema(s: FlyteSchema): + df = s.open().all() + print(df.to_markdown()) + + +@workflow +def full_hive_demo_wf() -> FlyteSchema: + s = hive_task_w_out() + demo_schema = demo_all(ds="2020-01-01", earlier_schema=s) + print_schema(s=demo_schema) + return demo_schema diff --git a/docs/examples/house_price_prediction/Dockerfile b/docs/examples/house_price_prediction/Dockerfile new file mode 100644 index 000000000..ed26329d4 --- /dev/null +++ b/docs/examples/house_price_prediction/Dockerfile @@ -0,0 +1,49 @@ +FROM ubuntu:focal + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN : \ + && apt-get update \ + && apt install -y software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa + +RUN : \ + && apt-get update \ + && apt-get install -y python3.8 python3-pip python3-venv make build-essential libssl-dev curl vim + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/house_price_prediction/README.md b/docs/examples/house_price_prediction/README.md new file mode 100644 index 000000000..5ebece507 --- /dev/null +++ b/docs/examples/house_price_prediction/README.md @@ -0,0 +1,49 @@ +# House Price Regression + +```{eval-rst} +.. tags:: Data, MachineLearning, DataFrame, Intermediate +``` + +House Price Regression refers to the prediction of house prices based on various factors, using the XGBoost Regression model (in our case). +In this example, we will train our data on the XGBoost model to predict house prices in multiple regions. + +(flyte's-role)= + +## Where Does Flyte Fit In? + +- Orchestrates the machine learning pipeline. +- Helps cache the output state between {py:func}`tasks `. +- Easier backtracking to the error source. +- Provides a Rich UI to view and manage the pipeline. + +House price prediction pipeline for one region doesn't require a {py:func}`~flytekit:flytekit.dynamic` workflow. When multiple regions are involved, to iterate through the regions at run-time and thereby build the DAG, Flyte workflow has to be {py:func}`~flytekit:flytekit.dynamic`. + +```{tip} +Refer to {ref}`dynamic_workflows` section to learn more about dynamic workflows. +``` + +## Dataset + +We will create a custom dataset to build our model by referring to the [Sagemaker example](https://github.com/aws/amazon-sagemaker-examples/blob/master/advanced_functionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb). + +The dataset will have the following columns: + +- Price +- House Size +- Number of Bedrooms +- Year Built +- Number of Bathrooms +- Number of Garage Spaces +- Lot Size + +## Takeaways + +- An in-depth dive into dynamic workflows +- How the Flyte type-system works + +## Examples + +```{auto-examples-toc} +house_price_predictor +multiregion_house_price_predictor +``` diff --git a/docs/examples/house_price_prediction/house_price_prediction/__init__.py b/docs/examples/house_price_prediction/house_price_prediction/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/house_price_prediction/house_price_prediction/house_price_predictor.py b/docs/examples/house_price_prediction/house_price_prediction/house_price_predictor.py new file mode 100644 index 000000000..bc23f1461 --- /dev/null +++ b/docs/examples/house_price_prediction/house_price_prediction/house_price_predictor.py @@ -0,0 +1,269 @@ +# %% [markdown] +# (single_region_house_prediction)= +# +# # Predicting House Price in a Region Using XGBoost +# +# [XGBoost](https://xgboost.readthedocs.io/en/latest/) is an optimized distributed gradient boosting library designed to be efficient, flexible, and portable. +# It uses [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) technique to implement Machine Learning algorithms. +# +# In this tutorial, we will understand how to predict house prices using XGBoost, and Flyte. +# +# We will split the generated dataset into train, test and validation set. +# +# Next, we will create three Flyte tasks, that will: +# +# 1. Generate house details, and split the dataset. +# 2. Train the model using XGBoost. +# 3. Generate predictions. +# +# Let's get started with the example! + +# %% [markdown] +# Install the following libraries before running the model (locally): +# +# ```python +# pip install scikit-learn +# pip install joblib +# pip install xgboost +# ``` + +# %% +import os + +# %% [markdown] +# First, let's import the required packages into the environment. +# %% +import typing +from typing import Tuple + +import flytekit +import joblib +import numpy as np +import pandas as pd +from flytekit import Resources, task, workflow +from flytekit.types.file import JoblibSerializedFile +from sklearn.model_selection import train_test_split +from xgboost import XGBRegressor + +# %% [markdown] +# We initialize a variable to represent columns in the dataset. The other variables help generate the dataset. +# %% +NUM_HOUSES_PER_LOCATION = 1000 +COLUMNS = [ + "PRICE", + "YEAR_BUILT", + "SQUARE_FEET", + "NUM_BEDROOMS", + "NUM_BATHROOMS", + "LOT_ACRES", + "GARAGE_SPACES", +] +MAX_YEAR = 2021 +# divide the data into train, validation, and test datasets in specific ratio. +SPLIT_RATIOS = [0.6, 0.3, 0.1] + + +# %% [markdown] +# ## Data Generation +# +# We define a function to compute the price of a house based on multiple factors (`number of bedrooms`, `number of bathrooms`, `area`, `garage space`, and `year built`). + +# %% +def gen_price(house) -> int: + _base_price = int(house["SQUARE_FEET"] * 150) + _price = int( + _base_price + + (10000 * house["NUM_BEDROOMS"]) + + (15000 * house["NUM_BATHROOMS"]) + + (15000 * house["LOT_ACRES"]) + + (15000 * house["GARAGE_SPACES"]) + - (5000 * (MAX_YEAR - house["YEAR_BUILT"])) + ) + return _price + + +# %% [markdown] +# Next, using the above function, we generate a DataFrame object that constitutes all the house details. +# %% +def gen_houses(num_houses) -> pd.DataFrame: + _house_list = [] + for _ in range(num_houses): + _house = { + "SQUARE_FEET": int(np.random.normal(3000, 750)), + "NUM_BEDROOMS": np.random.randint(2, 7), + "NUM_BATHROOMS": np.random.randint(2, 7) / 2, + "LOT_ACRES": round(np.random.normal(1.0, 0.25), 2), + "GARAGE_SPACES": np.random.randint(0, 4), + "YEAR_BUILT": min(MAX_YEAR, int(np.random.normal(1995, 10))), + } + _price = gen_price(_house) + # column names/features + _house_list.append( + [ + _price, + _house["YEAR_BUILT"], + _house["SQUARE_FEET"], + _house["NUM_BEDROOMS"], + _house["NUM_BATHROOMS"], + _house["LOT_ACRES"], + _house["GARAGE_SPACES"], + ] + ) + # convert the list to a DataFrame + _df = pd.DataFrame( + _house_list, + columns=COLUMNS, + ) + return _df + + +# %% [markdown] +# ## Data Preprocessing and Splitting +# +# We split the data into train, test, and validation subsets. +# %% +def split_data( + df: pd.DataFrame, seed: int, split: typing.List[float] +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + + seed = seed + val_size = split[1] # 0.3 + test_size = split[2] # 0.1 + + num_samples = df.shape[0] + # retain the features, skip the target column + x1 = df.values[:num_samples, 1:] + # retain the target column + y1 = df.values[:num_samples, :1] + + # divide the features and target column into random train and test subsets, based on `test_size` + x_train, x_test, y_train, y_test = train_test_split(x1, y1, test_size=test_size, random_state=seed) + # divide the train data into train and validation subsets, based on `test_size` + x_train, x_val, y_train, y_val = train_test_split( + x_train, + y_train, + test_size=(val_size / (1 - test_size)), # here, `test_size` computes to 0.3 + random_state=seed, + ) + + # reassemble the datasets by placing `target` as first column and `features` in the subsequent columns + _train = np.concatenate([y_train, x_train], axis=1) + _val = np.concatenate([y_val, x_val], axis=1) + _test = np.concatenate([y_test, x_test], axis=1) + + # return three DataFrames with train, test, and validation data + return ( + pd.DataFrame( + _train, + columns=COLUMNS, + ), + pd.DataFrame( + _val, + columns=COLUMNS, + ), + pd.DataFrame( + _test, + columns=COLUMNS, + ), + ) + + +# %% [markdown] +# Next, we create a `NamedTuple` to map a variable name to its respective data type. +# %% +dataset = typing.NamedTuple( + "GenerateSplitDataOutputs", + train_data=pd.DataFrame, + val_data=pd.DataFrame, + test_data=pd.DataFrame, +) + + +# %% [markdown] +# We define a task to call the aforementioned functions. + +# %% +@task(cache=True, cache_version="0.1", limits=Resources(mem="600Mi")) +def generate_and_split_data(number_of_houses: int, seed: int) -> dataset: + _houses = gen_houses(number_of_houses) + return split_data(_houses, seed, split=SPLIT_RATIOS) + + +# %% [markdown] +# ## Training +# +# We fit an `XGBRegressor` model on our data, serialize the model using `joblib`, and return a {py:obj}`~flytekit:flytekit.types.file.JoblibSerializedFile`. +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="600Mi")) +def fit(loc: str, train: pd.DataFrame, val: pd.DataFrame) -> JoblibSerializedFile: + + # fetch the features and target columns from the train dataset + x = train[train.columns[1:]] + y = train[train.columns[0]] + + # fetch the features and target columns from the validation dataset + eval_x = val[val.columns[1:]] + eval_y = val[val.columns[0]] + + m = XGBRegressor() + # fit the model to the train data + m.fit(x, y, eval_set=[(eval_x, eval_y)]) + + working_dir = flytekit.current_context().working_directory + fname = os.path.join(working_dir, f"model-{loc}.joblib.dat") + joblib.dump(m, fname) + + # return the serialized model + return JoblibSerializedFile(path=fname) + + +# %% [markdown] +# ## Generating Predictions +# +# Next, we unserialize the XGBoost model using `joblib` to generate the predictions. +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="600Mi")) +def predict( + test: pd.DataFrame, + model_ser: JoblibSerializedFile, +) -> typing.List[float]: + + # load the model + model = joblib.load(model_ser) + + # load the test data + x_df = test[test.columns[1:]] + + # generate predictions + y_pred = model.predict(x_df).tolist() + + # return the predictions + return y_pred + + +# %% [markdown] +# Lastly, we define a workflow to run the pipeline. +# %% +@workflow +def house_price_predictor_trainer(seed: int = 7, number_of_houses: int = NUM_HOUSES_PER_LOCATION) -> typing.List[float]: + + # generate the data and split it into train test, and validation data + split_data_vals = generate_and_split_data(number_of_houses=number_of_houses, seed=seed) + + # fit the XGBoost model + model = fit(loc="NewYork_NY", train=split_data_vals.train_data, val=split_data_vals.val_data) + + # generate predictions + predictions = predict(model_ser=model, test=split_data_vals.test_data) + + return predictions + + +# %% [markdown] +# ## Running the Model Locally +# +# We can run the workflow locally provided the required libraries are installed. The output would be a list of house prices, generated using the XGBoost model. +# +# %% +if __name__ == "__main__": + print(house_price_predictor_trainer()) diff --git a/docs/examples/house_price_prediction/house_price_prediction/multiregion_house_price_predictor.py b/docs/examples/house_price_prediction/house_price_prediction/multiregion_house_price_predictor.py new file mode 100644 index 000000000..8c57dcb3a --- /dev/null +++ b/docs/examples/house_price_prediction/house_price_prediction/multiregion_house_price_predictor.py @@ -0,0 +1,155 @@ +# %% [markdown] +# # Predicting House Price in Multiple Regions Using XGBoost and Dynamic Workflows +# +# In this tutorial, we will understand how to predict house prices in multiple regions using XGBoost, and {ref}`dynamic workflows ` in Flyte. +# +# We will split the generated dataset into train, test and validation set. +# +# Next, we will create two dynamic workflows in Flyte, that will: +# +# 1. Generate and split the data for multiple regions. +# 2. Train the model using XGBoost and generate predictions. +# +# Let's get started with the example! + +# %% [markdown] +# First, let's import the required packages into the environment. +# %% +import typing + +import pandas as pd +from flytekit import Resources, dynamic, workflow + +# %% [markdown] +# We define a `try-catch` block to import data preprocessing functions from {ref}`here `. +# %% +try: + from .house_price_predictor import fit, generate_and_split_data, predict +except ImportError: + from house_price_predictor import fit, generate_and_split_data, predict + +# %% [markdown] +# We initialize a variable to represent columns in the dataset. The other variables help generate the dataset. +# %% +NUM_HOUSES_PER_LOCATION = 1000 +COLUMNS = [ + "PRICE", + "YEAR_BUILT", + "SQUARE_FEET", + "NUM_BEDROOMS", + "NUM_BATHROOMS", + "LOT_ACRES", + "GARAGE_SPACES", +] +# initialize location names to predict house prices in these regions. +LOCATIONS = [ + "NewYork_NY", + "LosAngeles_CA", + "Chicago_IL", + "Houston_TX", + "Dallas_TX", + "Phoenix_AZ", + "Philadelphia_PA", + "SanAntonio_TX", + "SanDiego_CA", + "SanFrancisco_CA", +] + +# %% [markdown] +# ## Data Generation and Preprocessing +# +# We call the {ref}`data generation ` and {ref}`data preprocessing ` functions to generate train, test, and validation data. +# First, let's create a `NamedTuple` that maps variable names to their respective data types. +# %% +dataset = typing.NamedTuple( + "GenerateSplitDataOutputs", + train_data=typing.List[pd.DataFrame], + val_data=typing.List[pd.DataFrame], + test_data=typing.List[pd.DataFrame], +) + + +# %% [markdown] +# Next, we create a {py:func}`~flytekit:flytekit.dynamic` workflow to generate and split the data for multiple regions. + +# %% +@dynamic(cache=True, cache_version="0.1", limits=Resources(mem="600Mi")) +def generate_and_split_data_multiloc( + locations: typing.List[str], + number_of_houses_per_location: int, + seed: int, +) -> dataset: + train_sets = [] # create empty lists for train, validation, and test subsets + val_sets = [] + test_sets = [] + for _ in locations: + _train, _val, _test = generate_and_split_data(number_of_houses=number_of_houses_per_location, seed=seed) + train_sets.append( + _train, + ) + val_sets.append( + _val, + ) + test_sets.append( + _test, + ) + # split the dataset into train, validation, and test subsets + return train_sets, val_sets, test_sets + + +# %% [markdown] +# ## Training and Generating Predictions +# +# We create another {py:func}`~flytekit:flytekit.dynamic` workflow to train the model and generate predictions. +# We can use two different methods to fit the model and generate predictions, but including them in the same dynamic workflow will parallelize the tasks together, i.e., the two tasks together run in parallel for all the regions. +# %% +@dynamic(cache=True, cache_version="0.1", limits=Resources(mem="600Mi")) +def parallel_fit_predict( + multi_train: typing.List[pd.DataFrame], + multi_val: typing.List[pd.DataFrame], + multi_test: typing.List[pd.DataFrame], +) -> typing.List[typing.List[float]]: + preds = [] + + # generate predictions for multiple regions + for loc, train, val, test in zip(LOCATIONS, multi_train, multi_val, multi_test): + model = fit(loc=loc, train=train, val=val) + preds.append(predict(test=test, model_ser=model)) + + return preds + + +# %% [markdown] +# Lastly, we define a workflow to run the pipeline. +# %% +@workflow +def multi_region_house_price_prediction_model_trainer( + seed: int = 7, number_of_houses: int = NUM_HOUSES_PER_LOCATION +) -> typing.List[typing.List[float]]: + + # generate and split the data + split_data_vals = generate_and_split_data_multiloc( + locations=LOCATIONS, + number_of_houses_per_location=number_of_houses, + seed=seed, + ) + + # fit the XGBoost model for multiple regions in parallel + # generate predictions for multiple regions + predictions = parallel_fit_predict( + multi_train=split_data_vals.train_data, + multi_val=split_data_vals.val_data, + multi_test=split_data_vals.test_data, + ) + + return predictions + + +# %% [markdown] +# ## Running the Model Locally +# +# We can run the workflow locally provided the required libraries are installed. The output would be a list of lists of house prices based on region, generated using the XGBoost model. +# +# %% +if __name__ == "__main__": + print(multi_region_house_price_prediction_model_trainer()) diff --git a/docs/examples/house_price_prediction/requirements.in b/docs/examples/house_price_prediction/requirements.in new file mode 100644 index 000000000..23184073a --- /dev/null +++ b/docs/examples/house_price_prediction/requirements.in @@ -0,0 +1,9 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +xgboost +joblib +scikit-learn +tabulate +matplotlib diff --git a/docs/examples/house_price_prediction/requirements.txt b/docs/examples/house_price_prediction/requirements.txt new file mode 100644 index 000000000..6294a9a9f --- /dev/null +++ b/docs/examples/house_price_prediction/requirements.txt @@ -0,0 +1,309 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +attrs==22.1.0 + # via visions +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.1 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==38.0.1 + # via + # pyopenssl + # secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==5.0.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==1.1.14 + # via flytekit +flytekit==1.1.1 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.1.1 + # via -r ../../../common/requirements-common.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +htmlmin==0.1.12 + # via pandas-profiling +idna==3.3 + # via requests +imagehash==4.3.0 + # via visions +importlib-metadata==4.11.3 + # via + # flytekit + # keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.1.0 + # via + # -r requirements.in + # pandas-profiling + # phik + # scikit-learn +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via + # -r ../../../common/requirements-common.in + # -r requirements.in + # missingno + # pandas-profiling + # phik + # seaborn +missingno==0.5.1 + # via pandas-profiling +multimethod==1.8 + # via + # pandas-profiling + # visions +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +networkx==2.8.6 + # via visions +numpy==1.22.3 + # via + # imagehash + # matplotlib + # missingno + # pandas + # pandas-profiling + # patsy + # phik + # pyarrow + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions + # xgboost +packaging==21.3 + # via + # marshmallow + # matplotlib + # statsmodels +pandas==1.4.2 + # via + # flytekit + # pandas-profiling + # phik + # seaborn + # statsmodels + # visions +pandas-profiling==3.3.0 + # via flytekitplugins-deck-standard +patsy==0.5.2 + # via statsmodels +phik==0.12.2 + # via pandas-profiling +pillow==9.1.0 + # via + # imagehash + # matplotlib + # visions +plotly==5.10.0 + # via flytekitplugins-deck-standard +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pydantic==1.9.2 + # via pandas-profiling +pyopenssl==22.0.0 + # via flytekit +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pywavelets==1.3.0 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # pandas-profiling +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # docker + # flytekit + # pandas-profiling + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +scikit-learn==1.0.2 + # via sklearn +scipy==1.8.0 + # via + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # seaborn + # statsmodels + # xgboost +seaborn==0.11.2 + # via + # missingno + # pandas-profiling +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # grpcio + # patsy + # python-dateutil +sklearn==0.0 + # via -r requirements.in +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +statsmodels==0.13.2 + # via pandas-profiling +tabulate==0.8.9 + # via -r requirements.in +tangled-up-in-unicode==0.2.0 + # via + # pandas-profiling + # visions +tenacity==8.0.1 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +tqdm==4.64.1 + # via pandas-profiling +typing-extensions==4.1.1 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +visions[type_image_path]==0.7.5 + # via pandas-profiling +websocket-client==1.4.1 + # via docker +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +xgboost==1.5.2 + # via -r requirements.in +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/k8s_dask_plugin/Dockerfile b/docs/examples/k8s_dask_plugin/Dockerfile new file mode 100644 index 000000000..63e74c2b1 --- /dev/null +++ b/docs/examples/k8s_dask_plugin/Dockerfile @@ -0,0 +1,46 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM ubuntu:focal +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python3 and other basics +RUN apt-get update \ + && apt-get install -y software-properties-common \ + && add-apt-repository ppa:ubuntu-toolchain-r/test \ + && add-apt-repository -y ppa:deadsnakes/ppa \ + && apt-get install -y \ + build-essential \ + curl \ + git \ + libssl-dev \ + make \ + python3-pip \ + python3.11 \ + python3.11-venv \ + && rm -rf /var/lib/apt/lists/* \ + && : + +ENV VENV /opt/venv +# Virtual environment +RUN python3.11 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/k8s_dask_plugin/README.md b/docs/examples/k8s_dask_plugin/README.md new file mode 100644 index 000000000..616bd13a4 --- /dev/null +++ b/docs/examples/k8s_dask_plugin/README.md @@ -0,0 +1,241 @@ +(plugins-dask-k8s)= + +# Dask + +```{eval-rst} +.. tags:: Dask, Integration, DistributedComputing, Data, Advanced +``` + +Flyte can natively execute [Dask](https://www.dask.org/) jobs on a Kubernetes Cluster, +effortlessly managing the lifecycle of a virtual Dask cluster. +This functionality is achieved by leveraging the open-sourced +[Dask Kubernetes Operator](https://kubernetes.dask.org/en/latest/operator.html), +and no additional sign-ups for services are required. +The process is akin to running an ephemeral Dask cluster, +which is created specifically for the Flyte task and subsequently torn down upon completion. + +In the Flyte Kubernetes environment, the cost is amortized due to faster pod creation compared to machines. +However, the performance may be affected by the penalty of downloading Docker images. +Additionally, it's essential to keep in mind that starting a pod is not as swift as running a process. + +Flytekit enables writing Dask code natively as a task, +with the `Dask()` config automatically configuring the Dask cluster. +The example provided in this section offers a hands-on tutorial for writing Dask Flyte tasks. + +## Why use Kubernetes Dask? + +Managing Python dependencies can be challenging, but Flyte simplifies the process +by enabling easy versioning and management of dependencies through containers. +The Kubernetes Dask plugin extends the benefits of containerization to Dask without +requiring the management of specialized Dask clusters. + +Pros: + +1. Simple to get started, providing complete isolation between workloads. +2. Each job runs in isolation with its own virtual cluster, eliminating the complexities of dependency management. +3. Flyte takes care of all the management tasks. + +Cons: + +1. Short-running, bursty jobs may not be the best fit due to container overhead. +2. Interactive Dask capabilities are not available with Flyte Kubernetes Dask; + instead, it is better suited for running adhoc and scheduled jobs. + +## Install the plugin + +Install `flytekitplugins-dask` using `pip` in your environment. + +``` +pip install flytekitplugins-dask +``` + +:::{note} +To enable Flyte to build the Docker image for you using `ImageSpec`, install `flytekitplugins-envd`. +::: + +## Implementation details + +### Local execution + +When executing the Dask task on your local machine, +it will utilize a local [distributed client](https://distributed.dask.org/en/stable/client.html). +If you intend to link to a remote cluster during local development, simply define the `DASK_SCHEDULER_ADDRESS` +environment variable with the URL of the remote scheduler. +The `Client()` will then automatically connect to the cluster. + +### Remote execution + +#### Step 1: Deploy Dask plugin in the Flyte backend + +Flyte Dask utilizes the [Dask Kubernetes operator](https://kubernetes.dask.org/en/latest/operator.html) +in conjunction with a custom-built +[Flyte Dask plugin](https://pkg.go.dev/github.com/flyteorg/flyteplugins@v1.0.28/go/tasks/plugins/k8s/dask). +To leverage this functionality, you need to enable the backend plugin in your deployment. +You can follow the steps mentioned in the {ref}`flyte:deployment-plugin-setup-k8s` section +to enable the Flyte Dask plugin for your deployment. + +#### Step 2: Compute setup + +Ensure that your Kubernetes cluster has sufficient resources available. +Depending on the resource requirements of your Dask job (including the job runner, scheduler and workers), +you may need to adjust the resource quotas for the namespace accordingly. + +### Resource specification + +It's recommended to define `limits` as this will establish the +`--nthreads` and `--memory-limit` parameters for the workers, +in line with the suggested practices by Dask +(refer to [best practices](https://kubernetes.dask.org/en/latest/kubecluster.html?highlight=--nthreads#best-practices)). +When configuring resources, the subsequent hierarchy is observed across all components of the Dask job, +which encompasses the job-runner pod, scheduler pod, and worker pods: + +1. In the absence of specified resources, the + [platform resources](https://github.com/flyteorg/flyte/blob/1e3d515550cb338c2edb3919d79c6fa1f0da5a19/charts/flyte-core/values.yaml#L520-L531) + will be used. + +2. When employing task resources, those will be enforced across all segments of the Dask job. + You can achieve this using the following code snippet: + + > ```python + > from flytekit import Resources, task + > from flytekitplugins.dask import Dask + > + > @task( + > task_config=Dask(), + > limits=Resources(cpu="1", mem="10Gi") # Applied to all components + > ) + > def my_dask_task(): + > ... + > ``` + +3. When resources are designated for individual components, they hold the highest precedence. + + > ```python + > from flytekit import Resources, task + > from flytekitplugins.dask import Dask, Scheduler, WorkerGroup + > + > @task( + > task_config=Dask( + > scheduler=Scheduler( + > limits=Resources(cpu="1", mem="2Gi"), # Applied to the job pod + > ), + > workers=WorkerGroup( + > limits=Resources(cpu="4", mem="10Gi"), # Applied to the scheduler and worker pods + > ), + > ), + > ) + > def my_dask_task(): + > ... + > ``` + +### Images + +By default, all components of the deployed `dask` job (job runner pod, scheduler pod and worker pods) will all use the +the image that was used while registering (this image should have `dask[distributed]` installed in its Python +environment). This helps keeping the Python environments of all cluster components in sync. +However, there is the possibility to specify different images for the components. This allows for use cases such as using +different images between tasks of the same workflow. While it is possible to use different images for the different +components of the `dask` job, it is not advised, as this can quickly lead to Python environments getting our of sync. + +As the default behavior, all components of the deployed Dask job, +including the job runner pod, scheduler pod and worker pods, +will employ the image that was utilized during registration. +This image must have `dask[distributed]` installed in its Python environment, +ensuring consistency across the Python environments of all cluster components. + +However, there exists the option to specify distinct images for these components. +This accommodation caters to scenarios where diverse images are required for tasks within the same workflow. +It is important to note that while it is technically possible to implement varying images +for different components of the dask job, this approach is not recommended. +Doing so can rapidly lead to discrepancies in Python environments. + +> ```python +> from flytekit import Resources, task +> from flytekitplugins.dask import Dask, Scheduler, WorkerGroup +> +> @task( +> task_config=Dask( +> scheduler=Scheduler( +> image="my_image:0.1.0", # Will be used by the job pod +> ), +> workers=WorkerGroup( +> image="my_image:0.1.0", # Will be used by the scheduler and worker pods +> ), +> ), +> ) +> def my_dask_task(): +> ... +> ``` + +### Environment variables + +Environment variables configured within the `@task` decorator will be propagated to all components of the Dask job, +encompassing the job runner pod, scheduler pod and worker pods. + +> ```python +> from flytekit import Resources, task +> from flytekitplugins.dask import Dask +> +> @task( +> task_config=Dask(), +> env={"FOO": "BAR"} # Will be applied to all components +> ) +> def my_dask_task(): +> ... +> ``` + +### Labels and annotations + +Labels and annotations specified within a {ref}`launch plan ` will be inherited by all components of the dask job, +which include the job runner pod, scheduler pod and worker pods. + +> ```python +> from flytekit import Resources, task, workflow, Labels, Annotations +> from flytekitplugins.dask import Dask +> +> @task(task_config=Dask()) +> def my_dask_task(): +> ... +> +> @workflow +> def my_dask_workflow(): +> my_dask_task() +> +> # Labels and annotations will be passed on to all dask cluster components +> my_launch_plan = my_dask_workflow.create_launch_plan( +> labels=Labels({"myexecutionlabel": "bar", ...}), +> annotations=Annotations({"region": "SEA", ...}), +> ) +> ``` + +### Interruptible tasks + +The Dask backend plugin offers support for execution on interruptible nodes. +When `interruptible==True`, the plugin will incorporate the specified tolerations and node selectors into all worker pods. +It's important to be aware that neither the job runner nor the scheduler will be deployed on interruptible nodes. + +> ```python +> from flytekit import Resources, task, workflow, Labels, Annotations +> from flytekitplugins.dask import Dask +> +> @task( +> task_config=Dask(), +> interruptible=True, +> ) +> def my_dask_task(): +> ... +> ``` + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/k8s_dask_plugin/k8s_dask_plugin/dask_example.py \ + hello_dask --size 1000 +``` + +```{auto-examples-toc} +dask_example +``` diff --git a/docs/examples/k8s_dask_plugin/k8s_dask_plugin/__init__.py b/docs/examples/k8s_dask_plugin/k8s_dask_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/k8s_dask_plugin/k8s_dask_plugin/dask_example.py b/docs/examples/k8s_dask_plugin/k8s_dask_plugin/dask_example.py new file mode 100644 index 000000000..2dcfb0786 --- /dev/null +++ b/docs/examples/k8s_dask_plugin/k8s_dask_plugin/dask_example.py @@ -0,0 +1,56 @@ +# %% [markdown] +# (dask_task)= +# +# # Running a Dask Task +# +# The plugin establishes a distinct virtual and short-lived cluster for each Dask task, with Flyte overseeing the entire cluster lifecycle. +# +# To begin, import the required dependencies. +# %% +from flytekit import ImageSpec, Resources, task + +# %% [markdown] +# Create an `ImageSpec` to encompass all the dependencies needed for the Dask task. +# %% +custom_image = ImageSpec(name="flyte-dask-plugin", registry="ghcr.io/flyteorg", packages=["flytekitplugins-dask"]) + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# The following imports are required to configure the Dask cluster in Flyte. +# You can load them on demand. +# %% +if custom_image.is_container(): + from dask import array as da + from flytekitplugins.dask import Dask, WorkerGroup + + +# %% [markdown] +# When executed locally, Flyte launches a Dask cluster on the local environment. +# However, when executed remotely, Flyte triggers the creation of a cluster with a size determined by the +# specified {py:class}`~flytekitplugins.dask.Dask` configuration. +# %% +@task( + task_config=Dask( + workers=WorkerGroup( + number_of_workers=10, + limits=Resources(cpu="4", mem="10Gi"), + ), + ), + limits=Resources(cpu="1", mem="2Gi"), + container_image=custom_image, +) +def hello_dask(size: int) -> float: + # Dask will automatically generate a client in the background using the Client() function. + # When running remotely, the Client() function will utilize the deployed Dask cluster. + array = da.random.random(size) + return float(array.mean().compute()) + + +# %% [markdown] +# You can execute the task locally as follows: +# %% +if __name__ == "__main__": + print(hello_dask(size=1000)) diff --git a/docs/examples/k8s_dask_plugin/requirements.in b/docs/examples/k8s_dask_plugin/requirements.in new file mode 100644 index 000000000..6985b1709 --- /dev/null +++ b/docs/examples/k8s_dask_plugin/requirements.in @@ -0,0 +1,2 @@ +flytekitplugins-dask +flytekitplugins-envd diff --git a/docs/examples/k8s_dask_plugin/requirements.txt b/docs/examples/k8s_dask_plugin/requirements.txt new file mode 100644 index 000000000..ba00fd4a8 --- /dev/null +++ b/docs/examples/k8s_dask_plugin/requirements.txt @@ -0,0 +1,393 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.3 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # dask + # distributed + # flytekit + # rich-click +cloudpickle==2.2.1 + # via + # dask + # distributed + # flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dask[distributed]==2023.8.1 + # via + # distributed + # flytekitplugins-dask +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +distributed==2023.8.1 + # via dask +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.39 + # via flytekitplugins-envd +flyteidl==1.5.16 + # via + # flytekit + # flytekitplugins-dask +flytekit==1.9.0 + # via + # flytekitplugins-dask + # flytekitplugins-envd +flytekitplugins-dask==1.9.0 + # via -r requirements.in +flytekitplugins-envd==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # dask + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # dask + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via + # cookiecutter + # distributed +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +locket==1.0.0 + # via + # distributed + # partd +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +msgpack==1.0.5 + # via distributed +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # dask + # distributed + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +partd==1.4.0 + # via dask +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.1 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.5 + # via distributed +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # dask + # distributed + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via + # distributed + # flytekit +statsd==3.3.0 + # via flytekit +tblib==2.0.0 + # via distributed +text-unidecode==1.3 + # via python-slugify +toolz==0.12.0 + # via + # dask + # distributed + # partd +tornado==6.3.3 + # via distributed +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # distributed + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.2 + # via + # docker + # kubernetes +wheel==0.41.2 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zict==3.0.0 + # via distributed +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/k8s_pod_plugin/Dockerfile b/docs/examples/k8s_pod_plugin/Dockerfile new file mode 100644 index 000000000..eb4c61a26 --- /dev/null +++ b/docs/examples/k8s_pod_plugin/Dockerfile @@ -0,0 +1,37 @@ +FROM python:3.8-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/k8s_pod_plugin/README.md b/docs/examples/k8s_pod_plugin/README.md new file mode 100644 index 000000000..e7b0e8dc8 --- /dev/null +++ b/docs/examples/k8s_pod_plugin/README.md @@ -0,0 +1,35 @@ +# Kubernetes Pods + +```{eval-rst} +.. tags:: Integration, Kubernetes, Advanced +``` + +Flyte tasks, represented by the {py:func}`@task ` decorator, are essentially single functions that run in one container. +However, there may be situations where you need to run a job with more than one container or require additional capabilities, such as: + +- Running a hyper-parameter optimizer that stores state in a Redis database +- Simulating a service locally +- Running a sidecar container for logging and monitoring purposes +- Running a pod with additional capabilities, such as mounting volumes + +To support these use cases, Flyte provides a Pod configuration that allows you to customize the pod specification used to run the task. +This simplifies the process of implementing the Kubernetes pod abstraction for running multiple containers. + +:::{note} +A Kubernetes pod will not exit if it contains any sidecar containers (containers that do not exit automatically). +You do not need to write any additional code to handle this, as Flyte automatically manages pod tasks. +::: + +## Installation + +To use the Flytekit pod plugin, run the following command: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-pod +``` + +```{auto-examples-toc} +pod +``` diff --git a/docs/examples/k8s_pod_plugin/k8s_pod_plugin/__init__.py b/docs/examples/k8s_pod_plugin/k8s_pod_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/k8s_pod_plugin/k8s_pod_plugin/pod.py b/docs/examples/k8s_pod_plugin/k8s_pod_plugin/pod.py new file mode 100644 index 000000000..0a6d8994c --- /dev/null +++ b/docs/examples/k8s_pod_plugin/k8s_pod_plugin/pod.py @@ -0,0 +1,240 @@ +# %% [markdown] +# # Pod Example +# +# Pod configuration for a Flyte task allows you to run multiple containers within a single task. +# They provide access to a fully customizable [Kubernetes pod spec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.20/#podspec-v1-core), +# which can be used to modify the runtime of the task execution. +# +# The primary container is the main driver for Flyte task execution and is responsible for producing inputs and outputs. +# +# Pod tasks accept arguments that are commonly used with container tasks, such as resource specifications. +# However, these arguments only apply to the primary container. +# To customize the other containers that are brought up during task execution, you can define a complete pod spec using the +# [Kubernetes Python client library](https://github.com/kubernetes-client/python)'s, +# [V1PodSpec](https://github.com/kubernetes-client/python/blob/master/kubernetes/client/models/v1_pod_spec.py). +# %% [markdown] +# First, we import the necessary libraries for use in the following examples. +# %% +import os +import time +from typing import List + +from flytekit import Resources, TaskMetadata, dynamic, map_task, task, workflow +from flytekitplugins.pod import Pod +from kubernetes.client.models import ( + V1Container, + V1EmptyDirVolumeSource, + V1PodSpec, + V1ResourceRequirements, + V1Volume, + V1VolumeMount, +) + + +# %% [markdown] +# ## Add additional properties to the task container +# +# In this example, we define a simple pod specification. +# The `containers` field is set to an empty list, signaling to Flyte to insert a placeholder primary container. +# The `node_selector` field specifies the nodes on which the task pod should be run. +# %% +@task( + task_config=Pod( + pod_spec=V1PodSpec( + containers=[], + node_selector={"node_group": "memory"}, + ), + ), + requests=Resources( + mem="1G", + ), +) +def pod_task() -> str: + return "Hello from pod task!" + + +@workflow +def pod_workflow() -> str: + return pod_task() + + +# %% [markdown] +# :::{note} +# To configure default settings for all pods Flyte creates, including tasks for pods, containers, PyTorch, Spark, Ray, and Dask, +# [configure a default Kubernetes pod template](https://docs.flyte.org/en/latest/deployment/cluster_config/general.html#using-default-k8s-podtemplates). +# ::: + +# %% [markdown] +# ## Multiple containers +# +# We define a simple pod spec with a shared volume that is mounted in both the primary and secondary containers. +# The secondary container writes a file that the primary container waits for and reads before completing. +# +# First, we define the shared data path: +# %% +_SHARED_DATA_PATH = "/data/message.txt" + + +# %% [markdown] +# We define a pod spec with two containers. +# While pod tasks generally allow you to customize Kubernetes container attributes, you can still use Flyte directives to specify resources and the image. +# Unless you specify the `container_image` task attribute, the default image built for Flyte tasks will be used. +# %% +@task( + task_config=Pod( + pod_spec=V1PodSpec( + containers=[ + V1Container( + name="primary", + resources=V1ResourceRequirements( + requests={"cpu": "1", "memory": "100Mi"}, + limits={"cpu": "1", "memory": "100Mi"}, + ), + volume_mounts=[ + V1VolumeMount( + name="shared-data", + mount_path="/data", + ) + ], + ), + V1Container( + name="secondary", + image="alpine", + command=["/bin/sh"], + args=[ + "-c", + "echo hi pod world > {}".format(_SHARED_DATA_PATH), + ], + resources=V1ResourceRequirements( + requests={"cpu": "1", "memory": "100Mi"}, + limits={"cpu": "1", "memory": "100Mi"}, + ), + volume_mounts=[ + V1VolumeMount( + name="shared-data", + mount_path="/data", + ) + ], + ), + ], + volumes=[ + V1Volume( + name="shared-data", + empty_dir=V1EmptyDirVolumeSource(medium="Memory"), + ) + ], + ), + ), + requests=Resources( + mem="1G", + ), +) +def multiple_containers_pod_task() -> str: + # The code defined in this task will get injected into the primary container. + while not os.path.isfile(_SHARED_DATA_PATH): + time.sleep(5) + + with open(_SHARED_DATA_PATH, "r") as shared_message_file: + return shared_message_file.read() + + +@workflow +def multiple_containers_pod_workflow() -> str: + txt = multiple_containers_pod_task() + return txt + + +# %% [markdown] +# ## Pod configuration in a map task +# +# To use a pod task as part of a map task, you can send the pod task definition to the `map_task`. +# This will run the pod task across a collection of inputs. +# %% +@task( + task_config=Pod( + pod_spec=V1PodSpec( + containers=[ + V1Container( + name="primary", + resources=V1ResourceRequirements( + requests={"cpu": ".5", "memory": "500Mi"}, + limits={"cpu": ".5", "memory": "500Mi"}, + ), + ) + ], + init_containers=[ + V1Container( + image="alpine", + name="init", + command=["/bin/sh"], + args=["-c", 'echo "I\'m a customizable init container"'], + resources=V1ResourceRequirements( + limits={"cpu": ".5", "memory": "500Mi"}, + ), + ) + ], + ), + ) +) +def map_pod_task(int_val: int) -> str: + return str(int_val) + + +@task +def coalesce(list_of_strings: List[str]) -> str: + coalesced = ", ".join(list_of_strings) + return coalesced + + +@workflow +def map_pod_workflow(list_of_ints: List[int]) -> str: + mapped_out = map_task(map_pod_task, metadata=TaskMetadata(retries=1))(int_val=list_of_ints) + coalesced = coalesce(list_of_strings=mapped_out) + return coalesced + + +# %% [markdown] +# ## Pod configuration in a dynamic workflow +# +# To use a pod task in a dynamic workflow, simply pass the pod task config to the annotated dynamic workflow. +# %% +@task +def stringify(val: int) -> str: + return f"{val} served courtesy of a dynamic pod task!" + + +@dynamic( + task_config=Pod( + pod_spec=V1PodSpec( + containers=[ + V1Container( + name="primary", + resources=V1ResourceRequirements( + requests={"cpu": ".5", "memory": "450Mi"}, + limits={"cpu": ".5", "memory": "500Mi"}, + ), + ) + ], + ), + ) +) +def dynamic_pod_task(val: int) -> str: + return stringify(val=val) + + +@workflow +def dynamic_pod_workflow(val: int = 6) -> str: + txt = dynamic_pod_task(val=val) + return txt + + +# %% [markdown] +# You can execute workflows locally as follows: +# +# %% +if __name__ == "__main__": + print(f"Running {__file__}...") + print(f"Calling pod_workflow()... {pod_workflow()}") + print(f"Calling multiple_containers_pod_workflow()... {multiple_containers_pod_workflow()}") + print(f"Calling map_pod_workflow()... {map_pod_workflow()}") + print(f"Calling dynamic_pod_workflow()... {dynamic_pod_workflow()}") diff --git a/docs/examples/k8s_pod_plugin/requirements.in b/docs/examples/k8s_pod_plugin/requirements.in new file mode 100644 index 000000000..490cfc0e9 --- /dev/null +++ b/docs/examples/k8s_pod_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-pod diff --git a/docs/examples/k8s_pod_plugin/requirements.txt b/docs/examples/k8s_pod_plugin/requirements.txt new file mode 100644 index 000000000..f11d7902a --- /dev/null +++ b/docs/examples/k8s_pod_plugin/requirements.txt @@ -0,0 +1,423 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +appnope==0.1.3 + # via + # ipykernel + # ipython +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +attrs==22.2.0 + # via visions +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +cachetools==5.3.0 + # via google-auth +certifi==2022.12.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.0.1 + # via requests +click==8.1.3 + # via + # cookiecutter + # flytekit +cloudpickle==2.2.1 + # via flytekit +comm==0.1.2 + # via ipykernel +contourpy==1.0.7 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.8 + # via flytekit +cryptography==39.0.1 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +debugpy==1.6.6 + # via ipykernel +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==6.0.1 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +executing==1.2.0 + # via stack-data +flyteidl==1.3.7 + # via flytekit +flytekit==1.3.2 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard + # flytekitplugins-pod +flytekitplugins-deck-standard==1.3.2 + # via -r ../../../common/requirements-common.in +flytekitplugins-pod==1.3.2 + # via -r requirements.in +fonttools==4.38.0 + # via matplotlib +gitdb==4.0.10 + # via gitpython +gitpython==3.1.30 + # via flytekit +google-auth==2.16.0 + # via kubernetes +googleapis-common-protos==1.58.0 + # via + # flyteidl + # flytekit + # grpcio-status +grpcio==1.51.1 + # via + # flytekit + # grpcio-status +grpcio-status==1.51.1 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via requests +imagehash==4.3.1 + # via visions +importlib-metadata==6.0.0 + # via + # flytekit + # jupyter-client + # keyring + # markdown +importlib-resources==5.10.2 + # via keyring +ipykernel==6.21.1 + # via ipywidgets +ipython==8.10.0 + # via + # ipykernel + # ipywidgets +ipywidgets==8.0.4 + # via flytekitplugins-deck-standard +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.2.0 + # via + # flytekit + # phik +jupyter-client==8.0.2 + # via ipykernel +jupyter-core==5.2.0 + # via + # ipykernel + # jupyter-client +jupyterlab-widgets==3.0.5 + # via ipywidgets +keyring==23.13.1 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==25.3.0 + # via + # flytekit + # flytekitplugins-pod +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.2 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.6.3 + # via + # -r ../../../common/requirements-common.in + # phik + # seaborn + # ydata-profiling +matplotlib-inline==0.1.6 + # via + # ipykernel + # ipython +more-itertools==9.0.0 + # via jaraco-classes +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.2.0 + # via flytekit +nest-asyncio==1.5.6 + # via ipykernel +networkx==3.0 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # scipy + # seaborn + # statsmodels + # visions + # ydata-profiling +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.0 + # via + # docker + # ipykernel + # marshmallow + # matplotlib + # statsmodels +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +pandas-profiling==3.6.6 + # via flytekitplugins-deck-standard +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==9.4.0 + # via + # imagehash + # matplotlib + # visions +platformdirs==3.0.0 + # via jupyter-core +plotly==5.13.0 + # via flytekitplugins-deck-standard +prompt-toolkit==3.0.36 + # via ipython +protobuf==4.21.12 + # via + # flyteidl + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.4 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==10.0.1 + # via flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.4 + # via ydata-profiling +pygments==2.14.0 + # via ipython +pyopenssl==23.0.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # jupyter-client + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.4 + # via flytekit +python-slugify==8.0.0 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.7.1 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # ydata-profiling +pyzmq==25.0.0 + # via + # ipykernel + # jupyter-client +regex==2022.10.31 + # via docker-image-py +requests==2.28.2 + # via + # cookiecutter + # docker + # flytekit + # kubernetes + # requests-oauthlib + # responses + # ydata-profiling +requests-oauthlib==1.3.1 + # via kubernetes +responses==0.22.0 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +scipy==1.9.3 + # via + # imagehash + # phik + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +six==1.16.0 + # via + # asttokens + # google-auth + # kubernetes + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.13.5 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.1 + # via plotly +text-unidecode==1.3 + # via python-slugify +toml==0.10.2 + # via responses +tornado==6.2 + # via + # ipykernel + # jupyter-client +tqdm==4.64.1 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +types-toml==0.10.8.3 + # via responses +typing-extensions==4.4.0 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.14 + # via + # docker + # flytekit + # kubernetes + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.5.1 + # via + # docker + # kubernetes +wheel==0.38.4 + # via + # -r ../../../common/requirements-common.in + # flytekit +widgetsnbextension==4.0.5 + # via ipywidgets +wrapt==1.14.1 + # via + # deprecated + # flytekit +ydata-profiling==4.0.0 + # via pandas-profiling +zipp==3.13.0 + # via + # importlib-metadata + # importlib-resources + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/k8s_spark_plugin/Dockerfile b/docs/examples/k8s_spark_plugin/Dockerfile new file mode 100644 index 000000000..b661db239 --- /dev/null +++ b/docs/examples/k8s_spark_plugin/Dockerfile @@ -0,0 +1,48 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM apache/spark-py:3.3.1 +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND=noninteractive +ARG spark_uid=1001 + +## Install Python3 and other basics +USER 0 +RUN apt-get update && apt-get install -y python3 python3-venv make build-essential libssl-dev python3-pip curl wget + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" +RUN pip3 install wheel + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +RUN wget https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.2.2/hadoop-aws-3.2.2.jar -P /opt/spark/jars && \ + wget https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar -P /opt/spark/jars + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag + +# Set /root user and group +RUN chown -R ${spark_uid}:${spark_uid} /root + +# For spark we want to use the default entrypoint which is part of the +# distribution, also enable the virtualenv for this image. +ENTRYPOINT ["/opt/entrypoint.sh"] + +ENV HOME /root +USER ${spark_uid} diff --git a/docs/examples/k8s_spark_plugin/README.md b/docs/examples/k8s_spark_plugin/README.md new file mode 100644 index 000000000..5a757ff5c --- /dev/null +++ b/docs/examples/k8s_spark_plugin/README.md @@ -0,0 +1,192 @@ +(plugins-spark-k8s)= + +# Spark + +```{eval-rst} +.. tags:: Spark, Integration, DistributedComputing, Data, Advanced +``` + +Flyte has the capability to directly execute Spark jobs on a Kubernetes Cluster. +The cluster handles the lifecycle, initiation and termination of virtual clusters. +It harnesses the open-source [Spark on Kubernetes operator](https://github.com/GoogleCloudPlatform/spark-on-k8s-operator) +and can be enabled without requiring any service subscription. +This functionality is akin to operating a transient spark cluster +— a cluster type established specifically for a Spark job and taken down upon completion. + +While these clusters are optimal for production workloads, they do come with the additional cost of setup and teardown. +In the Flyte environment, this cost is spread out over time due to the swiftness of creating pods compared to a full machine. +However, keep in mind that the performance might be impacted by the need to download Docker images, and starting a pod is not as immediate as running a process. + +With Flytekit, you can compose PySpark code natively as a task. +The Spark cluster will be automatically configured using the specified Spark configuration. +The examples provided in this section offer a hands-on tutorial for writing PySpark tasks. + +:::{note} +This plugin has been rigorously tested at scale, successfully managing more than 100,000 Spark Jobs through Flyte at Lyft. +However, please bear in mind that this functionality requires a significant Kubernetes capacity and meticulous configuration. + +For optimal results, we highly recommend adopting the +[multi-cluster mode](https://docs.flyte.org/en/latest/deployment/configuration/performance.html#multi-cluster-mode). +Additionally, consider enabling {std:ref}`resource quotas ` +for Spark Jobs that are both large in scale and executed frequently. + +Nonetheless, it is important to note that extremely short-duration jobs might not be the best fit for this setup. +In such cases, utilizing a pre-spawned cluster could be more advantageous. +A job can be considered "short" if its runtime is less than 2 to 3 minutes. +In these situations, the cost of initializing pods might outweigh the actual execution cost. +::: + +## Why use Kubernetes Spark? + +Managing Python dependencies can be challenging, but Flyte simplifies the process +by enabling easy versioning and management of dependencies through containers. +The Kubernetes Spark plugin extends the benefits of containerization to Spark without +requiring the management of specialized Spark clusters. + +Pros: + +1. Simple to get started, providing complete isolation between workloads. +2. Each job runs in isolation with its own virtual cluster, eliminating the complexities of dependency management. +3. Flyte takes care of all the management tasks. + +Cons: + +1. Short-running, bursty jobs may not be the best fit due to container overhead. +2. Interactive Spark capabilities are not available with Flyte Kubernetes Dask; + instead, it is better suited for running adhoc and scheduled jobs. + +## Implementation details + +### Step 1: Deploy Spark plugin in the Flyte backend + +Flyte Spark employs the Spark on K8s operator in conjunction with a bespoke +[Flyte Spark Plugin](https://pkg.go.dev/github.com/flyteorg/flyteplugins@v0.5.25/go/tasks/plugins/k8s/spark). + +This plugin serves as a backend component and necessitates activation within your deployment. +To enable it, follow the instructions outlined in the {ref}`flyte:deployment-plugin-setup-k8s` section. + +:::{note} +Refer to [this guide](https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/master/docs/gcp.md) to use GCP instead of AWS. +::: + +### Step 2: Environment Setup + +Install `flytekitplugins-spark` using `pip` in your environment. + +```bash +pip install flytekitplugins-spark +``` + +:::{note} +To enable Flyte to build the Docker image for you using `ImageSpec`, install `flytekitplugins-envd`. +::: + +Ensure that your Kubernetes cluster has sufficient resources available. +Depending on the resource requirements of your Spark job across the driver and executors, +you may need to adjust the resource quotas for the namespace accordingly. + +### Step 3: Optionally, set up visibility + +Whenever a Spark job is executed, you have the opportunity to access a Spark application UI link for +real-time job monitoring. Additionally, for past executions, you can leverage the +Spark history server to access the stored history of Spark executions. + +Furthermore, Flyte offers the capability to generate direct links to both the Spark driver logs and individual Spark executor logs. + +These Spark-related features, including the Spark history server and Spark UI links, are seamlessly displayed on the Flyte Console. +Their availability is contingent upon the following configuration settings: + +#### Configure the Spark history link within the UI + +To access the Spark history UI link within the Flyte Console, +it's necessary to configure a variable in the Spark section of the Flyteplugins configuration. +Here's an example of how to set it up: + +``` +plugins: + spark: + spark-history-server-url: +``` + +You can explore various configuration options by referring to +[this link](https://github.com/flyteorg/flyteplugins/blob/master/go/tasks/plugins/k8s/spark/config.go). + +#### Configure the Spark application UI + +To obtain a link for the ongoing Spark drivers and the Spark application UI, +you must set up Kubernetes to allow wildcard ingress access using `*.my-domain.net`. +Additionally, you should configure the Spark on Kubernetes operator to +establish a new ingress route for each application. + +This can be achieved through the `ingress-url-format` command-line option of the Spark Operator. +You can find more details about this option in the source code +[here](https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/d38c904a4dd84e849408153cdf4d7a30a7be5a07/main.go#L62). + +#### Configure the Spark driver and executor logs + +The logs can be configured by adjusting the `logs` configuration within the Spark plugin settings. +The Spark plugin utilizes the same default log configuration outlined in the section on {ref}`configure-logging`. + +The SparkPlugin offers the capability to segregate user (Spark user code) and system (Spark core logs) logs, +thus enhancing visibility into Spark operations. +This is, however, feasible only if you can route the spark user logs separately from the core logs. +It's important to note that Flyte does not perform automatic log separation. You can review the configuration structure +[here](https://github.com/flyteorg/flyteplugins/blob/master/go/tasks/plugins/k8s/spark/config.go#L31-L36). + +- _Mixed_: Provides unseparated logs from the Spark driver (combining both user and system logs), following the standard structure of all log plugins. + You can obtain links to the Kubernetes dashboard or a preferred log aggregator as long as it can generate standardized links. +- _User_: Offers logs from the driver with separation (subject to log separation availability). +- _System_: Covers logs from executors, typically without individual links for each executor; + instead, it provides a prefix where all executor logs are accessible. +- _AllUser_: Encompasses all user logs across spark-submit, driver and executor. + +Log configuration example: + +```yaml +plugins: + spark: + logs: + user: + kubernetes-enabled: true + kubernetes-url: + mixed: + cloudwatch-enabled: true + cloudwatch-template-uri: "https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logStream:group=;prefix=var.log.containers.{{.podName}};streamFilter=typeLogStreamPrefix" + system: + cloudwatch-enabled: true + cloudwatch-template-uri: "https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logStream:group=;prefix=system_log.var.log.containers.{{.podName}};streamFilter=typeLogStreamPrefix" + all-user: + cloudwatch-enabled: true + cloudwatch-template-uri: "https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logStream:group=;prefix=var.log.containers.{{.podName}};streamFilter=typeLogStreamPrefix" +``` + +#### Additional configuration + +The Spark plugin provides support for a range of extended configuration options. +For instance, if you wish to enable specific Spark features as defaults for all Spark applications, +you can apply default Spark configurations. + +For more comprehensive information, please consult the [configuration structure](https://github.com/flyteorg/flyteplugins/blob/c528bb88937b4732c9cb5537ed8ea6943ff4fb56/go/tasks/plugins/k8s/spark/config.go#L24-L29). + +## Run the examples on the Flyte cluster + +To run the provided examples on the Flyte cluster, use any of the following commands: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/k8s_spark_plugin/k8s_spark_plugin/pyspark_pi.py \ + my_spark +``` + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/k8s_spark_plugin/k8s_spark_plugin/dataframe_passing.py \ + my_smart_structured_dataset +``` + +(spark-examples)= + +```{auto-examples-toc} +pyspark_pi +dataframe_passing +``` diff --git a/docs/examples/k8s_spark_plugin/k8s_spark_plugin/__init__.py b/docs/examples/k8s_spark_plugin/k8s_spark_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/k8s_spark_plugin/k8s_spark_plugin/dataframe_passing.py b/docs/examples/k8s_spark_plugin/k8s_spark_plugin/dataframe_passing.py new file mode 100644 index 000000000..368d5558a --- /dev/null +++ b/docs/examples/k8s_spark_plugin/k8s_spark_plugin/dataframe_passing.py @@ -0,0 +1,126 @@ +# %% [markdown] +# (intermediate_spark_dataframes_passing)= +# +# # Converting a Spark DataFrame to a Pandas DataFrame +# +# This example shows the process of returning a Spark dataset from a Flyte task +# and then utilizing it as a Pandas DataFrame. + +# %% [markdown] +# To begin, import the libraries. +# %% +import flytekit +import pandas +from flytekit import ImageSpec, Resources, kwtypes, task, workflow +from flytekit.types.structured.structured_dataset import StructuredDataset +from flytekitplugins.spark import Spark + +try: + from typing import Annotated +except ImportError: + from typing_extensions import Annotated + +# %% [markdown] +# Create an `ImageSpec` to automate the retrieval of a prebuilt Spark image. +# %% +custom_image = ImageSpec(name="flyte-spark-plugin", registry="ghcr.io/flyteorg") + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# In this particular example, +# we specify two column types: `name: str` and `age: int` +# that we extract from the Spark DataFrame. +# %% +columns = kwtypes(name=str, age=int) + + +# %% [markdown] +# To create a Spark task, add {py:class}`~flytekitplugins.spark.Spark` config to the Flyte task. +# +# The `spark_conf` parameter can encompass configuration choices commonly employed when setting up a Spark cluster. +# Additionally, if necessary, you can provide `hadoop_conf` as an input. +# +# Create a task that yields a Spark DataFrame. +# %% +@task( + task_config=Spark( + spark_conf={ + "spark.driver.memory": "1000M", + "spark.executor.memory": "1000M", + "spark.executor.cores": "1", + "spark.executor.instances": "2", + "spark.driver.cores": "1", + } + ), + limits=Resources(mem="2000M"), + container_image=custom_image, +) +def spark_df() -> Annotated[StructuredDataset, columns]: + """ + This task returns a Spark dataset that conforms to the defined schema. + """ + sess = flytekit.current_context().spark_session + return StructuredDataset( + dataframe=sess.createDataFrame( + [ + ("Alice", 5), + ("Bob", 10), + ("Charlie", 15), + ], + ["name", "age"], + ) + ) + + +# %% [markdown] +# `spark_df` represents a Spark task executed within a Spark context, leveraging an active Spark cluster. +# +# This task yields a `pyspark.DataFrame` object, even though the return type is specified as +# {ref}`StructuredDataset `. +# The Flytekit type system handles the automatic conversion of the `pyspark.DataFrame` into a `StructuredDataset` object. +# The `StructuredDataset` object serves as an abstract representation of a DataFrame, adaptable to various DataFrame formats. + + +# %% [markdown] +# Create a task to consume the Spark DataFrame. +# %% +@task +def sum_of_all_ages(sd: Annotated[StructuredDataset, columns]) -> int: + df: pandas.DataFrame = sd.open(pandas.DataFrame).all() + return int(df["age"].sum()) + + +# %% [markdown] +# The `sum_of_all_ages` task accepts a parameter of type `StructuredDataset`. +# By utilizing the `open` method, you can designate the DataFrame format, which, in our scenario, is `pandas.DataFrame`. +# When `all` is invoked on the structured dataset, the executor will load the data into memory (or download it if executed remotely). + + +# %% [markdown] +# Lastly, define a workflow. +# %% +@workflow +def spark_to_pandas_wf() -> int: + df = spark_df() + return sum_of_all_ages(sd=df) + + +# %% [markdown] +# You can execute the code locally. +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running my_smart_schema()-> {spark_to_pandas_wf()}") + +# %% [markdown] +# New DataFrames can be dynamically loaded through the type engine. +# To register a custom DataFrame type, you can define an encoder and decoder for `StructuredDataset` as outlined in the {ref}`structured_dataset_example` example. +# +# Existing DataFrame plugins include: +# +# - {ref}`Modin ` +# - [Vaex](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-vaex/README.md) +# - [Polars](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-polars/README.md) diff --git a/docs/examples/k8s_spark_plugin/k8s_spark_plugin/pyspark_pi.py b/docs/examples/k8s_spark_plugin/k8s_spark_plugin/pyspark_pi.py new file mode 100644 index 000000000..c50e51f7c --- /dev/null +++ b/docs/examples/k8s_spark_plugin/k8s_spark_plugin/pyspark_pi.py @@ -0,0 +1,104 @@ +# %% [markdown] +# (spark_task)= +# +# # Running a Spark Task +# +# To begin, import the necessary dependencies. +# %% +import datetime +import random +from operator import add + +import flytekit +from flytekit import ImageSpec, Resources, task, workflow +from flytekitplugins.spark import Spark + +# %% [markdown] +# Create an `ImageSpec` to automate the retrieval of a prebuilt Spark image. +# %% +custom_image = ImageSpec(name="flyte-spark-plugin", registry="ghcr.io/flyteorg") + + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# To create a Spark task, add {py:class}`~flytekitplugins.spark.Spark` config to the Flyte task. +# +# The `spark_conf` parameter can encompass configuration choices commonly employed when setting up a Spark cluster. +# Additionally, if necessary, you can provide `hadoop_conf` as an input. +# %% +@task( + task_config=Spark( + # This configuration is applied to the Spark cluster + spark_conf={ + "spark.driver.memory": "1000M", + "spark.executor.memory": "1000M", + "spark.executor.cores": "1", + "spark.executor.instances": "2", + "spark.driver.cores": "1", + } + ), + limits=Resources(mem="2000M"), + container_image=custom_image, +) +def hello_spark(partitions: int) -> float: + print("Starting Spark with Partitions: {}".format(partitions)) + + n = 1 * partitions + sess = flytekit.current_context().spark_session + count = sess.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add) + + pi_val = 4.0 * count / n + return pi_val + + +# %% [markdown] +# The `hello_spark` task initiates a new Spark cluster. +# When executed locally, it sets up a single-node client-only cluster. +# However, when executed remotely, it dynamically scales the cluster size based on the specified Spark configuration. +# +# For this particular example, +# we define a function upon which the map-reduce operation is invoked within the Spark cluster. +# %% +def f(_): + x = random.random() * 2 - 1 + y = random.random() * 2 - 1 + return 1 if x**2 + y**2 <= 1 else 0 + + +# %% [markdown] +# Additionally, we specify a standard Flyte task that won't be executed on the Spark cluster. +# %% +@task(cache_version="2") +def print_every_time(value_to_print: float, date_triggered: datetime.datetime) -> int: + print("My printed value: {} @ {}".format(value_to_print, date_triggered)) + return 1 + + +# %% [markdown] +# Finally, define a workflow that connects your tasks in a sequence. +# Remember, Spark and non-Spark tasks can be chained together as long as their parameter specifications match. +# %% +@workflow +def my_spark(triggered_date: datetime.datetime = datetime.datetime.now()) -> float: + """ + Using the workflow is still as any other workflow. As image is a property of the task, the workflow does not care + about how the image is configured. + """ + pi = hello_spark(partitions=1) + print_every_time(value_to_print=pi, date_triggered=triggered_date) + return pi + + +# %% [markdown] +# You can execute the workflow locally. +# Certain aspects of Spark, such as links to {ref}`Hive ` meta stores, may not work in the local execution, +# but these limitations are inherent to using Spark and are not introduced by Flyte. +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print( + f"Running my_spark(triggered_date=datetime.datetime.now()) {my_spark(triggered_date=datetime.datetime.now())}" + ) diff --git a/docs/examples/k8s_spark_plugin/requirements.txt b/docs/examples/k8s_spark_plugin/requirements.txt new file mode 100644 index 000000000..0f67a112c --- /dev/null +++ b/docs/examples/k8s_spark_plugin/requirements.txt @@ -0,0 +1,2 @@ +flytekitplugins-spark +flytekitplugins-envd diff --git a/docs/examples/kfmpi_plugin/Dockerfile b/docs/examples/kfmpi_plugin/Dockerfile new file mode 100644 index 000000000..807ad98c7 --- /dev/null +++ b/docs/examples/kfmpi_plugin/Dockerfile @@ -0,0 +1,76 @@ +FROM ubuntu:focal +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python3.10 and other libraries +RUN apt-get update \ + && apt-get install -y software-properties-common \ + && add-apt-repository ppa:ubuntu-toolchain-r/test \ + && add-apt-repository -y ppa:deadsnakes/ppa \ + && apt-get install -y \ + build-essential \ + cmake \ + g++-7 \ + curl \ + git \ + wget \ + python3.10 \ + python3.10-venv \ + python3.10-dev \ + make \ + libssl-dev \ + python3-pip \ + python3-wheel \ + libuv1 + +# Virtual environment +ENV VENV /opt/venv +RUN python3.10 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +# Install wheel after venv is activated +RUN pip3 install wheel + +# Install Open MPI +RUN wget --progress=dot:mega -O /tmp/openmpi-4.1.4-bin.tar.gz https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.4.tar.gz && \ + cd /tmp && tar -zxf /tmp/openmpi-4.1.4-bin.tar.gz && \ + mkdir openmpi-4.1.4/build && cd openmpi-4.1.4/build && ../configure --prefix=/usr/local && \ + make -j all && make install && ldconfig && \ + mpirun --version + +# Allow OpenSSH to talk to containers without asking for confirmation +RUN mkdir -p /var/run/sshd +RUN cat /etc/ssh/ssh_config | grep -v StrictHostKeyChecking > /etc/ssh/ssh_config.new && \ + echo " StrictHostKeyChecking no" >> /etc/ssh/ssh_config.new && \ + mv /etc/ssh/ssh_config.new /etc/ssh/ssh_config + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Install TensorFlow +# In case you encounter the "The TensorFlow library was compiled to use AVX instructions, which are not present on your machine" error, +# you can resolve it by installing TensorFlow using the following RUN instruction: +# RUN wget https://tf.novaal.de/westmere/tensorflow-2.8.0-cp310-cp310-linux_x86_64.whl && pip install tensorflow-2.8.0-cp310-cp310-linux_x86_64.whl +# Otherwise: +RUN pip install tensorflow==2.8.0 + +# Enable GPU +# ENV HOROVOD_GPU_OPERATIONS NCCL +RUN HOROVOD_WITH_MPI=1 pip install --no-cache-dir horovod==0.28.1 + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/kfmpi_plugin/README.md b/docs/examples/kfmpi_plugin/README.md new file mode 100644 index 000000000..ad23c4729 --- /dev/null +++ b/docs/examples/kfmpi_plugin/README.md @@ -0,0 +1,57 @@ +(kf-mpi-op)= + +# MPI + +```{eval-rst} +.. tags:: Integration, DistributedComputing, MachineLearning, KubernetesOperator, Advanced +``` + +In this section, you'll find a demonstration of running Horovod code with the Kubeflow MPI API. + +## Horovod + +[Horovod](http://horovod.ai/) stands as a distributed deep learning training framework compatible with +TensorFlow, Keras, PyTorch and Apache MXNet. Its primary objective is to enhance the speed and usability +of distributed deep learning through the implementation of ring-allreduce. This technique necessitates +just a few minimal modifications to the user's code, thereby simplifying the process of distributed deep learning. + +## MPI (Message Passing Interface) + +The Flyte platform employs the [Kubeflow training operator](https://github.com/kubeflow/training-operator), +to facilitate streamlined execution of all-reduce-style distributed training on Kubernetes. +This integration offers a straightforward interface for conducting distributed training through the utilization of MPI. + +The combined power of MPI and Horovod can be harnessed to streamline the complexities of distributed training. +The MPI API serves as a convenient encapsulation to execute Horovod scripts, thereby enhancing the overall efficiency of the process. + +## Install the plugin + +Install the MPI plugin by running the following command: + +``` +pip install flytekitplugins-kfmpi +``` + +## Build a Docker image + +The Dockerfile should include installation commands for various components, including MPI and Horovod. + +```{literalinclude} ../../../examples/kfmpi_plugin/Dockerfile +:language: docker +:emphasize-lines: 40-51,66 +``` + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + --image ghcr.io/flyteorg/flytecookbook:kfmpi_plugin-latest \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/kfmpi_plugin/kfmpi_plugin/mpi_mnist.py \ + horovod_training_wf +``` + +```{auto-examples-toc} +mpi_mnist +``` diff --git a/docs/examples/kfmpi_plugin/kfmpi_plugin/__init__.py b/docs/examples/kfmpi_plugin/kfmpi_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/kfmpi_plugin/kfmpi_plugin/mpi_mnist.py b/docs/examples/kfmpi_plugin/kfmpi_plugin/mpi_mnist.py new file mode 100644 index 000000000..ae6f4ac3f --- /dev/null +++ b/docs/examples/kfmpi_plugin/kfmpi_plugin/mpi_mnist.py @@ -0,0 +1,179 @@ +# %% [markdown] +# # Running Distributed Training Using Horovod and MPI +# +# This example demonstrates how to conduct distributed training of a CNN on MNIST data. + +# %% [markdown] +# To begin, import the necessary dependencies. +# %% +import os +import pathlib + +import flytekit +import tensorflow as tf +from flytekit import Resources, task, workflow +from flytekit.core.base_task import IgnoreOutputs +from flytekit.types.directory import FlyteDirectory +from flytekitplugins.kfmpi import Launcher, MPIJob, Worker + + +# %% [markdown] +# In the context of this example, we define a training step that will be invoked during the training loop. +# In this step, the training loss is calculated and the model weights are adjusted using gradients. +# %% +@tf.function +def training_step(images, labels, first_batch, mnist_model, loss, opt): + import horovod.tensorflow as hvd + + with tf.GradientTape() as tape: + probs = mnist_model(images, training=True) + loss_value = loss(labels, probs) + + # Add Horovod Distributed GradientTape — a tape that wraps another tf.GradientTape, + # using an allreduce to combine gradient values before applying gradients to model weights. + tape = hvd.DistributedGradientTape(tape) + + grads = tape.gradient(loss_value, mnist_model.trainable_variables) + opt.apply_gradients(zip(grads, mnist_model.trainable_variables)) + + # Broadcast initial variable states from rank 0 to all other processes. + # This is necessary to ensure consistent initialization of all workers when + # training is started with random weights or restored from a checkpoint. + # + # Note: Broadcast should be done after the first gradient step to ensure optimizer + # initialization. + if first_batch: + hvd.broadcast_variables(mnist_model.variables, root_rank=0) + hvd.broadcast_variables(opt.variables(), root_rank=0) + + return loss_value + + +# %% [markdown] +# To create an MPI task, add {py:class}`~flytekitplugins.kfmpi.MPIJob` config to the Flyte task. +# The configuration given in the `MPIJob` constructor will be used to set up the distributed training environment. +# +# Broadly, let us define a task that executes the following operations: +# +# 1. Loads the MNIST data +# 2. Prepares the data for training +# 3. Initializes a CNN model +# 4. Invokes the `training_step()` function to train the model +# 5. Saves the model, checkpoint history, and returns the result +# +# :::{note} +# For running Horovod code specifically, an alternative to using the `MPIJob` configuration is to employ the +# [`HorovodJob`](https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-kf-mpi/flytekitplugins/kfmpi/task.py#L222) +# configuration. Internally, the `HorovodJob` configuration utilizes the `horovodrun` command, +# while the `MPIJob` configuration utilizes `mpirun`. +# ::: +# %% +@task( + task_config=MPIJob( + launcher=Launcher( + replicas=1, + ), + worker=Worker( + replicas=1, + ), + ), + retries=3, + cache=True, + cache_version="0.1", + requests=Resources(cpu="1", mem="1000Mi"), + limits=Resources(cpu="2"), +) +def horovod_train_task(batch_size: int, buffer_size: int, dataset_size: int) -> FlyteDirectory: + import horovod.tensorflow as hvd + + hvd.init() + + (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(path="mnist-%d.npz" % hvd.rank()) + + dataset = tf.data.Dataset.from_tensor_slices( + ( + tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32), + tf.cast(mnist_labels, tf.int64), + ) + ) + dataset = dataset.repeat().shuffle(buffer_size).batch(batch_size) + + mnist_model = tf.keras.Sequential( + [ + tf.keras.layers.Conv2D(32, [3, 3], activation="relu"), + tf.keras.layers.Conv2D(64, [3, 3], activation="relu"), + tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), + tf.keras.layers.Dropout(0.25), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dropout(0.5), + tf.keras.layers.Dense(10, activation="softmax"), + ] + ) + loss = tf.losses.SparseCategoricalCrossentropy() + + # Adjust learning rate based on number of GPUs + opt = tf.optimizers.Adam(0.001 * hvd.size()) + + checkpoint_dir = ".checkpoint" + pathlib.Path(checkpoint_dir).mkdir(exist_ok=True) + + checkpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt) + + # Adjust number of steps based on number of GPUs + for batch, (images, labels) in enumerate(dataset.take(dataset_size // hvd.size())): + loss_value = training_step(images, labels, batch == 0, mnist_model, loss, opt) + + if batch % 10 == 0 and hvd.local_rank() == 0: + print("Step #%d\tLoss: %.6f" % (batch, loss_value)) + + if hvd.rank() != 0: + raise IgnoreOutputs("I am not rank 0") + + working_dir = flytekit.current_context().working_directory + checkpoint_prefix = pathlib.Path(os.path.join(working_dir, "checkpoint")) + checkpoint.save(checkpoint_prefix) + + tf.keras.models.save_model( + mnist_model, + str(working_dir), + overwrite=True, + include_optimizer=True, + save_format=None, + signatures=None, + options=None, + save_traces=True, + ) + return FlyteDirectory(path=str(working_dir)) + + +# %% [markdown] +# Lastly, define a workflow. +# %% +@workflow +def horovod_training_wf(batch_size: int = 128, buffer_size: int = 10000, dataset_size: int = 10000) -> FlyteDirectory: + """ + :param batch_size: Represents the number of consecutive elements of the dataset to combine in a single batch. + :param buffer_size: Defines the size of the buffer used to hold elements of the dataset used for training. + :param dataset_size: The number of elements of this dataset that should be taken to form the new dataset when + running batched training. + """ + return horovod_train_task(batch_size=batch_size, buffer_size=buffer_size, dataset_size=dataset_size) + + +# %% [markdown] +# You can execute the workflow locally. +# %% +if __name__ == "__main__": + model, plot, logs = horovod_training_wf() + print(f"Model: {model}, plot PNG: {plot}, Tensorboard Log Dir: {logs}") + +# %% [markdown] +# :::{note} +# In the context of distributed training, it's important to acknowledge that return values from various workers could potentially vary. +# If you need to regulate which worker's return value gets passed on to subsequent tasks in the workflow, +# you have the option to raise an +# [IgnoreOutputs exception](https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.core.base_task.IgnoreOutputs.html) +# for all remaining ranks. +# ::: +# %% diff --git a/docs/examples/kfmpi_plugin/requirements.in b/docs/examples/kfmpi_plugin/requirements.in new file mode 100644 index 000000000..a41edca38 --- /dev/null +++ b/docs/examples/kfmpi_plugin/requirements.in @@ -0,0 +1,2 @@ +flytekitplugins-kfmpi +flytekitplugins-envd diff --git a/docs/examples/kfmpi_plugin/requirements.txt b/docs/examples/kfmpi_plugin/requirements.txt new file mode 100644 index 000000000..08f281af6 --- /dev/null +++ b/docs/examples/kfmpi_plugin/requirements.txt @@ -0,0 +1,348 @@ +# +# This file is autogenerated by pip-compile with Python 3.10 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.2 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.38 + # via flytekitplugins-envd +flyteidl==1.5.16 + # via flytekit +flytekit==1.9.0 + # via + # flytekitplugins-envd + # flytekitplugins-kfmpi +flytekitplugins-envd==1.9.0 + # via -r requirements.in +flytekitplugins-kfmpi==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.0 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/kfpytorch_plugin/Dockerfile b/docs/examples/kfpytorch_plugin/Dockerfile new file mode 100644 index 000000000..b6b90afe6 --- /dev/null +++ b/docs/examples/kfpytorch_plugin/Dockerfile @@ -0,0 +1,36 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM python:3.10-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND noninteractive +ENV TERM linux + +RUN apt-get update && apt-get install -y make build-essential libssl-dev curl + +# Install gcc, g++ and make +RUN apt-get update && apt-get install --no-install-recommends -y gcc g++ +RUN echo 'installing make' \ + && apt-get install make + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/kfpytorch_plugin/README.md b/docs/examples/kfpytorch_plugin/README.md new file mode 100644 index 000000000..cbaa60002 --- /dev/null +++ b/docs/examples/kfpytorch_plugin/README.md @@ -0,0 +1,34 @@ +(kf-pytorch-op)= + +# PyTorch Distributed + +```{eval-rst} +.. tags:: Integration, DistributedComputing, MachineLearning, KubernetesOperator, Advanced +``` + +The Kubeflow PyTorch plugin leverages the [Kubeflow training operator](https://github.com/kubeflow/training-operator) +to offer a highly streamlined interface for conducting distributed training using different PyTorch backends. + +## Install the plugin + +To use the PyTorch plugin, run the following command: + +``` +pip install flytekitplugins-kfpytorch +``` + +To enable the plugin in the backend, follow instructions outlined in the {std:ref}`flyte:deployment-plugin-setup-k8s` guide. + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/kfpytorch_plugin/kfpytorch_plugin/pytorch_mnist.py \ + pytorch_training_wf +``` + +```{auto-examples-toc} +pytorch_mnist +``` diff --git a/docs/examples/kfpytorch_plugin/kfpytorch_plugin/__init__.py b/docs/examples/kfpytorch_plugin/kfpytorch_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/kfpytorch_plugin/kfpytorch_plugin/pytorch_mnist.py b/docs/examples/kfpytorch_plugin/kfpytorch_plugin/pytorch_mnist.py new file mode 100644 index 000000000..9fd59461e --- /dev/null +++ b/docs/examples/kfpytorch_plugin/kfpytorch_plugin/pytorch_mnist.py @@ -0,0 +1,388 @@ +# %% [markdown] +# # Run PyTorch Distributed +# +# This example is based on the default MNIST example found in the Kubeflow's PyTorch guide +# [here](https://github.com/kubeflow/training-operator/blob/master/examples/pytorch/mnist/mnist.py). +# +# To begin, import the required dependencies. +# %% +import os +import typing +from dataclasses import dataclass +from typing import Tuple + +import flytekit +from dataclasses_json import dataclass_json +from flytekit import ImageSpec, Resources, task, workflow +from flytekit.types.directory import TensorboardLogs +from flytekit.types.file import PNGImageFile, PythonPickledFile + +WORLD_SIZE = int(os.environ.get("WORLD_SIZE", 1)) + +# %% [markdown] +# Create an `ImageSpec` to encompass all the dependencies needed for the PyTorch task. +# %% +custom_image = ImageSpec( + name="flyte-kfpytorch-plugin", + packages=["torch", "torchvision", "flytekitplugins-kfpytorch", "matplotlib", "tensorboardX"], + registry="ghcr.io/flyteorg", +) + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# The following imports are required to configure the PyTorch cluster in Flyte. +# You can load them on demand. +# %% +if custom_image.is_container(): + import matplotlib.pyplot as plt + import torch + import torch.nn.functional as F + from flytekitplugins.kfpytorch import PyTorch, Worker + from tensorboardX import SummaryWriter + from torch import distributed as dist + from torch import nn, optim + from torchvision import datasets, transforms + +# %% [markdown] +# You can activate GPU support by either using the base image that includes the necessary GPU dependencies +# or by initializing the [CUDA parameters](https://github.com/flyteorg/flytekit/blob/master/flytekit/image_spec/image_spec.py#L34-L35) +# within the `ImageSpec`. +# +# Adjust memory, GPU usage and storage settings based on whether you are +# registering against the demo cluster or not. +# %% +if os.getenv("SANDBOX") != "": + cpu_request = "500m" + mem_request = "500Mi" + gpu_request = "0" + mem_limit = "500Mi" + gpu_limit = "0" +else: + cpu_request = "500m" + mem_request = "4Gi" + gpu_request = "1" + mem_limit = "8Gi" + gpu_limit = "1" + + +# %% [markdown] +# In this example, we create a model. +# %% +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 20, 5, 1) + self.conv2 = nn.Conv2d(20, 50, 5, 1) + self.fc1 = nn.Linear(4 * 4 * 50, 500) + self.fc2 = nn.Linear(500, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 50) + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return F.log_softmax(x, dim=1) + + +# %% [markdown] +# We define a trainer. +# %% +def train(model, device, train_loader, optimizer, epoch, writer, log_interval): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = F.nll_loss(output, target) + loss.backward() + optimizer.step() + if batch_idx % log_interval == 0: + print( + "Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format( + epoch, + batch_idx * len(data), + len(train_loader.dataset), + 100.0 * batch_idx / len(train_loader), + loss.item(), + ) + ) + niter = epoch * len(train_loader) + batch_idx + writer.add_scalar("loss", loss.item(), niter) + + +# %% [markdown] +# We define a test function to test the trained model. +# %% +def test(model, device, test_loader, writer, epoch): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + data, target = data.to(device), target.to(device) + output = model(data) + test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss + pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability + correct += pred.eq(target.view_as(pred)).sum().item() + + test_loss /= len(test_loader.dataset) + print("\naccuracy={:.4f}\n".format(float(correct) / len(test_loader.dataset))) + accuracy = float(correct) / len(test_loader.dataset) + writer.add_scalar("accuracy", accuracy, epoch) + return accuracy + + +# %% [markdown] +# We define a couple of auxiliary functions, initialize hyperparameters +# and create a `NamedTuple` to capture the outputs of the PyTorch task. +# %% +def epoch_step(model, device, train_loader, test_loader, optimizer, epoch, writer, log_interval): + train(model, device, train_loader, optimizer, epoch, writer, log_interval) + return test(model, device, test_loader, writer, epoch) + + +def should_distribute(): + return dist.is_available() and WORLD_SIZE > 1 + + +def is_distributed(): + return dist.is_available() and dist.is_initialized() + + +@dataclass_json +@dataclass +class Hyperparameters(object): + """ + Args: + backend: Distributed backend to use + sgd_momentum: SGD momentum (default: 0.5) + seed: random seed (default: 1) + log_interval: how many batches to wait for before logging training status + batch_size: input batch size for training (default: 64) + test_batch_size: input batch size for testing (default: 1000) + epochs: number of epochs to train (default: 10) + learning_rate: learning rate (default: 0.01) + """ + + backend: str = dist.Backend.GLOO + sgd_momentum: float = 0.5 + seed: int = 1 + log_interval: int = 10 + batch_size: int = 64 + test_batch_size: int = 1000 + epochs: int = 10 + learning_rate: float = 0.01 + + +TrainingOutputs = typing.NamedTuple( + "TrainingOutputs", + epoch_accuracies=typing.List[float], + model_state=PythonPickledFile, + logs=TensorboardLogs, +) + + +# %% [markdown] +# To create a PyTorch task, add {py:class}`~flytekitplugins.kfpytorch.PyTorch` config to the Flyte task. +# %% +@task( + task_config=PyTorch(worker=Worker(replicas=2)), + retries=2, + cache=True, + cache_version="0.1", + requests=Resources(cpu=cpu_request, mem=mem_request, gpu=gpu_request), + limits=Resources(mem=mem_limit, gpu=gpu_limit), + container_image=custom_image, +) +def mnist_pytorch_job(hp: Hyperparameters) -> TrainingOutputs: + log_dir = os.path.join(flytekit.current_context().working_directory, "logs") + writer = SummaryWriter(log_dir) + + torch.manual_seed(hp.seed) + + use_cuda = torch.cuda.is_available() + print(f"Use cuda {use_cuda}") + device = torch.device("cuda" if use_cuda else "cpu") + + print("Using device: {}, world size: {}".format(device, WORLD_SIZE)) + + if should_distribute(): + print("Using distributed PyTorch with {} backend".format(hp.backend)) + dist.init_process_group(backend=hp.backend) + + # Load data + kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {} + train_loader = torch.utils.data.DataLoader( + datasets.MNIST( + os.path.join(flytekit.current_context().working_directory, "data"), + train=True, + download=True, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ), + batch_size=hp.batch_size, + shuffle=True, + **kwargs, + ) + test_loader = torch.utils.data.DataLoader( + datasets.MNIST( + os.path.join(flytekit.current_context().working_directory, "data"), + train=False, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ), + batch_size=hp.test_batch_size, + shuffle=False, + **kwargs, + ) + + # Train the model + model = Net().to(device) + + if is_distributed(): + Distributor = nn.parallel.DistributedDataParallel if use_cuda else nn.parallel.DistributedDataParallelCPU + model = Distributor(model) + + optimizer = optim.SGD(model.parameters(), lr=hp.learning_rate, momentum=hp.sgd_momentum) + + accuracies = [ + epoch_step( + model, + device, + train_loader, + test_loader, + optimizer, + epoch, + writer, + hp.log_interval, + ) + for epoch in range(1, hp.epochs + 1) + ] + + # Save the model + model_file = os.path.join(flytekit.current_context().working_directory, "mnist_cnn.pt") + torch.save(model.state_dict(), model_file) + + return TrainingOutputs( + epoch_accuracies=accuracies, + model_state=PythonPickledFile(model_file), + logs=TensorboardLogs(log_dir), + ) + + +# %% [markdown] +# The `torch.save` function is utilized to save the model's `state_dict` in accordance with the guidelines outlined in the +# [PyTorch documentation](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-and-loading-models). +# Typically, the file is given a `.pt` extension. +# +# Additionally, an output variable named `logs` will be generated. +# These logs can be employed for visualizing the training process in Tensorboard. +# They constitute the outcomes of the `SummaryWriter` interface. +# %% + + +# %% [markdown] +# Next, we generate an accuracy plot in the form of a PNG image. +# %% +@task(container_image=custom_image) +def plot_accuracy(epoch_accuracies: typing.List[float]) -> PNGImageFile: + plt.plot(epoch_accuracies) + plt.title("Accuracy") + plt.ylabel("accuracy") + plt.xlabel("epoch") + accuracy_plot = os.path.join(flytekit.current_context().working_directory, "accuracy.png") + plt.savefig(accuracy_plot) + return PNGImageFile(accuracy_plot) + + +# %% [markdown] +# In the end, we combine the training and plotting processes within a single pipeline. +# In this setup, the training is executed initially, succeeded by the accuracy plotting phase. +# Data is exchanged between these steps, and the workflow produces both the image and the serialized model as its outputs. +# %% +@workflow +def pytorch_training_wf( + hp: Hyperparameters = Hyperparameters(epochs=2, batch_size=128), +) -> Tuple[PythonPickledFile, PNGImageFile, TensorboardLogs]: + accuracies, model, logs = mnist_pytorch_job(hp=hp) + plot = plot_accuracy(epoch_accuracies=accuracies) + return model, plot, logs + + +# %% [markdown] +# Running the model locally requires minimal modifications, +# as long as the code handles the resolution of whether it should be run in a distributed manner or not. +# %% +if __name__ == "__main__": + model, plot, logs = pytorch_training_wf() + print(f"Model: {model}, plot PNG: {plot}, Tensorboard Log Dir: {logs}") + +# %% [markdown] +# (pytorch_tensorboard)= +# :::{note} +# During local execution, the output of the process appears as follows: +# +# ``` +# Model: /tmp/flyte/20210110_214129/mock_remote/8421ae4d041f76488e245edf3f4360d5/my_model.h5, plot PNG: /tmp/flyte/20210110_214129/mock_remote/cf6a2cd9d3ded89ed814278a8fb3678c/accuracy.png, Tensorboard Log Dir: /tmp/flyte/20210110_214129/mock_remote/a4b04e58e21f26f08f81df24094d6446/ +# ``` +# +# To visualize the training progress, utilize the Tensorboard log directory path as input for Tensorboard, as demonstrated below: +# +# ``` +# tensorboard --logdir /tmp/flyte/20210110_214129/mock_remote/a4b04e58e21f26f08f81df24094d6446/ +# ``` +# +# When executing remotely on the Flyte-hosted environment, the workflow execution outputs can be retrieved. +# You can obtain the outputs, which will be in the form of a path to a storage system such as S3, GCS, Minio, etc. +# To visualize the outcomes, you can point Tensorboard on your local machine to these storage locations. +# ::: +# +# ## Pytorch elastic training (torchrun) +# +# Flyte supports distributed training through [torch elastic](https://pytorch.org/docs/stable/elastic/run.html) using `torchrun`. +# You can carry out elastic training on a single node with a local worker group of size four, +# analogous to executing `torchrun --nproc-per-node=4 --nnodes=1 ....`. +# +# The process involves adding {py:class}`~flytekitplugins.kfpytorch.Elastic` configuration to the Flyte task. +# +# ```python +# from flytekitplugins.kfpytorch import Elastic +# +# @task( +# task_config=Elastic( +# nnodes=1, +# nproc_per_node=4, +# ) +# ) +# def task(): +# ... +# ``` +# +# This initializes four worker processes, whether executed locally or remotely within a Kubernetes pod within a Flyte cluster. +# For distributed elastic training across multiple nodes, the Elastic task configuration can be utilized as follows: +# +# ```python +# from flytekitplugins.kfpytorch import Elastic +# +# @task( +# task_config=Elastic( +# nnodes=2, +# nproc_per_node=4, +# ), +# ) +# def train(): +# ``` +# +# This configuration runs distributed training on two nodes, each with four worker processes. +# +# :::{note} +# In the context of distributed training, it's important to acknowledge that return values from various workers could potentially vary. +# If you need to regulate which worker's return value gets passed on to subsequent tasks in the workflow, +# you have the option to raise an +# [IgnoreOutputs exception](https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.core.base_task.IgnoreOutputs.html) +# for all remaining ranks. +# ::: diff --git a/docs/examples/kfpytorch_plugin/requirements.in b/docs/examples/kfpytorch_plugin/requirements.in new file mode 100644 index 000000000..dc8618209 --- /dev/null +++ b/docs/examples/kfpytorch_plugin/requirements.in @@ -0,0 +1,6 @@ +flytekitplugins-kfpytorch +flytekitplugins-envd +tensorboardX +torch +torchvision +matplotlib diff --git a/docs/examples/kfpytorch_plugin/requirements.txt b/docs/examples/kfpytorch_plugin/requirements.txt new file mode 100644 index 000000000..8b78d1aac --- /dev/null +++ b/docs/examples/kfpytorch_plugin/requirements.txt @@ -0,0 +1,396 @@ +# +# This file is autogenerated by pip-compile with Python 3.10 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.2 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via + # flytekit + # flytekitplugins-kfpytorch +contourpy==1.1.0 + # via matplotlib +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.38 + # via flytekitplugins-envd +filelock==3.12.2 + # via torch +flyteidl==1.5.16 + # via + # flytekit + # flytekitplugins-kfpytorch +flytekit==1.9.0 + # via + # flytekitplugins-envd + # flytekitplugins-kfpytorch +flytekitplugins-envd==1.9.0 + # via -r requirements.in +flytekitplugins-kfpytorch==1.9.0 + # via -r requirements.in +fonttools==4.42.0 + # via matplotlib +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via + # cookiecutter + # torch +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.7.2 + # via -r requirements.in +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +mpmath==1.3.0 + # via sympy +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +networkx==3.1 + # via torch +numpy==1.25.2 + # via + # contourpy + # flytekit + # matplotlib + # pandas + # pyarrow + # tensorboardx + # torchvision +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow + # matplotlib + # tensorboardx +pandas==1.5.3 + # via flytekit +pillow==10.0.0 + # via + # matplotlib + # torchvision +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.0 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # tensorboardx +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # torchvision +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +sympy==1.12 + # via torch +tensorboardx==2.6.2 + # via -r requirements.in +text-unidecode==1.3 + # via python-slugify +torch==2.0.1 + # via + # -r requirements.in + # torchvision +torchvision==0.15.2 + # via -r requirements.in +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # torch + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/kftensorflow_plugin/Dockerfile b/docs/examples/kftensorflow_plugin/Dockerfile new file mode 100644 index 000000000..10ae82483 --- /dev/null +++ b/docs/examples/kftensorflow_plugin/Dockerfile @@ -0,0 +1,53 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM tensorflow/tensorflow:latest +# You can enable GPU support by replacing the above line with: +# FROM tensorflow/tensorflow:latest-gpu + +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND noninteractive +ENV TERM linux + +# Install Python3.10 and other libraries +RUN apt-get update \ + && apt-get install -y software-properties-common \ + && apt-get install -y \ + build-essential \ + git \ + wget \ + python3.10 \ + python3.10-venv \ + python3.10-dev \ + libssl-dev \ + python3-pip \ + python3-wheel \ + libuv1 + +# Virtual environment +ENV VENV /opt/venv +RUN python3.10 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install wheel after venv is activated +RUN pip3 install wheel + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Install TensorFlow +RUN pip install tensorflow + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/kftensorflow_plugin/README.md b/docs/examples/kftensorflow_plugin/README.md new file mode 100644 index 000000000..a137c40db --- /dev/null +++ b/docs/examples/kftensorflow_plugin/README.md @@ -0,0 +1,34 @@ +(kftensorflow-plugin)= + +# TensorFlow Distributed + +```{eval-rst} +.. tags:: Integration, DistributedComputing, MachineLearning, KubernetesOperator, Advanced +``` + +TensorFlow operator is useful to natively run distributed TensorFlow training jobs on Flyte. +It leverages the [Kubeflow training operator](https://github.com/kubeflow/training-operator). + +## Install the plugin + +To install the Kubeflow TensorFlow plugin, run the following command: + +``` +pip install flytekitplugins-kftensorflow +``` + +To enable the plugin in the backend, follow instructions outlined in the {std:ref}`flyte:deployment-plugin-setup-k8s` guide. + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/kftensorflow_plugin/kftensorflow_plugin/tf_mnist.py \ + mnist_tensorflow_workflow +``` + +```{auto-examples-toc} +tf_mnist +``` diff --git a/docs/examples/kftensorflow_plugin/kftensorflow_plugin/__init__.py b/docs/examples/kftensorflow_plugin/kftensorflow_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/kftensorflow_plugin/kftensorflow_plugin/tf_mnist.py b/docs/examples/kftensorflow_plugin/kftensorflow_plugin/tf_mnist.py new file mode 100644 index 000000000..3b5f69783 --- /dev/null +++ b/docs/examples/kftensorflow_plugin/kftensorflow_plugin/tf_mnist.py @@ -0,0 +1,248 @@ +# %% [markdown] +# # Run Distributed TensorFlow Training +# +# When you need to scale up model training using TensorFlow, you can utilize the +# {py:class}`~tensorflow:tf.distribute.Strategy` to distribute your training across multiple devices. +# Several strategies are available within this API, all of which can be employed as needed. +# In this example, we employ the {py:class}`~tensorflow:tf.distribute.MirroredStrategy` to train an MNIST model using a CNN. +# +# The `MirroredStrategy` enables synchronous distributed training across multiple GPUs on a single machine. +# For a deeper understanding of distributed training with TensorFlow, refer to the +# [distributed training with TensorFlow](https://www.tensorflow.org/guide/distributed_training) in the TensorFlow documentation. +# +# To begin, load the libraries. +# %% +import os +from dataclasses import dataclass +from typing import NamedTuple, Tuple + +from dataclasses_json import dataclass_json +from flytekit import ImageSpec, Resources, task, workflow +from flytekit.types.directory import FlyteDirectory + +# %% [markdown] +# Create an `ImageSpec` to encompass all the dependencies needed for the TensorFlow task. +# %% +custom_image = ImageSpec( + name="kftensorflow-flyte-plugin", + packages=["tensorflow", "tensorflow-datasets", "flytekitplugins-kftensorflow"], + registry="ghcr.io/flyteorg", +) + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# The following imports are required to configure the TensorFlow cluster in Flyte. +# You can load them on demand. +# %% +if custom_image.is_container(): + import tensorflow as tf + import tensorflow_datasets as tfds + from flytekitplugins.kftensorflow import PS, Chief, TfJob, Worker + +# %% [markdown] +# You can activate GPU support by either using the base image that includes the necessary GPU dependencies +# or by initializing the [CUDA parameters](https://github.com/flyteorg/flytekit/blob/master/flytekit/image_spec/image_spec.py#L34-L35) +# within the `ImageSpec`. +# +# For this example, we define the `MODEL_FILE_PATH` variable to indicate the storage location for the model file. +# %% +MODEL_FILE_PATH = "saved_model/" + + +# %% [markdown] +# We initialize a data class to store the hyperparameters. +# %% +@dataclass_json +@dataclass +class Hyperparameters(object): + batch_size_per_replica: int = 64 + buffer_size: int = 10000 + epochs: int = 10 + + +# %% [markdown] +# We use the [MNIST](https://www.tensorflow.org/datasets/catalog/mnist) dataset to train our model. +# %% +def load_data( + hyperparameters: Hyperparameters, +) -> Tuple[tf.data.Dataset, tf.data.Dataset, tf.distribute.Strategy]: + datasets, _ = tfds.load(name="mnist", with_info=True, as_supervised=True) + mnist_train, mnist_test = datasets["train"], datasets["test"] + + strategy = tf.distribute.MirroredStrategy() + print("Number of devices: {}".format(strategy.num_replicas_in_sync)) + + # strategy.num_replicas_in_sync returns the number of replicas; helpful to utilize the extra compute power by increasing the batch size + BATCH_SIZE = hyperparameters.batch_size_per_replica * strategy.num_replicas_in_sync + + def scale(image, label): + image = tf.cast(image, tf.float32) + image /= 255 + + return image, label + + # Fetch train and evaluation datasets + train_dataset = mnist_train.map(scale).shuffle(hyperparameters.buffer_size).batch(BATCH_SIZE) + eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE) + + return train_dataset, eval_dataset, strategy + + +# %% [markdown] +# We create and compile a model in the context of [Strategy.scope](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy#scope). +# %% +def get_compiled_model(strategy: tf.distribute.Strategy) -> tf.keras.Model: + with strategy.scope(): + model = tf.keras.Sequential( + [ + tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)), + tf.keras.layers.MaxPooling2D(), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(64, activation="relu"), + tf.keras.layers.Dense(10), + ] + ) + + model.compile( + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + optimizer=tf.keras.optimizers.Adam(), + metrics=["accuracy"], + ) + + return model + + +# %% [markdown] +# We define a function for decaying the learning rate. +# %% +def decay(epoch: int): + if epoch < 3: + return 1e-3 + elif epoch >= 3 and epoch < 7: + return 1e-4 + else: + return 1e-5 + + +# %% [markdown] +# We define the `train_model` function to initiate model training with three callbacks: +# +# - {py:class}`~tensorflow:tf.keras.callbacks.TensorBoard` to log the training metrics +# - {py:class}`~tensorflow:tf.keras.callbacks.ModelCheckpoint` to save the model after every epoch +# - {py:class}`~tensorflow:tf.keras.callbacks.LearningRateScheduler` to decay the learning rate +# %% +def train_model( + model: tf.keras.Model, + train_dataset: tf.data.Dataset, + hyperparameters: Hyperparameters, +) -> Tuple[tf.keras.Model, str]: + # Define the checkpoint directory to store checkpoints + checkpoint_dir = "./training_checkpoints" + + # Define the name of the checkpoint files + checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") + + # Define a callback for printing the learning rate at the end of each epoch + class PrintLR(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs=None): + print("\nLearning rate for epoch {} is {}".format(epoch + 1, model.optimizer.lr.numpy())) + + # Put all the callbacks together + callbacks = [ + tf.keras.callbacks.TensorBoard(log_dir="./logs"), + tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix, save_weights_only=True), + tf.keras.callbacks.LearningRateScheduler(decay), + PrintLR(), + ] + + # Train the model + model.fit(train_dataset, epochs=hyperparameters.epochs, callbacks=callbacks) + + # Save the model + model.save(MODEL_FILE_PATH, save_format="tf") + + return model, checkpoint_dir + + +# %% [markdown] +# We define the `test_model` function to evaluate loss and accuracy on the test dataset. +# %% +def test_model(model: tf.keras.Model, checkpoint_dir: str, eval_dataset: tf.data.Dataset) -> Tuple[float, float]: + model.load_weights(tf.train.latest_checkpoint(checkpoint_dir)) + + eval_loss, eval_acc = model.evaluate(eval_dataset) + + return eval_loss, eval_acc + + +# %% [markdown] +# To create a TensorFlow task, add {py:class}`~flytekitplugins.kftensorflow.TfJob` config to the Flyte task. +# %% +training_outputs = NamedTuple("TrainingOutputs", accuracy=float, loss=float, model_state=FlyteDirectory) + +if os.getenv("SANDBOX") != "": + resources = Resources(gpu="0", mem="1000Mi", storage="500Mi", ephemeral_storage="500Mi") +else: + resources = Resources(gpu="2", mem="10Gi", storage="10Gi", ephemeral_storage="500Mi") + + +@task( + task_config=TfJob(worker=Worker(replicas=1), ps=PS(replicas=1), chief=Chief(replicas=1)), + retries=2, + cache=True, + cache_version="2.2", + requests=resources, + limits=resources, + container_image=custom_image, +) +def mnist_tensorflow_job(hyperparameters: Hyperparameters) -> training_outputs: + train_dataset, eval_dataset, strategy = load_data(hyperparameters=hyperparameters) + model = get_compiled_model(strategy=strategy) + model, checkpoint_dir = train_model(model=model, train_dataset=train_dataset, hyperparameters=hyperparameters) + eval_loss, eval_accuracy = test_model(model=model, checkpoint_dir=checkpoint_dir, eval_dataset=eval_dataset) + return training_outputs(accuracy=eval_accuracy, loss=eval_loss, model_state=MODEL_FILE_PATH) + + +# %% [markdown] +# The task is initiated using `TFJob` with specific values configured: +# +# - `num_workers`: specifies the number of worker replicas to be launched in the cluster for this job +# - `num_ps_replicas`: determines the count of parameter server replicas to utilize +# - `num_chief_replicas`: defines the number of chief replicas to be employed +# +# For our example, with `MirroredStrategy` leveraging an all-reduce algorithm to communicate variable updates across devices, +# the parameter `num_ps_replicas` does not hold significance. +# +# :::{note} +# If you're interested in exploring the diverse TensorFlow strategies available for distributed training, +# you can find comprehensive information in the +# [types of strategies](https://www.tensorflow.org/guide/distributed_training#types_of_strategies) +# section of the TensorFlow documentation. +# ::: +# +# Lastly, define a workflow to invoke the tasks. +# %% +@workflow +def mnist_tensorflow_workflow( + hyperparameters: Hyperparameters = Hyperparameters(batch_size_per_replica=64), +) -> training_outputs: + return mnist_tensorflow_job(hyperparameters=hyperparameters) + + +# %% [markdown] +# You can run the code locally. +# %% +if __name__ == "__main__": + print(mnist_tensorflow_workflow()) + +# %% [markdown] +# :::{note} +# In the context of distributed training, it's important to acknowledge that return values from various workers could potentially vary. +# If you need to regulate which worker's return value gets passed on to subsequent tasks in the workflow, +# you have the option to raise an +# [IgnoreOutputs exception](https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.core.base_task.IgnoreOutputs.html) +# for all remaining ranks. +# ::: diff --git a/docs/examples/kftensorflow_plugin/requirements.in b/docs/examples/kftensorflow_plugin/requirements.in new file mode 100644 index 000000000..c03bf9dee --- /dev/null +++ b/docs/examples/kftensorflow_plugin/requirements.in @@ -0,0 +1,3 @@ +tensorflow-datasets +flytekitplugins-kftensorflow +flytekitplugins-envd diff --git a/docs/examples/kftensorflow_plugin/requirements.txt b/docs/examples/kftensorflow_plugin/requirements.txt new file mode 100644 index 000000000..2437ccb70 --- /dev/null +++ b/docs/examples/kftensorflow_plugin/requirements.txt @@ -0,0 +1,396 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +absl-py==1.4.0 + # via + # array-record + # tensorflow-datasets + # tensorflow-metadata +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +array-record==0.4.1 + # via tensorflow-datasets +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.2 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # flytekit + # rich-click + # tensorflow-datasets +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl + # secretstorage +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +dm-tree==0.1.8 + # via tensorflow-datasets +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.38 + # via flytekitplugins-envd +etils[enp,epath,epy]==1.4.1 + # via + # array-record + # tensorflow-datasets +flyteidl==1.5.16 + # via flytekit +flytekit==1.9.0 + # via + # flytekitplugins-envd + # flytekitplugins-kftensorflow +flytekitplugins-envd==1.9.0 + # via -r requirements.in +flytekitplugins-kftensorflow==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.37 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status + # tensorflow-metadata +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +importlib-resources==6.0.1 + # via etils +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # etils + # flytekit + # pandas + # pyarrow + # tensorflow-datasets +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +promise==2.3 + # via tensorflow-datasets +protobuf==4.24.0 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # tensorflow-datasets + # tensorflow-metadata +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.5 + # via tensorflow-datasets +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # tensorflow-datasets +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # promise + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +tensorflow-datasets==4.9.2 + # via -r requirements.in +tensorflow-metadata==1.13.1 + # via tensorflow-datasets +termcolor==2.3.0 + # via tensorflow-datasets +text-unidecode==1.3 + # via python-slugify +toml==0.10.2 + # via tensorflow-datasets +tqdm==4.66.1 + # via tensorflow-datasets +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # etils + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit + # tensorflow-datasets +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via + # etils + # importlib-metadata diff --git a/docs/examples/mlflow_plugin/Dockerfile b/docs/examples/mlflow_plugin/Dockerfile new file mode 100644 index 000000000..fa39c8f4a --- /dev/null +++ b/docs/examples/mlflow_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/mlflow_plugin/README.md b/docs/examples/mlflow_plugin/README.md new file mode 100644 index 000000000..1510cc976 --- /dev/null +++ b/docs/examples/mlflow_plugin/README.md @@ -0,0 +1,52 @@ +(mlflow)= + +# MLFlow + +```{eval-rst} +.. tags:: Integration, Data, Metrics, Intermediate +``` + +The MLflow Tracking component is an API and UI for logging parameters, +code versions, metrics, and output files when running your machine learning code and for later visualizing the results + +First, install the Flyte MLflow plugin: + +```{eval-rst} +.. prompt:: bash $ + + pip install flytekitplugins-mlflow +``` + +To log the metrics and parameters to Flyte deck, add {py:func}`@mlflow_autolog ` to the task. For example + +```python +@task(disable_deck=False) +@mlflow_autolog(framework=mlflow.keras) +def train_model(epochs: int): +... +``` + +To log the metric and parameters to a remote mlflow server, add default environment variable [MLFLOW_TRACKING_URI](https://mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server) to the flytepropeller config map. + +```{eval-rst} +.. prompt:: bash $ + + kubectl edit cm flyte-propeller-config +``` + +```yaml +plugins: + k8s: + default-cpus: 100m + default-env-vars: + - MLFLOW_TRACKING_URI: postgresql+psycopg2://postgres:@postgres.flyte.svc.cluster.local:5432/flyteadmin +``` + +:::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/f4b53a550bed70d9d7722d523e0b7568b781fc7d/flytesnacks/integrations/mlflow/server.png +:alt: MLflow UI +:class: with-shadow +::: + +```{auto-examples-toc} +mlflow_example +``` diff --git a/docs/examples/mlflow_plugin/mlflow_plugin/__init__.py b/docs/examples/mlflow_plugin/mlflow_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/mlflow_plugin/mlflow_plugin/mlflow_example.py b/docs/examples/mlflow_plugin/mlflow_plugin/mlflow_example.py new file mode 100644 index 000000000..24b422730 --- /dev/null +++ b/docs/examples/mlflow_plugin/mlflow_plugin/mlflow_example.py @@ -0,0 +1,71 @@ +# %% [markdown] +# (mlflow_example)= +# +# # MLflow Example +# +# MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. +# +# Flyte provides an easy-to-use interface to log the task's metrics and parameters to either Flyte Deck or MLflow server. + +# %% +import mlflow.keras +import tensorflow as tf + +# %% [markdown] +# Let's first import the libraries. +# %% +from flytekit import task, workflow +from flytekitplugins.mlflow import mlflow_autolog + + +# %% [markdown] +# Run a model training here and generate metrics and parameters. +# Add `mlflow_autolog` to the task, then flyte will automatically log the metric to the Flyte Deck. +# %% +@task(disable_deck=False) +@mlflow_autolog(framework=mlflow.keras) +def train_model(epochs: int): + # Refer to https://www.tensorflow.org/tutorials/keras/classification + fashion_mnist = tf.keras.datasets.fashion_mnist + (train_images, train_labels), (_, _) = fashion_mnist.load_data() + train_images = train_images / 255.0 + + model = tf.keras.Sequential( + [ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dense(10), + ] + ) + + model.compile( + optimizer="adam", + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=["accuracy"], + ) + model.fit(train_images, train_labels, epochs=epochs) + + +# %% [markdown] +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/f4b53a550bed70d9d7722d523e0b7568b781fc7d/flytesnacks/integrations/mlflow/metrics.png +# :alt: Model Metrics +# :class: with-shadow +# ::: +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/f4b53a550bed70d9d7722d523e0b7568b781fc7d/flytesnacks/integrations/mlflow/params.png +# :alt: Model Parameters +# :class: with-shadow +# ::: + +# %% [markdown] +# Finally, we put everything together into a workflow: +# +# %% +@workflow +def ml_pipeline(epochs: int): + train_model(epochs=epochs) + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + ml_pipeline(epochs=5) diff --git a/docs/examples/mlflow_plugin/requirements.in b/docs/examples/mlflow_plugin/requirements.in new file mode 100644 index 000000000..c5f8e5ea3 --- /dev/null +++ b/docs/examples/mlflow_plugin/requirements.in @@ -0,0 +1,3 @@ +flytekitplugins-mlflow +tensorflow +protobuf==3.20.1 diff --git a/docs/examples/mlflow_plugin/requirements.txt b/docs/examples/mlflow_plugin/requirements.txt new file mode 100644 index 000000000..da24d689b --- /dev/null +++ b/docs/examples/mlflow_plugin/requirements.txt @@ -0,0 +1,411 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile --resolver=backtracking requirements.in +# +absl-py==1.4.0 + # via + # tensorboard + # tensorflow +alembic==1.11.1 + # via mlflow +arrow==1.2.3 + # via jinja2-time +astunparse==1.6.3 + # via tensorflow +binaryornot==0.4.4 + # via cookiecutter +blinker==1.6.2 + # via flask +cachetools==5.3.1 + # via google-auth +certifi==2023.5.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via requests +click==8.1.3 + # via + # cookiecutter + # databricks-cli + # flask + # flytekit + # mlflow +cloudpickle==2.2.1 + # via + # flytekit + # mlflow +contourpy==1.1.0 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.1 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +databricks-cli==0.17.7 + # via mlflow +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via + # flytekit + # mlflow +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +entrypoints==0.4 + # via mlflow +flask==2.3.2 + # via mlflow +flatbuffers==1.12 + # via tensorflow +flyteidl==1.2.11 + # via flytekit +flytekit==1.2.11 + # via flytekitplugins-mlflow +flytekitplugins-mlflow==1.7.0 + # via -r requirements.in +fonttools==4.40.0 + # via matplotlib +gast==0.4.0 + # via tensorflow +gitdb==4.0.10 + # via gitpython +gitpython==3.1.31 + # via + # flytekit + # mlflow +google-auth==2.21.0 + # via + # google-auth-oauthlib + # kubernetes + # tensorboard +google-auth-oauthlib==0.4.6 + # via tensorboard +google-pasta==0.2.0 + # via tensorflow +googleapis-common-protos==1.56.4 + # via + # flyteidl + # grpcio-status +greenlet==2.0.2 + # via sqlalchemy +grpcio==1.48.2 + # via + # flytekit + # grpcio-status + # tensorboard + # tensorflow +grpcio-status==1.48.2 + # via flytekit +gunicorn==20.1.0 + # via mlflow +h5py==3.9.0 + # via tensorflow +idna==3.4 + # via requests +importlib-metadata==6.7.0 + # via + # alembic + # flask + # flytekit + # keyring + # markdown + # mlflow +importlib-resources==5.12.0 + # via + # alembic + # keyring + # matplotlib +itsdangerous==2.1.2 + # via flask +jaraco-classes==3.2.3 + # via keyring +jinja2==3.1.2 + # via + # cookiecutter + # flask + # jinja2-time + # mlflow +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.3.1 + # via + # flytekit + # scikit-learn +keras==2.9.0 + # via tensorflow +keras-preprocessing==1.1.2 + # via tensorflow +keyring==24.2.0 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==26.1.0 + # via flytekit +libclang==16.0.0 + # via tensorflow +mako==1.2.4 + # via alembic +markdown==3.4.3 + # via + # mlflow + # tensorboard +markupsafe==2.1.3 + # via + # jinja2 + # mako + # werkzeug +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.7.2 + # via mlflow +mlflow==2.4.1 + # via flytekitplugins-mlflow +more-itertools==9.1.0 + # via jaraco-classes +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.23.5 + # via + # contourpy + # flytekit + # h5py + # keras-preprocessing + # matplotlib + # mlflow + # opt-einsum + # pandas + # pyarrow + # scikit-learn + # scipy + # tensorboard + # tensorflow +oauthlib==3.2.2 + # via + # databricks-cli + # requests-oauthlib +opt-einsum==3.3.0 + # via tensorflow +packaging==23.1 + # via + # docker + # marshmallow + # matplotlib + # mlflow + # plotly + # tensorflow +pandas==1.5.3 + # via + # flytekit + # mlflow +pillow==10.0.0 + # via matplotlib +plotly==5.15.0 + # via flytekitplugins-mlflow +protobuf==3.20.1 + # via + # -r requirements.in + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # mlflow + # protoc-gen-swagger + # tensorboard + # tensorflow +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==10.0.1 + # via + # flytekit + # mlflow +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pyjwt==2.7.0 + # via databricks-cli +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # mlflow + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # mlflow + # responses +querystring-parser==1.2.4 + # via mlflow +regex==2023.6.3 + # via docker-image-py +requests==2.31.0 + # via + # cookiecutter + # databricks-cli + # docker + # flytekit + # kubernetes + # mlflow + # requests-oauthlib + # responses + # tensorboard +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +responses==0.23.1 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +scikit-learn==1.3.0 + # via mlflow +scipy==1.10.1 + # via + # mlflow + # scikit-learn +six==1.16.0 + # via + # astunparse + # databricks-cli + # google-auth + # google-pasta + # grpcio + # keras-preprocessing + # kubernetes + # python-dateutil + # querystring-parser + # tensorflow +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +sqlalchemy==2.0.18 + # via + # alembic + # mlflow +sqlparse==0.4.4 + # via mlflow +statsd==3.3.0 + # via flytekit +tabulate==0.9.0 + # via databricks-cli +tenacity==8.2.2 + # via plotly +tensorboard==2.9.0 + # via tensorflow +tensorboard-data-server==0.6.1 + # via tensorboard +tensorboard-plugin-wit==1.8.1 + # via tensorboard +tensorflow==2.9.0 + # via -r requirements.in +tensorflow-estimator==2.9.0 + # via tensorflow +tensorflow-io-gcs-filesystem==0.32.0 + # via tensorflow +termcolor==2.3.0 + # via tensorflow +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +types-pyyaml==6.0.12.10 + # via responses +typing-extensions==4.7.1 + # via + # alembic + # flytekit + # sqlalchemy + # tensorflow + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # databricks-cli + # docker + # flytekit + # google-auth + # kubernetes + # requests + # responses +websocket-client==1.6.1 + # via + # docker + # kubernetes +werkzeug==2.3.6 + # via + # flask + # tensorboard +wheel==0.40.0 + # via + # astunparse + # flytekit + # tensorboard +wrapt==1.15.0 + # via + # deprecated + # flytekit + # tensorflow +zipp==3.15.0 + # via + # importlib-metadata + # importlib-resources + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/mmcloud_plugin/Dockerfile b/docs/examples/mmcloud_plugin/Dockerfile new file mode 100644 index 000000000..ebdcf8f06 --- /dev/null +++ b/docs/examples/mmcloud_plugin/Dockerfile @@ -0,0 +1,27 @@ +FROM python:3.11-slim-bookworm +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/mmcloud_plugin/README.md b/docs/examples/mmcloud_plugin/README.md new file mode 100644 index 000000000..6a8d3c98d --- /dev/null +++ b/docs/examples/mmcloud_plugin/README.md @@ -0,0 +1,80 @@ +# Memory Machine Cloud + +```{eval-rst} +.. tags:: AWS, GCP, AliCloud, Integration, Advanced +``` + +[MemVerge](https://memverge.com/) [Memory Machine Cloud](https://www.mmcloud.io/) (MMCloud)—available on AWS, GCP, and AliCloud—empowers users to continuously optimize cloud resources during runtime, safely execute stateful tasks on spot instances, and monitor resource usage in real time. These capabilities make it an excellent fit for long-running batch workloads. + +Flyte can be integrated with MMCloud, allowing you to execute Flyte tasks using MMCloud. + +## Installation + +To install the plugin, run the following command: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-mmcloud +``` + +To get started with MMCloud, refer to the [MMCloud User Guide](https://docs.memverge.com/mmce/current/userguide/olh/index.html). + +## Configuring the backend to get MMCloud working + +The MMCloud plugin is [enabled in FlytePropeller's configuration](https://docs.flyte.org/en/latest/deployment/plugins/memverge/mmcloud.html). + +## Getting Started + +This plugin allows executing `PythonFunctionTask` using MMCloud without changing any function code. + +```{eval-rst} +.. testcode:: awsbatch-quickstart + from flytekitplugins.mmcloud import MMCloudConfig + + @task(task_config=MMCloudConfig()) + def to_str(i: int) -> str: + return str(i) +``` + +[Resource](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/productionizing/customizing_resources.html) (cpu and mem) requests and limits, [container](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/customizing_dependencies/multi_images.html) images, and [environment](https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.task.html) variable specifications are supported. + +[ImageSpec](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/customizing_dependencies/image_spec.html) may be used to define images to run tasks. + +### Credentials + +The following [secrets](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/productionizing/use_secrets.html) are required to be defined for the agent server: +* `mmc_address`: MMCloud OpCenter address +* `mmc_username`: MMCloud OpCenter username +* `mmc_password`: MMCloud OpCenter password + +### Defaults + +Compute resources: +* If only requests are specified, there are no limits. +* If only limits are specified, the requests are equal to the limits. +* If neither resource requests nor limits are specified, the default requests used for job submission are `cpu="1"` and `mem="1Gi"`, and there are no limits. + +### Agent Image + +Install `flytekitplugins-mmcloud` in the agent image. + +A `float` binary (obtainable via the OpCenter) is required. Copy it to the agent image `PATH`. + +Sample `Dockerfile` for building an agent image: +```dockerfile +FROM python:3.11-slim-bookworm + +WORKDIR /root +ENV PYTHONPATH /root + +# flytekit will autoload the agent if package is installed. +RUN pip install flytekitplugins-mmcloud +COPY float /usr/local/bin/float + +CMD pyflyte serve --port 8000 +``` + +```{auto-examples-toc} +example +``` diff --git a/docs/examples/mmcloud_plugin/mmcloud_plugin/__init__.py b/docs/examples/mmcloud_plugin/mmcloud_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/mmcloud_plugin/mmcloud_plugin/example.py b/docs/examples/mmcloud_plugin/mmcloud_plugin/example.py new file mode 100644 index 000000000..f0f89f21d --- /dev/null +++ b/docs/examples/mmcloud_plugin/mmcloud_plugin/example.py @@ -0,0 +1,42 @@ +# %% [markdown] +# # Memory Machine Cloud +# +# This example shows how to use the MMCloud plugin to execute tasks on MemVerge Memory Machine Cloud. + +# %% +from flytekit import Resources, task, workflow +from flytekitplugins.mmcloud import MMCloudConfig + +# %% [markdown] +# `MMCloudConfig` configures `MMCloudTask`. Tasks specified with `MMCloudConfig` will be executed using MMCloud. Tasks will be executed with requests `cpu="1"` and `mem="1Gi"` by default. + +# %% +@task(task_config=MMCloudConfig()) +def to_str(i: int) -> str: + return str(i) + + +@task(task_config=MMCloudConfig()) +def to_int(s: str) -> int: + return int(s) + + +# %% [markdown] +# [Resource](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/productionizing/customizing_resources.html) (cpu and mem) requests and limits, [container](https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/customizing_dependencies/multi_images.html) images, and [environment](https://docs.flyte.org/projects/flytekit/en/latest/generated/flytekit.task.html) variable specifications are supported. + +# %% +@task( + task_config=MMCloudConfig(submit_extra="--migratePolicy [enable=true]"), + requests=Resources(cpu="1", mem="1Gi"), + limits=Resources(cpu="2", mem="4Gi"), + environment={"KEY": "value"}, +) +def concatenate_str(s1: str, s2: str) -> str: + return s1 + s2 + + +@workflow +def concatenate_int_wf(i1: int, i2: int) -> int: + i1_str = to_str(i=i1) + i2_str = to_str(i=i2) + return to_int(s=concatenate_str(s1=i1_str, s2=i2_str)) diff --git a/docs/examples/mmcloud_plugin/requirements.in b/docs/examples/mmcloud_plugin/requirements.in new file mode 100644 index 000000000..b490acb14 --- /dev/null +++ b/docs/examples/mmcloud_plugin/requirements.in @@ -0,0 +1,2 @@ +flytekitplugins-envd +flytekitplugins-mmcloud diff --git a/docs/examples/mmcloud_plugin/requirements.txt b/docs/examples/mmcloud_plugin/requirements.txt new file mode 100644 index 000000000..40e1151f7 --- /dev/null +++ b/docs/examples/mmcloud_plugin/requirements.txt @@ -0,0 +1,355 @@ +# +# This file is autogenerated by pip-compile with Python 3.9 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.9.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.4 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.18.2 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.16.0 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.1 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.4 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl + # secretstorage +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.3 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.40 + # via flytekitplugins-envd +flyteidl==1.5.21 + # via flytekit +flytekit==1.9.1 + # via + # flytekitplugins-envd + # flytekitplugins-mmcloud +flytekitplugins-envd==1.9.1 + # via -r requirements.in +flytekitplugins-mmcloud==1.10.0b0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.9.2 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.9.2 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.37 + # via flytekit +google-api-core==2.12.0 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.23.2 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.1.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.11.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.6.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +jsonpickle==3.0.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==28.1.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.24.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.26.0 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.8.2 + # via msal-extensions +protobuf==4.24.3 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3.post1 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.3 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.9.2 + # via flytekit +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # azure-core + # isodate + # kubernetes + # python-dateutil +smmap==5.0.1 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.8.0 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.17 + # via + # botocore + # docker + # flytekit + # kubernetes + # requests +websocket-client==1.6.3 + # via + # docker + # kubernetes +wheel==0.41.2 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.17.0 + # via importlib-metadata diff --git a/docs/examples/mnist_classifier/Dockerfile b/docs/examples/mnist_classifier/Dockerfile new file mode 100644 index 000000000..30b0501c7 --- /dev/null +++ b/docs/examples/mnist_classifier/Dockerfile @@ -0,0 +1,40 @@ +FROM pytorch/pytorch:1.9.0-cuda10.2-cudnn7-runtime +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Set your wandb API key and user name. Get the API key from https://wandb.ai/authorize. +# ENV WANDB_API_KEY +# ENV WANDB_USERNAME + +# Install the AWS cli for AWS support +RUN pip install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y make build-essential libssl-dev curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/mnist_classifier/README.md b/docs/examples/mnist_classifier/README.md new file mode 100644 index 000000000..921d59f3f --- /dev/null +++ b/docs/examples/mnist_classifier/README.md @@ -0,0 +1,95 @@ +(mnist-classifier-training)= + +# MNIST Classification With PyTorch and W&B + +```{eval-rst} +.. tags:: MachineLearning, GPU, Advanced +``` + +## PyTorch + +[Pytorch](https://pytorch.org) is a machine learning framework that accelerates the path from research prototyping +to production deployment. You can build *Tensors* and *Dynamic neural networks* in Python with strong GPU acceleration +using PyTorch. + +In a nutshell, it is a Python package that provides two high-level features: + +- Tensor computation (like NumPy) with strong GPU acceleration +- Deep neural networks built on a tape-based autograd system + +Flyte directly has no unique understanding of PyTorch. As per Flyte, PyTorch is just a Python library. +However, when merged with Flyte, the combo helps utilize and bootstrap the infrastructure for PyTorch and ensures that things work well! +Additionally, it also offers other benefits of using tasks and workflows -- checkpointing, separation of concerns, and auto-memoization. + +## Model Development + +Some basics of model development are outlined in the following video, in addition to: + +- Bias Variance trade-off +- Model families +- Data parallelism +- Model parallelism, and +- PyTorch parallelism + +```{youtube} FuMtJOMh5uQ +``` + +## Specify GPU Requirement + +One of the necessary directives applicable when working on deep learning models is explicitly requesting one or more GPUs. +This can be done by giving a simple directive to the task declaration as follows: + +```{code-block} python +from flytekit import Resources, task + +@task(requests=Resources(gpu="1"), limits=Resources(gpu="1")) +def my_deep_learning_task(): + ... +``` + +```{tip} +It is recommended to use the same `requests` and `limits` for a GPU as automatic GPU scaling is not supported. +``` + +Moreover, to utilize the power of a GPU, ensure that your Flyte backend has GPU nodes provisioned. + +## Distributed Data-Parallel Training + +Flyte also supports distributed training for PyTorch models, but this is not native. This is achieved using one of the optional plugins, such as: + +- Natively on Kubernetes using {ref}`kf-pytorch-op` +- On AWS using {ref}`aws-sagemaker` training + +## Weights & Biases Integration + +[Weights & Biases](https://wandb.ai/site), or simply, `wandb` helps build better models faster with experiment tracking, dataset versioning, and model management. + +We'll use `wandb` alongside PyTorch to track our ML experiment and its concerned model parameters. + +```{note} +Before running the example, create a `wandb` account and log in to access the API. +If you're running the code locally, run the command `wandb login`. +If it's a remote cluster, you have to include the API key in the Dockerfile. +``` + +(pytorch-dockerfile)= + +## PyTorch Dockerfile for Deployment + +It is essential to build the Dockerfile with GPU support to use a GPU within PyTorch. +The example in this section uses a simple `nvidia-supplied GPU Docker image` as the base, and the rest of the construction is similar to the other Dockerfiles. + +```{literalinclude} ../../../examples/mnist_classifier/Dockerfile +:language: docker +``` + +```{note} +Run your code in the `ml_training` directory, both locally and within the sandbox. +``` + +## Examples + +```{auto-examples-toc} +pytorch_single_node_and_gpu +pytorch_single_node_multi_gpu +``` diff --git a/docs/examples/mnist_classifier/mnist_classifier/__init__.py b/docs/examples/mnist_classifier/mnist_classifier/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_and_gpu.py b/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_and_gpu.py new file mode 100644 index 000000000..0d9382fdb --- /dev/null +++ b/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_and_gpu.py @@ -0,0 +1,372 @@ +# %% [markdown] +# (pytorch_single_node_and_gpu)= +# +# # Single Node, Single GPU Training +# +# Training a model on a single node on one GPU is as trivial as writing any Flyte task and simply setting the GPU to `1`. +# As long as the Docker image is built correctly with the right version of the GPU drivers and the Flyte backend is +# provisioned to have GPU machines, Flyte will execute the task on a node that has GPU(s). +# +# Currently, Flyte does not provide any specific task type for PyTorch (though it is entirely possible to provide a task-type +# that supports *PyTorch-Ignite* or *PyTorch Lightning* support, but this is not critical). One can request for a GPU, simply +# by setting GPU="1" resource request and then at runtime, the GPU will be provisioned. +# +# In this example, we'll see how we can create any PyTorch model, train it using Flyte and a specialized container. The following video will outline the basics of this process. +# +# ```{eval-rst} +# .. youtube:: sJrERMVtxL4 +# ``` + +# %% [markdown] +# First, let's import the libraries. +# %% +import json +import os +import typing +from dataclasses import dataclass + +import torch +import torch.nn.functional as F +import wandb +from dataclasses_json import dataclass_json +from flytekit import Resources, task, workflow +from flytekit.types.file import PythonPickledFile +from torch import distributed as dist +from torch import nn, optim +from torchvision import datasets, transforms + +# %% [markdown] +# Let's define some variables to be used later. The following variables are specific to `wandb`: +# +# - `NUM_BATCHES_TO_LOG`: Number of batches to log from the test data for each test step +# - `LOG_IMAGES_PER_BATCH`: Number of images to log per test batch +# %% +NUM_BATCHES_TO_LOG = 10 +LOG_IMAGES_PER_BATCH = 32 + + +# %% [markdown] +# If running remotely, copy your `wandb` API key to the Dockerfile under the environment variable `WANDB_API_KEY`. +# This function logs into `wandb` and initializes the project. If you built your Docker image with the +# `WANDB_USERNAME`, this will work. Otherwise, replace `my-user-name` with your `wandb` user name. +# +# We'll call this function in the `pytorch_mnist_task` defined below. +# %% +def wandb_setup(): + wandb.login() + wandb.init( + project="mnist-single-node-single-gpu", + entity=os.environ.get("WANDB_USERNAME", "my-user-name"), + ) + + +# %% [markdown] +# ## Creating the Network +# +# We use a simple PyTorch model with {py:class}`torch:torch.nn.Conv2d` and {py:class}`torch:torch.nn.Linear` layers. +# Let's also use {py:func}`torch:torch.nn.functional.relu`, {py:func}`torch:torch.nn.functional.max_pool2d`, and +# {py:func}`torch:torch.nn.functional.relu` to define the forward pass. +# %% +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 20, 5, 1) + self.conv2 = nn.Conv2d(20, 50, 5, 1) + self.fc1 = nn.Linear(4 * 4 * 50, 500) + self.fc2 = nn.Linear(500, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 50) + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return F.log_softmax(x, dim=1) + + +# %% [markdown] +# ## The Data Loader + +# %% +def mnist_dataloader(batch_size, train=True, **kwargs): + return torch.utils.data.DataLoader( + datasets.MNIST( + "./data", + train=train, + download=True, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ), + batch_size=batch_size, + shuffle=True, + **kwargs, + ) + + +# %% [markdown] +# ## Training +# +# We define a `train` function to enclose the training loop per epoch, i.e., this gets called for every successive epoch. +# Additionally, we log the loss and epoch progression, which can later be visualized in a `wandb` dashboard. +# %% +def train(model, device, train_loader, optimizer, epoch, log_interval): + model.train() + + # hooks into the model to collect gradients and the topology + wandb.watch(model) + + # loop through the training batches + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(device), target.to(device) # device conversion + optimizer.zero_grad() # clear gradient + output = model(data) # forward pass + loss = F.nll_loss(output, target) # compute loss + loss.backward() # propagate the loss backward + optimizer.step() # update the model parameters + + if batch_idx % log_interval == 0: + print( + "Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format( + epoch, + batch_idx * len(data), + len(train_loader.dataset), + 100.0 * batch_idx / len(train_loader), + loss.item(), + ) + ) + + # log epoch and loss + wandb.log({"loss": loss, "epoch": epoch}) + + +# %% [markdown] +# We define a test logger function which will be called when we run the model on test dataset. +# %% +def log_test_predictions(images, labels, outputs, predicted, my_table, log_counter): + """ + Convenience function to log predictions for a batch of test images + """ + + # obtain confidence scores for all classes + scores = F.softmax(outputs.data, dim=1) + + # assign ids based on the order of the images + for i, (image, pred, label, score) in enumerate( + zip(*[x.cpu().numpy() for x in (images, predicted, labels, scores)]) + ): + # add required info to data table: id, image pixels, model's guess, true label, scores for all classes + my_table.add_data(f"{i}_{log_counter}", wandb.Image(image), pred, label, *score) + if i == LOG_IMAGES_PER_BATCH: + break + + +# %% [markdown] +# ## Evaluation +# +# We define a `test` function to test the model on the test dataset. +# +# We log `accuracy`, `test_loss`, and a `wandb` [table](https://docs.wandb.ai/guides/data-vis/log-tables). +# The `wandb` table can help in depicting the model's performance in a structured format. +# %% +def test(model, device, test_loader): + # ``wandb`` tabular columns + columns = ["id", "image", "guess", "truth"] + for digit in range(10): + columns.append("score_" + str(digit)) + my_table = wandb.Table(columns=columns) + + model.eval() + + # hooks into the model to collect gradients and the topology + wandb.watch(model) + + test_loss = 0 + correct = 0 + log_counter = 0 + + # disable gradient + with torch.no_grad(): + + # loop through the test data loader + for images, targets in test_loader: + images, targets = images.to(device), targets.to(device) # device conversion + outputs = model(images) # forward pass -- generate predictions + test_loss += F.nll_loss(outputs, targets, reduction="sum").item() # sum up batch loss + _, predicted = torch.max(outputs.data, 1) # get the index of the max log-probability + correct += (predicted == targets).sum().item() # compare predictions to true label + + # log predictions to the ``wandb`` table + if log_counter < NUM_BATCHES_TO_LOG: + log_test_predictions(images, targets, outputs, predicted, my_table, log_counter) + log_counter += 1 + + # compute the average loss + test_loss /= len(test_loader.dataset) + + print("\naccuracy={:.4f}\n".format(float(correct) / len(test_loader.dataset))) + accuracy = float(correct) / len(test_loader.dataset) + + # log the average loss, accuracy, and table + wandb.log({"test_loss": test_loss, "accuracy": accuracy, "mnist_predictions": my_table}) + + return accuracy + + +# %% [markdown] +# ## Hyperparameters +# +# We define a few hyperparameters for training our model. +# %% +@dataclass_json +@dataclass +class Hyperparameters(object): + """ + Args: + backend: pytorch backend to use, e.g. "gloo" or "nccl" + sgd_momentum: SGD momentum (default: 0.5) + seed: random seed (default: 1) + log_interval: how many batches to wait before logging training status + batch_size: input batch size for training (default: 64) + test_batch_size: input batch size for testing (default: 1000) + epochs: number of epochs to train (default: 10) + learning_rate: learning rate (default: 0.01) + """ + + backend: str = dist.Backend.GLOO + sgd_momentum: float = 0.5 + seed: int = 1 + log_interval: int = 10 + batch_size: int = 64 + test_batch_size: int = 1000 + epochs: int = 10 + learning_rate: float = 0.01 + + +# %% [markdown] +# ## Training and Evaluating +# +# The output model using {py:func}`torch:torch.save` saves the `state_dict` as described +# [in pytorch docs](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-and-loading-models). +# A common convention is to have the `.pt` extension for the model file. +# +# :::{note} +# Note the usage of `requests=Resources(gpu="1")`. This will force Flyte to allocate this task onto a machine with GPU(s). +# The task will be queued up until a machine with GPU(s) can be procured. Also, for the GPU Training to work, the +# Dockerfile needs to be built as explained in the {ref}`pytorch-dockerfile` section. +# ::: +# %% +TrainingOutputs = typing.NamedTuple( + "TrainingOutputs", + epoch_accuracies=typing.List[float], + model_state=PythonPickledFile, +) + +# %% [markdown] +# Set memory, gpu and storage depending on whether we are trying to register against sandbox or not... +# %% +if os.getenv("SANDBOX") != "": + print(f"SANDBOX ENV: '{os.getenv('SANDBOX')}'") + + mem = "100Mi" + gpu = "0" + storage = "500Mi" +else: + print(f"SANDBOX ENV: '{os.getenv('SANDBOX')}'") + + mem = "3Gi" + gpu = "1" + storage = "1Gi" + + +@task( + retries=2, + cache=True, + cache_version="1.0", + requests=Resources(gpu=gpu, mem=mem, storage=storage), + limits=Resources(gpu=gpu, mem=mem, storage=storage), +) +def pytorch_mnist_task(hp: Hyperparameters) -> TrainingOutputs: + wandb_setup() + + # store the hyperparameters' config in ``wandb`` + wandb.config.update(json.loads(hp.to_json())) + + # set random seed + torch.manual_seed(hp.seed) + + # ideally, if GPU training is required, and if cuda is not available, we can raise an exception + # however, as we want this algorithm to work locally as well (and most users don't have a GPU locally), we will fallback to using a CPU + use_cuda = torch.cuda.is_available() + print(f"Use cuda {use_cuda}") + device = torch.device("cuda" if use_cuda else "cpu") + + # load data + kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {} + training_data_loader = mnist_dataloader(hp.batch_size, train=True, **kwargs) + test_data_loader = mnist_dataloader(hp.batch_size, train=False, **kwargs) + + # train the model + model = Net().to(device) + + optimizer = optim.SGD(model.parameters(), lr=hp.learning_rate, momentum=hp.sgd_momentum) + + # run multiple epochs and capture the accuracies for each epoch + # train the model: run multiple epochs and capture the accuracies for each epoch + accuracies = [] + for epoch in range(1, hp.epochs + 1): + train(model, device, training_data_loader, optimizer, epoch, hp.log_interval) + accuracies.append(test(model, device, test_data_loader)) + + # after training the model, we can simply save it to disk and return it from the Flyte task as a {py:class}`flytekit.types.file.FlyteFile` + # type, which is the ``PythonPickledFile``. ``PythonPickledFile`` is simply a decorator on the ``FlyteFile`` that records the format + # of the serialized model as ``pickled`` + model_file = "mnist_cnn.pt" + torch.save(model.state_dict(), model_file) + + return TrainingOutputs(epoch_accuracies=accuracies, model_state=PythonPickledFile(model_file)) + + +# %% [markdown] +# Finally, we define a workflow to run the training algorithm. We return the model and accuracies. +# %% +@workflow +def pytorch_training_wf(hp: Hyperparameters = Hyperparameters(epochs=10, batch_size=128)) -> TrainingOutputs: + return pytorch_mnist_task(hp=hp) + + +# %% [markdown] +# ## Running the Model Locally +# +# It is possible to run the model locally with almost no modifications (as long as the code takes care of resolving +# if the code is distributed or not). This is how we can do it: +# %% +if __name__ == "__main__": + model, accuracies = pytorch_training_wf(hp=Hyperparameters(epochs=10, batch_size=128)) + print(f"Model: {model}, Accuracies: {accuracies}") + +# %% [markdown] +# ## Weights & Biases Report +# +# Lastly, let's look at the reports that are generated by the model. +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/tutorials/pytorch/single-node/wandb_graphs.png +# :alt: Wandb Graphs +# :class: with-shadow +# +# Wandb Graphs +# ::: +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/tutorials/pytorch/single-node/wandb_table.png +# :alt: Wandb Table +# :class: with-shadow +# +# Wandb Table +# ::: +# +# You can refer to the complete `wandb` report [here](https://wandb.ai/samhita-alla/pytorch-single-node/reports/PyTorch-Single-Node-Training-Report--Vmlldzo4NzUwNjA). +# +# :::{tip} +# A lot more customizations can be done to the report according to your requirement! +# ::: +# diff --git a/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_multi_gpu.py b/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_multi_gpu.py new file mode 100644 index 000000000..4a1856643 --- /dev/null +++ b/docs/examples/mnist_classifier/mnist_classifier/pytorch_single_node_multi_gpu.py @@ -0,0 +1,432 @@ +# %% [markdown] +# # Single Node, Multi GPU Training +# +# When you need to scale up model training in pytorch, you can use the {py:class}`~torch:torch.nn.DataParallel` for +# single node, multi-gpu/cpu training or {py:class}`~torch:torch.nn.parallel.DistributedDataParallel` for multi-node, +# multi-gpu training. +# +# This tutorial will cover how to write a simple training script on the MNIST dataset that uses +# `DistributedDataParallel` since its functionality is a superset of `DataParallel`, supporting both single- and +# multi-node training, and this is the [recommended way](https://pytorch.org/docs/stable/notes/cuda.html#cuda-nn-ddp-instead) +# of distributing your training workload. Note, however, that this tutorial will only work for single-node, multi-gpu +# settings. +# +# For training on a single node and gpu see +# {ref}`this tutorial `, and for more +# information on distributed training, check out the +# [pytorch documentation](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html). +# +# The following video has further explanation: +# +# ```{eval-rst} +# .. youtube:: nTMFb7TmArI +# ``` + +# %% [markdown] +# Import the required libraries. +# %% +import json +import os +import typing + +import torch +import torch.nn.functional as F +import wandb +from flytekit import Resources, task, workflow +from flytekit.types.file import PythonPickledFile + +# %% [markdown] +# We'll re-use certain classes and functions from the +# {ref}`single node and gpu tutorial ` +# such as the `Net` model architecture, `Hyperparameters`, and `log_test_predictions`. +# %% +from mnist_classifier.pytorch_single_node_and_gpu import Hyperparameters, Net, log_test_predictions +from torch import distributed as dist +from torch import multiprocessing as mp +from torch import nn, optim +from torchvision import datasets, transforms + +# %% [markdown] +# Let's define some variables to be used later. +# +# `WORLD_SIZE` defines the total number of GPUs we want to use to distribute our training job and `DATA_DIR` +# specifies where the downloaded data should be written to. +# %% +WORLD_SIZE = 2 +DATA_DIR = "./data" + +# %% [markdown] +# The following variables are specific to `wandb`: +# +# - `NUM_BATCHES_TO_LOG`: Number of batches to log from the test data for each test step +# - `LOG_IMAGES_PER_BATCH`: Number of images to log per test batch +# %% +NUM_BATCHES_TO_LOG = 10 +LOG_IMAGES_PER_BATCH = 32 + + +# %% [markdown] +# If running remotely, copy your `wandb` API key to the Dockerfile under the environment variable `WANDB_API_KEY`. +# This function logs into `wandb` and initializes the project. If you built your Docker image with the +# `WANDB_USERNAME`, this will work. Otherwise, replace `my-user-name` with your `wandb` user name. +# +# We'll call this function in the `pytorch_mnist_task` defined below. +# %% +def wandb_setup(): + wandb.login() + wandb.init( + project="mnist-single-node-multi-gpu", + entity=os.environ.get("WANDB_USERNAME", "my-user-name"), + ) + + +# %% [markdown] +# ## Re-Using the Network From the Single GPU Example +# +# We'll use the same neural network architecture as the one we define in the +# {ref}`single node and gpu tutorial `. + +# %% [markdown] +# ## Data Downloader +# +# We'll use this helper function to download the training and test sets before-hand to avoid race conditions when +# initializing the train and test dataloaders during training. +# %% +def download_mnist(data_dir): + for train in [True, False]: + datasets.MNIST(data_dir, train=train, download=True) + + +# %% [markdown] +# ## The Data Loader +# +# This function will be called in the training function to be distributed across all available GPUs. Note that +# we set `download=False` here to avoid race conditions as mentioned above. +# %% +def mnist_dataloader( + data_dir, + batch_size, + train=True, + distributed=False, + rank=None, + world_size=None, + **kwargs, +): + dataset = datasets.MNIST( + data_dir, + train=train, + download=False, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307), (0.3081))]), + ) + if distributed: + assert rank is not None, "rank needs to be specified when doing distributed training." + sampler = torch.utils.data.distributed.DistributedSampler( + dataset, + rank=rank, + num_replicas=1 if world_size is None else world_size, + shuffle=True, + ) + else: + sampler = None + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + sampler=sampler, + **kwargs, + ) + + +# %% [markdown] +# ## Training +# +# We define a `train` function to enclose the training loop per epoch, and we log the loss and epoch progression, +# which can later be visualized in a `wandb` dashboard. +# %% +def train(model, rank, train_loader, optimizer, epoch, log_interval): + model.train() + + # hooks into the model to collect gradients and the topology + if rank == 0: + wandb.watch(model) + + # loop through the training batches + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(rank), target.to(rank) # device conversion + optimizer.zero_grad() # clear gradient + output = model(data) # forward pass + loss = F.nll_loss(output, target) # compute loss + loss.backward() # propagate the loss backward + optimizer.step() # update the model parameters + + if rank == 0 and batch_idx % log_interval == 0: + # log epoch and loss + print( + "Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format( + epoch, + batch_idx * len(data), + len(train_loader.dataset), + 100.0 * batch_idx / len(train_loader), + loss.item(), + ) + ) + wandb.log({"loss": loss, "epoch": epoch}) + + +# %% [markdown] +# ## Evaluation +# +# We define a `test` function to test the model on the test dataset, logging `accuracy`, and `test_loss` to a +# `wandb` [table](https://docs.wandb.ai/guides/data-vis/log-tables), which helps us visualize the model's +# performance in a structured format. +# %% +def test(model, rank, test_loader): + model.eval() + + # define ``wandb`` tabular columns and hooks into the model to collect gradients and the topology + columns = ["id", "image", "guess", "truth", *[f"score_{i}" for i in range(10)]] + if rank == 0: + my_table = wandb.Table(columns=columns) + wandb.watch(model) + + test_loss = 0 + correct = 0 + log_counter = 0 + + # disable gradient + with torch.no_grad(): + + # loop through the test data loader + total = 0.0 + for images, targets in test_loader: + total += len(targets) + images, targets = images.to(rank), targets.to(rank) # device conversion + outputs = model(images) # forward pass -- generate predictions + test_loss += F.nll_loss(outputs, targets, reduction="sum").item() # sum up batch loss + _, predicted = torch.max(outputs.data, 1) # get the index of the max log-probability + correct += (predicted == targets).sum().item() # compare predictions to true label + + # log predictions to the ``wandb`` table + if log_counter < NUM_BATCHES_TO_LOG: + if rank == 0: + log_test_predictions(images, targets, outputs, predicted, my_table, log_counter) + log_counter += 1 + + # compute the average loss + test_loss /= total + accuracy = float(correct) / total + + if rank == 0: + print("\ntest_loss={:.4f}\naccuracy={:.4f}\n".format(test_loss, accuracy)) + # log the average loss, accuracy, and table + wandb.log( + { + "test_loss": test_loss, + "accuracy": accuracy, + "mnist_predictions": my_table, + } + ) + + return accuracy + + +# %% [markdown] +# ## Training and Evaluating + +# %% +TrainingOutputs = typing.NamedTuple( + "TrainingOutputs", + epoch_accuracies=typing.List[float], + model_state=PythonPickledFile, +) + + +# %% [markdown] +# ## Setting up Distributed Training +# +# `dist_setup` is a helper function that instantiates a distributed environment. We're pointing all of the +# processes across all available GPUs to the address of the main process. + +# %% +def dist_setup(rank, world_size, backend): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "8888" + dist.init_process_group(backend, rank=rank, world_size=world_size) + + +# %% [markdown] +# These global variables point to the location of where to save the model and validation accuracies. +# %% +MODEL_FILE = "./mnist_cnn.pt" +ACCURACIES_FILE = "./mnist_cnn_accuracies.json" + + +# %% [markdown] +# Then we define the `train_mnist` function. Note the conditionals that check for `rank == 0`. These parts of the +# functions are only called in the main process, which is the `0`th rank. The reason for this is that we only want the +# main process to perform certain actions such as: +# +# - log metrics via `wandb` +# - save the trained model to disk +# - keep track of validation metrics + +# %% +def train_mnist(rank: int, world_size: int, hp: Hyperparameters): + # store the hyperparameters' config in ``wandb`` + if rank == 0: + wandb_setup() + wandb.config.update(json.loads(hp.to_json())) + + # set random seed + torch.manual_seed(hp.seed) + + use_cuda = torch.cuda.is_available() + print(f"Using distributed PyTorch with {hp.backend} backend") + print(f"Running MNIST training on rank {rank}, world size: {world_size}") + print(f"Use cuda: {use_cuda}") + dist_setup(rank, world_size, hp.backend) + print(f"Rank {rank + 1}/{world_size} process initialized.\n") + + # load data + kwargs = {"num_workers": 0, "pin_memory": True} if use_cuda else {} + print("Getting data loaders") + training_data_loader = mnist_dataloader( + DATA_DIR, + hp.batch_size, + train=True, + distributed=use_cuda, + rank=rank, + world_size=world_size, + **kwargs, + ) + test_data_loader = mnist_dataloader(DATA_DIR, hp.test_batch_size, train=False, **kwargs) + + # define the distributed model and optimizer + print("Defining model") + model = Net().cuda(rank) + model = nn.parallel.DistributedDataParallel(model, device_ids=[rank]) + + optimizer = optim.SGD(model.parameters(), lr=hp.learning_rate, momentum=hp.sgd_momentum) + + # train the model: run multiple epochs and capture the accuracies for each epoch + print(f"Training for {hp.epochs} epochs") + accuracies = [] + for epoch in range(1, hp.epochs + 1): + train(model, rank, training_data_loader, optimizer, epoch, hp.log_interval) + + # only compute validation metrics in the main process + if rank == 0: + accuracies.append(test(model, rank, test_data_loader)) + + # wait for the main process to complete validation before continuing the training process + dist.barrier() + + if rank == 0: + # tell wandb that we're done logging metrics + wandb.finish() + + # after training the model, we can simply save it to disk and return it from the Flyte + # task as a `flytekit.types.file.FlyteFile` type, which is the `PythonPickledFile`. + # `PythonPickledFile` is simply a decorator on the `FlyteFile` that records the format + # of the serialized model as `pickled` + print("Saving model") + torch.save(model.state_dict(), MODEL_FILE) + + # save epoch accuracies + print("Saving accuracies") + with open(ACCURACIES_FILE, "w") as fp: + json.dump(accuracies, fp) + + print(f"Rank {rank + 1}/{world_size} process complete.\n") + dist.destroy_process_group() # clean up + + +# %% [markdown] +# The output model using {py:func}`torch:torch.save` saves the `state_dict` as described +# [in pytorch docs](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-and-loading-models). +# A common convention is to have the `.pt` extension for the model file. +# +# :::{note} +# Note the usage of `requests=Resources(gpu=WORLD_SIZE)`. This will force Flyte to allocate this task onto a +# machine with GPU(s), which in our case is 2 gpus. The task will be queued up until a machine with GPU(s) can be +# procured. Also, for the GPU Training to work, the Dockerfile needs to be built as explained in the +# {ref}`pytorch-dockerfile` section. +# ::: + +# %% [markdown] +# ## Defining the `task` +# +# Next we define the flyte task that kicks off the distributed training process. Here we call the +# pytorch {py:func}`multiprocessing ` function to initiate a process on each +# available GPU. Since we're parallelizing the data, each process will contain a copy of the model and pytorch +# will handle syncing the weights across all processes on `optimizer.step()` calls. +# +# Read more about pytorch distributed training [here](https://pytorch.org/tutorials/beginner/dist_overview.html). + +# %% [markdown] +# Set memory, gpu and storage depending on whether we are trying to register against sandbox or not: +# %% +if os.getenv("SANDBOX") != "": + mem = "100Mi" + gpu = "0" + storage = "500Mi" + ephemeral_storage = "500Mi" +else: + mem = "30Gi" + gpu = str(WORLD_SIZE) + ephemeral_storage = "500Mi" + storage = "20Gi" + + +@task( + retries=2, + cache=True, + cache_version="1.2", + requests=Resources(gpu=gpu, mem=mem, storage=storage, ephemeral_storage=ephemeral_storage), + limits=Resources(gpu=gpu, mem=mem, storage=storage, ephemeral_storage=ephemeral_storage), +) +def pytorch_mnist_task(hp: Hyperparameters) -> TrainingOutputs: + print("Start MNIST training:") + + world_size = torch.cuda.device_count() + print(f"Device count: {world_size}") + download_mnist(DATA_DIR) + mp.spawn( + train_mnist, + args=(world_size, hp), + nprocs=world_size, + join=True, + ) + print("Training Complete") + with open(ACCURACIES_FILE) as fp: + accuracies = json.load(fp) + return TrainingOutputs(epoch_accuracies=accuracies, model_state=PythonPickledFile(MODEL_FILE)) + + +# %% [markdown] +# Finally, we define a workflow to run the training algorithm. We return the model and accuracies. +# %% +@workflow +def pytorch_training_wf(hp: Hyperparameters = Hyperparameters(epochs=10, batch_size=128)) -> TrainingOutputs: + return pytorch_mnist_task(hp=hp) + + +# %% [markdown] +# ## Running the Model Locally +# +# It is possible to run the model locally with almost no modifications (as long as the code takes care of resolving +# if the code is distributed or not). This is how to do it: +# %% +if __name__ == "__main__": + model, accuracies = pytorch_training_wf(hp=Hyperparameters(epochs=10, batch_size=128)) + print(f"Model: {model}, Accuracies: {accuracies}") + +# %% [markdown] +# ## Weights & Biases Report +# +# You can refer to the complete `wandb` report [here](https://wandb.ai/niels-bantilan/mnist-single-node-multi-gpu/reports/Pytorch-Single-node-Multi-GPU-Report--Vmlldzo5Mjk4Nzk). +# +# :::{tip} +# Many more customizations can be done to the report according to your requirements! +# ::: +# diff --git a/docs/examples/mnist_classifier/requirements.in b/docs/examples/mnist_classifier/requirements.in new file mode 100644 index 000000000..d7c2d67aa --- /dev/null +++ b/docs/examples/mnist_classifier/requirements.in @@ -0,0 +1,9 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +torch +torchvision +wandb +numpy<1.22.0 +pandas<=1.4.3 diff --git a/docs/examples/mnist_classifier/requirements.txt b/docs/examples/mnist_classifier/requirements.txt new file mode 100644 index 000000000..06fcafecf --- /dev/null +++ b/docs/examples/mnist_classifier/requirements.txt @@ -0,0 +1,231 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via + # requests + # sentry-sdk +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit + # wandb +cloudpickle==2.0.0 + # via flytekit +cookiecutter==1.7.3 + # via flytekit +croniter==1.3.4 + # via flytekit +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docker-pycreds==0.4.0 + # via wandb +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.6 + # via -r ../../../common/requirements-common.in +fonttools==4.33.2 + # via matplotlib +gitdb==4.0.9 + # via gitpython +gitpython==3.1.27 + # via wandb +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.21.6 + # via + # -r requirements.in + # matplotlib + # pandas + # pyarrow + # torchvision +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.3.5 + # via + # -r requirements.in + # flytekit +pathtools==0.1.2 + # via wandb +pillow==9.1.0 + # via + # matplotlib + # torchvision +poyo==0.5.0 + # via cookiecutter +promise==2.3 + # via wandb +protobuf==3.20.1 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # wandb +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.0 + # via wandb +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # croniter + # flytekit + # matplotlib + # pandas + # wandb +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # flytekit + # wandb +regex==2022.4.24 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses + # torchvision + # wandb +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +sentry-sdk==1.5.10 + # via wandb +setproctitle==1.2.3 + # via wandb +shortuuid==1.0.8 + # via wandb +six==1.16.0 + # via + # cookiecutter + # docker-pycreds + # grpcio + # promise + # python-dateutil + # wandb +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +torch==1.11.0 + # via + # -r requirements.in + # torchvision +torchvision==0.12.0 + # via -r requirements.in +typing-extensions==4.2.0 + # via + # flytekit + # torch + # torchvision + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses + # sentry-sdk +wandb==0.12.15 + # via -r requirements.in +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/modin_plugin/Dockerfile b/docs/examples/modin_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/modin_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/modin_plugin/README.md b/docs/examples/modin_plugin/README.md new file mode 100644 index 000000000..ea6b1d04d --- /dev/null +++ b/docs/examples/modin_plugin/README.md @@ -0,0 +1,30 @@ +(modin-integration)= + +# Modin + +```{eval-rst} +.. tags:: Integration, DataFrame, MachineLearning, Intermediate +``` + +Modin is a pandas-accelerator that helps handle large datasets. +Pandas works gracefully with small datasets since it is inherently single-threaded, and designed to work on a single CPU core. +With large datasets, the performance of pandas drops (becomes slow or runs out of memory) due to single core usage. +This is where Modin can be helpful. + +Instead of optimizing pandas workflows for a specific setup, we can speed up pandas workflows by utilizing all the resources (cores) available in the system using the concept of `parallelism`, which is possible through modin. [Here](https://modin.readthedocs.io/en/stable/getting_started/why_modin/pandas.html#scalablity-of-implementation) is a visual representation of how the cores are utilized in case of Pandas and Modin. + +## Installation + +```bash +pip install flytekitplugins-modin +``` + +## How is Modin different? + +Modin **scales** the Pandas workflows by changing only a **single line of code**. + +The plugin supports the usage of Modin DataFrame as an input to and output of a task/workflow, similar to how a pandas DataFrame can be used. + +```{auto-examples-toc} +knn_classifier +``` diff --git a/docs/examples/modin_plugin/modin_plugin/__init__.py b/docs/examples/modin_plugin/modin_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/modin_plugin/modin_plugin/knn_classifier.py b/docs/examples/modin_plugin/modin_plugin/knn_classifier.py new file mode 100644 index 000000000..41887ebad --- /dev/null +++ b/docs/examples/modin_plugin/modin_plugin/knn_classifier.py @@ -0,0 +1,114 @@ +# %% [markdown] +# # KNN Classifier +# +# In this example, let's understand how effortlessly the Modin DataFrames can be used with tasks and workflows in a simple classification pipeline. +# Modin uses [Ray](https://github.com/ray-project/ray/) or [Dask](https://dask.org/) as the compute engine. We will use Ray in this example. +# +# To install Modin with Ray as the backend, +# +# ```bash +# pip install modin[ray] +# ``` +# +# :::{note} +# To install Modin with Dask as the backend, +# +# ```bash +# pip install modin[dask] +# ``` +# ::: +# +# Let's dive right in! +# %% [markdown] +# Let's import the necessary dependencies. +# %% +from typing import List, NamedTuple + +import flytekitplugins.modin # noqa: F401 +import modin.pandas +import ray +from flytekit import task, workflow +from sklearn.datasets import load_wine +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split +from sklearn.neighbors import KNeighborsClassifier + +ray.shutdown() # close previous instance of ray (if any) +ray.init(num_cpus=2) # open a new instance of ray + + +split_data = NamedTuple( + "split_data", + train_features=modin.pandas.DataFrame, + test_features=modin.pandas.DataFrame, + train_labels=modin.pandas.DataFrame, + test_labels=modin.pandas.DataFrame, +) + + +# %% [markdown] +# We define a task that processes the wine dataset after loading it into the environment. +# %% +@task +def preprocess_data() -> split_data: + wine = load_wine(as_frame=True) + + # convert features and target (numpy arrays) into Modin DataFrames + wine_features = modin.pandas.DataFrame(data=wine.data, columns=wine.feature_names) + wine_target = modin.pandas.DataFrame(data=wine.target, columns=["target"]) + + # split the dataset + X_train, X_test, y_train, y_test = train_test_split(wine_features, wine_target, test_size=0.33, random_state=101) + + return split_data( + train_features=X_train, + test_features=X_test, + train_labels=y_train, + test_labels=y_test, + ) + + +# %% [markdown] +# Next, we define a task that: +# +# 1. trains a KNeighborsClassifier model, +# 2. fits the model to the data, and +# 3. predicts the output for the test dataset. +# %% +@task +def fit_and_predict( + X_train: modin.pandas.DataFrame, + X_test: modin.pandas.DataFrame, + y_train: modin.pandas.DataFrame, +) -> List[int]: + lr = KNeighborsClassifier() # create a KNeighborsClassifier model + lr.fit(X_train, y_train) # fit the model to the data + predicted_vals = lr.predict(X_test) # predict values for test data + return predicted_vals.tolist() + + +# %% [markdown] +# We compute accuracy of the model. +# %% +@task +def calc_accuracy(y_test: modin.pandas.DataFrame, predicted_vals_list: List[int]) -> float: + return accuracy_score(y_test, predicted_vals_list) + + +# %% [markdown] +# Lastly, we define a workflow. +# +# %% +@workflow +def pipeline() -> float: + split_data_vals = preprocess_data() + predicted_vals_output = fit_and_predict( + X_train=split_data_vals.train_features, + X_test=split_data_vals.test_features, + y_train=split_data_vals.train_labels, + ) + return calc_accuracy(y_test=split_data_vals.test_labels, predicted_vals_list=predicted_vals_output) + + +if __name__ == "__main__": + print(f"Accuracy of the model is {pipeline()}%") diff --git a/docs/examples/modin_plugin/requirements.in b/docs/examples/modin_plugin/requirements.in new file mode 100644 index 000000000..c109e76aa --- /dev/null +++ b/docs/examples/modin_plugin/requirements.in @@ -0,0 +1,6 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-modin +scikit-learn diff --git a/docs/examples/modin_plugin/requirements.txt b/docs/examples/modin_plugin/requirements.txt new file mode 100644 index 000000000..2418f13e8 --- /dev/null +++ b/docs/examples/modin_plugin/requirements.txt @@ -0,0 +1,349 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +aiosignal==1.2.0 + # via ray +arrow==1.2.3 + # via jinja2-time +attrs==22.1.0 + # via + # jsonschema + # ray + # visions +binaryornot==0.4.4 + # via cookiecutter +certifi==2022.9.24 + # via requests +cffi==1.15.1 + # via cryptography +chardet==5.0.0 + # via binaryornot +charset-normalizer==2.1.1 + # via requests +click==8.0.4 + # via + # cookiecutter + # flytekit + # ray +cloudpickle==2.2.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.7 + # via flytekit +cryptography==38.0.3 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +distlib==0.3.6 + # via virtualenv +docker==6.0.1 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +filelock==3.8.0 + # via + # ray + # virtualenv +flyteidl==1.1.22 + # via flytekit +flytekit==1.2.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard + # flytekitplugins-modin +flytekitplugins-deck-standard==1.2.3 + # via -r ../../../common/requirements-common.in +flytekitplugins-modin==1.2.3 + # via -r requirements.in +fonttools==4.38.0 + # via matplotlib +frozenlist==1.3.1 + # via + # aiosignal + # ray +fsspec==2022.10.0 + # via + # flytekitplugins-modin + # modin +googleapis-common-protos==1.56.4 + # via + # flyteidl + # grpcio-status +grpcio==1.43.0 + # via + # flytekit + # flytekitplugins-modin + # grpcio-status + # ray +grpcio-status==1.43.0 + # via + # flytekit + # flytekitplugins-modin +htmlmin==0.1.12 + # via pandas-profiling +idna==3.4 + # via requests +imagehash==4.3.1 + # via visions +importlib-metadata==5.0.0 + # via + # flytekit + # keyring + # markdown +jaraco-classes==3.2.3 + # via keyring +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.2.0 + # via + # flytekit + # phik + # scikit-learn +jsonschema==4.17.0 + # via ray +keyring==23.11.0 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.18.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.3 + # via + # -r ../../../common/requirements-common.in + # missingno + # pandas-profiling + # phik + # seaborn +missingno==0.5.1 + # via pandas-profiling +modin==0.16.2 + # via flytekitplugins-modin +more-itertools==9.0.0 + # via jaraco-classes +msgpack==1.0.4 + # via ray +multimethod==1.9 + # via + # pandas-profiling + # visions +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.2.0 + # via flytekit +networkx==2.8.8 + # via visions +numpy==1.23.4 + # via + # imagehash + # matplotlib + # missingno + # modin + # pandas + # pandas-profiling + # patsy + # phik + # pyarrow + # ray + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions +packaging==21.3 + # via + # docker + # marshmallow + # matplotlib + # modin + # statsmodels +pandas==1.5.1 + # via + # flytekit + # modin + # pandas-profiling + # phik + # seaborn + # statsmodels + # visions +pandas-profiling==3.4.0 + # via flytekitplugins-deck-standard +patsy==0.5.3 + # via statsmodels +phik==0.12.2 + # via pandas-profiling +pillow==9.3.0 + # via + # imagehash + # matplotlib + # visions +platformdirs==2.5.3 + # via virtualenv +plotly==5.11.0 + # via flytekitplugins-deck-standard +protobuf==3.20.3 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # ray +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.3 + # via modin +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pydantic==1.10.2 + # via pandas-profiling +pyopenssl==22.1.0 + # via flytekit +pyparsing==3.0.9 + # via + # matplotlib + # packaging +pyrsistent==0.19.2 + # via jsonschema +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.4 + # via flytekit +python-slugify==6.1.2 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.6 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # pandas-profiling + # ray +ray==2.0.1 + # via flytekitplugins-modin +regex==2022.10.31 + # via docker-image-py +requests==2.28.1 + # via + # cookiecutter + # docker + # flytekit + # pandas-profiling + # ray + # responses +responses==0.22.0 + # via flytekit +retry==0.9.2 + # via flytekit +scikit-learn==1.1.3 + # via -r requirements.in +scipy==1.9.3 + # via + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # statsmodels +seaborn==0.12.1 + # via + # missingno + # pandas-profiling +six==1.16.0 + # via + # grpcio + # patsy + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +statsmodels==0.13.5 + # via pandas-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.1.0 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +toml==0.10.2 + # via responses +tqdm==4.64.1 + # via pandas-profiling +types-toml==0.10.8 + # via responses +typing-extensions==4.4.0 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.12 + # via + # docker + # flytekit + # requests + # responses +virtualenv==20.16.6 + # via ray +visions[type_image_path]==0.7.5 + # via pandas-profiling +websocket-client==1.4.2 + # via docker +wheel==0.38.2 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.1 + # via + # deprecated + # flytekit +zipp==3.10.0 + # via importlib-metadata diff --git a/docs/examples/nlp_processing/Dockerfile b/docs/examples/nlp_processing/Dockerfile new file mode 100644 index 000000000..ed26329d4 --- /dev/null +++ b/docs/examples/nlp_processing/Dockerfile @@ -0,0 +1,49 @@ +FROM ubuntu:focal + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN : \ + && apt-get update \ + && apt install -y software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa + +RUN : \ + && apt-get update \ + && apt-get install -y python3.8 python3-pip python3-venv make build-essential libssl-dev curl vim + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/nlp_processing/README.md b/docs/examples/nlp_processing/README.md new file mode 100644 index 000000000..3b5d6c97b --- /dev/null +++ b/docs/examples/nlp_processing/README.md @@ -0,0 +1,42 @@ +# NLP Processing + +```{eval-rst} +.. tags:: MachineLearning, UI, Intermediate +``` + +This tutorial will demonstrate how to process text data and generate word embeddings and visualizations +as part of a Flyte workflow. It's an adaptation of the official Gensim [Word2Vec tutorial](https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html). + +## About Gensim + +Gensim is a popular open-source natural language processing (NLP) library used to process +large corpora (can be larger than RAM). +It has efficient multicore implementations of a number of algorithms such as [Latent Semantic Analysis](http://lsa.colorado.edu/papers/dp1.LSAintro.pdf), [Latent Dirichlet Allocation (LDA)](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf), +[Word2Vec deep learning](https://arxiv.org/pdf/1301.3781.pdf) to perform complex tasks including understanding +document relationships, topic modeling, learning word embeddings, and more. + +You can read more about Gensim [here](https://radimrehurek.com/gensim/). + +## Data + +The dataset used for this tutorial is the open-source [Lee Background Corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee_background.cor) +that comes with the Gensim library. + +## Step-by-Step Process + +The following points outline the modelling process: + +- Returns a preprocessed (tokenized, stop words excluded, lemmatized) corpus from the custom iterator. +- Trains the Word2vec model on the preprocessed corpus. +- Generates a bag of words from the corpus and trains the LDA model. +- Saves the LDA and Word2Vec models to disk. +- Deserializes the Word2Vec model, runs word similarity and computes word movers distance. +- Reduces the dimensionality (using tsne) and plots the word embeddings. + +Let's dive into the code! + +## Examples + +```{auto-examples-toc} +word2vec_and_lda +``` diff --git a/docs/examples/nlp_processing/nlp_processing/__init__.py b/docs/examples/nlp_processing/nlp_processing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/nlp_processing/nlp_processing/word2vec_and_lda.py b/docs/examples/nlp_processing/nlp_processing/word2vec_and_lda.py new file mode 100644 index 000000000..22b1bc4ec --- /dev/null +++ b/docs/examples/nlp_processing/nlp_processing/word2vec_and_lda.py @@ -0,0 +1,331 @@ +# %% [markdown] +# (word2vec_and_lda)= +# +# # Word Embeddings and Topic Modelling with Gensim +# +# This example creates six Flyte tasks that: +# +# 1. Generate the sample dataset. +# 2. Train the word2vec model. +# 3. Train the LDA model and display the words per topic. +# 4. Compute word similarities. +# 5. Compute word movers distance. +# 6. Reduce dimensions using t-SNE and generate a plot using FlyteDeck. + +# %% [markdown] +# First, we import the necessary libraries. +# %% +import logging +import os +import random +import typing +from dataclasses import dataclass +from typing import Dict, List + +import flytekit +import gensim +import nltk +import numpy as np +import plotly.graph_objects as go +import plotly.io as io +from dataclasses_json import dataclass_json +from flytekit import Resources, task, workflow +from flytekit.types.file import FlyteFile +from gensim import utils +from gensim.corpora import Dictionary +from gensim.models import LdaModel, Word2Vec +from gensim.parsing.preprocessing import STOPWORDS, remove_stopwords +from gensim.test.utils import datapath +from nltk.stem import WordNetLemmatizer +from nltk.tokenize import RegexpTokenizer +from sklearn.manifold import TSNE + +logger = logging.getLogger(__file__) + + +# %% [markdown] +# We define the output file type. +# %% +MODELSER_NLP = typing.TypeVar("model") +model_file = typing.NamedTuple("ModelFile", model=FlyteFile[MODELSER_NLP]) + +# %% [markdown] +# Next, we define the path to the lee corpus dataset (installed with gensim). +# %% +data_dir = os.path.join(gensim.__path__[0], "test", "test_data") +lee_train_file = os.path.join(data_dir, "lee_background.cor") + + +# %% [markdown] +# We declare `NamedTuple`s which will be used as signatures of the Flyte task outputs. +# The variable names and types correspond to the values of the unpacked tuples returned +# from the corresponding Flyte task. +# %% +plotdata = typing.NamedTuple( + "PlottingData", + x_values=List[float], + y_values=List[float], + labels=List[str], +) + + +workflow_outputs = typing.NamedTuple( + "WorkflowOutputs", + simwords=Dict[str, float], + distance=float, + topics=Dict[int, List[str]], +) + + +# %% [markdown] +# We sample sentences of similar contexts to compare using the trained model. +# %% +SENTENCE_A = "Australian cricket captain has supported fast bowler" +SENTENCE_B = "Fast bowler received support from cricket captain" + + +# %% [markdown] +# ## Data Generation +# +# The data pre-processor implements the following steps: +# +# 1. Turns all words to lowercase and remove stopwords. +# 2. Splits the document into tokens using a regular expression tokenizer from NLTK. +# 3. Removes numeric single-character tokens as they do not tend to be useful, and the dataset contains a lot of them. +# 4. Uses the WordNet lemmatizer from NLTK and returns a list of lemmatized tokens. +# %% +def pre_processing(line: str) -> List[str]: + tokenizer = RegexpTokenizer(r"\w+") + tokens = tokenizer.tokenize(remove_stopwords(line.lower())) + lemmatizer = WordNetLemmatizer() + return [lemmatizer.lemmatize(token) for token in tokens] + + +# %% [markdown] +# Now, we implement an iterator that calls the `pre_processing` function on each input sentence from the corpus +# and yield the processed results. +# %% +class MyCorpus: + """An iterator that yields sentences (lists of str).""" + + def __init__(self, path): + self.corpus_path = datapath(path) + + def __iter__(self): + for line in open(self.corpus_path): + yield pre_processing(line) + + +# %% [markdown] +# We define a Flyte task to generate the processed corpus containing a list of tokenized sentence lists. +# %% +@task +def generate_processed_corpus() -> List[List[str]]: + # download the required packages from the nltk library + nltk.download("wordnet") + nltk.download("omw-1.4") + sentences_train = MyCorpus(lee_train_file) + train_corpus = list(sentences_train) + return train_corpus + + +# %% [markdown] +# ## Hyperparameters +# +# Next, we create a dataclass comprising Word2Vec hyperparameters: +# +# - `min_count`: Prunes the dictionary and removes low-frequency words. +# - `vector_size`: Number of dimensions (N) of the N-dimensional space that gensim Word2Vec maps the words onto. +# Bigger size values require more training data but can lead to better (more accurate) models. +# - `workers`: For training parallelization to speed up training. +# - `compute_loss`: To toggle computation of loss while training the Word2Vec model. +# %% +@dataclass_json +@dataclass +class Word2VecModelHyperparams(object): + """ + Hyperparameters that can be used while training the word2vec model. + """ + + vector_size: int = 200 + min_count: int = 1 + workers: int = 4 + compute_loss: bool = True + + +# %% [markdown] +# LDA needs a similar dataclass: +# +# - `num_topics`: The number of topics to be extracted from the training corpus. +# - `alpha`: A-priori belief on document-topic distribution. In `auto` mode, the model learns this from the data. +# - `passes`: Controls how often the model is trained on the entire corpus or number of epochs. +# - `chunksize`: Controls how many documents are processed at a time in the training algorithm. Increasing the +# chunk size speeds up training, at least as long as the chunk of documents easily fits into memory. +# - `update_every`: Number of documents to be iterated through for each update. +# - `random_state`: Seed for reproducibility. +# %% +@dataclass_json +@dataclass +class LDAModelHyperparams(object): + """ + Hyperparameters that can be used while training the LDA model. + """ + + num_topics: int = 5 + alpha: str = "auto" + passes: int = 10 + chunksize: int = 100 + update_every: int = 1 + random_state: int = 100 + + +# %% [markdown] +# ## Training +# +# We initialize and train a Word2Vec model on the preprocessed corpus. +# %% +@task +def train_word2vec_model(training_data: List[List[str]], hyperparams: Word2VecModelHyperparams) -> model_file: + + model = Word2Vec( + training_data, + min_count=hyperparams.min_count, + workers=hyperparams.workers, + vector_size=hyperparams.vector_size, + compute_loss=hyperparams.compute_loss, + ) + training_loss = model.get_latest_training_loss() + logger.info(f"training loss: {training_loss}") + out_path = os.path.join(flytekit.current_context().working_directory, "word2vec.model") + model.save(out_path) + return (out_path,) + + +# %% [markdown] +# Next, we transform the documents to a vectorized form and compute the frequency of each word to generate a bag of +# words corpus for the LDA model to train on. We also create a mapping from word IDs to words to send it as an input to +# the LDA model for training. +# %% +@task +def train_lda_model(corpus: List[List[str]], hyperparams: LDAModelHyperparams) -> Dict[int, List[str]]: + id2word = Dictionary(corpus) + bow_corpus = [id2word.doc2bow(doc) for doc in corpus] + id_words = [[(id2word[id], count) for id, count in line] for line in bow_corpus] + logger.info(f"Sample of bag of words generated: {id_words[:2]}") + lda = LdaModel( + corpus=bow_corpus, + id2word=id2word, + num_topics=hyperparams.num_topics, + alpha=hyperparams.alpha, + passes=hyperparams.passes, + chunksize=hyperparams.chunksize, + update_every=hyperparams.update_every, + random_state=hyperparams.random_state, + ) + return dict(lda.show_topics(num_words=5)) + + +# %% [markdown] +# ## Word Similarities +# +# We deserialize the model from disk and compute the top 10 similar +# words to the given word in the corpus (we will use the word `computer` when running +# the workflow to output similar words). Note that since the model is trained +# on a small corpus, some of the relations might not be clear. +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="600Mi")) +def word_similarities(model_ser: FlyteFile[MODELSER_NLP], word: str) -> Dict[str, float]: + model = Word2Vec.load(model_ser.download()) + wv = model.wv + logger.info(f"Word vector for {word}:{wv[word]}") + return dict(wv.most_similar(word, topn=10)) + + +# %% [markdown] +# ## Sentence Similarity +# +# We compute Word Mover’s Distance (WMD) using the trained embeddings of words. +# This enables us to assess the distance between two documents in a meaningful way even when they have +# no words in common. +# WMD outputs a large value for two completely unrelated sentences and small value for two closely related +# sentences. +# Since we chose two similar sentences for comparison, the word movers distance +# should be small. You can try altering either `SENTENCE_A` or `SENTENCE_B` variables to be dissimilar +# to the other sentence, and check if the value computed is larger. +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="600Mi")) +def word_movers_distance(model_ser: FlyteFile[MODELSER_NLP]) -> float: + sentences = [SENTENCE_A, SENTENCE_B] + results = [] + for i in sentences: + result = [w for w in utils.tokenize(i) if w not in STOPWORDS] + results.append(result) + model = Word2Vec.load(model_ser.download()) + logger.info(f"Computing word movers distance for: {SENTENCE_A} and {SENTENCE_B} ") + return model.wv.wmdistance(*results) + + +# %% [markdown] +# ## Dimensionality Reduction and Plotting +# +# The word embeddings made by the model can be visualized after reducing the dimensionality to two with t-SNE. +# This task can take a few minutes to complete. +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="1000Mi")) +def dimensionality_reduction(model_ser: FlyteFile[MODELSER_NLP]) -> plotdata: + model = Word2Vec.load(model_ser.download()) + num_dimensions = 2 + vectors = np.asarray(model.wv.vectors) + labels = np.asarray(model.wv.index_to_key) + logger.info("Running dimensionality reduction using t-SNE") + tsne = TSNE(n_components=num_dimensions, random_state=0) + vectors = tsne.fit_transform(vectors) + x_vals = [float(v[0]) for v in vectors] + y_vals = [float(v[1]) for v in vectors] + labels = [str(l) for l in labels] + return x_vals, y_vals, labels + + +@task(cache_version="1.0", cache=True, limits=Resources(mem="600Mi"), disable_deck=False) +def plot_with_plotly(x: List[float], y: List[float], labels: List[str]): + layout = go.Layout(height=600, width=800) + fig = go.Figure( + data=go.Scattergl(x=x, y=y, mode="markers", marker=dict(color="aqua")), + layout=layout, + ) + indices = list(range(len(labels))) + selected_indices = random.sample(indices, 50) + for i in selected_indices: + fig.add_annotation( + text=labels[i], + x=x[i], + y=y[i], + showarrow=False, + font=dict(size=15, color="black", family="Sans Serif"), + ) + logger.info("Generating the Word Embedding Plot using Flyte Deck") + flytekit.Deck("Word Embeddings", io.to_html(fig, full_html=True)) + + +# %% [markdown] +# ## Running the Workflow +# +# Let's kick off a workflow! This will return the inference outputs of both gensim models: +# similar words, WMD and LDA topics. +# +# %% +@workflow +def nlp_workflow(target_word: str = "computer") -> workflow_outputs: + corpus = generate_processed_corpus() + model_wv = train_word2vec_model(training_data=corpus, hyperparams=Word2VecModelHyperparams()) + lda_topics = train_lda_model(corpus=corpus, hyperparams=LDAModelHyperparams()) + similar_words = word_similarities(model_ser=model_wv.model, word=target_word) + distance = word_movers_distance(model_ser=model_wv.model) + axis_labels = dimensionality_reduction(model_ser=model_wv.model) + plot_with_plotly(x=axis_labels.x_values, y=axis_labels.y_values, labels=axis_labels.labels) + return similar_words, distance, lda_topics + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(nlp_workflow()) diff --git a/docs/examples/nlp_processing/requirements.in b/docs/examples/nlp_processing/requirements.in new file mode 100644 index 000000000..a040dab33 --- /dev/null +++ b/docs/examples/nlp_processing/requirements.in @@ -0,0 +1,10 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +numpy +gensim +nltk +plotly +pyemd +scikit-learn diff --git a/docs/examples/nlp_processing/requirements.txt b/docs/examples/nlp_processing/requirements.txt new file mode 100644 index 000000000..219540c82 --- /dev/null +++ b/docs/examples/nlp_processing/requirements.txt @@ -0,0 +1,312 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# pip-compile requirements.in +# +arrow==1.2.2 + # via jinja2-time +attrs==22.1.0 + # via visions +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.1 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +click==8.1.2 + # via + # cookiecutter + # flytekit + # nltk +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==38.0.1 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==5.0.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==1.1.14 + # via flytekit +flytekit==1.1.1 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.1.1 + # via -r ../../../common/requirements-common.in +fonttools==4.32.0 + # via matplotlib +gensim==4.2.0 + # via -r requirements.in +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +htmlmin==0.1.12 + # via pandas-profiling +idna==3.3 + # via requests +imagehash==4.3.1 + # via visions +importlib-metadata==4.11.3 + # via + # flytekit + # keyring + # markdown +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.1.0 + # via + # nltk + # pandas-profiling + # phik + # scikit-learn +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via + # -r ../../../common/requirements-common.in + # missingno + # pandas-profiling + # phik + # seaborn +missingno==0.5.1 + # via pandas-profiling +multimethod==1.8 + # via + # pandas-profiling + # visions +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +networkx==2.8.7 + # via visions +nltk==3.7 + # via -r requirements.in +numpy==1.22.3 + # via + # -r requirements.in + # gensim + # imagehash + # matplotlib + # missingno + # pandas + # pandas-profiling + # patsy + # phik + # pyarrow + # pyemd + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions +packaging==21.3 + # via + # marshmallow + # matplotlib + # statsmodels +pandas==1.4.2 + # via + # flytekit + # pandas-profiling + # phik + # seaborn + # statsmodels + # visions +pandas-profiling==3.3.0 + # via flytekitplugins-deck-standard +patsy==0.5.3 + # via statsmodels +phik==0.12.2 + # via pandas-profiling +pillow==9.1.0 + # via + # imagehash + # matplotlib + # visions +plotly==5.10.0 + # via + # -r requirements.in + # flytekitplugins-deck-standard +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pydantic==1.9.2 + # via pandas-profiling +pyemd==0.5.1 + # via -r requirements.in +pyopenssl==22.0.0 + # via flytekit +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pywavelets==1.3.0 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # pandas-profiling +regex==2022.3.15 + # via + # docker-image-py + # nltk +requests==2.27.1 + # via + # cookiecutter + # docker + # flytekit + # pandas-profiling + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +scikit-learn==1.1.3 + # via -r requirements.in +scipy==1.8.0 + # via + # gensim + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # seaborn + # statsmodels +seaborn==0.11.2 + # via + # missingno + # pandas-profiling +six==1.16.0 + # via + # grpcio + # patsy + # python-dateutil +smart-open==6.2.0 + # via gensim +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +statsmodels==0.13.2 + # via pandas-profiling +tangled-up-in-unicode==0.2.0 + # via + # pandas-profiling + # visions +tenacity==8.1.0 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +tqdm==4.64.1 + # via + # nltk + # pandas-profiling +typing-extensions==4.1.1 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +visions[type_image_path]==0.7.5 + # via pandas-profiling +websocket-client==1.4.1 + # via docker +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/onnx_plugin/Dockerfile b/docs/examples/onnx_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/onnx_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/onnx_plugin/README.md b/docs/examples/onnx_plugin/README.md new file mode 100644 index 000000000..688bc46ea --- /dev/null +++ b/docs/examples/onnx_plugin/README.md @@ -0,0 +1,53 @@ +(onnx)= + +# ONNX + +```{eval-rst} +.. tags:: Integration, MachineLearning, Intermediate +``` + +Open Neural Network Exchange ([ONNX](https://github.com/onnx/onnx)) is an open standard format for representing machine learning +and deep learning models. It enables interoperability between different frameworks and streamlines the path from research to production. + +The flytekit onnx type plugin comes in three flavors: + +```{eval-rst} +.. tabbed:: ScikitLearn + + .. code-block:: + + pip install flytekitplugins-onnxpytorch + + This plugin enables the conversion from scikitlearn models to ONNX models. +``` + +```{eval-rst} +.. tabbed:: TensorFlow + + .. code-block:: + + pip install flytekitplugins-onnxtensorflow + + This plugin enables the conversion from tensorflow models to ONNX models. +``` + +```{eval-rst} +.. tabbed:: PyTorch + + .. code-block:: + + pip install flytekitplugins-onnxpytorch + + This plugin enables the conversion from pytorch models to ONNX models. +``` + +:::{note} +If you'd like to add support for a new framework, please create an issue and submit a pull request to the flytekit repo. +You can find the ONNX plugin source code [here](https://github.com/flyteorg/flytekit/tree/master/plugins). +::: + +```{auto-examples-toc} +pytorch_onnx +scikitlearn_onnx +tensorflow_onnx +``` diff --git a/docs/examples/onnx_plugin/onnx_plugin/__init__.py b/docs/examples/onnx_plugin/onnx_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/onnx_plugin/onnx_plugin/pytorch_onnx.py b/docs/examples/onnx_plugin/onnx_plugin/pytorch_onnx.py new file mode 100644 index 000000000..aecbbc856 --- /dev/null +++ b/docs/examples/onnx_plugin/onnx_plugin/pytorch_onnx.py @@ -0,0 +1,154 @@ +# %% [markdown] +# # PyTorch Example +# +# In this example, we will see how to convert a pytorch model to an ONNX model. +# +# First import the necessary libraries. +# %% +from pathlib import Path + +import flytekit +import numpy as np +import onnxruntime +import requests +import torch.nn.init as init +import torch.onnx +import torch.utils.model_zoo as model_zoo +import torchvision.transforms as transforms +from flytekit import task, workflow +from flytekit.types.file import JPEGImageFile, ONNXFile +from flytekitplugins.onnxpytorch import PyTorch2ONNX, PyTorch2ONNXConfig +from PIL import Image +from torch import nn +from typing_extensions import Annotated + + +# %% [markdown] +# Define a conv super resolution model. +# %% +class SuperResolutionNet(nn.Module): + def __init__(self, upscale_factor, inplace=False): + super(SuperResolutionNet, self).__init__() + + self.relu = nn.ReLU(inplace=inplace) + self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2)) + self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)) + self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1)) + self.conv4 = nn.Conv2d(32, upscale_factor**2, (3, 3), (1, 1), (1, 1)) + self.pixel_shuffle = nn.PixelShuffle(upscale_factor) + + self._initialize_weights() + + def forward(self, x): + x = self.relu(self.conv1(x)) + x = self.relu(self.conv2(x)) + x = self.relu(self.conv3(x)) + x = self.pixel_shuffle(self.conv4(x)) + return x + + def _initialize_weights(self): + init.orthogonal_(self.conv1.weight, init.calculate_gain("relu")) + init.orthogonal_(self.conv2.weight, init.calculate_gain("relu")) + init.orthogonal_(self.conv3.weight, init.calculate_gain("relu")) + init.orthogonal_(self.conv4.weight) + + +# %% [markdown] +# Define a `train` task to train the model. +# Note the annotated output put. +# This is a special annotation that tells Flytekit that this parameter is to be converted to an ONNX model with the given config. +# %% +@task +def train() -> Annotated[ + PyTorch2ONNX, + PyTorch2ONNXConfig( + args=torch.randn(1, 1, 224, 224, requires_grad=True), + export_params=True, # store the trained parameter weights inside + opset_version=10, # the ONNX version to export the model to + do_constant_folding=True, # whether to execute constant folding for optimization + input_names=["input"], # the model's input names + output_names=["output"], # the model's output names + dynamic_axes={ + "input": {0: "batch_size"}, + "output": {0: "batch_size"}, + }, # variable length axes + ), +]: + # Create the super-resolution model by using the above model definition. + torch_model = SuperResolutionNet(upscale_factor=3) + + # Load pretrained model weights + model_url = "https://s3.amazonaws.com/pytorch/test_data/export/superres_epoch100-44c6958e.pth" + + # Initialize model with the pretrained weights + map_location = lambda storage, loc: storage # noqa: E731 + if torch.cuda.is_available(): + map_location = None + torch_model.load_state_dict(model_zoo.load_url(model_url, map_location=map_location)) + + return PyTorch2ONNX(model=torch_model) + + +# %% [markdown] +# Define an `onnx_predict` task to generate a super resolution image from the model, given an input image. +# %% +@task +def onnx_predict(model_file: ONNXFile) -> JPEGImageFile: + ort_session = onnxruntime.InferenceSession(model_file.download()) + + img = Image.open( + requests.get( + "https://raw.githubusercontent.com/flyteorg/static-resources/main/flytekit/onnx/cat.jpg", + stream=True, + ).raw + ) + + img = transforms.Resize([224, 224])(img) + + img_ycbcr = img.convert("YCbCr") + img_y, img_cb, img_cr = img_ycbcr.split() + + to_tensor = transforms.ToTensor() + img_y = to_tensor(img_y) + img_y.unsqueeze_(0) + + # compute ONNX Runtime output prediction + ort_inputs = { + ort_session.get_inputs()[0].name: img_y.detach().cpu().numpy() if img_y.requires_grad else img_y.cpu().numpy() + } + ort_outs = ort_session.run(None, ort_inputs) + img_out_y = ort_outs[0] + + img_out_y = Image.fromarray(np.uint8((img_out_y[0] * 255.0).clip(0, 255)[0]), mode="L") + + # get the output image follow post-processing step from PyTorch implementation + final_img = Image.merge( + "YCbCr", + [ + img_out_y, + img_cb.resize(img_out_y.size, Image.BICUBIC), + img_cr.resize(img_out_y.size, Image.BICUBIC), + ], + ).convert("RGB") + + img_path = Path(flytekit.current_context().working_directory) / "cat_superres_with_ort.jpg" + final_img.save(img_path) + + return JPEGImageFile(path=str(img_path)) + + +# %% [markdown] +# Define a workflow to run the above tasks. +# %% +@workflow +def wf() -> JPEGImageFile: + model = train() + return onnx_predict(model_file=model) + + +# %% [markdown] +# Run the workflow locally. +# +# %% +if __name__ == "__main__": + print(f"Prediction: {wf()}") diff --git a/docs/examples/onnx_plugin/onnx_plugin/scikitlearn_onnx.py b/docs/examples/onnx_plugin/onnx_plugin/scikitlearn_onnx.py new file mode 100644 index 000000000..a90f40b28 --- /dev/null +++ b/docs/examples/onnx_plugin/onnx_plugin/scikitlearn_onnx.py @@ -0,0 +1,88 @@ +# %% [markdown] +# # ScikitLearn Example +# +# In this example, we will see how to convert a scikitlearn model to an ONNX model. +# +# First import the necessary libraries. +# %% +from typing import List, NamedTuple + +import numpy +import onnxruntime as rt +import pandas as pd +from flytekit import task, workflow +from flytekit.types.file import ONNXFile +from flytekitplugins.onnxscikitlearn import ScikitLearn2ONNX, ScikitLearn2ONNXConfig +from skl2onnx.common.data_types import FloatTensorType +from sklearn.datasets import load_iris +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from typing_extensions import Annotated + +# %% [markdown] +# Define a `NamedTuple` to hold the output schema. +# Note the annotation on the `model` field. +# This is a special annotation that tells Flytekit that this parameter is to be converted to an ONNX model with the given metadata. +# %% +TrainOutput = NamedTuple( + "TrainOutput", + [ + ( + "model", + Annotated[ + ScikitLearn2ONNX, + ScikitLearn2ONNXConfig( + initial_types=[("float_input", FloatTensorType([None, 4]))], + target_opset=12, + ), + ], + ), + ("test", pd.DataFrame), + ], +) + + +# %% [markdown] +# Define a `train` task that will train a scikitlearn model and return the model and test data. +# %% +@task +def train() -> TrainOutput: + iris = load_iris(as_frame=True) + X, y = iris.data, iris.target + X_train, X_test, y_train, _ = train_test_split(X, y) + model = RandomForestClassifier() + model.fit(X_train, y_train) + + return TrainOutput(test=X_test, model=ScikitLearn2ONNX(model)) + + +# %% [markdown] +# Define a `predict` task that will use the model to predict the labels for the test data. +# %% +@task +def predict( + model: ONNXFile, + X_test: pd.DataFrame, +) -> List[int]: + sess = rt.InferenceSession(model.download()) + input_name = sess.get_inputs()[0].name + label_name = sess.get_outputs()[0].name + pred_onx = sess.run([label_name], {input_name: X_test.to_numpy(dtype=numpy.float32)})[0] + return pred_onx.tolist() + + +# %% [markdown] +# Lastly define a workflow to run the above tasks. +# %% +@workflow +def wf() -> List[int]: + train_output = train() + return predict(model=train_output.model, X_test=train_output.test) + + +# %% [markdown] +# Run the workflow locally. +# +# %% +if __name__ == "__main__": + print(f"Predictions: {wf()}") diff --git a/docs/examples/onnx_plugin/onnx_plugin/tensorflow_onnx.py b/docs/examples/onnx_plugin/onnx_plugin/tensorflow_onnx.py new file mode 100644 index 000000000..c8f9d866b --- /dev/null +++ b/docs/examples/onnx_plugin/onnx_plugin/tensorflow_onnx.py @@ -0,0 +1,113 @@ +# %% [markdown] +# # TensorFlow Example +# +# In this example, we will see how to convert a tensorflow model to an ONNX model. +# +# First import the necessary libraries. + +# %% +from typing import List, NamedTuple + +import numpy as np +import onnxruntime as rt +import tensorflow as tf +from flytekit import Resources, task, workflow +from flytekit.types.file import ONNXFile +from flytekitplugins.onnxtensorflow import TensorFlow2ONNX, TensorFlow2ONNXConfig +from tensorflow.keras import datasets, layers, models +from typing_extensions import Annotated + +# %% [markdown] +# Define a `NamedTuple` to define the data schema. +# %% +DataOutput = NamedTuple( + "DataOutput", + [ + ("train_images", np.ndarray), + ("train_labels", np.ndarray), + ("test_images", np.ndarray), + ], +) + +# %% [markdown] +# Define a `load_data` task to load CIFAR10 data. +# %% +@task(cache=True, cache_version="0.0.2", requests=Resources(mem="1000Mi", cpu="2")) +def load_data() -> DataOutput: + (train_images, train_labels), (test_images, _) = datasets.cifar10.load_data() + + # Normalize pixel values to be between 0 and 1 + train_images, test_images = train_images[:1000] / 255.0, test_images[:100] / 255.0 + train_labels = train_labels[:1000] + + return DataOutput(train_images=train_images, train_labels=train_labels, test_images=test_images) + + +# %% [markdown] +# Define a `train` task to train a CNN model on the CIFAR10 dataset. +# Note the annotated output type. +# This is a special annotation that tells Flytekit that this parameter is to be converted to an ONNX model with the given metadata. +# %% +@task(requests=Resources(mem="1000Mi", cpu="2")) +def train( + train_images: np.ndarray, train_labels: np.ndarray +) -> Annotated[ + TensorFlow2ONNX, + TensorFlow2ONNXConfig( + input_signature=(tf.TensorSpec((None, 32, 32, 3), tf.double, name="input"),), + opset=13, + ), +]: + model = models.Sequential() + model.add(layers.Conv2D(32, (3, 3), activation="relu", input_shape=(32, 32, 3))) + model.add(layers.MaxPooling2D((2, 2))) + model.add(layers.Conv2D(64, (3, 3), activation="relu")) + model.add(layers.Flatten()) + model.add(layers.Dense(64, activation="relu")) + model.add(layers.Dense(10)) + + model.compile( + optimizer="adam", + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=["accuracy"], + ) + + model.fit(train_images, train_labels, epochs=2) + + return TensorFlow2ONNX(model=model) + + +# %% [markdown] +# Define an `onnx_predict` task to generate predictions for the test data using the ONNX model. +# %% +@task(requests=Resources(mem="1000Mi", cpu="2")) +def onnx_predict( + model: ONNXFile, + test_images: np.ndarray, +) -> List[np.ndarray]: + m = rt.InferenceSession(model.download(), providers=["CPUExecutionProvider"]) + onnx_pred = m.run([n.name for n in m.get_outputs()], {"input": test_images}) + + return onnx_pred + + +# %% [markdown] +# Define a workflow to run the tasks. +# %% +@workflow +def wf() -> List[np.ndarray]: + load_data_output = load_data() + model = train( + train_images=load_data_output.train_images, + train_labels=load_data_output.train_labels, + ) + onnx_preds = onnx_predict(model=model, test_images=load_data_output.test_images) + return onnx_preds + + +# %% [markdown] +# Run the workflow locally. +# +# %% +if __name__ == "__main__": + print(f"Predictions: {wf()}") diff --git a/docs/examples/onnx_plugin/requirements.in b/docs/examples/onnx_plugin/requirements.in new file mode 100644 index 000000000..0407008aa --- /dev/null +++ b/docs/examples/onnx_plugin/requirements.in @@ -0,0 +1,9 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-onnxpytorch>=1.1.1 +flytekitplugins-onnxtensorflow>=1.1.1 +flytekitplugins-onnxscikitlearn>=1.1.1 +onnxruntime +torchvision diff --git a/docs/examples/onnx_plugin/requirements.txt b/docs/examples/onnx_plugin/requirements.txt new file mode 100644 index 000000000..0a9794da1 --- /dev/null +++ b/docs/examples/onnx_plugin/requirements.txt @@ -0,0 +1,434 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +absl-py==1.2.0 + # via + # tensorboard + # tensorflow +arrow==1.2.2 + # via jinja2-time +astunparse==1.6.3 + # via tensorflow +attrs==22.1.0 + # via visions +binaryornot==0.4.4 + # via cookiecutter +cachetools==5.2.0 + # via google-auth +certifi==2022.6.15 + # via requests +cffi==1.15.1 + # via cryptography +chardet==5.0.0 + # via binaryornot +charset-normalizer==2.1.1 + # via requests +click==8.1.3 + # via + # cookiecutter + # flytekit +cloudpickle==2.1.0 + # via flytekit +coloredlogs==15.0.1 + # via onnxruntime +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.5 + # via flytekit +cryptography==37.0.4 + # via + # pyopenssl + # secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==5.0.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.14.1 + # via flytekit +flatbuffers==1.12 + # via + # onnxruntime + # tensorflow + # tf2onnx +flyteidl==1.1.12 + # via flytekit +flytekit==1.1.1 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard + # flytekitplugins-onnxpytorch + # flytekitplugins-onnxscikitlearn + # flytekitplugins-onnxtensorflow +flytekitplugins-deck-standard==1.1.1 + # via -r ../../../common/requirements-common.in +flytekitplugins-onnxpytorch==1.1.1 + # via -r requirements.in +flytekitplugins-onnxscikitlearn==1.1.1 + # via -r requirements.in +flytekitplugins-onnxtensorflow==1.1.1 + # via -r requirements.in +fonttools==4.36.0 + # via matplotlib +gast==0.4.0 + # via tensorflow +google-auth==2.11.0 + # via + # google-auth-oauthlib + # tensorboard +google-auth-oauthlib==0.4.6 + # via tensorboard +google-pasta==0.2.0 + # via tensorflow +googleapis-common-protos==1.56.4 + # via + # flyteidl + # grpcio-status +grpcio==1.47.0 + # via + # flytekit + # grpcio-status + # tensorboard + # tensorflow +grpcio-status==1.47.0 + # via flytekit +h5py==3.7.0 + # via tensorflow +htmlmin==0.1.12 + # via pandas-profiling +humanfriendly==10.0 + # via coloredlogs +idna==3.3 + # via requests +imagehash==4.2.1 + # via visions +importlib-metadata==4.12.0 + # via flytekit +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.1.0 + # via + # pandas-profiling + # phik + # scikit-learn +keras==2.9.0 + # via tensorflow +keras-preprocessing==1.1.2 + # via tensorflow +keyring==23.8.2 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +libclang==14.0.6 + # via tensorflow +markdown==3.4.1 + # via + # flytekitplugins-deck-standard + # tensorboard +markupsafe==2.1.1 + # via + # jinja2 + # pandas-profiling + # werkzeug +marshmallow==3.17.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.3 + # via + # -r ../../../common/requirements-common.in + # missingno + # pandas-profiling + # phik + # seaborn +missingno==0.5.1 + # via pandas-profiling +mpmath==1.2.1 + # via sympy +multimethod==1.8 + # via + # pandas-profiling + # visions +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +networkx==2.8.6 + # via visions +numpy==1.23.2 + # via + # h5py + # imagehash + # keras-preprocessing + # matplotlib + # missingno + # onnx + # onnxconverter-common + # onnxruntime + # opt-einsum + # pandas + # pandas-profiling + # phik + # pyarrow + # pywavelets + # scikit-learn + # scipy + # seaborn + # skl2onnx + # tensorboard + # tensorflow + # tf2onnx + # torchvision + # visions +oauthlib==3.2.1 + # via requests-oauthlib +onnx==1.12.0 + # via + # onnxconverter-common + # skl2onnx + # tf2onnx +onnxconverter-common==1.12.1 + # via skl2onnx +onnxruntime==1.12.1 + # via -r requirements.in +opt-einsum==3.3.0 + # via tensorflow +packaging==21.3 + # via + # marshmallow + # matplotlib + # onnxruntime + # tensorflow +pandas==1.4.3 + # via + # flytekit + # pandas-profiling + # phik + # seaborn + # visions +pandas-profiling==3.2.0 + # via flytekitplugins-deck-standard +phik==0.12.2 + # via pandas-profiling +pillow==9.2.0 + # via + # imagehash + # matplotlib + # torchvision + # visions +plotly==5.10.0 + # via flytekitplugins-deck-standard +protobuf==3.19.4 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # onnx + # onnxconverter-common + # onnxruntime + # protoc-gen-swagger + # skl2onnx + # tensorboard + # tensorflow +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.9.2 + # via pandas-profiling +pyopenssl==22.0.0 + # via flytekit +pyparsing==3.0.9 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.4 + # via flytekit +python-slugify==6.1.2 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.2.1 + # via + # flytekit + # pandas +pywavelets==1.3.0 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # pandas-profiling +regex==2022.8.17 + # via docker-image-py +requests==2.28.1 + # via + # cookiecutter + # docker + # flytekit + # pandas-profiling + # requests-oauthlib + # responses + # tensorboard + # tf2onnx + # torchvision +requests-oauthlib==1.3.1 + # via google-auth-oauthlib +responses==0.21.0 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +scikit-learn==1.1.2 + # via skl2onnx +scipy==1.9.0 + # via + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # seaborn + # skl2onnx +seaborn==0.11.2 + # via + # missingno + # pandas-profiling +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # astunparse + # google-auth + # google-pasta + # grpcio + # imagehash + # keras-preprocessing + # python-dateutil + # tensorflow + # tf2onnx +skl2onnx==1.12 + # via flytekitplugins-onnxscikitlearn +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +sympy==1.10.1 + # via onnxruntime +tangled-up-in-unicode==0.2.0 + # via + # pandas-profiling + # visions +tenacity==8.0.1 + # via plotly +tensorboard==2.9.1 + # via tensorflow +tensorboard-data-server==0.6.1 + # via tensorboard +tensorboard-plugin-wit==1.8.1 + # via tensorboard +tensorflow==2.9.1 + # via flytekitplugins-onnxtensorflow +tensorflow-estimator==2.9.0 + # via tensorflow +tensorflow-io-gcs-filesystem==0.26.0 + # via tensorflow +termcolor==1.1.0 + # via tensorflow +text-unidecode==1.3 + # via python-slugify +tf2onnx==1.12.0 + # via flytekitplugins-onnxtensorflow +threadpoolctl==3.1.0 + # via scikit-learn +torch==1.12.1 + # via + # flytekitplugins-onnxpytorch + # torchvision +torchvision==0.13.1 + # via -r requirements.in +tqdm==4.64.0 + # via pandas-profiling +typing-extensions==4.3.0 + # via + # flytekit + # onnx + # pydantic + # tensorflow + # torch + # torchvision + # typing-inspect +typing-inspect==0.8.0 + # via dataclasses-json +urllib3==1.26.12 + # via + # flytekit + # requests + # responses +visions[type_image_path]==0.7.4 + # via pandas-profiling +websocket-client==1.3.3 + # via docker +werkzeug==2.2.2 + # via tensorboard +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # astunparse + # flytekit + # tensorboard +wrapt==1.14.1 + # via + # deprecated + # flytekit + # tensorflow +zipp==3.8.1 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/pandera_plugin/Dockerfile b/docs/examples/pandera_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/pandera_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/pandera_plugin/README.md b/docs/examples/pandera_plugin/README.md new file mode 100644 index 000000000..c79e9bf8d --- /dev/null +++ b/docs/examples/pandera_plugin/README.md @@ -0,0 +1,101 @@ +# Pandera + +```{eval-rst} +.. tags:: Integration, DataFrame, Data, Intermediate +``` + +Flytekit python natively supports {ref}`many data types `, +including a {ref}`FlyteSchema ` type for +type-annotating pandas dataframes. The flytekit pandera plugin provides an alternative for +defining dataframe schemas by integrating with [pandera](https://pandera.readthedocs.io/en/stable/), +which is a runtime data validation tool for pandas dataframes. + +## Installation + +```bash +pip install flytekitplugins-pandera +``` + +## Quick Start + +Pandera provides a flexible and expressive interface for defining schemas for +tabular data, where you can define the types and other statistical properties +of a column. + +```{eval-rst} +.. testcode:: pandera-quickstart + + import pandas as pd + import pandera as pa + from pandera.typing import DataFrame, Series + + class Schema(pa.SchemaModel): + column_1: Series[int] = pa.Field(ge=0) + column_2: Series[float] = pa.Field(gt=0, lt=100) + column_3: Series[str] = pa.Field(str_startswith="prefix") + + @pa.check("column_3") + def check_str_length(cls, series): + return series.str.len() > 5 + + @pa.check_types + def processing_fn(df: DataFrame[Schema]) -> DataFrame[Schema]: + df["column_1"] = df["column_1"] * 2 + df["column_2"] = df["column_2"] * 0.5 + df["column_3"] = df["column_3"] + "_suffix" + return df + + raw_df = pd.DataFrame({ + "column_1": [1, 2, 3], + "column_2": [1.5, 2.21, 3.9], + "column_3": ["prefix_a", "prefix_b", "prefix_c"], + }) + processed_df = processing_fn(raw_df) + print(processed_df) +``` + +```{eval-rst} +.. testoutput:: pandera-quickstart + :options: +NORMALIZE_WHITESPACE + + column_1 column_2 column_3 + 0 2 0.750 prefix_a_suffix + 1 4 1.105 prefix_b_suffix + 2 6 1.950 prefix_c_suffix +``` + +Informative errors are raised if invalid data is passed into `processing_fn`, +indicating the failure case and the index where they were found in the dataframe: + +```{eval-rst} +.. testcode:: pandera-quickstart + + invalid_df = pd.DataFrame({ + "column_1": [-1, 2, -3], + "column_2": [1.5, 2.21, 3.9], + "column_3": ["prefix_a", "prefix_b", "prefix_c"], + }) + processing_fn(invalid_df) +``` + +```{eval-rst} +.. testoutput:: pandera-quickstart + :options: +NORMALIZE_WHITESPACE, +IGNORE_EXCEPTION_DETAIL + + Traceback (most recent call last): + ... + pandera.errors.SchemaError: error in check_types decorator of function 'processing_fn': )> failed element-wise validator 0: + + failure cases: + index failure_case + 0 0 -1 + 1 2 -3 + +``` + +## Using Pandera with Flytekit Python + +```{auto-examples-toc} +basic_schema_example +validating_and_testing_ml_pipelines +``` diff --git a/docs/examples/pandera_plugin/pandera_plugin/__init__.py b/docs/examples/pandera_plugin/pandera_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/pandera_plugin/pandera_plugin/basic_schema_example.py b/docs/examples/pandera_plugin/pandera_plugin/basic_schema_example.py new file mode 100644 index 000000000..13abd2981 --- /dev/null +++ b/docs/examples/pandera_plugin/pandera_plugin/basic_schema_example.py @@ -0,0 +1,135 @@ +# %% [markdown] +# (pandera_basic_schema_example)= +# +# # Basic Schema Example +# +# In this example we'll show you how to use {ref}`pandera.DataFrameModel ` +# to annotate dataframe inputs and outputs in your flyte tasks. + +# %% +import typing + +import flytekitplugins.pandera # noqa : F401 +import pandas as pd +import pandera as pa +from flytekit import task, workflow +from pandera.typing import DataFrame, Series + +# %% [markdown] +# ## A Simple Data Processing Pipeline +# +# Let's first define a simple data processing pipeline in pure python. + +# %% +def total_pay(df): + return df.assign(total_pay=df.hourly_pay * df.hours_worked) + + +def add_id(df, worker_id): + return df.assign(worker_id=worker_id) + + +def process_data(df, worker_id): + return add_id(df=total_pay(df=df), worker_id=worker_id) + + +# %% [markdown] +# As you can see, the `process_data` function is composed of two simpler functions: +# One that computes `total_pay` and another that simply adds an `id` column to +# a pandas dataframe. + +# %% [markdown] +# ## Defining DataFrame Schemas +# +# Next we define the schemas that provide type and statistical annotations +# for the raw, intermediate, and final outputs of our pipeline. + +# %% +class InSchema(pa.DataFrameModel): + hourly_pay: Series[float] = pa.Field(ge=7) + hours_worked: Series[float] = pa.Field(ge=10) + + @pa.check("hourly_pay", "hours_worked") + def check_numbers_are_positive(cls, series: Series) -> Series[bool]: + """Defines a column-level custom check.""" + return series > 0 + + class Config: + coerce = True + + +class IntermediateSchema(InSchema): + total_pay: Series[float] + + @pa.dataframe_check + def check_total_pay(cls, df: DataFrame) -> Series[bool]: + """Defines a dataframe-level custom check.""" + return df["total_pay"] == df["hourly_pay"] * df["hours_worked"] + + +class OutSchema(IntermediateSchema): + worker_id: Series[str] = pa.Field() + + +# %% [markdown] +# Columns are specified as class attributes with a specified data type using the +# type-hinting syntax, and you can place additional statistical constraints on the +# values of each column using {py:func}`~pandera.api.pandas.model_components.Field`. +# You can also define custom validation functions by decorating methods with +# {py:func}`~pandera.api.pandas.model_components.check` (column-level checks) or +# {py:func}`~pandera.api.pandas.model_components.dataframe_check` (dataframe-level checks), which automatically make them +# class methods. +# +# Pandera uses inheritance to make sure that {py:class}`~pandera.api.pandas.model.DataFrameModel` subclasses contain +# all of the same columns and custom check methods as their base class. Inheritance semantics +# apply to schema models so you can override column attributes or check methods in subclasses. This has +# the nice effect of providing an explicit graph of type dependencies as data +# flows through the various tasks in your workflow. + +# %% [markdown] +# ## Type Annotating Tasks and Workflows +# +# Finally, we can turn our data processing pipeline into a Flyte workflow +# by decorating our functions with the {py:func}`~flytekit.task` and {py:func}`~flytekit.workflow` decorators and +# annotating the inputs and outputs of those functions with the pandera schemas: + +# %% +@task +def dict_to_dataframe(data: dict) -> DataFrame[InSchema]: + """Helper task to convert a dictionary input to a dataframe.""" + return pd.DataFrame(data) + + +@task +def total_pay(df: DataFrame[InSchema]) -> DataFrame[IntermediateSchema]: # noqa : F811 + return df.assign(total_pay=df.hourly_pay * df.hours_worked) + + +@task +def add_ids(df: DataFrame[IntermediateSchema], worker_ids: typing.List[str]) -> DataFrame[OutSchema]: + return df.assign(worker_id=worker_ids) + + +@workflow +def process_data( # noqa : F811 + data: dict = { + "hourly_pay": [12.0, 13.5, 10.1], + "hours_worked": [30.5, 40.0, 41.75], + }, + worker_ids: typing.List[str] = ["a", "b", "c"], +) -> DataFrame[OutSchema]: + return add_ids(df=total_pay(df=dict_to_dataframe(data=data)), worker_ids=worker_ids) + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + result = process_data( + data={"hourly_pay": [12.0, 13.5, 10.1], "hours_worked": [30.5, 40.0, 41.75]}, + worker_ids=["a", "b", "c"], + ) + print(f"Running wf(), returns dataframe\n{result}\n{result.dtypes}") + + +# %% [markdown] +# Now your workflows and tasks are guarded against unexpected data at runtime! +# diff --git a/docs/examples/pandera_plugin/pandera_plugin/validating_and_testing_ml_pipelines.py b/docs/examples/pandera_plugin/pandera_plugin/validating_and_testing_ml_pipelines.py new file mode 100644 index 000000000..316de857a --- /dev/null +++ b/docs/examples/pandera_plugin/pandera_plugin/validating_and_testing_ml_pipelines.py @@ -0,0 +1,367 @@ +# %% [markdown] +# # Validating and Testing Machine Learning Pipelines +# +# ```{eval-rst} +# .. tags:: Integration, DataFrame, MachineLearning, Intermediate +# ``` +# +# In this example we'll show you how to use {ref}`pandera.SchemaModel ` +# to annotate dataframe inputs and outputs in an [sklearn](https://scikit-learn.org/stable/) +# model-training pipeline. +# +# At a high-level, the pipeline architecture involves fetching, parsing, and splitting data, then training +# a model on the training set and evaluating the trained model on the test set to produce metrics: +# +# ```{eval-rst} +# .. mermaid:: +# +# flowchart LR +# fetch(fetch raw data) --> raw[(raw data)] +# raw --> parse(parse data) +# parse --> parsed_data[(parsed data)] +# parsed_data --> split(split data) +# split --> train[(training set)] +# split --> test[(test set)] +# train --> train_model(train model) +# train_model --> model[(model)] +# test --> evaluate(evaluate model) +# model --> evaluate +# evaluate --> metrics +# +# style fetch fill:#fff2b2,stroke:#333 +# style parse fill:#fff2b2,stroke:#333 +# style split fill:#fff2b2,stroke:#333 +# style train_model fill:#fff2b2,stroke:#333 +# style evaluate fill:#fff2b2,stroke:#333 +# +# ``` +# +# First, let's import all the necessary dependencies including `pandas`, `pandera`, `sklearn`, +# and `hypothesis`. + +# %% +import typing + +import flytekitplugins.pandera # noqa: F401 +import joblib +import pandas as pd +import pandera as pa +from flytekit import task, workflow +from flytekit.types.file import JoblibSerializedFile +from pandera.typing import DataFrame, Index, Series # noqa: F401 +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +# %% [markdown] +# We also need to import the `pandera` flytekit plugin to enable dataframe runtime type-checking: + +# %% [markdown] +# ## The Dataset: UCI Heart Disease +# +# Before building out our pipeline it's good practice to take a closer look at our data. We'll be +# modeling the [UCI Heart Disease](https://archive.ics.uci.edu/ml/datasets/heart+Disease) dataset. +# +# From the data documentation in the link above, we can see that this is a tabular dataset where each +# row contains data pertaining to a patient. There are 14 variables where 13 of them are features and one of them +# is a binary target variable indicating the presence or absence of heart disease. +# +# ```{eval-rst} +# .. list-table:: UCI Dataset Variables +# :widths: 25 25 +# :header-rows: 1 +# +# * - variable +# - description +# * - ``age`` +# - age in years +# * - ``sex`` +# - 1 = male; 0 = female +# * - ``cp`` +# - chest pain type (4 values) +# * - ``trestbps`` +# - resting blood pressure +# * - ``chol`` +# - serum cholestoral in mg/dl +# * - ``fbs`` +# - fasting blood sugar > 120 mg/dl +# * - ``restecg`` +# - resting electrocardiographic results (values 0,1,2) +# * - ``thalach`` +# - maximum heart rate achieved +# * - ``exang`` +# - exercise induced angina +# * - ``oldpeak`` +# - ST (stress test) depression induced by exercise relative to rest +# * - ``slope`` +# - the slope of the peak exercise ST segment +# * - ``ca`` +# - number of major vessels (0-3) colored by flourosopy +# * - ``thal`` +# - 3 = normal; 6 = fixed defect; 7 = reversible defect +# * - ``target`` +# - the predicted attribute +# ``` +# +# In practice, we'd want to do a little data exploration to first to get a sense of the distribution of variables. +# A useful resource for this is the [Kaggle](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset) version of this dataset, +# which has been slightly preprocessed to be model-ready. +# +# :::{Note} +# We'll be using the data provided by the UCI data repository since we want to work with a dataset that +# requires some preprocessing to be model-ready. +# ::: +# +# Once we've gotten a rough sense of the statistical properties of the data, we can encode that domain knowledge into +# a pandera schema: + +# %% +class RawData(pa.SchemaModel): + age: Series[int] = pa.Field(in_range={"min_value": 0, "max_value": 200}) + sex: Series[int] = pa.Field(isin=[0, 1]) + cp: Series[int] = pa.Field( + isin=[ + 1, # typical angina + 2, # atypical angina + 3, # non-anginal pain + 4, # asymptomatic + ] + ) + trestbps: Series[int] = pa.Field(in_range={"min_value": 0, "max_value": 200}) + chol: Series[int] = pa.Field(in_range={"min_value": 0, "max_value": 600}) + fbs: Series[int] = pa.Field(isin=[0, 1]) + restecg: Series[int] = pa.Field( + isin=[ + 0, # normal + 1, # having ST-T wave abnormality + 2, # showing probable or definite left ventricular hypertrophy by Estes' criteria + ] + ) + thalach: Series[int] = pa.Field(in_range={"min_value": 0, "max_value": 300}) + exang: Series[int] = pa.Field(isin=[0, 1]) + oldpeak: Series[float] = pa.Field(in_range={"min_value": 0, "max_value": 10}) + slope: Series[int] = pa.Field( + isin=[ + 1, # upsloping + 2, # flat + 3, # downsloping + ] + ) + ca: Series[int] = pa.Field(isin=[0, 1, 2, 3]) + thal: Series[int] = pa.Field( + isin=[ + 3, # normal + 6, # fixed defect + 7, # reversible defect + ] + ) + target: Series[int] = pa.Field(ge=0, le=4) + + class Config: + coerce = True + + +# %% [markdown] +# As we can see, the `RawData` schema serves as both documentation and a means of enforcing some minimal quality +# checks relating to the data type of each variable as well as additional constraints about their allowable values. +# +# ## Fetching the Raw Data +# +# Now we're ready to write our first Flyte task: + +# %% +@task +def fetch_raw_data() -> DataFrame[RawData]: + data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data" + return ( + pd.read_csv(data_url, header=None, names=RawData.to_schema().columns.keys()) + # Demo: remove these lines to see what happens! + .replace({"ca": {"?": None}, "thal": {"?": None}}) + .dropna(subset=["ca", "thal"]) + .astype({"ca": float, "thal": float}) + ) + + +# %% [markdown] +# This function fetches the raw data from `data_url` and cleaning up invalid values such as `"?"`, dropping records +# with null values, and casting the `ca` and `thal` columns into floats since those columns serialize the number +# values in the float format, e.g. `3.0`, so we cast them into floats before pandera coerces them into integer values. +# +# :::{Note} +# We're using the generic type `` pandera.typing.DataFrame` `` and supplying the `RawData` schema model to +# specify the expected fields of the dataframe. This is pandera's syntax for adding type annotations to dataframes. +# ::: +# +# ## Parsing the Raw Data +# +# For this relatively simple dataset, the only pre-processing we need to do is to convert the `target` column from +# having values ranging from `` 0 - 4` `` to a binary representation where `0` represents absence of heart disease and +# `1` represents presence of heart disease. +# +# Here we can use inheritance to define a `ParsedData` schema by overriding just the `target` attribute: + +# %% +class ParsedData(RawData): + target: Series[int] = pa.Field(isin=[0, 1]) + + +@task +def parse_raw_data(raw_data: DataFrame[RawData]) -> DataFrame[ParsedData]: + return raw_data.assign(target=lambda _: (_.target > 0).astype(int)) + + +# %% [markdown] +# As we can see the `parse_raw_data` function takes in a dataframe of type `DataFrame[RawData]` and outputs another +# dataframe of type `DataFrame[ParsedData]`. The actual parsing logic is very simple, but you can imagine many cases +# in which this would involve other steps, such as one-hot-encoding. As we'll see later, since we're using a tree-based +# sklearn estimator, we can preserve the integer representations of the features since these class of estimators handles +# the discretization of these types of variables. +# +# ## Splitting the Data +# +# Now it's time to split the data into a training and test set. Here we'll showcase the utility of +# {ref}`named outputs ` combined with pandera schemas. + +# %% +DataSplits = typing.NamedTuple("DataSplits", training_set=DataFrame[ParsedData], test_set=DataFrame[ParsedData]) + + +@task +def split_data(parsed_data: DataFrame[ParsedData], test_size: float, random_state: int) -> DataSplits: + training_set = parsed_data.sample(frac=test_size, random_state=random_state) + test_set = parsed_data[~parsed_data.index.isin(training_set.index)] + return training_set, test_set + + +# %% [markdown] +# As we can see above, we're defining a `DataSplits` named tuple consisting of a `training_set` key and `test_set` +# key. Since we assume that the training and test sets are drawn from the same distribution, we can specify the +# type of these data splits as `DataFrame[ParsedData]`. +# +# ## Training a Model +# +# Next we'll train a `RandomForestClassifier` to predict the absence/presence of heart disease: + +# %% +def get_features_and_target(dataset): + """Helper function for separating feature and target data.""" + X = dataset[[x for x in dataset if x != "target"]] + y = dataset["target"] + return X, y + + +@task +def train_model(training_set: DataFrame[ParsedData], random_state: int) -> JoblibSerializedFile: + model = RandomForestClassifier(n_estimators=100, random_state=random_state) + X, y = get_features_and_target(training_set) + model.fit(X, y) + model_fp = "/tmp/model.joblib" + joblib.dump(model, model_fp) + return JoblibSerializedFile(path=model_fp) + + +# %% [markdown] +# This task serializes the model with joblib and returns a `JoblibSerializedFile` type, which is understood and +# automatically handled by Flyte so that the pointer to the actual serialized file can be passed onto the last +# step of the pipeline. +# +# ## Model Evaluation +# +# Next we assess the accuracy score of the model on the test set: + +# %% +@task +def evaluate_model(model: JoblibSerializedFile, test_set: DataFrame[ParsedData]) -> float: + with open(model, "rb") as f: + model = joblib.load(f) + X, y = get_features_and_target(test_set) + preds = model.predict(X) + return accuracy_score(y, preds) + + +# %% [markdown] +# Finally, we put all of the pieces together in a Flyte workflow: + +# %% +@workflow +def pipeline(data_random_state: int, model_random_state: int) -> float: + raw_data = fetch_raw_data() + parsed_data = parse_raw_data(raw_data=raw_data) + training_set, test_set = split_data(parsed_data=parsed_data, test_size=0.2, random_state=data_random_state) + model = train_model(training_set=training_set, random_state=model_random_state) + return evaluate_model(model=model, test_set=test_set) + + +# %% [markdown] +# ## Unit Testing Pipeline Code +# +# A powerful feature offered by pandera is [data synthesis strategies](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html). +# It harnesses the power of [hypothesis](https://hypothesis.readthedocs.io/en/latest/), which is a property-based +# testing library that makes it easy to test your code not based on hand-crafted test cases, but by defining a +# contract of types and assertions that your code needs to fulfill. `pandera` strategies enables us to sample valid +# data under the constraints of our `SchemaModel` so that we can make sure the code implementation is correct. +# +# Typically we'd want to define these tests under the conventions of a testing framework like [pytest](https://docs.pytest.org/en/6.2.x/), +# but in this example we'll just define it under a `if __name__ == "__main__"` block: + +# %% +if __name__ == "__main__": + import hypothesis + import hypothesis.strategies as st + from hypothesis import given + + class PositiveExamples(ParsedData): + target: Series[int] = pa.Field(eq=1) + + class NegativeExamples(ParsedData): + target: Series[int] = pa.Field(eq=0) + + @given( + PositiveExamples.strategy(size=5), + NegativeExamples.strategy(size=5), + st.integers(min_value=0, max_value=2**32), + ) + @hypothesis.settings( + deadline=1000, + suppress_health_check=[ + hypothesis.HealthCheck.too_slow, + hypothesis.HealthCheck.filter_too_much, + ], + ) + def test_train_model(positive_examples, negative_examples, model_random_state): + training_set = pd.concat([positive_examples, negative_examples]) + model = train_model(training_set=training_set, random_state=model_random_state) + model = joblib.load(model) + X, _ = get_features_and_target(training_set) + preds = model.predict(X) + for pred in preds: + assert pred in {0, 1} + + def run_test_suite(): + test_train_model() + print("tests completed!") + + run_test_suite() + + +# %% [markdown] +# In the test code above, we can see that we're defining two schema models for the purposes of testing: `PositiveExamples` +# and `NegativeExamples`. We're doing this so that we guarantee that we can control the class balance of the sampled +# data. +# +# Then, we define a test called `test_train_model`, which is decorated with `@given` so that `hypothesis` can +# handle the sampling and shrinkage of test cases, trying to find a scenario that falsifies the assumptions in the +# test function body. In this case, all we're doing is checking the following properties of the `train_model` +# task: +# +# - With some sample of data, it can output a trained `model` +# - That model can be used to generate predictions in the set `{0, 1}` +# +# ## Conclusion +# +# In this example we've learned how to: +# +# 1. Define pandera `SchemaModel` s to document and enforce the properties of dataframes as they pass through our +# model-training pipeline. +# 2. Type-annotate `flytekit` tasks to automatically type-check dataframes at runtime using the `flytekitplugins.pandera` plugin. +# 3. Use pandera `SchemaModel` s in unit tests to make sure that our code is working as expected under certain operating assumptions. +# diff --git a/docs/examples/pandera_plugin/requirements.in b/docs/examples/pandera_plugin/requirements.in new file mode 100644 index 000000000..6cc17e15b --- /dev/null +++ b/docs/examples/pandera_plugin/requirements.in @@ -0,0 +1,10 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-pandera>=0.16.0 +hypothesis +joblib +pandas +pandera>=0.7.0 +scikit-learn diff --git a/docs/examples/pandera_plugin/requirements.txt b/docs/examples/pandera_plugin/requirements.txt new file mode 100644 index 000000000..7d5313e88 --- /dev/null +++ b/docs/examples/pandera_plugin/requirements.txt @@ -0,0 +1,413 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile requirements.in +# +appnope==0.1.3 + # via ipython +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +attrs==22.2.0 + # via + # hypothesis + # visions +backcall==0.2.0 + # via ipython +binaryornot==0.4.4 + # via cookiecutter +cachetools==5.3.0 + # via google-auth +certifi==2022.12.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via requests +click==8.1.3 + # via + # cookiecutter + # flytekit +cloudpickle==2.2.1 + # via flytekit +contourpy==1.0.7 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.8 + # via flytekit +cryptography==39.0.2 + # via pyopenssl +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via + # ipython + # retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==6.0.1 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +exceptiongroup==1.1.1 + # via hypothesis +executing==1.2.0 + # via stack-data +flyteidl==1.3.14 + # via flytekit +flytekit==1.4.2 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard + # flytekitplugins-pandera +flytekitplugins-deck-standard==1.4.2 + # via -r ../../../common/requirements-common.in +flytekitplugins-pandera==1.4.2 + # via -r requirements.in +fonttools==4.39.2 + # via matplotlib +gitdb==4.0.10 + # via gitpython +gitpython==3.1.31 + # via flytekit +google-auth==2.16.2 + # via kubernetes +googleapis-common-protos==1.59.0 + # via + # flyteidl + # flytekit + # grpcio-status +grpcio==1.51.3 + # via + # flytekit + # grpcio-status +grpcio-status==1.51.3 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +hypothesis==6.70.0 + # via -r requirements.in +idna==3.4 + # via requests +imagehash==4.3.1 + # via + # visions + # ydata-profiling +importlib-metadata==6.1.0 + # via + # flytekit + # keyring + # markdown +importlib-resources==5.12.0 + # via keyring +ipython==8.11.0 + # via ipywidgets +ipywidgets==8.0.5 + # via flytekitplugins-deck-standard +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.2.0 + # via + # -r requirements.in + # flytekit + # phik + # scikit-learn +jupyterlab-widgets==3.0.6 + # via ipywidgets +keyring==23.13.1 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==26.1.0 + # via flytekit +markdown==3.4.3 + # via flytekitplugins-deck-standard +markupsafe==2.1.2 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.6.3 + # via + # -r ../../../common/requirements-common.in + # phik + # seaborn + # ydata-profiling +matplotlib-inline==0.1.6 + # via ipython +more-itertools==9.1.0 + # via jaraco-classes +multimethod==1.9.1 + # via + # pandera + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.3.1 + # via flytekit +networkx==3.0 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # pandera + # patsy + # phik + # pyarrow + # pywavelets + # scikit-learn + # scipy + # seaborn + # statsmodels + # visions + # ydata-profiling +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.0 + # via + # docker + # marshmallow + # matplotlib + # pandera + # statsmodels +pandas==1.5.3 + # via + # -r requirements.in + # flytekit + # pandera + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +pandas-profiling==3.6.6 + # via flytekitplugins-deck-standard +pandera==0.14.5 + # via + # -r requirements.in + # flytekitplugins-pandera +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==9.4.0 + # via + # imagehash + # matplotlib + # visions +plotly==5.13.1 + # via flytekitplugins-deck-standard +prompt-toolkit==3.0.38 + # via ipython +protobuf==4.22.1 + # via + # flyteidl + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==10.0.1 + # via flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.7 + # via + # pandera + # ydata-profiling +pygments==2.14.0 + # via ipython +pyopenssl==23.0.0 + # via flytekit +pyparsing==3.0.9 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.7.1 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # responses + # ydata-profiling +regex==2023.3.22 + # via docker-image-py +requests==2.28.2 + # via + # cookiecutter + # docker + # flytekit + # kubernetes + # requests-oauthlib + # responses + # ydata-profiling +requests-oauthlib==1.3.1 + # via kubernetes +responses==0.23.1 + # via flytekit +retry==0.9.2 + # via flytekit +rsa==4.9 + # via google-auth +scikit-learn==1.2.2 + # via -r requirements.in +scipy==1.9.3 + # via + # imagehash + # phik + # scikit-learn + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +six==1.16.0 + # via + # asttokens + # google-auth + # kubernetes + # patsy + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via + # flytekit + # hypothesis +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.13.5 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.2 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +tqdm==4.64.1 + # via ydata-profiling +traitlets==5.9.0 + # via + # ipython + # ipywidgets + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +types-pyyaml==6.0.12.8 + # via responses +typing-extensions==4.5.0 + # via + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.8.0 + # via + # dataclasses-json + # pandera +urllib3==1.26.15 + # via + # docker + # flytekit + # kubernetes + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.5.1 + # via + # docker + # kubernetes +wheel==0.40.0 + # via + # -r ../../../common/requirements-common.in + # flytekit +widgetsnbextension==4.0.6 + # via ipywidgets +wrapt==1.15.0 + # via + # deprecated + # flytekit + # pandera +ydata-profiling==4.1.1 + # via pandas-profiling +zipp==3.15.0 + # via + # importlib-metadata + # importlib-resources + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/papermill_plugin/Dockerfile b/docs/examples/papermill_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/papermill_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/papermill_plugin/README.md b/docs/examples/papermill_plugin/README.md new file mode 100644 index 000000000..293623a6f --- /dev/null +++ b/docs/examples/papermill_plugin/README.md @@ -0,0 +1,33 @@ +# Papermill + +```{eval-rst} +.. tags:: Integration, Jupyter, Intermediate +``` + +It is possible to run a Jupyter notebook as a Flyte task using [papermill](https://github.com/nteract/papermill). +Papermill executes the notebook as a whole, so before using this plugin, it is essential to construct your notebook as +recommended by papermill. When using this plugin, there are a few important things to keep in mind: + +1. This plugin can be used for any task - type. + : - It can be python code, which can be a tensorflow model, a data transformation, etc - but things that run in a container + and you would typically write in a `@task`. + - It can be a {py:func}`~flytekit.dynamic` workflow. + - It can be a any other plugin like `spark`, `sagemaker` etc, **ensure that the plugin is installed as well** +2. Flytekit will execute the notebook and capture the output notebook as an *.ipynb* file and an HTML rendered notebook as well +3. Flytekit will pass the inputs into the notebook as long as you have the first cell annotated as `parameters` and inputs are specified +4. Flytekit will read the outputs from the notebook, as long as you use annotate the notebook with `outputs` and outputs are specified + +## Installation + +To use the flytekit papermill plugin simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-papermill +``` + +```{auto-examples-toc} +simple +nb_simple +``` diff --git a/docs/examples/papermill_plugin/papermill_plugin/__init__.py b/docs/examples/papermill_plugin/papermill_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/papermill_plugin/papermill_plugin/nb_simple.ipynb b/docs/examples/papermill_plugin/papermill_plugin/nb_simple.ipynb new file mode 100644 index 000000000..f80b2645d --- /dev/null +++ b/docs/examples/papermill_plugin/papermill_plugin/nb_simple.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Papermill Notebook\n", + "\n", + "This notebook is used in the previous example as a `NotebookTask` to demonstrate\n", + "how to use the papermill plugin.\n", + "\n", + "The cell below has the `parameters` tag, defining the inputs to the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "v = 3.14" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we do some computation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "square = v*v\n", + "print(square)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we use the `flytekitplugins.papermill` package to record the outputs\n", + "so that flyte understands which state to serialize and pass into a downstream\n", + "task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "outputs" + ] + }, + "outputs": [], + "source": [ + "from flytekitplugins.papermill import record_outputs\n", + "\n", + "record_outputs(square=square)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/papermill_plugin/papermill_plugin/simple.py b/docs/examples/papermill_plugin/papermill_plugin/simple.py new file mode 100644 index 000000000..666f8bb8e --- /dev/null +++ b/docs/examples/papermill_plugin/papermill_plugin/simple.py @@ -0,0 +1,83 @@ +# %% [markdown] +# # Jupyter Notebook Tasks +# +# In this example, we will show how to create a flyte task that runs a simple notebook, accepts one input variable, transforms it, and produces +# one output. This can be generalized to multiple inputs and outputs. +# %% +import math +import os +import pathlib + +from flytekit import kwtypes, task, workflow +from flytekitplugins.papermill import NotebookTask + +# %% [markdown] +# ## How to specify inputs and outputs +# +# 1. After you are satisfied with the notebook, ensure that the first cell only has the input variables for the notebook. Now add the tag `parameters` for the first cell. +# +# ```{image} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/parameters.png +# :alt: Example of "parameters tag" added to the cell with input variables +# ``` +# +# 2. Typically at the last cell of the notebook (which does not need to be the last cell), add a tag `outputs` for the intended cell. +# +# ```{image} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/user_guide/outputs.png +# :alt: Example of "parameters tag" added to the cell with input variables +# ``` +# +# 3. In a python file, create a new task at the `module` level. +# An example task is shown below: +# %% +nb = NotebookTask( + name="simple-nb", + notebook_path=os.path.join(pathlib.Path(__file__).parent.absolute(), "nb_simple.ipynb"), + render_deck=True, + inputs=kwtypes(v=float), + outputs=kwtypes(square=float), +) +# %% [markdown] +# :::{note} +# - Note the notebook_path. This is the absolute path to the actual notebook. +# - Note the inputs and outputs. The variable names match the variable names in the jupyter notebook. +# - You can see the notebook on Flyte deck if `render_deck` is set to true. +# ::: + +# %% [markdown] +# :::{figure} https://i.imgur.com/ogfVpr2.png +# :alt: Notebook +# :class: with-shadow +# ::: +# +# ## Other tasks +# +# You can definitely declare other tasks and seamlessly work with notebook tasks. The example below shows how to declare a task that accepts the squared value from the notebook and provides a sqrt: +# %% +@task +def square_root_task(f: float) -> float: + return math.sqrt(f) + + +# %% [markdown] +# Now treat the notebook task as a regular task: +# %% +@workflow +def nb_to_python_wf(f: float) -> float: + out = nb(v=f) + return square_root_task(f=out.square) + + +# %% [markdown] +# And execute the task locally as well: +# %% +if __name__ == "__main__": + print(nb_to_python_wf(f=3.14)) + +# %% [markdown] +# ## Why Are There 3 Outputs? +# +# On executing, you should see 3 outputs instead of the expected one, because this task generates 2 implicit outputs. +# +# One of them is the executed notebook (captured) and a rendered (HTML) of the executed notebook. In this case they are called +# `nb-simple-out.ipynb` and `nb-simple-out.html`, respectively. +# diff --git a/docs/examples/papermill_plugin/requirements.in b/docs/examples/papermill_plugin/requirements.in new file mode 100644 index 000000000..1a4d751b6 --- /dev/null +++ b/docs/examples/papermill_plugin/requirements.in @@ -0,0 +1,5 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-papermill>=0.16.0 diff --git a/docs/examples/papermill_plugin/requirements.txt b/docs/examples/papermill_plugin/requirements.txt new file mode 100644 index 000000000..d60f54573 --- /dev/null +++ b/docs/examples/papermill_plugin/requirements.txt @@ -0,0 +1,331 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +ansiwrap==0.8.4 + # via papermill +appnope==0.1.3 + # via + # ipykernel + # ipython +arrow==1.2.2 + # via jinja2-time +asttokens==2.0.5 + # via stack-data +attrs==21.4.0 + # via jsonschema +backcall==0.2.0 + # via ipython +beautifulsoup4==4.11.1 + # via nbconvert +binaryornot==0.4.4 + # via cookiecutter +bleach==5.0.0 + # via nbconvert +certifi==2021.10.8 + # via requests +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit + # papermill +cloudpickle==2.0.0 + # via flytekit +cookiecutter==1.7.3 + # via flytekit +croniter==1.3.4 + # via flytekit +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +debugpy==1.6.0 + # via ipykernel +decorator==5.1.1 + # via + # ipython + # retry +defusedxml==0.7.1 + # via nbconvert +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +entrypoints==0.4 + # via + # jupyter-client + # nbconvert + # papermill +executing==0.8.3 + # via stack-data +fastjsonschema==2.15.3 + # via nbformat +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-papermill +flytekitplugins-papermill==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +ipykernel==6.13.0 + # via flytekitplugins-papermill +ipython==8.2.0 + # via ipykernel +jedi==0.18.1 + # via ipython +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time + # nbconvert +jinja2-time==0.2.0 + # via cookiecutter +jsonschema==4.4.0 + # via nbformat +jupyter-client==7.2.2 + # via + # ipykernel + # nbclient +jupyter-core==4.9.2 + # via + # jupyter-client + # nbconvert + # nbformat +jupyterlab-pygments==0.2.1 + # via nbconvert +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via + # jinja2 + # nbconvert +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +matplotlib-inline==0.1.3 + # via + # ipykernel + # ipython +mistune==0.8.4 + # via nbconvert +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +nbclient==0.6.0 + # via + # nbconvert + # papermill +nbconvert==6.5.0 + # via flytekitplugins-papermill +nbformat==5.3.0 + # via + # nbclient + # nbconvert + # papermill +nest-asyncio==1.5.5 + # via + # ipykernel + # jupyter-client + # nbclient +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow +packaging==21.3 + # via + # ipykernel + # marshmallow + # matplotlib + # nbconvert +pandas==1.4.2 + # via flytekit +pandocfilters==1.5.0 + # via nbconvert +papermill==2.3.4 + # via flytekitplugins-papermill +parso==0.8.3 + # via jedi +pexpect==4.8.0 + # via ipython +pickleshare==0.7.5 + # via ipython +pillow==9.1.0 + # via matplotlib +poyo==0.5.0 + # via cookiecutter +prompt-toolkit==3.0.29 + # via ipython +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.0 + # via ipykernel +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pygments==2.11.2 + # via + # ipython + # nbconvert +pyparsing==3.0.8 + # via + # matplotlib + # packaging +pyrsistent==0.18.1 + # via jsonschema +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # jupyter-client + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # flytekit + # papermill +pyzmq==22.3.0 + # via jupyter-client +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # papermill + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +six==1.16.0 + # via + # bleach + # cookiecutter + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +soupsieve==2.3.2 + # via beautifulsoup4 +stack-data==0.2.0 + # via ipython +statsd==3.3.0 + # via flytekit +tenacity==8.0.1 + # via papermill +text-unidecode==1.3 + # via python-slugify +textwrap3==0.9.2 + # via ansiwrap +tinycss2==1.1.1 + # via nbconvert +tornado==6.1 + # via + # ipykernel + # jupyter-client +tqdm==4.64.0 + # via papermill +traitlets==5.1.1 + # via + # ipykernel + # ipython + # jupyter-client + # jupyter-core + # matplotlib-inline + # nbclient + # nbconvert + # nbformat +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wcwidth==0.2.5 + # via prompt-toolkit +webencodings==0.5.1 + # via + # bleach + # tinycss2 +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/pima_diabetes/Dockerfile b/docs/examples/pima_diabetes/Dockerfile new file mode 100644 index 000000000..ed26329d4 --- /dev/null +++ b/docs/examples/pima_diabetes/Dockerfile @@ -0,0 +1,49 @@ +FROM ubuntu:focal + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +RUN : \ + && apt-get update \ + && apt install -y software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa + +RUN : \ + && apt-get update \ + && apt-get install -y python3.8 python3-pip python3-venv make build-essential libssl-dev curl vim + +# This is necessary for opencv to work +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev ffmpeg + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN ${VENV}/bin/pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# Copy over the helper script that the SDK relies on +RUN cp ${VENV}/bin/flytekit_venv /usr/local/bin/ +RUN chmod a+x /usr/local/bin/flytekit_venv + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/pima_diabetes/README.md b/docs/examples/pima_diabetes/README.md new file mode 100644 index 000000000..0a6989866 --- /dev/null +++ b/docs/examples/pima_diabetes/README.md @@ -0,0 +1,50 @@ +# Diabetes Classification + +```{eval-rst} +.. tags:: MachineLearning, Intermediate +``` + +The workflow demonstrates how to train an XGBoost model. The workflow is designed for the [Pima Indian Diabetes dataset](https://github.com/jbrownlee/Datasets/blob/master/pima-indians-diabetes.names). + +An example dataset is available [here](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv). + +## Why a Workflow? + +One common question when you read through the example would be whether it is really required to split the training of XGBoost into multiple steps. The answer is complicated, but let us try and understand the pros and cons of doing so. + +### Pros: + +- Each task/step is standalone and can be used for other pipelines +- Each step can be unit tested +- Data splitting, cleaning and processing can be done using a more scalable system like Spark +- State is always saved between steps, so it is cheap to recover from failures, especially if ``caching=True`` +- High visibility + +### Cons: + +- Performance for small datasets is a concern because the intermediate data is durably stored and the state is recorded, and each step is essentially a checkpoint + +## Steps of the Pipeline + +1. Gather data and split it into training and validation sets +2. Fit the actual model +3. Run a set of predictions on the validation set. The function is designed to be more generic, it can be used to simply predict given a set of observations (dataset) +4. Calculate the accuracy score for the predictions + + +## Takeaways + +- Usage of FlyteSchema Type. Schema type allows passing a type safe vector from one task to task. The vector is directly loaded into a pandas dataframe. We could use an unstructured Schema (By simply omitting the column types). This will allow any data to be accepted by the training algorithm. +- We pass the file (that is auto-loaded) as a CSV input. + +Run workflows in this directory with the custom-built base image: + +```{prompt} bash $ +pyflyte run --remote --image ghcr.io/flyteorg/flytecookbook:pima_diabetes-latest diabetes.py diabetes_xgboost_model +``` + +## Examples + +```{auto-examples-toc} +diabetes +``` diff --git a/docs/examples/pima_diabetes/pima_diabetes/__init__.py b/docs/examples/pima_diabetes/pima_diabetes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/pima_diabetes/pima_diabetes/diabetes.py b/docs/examples/pima_diabetes/pima_diabetes/diabetes.py new file mode 100644 index 000000000..107549b78 --- /dev/null +++ b/docs/examples/pima_diabetes/pima_diabetes/diabetes.py @@ -0,0 +1,228 @@ +# %% [markdown] +# # Train and Validate a Diabetes Classification XGBoost Model +# +# Watch a demo of sandbox creation and a sample execution of the pima diabetes pipeline below. +# +# ```{eval-rst} +# .. youtube:: YEvs0MHXZnY +# +# ``` +# %% +import typing +from collections import OrderedDict +from dataclasses import dataclass +from typing import Tuple + +import joblib +import pandas as pd +from dataclasses_json import dataclass_json +from flytekit import Resources, task, workflow +from flytekit.types.file import FlyteFile +from flytekit.types.schema import FlyteSchema +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split +from xgboost import XGBClassifier + +# %% [markdown] +# Since we are working with a specific dataset, we will create a strictly typed schema for the dataset. +# If we wanted a generic data splitter we could use a Generic schema without any column type and name information +# [Example file](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv) +# CSV Columns +# +# 1. Number of times pregnant +# 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test +# 3. Diastolic blood pressure (mm Hg) +# 4. Triceps skin fold thickness (mm) +# 5. 2-Hour serum insulin (mu U/ml) +# 6. Body mass index (weight in kg/(height in m)^2) +# 7. Diabetes pedigree function +# 8. Age (years) +# 9. Class variable (0 or 1) +# +# Example Row: 6,148,72,35,0,33.6,0.627,50,1 +# the input dataset schema +# %% +DATASET_COLUMNS = OrderedDict( + { + "#preg": int, + "pgc_2h": int, + "diastolic_bp": int, + "tricep_skin_fold_mm": int, + "serum_insulin_2h": int, + "bmi": float, + "diabetes_pedigree": float, + "age": int, + "class": int, + } +) +# %% [markdown] +# The first 8 columns are features +# %% +FEATURE_COLUMNS = OrderedDict({k: v for k, v in DATASET_COLUMNS.items() if k != "class"}) +# %% [markdown] +# The last column is the class +# %% +CLASSES_COLUMNS = OrderedDict({"class": int}) + + +# %% [markdown] +# Let us declare a task that accepts a CSV file with the previously defined +# columns and converts it to a typed schema. +# An example CSV file is available at +# [here](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv) +# %% +@task(cache_version="1.0", cache=True, limits=Resources(mem="200Mi")) +def split_traintest_dataset( + dataset: FlyteFile[typing.TypeVar("csv")], seed: int, test_split_ratio: float +) -> Tuple[ + FlyteSchema[FEATURE_COLUMNS], + FlyteSchema[FEATURE_COLUMNS], + FlyteSchema[CLASSES_COLUMNS], + FlyteSchema[CLASSES_COLUMNS], +]: + """ + Retrieves the training dataset from the given blob location and then splits it using the split ratio and returns the result + This splitter is only for the dataset that has the format as specified in the example csv. The last column is assumed to be + the class and all other columns 0-8 the features. + + The data is returned as a schema, which gets converted to a parquet file in the back. + """ + column_names = [k for k in DATASET_COLUMNS.keys()] + df = pd.read_csv(dataset, names=column_names) + + # Select all features + x = df[column_names[:8]] + # Select only the classes + y = df[[column_names[-1]]] + + # split data into train and test sets + return train_test_split(x, y, test_size=test_split_ratio, random_state=seed) + + +# %% [markdown] +# It is also possible to defined the output file type. This is useful in +# combining tasks, where one task may only accept models serialized in `.joblib.dat` +# %% +MODELSER_JOBLIB = typing.TypeVar("joblib.dat") + + +# %% [markdown] +# It is also possible in Flyte to pass custom objects, as long as they are +# declared as ``` dataclass``es and also decorated with ``@dataclass_json ```. +# %% +@dataclass_json +@dataclass +class XGBoostModelHyperparams(object): + """ + These are the xgboost hyper parameters available in scikit-learn library. + """ + + max_depth: int = 3 + learning_rate: float = 0.1 + n_estimators: int = 100 + objective: str = "binary:logistic" + booster: str = "gbtree" + n_jobs: int = 1 + + +model_file = typing.NamedTuple("Model", model=FlyteFile[MODELSER_JOBLIB]) +workflow_outputs = typing.NamedTuple("WorkflowOutputs", model=FlyteFile[MODELSER_JOBLIB], accuracy=float) + + +@task(cache_version="1.0", cache=True, limits=Resources(mem="200Mi")) +def fit( + x: FlyteSchema[FEATURE_COLUMNS], + y: FlyteSchema[CLASSES_COLUMNS], + hyperparams: XGBoostModelHyperparams, +) -> model_file: + """ + This function takes the given input features and their corresponding classes to train a XGBClassifier. + NOTE: We have simplified the number of hyper parameters we take for demo purposes + """ + x_df = x.open().all() + y_df = y.open().all() + + # fit model no training data + m = XGBClassifier( + n_jobs=hyperparams.n_jobs, + max_depth=hyperparams.max_depth, + n_estimators=hyperparams.n_estimators, + booster=hyperparams.booster, + objective=hyperparams.objective, + learning_rate=hyperparams.learning_rate, + ) + m.fit(x_df, y_df) + + # TODO model Blob should be a file like object + fname = "model.joblib.dat" + joblib.dump(m, fname) + return (fname,) + + +@task(cache_version="1.0", cache=True, limits=Resources(mem="200Mi")) +def predict( + x: FlyteSchema[FEATURE_COLUMNS], + model_ser: FlyteFile[MODELSER_JOBLIB], +) -> FlyteSchema[CLASSES_COLUMNS]: + """ + Given a any trained model, serialized using joblib (this method can be shared!) and features, this method returns + predictions. + """ + model = joblib.load(model_ser) + # make predictions for test data + x_df = x.open().all() + y_pred = model.predict(x_df) + + col = [k for k in CLASSES_COLUMNS.keys()] + y_pred_df = pd.DataFrame(y_pred, columns=col, dtype="int64") + y_pred_df.round(0) + return y_pred_df + + +@task(cache_version="1.0", cache=True, limits=Resources(mem="200Mi")) +def score(predictions: FlyteSchema[CLASSES_COLUMNS], y: FlyteSchema[CLASSES_COLUMNS]) -> float: + """ + Compares the predictions with the actuals and returns the accuracy score. + """ + pred_df = predictions.open().all() + y_df = y.open().all() + # evaluate predictions + acc = accuracy_score(y_df, pred_df) + print("Accuracy: %.2f%%" % (acc * 100.0)) + return float(acc) + + +# %% [markdown] +# Workflow sample here +# %% +@workflow +def diabetes_xgboost_model( + dataset: FlyteFile[ + typing.TypeVar("csv") + ] = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv", + test_split_ratio: float = 0.33, + seed: int = 7, +) -> workflow_outputs: + """ + This pipeline trains an XGBoost mode for any given dataset that matches the schema as specified in + https://github.com/jbrownlee/Datasets/blob/master/pima-indians-diabetes.names. + """ + x_train, x_test, y_train, y_test = split_traintest_dataset( + dataset=dataset, seed=seed, test_split_ratio=test_split_ratio + ) + model = fit( + x=x_train, + y=y_train, + hyperparams=XGBoostModelHyperparams(max_depth=4), + ) + predictions = predict(x=x_test, model_ser=model.model) + return model.model, score(predictions=predictions, y=y_test) + + +# %% [markdown] +# The entire workflow can be executed locally as follows. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(diabetes_xgboost_model()) diff --git a/docs/examples/pima_diabetes/requirements.in b/docs/examples/pima_diabetes/requirements.in new file mode 100644 index 000000000..23184073a --- /dev/null +++ b/docs/examples/pima_diabetes/requirements.in @@ -0,0 +1,9 @@ +flytekit>=0.32.3 +wheel +matplotlib +flytekitplugins-deck-standard +xgboost +joblib +scikit-learn +tabulate +matplotlib diff --git a/docs/examples/pima_diabetes/requirements.txt b/docs/examples/pima_diabetes/requirements.txt new file mode 100644 index 000000000..d1a80d985 --- /dev/null +++ b/docs/examples/pima_diabetes/requirements.txt @@ -0,0 +1,205 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==1.7.3 + # via flytekit +croniter==1.3.4 + # via flytekit +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via -r ../../../common/requirements-common.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +joblib==1.1.0 + # via + # -r requirements.in + # scikit-learn +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via + # -r ../../../common/requirements-common.in + # -r requirements.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow + # scikit-learn + # scipy + # xgboost +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +pillow==9.1.0 + # via matplotlib +poyo==0.5.0 + # via cookiecutter +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +scikit-learn==1.0.2 + # via sklearn +scipy==1.8.0 + # via + # scikit-learn + # xgboost +six==1.16.0 + # via + # cookiecutter + # grpcio + # python-dateutil +sklearn==0.0 + # via -r requirements.in +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +tabulate==0.8.9 + # via -r requirements.in +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +xgboost==1.5.2 + # via -r requirements.in +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/productionizing/Dockerfile b/docs/examples/productionizing/Dockerfile new file mode 100644 index 000000000..dde00f12f --- /dev/null +++ b/docs/examples/productionizing/Dockerfile @@ -0,0 +1,28 @@ +FROM python:3.11-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +WORKDIR /root + +RUN apt-get update && apt-get install -y build-essential + +# Virtual environment +ENV VENV /opt/venv +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/productionizing/README.md b/docs/examples/productionizing/README.md new file mode 100644 index 000000000..d9c4f64fd --- /dev/null +++ b/docs/examples/productionizing/README.md @@ -0,0 +1,20 @@ +(deployment_workflow)= + +# Productionize + +In this section, you will learn how to take Flyte pipelines into production. +You will explore concepts such as customizing resources, notifications, scheduling, +GPU configuration, secrets, spot instances and more. + +```{auto-examples-toc} +customizing_resources +reference_task +reference_launch_plan +lp_notifications +lp_schedules +configure_logging_links +configure_use_gpus +spot_instances +use_secrets +workflow_labels_annotations +``` diff --git a/docs/examples/productionizing/productionizing/__init__.py b/docs/examples/productionizing/productionizing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/productionizing/productionizing/configure_logging_links.py b/docs/examples/productionizing/productionizing/configure_logging_links.py new file mode 100644 index 000000000..704a37c3c --- /dev/null +++ b/docs/examples/productionizing/productionizing/configure_logging_links.py @@ -0,0 +1,123 @@ +# %% [markdown] +# (configure-logging)= +# +# # Configuring Logging Links in UI +# +# ```{eval-rst} +# .. tags:: Deployment, Intermediate, UI +# ``` +# +# To debug your workflows in production, you want to access logs from your tasks as they run. +# These logs are different from the core Flyte platform logs, are specific to execution, and may vary from plugin to plugin; +# for example, Spark may have driver and executor logs. +# +# Every organization potentially uses different log aggregators, making it hard to create a one-size-fits-all solution. +# Some examples of the log aggregators include cloud-hosted solutions like AWS CloudWatch, GCP Stackdriver, Splunk, Datadog, etc. +# +# Flyte provides a simplified interface to configure your log provider. Flyte-sandbox +# ships with the Kubernetes dashboard to visualize the logs. This may not be safe for production, hence we recommend users +# explore other log aggregators. +# +# ## How to configure? +# +# To configure your log provider, the provider needs to support `URL` links that are shareable and can be templatized. +# The templating engine has access to [these](https://github.com/flyteorg/flyteplugins/blob/b0684d97a1cf240f1a44f310f4a79cc21844caa9/go/tasks/pluginmachinery/tasklog/plugin.go#L7-L16) parameters. +# +# The parameters can be used to generate a unique URL to the logs using a templated URI that pertain to a specific task. The templated URI has access to the following parameters: +# +# ```{eval-rst} +# .. list-table:: Parameters to generate a templated URI +# :widths: 25 50 +# :header-rows: 1 +# +# * - Parameter +# - Description +# * - ``{{ .podName }}`` +# - Gets the pod name as it shows in k8s dashboard +# * - ``{{ .podUID }}`` +# - The pod UID generated by the k8s at runtime +# * - ``{{ .namespace }}`` +# - K8s namespace where the pod runs +# * - ``{{ .containerName }}`` +# - The container name that generated the log +# * - ``{{ .containerId }}`` +# - The container id docker/crio generated at run time +# * - ``{{ .logName }}`` +# - A deployment specific name where to expect the logs to be +# * - ``{{ .hostname }}`` +# - The hostname where the pod is running and logs reside +# * - ``{{ .podRFC3339StartTime }}`` +# - The pod creation time (in RFC3339 format, e.g. "2021-01-01T02:07:14Z", also conforming to ISO 8601) +# * - ``{{ .podRFC3339FinishTime }}`` +# - Don't have a good mechanism for this yet, but approximating with ``time.Now`` for now +# * - ``{{ .podUnixStartTime }}`` +# - The pod creation time (in unix seconds, not millis) +# * - ``{{ .podUnixFinishTime }}`` +# - Don't have a good mechanism for this yet, but approximating with ``time.Now`` for now +# ``` +# +# The parameterization engine uses Golangs native templating format and hence uses `{{ }}`. An example configuration can be seen as follows: +# +# ```yaml +# task_logs: +# plugins: +# logs: +# templates: +# - displayName: +# templateUris: +# - "https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logEventViewer:group=/flyte-production/kubernetes;stream=var.log.containers.{{.podName}}_{{.namespace}}_{{.containerName}}-{{.containerId}}.log" +# - "https://some-other-source/home?region=us-east-1#logEventViewer:group=/flyte-production/kubernetes;stream=var.log.containers.{{.podName}}_{{.namespace}}_{{.containerName}}-{{.containerId}}.log" +# messageFormat: 0 # this parameter is optional, but use 0 for "unknown", 1 for "csv", or 2 for "json" +# ``` +# +# :::{tip} +# Since helm chart uses the same templating syntax for args (like `{{ }}`), compiling the chart results in helm replacing Flyte log link templates as well. To avoid this, you can use escaped templating for Flyte logs in the helm chart. +# This ensures that Flyte log link templates remain in place during helm chart compilation. +# For example: +# +# If your configuration looks like this: +# +# `https://someexample.com/app/podName={{ "{{" }} .podName {{ "}}" }}&containerName={{ .containerName }}` +# +# Helm chart will generate: +# +# `https://someexample.com/app/podName={{.podName}}&containerName={{.containerName}}` +# +# Flytepropeller pod would be created as: +# +# `https://someexample.com/app/podName=pname&containerName=cname` +# ::: +# +# This code snippet will output two logs per task that use the log plugin. +# However, not all task types use the log plugin; for example, the SageMaker plugin uses the log output provided by Sagemaker, and the Snowflake plugin will use a link to the snowflake console. +# +# ## Datadog Integration +# +# To send your Flyte workflow logs to Datadog, you can follow these steps: +# +# 1. Enable collection of logs from containers and collection of logs using files. The precise configuration steps will vary depending on your specific setup. +# +# For instance, if you're using Helm, use the following config: +# +# ```yaml +# logs: +# enabled: true +# containerCollectAll: true +# containerCollectUsingFiles: true +# ``` +# +# If you're using environment variables, use the following config: +# +# ```yaml +# DD_LOGS_ENABLED: "false" +# DD_LOGS_CONFIG_CONTAINER_COLLECT_ALL: "true" +# DD_LOGS_CONFIG_K8S_CONTAINER_USE_FILE: "true" +# DD_CONTAINER_EXCLUDE_LOGS: "name:datadog-agent" # This is to avoid tracking logs produced by the datadog agent itself +# ``` +# +# :::{warning} +# The boolean values have to be represented as strings. +# ::: +# +# 2. The Datadog [guide](https://docs.datadoghq.com/containers/kubernetes/log/?tab=daemonset) includes a section on mounting volumes. It is essential (and a prerequisite for proper functioning) to map the volumes "logpodpath" and "logcontainerpath" as illustrated in the linked example. While the "pointerdir" volume is optional, it is recommended that you map it to prevent the loss of container logs during restarts or network issues (as stated in the guide). +# diff --git a/docs/examples/productionizing/productionizing/configure_use_gpus.py b/docs/examples/productionizing/productionizing/configure_use_gpus.py new file mode 100644 index 000000000..ee3315ba1 --- /dev/null +++ b/docs/examples/productionizing/productionizing/configure_use_gpus.py @@ -0,0 +1,38 @@ +# %% [markdown] +# (configure-gpus)= +# +# # Configuring Flyte to Access GPUs +# +# ```{eval-rst} +# .. tags:: Deployment, Infrastructure, GPU, Intermediate +# ``` +# +# Along with the simpler resources like CPU/Memory, you may want to configure and access GPU resources. Flyte +# allows you to configure the GPU access poilcy for your cluster. GPUs are expensive and it would not be ideal to +# treat machines with GPUs and machines with CPUs equally. You may want to reserve machines with GPUs for tasks +# that explicitly request GPUs. To achieve this, Flyte uses the Kubernetes concept of [taints and tolerations](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/). +# +# Kubernetes can automatically apply tolerations for extended resources like GPUs using the [ExtendedResourceToleration plugin](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/#extendedresourcetoleration), enabled by default in some cloud environments. Make sure the GPU nodes are tainted with a key matching the resource name, i.e., `key: nvidia.com/gpu`. +# +# You can also configure Flyte backend to apply specific tolerations. This configuration is controlled under generic k8s plugin configuration as can be found [here](https://github.com/flyteorg/flyteplugins/blob/5a00b19d88b93f9636410a41f81a73356a711482/go/tasks/pluginmachinery/flytek8s/config/config.go#L120). +# +# The idea of this configuration is that whenever a task that can execute on Kubernetes requests for GPUs, it automatically +# adds the matching toleration for that resource (in this case, `gpu`) to the generated PodSpec. +# As it follows here, you can configure it to access specific resources using the tolerations for all resources supported by +# Kubernetes. +# +# Here's an example configuration: +# +# ```yaml +# plugins: +# k8s: +# resource-tolerations: +# - nvidia.com/gpu: +# - key: "key1" +# operator: "Equal" +# value: "value1" +# effect: "NoSchedule" +# ``` +# +# Getting this configuration into your deployment will depend on how Flyte is deployed on your cluster. If you use the default Opta/Helm route, you'll need to amend your Helm chart values ([example](https://github.com/flyteorg/flyte/blob/cc127265aec490ad9537d29bd7baff828043c6f5/charts/flyte-core/values.yaml#L629)) so that they end up [here](https://github.com/flyteorg/flyte/blob/3d265f166fcdd8e20b07ff82b494c0a7f6b7b108/deployment/eks/flyte_helm_generated.yaml#L521). +# diff --git a/docs/examples/productionizing/productionizing/customizing_resources.py b/docs/examples/productionizing/productionizing/customizing_resources.py new file mode 100644 index 000000000..1484ca880 --- /dev/null +++ b/docs/examples/productionizing/productionizing/customizing_resources.py @@ -0,0 +1,144 @@ +# %% [markdown] +# # Customizing Task Resources +# +# ```{eval-rst} +# .. tags:: Deployment, Infrastructure, Basic +# ``` +# +# One of the reasons to use a hosted Flyte environment is the potential of leveraging CPU, memory and storage resources, far greater than what's available locally. +# Flytekit makes it possible to specify these requirements declaratively and close to where the task itself is declared. + +# %% [markdown] +# In this example, the memory required by the function increases as the dataset size increases. +# Large datasets may not be able to run locally, so we would want to provide hints to the Flyte backend to request for more memory. +# This is done by decorating the task with the hints as shown in the following code sample. +# +# Tasks can have `requests` and `limits` which mirror the native [equivalents in Kubernetes](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#requests-and-limits). +# A task can possibly be allocated more resources than it requests, but never more than its limit. +# Requests are treated as hints to schedule tasks on nodes with available resources, whereas limits +# are hard constraints. +# +# For either a request or limit, refer to the {py:class}`flytekit:flytekit.Resources` documentation. +# +# The following attributes can be specified for a `Resource`. +# +# 1. `cpu` +# 2. `mem` +# 3. `gpu` +# +# To ensure that regular tasks that don't require GPUs are not scheduled on GPU nodes, a separate node group for GPU nodes can be configured with [taints](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/). +# +# To ensure that tasks that require GPUs get the needed tolerations on their pods, set up FlytePropeller using the following [configuration](https://github.com/flyteorg/flytepropeller/blob/v0.10.5/config.yaml#L51,L56). Ensure that this toleration config matches the taint config you have configured to protect your GPU providing nodes from dealing with regular non-GPU workloads (pods). +# +# The actual values follow the [Kubernetes convention](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#resource-units-in-kubernetes). +# Let's look at an example to understand how to customize resources. + +# %% [markdown] +# Import the dependencies. +# %% +import typing + +from flytekit import Resources, task, workflow + + +# %% [markdown] +# Define a task and configure the resources to be allocated to it. +# %% +@task(requests=Resources(cpu="1", mem="100Mi"), limits=Resources(cpu="2", mem="150Mi")) +def count_unique_numbers(x: typing.List[int]) -> int: + s = set() + for i in x: + s.add(i) + return len(s) + + +# %% [markdown] +# Define a task that computes the square of a number. +# %% +@task +def square(x: int) -> int: + return x * x + + +# %% [markdown] +# You can use the tasks decorated with memory and storage hints like regular tasks in a workflow. +# %% +@workflow +def my_workflow(x: typing.List[int]) -> int: + return square(x=count_unique_numbers(x=x)) + + +# %% [markdown] +# You can execute the workflow locally. +# %% +if __name__ == "__main__": + print(count_unique_numbers(x=[1, 1, 2])) + print(my_workflow(x=[1, 1, 2])) + +# %% [markdown] +# :::{note} +# To alter the limits of the default platform configuration, change the [admin config](https://github.com/flyteorg/flyte/blob/b16ffd76934d690068db1265ac9907a278fba2ee/deployment/eks/flyte_helm_generated.yaml#L203-L213) and [namespace level quota](https://github.com/flyteorg/flyte/blob/b16ffd76934d690068db1265ac9907a278fba2ee/deployment/eks/flyte_helm_generated.yaml#L214-L240) on the cluster. +# ::: + +# %% [markdown] +# (resource_with_overrides)= +# +# ## Using `with_overrides` +# +# You can use the `with_overrides` method to override the resources allocated to the tasks dynamically. +# Let's understand how the resources can be initialized with an example. + +# %% [markdown] +# Import the dependencies. +# %% +import typing # noqa: E402 + +from flytekit import Resources, task, workflow # noqa: E402 + + +# %% [markdown] +# Define a task and configure the resources to be allocated to it. +# You can use tasks decorated with memory and storage hints like regular tasks in a workflow. +# %% +@task(requests=Resources(cpu="1", mem="200Mi"), limits=Resources(cpu="2", mem="350Mi")) +def count_unique_numbers_1(x: typing.List[int]) -> int: + s = set() + for i in x: + s.add(i) + return len(s) + + +# %% [markdown] +# Define a task that computes the square of a number. +# %% +@task +def square_1(x: int) -> int: + return x * x + + +# %% [markdown] +# The `with_overrides` method overrides the old resource allocations. +# %% +@workflow +def my_pipeline(x: typing.List[int]) -> int: + return square_1(x=count_unique_numbers_1(x=x)).with_overrides(limits=Resources(cpu="6", mem="500Mi")) + + +# %% [markdown] +# You can execute the workflow locally. +# %% +if __name__ == "__main__": + print(count_unique_numbers_1(x=[1, 1, 2])) + print(my_pipeline(x=[1, 1, 2])) + +# %% [markdown] +# You can see the memory allocation below. The memory limit is `500Mi` rather than `350Mi`, and the +# CPU limit is 4, whereas it should have been 6 as specified using `with_overrides`. +# This is because the default platform CPU quota for every pod is 4. +# +# :::{figure} https://raw.githubusercontent.com/flyteorg/static-resources/main/flytesnacks/core/resource_allocation.png +# :alt: Resource allocated using "with_overrides" method +# +# Resource allocated using "with_overrides" method +# ::: +# diff --git a/docs/examples/productionizing/productionizing/lp_notifications.py b/docs/examples/productionizing/productionizing/lp_notifications.py new file mode 100644 index 000000000..269fdce4c --- /dev/null +++ b/docs/examples/productionizing/productionizing/lp_notifications.py @@ -0,0 +1,199 @@ +# %% [markdown] +# # Notifications +# +# ```{eval-rst} +# .. tags:: Intermediate +# +# ``` + +# %% [markdown] +# When a workflow is completed, users can be notified by: +# +# - Email +# - [Pagerduty](https://support.pagerduty.com/docs/email-integration-guide#integrating-with-a-pagerduty-service) +# - [Slack](https://slack.com/help/articles/206819278-Send-emails-to-Slack) +# +# The content of these notifications is configurable at the platform level. +# +# ## Code Example +# +# When a workflow reaches a specified [terminal workflow execution phase](https://github.com/flyteorg/flytekit/blob/v0.16.0b7/flytekit/core/notification.py#L10,L15), +# the {py:class}`flytekit:flytekit.Email`, {py:class}`flytekit:flytekit.PagerDuty`, or {py:class}`flytekit:flytekit.Slack` +# objects can be used in the construction of a {py:class}`flytekit:flytekit.LaunchPlan`. + +# %% +from datetime import timedelta + +# %% [markdown] +# Consider the following example workflow: +# %% +from flytekit import Email, FixedRate, LaunchPlan, PagerDuty, Slack, WorkflowExecutionPhase, task, workflow + + +@task +def double_int_and_print(a: int) -> str: + return str(a * 2) + + +@workflow +def int_doubler_wf(a: int) -> str: + doubled = double_int_and_print(a=a) + return doubled + + +# %% [markdown] +# Here are three scenarios that can help deepen your understanding of how notifications work: +# +# 1. Launch Plan triggers email notifications when the workflow execution reaches the `SUCCEEDED` phase. +# %% +int_doubler_wf_lp = LaunchPlan.get_or_create( + name="email_notifications_lp", + workflow=int_doubler_wf, + default_inputs={"a": 4}, + notifications=[ + Email( + phases=[WorkflowExecutionPhase.SUCCEEDED], + recipients_email=["admin@example.com"], + ) + ], +) + +# %% [markdown] +# 2. Notifications shine when used for scheduled workflows to alert for failures. +# %% +int_doubler_wf_scheduled_lp = LaunchPlan.get_or_create( + name="int_doubler_wf_scheduled", + workflow=int_doubler_wf, + default_inputs={"a": 4}, + notifications=[ + PagerDuty( + phases=[WorkflowExecutionPhase.FAILED, WorkflowExecutionPhase.TIMED_OUT], + recipients_email=["abc@pagerduty.com"], + ) + ], + schedule=FixedRate(duration=timedelta(days=1)), +) + + +# %% [markdown] +# 3. Notifications can be combined with different permutations of terminal phases and recipient targets. + +# %% +wacky_int_doubler_lp = LaunchPlan.get_or_create( + name="wacky_int_doubler", + workflow=int_doubler_wf, + default_inputs={"a": 4}, + notifications=[ + Email( + phases=[WorkflowExecutionPhase.FAILED], + recipients_email=["me@example.com", "you@example.com"], + ), + Email( + phases=[WorkflowExecutionPhase.SUCCEEDED], + recipients_email=["myboss@example.com"], + ), + Slack( + phases=[ + WorkflowExecutionPhase.SUCCEEDED, + WorkflowExecutionPhase.ABORTED, + WorkflowExecutionPhase.TIMED_OUT, + ], + recipients_email=["myteam@slack.com"], + ), + ], +) + +# %% [markdown] +# 4. You can use pyflyte register to register the launch plan and lauch it in flyteconsole to get the notifications. +# +# ``` +# pyflyte register lp_notifications.py +# ``` +# +# Choose the launch plan with notifications config +# :::{figure} https://i.ibb.co/cLT5tRX/lp.png +# :alt: Notifications Launch Plan +# :class: with-shadow +# ::: + +# %% [markdown] +# ### Future work +# +# Work is ongoing to support a generic event egress system that can be used to publish events for tasks, workflows, and +# workflow nodes. When this is complete, generic event subscribers can asynchronously process these events for a rich +# and fully customizable experience. +# +# ## Platform Configuration Changes +# +# The `notifications` top-level portion of the Flyteadmin config specifies how to handle notifications. +# +# As in schedules, the handling of notifications is composed of two parts— one part handles enqueuing notifications asynchronously. The other part handles processing pending notifications and sends out emails and alerts. +# +# This is only supported for Flyte instances running on AWS. +# +# ### Config +# #### For Sandbox +# To publish notifications, you'll need to register a sendgrid api key from [sendgrid](https://sendgrid.com/), it's free for 100 emails per day. +# You have to add notifications config in your sandbox config file. +# +# ```yaml +# # config-sandbox.yaml +# notifications: +# type: sandbox # noqa: F821 +# emailer: +# emailServerConfig: +# serviceName: sendgrid +# apiKeyEnvVar: SENDGRID_API_KEY +# subject: "Notice: Execution \"{{ workflow.name }}\" has {{ phase }} in \"{{ domain }}\"." +# sender: "flyte-notifications@company.com" +# body: > +# Execution \"{{ workflow.name }} [{{ name }}]\" has {{ phase }} in \"{{ domain }}\". View details at +# +# http://flyte.company.com/console/projects/{{ project }}/domains/{{ domain }}/executions/{{ name }}. {{ error }} +# ``` +# +# Note that you should set and export the `SENDGRID_API_KEY` environment variable in your shell. +# +# #### For AWS +# To publish notifications, you'll need to set up an [SNS topic](https://aws.amazon.com/sns/?whats-new-cards.sort-by=item.additionalFields.postDateTime&whats-new-cards.sort-order=desc). +# +# To process notifications, you'll need to set up an [AWS SQS](https://aws.amazon.com/sqs/) queue to consume notification events. This queue must be configured as a subscription to your SNS topic you created above. +# +# To publish notifications, you'll need a [verified SES email address](https://docs.aws.amazon.com/ses/latest/DeveloperGuide/verify-addresses-and-domains.html) which will be used to send notification emails and alerts using email APIs. +# +# The role you use to run Flyteadmin must have permissions to read and write to your SNS topic and SQS queue. +# +# Let's look into the following config section and explain what each value represents: +# +# ```bash +# notifications: +# type: "aws" # noqa: F821 +# region: "us-east-1" +# publisher: +# topicName: "arn:aws:sns:us-east-1:{{ YOUR ACCOUNT ID }}:{{ YOUR TOPIC }}" +# processor: +# queueName: "{{ YOUR QUEUE NAME }}" +# accountId: "{{ YOUR ACCOUNT ID }}" +# emailer: +# subject: "Notice: Execution \"{{ workflow.name }}\" has {{ phase }} in \"{{ domain }}\"." +# sender: "flyte-notifications@company.com" +# body: > +# Execution \"{{ workflow.name }} [{{ name }}]\" has {{ phase }} in \"{{ domain }}\". View details at +# +# http://flyte.company.com/console/projects/{{ project }}/domains/{{ domain }}/executions/{{ name }}. {{ error }} +# ``` +# +# - **type**: AWS is the only cloud back-end supported for executing scheduled workflows; hence `"aws"` is the only valid value. By default, the no-op executor is used. +# - **region**: Specifies the region AWS clients should use when creating SNS and SQS clients. +# - **publisher**: Handles pushing notification events to your SNS topic. +# : - **topicName**: This is the arn of your SNS topic. +# - **processor**: Handles recording notification events and enqueueing them to be processed asynchronously. +# : - **queueName**: Name of the SQS queue which will capture pending notification events. +# - **accountId**: AWS [account id](https://docs.aws.amazon.com/IAM/latest/UserGuide/console_account-alias.html#FindingYourAWSId). +# - **emailer**: Encloses config details for sending and formatting emails used as notifications. +# : - **subject**: Configurable subject line used in notification emails. +# - **sender**: Your verified SES email sender. +# - **body**: Configurable email body used in notifications. +# +# The complete set of parameters that can be used for email templating are checked in [here](https://github.com/flyteorg/flyteadmin/blob/a84223dab00dfa52d8ba1ed2d057e77b6c6ab6a7/pkg/async/notifications/email.go#L18,L30). +# diff --git a/docs/examples/productionizing/productionizing/lp_schedules.py b/docs/examples/productionizing/productionizing/lp_schedules.py new file mode 100644 index 000000000..7620c9b3e --- /dev/null +++ b/docs/examples/productionizing/productionizing/lp_schedules.py @@ -0,0 +1,201 @@ +# %% [markdown] +# (scheduling_launch_plan)= +# +# # Scheduling Workflows +# +# ```{eval-rst} +# .. tags:: Basic +# ``` +# +# {ref}`flyte:divedeep-launchplans` can be set to run automatically on a schedule using the Flyte Native Scheduler. +# For workflows that depend on knowing the kick-off time, Flyte supports passing in the scheduled time (not the actual time, which may be a few seconds off) as an argument to the workflow. +# +# Check out a demo of how the Native Scheduler works: +# +# ```{eval-rst} +# .. youtube:: sQoCp2qSQK4 +# ``` +# +# :::{note} +# Native scheduler doesn't support [AWS syntax](http://docs.aws.amazon.com/AmazonCloudWatch/latest/events/ScheduledEvents.html#CronExpressions). +# ::: + +# %% [markdown] +# Consider the following example workflow: +# %% +from datetime import datetime + +from flytekit import task, workflow + + +@task +def format_date(run_date: datetime) -> str: + return run_date.strftime("%Y-%m-%d %H:%M") + + +@workflow +def date_formatter_wf(kickoff_time: datetime): + formatted_kickoff_time = format_date(run_date=kickoff_time) + print(formatted_kickoff_time) + + +# %% [markdown] +# The `date_formatter_wf` workflow can be scheduled using either the `CronSchedule` or the `FixedRate` object. +# +# (cron-schedules)= +# +# ## Cron Schedules +# +# [Cron](https://en.wikipedia.org/wiki/Cron) expression strings use this {ref}`syntax `. +# An incorrect cron schedule expression would lead to failure in triggering the schedule. +# %% +from flytekit import CronSchedule, LaunchPlan # noqa: E402 + +# creates a launch plan that runs every minute. +cron_lp = LaunchPlan.get_or_create( + name="my_cron_scheduled_lp", + workflow=date_formatter_wf, + schedule=CronSchedule( + # Note that the ``kickoff_time_input_arg`` matches the workflow input we defined above: kickoff_time + # But in case you are using the AWS scheme of schedules and not using the native scheduler then switch over the schedule parameter with cron_expression + schedule="*/1 * * * *", # Following schedule runs every min + kickoff_time_input_arg="kickoff_time", + ), +) + +# %% [markdown] +# The `kickoff_time_input_arg` corresponds to the workflow input `kickoff_time`. +# Specifying this argument means that Flyte will pass in the kick-off time of the +# cron schedule into the `kickoff_time` argument of the `date_formatter_wf` workflow. + +# %% [markdown] +# ## Fixed Rate Intervals +# +# If you prefer to use an interval rather than a cron scheduler to schedule your workflows, you can use the fixed-rate scheduler. +# A fixed-rate scheduler runs at the specified interval. +# +# Here's an example: + +# %% +from datetime import timedelta # noqa: E402 + +from flytekit import FixedRate, LaunchPlan # noqa: E402 + + +@task +def be_positive(name: str) -> str: + return f"You're awesome, {name}" + + +@workflow +def positive_wf(name: str): + reminder = be_positive(name=name) + print(f"{reminder}") + + +fixed_rate_lp = LaunchPlan.get_or_create( + name="my_fixed_rate_lp", + workflow=positive_wf, + # Note that the workflow above doesn't accept any kickoff time arguments. + # We just omit the ``kickoff_time_input_arg`` from the FixedRate schedule invocation + schedule=FixedRate(duration=timedelta(minutes=10)), + fixed_inputs={"name": "you"}, +) + +# %% [markdown] +# This fixed-rate scheduler runs every ten minutes. Similar to a cron scheduler, a fixed-rate scheduler also accepts `kickoff_time_input_arg` (which is omitted in this example). +# +# (activating-schedules)= +# +# ## Activating a Schedule +# +# After initializing your launch plan, [activate the specific version of the launch plan](https://docs.flyte.org/projects/flytectl/en/latest/gen/flytectl_update_launchplan.html) so that the schedule runs. +# +# ```bash +# flytectl update launchplan -p flyteexamples -d development {{ name_of_lp }} --version --activate +# ``` + +# %% [markdown] +# Verify if your launch plan was activated: +# +# ```bash +# flytectl get launchplan -p flytesnacks -d development +# ``` + +# %% [markdown] +# ## Deactivating a Schedule +# +# You can [archive/deactivate the launch plan](https://docs.flyte.org/projects/flytectl/en/latest/gen/flytectl_update_launchplan.html) to deschedule any scheduled job associated with it. +# +# ```bash +# flytectl update launchplan -p flyteexamples -d development {{ name_of_lp }} --version --archive +# ``` + +# %% [markdown] +# ## Platform Configuration Changes For AWS Scheduler +# +# The Scheduling feature can be run using the Flyte native scheduler which comes with Flyte. If you intend to use the AWS scheduler then it requires additional infrastructure to run, so these will have to be created and configured. The following sections are only required if you use the AWS scheme for the scheduler. You can still run the Flyte native scheduler on AWS. +# +# ### Setting up Scheduled Workflows +# +# To run workflow executions based on user-specified schedules, you'll need to fill out the top-level `scheduler` portion of the flyteadmin application configuration. +# +# In particular, you'll need to configure the two components responsible for scheduling workflows and processing schedule event triggers. +# +# :::{note} +# This functionality is currently only supported for AWS installs. +# ::: +# +# #### Event Scheduler +# +# To schedule workflow executions, you'll need to set up an [AWS SQS](https://aws.amazon.com/sqs/) queue. A standard-type queue should suffice. The flyteadmin event scheduler creates [AWS CloudWatch](https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/Create-CloudWatch-Events-Scheduled-Rule.html) event rules that invoke your SQS queue as a target. +# +# With that in mind, let's take a look at an example `eventScheduler` config section and dive into what each value represents: +# +# ```bash +# scheduler: +# eventScheduler: +# scheme: "aws" +# region: "us-east-1" +# scheduleRole: "arn:aws:iam::{{ YOUR ACCOUNT ID }}:role/{{ ROLE }}" +# targetName: "arn:aws:sqs:us-east-1:{{ YOUR ACCOUNT ID }}:{{ YOUR QUEUE NAME }}" +# scheduleNamePrefix: "flyte" +# ``` + +# %% [markdown] +# - **scheme**: in this case because AWS is the only cloud back-end supported for scheduling workflows, only `"aws"` is a valid value. By default, the no-op scheduler is used. +# - **region**: this specifies which region initialized AWS clients should use when creating CloudWatch rules. +# - **scheduleRole** This is the IAM role ARN with permissions set to `Allow` +# : - `events:PutRule` +# - `events:PutTargets` +# - `events:DeleteRule` +# - `events:RemoveTargets` +# - **targetName** this is the ARN for the SQS Queue you've allocated to scheduling workflows. +# - **scheduleNamePrefix** this is an entirely optional prefix used when creating schedule rules. Because of AWS naming length restrictions, scheduled rules are a random hash and having a shared prefix makes these names more readable and indicates who generated the rules. +# +# #### Workflow Executor +# +# Scheduled events which trigger need to be handled by the workflow executor, which subscribes to triggered events from the SQS queue configured above. +# +# :::{NOTE} +# Failure to configure a workflow executor will result in all your scheduled events piling up silently without ever kicking off workflow executions. +# ::: +# +# Again, let's break down a sample config: +# +# ```bash +# scheduler: +# eventScheduler: +# ... +# workflowExecutor: +# scheme: "aws" +# region: "us-east-1" +# scheduleQueueName: "{{ YOUR QUEUE NAME }}" +# accountId: "{{ YOUR ACCOUNT ID }}" +# ``` + +# %% [markdown] +# - **scheme**: in this case because AWS is the only cloud back-end supported for executing scheduled workflows, only `"aws"` is a valid value. By default, the no-op executor is used and in case of sandbox we use `"local"` scheme which uses the Flyte native scheduler. +# - **region**: this specifies which region AWS clients should use when creating an SQS subscriber client. +# - **scheduleQueueName**: this is the name of the SQS Queue you've allocated to scheduling workflows. +# - **accountId**: Your AWS [account id](https://docs.aws.amazon.com/IAM/latest/UserGuide/console_account-alias.html#FindingYourAWSId). diff --git a/docs/examples/productionizing/productionizing/reference_launch_plan.py b/docs/examples/productionizing/productionizing/reference_launch_plan.py new file mode 100644 index 000000000..80c4c4554 --- /dev/null +++ b/docs/examples/productionizing/productionizing/reference_launch_plan.py @@ -0,0 +1,72 @@ +# %% [markdown] +# # Reference Launch Plan +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# A {py:func}`flytekit.reference_launch_plan` references previously defined, serialized, and registered Flyte launch plans. +# You can reference launch plans from other projects and create workflows that use launch plans declared by others. +# +# The following example illustrates how to use reference launch plans. +# +# :::{note} +# Reference launch plans cannot be run locally. You must mock them out. +# ::: + +# %% +from typing import List + +from flytekit import reference_launch_plan, workflow +from flytekit.types.file import FlyteFile + + +@reference_launch_plan( + project="flytesnacks", + domain="development", + name="advanced_composition.files.normalize_csv_file", + version="{{ registration.version }}", +) +def normalize_csv_file( + csv_url: FlyteFile, + column_names: List[str], + columns_to_normalize: List[str], + output_location: str, +) -> FlyteFile: + ... + + +@workflow +def reference_lp_wf() -> FlyteFile: + return normalize_csv_file( + csv_url="https://people.sc.fsu.edu/~jburkardt/data/csv/biostats.csv", + column_names=["Name", "Sex", "Age", "Heights (in)", "Weight (lbs)"], + columns_to_normalize=["Age"], + output_location="", + ) + + +# %% [markdown] +# It's important to verify that the workflow interface corresponds to that of the referenced workflow. +# +# :::{note} +# The macro `{{ registration.version }}` is populated by `flytectl register` during registration. +# Generally, it is unnecessary for reference launch plans, as it is preferable to bind to a specific version of the task or launch plan. +# However, in this example, we are registering both the launch plan `core.flyte_basics.files.normalize_csv_file` and the workflow that references it. +# Therefore, we need the macro to be updated to the version of a specific Flytesnacks release. +# This is why `{{ registration.version }}` is used. +# +# A typical reference launch plan would resemble the following: +# +# ```python +# @reference_launch_plan( +# project="flytesnacks", +# domain="development", +# name="core.flyte_basics.files.normalize_csv_file", +# version="d06cebcfbeabc02b545eefa13a01c6ca992940c8", # If using GIT for versioning OR 0.16.0, if semver +# ) +# def normalize_csv_file(...): +# ... +# ``` +# ::: +# diff --git a/docs/examples/productionizing/productionizing/reference_task.py b/docs/examples/productionizing/productionizing/reference_task.py new file mode 100644 index 000000000..7470cd7b5 --- /dev/null +++ b/docs/examples/productionizing/productionizing/reference_task.py @@ -0,0 +1,70 @@ +# %% [markdown] +# # Reference Task +# +# ```{eval-rst} +# .. tags:: Intermediate +# ``` +# +# A {py:func}`flytekit.reference_task` references the Flyte tasks that have already been defined, serialized, and registered. +# You can reference tasks from other projects and create workflows that use tasks declared by others. +# These tasks can be in their own containers, python runtimes, flytekit versions, and even different languages. +# +# The following example illustrates how to use reference tasks. +# +# :::{note} +# Reference tasks cannot be run locally. You must mock them out. +# ::: +# %% +from typing import List + +from flytekit import reference_task, workflow +from flytekit.types.file import FlyteFile + + +@reference_task( + project="flytesnacks", + domain="development", + name="advanced_composition.files.normalize_columns", + version="{{ registration.version }}", +) +def normalize_columns( + csv_url: FlyteFile, + column_names: List[str], + columns_to_normalize: List[str], + output_location: str, +) -> FlyteFile: + ... + + +@workflow +def wf() -> FlyteFile: + return normalize_columns( + csv_url="https://people.sc.fsu.edu/~jburkardt/data/csv/biostats.csv", + column_names=["Name", "Sex", "Age", "Heights (in)", "Weight (lbs)"], + columns_to_normalize=["Age"], + output_location="", + ) + + +# %% [markdown] +# :::{note} +# The macro `{{ registration.version }}` is populated by `flytectl register` during registration. +# Generally, it is unnecessary for reference tasks, as it is preferable to bind to a specific version of the task or launch plan. +# However, in this example, we are registering both the task `core.flyte_basics.files.normalize_columns` and the workflow that references it. +# Therefore, we need the macro to be updated to the version of a specific Flytesnacks release. +# This is why `{{ registration.version }}` is used. +# +# A typical reference task would resemble the following: +# +# ```python +# @reference_task( +# project="flytesnacks", +# domain="development", +# name="core.flyte_basics.files.normalize_columns", +# version="d06cebcfbeabc02b545eefa13a01c6ca992940c8", # If using GIT for versioning OR 0.16.0, if semver +# ) +# def normalize_columns(...): +# ... +# ``` +# ::: +# diff --git a/docs/examples/productionizing/productionizing/spot_instances.py b/docs/examples/productionizing/productionizing/spot_instances.py new file mode 100644 index 000000000..303bebb61 --- /dev/null +++ b/docs/examples/productionizing/productionizing/spot_instances.py @@ -0,0 +1,70 @@ +# %% [markdown] +# # Using Spot/Preemptible Instances +# +# ```{eval-rst} +# .. tags:: AWS, GCP, Intermediate +# +# ``` +# %% [markdown] +# ## What Are Spot/Preemptible Instances? +# +# Spot instances are unused EC2 capacity in AWS. [Spot instances](https://aws.amazon.com/ec2/spot/?cards.sort-by=item.additionalFields.startDateTime&cards.sort-order=asc) can result in up to 90% savings on on-demand prices. The caveat is that these instances can be preempted at any point and no longer be available for use. This can happen due to: +# +# - Price – The spot price is greater than your maximum price. +# - Capacity – If there are not enough unused EC2 instances to meet the demand for spot instances, Amazon EC2 interrupts spot instances. Amazon EC2 determines the order in which the instances are interrupted. +# - Constraints – If your request includes a constraint such as a launch group or an Availability Zone group, these spot instances are terminated as a group when the constraint can no longer be met. +# +# Generally, most spot instances are obtained for around 2 hours (median), with the floor being about 20 minutes and the ceiling of unbounded duration. +# +# :::{note} +# Spot Instances are called `Preemptible Instances` in the GCP terminology. +# ::: +# +# ### Setting up Spot Instances +# +# - AWS: +# - GCP: +# +# If an auto-scaling group (ASG) is set up, you may want to isolate the tasks you want to trigger on spot/preemptible instances from the regular workloads. +# This can be done by setting taints and tolerations using the [config](https://github.com/flyteorg/flyteplugins/blob/60b94c688ef2b98aa53a9224b529ac672af04540/go/tasks/pluginmachinery/flytek8s/config/config.go#L84-L92) available at `flyteorg/flyteplugins` repo. +# +# :::{admonition} What's an ASG for a spot/preemptible instance? +# When your spot/preemptible instance is terminated, ASG attempts to launch a replacement instance to maintain the desired capacity for the group. +# ::: + +# %% [markdown] +# ## What Are Interruptible Tasks? +# +# If specified, the `interruptible flag` is added to the task definition and signals to the Flyte engine that it may be scheduled on machines that may be preempted, such as AWS spot instances. This is low-hanging fruit for any cost-savings initiative. +# +# ### Setting Interruptible +# +# To run your workload on a spot/preemptible instance, you can set interruptible to `True`. In case you would like to automatically retry in case the node gets preemted, please also make sure to set at least one retry. For example: +# +# ```python +# @task(cache_version='1', interruptible=True, retries=1) +# def add_one_and_print(value_to_print: int) -> int: +# return value_to_print + 1 +# ``` + +# %% [markdown] +# By setting this value, Flyte will schedule your task on an auto-scaling group (ASG) with only spot instances. +# +# :::{note} +# If you set `retries=n`, for instance, and the task gets preempted repeatedly, Flyte will retry on a preemptible/spot instance `n-1` times and for the last attempt will retry your task on a non-spot (regular) instance. Please note that tasks will only be retried if at least one retry is allowed using the `retries` parameter in the `task` decorator. +# ::: +# +# ### Which Tasks Should Be Set To Interruptible? +# +# Most Flyte workloads should be good candidates for spot instances. +# If your task does NOT exhibit the following properties, you can set `interruptible` to true. +# +# - Time-sensitive: It needs to run now and can not have any unexpected delays. +# - Side Effects: The task is not idempotent, and retrying will cause issues. +# - Long-Running Tasks: The task takes > 2 hours. Having an interruption during this time frame could potentially waste a lot of computation. +# +# In a nutshell, you should use spot/preemptible instances when you want to reduce the total cost of running jobs at the expense of potential delays in execution due to restarts. + +# %% [markdown] +# % TODO: Write "How to Recover From Interruptions?" section +# diff --git a/docs/examples/productionizing/productionizing/use_secrets.py b/docs/examples/productionizing/productionizing/use_secrets.py new file mode 100644 index 000000000..afeda22e9 --- /dev/null +++ b/docs/examples/productionizing/productionizing/use_secrets.py @@ -0,0 +1,426 @@ +# %% [markdown] +# (secrets)= +# +# # Using Secrets in a Task +# +# ```{eval-rst} +# .. tags:: Kubernetes, Intermediate +# ``` +# +# Flyte supports running a variety of tasks, from containers to SQL queries and +# service calls, and it provides a native Secret construct to request and access +# secrets. +# +# This example explains how you can access secrets in a Flyte Task. Flyte provides +# different types of secrets, but for users writing Python tasks, you can only access +# secure secrets either as environment variables or as a file injected into the +# running container. + +# %% [markdown] +# ## Creating Secrets with a Secrets Manager +# +# :::{admonition} Prerequisites +# :class: important +# +# - Install [kubectl](https://kubernetes.io/docs/tasks/tools/). +# - Have access to a Flyte cluster, for e.g. with `flytectl demo start` as +# described {ref}`here `. +# ::: +# +# The first step to using secrets in Flyte is to create one on the backend. +# By default, Flyte uses the K8s-native secrets manager, which we'll use in this +# example, but you can also {ref}`configure different secret managers `. +# +# First, we use `kubectl` to create a secret called `user-info` with a +# `user_secret` key: +# +# ```{eval-rst} +# .. prompt:: bash $ +# +# kubectl create secret -n - generic user-info --from-literal=user_secret=mysecret +# ``` +# +# :::{note} +# Be sure to specify the correct Kubernetes namespace when creating a secret. If you plan on accessing +# the secret in the `flytesnacks` project under the `development` domain, replace `-` +# with `flytesnacks-development`. This is because secrets need to be in the same namespace as the +# workflow execution. +# ::: +# +# :::{important} +# The imperative command above is useful for creating secrets in an ad hoc manner, +# but it may not be the most secure or sustainable way to do so. You can, however, +# define secrets using a [configuration file](https://kubernetes.io/docs/tasks/configmap-secret/managing-secret-using-config-file/) +# or tools like [Kustomize](https://kubernetes.io/docs/tasks/configmap-secret/managing-secret-using-kustomize/). +# ::: + +# %% [markdown] +# ## Using Secrets in Tasks +# +# Once you've defined a secret on the Flyte backend, `flytekit` exposes a class +# called {py:class}`~flytekit.Secret`s, which allows you to request a secret +# from the configured secret manager. + +# %% +import os +from typing import Tuple + +import flytekit +from flytekit import Secret, task, workflow +from flytekit.testing import SecretsManager + +secret = Secret( + group="", + key="", + mount_requirement=Secret.MountType.ENV_VAR, +) + +# %% [markdown] +# Secrets consists of `group`, `key`, and `mounting_requirement` arguments, +# where a secret group can have multiple secrets associated with it. +# If the `mounting_requirement` argument is not specified, the secret will +# be injected as an environment variable by default. +# +# In the code below we specify two variables, `SECRET_GROUP` and +# `SECRET_NAME`, which maps onto the `user-info` secret that we created +# with `kubectl` above, with a key called `user_secret`. + +# %% +SECRET_GROUP = "user-info" +SECRET_NAME = "user_secret" + + +# %% [markdown] +# Now we declare the secret in the `secret_requests` argument of the +# {py:func}`@task ` decorator. The request tells Flyte to make +# the secret available to the task. +# +# The secret can then be accessed inside the task using the +# {py:class}`~flytekit.ExecutionParameters` object, which is returned by +# invoking the {py:func}`flytekit.current_context` function, as shown below. +# +# At runtime, flytekit looks inside the task pod for an environment variable or +# a mounted file with a predefined name/path and loads the value. + +# %% +@task(secret_requests=[Secret(group=SECRET_GROUP, key=SECRET_NAME)]) +def secret_task() -> str: + context = flytekit.current_context() + secret_val = context.secrets.get(SECRET_GROUP, SECRET_NAME) + print(secret_val) + return secret_val + + +# %% [markdown] +# :::{warning} +# Never print secret values! The example above is just for demonstration purposes. +# ::: +# +# :::{note} +# - In case Flyte fails to access the secret, an error is raised. +# - The `Secret` group and key are required parameters during declaration +# and usage. Failure to specify will cause a {py:class}`ValueError`. +# ::: +# +# ### Multiple Keys Grouped into One Secret +# +# In some cases you may have multiple secrets and sometimes, they maybe grouped +# as one secret in the SecretStore. +# +# For example, In Kubernetes secrets, it is possible to nest multiple keys under +# the same secret: +# +# ```{eval-rst} +# .. prompt:: bash $ +# +# kubectl create secret generic user-info \ +# --from-literal=user_secret=mysecret \ +# --from-literal=username=my_username \ +# --from-literal=password=my_password +# ``` +# +# In this case, the secret group will be `user-info`, with three available +# secret keys: `user_secret`, `username`, and `password`. + +# %% +USERNAME_SECRET = "username" +PASSWORD_SECRET = "password" + + +# %% [markdown] +# The Secret structure allows passing two fields, matching the key and the group, as previously described: +# %% +@task( + secret_requests=[ + Secret(key=USERNAME_SECRET, group=SECRET_GROUP), + Secret(key=PASSWORD_SECRET, group=SECRET_GROUP), + ] +) +def user_info_task() -> Tuple[str, str]: + context = flytekit.current_context() + secret_username = context.secrets.get(SECRET_GROUP, USERNAME_SECRET) + secret_pwd = context.secrets.get(SECRET_GROUP, PASSWORD_SECRET) + print(f"{secret_username}={secret_pwd}") + return secret_username, secret_pwd + + +# %% [markdown] +# :::{warning} +# Never print secret values! The example above is just for demonstration purposes. +# ::: +# +# ### Mounting Secrets as Files or Environment Variables +# +# It is also possible to make Flyte mount the secret as a file or an environment +# variable. +# +# The file type is useful for large secrets that do not fit in environment variables, +# which are typically asymmetric keys (like certs, etc). Another reason may be that a +# dependent library requires the secret to be available as a file. +# In these scenarios you can specify the `mount_requirement=Secret.MountType.FILE`. +# +# In the following example we force the mounting to be an environment variable: + +# %% +@task( + secret_requests=[ + Secret( + group=SECRET_GROUP, + key=SECRET_NAME, + mount_requirement=Secret.MountType.ENV_VAR, + ) + ] +) +def secret_file_task() -> Tuple[str, str]: + secret_manager = flytekit.current_context().secrets + + # get the secrets filename + f = secret_manager.get_secrets_file(SECRET_GROUP, SECRET_NAME) + + # get secret value from an environment variable + secret_val = secret_manager.get(SECRET_GROUP, SECRET_NAME) + + # returning the filename and the secret_val + return f, secret_val + + +# %% [markdown] +# These tasks can be used in your workflow as usual +# %% +@workflow +def my_secret_workflow() -> Tuple[str, str, str, str, str]: + x = secret_task() + y, z = user_info_task() + f, s = secret_file_task() + return x, y, z, f, s + + +# %% [markdown] +# ### Testing with Mock Secrets +# +# The simplest way to test secret accessibility is to export the secret as an +# environment variable. There are some helper methods available to do so: + +# %% +if __name__ == "__main__": + sec = SecretsManager() + os.environ[sec.get_secrets_env_var(SECRET_GROUP, SECRET_NAME)] = "value" + os.environ[sec.get_secrets_env_var(SECRET_GROUP, USERNAME_SECRET)] = "username_value" + os.environ[sec.get_secrets_env_var(SECRET_GROUP, PASSWORD_SECRET)] = "password_value" + x, y, z, f, s = my_secret_workflow() + assert x == "value" + assert y == "username_value" + assert z == "password_value" + assert f == sec.get_secrets_file(SECRET_GROUP, SECRET_NAME) + assert s == "value" + +# %% [markdown] +# ## Using Secrets in Task Templates +# +# For task types that connect to a remote database, you'll need to specify +# secret request as well. For example, for the {py:class}`~flytekitplugins.sqlalchemy.SQLAlchemyTask` +# you need to: +# +# 1. Specify the `secret_requests` argument. +# 2. Configure the {py:class}`~flytekitplugins.sqlalchemy.SQLAlchemyConfig` to +# declare which secret maps onto which connection argument. +# +# ```python +# from flytekit import kwtypes +# from flytekitplugins.sqlalchemy import SQLAlchemyTask, SQLAlchemyConfig +# +# +# # define the secrets +# secrets = { +# "username": Secret(group="", key=""), +# "password": Secret(group="", key=""), +# } +# +# +# sql_query = SQLAlchemyTask( +# name="sql_query", +# query_template="""SELECT * FROM my_table LIMIT {{ .inputs.limit }}""", +# inputs=kwtypes(limit=int), +# +# # request secrets +# secret_requests=[*secrets.values()], +# +# # specify username and password credentials in the configuration +# task_config=SQLAlchemyConfig( +# uri="", +# secret_connect_args=secrets, +# ), +# ) +# ``` + +# %% [markdown] +# :::{note} +# Here the `secret_connect_args` map to the +# [SQLAlchemy engine configuration](https://docs.sqlalchemy.org/en/20/core/engines.html) +# argument names for the username and password. +# ::: +# +# You can then use the `sql_query` task inside a workflow to grab data and +# perform downstream transformations on it. + +# %% [markdown] +# ## How Secrets Injection Works +# +# The rest of this page describes how secrets injection works under the hood. +# For a simple task that launches a Pod, the flow would look something like this: +# +# ```{image} https://mermaid.ink/img/eyJjb2RlIjoic2VxdWVuY2VEaWFncmFtXG4gICAgUHJvcGVsbGVyLT4-K1BsdWdpbnM6IENyZWF0ZSBLOHMgUmVzb3VyY2VcbiAgICBQbHVnaW5zLT4-LVByb3BlbGxlcjogUmVzb3VyY2UgT2JqZWN0XG4gICAgUHJvcGVsbGVyLT4-K1Byb3BlbGxlcjogU2V0IExhYmVscyAmIEFubm90YXRpb25zXG4gICAgUHJvcGVsbGVyLT4-K0FwaVNlcnZlcjogQ3JlYXRlIE9iamVjdCAoZS5nLiBQb2QpXG4gICAgQXBpU2VydmVyLT4-K1BvZCBXZWJob29rOiAvbXV0YXRlXG4gICAgUG9kIFdlYmhvb2stPj4rUG9kIFdlYmhvb2s6IExvb2t1cCBnbG9iYWxzXG4gICAgUG9kIFdlYmhvb2stPj4rUG9kIFdlYmhvb2s6IEluamVjdCBTZWNyZXQgQW5ub3RhdGlvbnMgKGUuZy4gSzhzLCBWYXVsdC4uLiBldGMuKVxuICAgIFBvZCBXZWJob29rLT4-LUFwaVNlcnZlcjogTXV0YXRlZCBQb2RcbiAgICBcbiAgICAgICAgICAgICIsIm1lcm1haWQiOnt9LCJ1cGRhdGVFZGl0b3IiOmZhbHNlfQ +# :target: https://mermaid.ink/img/eyJjb2RlIjoic2VxdWVuY2VEaWFncmFtXG4gICAgUHJvcGVsbGVyLT4-K1BsdWdpbnM6IENyZWF0ZSBLOHMgUmVzb3VyY2VcbiAgICBQbHVnaW5zLT4-LVByb3BlbGxlcjogUmVzb3VyY2UgT2JqZWN0XG4gICAgUHJvcGVsbGVyLT4-K1Byb3BlbGxlcjogU2V0IExhYmVscyAmIEFubm90YXRpb25zXG4gICAgUHJvcGVsbGVyLT4-K0FwaVNlcnZlcjogQ3JlYXRlIE9iamVjdCAoZS5nLiBQb2QpXG4gICAgQXBpU2VydmVyLT4-K1BvZCBXZWJob29rOiAvbXV0YXRlXG4gICAgUG9kIFdlYmhvb2stPj4rUG9kIFdlYmhvb2s6IExvb2t1cCBnbG9iYWxzXG4gICAgUG9kIFdlYmhvb2stPj4rUG9kIFdlYmhvb2s6IEluamVjdCBTZWNyZXQgQW5ub3RhdGlvbnMgKGUuZy4gSzhzLCBWYXVsdC4uLiBldGMuKVxuICAgIFBvZCBXZWJob29rLT4-LUFwaVNlcnZlcjogTXV0YXRlZCBQb2RcbiAgICBcbiAgICAgICAgICAgICIsIm1lcm1haWQiOnt9LCJ1cGRhdGVFZGl0b3IiOmZhbHNlfQ +# ``` +# +# Breaking down this sequence diagram: +# +# 1. Flyte invokes a plugin to create the K8s object. This can be a Pod or a more complex CRD (e.g. Spark, PyTorch, etc.) +# +# :::{note} +# The plugin ensures that the labels and annotations are passed to any Pod that is spawned due to the creation of the CRD. +# ::: +# +# 2. Flyte applies labels and annotations that are referenced to all secrets the task is requesting access to. Note that secrets are not case sensitive. +# +# 3. Flyte sends a `POST` request to `ApiServer` to create the object. +# +# 4. Before persisting the Pod, `ApiServer` invokes all the registered Pod Webhooks and Flyte's Pod Webhook is called. +# +# 5. Using the labels and annotiations attached in **step 2**, Flyte Pod Webhook looks up globally mounted secrets for each of the requested secrets. +# +# 6. If found, the Pod Webhook mounts them directly in the Pod. If not found, the Pod Webhook injects the appropriate annotations to load the secrets for K8s (or Vault or Confidant or any secret management system plugin configured) into the task pod. +# +# Once the secret is injected into the task pod, Flytekit can read it using the secret manager. +# +# The webhook is included in all overlays in the Flytekit repo. The deployment file creates two things; a **Job** and a **Deployment**. +# +# 1. `flyte-pod-webhook-secrets` **Job**: This job runs `flytepropeller webhook init-certs` command that issues self-signed CA Certificate as well as a derived TLS certificate and its private key. Ensure that the private key is in lower case, that is, `my_token` in contrast to `MY_TOKEN`. It stores them into a new secret `flyte-pod-webhook-secret`. +# 2. `flyte-pod-webhook` **Deployment**: This deployment creates the Webhook pod which creates a MutatingWebhookConfiguration on startup. This serves as the registration contract with the ApiServer to know about the Webhook before it starts serving traffic. +# +# ## Secret Discovery +# +# Flyte identifies secrets using a secret group and a secret key, which can +# be accessed by {py:func}`flytekit.current_context` in the task function +# body, as shown in the code examples above. +# +# Flytekit relies on the following environment variables to load secrets (defined [here](https://github.com/flyteorg/flytekit/blob/9d313429c577a919ec0ad4cd397a5db356a1df0d/flytekit/configuration/internal.py#L141-L159)). When running tasks and workflows locally you should make sure to store your secrets accordingly or to modify these: +# +# - `FLYTE_SECRETS_DEFAULT_DIR`: The directory Flytekit searches for secret files. **Default:** `"/etc/secrets"` +# - `FLYTE_SECRETS_FILE_PREFIX`: a common file prefix for Flyte secrets. **Default:** `""` +# - `FLYTE_SECRETS_ENV_PREFIX`: a common env var prefix for Flyte secrets. **Default:** `"_FSEC_"` +# +# When running a workflow on a Flyte cluster, the configured secret manager will use the secret Group and Key to try and retrieve a secret. +# If successful, it will make the secret available as either file or environment variable and will if necessary modify the above variables automatically so that the task can load and use the secrets. +# +# (configure_secret_management)= +# +# ## Configuring a Secret Management System Plugin +# +# When a task requests a secret, Flytepropeller will try to retrieve secrets in the following order: +# +# 1. Checking for global secrets, i.e. secrets mounted as files or environment variables on the `flyte-pod-webhook` pod +# 2. Checking with an additional configurable secret manager. +# +# :::{important} +# The global secrets take precedence over any secret discoverable by the secret manager plugins. +# ::: +# +# The following secret managers are available at the time of writing: +# +# - [K8s secrets](https://kubernetes.io/docs/concepts/configuration/secret/#creating-a-secret) (**default**): `flyte-pod-webhook` will try to look for a K8s secret named after the secret Group and retrieve the value for the secret Key. +# - [AWS Secret Manager](https://docs.aws.amazon.com/secretsmanager/latest/userguide/create_secret.html): `flyte-pod-webhook` will add the AWS Secret Manager sidecar container to a task Pod which will mount the secret. +# - [Vault Agent Injector](https://developer.hashicorp.com/vault/tutorials/getting-started/getting-started-first-secret#write-a-secret) : `flyte-pod-webhook` will annotate the task Pod with the respective Vault annotations that trigger an existing Vault Agent Injector to retrieve the specified secret Key from a vault path defined as secret Group. +# +# You can configure the additional secret manager by defining `secretManagerType` to be either 'K8s', 'AWS' or 'Vault' in +# the [core config](https://github.com/flyteorg/flyte/blob/master/kustomize/base/single_cluster/headless/config/propeller/core.yaml#L34) of the Flytepropeller. +# +# When using the K8s secret manager plugin, which is enabled by default, the secrets need to be available in the same namespace as the task execution +# (for example `flytesnacks-development`). K8s secrets can be mounted as either files or injected as environment variables into the task pod, +# so if you need to make larger files available to the task, then this might be the better option. +# +# Furthermore, this method also allows you to have separate credentials for different domains but still using the same name for the secret. +# +# ### AWS Secrets Manager +# +# When using the AWS secret management plugin, secrets need to be specified by naming them in the format +# `:`, where the secret string is a plain-text value, **not** key/value json. +# +# ### Vault Secrets Manager +# +# When using the Vault secret manager, make sure you have Vault Agent deployed on your cluster as described in this +# [step-by-step tutorial](https://learn.hashicorp.com/tutorials/vault/kubernetes-sidecar). +# Vault secrets can only be mounted as files and will become available under `"/etc/flyte/secrets/SECRET_GROUP/SECRET_NAME"`. +# +# Vault comes with various secrets engines. Currently Flyte supports working with both version 1 and 2 of the `Key Vault engine ` as well as the `databases secrets engine `. +# You can use use the `group_version` parameter to specify which secret backend engine to use. Available choices are: "kv1", "kv2", "db": + +# %% [markdown] +# How to request secrets with the Vault secret manager +# %% +secret = Secret( + group="", + key="", + group_version="", +) + +# %% [markdown] +# The group parameter is used to specify the path to the secret in the Vault backend. For example, if you have a secret stored in Vault at `"secret/data/flyte/secret"` then the group parameter should be `"secret/data/flyte"`. +# When using either of the Key Vault engine versions, the secret key is the name of a specific secret entry to be retrieved from the group path. +# When using the database secrets engine, the secret key itself is arbitrary but is required by Flyte to name and identify the secret file. It is arbitray because the database secrets engine returns always two keys, `username` and `password` and we need to retrieve a matching pair in one request. +# +# **Configuration** +# +# You can configure the Vault role under which Flyte will try to read the secret by setting webhook.vaultSecretManager.role (default: `"flyte"`). +# There is also a deprecated `webhook.vaultSecretManager.kvVersion` setting in the configmap that can be used to specify the version but only for the Key Vault backend engine. +# Available choices are: "1", "2". Note that the version number needs to be an explicit string (e.g. `"1"`). +# +# **Annotations** +# +# By default, `flyte-pod-webhook` injects following annotations to task pod: +# +# 1. `vault.hashicorp.com/agent-inject` to configure whether injection is explicitly enabled or disabled for a pod. +# 2. `vault.hashicorp.com/secret-volume-path` to configure where on the filesystem a secret will be rendered. +# 3. `vault.hashicorp.com/role` to configure the Vault role used by the Vault Agent auto-auth method. +# 4. `vault.hashicorp.com/agent-pre-populate-only` to configure whether an init container is the only injected container. +# 5. `vault.hashicorp.com/agent-inject-secret` to configure Vault Agent to retrieve the secrets from Vault required by the container. +# 6. `vault.hashicorp.com/agent-inject-file` to configure the filename and path in the secrets volume where a Vault secret will be written. +# 7. `vault.hashicorp.com/agent-inject-template` to configure the template Vault Agent should use for rendering a secret. +# +# It is possible to add extra annotations or override the existing ones in Flyte either at the task level using pod annotations or at the installation level. +# If Flyte administrator wants to set up annotations for the entire system, they can utilize `webhook.vaultSecretManager.annotations` to accomplish this. +# +# ## Scaling the Webhook +# +# ### Vertical Scaling +# +# To scale the Webhook to be able to process the number/rate of pods you need, you may need to configure a vertical [pod +# autoscaler](https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler). +# +# ### Horizontal Scaling +# +# The Webhook does not make any external API Requests in response to Pod mutation requests. It should be able to handle traffic +# quickly. For horizontal scaling, adding additional replicas for the Pod in the +# deployment should be sufficient. A single `MutatingWebhookConfiguration` object will be used, the same TLS certificate +# will be shared across the pods and the Service created will automatically load balance traffic across the available pods. +# diff --git a/docs/examples/productionizing/productionizing/workflow_labels_annotations.py b/docs/examples/productionizing/productionizing/workflow_labels_annotations.py new file mode 100644 index 000000000..92173f703 --- /dev/null +++ b/docs/examples/productionizing/productionizing/workflow_labels_annotations.py @@ -0,0 +1,60 @@ +# %% [markdown] +# # Adding Workflow Labels and Annotations +# +# ```{eval-rst} +# .. tags:: Kubernetes, Intermediate +# ``` +# +# In Flyte, workflow executions are created as Kubernetes resources. These can be extended with +# [labels](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/) and +# [annotations](https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/). +# +# **Labels** and **annotations** are key value pairs which can be used to identify workflows for your own uses. +# +# :::{Warning} +# Note that adding labels and annotations to your K8s resources may have side-effects depending on webhook behavior on your execution clusters. +# ::: +# +# Labels are meant to be used as identifying attributes, whereas annotations are arbitrary, *non-identifying* metadata. +# +# Using labels and annotations is entirely optional. They can be used to categorize and identify workflow executions. +# +# Labels and annotations are optional parameters to launch plan and execution invocations. When an execution +# defines labels and/or annotations *and* the launch plan does as well, the execution spec values will be preferred. +# +# ## Launch Plan Usage Example +# +# ```python +# from flytekit import Labels, Annotations +# +# @workflow +# class MyWorkflow(object): +# ... +# +# my_launch_plan = MyWorkflow.create_launch_plan( +# labels=Labels({"myexecutionlabel": "bar", ...}), +# annotations=Annotations({"region": "SEA", ...}), +# ... +# ) +# +# my_launch_plan.execute(...) +# ``` +# +# ## Execution Example +# +# ```python +# from flytekit import Labels, Annotations +# +# @workflow +# class MyWorkflow(object): +# ... +# +# my_launch_plan = MyWorkflow.create_launch_plan(...) +# +# my_launch_plan.execute( +# labels=Labels({"myexecutionlabel": "bar", ...}), +# annotations=Annotations({"region": "SEA", ...}), +# ... +# ) +# ``` +# diff --git a/docs/examples/productionizing/requirements.in b/docs/examples/productionizing/requirements.in new file mode 100644 index 000000000..3cfb26e38 --- /dev/null +++ b/docs/examples/productionizing/requirements.in @@ -0,0 +1 @@ +flytekit diff --git a/docs/examples/productionizing/requirements.txt b/docs/examples/productionizing/requirements.txt new file mode 100644 index 000000000..8ed90cfce --- /dev/null +++ b/docs/examples/productionizing/requirements.txt @@ -0,0 +1,340 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.29.3 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.7 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +flyteidl==1.5.17 + # via flytekit +flytekit==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.53.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.24.2 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +websocket-client==1.6.2 + # via + # docker + # kubernetes +wheel==0.41.2 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/ray_plugin/Dockerfile b/docs/examples/ray_plugin/Dockerfile new file mode 100644 index 000000000..4f1f15e06 --- /dev/null +++ b/docs/examples/ray_plugin/Dockerfile @@ -0,0 +1,26 @@ +# ###################### +# NOTE: For CI/CD only # +######################## +FROM rayproject/ray:2.5.1-py310-cpu +LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +USER root + +RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev build-essential + +# Install Python dependencies +COPY requirements.in /root +RUN pip install -r /root/requirements.in + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/ray_plugin/README.md b/docs/examples/ray_plugin/README.md new file mode 100644 index 000000000..3402ca58d --- /dev/null +++ b/docs/examples/ray_plugin/README.md @@ -0,0 +1,82 @@ +(kube-ray-op)= + +# Ray + +```{eval-rst} +.. tags:: Integration, DistributedComputing, KubernetesOperator, Advanced +``` + +[KubeRay](https://github.com/ray-project/kuberay) is an open-source toolkit designed to facilitate the execution of +Ray applications on Kubernetes. It offers a range of tools that enhance the operational aspects of +running and overseeing Ray on Kubernetes. + +Key components include: + +- Ray Operator +- Backend services for cluster resource creation and deletion +- Kubectl plugin/CLI for CRD object management +- Seamless integration of Jobs and Serving functionality with Clusters + +## Install the plugin + +To install the Ray plugin, run the following command: + +``` +pip install flytekitplugins-ray +``` + +To enable the plugin in the backend, refer to the instructions provided in the {std:ref}`flyte:deployment-plugin-setup-k8s` guide. + +## Implementation details + +### Submit a Ray job to existing cluster + +```{eval-rst} +.. testcode:: ray-quickstart-1 + import ray + from flytekit import task + from flytekitplugins.ray import RayJobConfig + + @ray.remote + def f(x): + return x * x + + @task( + task_config=RayJobConfig( + address= + runtime_env={"pip": ["numpy", "pandas"]} + ) + ) + def ray_task() -> typing.List[int]: + futures = [f.remote(i) for i in range(5)] + return ray.get(futures) + +``` + +### Create a Ray cluster managed by Flyte and run a Ray Job on the cluster + +```{eval-rst} +.. testcode:: ray-quickstart-2 + import ray + from flytekit import task + from flytekitplugins.ray import RayJobConfig, WorkerNodeConfig, HeadNodeConfig + + @task(task_config=RayJobConfig(worker_node_config=[WorkerNodeConfig(group_name="test-group", replicas=10)])) + def ray_task() -> typing.List[int]: + futures = [f.remote(i) for i in range(5)] + return ray.get(futures) +``` + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/ray_plugin/ray_plugin/ray_example.py \ + ray_workflow --n 10 +``` + +```{auto-examples-toc} +ray_example +``` diff --git a/docs/examples/ray_plugin/ray_plugin/__init__.py b/docs/examples/ray_plugin/ray_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/ray_plugin/ray_plugin/ray_example.py b/docs/examples/ray_plugin/ray_plugin/ray_example.py new file mode 100644 index 000000000..9f3a2fc41 --- /dev/null +++ b/docs/examples/ray_plugin/ray_plugin/ray_example.py @@ -0,0 +1,131 @@ +# %% [markdown] +# # Running Ray Tasks +# +# The Ray task offers the capability to execute a Ray job either on a pre-existing Ray cluster +# or by creating a new Ray cluster using the Ray operator. +# +# :::{Warning} +# **Known Bugs and Compatibility Issue with Kuberay Operator Versions** +# Please note that there have been reports of various bugs and compatibility issues with recent versions of the Kuberay operator. +# - Kuberay Operator Version 0.4: In this version, the reconcile logic of the operator was changed. +# As a result, when running a Ray job, the status of the job will always remain "pending." +# It is important to note that this change was not thoroughly tested by the Kuberay community before its release. +# - Kuberay Operator Version 0.5: In this version, an init container is injected into the worker nodes. +# However, the operator failed to set the required resource limits (CPU, memory) for this init container. +# Consequently, running Ray jobs becomes problematic in Flyte because Kubernetes imposes resource quotas in every project-domain namespace, +# and these quotas necessitate that every container sets the CPU and memory limits. +# +# **Given these issues, it is crucial to be aware that the current Ray extension is compatible only +# with version 0.3.0 and 0.5.2+ of the Kuberay operator.** It is recommended to use this specific version to +# ensure the proper functioning of the Ray extension. +# ::: +# +# To begin, load the libraries. +# %% +import typing + +from flytekit import ImageSpec, Resources, task, workflow + +# %% [markdown] +# Create an `ImageSpec` to encompass all the dependencies needed for the Ray task. +# %% +custom_image = ImageSpec( + name="ray-flyte-plugin", + registry="ghcr.io/flyteorg", + packages=["flytekitplugins-ray"], +) + +# %% [markdown] +# :::{note} +# To upload the image to the local registry in the demo cluster, indicate the registry as `localhost:30000`. +# ::: +# +# The following imports are required to configure the Ray cluster in Flyte. +# You can load them on demand. +# %% +if custom_image.is_container(): + import ray + from flytekitplugins.ray import HeadNodeConfig, RayJobConfig, WorkerNodeConfig + + +# %% [markdown] +# In this example, we define a [remote function](https://docs.ray.io/en/latest/ray-core/tasks.html#tasks) +# that will be executed asynchronously in the Ray cluster. +# %% +@ray.remote +def f(x): + return x * x + + +# %% [markdown] +# Include both {py:class}`~flytekitplugins.ray.HeadNodeConfig` and +# {py:class}`~flytekitplugins.ray.WorkerNodeConfig` in {py:class}`~flytekitplugins.ray.RayJobConfig`. +# These configurations will subsequently be employed by the Ray operator to establish a Ray cluster before task execution. +# +# Here's a breakdown of the parameters: +# +# - `ray_start_params`: These are the [parameters](https://docs.ray.io/en/latest/ray-core/api/doc/ray.init.html) +# used in the Ray `init` method, encompassing the address and object-store-memory settings. +# - `replicas`: Specifies the desired number of replicas for the worker group. The default is 1. +# - `group_name`: A RayCluster can host multiple worker groups, each differentiated by its name. +# - `runtime_env`: The [runtime environment](https://docs.ray.io/en/latest/ray-core/handling-dependencies.html#runtime-environments) +# definition outlines the necessary dependencies for your Ray application to function. +# This environment is dynamically installed on the cluster at runtime. +# %% +ray_config = RayJobConfig( + head_node_config=HeadNodeConfig(ray_start_params={"log-color": "True"}), + worker_node_config=[WorkerNodeConfig(group_name="ray-group", replicas=1)], + runtime_env={"pip": ["numpy", "pandas"]}, # or runtime_env="./requirements.txt" +) + + +# %% [markdown] +# Create a Ray task. The task is invoked on the Ray head node, while `f.remote(i)` executes asynchronously on distinct Ray workers. +# %% +@task( + task_config=ray_config, + requests=Resources(mem="2Gi", cpu="2"), + container_image=custom_image, +) +def ray_task(n: int) -> typing.List[int]: + futures = [f.remote(i) for i in range(n)] + return ray.get(futures) + + +# %% [markdown] +# :::{note} +# The `Resources` section here is utilized to specify the resources allocated to the worker nodes. +# ::: +# +# Lastly, define a workflow to call the Ray task. +# %% +@workflow +def ray_workflow(n: int) -> typing.List[int]: + return ray_task(n=n) + + +# %% [markdown] +# You have the option to execute the code locally, +# during which Flyte generates a self-contained Ray cluster on your local environment. +# %% +if __name__ == "__main__": + print(ray_workflow(n=10)) + +# %% [markdown] +# ## Troubleshoot +# +# If you observe that the head and worker pods are not being generated, you need to ensure that `ray[default]` is installed since it supports +# the cluster and dashboard launcher. +# +# Another potential error might involve ingress issues, as indicated in the kuberay-operator logs. +# If you encounter an error resembling the following: +# +# ```` +# ERROR controllers.RayCluster Ingress create error! +# { +# "Ingress.Error": "Internal error occurred: failed calling webhook "validate.nginx.ingress.kubernetes.io": failed to call webhook: Post "": no endpoints available for service "nginx-ingress-ingress-nginx-controller-admission"", +# "error": "Internal error occurred: failed calling webhook "validate.nginx.ingress.kubernetes.io": failed to call webhook: Post "": no endpoints available for service "nginx-ingress-ingress-nginx-controller-admission"" +# } +# ```` +# +# You must ensure that the ingress controller is [installed](https://docs.flyte.org/en/latest/deployment/gcp/manual.html#ingress). diff --git a/docs/examples/ray_plugin/requirements.in b/docs/examples/ray_plugin/requirements.in new file mode 100644 index 000000000..512867625 --- /dev/null +++ b/docs/examples/ray_plugin/requirements.in @@ -0,0 +1,2 @@ +flytekitplugins-ray +flytekitplugins-envd diff --git a/docs/examples/ray_plugin/requirements.txt b/docs/examples/ray_plugin/requirements.txt new file mode 100644 index 000000000..924bcfa7f --- /dev/null +++ b/docs/examples/ray_plugin/requirements.txt @@ -0,0 +1,420 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile requirements.in +# +adlfs==2023.8.0 + # via flytekit +aiobotocore==2.5.4 + # via s3fs +aiohttp==3.8.5 + # via + # adlfs + # aiobotocore + # aiohttp-cors + # gcsfs + # ray + # s3fs +aiohttp-cors==0.7.0 + # via ray +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via + # aiohttp + # ray +arrow==1.2.3 + # via cookiecutter +async-timeout==4.0.3 + # via aiohttp +attrs==23.1.0 + # via + # aiohttp + # jsonschema + # referencing +azure-core==1.29.2 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.14.0 + # via adlfs +azure-storage-blob==12.17.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +blessed==1.20.0 + # via gpustat +botocore==1.31.17 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.7.22 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.2.0 + # via binaryornot +charset-normalizer==3.2.0 + # via + # aiohttp + # requests +click==8.1.6 + # via + # cookiecutter + # flytekit + # ray + # rich-click +cloudpickle==2.2.1 + # via flytekit +colorful==0.5.5 + # via ray +cookiecutter==2.3.0 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.3 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +distlib==0.3.7 + # via virtualenv +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +envd==0.3.38 + # via flytekitplugins-envd +filelock==3.12.2 + # via + # ray + # virtualenv +flyteidl==1.5.16 + # via + # flytekit + # flytekitplugins-ray +flytekit==1.9.0 + # via + # flytekitplugins-envd + # flytekitplugins-ray +flytekitplugins-envd==1.9.0 + # via -r requirements.in +flytekitplugins-ray==1.9.0 + # via -r requirements.in +frozenlist==1.4.0 + # via + # aiohttp + # aiosignal + # ray +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.32 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage + # opencensus +google-auth==2.22.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.60.0 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +gpustat==1.1 + # via ray +grpcio==1.53.0 + # via + # flytekit + # grpcio-status + # ray +grpcio-status==1.53.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.8.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.3.0 + # via keyring +jinja2==3.1.2 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.2 + # via flytekit +jsonschema==4.19.0 + # via ray +jsonschema-specifications==2023.7.1 + # via jsonschema +keyring==24.2.0 + # via flytekit +kubernetes==27.2.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.20.1 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via + # dataclasses-json + # flytekit +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==10.1.0 + # via jaraco-classes +msal==1.23.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +msgpack==1.0.5 + # via ray +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.25.2 + # via + # flytekit + # pandas + # pyarrow + # ray +nvidia-ml-py==12.535.77 + # via gpustat +oauthlib==3.2.2 + # via + # kubernetes + # requests-oauthlib +opencensus==0.11.2 + # via ray +opencensus-context==0.1.3 + # via opencensus +packaging==23.1 + # via + # docker + # marshmallow + # ray +pandas==1.5.3 + # via flytekit +platformdirs==3.10.0 + # via virtualenv +portalocker==2.7.0 + # via msal-extensions +prometheus-client==0.17.1 + # via ray +protobuf==4.24.0 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # ray +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.5 + # via gpustat +py-spy==0.3.14 + # via ray +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.12 + # via ray +pygments==2.16.1 + # via rich +pyjwt[crypto]==2.8.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0.1 + # via + # cookiecutter + # flytekit + # kubernetes + # ray +ray[default]==2.6.3 + # via flytekitplugins-ray +referencing==0.30.2 + # via + # jsonschema + # jsonschema-specifications +regex==2023.8.8 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # ray + # requests-oauthlib +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +rich==13.5.2 + # via + # cookiecutter + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rpds-py==0.9.2 + # via + # jsonschema + # referencing +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +six==1.16.0 + # via + # azure-core + # blessed + # google-auth + # isodate + # kubernetes + # python-dateutil +smart-open==6.3.0 + # via ray +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # pydantic + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests +virtualenv==20.21.0 + # via ray +wcwidth==0.2.6 + # via blessed +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.41.1 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.16.2 + # via importlib-metadata diff --git a/docs/examples/sagemaker_pytorch_plugin/Dockerfile b/docs/examples/sagemaker_pytorch_plugin/Dockerfile new file mode 100644 index 000000000..b5d8b2253 --- /dev/null +++ b/docs/examples/sagemaker_pytorch_plugin/Dockerfile @@ -0,0 +1,48 @@ +# We use devel because plugins_sagemaker-training needs gcc to build +# TODO get rid of plugins_sagemaker-training +FROM python:3.8-slim-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root +ENV DEBIAN_FRONTEND noninteractive +ENV TERM linux + +# Install basics +RUN apt-get update && apt-get install -y make build-essential libssl-dev curl + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip install awscli + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Install gcc , g++ and make +RUN apt-get update && apt-get install --no-install-recommends -y gcc g++ +RUN echo 'installing make' \ + && apt-get install make + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Setup Sagemaker entrypoints +ENV SAGEMAKER_PROGRAM /opt/venv/bin/flytekit_sagemaker_runner.py + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/sagemaker_pytorch_plugin/README.md b/docs/examples/sagemaker_pytorch_plugin/README.md new file mode 100644 index 000000000..836813abe --- /dev/null +++ b/docs/examples/sagemaker_pytorch_plugin/README.md @@ -0,0 +1,79 @@ +# AWS Sagemaker Pytorch + +```{eval-rst} +.. tags:: Integration, MachineLearning, AWS, Advanced +``` + +This plugin shows an example of using Sagemaker custom training, with Pytorch distributed training. + +## Installation + +To use the Flytekit AWS Sagemaker plugin, simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-awssagemaker + +``` + +## Creating a Dockerfile for Sagemaker Custom Training [Required] + +The dockerfile for Sagemaker custom training is similar to any regular dockerfile, except for the difference in using the Nvidia cuda base to use GPU's. + +:::{note} +If using CPU for training, then the special dockerfile is NOT REQUIRED. If GPU or TPUs are required, the dockerfile differs only in the driver setup. The following dockerfile is enabled for GPU accelerated training using CUDA. +The checked in version of docker file uses python:3.8-slim-buster for faster CI, but you can use the Dockerfile pasted below which uses cuda base. +Additionally, the requirements.in uses the cpu version of pytorch. Remove the + cpu for torch and torchvision in requirements.in and make all requirements as shown below: +::: + +```{eval-rst} +.. prompt:: bash + + make -C integrations/aws/sagemaker_pytorch requirements + +``` + +```{code-block} docker +:emphasize-lines: 23-24 +:linenos: true + +FROM pytorch/pytorch:1.7.0-cuda11.0-cudnn8-devel +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip install awscli + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY sagemaker_pytorch/requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Setup Sagemaker entrypoints +ENV SAGEMAKER_PROGRAM /opt/venv/bin/flytekit_sagemaker_runner.py + +# Copy the makefile targets to expose on the container. This makes it easier to register. +COPY in_container.mk /root/Makefile +COPY sagemaker_pytorch/sandbox.config /root + +# Copy the actual code +COPY sagemaker_pytorch/ /root/sagemaker_pytorch + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag +``` + +```{auto-examples-toc} +sagemaker_pytorch_distributed_training +``` diff --git a/docs/examples/sagemaker_pytorch_plugin/requirements.in b/docs/examples/sagemaker_pytorch_plugin/requirements.in new file mode 100644 index 000000000..d657ebb61 --- /dev/null +++ b/docs/examples/sagemaker_pytorch_plugin/requirements.in @@ -0,0 +1,8 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-awssagemaker>=0.16.0 +torch==1.8.1 +torchvision==0.9.1 +tensorboardX diff --git a/docs/examples/sagemaker_pytorch_plugin/requirements.txt b/docs/examples/sagemaker_pytorch_plugin/requirements.txt new file mode 100644 index 000000000..264f8ba72 --- /dev/null +++ b/docs/examples/sagemaker_pytorch_plugin/requirements.txt @@ -0,0 +1,619 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +adlfs==2023.4.0 + # via flytekit +aiobotocore==2.5.0 + # via s3fs +aiohttp==3.8.4 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +appnope==0.1.3 + # via + # ipykernel + # ipython +arrow==1.2.3 + # via jinja2-time +asttokens==2.2.1 + # via stack-data +async-timeout==4.0.2 + # via aiohttp +attrs==23.1.0 + # via + # aiohttp + # visions +azure-core==1.27.1 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.13.0 + # via adlfs +azure-storage-blob==12.16.0 + # via adlfs +backcall==0.2.0 + # via ipython +bcrypt==4.0.1 + # via paramiko +binaryornot==0.4.4 + # via cookiecutter +boto3==1.26.76 + # via sagemaker-training +botocore==1.29.76 + # via + # aiobotocore + # boto3 + # s3transfer +cachetools==5.3.1 + # via google-auth +certifi==2023.5.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography + # pynacl +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via + # aiohttp + # requests +click==8.1.3 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +comm==0.1.3 + # via ipykernel +contourpy==1.1.0 + # via matplotlib +cookiecutter==2.1.1 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.1 + # via + # azure-identity + # azure-storage-blob + # msal + # paramiko + # pyjwt + # pyopenssl +cycler==0.11.0 + # via matplotlib +dacite==1.8.1 + # via ydata-profiling +dataclasses-json==0.5.8 + # via flytekit +debugpy==1.6.7 + # via ipykernel +decorator==5.1.1 + # via + # gcsfs + # ipython + # retry2 +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +executing==1.2.0 + # via stack-data +flyteidl==1.5.11 + # via flytekit +flytekit==1.7.0 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-awssagemaker + # flytekitplugins-deck-standard +flytekitplugins-awssagemaker==1.7.0 + # via -r requirements.in +flytekitplugins-deck-standard==1.7.0 + # via -r ../../../common/requirements-common.in +fonttools==4.40.0 + # via matplotlib +frozenlist==1.3.3 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gevent==22.10.2 + # via sagemaker-training +gitdb==4.0.10 + # via gitpython +gitpython==3.1.31 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.20.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.2 + # via google-cloud-storage +google-cloud-storage==2.9.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.59.1 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +greenlet==2.0.2 + # via gevent +grpcio==1.54.2 + # via + # flytekit + # grpcio-status +grpcio-status==1.54.2 + # via flytekit +htmlmin==0.1.12 + # via ydata-profiling +idna==3.4 + # via + # requests + # yarl +imagehash==4.3.1 + # via + # visions + # ydata-profiling +importlib-metadata==6.7.0 + # via + # flytekit + # jupyter-client + # keyring + # markdown +importlib-resources==5.12.0 + # via + # keyring + # matplotlib +inotify-simple==1.2.1 + # via sagemaker-training +ipykernel==6.23.2 + # via ipywidgets +ipython==8.12.2 + # via + # ipykernel + # ipywidgets +ipywidgets==8.0.6 + # via flytekitplugins-deck-standard +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.2.3 + # via keyring +jedi==0.18.2 + # via ipython +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # ydata-profiling +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.1 + # via + # boto3 + # botocore +joblib==1.2.0 + # via + # flytekit + # phik +jupyter-client==8.2.0 + # via ipykernel +jupyter-core==5.3.1 + # via + # ipykernel + # jupyter-client +jupyterlab-widgets==3.0.7 + # via ipywidgets +keyring==24.0.1 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +kubernetes==26.1.0 + # via flytekit +markdown==3.4.3 + # via flytekitplugins-deck-standard +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via + # jinja2 + # werkzeug +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.7.1 + # via + # -r ../../../common/requirements-common.in + # phik + # seaborn + # wordcloud + # ydata-profiling +matplotlib-inline==0.1.6 + # via + # ipykernel + # ipython +mdurl==0.1.2 + # via markdown-it-py +more-itertools==9.1.0 + # via jaraco-classes +msal==1.22.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +multimethod==1.9.1 + # via + # visions + # ydata-profiling +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +nest-asyncio==1.5.6 + # via ipykernel +networkx==3.1 + # via visions +numpy==1.23.5 + # via + # contourpy + # flytekit + # imagehash + # matplotlib + # pandas + # patsy + # phik + # pyarrow + # pywavelets + # sagemaker-training + # scipy + # seaborn + # statsmodels + # tensorboardx + # torch + # torchvision + # visions + # wordcloud + # ydata-profiling +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.1 + # via + # docker + # ipykernel + # marshmallow + # matplotlib + # plotly + # statsmodels + # tensorboardx +pandas==1.5.3 + # via + # flytekit + # phik + # seaborn + # statsmodels + # visions + # ydata-profiling +paramiko==3.2.0 + # via sagemaker-training +parso==0.8.3 + # via jedi +patsy==0.5.3 + # via statsmodels +pexpect==4.8.0 + # via ipython +phik==0.12.3 + # via ydata-profiling +pickleshare==0.7.5 + # via ipython +pillow==9.5.0 + # via + # imagehash + # matplotlib + # torchvision + # visions + # wordcloud +platformdirs==3.7.0 + # via jupyter-core +plotly==5.15.0 + # via flytekitplugins-deck-standard +portalocker==2.7.0 + # via msal-extensions +prompt-toolkit==3.0.38 + # via ipython +protobuf==4.23.3 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # sagemaker-training + # tensorboardx +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.5 + # via + # ipykernel + # sagemaker-training +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pydantic==1.10.9 + # via ydata-profiling +pygments==2.15.1 + # via + # ipython + # rich +pyjwt[crypto]==2.7.0 + # via msal +pynacl==1.5.0 + # via paramiko +pyopenssl==23.2.0 + # via flytekit +pyparsing==3.1.0 + # via matplotlib +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # jupyter-client + # kubernetes + # matplotlib + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pywavelets==1.4.1 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # responses + # ydata-profiling +pyzmq==25.1.0 + # via + # ipykernel + # jupyter-client +regex==2023.6.3 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # responses + # ydata-profiling +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +responses==0.23.1 + # via flytekit +retry2==0.9.5 + # via flytekitplugins-awssagemaker +retrying==1.3.4 + # via sagemaker-training +rich==13.4.2 + # via + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +s3transfer==0.6.1 + # via boto3 +sagemaker-training==3.9.2 + # via flytekitplugins-awssagemaker +scipy==1.10.1 + # via + # imagehash + # phik + # sagemaker-training + # statsmodels + # ydata-profiling +seaborn==0.12.2 + # via ydata-profiling +six==1.16.0 + # via + # asttokens + # azure-core + # azure-identity + # google-auth + # isodate + # kubernetes + # patsy + # python-dateutil + # retrying + # sagemaker-training +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +stack-data==0.6.2 + # via ipython +statsd==3.3.0 + # via flytekit +statsmodels==0.14.0 + # via ydata-profiling +tangled-up-in-unicode==0.2.0 + # via visions +tenacity==8.2.2 + # via plotly +tensorboardx==2.6.1 + # via -r requirements.in +text-unidecode==1.3 + # via python-slugify +torch==1.8.1 + # via + # -r requirements.in + # torchvision +torchvision==0.9.1 + # via -r requirements.in +tornado==6.3.2 + # via + # ipykernel + # jupyter-client +tqdm==4.65.0 + # via ydata-profiling +traitlets==5.9.0 + # via + # comm + # ipykernel + # ipython + # ipywidgets + # jupyter-client + # jupyter-core + # matplotlib-inline +typeguard==2.13.3 + # via ydata-profiling +types-pyyaml==6.0.12.10 + # via responses +typing-extensions==4.6.3 + # via + # aioitertools + # azure-core + # azure-storage-blob + # flytekit + # ipython + # pydantic + # rich + # torch + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests + # responses +visions[type_image_path]==0.7.5 + # via ydata-profiling +wcwidth==0.2.6 + # via prompt-toolkit +websocket-client==1.6.0 + # via + # docker + # kubernetes +werkzeug==2.3.6 + # via sagemaker-training +wheel==0.40.0 + # via + # -r ../../../common/requirements-common.in + # flytekit +widgetsnbextension==4.0.7 + # via ipywidgets +wordcloud==1.9.2 + # via ydata-profiling +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +ydata-profiling==4.3.1 + # via flytekitplugins-deck-standard +zipp==3.15.0 + # via + # importlib-metadata + # importlib-resources +zope-event==4.6 + # via gevent +zope-interface==6.0 + # via gevent + +# The following packages are considered to be unsafe in a requirements file: +# pip +# setuptools diff --git a/docs/examples/sagemaker_pytorch_plugin/sagemaker_pytorch_plugin/__init__.py b/docs/examples/sagemaker_pytorch_plugin/sagemaker_pytorch_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/sagemaker_pytorch_plugin/sagemaker_pytorch_plugin/sagemaker_pytorch_distributed_training.py b/docs/examples/sagemaker_pytorch_plugin/sagemaker_pytorch_plugin/sagemaker_pytorch_distributed_training.py new file mode 100644 index 000000000..089fce526 --- /dev/null +++ b/docs/examples/sagemaker_pytorch_plugin/sagemaker_pytorch_plugin/sagemaker_pytorch_distributed_training.py @@ -0,0 +1,406 @@ +# %% [markdown] +# # Distributed Pytorch on Sagemaker +# +# This example is adapted from +# [this sagemaker example](https://github.com/aws/amazon-sagemaker-examples/blob/89831fcf99ea3110f52794db0f6433a4013a5bca/sagemaker-python-sdk/pytorch_mnist/mnist.py): +# +# It shows how distributed training can be completely performed on the user side with minimal changes using Flyte. +# +# :::{NOTE} +# Flytekit will be adding further simplifications to make writing a distributed training algorithm even simpler, but +# this example basically provides the full details. +# ::: +# %% +import logging +import os +import typing +from dataclasses import dataclass + +import flytekit +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn as nn +import torch.nn.functional as functional +import torch.optim as optim +from dataclasses_json import dataclass_json +from flytekit import task, workflow +from flytekit.types.file import PythonPickledFile +from flytekitplugins.awssagemaker import ( + AlgorithmName, + AlgorithmSpecification, + InputContentType, + InputMode, + SagemakerTrainingJobConfig, + TrainingJobResourceConfig, +) +from torchvision import datasets, transforms + + +@dataclass_json +@dataclass +class Hyperparameters(object): + """ + Args: + batch_size: input batch size for training (default: 64) + test_batch_size: input batch size for testing (default: 1000) + epochs: number of epochs to train (default: 10) + learning_rate: learning rate (default: 0.01) + sgd_momentum: SGD momentum (default: 0.5) + seed: random seed (default: 1) + log_interval: how many batches to wait before logging training status + dir: directory where summary logs are stored + """ + + backend: str = dist.Backend.GLOO + sgd_momentum: float = 0.5 + seed: int = 1 + log_interval: int = 10 + batch_size: int = 64 + test_batch_size: int = 1000 + epochs: int = 10 + learning_rate: float = 0.01 + + +@dataclass +class TrainingArgs(Hyperparameters): + """ + These are training arguments that contain additional metadata beyond the hyper parameters useful especially in + distributed training: + """ + + hosts: typing.List[int] = None + current_host: int = 0 + num_gpus: int = 0 + data_dir: str = "/tmp" + model_dir: str = "/tmp" + + def is_distributed(self) -> bool: + return len(self.hosts) > 1 and self.backend is not None + + +# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5) + self.conv2 = nn.Conv2d(10, 20, kernel_size=5) + self.conv2_drop = nn.Dropout2d() + self.fc1 = nn.Linear(320, 50) + self.fc2 = nn.Linear(50, 10) + + def forward(self, x): + x = functional.relu(functional.max_pool2d(self.conv1(x), 2)) + x = functional.relu(functional.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) + x = x.view(-1, 320) + x = functional.relu(self.fc1(x)) + x = functional.dropout(x, training=self.training) + x = self.fc2(x) + return functional.log_softmax(x, dim=1) + + +def _get_train_data_loader(batch_size, training_dir, is_distributed, **kwargs): + logging.info("Get train data loader") + dataset = datasets.MNIST( + training_dir, + train=True, + download=False, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ) + logging.info("Dataset is downloaded. Creating a train_sampler") + train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if is_distributed else None + logging.info("Train_sampler is successfully created. Creating a DataLoader") + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=train_sampler is None, + sampler=train_sampler, + **kwargs, + ) + + +def _get_test_data_loader(test_batch_size, training_dir, **kwargs): + logging.info("Get test data loader") + return torch.utils.data.DataLoader( + datasets.MNIST( + training_dir, + train=False, + download=False, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs, + ) + + +def _average_gradients(model): + # Gradient averaging. + size = float(dist.get_world_size()) + for param in model.parameters(): + dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM) + param.grad.data /= size + + +def configure_model(model, is_distributed, gpu): + if is_distributed: + # multi-machine multi-gpu case + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu], output_device=gpu) + else: + # single-machine multi-gpu case or single-machine or multi-machine cpu case + model = torch.nn.DataParallel(model) + return model + + +# %% [markdown] +# The Actual Trainer +# %% +def train(gpu: int, args: TrainingArgs): + logging.basicConfig(level="INFO") + is_distributed = args.is_distributed() + logging.warning("Distributed training - {}".format(is_distributed)) + use_cuda = args.num_gpus > 0 + logging.warning("Number of gpus available - {}".format(args.num_gpus)) + kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {} + device = torch.device("cuda" if use_cuda else "cpu") + + rank = 0 + + if is_distributed: + # Initialize the distributed environment + world_size = len(args.hosts) * args.num_gpus + os.environ["WORLD_SIZE"] = str(world_size) + rank = args.hosts.index(args.current_host) * args.num_gpus + gpu + os.environ["RANK"] = str(rank) + dist.init_process_group(backend=args.backend, init_method="env://", rank=rank, world_size=world_size) + logging.info( + "Initialized the distributed environment: '{}' backend on {} nodes. ".format( + args.backend, dist.get_world_size() + ) + + "Current host rank is {}. Number of gpus: {}".format(dist.get_rank(), args.num_gpus) + ) + torch.cuda.set_device(gpu) + + # set the seed for generating random numbers + torch.manual_seed(args.seed) + if use_cuda: + torch.cuda.manual_seed(args.seed) + + train_loader = _get_train_data_loader(args.batch_size, args.data_dir, is_distributed, **kwargs) + test_loader = _get_test_data_loader(args.test_batch_size, args.data_dir, **kwargs) + + logging.info( + "Processes {}/{} ({:.0f}%) of train data".format( + len(train_loader.sampler), + len(train_loader.dataset), + 100.0 * len(train_loader.sampler) / len(train_loader.dataset), + ) + ) + + logging.info( + "Processes {}/{} ({:.0f}%) of test data".format( + len(test_loader.sampler), + len(test_loader.dataset), + 100.0 * len(test_loader.sampler) / len(test_loader.dataset), + ) + ) + + model = Net().to(device) + + model = configure_model(model, is_distributed, gpu) + + optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=args.sgd_momentum) + + logging.info("[rank {}|local-rank {}] Totally {} epochs".format(rank, gpu, args.epochs + 1)) + for epoch in range(1, args.epochs + 1): + model.train() + for batch_idx, (data, target) in enumerate(train_loader, 1): + if use_cuda: + data, target = ( + data.cuda(non_blocking=True), + target.cuda(non_blocking=True), + ) + optimizer.zero_grad() + output = model(data) + loss = functional.nll_loss(output, target) + loss.backward() + if is_distributed and not use_cuda: + # average gradients manually for multi-machine cpu case only + _average_gradients(model) + optimizer.step() + if batch_idx % args.log_interval == 0: + if not is_distributed or (is_distributed and rank == 0): + logging.info( + "[rank {}|local-rank {}] Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}".format( + rank, + gpu, + epoch, + batch_idx * len(data), + len(train_loader.sampler), + 100.0 * batch_idx / len(train_loader), + loss.item(), + ) + ) + test(model, test_loader, device) + + if not is_distributed or (is_distributed and rank == 0): + save_model(model, args.model_dir) + + +# %% [markdown] +# Lets test the trained model +# %% +def test(model, test_loader, device): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + if device.type == "cuda": + data, target = ( + data.cuda(non_blocking=True), + target.cuda(non_blocking=True), + ) + output = model(data) + test_loss += functional.nll_loss(output, target, size_average=False).item() # sum up batch loss + pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability + correct += pred.eq(target.view_as(pred)).sum().item() + + test_loss /= len(test_loader.dataset) + logging.info( + "Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( + test_loss, + correct, + len(test_loader.dataset), + 100.0 * correct / len(test_loader.dataset), + ) + ) + + +def model_fn(model_dir): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = torch.nn.DataParallel(Net()) + with open(os.path.join(model_dir, "model.pth"), "rb") as f: + model.load_state_dict(torch.load(f)) + return model.to(device) + + +# %% [markdown] +# Save the model to a local path +# %% +def save_model(model, model_dir) -> PythonPickledFile: + logging.info("Saving the model.") + path = os.path.join(model_dir, "model.pth") + # recommended way from http://pytorch.org/docs/master/notes/serialization.html + torch.save(model.cpu().state_dict(), path) + print(f"Model saved to {path}") + return path + + +def download_training_data(training_dir): + logging.info("Downloading train data") + datasets.MNIST( + training_dir, + train=True, + download=True, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ) + + +def download_test_data(training_dir): + logging.info("Downloading test data") + datasets.MNIST( + training_dir, + train=False, + download=True, + transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), + ) + + +# https://github.com/aws/amazon-sagemaker-examples/blob/89831fcf99ea3110f52794db0f6433a4013a5bca/sagemaker-python-sdk/pytorch_mnist/mnist.py +@task( + task_config=SagemakerTrainingJobConfig( + algorithm_specification=AlgorithmSpecification( + input_mode=InputMode.FILE, + algorithm_name=AlgorithmName.CUSTOM, + algorithm_version="", + input_content_type=InputContentType.TEXT_CSV, + ), + training_job_resource_config=TrainingJobResourceConfig( + instance_type="ml.p3.8xlarge", + instance_count=2, + volume_size_in_gb=25, + ), + ), + cache_version="1.0", + cache=True, +) +def mnist_pytorch_job(hp: Hyperparameters) -> PythonPickledFile: + # pytorch's save() function does not create a path if the path specified does not exist + # therefore we must pass an existing path + + ctx = flytekit.current_context() + data_dir = os.path.join(ctx.working_directory, "data") + model_dir = os.path.join(ctx.working_directory, "model") + os.makedirs(data_dir, exist_ok=True) + os.makedirs(model_dir, exist_ok=True) + args = TrainingArgs( + hosts=ctx.distributed_training_context.hosts, + current_host=ctx.distributed_training_context.current_host, + num_gpus=torch.cuda.device_count(), + batch_size=hp.batch_size, + test_batch_size=hp.test_batch_size, + epochs=hp.epochs, + learning_rate=hp.learning_rate, + sgd_momentum=hp.sgd_momentum, + seed=hp.seed, + log_interval=hp.log_interval, + backend=hp.backend, + data_dir=data_dir, + model_dir=model_dir, + ) + + # Data shouldn't be downloaded by the functions called in mp.spawn due to race conditions + # These can be replaced by Flyte's blob type inputs. Note that the data here are assumed + # to be accessible via a local path: + download_training_data(args.data_dir) + download_test_data(args.data_dir) + + if len(args.hosts) > 1: + # Config MASTER_ADDR and MASTER_PORT for PyTorch Distributed Training + os.environ["MASTER_ADDR"] = args.hosts[0] + os.environ["MASTER_PORT"] = "29500" + os.environ["NCCL_SOCKET_IFNAME"] = ctx.distributed_training_context.network_interface_name + os.environ["NCCL_DEBUG"] = "INFO" + # The function is called as fn(i, *args), where i is the process index and args is the passed + # through tuple of arguments. + # https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn + mp.spawn(train, nprocs=args.num_gpus, args=(args,)) + else: + # Config for Multi GPU with a single instance training + if args.num_gpus > 1: + gpu_devices = ",".join([str(gpu_id) for gpu_id in range(args.num_gpus)]) + os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices + train(-1, args) + + pth = os.path.join(model_dir, "model.pth") + print(f"Returning model @ {pth}") + return pth + + +# %% [markdown] +# ## Create a Pipeline +# +# Now the training and the plotting can be put together into a pipeline, where the training is performed first +# followed by the plotting of the accuracy. Data is passed between them and the workflow itself outputs the image and +# the serialize model: +# +# %% +@workflow +def pytorch_training_wf(hp: Hyperparameters) -> PythonPickledFile: + return mnist_pytorch_job(hp=hp) + + +if __name__ == "__main__": + model = pytorch_training_wf(hp=Hyperparameters(epochs=2, batch_size=128)) + print(f"Model: {model}") diff --git a/docs/examples/sagemaker_training_plugin/Dockerfile b/docs/examples/sagemaker_training_plugin/Dockerfile new file mode 100644 index 000000000..297bbe705 --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/Dockerfile @@ -0,0 +1,40 @@ +FROM python:3.8-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +# Setup a virtual environment +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Setup Sagemaker entrypoints +ENV SAGEMAKER_PROGRAM /opt/venv/bin/flytekit_sagemaker_runner.py + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/sagemaker_training_plugin/README.md b/docs/examples/sagemaker_training_plugin/README.md new file mode 100644 index 000000000..a5974a721 --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/README.md @@ -0,0 +1,98 @@ +(aws-sagemaker)= + +# AWS Sagemaker Training + +```{eval-rst} +.. tags:: Integration, MachineLearning, AWS, Advanced +``` + +This section provides examples of Flyte Plugins that are designed to work with +AWS Hosted services like Sagemaker, EMR, Athena, Redshift etc + +## Installation + +To use the flytekit aws sagemaker plugin simply run the following: + +```{eval-rst} +.. prompt:: bash + + pip install flytekitplugins-awssagemaker + +``` + +## Builtin Algorithms + +Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types. Built-in algorithms are the fastest to get started with, as they are already pre-built and optimized on Sagemaker. To understand how they work and the various options available please refer to Amazon Sagemaker Official Documentation + +Flyte Sagemaker plugin intends to greatly simplify using Sagemaker for training. We have tried to distill the API into a meaningful subset that makes it easier for users to adopt and run with Sagemaker. Due to the nature of the Sagemaker built-in algorithms, it is possible to run them completely from a local notebook using Flyte. This is because, Flyte will automatically use a pre-built Image for the given algorithm. + +The Algorithm Images are configured in FlytePlugins in the plugin Configuration here. In the default setting, we have configured XGBoost. + +:::{note} +Sagemaker Builtin Algorithms do not require explicit docker images to be specified. +::: + +## Training a custom model + +Flyte Sagemaker plugin intends to greatly simplify using Sagemaker for training. We have tried to distill the API into a meaningful subset that makes it easier for users to adopt and run with Sagemaker. Training code that runs on Sagemaker looks almost identical to writing any other task on Flyte. Once a custom job is defined, hyper parameter optimization for pre-built algorithms or custom jobs is identical. Users need to wrap their training tasks into an HPO Task and launch it. + +:::{note} +Sagemaker custom algorithms work by building our own Docker images. These images need to be pushed to ECR for Sagemaker to access them. Thus, this examples need to be compiled and pushed to your own AWS ECR docker registry to actually execute on Sagemaker. +::: + +When a remote execution is triggered, the Sagemaker API is invoked to launch a job in and users function `@task` is invoked and all parameters are passed to it. The returns are automatically captured. The users can provide a special predicate that marks when to capture outputs from Rank-0 task in distributed training. + +## Prerequisites + +Before following this example, make sure that +- SageMaker plugins are [enabled in flytepropeller's config](https://github.com/flyteorg/flytepropeller/blob/fe1981b1f10e19f0a189509c3826f2d8f5a75da0/propeller-config.yaml#L36-L39) +- You have your [AWS role set up correctly for SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html) +- [AWS SageMaker k8s operator](https://github.com/aws/amazon-sagemaker-operator-for-k8s) is installed in your k8s cluster + +### Creating a Dockerfile for Sagemaker Custom Training [Required] + +```{code-block} docker +:emphasize-lines: 22-23 +:linenos: true + +FROM python:3.8-buster +LABEL org.opencontainers.image.source https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip install awscli + +# Setup a virtual environment +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY aws/sagemaker_training/requirements.txt /root +RUN pip install -r /root/requirements.txt + +# Setup Sagemaker entrypoints +ENV SAGEMAKER_PROGRAM /opt/venv/bin/flytekit_sagemaker_runner.py + +# Copy the makefile targets to expose on the container. This makes it easier to register. +COPY in_container.mk /root/Makefile +COPY aws/sagemaker_training/sandbox.config /root + +# Copy the actual code +COPY aws/sagemaker_training/ /root/sagemaker_training + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag +``` + +```{auto-examples-toc} +sagemaker_builtin_algo_training +sagemaker_custom_training +``` diff --git a/docs/examples/sagemaker_training_plugin/requirements.in b/docs/examples/sagemaker_training_plugin/requirements.in new file mode 100644 index 000000000..7bdbc9a22 --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/requirements.in @@ -0,0 +1,7 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-awssagemaker>=0.16.0 +tensorflow +tensorflow-datasets diff --git a/docs/examples/sagemaker_training_plugin/requirements.txt b/docs/examples/sagemaker_training_plugin/requirements.txt new file mode 100644 index 000000000..a2ec363fc --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/requirements.txt @@ -0,0 +1,356 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +absl-py==1.0.0 + # via + # tensorboard + # tensorflow + # tensorflow-datasets + # tensorflow-metadata +arrow==1.2.2 + # via jinja2-time +astunparse==1.6.3 + # via tensorflow +bcrypt==3.2.0 + # via paramiko +binaryornot==0.4.4 + # via cookiecutter +boto3==1.21.39 + # via sagemaker-training +botocore==1.24.39 + # via + # boto3 + # s3transfer +cachetools==5.0.0 + # via google-auth +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via + # bcrypt + # cryptography + # pynacl +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==36.0.2 + # via + # paramiko + # secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +dill==0.3.4 + # via tensorflow-datasets +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flatbuffers==2.0 + # via tensorflow +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-awssagemaker +flytekitplugins-awssagemaker==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +gast==0.5.3 + # via tensorflow +gevent==21.12.0 + # via sagemaker-training +google-auth==2.6.4 + # via + # google-auth-oauthlib + # tensorboard +google-auth-oauthlib==0.4.6 + # via tensorboard +google-pasta==0.2.0 + # via tensorflow +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status + # tensorflow-metadata +greenlet==1.1.2 + # via gevent +grpcio==1.44.0 + # via + # flytekit + # grpcio-status + # tensorboard + # tensorflow +grpcio-status==1.44.0 + # via flytekit +h5py==3.6.0 + # via tensorflow +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +inotify-simple==1.2.1 + # via sagemaker-training +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.0 + # via + # boto3 + # botocore +keras==2.8.0 + # via tensorflow +keras-preprocessing==1.1.2 + # via tensorflow +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +libclang==13.0.0 + # via tensorflow +markdown==3.3.6 + # via tensorboard +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # h5py + # keras-preprocessing + # matplotlib + # opt-einsum + # pandas + # pyarrow + # sagemaker-training + # scipy + # tensorboard + # tensorflow + # tensorflow-datasets +oauthlib==3.2.0 + # via requests-oauthlib +opt-einsum==3.3.0 + # via tensorflow +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +paramiko==2.10.3 + # via sagemaker-training +pillow==9.1.1 + # via matplotlib +promise==2.3 + # via tensorflow-datasets +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger + # sagemaker-training + # tensorboard + # tensorflow + # tensorflow-datasets + # tensorflow-metadata +protoc-gen-swagger==0.1.0 + # via flyteidl +psutil==5.9.0 + # via sagemaker-training +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pyasn1==0.4.8 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 + # via google-auth +pycparser==2.21 + # via cffi +pynacl==1.5.0 + # via paramiko +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # requests-oauthlib + # responses + # tensorboard + # tensorflow-datasets +requests-oauthlib==1.3.1 + # via google-auth-oauthlib +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +retrying==1.3.3 + # via sagemaker-training +rsa==4.8 + # via google-auth +s3transfer==0.5.2 + # via boto3 +sagemaker-training==3.9.2 + # via flytekitplugins-awssagemaker +scipy==1.8.0 + # via sagemaker-training +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # absl-py + # astunparse + # bcrypt + # google-auth + # google-pasta + # grpcio + # keras-preprocessing + # paramiko + # promise + # python-dateutil + # retrying + # sagemaker-training + # tensorflow + # tensorflow-datasets +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +tensorboard==2.8.0 + # via tensorflow +tensorboard-data-server==0.6.1 + # via tensorboard +tensorboard-plugin-wit==1.8.1 + # via tensorboard +tensorflow==2.8.0 + # via -r requirements.in +tensorflow-datasets==4.5.2 + # via -r requirements.in +tensorflow-io-gcs-filesystem==0.24.0 + # via tensorflow +tensorflow-metadata==1.7.0 + # via tensorflow-datasets +termcolor==1.1.0 + # via + # tensorflow + # tensorflow-datasets +text-unidecode==1.3 + # via python-slugify +tf-estimator-nightly==2.8.0.dev2021122109 + # via tensorflow +tqdm==4.64.0 + # via tensorflow-datasets +typing-extensions==4.1.1 + # via + # flytekit + # tensorflow + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # botocore + # flytekit + # requests + # responses +werkzeug==2.1.1 + # via + # sagemaker-training + # tensorboard +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # astunparse + # flytekit + # tensorboard +wrapt==1.14.0 + # via + # deprecated + # flytekit + # tensorflow +zipp==3.8.0 + # via importlib-metadata +zope-event==4.5.0 + # via gevent +zope-interface==5.4.0 + # via gevent + +# The following packages are considered to be unsafe in a requirements file: +# pip +# setuptools diff --git a/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/__init__.py b/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_builtin_algo_training.py b/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_builtin_algo_training.py new file mode 100644 index 000000000..4640f3452 --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_builtin_algo_training.py @@ -0,0 +1,206 @@ +# %% [markdown] +# # Built-in Sagemaker Algorithms +# +# This example will show how it is possible to work with built-in algorithms with Amazon Sagemaker and perform hyper-parameter optimization using Sagemaker HPO. +# +# ## Defining an XGBoost Training Job +# +# We will create a job that will train an XGBoost model using the prebuilt algorithms @Sagemaker. +# Refer to [Sagemaker XGBoost docs here](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). +# To understand more about XGBoost refer [here](https://xgboost.readthedocs.io/en/latest/). +# %% +import typing + +from flytekit import TaskMetadata +from flytekitplugins.awssagemaker import ( + AlgorithmName, + AlgorithmSpecification, + ContinuousParameterRange, + HPOJob, + HyperparameterScalingType, + HyperparameterTuningJobConfig, + HyperparameterTuningObjective, + HyperparameterTuningObjectiveType, + HyperparameterTuningStrategy, + InputContentType, + InputMode, + IntegerParameterRange, + ParameterRangeOneOf, + SagemakerBuiltinAlgorithmsTask, + SagemakerHPOTask, + SagemakerTrainingJobConfig, + TrainingJobEarlyStoppingType, + TrainingJobResourceConfig, +) + +# %% [markdown] +# Below is the definition of the values of some hyperparameters, which will be used by the TrainingJob. +# These hyper-parameters are commonly used by the XGboost algorithm. Here we bootstrap them with some default values, which are usually selected or "tuned - refer to next section". +# %% +xgboost_hyperparameters: typing.Dict[str, str] = { + "num_round": "100", + "base_score": "0.5", + "booster": "gbtree", + "csv_weights": "0", + "dsplit": "row", + "grow_policy": "depthwise", + "lambda_bias": "0.0", + "max_bin": "256", + "normalize_type": "tree", + "objective": "reg:linear", + "one_drop": "0", + "prob_buffer_row": "1.0", + "process_type": "default", + "refresh_leaf": "1", + "sample_type": "uniform", + "scale_pos_weight": "1.0", + "silent": "0", + "skip_drop": "0.0", + "tree_method": "auto", + "tweedie_variance_power": "1.5", + "updater": "grow_colmaker,prune", +} + +# %% [markdown] +# Below is the definition of the actual algorithm (XGBOOST) and the version of the algorithm to use: +# %% +alg_spec = AlgorithmSpecification( + input_mode=InputMode.FILE, + algorithm_name=AlgorithmName.XGBOOST, + algorithm_version="0.90", + input_content_type=InputContentType.TEXT_CSV, +) + +# %% [markdown] +# Finally, the Flytekit plugin called SdkBuiltinAlgorithmTrainingJobTask will be used to create a task that wraps the algorithm. +# This task does not have a user-defined function as the actual algorithm is pre-defined in Sagemaker, but still has the same set of properties like any other FlyteTask: +# Caching, Resource specification, Versioning, etc. +# %% +xgboost_train_task = SagemakerBuiltinAlgorithmsTask( + name="xgboost_trainer", + task_config=SagemakerTrainingJobConfig( + algorithm_specification=alg_spec, + training_job_resource_config=TrainingJobResourceConfig( + instance_type="ml.m4.xlarge", + instance_count=1, + volume_size_in_gb=25, + ), + ), + metadata=TaskMetadata(cache_version="1.0", cache=True), +) + + +# %% [markdown] +# You can execute the task without needing to create a workflow. +# To trigger an execution, you will need to provide: +# +# Project (flyteexamples): the project under which the execution will be created +# +# Domain (development): the domain where the execution will be created, under the project +# +# Inputs: the actual inputs +# +# Pre-built algorithms have a restrictive set of inputs. They always expect: +# +# 1. Training data set +# 2. Validation data set +# 3. Static set of hyper parameters as a dictionary +# +# In this case we have taken the [PIMA Diabetes dataset](https://www.kaggle.com/kumargh/pimaindiansdiabetescsv) +# and split it and uploaded to an s3 bucket: +# %% +def execute_training(): + xgboost_train_task( + static_hyperparameters=xgboost_hyperparameters, + train="", + validation="", + ) + + +# %% [markdown] +# ## Optimizing the Hyper-Parameters +# +# Amazon Sagemaker offers automatic hyper-parameter blackbox optimization using the HPO Service. +# This technique is highly effective to figure out the right set of hyper-parameters to use that +# improve the overall accuracy of the model (or minimize the error). +# Flyte makes it extremely effective to optimize a model using Amazon Sagemaker HPO. This example will show how +# this can be done for the prebuilt algorithm training done in the previous section: +# +# ## Defining an HPO Task That Wraps the Training Task +# +# To start with hyper-parameter optimization, once a training task is created, wrap it in +# SagemakerHPOTask as follows: + +# %% +xgboost_hpo_task = SagemakerHPOTask( + name="xgboost_hpo", + task_config=HPOJob( + max_number_of_training_jobs=10, + max_parallel_training_jobs=5, + tunable_params=["num_round", "max_depth", "gamma"], + ), + training_task=xgboost_train_task, + metadata=TaskMetadata(cache=True, cache_version="1.0", retries=2), +) + + +# %% [markdown] +# ## Launch the HPO Job +# +# Just like the Training Job, the HPO job can be launched directly from the notebook. The inputs for an HPO job that wraps a +# training job are the combination of all inputs for the training job, i.e. +# +# 1. "train" dataset, "validation" dataset and "static hyper parameters" for the Training job, +# 2. HyperparameterTuningJobConfig, which consists of ParameterRanges, for the parameters that should be tuned, +# 3. Tuning strategy - Bayesian OR Random (or others as described in Sagemaker), +# 4. Stopping condition, and +# 5. Objective metric name and type (whether to minimize, etc). +# +# When launching the TrainingJob and HPOJob, we need to define the inputs, which are directly related to algorithm outputs. We use the inputs +# and the version information to decide cache hit/miss. +# %% +def execute(): + # TODO Local execution of hpo task is not supported. To add example of remote execution + xgboost_hpo_task( + # These 3 parameters are implicitly extracted from the training task itself + static_hyperparameters=xgboost_hyperparameters, + train="s3://demo/test-datasets/pima/train", + validation="s3://demo/test-datasets/pima/validation", + # The following parameteres are specific for hyper parameter tuning and allow to modify tuning at launch time + hyperparameter_tuning_job_config=HyperparameterTuningJobConfig( + tuning_strategy=HyperparameterTuningStrategy.BAYESIAN, + tuning_objective=HyperparameterTuningObjective( + objective_type=HyperparameterTuningObjectiveType.MINIMIZE, + metric_name="validation:error", + ), + training_job_early_stopping_type=TrainingJobEarlyStoppingType.AUTO, + ), + # The following parameters are tunable parameters are specified during the configuration of the task + # this section provides the ranges to be sweeped + num_round=ParameterRangeOneOf( + param=IntegerParameterRange(min_value=3, max_value=10, scaling_type=HyperparameterScalingType.LINEAR) + ), + max_depth=ParameterRangeOneOf( + param=IntegerParameterRange(min_value=5, max_value=7, scaling_type=HyperparameterScalingType.LINEAR) + ), + gamma=ParameterRangeOneOf( + param=ContinuousParameterRange( + min_value=0.0, + max_value=0.3, + scaling_type=HyperparameterScalingType.LINEAR, + ) + ), + ) + + +# %% [markdown] +# Register and launch the task standalone. +# +# ```python +# hpo_exc = xgboost_hpo_task.register_and_launch( +# "flyteexamples", +# "development", +# inputs=hpo_inputs, +# ) +# ``` +# diff --git a/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_custom_training.py b/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_custom_training.py new file mode 100644 index 000000000..a468bb537 --- /dev/null +++ b/docs/examples/sagemaker_training_plugin/sagemaker_training_plugin/sagemaker_custom_training.py @@ -0,0 +1,219 @@ +# %% [markdown] +# # Custom Sagemaker Algorithms +# +# This script shows an example of how to simply convert your tensorflow training scripts to run on Amazon Sagemaker +# with very few modifications. +# %% +import typing +from typing import Tuple + +import matplotlib.pyplot as plt +import tensorflow as tf +import tensorflow_datasets as tfds +from flytekit import task, workflow +from flytekit.types.directory import TensorboardLogs + +# %% [markdown] +# ## Training Algorithm +# +# In this custom algorithm, we will train MNIST using tensorflow. +# The Training will produce 3 outputs: +# +# 1. The serialized model in HDF5 format +# 2. A log dictionary which is the Keras - `History.history`. This contains the accuracies and loss values +# 3. Tensorboard Logs: We will also output a directory that contains Tensorboard compatible logs. Flyte will collect +# these logs and make them available for visualization in tensorboard - locally or if running remote. +# +# Refer to section {ref}`sagemaker_tensorboard` to visualize the outputs of this example. +# +# ## \[Optional\]: Create Specialized Type Aliases for Files in Specific Formats +# +# The trained model will be serialized using HDF5 encoding. We can create a type for such a file. This type is simply +# an informative way of understanding what type of file is generated and later on helps in matching up tasks that can +# work on similar files. This completely optional. +# +# ```python +# HDF5EncodedModelFile = FlyteFile[typing.TypeVar("hdf5")] +# ``` +# +# But this type alias is already available from Flytekit's type engine, so it can just be imported. +# We will also import `PNGImageFile` which will be used in the next task: +# %% +from flytekit.types.file import HDF5EncodedFile, PNGImageFile +from flytekitplugins.awssagemaker import ( + AlgorithmName, + AlgorithmSpecification, + InputContentType, + InputMode, + SagemakerTrainingJobConfig, + TrainingJobResourceConfig, +) + +# %% [markdown] +# We can create a named tuple to name the specific outputs from the training. This is optional as well, but +# is useful to have human readable names for the outputs. In the absence of this, flytekit will name all outputs as +# `o0, o1, ...` +# %% +TrainingOutputs = typing.NamedTuple("TrainingOutputs", model=HDF5EncodedFile, epoch_logs=dict, logs=TensorboardLogs) + + +# %% [markdown] +# ## Actual Algorithm +# +# To ensure that the code runs on Sagemaker, create a Sagemaker task config using the class +# `SagemakerTrainingJobConfig` +# %% +def normalize_img(image, label): + """Normalizes images: `uint8` -> `float32`.""" + return tf.cast(image, tf.float32) / 255.0, label + + +@task( + task_config=SagemakerTrainingJobConfig( + algorithm_specification=AlgorithmSpecification( + input_mode=InputMode.FILE, + algorithm_name=AlgorithmName.CUSTOM, + algorithm_version="", + input_content_type=InputContentType.TEXT_CSV, + ), + training_job_resource_config=TrainingJobResourceConfig( + instance_type="ml.m4.xlarge", + instance_count=1, + volume_size_in_gb=25, + ), + ), + cache_version="1.0", + cache=True, +) +def custom_training_task(epochs: int, batch_size: int) -> TrainingOutputs: + (ds_train, ds_test), ds_info = tfds.load( + "mnist", + split=["train", "test"], + shuffle_files=True, + as_supervised=True, + with_info=True, + ) + + ds_train = ds_train.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) + ds_train = ds_train.cache() + ds_train = ds_train.shuffle(ds_info.splits["train"].num_examples) + ds_train = ds_train.batch(batch_size) + ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) + + ds_test = ds_test.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) + ds_test = ds_test.batch(batch_size) + ds_test = ds_test.cache() + ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) + + model = tf.keras.models.Sequential( + [ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dense(10), + ] + ) + model.compile( + optimizer=tf.keras.optimizers.Adam(0.001), + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=[tf.keras.metrics.SparseCategoricalAccuracy()], + ) + + log_dir = "/tmp/training-logs" + tb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir) + + history = model.fit( + ds_train, + epochs=epochs, + validation_data=ds_test, + callbacks=[tb_callback], + ) + + serialized_model = "my_model.h5" + model.save(serialized_model, overwrite=True) + + return TrainingOutputs( + model=HDF5EncodedFile(serialized_model), + epoch_logs=history.history, + logs=TensorboardLogs(log_dir), + ) + + +# %% [markdown] +# ## Plot the Metrics +# +# In the following task we will use the history logs from the training in the previous step and plot the curves using +# matplotlib. Images will be output as png. +# %% +PlotOutputs = typing.NamedTuple("PlotOutputs", accuracy=PNGImageFile, loss=PNGImageFile) + + +@task +def plot_loss_and_accuracy(epoch_logs: dict) -> PlotOutputs: + # summarize history for accuracy + plt.plot(epoch_logs["sparse_categorical_accuracy"]) + plt.plot(epoch_logs["val_sparse_categorical_accuracy"]) + plt.title("Sparse Categorical accuracy") + plt.ylabel("accuracy") + plt.xlabel("epoch") + plt.legend(["train", "test"], loc="upper left") + accuracy_plot = "accuracy.png" + plt.savefig(accuracy_plot) + # summarize history for loss + plt.plot(epoch_logs["loss"]) + plt.plot(epoch_logs["val_loss"]) + plt.title("model loss") + plt.ylabel("loss") + plt.xlabel("epoch") + plt.legend(["train", "test"], loc="upper left") + loss_plot = "loss.png" + plt.savefig(loss_plot) + + return PlotOutputs(accuracy=PNGImageFile(accuracy_plot), loss=PNGImageFile(loss_plot)) + + +# %% [markdown] +# The workflow takes in the hyperparams, in this case the epochs and the batch_size, and outputs the trained model +# and the plotted curves: +# %% +@workflow +def mnist_trainer( + epochs: int = 5, batch_size: int = 128 +) -> Tuple[HDF5EncodedFile, PNGImageFile, PNGImageFile, TensorboardLogs]: + model, history, logs = custom_training_task(epochs=epochs, batch_size=batch_size) + accuracy, loss = plot_loss_and_accuracy(epoch_logs=history) + return model, accuracy, loss, logs + + +# %% [markdown] +# As long as you have tensorflow setup locally, it will run like a regular python script. +# %% +if __name__ == "__main__": + model, accuracy, loss, logs = mnist_trainer() + print(f"Model: {model}, Accuracy PNG: {accuracy}, loss PNG: {loss}, Tensorboard Log Dir: {logs}") + +# %% [markdown] +# (sagemaker_tensorboard)= +# +# ## Rendering the Output Logs in Tensorboard +# +# When running locally, the output of execution looks like: +# +# ``` +# Model: /tmp/flyte/20210110_214129/mock_remote/8421ae4d041f76488e245edf3f4360d5/my_model.h5, Accuracy PNG: /tmp/flyte/20210110_214129/mock_remote/cf6a2cd9d3ded89ed814278a8fb3678c/accuracy.png, loss PNG: /tmp/flyte/20210110_214129/mock_remote/267c9dd17d4d4e7c9c8bb8b12ef1e3d2/loss.png, Tensorboard Log Dir: /tmp/flyte/20210110_214129/mock_remote/a4b04e58e21f26f08f81df24094d6446/ +# ``` +# +# You can use the `Tensorboard Log Dir: /tmp/flyte/20210110_214129/mock_remote/a4b04e58e21f26f08f81df24094d6446/` as +# an input to tensorboard to visualize the training as follows: +# +# ```{eval-rst} +# .. prompt:: bash +# +# tensorboard --logdir /tmp/flyte/20210110_214129/mock_remote/a4b04e58e21f26f08f81df24094d6446/ +# +# ``` +# +# If running remotely (executing on Flyte hosted environment), the workflow execution outputs can be retrieved. +# +# You can retrieve the outputs - which will be a path to a blob store like S3, GCS, minio, etc. Tensorboad can be +# pointed to on your local laptop to visualize the results. +# diff --git a/docs/examples/snowflake_plugin/Dockerfile b/docs/examples/snowflake_plugin/Dockerfile new file mode 100644 index 000000000..a9ba25521 --- /dev/null +++ b/docs/examples/snowflake_plugin/Dockerfile @@ -0,0 +1,23 @@ +FROM python:3.8-slim-buster +LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + + +# Install Python dependencies +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt +RUN pip freeze + +# Copy the actual code +COPY . /root/ + + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/snowflake_plugin/README.md b/docs/examples/snowflake_plugin/README.md new file mode 100644 index 000000000..d1539cac2 --- /dev/null +++ b/docs/examples/snowflake_plugin/README.md @@ -0,0 +1,37 @@ +(plugins-snowflake)= + +# Snowflake + +```{eval-rst} +.. tags:: Integration, Data, Advanced, SQL +``` + +Flyte can be seamlessly integrated with the [Snowflake](https://www.snowflake.com) service, +providing you with a straightforward means to query data in Snowflake. + +## Install the plugin + +To use the Snowflake plugin, run the following command: + +``` +pip install flytekitplugins-snowflake +``` + +If you intend to run the plugin on the Flyte cluster, you must first set it up on the backend. +Please refer to the +{std:ref}`Snowflake plugin setup guide ` +for detailed instructions. + +## Run the example on the Flyte cluster + +To run the provided example on the Flyte cluster, use the following command: + +``` +pyflyte run --remote \ + https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/snowflake_plugin/snowflake_plugin/snowflake.py \ + snowflake_wf --nation_key 10 +``` + +```{auto-examples-toc} +snowflake +``` diff --git a/docs/examples/snowflake_plugin/requirements.in b/docs/examples/snowflake_plugin/requirements.in new file mode 100644 index 000000000..029e7ddd5 --- /dev/null +++ b/docs/examples/snowflake_plugin/requirements.in @@ -0,0 +1 @@ +flytekitplugins-snowflake diff --git a/docs/examples/snowflake_plugin/requirements.txt b/docs/examples/snowflake_plugin/requirements.txt new file mode 100644 index 000000000..36f5b7a5c --- /dev/null +++ b/docs/examples/snowflake_plugin/requirements.txt @@ -0,0 +1,359 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile --resolver=backtracking requirements.in +# +adlfs==2023.4.0 + # via flytekit +aiobotocore==2.5.1 + # via s3fs +aiohttp==3.8.4 + # via + # adlfs + # aiobotocore + # gcsfs + # s3fs +aioitertools==0.11.0 + # via aiobotocore +aiosignal==1.3.1 + # via aiohttp +arrow==1.2.3 + # via jinja2-time +async-timeout==4.0.2 + # via aiohttp +attrs==23.1.0 + # via aiohttp +azure-core==1.27.1 + # via + # adlfs + # azure-identity + # azure-storage-blob +azure-datalake-store==0.0.53 + # via adlfs +azure-identity==1.13.0 + # via adlfs +azure-storage-blob==12.16.0 + # via adlfs +binaryornot==0.4.4 + # via cookiecutter +botocore==1.29.161 + # via aiobotocore +cachetools==5.3.1 + # via google-auth +certifi==2023.5.7 + # via + # kubernetes + # requests +cffi==1.15.1 + # via + # azure-datalake-store + # cryptography +chardet==5.1.0 + # via binaryornot +charset-normalizer==3.1.0 + # via + # aiohttp + # requests +click==8.1.3 + # via + # cookiecutter + # flytekit + # rich-click +cloudpickle==2.2.1 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.4.1 + # via flytekit +cryptography==41.0.1 + # via + # azure-identity + # azure-storage-blob + # msal + # pyjwt + # pyopenssl + # secretstorage +dataclasses-json==0.5.9 + # via flytekit +decorator==5.1.1 + # via gcsfs +deprecated==1.2.14 + # via flytekit +diskcache==5.6.1 + # via flytekit +docker==6.1.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.15 + # via flytekit +flyteidl==1.5.12 + # via flytekit +flytekit==1.7.1b1 + # via flytekitplugins-snowflake +flytekitplugins-snowflake==1.7.0 + # via -r requirements.in +frozenlist==1.3.3 + # via + # aiohttp + # aiosignal +fsspec==2023.6.0 + # via + # adlfs + # flytekit + # gcsfs + # s3fs +gcsfs==2023.6.0 + # via flytekit +gitdb==4.0.10 + # via gitpython +gitpython==3.1.37 + # via flytekit +google-api-core==2.11.1 + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.21.0 + # via + # gcsfs + # google-api-core + # google-auth-oauthlib + # google-cloud-core + # google-cloud-storage + # kubernetes +google-auth-oauthlib==1.0.0 + # via gcsfs +google-cloud-core==2.3.3 + # via google-cloud-storage +google-cloud-storage==2.10.0 + # via gcsfs +google-crc32c==1.5.0 + # via google-resumable-media +google-resumable-media==2.5.0 + # via google-cloud-storage +googleapis-common-protos==1.59.1 + # via + # flyteidl + # flytekit + # google-api-core + # grpcio-status +grpcio==1.56.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.56.0 + # via flytekit +idna==3.4 + # via + # requests + # yarl +importlib-metadata==6.7.0 + # via + # flytekit + # keyring +isodate==0.6.1 + # via azure-storage-blob +jaraco-classes==3.2.3 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.1 + # via botocore +joblib==1.3.1 + # via flytekit +keyring==24.2.0 + # via flytekit +kubernetes==26.1.0 + # via flytekit +markdown-it-py==3.0.0 + # via rich +markupsafe==2.1.3 + # via jinja2 +marshmallow==3.19.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +mdurl==0.1.2 + # via markdown-it-py +more-itertools==9.1.0 + # via jaraco-classes +msal==1.22.0 + # via + # azure-datalake-store + # azure-identity + # msal-extensions +msal-extensions==1.0.0 + # via azure-identity +multidict==6.0.4 + # via + # aiohttp + # yarl +mypy-extensions==1.0.0 + # via typing-inspect +natsort==8.4.0 + # via flytekit +numpy==1.24.4 + # via + # flytekit + # pandas + # pyarrow +oauthlib==3.2.2 + # via requests-oauthlib +packaging==23.1 + # via + # docker + # marshmallow +pandas==1.5.3 + # via flytekit +portalocker==2.7.0 + # via msal-extensions +protobuf==4.23.3 + # via + # flyteidl + # google-api-core + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +pyarrow==10.0.1 + # via flytekit +pyasn1==0.5.0 + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.3.0 + # via google-auth +pycparser==2.21 + # via cffi +pygments==2.15.1 + # via rich +pyjwt[crypto]==2.7.0 + # via msal +pyopenssl==23.2.0 + # via flytekit +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # kubernetes + # pandas +python-json-logger==2.0.7 + # via flytekit +python-slugify==8.0.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2023.3 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # kubernetes + # responses +regex==2023.6.3 + # via docker-image-py +requests==2.31.0 + # via + # azure-core + # azure-datalake-store + # cookiecutter + # docker + # flytekit + # gcsfs + # google-api-core + # google-cloud-storage + # kubernetes + # msal + # requests-oauthlib + # responses +requests-oauthlib==1.3.1 + # via + # google-auth-oauthlib + # kubernetes +responses==0.23.1 + # via flytekit +rich==13.4.2 + # via + # flytekit + # rich-click +rich-click==1.6.1 + # via flytekit +rsa==4.9 + # via google-auth +s3fs==2023.6.0 + # via flytekit +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # azure-core + # azure-identity + # google-auth + # isodate + # kubernetes + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +types-pyyaml==6.0.12.10 + # via responses +typing-extensions==4.7.1 + # via + # azure-core + # azure-storage-blob + # flytekit + # typing-inspect +typing-inspect==0.9.0 + # via dataclasses-json +urllib3==1.26.16 + # via + # botocore + # docker + # flytekit + # google-auth + # kubernetes + # requests + # responses +websocket-client==1.6.1 + # via + # docker + # kubernetes +wheel==0.40.0 + # via flytekit +wrapt==1.15.0 + # via + # aiobotocore + # deprecated + # flytekit +yarl==1.9.2 + # via aiohttp +zipp==3.15.0 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/docs/examples/snowflake_plugin/snowflake_plugin/__init__.py b/docs/examples/snowflake_plugin/snowflake_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/snowflake_plugin/snowflake_plugin/snowflake.py b/docs/examples/snowflake_plugin/snowflake_plugin/snowflake.py new file mode 100644 index 000000000..4a466b3f0 --- /dev/null +++ b/docs/examples/snowflake_plugin/snowflake_plugin/snowflake.py @@ -0,0 +1,89 @@ +# %% [markdown] +# # Quering data in Snowflake +# +# This example shows how to use the `SnowflakeTask` to execute a query in Snowflake. +# +# To begin, import the required libraries. +# %% +from flytekit import kwtypes, workflow +from flytekitplugins.snowflake import SnowflakeConfig, SnowflakeTask + +# %% [markdown] +# Instantiate a {py:class}`~flytekitplugins.snowflake.SnowflakeTask` to execute a query. +# Incorporate {py:class}`~flytekitplugins.snowflake.SnowflakeConfig` within the task to define the appropriate configuration. +# %% +snowflake_task_no_io = SnowflakeTask( + name="sql.snowflake.no_io", + inputs={}, + query_template="SELECT 1", + output_schema_type=None, + task_config=SnowflakeConfig( + account="", + database="SNOWFLAKE_SAMPLE_DATA", + schema="TPCH_SF1000", + warehouse="COMPUTE_WH", + ), +) + +# %% [markdown] +# :::{note} +# For successful registration, ensure that your Snowflake task is assigned a unique +# name within your project/domain for your Flyte installation. +# ::: +# +# In practical applications, our primary focus is often on utilizing Snowflake to query datasets. +# Here, we employ the `SNOWFLAKE_SAMPLE_DATA`, a default dataset in the Snowflake service. +# You can find more details about it [here](https://docs.snowflake.com/en/user-guide/sample-data.html). +# The data adheres to the following schema: +# +# ```{eval-rst} +# +----------------------------------------------+ +# | C_CUSTKEY (int) | +# +----------------------------------------------+ +# | C_NAME (string) | +# +----------------------------------------------+ +# | C_ADDRESS (string) | +# +----------------------------------------------+ +# | C_NATIONKEY (int) | +# +----------------------------------------------+ +# | C_PHONE (string) | +# +----------------------------------------------+ +# | C_ACCTBAL (float) | +# +----------------------------------------------+ +# | C_MKTSEGMENT (string) | +# +----------------------------------------------+ +# | C_COMMENT (string) | +# +----------------------------------------------+ +# ``` +# +# Let us explore how we can parameterize our query to filter results for a specific country. +# This country will be provided as user input, using a nation key to specify it. +# %% +snowflake_task_templatized_query = SnowflakeTask( + name="sql.snowflake.w_io", + # Define inputs as well as their types that can be used to customize the query. + inputs=kwtypes(nation_key=int), + task_config=SnowflakeConfig( + account="", + database="SNOWFLAKE_SAMPLE_DATA", + schema="TPCH_SF1000", + warehouse="COMPUTE_WH", + ), + query_template="SELECT * from CUSTOMER where C_NATIONKEY = {{ .inputs.nation_key }} limit 100", +) + + +@workflow +def snowflake_wf(nation_key: int): + return snowflake_task_templatized_query(nation_key=nation_key) + + +# %% [markdown] +# To review the query results, access the Snowflake console at: +# `https://.snowflakecomputing.com/console#/monitoring/queries/detail`. +# +# You can also execute the task and workflow locally. +# %% +if __name__ == "__main__": + print(snowflake_task_no_io()) + print(snowflake_wf(nation_key=10)) diff --git a/docs/examples/sql_plugin/Dockerfile b/docs/examples/sql_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/sql_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/sql_plugin/README.md b/docs/examples/sql_plugin/README.md new file mode 100644 index 000000000..58fd1746c --- /dev/null +++ b/docs/examples/sql_plugin/README.md @@ -0,0 +1,14 @@ +# SQL + +```{eval-rst} +.. tags:: Integration, Data, SQL, Intermediate +``` + +Flyte tasks are not always restricted to running user-supplied containers, nor even containers at all. Indeed, this is +one of the most important design decisions in Flyte. Non-container tasks can have arbitrary targets for execution -- +an API that executes SQL queries like SnowFlake, BigQuery, a synchronous WebAPI, etc. + +```{auto-examples-toc} +sqlite3_integration +sql_alchemy +``` diff --git a/docs/examples/sql_plugin/requirements.in b/docs/examples/sql_plugin/requirements.in new file mode 100644 index 000000000..5a2140931 --- /dev/null +++ b/docs/examples/sql_plugin/requirements.in @@ -0,0 +1,6 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-sqlalchemy>=0.20.1 +psycopg2-binary diff --git a/docs/examples/sql_plugin/requirements.txt b/docs/examples/sql_plugin/requirements.txt new file mode 100644 index 000000000..cc1dec0a8 --- /dev/null +++ b/docs/examples/sql_plugin/requirements.txt @@ -0,0 +1,204 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# /Library/Developer/CommandLineTools/usr/bin/make requirements.txt +# +arrow==1.2.2 + # via jinja2-time +binaryornot==0.4.4 + # via cookiecutter +certifi==2021.10.8 + # via requests +cffi==1.15.0 + # via cryptography +chardet==4.0.0 + # via binaryornot +charset-normalizer==2.0.12 + # via requests +checksumdir==1.2.0 + # via flytekit +click==8.1.2 + # via + # cookiecutter + # flytekit +cloudpickle==2.0.0 + # via flytekit +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.4 + # via flytekit +cryptography==37.0.3 + # via secretstorage +cycler==0.11.0 + # via matplotlib +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.13 + # via flytekit +flyteidl==0.24.21 + # via flytekit +flytekit==0.32.3 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-sqlalchemy +flytekitplugins-sqlalchemy==0.32.3 + # via -r requirements.in +fonttools==4.32.0 + # via matplotlib +googleapis-common-protos==1.56.0 + # via + # flyteidl + # grpcio-status +greenlet==1.1.2 + # via sqlalchemy +grpcio==1.44.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.44.0 + # via flytekit +idna==3.3 + # via requests +importlib-metadata==4.11.3 + # via keyring +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.1 + # via + # cookiecutter + # jinja2-time +jinja2-time==0.2.0 + # via cookiecutter +keyring==23.5.0 + # via flytekit +kiwisolver==1.4.2 + # via matplotlib +markupsafe==2.1.1 + # via jinja2 +marshmallow==3.15.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.1 + # via -r ../../../common/requirements-common.in +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +numpy==1.22.3 + # via + # matplotlib + # pandas + # pyarrow +packaging==21.3 + # via + # marshmallow + # matplotlib +pandas==1.4.2 + # via flytekit +pillow==9.1.0 + # via matplotlib +protobuf==3.20.0 + # via + # flyteidl + # flytekit + # googleapis-common-protos + # grpcio-status + # protoc-gen-swagger +protoc-gen-swagger==0.1.0 + # via flyteidl +psycopg2-binary==2.9.3 + # via -r requirements.in +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycparser==2.21 + # via cffi +pyparsing==3.0.8 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # croniter + # flytekit + # matplotlib + # pandas +python-json-logger==2.0.2 + # via flytekit +python-slugify==6.1.1 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # pandas +pyyaml==6.0 + # via + # cookiecutter + # flytekit +regex==2022.3.15 + # via docker-image-py +requests==2.27.1 + # via + # cookiecutter + # flytekit + # responses +responses==0.20.0 + # via flytekit +retry==0.9.2 + # via flytekit +secretstorage==3.3.2 + # via keyring +six==1.16.0 + # via + # grpcio + # python-dateutil +sortedcontainers==2.4.0 + # via flytekit +sqlalchemy==1.4.35 + # via flytekitplugins-sqlalchemy +statsd==3.3.0 + # via flytekit +text-unidecode==1.3 + # via python-slugify +typing-extensions==4.1.1 + # via + # flytekit + # typing-inspect +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.9 + # via + # flytekit + # requests + # responses +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +wrapt==1.14.0 + # via + # deprecated + # flytekit +zipp==3.8.0 + # via importlib-metadata diff --git a/docs/examples/sql_plugin/sql_plugin/__init__.py b/docs/examples/sql_plugin/sql_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/sql_plugin/sql_plugin/sql_alchemy.py b/docs/examples/sql_plugin/sql_plugin/sql_alchemy.py new file mode 100644 index 000000000..e2dca837d --- /dev/null +++ b/docs/examples/sql_plugin/sql_plugin/sql_alchemy.py @@ -0,0 +1,94 @@ +# %% [markdown] +# (sql_alchemy)= +# +# # SQLAlchemy +# +# SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. +# +# Flyte provides an easy-to-use interface to utilize SQLAlchemy to connect to various SQL Databases. In this example, we use a Postgres DB to understand how SQLAlchemy can be used within Flyte. +# +# This task will run with a pre-built container, and thus users needn't build one. +# You can simply implement the task, then register and execute it immediately. +# +# First, install the Flyte Sqlalchemy plugin: +# +# ```{eval-rst} +# .. prompt:: bash $ +# +# pip install flytekitplugins-sqlalchemy +# +# ``` + +# %% [markdown] +# Let's first import the libraries. +# %% +from flytekit import kwtypes, task, workflow +from flytekit.types.schema import FlyteSchema +from flytekitplugins.sqlalchemy import SQLAlchemyConfig, SQLAlchemyTask + +# %% [markdown] +# First we define a `SQLALchemyTask`, which returns the first `n` records from the `rna` table of the +# [RNA central database](https://rnacentral.org/help/public-database) . Since this database is public, we can +# hard-code the database URI, including the user and password in a string. +# +# :::{note} +# The output of SQLAlchemyTask is a `FlyteSchema` by default. +# ::: +# +# :::{caution} +# **Never** store passwords for proprietary or sensitive databases! If you need to store and access secrets in a task, +# Flyte provides a convenient API. See {ref}`secrets` for more details. +# ::: + +# %% +DATABASE_URI = "postgresql://reader:NWDMCE5xdipIjRrp@hh-pgsql-public.ebi.ac.uk:5432/pfmegrnargs" + +# Here we define the schema of the expected output of the query, which we then re-use in the `get_mean_length` task. +DataSchema = FlyteSchema[kwtypes(sequence_length=int)] + +sql_task = SQLAlchemyTask( + "rna", + query_template=""" + select len as sequence_length from rna + where len >= {{ .inputs.min_length }} + and len <= {{ .inputs.max_length }} + limit {{ .inputs.limit }} + """, + inputs=kwtypes(min_length=int, max_length=int, limit=int), + output_schema_type=DataSchema, + task_config=SQLAlchemyConfig(uri=DATABASE_URI), +) + + +# %% [markdown] +# Next, we define a task that computes the mean length of sequences in the subset of RNA sequences that our query +# returned. +# Note for those running this in your live Flyte backend via `pyflyte run`. `run` will use the default flytekit +# image if one is not specified. The default flytekit image does not have the sqlalchemy flytekit plugin installed. +# To correctly kick off an execution of this task, you'll need to use the following command. +# +# ``` +# pyflyte --config ~/.flyte/your-config.yaml run --destination-dir /app --remote --image ghcr.io/flyteorg/flytekit:py3.8-sqlalchemy-latest integrations/flytekit_plugins/sql/sql_alchemy.py my_wf --min_length 3 --max_length 100 --limit 50 +# ``` +# +# Note also we added the `destination-dir` argument, since by default `pyflyte run` copies code into `/root` which +# is not what that image's workdir is set to. +# %% +@task +def get_mean_length(data: DataSchema) -> float: + dataframe = data.open().all() + return dataframe["sequence_length"].mean().item() + + +# %% [markdown] +# Finally, we put everything together into a workflow: +# +# %% +@workflow +def my_wf(min_length: int, max_length: int, limit: int) -> float: + return get_mean_length(data=sql_task(min_length=min_length, max_length=max_length, limit=limit)) + + +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(my_wf(min_length=50, max_length=200, limit=5)) diff --git a/docs/examples/sql_plugin/sql_plugin/sqlite3_integration.py b/docs/examples/sql_plugin/sql_plugin/sqlite3_integration.py new file mode 100644 index 000000000..904ebec6d --- /dev/null +++ b/docs/examples/sql_plugin/sql_plugin/sqlite3_integration.py @@ -0,0 +1,84 @@ +# %% [markdown] +# (integrations_sql_sqlite3)= +# +# # Sqlite3 +# +# The following example shows how you can write SQLite3 queries using the SQLite3Task, which is bundled as part of the +# core flytekit. Since SQL Queries are portable across workflows and Flyte installations (as long as the data exists), +# this task will always run with a pre-built container, specifically the [flytekit container](https://github.com/flyteorg/flytekit/blob/v0.19.0/Dockerfile.py38) +# itself. Therefore, users are not required to build a container for SQLite3. You can simply implement the task - register and +# execute it immediately. +# +# In some cases local execution is not possible - e.g. Snowflake. But for SQLlite3 local execution is also supported. +# %% +import pandas +from flytekit import kwtypes, task, workflow +from flytekit.extras.sqlite3.task import SQLite3Config, SQLite3Task + +# %% [markdown] +# SQLite3 queries in flyte produce a Schema output. The data in this example is +# taken from [here](https://www.sqlitetutorial.net/sqlite-sample-database/). +# %% +from flytekit.types.schema import FlyteSchema + +EXAMPLE_DB = "https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip" + +# %% [markdown] +# the task is declared as a regular task. Alternatively it can be declared within a workflow just at the point of using +# it (example later) +# %% +sql_task = SQLite3Task( + name="sqlite3.sample", + query_template="select TrackId, Name from tracks limit {{.inputs.limit}}", + inputs=kwtypes(limit=int), + output_schema_type=FlyteSchema[kwtypes(TrackId=int, Name=str)], + task_config=SQLite3Config(uri=EXAMPLE_DB, compressed=True), +) + + +# %% [markdown] +# As described elsewhere FlyteSchemas can be easily be received as pandas Dataframe and Flyte will autoconvert them +# %% +@task +def print_and_count_columns(df: pandas.DataFrame) -> int: + return len(df[df.columns[0]]) + + +# %% [markdown] +# The task can be used normally in the workflow, passing the declared inputs +# %% +@workflow +def wf() -> int: + return print_and_count_columns(df=sql_task(limit=100)) + + +# %% [markdown] +# It can also be executed locally. +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running main {wf()}") + + +# %% [markdown] +# As mentioned earlier it is possible to also write the SQL Task inline as follows +# %% +@workflow +def query_wf() -> int: + df = SQLite3Task( + name="sqlite3.sample_inline", + query_template="select TrackId, Name from tracks limit {{.inputs.limit}}", + inputs=kwtypes(limit=int), + output_schema_type=FlyteSchema[kwtypes(TrackId=int, Name=str)], + task_config=SQLite3Config(uri=EXAMPLE_DB, compressed=True), + )(limit=100) + return print_and_count_columns(df=df) + + +# %% [markdown] +# It can also be executed locally. +# +# %% +if __name__ == "__main__": + print(f"Running {__file__} main...") + print(f"Running main {query_wf()}") diff --git a/docs/examples/test b/docs/examples/test new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/docs/examples/test @@ -0,0 +1 @@ + diff --git a/docs/examples/testing/README.md b/docs/examples/testing/README.md new file mode 100644 index 000000000..800ea97b5 --- /dev/null +++ b/docs/examples/testing/README.md @@ -0,0 +1,12 @@ +(testing)= + +# Testing Flyte Tasks and Workflows + +The `flytekit` python SDK provides a few utilities for making it easier to test +your tasks and workflows in your test suite. For more details, you can also refer +to the {py:mod}`~flytekit.testing` module in the API reference. + + +```{auto-examples-toc} +mocking +``` diff --git a/docs/examples/testing/testing/__init__.py b/docs/examples/testing/testing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/testing/testing/mocking.py b/docs/examples/testing/testing/mocking.py new file mode 100644 index 000000000..5c5f9a23a --- /dev/null +++ b/docs/examples/testing/testing/mocking.py @@ -0,0 +1,74 @@ +# %% [markdown] +# # Mock Tasks for Testing +# +# A lot of the tasks that you write you can run locally, but some of them you will not be able to, usually because they +# are tasks that depend on a third-party only available on the backend. Hive tasks are a common example, as most users +# will not have access to the service that executes Hive queries from their development environment. However, it's still +# useful to be able to locally run a workflow that calls such a task. In these instances, flytekit provides a couple +# of utilities to help navigate this. + +# %% +import datetime + +import pandas +from flytekit import SQLTask, TaskMetadata, kwtypes, task, workflow +from flytekit.testing import patch, task_mock +from flytekit.types.schema import FlyteSchema + +# %% [markdown] +# This is a generic SQL task (and is by default not hooked up to any datastore nor handled by any plugin), and must +# be mocked. +# %% +sql = SQLTask( + "my-query", + query_template="SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds }}' LIMIT 10", + inputs=kwtypes(ds=datetime.datetime), + outputs=kwtypes(results=FlyteSchema), + metadata=TaskMetadata(retries=2), +) + + +# %% [markdown] +# This is a task that can run locally +# %% +@task +def t1() -> datetime.datetime: + return datetime.datetime.now() + + +# %% [markdown] +# Declare a workflow that chains these two tasks together. +# %% +@workflow +def my_wf() -> FlyteSchema: + dt = t1() + return sql(ds=dt) + + +# %% [markdown] +# Without a mock, calling the workflow would typically raise an exception, but with the `task_mock` construct, which +# returns a `MagicMock` object, we can override the return value. +# %% +def main_1(): + with task_mock(sql) as mock: + mock.return_value = pandas.DataFrame(data={"x": [1, 2], "y": ["3", "4"]}) + assert (my_wf().open().all() == pandas.DataFrame(data={"x": [1, 2], "y": ["3", "4"]})).all().all() + + +# %% [markdown] +# There is another utility as well called `patch` which offers the same functionality, but in the traditional Python +# patching style, where the first argument is the `MagicMock` object. +# +# %% +def main_2(): + @patch(sql) + def test_user_demo_test(mock_sql): + mock_sql.return_value = pandas.DataFrame(data={"x": [1, 2], "y": ["3", "4"]}) + assert (my_wf().open().all() == pandas.DataFrame(data={"x": [1, 2], "y": ["3", "4"]})).all().all() + + test_user_demo_test() + + +if __name__ == "__main__": + main_1() + main_2() diff --git a/docs/examples/whylogs_plugin/Dockerfile b/docs/examples/whylogs_plugin/Dockerfile new file mode 100644 index 000000000..fe7f46836 --- /dev/null +++ b/docs/examples/whylogs_plugin/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.8-buster + +WORKDIR /root +ENV VENV /opt/venv +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 +ENV PYTHONPATH /root + +# Install the AWS cli separately to prevent issues with boto being written over +RUN pip3 install awscli + +# Install gcloud for GCP +RUN apt-get update && apt-get install -y curl + +WORKDIR /opt +RUN curl https://sdk.cloud.google.com > install.sh +RUN bash /opt/install.sh --install-dir=/opt +ENV PATH $PATH:/opt/google-cloud-sdk/bin +WORKDIR /root + +ENV VENV /opt/venv +# Virtual environment +RUN python3 -m venv ${VENV} +ENV PATH="${VENV}/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt /root/. +RUN pip install -r /root/requirements.txt + +# Copy the actual code +COPY . /root/ + +# This tag is supplied by the build script and will be used to determine the version +# when registering tasks, workflows, and launch plans +ARG tag +ENV FLYTE_INTERNAL_IMAGE $tag diff --git a/docs/examples/whylogs_plugin/README.md b/docs/examples/whylogs_plugin/README.md new file mode 100644 index 000000000..f1b33d1cc --- /dev/null +++ b/docs/examples/whylogs_plugin/README.md @@ -0,0 +1,102 @@ +# WhyLogs + +```{eval-rst} +.. tags:: Intermediate, Data, DataFrame, Intermediate +``` + +whylogs is an open source software that allows you to log and inspect differents aspects of your data and ML models. +It creates efficient and mergeable statistical summaries of your datasets, called profiles, that have similar properties +to logs produced by regular software applications. + +## whylogs with Flyte + +The integration we've built consists on a type registration and also renderers. + +### whylogs Flyte Type + +The first part of the integration consists on the ability to pass a `DatasetProfileView` in and out of +the desired tasks. whylogs' DatasetProfileView is the representation of a snapshot of your dataset. +With this integration under Flyte's type system, users will benefit from profiling their desired dataset once +and be able to use the statistical representation for many validations and drift detection systems downstream. + +To be able use it, pass in a `pandas.DataFrame` to a task and call: + +```python +@task +def profiling_task(data: pd.DataFrame) -> DatasetProfileView: + results = why.log(data) + return results.view() +``` + +This will grant any downstream task the ability to ingest the profiled dataset and use +basically anything from whylogs' api, such as transforming it back to a pandas DataFrame: + +```python +@task +def consume_profile_view(profile_view: DatasetProfileView) -> pd.DataFrame: + return profile_view.to_pandas() +``` + +## Renderers + +The Summary Drift Report is a neat HTML report containing information on the distribution and drift +detection of a target and a reference profile. It makes it easy for users to compare a new read dataset +against the one that was used to train the model that's in production. + +To use it, simply take in the two desired `pandas.DataFrame` objects and call: + +```python +renderer = WhylogsSummaryDriftRenderer() +report = renderer.to_html(target_data=new_data, reference_data=reference_data) +flytekit.Deck("summary drift", report) +``` + +The other report that can be generated with our integration is the Constraints Report. With it, users will +have a neat view on a Flyte Deck that will give intuition on which are the passed and failing constraints, enabling +them to act quicker on potentially wrong results. + +```python +from whylogs.core.constraints.factories import greater_than_number + +@task +def constraints_report(profile_view: DatasetProfileView) -> bool: + builder = ConstraintsBuilder(dataset_profile_view=profile_view) + builder.add_constraint(greater_than_number(column_name="my_column", number=10.0)) + + constraints = builder.build() + + renderer = WhylogsConstraintsRenderer() + flytekit.Deck("constraints", renderer.to_html(constraints=constraints)) + + return constraints.validate() +``` + +Since we need a `Constraints` object to create this report, users can also return a boolean to whether their dataset +passed the validations or not, and take actions depending on this result downstream, as the code snippet above showed. +Other use-case would be to return the constraints report itself and parse it to provide more information to other +systems automatically. + +```python +constraints = builder.build() +constraints.report() + +>> [('my_column greater than number 10.0', 0, 1)] +``` + +## Installing the plugin + +In order to have the whylogs plugin installed, simply run: + +```bash +pip install flytekitplugins-whylogs +``` + +And you should then have it available to use on your environment! + +With any questions or demands, feel free to join our community [Slack]. + +[slack]: http://join.slack.whylabs.ai/ + +```{auto-examples-toc} +whylogs_example +``` diff --git a/docs/examples/whylogs_plugin/requirements.in b/docs/examples/whylogs_plugin/requirements.in new file mode 100644 index 000000000..3f8bc07aa --- /dev/null +++ b/docs/examples/whylogs_plugin/requirements.in @@ -0,0 +1,9 @@ +flytekit +wheel +matplotlib +flytekitplugins-deck-standard +flytekitplugins-whylogs>=1.1.1b0 +scikit-learn +whylogs[s3] +whylogs[mlflow] +whylogs[whylabs] diff --git a/docs/examples/whylogs_plugin/requirements.txt b/docs/examples/whylogs_plugin/requirements.txt new file mode 100644 index 000000000..bab22361c --- /dev/null +++ b/docs/examples/whylogs_plugin/requirements.txt @@ -0,0 +1,358 @@ +# +# This file is autogenerated by pip-compile with python 3.9 +# To update, run: +# +# pip-compile requirements.in +# +arrow==1.2.2 + # via jinja2-time +attrs==22.1.0 + # via visions +binaryornot==0.4.4 + # via cookiecutter +boto3==1.24.45 + # via whylogs +botocore==1.27.45 + # via + # boto3 + # s3transfer +certifi==2022.6.15 + # via requests +cffi==1.15.1 + # via cryptography +chardet==5.0.0 + # via binaryornot +charset-normalizer==2.1.0 + # via requests +click==8.1.3 + # via + # cookiecutter + # databricks-cli + # flytekit + # mlflow-skinny +cloudpickle==2.1.0 + # via + # flytekit + # mlflow-skinny +cookiecutter==2.1.1 + # via flytekit +croniter==1.3.5 + # via flytekit +cryptography==37.0.4 + # via + # pyopenssl + # secretstorage +cycler==0.11.0 + # via matplotlib +databricks-cli==0.17.0 + # via mlflow-skinny +dataclasses-json==0.5.7 + # via flytekit +decorator==5.1.1 + # via retry +deprecated==1.2.13 + # via flytekit +diskcache==5.4.0 + # via flytekit +docker==5.0.3 + # via flytekit +docker-image-py==0.1.12 + # via flytekit +docstring-parser==0.14.1 + # via flytekit +entrypoints==0.4 + # via mlflow-skinny +flake8==4.0.1 + # via whylogs +flyteidl==1.1.9 + # via flytekit +flytekit==1.1.0 + # via + # -r ../../../common/requirements-common.in + # flytekitplugins-deck-standard +flytekitplugins-deck-standard==1.1.0 + # via -r ../../../common/requirements-common.in +flytekitplugins-whylogs==1.1.1b0 + # via -r requirements.in +fonttools==4.34.4 + # via matplotlib +gitdb==4.0.9 + # via gitpython +gitpython==3.1.27 + # via mlflow-skinny +googleapis-common-protos==1.56.4 + # via + # flyteidl + # grpcio-status +grpcio==1.47.0 + # via + # flytekit + # grpcio-status +grpcio-status==1.47.0 + # via flytekit +htmlmin==0.1.12 + # via pandas-profiling +idna==3.3 + # via requests +imagehash==4.2.1 + # via visions +importlib-metadata==4.12.0 + # via + # flytekit + # mlflow-skinny +jeepney==0.8.0 + # via + # keyring + # secretstorage +jinja2==3.1.2 + # via + # cookiecutter + # jinja2-time + # pandas-profiling +jinja2-time==0.2.0 + # via cookiecutter +jmespath==1.0.1 + # via + # boto3 + # botocore +joblib==1.1.0 + # via + # pandas-profiling + # phik + # scikit-learn +keyring==23.7.0 + # via flytekit +kiwisolver==1.4.4 + # via matplotlib +markdown==3.4.1 + # via flytekitplugins-deck-standard +markupsafe==2.1.1 + # via + # jinja2 + # pandas-profiling +marshmallow==3.17.0 + # via + # dataclasses-json + # marshmallow-enum + # marshmallow-jsonschema +marshmallow-enum==1.5.1 + # via dataclasses-json +marshmallow-jsonschema==0.13.0 + # via flytekit +matplotlib==3.5.2 + # via + # -r ../../../common/requirements-common.in + # missingno + # pandas-profiling + # phik + # seaborn +mccabe==0.6.1 + # via flake8 +missingno==0.5.1 + # via pandas-profiling +mlflow-skinny==1.27.0 + # via whylogs +multimethod==1.8 + # via + # pandas-profiling + # visions +mypy-extensions==0.4.3 + # via typing-inspect +natsort==8.1.0 + # via flytekit +networkx==2.8.5 + # via visions +numpy==1.23.1 + # via + # imagehash + # matplotlib + # missingno + # pandas + # pandas-profiling + # phik + # pyarrow + # scikit-learn + # scipy + # seaborn + # visions +oauthlib==3.2.1 + # via databricks-cli +packaging==21.3 + # via + # marshmallow + # matplotlib + # mlflow-skinny +pandas==1.4.3 + # via + # flytekit + # pandas-profiling + # phik + # seaborn + # visions +pandas-profiling==3.2.0 + # via flytekitplugins-deck-standard +phik==0.12.2 + # via pandas-profiling +pillow==9.2.0 + # via + # imagehash + # matplotlib + # visions +plotly==5.9.0 + # via flytekitplugins-deck-standard +protobuf==3.20.1 + # via + # flyteidl + # flytekit + # flytekitplugins-whylogs + # googleapis-common-protos + # grpcio-status + # mlflow-skinny + # protoc-gen-swagger + # whylogs +protoc-gen-swagger==0.1.0 + # via flyteidl +py==1.11.0 + # via retry +pyarrow==6.0.1 + # via flytekit +pycodestyle==2.8.0 + # via flake8 +pycparser==2.21 + # via cffi +pydantic==1.9.1 + # via pandas-profiling +pyflakes==2.4.0 + # via flake8 +pyjwt==2.4.0 + # via databricks-cli +pyopenssl==22.0.0 + # via flytekit +pyparsing==3.0.9 + # via + # matplotlib + # packaging +python-dateutil==2.8.2 + # via + # arrow + # botocore + # croniter + # flytekit + # matplotlib + # pandas + # whylabs-client +python-json-logger==2.0.4 + # via flytekit +python-slugify==6.1.2 + # via cookiecutter +pytimeparse==1.1.8 + # via flytekit +pytz==2022.1 + # via + # flytekit + # mlflow-skinny + # pandas +pywavelets==1.3.0 + # via imagehash +pyyaml==6.0 + # via + # cookiecutter + # flytekit + # mlflow-skinny + # pandas-profiling +regex==2022.7.25 + # via docker-image-py +requests==2.28.1 + # via + # cookiecutter + # databricks-cli + # docker + # flytekit + # mlflow-skinny + # pandas-profiling + # responses +responses==0.21.0 + # via flytekit +retry==0.9.2 + # via flytekit +s3transfer==0.6.0 + # via boto3 +scikit-learn==1.1.2 + # via -r requirements.in +scipy==1.9.0 + # via + # imagehash + # missingno + # pandas-profiling + # phik + # scikit-learn + # seaborn +seaborn==0.11.2 + # via + # missingno + # pandas-profiling +secretstorage==3.3.3 + # via keyring +six==1.16.0 + # via + # databricks-cli + # grpcio + # imagehash + # python-dateutil +smmap==5.0.0 + # via gitdb +sortedcontainers==2.4.0 + # via flytekit +statsd==3.3.0 + # via flytekit +tabulate==0.8.10 + # via databricks-cli +tangled-up-in-unicode==0.2.0 + # via + # pandas-profiling + # visions +tenacity==8.0.1 + # via plotly +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.1.0 + # via scikit-learn +tqdm==4.64.0 + # via pandas-profiling +typing-extensions==4.3.0 + # via + # flytekit + # pydantic + # typing-inspect + # whylogs +typing-inspect==0.7.1 + # via dataclasses-json +urllib3==1.26.11 + # via + # botocore + # flytekit + # requests + # responses + # whylabs-client +visions[type_image_path]==0.7.4 + # via pandas-profiling +websocket-client==1.3.3 + # via docker +wheel==0.37.1 + # via + # -r ../../../common/requirements-common.in + # flytekit +whylabs-client==0.3.0 + # via whylogs +whylogs[mlflow,s3,whylabs]==1.0.9 + # via + # -r requirements.in + # flytekitplugins-whylogs +whylogs-sketching==3.4.1.dev2 + # via whylogs +wrapt==1.14.1 + # via + # deprecated + # flytekit +zipp==3.8.1 + # via importlib-metadata diff --git a/docs/examples/whylogs_plugin/whylogs_plugin/__init__.py b/docs/examples/whylogs_plugin/whylogs_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/docs/examples/whylogs_plugin/whylogs_plugin/whylogs_example.py b/docs/examples/whylogs_plugin/whylogs_plugin/whylogs_example.py new file mode 100644 index 000000000..0d79339cf --- /dev/null +++ b/docs/examples/whylogs_plugin/whylogs_plugin/whylogs_example.py @@ -0,0 +1,148 @@ +# %% [markdown] +# # whylogs Example +# +# This examples shows users how to profile pandas DataFrames with whylogs, +# pass them within tasks and also use our renderers to create a SummaryDriftReport +# and a ConstraintsReport with failed and passed constraints. + +# %% [markdown] +# First, let's make all the necessary imports for our example to run properly +# %% +import os + +import flytekit +import numpy as np +import pandas as pd +import whylogs as why +from flytekit import conditional, task, workflow +from flytekitplugins.whylogs.renderer import WhylogsConstraintsRenderer, WhylogsSummaryDriftRenderer +from flytekitplugins.whylogs.schema import WhylogsDatasetProfileTransformer # noqa +from sklearn.datasets import load_diabetes +from whylogs.core import DatasetProfileView +from whylogs.core.constraints import ConstraintsBuilder +from whylogs.core.constraints.factories import ( + greater_than_number, + mean_between_range, + null_percentage_below_number, + smaller_than_number, +) + + +# %% [markdown] +# Next thing is defining a task to read our reference dataset. +# For this, we will take scikit-learn's entire example Diabetes dataset +# %% +@task +def get_reference_data() -> pd.DataFrame: + diabetes = load_diabetes() + df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names) + df["target"] = pd.DataFrame(diabetes.target) + return df + + +# %% [markdown] +# To some extent, we wanted to show kinds of drift in our example, +# so in order to reproduce some of what real-life data behaves +# we will take an arbitrary subset of the reference dataset +# %% +@task +def get_target_data() -> pd.DataFrame: + diabetes = load_diabetes() + df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names) + df["target"] = pd.DataFrame(diabetes.target) + return df.mask(df["age"] < 0.0).dropna(axis=0) + + +# %% [markdown] +# Now we will define a task that can take in any pandas DataFrame +# and return a `DatasetProfileView`, which is our data profile. +# With it, users can either visualize and check overall statistics +# or even run a constraint suite on top of it. +# %% +@task +def create_profile_view(df: pd.DataFrame) -> DatasetProfileView: + result = why.log(df) + return result.view() + + +# %% [markdown] +# And we will also define a constraints report task +# that will run some checks in our existing profile. +# %% +@task +def constraints_report(profile_view: DatasetProfileView) -> bool: + builder = ConstraintsBuilder(dataset_profile_view=profile_view) + builder.add_constraint(greater_than_number(column_name="age", number=-11.0)) + builder.add_constraint(smaller_than_number(column_name="bp", number=20.0)) + builder.add_constraint(mean_between_range(column_name="s3", lower=-1.5, upper=1.5)) + builder.add_constraint(null_percentage_below_number(column_name="sex", number=0.0)) + + constraints = builder.build() + + renderer = WhylogsConstraintsRenderer() + flytekit.Deck("constraints", renderer.to_html(constraints=constraints)) + + return constraints.validate() + + +# %% [markdown] +# This is a representation of a prediction task. Since we are looking +# to take some of the complexity away from our demonstrations, +# our model prediction here will be represented by generating a bunch of +# random numbers with numpy. This task will take place if we pass our +# constraints suite. +# %% +@task +def make_predictions(input_data: pd.DataFrame, output_path: str) -> str: + input_data["predictions"] = np.random.random(size=len(input_data)) + if not os.path.exists(output_path): + os.makedirs(output_path) + input_data.to_csv(os.path.join(output_path, "predictions.csv")) + return f"wrote predictions successfully to {output_path}" + + +# %% [markdown] +# Lastly, if the constraint checks fail, we will create a FlyteDeck +# with the Summary Drift Report, which can provide further intuition into +# whether there was a data drift to the failed constraint checks. +# %% +@task +def summary_drift_report(new_data: pd.DataFrame, reference_data: pd.DataFrame) -> str: + renderer = WhylogsSummaryDriftRenderer() + report = renderer.to_html(target_data=new_data, reference_data=reference_data) + flytekit.Deck("summary drift", report) + return f"reported summary drift for target dataset with n={len(new_data)}" + + +# %% [markdown] +# Finally, we can then create a Flyte workflow that will +# chain together our example data pipeline +# +# %% +@workflow +def wf() -> str: + # 1. Read data + target_df = get_target_data() + + # 2. Profile data and validate it + profile_view = create_profile_view(df=target_df) + validated = constraints_report(profile_view=profile_view) + + # 3. Conditional actions if data is valid or not + return ( + conditional("stop_if_fails") + .if_(validated.is_false()) + .then( + summary_drift_report( + new_data=target_df, + reference_data=get_reference_data(), + ) + ) + .else_() + .then(make_predictions(input_data=target_df, output_path="./data")) + ) + + +# %% +if __name__ == "__main__": + wf() diff --git a/docs/feature_engineering.md b/docs/feature_engineering.md index b34a73891..b378c3de3 100644 --- a/docs/feature_engineering.md +++ b/docs/feature_engineering.md @@ -10,9 +10,9 @@ Explore how features can be engineered with the power of Flyte. :header-rows: 0 :widths: 20 30 -* - {doc}`EDA and Feature Engineering With Papermill ` +* - {doc}`EDA and Feature Engineering With Papermill ` - How to use Jupyter notebook within Flyte -* - {doc}`Data Cleaning and Feature Serving With Feast ` +* - {doc}`Data Cleaning and Feature Serving With Feast ` - How to use Feast to serve data in Flyte ``` @@ -22,6 +22,6 @@ Explore how features can be engineered with the power of Flyte. :caption: Contents :hidden: -auto_examples/exploratory_data_analysis/index -auto_examples/feast_integration/index +examples/exploratory_data_analysis/README +examples/feast_integration/README ``` diff --git a/docs/index.md b/docs/index.md index 0ebdc325b..58dea290b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -404,14 +404,14 @@ getting_started/core_use_cases 📖 User Guide 🌳 Environment Setup -🔤 Basics -⌨️ Data Types and IO -🔮 Advanced Composition -🧩 Customizing Dependencies -🏡 Development Lifecycle -⚗️ Testing -🚢 Productionizing -🏗 Extending +🔤 Basics +⌨️ Data Types and IO +🔮 Advanced Composition +🧩 Customizing Dependencies +🏡 Development Lifecycle +⚗️ Testing +🚢 Productionizing +🏗 Extending 📝 Contributing ``` @@ -433,34 +433,34 @@ flyte_lab :hidden: Integrations -auto_examples/airflow_plugin/index -auto_examples/athena_plugin/index -auto_examples/aws_batch_plugin/index -auto_examples/sagemaker_pytorch_plugin/index -auto_examples/sagemaker_training_plugin/index -auto_examples/bigquery_plugin/index -auto_examples/k8s_dask_plugin/index -auto_examples/databricks_plugin/index -auto_examples/dbt_plugin/index -auto_examples/dolt_plugin/index -auto_examples/duckdb_plugin/index -auto_examples/greatexpectations_plugin/index -auto_examples/hive_plugin/index -auto_examples/k8s_pod_plugin/index -auto_examples/mlflow_plugin/index -auto_examples/mmcloud_plugin/index -auto_examples/modin_plugin/index -auto_examples/kfmpi_plugin/index -auto_examples/onnx_plugin/index -auto_examples/papermill_plugin/index -auto_examples/pandera_plugin/index -auto_examples/kfpytorch_plugin/index -auto_examples/ray_plugin/index -auto_examples/snowflake_plugin/index -auto_examples/k8s_spark_plugin/index -auto_examples/sql_plugin/index -auto_examples/kftensorflow_plugin/index -auto_examples/whylogs_plugin/index +examples/airflow_plugin/README +examples/athena_plugin/README +examples/aws_batch_plugin/README +examples/sagemaker_pytorch_plugin/README +examples/sagemaker_training_plugin/README +examples/bigquery_plugin/README +examples/k8s_dask_plugin/README +examples/databricks_plugin/README +examples/dbt_plugin/README +examples/dolt_plugin/README +examples/duckdb_plugin/README +examples/greatexpectations_plugin/README +examples/hive_plugin/README +examples/k8s_pod_plugin/README +examples/mlflow_plugin/README +examples/mmcloud_plugin/README +examples/modin_plugin/README +examples/kfmpi_plugin/README +examples/onnx_plugin/README +examples/papermill_plugin/README +examples/pandera_plugin/README +examples/kfpytorch_plugin/README +examples/ray_plugin/README +examples/snowflake_plugin/README +examples/k8s_spark_plugin/README +examples/sql_plugin/README +examples/kftensorflow_plugin/README +examples/whylogs_plugin/README ``` ```{toctree} diff --git a/docs/integrations.md b/docs/integrations.md index 83bb5e878..4a53babbf 100644 --- a/docs/integrations.md +++ b/docs/integrations.md @@ -18,27 +18,27 @@ Flytekit functionality. These plugins can be anything and for comparison can be :header-rows: 0 :widths: 20 30 -* - {doc}`SQL ` +* - {doc}`SQL ` - Execute SQL queries as tasks. -* - {doc}`Great Expectations ` +* - {doc}`Great Expectations ` - Validate data with `great_expectations`. -* - {doc}`Papermill ` +* - {doc}`Papermill ` - Execute Jupyter Notebooks with `papermill`. -* - {doc}`Pandera ` +* - {doc}`Pandera ` - Validate pandas dataframes with `pandera`. -* - {doc}`Modin ` +* - {doc}`Modin ` - Scale pandas workflows with `modin`. -* - {doc}`Dolt ` +* - {doc}`Dolt ` - Version your SQL database with `dolt`. -* - {doc}`DBT ` +* - {doc}`DBT ` - Run and test your `dbt` pipelines in Flyte. -* - {doc}`WhyLogs ` +* - {doc}`WhyLogs ` - `whylogs`: the open standard for data logging. -* - {doc}`MLFlow ` +* - {doc}`MLFlow ` - `mlflow`: the open standard for model tracking. -* - {doc}`ONNX ` +* - {doc}`ONNX ` - Convert ML models to ONNX models seamlessly. -* - {doc}`DuckDB ` +* - {doc}`DuckDB ` - Run analytical queries using DuckDB. ``` @@ -64,19 +64,19 @@ orchestrated by Flyte itself, within its provisioned Kubernetes clusters. :header-rows: 0 :widths: 20 30 -* - {doc}`K8s Pods ` +* - {doc}`K8s Pods ` - Execute K8s pods for arbitrary workloads. -* - {doc}`K8s Cluster Dask Jobs ` +* - {doc}`K8s Cluster Dask Jobs ` - Run Dask jobs on a K8s Cluster. -* - {doc}`K8s Cluster Spark Jobs ` +* - {doc}`K8s Cluster Spark Jobs ` - Run Spark jobs on a K8s Cluster. -* - {doc}`Kubeflow PyTorch ` +* - {doc}`Kubeflow PyTorch ` - Run distributed PyTorch training jobs using `Kubeflow`. -* - {doc}`Kubeflow TensorFlow ` +* - {doc}`Kubeflow TensorFlow ` - Run distributed TensorFlow training jobs using `Kubeflow`. -* - {doc}`MPI Operator ` +* - {doc}`MPI Operator ` - Run distributed deep learning training jobs using Horovod and MPI. -* - {doc}`Ray Task ` +* - {doc}`Ray Task ` - Run Ray jobs on a K8s Cluster. ``` @@ -94,23 +94,23 @@ the Flyte task that use the respective plugin. :header-rows: 0 :widths: 20 30 -* - {doc}`AWS Sagemaker: Model Training ` +* - {doc}`AWS Sagemaker: Model Training ` - Train models with built-in or define your own custom algorithms. -* - {doc}`AWS Sagemaker: Pytorch Training ` +* - {doc}`AWS Sagemaker: Pytorch Training ` - Train Pytorch models using Sagemaker, with support for distributed training. -* - {doc}`AWS Athena ` +* - {doc}`AWS Athena ` - Execute queries using AWS Athena -* - {doc}`AWS Batch ` +* - {doc}`AWS Batch ` - Running tasks and workflows on AWS batch service -* - {doc}`Hive ` +* - {doc}`Hive ` - Run Hive jobs in your workflows. -* - {doc}`MMCloud ` +* - {doc}`MMCloud ` - Execute tasks using MemVerge Memory Machine Cloud -* - {doc}`Snowflake ` +* - {doc}`Snowflake ` - Run Snowflake jobs in your workflows. -* - {doc}`Databricks ` +* - {doc}`Databricks ` - Run Databricks jobs in your workflows. -* - {doc}`BigQuery ` +* - {doc}`BigQuery ` - Run BigQuery jobs in your workflows. ``` @@ -145,7 +145,7 @@ Because Flyte uses executable docker containers as the smallest unit of compute, :header-rows: 0 :widths: 20 30 -* - {doc}`Raw Container Tasks ` +* - {doc}`Raw Container Tasks ` - Execute arbitrary containers: You can write C++ code, bash scripts and any containerized program. ``` @@ -172,6 +172,6 @@ constructs natively within other orchestration tools. :header-rows: 0 :widths: 20 30 -* - {doc}`Airflow ` +* - {doc}`Airflow ` - Trigger Flyte executions from Airflow. ``` diff --git a/docs/ml_training.md b/docs/ml_training.md index cd909b2ec..94dafadc7 100644 --- a/docs/ml_training.md +++ b/docs/ml_training.md @@ -6,15 +6,15 @@ Understand how machine learning models can be trained from within Flyte, with an :header-rows: 0 :widths: 20 30 -* - {doc}`Diabetes Classification ` +* - {doc}`Diabetes Classification ` - Train an XGBoost model on the Pima Indians Diabetes Dataset. -* - {doc}`House Price Regression ` +* - {doc}`House Price Regression ` - Use dynamic workflows to train a multiregion house price prediction model using XGBoost. -* - {doc}`MNIST Classification ` +* - {doc}`MNIST Classification ` - Train a neural network on MNIST with PyTorch and W&B -* - {doc}`NLP Processing with Gensim ` +* - {doc}`NLP Processing with Gensim ` - Word embedding and topic modelling on lee background corpus with Gensim -* - {doc}`Forecast Sales Using Rossmann Store Sales ` +* - {doc}`Forecast Sales Using Rossmann Store Sales ` - Forecast sales data with data-parallel distributed training using Horovod on Spark. ``` @@ -23,9 +23,9 @@ Understand how machine learning models can be trained from within Flyte, with an :caption: Contents :hidden: -auto_examples/pima_diabetes/index -auto_examples/house_price_prediction/index -auto_examples/mnist_classifier/index -auto_examples/nlp_processing/index -auto_examples/forecasting_sales/index +examples/pima_diabetes/README +examples/house_price_prediction/README +examples/mnist_classifier/README +examples/nlp_processing/README +examples/forecasting_sales/README ``` diff --git a/docs/tutorials.md b/docs/tutorials.md index 559a71554..00e5be8a4 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -28,15 +28,15 @@ Train machine learning models from using your framework of choice. :header-rows: 0 :widths: 20 30 -* - {doc}`Diabetes Classification ` +* - {doc}`Diabetes Classification ` - Train an XGBoost model on the Pima Indians Diabetes Dataset. -* - {doc}`House Price Regression ` +* - {doc}`House Price Regression ` - Use dynamic workflows to train a multiregion house price prediction model using XGBoost. -* - {doc}`MNIST Classification ` +* - {doc}`MNIST Classification ` - Train a neural network on MNIST with PyTorch and W&B -* - {doc}`NLP Processing with Gensim ` +* - {doc}`NLP Processing with Gensim ` - Word embedding and topic modelling on lee background corpus with Gensim -* - {doc}`Sales Forecasting ` +* - {doc}`Sales Forecasting ` - Use the Rossmann Store data to forecast sales with distributed training using Horovod on Spark. ``` @@ -48,9 +48,9 @@ Engineer the data features to improve your model accuracy. :header-rows: 0 :widths: 20 30 -* - {doc}`EDA and Feature Engineering With Papermill ` +* - {doc}`EDA and Feature Engineering With Papermill ` - How to use Jupyter notebook within Flyte -* - {doc}`Data Cleaning and Feature Serving With Feast ` +* - {doc}`Data Cleaning and Feature Serving With Feast ` - How to use Feast to serve data in Flyte ``` @@ -62,7 +62,7 @@ Perform computational biology with Flyte. :header-rows: 0 :widths: 20 30 -* - {doc}`Nucleotide Sequence Querying with BLASTX ` +* - {doc}`Nucleotide Sequence Querying with BLASTX ` - Use BLASTX to Query a Nucleotide Sequence Against a Local Protein Database ``` diff --git a/docs/userguide.md b/docs/userguide.md index 844c31432..8d27e5c71 100644 --- a/docs/userguide.md +++ b/docs/userguide.md @@ -37,20 +37,20 @@ Want to contribute or update an example? Check out the {doc}`Contribution Guide * - {doc}`🌳 Environment Setup ` - Set up a development environment to run the examples in the user guide. -* - {doc}`🔤 Basics ` +* - {doc}`🔤 Basics ` - Learn about tasks, workflows, launch plans, caching and managing files and directories. -* - {doc}`⌨️ Data Types and IO ` +* - {doc}`⌨️ Data Types and IO ` - Improve pipeline robustness with Flyte's portable and extensible type system. -* - {doc}`🔮 Advanced Composition ` +* - {doc}`🔮 Advanced Composition ` - Implement conditionals, nested and dynamic workflows, map tasks and even recursion! -* - {doc}`🧩 Customizing Dependencies ` +* - {doc}`🧩 Customizing Dependencies ` - Provide custom dependencies to run your Flyte entities. -* - {doc}`🏡 Development Lifecycle ` +* - {doc}`🏡 Development Lifecycle ` - Develop and test locally on the demo cluster. -* - {doc}`⚗️ Testing ` +* - {doc}`⚗️ Testing ` - Test tasks and workflows with Flyte's testing utilities. -* - {doc}`🚢 Productionizing ` +* - {doc}`🚢 Productionizing ` - Ship and configure your machine learning pipelines on a production Flyte installation. -* - {doc}`🏗 Extending ` +* - {doc}`🏗 Extending ` - Define custom plugins that aren't currently supported in the Flyte ecosystem. ```