From 230dc29a53acde1f229359dd79cfddb567dad5a8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 12:29:49 -0400 Subject: [PATCH 001/121] refactor: update docstrings and file ordering --- src/autora/experimentalist/pooler/grid.py | 228 +++++++++++++++++++++- 1 file changed, 223 insertions(+), 5 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index dadc2a4a..b9e5a868 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,19 +1,237 @@ +"""""" from itertools import product -from typing import List +from typing import Sequence -from autora.variable import IV +import pandas as pd +from autora.state.delta import Result, wrap_to_use_state +from autora.variable import Variable, VariableCollection -def grid_pool(ivs: List[IV]): - """Creates exhaustive pool from discrete values using a Cartesian product of sets""" + +def grid_pool(ivs: Sequence[Variable]) -> product: + """ + Low level function to create an exhaustive pool from discrete values + using a Cartesian product of sets. + """ # Get allowed values for each IV l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " + f"grid_pool requires allowed_values to be set, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) # Return Cartesian product of all IV values return product(*l_iv_values) + + +def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: + """ + + Args: + variables: the description of all the variables in the AER experiment. + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed values, we get exactly those values + back when running the executor: + >>> grid_pool_from_variables(variables=VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] + ... )) + {'conditions': x + 0 1 + 1 2 + 2 3} + + The allowed_values must be specified: + >>> grid_pool_from_variables( + ... variables=VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + With two independent variables, we get the cartesian product: + >>> grid_pool_from_variables(variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ]))["conditions"] + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> grid_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + + We can specify arrays of allowed values: + >>> grid_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]))["conditions"] + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + """ + raw_conditions = grid_pool(variables.independent_variables) + iv_names = [v.name for v in variables.independent_variables] + conditions = pd.DataFrame(raw_conditions, columns=iv_names) + return Result(conditions=conditions) + + +grid_pool_executor = wrap_to_use_state(grid_pool_from_variables) +grid_pool_executor.__doc__ = """ + +Args: + state: a [autora.state.delta.State][] with a `variables` field + kwargs: ignored + +Returns: the [autora.state.delta.State][] with an updated `conditions` field. + +Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + ... we get exactly those values back when running the executor: + >>> grid_pool_executor(s).conditions + x + 0 1 + 1 2 + 2 3 + + The allowed_values must be specified: + >>> grid_pool_executor( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + With two independent variables, we get the cartesian product: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + >>> grid_pool_executor(t).conditions + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> grid_pool_executor(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> grid_pool_executor(u).conditions + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + If you require a different type than the pd.DataFrame, then you can instruct the State object + to convert it (if you have a constructor for the desired type which is compatible with the + DataFrame): + + We define a state object with the fields we need: + >>> from typing import Optional + >>> @dataclass(frozen=True) + ... class T(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: Optional[np.array] = field(default=None, + ... metadata={"delta": "replace", "converter": np.asarray}) + + >>> t = T( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + The returned DataFrame is converted into the array format: + >>> grid_pool_executor(t).conditions + array([[1], + [2], + [3]]) + + This also works for multiple variables: + >>> t = T( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + + >>> grid_pool_executor(t).conditions + array([[1, 3], + [1, 4], + [2, 3], + [2, 4]]) +""" From ba578261f2ce2a74db5e7864ea9c8962d85b7a29 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:17:58 -0400 Subject: [PATCH 002/121] refactor: reorder random_pooler file --- .../experimentalist/pooler/random_pooler.py | 172 +++++++++++++++++- 1 file changed, 170 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 78ad104e..3c31ab40 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,10 +1,12 @@ import random -from typing import Iterable, List, Tuple +from typing import Iterable, List, Tuple, Type import numpy as np +import pandas as pd +from autora.state.delta import Result, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -from autora.variable import IV +from autora.variable import IV, ValueType, VariableCollection def random_pool( @@ -50,3 +52,169 @@ def random_pool( random_pooler = deprecated_alias(random_pool, "random_pooler") + + +@wrap_to_use_state +def random_pool_from_variables( + variables: VariableCollection, + num_samples=5, + random_state=None, + fmt: Type = pd.DataFrame, + duplicates: bool = True, +): + """ + + Args: + variables: + fmt: the output type required + + Returns: + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed_values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=range(10)) + ... ])) + + ... we get some of those values back when running the experimentalist: + >>> random_pool_from_variables(s, random_state=1).conditions + x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0 + + With one independent variable "x", and a value_range: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ])) + + ... we get a sample of the range back when running the experimentalist: + >>> random_pool_from_variables(t, random_state=1).conditions + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> random_pool_from_variables( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ])) + >>> random_pool_from_variables(t, + ... num_samples=10, duplicates=True, random_state=1).conditions + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> random_pool_from_variables(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> random_pool_from_variables(u, random_state=1).conditions + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + The output can be in several formats. The default is pd.DataFrame. + Alternative: `np.recarray`: + >>> random_pool_from_variables(s, fmt=np.recarray, random_state=1).conditions + rec.array([(4,), (5,), (7,), (9,), (0,)], + dtype=[('x', '>> random_pool_from_variables(t, fmt=np.recarray, random_state=1).conditions + rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], + dtype=[('x1', '>> random_pool_from_variables(t, fmt=np.array, random_state=1).conditions + array([[ 2, 72], + [ 3, 411], + [ 4, 474], + [ 4, 125], + [ 1, 156]]) + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=duplicates + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + iv_names = [v.name for v in variables.independent_variables] + if fmt is pd.DataFrame: + conditions = pd.DataFrame(raw_conditions) + elif fmt is np.recarray: + conditions = np.core.records.fromarrays( + [raw_conditions[n] for n in iv_names], names=iv_names + ) # type: ignore + elif fmt is np.array: + conditions = np.column_stack([raw_conditions[n] for n in iv_names]) + else: + raise NotImplementedError("fmt=%s is not supported" % (fmt)) + + return Result(conditions=conditions) From af49e59d433459af33d2893ae7e0c65a40d18c53 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:27:56 -0400 Subject: [PATCH 003/121] refactor: reorganize random_pool to use pd.DataFrame as default and have two separate functions --- .../experimentalist/pooler/random_pooler.py | 187 ++++++++++++------ 1 file changed, 127 insertions(+), 60 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 3c31ab40..1a9b1217 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,5 +1,5 @@ import random -from typing import Iterable, List, Tuple, Type +from typing import Iterable, List, Tuple import numpy as np import pandas as pd @@ -54,16 +54,132 @@ def random_pool( random_pooler = deprecated_alias(random_pool, "random_pooler") -@wrap_to_use_state def random_pool_from_variables( variables: VariableCollection, num_samples=5, random_state=None, - fmt: Type = pd.DataFrame, duplicates: bool = True, -): +) -> pd.DataFrame: """ + Args: + variables: the description of all the variables in the AER experiment. + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + duplicates: if True, allow repeated values + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed_values we get some of those values + back when running the experimentalist: + >>> random_pool_from_variables( + ... variables=VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=range(10)) + ... ]), random_state=1) + {'conditions': x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0} + + + ... we get a sample of the range back when running the experimentalist: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ]), random_state=1)["conditions"] + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ]), num_samples=10, duplicates=True, random_state=1)["conditions"] + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + + >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]), random_state=1)["conditions"] + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=duplicates + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + conditions = pd.DataFrame(raw_conditions) + return Result(conditions=conditions) + + +random_pool_executor = wrap_to_use_state(random_pool_from_variables) +random_pool_executor.__doc__ = """ + Args: variables: fmt: the output type required @@ -91,7 +207,7 @@ def random_pool_from_variables( ... ])) ... we get some of those values back when running the experimentalist: - >>> random_pool_from_variables(s, random_state=1).conditions + >>> random_pool_executor(s, random_state=1).conditions x 0 4 1 5 @@ -106,7 +222,7 @@ def random_pool_from_variables( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> random_pool_from_variables(t, random_state=1).conditions + >>> random_pool_executor(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -117,7 +233,7 @@ def random_pool_from_variables( The allowed_values or value_range must be specified: - >>> random_pool_from_variables( + >>> random_pool_executor( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -129,7 +245,7 @@ def random_pool_from_variables( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_pool_from_variables(t, + >>> random_pool_executor(t, ... num_samples=10, duplicates=True, random_state=1).conditions x1 x2 0 2 434 @@ -144,7 +260,7 @@ def random_pool_from_variables( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool_from_variables(S( + >>> random_pool_executor(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -161,60 +277,11 @@ def random_pool_from_variables( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> random_pool_from_variables(u, random_state=1).conditions + >>> random_pool_executor(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 2 5.2 4 23.0 3 9.0 3 29.0 4 -9.4 3 22.0 - - The output can be in several formats. The default is pd.DataFrame. - Alternative: `np.recarray`: - >>> random_pool_from_variables(s, fmt=np.recarray, random_state=1).conditions - rec.array([(4,), (5,), (7,), (9,), (0,)], - dtype=[('x', '>> random_pool_from_variables(t, fmt=np.recarray, random_state=1).conditions - rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], - dtype=[('x1', '>> random_pool_from_variables(t, fmt=np.array, random_state=1).conditions - array([[ 2, 72], - [ 3, 411], - [ 4, 474], - [ 4, 125], - [ 1, 156]]) - - """ - rng = np.random.default_rng(random_state) - - raw_conditions = {} - for iv in variables.independent_variables: - if iv.allowed_values is not None: - raw_conditions[iv.name] = rng.choice( - iv.allowed_values, size=num_samples, replace=duplicates - ) - elif (iv.value_range is not None) and (iv.type == ValueType.REAL): - raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) - - else: - raise ValueError( - "allowed_values or [value_range and type==REAL] needs to be set for " - "%s" % (iv) - ) - - iv_names = [v.name for v in variables.independent_variables] - if fmt is pd.DataFrame: - conditions = pd.DataFrame(raw_conditions) - elif fmt is np.recarray: - conditions = np.core.records.fromarrays( - [raw_conditions[n] for n in iv_names], names=iv_names - ) # type: ignore - elif fmt is np.array: - conditions = np.column_stack([raw_conditions[n] for n in iv_names]) - else: - raise NotImplementedError("fmt=%s is not supported" % (fmt)) - - return Result(conditions=conditions) +""" From c206cc29dea7828331070f7058777e2336ed2712 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:52:01 -0400 Subject: [PATCH 004/121] refactor: remake random sampler to use a result object to be used in a pipeline --- .../experimentalist/sampler/random_sampler.py | 105 ++++++++++++++++-- 1 file changed, 97 insertions(+), 8 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 7e28d2c3..c77f5c2a 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,20 +1,34 @@ import random -from typing import Iterable, Sequence, Union +from typing import Iterable, Optional +import pandas as pd + +from autora.state.delta import Result, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): +def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): """ - Uniform random sampling without replacement from a pool of conditions. - Args: - conditions: Pool of conditions - n: number of samples to collect - Returns: Sampled pool - """ + Examples: + From a range: + >>> random.seed(1) + >>> random_sample(range(100), num_samples=5) + [53, 37, 65, 51, 4] + >>> random.seed(1) + >>> random_sample([1,2,3,4,5,6,7,8,9,10], num_samples=5) + [7, 9, 10, 8, 6] + + >>> random.seed(1) + >>> random_sample(filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), + ... num_samples=5) + [375, 390, 600, 285, 885] + + """ + if random_state is not None: + random.seed(random_state) if isinstance(conditions, Iterable): conditions = list(conditions) random.shuffle(conditions) @@ -24,3 +38,78 @@ def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): random_sampler = deprecated_alias(random_sample, "random_sampler") + + +def random_sample_from_conditions( + conditions, + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> Result: + """ + Take a random sample from some conditions. + + Args: + conditions: the conditions to sample from + num_samples: + random_state: + replace: + + Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> random.seed(1) + >>> random_sample_from_conditions( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + {'conditions': x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196} + + """ + return Result( + conditions=pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=replace + ) + ) + + +random_sample_executor = wrap_to_use_state(random_sample_from_conditions) +random_sample_executor.__doc__ = """ +Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + >>> from autora.experimentalist.pooler.grid import grid_pool_executor + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=range(100)) + ... ])) + + ... we can update the state with a sample from the allowed values: + >>> s_ = grid_pool_executor(s) + >>> random_sample_executor(s_, num_samples=5, random_state=1 + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + S(variables=..., conditions= x + 80 80 + 84 84 + 33 33 + 81 81 + 93 93) + +""" From b735ce43aec1bdf24e4f1a1ec2b6c07ef4bb8859 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:05:08 -0400 Subject: [PATCH 005/121] test: update doctests to support windows --- src/autora/experimentalist/pooler/grid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index b9e5a868..d9b8a37d 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -220,7 +220,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: >>> grid_pool_executor(t).conditions array([[1], [2], - [3]]) + [3]]...) This also works for multiple variables: >>> t = T( @@ -233,5 +233,5 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: array([[1, 3], [1, 4], [2, 3], - [2, 4]]) + [2, 4]]...) """ From 96533ed2bdeafb408647d8905984d9749f08a63c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:29:10 -0400 Subject: [PATCH 006/121] revert: changes to grid_pool function --- src/autora/experimentalist/pooler/grid.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index d9b8a37d..46c204ad 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -9,15 +9,12 @@ def grid_pool(ivs: Sequence[Variable]) -> product: - """ - Low level function to create an exhaustive pool from discrete values - using a Cartesian product of sets. - """ + """Creates exhaustive pool from discrete values using a Cartesian product of sets""" # Get allowed values for each IV l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"grid_pool requires allowed_values to be set, " + f"gridsearch_pool only supports independent variables with discrete allowed values, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) From e1b2c547c1b7519d2cee0dcc62ea57103f1aa2f0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:29:25 -0400 Subject: [PATCH 007/121] docs: update docstrings and tests to work --- src/autora/experimentalist/pooler/grid.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index 46c204ad..20e49d68 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -24,10 +24,11 @@ def grid_pool(ivs: Sequence[Variable]) -> product: def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: - """ + """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: - variables: the description of all the variables in the AER experiment. + variables: a VariableCollection with `independent_variables` – a sequence of Variable + objects, each of which has an attribute `allowed_values` containing a sequence of values. Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field @@ -53,7 +54,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... variables=VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: >>> grid_pool_from_variables(variables=VariableCollection(independent_variables=[ @@ -74,7 +75,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... ])) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... We can specify arrays of allowed values: @@ -147,7 +148,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: >>> t = S( @@ -170,7 +171,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... ]))) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... We can specify arrays of allowed values: From 8b91e48719b82d27055714b7e5dbaf4cf08d93a0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:34:16 -0400 Subject: [PATCH 008/121] revert: changes to random_sample function --- src/autora/experimentalist/sampler/random_sampler.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index c77f5c2a..7f8a3a3f 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,5 +1,5 @@ import random -from typing import Iterable, Optional +from typing import Iterable, Optional, Sequence, Union import pandas as pd @@ -7,9 +7,14 @@ from autora.utils.deprecation import deprecated_alias -def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): +def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): """ + Uniform random sampling without replacement from a pool of conditions. + Args: + conditions: Pool of conditions + num_samples: number of samples to collect + Returns: Sampled pool Examples: From a range: @@ -27,8 +32,6 @@ def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] [375, 390, 600, 285, 885] """ - if random_state is not None: - random.seed(random_state) if isinstance(conditions, Iterable): conditions = list(conditions) random.shuffle(conditions) From 3d08b873a3b81f76545a5beba0648fbb1fa9fd90 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:43:21 -0400 Subject: [PATCH 009/121] docs: add explanation on wrapper. --- src/autora/state/wrapper.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 13bf5528..74ecbade 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -1,6 +1,8 @@ -"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)`. +"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)` so that $n$ processes $f_i$ on states $S$ can be represented as $$f_n(...(f_1(f_0(S))))$$ + +These are special cases of the [autora.state.delta.wrap_to_use_state][] function. """ from __future__ import annotations From 00d15d8431f255a1ac6d760c9087941932457b04 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 10:21:22 -0400 Subject: [PATCH 010/121] rename: update executors to use a new naming convention --- ...Workflows using Functions and States.ipynb | 12 +- docs/experimentalists/pooler/grid/index.md | 5 +- .../pooler/grid/quickstart.md | 2 +- docs/experimentalists/pooler/random/index.md | 5 +- .../pooler/random/quickstart.md | 2 +- docs/experimentalists/sampler/random/index.md | 5 +- .../sampler/random/quickstart.md | 2 +- src/autora/experimentalist/pooler/grid.py | 131 +----------------- .../experimentalist/pooler/random_pooler.py | 113 +-------------- .../experimentalist/sampler/random_sampler.py | 54 ++------ tests/test_experimentalist_random.py | 20 +-- 11 files changed, 49 insertions(+), 302 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 6af80629..e57f9232 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -1279,7 +1279,7 @@ "\n", "A more complicated experimentalist can be constructed using a pooling function and sampler(s), which are chained\n", "together.\n", - "In this example, the `grid_pool_executor` requires explicit specification of the allowed states, so we add those to\n", + "In this example, the `grid_pool` requires explicit specification of the allowed states, so we add those to\n", "the `variables` attribute:" ] }, @@ -1335,7 +1335,7 @@ ], "source": [ "from autora.experimentalist.sampler.random_sampler import random_sample_executor\n", - "from autora.experimentalist.pooler.grid import grid_pool_executor\n", + "from autora.experimentalist.pooler.grid import grid_pool\n", "\n", "variables = VariableCollection(\n", " independent_variables=[Variable(name=\"x\",\n", @@ -1348,7 +1348,7 @@ "\n", "# The experimentalist is built of two functions acting in sequence.\n", "# The first makes a full list of all allowable conditions:\n", - "r = grid_pool_executor(r)\n", + "r = grid_pool(r)\n", "print(f\"After pooler: {r.conditions=}\")\n", "\n", "# The second samples ten of those allowable conditions.\n", @@ -1388,7 +1388,7 @@ "source": [ "r = Snapshot(variables=variables)\n", "\n", - "r = random_sample_executor(grid_pool_executor(r), num_samples=50, random_state=1) # experimentalist\n", + "r = random_sample_executor(grid_pool(r), num_samples=50, random_state=1) # experimentalist\n", "r = experiment_runner(r)\n", "r = theorist(r)\n", "\n", @@ -1420,7 +1420,7 @@ ], "source": [ "def experimentalist(state):\n", - " return random_sample_executor(grid_pool_executor(state), num_samples=50, random_state=1)\n", + " return random_sample_executor(grid_pool(state), num_samples=50, random_state=1)\n", "\n", "r = Snapshot(variables=variables)\n", "\n", @@ -1459,7 +1459,7 @@ "from autora.experimentalist.pipeline import Pipeline as ExperimentalistPipeline\n", "\n", "experimentalist = ExperimentalistPipeline(\n", - " [(\"pool\", grid_pool_executor),\n", + " [(\"pool\", grid_pool),\n", " (\"sample\", random_sample_executor)],\n", " params={\"sample\": {\"num_samples\": 50, \"random_state\": 1}}\n", ")\n", diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/pooler/grid/index.md index 97b314aa..ddd78f87 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/pooler/grid/index.md @@ -22,12 +22,13 @@ This means that there are various combinations that these variables can form, th ### Example Code + ```python -from autora.experimentalist.pooler.grid import grid_pool +from autora.experimentalist.pooler.grid import grid_pool_from_ivs from autora.variable import Variable iv_1 = Variable(allowed_values=[1, 2, 3]) iv_2 = Variable(allowed_values=[4, 5, 6]) -pool = grid_pool([iv_1, iv_2]) +pool = grid_pool_from_ivs([iv_1, iv_2]) ``` diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/pooler/grid/quickstart.md index 740bb904..646b9e60 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/pooler/grid/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the grid pooler via: ```python -from autora.experimentalist.pooler.grid import grid_pool +from autora.experimentalist.pooler.grid import grid_pool_from_ivs ``` diff --git a/docs/experimentalists/pooler/random/index.md b/docs/experimentalists/pooler/random/index.md index 59fe7450..9abd84be 100644 --- a/docs/experimentalists/pooler/random/index.md +++ b/docs/experimentalists/pooler/random/index.md @@ -22,8 +22,9 @@ This means that there are 9 possible combinations for these variables (3x3), fro | 3 | (3,4) | (3,5) | X | ### Example Code + ```python -from autora.experimentalist.pooler.random_pooler import random_pool +from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs -pool = random_pool([1, 2, 3],[4, 5, 6], n=3) +pool = random_pool_from_ivs([1, 2, 3], [4, 5, 6], n=3) ``` diff --git a/docs/experimentalists/pooler/random/quickstart.md b/docs/experimentalists/pooler/random/quickstart.md index 4219f89b..c98a1225 100644 --- a/docs/experimentalists/pooler/random/quickstart.md +++ b/docs/experimentalists/pooler/random/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the random pooler via: ```python -from autora.experimentalist.pooler.random_pooler import random_pool +from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs ``` diff --git a/docs/experimentalists/sampler/random/index.md b/docs/experimentalists/sampler/random/index.md index e20be0d5..c50c93d4 100644 --- a/docs/experimentalists/sampler/random/index.md +++ b/docs/experimentalists/sampler/random/index.md @@ -3,8 +3,9 @@ Uniform random sampling without replacement from a pool of conditions. ### Example Code + ```python -from autora.experimentalist.sampler.random_sampler import random_sample +from autora.experimentalist.sampler.random_sampler import random_sample_from_conditions_iterable -pool = random_sample([1, 1, 2, 2, 3, 3], n=2) +pool = random_sample_from_conditions_iterable([1, 1, 2, 2, 3, 3], n=2) ``` diff --git a/docs/experimentalists/sampler/random/quickstart.md b/docs/experimentalists/sampler/random/quickstart.md index a9337826..97653206 100644 --- a/docs/experimentalists/sampler/random/quickstart.md +++ b/docs/experimentalists/sampler/random/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the random sampler via: ```python -from autora.experimentalist.sampler.random_sampler import random_sample +from autora.experimentalist.sampler.random_sampler import random_sample_from_conditions_iterable ``` diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index 20e49d68..eacd3e78 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -8,7 +8,7 @@ from autora.variable import Variable, VariableCollection -def grid_pool(ivs: Sequence[Variable]) -> product: +def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: """Creates exhaustive pool from discrete values using a Cartesian product of sets""" # Get allowed values for each IV l_iv_values = [] @@ -101,135 +101,10 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: [2222 rows x 3 columns] """ - raw_conditions = grid_pool(variables.independent_variables) + raw_conditions = grid_pool_from_ivs(variables.independent_variables) iv_names = [v.name for v in variables.independent_variables] conditions = pd.DataFrame(raw_conditions, columns=iv_names) return Result(conditions=conditions) -grid_pool_executor = wrap_to_use_state(grid_pool_from_variables) -grid_pool_executor.__doc__ = """ - -Args: - state: a [autora.state.delta.State][] with a `variables` field - kwargs: ignored - -Returns: the [autora.state.delta.State][] with an updated `conditions` field. - -Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=[1, 2, 3]) - ... ])) - - ... we get exactly those values back when running the executor: - >>> grid_pool_executor(s).conditions - x - 0 1 - 1 2 - 2 3 - - The allowed_values must be specified: - >>> grid_pool_executor( - ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) - Traceback (most recent call last): - ... - AssertionError: gridsearch_pool only supports independent variables with discrete... - - With two independent variables, we get the cartesian product: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4]), - ... ])) - >>> grid_pool_executor(t).conditions - x1 x2 - 0 1 3 - 1 1 4 - 2 2 3 - 3 2 4 - - If any of the variables have unspecified allowed_values, we get an error: - >>> grid_pool_executor(S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ]))) - Traceback (most recent call last): - ... - AssertionError: gridsearch_pool only supports independent variables with discrete... - - - We can specify arrays of allowed values: - >>> u = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ])) - >>> grid_pool_executor(u).conditions - x y z - 0 -10.0 3 20.0 - 1 -10.0 3 21.0 - 2 -10.0 3 22.0 - 3 -10.0 3 23.0 - 4 -10.0 3 24.0 - ... ... .. ... - 2217 10.0 4 26.0 - 2218 10.0 4 27.0 - 2219 10.0 4 28.0 - 2220 10.0 4 29.0 - 2221 10.0 4 30.0 - - [2222 rows x 3 columns] - - If you require a different type than the pd.DataFrame, then you can instruct the State object - to convert it (if you have a constructor for the desired type which is compatible with the - DataFrame): - - We define a state object with the fields we need: - >>> from typing import Optional - >>> @dataclass(frozen=True) - ... class T(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: Optional[np.array] = field(default=None, - ... metadata={"delta": "replace", "converter": np.asarray}) - - >>> t = T( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=[1, 2, 3]) - ... ])) - - The returned DataFrame is converted into the array format: - >>> grid_pool_executor(t).conditions - array([[1], - [2], - [3]]...) - - This also works for multiple variables: - >>> t = T( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4]), - ... ])) - - >>> grid_pool_executor(t).conditions - array([[1, 3], - [1, 4], - [2, 3], - [2, 4]]...) -""" +grid_pool = wrap_to_use_state(grid_pool_from_variables) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 1a9b1217..fb077fb3 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -9,7 +9,7 @@ from autora.variable import IV, ValueType, VariableCollection -def random_pool( +def random_pool_from_ivs( ivs: List[IV], num_samples: int = 1, duplicates: bool = True ) -> Iterable: """ @@ -51,7 +51,7 @@ def random_pool( return iter(l_samples) -random_pooler = deprecated_alias(random_pool, "random_pooler") +random_pooler = deprecated_alias(random_pool_from_ivs, "random_pooler") def random_pool_from_variables( @@ -177,111 +177,4 @@ def random_pool_from_variables( return Result(conditions=conditions) -random_pool_executor = wrap_to_use_state(random_pool_from_variables) -random_pool_executor.__doc__ = """ - - Args: - variables: - fmt: the output type required - - Returns: - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed_values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=range(10)) - ... ])) - - ... we get some of those values back when running the experimentalist: - >>> random_pool_executor(s, random_state=1).conditions - x - 0 4 - 1 5 - 2 7 - 3 9 - 4 0 - - With one independent variable "x", and a value_range: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", value_range=(-5, 5)) - ... ])) - - ... we get a sample of the range back when running the experimentalist: - >>> random_pool_executor(t, random_state=1).conditions - x - 0 0.118216 - 1 4.504637 - 2 -3.558404 - 3 4.486494 - 4 -1.881685 - - - - The allowed_values or value_range must be specified: - >>> random_pool_executor( - ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - With two independent variables, we get independent samples on both axes: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=range(1, 5)), - ... Variable(name="x2", allowed_values=range(1, 500)), - ... ])) - >>> random_pool_executor(t, - ... num_samples=10, duplicates=True, random_state=1).conditions - x1 x2 - 0 2 434 - 1 3 212 - 2 4 137 - 3 4 414 - 4 1 129 - 5 1 205 - 6 4 322 - 7 4 275 - 8 1 43 - 9 2 14 - - If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool_executor(S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ]))) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - - We can specify arrays of allowed values: - >>> u = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ])) - >>> random_pool_executor(u, random_state=1).conditions - x y z - 0 -0.6 3 29.0 - 1 0.2 4 24.0 - 2 5.2 4 23.0 - 3 9.0 3 29.0 - 4 -9.4 3 22.0 -""" +random_pool = wrap_to_use_state(random_pool_from_variables) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 7f8a3a3f..5e5cac82 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -7,7 +7,9 @@ from autora.utils.deprecation import deprecated_alias -def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): +def random_sample_from_conditions_iterable( + conditions: Union[Iterable, Sequence], num_samples: int = 1 +): """ Uniform random sampling without replacement from a pool of conditions. Args: @@ -19,16 +21,18 @@ def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): Examples: From a range: >>> random.seed(1) - >>> random_sample(range(100), num_samples=5) + >>> random_sample_from_conditions_iterable(range(100), num_samples=5) [53, 37, 65, 51, 4] >>> random.seed(1) - >>> random_sample([1,2,3,4,5,6,7,8,9,10], num_samples=5) + >>> random_sample_from_conditions_iterable([1,2,3,4,5,6,7,8,9,10], num_samples=5) [7, 9, 10, 8, 6] >>> random.seed(1) - >>> random_sample(filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), - ... num_samples=5) + >>> random_sample_from_conditions_iterable( + ... filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), + ... num_samples=5 + ... ) [375, 390, 600, 285, 885] """ @@ -40,7 +44,9 @@ def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): return samples -random_sampler = deprecated_alias(random_sample, "random_sampler") +random_sampler = deprecated_alias( + random_sample_from_conditions_iterable, "random_sampler" +) def random_sample_from_conditions( @@ -81,38 +87,4 @@ def random_sample_from_conditions( ) -random_sample_executor = wrap_to_use_state(random_sample_from_conditions) -random_sample_executor.__doc__ = """ -Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - >>> from autora.experimentalist.pooler.grid import grid_pool_executor - - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=range(100)) - ... ])) - - ... we can update the state with a sample from the allowed values: - >>> s_ = grid_pool_executor(s) - >>> random_sample_executor(s_, num_samples=5, random_state=1 - ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - S(variables=..., conditions= x - 80 80 - 84 84 - 33 33 - 81 81 - 93 93) - -""" +random_sample = wrap_to_use_state(random_sample_from_conditions) diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py index a81ad483..96aacd57 100644 --- a/tests/test_experimentalist_random.py +++ b/tests/test_experimentalist_random.py @@ -4,9 +4,11 @@ import pytest from autora.experimentalist.pipeline import make_pipeline -from autora.experimentalist.pooler.grid import grid_pool -from autora.experimentalist.pooler.random_pooler import random_pool -from autora.experimentalist.sampler.random_sampler import random_sample +from autora.experimentalist.pooler.grid import grid_pool_from_ivs +from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs +from autora.experimentalist.sampler.random_sampler import ( + random_sample_from_conditions_iterable, +) from autora.variable import DV, IV, ValueType, VariableCollection @@ -20,7 +22,9 @@ def test_random_pooler_experimentalist(metadata): """ num_samples = 10 - conditions = random_pool(metadata.independent_variables, num_samples=num_samples) + conditions = random_pool_from_ivs( + metadata.independent_variables, num_samples=num_samples + ) conditions = np.array(list(conditions)) @@ -43,8 +47,8 @@ def test_random_sampler_experimentalist(metadata): # ---Implementation 1 - Pool using Callable via partial function---- # Set up pipeline functions with partial - pooler_callable = partial(grid_pool, ivs=metadata.independent_variables) - sampler = partial(random_sample, num_samples=n_trials) + pooler_callable = partial(grid_pool_from_ivs, ivs=metadata.independent_variables) + sampler = partial(random_sample_from_conditions_iterable, num_samples=n_trials) pipeline_random_samp = make_pipeline( [pooler_callable, weber_filter, sampler], ) @@ -81,8 +85,8 @@ def test_random_sampler_experimentalist(metadata): def test_random_experimentalist_generator(metadata): n_trials = 25 # Number of trails for sampler to select - pooler_generator = grid_pool(metadata.independent_variables) - sampler = partial(random_sample, num_samples=n_trials) + pooler_generator = grid_pool_from_ivs(metadata.independent_variables) + sampler = partial(random_sample_from_conditions_iterable, num_samples=n_trials) pipeline_random_samp_poolgen = make_pipeline( [pooler_generator, weber_filter, sampler] ) From 6a57124cd7e620a0776aa3055a520307d25b6c9e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 10:36:24 -0400 Subject: [PATCH 011/121] Revert "docs: remove notebook which doesn't yet work" This reverts commit 25e24f8e87a1e93402dcb76f5da29746c38f6653. --- ...Introduction to Functions and States.ipynb | 566 ++++++++++++++++++ 1 file changed, 566 insertions(+) create mode 100644 docs/cycle/Basic Introduction to Functions and States.ipynb diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb new file mode 100644 index 00000000..eb6bd33a --- /dev/null +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Introduction to Functions and States" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the functions and objects in `autora.state`, we can build flexible pipelines and cycles which operate on state\n", + "objects.\n", + "\n", + "## Theoretical Overview\n", + "\n", + "The fundamental idea is this:\n", + "- We define a \"state\" object $S$ which can be modified with a \"delta\" (a new result) $\\Delta S$.\n", + "- A new state at some point $i+1$ is $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", + "- The cycle state after $n$ steps is thus $$S_n = S_{0} + \\sum^{n}_{i=1} \\Delta S_{i}$$\n", + "\n", + "To represent $S$ and $\\Delta S$ in code, you can use `autora.state.delta.State` and `autora.state.delta.Delta`\n", + "respectively. To operate on these, we define functions.\n", + "\n", + "- Each operation in an AER cycle (theorist, experimentalist, experiment_runner, etc.) is implemented as a\n", + "function with $n$ arguments $s_j$ which are members of $S$ and $m$ others $a_k$ which are not.\n", + " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", + "- There is a wrapper function $h$ (`autora.state.delta.wrap_to_use_state`) which changes the signature of $f$ to\n", + "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", + " $$h\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", + "S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta\n", + "S_{i+1} = S_{i+1}\\right]$$\n", + "\n", + "- Assuming that the other arguments $a_k$ are provided by partial evaluation of the $f^\\prime$, the full AER cycle can\n", + "then be represented as:\n", + " $$S_n = f_n^\\prime(...f_2^\\prime(f_1^\\prime(S_0)))$$\n", + "\n", + "There are additional helper functions to wrap common experimentalists, experiment runners and theorists so that we\n", + "can define a full AER cycle using python notation as shown in the following example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "First initialize the State. In this case, we use the pre-defined `StandardState` which implements the standard AER\n", + "naming convention.\n", + "There are two variables `x` with a range [-10, 10] and `y` with an unspecified range." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state.bundled import StandardState\n", + "from autora.variable import VariableCollection, Variable\n", + "\n", + "s_0 = StandardState(\n", + " variables=VariableCollection(\n", + " independent_variables=[Variable(\"x\", value_range=(-10, 10))],\n", + " dependent_variables=[Variable(\"y\")]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the experimentalist. Use a standard function `random_pool_executor`.\n", + "This gets 5 independent random samples (by default, configurable using an argument)\n", + "from the value_range of the independent variables, and returns them in a DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "experimentalist = random_pool" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the experiment runner. This calculates a linear function, adds noise, assigns the value to the `y` column\n", + " in a new DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from autora.state.delta import Delta, wrap_to_use_state\n", + "\n", + "rng = np.random.default_rng(180)\n", + "\n", + "@wrap_to_use_state\n", + "def experiment_runner(conditions: pd.DataFrame, c=[2, 4]):\n", + " x = conditions[\"x\"]\n", + " noise = rng.normal(0, 1, len(x))\n", + " y = c[0] + (c[1] * x) + noise\n", + " experiment_data = conditions.assign(y = y)\n", + " return Delta(experiment_data=experiment_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify a theorist, using a standard LinearRegression from scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from autora.state.wrapper import theorist_from_estimator\n", + "\n", + "theorist = theorist_from_estimator(LinearRegression(fit_intercept=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the cycle: run the experimentalist, experiment_runner and theorist ten times." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_ = s_0\n", + "for i in range(10):\n", + " s_ = experimentalist(s_)\n", + " s_ = experiment_runner(s_)\n", + " s_ = theorist(s_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The experiment_data has 50 entries (10 cycles and 5 samples per cycle):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y\n", + "0 -4.451978 -15.373958\n", + "1 0.323487 2.561481\n", + "2 -2.867211 -10.516852\n", + "3 -2.030568 -5.247614\n", + "4 2.913797 12.957584\n", + "5 -7.340735 -27.820030\n", + "6 -6.019243 -21.600574\n", + "7 -8.893466 -31.496807\n", + "8 6.613056 27.020377\n", + "9 4.825417 21.875249\n", + "10 -9.992198 -36.097453\n", + "11 -1.097681 -3.538933\n", + "12 6.572045 29.078863\n", + "13 -3.039432 -9.749266\n", + "14 6.313866 28.311789\n", + "15 2.804555 12.014208\n", + "16 -7.008751 -27.139038\n", + "17 3.286213 14.225707\n", + "18 -8.826214 -30.646008\n", + "19 -9.652346 -37.233317\n", + "20 -0.370936 -0.088444\n", + "21 -6.641559 -25.624469\n", + "22 -7.938631 -29.646345\n", + "23 1.277432 7.965713\n", + "24 2.684480 14.171408\n", + "25 -0.450963 -0.932371\n", + "26 -4.497923 -13.955542\n", + "27 -8.923897 -31.592700\n", + "28 -9.873687 -37.661495\n", + "29 5.831155 26.193081\n", + "30 2.985742 14.107186\n", + "31 -0.399053 1.001974\n", + "32 5.995893 26.435367\n", + "33 2.131670 11.344637\n", + "34 -1.639935 -4.308918\n", + "35 -2.326959 -5.789104\n", + "36 -1.035607 -3.114820\n", + "37 -8.758742 -31.689823\n", + "38 0.366747 4.527129\n", + "39 1.926732 9.679125\n", + "40 3.577052 15.611630\n", + "41 -9.588634 -37.731120\n", + "42 -7.100105 -27.600941\n", + "43 2.469015 11.837649\n", + "44 -1.727297 -5.464983\n", + "45 4.894551 21.937380\n", + "46 -3.799161 -12.654000\n", + "47 2.707062 11.246337\n", + "48 -2.013533 -7.202246\n", + "49 -5.757174 -22.951716" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_.experiment_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fitted coefficients are close to the original intercept = 2, gradient = 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.03390614] [[3.97374104]]\n" + ] + } + ], + "source": [ + "print(s_.model.intercept_, s_.model.coef_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 38a557740f5fbededbadbd5435170a8227492660 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 11:45:59 -0400 Subject: [PATCH 012/121] fix: if there is no model available, return None --- src/autora/state/bundled.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/autora/state/bundled.py b/src/autora/state/bundled.py index 07cfbd1c..e442510d 100644 --- a/src/autora/state/bundled.py +++ b/src/autora/state/bundled.py @@ -132,6 +132,10 @@ class StandardState(State): >>> (s + dm1 + dm2).model DummyClassifier(constant=3) + If there is no model, `None` is returned: + >>> print(s.model) + None + `models` can also be updated using a Delta with a single `model`: >>> dm3 = Delta(model=DummyClassifier(constant=4)) >>> (s + dm1 + dm3).model @@ -165,4 +169,7 @@ class StandardState(State): @property def model(self): """Alias for the last model in the `models`.""" - return self.models[-1] + try: + return self.models[-1] + except IndexError: + return None From 4824af9576a6b275c4962b489c3c009629db60ea Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 11:49:19 -0400 Subject: [PATCH 013/121] docs: update notebook to use new format --- ...Workflows using Functions and States.ipynb | 696 ++++-------------- 1 file changed, 141 insertions(+), 555 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index e57f9232..5563ef49 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -11,202 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using the functions in `autora.state`, we can build flexible pipelines and cycles which operate on state objects.\n", - "\n", - "The fundamental idea is this:\n", - "- We define a \"state\" object $S$ which can be modified with a \"delta\" (a new result) $\\Delta S$.\n", - "- A new state at some point $i+1$ is $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", - "- The cycle state after $n$ steps is thus $$S_n = S_{0} + \\sum^{n}_{i=1} \\Delta S_{i}$$\n", - "\n", - "To represent $S$ and $\\Delta S$ in code, you can use `autora.state.delta.State` and `autora.state.delta.Delta`\n", - "respectively. To operate on these, we define functions.\n", - "\n", - "- Each operation in an AER cycle (theorist, experimentalist, experiment_runner, etc.) is implemented as a\n", - "function with $n$ arguments $s_j$ which are members of $S$ and $m$ others $a_k$ which are not.\n", - " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", - "- There is a wrapper function $h$ (`autora.state.delta.wrap_to_use_state`) which changes the signature of $f$ to\n", - "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", - " $$h\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", - "S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta\n", - "S_{i+1} = S_{i+1}\\right]$$\n", - "\n", - "- Assuming that the other arguments $a_k$ are provided by partial evaluation of the $f^\\prime$, the full AER cycle can\n", - "then be represented as:\n", - " $$S_n = f_n^\\prime(...f_2^\\prime(f_1^\\prime(S_0)))$$\n", - "\n", - "There are additional helper functions to wrap common experimentalists, experiment runners and theorists so that we\n", - "can define a full AER cycle using python notation as follows:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First initialize the State. There are two variables `x` with a range [-10, 10] and `y` with an unspecified range." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.state.bundled import StandardState\n", - "from autora.variable import VariableCollection, Variable\n", - "\n", - "s_0 = StandardState(\n", - " variables=VariableCollection(\n", - " independent_variables=[Variable(\"x\", value_range=(-10, 10))],\n", - " dependent_variables=[Variable(\"y\")]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify the experimentalist. Use a standard function `random_pool_executor`.\n", - "This gets 5 independent random samples (by default, configurable using an argument)\n", - "from the value_range of the independent variables, and returns them in a DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool_executor\n", - "experimentalist = random_pool_executor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify the experiment runner. This calculates a linear function, adds noise, assigns the value to the `y` column\n", - " in a new DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from autora.state.delta import Delta, wrap_to_use_state\n", - "\n", - "rng = np.random.default_rng(180)\n", - "\n", - "@wrap_to_use_state\n", - "def experiment_runner(conditions: pd.DataFrame, c=[2, 4]):\n", - " x = conditions[\"x\"]\n", - " noise = rng.normal(0, 1, len(x))\n", - " y = c[0] + (c[1] * x) + noise\n", - " experiment_data = conditions.assign(y = y)\n", - " return Delta(experiment_data=experiment_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify a theorist, using a standard LinearRegression from scikit-learn." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "from autora.state.wrapper import theorist_from_estimator\n", - "\n", - "theorist = theorist_from_estimator(LinearRegression(fit_intercept=True))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define the cycle: run the experimentalist, experiment_runner and theorist ten times." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_ = s_0\n", - "for i in range(10):\n", - " s_ = experimentalist(s_)\n", - " s_ = experiment_runner(s_)\n", - " s_ = theorist(s_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The experiment_data has 50 entries (10 cycles and 5 samples per cycle):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_.experiment_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The fitted coefficients are close to the original intercept = 2, gradient = 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2.07479102] [[4.01094241]]\n" - ] - } - ], - "source": [ - "print(s_.model.intercept_, s_.model.coef_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Optional\n", - "from dataclasses import field, dataclass\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "from sklearn.base import BaseEstimator\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "\n", - "from autora.state.delta import State, Delta, wrap_to_use_state\n" + "Using the functions in `autora.state`, we can build flexible pipelines and cycles which operate on state objects.\n" ] }, { @@ -229,11 +34,7 @@ "\n", "### Defining The State\n", "\n", - "We define the state as a dataclass, subclassed from `autora.state.delta.State` with fields representing the variables,\n", - "parameters, experimental data, (possibly) conditions, and (possibly) a model.\n", - "\n", - "This state has no \"history\"; it represents a snapshot of the data at one time. Other exemplar state objects are\n", - "available in the subpackage `autora.state` and include some with in-built histories." + "We use the standard State object bundled with `autora`: `StandardState`\n" ] }, { @@ -242,14 +43,12 @@ "metadata": {}, "outputs": [], "source": [ - "@dataclass(frozen=True)\n", - "class Snapshot(State):\n", - " variables: VariableCollection = field(metadata={\"delta\": \"replace\"})\n", - " experiment_data: pd.DataFrame = field(default_factory=pd.DataFrame, metadata={\"delta\": \"extend\"})\n", - " conditions: pd.Series = field(default_factory=pd.Series, metadata={\"delta\": \"replace\"})\n", - " model: Optional[BaseEstimator] = field(default=None, metadata={\"delta\": \"replace\"})\n", + "import numpy as np\n", + "import pandas as pd\n", + "from autora.variable import VariableCollection, Variable\n", + "from autora.state.bundled import StandardState\n", "\n", - "s = Snapshot(\n", + "s = StandardState(\n", " variables=VariableCollection(independent_variables=[Variable(\"x\", value_range=(-15,15))],\n", " dependent_variables=[Variable(\"y\")]),\n", " conditions=pd.DataFrame({\"x\": np.linspace(-15,15,101)}),\n", @@ -265,9 +64,7 @@ { "data": { "text/plain": [ - "Snapshot(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), experiment_data=Empty DataFrame\n", - "Columns: [x, y]\n", - "Index: [], conditions= x\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", "0 -15.0\n", "1 -14.7\n", "2 -14.4\n", @@ -280,7 +77,9 @@ "99 14.7\n", "100 15.0\n", "\n", - "[101 rows x 1 columns], model=None)" + "[101 rows x 1 columns], experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])" ] }, "execution_count": null, @@ -296,45 +95,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Defining The Experiment Runner\n", - "\n", - "For this example, we'll use a polynomial of degree 3 as our \"ground truth\" function. We're also using pandas\n", - "DataFrames and Series as our data interchange format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "coefs = [432, -144, -3, 1] # from https://www.maa.org/sites/default/files/0025570x28304.di021116.02p0130a.pdf\n", - "\n", - "def ground_truth(x: pd.Series) -> pd.Series:\n", - " y = pd.Series(coefs[0] + coefs[1] * x + coefs[2] * x**2 + coefs[3] * x**3, name=\"y\")\n", - " return y\n", + "Given this state, we define a two part AER pipeline consisting of an experiment runner and a theorist. We'll just\n", + "reuse the initial seed `conditions` in this example.\n", "\n", - "def noisy_observation(x: pd.Series, std=1000, rng=None) -> pd.Series:\n", - " if rng is None:\n", - " rng = np.random.default_rng()\n", - " y = ground_truth(x) + rng.normal(0, std, len(x))\n", - " return y\n", + "First we define and test the experiment runner.\n", "\n", - "def noisy_observation_df(df: pd.DataFrame, std=100, rng=None) -> pd.DataFrame:\n", - " y = pd.DataFrame({\"y\": noisy_observation(df[\"x\"], std=std, rng=rng)})\n", - " return y" + "The key part here is that both the experiment runner and the theorist are functions which operate on the `State`.\n", + "We use the wrapper function `wrap_to_use_state` that wraps the experiment_runner and makes it operate on the\n", + "fields of the `State` rather than the `conditions` and `experiment_data` directly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Given this state, we define a two part AER pipeline consisting of an experiment runner and a theorist. We'll just\n", - "reuse the initial seed `conditions` in this example.\n", - "\n", - "First we define and test the experiment runner.\n", + "### Defining The Experiment Runner\n", "\n", - "The key part here is that both the experiment runner and the theorist are functions which operate on the `State`. Therefore, we use a wrapper function `experiment_runner_from_x_to_y_function` that wraps the previously defined `noisy_observation_df` function and returns a function with the same functionality, but operating on the `State`. In this case, we want to use the `State` field `conditions` as input and extend the `State` field `experiment_data`." + "For this example, we'll use a polynomial of degree 3 as our \"ground truth\" function. We're also using pandas\n", + "DataFrames and Series as our data interchange format." ] }, { @@ -343,7 +121,20 @@ "metadata": {}, "outputs": [], "source": [ - "experiment_runner = experiment_runner_from_x_to_y_function(noisy_observation_df)" + "from autora.state.delta import wrap_to_use_state, Delta\n", + "\n", + "def ground_truth(x: pd.Series, c=(432, -144, -3, 1)):\n", + " return c[0] + c[1] * x + c[2] * x**2 + c[3] * x**3\n", + "\n", + "@wrap_to_use_state\n", + "def experiment_runner(conditions, std=100., random_state=None):\n", + " \"\"\"Coefs from https://www.maa.org/sites/default/files/0025570x28304.di021116.02p0130a.pdf\"\"\"\n", + " rng = np.random.default_rng(random_state)\n", + " x = conditions[\"x\"]\n", + " noise = rng.normal(0, std, len(x))\n", + " y = (ground_truth(x) + noise)\n", + " experiment_data = conditions.assign(y = y)\n", + " return Delta(experiment_data=experiment_data)" ] }, { @@ -387,27 +178,27 @@ " \n", " 0\n", " -15.0\n", - " -1456.979354\n", + " -1458.607761\n", " \n", " \n", " 1\n", " -14.7\n", - " -1275.903805\n", + " -1275.827665\n", " \n", " \n", " 2\n", " -14.4\n", - " -1101.590466\n", + " -1102.085834\n", " \n", " \n", " 3\n", " -14.1\n", - " -936.923388\n", + " -937.199684\n", " \n", " \n", " 4\n", " -13.8\n", - " -780.252340\n", + " -782.085722\n", " \n", " \n", " ...\n", @@ -417,27 +208,27 @@ " \n", " 96\n", " 13.8\n", - " 502.578979\n", + " 500.917990\n", " \n", " \n", " 97\n", " 14.1\n", - " 609.939995\n", + " 608.249467\n", " \n", " \n", " 98\n", " 14.4\n", - " 723.255149\n", + " 720.981531\n", " \n", " \n", " 99\n", " 14.7\n", - " 843.905227\n", + " 842.599674\n", " \n", " \n", " 100\n", " 15.0\n", - " 972.154947\n", + " 971.996572\n", " \n", " \n", "\n", @@ -446,17 +237,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1456.979354\n", - "1 -14.7 -1275.903805\n", - "2 -14.4 -1101.590466\n", - "3 -14.1 -936.923388\n", - "4 -13.8 -780.252340\n", + "0 -15.0 -1458.607761\n", + "1 -14.7 -1275.827665\n", + "2 -14.4 -1102.085834\n", + "3 -14.1 -937.199684\n", + "4 -13.8 -782.085722\n", ".. ... ...\n", - "96 13.8 502.578979\n", - "97 14.1 609.939995\n", - "98 14.4 723.255149\n", - "99 14.7 843.905227\n", - "100 15.0 972.154947\n", + "96 13.8 500.917990\n", + "97 14.1 608.249467\n", + "98 14.4 720.981531\n", + "99 14.7 842.599674\n", + "100 15.0 971.996572\n", "\n", "[101 rows x 2 columns]" ] @@ -486,8 +277,13 @@ "metadata": {}, "outputs": [], "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from autora.state.wrapper import theorist_from_estimator\n", + "from sklearn.pipeline import make_pipeline as make_theorist_pipeline\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", "# Completely standard scikit-learn pipeline regressor\n", - "regressor = make_pipeline(PolynomialFeatures(degree=5), LinearRegression())\n", + "regressor = make_theorist_pipeline(PolynomialFeatures(degree=5), LinearRegression())\n", "theorist = theorist_from_estimator(regressor)\n", "\n", "def get_equation(r):\n", @@ -511,7 +307,7 @@ "metadata": {}, "outputs": [], "source": [ - "t = theorist(experiment_runner(s, rng=np.random.default_rng(1)))" + "t = theorist(experiment_runner(s, random_state=1))" ] }, { @@ -621,9 +417,9 @@ "metadata": {}, "outputs": [], "source": [ - "def pipeline(state: State, rng=None) -> State:\n", + "def pipeline(state: StandardState, random_state=None) -> StandardState:\n", " s_ = state\n", - " t_ = experiment_runner(s_, rng=rng)\n", + " t_ = experiment_runner(s_, random_state=random_state)\n", " u_ = theorist(t_)\n", " return u_" ] @@ -716,7 +512,7 @@ } ], "source": [ - "u = pipeline(s, rng=np.random.default_rng(1))\n", + "u = pipeline(s, random_state=1)\n", "get_equation(u.model)" ] }, @@ -766,27 +562,27 @@ " \n", " 1\n", " x\n", - " -143.576922\n", + " -145.738569\n", " \n", " \n", " 2\n", " x^2\n", - " -2.506925\n", + " -2.898667\n", " \n", " \n", " 3\n", " x^3\n", - " 0.998978\n", + " 1.042038\n", " \n", " \n", " 4\n", " x^4\n", - " -0.002146\n", + " -0.000893\n", " \n", " \n", " 5\n", " x^5\n", - " -0.000055\n", + " -0.000218\n", " \n", " \n", "\n", @@ -795,11 +591,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -143.576922\n", - "2 x^2 -2.506925\n", - "3 x^3 0.998978\n", - "4 x^4 -0.002146\n", - "5 x^5 -0.000055" + "1 x -145.738569\n", + "2 x^2 -2.898667\n", + "3 x^3 1.042038\n", + "4 x^4 -0.000893\n", + "5 x^5 -0.000218" ] }, "execution_count": null, @@ -808,7 +604,7 @@ } ], "source": [ - "u_ = pipeline(pipeline(s, rng=np.random.default_rng(1)))\n", + "u_ = pipeline(pipeline(s, random_state=1), random_state=2)\n", "get_equation(u_.model)" ] }, @@ -858,27 +654,27 @@ " \n", " 1\n", " x\n", - " -143.576922\n", + " -145.738569\n", " \n", " \n", " 2\n", " x^2\n", - " -2.506925\n", + " -2.898667\n", " \n", " \n", " 3\n", " x^3\n", - " 0.998978\n", + " 1.042038\n", " \n", " \n", " 4\n", " x^4\n", - " -0.002146\n", + " -0.000893\n", " \n", " \n", " 5\n", " x^5\n", - " -0.000055\n", + " -0.000218\n", " \n", " \n", "\n", @@ -887,11 +683,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -143.576922\n", - "2 x^2 -2.506925\n", - "3 x^3 0.998978\n", - "4 x^4 -0.002146\n", - "5 x^5 -0.000055" + "1 x -145.738569\n", + "2 x^2 -2.898667\n", + "3 x^3 1.042038\n", + "4 x^4 -0.000893\n", + "5 x^5 -0.000218" ] }, "execution_count": null, @@ -900,7 +696,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -910,19 +706,20 @@ } ], "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "\n", "def show_best_fit(state):\n", - " fig, ax = plt.subplots(1,1)\n", + " state.experiment_data.plot.scatter(\"x\", \"y\", s=1, alpha=0.5, c=\"gray\")\n", "\n", " observed_x = state.experiment_data[[\"x\"]].sort_values(by=\"x\")\n", " observed_x = pd.DataFrame({\"x\": np.linspace(observed_x[\"x\"].min(), observed_x[\"x\"].max(), 101)})\n", "\n", " plt.plot(observed_x, state.model.predict(observed_x), label=\"best fit\")\n", - "\n", + " \n", " allowed_x = pd.Series(np.linspace(*state.variables.independent_variables[0].value_range, 101), name=\"x\")\n", " plt.plot(allowed_x, ground_truth(allowed_x), label=\"ground truth\")\n", - "\n", - " state.experiment_data.plot.scatter(\"x\", \"y\", s=1, alpha=0.75, c=\"black\", ax=ax, zorder=2)\n", - "\n", + " \n", " plt.legend()\n", "\n", "def show_coefficients(state):\n", @@ -947,7 +744,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -979,7 +776,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -989,7 +786,7 @@ } ], "source": [ - "def cycle(state: State) -> State:\n", + "def cycle(state: StandardState) -> StandardState:\n", " s_ = state\n", " while True:\n", " s_ = experiment_runner(s_)\n", @@ -1017,7 +814,7 @@ "outputs": [], "source": [ "v0 = s\n", - "def cycle(state: State) -> State:\n", + "def cycle(state: StandardState) -> StandardState:\n", " s_ = state\n", " while True:\n", " print(\"#-- running experiment_runner --#\\n\")\n", @@ -1047,12 +844,14 @@ "output_type": "stream", "text": [ "v0.model=None, \n", - "v0.experiment_data.shape=(0, 2)\n" + "v0.experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: []\n" ] } ], "source": [ - "print(f\"{v0.model=}, \\n{v0.experiment_data.shape=}\")" + "print(f\"{v0.model=}, \\n{v0.experiment_data=}\")" ] }, { @@ -1071,16 +870,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running experiment_runner --#\n", + "#-- running theorist --#\n", + "\n", + "v1.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", + " ('linearregression', LinearRegression())]), \n", + "v1.experiment_data= x y\n", + "0 -15.0 -1211.930218\n", + "1 -14.7 -1251.680229\n", + "2 -14.4 -971.099010\n", + "3 -14.1 -885.923940\n", + "4 -13.8 -949.358016\n", + ".. ... ...\n", + "298 13.8 683.771393\n", + "299 14.1 689.553131\n", + "300 14.4 745.739431\n", + "301 14.7 914.039795\n", + "302 15.0 981.490063\n", "\n", - "v1.model=None, \n", - "v1.experiment_data.shape=(101, 2)\n" + "[303 rows x 2 columns]\n" ] } ], "source": [ "v1 = next(cycle_generator)\n", - "print(f\"{v1.model=}, \\n{v1.experiment_data.shape=}\")" + "print(f\"{v1.model=}, \\n{v1.experiment_data=}\")" ] }, { @@ -1099,11 +912,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running theorist --#\n", + "#-- running experiment_runner --#\n", "\n", "v2.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", " ('linearregression', LinearRegression())]), \n", - "v2.experiment_data.shape=(101, 2)\n" + "v2.experiment_data.shape=(404, 2)\n" ] } ], @@ -1128,11 +941,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running experiment_runner --#\n", + "#-- running theorist --#\n", "\n", "v3.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", " ('linearregression', LinearRegression())]), \n", - "v3.experiment_data.shape=(202, 2)\n" + "v3.experiment_data.shape=(404, 2)\n" ] } ], @@ -1146,29 +959,8 @@ "metadata": {}, "source": [ "## Adding The Experimentalist\n", - "\n", - "### Single Function Experimentalists\n", "Modifying the code to use a custom experimentalist is simple.\n", - "We define an experimentalist which adds some random observations each cycle:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool_executor\n", - "\n", - "experimentalist = random_pool_executor" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "If we call the experimentalist with `num_samples=4`, the state is returned with new\n", - "conditions:" + "We define an experimentalist which adds four observations each cycle:" ] }, { @@ -1179,13 +971,14 @@ { "data": { "text/plain": [ - "Snapshot(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), params={}, experiment_data=Empty DataFrame\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 7.157436\n", + "1 6.395671\n", + "2 9.084903\n", + "3 10.618956\n", + "4 14.665249, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", - "Index: [], conditions= x\n", - "0 0.354649\n", - "1 13.513911\n", - "2 -10.675212\n", - "3 13.459483, model=None)" + "Index: [], models=[])" ] }, "execution_count": null, @@ -1194,14 +987,10 @@ } ], "source": [ - "experimentalist(s, num_samples=4, random_state=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can call the experimentalist as part of the cycle:" + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "\n", + "experimentalist = random_pool\n", + "experimentalist(s)" ] }, { @@ -1211,7 +1000,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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ZnE4mfRMsHGec3gOwWKH9TXDpUy6N0crILeCFmRv5ftUewJhz78UBrbikWc0zbCkiVd7MB2HFBOh0K1z5Zrm8hAKUyRSgqqb07DxujV/B+r1ZeFktPNWvJTcFrcYy60HI3W+Ejq4jocf/gY+Hdu5OWQMLXjT6sIBx5d4lT0LHW1y6au+vbQf4vx/WsevgEQAGnxfDE1e2INC30rSmE5Gy9k4HyNgOg76C5leWy0soQJlMAarq2Z1xhKGfLmPnwSOEV/Phw8Gt6LzlNWOQOEBEc7h6PNTpaG6hZWXXEvjlIUhdZzyu2Rr6vgZ1zy/1LvIK7bz+62Y++TMJpxNiwv154/p2nFdfvaNE5F8O7YS324LFBo/sBL/y+W515ftbl8KInKOtadlc++Fidh48Qp0wf34c2pDOv998LDxZoPsDcNfvlSc8AdTrCnf+blwJ4xcCaevgszhjjFd+dql24edt4/G+Lfj69vOpHerP7oyjXP/REl6evYnCY32kREQAo7EwQEzncgtPrlKAEjkHa3Yf5rqPlpCWlU+TyEB+vNJKzLe9Yc9yI1jcONUYKO5VCfsfWW3GZcT3rIIONxvLEibC+91gx++l3k3XRtWZfX93ru1YB6cTPli4ncEfL2Xv4aPlVLiIeJztvxk/G11qbh0nUIASOUsrd2Zw44SlHD5SSNuYUKZ32Uz17wdCbjpEtoQ7FkDTy80us/xVqwH93oVhP0FoXchMhs/7wcwHID+nVLsI8vPmteva8sGQDgT5eZGw6xBXvL2IeRvSzryxiFRu9kJI+sO43/gSc2s5gQKUyFlYtyeTWyauILfATreG4Uxttohqcx8ypkZpNRBun1suPUpcNWPGDAYMGMCMGTPK/8UaXAQjlkCn24zHKz6BDy+EfatLvYs+rWsx857utK0TQubRQm7/fCXP/7xBp/REqrI9KyE/C/zDoVY7s6sppgAl4qKtadnc/NkysvOL6FI/lM9r/4DvonHGkxc/AgM/BZ9q5hZ5THx8PAsWLCA+Pr5iXtA3EK58A27+0Zio+FASfHIZLHnf6CtVCnWrB/Dt3d249YIGAHzyZxJDPlnGgZz88qxcRNzV9mPjnxr2MIYOuAkFKBEX7DqYy5BPlnHoSCHtawfyZY1JeK34yHiy98tGp2436u00fPhwevbsyfDhwyv2hRv2gLsXGd3LHYUw5zH45kY4klGqzX28rIy9qgUfDe1IoK8Xy5MyuOrdP1mz+3C5li0ibuj4+KfG7jP+CdTGoFyojUHllJJ5lOs+XMKeQ0dpFenHtMgJ+Gz7xbistv/70Haw2SW6H6fTOJU35/+MOf+Ca8N1kyDmvFLvYlt6Dnd+sZId+3Px8bLyfP9WXN8pphyLFhG3kXsQXm0EOGHMRgiOLteXUxsDkTKWk1/ELRNXsOfQURqF+/JdzU+N8GTzhcFfKTydisViXKl3+zwIbwRZeyH+Clj1Ral30TgykB9HXkCv5jUpKHLw8HdrefanDdgd+ttPpNLbNg9wQs1W5R6eXKUAJXIGdoeT+79ZzabUbCKqeTOj3lT8ts4Emw/cOAVi+5hdovur1dbohdXsSuNI1IxRMOsh4+qaUgjy8+bjoR0Z3aspAJ/9lcQdn68kJ7+oPKsWEbNtnWP8bOJ+VzQrQImcwUu/bGTexnR8vCzMbPYL1TZOMU7bXTsRGvU0uzzP4RsE138BPR83Hi//GD6/GnL2l2pzq9XCfb2a8N6N7fH1svLbpnSu/WCx+kWJVFb2omNHoICmcebWchIKUCKn8c3yZCYsSgJgRuu/iPz7M+OJq8eX21xMlZrVChc/DIMng08Q7PoLJvSE9I2l3sWVbaKZcldXagT6sik1m6vf+0uDy0Uqoz3LIS8T/MOgTunHTVYUBSiRU1i8/QBPTF8PwMQWiTTb+J7xRJ9XoN0NJlZWCTS7Au74zRgXlbkbPo2DHQtLvXm7mFB+HHUBzaKCOJCTz+CPl7Jgc3r51SsiFW/LsdN3jXu5VfuC4xSgRE4iJfMoo75eTZHDycON99Aj6TXjiR7/B13uMre4yiKiqTG4vG5XyM+ELwfC6q9KvXntUH++G9GN7k1qcLTQzu2TVjJ15e5yLFhEKtTWX42fTdzv9B0oQIn8R6HdwT1fryYjt4DLamYzYv/zWJwOaHeTcfpJyk5AOAydbnRvdxTBj/+D354vddPNQF8vPh12HgPa18bucPLwd2t5d/5W1J1FxMMd3g3pG8Bidbv+T8cpQIn8y2tzNrNy1yGiffN53/IKlvwsiOlidNh2oyaZlYa3Hwz4BLo/YDz+41X4caQxgLQUfLysvH59W/7Xw5g65/W5W3jyx/U41OZAxHMdv/quTmfjDy03pAAlcoK5G9L46I8dWHHwY9RneB/eDsF1YNCX4OVrdnmVl9UKl46Fq94x/uJM/Aqm3gyFeaXa3GKx8HDvZjzTryUWC3y5NJnRUxM1h56Ip9py7PSdG0/IrgAlcszujCM8MDURgK/qzSQibRF4+cMNX0NgpLnFVRUdhxmtDmy+sHmmMS4qL6vUmw/rVp+3B7fHy2rhx8R93P1FAnmF9nIsWETKXOFRSPrDuO+m459AAUoEMMY9jZq8mqy8IkZGrKVr2mTjiWs+MJpASsVpfiXc9P2xNgd/wqQrS90rCqBf22gm3NwJXy8r8zelM+yz5WTnla5hp4i4gaRFUHTUmPqpZkuzqzklBSgRYPyCbazZfZgWfhk8kD/eWHjhGGh5jbmFVVUNusPwnyGgBqSsgYm9IXNvqTfv2SySz2/tTKCvF8uSMhjyyTIOHykox4JFpMyc2H3cjcedKkBJlbd2z2He+20bXhTxZdjHWAuyjUHjxztmizmi28GtcyAkBg5uM+bQO5xc6s27NKzO5DvOJyzAm7V7MrlxwjIychWiRNya03nC+Cf3PX0HClBSxeUV2hkzdQ1FDifja80m/NBa8AuBgZ+Azcvs8qRGY7hlFoTWg0M7YWJfyEgq9eat64TwzZ1dqRHow4aULG6csJQDOfnlV6+InJv9myAz2RgH2eAis6s5LQUoqdJem7OZbek59K22icsPHRv31O9dCK1rbmHyj9C6cMsvx7qWJ0N8Xzi4vdSbx0YF8c2dXYkMMqZ+GfzxUtKzSnd1n4hUsOPdxxtcBD7VzK3lDBSgpMpauuMgn/6VRHUyecP7Ayw4oeMt0OJqs0uTfwupbRyJqhELWXth4hWwf0upN28cGciUu7pSK8SPbek5DP54KWkKUSLuZ/Mvxk83P30HClBSReXmF/Hgt2twOp18EfEFvnn7IaI5xL1odmlyKkFRMHwmRLaEnFSYdJVLR6Ia1KjGlDu7UjvUnx0HcrlxwlLSsxWiRNxGTjrsXmbcj73C3FpKQQFKqqQ35m5hz6Gj3BK0nBbZi8HmA9d+Cj4BZpcmpxMYAcN+KhmiXBgTVbd6AN/ceT7RIX5s35/LkAnLNCZKxF1s/gVwQnR746izm1OAkipn/d5MJv6VRASH+T9LvLHw4kfcut+InKBadbj5R4hoZpzOm3QVHNpV6s1jwgOYfOf5RAX7sTU9hyG6Ok/EPWyaafxs1tfcOkpJAUqqFLvDyeM/rMPhhI9rfIN3QSZEtYEL7jO7NHFFYATcPAOqN4bM3UaIytxT6s3rVa/G5DvPJzLIl81p2dw4YSmHFKJEzJOfAzsWGvebXWlqKaWlACVVylfLdrFmTyYDfFfSPucPsHrB1ePB5m12aeKqoJrG6bywBnB4lxGislNLvXmDGkaIqhFoXJ03bKI6louYZvt8sOdDeEPj6LIHUICSKiMtK49XZ28mlGxe8I03Fl44Gmq1MbUuOQfB0UbH8tC6kLEDvrgGjmSUevNGEYF8fUeX4mabt8Wv5GiB5s4TqXAnnr5z4+7jJ1KAkirj2Z83kJ1fxJsh3+BfkGH8lXPRQ2aXJecqpI5xOi8wCtI3wFfXQn52qTdvWjOIL27rQpCvF8t3ZnDnFyvJL1KIEqkw9kLYMtu4H+sZ459AAUqqiN+37Gfm2hR6WNfQM38BWKzGqTsvX7NLk7IQ3gBung7+YbA3Ab65EQpL36KgVe0QJt5yHv7eNhZtPcC9k1dTZHeUX70i8o9df0FepjH3ZUxns6spNQUoqfQK7Q6e+3kDPhTyetDXxsIud0OdTuYWJmUrsjnc9D34BEHSH/DtcOMv21LqVD+cCTd3wsdmZc7faTz83VocDmf51Ssihk2zjJ+xfcBqM7cWFyhASaU3eXky29JzGOn/K9Xzd0O1SOjxmNllSXmo3RFu/Aa8/GDLL/DjKHCU/kjShU1qMH5IB2xWC9NW7+WFWRtxOhWiRMqN03nC+CfPuPruOAUoqdQyjxTy5twtRHGQ/1mnGQsvexb8gs0tTMpP/Qvh+s/BYoO138C8sS5tflmLmrwy0Liw4NM/k3h/Yem7nYuIi1LWQNYe8K4GDS82uxqXKEBJpfbOb1s5dKSQcYFT8bYfhZgu0GaQ2WVJeWsaZ4xxA1j8Lvz1jkubD+xYhyf6Ngfg1Tmb+XpZcllXKCLwz9GnxpeCt7+5tbhIAUoqraQDuXy+ZCddLBvpWbQIsMAVr4JVv/ZVQrsbjKONAHOfhMTJLm1+e/eGjOzZCIAnpq9j1rqUsq5QRDYfG//kId3HT6RvEqm0Xpy1EYe9iNcCvzQWdLoVarU1tyipWBfcB11HGfd/HAlbfnVp8wcvj+WGznVxOOH+bxJZsv1gORQpUkVl7IC09cbp9iaXm12NyxSgpFJavP0AczekcbPXPGIKk8A/HC55wuyyxAyXPWectnXa4dthRpuDUrJYLDzfvxW9W0ZRYHdw5xcr2ZSaVY7FilQhf083fja4CALCTS3lbChASaXjdDoZN2sTweTykO+xgeOXPumR/4NKGbAe6/nV6FIoPAJfXW/85VtKNquFtwa3o3P9cLLzihj22XL2Hj5ajgWLVBEbphs/W/Y3s4qzpgAllc6cv1NZtzeTe3x/JsCeDRHNocMws8sSM9m84fpJxsTRRw7Al9dCbulPx/l525hwcyea1gwkLSufYZ8t5/ARTT4sctYydhhX4Fls0Owqs6s5KwpQUqnYHU5e/9VoWzDcdmxqgF5Pe1RzNiknvkEw5FsIiYGM7TB5MBSW/khSSIA38bd0JirYj23pOdw+aSV5hZryReSsFJ++6w7VqptaytlSgJJKZcaavWxNz+Fhvx/wduRD3W7GJe0iAEFRRrdyvxDYsxy+vx0cpQ9B0aH+TLq1M0F+XqzcdYgHpq5Rt3KRs1F8+u4aU8s4FwpQUmkU2h28OXcrjS176M9CY+Flz3jMzN5SQSJiYfBksPnApp9hzuMubR4bFcTHQzvhbbMwc10KL83eVE6FilRSGUkef/oOFKCkEpm6cjfJGUd4wu9brDiMaQE8aGJKqUD1L4BrPjLuL/sAln3s0uZdG1Xn1WuNlhgf/7GDz5fsLOMCRSqx40efPPj0HShASSWRV2jn3fnb6GjZTA/nCrBY4dKnzC5L3FmrAf/8jsx+BLbMcWnz/u1r8+DlTQF4esbfzN2QVtYVilROx8c/tehvZhXnTAFKKoUvl+4iNesoY/2mGAvaD4WIpuYWJe7vwtHG74rTAd/eAilrXdp8ZM/GDD4vBocT7pm8irV7DpdPnSKVRUYSpCQap++ae+7pO6hkAerpp5/GYrGUuDVr1qz4+by8PEaOHEn16tUJDAxk4MCBpKWV/KsxOTmZvn37EhAQQGRkJA899BBFRUUV/VbEBXmFdj78fTuXWFfT1rkJvPyhx6NmlyWewGKBK9+EBhdDYS58fT1k7nVhcwvP9W/FRU0jyCt0cNukleoRJXI6G340fta/EKrVMLeWc1SpAhRAy5YtSUlJKb79+eefxc+NHj2an376iW+//Zbff/+dffv2MWDAgOLn7XY7ffv2paCggMWLFzNp0iTi4+MZO9a12dylYn2zPJkDOfk85DvdWND5DgiONrUm8SA2b7j+c6gRC9kp8PUgyM8p9ebeNivjb2xPs6gg9mfnc1v8CrLzCsuxYBEP5uHNM09U6QKUl5cXUVFRxbcaNYyEm5mZyaeffsobb7zBJZdcQseOHZk4cSKLFy9m6dKlAPz6669s2LCBL7/8knbt2tGnTx+ee+45xo8fT0GBmua5o4IiBx//sYMe1kSaO7eBdwB0u9fsssTT+IfCkKlQLQLS1sG0O1xqbxDk582nw88jIsiXTanZ3DN5NUV2R/nVK+KJMpJg32pjjKoHX313XKULUFu3biU6OpqGDRsyZMgQkpOTAUhISKCwsJBevXoVr9usWTPq1q3LkiVLAFiyZAmtW7emZs2axevExcWRlZXF33//fcrXzM/PJysrq8RNKsb01XvZl3mUB44ffep0KwRGmFqTeKiw+jD4a7D5GjPEz3vapc1rh/rz6bBO+HlbWbh5P8/+vAGnUz2iRIqdePquEvw7XakCVJcuXYiPj2f27Nl88MEHJCUl0b17d7Kzs0lNTcXHx4fQ0NAS29SsWZPU1FQAUlNTS4Sn488ff+5Uxo0bR0hISPEtJiambN+YnJTd4eSD37dzkXUtrZ1bwcsPLrjP7LLEk8V0NubNA1j8Dqz6olSbzZgxgwEDBrBz1R+8Nag9Fgt8vmQX8Yt3ll+tIp5m/XfGTw9unnmiShWg+vTpw3XXXUebNm2Ii4tj1qxZHD58mKlTp5br6z722GNkZmYW33bv3l2uryeGWetSSDqQwwM+PxgLOt0KgZHmFiWer811cNHDxv2f74edf552dYD4+HgWLFhAfHw8vVtF8Vgf4+KV537ewMLN6eVYrIiHSN8EqevA6u3x7QuOq1QB6t9CQ0Np2rQp27ZtIyoqioKCAg4fPlxinbS0NKKiogCIior6z1V5xx8fX+dkfH19CQ4OLnGT8uV0Ohm/YBsXWNfTli06+iRlq8djxl/JjiKYchMc3H7a1YcPH07Pnj0ZPnw4AHd0b8j1neoY7Q2+Xs3WtOwKKFrEja07diCjyWUQEG5uLWWkUgeonJwctm/fTq1atejYsSPe3t7Mnz+/+PnNmzeTnJxM165dAejatSvr1q0jPf2fvxjnzp1LcHAwLVq0qPD65dTmb0xnU2oWY7yPHX3qONyY50ykLFit0P8DiO4ARw8ZEw/nZZ5y9X79+jFt2jT69esHGO0Nnu/fms4NwsnOL+LWSSvIyNWFKFJFOZ2w7lvjfuvrzK2lDFWqAPXggw/y+++/s3PnThYvXsw111yDzWbjhhtuICQkhNtuu40xY8awYMECEhISuOWWW+jatSvnn38+AJdffjktWrRg6NChrFmzhjlz5vDEE08wcuRIfH19TX53cpzT6WT8wm10tW6go2WTMaeZjj5JWfP2hxsmQ1A0HNgC393q0pV5Pl5WPrypI3XDA9idcZS7v0ggv6j024t4uu37c8jNL4Ldy+BwMvgEQWwfs8sqM5UqQO3Zs4cbbriB2NhYrr/+eqpXr87SpUuJiDBG+7/55ptceeWVDBw4kIsuuoioqCimTZtWvL3NZuPnn3/GZrPRtWtXbrrpJm6++WaeffZZs96SnMTKXYdYnXyYe72mGws6DFPfJykfQVFww9dGc9Zt82Cuaz3hwqv58OmwTgT5erF8ZwZP/LBeV+ZJlTBjxgwu6NWXJjc9y75Fk4yFza8y/jCpJCxO/d9c5rKysggJCSEzM1PjocrBXV+sZN+GJfzk+wRYveDeRAjVlY9SjtZPg+9uMe5fPR7a3+TS5gs3p3Nr/AocTniib3Nu796wHIoUcR99+/Xnl1/n4V+3Ndm3pWI9mgE3TYPGl5pd2mm58v1dqY5ASeWXfPAIv25I406vn40FrQYqPEn5azUALn7EuP/T/ZC81KXNe8RG8nhfYxzli7M28vuW/WVcoIh7aXNJP/zqtqHfhc2N8FQt0pgyqRJRgBKPMnFxErXZT1/bcmNBt3vMLUiqjosfheb9wFEI3wwxxnS44NYL6hdfmTfq61Vs31/66WJEPE1erQ4Etu7FgYQ5zNhcaPyxa/Myu6wypQAlHiMrr5CpK3Zzm20WVhzQsCdEtTa7LKkqrFa45kPjd+7IAfjmRijILfXmxyce7lQvjOy8Im6ftJLMI5ozTyofh8PJoq37yVs3hxVbUohPLDT6q1UyClDiMaYs341XQSaDvX43FlygOe+kgvlUg8GTIaCG0RRw+v+MS7RLydfLxodDO1I71J+kA7mMmrxKc+ZJpbMxNYsDOQX0al+XS+rbGH5hXaMlSCWjACUeocjuIH7xTobY5uFPHtRsbRyBEqlooTEw6Eujo/KG6bDoNZc2rxHoy4SbO+HvbWPR1gO89Mum8qlTxCR/bDkAwDMdDjFtUAD9htwBFovJVZU9BSjxCLP/TmX/4Sxu9f7VWNDtnkr5P6R4iHpdoe+x4PTb87Bplkubt4gO5rXr2gLwyZ9JfJ+wp6wrFDHNoq37qUEmLfNWGQsqUfPMEylAiUf49M8k+tv+pAaHjcaGrQaYXZJUdR2Hw3l3GPen3QHpG13avG+bWtxzSWMAHvthHauTD5VxgSIV70hBESt3HuIa2yKsTjvUOQ+qNzK7rHKhACVub3XyIRKTM7jT69hf+eePAJu3uUWJAPQeB/W7Q0EOTL4BjmS4tPnoXk3p1bwmBUUO7voigbSsvHIqVKRiLNuRQYHdzo0+fxgLXOyZ5kkUoMTtfbF0Fxdb19DYshd8g42//EXcgc0brv8cQuvCoSRjuhd7Uak3t1otvDmoLU0iA0nPzueuLxLIK9R0L+K5ft+yn3aW7TRw7jE6+LesvGcLFKDErR3KLeDntSkMtx0b+9R+KPipu7u4kYBw48o87wDYsQDmPeXS5kF+3ky4uRMh/t4k7j7M2B813Yt4rkVb93O9baHxoMXVlfrfawUocWvfJeyhln0fPWxrcGKB824zuySR/4pqBf0/MO4veQ/WTHFp8/o1qvHODe2xWmDqyj18sXRXORQpUr72Hj7K3v0ZXGVbYiyoxKfvQAFK3JjD4eSrZbsYapsLgKXJZZV2MKJUAi37Q/cHjfsz7oG9q1za/OKmETzSuxkAz/60gaU7DpZxgSLla9GW/fS2riDIchRC60G9C8wuqVwpQInb+mv7AdIOZnCd7dhgxONXPIm4q56PQ9PeYM83pnvJSXdp8zsvashVbaMpcjgZ+dUq9h4+Wk6FipS9P048fdf+JqN7fyVWud+deLQvl+7iattiQiy5EFYfGvcyuySR07NaYcDHUKMpZO+DKUOhqKDUm1ssFl4Z2IYWtYI5mFvAXV+s1KBy8Qh2h5OkrRvoZttgDLdoe4PZJZU7BShxS6mZeczbmMaw44PHz7uj0v81I5WEXwgM/tq4YnT3Upj9iEub+/vY+GhoR8Kr+bB+bxb/98M6DSoXt7cq+RC9i34zHjTsYXTsr+T0jSRu6ZsVybR3bqK5Ndm4FLb9ELNLEim9Gk1g4CeABVZ+BisnurR5THgA793YHpvVwrRVe4lfvLNcyhQpK/M2pDDw2HALSyUfPH6cApS4nSK7g2+W72aY17GjT22uA/8wc4sScVXTOLjkCeP+rIcgealLm3drVIPH+hiDyp+fuVGDysWtZaybSx3LAQq8g6FZX7PLqRAKUOJ25m1Mx56VQh/bCmOBBo+Lp+r+gNELx1FojIfK3OvS5rdd2ID+7aKxHxtUvk+DysUNJR3I5aKcX4wHrQaCt7+5BVUQBShxO1NX7uZG2294YYe6XaFWG7NLEjk7Fgtc/T5EtoTcdJhyExSWfroWi8XCuAH/DCq/+0t1Khf3szhxA3FW4w9en863mFxNxVGAEreSlpXHos0p3OB1bDDiebebW5DIufINhMFfGaeh962Cn0eDC4PCjw8qDwvwZu2eTJ6Yrk7l4l5sa77Ex2InPaQ11GprdjkVRgFK3Mq0VXvpbllDlOUQBFSH5v3MLknk3IU3gGsngsUKa76G5R+7tLkxqLwDVovRnf9LdSoXN5GZk0f3rJ8BsHWuWn/wKkCJ23A6nXy7cjeDbQuMBW1vAC8fc4sSKSuNesJlzxn3Zz8GSYtc2vyCxjV49Nig8md+2sDKnRllXaGIyzb++T21LQfItgRSvfMgs8upUApQ4jZWJR8i+8AeLrGuNhZ0uNncgkTKWteR0GYQOO3w7TA4nOzS5nd0b8iVbWpR5HAy4qtVpGWVfjyVSHkIXDcJgE1R/arM4PHjFKDEbXy7cg/X2hbhZXFAzPkQEWt2SSJly2KBq942xokcOQjf3AgFR1zY3MIr17YhtmYQ+7PzGfFlAgVFjnIsWOTUCg8k0SJ3OQAB3are1dIKUOIWjhQU8dOavQw6fvruNEefZsyYwYABA5gxY0YFVSdShrz9YdBXEFADUtfBjFEuDSoP8PHio6EdCfbzYlXyYZ756e9yLFbk1NIWfIgVJ8ssbWjesr3Z5VQ4BShxC7+sS6V10XrqW9Nw+gQZM9ufQnx8PAsWLCA+Pr7C6hMpU6ExcP3nYPWC9d/D4ndc2rx+jWq8Pbg9Fgt8tSyZqSt2l1OhIqdQlE/Y5m8A2FznOqxWi8kFVTwFKHELU1fuLj76ZGl9LfhUO+W6w4cPp2fPngwfPryCqhMpB/UvgN4vGffnPQ3b5rm0ec9mkYzu1RSAJ35cz9o9h8u2PpHTcG6YQbWiw6Q6w4jqPMDsckyhACWm23Uwl41JyVxhNc6l02Hoadfv168f06ZNo18/tTgQD3fe7cbpaqcDvrsVDm53afNRPRvTq3lNCooc3P1FAgdy8supUJGSji6ZAMC3zku4MDbK5GrMoQAlpvsuYQ/9bX/haymEmq0guoPZJYlUDIsFrngN6nSGvExjUHl+dqk3t1otvDGoLQ1rVGNfZh73fL2aIrsGlUs5S11PQMoyipxWkupeR4CPl9kVmUIBSkzldDr5YdUebjhx8Lil6p1LlyrMyxcGfQFBtWD/Jph2FzhKH4KC/bz5aGhHqvnYWLLjIC/P3lSOxYoAS98HYLbjPLq1b21yMeZRgBJTrUo+RFjmBppbk3HafKH1dWaXJFLxgqJg0Jdg84HNM+H3l13avEnNIF67zphCY8KiJGas2VceVYpAdhrOtd8CMMnZl8ta1DS5IPMoQImppq/ex0DbHwBYml8FAeEmVyRikjqdjB5RAL+/BBtca9PRp3UtRvRoBMAj361lU2pWWVcoAis/xeIoYJWjMYGNuhLi7212RaZRgBLTFNodzFm7m6tsS4wFbW8wtyARs7W7Ec7/n3H/h7shzbUeTw9eHkv3JjU4Wmjnri8SyDxSWA5FSpVVeBRWfALAJ0VX0LdNtMkFmUsBSkzz59YDtM5bQXVLNs5qkdCwh9kliZjvsuegwcVQmAuTb4AjpZ/zzma18M7g9tQJ82fXwSPcP2U1Dkfpm3SKnNbaqXDkIHucNfjN0rlKn74DBSgx0fTEvVxj+xMAS+vrwFY1r+QQKcHmBdfFQ1h9OLzLmDPPXlTqzcOq+fDhTR3x9bKyYPN+3pq3pdxKlSrE6SwePB5fFEe3JlFV+vQdKECJSXLzi1jy9w4us64yFrStWrN4i5xWQDgMngw+gZD0B8z5P5c2b1U7hJcGGldHvfPbNp55b5KmP5Jzs30+7N/EEfyZYu/JFa1rmV2R6RSgxBTzNqbR07EEX0shzojmENXG7JJE3EvNFnDNR8b95R9BQrxLm1/Tvg7Du9UH4PXxHzNv/m+a/kjO3hLj6NM3RReTZ6tW5U/fgQKUmGT66r0MsC0CwNJ2kHo/iZxM8yuh5xPG/ZkPwq7FLm3+eN/mdK4fjl+LS/Gr14ZBQ07f5V/kpNI3wvb5OLAy0R5H9yYRVf70HShAiQkO5uSzfetGulg34cQCra83uyQR93XRg9CiPzgKYcpQOJxc6k29bVbGD+lAw44XE3DFIyw4UhenU4PKxUVL3gPgL68u7HbW1Om7YxSgpMLNXJfCVZZjg8cbdIeQ2iZXJOLGLBbo/75xmvvIAZh8IxTklnrziCBfPripAz42K7P/TuX9ha7NtydV3OHdsOYbAN7MjcPbZtHpu2MUoKTC/XjC6TvaDDa3GBFP4FMNBn8N1SIgbZ3RI8qF6V7a1w3jmatbAvDar5tZuDm9vCqVymbxO+AoIjmkE6ucTXX67gQKUFKh9h0+SuHuBBpZU3B6+UOLfmaXJOIZQmOM6V6s3rBxBiwc59LmN3Suyw2d6+J0wr2TV7PzQOmPYkkVlZMOqz4H4I28qwC4qq1O3x2nACUV6pf1qf8MHm/WF3yDTK5IxIPUPf+f6V7+eAXWfefS5k/3a0GHuqFk5RVx1xcJ5OaXvr+UVEFL3oOiPHJqtGN6ZmOq+diIaxlldlVuQwFKKtTstXu40rbUeNBWp+9EXNZ+CHS7x7j/40jYk1DqTX29bHxwU0cignzZnJbNQ9+t0aByObkjGbDiUwC+rzYYsHBF61oE+Kjh8XEKUFJhUjPz8NnzFzUsWTj8wjV1i8jZ6vUMNO0NRXnwzY2Qta/Um9YM9uPDmzrgbbMwa10qH/yuQeVyEss/hoIcHJEteW1nAwAGdqxjclHuRQFKKswv61O40mocfbK27Ac2DUQUOStWGwyYABHNIScVJg926cq8jvXCebqfMaj81TkaVC7/kp8NSz8AIKHurWTn26kT5k/n+uEmF+ZeFKCkwsxZu5vethXGg5YDzC1GxNP5BcON30BAdUhZAz/c5dKVeTd2rsvg82I0qFz+a+VnkHcYqjfmvTQjaA/oUAerVQ2PT6QAJRUiLSsPvz2LCLPkYA+IgPoXml2SiOcLqw+DvgKbD2z8CX57ttSbWiwWnrm6ZfGg8js+X0mOBpVL4VFYbDTOzOw4ikXbMgAY2EH9+v5NAUoqxOz1qcWn72wt+xunIETk3NXrCv2MLzz+fBNWf1nqTX29bHx4U0cig3zZmp7DmCmJOBwaVF6lrfgEctMhpC5T87vicEKnemHUq17N7MrcjgKUVIg5a3dxuXWl8aCVTt+JlKm2g+Cih4z7P90PO/8s9aaRwX58NLQjPjYrv25I493ftpVPjeL+8rJg0RsAOC9+mG8T0wANHj8VBSgpd+nZeVTb/TvBliPYA2tBzPlmlyRS+fT4P2h5zbE5826Cg6W/uq593TCev6YVAG/O28Kvf6eWV5XizpaMh6MZUL0J62tcwZa0HHy9rPRto+aZJ6MAJWVuxowZDBgwgBkzZgAwZ30qfa1LALC1ugas+rUTKXNWK/T/AGp3hKOH4KtrIfdgqTe/vlMMw7vVB2D0lES2pGWXU6HilnIPFk8azCWP832iEaIvbxlFsJ+umD4ZfZOdwvjx46lfvz5+fn506dKF5cuXm12Sx4iPj2fBggXEx8cDMHftLnpZVxlP6uo7kfLj7Q+DJ0NIXcjYYfSIKswr9eaP921O14bVyS2wc/uklRzKLSjHYsWt/PkGFORAVBuONr6Saav2AHCtTt+dkgLUSUyZMoUxY8bw1FNPsWrVKtq2bUtcXBzp6eqVUhrDhw+nZ8+eDB8+nAM5+QQm/0agJY+ioDpQp5PZ5YlUbkE1Yci34BsCu5fCj/8rdXsDb5uV8UM6UCfMn+SMI4yavIoie+lbI4iHytwLyycY9y8dy09rU8nKK6JueADdG9cwtzY3pgB1Em+88QZ33HEHt9xyCy1atODDDz8kICCAzz77zOzSPEK/fv2YNm0a/fr147eN6Vxx7PSdV+sBYFEfEZFyF9kMBn0BVi9Y/z389lypNw2v5sMnwzoR4GPjr20HeX7mxnIsVNzCH6+CPR/qdoPGvfhy2S4AhnSpq95Pp6EA9S8FBQUkJCTQq1ev4mVWq5VevXqxZMkSEyvzTL+v38ml1tXGA119J1JxGl4M/d417v/5BiTEl3rTZlHBvHF9OwDiF+9kyorksq9P3MPB7bD6C+P+pU+yZk8ma/dk4uNl5bpOMebW5uYUoP7lwIED2O12atasWWJ5zZo1SU09+ZUp+fn5ZGVllbgJHC2w45P0K/6WAgqC60GtdmaXJFK1tLsRLn7EuP/zGNg6t9Sb9m4Vxf29mgDwxPT1vD7hqxIXh0gl8dvz4CiCxpdBvW58sdQ4+nRl61qEV/MxuTj3pgBVBsaNG0dISEjxLSZGqR1g0db9XOo0Bt976/SdiDl6PAZtbwCnHabeDHtXlXrTey9pwhWtoyi0O3nuzQ+Y/9tvxReHSCWwawn8PQ2wwKVjOXykgJ/WGBNT39S1nrm1eQAFqH+pUaMGNpuNtLS0EsvT0tKIioo66TaPPfYYmZmZxbfdu3dXRKlub8H6ZHpYEwGwtLjK3GJEqiqLBa56Bxr2hMIj8PX1xhV6pWC1Wnjtura0jA7Gp/kl+NVtw6AhQ8u5YKkQDgfMftS432Eo1GrDdwl7yC9y0KJWMO1jQk0tzxMoQP2Lj48PHTt2ZP78+cXLHA4H8+fPp2vXrifdxtfXl+Dg4BK3qs7ucHJ083wCLXnkB0RBdAezSxKpurx8jEHlUW0gdz98ORByD5Rq0wAfLz4Z1ol6HS7C/4pHmJtTB7ume/F8a76GlETwCYJLnsThcPLlsdN3Q7vWw6IzBmekAHUSY8aMYcKECUyaNImNGzcyYsQIcnNzueWWW8wuzWOsSj5Et4JjV9+17KfTdyJm8w0y2hsc7xH19fVQkFuqTWuF+PPx0I74eFmZtzGdV+ZsKudipVzlZ8P8YxNPX/wQBEby1/YD7Dx4hCBfL65uF21ufR5CAeokBg0axGuvvcbYsWNp164diYmJzJ49+z8Dy+XU5v29l162BABsOn0n4h6CouCm78E/DPYmwLfDwV5Yqk3b1w3j1WvbAPDR7zuYulJDFTzWotchJw3CGkCXuwH4Yolx9GlgxzoE+HiZWZ3HUIA6hVGjRrFr1y7y8/NZtmwZXbp0Mbskj+F0Oklbt4BwSw4FPqFGbxERcQ8RTeGGKeDlD1t/hR9HlrrR5tXtanPPJY0BePyHdSzdUfqpYsRNZCQZc94BxL0AXr7sOpjLvI3GuN+bzq9rYnGeRQFKyty29Bza5hizwVuaXQE2/TUj4lbqdoHrJ4HFBmunwK+Pg7N045pG92pK3za1KLQ7ufvLBJIOlO40oLiJuU+CvQAaXAyxVwDw8R87cDihZ2wEjSODTC7QcyhASZn79e9U4mwrAPBu2c/kakTkpJrGGZMPAyx93zitUwpWq4XXr2tLu5hQDh8p5Nb4FRw+ojnzPMK2+bDxJ7BYofc4sFjYn53PtwnGvHd3X9zI5AI9iwKUlLmktX8Sbcmg0OZvXDotIu6p7SCIG2fc/+05WDmxVJv5eduYcHMnaof6k3Qgl7u+SKCgSHPmubWCI/DzaON+5zuhZksAJi3eSUGRg3YxoXRuEG5igZ5HAUrKVHpWHg0P/AaAvdFl4O1nckUiclpd/wfdHzDu/zzamDuvFCKCfPl0eCcCfb1YlpTBo9PW4izlaUAxwe8vweFdEFwHLnkCgJz8Ij5fshMwjj6pdYFrFKCkTC3YlEac1Th959f6apOrEZFSueRJ6DgccMK0O2HzL6XarFlUMO/d2B6b1cK0VXt5a97Wci1TzlLKWlj8nnG/72tGSwvgm+XJZOUV0bBGNS5voavMXaUAJWVq47oVNLKmUGTxhiaXm12OiJSGxQJ934DW1xvzok0dBtsXlGrTHrGRPN+/FQBvz9/Kt2pv4F4cdvjpXmMqnxb9IbYPAAVFDj79MwmAOy9qiNWqo0+uUoCSMlNQ5CBs168AHKlzIfipI7uIx7DajEHlza4Eez58cyMkLy3Vpjd0rsv/ehgDkB+bto4/t5auy7lUgOUfw77V4BsCfV4uXjxjzT5SMvOIDPLlmg61TSzQcylASZlZuTODS1gGQGDba0yuRkRcZvOCaz+DRpca8+Z9dZ3x5VsKD14eS7+20RQ5nIz4MoFNqVnlXKyc0eHdMP854/5lzxiNVAGHw8lHv28H4NYLG+DrZTOrQo/mcoAaNmwYf/zxR3nUIh5u5br1tLbuxIEFa7M+ZpcjImfDyxcGfWk0wM3Pgs/7Q8qaM25mtVp49bo2dGkQTnZ+EcM/W8G+w0fLv145OYcDfroPCnON/5YdhhU/9cv6VLam5xDk68WNXdQ482y5HKAyMzPp1asXTZo04cUXX2Tv3r3lUZd4IPvmOQBkhreBwEiTqxGRs+YTADdOgTrnQd5h+PxqYyDyGfh62fh4aCeaRAaSmpXHzZ8tV48os6yYANvng5cfXPU2WI2v+yK7g9fnbgbgtu4NCPbzNrNKj+ZygJo+fTp79+5lxIgRTJkyhfr169OnTx++++47CgtLN6eSVD67M47QOteYPNi/1ZUmVyMi58wv2Jg3r3YnOHoIPu8HqevOuFlIgDeTbu1MVLAf29JzuH3SSvIK7RVQsBRL3wi/Pmncv/x5Y/qeY6at2suO/bmEBXhz24UNTCqwcjirMVARERGMGTOGNWvWsGzZMho3bszQoUOJjo5m9OjRbN2qS1mrmkUbdnGhdT0Afi2uMLkaESkTfiEwdBrU7miEqEn9IHX9GTeLDvXn89s6E+znxcpdh7hn8mqK7Gq0WSGK8uH7O4wLARpfBufdXvxUfpGdt+ZtAWBkz8YE6ejTOTmnQeQpKSnMnTuXuXPnYrPZuOKKK1i3bh0tWrTgzTffLKsaxQPsXzsXP0sh2b5RxR1uRaQS8AuBm6ZBdAc4mmEciSrFmKimNYP4ZNh5+HhZmbshjSd/XK9GmxXht+chbR0EVIerxxstKo75amky+zLziAr246bz65lYZOXgcoAqLCzk+++/58orr6RevXp8++233H///ezbt49JkyYxb948pk6dyrPPPlse9Yobyiu0E5W6EICixnEl/ocVkUrAPxSG/mCEqCMHYdJVsGflGTfr3CCcdwa3x2qByct388qczeVfa1WW9Acsfte43+9dCPqnOWZufhHjF2wD4N5Lm+DnrSvvzpXLAapWrVrccccd1KtXj+XLl7Ny5UruvvtugoP/6fnTs2dPQkNDy7JOcWNLtx/gYssqAELbXWVyNSJSLvxD4ebpEHM+5GUaA8t3/nnGzXq3iuLFa1oD8MHC7Xx47PJ5KWNHMuCHEYDTuOKuWd8ST3/2ZxIHcwuoXz2A6zrVMafGSsblAPXmm2+yb98+xo8fT7t27U66TmhoKElJSedam3iIzYl/EWU5RL7VH0v97maXIyLl5fiYqAYXQ0EOfDkQts4742aDO9flsT7NAHjpl018vSy5vCutWhx2+O5WyNoD4Q0h7sUSTx8+UsDHf+wAYPRlTfG2qQVkWXD5Uxw6dCh+fpogVgxOpxPv7Ub7gsNRF2jyYJHKzqca3DgVmsRBUR5MHgwbZpxxs7subsSIY93KH5++jp/W7CvvSquOBS/AjgXgHQDXfwG+gSWefnPuFrLzi2gWFcRVbaJNKrLyUQyVc7LjQC6d8o3u46Ht+plcjYhUCG8/o9lmi6vBUQjfDoOVn51xs4fjYrmxS12cThg9JZF5G9IqoNhKbuNPsOh1436/dyGqVYmn/96XyRdLdwEw9soWmvOuDClAyTlZtmY9bazG6VrfFuo+LlJlePnAwM+gw83gdMDPo2Hhy3CaK+0sFgvPXd2qeMqX/321ioWb0yuw6Epm/5Zj456A80dC62tLPO1wOBn74984nHBlm1p0a1zDhCIrLwUoOSd5G2YDkB7cSt3HRaoamxdc9Q5c9JDxeOGLMHOMMSbnVJtYLbxxfVv6tIqiwO7gri8S+GubJh92WX42TBkCBdlQ70Jjrrt/+X7VHhJ2HSLAx8YTfVuYUGTlpgAlZy2v0E69g8a8iJbY3iZXIyKmsFjgkifgitcAi3Eq79thUHjqefC8bFbeHtyeXs0jyS9ycPuklSxPyqi4mj2dvcholnlgCwRFw3UTwVayKWbmkUJe+mUTAPdd2oSoEI1PLWsKUHLWEren0A1jaocaHa42uRoRMVXnO459kfsY43Li+0L2qcc4+XhZGT+kAxc3jeBooZ1bJi5nxU6FqDNyOo2jfFt+Mea5G/TFSY/+vzF3MwdzC2gcGcgtF2jKlvKgACVnLXn1HPwtBRzyisQS1drsckTEbC2vMRpu+ofB3gT45NLTTv3i62Xjo6EduaBxdXIL7Az7bDlLth+swII90O8vw6pJYLHCwE+hTqf/rHLiwPFn+7XEx0tf9eVBn6qcNf9dvwFwqHYPdR8XEUP9C+H2+RDeCDJ3w2dxsOXXU67u523jk5vPo3uTGhwpsHNL/HL+3KoxUSeVEA8Lxxn3r3gNmv934vaCIgcPfbtWA8crgAKUnJWDOfm0OrICgPC2mjxYRE5QvRHcPg/qdzcabk4eBIvfO+UVev4+Nibc3ImesRHkFTq4ddIKXZ33b5t/Ma50BGPQ/nm3nXS1t+ZtYUNKFmEB3oy9SgPHy5MClJyVxDWraGBNoxAvQlv2MrscEXE3AeHGJMTthxptDn593OiWnZ9z0tX9vG18OLQjl7WoSUGRgzs/T2DO36kVXLSb2rEQvr3F+Bzb3wQ9Hz/pait2ZhRPlTNuQGsigzRwvDwpQMlZyV7/CwB7g9qCb5DJ1YiIW/LyMZo79nkFrF7w9zT4pBcc2HbS1X29bLw/pANXtDZaHIz4MoGpK3dXcNFuZus8+HoQFB2Fpr3hyrdOOmQiJ7+IMVMTcTjh2o516N2qVsXXWsUoQInLnE4nEWnGJKKORpeaXI2IuDWLBbrcBcNnQmAU7N8IE3rCppknXd3bZuWdwe25vlMdHE54+Lu1fFRVJyDe/At8c4MxZU5sX7j+8/+0KzjuuZ82sDvjKLVD/XlKp+4qhAKUuGxHykHa240ra6I7XWVyNSLiEeqeD3f9AXW7QX4WfHMj/PIIFOb9Z1Uvm5WXB7bhrosbAjDul02Mm7UR52m6nFc6G2bAlJvAXmBMmXP9JPDyPemqv/6dypSVu7FY4I3r2xLkd/KQJWVLAUpctnXlXAIs+RyyhuNXW+0LRKSUgmrCsBnQdZTxeNmHMOESSN/4n1UtFguP9WnOY32aAfDRHzt44Ns1FBQ5KrJic6yZAt8OB0cRtLrWmDLnFEeekg7k8uC3awC4s3tDujSsXoGFVm0KUOIyy7b5AKRFXqj2BSLiGps3xL0AQ76DahGQ/jd83AOWTzjpVXp3XdyIVwa2wWa1MG3VXm7+bBmHjxRUfN0VweGA+c/BD3eC0w5tb4QBHxtT5pxEdl4hd3y+kqy8ItrXDWXM5U0ruOCqTQFKXFJQ5KBh5hIAAlrEmVyNiHisJpfBiMXQ+DJjjM+sB+Gra+Fw8n9Wvf68GD4d1olAXy+W7shgwAeL2XUw14Siy1FBLnx7Myx6zXh8wf1w9Xiw2k66ut3h5P5vEtmWnkNUsB8f3dQRX6+TryvlQwFKXPL3xr9pYtmDHSt1Oqr/k4icg8BIGPIt9H4ZbL6wbR6MPx+WfWQcjTlBj9hIvhvRlegQP3bsz+Wa9xezsrJM/ZK5Bz7rbUyBY/OB/h8akwNbT/0V/fqvm5m/KR1fLysfDe1IZLBaFlQ0BShxyf7EWQAk+zXHWi3c5GpExONZLHD+3TDiL6jbFQpz4ZeHYWJv2L+5xKrNooKZPvICWtcOISO3gBsmLOWLpbvcfnD5jBkzGDBgADNmzPjvk9vmwcc9IXUtBNSAYT9DuxtOv781+3h/oXFl4ivXtqFtTGg5VC1nogAlLgnc8zsAuXV7mFuIiFQuNZrA8FnGFCU+gbB7GXxwAfz6BORlFq8WGezHlLvO54rWURTanTw5fT0PfbeWvEK7icWfXnx8PAsWLCA+Pv6fhQVHYNZD8OVAyE2HyJZw5wKo2+W0+/pjy/7iQeN3XdyQq9vVLsfK5XQUoKTUco4cpXXeKgAiO/Q1uRoRqXSsVuh8B4xcZjSNdBTC4nfhnfaw4lOwFwEQ4OPF+Bs78FifZlgt8F3CHq79cDG7M46Y/AZObvjw4fTs2ZPhw4cbC/atho8vhuUfG48732VMfRNa97T7WbztAHd8vpKCIgdxLWvycFyz8i1cTsvidPdjnx4oKyuLkJAQMjMzCQ4ONrucMrNq0Sw6zL+BwwQTOnbXac/Pi4ics61zYc7/wYEtxuOI5sbYoCaXF18B/Ne2A9wzeTUZuQWE+Hvz8sDW7tuFOz8H/nwT/nrLaFEQGAX9x0PjM0+HtTwpg2GfLedooZ1Lm0XywU0d8fHSv8FlzZXvb336UmpHN84BICmki8KTiJS/41fq9XkF/EKNLuZfX28cvdn4MzgcXNC4Bj/dcyFt64SQebSQu79cxSPfrSU3v8js6v/hcMDqr+DdjsZVdo4ioznm/5aUKjwl7MrglolGeLq4aQTv39RB4ckN6L+AlFqkpm8RkYpm8zamgrl3NXS7B7yrQcoamDIEPrwQ1n9P7SAvvr27GyN6NMJigSkrd3Plu3+yZvdhs6uHnX/ChB7w4/8gJxXC6sP1X8B1k4wJl8/g9y37GfbZCnIL7FzYuAYfDVW7AnehU3jloDKewss6kErwe7EA7L9rHRG1Tn+uXkSkXOQehKXjYdnHUJBtLAusCe2HQsdhLDlYjTFTE0nJzMPLauGOixpy36VN8POuwNBhL4KNM2Dp+7BnhbHMNxguesgIg6eYkuVETqeTiX/t5PmZG3A4oWvD6nw2/Dz8fRSeypMr398KUOWgMgaoNbM/o+3S0Wy31qPR2LVmlyMiVd3RQ0a/qBWfGlexAWCBxr3IbX4dY/+O5vsNWQDUDQ/ghWta0b1JRPnWlHsQ1nxt1JW521hm8zHCXY/HILB0r19Q5OCpGeuZvNzYx3Ud6/D8Na105KkCKECZrDIGqIT3htLxwAz+rDGIC0d9bHY5IiKGogLYPAtWfgZJv/+z3OrN/sjz+WR/S6bltmY/YfRvF83/XdG8bJtOZu6FTTONI067/gLnsQagATXgvNvgvNuNhqGllJ6Vxz2TV7MsKQOLBf6vT3Nu794Ai6bNqhAKUCarjAEq5dmm1HKksbzbh3S+/PRN3kRETHFwO6z+wujofXBbiad2OSNZ7WjMOkss9dtezNWX9SQ4ONS1/duL4MBmow3BvtXG6bmUNSXXqdXWCE2trwfv0gc1p9PJtwl7eP7nDWTlFRHo68W7N7SnZ7PShy85dwpQJqtsAerQns2EfdKZQqeNzPu2UiNcs32LiJvbvxk2/Wxcrbdv1UlXOeodim/1elhDYyColnG6zWozBq5bbEYDz9x0yNlv/Dy8G4qO/msvFojpAs2vguZXGoPEXbQ74wj/98M6Fm09AEDr2iG8cX1bmtQMcnlfcm5c+f4++RTPIifYk/ALYcAmr1haKzyJiCeIiDVu3R+Ao4dh3yqcu5dzYNNf+KauJphs/AsPQ+phSF1zhp2dwCcQarWD6HYQ3R7qXwhBUWdVYuaRQuIX7+SjP7ZzpMCOr5eV0Zc15fYLG+Bl00Xy7k4BSs7IcmxcwYHIbiZXIiJyFvxDodElWBpdQkQPKLI7+H7pBqYvXIpP7j5qWw5Q05ZF88gA2tSqRvUAKxZHkXHlXGAkVIswfgZFQ3jDc+6DdyAnn08WJfHl0l3kHOtX1bl+OC8NbE3DiMBzf79SIRSg5PQcDupmGpfh+seq/5OIeD4vm5WBF7Si3/ktmLUuhc/+TGLNnkzYA+yB+tUD6NumFle0qEWLWsFlMoA7v8jOX9sO8Mu6VH5au4+8QmOwebOoIP7XszFXtq6F1aqB4p5EY6DKQWUaA5WxbTnhX15GttMf+4PbCQ2qZnZJIiJlyul0sir5EJ/9tZN5G9LIL3IUP1cnzJ9O9cLoUC+M9jFhNKsVhHcpTq/lFdrZvj+HTSnZ/L5lP79tSi8+2gTQNiaUUT0bc2mzSAUnN6IxUFJmUlbPJhz426c15ys8iUglZLFY6FgvnI71wsnNL2L+pnRmrU1hweZ09hw6yp5DR5meuA8Ab5uFyCA/IoJ8qRnsS/VAX5xOJ/mFDvKLHBwttLPzYC47D+Ti+NfhiZrBvsS1jKJv61p0bhCu1gQeTgFKTstn1x8AHI66wORKRETKXzVfL/q1jaZf22hy8otYtesQq5IPsTr5MKuTD5GVV8Tew0fZe/jfV+P9V2iAN7E1g2gXE0pcqyja1QnV0aZKRAFKTq0wj7o5xtUpIa0uM7kYEZGKFejrxUVNI7ioqdFB3OFwkpKVR1pWHulZ+aRn53EwpwAvqwVfbyu+XjZ8vazUDvMntmYQEUG+OspUiSlAySllbF5EOAWkOsNo2eY8s8sRETGV1Wqhdqg/tUP9zS5F3IAaTcgpHVz7KwDrfdsT7O9jcjUiIiLuo1IFqPr162OxWErcXnrppRLrrF27lu7du+Pn50dMTAyvvPLKf/bz7bff0qxZM/z8/GjdujWzZs2qqLfgVvz3LAIgq5bGP4mIiJyoUgUogGeffZaUlJTi2z333FP8XFZWFpdffjn16tUjISGBV199laeffpqPP/5nctzFixdzww03cNttt7F69Wr69+9P//79Wb9+vRlvxzxHDxF9ZBMAIS17mVyMiIiIe6l0Y6CCgoKIijp5W/2vvvqKgoICPvvsM3x8fGjZsiWJiYm88cYb3HnnnQC8/fbb9O7dm4ceegiA5557jrlz5/Lee+/x4YcfVtj7MFvu5oVUw8kWR23aNG9udjkiIiJupdIdgXrppZeoXr067du359VXX6Wo6J/GZUuWLOGiiy7Cx+ef8TxxcXFs3ryZQ4cOFa/Tq1fJIy5xcXEsWbLklK+Zn59PVlZWiZuny1g/F4C/fdsREeRrcjUiIiLupVIdgbr33nvp0KED4eHhLF68mMcee4yUlBTeeOMNAFJTU2nQoEGJbWrWrFn8XFhYGKmpqcXLTlwnNTX1lK87btw4nnnmmTJ+N+by22sExuyoriZXIiIi4n7c/gjUo48++p+B4f++bdpkjNUZM2YMPXr0oE2bNtx99928/vrrvPvuu+Tn55drjY899hiZmZnFt927d5fr65W7nP1EHN0BQGjzHubWIiIi4obc/gjUAw88wPDhw0+7TsOGDU+6vEuXLhQVFbFz505iY2OJiooiLS2txDrHHx8fN3WqdU41rgrA19cXX9/Kc5orf/sf+AIbHXVp36yR2eWIiIi4HbcPUBEREURERJzVtomJiVitViIjIwHo2rUrjz/+OIWFhXh7ewMwd+5cYmNjCQsLK15n/vz53H///cX7mTt3Ll27Vp1TWRl//0YtYK1Xa64PU8M4ERGRf3P7U3iltWTJEt566y3WrFnDjh07+Oqrrxg9ejQ33XRTcTi68cYb8fHx4bbbbuPvv/9mypQpvP3224wZM6Z4P/fddx+zZ8/m9ddfZ9OmTTz99NOsXLmSUaNGmfXWKpzP7r8AyIo6X9MQiIiInESlCVC+vr588803XHzxxbRs2ZIXXniB0aNHl+jxFBISwq+//kpSUhIdO3bkgQceYOzYscUtDAC6devG119/zccff0zbtm357rvvmD59Oq1atTLjbVW8nHSqH03C4bQQ3KyH2dWIiFQKM2bMYMCAAcyYMcPsUqSMWJxOp9PsIiqbrKwsQkJCyMzMJDg42OxyXFK09nu8pt3KBkc9bP/7i9ioILNLEhHxeAMGDGDBggX07NmTadOmmV2OnIIr399uPwZKKtahDfOJABKsrRgSGWh2OSIilcLxi6HOdFGUeA4FKCnBK9kY/5QZ2RmrVeOfRETKQr9+/ejXr5/ZZUgZqjRjoKQMZKcSdmQnDqeFwNiLza5GRETEbSlASTFH0p8AbHDWo22T+uYWIyIi4sYUoKRY1qYFAKykJa1qh5hcjYiIiPtSgJJi1l3GEaj9NTrjbdOvhoiIyKnoW1IMWSkE5+7E7rTg2+gCs6sRERFxawpQYthpHH3621mfVo3qmlyMiIiIe1OAEgDytv0OwFJHC9rHhJlcjYiIiHtTgBIAHDsWAbArqD1h1XxMrkZERMS9KUAJZKcRkGP0f7LV72p2NSIiIm5PAUogeTEAm5x1adFA459ERETORAFKsO80AtRyRywd62n8k4iIyJkoQAkFO4z579Z7taBRhCYQFhERORMFqKouLxO/gxsAKKx9viYQFhERKQUFqKpu93IsONnpqEmjho3NrkZERMQjKEBVdbuM8U8rNP5JRESk1BSgqrjj459WOpvRNibU3GJEREQ8hAJUVVaYhy11FQAHq3ck0NfL5IJEREQ8gwJUVbZvFTZHIfudIUQ1aGF2NSIiIh5DAaoq23VC/6f64SYXIyIi4jkUoKqw4w00Vzia0bGuApSIiEhpKUBVUTOmT+fal39hxuZCtvq1Jibc3+ySREREPIZGDVdR8R+/y8IdedgdvtTq3RaLRQ00RURESksBqooa3rMpJP1BxzaNCKtX3exyREREPIpO4VVR/erlMm1QAD6Nu9GuTqjZ5YiIiHgUBaiqyOnEcWwA+UpnLK3rhJhckIiIiGdRgKqKMnZgPbKffKcX2dXbEuTnbXZFIiIiHkUBqipKXgLAWmdDWtaNNLkYERERz6MAVRUlLwVgpSNW89+JiIicBQWoKsi5ezkACY6mtFOAEhERcZkCVFVz9BCWA5sBWG+NJTYqyOSCREREPI8CVFWzZyUAOxxR1K4dg7dNvwIiIiKu0rdnVbN7GQCrnDp9JyIicrYUoKqaYwEqwdFEA8hFRETOkgJUVWIvwrknATAGkLdXgBIRETkrClBVSfrfWApzyXL6c9C/AXXC/M2uSERExCMpQFUlx9oXrHY0oU1MGBaLxeSCREREPJMCVFVyfAC5owntYsJMLkZERMRzKUBVJccHkDub0jZGEwiLiIicLQWoqiI7FQ4n43BaSHQ0UgsDERGRc6AAVVUcG/+02RlDjeo1CA3wMbkgERERz6UAVVWo/5OIiEiZUYCqKk6YQLhtnVBzaxEREfFwClBVQWEepCQCxgDyNnU0gFxERORcKEBVBSlrwF7Afmcwe4ikRXSw2RWJiIh4NAWoqqC4/1NTGkUEEeDjZXJBIiIink0Bqio4oYFma52+ExEROWcKUJWd0wl7VgDGFXitaytAiYiInCsFqMouczfkpFGEjXXOhhpALiIiUgYUoCq7PSsB2OiIodDiQ4taClAiIiLnSgGqstubAMBqRxMaRwbi72MzuSARERHP5zEB6oUXXqBbt24EBAQQGhp60nWSk5Pp27cvAQEBREZG8tBDD1FUVFRinYULF9KhQwd8fX1p3Lgx8fHx/9nP+PHjqV+/Pn5+fnTp0oXly5eXwzuqIMeOQCU6GtG6dqi5tYiIiFQSHhOgCgoKuO666xgxYsRJn7fb7fTt25eCggIWL17MpEmTiI+PZ+zYscXrJCUl0bdvX3r27EliYiL3338/t99+O3PmzCleZ8qUKYwZM4annnqKVatW0bZtW+Li4khPTy/391jm7IXFDTQTnY1pXVv9n0RERMqCxel0Os0uwhXx8fHcf//9HD58uMTyX375hSuvvJJ9+/ZRs2ZNAD788EMeeeQR9u/fj4+PD4888ggzZ85k/fr1xdsNHjyYw4cPM3v2bAC6dOnCeeedx3vvvQeAw+EgJiaGe+65h0cffbRUNWZlZRESEkJmZibBwSaGln2J8PHFZFGNtnkf8d2IC+lYL8y8ekRERNyYK9/fHnME6kyWLFlC69ati8MTQFxcHFlZWfz999/F6/Tq1avEdnFxcSxZsgQwjnIlJCSUWMdqtdKrV6/idTzK3mOn7+wNsVistKilI1AiIiJlodK0pE5NTS0RnoDix6mpqaddJysri6NHj3Lo0CHsdvtJ19m0adMpXzs/P5/8/Pzix1lZWef0XsrMnmMDyJ2NaRIZpAHkIiIiZcTUI1CPPvooFovltLfTBRd3MW7cOEJCQopvMTExZpdkOH4EytFYHchFRETKkKlHoB544AGGDx9+2nUaNmxYqn1FRUX952q5tLS04ueO/zy+7MR1goOD8ff3x2azYbPZTrrO8X2czGOPPcaYMWOKH2dlZZkfoo4ehgNbAFjjaMR96kAuIiJSZkwNUBEREURERJTJvrp27coLL7xAeno6kZGRAMydO5fg4GBatGhRvM6sWbNKbDd37ly6du0KgI+PDx07dmT+/Pn0798fMAaRz58/n1GjRp3ytX19ffH19S2T91Fm9q0CYA81ySBYR6BERETKkMcMIk9OTiYxMZHk5GTsdjuJiYkkJiaSk5MDwOWXX06LFi0YOnQoa9asYc6cOTzxxBOMHDmyONzcfffd7Nixg4cffphNmzbx/vvvM3XqVEaPHl38OmPGjGHChAlMmjSJjRs3MmLECHJzc7nllltMed9n7dj4pwR7I2xWiwaQi4iIlCGPGUQ+duxYJk2aVPy4ffv2ACxYsIAePXpgs9n4+eefGTFiBF27dqVatWoMGzaMZ599tnibBg0aMHPmTEaPHs3bb79NnTp1+OSTT4iLiyteZ9CgQezfv5+xY8eSmppKu3btmD179n8Glru9vf800GwSGYiftwaQi4iIlBWP6wPlCUzvA+V0wquN4cgBrsl/hsYdevLqdW0rvg4REREPUiX7QMkJDu+CIwcowosNznoa/yQiIlLGFKAqo2Pz32221CcfH1rpCjwREZEypQBVGe01BpCvKGyI1QLNozSAXEREpCwpQFVGe/5poNmgRjV1IBcRESljClCVTVEBpKwBINHZiJbROn0nIiJS1hSgKpu0dWDPJ8cazE5nFC2idfpORESkrClAVTZ7jQ7kf1saAxZaKkCJiIiUOQWoyuZYgFqaXx9AHchFRETKgQJUZbNvNQBrHQ2ICvajeqCbzdEnIiJSCShAVSb5OXBgMwBrHQ01/klERKScKEBVJqlrwekg0yuC/YRp/JOIiEg5UYCqTI6dvttoaQRo/JOIiEh5UYCqTI4NIF+cVw9APaBERETKiQJUZXLsCFSivT5Bvl7UCfM3uSAREZHKSQGqsjh6GDK2A8YA8ubRwVitFnNrEhERqaS8zC5AykhKIgCHfKI5nBek8U8iUqXZ7XYKCwvNLkPcjLe3NzZb2cwPqwBVWRw7fbfJagwg1xV4IlIVOZ1OUlNTOXz4sNmliJsKDQ0lKioKi+XcztIoQFUWxwLUkmMDyNUDSkSqouPhKTIykoCAgHP+kpTKw+l0cuTIEdLT0wGoVavWOe1PAaqy2GsEqBUF9fC2WWgSGWRyQSIiFctutxeHp+rVq5tdjrghf3/j4qr09HQiIyPP6XSeBpFXBrkHIDMZgPWOBjSJDMLHS/9pRaRqOT7mKSAgwORKxJ0d//041zFy+patDPYlApDhX49sAjT+SUSqNJ22k9Mpq98PBajKYJ/RQHOLtTGg8U8iIp6mR48e3H///WaXAcD06dNp3LgxNpuN+++/n/j4eEJDQ80uy+0oQFUGxwaQL82rC6gDuYiIlLRw4UIsFkuprk686667uPbaa9m9ezfPPfccgwYNYsuWLcXPP/3007Rr1678ivUQGkReGRwLUIuOGAGqeS0NIBcREdfl5OSQnp5OXFwc0dHRxcuPD76Wf+gIlKfLSoHsFJwWK6OHDuS5q1sS5OdtdlUiIuKioqIiRo0aRUhICDVq1ODJJ5/E6XQWP5+fn8+DDz5I7dq1qVatGl26dGHhwoXFz+/atYurrrqKsLAwqlWrRsuWLZk1axY7d+6kZ8+eAISFhWGxWBg+fPh/Xn/hwoUEBRl/gF9yySVYLBYWLlxY4hRefHw8zzzzDGvWrMFisWCxWIiPjy+vj8St6QiUpzt29MkS0YwLW9TjQpPLERFxJ06nk6OFdlNe29/b5tKA5UmTJnHbbbexfPlyVq5cyZ133kndunW54447ABg1ahQbNmzgm2++ITo6mh9++IHevXuzbt06mjRpwsiRIykoKOCPP/6gWrVqbNiwgcDAQGJiYvj+++8ZOHAgmzdvJjg4+KRHlLp168bmzZuJjY3l+++/p1u3boSHh7Nz587idQYNGsT69euZPXs28+bNAyAkpGoOG1GA8nTHAhTRHcytQ0TEDR0ttNNi7BxTXnvDs3EE+JT+azYmJoY333wTi8VCbGws69at48033+SOO+4gOTmZiRMnkpycXHxq7cEHH2T27NlMnDiRF198keTkZAYOHEjr1q0BaNiwYfG+w8PDAYiMjDzlgHAfHx8iIyOL14+KivrPOv7+/gQGBuLl5XXS56sSBShPd+wKPKLbmVqGiIicm/PPP7/EEauuXbvy+uuvY7fbWbduHXa7naZNm5bYJj8/v7hp6L333suIESP49ddf6dWrFwMHDqRNmzYV+h6qEgUoT+Z06giUiMhp+Hvb2PBsnGmvXVZycnKw2WwkJCT8p3t2YGAgALfffjtxcXHMnDmTX3/9lXHjxvH6669zzz33lFkd8g8FKE+WtReOHASrF9RsaXY1IiJux2KxuHQazUzLli0r8Xjp0qU0adIEm81G+/btsdvtpKen071791PuIyYmhrvvvpu7776bxx57jAkTJnDPPffg4+MDGNPdnCsfH58y2Y+n01V4nixljfEzojl4+zFjxgwGDBjAjBkzzK1LRERclpyczJgxY9i8eTOTJ0/m3Xff5b777gOgadOmDBkyhJtvvplp06aRlJTE8uXLGTduHDNnzgTg/vvvZ86cOSQlJbFq1SoWLFhA8+bNAahXrx4Wi4Wff/6Z/fv3k5OTc9Z11q9fn6SkJBITEzlw4AD5+fnn/uY9kAKUJzseoGq1BYzLSxcsWFBlLykVEfFkN998M0ePHqVz586MHDmS++67jzvvvLP4+YkTJ3LzzTfzwAMPEBsbS//+/VmxYgV16xo9AO12OyNHjqR58+b07t2bpk2b8v777wNQu3ZtnnnmGR599FFq1qzJqFGjzrrOgQMH0rt3b3r27ElERASTJ08+tzfuoSzOE5tMSJnIysoiJCSEzMxMgoPLcVqVrwfBltnQ51XociczZswgPj6e4cOH069fv/J7XRERN5SXl0dSUhINGjTAz8/P7HLETZ3u98SV72/PODEsJ/evI1D9+vVTcBIREakAOoXnqbLTIDsFsEBUK7OrERERqVIUoDxV6lrjZ42m4FPN3FpERESqGAUoT5WSaPw8dvpOREREKo4ClKf61/gnERERqTgKUJ5KAUpERMQ0ClCe6EgGHE427ke1NrcWERGRKkgByhMdH0AeVh/8Q82sREREpEpSgPJEOn0nIiJiKgUoT6QAJSIiJouPjyc0NNTsMhg+fDj9+/ev8NdVgPJEClAiIuLmdu7cicViITEx0S33d64UoDxNXhYc3Gbcj1KAEhGpqgoKCswuoUx46vtQgPI0aeuNn8G1ITDC3FpERKRMZGdnM2TIEKpVq0atWrV488036dGjB/fff3/xOvXr1+e5557j5ptvJjg4mDvvvBOA77//npYtW+Lr60v9+vV5/fXXS+zbYrEwffr0EstCQ0OJj48H/jmyM23aNHr27ElAQABt27ZlyZIlJbaJj4+nbt26BAQEcM0113Dw4MHTvqcGDRoA0L59eywWCz169AD+OeX2wgsvEB0dTWxsbKnqPNX+jnvttdeoVasW1atXZ+TIkRQWFp62vnOlyYQ9TcqxK/B0+k5E5MycTig8Ys5reweAxVKqVceMGcNff/3FjBkzqFmzJmPHjmXVqlW0a9euxHqvvfYaY8eO5amnngIgISGB66+/nqeffppBgwaxePFi/ve//1G9enWGDx/uUrmPP/44r732Gk2aNOHxxx/nhhtuYNu2bXh5ebFs2TJuu+02xo0bR//+/Zk9e3ZxDaeyfPlyOnfuzLx582jZsiU+Pj7Fz82fP5/g4GDmzp1b6vpOt78FCxZQq1YtFixYwLZt2xg0aBDt2rXjjjvucOkzcIUClKfR+CcRkdIrPAIvRpvz2v+3r1RzlWZnZzNp0iS+/vprLr30UgAmTpxIdPR/677kkkt44IEHih8PGTKESy+9lCeffBKApk2bsmHDBl599VWXA9SDDz5I3759AXjmmWdo2bIl27Zto1mzZrz99tv07t2bhx9+uPh1Fi9ezOzZs0+5v4gI4yxJ9erViYqKKvFctWrV+OSTT0qEoDM53f7CwsJ47733sNlsNGvWjL59+zJ//vxyDVA6hedpFKBERCqVHTt2UFhYSOfOnYuXhYSEFJ/aOlGnTp1KPN64cSMXXHBBiWUXXHABW7duxW63u1RHmzZtiu/XqlULgPT09OLX6dKlS4n1u3bt6tL+T9S6dWuXwtOZtGzZEpvNVvy4Vq1axbWXFx2B8iSFR2H/JuO+ApSIyJl5BxhHgsx67TJWrdqZj2j9m8Viwel0llh2svFB3t7eJbYBcDgcLr9eaZzsfZS2zpM5sfbj+yqv2o9TgPIkaRvAaYdqERBUy+xqRETcn8VSqtNoZmrYsCHe3t6sWLGCunXrApCZmcmWLVu46KKLTrtt8+bN+euvv0os++uvv2jatGnxEZmIiAhSUlKKn9+6dStHjrg2Lqx58+YsW7asxLKlS5eedpvjR5hKeyTsTHW6ur/ypgDlSVISjZ+12pZ6YKKIiLi3oKAghg0bxkMPPUR4eDiRkZE89dRTWK3W4iNBp/LAAw9w3nnn8dxzzzFo0CCWLFnCe++9x/vvv1+8ziWXXMJ7771H165dsdvtPPLII/85YnMm9957LxdccAGvvfYaV199NXPmzDnt+CeAyMhI/P39mT17NnXq1MHPz4+QkJBTrn+mOl3dX3nTGChPkncYvPx1+k5EpJJ544036Nq1K1deeSW9evXiggsuoHnz5vj5+Z12uw4dOjB16lS++eYbWrVqxdixY3n22WdLDCB//fXXiYmJoXv37tx44408+OCDBAS4dnrx/PPPZ8KECbz99tu0bduWX3/9lSeeeOK023h5efHOO+/w0UcfER0dzdVXX33a9c9Up6v7K3dOD/H88887u3bt6vT393eGhIScdB3gP7fJkyeXWGfBggXO9u3bO318fJyNGjVyTpw48T/7ee+995z16tVz+vr6Ojt37uxctmyZS7VmZmY6AWdmZqZL25WKvcjpzMsu+/2KiHi4o0ePOjds2OA8evSo2aWcs5ycHGdISIjzk08+MbuUSud0vyeufH97zBGogoICrrvuOkaMGHHa9SZOnEhKSkrx7cT5cZKSkujbty89e/YkMTGR+++/n9tvv505c+YUrzNlyhTGjBnDU089xapVq2jbti1xcXHlPpq/1Kw28A00uwoRESlDq1evZvLkyWzfvp1Vq1YxZMgQAPOPssgpecwYqGeeeQaguCPpqYSGhv6nP8RxH374IQ0aNCju0tq8eXP+/PNP3nzzTeLi4gDjMOodd9zBLbfcUrzNzJkz+eyzz3j00UfL6N2IiIiU9Nprr7F582Z8fHzo2LEjixYtokaNGmaXJafgMUegSmvkyJHUqFGDzp0789lnn5W4JHLJkiX06tWrxPpxcXHF7eoLCgpISEgosY7VaqVXr17/aWl/ovz8fLKyskrcRERESqt9+/YkJCSQk5NDRkYGc+fOpXXr1maXJafhMUegSuPZZ5/lkksuISAggF9//ZX//e9/5OTkcO+99wKQmppKzZo1S2xTs2ZNsrKyOHr0KIcOHcJut590nU2bNp3ydceNG1d8hExEREQqP1OPQD366KNYLJbT3k4XXP7tySef5IILLqB9+/Y88sgjPPzww7z66qvl+A4Mjz32GJmZmcW33bt3l/trioiIiHlMPQL1wAMPnHGunoYNG571/rt06cJzzz1Hfn4+vr6+REVFkZaWVmKdtLQ0goOD8ff3x2azYbPZTrrOqcZVAfj6+uLr63vWdYqISNlx/qubtciJyur3w9QAFRERUTw5YHlITEwkLCysONx07dqVWbNmlVhn7ty5xfP5HB+4N3/+/OKr9xwOB/Pnz2fUqFHlVqeIiJy7400Xjxw5gr+/v8nViLs63t3c1Wai/+YxY6CSk5PJyMggOTkZu91OYmIiAI0bNyYwMJCffvqJtLQ0zj//fPz8/Jg7dy4vvvgiDz74YPE+7r77bt577z0efvhhbr31Vn777TemTp3KzJkzi9cZM2YMw4YNo1OnTnTu3Jm33nqL3Nzc4qvyRETEPdlsNkJDQ4vbzgQEBJyxk7dUHU6nkyNHjpCenk5oaGiJyYfPhscEqLFjxzJp0qTix+3btwdgwYIF9OjRA29vb8aPH8/o0aNxOp00bty4uCXBcQ0aNGDmzJmMHj2at99+mzp16vDJJ58UtzAAGDRoEPv372fs2LGkpqbSrl07Zs+e/Z+B5SIi4n6OD7dwm9594nZO1+7IFRanThaXuaysLEJCQsjMzCQ4ONjsckREqhy73U5hYaHZZYib8fb2Pu2RJ1e+vz3mCJSIiEhpHb8oSKS8VLpGmiIiIiLlTQFKRERExEUKUCIiIiIu0hiocnB8XL7mxBMREfEcx7+3S3N9nQJUOcjOzgYgJibG5EpERETEVdnZ2YSEhJx2HbUxKAcOh4N9+/YRFBRU5k3csrKyiImJYffu3WqRcAb6rEpPn1Xp6bMqPX1WpafPqvTK87NyOp1kZ2cTHR2N1Xr6UU46AlUOrFYrderUKdfXCA4O1v9kpaTPqvT0WZWePqvS02dVevqsSq+8PqszHXk6ToPIRURERFykACUiIiLiIgUoD+Pr68tTTz2Fr6+v2aW4PX1WpafPqvT0WZWePqvS02dVeu7yWWkQuYiIiIiLdARKRERExEUKUCIiIiIuUoASERERcZEClIiIiIiLFKA8xAsvvEC3bt0ICAggNDT0pOtYLJb/3L755puKLdRNlObzSk5Opm/fvgQEBBAZGclDDz1EUVFRxRbqhurXr/+f36OXXnrJ7LLcxvjx46lfvz5+fn506dKF5cuXm12S23n66af/8zvUrFkzs8tyC3/88QdXXXUV0dHRWCwWpk+fXuJ5p9PJ2LFjqVWrFv7+/vTq1YutW7eaU6zJzvRZDR8+/D+/Z717966w+hSgPERBQQHXXXcdI0aMOO16EydOJCUlpfjWv3//iinQzZzp87Lb7fTt25eCggIWL17MpEmTiI+PZ+zYsRVcqXt69tlnS/we3XPPPWaX5BamTJnCmDFjeOqpp1i1ahVt27YlLi6O9PR0s0tzOy1btizxO/Tnn3+aXZJbyM3NpW3btowfP/6kz7/yyiu88847fPjhhyxbtoxq1aoRFxdHXl5eBVdqvjN9VgC9e/cu8Xs2efLkiivQKR5l4sSJzpCQkJM+Bzh/+OGHCq3H3Z3q85o1a5bTarU6U1NTi5d98MEHzuDgYGd+fn4FVuh+6tWr53zzzTfNLsMtde7c2Tly5Mjix3a73RkdHe0cN26ciVW5n6eeesrZtm1bs8twe//+N9vhcDijoqKcr776avGyw4cPO319fZ2TJ082oUL3cbLvt2HDhjmvvvpqU+pxOp1OHYGqZEaOHEmNGjXo3Lkzn332GU61+TqpJUuW0Lp1a2rWrFm8LC4ujqysLP7++28TK3MPL730EtWrV6d9+/a8+uqrOrWJcVQzISGBXr16FS+zWq306tWLJUuWmFiZe9q6dSvR0dE0bNiQIUOGkJycbHZJbi8pKYnU1NQSv2MhISF06dJFv2OnsHDhQiIjI4mNjWXEiBEcPHiwwl5bkwlXIs8++yyXXHIJAQEB/Prrr/zvf/8jJyeHe++91+zS3E5qamqJ8AQUP05NTTWjJLdx77330qFDB8LDw1m8eDGPPfYYKSkpvPHGG2aXZqoDBw5gt9tP+nuzadMmk6pyT126dCE+Pp7Y2FhSUlJ45pln6N69O+vXrycoKMjs8tzW8X97TvY7VtX/XTqZ3r17M2DAABo0aMD27dv5v//7P/r06cOSJUuw2Wzl/voKUCZ69NFHefnll0+7zsaNG0s9+PLJJ58svt++fXtyc3N59dVXK02AKuvPqypx5bMbM2ZM8bI2bdrg4+PDXXfdxbhx40yfOkE8Q58+fYrvt2nThi5dulCvXj2mTp3KbbfdZmJlUpkMHjy4+H7r1q1p06YNjRo1YuHChVx66aXl/voKUCZ64IEHGD58+GnXadiw4Vnvv0uXLjz33HPk5+dXii++svy8oqKi/nP1VFpaWvFzlc25fHZdunShqKiInTt3EhsbWw7VeYYaNWpgs9mKf0+OS0tLq5S/M2UpNDSUpk2bsm3bNrNLcWvHf4/S0tKoVatW8fK0tDTatWtnUlWeo2HDhtSoUYNt27YpQFV2ERERRERElNv+ExMTCQsLqxThCcr28+ratSsvvPAC6enpREZGAjB37lyCg4Np0aJFmbyGOzmXzy4xMRGr1Vr8OVVVPj4+dOzYkfnz5xdf3epwOJg/fz6jRo0ytzg3l5OTw/bt2xk6dKjZpbi1Bg0aEBUVxfz584sDU1ZWFsuWLTvjFdgCe/bs4eDBgyXCZ3lSgPIQycnJZGRkkJycjN1uJzExEYDGjRsTGBjITz/9RFpaGueffz5+fn7MnTuXF198kQcffNDcwk1yps/r8ssvp0WLFgwdOpRXXnmF1NRUnnjiCUaOHFlpAufZWLJkCcuWLaNnz54EBQWxZMkSRo8ezU033URYWJjZ5ZluzJgxDBs2jE6dOtG5c2feeustcnNzueWWW8wuza08+OCDXHXVVdSrV499+/bx1FNPYbPZuOGGG8wuzXQ5OTkljsQlJSWRmJhIeHg4devW5f777+f555+nSZMmNGjQgCeffJLo6Ogq2ZLmdJ9VeHg4zzzzDAMHDiQqKort27fz8MMP07hxY+Li4iqmQNOu/xOXDBs2zAn857ZgwQKn0+l0/vLLL8527do5AwMDndWqVXO2bdvW+eGHHzrtdru5hZvkTJ+X0+l07ty509mnTx+nv7+/s0aNGs4HHnjAWVhYaF7RbiAhIcHZpUsXZ0hIiNPPz8/ZvHlz54svvujMy8szuzS38e677zrr1q3r9PHxcXbu3Nm5dOlSs0tyO4MGDXLWqlXL6ePj46xdu7Zz0KBBzm3btpldlltYsGDBSf9tGjZsmNPpNFoZPPnkk86aNWs6fX19nZdeeqlz8+bN5hZtktN9VkeOHHFefvnlzoiICKe3t7ezXr16zjvuuKNEa5ryZnE6dZ27iIiIiCvUB0pERETERQpQIiIiIi5SgBIRERFxkQKUiIiIiIsUoERERERcpAAlIiIi4iIFKBEREREXKUCJiIiIuEgBSkRERMRFClAiIiIiLlKAEhE5g/379xMVFcWLL75YvGzx4sX4+Pgwf/58EysTEbNoLjwRkVKYNWsW/fv3Z/HixcTGxtKuXTuuvvpq3njjDbNLExETKECJiJTSyJEjmTdvHp06dWLdunWsWLECX19fs8sSERMoQImIlNLRo0dp1aoVu3fvJiEhgdatW5tdkoiYRGOgRERKafv27ezbtw+Hw8HOnTvNLkdETKQjUCIipVBQUEDnzp1p164dsbGxvPXWW6xbt47IyEizSxMREyhAiYiUwkMPPcR3333HmjVrCAwM5OKLLyYkJISff/7Z7NJExAQ6hScicgYLFy7krbfe4osvviA4OBir1coXX3zBokWL+OCDD8wuT0RMoCNQIiIiIi7SESgRERERFylAiYiIiLhIAUpERETERQpQIiIiIi5SgBIRERFxkQKUiIiIiIsUoERERERcpAAlIiIi4iIFKBEREREXKUCJiIiIuEgBSkRERMRFClAiIiIiLvp/FC9OvxAdh9UAAAAASUVORK5CYII=", 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", 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FFKAqp7V7Mrn500Xsy86lSqAP7w9tS8e6J7QkbZwJP46ErD3m49g2cOlYqNujfASnfzMMSE40g9S6HwEDsJljpLo/CtXqn9Np0zNzeHzqamavNacHaV49hLeGtKFuRNBZjhSRSm3DLzBpiHn18P0rSuUl3Pn+1p99IiVg6Y6DDPkwkX3ZuTSODuaH4V3/CU9HDsCUO+HL68zwFF4Hrvsc7pgL9S4pn+EJzLpqd4HBn8M9C6DpAMCA1d/CuxfAtHvNq/zcFBnix4c3teOt69sQFuDN6t2Z9HnrLyYvTkZ/z4nIaW2eY/6sd4m1dRyjFqhSoBaoyuWvTfu48/MlHMlz0r52OJ/EX0Co/7EJJNf+ANNHw+G9YLND5+HQ/THw8dAr0VJWwNznzXlYwLxy75InoN0t53TVXmpGDqO+TiocG3Vli2jGXd1SE3CKyMneagsHtsDgidCkb6m8hLrwLKYAVXnMXJPKvV8uJ8/p4qIG1fjfTe0I8PGCgjyY+ag5SBwgogn0fxdqtLO24JKyIxF+eQhSV5mPo1pAn1egVie3T+VyGXz451ZembmBApdB9TB/3hvallY1w0q2ZhHxXAe3w5utwOaAR7aDX+l8t6oLT6QM/LIqhXsmLiPP6aJ3s2g+HtbeDE+ZKZDQ51h4ssFFo+E/v1ec8ARQuzPc+bt5JYxfKKStgk97mWO8crPcOpXdbuOubvWYck8X4qoGsPvQUa79IJEvFuxQl56ImI5339XsUGrhyV0KUCLn4PeNe7nvq+U4XQYD21bnnRva4OvlMKcC+N/FsGuRGSxu+NocKO5VAddKtDvMy4jvXQZtbza3LR0P73WBrb+7fbqWNcKYdu+FXN40ijyni/9OXc3ob1ZwNM9ZwoWLiMfZ8pv5s96l1tZxAgUoETct2X6A/3y+hHynQZ8WMbx8TStzUsjFn8CEq+BwOkQ2MweJN7zc6nJLX2A16Pc2DPsRwmpBRjJ81s8c+5Wb7dapQvy8+d9N7Xj0isbYbTBl2W6ufu9vdh44UkrFi0i558yHbX+Y9+uXjwHkoAAl4pY1ezK4JWExOfkuujWM4PXBrXHYgN9fgumjzKVRmg+C22eXyhwl7po2bRoDBw5k2rRppf9idS6GuxOh/W3m48UfwwcXwp7lbp3GZrPxn271mHh7J6oF+bI+NYv+7/7Nwq37S6FoESn3di2B3EzwrwIxra2uppAClEgxbdt3mGGfLiIrp4AL4sL54MZ2+NiBGY/C3OfMnbo9AoM+AZ/TLBRcxhISEpg7dy4JCQll84K+QdD3Nbj5B3Oh4oPb4OPLIPE9c14pN3SuV5Uf7+1K8+ohHDicx42fLOSrRcmlVLiIlFtbjo1/qtvdHDpQTihAiRTDoSN53JqwmH3ZeTSLDeGT+Avwdxjwwz2w8H1zp94vmjN1l6N5neLj4+nRowfx8fFl+8J1u8Ndf5qzl7vyzSsSv7rBnBPLDTGh/nzzny70aRlDvtNgzJRVPDVtDQVOV+nULSLlz/HxT/XLz/gn0DQGpULTGFQs+U4Xwz5dxPwt+6ke5s/U4V2J8Ae+uQU2TDcvqx3wHrQaYnWp5Y9hmF15Mx8z1/wLqQ7XToCaF7h5GoO3f9vMa7M3AnBp40jevqGNedWjiFRch/fDy/UAA0atg5DYUn05TWMgUkIMw+CpY4vgBvg4+HhYeyICHPDdbWZ4cvjCkIkKT6djs5lX6t3+K1SpB5m7IeFKWPa5m6excd+lDXh/aFt8vezMWZ/O9R8uYG9WbikVLiLlwuZfAQOimpd6eHKXApTIGXyWuIOJC5Ox2eDNIW1oEhUEP95nrg3n8IEbJkOjK6wus/yLaWXOhdW4r9kSNW0E/PyQeXWNG65oEcOXd3QiPMCbFbsyGPj+32zZ696VfiLiQTbNNH82KH9XNCtAiZzGn5v28sxPawF4pHdjLmsSCbMeh6SJZrfdNeOhXg+Lq/QgvsHmGoA9HjcfL/oQPusP2XvdOk272uFMuacrtasGsPPAUQa9P5+lO9wbWyUiHsBZcKwFCmjYy9paTkEBSuQU9hw6yr2TzIkyB7WtwX8urmtOVbDgPXOH/u+W2lpMFZrdDt0ehiGTwCcYdvwNH/WA9HVunaZOtUC+u7sLrWuGcehIPkM/Xsi8DemlVLSIWGLXIsjJAP9wqOHeuMmyoAAl8i/5Thf3TlrOoSP5tKgeyvMDm2Nb/DHMe97c4YqXoPX11hbp6RpfCXf8Zo6LytgJn/SCrfPcOkW1IF8m3dGJ7o0iyMl3cfuEJUxbsad06hWRsrfxWPdd/Z7lavqC4xSgRP7l1VkbWbrjIMG+Xrx7Q1t8t8+FXx42n+z+GHT8j7UFVhQRDc3B5bU6Q24GfDEIlk906xT+Pg4+vKk9/VrFUuAyuP+r5Xy+YEcpFSwiZWrTLPNng/LXfQcKUCJFzN2Qzge/bwHgpWtaUsvYA9/cCoYLWt9odj9JyQmoAjdNNWdvdxWY82r99n9uTbrp42XnjcGtualTbQwDnpi6mnfnbi69mkWk9B3aCelrwWYvd/M/HacAJXJMSsZRRk1OAmBY59pcUd8fJg0xW0dqdjRn2C5Hk2RWGN5+MPBjuGi0+fiPl+GH4eYA0mKy2208078Z911SH4CXZ27gtVkb0DR3Ih7q+NV3NTqYf2iVQwpQIoDTZXD/pCQOHsmnefUQHruioTnX0/5NEFIDBn8BXr5Wl1lx2e1w6Vi46i3zL86kifD1zZCfU+xT2Gw2Rl3eiEevaAzAW79t5oUZ6xWiRDzRxmPdd+V4QXYFKBHgk7+2smj7AYJ8vXjn+rb4zn3avHzWyx+u/xKCIq0usXJoN8yc6sDha05U+sUgyMl06xT/6VaPJ69qCsD/ft/K0z+uVYgS8ST5R2HbH+b9cjr+CRSgRNiUlsUrs8wlQp7o24S41JmQ+I755NXvm5NAStlp0hdu/O7YNAd/wYS+bs8VdUvXOjx3dXMAEuZv5/Gpq3G5FKJEPMK2P6HgqLn0U1Qzq6s5LQUoqdQKnC4e/GYFeQUuejSK4Lp6TvjxfvPJC0dBs6utLbCyqnMRxP8EAdUgZQWM7w0Zu906xdCOtXnpmpbYbPDlwmTGTlutligRT3Di7OPleNypApRUah/8voUVuzII8fPihQFNsH13O+RmmoPGj8+YLdaIbQ23zoTQmrB/s7mG3qFkt05xXfuavHJNK2w2+GJBMk9NW6MQJVKeGcYJ45/Kb/cdKEBJJbZ2TyZvztkEwNP9mxG19FXYvQT8QmHQx+DwsrhCoVp9uOVnCKsNB7fD+D5wYJtbpxjUrgYvDjJboiYk7uCZnzQmSqTc2rseMpLNcZB1Lra6mjNSgJJKKf9Y112+0+DyplEMCNkEf71hPtnvbQirZWl9coKwWnDLL8dmLU+GhD6wf4tbp7iufU3GXd0CgPF/b+e56esUokTKo+Ozj9e5GHwCra3lLBSgpFL6+M9trE3JJDzAm+d7RWP7/j+AAe1ugab9rS5P/i20utkSVa0RZO6G8VfC3o1unWJIh1qFA8s//msbr89273gRKQMbfjF/lvPuO1CAkkpo54EjvDnH/PL875VNqDZnNGSnQUQT6PW8xdXJaQVHQ/x0iGwG2akw4Sq3W6KGdqzN0/3Mq3re+m0z//vdveNFpBRlp8POheb9RldaW0sxKEBJpfP0j2vIyXfRsU4VBnr/DRtngMMHrvkEfAKsLk/OJCgChv1YNES5OSZqWJc4Hu7dCIBxv6znC62dJ1I+bPgFMCC2jdnqXM4pQEmlMmtNKr+uS8fLbmPc5ZHYfnnEfKLbI+V6vhE5QWBVuPkHiGhsdudNuAoOuheC7ulen3u61wPgiR9W8/3yXaVRqYi4Y/1082fjPtbWUUwKUFJpHMkr4Okf1wJw58V1qbvoKcg5BNEtoev9ltYmbgqKgJunQdX6kLHTDFEZ7oWgh3o1Ir5LHIYBD36zkllrUkupWBE5q9xs2DrPvN+4r6WlFJcClFQab87ZxO5DR6kR7s/9MWth3TSwe0H/d8HhbXV54q7gKLM7L7wOHNphhqis4ocgm83G2L5NuaZdDZwugxGTlrNg6/5SLFhETmvLHHDmQpW6ZuuyB1CAkkphY1oWn/xpjpV5rlcsvrMeNp+48AGIaWlhZXJeQmLNGcvDasGBrfD51XDkQLEPt9ttvDCwBZc1jSKvwMUdE5awZk9GKRYsIqd0YvddOZ59/EQKUFIpPPvTWgpcBpc1jaLb1tfg8F7zr5yLH7K6NDlfoTXM7rygaEhfCxOvgdysYh/u5bDz9vVt6FCnClm5BQz7dDE79h8uxYJFpAhnvnkxD0Ajzxj/BApQUgnM25DOn5v24eOw83/NU2HlZLDZza47L1+ry5OSUKUO3DwV/MNh91L46gbIzyn24X7eDj4e1p4mMSHsy87lxk8Wkp5Z/ONF5Dzs+BtyMsy1L2t2sLqaYlOAkgqtwOniuenrALi1UwxRf401n+h4F9Rob2FlUuIim8CN34FPMGz7A76JN/+yLaYQP28m3HoBtasGsPPAUeLHLyYrp/jHi8g5Wv+z+bPRFWB3WFuLGxSgpEL7avFONqVnEx7gzf1Bc+DAFgiMhO6PWl2alIbq7eCGr8DLDzb+Aj+MAJer2IdHBvvx+a0dqRbkw9qUTO6ZuIy8guIfLyJuMowTxj95xtV3xylASYWVlZNfuFzHo11D8J//qvnEZc+AX4iFlUmpirsQrvsMbA5Y+RX8Otatw2tVDeDT+AsI8HHw56Z9jJmyUuvmiZSWlBWQuQu8A6FuN6urcYsClFRY783bwv7DedStFsg1B/4H+YehZkdoOdjq0qS0NexljnEDmP82/P2WW4e3rBHGu0Pb4rDbmLJsN6/M2lAKRYpIYetT/UvB29/aWtykACUV0q6DR/jkL3PagpfaZ2FfMwWwwZUvg12/9pVC6+vN1kaA2U9A0iS3Du/RKJJxV7cA4N25W/hcS76IlLwNx8Y/ecjs4yfSN4lUSK/O2khegYuudcJot/bYAsHtb4WYVtYWJmWr6/3QeYR5/4fhsHGWW4dfd0FNRvZsAMCTP6zmt/VpJV2hSOV1YCukrTa72xtcbnU1blOAkgpnU1oWU5N2A/By3GJs6WvBvwpc8l+LKxNLXPas2W1rOOGbYeY0B264/9IGXNuuBi4DRny5XBNtipSUNVPNn3UuhoAqlpZyLhSgpMJ5/deNGAYMaBxI7PLXzY2XPuGR/4NKCbAfm/Or3qWQfwQmXmf+5VtMNpuN5we2oGv9qhzJc3JrwmJSMo6WYsEilcTaqebPZgOsrOKcKUBJhbJmTwY/r0rFZoMnwmeZiwVHNIG2w6wuTazk8IbrJpgLRx/ZB19cA4eLv+6dt8POe0Pb0SAyiLTMXG5NWEJ2bkEpFixSwR3Yal6BZ3NA46usruacKEBJhXJ82oKbmnhRddUn5saeT3nU5GxSSnyDYeg3EFrTnA9s0hDIL35LUqi/N5/GX0C1IF/WpWQyfOIyCpyaI0rknBR2310EgVUtLeVcKUBJhbE8+SC/rkvHboMHfb+Hghyo1cW8pF0EIDjanK3cLxR2LYLvbgeXs9iH16wSwCfD2uPnbef3jXv5v2Oz3IuImwq77662tIzzoQAlFcZrx1qf7m5aQMj6yebGy572mJW9pYxENIIhk8DhA+t/gpmPu3V4q5phvDG4NQAJ87fzeeL2kq9RpCI7sM3ju+9AAUoqiIVb9/Pnpn142W0MNyaC4TKXBfCghSmlDMV1hav/Z95f+D4s/NCtw3s3j+GhXo0AeOrHtfy5aW9JVyhScR1vffLg7jtQgJIK4njr00NNDxKwdSbY7HDpkxZXJeVa84H//I7MeAQ2znTr8Hu612Ng2+o4XQb3TFzG5vSsUihSpAI6Pv6p6QArqzhvClDi8ZZsP8DCbQfwdkD8kQRzY5ubIKKhpXWJB7jwAfN3xXDBN7dAyspiH2qz2Rg3sAUXxIWTlVPArQlLOHg4rxSLFakADmyDlCSz+66J53bfQQULUE899RQ2m63IrXHjxoXP5+TkMHz4cKpWrUpQUBCDBg0iLa3ozMLJycn06dOHgIAAIiMjeeihhygo0OXK5dl787YA8Fi9ZHz3LAIvf+g+xuKqxCPYbND3dajTzVwr8cvrIGN3sQ/39XLwwY3tqFnFn+QDR7h74lLydWWeyOmt/cH8GXchBFaztpbzVKECFECzZs1ISUkpvP3111+Fzz3wwAP8+OOPfPPNN/z+++/s2bOHgQMHFj7vdDrp06cPeXl5zJ8/nwkTJpCQkMDYse6t5i5lZ11KJr+tT8duM7gh59haZx3ugJBYawsTz+Hwhus+g2qNICsFvhwMudnFPrxqkC+fDLuAQB8HC7Ye4Okf15RisSIezsMnzzxRhQtQXl5eREdHF96qVTMTbkZGBp988gmvvfYal1xyCe3atWP8+PHMnz+fBQsWADBr1izWrl3LF198QevWrbniiit49tlneffdd8nLU9N8efT+sdanB+sk45u+ArwDoMt9FlclHsc/DIZ+DYERkLYKptzh1vQGDaOCeXNIG2w2+GJBshYeFjmVA9tgz3JzjKoHX313XIULUJs2bSI2Npa6desydOhQkpOTAVi6dCn5+fn07NmzcN/GjRtTq1YtEhMTAUhMTKRFixZERUUV7tOrVy8yMzNZs+b0f1Xm5uaSmZlZ5Calb8f+w/y0cg9gEF/wtbmx/a0QFGFpXeKhwuNgyJfg8DVXiP/1KbcO79k06p8r86atYf6WfSVfo4gnO7H7rgL8O12hAlTHjh1JSEhgxowZvP/++2zbto2LLrqIrKwsUlNT8fHxISwsrMgxUVFRpKamApCamlokPB1//vhzpzNu3DhCQ0MLbzVr1izZNyan9L8/tuIyYHjNHQSkLwcvP+h6v9VliSer2cFcNw9g/luw7PNiHTZt2jQGDhxI9Yw1DGgdW3hl3o79h0uxWBEPs/pb86cHT555Ii+rCyhJV1xxReH9li1b0rFjR2rXrs3XX3+Nv79/qb3uo48+yqhRowofZ2ZmKkSVsvTMHL5dsgswuItj/1O2vxWCIi2tSyqAltfCvo3wx0vw00ioUsf8i/kMEhISmDt3LgBfTv6GbfsOs2JXBnd+tpQp93Qh0LdC/VMr4r709ZC6CuzeHj99wXEVqgXq38LCwmjYsCGbN28mOjqavLw8Dh06VGSftLQ0oqOjAYiOjj7pqrzjj4/vcyq+vr6EhIQUuUnp+vivbeQ5XdwSvYPgvcvU+iQlq/uj5l/JrgKYfCPs33LG3ePj4+nRowfx8fH4eTv48Ob2RAT7siEtiwe/WYFhGGVUuEg5terYMIsGl0FAFWtrKSEVOkBlZ2ezZcsWYmJiaNeuHd7e3syZM6fw+Q0bNpCcnEznzp0B6Ny5M6tWrSI9Pb1wn9mzZxMSEkLTpk3LvH45tYyj+UxcsAMwuN/rO3Nju3hznTORkmC3w4D3IbYtHD1oLjyck3Ha3fv168eUKVPo168fAFEhfnxwYzt8HHZ+WZ3KO79tLqvKRcofw4BV35j3W1xrbS0lqEIFqAcffJDff/+d7du3M3/+fK6++mocDgfXX389oaGh3HbbbYwaNYq5c+eydOlSbrnlFjp37kynTp0AuPzyy2natCk33XQTK1asYObMmfz3v/9l+PDh+Pr6Wvzu5LhvluzkcJ6T66puJ2zfUnNNM7U+SUnz9ofrJ0FwrNml9+2tbl2Z1652OM/0bwbAq7M38uvatLMcIVKxJO08RErGUdi5EA4lg08wNLri7Ad6iAoVoHbt2sX1119Po0aNuO6666hatSoLFiwgIsIc7f/666/Tt29fBg0axMUXX0x0dDRTpkwpPN7hcPDTTz/hcDjo3LkzN954IzfffDPPPPOMVW9J/qXA6WL839sBGO137IqOtsM075OUjuBouP5Lc3LWzb/CbPfmhBvSoRY3daoNwMjJSVruRSqNadOmcXmffrS6dRy7/5hgbmxylfmHSQVhM9Q5X+IyMzMJDQ0lIyND46FK2IzVKdz1xTK6BiQz0TUG7F5wXxKEadC+lKLVU+DbW8z7/d+FNjcW+9B8p4uhHy9k0bYD1I0IZOrwroT4eZdSoSLlQ59+A/hl1q8E1G5B5q2p2I8egBunQP1LrS7tjNz5/q5QLVBS8X3613YAngg/Npat+SCFJyl9zQdCt0fM+z+OhOQFxT7U22HnvaFtiQn1Y+vew4yavAKXS3+3SsXWskc//Gq1pH/XJmZ4Cow0l0yqQBSgxGOs2pXBou0HiHPso9GBYwGqy73WFiWVR7cx0KQfuPLhq6HmmI5iqhbkaw4q97Lz67o03pmrQeVSseVWb0tQi56kL53JtA355h+7joo1nYcClHiM8X9vA+CZyN+xGS6o2wOiW1hclVQadjtc/YH5O3dkH3x1A+QVf6LMVjXD+L8BzQF4/deNzFmnQeVSMRmGwd+b95GzaiaLN6aQkJRvzq9WwShAiUdIz8zhx5V7CCWbrlm/mBu7as07KWM+gTBkEgRUMycFnHqPeYl2MV3XviY3daqNYZiDyrft00zlUvFs2ZtNWmYuPdvU5JI4B/EX1jKnBKlgFKDEI3yxYAf5ToNHqv2No+AIRLUwW6BEylpYTRj8hTmj8tqp8Ocrbh3+RN+mtK8dTlZOAXd+toTDuQWlU6eIRf7aZK4D+WTrg0wZHEC/oXeAzWZxVSVPAUrKvZx8J18sTMaHfAblTzc3drm3Qv4PKR6idmfocyw4/fZ/sP7nYh/q42UOKo8M9mVTejaPfLdSM5VLhfLX5v1UI4MWucvNDRVo8swTKUBJuTd9ZQoHDudxS9AifHP3mRMbNh9odVlS2bWLhwvuMO9PuQPS1xX70MgQP94b2hYvu42fVqbwyV/bSqdGkTJW4HSxYOt+rnb8iR0n1LgAqtazuqxSoQAl5d7EhTuw4eI/Psf+yu90Nzg0j46UA73HQdxFkJcNk66HIweKfWj7uCr8t08TAMb9sp4FW/eXVpUiZWbFrgyyc/MZ4v27ucGNOdM8jQKUlGtr92SyLPkQlzhWUuXINvANMf/yFykPHN5w3WcQVgsObjOXe3EWf0zTsC5xDGgdi9NlMOLLZaRm5JRisSKl7+/N+2ht20I9dpsz+DeruL0FClBSrn25aAcAo0LnmRva3AR+mt1dypGAKuaVed4BsHUu/PpksQ+12WyMG9iSxtHB7MvO456JS8krcJVisSKl6+/N+7jOMc980LR/hf73WgFKyq3DuQVMXb6H2rZUmh1ZBNjggtusLkvkZNHNYcD75v3Ed2DF5GIf6u/j4IMb2xHs58Wy5EM8/3Pxx1KJlCdH8gpYm5zKVY5Ec0MF7r4DBSgpx6at2EN2bgEjguaZGxpcVmEHI0oF0GwAXPSgeX/avbB7WbEPjasWyOvXtQYgYf52fkjaXfL1iZSyRdsOcKmxiGDbUYyw2lC7q9UllSoFKCmXDMPgiwU78CeHfsZcc+PxK55Eyqsej0PD3uDMNZd7yU4v9qE9m0YxvIf5B8KY71axITWrtKoUKRUndt/Z2txozt5fgVXsdycea+WuDNbsyWSQdyK+BVkQHgf1e1pdlsiZ2e0w8EOo1hCy9sDkm6Agr9iHj7qsERfWr8bRfCd3f7GUrJz8UixWpGRt2rCGLo61GNig1fVWl1PqFKCkXJq4cAdgcHfAb+aGC+6o8H/NSAXhFwpDvjSvGN25AGY8UuxDHXYbbw5pTUyoH1v3HeahbzTJpniGvVm5tDlgTjWTX/tic8b+Ck7fSFLuZBzN58cVKbS3baB67hbzUtg2Q60uS6T4qjWAQR8DNljyKSwZX+xDqwb58t7Qtng7bMxYk8rHf2qSTSn/ft+QxiDHHwD4tL/Z4mrKhgKUlDs/JO3maL6TEUHHxj61vBb8w60tSsRdDXvBJf817//8ECQvKPahbWqF80TfpgC8MGM9i7YVf4JOESvsWT6TGrZ95DiCoXEfq8spEwpQUu58s2QXERzk4oJjl8Jq8Lh4qotGm3PhuPLN8VAZxb+67qZOtenX6p9JNtOzNMmmlE/5ThcNdk8BIKtBf/D2t7iisqEAJeXK+tRMVu3O4Ebv37AbBVCrM8S0tLoskXNjs0H/9yCyGRxOh8k3Qn7xgpA5yWYL6kcGkZ6Vy32TllPg1CSbUv6sXL+RS42FAFS5qPL8wasAJeXKt0t24cDJMJ955oYLbre0HpHz5hsEQyaa3dB7lsFPD0AxB4YH+nrxwY1tCfBxsGDrAV6bvbGUixVxX1bieHxsTrb7NcVRvbXV5ZQZBSgpN/KdLqYm7aabfQVhzv0QUBWa9LO6LJHzV6UOXDMebHZY8SUs+rDYh9aPDObFQWYr7HvztvDr2rTSqlLEfS4nTXZ/B8CBphV75vF/U4CScuP3DXvZl53Hzb7HVvFudT14+VhblEhJqdcDLnvWvD/jUdj2Z7EPvapVLPFd4gAY9XUSOw8cKYUCRdy3b/lPRBl7OWQEUrebApSIJb5ZutMcPG4cWwKjbeW4FFYqkc7DoeVgMJzwzTA4lFzsQx+7sgmta4aRmVPA3ROXkpPvLMVCRYonZ8FHAPwZ2Iuw0FCLqylbClBSLuzPzmXOunSucfyJHSfU7AQRjawuS6Rk2Wxw1ZsQ0wqO7IevboC84rUm+XjZeXdoW8IDvFm9O5Nnf1pbysWKnMXB7cTu/QuAjGY3WVxM2VOAknJh2oo9FLhc3HS8++4MrU/Tpk1j4MCBTJs2rYyqEylB3v4weCIEVIPUVTBtRLEHlVcP8+f1wa2x2WDiwmS+X76rlIsVOb2CxeOxY/Cnsznt2ra3upwypwAl5cI3S3bRyb6OWFcK+ASbK9ufRkJCAnPnziUhIaHM6hMpUWE14brPwO4Fq7+D+W8V+9DujSK595IGADw2ZTUb07TosFigIBfX0s8AmO57JY2jgy0uqOwpQInl1uzJYG1KJtd7zTM3tLgGfAJPu398fDw9evQgPj6+TOoTKRVxXaH3C+b9X5+Czb8W+9D7L21QuOjwXV8s5XBuQenUKHI6637EJ/cAqUY4jiZXYrPZrK6ozClAieW+W7qbELK50mFOxEbbM/el9+vXjylTptCvn6Y4EA93we1md7Xhgm9vhf1binXY8UWHo0P82Lr3MGOmrNKiw1KmjMUfA/CVswfdGsdYXI01FKDEUk6XwbQVexjg+BtvIx+imkNsW6vLEikbNhtc+QrU6AA5Geag8tzidclVDfLlnRva4LDb+HHFHr5YsKOUixU5JnU1tuRECgw73xk96Vq/mtUVWUIBSiyVuGU/+7JzuNF7nrmh7c3ml4pIZeHlC4M/h+AY2LsepvwHXMVbsqV9XBXG9G4MwLM/rWPlrkOlWKjIMQveA2CG6wLi6tYn0NfL4oKsoQAllvohaTctbNtoyA5w+EKLa60uSaTsBUfD4C/A4QMbpsPvLxb70NsvqsPlTaPIc7q4+4tlHDqSV4qFSqWXlQarvgHgk4Ir6dUs2uKCrKMAJZbJyXcyY00qgxx/mBuaXAUBVawtSsQqNdqbc0QB/P4CrC3eNB02m42Xr21FrSoB7D50lNFfr8Dl0ngoKSVLPgFnHstc9UmiAZc3i7K6IssoQIll5m3Yy9GcHPp7LTA3tLre2oJErNb6Buh0j3n/+7sgbU2xDvt99i/4zHuNvK2LmLM+nf/9sbUUi5RKK/8oHBs8/nHBlbSvHU5ksJ/FRVlHAUosM22FuXBwOJkQGAl1u1tdkoj1LnsW6nSD/MMw6Xo4cuCshyQkJLA08S9q7DWvZH155noWbN1f2pVKZbPyaziyn72OSGa6LqjU3XegACUWycrJZ866dK52mMsA0OJacFTOgYgiRTi84NoECI+DQzvMNfOcZ57n6fjcaI+PvJuBbarjMuDeSctJz8opk5KlEjCMwsHjH+ZehhOHApTVBUjlNGtNGr4FWVzuOLZwcKvB1hYkUp4EVIEhk8AnCLb9ATMfO+Pux+dG69+/P/93dXMaRgWxNyuX+yclUeB0afkjOX9b5sDe9eQ7AviqoAfNq4dQs0qA1VVZSgFKLPHDij1c4ViED/kQ0QSiW1pdkkj5EtUUrv6feX/R/2BpQrEOC/Dx4r2h7QjwcZC4dT+v/7pRyx/J+Us0W5/mBvQiiwB6V/LWJ1CAEgvsy87l7837GOj409zQarDmfhI5lSZ9ocd/zfvTH4Qd84t1WP3IIF4YZP5R8u7cLbTtOUDLH8m5S18HW+Zg2Oy8cKAbAL2bK0ApQEmZ+3lVCjFGOh3t6wEbtLjO6pJEyq+LH4SmA8CVD5NvgkPJxTqsX6tYbu5cG4Bv9kbz1idfaPkjOTeJ7wCQEn0pW52R1I8Mon5k5Vs8+N8UoKTM/ZC0h/72v80HdS6C0OrWFiRSntlsMOA9s5v7yD6YdAPkHS7WoY/3aUKrGqFkHM1n+MRl5BY4S7lYqXAO7YQVXwHwhd0M4Oq+MylASZlKyTjK0h0H/um+aznE2oJEPIFPIAz5EgIjIG2VOUdUMZZ78fVy8O7QtoT6e7NiVwbPTV9XBsVKhTL/LXAV4Kx9EeOTIwF13x2nACVlasbqVFratlLPngJe/tBUXQoixRJW01zuxe4N66bBvHHFOqxGeABvDG4NwGeJO/ghaXcpFikVSnY6LPsMgGW1b+NovpPqYf40iw2xuLDyQQFKytQvq1P/aX1q3Ad81Y8uUmy1Ov2z3MsfL8Gqb4t1WI/GkYzoUR+AR6esYnN6VmlVKBVJ4jtQkAPV2/NZqjmernfzaGy66AdQgJIylJ6Vw7Lte+nrOL50i7rvRNzWZih0ude8/8Nw2LW0WIc9cFlDutSrypE8J3d9sYzDuWeenFMquSMHYPEnABzt/ACz16UB5sUJYlKAkjIzc00aHW1rqWbLBP8qWrpF5Fz1fBoa9jZbB766ATL3nPUQh93Gm0PaEBXiy+b0bMZMWYVhaNFhOY1FH0JeNkQ155fcVuTku6hbLZCWNUKtrqzcUICSMvPLqhT62o+1PjXtBw5vawsS8VR2Bwz8yJyENjsVJg0p1pV5EcG+vHtDW7zsNn5csYfPEneUQbHicXKzYMH75v2LRvF9khnQ+7euru67EyhASZnYn53L0m3p9HYsNjc0G2htQSKezi8EbvgKAqpCygr4/j/FujKvfVwVxlzRGID/m76WZckHS7tS8TRLPoWcQ1C1Puk1e/P35n0A9G+t7rsTKUBJmZi9No3OrCLclg2BkRB3odUliXi+8DgYPBEcPrDuR/jtmWIddtuFdbiyRTT5ToPhE5exPzu3dOsUz5F/FOabE2dy4QP8tCodlwGta4YRVy3Q2trKGQUoKRM/r079Z/B40/5mF4SInL/anaHfsS+8v16H5V+c9RCbzcaLg1pSt1ogKRk53P9VEk6XxkMJsPhjOJwOobWg5eDCaS8GqPXpJApQUuoyjuSzZHMKl9uXmBuaq/tOpES1GgwXP2Te/3EkbP/rrIcE+3nz/o3t8Pd28Nfmfbw2e0Pp1ijlX04m/Pmaeb/7I2w7mMeKXRk47Db66uq7kyhASambvS6NLqwgxHYEgmOhZierSxKpeLo/Bs2uPrZm3o2wf8tZD2kUHcwLg1oA5qLDs9emlXaVUp4lvgtHD0DVBtByCFOXm61PF9avRrUgX4uLK38UoKTETZs2jYEDBzJt2jTg2NV3jkTzyWYDwK5fO5ESZ7fDgPehejs4ehAmXgOH95/1sP6tqxPfJQ6AUV8nsX1f8dbZkwrm8P7CRYO55HEMu+Of7rs2an06FX2Tnca7775LXFwcfn5+dOzYkUWLFlldksdISEhg7ty5JCQkkJWTz6JNe+hpX2Y+qavvREqPtz8MmWSOXzmw1ZwjKj/nrIc9dmUT2tYKIyungLu+WMrRPC06XOn89Zo571N0S2jSn6Sdh9i+/wj+3g4ub6q1705FAeoUJk+ezKhRo3jyySdZtmwZrVq1olevXqSnp1tdmkeIj4+nR48exMfH8/vGvXQ1lhFkyzH/Ua/R3uryRCq24CgY+g34hsLOBfDDPWed3sDHy857Q9tRLciH9alZPP69JtmsVDJ2w6KPzPuXjgW7nR+Ozf10WdMoAn29LCyu/FKAOoXXXnuNO+64g1tuuYWmTZvywQcfEBAQwKeffmp1aR6hX79+TJkyhX79+vHr2rSi3XeahE2k9EU2hsGfg90LVn8Hvz171kOiQ/14+/q2OOw2pizfzecLNMlmpfHHy+DMhVpdoH5PcvKdTD3WfXd1m+oWF1d+KUD9S15eHkuXLqVnz56F2+x2Oz179iQxMdHCyjxPvtNF4vpkLrUvNzfo6juRslO3G/R727z/12uwNOGsh3SuV5Uxvc1JNp/5cS1LdxwoxQKlXNi/BZZ/bt6/9Amw2Zi5JpVDR/KJCfXj4oYR1tZXjilA/cu+fftwOp1ERUUV2R4VFUVqauopj8nNzSUzM7PITWDJ9oNckLcYf1seRngdiGltdUkilUvrG6DbI+b9n0bBptlnPeT2i+rQp2UMBS6Du79YRnqmOYbq3xeHSAXx2/+BqwDqXwa1uwAwaVEyANe1r4nDrl6D01GAKgHjxo0jNDS08FazZk2rSyoXZq9NK1y6xabuOxFrdH8UWl0PhhO+vhl2Lzvj7jabjZcGtaRhVBDpWbncM3EZeQWuIheHSAWxIxHWTAFs5tgnYOvebBZsPYDdBtddoO+yM1GA+pdq1arhcDhISys6H0paWhrR0ae+EuHRRx8lIyOj8LZz586yKLVcMwyD39cm092eZG5ocpWl9YhUWjYbXPUW1O0B+Ufgy+vMK/TOINDXiw9ubEewrxdLdhzkuelri1wcIhWAywUzxpj3294EMS0BmLzY/P7q3iiS6mH+VlXnERSg/sXHx4d27doxZ86cwm0ul4s5c+bQuXPnUx7j6+tLSEhIkVtltyk9m9oZiwmy5eAKjoXYtlaXJFJ5efmYg8qjW8LhvfDFIDi874yH1I0I4rXBrQGYkLiD3Ng2hReHSAWw4ktISQKfYLjkCQDyClx8u3QXAEPU+nRWClCnMGrUKD766CMmTJjAunXruPvuuzl8+DC33HKL1aV5jNlr0+htN7vv7E2uUvediNV8g83pDY7PEfXldZB35kkzL2saxX2XNgDg8amrWbHzUBkUKqUuNwvmHFt4uttDEBQJmP9u7z+cR2SwL5c0jrSwQM+gAHUKgwcP5pVXXmHs2LG0bt2apKQkZsyYcdLAcjm9OWv20NOx1HzQpK+1xYiIKTgabvwO/MNh91L4Jh6c+Wc8ZOSlDejZJJK8Ahd3fbGUvVm5ZVOrlJ4/X4XsNAivAx3vKtx84uBxL4fiwdnoEzqNESNGsGPHDnJzc1m4cCEdO3a0uiSPkZ6Vg++eBVSxZePyq2LOLSIi5UNEQ7h+Mnj5w6ZZ8MPwM060abfbeG1wa+pGBJKSkcPwicvId555Yk4pxw5sM9e8A+j1HHiZa9wl7z/CX5v3YbPBYHXfFYsClJS439alc7l9CQD2xleCQ7PYipQrtTrCdRPA5oCVk2HW43CGmcdD/Lz58Kb2BPl6sWj7AZ79aW0ZFislavYT4MyDOt2g0ZWFm79abLY+XVi/GjWrBFhVnUdRgJISN3tNKr2OTV+g7juRcqphL3PxYYAF75ndOmdQPzKIN44NKv8scQdfHevuEQ+yeQ6s+xFsdug9rnBsam6Bk6+XmIPHb+hQy8oKPYoClJSoI3kFZGxZRKztAC6vAPPSaREpn1oNhl7jzPu/PQtLxp9x955Noxh1WUMAnvhhNUu2a6Zyj5F3BH56wLzf4U6Ialb41I8rUtiXnUtUiC+XNtFY3+JSgJIS9ffm/VzCQgBsDS8Hbz+LKxKRM+p8D1w02rz/0wPm2nlncO8l9enTIoZ8p8FdXyxl96GjZVCknLffX4BDOyCkBlzy38LNhmHw8Z/mvGDxXerg46VYUFz6pKREzV2fRq9j0xfYNHmmiGe45AloFw8YMOVO2PDLaXe12Wy8fG1LmsaEsC87jzs/W8LRPGeZlSrnIGUlzH/HvN/nFXNKi2Pmb9nP+tQs/L0d6r5zkwKUlBjDMNi+bhn17ClM3Wgw8InPtG6WiCew2aDPa9DiOnNdtK+HwZa5p909wMeLD29uR9VAH9bsyeTBb1dgnGEQuljI5YQf7zOX8mk6ABpdUeTp461P17WvQWiAtwUFei4FKCkxm9KzaXPkbwAmbAxm7u9/at0sEU9hd5iDyhv3BWcufHUDJC847e41wgN4/8Z2eDtsTF+Zwtu/bS7DYqXYFn0Ie5aDbyhc8WKRpzanZzF3w15sNrilax2LCvRcClBSIqZNm8bga6/Bd7O52vstN1yrdbNEPI3DC675FOpdaq6bN/Fa88v3NDrUqcKz/ZsD8Nrsjfy0ck9ZVSrFcWgnzHnWvH/Z0+ZEqif45K/t5lNNooirFljGxXk+twPUsGHD+OOPP0qjFvFgCQkJrF3yN/NW7sbARr87H9O6WSKeyMsXBn9hToCbmwmfDYCUFafdfUiHWtx2odl6MfrrFSRpuZfyweWCH++H/MPmf8u2w4o8vT87lynLzKkLbr+orhUVejy3A1RGRgY9e/akQYMGPP/88+zevbs06hIPM3joTdSpFUN8a29yo9oWrq0kIh7IJwBumAw1LoCcQ/BZf3Mg8mk8dmUTLmkcSW6Bizs+W8IeXZlnvcUfwZY54OUHV70J9qJf9xMXJpNb4KJljVAuiAu3qEjP5naAmjp1Krt37+buu+9m8uTJxMXFccUVV/Dtt9+Sn3/mNZWk4gpr3Jl3rq1Ov0be+DW78uwHiEj55hdirptXvT0cPQif9YPUVafc1WG38eaQ1jSKCmZvVi63T1jC4dyCMi5YCqWvg1lPmPcv/z9z+Z4T5OQ7+SxxBwC3XVgHmxZ7PyfnNAYqIiKCUaNGsWLFChYuXEj9+vW56aabiI2N5YEHHmDTpk0lXaeUc3+v28mF9tXmg4a9rS1GREqGXyjcNAWqtzND1IR+kLr6lLsG+3nz8bD2VAvyYW1KJvd/tRynS1fmlbmCXPjuDvNCgPqXwQW3n7TL5MU72ZedS2yoH1e2iLGgyIrhvAaRp6SkMHv2bGbPno3D4eDKK69k1apVNG3alNdff72kapRyzjAMjq6fg58tn5yA2CIz3IqIh/MLhRunQGxbOHrAbIk6zZiomlUC+N9N7fHxsvPrunStmWeF3/4P0lZBQFXo/27hci3H5eQ7eW+eecXk3T3q4+3QtWTnyu1PLj8/n++++46+fftSu3ZtvvnmG0aOHMmePXuYMGECv/76K19//TXPPPNMadQr5dDGtGza5Jizj3s1ufKk/2FFxMP5h8FN35sh6sh+mHAV7Fpyyl3b1Q7n9etaA5Awfzuf/rWt7Oqs7Lb9AfPfNu/3exuCT16WZdKiZNIyzdan69rXKOMCKxYvdw+IiYnB5XJx/fXXs2jRIlq3bn3SPj169CAsLKwEyhNPMG99Gv0d5qXOXo2vOMveIuKR/MPg5qkw8TrYucAcWH7DZIi78KRd+7SMYdfBxoz7ZT3PTl9L9XB/ejWLPmk/KUFHDsD3dwOGecVd4z4n7WK2Pm0BYPgl9fH1cpRxkRWL2y1Qr7/+Onv27OHdd989ZXgCCAsLY9s2/dVRWWxfnUi07SD5Dv9T/mMqIhXE8TFRdbpBXjZ8MQg2/XrKXe+8uC5DO9bCMOD+r5ZreoPS5HLCt7dC5i6oUhd6PX/K3SYuTGZvVi7Vw/y5tl3NMi6y4nE7QN100034+WmBWDFl5eQTnWYu+ZBXu5sWDxap6HwC4YavoUEvKMiBSUNg7clLNtlsNp7u14zujSLIyXdxW8Jitu87bEHBlcDc52DrXPAOgOs+B9+gk3Y5mufk/WOtTyMuqa9Fg0uAPkE5L39v3k8P2zIAApv3tbgaESkT3n7mZJtN+4MrH74ZBks+PWk3L4edd25oS/PqIew/nMfNny4iPSvHgoIrsHU/wp+vmvf7vQ3RzU+528SFO9iXnUuNcH+uaaexTyVBAUrOy/K162hpP9Zd27CXtcWISNnx8oFBn0Lbm8FwwU8PwLwX4V+LCgf5ejE+vgO1qgSQfOAIt4xfTLbmiCoZezceG/cEdBoOLa455W5H8gr44Hez9eneS3TlXUnRpyjnxbF5FgAZVVpq9nGRysbhBVe9BRc/ZD6e9zxMH2WOyTlBRLAvn93agaqBPqzZk8ldny8lr8BlQcEVSG4WTB4KeVlQ+0JzrbvT+OTPbezLzqNWlQAGtlXrU0lRgJJzlrz/CG2OmtMX+DU/+YoPEakEbDa45L9w5SuAzezK+2YY5BddziWuWiDjb7mAAB8Hf23ex4PfrMCliTbPjbPAnCxz30YIjoVrx4PD+5S7pmbkFF55N/ryhmp9KkH6JOWcJW7YxYV2c2kH36YKUCKVWoc7jn2R+5jjchL6QFZakV1a1gjj/Rvb4WW3MW3FHp6ctgbDUIhyi2GYrXwbfzHXuRv8+Rlb/1+csZ6j+U7a1Q6nX6vYMiy04lOAknO2b/Vs/G15ZPlGQdSpBy6KSCXS7Gpzwk3/cNi9FD6+9KSlX7o1jODV61phs8HnC3bw8swNFhXroX5/EZZNAJsdBn0CNdqfdtdlyQf5fvluAJ68qqnWvCthClByTpwugyp7/gAgJ+5SzT4uIqa4C+H2OVClHmTshE97wcZZRXbp37o6/zfA/KPrvXlbCi+vl7NYmgDzxpn3r3wFmpz+ymeXy+DpH82ldK5pV4OWNcJKv75KRgFKzsmaPRl0cpmzj1dppe47ETlB1Xpw+68Qd5E54eakwTD/nSJX6A3tWJsxVzQGzG6miQt3WFWtZ9jwi3mlI5iD9i+47Yy7T03azYqdhwj0cfBwr0ZlUGDlowAl52TlyuXUsadRgBeOet2sLkdEypuAKuYixG1uMqc5mPW4OVt2bnbhLnd1q8fwHvUA+O/U1Xy7dJdV1ZZvW+fBN7eYn2ObG6HH42fc/XBuAS/8sh4wl2yJDNEEx6VBAUrOSf4Gs0l+b5U24BtscTUiUi55+ZiTO17xEti9YM0U+Lgn7NtcuMuDlzdiWOfaGAY89O0Kvl+uEFXEpl/hy8FQcBQa9oa+b5x1yMRbczaRnpVLrSoB3Nq1TtnUWQkpQInbjuY5iTuYCIBPo8strkZEyjWbDTr+B+KnQ1A07F0HH/WA9dOPPW3jyauaccOxdfNGf72CH5J2W1x0ObHhF/jqenPJnEZ94LrPTjtdwXHLkw/y0Z9bAXiib1P8vLVgcGlRgBK3Ld6SQkebOTixSqsrLK5GRDxCrU7wnz+gVhfIzYSvboBfHoH8HOx2G//XvznXd6iJy4AHJifx44o9VldsrbXTYPKN4Mwzl8y5bgJ4+Z7xkJx8Jw99uxKXAf1bx3JZ06gyKrZyUoASt+1c/isBtlwyvapi0/QFIlJcwVEwbBp0HmE+XvgBfHQJpK/Dbrfx3IAWDG5vhqiRk5OYVllD1IrJ8E08uAqg+TXmkjlnaXkCeOPXTWxOz6ZakC9PXdWs9Ous5BSgxG2+O+YCcCi2m6YvEBH3OLyh13Mw9FsIjID0NfBhd1j0EXYbjBvYgmva1cDpMrj/q+VMXpxsdcVlx+WCOc/C93eC4YRWN8DAD80lc85iefJBPvzDnA7iuaubEx7oU9rVVnoKUOKWvVm5tDi6BIDwluq+E5Fz1OAyuHs+1L/MHOPz84Mw8RrsmTt5aVBLhh4bE/XId6v49K9tVldb+vIOwzc3w5+vmI+7joT+74L97GOY/t1116tZdOnWKoAClLhp2apVNLLvwomd4KY9rS5HRDxZUCQM/QZ6vwgOX9j8K7zbCfviD/m//k254yLzCrJnflrLu3M3n+VkHixjF3za21wCx+EDAz4wFwe2F+8r+vXZG9V1ZwEFKHFLxuqZAKQENTPneREROR82G3S6C+7+G2p1hvzD8MvD2MZfwWMX2BnZswEAL8/cwPM/r/PIBYinTZvGwIEDmTZt2slPbv4VPuwBqSshoBoM+wlaX1/sc89ak8r//jCvulPXXdlSgJJiMwyDiNQ/ASioc4nF1YhIhVKtAcT/bC5R4hMEOxdi++BCRro+46nLawDw4R9beeDrJHILnBYX656EhATmzp1LQkLCPxvzjsDPD8EXg+BwOkQ2gzvnQq2OxT7vtn2HGf31CgDiu8Sp666MKUBJse3al0k7ZxIA0e20fIuIlDC7HTrcAcMXmpNGuvJh/tvEL7maKResx9fu4oekPdwyfjGZOflWV1ts8fHx9OjRg/j4eHPDnuXwYTdY9KH5uMN/zKVvwmoV+5xH8gq46/OlZOUW0L52OI9d2aTkC5czshmG4XntoeVcZmYmoaGhZGRkEBISYnU5Jea3mT9wSeLNZNhCCH1iR7H750VEzsmm2TDzMdi3EYDDoQ148OBAfslrSePoEBJu6UB0qActU5KbDX+9Dn+/YU5REBQNA96F+u6NJzUMg5GTk/ghaQ8Rwb5Mv/dCLddSQtz5/tY3oBRbwcbZAOyu2lnhSURK3/Er9a54CfzCCMzYxPv2F/nF7wlqpf/GgHf+YMXOQ1ZXeXYuFyyfCG+3M6+ycxWYk2Pek+h2eAJImL+dH5L24LDbePeGtgpPFtG3oBSLYRjUPDAfAK+Gl1lcjYhUGg5vcymY+5ZDl3vBO5AmbOVDn9cZnzuK8R++ytSl5Xiag+1/wUfd4Yd7IDsVwuPgus/h2gnndCHOjNWpPPuTuRLEY1c2oUMdXcxjFXXhlYKK2IWXvDOZWp+0AODofevwrxJrcUUiUikd3g8L3sVY+D9sedkApBthbKo+gE7XjMJRpbbFBQLOAlg3DRa8B7sWm9t8Q+Dih8wweJYlWU7nr037uDVhMXlOF9e2q8FL17TEpsmMS5Q7399nn95UBNi5bAa1gB1ecdRWeBIRqwRWhUvHYutyL64FH3B0/odE5h8gck8CrrcmkFfnEnza3gANLge/Mv4D9vB+WPElLPwfZOw0tzl8oM1N0P1RCIo451Mv3XGQOz9fQp7TxRXNoxk3sIXCk8UUoKRYbFt/B2BfRGfKwd93IlLZ+Ydj7/EogReNZunsieQt+JjOttX4bJsD2+aA3RvqdoPGfaHRFRBcSpf4Z+yG9dPNFqcdf4PhMrcHVIMLboMLbjcnDD0P61IyuWX8Io7kObmoQTXeGNIaL4dG4FhNAUrOyjAMamcsAsC/yaUWVyMicgIvH9pdcQvr2wxi2OfT6ZjxM73ti6lLijlJ5eZf4aeR5tijGhdAjQ5Qoz1ENAKfQPdey1kA+zaY0xDsWW52z6WsKLpPTCszNLW4DrzPf3D3mj0ZDPt0MZk5BbSrHc7/bmqHr9fZl3eR0qcxUKWgoo2BSt68hlpfdCHfcOB8eBt+gaFWlyQicpIjeQU8PW0tk5fspJ5tN7dWXcM1AUn4pied+oCAqhBaA0JrQnCM2d1md5gD120OyMkwJ7nM3mv+PLQTCo7+6yQ2qNkRmlwFTfqaQa2EzN2QzoiJyzic56RJTAhf3dmJUH/vEju/nExjoKREpSw3xz9t9m1CE4UnESmnAny8ePGalnSpX5XHv/fi8X3Veda7N4/1iGVozf04di82W412L4GjB+HIfvP271akM/EJgpjWENsaYttA3IWl0j345cJknvhhNU6XQZd6VXn/xnYKT+WMApSclXfyHwAciu5icSUiImfXv3V12tQM55HvVpK4dT9jZ+3iu5phvDToHhp1DzZ3OnrIHOidsctsWTqcbs7P5MwHl9OcBd03xBy/FBhh/gyOhSp1S3UePJfL4KWZG/jg9y0ADGxbnRcGtsTHS2Oeyht14ZWCitSFZ7icZDxTizCyWd37G5p3utzqkkREisUwDCYv3slz09eRlVuAt8NGfJc4RvRoQGhA+WvN2XXwCA99Y4Y+gJE9G3D/pQ10tV0Z0kzkUmJ2rVtEGNlkG/7Ub32x1eWIiBSbzWZjSIdazB7VjcuaRpHvNPjoz210e2Uun/61jbwCl9UlAseDXjK93/iTxK378fd28Oq1rRjZs6HCUzmmACVnlL5iBgAb/Frh56flAkTE80SH+vHhTe0Yf8sFNIwK4tCRfJ75aS2Xvf47U5btIt9pXZDac+got09YwiPfrSI717zS7pf7L2JQuxqW1STFozFQckYBu/4CILv6hRZXIiJy7mw2Gz0aRXJR/Wp8u3QXr87eyI79Rxj19QpenrmBW7rGMaRDLUL8yqZrLzUjh/fmbearRTvJc7rwcdgZfXlDbr+oLg67Wp08gcZAlYKKMgbKyD9K7nO18COPlf1n07JNB6tLEhEpEYdzC0iYv53xf29nX3YuAEG+XgxqW51+rWNpUzMceykEmd2HjvLRH1v5clFyYRdixzpVeKZ/cxpFB5f464l73Pn+VoAqBRUlQO1ZPoPYHwaTZoQT9t/N+HqrwVJEKpbcAic/LN/Dh39uZXN6duH26mH+9GkZQ69m0bSoHnpeV8GlZ+YwfVUKP61MYemOg4XbL4gL54HLGtKlXrXzeg9ScjQPlJSIQ6tnEwusD2hHN4UnEamAfL0cXHdBTa5pV4M/N+/jh+W7mbU2jd2HjvLhH1v58I+t+HrZaVkjlLa1wmlVM4yYUD8iQ/yICPItDFaGYZDvNDiSV8CWvdmsTcliXUoma/ZksnLXIY43Vdhs0KlOVYb3qE/X+lU1SNyDVahvxbi4OHbs2FFk27hx4xgzZkzh45UrVzJ8+HAWL15MREQE9957Lw8//HCRY7755hueeOIJtm/fToMGDXjxxRe58sory+Q9lCdBu83xT4c1/klEKji73Ua3hhF0axhBTr6TeRvS+XFFCn9v2cehI/ks3n6QxdsPnnRciJ8XTpdBToELp+v0HTpta4XRt2UsV7aIITpUF+RUBBUqQAE888wz3HHHHYWPg4P/6VPOzMzk8ssvp2fPnnzwwQesWrWKW2+9lbCwMO68804A5s+fz/XXX8+4cePo27cvX375JQMGDGDZsmU0b968zN+PZY4epEbOBgDCmvW0uBgRkbLj5+2gd/MYejePwTAMtu47zLIdB1mWfJC1KVnszcxhb3Yu+U6DzJyCk46PCvGlSUwITWJCaBoTQtva4VQP87fgnUhpqlBjoOLi4hg5ciQjR4485fPvv/8+jz/+OKmpqfj4+AAwZswYpk6dyvr16wEYPHgwhw8f5qeffio8rlOnTrRu3ZoPPvigWHVUhDFQh5Z+R9iPt7LRVZ2Yx1YQXEZXpoiIeAKXy+DQ0XwOHM7Fy27H38eBn7cDf2+HZg33YJV6Is0XXniBqlWr0qZNG15++WUKCv756yAxMZGLL764MDwB9OrViw0bNnDw4MHCfXr2LNri0qtXLxITE0/7mrm5uWRmZha5ebqDa34FYIN/G4UnEZF/sdttVAn0oX5kMHHVAokK8SPU31vhqRKpUF149913H23btqVKlSrMnz+fRx99lJSUFF577TUAUlNTqVOnTpFjoqKiCp8LDw8nNTW1cNuJ+6Smpp72dceNG8fTTz9dwu/GWgF7zMB4JFbr34mIiPxbuY/KY8aMwWaznfF2vPtt1KhRdO/enZYtW3LXXXfx6quv8vbbb5Obm1uqNT766KNkZGQU3nbu3Fmqr1fqsvcSlbMNgPCmPSwuRkREpPwp9y1Qo0ePJj4+/oz71K1b95TbO3bsSEFBAdu3b6dRo0ZER0eTlpZWZJ/jj6Ojowt/nmqf48+fiq+vL76+vmd7Kx7j8KbfCQTWuWrRuvGpP1sREZHKrNwHqIiICCIiIs7p2KSkJOx2O5GRkQB07tyZxx9/nPz8fLy9zXE9s2fPplGjRoSHhxfuM2fOnCID0WfPnk3nzp3P7414kINrfiMQWOPTkmuCdbmtiIjIv5X7LrziSkxM5I033mDFihVs3bqViRMn8sADD3DjjTcWhqMbbrgBHx8fbrvtNtasWcPkyZN58803GTVqVOF57r//fmbMmMGrr77K+vXreeqpp1iyZAkjRoyw6q2VOb/dfwOQFVN5QqOIiIg7KkyA8vX15auvvqJbt240a9aM5557jgceeIAPP/ywcJ/Q0FBmzZrFtm3baNeuHaNHj2bs2LGFc0ABdOnShS+//JIPP/yQVq1a8e233zJ16tTKMwdUdjrVjm7HZdgIb9zd6mpERCqEadOmMXDgQKZNm2Z1KVJCKtQ8UOWFJ88DlbfiW3y+v421rtr435dInWqBVpckIuIxpk2bRkJCAvHx8fTr169w+8CBA5k7dy49evRgypQpFlYoZ6K18OScHVzzG1FAkqMF11cNsLocERGPkpCQwNy5cwGKBKjjF0Od7aIo8RwKUFKE9y5z/FNGVEctciki4qbTBaV+/foVCVTi+RSg5B9ZqVQ5Yo5/Cm7UzepqREQ8joJS5VFhBpHL+XNt+wuAtUZtWjeMs7YYERGRckwBSgodWmf22y+1NaNxdLDF1YiIiJRfClBSyGvHnwDsq9YBL4d+NURERE5H35Jiykwh5MgOnIYNv3oXWl2NiIhIuaYAJabt5vinNUYcTevVsrgYERGR8k0BSgDI2fw7AAtcTWlbM9ziakRERMo3BSgBwLnVHP+0I7gtoQHeFlcjIiJSvilACWSlEZhtzv/kiOtkdTUiIiLlngKUQPJ8ANYbtWhaR+OfREREzkYBSnBtNwPUIlcj2tbW+CcREZGzUYAScrea69+t9mpK/Yggi6sREREp/xSgKrucDPz2rwUgt3on7HYtICwiInI2ClCV3c5F2DDY7oqiXp16VlcjIiLiERSgKrsd5vinxa5GtK2l8U8iIiLFoQBVyeVvM8c/LTYa07pWmLXFiIiIeAgFqMosPwdHyjIA9oa3I8RPE2iKiIgUhwJUZbZnGXZXPnuNUKLrNLG6GhEREY+hAFWZ7fhn/qc2tatYXIyIiIjnUICqxFyFA8gbawC5iIiIGxSgKqlpU6cy6IVfmLYhn3U+zalbLdDqkkRERDyGl9UFiDUSPnybeVtzcLp8qXpZS02gKSIi4gYFqEoqvkdD2PYH7VrWI6hWNavLERER8Sjqwquk+tU+zJTBAfjU70KrmqFWlyMiIuJRFKAqI8PAtSMRMGcgb1kjzNp6REREPIwCVGV0YCv2w+nkGl4cCGtBlUAfqysSERHxKApQlVGy2fq00qhLk1qRFhcjIiLieRSgKqPkBQAscTWiVQ2NfxIREXGXAlQlZOxcBMBSV0Na1wyzthgREREPpABV2Rw9iG3fBgBW0JBmsWqBEhERcZcCVGWzawkAW13RRERVx9/HYXFBIiIinkcBqrLZuRCAZUZDWqn7TkRE5JwoQFU2xwLUUlcDWmsCTRERkXOiAFWZOAswdi0FzAHkaoESERE5NwpQlUn6Gmz5h8k0/NntXYsGkcFWVyQiIuKRFKAqk2PTFyx3NaBZ9XAcdpvFBYmIiHgmBajK5PgAclcDzf8kIiJyHhSgKpPjA8iNhrTUDOQiIiLnTAGqsshKhUPJuAwbSa56tKoRZnVFIiIiHksBqrI4Nv5pg1ET38AwaoT7W1yQiIiI51KAqixOmP+pVc0wbDYNIBcRETlXClCVxQkLCGv8k4iIyPlRgKoM8nMgJQnQAHIREZGSoABVGaSsAGce+4wQko1ImldXgBIRETkfClCVQeH4p4ZEhfgRGexncUEiIiKeTQGqMjhhAs0Wan0SERE5bwpQFZ1hwK7FgHkFnrrvREREzp8CVEWXsROy0yjAwSqjrlqgRERESoACVEW3awkA61w1ycVHAUpERKQEKEBVdLuXArDc1YDIYF8iQzSAXERE5Hx5TIB67rnn6NKlCwEBAYSFhZ1yn+TkZPr06UNAQACRkZE89NBDFBQUFNln3rx5tG3bFl9fX+rXr09CQsJJ53n33XeJi4vDz8+Pjh07smjRolJ4R2XkWAtUkqueWp9ERERKiMcEqLy8PK699lruvvvuUz7vdDrp06cPeXl5zJ8/nwkTJpCQkMDYsWML99m2bRt9+vShR48eJCUlMXLkSG6//XZmzpxZuM/kyZMZNWoUTz75JMuWLaNVq1b06tWL9PT0Un+PJc6ZXziBZpJRXwPIRURESojNMAzD6iLckZCQwMiRIzl06FCR7b/88gt9+/Zlz549REVFAfDBBx/wyCOPsHfvXnx8fHjkkUeYPn06q1evLjxuyJAhHDp0iBkzZgDQsWNHLrjgAt555x0AXC4XNWvW5N5772XMmDHFqjEzM5PQ0FAyMjIICQkpgXd9jvYkwYfdyCKQljn/48ObO3BZ0yjr6hERESnH3Pn+9pgWqLNJTEykRYsWheEJoFevXmRmZrJmzZrCfXr27FnkuF69epGYmAiYrVxLly4tso/dbqdnz56F+3iU3Wb33XJXXQzs6sITEREpIV5WF1BSUlNTi4QnoPBxamrqGffJzMzk6NGjHDx4EKfTecp91q9ff9rXzs3NJTc3t/BxZmbmeb2XErPr+ADy+lQL8iUqxNfigkRERCoGS1ugxowZg81mO+PtTMGlvBg3bhyhoaGFt5o1a1pdkmn38QHk9WlRPQSbzWZxQSIiIhWDpS1Qo0ePJj4+/oz71K1bt1jnio6OPulqubS0tMLnjv88vu3EfUJCQvD398fhcOBwOE65z/FznMqjjz7KqFGjCh9nZmZaH6KOHoJ9GwFY4arHjeq+ExERKTGWBqiIiAgiIiJK5FydO3fmueeeIz09ncjISABmz55NSEgITZs2Ldzn559/LnLc7Nmz6dy5MwA+Pj60a9eOOXPmMGDAAMAcRD5nzhxGjBhx2tf29fXF17ecdY/tWWb+sEdzgBBdgSciIlKCPGYQeXJyMklJSSQnJ+N0OklKSiIpKYns7GwALr/8cpo2bcpNN93EihUrmDlzJv/9738ZPnx4Ybi566672Lp1Kw8//DDr16/nvffe4+uvv+aBBx4ofJ1Ro0bx0UcfMWHCBNatW8fdd9/N4cOHueWWWyx53+fs2PinJflmC16LGgpQIiIiJcVjBpGPHTuWCRMmFD5u06YNAHPnzqV79+44HA5++ukn7r77bjp37kxgYCDDhg3jmWeeKTymTp06TJ8+nQceeIA333yTGjVq8PHHH9OrV6/CfQYPHszevXsZO3YsqamptG7dmhkzZpw0sLzcK7wCrx7VgnyI1gzkIiIiJcbj5oHyBJbPA2UY8HJ9OLKPq3OfJrRhFxJu6VD2dYiIiHiQSjkPlJzg0A44so8Cmxdrjdqa/0lERKSEKUBVRMfWv9tir0MuPhpALiIiUsIUoCqi3eYA8oV5dQBoFmvhcjIiIiIVkAJURXSsBWqZsz4hfl5UD/O3uCAREZGKRQGqoinIg5QVACQZ9WgaqxnIRURESpoCVEWTtgqcuRxxhLDdiKZZrMY/iYiIlDQFqIpmtzkD+QavhoCNpjEa/yQiIlLSFKAqmmMBakFuHABNNYBcRESkxClAVTR7lgOwJK82Pg479SKCLC5IRESk4lGAqkhys2HfBgBWuurSICoIHy/9JxYRESlp+natSFJXguEiyyeSvYRr/JOIiEgpUYCqSI513232qg9o/JOIiEhpUYCqSI4NIF+UFwegFigREZFSogBVkRxrgfr7SE0AmqgFSkREpFQoQFUURw/BgS2AOYC8VpUAQvy8ra1JRESkgvKyugApISlJAGT6VedQTjCd1H0nIpWY0+kkPz/f6jKknPH29sbhcJTIuRSgKopj3XdbfRoAGkAuIpWTYRikpqZy6NAhq0uRciosLIzo6OjzXidWAaqiKJxAsw6gAeQiUjkdD0+RkZEEBARoMXUpZBgGR44cIT09HYCYmJjzOp8CVEWx2wxQ87KqA2qBEpHKx+l0FoanqlWrWl2OlEP+/v4ApKenExkZeV7deRpEXhEc3gcZyQCscMYRFuBNTKifxUWJiJSt42OeAgICLK5EyrPjvx/nO0ZOAaoi2JMEQGZgHbIIoGlMiJqtRaTS0r9/ciYl9fuhAFUR7DEn0Nx+fAC5xj+JiHiU7t27M3LkSKvLAGDq1KnUr18fh8PByJEjSUhIICwszOqyyh0FqIrg2ADyZQXHBpBr/JOIiJxg3rx52Gy2Yl2d+J///IdrrrmGnTt38uyzzzJ48GA2btxY+PxTTz1F69atS69YD6FB5BXBsQA1J9McQN4sNtTKakRExENlZ2eTnp5Or169iI2NLdx+fPC1/EMtUJ4uMwWyUjBsdtYTh4+XnboRgVZXJSIibiooKGDEiBGEhoZSrVo1nnjiCQzDKHw+NzeXBx98kOrVqxMYGEjHjh2ZN29e4fM7duzgqquuIjw8nMDAQJo1a8bPP//M9u3b6dGjBwDh4eHYbDbi4+NPev158+YRHBwMwCWXXILNZmPevHlFuvASEhJ4+umnWbFiBTabDZvNRkJCQml9JOWaWqA83bHWJ1tEYxbe1Y+0rBy8HcrFIiJgzv1zNN9pyWv7ezvcGrA8YcIEbrvtNhYtWsSSJUu48847qVWrFnfccQcAI0aMYO3atXz11VfExsby/fff07t3b1atWkWDBg0YPnw4eXl5/PHHHwQGBrJ27VqCgoKoWbMm3333HYMGDWLDhg2EhIScskWpS5cubNiwgUaNGvHdd9/RpUsXqlSpwvbt2wv3GTx4MKtXr2bGjBn8+uuvAISGVs5eDwUoT3csQBHbFrvdRkyomllFRI47mu+k6diZlrz22md6EeBT/K/ZmjVr8vrrr2Oz2WjUqBGrVq3i9ddf54477iA5OZnx48eTnJxc2LX24IMPMmPGDMaPH8/zzz9PcnIygwYNokWLFgDUrVu38NxVqlQBIDIy8rQDwn18fIiMjCzcPzo6+qR9/P39CQoKwsvL65TPVyYKUJ7u2BV4xLa2tAwRETk/nTp1KtJi1blzZ1599VWcTierVq3C6XTSsGHDIsfk5uYWThp63333cffddzNr1ix69uzJoEGDaNmyZZm+h8pEAcqTGUaRFigRESnK39vB2md6WfbaJSU7OxuHw8HSpUtPmj07KCgIgNtvv51evXoxffp0Zs2axbhx43j11Ve59957S6wO+YcClCfL3A1H9oPdC6KaWV2NiEi5Y7PZ3OpGs9LChQuLPF6wYAENGjTA4XDQpk0bnE4n6enpXHTRRac9R82aNbnrrru46667ePTRR/noo4+499578fHxAczlbs6Xj49PiZzH02m0sSdLWWH+jGgC3n5MmzaNgQMHMm3aNGvrEhERtyUnJzNq1Cg2bNjApEmTePvtt7n//vsBaNiwIUOHDuXmm29mypQpbNu2jUWLFjFu3DimT58OwMiRI5k5cybbtm1j2bJlzJ07lyZNmgBQu3ZtbDYbP/30E3v37iU7O/uc64yLi2Pbtm0kJSWxb98+cnNzz//NeyAFKE92PEDFtALMy0vnzp1baS8pFRHxZDfffDNHjx6lQ4cODB8+nPvvv58777yz8Pnx48dz8803M3r0aBo1asSAAQNYvHgxtWrVAszWpeHDh9OkSRN69+5Nw4YNee+99wCoXr06Tz/9NGPGjCEqKooRI0acc52DBg2id+/e9OjRg4iICCZNmnR+b9xD2YwTJ5mQEpGZmUloaCgZGRmEhJTirOBfDoaNM+CKl6HjnUybNo2EhATi4+Pp169f6b2uiEg5lJOTw7Zt26hTpw5+flpQXU7tTL8n7nx/e0bHsJzav1qg+vXrp+AkIiJSBtSF56my0iArBbBBdHOrqxEREalUFKA8VepK82e1huCjpVtERETKkgKUp0pJMn8e674TERGRsqMA5an+Nf5JREREyo4ClKdSgBIREbGMApQnOnIADiWb96NbWFuLiIhIJaQA5YmODyAPjwP/MCsrERERqZQUoDyRuu9EREQspQDliRSgRETEYgkJCYSFhVldBvHx8QwYMKDMX1cByhMpQImISDm3fft2bDYbSUlJ5fJ850sBytPkZML+zeb9aAUoEZHKKi8vz+oSSoSnvg8FKE+Tttr8GVIdgiKsrUVEREpEVlYWQ4cOJTAwkJiYGF5//XW6d+/OyJEjC/eJi4vj2Wef5eabbyYkJIQ777wTgO+++45mzZrh6+tLXFwcr776apFz22w2pk6dWmRbWFgYCQkJwD8tO1OmTKFHjx4EBATQqlUrEhMTixyTkJBArVq1CAgI4Oqrr2b//v1nfE916tQBoE2bNthsNrp37w780+X23HPPERsbS6NGjYpV5+nOd9wrr7xCTEwMVatWZfjw4eTn55+xvvOlxYQ9TcqxK/DUfScicnaGAflHrHlt7wCw2Yq166hRo/j777+ZNm0aUVFRjB07lmXLltG6desi+73yyiuMHTuWJ598EoClS5dy3XXX8dRTTzF48GDmz5/PPffcQ9WqVYmPj3er3Mcff5xXXnmFBg0a8Pjjj3P99dezefNmvLy8WLhwIbfddhvjxo1jwIABzJgxo7CG01m0aBEdOnTg119/pVmzZvj4+BQ+N2fOHEJCQpg9e3ax6zvT+ebOnUtMTAxz585l8+bNDB48mNatW3PHHXe49Rm4QwHK02j8k4hI8eUfgedjrXntx/YUa63SrKwsJkyYwJdffsmll14KwPjx44mNPbnuSy65hNGjRxc+Hjp0KJdeeilPPPEEAA0bNmTt2rW8/PLLbgeoBx98kD59+gDw9NNP06xZMzZv3kzjxo1588036d27Nw8//HDh68yfP58ZM2ac9nwREWYvSdWqVYmOji7yXGBgIB9//HGREHQ2ZzpfeHg477zzDg6Hg8aNG9OnTx/mzJlTqgFKXXieRgFKRKRC2bp1K/n5+XTo0KFwW2hoaGHX1onat29f5PG6devo2rVrkW1du3Zl06ZNOJ1Ot+po2bJl4f2YmBgA0tPTC1+nY8eORfbv3LmzW+c/UYsWLdwKT2fTrFkzHA5H4eOYmJjC2kuLWqA8Sf5R2LvevK8AJSJydt4BZkuQVa9dwgIDz96i9W82mw3DMIpsO9X4IG9v7yLHALhcLrdfrzhO9T6KW+epnFj78XOVVu3HKUB5krS1YDghMAKCY6yuRkSk/LPZitWNZqW6devi7e3N4sWLqVWrFgAZGRls3LiRiy+++IzHNmnShL///rvItr///puGDRsWtshERESQkpJS+PymTZs4csS9cWFNmjRh4cKFRbYtWLDgjMccb2EqbkvY2ep093ylTQHKk6QkmT9jWhV7YKKIiJRvwcHBDBs2jIceeogqVaoQGRnJk08+id1uL2wJOp3Ro0dzwQUX8OyzzzJ48GASExN55513eO+99wr3ueSSS3jnnXfo3LkzTqeTRx555KQWm7O577776Nq1K6+88gr9+/dn5syZZxz/BBAZGYm/vz8zZsygRo0a+Pn5ERoaetr9z1anu+crbRoD5UlyDoGXv7rvREQqmNdee43OnTvTt29fevbsSdeuXWnSpAl+fn5nPK5t27Z8/fXXfPXVVzRv3pyxY8fyzDPPFBlA/uqrr1KzZk0uuugibrjhBh588EECAtzrXuzUqRMfffQRb775Jq1atWLWrFn897//PeMxXl5evPXWW/zvf/8jNjaW/v37n3H/s9Xp7vlKneEh/u///s/o3Lmz4e/vb4SGhp5yH+Ck26RJk4rsM3fuXKNNmzaGj4+PUa9ePWP8+PEnneedd94xateubfj6+hodOnQwFi5c6FatGRkZBmBkZGS4dVyxOAsMIyer5M8rIuLhjh49aqxdu9Y4evSo1aWct+zsbCM0NNT4+OOPrS6lwjnT74k7398e0wKVl5fHtddey913333G/caPH09KSkrh7cT1cbZt20afPn3o0aMHSUlJjBw5kttvv52ZM2cW7jN58mRGjRrFk08+ybJly2jVqhW9evUq9dH8xWZ3gG+Q1VWIiEgJWr58OZMmTWLLli0sW7aMoUOHAljfyiKn5TFjoJ5++mmAwhlJTycsLOyk+SGO++CDD6hTp07hLK1NmjThr7/+4vXXX6dXr16A2Yx6xx13cMsttxQeM336dD799FPGjBlTQu9GRESkqFdeeYUNGzbg4+NDu3bt+PPPP6lWrZrVZclpeEwLVHENHz6catWq0aFDBz799NMil0QmJibSs2fPIvv36tWrcLr6vLw8li5dWmQfu91Oz549T5rS/kS5ublkZmYWuYmIiBRXmzZtWLp0KdnZ2Rw4cIDZs2fTokULq8uSM/CYFqjieOaZZ7jkkksICAhg1qxZ3HPPPWRnZ3PfffcBkJqaSlRUVJFjoqKiyMzM5OjRoxw8eBCn03nKfdavX3/a1x03blxhC5mIiIhUfJa2QI0ZMwabzXbG25mCy7898cQTdO3alTZt2vDII4/w8MMP8/LLL5fiOzA9+uijZGRkFN527txZ6q8pIiIi1rG0BWr06NFnXaunbt2653z+jh078uyzz5Kbm4uvry/R0dGkpaUV2SctLY2QkBD8/f1xOBw4HI5T7nO6cVUAvr6++Pr6nnOdIiJScox/zWYtcqKS+v2wNEBFREQULg5YGpKSkggPDy8MN507d+bnn38uss/s2bML1/M5PnBvzpw5hVfvuVwu5syZw4gRI0qtThEROX/HJ108cuQI/v7+Flcj5dXx2c3dnUz03zxmDFRycjIHDhwgOTkZp9NJUlISAPXr1ycoKIgff/yRtLQ0OnXqhJ+fH7Nnz+b555/nwQcfLDzHXXfdxTvvvMPDDz/Mrbfeym+//cbXX3/N9OnTC/cZNWoUw4YNo3379nTo0IE33niDw4cPF16VJyIi5ZPD4SAsLKxw2pmAgICzzuQtlYdhGBw5coT09HTCwsKKLD58LjwmQI0dO5YJEyYUPm7Tpg0Ac+fOpXv37nh7e/Puu+/ywAMPYBgG9evXL5yS4Lg6deowffp0HnjgAd58801q1KjBxx9/XDiFAcDgwYPZu3cvY8eOJTU1ldatWzNjxoyTBpaLiEj5c3y4RbmZu0/KnTNNd+QOm6HO4hKXmZlJaGgoGRkZhISEWF2OiEil43Q6yc/Pt7oMKWe8vb3P2PLkzve3x7RAiYiIFNfxi4JESkuFm0hTREREpLQpQImIiIi4SQFKRERExE0aA1UKjo/L15p4IiIinuP493Zxrq9TgCoFWVlZANSsWdPiSkRERMRdWVlZhIaGnnEfTWNQClwuF3v27CE4OLjEJ3HLzMykZs2a7Ny5U1MknIU+q+LTZ1V8+qyKT59V8emzKr7S/KwMwyArK4vY2Fjs9jOPclILVCmw2+3UqFGjVF8jJCRE/5MVkz6r4tNnVXz6rIpPn1Xx6bMqvtL6rM7W8nScBpGLiIiIuEkBSkRERMRNClAextfXlyeffBJfX1+rSyn39FkVnz6r4tNnVXz6rIpPn1XxlZfPSoPIRURERNykFigRERERNylAiYiIiLhJAUpERETETQpQIiIiIm5SgPIQzz33HF26dCEgIICwsLBT7mOz2U66ffXVV2VbaDlRnM8rOTmZPn36EBAQQGRkJA899BAFBQVlW2g5FBcXd9Lv0QsvvGB1WeXGu+++S1xcHH5+fnTs2JFFixZZXVK589RTT530O9S4cWOryyoX/vjjD6666ipiY2Ox2WxMnTq1yPOGYTB27FhiYmLw9/enZ8+ebNq0yZpiLXa2zyo+Pv6k37PevXuXWX0KUB4iLy+Pa6+9lrvvvvuM+40fP56UlJTC24ABA8qmwHLmbJ+X0+mkT58+5OXlMX/+fCZMmEBCQgJjx44t40rLp2eeeabI79G9995rdUnlwuTJkxk1ahRPPvkky5Yto1WrVvTq1Yv09HSrSyt3mjVrVuR36K+//rK6pHLh8OHDtGrVinffffeUz7/00ku89dZbfPDBByxcuJDAwEB69epFTk5OGVdqvbN9VgC9e/cu8ns2adKksivQEI8yfvx4IzQ09JTPAcb3339fpvWUd6f7vH7++WfDbrcbqamphdvef/99IyQkxMjNzS3DCsuf2rVrG6+//rrVZZRLHTp0MIYPH1742Ol0GrGxsca4ceMsrKr8efLJJ41WrVpZXUa59+9/s10ulxEdHW28/PLLhdsOHTpk+Pr6GpMmTbKgwvLjVN9vw4YNM/r3729JPYZhGGqBqmCGDx9OtWrV6NChA59++imGpvk6pcTERFq0aEFUVFThtl69epGZmcmaNWssrKx8eOGFF6hatSpt2rTh5ZdfVtcmZqvm0qVL6dmzZ+E2u91Oz549SUxMtLCy8mnTpk3ExsZSt25dhg4dSnJystUllXvbtm0jNTW1yO9YaGgoHTt21O/YacybN4/IyEgaNWrE3Xffzf79+8vstbWYcAXyzDPPcMkllxAQEMCsWbO45557yM7O5r777rO6tHInNTW1SHgCCh+npqZaUVK5cd9999G2bVuqVKnC/PnzefTRR0lJSeG1116zujRL7du3D6fTecrfm/Xr11tUVfnUsWNHEhISaNSoESkpKTz99NNcdNFFrF69muDgYKvLK7eO/9tzqt+xyv7v0qn07t2bgQMHUqdOHbZs2cJjjz3GFVdcQWJiIg6Ho9RfXwHKQmPGjOHFF1884z7r1q0r9uDLJ554ovB+mzZtOHz4MC+//HKFCVAl/XlVJu58dqNGjSrc1rJlS3x8fPjPf/7DuHHjLF86QTzDFVdcUXi/ZcuWdOzYkdq1a/P1119z2223WViZVCRDhgwpvN+iRQtatmxJvXr1mDdvHpdeemmpv74ClIVGjx5NfHz8GfepW7fuOZ+/Y8eOPPvss+Tm5laIL76S/Lyio6NPunoqLS2t8LmK5nw+u44dO1JQUMD27dtp1KhRKVTnGapVq4bD4Sj8PTkuLS2tQv7OlKSwsDAaNmzI5s2brS6lXDv+e5SWlkZMTEzh9rS0NFq3bm1RVZ6jbt26VKtWjc2bNytAVXQRERFERESU2vmTkpIIDw+vEOEJSvbz6ty5M8899xzp6elERkYCMHv2bEJCQmjatGmJvEZ5cj6fXVJSEna7vfBzqqx8fHxo164dc+bMKby61eVyMWfOHEaMGGFtceVcdnY2W7Zs4aabbrK6lHKtTp06REdHM2fOnMLAlJmZycKFC896BbbArl272L9/f5HwWZoUoDxEcnIyBw4cIDk5GafTSVJSEgD169cnKCiIH3/8kbS0NDp16oSfnx+zZ8/m+eef58EHH7S2cIuc7fO6/PLLadq0KTfddBMvvfQSqamp/Pe//2X48OEVJnCei8TERBYuXEiPHj0IDg4mMTGRBx54gBtvvJHw8HCry7PcqFGjGDZsGO3bt6dDhw688cYbHD58mFtuucXq0sqVBx98kKuuuoratWuzZ88ennzySRwOB9dff73VpVkuOzu7SEvctm3bSEpKokqVKtSqVYuRI0fyf//3fzRo0IA6derwxBNPEBsbWymnpDnTZ1WlShWefvppBg0aRHR0NFu2bOHhhx+mfv369OrVq2wKtOz6P3HLsGHDDOCk29y5cw3DMIxffvnFaN26tREUFGQEBgYarVq1Mj744APD6XRaW7hFzvZ5GYZhbN++3bjiiisMf39/o1q1asbo0aON/Px864ouB5YuXWp07NjRCA0NNfz8/IwmTZoYzz//vJGTk2N1aeXG22+/bdSqVcvw8fExOnToYCxYsMDqksqdwYMHGzExMYaPj49RvXp1Y/DgwcbmzZutLqtcmDt37in/bRo2bJhhGOZUBk888YQRFRVl+Pr6GpdeeqmxYcMGa4u2yJk+qyNHjhiXX365ERERYXh7exu1a9c27rjjjiJT05Q2m2HoOncRERERd2geKBERERE3KUCJiIiIuEkBSkRERMRNClAiIiIiblKAEhEREXGTApSIiIiImxSgRERERNykACUiIiLiJgUoERERETcpQImIiIi4SQFKROQs9u7dS3R0NM8//3zhtvnz5+Pj48OcOXMsrExErKK18EREiuHnn39mwIABzJ8/n0aNGtG6dWv69+/Pa6+9ZnVpImIBBSgRkWIaPnw4v/76K+3bt2fVqlUsXrwYX19fq8sSEQsoQImIFNPRo0dp3rw5O3fuZOnSpbRo0cLqkkTEIhoDJSJSTFu2bGHPnj24XC62b99udTkiYiG1QImIFENeXh4dOnSgdevWNGrUiDfeeINVq1YRGRlpdWkiYgEFKBGRYnjooYf49ttvWbFiBUFBQXTr1o3Q0FB++uknq0sTEQuoC09E5CzmzZvHG2+8weeff05ISAh2u53PP/+cP//8k/fff9/q8kTEAmqBEhEREXGTWqBERERE3KQAJSIiIuImBSgRERERNylAiYiIiLhJAUpERETETQpQIiIiIm5SgBIRERFxkwKUiIiIiJsUoERERETcpAAlIiIi4iYFKBERERE3KUCJiIiIuOn/AVUnMer5Q/B5AAAAAElFTkSuQmCC", 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", 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", 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", 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" ] @@ -1263,214 +1052,11 @@ "source": [ "u0 = s\n", "for i in range(5):\n", - " u0 = experimentalist(u0, num_samples=5, random_state=i)\n", + " u0 = experimentalist(u0, num_samples=10)\n", " u0 = experiment_runner(u0)\n", " u0 = theorist(u0)\n", " show_best_fit(u0)\n", - " plt.title(f\"{i=}\")\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Chained Experimentalists\n", - "\n", - "A more complicated experimentalist can be constructed using a pooling function and sampler(s), which are chained\n", - "together.\n", - "In this example, the `grid_pool` requires explicit specification of the allowed states, so we add those to\n", - "the `variables` attribute:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After pooler: r.conditions= x\n", - "0 -15.00\n", - "1 -14.95\n", - "2 -14.90\n", - "3 -14.85\n", - "4 -14.80\n", - ".. ...\n", - "596 14.80\n", - "597 14.85\n", - "598 14.90\n", - "599 14.95\n", - "600 15.00\n", - "\n", - "[601 rows x 1 columns]\n", - "After sampler: r.conditions= x\n", - "446 7.30\n", - "404 5.20\n", - "509 10.45\n", - "455 7.75\n", - "201 -4.95\n", - ".. ...\n", - "439 6.95\n", - "9 -14.55\n", - "189 -5.55\n", - "373 3.65\n", - "517 10.85\n", - "\n", - "[100 rows x 1 columns]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from autora.experimentalist.sampler.random_sampler import random_sample_executor\n", - "from autora.experimentalist.pooler.grid import grid_pool\n", - "\n", - "variables = VariableCollection(\n", - " independent_variables=[Variable(name=\"x\",\n", - " allowed_values=np.linspace(-15, 15, 601),\n", - " value_range=(-15, 15))],\n", - " dependent_variables=[Variable(\"y\")]\n", - ")\n", - "\n", - "r = Snapshot(variables=variables)\n", - "\n", - "# The experimentalist is built of two functions acting in sequence.\n", - "# The first makes a full list of all allowable conditions:\n", - "r = grid_pool(r)\n", - "print(f\"After pooler: {r.conditions=}\")\n", - "\n", - "# The second samples ten of those allowable conditions.\n", - "r = random_sample_executor(r, num_samples=100, random_state=1)\n", - "print(f\"After sampler: {r.conditions=}\")\n", - "\n", - "# ... then we continue with the experiment_runner and the theorist.\n", - "r = experiment_runner(r)\n", - "r = theorist(r)\n", - "\n", - "show_best_fit(r)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Experimentalists could be chained together as a single line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "r = Snapshot(variables=variables)\n", - "\n", - "r = random_sample_executor(grid_pool(r), num_samples=50, random_state=1) # experimentalist\n", - "r = experiment_runner(r)\n", - "r = theorist(r)\n", - "\n", - "show_best_fit(r)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The experimentalists could also be chained together using a `def`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def experimentalist(state):\n", - " return random_sample_executor(grid_pool(state), num_samples=50, random_state=1)\n", - "\n", - "r = Snapshot(variables=variables)\n", - "\n", - "r = experimentalist(r)\n", - "r = experiment_runner(r)\n", - "r = theorist(r)\n", - "\n", - "show_best_fit(r)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The experimentalists can be chained together using the `autora.experimentalist.pipeline.Pipeline`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# The experimentalist is built of two functions acting in sequence.\n", - "from autora.experimentalist.pipeline import Pipeline as ExperimentalistPipeline\n", - "\n", - "experimentalist = ExperimentalistPipeline(\n", - " [(\"pool\", grid_pool),\n", - " (\"sample\", random_sample_executor)],\n", - " params={\"sample\": {\"num_samples\": 50, \"random_state\": 1}}\n", - ")\n", - "\n", - "r = Snapshot(variables=variables)\n", - "\n", - "r = experimentalist(r)\n", - "r = experiment_runner(r)\n", - "r = theorist(r)\n", - "\n", - "show_best_fit(r)\n" + " plt.title(f\"{i=}\")\n" ] }, { From fac91cb03ea1c2d4b3f6c2c82f83e3bad32627a8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:16:06 -0400 Subject: [PATCH 014/121] refactor: change to using singledispatch for random_pool --- .../experimentalist/pooler/random_pooler.py | 51 ++++++++++++------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index fb077fb3..256fefb7 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,14 +1,29 @@ import random +from functools import singledispatch from typing import Iterable, List, Tuple import numpy as np import pandas as pd -from autora.state.delta import Result, wrap_to_use_state +from autora.state.delta import Result, State, wrap_to_use_state from autora.utils.deprecation import deprecated_alias from autora.variable import IV, ValueType, VariableCollection +@singledispatch +def random_pool(s, **kwargs): + """Function to create a sequence of conditions randomly sampled from independent variables.""" + raise NotImplementedError( + "%s (type=%s) is not implemented for random_pool" % (s, type(s)) + ) + + +@random_pool.register(State) +def _random_pool_on_state(s, **kwargs): + return wrap_to_use_state(random_pool_from_variables)(s, **kwargs) + + +@random_pool.register(list) def random_pool_from_ivs( ivs: List[IV], num_samples: int = 1, duplicates: bool = True ) -> Iterable: @@ -30,7 +45,7 @@ def random_pool_from_ivs( ) l_iv_values.append(iv.allowed_values) - # Check to ensure infinite search won't occur if duplicates not allowed + # Check to ensure infinite search won't occur if replace not allowed if not duplicates: l_pool_len = [len(set(s)) for s in l_iv_values] n_combinations = np.product(l_pool_len) @@ -54,11 +69,12 @@ def random_pool_from_ivs( random_pooler = deprecated_alias(random_pool_from_ivs, "random_pooler") +@random_pool.register(VariableCollection) def random_pool_from_variables( variables: VariableCollection, num_samples=5, random_state=None, - duplicates: bool = True, + replace: bool = True, ) -> pd.DataFrame: """ @@ -66,7 +82,7 @@ def random_pool_from_variables( variables: the description of all the variables in the AER experiment. num_samples: the number of conditions to produce random_state: the seed value for the random number generator - duplicates: if True, allow repeated values + replace: if True, allow repeated values Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field @@ -79,8 +95,8 @@ def random_pool_from_variables( With one independent variable "x", and some allowed_values we get some of those values back when running the experimentalist: - >>> random_pool_from_variables( - ... variables=VariableCollection( + >>> random_pool( + ... VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=range(10)) ... ]), random_state=1) {'conditions': x @@ -92,8 +108,8 @@ def random_pool_from_variables( ... we get a sample of the range back when running the experimentalist: - >>> random_pool_from_variables( - ... variables=VariableCollection(independent_variables=[ + >>> random_pool( + ... VariableCollection(independent_variables=[ ... Variable(name="x", value_range=(-5, 5)) ... ]), random_state=1)["conditions"] x @@ -106,17 +122,16 @@ def random_pool_from_variables( The allowed_values or value_range must be specified: - >>> random_pool_from_variables( - ... variables=VariableCollection(independent_variables=[Variable(name="x")])) + >>> random_pool(VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... ValueError: allowed_values or [value_range and type==REAL] needs to be set... With two independent variables, we get independent samples on both axes: - >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + >>> random_pool(VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), - ... ]), num_samples=10, duplicates=True, random_state=1)["conditions"] + ... ]), num_samples=10, replace=True, random_state=1)["conditions"] x1 x2 0 2 434 1 3 212 @@ -130,8 +145,8 @@ def random_pool_from_variables( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool_from_variables( - ... variables=VariableCollection(independent_variables=[ + >>> random_pool( + ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), ... ])) @@ -142,7 +157,8 @@ def random_pool_from_variables( We can specify arrays of allowed values: - >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + >>> random_pool( + ... VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), @@ -162,7 +178,7 @@ def random_pool_from_variables( for iv in variables.independent_variables: if iv.allowed_values is not None: raw_conditions[iv.name] = rng.choice( - iv.allowed_values, size=num_samples, replace=duplicates + iv.allowed_values, size=num_samples, replace=replace ) elif (iv.value_range is not None) and (iv.type == ValueType.REAL): raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) @@ -175,6 +191,3 @@ def random_pool_from_variables( conditions = pd.DataFrame(raw_conditions) return Result(conditions=conditions) - - -random_pool = wrap_to_use_state(random_pool_from_variables) From 38eebaae0ae4f709457273d2beda649faecadfff Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:26:45 -0400 Subject: [PATCH 015/121] revert: undo name change in tests --- tests/test_experimentalist_random.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py index 96aacd57..7465a2e0 100644 --- a/tests/test_experimentalist_random.py +++ b/tests/test_experimentalist_random.py @@ -5,7 +5,7 @@ from autora.experimentalist.pipeline import make_pipeline from autora.experimentalist.pooler.grid import grid_pool_from_ivs -from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs +from autora.experimentalist.pooler.random_pooler import random_pool from autora.experimentalist.sampler.random_sampler import ( random_sample_from_conditions_iterable, ) @@ -22,9 +22,7 @@ def test_random_pooler_experimentalist(metadata): """ num_samples = 10 - conditions = random_pool_from_ivs( - metadata.independent_variables, num_samples=num_samples - ) + conditions = random_pool(metadata.independent_variables, num_samples=num_samples) conditions = np.array(list(conditions)) From 790164ecfe4aaf79367b66cccf719b3434251939 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:30:48 -0400 Subject: [PATCH 016/121] docs: update signatures --- src/autora/experimentalist/pooler/random_pooler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 256fefb7..e7174c0b 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -14,12 +14,12 @@ def random_pool(s, **kwargs): """Function to create a sequence of conditions randomly sampled from independent variables.""" raise NotImplementedError( - "%s (type=%s) is not implemented for random_pool" % (s, type(s)) + "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) @random_pool.register(State) -def _random_pool_on_state(s, **kwargs): +def _random_pool_on_state(s: State, **kwargs) -> State: return wrap_to_use_state(random_pool_from_variables)(s, **kwargs) From 03f639af9f3a2f2ab25ed0756f05c449e2cc1ff4 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:41:49 -0400 Subject: [PATCH 017/121] refactor: update random_sampler to use singledispatch --- .../experimentalist/sampler/random_sampler.py | 44 +++++++++++++------ 1 file changed, 30 insertions(+), 14 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 5e5cac82..1d6e9d15 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,14 +1,32 @@ import random +from functools import singledispatch from typing import Iterable, Optional, Sequence, Union +import numpy as np import pandas as pd -from autora.state.delta import Result, wrap_to_use_state +from autora.state.delta import Result, State, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -def random_sample_from_conditions_iterable( - conditions: Union[Iterable, Sequence], num_samples: int = 1 +@singledispatch +def random_sample(s, **kwargs): + """Function to create a sequence of conditions randomly sampled from independent variables.""" + raise NotImplementedError( + "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) + ) + + +@random_sample.register(State) +def random_sample_on_state(s: State, **kwargs) -> State: + return wrap_to_use_state(random_sample_from_conditions)(s, **kwargs) + + +@random_sample.register(list) +@random_sample.register(range) +@random_sample.register(filter) +def random_sample_on_iterable_conditions( + conditions: Union[Sequence], num_samples: int = 1 ): """ Uniform random sampling without replacement from a pool of conditions. @@ -21,15 +39,15 @@ def random_sample_from_conditions_iterable( Examples: From a range: >>> random.seed(1) - >>> random_sample_from_conditions_iterable(range(100), num_samples=5) + >>> random_sample(range(100), num_samples=5) [53, 37, 65, 51, 4] >>> random.seed(1) - >>> random_sample_from_conditions_iterable([1,2,3,4,5,6,7,8,9,10], num_samples=5) + >>> random_sample([1,2,3,4,5,6,7,8,9,10], num_samples=5) [7, 9, 10, 8, 6] >>> random.seed(1) - >>> random_sample_from_conditions_iterable( + >>> random_sample( ... filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), ... num_samples=5 ... ) @@ -44,13 +62,14 @@ def random_sample_from_conditions_iterable( return samples -random_sampler = deprecated_alias( - random_sample_from_conditions_iterable, "random_sampler" -) +random_sampler = deprecated_alias(random_sample, "random_sampler") +@random_sample.register(pd.DataFrame) +@random_sample.register(np.ndarray) +@random_sample.register(np.recarray) def random_sample_from_conditions( - conditions, + conditions: Union[pd.DataFrame, np.ndarray, np.recarray], num_samples: int = 1, random_state: Optional[int] = None, replace: bool = False, @@ -70,7 +89,7 @@ def random_sample_from_conditions( From a pd.DataFrame: >>> import pandas as pd >>> random.seed(1) - >>> random_sample_from_conditions( + >>> random_sample( ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) {'conditions': x 67 167 @@ -85,6 +104,3 @@ def random_sample_from_conditions( conditions, random_state=random_state, n=num_samples, replace=replace ) ) - - -random_sample = wrap_to_use_state(random_sample_from_conditions) From a5b659c50f0b0e1681c63baf6878035f397e14e9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:45:43 -0400 Subject: [PATCH 018/121] docs: update random_pool docs --- src/autora/experimentalist/pooler/random_pooler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index e7174c0b..f4836edc 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -12,14 +12,14 @@ @singledispatch def random_pool(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from independent variables.""" + """Function to create a sequence of conditions randomly sampled from given conditions.""" raise NotImplementedError( "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) @random_pool.register(State) -def _random_pool_on_state(s: State, **kwargs) -> State: +def random_pool_on_state(s: State, **kwargs) -> State: return wrap_to_use_state(random_pool_from_variables)(s, **kwargs) From 4dc67ccabf149c48fd78b5760073260c4f93ed46 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:46:14 -0400 Subject: [PATCH 019/121] Revert "docs: update random_pool docs" This reverts commit a5b659c50f0b0e1681c63baf6878035f397e14e9. --- src/autora/experimentalist/pooler/random_pooler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index f4836edc..e7174c0b 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -12,14 +12,14 @@ @singledispatch def random_pool(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from given conditions.""" + """Function to create a sequence of conditions randomly sampled from independent variables.""" raise NotImplementedError( "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) @random_pool.register(State) -def random_pool_on_state(s: State, **kwargs) -> State: +def _random_pool_on_state(s: State, **kwargs) -> State: return wrap_to_use_state(random_pool_from_variables)(s, **kwargs) From 88cc5dd6c0de22d6b0983f3c621f4276541feb39 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 12:46:38 -0400 Subject: [PATCH 020/121] docs: update random_sample docs --- src/autora/experimentalist/sampler/random_sampler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 1d6e9d15..19e2d841 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -11,7 +11,7 @@ @singledispatch def random_sample(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from independent variables.""" + """Function to create a sequence of conditions randomly sampled from conditions.""" raise NotImplementedError( "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) From 686145ec7c9718d42ae812bfead24cb2eaacaa6b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 15:39:15 -0400 Subject: [PATCH 021/121] refactor: move random_pool, random_sample to experimentalist.random_ --- .../experimentalist/pooler/random_pooler.py | 149 +------- src/autora/experimentalist/random_.py | 340 ++++++++++++++++++ .../experimentalist/sampler/random_sampler.py | 88 +---- 3 files changed, 348 insertions(+), 229 deletions(-) create mode 100644 src/autora/experimentalist/random_.py diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index e7174c0b..78ad104e 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,30 +1,13 @@ import random -from functools import singledispatch from typing import Iterable, List, Tuple import numpy as np -import pandas as pd -from autora.state.delta import Result, State, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -from autora.variable import IV, ValueType, VariableCollection +from autora.variable import IV -@singledispatch -def random_pool(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from independent variables.""" - raise NotImplementedError( - "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) - ) - - -@random_pool.register(State) -def _random_pool_on_state(s: State, **kwargs) -> State: - return wrap_to_use_state(random_pool_from_variables)(s, **kwargs) - - -@random_pool.register(list) -def random_pool_from_ivs( +def random_pool( ivs: List[IV], num_samples: int = 1, duplicates: bool = True ) -> Iterable: """ @@ -45,7 +28,7 @@ def random_pool_from_ivs( ) l_iv_values.append(iv.allowed_values) - # Check to ensure infinite search won't occur if replace not allowed + # Check to ensure infinite search won't occur if duplicates not allowed if not duplicates: l_pool_len = [len(set(s)) for s in l_iv_values] n_combinations = np.product(l_pool_len) @@ -66,128 +49,4 @@ def random_pool_from_ivs( return iter(l_samples) -random_pooler = deprecated_alias(random_pool_from_ivs, "random_pooler") - - -@random_pool.register(VariableCollection) -def random_pool_from_variables( - variables: VariableCollection, - num_samples=5, - random_state=None, - replace: bool = True, -) -> pd.DataFrame: - """ - - Args: - variables: the description of all the variables in the AER experiment. - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - With one independent variable "x", and some allowed_values we get some of those values - back when running the experimentalist: - >>> random_pool( - ... VariableCollection( - ... independent_variables=[Variable(name="x", allowed_values=range(10)) - ... ]), random_state=1) - {'conditions': x - 0 4 - 1 5 - 2 7 - 3 9 - 4 0} - - - ... we get a sample of the range back when running the experimentalist: - >>> random_pool( - ... VariableCollection(independent_variables=[ - ... Variable(name="x", value_range=(-5, 5)) - ... ]), random_state=1)["conditions"] - x - 0 0.118216 - 1 4.504637 - 2 -3.558404 - 3 4.486494 - 4 -1.881685 - - - - The allowed_values or value_range must be specified: - >>> random_pool(VariableCollection(independent_variables=[Variable(name="x")])) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - With two independent variables, we get independent samples on both axes: - >>> random_pool(VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=range(1, 5)), - ... Variable(name="x2", allowed_values=range(1, 500)), - ... ]), num_samples=10, replace=True, random_state=1)["conditions"] - x1 x2 - 0 2 434 - 1 3 212 - 2 4 137 - 3 4 414 - 4 1 129 - 5 1 205 - 6 4 322 - 7 4 275 - 8 1 43 - 9 2 14 - - If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool( - ... VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ])) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - - We can specify arrays of allowed values: - - >>> random_pool( - ... VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ]), random_state=1)["conditions"] - x y z - 0 -0.6 3 29.0 - 1 0.2 4 24.0 - 2 5.2 4 23.0 - 3 9.0 3 29.0 - 4 -9.4 3 22.0 - - - """ - rng = np.random.default_rng(random_state) - - raw_conditions = {} - for iv in variables.independent_variables: - if iv.allowed_values is not None: - raw_conditions[iv.name] = rng.choice( - iv.allowed_values, size=num_samples, replace=replace - ) - elif (iv.value_range is not None) and (iv.type == ValueType.REAL): - raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) - - else: - raise ValueError( - "allowed_values or [value_range and type==REAL] needs to be set for " - "%s" % (iv) - ) - - conditions = pd.DataFrame(raw_conditions) - return Result(conditions=conditions) +random_pooler = deprecated_alias(random_pool, "random_pooler") diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py new file mode 100644 index 00000000..7a29621b --- /dev/null +++ b/src/autora/experimentalist/random_.py @@ -0,0 +1,340 @@ +import random +from functools import singledispatch +from typing import Optional, Union + +import numpy as np +import pandas as pd + +from autora.state.delta import Result, State, wrap_to_use_state +from autora.variable import ValueType, VariableCollection + + +@singledispatch +def random_pool(s, **kwargs): + """Function to create a sequence of conditions randomly sampled from independent variables.""" + raise NotImplementedError( + "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) + ) + + +@random_pool.register(State) +def random_pool_on_state( + s: State, + num_samples: int = 5, + random_state: Optional[int] = None, + replace: bool = True, +) -> State: + """ + + Args: + variables: + fmt: the output type required + + Returns: + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed_values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=range(10)) + ... ])) + + ... we get some of those values back when running the experimentalist: + >>> random_pool(s, random_state=1).conditions + x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0 + + With one independent variable "x", and a value_range: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ])) + + ... we get a sample of the range back when running the experimentalist: + >>> random_pool(t, random_state=1).conditions + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> random_pool( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ])) + >>> random_pool(t, + ... num_samples=10, replace=True, random_state=1).conditions + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> random_pool(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> random_pool(u, random_state=1).conditions + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + """ + return wrap_to_use_state(random_pool_on_variables)( + s, num_samples=num_samples, random_state=random_state, replace=replace + ) + + +@random_pool.register(VariableCollection) +def random_pool_on_variables( + variables: VariableCollection, + num_samples: int = 5, + random_state: Optional[int] = None, + replace: bool = True, +) -> pd.DataFrame: + """ + + Args: + variables: the description of all the variables in the AER experiment. + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed_values we get some of those values + back when running the experimentalist: + >>> random_pool( + ... VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=range(10)) + ... ]), random_state=1) + {'conditions': x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0} + + + ... we get a sample of the range back when running the experimentalist: + >>> random_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ]), random_state=1)["conditions"] + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> random_pool(VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> random_pool(VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ]), num_samples=10, replace=True, random_state=1)["conditions"] + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> random_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + + >>> random_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]), random_state=1)["conditions"] + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=replace + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + conditions = pd.DataFrame(raw_conditions) + return Result(conditions=conditions) + + +@singledispatch +def random_sample(s, **kwargs): + """Function to create a sequence of conditions randomly sampled from conditions.""" + raise NotImplementedError( + "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) + ) + + +@random_sample.register(State) +def random_sample_on_state(s: State, **kwargs) -> State: + return wrap_to_use_state(random_sample_on_conditions)(s, **kwargs) + + +@random_sample.register(list) +@random_sample.register(tuple) +def random_sample_on_list( + conditions: Union[list, tuple], + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> list: + """ + Examples: + >>> random_sample([1, 1, 2, 2, 3, 3], num_samples=2, random_state=1, replace=True) + [1, 3] + + >>> random_sample((1, 1, 2, 2, 3, 3), num_samples=3, random_state=1, replace=True) + [1, 3, 3] + + + """ + + if random_state is not None: + random.seed(random_state) + + assert replace is True, "random.choices only supports choice with replacement." + return random.choices(conditions, k=num_samples) + + +@random_sample.register(pd.DataFrame) +@random_sample.register(np.ndarray) +@random_sample.register(np.recarray) +def random_sample_on_conditions( + conditions: Union[pd.DataFrame, np.ndarray, np.recarray], + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> Result: + """ + Take a random sample from some conditions. + + Args: + conditions: the conditions to sample from + num_samples: + random_state: + replace: + + Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> random.seed(1) + >>> random_sample( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + {'conditions': x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196} + + """ + return Result( + conditions=pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=replace + ) + ) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 19e2d841..7e28d2c3 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,59 +1,20 @@ import random -from functools import singledispatch -from typing import Iterable, Optional, Sequence, Union +from typing import Iterable, Sequence, Union -import numpy as np -import pandas as pd - -from autora.state.delta import Result, State, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -@singledispatch -def random_sample(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from conditions.""" - raise NotImplementedError( - "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) - ) - - -@random_sample.register(State) -def random_sample_on_state(s: State, **kwargs) -> State: - return wrap_to_use_state(random_sample_from_conditions)(s, **kwargs) - - -@random_sample.register(list) -@random_sample.register(range) -@random_sample.register(filter) -def random_sample_on_iterable_conditions( - conditions: Union[Sequence], num_samples: int = 1 -): +def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): """ Uniform random sampling without replacement from a pool of conditions. Args: conditions: Pool of conditions - num_samples: number of samples to collect + n: number of samples to collect Returns: Sampled pool - Examples: - From a range: - >>> random.seed(1) - >>> random_sample(range(100), num_samples=5) - [53, 37, 65, 51, 4] - - >>> random.seed(1) - >>> random_sample([1,2,3,4,5,6,7,8,9,10], num_samples=5) - [7, 9, 10, 8, 6] - - >>> random.seed(1) - >>> random_sample( - ... filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), - ... num_samples=5 - ... ) - [375, 390, 600, 285, 885] - """ + if isinstance(conditions, Iterable): conditions = list(conditions) random.shuffle(conditions) @@ -63,44 +24,3 @@ def random_sample_on_iterable_conditions( random_sampler = deprecated_alias(random_sample, "random_sampler") - - -@random_sample.register(pd.DataFrame) -@random_sample.register(np.ndarray) -@random_sample.register(np.recarray) -def random_sample_from_conditions( - conditions: Union[pd.DataFrame, np.ndarray, np.recarray], - num_samples: int = 1, - random_state: Optional[int] = None, - replace: bool = False, -) -> Result: - """ - Take a random sample from some conditions. - - Args: - conditions: the conditions to sample from - num_samples: - random_state: - replace: - - Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions - - Examples: - From a pd.DataFrame: - >>> import pandas as pd - >>> random.seed(1) - >>> random_sample( - ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) - {'conditions': x - 67 167 - 71 171 - 64 164 - 63 163 - 96 196} - - """ - return Result( - conditions=pd.DataFrame.sample( - conditions, random_state=random_state, n=num_samples, replace=replace - ) - ) From 09fdaede6916a830f7e362a483e637db76486784 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 15:39:31 -0400 Subject: [PATCH 022/121] refactor: move grid_pool to experimentalist.grid_ --- src/autora/experimentalist/grid_.py | 124 ++++++++++++++++++++++ src/autora/experimentalist/pooler/grid.py | 97 +---------------- 2 files changed, 127 insertions(+), 94 deletions(-) create mode 100644 src/autora/experimentalist/grid_.py diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py new file mode 100644 index 00000000..fc3131fe --- /dev/null +++ b/src/autora/experimentalist/grid_.py @@ -0,0 +1,124 @@ +"""""" +from functools import singledispatch +from itertools import product +from typing import Sequence + +import pandas as pd + +from autora.state.delta import Result, State, wrap_to_use_state +from autora.variable import Variable, VariableCollection + + +@singledispatch +def grid_pool(s, **kwargs): + """Function to create a sequence of conditions sampled from a grid of independent variables.""" + raise NotImplementedError( + "grid_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) + ) + + +@grid_pool.register(State) +def grid_pool_on_state(s: State) -> State: + return wrap_to_use_state(grid_pool_from_variables)(s) + + +@grid_pool.register(list) +@grid_pool.register(tuple) +def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: + """Creates exhaustive pool from discrete values using a Cartesian product of sets""" + # Get allowed values for each IV + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"gridsearch_pool only supports independent variables with discrete allowed values, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Return Cartesian product of all IV values + return product(*l_iv_values) + + +@grid_pool.register(VariableCollection) +def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: + """Creates exhaustive pool of conditions given a definition of variables with allowed_values. + + Args: + variables: a VariableCollection with `independent_variables` – a sequence of Variable + objects, each of which has an attribute `allowed_values` containing a sequence of values. + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed values, we get exactly those values + back when running the executor: + >>> grid_pool(VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] + ... )) + {'conditions': x + 0 1 + 1 2 + 2 3} + + The allowed_values must be specified: + >>> grid_pool(VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + AssertionError: gridsearch_pool only supports independent variables with discrete... + + With two independent variables, we get the cartesian product: + >>> grid_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ]))["conditions"] + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> grid_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + AssertionError: gridsearch_pool only supports independent variables with discrete... + + + We can specify arrays of allowed values: + >>> grid_pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]))["conditions"] + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + """ + raw_conditions = grid_pool_from_ivs(variables.independent_variables) + iv_names = [v.name for v in variables.independent_variables] + conditions = pd.DataFrame(raw_conditions, columns=iv_names) + return Result(conditions=conditions) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index eacd3e78..dadc2a4a 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,14 +1,10 @@ -"""""" from itertools import product -from typing import Sequence +from typing import List -import pandas as pd +from autora.variable import IV -from autora.state.delta import Result, wrap_to_use_state -from autora.variable import Variable, VariableCollection - -def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: +def grid_pool(ivs: List[IV]): """Creates exhaustive pool from discrete values using a Cartesian product of sets""" # Get allowed values for each IV l_iv_values = [] @@ -21,90 +17,3 @@ def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: # Return Cartesian product of all IV values return product(*l_iv_values) - - -def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: - """Creates exhaustive pool of conditions given a definition of variables with allowed_values. - - Args: - variables: a VariableCollection with `independent_variables` – a sequence of Variable - objects, each of which has an attribute `allowed_values` containing a sequence of values. - - Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - With one independent variable "x", and some allowed values, we get exactly those values - back when running the executor: - >>> grid_pool_from_variables(variables=VariableCollection( - ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] - ... )) - {'conditions': x - 0 1 - 1 2 - 2 3} - - The allowed_values must be specified: - >>> grid_pool_from_variables( - ... variables=VariableCollection(independent_variables=[Variable(name="x")])) - Traceback (most recent call last): - ... - AssertionError: gridsearch_pool only supports independent variables with discrete... - - With two independent variables, we get the cartesian product: - >>> grid_pool_from_variables(variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4]), - ... ]))["conditions"] - x1 x2 - 0 1 3 - 1 1 4 - 2 2 3 - 3 2 4 - - If any of the variables have unspecified allowed_values, we get an error: - >>> grid_pool_from_variables( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ])) - Traceback (most recent call last): - ... - AssertionError: gridsearch_pool only supports independent variables with discrete... - - - We can specify arrays of allowed values: - >>> grid_pool_from_variables( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ]))["conditions"] - x y z - 0 -10.0 3 20.0 - 1 -10.0 3 21.0 - 2 -10.0 3 22.0 - 3 -10.0 3 23.0 - 4 -10.0 3 24.0 - ... ... .. ... - 2217 10.0 4 26.0 - 2218 10.0 4 27.0 - 2219 10.0 4 28.0 - 2220 10.0 4 29.0 - 2221 10.0 4 30.0 - - [2222 rows x 3 columns] - - """ - raw_conditions = grid_pool_from_ivs(variables.independent_variables) - iv_names = [v.name for v in variables.independent_variables] - conditions = pd.DataFrame(raw_conditions, columns=iv_names) - return Result(conditions=conditions) - - -grid_pool = wrap_to_use_state(grid_pool_from_variables) From 451bad82f4dcfbded3c7cd2f8143e91be4094ff5 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 15:39:50 -0400 Subject: [PATCH 023/121] test: revert tests to old behavior --- tests/test_experimentalist_random.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py index 7465a2e0..a81ad483 100644 --- a/tests/test_experimentalist_random.py +++ b/tests/test_experimentalist_random.py @@ -4,11 +4,9 @@ import pytest from autora.experimentalist.pipeline import make_pipeline -from autora.experimentalist.pooler.grid import grid_pool_from_ivs +from autora.experimentalist.pooler.grid import grid_pool from autora.experimentalist.pooler.random_pooler import random_pool -from autora.experimentalist.sampler.random_sampler import ( - random_sample_from_conditions_iterable, -) +from autora.experimentalist.sampler.random_sampler import random_sample from autora.variable import DV, IV, ValueType, VariableCollection @@ -45,8 +43,8 @@ def test_random_sampler_experimentalist(metadata): # ---Implementation 1 - Pool using Callable via partial function---- # Set up pipeline functions with partial - pooler_callable = partial(grid_pool_from_ivs, ivs=metadata.independent_variables) - sampler = partial(random_sample_from_conditions_iterable, num_samples=n_trials) + pooler_callable = partial(grid_pool, ivs=metadata.independent_variables) + sampler = partial(random_sample, num_samples=n_trials) pipeline_random_samp = make_pipeline( [pooler_callable, weber_filter, sampler], ) @@ -83,8 +81,8 @@ def test_random_sampler_experimentalist(metadata): def test_random_experimentalist_generator(metadata): n_trials = 25 # Number of trails for sampler to select - pooler_generator = grid_pool_from_ivs(metadata.independent_variables) - sampler = partial(random_sample_from_conditions_iterable, num_samples=n_trials) + pooler_generator = grid_pool(metadata.independent_variables) + sampler = partial(random_sample, num_samples=n_trials) pipeline_random_samp_poolgen = make_pipeline( [pooler_generator, weber_filter, sampler] ) From 99421809dc2c4a2c148c3c52995d61d2ac49b527 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:08:01 -0400 Subject: [PATCH 024/121] docs: update standard example for grid pool --- docs/experimentalists/pooler/grid/index.md | 7 ++++--- src/autora/experimentalist/grid_.py | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/pooler/grid/index.md index ddd78f87..2a56cd7c 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/pooler/grid/index.md @@ -24,11 +24,12 @@ This means that there are various combinations that these variables can form, th ### Example Code ```python -from autora.experimentalist.pooler.grid import grid_pool_from_ivs -from autora.variable import Variable +from autora.experimentalist.grid_ import grid_pool +from autora.variable import Variable, VariableCollection iv_1 = Variable(allowed_values=[1, 2, 3]) iv_2 = Variable(allowed_values=[4, 5, 6]) +variables = VariableCollection(independent_variables=[iv_1, iv_2]) -pool = grid_pool_from_ivs([iv_1, iv_2]) +pool = grid_pool(variables) ``` diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index fc3131fe..3c071096 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -1,4 +1,4 @@ -"""""" +"""Tools to make grids of experimental conditions.""" from functools import singledispatch from itertools import product from typing import Sequence From c2dcce9c81c8aaa09a3a7ac8cf151e9dd7dfe118 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:08:18 -0400 Subject: [PATCH 025/121] docs: update docstring for random_ module --- src/autora/experimentalist/random_.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 7a29621b..4fa4f006 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -1,3 +1,4 @@ +"""Tools to make randomly sampled experimental conditions.""" import random from functools import singledispatch from typing import Optional, Union From 4911177a86381bee62d715bf53f5777a77e80533 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:08:35 -0400 Subject: [PATCH 026/121] docs: add deprecation warning to old pooler and sampler files --- src/autora/experimentalist/pooler/grid.py | 7 +++++++ src/autora/experimentalist/pooler/random_pooler.py | 7 +++++++ src/autora/experimentalist/sampler/random_sampler.py | 7 +++++++ 3 files changed, 21 insertions(+) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index dadc2a4a..7bd31d1e 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,8 +1,15 @@ +import logging from itertools import product from typing import List from autora.variable import IV +_logger = logging.getLogger(__name__) +_logger.warning( + "`autora.experimentalist.pooler.grid` is deprecated. " + "Use the functions in `autora.experimentalist.grid_` instead." +) + def grid_pool(ivs: List[IV]): """Creates exhaustive pool from discrete values using a Cartesian product of sets""" diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 78ad104e..b9c7ec83 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,3 +1,4 @@ +import logging import random from typing import Iterable, List, Tuple @@ -6,6 +7,12 @@ from autora.utils.deprecation import deprecated_alias from autora.variable import IV +_logger = logging.getLogger(__name__) +_logger.warning( + "`autora.experimentalist.pooler.random_pooler` is deprecated. " + "Use the functions in `autora.experimentalist.random_` instead." +) + def random_pool( ivs: List[IV], num_samples: int = 1, duplicates: bool = True diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 7e28d2c3..0076ddbb 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,8 +1,15 @@ +import logging import random from typing import Iterable, Sequence, Union from autora.utils.deprecation import deprecated_alias +_logger = logging.getLogger(__name__) +_logger.warning( + "`autora.experimentalist.sampler.random_sampler` is deprecated. " + "Use the functions in `autora.experimentalist.random_` instead." +) + def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): """ From 31dd73b63c4ac82a9221554ad43a94fc0a25f99b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:11:08 -0400 Subject: [PATCH 027/121] docs: add introductory documentation --- docs/experimentalists/sampler/random/index.md | 4 ++-- docs/experimentalists/sampler/random/quickstart.md | 4 +++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/experimentalists/sampler/random/index.md b/docs/experimentalists/sampler/random/index.md index c50c93d4..9d1abc22 100644 --- a/docs/experimentalists/sampler/random/index.md +++ b/docs/experimentalists/sampler/random/index.md @@ -5,7 +5,7 @@ Uniform random sampling without replacement from a pool of conditions. ### Example Code ```python -from autora.experimentalist.sampler.random_sampler import random_sample_from_conditions_iterable +from autora.experimentalist.random_ import random_sample -pool = random_sample_from_conditions_iterable([1, 1, 2, 2, 3, 3], n=2) +pool = random_sample([1, 1, 2, 2, 3, 3], num_samples=2) ``` diff --git a/docs/experimentalists/sampler/random/quickstart.md b/docs/experimentalists/sampler/random/quickstart.md index 97653206..5da12467 100644 --- a/docs/experimentalists/sampler/random/quickstart.md +++ b/docs/experimentalists/sampler/random/quickstart.md @@ -10,5 +10,7 @@ You will need: you can import the random sampler via: ```python -from autora.experimentalist.sampler.random_sampler import random_sample_from_conditions_iterable +from autora.experimentalist.random_ import random_sample + +pool = random_sample([1, 1, 2, 2, 3, 3], num_samples=2) ``` From edd634bf1c1fc7384f353448b2a20ff27614d4f2 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:12:03 -0400 Subject: [PATCH 028/121] docs: update introductory notebooks --- ...Introduction to Functions and States.ipynb | 304 +++++++++--------- ...Workflows using Functions and States.ipynb | 4 +- 2 files changed, 154 insertions(+), 154 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index eb6bd33a..45e43abe 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -84,7 +84,7 @@ "metadata": {}, "outputs": [], "source": [ - 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"[2.03390614] [[3.97374104]]\n" + "[2.08507109] [[3.9511443]]\n" ] } ], diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 5563ef49..347cb1fc 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using the functions in `autora.state`, we can build flexible pipelines and cycles which operate on state objects.\n" + "Using the functions and objects in `autora`, we can build flexible pipelines and cycles." ] }, { @@ -987,7 +987,7 @@ } ], "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.experimentalist.random_ import random_pool\n", "\n", "experimentalist = random_pool\n", "experimentalist(s)" From eba68b094844679a8c2aa013d12777d75adbf698 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 12 Jul 2023 16:29:26 -0400 Subject: [PATCH 029/121] docs: update docs to use new locations --- docs/experimentalists/pooler/grid/quickstart.md | 2 +- docs/experimentalists/pooler/random/index.md | 4 ++-- docs/experimentalists/pooler/random/quickstart.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/pooler/grid/quickstart.md index 646b9e60..444deeec 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/pooler/grid/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the grid pooler via: ```python -from autora.experimentalist.pooler.grid import grid_pool_from_ivs +from autora.experimentalist.grid_ import grid_pool ``` diff --git a/docs/experimentalists/pooler/random/index.md b/docs/experimentalists/pooler/random/index.md index 9abd84be..31d11bbd 100644 --- a/docs/experimentalists/pooler/random/index.md +++ b/docs/experimentalists/pooler/random/index.md @@ -24,7 +24,7 @@ This means that there are 9 possible combinations for these variables (3x3), fro ### Example Code ```python -from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs +from autora.experimentalist.random_ import random_pool -pool = random_pool_from_ivs([1, 2, 3], [4, 5, 6], n=3) +pool = random_pool([1, 2, 3], [4, 5, 6], n=3) ``` diff --git a/docs/experimentalists/pooler/random/quickstart.md b/docs/experimentalists/pooler/random/quickstart.md index c98a1225..f61d33e9 100644 --- a/docs/experimentalists/pooler/random/quickstart.md +++ b/docs/experimentalists/pooler/random/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the random pooler via: ```python -from autora.experimentalist.pooler.random_pooler import random_pool_from_ivs +from autora.experimentalist.random_ import random_pool ``` From e21097558b4fab91454e7b3cf110bb7c92d25856 Mon Sep 17 00:00:00 2001 From: benwandrew Date: Fri, 14 Jul 2023 08:29:19 -0400 Subject: [PATCH 030/121] docs: update docstring for n-->num_samples know we're deprecating, but just cleaning up anyway --- src/autora/experimentalist/pooler/random_pooler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index b9c7ec83..f758abf1 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -21,7 +21,7 @@ def random_pool( Creates combinations from lists of discrete values using random selection. Args: ivs: List of independent variables - n: Number of samples to sample + num_samples: Number of samples to sample duplicates: Boolean if duplicate value are allowed. """ From b9ed3f33db98c5b5763be06431b06e63050e9e0d Mon Sep 17 00:00:00 2001 From: benwandrew Date: Fri, 14 Jul 2023 08:51:06 -0400 Subject: [PATCH 031/121] docs: update docstring again, being deprecated but still cleaning up --- src/autora/experimentalist/pooler/grid.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index 7bd31d1e..2a8eeb22 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -17,7 +17,7 @@ def grid_pool(ivs: List[IV]): l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " + f"grid_pool only supports independent variables with discrete allowed values, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) From 3aba5bfc49965fd21a183095fb52d4fef885a873 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 09:33:13 -0400 Subject: [PATCH 032/121] docs: remove broken bit of example code --- ...Workflows using Functions and States.ipynb | 111 +++++++++--------- 1 file changed, 55 insertions(+), 56 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 347cb1fc..6a8c56c0 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -178,27 +178,27 @@ " \n", " 0\n", " -15.0\n", - " -1458.607761\n", + " -1457.949701\n", " \n", " \n", " 1\n", " -14.7\n", - " -1275.827665\n", + " -1275.900522\n", " \n", " \n", " 2\n", " -14.4\n", - " -1102.085834\n", + " -1101.584447\n", " \n", " \n", " 3\n", " -14.1\n", - " -937.199684\n", + " -938.510951\n", " \n", " \n", " 4\n", " -13.8\n", - " -782.085722\n", + " -780.229165\n", " \n", " \n", " ...\n", @@ -208,27 +208,27 @@ " \n", " 96\n", " 13.8\n", - " 500.917990\n", + " 500.274061\n", " \n", " \n", " 97\n", " 14.1\n", - " 608.249467\n", + " 608.306420\n", " \n", " \n", " 98\n", " 14.4\n", - " 720.981531\n", + " 720.885521\n", " \n", " \n", " 99\n", " 14.7\n", - " 842.599674\n", + " 843.944513\n", " \n", " \n", " 100\n", " 15.0\n", - " 971.996572\n", + " 971.655807\n", " \n", " \n", "\n", @@ -237,17 +237,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1458.607761\n", - "1 -14.7 -1275.827665\n", - "2 -14.4 -1102.085834\n", - "3 -14.1 -937.199684\n", - "4 -13.8 -782.085722\n", + "0 -15.0 -1457.949701\n", + "1 -14.7 -1275.900522\n", + "2 -14.4 -1101.584447\n", + "3 -14.1 -938.510951\n", + "4 -13.8 -780.229165\n", ".. ... ...\n", - "96 13.8 500.917990\n", - "97 14.1 608.249467\n", - "98 14.4 720.981531\n", - "99 14.7 842.599674\n", - "100 15.0 971.996572\n", + "96 13.8 500.274061\n", + "97 14.1 608.306420\n", + "98 14.4 720.885521\n", + "99 14.7 843.944513\n", + "100 15.0 971.655807\n", "\n", "[101 rows x 2 columns]" ] @@ -298,7 +298,7 @@ "source": [ "### Directly Chaining State Based Functions\n", "\n", - "Now we run the theorist on the result of the experiment_runner (by chaining the two functions)." + "Now we run the theorist on the result of the experiment runner (by chaining the two functions)." ] }, { @@ -520,7 +520,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Since the pipeline function operates on the `State` itself and returns a `State`, we can chain these pipelines in the same fashion as we chain the theorist and experiment_runner:" + "Since the pipeline function operates on the `State` itself and returns a `State`, we can chain these pipelines in the same fashion as we chain the theorist and experiment runner:" ] }, { @@ -744,7 +744,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -776,7 +776,7 @@ "outputs": [ { "data": { - "image/png": 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", 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mZrZv386SJUsA+Ne//oVhGCxbtsxs873vfY/e3l5zD69169Yxd+7cIYcURfiKj4+nsbGR+Ph487GoqChcLtfYjiwqRfvRHZzi2cZCDjJLr4ZXIQvM4MijNCpIp0KlU6em0EUUKdNyURHxHD5WjY4ikh5yM6bQfPwYcXSRoTWSrdUzjUZiNRcLtDIWUAZvbeLrQL1KosQo4PCbfyLvtDVj9/P6yZp/5nQ68Xg8PtXKm5qaqK2tJTU1dUTnlpErIUJLZ2cnhmHQ2dlp+/2WtjZO1/t2LjiqTR/Lrg0QVgFUe3u7eZcJfYnjJSUlTJkyhenTp3Pbbbfx//7f/2POnDnMnDmTH/zgB2RlZXHxxRcDMH/+fM4//3xuuOEGHnvsMXp7e7n11lv5/Oc/b755fuELX+Dee+/l+uuv59vf/jYffvghDz/8MA899NB4/MgiCPz50LRLZg6WRNXMDKOMU7S9xD/xEBf1Gzk6qHI4Gl9ISWsyjdoUerRYfvSD77D2h/ebbe6+5W4A7r33XvOxL916t8+/v/v9H1DR0MLvf/kTUlQjuVQx31nJHOMIaVoLq7St8MZWet9wssaYzV4KqNDH981pMAcOHEApZdaFAjh06BBdXV0+7w/+kJErIUJLR0cHXV1dPqVM+tv3zt85XXNRo1Jo1aaMce98hVUAtW3bNs455xzz33fccQcA11xzDWvXruVb3/oWHR0d3HjjjTQ3N7Ny5UpeffVVoqOjzef88Y9/5NZbb+UTn/gEuq5z6aWX8otf/ML8flJSEq+//jq33HILS5YsIS0tjbvuumvQEgYi/Fi3aWlubsbj8dDc3Gy2Cfo+d0rRsOcNGtf9lNt5xxxhcqkI3jYWUcJ8arUM/t9dP2COU2fTvfcSBUTRS6Rz5Gs/Ihw606em4HBE0Uome8jk0rv+znfvuZ9ETyOzOcppjn3kUc2p+n5OZT8uFcGHD39EujGVOi0DtNBYc2KXn5aamkpHR4c5AuXvyJK3lpTUlBIiNBw+fBiXy8Xhw4dtv2/s/QcA76uFoI9v6kFYBVDFxcVDJvVqmsZ9993HfffdN2ibKVOm8Kc//WnI65x88sm8/fbbo+6nCG3WbVq8hVb7F1wNFk0Z5KpylqttpD73EKmAoTQ2qwW8rxZx4fU/4J21/4umQRyMKljyuy+aRrSu6NFT2EcKl3z/eUp2b2f78z9npVbCXL2Ck5o2cJIGtSqZjcapdLfUEp00dfiTj7GUlBRqamrMaXZ/R5aysrJk5EmIEDLUvqOGUixo3QQaHNFmjnXXBgirAEqIkbIbiejo6MAwDHOIeKwCqOMfvMwXjWf6cps06FYRvB33STa2zyZCj0DTYG5ezrjlc0c4dIqKlvL3vxfwJzUHh9HJmVPqObnpdaZqzXxOW0/XQwvZmfEpYlUKnVrS+PQzIsInRxHgww8/pKenhw8//JCLLrrI75ElyYESInzEGU0k6+00qniatZHlOwZDaIzJCxEkdvumpaWlERkZSVpaGhD8kgVJqpk1nlfI+McXmKVX06TiWeu5gENXvscnv/VnIh0RIbcITtc0lCOOc257nJ9rN/Cw8Xn2GDOIoYfC43/lm/wvqzzrSDCax7xv3npQ3iPYVzD3h+yrJ0T4KFB903pb1cKQSCmQESgxodmNRBQUFOByuSgoKAD6prKUUgHfpsXoauF0z2Y+oW3BqRv0KAd/NYrZqZ9MtFPj2oL8gF4vWJwaNDuyeFZ9hgvOWoz+3q841bWFFfpuVrCb3T8/SqyaSaeWOCb98SdYsua5DUZyoIQID0rBcv1DAA4SGq9XCaDEhGaX43LgwAEqKyuJioqisLAwKCNQtbvWw99vZrXeV3fsX54iYj/9E/a//E+ih3luqNI1jdPP/TSc+2nuu+s7LDe28knHNhY1b2CB0njDWMI2fUnQ+2G3L6Gu6xiGga733ZUOt5LHS3KghAgP0UYLGXoT7cTQqKcP/4QxMP5jYEIE0c6dO3nqqafYuXOn+Vh1dTXd3d1UV1cH/HoO1ctpnveY+tdLmeqp5ZiRxv/zXM+bjk9w+qmnBfx640U5YnjXeRb3G1/m/ajlODTFKn0b/6WeYMcfvkNvd3vQrm23QtK70tZ7jIuLIyYmhri4OLNNVVUV77zzDlVVVUM+JoQIPbPVEQAOJq1AaaEx9iMBlJjQvNsC7N6923wsWDWeUlQ9V6lnuUB/F4B1MRfwa+1qPM4kdC2EtoQJIMMRx9Lv/JMfGjfwgTGHGK2HxaW/pvF/ipjmKWesNqzyFt3zHltbW2lvb6e1tdVss2nTJt555x2fTYglB0qI0KeU4jStr3hmxEn/Mc69+TcJoMSElpOTQ1xcnM+2AMGo8XRk3W+4Sf2JGdpxjqtk/ln4COd+889ETYJXmKZpuB0J/F3/FA8bn6eaVDJUHV/W/8JFxkvEqLYx79OhQ4dwu90+hTWrq6txuVw+I4+zZs0iPz9fcqCEGCcbN27kkUceYePGjYO2iVQdzNCP41IRzFlx8Zj1bTihMQ4mRIBYl6XbbQswVJ2RkdKVh9OMLczc9B5o8IaniHX6OfzokqtP+NzhRtc0mh1ZxN62g41P38eyqj9win6QhaqMHX+MQVMaSnMMf6IAsAuSIyIi0DTNp/yB5EAJMb62bdtGR0cH27Zto7i42LbNDKMMHLBNzWNF7PiUT7EzCe6PxWRinZKxFs2EwI1ARasuPm28xGr9PQAed6/hX45PEDXO1XHHW1JSEsVffpCf8CU2GwuI0npZfPAXXGE8T4LROG79mj9/PikpKcyfP3/c+iCE8DVcSoVSilO1vQDsZc6Y9csfMgIlJhTrsvSmpiY8Hg9NTU0BvU7L0V1co55mmt5Em4rm7UU/ovzDCpwTNNdpNJQezatqNduMk/ii9k/m6BX8l3qSv3uK8fS6cESM4UbNQF1dHR0dHdTV1Y3pdYUQgxvuhvbw/l3M1StwK51aPXMsuzYsGYESE0pWVhYrV640p2WCke9Ut+8dtCcuYJrWRKmRyY/V9Vz42etDrhhmKNA1jUZHDq4vb+Z1z1IcmuIz+huU/8/pVB/YMaZ9KSsrw+VyUVZWNqbXFUL0Gc2q1+pNfVuvbVdzcWtje9M1HAmgxIRmXZ11ojKMKuKeuZRE2ikxZvO/2udxOkLrRR2KpmXl8q7jTH7u+QKNKp6Z7lKm/PGTzPLsH7eVenakrIEQwWO36tUwDJ9jf0opsir+CcBu5o1NJ0dAAigxoVg/AAM5ApVjlPGfPEcs3Wx1FPGs9h9ETvJ8p5HQNGhxTuMRruWDyKVEab1cpb/MhcY/iVDB38jZjvXvRcoaCBE8dqtehwqgDu3Zzmx1lB7loEbPHrN++ksCKDGhBOsDcKbnINfxNyI1D29Hnsmsr7006ZPFR02PpPDbr/NWwXfpVFEs1T/iBvVHDmx5ecy7Yq0NlZCQgNPpJCEhYcz7IoTwVbP5aaBv9Z1Hixzn3gwkAZSYUILxAXjkX0/wRe0ldE3xV8+ZFN32F1KT5AP2RDgcOmd94ds8xLUcNLJJ11qY/cqVzHPvBjXwTjRY6uvr6enpMVdperf5OXDggNlGpvWECIyR3OAqpcit6pu++5C5we7aqEgAJSaUyspKGhoaqKysDMj5soxyct+6A11TPO85ix2OpSTEhutudqHHrcfxR/2zvGL0bQdzuWMda4x/4hyjKb24uDh0XTe3fGlpaaG3t5eWlhazjUzrCREYI7nBjVQd5KkKepST4yE4fQcSQIkwZjcyYFf3abSmGtVcwws4MXjJs5ydjqU4ZdYu4DTNwfv6cjYt+n90qihO1fdzvfozR3a+NeZ9yc/PJykpifz8fPMxqVYuRGC0tbXhdrtpaxt+d4JZqu+GZV/8MgwtYpjW40PqQImw5R0ZAMyyBe3t7RiGQXv7iW1mm2oc51qeJ1JzsylyBVt6TpcaT0GkabDi0q/yw13HuML4O7P0Glx//Qw7jt4T1OseP34cj8fD8ePHAcy7Y6fz32+NUq1ciMDwdwRKKcXpfLx/6cKLYWvgN34PBBmBEmHLbmSgp6cHpRQ9PT2jPm/D4e1cw1+J0Xp4x3MSC259NuSCJ+3jolNav+JTdo+FG7cex//pn+Nt4+S+CuYffI8lni1oKnB1vPrr6uryOdq9wUsOlBCBsXPnTsrKyti5c+eQ7aJUG3n6cbpVBHPP+twY9W7kJIASYctaNBMwh4b9GSK209VYhfHHy0nQuthmFPBPx3mkJMYHpL+BpOu6zxH67tr6H8OW5mSD/gnezrkBgE/pm/gP42Wc6sT3LrSy/s7scuhKSkrYvn07JSUlAb++EJNJbW0tHo+H2traIdvNNvqm7z5KXE50fPKA70dHR/scx4sEUGJCOZEgwqF6qfnNJaQbdRwxpvGCdiGRITqYk5iY6HO0YxdkhcsolaZpnPmln7LtjF/TpmIo0g9xjXqGiiBXL7fLoevo6KCrq4uOjg5ARqSEGK2YmBifox2lFMu1vuk7beEltm28G4L33xh8PEgAJSaUUQdQSlFsvMVM10c0qXie4FIi9PF5eXjzb/rn4VgDn5kzZxITE8PMmTMHPY/d78Kf349d4DVeTj3vCzzCVZSrdHK0epL+dAEpnuNBu5536tc6Bdw/4JRVeUKMjq7raJo25HtLjNFKrl5Hp4pk7pmftW3T3d3tcxwv4/8OKUQIONkoYaW+kx7lYOcZv8QxjtuzTJ06FU3TmDp16qBtjh8/Tk9Pj5n8DBAbG+tztJOWluZztDNnzhycTidz5oTGzue9ejxPaJfzgTGHBLq4RfsTMzwHg3Kt7u5uPB6PzxtzZWUl3d3d5rSerMoTYnTsbg6t8tVhALaok4iOsx9hlxEoIYbgzzRJoKZScj1HuER/A4D1s++keLX9sHGwWEd8mpubUUrR3NxstrGOHEVGRvocAfLy8oiOjiYvLw+wn+ZbuXIls2fPZuXKlYP2JxCJ+AGnRfIP/ULeSzwfh6a4Vn+RIs9WDLc7oJdpaGjwOcKJ59UJIfrU1dWhlKKurs72+729vZyplwCwl8Fv4BwOh89xvEgAJUKSP9MkgUjubTiygyu0vi1E/uT5JOd/8RujPtdoWYMhl8vlc4SBbxgxMTFERET45BKsWLHC/AKIj4/H4XAQH//vJHi7SttW1dXVeDweqqtDa+mwpjlYdtufedJzIQCf1t9m18OX4Oo6sZIVw7GO7ElSuRD+2blzJ0899ZS56q63t9fnaLVn00tM1ZppVnE06NMGPW90dDSapkkSuRB2/JkmsSb3jpRT9dD9xy8So/XwrrGQfY5F6EHe384uids6upSRkYGmaWRkZJhtUlNT0TSN1NTUAc8bzNKlS8nLy2Pp0qXmY3V1dbhcLvMO0Ltcv/+yfeuIWCglnmu6TplzHr80LselnBS1vcWRh85DV8EbLbO+6QeyWKsQE9nu3bupqKhg9+7dfrV3ffBnADYZhSht8NGlFStWMGvWLPNmcbxIIU0RkvwpXtjc3ExPT4/PVJfflGKlsYlsdwU1KoV/aqvGpMq4XRK3dcRpzpw5dHd3++Qg5eTk0NbWRk5ODtC3BUlMTIy5BQkMLCxaWFhIYWGhz/XT09Npa2sjPT0dgE984hPs3r2bRYsWmW2ioqLo7OwkKqovDywmJobOzs4hV86MtQZHNo94ruRGnmNezx6+aDTwZ+1ievXB879Gy/r/x65Ya1VVFaWlpcyaNUuKbgrxMe/7Sv/3l0EpNye1vAkafKQPnX9p9942HmQESoSt+vp6lFKjGgmYZRzkbH0HbqXze+NSnHpw5tL9WdFmrWly6NAhWlpazGAI+vZoMwzD3KOtqKiIJUuWUFRUZLbxp8pvQUEB2dnZFBQUAH1vRF/84hd93oySk5NxOBwkJycDfSNiDofDZ0TMn2TQYOtwpFJ72d+pIY2Zeg038GeijdagXzc+Ph5d132mRjdt2sQ777zDpk2bgn59IcKF3fvLYNKMGuK0bipUGm3alCHbhkopEQmgREiye4FYH/N4PD5HfyUZDVyuvQrA+uyb8DgHr6V0oqy5S3Yr5RITE9E0zUz2TkpKIiIigqSkJLPNokWLyMnJMe/k6urqKCsr80nG9GefKX82W05LSyMhIWHIlXrWlYLjtSom/6TTUF9axwEjh3SthZv5E/vffz2o13S5XBiG4ZOj1tLSgtvtNgNca+6HEJPRSF4Hi9kLwLuqaNh0gVApJSIBlAhJdi8Q62OjqfnU29HEZ3mZSM3Nm55Ciq+9L7Adt/COUniPdgFUXFwcuq6b03Hp6enExsaa02ww8E7OLrfAmjc22ru02NhYdF03+2hXGyk7O5ukpCSys/t2SXd/vBrOHeBVcf7IzJnFn/VL2GHkk6h1Mv3lK5niqQna9exWSVrzzUaa+yHEROTv60BTPZym9QVQpdrw5UFCpZSI5ECJkOR9YfR/gVgfMwzD5+iP/U/eyklaPRUqjfV6MWdHBnfEZOrUqbS1tZkjNda912DgirqKigo6OjqoqKgY9Lx2uQXWvDG7zZaLiopITEwc8o3Hev38/Hy6urrIz88322RnZ9PY2GgGUJGRkbhcLp+yCmNKi+Af+ho6jQ2s0D/kZp7mfz2XUOOYEfBLxcbG0tnZOWS9rRHlfggxQeXk5NDU1GTmbg7azlOO02FwwDkXl2f4rbNCZYNvCaBESArGC+TIO89yUu1LGEpjrXExTmfwa4hUV1fjdrvNkgBJSUl0dnb6TM/FxcURHR1tjkD58+HrTxKlXRDqz+/Ven1vXlX/fKcDBw5QUVFBVFQUhYWF4zoCZdIcrNNXEZGQyWlt6/iy9jxPeC4K+GUaGxt9jgBbt26lpqaG7u5uCgsLaWpqMr+EmKw6OzsxDIPOzs4h252m7QKgec4l8NHAFbW6rmMYRkjsjtBfaPVGiCGcSKJupOoiaUNfjadnjXODkvdkV9wyPj4eTdPMKbzk5GQiIyPNBG0YmBA+ksTLodhttmxlN81nvb7dcLk158cuqXw8toTRNJ0ltz3D3zxnAXCd/iLv/+H7Ab2G3chnUlISTqfTDIx37NhBY2MjO3YEd+8+IUKZP6VmIowOTtLLcCudgnOutm1jfc1JErkQI1RdXY3L5Rp5gUelOMvYxBTVwiGms9dxclD6t3z5cqZMmcLy5cvNx6wBk135gfHkTzKmXSBmzfmxJsID5Obmomkaubm5wNhVD3Y4HOx0LOHPxicBOK30Ebb87+2j2mDaX+np6cTFxZl5a6mpqTgcDp+6XUJMNl1dXbjdbp+UBatZqu+95321gOSp2X6dN1SSyGUKT4SN1tZWn6O/co0yVui76FUOms7/BY7X3wpIfzRNQyllrhgpLi6muLjYp431DcQuB8kuV2k0RlOLyG6azx/WKcQVK1YMqCfldrvRdd2c1ktJSaG+vp6UlJQRXWs0NE3jgGMRf3BHcLXjFZYde5xq4yxK9CVBKQi6a9cumpqa2LVrF8XFxaxatcr8fyHEZNXc3IzH4xm0Vp/hMVihlQCwk/mcMch54uLi6OjoMG88/SnZMhZkBEqEJLsh2tGULYhRHXz245IFGzOvY+nycwLWR2uFbrs+W1ew2Y3mBGpFiT93ZdY++jPN58950tPTycvL81k5aJ3S1DTN/PIK9qjUEec83p17JwAX629xmvEeBGEkyhrcHzhwgB07dgy5ZY4QE4nd+5+32Gz/orP97X3vVXK1OtpVNHV65qDnnjFjBtHR0cyY0bcoxJ+SLWNBRqBESArIqIxSnGO8SaLexS5jJmdc88MA9rDvLqilpcW8CyopKeHgwYO0traafV66dOmAkRkra2L3aKta+zOaFKjRLut57M6blpZGU1OTWU/KWgUd+oKrrq6uoK7eO+OK7/DeX+NYuvMHXKhvxml4MNxu9AAWAbWW1Ni8eTM9PT1s3rx5wKikEBOR3fvfcHvfdW5ZC8BbajFKH/z1aF1oM9qR80CTAEqEJLsXiD/Lx/s7sGEtS/WPcKkI/soF3BMT2I0nrYmNdgmT1qkuf4Kj0QY5/qywC9Qbj/U8due1TleuWLGCzMxMnzajKUUxGqd/5qs8WrKbL/Mc5+nvs/2Rz1P01T8H7PzWn2O0RV6FCFcj3Zu0pbmRRS1vgAZ7tXlDtrWWTZEyBkIMwe4FkpycTFdXl88KtsFEKBdpm+4B4BljFZoz8Lt2W/dI8ydB3J/gKJh3V4F64xnNeeyeYw08IiIi6O3tDUpF83pHNr92f46b9OdY0rKODx6+DNTJMMSmpaOVnp5OTU2Nz2ibEBPZSBfI7Fv3BKdrPRw2MmnXkxkqMzFUpuysJIASIclupKa1tRWllF9J5EuM7UzRmzlsZHLQMS8of+hRUVH09PSYm+76U6TSn+AoVO6uRsIuMNy0aROlpaVUV1dz2WWXsXPnTnM60zsql5qaSk1NjblaLdilDxqdWfza8zlu4llOaXuDNqOJjXoxaIG9nl2tqI0bN5o/v0zriXBnfY/25/2vv5T9zwDwjjpl2IUdoTJlZyUBlAhJdvPp3mJswxVlSzHqWK2/B8Az6kKcAVh1lZaWRn19vc/+cLm5uRw+fNhcpu9P4BOOwZE/7N7gWlpa6O3tNWtFebd1AMwAyvr/1DqqFwxNjkw+PONRTnrnVs7WS9AMxRt64BYXgP32N9u2baOjo4Nt27ZJACXC3onkU0Ya7cxV++lVDo458oLQu7EhAZQISfX19bS1tVFfX28+5k++jKY8XKg2gAZvxa2mpyNpyKFhf82cORO3283MmTPNxwoKCnC5XBQUFATgCuHNLjC0bgFjV2E9NTWVjo6OMa+XdMonr6DE4WD+mzdzlr4TZWi4e4IXtMHARHO7ETkhwoX1psk64jyUucZ+cMCH8afj6Ywa9lqBWvwSaFLGQIQkuzt4fxQYH5GvV9GgEpj9hZ8RqJI/ra2ttLe3+0wfVlZW0tDQQGVlZWAuMsFYt4Cxq7BeWFhIXl6e+ZhdRXO7Cu+BUHTu53hMXY5LOTlbL2HXLy4DFbykb2vJBtlwWIQzawmUqqoquru7h68OrgzO1j8AQD/FvvK4VahsHmwlAZQISRkZGURGRpKRkeH3c1qqDvFp7Q0A/mhcSHb20BtYjkRZWRlut5uysrJB24TK9gKhwvqmZ/f7sSaHRkdH+xwheAEUQLMjg8fU5fQoB6e0v8nZxluggrMi0Loyb9GiReTk5MiGw2JC8HflaapRQ6rWRj3JLDzrM36de7T16oJtQgVQ99xzj0+xPk3TmDfv38sju7u7ueWWW0hNTSU+Pp5LL72U48eP+5yjvLycNWvWEBsby9SpU/nmN785vhukTlLHjh2ju7ubY8eO+f+cZ79BjNbDVmMuDQ7/tgTwV2pqKpqm+Uw1WfewC5XtBUKF9U3P7vdjDbLs8ty8VdyH2g7iRDQ7Mvj1x0FUsb6DM4MURFnzu+yKjwoRLqw3RP7OGizmQwAOZf0Hzgj/bopC9eZ0wuVALVy4kPXr15v/7j8VcPvtt/Pyyy/z3HPPkZSUxK233spnPvMZc3Naj8fDmjVrmDZtGu+++y7V1dVcffXVRERE8KMf/WjMf5bJrLu7G6UU3d3dfrU/tmMdJzW/gUdp/JNz0AO8W8eaNWsGbM1hzfsJ1ZUiocLu91NXV0dZWRkJCQlkZWUNqO4OA3PfHA4HHo8noNXLmx3T+PCMRzjpna9yrv4BHkMPeLFN6x263UKJ0RZRFWKsWf9+/VkA4lDdnK7tASD7nC/5fa1QzYGacAGU0+lk2rRpAx5vaWnhf//3f/nTn/7EueeeC8ATTzzB/Pnzee+99zj99NN5/fXX2bt3L+vXrycjI4OioiLuv/9+vv3tb3PPPfcMOoXgcrl8/mhGulebGMgbOPkVQCkDzyvfAeBFYwXK6V+hzaFERkbS09Nj/j+fzCvsAsXu92NdmZeYmEhTU5PPpsSRkZG4XC7z/0WwilSe8skr+cXbb/MVnuGT+ja2Pno1S279v4Beoz+7hRIjScQVYjzZ/f0OJ89TisOhKDHyKZrj/8KJUNn7zmpCTeEBHDx4kKysLGbNmsWVV15JeXk5ANu3b6e3t5dVq1aZbefNm8f06dPZvHkz0Lf9wqJFi3zyblavXk1rayt79uwZ9JoPPPAASUlJ5pd3WbsYveG2AOhvulFGXu8h2lQMH+hLAnL9zMxMNE0jM3Pw/ZnEicvJySEuLo6cnL58tbPPPpvZs2dz9tlnm20SExPRNM0MqoK5f16TI5PfqMvwKI2lTS+z7dfXm6vmAq29vR3DMHz2CbOWfhAiVA23UbCVx+2mWN8GwBYGD56SkpJ8jhC6hTQnVAC1bNky1q5dy6uvvsqvf/1rjhw5wplnnklbWxs1NTVERkYOqGKdkZFBTU0NADU1NQOSlr3/9raxc+edd9LS0mJ+jSRvR5wYh+rlUx8njr8/40s49MB8qHpfqKH2gp1o/Fmpl5ubS3R0tHljEuwNiOsd2fxWfRZDaZxW/zeKjO1BCaKUUuaXV35+PklJSWbpByFC1Uhrtn349l/J0eppVnHU6IMv8PHmHPfPPQ7VVXgTagrvggsuMP/75JNPZtmyZcyYMYNnn32WmJiYoF03KirKrEYtRme0uR8Ljd2k6y2UM43TLr+Td37604D0Z9GiRcNuAixOnD95Y9YFBdYK8MFQ68hl60n3smz3XVysv4XbcLJHD2ytJu9Uf/8p//LyclpaWsyRcyFCgd3780hmCQB4//cAvGGcCs7Bb37sdiMI1fSICTUCZZWcnExBQQGHDh1i2rRp9PT0DBhuPH78uJkzNW3atAGr8rz/tsurEoGzadMm88tfMaqdC7W+9mVLvkuCn3swWc2YMQNN05gxY4b5WEpKivkFobsKJNz5szzZmliem5tLVFRU0KfKl136X2yZ/z0APqv/izmevQE9v92qpePHj+PxeAa8DwkxnuxW0NqVHBlMhNHJos73AfhInztk27F6fQfChA6g2tvbOXz4MJmZmSxZsoSIiAg2bNhgfn///v2Ul5ezfPlyAJYvX87u3bupra0126xbt47ExEQWLFgw5v2fTOrq6nC5XNTV1fn9nGXG+0RpvWwx5rPiwqtGfe3zzjuPc889l/POO898zFrkUEoUjJ8zzjiDWbNmccYZZwCwYsUKVq5cyYoVK4J+7WWXf4u1njUAfMHxGlv+eF9QrxcbG+tzlMBdhAK7KbS0tDQcDofP9laDmaMOoGuKrcY8evT4IduO5ev7RE2oKbxvfOMbXHTRRcyYMYOqqiruvvtuHA4HV1xxBUlJSVx//fXccccdTJkyhcTERL761a+yfPlyTj/9dKDvg3TBggVcddVV/PjHP6ampobvf//73HLLLTJFF2R2+SBDqdq7mbP1Egyl8Tpns8wx+nuBTZs2cfjwYZ+VT9ZtR6REwfgpLCz0yYmylj4AiIiIoLe3l4iICKDvrri7u9uvu+PhHHXO5U/uXr7geJ1lBx9kt3ExlY7g/B1ERUXhcDjM9xu7UgdWUvpABJvda66trQ2PxzN8nqgyOEfzJo8XDXutUJ2uszOhAqiKigquuOIKGhoaSE9PZ+XKlbz33ntmobqHHnoIXde59NJLcblcrF69ml/96lfm8x0OBy+99BI333wzy5cvJy4ujmuuuYb77gvuXafAXInUf0XSUFpe+gFZwOvGUowTLFvQ0tKC2+32Wflk/dAOpxf1RLdhwwba2tqora01/x9FR0fT29trBkyj3QpoMAccC3nOcHOZ/i/+U/s7v/V8huOOGcM/cYQ8Ho/5BdDR0UFXVxcdHR2DPidUa+SIicNuI3Dv++VwK0anGpWk6a3UMoUGfWKlwkyoAOrpp58e8vvR0dE8+uijPProo4O2mTFjBq+88kqguyaGYd1odShTjFrmd26lVznYop96wtdeunSpJIyHEbsaYTExMbS3t5uLRfzZeHokNE1jj15IbvoUTq/7CzfwV37tuTwg5+6v/4rgwVhHnGR0VARbTk4OTU1NZrkR8L8e2+nsAOBQzmegcviZgnDaZHtC50CJ8OH3B55SFKu+xPGXjTNAP/EpGrul8yJ0LVq0iJiYGJ+ANycnh+joaJ83+EDTNI3Tbvot/zROx6EpbtSeY/ebzwftetC3fY3b7fbZxsaajxeq+4SJ8GXNvbOWGwH/3rOjjVYW64dwK51Z59/i17W3bt1KWVkZW7duPYGfYGxMqBEoER5OZEnsVKOKRfoRulQkO/QiuQOYhJYsWUJKSorPiEtpaSldXV1mUBGM7V4AdIeDLfrpRHtcnOPYQf6/buKj6KGTYk9EVVUVHo/HJ4lcRpxEsFmnhUdbCXyB8RE4YJNaxNk5/v29JiUl0dDQ4FNIM1RJACXG3KhzNpRiFe8AsGXqZej1EaO6vqZpKKXMZfGShBte7P5+rNN6wdruBUDTdN5ynE200cNyfQ/Zr1xDDF+gSw/8G75dsULJxxPBZg3SR1MJvLO9mXM+rjy+nZM5e5j2XitWrCAzMzMsbhDkBl6MudFWlc0yjjJHr6RVxTLvs98f9fW9w9Deo5QoCC92fz/x8fE+R7tNiQNK03ldX8WeiEUkaF3cwDNEGYGvWu9dVeg9gpQ2EGNvNO/Ze17+NfFaN0eMDNr0wUsdWAtnhtOUtARQYsyN5gVi9PZwvvY2AC8YZzMtY/QvrpycHBwOh5kvE6rbBAj/aZpmfoH9ogS7CscndlEH02/9BwecBaRo7VzHc0QYnYE598dSUlLQNM0s6Ap9ZTfeeecds+isBFQi0Kw3ldu3b+fdd99l+/btfj3fcLvJ+mgtAP/idBjiRsbuJiFcSAAlxtxo3vD3/PMxcrU66lUihx1DV7Ltz+5Ds7CwkLy8PDNpPJzueIT9iGF6ejpRUVFmyRK7/++BXpkHkJA0hak3vcghI4sMrZmreJ76qqMBO7+u6yilfH6O+vp6enp6qK+vB2QEVQSed7rOux/d7t276erqMgsLD+fDN58jW9XQrOKo0Icu9xHM6fZgkwBKjLmSkhK2b99OSUmJX+2Vp5e0kr7SE383zkbX/P+ztfvQrKyspKGhgcrKSv87LUKG3YjhihUrzC+AOXPm4HQ6mTNnjtkmWNN6yWnTeFq/mGMqnVytjrbfX4SuAlN/yq6sgXUKerQJvkIMpqKigo6ODrP200iDHOf7vwZgnVoG2tCp1sHeHDyYJIASIW/Pa4+TadTQqBIoH2EF6IBP24hxZzdiaH0sMTGR+Ph4EhMTzTbBnCrwaNH8gc9Sq5KZaRzlEuMlNOUe/omjoGkauq6bgeCBAweoqKjgwIEDQbmemHwWLVpETk6OWSpkJKO3sUYLC1w7cSudj7R5w7ZfuHAhMTExLFy48MQ6PQ7kU0WMuezsbFJTU8nOzh6+sTJI2v4IAC8aZ6JrI7tLsdvwckTXF2HJroJ3sKcK3HoMj3MZzcRzkl7GhcZroAJ/raVLl5KXl8fSpUsBOHbsGC6Xi2PHjgX8WmJySk9PJy8vz5wSt456DqVI7QJgR8LZGH7U6UtJSWHq1Kk+eX7hQgIoMeZGMoU2zagk13OMZhVHqWPOsO2t7IaHR7MkV4Qf61Sd98PAewwGl57A8f/4E+0qmlP1/ZxrvIG7NzDTeYOxq6H24osv8uMf/5gXX3wxqNcWE9P69evZuHEj69evBzBzobzHwThVN2drHwAQd/bX/LpWOE9BSwAlxpw/+3sBoBTnshmAkqwrRjz6BNDZ2elzBFl1NxnExcURHR1NXFyc+ZjH40HTNJ8RqGBM68095Wx+oy7HpSI4U99FyS+/iBHAUa8333yTw4cP8+abbwIwZcoUNE1jypQpZpt9+/bR1dXFvn37AnZdMXlUV1fj8Xiorq4e0fPmGAeI1Dzsc85j/qnn+PWccM5JlQBKjLm4uDhiYmJ8PtzsTDWqmKNX0K5iWHDJN/06t3XKzm7aRlbdTXxFRUUsWbKEoqIi8zG3241SyucuOliJ5e2OVH6jLsOtdE5teY2tv7nJr30e/dHU1ORztDN//nxiYmKYP38+IKUOxMiMJndUU25WaVsAaCu6MXg12EKIVCIXY660tJTm5uahl10rxdm8B8D2jM9y9lT/dvGOiYmhu7vb3FQ2NTWVhoYGUlNTT7jfInzYVetOTk6mtbWV5ORk87HY2Fh6enqIjY0NeB+aHNP4nedSbtaeY1nts5R6PskBZ+A3rG5oaEApRUNDg/nYRRddxEUXXWT+2656u1TgF4MZzcq4bKOcKXoblSqVovOu8vt5RUVFJCYmhuWMgIxAiTHnfaPv/4ZvlWocZ4F+lE4VxZxPf8vvc1un7KKjo3E4HD5J5EJ4FRcXM3v2bIqLi4Ny/lpHLu/N/TYAVzjWMd0T+JVydh92O3fu5KmnnmLnzp2A/bS11I+anIIxGqk8bs79+IZ3nbGcyMhIv58bzjMCEkCJkLSS9wF4xVhOVvZ0v59nTXb0vpBH8oIWE1NHRweGYfjk3llXG1m3hAmE06/4Lpun3wjAdfpLTPMErtDmYDZt2kRpaalZrdzuQ0pyAScnfwLnkY5AffivPzJTr6FFxVLmmD1oO+9IbzBGfMeDBFAi5JTvepMi/RA9ysEe/aQRPdeaA5WWlkZCQgJpaYPvxSQmh7i4OHRd98m9s36YBKtu2OnX/g9/85wFwPXa39i57o8BO7fdhsPd3d0opczNle2E852/GD1/AuesrCyio6P9+9tQitgtvwDgFbUCbYjFPhMtgJIcKBFymtY/xHRgg3EqOEc2ctTT0+NzDOf5dRFYaWlpNDU1+QTT1l3nU1JSaGtrC3hNGk3X2eVYQqzRzWr9fea9818kqCtoc5x4YG+3UCI6Opr29nbzRmLnzp3s3r2bRYsWmVsYicnJLj/QyuPx4Ha7/aqZlmLUMtt9iE4VxQF96MKZ/iyACKfcPAmgRNCN5AURpdo5qWUjaLBdLxrxtaybyPrzZiEmB7tguq6ujrKyMhISEsjKyqKpqQmllM8bvMPhwOPxnPhWE5rGe/oKYgwXZ+k7uZFn+ZXxBbr05BM7rw3rnf6bb75JU1MTjY2NEkAJH3bvz/v37/c5DmUlWwF41TgdnEOXA/GnmK3dgodQJQGUCLqRvCAWGHtx6IrNxgI8jqHLHNiJjIzE7XZLzpMYwC6Y3r17t7nfV2FhoW3OnNPpxOPx+FWFeViaxht6MVMiNU7qKeEG9Sy/Ma448fNa1NTUoJQy99CzjsyCjEqJPt58uerqai677LIRPTfBaOAU/QA9ysGHfqRbREVF4XK5iIqKGrSNdVQ4lEkOlAg6687eg3GoHj6h9d3NbGLpqK4VFxeHpmnD1pgSAiAnJ4e4uDhycnIAzD3m+udA2eUYnRDNwYxb/sYeI48UrZ1r+QvVR4e/0x8Ja5/nzp1LTEwMc+fONdt4g8fdu3cH9NoivBw7dozu7u5RbQW0TG0HYFvyapQ+eFDkNXv2bKKiopg9+9+J5tZVgeGUmycBlAi6vXv30tTUxN69e4dsN8s4SJzm4pCeR5s+Zci2g1mwYAEpKSksWLAAkAKCYmjeLSS8o0tpaWlERkYGfdFBQtIU/qpfxGEji2laE8ba/6C+pjxo16upqaG7u9sckYKBwaOYnOxGJ/0Ra7SwQv8QQ2lMXf1tv56zYsUKVq5cyYoVK8zHwrmchgRQIug6OjpQSg25dUv/KrY1C29AH2UVW+sHYklJCdu3b6ekpGRU5xMTm3VFUkFBATk5ORQUFJhtglWt3NAi+LN+McdUOtmqhrbfXYSmeod/4ihUVVWhlPK5kejs7MQwDLNmmtxshD9//h9a24xko+D+lqgSADYai8lfUOTXc7w5h3V1deZj4VxOQwIoEVB2L2B/pkCyjGNM1Zo5rpJZcuH1fl3L7oMtnDemFGPPOl1gt9G0t6q99xhIHi2aP3Ap9SQz01PGxcbLaGroqe5gkZuN8OfPaI61jbfUxVAlL6xqj+41Nw1+T1vi9/O2bt1KWVkZW7duNR8Lpyk7KwmgREDZvQkbhuFztFKGQTF9o0//NFb4/UHl3Ty1/yaq1o0p7fZEE2IwdnfDGRkZOBwOMjIygnJNtx5Ly2efo4U4TtZLOd9YBypwmw8Pxvra8HuTbxGy/BnNsd5k+rMyzqryhXtwaIp3jEX0Ovy/WZ1ohY1lFZ4Ydwc3/4MCvYp2FT1kFVur1tZWn6MdKWMgwsHsk05jX/f/4XzxCk7T99FlROJxB246T9d1DMPwSY7fvn07+/bto6mpiaysLLq6unC73XR1dQXsumJs+fN+ZzfKOhLVh0oobHwdNHiL00f0XLtabOFMRqBEQNmN+FhrM1m53v0NAOuM09A0/2N6u6rRMuIkToTdFMhok2xHav6pn+A36nJcysnZ+k52/PIqGOQ1M1J2o8A7d+6kq6vL3C/PbqsbMfH4uyp6MLX/uAddU2w0ikY0+gQT7/1ZRqBEQI10xKfu2H4Wtm8GDfboC0Z0rdjYWFwul8+2ADLiJE6EXQ2apUuXmvWSgq3NkcZvPJfxFZ7h1OZ/UulpZ4fj1IAnsNuJi4ujoaHBpwRIOFWFFv7ZtWsXTU1N7Nq1i+LiYjRNQynl199YnNFCYesbGEpjE6eN+NoT7f1ZAigRUCMtznf01UdI1xTvGQvwOEa2P1JmZiadnZ1kZmaOtrtC+LB7gy8sLPT5W46NjaWzszNo+3k1OTL5nedSbtae49OOt3EZUexznBzw68THx9PS0jLkxsnhVBVaDGQXAHunaL3H4WYI+jtdbQUNtsadjbsrcBtuhyuZwhMBZbfKYjCacpNf+TcA3lNFI76WXU0RIQLNurI0NzcXp9NJbm5u0K5Z68hl89zvAPA5fT2zPB8F/BreHBjv0W6qUla1hje7KenMzEwcDseIbzwTjCZW6rvwKI3kC3/g13O82x+d8DZIIUoCKBFQSikMw/DrbibLOEYy7VSRTpNj5Cuc7Ja/Si0bEWjWD6Hq6mrcbjfV1dVmm2CUOlh+xZ085TkfgKv0V9j2l58G7NwwMC/K4/GYX17+JBxbX3PyGgwddvlOkZGRaJo24pVwZ6i+ldLvJ6xi7kmn+vWc/Px8nE4n+fn5I7pWuJApPBFQ3m0whp1PV4qV9G0DcHjG59HLR57jYTddKFMOItCseVHx8fG0tbX5TH3Fx8fT3d095HTYaBxyLOA5o4fL9H9xyu7/xwdRwZs2OX78uM8R/NuXzPqak9dg6Dh06BAtLS0cOnSI4uJiAA4cOIBSigMHDvh9nkSjgdP1vbiVTvqn/Bt9AqitrcXtdlNbWzvSrocFGYESAZWRkUFkZOSwNXMSVBPz9HJcKoK5F948qmvZ7eUVzlVtRWiyjnRmZ2eTlJREdna22SYiIsLnGCiaBnv0Qv7uORNdU5y87U5SPcEZ2bFb1Wq1c+dOnnrqKXPlHgx8zclrMHQkJSXhdDpJSkoyHxtJztPHDSlW7wLwmlpG/jz/N54eqxWs40UCKBFQ/m5MWaj6gp4dSZ9gakb2kG29rB9SixYtIicnx2d1VDhXtRXhITs7m9TUVJ8AyjtFMtql4UPRNI0Sx6lsSTofp2Zwg/YXkjyBv6O329Jj/fr1bNy4kfXr1wP2Ny3ymgtddlsTjdT+t55lsX6QbhXBNu2UET03JycHp9M5YfdblABKnBBrvkNraytKqSGLW0aoblZqfXewCWf5P/rknR7xHgsLC/niF7/o12o/IQLFLi9otPuJ+U3TWHLr/7E97iyiNDdf1p4l0VMf0EvYbbl07NgxPB6PeUPkzwbE4bw5bLizvh9bd2YYKU15iHvrXgD+ZhSDHjWi57e3t6OUor29fVTXD3WSAyVOSElJCQcPHqS1tZWsrCx6e/uqJ3uPdmZ6DhPlcLPLmMWiJWf7fa2YmBiam5uDsieZEP6yywvy5+/+RDkjIjnpa8+y6YerWKF/yA08y6EPLgna9WBgonldXR0dHR0+m8Fa+ZM3JYLDmn92otvzzDAOk0MlDSqBjxwLRjziMtG2brGSESgRUMPlghhuN8V6X4mDt1kyogKBbW1tKKVGvQWBEMGSnp5OZGQk6enpAOZWFYHesiIqKob1+ireN+YTr3Uz9R9XEms0B/Qa/XnLF3iP9fX19PT0UF8/+OiXTOmNDbvVjtZVdyeyPY9D9XCRthGAvxir0LWhSxFER0f7HKHv7z8hIWHCbN1iJSNQ4oQUFRWRmJjo993mR5teYIHWSJOKp073L/fJy26KQYixZrfKbMWKFWRmZpqvA7uq3gGj6fxT/yQRRi+L9UN8iWf5rXEF3XrgazV5Ry68x6D+XGJErKP/gLm34b59+yguLqa+vh6PxzNkwDuYBcaHTNHbKdOyqXLkDTvaYpecPtLPh3AjAZQ4IdbKzcMFOb1b1wKw0TgFzTmyAdDY2Fh6enqCVgFaCH/YTVFZXwcVFRUopaioqDAfi4uLo6OjIzDBh+bkH/qFxDr+yVzPQa5Xz/B74/Mnfl6LYCbHi8Dr6elBKWWuerMGwP6KVh2s0TYBUHXqnejbhs9nm4w3uDKFJ8aMU3WxoK1vOexeff6In19cXMzs2bPNeiZCjAd/pqjspjMCng+iOZl688vsN3JJ01q5jmepLt0TmHMP4kRGNERg2W3Mm5KSgsPhICUlBbDfRNofS41tRGm9bDXmcfr5V466jxN9QYEEUGLM5BlHiNA87DRm49ZHfhcuq+5EuEhOTsbhcJCcnBzU66SkZfCM/mkOGVlkaM3o/3cRNUcDv+2Ll90og1QiHx91dXWUlZX5JPS7XC4MwzihUaBju97kXL2vyPFrnIXuGH2YMNG3ApIASowJpRRn8gEA71E0vp0RIsjy8/NJSkry2cIiJiYGTdMCvopUaZH8Wf8MpUYmGaoBtfZTRKjOgF7DvJZNnktJSQnbt2+npKTE9t/CV6ACTLt9R+vr61FKjXqEUHl68bx4OwAve5bjcZxY5Xt/tgIKZxJAiRNiV5nYTrJRz3S9lnYVQ40+fFE17+q8kazSEyJUeO+6+9eF6urqQik1qhVRwzG0SJ7SPkO5lkWmquMq9TwRKvDXsSvXUF9fT1tbm0zr+Wm001rWwMtuSti7j2H//QxH4sO/P0Re72GaVRzv60tHdY7+JnpVegmgxAnZtGkTpaWlbNq0ach2J7EPgN0pq2CY5bDQt2O4pmkj3jFciFBg98HR2dnpcww0pUfhvO4lKrRp5Gp1fDFIQZSVNS8qNjYWXdd9FnuMZtRlok4FjjaosAZediUCHA6Hz3EkolQXM3c9BMDTxmp0fWRrzLzbxfTfNmaik1V44oS43W6UUkOu0nEoF2d+XHk8aeWX4OU3hz3vmjVrKC0t9XmTqaqqMh+TGjMilFlX5cHAhN6oqChcLhdRUSOr7jzkdafPpvKaFzn2xIVM12r5onqe+qobA3Z+O9bA0LqUHuyX3FtZX9/+PCcc2f1t+MO6+tMuUE1LS6O6unpUdZdOM94nXu/kIz2fKmYw0hCsoKCAgwcPMmfOHPOxib6xtIxADeLRRx8lLy+P6Oholi1bxvvvvz/eXQpJ/tzx5BhHidJ6+cjIZf4pZ/l1XruVTpJbIcLZrFmzcDqd5gdgVlYWDocj4B8s2XkFrOUyjql0pmu1uH53QVBHoqzTRr29vSilRlyVfaKv2PIKZA5UY2OjTw5UY2MjSikaGxtHdK4pxnHO1bdjKI32VT/GMYrUCbs9ImUKbxJ65plnuOOOO7j77rv54IMPKCwsZPXq1dTWBn4Dz3A37LSEUqxgBwCbOAVtiJ3ehZjIcnJyyM7ONveRi4mJISIiIihbExl6tBlEZauaMZvOA/tk+dbWVtrb24fcI9P6YWu3TH8iGG2gaL2BtHvv7e7u9jn6Q1MeLlRvAPB20qc49YxP+PU8a6kOu4TxiV6VXqbwbPzsZz/jhhtu4LrrrgPgscce4+WXX+bxxx/nO9/5zoD2LpfLZ9noUG8SE01sbCydnZ2DFrc8XPIWc/RKulUEFfqMQc+j6zqGYaAPEWBN9Kq2YmKzTsHExcURHR0dtKrehh7NWuMy/pPnma4d54vqef7IpUG5Vn/Hjx9HKcXx48fNx8rKynC73ZSVlQF9i092797NokWLzLIk1qmt0U51hbrR7hVo3dcuUFPABcZHzNaraFAJ5F/xP34/z7oic6KXLLAjwwEWPT09bN++nVWrVpmP6brOqlWr2Lx5s+1zHnjgAZKSksyv3NzcserumLIbevbebQy2TLVx0xMAvK2KQBs8Xven4NtEv5sRk4vdlEegGXo06toXzem8q9Vz1JYHr04U2K8Es+6p9+6771JaWsq777476HkmahL5aN/Hmpub6enpobm5GYDU1FQ0TSM1NXXUfWk8upeLtX8B8GfjfLIz/f9btNYEq6yspKGhgcrKylH3J9xIAGXhXVGSkZHh83hGRgY1NTW2z7nzzjtpaWkxv44dOzYWXR1zdkPPQ5Xv73V1UVC/DoDdzBubTgoRoqyvH7spD7sK5icqJ28ua7mMo8ZUsrV6eOJCako/DNj5R8MuT8oaME3mnEe78jDWGk/Nzc0opcyAaqQ0ZdD45xuI1nrZbCygzjGyG39viY7+pTomm8n7kwdQVFRUQFfShKqRDj3vffM5CmmnRqXQqqciFZ3EZGZ9/XiDp/4rWOPj4+nu7iY+/sQKGFoZejR/MC7jSuOvzNKrqf/Dp4hRl9KlJwb0OoNpaGjwOWZmZtLZ2elTpmSirrobja1bt1JTU0N3d7c5xWkd2TvRshj5xn7yuz+kTUXzqnYu+gjfoL2DCt7BhsmYYiEjUBZpaWk4HA6f+Xvom9efNm3aOPUqPKmdTwPwtlqMpsmfmpjcrFM3hw4doqWlxVzmDdDS0uJzDCRDj+Ip/VJK9Rmk0cR/8iyxRuCvY8daGLegoICcnBwKCgrMNtYcH3+SyMNxms+fPtsVyfTmhw6VJ+qvONXKJdoGAP7P+BS6PvL9GZcuXUpeXh5Ll/YV3JyMKRbyqWYRGRnJkiVL2LBhg/mYYRhs2LCB5cuXj2PPxt9IVo84lYuF7e8BcFDLH6a1VB4Xk09SUhJOp9On8GCwixEqLZKEL/+TQ/pM0rRWbuBp4o2RLXkfDWsOlF2+TFdXF26326zU7s8Hst17UqgHVf5MTdoVyYyIiPA5jppSnGe8QYzWww5nITWO6aM6jexNKlN4tu644w6uueYaTj31VE477TR+/vOf09HRYa7Km6xGMoWXbZQToXs44JhDrzH8dERkZCQulytwO9ULEeJWrFhBZmamz+tp5syZuN1uZs6cGbTrpmdko3/lNXb/4nwW6aV8WT3NwS2rg3Y9GLgFTGVlJS0tLT4BVHNzMx6PZ8icHmuxTbtpUGvxxlArwNvR0UF3d7c50gbw9NNPc/jwYWbPns3nP/95srOzaWxs9FlgYJ2y0zQNpdSIbzrzjf2crB+mQ0URc+mvcDz7/Kh+DruVlJONBFA2Lr/8curq6rjrrruoqamhqKiIV199dUBi+WQzkmXFp7ELgPrZl8DBwauUe8XGxuJyuQYthyDERGP3ehqrPJLUtAwe0S+i23iNpfpH5L5yNanqszQ4grN1kvXDv7a2FqWUT209a5BlF/hY86QqKiro6OigoqLCPI91Ob1dNWzruccyyLIrX3H48GHcbjeHDx8G4MCBA1RUVBAVFTVocGK3sfNwmsr3cIm2HoA/GJ/i5vknAaMLoHbv3m3+3idrACVTeIO49dZbOXr0KC6Xiy1btrBs2bLx7lLYiDbaWKAfpVc5KPjEtX49Jzk5GYfDQXJyclD7JkQ48eZdBiP/UtMcvKyfz9vGyURrvXxZe5ZpnrFZQewdMeo/cmQtZbJ+/Xo2btzI+vXrzTbWjYtzcnKIi4szi5PCwNWNdvWJrFN/YzkVaJfbZZ26PXbsGC6XK6Aruo2eLlr/7ypitR62GPOocQxel88fdr/7yUZGoITf/L1Lm60OgwYfxp7G4ozg1bgRYqKxjpbk5ubS3d0dtNpymqazXj+XyLg0lnX8ixv4C3/wfCoo1xqONYA6cuSIzxH6pr8MwzCnv+rq6ujs7KSurs5sYw2Y7MpFWNMR7NITrKNd/rz/jXZayzr65v35+k/znah9f7iNhb2HaVAJvKSdh/ME0029v2MpYyCEH/xaZqwMVmp9W7d4Tv683+fu6enxOQoxGVk/yO3yZQJN13ROue1Z/nHfxfyH4x2u1V7kgye/xSlX+1+VOhCsQYSduLg4GhoazOmvlpYWent7fVYtWgMmu+BoNFXP/dkY159prU2bNnH48GGqq6u57LLLgIGr7kYzPTeUaUYFCyueBeD3xmcCEvSMtqL6RCIBlAioJKOBaXoTzSqOk4o/5/fz8vPz6erqIj9/+BV7QkxU1g/y5uZmXC7XqIsl+isiIoIPHEtp88RypeN1TjnyGz74dS2oGTBGJUjsKphbWfcPtHvfqKiooLKy0ixGahcc+TOaZM1H82erkkWLFvkc7a7V0tKC2+32Cfp0XUfTNLNEQSADqBjVweW8DMD6pM/S1RKYmnwTdaudkZAcKOE3f+qyLKRvm4i3jSKiY/xPCJfhYCEGamxsRClFY2PwSw1omsZBx0n81nMxhtI4pfZvnGO8ga6GXwQSqOv3Pw6mf1DR2dmJYRg+xSQPHTqE2+32qa9l5U8pAWsZBW9i94EDB/z5cUzW/Kr4+Hg0TfMpltra2opSKuD7qGrKYLWxgWStg4+02Sy9/mFGUykmkDWoJhL5bQi/HThwgB07dgz6BtLd2cYZWt/qu4+0Ats2g7HuxC6EsK/9E8xaUZoG1c5ZvLfkQVzKyVn6Tj5lvIJTDdyqKdDsNqftf4SB+8EdOXKE5uZmnzwph8Phc7RLBrcW7bRj3U7FbuTI2ubNN9/k8OHDvPnmm2Yb68hVdXU1breb6upqs013d7fPMVAWGTsp1A/RrqLpveT3JCWOrsJ9UVERMTExQ948T0Zyuy/8tm3bNjo6Oti2bRvFxcUDvr/3zec4RXNxTKXToScPeh6n04nb7fYZbZLhYCEGys3N5fDhwz5J5LNnz2bfvn3Mnj07aNc94z+uZ3tCKgUbb2KxfogUo5368quDdj07djlRLS0tKKXMIKapqcnnCHDSSSexb98+5s+fD/iXu2k3pWfNZ7KbLrS2scvl3LlzJ+Xl5RiGQWFh4ZiN5pS9/Wcu0d8A4HfGJdx+8imjPtdFF13ERRddFKiuTRgyAiX85q0Q7D1a6R/+BYDN6uQhh+G9pQr6lywI9erBQoyHFStWsHLlSlasWGE+tn//frq6uti/f39Qr73knIt5hKuoVlPI02vQHz+PhDGoWu5lNypjnebz7kHafy9S7x5y3s3fraUPrOcC+yk96zJ9uzQDa5tTTz2VKVOmcOqpp5ptjh49isfj4ejRo4P+XIGWqJqYuuE2AJ71nEPLCdb3kvdnezICJfxmXWbcn6bcLGjfAhqUakNPw7lcLp8j+LfCRYjJxm5k1jrVlZSUREtLS1Cm9Hr1eH5rfIHPGf9gvl7OzfyJJzyXUOcYn/Ik7e3tPsfk5GS6urp8bsaOHz+OUsrcz9RuVMiumKVVeXk5LS0tlJeXA/abP1tzsIqLiweMzlvfN4O94jhKdfE59SKxWjfvGfPZ7Vh8wiUL5P3ZnoxACb8NNfQ8zagkUnNz2MiiWxt6nt1uJEtyoITwj3XJe1xcHJqmDRkMnBA9kqf1S9gReSoxWg83ac8yx7MXArTE/kQ0NzejlPJZpWh9n8rIyCAyMtJnJ4nY2Fh0XTd3PrBbINPQ0IDH46GhoQGAffv20dTUxL59+8w21tEta06UnaFuRE+UpjysNtaTrdVzjIyA1HsCeX8ejARQYlDWN4OhXvgn0/em8p5aNKoNgSfjTt5CjEZWVhbR0dHma6W9vR2llDkqEwy65mD+11/h756V6JriC/qrlPzyC7hd9tP5Y8W6RQwMfJ/as2cPXV1d7Nmzx2xjtwWMVUpKCpqmkZKSAvTlYimlfHKympqa8Hg8Zg7W1q1bKSsrY+vWrQH6CUdmqbGVxfpB2lU0NRc+QYTuGNV5rFOl8v5sTwIoMShvguTu3buHbOdQPZyq9ZUvOKrnDXtebw0X71EI4b8VK1aYX9BXCykmJiboNdSio6LY4VjKbz0X41Y6RQ2vcOTBc4lQwcvlGQ1rAGWXMlBfX4/L5TJHjjZt2sQ777zDpk2bzDbeQpzeo912U9aK4dXV1Xg8Hp8VdmPl4N//hwv0dwF41PgcS09bMcwzBpeYmOhzFPYkgBKDWrRoETk5OT5F4exkGsdwagYHHXPo1YefRkhISEDTtCEL0gkh/JOSksLUqVPNkZJg0jSNaucsHlJX0aLimNOzl+vU0yQYTcM/eYz4U4TSu4rPe7Tbey4yMhKllDlVasda/DOY03NDOfKvJ5iz40cA/M5zEV2OtBM633j9HOFGAigxqPT0dPLy8khPTx+y3Sn0DY03zPRvD622tjaUUj57Uwkh/GMtzGhXIduuflQgdTpSOf65lykjm0ytka/wFDmeI8M/MURZk9MBMwnde6yoqMDj8fhM+82ZMwen08mcOXPGsLe+phmV5Lz1daBvxV2FY86oimX2J1tr+UcCKDEof6r1OlUXi/W+1Rl5Z1/l13nthtSFEP6xJvRWVlbS0NBAZWWl2ca7Uqz/irFAK1i4mLivvME7xklEa71cr/+Nkl//J9oYVS73lz/BpN2olfUxu7pU9fX1uN3uASUSxkqKUcdV/I0IPLwZVcxuxynoAUgaT01NRdM0UlNTT/xkE9iIA6hrrrmGt956Kxh9EWEox9O3xHeHkc+0XPvCftZVMd6VL96jEMJ/1oReuzpH/m6LcqLSp2awTj+P//OsxlAaRcef51LjH0SpzuGfPEb82aR4tLwr9LzHsZSgmrmavxKr9bDVsZiTb/kjTi0wKyOzs7NJSkoiO3t8ylWEixEHUC0tLaxatYo5c+bwox/9yOeuR0ws/ux9d6r2IQAfsHDQNtYtGYqLi5k9e7ZtNXMhxMh0dHRgGIbPtiRTp071OVpLHwSSrkGpcyE/M75Ii4pjoV7GDeqPpBljn0g9WbSU7eRq9ReStQ52GbPI+fJfSBnlNi3ewqD9C4RmZ2eTmpo6ZAAlxTVHEUC98MILVFZWcvPNN/PMM8+Ql5fHBRdcwF/+8pegRPhi/NTV1VFWVkZdXZ3t948d2s1C/ShupVOt59q2gYF3gIWFhXzxi1+ksLAw8J0WYpKJi4tD13WfOlDW5f3e7wWtVhTQ4Uyn9orX2GdMJ1Vr4xbtz+z831sxemWqPpBSVAM8uYY0rZV9xnSe1f6DzKmjTxq3WxXtLRg6VJ6qNRdvMhpVDlR6ejp33HEHO3fuZMuWLeTn53PVVVeRlZXF7bffzsGDBwPdTzEO7DbG7K/inT8BsF3NxaMNfmcb7IRWISaztLQ0EhISSEv794doamoqDofDzGGxS5IOhjnzFvFn/VL+5jkLgMJj/8eRn5xJjArudSeLKaqOa9VzJKk2dhkzeUq7lIgT3FPPbjsca56d3WiTFNc8wa1cqqurWbduHevWrcPhcHDhhReye/duFixYwI9//GNuv/32QPVTjIPW1lafo1X6sdcA2MW8Ic8TzBwEISa7oqIiEhMTfT7IvJvWekuQjOVr0KFp7HScyhHPdP5T/xuze/bzFXWUZ4wLqPCjTpywl2bUcg1/IV7rZpc2l2e0C4kMQMa43Yo76xZCdlu5yAbwoxiB6u3t5fnnn+dTn/oUM2bM4LnnnuO2226jqqqKJ598kvXr1/Pss89y3333BaO/IkRUlu4j33MYt9Kp0YdONJSkcSHGlt3KvLGkadDmnErDF//FbscC4rVurtf/xipjAx2Nkhs1UlXv/YXreJZ4rZv3jXlM+fLLAQmeYGDFdTsy2mRvxCNQmZmZGIbBFVdcwfvvv2+bYHzOOef4VGsV4SknJ4fy8nJzp/H+yjc9TTZQouZg6EMnpmqaZn4JIQLLn41eNU1DKTXmr8HZc+bR9c2NPPnDa7lCf40V+i6af3EamcZqqh2D502KjynF4Rd+xMwdP0bXFO8YJ/GKdh4/mjZ0bb6RqK+vRynls4qzqqqK0tJSZs2aZY40TfbRJjsjDqAeeughLrvsMqKjowdtk5yczJEj4VtUTfRpaGhAKWW7RDfl6KsA7GLusOeJj4+nsbGR+PjRrRIRQgzOOyrQf3TAOq03ffp0ysvLmT59utlmrIKqmOgoypzz+B9PLp9TrzBXP8aN+nNs9Cxms748qNcOZ7rysNLYxOySbaDBXzxns8OxlCgtsNXB7XJU/QnKxSgCqKuu8q9Yogh/XV1dPkcvh9HNPPURhtKo1geOTlmlpaXR1NTkk+QqhAgMf0YHpk6dSktLi1nWAPzb8iSQDEccf1SXcVZaHcV1f6RY38FidYCPXslm3vlfGZM+hIso1cWFxuucrB/GozT+kfk1dtU4iAxw8AR9+XK7d+/22bLLLigXA0klcjEo74iRdeQoW/XtF7U/cgFubfCRSLO9HzVFhBCBY11i3tHRQVdXl0+tKOvqK2vB22BwaLDq1l/ygPoS+4zpJGkdzHv/exz+8ZkkqOagXTecNOx5g+vU05ysH6ZNxfDPkx/m4i/fiyNIA4WdnZ0YhmGWvICBxVqFPQmgxKAGW7lTxD4AWmdd6Nd5xjuhVYjJxpr0GxcXR0xMjE8dqKysLBwOh/kh6XA4fI7BZDji+bP+WX7n+TQdKorZ3R/yVfUkp3i2EqEmZ90oTRksNraT/NwlZGhNlBkZ/I/6Tz516TUBnWYdqyr1k8EJlTEQE4c1aRAGFuMD0JWLQu0wADPPvAIOPDXsue3ufoUQwWOd1rMrdWCdWh/rciMODaqcs6m44k2a/nI7p/ds5iLtbc5UH7DvhWR05cHQgh/MhYJ41coFxnoW6GUA/N2zgs366cTogZ9ejYiIoKenx8x5svvbEP6RAEoA/icNZhqV6LrigLOAghz7ve+surq6cLvdA3KphBBjwy5PyvrBaZdUHhERQW9vb1CL4M4tmI/nO//kv+++jc9o6yjQK0ku+SHXqKm8ps6iSpvAq/U8bo6+9jBfVk8Rr3fTqmL4V/53+eBwMzEB2tfOKjs7m/LycjOlQlbYjZ4EUALwP2mwkI8AaJphP31n9yZsV6hNCBFaYmJi6Ozs9NnSY6xGpRy6hss5hafU5WR4yrlcX8d0rZYb+AsHjFyOvjOfGWdcFtQ+jLV0dZzKHy9jhusQaPCBkc9zrOGBq77GznvvDdp1rUVWxehJACUA/+5CNNXLYu0AALkrP2/bxm5lz9KlSwes8hBCjC/rqHNsbCydnZ3jWvDWoUG9czo9N2/n/x65hUv0NynQj8H6G6l48wFyjMVUaDP6KnWGqRjVwVJjG+fo28EFzSqOtZ5PUevMJToIq+ys/NnnTvhHAijhtwyjEqdusN/IZe7M+bZt7Ib8CwsLZeNgIUKM90PU7XYDY7dfnj+ypqZT6lzIA8Z85nn2cbH+Fjm9R7heO8Ixlc47xhI8nU3j3c0RiVPtHPrDrXxVPUOM3jca/1rUeWRe+t+0/Pkpogh+8ASQkJCA0+kkISFhTK43kckqPAHYbxZptYj9AGxXCwZt43Q6fY5CiNBUUVFBR0cHFRUVAObUXf8pvPEWqeuUOhdS/6VtvJZ2LS0qllytjiv0V+n58VyWezaRrBrHu5tDilftLPe8y1fV4+SX/h8xWg87jVn8wHMTq779LCcX+JdLOhp2KytlBCpw5FNOAMMnkbc0N3Kq1pf/VK5PH/B9L4/H43MUQoQm75S693j22WcPmGp3Op243W6fG6P+/x4rM3NzmHnrw3z37ilkeso5T3uPuXoF5+lbOI8tHPJks/8vGjOKrx7Tfg1GVx7qt/2VlnfX8l/qbZx63+hSCXP5h+dMXI4kIjWFI0D72Q0mNjaWtrY2n2lZKZIZOBJACWD4Yd0Dm/7GUs1DmZGByzH40K/T6aSnp0dGoIQIcdap9fT0dPLy8khP//c+a3FxcbS0tJj1o8b7BilKh0Z9Ok+pGZxz+kKMTY9ytl5Cvl4JHz4IHz7IpcYs9pBPrZ4JY1RlHQDDw1R1nBnGUU7XdjLlpTbSADR435jHK+pMvvm9B1D/8yMiGcN+iaCRTzkBwIEDB6ioqCAqKso2X0l99AoA27HPffKKjIyks7OTyMihNxgWQoQWu1Fo6yq8sd7+ZTBOTXHu+Zdyz3sfsl6dTbbnGGc497HY2MNJeikn0VeBven+f3C2ZybHtBxatGTw9IIjQCUZlCJRtZKhqpmhjtF2/++4mTYzMaZOJfJe/Hlsas0iwhGJUzOIiw5eOQg70dHRtLe3++xdK/vcBY4EUAKA6upqXC4X1dXVA76nlMG81s2gwVFtxpDnycrKoru7W16YQoQZu6md5ORkurq6SE5OHqdeDU3TIEaDRj2XxT/4HSV797LlmZ+wkMOcqu8nxWiiWG8CPgCg5/4/czx6Fis9iTSRRKcWS/Oe9UxRjXQSiwcdertBd4JSRNFDLF24yraQq8qJVR2kqkaO/vQsUjsOczvtoNH3paBVxfCesZCtLOLqr93LRelJfHDvvTBGCeJWK1askH3ugkgCKAHYVx33SjCaSNQ7aFAJtGkpQ55nxYoVZGZmyotTiDBjV8okOTmZxsZGM4Aai8Kao6XrGqectJAXn59JLTN5yVjNJ5fOpea9Z5mnlTFPO0aC1kVu935y+y+feu4ffLX/iX74SwDu7v/Y2sf5T/h3sPTxQkW30vlQ5bFDzSXj9M/xr/c/It7Ri6ZBbnpS8H7YQVjr8MkK6OCSAEoAQxe7nKWOALBVLYBhNhqVqrZCTCz9p+vS09Oprq72yZMKVXG6h0+uuYx7t+3lsDqJv+Dk02vOo+nwdur3bCSVZlJpIV1rZqrWRKJmv1NCh4qiiQQaVQKNKpEKMojPX05s7iL++eY2EvReHJriujWfYeu24BXA9Ieu63g8niE3hJYpvMCRAEoAfcnfvb29A5K/lVIs1fcCcIiZ49E1IUSI6OzsRCllO1IdyjQNYnFzxtKlsHQp9957nLKPv/fd7/+A7973P/QqDQ3F1VddiafXzZ///EcMzYmOxt13fp3f//ePzPqdd19zBwCb3353XH6eweTn53P48GFmzx68NIJM4QWOBFAC6Ev+7u3tHZD8HaXaydHq6VYRNOpTx6l3QojxEBcXR0xMjLkKzzs11H+rJl3XMQxjyFGPUBbh0InX/z3yvji/r0zLyw4D6Hs8MToiLIqfOxwO80sEnwRQAhg8ByrXOAYO2B93Kqpr+D+XqqoqSktLmTVrlgwPCxHmrBsOJycn09ra6pNUPm3aNKqrq5k2bdo49VJ4DbUYyEum8AJHAigBDL48efHHxTPd+efD7pZhzyMvTiEmDmtOY1paGk1NTaSlpZmPZWdn09nZSXZ29nh0cdKyS+iPiIhA07Qhk/xlCi9wJIASg9IMFwv1MgylMWvlZ2H3/w5o43A48Hg85pCxvDiFmDisI8rZ2dk0Njb6BEvWUapQXqk3kaSkpFBXV0dKyr9XRp9xxhnDbtwuC30CRwIoMahMVQkafKhmcvLUHNs21gBKXpxCTBzWEWV/9lGLjo6mt7fXp3ijdXm9GDlrrllKSgqNjY0+AZS1bIGkVARXeGb9DSIvLw9N03y+/vu//9unza5duzjzzDOJjo4mNzeXH//4xwPO89xzzzFv3jyio6NZtGgRr7zyylj9CCHlJA4CsFsVDNrGu8dS/72WhBATg3WLp1mzZpGfn+8zwlxSUsL27dspKSkB7DewDZUK5uHM+nutqKjA7Xabm0HDwE3hvQFwaWnp2Hd4EphQARTAfffdR3V1tfn11a/+u0Raa2sr5513HjNmzGD79u385Cc/4Z577uG3v/2t2ebdd9/liiuu4Prrr2fHjh1cfPHFXHzxxXz44Yfj8eMEjfWFZtXS1Mgp2n4AKvTcQc8za9YsYmJiZMpOiAnInxEnq6ysLKKjo31GPOxW74mRsW6rY/c7tQazdgGvCJwJN4WXkJAw6GqQP/7xj/T09PD4448TGRnJwoULKSkp4Wc/+xk33ngjAA8//DDnn38+3/zmNwG4//77WbduHb/85S957LHHxuznCLbhkr33v/sCp2lujhoZdOvxg57n+PHj9PT0cPz48aD1VQgxPqw5jXbvG9YcqIKCAlwuFwUF/x65lim8wJs+fTqlpaVMnz7dfKyjo4Ouri46OjoASakItgk3AvXf//3fpKamsnjxYn7yk5/gdrvN723evJmzzjrLp9bR6tWr2b9/P01NTWabVatW+Zxz9erVbN68edBrulwuWltbfb5CnXVo3srY/xoAHzCPoQqgeH+XsnmwEBNPVlYWK1euND+E7UY0rG3sRq0Mw/A5ihNXUFBAdna2T6AqxtaEGoH62te+ximnnMKUKVN49913ufPOO6muruZnP/sZADU1Ncyc6VtNOyMjw/xeSkoKNTU15mP929TU1Ax63QceeIB77x3fEv4jVVlZSUNDA5WVlQP2SvJ4PMxp7QsYjzLd7ummmJgYIiIiiImJCVpfhRChwZ8RDbuVuOFebHOsWRfnQF+eaWdnp5lvavcebi18KoIr5P+av/Od7wxIDLd+ffRRX62iO+64g+LiYk4++WRuuukmHnzwQR555BFcLldQ+3jnnXfS0tJifh07diyo1wsE61Bvf/t3vE0qLbSraFr1KcOeSxJDhRBe1hEp6KsfpWmaWT/KO/I92Aj4ZGeXdB8VFeVztFNUVMSSJUsoKioKav9En5Afgfr617/OtddeO2SbwRLkli1bhtvtpqysjLlz5zJt2rQBuTref3vzpgZrM1SV3aioqCH/qENRc3MzPT09NDc3D/heY8lLAHyg5qL04bcEkLwGIcRQrPWJsrKyOHz4sE+QFR0dTXd3t0/5g8nKLkE8JiaG5uZmc7Tfmnsmxl7IB1Dp6emj3vm7pKQEXdeZOrVvD7fly5fzve99z6fI27p165g7d65ZS2P58uVs2LCB2267zTzPunXrWL58+Yn9ICGmvr4epRT19fUDvpdW/SYAB/zYPDguLo7o6GgZMhZikvKn1lB6ejp5eXnme7ndEvyoqCi6u7vD7mY0EKxJ9k6nE4/H47O5u6Zp6LputrGbTpWdIMZWyAdQ/tq8eTNbtmzhnHPOISEhgc2bN3P77bfzxS9+0QyOvvCFL3Dvvfdy/fXX8+1vf5sPP/yQhx9+mIceesg8z3/9139x9tln8+CDD7JmzRqefvpptm3b5lPqYCKwLok1qR4K3AdBg1o9c9jzyF2QEJObPx/aJSUlHDx4kNbWVrKysszFPf0X+XiTzr3HyZQ35c2z9ebf2k3hLV26dNgq47ITxNiaMAFUVFQUTz/9NPfccw8ul4uZM2dy++23c8cdd5htkpKSeP3117nllltYsmQJaWlp3HXXXWYJA+gbav7Tn/7E97//fb773e8yZ84cXnjhBU466aTx+LGCZrDCdunGcXRdcdg5m17P8InhskxWiMltNB/adlNUU6ZMob6+nilT+vIuY2Ji6OjomHALVOwCQ+9MgPcYGxtLT0+PT4Fia5VxO/J+PLYmTAB1yimn8N577w3b7uSTT+btt98ess1ll13GZZddFqiuhZW5qq9ibWPm2VAxTGMhxKTnz4e2dQ+9KVOmUF1dbQZLAJdccok5FQjQ3d3tc5wooqKi6Orq8pmqtI7IFRYWDjvaJMbfxB8bFX5TSnGqvg+AKYsvGufeCCEmCmttqOTkZCIjI0lOTh70OR6Px+c4USQkJKBpms8KRO/qRO/RW6Ovfw7UcLtHiLEnAdQk5R0+7j+MHGs0k6R10kI8swrPGq+uCSEmGH+2FLFuQ+INroYKsux4g47+wUcoSUlJweFw+GwCvHDhQqZMmcLChQsB+0LHsq9d6JEAapKyy0HIU+UAlCadjuYY+OZjt0moEEKMlD8FH+fMmUNycjJz5swxH7O+b1lHbiD0Ni629tFuBeKhQ4doaWkxk/H7F8n0kn3tQk9ohugi6OyGxws/3jxYn7t6XPokhJiYrCv1rDlRMDBPyq5NcnIyTU1N5qhUQkICTU1NPiM1oTb1Z83tsgvwkpKSqK+vJykpadDzSIJ46JEASgCgGS7m6hV4lMas5Z+2bZORkUF1dfWArW6EEGIo1pV6dvvlWR+za2NNtu7p6fE52omPj6e9vZ34+ME3RQ9myYS6ujrKyspISEggKyvLdiR/xYoVZGZmmr+f2NhYdF33WYUnQo8EUAKADFUFGuxRMzk5xT5AWrNmjc+dlBBC+MM6emJX+sCa9+MNnvrXirLmN+Xn59PV1UV+fr7ZxhoM2Y342O01Z+V0OnG73ea1rMUu7cTExNDV1eVTeuHdd9+lrq6OtrY2CgsLiYmJob293aeN9fdTUVFBR0eHzzSfCD2SAyUAmE9fYuIeNWeYlkIIcWLs9suzjjjZBRGZmZlERUWRmdlX5NdutZp3lwnvcerUqTgcDnNHCu/z+h/tREZG+hxTU1N9jnZt7Par6+npQSlljpLNnz+flJQU5s+fP+i1Fy1aRE5Ojk8ZA1mFF3pkBErQ2dXJEq1vQ+ZKPXvQdrJNgBAiWKyjUt7goX8QUVBQgMvloqCgALBfrWZNNC8sLETXdZ/zWKf+IiMj6e7uNgMhGLhoJjIyEk3TfNrMnDmTw4cPM3Nm37ZXdvlXsbGxtLS0mNNxdkGflV3RTHn/DT0SQE0Cw+1VtX/rBhZrLupVIh3a4EmMdm9WQggRCNZpLLsgov/qtMLCQnbu3El5eTmGYZhtMzMzKS8vN0eprM+xYxf4WB9raWlBKUVLS4vZpr29HaUU7e3tgP3IlnUPO7vpS3/2E5RtWkKPBFCTwHB3Lh17XwOgRBWAPvj8vl1SpxBCjJfa2lo8Hg+1tbXmY6tWrfLJ1ezo6KCrq4uOjg6zTWZmJtXV1WaQFRkZSW9vr8/okjVhferUqZSXl/tMBVqn8JKTk2ltbfWpXWXN0xrtJsCyCi/0SAA1CQx35zK19l0ASpkx5HlkBEoIMZ6spQ2SkpLo7Owccvl/V1cXbrebrq4u87GEhARqa2vN9zK7RPOoqCh6enrMfCa7qcC0tDSampp8aj31/zf4N2Uno0vhSQKoSU4ZvRRohwGo14cuT+DPULgQQgSLdRQ8Ozubzs5On1pRmzZtorS0lOrqai677DLbUgfl5eW43W7Ky/uKB1sTz6EvGOrs7DSDIbv3v6KiIhITE83Ax652lT/BkYwuhScJoCaBkpISDh48SGtr64AXabo6DhrsN3LpdUQPeR67oXAhhBgr1mDEGsBAX65Sb2+vmau0dOnSARvzWqfjent7fY7+sgY+kuYwuUgANQnU19fT1tZGfX39gO/NVmUA7GH2sOfxZ/sFIYQIFn9GaqwBk10yutvtRtd1M78pOTmZrq4un9wl68iVXbBmTf62G22S1XMTlwRQk0B9fT0ej2dAAGUoxSl63/YtFeQMex674WkhhBgvdsGJNWCyW+Fm3TrFbiowUMnfkt80cUkANQl0dnb6HL0iVQdTtRa6iKRZnzLgedaKvjI8LYQIJf4EJ3ZBjnXrFLvRpdEkf9ulS0h+08QlAdQkMNjmmllGJTjgcGwRqmvgn4LD4cAwDLOQnNxJCSFCiT/Bid37lvV5duexPs9uJEuCo8lNAqhJbIHWt32La8Y58NHA5ElrbRR5sxBChJvRvm9ZnzfUYhwvu5EsMXFJADVJGcqgSOsb1s5ZugY+emF8OySEEGHOLljzp8q4CE+ymfAklWg0EKX1Uq2mkDHzZNs23t3C++8aLoQQk1F2djapqakjXkTjzcEqLS0NUs/EeJERqEkqTx0DYKeaQ6Zmv32Ld/8mbxK5EEJMVnaLaGQPu8lNAqhJSClFoXYAgCNDbN+SlpZGS0uLz7YEQggxGY22xpPkjk5cEkBNQMPdFR09coB8vQqP0mjU0wc9T3p6OjU1NaSnD95GCCEmA39W6onJRQKoCWi41SIV214hD9in8nDrUYOep6Kigo6ODioqKoLXWSGECFMyujS5SQA1CUWUvw3AR8wcsp13K4T+e0gJIYSwJyvuJhcJoCagoWqRuN0eZrVtBw0qtaFf4HZ7SAkhhLAn+95NLhJATUBDDSsf3LON+Voz3SqCVm3g9i1CCCFGR3KiJhcJoCagoYaR63e9DsBuNRtDl//9QggRKJITNblIgZ8JaP369WzcuJH169cP+F5M5TsAHByifIEQQgghhiYB1ARUVVWFx+OhqqrK53FDKeZ27QSgRsscj64JIYQQE4IEUBOQw+HwOXpFG60kaF20EkebluzzPe3jauTaIFXJhRBCCPFvEkBNQFFRUT5HrxzVNyJ1NH4xaL7/6yWAEkIIIfwnAdQEZBiGz9FrrnYEAHfeWWPeJyGEEGIikQBqAvJudtl/00uPUhRqhwHIOeX8Ac9xOp0+RyGEEEIMTgKoSSLeaCJK66VeSyF95skDvh8REeFzFEIIIcTgJICagOySyKervv3sKpJPA5s8J7fb7XMUQgghxOAkgJqAent7fY4AC7RSAByzz7Z9Tl5eHk6nk7y8vKD3TwghhAh3EkBNAoYyWPhxAvn0Uy+wbeNwOHA6nQNKHwghhBBiIAmgJoEkTx0OTVFuTCVpmv0eTXV1dbhcLurq6sa4d0IIIUT4kQBqEsijL/9pD7MHbRMfH4+u68THx49Vt4QQQoiwJQHUBKeUYpF2CIBycgZtl5aWRkJCAmlpaWPVNSGEECJsSdGfCa6iopw5eiUAjXr6oO2KiopITExk1iz7KT4hhBBC/JsEUBPcsR3ryQUOGtn0OKIHbZeVlUVWVtbYdUwIIYQIYzKFN8F5jrwNwH7yxrcjQgghxAQSNgHUD3/4Q8444wxiY2NJTk62bVNeXs6aNWuIjY1l6tSpfPOb3xxQGHLjxo2ccsopREVFkZ+fz9q1awec59FHHyUvL4/o6GiWLVvG+++/H4SfKPiUgqzm7QBUMPToUlVVFe+88w5VVVVj0TUhhBAirIVNANXT08Nll13GzTffbPt9j8fDmjVr6Onp4d133+XJJ59k7dq13HXXXWabI0eOsGbNGs455xxKSkq47bbb+NKXvsRrr71mtnnmmWe44447uPvuu/nggw8oLCxk9erV1NbWBv1nHI2hAh9DuZmtyoGh858ASktLOXToEKWlpUHppxBCCDGRhE0Ade+993L77bezaNEi2++//vrr7N27l6eeeoqioiIuuOAC7r//fh599FF6enoAeOyxx5g5cyYPPvgg8+fP59Zbb+Wzn/0sDz30kHmen/3sZ9xwww1cd911LFiwgMcee4zY2Fgef/zxMfk5R2qowCfFqAfgsJFFrzZ4/hPArFmzyM/PlyRyIYQQwg9hE0ANZ/PmzSxatIiMjAzzsdWrV9Pa2sqePXvMNqtWrfJ53urVq9m8eTPQN8q1fft2nza6rrNq1SqzjR2Xy0Vra6vP11hJSEjA6XSSkJAw4HszPq7/9JEf+U9ZWVmsXLlSEsmFEEIIP0yYAKqmpsYneALMf9fU1AzZprW1la6uLurr6/F4PLZtvOew88ADD5CUlGR+5ebmBuJH8ktbWxtut5u2tjafx5XC3L6lguwBz0tJSfE5CiGEEMJ/4xpAfec730HTtCG/Pvroo/Hsol/uvPNOWlpazK9jx46N2bUHG4EylIe5el8/GvWBxTF1Xfc5CiGEEMJ/41oH6utf/zrXXnvtkG38zcmZNm3agNVyx48fN7/nPXof698mMTGRmJgYHA4HDofDto33HHaioqKIioryq5+BtnPnTsrLyzEMg8LCQvPxFKMeHHDMkUuPETPgeY2NjT5HIYQQQvhvXAOo9PR00tOHXh3mr+XLl/PDH/6Q2tpapk6dCsC6detITExkwYIFZptXXnnF53nr1q1j+fLlAERGRrJkyRI2bNjAxRdfDIBhGGzYsIFbb701IP0MtIaGBjweDw0NDT6PT/84/6k+7TSwWUAYGxtLR0cHsbGxY9FNIYQQYkIJm/mb8vJySkpKKC8vx+PxUFJSQklJCe3t7QCcd955LFiwgKuuuoqdO3fy2muv8f3vf59bbrnFHB266aabKC0t5Vvf+hYfffQRv/rVr3j22We5/fbbzevccccd/O53v+PJJ59k37593HzzzXR0dHDdddeNy889nMzMTJxOJ5mZmT6PL9T6VuVF559l+7wZM2YQFRXFjBkzgt5HIYQQYqIJm61c7rrrLp588knz34sXLwbgjTfeoLi4GIfDwUsvvcTNN9/M8uXLiYuL45prruG+++4znzNz5kxefvllbr/9dh5++GFycnL4/e9/z+rVq802l19+OXV1ddx1113U1NRQVFTEq6++OiCxPFQ0NTXh8XhoamoyH3MbHgq0vhGoGad8Et79/YDnxcXFERMTQ1xc3Jj1VQghhJgowiaAWrt2rW3V8P5mzJgxYIrOqri4mB07dgzZ5tZbbw3ZKTur9vZ2lFLmSBz05T/pDkWZyiAvdeAKPIDs7GwaGxvJzrb/vhBCCCEGFzZTeMKey+XyOQJMpxKAfWrwBPzByh8IIYQQYnhhMwIl7Hk8Hp8jwIKP85+G2v/Ou7pRKo8LIYQQIycjUBNMRVUV87S++k8Nw+x/J4QQQojRkQBqgjm6YwO6pjhqZODSBi9RIJsHCyGEEKMnU3gTjPvIO8Dw+9/JFJ4QQggxejICNcGkN34AwDGb/e+EEEIIERgSQIWZqqoq3nnnHaqqqgZ8r7axkTmewwA06alDnkem8IQQQojRkym8MOMNfACysnxX2R3Z8SZTNQ/HVQqdWrz5uK7rGIbhs3GwTOEJIYQQoycBVJgZKvDpPNSX/7RP5YGumY8bhuFzhL7gyxqACSGEEMI/EkCFmaECn6T67QCUS/6TEEIIEVSSAzVBuBUU9OwDoN5S/8nhcPgchRBCCHFiJICaIKKMduK1btqIpZ0kn+9pmuZzFEIIIcSJkSm8MLNx40Z2797NokWLKC4uNh+fpmoAOBZ3MqpT4mIhhBAimOSTNszs2LGDxsZGduzY4fP4LK0CgJ7sZQOeExMT43MUQgghxImRACrMxMfHo2ka8fH/LlPgUXCy1lfaIG3h2QOek5aWhsPhIC0tbcz6KYQQQkxkEkCFmeTkZCIjI0lOTjYf041u0rRWenCSveCMAc+JiYnB6XTKCJQQQggRIBJATQBTVR0AR6PnoUXYB0mSQC6EEEIEjgRQYaa2thaXy0Vtba352Az68p86MpbaPqerq4ve3l66urrGpI9CCCHERCcBVJhpaGjwOSoFC7W+/ewS5p5l+5yenh6foxBCCCFOjARQYUYp5XP0KDd5+nEMpTG98Bzb52RkZBAZGUlGRsaY9VMIIYSYyCSACjPWophTjHoADqssIuJSbJ/T0tKCYRi0tLSMTSeFEEKICU4CqDBjHYGaTiUAB5gx6HOSkpLQdZ2kpKRB2wghhBDCfxJAhbl5WhkAVWQO2kZGoIQQQojAkq1cwlh1bT3ztHIAmvTBi2QuWrTI5yiEEEKIEyMjUGHsyK63cGiKajWFLi1u0Hbp6enk5eWRnp4+hr0TQgghJi4ZgQpj3YffBWC/Gjz/CaC0tJRDh/q2esnKygp6v4QQQoiJTgKoMJZY37ehcAVDB0WzZs3yOQohhBDixEgAFaY8SiO/Zx9o0KANvUlwVlaWjDwJIYQQASQ5UGHKYXSSrHXQpSJp0ZJ9v+dw+ByFEEIIEVgSQIWp9I83EN6vpqM030Bp+vTpOBwOpk+fPh5dE0IIISY8mcILU9OpAuAIOQO+V1hYiK7rUrZACCGECBIZgQpDSv27gOZxbeD+djt37qSsrIydO3eOcc+EEEKIyUECqDDkVgb5et8IVLM2ZcD3Kyoq8Hg8VFRUjHXXhBBCiElBAqgwlGQ0AlDlyMalxQz4viSRCyGEEMElAVQYyqIGgPqUQtvvL1u2jClTprBs2bKx7JYQQggxaUgSeRjK/3j/O8eM06GhecD3CwoKcDqdUjhTCCGECBIZgQozvUrjJO0IAFknnW3bpqSkhO3bt1NSUjKGPRNCCCEmDwmgwkyk0U6c5qJNxZAy4+Tx7o4QQggxKckUXpjJULUAfKRmsFS3j3+LiopITEyUKTwhxKTl8Xjo7e0d726IEBMRERGwBVYSQIWZPCoBOEo2SwdpI3vfCSEmK6UUNTU1NDc3j3dXRIhKTk5m2rRpaJp2QueRACqMeAzF/I/zn2q1qePcGyGECD3e4Gnq1KnExsae8IekmDiUUnR2dlJb2zeTk5mZeULnkwAqjBwqPcxcvQ5DabYFNIUQYjLzeDxm8JSamjre3REhKCamr3ZibW0tU6dOPaHpPEkiDyPVe94CoFRl0atFjXNvhBAitHhznmJjY8e5JyKUef8+TjRHTgKoMJLSsAOAQ+SOc0+EECJ0ybSdGEqg/j7CJoD64Q9/yBlnnEFsbCzJycm2bTRNG/D19NNP+7TZuHEjp5xyClFRUeTn57N27doB53n00UfJy8sjOjqaZcuW8f777wfhJxq5wku/w1+4kKO6rK4TQgghxlPYBFA9PT1cdtll3HzzzUO2e+KJJ6iurja/Lr74YvN7R44cYc2aNZxzzjmUlJRw22238aUvfYnXXnvNbPPMM89wxx13cPfdd/PBBx9QWFjI6tWrzaSzcZWUzR5tHjXaNPMhbyQtd1xCCBG+iouLue2228a7GwC88MIL5Ofn43A4uO2221i7du2gAxeTWdgEUPfeey+33347ixYtGrKdd3mi9ys6Otr83mOPPcbMmTN58MEHmT9/Prfeeiuf/exneeihh8w2P/vZz7jhhhu47rrrWLBgAY899hixsbE8/vjjQfvZToRSyucohBBCWG3cuBFN0/wq7/DlL3+Zz372sxw7doz777+fyy+/nAMHDpjfv+eeeygqKgpeZ8NE2ARQ/rrllltIS0vjtNNO4/HHH/cJLDZv3syqVat82q9evZrNmzcDfaNc27dv92mj6zqrVq0y29hxuVy0trb6fAkhhBDhpr29ndraWlavXk1WVhYJCQnExMQwdaqUzrGaUAHUfffdx7PPPsu6deu49NJL+cpXvsIjjzxifr+mpoaMjAyf52RkZNDa2kpXVxf19fV4PB7bNjU1NYNe94EHHiApKcn8ys2VJG8hhAgFSik6e9zj8jXSmQG3282tt95KUlISaWlp/OAHP/A5h8vl4hvf+AbZ2dnExcWxbNkyNm7caH7/6NGjXHTRRaSkpBAXF8fChQt55ZVXKCsr45xzzgEgJSUFTdO49tprB1x/48aNJCQkAHDuueeiaRobN270mcJbu3Yt9957Lzt37jRzje1yiSeDca0D9Z3vfIf/+Z//GbLNvn37mDdvnl/n+8EPfmD+9+LFi+no6OAnP/kJX/va106on8O58847ueOOO8x/t7a2jlkQ5XQ6cbvdOJ1S0ksIIay6ej0suOu14RsGwd77VhMb6f9785NPPsn111/P+++/z7Zt27jxxhuZPn06N9xwAwC33nore/fu5emnnyYrK4u//e1vnH/++ezevZs5c+Zwyy230NPTw1tvvUVcXBx79+4lPj6e3Nxcnn/+eS699FL2799PYmKiWQ+pvzPOOIP9+/czd+5cnn/+ec444wymTJlCWVmZ2ebyyy/nww8/5NVXX2X9+vUAJCUlndgvKkyN66fu17/+ddsouL8T2c9t2bJl3H///bhcLqKiopg2bRrHjx/3aXP8+HHzj8nhcOBwOGzbTJs2jcFERUURFTU+dZmio6Npb2/3yfUSQggRfnJzc3nooYfQNI25c+eye/duHnroIW644QbKy8t54oknKC8vN7fq+sY3vsGrr77KE088wY9+9CPKy8u59NJLzVzh/p+fU6b0FV+eOnXqoAnhkZGR5lTdlClTbD/3YmJiiI+Px+l0Dvm5OBmMawCVnp5Oenp60M5fUlJCSkqKGdwsX76cV155xafNunXrWL58OdD3x7NkyRI2bNhgrt4zDIMNGzZw6623Bq2fJyI2NpaOjg4pHCeEEDZiIhzsvW/1uF17JE4//XSfFdXLly/nwQcfxOPxsHv3bjweDwUFBT7PcblcZtX1r33ta9x88828/vrrrFq1iksvvZSTTz75xH8QYSts5n3Ky8tpbGykvLwcj8dDSUkJAPn5+cTHx/Piiy9y/PhxTj/9dKKjo1m3bh0/+tGP+MY3vmGe46abbuKXv/wl3/rWt/jP//xP/vWvf/Hss8/y8ssvm23uuOMOrrnmGk499VROO+00fv7zn9PR0cF111031j+yX+bPn4/b7Wb+/Pnj3RUhhAg5mqaNaBotVLW3t+NwONi+ffuA7Ufi4+MB+NKXvsTq1at5+eWXef3113nggQd48MEH+epXvzoeXZ7wwuav6q677uLJJ580/7148WIA3njjDYqLi4mIiODRRx/l9ttvRylFfn6+WZLAa+bMmbz88svcfvvtPPzww+Tk5PD73/+e1av/fXdy+eWXU1dXx1133UVNTQ1FRUW8+uqrAxLLQ4XT6SQhIUFyoIQQIsxt2bLF59/vvfcec+bMweFwsHjxYjweD7W1tZx55pmDniM3N5ebbrqJm266iTvvvJPf/e53fPWrXyUyMhLo2y/wREVGRgbkPOEubD51165dO2Sm//nnn8/5558/7HmKi4vZsWPHkG1uvfXWkJ2ys/LOcZ9IrpgQQojxV15ezh133MGXv/xlPvjgAx555BEefPBBAAoKCrjyyiu5+uqrefDBB1m8eDF1dXVs2LCBk08+mTVr1nDbbbdxwQUXUFBQQFNTE2+88YY5OzFjxgw0TeOll17iwgsvNHOZRiMvL48jR45QUlJCTk4OCQkJ45YHPJ4mVBmDySgrK4uVK1eaSYVCCCHC09VXX01XVxennXYat9xyC//1X//FjTfeaH7/iSee4Oqrr+brX/86c+fO5eKLL2br1q1Mnz4d6BtduuWWW5g/fz7nn38+BQUF/OpXvwIgOzube++9l+985ztkZGSc0CDBpZdeyvnnn88555xDeno6f/7zn0/sBw9TmpIS1gHX2tpKUlISLS0tJCYmBvTc9957r/nfd999N1VVVZSWljJr1iwJooQQk1p3dzdHjhxh5syZsjJZDGqov5ORfH6HzRSesFdaWsqhQ4cAJIASQgghxogEUGFOcqCEEEKIsScBVJjLysqSkSchhBBijEkSuRBCCCHECEkAJYQQQggxQhJACSGEEEKMkARQQgghhBAjJAGUEEIIIcQISQAlhBBCCDFCEkCFGV3XfY5CCCHEeFi7di3Jycnj3Q2uvfZaLr744jG/rnwKhxlvaflAbxEjhBBCBFJZWRmaplFSUhKS5ztREkCFGbfb7XMUQggxOfX09Ix3FwIiXH8OCaDCjHfvZ9kDWggh/KAU9HSMz9cI3qfb2tq48soriYuLIzMzk4ceeoji4mJuu+02s01eXh73338/V199NYmJidx4440APP/88yxcuJCoqCjy8vJ48MEHfc6taRovvPCCz2PJycmsXbsW+PfIzl//+lfOOeccYmNjKSwsZPPmzT7PWbt2LdOnTyc2NpZLLrmEhoaGIX+mmTNnArB48WI0TaO4uBj495TbD3/4Q7Kyspg7d65f/RzsfF4//elPyczMJDU1lVtuuYXe3t4h+3eiZCuXMDN37lz27dtn/sEJIYQYQm8n/Gictrv6bhVExvnV9I477mDTpk384x//ICMjg7vuuosPPviAoqIin3Y//elPueuuu7j77rsB2L59O5/73Oe45557uPzyy3n33Xf5yle+QmpqKtdee+2Iuvu9732Pn/70p8yZM4fvfe97XHHFFRw6dAin08mWLVu4/vrreeCBB7j44ot59dVXzT4M5v333+e0005j/fr1LFy4kMjISPN7GzZsIDExkXXr1vndv6HO98Ybb5CZmckbb7zBoUOHuPzyyykqKuKGG24Y0e9gJCSACjMOh4OoqCgcDsd4d0UIIUQAtLW18eSTT/KnP/2JT3ziEwA88cQTtvucnnvuuXz96183/33llVfyiU98gh/84AcAFBQUsHfvXn7yk5+MOID6xje+wZo1awC49957WbhwIYcOHWLevHk8/PDDnH/++XzrW98yr/Puu+/y6quvDnq+9PR0AFJTU5k2bZrP9+Li4vj973/vEwQNZ6jzpaSk8Mtf/hKHw8G8efNYs2YNGzZskABKCCGEGJWI2L6RoPG6th9KS0vp7e3ltNNOMx9LSkqynWk49dRTff69b98+Pv3pT/s8tmLFCn7+85/j8XhGdLN98sknm/+dmZkJQG1tLfPmzWPfvn1ccsklPu2XL18+ZAA1lEWLFo0oeBrOwoULfX7WzMxMdu/eHbDz25EAKsxkZ2fT2NhIdnb2eHdFCCFCn6b5PY0WDuLiRv6zaJo2IG/WLj8oIiLC5zkAhmGM+Hr+sPs5/O2nnf59954rWH33kiTyMFNZWUlDQwOVlZXj3RUhhBABMGvWLCIiIti6dav5WEtLCwcOHBj2ufPnz2fTpk0+j23atImCggJzRCY9PZ3q6mrz+wcPHqSzs3NEfZw/fz5btmzxeey9994b8jneESaPx+PXNYbr50jPF2wyAiWEEEKMo4SEBK655hq++c1vMmXKFKZOncrdd9+NruvmSNBgvv71r7N06VLuv/9+Lr/8cjZv3swvf/lLfvWrX5ltzj33XH75y1+yfPlyPB4P3/72tweM2Azna1/7GitWrOCnP/0pn/70p3nttdeGnb6bOnUqMTExvPrqq+Tk5BAdHU1SUtKg7Yfr50jPF2wyAhVmioqKWLJkyYCVGUIIIcLXz372M5YvX86nPvUpVq1axYoVK5g/fz7R0dFDPu+UU07h2Wef5emnn+akk07irrvu4r777vNJIH/wwQfJzc3lzDPP5Atf+ALf+MY3iI31Lz/L6/TTT+d3v/sdDz/8MIWFhbz++ut8//vfH/I5TqeTX/ziF/zmN78hKytrQK6W1XD9HOn5gk1TUlAo4FpbW0lKSqKlpUUqhgshxBjp7u7myJEjzJw5c9jAI9R1dHSQnZ3Ngw8+yPXXXz/e3ZlQhvo7Gcnnt0zhCSGEEONsx44dfPTRR5x22mm0tLRw3333AYz7KIsYnARQQgghRAj46U9/yv79+4mMjGTJkiW8/fbbpKWljXe3xCAkgBJCCCHG2eLFi9m+fft4d0OMgCSRCyGEEEKMkARQQgghJhRZGyWGEqi/DwmghBBCTAjemkEjLRIpJhfv38dIa2FZSQ6UEEKICcHhcJCcnExtbS0AsbGxwxaiFJOHUorOzk5qa2tJTk4e0T6BdiSAEkIIMWFMmzYNwAyihLBKTk42/05OhARQQgghJgxN08jMzGTq1Kl+b0QrJo+IiIgTHnnykgBKCCHEhONwOAL2QSmEHUkiF0IIIYQYIQmghBBCCCFGSAIoIYQQQogRkhyoIPAW6WptbR3nngghhBDCX97PbX+KbUoAFQRtbW0A5ObmjnNPhBBCCDFSbW1tJCUlDdlGU1LzPuAMw6CqqoqEhISAF3FrbW0lNzeXY8eOkZiYGNBzTzTyu/Kf/K78J78r/8nvyn/yu/JfMH9XSina2trIyspC14fOcpIRqCDQdZ2cnJygXiMxMVFeZH6S35X/5HflP/ld+U9+V/6T35X/gvW7Gm7kyUuSyIUQQgghRkgCKCGEEEKIEZIAKsxERUVx9913ExUVNd5dCXnyu/Kf/K78J78r/8nvyn/yu/JfqPyuJIlcCCGEEGKEZARKCCGEEGKEJIASQgghhBghCaCEEEIIIUZIAighhBBCiBGSACpM/PCHP+SMM84gNjaW5ORk2zaapg34evrpp8e2oyHCn99XeXk5a9asITY2lqlTp/LNb34Tt9s9th0NQXl5eQP+jv77v/97vLsVMh599FHy8vKIjo5m2bJlvP/+++PdpZBzzz33DPgbmjdv3nh3KyS89dZbXHTRRWRlZaFpGi+88ILP95VS3HXXXWRmZhITE8OqVas4ePDg+HR2nA33u7r22msH/J2df/75Y9Y/CaDCRE9PD5dddhk333zzkO2eeOIJqqurza+LL754bDoYYob7fXk8HtasWUNPTw/vvvsuTz75JGvXruWuu+4a456Gpvvuu8/n7+irX/3qeHcpJDzzzDPccccd3H333f+/vfsNaWp94AD+/U2alv/Spk4Dh2aNLN0s2epFRClOX1yMemFRsiIiaiGlFhWJ2B8tBZMi9FUFvhAi6EVhkA2FwiVhLCwqcigWullGiJZW87kvLg38XW967k2fk30/MNiejZ0vDw9nX852zvDkyROYTCbYbDYMDg7KjqY6q1atmrSGHj58KDuSKoyOjsJkMuHKlStTPl9dXY1Lly6hoaEBHR0dCA0Nhc1mw9jY2BwnlW+6uQKA3NzcSeusqalp7gIK+qVcu3ZNREZGTvkcAHHr1q05zaN2/zRfzc3NQqPRCK/XGxirr68XERERYnx8fA4Tqo/BYBAXL16UHUOVLBaLcDgcgcd+v18kJCSIqqoqianUp7y8XJhMJtkxVO//99kTExNCr9eLmpqawNjHjx9FcHCwaGpqkpBQPab6fLPb7SI/P19KHiGE4BGoecbhcECn08FiseDq1asQvMzXlFwuF9LS0hAXFxcYs9lsGB4exvPnzyUmU4fz589jyZIlyMjIQE1NDb/axF9HNTs7O5GdnR0Y02g0yM7OhsvlkphMnV6/fo2EhAQkJydj586d6Ovrkx1J9Xp6euD1eietscjISFitVq6xf9DW1obY2FgYjUYcOHAAQ0NDc7Zt/pnwPHL69Gls3rwZixYtwr1793Dw4EGMjIygqKhIdjTV8Xq9k8oTgMBjr9crI5JqFBUVYc2aNYiOjkZ7eztOnDiBgYEB1NbWyo4m1fv37+H3+6dcNy9fvpSUSp2sViuuX78Oo9GIgYEBVFRUYMOGDXj27BnCw8Nlx1Ot7/ueqdbY775fmkpubi62bt2KpKQkeDwenDx5Enl5eXC5XAgKCpr17bNASXT8+HFcuHDhh6958eLFjH98WVZWFrifkZGB0dFR1NTUzJsC9bPn63eiZO6Ki4sDY+np6dBqtdi/fz+qqqqk/3UC/Rry8vIC99PT02G1WmEwGHDjxg3s3btXYjKaT7Zv3x64n5aWhvT0dCxbtgxtbW3Iysqa9e2zQElUUlKC3bt3//A1ycnJ//r9rVYrzpw5g/Hx8Xnxwfcz50uv1//t7Cmfzxd4br75L3NntVrx7ds39Pb2wmg0zkK6X4NOp0NQUFBgnXzn8/nm5Zr5mRYvXowVK1agu7tbdhRV+76OfD4f4uPjA+M+nw9ms1lSql9HcnIydDoduru7WaDmu5iYGMTExMza+7vdbkRFRc2L8gT83Plav349zp07h8HBQcTGxgIAWlpaEBERgdTU1J+yDTX5L3Pndruh0WgC8/S70mq1WLt2LZxOZ+Ds1omJCTidThw6dEhuOJUbGRmBx+NBYWGh7CiqlpSUBL1eD6fTGShMw8PD6OjomPYMbALevn2LoaGhSeVzNrFA/SL6+vrw4cMH9PX1we/3w+12AwBSUlIQFhaG27dvw+fzYd26dQgJCUFLSwsqKytRWloqN7gk081XTk4OUlNTUVhYiOrqani9Xpw6dQoOh2PeFM5/w+VyoaOjA5s2bUJ4eDhcLheOHDmCXbt2ISoqSnY86YqLi2G325GZmQmLxYK6ujqMjo5iz549sqOpSmlpKf744w8YDAb09/ejvLwcQUFB2LFjh+xo0o2MjEw6EtfT0wO3243o6GgkJibi8OHDOHv2LJYvX46kpCSUlZUhISHht7wkzY/mKjo6GhUVFdi2bRv0ej08Hg+OHTuGlJQU2Gy2uQko7fw/UsRutwsAf7u1trYKIYS4e/euMJvNIiwsTISGhgqTySQaGhqE3++XG1yS6eZLCCF6e3tFXl6eWLhwodDpdKKkpER8/fpVXmgV6OzsFFarVURGRoqQkBCxcuVKUVlZKcbGxmRHU43Lly+LxMREodVqhcViEY8ePZIdSXUKCgpEfHy80Gq1YunSpaKgoEB0d3fLjqUKra2tU+6b7Ha7EOKvSxmUlZWJuLg4ERwcLLKyssSrV6/khpbkR3P16dMnkZOTI2JiYsSCBQuEwWAQ+/btm3Rpmtn2PyF4njsRERGRErwOFBEREZFCLFBERERECrFAERERESnEAkVERESkEAsUERERkUIsUEREREQKsUARERERKcQCRURERKQQCxQRERGRQixQRERERAqxQBEREREpxAJFRDSNd+/eQa/Xo7KyMjDW3t4OrVYLp9MpMRkRycI/EyYimoHm5mZs2bIF7e3tMBqNMJvNyM/PR21trexoRCQBCxQR0Qw5HA7cv38fmZmZ6OrqwuPHjxEcHCw7FhFJwAJFRDRDnz9/xurVq/HmzRt0dnYiLS1NdiQikoS/gSIimiGPx4P+/n5MTEygt7dXdhwikohHoIiIZuDLly+wWCwwm80wGo2oq6tDV1cXYmNjZUcjIglYoIiIZuDo0aO4efMmnj59irCwMGzcuBGRkZG4c+eO7GhEJAG/wiMimkZbWxvq6urQ2NiIiIgIaDQaNDY24sGDB6ivr5cdj4gk4BEoIiIiIoV4BIqIiIhIIRYoIiIiIoVYoIiIiIgUYoEiIiIiUogFioiIiEghFigiIiIihVigiIiIiBRigSIiIiJSiAWKiIiISCEWKCIiIiKFWKCIiIiIFPoTGpVEOq9twnoAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -831,7 +831,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "At the outset, we have no model and an emtpy experiment_data dataframe." + "At the outset, we have no model and an emtpy `experiment_data` dataframe." ] }, { @@ -870,24 +870,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running theorist --#\n", + "#-- running experiment_runner --#\n", "\n", - "v1.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", - " ('linearregression', LinearRegression())]), \n", + "v1.model=None, \n", "v1.experiment_data= x y\n", - "0 -15.0 -1211.930218\n", - "1 -14.7 -1251.680229\n", - "2 -14.4 -971.099010\n", - "3 -14.1 -885.923940\n", - "4 -13.8 -949.358016\n", + "0 -15.0 -1386.402949\n", + "1 -14.7 -1073.690228\n", + "2 -14.4 -1072.951606\n", + "3 -14.1 -1096.806703\n", + "4 -13.8 -838.977013\n", ".. ... ...\n", - "298 13.8 683.771393\n", - "299 14.1 689.553131\n", - "300 14.4 745.739431\n", - "301 14.7 914.039795\n", - "302 15.0 981.490063\n", + "96 13.8 384.625949\n", + "97 14.1 559.333146\n", + "98 14.4 795.556490\n", + "99 14.7 920.071641\n", + "100 15.0 907.742229\n", "\n", - "[303 rows x 2 columns]\n" + "[101 rows x 2 columns]\n" ] } ], @@ -912,11 +911,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running experiment_runner --#\n", + "#-- running theorist --#\n", "\n", "v2.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", " ('linearregression', LinearRegression())]), \n", - "v2.experiment_data.shape=(404, 2)\n" + "v2.experiment_data.shape=(101, 2)\n" ] } ], @@ -945,13 +944,13 @@ "\n", "v3.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", " ('linearregression', LinearRegression())]), \n", - "v3.experiment_data.shape=(404, 2)\n" + "v3.experiment_data.shape=(202, 2)\n" ] } ], "source": [ "v3 = next(cycle_generator)\n", - "print(f\"{v3.model=}, \\n{v3.experiment_data.shape=}\")" + "print(f\"{v3.model=}, \\n{v3.experiment_data.shape=}\")\n" ] }, { @@ -959,8 +958,8 @@ "metadata": {}, "source": [ "## Adding The Experimentalist\n", - "Modifying the code to use a custom experimentalist is simple.\n", - "We define an experimentalist which adds four observations each cycle:" + "\n", + "Modifying the code to use a custom experimentalist is simple. We define an experimentalist which adds four observations each cycle:\n" ] }, { @@ -972,11 +971,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 7.157436\n", - "1 6.395671\n", - "2 9.084903\n", - "3 10.618956\n", - "4 14.665249, experiment_data=Empty DataFrame\n", + "0 -10.546793\n", + "1 9.032887\n", + "2 -0.802825\n", + "3 -12.571801\n", + "4 1.990531, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])" ] @@ -1000,7 +999,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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oNvpMDezckCcubwfAK3M38uWfWtpIRM7SkRzYuczYjr3Q3CxHqYASqWoOB/zxDnx8FeTvg8gOcMcvcP5YZq7dw1PT1wDwQHIrruteTS0KqpNPIPR6GO5KgbjeRoPOBc8YvVnS/wBgWI+m3NWrGQDjv1nJok17zEwsIu5u60Jw2I0GwqExZqcBVECJVK3iAmOi+MyHjP/ZOw6B2+ZCZDtStx9gzBd/4XDADec2dhYYbqt+M7jxW2N+VFAk7N0EH/Y37jAsLuT+vq24tGMDiksc3PHJMjZl553+mCIi5XG2L3CNy3egAkqk6uRlw5TL4K+PwWKFvk/Dle+Atx87D+Rz25SlHCkqoXercB6/rB2WCiyb4vIsFmN+1Kgl0Ol6cJTAwpfg/Yuw7lnHC9d0omuTuhw8UsztH/9J7pEisxOLiDvaPN94dIH+T6VUQIlUhf3b4L8Xw/bFRs+koV9Cj9Fk7NrF3AW/Mfz9FPbkFdKmQTBvXN/F8xpN+ofClW/DtR+Bfz1jmZj/uxC/Ze/yfzd0oUGIH2m7D3Hf56mUlOjOPBGphJwdRisVi7XK1747Gx72r7iICfZuhg8vMe5Mq9sURvxsLDMAbN68mZd+z2bjnnzqB/rw/rBuBPp68AIAba8w5ka16GvMjZr5MGEzbuO9wS3w8bIyb102r+jOPBGpjLRfjMfoLsYvay5CBZTI2chea8z7yd0BYS3h5p8grLnz5d/3BbLmoB9eVgtv39CVhqH+JoatIXWi4Ppp0P954+7DtT/Q/screOsibwBem7eR+euzTQ4pIm4j7ejlOxdpX1BKBZTImdq1whh5ysuCyPYwfAYEH1uG5beNu3n3j10APHlFe7rH1jMrac2zWCDhdrhlFoTEwL40khYN5cUWqwAY+0UqGQfyTQ4pIi7P4Tg2AuVC859ABZTImdn1tzFhPH8fRHeGYT9AULjz5Z0H8rln6l+UOGBwtxiuT3DDdgVVoVFXuONXaH4xFB9h0PZneDPkUw4ezufuqX9RZC8xO6GIuLKsVcYKCN4B0Ogcs9OUoQJKpLL2b4VPrzYau8UkwE3/g4Bjo0uFxSXc9ely9h8uon3DYJ64ol2Zb8/IyGDhwoVkZGTUcHCTBNQzLun1fhSwMKBgOp/4Pc/Gbdt5cfYGs9OJiCvbOMd4jL0QvHzNzfIPKqBEKuPQHqNBZl4WRLQzCgO/kDK7TPppLSu2HyDE35u3h3bFz9tW5vW0tDQ2bdpEWlpaTSY3l9UKFz4AQz4D70DOZSXf+kxk9m8LWbRZTTZF5CQ2zTMem/cxN0c5VECJVFRBHnx6DezbDCGN4YavT7gjZNbqTD78fSsAL13biZh6ASccJi4ujubNmxMXF1cDoV1M60vg1tkQEkMz6y6+9X6MqVM/Yv+hQrOTiYirOZIL243VDUrvbHYlKqBEKqK4EKbdBBnLjT5HN34DwWXXsNu+7zAPfLkCgBHnx9KnTWS5h4qOjua8884jOjq63Nc9XlR7GPEz9kbdCbEc5qWif/PtlBdxONQfSkSOs+UXKCk2lm+pF2t2mhOogBI5HYcDfhwDm+cZExmHfglhLcrsUmwv4d7P/yL3SDHxMaE82K+1OVndRVAEtuE/cqDZQLwtdm7J/g+rpj1pfNYiInBs/pMLjj6BCiiR01vyHqR+anTBvfYjaNTthF1em7eR5ekHqOPnxevXdcbb0zqNn6VyJ857+RI69EP+anQDAB3WvkTe/x6AEt2ZJ1LrORzHzX+62NwsJ6F/5UVOZdsimDXe2L74SWhx4v/Ii9P28sb8TQA8fWWHcuc91XYnnThvtdLh5tf5IPA2AIJS38Px9S3GoswiUnvtXmc0KPbyg6Y9zU5TLhVQIieTmwHThhnX4NsPgsTRJ+ySc7iI+75IpcQBV3dtxOWdaum8ptM41cR5L5uVC4c/wbiSuyl02LCs/hamDoHCwyYkFRGXsGmu8dikJ3i75goOKqBEylNcYEwaP5RttCu4/HWju/Y/PPq/VWTkHKFp/QCeuLxdOQcSOP3E+WbhQbTreys3Fz3IYYcvbP4ZPhlk3IUjIrVPaQFVzqi/q1ABJVKenx6CHUuNHk9DPgGfwBN2+V/qTn5YkYHNauGVIZ09e5HgGjCsR1OOxFzADYXjOWwJgPRF8PFAOLzP7GgiUpMK8ozpE+CyE8hBBZTIif7+EpZ9CFhg0H+h3omXnXYeyOfR74x13e65qAXxMaE1m9ED2awWnh3UkVW21lxz5BEKvENh5zJjyZy83WbHE5GasnUh2AshtInRwsBFqYASOd6BdJg+zti+8MFyh49LShzcP20FB48U07lxKKN6N6vhkJ6reUQQ9/ZpwWpHLNcVTaAkMMJYC2vyJXAwy+x4IlITNh3XvqCcqROuQgWUSKkSO3x7JxTkGItWXvBgubtNXrSVlLS9+HvbePnaeLzUsqBK3XFBHG0bBLP8SBTPRL4EwY1gzwYOv9OHzE1/mx1PRKqTw3Gs/5MLz38CFVAix/z+Kmz7HXyC4Kp3wXbinKZN2Qd5duY6AB4Z0IamYSfOjZKz42WzMumqDlgt8P4aK0t6fUSBbxgBh7ZT59uhupwn4sn2boYD28DqDU3PNzvNKamAEgHISIX5Txvb/Z8td95Tkb2EsdNWUFBcwgUtwxma0LhmM9YinWJCuSmxKQDj5uaQdelHFPjWJ/BQOnx0ubGos4h4Hmf7gkTwDTI3y2mogBIpPAxf32b0e2pzGcQPLXe3t+Zv5u8dOQT7efHcoI5YXPjavCe4P7kVDUL82L4vn8931sf39jlQpwFkr4Epl8OhvWZHFJGqtmGm8eii3cePpwJKZO5E2LsRgqLgstfKnbS4amcOr/+8EYCnBrYnKsSvplPWOkG+Xjx+tLfWe7+lsckeCcN+NP47Za+GT66EIzkmpxSRKnMk17gDD6BVf3OzVIAKKKnd0v+AJe8a2wPfgoB6J+xSUGxn3LQVFJc46NcuSt3Ga1DftpFc1DqCIruDid+vwlG/GQz7AQLCYNcK+PRaKDxkdkwRqQqbf4aSIqjX7IQF212RCiipvYoL4Yd7je3ON0DzPuXu9urcjazPOkj9QB/+fWV7XbqrQRaLhccva4evl5XfN+3lh793QXhLuPFbo8np9j/g86FaO0/EE5RevnOD0SdQASW12e+vGgtWBoTBxU+Vu0vq9gO888tmAJ6+sj1hQb41mVCAxvUDGNXbaKb37x/XkFdQDA06wtCvwDsQ0ubDV7eAvdjkpCJyxkrssHG2sd2yn7lZKkgFlNROezbCr88Z2/2fLffS3ZEiO+OmGQsFXxEfTb/2DWo4pJS648I4mtYPIPtggXMuGjHd4brPwOYL636E/90FJSXmBhWRM7NjKRzea4wsNz7X7DQVogJKah+HA34YYywV0DwJ2g8qd7eX5mxg8+5DhNfx1ULBJvP1sjHhsrYAfLBwC5t35xkvxPWCa6eA1Qv+/gJmP2r89xUR97L+J+Ox+cVg8zY3SwWpgJLa569PYNtC8A6AAS+We9fdn1v38d5vaQD856oOhAb41HRK+YeLWkfSu1U4RXYHT/6wBkdpodSqP1zxlrH9x5vGpVkRcS9uNv8JVEBJbZO32xilAOj9L6jb9IRdDhcWc/+XK3A44OqujejTJrJmM8pJTbisHT42K79s2M3ctdnHXug0GPoebYQ6dyL89ak5AUWk8vZtMeajWmwnvZnHFamAktrl56fgyAGI6gAJI8vd5bmZ69m69zANQvx47NK2NZtPTik2LJBbz48F4Onpaygoth97scdo6HGPsf393bBhlgkJRaTSSkefmvQA/7rmZqkEFVBSe+z6G5Z/ZGxf8kK5a92lbN7L5EVbAfjPoI6E+LvHtfjaZFTv5oTX8WXr3sNMOfrfyinpCeh0HTjsMG0YbF9iSkYRqYTS+U9ucvddKRVQUjs4HDBzPOCAdleVe5dHXoFx6Q7guu6NubBleA2HlIoI8vXigeRWALw+bxN78o7rAWW1wuWvQ4u+UJwPnw02FicVEdd0JMdYxB3cav4TqICS2mLtD8bEcS8/uPiJcnf5949r2Hkgn0Z1/XlkQJsaDiiVcXWXRrRvGMzBgmJenL2+7Is2b7hmMkR3hvx98MkgLT4s4qo2zTPWIQ1rCfWbmZ2mUlRAiecrLjg2cbzH3RDa+IRd5q/L5vOl2wF44ZpOBPmeeHlPXIfVamHiZUZriS+WbmftrtyyO/gEwvXTjP/W+7cYI1GFh01IKiKnVDr/yc0u34EKKKkN/ngLDmyDOg2g55gTXj5wuJCHvv4bgFt6xnJuXP0aDihn4pym9bikQxQlDnhmxtpjbQ1KBUXA0K/BLxR2/gnfjDC6HYuIa7AXH+s+7maX70AFlHi6g1nw64vGdp+J4BtU5mWHw8Ej360i+2ABceGBPNivlQkh5Uw91K81PjYrv23cw4INu0/cIbwlXPf5sW7lM8fXfEgRKV96CuTvN+68a9Td7DSVpgJKPNv8f0PhQYjuAh0Hn/Dy9ysymP73LmxWCy9fG4+ft82EkHKmmtQPZHjPpgA8PX0txfZylnJpkghXvmNsL/k/WPx/NRdQRE5u7ffGY6sB5d4V7epUQInn2rPR6DoO0G+ScYfWcTIO5PPYd6sAuOeiFnSKCa3hgFIVRvVuTt0AbzZl5znnsZ2g/VVGiwOAmQ/Dhtk1F1BETlRSAmt/NLbbXm5uljOkAko81/xnwFECLfuf0LagpMTB/V+uIPdIMZ1iQhnV273u/pBjQvy9ubdPCwBembuBvILi8nfseS90vtH4O/HVzZC5qgZTikgZGcvhYAb41IHYC81Oc0ZUQIln2vU3rP7G2L7o0RNefu+3NBZt3ou/t42Xr+2El03/K7iz6xOa0LR+AHvyCnn317Tyd7JYYMBL0PR8KMwz7sw7mFWzQUXEUHr5rmVf8PYzN8sZ0k8N8Uzzj66L1n4QRLUv89KqnTm8cLR30MTL2hIXHvTP7xY34+Nl5aF+rQF479c0snKPlL+jlw8M/hjqt4DcHTB1iNobiNQ0h8PozQfQ5jJzs5wFFVDiebYvMXqLWGzQ619lXjpUUMw9n/9Fkd1Bv3ZRDD4nxqSQUtX6tY+ia5O65BfZeXnOhpPv6F8Xrv8C/OsZlxG+G2n8gy4iNSN7DexLM+6ObX6x2WnOmAoo8TzznjQe46+HsOZlXprwv9Wk7T5EVLAfk67qgMViMSGgVAeLxcK/LjFGoab9uZ2NWQdPvnP9ZjDkU7B6w5rv4JdnayakiMCao5fvmvc5obWMO1EBJZ4lbQFs/Q1sPnDhQ2Ve+mrZDr5evgOrBV4dEk/dQB9zMkq16dqkHsntIilxwHOz1p965yY94NKXjO0Fk2D1t9UfUESOu3znnnfflVIBJZ7D4Tg2+tT1Zgg9dnluU3aes2XBmKSWJKjbuMd6ILk1VgvMWZPFn1v3nXrnLjfBuaOM7W9HQsZf1R9QpDbbuxmyV4PVC1omm53mrHhUAfX4449jsVjKfLVu3dr5+pEjRxg1ahT169cnKCiIQYMGkZVV9i6c9PR0BgwYQEBAABERETzwwAMUF5/ktmhxLRtmwc5l4B0A549zPn2ooJi7Pl1GfpGdHs3qM6p381McRNxd84gg59y2ST+tO3GJl3/q+xQ0T4LifJh6PRzMrIGUIrVU6ehT0/MhoJ65Wc6SRxVQAO3atWPXrl3Or4ULFzpfu++++/jhhx/48ssv+eWXX8jIyOCqq65yvm632xkwYACFhYUsWrSIKVOmMHnyZCZMmGDGW5HKcDjgtxeM7XNugzqRR5928NDXf7MhK4/wOr68Mjgem1XznjzdmKSW+HlbWbZtP7PXnKZVgdUGV39grAZ/MAM+vx6KTnIXn4icHQ+4+66UxxVQXl5eREVFOb/CwsIAyMnJ4b///S8vvfQSF110EV27duXDDz9k0aJF/PHHHwDMnj2bNWvW8MknnxAfH0///v156qmnePPNNyksLDTzbcnpbF0IO5Yad3UkjnY+/cpPK/jx713YrPDW0C5EBLtnvxGpnMhgP249LxaA52etx15ymlEovxBjzTz/usYo5o/36c48kaqWs9NY2BsLtB5gdpqz5nEF1MaNG4mOjiYuLo6hQ4eSnp4OwLJlyygqKiIpKcm5b+vWrWncuDEpKSkApKSk0KFDByIjI537JCcnk5uby+rVq096zoKCAnJzc8t8SQ0rHX3qcqNz9Gnp1n288dtOAK5v48c5Td17uFgq5/YLmhHibyzx8s3yHaf/hvrN4JrJRvuLFZ/BH29Xe0aRWmXd0aVbYhKgTpS5WaqARxVQCQkJTJ48mZkzZ/L222+zZcsWzj//fA4ePEhmZiY+Pj6EhoaW+Z7IyEgyM405D5mZmWWKp9LXS187mUmTJhESEuL8iolRb6EatWOZcfedxQY97nE+HVM3gDaRgSRGe3PnRa1P/v3ikUL8vZ1L9LwydyNHiuyn/6a4XpB8tAnr7Edg8/zqCyhS26z+znh007Xv/smjCqj+/ftzzTXX0LFjR5KTk5kxYwYHDhxg2rRp1Xre8ePHk5OT4/zavv0kC5pK9Vh49Fb0jtdC3SbOp6NC/Phm9AV8cGcfGjZsaFI4MdNNiU1pEOLHzgP5fPLHtop9U8KdED/UWDPvy+FGwz8ROTs5OyF9kbHddqCpUaqKRxVQ/xQaGkrLli3ZtGkTUVFRFBYWcuDAgTL7ZGVlERVlDCVGRUWdcFde6Z9L9ymPr68vwcHBZb6khmSvPTosbIHz7jvhZR8vK/4+tprPJS7Bz9vGmCRjoeE352/i4JGi039T6Zp5DbvBkQPGnXkFp2jKKSKnV9pnrXEPCPGMX2g9uoDKy8tj8+bNNGjQgK5du+Lt7c28efOcr69fv5709HQSExMBSExMZOXKlWRnZzv3mTNnDsHBwbRt27bG80sFLHzZeGxzGYS3MjeLuKRBXRrRLDyQ/YeLeP+3LRX7Jm8/GPwJBEXB7rXw7Z1QUlK9QUU82aqvjcf2V516PzfiUQXU/fffzy+//MLWrVtZtGgRV155JTabjeuuu46QkBBuvfVWxo4dy/z581m2bBk333wziYmJnHvuuQD07duXtm3bcuONN7JixQpmzZrFo48+yqhRo/D19TX53ckJ9m2BlV8Z2+ePNTeLuCwvm5VxfY3i+v3f0tibV1CxbwxuYCz3YvMxRjkXvliNKUU82L4txrqTFqvHXL4DDyugduzYwXXXXUerVq249tprqV+/Pn/88Qfh4eEAvPzyy1x66aUMGjSICy64gKioKL755hvn99tsNn788UdsNhuJiYnccMMN3HTTTTz55JNmvSU5lUWvgcMOzfpAdGez04gL698+ig4NQzhUaOetBZsr/o2NusGAo4XTz0/DhtnVE1DEk60++nM29kIICjc3SxWyOE7bplcqKzc3l5CQEHJycjQfqroc2gMvtQV7AQyfDk3PMzuRuLhfN+zmpg+W4GOzMv+BXjQM9a/4N/94H/z5AfiGwO3zjZYHIlIxb/eErFVw+evG8kkurDI/vz1qBEpqkWUfGsVTdGdo0tPsNOIGzm8Rxrlx9Si0l/Da3I2V++Z+zxq9awpyjE7lmlQuUjHZ64ziyeoNrS81O02VUgEl7qe4EJa8b2yfe5dx15TIaVgsFh5INvqBfbV8B2m78yr+zV4+cO1HUKcB7F4H341Up3KRiii9fNe8j9uvffdPKqDE/az+FvIyjTukPGhColS/rk3qktQmAnuJg5crOwpVJwqu/diYVL72h2N3gIpI+RwOWHW0gGo/yNws1UAFlLgXhwP+eNPY7j7CGBkQqYSxFxt35P2wIoM1GZVcdinmHLjkeWP756dg07xT7y9Sm2WuhL0bwcsPWvU3O02VUwEl7iU9BXatMP6H7Hqz2WnEDbWNDuayTtEAvDB7feUP0HW4MRHWUQJf3wr7K9jhXKS2Ke391DIZfOuYm6UaqIAS9/LHW8ZjpyEQWN/cLOK27ktqgc1q4ed12Szbtr/yB+j/PER3gfz98MUNUJRf9SFF3JnDcWz+UzvPaZ55PBVQ4j72b4V1043thJGmRhH3FhcexNVdGgHwwqwzGIXy9oPBH0NAGGT+bbQ50KRykWO2L4YD6eATBC36mp2mWqiAEvex+F3jskmziyCitdlpxM3dk9QCH5uVlLS9/L5pT+UPENIIrvnQ6K68Yiosfb/qQ4q4qxVTjce2V4BPgLlZqokKKHEPR3Jh+UfG9rmjzM0iHqFhqD/XJzQG4PlZ6zmjnsKxF8DFR1cqmDketi+pwoQibqroCKw6unhwpyHmZqlGKqDEPaz4HAoPQlhLYwRKpAqM6t0cf28bqdsPMHdt9um/oTyJo412GiVFMO0myDvD44h4ivUzjKazITHQxHNXiVABJa7P4TA6jwOcMwKs+msrVSO8ji/DezYF4MXZ6ykpOYNRKIsFrngDwlrBwV3w5c1gL67aoCLuZMXnxmPHwR7977XnvjPxHNsXQ/Ya8A6AToPNTiMe5o4L4qjj68W6zINMX7nrzA7iWwcGf2JMmN22EOZOrNqQIu4iLxs2zTW2PfjyHaiAEnfw5wfGY/tB4BdibhbxOKEBPoy4IA6Al+dsoNhecmYHCm8JA4+22Uh5w+iYL1LbrPwKHHZo2A3CWpidplqpgBLXdmgvrP7O2O52i6lRxHPdcl4s9QJ9SNtziG/+2nnmB2p7BfS819j+bhTsPoMWCSLubMVnxqOHjz6BCihxdSs+A3sBNIiHhl3MTiMeKsjXi5EXNgPg1bkbKSi2n/nBLpoATc+HokNGk82Cg1WUUsTFZa4ylm+xenvk2nf/pAJKXFdJCfx5dPK4Rp+kmt2Y2ITIYF92Hsjni6Xbz/xANi+4+kOoEw17NsD/RqnJptQOfx+dPN4yGQLqmZulBqiAEte19VfYtxl8g2vFbzNiLj9vG6MvMuZsvP7zJvILz2IUKigcrp1i/Ca+5n+Q8mYVpRRxUfZi+HuasR1/vblZaogKKHFdpZPHOw4G3yBzs0itMLhbDI3q+rP7YAEfpWw9u4PFdId+k4ztORNg6+9nnU/EZaUtgLws8K8HzS82O02NUAElrulg5rF177rdbG4WqTV8vKzc28cYhXr7l80cPFJ0dgc85zbocK1xV9KXwyH3DNskiLi61E+Nx/aDwMvH3Cw1RAWUuKa/PoaSYog5FyLbmZ1GapErOzckLjyQA4eL+O/CLWd3MIsFLnsFItrCoWyjiLKfZVEm4moO7YF1PxrbXW40N0sNUgElrqek5Ni6dxp9khrmZbMy9uKWALz/2xb2Hyo8uwP6BBpNNn2DYfsfxuU8EU+yYirYC427pRt0MjtNjVEBJa5n629wIB18Q4y+OiI17JL2DWjbIJi8gmLe+XXz2R+wfjO48h1j+4+3YNU3Z39MEVfgcMCyKcZ212HmZqlhKqDE9aQebcTW/irw9jc3i9RKVquF+5ONUagpi7aSnXvk7A/aegCcd5+x/b/RkL3u7I8pYrb0FNi7EbwDof3VZqepUSqgxLUcyTVu+wbofIO5WaRW690qgi6NQzlSVMIb8zdV0UEfhdgLjCab025Uk01xf8smG4/trwK/YFOj1DQVUOJa1nwHxfkQ1hIadjU7jdRiFouF+5NbATB1STrb9x0++4PavGDQB2qyKZ4hf/+xX3i7Djc1ihlUQIlr+evorbDxQ407mERM1KNZGOc1D6PI7uDVeRur5qBB4XDtR2qyKe7v72lQfAQi29fKX3hVQInr2LvZuEvJYjWaZ4q4gNJRqG+W72BTdhVdcos5R002xb0dP3m8y7Ba+QuvCihxHaWN2JonQXADc7OIHBUfE0rftpGUOOClORuq7sDn3Gb8oqAmm+KOdvwJ2avByw86Xmt2GlOogBLXUGKHFUcXoqwl6yiJ+xjXtxUWC8xYmcnKHTlVc1CLBS59BSLaHW2yOQyKz7LnlEhNWT7ZeGx3JfiHmpnENCqgxDWkLYDcneAXCq0uMTuNSBmtouowML4hAM/PXl91B/YJgMEfGz3Pti+GOY9V3bFFqsuRnGO9zLrUrt5Px1MBJa6htPdTh2vAy9fcLCLlGJPUAi+rhV837OaPtL1Vd+Djm2wufgdWflV1xxapDqmfQdFhCG8Njc81O41pVECJ+fIPHFtHqfNQU6OInEyT+oEM6R4DwHMz1+GoyvYDrS+B88cZ29/fDVmrq+7YIlWppASWvGtsd7+9Vk4eL6UCSsy35jvjVtiItsZaSiIu6p6LWuDnbWV5+gHmrc2u2oP3fgTiehu/2X9xg/GLhYir2TQX9qUZl507DTE7jalUQIn5Si9ZdBxcq3+bEdcXEezH8B6xADw/az32kiochbLaYNB/ISTG+AH17Z3Gb/sirmTx0cvNXW40FsquxVRAiblydsLWhcZ2+0HmZhGpgJEXNiPYz4v1WQf5X+rOqj14YH1jUrnNFzb8BL+9WLXHFzkbezbC5nmAxWjDUcupgBJzrf4GcEDjHhAaY3YakdMKCfDmzl7NAHhx9gYKiu1Ve4LozjDgaOE0/2nYOLdqjy9ypkrnPrXsB/Vizc3iAlRAiblWfmk8dqhdq3iLe7u5RyyRwb7sPJDPZ4vTq/4EXW48uraYA76+FfZvrfpziFTGkdxjd0sn3GFuFhehAkrMs3sD7FoBVi9oO9DsNCIV5u9j494+LQF44+dN5BUUV/1J+j9nrC925IAxqbywChYzFjlTqZ9BYR6EtYK4XmancQkqoMQ8q45OHm92kTH3Q8SNXNOtEbFhgew9VMi7v6ZV/Qm8fI1FhwPCIHMl/DjGWH9MpKYd37ogoXa3LjieCigxh8Nx7O67DteYm0XkDHjbrDxwdKHh939LI/vgkao/SUgjuGYyWGzw9xew+P+q/hwip7N5HuzbbLQu6Fi7WxccTwWUmCPjL+N/SC9/Ld0ibqt/+yg6xYRyuNDO6/M2Vc9JYs+Hvk8Z27P+BVt/r57ziJxMypvGY+cbwDfI3CwuRAWUmKN09Kn1JfofUtyWxWLh4X6tAZi6JJ0tew5Vz4nOvcsYqXXYjUWHc6q4fYLIyWSkQtp8YxRUk8fLUAElNa/EDqu+NrZ1+U7cXGKz+vRqFU5xiYMXZlXhQsPHs1jgslchsj0c2g3TboSiarhkKPJPv79qPLa/Cuo2MTeLi1EBJTVv60LIywS/UGjWx+w0ImftoX6tsVhg+spd/JW+v3pO4hMIgz8x/r/ZuQxmjNOkcqle+7YYS20B9LzX1CiuSAWU1LzS3k9trwAvH3OziFSBNg2CGdSlEQDPzFhbtQsNH69eLFz9AVis8NcnsPT96jmPCEDKG+AogeZJENXB7DQuRwWU1Cx7Eaz9wdhW80zxIOP6tsTXy8rSrfuZvSar+k7UvA8kPW5sz3xYk8qleuTtNop00OjTSaiAkpq15RejMWBgBDTpaXYakSrTIMSfW88zlrd49qd1FNmrcSHgHvcYa0eWFB+dVL6j+s4ltdOSd6H4CER3gabnm53GJamAkpq15n/GY5vLjNXnRTzInb2aUS/Qh7Q9h/h8STUs8VLKYoHL3zAuqxzabXQqL8qvvvNJ7VKQd6xx5nlj1DjzJFRASc2xF8PaH43ttleYm0WkGgT7eXNvnxYAvDx3I7lHiqrvZD4BMPhT8K9n9FX7/h5NKpeqsfwj40pBvWbQ+lKz07gsFVBSc7YthPx9EFBfl+/EY12f0Ji48ED2HSrkrfmbq/dkdZvAtVOMHj0rp8Gi16r3fOL57EXHGmf2uFtXCk5BBZTUnNLLd60vBZuXuVlEqom3zcq/+rcB4IPft7B9XzUvAhx7AfR/1tieMxE2zK7e84lnS/0UcncY81Q7XWd2GpemAkpqRon92N13unwnHq5PmwgS4+pTWFzCc9XVXPN459wGXYcDDvj6Vti9ofrPKZ6nuBB+fcHYPu8+8PYzN4+LUwElNSM9xZjs6hdq/MYs4sEsFguPDGiDxQI/rMhg2bZqaq557ITQ/3lo3AMKcuHz6yC/ms8pnuevjyFnOwRFQbebzU7j8lRASc0oc/nO29wsIjWgfcMQrulqNNd88ofVlJRU8wRvLx+49iMIiYG9m+CrW4wbN0QqorgAfnvR2D5/LHj7m5vHDaiAkupXUgJrvje2dflOapH7k1sR6GNjxY4cvv2rBhYADgqHIZ+BdwBs/hlm/av6zymeYflHkLsT6kRDl2Fmp3ELKqBO4s0336Rp06b4+fmRkJDAkiVLzI7kvnYsMda+8w2BuAvNTiNSYyLq+DHqouYAPDdrHYcKamBEqEFHuOo9Y3vJ/2m5Fzm9oiP/GH3S3KeKUAFVji+++IKxY8cyceJEli9fTqdOnUhOTiY7O9vsaO6p9PJdq/7g5WtuFpEadkvPWGLq+ZOVW8BbCzbVzEnbXAp9JhjbMx6EzfNr5rzinpZNhoO7ILgRdLnJ7DRuQwVUOV566SVGjBjBzTffTNu2bXnnnXcICAjggw8+MDua+ykpOVZA6fKd1EJ+3jYeuaQtAO/9uoVtew/VzInPGwsdB4PDbiz3sqeGijdxL0X5sPAlY/uCcfoltxJUQP1DYWEhy5YtIykpyfmc1WolKSmJlJSUcr+noKCA3NzcMl9yVMZy47q6TxA0u8jsNCKmSG4XyXnNwyi0l/Dv6Wtr5qQWC1z2GjTqDkdy4LNr4fC+mjm3uI8/P4C8LAhpDPE3mJ3GraiA+oc9e/Zgt9uJjIws83xkZCSZmZnlfs+kSZMICQlxfsXExNREVPdQ2vupRV9dV5day2Kx8PjlbfGyWpizJotfNuyumRN7+8GQT4078/ZtNtbMKy6omXOL68s/AL8+b2xfcL9xJ6dUmAqoKjB+/HhycnKcX9u3bzc7kutY/5Px2HqAuTlETNY8og7DejQF4InvV1NQbK+ZEwdFwPXTwDcYtv2uNfPkmN9eNPqFhbeG+KFmp3E7KqD+ISwsDJvNRlZWVpnns7KyiIqKKvd7fH19CQ4OLvMlwN7NsGc9WL2gxcVmpxEx3b1JLQgL8iVtzyH+u3BLzZ04si1cM9lYM+/vz+GX52ru3OKa9m+Dxe8Y2xc/peW1zoAKqH/w8fGha9euzJs3z/lcSUkJ8+bNIzEx0cRkbmjddOOx6XngF2JuFhEXEOznzSMDWgPw+rxN7DyQX3Mnb94HLj06WXjBM7Dii5o7t7ieeU+CvRBiL9QvuGdIBVQ5xo4dy3vvvceUKVNYu3YtI0eO5NChQ9x8s1rbV8r6GcZjK12+Eyk1ML4h3WPrkV9k56kf1tTsybsOhx73GNvfj4atv9fs+cU17FgGq74CLND338YNB1JpKqDKMXjwYF544QUmTJhAfHw8qampzJw584SJ5XIKh/bA9sXGdqv+5mYRcSEWi4WnrmiPzWph5upM5q+v4f5ySU9Am8uN0YfPr4PsdTV7fjGXwwGzHzW2O11nNF6VM6IC6iRGjx7Ntm3bKCgoYPHixSQkJJgdyb1smAWOEojqCKG6K1HkeK2i6nBLz6YAPPbdKvILa2hCOYDVCle9e6y9wadXQ+6umju/mGvddEhfBF5+cNGjZqdxayqgpHo4L99dYm4OERc1Jqkl0SF+7Nifz2s/b6zZk3v7w3WfQ71mkLMdPr0Gjqh/nccrLoQ5RzvUJ46GkIbm5nFzKqCk6hXlGwuZArRWASVSnkBfL564oj0A7/2axvrMgzUcoD7c8DUEhkPWSph2o/EDVjzXH28Z/cACw6HnvWancXsqoKTqpf0CRYeNdZWidH1d5GQubhtJ37aRFJc4GP/N35SU1HB/pnqxMPRL8A6EtAXw/d3qEeWpDqTDL88a2xc/CX5qt3O2VEBJ1Vt/tH1Bq/66u0PkNB6/vB2BPjaWpx/gk8Xbaj5AdGe4dsqxHlFzHqv5DFL9Zo43frFt3MOYPC5nTQWUVK2SElg/09jW5TuR04oO9eeh/kZvqGd/WlezvaFKtbgYrnjD2F70Oix8peYzSPVZPxPW/Wg0Nb70Jf1iW0VUQEnV2rkMDmUby0Y0Oc/sNCJu4YaEJnRrUpdDhXYe/XYlDjMuo8Vfb/QEApg7EZZ/VPMZpOoVHoafHjC2E0dBRBtz83gQFVBStUov3zVP0sKUIhVktVr4z6CO+NiszF+/m+9Sd5oTpMfd0HOMsf3DvccWAxf39dsLxvyn4EZwwYNmp/EoKqCkamnxYJEz0jwiiHuTWgDw+PdryMo9Yk6QpMeh841GH7evbjFuChH3tHs9/P6asd3/P+AbZG4eD6MCSqrO/m2we50xGbV5H7PTiLidOy6Io0PDEHLyi/jXNyZdyrNY4NJXoPWlRrfyqdfB9iU1n0POTondGEUsKYIWycZ/T6lSKqCk6myaYzzGJIB/XXOziLghL5uVF67phI/Nyrx12Xyz3KRLeTYvGPRfiOsNRYfgk0GQ8Zc5WeTM/PEWpKeATxBc8pwmjlcDFVBSdTYeLaC0srfIGWsVVee4S3mrzbkrD8DbD4Z8atz2XpALH18JWTW8+LGcmd3rYd5Txnby01C3qalxPJUKKKkaRUeOzZVo0dfcLCJu7o4L4oiPCeVgQTHjpqXWfIPNUj6BcP0X0LAr5O+Hj66APZvMySIVYy+Gb+8Ae4FxM0+XYWYn8lgqoKRqbFsIxflQJxoi25mdRsStedmsvDw4Hn9vG3+k7eO/C7eYF8Yv2FjyJbKD0aJkymWwd7N5eeTUFr5sXG71C4HLX9elu2qkAkqqxvGX7/Q/rMhZiw0L5LFL2wLw/Kz1rMkwcbFf/7pw03cQ1goOZsDkS1VEuaJdf8Mv/zG2+z8PwdHm5vFwlS6ghg0bxq+//lodWcSdbZxtPOrynUiVua57DEltIii0l3D31OUcLiw2L0xgGAz74bgiaoCKKFdSdAS+vRNKio077jpea3Yij1fpAionJ4ekpCRatGjBM888w86dJt0lIq5j72bYlwZWb4i70Ow0Ih7DYrHw7KCORNTxZfPuQzzxvcmTuOtEwvAfIbw1HNwFH14Cezaam0kMs8ZD9moIqG+0odCVgGpX6QLqu+++Y+fOnYwcOZIvvviCpk2b0r9/f7766iuKioqqI6O4utLRpyY9wLeOuVlEPEz9IF9eGRKPxQJf/Lmd71dkmBsoKAKG/QgRbSEv0xiJ2r3B3Ey13d9fwp8fABa48l0ICjc7Ua1wRnOgwsPDGTt2LCtWrGDx4sU0b96cG2+8kejoaO677z42btRvJLWKLt+JVKsezcIY3bs5AP/6ZiVpu/PMDRQUblzOi2gHeVnwYX/YtcLcTLXV7vVGw0yAC+6HFknm5qlFzmoS+a5du5gzZw5z5szBZrNxySWXsHLlStq2bcvLL79cVRnFlRUegq2/G9sqoESqzb19WtA9th55BcWM/GQ5+YV2cwOVzolq0AkO74HJl0H6H+Zmqm0KD8G0m4xmp7EXQK/xZieqVSpdQBUVFfH1119z6aWX0qRJE7788kvGjBlDRkYGU6ZMYe7cuUybNo0nn3yyOvKKq9nym9FvJLQxhLUwO42Ix/KyWXnjus6EBfmyPusgj3xn0lIvxwusbxRRjXtAQQ58NBA2zjU3U23hcMCP9xnLZwVFGZ3jrTazU9UqlS6gGjRowIgRI2jSpAlLlizhzz//5M477yQ4ONi5T+/evQkNDa3KnOKqjr98p0mLItUqItiP16/rjNUC3yzfySeL082OZPQbuuFraH6x0Qtu6hBY/a3ZqTzfnx/A318Ya49e/YExN01qVKULqJdffpmMjAzefPNN4uPjy90nNDSULVtMbPwmNcPhOK7/ky7fidSExGb1eahfawCe+H41i9P2mpwI8AmAIZ9BuyuNxWu/vBkWv2t2Ks+1aR7MeMDY7vMYNO1pbp5aqtIF1I033oifn191ZBF3s3s95KSDzReanm92GpFa4/YL4risUzTFJQ7u+nQ5O/YfNjsSePkYl5G6Dgcc8NMDMPtRKCmp9lNnZGSwcOFCMjJMvkOxJmSthmnDwGGHjoOh5xizE9Va6kQuZ27T0bkOTc8zfgMVkRphsVh4blBH2kUHs/dQISM+WkZegYlNNktZbUYPooseM/686HX4+hajyWM1SktLY9OmTaSlpVXreUyXuws+vRYKD0KTnlqqxWQqoOTMbf7ZeGyu22ZFapq/j413b+pGWJAPa3flcvdnyym2V/9oz2lZLMbt9Fe+azTXXf0tfDwQDu+rtlPGxcXRvHlz4uLiqu0cpivIg6mDIXcH1G8Bgz8BL1+zU9VqKqDkzBQdgW1H2xc0621uFpFaqmGoP+8POwc/byvz1+/miR/WmH9nXqlOg43J5b7BkJ4C7/eB7HXVcqro6GjOO+88oqM9dO03ezF8fZvRayugPgydBgH1zE5V66mAkjOTngLFR6BOA2NZBxExRXxMKK8M7ozFAh//sY23f3Gh9eniLoRbZkFIY2O5p/f7wLrpZqdyL/Zi+PZ22PCTMd/0us+hngePtLkRFVByZtLmG49xvXUNXsRk/dpH8eiAtgA8N3M9ny9xgfYGpSLbwu3zjRtNCvPg8+vhl+dqZHK52yuxw3d3wqqvjcuh106BmO5mp5KjVEDJmSmd/9TsInNziAgAt54Xy8hezQD417crmblql8mJjhMYBjd+C91vN/48/2n4chgcyTU3lysrscN3I2Hll2D1MoqnVv3NTiXHUQEllZeXDZkrje24XqZGEZFjHkxuxZBzYihxwN1T/2LumiyzIx1j84ZLnofLXjNGU9Z+D/93AWSkmp3M9ZTY4bu7jEaZVi+4ZjK0HmB2KvkHFVBSeWm/GI9RHbTqt4gLsVgsPH1lBy7t2IAiu9Ejav66bLNjldV1GNw8A0JiYP8W+O/FsPj/jMa8AsUF8M0I+PvzY13G21xmdiophwooqTxdvhNxWTarhVcGx3NJhygK7SXc8fEy1xqJAmMezx2/QqsBYC+Enx6EL26A/P1mJzPX4X3GeoKrvjZGnq7+L7S9wuxUchIqoKRyHA4VUCIuzstm5dUhnUluF2kUUZ8s43+pO82OVVZAPRjyKfR71rikt+5HeKvHseWhapu9m+H9JEhfZLR+uOFrY2kccVkqoKRydq+DvEzw8oOYc81OIyIn4W2z8sb1XRgYH429xMGYL1L5KGWr2bHKsljg3DvhtjlQrxkczIBPr4bvRkH+AbPT1Zz0xUbxtG+zcWnz1tmaX+oGVEBJ5ZSOPjXpCd5aE1HElXnbrLx0bTw3JTbB4YAJ/1vNMzPWUlLiYvONojvDnQvh3FGABVI/gbcSPX80yuEw5n9NuRTy9xmfw23zIKKN2cmkAlRASeU4L9+p+7iIO7BaLTxxeTvGXtwSgHd/TeOuT5dzuNAF1s47nk8A9HsGbv7JaBRZOhr1xY1w4MS+Vm6/gPChPTB1iDH/y14IrS+F4dOhTqTZyaSCVEBJxRUXwNbS5Vs0/0nEXVgsFu7p04JXBsfjY7Myc3UmV721iK17Dpkd7URNEuHO3+Hcu8BiNdodvNHdaL553KLEbr2A8Ob58HZP2DDT6C7e/zljbTufQLOTSSWogJKKS/8DivMhKBIi2pqdRkQqaWDnhnw6IoGwIF/WZR7ksjcWMsfV7tCDo6NRk+CO34zpAsX5RvPNN7vDqm+gpMQ9FxDOPwAzHoSPrzTmkoa1hBHzIOEOrejghlRAScVp+RYRt3dO03pMv+c8ujapy8EjxYz46E8e+Xal613SA4hqb1zWGvRfqBMNB7bBVzfDO+cRfeBPzuvRwz0WEC4pgWVT4PUusOT/AAd0HQ63/2L00xO3pAJKKk7tC0Q8QmSwH1NHnMtt58UC8OnidC59bSF/bt1ncrJyWCzQ4WoYvRR6jTdu8c9eDdNuhHcvgLU/Gp27XdX2pfD+RfDDPXB4rzHqdMM3cNmrxkibuC2Lw6H2r1UtNzeXkJAQcnJyCA4ONjtO1Ti8D56LAxwwbj3UiTI7kYhUgd837WHctBVk5hrzi65PaMxD/VoT4u9tcrKTyN8PKW/CH28bixMDhDaBc26Fzjca/aXMVtovb9FrkLbAeM6nDvR62LhcZ3PRz1Yq9fNbBVQ18MgCas33xm98Ya1g9BKz04hIFTpwuJBJM9bxxZ/bAagX6MOYpBZc170x3jYXvVBxeB8seh3+/C8cyTGe8/KD9oOg8w0QkwBWW81mKi6E1d8aubKOrhdqsUGn66DPBN1h5wZUQJnMIwuoGQ/AknfhnBEw4AWz04hINfgjbS+PfLuSzbuNu/PiwgK5u09zLusYjZerFlKFh2HVV7DkPcj8+9jzgRHQ5lJoczk0PR9sXtVz/uJCY5Rp9bewfvqxYs47ELrcBOeOhLpNqufcUuVUQJnMIwuoNxOMLuTXfgxtLzc7jYhUkyJ7CZ8v3c4rczaw91AhADH1/LntvDiu7NKQYD8XvfzkcMCOpbBssrEsTGkhA+AbYqy/1zjBGJlq2PXMWwYUF0DWKti5HLYvgY2zyp4rKAq63wbdbnWNy4lSKSqgTOZxBdTBLHixJWCBB9P0j4JILXDwSBEfpWzjg4VbnIVUgI+NyztFc3l8NAmx9bFZzb0b93BhMTv357Njfz6F9hKS2x2dm1lcCFt/hTX/g3XTjcnbx7PYjFGh0MbHvuo0AJuPcdnP6m0s5nskB/KyIC/beNyXZhRP9sKyxwuKNEa62g2Exok1f+lQqowKKJN5XAG18iv4+lbjdts7F5qdRkRqUH6hnS+XbefjlG1szM5zPh8W5EtSmwjOaxFGj2Zh1Av0qZLzZWRkkJaWRlxcHNHR0RwpsrNt72G27Dl09CuPrXsOs2XvIXYfLHB+X+N6Afz6YDkrJNiLjUt7O5Yavey2L4HcHWcX0r+eMYrVsAvEXgiNz1XR5CFUQJnM4wqo7++B5VMgcTQkP212GhExgcPhYOnW/Xy9bAczV2eSk19U5vUm9QPo2CiUVpFBNA0LpHG9AMLr+FIv0Adfr/KLi8LiEnKPFLEnr4A9BwvJzD1Cyop1pGUdoMAriBy7Dxk5+Zzqp1QdXy8a1QsgNiyAN6/vgqUiPepyM2DfFmOJmNKvvCwoKTJaItiLjG2/EGN0KTDceAxpCA3ioW5T9cLzUCqgTOZxBdSr8bB/C1w/DVomm51GRExWWFxCStpeft2wm4Ub97A+6+Ap9/fxsuLnZcXHy0aJw4G9xEF+kZ3C4pIKna+OnxexYYHEhQUSGxZEbHggTesH0KReICEBLjonS9xSZX5+V9NtCeIxDmw3iieLzbi2LyK1no+XlQtbhnNhy3DAaIPw944cVu7MYfPuPLbtPcz2fYfZe6gQe4mDwuKSo8XSid3OLRaoG+BD/UAfokL8aBDiR3SoP03qB9C4XgBN6gdSP9CnYiNLIjVIBZSc2tbfjMfozuDnAaNpIlLlQgN8uKBlOBccLahKlZQ4yMkv4lBhMUeKSiiyl2CzWrBaLPj72Ajy9SLQx+a6LRJETkEFlJzall+Nx9gLzM0hIm7HarVQN9CHulU0wVzElajsl5NzOFRAiYiIlEMFlJzcvjTI3Wn0RolJMDuNiIiIy1ABJSdXOvrU6BytGi4iInIcFVBycrp8JyIiUi6PKqCaNm2KxWIp8/Wf//ynzD5///03559/Pn5+fsTExPDcc8+dcJwvv/yS1q1b4+fnR4cOHZgxY0ZNvQXX4XAcuwNPBZSIiEgZHlVAATz55JPs2rXL+XX33Xc7X8vNzaVv3740adKEZcuW8fzzz/P444/z7rvvOvdZtGgR1113Hbfeeit//fUXAwcOZODAgaxatcqMt2Oe3evg0G7w8oeG3cxOIyIi4lI8ro1BnTp1iIqKKve1Tz/9lMLCQj744AN8fHxo164dqampvPTSS9x+++0AvPrqq/Tr148HHngAgKeeeoo5c+bwxhtv8M4779TY+zDdlqOjT43PBS/dgiwiInI8jxuB+s9//kP9+vXp3Lkzzz//PMXFxzrfpqSkcMEFF+Djc6wgSE5OZv369ezfv9+5T1JSUpljJicnk5KSctJzFhQUkJubW+bL7W373Xhsep65OURERFyQR41A3XPPPXTp0oV69eqxaNEixo8fz65du3jppZcAyMzMJDY2tsz3REZGOl+rW7cumZmZzueO3yczM/Ok5500aRJPPPFEFb8bEzkcsG2Rsd2kp7lZREREXJDLj0A9/PDDJ0wM/+fXunXrABg7diy9evWiY8eO3Hnnnbz44ou8/vrrFBQUVGvG8ePHk5OT4/zavn17tZ6v2u3dDIeyweYLDbuYnUZERMTluPwI1Lhx4xg+fPgp94mLiyv3+YSEBIqLi9m6dSutWrUiKiqKrKysMvuU/rl03tTJ9jnZvCoAX19ffH19T/dW3Me2hcZjo3PAy4Pel4iISBVx+QIqPDyc8PDw0+9YjtTUVKxWKxEREQAkJibyyCOPUFRUhLe3NwBz5syhVatW1K1b17nPvHnzGDNmjPM4c+bMITEx8ezeiDtxXr7rYW4OERERF+Xyl/AqKiUlhVdeeYUVK1aQlpbGp59+yn333ccNN9zgLI6uv/56fHx8uPXWW1m9ejVffPEFr776KmPHjnUe595772XmzJm8+OKLrFu3jscff5w///yT0aNHm/XWap4KKBERkVNy+RGoivL19eXzzz/n8ccfp6CggNjYWO67774yxVFISAizZ89m1KhRdO3albCwMCZMmOBsYQDQo0cPPvvsMx599FH+9a9/0aJFC7777jvat29vxtuqeQfSIWc7WL0gprvZaURERFySxeFwOMwO4Wlyc3MJCQkhJyeH4OBgs+NUzorP4ds7jPlPt801O42IiEiNqczPb4+5hCdVpLT/ky7fiYiInJQKKClL/Z9EREROSwWUHHMwE/ZuAiwQk2B2GhEREZelAkqOKR19imoP/qGmRhEREXFlKqDkGF2+ExERqRAVUHKM+j+JiIhUiAooMRzeB9mrje3GKqBERERORQWUGNL/MB7DWkLQmS2dIyIiUluogBKDs/+T5j+JiIicjgooMaiAEhERqTAVUAIFebDrb2O7SaK5WURERNyACiiBncvAYYfgRhDSyOw0IiIiLk8FlMD2xcZjY3UfFxERqQgVUHLsDryYc83NISIi4iZUQNV2JXbYsdTY1giUiLiZjIwMFi5cSEZGhtlRpJbxMjuAmCx7LRTkgk8QRLQzO42ISKWkpaWxadMmAKKjo01OI7WJCqjabvvRy3cNu4JNfx1ExL3ExcWVeRSpKfqJWdttX2I8Ntb8JxFxP9HR0Rp5ElNoDlRt55xArvlPIiIiFaUCqjY7mAkHtoHFCo3OMTuNiIiI21ABVZuVjj5FtAO/YHOziIiIuBEVULWZGmiKiIicERVQtZkaaIqIiJwRFVC1VeFhyDy6gLBGoERERCpFBVRttXMZlBRDnWgIiTE7jYiIiFtRAVVbHT//yWIxN4uIiIibUQFVW5UWUOr/JCIiUmkqoGqjkhIVUCIiImdBBVRttGc9HMkB7wCI6mB2GhEREbejAqo2Sj9+AWFvc7OIiIi4IRVQtdGOP43HmO7m5hAREXFTKqBqo51HC6hGKqBERETOhAqo2ib/AOxeZ2w36mZqFBEREXelAqq2yVhuPNZtCoFhpkYRERFxVyqgapvS+U+NzjE3h4iIiBtTAVXb7FhqPKqAEhEROWMqoGoTh+O4Akrzn0RERM6UCqjaZF8a5O8Hmy9EqoGmiIjImVIBVZuUjj5Fx4OXj6lRRERE3JkKqNpE859ERESqhAqo2qT0DryGXc3NISIi4uZUQNUWhYcha5WxrREoERGRs6ICqrbYtQJKiiEoCkIamZ1GRETEramAqi2Ob19gsZibRURExM2pgKotNIFcRESkyqiAqi20hIuIiEiVUQFVG+TshIMZYLEZPaBERETkrKiAqg1KL99FtgWfQHOziIiIeAAVULWB5j+JiIhUKRVQtcHOZcajCigREZEqoQLK09mLICPV2G7YzdQoIiIinkIFlKfLXgvF+eAbDPWbm51GRETEI6iA8nQZy43H6Hiw6j+3iIhIVdBPVE+3s7SA6mJuDhEREQ/iNgXU008/TY8ePQgICCA0NLTcfdLT0xkwYAABAQFERETwwAMPUFxcXGafBQsW0KVLF3x9fWnevDmTJ08+4ThvvvkmTZs2xc/Pj4SEBJYsWVIN76iGlI5ANexqbg4REREP4jYFVGFhIddccw0jR44s93W73c6AAQMoLCxk0aJFTJkyhcmTJzNhwgTnPlu2bGHAgAH07t2b1NRUxowZw2233casWbOc+3zxxReMHTuWiRMnsnz5cjp16kRycjLZ2dnV/h6rXOFhyFpjbDfUCJSIiEhVsTgcDofZISpj8uTJjBkzhgMHDpR5/qeffuLSSy8lIyODyMhIAN555x0eeughdu/ejY+PDw899BDTp09n1apVzu8bMmQIBw4cYObMmQAkJCRwzjnn8MYbbwBQUlJCTEwMd999Nw8//HCFMubm5hISEkJOTg7BwcFV8K7PUPpi+KAvBEbA/Ru0iLCIiMgpVObnt9uMQJ1OSkoKHTp0cBZPAMnJyeTm5rJ69WrnPklJSWW+Lzk5mZSUFMAY5Vq2bFmZfaxWK0lJSc593Irz8l0XFU8iIiJVyMvsAFUlMzOzTPEEOP+cmZl5yn1yc3PJz89n//792O32cvdZt27dSc9dUFBAQUGB88+5ubln9V6qjCaQi4iIVAtTR6AefvhhLBbLKb9OVbi4ikmTJhESEuL8iomJMTuS4fgRKBEREakypo5AjRs3juHDh59yn7i4uAodKyoq6oS75bKyspyvlT6WPnf8PsHBwfj7+2Oz2bDZbOXuU3qM8owfP56xY8c6/5ybm2t+EZV/APZuMrY1AiUiIlKlTC2gwsPDCQ8Pr5JjJSYm8vTTT5OdnU1ERAQAc+bMITg4mLZt2zr3mTFjRpnvmzNnDomJiQD4+PjQtWtX5s2bx8CBAwFjEvm8efMYPXr0Sc/t6+uLr69vlbyPKrMr1XgMbQyB9U2NIiIi4mncZhJ5eno6qamppKenY7fbSU1NJTU1lby8PAD69u1L27ZtufHGG1mxYgWzZs3i0UcfZdSoUc7i5s477yQtLY0HH3yQdevW8dZbbzFt2jTuu+8+53nGjh3Le++9x5QpU1i7di0jR47k0KFD3Hzzzaa87zO2U/2fREREqovbTCKfMGECU6ZMcf65c+fOAMyfP59evXphs9n48ccfGTlyJImJiQQGBjJs2DCefPJJ5/fExsYyffp07rvvPl599VUaNWrE+++/T3JysnOfwYMHs3v3biZMmEBmZibx8fHMnDnzhInlLm/nMuNRl+9ERESqnNv1gXIHLtEH6qW2kLsThk+HpueZk0FERMSN1Mo+UHKcg1lG8YQFGnQyO42IiIjHUQHliUrbF4S3At865mYRERHxQCqgPJEaaIqIiFQrFVCeSA00RUREqpUKKE/jcBzXwkAFlIiISHVQAeVp9m+F/H1g9YbI9manERER8UgqoDxN6eW7qPbg5WLd0UVERDyECihPk5FqPEZ3NjWGiIiIJ1MB5WlK18BrEG9mChEREY+mAsqTOBywa4WxHR1vahQRERFPpgLKk+zfCkdywOYD4W3MTiMiIuKxVEB5ktLLdxFtwcvH1CgiIiKeTAWUJ9HlOxERkRqhAsqTlN6BpwWERUREqpUKKE9x/ARy3YEnIiJSrVRAeYqc7Uc7kHsZc6BERESk2qiA8hSll+8i2oC3n6lRREREPJ2X2QGkiujynYiIk91up6ioyOwY4mK8vb2x2WxVciwVUJ7C2YFcE8hFpPZyOBxkZmZy4MABs6OIiwoNDSUqKgqLxXJWx1EB5QkcDq2BJyICzuIpIiKCgICAs/4hKZ7D4XBw+PBhsrOzAWjQoMFZHU8FlCfIzYDDe8Big8h2ZqcRETGF3W53Fk/169c3O464IH9/fwCys7OJiIg4q8t5mkTuCUrnP4W3Bm9/c7OIiJikdM5TQECAyUnElZX+/TjbOXIqoDxB6fwndSAXEdFlOzmlqvr7oQLKE6gDuYiIW+vVqxdjxowxOwYA3333Hc2bN8dmszFmzBgmT55MaGio2bFcjgooT6AWBiIicgoLFizAYrFU6O7EO+64g6uvvprt27fz1FNPMXjwYDZs2OB8/fHHHyc+Pr76wroJTSJ3dwczIS8TLFaIam92GhERcWN5eXlkZ2eTnJxMdHS08/nSyddyjEag3F3p6FNYS/AJNDeLiIicseLiYkaPHk1ISAhhYWE89thjOBwO5+sFBQXcf//9NGzYkMDAQBISEliwYIHz9W3btnHZZZdRt25dAgMDadeuHTNmzGDr1q307t0bgLp162KxWBg+fPgJ51+wYAF16tQB4KKLLsJisbBgwYIyl/AmT57ME088wYoVK7BYLFgsFiZPnlxdH4lL0wiUu9P8JxGRk3I4HOQX2U05t7+3rVITlqdMmcKtt97KkiVL+PPPP7n99ttp3LgxI0aMAGD06NGsWbOGzz//nOjoaL799lv69evHypUradGiBaNGjaKwsJBff/2VwMBA1qxZQ1BQEDExMXz99dcMGjSI9evXExwcXO6IUo8ePVi/fj2tWrXi66+/pkePHtSrV4+tW7c69xk8eDCrVq1i5syZzJ07F4CQkJCz+6DclAood+fsQB5vZgoREZeUX2Sn7YRZppx7zZPJBPhU/MdsTEwML7/8MhaLhVatWrFy5UpefvllRowYQXp6Oh9++CHp6enOS2v3338/M2fO5MMPP+SZZ54hPT2dQYMG0aFDBwDi4uKcx65Xrx4AERERJ50Q7uPjQ0REhHP/qKioE/bx9/cnKCgILy+vcl+vTVRAubvSS3hqYSAi4tbOPffcMiNWiYmJvPjii9jtdlauXIndbqdly5ZlvqegoMDZNPSee+5h5MiRzJ49m6SkJAYNGkTHjh1r9D3UJiqg3NmhPZC709iO1ARyEZF/8ve2sebJZNPOXVXy8vKw2WwsW7bshO7ZQUFBANx2220kJyczffp0Zs+ezaRJk3jxxRe5++67qyyHHKMCyp1lrjQe68WBX7C5WUREXJDFYqnUZTQzLV68uMyf//jjD1q0aIHNZqNz587Y7Xays7M5//zzT3qMmJgY7rzzTu68807Gjx/Pe++9x913342Pjw9gLHdztnx8fKrkOO5Od+G5s9ICKqqDuTlEROSspaenM3bsWNavX8/UqVN5/fXXuffeewFo2bIlQ4cO5aabbuKbb75hy5YtLFmyhEmTJjF9+nQAxowZw6xZs9iyZQvLly9n/vz5tGnTBoAmTZpgsVj48ccf2b17N3l5eWecs2nTpmzZsoXU1FT27NlDQUHB2b95N6QCyp1l/m08qoASEXF7N910E/n5+XTv3p1Ro0Zx7733cvvttztf//DDD7npppsYN24crVq1YuDAgSxdupTGjRsDxujSqFGjaNOmDf369aNly5a89dZbADRs2JAnnniChx9+mMjISEaPHn3GOQcNGkS/fv3o3bs34eHhTJ069ezeuJuyOI5vMiFVIjc3l5CQEHJycggOrsZLa28mwO51cP2X0LJv9Z1HRMQNHDlyhC1bthAbG4ufn5/ZccRFnervSWV+fmsEyl0V5cOeo631NQIlIiJSo1RAuavsNeAogYAwqFO7e3GIiIjUNBVQ7ur4CeSV6HQrIiIiZ08FlLvadXQCeQM1SRMREalpKqDclXMESgWUiIhITVMB5Y5K7JC12tjWBHIREZEapwLKHe3bAkWHwMsf6jc3O42IiEitowLKHWUeXUA4sh1Yq26tJREREakYFVDuSEu4iIiImEoFlDtSASUiIiabPHkyoaGhZsdg+PDhDBw4sMbPqwLKHekOPBERcXFbt27FYrGQmprqksc7Wyqg3M3BLMjLAovVmAMlIiK1UmFhodkRqoS7vg8VUO6mdPSpfnPwCTA3i4iIVImDBw8ydOhQAgMDadCgAS+//DK9evVizJgxzn2aNm3KU089xU033URwcDC33347AF9//TXt2rXD19eXpk2b8uKLL5Y5tsVi4bvvvivzXGhoKJMnTwaOjex888039O7dm4CAADp16kRKSkqZ75k8eTKNGzcmICCAK6+8kr17957yPcXGxgLQuXNnLBYLvXr1Ao5dcnv66aeJjo6mVatWFcp5suOVeuGFF2jQoAH169dn1KhRFBUVnTLf2fKq1qNL1cs82oFc859ERE7P4YCiw+ac2zugwkttjR07lt9//53vv/+eyMhIJkyYwPLly4mPjy+z3wsvvMCECROYOHEiAMuWLePaa6/l8ccfZ/DgwSxatIi77rqL+vXrM3z48ErFfeSRR3jhhRdo0aIFjzzyCNdddx2bNm3Cy8uLxYsXc+uttzJp0iQGDhzIzJkznRlOZsmSJXTv3p25c+fSrl07fHx8nK/NmzeP4OBg5syZU+F8pzre/PnzadCgAfPnz2fTpk0MHjyY+Ph4RowYUanPoDJUQLkbTSAXEam4osPwTLQ55/5XBvgEnna3gwcPMmXKFD777DP69OkDwIcffkh09Im5L7roIsaNG+f889ChQ+nTpw+PPfYYAC1btmTNmjU8//zzlS6g7r//fgYMGADAE088Qbt27di0aROtW7fm1VdfpV+/fjz44IPO8yxatIiZM2ee9Hjh4eEA1K9fn6iosoveBwYG8v7775cpgk7nVMerW7cub7zxBjabjdatWzNgwADmzZtXrQWULuG5G00gFxHxKGlpaRQVFdG9e3fncyEhIc5LW8fr1q1bmT+vXbuWnj17lnmuZ8+ebNy4EbvdXqkcHTse+7nSoEEDALKzs53nSUhIKLN/YmJipY5/vA4dOlSqeDqddu3aYbMd64vYoEEDZ/bqohEod1KQB3s3GdsagRIROT3vAGMkyKxzV7HAwNOPaP2TxWLB4XCUea68+UHe3t5lvgegpKSk0ueriPLeR0Vzluf47KXHqq7spVRAuZPsNYADgqIgKMLsNCIirs9iqdBlNDPFxcXh7e3N0qVLady4MQA5OTls2LCBCy644JTf26ZNG37//fcyz/3++++0bNnSOSITHh7Orl27nK9v3LiRw4crNy+sTZs2LF68uMxzf/zxxym/p3SEqaIjYafLWdnjVTcVUO5EE8hFRDxOnTp1GDZsGA888AD16tUjIiKCiRMnYrVanSNBJzNu3DjOOeccnnrqKQYPHkxKSgpvvPEGb731lnOfiy66iDfeeIPExETsdjsPPfTQCSM2p3PPPffQs2dPXnjhBa644gpmzZp1yvlPABEREfj7+zNz5kwaNWqEn58fISEhJ93/dDkre7zqpjlQ7uRIjrGAsAooERGP8tJLL5GYmMill15KUlISPXv2pE2bNvj5+Z3y+7p06cK0adP4/PPPad++PRMmTODJJ58sM4H8xRdfJCYmhvPPP5/rr7+e+++/n4CAyl1ePPfcc3nvvfd49dVX6dSpE7Nnz+bRRx895fd4eXnx2muv8X//939ER0dzxRVXnHL/0+Ws7PGqncNN/Pvf/3YkJiY6/P39HSEhIeXuA5zwNXXq1DL7zJ8/39G5c2eHj4+Po1mzZo4PP/zwhOO88cYbjiZNmjh8fX0d3bt3dyxevLhSWXNychyAIycnp1LfVyH2YoejIK/qjysi4uby8/Mda9asceTn55sd5azl5eU5QkJCHO+//77ZUTzOqf6eVObnt9uMQBUWFnLNNdcwcuTIU+734YcfsmvXLufX8evjbNmyhQEDBtC7d29SU1MZM2YMt912G7NmzXLu88UXXzB27FgmTpzI8uXL6dSpE8nJydU+m7/CrDaXv54vIiKV89dffzF16lQ2b97M8uXLGTp0KID5oyxyUm4zB+qJJ54AcHYkPZnQ0NAT+kOUeuedd4iNjXV2aW3Tpg0LFy7k5ZdfJjk5GTCGUUeMGMHNN9/s/J7p06fzwQcf8PDDD1fRuxERESnrhRdeYP369fj4+NC1a1d+++03wsLCzI4lJ+E2I1AVNWrUKMLCwujevTsffPBBmVsiU1JSSEpKKrN/cnKys119YWEhy5YtK7OP1WolKSnphJb2xysoKCA3N7fMl4iISEV17tyZZcuWkZeXx759+5gzZw4dOmi+qytzmxGoinjyySe56KKLCAgIYPbs2dx1113k5eVxzz33AJCZmUlkZGSZ74mMjCQ3N5f8/Hz279+P3W4vd59169ad9LyTJk1yjpCJiIiI5zN1BOrhhx/GYrGc8utUhcs/PfbYY/Ts2ZPOnTvz0EMP8eCDD/L8889X4zswjB8/npycHOfX9u3bq/2cIiIiYh5TR6DGjRt32rV64uLizvj4CQkJPPXUUxQUFODr60tUVBRZWVll9snKyiI4OBh/f39sNhs2m63cfU42rwrA19cXX1/fM84pIiJVx/GPbtYix6uqvx+mFlDh4eHOxQGrQ2pqKnXr1nUWN4mJicyYMaPMPnPmzHGu51M6cW/evHnOu/dKSkqYN28eo0ePrracIiJy9kqbLh4+fBh/f3+T04irKu1uXtlmov/kNnOg0tPT2bdvH+np6djtdlJTUwFo3rw5QUFB/PDDD2RlZXHuuefi5+fHnDlzeOaZZ7j//vudx7jzzjt54403ePDBB7nlllv4+eefmTZtGtOnT3fuM3bsWIYNG0a3bt3o3r07r7zyCocOHXLelSciIq7JZrMRGhrqbDsTEBBw2k7eUns4HA4OHz5MdnY2oaGhZRYfPhNuU0BNmDCBKVOmOP/cuXNnAObPn0+vXr3w9vbmzTff5L777sPhcNC8eXNnS4JSsbGxTJ8+nfvuu49XX32VRo0a8f777ztbGAAMHjyY3bt3M2HCBDIzM4mPj2fmzJknTCwXERHXUzrdwmV694nLOVW7o8qwOHSxuMrl5uYSEhJCTk4OwcHBZscREal17HY7RUVFZscQF+Pt7X3KkafK/Px2mxEoERGRiiq9KUikunhcI00RERGR6qYCSkRERKSSVECJiIiIVJLmQFWD0nn5WhNPRETEfZT+3K7I/XUqoKrBwYMHAYiJiTE5iYiIiFTWwYMHCQkJOeU+amNQDUpKSsjIyKBOnTpV3sQtNzeXmJgYtm/frhYJp6HPquL0WVWcPquK02dVcfqsKq46PyuHw8HBgweJjo7Gaj31LCeNQFUDq9VKo0aNqvUcwcHB+p+sgvRZVZw+q4rTZ1Vx+qwqTp9VxVXXZ3W6kadSmkQuIiIiUkkqoEREREQqSQWUm/H19WXixIn4+vqaHcXl6bOqOH1WFafPquL0WVWcPquKc5XPSpPIRURERCpJI1AiIiIilaQCSkRERKSSVECJiIiIVJIKKBEREZFKUgHlJp5++ml69OhBQEAAoaGh5e5jsVhO+Pr8889rNqiLqMjnlZ6ezoABAwgICCAiIoIHHniA4uLimg3qgpo2bXrC36P//Oc/ZsdyGW+++SZNmzbFz8+PhIQElixZYnYkl/P444+f8HeodevWZsdyCb/++iuXXXYZ0dHRWCwWvvvuuzKvOxwOJkyYQIMGDfD39ycpKYmNGzeaE9Zkp/ushg8ffsLfs379+tVYPhVQbqKwsJBrrrmGkSNHnnK/Dz/8kF27djm/Bg4cWDMBXczpPi+73c6AAQMoLCxk0aJFTJkyhcmTJzNhwoQaTuqannzyyTJ/j+6++26zI7mEL774grFjxzJx4kSWL19Op06dSE5OJjs72+xoLqddu3Zl/g4tXLjQ7Egu4dChQ3Tq1Ik333yz3Nefe+45XnvtNd555x0WL15MYGAgycnJHDlypIaTmu90nxVAv379yvw9mzp1as0FdIhb+fDDDx0hISHlvgY4vv322xrN4+pO9nnNmDHDYbVaHZmZmc7n3n77bUdwcLCjoKCgBhO6niZNmjhefvlls2O4pO7duztGjRrl/LPdbndER0c7Jk2aZGIq1zNx4kRHp06dzI7h8v75b3ZJSYkjKirK8fzzzzufO3DggMPX19cxdepUExK6jvJ+vg0bNsxxxRVXmJLH4XA4NALlYUaNGkVYWBjdu3fngw8+wKE2X+VKSUmhQ4cOREZGOp9LTk4mNzeX1atXm5jMNfznP/+hfv36dO7cmeeff16XNjFGNZctW0ZSUpLzOavVSlJSEikpKSYmc00bN24kOjqauLg4hg4dSnp6utmRXN6WLVvIzMws83csJCSEhIQE/R07iQULFhAREUGrVq0YOXIke/furbFzazFhD/Lkk09y0UUXERAQwOzZs7nrrrvIy8vjnnvuMTuay8nMzCxTPAHOP2dmZpoRyWXcc889dOnShXr16rFo0SLGjx/Prl27eOmll8yOZqo9e/Zgt9vL/Xuzbt06k1K5poSEBCZPnkyrVq3YtWsXTzzxBOeffz6rVq2iTp06ZsdzWaX/9pT3d6y2/7tUnn79+nHVVVcRGxvL5s2b+de//kX//v1JSUnBZrNV+/lVQJno4Ycf5tlnnz3lPmvXrq3w5MvHHnvMud25c2cOHTrE888/7zEFVFV/XrVJZT67sWPHOp/r2LEjPj4+3HHHHUyaNMn0pRPEPfTv39+53bFjRxISEmjSpAnTpk3j1ltvNTGZeJIhQ4Y4tzt06EDHjh1p1qwZCxYsoE+fPtV+fhVQJho3bhzDhw8/5T5xcXFnfPyEhASeeuopCgoKPOIHX1V+XlFRUSfcPZWVleV8zdOczWeXkJBAcXExW7dupVWrVtWQzj2EhYVhs9mcf09KZWVleeTfmaoUGhpKy5Yt2bRpk9lRXFrp36OsrCwaNGjgfD4rK4v4+HiTUrmPuLg4wsLC2LRpkwooTxceHk54eHi1HT81NZW6det6RPEEVft5JSYm8vTTT5OdnU1ERAQAc+bMITg4mLZt21bJOVzJ2Xx2qampWK1W5+dUW/n4+NC1a1fmzZvnvLu1pKSEefPmMXr0aHPDubi8vDw2b97MjTfeaHYUlxYbG0tUVBTz5s1zFky5ubksXrz4tHdgC+zYsYO9e/eWKT6rkwooN5Gens6+fftIT0/HbreTmpoKQPPmzQkKCuKHH34gKyuLc889Fz8/P+bMmcMzzzzD/fffb25wk5zu8+rbty9t27blxhtv5LnnniMzM5NHH32UUaNGeUzBeSZSUlJYvHgxvXv3pk6dOqSkpHDfffdxww03ULduXbPjmW7s2LEMGzaMbt260b17d1555RUOHTrEzTffbHY0l3L//fdz2WWX0aRJEzIyMpg4cSI2m43rrrvO7Gimy8vLKzMSt2XLFlJTU6lXrx6NGzdmzJgx/Pvf/6ZFixbExsby2GOPER0dXStb0pzqs6pXrx5PPPEEgwYNIioqis2bN/Pggw/SvHlzkpOTayagaff/SaUMGzbMAZzwNX/+fIfD4XD89NNPjvj4eEdQUJAjMDDQ0alTJ8c777zjsNvt5gY3yek+L4fD4di6daujf//+Dn9/f0dYWJhj3LhxjqKiIvNCu4Bly5Y5EhISHCEhIQ4/Pz9HmzZtHM8884zjyJEjZkdzGa+//rqjcePGDh8fH0f37t0df/zxh9mRXM7gwYMdDRo0cPj4+DgaNmzoGDx4sGPTpk1mx3IJ8+fPL/ffpmHDhjkcDqOVwWOPPeaIjIx0+Pr6Ovr06eNYv369uaFNcqrP6vDhw46+ffs6wsPDHd7e3o4mTZo4RowYUaY1TXWzOBy6z11ERESkMtQHSkRERKSSVECJiIiIVJIKKBEREZFKUgElIiIiUkkqoEREREQqSQWUiIiISCWpgBIRERGpJBVQIiIiIpWkAkpERESkklRAiYiIiFSSCigRkdPYvXs3UVFRPPPMM87nFi1ahI+PD/PmzTMxmYiYRWvhiYhUwIwZMxg4cCCLFi2iVatWxMfHc8UVV/DSSy+ZHU1ETKACSkSkgkaNGsXcuXPp1q0bK1euZOnSpfj6+podS0RMoAJKRKSC8vPzad++Pdu3b2fZsmV06NDB7EgiYhLNgRIRqaDNmzeTkZFBSUkJW7duNTuOiJhII1AiIhVQWFhI9+7diY+Pp1WrVrzyyiusXLmSiIgIs6OJiAlUQImIVMADDzzAV199xYoVKwgKCuLCCy8kJCSEH3/80exoImICXcITETmNBQsW8Morr/Dxxx8THByM1Wrl448/5rfffuPtt982O56ImEAjUCIiIiKVpBEoERERkUpSASUiIiJSSSqgRERERCpJBZSIiIhIJamAEhEREakkFVAiIiIilaQCSkRERKSSVECJiIiIVJIKKBEREZFKUgElIiIiUkkqoEREREQqSQWUiIiISCX9P0wudjuffMkQAAAAAElFTkSuQmCC", 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", 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FxcY/gQookcqRuYfC9y7Gd9dicgw/HvadwIBRk8nPPsjixYtJSUmxOmHVCgqHoR/BFe+CbzCxRzYwz/8hhjl+4sW5W5j8k4ooEamgrBTYtxmwQWPXac1XASVytjJ2UPxBf7wOJrLHqMMdvs8ycuQYIoL9SEpKIjExkaSkJKtTVj2bDTpcCyOXQOML8C7O5WnvD3jN+w3en79WRZSIVExJ61NMJwgIszTK31XyAlsiNcy+rRj/uxx7dgo7iiO5y/sx3rj9cuqF+gMQFxdXZlsjhMbCjV/Dsjfhp8e4jKW0se1k5M9jALi3bzNs1blMjYi4t5L5n5q4zvgnUAuUSMWlrsOYMgBbdgpbi+sxnMeZdPNAGtUNLD0kJiaGHj16EBPjJnM+VRa7Hc67C0bMhloxNLHv5WufR9iz4H3eWrjd6nQi4i4MwyXHP4EKKJGKSV0HUwdiO7KfdcWNuL5wIo9ffxHt6odYncy1NOgGd/wKTfrgb8vnJZ93CJo/gY9+22Z1MhFxB2nr4XA6eAdAbDer05ShAkrEWQd3wkdDIDeTlcXNGJb/EOMGn0fvluVbCDglJaVmDCwvEVgXhn0Ovf4PAxvDvebRaM5wvlu+3upkIuLqts0zt40vAC9fa7P8gwooEWfk7IMPr4CcNLYYDbg5/36uvaAd13VtUO5T1KiB5SXsDuj1AAz9iHy7Pz0cG2g7ewjLViyxOpmIuLLEn8xt077W5jgJFVAi5ZWXDR9fBRlJpNgiuDHvATo1b8QD/Vs6dZq4uDiaNm1aswaWH2NrNQiv2+aR4R1FI1sqrb8fQtLSr62OJSKuKDcTkpeZ+yqgRNxUYR7MuAH2ribLHsKw3AcIqFOP167thMPu3BVlnjaw3NkuSXt0O2rdtYgtvm0Jth2hwY8j2L/ovSpOKSJuJ2khGEVQp6nLLN/ydyqgRM7EMOCbuyBpIfl2f244Op59PrG8d1MXQgK8rU5nuYp0SXoHR1L/7rn87NMbL4qp+/N4jsx92vysRUTg+Pinphdbm+MUVECJnMnSN2HtDIptXtySew9rjSa8cFV7mkXWsjqZSyhPl+TJWqkCAwNpO3o6//MaAkDAkucp/OYeKCqs8swi4uIMAxLnm/vNXK/7DlRAiZxe0i8w7xEAnjNu5Nfi9tx8fiMGtIu2OJjrKE+X5KlaqSKC/Tnv9td4mlsoNmx4/TkNY8YwyD9S1bFFxJWlbYDsFPDyh4Y9rE5zUiqgRE7l4F/w2QgwipnvexHv5valY2woDw5oZXUyt3O6VqqmEUFcOOz/GFV4L7mGN7atc45NE5FlQVIRcQmJJdMX9ARvP2uznIIKKJGTyT8CM4bB0QxSAltxZ+aNhAb48Oawc/Dx0o+Ns87UStWjWV3Ov3QEw/L/jywjAJKXwP8ugyMZ1ZxURFzCtpLpC1xz/BOogBI5kWHAt/dA6jryfcMYcuBO8vDh+SHtS9e4k8p3Q/eGtItP4Lr8h8kwakHKnzDlEshOtTqaiFSn3CzYdWz6Ahcd/wQqoEROtHIqrJuJYXNwV+EY9lKHYd0a0K9NlNXJPN7DA1tRu0kXrsl/hH22MNi3CaYMgEPJVkcTkeqy4xcoLoSwJhDmuvPlqYAS+bv922DOgwB8XvtWfjzclCbhgTw8sLXFwWoGL4ed16/rRF7tZlyZ+wj7HJGQkQQfDICMHVbHE5HqUDJ9QTPX7b4DFVAixxXmwxe3QuFRUut25/6Unng7bLx6bSf8fRxWp6sxagf68J8bu7DfK4ZLDz/MAb8GkLWb3P9cTNrmFVbHE5GqZBguvXzL36mAEimx8BnYu5oiv9pcv28EBnbG92tB23ohViercVpFB/PC1e1JpQ79Dz1Ahk8Mfrn7CJl1vbmYs4h4pvRNkLUHvPygkWtOX1BCBZQIwI5fYfErALwacBdJecF0aVibW3u6bv+7pxvUPobbejZmH7UZfPQRsv3q4Ze7D6YOUhEl4qlKpi9o1AO8XfuiHRVQIkcPwld3AAbb6l3Baykt8fO288LVHZxe504q1/39W9K1URjJBSHc7v00xWFNIHMXTL3UnKdLRDzL1h/NbbN+1uYoBxVQIrPvg6zdFIQ05trkwQBM6N+SxnUDrc0leDvsvHF9J+oG+bJ0nw+Phz2PUacpZCab80Rl7bU6oohUliMZkLzU3G/e39os5aACSmq2rXNh3WcYNjuPe4/hQIE33ePCuCm+kdXJ5JiIYD/evL4TDruNaevz+Kbju1C7kdmN97/L4fB+qyOKSGXYNg+MYohoA7UbWp3mjFRASc2VlwPfjwVgS+Ob+Gh3OH7edp4f0gG7uu5cSre4Ooy9uDkAD8zdx/YB0yG4HuzfAh9eAUcPWRtQRM7eltnmtsUAa3OUkwooqbl+fgoyd1EU0oCbki4CYNzFLWhQJ8DiYHIyIy9swgXNw8ktKObf3+3j6HVfQGA4pK6Fj682C2IRcU+F+ZA439xXASXiwnavhOXvAPBG4GjScx20rx/Czec3sjaXnJLdbuPlazoQUcuXxPQcHv41H26cBX6hsHsFfHodFORaHVNEKuKvxZCfDYEREHOO1WnKRQWU1DxFBfDNXYDBnoaXMzmpPl52G89e2R4vh34kXFndIF9eu64Tdht8sWo3s/bWhhu+BJ8g2LEIvrwNiousjikiztryg7lt0R/s7vF72D1SilSmJa9B+gYM/zr8K+UKAG6/II7WMcEWB5Py6B5Xh7svagbAw7PWk+zfCq79BBw+sOkb+H6cOZuxiLgHw4Atc8z95u7RfQcqoKSmObAdFj4HwNdRo9mS7UODsIDSP8jiHkb3bsq5jWqTk1fIXZ/+SUHDnnDle4ANVk6BBc9YHVFEyittgzk1iZcfxPWyOk25qYCSmmXuw1CUR069nozb0gKAJy5vg5+31rpzJ14OO69c24lgPy/W7DrE5Hlboc1gGPiSecCi52H5u5ZmFJFy2nqs+y6uF/i4z0U8KqCk5tj+M2yZjWH34sGjwygqhoHtounVIsLqZFIB9UL9eXZIewDe/mU7S7cfgHNvgd4PmQf8cD+s/8LChCJSLqXjn9yn+w5UQElNUVQIc/4PgC2x1/JtSjBBvl48Mqi1xcHkbFzSLpqhXWIxDBg3czWZRwvggvug6+3mAV/dATsXWxtSRE4tOw32rDT33WD28b9TASU1w8opsG8Txf5h3PqXOefT2IubExXiZ3EwOVsTL21NwzoBpGTmMvHr9WCzQf9nodVlUJQPn14P6ZutjikiJ7P12ODxmHOgVpS1WZykAko8WkpKCssW/EDx/CcB+DbsZnbn+tIyqhY3xbv+UgFyZoG+Xkwe2hGH3cbXq1P4evUesDvgyv9AbHfIzYSPr9K6eSKuqLT77hJrc1SACijxaElJSQSvegt7Xia5YS0Zl9QRgMcva6M5nzzIOQ1qc1efpoA5tcGeQ0fB2x+umw51mkHmLnO28twsi5OKSKn8I5C00Nxv4V7dd6ACSjxc89AiWuWYY2CeKb6JQsPBZR1i6BZXx+JkUtlG925KpwahZOcWct9nayguNiAgDG743JzdOG0dzLzJnEhVRKy3fT4UHoWQBhDZ1uo0TlMBJR4t4s9XsRnF7Inqy/9SGxHg4+D/LmlldSypAl4OO5Ov6Yi/t4Ml2w8wbelO84HajWDYTPAOhKQFmmhTxFVs/Mbctr7MHLvoZlRAiefa8Stsn49h92LUfnPG8bv6NNPAcQ/WqG4gDw00C+Rnf9hMYvqxBYZjOsFVH4DNDqummbPRi4h1CvOODyBvdam1WSpIBZR4JsOAn82B43+GX87qnNo0qhPAv3o0sjaXVLlh3RpwQfNw8gqLGTtzNQVFxeYDLfpDwiRzf96jx//3KyLVb8ciyMuCoCio39XqNBWiAko807a5sGs5xV5+3LWnLwAPDWyNr5dmHPd0NpuN54e0J8Tfm7W7M3lrwfbjD3a/49gcUQZ8eTvsXmlZTpEabePX5rbVILdZPPif3DO1yOkUF8OxaQvm1RrMnsIQzm9ah76tNON4TREV4scTl7cB4PWft7F+T+bxBxMmQbN+5uDV6dfCoWSLUorUUEWFsPl7c99Nu+9ABZR4og1fQto6Cr2DuH9vH+w2eGRQa2xuOEhRKu6yDjEMaBtFYbHB+M/WkF94rCvP4WWOh4psC4fT4ZNrIS/b2rAiNUnyEjiaAf5h0LCH1WkqTAWUeJaiAljwNADTvQaTSRDXdW1Ay6hgi4NJdbPZbDw1uC11An3YnJrNa/O3HX/QtxZcP8Oc3iB9A3xxGxQXWRdWpCYpGX/Y8hLzPzRuSgWUeJbVn0BGErk+YUw62Idavl6Mvbi51anEInWCfHlqsDm/zNu/bGfNrkPHHwypb0606fA1V4Of/7g1IUVqkuJi2PStud/qcmuznCUVUOI5CnLhl+cAeKtoMEfwY3SfptQJ8rU4mFhpQLtoLusQQ9Gxrrzcgr+1NNXvAoPfMvd/exX+/NiakCI1xe7fIScVfIMh7kKr05wVjyqgHnvsMWw2W5lby5YtSx/Pzc1l1KhR1KlTh6CgIIYMGUJaWlqZcyQnJzNw4EACAgKIiIjgvvvuo7CwsLrfilTEqv9B1h6yfSN59/AF1Av1Z/h5jaxOJS7g8cvaUDfIl23pOWW78gDaXQUX3G/uf3sP/LW0+gOK1BSbjnXfNU8AL/f+z61HFVAAbdq0Ye/evaW3xYsXlz5277338u233/LZZ5/xyy+/kJKSwpVXXln6eFFREQMHDiQ/P58lS5Ywbdo0pk6dysSJE614K+KMwnyzBQGYfHQQefhwf/8W+Hlr2gKB2oE+pV157/yzKw+g14PQ+nIoLoAZw+DgX9UfUsTTGcbxAqrVZdZmqQQeV0B5eXkRFRVVeqtbty4AmZmZvP/++7z88sv06dOHzp07M2XKFJYsWcKyZcsAmDt3Lhs3buSjjz6iY8eODBgwgCeffJI333yT/Px8K9+WnMnaGZC1myyvOnyc35MO9UO4tH0MKSkpLF68mJSUFKsTisX6t43i0g4xFBtw3+dryCv8W1ee3Q6D34HoDnDkAHx6PeQfti6siCfau8acNsTLH5peZHWas+ZxBdS2bduIiYkhLi6OYcOGkZxszvGycuVKCgoK6Nu3b+mxLVu2pEGDBixdajbZL126lHbt2hEZGVl6TEJCAllZWWzYsOGUr5mXl0dWVlaZm1SjokJY/DIAb+T2Jw8fHhrYGrvdRlJSEomJiSQlJVkcUlzB45e1oU6gD1vTTtKV5xMA135ybOHh9fDVHeaAVxGpHCWtT836gk+gtVkqgUcVUN26dWPq1KnMmTOHt99+mx07dtCzZ0+ys7NJTU3Fx8eH0NDQMs+JjIwkNTUVgNTU1DLFU8njJY+dyqRJkwgJCSm9xcbGVu4bk9PbOAsyksix1+Kjwovo1zqSro3DAIiLi6Np06bExcVZm1FcQliZrrykshNsgnll3tAPwe5t/rJf9IIFKUU8kGHA+i/N/daDLY1SWTyqgBowYABXX3017du3JyEhgdmzZ3Po0CFmzpxZpa/74IMPkpmZWXrbtWtXlb6e/E1xMfz6EgDv5iWQZ/fn/v7HLxyIiYmhR48exMTEWJVQXMyAdtFc0i6KomKD+z9fe3ytvBINusOgyeb+wmeOX3ItIhWXsgoO7gDvAGgxwOo0lcKjCqh/Cg0NpXnz5iQmJhIVFUV+fj6HDh0qc0xaWhpRUVEAREVFnXBVXsnXJcecjK+vL8HBwWVuUk22/gDpGzli82daUT+u6RJL04ggq1OJi3v8sraEBnizcW8W7/6y/cQDzrkRut1h7n/5b0g7dRe+iJRDSetT8/4e0X0HHl5A5eTksH37dqKjo+ncuTPe3t7Mnz+/9PEtW7aQnJxMfHw8APHx8axbt4709PTSY+bNm0dwcDCtW7eu9vxyBoYBi14EYGrBxeR7BzOmbzOLQ4k7CK/ly2OXmmvlvTY/ka1pJ1nKpd/T0PhCKDgM06+DIxnVnFLEQxQXHy+g2l1lbZZK5FEF1Pjx4/nll1/YuXMnS5Ys4YorrsDhcHDdddcREhLCLbfcwtixY1mwYAErV67k5ptvJj4+nu7duwPQr18/WrduzY033siaNWv48ccfefjhhxk1ahS+vu49X4VHSloAKavIxYf3Cwdwa484IoP9rE4lbuLyjjFc1DKC/KJi7vt8LUXFRtkDHF5w9VQIbQiH/oLP/2VesCAizkleCtkp4BsCTfue+Xg34VEF1O7du7nuuuto0aIF11xzDXXq1GHZsmWEh4cDMHnyZAYNGsSQIUO44IILiIqK4ssvvyx9vsPh4LvvvsPhcBAfH88NN9zATTfdxBNPPGHVW5LT+dW88m56YW+MwHAubx6gKQuk3Gw2G09f0Y5avl6s2XWIKb/tOPGggDDzyjzvALNgn/9YtecUcXvrvzC3rQa5/eSZf2czDMM482HijKysLEJCQsjMzNR4qCqSvvYnIr4cQiF2eua+yq2DetKCPSQmJtK0aVN69HDfFb6lek1fkcyDX67Dz9vO3DEX0qBOwIkHbfgKPhth7g9536O6IUSqVFEhvNTcnF/thi9dfv4nZ/5+e1QLlNQgS94E4Pui7thD63ND9waaskAq5NpzY4mPq0NuQTETvlzLSf9P2eYK6DHW3P96tDkhoIic2Y6FZvEUUNccU+hBVECJ+8lOJTx9EQDvFw7g3oub4+vl0JQFUiE2m41JV7bDz9vOku0HmPnHKaYh6fMwNL0YCo/Cp8Pg8P7qDSrijkoGj7cZbI4r9CAqoMT9rHgPW3Ehvxc352h4B67oVM/qROLmGtUNZNzFLQB46vtNpGXlnniQ3QFD/gthTSBzF3x+swaVi5xOQe7xedTaDrE2SxVQASXuJf8Ixb9/AMD7hZdwX0ILHHabxaHEE/yrR2M61A8hO7eQR2atP3lXnn8oXPsxeAfCjkUaVC5yOok/QV4WBNeD2O5Wp6l0KqDEvaz9FHtuBruKw9lX7yIubh155ueIlIPDbuPZIe3xstuYuzGNH9afYvmmiFYw+C1zf8nrsO7z6gsp4k5Krr5rc4W5YLeH8bx3JJ6ruBiWvQ3AwtpDGN+/DTabWp+k8rSKDmZkryYATPx6A4eO5J/8wDaD4fwx5v43d0Hq+mrJJ+I28nJg6xxz3wO770AFlLiT7fNh/1bwqcWNIx8ivkkdqxOJBxrdpylNwgPZn5PHQ5+vPPXcYhdNhCZ9oOAIzBimmcpF/m7TN+bPRlgTiOlkdZoqoQJK3MdSc+oCzrkJ/DS/llQNXy8Hzw1pj80G32/M4Mc1f5GUlHTigXaHOSdUaAM4uBO+vA2Ki6o9r4hLWv2Jue14HXhoT4EKKHEPaRvNmaBtduj2b6vTiIfr0iiMG7s3BGDOgdrExDY6+YEBYTD0Y/DyMwfMLny2+kKKuKpDybDzV3O//VBrs1QhFVDiHv5439y2HAi1G1qbRWqE+/u3JDrEj31HDKavzzr1gdHt4dLXzP1Fz8OWH6onoIirWjvD3DbqabbQeigVUOL68nJgzbEfyHNvtTaL1BhBvl48fUVbAN5fvIO1uw+d+uAOQ6Hr7eb+l7fDge1VH1DEFRkGrJ5u7ne83tosVUwFlLi+dTMhPxvqNPW4pQDEtfVpGcllHWIoNuCBL9ZRUFR86oP7PW3OdZOXBTNuMAt/kZpm9++Qsd2cK63VZVanqVIqoMS1GQYcmziTLv/y2MGI4romXtqa0ABvNu3N4j+LTjKYvISXD1wzDYIiIX2jOb2B1mqXmqZk8Hjry8A3yNosVUwFlLi23X9A2jpzkG6H66xOIzVQ3SBfJg5qDcCr87eRtO80LUu1ouCa/4HdCzZ8CcveqqaUIi6gINf8voca8ftaBZS4tpLB422uNK94ErHAFZ3q0bNZXfILi5nw5TqKi0/TstSgOyQ8Y+7PfQR2Lq6ekCJW2zIbcjMhJNYcQO7hVECJ6zqScXwl73NvsTaL1Gg2m41nrmiHv7eDFTsy+PT3Xad/Qtfbod01YBTBZyMg6yQTcYp4mjWfmtv2Qz1y6ZZ/8vx3KO5r9SdQlAdR7aFeZ6vTSA0XGxbA+IQWAEyavYnUzNxTH2yzwaWvQmRbOLwPZg6HwlMsCyPiCXLSzbnQADpca22WaqICSlxTcTH8ocHj4lpGnNeIDrGhZOcV8sjX6zFON0jcJ8AcD+UbArtXwI//V31BRarb2plmi2v9c6FuM6vTVAsVUOKadi4yL4X1qQXtrrY6jQgADruN54a0w8tuY97GNH5Yn3r6J9RpAlf+x9z//b3jXRwinsQwYNX/zH0Pn/vp71RAiWv6/djg8Q7XevylsOJeWkYFc2evJgBM/HoDh46coWuuRX+48AFz/9t7YO/aKk4oUs2Sl8H+LeAdAG2vsjpNtVEBJa7n8H7zag6ALjdbm0XkJEb1aUqT8ED25+TxzOxNZ37ChQ9A075QmAszb4SjB6s+pEh1WTnV3LYdUqMWelcBJa5n7UwoLoSYcyCyjdVpRE7g6+XguSHtsdlg5h+7+S1x/+mfYHfAle+Z64Id3Alf/tsc5yfi7o5kwIavzP3ONes/vCqgxLUYBvz5kbnfaZi1WUROo0ujMG7sbi5s/eCX6ziSX3j6JwSEwdCPzElht/0Ii16ohpQiVWztTPNq6ch2UO8cq9NUKxVQ4lr2rob0DeDwNZuDRVzY/f1bEhPiR3LGEV6eu/XMT4juAIMmm/sLJ8G2eVUbUKQqGcbx7rvOw2vc1dIqoMS1/PmxuW01CPxrW5tF5AyCfL14+op2AHzw2w5W7zp05id1vN6cmgMDvrgVMnZUaUaRKrNrBezbBF7+0P4aq9NUOxVQ4joKcmHdZ+Z+R3XfiXvo3TKCKzrVo9iABz5fS35hOcY29X8W6nWB3EPmoPL8I1WeU6TSlRk8HmJpFCuogBLXsWW2+QcluB7E9bI6jUi5PTKoNWGBPmxJy+bthdvP/AQvX3OSzYC6kLoOvh9rdoeIuIujB48vHNx5hKVRrKICSlxHyeDxDteZVy2JuImwQB8eu8y8YvSNBdvYmpZ95ieF1IOrp4DNDmumw+//reKUIpVo7WfmtBwRbaB+F6vTWEIFlLiGzD2w/WdzvwbNZCue49L20fRtFUFBkcH9n6+lqLgcLUqNL4C+j5v7cx40x5SIuDrDgJVTzP3OI2rc4PESKqDENayZDhjQ4Dxz+QsRN2Oz2XhqcDtq+XqxetchpvxWzsHh590FrQdDcQHMvAmy06o0p8hZS14G6RvNKTna19yltlRAifUMA1Yfu/pOcz+JG4sK8eOhga0AeHHuFnbuP3zmJ9lscPkbULcFZO+Fz2+GooIqTipyFpa/Y27bX1Ojr5ZWASXWS14GGUngHWj+T1zEjQ09N5bzm9Yht6CYCV+upbg8XXm+teDaj83Fs//6DeZNrPqgIhWRuRs2fWvud7vD2iwWUwEl1ls7w9y2vlwLB4vbs9lsPHtle/y9HSxLyuDjFcnle2LdZnDF2+b+srfMQboirub3/4JRBI161viltlRAibUK82HjLHO/Bk7EJp4pNiyA+/u3AODZ2ZvYfbCc8zy1uhR6jjP3v7kLUtdXUUKRCig4enzupxre+gQqoMRq2+eb84kERZpXJIl4iOHxjTi3UW0O5xfx4JfrMMo7z1Pvh6DJRVB4FGYMM38+RFzBus/M78eQBtBigNVpLKcCSqxVMvN42yGa+0k8it1u47kh7fH1svPrtv3M/GNXOZ/ogCH/hdCGcHAnfHEbFBdVaVaRMzIMWP6uud/1Nv2+RgWUWCkvBzbPNvfbXWVtFpEqEBcexLh+zQF46rtN7M08Wr4nBoTB0I/My8QT55kLD4tYaediSFsP3gFwzo1Wp3EJKqDEOpu/N7spwuIg5hyr04hUiVt6xNExNpTsvEImfOFEV150e7j0NXN/0QvHr3wSsULJ1AUdrq3RUxf8nQoosU5J9127a2rsTLbi+Rx2Gy9e3QEfLzu/bN1X/q48gA5Dofud5v5Xd8C+LVUTUuR0Dv5lrlUK0PXf1mZxISqgxBo5+44v3dKu5s5kKzVD04ggxl18vCsv5VA5u/IALn7CvGQ8Pwc+vR5yM6sopcgprPgPGMUQ1xsiWlqdxmWogBJrbJxlziUS0wnqNrU6jUiVu7VnHJ0amF15D3yxtvxdeQ5vuGoKBNeHA4nw5b+huLhqw4qUOHoIVk4z97uPtDSKq1EBJdZYO9PcqvVJagiH3cYLV5ldeb9u28/0FU505QWFw9APweELW3+AX56ruqAif/fH+5CfDRGtoVk/q9O4FBVQUv0ydsDuFYDNnL5ApIZoGhHE/QnmBJtPfb+RXRnlnGAToN45cOkr5v4vz2pQuVS9gqOw7Njs+OeP0VjVf1ABJdVv/efmtvEFUCvK2iwi1ezm8xvTtVEYR/KLGP/ZmvKtlVei4/XHZ4D+6g5I31Q1IUXAXOT98D5z4sy2V1qdxuWogJLqt+4Lc6ulW6QGcthtvHB1ewJ8HCzfkcHUJTudO0G/p44PKp9+HRzJqJKcUsMVFcKS183980abY/GkDBVQUr3SN8O+TWD3hpaDrE4jYomGdQL5v0taAfDcnM0kpueU/8kOb7h6GoQ2gIM74ItbzD92IpVp4yxzJvyAOtBJE2eejAooqV4bvza3TXqDf6ilUUSsNKxbA3o2q0teYTFjZ66moMiJK+sC68C1n5izQm//GeY/VmU5pQYyDPjtFXO/2x3gE2BpHFelAkqqV0kB1XqwpTFErGazmVflhfh7s3Z3Jq//nOjcCaLaweVvmvtLXoc1n1Z+SKmZts+H1HXgHQjn3mp1GpelAkqqz/5tkL4B7F7Q8hKr04hYLirEjycHtwXgzQWJ/Jl80LkTtL0Seo4397+5G3b/UckJpUZa/Iq57TzCXJdRTkoFlFSfDbPMbVwvraUkcsxlHWK4rEMMRcUGY2eu4Ui+k+OZej8ELQZCUZ45U3nmnqoJKjVD8nLY+av5H934O61O49JUQEn1UfedyEk9eXlbooL92LH/ME9/7+TUBHY7XPmuOdFhTppZROU7Mb+UyN8teNrcdrweQupbm8XFqYCS6nFgO6StA5sDWg60Oo2ISwkJ8ObFqzsA8PHyZOZtTHPuBL614Lrp4B8Ge1fDN6PNgcAizti5GHb8Yl4lfcF9VqdxeSqgpHpsnGVu4y5Un7rISfRoVpfbejYG4IEv1pKenevcCWo3Mpd7sXvB+i/g1xcrP6R4LsOABc+Y++fcZE6TIaelAkqqR8n4p9aXWxpDxJWNT2hBq+hgMg7nM/6ztc7NUg7QqAdc8oK5//NTx3/uRM5kxy/w12/meos9x1mdxi2ogDqFN998k0aNGuHn50e3bt1YsWKF1ZHcV0YSpK491n13qdVpRFyWr5eDV6/tiK+XnUVb9zHF2VnKAbr8q+xyL3tWVWpG8UCGAT8fG/vU5WYIqWdtHjehAuokZsyYwdixY3n00UdZtWoVHTp0ICEhgfT0dKujuaeSweONepgTAIrIKTWPrMVDA4/NUv7DZtbvyXT+JP2ehqZ9ofCoOag8K6WSU4pHSZxvLvDu5Qc97rU6jdtQAXUSL7/8Mrfddhs333wzrVu35p133iEgIIAPPvjA6mjuqaSAajPY0hgi7uLG7g3p2yqS/KJi7p7+J4fznJzawOEFV30A4a0gey98MhTyD1dNWHFvhnH8yrtzb9UC705QAfUP+fn5rFy5kr59+5beZ7fb6du3L0uXLj3pc/Ly8sjKyipzk2MO7oSUP8FmV/edSDmZs5S3JyrYj6T9h3nsmw3On8QvBK7/1FzLLHUtfHk7FDuxXIzUDFvnQMoqc1mg88dYncatqID6h/3791NUVERkZGSZ+yMjI0lNTT3pcyZNmkRISEjpLTY2tjqiuodN35nbhudDULi1WUTcSO1AH165tiM2G3y2cjdfr67ABJm1G5lr5jl8YPN38NPESs8pbqy4COY/Ye53vU2/o52kAqoSPPjgg2RmZpbedu3aZXUk17H5e3PbSq1PIs7qHleHu3o3BeD/vlzHjv0V6IZr0L3smnm/v1+JCcWt/fkhpG8Ev1C1PlWACqh/qFu3Lg6Hg7S0shPZpaWlERV18r5hX19fgoODy9wEOHwAdi0z91sMsDaLiJu6+6JmdG0cxuH8Iu78eBW5BUXOn6T9NeaSLwCzx8PWuZUbUtxPXvbxK+8ufEDz81WACqh/8PHxoXPnzsyfP7/0vuLiYubPn098fLyFydzQ1jlgFJurxmtSNpEK8XLYef26TtQJ9GHT3iye/G5jxU50wX3QcZj5M/n5zbB3beUGFffy26twOB3C4szB4+I0FVAnMXbsWN577z2mTZvGpk2bGDlyJIcPH+bmm2+2Opp7Kem+a6GlW0TORmSwH5OHmuOhPl6ezDdrKjAtgc0Gg16BxhdAfg58co0WHq6pMvfAkjfM/b6Pg5ePtXnclAqokxg6dCgvvvgiEydOpGPHjqxevZo5c+acMLBcTiP/CGz/2dxveYm1WUQ8wAXNwxnVyxwP9eAXa0lMz3b+JF4+cM2HEN7SnN7g46vg6KHKDSqu7+enzDnCGpyn8alnQQXUKYwePZq//vqLvLw8li9fTrdu3ayO5F6SFpo/oCGxENXe6jQiHmFM32bEx9XhcH4Rd3y0yvn5oQD8Q2HYZxAUaQ4gnnEDFOZVelZxUXvXwJrp5n6/p8yWSakQFVBSNbaUdN9doh9QkUri5bDz2nWdiAz2JTE9hwe+WIthOLleHphjEod9Dj61YOev5pIvmiPK8xkG/PgQYEDbq6B+Z6sTuTUVUFL5iotgyxxzX913IpUqvJYvbw07By+7je/W7mXKbzsrdqLo9nDtR2D3hg1fwtyHKzWnuKCNX5sFs8MX+j5qdRq3pwJKKt+uFXBkvzkTcsPzrU4j4nE6NwwrXS/v6dmbWLr9QMVOFNcLBr9t7i978/jAYvE8uVkwZ4K532OMroyuBCqgpPKVdN81SwCHt7VZRDzUiPMacXnHGIqKDUZ9soo9h45W7ETtr4aLnzT35z4Eaz6tvJDiOhZOMi8cqN0Yeoy1Oo1HUAEllcswYPNsc1/ddyJVxmaz8eyV7WkTE0zG4Xz+/eEfFZtkE+C8u6D7KHN/1p2w5YfKCyrW27sGlr9j7g98Cbz9rM3jIVRASeXavxUytptrbzXte+bjRaTC/H0cvHtjZ8ICfVi/J4sJFR1UbrOZV2R1uB6MIvhsBOz8rdLzigWKi+C7e80JVNtcCU0vsjqRx1ABJZVr87HFgxtfAL61rM0iUgPUrx3Am9efg8NuY9bqFN7+ZXvFTmS3w2Wvm1fOFubC9GvNlgtxbyunwp6V5hWXCc9YncajqICSylXSfddC3Xci1SW+SR0eu6wNAM/P2cKc9akVO5HDC66aAg17QF4WfHgl7E+sxKRSrXLSYf7j5v5Fj0BwtLV5PIwKKKk8OfvM/+mAFg8WqWY3dm/I8PiGANw7YzXr92RW7ETefnDddIjuYF5N+7/L4ODOygsq1WfOBMjNhOiOWu+uCqiAksqzfT5gmIsHB8dYnUakxnlkUGt6NqvL0YIibvvfH6Rm5lbsRH7BcMOXULcFZO2BaZdp3Tx3s/5LWP8F2BwwaDLYHVYn8jgqoKTybP3R3DZLsDaHSA3l5bDzxvXn0CQ8kL2Zudw89XdyKrLcC0BgXbjpa/Oy90N/wbRLIbuCXYNSvbJT4ftjUxX0HAf1zrE2j4dSASWVo6jwWAsU0KyftVlEarAQf2+m3tyVukE+bNqbxZ0fr6KgqILLtARHw/BvIaSBeXXt/y6Hw/srN7BULsOAb+6GowfNbtgL7rM6kcdSASWVY/cKs6/dvzbU72J1GpEaLTYsgA9GnIu/t4NFW/fx0FfrKja9AUBoLAz/GmpFw77N8L/BcCSjUvNKJfrzQ9j2o7lcyxXvgpeP1Yk8ltMF1PDhw1m0aFFVZBF3tm2uuW3aV33tIi6gff1Q3ri+E3YbzPxjNy/P21rxk4XFmS1RgRGQts4cE3W4gsvHSNU5uBPmPGju93kYIlpZGsfTOV1AZWZm0rdvX5o1a8YzzzzDnj0aWCjAtnnmVt13Ii7jolaRPDW4HQCv/5zIB4t3VPxkdZv9o4i61LzyVlxDcRHMGgX5OdDgPIgfZXUij+d0ATVr1iz27NnDyJEjmTFjBo0aNWLAgAF8/vnnFBQUVEVGcXWZuyFtPWDT7OMiLub6bg0Y3685AE98t5Gv/txd8ZNFtIQR30NQFKRvgGmDzLmGxHqLXoC/FoN3IAx+Sz0B1aBCY6DCw8MZO3Ysa9asYfny5TRt2pQbb7yRmJgY7r33XrZt21bZOcWVlbQ+1T8XAsKszSIiJxjVuyn/Or8xAOM/W8u8jWkVP1l4c7h5NtSKMcdETR2oq/OsljgfFj5r7g98CcIaW5unhjirQeR79+5l3rx5zJs3D4fDwSWXXMK6deto3bo1kydPrqyM4upKxj81V/ediCuy2Ww8PLAVV3aqR1GxwaiPV7Fwy1m0HNVpAjd/D8H1zfUvP+ivyTatkrkbvrgVMKDzCOh4ndWJagynC6iCggK++OILBg0aRMOGDfnss88YM2YMKSkpTJs2jZ9++omZM2fyxBNPVEVecTWFeZC00NzX+CcRl2W323j+qvYMaBtFflExt3+4kt8Sz2JKgrA4s4gKbQgHd5hFVPqmygssZ1aYby78fDQDotpD/+esTlSjOF1ARUdHc9ttt9GwYUNWrFjBH3/8wR133EFwcHDpMb179yY0NLQyc4qr+us3KDhijomIam91GhE5DS+HnVev7UTfVhHkFxZzy7TfWZZ0FlfT1W4E//oRIlpD9l6YMgB2r6y0vHIG8ybC7t/BNwSu+Z+5DI9UG6cLqMmTJ5OSksKbb75Jx44dT3pMaGgoO3acxdUe4j62Huu+a3Yx2GzWZhGRM/LxsvPmsHO4sHk4uQXFjJiygsXbzqIlKjjaHFher4s5eeO0S4+3SkvVWf8FLH/b3L/iHY17soDTBdSNN96In5+qXDmmZPyTuu9E3Iavl4N3b+xMrxZmEfWvab/z8+azGFgeEGYu+xLXCwoOw8dXw7rPKy2v/MOuFTDrTnP//Hug5SXW5qmhNBO5VNyB7ebyDnZv8xeniLgNP2+ziLq4dST5hcX8+8OV/LBub8VP6BsE18+EVpdBUT58cQv8+rK5tEgVS0lJYfHixaSkpFT5a1kuIwmmXwuFuea6o30mWp2oxlIBJRVXMn1Bw3hz9XYRcSu+Xg7eGnYOA9tHU1BkMOqTVXyyPLniJ/TyhaunQvdjrSPzH4fvxphrZVahpKQkEhMTSUpKqtLXsdyRDPjoKjhywFzn7qoPwOFldaoaSwWUVNz2n82tJs8UcVveDjuvDu3ItefGUmzA/321jtfmb6v42nl2B/SfdOyKMBusnArTh0JedmXGLiMuLo6mTZsSFxdXZa9huYJc+PR6s9U/JNZs7fMNsjpVjaYCSiqmMA92/mruN+ljbRYROSteDjuTrmzHXX2aAvDyvK088vV6CouKK37S7nfA0I/Ayx8Sf4L3EyCjai4uiomJoUePHsTExFTJ+S1XXAyzRkLyUvANhmGfQa0oq1PVeCqgpGJ2rTCnLwiMgIg2VqcRkbNks9kY168Fj1/WBpsNPlqWzM1Tfycr9yyW6Go1yLxCLzDCXPrlP72Ot1xL+RQXw7d3wYYvwe4FQz/UIsEuQgWUVEzJL8EmvcGubyMRTzH8vEa8c0Nn/L0d/LptP1e+tYTkA0cqfsL6neHfv0C9zpB7CD4aAkter5bB5W6vpHj68yOw2eGKd3XBjgvRXz6pmNICSt13Ip4moU0Un90RT1SwH4npOVz25mIWbd1X8RMGx8CI2dBxGBjFMPdh+PJ2yD9ceaE9TXExfHv38eLpyveg3VVWp5K/UQElzju8H/auMff1vyERj9S2Xghfjz6f9vVDOHSkgOFTVvD6/G0UF1ew5cjbDy5/0xxcbnPAupnwn96QtqFyg3uC4mL47h7488NjLU//UfHkglRAifOSFgIGRLbVQEYRDxYZ7MfMf8dzXddYDANemreVW//3BxmH8yt2QpvNHFw+/Btz+af9W+C9PvDHB+rSK1GYD1/fCav+d7x4an+11ankJFRAifP+Pv5JRDyan7eDSVe25/kh7fHxsvPz5nQSXlnEr9vOokuvUQ8Y+Rs0vdicEPK7e48tinuosmK7pyMZ8NGVsGa62Up3xbsqnlyYCihxjmH8rYC6yNosIlJtrjk3lq/uPI+mEUHsy87jxvdX8OR3G8ktKKrYCQPrmnMZXfykeXXZxlnw9nmw7adKze02MpLg/X6w81eKvQNZ32EiKXV7WJ1KTkMFlDhn32Zz1XUvP2gQb3UaEalGbWJC+HZ0D27s3hCA9xfvYMCrv7I86UDFTmi3w/l3s++yjznqHw1Ze+DjIfD1KI9vjTIMg/05eazYkcGi+d9x9O3ecGAbh7wjeDjwcSZsiOXumevIK6xggSpVTnPAi3NKWp8anm8OChWRGsXfx8GTg9vSu2U4D365jh37DzP0P8u4vlsDHkhoSUiAt9Pn3JITxM7we+lt/Ea9Xd+YV54l/gyXvgLNEyr/TVSzomKDbenZrN2dyfo9mWxIySIxPYfMo/mMcPzIg16f4GsrZG1xY27JHs++7NrmE7MLyS0oxtfLYe0bkJNSASXO0fQFIgL0aRnJvLFhTJq9mekrkvlkeTI/rNvLuH4tuK5rAxx2W7nPVbIEiy3uUigYaQ6izkiCT66BloMg4Wmo3ajMc1JSUkhKSiIuLs7lZiAvLCpmze5MliUd4PedGaz86yDZuWXXA6xDJh94v0sfx2oA1gT15IdmjzMssBa+Xg5yD2eTdegAGfvSCGlQ34J3IWdiMyq84JGcSlZWFiEhIWRmZhIc7EGL7BbkwnONoPAojFwKka2tTiQiLmDp9gNM/Ho929JzAGgZVYsJA1pyYfNwbLbyF1Kl8o/Agqdh2dtgFIHDF86/B3rcCz4BACxevJjExESaNm1Kjx7WjhUyDIOk/YdZtHUfi7ftZ/mODHLyyhZMgT4O2tYLoX39EPp4reXc1Q/jdXSf+d4SnoZzbzWvUjzGld5fTeLM328VUFXAYwuopIXwv8vNy4/HbS7zwy4iNVtBUTEfL/uLl+dtJetYa0uXhrUZ26855zWpW7GTpm+CH+6HHYvMr4Prw0UTod1VpKSmWdoClZNXyJLE/Szcuo9ftuxjz6GjZR4PDfAmPq4OXRuHcW6jMFpG1cIr9yD8/CSsnGIeFNEahrx/0v+MunILmydTAWUxjy2g5j4CS16DDtfDFW9bnUZEXFDG4XzeWpDIh8v+Iq/QXIy4c8Pa3NqjMf3aRDnVtQeYV/5u+hZ+fAgyk8376jSDC++HtkPAXj3jgwzDYGtaDgu3pLNwyz7++CuDgqLjfz59HHbObVybns3C6dG0Lq2jg7GXvNeiQnOuqwVPm8vZAHS9HS5+Arz9qyW/lI8KKIt5bAH1dg9IWwdX/ldzk4jIaaVl5fLWgkSmr9hFfpFZSMWG+XNDt4ZccU49Imo5eRFKwVGzS2/Ja3D0oHlfSSHV5gpwOD94/UwOHclnceJ+ft26n0Xb9rE3M7fM4w3rBNCreTi9WkTQLS6MAJ+TDCtO+gXmTID0jebXkW1hwHPmXFjiclRAWcwjC6jD++GFJub++G0QFGFtHhFxC+nZuXy09C8+XPYXB48UAOCw2+jVPJzBnerRq0U4tfycKH7ysmH5u7D0jeOFVFAkdB5h3oIr3t2VnVvAH38dZOn2AyzZvp8NKVllJkj39bIT36QOFx4rmhrXDTz5iYqLYPP35qLJu1eY9/nXhj4PwzkjwKHrt1yVCiiLeWQBteErc6bgiNZw51Kr04iImzmaX8TXq/cw849drEo+VHq/j8MsSvq2iiC+SR2ahAeVb+B5bhaseBeW/wcOp5v32RzQcqC5aHGT3uDle8qnG4bBzgNHWLv7EH8mH+L3nRls2pvFP5f6axFZi57N6nJB83C6Ng7Dz/s0XYZ5ObD2U1j6pnkVIYDDxyzsej0IAWFnfl9iKRVQFvPIAuq7e80+/G4jYcCzVqcRETe2fV8OX6zczZz1qSTtP1zmsbpBPnRpGEabmGBaRQfTMroW0SH+px47VZgPm7+F39+Hv347fr9PLWieQHGrS0mL6MFf2Ta278tha2o2W9Ky2bQ3m8yjBSecLjbMn/i4OpzftC7xcXWICD5DV2NuFmz90ZxJPfEnc2kaAL9Q88q6rrdDrcjyfzhiKRVQFvPIAur1znAgEa6dDi0vsTqNiHiIxPQcftyQym+J+1n518HSged/52W3ERPqT0yoH7UDfAjx9ybY3xtvhw27zbzlFxVTK3Mr7dNm0SZzIbWLjs+Onmd4sd5ozMri5qwsbsaq4mbsozY+XnbaxATToX4oXRrV5txGYUSeqWA6kgEpq2DPn2b3XNIvUJR3/PGwJtDt32YrmG9QZX1MUk1UQFnM4wqozD0wubW5Mvj9O8A/1OpEIuKB8gqLWLs7k9XJh9i0N4uNe7PYvi+nzNVu5WGjmE62RBIcvzPAvoIG9hMXPi70DcFeuyH20AYQ2hBqRZndbXaHOSDd7gW5mZCTBjnp5jZjBxzcceIL1mkKrQdDm8HmIHFN8eK2nPn7rZFscmY7fzW30R1VPIlIlfH1clDPJ5c8ewoDe8YRE9ORomKD9Oxcdh88Ssqho2QeLSDzSAFZuQUUFBkYhkGRYeDjcODvY8ff20FIgA/hQV2oGzQcWy1fioxUHHt+h10rzFvaerzyMiF1rXlzVlgc1OsMMedA3IXm2FAVTTWOCig5s6RfzG3jC6zNISIeLykpicTERABiYmJw2G1Eh/gTHXI28yU1gbpNoMO15pd5OXAo+fgtM9lsZSoqgOJC81ZUAH7B5hV+QRHmNjgGotprMLgAKqDkTAzj+CzAKqBEpIqVrItXsq0SvkHm7N9ajkrOggooOb2MJMjaDXZvaBBvdRoR8XAxMTFaukTcgt3qAOLiSlqfYruWLuIpIiJS06mAktNT952IiMgJVEDJqWn8k4iIyEmpgJJTS98ER/aDdwDU62J1GhEREZehAkpOraT1qUE8ePlYm0VERMSFeFQB1ahRI2w2W5nbs8+WXbdt7dq19OzZEz8/P2JjY3n++edPOM9nn31Gy5Yt8fPzo127dsyePbu63oJr2aH5n0RERE7GowoogCeeeIK9e/eW3u66667Sx7KysujXrx8NGzZk5cqVvPDCCzz22GP85z//KT1myZIlXHfdddxyyy38+eefDB48mMGDB7N+/Xor3o51igph52JzXwWUiIhIGR43D1StWrWIioo66WMff/wx+fn5fPDBB/j4+NCmTRtWr17Nyy+/zO233w7Aq6++Sv/+/bnvvvsAePLJJ5k3bx5vvPEG77zzTrW9D8ulroG8LPANgegOVqcRERFxKR7XAvXss89Sp04dOnXqxAsvvEBhYWHpY0uXLuWCCy7Ax+f4eJ6EhAS2bNnCwYMHS4/p27dvmXMmJCSwdOnS6nkDrmLHsfXvGp1vLq4pIiIipTyqBeruu+/mnHPOISwsjCVLlvDggw+yd+9eXn75ZQBSU1Np3LhxmedERkaWPla7dm1SU1NL7/v7Mampqad83by8PPLy8kq/zsrKqqy3ZJ2/fjO3jXpYm0NERMQFuXwL1IQJE04YGP7P2+bNmwEYO3YsvXr1on379txxxx289NJLvP7662WKm6owadIkQkJCSm+xsbFV+npVrrgIkpeZ+w3PtzaLiIiIC3L5Fqhx48YxYsSI0x5zqkUnu3XrRmFhITt37qRFixZERUWRlpZW5piSr0vGTZ3qmFONqwJ48MEHGTt2bOnXWVlZ7l1Epa47Nv4pGKLaWZ1GRETE5bh8ARUeHk54eHiFnrt69WrsdjsREREAxMfH89BDD1FQUIC3tzcA8+bNo0WLFtSuXbv0mPnz5zNmzJjS88ybN4/4+FMvpOvr64uvr2+FMrqkv5aY2wbdNf5JRETkJFy+C6+8li5dyiuvvMKaNWtISkri448/5t577+WGG24oLY6uv/56fHx8uOWWW9iwYQMzZszg1VdfLdN6dM899zBnzhxeeuklNm/ezGOPPcYff/zB6NGjrXpr1a9k/FPD86zNISIi4qJcvgWqvHx9ffn000957LHHyMvLo3Hjxtx7771liqOQkBDmzp3LqFGj6Ny5M3Xr1mXixImlUxgAnHfeeXzyySc8/PDD/N///R/NmjVj1qxZtG3b1oq3Vf2Ki4+3QDXUAHIREZGTsRmGYVgdwtNkZWUREhJCZmYmwcHBVsdxTtpGeDveXP9uQjI4vK1OJCIiUi2c+fvtMV14UklKuu9iu6p4EhEROQUVUFJW6fgndd+JiIicigooOc4wYKcGkIuIiJyJCig57sB2OJwODl+o19nqNCIiIi5LBZQc99dic1u/C3j7WZtFRETEhamAkuNKpy/Q8i0iIiKnowJKTBr/JCIiUm4qoMR0KBmydoPdy5zCQERERE5JBZSYSqYviOkEPoHWZhEREXFxKqDEVDr/k8Y/iYiInIkKKDHtVAElIiJSXiqgBLLT4OAOwAYNulmdRkRExOWpgBLYtczcRrYBvxBrs4iIiLgBFVACycvNbaxan0RERMpDBZRA8lJz2yDe2hwiIk5KSUlh8eLFpKSkWB1FahgvqwOIxfKPQOpac1/jn0TEzSQlJZGYmAhATEyMxWmkJlEBVdPtWQnFhVArBkJirU4jIuKUuLi4MluR6qICqqZLPjaAvEE3sNmszSIi4qSYmBi1PIklNAaqpiu5Ak/jn0RERMpNBVRNVlwMu34393UFnoiISLmpgKrJ9m2CvEzwDoTItlanERERcRsqoGqykukLYs8Fh4bDiYiIlJcKqJqsdALN7tbmEBERcTMqoGqyXX+7Ak9ERETKTQVUTZWVAoeSwWaH+udanUZERMStqICqqUrmf4psC761rM0iIiLiZlRA1VS7jo1/aqDxTyIiIs5SAVVTlbRAaf4nERERp6mAqonyciB1nbmvGchFREScpgKqJtrzBxhF5uLBIfWsTiMiIuJ2VEDVRLtWmFt134mIiFSICqiaaHfJ+nddrc0hIiLiplRA1TSGcbyAqt/F2iwiIiJuSgVUTXNgOxw9CF5+ENnO6jQiIiJuSQVUTVPS+hTdEbx8LI0iIiLirlRA1TTqvhMRETlrKqBqmtICSuvfiYiIVJQKqJok/zCkbTD3dQWeiIhIhamAqklS/jQn0AyuB8ExVqcRERFxWyqgahKNfxIREakUKqBqkt1/mFuNfxIRETkrKqBqCsM4voRLfY1/EhERORsqoGqKQ8lwOB3s3hDd3uo0IiIibk0FVE1RMv4pqh14+1ubRURExM2pgKopNP5JRESk0qiAqil2Hxv/pPmfREREzpoKqJqgIBf2rjX3NYWBiIjIWVMBVROkroXiAggMh9CGVqcRERFxeyqgaoLSCTS7gs1mbRYREREPoAKqJiid/0nddyIiIpVBBVRNoCvwREREKpUKKE+XnQpZu8Fmh5hOVqcRERHxCCqgPN2eVeY2vCX4BlmbRURExEOogPJ0KccKqJhzrM0hIiLiQVRAebo9K81tPXXfiYiIVBa3KaCefvppzjvvPAICAggNDT3pMcnJyQwcOJCAgAAiIiK47777KCwsLHPMwoULOeecc/D19aVp06ZMnTr1hPO8+eabNGrUCD8/P7p168aKFSuq4B1VA8OAlD/N/Xqdrc0iIiLiQdymgMrPz+fqq69m5MiRJ328qKiIgQMHkp+fz5IlS5g2bRpTp05l4sSJpcfs2LGDgQMH0rt3b1avXs2YMWO49dZb+fHHH0uPmTFjBmPHjuXRRx9l1apVdOjQgYSEBNLT06v8PVa6gzvg6EFw+EBEG6vTiIiIeAybYRiG1SGcMXXqVMaMGcOhQ4fK3P/DDz8waNAgUlJSiIyMBOCdd97hgQceYN++ffj4+PDAAw/w/fffs379+tLnXXvttRw6dIg5c+YA0K1bN84991zeeOMNAIqLi4mNjeWuu+5iwoQJ5cqYlZVFSEgImZmZBAcHV8K7rqB1n8MXt0C9LnDbfOtyiIiIuAFn/n67TQvUmSxdupR27dqVFk8ACQkJZGVlsWHDhtJj+vbtW+Z5CQkJLF26FDBbuVauXFnmGLvdTt++fUuPOZm8vDyysrLK3FxCyRV49TSAXEREpDJ5TAGVmppapngCSr9OTU097TFZWVkcPXqU/fv3U1RUdNJjSs5xMpMmTSIkJKT0FhsbWxlv6eyVDiDX+CcREZHKZGkBNWHCBGw222lvmzdvtjJiuTz44INkZmaW3nbt2mV1JCgqhL1rzH1NYSAiIlKpvKx88XHjxjFixIjTHhMXF1euc0VFRZ1wtVxaWlrpYyXbkvv+fkxwcDD+/v44HA4cDsdJjyk5x8n4+vri6+tbrpzVZt8mKDwKvsFQp6nVaURERDyKpQVUeHg44eHhlXKu+Ph4nn76adLT04mIiABg3rx5BAcH07p169JjZs+eXeZ58+bNIz4+HgAfHx86d+7M/PnzGTx4MGAOIp8/fz6jR4+ulJzVpmT8U0xHsHtMT62IiIhLcJu/rMnJyaxevZrk5GSKiopYvXo1q1evJicnB4B+/frRunVrbrzxRtasWcOPP/7Iww8/zKhRo0pbh+644w6SkpK4//772bx5M2+99RYzZ87k3nvvLX2dsWPH8t577zFt2jQ2bdrEyJEjOXz4MDfffLMl77vCSsY/qftORESk0lnaAuWMiRMnMm3atNKvO3UyZ9ZesGABvXr1wuFw8N133zFy5Eji4+MJDAxk+PDhPPHEE6XPady4Md9//z333nsvr776KvXr1+e///0vCQkJpccMHTqUffv2MXHiRFJTU+nYsSNz5sw5YWC5yytZwkUDyEVERCqd280D5Q4snwcq/whMqg9GEdy7AULqV38GERERN1Mj54GSv0ldZxZPQZEQXM/qNCIiIh5HBZQn+vv4J5vN2iwiIiIeSAWUJ9L4JxERkSqlAsoTlc5A3snaHCIiIh5KBZSnOZIBGUnmvqYwEBERqRIqoDxNyp/mtnZjCAizNouIiIiHUgHlaUrHP6n1SUREpKqogPI0KavNbYzGP4mIiFQVFVCeZu8acxvd0dIYIiIinkwFlCc5fAAyd5n70e2tzSIiIuLBVEB5kr3HBpCHxYFfiLVZREREPJgKKE9SMv5J3XciIiJVSgWUJykZ/xTT0dIYIiIink4FlCfZu9rcqgVKRESkSqmA8hRHMuBQsrkf3cHaLCIiIh5OBZSnKGl9qt0Y/EOtTCIiIuLxvKwOIJWkdALNjlamEBFxCUVFRRQUFFgdQ1yMt7c3DoejUs6lAspTaPyTiAiGYZCamsqhQ4esjiIuKjQ0lKioKGw221mdRwWUp1ALlIhIafEUERFBQEDAWf+RFM9hGAZHjhwhPT0dgOjo6LM6nwooT3AkAw79Ze5rALmI1FBFRUWlxVOdOnWsjiMuyN/fH4D09HQiIiLOqjtPg8g9Qcn8T7UbgX9tS6OIiFilZMxTQECAxUnElZV8f5ztGDkVUJ5A459EREqp205Op7K+P1RAeQKNfxIRcWu9evVizJgxVscAYNasWTRt2hSHw8GYMWOYOnUqoaGhVsdyOSqgPEFpC5TGP4mIyIkWLlyIzWYr19WJ//73v7nqqqvYtWsXTz75JEOHDmXr1q2ljz/22GN07Nix6sK6CQ0id3dHD8LBnea+uvBEROQs5OTkkJ6eTkJCAjExMaX3lwy+luPUAuXuSgaQhzaAgDBrs4iISIUVFhYyevRoQkJCqFu3Lo888giGYZQ+npeXx/jx46lXrx6BgYF069aNhQsXlj7+119/cemll1K7dm0CAwNp06YNs2fPZufOnfTu3RuA2rVrY7PZGDFixAmvv3DhQmrVqgVAnz59sNlsLFy4sEwX3tSpU3n88cdZs2YNNpsNm83G1KlTq+ojcWlqgXJ3JQWUWp9ERE5gGAZHC4oseW1/b4dTA5anTZvGLbfcwooVK/jjjz+4/fbbadCgAbfddhsAo0ePZuPGjXz66afExMTw1Vdf0b9/f9atW0ezZs0YNWoU+fn5LFq0iMDAQDZu3EhQUBCxsbF88cUXDBkyhC1bthAcHHzSFqXzzjuPLVu20KJFC7744gvOO+88wsLC2LlzZ+kxQ4cOZf369cyZM4effvoJgJCQkLP7oNyUCih3pwHkIiKndLSgiNYTf7TktTc+kUCAT/n/zMbGxjJ58mRsNhstWrRg3bp1TJ48mdtuu43k5GSmTJlCcnJyadfa+PHjmTNnDlOmTOGZZ54hOTmZIUOG0K5dOwDi4uJKzx0WZvZQREREnHJAuI+PDxEREaXHR0VFnXCMv78/QUFBeHl5nfTxmkQFlLvTFAYiIh6he/fuZVqs4uPjeemllygqKmLdunUUFRXRvHnzMs/Jy8srnTT07rvvZuTIkcydO5e+ffsyZMgQ2rdvX63voSZRAeXOcjMhI8ncVwElInICf28HG59IsOy1K0tOTg4Oh4OVK1eeMHt2UFAQALfeeisJCQl8//33zJ07l0mTJvHSSy9x1113VVoOOU4FlDtL22Bug+tBoJYtEBH5J5vN5lQ3mpWWL19e5utly5bRrFkzHA4HnTp1oqioiPT0dHr27HnKc8TGxnLHHXdwxx138OCDD/Lee+9x11134ePjA5jL3ZwtHx+fSjmPu9NVeO5s71pzG6UmWhERd5ecnMzYsWPZsmUL06dP5/XXX+eee+4BoHnz5gwbNoybbrqJL7/8kh07drBixQomTZrE999/D8CYMWP48ccf2bFjB6tWrWLBggW0atUKgIYNG2Kz2fjuu+/Yt28fOTk5Fc7ZqFEjduzYwerVq9m/fz95eXln/+bdkAood5a6ztxGtbM2h4iInLWbbrqJo0eP0rVrV0aNGsU999zD7bffXvr4lClTuOmmmxg3bhwtWrRg8ODB/P777zRo0AAwW5dGjRpFq1at6N+/P82bN+ett94CoF69ejz++ONMmDCByMhIRo8eXeGcQ4YMoX///vTu3Zvw8HCmT59+dm/cTdmMv08yIZUiKyuLkJAQMjMzCQ4OrroXeqeHWUQN/QhaXVp1ryMi4gZyc3PZsWMHjRs3xs/Pz+o44qJO933izN9vtUC5q8J8SN9s7qsLT0REpFqpgHJX+zZDcQH4hZizkIuIiEi1UQHlrkrHP7UHJ2a6FRERkbOnAspdpZZcgacB5CIiItVNBZS70hQGIiIillEB5Y6KizWFgYiIiIVUQLmjQzshPxscPhDewuo0IiIiNY4KKHdU0voU0Qoc3tZmERERqYFUQLkjjX8SERGxlAood1TSAhXdwdocIiJSY02dOpXQ0FCrYzBixAgGDx5c7a+rAsodaQoDERFxcTt37sRms7F69WqXPN/ZUgHlbnL2QfZewAaRbaxOIyIiFsnPz7c6QqVw1/ehAsrdlLQ+hcWBby1rs4iISKXIzs5m2LBhBAYGEh0dzeTJk+nVqxdjxowpPaZRo0Y8+eST3HTTTQQHB3P77bcD8MUXX9CmTRt8fX1p1KgRL730Uplz22w2Zs2aVea+0NBQpk6dChxv2fnyyy/p3bs3AQEBdOjQgaVLl5Z5ztSpU2nQoAEBAQFcccUVHDhw4LTvqXHjxgB06tQJm81Gr169gONdbk8//TQxMTG0aNGiXDlPdb4SL774ItHR0dSpU4dRo0ZRUFBw2nxny6tKzy6Vr3T8kwaQi4ickWFAwRFrXts7oNxLbY0dO5bffvuNb775hsjISCZOnMiqVavo2LFjmeNefPFFJk6cyKOPPgrAypUrueaaa3jssccYOnQoS5Ys4c4776ROnTqMGDHCqbgPPfQQL774Is2aNeOhhx7iuuuuIzExES8vL5YvX84tt9zCpEmTGDx4MHPmzCnNcCorVqyga9eu/PTTT7Rp0wYfH5/Sx+bPn09wcDDz5s0rd77TnW/BggVER0ezYMECEhMTGTp0KB07duS2225z6jNwhgood6PxTyIi5VdwBJ6Jsea1/y8FfALPeFh2djbTpk3jk08+4aKLLgJgypQpxMScmLtPnz6MGzeu9Othw4Zx0UUX8cgjjwDQvHlzNm7cyAsvvOB0ATV+/HgGDhwIwOOPP06bNm1ITEykZcuWvPrqq/Tv35/777+/9HWWLFnCnDlzTnm+8PBwAOrUqUNUVFSZxwIDA/nvf/9bpgg6k9Odr3bt2rzxxhs4HA5atmzJwIEDmT9/fpUWUOrCczelM5DrCjwREU+QlJREQUEBXbt2Lb0vJCSktGvr77p06VLm602bNnH++eeXue/8889n27ZtFBUVOZWjffvjPRvR0dEApKenl75Ot27dyhwfHx/v1Pn/rl27dk4VT2fSpk0bHA5H6dfR0dGl2auKWqDcSf5h2L/N3FcLlIjImXkHmC1BVr12JQsMPHOL1j/ZbDYMwyhz38nGB3l7H5+Y2Xas67G4uNjp1yuPk72P8uY8mb9nLzlXVWUvoQLKnaRtAAwIioRakVanERFxfTZbubrRrBQXF4e3tze///47DRo0ACAzM5OtW7dywQUXnPa5rVq14rfffitz32+//Ubz5s1LW2TCw8PZu3dv6ePbtm3jyBHnxoW1atWK5cuXl7lv2bJlp31OSQtTeVvCzpTT2fNVNRVQ7iRVM5CLiHiaWrVqMXz4cO677z7CwsKIiIjg0UcfxW63l7YEncq4ceM499xzefLJJxk6dChLly7ljTfe4K233io9pk+fPrzxxhvEx8dTVFTEAw88cEKLzZncfffdnH/++bz44otcfvnl/Pjjj6cd/wQQERGBv78/c+bMoX79+vj5+RESEnLK48+U09nzVTWNgXInuZng5a/uOxERD/Pyyy8THx/PoEGD6Nu3L+effz6tWrXCz8/vtM8755xzmDlzJp9++ilt27Zl4sSJPPHEE2UGkL/00kvExsbSs2dPrr/+esaPH09AgHPdi927d+e9997j1VdfpUOHDsydO5eHH374tM/x8vLitdde49133yUmJobLL7/8tMefKaez56tyhpt46qmnjPj4eMPf398ICQk56THACbfp06eXOWbBggVGp06dDB8fH6NJkybGlClTTjjPG2+8YTRs2NDw9fU1unbtaixfvtyprJmZmQZgZGZmOvW8cikqNIy8nMo/r4iImzt69KixceNG4+jRo1ZHOWs5OTlGSEiI8d///tfqKB7ndN8nzvz9dpsWqPz8fK6++mpGjhx52uOmTJnC3r17S29/Xx9nx44dDBw4kN69e7N69WrGjBnDrbfeyo8//lh6zIwZMxg7diyPPvooq1atokOHDiQkJFT5aP5ysztcvj9fRESc8+effzJ9+nS2b9/OqlWrGDZsGID1rSxySm4zBurxxx8HKJ2R9FRCQ0NPmB+ixDvvvEPjxo1LZ2lt1aoVixcvZvLkySQkJABmM+ptt93GzTffXPqc77//ng8++IAJEyZU0rsREREp68UXX2TLli34+PjQuXNnfv31V+rWrWt1LDkFt2mBKq9Ro0ZRt25dunbtygcffFDmksilS5fSt2/fMscnJCSUTlefn5/PypUryxxjt9vp27fvCVPa/11eXh5ZWVllbiIiIuXVqVMnVq5cSU5ODhkZGcybN4927TTe1ZW5TQtUeTzxxBP06dOHgIAA5s6dy5133klOTg533303AKmpqURGlr38PzIykqysLI4ePcrBgwcpKio66TGbN28+5etOmjSptIVMREREPJ+lLVATJkzAZrOd9na6wuWfHnnkEc4//3w6derEAw88wP33388LL7xQhe/A9OCDD5KZmVl627VrV5W/poiIiFjH0haocePGnXGtnri4uAqfv1u3bjz55JPk5eXh6+tLVFQUaWlpZY5JS0sjODgYf39/HA4HDofjpMecalwVgK+vL76+vhXOKSIilcf4x2zWIn9XWd8flhZQ4eHhpYsDVoXVq1dTu3bt0uImPj6e2bNnlzlm3rx5pev5lAzcmz9/funVe8XFxcyfP5/Ro0dXWU4RETl7JZMuHjlyBH9/f4vTiKsqmd3c2clE/8ltxkAlJyeTkZFBcnIyRUVFrF69GoCmTZsSFBTEt99+S1paGt27d8fPz4958+bxzDPPMH78+NJz3HHHHbzxxhvcf//9/Otf/+Lnn39m5syZfP/996XHjB07luHDh9OlSxe6du3KK6+8wuHDh0uvyhMREdfkcDgIDQ0tnXYmICDgjDN5S81hGAZHjhwhPT2d0NDQMosPV4TbFFATJ05k2rRppV936tQJgAULFtCrVy+8vb158803uffeezEMg6ZNm5ZOSVCicePGfP/999x77728+uqr1K9fn//+97+lUxgADB06lH379jFx4kRSU1Pp2LEjc+bMOWFguYiIuJ6S4RYuM3efuJzTTXfkDJuhzuJKl5WVRUhICJmZmQQHB1sdR0SkxikqKqKgoMDqGOJivL29T9vy5Mzfb7dpgRIRESmvkouCRKqKx02kKSIiIlLVVECJiIiIOEkFlIiIiIiTNAaqCpSMy9eaeCIiIu6j5O92ea6vUwFVBbKzswGIjY21OImIiIg4Kzs7m5CQkNMeo2kMqkBxcTEpKSnUqlWr0idxy8rKIjY2ll27dmmKhDPQZ1V++qzKT59V+emzKj99VuVXlZ+VYRhkZ2cTExOD3X76UU5qgaoCdrud+vXrV+lrBAcH64esnPRZlZ8+q/LTZ1V++qzKT59V+VXVZ3WmlqcSGkQuIiIi4iQVUCIiIiJOUgHlZnx9fXn00Ufx9fW1OorL02dVfvqsyk+fVfnpsyo/fVbl5yqflQaRi4iIiDhJLVAiIiIiTlIBJSIiIuIkFVAiIiIiTlIBJSIiIuIkFVBu4umnn+a8884jICCA0NDQkx5js9lOuH366afVG9RFlOfzSk5OZuDAgQQEBBAREcF9991HYWFh9QZ1QY0aNTrh++jZZ5+1OpbLePPNN2nUqBF+fn5069aNFStWWB3J5Tz22GMnfA+1bNnS6lguYdGiRVx66aXExMRgs9mYNWtWmccNw2DixIlER0fj7+9P37592bZtmzVhLXamz2rEiBEnfJ/179+/2vKpgHIT+fn5XH311YwcOfK0x02ZMoW9e/eW3gYPHlw9AV3MmT6voqIiBg4cSH5+PkuWLGHatGlMnTqViRMnVnNS1/TEE0+U+T666667rI7kEmbMmMHYsWN59NFHWbVqFR06dCAhIYH09HSro7mcNm3alPkeWrx4sdWRXMLhw4fp0KEDb7755kkff/7553nttdd45513WL58OYGBgSQkJJCbm1vNSa13ps8KoH///mW+z6ZPn159AQ1xK1OmTDFCQkJO+hhgfPXVV9Wax9Wd6vOaPXu2YbfbjdTU1NL73n77bSM4ONjIy8urxoSup2HDhsbkyZOtjuGSunbtaowaNar066KiIiMmJsaYNGmShalcz6OPPmp06NDB6hgu75+/s4uLi42oqCjjhRdeKL3v0KFDhq+vrzF9+nQLErqOk/19Gz58uHH55ZdbkscwDEMtUB5m1KhR1K1bl65du/LBBx9gaJqvk1q6dCnt2rUjMjKy9L6EhASysrLYsGGDhclcw7PPPkudOnXo1KkTL7zwgro2MVs1V65cSd++fUvvs9vt9O3bl6VLl1qYzDVt27aNmJgY4uLiGDZsGMnJyVZHcnk7duwgNTW1zPdYSEgI3bp10/fYKSxcuJCIiAhatGjByJEjOXDgQLW9thYT9iBPPPEEffr0ISAggLlz53LnnXeSk5PD3XffbXU0l5OamlqmeAJKv05NTbUiksu4++67OeeccwgLC2PJkiU8+OCD7N27l5dfftnqaJbav38/RUVFJ/2+2bx5s0WpXFO3bt2YOnUqLVq0YO/evTz++OP07NmT9evXU6tWLavjuayS3z0n+x6r6b+XTqZ///5ceeWVNG7cmO3bt/N///d/DBgwgKVLl+JwOKr89VVAWWjChAk899xzpz1m06ZN5R58+cgjj5Tud+rUicOHD/PCCy94TAFV2Z9XTeLMZzd27NjS+9q3b4+Pjw///ve/mTRpkuVLJ4h7GDBgQOl++/bt6datGw0bNmTmzJnccsstFiYTT3LttdeW7rdr14727dvTpEkTFi5cyEUXXVTlr68CykLjxo1jxIgRpz0mLi6uwufv1q0bTz75JHl5eR7xh68yP6+oqKgTrp5KS0srfczTnM1n161bNwoLC9m5cyctWrSognTuoW7dujgcjtLvkxJpaWke+T1TmUJDQ2nevDmJiYlWR3FpJd9HaWlpREdHl96flpZGx44dLUrlPuLi4qhbty6JiYkqoDxdeHg44eHhVXb+1atXU7t2bY8onqByP6/4+Hiefvpp0tPTiYiIAGDevHkEBwfTunXrSnkNV3I2n93q1aux2+2ln1NN5ePjQ+fOnZk/f37p1a3FxcXMnz+f0aNHWxvOxeXk5LB9+3ZuvPFGq6O4tMaNGxMVFcX8+fNLC6asrCyWL19+xiuwBXbv3s2BAwfKFJ9VSQWUm0hOTiYjI4Pk5GSKiopYvXo1AE2bNiUoKIhvv/2WtLQ0unfvjp+fH/PmzeOZZ55h/Pjx1ga3yJk+r379+tG6dWtuvPFGnn/+eVJTU3n44YcZNWqUxxScFbF06VKWL19O7969qVWrFkuXLuXee+/lhhtuoHbt2lbHs9zYsWMZPnw4Xbp0oWvXrrzyyiscPnyYm2++2epoLmX8+PFceumlNGzYkJSUFB599FEcDgfXXXed1dEsl5OTU6YlbseOHaxevZqwsDAaNGjAmDFjeOqpp2jWrBmNGzfmkUceISYmpkZOSXO6zyosLIzHH3+cIUOGEBUVxfbt27n//vtp2rQpCQkJ1RPQsuv/xCnDhw83gBNuCxYsMAzDMH744QejY8eORlBQkBEYGGh06NDBeOedd4yioiJrg1vkTJ+XYRjGzp07jQEDBhj+/v5G3bp1jXHjxhkFBQXWhXYBK1euNLp162aEhIQYfn5+RqtWrYxnnnnGyM3NtTqay3j99deNBg0aGD4+PkbXrl2NZcuWWR3J5QwdOtSIjo42fHx8jHr16hlDhw41EhMTrY7lEhYsWHDS303Dhw83DMOcyuCRRx4xIiMjDV9fX+Oiiy4ytmzZYm1oi5zuszpy5IjRr18/Izw83PD29jYaNmxo3HbbbWWmpqlqNsPQde4iIiIiztA8UCIiIiJOUgElIiIi4iQVUCIiIiJOUgElIiIi4iQVUCIiIiJOUgElIiIi4iQVUCIiIiJOUgElIiIi4iQVUCIiIiJOUgElIiIi4iQVUCIiZ7Bv3z6ioqJ45plnSu9bsmQJPj4+zJ8/38JkImIVrYUnIlIOs2fPZvDgwSxZsoQWLVrQsWNHLr/8cl5++WWro4mIBVRAiYiU06hRo/jpp5/o0qUL69at4/fff8fX19fqWCJiARVQIiLldPToUdq2bcuuXbtYuXIl7dq1szqSiFhEY6BERMpp+/btpKSkUFxczM6dO62OIyIWUguUiEg55Ofn07VrVzp27EiLFi145ZVXWLduHREREVZHExELqIASESmH++67j88//5w1a9YQFBTEhRdeSEhICN99953V0UTEAurCExE5g4ULF/LKK6/w4YcfEhwcjN1u58MPP+TXX3/l7bfftjqeiFhALVAiIiIiTlILlIiIiIiTVECJiIiIOEkFlIiIiIiTVECJiIiIOEkFlIiIiIiTVECJiIiIOEkFlIiIiIiTVECJiIiIOEkFlIiIiIiTVECJiIiIOEkFlIiIiIiTVECJiIiIOOn/Aei6q/l82QOjAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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" ] @@ -1056,7 +1055,7 @@ " u0 = experiment_runner(u0)\n", " u0 = theorist(u0)\n", " show_best_fit(u0)\n", - " plt.title(f\"{i=}\")\n" + " plt.title(f\"{i=}\")" ] }, { From dc83b55463dc28d48b54d7e454808ddf01969d48 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 09:35:15 -0400 Subject: [PATCH 033/121] docs: update execution of notebook --- ...Workflows using Functions and States.ipynb | 86 +++++++++---------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 6a8c56c0..2719f548 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -178,27 +178,27 @@ " \n", " 0\n", " -15.0\n", - " -1457.949701\n", + " -1458.277776\n", " \n", " \n", " 1\n", " -14.7\n", - " -1275.900522\n", + " -1275.239274\n", " \n", " \n", " 2\n", " -14.4\n", - " -1101.584447\n", + " -1102.572539\n", " \n", " \n", " 3\n", " -14.1\n", - " -938.510951\n", + " -935.381331\n", " \n", " \n", " 4\n", " -13.8\n", - " -780.229165\n", + " -780.490659\n", " \n", " \n", " ...\n", @@ -208,27 +208,27 @@ " \n", " 96\n", " 13.8\n", - " 500.274061\n", + " 500.506401\n", " \n", " \n", " 97\n", " 14.1\n", - " 608.306420\n", + " 609.386647\n", " \n", " \n", " 98\n", " 14.4\n", - " 720.885521\n", + " 721.981947\n", " \n", " \n", " 99\n", " 14.7\n", - " 843.944513\n", + " 843.750465\n", " \n", " \n", " 100\n", " 15.0\n", - " 971.655807\n", + " 972.798407\n", " \n", " \n", "\n", @@ -237,17 +237,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1457.949701\n", - "1 -14.7 -1275.900522\n", - "2 -14.4 -1101.584447\n", - "3 -14.1 -938.510951\n", - "4 -13.8 -780.229165\n", + "0 -15.0 -1458.277776\n", + "1 -14.7 -1275.239274\n", + "2 -14.4 -1102.572539\n", + "3 -14.1 -935.381331\n", + "4 -13.8 -780.490659\n", ".. ... ...\n", - "96 13.8 500.274061\n", - "97 14.1 608.306420\n", - "98 14.4 720.885521\n", - "99 14.7 843.944513\n", - "100 15.0 971.655807\n", + "96 13.8 500.506401\n", + "97 14.1 609.386647\n", + "98 14.4 721.981947\n", + "99 14.7 843.750465\n", + "100 15.0 972.798407\n", "\n", "[101 rows x 2 columns]" ] @@ -744,7 +744,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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gQaqrq1m+fLn5WFJSEosWLWLjxo0AbNy4keTkZDN4Ali+fDm6rvPBBx+Y+5x22mlERkaa+6xYsYI9e/bQ1NRke223201zc7PflwgfdlPn4+Pj0TSN+Ph4YOyOwY83vgWLj164eO/evZSXl5u9gdIjJcT4VVJSQnl5OSUlJaPdlAGNmwCquroagMmTJ/s9PnnyZPO56upqMjIy/J53Op2kpKT47WN3jqOvYXX//feTlJRkfuXm5g7/BYkRE8gHsi+gPjqwFsHny4U6ulzE4cOHcbvdHD58GLCvVCyEGB9ycnKIi4sjJydntJsyoHETQI2mO+64A5fLZX753uhFeLD7QG5sbEQpRWNjIyDLtIwUX6X3oyu++2Y+Hj0DUggxPtXV1dHe3k5dXV3fO42RXNSwyoHqz5QpUwCoqakhMzPTfLympoaioiJzn9raWr/jPB4PjY2N5vFTpkyhpqbGbx/f9759rKKiovyWpxDhpa6ujkOHDpGQkGDm1FhzccZCErmuPESpdj5+82lmeHeTSAvRdLH14Ytweto539uIgYabSLY+8jEneVvpJIpmEtj19vNMyp6JQ3Xj1cIrjyshIQG3223mQEkdKCHGr7q6Otxud78BVJ4q5TS1mc2Mbo7UuAmgpk+fzpQpU1i7dq0ZMDU3N/PBBx9w0003AbB48WKOHDnCli1bWLBgAQD/+c9/MAyDRYsWmfv86Ec/8ksYXrNmDbNmzWLSpEkj/8JE0FmTkH1j7oCZtDjqNZ6UQYxqYfPz/4Oj/H2+ZhSTqTWCBqyH44/uO/al3B39mKvE//u3XgHgx0CFkcY+lctHz+okGXW0asljOqhqaGjw21qTykESy4UYL+Lj42lsbDTzT+3MVAeYrlfyrvf4EWxZb2EVQLW2tpp3ntCTOF5cXExKSgpTp07l1ltv5ac//SkzZ85k+vTp/OQnPyErK4sLL7wQgNmzZ7Ny5UpuuOEGHn/8cbq7u7nlllv48pe/bL7pXn755dxzzz1cf/31fP/732f79u08+uijPPLII6PxkkUIWHswcnJyaGpq8htzH40ASlMeil9/itO8b7FA20Oi1g47fU/2bI6oOGqdmRzuSqKRJNxEMX3WXLSoOD4q2Y2Gwkk3c2dO59DencTQQZrmIsvpYrK3hkStnWytnmytHnZv5QQNupWD7cYMPvqrQfZJF/R0j2vaiL3ugVh7/yoqKmhoaKCiosIMeKVXSoiJwehwsUDbDcABbXTzIMMqgPrwww8544wzzO9XrVoFwDXXXMOTTz7J9773Pdra2vja177GkSNHOOWUU1i9ejXR0dHmMc888wy33HILZ511Frquc/HFF/OrX/3KfD4pKYk33niDm2++mQULFpCWlsadd94pJQzClF3PhLUHo6ysDJfLRVlZmXncSA3ZeT3dpHormcNeFmk7iNvoNnuOWlU0pdFzack4kfc/6aRVT0RpEdz9k7v42z33mOe48sqfALBm52ePff2qu1h71D533XkXXkNx5z13Ea9cTFa1zIlzkduxkylaE/O1fbD3l7D3l3xdTWKLMZt92swxk2twtPr6elpaWqivrzcfk1IHQoQn69/qkSNH8Hq9HDlyxHb/g++9yDGah1IjE6VH2+4zUsIqgFq2bFm/H2iapnHvvfdy77339rlPSkoKf/3rX/u9zvHHH8+777475HaKscOuZ8I3LT4qKorCwkLKy8vxer3mMB6EvgdKV9188PTdTD3wNLfon431V5LBe8ZcDjCdTj2Ju+64G4A3Pw2GhtMv5NA1HLqDDlI4RArX/OAu7r77HjTVSZZRyYLYCuZ0FjNFa+I87T3gPSrvW8Mc7xwO6tPp0BIGvMZIsFuXsKWlBY/HYy42Kj1SQoQH69+qtTacVdfH/wTgPTUPxyh3lIdVACXEYNmtm1ZVVYXb7aaqqgoY2RpPEUY7xxnbOUvfTNz+nhl9jSqB/xgnckCfwXfvfJAdP70v5O3w0TRAi6ZSn8ENdzzFEZeL3zz4I+axm1P1j8kyKrlE76mPtck7m/0b5pG/+MIRa58du3UJrT1QvmBqrC0+KoTwZ32P7m/Gc4TqYrqrp67jfu2YYd1QBoMEUGLCsa7s7XQ66erqMhe0DQVddfH+727kdv5OlKPnugf1adTO/QqrSxrB6ejZzzG6lUWSk5Joc6bzPulsUEs567h0Ij9+hkXaLk7Sd8Ga66j4z4+Y4Z3PJ/qMUUk+t1uX0JoXtX//flwuF/v372fZsmUj3kYhRPBNNiqJ1rs4ZEzG0GNwjHJ7JIAS45rdUE50dDStra1mblxI6wwpL9O9+7hAX0dSTTtosNmYxXpO4tY7f8V0XWf19nsGPs8ocGg6p3/xFu7Z0cAbxjLmeHexQv+AbG8lV+mVuFQcrxuLcbfaV+gPFbv8tLa2Njo6Osz6UUlJSdTX15OUlNTvuSRXSojRZX2P1jQNpRSazUSWWfTs9546ftSH70ACKDHO2U15z83NpbW1NeQV4z9+6x98VT1NtqNn+v1BPY//5zmFI3o6mqah6WFUx1aPZqc+n2W3/Im3Xvs9efueYrpezZe0N2l+cC5zjMXs02fRrY1OPbSKigrcbjcVFRUAFBQU4Ha7KSgo6Pc4yZUSYnRZh/D6msDjVN3m7Lt92jEj2MK+hdE7uBCDd/TQjs/hw4fp7OwMWcV4TXWz+Zdf5vi3rydba6BKpfB742Km/nALLkeG7Z1VuEhPTeWMq37IE/plPGpcxn4ji0TauER/k1vUnynw7sDo6hj4REHmcrn8tnY/dzuyLIwQoysrK4tTTjllwBuYDKOKGK2Lw2oyXj12hFrXP+mBEhNOZ2cnSik6OzuDfu5J3iq+rP2bjCNHMJTG/zNO5WNHEehOHCHMsRppDk3jiCOTv6hLOev4KWQXP0q+Xsll2utU/3chzUt/NKr1pCoqKnC5XAMGUFlZWdLzJMQo2rZtGyUlJcybN8+s62bnWPYBsDftLByNI9W6/kkPlBjXvF4vbrfbb4adL3AKZgDV0dbCIu8GvqX/jQztCGV6NnvP+zsfO08EbfwETlYOTeP0L9zIX/Qv82vvl6hWk5hi1FDw7re42HiJZKN+4JOEQF1dHUopv+Ugtm3bxtNPP822bdvMxyorK1m/fj2VlZWj0UwhJrzNmzdz6NAhNm/e3Oc+PcN3uwCIn3/xSDVtQBJAiXHt0KFDdHZ2cujQIfOx7u5uv+1wRRhtVD10Civ1D/AqjX8YZ5DxnU0ce9LngnL+cODQoNGZw2+1a1mb+VXaVBTH6Qf5tvYXTva+R2fzyAZSdj9juzfq4uJitmzZQnFx8Yi2TwjRQymFYRj91nhMN6qI1bo4bKRReNKykWvcACSAEuOa1+tFKRWyGk+TvFV8k78wwzhEvUrkUXU1OxzziY7tex2n8cyhaZz19YeovfY9XvUuBmCF/j4dj5xArrc0ZJXNfXll/eWXJSUlERERMeDMPCHEyDly5AhKqT4rjwPM4gAA76lCoiPHTo/+2GmJEEFgnZZuVzPI4XDg9XpxOIZeRcTr8TDHW8wl+n8A2B0xhxe6l+EZ5aUFxorp0/N5yrGYj405XMK/ydcr+Yr+EluNmazVlwX9etaZOxEREX4LggOkp6dTXV1Nenq6+VhRURGJiYmSRC7EKOno6PDbWhnuNhZqPYuC7hvlte+spAdKjCsbNmxg/fr1bNiwAYC4uDi/LQx/mZbOjja2PXKRGTz903sax3x3HR5NgqejaRp0OZL4i34pb2bdSIeKZL6+j5vVU+x86RcQwoWa7Ybwtm3bRmNjo18OVKAzgIQQo6P0vReI1dyUqzTc+thYTspHAigxrrhcLjwejzmdPTExEU3TSExMNPcZzkLBmuqm9OGzOaHtHdzKye+MS/jYeSIRkaNT/ygcODSN5V/7H/6HG/jAOJYYrYs5xT9l34NnEKtaRqwddr2RQoixrWvr8wC8Y8zHOcYqwEgAJcaV/Px8kpKSyM/PB+xnYw2Vw+jkcuMF5nRvp4UYHlOXU+sIbTHO8UTTo3hNP5ffeb9Au4piZnsx31B/Ybp3b8hyo45m1/MYyCw8maknxOjobm0kv7ln7bu9Wv4ot6Y3CaDEuNLe3o5hGOZK3sGacRdltPBV/ka+XkktKdRf8v9ocaQNu70Tja5BrTOPQ196k22OucRpbq7WX+Fzxpt0uoYf5PYnPj7ebwu9Z+HZBUu+auWlpaUhbZ8QE1F/IwIH3v4bkXjYY+Sg9JiRbtqAJIAS40qgBRQHo3TnJr7Gs0zRmjhgZKK+8gbT5y4K2vknojlzj2fW99/mD94L6FIOlugltPxyEalGTciuaZesav19sQuWpFq5EKND3/EPADaqQvQxNnwHEkCJcaampgalFDU1wfkgjjRaSXr+i6RoLewwpvFX/WImT50ZlHNPdNGREVQ6j+G/1VcpNTJJVw18g79yvPcjNBX8BHNfKYujS1pYf1/s1k6sq6vj0KFDQRkGFmIiG8xweLRqJ79tKwAH9emhbtqQSAAlxpXhzrA7WoTRxvU8RyoudhtTeUH/LwwtctjnFf6UI44/65eyLm4luqa4SF/HBcYrtDeUh/za1qBq7969lJeXs3fvXnOfkpISysvLKSkpCXl7hBjPBlO4Ntf4BF1TlOjHoo3R910JoMS4EhMT47cdqgijja/yHBmaiwOO6fxd/y+UFjHwgWJIIjSd025/loe8l9OioinU99P5m6WUb10T0uv6aoH5tvX19XR1dVFf/1nl9JycHOLi4sjJyQlpW4QY7+rr62lpafH7++rLfHYAUDvt/NFaUnNAEkCJcSUvL4+oqCjy8vKGfI7KT/ZyHX8nQzvCPiOblBtfGzc9T4FU7B4tuq7R6pzCQ+p69hg5pKgjTHnpSxzr3T4is/Sgp16Yrut+dcN8Q3rOTxeDlll5QgzNkSNH8Hq9/VYdB4hTzczWy/AonRmnXzEyjRsCCaDEuJKenk5cXJxftenBcDXW0/XUF8jUGjlgZPKcfiGT0ke2yKJdkBMVFeW3HSpd1/22Y5HmiOJp/RLejlmOUzO4VH+DzxlrcKquUWmPNS9KZuUJMTROpxNN08ybkb5MMw4BsC2yiOl5YzP/CSSAEuPMxx9/TGNjIx9//PHgD1Zeyh+/iDzjMNVqEs/qF+LVRr5Apt203kCKfwbSuxTMHLFQcmoaS29/nn9P/wFu5WSJvp3LjBdprtgT1OtYc6Da2towDIO2tjZzn4qKChoaGsyZejIrT4ihSUlJweFwkJKS0vdOSrFQ68k3bMm/cGQaNkQSQIlxpampyW8bKKUUS4z3mNv1Ma0qhqe4GI82duqOBBJA+e7q+ru7s55nLA/pOZ0OzrnmDn6hrqNGJTNDr4Q/nEm6URWya/pWhT86wLTmbcjyL0IMTXl5OR6Ph/LyvieIJKkm8rQaOlUEx55x2Qi2bvAkgBICmG2U8Dl9M93KwaGzfkennjjwQSESHR3ttwXMRXF9W7uhOGtCtF1AFayhwJHkdSTwG65mmzGDRFr5Os9S4N0Zkryo1tZWv63v34Zh+D1mJXlRQgzM4/H4be3MUAcAWG/MY0pGhu0+kZGRftvRIgGUmPAyvYe4VH8TgOKiezjutItG9PrW4MiuXpH1saKiImJiYigqKjL3sQZHGRkZaJpGxlFvQtYhvKlTp6JpGlOnTg366womp+7kH/qFvB3zORya4jJ9NUuN9ShPcPOi7P7vOzo6UEr1uVo8SF6UEIEYqCddebpYovUs9v0xc/o8z1jpOZcASoStYNz17968lmu1/wfAc8ZyFl70zWA1L2DWNxW7D3HrkjQLFixgyZIlLFiwwNzHeneXnZ1NUlIS2dnZ5j7Wczc0NKCUoqGhIfgvLMicGiy5/Tke916IV2ks1zdz4JEVdLU0Bu0adkv/WHul7H7vJC9KiN6sfysDBVAHP3iZNK2ZBpVAk8O+9ymQ84wUCaBE2BpMUTY7unKT8upXidS8vG0UsUufF9wG2rAOs0HvuylfguXRiZbWYb0NGzaYXz4ZGRk4HA6zx6moqIgFCxb49VLNnDkTp9PJzJk91dTthqystbTs2jxas/kinA5qnDP4hXE1rSqa/LaPqHv0NGJVS8iuaX39dr1NkhclRG/W9+iB1iZt3/R/ALxlLMDZT+fSWElF6H8uoRBjyLZt2ygpKWHevHkUFhbS1tZGR0eH34ypgCnFucYaMvRGSo1M3tZPG5HuYLtZcNZgZPr06Xg8HqZP/2z67uTJkykrK2Py5MlAz/Iibrfbb3mR5cuXU1pa2m8vyJe//GW/7+Pj42ltbfVbYHf27Nns2rWL2bNnA2Pnbu9obmcqD3qv4+s8T7bnMNerv/GMupBGve+71qHSdR2v12v+fOyWexFCDE+k6qTA9S4AO7VZ/e6blpZGe3s7aWmju6C79ECJsLF582YOHTrE5s2bgZ7cFI/H029uSl+O825lgb6XNhXN89r5KC009xLW3ovY2Fi/LfTugbLrObJKT08nKirKr96VtRckkLycBQsWkJKS4jcUWFNTQ1dXl7k+nF2PmC948G2teVwjQXPE0HLV6+zSjiFFa+VrPEe2URb061jvmvfu3UtFRYXfci9CiN6ys7NJTU31SyPoc1+jjEg87DZy8epx/e6blpZGQkKCBFBCBMo686KsrAyv10tZ2eA+NFM9FVzsWAfA3sUP4Nbj+91/OKxdzXazR3y9P76t3XBQV1eX33bp0qXmV1/s8nKsOQn79+/H5XKxf/9+cx9rGzMyMoiKivJLRrfWcxmtXqqC/Jmk3rKGdd4iorRuvsIL7P3XL0J6zcrKSjo7O/1yoGQWnhC9WWuo9edEtgOwXp2APsBgQCA3mSNBAigRNqx3HXbJ1gP5ZE8x1+kvAfAP4wzmr7wm6O08Wnp6OpqmmT1FdoFGVFQUDoej3/H8yZMnExkZaQ7hDTXnxtorZRfQBXJ3Zw3oRnOYLyM1lbWOM3nRexq6pij46Kcs8G4O2fIvviHjo4eOZRaeEL21tbXR2dk5YJpFvHIxRz9Et3JQpk8b8LxjJedQcqBE2MjOzqaxsTGg7mA7bncHXc9/hTjNzYfGLHbohXwxyG20qq+vRynlV4Sxs7PT7w9/4cKFZm5XX1wuF4Zh4HK5+tynsrLSzIHKysqiuLiYffv20dzcbF7P1xvl26alpdHU1OQXLBUVFZGYmGjuExcXR0xMjN/6cPn5+XR0dJCfnw/0zhOCnuG87u7uERnWc2qwzXEiTd5Erne8wuf1d0kwWnlHPy3o17JLhLXmRVnz9YSYqAK5qZphHAAdtkYvxNEVPmFJ+LRUTHh79+6lvLycqKioIX0offTkd1nsPUCTimO1fhZooe+AteY3LV26lMzMTL9htcLCwgFfjy+46i/I8vWCQE+gZrfyeVZWll/wZheUWvexBlQA7e3tGIZBe3s7ADk5OZSVlZGTk2Puk56eTlVV1ZDXJRwsXYPDjgJeO+ZOPrf/Z5yubyXeaMfb+V0c0cFL+NZ1HcMw/ILFo4cqCgsL2bx5M9XV1XR2dpo/W2uAK8R4Yvf7feTIEbq6uvpfPFgZLNWKAXDPvRS2Hg59Y4NEAigxJtn9MdbX19PV1eUXEAQq3tvAosqnQYNn1Pl49eiBDwqCxMRE2traSEzsqWxuDU4CFUiQZe1dsg6z2bF+8NsJpM12MwDb2tpQSg1tluQQaRqce9Xt/Pwnh/m2/lcW6Hs49MvlZH3j1ZBe1zoj1LcczNF339YAV4jxxO73u7GxEaUUjY1912pLUzVkaEdoVPEUnnUp/9n64Ii0NxgkB0qMSXY5JWlpaURGRg565oWmurlcexldU2xKOZ8GR2g+vKz1k6Cn50nXdbMHKpBkY7t9hpKkvHDhQvLy8li4cOFgX8qAAknibG5u9tuOpG7nJB4yrqFJxZPXuZu6X51BjApOIGdXisI6I9SuermUPxDjmd2klUAWL5+rdgOwzlhAYpz97LvRqjs3EOmBEmOStTcFeu7yu7q6BtWjoZTiNO96Mh2NHNayOO4rv+HfDz0a9PaC/TpP1vymQHoh7PYZynGB9FrZDc8NhV37oqOj6ejoMIt/jmROFIByxvNL79XcxLNke8q4Sv2dv3IRrVpS0K915MgRvF6vOVQRERGBpml+r7WlpQWPx0NLS+iKfgoxWux6qgfqBXe3NnKy1jP7bqc2iy/0ce6YmBja2tr8bk7HAgmgRNgoLy9HKdXvSt5WmUYZZzi20q0cuC/4PbHxyUFrj9PpxOPxmIv12uX8WIMYu8DQym6foR43kECG5+yGU60Bk921TzrpJL/gcd68eX4FOkeC7oim/pJ/0fn8JeRpVVynnuP/QjB1wJcL5tvOnj0bj8fj91qH8vMRYjzb88afOF7rZo+RQ/cAtZ/GIgmgxJhk16Mx2AUkaypKuVx7DYDnjc9xxfzgzshKTk6mvr6e5ORkAM4777wBK4EHErDY7TPU44LB7mdhHY6yu7bveV+A2dTURFdXF01NTeY+mqahlAppFfjj5syj9NrV7PjzfzFX/4Tr1HPU7ryYjDnB+32wzsyzJtnD0INVIcLBoGeeKkXSzqcBeFudiN7Pe8BY7YEaWwOKQnzKLl/EboisL0opKp+5mQStg+1GHvscxwa9jb7cJt+4fCC1ScKx4KJdbkMgw1G+530/r9raWrxeL7W1teY+I7Wm1Yy8PP6mXcRHxkwStQ7in7+Eyo/+HbTzW4P7iooKXC6XXwHBQH72Uk9KhKuSkhLKy8spKSkJaP+K7e8wzXOIThXB4QFqP1nfa8cK6YESY1Igs8P6k+atZH77e3QrB69qy9GGWbLArqfE7XajlMLtdgd8HrvaTGOdXc+JdTjKruekvLyctra2fodcFy1aNGANrGCJ0HVeUJ/HbbzOYn0njn9dRaa6kCo9d9jnthZ1rampQSllLocDgeWxyTCfCFc5OTk0NTX5lTLpT/3b/0s2ny4c7HT0u6+vF9u3HSvGVmuE+NRwFgrWVRdf1nt6F15QZ9DpSBx2e5xOJ93d3X5/wK2trX7bicwuMLTWrrJLKJ00aZL5BaFPNI/UNF7VV6IiElnS/T7X8SL/Z/wXh/XpAx88CHZV8gMJjkI1DCtEqBUXF+NyuSguLmbZsmX97utUXRTUrwFgqzZ3wHPbzWodC8ZWf5gQRxlqXsxJxibStGY+0XPZow/8xxmIGTNm4HQ6/T78rAsDBzJEE6w1nEZ7KDCQoab09HTy8vLMpHq7ZWOs3f6DGaYdqggN5n77n7wddQYRmpdreIlp3gMhu55PXV0dhw4doq6uLuTXEiLUrO9BvlUS+lstwSfH+IQY3JRqubj1gW9wnU4nmqaNuR4oCaDEmBQXF0d0dLTf8iGBSPTWskLfhKE0Os/9JWj9dw0HqqmpCa/X65cAfcIJJ5CSksIJJ5wAjGz+ymjnyljzouwCQ2sb7YKjefPmkZOTY/ZSjVS9l6T4WE689Tn+5V2KQ1Ncrf2LA6//LqTXHGyOiBBjWXFxMVu2bKG4uHhwByrFYrYCUJZ3Sb/J4z5Lly5lxowZ/S6ePhrGVQB19913o2ma39exx36WPNzZ2cnNN99Mamoq8fHxXHzxxX45CgBlZWWcd955xMbGkpGRwXe/+92Q3g0Le7Gxsei6bvbuBKKt5Qhf0nqG7l42TmHWicuD1h67LmTrLDO7ZGurYAU+gVwrlKwJ83YJ9NaJANYeO+gp83DllVeaeW52+4RKXEwUHzpONhchPmbjD8j37g7Z9ZKSktB1naSknjpUo92LKEQwBXrzk6gaKdDLcasIClbcENC5re8TY8W4CqAA5s6dS1VVlfm1fv1687nbbruNl19+mb///e+8/fbbVFZW8oUvfFa6y+v1ct5559HV1cV7773HU089xZNPPsmdd945Gi9lQtu5cydNTU3s3Lkz4GO2/fUnZGsNVKpUtjnmD+v6vg853zYmJgZN0/ym0dpN5R9oFl6wqlGPldXI+2OdqXf66adzzDHHcPrpp5v7WIOI3NxcoqKiyM39LLHbbugvWByaYqvjRJ7zngnAFfprzPIG/js3GAcPHqSjo4ODBw8CsGHDBtavX8+GDRtCcj0hgsUu2Lf2OvtGCwYaNZjzaeXxLfGnkzVl7L5/BWJsDSgGgdPpZMqUKb0ed7lc/OlPf+Kvf/0rZ57Z82b5xBNPMHv2bN5//31OPvlk3njjDXbu3Mmbb77J5MmTKSoq4r777uP73/8+d999d0jewIW9hoYGlFI0NDQEtH/Zvm2cWPkMaPCCOhulD+9X27eGmW+7ZMmSXrPFrAFCIDV8JlI1amvStF1ldOvMtPT0dKqrq/2KkTqdTrq6ukKW/+DUYIejiNeSJnOu6298WV/Nc96BV5AfLN/P3Ld1uVx4PJ6AckaEGE12M0itEx7S0tJob2/vd6kth+rmlE8XDo446bqArz9W66ONux6offv2mZWRr7jiCsrKygDYsmUL3d3dLF/+2bDOsccey9SpU9m4cSMAGzduZN68eUyePNncZ8WKFTQ3N7Njx44+r+l2u2lubvb7EsMTyBpKPkopml5YRaTm5X1jDo167wB6sOLj49E0jfj4eKAnB8r35WMdRgtkeG60h95GUiC9ZNb/D7vSB9Yq36Hg0GD5LY/xpPccAC7VX2ffP38esusB5Ofnk5SURH5+vvmYDOuJsSiQ963q6mq8Xi/V1dV97pNjfEKc5uaAkcn8pecEfP3Rzvnsy7jqgVq0aBFPPvkks2bNoqqqinvuuYdTTz2V7du3U11dTWRkpFk12mfy5MnmD7y6utovePI973uuL/fffz/33HNPcF/MBDOcO4wUo4bCzg/pUg7e0k6BIFS1joqKQtd1s8Dj1q1baW5uZuvWreYUXesdmExTHzzr/4e19MFIioxwcNAxhye9cK3j38zc9j/sU8HribIm0ZeVleFyucybPOh9pz9W77zFxBLI+9bRC2nbUoqlbAFgnTqR6/uo/WRXc2+s1kcbVwHUOed8FtEef/zxLFq0iGnTpvH888+HtAT8HXfcwapVq8zvm5ub/XI4xMACKTJoS3m5SPu0nkj2FXRVxQelPdbFYVNTU2lrayM1NbXPYyQ4Gj67Yb6YmBg6OjrMv2Fd1zEMIyQz9XRNcdAxhye8Gtc5XmPmxw8wx/gcOx3BD+gOHz6M1+vl8OHD5mPWHLkh/10IEWKDDe4PfbSGmXoF7SqKMkffgZBdADVW31vH3RDe0ZKTkykoKGD//v1MmTKFrq4u8wPRp6amxsyZmjJlSq9Zeb7v7fKqfKKiokhMTPT7EoMz1KGtfO9ucrU66khh3uX3Ba091rojy5cvZ9myZX5DwGJkWIdzA01WHSpdUxxyHMvLqT05Gpfoa5jr/Tjo17Er63B0BX6YWEO+IrwMdlit+e3HAHjTOBG9n/IyKSkpftuxbFwHUK2trRw4cIDMzEwWLFhAREQEa9euNZ/fs2cPZWVlLF68GIDFixdTUlLit1bXmjVrSExMZM6cOSPe/olkKEUGnUYHX9DfAqDsxB8SG588pGvbLVJsrTsSDrPexivrLDyHw+G3DQVdg3O+8TAvp10PwBf1N5njHfn6TfJ7J8Yq61qX/YlSbcxxvQPAx9px/e6bkJCAw+EY9kzlkTCuAqjvfOc7vP322xw6dIj33nuPiy66CIfDwWWXXUZSUhLXX389q1at4q233mLLli1cd911LF68mJNPPhmAs88+mzlz5nDVVVexbds2Xn/9dX784x9z8803h3yx04luKEUGlxjvE6N18ZExkxPOvX7I17YLoMZq3ZGJyFrawDdJI9STNZwOnXNuepA/ec8Denqi9r8UvMRyu9+77OxsUlNTyc7OBiSpXIxdgax16TPT2IdTM9hszMKj999zbLfk01g1rgKo8vJyLrvsMmbNmsWXvvQlUlNTef/9980p0Y888gif//znufjiiznttNOYMmUKL774onm8w+HglVdeweFwsHjxYq688kquvvpq7r333tF6SROGtcjgQHZvXssZjq14lcYa7XS0YeTD+HJrjs6T27ZtG08//TTbtm0D5INsNBUUFJCTk0NBQQEwMj1QPk6HTpljFk94zgUgv/h/ONbb94zcwbCWygDYu3cv5eXl7N27FxhGtWchQqy+vh632019fX2/++nKwxnaJgDWqwUDzvGxm506Vo2rJPJnn3223+ejo6N57LHHeOyxx/rcZ9q0abz22mvBbpoYQE1NDV1dXb1y0OwopTDe+AkAa9RJw14suLu7228LsHnzZqqrq+ns7KSwsFCSeUfR0XlBhYWF5OTkUFZWFvCq78Ola3DIOZsnvYprHf/mUv11nvVq7HEEf1j/8OHDuN1uv8RyK5mZJ0aa3e9coGvfZRqHSdFbqSKVI47JA/baWFd4GMvGfgvFhFBfX4/X6x3wbgZ6yhbM6d5Bp4pgi37CsK9ttwRBUlIS9fX1Zo/YWJ1GOxFNnToVl8vF1KlTzcccDgderzdkvVK6pih1zObV5BTOO/IMX9ZX8zfv8MtlWFmnghcVFZGYmOj3e1dcXMy+fftobm6WAEqMiDfffJOysjJKS0u5+uqrAz/wqNIFe3IvRS8f+G8mnN5rx9UQnggP1uEx6ClGevS2T8rgPHoSx19Rp+LRBl+ewndn49suWrSIlJQUFi1aZO6zdOlSTjnlFEkiHwOseUF2uRe+gqe+bSg4NPjczb/mae/ZAFym/5vS1b8J6jW8Xq/fdsuWLbz33nts2bKlz2NkeFmEWm1tLV6v12+CVSCSVAOz9TLcKoJZ594cotaNHumBEiPOlzAODDpJO8v4hGP0Ko4Qzy597pCun5ycTH19vVlUddmyZWZxTPM6Y7TuyERkXf4mJyeHpqYmvyG8gAPwYYqMcLDXcRx/9Rpc7niTGe//iIOOiKCd35oXVVJSQnd3NyUlJZx//vlA74W2ZXhZhFpSUhLt7e0B56j6FKrtoMF/jAWckxnYkHs4/T5LD5QYcfPmzSMnJ2fw1aaVhwu0nt6nPQU3YmhD++DSNM38AvseMTF2WGsh2eVIWGe0+dbj6m9drqFyaLDLcTzPe88AYNr675NnHAj6dQAiIiL8tgD79+/H5XKZHzJSK0qEmnVlhkA015SxVOt5T/1IOz7g48Lp91l6oMSIs6s2HYh87x4yHEcoV2nM/8J3WP0/Dwzp+vHx8TQ2NprDPcPpEROhF8iSOQkJCXR2dpq1Yy666CIz6TUUnBqUOE4gOTaOs9tf4Ur1L/5iXEiZPj2o14mNjaW9vd3sbYLedbGkt1SEWmtrK4Zh0NraGvAx+155kAWal4+MmXTrgQ+th9Pvs/RAiRG3bt06fv3rX7Nu3bqAj2moreAC/W0AXlZnEBk99KV50tLSSEhIMHsncnJyiIuLG7FZXWJ47PLRlixZwowZM1iyZEmfx9lNFhgOp6ZY+q0neDNmJQ5NcRX/jxzjk6Cc26ehocFvCz3DlIZhmMOV0oMqQq2lpQWllDmMPpDO1iMUHP47AG+rk/otXWBXRiZcSAAlRtzWrVtpbGxk69atAR+z94X7iNc62WVMpVYfXqBTVFTEggULKCoqAsJr2qwIzIYNG1i/fj0bNmwwH7MuCRMMcdGRLPrWX3jVezJOzeAqXqJy00tBO79draja2lqUUmZC7+bNmzl06BCbN28O2nWFOFpnZ6ffdiA7X/k1CbRzwMjE5Zjc776TJk1C0zQmTZoEhNekCAmgxIhLTU3F4XD0uzDv0eqryiiqfgGANZzCgJXYBimcxtyFPWsle5fLhcfjGbBGTTCKcibERLHRsZTXvQuJ1LykvvZVJhuhe/O3BoJJSUk4nc5BJ/gKEQqa8pK950kA3lBLBny77ujoQClllu4Y7Bp7o0luucWIKywsRNf1gJPI9/3zpyzWuigxptOoT2Yw4ZOu6xiG4TdsY53lEU5j7sKe73fJt508eTKNjY1MnvzZ3a9drShr2YChitAU7zhOxen1cJZjK9fwIk8YX6RO73sR8iFfKyICt9ttJpYvXbqUzMxMuQEQIaNpGkopv2WH+jLFKGeyXk89SdQ4cgd8v7YWMg6nOlASQIkRt3fvXioqKoiKihowaduh3MyvebFnKixLAvoDPppdAOUbrguHxSpFb3ZVka0TE1wuF4ZhDNgDFUyRGvzHcSZxDgcnez/kWvUCT6gvBv06vsVbA1nEVYhgsBtK7mNHzuADAHbmXoZWPvAgl3WmaTjd0MoQnhhxdXV1uN1u6urqBtz3OG8J0Vo3eyKOpUnPGPS17BIUrXWFRHgJpIvfrlSGXbFNuwV9hyNSU0y/+QU2GnOI1dxcrV6kcd/7QTm3j/UDJ5D18sIpr0SMvqH+vqQYtczUy2lTUcz+/K0BHZOcnIzD4TDr8oUTCaDEiIuPj0fX9QGrRuvKzUp9IwDdp/5gSB9yubm5REVFkZubaz4mPVDhLZCctcLCQq688kq/XqmoqCg0TfOrZRPsmXkAk1OSeVlbyWZjFglaB86/XozrYOATJgZiTeitr6+npaWl32WQwimvRIw+6++L729moDpQJ3+6bMvWtM+TPjkzoGtZZ0WHEwmgxIizTsPuyzzvx0Rr3eyOmMPcUy4Y0rWsS7KA/+K0YuKIi4tD13Xi4uLMx6wJ2cEKqKJ0+Kd2HsXGMSSqVtRfLiBRHRnWOftSU1OD1+v1W4jb2oMgEyVEX+x6m5qamqitraWpqQkIrNJ/nHGEhfpuvEoj55zbA76+dVZ0OJEASoy46upqlFJUV1f3uY9DdXKO3jP04T3tB2gBfqDZFRmUNezGl6H2ptjd6VpzO4JZ6iBKh+e0C9jFDJKViyvVC8Sr4OdkWRcgBulxEoGz+13Zvn07HR0dbN++PeDznKCKAVhnnEBefuDLbIXze7QkkYuQsyb9BvIhNc9bQpSjm2Ijn8Kl5wd8rYiICLq6usz8ELuEY7sV7kX4sJulY/dztrL7uUdGRtLV1WUG3MEWrYPjmpfY9+R5zNQOc7n6J89wcVCv4XA48Hg8frMLfTl+vkTzcFpfTIwsu7+nrq4uv+1AakpLWKb1DFN/oJ3AWX3sN5jZfOFAAigRcoN9866rLmPlp71Pb7GYokEMp1j/8O2uHU6zPERvdj+/QH7H7I5bvHgxJSUlZrK53azN4SrIm8auy1+i9OnPM0Ov4jL1Tzobvh2080dERODxePpcL2/ZsmVhNTVcjKxgvB9WvvxTJmuKd43j6Xb0nVsqAZQQgzTYpO19/3qIJVo3JcZ0XPrgEgtjY2NxuVzm2mGSMD4xDDVAWLZsGcuWLTO/T0lJob6+npSUlGA2j9kF+fyQL3Gj8Tem6rVU/e4cotV5dGqxAx88ALshPKUUhmGYQ5N2H5Lbtm0zg0dZA3LiCKS3djCiVCvzGt8ADTawsN997UYfgt2ekSQ5UCLkNm3aRGlpKZs2bRpwX015OK7ieQDeZtGg71SsRdmkZMHEEKw8iqioKBwOx6BWnQ/43A4Hj3MZlSqVTM9hvmj8iyjVMfCBQ9DU1IRSykwCtlsvz1q9XUwM1pyn4Za4KDK24dQM3jPm4nb0Xw3frvJ/OOfrSQAlgsruj7Gurg6lVEB1n/K8B0jU2ik1MmnQA5sGezTrbBGZfSQGw+l0YhhGyNZFjHI4eExdTo2axDF6JRcZrxCpAltfbDCsvVJ26+XJItoTk/U9cTgBTJRq5yztQwDeZdGA+ycmJvpt7doTTmQITwSVXS5KZGQk3d3dAybqKuVlpd6z+OubLB7SmncJCQkcOXLEHLKTfCfhE8hQgXWh3lCIdERwYMUzOP79ZWbph/kv4zWMjjvQYxIHPniIjhw5gtfr5ciRI+Zjsoi2gOGlOcwzPiZC97Itcj4dXckDLtuSkZFBS0sLGRmfFUUO5/do6YESQWV3NxHojI4s4zAZ2hFqSaFKnzqk60dERKBpml9CrRAQ2J12RkYGmqb5vcGHwpKTl/KoupIjKo65+iE+eey/UF1tQTu/dajELk9K8gMnJmvl+r1791JeXs7evXsHdZ4I1cFyrSctQ5323YDudw8fPozH4+Hw4cODbfaYJAGUCCq7XBRrXpIdpQw+R0/v08GZ14E2+DWUANLT04mMjCQ9PX1I7RfjVyBDBe3t7X5bgGnTpqFpGtOmTQtqe5yOaB42rqZZxTC9dSsHf3MhDhWc9e2s9dB8QdLRwZI1P1CWe5mYKisrcbvdg/65H2eUEKV52GIUULj03ICOscuBCmcSQImQC2S9sVSjmul6DUdUHMed/82AzmsXmBUUFJCTk0NBQcEwWizGo0ASze1+V88++2zOPPNMzj77bCC4HwIOZwwPGVfTpqKY0byJs4016Mo77PNae5yys7OJiooiOzvb3KevWlHhmMwrApednU1qaqr5uzDYmk8ATVWHWKH1LBr8FosDnuyTkJCApmnjptdTAigRcgOt5K0MgzPoqfv0hlpMXOKkgM5r92Ens+7EcMyePZtJkyYxe/Zs87ENGzawfv16NmzYEJJr6s443j3xN3SqCE7Sd7HcWIumhl8J/WgVFRW43W6/5Ys++ugjGhsb+eijjwAZ0pso9u7dS0VFhTlkZze8O5DSF+4kSutmq5FPqx54yQ9N09B1XepACREs2997jXn6ITpUJHv1YwM+zul00t3d7ZcEKwUDxXC0t7djGIbfEJ7L5cLj8eBy9SzDMtANwVCsPP9LvOrpYnnxt1msb8drOMC4M2jn97XdtwVoa2vz28rNx8RQV1eH2+02Z0UP9ve5urSEwrqXQYM1nDqoYGjhwoV+hWvDnfRAiVHn2fAbAN40FuLVAl9Sw26F8HBeV0mMTQsXLiQvL4+FC3uKBE6aNMlvGyznXXQlDxpX0a0cnKJvY/8froYgBmlW1hzCQHqgJE8qvNj9vLq6ulBKDWrI7mhVL92JUzNYb8yjXR/c30B6ejp5eXnjJkdVAigRVIN9gz18YAeF7T3Dd9v1wBegBJg6dSrR0dFMnTq0GXtCWNmtDG9907f23ASTxzmJB40r8Sid/KpXONV4N2RBVExMjN+2oqKChoYGv2E+K8mTCi/WGXcAra2tftvBiDOOML/5PxhK4x1t8aArzdi1J5zJEJ4IqsGue1ex+pfkaoqNxly6HPGDutbSpUvJzMyU4ToRNHY1aYqLi9m3bx/Nzc0BL4Y9HG5HKg97r+B2/WnO1D/Eazh4T18S9Ov4hr5924qKClwul18AZa2dJUPk4U/Xdbxe75DWezxVbQQNNiecRXfbwO/XoVhbciyRAEoElXVmT3805eG42p6x9A+YP/D+loUow7kAmwgfbW1tdHZ2mj1O2dnZlJWV+c1oCyZNg3ZHOv+a9mMuOPRTPqd/gGHoPT1RQUy+9RXV9G0bGhpQStHQ0GDuYw0e5W9ubLMGvLGxsei6bq4NCoHNiraT5K1jkb6LbuVg8gX3wl//Mej2FRUVkZiYOG4C8PEZFopRc/Qq8APJNQ4Sr3XwiZ7LEX3gMXHrHbPkY4iREBcXR3R0NHFxcQAUFhaSl5cX0gV4NQ0uvPZ2fum9FIAV+kb2P7MqqMN5vpsc39ZaO8qO/M2NbdYh1vLyctra2igvLzf3GcokCKUUy1kPwObUC8ibGVi6RXR0tN92vOWoSg+UCKpA3oShp3DmWZ/WEamZcy3ajsHnkwx2uFCIobDeNR+dKxTaIEqj2ZnFo95L+Lbj7+Tv/zMHnnWCigtqT5SPb5jl6OEW62uXv7mxzTrEmpOTQ1NTk996h4EUNrZKNyo5Tj9Iu4pixsV3B3xccnIyHR0dJCcnB3xMOJEeKDEsQ70jTTFqmarV4iKOeed8PaBjrLOG7CpL2606L8RwBHLXnJWVhaZpQQ8qNA2aHLn82vtFAI7Z878Uqa1BvYaPXakDK7uZetIrNXZYf1ftynIMlqerk8+zDoCX1alMyQ68Iv94q/tkJQGUGBZrl3FrayuGYQw4w2MxWwDYnfkFYuICK9wXFxeHpmnmUIrdB1tJSQnl5eWUlJQM5eUIMSC7vBLDMFBKhSSxXNOg3jGVf2bcDMAF2joKvaEJoqysf992taJkZt7oGKnAtfiFB5im11CnktitzxnUsfn5+SQlJZGfnx+i1o0uGcITw2LtMo6Pj6exsZH4+L5naEQZzSzU9+BROnnnfjvgay1dunTAImy+58ZLoTYxsqxJuHbs8kpqamr8tsGma3D+13/KP3/n4aL633Oh/hbKG/q7+qamJmpra0lNTQXsC9XKzLzREchwanZ2No2NjUOe8OBUbmbteQKAF9VZoA8uZAhGD9hYJgGUGJahzMopVNtBg/WqkGW5MwM/rrBwwJyTQPYRoi+BfCjZBelTp06lrKzMrEkWERFBd3e330LXw+V06Jx/03/z+7ur+brj/3GR/h9KX7gbCF0gtWPHDtxuNzt27OD8889n7969lJSU4PF4JAdqlNkFruvWrTNvMpctWzbs6vJF3o9IcLSz05hKlT41hL9p4UkCKBFUNTU1eL3ePu/EXY21nKH1DN9t0QpZNoJtE2IggfSm2AXp1157rd/30dHRdHd3m7OPgsXp0Kl05PN7z3/xdee/mFHyCPOMsyhxhOamwe12+223bt1Kc3MzW7duZdmyZUDPWoEHDhygqqqKSy65JCTtEL3Z3bxafz7l5eVUVFQM6fcw1nCxUu8pcvwqZ6Jpg8/4GW9lC6wkgBJBNdDClDv//b8s1rrZY+TQMshlAIQItWDVOYqOjqa1tTXoARSArikqnTP5X8/5fM35Ml/Q14KXkAVRR/N6vX5bgPr6erq6uqivrw/59UX/rLOg9+zZ47cNlFKKM9W7OHTF5rhltLcHvmDw0cZ73TBJIhdB1V+NEaUUWfv/BsB6FvQ7M8NuSrUQ4SI3N5fo6Ghyc3NDcn5dU1Q4C3gp5XoAvqCv5XhvcUiudTS7G6S0tDQiIyNJS0sL+fVF/3wz3ob7vplhVLJA30uniiDziw8EdMxQC3SGM/l0EiMm3mhkmiqnVUVTofc/FTbUy2UIEUzWGVE1NTV0dXX5DWU7HA6/7XDpmuK8mx/kD57zAXpyov7+46Cc28davNbu73Lp0qWccsopLF26FJCyBqMpLi4OXdfNmcpD4e5s47+0tQC8pJaRM31WQMelpKT4bScCCaDEiJnPdgDeNk5AaTJ6LMYP61R+u4Ky1sV7gyHCoVPuLOD3ngsAmLHj1xx47o6gVSy3Viu3U1dXx6FDh6irqwOkrMFoamlpwev1DjlpHKD4mZ+Qo9VTrSaxW58d8HHWpYEmAgmgxIjQlZtTtWIAdgzij1KIcGAt6pqWlkZCQoLfsNZQKkAHoicnKp/feS8E4Jhdv2WB2hLUZV/68/bbb3PgwAHefvttwL7Yphi+QIoE+9YxPHo9w8E4vOcj5pc9CcDzxgq0QdzoyhCeEIMUaHd9nreUSM3L3ohZdOhJA543KirKbyvEWGYt6pqdnU1qamrIFhy20jVFtWMGL35abPPz2jssVJtHJIhqbm722w536rywf1/dvHkzhw4dYvPmzYB9QDWUde6OOpi2F24hUvPyrnE8jY7MQR2ekZGBpmlkZGQM/tphSgKoPjz22GPk5eURHR3NokWL2LRp02g3aUwqLi5my5YtFBcX97mPUooz9J4/+ua5VwV03sTERDRNIzExMRjNFGJEHb1eno81Byr4OVFwwY0/48XJ3wLgXG09i42NEOI8QuvMvP3791NWVhbQguLCnt0wqHVYONirLmQbBzm2awdtKoq3taWD7knKzs4mKSlpxG4axgIJoGw899xzrFq1irvuuouPPvqIwsJCVqxYQW1t7Wg3LSwlGXXkaPW4VCzHnX1tQMfMmTOHSZMmMWfO4JYOEGKsWrhwISkpKSxcuBDAvDkI5k2CQ9e44Ov38ojnUgylcbb+Pgee+OqIDecBlJWVoZSirKzMfEwSyz8TyP+F3Tqf1mFhp9OJ1+s1E/yHw6ncXKK9AUDxzFvw6oPP0ysqKmLBggUUFRUNuz3hQgIoGw8//DA33HAD1113HXPmzOHxxx8nNjaWP//5z6PdtDEnkKGKE/kYgLfUiUTHBpYX4cuhCMabgxChZv1QtPswKSgoYP78+RQUFAA9yeSapgU1qRx6giiXM5uHvJfhVRrHHH6BM4x1aGpkZrRaZ+6BJJYfbaj/F9bfqfLycjwej9+SQkN1svE+SVo7+xzHcNKlPxj2+SYK+XSy6OrqYsuWLdxxxx3mY7qus3z5cjZu3Gh7jNvtNiv1wme5ABPBpk2bqKqqoqOjw3YJlarDB1ii9XQx79aODfi8sr6WCCfWJWDsCggWFxezb98+mpubycrKCmiG21BpGrQ5p/AL75V8R3+G0/StOA0Pa/Uzg34tqxkzZnDgwAG/v11rYvm2bdvMJUf6WnopkHUJw1Eg7212SwpZf6c6Ozv9tkOV7K3hLH0LXqXhPe+XREREDnxQgG0e76QHyqK+vh6v18vkyZP9Hp88eTLV1dW2x9x///0kJSWZX6EqnjcW1dTUoJTqc+mWg2t+j0NTfGTMxK33vcCwEOHMbsjFqq2tjY6ODtra2oDQF6DUNOh0pPPqsf9Nl3KwRC/hHON1VFdbSK7nU1NTg8fj8XtPsCaWWxOioXcv3njttbJOOLBj9/tk/f+xqwo/WC1H6rlUew2AfxmncuwJpw35XIH8DYw3EkAFwR133IHL5TK/Dh8+PNpNGhMMr5dph18CYBODW2YikOR0IcaKQD4U4+LiiImJMYscFhQUkJOTYw7phYKmwQWXfZ3/Ma6lQ0Vyor6bT351LkaHK2TX9AWIvi1grsnmG26yq5O1YcMG8wsmdjkEu98n6/9PMOx98mamaE2UqQw+dhQN6ljfz8W3DeRvYLyRAMoiLS0Nh8PRq0elpqaGKVOm2B4TFRVFYmKi39dE0d9d0I6N/yZb1dCiYqjVJ84flRB2rPmCdjP1IiIi/LbBYjiT+G/jOppVDHmtxVQ8+jmilf16lcNlV+9q//79eDwec4jHrk6Wy+Wiu7sbl6snuBuv5RDsksgDSSwvKyujs7PTLzl/ONK8FSw4shqv0nhOnQeDLG5sl+s20UgAZREZGcmCBQtYu3at+ZhhGKxdu5bFixePYsvCT8empwB4xygasPK4tQjbRJzRIca3QAKC+Ph4v20w6c44fmFcR4NKILdzD5eqfxKrWoN+HTvWG63a2lpcLpffzOb8/HySkpLIz88Hxu+QkF3vunW40q7G00ALtQ+GU7m57NOhuxfUmbQ7Br+we6gmQYSTiRs69mPVqlVcc801nHjiiZx00kn88pe/pK2tjeuuu260mzaqAkn8NCkP81zrQIOd+sDJ47qu4/V6zUUwx/sq3mLisSYPFxUVkZiY6BcgZGZm0t7eTmbm4IoYBsrpjGbX2c+Rv/pK8rRqrlT/4DkuDMm1+lNeXo5Sym8GWVlZGS6Xy+xhsXsPGNR70CCMdsK6dbhy7dq1tLS0UFtba77OYOQ8QU9dvtOMd0nRWzigT2cn8xio4pOu6xiG4bdIcagq64cTCaBsXHrppdTV1XHnnXdSXV1NUVERq1ev7pVYPtH4CrcBA755ZRmHidG7+ETPpc1IHvAPNDk5mYaGBpKTk4PTWCHGGGtAYBcgFBQU4Ha7Q5oXdcripXwY8086XryU6Xo116jnObLv0pBdz05ERARer9dvqLKyshKv19vvMNZg3oP6Yw3ERnIGWXZ2No2NjX6lX44ezi0sLLTNIwuWLOMTTtU/pks5MC58HO2frw94jN0i0unp6bS0tJCenh70NoYLGcLrwy233MInn3yC2+3mgw8+YNGiRaPdpFE3b948cnJymDdv3oD7nkRP13P1jC8GVNHWuhyEEBPR3r17KS8vZ+/evSG9zolFRfyvdhnbjTwmaa1EPXMBU4yKgQ8MErveC2tiuV1eUCDvQdbjAlkWZajDhYHkLln3sct9q6+vp6Wlhfr6eiB068pVlu7k8k+H7p4zVjDz+JOHfK709HRiY2MlgBIiEIWFhVx55ZUD3vlFGq0U6qV4lE7+574S0LlDlTwrRDhxuVx4PB4zkdo3ZHL00EmwROsaz2hfYKMxhxg6uY5/MM0YmZIBdsNR1vUv33zzTdatW8ebb75p7pOenk5eXp75oW0XwFjziexyjpKSknA6nSQl9azLOdQZZIGUWrDuYy1nAXDkyBG8Xi9HjhwB7Ht8hsvd2UbHM1cSr3Wy1cjngGN4vZz79+/H5XJN6CV7ZAhPBGzdunVmt/eyZcv63G+m2gcabI87maLJUwM6d15eHqWlpeTl5QWnsUKEoYULF5p/YwC5ubmUlZWFrLZctA4vq3NwR6SyrPtdruUl/u49u2fplyD3fhzN4XDg9Xr91gH09b74tg0NDXi9XhoaGsx9NmzYwIEDB6iqquKSSy7pVZwUAitUGayh0kCuFcg+1h65iIgIurq6gnpD+fEfv8FC7wEaVQKvamejacMLyu1KUUw0EkCJgH344Ye0tbXx4Ycf9h1AKYPTtY8A0OZfEfC5ly5dSmZm5ribcSPEYBQWFvr18DY1NaGUoqmpKWTXjNIU8297gefuv5RLHW9xif4G+//vFvKv/FXIrhlIQnR+fj67du0yZ+VBT3DV1dVlBll2rLlldsn6wSqRYL2WXZK7dZ+Ojg48Ho/fbDprj1NmZiZlZWVBm0ww2VvGwvqXMJTGk+pCvI7oQR2vaRpKKb8hxbS0NJqamkJWCDYcSAAlAhbIcEKyUUu67qJeJTL39EtGqmlCjEvx8fG0tLSEpKzB0ZJio9jumE+DN5lvOP5JfunTHHisAqc6Do82OsPqZWVldHR0+NU9stYeio2NRdd1YmNjB3XuUC0VFUiSe01NDV6v16/WYHt7u9+2vr4epVS/gWKgooxWrtJeBuD9nOtoqUwe9DkyMzOpqqryC+jsAtOJRnKgRMB8RUb7u+MoYgcA76r5OCOjAj73eF22QYjhSE5OJjIyckRmpzo1qHVM57+9V+NWTo5peIuLjJeJVu0hvzb0Tpy2Dun5ntN13dwnkDycob63WGsxBZIwnpOTQ1xcHDk5OeZj69at49e//jXr1q0DAqvnFKxZeJry8EX1CnGam+2RhZx07QNDOs+UKVOIjo72KyY9ESuPW0kAJYKmqb6axdp2APZqMwd17HgtmifEcFlnYvmCqVAEVZoGbmcaby36A0dUHHP0Q1yp/k6SCt0Q4mBYi20Gkodj994SSOFK60y9QAIxXy2no6tzf/jhhzQ2NvLhhx/2e9zR22BQhsESYwP5eiW1KonJ1z2Nc4g5Vfv376ejo2NCJ4zbkSE8ETDfQst9dSvv+c9fOFnzstvIpdMxuOVspHCmEPaUUn7fL1u2zC/RPBRWnvsF1k+azNR/X8tUvZbr1bPUfvS5kF0PPnud1td7tLq6Otra2qirqwOgsbERr9dLY2OjuU8gk12shSs3b95MdXU1nZ2d5tCbNThramqitraW1NTUPq9lt36f9XXZvU6Px+O3DYYP/vJDlutb8Cid/1MXcntmYBN67NgFqqNdfHQskB4oEbDW1la/rVXivpcA2MTxgz53IN3jQkw01gWIoXcCdKhmQ52yeCm/1a7kQ6OABK2D1H9dwSzvzqBeoz92tZB2796N2+1m9+7dAGa5B98Wevf42JUx2LZtG4cOHTJ7nJRSGIbRb/C2Y8cOOjo62LFjh/nY5s2baWxsNHup7Op4hbIURV/SveWcfOh3ADyhLqDVkTrAEf3TNM388pG0CwmgxCD0d4foNDqY070DQ2kc1qf1ex5rvReQP0Yh7DQ3N9Pa2upXYNY6JJWWloamaSGZDRWjw4v6+fzLuwQHii/rq1nqXY+uhrecSCDs3m8CqY/U1dXlt7UWqQSoqqrC6/VSVVUF2M92tPa42wV01tmEBw4cwO12c+DAAXOfYK5hF4hY4whf0f4JwIve06l0TB/0OaxDiunp6URFRfkVzZS0CxnCE0GSZxwEB2xRBXj0/qfIxsTE4Ha7/RahDNWsGCHCWVlZGR6Px28mmlVLSwtKqWFPye9LlKbY7FiMN/U4Lqj/A8v1TUw2GuhuvpmIxLG3vJU1yGpra8MwDL+EbGuvkNvt9tvanWfatGkcOHCAadM+u0G0BlV25wnWGnaBcKoOruUForVuiqMX8XHn/AGX0bITGRmJx+MxezXtysxI2oX0QIkgUEqxWOvpCt/K3AH3z8rKIjo6ute6YBN9RocQVrNnzyYmJobZs2ebj1mHpEJRtdrKqSkuuPkB7jeupUXFME8/QPMvl3Bk/wchu+ZQ+Ypz+rZtbW0opfwCKGsvuN1KCNHR0X7b1tZWlFJ+KQzW84RqCZZAtDY38kXjZdI1F3uNHGbc9OyQi2VaC3vK+7M96YESwxatmpmhV9GpIqjRswfcX4pmChGY888/n/PPP9/vMetSIDk5ORw4cMBv6nwo6LqGxzmJ+71f5Xr1D46hCvfT5zFDraRUH9ys21CyDuHZlQSwBlmxsbG43W6/elK+ZVV826qqKgzDMIf9oPfwXCCJ8KHQ2d5C2W/+izl6OfUqkb9r5/OjpJQhny86Opru7m4zeBT2pAdKDFuB0TO1dWfiKRijVHRPiInCmlju8XhwOBxBncHVnyhHBH/ULuM9x0Ki6OYq7WWWetfjUCNz/WCwFq5MTk7G4XD4lYawDvPZ9fRZg7XRoCmDfb++iDldJTSrWP7El/DoMQMf2I9Aav4JCaBEP+xqo/SiDE7RiwFwFl0a0HklYVyIoSsqKmLBggUUFRUB9sUbQy1ah+O+8yr/Sr4KgOX6Jr6kXqSz5sAAR44Nvp4m39btdmMYhl/uUihKCwSdMjjNeJt5HZtpV1H8Xn2JTn1wJWTsxMTEEBERYeapBvRZMAFJACX65FuWoKSkpM99Eo16MjQXTSqe2adeFNB5ZfaGEENnzUdpb2/HMAyzNwV6BwihkBgTxee/9Wt+6v0KTSqeAq0cz+9OJdf4JGTXDBZr+YPq6mqUUlRXV5v7jEb5gUFRipON91imb6VLOdl3xuO0O4Y+bHe0jo4Ouru7zaHJQD4LJqJB/2Zcc801vPPOO6Foixhj5s2bR05OTr8F++bQU+9kg1FIRKSMlwsRaoHUTBuJAAp68qK8zmQeUF9lq5FPPG18RXuBRd6NY3pIz5okbTc8N5Kz5wbL8HpZ4P2AFfomPEqnZPEjFC77QtDObx2aDOSzYCIadBK5y+Vi+fLlTJs2jeuuu45rrrmG7OyBE4dF+LGuDG/V1uJiqfYxAHv0mXw+wPP6hvAAmdUhxCBZ/36ys7NpbGz0ex8e6dycaIfO39UFVMYf4rzWF1ipbyRfldF6+EvE58qHblApL1sf/RKfd7yHoTT+oL7ATSuvHvLpHA4HXq/XTKiHniVzOjo6zCVzBvosmKgG3QP10ksvUVFRwU033cRzzz1HXl4e55xzDv/4xz/MaF6MDwPd6e5c9zzxWiflKo0WbVLA55UhPCGGzvr3U1FRQUNDAxUVFeY++fn5xMTEmB+AIzG9PkpTrFz1J37mvY4GlUC+VkHEn87k0KsPwwjPShu3lIflxloWNL9Jt3LwO3UJtY6hL9EC9r2VdsPCorchDe6mp6ezatUqtm3bxgcffEB+fj5XXXUVWVlZ3Hbbbezbty/Y7RSjYMOGDaxfv54NGzbYPu/Y1VPtdqMq7PeN2VpjRWqKCDF0gfz9TJo0iYyMDCZN6rmx8SUDH128NhQcn5Y6eFB9lfXGcUTRRd7me/i88W9iVNvAJxB90lU35xv/Zqm+nU4VwWPqy9Q7hj9xYMzneo1hw/ofq6qqYs2aNaxZswaHw8G5555LSUkJc+bM4ZFHHglWG8UocblceDwev3WmzOeONDK3bRMApVr/SwVY8w2EEMFjnZUHvRfLtdY9CrVoh8ZqfQX/nPJtOlUEC/TdfF39HzOM/dIbNQQNVWVcbPyLE/R9tKgY9q34P1yO4FSBt1vj1O53SvQ26ACqu7ubF154gc9//vNMmzaNv//979x6661UVlby1FNP8eabb/L8889z7733hqK9YgTl5+eTlJRkDgMcbc/bzxOldXPQmEKnlmBz9GesFX2FEMFj1yNlXXA4JiYGTdNC3gN1tAhNcdGN97L57BfZbkwjSWvnKu1fHHj0HGJVaJadGY/2f/Q2xu9PZ47+CY0qgce4nHlLzgna+a3r3oGMEgRq0EnkmZmZGIbBZZddxqZNm2wj1DPOOMOvIJkIT/2Ng0fsfgmADzgOBsirSEhIwO12m3fDQojQsq4tuWTJEkpKSkZlFtWpS0/jh2suIc+7l2v01zjmyEZuUlv5l3EW+/SCEW9POMn0fkLu/7uYKK2bA0YWz2nn063HBfUaSUlJ1NXVkZSUZD5WWVlJaWkpM2bMkCCqH4MOoB555BEuueSSfnsTkpOTOXjw4LAaJkaf3SrmAJrqZm77ZtDgkwGG76AnZ66lpcVvJW8hROhYF3pNT08nLy/P728wKioKt9ttruMWSlGaQZUzn3u9N3Gp/gbHs4fLtVfZZWznyP5zSc4/KeRtCCvK4HhjKxfpbwPwUcwSXu5YCFrwh2DtgmuZKR2YQQ/hXXXVVTIUM0HU19fj9Xp7BVAZRhWRmodP9Fw69YF7lZYuXWp+CSFCzzqD1roAMUBqaiqappGamjpi7Yp0RDDt9nf4rfcLtKgYZuufkPj02ez501eJUh0j1o6xrLZsD/9lvGoGTxuyv0Lhd14JWvBknZFpF1zLTOnAyGLCok+dnZ1+W5957AagKudcODzweax3w0KI0LL2ILS1tdHZ2em3oG57eztKKXOIXtM0lFIhLXUAkBQfTZ0zj597b+RU4z3OdXzArMN/5+sqntXGqeyfqMN6SpFplBH759OZr3fQqqJ5wriQb94Q3AlZ1gWP7Xqb5D07MBJAiT7ZVedFdbNQ2wVA9tLL4Nl/DXgeGU8XYmRZc6Di4uKIjo42FyCG3j0R1g/WUIt2aHygluJY+DVmbL6bmdphLtP+zWG1hYoNs3tm64U4mBsrHMrNqcYGTv90XdFtxjG8rH0OrzP4leQjIyPp6uoiMjIS6D1jUwROCj+IPtktZTDFqCRS83LQkUfurPkBnUcWDxZiZFlnUWVnZ5OamupXrTwzM5OoqCgyMzNHq5noGqw474tMuu19HvNeTL1KJFerJXvNjVxqvMBko2rU2jYSlOFl68u/5Ub1f5yuF9OtHKyf+g3+qf8XXj00y/Acd9xxxMTEcNxxxwG9Z2yKwEkAJQbleHp6n2pzA59GK+PpQowuuw/JgoICcnJyKCjoGTLz9U4d3Us1UtKS4ql3TuMhvsqT3nNoU1Ecq5dxo/Y3Lvb+k5oP/zXu6kfFG03su38J87fcQZrWTKmRycNcxylfuT+kw6gOh4OoqCizJpj0QA2dDOEJILBhNl11c6K2B4CcUy4P+Nwyni7EyNq2bZs5s6qwsLDXkB70DqoiIyNpa2szh3ZGQ7QOn+izOXjZfez6v9v5L30Dx+kH4ZWrKF9zDFON4zmsTUNp4XvvH6E6WGBsYYW+CbqhTUXxD+MsDjoK0EbhdUkP1NBJACWAwKatTjbKidC97DZyOTb/eNt97BamFEKMrJKSEsrLy4GehWDtbmKsQZXT6UTTNL+CiqmpqTQ0NIzoTD2A4wpm8oJzAR95T2CeUcIF+rvkuA9wnXaAKpXCBuMEul3hNbwXpdrY8tg1fEe9QqTuAeCDhM+R+6UHOPTn/yMUfU52EwOKiopITEzs9fOXEYLBkwBKAIF14xZ+Ovtui5rLsX3tU1jIrl27mD17dghaKYQIhK+mj29r18NsDaqWLl3aqx7QaC/DFO3Q2MfxlF/1MAdefZjF9f8gU2vki9qbeB6Zy5nGcezUZlOjTRmV9gUiznBxgirmdO0jHHUKNCg2juF1Tuf7t4d2ybPIyEjcbne/vYoyQjB0EkAJYOBu3Kb6GhZ8OnxXpk/r8zyHDx+ms7OTw4cDqG8ghAiJwsJCCgsLze8D6WG2qwfk++AdzWE9gFkzpjHrm4/yg7tSmeyt4AxtM0X6AU7Vt3Eq26hTSez4Uw1TFl82JmbvOZWbrf/4HxL2/IPvaHvxdS9tjTyRNZ3H0a5PCnm5CLAPgKVIZvBIACUAaGpqora2ts+u+r3v/p1FmsFeI4cuR99Jptap0UKI0RfIME1xcTH79u2jubnZ/GC1m4k7mmJ0RbOexYvqQlrOOoX6Nx7mTH0L6ZqL9MN/g8N/40aVzDZjFoe1bLyt9QOfNEha6w+T5T3EbPZzkraDyO09/2fdysE7RiEfaAv44Q8f5F/33BOS4To7KSkp1NfXk5KSYj5mHW2QMjNDJwGUAGD37t10dHSwe/duzj///F7PR+59FYAtqv+hudmzZ+PxeGQIT4gxxG6YxvrBaVds0+12+22jo6Pp7Owc9dUoHJri1FPP5J7/vMsmtYhEbz2L4w5zQsd7TNaOcLb2Qc+OD77IZUYO+5hGHWm0fLKVhOy5w2+AYdBcU0qGt4JsKpnLAeJ/8zA3HJUDvlefQXXeRfznQBeaM2L41xyCiy66yPwZ+1RUVNDQ0EBFRQWFhYXSIzUMEkAJYIA7TeVh9qdr3/U3fAf9L0AshBg77D44ByqimZKSQlVVlV+PxmiL1gy6nCks+8GjVDUc4be/vIsZfEKhto9ZejkFejkF9CTU88T/oxsnVxiTqSGFZhIo+dtPmOr9BLcWjYHO4S3/RtOdpBi1OPEQpdyU/ON+jvMWk0gL2dTSfu9vSaSTm44KmAylsUtNpUTNZL82gx/d9QAFmsZb99wzYv8XSUlJuFwuv4WBrayBsiSRD50EUALoP1k01VtLtKObMpVBh5bQb/dzW1sbHR0dfnexQoixx65aeUxMjF8dqIULF/ollrtcLpRSuFyukW9wADJTk3E7U9hFCjvUfC65/HLW/N9DTKWSaVoVBVo5iVo7+XoF+VT0HLTnPeYdXT3g5b8B8E3fG50GbMd/H8CtnBxUmewjl1I1jS/e+GP+8cc/mc+PRhqDNYXCLki2VqWXJPKhkwBKAH0s2/KpY7WeP8AP1Rw0XXKbhBgPrB+c1untAMuWLWPZsmXm9zExMbS1tRETE2M+NlZLl+iarxxCJtvJZDsw87t3sO3AbtY8/7/E00oCbeQkajhbykmiDR2DCB0ceNEND+1E00wsKi6D6lZFK3HUM4nPffErTJ15HM/94gHzetnZOaP3Yj8VGRmJpmlm0r9d75Ldz1kMjQRQE1SgiYNKGSzSdgBwUJs+4Hnt7mKFEGNfIHlSdkP9uq7j9XrR9bFf3DIpNpLCecfz0ovptJFODXDFd+/inqOG2e666y4A/8e+77/PcYULR6zNg2HNQbX7mUqPU/BIADVBBZo4mGTUk6B3UMckWrXkAc8rdzdCjB/W9wm7Ypv99V6LkVVXV0dbWxt1dXWj3ZQJQQKoCSrQxMGZqmcB4ENpp0ODDN8JMZFY3yfsim1GRETg9XqJiBidmWYTlV2V8bq6Orq6uiSAGiESQE1QgXTjKmVwsr4dgNjCi+A/23rtExkZSVdXlznmLlNihRg/rO8T1gKdANOmTePAgQNMm9b/DF0RXBEREXR1dfkFrunp6bS0tPgVQxWhIwGU6FO8cYQ0vRmXimXWonNsAyjrH7FMiRVi/LIuUgzQ2tqKUorW1lag900V2PeWiOGJi4ujq6vLL9906dKlZGZmyvvvCBn7WX+DkJeXh6Zpfl///d//7bfPxx9/zKmnnkp0dDS5ubk88MADvc7z97//nWOPPZbo6GjmzZvHa6+9NlIvYUyZoQ4CsEnNxRkZZbtPR0eH3zYrK4tTTjlFep+EGId8ixSXlJSYjyUlJeF0Os3aQ74aUWOpVtR4NH36dGJiYpg+/bPJPfL+O7LGXQ/Uvffeyw033GB+f/TiuM3NzZx99tksX76cxx9/nJKSEr7yla+QnJzM1772NQDee+89LrvsMu6//34+//nP89e//pULL7yQjz76iOOOO27EX89oUYbBSXrP7Ls9HMPn+thP13UMwwiLGThCiOGxLlIMvXs9fPk3R+fh+Ap0DlSoU/TN+l4r646OvnEXQCUkJDBliv3K3M888wxdXV38+c9/JjIykrlz51JcXMzDDz9sBlCPPvooK1eu5Lvf/S4A9913H2vWrOE3v/kNjz/++Ii9jlAbqIzBvo/fo0Crp0NF0qhn9HmejIwMqqqqyMjoex8hxPhglwNlzZMKp7IG4aytrQ2llBQtHkXj7jf8v//7v0lNTWX+/Pn84he/wOPxmM9t3LiR0047zW9sfsWKFezZs4empiZzn+XLl/udc8WKFWzcuLHPa7rdbpqbm/2+xjpfsndpaant8/Uf/hOAzWo2Sus7zk5OTiYqKork5ORQNFMIEWZmzJiB0+n0y8PxBVMSVA2db/1B31bKR4y+cfXb/K1vfYtnn32Wt956i69//ev8/Oc/53vf+575fHV1NZMnT/Y7xvd9dXV1v/v4nrdz//33k5SUZH7l5uYG6yWFjHVFbqv0qrcA2M0x/Z7HuiyAEGJisyaVQ89MPYfDYc7Uk4Bq8CZPnozD4TA/n1JSUtA0TXLNRtGY/+39wQ9+0Csx3Pq1e/duAFatWsWyZcs4/vjjufHGG3nooYf49a9/ba4kHip33HEHLpfL/AqHMemjV+S2qirbz0zvAQylUavbD4f6ZGdnk5qaSnZ2dqiaKoQII9akcoDly5ezbNkys3fft+zLWFv+ZazwzWo+ukSB2+3GMAzz8yw5OZnIyEjp/R9FYz4H6vbbb+faa6/td5++pmwuWrQIj8fDoUOHmDVrFlOmTKGmpsZvH9/3vrypvvbpK68KICoqiqgo+1lqY1VFRQUul8s2gPrk/RfJBLar6Xj16AHP4wvErLkRQojxz5pPaTeVvq6ujkOHDpGQkEBWVpbt4uVS6uAz0dHRdHd3m8N10HshZ7tlswJdoksEx5gPoNLT04dcFKy4uBhd180E58WLF/OjH/2I7u5uM7Jfs2YNs2bNYtKkSeY+a9eu5dZbbzXPs2bNGhYvXjy8FzLG1NfXo5Sivr6+13PRpWsAKFEFA56nra2Njo4OSWQUYoKyFs+1K9K7efNmqqur6ezspLCwkKioKNxut9+N50SeqWf9/7D7v0hKSqK9vd3s2bNbNksKGY+sMT+EF6iNGzfyy1/+km3btlFaWsozzzzDbbfdxpVXXmkGR5dffjmRkZFcf/317Nixg+eee45HH32UVatWmef59re/zerVq3nooYfYvXs3d999Nx9++CG33HLLaL20kLBbFBQA5WF2x1YAyvWBVxeXxYOFmNhmzJhBfn6++UFeWVnJ+vXrqaysNPfxTdzxbU8++WRSUlI4+eSTzX2seVETKU/KNyzn2/p64Y7ujbMO2fl69Y4uF2H9WYjQGvM9UIGKiori2Wef5e6778btdjN9+nRuu+02v+AoKSmJN954g5tvvpkFCxaQlpbGnXfeaZYwAFiyZAl//etf+fGPf8wPf/hDZs6cyUsvvTTuakD1NdV4klFHlN5NlZZBBwkM1JkuiwcLMbFZe5zsekHS0tJoamoiLS0NgPb2dgzDoL293TzOGjRMpFlmDocDr9fbKzfs6Bwx682qr6gpYKZPBLJElwiecRNAnXDCCbz//vsD7nf88cfz7rvv9rvPJZdcwiWXXBKspo06u3Hxvt6c8umpPn44/XS0OslFEEIMjt1yToHcaMXExNDa2kpMTEzI2zia7HK9CgsL2bVrF7NnzwZg0qRJtLS0mKMn0Pv/0K6oqRhZ4yaAEn2zuyO0G8JTSrFQ2wVA3LzP2659F8i5hRATl10viPUxu4DKmvczXlc5sMtv8uWH+crlWGfc2bEraipGlgRQE4DdHaG1yxggxnCRrrtoJYaCRSsDCqBk8WAhxGDZBVkxMTG0tbWZPVApKSnU19f71Tkar0FVXV0dSikzn8k64w5636zKjLvRJwHUBGD3ZhUREYHX6/WrMzKNMgD2xZ/E/Mje5Qvsgi4ZcxdCDJbdh39UVBQOh8OcidbV1eW3hZ73Lbfb7fe+FW7s3kdjY2NxuVzExsYCPUtklZWV+S2RZb1Zld7/0ScB1ATV2dnptwWYzx4AVMFK22NkjSshRDDYffjn5+fT0dFBfn6++f2uXbvM7wHy8vI4cOAAeXl5AMTHx9Pa2kp8fPzIvoBBcDqdeDwenM6+P26tr2P58uVmgOljvVmV3v/RJ5+EAgCH0cEs/TBepZG/5Au2+9itcSWEEINlN92+rq6O9vZ2cxhr0qRJZGRk+CVSW5eJSUxMRNM0EhMTR/YF9MG3NNbRS2T5Zs35tpMnT0bTNL8lwxYuXEheXh4LFy4M+FpZWVmccsop0vs0iqQHSgCQqSpBg+1qBoVp9lXXHQ6H+SWEEENlN/Tvcrno7u42837seliUUhiGYSZg2627FyzWniO72XPWx0444QRKSkr8ZsZZk8ZPOumkXvtYE8JleC48SA+UAGAOPX+s/VUfr6qqwu12U1VVNVLNEkJMENZeGLseFusSMPPnzyclJYX58+eb+/gCnqMDn6O3ENhafBkZGWiaZuYh2R1jLRC6b98+mpqa2Ldvn7lPfn4+MTEx5lDk3r17qaioYO/evX1eWwpihgfpgRK0trg4Qev5Y67op/r4RF5qQQgRWtZemG3btpk9Nb7H09PTaWlpMZf3Kigo6JVWkJGRQVVVlRn42L1vJSYm0tTU1O/QX1dXF0opM4k9JiaGlpYWvzpVCQkJuN1uc8iuoaEBpRQNDQ3mPp2dnRiGYeabWnvaRPiSHijB3vdfI0rrpkKl0qn1nYw5adIkHA6HX06CEEKEgq/SdklJiflYQUEB2dnZFBT09JRv2LCB9evXs2HDhj7PY9dzdPrpp3PMMcdw+umnA5gz/45em88X4Pi2aWlpOBwOs5o69Eys0TTNnFgzd+5cYmJimDt3rrlPfX09XV1d5rqjgeQ7+YbwSktL+9xHjD7pgRJ07X4dgGJjFjj7rj4eExOD0+kc95WChRCjz67SdkVFBQ0NDVRUVFBYWIjL5cLj8fj15jQ2NqKUorGxEbDvbUpPTycvL8/syTrmmGM4cOAAxxxzjLlPVFQU3d3dfkGVldvtRillFrw8//zzOf/88/32sQ4pBlIAU2bYhQcJoCaA/gquKaXIbXwPgFJtWr/nkYWDhRAjxRrkALS1tdHR0UFbWxvQ05tjTciOiIigs7PTrBU1ffp0Ojs7mT59urlPcXEx+/bto7m5maysLJYuXUpmZqZfwOLxePy2ra2tGIbhl7De0tLit7V7r/X1UPlysKQA5vghAdQE0N+MjkjVTjY1uJWTI3q63eEmWThYCDFS7N63rDdxdr05aWlptLe3m0NtNTU1dHV1UVNTM6jrx8TE0NnZafa4x8fH09jY6Fdzyul00tXVZfYuWQMz6Clb0NjYaJYtCGSGnczCCw8SQE0ACQkJOJ1Ov9okPplGJTjgY3UMht7/r4NUHRdCjJShLkqclpZGU1OTGUAlJSVRX19PUlKSuU9sbCy6rpuVv+0CltNPP71X75bV4sWL/fapr6+npaXFzHeC3gFcIMNzMoQXHiSAmgC2bdtGWVkZhmH0ulubrR0AYBfH2B0qhBCjIpBFie1kZ2fT2NhIdnY2gO3wXHl5OW1tbZSXlwP2N5nWIUS7Ibxly5axbNky83u7fawBXCCvQW5Ww4MEUBNAVVUVXq+3d/0m5WW+1lOvpErL7nXceF24UwgxfrW0tODxeMy8pLq6Og4dOkRCQoIZlFgT1K3HQO/huPT0dJqbm/1ysqysZRbAPoAT44MEUBNAX/WbEo0GovRuqrV0OojHOv8uOjqa9vZ2oqN7FhaW5EchxFhi955kHf567733qKuro6WlxeyBt+ZOBTJkFkggZLeP9CaNXxJATQB2q5oDzFCfAHA4ZQlaY+/yBdb6KZLYKIQYS+zek6wBSyAFgO2CHGu+lQy9CSsJoCYAuzcQpWC+vgeAqNkrYMP+XsdFR0fT2tpq9kBJYqMQYiwJtOdooGRwOxIMiYFIADVBRah2pmq1dCkHMxedBxse7bXPnDlzKCkpYc6cOYC8oQghxpZA3pMCKVwpxFBIdvAElWlUAvCxyicmIdl2n7KyMlwuF2VlZSPYMiGEEGLskwBqHKqsrGT9+vVUVlb2uU8g5QsqKyvxer39nkcIIcJNIO+RQgxEhvDGIbtquEdra21hvrYXgCqt7+5vu0U4hRAi3MmEGBEMEkCNQ21tbXR2dprrRVnt+eDfnKB1U60m0a71rk7uEx8fT0dHh9/SBUIIEe5kQowIBgmgxqG4uDiio6P7XPTXvfsNALaqWWh67/IFPrNnz8bj8TB79uyQtFMIIUaDTIgRwSAB1Dg00HpRmQ3vA1DKtH7P097ejmEYtLe3B72NQggx3kix4YlFksjHId/SBXV1db2eqzx8gDzjMIbSaNL7XpJACCHE4Phyq0pLS0e7KWIESA/UOFRSUmIukmmtf3J486tkAbvUNLx6ZL/nsS7KKYQQom+SWzWxSAA1DuXk5NDU1EROTk6v5/SD6wDY2U/5Ah+7BTaFEELYk9yqiUUCqHGor9wlpRQzWj4EoJyBe5XkbkoIIYSwJwHUBBKtWknVXLSrKFr0FL/nHA4HXq/Xr+aT3E0JIYQQ9iSAGof6moWXZVSAA/bHFqE6/ecPJCYm0tTURGJi4kg2VQghhAhLMgtvApmlHQTAPfW0Xs/FxMSgaRoxMTEj3SwhhBAi7EgANQ49//zzrF27lueff958TCmDQq1n6YIpJ5zb65gjR46glOLIkSMj1UwhhBAibEkANQ65XC6/LUCi0UC01k0dKeTMLOp1TEdHh99WCCGEEH2TAGqCmKp66kKVJZ+Epvf+sWua5rcVQgjxmcrKStavX09lZeVoN0WMEZJEPkEc9+nwnZ5/pu3zDocDwzD8ZuEJIYTo4asyDsjsZAFIADUhaKqLY/XDAEw/6TzbfeLi4jhy5EifCxALIcREJnXxhJUEUBNAqlELOuwxcpiV0bs6OcCyZcsoKSlh3rx5I9w6IYQY+6QunrCSAGoCOIZPANhBPrP62KewsLDXunlCCCGEsCdJ5OOcMgwKtX0AHMa+90kIIYQQgyMB1Dh3aF8JmVojbuWk2bJ8ixBCCCGGRgKoca566+sAbFczMDQZsRVCCCGCIWwCqJ/97GcsWbKE2NhYkpOTbfcpKyvjvPPOIzY2loyMDL773e/i8Xj89lm3bh0nnHACUVFR5Ofn8+STT/Y6z2OPPUZeXh7R0dEsWrSITZs2heAVBcdAtUkiDq8HYJ+aNpLNEkIIIca1sAmgurq6uOSSS7jppptsn/d6vZx33nl0dXXx3nvv8dRTT/Hkk09y5513mvscPHiQ8847jzPOOIPi4mJuvfVWvvrVr/L666+b+zz33HOsWrWKu+66i48++ojCwkJWrFhBbW1tyF/jUPhqk5SWlvZ6TinFjLatAFTqMntECCGECJawCaDuuecebrvttj6n2b/xxhvs3LmTp59+mqKiIs455xzuu+8+HnvsMbq6ugB4/PHHmT59Og899BCzZ8/mlltu4Ytf/CKPPPKIeZ6HH36YG264geuuu445c+bw+OOPExsby5///OcReZ2DlZCQgNPpJCEhoddzkaqNFJppV5G0askj3zghhBBinAqbAGogGzduZN68eUyePNl8bMWKFTQ3N7Njxw5zn+XLl/sdt2LFCjZu3Aj09HJt2bLFbx9d11m+fLm5jx23201zc7Pf10ipqKigoaGBioqKXs9NMaoAKFH5KE0qjAshhBDBMm4CqOrqar/gCTC/r66u7nef5uZmOjo6qK+vx+v12u7jO4ed+++/n6SkJPMrNzc3GC8pIG1tbXR2dtLW1tbruZlaT/2nffTOf4qIiPDbCiGEECJwoxpA/eAHP0DTtH6/du/ePZpNDMgdd9yBy+Uyvw4fPjxi1+7o6KC7u5uOjg7/J5Si6NP6T9XalF7HxcbG+m2FEEIIEbhRndd+++23c+211/a7T6DrDk2ZMqXXbLmamhrzOd/W99jR+yQmJhITE4PD4cDhcNju4zuHnaioKKKiogJqZ7C1trZiGAatra1+j0cbzSToHbQQS5uW1Os43+xE6yxFIYQQQgxsVAOo9PR00tPTg3KuxYsX87Of/Yza2loyMjIAWLNmDYmJicyZM8fc57XXXvM7bs2aNSxevBiAyMhIFixYwNq1a7nwwgsBMAyDtWvXcssttwSlncEWHx9PY2Mj8fHxfo9PUT35T6Vx86G9d0djUlIS7e3tJCX1Dq6EEEII0b+wyYEqKyujuLiYsrIyvF4vxcXFFBcXmz0vZ599NnPmzOGqq65i27ZtvP766/z4xz/m5ptvNnuHbrzxRkpLS/ne977H7t27+e1vf8vzzz/PbbfdZl5n1apV/OEPf+Cpp55i165d3HTTTbS1tXHdddeNyuseSExMDE6nk5iYGL/HC7RDAHTlnjIKrRJCCCHGt7ApTX3nnXfy1FNPmd/Pnz8fgLfeeotly5bhcDh45ZVXuOmmm1i8eDFxcXFcc8013HvvveYx06dP59VXX+W2227j0UcfJScnhz/+8Y+sWLHC3OfSSy+lrq6OO++8k+rqaoqKili9enWvxPKxoqysDLfbTVlZmfmYUgaF2gEAphR9Dva81uu4hoYGlFI0NDSMWFuFEEKI8SJsAqgnn3zStmr40aZNm9ZriM5q2bJlbN26td99brnlljE7ZGfl64E7OgcqznARq7tpUAnkFJwA9P4/iY2Nxe12SxK5EEIIMQRhM4QnApetempCfazy0XT7+k+nn346xxxzDKeffvpINk0IIYQYF8KmB0oEbtan+U8HmcoZfexTWFhIYWHhiLVJCCGEGE+kB2qcaW1t4TitZ128Om1s5m0JIYQQ4U4CqHFm/5a1RGkeqtUkOrW40W6OEEIIMS5JADXOtO15C+hZ/w5NG+XWCCGEEOOTBFDjTHLtZgAOkTPKLRFCCCHGLwmgwkxlZSXr16+nsrKy13NtrS3kd+8BoEHLGOmmCSGEEBOGBFBhprS0lP3791NaWtrruf1b1xGleahRybg1qe8khBBChIqUMQgzvsWV7RZZbt3zNgDb1TGgf5b/pGkaSik0yYkSQgghgkICqDCTlZVFVlaW7XOJNZsA+IRsv8djY2Npa2uTquNCCCFEkMgQXpjpKwdKKS8zu3YCves/ud1uv60QQgghhkcCqDCzYcMG8+tocUYz0Vo3jSTRocX7Pef1ev22QgghhBgeCaDCTF1dHW63m7q6Or/HM1U1AJ/EF/XKdYqMjPTbCiGEEGJ4JIAKM+3t7SilaG9v93s8X/sEAE/u4l7HJCYmomkaiYmJI9JGIYQQYryTACrMdHR0+G0BlFLM0w4AMHneWb2OWbp0KTNmzGDp0qUj00ghhBBinJMAKsw4nU6/LUCM0Uyc5sZFPLnHLhitpgkhhBAThgRQYcYuIXyKL/8p7ng03dHrmJKSEsrLyykpKRmZRgohhBDjnNSBCjO6ruP1etH1z2LfY7QyANzZvfOfAObNm+e3FUIIIcTwSA9UmFNKcfyn+U9p88603Sc9PZ28vDzS09NHsmlCCCHEuCU9UGGmu7vbbxutWkjU2mlV0UybfbLtMb7184A+q5gLIYQQInASQIW5DKMWHLBdzeBkp/2Ps7/184QQQggxeBJAhbljPq3/dICp2Pc/9b9+nhBCCCEGT3KgwpjXa5j1n6q1KaPcGiGEEGLikAAqjB3cvZUUrZUOFUmbljTazRFCCCEmDAmgwljtzrcB2KnyUFrv+k9CCCGECA0JoMKY4/D7ABwgd5RbIoQQQkwsEkCFsezmbQBUI/lPQgghxEiSACpMaUYXOVRjKA2XnjLazRFCCCEmFAmgwtQkox6AfSobQ4sY5dYIIYQQE4sEUGEqmyoA9qppo9wSIYQQYuKRACpMzfq0gGa51rtAZlxcnN9WCCGEEMElAVQYUsrLLK0MgCN6aq/nY2Nj0TSN2NjYkW6aEEIIMSFIABWG4o0jODWDai2dLq13kJSenk5UVBTp6emj0DohhBBi/JO18MJQlurJf6pMLITm3s8XFBTgdrspKCgY4ZYJIYQQE4P0QIWhY7TDABi59ssHt7S04PF4aGlpGclmCSGEEBOGBFBhRinFXO0gAJPnnm67z4wZM8jPz2fGjBkj2TQhhBBiwpAAKsxEGy3Ea500q1hyCk4Y7eYIIYQQE5IEUGEmQ9UCPQsIaw77FLbS0lL2799PaWnpSDZNCCGEmDAkiTzM5NGT/1SqcrHPgMIcupMhPCGEECI0JIAKI4bX4Di9J/+pRp/c535ZWVlkZfUusCmEEEKI4JAhvDBSdnAXGdoRupSDVi15tJsjhBBCTFgSQIWR6pJ1AOxRUzE06TwUQgghRkvYBFA/+9nPWLJkCbGxsSQnJ9vuo2lar69nn33Wb59169ZxwgknEBUVRX5+Pk8++WSv8zz22GPk5eURHR3NokWL2LRpUwhe0eBF1m0HYB9TR7klQgghxMQWNt0YXV1dXHLJJSxevJg//elPfe73xBNPsHLlSvP7o4OtgwcPct5553HjjTfyzDPPsHbtWr761a+SmZnJihUrAHjuuedYtWoVjz/+OIsWLeKXv/wlK1asYM+ePWRkZITs9QXihBt+y8P3xNKtO0a1HUIIMdZ5vV66u7tHuxlijImIiMDhCM5naNgEUPfccw+AbY/R0ZKTk5kyZYrtc48//jjTp0/noYceAmD27NmsX7+eRx55xAygHn74YW644Qauu+4685hXX32VP//5z/zgBz8I0qsZIk2jRUsc3TYIIcQYppSiurqaI0eOjHZTxBjlixM0TRvWecImgArUzTffzFe/+lVmzJjBjTfeyHXXXWf+J23cuJHly5f77b9ixQpuvfVWoKeXa8uWLdxxxx3m87qus3z5cjZu3Dhir0EIIcTQ+IKnjIwMYmNjh/0hKcYPpRTt7e3U1vbUU8zMzBzW+cZVAHXvvfdy5plnEhsbyxtvvME3vvENWltb+da3vgX0/GFNnuw//X/y5Mk0NzfT0dFBU1MTXq/Xdp/du3f3eV23243b7Ta/b262WeE3RBwOB16vN2hdkkIIEa68Xq8ZPKWmpo52c8QYFBMTA0BtbS0ZGRnD+uwc1STyH/zgB7aJ30d/9Re4WP3kJz9h6dKlzJ8/n+9///t873vf4xe/+EUIX0GP+++/n6SkJPMrNzc35Nf0UUr5bYUQYqLy5TzFxsaOckvEWOb7/Rhujtyo9kDdfvvtXHvttf3uM5xq2osWLeK+++7D7XYTFRXFlClTqKmp8dunpqaGxMREYmJicDgcOBwO2336yqsCuOOOO1i1apX5fXNz84gFUREREbjdbiIiIkbkekIIMdbJsJ3oT7B+P0Y1gEpPTyc9PT1k5y8uLmbSpElERUUBsHjxYl577TW/fdasWcPixYsBiIyMZMGCBaxdu5YLL7wQAMMwWLt2Lbfcckuf14mKijKvMdIkgBJCCCFGXtjUgSorK6O4uJiysjK8Xi/FxcUUFxfT2toKwMsvv8wf//hHtm/fzv79+/nd737Hz3/+c775zW+a57jxxhspLS3le9/7Hrt37+a3v/0tzz//PLfddpu5z6pVq/jDH/7AU089xa5du7jppptoa2szZ+WNNb7ASQIoIYQIX8uWLTMnNI22l156ifz8fBwOB7feeitPPvlkn/UXJ7KwSSK/8847eeqpp8zv58+fD8Bbb73FsmXLiIiI4LHHHuO2225DKUV+fr5ZksBn+vTpvPrqq9x22208+uij5OTk8Mc//tEsYQBw6aWXUldXx5133kl1dTVFRUWsXr26V2L5WJGZmUl7e/uwZxMIIYQYv9atW8cZZ5xBU1PTgMHQ17/+da677jq+9a1vkZCQgNPp5NxzzzWfv/vuu3nppZcoLi4ObaPHuLAJoJ588sl+a0CtXLnSr4BmX5YtW8bWrVv73eeWW27pd8huLFm6dCmZmZnDyhUTQgghAFpbW6mtrWXFihV+i9L7Zq+Jz4TNEJ6wl5WVxSmnnOL3iy6EEKKHUor2Ls+ofA12drTH4+GWW24hKSmJtLQ0fvKTn/idw+12853vfIfs7Gzi4uJYtGgR69atM5//5JNPOP/885k0aRJxcXHMnTuX1157jUOHDnHGGWcAMGnSJDRNs53AtW7dOhISEgA488wz0TSNdevW+Q3hPfnkk9xzzz1s27bNnC0/UIHr8SpseqCEvcrKSkpLS5kxY4YEUUIIYdHR7WXOna+PyrV33ruC2MjAP2afeuoprr/+ejZt2sSHH37I1772NaZOnWqmotxyyy3s3LmTZ599lqysLP75z3+ycuVKSkpKmDlzJjfffDNdXV288847xMXFsXPnTuLj48nNzeWFF17g4osvZs+ePebMc6slS5awZ88eZs2axQsvvMCSJUtISUnh0KFD5j6XXnop27dvZ/Xq1bz55psAJCUlDe8/KkxJABXmiouL2bdvH83NzRJACSFEGMvNzeWRRx5B0zRmzZpFSUkJjzzyCDfccANlZWU88cQTlJWVme/13/nOd1i9ejVPPPEEP//5zykrK+Piiy9m3rx5gH8ZoJSUFAAyMjL6zIGKjIw013xNSUmxLd8TExNDfHw8Tqez3/I+E4EEUEIIIcatmAgHO+9dMfCOIbr2YJx88sl+NYoWL17MQw89hNfrpaSkBK/XS0FBgd8xbrfbrLr+rW99i5tuuok33niD5cuXc/HFF3P88ccP/4UIWxJAhbmioiISExMliVwIIWxomjaoYbSxqrW1FYfDwZYtW3otPxIfHw/AV7/6VVasWMGrr77KG2+8wf33389DDz3kV85HBE/4/1ZNcFlZWTJ0J4QQ48AHH3zg9/3777/PzJkzcTgczJ8/H6/XS21tLaeeemqf58jNzeXGG2/kxhtv5I477uAPf/gD3/zmN4mMjAR61gscrsjIyKCcJ9zJLDwhhBBiDCgrK2PVqlXs2bOHv/3tb/z617/m29/+NgAFBQVcccUVXH311bz44oscPHiQTZs2cf/99/Pqq68CcOutt/L6669z8OBBPvroI9566y1mz54NwLRp09A0jVdeeYW6ujqzCPVQ5OXlcfDgQYqLi6mvr8ftdg//xYchCaCEEEKIMeDqq6+mo6ODk046iZtvvplvf/vbfO1rXzOff+KJJ7j66qu5/fbbmTVrFhdeeCGbN29m6tSpQE/v0s0338zs2bNZuXIlBQUF/Pa3vwUgOzube+65hx/84AdMnjx5WLUOL774YlauXMkZZ5xBeno6f/vb34b3wsOUpgZbqEIMqLm5maSkJFwuF4mJiUE99z333GP++6677grquYUQIpx1dnZy8OBBpk+fTnR09Gg3R4xR/f2eDObzW3qghBBCCCEGSQKoMOOb4nr0VFchhBBCjCwJoIQQQgghBkkCqDAjPVBCCCHE6JMASgghhBBikCSACjOGYfhthRBCCDHyJIAKM7qu+22FEEIIMfLkUzjMSA6UEEIIMfokgAozvkUkrYtJCiGEECPpySefJDk5ebSbwbXXXsuFF1444teVACrMpKWloWkaaWlpo90UIYQQok+HDh1C0zSKi4vH5PmGSwKoMJOcnExkZOSYiPqFEEKMnq6urtFuQlCE6+uQAEoIIYQYZS0tLVxxxRXExcWRmZnJI488wrJly7j11lvNffLy8rjvvvu4+uqrSUxMNBcafuGFF5g7dy5RUVHk5eXx0EMP+Z1b0zReeuklv8eSk5N58skngc96dl588UXOOOMMYmNjKSwsZOPGjX7HPPnkk0ydOpXY2FguuugiGhoa+n1N06dPB2D+/PlomsayZcuAz4bcfvazn5GVlcWsWbMCamdf5/N58MEHyczMJDU1lZtvvpnu7u5+2zdczpCeXYSEJJALIUSAlILu9tG5dkQsBPh+vWrVKjZs2MC//vUvJk+ezJ133slHH31EUVGR334PPvggd955p7mY/JYtW/jSl77E3XffzaWXXsp7773HN77xDVJTU7n22msH1dwf/ehHPPjgg8ycOZMf/ehHXHbZZezfvx+n08kHH3zA9ddfz/3338+FF17I6tWrB1zQftOmTZx00km8+eabzJ07l8jISPO5tWvXkpiYyJo1awJuX3/ne+utt8jMzOStt95i//79XHrppRQVFXHDDTcM6v9gMCSACjNxcXFER0cTFxc32k0RQoixr7sdfp41Otf+YSVEDvxe3dLSwlNPPcVf//pXzjrrLACeeOIJsrJ6t/vMM8/k9ttvN7+/4oorOOuss/jJT34CQEFBATt37uQXv/jFoAOo73znO5x33nkA3HPPPcydO5f9+/dz7LHH8uijj7Jy5Uq+973vmdd57733WL16dZ/nS09PByA1NZUpU6b4PRcXF8cf//hHvyBoIP2db9KkSfzmN7/B4XBw7LHHct5557F27dqQBlAyhBdmioqKWLBgQa+7EiGEEOGptLSU7u5uTjrpJPOxpKQkc2jraCeeeKLf97t27WLp0qV+jy1dupR9+/bh9XoH1Y7jjz/e/HdmZiYAtbW15nUWLVrkt//ixYsHdf6jzZs3b1DB00Dmzp3rNzs9MzPTbHuoSA+UEEKI8SsitqcnaLSuHWRDGX3QNA2llN9jdvlBERERfsdA6Fa9sHsdgbbTztFt950r1Ct2SAAVZkpLS9m/fz+AbfeuEEKIo2haQMNoo2nGjBlERESwefNmpk6dCoDL5WLv3r2cdtpp/R47e/ZsNmzY4PfYhg0bKCgoMHtk0tPTqaqqMp/ft28f7e2DywubPXs2H3zwgd9j77//fr/H+HqYAu0JG6idgz1fqEkAFWYSEhJwOp0kJCSMdlOEEEIEQUJCAtdccw3f/e53SUlJISMjg7vuugtd1wecNHT77bezcOFC7rvvPi699FI2btzIb37zG37729+a+5x55pn85je/YfHixXi9Xr7//e/36rEZyLe+9S2WLl3Kgw8+yAUXXMDrr7/eb/4TQEZGBjExMaxevZqcnByio6NJSkrqc/+B2jnY84Wa5ECFmZaWFjweDy0tLaPdFCGEEEHy8MMPs3jxYj7/+c+zfPlyli5dyuzZs4mOju73uBNOOIHnn3+eZ599luOOO44777yTe++91y+B/KGHHiI3N5dTTz2Vyy+/nO985zvExg5uePHkk0/mD3/4A48++iiFhYW88cYb/PjHP+73GKfTya9+9St+//vfk5WVxQUXXNDv/gO1c7DnCzVNWQccxbA1NzeTlJSEy+UiMTExqOeurKyktLSUGTNmyBCeEEIcpbOzk4MHDzJ9+vQBA4+xrq2tjezsbB566CGuv/760W7OuNLf78lgPr9lCC/MZGVlSeAkhBDjzNatW9m9ezcnnXQSLpeLe++9F2DUe1lE3ySAEkIIIcaABx98kD179hAZGcmCBQt49913Zd3TMUwCKCGEEGKUzZ8/ny1btox2M8QgSBK5EEIIIcQgSQAlhBBCCDFIEkAJIYQYV2RyuehPsH4/JIASQggxLviKLg62yraYWHy/H4MtJmolSeRCCCHGBYfDQXJysrmIbGxs7ICVvMXEoZSivb2d2tpakpOT/RYfHgoJoIQQQowbU6ZMATCDKCGskpOTzd+T4ZAASgghxLihaRqZmZlkZGTQ3d092s0RY0xERMSwe558JIASQggx7jgcjqB9UAphR5LIhRBCCCEGSQIoIYQQQohBkgBKCCGEEGKQJAcqBHxFupqbm0e5JUIIIYQIlO9zO5BimxJAhUBLSwsAubm5o9wSIYQQQgxWy/9v795Dmur/OIC/nbSZl83My1wX0y4ry1vJVkFEKalEFEVUVKyIiLKi7EJFale7CCVF5F+PRX9YERQUBZUkFVsWxqKiIiXR0K0bYdrFmt/fHw8N/OmjO8+jfo/2fsFgnh22Nx++zjfHnbMvX2AwGDrdx0/wmvfdrrW1FfX19QgJCen2i7g1NjZi2LBhqKurg16v79bn7m84K99xVr7jrHzHWfmOs/JdT85KCIEvX77AZDJBo+n8U048AtUDNBoNhg4d2qOvodfr+UvmI87Kd5yV7zgr33FWvuOsfNdTs+rqyNNv/BA5ERERkUIsUEREREQKsUD1MTqdDvn5+dDpdLKjqB5n5TvOynecle84K99xVr5Ty6z4IXIiIiIihXgEioiIiEghFigiIiIihVigiIiIiBRigSIiIiJSiAWqjzh48CCmTp2KwMBAhIaGdriPn59fu9v58+d7N6hK+DKv2tpazJ49G4GBgYiMjMS2bdvw69ev3g2qQiNGjGi3jg4fPiw7lmqcOnUKI0aMQEBAAKxWKx4+fCg7kurs2bOn3RoaO3as7FiqcPfuXcyZMwcmkwl+fn64cuVKm8eFEMjLy0N0dDQGDhyI9PR0vH79Wk5Yybqa1YoVK9qts8zMzF7LxwLVR7S0tGDhwoVYu3Ztp/uVlJSgoaHBe5s3b17vBFSZrubl8Xgwe/ZstLS0wG634+zZszhz5gzy8vJ6Oak67du3r8062rBhg+xIqnDhwgXk5OQgPz8fjx8/RlJSEjIyMvDu3TvZ0VRn/PjxbdbQ/fv3ZUdShebmZiQlJeHUqVMdPn706FGcOHECxcXFqKioQFBQEDIyMvD9+/deTipfV7MCgMzMzDbrrLS0tPcCCupTSkpKhMFg6PAxAOLy5cu9mkft/mle169fFxqNRrhcLu+206dPC71eL378+NGLCdUnJiZGHD9+XHYMVbJYLCI7O9v7s8fjESaTSRw6dEhiKvXJz88XSUlJsmOo3v+/Z7e2tgqj0SgKCwu92z5//ix0Op0oLS2VkFA9Ovr7ZrPZxNy5c6XkEUIIHoHqZ7KzsxEeHg6LxYK//voLgpf56pDD4UBCQgKioqK82zIyMtDY2Ijnz59LTKYOhw8fxuDBg5GSkoLCwkL+axN/H9WsrKxEenq6d5tGo0F6ejocDofEZOr0+vVrmEwmxMXFYenSpaitrZUdSfXevHkDl8vVZo0ZDAZYrVausX9QXl6OyMhImM1mrF27Fh8/fuy11+aXCfcj+/btw8yZMxEYGIibN29i3bp1aGpqwsaNG2VHUx2Xy9WmPAHw/uxyuWREUo2NGzdi4sSJCAsLg91ux86dO9HQ0IBjx47JjibVhw8f4PF4Olw3L1++lJRKnaxWK86cOQOz2YyGhgbs3bsX06ZNw7NnzxASEiI7nmr9fu/paI396e9LHcnMzMT8+fMRGxuL6upq7Nq1C1lZWXA4HPD39+/x12eBkmjHjh04cuRIp/u8ePHC5w9f5ubmeu+npKSgubkZhYWF/aZAdfe8/iRKZpeTk+PdlpiYCK1WizVr1uDQoUPSvzqB+oasrCzv/cTERFitVsTExODixYtYtWqVxGTUnyxevNh7PyEhAYmJiRg5ciTKy8uRlpbW46/PAiXRli1bsGLFik73iYuL+9fPb7VasX//fvz48aNf/OHrznkZjcZ2Z0+53W7vY/3Nf5md1WrFr1+/UFNTA7PZ3APp+obw8HD4+/t718lvbre7X66Z7hQaGooxY8agqqpKdhRV+72O3G43oqOjvdvdbjeSk5Mlpeo74uLiEB4ejqqqKhao/i4iIgIRERE99vxOpxODBg3qF+UJ6N55TZkyBQcPHsS7d+8QGRkJALh16xb0ej3i4+O75TXU5L/Mzul0QqPReOf0p9JqtZg0aRLKysq8Z7e2trairKwM69evlxtO5ZqamlBdXY3ly5fLjqJqsbGxMBqNKCsr8xamxsZGVFRUdHkGNgFv377Fx48f25TPnsQC1UfU1tbi06dPqK2thcfjgdPpBACMGjUKwcHBuHr1KtxuNyZPnoyAgADcunULBQUF2Lp1q9zgknQ1r1mzZiE+Ph7Lly/H0aNH4XK5sHv3bmRnZ/ebwvlvOBwOVFRUYMaMGQgJCYHD4cDmzZuxbNkyDBo0SHY86XJycmCz2ZCamgqLxYKioiI0Nzdj5cqVsqOpytatWzFnzhzExMSgvr4e+fn58Pf3x5IlS2RHk66pqanNkbg3b97A6XQiLCwMw4cPx6ZNm3DgwAGMHj0asbGxyM3Nhclk+iMvSdPZrMLCwrB3714sWLAARqMR1dXV2L59O0aNGoWMjIzeCSjt/D9SxGazCQDtbnfu3BFCCHHjxg2RnJwsgoODRVBQkEhKShLFxcXC4/HIDS5JV/MSQoiamhqRlZUlBg4cKMLDw8WWLVvEz58/5YVWgcrKSmG1WoXBYBABAQFi3LhxoqCgQHz//l12NNU4efKkGD58uNBqtcJisYgHDx7IjqQ6ixYtEtHR0UKr1YohQ4aIRYsWiaqqKtmxVOHOnTsdvjfZbDYhxN+XMsjNzRVRUVFCp9OJtLQ08erVK7mhJelsVl+/fhWzZs0SERERYsCAASImJkasXr26zaVpepqfEDzPnYiIiEgJXgeKiIiISCEWKCIiIiKFWKCIiIiIFGKBIiIiIlKIBYqIiIhIIRYoIiIiIoVYoIiIiIgUYoEiIiIiUogFioiIiEghFigiIiIihVigiIi68P79exiNRhQUFHi32e12aLValJWVSUxGRLLwu/CIiHxw/fp1zJs3D3a7HWazGcnJyZg7dy6OHTsmOxoRScACRUTko+zsbNy+fRupqal4+vQpHj16BJ1OJzsWEUnAAkVE5KNv375hwoQJqKurQ2VlJRISEmRHIiJJ+BkoIiIfVVdXo76+Hq2traipqZEdh4gk4hEoIiIftLS0wGKxIDk5GWazGUVFRXj69CkiIyNlRyMiCVigiIh8sG3bNly6dAlPnjxBcHAwpk+fDoPBgGvXrsmORkQS8F94RERdKC8vR1FREc6dOwe9Xg+NRoNz587h3r17OH36tOx4RCQBj0ARERERKcQjUEREREQKsUARERERKcQCRURERKQQCxQRERGRQixQRERERAqxQBEREREpxAJFREREpBALFBEREZFCLFBERERECrFAERERESnEAkVERESkEAsUERERkUL/A+Ct+hGwhKBBAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -874,17 +874,17 @@ "\n", "v1.model=None, \n", "v1.experiment_data= x y\n", - "0 -15.0 -1386.402949\n", - "1 -14.7 -1073.690228\n", - "2 -14.4 -1072.951606\n", - "3 -14.1 -1096.806703\n", - "4 -13.8 -838.977013\n", + "0 -15.0 -1545.935365\n", + "1 -14.7 -1144.076706\n", + "2 -14.4 -1146.527730\n", + "3 -14.1 -1100.649495\n", + "4 -13.8 -746.834562\n", ".. ... ...\n", - "96 13.8 384.625949\n", - "97 14.1 559.333146\n", - "98 14.4 795.556490\n", - "99 14.7 920.071641\n", - "100 15.0 907.742229\n", + "96 13.8 521.681151\n", + "97 14.1 674.091679\n", + "98 14.4 770.699562\n", + "99 14.7 848.473161\n", + "100 15.0 953.358913\n", "\n", "[101 rows x 2 columns]\n" ] @@ -940,7 +940,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-- running theorist --#\n", + "#-- running experiment_runner --#\n", "\n", "v3.model=Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", " ('linearregression', LinearRegression())]), \n", @@ -971,11 +971,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -10.546793\n", - "1 9.032887\n", - "2 -0.802825\n", - "3 -12.571801\n", - "4 1.990531, experiment_data=Empty DataFrame\n", + "0 0.787469\n", + "1 -11.056959\n", + "2 -12.028324\n", + "3 0.278927\n", + "4 7.568485, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])" ] @@ -999,7 +999,7 @@ "outputs": [ { "data": { - "image/png": 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oNvpMDezckCcubwfAK3M38uWfWtpIRM7SkRzYuczYjr3Q3CxHqYASqWoOB/zxDnx8FeTvg8gOcMcvcP5YZq7dw1PT1wDwQHIrruteTS0KqpNPIPR6GO5KgbjeRoPOBc8YvVnS/wBgWI+m3NWrGQDjv1nJok17zEwsIu5u60Jw2I0GwqExZqcBVECJVK3iAmOi+MyHjP/ZOw6B2+ZCZDtStx9gzBd/4XDADec2dhYYbqt+M7jxW2N+VFAk7N0EH/Y37jAsLuT+vq24tGMDiksc3PHJMjZl553+mCIi5XG2L3CNy3egAkqk6uRlw5TL4K+PwWKFvk/Dle+Atx87D+Rz25SlHCkqoXercB6/rB2WCiyb4vIsFmN+1Kgl0Ol6cJTAwpfg/Yuw7lnHC9d0omuTuhw8UsztH/9J7pEisxOLiDvaPN94dIH+T6VUQIlUhf3b4L8Xw/bFRs+koV9Cj9Fk7NrF3AW/Mfz9FPbkFdKmQTBvXN/F8xpN+ofClW/DtR+Bfz1jmZj/uxC/Ze/yfzd0oUGIH2m7D3Hf56mUlOjOPBGphJwdRisVi7XK1747Gx72r7iICfZuhg8vMe5Mq9sURvxsLDMAbN68mZd+z2bjnnzqB/rw/rBuBPp68AIAba8w5ka16GvMjZr5MGEzbuO9wS3w8bIyb102r+jOPBGpjLRfjMfoLsYvay5CBZTI2chea8z7yd0BYS3h5p8grLnz5d/3BbLmoB9eVgtv39CVhqH+JoatIXWi4Ppp0P954+7DtT/Q/screOsibwBem7eR+euzTQ4pIm4j7ejlOxdpX1BKBZTImdq1whh5ysuCyPYwfAYEH1uG5beNu3n3j10APHlFe7rH1jMrac2zWCDhdrhlFoTEwL40khYN5cUWqwAY+0UqGQfyTQ4pIi7P4Tg2AuVC859ABZTImdn1tzFhPH8fRHeGYT9AULjz5Z0H8rln6l+UOGBwtxiuT3DDdgVVoVFXuONXaH4xFB9h0PZneDPkUw4ezufuqX9RZC8xO6GIuLKsVcYKCN4B0Ogcs9OUoQJKpLL2b4VPrzYau8UkwE3/g4Bjo0uFxSXc9ely9h8uon3DYJ64ol2Zb8/IyGDhwoVkZGTUcHCTBNQzLun1fhSwMKBgOp/4Pc/Gbdt5cfYGs9OJiCvbOMd4jL0QvHzNzfIPKqBEKuPQHqNBZl4WRLQzCgO/kDK7TPppLSu2HyDE35u3h3bFz9tW5vW0tDQ2bdpEWlpaTSY3l9UKFz4AQz4D70DOZSXf+kxk9m8LWbRZTTZF5CQ2zTMem/cxN0c5VECJVFRBHnx6DezbDCGN4YavT7gjZNbqTD78fSsAL13biZh6ASccJi4ujubNmxMXF1cDoV1M60vg1tkQEkMz6y6+9X6MqVM/Yv+hQrOTiYirOZIL243VDUrvbHYlKqBEKqK4EKbdBBnLjT5HN34DwWXXsNu+7zAPfLkCgBHnx9KnTWS5h4qOjua8884jOjq63Nc9XlR7GPEz9kbdCbEc5qWif/PtlBdxONQfSkSOs+UXKCk2lm+pF2t2mhOogBI5HYcDfhwDm+cZExmHfglhLcrsUmwv4d7P/yL3SDHxMaE82K+1OVndRVAEtuE/cqDZQLwtdm7J/g+rpj1pfNYiInBs/pMLjj6BCiiR01vyHqR+anTBvfYjaNTthF1em7eR5ekHqOPnxevXdcbb0zqNn6VyJ857+RI69EP+anQDAB3WvkTe/x6AEt2ZJ1LrORzHzX+62NwsJ6F/5UVOZdsimDXe2L74SWhx4v/Ii9P28sb8TQA8fWWHcuc91XYnnThvtdLh5tf5IPA2AIJS38Px9S3GoswiUnvtXmc0KPbyg6Y9zU5TLhVQIieTmwHThhnX4NsPgsTRJ+ySc7iI+75IpcQBV3dtxOWdaum8ptM41cR5L5uVC4c/wbiSuyl02LCs/hamDoHCwyYkFRGXsGmu8dikJ3i75goOKqBEylNcYEwaP5RttCu4/HWju/Y/PPq/VWTkHKFp/QCeuLxdOQcSOP3E+WbhQbTreys3Fz3IYYcvbP4ZPhlk3IUjIrVPaQFVzqi/q1ABJVKenx6CHUuNHk9DPgGfwBN2+V/qTn5YkYHNauGVIZ09e5HgGjCsR1OOxFzADYXjOWwJgPRF8PFAOLzP7GgiUpMK8ozpE+CyE8hBBZTIif7+EpZ9CFhg0H+h3omXnXYeyOfR74x13e65qAXxMaE1m9ED2awWnh3UkVW21lxz5BEKvENh5zJjyZy83WbHE5GasnUh2AshtInRwsBFqYASOd6BdJg+zti+8MFyh49LShzcP20FB48U07lxKKN6N6vhkJ6reUQQ9/ZpwWpHLNcVTaAkMMJYC2vyJXAwy+x4IlITNh3XvqCcqROuQgWUSKkSO3x7JxTkGItWXvBgubtNXrSVlLS9+HvbePnaeLzUsqBK3XFBHG0bBLP8SBTPRL4EwY1gzwYOv9OHzE1/mx1PRKqTw3Gs/5MLz38CFVAix/z+Kmz7HXyC4Kp3wXbinKZN2Qd5duY6AB4Z0IamYSfOjZKz42WzMumqDlgt8P4aK0t6fUSBbxgBh7ZT59uhupwn4sn2boYD28DqDU3PNzvNKamAEgHISIX5Txvb/Z8td95Tkb2EsdNWUFBcwgUtwxma0LhmM9YinWJCuSmxKQDj5uaQdelHFPjWJ/BQOnx0ubGos4h4Hmf7gkTwDTI3y2mogBIpPAxf32b0e2pzGcQPLXe3t+Zv5u8dOQT7efHcoI5YXPjavCe4P7kVDUL82L4vn8931sf39jlQpwFkr4Epl8OhvWZHFJGqtmGm8eii3cePpwJKZO5E2LsRgqLgstfKnbS4amcOr/+8EYCnBrYnKsSvplPWOkG+Xjx+tLfWe7+lsckeCcN+NP47Za+GT66EIzkmpxSRKnMk17gDD6BVf3OzVIAKKKnd0v+AJe8a2wPfgoB6J+xSUGxn3LQVFJc46NcuSt3Ga1DftpFc1DqCIruDid+vwlG/GQz7AQLCYNcK+PRaKDxkdkwRqQqbf4aSIqjX7IQF212RCiipvYoL4Yd7je3ON0DzPuXu9urcjazPOkj9QB/+fWV7XbqrQRaLhccva4evl5XfN+3lh793QXhLuPFbo8np9j/g86FaO0/EE5RevnOD0SdQASW12e+vGgtWBoTBxU+Vu0vq9gO888tmAJ6+sj1hQb41mVCAxvUDGNXbaKb37x/XkFdQDA06wtCvwDsQ0ubDV7eAvdjkpCJyxkrssHG2sd2yn7lZKkgFlNROezbCr88Z2/2fLffS3ZEiO+OmGQsFXxEfTb/2DWo4pJS648I4mtYPIPtggXMuGjHd4brPwOYL636E/90FJSXmBhWRM7NjKRzea4wsNz7X7DQVogJKah+HA34YYywV0DwJ2g8qd7eX5mxg8+5DhNfx1ULBJvP1sjHhsrYAfLBwC5t35xkvxPWCa6eA1Qv+/gJmP2r89xUR97L+J+Ox+cVg8zY3SwWpgJLa569PYNtC8A6AAS+We9fdn1v38d5vaQD856oOhAb41HRK+YeLWkfSu1U4RXYHT/6wBkdpodSqP1zxlrH9x5vGpVkRcS9uNv8JVEBJbZO32xilAOj9L6jb9IRdDhcWc/+XK3A44OqujejTJrJmM8pJTbisHT42K79s2M3ctdnHXug0GPoebYQ6dyL89ak5AUWk8vZtMeajWmwnvZnHFamAktrl56fgyAGI6gAJI8vd5bmZ69m69zANQvx47NK2NZtPTik2LJBbz48F4Onpaygoth97scdo6HGPsf393bBhlgkJRaTSSkefmvQA/7rmZqkEFVBSe+z6G5Z/ZGxf8kK5a92lbN7L5EVbAfjPoI6E+LvHtfjaZFTv5oTX8WXr3sNMOfrfyinpCeh0HTjsMG0YbF9iSkYRqYTS+U9ucvddKRVQUjs4HDBzPOCAdleVe5dHXoFx6Q7guu6NubBleA2HlIoI8vXigeRWALw+bxN78o7rAWW1wuWvQ4u+UJwPnw02FicVEdd0JMdYxB3cav4TqICS2mLtD8bEcS8/uPiJcnf5949r2Hkgn0Z1/XlkQJsaDiiVcXWXRrRvGMzBgmJenL2+7Is2b7hmMkR3hvx98MkgLT4s4qo2zTPWIQ1rCfWbmZ2mUlRAiecrLjg2cbzH3RDa+IRd5q/L5vOl2wF44ZpOBPmeeHlPXIfVamHiZUZriS+WbmftrtyyO/gEwvXTjP/W+7cYI1GFh01IKiKnVDr/yc0u34EKKKkN/ngLDmyDOg2g55gTXj5wuJCHvv4bgFt6xnJuXP0aDihn4pym9bikQxQlDnhmxtpjbQ1KBUXA0K/BLxR2/gnfjDC6HYuIa7AXH+s+7maX70AFlHi6g1nw64vGdp+J4BtU5mWHw8Ej360i+2ABceGBPNivlQkh5Uw91K81PjYrv23cw4INu0/cIbwlXPf5sW7lM8fXfEgRKV96CuTvN+68a9Td7DSVpgJKPNv8f0PhQYjuAh0Hn/Dy9ysymP73LmxWCy9fG4+ft82EkHKmmtQPZHjPpgA8PX0txfZylnJpkghXvmNsL/k/WPx/NRdQRE5u7ffGY6sB5d4V7epUQInn2rPR6DoO0G+ScYfWcTIO5PPYd6sAuOeiFnSKCa3hgFIVRvVuTt0AbzZl5znnsZ2g/VVGiwOAmQ/Dhtk1F1BETlRSAmt/NLbbXm5uljOkAko81/xnwFECLfuf0LagpMTB/V+uIPdIMZ1iQhnV273u/pBjQvy9ubdPCwBembuBvILi8nfseS90vtH4O/HVzZC5qgZTikgZGcvhYAb41IHYC81Oc0ZUQIln2vU3rP7G2L7o0RNefu+3NBZt3ou/t42Xr+2El03/K7iz6xOa0LR+AHvyCnn317Tyd7JYYMBL0PR8KMwz7sw7mFWzQUXEUHr5rmVf8PYzN8sZ0k8N8Uzzj66L1n4QRLUv89KqnTm8cLR30MTL2hIXHvTP7xY34+Nl5aF+rQF479c0snKPlL+jlw8M/hjqt4DcHTB1iNobiNQ0h8PozQfQ5jJzs5wFFVDiebYvMXqLWGzQ619lXjpUUMw9n/9Fkd1Bv3ZRDD4nxqSQUtX6tY+ia5O65BfZeXnOhpPv6F8Xrv8C/OsZlxG+G2n8gy4iNSN7DexLM+6ObX6x2WnOmAoo8TzznjQe46+HsOZlXprwv9Wk7T5EVLAfk67qgMViMSGgVAeLxcK/LjFGoab9uZ2NWQdPvnP9ZjDkU7B6w5rv4JdnayakiMCao5fvmvc5obWMO1EBJZ4lbQFs/Q1sPnDhQ2Ve+mrZDr5evgOrBV4dEk/dQB9zMkq16dqkHsntIilxwHOz1p965yY94NKXjO0Fk2D1t9UfUESOu3znnnfflVIBJZ7D4Tg2+tT1Zgg9dnluU3aes2XBmKSWJKjbuMd6ILk1VgvMWZPFn1v3nXrnLjfBuaOM7W9HQsZf1R9QpDbbuxmyV4PVC1omm53mrHhUAfX4449jsVjKfLVu3dr5+pEjRxg1ahT169cnKCiIQYMGkZVV9i6c9PR0BgwYQEBAABERETzwwAMUF5/ktmhxLRtmwc5l4B0A549zPn2ooJi7Pl1GfpGdHs3qM6p381McRNxd84gg59y2ST+tO3GJl3/q+xQ0T4LifJh6PRzMrIGUIrVU6ehT0/MhoJ65Wc6SRxVQAO3atWPXrl3Or4ULFzpfu++++/jhhx/48ssv+eWXX8jIyOCqq65yvm632xkwYACFhYUsWrSIKVOmMHnyZCZMmGDGW5HKcDjgtxeM7XNugzqRR5928NDXf7MhK4/wOr68Mjgem1XznjzdmKSW+HlbWbZtP7PXnKZVgdUGV39grAZ/MAM+vx6KTnIXn4icHQ+4+66UxxVQXl5eREVFOb/CwsIAyMnJ4b///S8vvfQSF110EV27duXDDz9k0aJF/PHHHwDMnj2bNWvW8MknnxAfH0///v156qmnePPNNyksLDTzbcnpbF0IO5Yad3UkjnY+/cpPK/jx713YrPDW0C5EBLtnvxGpnMhgP249LxaA52etx15ymlEovxBjzTz/usYo5o/36c48kaqWs9NY2BsLtB5gdpqz5nEF1MaNG4mOjiYuLo6hQ4eSnp4OwLJlyygqKiIpKcm5b+vWrWncuDEpKSkApKSk0KFDByIjI537JCcnk5uby+rVq096zoKCAnJzc8t8SQ0rHX3qcqNz9Gnp1n288dtOAK5v48c5Td17uFgq5/YLmhHibyzx8s3yHaf/hvrN4JrJRvuLFZ/BH29Xe0aRWmXd0aVbYhKgTpS5WaqARxVQCQkJTJ48mZkzZ/L222+zZcsWzj//fA4ePEhmZiY+Pj6EhoaW+Z7IyEgyM405D5mZmWWKp9LXS187mUmTJhESEuL8iolRb6EatWOZcfedxQY97nE+HVM3gDaRgSRGe3PnRa1P/v3ikUL8vZ1L9LwydyNHiuyn/6a4XpB8tAnr7Edg8/zqCyhS26z+znh007Xv/smjCqj+/ftzzTXX0LFjR5KTk5kxYwYHDhxg2rRp1Xre8ePHk5OT4/zavv0kC5pK9Vh49Fb0jtdC3SbOp6NC/Phm9AV8cGcfGjZsaFI4MdNNiU1pEOLHzgP5fPLHtop9U8KdED/UWDPvy+FGwz8ROTs5OyF9kbHddqCpUaqKRxVQ/xQaGkrLli3ZtGkTUVFRFBYWcuDAgTL7ZGVlERVlDCVGRUWdcFde6Z9L9ymPr68vwcHBZb6khmSvPTosbIHz7jvhZR8vK/4+tprPJS7Bz9vGmCRjoeE352/i4JGi039T6Zp5DbvBkQPGnXkFp2jKKSKnV9pnrXEPCPGMX2g9uoDKy8tj8+bNNGjQgK5du+Lt7c28efOcr69fv5709HQSExMBSExMZOXKlWRnZzv3mTNnDsHBwbRt27bG80sFLHzZeGxzGYS3MjeLuKRBXRrRLDyQ/YeLeP+3LRX7Jm8/GPwJBEXB7rXw7Z1QUlK9QUU82aqvjcf2V516PzfiUQXU/fffzy+//MLWrVtZtGgRV155JTabjeuuu46QkBBuvfVWxo4dy/z581m2bBk333wziYmJnHvuuQD07duXtm3bcuONN7JixQpmzZrFo48+yqhRo/D19TX53ckJ9m2BlV8Z2+ePNTeLuCwvm5VxfY3i+v3f0tibV1CxbwxuYCz3YvMxRjkXvliNKUU82L4txrqTFqvHXL4DDyugduzYwXXXXUerVq249tprqV+/Pn/88Qfh4eEAvPzyy1x66aUMGjSICy64gKioKL755hvn99tsNn788UdsNhuJiYnccMMN3HTTTTz55JNmvSU5lUWvgcMOzfpAdGez04gL698+ig4NQzhUaOetBZsr/o2NusGAo4XTz0/DhtnVE1DEk60++nM29kIICjc3SxWyOE7bplcqKzc3l5CQEHJycjQfqroc2gMvtQV7AQyfDk3PMzuRuLhfN+zmpg+W4GOzMv+BXjQM9a/4N/94H/z5AfiGwO3zjZYHIlIxb/eErFVw+evG8kkurDI/vz1qBEpqkWUfGsVTdGdo0tPsNOIGzm8Rxrlx9Si0l/Da3I2V++Z+zxq9awpyjE7lmlQuUjHZ64ziyeoNrS81O02VUgEl7qe4EJa8b2yfe5dx15TIaVgsFh5INvqBfbV8B2m78yr+zV4+cO1HUKcB7F4H341Up3KRiii9fNe8j9uvffdPKqDE/az+FvIyjTukPGhColS/rk3qktQmAnuJg5crOwpVJwqu/diYVL72h2N3gIpI+RwOWHW0gGo/yNws1UAFlLgXhwP+eNPY7j7CGBkQqYSxFxt35P2wIoM1GZVcdinmHLjkeWP756dg07xT7y9Sm2WuhL0bwcsPWvU3O02VUwEl7iU9BXatMP6H7Hqz2WnEDbWNDuayTtEAvDB7feUP0HW4MRHWUQJf3wr7K9jhXKS2Ke391DIZfOuYm6UaqIAS9/LHW8ZjpyEQWN/cLOK27ktqgc1q4ed12Szbtr/yB+j/PER3gfz98MUNUJRf9SFF3JnDcWz+UzvPaZ55PBVQ4j72b4V1043thJGmRhH3FhcexNVdGgHwwqwzGIXy9oPBH0NAGGT+bbQ50KRykWO2L4YD6eATBC36mp2mWqiAEvex+F3jskmziyCitdlpxM3dk9QCH5uVlLS9/L5pT+UPENIIrvnQ6K68Yiosfb/qQ4q4qxVTjce2V4BPgLlZqokKKHEPR3Jh+UfG9rmjzM0iHqFhqD/XJzQG4PlZ6zmjnsKxF8DFR1cqmDketi+pwoQibqroCKw6unhwpyHmZqlGKqDEPaz4HAoPQlhLYwRKpAqM6t0cf28bqdsPMHdt9um/oTyJo412GiVFMO0myDvD44h4ivUzjKazITHQxHNXiVABJa7P4TA6jwOcMwKs+msrVSO8ji/DezYF4MXZ6ykpOYNRKIsFrngDwlrBwV3w5c1gL67aoCLuZMXnxmPHwR7977XnvjPxHNsXQ/Ya8A6AToPNTiMe5o4L4qjj68W6zINMX7nrzA7iWwcGf2JMmN22EOZOrNqQIu4iLxs2zTW2PfjyHaiAEnfw5wfGY/tB4BdibhbxOKEBPoy4IA6Al+dsoNhecmYHCm8JA4+22Uh5w+iYL1LbrPwKHHZo2A3CWpidplqpgBLXdmgvrP7O2O52i6lRxHPdcl4s9QJ9SNtziG/+2nnmB2p7BfS819j+bhTsPoMWCSLubMVnxqOHjz6BCihxdSs+A3sBNIiHhl3MTiMeKsjXi5EXNgPg1bkbKSi2n/nBLpoATc+HokNGk82Cg1WUUsTFZa4ylm+xenvk2nf/pAJKXFdJCfx5dPK4Rp+kmt2Y2ITIYF92Hsjni6Xbz/xANi+4+kOoEw17NsD/RqnJptQOfx+dPN4yGQLqmZulBqiAEte19VfYtxl8g2vFbzNiLj9vG6MvMuZsvP7zJvILz2IUKigcrp1i/Ca+5n+Q8mYVpRRxUfZi+HuasR1/vblZaogKKHFdpZPHOw4G3yBzs0itMLhbDI3q+rP7YAEfpWw9u4PFdId+k4ztORNg6+9nnU/EZaUtgLws8K8HzS82O02NUAElrulg5rF177rdbG4WqTV8vKzc28cYhXr7l80cPFJ0dgc85zbocK1xV9KXwyH3DNskiLi61E+Nx/aDwMvH3Cw1RAWUuKa/PoaSYog5FyLbmZ1GapErOzckLjyQA4eL+O/CLWd3MIsFLnsFItrCoWyjiLKfZVEm4moO7YF1PxrbXW40N0sNUgElrqek5Ni6dxp9khrmZbMy9uKWALz/2xb2Hyo8uwP6BBpNNn2DYfsfxuU8EU+yYirYC427pRt0MjtNjVEBJa5n629wIB18Q4y+OiI17JL2DWjbIJi8gmLe+XXz2R+wfjO48h1j+4+3YNU3Z39MEVfgcMCyKcZ212HmZqlhKqDE9aQebcTW/irw9jc3i9RKVquF+5ONUagpi7aSnXvk7A/aegCcd5+x/b/RkL3u7I8pYrb0FNi7EbwDof3VZqepUSqgxLUcyTVu+wbofIO5WaRW690qgi6NQzlSVMIb8zdV0UEfhdgLjCab025Uk01xf8smG4/trwK/YFOj1DQVUOJa1nwHxfkQ1hIadjU7jdRiFouF+5NbATB1STrb9x0++4PavGDQB2qyKZ4hf/+xX3i7Djc1ihlUQIlr+evorbDxQ407mERM1KNZGOc1D6PI7uDVeRur5qBB4XDtR2qyKe7v72lQfAQi29fKX3hVQInr2LvZuEvJYjWaZ4q4gNJRqG+W72BTdhVdcos5R002xb0dP3m8y7Ba+QuvCihxHaWN2JonQXADc7OIHBUfE0rftpGUOOClORuq7sDn3Gb8oqAmm+KOdvwJ2avByw86Xmt2GlOogBLXUGKHFUcXoqwl6yiJ+xjXtxUWC8xYmcnKHTlVc1CLBS59BSLaHW2yOQyKz7LnlEhNWT7ZeGx3JfiHmpnENCqgxDWkLYDcneAXCq0uMTuNSBmtouowML4hAM/PXl91B/YJgMEfGz3Pti+GOY9V3bFFqsuRnGO9zLrUrt5Px1MBJa6htPdTh2vAy9fcLCLlGJPUAi+rhV837OaPtL1Vd+Djm2wufgdWflV1xxapDqmfQdFhCG8Njc81O41pVECJ+fIPHFtHqfNQU6OInEyT+oEM6R4DwHMz1+GoyvYDrS+B88cZ29/fDVmrq+7YIlWppASWvGtsd7+9Vk4eL6UCSsy35jvjVtiItsZaSiIu6p6LWuDnbWV5+gHmrc2u2oP3fgTiehu/2X9xg/GLhYir2TQX9qUZl507DTE7jalUQIn5Si9ZdBxcq3+bEdcXEezH8B6xADw/az32kiochbLaYNB/ISTG+AH17Z3Gb/sirmTx0cvNXW40FsquxVRAiblydsLWhcZ2+0HmZhGpgJEXNiPYz4v1WQf5X+rOqj14YH1jUrnNFzb8BL+9WLXHFzkbezbC5nmAxWjDUcupgBJzrf4GcEDjHhAaY3YakdMKCfDmzl7NAHhx9gYKiu1Ve4LozjDgaOE0/2nYOLdqjy9ypkrnPrXsB/Vizc3iAlRAiblWfmk8dqhdq3iLe7u5RyyRwb7sPJDPZ4vTq/4EXW48uraYA76+FfZvrfpziFTGkdxjd0sn3GFuFhehAkrMs3sD7FoBVi9oO9DsNCIV5u9j494+LQF44+dN5BUUV/1J+j9nrC925IAxqbywChYzFjlTqZ9BYR6EtYK4XmancQkqoMQ8q45OHm92kTH3Q8SNXNOtEbFhgew9VMi7v6ZV/Qm8fI1FhwPCIHMl/DjGWH9MpKYd37ogoXa3LjieCigxh8Nx7O67DteYm0XkDHjbrDxwdKHh939LI/vgkao/SUgjuGYyWGzw9xew+P+q/hwip7N5HuzbbLQu6Fi7WxccTwWUmCPjL+N/SC9/Ld0ibqt/+yg6xYRyuNDO6/M2Vc9JYs+Hvk8Z27P+BVt/r57ziJxMypvGY+cbwDfI3CwuRAWUmKN09Kn1JfofUtyWxWLh4X6tAZi6JJ0tew5Vz4nOvcsYqXXYjUWHc6q4fYLIyWSkQtp8YxRUk8fLUAElNa/EDqu+NrZ1+U7cXGKz+vRqFU5xiYMXZlXhQsPHs1jgslchsj0c2g3TboSiarhkKPJPv79qPLa/Cuo2MTeLi1EBJTVv60LIywS/UGjWx+w0ImftoX6tsVhg+spd/JW+v3pO4hMIgz8x/r/ZuQxmjNOkcqle+7YYS20B9LzX1CiuSAWU1LzS3k9trwAvH3OziFSBNg2CGdSlEQDPzFhbtQsNH69eLFz9AVis8NcnsPT96jmPCEDKG+AogeZJENXB7DQuRwWU1Cx7Eaz9wdhW80zxIOP6tsTXy8rSrfuZvSar+k7UvA8kPW5sz3xYk8qleuTtNop00OjTSaiAkpq15RejMWBgBDTpaXYakSrTIMSfW88zlrd49qd1FNmrcSHgHvcYa0eWFB+dVL6j+s4ltdOSd6H4CER3gabnm53GJamAkpq15n/GY5vLjNXnRTzInb2aUS/Qh7Q9h/h8STUs8VLKYoHL3zAuqxzabXQqL8qvvvNJ7VKQd6xx5nlj1DjzJFRASc2xF8PaH43ttleYm0WkGgT7eXNvnxYAvDx3I7lHiqrvZD4BMPhT8K9n9FX7/h5NKpeqsfwj40pBvWbQ+lKz07gsFVBSc7YthPx9EFBfl+/EY12f0Ji48ED2HSrkrfmbq/dkdZvAtVOMHj0rp8Gi16r3fOL57EXHGmf2uFtXCk5BBZTUnNLLd60vBZuXuVlEqom3zcq/+rcB4IPft7B9XzUvAhx7AfR/1tieMxE2zK7e84lnS/0UcncY81Q7XWd2GpemAkpqRon92N13unwnHq5PmwgS4+pTWFzCc9XVXPN459wGXYcDDvj6Vti9ofrPKZ6nuBB+fcHYPu8+8PYzN4+LUwElNSM9xZjs6hdq/MYs4sEsFguPDGiDxQI/rMhg2bZqaq557ITQ/3lo3AMKcuHz6yC/ms8pnuevjyFnOwRFQbebzU7j8lRASc0oc/nO29wsIjWgfcMQrulqNNd88ofVlJRU8wRvLx+49iMIiYG9m+CrW4wbN0QqorgAfnvR2D5/LHj7m5vHDaiAkupXUgJrvje2dflOapH7k1sR6GNjxY4cvv2rBhYADgqHIZ+BdwBs/hlm/av6zymeYflHkLsT6kRDl2Fmp3ELKqBO4s0336Rp06b4+fmRkJDAkiVLzI7kvnYsMda+8w2BuAvNTiNSYyLq+DHqouYAPDdrHYcKamBEqEFHuOo9Y3vJ/2m5Fzm9oiP/GH3S3KeKUAFVji+++IKxY8cyceJEli9fTqdOnUhOTiY7O9vsaO6p9PJdq/7g5WtuFpEadkvPWGLq+ZOVW8BbCzbVzEnbXAp9JhjbMx6EzfNr5rzinpZNhoO7ILgRdLnJ7DRuQwVUOV566SVGjBjBzTffTNu2bXnnnXcICAjggw8+MDua+ykpOVZA6fKd1EJ+3jYeuaQtAO/9uoVtew/VzInPGwsdB4PDbiz3sqeGijdxL0X5sPAlY/uCcfoltxJUQP1DYWEhy5YtIykpyfmc1WolKSmJlJSUcr+noKCA3NzcMl9yVMZy47q6TxA0u8jsNCKmSG4XyXnNwyi0l/Dv6Wtr5qQWC1z2GjTqDkdy4LNr4fC+mjm3uI8/P4C8LAhpDPE3mJ3GraiA+oc9e/Zgt9uJjIws83xkZCSZmZnlfs+kSZMICQlxfsXExNREVPdQ2vupRV9dV5day2Kx8PjlbfGyWpizJotfNuyumRN7+8GQT4078/ZtNtbMKy6omXOL68s/AL8+b2xfcL9xJ6dUmAqoKjB+/HhycnKcX9u3bzc7kutY/5Px2HqAuTlETNY8og7DejQF4InvV1NQbK+ZEwdFwPXTwDcYtv2uNfPkmN9eNPqFhbeG+KFmp3E7KqD+ISwsDJvNRlZWVpnns7KyiIqKKvd7fH19CQ4OLvMlwN7NsGc9WL2gxcVmpxEx3b1JLQgL8iVtzyH+u3BLzZ04si1cM9lYM+/vz+GX52ru3OKa9m+Dxe8Y2xc/peW1zoAKqH/w8fGha9euzJs3z/lcSUkJ8+bNIzEx0cRkbmjddOOx6XngF2JuFhEXEOznzSMDWgPw+rxN7DyQX3Mnb94HLj06WXjBM7Dii5o7t7ieeU+CvRBiL9QvuGdIBVQ5xo4dy3vvvceUKVNYu3YtI0eO5NChQ9x8s1rbV8r6GcZjK12+Eyk1ML4h3WPrkV9k56kf1tTsybsOhx73GNvfj4atv9fs+cU17FgGq74CLND338YNB1JpKqDKMXjwYF544QUmTJhAfHw8qampzJw584SJ5XIKh/bA9sXGdqv+5mYRcSEWi4WnrmiPzWph5upM5q+v4f5ySU9Am8uN0YfPr4PsdTV7fjGXwwGzHzW2O11nNF6VM6IC6iRGjx7Ntm3bKCgoYPHixSQkJJgdyb1smAWOEojqCKG6K1HkeK2i6nBLz6YAPPbdKvILa2hCOYDVCle9e6y9wadXQ+6umju/mGvddEhfBF5+cNGjZqdxayqgpHo4L99dYm4OERc1Jqkl0SF+7Nifz2s/b6zZk3v7w3WfQ71mkLMdPr0Gjqh/nccrLoQ5RzvUJ46GkIbm5nFzKqCk6hXlGwuZArRWASVSnkBfL564oj0A7/2axvrMgzUcoD7c8DUEhkPWSph2o/EDVjzXH28Z/cACw6HnvWancXsqoKTqpf0CRYeNdZWidH1d5GQubhtJ37aRFJc4GP/N35SU1HB/pnqxMPRL8A6EtAXw/d3qEeWpDqTDL88a2xc/CX5qt3O2VEBJ1Vt/tH1Bq/66u0PkNB6/vB2BPjaWpx/gk8Xbaj5AdGe4dsqxHlFzHqv5DFL9Zo43frFt3MOYPC5nTQWUVK2SElg/09jW5TuR04oO9eeh/kZvqGd/WlezvaFKtbgYrnjD2F70Oix8peYzSPVZPxPW/Wg0Nb70Jf1iW0VUQEnV2rkMDmUby0Y0Oc/sNCJu4YaEJnRrUpdDhXYe/XYlDjMuo8Vfb/QEApg7EZZ/VPMZpOoVHoafHjC2E0dBRBtz83gQFVBStUov3zVP0sKUIhVktVr4z6CO+NiszF+/m+9Sd5oTpMfd0HOMsf3DvccWAxf39dsLxvyn4EZwwYNmp/EoKqCkamnxYJEz0jwiiHuTWgDw+PdryMo9Yk6QpMeh841GH7evbjFuChH3tHs9/P6asd3/P+AbZG4eD6MCSqrO/m2we50xGbV5H7PTiLidOy6Io0PDEHLyi/jXNyZdyrNY4NJXoPWlRrfyqdfB9iU1n0POTondGEUsKYIWycZ/T6lSKqCk6myaYzzGJIB/XXOziLghL5uVF67phI/Nyrx12Xyz3KRLeTYvGPRfiOsNRYfgk0GQ8Zc5WeTM/PEWpKeATxBc8pwmjlcDFVBSdTYeLaC0srfIGWsVVee4S3mrzbkrD8DbD4Z8atz2XpALH18JWTW8+LGcmd3rYd5Txnby01C3qalxPJUKKKkaRUeOzZVo0dfcLCJu7o4L4oiPCeVgQTHjpqXWfIPNUj6BcP0X0LAr5O+Hj66APZvMySIVYy+Gb+8Ae4FxM0+XYWYn8lgqoKRqbFsIxflQJxoi25mdRsStedmsvDw4Hn9vG3+k7eO/C7eYF8Yv2FjyJbKD0aJkymWwd7N5eeTUFr5sXG71C4HLX9elu2qkAkqqxvGX7/Q/rMhZiw0L5LFL2wLw/Kz1rMkwcbFf/7pw03cQ1goOZsDkS1VEuaJdf8Mv/zG2+z8PwdHm5vFwlS6ghg0bxq+//lodWcSdbZxtPOrynUiVua57DEltIii0l3D31OUcLiw2L0xgGAz74bgiaoCKKFdSdAS+vRNKio077jpea3Yij1fpAionJ4ekpCRatGjBM888w86dJt0lIq5j72bYlwZWb4i70Ow0Ih7DYrHw7KCORNTxZfPuQzzxvcmTuOtEwvAfIbw1HNwFH14Cezaam0kMs8ZD9moIqG+0odCVgGpX6QLqu+++Y+fOnYwcOZIvvviCpk2b0r9/f7766iuKioqqI6O4utLRpyY9wLeOuVlEPEz9IF9eGRKPxQJf/Lmd71dkmBsoKAKG/QgRbSEv0xiJ2r3B3Ey13d9fwp8fABa48l0ICjc7Ua1wRnOgwsPDGTt2LCtWrGDx4sU0b96cG2+8kejoaO677z42btRvJLWKLt+JVKsezcIY3bs5AP/6ZiVpu/PMDRQUblzOi2gHeVnwYX/YtcLcTLXV7vVGw0yAC+6HFknm5qlFzmoS+a5du5gzZw5z5szBZrNxySWXsHLlStq2bcvLL79cVRnFlRUegq2/G9sqoESqzb19WtA9th55BcWM/GQ5+YV2cwOVzolq0AkO74HJl0H6H+Zmqm0KD8G0m4xmp7EXQK/xZieqVSpdQBUVFfH1119z6aWX0qRJE7788kvGjBlDRkYGU6ZMYe7cuUybNo0nn3yyOvKKq9nym9FvJLQxhLUwO42Ix/KyWXnjus6EBfmyPusgj3xn0lIvxwusbxRRjXtAQQ58NBA2zjU3U23hcMCP9xnLZwVFGZ3jrTazU9UqlS6gGjRowIgRI2jSpAlLlizhzz//5M477yQ4ONi5T+/evQkNDa3KnOKqjr98p0mLItUqItiP16/rjNUC3yzfySeL082OZPQbuuFraH6x0Qtu6hBY/a3ZqTzfnx/A318Ya49e/YExN01qVKULqJdffpmMjAzefPNN4uPjy90nNDSULVtMbPwmNcPhOK7/ky7fidSExGb1eahfawCe+H41i9P2mpwI8AmAIZ9BuyuNxWu/vBkWv2t2Ks+1aR7MeMDY7vMYNO1pbp5aqtIF1I033oifn191ZBF3s3s95KSDzReanm92GpFa4/YL4risUzTFJQ7u+nQ5O/YfNjsSePkYl5G6Dgcc8NMDMPtRKCmp9lNnZGSwcOFCMjJMvkOxJmSthmnDwGGHjoOh5xizE9Va6kQuZ27T0bkOTc8zfgMVkRphsVh4blBH2kUHs/dQISM+WkZegYlNNktZbUYPooseM/686HX4+hajyWM1SktLY9OmTaSlpVXreUyXuws+vRYKD0KTnlqqxWQqoOTMbf7ZeGyu22ZFapq/j413b+pGWJAPa3flcvdnyym2V/9oz2lZLMbt9Fe+azTXXf0tfDwQDu+rtlPGxcXRvHlz4uLiqu0cpivIg6mDIXcH1G8Bgz8BL1+zU9VqKqDkzBQdgW1H2xc0621uFpFaqmGoP+8POwc/byvz1+/miR/WmH9nXqlOg43J5b7BkJ4C7/eB7HXVcqro6GjOO+88oqM9dO03ezF8fZvRayugPgydBgH1zE5V66mAkjOTngLFR6BOA2NZBxExRXxMKK8M7ozFAh//sY23f3Gh9eniLoRbZkFIY2O5p/f7wLrpZqdyL/Zi+PZ22PCTMd/0us+hngePtLkRFVByZtLmG49xvXUNXsRk/dpH8eiAtgA8N3M9ny9xgfYGpSLbwu3zjRtNCvPg8+vhl+dqZHK52yuxw3d3wqqvjcuh106BmO5mp5KjVEDJmSmd/9TsInNziAgAt54Xy8hezQD417crmblql8mJjhMYBjd+C91vN/48/2n4chgcyTU3lysrscN3I2Hll2D1MoqnVv3NTiXHUQEllZeXDZkrje24XqZGEZFjHkxuxZBzYihxwN1T/2LumiyzIx1j84ZLnofLXjNGU9Z+D/93AWSkmp3M9ZTY4bu7jEaZVi+4ZjK0HmB2KvkHFVBSeWm/GI9RHbTqt4gLsVgsPH1lBy7t2IAiu9Ejav66bLNjldV1GNw8A0JiYP8W+O/FsPj/jMa8AsUF8M0I+PvzY13G21xmdiophwooqTxdvhNxWTarhVcGx3NJhygK7SXc8fEy1xqJAmMezx2/QqsBYC+Enx6EL26A/P1mJzPX4X3GeoKrvjZGnq7+L7S9wuxUchIqoKRyHA4VUCIuzstm5dUhnUluF2kUUZ8s43+pO82OVVZAPRjyKfR71rikt+5HeKvHseWhapu9m+H9JEhfZLR+uOFrY2kccVkqoKRydq+DvEzw8oOYc81OIyIn4W2z8sb1XRgYH429xMGYL1L5KGWr2bHKsljg3DvhtjlQrxkczIBPr4bvRkH+AbPT1Zz0xUbxtG+zcWnz1tmaX+oGVEBJ5ZSOPjXpCd5aE1HElXnbrLx0bTw3JTbB4YAJ/1vNMzPWUlLiYvONojvDnQvh3FGABVI/gbcSPX80yuEw5n9NuRTy9xmfw23zIKKN2cmkAlRASeU4L9+p+7iIO7BaLTxxeTvGXtwSgHd/TeOuT5dzuNAF1s47nk8A9HsGbv7JaBRZOhr1xY1w4MS+Vm6/gPChPTB1iDH/y14IrS+F4dOhTqTZyaSCVEBJxRUXwNbS5Vs0/0nEXVgsFu7p04JXBsfjY7Myc3UmV721iK17Dpkd7URNEuHO3+Hcu8BiNdodvNHdaL553KLEbr2A8Ob58HZP2DDT6C7e/zljbTufQLOTSSWogJKKS/8DivMhKBIi2pqdRkQqaWDnhnw6IoGwIF/WZR7ksjcWMsfV7tCDo6NRk+CO34zpAsX5RvPNN7vDqm+gpMQ9FxDOPwAzHoSPrzTmkoa1hBHzIOEOrejghlRAScVp+RYRt3dO03pMv+c8ujapy8EjxYz46E8e+Xal613SA4hqb1zWGvRfqBMNB7bBVzfDO+cRfeBPzuvRwz0WEC4pgWVT4PUusOT/AAd0HQ63/2L00xO3pAJKKk7tC0Q8QmSwH1NHnMtt58UC8OnidC59bSF/bt1ncrJyWCzQ4WoYvRR6jTdu8c9eDdNuhHcvgLU/Gp27XdX2pfD+RfDDPXB4rzHqdMM3cNmrxkibuC2Lw6H2r1UtNzeXkJAQcnJyCA4ONjtO1Ti8D56LAxwwbj3UiTI7kYhUgd837WHctBVk5hrzi65PaMxD/VoT4u9tcrKTyN8PKW/CH28bixMDhDaBc26Fzjca/aXMVtovb9FrkLbAeM6nDvR62LhcZ3PRz1Yq9fNbBVQ18MgCas33xm98Ya1g9BKz04hIFTpwuJBJM9bxxZ/bAagX6MOYpBZc170x3jYXvVBxeB8seh3+/C8cyTGe8/KD9oOg8w0QkwBWW81mKi6E1d8aubKOrhdqsUGn66DPBN1h5wZUQJnMIwuoGQ/AknfhnBEw4AWz04hINfgjbS+PfLuSzbuNu/PiwgK5u09zLusYjZerFlKFh2HVV7DkPcj8+9jzgRHQ5lJoczk0PR9sXtVz/uJCY5Rp9bewfvqxYs47ELrcBOeOhLpNqufcUuVUQJnMIwuoNxOMLuTXfgxtLzc7jYhUkyJ7CZ8v3c4rczaw91AhADH1/LntvDiu7NKQYD8XvfzkcMCOpbBssrEsTGkhA+AbYqy/1zjBGJlq2PXMWwYUF0DWKti5HLYvgY2zyp4rKAq63wbdbnWNy4lSKSqgTOZxBdTBLHixJWCBB9P0j4JILXDwSBEfpWzjg4VbnIVUgI+NyztFc3l8NAmx9bFZzb0b93BhMTv357Njfz6F9hKS2x2dm1lcCFt/hTX/g3XTjcnbx7PYjFGh0MbHvuo0AJuPcdnP6m0s5nskB/KyIC/beNyXZhRP9sKyxwuKNEa62g2Exok1f+lQqowKKJN5XAG18iv4+lbjdts7F5qdRkRqUH6hnS+XbefjlG1szM5zPh8W5EtSmwjOaxFGj2Zh1Av0qZLzZWRkkJaWRlxcHNHR0RwpsrNt72G27Dl09CuPrXsOs2XvIXYfLHB+X+N6Afz6YDkrJNiLjUt7O5Yavey2L4HcHWcX0r+eMYrVsAvEXgiNz1XR5CFUQJnM4wqo7++B5VMgcTQkP212GhExgcPhYOnW/Xy9bAczV2eSk19U5vUm9QPo2CiUVpFBNA0LpHG9AMLr+FIv0Adfr/KLi8LiEnKPFLEnr4A9BwvJzD1Cyop1pGUdoMAriBy7Dxk5+Zzqp1QdXy8a1QsgNiyAN6/vgqUiPepyM2DfFmOJmNKvvCwoKTJaItiLjG2/EGN0KTDceAxpCA3ioW5T9cLzUCqgTOZxBdSr8bB/C1w/DVomm51GRExWWFxCStpeft2wm4Ub97A+6+Ap9/fxsuLnZcXHy0aJw4G9xEF+kZ3C4pIKna+OnxexYYHEhQUSGxZEbHggTesH0KReICEBLjonS9xSZX5+V9NtCeIxDmw3iieLzbi2LyK1no+XlQtbhnNhy3DAaIPw944cVu7MYfPuPLbtPcz2fYfZe6gQe4mDwuKSo8XSid3OLRaoG+BD/UAfokL8aBDiR3SoP03qB9C4XgBN6gdSP9CnYiNLIjVIBZSc2tbfjMfozuDnAaNpIlLlQgN8uKBlOBccLahKlZQ4yMkv4lBhMUeKSiiyl2CzWrBaLPj72Ajy9SLQx+a6LRJETkEFlJzall+Nx9gLzM0hIm7HarVQN9CHulU0wVzElajsl5NzOFRAiYiIlEMFlJzcvjTI3Wn0RolJMDuNiIiIy1ABJSdXOvrU6BytGi4iInIcFVBycrp8JyIiUi6PKqCaNm2KxWIp8/Wf//ynzD5///03559/Pn5+fsTExPDcc8+dcJwvv/yS1q1b4+fnR4cOHZgxY0ZNvQXX4XAcuwNPBZSIiEgZHlVAATz55JPs2rXL+XX33Xc7X8vNzaVv3740adKEZcuW8fzzz/P444/z7rvvOvdZtGgR1113Hbfeeit//fUXAwcOZODAgaxatcqMt2Oe3evg0G7w8oeG3cxOIyIi4lI8ro1BnTp1iIqKKve1Tz/9lMLCQj744AN8fHxo164dqampvPTSS9x+++0AvPrqq/Tr148HHngAgKeeeoo5c+bwxhtv8M4779TY+zDdlqOjT43PBS/dgiwiInI8jxuB+s9//kP9+vXp3Lkzzz//PMXFxzrfpqSkcMEFF+Djc6wgSE5OZv369ezfv9+5T1JSUpljJicnk5KSctJzFhQUkJubW+bL7W373Xhsep65OURERFyQR41A3XPPPXTp0oV69eqxaNEixo8fz65du3jppZcAyMzMJDY2tsz3REZGOl+rW7cumZmZzueO3yczM/Ok5500aRJPPPFEFb8bEzkcsG2Rsd2kp7lZREREXJDLj0A9/PDDJ0wM/+fXunXrABg7diy9evWiY8eO3Hnnnbz44ou8/vrrFBQUVGvG8ePHk5OT4/zavn17tZ6v2u3dDIeyweYLDbuYnUZERMTluPwI1Lhx4xg+fPgp94mLiyv3+YSEBIqLi9m6dSutWrUiKiqKrKysMvuU/rl03tTJ9jnZvCoAX19ffH19T/dW3Me2hcZjo3PAy4Pel4iISBVx+QIqPDyc8PDw0+9YjtTUVKxWKxEREQAkJibyyCOPUFRUhLe3NwBz5syhVatW1K1b17nPvHnzGDNmjPM4c+bMITEx8ezeiDtxXr7rYW4OERERF+Xyl/AqKiUlhVdeeYUVK1aQlpbGp59+yn333ccNN9zgLI6uv/56fHx8uPXWW1m9ejVffPEFr776KmPHjnUe595772XmzJm8+OKLrFu3jscff5w///yT0aNHm/XWap4KKBERkVNy+RGoivL19eXzzz/n8ccfp6CggNjYWO67774yxVFISAizZ89m1KhRdO3albCwMCZMmOBsYQDQo0cPPvvsMx599FH+9a9/0aJFC7777jvat29vxtuqeQfSIWc7WL0gprvZaURERFySxeFwOMwO4Wlyc3MJCQkhJyeH4OBgs+NUzorP4ds7jPlPt801O42IiEiNqczPb4+5hCdVpLT/ky7fiYiInJQKKClL/Z9EREROSwWUHHMwE/ZuAiwQk2B2GhEREZelAkqOKR19imoP/qGmRhEREXFlKqDkGF2+ExERqRAVUHKM+j+JiIhUiAooMRzeB9mrje3GKqBERERORQWUGNL/MB7DWkLQmS2dIyIiUluogBKDs/+T5j+JiIicjgooMaiAEhERqTAVUAIFebDrb2O7SaK5WURERNyACiiBncvAYYfgRhDSyOw0IiIiLk8FlMD2xcZjY3UfFxERqQgVUHLsDryYc83NISIi4iZUQNV2JXbYsdTY1giUiLiZjIwMFi5cSEZGhtlRpJbxMjuAmCx7LRTkgk8QRLQzO42ISKWkpaWxadMmAKKjo01OI7WJCqjabvvRy3cNu4JNfx1ExL3ExcWVeRSpKfqJWdttX2I8Ntb8JxFxP9HR0Rp5ElNoDlRt55xArvlPIiIiFaUCqjY7mAkHtoHFCo3OMTuNiIiI21ABVZuVjj5FtAO/YHOziIiIuBEVULWZGmiKiIicERVQtZkaaIqIiJwRFVC1VeFhyDy6gLBGoERERCpFBVRttXMZlBRDnWgIiTE7jYiIiFtRAVVbHT//yWIxN4uIiIibUQFVW5UWUOr/JCIiUmkqoGqjkhIVUCIiImdBBVRttGc9HMkB7wCI6mB2GhEREbejAqo2Sj9+AWFvc7OIiIi4IRVQtdGOP43HmO7m5hAREXFTKqBqo51HC6hGKqBERETOhAqo2ib/AOxeZ2w36mZqFBEREXelAqq2yVhuPNZtCoFhpkYRERFxVyqgapvS+U+NzjE3h4iIiBtTAVXb7FhqPKqAEhEROWMqoGoTh+O4Akrzn0RERM6UCqjaZF8a5O8Hmy9EqoGmiIjImVIBVZuUjj5Fx4OXj6lRRERE3JkKqNpE859ERESqhAqo2qT0DryGXc3NISIi4uZUQNUWhYcha5WxrREoERGRs6ICqrbYtQJKiiEoCkIamZ1GRETEramAqi2Ob19gsZibRURExM2pgKotNIFcRESkyqiAqi20hIuIiEiVUQFVG+TshIMZYLEZPaBERETkrKiAqg1KL99FtgWfQHOziIiIeAAVULWB5j+JiIhUKRVQtcHOZcajCigREZEqoQLK09mLICPV2G7YzdQoIiIinkIFlKfLXgvF+eAbDPWbm51GRETEI6iA8nQZy43H6Hiw6j+3iIhIVdBPVE+3s7SA6mJuDhEREQ/iNgXU008/TY8ePQgICCA0NLTcfdLT0xkwYAABAQFERETwwAMPUFxcXGafBQsW0KVLF3x9fWnevDmTJ08+4ThvvvkmTZs2xc/Pj4SEBJYsWVIN76iGlI5ANexqbg4REREP4jYFVGFhIddccw0jR44s93W73c6AAQMoLCxk0aJFTJkyhcmTJzNhwgTnPlu2bGHAgAH07t2b1NRUxowZw2233casWbOc+3zxxReMHTuWiRMnsnz5cjp16kRycjLZ2dnV/h6rXOFhyFpjbDfUCJSIiEhVsTgcDofZISpj8uTJjBkzhgMHDpR5/qeffuLSSy8lIyODyMhIAN555x0eeughdu/ejY+PDw899BDTp09n1apVzu8bMmQIBw4cYObMmQAkJCRwzjnn8MYbbwBQUlJCTEwMd999Nw8//HCFMubm5hISEkJOTg7BwcFV8K7PUPpi+KAvBEbA/Ru0iLCIiMgpVObnt9uMQJ1OSkoKHTp0cBZPAMnJyeTm5rJ69WrnPklJSWW+Lzk5mZSUFMAY5Vq2bFmZfaxWK0lJSc593Irz8l0XFU8iIiJVyMvsAFUlMzOzTPEEOP+cmZl5yn1yc3PJz89n//792O32cvdZt27dSc9dUFBAQUGB88+5ubln9V6qjCaQi4iIVAtTR6AefvhhLBbLKb9OVbi4ikmTJhESEuL8iomJMTuS4fgRKBEREakypo5AjRs3juHDh59yn7i4uAodKyoq6oS75bKyspyvlT6WPnf8PsHBwfj7+2Oz2bDZbOXuU3qM8owfP56xY8c6/5ybm2t+EZV/APZuMrY1AiUiIlKlTC2gwsPDCQ8Pr5JjJSYm8vTTT5OdnU1ERAQAc+bMITg4mLZt2zr3mTFjRpnvmzNnDomJiQD4+PjQtWtX5s2bx8CBAwFjEvm8efMYPXr0Sc/t6+uLr69vlbyPKrMr1XgMbQyB9U2NIiIi4mncZhJ5eno6qamppKenY7fbSU1NJTU1lby8PAD69u1L27ZtufHGG1mxYgWzZs3i0UcfZdSoUc7i5s477yQtLY0HH3yQdevW8dZbbzFt2jTuu+8+53nGjh3Le++9x5QpU1i7di0jR47k0KFD3Hzzzaa87zO2U/2fREREqovbTCKfMGECU6ZMcf65c+fOAMyfP59evXphs9n48ccfGTlyJImJiQQGBjJs2DCefPJJ5/fExsYyffp07rvvPl599VUaNWrE+++/T3JysnOfwYMHs3v3biZMmEBmZibx8fHMnDnzhInlLm/nMuNRl+9ERESqnNv1gXIHLtEH6qW2kLsThk+HpueZk0FERMSN1Mo+UHKcg1lG8YQFGnQyO42IiIjHUQHliUrbF4S3At865mYRERHxQCqgPJEaaIqIiFQrFVCeSA00RUREqpUKKE/jcBzXwkAFlIiISHVQAeVp9m+F/H1g9YbI9manERER8UgqoDxN6eW7qPbg5WLd0UVERDyECihPk5FqPEZ3NjWGiIiIJ1MB5WlK18BrEG9mChEREY+mAsqTOBywa4WxHR1vahQRERFPpgLKk+zfCkdywOYD4W3MTiMiIuKxVEB5ktLLdxFtwcvH1CgiIiKeTAWUJ9HlOxERkRqhAsqTlN6BpwWERUREqpUKKE9x/ARy3YEnIiJSrVRAeYqc7Uc7kHsZc6BERESk2qiA8hSll+8i2oC3n6lRREREPJ2X2QGkiujynYiIk91up6ioyOwY4mK8vb2x2WxVciwVUJ7C2YFcE8hFpPZyOBxkZmZy4MABs6OIiwoNDSUqKgqLxXJWx1EB5QkcDq2BJyICzuIpIiKCgICAs/4hKZ7D4XBw+PBhsrOzAWjQoMFZHU8FlCfIzYDDe8Big8h2ZqcRETGF3W53Fk/169c3O464IH9/fwCys7OJiIg4q8t5mkTuCUrnP4W3Bm9/c7OIiJikdM5TQECAyUnElZX+/TjbOXIqoDxB6fwndSAXEdFlOzmlqvr7oQLKE6gDuYiIW+vVqxdjxowxOwYA3333Hc2bN8dmszFmzBgmT55MaGio2bFcjgooT6AWBiIicgoLFizAYrFU6O7EO+64g6uvvprt27fz1FNPMXjwYDZs2OB8/fHHHyc+Pr76wroJTSJ3dwczIS8TLFaIam92GhERcWN5eXlkZ2eTnJxMdHS08/nSyddyjEag3F3p6FNYS/AJNDeLiIicseLiYkaPHk1ISAhhYWE89thjOBwO5+sFBQXcf//9NGzYkMDAQBISEliwYIHz9W3btnHZZZdRt25dAgMDadeuHTNmzGDr1q307t0bgLp162KxWBg+fPgJ51+wYAF16tQB4KKLLsJisbBgwYIyl/AmT57ME088wYoVK7BYLFgsFiZPnlxdH4lL0wiUu9P8JxGRk3I4HOQX2U05t7+3rVITlqdMmcKtt97KkiVL+PPPP7n99ttp3LgxI0aMAGD06NGsWbOGzz//nOjoaL799lv69evHypUradGiBaNGjaKwsJBff/2VwMBA1qxZQ1BQEDExMXz99dcMGjSI9evXExwcXO6IUo8ePVi/fj2tWrXi66+/pkePHtSrV4+tW7c69xk8eDCrVq1i5syZzJ07F4CQkJCz+6DclAood+fsQB5vZgoREZeUX2Sn7YRZppx7zZPJBPhU/MdsTEwML7/8MhaLhVatWrFy5UpefvllRowYQXp6Oh9++CHp6enOS2v3338/M2fO5MMPP+SZZ54hPT2dQYMG0aFDBwDi4uKcx65Xrx4AERERJ50Q7uPjQ0REhHP/qKioE/bx9/cnKCgILy+vcl+vTVRAubvSS3hqYSAi4tbOPffcMiNWiYmJvPjii9jtdlauXIndbqdly5ZlvqegoMDZNPSee+5h5MiRzJ49m6SkJAYNGkTHjh1r9D3UJiqg3NmhPZC709iO1ARyEZF/8ve2sebJZNPOXVXy8vKw2WwsW7bshO7ZQUFBANx2220kJyczffp0Zs+ezaRJk3jxxRe5++67qyyHHKMCyp1lrjQe68WBX7C5WUREXJDFYqnUZTQzLV68uMyf//jjD1q0aIHNZqNz587Y7Xays7M5//zzT3qMmJgY7rzzTu68807Gjx/Pe++9x913342Pjw9gLHdztnx8fKrkOO5Od+G5s9ICKqqDuTlEROSspaenM3bsWNavX8/UqVN5/fXXuffeewFo2bIlQ4cO5aabbuKbb75hy5YtLFmyhEmTJjF9+nQAxowZw6xZs9iyZQvLly9n/vz5tGnTBoAmTZpgsVj48ccf2b17N3l5eWecs2nTpmzZsoXU1FT27NlDQUHB2b95N6QCyp1l/m08qoASEXF7N910E/n5+XTv3p1Ro0Zx7733cvvttztf//DDD7npppsYN24crVq1YuDAgSxdupTGjRsDxujSqFGjaNOmDf369aNly5a89dZbADRs2JAnnniChx9+mMjISEaPHn3GOQcNGkS/fv3o3bs34eHhTJ069ezeuJuyOI5vMiFVIjc3l5CQEHJycggOrsZLa28mwO51cP2X0LJv9Z1HRMQNHDlyhC1bthAbG4ufn5/ZccRFnervSWV+fmsEyl0V5cOeo631NQIlIiJSo1RAuavsNeAogYAwqFO7e3GIiIjUNBVQ7ur4CeSV6HQrIiIiZ08FlLvadXQCeQM1SRMREalpKqDclXMESgWUiIhITVMB5Y5K7JC12tjWBHIREZEapwLKHe3bAkWHwMsf6jc3O42IiEitowLKHWUeXUA4sh1Yq26tJREREakYFVDuSEu4iIiImEoFlDtSASUiIiabPHkyoaGhZsdg+PDhDBw4sMbPqwLKHekOPBERcXFbt27FYrGQmprqksc7Wyqg3M3BLMjLAovVmAMlIiK1UmFhodkRqoS7vg8VUO6mdPSpfnPwCTA3i4iIVImDBw8ydOhQAgMDadCgAS+//DK9evVizJgxzn2aNm3KU089xU033URwcDC33347AF9//TXt2rXD19eXpk2b8uKLL5Y5tsVi4bvvvivzXGhoKJMnTwaOjex888039O7dm4CAADp16kRKSkqZ75k8eTKNGzcmICCAK6+8kr17957yPcXGxgLQuXNnLBYLvXr1Ao5dcnv66aeJjo6mVatWFcp5suOVeuGFF2jQoAH169dn1KhRFBUVnTLf2fKq1qNL1cs82oFc859ERE7P4YCiw+ac2zugwkttjR07lt9//53vv/+eyMhIJkyYwPLly4mPjy+z3wsvvMCECROYOHEiAMuWLePaa6/l8ccfZ/DgwSxatIi77rqL+vXrM3z48ErFfeSRR3jhhRdo0aIFjzzyCNdddx2bNm3Cy8uLxYsXc+uttzJp0iQGDhzIzJkznRlOZsmSJXTv3p25c+fSrl07fHx8nK/NmzeP4OBg5syZU+F8pzre/PnzadCgAfPnz2fTpk0MHjyY+Ph4RowYUanPoDJUQLkbTSAXEam4osPwTLQ55/5XBvgEnna3gwcPMmXKFD777DP69OkDwIcffkh09Im5L7roIsaNG+f889ChQ+nTpw+PPfYYAC1btmTNmjU8//zzlS6g7r//fgYMGADAE088Qbt27di0aROtW7fm1VdfpV+/fjz44IPO8yxatIiZM2ee9Hjh4eEA1K9fn6iosoveBwYG8v7775cpgk7nVMerW7cub7zxBjabjdatWzNgwADmzZtXrQWULuG5G00gFxHxKGlpaRQVFdG9e3fncyEhIc5LW8fr1q1bmT+vXbuWnj17lnmuZ8+ebNy4EbvdXqkcHTse+7nSoEEDALKzs53nSUhIKLN/YmJipY5/vA4dOlSqeDqddu3aYbMd64vYoEEDZ/bqohEod1KQB3s3GdsagRIROT3vAGMkyKxzV7HAwNOPaP2TxWLB4XCUea68+UHe3t5lvgegpKSk0ueriPLeR0Vzluf47KXHqq7spVRAuZPsNYADgqIgKMLsNCIirs9iqdBlNDPFxcXh7e3N0qVLady4MQA5OTls2LCBCy644JTf26ZNG37//fcyz/3++++0bNnSOSITHh7Orl27nK9v3LiRw4crNy+sTZs2LF68uMxzf/zxxym/p3SEqaIjYafLWdnjVTcVUO5EE8hFRDxOnTp1GDZsGA888AD16tUjIiKCiRMnYrVanSNBJzNu3DjOOeccnnrqKQYPHkxKSgpvvPEGb731lnOfiy66iDfeeIPExETsdjsPPfTQCSM2p3PPPffQs2dPXnjhBa644gpmzZp1yvlPABEREfj7+zNz5kwaNWqEn58fISEhJ93/dDkre7zqpjlQ7uRIjrGAsAooERGP8tJLL5GYmMill15KUlISPXv2pE2bNvj5+Z3y+7p06cK0adP4/PPPad++PRMmTODJJ58sM4H8xRdfJCYmhvPPP5/rr7+e+++/n4CAyl1ePPfcc3nvvfd49dVX6dSpE7Nnz+bRRx895fd4eXnx2muv8X//939ER0dzxRVXnHL/0+Ws7PGqncNN/Pvf/3YkJiY6/P39HSEhIeXuA5zwNXXq1DL7zJ8/39G5c2eHj4+Po1mzZo4PP/zwhOO88cYbjiZNmjh8fX0d3bt3dyxevLhSWXNychyAIycnp1LfVyH2YoejIK/qjysi4uby8/Mda9asceTn55sd5azl5eU5QkJCHO+//77ZUTzOqf6eVObnt9uMQBUWFnLNNdcwcuTIU+734YcfsmvXLufX8evjbNmyhQEDBtC7d29SU1MZM2YMt912G7NmzXLu88UXXzB27FgmTpzI8uXL6dSpE8nJydU+m7/CrDaXv54vIiKV89dffzF16lQ2b97M8uXLGTp0KID5oyxyUm4zB+qJJ54AcHYkPZnQ0NAT+kOUeuedd4iNjXV2aW3Tpg0LFy7k5ZdfJjk5GTCGUUeMGMHNN9/s/J7p06fzwQcf8PDDD1fRuxERESnrhRdeYP369fj4+NC1a1d+++03wsLCzI4lJ+E2I1AVNWrUKMLCwujevTsffPBBmVsiU1JSSEpKKrN/cnKys119YWEhy5YtK7OP1WolKSnphJb2xysoKCA3N7fMl4iISEV17tyZZcuWkZeXx759+5gzZw4dOmi+qytzmxGoinjyySe56KKLCAgIYPbs2dx1113k5eVxzz33AJCZmUlkZGSZ74mMjCQ3N5f8/Hz279+P3W4vd59169ad9LyTJk1yjpCJiIiI5zN1BOrhhx/GYrGc8utUhcs/PfbYY/Ts2ZPOnTvz0EMP8eCDD/L8889X4zswjB8/npycHOfX9u3bq/2cIiIiYh5TR6DGjRt32rV64uLizvj4CQkJPPXUUxQUFODr60tUVBRZWVll9snKyiI4OBh/f39sNhs2m63cfU42rwrA19cXX1/fM84pIiJVx/GPbtYix6uqvx+mFlDh4eHOxQGrQ2pqKnXr1nUWN4mJicyYMaPMPnPmzHGu51M6cW/evHnOu/dKSkqYN28eo0ePrracIiJy9kqbLh4+fBh/f3+T04irKu1uXtlmov/kNnOg0tPT2bdvH+np6djtdlJTUwFo3rw5QUFB/PDDD2RlZXHuuefi5+fHnDlzeOaZZ7j//vudx7jzzjt54403ePDBB7nlllv4+eefmTZtGtOnT3fuM3bsWIYNG0a3bt3o3r07r7zyCocOHXLelSciIq7JZrMRGhrqbDsTEBBw2k7eUns4HA4OHz5MdnY2oaGhZRYfPhNuU0BNmDCBKVOmOP/cuXNnAObPn0+vXr3w9vbmzTff5L777sPhcNC8eXNnS4JSsbGxTJ8+nfvuu49XX32VRo0a8f777ztbGAAMHjyY3bt3M2HCBDIzM4mPj2fmzJknTCwXERHXUzrdwmV694nLOVW7o8qwOHSxuMrl5uYSEhJCTk4OwcHBZscREal17HY7RUVFZscQF+Pt7X3KkafK/Px2mxEoERGRiiq9KUikunhcI00RERGR6qYCSkRERKSSVECJiIiIVJLmQFWD0nn5WhNPRETEfZT+3K7I/XUqoKrBwYMHAYiJiTE5iYiIiFTWwYMHCQkJOeU+amNQDUpKSsjIyKBOnTpV3sQtNzeXmJgYtm/frhYJp6HPquL0WVWcPquK02dVcfqsKq46PyuHw8HBgweJjo7Gaj31LCeNQFUDq9VKo0aNqvUcwcHB+p+sgvRZVZw+q4rTZ1Vx+qwqTp9VxVXXZ3W6kadSmkQuIiIiUkkqoEREREQqSQWUm/H19WXixIn4+vqaHcXl6bOqOH1WFafPquL0WVWcPquKc5XPSpPIRURERCpJI1AiIiIilaQCSkRERKSSVECJiIiIVJIKKBEREZFKUgHlJp5++ml69OhBQEAAoaGh5e5jsVhO+Pr8889rNqiLqMjnlZ6ezoABAwgICCAiIoIHHniA4uLimg3qgpo2bXrC36P//Oc/ZsdyGW+++SZNmzbFz8+PhIQElixZYnYkl/P444+f8HeodevWZsdyCb/++iuXXXYZ0dHRWCwWvvvuuzKvOxwOJkyYQIMGDfD39ycpKYmNGzeaE9Zkp/ushg8ffsLfs379+tVYPhVQbqKwsJBrrrmGkSNHnnK/Dz/8kF27djm/Bg4cWDMBXczpPi+73c6AAQMoLCxk0aJFTJkyhcmTJzNhwoQaTuqannzyyTJ/j+6++26zI7mEL774grFjxzJx4kSWL19Op06dSE5OJjs72+xoLqddu3Zl/g4tXLjQ7Egu4dChQ3Tq1Ik333yz3Nefe+45XnvtNd555x0WL15MYGAgycnJHDlypIaTmu90nxVAv379yvw9mzp1as0FdIhb+fDDDx0hISHlvgY4vv322xrN4+pO9nnNmDHDYbVaHZmZmc7n3n77bUdwcLCjoKCgBhO6niZNmjhefvlls2O4pO7duztGjRrl/LPdbndER0c7Jk2aZGIq1zNx4kRHp06dzI7h8v75b3ZJSYkjKirK8fzzzzufO3DggMPX19cxdepUExK6jvJ+vg0bNsxxxRVXmJLH4XA4NALlYUaNGkVYWBjdu3fngw8+wKE2X+VKSUmhQ4cOREZGOp9LTk4mNzeX1atXm5jMNfznP/+hfv36dO7cmeeff16XNjFGNZctW0ZSUpLzOavVSlJSEikpKSYmc00bN24kOjqauLg4hg4dSnp6utmRXN6WLVvIzMws83csJCSEhIQE/R07iQULFhAREUGrVq0YOXIke/furbFzazFhD/Lkk09y0UUXERAQwOzZs7nrrrvIy8vjnnvuMTuay8nMzCxTPAHOP2dmZpoRyWXcc889dOnShXr16rFo0SLGjx/Prl27eOmll8yOZqo9e/Zgt9vL/Xuzbt06k1K5poSEBCZPnkyrVq3YtWsXTzzxBOeffz6rVq2iTp06ZsdzWaX/9pT3d6y2/7tUnn79+nHVVVcRGxvL5s2b+de//kX//v1JSUnBZrNV+/lVQJno4Ycf5tlnnz3lPmvXrq3w5MvHHnvMud25c2cOHTrE888/7zEFVFV/XrVJZT67sWPHOp/r2LEjPj4+3HHHHUyaNMn0pRPEPfTv39+53bFjRxISEmjSpAnTpk3j1ltvNTGZeJIhQ4Y4tzt06EDHjh1p1qwZCxYsoE+fPtV+fhVQJho3bhzDhw8/5T5xcXFnfPyEhASeeuopCgoKPOIHX1V+XlFRUSfcPZWVleV8zdOczWeXkJBAcXExW7dupVWrVtWQzj2EhYVhs9mcf09KZWVleeTfmaoUGhpKy5Yt2bRpk9lRXFrp36OsrCwaNGjgfD4rK4v4+HiTUrmPuLg4wsLC2LRpkwooTxceHk54eHi1HT81NZW6det6RPEEVft5JSYm8vTTT5OdnU1ERAQAc+bMITg4mLZt21bJOVzJ2Xx2qampWK1W5+dUW/n4+NC1a1fmzZvnvLu1pKSEefPmMXr0aHPDubi8vDw2b97MjTfeaHYUlxYbG0tUVBTz5s1zFky5ubksXrz4tHdgC+zYsYO9e/eWKT6rkwooN5Gens6+fftIT0/HbreTmpoKQPPmzQkKCuKHH34gKyuLc889Fz8/P+bMmcMzzzzD/fffb25wk5zu8+rbty9t27blxhtv5LnnniMzM5NHH32UUaNGeUzBeSZSUlJYvHgxvXv3pk6dOqSkpHDfffdxww03ULduXbPjmW7s2LEMGzaMbt260b17d1555RUOHTrEzTffbHY0l3L//fdz2WWX0aRJEzIyMpg4cSI2m43rrrvO7Gimy8vLKzMSt2XLFlJTU6lXrx6NGzdmzJgx/Pvf/6ZFixbExsby2GOPER0dXStb0pzqs6pXrx5PPPEEgwYNIioqis2bN/Pggw/SvHlzkpOTayagaff/SaUMGzbMAZzwNX/+fIfD4XD89NNPjvj4eEdQUJAjMDDQ0alTJ8c777zjsNvt5gY3yek+L4fD4di6daujf//+Dn9/f0dYWJhj3LhxjqKiIvNCu4Bly5Y5EhISHCEhIQ4/Pz9HmzZtHM8884zjyJEjZkdzGa+//rqjcePGDh8fH0f37t0df/zxh9mRXM7gwYMdDRo0cPj4+DgaNmzoGDx4sGPTpk1mx3IJ8+fPL/ffpmHDhjkcDqOVwWOPPeaIjIx0+Pr6Ovr06eNYv369uaFNcqrP6vDhw46+ffs6wsPDHd7e3o4mTZo4RowYUaY1TXWzOBy6z11ERESkMtQHSkRERKSSVECJiIiIVJIKKBEREZFKUgElIiIiUkkqoEREREQqSQWUiIiISCWpgBIRERGpJBVQIiIiIpWkAkpERESkklRAiYiIiFSSCigRkdPYvXs3UVFRPPPMM87nFi1ahI+PD/PmzTMxmYiYRWvhiYhUwIwZMxg4cCCLFi2iVatWxMfHc8UVV/DSSy+ZHU1ETKACSkSkgkaNGsXcuXPp1q0bK1euZOnSpfj6+podS0RMoAJKRKSC8vPzad++Pdu3b2fZsmV06NDB7EgiYhLNgRIRqaDNmzeTkZFBSUkJW7duNTuOiJhII1AiIhVQWFhI9+7diY+Pp1WrVrzyyiusXLmSiIgIs6OJiAlUQImIVMADDzzAV199xYoVKwgKCuLCCy8kJCSEH3/80exoImICXcITETmNBQsW8Morr/Dxxx8THByM1Wrl448/5rfffuPtt982O56ImEAjUCIiIiKVpBEoERERkUpSASUiIiJSSSqgRERERCpJBZSIiIhIJamAEhEREakkFVAiIiIilaQCSkRERKSSVECJiIiIVJIKKBEREZFKUgElIiIiUkkqoEREREQqSQWUiIiISCX9P0wudjuffMkQAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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7UIESqTabd6WT8ubFXFn6PQCHOt9JyMhPwLMWryFnsThnLh+3HBIHO08vnvUgfDYMDmeQFBvC9Nv6cEWXWAwDXp6zjdGTlnEg/2SL+YmI/E3Kb86vTc4yN8dfqECJVIMNO3ZR+tEFnG0spwRPcge9Rb0hj7rkIbsa4R8GV3wGQ14GD1/n9Adv9YCtP+PrZePZYe148bL2+HraWLD9AINfW8Cy1GyzU4uIO8jfD1kbnddVoERqj2270rB8cjHtLMnkWYIoufp/BHcbYXasM89igc7Xwc2/QVRbOJINU6+AuU+Aw86wTg35YVwvEiICyMwr5qr3l/D50jSzU4uIq9u5wPk1so3zP2suQgVK5DTs3J2GY9KFtGYHuZYgPK77iYCEnmbHMld4ItwwF7qNcX6/4AX47DIozKZ5ZCA/jO3FkHbRlDkM/vvdeh7930bK7A5zM4uI6yof/+Q6e59ABUrkX9u7dzclH11AC1LJsQRjHT0dv9j2ZsdyDR7eMOhZGPbh0UN6c+G9s2HfWvy9PXj9yg7cfX5zACYv2snoScvJLSw1ObSIuKTUY+OfXGcAOahAifwrWZn7OPLBEJobO8m2hGCM+onAuHZmx3I9bS+FG+ZAvcaQkwYfng/rv8ZisTCuXzPeuboTfl42FiYfYOhbf5B6oMDsxCLiSnLSIDsFLDaI62F2mgpUoESqqLAwn6z3LyHB2MlB6mG/5ifqNVZ5OqGoNnDTfGh2vnPOqG+uh9+fB8NgYJsovhnTkwYhvqQeKODiNxYy5cf5/5gVvVKzpYtI7bPjV+fXhp3BJ9jcLH+jAiVSBQ67nU1vjqBN2SYO40fJVd8S3qSt2bFcn289uHIa9Bjn/P7XJ+GHsVBWQsvoIL4f24u2DYLJKSrjyUX5fLN4a4WHV3q2dBGpXY4VqKbnmpvjOFSgRKpg+Qe307lgPiWGjfQB7xPdvKPZkdyH1QYDnoLBL4LFCms+g08vgSOHCA/0ZtpN3ekeF0ipYeHlFYVMW/bnGXpVni1dRNyfvQxS5juvN+1napTjUYESqaTVXz9Ht32fArCu01Mk9hhiciI31eUGuOpL8Apwnp784QDI3Yu/twef3NSbSzs1xGHA/d+u57W52zEM49/Pli4i7it9tXMNPJ9gaOB6/1lVgRKphO2/f0G79U8D8HvsGDpfOMbkRG6u2Xlw3UwIjIEDW+GjAXBgO542K89f2o7b+iUA8NLsbTzz8xYMQ8u/iNQ5O+Y6v8b3de7BdjEqUCKncGDnBmJ+vR2bxWBB0GB6j37a7Ei1Q1RbuH4W1G8GubudJSp9NRaLhbvOT+ThIc71A9/7PYWHvt+AQ2voidQtLjz+CVSgRE6q7Mhhjnx6Ff4Usc7Wmk63foTVpr821SYk1rknKjoJCg/C5Asg1Tnr8PW9m/B/w9piscBnS9O4+6u1mnBTpK44kgN7Vjivu+D4J1CBEjkxw2DbRzcSW7aL/UYIwSM/xc/Hx+xUtY9/GIz6ERr3gZLD8Okw2PozAMO7NOKV4UnYrBa+Xb2XcZ+vpqRMJUqktste9T0YdkpD4p3/0XJBKlAiJ5A843Va7f+ZMsPK1j6vEddYZ4DVGJ8gGPE1tBgC9mL4YiRs/hGAi5Ia8M7VnfCyWZm5MYNxn6+iVHuiRGq14k3O/0TtD2xjcpITU4ESOY7s7YtptPwxAH6JvoXe/S8yOVEd4OkDl02BNsPAUQpfjYaN3wNwXqtI3h/VGS8PK7M2ZapEidRmhkF47joAvFsPMjnMialAifyNvSAb+7Rr8KKMhR496HftE2ZHqjtsHnDxe9D2cnCUwdfXwYZvADi7eTjvjeyEl4eVXzZmcvvU1SpRIrVRdgoe+elg9aR+R9f9z6sKlMjfpEwZQ7g9i11GFDGjPsLX28PsSHWLzQMufgeSRoBhh29ugHVfAtA3MYJ3RzoP5/28IYM7pqlEidQW5Us2LfkKAKNRd/DyNznVialAifzFvj8+p1nWTMoMK9t6v0x8rCZuNIXVBhe+AR2vAcMB391cvifqnL+UqBnrM7jry7XYNcWBiNs7tmRT3sbZAHybl2hyopNTgRI5qiwnHf859wIwPeRK+vd33WPvdYLVCkNe/bNEfXsTbJkOwDktInj76o54WC38b206D32/XpNtiri5+Ph4msXHEXdkIwCOJq45fcExKlAiAIbBno9vIMg4zCaa0G3Us1gsFrNTidUKQ16BdsOdY6K+Gg3JcwA4t2Ukr17RAasFpi7bzVPTN6tEibixmJgYujX0wNc4wn4jiObte5gd6aRUoESAjPnv0Tj7D4oNT/b0fYWo0CCzI8kxVhtc9Ba0ugjsJTBtBKT+DsDgdtE8e0k7AD5YmMprc5PNTCoip+ngWuf0BUst7WjTsJ7JaU5OBUrqvNIDqQT99ggAP9S/lvPOPtvkRPIPNg+45ANoPgjKiuDzK2D3MgAu7xLLxKPLvrw8ZxsfLkw1M6mInAZb8iwAMsN7Y7O69lEAFSip2wyDzE9uwI8jrKQlfa95VIfuXJWHF1w2GeLPgdIC+OwyyNoMwHW9m3DXec0BeOKnTXy3eo+JQUXkX8ndS1jBdhyGBb9WA8xOc0oqUFKnHVj0MQ1zV3DE8OJg/5eJCHHdU2YF52SbV3wGDbtCUQ58cjEc2gXAuH4J3NC7CQD3fLWO+VuzTAwqIlVVumUmAKuNBDq3amZymlNTgZI6yyjMxnPOwwB8G3AF5/XqbnIiqRQvf7jqCwhvCYf3OUtU/n4sFgv//U9LhibFUOYwuPWzVazdnWN2WhGppMPrZwCwzKMzCREBJqc5NRUoqbN2f30/wUYu2x0xeLe+SIfu3IlfKIz8FoIbQfYO+GwYFOVhtVp47tL29GkWRmGJnWsnLydlf77ZaUXkVEqLCExfCMDhRv3c4t9jFSipk/Ys/4lGKV8A8HPkLfRMamVyIqmyoBgY+R34hcG+tTDtKigrxsvDyttXd6Jdw2CyC0oY+eEysvKKzE4rIiezayGejiIyjHo0buUeRwNUoKTusZfBrAcB+ImzuPHGccTEaMZxtxSWAFd/A16BsHMBfD8GHA4CvD34aHQXGtf3Y2/OEa6bspyC4jKz04rICZRsdo5/mmdPokdCmMlpKkcFSuqc/XNfp2HpTnIMf+xnPYCvl83sSHI6YpJg+Mdg9XAu9zL3UQDCArz5+Lpu1Pf3YsPePG6bupoyrZsn4noMA/vRAeTr/boRG+pncqDKUYGSOsXI3UvA4v8D4Iewm7ioX0+TE0m1aNrPuXYewB+vwtL3AGhU34/3R3XG28PKr1uyePTHjZqtXMTVHNiOb8Fuig0PPJudY3aaSlOBkjpl77cP4mscYbXRjH5X3W12HKlOSVdCv4ec13++Fzb/BEDHRvV49YokLBb4dEka7/2eYmJIEfmH7c7JM5c6WtKpeSOTw1SeCpTUGaV71hKz63sANrd7gNj6rn+arFRRn7uh02jAgG+uh93LARjYJpoH/9MSgGd+3sJP69LNyygiFRyb/2m+I4meTeubnKbyVKCkbjAM9n97D1YMfrH04qIhF5mdSGqCxQL/eRGaDXAu+TL1Cji0E4DrezdhdM/GANz15VrWaI4oEfMV5WHbvRiAnaG9CQvwNjlQ5alASZ1QsPFnYrKXUmx4cOSsB/H39jA7ktQUmwdc+hFEtYXCA/D5cCjKxWKx8PCQVpzbIoLiMgc3TFnB3pwjZqcVqdtS5mE1ykhxRBHXvK3ZaapEBUpqP3sZhdP/C8AP3hcw5KweJgeSGucdAFd+AYHRsH8LfDkK7KXYrBZevbIDLaICOZBfzA1TVmh6AxEzbXOOf5rn6ECvpu4xfcExKlBS6x3640PCj6RyyAggcvCDeNj0Y18nBDeAK6eBpx+kzIMZ94BhEODtwQejOhMW4MXmfXncMW0NdofOzBM54xwO7EcL1G9GB7rFh5ocqGr0m0Rqt+LDePz2DAA/BF/NWe0STA4kZ1RMEgz7ELDAykmw+E0AGtbz492RnfHysDJncyb/N3OLqTFF6qT01dgKs8g3fCiL7U6gj6fZiapEBUpqtayZzxFoP0SKI4rOw+52i/WVpJq1+A8MeMp5fdZD5YcMOsXV4/lL2wHw3u8pfLVit1kJReqmLc6pRuY72tMrsYHJYapOBUpqr4IDBK1xTqg4L/ZW2sSFmxxITNP91j+nN/j6Oshy7nG6KKkBt/dz7pV88LsNrEo7ZF5GkTrG2DIdgFn2zpzd3P3+fVaBklor4+f/w8coYp0jnvMuvsHsOGImiwUGPQ9xvaHkMEwdDoXZAIzv35zzW0VSYndw8ycrycjVwsMiNe5AMpYDWyk1bKzz7Uqr6CCzE1WZCpTUTvlZ1Ns4BYCVTW6hUZi/yYHEdB5ecPnHEBLnnBvqy2vAXorVauGl4UkkRgay/3AxN32ygqJSu9lpRWq3rc69T4sdrejQvDFWq/sNr1CBklopY8azeBvFrHEk0P/Cq82OI67Cv77zzDyvANi5AH6+D4AAbw/ev6YzIX6erNuTy/3frNOaeSI16djhO0dnooxs0tPdb3UAFSipfQ5nELrpEwBWNx1DbH3tfZK/iGwFwz4ALLDiQ1j+AeBcePitqzpis1r4fk067y/QmnkiNeJwJsbuZQDMsXfEP383KSnu9/dNBUpqnX3Tn8GLElY6mnP+hVeZHUdcUeIgOHei8/rP98GuRQD0TAhj4pBWADz78xYWbj9gVkKR2mvbz1gwWOuIJySiIUktmhIfH292qipTgZJaxcjdS/0tnwGwrtlYGtTzMzmRuKzed0KbYeAoc46Hyt0DwDU94risU0McBoybuord2YUmBxWpZf5y9t25rWPo3bs3MTExJoeqOhUoqVUypj+DF6Usd7Rg4AWXmx1HXJnFAhe+4Vwzr2A/TBsBpUewWCw8MbQN7RoGk1NYys2frORIiQaVi1SL4sMYKfMB5/ins5tHmJvnNKhASa1h5OwmbNtUADY0H0t0iPY+ySl4+cHwz8A3FPatgR/vAMPAx9PGO1d3or6/F5v25fHAtxpULlItkudisZeQ6ohkn1ccHRqFmJ3oX6tVBerRRx/FYrFUuLRo0aL8/qKiIsaOHUv9+vUJCAhg2LBhZGZmVniOtLQ0Bg8ejJ+fHxEREdxzzz2UlWmxUXeQ8csLeFLGUqMl/7lQe5+kkurFweVTwGKDdV/AkrcAiAnx5c0Rfw4q/3BhqslBRWqBv5x917NpGJ5uvDap+yY/gdatW7Nv377yy8KFC8vvu/POO/nxxx/56quv+O2330hPT+eSSy4pv99utzN48GBKSkpYtGgRU6ZMYfLkyUycONGMtyJVUZhN6JZpAGxseiORQT4mBxK30uQsGPC08/qshyH1dwC6x9fnocEtAXjm5y0sSTloVkIR92cvhW2/AEdnH090v9nH/6rWFSgPDw+ioqLKL2FhYQDk5uby4Ycf8tJLL9GvXz86derEpEmTWLRoEUuWLAFg1qxZbNq0iU8//ZSkpCQGDRrEE088wZtvvklJSYmZb0tOYf+vb+BtFLHREUe/QcPNjiPuqNvN0P5KMOzw1bXlg8pH92zMxR0aYHcYjPt8lWYqF/m3di6E4lwOGEGsNppxVjMVKJeyfft2YmJiiI+PZ8SIEaSlpQGwcuVKSktL6d+/f/m2LVq0oFGjRixevBiAxYsX07ZtWyIjI8u3GTBgAHl5eWzcuPGEr1lcXExeXl6Fi5xBJYX4rnbO5bM0ZiSNwwNMDiRuyWKBIS87B5UXHoAvRkJpERaLhacvbkuLqEAO5Jdw62crKSlzmJ1WxP0cXTx4jr0jjcMCiQ1173GqtapAdevWjcmTJzNz5kzefvttUlNT6dOnD4cPHyYjIwMvLy9CQkIqPCYyMpKMjAwAMjIyKpSnY/cfu+9EnnnmGYKDg8svsbGx1fvG5KQOLZpMgD2XNEc4XQdfZ3YccTPp6eksXLjQOROypy8M/xR860H6Kvj5HgB8vZyDygN9PFiVlsNT0zeZnFrEzTjssOl/AMx0dOGcFu579t0xtapADRo0iMsuu4x27doxYMAAZsyYQU5ODl9++WWNvu4DDzxAbm5u+WX37t01+nryF/YyWPQ6AL+GDqdNbH2TA4m7SUlJITk5+c+ZkOs1hmEfAhZY9TGsnAxA4zB/Xr48CYApi3fx3eo9ZsQVcU9pi6Egizz8+cPRlnNbqkC5tJCQEJo3b05ycjJRUVGUlJSQk5NTYZvMzEyioqIAiIqK+sdZece+P7bN8Xh7exMUFFThImdG/upvqFeSzkEjkOYDbjE7jrih+Ph4EhISKs6EnHAunPuw8/qMe2DPCgD6t4rktn4JADzw7Xo279PhepFK2fg9AL+UdcLHx4cujUPNzVMNanWBys/PZ8eOHURHR9OpUyc8PT2ZO3du+f1bt24lLS2NHj16ANCjRw/Wr19PVlZW+TazZ88mKCiIVq1anfH8cgqGQeG8FwD42e9CerRoaHIgcUcxMSeYCbn3BGgxBOwlzpnKC5zLuozv35w+zcIoKnVw62eryCsqNSG1iBtx2GHTDwBMd3Snb2KEW09fcIz7v4O/uPvuu/ntt9/YuXMnixYt4uKLL8Zms3HllVcSHBzM9ddfz4QJE5g3bx4rV67k2muvpUePHnTv3h2A888/n1atWjFy5EjWrl3LL7/8wkMPPcTYsWPx9vY2+d3J3xVtm0NEwTYKDW/C+43FYrGYHUlqE4sFhr4N9RMgby98cz047NisFl69ogMxwT6kHijg3q80yabISR09fHcYf/5wtKF/LTh8B7WsQO3Zs4crr7ySxMRELr/8curXr8+SJUsID3eeKvnyyy8zZMgQhg0bxllnnUVUVBTffvtt+eNtNhs//fQTNpuNHj16cPXVV3PNNdfw+OOPm/WW5CSyf3kegOme59G/k/YQSg3wCXIOKvf0g5T5MM85V1SovxdvjuiIp83CzI0ZmmRT5GSOHr6bWdYJh9WTvm68fMtfWQz916na5eXlERwcTG5ursZD1RBHxias7/TAblj4/uwZDOvX0+xIUput/9q5BwrgymmQOAiAKYt28sj/NuJhtTDtpu50rgXjOkSqlcMOL7aAgixGl9zLkbh+fHFzD7NTnVBVfn/Xqj1QUnfsm/MaAPPowsBeXU1OI7Ve20uh683O69/eDNnOM/au6RHHBe1jKHMYjP18FQfyi00MKeKCjh6+K7AEHD18F3nqx7gJFShxP0dyCNvxHQDpiaPw9/YwOZDUCec/CQ27QnEufHENlB7BYrHw7CVtaRruT2ZeMXdMW43doZ36IuWOHb6zd6IUj1oxfcExKlDidg4s/Ahvo4gtjlj6nn+x2XGkrvDwgssmg18YZK53Tm8A+Ht78M7VnfD1tPFH8kFem7vd3JwiruIvZ9/9WNaN+DB/4mvRShEqUOJeHHasy98HYFn4pTQK8zc5kNQpwQ3g0g/BYoXVn8DqTwFoFhnI05e0AeC1X7ezYPt+M1OKuIajh+8Krc7Dd7Vp7xOoQImbKdz0M6El6eQafjQ7T8u2iAni+8I5/3Ven34XZKwH4OIODbmyayMMA8ZPW6NFh0WOHr6b7eh89PBd7Rn/BCpQ4mayf30DgF+8B9A9UWsOikl63wUJ50FZkXOSzaJcAB65oBWtooM4WFDCbVNXUWbXosNSRznssNm59t13JV0J9vWkc1w9k0NVLxUocRv2rK00zF6Mw7Dg1eNGTZwp5rFa4ZL3IDjWeUbeD2PBMPDxtPHWiI4EeHuwfOchXpi1zeykIubYuRDyMzliC+IPRxv6JobjUQtmH/+r2vVupFZLn/UqAL9ZOnF+r24mp5E6zy8ULpsCVk/Y/CMsfhNwLjr83KXtAHjntx3M3Zx5smcRqZ3WfQnAHEuPWnn4DlSgxF0U5RG2wzlr/L7Ea/Dz0tQF4gIadoKBzzivz3kE0pYC8J+20Yzu2RiAu75ay96cIyYFFDFB6ZHys+8+LuiGl83KOYnhJoeqfipQ4hYOLv4YX+MI2x0N6HP+pWbHEflTlxugzTBwlMFXo8sXHX7gPy1o1zCYnMJSbvt8FaUaDyV1xbaZUHKYPO8oVhjOxbcDfTzNTlXtVKDE9RkGZcsnA7C0/kXE1tfUBeJCLBa44FWo3wwOp8O3N4HDgbeHjTeu7Eigjwer0nJ44ZetZicVOTOOHr6bQR8MrAxsE2VyoJqhAiUur3T3SiILt1NseBLdZ5TZcUT+yTsQLv8YPHxhx1xY8AIAjer78fzR8VDv/p7Cr1s0HkpqucJs2D4bgA/zuuBhtXBeq9o3/glUoMQN7Pv1HQB+tfbgrPbNTU4jcgKRrWDIS87r856GlPkADGzz53ioCV+uJV3joaQ22/gdOErZH5DIdqMhPZrWJ8TPy+xUNUIFSlxbcT7hu34CIKfllXjWstNgpZZJugo6XA0Y8M0NkLcP+Nt4qKmrNR5Kaq+jh+9+dPQCYFCbaDPT1Cj9NhKXdnDpVHyNI6Q4oul97kVmxxE5tf+8AJFtoGC/s0TZyyqMh1q56xAvan4oqY0O7YTdSzCw8G52R6wWOL917Tx8BypQ4uKKl04CYFnoEA0eF/fg6eucH8orAHYthPlPA87xUM8N+3N+qHlbs8xMKVL91n8FQHq9LmQSSpfGoYQFeJscquaoQInLKt27jpiCjZQYNiJ6jzY7jkjlhSXAha85ry94sXxQ7aC20VzTIw6Au75cq/XypPYwjPLDd9/Zjx2+q51n3x2jAiUua+/RweO/WbvSp0Mrk9OIVFGbYdDlRuf1b2+E3D0A/Pc/LWkdE0R2QQm3T12t9fKkdti3Fg5sw7B5825Wa8B5AkVtpgIlrqn0COEpzplssxM1eFzc1ICnIDoJjhyCr64Feyk+njbevMq5Xt6yndm8Mme72SlFTt+6LwDYFXY2h/GjQ6MQooJ9TA5Vs/RbSVzSwWVf4m/kk+YIp+d5w8yOI/LveHjDZZPBOxj2LIM5jwLO9fKeuaQtAG/OT2bB9v3mZRQ5XWUl5QXqq9K6cfgOVKDERR05Onh8SchgYusHmJxG5DSENoGhbzmvL34DtkwH4IL2MVzVrRGGAXd+sYaswxoPJW5q289QeBCHfwTv7YsHavf0BceoQInLcRxMpWHeauyGhaDumnlcaoGWQ6D7WOf178c4T/cGJg5pRYuoQA7klzB+2hrsDsO8jCL/1upPAdgUMZhSw0bbBsHEhvqZHKrmqUCJy9n7+2QAltKWszu3NzeMSHXp/yg07AJFuc5Fh8uK8fG08cZVHfH1tLFox0HenJdsdkqRqslLh+Q5ALyT1xOAi5JizEx0xqhAiWsxDHw3O+cSSYu9EF8vm8mBRKqJhxdcOgl860H6apj1MAAJEQE8ObQNAK/M2caSlINmphSpmjWfg+GgOKYrP+31d66t3V4FSuSMK0pdTFjJXgoMbxLOutLsOCLVKyQWLn7XeX3Zu851w4BhnRpyaaeGOAy4Y9pqDuYXlz8kPT2dhQsXkp6ebkZikRMzjPLDdwsDBwHQI74+kUG1++y7Y1SgxKWkHz1897tHTzo1a2BuGJGa0HwA9BrvvP7DbXBwBwCPX9SapuH+ZOYVc9dXa3EcHQ+VkpJCcnIyKSkpJgUWOYFdf8ChVPAK4LV9zrmf6srhO1CBEldSVkzkLucZSoUtL8VisZgcSKSG9HsYGvWEksPw1SgoPYKflwdvjuiIt4eV+Vv3894CZ2GKj48nISGB+Ph4k0OLOB3bK1r4x3sA5MRfwNqsMrxsVga2rv1n3x2jAiUuI3v1//A38kk3QunaVwsHSy1m84BLPwS/MMhYDzPvB6BFVBCPXuj8n/zzv2xl5a5sYmJi6N27NzExded/9uLaUlJS2LVtA947ZgLwo+1cAPomhhPs52lmtDNKBUpcRu6STwBYFtCf2LBAk9OI1LCgGBj2PmCBlZPL1xG7okssF7aPwe4wuO3z1eQUlpQ/ROOhxBXEx8fT1W83NkcxRlgi7ySHAnBRUt0adqECJS7ByN9P7MGFAPh0HmFyGpEzpGk/OPte5/Ufx8P+rVgsFp66uA2N6/uRnlvE3V+twzA0HkpcR0xMDM0OLwFgT+Nh7M0twt/LxrktI0xOdmapQIlL2LPwUzyws8GIp3fP3mbHETlzzr4PmpwFpQXw5SgoKSDQx5M3ruqIl83KnM2ZfLgwFdB4KHERGRtg7wqwevB5UQ8ABrSJwsezbk07owIlLsFydB2lbVGDCfD2MDmNyBlktcGwDyEgEvZvhul3gWHQpkEwDw1pCcD/zdzC2t05Gg8lrmH5BwA4Eofw5WbnlBt17fAdqECJCyjN2EzDws2UGjaie11tdhyRMy8gAi79CCxWWDsVVn0MwMjucQxqE0Wp3WDs56vIPVJaqafTWCmpMUW55eP11kVfysGCEur7e9GraX2Tg515KlBiur0LnIPHl1jb06V1c5PTiJikcW/n9AYAM+6BfeuwWCw8O6wdsaG+7Dl0hPu+/nM81MlorJTUmLXTnIebw1vw0R7nXqfB7aLxsNW9OlH33rG4FsPAb/sPAGQ2GlIn/xKKlOs1HpoPBHsxfHkNFOUS7OvJG1d2xNNmYebGDD5evOuUT6OxUlIjDKP88N2R9tcyc1MmAJd2amhmKtPot5WYqnjPGiJK9lBkeNK0z2VmxxExl9UKQ9+G4EbOGZ6/vxUMg/axITwwyDke6qnpm9mwN/ekT6OxUlIjUn+HA9vAK4AfjN6UlDloERVI2wbBZiczhQqUmGrPgs8AWGzrTFLTWJPTiLgAv1C4bDJYPWHLT7DkLQCu7dWY81tFUmJ3MPbzVRwuqtx4KJFqc3TvE+2G8/naHAAu6xxbZ1eNUIES8xgGwSk/As6lAOrqX0KRf2jYCQY+47w+eyKkLcFisfD8pe1pEOLLroOF3P/t+kqNhxKpFnnpsMW51FZKkytYtycXT5uFoXVo7bu/U4ES0xSkLiWsLIMCw5sWZ11qdhwR19LlBmgzDBxl8NVoyN9PsJ8nb1zVAQ+rhenr9vHpklOPhxKpFisng2GHuF58mhIAQP+WkRTnHayzZ3yqQIlp9i50Hr5b6tmNFrF1awZbkVOyWOCC1yCsORzeB99cDw47HRrV4/5BLQB44qdTj4cSOW32UmeBAso6Xsd3q/cAcHnn2Dp9xqcKlJjD4SBs1wwACptfqMN3IsfjHQCXfwKe/pD6G8x7GoDrezehf0vneKhbP1tFnsZDSU3a/CPkZ4J/BHPowqHCUiKDvOnTLOyEZ3zWhbnIVKDEFLnbFhBqP0Ce4Uvrs4eZHUfEdUW0gAtfc15f8AJs+wWLxcILl7WjQYgvadmF3P9N5eaHEvlXlr7j/NppFF+sck5dMKxjQzxs1hOe8VkX9kypQMkZl56ezpaf3wZguU9PmkSGmpxIxMW1vRS63uS8/u1NcGgnIX5evHFVBzxtFmasr9z8UCJVtnsZ7F4KNi/2txjJb9v2A86z706mLsxFpgIlZ1xK8naa5f4BQEmLoeaGEXEX5z8FDTpDUY5zks3SoqPjoZzzQz05fRNrd+eYGlFqoUWvO7+2vYyvtpXiMKBL43o0CfM/6cPqwlxkKlByxkWTQSh5HDICSDp7qNlxRNyDhxdcPgX86sO+tTDjbgCu69WYAa0j/1wvr7Dy46HqwjgVOQ3Zqc65yABH97F8tcI5ePxUe5/qChUoOePKts4EYIVfb6JDg0xOI+JGghvCsA+diw6v/gRWTsFisfDcpe1pFOrHnkNHuOurNZUeD1UXxqnIaVjyNhgOaHoufxyOIPVAAQHeHgxuG212MpegAiVnlr2M6PQ5AJS2vNjkMCJuqOk50O8h5/UZd8PeVQT7evLWiI54eViZszmL9xdUrhDVhXEq8i8VZjtLOkDP28rH2A3r2AB/bw8Tg7kOFSg5o3K2/EaQkUe2EUD73oPNjiPinnrdCYmDwV7iHA9VcJA2DYJ55IJWAPzfzK0s35l9yqepC+NU5PhOefh25SQoLYTINuwN7cbczc6z70b2iDuDKV2bCpScUZnLvgJglU8PGoQGmpxGxE1ZrXDx2xAaD7m74dsbwGHnqq6NuCgpBrvDYNznqziQX2x2UnFRJz18W1YMS991Xu95G58vS8NhQM+m9UmI0L/bx6hAyZnjcBC+ZzYAJc2190nktPgEw/BPwcMXdvwKvz6JxWLh6YvbkhARQGZeMXdMW43dofmh5J9Oevh2/dfOiTMDoylucRHTlu0G4BrtfapABUrOmLyUZYTaD5Bv+NC694VmxxFxf5Gt4aI3nNcXvgSbfsDf24O3R3TEz8vGH8kHeXn2NnMziks64eFbw4DFR3+mut3Mz5uyOVhQQlSQD/1bRp75oC5MBUrOmL2LvwRgpVcX4iLrm5xGxLVVeoqBtpdCj3HO69/fCllbaBYZyDOXtAXgjXnJ5eNXRE5p2y+Qtcm5fFCn0Xy8eCcAV3VrhIdNleGv9GnImWEY1NvlnL6gMH6gyWFEXF+Vphjo/xg0OQtK8mHaVVCUy0VJDRh19JDLnV+sYXd2YQ0nFrdnGPDb/zmvd72BDdlWVqXl4GmzcEVXzf30dypQckbk79lAVNleig0Pmve5xOw4Ii6vSlMM2Dzg0kkQHAvZO5zLvTgcPDi4FUmxIeQVlTHms5UUldprPri4r+S5kL7KOa6ux218cnTqgoFtookI9DE5nOtRgZIzIu0P5+G71Z5JNG2oU6ZFTqXKUwz4h8HwT8DmDdtmwm/P4uVh5c0RHann58mGvXk8+r+NNRta3Ndf9z51uZ5cawg/rN0LaPD4iahAyRkRkDIDgNy4ASYnEanFYjrABa84r//2f7DpfzQI8eXVKzpgscC05buZtizN1IjiolJ/gz3LwMMHet7G1OVpFJU6aBEVSOe4emanc0kqUCfw5ptv0rhxY3x8fOjWrRvLli0zO5LbKsxMoVFJMnbDQuOel5kdR6R2S7oKuo1xXv/uFsjcyFnNw7n7/EQAJv6wUYsOyz/99pzza6fRFPuGM+mPVACu790Ei8ViYjDXpQJ1HF988QUTJkzgkUceYdWqVbRv354BAwaQlZVldjS3tHPhFwCss7WmeXxjc8OI1AXnP+kcVF5aAFOvhMJsxpzdlP4tIymxOxjz6UoOapJNOWbnQtj1B9i8oNcd/LAmncy8YiKDvLkoqYHZ6VyWCtRxvPTSS9x4441ce+21tGrVinfeeQc/Pz8++ugjs6O5Je/k6QAcaHie/icjcibYPOCyKRASBzm74KvRWA07Lw1vT5Mwf9Jzi7h92mrK7A6zk4orODb2qcNIHAHRvP+788zP63o1wctDNeFE9Mn8TUlJCStXrqR///7lt1mtVvr378/ixYtNTOaeSnMzaHJkAwCR3S41OY1IHeIXCldOdc7nk/obzHqIIB9P3rm6E76ezkk2X5ilSTbrvLQlkPo7WD2h953M25rF9qx8Arw9uLJbI7PTuTQVqL85cOAAdrudyMiKM65GRkaSkZFx3McUFxeTl5dX4SJOuxZ/jRWDjTSldcvWZscRqVsiW8PF7zivL30bVn1MYlQgz13aDoB3ftvB9HX7TAwopjIMmPeU83rSVRASy7tH9z6N6NaIIB9PE8O5PhWoavDMM88QHBxcfomN1YRjx5Rt/hmA3RF9sVl1+E7kjGt1IfR9wHn9pwmwcyEXtI/hxj5NALj7q7Vs3qf/9NVJO+Y69z7ZvOCsu1mddohlqdl42ixc28v581HpGfHrIBWovwkLC8Nms5GZWXHpg8zMTKKioo77mAceeIDc3Nzyy+7du89EVJdnlBTSONd59mJQe619J2Kas++D1peAoxS+GAnZqdw3sAW9E8I4Umrnpk9WkFNYYnZKOZMcDpjzqPN615sgpBHvHd37dFFSA6KCnRNnVmlG/DpGBepvvLy86NSpE3Pnzi2/zeFwMHfuXHr06HHcx3h7exMUFFThIrB39Sx8KGGfEUpS515mxxGpuywWGPoWxHSEI9kw9Qo8Sg/z+pUdiA31ZXf2EW6bqkHldcqGbyBjPXgHQZ+72HmggJkbncNUbjrrz9nvqzQjfh2jAnUcEyZM4P3332fKlCls3ryZMWPGUFBQwLXXXmt2NLeSs+Z/AGwJ6omft46li1SXf3VYxdMXrvgcAmNg/xb4+jrq+Vh59+rO+HraWLD9AM//srXmQovrKCuGXx93Xu91B/iF8v6CFAwD+rWIoHlkYPmmVZ4Rvw5RgTqO4cOH88ILLzBx4kSSkpJYs2YNM2fO/MfAcjkJwyAq8zcALImDTA4jUrv868MqQdFw5efOtc6S58CsB2kVE8TzlzkHlb/7ewo/rNlbA4nFpayYBDlpEBAF3cewN+cIX63YA8DNZ2lPU2WpQJ3AuHHj2LVrF8XFxSxdupRu3bqZHcmtZO9YSZjjAEcML1r1GGx2HJFa5bQOq8R0+MuZee/A0ncZ0i6GW85uCsC9X6/TTOW1WVEe/H501vG+94OXP2/OS6bE7qBHfH26xdc3N58bUYGSGrFn2XcArPPuSER9raMkUp1O+7BK66Fw7iPO6zPvh60/c8+ARPq1iKC4zMGNH68gI7eo2vKKC1n0OhQehPoJ0GEku7ML+WqF88SnO89rbnI496ICJTXCf+ccAArizjU5iYgcV+87oeM1YDjg6+uwZazl1SuSaBYRQNbhYm76ZAVFpXazU0p1ytsHi990Xj/3EbB58Oa8ZErtBr0TwujaJNTcfG5GBUqqXWF2Ok2KnYNRG3UfWn675hMRcSEWCwx+CeLPgdJC+Hw4gcWZfDiqCyF+nqzbk8u9X6/DMAxAf39rhdkTnesjNuwCLS8g7WAhX610jn2687xmJodzPypQUu12LPoOq8Vgi6UpTeP//Eup+UREXIzNEy6fAhGtID+D0ikXs3f9Ap4YEIeH1cL/1qbz1vwdgP7+ur2df8D6LwEL/Od5sFh4/dft2B0GZzcPp1Oc9j5VlQqUVL+tztnHM6L6Vlg8WPOJiLggn2C46ksIiMQzextxSx4ktDidRy50Lr30/C9bmbF+n/7+ujN7Gfx8r/N6p9EQ04HUAwV8u9p5xqXGPv07KlBSrRwlRTQ9vByAkKQLKtyn+UREXFRILFz1JQ5PP2KKttNh13uM7BrL6J6NAbjzizVk2f3+8fdXh/XcxIqPIHMD+NaDcycC8Ppc596nfi0iSIoNMTefm1KBkmq1c+Uv+FFEplGP1p3OMjuOiFRWTBLWKz4Dqwd+O2bA7Id5eEir8jPzbpiygj2HCis8RIf13ED+fpj3pPN6v4fBL5TtmYf5/uh8X3f2196nf0sFSqpV3rqfANgW1BNPD5vJaUSkSpr2g4uOnqW1+A1sS9/itSs70CIqkAP5xVw/eQWHi0rLN9dhPTcw9zEoyoWods7Dd8BTMzbjMGBA60jaNgw2N58bU4GS6mMYRB+dfZzEAeZmEZF/p/0V0P9R5/Vf/kvAtu/5aHQXwgO92Zp5mHGf/7lmng7Lu7g9K2H1J87r/3kBrDZ+27af+Vv342mzcP+glubmc3MqUFJtDu3eRKQjk2LDg+bdh5gdR0T+rV7joevNzuvf3UzM/oV8OKozPp5Wftu2n4e+31A+vYG4KHsp/HiH83r7q6BRN8rsDp6avgmAa3o0pkmYv4kB3Z8KlFSb3ct+BGCjZxsiw7QcgIjbslhg4DPQZhg4yuCLkbSzb+K1KzpgtcC05bt5/ddks1PKySx4CTLXg28onOdcOPiLFbvZlplPiJ8nt/fTvE+nSwVKqo1H6lwAcmM0eFzE7VltcPG70Ox8KDsCnw/n/NBMHruoDQAvzd7Gl0eXABEXk7kRfn/eef0/z0NAOIeLSnlp1jYAxp/bjGA/TxMD1g4qUFIt7MWFNClYA0BY0n/MDSMi1cPmCZdNgUY9oTgPPrmEkQkljOnrXHj4gW/X89u2/SaHlArsZfD9reAohcTBzr2IwFvzd3CwoIT4cH9GdI8zOWTtoAIl1SJl1Wx8KSGTUFq262p2HBGpLl5+cNU0iG4PhQfg46Hc09WHoUkx2B0GYz5dyYa9uWanlGMWvQr71oBPCAx5CSwWdmcX8uHCVAAe/E9LPG361V8d9ClKtchb55x9PDmoOx6avkCkdvEJhqu/hfrNIG8P1o8v5Lnz6tMroT6FJXZGfbSMlP35ZqeUrC0w/1nn9UH/B4FRADzx0yZKyhz0SqhPvxYRJgasXVSgpFqEZy0EwJLQ3+QkIlIj/MNg1P+gXhPI2YXXpxfy7kUxtI4J4mBBCSM/XMa+3CNmp6y77GXww1iwl0CzAdBuOAAzN2Qwa1MmHlYLE4e0rrC8lpweFSg5bQf3JtPIvpsyw0qzHpq+QKTWCoqB0T9BSBwcSiVg2sV8PDyOJmH+7M05wjUfLuNQQYnZKeum3/4P9q4A72C44BWwWDhcVMoj/9sAwM1nx5MYFWhuxlpGBUpO286l/wNgm2cLwsMjTU4jIjUquCGM+hGCY+FgMvW/upTPrownKsiH7Vn5jJ68nILiMrNT1i0p8/88627IS86ii3Mh6My8YhrX9+M2TVtQ7VSg5LTZUn4F4FB0H5OTiMgZUS/OeTgvMAYObCXmu0v5/Io4Qvw8Wbs7h5s/WUlRqd3slHVDfhZ8cyNgQMdR0PZSAFbuOsQnS3YB8PTFbfHx1NjU6qYCJaelrKSYhPwVAIS21/QFInVGaLzzcN7REhX/02V8dlkD/LxsLEw+wK2fraKkzGF2ytrN4YBvb4KCLIhoBQOdA8hLyhz899v1GAYM69iQnglhJgetnapcoEaNGsXvv/9eE1nEDSWvnkcAR8gmkOYdtAdKpE6p3xSunQEhjSA7hdYzr+DTSyLx9rDy65Ysbpu6ilK7SlSN+eNlSJkHHr5w6STnlBPA+wtS2Jp5mFB/Lx4crPXuakqVC1Rubi79+/enWbNmPP300+zdu7cmcombyD06fcGOwG7YbNpFLFLnhDaBa3927pHKTaPj3Cv59KJ6eNms/LIxkzu/WIPdoXXzql3aEvj1Kef1wS9ARAsAtmTk8erc7QA8PKQlof5eZiWs9apcoL7//nv27t3LmDFj+OKLL2jcuDGDBg3i66+/prS0tCYyigsLy3ROX+Bo2s/kJCJimuCGzhIV3gIO76PL/Kv5dIgPnjYLP63bxz1fr8WhElUp6enpLFy4kPT09BNvlLMbvhgJhh3aXg5JIwAoKrVz+9TVlJQ56NcigqFJDc5Q6rrpX42BCg8PZ8KECaxdu5alS5eSkJDAyJEjiYmJ4c4772T79u3VnVNcUN7+vTQtcy4o2rjrBSanERFTBUbB6OkQ1RYK9tN13kg+61eMzWrh21V7uefrddoTVQkpKSkkJyeTkpJy/A2K82Hqlc5xT5FtYMjLzsWfgadnbGZbZj5hAd48d2k7zflUw05rEPm+ffuYPXs2s2fPxmaz8Z///If169fTqlUrXn755erKKC5q57IfAdhujScyppHJaUTEdP5hzhLVuA+UHKbrHzfyVa+92KwWvlm1h/FfrNGYqFOIj48nISGB+Pj4f97psMO3N0LmevAPhyungXcAAHM3Z/LxYudZdy9e3p6wAO8zGbtOqnKBKi0t5ZtvvmHIkCHExcXx1VdfMX78eNLT05kyZQpz5szhyy+/5PHHH6+JvOJCypLnAZAZ3tPkJCLiMnyCYcTX0OoicJTScfnd/NRlPZ42Cz+uTee2z1fr7LyTiImJoXfv3sTExPzzzrmPwdYZYPOGK6ZCSCwAWXlF3PP1OgCu69WEs5uHn8nIdVaVC1R0dDQ33ngjcXFxLFu2jBUrVnDLLbcQFBRUvs0555xDSEhIdeYUV2MYNMxZDoBfi3NNDiMiLsXTx3lWWJcbAWi59mnmtJmDjw1mbsxgzKeaJ6rKVn8Gf7zqvH7RmxDbBQCHw+Cur9aSXVBCy+gg7huUaGLIuqXKBerll18mPT2dN998k6SkpONuExISQmpq6ulmExe2N3ktEcZBig1PErucZ3YcEXE1Vhv853no9zAAcVs/ZGHjDwn1KGbulixumLKCfM1YXjnbZ8OPdzivn3UvtLus/K63f9vBgu0H8Paw8toVSXhrMfczpsoFauTIkfj4+NREFnEje1fNBGCbd2v8A7S+kogch8UCZ90Nl3wANm/C9s5lQdizJHhlszD5AFe9v4QD+cVmp3RtKb/BF1eDoxTaDIO+D5TfNXtTJi/M2grAoxe2pllkYOXO4pNqoZnI5V/xTHNOppof08vkJCLi8tpd5pxw0z8C/5yt/Oz3KH39Uli3J5fL3lnM7uxCsxO6prQlMPUKKCuC5oPg4nfB6vy1vS3zMOOnrcYwYGT3OK7s6jyR55Rn8Um1UYGSKisrLaFpwWoAQtsNMDmNiLiFhp3hpnkQ2RbPogNM4nHGBvxG6oF8Lnl7EZvS88xO6Fr2roRPL4XSQmjaDy6bDDZPAA4VlHDDlBUUlNjpHh/KxAtalT/spGfxSbVSgZIq2772D4IoJA9/EtppD5SIVFJwQ7huJrS8AIujhHvK3uWjwPcpOJzL8HcX8/u2/WYndA0Z6+GTS6DkMMT1huGfOQfmA6V2B7d+toq07EJiQ315a0QnPG1//io/6Vl8Uq1UoKTKDq2fBUCKfwdsHh4mpxERt+IdAJd/Auc9ARYb/UrnMyvgUSJKdnHt5OV8vHin2QnNlboAJg2Gohxo2AWumla+xp1hGDzyv40sTjmIv5eND67poqVaTKQCJVUWmP4HAKVxZ5ucRETcksUCvW6H0T9BQBQNy9KY4TORi/iNiT9sYOIPGyirixNubvgGPr0EinOhUQ/nfFrezpN0DMPg2Zlb+HxpGhYLvDw8icQoncBjJhUoqZLDh3NpXrIRgIadBpmcRkTcWlxPuGUBNDkLb+MIL3m9w9uer/DT4vVcO3k5uUfq0Pqqi96Ar68Dewm0vBBGfg++IeV3v/5rMu/+5hwY/uTQNpzfOsqcnFJOBUqqZOvyOXhbysiy1Cc6vo3ZcUTE3QVEOMtCv4fA6sEg23Jmed+H945fuPCNhWxMzzU7Yc2yl8HMB2DWg87vu93iHDDu+ed0QR8sSOGl2dsAeGhwS0Z0izMhqPydCpRUyZEtcwHYU69b+QKWIiKnxWqDs+6BG3+F8JaEWXL5wOtFxua+zOi3ZjFtWRqGUQsXIs5Lh48vhCVvOb8/7wkY+Kzz8zjq0yW7eHL6ZgDuOq85N/SpeHad5n0yjwqUVEnEgSUA2BLOMTmJiNQ60e3hpvnQ8zYMLFzu8RszbXey4oc3uPvL1RSW1KKZy7fPgXd6w64/wCvAufRNr9sr/Mf0gwUpPPT9BgBuObsp4/ol/ONpNO+TeVSgpNKyMtNpZnf+JW3S+T8mpxGRWsnTB85/Esu1P2OEt6C+5TAveL7L8I23cMern7Fhr5sf0rOXwpxH4bNhUHgQotrCTb9Bm0vKN3E4DB7/cVP5nqfrejXhvoGJWI6z11/zPpnHYtTK/aLmysvLIzg4mNzc3AqLLLu7ZdM/ouvyO9lla0Tcw+vNjiMitZ29FJa8jX3eM9jKCikzrEx19Kek5wRGnd8ND5ub7QPYvRymT4CMdc7vu9wA5z9VYbxTUamdCV+uYcb6DAAeGNSCm86KP255kupXld/fbvbTJ2ay75gPQGZYD3ODiEjdYPOEXrdju20Fxc0vwMPiYKRtFlctuZAfXriR1N1pZiesnIKD8L/b4MP+zvLkE+wcKD74xQrlKaewhJEfLmXG+gw8bRZevSKJm89uqvLkolSgpNIaHloGgE9iP5OTiEidEtwA76s+xRj1IwfrJeFrKWHYka8J+6AryybdQ3FuptkJj89eCis+gjc6waqPnbcljYBxK6H1xRU2Xb4zm/+8uoDlOw8R6O3BlOu6clFSAxNCS2XpEF4NqI2H8NJ3JRMzqRN2w8KRu1IICAo1O5KI1EWGwcE1P5I/4xHiSp1jMkvwJDv+IqLOuwOi25kcECgpdBamRa9D3h7nbZFt4D8vQFzFPfhldgdvzEvmtbnbcRgQV9+Pd0d2okVU7fjd4W6q8vtb63BIpaStnk0MkOLZjGYqTyJiFouF+h0uJLT9YJbNmETAirdoxQ6iUr6Gd7+mOKYb3t2ug8RBzkNlZ1LBAVgxCZa+7RwgDuAfAX0mQJcbwVbxV+7enCPcOW0Ny3ZmA3BJhwY8PrQNAd761ewO9KcklWLsXADAofAuJicREQGL1UbXITeQd+41fPT9d4RvmsRA6zK805fCd0sxbF5Ymp7rPFSWOAh8amiPTmE2bPkJNnwLqb+DYXfeXq8x9LzdecjuL+OcAIrL7Ez+Yyev/5pMfnEZ/l42nry4DRd3aFgzGaVGqEDJKRmGQYOcVQAEJGr+JxFxHUG+Xlx35XC2ZAzizh9+p2natwyxLaEZe2Hbz86L1cM5x1Rsd2h09BIQ8e9e8EgO7F3pvKQtcZYmx1+WnInpCD3GQquh/9jjZBgGszdl8tSMzew6WAhAUmwIr16RRFx9/3+XR0yjMVA1oLaNgdqVup24KZ2xGxZK707FJ7Ce2ZFERI5r8Y6DvDRrC7lp6xlsW8oQ2xKaWo4zS7dfGNSLg5A459fAGPDwcpYtq6dzNvCiHMjPgsMZkJ8J2SlwYNs/nyuyjXNPV+uLoX7Tf9xtGAbLUrN5/ddkFiYfACA80Jt7ByQyrGNDrFadZecqNAZKqkV6ejopKSkU7lxKHLDTK4GmKk8i4sJ6NK3Pl7f05I/kRF6a3ZaX0y6lAfvpZN3GwMAUenpuJyQ/GQoPOC97V1b9Reo1hoZdoEFnaHoOhCced7OiUjs/rNnLpD92siXjMABeHlZu6N2EW89J0FgnN6c/PTmhY0sENEz/A4CciO4mJxIROTWLxULvZmH0SqjP6t05fLpkFz+ti+R/ub0AqGcrYkhsMf2jjtAxKI/AI3ude5gcZc6pBxylzkV+fYKdh/oCoyAgEoJjISYJ/MNO+NpFpXaWpmYzb0sW36/ZS06h8/Cej6eVizs05Na+TYkN9TsTH4PUMBUoOaH4+HgMw6DRzk0ABLToa24gEZEqsFgsdGxUj46N6vHQ4FZ8vXI305bvJmU/fLLTh092BgNRtIzuTFJsMG0aBNOuQQiJUYF4eZx6mkTDMDiQX0JyVj7bMg+zYPt+/kg+yJFSe/k2DUJ8GdUzjss7xxLi51WD71bONI2BqgG1aQzUjuRtNP20C3bDgv2eVLwCdAhPRNxbclY+szdlMntTBqt35/D334IeVgv1A7wIC/CmfoA3Yf5eeNqsFJXZKS51UFRmJ6ewlJT9+eQV/XOB46ggH85pEc55rSI5u3kENo1xchsaAyXVZs+a2TQFdnklEK/yJCK1QEJEAAkRAYzp25Ssw0Ws2nWIdXtyWb/XeckpLCUzr5jMvOJTPpfFArH1/EiICKBTXD3OSYygZXSgll+pA1Sg5OR2Occ/5UV2MzmIiEj1iwj0YWCbaAa2iQach+Uy84rZf7iYA/nHLiU4DANvDyvenja8PawEeHvQJMyfJmH++HjaTH4XYgYVKDkhu8Mg7rBz/qegllr/TkRqP4vFQlSwD1HBPqfeWOo0LSYsJ7Q9eRuN2YfdsOAZ1crsOCIi1S49PZ2FCxeSnn6cuaJETkIFSk5o37q5AOywNGLXvoMmpxERqX7HpmtJSUkxO4q4GR3CkxOypS0EICOoHQnx8SanERGpfvFH/22L179xUkW1ag9U48aNsVgsFS7PPvtshW3WrVtHnz598PHxITY2lueee+4fz/PVV1/RokULfHx8aNu2LTNmzDhTb8FlGIZBozzn+KeYLhcRExNjciIRkeoXExND7969T/vfuOMdCtThwdqtVhUogMcff5x9+/aVX2677bby+/Ly8jj//POJi4tj5cqVPP/88zz66KO899575dssWrSIK6+8kuuvv57Vq1czdOhQhg4dyoYNG8x4O6ZJTd1BY/bhMCw06tDf7DgiIi7teIcCdXiwdqt1h/ACAwOJioo67n2fffYZJSUlfPTRR3h5edG6dWvWrFnDSy+9xE033QTAq6++ysCBA7nnnnsAeOKJJ5g9ezZvvPEG77zzzhl7H2bbu3YO8cAur6Y00fxPIiIndbxDgTo8WLvVuj1Qzz77LPXr16dDhw48//zzlJX9OUvs4sWLOeuss/Dy+nM6/QEDBrB161YOHTpUvk3//hX3uAwYMIDFixef8DWLi4vJy8urcHF3xi7n+z0U3sXkJCIiru94hwKr6/CguKZatQfq9ttvp2PHjoSGhrJo0SIeeOAB9u3bx0svvQRARkYGTZo0qfCYyMjI8vvq1atHRkZG+W1/3SYjI+OEr/vMM8/w2GOPVfO7MY9hGETnOsc/+TXrY3IaERER1+Pye6Duv//+fwwM//tly5YtAEyYMIG+ffvSrl07brnlFl588UVef/11iotPPR3/6XjggQfIzc0tv+zevbtGX6+m7UlPp6nD+R4aJ2kCTRERkb9z+T1Qd911F6NHjz7pNic6vtytWzfKysrYuXMniYmJREVFkZmZWWGbY98fGzd1om1ONK4KwNvbG29v71O9Fbexc808Yi0Ge20NaFAv2uw4IiIiLsflC1R4eDjh4eH/6rFr1qzBarUSEREBQI8ePXjwwQcpLS3F09MTgNmzZ5OYmEi9evXKt5k7dy7jx48vf57Zs2fTo0eP03sjbqQkdREAB0M70sDkLCIiIq7I5Q/hVdbixYt55ZVXWLt2LSkpKXz22WfceeedXH311eXl6KqrrsLLy4vrr7+ejRs38sUXX/Dqq68yYcKE8ue54447mDlzJi+++CJbtmzh0UcfZcWKFYwbN86st3bGhWc7xz95xfc0OYmIiIhrcvk9UJXl7e3NtGnTePTRRykuLqZJkybceeedFcpRcHAws2bNYuzYsXTq1ImwsDAmTpxYPoUBQM+ePfn888956KGH+O9//0uzZs34/vvvadOmjRlv64zbd/AQifbtYIGGSeeaHUdERMQlWQzDMMwOUdvk5eURHBxMbm4uQUFBZsepkgVzfqDPwms4ZAmh3sSdYLGYHUlEROSMqMrv71pzCE+qR2HyHwDsC+mg8iQiInICKlBSQciBlQBY4+rOoHkREZGqUoGScgfyCmlZugmAmLaa/0lEROREVKCk3JZ1ywiyFFKIL0GNO5gdR0RExGWpQEm5vK2/A5Ae2AZsteYETRERkWqnAiXlAjKXA1DaoLvJSURERFybCpQAUFhcSrPiDQCEtT7b5DQiIiKuTQVKANi8ZSPRlmzKsBGe2MvsOCIiIi5NBUoAOLhxPgC7fZqDl5+5YURERFycCpQA4Jm+DICCyC4mJxEREXF9KlCCw2EQm78WgIDmZ5mcRkRExPWpQAnJaWkksAeAhm01gFxERORUVKCEPesXAJBhi8EjKMLkNCIiIq5PBUoo3bUUgIOhSeYGERERcRMqUEJotnP8k2djTaApIiJSGSpQdVxWTgEt7VsBiGmjAeQiIiKVoQJVx23bsJwASxGF+BIQ287sOCIiIm5BBaqOy93+BwD7AlqB1WZyGhEREfegAlXH+WSsAqAsRhNoioiIVJYKVB12pMROfJFzAeHQFr1NTiMiIuI+VKDqsI3JKTSxZAAQ1kILCIuIiFSWClQdlrHp6ASanrFY/EJNTiMiIuI+VKDqMCPNuYBwXlgHk5OIiIi4FxWoOsrhMIjMWweAb3wPk9OIiIi4FxWoOiolK5c2RjIAUZpAU0REpEpUoOqo1I3L8LMUU2jxwzOyldlxRERE3IoKVB11JGUxABmBbcCqHwMREZGq0G/OOipg/2oA7A06m5xERETE/ahA1UGFJWXEF28CoH6LPianERERcT8qUHXQpu07aGzJBCC0eU+T04iIiLgfFag6aP/mhQCke8WBb4i5YURERNyQClQdZOxZDsDh+knmBhEREXFTKlB1jGEYhOc6FxD2btzV5DQiIiLuSQWqjtl7qIAWDucEmtGtNYBcRETk31CBqmO2b1xNoOUIxXjjHd3a7DgiIiJuSQWqjsndsQSADP9EsHmYnEZERMQ9qUDVMd6Zzgk0S6I6mpxERETEfalA1SHFZXYaFmwGIKRZd5PTiIiIuC8VqDpkc1oWLSy7AAhL7GFyGhEREfelAlWH7N68DE+LnVxrCJaQOLPjiIiIuC0VqDqkeNcyALJD2oLFYnIaERER96UCVYcEHVwHgLVhJ5OTiIiIuDcVqDoi63ARzUq3AhDeQgsIi4iInA4VqDpiQ/JOmlgzAfBr3MXkNCIiIu5NBaqOOLh1MQD7vRqCX6jJaURERNybClQdYUlfBUB+WHuTk4iIiLg/Fag6wDAMwvM2AOAd19XkNCIiIu5PBaoO2HmggDbGdgAiWmoAuYiIyOlSgaoDkrdtpL7lMKV44BHdzuw4IiIibk8Fqg7ITV4KQJZfM/D0MTmNiIiI+1OBqgO8MlcDUBSRZG4QERGRWkIFqpYrsztoULARgID4bianERERqR1UoGq5bftyaEUqoBnIRUREqosKVC23a8sqfC0lFFr8sIY1MzuOiIhIraACVcvl71wJwIGAFmDVH7eIiEh10G/UWs5n/1oAyqI0A7mIiEh1UYGqxYpK7TQ8shWAkAQNIBcREakublOgnnrqKXr27Imfnx8hISHH3SYtLY3Bgwfj5+dHREQE99xzD2VlZRW2mT9/Ph07dsTb25uEhAQmT578j+d58803ady4MT4+PnTr1o1ly5bVwDuqeRv3HKSlZRcA9Zp2MTmNiIhI7eE2BaqkpITLLruMMWPGHPd+u93O4MGDKSkpYdGiRUyZMoXJkyczceLE8m1SU1MZPHgw55xzDmvWrGH8+PHccMMN/PLLL+XbfPHFF0yYMIFHHnmEVatW0b59ewYMGEBWVlaNv8fqtnvrKnwspRRa/LGExpsdR0REpNawGIZhmB2iKiZPnsz48ePJycmpcPvPP//MkCFDSE9PJzIyEoB33nmH++67j/379+Pl5cV9993H9OnT2bBhQ/njrrjiCnJycpg5cyYA3bp1o0uXLrzxxhsAOBwOYmNjue2227j//vsrlTEvL4/g4GByc3MJCgqqhnf970x79ymu2Pccu4O7EHvnHNNyiIiIuIOq/P52mz1Qp7J48WLatm1bXp4ABgwYQF5eHhs3bizfpn///hUeN2DAABYvXgw493KtXLmywjZWq5X+/fuXb3M8xcXF5OXlVbi4At/96wEwojWAXEREpDrVmgKVkZFRoTwB5d9nZGScdJu8vDyOHDnCgQMHsNvtx93m2HMczzPPPENwcHD5JTY2tjre0mnJPVJKXIlzAHloMw0gFxERqU6mFqj7778fi8Vy0suWLVvMjFgpDzzwALm5ueWX3bt3mx2JjWn7aWlJAyCgSWeT04iIiNQuHma++F133cXo0aNPuk18fOUGP0dFRf3jbLnMzMzy+459PXbbX7cJCgrC19cXm82GzWY77jbHnuN4vL298fb2rlTOM2XPtlX0tJRRaA3Ar14Ts+OIiIjUKqYWqPDwcMLDw6vluXr06MFTTz1FVlYWERERAMyePZugoCBatWpVvs2MGTMqPG727Nn06NEDAC8vLzp16sTcuXMZOnQo4BxEPnfuXMaNG1ctOc+UkjTnDOTZwa3xs1hMTiMiIlK7uM0YqLS0NNasWUNaWhp2u501a9awZs0a8vPzATj//PNp1aoVI0eOZO3atfzyyy889NBDjB07tnzv0C233EJKSgr33nsvW7Zs4a233uLLL7/kzjvvLH+dCRMm8P777zNlyhQ2b97MmDFjKCgo4NprrzXlff9bAdnOMw0tMUnmBhEREamFTN0DVRUTJ05kypQp5d936NABgHnz5tG3b19sNhs//fQTY8aMoUePHvj7+zNq1Cgef/zx8sc0adKE6dOnc+edd/Lqq6/SsGFDPvjgAwYMGFC+zfDhw9m/fz8TJ04kIyODpKQkZs6c+Y+B5a4su6CE+NLtYNUAchERkZrgdvNAuQOz54H6ffMeuk9rh5fFDnesg3pxZzyDiIiIu6mT80DJnzK2r8bLYqfAGgQhjcyOIyIiUuuoQNVCpbuPDiAPaQUaQC4iIlLtVKBqocBs5wzklpgOJicRERGpnVSgaplDBSXElyYDUC9BA8hFRERqggpULbNp934SLc6Z0P0bawZyERGRmqACVctkbFuBp8XOYVswBDc0O46IiEitpAJVy5TuXgXAoeDWGkAuIiJSQ1SgapmAQxsBsES3NzmJiIhI7aUCVYvkHimlUYlzAHlogsY/iYiI1BQVqFpk0+4Dfw4gj+tkchoREZHaSwWqFtmzfQ3eljIKrf5Qr7HZcURERGotFahapHj3agAOBbbQAHIREZEapAJVi/gedA4gN6LampxERESkdlOBqiXyikppWOwcQB4SrwHkIiIiNUkFqpbYuCeHVpZdAAQ07mhyGhERkdpNBaqW2L1jI4GWI5RYvCAs0ew4IiIitZoKVC1RsOvoAHL/BLB5mJxGRESkdlOBqiW8D6wHoCxSA8hFRERqmnZV1AL5xWU0KNoOVghqogk0RUTsdjulpaVmxxAX4+npic1mq5bnUoGqBTan59LKshOAwMYqUCJSdxmGQUZGBjk5OWZHERcVEhJCVFQUltOcL1EFqhZISUmmiyUPB1asEa3MjiMiYppj5SkiIgI/P7/T/iUptYdhGBQWFpKVlQVAdHT0aT2fClQtUJDmHECe7RtHmJefyWlERMxht9vLy1P9+vXNjiMuyNfXF4CsrCwiIiJO63CeBpHXAp5ZzgHkxeEaQC4iddexMU9+fvqPpJzYsZ+P0x0jpwLl5krtDiILtgLg16iDyWlERMynw3ZyMtX186EC5eaSs/JpyU4AQuI1gFxExB317duX8ePHmx0DgO+//56EhARsNhvjx49n8uTJhISEmB3L5ahAubntO3cTa90PgCW6nclpRETEFc2fPx+LxVKpsxNvvvlmLr30Unbv3s0TTzzB8OHD2bZtW/n9jz76KElJSTUX1k1oELmby0ldBcAhr2jq+dYzOY2IiLiz/Px8srKyGDBgADExMeW3Hxt8LX/SHih3l7EWgIJQTV8gIuLOysrKGDduHMHBwYSFhfHwww9jGEb5/cXFxdx99900aNAAf39/unXrxvz588vv37VrFxdccAH16tXD39+f1q1bM2PGDHbu3Mk555wDQL169bBYLIwePfofrz9//nwCAwMB6NevHxaLhfnz51c4hDd58mQee+wx1q5di8ViwWKxMHny5Jr6SFya9kC5McMwqJe3BQDPhhpALiLyd4ZhcKTUbspr+3raqjRgecqUKVx//fUsW7aMFStWcNNNN9GoUSNuvPFGAMaNG8emTZuYNm0aMTExfPfddwwcOJD169fTrFkzxo4dS0lJCb///jv+/v5s2rSJgIAAYmNj+eabbxg2bBhbt24lKCjouHuUevbsydatW0lMTOSbb76hZ8+ehIaGsnPnzvJthg8fzoYNG5g5cyZz5swBIDg4+PQ+KDelAuXG9hw6QqIjBawQ2rSz2XFERFzOkVI7rSb+Ysprb3p8AH5elf81Gxsby8svv4zFYiExMZH169fz8ssvc+ONN5KWlsakSZNIS0srP7R29913M3PmTCZNmsTTTz9NWloaw4YNo21b55Q28fHx5c8dGhoKQERExAkHhHt5eREREVG+fVRU1D+28fX1JSAgAA8Pj+PeX5eoQLmxLXuyOMeyDwDPBu1NTiMiIqeje/fuFfZY9ejRgxdffBG73c769eux2+00b968wmOKi4vLJw29/fbbGTNmDLNmzaJ///4MGzaMdu10clFNUYFyY1nJa/CwOMi3BRMQeHpT0ouI1Ea+njY2PT7AtNeuLvn5+dhsNlauXPmP2bMDAgIAuOGGGxgwYADTp09n1qxZPPPMM7z44ovcdttt1ZZD/qQC5cZK09cBkBfcggBNHCci8g8Wi6VKh9HMtHTp0grfL1myhGbNmmGz2ejQoQN2u52srCz69OlzwueIjY3llltu4ZZbbuGBBx7g/fff57bbbsPLywtwLndzury8vKrledydzsJzY36HnAPILZGtTU4iIiKnKy0tjQkTJrB161amTp3K66+/zh133AFA8+bNGTFiBNdccw3ffvstqampLFu2jGeeeYbp06cDMH78eH755RdSU1NZtWoV8+bNo2XLlgDExcVhsVj46aef2L9/P/n5+f86Z+PGjUlNTWXNmjUcOHCA4uLi03/zbkgFyk0dKiihUWkKACHxHU1OIyIip+uaa67hyJEjdO3albFjx3LHHXdw0003ld8/adIkrrnmGu666y4SExMZOnQoy5cvp1GjRoBz79LYsWNp2bIlAwcOpHnz5rz11lsANGjQgMcee4z777+fyMhIxo0b969zDhs2jIEDB3LOOecQHh7O1KlTT++NuymL8ddJJqRa5OXlERwcTG5uLkFBQdX+/Onp6fy4dCtXrLmKYEshWZd8y7Y8b+Lj4ytMfCYiUpcUFRWRmppKkyZN8PHxMTuOuKiT/ZxU5fe3exwYlgpSUlJI3bGZYEshdmxsz7GSnJIMoAIlIiJyBugQnhuKj48nzJIDwCG/JjRJSCQhIaHCnB8iIiJSc7QHyg3FxMQQVrIXgNLwVsTExGjPk4iIyBmkPVBuqKjUTsSR7QD4N0oyN4yIiEgdpALlhrZlHqYFuwAIjEsyN4yIiEgdpALlhramZdDYkgmAJaqtyWlERETqHhUoN3Ro51qsFoN8z1AIiDA7joiISJ2jAuWG4h2pABTWa2lyEhERkbpJBcoN9a+3H4CIhE4mJxEREambVKDcUcYG51eNfxIREZNMnjyZkJAQs2MwevRohg4desZfVwXK3TgckLnReT2yjblZRERETmDnzp1YLBbWrFnjks93ulSg3E3OLig5DDYvCGtmdhoRETFJSUmJ2RGqhbu+DxUod5N59PBdeCLYPM3NIiIi1eLw4cOMGDECf39/oqOjefnll+nbty/jx48v36Zx48Y88cQTXHPNNQQFBXHTTTcB8M0339C6dWu8vb1p3LgxL774YoXntlgsfP/99xVuCwkJYfLkycCfe3a+/fZbzjnnHPz8/Gjfvj2LFy+u8JjJkyfTqFEj/Pz8uPjiizl48OBJ31OTJk0A6NChAxaLhb59+wJ/HnJ76qmniImJITExsVI5T/R8x7zwwgtER0dTv359xo4dS2lp6UnznS4t5eJujo1/itT4JxGRUzIMKC0057U9/cBiqdSmEyZM4I8//uB///sfkZGRTJw4kVWrVpGUlFRhuxdeeIGJEyfyyCOPALBy5Uouv/xyHn30UYYPH86iRYu49dZbqV+/PqNHj65S3AcffJAXXniBZs2a8eCDD3LllVeSnJyMh4cHS5cu5frrr+eZZ55h6NChzJw5szzDiSxbtoyuXbsyZ84cWrdujZeXV/l9c+fOJSgoiNmzZ1c638meb968eURHRzNv3jySk5MZPnw4SUlJ3HjjjVX6DKpCBcrdHNsDFaXxTyIip1RaCE+btFbof9PBy/+Umx0+fJgpU6bw+eefc+655wIwadKk465x2q9fP+66667y70eMGMG5557Lww8/DEDz5s3ZtGkTzz//fJUL1N13383gwYMBeOyxx2jdujXJycm0aNGCV199lYEDB3LvvfeWv86iRYuYOXPmCZ8vPDwcgPr16xMVFVXhPn9/fz744IMKJehUTvZ89erV44033sBms9GiRQsGDx7M3Llza7RA6RCeuzlWoDSAXESkVkhJSaG0tJSuXbuW3xYcHFx+aOuvOnfuXOH7zZs306tXrwq39erVi+3bt2O326uUo127duXXo6OjAcjKyip/nW7dulXYvkePHlV6/r9q27ZtlcrTqbRu3RqbzVb+fXR0dHn2mqI9UO6kKA8O7XRe1xQGIiKn5unn3BNk1mtXM3//U+/R+juLxYJhGBVuO974IE/PP8fVWo4eenQ4HFV+vco43vuobM7j+Wv2Y89VU9mPUYFyJ1mbnF8DY8Av1NwsIiLuwGKp1GE0M8XHx+Pp6cny5ctp1KgRALm5uWzbto2zzjrrpI9t2bIlf/zxR4Xb/vjjD5o3b16+RyY8PJx9+/aV3799+3YKC6s2Lqxly5YsXbq0wm1Lliw56WOO7WGq7J6wU+Ws6vPVNBUod5Kx3vlV459ERGqNwMBARo0axT333ENoaCgRERE88sgjWK3W8j1BJ3LXXXfRpUsXnnjiCYYPH87ixYt54403eOutt8q36devH2+88QY9evTAbrdz3333/WOPzancfvvt9OrVixdeeIGLLrqIX3755aTjnwAiIiLw9fVl5syZNGzYEB8fH4KDg0+4/alyVvX5aprGQLmT4jznLuHI1mYnERGRavTSSy/Ro0cPhgwZQv/+/enVqxctW7bEx8fnpI/r2LEjX375JdOmTaNNmzZMnDiRxx9/vMIA8hdffJHY2Fj69OnDVVddxd13342fX9UOL3bv3p3333+fV199lfbt2zNr1iweeuihkz7Gw8OD1157jXfffZeYmBguuuiik25/qpxVfb4aZ7iJJ5980ujRo4fh6+trBAcHH3cb4B+XqVOnVthm3rx5RocOHQwvLy+jadOmxqRJk/7xPG+88YYRFxdneHt7G127djWWLl1apay5ubkGYOTm5lbpcZViLzOM4vzqf14RETd35MgRY9OmTcaRI0fMjnLa8vPzjeDgYOODDz4wO0qtc7Kfk6r8/nabPVAlJSVcdtlljBkz5qTbTZo0iX379pVf/ro+TmpqKoMHD+acc85hzZo1jB8/nhtuuIFffvmlfJsvvviCCRMm8Mgjj7Bq1Srat2/PgAEDanw0f6VZbS5/PF9ERKpm9erVTJ06lR07drBq1SpGjBgBYP5eFjkhtxkD9dhjjwGUz0h6IiEhIf+YH+KYd955hyZNmpTP0tqyZUsWLlzIyy+/zIABAwDnbtQbb7yRa6+9tvwx06dP56OPPuL++++vpncjIiJS0QsvvMDWrVvx8vKiU6dOLFiwgLCwMLNjyQm4zR6oyho7dixhYWF07dqVjz76qMIpkYsXL6Z///4Vth8wYED5dPUlJSWsXLmywjZWq5X+/fv/Y0p7ERGR6tKhQwdWrlxJfn4+2dnZzJ49m7ZtNV2NK3ObPVCV8fjjj9OvXz/8/PyYNWsWt956K/n5+dx+++0AZGRkEBkZWeExkZGR5OXlceTIEQ4dOoTdbj/uNlu2bDnh6xYXF1NcXFz+fV5eXjW+KxEREXE1pu6Buv/++7FYLCe9nKy4/N3DDz9Mr1696NChA/fddx/33nsvzz//fA2+A6dnnnmG4ODg8ktsbGyNv6aIiIiYx9Q9UHfdddcp1+qJj4//18/frVs3nnjiCYqLi/H29iYqKorMzMwK22RmZhIUFISvry82mw2bzXbcbU40rgrggQceYMKECeXf5+XlqUSJiJjE+Nts1iJ/VV0/H6YWqPDw8PLFAWvCmjVrqFevHt7e3oBz3Z4ZM2ZU2Gb27Nnl6/kcG7g3d+7c8rP3HA4Hc+fOZdy4cSd8HW9v7/LXEBERcxybdLGwsBBfX1+T04irOja7eVUnE/07txkDlZaWRnZ2NmlpadjtdtasWQNAQkICAQEB/Pjjj2RmZtK9e3d8fHyYPXs2Tz/9NHfffXf5c9xyyy288cYb3HvvvVx33XX8+uuvfPnll0yfPr18mwkTJjBq1Cg6d+5M165deeWVVygoKCg/K09ERFyTzWYjJCSkfNoZPz+/U87kLXWHYRgUFhaSlZVFSEhIhcWH/w23KVATJ05kypQp5d936NABgHnz5tG3b188PT158803ufPOOzEMg4SEhPIpCY5p0qQJ06dP58477+TVV1+lYcOGfPDBB+VTGAAMHz6c/fv3M3HiRDIyMkhKSmLmzJn/GFguIiKu59hwC5eZu09czsmmO6oKi6GDxdUuLy+P4OBgcnNzCQoKMjuOiEidY7fbKS0tNTuGuBhPT8+T7nmqyu9vt9kDJSIiUlnHTgoSqSm1biJNERERkZqmAiUiIiJSRSpQIiIiIlWkMVA14Ni4fC3pIiIi4j6O/d6uzPl1KlA14PDhwwCajVxERMQNHT58mODg4JNuo2kMaoDD4SA9PZ3AwMBqn8Tt2DIxu3fv1hQJp6DPqvL0WVWePqvK02dVefqsKq8mPyvDMDh8+DAxMTFYrScf5aQ9UDXAarXSsGHDGn2NoKAg/SWrJH1WlafPqvL0WVWePqvK02dVeTX1WZ1qz9MxGkQuIiIiUkUqUCIiIiJVpALlZry9vXnkkUfw9vY2O4rL02dVefqsKk+fVeXps6o8fVaV5yqflQaRi4iIiFSR9kCJiIiIVJEKlIiIiEgVqUCJiIiIVJEKlIiIiEgVqUC5iaeeeoqePXvi5+dHSEjIcbexWCz/uEybNu3MBnURlfm80tLSGDx4MH5+fkRERHDPPfdQVlZ2ZoO6oMaNG//j5+jZZ581O5bLePPNN2ncuDE+Pj5069aNZcuWmR3J5Tz66KP/+Blq0aKF2bFcwu+//84FF1xATEwMFouF77//vsL9hmEwceJEoqOj8fX1pX///mzfvt2csCY71Wc1evTof/ycDRw48IzlU4FyEyUlJVx22WWMGTPmpNtNmjSJffv2lV+GDh16ZgK6mFN9Xna7ncGDB1NSUsKiRYuYMmUKkydPZuLEiWc4qWt6/PHHK/wc3XbbbWZHcglffPEFEyZM4JFHHmHVqlW0b9+eAQMGkJWVZXY0l9O6desKP0MLFy40O5JLKCgooH379rz55pvHvf+5557jtdde45133mHp0qX4+/szYMAAioqKznBS853qswIYOHBghZ+zqVOnnrmAhriVSZMmGcHBwce9DzC+++67M5rH1Z3o85oxY4ZhtVqNjIyM8tvefvttIygoyCguLj6DCV1PXFyc8fLLL5sdwyV17drVGDt2bPn3drvdiImJMZ555hkTU7meRx55xGjfvr3ZMVze3//NdjgcRlRUlPH888+X35aTk2N4e3sbU6dONSGh6zje77dRo0YZF110kSl5DMMwtAeqlhk7dixhYWF07dqVjz76CEPTfB3X4sWLadu2LZGRkeW3DRgwgLy8PDZu3GhiMtfw7LPPUr9+fTp06MDzzz+vQ5s492quXLmS/v37l99mtVrp378/ixcvNjGZa9q+fTsxMTHEx8czYsQI0tLSzI7k8lJTU8nIyKjwMxYcHEy3bt30M3YC8+fPJyIigsTERMaMGcPBgwfP2GtrMeFa5PHHH6dfv374+fkxa9Ysbr31VvLz87n99tvNjuZyMjIyKpQnoPz7jIwMMyK5jNtvv52OHTsSGhrKokWLeOCBB9i3bx8vvfSS2dFMdeDAAex2+3F/brZs2WJSKtfUrVs3Jk+eTGJiIvv27eOxxx6jT58+bNiwgcDAQLPjuaxj//Yc72esrv+7dDwDBw7kkksuoUmTJuzYsYP//ve/DBo0iMWLF2Oz2Wr89VWgTHT//ffzf//3fyfdZvPmzZUefPnwww+XX+/QoQMFBQU8//zztaZAVffnVZdU5bObMGFC+W3t2rXDy8uLm2++mWeeecb0pRPEPQwaNKj8ert27ejWrRtxcXF8+eWXXH/99SYmk9rkiiuuKL/etm1b2rVrR9OmTZk/fz7nnntujb++CpSJ7rrrLkaPHn3SbeLj4//183fr1o0nnniC4uLiWvGLrzo/r6ioqH+cPZWZmVl+X21zOp9dt27dKCsrY+fOnSQmJtZAOvcQFhaGzWYr/zk5JjMzs1b+zFSnkJAQmjdvTnJystlRXNqxn6PMzEyio6PLb8/MzCQpKcmkVO4jPj6esLAwkpOTVaBqu/DwcMLDw2vs+desWUO9evVqRXmC6v28evTowVNPPUVWVhYREREAzJ49m6CgIFq1alUtr+FKTuezW7NmDVartfxzqqu8vLzo1KkTc+fOLT+71eFwMHfuXMaNG2duOBeXn5/Pjh07GDlypNlRXFqTJk2Iiopi7ty55YUpLy+PpUuXnvIMbIE9e/Zw8ODBCuWzJqlAuYm0tDSys7NJS0vDbrezZs0aABISEggICODHH38kMzOT7t274+Pjw+zZs3n66ae5++67zQ1uklN9Xueffz6tWrVi5MiRPPfcc2RkZPDQQw8xduzYWlM4/43FixezdOlSzjnnHAIDA1m8eDF33nknV199NfXq1TM7nukmTJjAqFGj6Ny5M127duWVV16hoKCAa6+91uxoLuXuu+/mggsuIC4ujvT0dB555BFsNhtXXnml2dFMl5+fX2FPXGpqKmvWrCE0NJRGjRoxfvx4nnzySZo1a0aTJk14+OGHiYmJqZNT0pzsswoNDeWxxx5j2LBhREVFsWPHDu69914SEhIYMGDAmQlo2vl/UiWjRo0ygH9c5s2bZxiGYfz8889GUlKSERAQYPj7+xvt27c33nnnHcNut5sb3CSn+rwMwzB27txpDBo0yPD19TXCwsKMu+66yygtLTUvtAtYuXKl0a1bNyM4ONjw8fExWrZsaTz99NNGUVGR2dFcxuuvv240atTI8PLyMrp27WosWbLE7EguZ/jw4UZ0dLTh5eVlNGjQwBg+fLiRnJxsdiyXMG/evOP+2zRq1CjDMJxTGTz88MNGZGSk4e3tbZx77rnG1q1bzQ1tkpN9VoWFhcb5559vhIeHG56enkZcXJxx4403VpiapqZZDEPnuYuIiIhUheaBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhE5hf379xMVFcXTTz9dftuiRYvw8vJi7ty5JiYTEbNoLTwRkUqYMWMGQ4cOZdGiRSQmJpKUlMRFF13ESy+9ZHY0ETGBCpSISCWNHTuWOXPm0LlzZ9avX8/y5cvx9vY2O5aImEAFSkSkko4cOUKbNm3YvXs3K1eupG3btmZHEhGTaAyUiEgl7dixg/T0dBwOBzt37jQ7joiYSHugREQqoaSkhK5du5KUlERiYiKvvPIK69evJyIiwuxoImICFSgRkUq45557+Prrr1m7di0BAQGcffbZBAcH89NPP5kdTURMoEN4IiKnMH/+fF555RU++eQTgoKCsFqtfPLJJyxYsIC3337b7HgiYgLtgRIRERGpIu2BEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKlKBEhEREakiFSgRERGRKvp/eAeJN0kio7cAAAAASUVORK5CYII=", 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", + "image/png": 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" ] From 365adcde6500ec5756f2c0ef227c86906b5683d0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 09:49:04 -0400 Subject: [PATCH 034/121] docs: fix argument name --- docs/experimentalists/pooler/random/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/experimentalists/pooler/random/index.md b/docs/experimentalists/pooler/random/index.md index 31d11bbd..2500e7b9 100644 --- a/docs/experimentalists/pooler/random/index.md +++ b/docs/experimentalists/pooler/random/index.md @@ -26,5 +26,5 @@ This means that there are 9 possible combinations for these variables (3x3), fro ```python from autora.experimentalist.random_ import random_pool -pool = random_pool([1, 2, 3], [4, 5, 6], n=3) +pool = random_pool([1, 2, 3], [4, 5, 6], num_samples=3) ``` From 00085157f00541c04b86da8fe597a4bfc5619934 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 09:59:10 -0400 Subject: [PATCH 035/121] Apply suggestions from code review Co-authored-by: benwandrew --- src/autora/experimentalist/grid_.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 3c071096..ed6bfdba 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -30,7 +30,7 @@ def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " + f"grid_pool only supports independent variables with discrete allowed values, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) @@ -70,7 +70,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: >>> grid_pool(VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... - AssertionError: gridsearch_pool only supports independent variables with discrete... + AssertionError: grid_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: >>> grid_pool( @@ -92,7 +92,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... ])) Traceback (most recent call last): ... - AssertionError: gridsearch_pool only supports independent variables with discrete... + AssertionError: grid_pool only supports independent variables with discrete... We can specify arrays of allowed values: From 396e5d706ae8bd1aec3b1ca57927e818e94084f7 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:04:48 -0400 Subject: [PATCH 036/121] =?UTF-8?q?refactor:=20return=20dataframe=20from?= =?UTF-8?q?=20grid=5Fpool=5Ffrom=5Fivs=20=E2=80=93=20use=20in=20other=20fu?= =?UTF-8?q?nctions?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/autora/experimentalist/grid_.py | 46 +++++++++++++++++++++++++---- 1 file changed, 40 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 3c071096..9ade2fc1 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -24,19 +24,55 @@ def grid_pool_on_state(s: State) -> State: @grid_pool.register(list) @grid_pool.register(tuple) -def grid_pool_from_ivs(ivs: Sequence[Variable]) -> product: - """Creates exhaustive pool from discrete values using a Cartesian product of sets""" +def grid_pool_from_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: + """ + Creates exhaustive pool from discrete values using a Cartesian product of sets + + Examples: + >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2])]) + x + 0 1 + 1 2 + + >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2]), + ... Variable("y", allowed_values=["a","b"])]) + x y + 0 1 a + 1 1 b + 2 2 a + 3 2 b + + >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2]), + ... Variable("y", allowed_values=["a","b"]), + ... Variable("z", allowed_values=[3.0,4.0])]) + x y z + 0 1 a 3.0 + 1 1 a 4.0 + 2 1 b 3.0 + 3 1 b 4.0 + 4 2 a 3.0 + 5 2 a 4.0 + 6 2 b 3.0 + 7 2 b 4.0 + + + """ # Get allowed values for each IV l_iv_values = [] + l_iv_names = [] for iv in ivs: assert iv.allowed_values is not None, ( f"gridsearch_pool only supports independent variables with discrete allowed values, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) + l_iv_names.append(iv.name) # Return Cartesian product of all IV values - return product(*l_iv_values) + pool = product(*l_iv_values) + result = pd.DataFrame(pool, columns=l_iv_names) + + return result @grid_pool.register(VariableCollection) @@ -118,7 +154,5 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: [2222 rows x 3 columns] """ - raw_conditions = grid_pool_from_ivs(variables.independent_variables) - iv_names = [v.name for v in variables.independent_variables] - conditions = pd.DataFrame(raw_conditions, columns=iv_names) + conditions = grid_pool_from_ivs(variables.independent_variables) return Result(conditions=conditions) From 331698d953bf84e390e291d2af68f5046ec45dde Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:12:49 -0400 Subject: [PATCH 037/121] Apply suggestions from code review Co-authored-by: benwandrew --- src/autora/experimentalist/random_.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 4fa4f006..12246578 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -28,8 +28,10 @@ def random_pool_on_state( """ Args: - variables: - fmt: the output type required + s: a State object with the desired fields + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values Returns: From 4b5bb143e8a56eff03ae439e7d3757ba8e7c6f0d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:15:01 -0400 Subject: [PATCH 038/121] docs: update docstring for grid_pool --- src/autora/experimentalist/grid_.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 9ade2fc1..f2264f8b 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -10,8 +10,13 @@ @singledispatch -def grid_pool(s, **kwargs): - """Function to create a sequence of conditions sampled from a grid of independent variables.""" +def grid_pool(s, **___): + """ + Function to create a sequence of conditions sampled from a grid of independent variables. + + Depending on the type of the first argument, this will return a different result-type. + + """ raise NotImplementedError( "grid_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) From 7875985ff1bb41136d5bcd8e5ff2a4fca15a5aaa Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:15:15 -0400 Subject: [PATCH 039/121] test: add doctests fr grid_pool_on_state --- src/autora/experimentalist/grid_.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index f2264f8b..3724fb94 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -24,6 +24,29 @@ def grid_pool(s, **___): @grid_pool.register(State) def grid_pool_on_state(s: State) -> State: + """ + + Args: + s: a State object with a `variables` field. + + Returns: a State object updated with the new conditions. + + Examples: + >>> from autora.state.bundled import StandardState + >>> s = StandardState(variables=VariableCollection( + ... independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4])])) + >>> grid_pool(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + StandardState(..., conditions= + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4, ...) + + """ + return wrap_to_use_state(grid_pool_from_variables)(s) From 3e112687a794863b038618a77964738fc51f37d7 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:15:38 -0400 Subject: [PATCH 040/121] test: update doctests fr grid_pool_on_ivs --- src/autora/experimentalist/grid_.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 3724fb94..90e1f480 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -56,23 +56,31 @@ def grid_pool_from_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: """ Creates exhaustive pool from discrete values using a Cartesian product of sets + Args: + ivs: Variable objects, each of which has an attribute `allowed_values` + containing a sequence of values. + + Returns: A pd.DataFrame with the exhaustive pool of allowed values + + + Examples: - >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2])]) + >>> grid_pool([Variable("x", allowed_values=[1,2])]) x 0 1 1 2 - >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2]), - ... Variable("y", allowed_values=["a","b"])]) + >>> grid_pool([Variable("x", allowed_values=[1,2]), + ... Variable("y", allowed_values=["a","b"])]) x y 0 1 a 1 1 b 2 2 a 3 2 b - >>> grid_pool_from_ivs([Variable("x", allowed_values=[1,2]), - ... Variable("y", allowed_values=["a","b"]), - ... Variable("z", allowed_values=[3.0,4.0])]) + >>> grid_pool([Variable("x", allowed_values=[1,2]), + ... Variable("y", allowed_values=["a","b"]), + ... Variable("z", allowed_values=[3.0,4.0])]) x y z 0 1 a 3.0 1 1 a 4.0 From 4a535cb55f288c853ee34fb23d8b80a61f8c95b9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:15:55 -0400 Subject: [PATCH 041/121] test: update docstrings for grid_pool_from_variables --- src/autora/experimentalist/grid_.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 90e1f480..5c16e630 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -112,12 +112,13 @@ def grid_pool_from_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: @grid_pool.register(VariableCollection) -def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: +def grid_pool_from_variables(variables: VariableCollection) -> Result: """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: variables: a VariableCollection with `independent_variables` – a sequence of Variable - objects, each of which has an attribute `allowed_values` containing a sequence of values. + objects, each of which has an attribute `allowed_values` containing a sequence of + values. Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field From d4a5987a945659f93b16f59f7850588f390563a7 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:16:40 -0400 Subject: [PATCH 042/121] refactor: rename functions to _on --- src/autora/experimentalist/grid_.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 5c16e630..983ec350 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -47,12 +47,12 @@ def grid_pool_on_state(s: State) -> State: """ - return wrap_to_use_state(grid_pool_from_variables)(s) + return wrap_to_use_state(grid_pool_on_variables)(s) @grid_pool.register(list) @grid_pool.register(tuple) -def grid_pool_from_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: +def grid_pool_on_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: """ Creates exhaustive pool from discrete values using a Cartesian product of sets @@ -112,7 +112,7 @@ def grid_pool_from_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: @grid_pool.register(VariableCollection) -def grid_pool_from_variables(variables: VariableCollection) -> Result: +def grid_pool_on_variables(variables: VariableCollection) -> Result: """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: @@ -191,5 +191,5 @@ def grid_pool_from_variables(variables: VariableCollection) -> Result: [2222 rows x 3 columns] """ - conditions = grid_pool_from_ivs(variables.independent_variables) + conditions = grid_pool_on_ivs(variables.independent_variables) return Result(conditions=conditions) From 00a31224210788e126267a2d764ba2bf6b51a534 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:17:35 -0400 Subject: [PATCH 043/121] Update src/autora/experimentalist/grid_.py Co-authored-by: benwandrew --- src/autora/experimentalist/grid_.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index ed6bfdba..d418f662 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -57,7 +57,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: >>> import numpy as np With one independent variable "x", and some allowed values, we get exactly those values - back when running the executor: + back when running the experimentalist: >>> grid_pool(VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] ... )) From 000c66f54aff054189ad65e18cfd2ca992b7b01a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:19:50 -0400 Subject: [PATCH 044/121] test: update doctest formatting --- src/autora/experimentalist/random_.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 4fa4f006..0850df7b 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -143,7 +143,7 @@ def random_pool_on_variables( num_samples: int = 5, random_state: Optional[int] = None, replace: bool = True, -) -> pd.DataFrame: +) -> Result: """ Args: @@ -166,13 +166,13 @@ def random_pool_on_variables( >>> random_pool( ... VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=range(10)) - ... ]), random_state=1) - {'conditions': x + ... ]), random_state=1)["conditions"] + x 0 4 1 5 2 7 3 9 - 4 0} + 4 0 ... we get a sample of the range back when running the experimentalist: From da6b550db44d0e4fd701b0e11de81b6e62f7b455 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:20:52 -0400 Subject: [PATCH 045/121] test: update doctest formatting --- src/autora/experimentalist/grid_.py | 6 +++--- src/autora/experimentalist/random_.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 983ec350..f83bb9a7 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -133,11 +133,11 @@ def grid_pool_on_variables(variables: VariableCollection) -> Result: back when running the executor: >>> grid_pool(VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] - ... )) - {'conditions': x + ... ))["conditions"] + x 0 1 1 2 - 2 3} + 2 3 The allowed_values must be specified: >>> grid_pool(VariableCollection(independent_variables=[Variable(name="x")])) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 0850df7b..c0335356 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -325,13 +325,13 @@ def random_sample_on_conditions( >>> import pandas as pd >>> random.seed(1) >>> random_sample( - ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) - {'conditions': x + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180)["conditions"] + x 67 167 71 171 64 164 63 163 - 96 196} + 96 196 """ return Result( From ad199f2720e32d266cc69ca45a4c36e1a167da64 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:22:10 -0400 Subject: [PATCH 046/121] docs: update docstring formatting --- src/autora/experimentalist/random_.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index c0335356..3f7dcd52 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -92,8 +92,7 @@ def random_pool_on_state( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_pool(t, - ... num_samples=10, replace=True, random_state=1).conditions + >>> random_pool(t, num_samples=10, replace=True, random_state=1).conditions x1 x2 0 2 434 1 3 212 From 30be1fa0378f851be294f8212c32124cab77cdcf Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:22:54 -0400 Subject: [PATCH 047/121] Update src/autora/experimentalist/random_.py Co-authored-by: benwandrew --- src/autora/experimentalist/random_.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 12246578..426b8adc 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -177,7 +177,7 @@ def random_pool_on_variables( 4 0} - ... we get a sample of the range back when running the experimentalist: + ... With one independent variable "x", and a value_range we get a sample of the range back when running the experimentalist: >>> random_pool( ... VariableCollection(independent_variables=[ ... Variable(name="x", value_range=(-5, 5)) From ad8cf3043fdb1a547a20716784a62182b603dea8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:28:32 -0400 Subject: [PATCH 048/121] =?UTF-8?q?chore:=20remove=20unecessary=20random?= =?UTF-8?q?=5Fsample=5Fon=5Flist=20=E2=80=93=20just=20use=20random.choices?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/autora/experimentalist/random_.py | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 3f7dcd52..1fc3e27d 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -1,5 +1,4 @@ """Tools to make randomly sampled experimental conditions.""" -import random from functools import singledispatch from typing import Optional, Union @@ -273,32 +272,6 @@ def random_sample_on_state(s: State, **kwargs) -> State: return wrap_to_use_state(random_sample_on_conditions)(s, **kwargs) -@random_sample.register(list) -@random_sample.register(tuple) -def random_sample_on_list( - conditions: Union[list, tuple], - num_samples: int = 1, - random_state: Optional[int] = None, - replace: bool = False, -) -> list: - """ - Examples: - >>> random_sample([1, 1, 2, 2, 3, 3], num_samples=2, random_state=1, replace=True) - [1, 3] - - >>> random_sample((1, 1, 2, 2, 3, 3), num_samples=3, random_state=1, replace=True) - [1, 3, 3] - - - """ - - if random_state is not None: - random.seed(random_state) - - assert replace is True, "random.choices only supports choice with replacement." - return random.choices(conditions, k=num_samples) - - @random_sample.register(pd.DataFrame) @random_sample.register(np.ndarray) @random_sample.register(np.recarray) From ca5d17596c2885f2c68e639c554d9580cea6ea84 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:28:39 -0400 Subject: [PATCH 049/121] docs: update docstrings --- src/autora/experimentalist/random_.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 1fc3e27d..b9b8c989 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -286,9 +286,9 @@ def random_sample_on_conditions( Args: conditions: the conditions to sample from - num_samples: - random_state: - replace: + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions From cb4b26fd30c10e4ae4844d4d649892a5f67a34b6 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:38:23 -0400 Subject: [PATCH 050/121] docs: update grid docstrings --- src/autora/experimentalist/grid_.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index ae93261b..976912e1 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -12,7 +12,7 @@ @singledispatch def grid_pool(s, **___): """ - Function to create a sequence of conditions sampled from a grid of independent variables. + Create an exhaustive pool of conditions. Depending on the type of the first argument, this will return a different result-type. @@ -25,11 +25,12 @@ def grid_pool(s, **___): @grid_pool.register(State) def grid_pool_on_state(s: State) -> State: """ + Create an exhaustive pool of conditions. Args: s: a State object with a `variables` field. - Returns: a State object updated with the new conditions. + Returns: a State object updated with the new conditions Examples: >>> from autora.state.bundled import StandardState @@ -54,7 +55,7 @@ def grid_pool_on_state(s: State) -> State: @grid_pool.register(tuple) def grid_pool_on_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: """ - Creates exhaustive pool from discrete values using a Cartesian product of sets + Create an exhaustive pool of conditions. Args: ivs: Variable objects, each of which has an attribute `allowed_values` From a278b04cf200bddf13f89da0d2af20f80e3f5f7d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:39:06 -0400 Subject: [PATCH 051/121] docs: update random_ docstrings --- src/autora/experimentalist/random_.py | 40 +++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 8d5d0a13..801b63da 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -11,7 +11,11 @@ @singledispatch def random_pool(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from independent variables.""" + """ + Create a sequence of conditions randomly sampled from independent variables. + + Depending on the type of the first argument, this will return a different result-type. + """ raise NotImplementedError( "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) @@ -25,6 +29,7 @@ def random_pool_on_state( replace: bool = True, ) -> State: """ + Create a sequence of conditions randomly sampled from independent variables. Args: s: a State object with the desired fields @@ -32,7 +37,7 @@ def random_pool_on_state( random_state: the seed value for the random number generator replace: if True, allow repeated values - Returns: + Returns: a State object updated with the new conditions Examples: >>> from autora.state.delta import State @@ -145,6 +150,7 @@ def random_pool_on_variables( replace: bool = True, ) -> Result: """ + Create a sequence of conditions randomly sampled from independent variables. Args: variables: the description of all the variables in the AER experiment. @@ -175,7 +181,8 @@ def random_pool_on_variables( 4 0 - ... With one independent variable "x", and a value_range we get a sample of the range back when running the experimentalist: + ... with one independent variable "x", and a value_range, + we get a sample of the range back when running the experimentalist: >>> random_pool( ... VariableCollection(independent_variables=[ ... Variable(name="x", value_range=(-5, 5)) @@ -263,7 +270,11 @@ def random_pool_on_variables( @singledispatch def random_sample(s, **kwargs): - """Function to create a sequence of conditions randomly sampled from conditions.""" + """ + Take a random sample from some input conditions. + + Depending on the type of the first argument, this will return a different result-type. + """ raise NotImplementedError( "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) ) @@ -271,6 +282,24 @@ def random_sample(s, **kwargs): @random_sample.register(State) def random_sample_on_state(s: State, **kwargs) -> State: + """ + Take a random sample from some input conditions. + + Args: + s: a State object with a `variables` field. + + Returns: a State object updated with the new conditions + + Examples: + >>> from autora.state.bundled import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": range(100, 200)})) + >>> random_sample(s, random_state=1, replace=False, num_samples=3).conditions + x + 80 180 + 84 184 + 33 133 + + """ return wrap_to_use_state(random_sample_on_conditions)(s, **kwargs) @@ -284,7 +313,7 @@ def random_sample_on_conditions( replace: bool = False, ) -> Result: """ - Take a random sample from some conditions. + Take a random sample from some input conditions. Args: conditions: the conditions to sample from @@ -297,7 +326,6 @@ def random_sample_on_conditions( Examples: From a pd.DataFrame: >>> import pandas as pd - >>> random.seed(1) >>> random_sample( ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180)["conditions"] x From f866d6dbed7ed5d160afa50d5e5eea1db789a5f4 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:41:17 -0400 Subject: [PATCH 052/121] docs: update random_ docstrings --- src/autora/experimentalist/random_.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 801b63da..5f01d634 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -321,7 +321,8 @@ def random_sample_on_conditions( random_state: the seed value for the random number generator replace: if True, allow repeated values - Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions + Returns: a Result object with a field `conditions` containing a DataFrame of the sampled + conditions Examples: From a pd.DataFrame: From 228cceddf644d19f2b17a0165d1a48f0de7a8190 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 17 Jul 2023 10:48:34 -0400 Subject: [PATCH 053/121] docs: remove extra spaces --- src/autora/experimentalist/grid_.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 976912e1..83eee012 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -63,8 +63,6 @@ def grid_pool_on_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: Returns: A pd.DataFrame with the exhaustive pool of allowed values - - Examples: >>> grid_pool([Variable("x", allowed_values=[1,2])]) x From dfa5484a447cb172354cbadd5c43b07f170c4174 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 14:29:34 -0400 Subject: [PATCH 054/121] fix: support **kwargs on state function call --- src/autora/experimentalist/grid_.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 83eee012..74f2781d 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -23,7 +23,7 @@ def grid_pool(s, **___): @grid_pool.register(State) -def grid_pool_on_state(s: State) -> State: +def grid_pool_on_state(s: State, **kwargs) -> State: """ Create an exhaustive pool of conditions. @@ -48,7 +48,7 @@ def grid_pool_on_state(s: State) -> State: """ - return wrap_to_use_state(grid_pool_on_variables)(s) + return wrap_to_use_state(grid_pool_on_variables)(s, **kwargs) @grid_pool.register(list) From 5e70c7bfc603ff7b9416f21dc93c638e0a572833 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 14:32:51 -0400 Subject: [PATCH 055/121] refactor: simplify grid_pool to make fundamental function the one with the shorter name, and add _on_state (placeholder extension) to the version which works on the state object --- src/autora/experimentalist/grid_.py | 102 ++++++---------------------- 1 file changed, 21 insertions(+), 81 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 74f2781d..b8736b81 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -1,28 +1,12 @@ """Tools to make grids of experimental conditions.""" -from functools import singledispatch from itertools import product -from typing import Sequence import pandas as pd from autora.state.delta import Result, State, wrap_to_use_state -from autora.variable import Variable, VariableCollection +from autora.variable import VariableCollection -@singledispatch -def grid_pool(s, **___): - """ - Create an exhaustive pool of conditions. - - Depending on the type of the first argument, this will return a different result-type. - - """ - raise NotImplementedError( - "grid_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) - ) - - -@grid_pool.register(State) def grid_pool_on_state(s: State, **kwargs) -> State: """ Create an exhaustive pool of conditions. @@ -34,11 +18,12 @@ def grid_pool_on_state(s: State, **kwargs) -> State: Examples: >>> from autora.state.bundled import StandardState + >>> from autora.variable import Variable, VariableCollection >>> s = StandardState(variables=VariableCollection( ... independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2", allowed_values=[3, 4])])) - >>> grid_pool(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> grid_pool_on_state(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE StandardState(..., conditions= x1 x2 0 1 3 @@ -48,70 +33,10 @@ def grid_pool_on_state(s: State, **kwargs) -> State: """ - return wrap_to_use_state(grid_pool_on_variables)(s, **kwargs) - - -@grid_pool.register(list) -@grid_pool.register(tuple) -def grid_pool_on_ivs(ivs: Sequence[Variable]) -> pd.DataFrame: - """ - Create an exhaustive pool of conditions. - - Args: - ivs: Variable objects, each of which has an attribute `allowed_values` - containing a sequence of values. - - Returns: A pd.DataFrame with the exhaustive pool of allowed values - - Examples: - >>> grid_pool([Variable("x", allowed_values=[1,2])]) - x - 0 1 - 1 2 - - >>> grid_pool([Variable("x", allowed_values=[1,2]), - ... Variable("y", allowed_values=["a","b"])]) - x y - 0 1 a - 1 1 b - 2 2 a - 3 2 b - - >>> grid_pool([Variable("x", allowed_values=[1,2]), - ... Variable("y", allowed_values=["a","b"]), - ... Variable("z", allowed_values=[3.0,4.0])]) - x y z - 0 1 a 3.0 - 1 1 a 4.0 - 2 1 b 3.0 - 3 1 b 4.0 - 4 2 a 3.0 - 5 2 a 4.0 - 6 2 b 3.0 - 7 2 b 4.0 + return wrap_to_use_state(grid_pool)(s, **kwargs) - """ - # Get allowed values for each IV - l_iv_values = [] - l_iv_names = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"grid_pool only supports independent variables with discrete allowed values, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - l_iv_names.append(iv.name) - - # Return Cartesian product of all IV values - pool = product(*l_iv_values) - result = pd.DataFrame(pool, columns=l_iv_names) - - return result - - -@grid_pool.register(VariableCollection) -def grid_pool_on_variables(variables: VariableCollection) -> Result: +def grid_pool(variables: VariableCollection) -> Result: """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: @@ -190,5 +115,20 @@ def grid_pool_on_variables(variables: VariableCollection) -> Result: [2222 rows x 3 columns] """ - conditions = grid_pool_on_ivs(variables.independent_variables) + ivs = variables.independent_variables + # Get allowed values for each IV + l_iv_values = [] + l_iv_names = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"grid_pool only supports independent variables with discrete allowed values, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + l_iv_names.append(iv.name) + + # Return Cartesian product of all IV values + pool = product(*l_iv_values) + conditions = pd.DataFrame(pool, columns=l_iv_names) + return Result(conditions=conditions) From 6730ca6a5d176a96e2fa4689e5c99da89210a3d0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 14:35:08 -0400 Subject: [PATCH 056/121] refactor: simplify random_pool and random_sample to make fundamental function the one with the shorter name, and add _on_state (placeholder extension) to the version which works on the state object --- src/autora/experimentalist/random_.py | 56 +++++++-------------------- 1 file changed, 13 insertions(+), 43 deletions(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 5f01d634..795b4e7f 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -1,5 +1,4 @@ """Tools to make randomly sampled experimental conditions.""" -from functools import singledispatch from typing import Optional, Union import numpy as np @@ -9,24 +8,12 @@ from autora.variable import ValueType, VariableCollection -@singledispatch -def random_pool(s, **kwargs): - """ - Create a sequence of conditions randomly sampled from independent variables. - - Depending on the type of the first argument, this will return a different result-type. - """ - raise NotImplementedError( - "random_pool doesn't have an implementation for %s (type=%s)" % (s, type(s)) - ) - - -@random_pool.register(State) def random_pool_on_state( s: State, num_samples: int = 5, random_state: Optional[int] = None, replace: bool = True, + **kwargs, ) -> State: """ Create a sequence of conditions randomly sampled from independent variables. @@ -60,7 +47,7 @@ def random_pool_on_state( ... ])) ... we get some of those values back when running the experimentalist: - >>> random_pool(s, random_state=1).conditions + >>> random_pool_on_state(s, random_state=1).conditions x 0 4 1 5 @@ -75,7 +62,7 @@ def random_pool_on_state( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> random_pool(t, random_state=1).conditions + >>> random_pool_on_state(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -86,7 +73,7 @@ def random_pool_on_state( The allowed_values or value_range must be specified: - >>> random_pool( + >>> random_pool_on_state( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -98,7 +85,7 @@ def random_pool_on_state( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_pool(t, num_samples=10, replace=True, random_state=1).conditions + >>> random_pool_on_state(t, num_samples=10, replace=True, random_state=1).conditions x1 x2 0 2 434 1 3 212 @@ -112,7 +99,7 @@ def random_pool_on_state( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool(S( + >>> random_pool_on_state(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -129,7 +116,7 @@ def random_pool_on_state( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> random_pool(u, random_state=1).conditions + >>> random_pool_on_state(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 @@ -137,13 +124,12 @@ def random_pool_on_state( 3 9.0 3 29.0 4 -9.4 3 22.0 """ - return wrap_to_use_state(random_pool_on_variables)( - s, num_samples=num_samples, random_state=random_state, replace=replace + return wrap_to_use_state(random_pool)( + s, num_samples=num_samples, random_state=random_state, replace=replace, **kwargs ) -@random_pool.register(VariableCollection) -def random_pool_on_variables( +def random_pool( variables: VariableCollection, num_samples: int = 5, random_state: Optional[int] = None, @@ -268,19 +254,6 @@ def random_pool_on_variables( return Result(conditions=conditions) -@singledispatch -def random_sample(s, **kwargs): - """ - Take a random sample from some input conditions. - - Depending on the type of the first argument, this will return a different result-type. - """ - raise NotImplementedError( - "random_sample doesn't have an implementation for %s (type=%s)" % (s, type(s)) - ) - - -@random_sample.register(State) def random_sample_on_state(s: State, **kwargs) -> State: """ Take a random sample from some input conditions. @@ -293,20 +266,17 @@ def random_sample_on_state(s: State, **kwargs) -> State: Examples: >>> from autora.state.bundled import StandardState >>> s = StandardState(conditions=pd.DataFrame({"x": range(100, 200)})) - >>> random_sample(s, random_state=1, replace=False, num_samples=3).conditions + >>> random_sample_on_state(s, random_state=1, replace=False, num_samples=3).conditions x 80 180 84 184 33 133 """ - return wrap_to_use_state(random_sample_on_conditions)(s, **kwargs) + return wrap_to_use_state(random_sample)(s, **kwargs) -@random_sample.register(pd.DataFrame) -@random_sample.register(np.ndarray) -@random_sample.register(np.recarray) -def random_sample_on_conditions( +def random_sample( conditions: Union[pd.DataFrame, np.ndarray, np.recarray], num_samples: int = 1, random_state: Optional[int] = None, From 848a8d9cefd7255a49a9f4deafa83114619bed42 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:11:48 -0400 Subject: [PATCH 057/121] refactor: rename state_fn_from_estimator --- docs/cycle/Basic Introduction to Functions and States.ipynb | 4 ++-- ... and Cyclical Workflows using Functions and States.ipynb | 6 +++--- src/autora/state/wrapper.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index 45e43abe..58294cad 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -131,9 +131,9 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state.wrapper import theorist_from_estimator\n", + "from autora.state.wrapper import state_fn_from_estimator\n", "\n", - "theorist = theorist_from_estimator(LinearRegression(fit_intercept=True))" + "theorist = state_fn_from_estimator(LinearRegression(fit_intercept=True))" ] }, { diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 2719f548..3151eb57 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -268,7 +268,7 @@ "### Defining The Theorist\n", "\n", "Now we define a theorist, which does a linear regression on the polynomial of degree 5. We define a regressor and a\n", - "method to return its feature names and coefficients, and then the theorist to handle it. Here, we use a different wrapper `theorist_from_estimator` that wraps the regressor and returns a function with the same functionality, but operating on `State` fields. In this case, we want to use the `State` field `experiment_data` and extend the `State` field `models`." + "method to return its feature names and coefficients, and then the theorist to handle it. Here, we use a different wrapper `state_fn_from_estimator` that wraps the regressor and returns a function with the same functionality, but operating on `State` fields. In this case, we want to use the `State` field `experiment_data` and extend the `State` field `models`." ] }, { @@ -278,13 +278,13 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state.wrapper import theorist_from_estimator\n", + "from autora.state.wrapper import state_fn_from_estimator\n", "from sklearn.pipeline import make_pipeline as make_theorist_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# Completely standard scikit-learn pipeline regressor\n", "regressor = make_theorist_pipeline(PolynomialFeatures(degree=5), LinearRegression())\n", - "theorist = theorist_from_estimator(regressor)\n", + "theorist = state_fn_from_estimator(regressor)\n", "\n", "def get_equation(r):\n", " t = r.named_steps['polynomialfeatures'].get_feature_names_out()\n", diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 74ecbade..bbe8c2c8 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -23,7 +23,7 @@ Executor = Callable[[State], State] -def theorist_from_estimator(estimator: BaseEstimator) -> Executor: +def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: """ Convert a scikit-learn compatible estimator into a function on a `State` object. From 27bd42ab04f43411c0db929d986c89d3fe072f1a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:12:55 -0400 Subject: [PATCH 058/121] refactor: rename state_fn_from_x_to_y_fn --- src/autora/state/wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index bbe8c2c8..7970fd5e 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -44,7 +44,7 @@ def theorist( return theorist -def experiment_runner_from_x_to_y_function(f: Callable[[X], Y]) -> Executor: +def state_fn_from_x_to_y_fn(f: Callable[[X], Y]) -> Executor: """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ values""" From 0caf0918f1dfb2bd78c5f12b97adfbec501eae33 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:13:25 -0400 Subject: [PATCH 059/121] refactor: rename state_fn_from_x_to_xy_fn --- src/autora/state/wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 7970fd5e..3d3c934a 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -58,7 +58,7 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return experiment_runner -def experiment_runner_from_x_to_xy_function(f: Callable[[X], XY]) -> Executor: +def state_fn_from_x_to_xy_fn(f: Callable[[X], XY]) -> Executor: """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` returns both $x$ and $y$ values in a complete dataframe.""" From c329501ddda794223434d511f3680166a04495d6 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:14:15 -0400 Subject: [PATCH 060/121] refactor: rename state_fn_from_experimentalist_pipeline --- src/autora/state/wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 3d3c934a..dc6143c0 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -71,7 +71,7 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return experiment_runner -def experimentalist_from_pipeline(pipeline: Pipeline) -> Executor: +def state_fn_from_experimentalist_pipeline(pipeline: Pipeline) -> Executor: """Wrapper for experimentalists of the form $f() \rarrow x$, where `f` returns both $x$ and $y$ values in a complete dataframe.""" From 6df547eb1dff3f7af3029368e9ac544af981300f Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:18:05 -0400 Subject: [PATCH 061/121] refactor: rename state_fn_from_pipeline --- src/autora/state/wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index dc6143c0..a3f3a6d5 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -71,7 +71,7 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return experiment_runner -def state_fn_from_experimentalist_pipeline(pipeline: Pipeline) -> Executor: +def state_fn_from_pipeline(pipeline: Pipeline) -> Executor: """Wrapper for experimentalists of the form $f() \rarrow x$, where `f` returns both $x$ and $y$ values in a complete dataframe.""" From b1a9178cae4e3b48ce81bae2ec2fdb9b6c5c3b4d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 18 Jul 2023 18:25:10 -0400 Subject: [PATCH 062/121] test: add a doctest for the estimator wrapper --- src/autora/state/wrapper.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index a3f3a6d5..8b06e15f 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -29,6 +29,27 @@ def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: Supports passing additional `**kwargs` which are used to update the estimator's params before fitting. + + Examples: + Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. + >>> from sklearn.linear_model import LinearRegression + >>> state_fn = state_fn_from_estimator(LinearRegression()) + + Define the state on which to operate (here an instance of the `StandardState`): + >>> from autora.state.bundled import StandardState + >>> from autora.variable import Variable, VariableCollection + >>> import pandas as pd + >>> s = StandardState( + ... variables=VariableCollection( + ... independent_variables=[Variable("x")], + ... dependent_variables=[Variable("y")]), + ... experiment_data=pd.DataFrame({"x": [1,2,3], "y":[3,6,9]}) + ... ) + + Run the function, which fits the model and adds the result to the `StandardState` + >>> state_fn(s).model.coef_ + array([[3.]]) + """ @wrap_to_use_state From 4ecfd141c2b7594eaee1ed0d9b606260d80e4843 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 19 Jul 2023 10:16:22 -0400 Subject: [PATCH 063/121] test: add doctests for dataframe version of experiment_runner wrapper --- src/autora/state/wrapper.py | 36 ++++++++++++++++++++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 8b06e15f..1dad1e04 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -65,9 +65,41 @@ def theorist( return theorist -def state_fn_from_x_to_y_fn(f: Callable[[X], Y]) -> Executor: +def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ - values""" + values, with inputs and outputs as a DataFrame or Series with correct column names. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state.bundled import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame (allowing the explicit inclusion of + metadata like column names). + >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(2 * c["x"] + 1, name="y") + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(c["x0"] + c["x1"], name="y") + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + """ @wrap_to_use_state def experiment_runner(conditions: pd.DataFrame, **kwargs): From 498bfd63c73f4050adf26958e04b153e9e50e7d2 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 19 Jul 2023 10:22:37 -0400 Subject: [PATCH 064/121] test: add doctests for dataframe version of experiment_runner wrapper for x -> x,y functions --- src/autora/state/wrapper.py | 37 +++++++++++++++++++++++++++++++++++-- 1 file changed, 35 insertions(+), 2 deletions(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 1dad1e04..6922a296 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -111,9 +111,42 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return experiment_runner -def state_fn_from_x_to_xy_fn(f: Callable[[X], XY]) -> Executor: +def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` - returns both $x$ and $y$ values in a complete dataframe.""" + returns both $x$ and $y$ values in a complete dataframe. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state.bundled import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame, allowing the explicit inclusion of + metadata like column names. + >>> def x_to_xy_fn_df(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=lambda df: 2 * df.x + 1) + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn_df)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_xy_fn(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=c.x0 + c.x1) + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + + """ @wrap_to_use_state def experiment_runner(conditions: pd.DataFrame, **kwargs): From 65cce2c0f5cdb1646645b8f6692dd324ae9eb449 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 19 Jul 2023 10:25:41 -0400 Subject: [PATCH 065/121] =?UTF-8?q?chore:=20remove=20wrapper=20for=20pipel?= =?UTF-8?q?ine=20=E2=80=93=20no=20longer=20compatible=20with=20StandardSta?= =?UTF-8?q?te,=20not=20useful?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/autora/state/wrapper.py | 22 +--------------------- 1 file changed, 1 insertion(+), 21 deletions(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 6922a296..23c94bc1 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -6,13 +6,11 @@ """ from __future__ import annotations -from typing import Callable, Iterable, TypeVar +from typing import Callable, TypeVar -import numpy as np import pandas as pd from sklearn.base import BaseEstimator -from autora.experimentalist.pipeline import Pipeline from autora.state.delta import Delta, State, wrap_to_use_state from autora.variable import VariableCollection @@ -155,21 +153,3 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return Delta(experiment_data=experiment_data) return experiment_runner - - -def state_fn_from_pipeline(pipeline: Pipeline) -> Executor: - """Wrapper for experimentalists of the form $f() \rarrow x$, where `f` - returns both $x$ and $y$ values in a complete dataframe.""" - - @wrap_to_use_state - def experimentalist(params): - conditions = pipeline(**params) - if isinstance(conditions, (pd.DataFrame, np.ndarray, np.recarray)): - conditions_ = conditions - elif isinstance(conditions, Iterable): - conditions_ = np.array(list(conditions)) - else: - raise NotImplementedError("type `%s` is not supported" % (type(conditions))) - return Delta(conditions=conditions_) - - return experimentalist From 807f4e8d2bc6a4643867691e4143703a63b6cc91 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 19 Jul 2023 15:08:42 -0400 Subject: [PATCH 066/121] docs: rename example functions to drop _df where unnecessary. --- src/autora/state/wrapper.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 23c94bc1..1bc3dd66 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -120,12 +120,12 @@ def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: The function can be defined on a DataFrame, allowing the explicit inclusion of metadata like column names. - >>> def x_to_xy_fn_df(c: pd.DataFrame) -> pd.Series: + >>> def x_to_xy_fn(c: pd.DataFrame) -> pd.Series: ... result = c.assign(y=lambda df: 2 * df.x + 1) ... return result We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn_df)(s).experiment_data + >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data x y 0 1 3 1 2 5 From 463c3900477540619eeab99f6ba43d2ff1f5ec8d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 21 Jul 2023 12:00:32 -0400 Subject: [PATCH 067/121] docs: rename experimentalist functions in preparation for aliasing --- src/autora/experimentalist/grid_.py | 103 +++++++++++++++-- .../experimentalist/random_/__init__.py | 1 + src/autora/experimentalist/random_/sample.py | 105 ++++++++++++++++++ 3 files changed, 199 insertions(+), 10 deletions(-) create mode 100644 src/autora/experimentalist/random_/__init__.py create mode 100644 src/autora/experimentalist/random_/sample.py diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index b8736b81..45185530 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -7,7 +7,7 @@ from autora.variable import VariableCollection -def grid_pool_on_state(s: State, **kwargs) -> State: +def _state(s: State, **kwargs) -> State: """ Create an exhaustive pool of conditions. @@ -23,7 +23,7 @@ def grid_pool_on_state(s: State, **kwargs) -> State: ... independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2", allowed_values=[3, 4])])) - >>> grid_pool_on_state(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> _state(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE StandardState(..., conditions= x1 x2 0 1 3 @@ -33,10 +33,10 @@ def grid_pool_on_state(s: State, **kwargs) -> State: """ - return wrap_to_use_state(grid_pool)(s, **kwargs) + return wrap_to_use_state(_result)(s, **kwargs) -def grid_pool(variables: VariableCollection) -> Result: +def _result(variables: VariableCollection) -> Result: """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: @@ -55,7 +55,7 @@ def grid_pool(variables: VariableCollection) -> Result: With one independent variable "x", and some allowed values, we get exactly those values back when running the experimentalist: - >>> grid_pool(VariableCollection( + >>> _result(VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] ... ))["conditions"] x @@ -64,13 +64,13 @@ def grid_pool(variables: VariableCollection) -> Result: 2 3 The allowed_values must be specified: - >>> grid_pool(VariableCollection(independent_variables=[Variable(name="x")])) + >>> _result(VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... AssertionError: grid_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: - >>> grid_pool( + >>> _result( ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2", allowed_values=[3, 4]), @@ -82,7 +82,7 @@ def grid_pool(variables: VariableCollection) -> Result: 3 2 4 If any of the variables have unspecified allowed_values, we get an error: - >>> grid_pool( + >>> _result( ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -93,7 +93,7 @@ def grid_pool(variables: VariableCollection) -> Result: We can specify arrays of allowed values: - >>> grid_pool( + >>> _result( ... VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), ... Variable(name="y", allowed_values=[3, 4]), @@ -114,6 +114,89 @@ def grid_pool(variables: VariableCollection) -> Result: [2222 rows x 3 columns] + """ + conditions = _base(variables=variables) + return Result(conditions=conditions) + + +def _base(variables: VariableCollection) -> pd.DataFrame: + """Creates exhaustive pool of conditions given a definition of variables with allowed_values. + + Args: + variables: a VariableCollection with `independent_variables` – a sequence of Variable + objects, each of which has an attribute `allowed_values` containing a sequence of + values. + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed values, we get exactly those values + back when running the experimentalist: + >>> _base(VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] + ... )) + x + 0 1 + 1 2 + 2 3 + + The allowed_values must be specified: + >>> _base(VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + AssertionError: grid_pool only supports independent variables with discrete... + + With two independent variables, we get the cartesian product: + >>> _base( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> _base( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + AssertionError: grid_pool only supports independent variables with discrete... + + + We can specify arrays of allowed values: + >>> _base( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + """ ivs = variables.independent_variables # Get allowed values for each IV @@ -131,4 +214,4 @@ def grid_pool(variables: VariableCollection) -> Result: pool = product(*l_iv_values) conditions = pd.DataFrame(pool, columns=l_iv_names) - return Result(conditions=conditions) + return conditions diff --git a/src/autora/experimentalist/random_/__init__.py b/src/autora/experimentalist/random_/__init__.py new file mode 100644 index 00000000..69d6f5a4 --- /dev/null +++ b/src/autora/experimentalist/random_/__init__.py @@ -0,0 +1 @@ +"""Tools to make randomly sampled experimental conditions.""" diff --git a/src/autora/experimentalist/random_/sample.py b/src/autora/experimentalist/random_/sample.py new file mode 100644 index 00000000..854d05a9 --- /dev/null +++ b/src/autora/experimentalist/random_/sample.py @@ -0,0 +1,105 @@ +from typing import Optional, Union + +import numpy as np +import pandas as pd + +from autora.state.delta import Result, State, wrap_to_use_state + + +def _state(s: State, **kwargs) -> State: + """ + Take a random sample from some input conditions. + + Args: + s: a State object with a `variables` field. + + Returns: a State object updated with the new conditions + + Examples: + >>> from autora.state.bundled import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": range(100, 200)})) + >>> _state(s, random_state=1, replace=False, num_samples=3).conditions + x + 80 180 + 84 184 + 33 133 + + """ + return wrap_to_use_state(_result)(s, **kwargs) + + +def _result( + conditions: Union[pd.DataFrame, np.ndarray, np.recarray], + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> Result: + """ + Take a random sample from some input conditions. + + Args: + conditions: the conditions to sample from + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: a Result object with a field `conditions` containing a DataFrame of the sampled + conditions + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> _result( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180)["conditions"] + x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196 + + """ + return Result( + conditions=_base( + conditions=conditions, + num_samples=num_samples, + random_state=random_state, + replace=replace, + ) + ) + + +def _base( + conditions: Union[pd.DataFrame, np.ndarray, np.recarray], + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> pd.DataFrame: + """ + Take a random sample from some input conditions. + + Args: + conditions: the conditions to sample from + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: a Result object with a field `conditions` containing a DataFrame of the sampled + conditions + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> _base( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196 + + """ + return pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=replace + ) From 6035eeac4982b69d96ec1180ca6ab5831b33966b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 24 Jul 2023 09:36:55 -0400 Subject: [PATCH 068/121] exploration: add some function naming options --- .../Function Naming Convention Options.ipynb | 489 ++++++++++++++++++ .../experimentalist/random_/__init__.py | 7 + .../{random_.py => random_/pool.py} | 141 +++-- 3 files changed, 555 insertions(+), 82 deletions(-) create mode 100644 docs/cycle/Function Naming Convention Options.ipynb rename src/autora/experimentalist/{random_.py => random_/pool.py} (75%) diff --git a/docs/cycle/Function Naming Convention Options.ipynb b/docs/cycle/Function Naming Convention Options.ipynb new file mode 100644 index 00000000..61b6b780 --- /dev/null +++ b/docs/cycle/Function Naming Convention Options.ipynb @@ -0,0 +1,489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Function Naming Convention Options\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "AutoRA is a framework for model discovery, which can propose and run experiments and analyse the resulting data,\n", + "fully autonomously. This process runs cyclically and we call the complete process the \"cycle\" and the individual\n", + "steps \"tasks\".\n", + "\n", + "Our original object-oriented approach for defining the cycle turned out to be too complicated for people to understand.\n", + "We've been building a simpler functional interface for it for defining the cycles.\n", + "\n", + "But we have a problem – the naming convention for the functions is difficult to agree on. The AER group (which is\n", + "developing AutoRA and related tools) has asked us to look over the current options and give some feedback.\n", + "\n", + "## The functional interface\n", + "A **state** is a description of all the data and metadata known about a particular phenomenon:\n", + "\n", + "- the domain of the variables,\n", + "- experimental conditions to be investigated,\n", + "- the experimental data, the newest model, and\n", + "- any other data the cycle might need.\n", + "\n", + "We define a state as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state.bundled import StandardState\n", + "from autora.variable import VariableCollection, Variable\n", + "\n", + "s_0 = StandardState(\n", + " variables=VariableCollection(\n", + " independent_variables=[Variable(\"x\", value_range=(-10, 10))],\n", + " dependent_variables=[Variable(\"y\")]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`s_0` doesn't have anything other than the metadata we gave it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions=None, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The functional interface sees the tasks as functions $f$ on state $S$ which return a new state.\n", + "A single task looks like:\n", + "$$ f(S_{i}) \\rightarrow S_{i+1} ,$$\n", + "\n", + "and a pipeline of such operations looks like:\n", + "$$S_n = f_n^\\prime(...f_2^\\prime(f_1^\\prime(S_0))) .$$\n", + "\n", + "One task we define is the experimentalist, which proposes new experimental conditions.\n", + "One experimentalist is the `random_pool` which takes variables and returns a series of conditions.\n", + "We define it just like that:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Optional\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "def random_pool(\n", + " variables: VariableCollection,\n", + " num_samples: int = 5,\n", + " random_state: Optional[int] = None,\n", + ") -> pd.DataFrame:\n", + " rng = np.random.default_rng(random_state)\n", + "\n", + " raw_conditions = {}\n", + " for iv in variables.independent_variables:\n", + " raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples)\n", + "\n", + " return pd.DataFrame(raw_conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And running it on the variables results in a series of conditions sampled uniformly between -10 and +10:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "random_pool(s_0.variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We still need to do some work so that it can run directly on $S$.\n", + "\n", + "$S$ is defined such that it can be added to: $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", + "\n", + "The way we package the random_pool function is to make its output into a `Delta`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state.delta import Delta\n", + "\n", + "\n", + "def random_pool_delta(\n", + " variables: VariableCollection,\n", + " num_samples: int = 5,\n", + " random_state: Optional[int] = None,\n", + "):\n", + " \"\"\"\n", + " Create a sequence of conditions randomly sampled from independent variables.\n", + "\n", + " Args:\n", + " variables: the description of all the variables in the AER experiment.\n", + " num_samples: the number of conditions to produce\n", + " random_state: the seed value for the random number generator\n", + " replace: if True, allow repeated values\n", + "\n", + " Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field\n", + "\n", + " \"\"\"\n", + " conditions = random_pool(\n", + " variables=variables,\n", + " num_samples=num_samples,\n", + " random_state=random_state,\n", + " )\n", + " return Delta(conditions=conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which can be run on the same inputs but produces a differently packaged output:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'conditions': x\n", + "0 -6.168705\n", + "1 1.143822\n", + "2 4.432569\n", + "3 9.079492\n", + "4 -8.734145}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_pool_delta(s_0.variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we define a wrapper which combines this with $S$, which uses a utility function offered by AutoRA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state.delta import State, wrap_to_use_state\n", + "\n", + "\n", + "def random_pool_state(\n", + " s: State,\n", + " num_samples: int = 5,\n", + " random_state: Optional[int] = None,\n", + " **kwargs,\n", + ") -> State:\n", + "\n", + " return wrap_to_use_state(random_pool_delta)(\n", + " s, num_samples=num_samples, random_state=random_state, **kwargs\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run the function directly on $S$, returning a new state with our conditions included:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 0.434308\n", + "1 -6.668831\n", + "2 -4.216564\n", + "3 -1.528779\n", + "4 -3.591671, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_pool_state(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The question is: what naming convention should these functions have, given that usually the `_state` version will be\n", + "used and that every contribution will need to follow the same convention? There might be multiple poolers and\n", + "samplers offered by an AutoRA module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 1: simple function names with conventional suffixes (or prefixes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 6.183674\n", + "1 2.837212\n", + "2 -3.392042\n", + "3 1.720430\n", + "4 -9.221208, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.random_ import random_pool_state\n", + "random_pool_state(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... or ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 -2.414414\n", + "1 0.188652\n", + "2 -2.501508\n", + "3 -0.528629\n", + "4 -5.542678, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.random_ import random_pool_s\n", + "random_pool_s(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 2: one state function per module\n", + "\n", + "This option is inspired by the scikit-learn `Regressor().fit(X, y)` syntax, but note that `pooler` in this case is a\n", + "module rather than a traditional object, and it shouldn't have any internal state which affects the fitting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 0.445504\n", + "1 0.106361\n", + "2 5.592574\n", + "3 6.849602\n", + "4 7.662081, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import autora.experimentalist.random_.pool as pooler\n", + "pooler.on_state(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 3: `run` functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 2.373031\n", + "1 9.041770\n", + "2 -8.355166\n", + "3 0.447124\n", + "4 7.788639, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pooler.run(s_0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 -6.426886\n", + "1 6.937435\n", + "2 6.747884\n", + "3 3.964029\n", + "4 -4.822142, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pooler.run_on_state(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 4...n: your suggestion?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/autora/experimentalist/random_/__init__.py b/src/autora/experimentalist/random_/__init__.py index 69d6f5a4..795207ed 100644 --- a/src/autora/experimentalist/random_/__init__.py +++ b/src/autora/experimentalist/random_/__init__.py @@ -1 +1,8 @@ """Tools to make randomly sampled experimental conditions.""" + +# Option 1 +from .pool import _state as random_pool_s +from .pool import _state as random_pool_state +from .pool import _state as random_pool_t +from .pool import _state as random_pool_task +from .pool import _state as random_pool_wf diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_/pool.py similarity index 75% rename from src/autora/experimentalist/random_.py rename to src/autora/experimentalist/random_/pool.py index 795b4e7f..2dd5d4ea 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_/pool.py @@ -1,14 +1,14 @@ -"""Tools to make randomly sampled experimental conditions.""" -from typing import Optional, Union +from dataclasses import dataclass, field +from typing import Optional import numpy as np import pandas as pd from autora.state.delta import Result, State, wrap_to_use_state -from autora.variable import ValueType, VariableCollection +from autora.variable import ValueType, Variable, VariableCollection -def random_pool_on_state( +def _state( s: State, num_samples: int = 5, random_state: Optional[int] = None, @@ -47,7 +47,7 @@ def random_pool_on_state( ... ])) ... we get some of those values back when running the experimentalist: - >>> random_pool_on_state(s, random_state=1).conditions + >>> _state(s, random_state=1).conditions x 0 4 1 5 @@ -62,7 +62,7 @@ def random_pool_on_state( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> random_pool_on_state(t, random_state=1).conditions + >>> _state(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -73,7 +73,7 @@ def random_pool_on_state( The allowed_values or value_range must be specified: - >>> random_pool_on_state( + >>> _state( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -85,7 +85,7 @@ def random_pool_on_state( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_pool_on_state(t, num_samples=10, replace=True, random_state=1).conditions + >>> _state(t, num_samples=10, replace=True, random_state=1).conditions x1 x2 0 2 434 1 3 212 @@ -99,7 +99,7 @@ def random_pool_on_state( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool_on_state(S( + >>> _state(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -116,7 +116,7 @@ def random_pool_on_state( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> random_pool_on_state(u, random_state=1).conditions + >>> _state(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 @@ -124,17 +124,44 @@ def random_pool_on_state( 3 9.0 3 29.0 4 -9.4 3 22.0 """ - return wrap_to_use_state(random_pool)( + return wrap_to_use_state(_result)( s, num_samples=num_samples, random_state=random_state, replace=replace, **kwargs ) -def random_pool( +def _result( + variables: VariableCollection, + num_samples: int, + random_state: Optional[int], + replace: bool, +): + """ + Create a sequence of conditions randomly sampled from independent variables. + + Args: + variables: the description of all the variables in the AER experiment. + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + """ + conditions = _base( + variables=variables, + num_samples=num_samples, + random_state=random_state, + replace=replace, + ) + return Result(conditions=conditions) + + +def _base( variables: VariableCollection, num_samples: int = 5, random_state: Optional[int] = None, replace: bool = True, -) -> Result: +) -> pd.DataFrame: """ Create a sequence of conditions randomly sampled from independent variables. @@ -144,7 +171,7 @@ def random_pool( random_state: the seed value for the random number generator replace: if True, allow repeated values - Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + Returns: the generated conditions as a dataframe Examples: >>> from autora.state.delta import State @@ -155,10 +182,10 @@ def random_pool( With one independent variable "x", and some allowed_values we get some of those values back when running the experimentalist: - >>> random_pool( + >>> _base( ... VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=range(10)) - ... ]), random_state=1)["conditions"] + ... ]), random_state=1) x 0 4 1 5 @@ -169,10 +196,10 @@ def random_pool( ... with one independent variable "x", and a value_range, we get a sample of the range back when running the experimentalist: - >>> random_pool( + >>> _base( ... VariableCollection(independent_variables=[ ... Variable(name="x", value_range=(-5, 5)) - ... ]), random_state=1)["conditions"] + ... ]), random_state=1) x 0 0.118216 1 4.504637 @@ -183,16 +210,16 @@ def random_pool( The allowed_values or value_range must be specified: - >>> random_pool(VariableCollection(independent_variables=[Variable(name="x")])) + >>> _base(VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... ValueError: allowed_values or [value_range and type==REAL] needs to be set... With two independent variables, we get independent samples on both axes: - >>> random_pool(VariableCollection(independent_variables=[ + >>> _base(VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), - ... ]), num_samples=10, replace=True, random_state=1)["conditions"] + ... ]), num_samples=10, replace=True, random_state=1) x1 x2 0 2 434 1 3 212 @@ -206,7 +233,7 @@ def random_pool( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool( + >>> _base( ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -218,12 +245,12 @@ def random_pool( We can specify arrays of allowed values: - >>> random_pool( + >>> _base( ... VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ]), random_state=1)["conditions"] + ... ]), random_state=1) x y z 0 -0.6 3 29.0 1 0.2 4 24.0 @@ -250,65 +277,15 @@ def random_pool( "%s" % (iv) ) - conditions = pd.DataFrame(raw_conditions) - return Result(conditions=conditions) - - -def random_sample_on_state(s: State, **kwargs) -> State: - """ - Take a random sample from some input conditions. - - Args: - s: a State object with a `variables` field. + return pd.DataFrame(raw_conditions) - Returns: a State object updated with the new conditions - Examples: - >>> from autora.state.bundled import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": range(100, 200)})) - >>> random_sample_on_state(s, random_state=1, replace=False, num_samples=3).conditions - x - 80 180 - 84 184 - 33 133 +# Option 2: +on_state = _state +to_result = _result +raw = _base - """ - return wrap_to_use_state(random_sample)(s, **kwargs) - -def random_sample( - conditions: Union[pd.DataFrame, np.ndarray, np.recarray], - num_samples: int = 1, - random_state: Optional[int] = None, - replace: bool = False, -) -> Result: - """ - Take a random sample from some input conditions. - - Args: - conditions: the conditions to sample from - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a Result object with a field `conditions` containing a DataFrame of the sampled - conditions - - Examples: - From a pd.DataFrame: - >>> import pandas as pd - >>> random_sample( - ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180)["conditions"] - x - 67 167 - 71 171 - 64 164 - 63 163 - 96 196 - - """ - return Result( - conditions=pd.DataFrame.sample( - conditions, random_state=random_state, n=num_samples, replace=replace - ) - ) +# Option 3: +run = _state +run_on_state = _state From 942bfaa6c3a279a2ab17583b8921f059fdec7bb3 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 24 Jul 2023 09:51:31 -0400 Subject: [PATCH 069/121] exploration: add more function naming options examples --- .../Function Naming Convention Options.ipynb | 516 ++++++++++++++++-- src/autora/experimentalist/grid_.py | 19 + .../experimentalist/random_/__init__.py | 5 + src/autora/experimentalist/random_/sample.py | 11 + 4 files changed, 500 insertions(+), 51 deletions(-) diff --git a/docs/cycle/Function Naming Convention Options.ipynb b/docs/cycle/Function Naming Convention Options.ipynb index 61b6b780..d3eb1889 100644 --- a/docs/cycle/Function Naming Convention Options.ipynb +++ b/docs/cycle/Function Naming Convention Options.ipynb @@ -11,8 +11,62 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/autoresearch/autora-core.git@feat/function-naming-options\r\n", + " Cloning https://github.com/autoresearch/autora-core.git (to revision feat/function-naming-options) to /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-req-build-sl7lbdwl\r\n", + " Running command git clone --filter=blob:none --quiet https://github.com/autoresearch/autora-core.git /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-req-build-sl7lbdwl\r\n", + " Running command git checkout -b feat/function-naming-options --track origin/feat/function-naming-options\r\n", + " Switched to a new branch 'feat/function-naming-options'\r\n", + " branch 'feat/function-naming-options' set up to track 'origin/feat/function-naming-options'.\r\n", + " Resolved https://github.com/autoresearch/autora-core.git to commit 6035eeac4982b69d96ec1180ca6ab5831b33966b\r\n", + " Installing build dependencies ... \u001b[?25ldone\r\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\r\n", + "\u001b[?25h Installing backend dependencies ... \u001b[?25ldone\r\n", + "\u001b[?25h 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autora-core\r\n", + " Building wheel for autora-core (pyproject.toml) ... \u001b[?25ldone\r\n", + "\u001b[?25h Created wheel for autora-core: filename=autora_core-3.3.1.dev114+g6035eea-py3-none-any.whl size=37926 sha256=959da0a15ad08f0e0f0e78aeb96863d3e004afff8e064d3ec0d6d2826f1dbd8e\r\n", + " Stored in directory: /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-ephem-wheel-cache-jyoqb5ma/wheels/bb/0a/d2/56e886daa68a6995d882ea047057679c671473e74b90f90b28\r\n", + "Successfully built autora-core\r\n", + "Installing collected packages: autora-core\r\n", + " Attempting uninstall: autora-core\r\n", + " Found existing installation: autora-core 3.1.1.dev2+gb5c9461\r\n", + " Not uninstalling autora-core at /Users/jholla10/Developer/autora-core/src, outside environment /Users/jholla10/Developer/autora-core/.venv\r\n", + " Can't uninstall 'autora-core'. No files were found to uninstall.\r\n", + "Successfully installed autora-core-3.3.1.dev114+g6035eea\r\n", + "\r\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\r\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" + ] + } + ], + "source": [ + "!pip install \"git+https://github.com/autoresearch/autora-core.git@feat/function-naming-options\"" + ] }, { "cell_type": "markdown", @@ -136,7 +190,70 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x\n", + "0 -2.291109\n", + "1 -6.603135\n", + "2 -8.877414\n", + "3 3.108730\n", + "4 8.169106" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_pool(s_0.variables)" ] @@ -202,11 +319,11 @@ "data": { "text/plain": [ "{'conditions': x\n", - "0 -6.168705\n", - "1 1.143822\n", - "2 4.432569\n", - "3 9.079492\n", - "4 -8.734145}" + "0 -1.235222\n", + "1 -6.908781\n", + "2 -2.617692\n", + "3 -4.960670\n", + "4 0.743513}" ] }, "execution_count": null, @@ -262,11 +379,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.434308\n", - "1 -6.668831\n", - "2 -4.216564\n", - "3 -1.528779\n", - "4 -3.591671, experiment_data=None, models=[])" + "0 5.510190\n", + "1 -9.734381\n", + "2 -9.247260\n", + "3 -3.880819\n", + "4 -7.846659, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -291,7 +408,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Option 1: simple function names with conventional suffixes (or prefixes)" + "## The problem and the options in the simplest case\n", + "\n", + "### Option 1: simple function names with conventional suffixes (or prefixes)" ] }, { @@ -303,11 +422,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 6.183674\n", - "1 2.837212\n", - "2 -3.392042\n", - "3 1.720430\n", - "4 -9.221208, experiment_data=None, models=[])" + "0 1.802195\n", + "1 -6.681581\n", + "2 -5.298816\n", + "3 -8.727805\n", + "4 9.767906, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -336,11 +455,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -2.414414\n", - "1 0.188652\n", - "2 -2.501508\n", - "3 -0.528629\n", - "4 -5.542678, experiment_data=None, models=[])" + "0 -0.084047\n", + "1 6.874185\n", + "2 -6.176624\n", + "3 -5.670282\n", + "4 -6.865156, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -353,16 +472,11 @@ "random_pool_s(s_0)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Option 2: one state function per module\n", + "### Option 2: one state function per module\n", "\n", "This option is inspired by the scikit-learn `Regressor().fit(X, y)` syntax, but note that `pooler` in this case is a\n", "module rather than a traditional object, and it shouldn't have any internal state which affects the fitting." @@ -377,11 +491,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.445504\n", - "1 0.106361\n", - "2 5.592574\n", - "3 6.849602\n", - "4 7.662081, experiment_data=None, models=[])" + "0 1.929206\n", + "1 5.592331\n", + "2 6.558775\n", + "3 -8.900612\n", + "4 6.128046, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -390,15 +504,15 @@ } ], "source": [ - "import autora.experimentalist.random_.pool as pooler\n", - "pooler.on_state(s_0)" + "import autora.experimentalist.random_.pool as pool\n", + "pool.on_state(s_0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Option 3: `run` functions" + "### Option 3: `run` functions" ] }, { @@ -410,11 +524,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 2.373031\n", - "1 9.041770\n", - "2 -8.355166\n", - "3 0.447124\n", - "4 7.788639, experiment_data=None, models=[])" + "0 -0.114128\n", + "1 2.292411\n", + "2 -5.499759\n", + "3 7.032079\n", + "4 -8.296732, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -423,7 +537,7 @@ } ], "source": [ - "pooler.run(s_0)" + "pool.run(s_0)" ] }, { @@ -435,11 +549,229 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -6.426886\n", - "1 6.937435\n", - "2 6.747884\n", - "3 3.964029\n", - "4 -4.822142, experiment_data=None, models=[])" + "0 -5.583620\n", + "1 -1.866155\n", + "2 -5.859761\n", + "3 0.061423\n", + "4 -1.798987, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pool.run_on_state(s_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More examples: using a grid and sample\n", + "\n", + "We can also construct a processing pipeline using multiple functions. In the following example, we have a state which\n", + " has a grid of allowable variable values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_0 = StandardState(\n", + " variables=VariableCollection(independent_variables=[\n", + " Variable(name=\"x\", allowed_values=np.linspace(-10, 10, 101)),\n", + " Variable(name=\"y\", allowed_values=[3, 4]),\n", + " Variable(name=\"z\", allowed_values=np.linspace(20, 30, 11))]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we generate the full list of possible conditions using the `grid` functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "0 -10.0 3 20.0\n", + "1 -10.0 3 21.0\n", + "2 -10.0 3 22.0\n", + "3 -10.0 3 23.0\n", + "4 -10.0 3 24.0\n", + "... ... .. ...\n", + "2217 10.0 4 26.0\n", + "2218 10.0 4 27.0\n", + "2219 10.0 4 28.0\n", + "2220 10.0 4 29.0\n", + "2221 10.0 4 30.0\n", + "\n", + "[2222 rows x 3 columns], experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.grid_ import grid_pool_state\n", + "grid_pool_state(s_0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the same options as before – shorter suffixes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "0 -10.0 3 20.0\n", + "1 -10.0 3 21.0\n", + "2 -10.0 3 22.0\n", + "3 -10.0 3 23.0\n", + "4 -10.0 3 24.0\n", + "... ... .. ...\n", + "2217 10.0 4 26.0\n", + "2218 10.0 4 27.0\n", + "2219 10.0 4 28.0\n", + "2220 10.0 4 29.0\n", + "2221 10.0 4 30.0\n", + "\n", + "[2222 rows x 3 columns], experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.grid_ import grid_pool_s\n", + "grid_pool_s(s_0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "0 -10.0 3 20.0\n", + "1 -10.0 3 21.0\n", + "2 -10.0 3 22.0\n", + "3 -10.0 3 23.0\n", + "4 -10.0 3 24.0\n", + "... ... .. ...\n", + "2217 10.0 4 26.0\n", + "2218 10.0 4 27.0\n", + "2219 10.0 4 28.0\n", + "2220 10.0 4 29.0\n", + "2221 10.0 4 30.0\n", + "\n", + "[2222 rows x 3 columns], experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.grid_ import grid_pool_wf\n", + "grid_pool_wf(s_0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "0 -10.0 3 20.0\n", + "1 -10.0 3 21.0\n", + "2 -10.0 3 22.0\n", + "3 -10.0 3 23.0\n", + "4 -10.0 3 24.0\n", + "... ... .. ...\n", + "2217 10.0 4 26.0\n", + "2218 10.0 4 27.0\n", + "2219 10.0 4 28.0\n", + "2220 10.0 4 29.0\n", + "2221 10.0 4 30.0\n", + "\n", + "[2222 rows x 3 columns], experiment_data=None, models=[])" ] }, "execution_count": null, @@ -448,14 +780,96 @@ } ], "source": [ - "pooler.run_on_state(s_0)" + "import autora.experimentalist.grid_ as grid\n", + "grid.on_state(s_0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "0 -10.0 3 20.0\n", + "1 -10.0 3 21.0\n", + "2 -10.0 3 22.0\n", + "3 -10.0 3 23.0\n", + "4 -10.0 3 24.0\n", + "... ... .. ...\n", + "2217 10.0 4 26.0\n", + "2218 10.0 4 27.0\n", + "2219 10.0 4 28.0\n", + "2220 10.0 4 29.0\n", + "2221 10.0 4 30.0\n", + "\n", + "[2222 rows x 3 columns], experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.run(s_0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Option 4...n: your suggestion?" + "However, we can also join this with some random sampling functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", + " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", + " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", + " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", + " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", + " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", + " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", + " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", + " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", + " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", + " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", + "1659 5.0 3 29.0\n", + "78 -9.4 4 21.0\n", + "912 -1.8 3 30.0\n", + "908 -1.8 3 26.0\n", + "856 -2.4 4 29.0, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.experimentalist.random_ import random_sample_state\n", + "random_sample_state(grid_pool_state(s_0), num_samples=5)\n" ] }, { diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 45185530..75bb9333 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -215,3 +215,22 @@ def _base(variables: VariableCollection) -> pd.DataFrame: conditions = pd.DataFrame(pool, columns=l_iv_names) return conditions + + +# Option 1: + +grid_pool_s = _state +grid_pool_state = _state +grid_pool_t = _state +grid_pool_task = _state +grid_pool_wf = _state + +# Option 2: +on_state = _state +to_result = _result +raw = _base + + +# Option 3: +run = _state +run_on_state = _state diff --git a/src/autora/experimentalist/random_/__init__.py b/src/autora/experimentalist/random_/__init__.py index 795207ed..3f533eb4 100644 --- a/src/autora/experimentalist/random_/__init__.py +++ b/src/autora/experimentalist/random_/__init__.py @@ -6,3 +6,8 @@ from .pool import _state as random_pool_t from .pool import _state as random_pool_task from .pool import _state as random_pool_wf +from .sample import _state as random_sample_s +from .sample import _state as random_sample_state +from .sample import _state as random_sample_t +from .sample import _state as random_sample_task +from .sample import _state as random_sample_wf diff --git a/src/autora/experimentalist/random_/sample.py b/src/autora/experimentalist/random_/sample.py index 854d05a9..21f02b5a 100644 --- a/src/autora/experimentalist/random_/sample.py +++ b/src/autora/experimentalist/random_/sample.py @@ -103,3 +103,14 @@ def _base( return pd.DataFrame.sample( conditions, random_state=random_state, n=num_samples, replace=replace ) + + +# Option 2: +on_state = _state +to_result = _result +raw = _base + + +# Option 3: +run = _state +run_on_state = _state From 1f72cc165bf489119534866720138bed1a57b5d8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 24 Jul 2023 09:53:23 -0400 Subject: [PATCH 070/121] docs: clear output from notebook --- .../Function Naming Convention Options.ipynb | 54 +------------------ 1 file changed, 1 insertion(+), 53 deletions(-) diff --git a/docs/cycle/Function Naming Convention Options.ipynb b/docs/cycle/Function Naming Convention Options.ipynb index d3eb1889..ff5e0536 100644 --- a/docs/cycle/Function Naming Convention Options.ipynb +++ b/docs/cycle/Function Naming Convention Options.ipynb @@ -11,59 +11,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/autoresearch/autora-core.git@feat/function-naming-options\r\n", - " Cloning https://github.com/autoresearch/autora-core.git (to revision feat/function-naming-options) to /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-req-build-sl7lbdwl\r\n", - " Running command git clone --filter=blob:none --quiet https://github.com/autoresearch/autora-core.git /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-req-build-sl7lbdwl\r\n", - " Running command git checkout -b feat/function-naming-options --track origin/feat/function-naming-options\r\n", - " Switched to a new branch 'feat/function-naming-options'\r\n", - " branch 'feat/function-naming-options' set up to track 'origin/feat/function-naming-options'.\r\n", - " Resolved https://github.com/autoresearch/autora-core.git to commit 6035eeac4982b69d96ec1180ca6ab5831b33966b\r\n", - " Installing build dependencies ... \u001b[?25ldone\r\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\r\n", - "\u001b[?25h Installing backend dependencies ... \u001b[?25ldone\r\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\r\n", - "\u001b[?25hRequirement already satisfied: pandas in /Users/jholla10/Developer/autora-core/.venv/lib/python3.8/site-packages (from autora-core==3.3.1.dev114+g6035eea) (2.0.2)\r\n", - "Requirement already satisfied: matplotlib in /Users/jholla10/Developer/autora-core/.venv/lib/python3.8/site-packages (from 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/Users/jholla10/Developer/autora-core/.venv/lib/python3.8/site-packages (from scikit-learn->autora-core==3.3.1.dev114+g6035eea) (1.10.1)\r\n", - "Requirement already satisfied: zipp>=3.1.0 in /Users/jholla10/Developer/autora-core/.venv/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib->autora-core==3.3.1.dev114+g6035eea) (3.15.0)\r\n", - "Requirement already satisfied: six>=1.5 in /Users/jholla10/Developer/autora-core/.venv/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib->autora-core==3.3.1.dev114+g6035eea) (1.16.0)\r\n", - "Building wheels for collected packages: autora-core\r\n", - " Building wheel for autora-core (pyproject.toml) ... \u001b[?25ldone\r\n", - "\u001b[?25h Created wheel for autora-core: filename=autora_core-3.3.1.dev114+g6035eea-py3-none-any.whl size=37926 sha256=959da0a15ad08f0e0f0e78aeb96863d3e004afff8e064d3ec0d6d2826f1dbd8e\r\n", - " Stored in directory: /private/var/folders/n5/6b48sz2j3yldl4mnglvsr6mh0000gq/T/pip-ephem-wheel-cache-jyoqb5ma/wheels/bb/0a/d2/56e886daa68a6995d882ea047057679c671473e74b90f90b28\r\n", - "Successfully built autora-core\r\n", - "Installing collected packages: autora-core\r\n", - " Attempting uninstall: autora-core\r\n", - " Found existing installation: autora-core 3.1.1.dev2+gb5c9461\r\n", - " Not uninstalling autora-core at /Users/jholla10/Developer/autora-core/src, outside environment /Users/jholla10/Developer/autora-core/.venv\r\n", - " Can't uninstall 'autora-core'. No files were found to uninstall.\r\n", - "Successfully installed autora-core-3.3.1.dev114+g6035eea\r\n", - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "!pip install \"git+https://github.com/autoresearch/autora-core.git@feat/function-naming-options\"" ] From bdb7aa831d04e80a438f302c495a4d28452bc585 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 26 Jul 2023 14:06:53 -0400 Subject: [PATCH 071/121] refactor: update more examples --- src/autora/experimentalist/grid_.py | 7 +++++++ src/autora/experimentalist/random_/pool.py | 7 +++++-- src/autora/experimentalist/random_/sample.py | 3 +++ 3 files changed, 15 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 75bb9333..915bc907 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -234,3 +234,10 @@ def _base(variables: VariableCollection) -> pd.DataFrame: # Option 3: run = _state run_on_state = _state + + +# Option 4: suggestion SMusslick +pool = _state + +# Option 5: user has to wrap everything by convention +grid_pool = _base diff --git a/src/autora/experimentalist/random_/pool.py b/src/autora/experimentalist/random_/pool.py index 2dd5d4ea..de04ac46 100644 --- a/src/autora/experimentalist/random_/pool.py +++ b/src/autora/experimentalist/random_/pool.py @@ -1,11 +1,10 @@ -from dataclasses import dataclass, field from typing import Optional import numpy as np import pandas as pd from autora.state.delta import Result, State, wrap_to_use_state -from autora.variable import ValueType, Variable, VariableCollection +from autora.variable import ValueType, VariableCollection def _state( @@ -289,3 +288,7 @@ def _base( # Option 3: run = _state run_on_state = _state + +# Option 4: suggestion SMusslick +pool = _state # shorter alias +random_pool = _state # longer alias diff --git a/src/autora/experimentalist/random_/sample.py b/src/autora/experimentalist/random_/sample.py index 21f02b5a..b71b6464 100644 --- a/src/autora/experimentalist/random_/sample.py +++ b/src/autora/experimentalist/random_/sample.py @@ -114,3 +114,6 @@ def _base( # Option 3: run = _state run_on_state = _state + +# Option 4: suggestion SMusslick +sample = _state From ec53bad61214eb4610323e3842ee3d32f87628fc Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 26 Jul 2023 16:00:25 -0400 Subject: [PATCH 072/121] refactor: add basic wrapper function to return deltas --- src/autora/state/delta.py | 54 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 85a62c23..c2c174ba 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -616,3 +616,57 @@ def _f(state_: S, /, **kwargs) -> S: return new_state return _f + + +def _map_outputs_to_delta(*output: str): + """ + Decorator maker to wrap outputs from a function as Deltas. + + Examples: + >>> @_map_outputs_to_delta("conditions") + ... def add_five(x): + ... xprime = [xi + 5 for xi in x] + ... return xprime + + >>> add_five([1, 2, 3]) + {'conditions': [6, 7, 8]} + + >>> @_map_outputs_to_delta("c") + ... def add_six(conditions): + ... new_conditions = [c + 5 for c in conditions] + ... return new_conditions + + >>> add_six([1, 2, 3]) + {'c': [6, 7, 8]} + + >>> @_map_outputs_to_delta("+1", "-1") + ... def plus_minus_1(x): + ... a = [xi + 1 for xi in x] + ... b = [xi - 1 for xi in x] + ... return a, b + + >>> plus_minus_1([1, 2, 3]) + {'+1': [2, 3, 4], '-1': [0, 1, 2]} + """ + + def _wrapper(f): + + if len(output) == 1: + + def _f(*args, **kwargs): + result = f(*args, **kwargs) + delta = Delta(**{output[0]: result}) + return delta + + else: + + def _f(*args, **kwargs): + result = f(*args, **kwargs) + assert isinstance(result, tuple) + assert len(output) == len(result) + delta = Delta(**dict(zip(output, result))) + return delta + + return _f + + return _wrapper From 881a09d842dce18647da2f0a13ca1807d4cd08c9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 26 Jul 2023 16:35:25 -0400 Subject: [PATCH 073/121] test: update output wrapping function with more doctests --- src/autora/state/delta.py | 62 ++++++++++++++++++++++++++++++++++----- 1 file changed, 55 insertions(+), 7 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index c2c174ba..48dc5b25 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -625,16 +625,14 @@ def _map_outputs_to_delta(*output: str): Examples: >>> @_map_outputs_to_delta("conditions") ... def add_five(x): - ... xprime = [xi + 5 for xi in x] - ... return xprime + ... return [xi + 5 for xi in x] >>> add_five([1, 2, 3]) {'conditions': [6, 7, 8]} >>> @_map_outputs_to_delta("c") ... def add_six(conditions): - ... new_conditions = [c + 5 for c in conditions] - ... return new_conditions + ... return [c + 5 for c in conditions] >>> add_six([1, 2, 3]) {'c': [6, 7, 8]} @@ -647,11 +645,52 @@ def _map_outputs_to_delta(*output: str): >>> plus_minus_1([1, 2, 3]) {'+1': [2, 3, 4], '-1': [0, 1, 2]} + + + If the wrong number of values are specified for the return, then there might be errors. + If multiple outputs are expected, but only a single output is returned, we get a warning: + >>> @_map_outputs_to_delta("1", "2") + ... def returns_single_result_when_more_expected(): + ... return "a" + >>> returns_single_result_when_more_expected() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError: function `` + has to return multiple values to match `('1', '2')`. Got `a` instead. + + If multiple outputs are expected, but the wrong number are returned, we get a warning: + >>> @_map_outputs_to_delta("1", "2", "3") + ... def returns_wrong_number_of_results(): + ... return "a", "b" + >>> returns_wrong_number_of_results() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError: function `` + has to return exactly `3` values to match `('1', '2', '3')`. Got `('a', 'b')` instead. + + However, if a single output is expected, and multiple are returned, these are treated as + a single object and no error occurs: + >>> @_map_outputs_to_delta("foo") + ... def returns_a_tuple(): + ... return "a", "b", "c" + >>> returns_a_tuple() + {'foo': ('a', 'b', 'c')} + + >>> @_map_outputs_to_delta() + ... def decorator_missing_arguments(): + ... return "a", "b", "c" + >>> decorator_missing_arguments() + Traceback (most recent call last): + ... + ValueError: `output` names must be specified. """ def _wrapper(f): - if len(output) == 1: + if len(output) == 0: + raise ValueError("`output` names must be specified.") + + elif len(output) == 1: def _f(*args, **kwargs): result = f(*args, **kwargs) @@ -662,8 +701,17 @@ def _f(*args, **kwargs): def _f(*args, **kwargs): result = f(*args, **kwargs) - assert isinstance(result, tuple) - assert len(output) == len(result) + assert isinstance(result, tuple), ( + "function `%s` has to return multiple values " + "to match `%s`. Got `%s` instead." % (f, output, result) + ) + assert len(output) == len(result), ( + "function `%s` has to return " + "exactly `%s` values " + "to match `%s`. " + "Got `%s` instead." + "" % (f, len(output), output, result) + ) delta = Delta(**dict(zip(output, result))) return delta From 4a84c054bef14fe9e769f531e7bed8c8f15cb0a1 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 26 Jul 2023 17:25:14 -0400 Subject: [PATCH 074/121] =?UTF-8?q?refactor:=20add=20more=20ways=20to=20ac?= =?UTF-8?q?cess=20the=20`on=5Fstate`=20wrapper=20=E2=80=93=20as=20a=20deco?= =?UTF-8?q?rator=20or=20a=20decorator=20factory=20directly?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...Introduction to Functions and States.ipynb | 489 +++++------------- src/autora/state/delta.py | 109 +++- src/autora/state/wrapper.py | 10 +- 3 files changed, 214 insertions(+), 394 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index 58294cad..ff66ab00 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -73,19 +73,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Specify the experimentalist. Use a standard function `random_pool_executor`.\n", + "Specify the experimentalist. Use a standard function `random_pool`.\n", "This gets 5 independent random samples (by default, configurable using an argument)\n", - "from the value_range of the independent variables, and returns them in a DataFrame." + "from the value_range of the independent variables, and returns them in a DataFrame.\n", + "To make this work as a function on the State objects, we wrap it in the `to_state_function`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 6.879258\n", + "1 -1.231564\n", + "2 0.149769\n", + "3 0.454804\n", + "4 -0.603432, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from autora.experimentalist.random_ import random_pool\n", - "experimentalist = random_pool" + "from autora.state.delta import on_state\n", + "\n", + "experimentalist = on_state(function=random_pool, output=[\"conditions\"])\n", + "s_1 = experimentalist(s_0)\n", + "s_1" ] }, { @@ -100,21 +121,91 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AssertionError", + "evalue": "function `` has to return multiple values to match `('e', 'x', 'p', 'e', 'r', 'i', 'm', 'e', 'n', 't', '_', 'd', 'a', 't', 'a')`. Got ` x y\n0 6.879258 30.075715\n1 -1.231564 -4.606542\n2 0.149769 2.340361\n3 0.454804 4.309657\n4 -0.603432 -0.251267` instead.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m observations\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Which does the following:\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mexperiment_runner\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms_1\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Developer/autora-core/src/autora/state/delta.py:613\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[0;34m(state_, **kwargs)\u001b[0m\n\u001b[1;32m 611\u001b[0m arguments_from_state \u001b[38;5;241m=\u001b[39m {k: \u001b[38;5;28mgetattr\u001b[39m(state_, k) \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m from_state}\n\u001b[1;32m 612\u001b[0m arguments \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(arguments_from_state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 613\u001b[0m delta \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 614\u001b[0m new_state \u001b[38;5;241m=\u001b[39m state_ \u001b[38;5;241m+\u001b[39m delta\n\u001b[1;32m 615\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", + "File \u001b[0;32m~/Developer/autora-core/src/autora/state/delta.py:706\u001b[0m, in \u001b[0;36moutputs_to_delta..decorator..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 705\u001b[0m result \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, \u001b[38;5;28mtuple\u001b[39m), (\n\u001b[1;32m 707\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` has to return multiple values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 708\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto match `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m`. Got `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (f, output, result)\n\u001b[1;32m 709\u001b[0m )\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(result), (\n\u001b[1;32m 711\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` has to return \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexactly `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (f, \u001b[38;5;28mlen\u001b[39m(output), output, result)\n\u001b[1;32m 716\u001b[0m )\n\u001b[1;32m 717\u001b[0m delta \u001b[38;5;241m=\u001b[39m Delta(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(output, result)))\n", + "\u001b[0;31mAssertionError\u001b[0m: function `` has to return multiple values to match `('e', 'x', 'p', 'e', 'r', 'i', 'm', 'e', 'n', 't', '_', 'd', 'a', 't', 'a')`. Got ` x y\n0 6.879258 30.075715\n1 -1.231564 -4.606542\n2 0.149769 2.340361\n3 0.454804 4.309657\n4 -0.603432 -0.251267` instead." + ] + } + ], "source": [ + "from autora.state.delta import on_state\n", "import numpy as np\n", "import pandas as pd\n", - "from autora.state.delta import Delta, wrap_to_use_state\n", "\n", - "rng = np.random.default_rng(180)\n", "\n", - "@wrap_to_use_state\n", - "def experiment_runner(conditions: pd.DataFrame, c=[2, 4]):\n", + "@on_state(output=\"experiment_data\")\n", + "def experiment_runner(conditions: pd.DataFrame, c=[2, 4], random_state = None):\n", + " rng = np.random.default_rng(random_state)\n", + " x = conditions[\"x\"]\n", + " noise = rng.normal(0, 1, len(x))\n", + " y = c[0] + (c[1] * x) + noise\n", + " observations = conditions.assign(y = y)\n", + " return observations\n", + "\n", + "# Which does the following:\n", + "experiment_runner(s_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A completely analogous definition would be:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state.delta import outputs_to_delta\n", + "\n", + "\n", + "@inputs_from_state\n", + "@outputs_to_delta(\"experiment_data\")\n", + "def experiment_runner_alt_1(conditions: pd.DataFrame, c=[2, 4]):\n", + " x = conditions[\"x\"]\n", + " noise = rng.normal(0, 1, len(x))\n", + " y = c[0] + (c[1] * x) + noise\n", + " xy = conditions.assign(y = y)\n", + " return xy\n", + "\n", + "# Which does the following:\n", + "experiment_runner_alt_1(s_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or alternatively:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def experiment_runner_alt_2_core(conditions: pd.DataFrame, c=[2, 4]):\n", " x = conditions[\"x\"]\n", " noise = rng.normal(0, 1, len(x))\n", " y = c[0] + (c[1] * x) + noise\n", - " experiment_data = conditions.assign(y = y)\n", - " return Delta(experiment_data=experiment_data)" + " xy = conditions.assign(y = y)\n", + " return xy\n", + "\n", + "experiment_runner_alt_2 = on_state(experiment_runner_alt_2_core, output=[\"experiment_data\"])\n", + "experiment_runner_alt_2(s_1)" ] }, { @@ -136,6 +227,24 @@ "theorist = state_fn_from_estimator(LinearRegression(fit_intercept=True))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theorist(experiment_runner(experimentalist(s_0)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -167,346 +276,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " x y\n", - "0 3.078100 14.746353\n", - "1 6.639407 27.825164\n", - "2 1.844467 8.329861\n", - "3 -1.349516 -2.523405\n", - "4 8.811023 36.546486\n", - "5 7.862659 32.993548\n", - "6 -8.139137 -30.080151\n", - "7 1.594910 10.456698\n", - "8 2.390949 10.131948\n", - "9 -4.160698 -14.069210\n", - "10 -1.913405 -3.782278\n", - "11 -4.096757 -15.535237\n", - "12 -6.323442 -22.503085\n", - "13 -7.184761 -26.330581\n", - "14 7.346259 32.441359\n", - "15 3.828251 16.108990\n", - "16 -3.618889 -13.579591\n", - "17 8.997905 37.072474\n", - "18 -9.708017 -34.173223\n", - "19 -4.900256 -18.224959\n", - "20 7.823543 32.689474\n", - "21 -3.686450 -13.804035\n", - "22 -1.823864 -5.187276\n", - "23 -1.525316 -3.245281\n", - "24 -6.245972 -21.550399\n", - "25 -8.256509 -32.154554\n", - "26 4.540280 22.197273\n", - "27 3.440114 17.863344\n", - "28 -2.067260 -6.435788\n", - "29 -5.254835 -18.150877\n", - "30 5.101388 22.569768\n", - "31 4.840714 21.961039\n", - "32 -9.833613 -36.882659\n", - "33 -6.525488 -23.283997\n", - "34 -5.134923 -18.288871\n", - "35 -7.964319 -28.338543\n", - "36 4.276729 18.134525\n", - "37 -0.102663 2.934492\n", - "38 6.689145 29.816722\n", - "39 1.865748 9.435187\n", - "40 8.380522 34.825511\n", - "41 -5.675485 -22.078524\n", - "42 7.275761 29.902523\n", - "43 5.581365 24.287050\n", - "44 8.144878 34.023720\n", - "45 -2.320579 -6.923142\n", - "46 -1.342632 -2.827881\n", - "47 -0.429666 -1.300576\n", - "48 8.596749 35.238883\n", - "49 -1.916867 -7.590488" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s_.experiment_data" ] @@ -522,25 +292,10 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2.08507109] [[3.9511443]]\n" - ] - } - ], + "outputs": [], "source": [ "print(s_.model.intercept_, s_.model.coef_)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 48dc5b25..5f17d11a 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -7,7 +7,7 @@ from collections import UserDict from dataclasses import dataclass, fields, replace from functools import singledispatch, wraps -from typing import Generic, List, TypeVar +from typing import Callable, Generic, List, Optional, Sequence, TypeVar import numpy as np import pandas as pd @@ -495,7 +495,7 @@ def append(a: List[T], b: T) -> List[T]: return a + [b] -def wrap_to_use_state(f): +def inputs_from_state(f): """Decorator to make target `f` into a function on a `State` and `**kwargs`. This wrapper makes it easier to pass arguments to a function from a State. @@ -508,7 +508,6 @@ def wrap_to_use_state(f): Returns: Examples: - >>> from autora.state.delta import State, Delta >>> from dataclasses import dataclass, field >>> import pandas as pd >>> from typing import List, Optional @@ -521,7 +520,7 @@ def wrap_to_use_state(f): We indicate the inputs required by the parameter names. The output must be a `Delta` object. >>> from autora.state.delta import Delta - >>> @wrap_to_use_state + >>> @inputs_from_state ... def experimentalist(conditions): ... new_conditions = [c + 10 for c in conditions] ... return Delta(conditions=new_conditions) @@ -536,7 +535,7 @@ def wrap_to_use_state(f): >>> from sklearn.base import BaseEstimator >>> from sklearn.linear_model import LinearRegression - >>> @wrap_to_use_state + >>> @inputs_from_state ... def theorist(experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs): ... ivs = [v.name for v in variables.independent_variables] ... dvs = [v.name for v in variables.dependent_variables] @@ -572,7 +571,7 @@ def wrap_to_use_state(f): Any parameters not provided by the state must be provided by default values or by the caller. If the default is specified: - >>> @wrap_to_use_state + >>> @inputs_from_state ... def experimentalist(conditions, offset=25): ... new_conditions = [c + offset for c in conditions] ... return Delta(conditions=new_conditions) @@ -582,7 +581,7 @@ def wrap_to_use_state(f): S(conditions=[26, 27, 28, 29]) If a default isn't specified: - >>> @wrap_to_use_state + >>> @inputs_from_state ... def experimentalist(conditions, offset): ... new_conditions = [c + offset for c in conditions] ... return Delta(conditions=new_conditions) @@ -618,26 +617,26 @@ def _f(state_: S, /, **kwargs) -> S: return _f -def _map_outputs_to_delta(*output: str): +def outputs_to_delta(*output: str): """ - Decorator maker to wrap outputs from a function as Deltas. + Decorator factory to wrap outputs from a function as Deltas. Examples: - >>> @_map_outputs_to_delta("conditions") + >>> @outputs_to_delta("conditions") ... def add_five(x): ... return [xi + 5 for xi in x] >>> add_five([1, 2, 3]) {'conditions': [6, 7, 8]} - >>> @_map_outputs_to_delta("c") + >>> @outputs_to_delta("c") ... def add_six(conditions): ... return [c + 5 for c in conditions] >>> add_six([1, 2, 3]) {'c': [6, 7, 8]} - >>> @_map_outputs_to_delta("+1", "-1") + >>> @outputs_to_delta("+1", "-1") ... def plus_minus_1(x): ... a = [xi + 1 for xi in x] ... b = [xi - 1 for xi in x] @@ -649,7 +648,7 @@ def _map_outputs_to_delta(*output: str): If the wrong number of values are specified for the return, then there might be errors. If multiple outputs are expected, but only a single output is returned, we get a warning: - >>> @_map_outputs_to_delta("1", "2") + >>> @outputs_to_delta("1", "2") ... def returns_single_result_when_more_expected(): ... return "a" >>> returns_single_result_when_more_expected() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS @@ -659,7 +658,7 @@ def _map_outputs_to_delta(*output: str): has to return multiple values to match `('1', '2')`. Got `a` instead. If multiple outputs are expected, but the wrong number are returned, we get a warning: - >>> @_map_outputs_to_delta("1", "2", "3") + >>> @outputs_to_delta("1", "2", "3") ... def returns_wrong_number_of_results(): ... return "a", "b" >>> returns_wrong_number_of_results() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS @@ -670,36 +669,39 @@ def _map_outputs_to_delta(*output: str): However, if a single output is expected, and multiple are returned, these are treated as a single object and no error occurs: - >>> @_map_outputs_to_delta("foo") + >>> @outputs_to_delta("foo") ... def returns_a_tuple(): ... return "a", "b", "c" >>> returns_a_tuple() {'foo': ('a', 'b', 'c')} - >>> @_map_outputs_to_delta() + If we fail to specify output names, an error is returned immediately. + >>> @outputs_to_delta() ... def decorator_missing_arguments(): ... return "a", "b", "c" - >>> decorator_missing_arguments() Traceback (most recent call last): ... ValueError: `output` names must be specified. + """ - def _wrapper(f): + def decorator(f): if len(output) == 0: raise ValueError("`output` names must be specified.") elif len(output) == 1: - def _f(*args, **kwargs): + @wraps(f) + def inner(*args, **kwargs): result = f(*args, **kwargs) delta = Delta(**{output[0]: result}) return delta else: - def _f(*args, **kwargs): + @wraps(f) + def inner(*args, **kwargs): result = f(*args, **kwargs) assert isinstance(result, tuple), ( "function `%s` has to return multiple values " @@ -715,6 +717,69 @@ def _f(*args, **kwargs): delta = Delta(**dict(zip(output, result))) return delta - return _f + return inner + + return decorator + + +def on_state( + function: Optional[Callable] = None, output: Optional[Sequence[str]] = None +): + """Decorator (factory) to make target `function` into a function on a `State` and `**kwargs`. + + This combines the functionality of `outputs_to_delta` and `inputs_from_state` + + Args: + function: the function to be wrapped + output: list specifying State field names for the return values of `function` + + Returns: + + Examples: + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> from typing import List, Optional + + The `State` it operates on needs to have the metadata described in the state module: + >>> @dataclass(frozen=True) + ... class S(State): + ... conditions: List[int] = field(metadata={"delta": "replace"}) + + We indicate the inputs required by the parameter names. + >>> def add_ten(conditions): + ... return [c + 10 for c in conditions] + >>> experimentalist = on_state(function=add_ten, output=["conditions"]) + + >>> experimentalist(S(conditions=[1,2,3,4])) + S(conditions=[11, 12, 13, 14]) + + You can also wrap functions which return a Delta object natively, by omitting the `output` + argument: + >>> @on_state() + ... def add_five(conditions): + ... return Delta(conditions=[c + 5 for c in conditions]) + + >>> add_five(S(conditions=[1, 2, 3, 4])) + S(conditions=[6, 7, 8, 9]) + + You can also use the @on_state(output=[]) as a decorator: + >>> @on_state(output=["conditions"]) + ... def add_six(conditions): + ... return [c + 6 for c in conditions] + + >>> add_six(S(conditions=[1, 2, 3, 4])) + S(conditions=[7, 8, 9, 10]) + + """ - return _wrapper + def decorator(f): + f_ = f + if output is not None: + f_ = outputs_to_delta(*output)(f_) + f_ = inputs_from_state(f_) + return f_ + + if function is None: + return decorator + else: + return decorator(function) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 1bc3dd66..ee0dd482 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -2,7 +2,7 @@ so that $n$ processes $f_i$ on states $S$ can be represented as $$f_n(...(f_1(f_0(S))))$$ -These are special cases of the [autora.state.delta.wrap_to_use_state][] function. +These are special cases of the [autora.state.delta.inputs_from_state][] function. """ from __future__ import annotations @@ -11,7 +11,7 @@ import pandas as pd from sklearn.base import BaseEstimator -from autora.state.delta import Delta, State, wrap_to_use_state +from autora.state.delta import Delta, State, inputs_from_state from autora.variable import VariableCollection S = TypeVar("S") @@ -50,7 +50,7 @@ def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: """ - @wrap_to_use_state + @inputs_from_state def theorist( experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs ): @@ -99,7 +99,7 @@ def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: 2 3 30 33 """ - @wrap_to_use_state + @inputs_from_state def experiment_runner(conditions: pd.DataFrame, **kwargs): x = conditions y = f(x, **kwargs) @@ -146,7 +146,7 @@ def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: """ - @wrap_to_use_state + @inputs_from_state def experiment_runner(conditions: pd.DataFrame, **kwargs): x = conditions experiment_data = f(x, **kwargs) From f4975bd56f346cecbc60147feb79189765a3c807 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 15 Aug 2023 17:41:21 +0200 Subject: [PATCH 075/121] feat: add a warning if a Delta field is not available on the State --- src/autora/state/delta.py | 133 +++++++++++++++++++++++++++----------- 1 file changed, 95 insertions(+), 38 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 5f17d11a..680643af 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -4,6 +4,7 @@ import dataclasses import inspect import logging +import warnings from collections import UserDict from dataclasses import dataclass, fields, replace from functools import singledispatch, wraps @@ -58,6 +59,13 @@ class State: >>> l + Delta(o="not a field") ListState(l=['a', 'b', 'c'], m=['x', 'y', 'z']) + ... but will trigger a warning: + >>> with warnings.catch_warnings(record=True) as w: + ... _ = l + Delta(o="not a field") + ... print(w[0].message) # doctest: +NORMALIZE_WHITESPACE + These fields: ['o'] could not be used to update ListState, + which has these fields & aliases: ['l', 'm'] + We can also use the `.update` method to do the same thing: >>> l.update(l=list("ghi"), m=list("rst")) ListState(l=['a', 'b', 'c', 'g', 'h', 'i'], m=['r', 's', 't']) @@ -225,11 +233,13 @@ class State: def __add__(self, other: Delta): updates = dict() + other_fields_unused = list(other.keys()) for self_field in fields(self): - other_value = _get_value(self_field, other) + other_value, key = _get_value(self_field, other) if other_value is None: continue + other_fields_unused.remove(key) self_field_key = self_field.name self_value = getattr(self, self_field_key) @@ -253,6 +263,17 @@ def __add__(self, other: Delta): "delta_behaviour=`%s` not implemented" % (delta_behavior) ) + if len(other_fields_unused) > 0: + warnings.warn( + "These fields: %s could not be used to update %s, " + "which has these fields & aliases: %s" + % ( + other_fields_unused, + type(self).__name__, + _get_field_names_and_aliases(self), + ), + ) + new = replace(self, **updates) return new @@ -262,7 +283,9 @@ def update(self, **kwargs): def _get_value(f, other: Delta): """ - Given a `State`'s `dataclasses.field` f, get a value from `other` + Given a `State`'s `dataclasses.field` f, get a value from `other` and report its name. + + Returns: a tuple (the value, the key associated with that value) Examples: >>> from dataclasses import field, dataclass, fields @@ -278,94 +301,128 @@ def _get_value(f, other: Delta): For a field with no aliases, we retrieve values with the base name: >>> f_a = fields(Example)[0] >>> _get_value(f_a, Delta(a=1)) - 1 + (1, 'a') ... and only the base name: >>> print(_get_value(f_a, Delta(b=2))) # no match for b - None + (None, None) Any other names are unimportant: >>> _get_value(f_a, Delta(b=2, a=1)) - 1 + (1, 'a') For fields with an alias, we retrieve values with the base name: >>> f_b = fields(Example)[1] >>> _get_value(f_b, Delta(b=[2])) - [2] + ([2], 'b') ... or for the alias name, transformed by the alias lambda function: >>> _get_value(f_b, Delta(ba=21)) - [21] + ([21], 'ba') We preferentially get the base name, and then any aliases: >>> _get_value(f_b, Delta(b=2, ba=21)) - 2 + (2, 'b') ... , regardless of their order in the `Delta` object: >>> _get_value(f_b, Delta(ba=21, b=2)) - 2 + (2, 'b') Other names are ignored: - >>> print(_get_value(f_b, Delta(a=1))) - None + >>> _get_value(f_b, Delta(a=1)) + (None, None) and the order of other names is unimportant: >>> _get_value(f_b, Delta(a=1, b=2)) - 2 + (2, 'b') For fields with multiple aliases, we retrieve values with the base name: >>> f_c = fields(Example)[2] >>> _get_value(f_c, Delta(c=[3])) - [3] + ([3], 'c') ... for any alias: >>> _get_value(f_c, Delta(ca=31)) - 31 + (31, 'ca') ... transformed by the alias lambda function : >>> _get_value(f_c, Delta(cb=32)) - [32] + ([32], 'cb') ... and ignoring any other names: >>> print(_get_value(f_c, Delta(a=1))) - None + (None, None) ... preferentially in the order base name, 1st alias, 2nd alias, ... nth alias: >>> _get_value(f_c, Delta(c=3, ca=31, cb=32)) - 3 + (3, 'c') >>> _get_value(f_c, Delta(ca=31, cb=32)) - 31 + (31, 'ca') >>> _get_value(f_c, Delta(cb=32)) - [32] + ([32], 'cb') >>> print(_get_value(f_c, Delta())) - None + (None, None) + """ key = f.name + aliases = f.metadata.get("aliases", {}) + + value, used_key = None, None - try: + if key in other.data.keys(): value = other.data[key] - return value - except KeyError: - pass - - try: - aliases = f.metadata["aliases"] - except KeyError: - return - - for alias_key, wrapping_function in aliases.items(): - try: - value = wrapping_function(other.data[alias_key]) - return value - except KeyError: - pass - - return + used_key = key + elif aliases: # ... is not an empty dict + for alias_key, wrapping_function in aliases.items(): + if alias_key in other.data: + value = wrapping_function(other.data[alias_key]) + used_key = alias_key + break # we only evaluate the first match + + return value, used_key + + +def _get_field_names_and_aliases(s: State): + """ + Get a list of field names and their aliases from a State object + + Args: + s: a State object + + Returns: a list of field names and their aliases on `s` + + Examples: + >>> from dataclasses import field + >>> @dataclass(frozen=True) + ... class SomeState(State): + ... l: List = field(default_factory=list) + ... m: List = field(default_factory=list) + >>> _get_field_names_and_aliases(SomeState()) + ['l', 'm'] + + >>> @dataclass(frozen=True) + ... class SomeStateWithAliases(State): + ... l: List = field(default_factory=list, metadata={"aliases": {"l1": None, "l2": None}}) + ... m: List = field(default_factory=list, metadata={"aliases": {"m1": None}}) + >>> _get_field_names_and_aliases(SomeStateWithAliases()) + ['l', 'l1', 'l2', 'm', 'm1'] + + """ + result = [] + + for f in fields(s): + name = f.name + result.append(name) + + aliases = f.metadata.get("aliases", {}) + result.extend(aliases) + + return result class Delta(UserDict, Generic[S]): From 19c6406322923216db7883e29b5c264dd4fc3bd3 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 15 Aug 2023 17:42:33 +0200 Subject: [PATCH 076/121] refactor: move random functions back to random_ file --- docs/experimentalists/pooler/random/index.md | 1 + .../pooler/random/quickstart.md | 1 + src/autora/experimentalist/random_.py | 174 +++++++++++ .../experimentalist/random_/__init__.py | 13 - src/autora/experimentalist/random_/pool.py | 294 ------------------ src/autora/experimentalist/random_/sample.py | 119 ------- 6 files changed, 176 insertions(+), 426 deletions(-) create mode 100644 src/autora/experimentalist/random_.py delete mode 100644 src/autora/experimentalist/random_/__init__.py delete mode 100644 src/autora/experimentalist/random_/pool.py delete mode 100644 src/autora/experimentalist/random_/sample.py diff --git a/docs/experimentalists/pooler/random/index.md b/docs/experimentalists/pooler/random/index.md index 2500e7b9..f9370e10 100644 --- a/docs/experimentalists/pooler/random/index.md +++ b/docs/experimentalists/pooler/random/index.md @@ -24,6 +24,7 @@ This means that there are 9 possible combinations for these variables (3x3), fro ### Example Code ```python + from autora.experimentalist.random_ import random_pool pool = random_pool([1, 2, 3], [4, 5, 6], num_samples=3) diff --git a/docs/experimentalists/pooler/random/quickstart.md b/docs/experimentalists/pooler/random/quickstart.md index f61d33e9..0687529a 100644 --- a/docs/experimentalists/pooler/random/quickstart.md +++ b/docs/experimentalists/pooler/random/quickstart.md @@ -10,5 +10,6 @@ You will need: you can import the random pooler via: ```python + from autora.experimentalist.random_ import random_pool ``` diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py new file mode 100644 index 00000000..12e83e1a --- /dev/null +++ b/src/autora/experimentalist/random_.py @@ -0,0 +1,174 @@ +from typing import Optional, Union + +import numpy as np +import pandas as pd + +from autora.variable import ValueType, VariableCollection + + +def pool( + variables: VariableCollection, + num_samples: int = 5, + random_state: Optional[int] = None, + replace: bool = True, +) -> pd.DataFrame: + """ + Create a sequence of conditions randomly sampled from independent variables. + + Args: + variables: the description of all the variables in the AER experiment. + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: the generated conditions as a dataframe + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed_values we get some of those values + back when running the experimentalist: + >>> pool( + ... VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=range(10)) + ... ]), random_state=1) + x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0 + + + ... with one independent variable "x", and a value_range, + we get a sample of the range back when running the experimentalist: + >>> pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ]), random_state=1) + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> pool(VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> pool(VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ]), num_samples=10, replace=True, random_state=1) + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + + >>> pool( + ... VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]), random_state=1) + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=replace + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + return pd.DataFrame(raw_conditions) + + +random_pool = pool +random_pool.__doc__ = """Alias for `pool`""" + + +def sample( + conditions: Union[pd.DataFrame, np.ndarray, np.recarray], + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> pd.DataFrame: + """ + Take a random sample from some input conditions. + + Args: + conditions: the conditions to sample from + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + replace: if True, allow repeated values + + Returns: a Result object with a field `conditions` containing a DataFrame of the sampled + conditions + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> sample( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196 + + """ + return pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=replace + ) + + +random_sample = sample +random_sample.__doc__ = """Alias for `sample`""" diff --git a/src/autora/experimentalist/random_/__init__.py b/src/autora/experimentalist/random_/__init__.py deleted file mode 100644 index 3f533eb4..00000000 --- a/src/autora/experimentalist/random_/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Tools to make randomly sampled experimental conditions.""" - -# Option 1 -from .pool import _state as random_pool_s -from .pool import _state as random_pool_state -from .pool import _state as random_pool_t -from .pool import _state as random_pool_task -from .pool import _state as random_pool_wf -from .sample import _state as random_sample_s -from .sample import _state as random_sample_state -from .sample import _state as random_sample_t -from .sample import _state as random_sample_task -from .sample import _state as random_sample_wf diff --git a/src/autora/experimentalist/random_/pool.py b/src/autora/experimentalist/random_/pool.py deleted file mode 100644 index de04ac46..00000000 --- a/src/autora/experimentalist/random_/pool.py +++ /dev/null @@ -1,294 +0,0 @@ -from typing import Optional - -import numpy as np -import pandas as pd - -from autora.state.delta import Result, State, wrap_to_use_state -from autora.variable import ValueType, VariableCollection - - -def _state( - s: State, - num_samples: int = 5, - random_state: Optional[int] = None, - replace: bool = True, - **kwargs, -) -> State: - """ - Create a sequence of conditions randomly sampled from independent variables. - - Args: - s: a State object with the desired fields - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a State object updated with the new conditions - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed_values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=range(10)) - ... ])) - - ... we get some of those values back when running the experimentalist: - >>> _state(s, random_state=1).conditions - x - 0 4 - 1 5 - 2 7 - 3 9 - 4 0 - - With one independent variable "x", and a value_range: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", value_range=(-5, 5)) - ... ])) - - ... we get a sample of the range back when running the experimentalist: - >>> _state(t, random_state=1).conditions - x - 0 0.118216 - 1 4.504637 - 2 -3.558404 - 3 4.486494 - 4 -1.881685 - - - - The allowed_values or value_range must be specified: - >>> _state( - ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - With two independent variables, we get independent samples on both axes: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=range(1, 5)), - ... Variable(name="x2", allowed_values=range(1, 500)), - ... ])) - >>> _state(t, num_samples=10, replace=True, random_state=1).conditions - x1 x2 - 0 2 434 - 1 3 212 - 2 4 137 - 3 4 414 - 4 1 129 - 5 1 205 - 6 4 322 - 7 4 275 - 8 1 43 - 9 2 14 - - If any of the variables have unspecified allowed_values, we get an error: - >>> _state(S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ]))) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - - We can specify arrays of allowed values: - >>> u = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ])) - >>> _state(u, random_state=1).conditions - x y z - 0 -0.6 3 29.0 - 1 0.2 4 24.0 - 2 5.2 4 23.0 - 3 9.0 3 29.0 - 4 -9.4 3 22.0 - """ - return wrap_to_use_state(_result)( - s, num_samples=num_samples, random_state=random_state, replace=replace, **kwargs - ) - - -def _result( - variables: VariableCollection, - num_samples: int, - random_state: Optional[int], - replace: bool, -): - """ - Create a sequence of conditions randomly sampled from independent variables. - - Args: - variables: the description of all the variables in the AER experiment. - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field - - """ - conditions = _base( - variables=variables, - num_samples=num_samples, - random_state=random_state, - replace=replace, - ) - return Result(conditions=conditions) - - -def _base( - variables: VariableCollection, - num_samples: int = 5, - random_state: Optional[int] = None, - replace: bool = True, -) -> pd.DataFrame: - """ - Create a sequence of conditions randomly sampled from independent variables. - - Args: - variables: the description of all the variables in the AER experiment. - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: the generated conditions as a dataframe - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - With one independent variable "x", and some allowed_values we get some of those values - back when running the experimentalist: - >>> _base( - ... VariableCollection( - ... independent_variables=[Variable(name="x", allowed_values=range(10)) - ... ]), random_state=1) - x - 0 4 - 1 5 - 2 7 - 3 9 - 4 0 - - - ... with one independent variable "x", and a value_range, - we get a sample of the range back when running the experimentalist: - >>> _base( - ... VariableCollection(independent_variables=[ - ... Variable(name="x", value_range=(-5, 5)) - ... ]), random_state=1) - x - 0 0.118216 - 1 4.504637 - 2 -3.558404 - 3 4.486494 - 4 -1.881685 - - - - The allowed_values or value_range must be specified: - >>> _base(VariableCollection(independent_variables=[Variable(name="x")])) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - With two independent variables, we get independent samples on both axes: - >>> _base(VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=range(1, 5)), - ... Variable(name="x2", allowed_values=range(1, 500)), - ... ]), num_samples=10, replace=True, random_state=1) - x1 x2 - 0 2 434 - 1 3 212 - 2 4 137 - 3 4 414 - 4 1 129 - 5 1 205 - 6 4 322 - 7 4 275 - 8 1 43 - 9 2 14 - - If any of the variables have unspecified allowed_values, we get an error: - >>> _base( - ... VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ])) - Traceback (most recent call last): - ... - ValueError: allowed_values or [value_range and type==REAL] needs to be set... - - - We can specify arrays of allowed values: - - >>> _base( - ... VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ]), random_state=1) - x y z - 0 -0.6 3 29.0 - 1 0.2 4 24.0 - 2 5.2 4 23.0 - 3 9.0 3 29.0 - 4 -9.4 3 22.0 - - - """ - rng = np.random.default_rng(random_state) - - raw_conditions = {} - for iv in variables.independent_variables: - if iv.allowed_values is not None: - raw_conditions[iv.name] = rng.choice( - iv.allowed_values, size=num_samples, replace=replace - ) - elif (iv.value_range is not None) and (iv.type == ValueType.REAL): - raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) - - else: - raise ValueError( - "allowed_values or [value_range and type==REAL] needs to be set for " - "%s" % (iv) - ) - - return pd.DataFrame(raw_conditions) - - -# Option 2: -on_state = _state -to_result = _result -raw = _base - - -# Option 3: -run = _state -run_on_state = _state - -# Option 4: suggestion SMusslick -pool = _state # shorter alias -random_pool = _state # longer alias diff --git a/src/autora/experimentalist/random_/sample.py b/src/autora/experimentalist/random_/sample.py deleted file mode 100644 index b71b6464..00000000 --- a/src/autora/experimentalist/random_/sample.py +++ /dev/null @@ -1,119 +0,0 @@ -from typing import Optional, Union - -import numpy as np -import pandas as pd - -from autora.state.delta import Result, State, wrap_to_use_state - - -def _state(s: State, **kwargs) -> State: - """ - Take a random sample from some input conditions. - - Args: - s: a State object with a `variables` field. - - Returns: a State object updated with the new conditions - - Examples: - >>> from autora.state.bundled import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": range(100, 200)})) - >>> _state(s, random_state=1, replace=False, num_samples=3).conditions - x - 80 180 - 84 184 - 33 133 - - """ - return wrap_to_use_state(_result)(s, **kwargs) - - -def _result( - conditions: Union[pd.DataFrame, np.ndarray, np.recarray], - num_samples: int = 1, - random_state: Optional[int] = None, - replace: bool = False, -) -> Result: - """ - Take a random sample from some input conditions. - - Args: - conditions: the conditions to sample from - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a Result object with a field `conditions` containing a DataFrame of the sampled - conditions - - Examples: - From a pd.DataFrame: - >>> import pandas as pd - >>> _result( - ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180)["conditions"] - x - 67 167 - 71 171 - 64 164 - 63 163 - 96 196 - - """ - return Result( - conditions=_base( - conditions=conditions, - num_samples=num_samples, - random_state=random_state, - replace=replace, - ) - ) - - -def _base( - conditions: Union[pd.DataFrame, np.ndarray, np.recarray], - num_samples: int = 1, - random_state: Optional[int] = None, - replace: bool = False, -) -> pd.DataFrame: - """ - Take a random sample from some input conditions. - - Args: - conditions: the conditions to sample from - num_samples: the number of conditions to produce - random_state: the seed value for the random number generator - replace: if True, allow repeated values - - Returns: a Result object with a field `conditions` containing a DataFrame of the sampled - conditions - - Examples: - From a pd.DataFrame: - >>> import pandas as pd - >>> _base( - ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) - x - 67 167 - 71 171 - 64 164 - 63 163 - 96 196 - - """ - return pd.DataFrame.sample( - conditions, random_state=random_state, n=num_samples, replace=replace - ) - - -# Option 2: -on_state = _state -to_result = _result -raw = _base - - -# Option 3: -run = _state -run_on_state = _state - -# Option 4: suggestion SMusslick -sample = _state From 7e24f1f86dd5ca3ba424cfc8e4b8d663dbe49650 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 15 Aug 2023 17:43:11 +0200 Subject: [PATCH 077/121] refactor: move grid functions back to grid_ file --- src/autora/experimentalist/grid_.py | 151 ++-------------------------- 1 file changed, 8 insertions(+), 143 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 915bc907..db953654 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -3,40 +3,10 @@ import pandas as pd -from autora.state.delta import Result, State, wrap_to_use_state from autora.variable import VariableCollection -def _state(s: State, **kwargs) -> State: - """ - Create an exhaustive pool of conditions. - - Args: - s: a State object with a `variables` field. - - Returns: a State object updated with the new conditions - - Examples: - >>> from autora.state.bundled import StandardState - >>> from autora.variable import Variable, VariableCollection - >>> s = StandardState(variables=VariableCollection( - ... independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4])])) - >>> _state(s) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - StandardState(..., conditions= - x1 x2 - 0 1 3 - 1 1 4 - 2 2 3 - 3 2 4, ...) - - """ - - return wrap_to_use_state(_result)(s, **kwargs) - - -def _result(variables: VariableCollection) -> Result: +def pool(variables: VariableCollection) -> pd.DataFrame: """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: @@ -55,90 +25,7 @@ def _result(variables: VariableCollection) -> Result: With one independent variable "x", and some allowed values, we get exactly those values back when running the experimentalist: - >>> _result(VariableCollection( - ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] - ... ))["conditions"] - x - 0 1 - 1 2 - 2 3 - - The allowed_values must be specified: - >>> _result(VariableCollection(independent_variables=[Variable(name="x")])) - Traceback (most recent call last): - ... - AssertionError: grid_pool only supports independent variables with discrete... - - With two independent variables, we get the cartesian product: - >>> _result( - ... VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4]), - ... ]))["conditions"] - x1 x2 - 0 1 3 - 1 1 4 - 2 2 3 - 3 2 4 - - If any of the variables have unspecified allowed_values, we get an error: - >>> _result( - ... VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ])) - Traceback (most recent call last): - ... - AssertionError: grid_pool only supports independent variables with discrete... - - - We can specify arrays of allowed values: - >>> _result( - ... VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ]))["conditions"] - x y z - 0 -10.0 3 20.0 - 1 -10.0 3 21.0 - 2 -10.0 3 22.0 - 3 -10.0 3 23.0 - 4 -10.0 3 24.0 - ... ... .. ... - 2217 10.0 4 26.0 - 2218 10.0 4 27.0 - 2219 10.0 4 28.0 - 2220 10.0 4 29.0 - 2221 10.0 4 30.0 - - [2222 rows x 3 columns] - - """ - conditions = _base(variables=variables) - return Result(conditions=conditions) - - -def _base(variables: VariableCollection) -> pd.DataFrame: - """Creates exhaustive pool of conditions given a definition of variables with allowed_values. - - Args: - variables: a VariableCollection with `independent_variables` – a sequence of Variable - objects, each of which has an attribute `allowed_values` containing a sequence of - values. - - Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - With one independent variable "x", and some allowed values, we get exactly those values - back when running the experimentalist: - >>> _base(VariableCollection( + >>> pool(VariableCollection( ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] ... )) x @@ -147,13 +34,13 @@ def _base(variables: VariableCollection) -> pd.DataFrame: 2 3 The allowed_values must be specified: - >>> _base(VariableCollection(independent_variables=[Variable(name="x")])) + >>> pool(VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... AssertionError: grid_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: - >>> _base( + >>> pool( ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2", allowed_values=[3, 4]), @@ -165,7 +52,7 @@ def _base(variables: VariableCollection) -> pd.DataFrame: 3 2 4 If any of the variables have unspecified allowed_values, we get an error: - >>> _base( + >>> pool( ... VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -176,7 +63,7 @@ def _base(variables: VariableCollection) -> pd.DataFrame: We can specify arrays of allowed values: - >>> _base( + >>> pool( ... VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), ... Variable(name="y", allowed_values=[3, 4]), @@ -217,27 +104,5 @@ def _base(variables: VariableCollection) -> pd.DataFrame: return conditions -# Option 1: - -grid_pool_s = _state -grid_pool_state = _state -grid_pool_t = _state -grid_pool_task = _state -grid_pool_wf = _state - -# Option 2: -on_state = _state -to_result = _result -raw = _base - - -# Option 3: -run = _state -run_on_state = _state - - -# Option 4: suggestion SMusslick -pool = _state - -# Option 5: user has to wrap everything by convention -grid_pool = _base +grid_pool = pool +grid_pool.__doc__ = """Alias for pool""" From ef2aa3563f29e06810141a9c1fb14c82c0902a58 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 15 Aug 2023 18:03:15 +0200 Subject: [PATCH 078/121] docs: update Basic Introduction to Functions and States.ipynb --- ...Introduction to Functions and States.ipynb | 493 ++++++++++++++++-- 1 file changed, 456 insertions(+), 37 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index ff66ab00..e47ccd76 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -27,9 +27,9 @@ "- Each operation in an AER cycle (theorist, experimentalist, experiment_runner, etc.) is implemented as a\n", "function with $n$ arguments $s_j$ which are members of $S$ and $m$ others $a_k$ which are not.\n", " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", - "- There is a wrapper function $h$ (`autora.state.delta.wrap_to_use_state`) which changes the signature of $f$ to\n", + "- There is a wrapper function $w$ (`autora.state.delta.wrap_to_use_state`) which changes the signature of $f$ to\n", "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", - " $$h\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", + " $$w\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", "S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta\n", "S_{i+1} = S_{i+1}\\right]$$\n", "\n", @@ -76,7 +76,7 @@ "Specify the experimentalist. Use a standard function `random_pool`.\n", "This gets 5 independent random samples (by default, configurable using an argument)\n", "from the value_range of the independent variables, and returns them in a DataFrame.\n", - "To make this work as a function on the State objects, we wrap it in the `to_state_function`." + "To make this work as a function on the State objects, we wrap it in the `on_state` function." ] }, { @@ -88,11 +88,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 6.879258\n", - "1 -1.231564\n", - "2 0.149769\n", - "3 0.454804\n", - "4 -0.603432, experiment_data=None, models=[])" + "0 5.479121\n", + "1 -1.222431\n", + "2 7.171958\n", + "3 3.947361\n", + "4 -8.116453, experiment_data=None, models=[])" ] }, "execution_count": null, @@ -105,7 +105,7 @@ "from autora.state.delta import on_state\n", "\n", "experimentalist = on_state(function=random_pool, output=[\"conditions\"])\n", - "s_1 = experimentalist(s_0)\n", + "s_1 = experimentalist(s_0, random_state=42)\n", "s_1" ] }, @@ -123,17 +123,24 @@ "metadata": {}, "outputs": [ { - "ename": "AssertionError", - "evalue": "function `` has to return multiple values to match `('e', 'x', 'p', 'e', 'r', 'i', 'm', 'e', 'n', 't', '_', 'd', 'a', 't', 'a')`. Got ` x y\n0 6.879258 30.075715\n1 -1.231564 -4.606542\n2 0.149769 2.340361\n3 0.454804 4.309657\n4 -0.603432 -0.251267` instead.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m observations\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Which does the following:\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mexperiment_runner\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms_1\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Developer/autora-core/src/autora/state/delta.py:613\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[0;34m(state_, **kwargs)\u001b[0m\n\u001b[1;32m 611\u001b[0m arguments_from_state \u001b[38;5;241m=\u001b[39m {k: \u001b[38;5;28mgetattr\u001b[39m(state_, k) \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m from_state}\n\u001b[1;32m 612\u001b[0m arguments \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(arguments_from_state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 613\u001b[0m delta \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 614\u001b[0m new_state \u001b[38;5;241m=\u001b[39m state_ \u001b[38;5;241m+\u001b[39m delta\n\u001b[1;32m 615\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", - "File \u001b[0;32m~/Developer/autora-core/src/autora/state/delta.py:706\u001b[0m, in \u001b[0;36moutputs_to_delta..decorator..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 705\u001b[0m result \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, \u001b[38;5;28mtuple\u001b[39m), (\n\u001b[1;32m 707\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` has to return multiple values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 708\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto match `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m`. Got `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (f, output, result)\n\u001b[1;32m 709\u001b[0m )\n\u001b[1;32m 710\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(result), (\n\u001b[1;32m 711\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` has to return \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexactly `\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m` values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (f, \u001b[38;5;28mlen\u001b[39m(output), output, result)\n\u001b[1;32m 716\u001b[0m )\n\u001b[1;32m 717\u001b[0m delta \u001b[38;5;241m=\u001b[39m Delta(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(output, result)))\n", - "\u001b[0;31mAssertionError\u001b[0m: function `` has to return multiple values to match `('e', 'x', 'p', 'e', 'r', 'i', 'm', 'e', 'n', 't', '_', 'd', 'a', 't', 'a')`. Got ` x y\n0 6.879258 30.075715\n1 -1.231564 -4.606542\n2 0.149769 2.340361\n3 0.454804 4.309657\n4 -0.603432 -0.251267` instead." - ] + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.479121\n", + "1 -1.222431\n", + "2 7.171958\n", + "3 3.947361\n", + "4 -8.116453, experiment_data= x y\n", + "0 5.479121 24.160713\n", + "1 -1.222431 -2.211546\n", + "2 7.171958 30.102304\n", + "3 3.947361 16.880769\n", + "4 -8.116453 -32.457650, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -142,7 +149,7 @@ "import pandas as pd\n", "\n", "\n", - "@on_state(output=\"experiment_data\")\n", + "@on_state(output=[\"experiment_data\"])\n", "def experiment_runner(conditions: pd.DataFrame, c=[2, 4], random_state = None):\n", " rng = np.random.default_rng(random_state)\n", " x = conditions[\"x\"]\n", @@ -152,36 +159,59 @@ " return observations\n", "\n", "# Which does the following:\n", - "experiment_runner(s_1)" + "experiment_runner(s_1, random_state=43)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A completely analogous definition would be:" + "A completely analogous definition, using the separate `@inputs_from_state` and `@outputs_to_delta(...)` decorators\n", + "rather than the combined `@on_state(...)` decorator would be:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.479121\n", + "1 -1.222431\n", + "2 7.171958\n", + "3 3.947361\n", + "4 -8.116453, experiment_data= x y\n", + "0 5.479121 24.221201\n", + "1 -1.222431 -3.929709\n", + "2 7.171958 31.438285\n", + "3 3.947361 18.730007\n", + "4 -8.116453 -32.416847, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from autora.state.delta import outputs_to_delta\n", + "from autora.state.delta import inputs_from_state, outputs_to_delta\n", "\n", "\n", "@inputs_from_state\n", "@outputs_to_delta(\"experiment_data\")\n", - "def experiment_runner_alt_1(conditions: pd.DataFrame, c=[2, 4]):\n", + "def experiment_runner_alt_1(conditions: pd.DataFrame, c=[2, 4], random_state=None):\n", " x = conditions[\"x\"]\n", + " rng = np.random.default_rng(random_state)\n", " noise = rng.normal(0, 1, len(x))\n", " y = c[0] + (c[1] * x) + noise\n", " xy = conditions.assign(y = y)\n", " return xy\n", "\n", "# Which does the following:\n", - "experiment_runner_alt_1(s_1)" + "experiment_runner_alt_1(s_1, random_state=42)" ] }, { @@ -195,10 +225,32 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.479121\n", + "1 -1.222431\n", + "2 7.171958\n", + "3 3.947361\n", + "4 -8.116453, experiment_data= x y\n", + "0 5.479121 23.727234\n", + "1 -1.222431 -3.425782\n", + "2 7.171958 30.108872\n", + "3 3.947361 17.792187\n", + "4 -8.116453 -30.609650, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def experiment_runner_alt_2_core(conditions: pd.DataFrame, c=[2, 4]):\n", + "def experiment_runner_alt_2_core(conditions: pd.DataFrame, c=[2, 4], random_state=None):\n", " x = conditions[\"x\"]\n", + " rng = np.random.default_rng(random_state)\n", " noise = rng.normal(0, 1, len(x))\n", " y = c[0] + (c[1] * x) + noise\n", " xy = conditions.assign(y = y)\n", @@ -228,19 +280,39 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "s_0" + "Now we can run the theorist on the output from the experiment_runner,\n", + "which itself uses the output from the experimentalist." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 -3.911881\n", + "1 -4.014468\n", + "2 8.621441\n", + "3 -5.956952\n", + "4 -4.300384, experiment_data= x y\n", + "0 -3.911881 -13.395744\n", + "1 -4.014468 -13.341993\n", + "2 8.621441 35.568485\n", + "3 -5.956952 -22.891165\n", + "4 -4.300384 -14.266465, models=[LinearRegression()])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "theorist(experiment_runner(experimentalist(s_0)))" ] @@ -249,7 +321,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Define the cycle: run the experimentalist, experiment_runner and theorist ten times." + "If we like, we can run the experimentalist, experiment_runner and theorist ten times." ] }, { @@ -276,7 +348,346 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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This process runs cyclically and we call the complete process the \"cycle\" and the individual\n", - "steps \"tasks\".\n", - "\n", - "Our original object-oriented approach for defining the cycle turned out to be too complicated for people to understand.\n", - "We've been building a simpler functional interface for it for defining the cycles.\n", - "\n", - "But we have a problem – the naming convention for the functions is difficult to agree on. The AER group (which is\n", - "developing AutoRA and related tools) has asked us to look over the current options and give some feedback.\n", - "\n", - "## The functional interface\n", - "A **state** is a description of all the data and metadata known about a particular phenomenon:\n", - "\n", - "- the domain of the variables,\n", - "- experimental conditions to be investigated,\n", - "- the experimental data, the newest model, and\n", - "- any other data the cycle might need.\n", - "\n", - "We define a state as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.state.bundled import StandardState\n", - "from autora.variable import VariableCollection, Variable\n", - "\n", - "s_0 = StandardState(\n", - " variables=VariableCollection(\n", - " independent_variables=[Variable(\"x\", value_range=(-10, 10))],\n", - " dependent_variables=[Variable(\"y\")]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`s_0` doesn't have anything other than the metadata we gave it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions=None, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The functional interface sees the tasks as functions $f$ on state $S$ which return a new state.\n", - "A single task looks like:\n", - "$$ f(S_{i}) \\rightarrow S_{i+1} ,$$\n", - "\n", - "and a pipeline of such operations looks like:\n", - "$$S_n = f_n^\\prime(...f_2^\\prime(f_1^\\prime(S_0))) .$$\n", - "\n", - "One task we define is the experimentalist, which proposes new experimental conditions.\n", - "One experimentalist is the `random_pool` which takes variables and returns a series of conditions.\n", - "We define it just like that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Optional\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "def random_pool(\n", - " variables: VariableCollection,\n", - " num_samples: int = 5,\n", - " random_state: Optional[int] = None,\n", - ") -> pd.DataFrame:\n", - " rng = np.random.default_rng(random_state)\n", - "\n", - " raw_conditions = {}\n", - " for iv in variables.independent_variables:\n", - " raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples)\n", - "\n", - " return pd.DataFrame(raw_conditions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And running it on the variables results in a series of conditions sampled uniformly between -10 and +10:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " x\n", - "0 -2.291109\n", - "1 -6.603135\n", - "2 -8.877414\n", - "3 3.108730\n", - "4 8.169106" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_pool(s_0.variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We still need to do some work so that it can run directly on $S$.\n", - "\n", - "$S$ is defined such that it can be added to: $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", - "\n", - "The way we package the random_pool function is to make its output into a `Delta`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.state.delta import Delta\n", - "\n", - "\n", - "def random_pool_delta(\n", - " variables: VariableCollection,\n", - " num_samples: int = 5,\n", - " random_state: Optional[int] = None,\n", - "):\n", - " \"\"\"\n", - " Create a sequence of conditions randomly sampled from independent variables.\n", - "\n", - " Args:\n", - " variables: the description of all the variables in the AER experiment.\n", - " num_samples: the number of conditions to produce\n", - " random_state: the seed value for the random number generator\n", - " replace: if True, allow repeated values\n", - "\n", - " Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field\n", - "\n", - " \"\"\"\n", - " conditions = random_pool(\n", - " variables=variables,\n", - " num_samples=num_samples,\n", - " random_state=random_state,\n", - " )\n", - " return Delta(conditions=conditions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "which can be run on the same inputs but produces a differently packaged output:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'conditions': x\n", - "0 -1.235222\n", - "1 -6.908781\n", - "2 -2.617692\n", - "3 -4.960670\n", - "4 0.743513}" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_pool_delta(s_0.variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we define a wrapper which combines this with $S$, which uses a utility function offered by AutoRA." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.state.delta import State, wrap_to_use_state\n", - "\n", - "\n", - "def random_pool_state(\n", - " s: State,\n", - " num_samples: int = 5,\n", - " random_state: Optional[int] = None,\n", - " **kwargs,\n", - ") -> State:\n", - "\n", - " return wrap_to_use_state(random_pool_delta)(\n", - " s, num_samples=num_samples, random_state=random_state, **kwargs\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run the function directly on $S$, returning a new state with our conditions included:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 5.510190\n", - "1 -9.734381\n", - "2 -9.247260\n", - "3 -3.880819\n", - "4 -7.846659, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_pool_state(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The question is: what naming convention should these functions have, given that usually the `_state` version will be\n", - "used and that every contribution will need to follow the same convention? There might be multiple poolers and\n", - "samplers offered by an AutoRA module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The problem and the options in the simplest case\n", - "\n", - "### Option 1: simple function names with conventional suffixes (or prefixes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 1.802195\n", - "1 -6.681581\n", - "2 -5.298816\n", - "3 -8.727805\n", - "4 9.767906, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.random_ import random_pool_state\n", - "random_pool_state(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... or ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -0.084047\n", - "1 6.874185\n", - "2 -6.176624\n", - "3 -5.670282\n", - "4 -6.865156, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.random_ import random_pool_s\n", - "random_pool_s(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 2: one state function per module\n", - "\n", - "This option is inspired by the scikit-learn `Regressor().fit(X, y)` syntax, but note that `pooler` in this case is a\n", - "module rather than a traditional object, and it shouldn't have any internal state which affects the fitting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 1.929206\n", - "1 5.592331\n", - "2 6.558775\n", - "3 -8.900612\n", - "4 6.128046, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import autora.experimentalist.random_.pool as pool\n", - "pool.on_state(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 3: `run` functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -0.114128\n", - "1 2.292411\n", - "2 -5.499759\n", - "3 7.032079\n", - "4 -8.296732, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pool.run(s_0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -5.583620\n", - "1 -1.866155\n", - "2 -5.859761\n", - "3 0.061423\n", - "4 -1.798987, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pool.run_on_state(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## More examples: using a grid and sample\n", - "\n", - "We can also construct a processing pipeline using multiple functions. In the following example, we have a state which\n", - " has a grid of allowable variable values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_0 = StandardState(\n", - " variables=VariableCollection(independent_variables=[\n", - " Variable(name=\"x\", allowed_values=np.linspace(-10, 10, 101)),\n", - " Variable(name=\"y\", allowed_values=[3, 4]),\n", - " Variable(name=\"z\", allowed_values=np.linspace(20, 30, 11))]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we generate the full list of possible conditions using the `grid` functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "0 -10.0 3 20.0\n", - "1 -10.0 3 21.0\n", - "2 -10.0 3 22.0\n", - "3 -10.0 3 23.0\n", - "4 -10.0 3 24.0\n", - "... ... .. ...\n", - "2217 10.0 4 26.0\n", - "2218 10.0 4 27.0\n", - "2219 10.0 4 28.0\n", - "2220 10.0 4 29.0\n", - "2221 10.0 4 30.0\n", - "\n", - "[2222 rows x 3 columns], experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.grid_ import grid_pool_state\n", - "grid_pool_state(s_0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have the same options as before – shorter suffixes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "0 -10.0 3 20.0\n", - "1 -10.0 3 21.0\n", - "2 -10.0 3 22.0\n", - "3 -10.0 3 23.0\n", - "4 -10.0 3 24.0\n", - "... ... .. ...\n", - "2217 10.0 4 26.0\n", - "2218 10.0 4 27.0\n", - "2219 10.0 4 28.0\n", - "2220 10.0 4 29.0\n", - "2221 10.0 4 30.0\n", - "\n", - "[2222 rows x 3 columns], experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.grid_ import grid_pool_s\n", - "grid_pool_s(s_0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "0 -10.0 3 20.0\n", - "1 -10.0 3 21.0\n", - "2 -10.0 3 22.0\n", - "3 -10.0 3 23.0\n", - "4 -10.0 3 24.0\n", - "... ... .. ...\n", - "2217 10.0 4 26.0\n", - "2218 10.0 4 27.0\n", - "2219 10.0 4 28.0\n", - "2220 10.0 4 29.0\n", - "2221 10.0 4 30.0\n", - "\n", - "[2222 rows x 3 columns], experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.grid_ import grid_pool_wf\n", - "grid_pool_wf(s_0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "0 -10.0 3 20.0\n", - "1 -10.0 3 21.0\n", - "2 -10.0 3 22.0\n", - "3 -10.0 3 23.0\n", - "4 -10.0 3 24.0\n", - "... ... .. ...\n", - "2217 10.0 4 26.0\n", - "2218 10.0 4 27.0\n", - "2219 10.0 4 28.0\n", - "2220 10.0 4 29.0\n", - "2221 10.0 4 30.0\n", - "\n", - "[2222 rows x 3 columns], experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import autora.experimentalist.grid_ as grid\n", - "grid.on_state(s_0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "0 -10.0 3 20.0\n", - "1 -10.0 3 21.0\n", - "2 -10.0 3 22.0\n", - "3 -10.0 3 23.0\n", - "4 -10.0 3 24.0\n", - "... ... .. ...\n", - "2217 10.0 4 26.0\n", - "2218 10.0 4 27.0\n", - "2219 10.0 4 28.0\n", - "2220 10.0 4 29.0\n", - "2221 10.0 4 30.0\n", - "\n", - "[2222 rows x 3 columns], experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid.run(s_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, we can also join this with some random sampling functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=None, allowed_values=array([-10. , -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4,\n", - " -8.2, -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6,\n", - " -6.4, -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8,\n", - " -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. ,\n", - " -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6,\n", - " 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n", - " 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2,\n", - " 4.4, 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. ,\n", - " 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8,\n", - " 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6,\n", - " 9.8, 10. ]), units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='y', value_range=None, allowed_values=[3, 4], units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='z', value_range=None, allowed_values=array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x y z\n", - "1659 5.0 3 29.0\n", - "78 -9.4 4 21.0\n", - "912 -1.8 3 30.0\n", - "908 -1.8 3 26.0\n", - "856 -2.4 4 29.0, experiment_data=None, models=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from autora.experimentalist.random_ import random_sample_state\n", - "random_sample_state(grid_pool_state(s_0), num_samples=5)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 6d315e946fe131f081a7c180e7eda18ce7f86ee3 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 15 Aug 2023 18:19:56 +0200 Subject: [PATCH 080/121] docs: update Linear and Cyclical Workflows using Functions and States.ipynb --- ...Workflows using Functions and States.ipynb | 100 +++++++++--------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 3151eb57..7550a08c 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -121,12 +121,12 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.state.delta import wrap_to_use_state, Delta\n", + "from autora.state.delta import on_state, Delta\n", "\n", "def ground_truth(x: pd.Series, c=(432, -144, -3, 1)):\n", " return c[0] + c[1] * x + c[2] * x**2 + c[3] * x**3\n", "\n", - "@wrap_to_use_state\n", + "@on_state\n", "def experiment_runner(conditions, std=100., random_state=None):\n", " \"\"\"Coefs from https://www.maa.org/sites/default/files/0025570x28304.di021116.02p0130a.pdf\"\"\"\n", " rng = np.random.default_rng(random_state)\n", @@ -178,27 +178,27 @@ " \n", " 0\n", " -15.0\n", - " -1458.277776\n", + " -1457.218119\n", " \n", " \n", " 1\n", " -14.7\n", - " -1275.239274\n", + " -1275.332030\n", " \n", " \n", " 2\n", " -14.4\n", - " -1102.572539\n", + " -1102.558433\n", " \n", " \n", " 3\n", " -14.1\n", - " -935.381331\n", + " -937.742130\n", " \n", " \n", " 4\n", " -13.8\n", - " -780.490659\n", + " -780.935825\n", " \n", " \n", " ...\n", @@ -208,27 +208,27 @@ " \n", " 96\n", " 13.8\n", - " 500.506401\n", + " 501.733867\n", " \n", " \n", " 97\n", " 14.1\n", - " 609.386647\n", + " 607.023667\n", " \n", " \n", " 98\n", " 14.4\n", - " 721.981947\n", + " 721.623458\n", " \n", " \n", " 99\n", " 14.7\n", - " 843.750465\n", + " 843.627156\n", " \n", " \n", " 100\n", " 15.0\n", - " 972.798407\n", + " 973.391517\n", " \n", " \n", "\n", @@ -237,17 +237,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1458.277776\n", - "1 -14.7 -1275.239274\n", - "2 -14.4 -1102.572539\n", - "3 -14.1 -935.381331\n", - "4 -13.8 -780.490659\n", + "0 -15.0 -1457.218119\n", + "1 -14.7 -1275.332030\n", + "2 -14.4 -1102.558433\n", + "3 -14.1 -937.742130\n", + "4 -13.8 -780.935825\n", ".. ... ...\n", - "96 13.8 500.506401\n", - "97 14.1 609.386647\n", - "98 14.4 721.981947\n", - "99 14.7 843.750465\n", - "100 15.0 972.798407\n", + "96 13.8 501.733867\n", + "97 14.1 607.023667\n", + "98 14.4 721.623458\n", + "99 14.7 843.627156\n", + "100 15.0 973.391517\n", "\n", "[101 rows x 2 columns]" ] @@ -744,7 +744,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -776,7 +776,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -874,17 +874,17 @@ "\n", "v1.model=None, \n", "v1.experiment_data= x y\n", - "0 -15.0 -1545.935365\n", - "1 -14.7 -1144.076706\n", - "2 -14.4 -1146.527730\n", - "3 -14.1 -1100.649495\n", - "4 -13.8 -746.834562\n", + "0 -15.0 -1646.530156\n", + "1 -14.7 -1336.437358\n", + "2 -14.4 -1055.375424\n", + "3 -14.1 -1100.425725\n", + "4 -13.8 -929.288485\n", ".. ... ...\n", - "96 13.8 521.681151\n", - "97 14.1 674.091679\n", - "98 14.4 770.699562\n", - "99 14.7 848.473161\n", - "100 15.0 953.358913\n", + "96 13.8 461.151029\n", + "97 14.1 512.259065\n", + "98 14.4 795.078025\n", + "99 14.7 930.233261\n", + "100 15.0 986.124289\n", "\n", "[101 rows x 2 columns]\n" ] @@ -959,7 +959,8 @@ "source": [ "## Adding The Experimentalist\n", "\n", - "Modifying the code to use a custom experimentalist is simple. We define an experimentalist which adds four observations each cycle:\n" + "Modifying the code to use a custom experimentalist is simple. We define an experimentalist which adds some observations\n", + "each cycle:\n" ] }, { @@ -971,11 +972,11 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.787469\n", - "1 -11.056959\n", - "2 -12.028324\n", - "3 0.278927\n", - "4 7.568485, experiment_data=Empty DataFrame\n", + "0 -3.681470\n", + "1 13.752780\n", + "2 -4.058959\n", + "3 10.911147\n", + "4 -1.159941, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])" ] @@ -987,8 +988,7 @@ ], "source": [ "from autora.experimentalist.random_ import random_pool\n", - "\n", - "experimentalist = random_pool\n", + "experimentalist = on_state(random_pool, output=[\"conditions\"])\n", "experimentalist(s)" ] }, @@ -999,7 +999,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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//z49evSgZ8+eRY7V6XRFWrBKa8iQISxatIjRo0ezbt062rdvj9Fo5NChQyxatIiVK1cWK6VwM+Hh4TRu3Jg1a9YUGU/k6elJv379+OSTT9DpdNSuXZs//vij2PgbUL9Q7rzzToYPH86BAwfw8/Pj888/x2g0Mn369CLHdu/eHcBce8vDw4MvvviCIUOG0LJlSwYOHIi/vz/x8fEsW7aM9u3b8+mnn5KTk8PQoUOpW7cub7zxBgDTp0/n999/Z/jw4ezdu7dI8ti4cWOio6OLlDEofE6ht956i3Xr1hEZGcnIkSNp2LAhFy5cYOfOnaxZs4YLFy6U6VqWl4qIq1WrVixcuJCJEydyxx134Obmxj333HPT5/n7+zN58mRmzJjB3XffzV133cWuXbv4888/i3Wd3X333bz66qsMHz6cdu3asXfvXubNm1csUalduzZeXl7MmjULd3d3XF1diYyMpH79+tSuXZvJkydz5swZPDw8+Pnnn687lmvNmjUoisJ9991XZPutjoH6/fffzTWr8vPz2bNnD6+//joA9957r/kPjLJ83oWN0WbynxCWp6QyBrfjn3/+Udq1a6c4OTkp/v7+ytixY5X09PQix2RkZCiAMnDgwJue79oyBoqiKHl5ecrbb7+tNGrUSHF0dFS8vb2VVq1aKdOnT1fS0tLMxwHK2LFji52zZs2axaZnf/DBB4qbm1uxKeRnz55V+vbtq7i4uCje3t7K448/ruzbt6/EKegXLlxQRowYofj6+iouLi5K586dlW3btpX4+iVNp1+3bp0SHR2teHp6Kk5OTkrt2rWVYcOGmUsRTJgwQTEYDMqWLVuKPG/79u2KnZ2dMmbMmGLv/YcfflDq1q2rODo6Ki1atChWkkFRFCU5OVkZO3asEhoaqtjb2ytBQUFK9+7dla+++qpIbICyePHiEuOmhDIGjRo1KvG99+7du9j2kv6tbieukkpNZGZmKg899JDi5eWlAGUqaWA0GpXp06crwcHBirOzs9KlSxdl3759xT5LOTk5yqRJk8zHtW/fXtm8eXOJn+Nff/1VadiwoWJnZ1ck1gMHDihRUVGKm5ub4ufnp4wcOdJcfuPaz9yAAQOUDh06lPp93MyNSpjc6udd2BadolTyyEIhhNny5cu5++672b17t8UU20tLSyM8PJx33nmHESNGaB3ObdPpdIwdO5ZPP/1U61BEBUlKSiIsLIwFCxYUa4ESoqLIUi5CaGjdunUMHDjQYpInULvrnn32Wd59991SzZYSQmsffvghTZo0keRJVCppgRJC2DRpgSqdm033v7pUhxBCBpELIYTg5tP9b2WigxC2TBIoIYRNk0b20rnZdP9rK40LUdVJF54QQgghRBnJIHIhhBBCiDKSLrwKYDKZSEhIwN3dvdyW2BBCCCFExVIUhYyMDEJCQooszVMSSaAqQEJCQrGFXIUQQghhHU6dOkX16tVveIwkUBWgcO2zU6dO4eHhoXE0QgghhCiN9PR0QkNDzd/jNyIJVAUo7Lbz8PCQBEoIIYSwMqUZfiODyIUQQgghykgSKCGEEEKIMpIESgghhBCijCSBEkIIIYQoI0mghBBCCCHKSBIoIYQQQogykgRKCCGEEKKMJIESQgghhCgjSaCEEEIIIcpIEighhBBCiDKSBEoIIYQQoowkgRJCCCGEKCNZTFgIK5edZyQ1Ow+DXoejwYCDnR4HOz0G/c0XwxRCCHFrJIESwgooikJCWg47T15kV3wq+xPSOJuRy9mMXDJyC4odr9dBiJcztXxdqeXnQrifG3fU8qFhiIckVkIIUQ4kgRLCQimKws74i/wak8Cq/ckkpedc91g7vQ6joqAo6s8mBU5fzOb0xWw2xl45zt3JjsgwH9rW9qNX4yBCvJwr+F0IIYRt0ilK4a9cUV7S09Px9PQkLS0NDw8PrcMRViYxLZsf/jvJrzEJnL6Ybd5up9fRINiDljW8aBbqRTUvZ/zdHfF3d8TNUf1bqMCkkFdgIjO3gPgLWcSdu8TJ85c4lJjB1rgLRVqrdDqIDPPh/hbV6Nk4GE9n+0p/r0IIYUnK8v0tCVQFkARK3IrTF7P4Yv0xFm8/TZ7RBICrg4HoxkHc2yyEyDBfnB0Mt3x+o0lhf0Iam4+dZ+2hFLbGXTDvc7DT07dldR7rGEZtf7fbfi9CCGGNJIHSmCRQoixS0nP4YPURftpxmgKT+t+xTZgPj7StSff6gbeVNN3I6YtZ/LY7gV93JXA4OQNQW6WiGgQyunM4rWr6VMjrCiGEpZIESmOSQInSMJkUFmw7xYw/D5KRo3atta/jy5Pd6nJnuG+lxaEoCttOXOSrDcdYczDFvL1rhD/P39WAeoHulRaLEEJoSRIojUkCJW4mNiWTF5bsZesJtRutWXVPXr67Ia1radvqE5uSwdcb4vh5p9oaptdB/9ahTPhfPQI9nDSNTQghKpokUBqTBEpcj6IofP/fSV7/4yB5RhMuDgYm9YhgWLtaFlVe4PjZTN5ZcZgV+5MAcLY3MKlHPYa1q4WdQervCiFskyRQGpMESpQkJ9/IC7/sZcnOMwB0rufPG/c3prq3i8aRXd/2Exd4Y/lBdsWnAtC0uidvPdCUhiHyuRZC2B5JoDQmCZS41qkLWTz+/Q4OJKaj18HzvRrwWMcwdDrLaXW6HpNJYdH2U7yxXB2rZdDrGNUpnPFRdXG0q5gB7kIIoQVJoDQmCZS42ta4C4z6fjupWfn4ujrwyUMtaFfbT+uwyiwlPYdXft/P8r1qt17DYA8+HtSCOgFS9kAIYRvK8v0tgxmEqEDrD6fwyHdbSM3Kp1l1T35/soNVJk8AAR5OfD64FV8OaYWPqwMHEtO555ONLNgaj/wdJoSoaiSBEqKC/Lk3kZH/t52cfBPd6gew8PG2NrF0SnSjIFY83ZEOdfzIzjcyZclexs7fSXpOvtahCSFEpbGqBGrDhg3cc889hISEoNPpWLp0aZH9iqIwdepUgoODcXZ2JioqiqNHjxY55sKFCwwePBgPDw+8vLwYMWIEmZmZRY7Zs2cPHTt2xMnJidDQUN55552KfmvCxvy84zRj5+8k36jQu2kwsx5uhZO97YwXCvBw4v8ebcOUXvWx0+tYvjeJPp/9S2xK5s2fLIQQNsCqEqhLly7RrFkzPvvssxL3v/POO3z88cfMmjWLLVu24OrqSnR0NDk5VxZhHTx4MPv372f16tX88ccfbNiwgVGjRpn3p6en06NHD2rWrMmOHTt49913eeWVV/jqq68q/P0J2/DTjtNMWrwbkwL9W1fn44EtcLCzqv9qpaLX6xjduTY/jWlHsKcTx89eos9n/7LmQLLWoQkhRIWz2kHkOp2OX375hT59+gBq61NISAiTJk1i8uTJAKSlpREYGMicOXMYOHAgBw8epGHDhmzbto3WrVsDsGLFCu666y5Onz5NSEgIX3zxBS+++CJJSUk4ODgAMGXKFJYuXcqhQ4dKFZsMIq+61h1K4bH/247RpDCsXS2m3t0QvQXVd6ooZzNyGTtvp7kw6PioujzVrW6VeO9CCNtRJQeRx8XFkZSURFRUlHmbp6cnkZGRbN68GYDNmzfj5eVlTp4AoqKi0Ov1bNmyxXxMp06dzMkTQHR0NIcPH+bixYslvnZubi7p6elFbqLq2RV/kSfm7cRoUnigRbUqkzwB+Ls7Mm9kJEPb1gTgwzVHmbAohtwCo8aRCSFExbCZBCopSZ1aHRgYWGR7YGCgeV9SUhIBAQFF9tvZ2eHj41PkmJLOcfVrXGvGjBl4enqab6Ghobf/hoRViU3J5NE528jON9K5nj9vP9i0yiRPhewNeqbf15h3+jbFTq/j15gEHvl2K2lZMrhcCGF7bCaB0tLzzz9PWlqa+Xbq1CmtQxKVKCU9h6HfbeXi5VIFnw9uiX0VXu6k/x2hzBneBjdHO7bEXaDvrE2cupCldVhCCFGu7LQOoLwEBQUBkJycTHBwsHl7cnIyzZs3Nx+TkpJS5HkFBQVcuHDB/PygoCCSk4sOgi38ufCYazk6OuLo6Fgu70NYl3yjiSfm7eRMajZhfq58N+wOXB0r4b9VXhakxkNu+uVbhnrT24GDGzi6g6MHOHuBVw0w2Fd8TFfpUNePxaPbMnz2NmJTMrn/803836NtZAkYIcTtO70DTv0HdXuAX13NwrCZBCosLIygoCDWrl1rTpjS09PZsmULY8aMAaBt27akpqayY8cOWrVqBcBff/2FyWQiMjLSfMyLL75Ifn4+9vbql87q1auJiIjA29u78t+YsGhvLDvI9pMXcXe047thd+DrVgGJdNYFOL4ekvZAyiE4exAungRKOf9Dbwc+4eBXT72FNIdaHcHFp/xjvUqDYA9+GduO4bO3cSgpg4FfbWbOo21oWUP+HwkhbsPeRbBlFpw7Avd8pFkYVpVAZWZmEhsba/45Li6OmJgYfHx8qFGjBuPHj+f111+nbt26hIWF8fLLLxMSEmKeqdegQQN69uzJyJEjmTVrFvn5+YwbN46BAwcSEhICwEMPPcT06dMZMWIEzz33HPv27eOjjz5i5syZWrxlYcF+jTnDnE0nAPhgQHPC/FzL58SKoiZLR1fB0dVwehsopuLHOXmCs/eV1iZHdzAVQG7mlRaprHOQn6X+ojl35Kon6yCoCYR1gvAuENYZ7ByKv8ZtCvZ0ZuHjbXl0zjZ2nLzIw99s4ZtHWtOujnVWYxdCWIBj69T78K6ahmFVZQzWr19P167FL9jQoUOZM2cOiqIwbdo0vvrqK1JTU+nQoQOff/459erVMx974cIFxo0bx++//45er6dv3758/PHHuLldWc9rz549jB07lm3btuHn58eTTz7Jc889V+o4pYyB7TuUlM79n20iO9/IuK51mBwdcfsnzb4IMfNh+3dwPrbovoCGUONO8G8AAZdvrqVIQkwmyEiAs4fh3FG19Sr+Pzh7TUkOJy9oeB807Q812oG+fMdwZeUV8Pj3O/jn6Dkc7PR8/lBLohoG3vyJQghxtbQzMLMhoINnj5d7S7osJqwxSaBsW3pOPvd+spET57PoWNePOcPbYLidGXcJu2DrN7DvZyjIVrfZu6otQ3X/p948q5dL7GYZyXDiH4j7G46sgsyrZph6VINmg+COx8Aj+PrnKKPcAiNPzt/FqgPJGPQ6Ph3Ugl5Nyu/8QogqYNc8+PUJCGkJo9aV++klgdKYJFC2beKiGJbsPEM1L2d+f7IDPq632PV19jCsfRUO/XFlW2BjaP2o2hLk6F4+Ad+MyQgnNsLexXDgN8hNU7fr7aFxX2j7BAQ3K5eXKjCamLx4N0tjErDT6/j0oZb0bFzy5AwhhCjm58fU31UdJ0H3qeV+ekmgNCYJlO1asS+R0T/sRK+DxaPb0qrmLTQfp52B9TMgZp46tkmnh8YPqi0+oW1Ap2H9qPwcOPInbPkS4jdf2V6rI3SZArU63PZLGE0KkxbFmJOozwe3pEcjSaKEEDdhMsH79eDSWRj6B4R1LPeXqJKVyIWoaGczcnnhl30APN65dtmTJ2M+rH8bPmkJu75Xk6f6d8MT/0Hfr6FGpLbJE4C9EzS6Hx5dASP/UhM7nUHt7pvTG75/ABJibuslDHod7/dvzn3NQygwKYydv5PVsn6eEOJmUvaryZO9i/rHpsYkgRKiFBRF4fkle7hwKY/6Qe6Mjypj7ZGkffB1V1j/JhTkQI22MGI1DJwH/uUwAL0iVGsFD34L4/eorWN6Ozi2Fr7qDIuHwfljt3xqg17H+/2acU+zEPKNCk/M28H6wyk3f6IQouoqnH1Xsz3YaV97URIoIUph8Y7TrDmYgr1Bx8wBzXG0M5TuicYC2PAufNUFkvaqZQf6fgvD/7SIv6BKxbM69H4fxm2DJv0BHez/BT6/E/56HfKzb+m0dgY9M/s3o3fTYPKNCqN/2MG2y4sRCyFEMcfXq/e1tS1fUEgSKCFu4kxqNq/+fgCAif+LoEFwKce1XTwJ30apSYYpHyJ6wxNboMmD2nfV3QqfcLWrcfRGqN0djHlqcvhZJBxZeUunVJOo5nSN8Ccn38Sjs7ex70xaOQcuhLB6+TlwcpP6WOP6T4UkgRLiJl75bT+ZuQW0qunNqE7hpXvS8b/VVqeEXWqNpQe+Vrvr3G2g9lFQY3j4Z+j/vVryIPUkzO8PCwZDekKZT+dgp+fzwa1oE+ZDRm4Bj3y3ldiUzAoIXAhhtU5tUcu8uAWpdfAsgCRQQtzAmgPJrD6QjJ1ex4wHmty83pOiwH9fwPf3Q/YFCGkBY/5VyxJYY6vT9eh00PBeGLsV2j2ljo869Ad83hb2/lTm0zk7GPh2aGuaVPPkwqU8hny7hYTUW+saFELYoOOF1ce7WMzvUkmghLiO7Dwjr/y+H4ARHcOoF3iTukz5ObD0CVgxBRQjNB2ojnUq7yKYlsTRDXq8Bo//A8HNIScVfh6hDjLPKtt4Jncne+Y+2oY6AW4kpuUw9LutpGXlV0TUQghrUziA3ELGP4EkUEJc16frjnL6YjYhnk483f0ms+5y0uD7PrB7vlrXKfpNuH8W2DtXSqyaC2wIj62BLs+rZQ8KB5kfXV2m0/i4OjD30TYEejhyNCWTkf+3nZx8YwUFLYSwClkXIHG3+ji8i6ahXE0SKCFKEJuSyVcbjgMw7d5GuDjcYN3tS+dh7r1q4UlHT3h4CbQdazHNzJXGYK8W23xsDfjVg8xkmPcgrHlFnY1YStW8nJn7aBvcHe3YeuICExbGYDRJvV8hqqzj6wFFXRPU3XKK7koCJcQ1FEVh6q/7yDcqdKsfQI8bLXqbkaQWmEyMARdfGPa7RTUxa6JaS3h8A7QZpf68cSbMvQfSE0t9ivpBHnz1SGscDHr+3JfEq7/vRxZNEKKKMo9/sqzfrZJACXGN5XuT2HTsPI52el65pxG667UkpcbDdz3h7EFwD1bHO5XTmnFWz94Z7noX+s0BB3eI3wSzOlwZx1AKbWv78sEA9XrO3XyS7/49UTGxCiEsl6LAsfXqYwv741QSKCGukldg4p2VhwB1uZYavi4lH3jxJHzXCy7GgVcNNXmy1IriWmp0Pzz+NwQ2gaxz6uzEfz5QfymWwt1NQ3jxLnXK8uvLDsiSL0JUNedjIS1eXdy8ZjutoylCEighrjJ/y0lOns/Cz82Rx69X8ynzrJoIpJ8G37owfAX4hFVuoNbEtzY8thpaPgIosHY6LBmlzloshcc6hvFQZA0UBZ76cZcU2hSiKimciFKzHTi4ahvLNSSBEuKy9Jx8Pv4rFoAJ/6uLq2MJA8dzM9SB0ReOgWcoDP0NPKtVcqRWyN4Z7v1EXRJGZ4C9i2DOXeoYspvQ6XRMv7cRHev6kZ1vZMTcbSSmSY0oIaqE2MsJVN0e2sZRAkmghLjsy7+PceFSHuH+rgxoHVr8gIJctdp24YDxIb+AR0ilx2nV7ngMHlmqrgl4Zodarf3Mzps+zd6g57PBLakX6EZyei6PztnOpdzSz+wTQlihvEtwYqP6uO7/tI2lBJJACQEkpmXzzT9xAEzpWR87wzX/NUxGtdsp7m9wcIPBP4HfTWpDiZKFdYKRf4FfBGQkwuy74PCKmz7Nw8me74bdgZ+bIwcT05m4KAaTlDcQwnbF/aOuuelZQy2NYmEkgRICmLn6CLkFJu6o5c3/SipbsPJFOLBUHcg44Ad1qr64dT7har2oOlHq+lYLBsH22Td9WnVvF74c0goHg56V+5P5cO3RSghWCKEJc/ddlEXW1ZMESlR5R5Iz+GnHaQCev6tB8bIFu36ALV+ojx/40uKm0lotJw8YtACaPwyKCf4YD3+9cdMZeq1qevPmA00A+HjtUZbtKX19KSGElVCUKwPI61he9x1IAiUEH689ikmB6EaBtKzhXXTn6R3wxwT1cZfnoXHfyg/Qlhns4b5PofNz6s8b3oFfx4HxxmvgPdiqOiM7qjMfJy2OkZl5Qtia87GQehIMDmq3vwWSBEpUaUeTM1i2V23BeLr7NX3sGcmw8GG1Dz6iN3R6VoMIqwCdDrq+APd8pM7Qi/kBFg65aZmDKb0a0LmePzn5Jkb+33bOZuRWUsBCiAp3dJV6X7Odumi5BZIESlRpn66LRVGgR8NAGoZ4XNlRkAeLh0JGgjp48f5ZoJf/LhWq1TAYOB/snODInzC/H+RmXvdwg17Hx4NaEO7vSmJaDmPn7yTfaKq8eIUQ5eb4ydOs+/sfEhIS1A0W3n0HkkCJKuzY2Ux+363+Z32q+zUz6lY+f3lxYA/1S93Jo4QziHIX0RMe/lmd6Ri3Ab7vA9kXr3u4p7M9Xw1pjZujHVvjLvDGsoPmfQkJCWzcuPHKL2QhhMWas+Ewo1ak8c6fB9TyBSf/VXdYYPmCQpJAiSrrs79iMSkQ1SCAxtU8r+zYsxi2fQPo4IGvpVxBZavVAR75DZy84PQ2mHOPWv39OuoEuPF+f3XNvDmbTrBkpzoh4Pjx48TGxnL8+PHKiFoIcRtiLzmQr+ioFuBr8eULCkkCJaqkE+cusTTmDHBN69PFk7Bsovq40zNqi4iofNVbwfDl4BoAyXthdq/rVi1PSEjA9WIsw+5Qy088v2Qv+86kER4eTp06dQgPv86SPEIIi5CTb2TXmUsA3HNHbYsvX1BIEihRJX22Tm196hrhT9PqXupGY4FaLDM3Haq3uTIzTGgjsBE8ukJdMuf8UZhzd4lJVGFLU7eAbLpG+JNbYOLx73fg7OlHhw4dCAmRavFCWLLtJy6SnW8k0MORiAC3KwPILXD5lqtJAiWqnFMXsliyS219evLq1qeNH8Cp/8DBHR74CgwlrIUnKpdvbRj2xw2TqMKWpjq1a/PhwBbU9HXhTGo2Ty+MwSiVyoWweH8fSQGgcz1/dOdjITXeossXFJIESlQ5326Mw2hS6FDH70rdp1PbYP1b6uPe74FPmHYBiqK8a12TRPUukkSFhISYW5o8ne2Z9XArnOz1bDhylk/+kkrlQli6v4+oYxw71wu40n1Xsx04uGoY1c1JAiWqlLSsfBZtPwXAqE6Xx8bkZsCSx0AxQuMHoekADSMUJSqSRMWqSVR6yRXIGwR78EYftVL5R2uPsv5wSiUGKoQoi4TUbI4kZ6LXQYc6fnDk8rqYFt59B5JAiSpm/tZ4svKM1A9yp2NdP3Xjiilw8YT65dz7fYsetFilXZtE/d99cOlciYf2bVWdhyJroCgwfmEMpy9mVW6sQohS2XC59alFDW88dZlw4nL5goheGkZVOpJAiSojr8DEnE1xAIzoEKaueXdsnbrWHTq4/0tw9tI0RnEThUmURzU4d/hynajUEg+ddk9Dmlb3JDUrnyfm7SS3wFiZkQohSuFK950/xK5VewL866sLjls4SaBElbFsbwLJ6bn4uztyb/MQyMtSF7AFaDMSarXXND5RSt614JFfwdUfkvbCvAfVbthrONoZ+HxwS7xc7NlzOo0Zyw9VfqxCiOvKN5rYeFRtRe5czx8O/6nuqGcd5WMkgRJVgqIofL1BbX0a2rYmjnYGWD9D7brzqAbdp2oboCgbv7owZOmVYps/DoL87GKHVfd2YWb/5oBaZHPZnpLHTQkhKl/MqVQycgvwcXWgSZDLleVbIu7SNrBSkgRKVAmbj53nQGI6TvZ6BkfWhIQY2PypurP3B+Dorml84hYENYYhS9SyEyf+URd+LsgrdljX+gGM6VIbgOd+3sOJc5cqO1IhRAn+Pqx233Ws64f+1GbITQMXP6jeWuPISkcSKFElfP2PupxHv1aheDvp4bcnQTFBowek2rg1q9YKBi8CO2eIXQNLx4Cp+ILCk/5Xjza1fMjMLeCJeTvJyZfxUEJorcj4p6u77/QGDaMqPUmghM07djaTdYfPotOpg8f57zNI2qN2//R6W+vwxO2q2Q4G/AB6O9j3kzqrUilaQNPOoOfjQS3wdXXgQGI6r/5xQKNghRAA5zJz2XsmDYCOdfzg8HJ1hxXMviskCZSweT/8dxKA7vUDqKVPgXVvqjui3wC3AA0jE+WmbhT0maU+3volbHi32CFBnk7MHNAcnQ7mb4nn990JlRykEKLQP0fV1qfG1Tzwzz4OqSfB4Ai1u2ocWelJAiVsWnaekZ93nAbg4TtrwqqXoCAHanWE5oM1jk6Uq6b9oOflFsV1b8C2b4od0qmeP2O71AHURYdPnpfxUEJo4a9DagLVqa7/ldan8C4WX338apJACZv2++4E0nMKCPVxppPhABz6A3QGuOtdKZhpi+4cDZ2eUR8vmwz7lxY7ZHxUXe6o5U1mbgHj5u+S+lBCVLJ8o4m/L68Q0L1B4JXq41Y2HlUSKGHTftiidt8Nbl0N/arn1Y13PAYBDTSMSlSori9Cq+GAAktGwclNRXbbGfR8NLAFXi727D2Txtt/HtYmTiGqqB0nL5Keo5YvaO6dC6e3qzuspP5TIUmghM3aczqVPafTcDDoedj+L0g5AM7e0GWK1qGJiqTTqUvyRPQGYy78OBBSihbRDPFy5r0HmwHw3b9xrD6QrEWkQlRJaw+q/9+6RPhjiF0FKBDSAjxCtA2sjCSBEjarcPB434YuuG26PDam64vg4qNhVKJS6A3Q9xuofgfkpKnVyq9ZfDiqYSCPtg8D4JmfdpOYVrwQpxCi/K09pHbfRTUIvFK+wEqKZ15NEihhk9Ky8vnt8iyr8XY/Q/ZFCGh4uWtHVAkOLjBoIfjUhrRTMK8f5KQXOWRKr/o0qaaulzd+QQxGk3KdkwkhykPcuUscP3sJO72OjrVc1PVIwarKFxSSBErYpCW7TpOTb6KH3wUCDv2gbuw5Awx22gYmKperLzz8s7puXvJeWDQEjPnm3Q52an0oVwcDW+Iu8Nm6WA2DFcL2FXbfRYb74H56AxRkg2cNCGyscWRlJwmUsDmKojBvSzwA0xzno1OMUP9udYqsqHp8wmDwYrB3hePr4Y8JRQpthvm58lof9Zf3h2uOsP3EBY0CFcL2/XW5+657/UA48Ju6seG9VjkrWhIoYXO2xl0gNiWTLg4HqXZ+E+jtocdrWocltBTSAh78DnR62PU9/Pthkd0PtKzOAy2qYVLg6QUxpGXll3weIcQtS8/JZ2uc+gdK93qeV8oXNLhXw6hunSRQwub8tOM0oDDddYm6odUw8AnXMiRhCSJ6Xim0ueYV2P9Lkd2v9mlMLV8XzqRmM2XJHhRFxkMJUZ42HDlLgUmhToAbNVO3Q246uAerkz2skCRQwqZcyi1g2d5EovQ7qZm9X11ktrCwohCRoyByjPp4yeNwaqt5l5ujHR8PaoG9Qcef+5JYtP2URkEKYZv+OljYfRcAB39VN9a/G/TWmYpYZ9RCXMeKfUnk5OXzguNidcOdo8E9UNughGWJfgPq9bpcI2oQXIgz72pa3YvJPSIAeOW3Axw7m6lVlELYFKNJYV1h9fF63nBombqjoXV234EkUMLGLN5xinv1mwhX4sHJE9o/rXVIwtIU1ogKbgZZ59RCmzlp5t0jO4bTvo4v2flGnl6wi7wCk4bBCmEbdsVf5GJWPp7O9rRSDqilZVx8oUY7rUO7ZZJACZtx6kIWO46nMMH+J3VD+6fVyuNCXMvRTa0R5R4MZw/BT4+CsQAAvV7H+/2a4+1iz74z6by/SpZ6EeJ2rb5cvqBzPX8Mh39XN9bvbdWlZSSBEjbj552nGWBYR01dCrgGQORorUMSlswjGAb9qI6Ti10Dq1407wrydOLtvk0B+HLDcTYePadVlEJYPUVRWLVfTaB6NPSDg3+oOxrcp2FUt08SKGETTCaFP3Yc40m7yzOrOj8LDq7aBiUsX0gLuH+W+njLLNj2rXlXj0ZBDI6sAcDERTFcuJSnRYRCWL2jKZnEnbuEg52ebq4n4FIKOHpCWCetQ7stNpVAvfLKK+h0uiK3+vXrm/fn5OQwduxYfH19cXNzo2/fviQnF11END4+nt69e+Pi4kJAQADPPPMMBQUFlf1WRBltibtA5/Q/CNSlYvKsAS2Hah2SsBaN+kC3l9THy59Ri21e9lLvhtT2dyUlI5fnfpbSBkLcipX7kgDoUMcPl6OXB49H9AI7Bw2jun02lUABNGrUiMTERPNt48aN5n0TJkzg999/Z/Hixfz9998kJCTwwAMPmPcbjUZ69+5NXl4emzZtYu7cucyZM4epU6dq8VZEGSzddoxRdmqzsL7TZKv/jykqWcfJ0KQ/KEZY9AicPwaAs4OBjwaqpQ1WH0hmwTYpbSBEWa08oCZQ0Q0D4ODl8U9WPPuukM0lUHZ2dgQFBZlvfn5+AKSlpfHtt9/ywQcf0K1bN1q1asXs2bPZtGkT//33HwCrVq3iwIED/PDDDzRv3pxevXrx2muv8dlnn5GXJ833lupSbgEuBxYSqEslzyUYmg3SOiRhbXQ6uPcTtaBfTlqRmXmNq3nyTLRa2uDV3w9wXEobCFFqpy9mse9MOnod9PROgPTT6rJKtbtpHdpts7kE6ujRo4SEhBAeHs7gwYOJj1fXRNuxYwf5+flERUWZj61fvz41atRg8+bNAGzevJkmTZoQGHilblB0dDTp6ens37//uq+Zm5tLenp6kZuoPGv2neZR1KJs9p3GS+uTuDX2TjDgB3APgXNH4OfHwGQE4LEO4bSrXVjaIEZKGwhRSoWDx1vX8sHzxJ/qxno9wN5Zw6jKh00lUJGRkcyZM4cVK1bwxRdfEBcXR8eOHcnIyCApKQkHBwe8vLyKPCcwMJCkJLV5MSkpqUjyVLi/cN/1zJgxA09PT/MtNDS0fN+YuKGzm+cRqj/LJXsfdK1k7JO4De5BMHAe2DnB0VWwdjpwubRB/2Z4Otuz90waM9cc0ThQIazDiv3qd2fPhoGw7/Ikn4Z9tAuoHNlUAtWrVy/69etH06ZNiY6OZvny5aSmprJo0aIKfd3nn3+etLQ08+3UKRknUVkuZmTT9ewPAGS3Hm0Tf9UIjVVrCfd9pj7+9yPYvQCAYE9n3nqgCQCz/j5mXhRVCFGy85m5bD+h/j+52+cUpMWDgxvUi9Y4svJhUwnUtby8vKhXrx6xsbEEBQWRl5dHampqkWOSk5MJCgoCICgoqNisvMKfC48piaOjIx4eHkVuonLs/+t7ausSyNC54dflCa3DEbaiyYPQcZL6+Len4PR2AHo1CaZfq+ooCkxYGEN6Tr6GQQph2dYcTMakQONqHgScvFz7qX5vm/lD16YTqMzMTI4dO0ZwcDCtWrXC3t6etWvXmvcfPnyY+Ph42rZtC0Dbtm3Zu3cvKSkp5mNWr16Nh4cHDRs2rPT4xU0oCtX3fgHAoZqDwdFd44CETen6EkTcpa6Zt/BhyFC7Iqbd24gaPi6cSc1m2q/XHxspRFW38vL4p54N/GD/5e67xg9qGFH5sqkEavLkyfz999+cOHGCTZs2cf/992MwGBg0aBCenp6MGDGCiRMnsm7dOnbs2MHw4cNp27Ytd955JwA9evSgYcOGDBkyhN27d7Ny5Upeeuklxo4di6Ojo8bvTlzr4u7fqVVwnEzFiZAesuadKGd6Pdz/JfhFQEYiLBwCBbm4Odoxc0Az9Dr4ZdcZftudoHWkQliczNwCcwX/Pt5xcOksOPtA7a4aR1Z+bCqBOn36NIMGDSIiIoL+/fvj6+vLf//9h7+/PwAzZ87k7rvvpm/fvnTq1ImgoCCWLFlifr7BYOCPP/7AYDDQtm1bHn74YR555BFeffVVrd6SuIG8de8CsNr1bqqFVNM4GmGTnDzU5V6cPOH0Vlg+GRSFVjV9GNetLgAv/bKXhNRsjQMVwrKsP5xCntFEmJ8r1U5dLp7Z8D4w2GsbWDnSKVJat9ylp6fj6elJWlqajIeqKKe2wrf/I1exY2nnFQzodofWEQlbdnQNzHsQUKD3+3DHY+QbTTw4azO7T6XSNtyXeY9FotfrtI5UCIvwxLwdLN+bxNiOoTyz9261rtrQPyCso9ah3VBZvr9tqgVKVB2X/v4YgN9M7el6RxONoxE2r24URE1TH//5HJz4F3uDng8HNMfZ3sDm4+f5dmOctjEKYSEu5Rbw1yF1LHF/78Nq8uQeDDXbaRxZ+ZIESlifiydxjlWbhHeEDCLA3UnjgESV0H48NO4LpgJ1uZe004T5uTL1HnWCybsrD3MwUYroCrH2UAo5+SZq+bpQI+Fy8cxGD4DeoG1g5UwSKGF9tnyJHhMbjE1o0bq91tGIqkKng3s/haAmkHVOnZmXn8PAO0KJahBIntHE+AUx5OQbtY5UCE39cXliRZ9GXugOX06gGvfVMKKKIQmUsC456Zh2zAVgrnIXPRsFaxyQqFIcXNTlXpy9IWEXLJuIDnirbxP83Bw4nJzBeysPax2lEJrJyMln/ZGzAPRz3wf5WeAdphaotTGSQAnrsut79PmZHDVVQwnvhqeL7czoEFbCuxY8OBt0eoiZB9u+wc/Nkbf7NgXgm41x/Bt7TtsYhdDI2oMp5BWYCPd3JeT05dl3jfuqLbg2RhIoYT2MBfDfLAC+NfYiukmIxgGJKqt2V4hS18ljxRQ4uZnuDQJ5KLIGAJMW7SY1K0/DAIXQxh971O67vg3d0B1do25sYjvFM68mCZSwHof+gLR4zivu/GrqQFSDwJs/R4iK0u7JooPK0xN4qXcDwvxcSUrP4aWl+5AqMaIqScvOZ8MRtfX1QYctYMqHwMYQ0EDjyCqGJFDCemxWF3j9wRhFs7AgfN2kOrzQkE4H936ifkFcSoFFj+CiNzJzQHMMeh1/7Enk1xipUi6qjtUHkskzmqgX6Ebg8ctFqpsN0jaoCiQJlLAOp7fD6a3kY8cPBf+jV2MZPC4sgIOrOqjcyQtOb4MVU2ge6sVTl6uUv/zrPs5IlXJRRSy73H03uHYenNkOOgM07a9xVBVHEihhHbZ+DcBvxracxYsejaT7TlgInzDo+y2gg+3fwc7vGdu1Ni1qeJGRU8DEhTEYTdKVJ2xbalYe/1xe++4e1qsb6/4P3AK0C6qCSQIlLF/WBfNK3t8X/I/moV4EezprHJQQV6kbBd1eVB8vm4Rd0i5m9m+Oi4OBLXEX+Oaf49rGJ0QFW7k/iQKTQsNAF3yO2n73HUgCJazBrh/AmMsJ+zrEKLXp2ThI64iEKK7DJIjoDcZcWPgItZyymHq3WqX8vVWHOZAgVcqF7Vq6S+2+e7zGGchIULu1I3ppG1QFkwRKWDaTSe0WAb7O7gLoiG4kCZSwQHo93P8F+NaB9NPw03AGtAomqkEg+UaF8Qt3SZVyYZMSUrP5L+48AP/L/0vd2Lgv2Nn2RB9JoIRlO74OLsaRb+fGkoJ21A9yJ8zPVeuohCiZkycMmAcObnDiH3Rrp5urlB9JzuRdqVIubNCvMQkoCnSp6YhL7HJ1Y/PB2gZVCSSBEpbtcuvTBpcosnGS1idh+QLqw31qyQ02fYLfyeW886BapfxbqVIubIyiKPyy6zQAYwL2Q0E2+NWzyaVbriUJlLBcaWfgsPrXzPsX1EWDZfyTsAqN+kC7p9THS8fSzeeCVCkXNulgYgZHkjNxsNPTKvXywsHNBtnk0i3XkgRKWK6dc0ExccG/DQcKqlHDx4X6Qe5aRyVE6XSfBmGdIP8SLHyYl7pXkyrlwuYUtj4NqFOA3anNgA6aDtA2qEoiCZSwTMZ82DEXgD+d7gKge4MAdFXgrxphIwx26qLDHtXhfCwuy8Yxs39TqVIubIbRpJg/x8Nc/lM3hncBz2raBVWJJIESlunwcshMQnEN4JOE+gB0ry/FM4WVcfWDAf8HBgc4vIzmJ769UqV86T5OX8zSOEAhbt3mY+dJycjFx1lP+KnLtZ9aPKxtUJVIEihhmS4PHk+p04+kSyZcHQy0CfPROCghbkG1VnDXe+rjv95gXI0TapXy3AImLtotVcqF1VpyuftufK14dBkJ4OwDDe7ROKrKIwmUsDwXT8Lx9YCO3+16ANCpnj8OdvJxFVaq1VBo+QigYPhlJJ/08sXFwcDWuAt8tUGqlAvrk5VXwMp9SQDcW7BS3dj8IZuv/XQ1+UYSlidmvnof3pmlJwwAdKtvu+spiSqi17sQ0gKyL1J95Uheu6s2AB+sPsy+M2kaBydE2aw+kMylPCMtvS7heXqdurHVME1jqmySQAnLYjKZE6jUiP7sO5OOTgddJYES1s7eCfp/Dy6+kLSHB5I+ILphAPlGhacX7CI7T6qUC+uxaPspACb7b0OnmKBmB/Crq3FUlUsSKGFZTmyAtHhw9GSV6Q4Amod64edWdZqFhQ3zClVn5un06GLm80HtXQS4O3Ls7CXeXH5Q6+iEKJVTF7L4N/Y8Bp2JNhf/UDe2Hq5tUBqQBEpYll3z1PsmfVl1RO3W6C6tT8KWhHdWa0QBrmtf4MuuJgC+/+8kfx1K1jIyIUpl8eXWpzEhx7HLrHqDxwtJAiUsR3YqHPwNgNwmD7Hx8pIX3aR8gbA17Z+GBveCKZ8Wm59iXBsPAJ79aQ/nMnM1Dk6I6zOaFBbvUGffPWx/eexTFRs8XkgSKGE59i+Bghzwb8CmrBrk5JsI9nSiQbBUHxc2RqeDPp+DXwRkJDIx9U0aBjhzLjOPZ3/aI1XKhcXaGHuOxLQc6jqlE5j8t7qxig0eLyQJlLAcu35Q71sMZu3hFECdfSfVx4VNcnSHAT+Agzv6+E38UGsZDnZ6/jqUwg9b4rWOTogSLdqmdt+9EFx1B48XkgRKWIaUg3BmB+jtUJr056+DagLVvYGMfxI2zL8e3P8FAD57vuHL5nEAvP7HAWJTMrSMTIhiLlzKY9WBJPSY6JCuLvReFQePF5IESliGwtanutEcvuRMQloOjnZ62tX20zYuISpag3ugw0QAuhx+jUE1M8gtMPHkjzHkFkhpA2E5ftl1hnyjwgi/A9hfSlQHj9e/W+uwNCMJlNCeMR/2LFQft3iYDUfOAnBnuC9O9gYNAxOiknR7CcK7osvP4rWcGdR0yeNgYjrvrjisdWRCAKAoirn7boT9KnVjq2FqfbMqShIoob2jq+HSWXD1h7r/Y/XeMwA0D3TQODAhKoneAA9+B141sEs7wc+Bs9Fh4puNceY/KITQ0u7TaRxOzqCp3SmCLm4HnQHueEzrsDQlCZTQXmHrU5P+ZBv1xJxRx36E6GV5C1GFuPiog8rtnPBL/Jvvaq4BYNLi3ZyX0gZCY/O3nATgBd/LM+8a3gee1TSMSHuSQAlt5aTDkRXq46b92RJ3nnwT+Drp6NC0jraxCVHZgpvBPR8B0DV5Do947+NsRi7P/SylDYR20rLy+TUmAW/SaZOpJvZEjtY2KAsgCZTQ1qE/1NpPvnUhuBkbjqjFM//XpDrVqlXtv25EFdVsILR5HIBpBR8TYUhizcEUvv/vpMaBiapq8Y5T5BaYeNprE3pjnroodmgbrcPSnCRQQlt7Fqn3TfuDTseGo+p4j071/DUMSgiNRb8BNdphyM9kgdenuJHF68sOcigpXevIRBVjMinM2xKPHQX0Vy73FkSOVovBVnGSQAntZCRD3OX+9CYPkpCaTWxKJnodtJfyBaIqM9hDvzngHoz3pePM9Z5NfkEBT/24i5x8KW0gKs/G2HPEnbtEH8eduOSmgGsANLpf67AsgiRQQjv7fgbFBNXvAJ9w82yjZqFeeLrYaxycEBpzD1QHlRscaJX9L8+6LONIciavLzugdWSiCinsOn7a7fLYpztGVMl170oiCZTQzt7L3XdN+gHwz1F1/FOnutJ9JwQA1VtD7/cBGG1aQFf9Ln74L56V+5M0DkxUBWdSs1l7MJmmumOEXtoHentoVXUrj19LEiihjXOxkLBLrSXS6AGMJoWNsZcTKBn/JMQVLR+B1iPQofCF8+eE6RJ59qc9JKRmax2ZsHE/bonHpMCzXn+pGxr3VVtGBSAJlNDK3sXqfe2u4ObP7tOppGXn4+FkR7PqntrGJoSl6fkW1GiLk/ESc10+xJidxtMLdlFgNGkdmbBReQUmFmyLJ1SXTPvsy2NV2z6hbVAWRhIoUfkU5aruu/4A5vFPHer6YWeQj6UQRdg5QL+54B5CDeMpPnKcxfYT5/n4r1itIxM26s99iZzLzONp55XoMEHt7mqdMmEm31Si8p3ZCReOg50z1L8LuJJAyfgnIa7jqkHl3XXbedpuCZ/8dZRNx85pHZmwMYqi8M0/cfiRRh/WqRs7TNA2KAskCZSofIWtT/XvAkd30rLziTmVCsj4JyFuqHorc6Xy8XZL6KHbyoSFMbLUiyhXW+MusPdMGiMcVmJnyoVqraFWB63DsjiSQInKZTLC/l/Ux5e777YcP49JgXA/V0K8nDUMTggr0PwhiBwDwIcOs/DKOMrkxbsxmWSpF1E+vtkYhxtZDLO7XLqgwwQpnFkCSaBE5YrfDJnJ4OQJtbsBsOnYeQDa1fHVMjIhrEeP1yGsE87k8I3DB+w6fJyv/zmudVTCBsSdu8Sag8k8ZFiLsykT/OpBxF1ah2WRJIESlevAr+p9RG91YCywuTCBkurjQpSOwU4dVO5Vk1BdCp/af8z7Kw+w4+QFrSMTVu67jXE4KHk84bRS3dB+POglVSiJXBVReUwmOPCb+rhRHwDOZuRyODkDgDvDpQVKiFJz8YFBP6LYu9LBsJ/n9d/z5PxdXLyUp3VkwkpdvJTH4h2nuN+wES/jBfCoZi50LIqTBEpUnlP/QWYSOHpCeFcANh9XW58aBHvg4+qgZXRCWJ/ARuge+BKA4XYr6Zy5jGd+2o2iyHgoUXbzt8aTl1/Ak47L1A1tx5l7CkRxkkCJyrN/qXpf/66ruu/UKdjta0vrkxC3pME90PUlAF61m0PGob/55p84jYMS1ia3wMicTSe4T/8v1UyJ4OytVsEX1yUJlKgcJhMcvNx917CPebMMIBeiHHSaDI37Yq8z8oXDTOat+JvtJ2Q8lCi9X2MSuJCRxUSHy7Ok2z0Fjm7aBmXhJIESlePUFshIBEcPdfkW4PTFLE6ez8Kg13FHLR+NAxTCiul0cO+nKMHN8dFl8qXdezw771/OSX0oUQpGk8Ln62J5wPAPoSSBix+0GaV1WBZPEihROQ4sVe8jeoGdI3Bl9l3T6p64O9lrFJgQNsLBBd2gHzG5BRKhP82LOe8z4ccdGKU+lLiJP/YkcOZ8OhPsL7c+dRgvrU+lIAmUqHhXz74rqftOxj8JUT48QtAP/BGTwZHuhl10PvkxH689qnVUwoKZTAqfrztGf8N6QjgLboHQeoTWYVkFSaCu47PPPqNWrVo4OTkRGRnJ1q1btQ7Jep3eBhkJ4OBuLp6pKIp5DS+p/yREOareCv39swB4zO5Pzq6fZV5rUohrrT6YzInk8zxlt1Td0HESOLhoGpO1kASqBAsXLmTixIlMmzaNnTt30qxZM6Kjo0lJSdE6NOt0dfedvRMAx89dIjk9Fwc7Pa1qemsXmxC2qPED0PVFAF61m838H+dy+mKWxkEJS6MoCp/+FctAwzoCdZfrPrUcqnVYVkMSqBJ88MEHjBw5kuHDh9OwYUNmzZqFi4sL3333ndahWR+T6Ur18cvFM+FK912rGt442Rs0CEwIG9fpGYyN+2GnM/GO6X3emPsrOflGraMSFuTvI2c5eiaFcXaXf0d3mmz+I1fcnCRQ18jLy2PHjh1ERUWZt+n1eqKioti8eXOJz8nNzSU9Pb3ITVx2Zjukn7ncfdfdvHlTbGH3nYx/EqJC6HQY7vuU3OA78NBl8dyFaby7ZKPWUQkLUdj69LBhDf66VPCqAc0f1josqyIJ1DXOnTuH0WgkMDCwyPbAwECSkpJKfM6MGTPw9PQ030JDQysjVOtw8Hf1vl60+S8bk0kxVyCX+k9CVCB7JxwH/0iOa3Vq6ZO5a/8kFm8+onVUwgJsPn6eoydPMa5w7FPn56TqeBlJAlUOnn/+edLS0sy3U6dOaR2SZVAUOHR5SYD6vc2bj6RkkJqVj4uDgabVvbSJTYiqws0fp2FLyLFzp5X+KK5/PsnueCmyWZUpisK7Kw/ztN0SvHSXILAxNBukdVhWRxKoa/j5+WEwGEhOTi6yPTk5maCgoBKf4+joiIeHR5GbAM4dgQvHwOAAda50iW6NU395t6rpjb1BPoJCVDj/CBwemk8Bdtyl/489cydwNkOKbFZVaw+mcPHUQYYYVqsberwOehmLWlby7XUNBwcHWrVqxdq1a83bTCYTa9eupW3bthpGZoUKW5/COoHTlaRyy+UEqo1UHxei0ujDO5F/98cADDEuZenXr5FXYNI4KlHZTCaF91YdZordAux1Rqjbw7w6hCgbSaBKMHHiRL7++mvmzp3LwYMHGTNmDJcuXWL48OFah2ZdSui+UxTF3ALVJkwSKCEqk3PrwVxoMxmAR9M+ZcH8bzSOSFS23/ck4Jm8lZ6GbSg6A/zvNa1Dslp2WgdgiQYMGMDZs2eZOnUqSUlJNG/enBUrVhQbWC5uID1RnYEHUK+XefOJ81mczcjFwaCnWaiXNrEJUYX59HqJxLPHCY5bwoPHXmLVqur06HGX1mGJSpBvNDFz1SE+tv8BAF2roRBQX+OorJe0QF3HuHHjOHnyJLm5uWzZsoXIyEitQ7IuR/5U76u1Bo9g8+atcersu+ahXlL/SQgt6HQEP/wVJ73b4qLLpdW/j7Nvz06toxKVYNH2U7RIXU1TfRyKgxt0eUHrkKyaJFCiYpi774r+ZbtFuu+E0J7BnhqjF3PSsR6+unS8lgwg8Uy81lGJCpSTb+TLNft4xn4hALqOk8DNX+OorJskUKL85aRD3Ab1cf27i+yS8U9CWAadozt+j/9Kgj6Y6qSQ+V0fsjIuah2WqCDf/HOcgdkLCNFdQPGoDneO0TokqycJlCh/sWvAmAc+tcGvnnlzQmo2py9mY9DraCnr3wmhOVefEPSPLOECHtQ1HiPu8wcx5Ut5A1uTnJ7DqvXrGGlQewZ0d70L9s4aR2X9JIES5e/wcvW+fm/Q6cybt51QW58ah3jg5ijzF4SwBEG1GpJ89/dkKY40yt7OkVmDwSRr5tmSd/48wEt8g73OiBJxV7GhFeLWSAIlypcxH46sUh9fVb4Arox/ukPqPwlhURq07sLOtp+Qpxiof341cf83Wl1JQFi93adS0e/5kTb6wxjtXND1ekfrkGyGJFCifJ3YCLlp4OoP1e8oskvGPwlhuTr0HMCyuq9hUnSEnVjEmZ+f1zokcZsURWHmb5t5wW4+AIZuL4CXrNVaXiSBEuWrcPZdvZ5FlgY4l5lLbEomIC1QQliq+x56gvkBEwCotu8Lzq98V+OIxO34bXcCdyV+gbcuk3y/hhA5WuuQbIokUKL8KAocvlz/6Zruu+2Xxz9FBLrj7SorfgthifR6HQ+Oeom5ruqqC76bXydzk1Qrt0bZeUZWLvuZ/nZ/A2B/30dgsNc4KtsiCZQoPykHIP002DlDeJciu6T+kxDWwcneQO8xb/OD3f0AuKyaTN7O+RpHJcrq81V7mJz7GQAFLYZCaBuNI7I9kkCJ8nNkpXof1qnYFFkZ/ySE9fBzc+TOkR+zgB7oUbD7bSymvUu0DkuU0sHEdPy3vEm4Pokc50DsekzXOiSbJAmUKD9HL8++q/u/IpszcvI5mJgOSAIlhLWoE+hBzYc/Z5GpG3pMKD8/hnLwd63DEjdhNCksWDCXRwzq72OnB2eBs9TdqwiSQInykX0RTm1RH9eLLrIr5lQqJgVq+LgQ6OGkQXBCiFvRto4/Ln0/ZomxAwaMmBYNu1KmRFikRf/sZXTqBwBkNXsUanfTOCLbVeYEaujQoWzYsKEiYhHWLHYtKCbwbwBeNYrs2nFSXR6ilVQfF8Lq3N0slIweH/KH8U4MSgHGBYPh6GqtwxIlSEzLxu2vFwjWXSDdpSYuvV/XOiSbVuYEKi0tjaioKOrWrcubb77JmTNnKiIuYW2u030HVxIoWb5FCOs0tGNdDrZ9nxXGOzCY8jD9OAgOr9A6LHGNX+d9zj26fzCix23gN+DgqnVINq3MCdTSpUs5c+YMY8aMYeHChdSqVYtevXrx008/kZ+fXxExCktnMqrr30Gx7jujSWFXfCoArWpIAiWEtZrcqxFrG7/NcmMb9KZ8TAsfhoN/aB2WuGz1f7von6x23aW2GIu+hsy6q2i3NAbK39+fiRMnsnv3brZs2UKdOnUYMmQIISEhTJgwgaNHj5Z3nMKSndkJWefB0RNCI4vsOpKcQWZuAa4OBiKC3DUKUAhxu3Q6HTMebMHvdV7jd+Od6E35KIuGwv6lWodW5SVdyMB3xeP46DJJca2Hb++pWodUJdzWIPLExERWr17N6tWrMRgM3HXXXezdu5eGDRsyc+bM8opRWLqjl8sX1O5arFBbYfddixreGPS6a58phLAidgY9Hw6+g8U1pvGLsT06pQDlp0dh709ah1ZlmUwKu757ipYc5pLOBe9hP4KdFCuuDGVOoPLz8/n555+5++67qVmzJosXL2b8+PEkJCQwd+5c1qxZw6JFi3j11VcrIl5hiQrHP13TfQewU8Y/CWFTHO0MzBrahnlBU/jJ2AmdYkT5+THYJhXLtfD30q/planW6Erv+Qn2/nU0jqjqsCvrE4KDgzGZTAwaNIitW7fSvHnzYsd07doVLy+vcghPWLyMJEjcrT6uU8IA8niZgSeErXFxsOPbR9vy0JeTyTrnyCN2q2HZJLWcScfJoJPW5spw4vBu7tg9FXSwv9YwGkU+qHVIVUqZE6iZM2fSr18/nJyuX8/Hy8uLuLi42wpMWInC1qeQluDmX2TX2YxcTp7PQqeD5qFelR+bEKLCeDrb83+P3clDX8HFC248bfcL/PU6ZF2EHq+DXsoMVqS8rAxYNAQ3XTaHHJvQcMh7WodU5ZT5Ez5kyJAbJk+iirlR993l1qd6Ae54OssilkLYGl83R+aNupM/fIbzav4QdeN/n8Fv48Aos7IrjMnE4a+GUst4knN44jt0HjpZKLjSyZ8I4tYV5MGx9erjuj2K7ZbxT0LYPj83R+aPvJMNvv2YmDeaAvQQMw/m94ecdK3Ds0lHfnyGJqlryVcMnOz6Gf4hNbUOqUqSBErcuvhNkJcBrgEQ3LzY7sIZeK0lgRLCpvm7OzJ/ZCS7fXsxKm8i2TjCsb/gu56QJsWWy1PSX19Q76g6YH9N3Zdo1fkejSOquiSBErcudq16Xyeq2HiH3AIje86kATKAXIiqIMDdiQWj2pIQ0Jl+uS9zFi9I2Q/fdIfEPVqHZxOy9i/Hf8MLAPzs8Qg9HpqgcURVmyRQ4tYd+0u9r9O92K59Z9LJKzDh6+pATV+XSg5MCKEFf3dHFoy6E0P1lvTJmc5RJRQyEmF2LziyUuvwrJqSsAv9T8MxYGKZvitdR70ntfU0JgmUuDUZSZC8D9BBeNdiu68e/6STKc1CVBleLg7MeyyS6mER9M2dyialMeRlwvwBsOE9UBStQ7Q+549xafaDOCk5bDQ1odrQr/Fxc9Q6qipPEihxa46tU++Dm4Grb7Hd/x5OAKCut6EyoxJCWAA3RzvmPtqGVhG1GJr7LPOMUYACf70Gix6B3EytQ7Qe54+R9VVP3PLPcdBUg5ReX9O8pv/NnycqnCRQ4tYUdt/V7lZsl6Io7Dqtzr7xVdIqMyohhIVwsjfw1SOtuadlTV7Mf5Tn80dg1NnBwd/g2//BheNah2j5zh8j95teuOSmcNhUnfWRX/NA2wZaRyUukwRKlJ3JBMcvt0CVMP4pJSOXzHwFgw66NZdlBYSoquwNet7v14xxXevwo7E7/XJeIt3OB1IOwFdd4OAfWodouc7Fkv9tLxyzkzlsqs68+p8y+q4ri7UnJCSwceNGEhISNAyyapMESpRd8l64dBbsXaF6m2K7Az2c2PdKT34d14GwGtU1CFAIYSl0Oh2ToyN44/7GxFCPqMzXOGpfH3LSYOFgWP4M5OdoHaZlORdLweze2Gclc8gUysyQ93mpf+ci40mPHz9ObGwsx49LS55WJIESZVfYfRfW8bqrfjs7GGhczbMSgxJCWLLBkTX5akhrMh38uCvjBRbY3afu2PoVfBsF545qG6ClOLkZ4zdR2F1K4pAplKmeb/L20O442BX9ug4PD6dOnTqEh4drFKiQBEqUnXn8U/HuOyGEuJ6ohoEseaIdgd7uTMkcwOOmKeQ5ekPSXviyM+z8v6o9S2/PIpS592LIuUiMKZyXPN7gs1HRJS6FFRISQocOHQgJCdEgUAGSQImyyrsE8f+pj0sYQC6EEDdSP8iD38Z1IDLMh5V5TemY/jonPVtD/iX47Un4oS+kntI6zMqlKLBuBiwZic6Ux3JjG17weIvPH4/G313KFVgqSaBE2Zz4F4x54FkDfGtrHY0Qwgr5uDrww2ORDLmzJsmKN12Tx/ODx2MoBic4thY+bwvbZ1eN1qi8S7BkFPz9FgCzCu7hA8/nmfN4ZwLcnTQOTtyIJFCibI4VLt/SDaRAphDiFtkb9LzWpzEfDWyOk4M9L6V040HeId2/pbrG5h/j4f/ug7NHtA614iTuVrsu9y4iHwNT8h9jkfdjzB/VTpInKyAJlCibG9R/EkKIsrqveTV+f7ID9YPc2XHJjxanJ7K6xngUO2eI+xu+aAsrX1Rn7dkKkwk2fQJfd4fzR0lSvBmS9zxHq/flp9HtCPCQ5MkaSAIlSi/1FJw7Ajo9hHXSOhohhI2o7e/G0rHtGdQmFKOiZ+SRNjzi9CFpNaLAVACbP4VPWsGuH9Tkw5qlJ8IPD8Cql8CUz0pja3rmvoVvo+7MeywSH9eSZzYLyyMJlCi9wtanaq3B2VvbWIQQNsXJ3sCMB5ryzSOt8XNz5J9z7rSMHcHPDT7C5FtXrT3361j4siMc+M36EqmCXNg4Ez5tDcfXkatz5Pn8ETyeP4H+nZrxyaAWONnL0lfWRBIoUXrSfSeEqGBRDQNZPaET9zQLwWhSmLTLnx7ZbxLbfAo4eqiLmC8aAl92goO/W34ipShw+E/4/E5Y8wrkZbJXV4+7cl7nZ93/mPFAU164qwF6vYwptTY6RakK0xwqV3p6Op6enqSlpeHh4aF1OOXDZIR3wiEnFR5dBTUib/oUIYS4Hcv2JDL99/2kZOQC8EB9F14J+BuPmG/UgeYAgY0h8nFo/CA4uGgY7TUUBU7+C/+8b/7jM9PBj6mX+vGLsT3h/u58+lBLGgTbyHeEjSjL97ckUBXAJhOohBj4qjM4uMNzJ8Bgp3VEQogqICMnn4/WHGX2phMYTQpO9npGt/FhtMOfOO34GvIy1QMdPaH5Q3DHCPCrq13AxgI4+Ks6SDxhFwAmvQOL7O7htfS7uIQzfVtW59X7GuHqKL9HLY0kUBqzyQTq349h9ctQNxoGL9I6GiFEFXM4KYOXf93H1rgLALg72jGurS/DXDbiuGs2XDxx5eDqbaDhvdDgHvCudVuvm5CQwPHjxwkPDzdX/S5pG2ePwIFf1WrqafEAKHZObHLrwfPJXYlXAvF1dWDqPQ25r3m124pJVJyyfH9L+itKJ+5v9T68s7ZxCCGqpIggdxaOupO/DqXw3qojHExMZ8b6ZGa5NGRwmwWMCI7De///wZEVcHqrelv1EgQ1hfp3Q822ENISHN3K9LqFi/YC5mTp+PHjHDt6BNeMY4QcTlATp7MHzc8xOfuyyfcBnj8VyakkF3Q6GBxZg2ej6+PpUnxZFmGdpAWqAthcC1RBHrxdS11qYfRGCGqidURCiCrMZFJYtjeRD1YfIe7cJQDs9DrubhrMyObONEzbgO7gb+oYJOWqQeY6vTpmKrQN+NcHr5rgVQO8QsHBtcTXSjh1gtOHdxHm54K/IQMSdpEbtxm7s/sxGHOuHKi3J7dmZ1ZzJy8fi+BinjqjrnE1D17v04TmoV4VdTlEOZIuPI3ZXAIV/x98Fw0uvjA5FvQyeVMIob0Co4nVB5L57t84tp24aN4e7ufKPc1C6BPhRNi59XBsHZzeBmnXX2PP5OBOgaLHYO+Awd5JTbZy0tSJM9fj4IapZgf2eXXlm5T6LD+aRYFJ/UptXM2Dp7rVJapBoMywsyKSQGnM5hKo9W/D+jehYR/oP1fraIQQNq7EMUY3sfd0GrP/jWPZ3kRyC660OjUI9qBjXT/a1vYl0icbl5SdcHo7XIiD1Hj1lnuTKud6e3ALBI9gCG5Ohm8TdhSEsSrZgz/3p3AxK998aMsaXozrVoeuEQHoZLkrqyMJlMZsLoGa3RtOboS7Z0LrR7WORghh4zZu3EhsbCx16tShQ4cOZXpuZm4Bq/Yn8dvuBP45eg6j6cpXnJ1eR6MQD+oGulM3wI06AW7U9HXBU5dFbtIRUs7EUz0kCH9fbwry88jRu3EWD2LT7TlxIYu4c5fYFZ/KoaSMIq8Z4O7I/S2r8WDL6tQNdC+XayC0IYPIRfnJy1IHYwKEyQByIUTFCw8PL3JfFm6OdjzQsjoPtKzO+cxcNsaeY1PseTYdP8epC9nsPp3G7tMltzg52PngYCggOz/pcuJ1/rqvUz/InXa1/egc4U/72r7YGWRoQ1UjCZS4sVP/gTEPPKqBT9l/mQkhRFmFhISUuuvuRnzdHLmveTVz2YBTF7LYeyaNo8mZHE3JIDYlk4TUbDJyC1AUyCswkVdQtLK5m6MdtfxcCPNzI8zPlYhAd+4M98HXzfG24xPWTRIocWNxG9T7sM4g/flCCCsW6uNCqI8LXDOR2GRSyMwrID07n3yjgouDAWcHA872BuylZUlchyRQ4saOX67/FNZJ2ziEEKKC6PU6PJzs8XCSGk2i9CS1FteXnQqJMepjSaCEEEIIM0mgxPUVFqHzrQOesvSAEEIIUUgSKHF9V49/EkIIIYSZTSVQtWrVQqfTFbm99dZbRY7Zs2cPHTt2xMnJidDQUN55551i51m8eDH169fHycmJJk2asHz58sp6C5bFnEBJ950QQghxNZtKoABeffVVEhMTzbcnn3zSvC89PZ0ePXpQs2ZNduzYwbvvvssrr7zCV199ZT5m06ZNDBo0iBEjRrBr1y769OlDnz592LdvnxZvRzuZKZByQH1cq6O2sQghhBAWxuZm4bm7uxMUFFTivnnz5pGXl8d3332Hg4MDjRo1IiYmhg8++IBRo0YB8NFHH9GzZ0+eeeYZAF577TVWr17Np59+yqxZsyrtfWiusPUpqAm4+mobixBCCGFhbK4F6q233sLX15cWLVrw7rvvUlBQYN63efNmOnXqhIODg3lbdHQ0hw8f5uLFi+ZjoqKiipwzOjqazZs3X/c1c3NzSU9PL3Kzeic2qve1pPtOCCGEuJZNtUA99dRTtGzZEh8fHzZt2sTzzz9PYmIiH3zwAQBJSUmEhYUVeU5gYKB5n7e3N0lJSeZtVx+TlJR03dedMWMG06dPL+d3o7GTm9T7Wu21jUMIIYSwQBbfAjVlypRiA8OvvR06dAiAiRMn0qVLF5o2bcro0aN5//33+eSTT8jNza3QGJ9//nnS0tLMt1OnTlXo61W4zLNw7rD6uEZbbWMRQgghLJDFt0BNmjSJYcOG3fCY6y04GRkZSUFBASdOnCAiIoKgoCCSk5OLHFP4c+G4qesdc71xVQCOjo44OtrQukgn/1XvAxqBi4+2sQghhBAWyOITKH9/f/z9/W/puTExMej1egICAgBo27YtL774Ivn5+djbqyX7V69eTUREBN7e3uZj1q5dy/jx483nWb16NW3bVqGWmMIESrrvhBBCiBJZfBdeaW3evJkPP/yQ3bt3c/z4cebNm8eECRN4+OGHzcnRQw89hIODAyNGjGD//v0sXLiQjz76iIkTJ5rP8/TTT7NixQref/99Dh06xCuvvML27dsZN26cVm+t8p24nEDVlARKCCGEKInFt0CVlqOjIwsWLOCVV14hNzeXsLAwJkyYUCQ58vT0ZNWqVYwdO5ZWrVrh5+fH1KlTzSUMANq1a8f8+fN56aWXeOGFF6hbty5Lly6lcePGWrytypd1AVL2q48lgRJCCCFKpFMURdE6CFuTnp6Op6cnaWlpeHh4aB1O2RxaBgseAr96MG6b1tEIIYQQlaYs398204Unyol03wkhhBA3JQmUKOpkYQHNDtrGIYQQQlgwSaDEFTlpkLRXfVyznbaxCCGEEBZMEihxRfwWUEzgHQYeIVpHI4QQQlgsSaDEFebuOxn/JIQQQtyIJFDiCvMAchn/JIQQQtyIJFBClZsJCbvUx9ICJYQQQtyQJFBCdXorKEbwDAWvGlpHI4QQQlg0SaCESuo/CSGEEKUmCZRQyQLCQgghRKlJAiUgPwfO7FAfSwuUEEIIcVOSQAl18LgxD1z9wSdc62iEEEIIiycJlIBT/6n3Ne4EnU7bWIQQQggrIAmUgPjLCVTondrGIYQQQlgJSaCqOpMJTm1RH9doq20sQgghhJWQBKqqO3cEsi+CnTMEN9U6GiGEEMIqSAJV1RWOf6reGgz22sYihBBCWAlJoKq6+KsGkAshhBCiVCSBqupkALkQQghRZpJAVWUZyXAxDtBB6B1aRyOEEEJYDUmgqrLC8U+BjcDJU9tYhBBCCCsiCVRVJuOfhBBCiFsiCVRVJuOfhBBCiFsiCVRVlXcJEnerj6UFSgghhCgTSaCqqjM7QDGCRzXwCtU6GiGEEMKqSAJVVcn4JyGEEOKWSQJVVcn4JyGEEOKWSQJVFZmMcGqr+lhaoIQQQogykwSqKko5AHkZ4OCu1oASQgghRJlIAlUVxV+1gLDeoG0sQgghhBWSBKoqOr1NvQ+N1DYOIYQQwkpJAlUVmRMoWf9OCCGEuBWSQFU1l87BhePq42qttI1FCCGEsFKSQFU1p7er934R4OytbSxCCCGElZIEqqo5fbl8QXXpvhNCCCFulSRQVY2MfxJCCCFumyRQVYnJCGd2qo+lBUoIIYS4ZZJAVSUpByEvUy2g6V9f62iEEEIIqyUJVFVSOP6pWkspoCmEEELcBkmgqpLCGXihbbSNQwghhLBykkBVJadkBp4QQghRHiSBqiqyLsD5o+pjSaCEEEKI2yIJVFVxZod671MbXHy0jUUIIYSwcpJAVRXm+k8y/kkIIYS4XZJAVRXm8U+ttY1DCCGEsAGSQFUFJtOVLrzq0gIlhBBC3C5JoKqCc0cgNx3sXSGgodbRCCGEEFZPEqiq4OoCmgY7bWMRQgghbIAkUFVB4QByGf8khBBClAtJoKqCU4UJlIx/EkIIIcqDJFC2LjcDzh5SH0sLlBBCCFEuJIGydQkxgAKeoeAWoHU0QgghhE2QBMrWFZYvqNZS2ziEEEIIGyIJlK0zJ1CttI1DCCGEsCFWk0C98cYbtGvXDhcXF7y8vEo8Jj4+nt69e+Pi4kJAQADPPPMMBQUFRY5Zv349LVu2xNHRkTp16jBnzpxi5/nss8+oVasWTk5OREZGsnXr1gp4R5UkYZd6HyItUEIIIUR5sZoEKi8vj379+jFmzJgS9xuNRnr37k1eXh6bNm1i7ty5zJkzh6lTp5qPiYuLo3fv3nTt2pWYmBjGjx/PY489xsqVK83HLFy4kIkTJzJt2jR27txJs2bNiI6OJiUlpcLfY7nLSIa0U4AOQpprHY0QQghhM3SKoihaB1EWc+bMYfz48aSmphbZ/ueff3L33XeTkJBAYGAgALNmzeK5557j7NmzODg48Nxzz7Fs2TL27dtnft7AgQNJTU1lxYoVAERGRnLHHXfw6aefAmAymQgNDeXJJ59kypQppYoxPT0dT09P0tLS8PDwKId3fYsO/wk/DgT/BjD2P+3iEEIIIaxAWb6/raYF6mY2b95MkyZNzMkTQHR0NOnp6ezfv998TFRUVJHnRUdHs3nzZkBt5dqxY0eRY/R6PVFRUeZjSpKbm0t6enqRm0U4s1O9lwHkQgghRLmymQQqKSmpSPIEmH9OSkq64THp6elkZ2dz7tw5jEZjiccUnqMkM2bMwNPT03wLDQ0tj7d0+2QGnhBCCFEhNE2gpkyZgk6nu+Ht0KFDWoZYKs8//zxpaWnm26lTp7QOCRRFZuAJIYQQFUTTlWUnTZrEsGHDbnhMeHh4qc4VFBRUbLZccnKyeV/hfeG2q4/x8PDA2dkZg8GAwWAo8ZjCc5TE0dERR0fHUsVZaS4ch5xUMDhAQCOtoxFCCCFsiqYJlL+/P/7+/uVyrrZt2/LGG2+QkpJCQIBacXv16tV4eHjQsGFD8zHLly8v8rzVq1fTtm1bABwcHGjVqhVr166lT58+gDqIfO3atYwbN65c4qw0heULgpqCnYO2sQghhBA2xmrGQMXHxxMTE0N8fDxGo5GYmBhiYmLIzMwEoEePHjRs2JAhQ4awe/duVq5cyUsvvcTYsWPNrUOjR4/m+PHjPPvssxw6dIjPP/+cRYsWMWHCBPPrTJw4ka+//pq5c+dy8OBBxowZw6VLlxg+fLgm7/uWSfedEEIIUWE0bYEqi6lTpzJ37lzzzy1atABg3bp1dOnSBYPBwB9//MGYMWNo27Ytrq6uDB06lFdffdX8nLCwMJYtW8aECRP46KOPqF69Ot988w3R0dHmYwYMGMDZs2eZOnUqSUlJNG/enBUrVhQbWG7xZAC5EEIIUWGsrg6UNdC8DpQxH2aEQkE2jNsOfnUrPwYhhBDCylTJOlDiKikH1eTJ0QN8amsdjRBCCGFzJIGyRYXddyEtQC//xEIIIUR5k29XW5RQWIFcBpALIYQQFUESKFskS7gIIYQQFUoSKFuTdwlSDqiPpQVKCCGEqBCSQNmaxD2gmMA9GDxCtI5GCCGEsEmSQNka8wBy6b4TQgghKookULYmMUa9D2mhaRhCCCGELZMEytYkxKj3Ic21jEIIIYSwaZJA2ZKcdDgfqz4Obq5pKEIIIYQtkwTKliTtARTwqAZu/lpHI4QQQtgsSaBsSWH3nbQ+CSGEEBVKEihbIgPIhRBCiEohCZQtkQHkQgghRKWQBMpW5GbIAHIhhBCikkgCZSsSZQC5EEIIUVnstA5AlJPC8U/S+iSEEBiNRvLz87UOQ1gYe3t7DAZDuZxLEihbIeOfhBACRVFISkoiNTVV61CEhfLy8iIoKAidTndb55EEylYk7FLvpQVKCFGFFSZPAQEBuLi43PaXpLAdiqKQlZVFSkoKAMHBwbd1PkmgbMHVA8ilBUoIUUUZjUZz8uTr66t1OMICOTs7A5CSkkJAQMBtdefJIHJbUDiA3D0E3AK0jkYIITRROObJxcVF40iEJSv8fNzuGDlJoGyBFNAUQggz6bYTN1Jenw9JoGyBDCAXQgir1qVLF8aPH691GAAsXbqUOnXqYDAYGD9+PHPmzMHLy0vrsCyOJFC2QEoYCCGEuIH169ej0+lKNTvx8ccf58EHH+TUqVO89tprDBgwgCNHjpj3v/LKKzRv3rzigrUSMojc2uVmwLmj6mNpgRJCCHEbMjMzSUlJITo6mpCQEPP2wsHX4gppgbJ2SXuRAeRCCGH9CgoKGDduHJ6envj5+fHyyy+jKIp5f25uLpMnT6ZatWq4uroSGRnJ+vXrzftPnjzJPffcg7e3N66urjRq1Ijly5dz4sQJunbtCoC3tzc6nY5hw4YVe/3169fj7u4OQLdu3dDpdKxfv75IF96cOXOYPn06u3fvRqfTodPpmDNnTkVdEosmLVDWrrD+k7Q+CSFEMYqikJ1v1OS1ne0NZRqwPHfuXEaMGMHWrVvZvn07o0aNokaNGowcORKAcePGceDAARYsWEBISAi//PILPXv2ZO/evdStW5exY8eSl5fHhg0bcHV15cCBA7i5uREaGsrPP/9M3759OXz4MB4eHiW2KLVr147Dhw8TERHBzz//TLt27fDx8eHEiRPmYwYMGMC+fftYsWIFa9asAcDT0/P2LpSVkgTK2hUOIJfxT0IIUUx2vpGGU1dq8toHXo3GxaH0X7OhoaHMnDkTnU5HREQEe/fuZebMmYwcOZL4+Hhmz55NfHy8uWtt8uTJrFixgtmzZ/Pmm28SHx9P3759adKkCQDh4eHmc/v4+AAQEBBw3QHhDg4OBAQEmI8PCgoqdoyzszNubm7Y2dmVuL8qkQTK2plLGDTXMgohhBC36c477yzSYtW2bVvef/99jEYje/fuxWg0Uq9evSLPyc3NNRcNfeqppxgzZgyrVq0iKiqKvn370rRp00p9D1WJJFDWLDfzygByaYESQohinO0NHHg1WrPXLi+ZmZkYDAZ27NhRrHq2m5sbAI899hjR0dEsW7aMVatWMWPGDN5//32efPLJcotDXCEJlDVLOQAo4BYI7oFaRyOEEBZHp9OVqRtNS1u2bCny83///UfdunUxGAy0aNECo9FISkoKHTt2vO45QkNDGT16NKNHj+b555/n66+/5sknn8TBwQFQl7u5XQ4ODuVyHmsns/CsWeJu9T5ImmiFEMLaxcfHM3HiRA4fPsyPP/7IJ598wtNPPw1AvXr1GDx4MI888ghLliwhLi6OrVu3MmPGDJYtWwbA+PHjWblyJXFxcezcuZN169bRoEEDAGrWrIlOp+OPP/7g7NmzZGZm3nKctWrVIi4ujpiYGM6dO0dubu7tv3krJAmUNUvao94HSwIlhBDW7pFHHiE7O5s2bdowduxYnn76aUaNGmXeP3v2bB555BEmTZpEREQEffr0Ydu2bdSoUQNQW5fGjh1LgwYN6NmzJ/Xq1ePzzz8HoFq1akyfPp0pU6YQGBjIuHHjbjnOvn370rNnT7p27Yq/vz8//vjj7b1xK6VTri4yIcpFeno6np6epKWl4eHhUXEv9GVndRB5vznQ6P6Kex0hhLACOTk5xMXFERYWhpOTk9bhCAt1o89JWb6/pQXKWhnzIeWg+li68IQQQohKJQmUtTp3BIy54OAO3mFaRyOEEEJUKZJAWavEy+OfghqDXv4ZhRBCiMok37zWKmmvei/dd0IIIUSlkwTKWskMPCGEEEIzkkBZI0W5kkBJC5QQQghR6SSBskap8ZCTBnp78K+vdTRCCCFElSMJlDUqbH0KqA92DtrGIoQQQlRBkkBZI/MMvGbaxiGEEEJUUZJAWSMZQC6EEEJjc+bMwcvLS+swGDZsGH369Kn015UEyhqZSxg00TYOIYQQ4jpOnDiBTqcjJibGIs93uySBsjaXzkP6GfVxYGNtYxFCCKGZvLw8rUMoF9b6PiSBsjZJu9V7n3BwqsCFioUQQlSajIwMBg8ejKurK8HBwcycOZMuXbowfvx48zG1atXitdde45FHHsHDw4NRo0YB8PPPP9OoUSMcHR2pVasW77//fpFz63Q6li5dWmSbl5cXc+bMAa607CxZsoSuXbvi4uJCs2bN2Lx5c5HnzJkzhxo1auDi4sL999/P+fPnb/iewsLUZcZatGiBTqejS5cuwJUutzfeeIOQkBAiIiJKFef1zlfovffeIzg4GF9fX8aOHUt+fv4N47tddhV6dlH+pAK5EEKUnqJAfpY2r23vAjpdqQ6dOHEi//77L7/99huBgYFMnTqVnTt30rx58yLHvffee0ydOpVp06YBsGPHDvr3788rr7zCgAED2LRpE0888QS+vr4MGzasTOG++OKLvPfee9StW5cXX3yRQYMGERsbi52dHVu2bGHEiBHMmDGDPn36sGLFCnMM17N161batGnDmjVraNSoEQ4OV2aNr127Fg8PD1avXl3q+G50vnXr1hEcHMy6deuIjY1lwIABNG/enJEjR5bpGpSFJFDWxjwDT8Y/CSHETeVnwZsh2rz2Cwng4HrTwzIyMpg7dy7z58+ne/fuAMyePZuQkOJxd+vWjUmTJpl/Hjx4MN27d+fll18GoF69ehw4cIB33323zAnU5MmT6d27NwDTp0+nUaNGxMbGUr9+fT766CN69uzJs88+a36dTZs2sWLFiuuez9/fHwBfX1+CgoKK7HN1deWbb74pkgTdzI3O5+3tzaefforBYKB+/fr07t2btWvXVmgCJV141sY8A09KGAghhC04fvw4+fn5tGnTxrzN09PT3LV1tdatWxf5+eDBg7Rv377Itvbt23P06FGMRmOZ4mja9ErPRnBwMAApKSnm14mMjCxyfNu2bct0/qs1adKkTMnTzTRq1AiDwWD+OTg42Bx7RZEWKGuSdwnOHVUfSxeeEELcnL2L2hKk1WuXM1fXm7doXUun06EoSpFtJY0Psre3L/IcAJPJVObXK42S3kdp4yzJ1bEXnquiYi8kCZQ1ST4AKOAaAO6BWkcjhBCWT6crVTealsLDw7G3t2fbtm3UqFEDgLS0NI4cOUKnTp1u+NwGDRrw77//Ftn277//Uq9ePXOLjL+/P4mJieb9R48eJSurbOPCGjRowJYtW4ps+++//274nMIWptK2hN0szrKer6JJAmVNCmfgSQFNIYSwGe7u7gwdOpRnnnkGHx8fAgICmDZtGnq93twSdD2TJk3ijjvu4LXXXmPAgAFs3ryZTz/9lM8//9x8TLdu3fj0009p27YtRqOR5557rliLzc089dRTtG/fnvfee4/77ruPlStX3nD8E0BAQADOzs6sWLGC6tWr4+TkhKen53WPv1mcZT1fRZMxUNYkJw3snKX7TgghbMwHH3xA27Ztufvuu4mKiqJ9+/Y0aNAAJyenGz6vZcuWLFq0iAULFtC4cWOmTp3Kq6++WmQA+fvvv09oaCgdO3bkoYceYvLkybi4lK178c477+Trr7/mo48+olmzZqxatYqXXnrphs+xs7Pj448/5ssvvyQkJIT77rvvhsffLM6ynq/CKVbi9ddfV9q2bas4Ozsrnp6eJR4DFLv9+OOPRY5Zt26d0qJFC8XBwUGpXbu2Mnv27GLn+fTTT5WaNWsqjo6OSps2bZQtW7aUKda0tDQFUNLS0sr0vFIxFihKbmb5n1cIIaxcdna2cuDAASU7O1vrUG5bZmam4unpqXzzzTdah2JzbvQ5Kcv3t9W0QOXl5dGvXz/GjBlzw+Nmz55NYmKi+Xb1+jhxcXH07t2brl27EhMTw/jx43nsscdYuXKl+ZiFCxcyceJEpk2bxs6dO2nWrBnR0dEVPpq/1PQGi+/PF0IIUTa7du3ixx9/5NixY+zcuZPBgwcDaN/KIq7LasZATZ8+HcBckfR6vLy8itWHKDRr1izCwsLMVVobNGjAxo0bmTlzJtHR0YDajDpy5EiGDx9ufs6yZcv47rvvmDJlSjm9GyGEEKKo9957j8OHD+Pg4ECrVq34559/8PPz0zoscR1W0wJVWmPHjsXPz482bdrw3XffFZkSuXnzZqKiooocHx0dbS5Xn5eXx44dO4oco9friYqKKlbS/mq5ubmkp6cXuQkhhBCl1aJFC3bs2EFmZiYXLlxg9erVNGkiBZMtmdW0QJXGq6++Srdu3XBxcWHVqlU88cQTZGZm8tRTTwGQlJREYGDR6f+BgYGkp6eTnZ3NxYsXMRqNJR5z6NCh677ujBkzzC1kQgghhLB9mrZATZkyBZ1Od8PbjRKXa7388su0b9+eFi1a8Nxzz/Hss8/y7rvvVuA7UD3//POkpaWZb6dOnarw1xRCCCGEdjRtgZo0adJN1+oJDw+/5fNHRkby2muvkZubi6OjI0FBQSQnJxc5Jjk5GQ8PD5ydnTEYDBgMhhKPud64KgBHR0ccHR1vOU4hhBDlR7mmmrUQVyuvz4emCZS/v795ccCKEBMTg7e3tzm5adu2LcuXLy9yzOrVq83r+RQO3Fu7dq159p7JZGLt2rWMGzeuwuIUQghx+wqLLmZlZeHs7KxxNMJSFVY3L2sx0WtZzRio+Ph4Lly4QHx8PEajkZiYGADq1KmDm5sbv//+O8nJydx55504OTmxevVq3nzzTSZPnmw+x+jRo/n000959tlnefTRR/nrr79YtGgRy5YtMx8zceJEhg4dSuvWrWnTpg0ffvghly5dMs/KE0IIYZkMBgNeXl7msjMuLi43reQtqg5FUcjKyiIlJQUvL68iiw/fCqtJoKZOncrcuXPNP7do0QKAdevW0aVLF+zt7fnss8+YMGECiqJQp04dc0mCQmFhYSxbtowJEybw0UcfUb16db755htzCQOAAQMGcPbsWaZOnUpSUhLNmzdnxYoVxQaWCyGEsDyFwy0spnafsDg3KndUFjpFOovLXXp6Op6enqSlpeHh4aF1OEIIUeUYjUby8/O1DkNYGHt7+xu2PJXl+9tqWqCEEEKI0iqcFCRERbG5QppCCCGEEBVNEighhBBCiDKSBEoIIYQQooxkDFQFKByXL2viCSGEENaj8Hu7NPPrJIGqABkZGQCEhoZqHIkQQgghyiojIwNPT88bHiNlDCqAyWQiISEBd3f3ci/ilp6eTmhoKKdOnZISCTch16r05FqVnlyr0pNrVXpyrUqvIq+VoihkZGQQEhKCXn/jUU7SAlUB9Ho91atXr9DX8PDwkP9kpSTXqvTkWpWeXKvSk2tVenKtSq+irtXNWp4KySByIYQQQogykgRKCCGEEKKMJIGyMo6OjkybNg1HR0etQ7F4cq1KT65V6cm1Kj25VqUn16r0LOVaySByIYQQQogykhYoIYQQQogykgRKCCGEEKKMJIESQgghhCgjSaCEEEIIIcpIEigr8cYbb9CuXTtcXFzw8vIq8RidTlfstmDBgsoN1EKU5nrFx8fTu3dvXFxcCAgI4JlnnqGgoKByA7VAtWrVKvY5euutt7QOy2J89tln1KpVCycnJyIjI9m6davWIVmcV155pdhnqH79+lqHZRE2bNjAPffcQ0hICDqdjqVLlxbZrygKU6dOJTg4GGdnZ6Kiojh69Kg2wWrsZtdq2LBhxT5nPXv2rLT4JIGyEnl5efTr148xY8bc8LjZs2eTmJhovvXp06dyArQwN7teRqOR3r17k5eXx6ZNm5g7dy5z5sxh6tSplRypZXr11VeLfI6efPJJrUOyCAsXLmTixIlMmzaNnTt30qxZM6Kjo0lJSdE6NIvTqFGjIp+hjRs3ah2SRbh06RLNmjXjs88+K3H/O++8w8cff8ysWbPYsmULrq6uREdHk5OTU8mRau9m1wqgZ8+eRT5nP/74Y+UFqAirMnv2bMXT07PEfYDyyy+/VGo8lu5612v58uWKXq9XkpKSzNu++OILxcPDQ8nNza3ECC1PzZo1lZkzZ2odhkVq06aNMnbsWPPPRqNRCQkJUWbMmKFhVJZn2rRpSrNmzbQOw+Jd+zvbZDIpQUFByrvvvmvelpqaqjg6Oio//vijBhFajpK+34YOHarcd999msSjKIoiLVA2ZuzYsfj5+dGmTRu+++47FCnzVaLNmzfTpEkTAgMDzduio6NJT09n//79GkZmGd566y18fX1p0aIF7777rnRtorZq7tixg6ioKPM2vV5PVFQUmzdv1jAyy3T06FFCQkIIDw9n8ODBxMfHax2SxYuLiyMpKanIZ8zT05PIyEj5jF3H+vXrCQgIICIigjFjxnD+/PlKe21ZTNiGvPrqq3Tr1g0XFxdWrVrFE088QWZmJk899ZTWoVmcpKSkIskTYP45KSlJi5AsxlNPPUXLli3x8fFh06ZNPP/88yQmJvLBBx9oHZqmzp07h9FoLPFzc+jQIY2iskyRkZHMmTOHiIgIEhMTmT59Oh07dmTfvn24u7trHZ7FKvzdU9JnrKr/XipJz549eeCBBwgLC+PYsWO88MIL9OrVi82bN2MwGCr89SWB0tCUKVN4++23b3jMwYMHSz348uWXXzY/btGiBZcuXeLdd9+1mQSqvK9XVVKWazdx4kTztqZNm+Lg4MDjjz/OjBkzNF86QViHXr16mR83bdqUyMhIatasyaJFixgxYoSGkQlbMnDgQPPjJk2a0LRpU2rXrs369evp3r17hb++JFAamjRpEsOGDbvhMeHh4bd8/sjISF577TVyc3Nt4ouvPK9XUFBQsdlTycnJ5n225nauXWRkJAUFBZw4cYKIiIgKiM46+Pn5YTAYzJ+TQsnJyTb5mSlPXl5e1KtXj9jYWK1DsWiFn6Pk5GSCg4PN25OTk2nevLlGUVmP8PBw/Pz8iI2NlQTK1vn7++Pv719h54+JicHb29smkico3+vVtm1b3njjDVJSUggICABg9erVeHh40LBhw3J5DUtyO9cuJiYGvV5vvk5VlYODA61atWLt2rXm2a0mk4m1a9cybtw4bYOzcJmZmRw7dowhQ4ZoHYpFCwsLIygoiLVr15oTpvT0dLZs2XLTGdgCTp8+zfnz54sknxVJEigrER8fz4ULF4iPj8doNBITEwNAnTp1cHNz4/fffyc5OZk777wTJycnVq9ezZtvvsnkyZO1DVwjN7tePXr0oGHDhgwZMoR33nmHpKQkXnrpJcaOHWszCeet2Lx5M1u2bKFr1664u7uzefNmJkyYwMMPP4y3t7fW4Wlu4sSJDB06lNatW9OmTRs+/PBDLl26xPDhw7UOzaJMnjyZe+65h5o1a5KQkMC0adMwGAwMGjRI69A0l5mZWaQlLi4ujpiYGHx8fKhRowbjx4/n9ddfp27duoSFhfHyyy8TEhJSJUvS3Oha+fj4MH36dPr27UtQUBDHjh3j2WefpU6dOkRHR1dOgJrN/xNlMnToUAUodlu3bp2iKIry559/Ks2bN1fc3NwUV1dXpVmzZsqsWbMUo9GobeAaudn1UhRFOXHihNKrVy/F2dlZ8fPzUyZNmqTk5+drF7QF2LFjhxIZGal4enoqTk5OSoMGDZQ333xTycnJ0To0i/HJJ58oNWrUUBwcHJQ2bdoo//33n9YhWZwBAwYowcHBioODg1KtWjVlwIABSmxsrNZhWYR169aV+Ltp6NChiqKopQxefvllJTAwUHF0dFS6d++uHD58WNugNXKja5WVlaX06NFD8ff3V+zt7ZWaNWsqI0eOLFKapqLpFEXmuQshhBBClIXUgRJCCCGEKCNJoIQQQgghykgSKCGEEEKIMpIESgghhBCijCSBEkIIIYQoI0mghBBCCCHKSBIoIYQQQogykgRKCCGEEKKMJIESQgghhCgjSaCEEEIIIcpIEighhLiJs2fPEhQUxJtvvmnetmnTJhwcHFi7dq2GkQkhtCJr4QkhRCksX76cPn36sGnTJiIiImjevDn33XcfH3zwgdahCSE0IAmUEEKU0tixY1mzZg2tW7dm7969bNu2DUdHR63DEkJoQBIoIYQopezsbBo3bsypU6fYsWMHTZo00TokIYRGZAyUEEKU0rFjx0hISMBkMnHixAmtwxFCaEhaoIQQohTy8vJo06YNzZs3JyIigg8//JC9e/cSEBCgdWhCCA1IAiWEEKXwzDPP8NNPP7F7927c3Nzo3Lkznp6e/PHHH1qHJoTQgHThCSHETaxfv54PP/yQ77//Hg8PD/R6Pd9//z3//PMPX3zxhdbhCSE0IC1QQgghhBBlJC1QQgghhBBlJAmUEEIIIUQZSQIlhBBCCFFGkkAJIYQQQpSRJFBCCCGEEGUkCZQQQgghRBlJAiWEEEIIUUaSQAkhhBBClJEkUEIIIYQQZSQJlBBCCCFEGUkCJYQQQghRRpJACSGEEEKU0f8DBlvIdMozGS0AAAAASUVORK5CYII=", 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", 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UAnjsscd45513eOyxx2jbti3r16+39OJcrn379tx3331MmDCBlJQUGjRowLx58zh+/DjffPON1bHDhg3jn3/+sXxJ2tnZ8fXXX3PHHXfQtGlTHnnkEerUqcPp06dZu3Yt7u7u/P777yiKwqOPPoqzszOff/45oJZ0WLJkCc8++yy9evWyGjpp1KgRI0eOZNu2bfj5+TF79mySk5OZM2eO5Zjx48fz22+/0a9fP0t5h4sXL7Jv3z5++uknjh8/bnX5emWpiLiK5wU988wzREVFodfruf/++6/7OAcHB958801GjRpFz549GTJkCPHx8cyZM+eKOVD9+vXj559/5t5776Vv377Ex8cza9YsIiIirJJjZ2dnIiIiWLhwIY0aNcLT05NmzZrRrFkzS9mHgoIC6tSpw6pVq4iPjy8xth07dpCamso999xT4msti61btzJs2DC8vLy47bbbrhgm79Spk+X1luXzLqoZrS7/E6KqKamMwc2eq6Tbfy+X9vb2Vjp06HDdc16tEvmXX36ptGnTRnF2dlZq1aqlNG/eXHnxxReVxMREyzHBwcFK3759r3hsSdWjf/75Z0Wn01kujy+WnZ2tjBw5UjEajUqtWrWUwYMHKykpKSVegp6Tk6O88MILir+/v2IwGJRbbrlFWbFiRYnPX9J/Q7t27VIGDBigeHl5KQaDQQkODlYGDx5sqTL98ccfX1GaQFEUJSEhQXF3d1fuvPPOK177ypUrlRYtWigGg0Fp3Lixsnjx4iueNzMzU5kwYYLSoEEDxdHRUfH29lY6deqkvPfee5bK8sXlAi6vPF3samUMXF1dS3ztTZs2vaK9pH+rm43rv/9GhYWFytNPP634+PgoOp2uzCUNZs6cqYSGhioGg0Fp27atsn79+is+S2azWXn77beV4OBgxWAwKK1atVKWLVtW4ud48+bNSps2bRRHR0erWE+dOqXce++9ioeHh2I0GpX77rtPSUxMLPEz99JLLyn16tWzKn9xo671+/vff19FKf3nXVQvOkWp5JmFQgiLgwcP0rRpU5YtW0bfvn21DgdQJ4ZHREQwePDgEocHbU1ISAjNmjVj2bJlWociKkheXh4hISG8/PLLPPvss1qHI2oImQMlhIbWrl1Lx44dq0zyBOrck6lTpzJjxowS5yEJUdXMmTMHBwcHnnzySa1DETWI9EAJIao16YEqnetd7q/X68tUOFSI6k4mkQshhLju5f7BwcHlstC2ENWF9EAJIYRgx44d16xW7uzsTOfOnSsxIiGqNkmghBBCCCHKSCaRCyGEEEKUkcyBqgBms5nExERq1ap1w0smCCGEEKJyKYpCZmYmgYGB2Nldu49JEqgKkJiYKOX7hRBCCBt18uRJqwXVSyIJVAWoVasWoP4DXL64pBBCCCGqroyMDIKCgizf49ciCVQFKB62c3d3lwRKCCGEsDGlmX4jk8iFEEIIIcpIEighhBBCiDKSBEoIIYQQoowkgRJCCCGEKCNJoIQQQgghykgSKCGEEEKIMrKpBGr9+vXcddddBAYGotPpWLp0qdV+RVGYOHEiAQEBODs706tXL2JiYqyOSU1NZejQobi7u+Ph4cHIkSPJysqyOmbv3r107doVJycngoKC+N///lfRL00IIYQQNsSmEqiLFy/SsmVLZsyYUeL+//3vf3zyySfMmjWLLVu24OrqSlRUFLm5uZZjhg4dyoEDB1i9ejXLli1j/fr1PPHEE5b9GRkZ9O7dm+DgYHbs2MH06dOZPHkyX375ZYW/PiGEEELYCMVGAcovv/xi+dlsNiv+/v7K9OnTLW1paWmKwWBQFixYoCiKohw8eFABlG3btlmO+fPPPxWdTqecPn1aURRFmTlzplK7dm0lLy/PcsxLL72khIeHlzq29PR0BVDS09Nv9OUJIYQQopKV5fvbpnqgriU+Pp6kpCR69eplaTMajbRv357o6GgAoqOj8fDwoG3btpZjevXqhZ2dHVu2bLEc061bNxwdHS3HREVFceTIES5cuFDic+fl5ZGRkWF1E0IIIUT1VW0SqKSkJAD8/Pys2v38/Cz7kpKS8PX1tdpvb2+Pp6en1TElnePy5/ivadOmYTQaLTdZSFgIIYSo3qpNAqWlCRMmkJ6ebrmdPHlS65CEEEIIUYGqzWLC/v7+ACQnJxMQEGBpT05OJjIy0nJMSkqK1eMKCwtJTU21PN7f35/k5GSrY4p/Lj7mvwwGAwaDoVxehxBlpiiQfxHyMsHOHpzcwV4+j0KIairjDORngVcDKMWivxWl2iRQoaGh+Pv7s2bNGkvClJGRwZYtWxg9ejQAHTt2JC0tjR07dtCmTRsA/v77b8xmM+3bt7cc8+qrr1JQUICDgwMAq1evJjw8nNq1a1f+CxMCoDAfzh2B5AOQvF+9T42D3HTIzQDFZHW4onek0KEW+Q7upLmEkmQI4YxjCOluDch0D8PewYDBQU+g0YkgTxeCarvg7KjX6MUJIUQZ7PoO1r4FrYfB3Z9qFoZNJVBZWVnExsZafo6Pj2f37t14enpSr149xo4dy5tvvknDhg0JDQ3l9ddfJzAwkP79+wPQpEkT+vTpw+OPP86sWbMoKCjgqaee4v777ycwMBCABx98kClTpjBy5Eheeukl9u/fz8cff8yHH36oxUsWNVlWChxdCUeWw7G1UJhzzcPN2GGHGQCdKR8H03kccs/jmhlPnctPqzgRbY5gg7k5s80tiFf8AR3ebgaaBNSibbAnt4TUJrKeBy6ONvVfhBCiJohfr94HtNQ0DJ2iKIqmEZTBunXr6NGjxxXtw4cPZ+7cuSiKwqRJk/jyyy9JS0ujS5cuzJw5k0aNGlmOTU1N5amnnuL333/Hzs6OgQMH8sknn+Dm5mY5Zu/evYwZM4Zt27bh7e3N008/zUsvvVTqODMyMjAajaSnp+Pu7n5zL1rULPkXYc+P6u3UNuCyX08nI/g1A7+mXPRozJYsb9aeKGTdiVzOFTqRgwEdCm7k4q7LIcStkBDnbJroE6lPAnXyT+CTE4ez2bpw7Cl8+aWwE0tMXTmuXBr+1tvpaFnXyB3NArijuT91a7tU0psghBBXUZAL79QDUx6M2QY+ja7/mDIoy/e3TSVQtkISKFFmaSdh21ewYx7kpl1qD4iE8Dsh/A4Uv2ZEx6Uye9Nx1h5JwWS+9Ktbt7Yz7UI9aV7HSPM6RiIC3UvuPTKbIXkfHPtbvSX8C6Z8y+4Uj0jWGHrxzYVIYjOsrzGJDPKgX4sA7m1VBy83mWMlhNBA/AaY1w/c/OD5I+U+B0oSKI1JAiVK7fwxdSz/wNJL85hqh0K7xyGiPxjrkFtg4rfdiczeFM/hpEzLQxv71yKqqT9RTf1pElAL3Y38R5J/UR0m3LMAYv8CRR0CxOBORrNhrKh1L0uOFLD1eCrF/1M42ttxd8tARnQKoVkd4029fCGEKJO1b8M/70KzgTBodrmfXhIojUkCJa4rNx3Wvwf/fg7mArUttBu0Hw2NosBOj8mssHj7Sd5ffZSzmXkAODvoGdSmLsM7hdDA1+0aT3ADMpNg70LY+R2cL1pDUm+AVg9xruUTLD/lxOLtp9h3Ot3ykLbBtRl1a316NfG9sQROCCHKYvYdkLAZ+n0EbR8p99NLAqUxSaDEVZlN6hUkf78JF8+qbfVvg9ungH9zy2Hrj57l7eWHLD1OdTycGd4pmCFt62F0cajgGM1wdAVs/KBoHhag00ObESjdJ7DzvD3zNh9n+b4zFBYNI7YM8mB873A6N/CSREoIUTHys9X5T+YCeHoneNUv96eQBEpjkkCJEqXGw89PwKmt6s9eDSHqbWh4u2Uc/2RqNq//up91R9TkyujswDO3NeThDsE42ldy3VtFgRObYMMHcGyN2mZwh67PQ/snSc6BOZuOM2/zcXIK1OHH9qGevNinMW2CpeSHEKKcHVsL3/WHWoEw7mCF1ICSBEpjkkDVLImJicTFxREWFmYph2FFUdShsT9egPxMNQnp8Qrc8hjoHYoOUViw9SRv/nGQ7HwTDnodD3cI4ZnbGuDh4njlOSvb8Y2w8hU4s0f92aMe9H4TmtxNSlYeM9ceY/6WBPJN6hyqga3rMuHOxnjLZHMhRHlZMxU2vA8thsCALyvkKSSB0pgkUDXLxo0biY2NpUGDBnTp0sV6Z04a/DEO9i9Rf67XUf3F96hnOSQ5I5eXluy19Dq1C/Hk3UEtCPV2raRXUEpms5oIrpkKmYlqW+N+0Pd9qOXP6bQcPv7rKIu2nwLA3cme8X0a82C7eujtZFhPCHGTvr5d7cG/+zNo/XCFPIUkUBqTBKpmuWoPVNI+WPAgpCeoc4h6TIAu48DuUsXvlQeSePGnvaTnFOBob8f43uE82iW0aicc+dmw8UN1jpS5EAxGiHoLWj0EOh07Ey7w+tL9HEjMAKBFXSP/G9SCxv7yuyCEuEF5WfBusPp/zrN7oHZIhTyNJFAakwRKELMaFo9Q12uqHQIDv4G6bS27zWaFj9fE8PEa9Wq3ZnXc+WBwJI38amkT741IPgC/PgWJO9WfQ2+Fe2aARxAms8L3/57gvZVHyMwrxFFvx/O9G/FY17CqnRwKIaqm2L/g+4FgrAfP7auwpynL93clz0oVogbY+hXMH6wmT6Hd4Il/rJKnrLxCRv+ww5I8PdI5hF/+r7NtJU8Afk3hsb/UuVD2zhD/D8zqAod+R2+nY3inENa8cCu3NfYl32Rm2p+HeeDLfzmZmq115EIIWxO/Qb0P7aptHJeRBEqI8mI2wYpXYPkLakHKVg/B0CXg7GE5JOF8NgNnbmblgWQc9XZMH9SCSXc1xUFvo7+Kdnro9DSM3gR12qhV1Bc+BMvGQUEOvrWc+Hp4W94Z0BxXRz1bj6fS56P1/LzzlNaRCyFsyfGiBCpEEighqhdTAfz0KPw7Q/35tonqREf7S1fQ7TuVTv+ZmziSnIlvLQMLR3XgvrZBGgVczrzqw6MrofNY9eft38BXPSHlEDqdjvvb1ePPZ7vRNrg2F/NNjFu0h1d+2UduUfkDIYS4qtwMSNytbksPlBDViKkQljwGB5eC3lFdXqDr81Y1SrbGp/LgV/+SejGf5nWM/P50F1rVq2a1kvQOakHQh38BV19IOagmUQeWAlDPy4WFozry7G0N0elg/pYE7psVLUN6QohrS4hWl7qqHQrGulpHYyEJlBA3w1QIvzyhJk92DjD4O3WNpsv8c/Qsw2ZvITOvkPahnix4ogN+7k7axFsZ6veE0ZshrDsUZMPi4WrpA7MJvZ2O525vxJwRt+Dh4sC+0+n0+3Qjaw+naB21EKKqil+v3leh3ieQBEqIG2cqhF9GqTWe7BxgyHcQ3sfqkBX7z/DYvG3kFpjpEe7DvEfb4Waw1yjgSuTmo87/6vS0+vOG92HB/WpdLKB7uC9/PNOVlkEepOcU8Oi8bXy1Pg65KFgIcQXL/Kdu2sbxH5JACXEjzCb49f9g/09gZw+D50H4HVaHLN93hv/7YScFJoW+zQP44uG2ODnoSzxdYmIiGzduJDExsTKirxx6e/UKvQFfg70TxKxSh/TOqVcf1vFwZtGoDjzQrh6KAm8tP8Qrv+yjoKiauRBCkJ0KZ/aq2yFdrn1sJZMESogbsXqiWpXbzh7umwuN+1rt3hBzlmd/3IVZUZc1+eSBVtdcyy4uLo7Y2Fji4uIqOHANtLhPnWBuDILUY/DN7XAiGgCDvZ63723G6/0i0OlgwdaTDJ+9lfTsAo2DFkJUCcc3AAp4h4N7gNbRWJEESoiy2vY1RH+mbt/7BTS5y2r3zoQLjPpuh6Xn6X+DWly3eGRYWBgNGjQgLCysoqLWVmAkPL5WLXWQcwG+vQcO/AKATqdjZJdQvh7WFldHPZuPnefemZtIOC+Ty4Wo8Y6tVe/r99A2jhJIAiVEWcSshuXj1e2er0PzQVa7jyRl8sicbWTnm+ja0JsPhrQsVeXtwMBAunTpUvJixNWFmw8MXwbhfcGUp1Zq3/yputgycFsTP34a3YlAoxNx5y4ycNZmDp3J0DZmIYS24ooSqDBJoISwXUn71C99xQyRD6mlCi5zMjWbh7/ZQnpOAa3qeTDroTYY7Eue81RjObqok+1veVz9edVrsOJldaFioEmAO7+M6Uy4Xy3OZuYx+ItotsanahiwEEIzqfFw4bg6VSKks9bRXEESKCFKI+MMzB+iLs8S0hX6fWhV5ykjt4BH5m4jJTOPcL9azBlxC6414Wq7G2GnhzunqxPMAbbMUifkmwoB8HN3YtGojrQNrk1mbiEPf7OFvw4maxiwEEITxb1PdduBoeotdSUJlBDXU5ivLk+ScRq8G6k9KJdVGDeZFZ5dsIvYlCz83A3Me7QdHi6O1zihQKdTSxwM+Ap0etizQK0XVZgHgNHFge9Gtue2xr7kFZoZ9f0Oftkly78IUaNU4flPIAmUENe3eiKc3g5OHvDgInC2riD+vxWHWXvkLAZ7O74a1hZ/YzUuklneWgxWE1K9IxxeptaKyr8IgLOjnlkPt2Fg67qYzArjFu1h0faTGgcshKgUZtOlAppVcP4TSAIlxLUd/A22fK5u3/sFeIZa7V6y4xRfrFdLD7x3X0ta1PWo5ACrgcZ9YehicHCFY3/DdwMgNx0AB70dz3X25rZgRxQFXvxpLwu2JmgcsBCiwiXuVhcnNxghsJXW0ZRIEighriY1Dn59St3u9MwVVcZ3Jlxgws/7AHiqRwPualmNr6CraGHdYdhScDLCyX/hu3stSdTx+Hi6OJ2md4g6LDrh53189+8J7WIVQlS8uL/V+9CualHeKkgSKCFKUpCrXnGXlw5B7eG2iVa7z2Xl8eR3O8g3mekd4ce42xtpE2d1EtROLXPg7Amnd1h6osLCwmjYsAGT7org8a5qD+DrS/czd1O8xgELIcrLgg0HGfTRSuavP6g2HFun3od11yqk65IESoiSrHoVzuxRv8wHzQa9g2WXuWg+TkpmHg183fhwSCR2paj1JEohoAUM/02dZ3Z6O3w/kEBPN7p06UKdOnV45c4mjO5eH4DJvx9k/parD+dVy+VxhKimlu85xfakQjYePq3Ogzy5Rd1Rv6e2gV2DJFBC/NfhP9Rq4wADvgRjXavdX26IY/1RddL4jAdbS7mC8ubfHIYVJVGntsH3AyBXLaip0+l4MSqcUbeqFdtfXbqPn3eWfHVetV4eR4hqRFEUDl1Qt3u3CIITm8FcAMZ64Fl1V2eQBEqIy2Wnwu9j1e1OT0PD26127zhxgekrjwAw5e6mhPtXvdok1UJACxj2q3rl46lt8MMgyMsC1CTq5T6NGd4xGEWBFxbv4YNFa6/oaar2y+MIUU3EpGRx7mIBBns7+rRpeFn5gu5W9faqGkmghLjc8vFwMQV8GkOP16x2pWcX8MyCXZjMCne1DGTILUEaBVlDBLS8lESd3AI/PqjOTUNNoibd1ZTBbetiVuCznRdZEn3E6uE1YnkcIaqBDTHnAGgX6omTg75KL99yOUmghCh28FfY/5Na2LH/THC4VM9JURReXLKH02k5BHu58Pa9zdBV4b+Mqo3ASHhoCTi6Qfw/8NOjYCoAwM5Ox7QBLbi9UW3M6Ph0Zw7Rx85rG68Qosw2xJwFoFtDH8hMgpSDgK5KTyAHSaCEUF08B8vGqdtdxkKdNla7F20/ycoDyTjodXz2QGtqOTlceQ5RMeq2hQd+BL0BjvwBS//Psnae3k7HzOEduD3Cj3yTwhPfbudAYrrGAQshSiuv0MS/ceofPl0beUPcOnVHQEtw8dQusFKQBEoIgOUvQPY58I2AW1+y2nU6LYc3lh0CYHxUOM3rGrWIsGYL7QqDv1UXFd23CJY/D4oCqMU2P32gFe1CPcnMK2T47G0knM/WOGAhRGnsOH6B3AIzPrUMhPvVUovpQpXvfQJJoISAA7+oN50e+n8O9gbLLkVReHnJXrLyCmkTXJuRXWRCsmbC+6hXRaKD7bPh7zcsu5wc9Hw1rC2N/WtxLiuPh2dv4WxmnnaxCiFKZX3R/KeuDb3RKQrE/qXu+M8FPFWRJFCiZstJUyeOA3R9Xp1zc5kFW0+yIeYcBns7pg9qgV7qPWmr2UC46yN1e8P7sPUryy6jswPfPtqOurWdOXE+m0fmbiUzt0CbOIUQpVI8/6lrQ29I3AXZ58HgrhYwruIkgRI127ppcPEseDWEbuOtdp1MzeatP9SquOOjwgnzcdMiQvFfbUZcukJy+Xg4sNSyy9fdie9GtsfL1ZH9pzP4vx92UmAyaxKmEOLazmflcSBRrfHWuYE3xK5Wd4TdalW8uKqSBErUXEn7YOuX6vad08He0bLLbFZ4acleLuabuCWkNo90Dr3KSYQmur0AbUcCCvz8OMRvsOwK9XZlziO34OygZ0PMOV5fuh+laL6UEKLq2BirDt81CXDHt5YTxBQlUA17axhV6UkCJWomsxn+eAEUMzS9F+pb1xtZsC2BzcfO4+Rgx/RBLWXorqrR6dSkt8ldYMpXa0Ql7bfsblHXg08faIWdDn7cdpKZ645pGKwQoiTF9Z+6NfRWr4Q+vUPd0aCXhlGVniRQomba+yOc/BccXKH3W1a7zmbm8c6fhwF4MaoxId6uWkQorsdODwO+hnqdIC9DrVaeftqyu1eEH5PvbgrA9JVH+HX36audSQhRyRRFuWz+k0/R1XcK+DUDd9sofisJlKh5ctJg1evqdveXwFjHavfbyw+RmVtIszruDO8UUunhiTJwcIIH5quV4zPPwPzBlnXzAIZ1DOGxLurw6/jFe9kSJ4U2hagKYlKySM7Iw2BvR9uQ2pcN31X9q++KSQIlap61b6k1n7zDof1oq13Rx87zy67T6HTwVv/mMnRnC5xrw9DF4OoLyfvhp0fAVGjZ/cqdTbijmT/5JjOjvt/BifMXNQxWCAGw/qja+9Q+zAsnPXBsjbqjgSRQQlRNSftg29fq9n8mjucXmnn9V3UezdD29WgZ5KFBgOKGeNSDB38Ee2e1jszyFyyFNu3sdHw4JJKWdY2kZRcwct52MqS8gRCaKp5A3u2K8gXtNI6s9CSBEjXL6omXJo6H3Wq166sNccSmZOHt5sj4qMYaBShuWJ02MOgbQAc75sDmTyy7igtt+rs7EZuSxdPzd1Eo5Q2E0ERugcmybmXXhj6Xhu/q97CJ8gXFJIESNcextepERTsHuG2S1a6Tqdl8+ncMAK/2bYLR2XZ+icVlGveFqLfV7dUT1QWii/i6O/H18LY4Odjxz9GzvL38sEZBClGzRcedJ6/QTKDRiUZ+bpfqP9nQ8B1IAiVqCrNZ/UIFuOUx8LSu6zTl94PkFpjpEOZJ/8g6JZxA2IwOo6HdE+r2z6MgcbdlV7M6Rj4cHAnA7E3x/Lg1ofLjE6KGW3c4BYDujX3RZZ+H0zvVHTZSvqCYJFCiZtj/EyTtVcfY/1NxfGPMOf46lIy9nY43+zdDp5OJ4zZNp4OoaVD/NijMgQUPQGaSZfcdzQMYd3sjAF5bup+t8alaRSpEjaMoCn8fUROoHuG+ELsGUMC/ObgHaBtcGUkCJaq/wrxLC892fhZcvSy7TGaFN4uWa3moQzANfGtpEaEob3p7uG+OeqVlZqKaRBXkWHY/3bMB/VoEUGhW+L8fdpCYlnONkwkhysuxsxc5mZqDo96Ozg28bHb4DiSBEjXBtq8hLQFqBUCH/7Pa9dOOkxxOysTdyZ5nb2uoUYCiQjgZ1SvznGtD4k74dYzlyjydTsf0QS2JCHDnXFY+o77bQW6BSeOAhaj+1hX1PrUP88TFXlfUA4VN1X8qJgmUqN5y0mD9dHW7xyvg6GLZlZVXyPSVRwF45raG1HZ1LOEEwqZ5hsHg78DOHvYvufRZAJwd9XzxcBtquziw73Q6r/yyT9bME6KCrb18+O7UdshJVf/YqWs75QuKSQIlqreNH0LOBbVSdcsHrXbNWneMc1l5hHi5MKxjiDbxiYoX2hX6fqBur30LDi2z7ArydGHGg63R2+n4eedp5mw6rk2MQtQAWXmFljmHPRr7wpHl6o6GvdVhdxsjCZSovrLOwpYv1O1ek61+QU+n5fDVhjgAJtzZBEd7+VWo1toMh/ZPqtu/jIKUQ5ZdnRp48+qdTQB4a/khNh87p0WEQlR7G2POUWBSCPFyIdTb9VICFX6HtoHdIPnWENXX5k/Uq7ACW0OjPla7pq84TF6hmfahnvSO8NMoQFGREhMT2bhxI4mJiWpD7zchpCvkZ8GPD6o9k0Ue6RzCgNZ1MJkVnp6/izPpMqlciPJWPP+pR2NfOBcL546qw+s2Vr6gmCRQonq6eO7Ski3dX1YvbS+y71Q6S3cnotPB6/0ipGxBNRUXF0dsbCxxcWpPI3oHuG+euuxLahz89CiY1YnjOp2Ot+9tTkSAO+cv5jP6+53kFcqkciHKi6Io1vOfjv6p7gjpos6BskGSQInqKfozKMiGgEh1fP0y7606AsA9LQNpVsc2f3HF9YWFhdGgQQPCwsIuNbp6wf3zwcFFrUr/12TLLicHPbMeaoO7kz27T6bx5rJDV55UCHFDDp7JIDkjD2cHPe1CPeFIUQIV3lfbwG6CJFCi+slOha1fqdu3vmTV+7TteCr/HD2LvZ2O54qKKYrqKTAwkC5duhAYGGi9w7853DND3d78Cez7ybKrnpcLH9/fCoDv/j3Bkh2nKitcIaq1dUfOAtC5gRdOBemQEK3uCO9zjUdVbZJAieon+jN1not/C6vJiYqiMH2l2vt0X9sggr1ctYpQaK3ZAOgyTt3+9SlI2m/Z1aOxr6Um2Cu/7ONgYoYWEQpRraw9fNn8p5hV6qLufs3VIXUbJQmUqF6yU2HLl+r2f3qfNsScY2t8Ko72djxzWwONAhRVRs/XoH5P9UKDhUOtJpU/e1tDuof7kFdoZvQPO8jILdAwUCFsW1p2PjsT1N+v7uG+Nn/1XTFJoET18u9MyM9U/7JpfGlsXVEUy9ynhzsEE2B01ipCUVXY6WHgN+pfwBeOw5LH1UWnATs7HR8NiaSOhzMnzmczfvEeKbIpxA36+3AKZgXC/WpRx83uUvVxSaCEqCJyLlyq+3Tri1a9T6sOJrP3VDoujnpGd6+vUYCiynHxhCHfg72TuibXP+9Ydnm4ODJjaGsc9DpWHkjmm43xGgYqhO1afTAZgN5N/SB+gzrFolaAepGPDatWCdTkyZPR6XRWt8aNG1v25+bmMmbMGLy8vHBzc2PgwIEkJydbnSMhIYG+ffvi4uKCr68v48ePp7CwsLJfirgR276GvAzwjYDG/SzNJrPC+0W9T492DsXbzaBVhKIqCmgJd32sbv/z7qWrg4DIIA9e6xsBwDt/HmbHiQslnUEIcRW5BSb+OapOIL89ws96+M7OtlMQ246+BE2bNuXMmTOW28aNGy37nnvuOX7//XcWL17MP//8Q2JiIgMGDLDsN5lM9O3bl/z8fDZv3sy8efOYO3cuEydO1OKliLIoyL0096nLc1a/mMv2JnI0OQt3J3se7xZ2lROIGq3l/dDuCXX75yfg/DHLrmEdg+nbIoBCs8JT83eSejFfoyCFsD3Rx86TnW/C392J5oHul5UvuFPbwMpBtUug7O3t8ff3t9y8vb0BSE9P55tvvuGDDz6gZ8+etGnThjlz5rB582b+/fdfAFatWsXBgwf5/vvviYyM5I477uCNN95gxowZ5OfLf5pV2r5FcDEF3OtA03stzWazwqd/xwLwRLcwjM4OWkUoqrreb0FQB7UXc9EwyM8G1CKb7w5sQZi3K2fScxm7cDdms8yHEqI0VhUN3/WK8EWXtBcyE8HBVV0VwMZVuwQqJiaGwMBAwsLCGDp0KAkJCQDs2LGDgoICevW6VDK+cePG1KtXj+hotR5FdHQ0zZs3x8/v0tIeUVFRZGRkcODAgas+Z15eHhkZGVY3UYnMZtj8mbrdYbRacbrIygNJxKaovU/DO4VoE5+wDfaOcN8ccPWB5P3wx/NQNHHczWDPzIda4+Rgx/qjZ/n8n2PXOZkQwmxW+OuQmkDdHuF/afiuQU9wcNIwsvJRrRKo9u3bM3fuXFasWMHnn39OfHw8Xbt2JTMzk6SkJBwdHfHw8LB6jJ+fH0lJSQAkJSVZJU/F+4v3Xc20adMwGo2WW1BQUPm+MHFtsavh3BEwuEPr4ZZmRVH4bK3a+zSiUwi1nKT3SVyHeyAMmg06O9gzH3bOs+xq7O/O1LubAfDB6qNsO56qVZRC2IQ9p9I4m5mHm8GeDmGecOh3dYcNVx+/XLVKoO644w7uu+8+WrRoQVRUFMuXLyctLY1FixZV6PNOmDCB9PR0y+3kyZMV+nziPzZ9ot63GQFO7pbmdUfOciAxAxdHPY90DtUmNmF7QrvBbUXzHpePh8Rdll33ta3Lva0uLTos86GEuLriq+9uDffBkBYHKQfBzsGmq49frlolUP/l4eFBo0aNiI2Nxd/fn/z8fNLS0qyOSU5Oxt/fHwB/f/8rrsor/rn4mJIYDAbc3d2tbqKSnN4BJzaqK3q3f9LSrCgKn/wdA6h1n2q7OmoVobBFnceqk1xN+ep8qGy1t0mn0/Fm/2aE+biSlJHL84tkPpQQV2MpXxDhBwd/VRvDbgXn2hpGVX6qdQKVlZXFsWPHCAgIoE2bNjg4OLBmzRrL/iNHjpCQkEDHjh0B6NixI/v27SMlJcVyzOrVq3F3dyciIqLS4xelUDz3qdkgMNaxNEcfO8+uhDQM9naM7Cq9T6KMdDro/znUDoG0BPjlSUuRTVeDPTMebI3B3o61R87y1YY4bWMVogqKP3eRmJQs7O10avXx4gQq4h5tAytH1SqBeuGFF/jnn384fvw4mzdv5t5770Wv1/PAAw9gNBoZOXIk48aNY+3atezYsYNHHnmEjh070qFDBwB69+5NREQEDz/8MHv27GHlypW89tprjBkzBoNBagdVORdOwMGl6nanp6x2FV9590C7evjWsv3JikIDzh4w+DvQGyBmJWz+2LKrSYA7k+5qCsD/Vh6R+lBC/Mfqg+q84Q5hXhhzTkHSXtDpq838J6hmCdSpU6d44IEHCA8PZ/DgwXh5efHvv//i4+MDwIcffki/fv0YOHAg3bp1w9/fn59//tnyeL1ez7Jly9Dr9XTs2JGHHnqIYcOGMXXqVK1ekriWfz9XF6QM6wH+zS3N24+nEh13Hge9jiek7pO4GQEt4M7p6vaaN+D4JsuuB9oFcVfLQExmhWcW7CI9R9bLE6JY8fDd7RF+cOg3tTGkC7h6aRhV+dIpssBTucvIyMBoNJKeni7zoSpKXia831hdEuChn6HBbZZdj8zZytojZ7n/liDeGdhCwyBFtaAo6hDe3h/BzR+e3ABuvgBk5hbQ95ONJKRmc0czf2YObY3usiWEhKiJzmflcctbf2FWYNPLPamzuB+c3g59P4BbRmod3jWV5fu7WvVAiRpkz49q8uTVEOr3tDQfTc5k7ZGz6HTw5K2y5p0oBzod9PsAfJpAVhIsGQlmEwC1nBz49IFW2Nvp+HN/EvO3JmgcrBDaW1O0eHDTQHfqcE5NntBZLbFVHUgCJWyPoqjr3gHc8pjVosFfF03ojYrwJ8TbVYvoRHXk6AqD56kVlOPXw7pLiw63DPLgpT7qmptTfz/IkaRMraIUokpYsV+d/9Q7wv9S7afgTlDL7xqPsj2SQAnbc3wjnD2sfplFPmBpTsnMZemuRABZ806UP5/wS4sOr58OsZeu6B3ZJZTu4T7kFZp5av5OcvJNGgUphLbScwrYEKMuHty3hf+l+U9N7tYwqoohCZSwPdu+Uu9bDAYno6X5280nyDeZaV3PgzbB1aPOiKhiWtwHbR4BFHXR4YwzANjZ6Xjvvpb41DIQk5LF1GVXX/pJiOpszaFkCkwKDX3daOB8ERLUtWZpcpe2gVUASaCEbclIhEPL1O12j1uas/ML+e7fEwBy5Z2oWH2mgV9zyD4HSx4DUyEA3m4GPhoSiU4HC7aeZPm+MxoHKkTlK/7c39k8oGj4ToG6t1jV6asuJIEStmXHXFBMUK8T+DW1NP+04xTpOQUEe7moi1YKUVEcnNX5UI5uahX8ddMsuzo38LZcvPDykr2cTsvRKkohKl1mbgHrj54DihKoalg883KSQAnbUZivJlAA7R6zNJvMCl9viAfUuSh6O7mMXFQwr/qX5kNteB9i/7LsGnd7I1oGeZCRW8jYH3dRaDJrFKQQlWvNoRTyTWbq+7jSyC0HThTVTauGw3cgCZSwJYd/h6xkcPODxpd+IVcfTCIhNRujswOD2tTVMEBRozQfBG0f5dJ8KPUCBge9HZ/cH4mbwZ5txy/w2dpYbeMUopJcPnynO/irWug4sLW6JFI1JAmUsB1bi0oXtBkB9pcWB/5yvVq64OEOwbg42msQmKixoornQ523mg8V7OXKm/2bAfDJmhi2HU/VMkohKlxWXiHrjqpX393ZPAD2LVZ3NL9Pw6gqliRQwjYkH4CEzepaSm1GWJp3n0xjZ0Iajno7hnUK1i4+UTM5OF02H2oT/POuZVf/VnUY0KoOZgWeXbCL9GxZ6kVUX38fTiG/0EyotyuNnVLh5BZAB03v1Tq0CiMJlLANO79V7xv3BfdAS/O3m48D0K9lgCwaLLRx+Xyo9dMhbp1l19T+zQj2ciExPZdXlu5DVs4S1dXyverw3R3N/NHtL1pjNrQruAdoGFXFkgRKVH2FebB3obrderil+VxWHsuKfmmHdwzRIDAhijQfVPTZVGDJ45CpLqTqZrDn4/vVpV7+2HuGn3ac0jZOISrAxbxC1h5JAYqG7/YvUXc0G6RhVBVPEihR9R1eBjkXwL0O1O9haf5xawL5JjMtgzxoGeShXXxCAPR5B3wj4GIK/Py4Zb28yCAPnru9EQCTfjtA/LmLWkYpRLlbd+QseYVm6nm60NT+FCTvBzsHiKh+1ccvJwmUqPp2fqfeRz4IdnoACk1mvv9XXbh1hMx9ElWBowvcNxccXCD+H9jwgWXXk7fWp0OYJ9n5Jp79cRf5hVLaQFQfVlffFfc+NewNztV7RQhJoETVlpZwaU5J5FBL8+qDySRl5OLl6qh2GQtRFfiEQ9/31e11b8NxtQ6O3k7Hh0MiMTo7sPdUOh/+dVTDIIUoP1l5hfx1SB2y7tvMH/b9pO5oPlDDqCqHJFCiatv1A6BAaDfwDLU0z4s+DsAD7ephsNdrE5sQJYl8EFo+qNbAWfIYXDwPQIDRmXcHNgdg1j/H2HzsnJZRClEuVu5PIq/QTJi3K804Cmkn1IXeG92hdWgVThIoUXWZTbD7B3W71TBL8+GkDP6NS0Vvp2Noh3oaBSfENdw5HbwaQmYiLB0NRVff9WkWwAPtglAUGLdwD2nZ+RoHKsTN+XWPWkD2nsg6l4bvGvdVh7SrOUmgRNUVtw7ST4KTEZr0szR/G60uGhzV1I8Ao7NGwQlxDQY3dT6U3gAxKyF6hmXX6/0iCPN2JSkjlwk/S2kDYbvOZuaxMUYtnnlPC18oLl9QjYtnXk4SKFF17SqaPN58sLqAK5CeU8AvO08DMExKF4iqzL8Z9ClaaPivSXBqBwAujpdKG/y5P4lF209qGKQQN27Z3kTMCrQM8iAkc4d6Baqzp9XV0tWZJFCiaspOhcN/qNutH7Y0L9lxipwCE439a9E+1FOj4IQopbaPQkR/MBfCT49AThoAzesaeb53OACTfztI3Nks7WIU4gb9ulsdvusfGQh7i5Zuadof9A7aBVWJJIESVdPeRWDKB/8WENASAEVRWLBVLV0wtH09dDqdlhEKcX06Hdz9CXgEq5Nrf3/GMh9qVLcwOoZ5kVNgYuzC3VLaQNiU4+cusvtkGnY66NfYHQ7+qu5oMUTbwCqRJFCiatr1vXrf+tLk8R0nLhCTkoWzg557WtXRKDAhysjJCPfNUQsLHvwVts8GwM5OxwdDWkppA2GTfiuaPN65gTc+CSug4CJ4NYCg9hpHVnkkgRJVT/IBSN6nfuE0u1RLZMFWda7IXS0DcHeqGV3Eopqo0wZ6TVa3V0yApH2AWtrgnQGXShtEHzuvUYBClJ6iKCzdrc5F7R9Z59LV0pEPqr2uNYQkUKLq2btIvW8UBS7qPKf07AKW7VX/4nmgnZQuEDao4xho1AdMebD4EchT5z3d0TyAwW3rqqUNFu2W0gaiytt/OoO4sxcx2NvRp04OnNgE6KDF/VqHVqkkgRJVi9l8qZJti8GW5qW7T5NXaKaxfy0iZd07YYt0OrhnJtQKhPMxsHy8Zdeku5oS4uXCmfRcXvlFShuIqu3Xot6nXhF+uB4qmjxevwcYa9bUCkmgRNWSsBkyToHBCA2jAOvJ4w+0k8njwoa5esGgb0BnB3vmw+4FarPhUmmD5fuS+GnHKY0DFaJkJrPC70WjAf1bBlg+w5cvtVVTSAIlqpa9C9X7iLvBwQmAXSfTOJyUicHejv4yeVzYuuBO0H2Cuv3HODirTh5vGeTBc7c3AmDSbwc4fu6iVhEKcVUbY8+RnJGHh4sD3R2PQHqC+gdv475ah1bpJIESVUdBLhy48lLYH4t6n/q1CMToLJPHRTXQ9Xl1fceCbLU+VEEOAE/eWp92oZ5k55t4duFuCkxS2kBULYuLCr/2j6yDw775amOzAZZixzWJJFCi6ohZCXnp4F4HgjsDkJFbwO97zgDwYPsgLaMTovzY6WHAV+DqA8n7YeUrAOjtdHw4JBJ3J3v2nEzj479iNA5UiEvSsvNZdSAZgMHNjXDwN3VHq4c0jEo7kkCJqqP46rvm94Gd+tH8dXciOQUmGvm50bpebQ2DE6Kc1fKHAV8COrU2VNE6YnU8nHm7qLTBjHWxbImT0gaiavhtTyL5JjMRAe5EXPgbCnPAu5FapqMGkgRKVA3ZqRCzSt2+7Oq7hdvU4bv7b5HJ46Iaqt8Tuo5Tt39/FlLjAHW4elAbtbTBcwt3k55ToGGQQqiK1228r21d2FVc+2lojar9dDlJoETVcPBXdekWv2bg1xSAQ2cy2H86Awe9jntl8riorrq/AkEdIC8DfnoUCtU6UJPvbkqwlwuJ6bm8KqUNhMYOJl72/3G9HDj5r3o1aQ1auuW/JIESVUPx8N1lvU9Lii7l7tXEj9qujlpEJUTF09urpQ2ca0PiLvhrEgBul5U2WLb3DD/vPK1xoKImW7xD7X26PcIPjwNFS2017A3uARpGpS1JoIT20hLU+k/ooNkgAApMZstSAYPa1NUwOCEqgbEu9P9c3f53Jhz+A4DIy0obTPx1PyfOS2kDUfnyC80s3aX+fzykpc+lpVvajtQwKu1JAiW0VzR5lpAuYKxDYmIiny/9h3NZ+Xi7GejWyEfb+ISoDOF3QMen1O2lo9U/LFBLG7QP9eRivolnfpTSBqLyrTmUzIXsAvzcDXTJ3wC5aWCsBw1u0zo0TUkCJbR34Bf1vtkAAOLi4vjz0AUA7m0ViINePqaihrhtknpFU266Oh/KVHBFaYOP/jqqdZSihimePD6gdV30O+eojW1HqOU4ajD5ZhLaSo2HM7vVyYhN7gbAM6AeRy8aABgow3eiJrF3hEFzwMkIp7bBmikABHo4887AFgDMXHeM6GNS2kBUjuSMXP45ehaAofXS1M+lnT20eljbwKoASaCEtg4uVe9DuoKrNwBbk0yYFGhex0hjf3ftYhNCC7WD1UWHATZ/CkdXAnBn8wCGtA2ylDZIy87XMEhRUyzefhKzAm2Da1M3rmiprSZ3gZuvtoFVAZJACW0dWKreN73X0rS46Oo7mTwuaqwm/aD9k+r2L6MgXf2dmHhXBGHeriRl5PLyEiltICqWyaywYKs6fPdwa69LV0u3fVTDqKoOSaCEdizDd3r1LxrUWiMHEtVaI3e3DNQ2PiG0dPtUCGwFORdg8SNgKsC1qLSBg17HigNJli83ISrC2sMpnE7LwcPFgTvZCPlZ4NVQHTEQkkAJDRUP34VeGr5bslNqPwkBgL1BnQ9lMMKprZb5UM3rGnmhdzgAU5cdICY5U8soRTX2/ZYTAAxuUxcHy+TxR2ts5fH/kgRKaKf46ruI/kBR7addUvtJCAvPUOg/Q93e/Ckc+ROAx7uG0bWhN7kFZp5esIvcApOGQYrqKOF8tmXy+CPB5yB5H9g7Qcv7NY6s6pAESmgjNR7O7LEavtsYc47zF/PxcnWU2k9CFGtyF7QfrW7/8iSkJWBnp+P9wS3xcnXkcFIm7/x5WNsYRbXzw9YTKAp0a+RDwNGiwplNB4CLp7aBVSGSQAltlDB8V1x5/K6WUvtJCCu3Ty2qD5UGi0dAYT6+tZx4776WAMzdfJy/DiaTmJjIxo0bSUxM1DRcYdtyC0ws3q5Op3i0hRPsX6LuaPeYhlFVPfItJbRRPHxXdPXdxbxCVh1IBuCeSJk8LoSVy+tDnd5hWS+vR2NfRnYJBWD8T3vYcTCW2NhY4uLitIxW2Lg/958h9WI+AUYnuqUtBXOBuuB1nTZah1alSAIlKl9q3KXhu8bq8N3qg8nkFJgI9nIhMshD2/iEqIpqB0P/Wer2vzPh4K8AvNgnnKaB7lzILmDOYYWw+vUJCwvTMFBh677/V11G6OE2vtjtKJo83vH/NIyoapIESlS+4tpPod3A1Qu4NHx3T2QddHKFhxAla3wndHpG3V46Bs4fw2Cv59MHWuHiqGfnqSz25PsTGCi9uOLGHEzMYMeJC9jb6XjINRpyUsGjHjTup3VoVY4kUKLyFc9/atofgHNZeWyIOQdAfxm+E+LabpsI9TpBfiYsGgYFOYT5uPHGPc0A+PCvo2yNT9U4SGGriksXREX44b7ra7Wx/ZM1ft27kkgCJSpX2smi4Ts7y180f+w9g8ms0KKukTAfN40DFKKK0zvAoNng6gPJ+2H5C4C6buSAVnUwK/Dsj7u4cFGWehFlk3oxn5+LavE9FXwCzh0Bx1qy7t1VSAIlKteR5ep9vY5XXH13T2QdraISwra4B8DAr9U/RHZ9r96AN/o3I9TblTPpuYz/aa8s9SLKZP6WE+QWmGlWx53Gx79TG1s/DE6yJmlJJIESlevwMvW+cV8ATpy/yK6ENOx0cFfLAA0DE8LGhHWHHq+o2388D0n7cDXY8+kDrXDU2/HXoWTmbj6uZYTChuQVmpgXrQ7fPdfChO7Y32qC3n6UxpFVXZJAicqTnQrHN6nb4XcC8OtutV5N5wbe+NZy0ioyIWxTl+ehYW8ozIWFD0NOGs3qGHnlzsYATFt+mH2n0jUOUtiC33YncjYzD393J3qkFdV9Cr8TaodoGldVJgmUqDwxq0AxgV8z8AxFURQZvhPiZtjZwb1fqFdJXYhXK5WbzQzvFELvCD/yTWbGzN9JRm6B1pGKKkxRFL7ZGA/Ak7e4Y7dvkbqj4xgNo6r6JIESlec/w3f7T2cQd/YiBns7opr6aRiYEDbMxRMGfwd6Axz9EzZ+gE6nY/qgltTxcCYhNZsJS/bJfChxVRtjz3E4KRMXRz33F/6u9mgGtlbnqoqrkgRKVI6CHIhdo24XDd8t26sO3/Vq4kctJwetIhPC9gVGQt/31O2/34Rjf2N0cWDG0NY46HX8se8M3/97QtMQRdX11Qa192lYpBGnXbPVxm4vgNTkuyZJoETliFsHBdngXhcCWqIoCsv2ngGgXwuZPC7ETWs9rOhycwV+GglpJ4kM8uClPup8qDeWHWL/aZkPJawdScpk/dGz6HTwpPMatb6YbwQ0ukPr0Ko8SaBE5bh8+E6nY/fJNE6n5eDiqKdHY19tYxOiurjzPQhoqVaPXvQwFOQysksovZrIfChRstlFc5/ubmzEY29R4cyuz6vz68Q1yTskKp7ZBEdWqNtF85/+KOp96tXEDycHqXArRLlwcILB34JzbUjcBX88jw54774W1PFw5sT5bF6S+lCiSHJGLr/sUi/kecFrE+RcAM8wyyLv4tokgbqKGTNmEBISgpOTE+3bt2fr1q1ah2S7Tm6F7HPg5AHBnTCbFZbvUxOovjJ8J0T5qh2iVirX2cHu72H7N3i4OPLZg61w0Ov4c38Sszcd1zpKUQV88U8c+SYzHeu5UvfwN2pjl+dk2ZZSkgSqBAsXLmTcuHFMmjSJnTt30rJlS6KiokhJSdE6NNtUPHzXqA/oHdh1Mo3E9FzcDPbc2shH29iEqI7q94TbJqnbf74MCf/Sql5tXusbAcC05YfYcULWy6vJzmXlMX+remHBlODd6LKS1TmqLe7XODLbIQlUCT744AMef/xxHnnkESIiIpg1axYuLi7Mnj1b69Bsj6LA4T/U7aLhu+Kr726PkOE7ISpM52choj+YC9RFhzPOMKxjMP1aBFBoVhjzwy7OZeVpHaXQyFcb4sgtMNO6jisNjxbNfer8LNg7ahuYDZEE6j/y8/PZsWMHvXr1srTZ2dnRq1cvoqOjS3xMXl4eGRkZVjdRJOWQWuDP3gka3GY9fNdchu+EqDA6HdwzQ72iKisZFg1DZ8rnnYEtCPNxJSkjl7E/7sZklvlQNc2Fi/l8V7Rsyxthh9ClnwRXX3XdO1FqkkD9x7lz5zCZTPj5WRd29PPzIykpqcTHTJs2DaPRaLkFBQVVRqi2oXjx4LDu4OjKjoQLJGfkUctgT9dG3pqGJkS1Z3CDId+DkxFObYU/nsfNUc+sh9rg7KBnY+w5Pv7rqNZRiko2Z1M82fkmWvg7ExE7S23sOAYcnLUNzMZIAlUOJkyYQHp6uuV28uRJrUOqOmJWqfeNogBYtqdo+K6pHwZ7Gb4TosJ51YeBRZPKd30HW7+kkV8tpg1oDsAnf8ey5lCyxkGKypKRW8CcokWmp4XsRnfhuNr71O5xTeOyRZJA/Ye3tzd6vZ7kZOv/UJKTk/H39y/xMQaDAXd3d6ubQF08+NQ2dbvB7ZjMCsv3q714d7UI1DAwIWqYhr2g1xR1e8UEiFtH/1Z1GNYxGICxC3cTf+6ihgGKyjJv03Eycwtp5uNAROwXamO38eDoqm1gNkgSqP9wdHSkTZs2rFmzxtJmNptZs2YNHTvKukBlErsGFDP4NgWPILYdT+VsZh7uTvZ0biDDd0JUqk5PQ4sh6oLei0dAajyv9Y2gTXBtMnMLefK7HWTnF2odpahAWXmFfLNJLZz5blA0uqwkdSHqNiO0DcxGSQJVgnHjxvHVV18xb948Dh06xOjRo7l48SKPPPKI1qHZlpiV6n2j3sCl4plRTf1xtJePnhCVSqeDuz5WF4nNuQALHsDRdJGZQ1vjU8vAkeRMXpJFh6u1ORvjScsuoJmXQkRc0VXl3V+RK+9ukHyLlWDIkCG89957TJw4kcjISHbv3s2KFSuumFgursFsgti/1O2GUZjNCisOqMN3d8rVd0Jow8EZ7v8B3Pzh7CH4+Qn83ByY8WBr7O10/L4nkW+KlvYQ1UvqxXy+WB8HwAd1/kGXmwY+TaDFYG0Ds2GSQF3FU089xYkTJ8jLy2PLli20b99e65Bsy6lt6l+5Th5Q9xZ2nbzA2Uz16rtODby0jk6Imss9UE2i9Ab1Ktm/JtMu1JPX+jYBYNqfh9kce65Mp0xMTGTjxo0kJiZWRMSiHMxYG0tWXiFd/E00jP9ebez5mlQdvwmSQImKcbRo+K5BL9Dbs6Jo8njPJr5y9Z0QWqvbFvrPVLc3fwI7v2V4pxAGtKqDyazwf/N3cjI1u9Sni4uLIzY2lri4uAoKWNyMUxeyLXWf3vFdja4gG+q0tRQ3FjdGEihRMS4rX6Aol4bv7mhW8pWMQohK1nwQdJ+gbi97Dt3xjbw9oDkt6xpJyy7g8W+3czGvdJPKw8LCaNCgAWFhYRUYsLhRH66OId9k5p56edSJXaA23jZRnRcnbpgkUKL8pZ+C5P2ADurfxsEzGZxMzcHJwY5usvadEFXHrS9Bs0FgLoSFD+GUcZwvHm6LTy0Dh5MyGbdoN+ZSVCoPDAykS5cuBAZKeZKq5nBSBj/vOgXAVKcF6MwFUP82CLtV48hsnyRQovwV9z7VvQVcvVhZNHx3ayMfXBztNQxMCGFFp4N7PlOHc3LTYP5g/B2ymfVQGxz1dqw8kMynf8dqHaW4CdNXHEFR4LmwUxgTVoGdPfSZpnVY1YIkUKL8xaxW74vKF/xZlED1keE7IaoeB2d4YAEYg+B8LPz4IG0CnXnz3mYAfPjXUf4sWr9S2JZtx1NZczgFg52J0TlfqY3tngCfcG0DqyYkgRLlqyAX4tap2w2jiE3JIiYlC3s7HT0bSxkIIaokN18YuhgMRkiIhqWjGdy6DiM6hQDw3KLd7D2VpmmIomzMZoU3lx0E4L2QbTheiAEXL3XYVpQLSaBE+TqxEQqyoVYg+DdnZdHk8U4NvDE6O2gcnBDiqnybwJDvwM4BDvwMaybzWt8mdA/3IbfAzGPztnMmPUfrKEUpLd5xkj2n0gkyZNP3/Fy1sefr4OyhZVjViiRQonwdLZr/1PB20OksCVSfpjJ8J0SVF3Yr3DND3d70MfY7vuHTB1rRyM+NlMw8Rs4t/ZV5Qjvp2QW8u+IIAF/W+RO7vAzwbwGth2kcWfUiCZQoX5eVLzidlsPeU+nodNC7qQzfCWETWg5RCywC/PkitY6v5pvht+Dt5sjBMxmMXbgbUymuzBPa+fCvo6RezKePVzKNE39WG+94V4pmljNJoET5SY2DC/HqVR6h3SxX390S4om3m0Hj4IQQpdb1BbW3QjHDT48QlLWXLx5ui6O9HasPJjNt+SGtIxRXcehMBt9GH8cOM/9z/hYdCjQbCMGdtA6t2pEESpSfY3+r90EdwFDLUjxThu+EsDE6HfT9EBr1gcJcmD+YNk5nmD6oBQBfb4xnziZZM6+qURSFSb8dwKzAu3WjcT+3Cxxrwe1vaB1atSQJlCg/x9aq9/V7cD4rj+3HUwEZvhPCJuntYdAc9Q+i3HT4fgD3BBfyYh/1Evipyw5KeYMq5rc9iWyNT6W+wzkGps1WG2+fAsY62gZWTUkCJcqHqQDi/lG36/fk78MpmBVoGuhO3dou2sYmhLgxji7w4I/gGwGZZ+D7AYxua+ShDvVQFHh24W62Ff2hJLSVnlPA28sPAQpzvH7ArjAHgjtDm0e0Dq3akgRKlI9T2yE/E5w9ISCSvw4lA9CrifQ+CWHTnGvDQ0vAWA/Ox6Kbfx9Toupxe4Qf+YVqeYPYlCyto6zx3lx2kOSMPJ40bqVe2hawd4K7PgE7+ZqvKPLOivJRPP+pfg9yTQrrj54D4PYISaCEsHnugfDwL2ohxsRd6BfczycDGhEZ5EF6TgHDZ2+VGlEaWnckhcU7TuGjS+cFZa7a2P1l8G6gaVzVnSRQonxYEqiebD52jpwCEwFGJ5oGumsblxCifHg3UJMogxESNuP888N8M7QZod6unE7L4eFvtpJ6MV/rKGuczNwCJvy8D4C5/ouxz0+HgJbQ8WmNI6v+JIESNy87FRJ3qtv1e7L6YAqgDt/pdDoNAxNClKuAlvDQT+DgCnHr8Fr+BN+NiMTf3YnYlCxGzNlKlhTarFRvLz/MmfRcHnHfTtMLf4NOD3d/pl4EICqUJFDi5sX/o9aL8WmC2S3g0vwnGb4TovoJagcPLlTn2BxdQd2/n+X7R1tT28WBvafSeXzednILTFpHadMSExPZuHEjiYmJ1zxuY8w5FmxNIEiXzKvKl2pjt/EQ0KISohSSQImbd9nw3d7T6ZzNzMPNYE+HME9t4xJCVIzQrjDkB3XdvINLabBpPPNGtMbNYE903HmeXrCLQpNZ6yhtVlxcHLGxscTFxV31mKy8Ql5ashd7Cplf+yvsC7LUkhPdxldipDWbJFDi5igKxBYlUA168tdBtffp1kY+GOxl2QAhqq2GveC+uerKA/sW02LLeL56KNJSrXzswt2SRN2gsLAwGjRoQFhYWIn7FUXhlZ/3cToth9ddfyUo+6A6N23gVzJ0V4kkgRI351wMZJwCvQHqdWL1weLhO1+NAxNCVLgm/eC+eWpP1IGf6bhrPLMeaI6DXseyvWd4fvEeWTfvBgQGBtKlSxcCAwNL3L9o+0l+25NIZ/1BhpmK1rq7+2PwqFeJUQpJoMTNKR6+C+5IQiYcSc5Eb6ejR7gkUELUCE36wZDvQO8IB3+l576XmHl/M+ztdPy6O5EXJIkqV0eSMpn02wFqk8EXrl+oa921HgZN79U6tBpHEihxc46tUe/r97RMHr8lpDYeLo4aBiWEqFThd6hzovQGOLyM2/e9yMwhTbG30/HLrtO8+NNeSaLKQXZ+IWPm7yS/oJB5Ht/gln8WvBtBn3e0Dq1GkgRK3LjCPDi+Ud2uf9ul4TupPi5EzdOoNzwwX02ijv5J791PMXNQQ/R2OpbsPMX4xXtkTtRNmvjrAWJTspjqsogWudvA3hkGzQZHV61Dq5HKnEANHz6c9evXV0Qswtac3AIF2eDqS3qtRmwtWhNLqo8LUUM16AVDF4OjG8Svp/f2x/n83nro7XT8vOs0Y+bvJK9QShzciEXbT/LTjlMM0q/nIfNvamP/meDfXNvAarAyJ1Dp6en06tWLhg0b8vbbb3P69OmKiEvYgrh16n39HqyLOYvJrNDQ141gL/lrSIgaK+xWGP67ZdmX3v+OYM69/jjq7Vh5IJnH5m0nO1+KbZbFlrjzvPrLPlrpYnjX8Ru1sdt4aDZA28BquDInUEuXLuX06dOMHj2ahQsXEhISwh133MFPP/1EQUFBRcQoqqr4op7I0FtZe1itPt6ziUweF6LGq9MaHlkB7nXhfAzdNjzEgns9cHbQsyHmHMO+2UpGrnxflMbxcxcZ9f0OvEznmOvyMXqlABr3g+6vaB1ajXdDc6B8fHwYN24ce/bsYcuWLTRo0ICHH36YwMBAnnvuOWJiYso7TlHV5GbAaXX5FlNIV/45ehaAnnL1nRACwKcRjFwJ3uGQcZo2fw3h1zsLqeVkz/YTF7j/i39JzsjVOsoqLT27gEfnbiM/O5Pv3T7GaEoF36Zw7xdgJ1OYtXZT/wJnzpxh9erVrF69Gr1ez5133sm+ffuIiIjgww8/LK8YRVV0YjMoJvAMY3eGGxeyC3B3sqdNcG2tIxNCVBXGuvDInxDUHnLTabR6OCu6ncDbzZGDZzK4d8YmjiRlah1llZRfaObJ73dw+twF5rl8RIPCWHD2VCfqG9y0Dk9wAwlUQUEBS5YsoV+/fgQHB7N48WLGjh1LYmIi8+bN46+//mLRokVMnTq1IuIVVYVl+K4bfxcN33Vr5IO9Xv1IlXYtJyFENefqBcN+g2YDwVxInfXjWdNiHfW9nUlMz2XQ55vZFHtO6yirFLNZ4ZVf9rEtLpmZhs+4xbxXXcD5wUVQO0Tr8ESRMtd8DwgIwGw288ADD7B161YiIyOvOKZHjx54eHiUQ3iiyro8gVpTNHzX+NLwXfFaTsBVq+kKIWoIBycY+A14NYB/3sW48zP+bHSckS6PsSEhm+Gzt/LOwBYMalNX60g1pygKr/+6nyU7EvjA4Utu021XS0M8sACCbtE6PHGZMvdAffjhhyQmJjJjxowSkycADw8P4uPjbzY2UVVdPAfJ+wBI8WrHoTMZ6HTq+nfFrreWkxCihtHpoMcrRfN3HHA8uox5ygRGNjFRaFZ4YfEe3vrjYI2uFaUoCpN/O8APW04w1WEu9+o3qmsNDp6nXt14Genl116ZE6iHH34YJyeniohF2IrjG9R736asOalWF44M8sDLzWA55HprOQkhaqiW98OIZeDmh93ZQ7yWOIaPWp4C4KsN8Tz8zVbOZ+VpHGTlUxSFqcsO8m10PK/bf8/D+r8AnZpwht9xxfHFvfxxcXGVH6wApBK5uBElzH+Ste+EEKVWrwOMWg/1OqLLy6D/kRdZ03IttRx1RMed565PN7L3VJrWUVYaRVGY9udhvtsUy/sOsxhp/6e6o9+H0HxQiY+RXn7tSQIlyq4ogSoI7mKZ/Hn5/CchhLiuWv5qwc0OYwCof+QrttT9mA6e2erk8lnRfP/vCRSleq+hl19o5oXFe/lu/UG+dnifAfqNoNND/1nQ9pGrPk56+bUnCZQom/TTcD4WdHZsNTchO9+Eby0DTQPdtY5MCGFr9A7Q5211PTcHV1wS/2WBaRyvBO0nv9DMa0v38/i32zlXTYf00rMLGD57K2t2HmK+49t01+9R17d74EeIfEDr8MR1SAIlyqZ4+C4gktVxahG8HuG+6HQ6DYMSQti0ZgNh9Eaoewu6vAyeOPs2a4K/xVufzV+HUujz0XrLagfVxcnUbAZ8vonk+H0sMUyhlV0sONdWe+Ua9dY6PFEKkkCJsilKoJTQW1l7pGj+kwzfCSFulmeYuvxLj1dBp6d+8gqiPV5nuOdBzmXl88jcbbzyyz7Sc2x/CZjoY+e5d+YmIs6v5nfDa9TXJYJ7HXh0pZQqsCGSQInSUxRLApXk1Y4T57Nx0Ovo0tBb48CEENWC3h5ufRFGrgLPMBwunmFK9pv86T+LAM4zf0sCvT74h9/2JNrk3Kj8QjPvrjjMiK838EzuF3zq+Bmu5EJIV3h8LfiEax2iKANJoETppcZBximwc2BFRjAA7UI9cTOUuR6rEEJcXd228OQm6PIc2NnTJG09G91e4kXjX6RmZvPMgl0Mm72V+HMXtY601I6dzWLA55tY9s9mFjlMYZj9anVH1+fh4aVQy0/T+ETZSQIlSi/+H/U+qB1/H8sCpHyBEKKCOLpAr8kwagMEdUBfmM3/5c1mW+2J9HXYzoaYs0R9uJ7Jvx3gbGbVnWReYDIze2M8/T9ZS7ek71hleImWdnHqfKcHF8NtE9WeN2Fz5F9NlF5x+YJ6XdiyNhWA7uE+13qEEELcHL8IdUHi3d/D6kl45sQzQ/8Bzzk35rWsQczdbGbhtpM82iWEJ7rWx+jioHXEgFrb6e/DKby1/BDe57bzs8NsGjqcVneGdIX+n4NHkLZBipsiCZQoHUWBeLUC+T7HluQXmqnj4Ux9H1kVXAhRwezsoPUwiLgHNn0C/86kQf5hfnR8k10OrZl+sQ8z1hbyXfQJHmhfj4faBxPk6aJZuAcTM3h7+SGOHzvIWPufGWQouvjGxRtd1NvQYrC6tI2waZJAidI5dxSyz4G9E3+cDwQS6dbIR8oXCCEqj5MRbnsd2j0B66fDjjm0KtjJfMedxNmF8Hleb+b+04mv1sdxWxM/hncMoXMDr0r5f6rQZOavQyl8G32c5Li9/J/9b9zjuAl7XdHafm1GoOs1WR26E9WCJFCidI5vVO/r3sLa2HTAevFgIYSoNLX8oO970HEMRM+A3T8QVnCc6Q5f8pphIT/md+H3Qx156GASdTxc6N3Ujz5N/Wkb4onermzJVGJiInFxcYSFhVmqfhe3hYaGkutoZMX+JBb8e5yAjD2MsF/BHY7bsNMVXSVYvyd0f0XKE1RDkkCJ0jmxGYB033bEHb6I3k5HpwZeGgclhKjRPEPVRKrnq7DzW9jyJcaMU4yy/4NR9n+QoPjxa1ZH/tzcjrmb6uHp6sSt4T60CvKgRV0PGgfUwmCvv+ZTFC/aCxAQEEDqxXyWRB9hfcx5jq9Jxzc3nrv1m1mo/5c6hvOXHhjeF7o9D3XaVOQ7IDSkU2yxmEYVl5GRgdFoJD09HXf3arDEiaLAB00g8wyrb/maxze40C7Ek0VPdtQ6MiGEuMRUCEf/hH0/wdGVUJhj2ZWGG9tM4Ww1h7PV3JjDSj3MegON/GoRYHTC281QdHMEIN9kJr/QzLkL6cQnpZJa6MjptBz8cuNpZRdLa7sY2uqOEGKXbHkOxVALXZN7oOP/gV/TSn/54uaV5ftbeqDE9V2Ih8wzYOfA0rMBQDq3ytV3QoiqRm8PTe5Sb3lZcHQF7P8Z4tbiUZDF7fod3K7fYTk8WfHg5DlfEs76kqzUJh8Hzit2FGJPIXYYdRdpygV66NLw1aVRT5eMq8G6ZIJi74SuURQ0G4SuYW9wcKrsVy00IgmUuL7jmwAwB7ZmXZxa/0nmPwkhqjSDGzQfpN5MBXBmjzoVISEaEv6FnFT8dGn46dJoy9FSn1ZxdENXpw0EtYO67dDV6wBO1WCkQZSZJFDi+ormP53xaM3FWBNero5EBMh/GEKIilHSxO2bondQq5vXbQudn1Hbci7AheNFtxNkJcWSkZaK0c0FVydHMBeqV/25+UEtf3DzB2NddN4Nwe7a86ZEzSAJlLi+E+oVeJsKGwPQrZEPdmW8kkUIIUrr8onb5ZJAlcS5tnoLbAWAW9FNiNKSBEpcW9pJSEsAnZ6FSQFAoQzfCSEqVFhYmNW9EFWRJFDi2oqG7wr8mrPjeCE6HXRt6K1xUEKI6iwwMLDiep6KlPswoahxJIES13ZCnUAe76p2czevY8TLzaBlREIIcdMqZZhQVGuSQIlrK0qg1uU2BKBbQxm+E0LYPhkmFDdLEihxdZnJcD4WBR0/nFH/Qusm85+EENVAZQwTiurNTusARBVW1PuU69mEE9mOuBnsaVXPQ9uYhBBCiCpAEihxdUUTyGOcWwLQIcwTB718ZIQQQohq9W0YEhKCTqezur3zzjtWx+zdu5euXbvi5OREUFAQ//vf/644z+LFi2ncuDFOTk40b96c5cuXV9ZLqFqKeqD+zqkPQJcGcvWdEEIIAdUsgQKYOnUqZ86csdyefvppy76MjAx69+5NcHAwO3bsYPr06UyePJkvv/zScszmzZt54IEHGDlyJLt27aJ///7079+f/fv3a/FytJOdCikHAViQUg+ALjKBXAghhACq4STyWrVq4e/vX+K+H374gfz8fGbPno2joyNNmzZl9+7dfPDBBzzxxBMAfPzxx/Tp04fx48cD8MYbb7B69Wo+++wzZs2aVWmvQ3NFw3cX3euTnOJGgNGJ+j6uGgclhBBCVA3VrgfqnXfewcvLi1atWjF9+nQKCwst+6Kjo+nWrRuOjo6WtqioKI4cOcKFCxcsx/Tq1cvqnFFRUURHR1/1OfPy8sjIyLC62bwE9fUeNTQH1OE7nU6WbxFCCCGgmvVAPfPMM7Ru3RpPT082b97MhAkTOHPmDB988AEASUlJhIaGWj3Gz8/Psq927dokJSVZ2i4/Jikp6arPO23aNKZMmVLOr0ZjJ7cAsOaiWiOli1QfF0IIISyqfA/Uyy+/fMXE8P/eDh8+DMC4cePo3r07LVq04Mknn+T999/n008/JS8vr0JjnDBhAunp6ZbbyZMnK/T5KlxBDiTuBuDXC0EAdJYJ5EIIIYRFle+Bev755xkxYsQ1j7laJdn27dtTWFjI8ePHCQ8Px9/fn+TkZKtjin8unjd1tWOuNq8KwGAwYDBUo+VNEneBuYBcgzcnc31pEuCOtyzfIoQQQlhU+QTKx8cHH58bu/pr9+7d2NnZ4evrC0DHjh159dVXKSgowMHBAYDVq1cTHh5O7dq1LcesWbOGsWPHWs6zevVqOnbseHMvxJZY5j81BXSyeLAQQgjxH1V+CK+0oqOj+eijj9izZw9xcXH88MMPPPfcczz00EOW5OjBBx/E0dGRkSNHcuDAARYuXMjHH3/MuHHjLOd59tlnWbFiBe+//z6HDx9m8uTJbN++naeeekqrl1b5EtT5T2uL5z/J8J0QQghhpcr3QJWWwWDgxx9/ZPLkyeTl5REaGspzzz1nlRwZjUZWrVrFmDFjaNOmDd7e3kycONFSwgCgU6dOzJ8/n9dee41XXnmFhg0bsnTpUpo1a6bFy6p8ZrNlAvnf2aE46u24JcRT46CEEEKIqkWnKIqidRDVTUZGBkajkfT0dNzd3bUOp2xSDsPM9hTaOdE4+0va1fdj/uMdtI5KCCGEqHBl+f6uNkN4opyc/BeAY47hFGIv5QuEEEKIEkgCJawVzX9aV7T+XdcGsnyLEEII8V+SQAlrRT1Q0QUN8HBxoGmgjQ1BCiGEEJVAEihxSVYKpMahoGOnuSGd6nthZyfLtwghhBD/JQmUuKTo6ruT9sFk4ErH+jL/SQghhCiJJFDikgR1+G5TfgMAOtX30jIaIYQQosqSBEpcUpRAbS1siL+7E2HerhoHJIQQQlRNkkAJVUEOnNkDwHalEZ3qe6HTyfwnIYQQoiSSQAnV6Z1gLiDVrjYnFV86yvCdEEIIcVWSQAlVUfmCLYUNAZ0kUEIIIcQ1SAIlVEUFNLebGhHs5ULd2i4aBySEEEJUXZJACVAUOLUVgO3mRnSS8gVCCCHENUkCJeD8Mci5QB6OHFRCpHyBEEIIcR2SQAk4tQ2AveYQCrCnQ5gkUEIIIcS1SAIlLAnULnNDwv1q4VPLoHFAQgghRNUmCZS4LIFqQKcG0vskhBBCXI8kUDVd/kVIPgAUJVAygVwIIYS4LkmgarrE3aCYOKN4kqLzol2op9YRCSGEEFWeJFA13WXDd83rGDE6O2gckBBCCFH1SQJV012WQHWQ8gVCCCFEqUgCVZMpilUC1VHKFwghhBClIglUTZZ+CrKSKVD0HNKF0TZE5j8JIYQQpSEJVA2Wum8VAIeUejSo44ubwV7jiIQQQgjbIAlUDZYTsx4omv8UJr1PQgghRGlJAlWD+eQdB9QK5LJ8ixBCCFF6kkDVVIV5OJw7BMBeGtI2uLbGAQkhhBC2QxKomippPzpTHqmKG7UCGlLLSeo/CSGEEKUlCVRNddkCwh1k+RYhhBCiTCSBqqkuL6Ap85+EEEKIMpEEqoYqTNgKwB6lAW1DZP6TEEIIURaSQNVEWSnYZyRgVnQU+LeS+U9CCCFEGUkCVROd2g5AjFKHFg3qaRyMEEIIYXskgaqJTqsJ1G4poCmEEELcEEmgaqC8E2oCtVepL+vfCSGEEDdAEqiaRlHQndkFwEXvFrjL/CchhBCizCSBqmlS43AsyCBPccCvQWutoxFCCCFskiRQNc3pnQAcVIK5pb6fxsEIIYQQtkkSqBom+7haQHOvEsYtMv9JCCGEuCGSQNUwuSfUBCqlVlOMLjL/SQghhLgRkkDVJKZCaqUeAMAp+BaNgxFCCCFslyRQNcnZQzgoeWQozoQ2bql1NEIIIYTNkgSqBime/7TfHEq7UG+NoxFCCCFslyRQNciFmH8BOO4Ujq+7k8bRCCGEELZLEqgaRH9mNwAm/1baBiKEEELYOEmgaoqCXLyzYwHwbNhB42CEEEII2yYJVA2Rd3o39pg4q7jTPKKZ1uEIIYQQNk0SqBoicf8mAI7YNSTIy0XjaIQQQgjbJglUDVFcQDPDszk6nU7jaIQQQgjbJglUDWG8sA8AQ3BbjSMRQgghbJ8kUDVAwcULBBaeAiC4eVeNoxFCCCFsnyRQNcCJ/ZsBOI0PYcHBGkcjhBBC2D5JoGqA80fVApqJrhHY2cn8JyGEEOJmSQJVA9if2QWAyT9S20CEEEKIakISqGrObFYIzD4IgHd4R42jEUIIIaoHSaCqubgTJwjgPADBzSSBEkIIIcqDJFDVXMIBdQJ5on1dHFw8tA1GCCGEqCYkgarmck7sACDDI0LjSIQQQojqQxKoas419QAAjkGtNY5ECCGEqD5sJoF666236NSpEy4uLnh4eJR4TEJCAn379sXFxQVfX1/Gjx9PYWGh1THr1q2jdevWGAwGGjRowNy5c684z4wZMwgJCcHJyYn27duzdevWCnhFFS85I5f6BbEA+Ie30zgaIYQQovqwmQQqPz+f++67j9GjR5e432Qy0bdvX/Lz89m8eTPz5s1j7ty5TJw40XJMfHw8ffv2pUePHuzevZuxY8fy2GOPsXLlSssxCxcuZNy4cUyaNImdO3fSsmVLoqKiSElJqfDXWN72HI0nyO4sAC7BbTSORgghhKg+dIqiKFoHURZz585l7NixpKWlWbX/+eef9OvXj8TERPz8/ACYNWsWL730EmfPnsXR0ZGXXnqJP/74g/3791sed//995OWlsaKFSsAaN++PbfccgufffYZAGazmaCgIJ5++mlefvnlUsWYkZGB0WgkPT0dd3f3cnjVN+bbH+YyLOZZUh0D8XzlkGZxCCGEELagLN/fNtMDdT3R0dE0b97ckjwBREVFkZGRwYEDByzH9OrVy+pxUVFRREdHA2ov144dO6yOsbOzo1evXpZjbInp9G4AcnxaaBuIEEIIUc3Yax1AeUlKSrJKngDLz0lJSdc8JiMjg5ycHC5cuIDJZCrxmMOHD1/1ufPy8sjLy7P8nJGRcVOvpTxczCvEN/MQ6KFWiAzfCSGEEOVJ0x6ol19+GZ1Od83btRKXqmLatGkYjUbLLSgoSOuQ2HMyjQhdPADuYW01jkYIIYSoXjTtgXr++ecZMWLENY8JCwsr1bn8/f2vuFouOTnZsq/4vrjt8mPc3d1xdnZGr9ej1+tLPKb4HCWZMGEC48aNs/yckZGheRK1J/YkneyKXkdApKaxCCGEENWNpgmUj48PPj4+5XKujh078tZbb5GSkoKvry8Aq1evxt3dnYiICMsxy5cvt3rc6tWr6dhRXeLE0dGRNm3asGbNGvr37w+ok8jXrFnDU089ddXnNhgMGAyGcnkd5SX12DYAspwCcXPx1DgaIYQQonqxmUnkCQkJ7N69m4SEBEwmE7t372b37t1kZWUB0Lt3byIiInj44YfZs2cPK1eu5LXXXmPMmDGW5ObJJ58kLi6OF198kcOHDzNz5kwWLVrEc889Z3mecePG8dVXXzFv3jwOHTrE6NGjuXjxIo888ogmr/tGmMwKDin7ADD7t9Q4GiGEEKL6sZlJ5BMnTmTevHmWn1u1agXA2rVr6d69O3q9nmXLljF69Gg6duyIq6srw4cPZ+rUqZbHhIaG8scff/Dcc8/x8ccfU7duXb7++muioqIsxwwZMoSzZ88yceJEkpKSiIyMZMWKFVdMLK/KDidl0MgcC3pwC5UJ5EIIIUR5s7k6ULZA6zpQ30Yfp9Ofd9DALhGGLoGGva7/ICGEEKKGq5F1oMQle+NOE6Y7o/4QIEN4QgghRHmTBKoayjq+CzudQp6LP7iVzyR9IYQQQlwiCVQ1czoth4BstXZWoVcTjaMRQgghqidJoKqZ7cdTaWZ3HIBzDoHaBiOEEEJUU5JAVTMnzmfTrKgCuVvDzhpHI4QQQlRPkkBVM890rUMjfSIAXs1u0zgaIYQQonqSBKq6Sd6PTjGDmz/UuvryM0IIIYS4cZJAVTdn9qj3Ur5ACCGEqDCSQFU3SXvVe0mghBBCiAojCVR1c6YogfJvrm0cQgghRDUmCVR1YiqAlEPqtiRQQgghRIWRBKo6ORcDpjwwuINHsNbRCCGEENWWJFDVSdI+9d6vGdjJP60QQghRUeRbtjpJkvlPQgghRGWQBKo6Ke6BkgRKCCGEqFCSQFUXiiIJlBBCCFFJJIGqLjISIScV7OzBt4nW0QghhBDVmiRQ1UXx/CefxmBv0DYWIYQQopqTBKq6kOE7IYQQotJIAlVdyBV4QgghRKWx1zoAUU6kB0oIISxMJhMFBQVahyGqGAcHB/R6fbmcSxKo6iA3HS4cV7f9mmkaihBCaElRFJKSkkhLS9M6FFFFeXh44O/vj06nu6nzSAJVHSQfUO+NQeDiqW0sQgihoeLkydfXFxcXl5v+khTVh6IoZGdnk5KSAkBAQMBNnU8SqOpAhu+EEAKTyWRJnry8vLQOR1RBzs7OAKSkpODr63tTw3kyibw6kAnkQghhmfPk4uKicSSiKiv+fNzsHDlJoKqDM5JACSFEMRm2E9dSXp8PSaBsXWE+nD2sbksCJYQQNql79+6MHTtW6zAAWLp0KQ0aNECv1zN27Fjmzp2Lh4eH1mFVOZJA2bpzR8GUDwYjeARrHY0QQogqaN26deh0ulJdnThq1CgGDRrEyZMneeONNxgyZAhHjx617J88eTKRkZEVF6yNkEnktu7yCeTSbS2EEOImZGVlkZKSQlRUFIGBgZb24snX4hLpgbJ1cgWeEEJUC4WFhTz11FMYjUa8vb15/fXXURTFsj8vL48XXniBOnXq4OrqSvv27Vm3bp1l/4kTJ7jrrruoXbs2rq6uNG3alOXLl3P8+HF69OgBQO3atdHpdIwYMeKK51+3bh21atUCoGfPnuh0OtatW2c1hDd37lymTJnCnj170Ol06HQ65s6dW1FvSZUmPVC2Tq7AE0KIq1IUhZwCkybP7eygL9OE5Xnz5jFy5Ei2bt3K9u3beeKJJ6hXrx6PP/44AE899RQHDx7kxx9/JDAwkF9++YU+ffqwb98+GjZsyJgxY8jPz2f9+vW4urpy8OBB3NzcCAoKYsmSJQwcOJAjR47g7u5eYo9Sp06dOHLkCOHh4SxZsoROnTrh6enJ8ePHLccMGTKE/fv3s2LFCv766y8AjEbjzb1RNkoSKFumKJf1QEkFciGE+K+cAhMRE1dq8twHp0bh4lj6r9mgoCA+/PBDdDod4eHh7Nu3jw8//JDHH3+chIQE5syZQ0JCgmVo7YUXXmDFihXMmTOHt99+m4SEBAYOHEjz5uof1GFhYZZze3qqRZZ9fX2vOiHc0dERX19fy/H+/v5XHOPs7Iybmxv29vYl7q9JJIGyZRmJkJsGdvbg01jraIQQQtyEDh06WPVYdezYkffffx+TycS+ffswmUw0atTI6jF5eXmWoqHPPPMMo0ePZtWqVfTq1YuBAwfSokWLSn0NNYkkULaseAkX70Zgb9A2FiGEqIKcHfQcnBql2XOXl6ysLPR6PTt27LiierabmxsAjz32GFFRUfzxxx+sWrWKadOm8f777/P000+XWxziEkmgbFnyfvXer6m2cQghRBWl0+nKNIympS1btlj9/O+//9KwYUP0ej2tWrXCZDKRkpJC165dr3qOoKAgnnzySZ588kkmTJjAV199xdNPP42joyOgLndzsxwdHcvlPLZOrsKzZcU9UL4R2sYhhBDipiUkJDBu3DiOHDnCggUL+PTTT3n22WcBaNSoEUOHDmXYsGH8/PPPxMfHs3XrVqZNm8Yff/wBwNixY1m5ciXx8fHs3LmTtWvX0qRJEwCCg4PR6XQsW7aMs2fPkpWVdcNxhoSEEB8fz+7duzl37hx5eXk3/+JtkCRQtqw4gfKTCeRCCGHrhg0bRk5ODu3atWPMmDE8++yzPPHEE5b9c+bMYdiwYTz//POEh4fTv39/tm3bRr169QC1d2nMmDE0adKEPn360KhRI2bOnAlAnTp1mDJlCi+//DJ+fn489dRTNxznwIED6dOnDz169MDHx4cFCxbc3Au3UTrl8iITolxkZGRgNBpJT0/H3d29Yp6kMA/eCgDFBM8dBGOdinkeIYSwEbm5ucTHxxMaGoqTk5PW4Ygq6lqfk7J8f0sPlK06e0RNnpw8wD3wuocLIYQQovxIAmWrLh++kyVchBBCiEolCZStSilOoOQKPCGEEKKySQJlq5IlgRJCCCG0IgmUrZIESgghhNCMJFC2KOssZCUDOlnCRQghhNCAJFC2qHj+k2coGNy0jUUIIYSogSSBskUyfCeEEEJoShIoWyQVyIUQQghNSQJli2QRYSGEEBqbO3cuHh4eWofBiBEj6N+/f6U/ryRQtsZUqFYhB0mghBBCVFnHjx9Hp9Oxe/fuKnm+myUJlK1JjYPCXHBwBY8QraMRQgihkfz8fK1DKBe2+jokgbI1xcN3vk3ATv75hBCiOsjMzGTo0KG4uroSEBDAhx9+SPfu3Rk7dqzlmJCQEN544w2GDRuGu7s7TzzxBABLliyhadOmGAwGQkJCeP/9963OrdPpWLp0qVWbh4cHc+fOBS717Pz888/06NEDFxcXWrZsSXR0tNVj5s6dS7169XBxceHee+/l/Pnz13xNoaGhALRq1QqdTkf37t2BS0Nub731FoGBgYSHh5cqzqudr9h7771HQEAAXl5ejBkzhoKCgmvGd7PsK/TsovzJFXhCCFF6igIF2do8t4NLqdcqHTduHJs2beK3337Dz8+PiRMnsnPnTiIjI62Oe++995g4cSKTJk0CYMeOHQwePJjJkyczZMgQNm/ezP/93//h5eXFiBEjyhTuq6++ynvvvUfDhg159dVXeeCBB4iNjcXe3p4tW7YwcuRIpk2bRv/+/VmxYoUlhqvZunUr7dq146+//qJp06Y4Ojpa9q1ZswZ3d3dWr15d6viudb61a9cSEBDA2rVriY2NZciQIURGRvL444+X6T0oC0mgbI1cgSeEEKVXkA1vB2rz3K8kgqPrdQ/LzMxk3rx5zJ8/n9tuuw2AOXPmEBh4Zdw9e/bk+eeft/w8dOhQbrvtNl5//XUAGjVqxMGDB5k+fXqZE6gXXniBvn37AjBlyhSaNm1KbGwsjRs35uOPP6ZPnz68+OKLlufZvHkzK1asuOr5fHx8APDy8sLf399qn6urK19//bVVEnQ91zpf7dq1+eyzz9Dr9TRu3Ji+ffuyZs2aCk2gZAzI1kgPlBBCVCtxcXEUFBTQrl07S5vRaLQMbV2ubdu2Vj8fOnSIzp07W7V17tyZmJgYTCZTmeJo0aKFZTsgIACAlJQUy/O0b9/e6viOHTuW6fyXa968eZmSp+tp2rQper3e8nNAQIAl9ooiPVC2JDcd0hPUbb8IbWMRQghb4OCi9gRp9dzlzNX1+j1a/6XT6VAUxaqtpPlBDg4OVo8BMJvNZX6+0ijpdZQ2zpJcHnvxuSoq9mKSQNmS5IPqvXtdcK6tbSxCCGELdLpSDaNpKSwsDAcHB7Zt20a9evUASE9P5+jRo3Tr1u2aj23SpAmbNm2yatu0aRONGjWy9Mj4+Phw5swZy/6YmBiys8s2L6xJkyZs2bLFqu3ff/+95mOKe5hK2xN2vTjLer6KJgmULUmR4TshhKhuatWqxfDhwxk/fjyenp74+voyadIk7OzsLD1BV/P8889zyy238MYbbzBkyBCio6P57LPPmDlzpuWYnj178tlnn9GxY0dMJhMvvfTSFT021/PMM8/QuXNn3nvvPe655x5Wrlx5zflPAL6+vjg7O7NixQrq1q2Lk5MTRqPxqsdfL86ynq+iyRwoW5KbDvbOMnwnhBDVzAcffEDHjh3p168fvXr1onPnzjRp0gQnJ6drPq5169YsWrSIH3/8kWbNmjFx4kSmTp1qNYH8/fffJygoiK5du/Lggw/ywgsv4OJStuHFDh068NVXX/Hxxx/TsmVLVq1axWuvvXbNx9jb2/PJJ5/wxRdfEBgYyD333HPN468XZ1nPV+EUG/Hmm28qHTt2VJydnRWj0VjiMcAVtwULFlgds3btWqVVq1aKo6OjUr9+fWXOnDlXnOezzz5TgoODFYPBoLRr107ZsmVLmWJNT09XACU9Pb1MjysVU6Gi5GWV/3mFEMLG5eTkKAcPHlRycnK0DuWmZWVlKUajUfn666+1DqXaudbnpCzf3zbTA5Wfn899993H6NGjr3ncnDlzOHPmjOV2+fo48fHx9O3blx49erB7927Gjh3LY489xsqVKy3HLFy4kHHjxjFp0iR27txJy5YtiYqKqvDZ/KVmp6/y4/lCCCHKZteuXSxYsIBjx46xc+dOhg4dCqB9L4u4KpuZAzVlyhQAS0XSq/Hw8LiiPkSxWbNmERoaaqnS2qRJEzZu3MiHH35IVFQUoHajPv744zzyyCOWx/zxxx/Mnj2bl19+uZxejRBCCGHtvffe48iRIzg6OtKmTRs2bNiAt7e31mGJq7CZHqjSGjNmDN7e3rRr147Zs2dbXRIZHR1Nr169rI6PioqylKvPz89nx44dVsfY2dnRq1evK0raCyGEEOWlVatW7Nixg6ysLFJTU1m9ejXNmzfXOixxDTbTA1UaU6dOpWfPnri4uLBq1Sr+7//+j6ysLJ555hkAkpKS8PPzs3qMn58fGRkZ5OTkcOHCBUwmU4nHHD58+KrPm5eXR15enuXnjIyMcnxVQgghhKhqNO2Bevnll9HpdNe8XStx+a/XX3+dzp0706pVK1566SVefPFFpk+fXoGvQDVt2jSMRqPlFhQUVOHPKYQQQgjtaNoD9fzzz193rZ6wsLAbPn/79u154403yMvLw2Aw4O/vT3JystUxycnJuLu74+zsjF6vR6/Xl3jM1eZVAUyYMIFx48ZZfs7IyJAkSgghNKL8p5q1EJcrr8+HpgmUj4+PZXHAirB7925q166NwWAA1HV7li9fbnXM6tWrLev5FE/cW7NmjeXqPbPZzJo1a3jqqaeu+jwGg8HyHEIIIbRRXHQxOzsbZ2dnjaMRVVVxdfOyFhP9L5uZA5WQkEBqaioJCQmYTCZ2794NQIMGDXBzc+P3338nOTmZDh064OTkxOrVq3n77bd54YUXLOd48skn+eyzz3jxxRd59NFH+fvvv1m0aBF//PGH5Zhx48YxfPhw2rZtS7t27fjoo4+4ePGi5ao8IYQQVZNer8fDw8NSdsbFxeW6lbxFzaEoCtnZ2aSkpODh4WG1+PCNsJkEauLEicybN8/yc6tWrQBYu3Yt3bt3x8HBgRkzZvDcc8+hKAoNGjSwlCQoFhoayh9//MFzzz3Hxx9/TN26dfn6668tJQwAhgwZwtmzZ5k4cSJJSUlERkayYsWKKyaWCyGEqHqKp1tUmdp9osq5VrmjstApMlhc7jIyMjAajaSnp+Pu7q51OEIIUeOYTCYKCgq0DkNUMQ4ODtfseSrL97fN9EAJIYQQpVV8UZAQFaXaFdIUQgghhKhokkAJIYQQQpSRJFBCCCGEEGUkc6AqQPG8fFnSRQghhLAdxd/bpbm+ThKoCpCZmQkg1ciFEEIIG5SZmYnRaLzmMVLGoAKYzWYSExOpVatWuRdxK14m5uTJk1Ii4TrkvSo9ea9KT96r0pP3qvTkvSq9inyvFEUhMzOTwMBA7OyuPctJeqAqgJ2dHXXr1q3Q53B3d5dfslKS96r05L0qPXmvSk/eq9KT96r0Kuq9ul7PUzGZRC6EEEIIUUaSQAkhhBBClJEkUDbGYDAwadIkDAaD1qFUefJelZ68V6Un71XpyXtVevJelV5Vea9kErkQQgghRBlJD5QQQgghRBlJAiWEEEIIUUaSQAkhhBBClJEkUEIIIYQQZSQJlI1466236NSpEy4uLnh4eJR4jE6nu+L2448/Vm6gVURp3q+EhAT69u2Li4sLvr6+jB8/nsLCwsoNtAoKCQm54nP0zjvvaB1WlTFjxgxCQkJwcnKiffv2bN26VeuQqpzJkydf8Rlq3Lix1mFVCevXr+euu+4iMDAQnU7H0qVLrfYrisLEiRMJCAjA2dmZXr16ERMTo02wGrveezVixIgrPmd9+vSptPgkgbIR+fn53HfffYwePfqax82ZM4czZ85Ybv3796+cAKuY671fJpOJvn37kp+fz+bNm5k3bx5z585l4sSJlRxp1TR16lSrz9HTTz+tdUhVwsKFCxk3bhyTJk1i586dtGzZkqioKFJSUrQOrcpp2rSp1Wdo48aNWodUJVy8eJGWLVsyY8aMEvf/73//45NPPmHWrFls2bIFV1dXoqKiyM3NreRItXe99wqgT58+Vp+zBQsWVF6AirApc+bMUYxGY4n7AOWXX36p1Hiququ9X8uXL1fs7OyUpKQkS9vnn3+uuLu7K3l5eZUYYdUTHBysfPjhh1qHUSW1a9dOGTNmjOVnk8mkBAYGKtOmTdMwqqpn0qRJSsuWLbUOo8r77//ZZrNZ8ff3V6ZPn25pS0tLUwwGg7JgwQINIqw6Svp+Gz58uHLPPfdoEo+iKIr0QFUzY8aMwdvbm3bt2jF79mwUKfNVoujoaJo3b46fn5+lLSoqioyMDA4cOKBhZFXDO++8g5eXF61atWL69OkytInaq7ljxw569eplabOzs6NXr15ER0drGFnVFBMTQ2BgIGFhYQwdOpSEhAStQ6ry4uPjSUpKsvqMGY1G2rdvL5+xq1i3bh2+vr6Eh4czevRozp8/X2nPLYsJVyNTp06lZ8+euLi4sGrVKv7v//6PrKwsnnnmGa1Dq3KSkpKskifA8nNSUpIWIVUZzzzzDK1bt8bT05PNmzczYcIEzpw5wwcffKB1aJo6d+4cJpOpxM/N4cOHNYqqamrfvj1z584lPDycM2fOMGXKFLp27cr+/fupVauW1uFVWcX/95T0Gavp/y+VpE+fPgwYMIDQ0FCOHTvGK6+8wh133EF0dDR6vb7Cn18SKA29/PLLvPvuu9c85tChQ6WefPn6669btlu1asXFixeZPn16tUmgyvv9qknK8t6NGzfO0taiRQscHR0ZNWoU06ZN03zpBGEb7rjjDst2ixYtaN++PcHBwSxatIiRI0dqGJmoTu6//37LdvPmzWnRogX169dn3bp13HbbbRX+/JJAaej5559nxIgR1zwmLCzshs/fvn173njjDfLy8qrFF195vl/+/v5XXD2VnJxs2Vfd3Mx71759ewoLCzl+/Djh4eEVEJ1t8Pb2Rq/XWz4nxZKTk6vlZ6Y8eXh40KhRI2JjY7UOpUor/hwlJycTEBBgaU9OTiYyMlKjqGxHWFgY3t7exMbGSgJV3f1/e3cQ0nQfx3H88xhNo6kozmaCIiwkSRokK08hwsKDKHSQoDE6dLKDpnlSIsgdOsSgQ929dkvqoGOCoIkIE0+CI7GQSuwQplHot8MDgjwP6e956vntqfcLvOwwP/wO481f//+FQiGFQqGf9v65XE4VFRW/RDxJP/a8WltbNTo6qvfv36u6ulqSNDExobKyMjU1Nf2Q31FI/s3Z5XI5FRUV7Z/T7yoQCOjChQvKZDL7d7fu7e0pk8no1q1bfscVuK2tLeXzeSUSCd9TClpDQ4PC4bAymcx+MH38+FFzc3OH3oEN6c2bN9rc3DwQnz8TAfU/sba2pg8fPmhtbU27u7vK5XKSpEgkomAwqGfPnundu3e6dOmSSkpKNDExoVQqpcHBQb/DPTnsvOLxuJqampRIJPTgwQO9fftWw8PD6u3t/WWC85+YnZ3V3Nyc2traVFpaqtnZWfX39+v69euqqKjwPc+727dvK5lMqqWlRbFYTOl0Wp8+fdKNGzd8Tysog4OD6uzsVH19vdbX13X37l0dO3ZM165d8z3Nu62trQNX4l69eqVcLqfKykrV1dWpr69P9+/f15kzZ9TQ0KCRkRGdPn36t3wkzffOqrKyUvfu3dPVq1cVDoeVz+c1NDSkSCSiK1eu/DcDvd3/ByfJZNIk/eUnm82amdmLFy8sGo1aMBi0kydP2vnz5+3Jkye2u7vrd7gnh52Xmdnq6qp1dHTYiRMnrKqqygYGBuzr16/+RheAhYUFu3jxopWXl1tJSYmdPXvWUqmUff782fe0gvHo0SOrq6uzQCBgsVjMXr586XtSwenp6bGamhoLBAJWW1trPT09trKy4ntWQchms3/72ZRMJs3sz0cZjIyM2KlTp6y4uNja29tteXnZ72hPvndW29vbFo/HLRQK2fHjx62+vt5u3rx54NE0P9sfZtznDgAA4ILnQAEAADgioAAAABwRUAAAAI4IKAAAAEcEFAAAgCMCCgAAwBEBBQAA4IiAAgAAcERAAQAAOCKgAAAAHBFQAHCIjY0NhcNhpVKp/ddmZmYUCASUyWQ8LgPgC9+FBwBH8Pz5c3V3d2tmZkaNjY2KRqPq6urSw4cPfU8D4AEBBQBH1Nvbq8nJSbW0tGhpaUnz8/MqLi72PQuABwQUABzRzs6Ozp07p9evX2thYUHNzc2+JwHwhP+BAoAjyufzWl9f197enlZXV33PAeARV6AA4Ai+fPmiWCymaDSqxsZGpdNpLS0tqbq62vc0AB4QUABwBHfu3NHTp0+1uLioYDCoy5cvq7y8XOPj476nAfCAP+EBwCGmpqaUTqc1NjamsrIyFRUVaWxsTNPT03r8+LHveQA84AoUAACAI65AAQAAOCKgAAAAHBFQAAAAjggoAAAARwQUAACAIwIKAADAEQEFAADgiIACAABwREABAAA4IqAAAAAcEVAAAACOCCgAAABH3wA+NdrvPu3t3wAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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", + "image/png": 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", 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" ] @@ -1051,11 +1051,11 @@ "source": [ "u0 = s\n", "for i in range(5):\n", - " u0 = experimentalist(u0, num_samples=10)\n", - " u0 = experiment_runner(u0)\n", + " u0 = experimentalist(u0, num_samples=10, random_state=42+i)\n", + " u0 = experiment_runner(u0, random_state=43+i)\n", " u0 = theorist(u0)\n", " show_best_fit(u0)\n", - " plt.title(f\"{i=}\")" + " plt.title(f\"{i=}, {len(u0.experiment_data)=}\")" ] }, { From dda49f3bf6bd6c52b69f3a48e0e7cff590a7c1c3 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:22:13 +0200 Subject: [PATCH 081/121] docs: add docstrings --- src/autora/state/delta.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 680643af..e23cd098 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -560,9 +560,9 @@ def inputs_from_state(f): It was inspired by the pytest "fixtures" mechanism. Args: - f: + f: the function which takes any arguments - Returns: + Returns: the function modified to take a State object as input and return a State object Examples: >>> from dataclasses import dataclass, field From cee922232124b919032f06bfa7f63be0d6914211 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:23:00 +0200 Subject: [PATCH 082/121] feat: add support and tests for dict and UserDict objects alongside Deltas --- src/autora/state/delta.py | 45 +++++++++++++++++++++++++++++++++------ 1 file changed, 39 insertions(+), 6 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index e23cd098..cc9701b5 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -374,13 +374,13 @@ def _get_value(f, other: Delta): value, used_key = None, None - if key in other.data.keys(): - value = other.data[key] + if key in other.keys(): + value = other[key] used_key = key elif aliases: # ... is not an empty dict for alias_key, wrapping_function in aliases.items(): - if alias_key in other.data: - value = wrapping_function(other.data[alias_key]) + if alias_key in other: + value = wrapping_function(other[alias_key]) used_key = alias_key break # we only evaluate the first match @@ -575,8 +575,7 @@ def inputs_from_state(f): ... conditions: List[int] = field(metadata={"delta": "replace"}) We indicate the inputs required by the parameter names. - The output must be a `Delta` object. - >>> from autora.state.delta import Delta + The output must be (compatible with) a `Delta` object. >>> @inputs_from_state ... def experimentalist(conditions): ... new_conditions = [c + 10 for c in conditions] @@ -588,6 +587,40 @@ def inputs_from_state(f): >>> experimentalist(S(conditions=[101,102,103,104])) S(conditions=[111, 112, 113, 114]) + If the output of the function is not a `Delta` object (or something compatible with its + interface), then an error is thrown. + >>> @inputs_from_state + ... def returns_bare_conditions(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return new_conditions + + >>> returns_bare_conditions(S(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + AssertionError: Output of must be a `Delta`, + `UserDict`, or `dict`. + + A dictionary can be returned and used: + >>> @inputs_from_state + ... def returns_a_dictionary(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return {"conditions": new_conditions} + >>> returns_a_dictionary(S(conditions=[2])) + S(conditions=[12]) + + ... as can an object which subclasses UserDict (like `Delta`) + >>> class MyDelta(UserDict): + ... pass + >>> @inputs_from_state + ... def returns_a_userdict(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return MyDelta(conditions=new_conditions) + >>> returns_a_userdict(S(conditions=[3])) + S(conditions=[13]) + + We recommend using the `Delta` object rather than a `UserDict` or `dict` as its + functionality may be expanded in future. + >>> from autora.variable import VariableCollection, Variable >>> from sklearn.base import BaseEstimator >>> from sklearn.linear_model import LinearRegression From 0a798a08a3fc61fe403752d3c5dd5a8aab91ed11 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:37:22 +0200 Subject: [PATCH 083/121] feat: add support and tests for dict and UserDict objects alongside Deltas --- src/autora/state/delta.py | 66 +++++++++++++++++++++++++++++++++------ 1 file changed, 57 insertions(+), 9 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index cc9701b5..7bc113a1 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -6,16 +6,25 @@ import logging import warnings from collections import UserDict +from collections.abc import Mapping from dataclasses import dataclass, fields, replace from functools import singledispatch, wraps -from typing import Callable, Generic, List, Optional, Sequence, TypeVar +from typing import Callable, Generic, List, Optional, Protocol, Sequence, TypeVar, Union import numpy as np import pandas as pd _logger = logging.getLogger(__name__) -S = TypeVar("S") T = TypeVar("T") +C = TypeVar("C", covariant=True) + + +class DeltaAddable(Protocol[C]): + def __add__(self: C, other: Union[Delta, Mapping]) -> C: + ... + + +S = TypeVar("S", bound=DeltaAddable) @dataclass(frozen=True) @@ -226,12 +235,9 @@ class State: >>> s.thing '1' - - - """ - def __add__(self, other: Delta): + def __add__(self, other: Union[Delta, Mapping]): updates = dict() other_fields_unused = list(other.keys()) for self_field in fields(self): @@ -281,7 +287,7 @@ def update(self, **kwargs): return self + Delta(**kwargs) -def _get_value(f, other: Delta): +def _get_value(f, other: Union[Delta, Mapping]): """ Given a `State`'s `dataclasses.field` f, get a value from `other` and report its name. @@ -366,6 +372,32 @@ def _get_value(f, other: Delta): >>> print(_get_value(f_c, Delta())) (None, None) + This works with dict objects: + >>> _get_value(f_a, dict(a=13)) + (13, 'a') + + ... with multiple keys: + >>> _get_value(f_b, dict(a=13, b=24, c=35)) + (24, 'b') + + ... and with aliases: + >>> _get_value(f_b, dict(ba=222)) + ([222], 'ba') + + This works with UserDicts: + >>> class MyDelta(UserDict): + ... pass + + >>> _get_value(f_a, MyDelta(a=14)) + (14, 'a') + + ... with multiple keys: + >>> _get_value(f_b, MyDelta(a=1, b=4, c=9)) + (4, 'b') + + ... and with aliases: + >>> _get_value(f_b, MyDelta(ba=234)) + ([234], 'ba') """ @@ -701,6 +733,9 @@ def _f(state_: S, /, **kwargs) -> S: arguments_from_state = {k: getattr(state_, k) for k in from_state} arguments = dict(arguments_from_state, **kwargs) delta = f(**arguments) + assert isinstance(delta, Mapping), ( + "Output of %s must be a `Delta`, `UserDict`, " "or `dict`." % f + ) new_state = state_ + delta return new_state @@ -843,7 +878,7 @@ def on_state( >>> experimentalist(S(conditions=[1,2,3,4])) S(conditions=[11, 12, 13, 14]) - You can also wrap functions which return a Delta object natively, by omitting the `output` + You can wrap functions which return a Delta object natively, by omitting the `output` argument: >>> @on_state() ... def add_five(conditions): @@ -852,7 +887,20 @@ def on_state( >>> add_five(S(conditions=[1, 2, 3, 4])) S(conditions=[6, 7, 8, 9]) - You can also use the @on_state(output=[]) as a decorator: + If you fail to declare outputs for a function which doesn't return a Delta: + >>> @on_state() + ... def missing_output_param(conditions): + ... return [c + 5 for c in conditions] + + ... an exception is raised: + >>> missing_output_param(S(conditions=[1, 2, 3, 4]) + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + AssertionError: Output of must be a `Delta`, + `UserDict`, or `dict`. + + You can use the @on_state(output=[]) as a decorator: >>> @on_state(output=["conditions"]) ... def add_six(conditions): ... return [c + 6 for c in conditions] From 351ff067978351cb53720dff9d8f92240c9a2bd7 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:39:16 +0200 Subject: [PATCH 084/121] chore: add docstring --- src/autora/state/delta.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 7bc113a1..c9acbae6 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -15,11 +15,14 @@ import pandas as pd _logger = logging.getLogger(__name__) + T = TypeVar("T") C = TypeVar("C", covariant=True) class DeltaAddable(Protocol[C]): + """A class which a Delta or other Mapping can be added to, returning the same class""" + def __add__(self: C, other: Union[Delta, Mapping]) -> C: ... From 70f442452d23fd961c8d7e2ee04d90e008cd9f20 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:41:29 +0200 Subject: [PATCH 085/121] docs: rename parameter to avoid shadowing `S` --- src/autora/state/delta.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index c9acbae6..144d434f 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -870,7 +870,7 @@ def on_state( The `State` it operates on needs to have the metadata described in the state module: >>> @dataclass(frozen=True) - ... class S(State): + ... class St(State): ... conditions: List[int] = field(metadata={"delta": "replace"}) We indicate the inputs required by the parameter names. @@ -878,8 +878,8 @@ def on_state( ... return [c + 10 for c in conditions] >>> experimentalist = on_state(function=add_ten, output=["conditions"]) - >>> experimentalist(S(conditions=[1,2,3,4])) - S(conditions=[11, 12, 13, 14]) + >>> experimentalist(St(conditions=[1,2,3,4])) + St(conditions=[11, 12, 13, 14]) You can wrap functions which return a Delta object natively, by omitting the `output` argument: @@ -887,8 +887,8 @@ def on_state( ... def add_five(conditions): ... return Delta(conditions=[c + 5 for c in conditions]) - >>> add_five(S(conditions=[1, 2, 3, 4])) - S(conditions=[6, 7, 8, 9]) + >>> add_five(St(conditions=[1, 2, 3, 4])) + St(conditions=[6, 7, 8, 9]) If you fail to declare outputs for a function which doesn't return a Delta: >>> @on_state() @@ -896,20 +896,19 @@ def on_state( ... return [c + 5 for c in conditions] ... an exception is raised: - >>> missing_output_param(S(conditions=[1, 2, 3, 4]) - ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> missing_output_param(St(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Traceback (most recent call last): ... AssertionError: Output of must be a `Delta`, `UserDict`, or `dict`. - You can use the @on_state(output=[]) as a decorator: + You can use the @on_state(output=[...]) as a decorator: >>> @on_state(output=["conditions"]) ... def add_six(conditions): ... return [c + 6 for c in conditions] - >>> add_six(S(conditions=[1, 2, 3, 4])) - S(conditions=[7, 8, 9, 10]) + >>> add_six(St(conditions=[1, 2, 3, 4])) + St(conditions=[7, 8, 9, 10]) """ From d29b4e2dffb6b16a1be6fe272015add33de2f9be Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:43:12 +0200 Subject: [PATCH 086/121] docs: rename parameter to avoid replacing `u` or shadowing pd/np --- src/autora/state/delta.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 144d434f..be836393 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -118,7 +118,6 @@ class State: CoerceStateList(o=None, p=['a', 'list']) With a converter, inputs are converted to the type output by the converter: - >>> import pandas as pd >>> @dataclass(frozen=True) ... class CoerceStateDataFrame(State): ... q: pd.DataFrame = field(default_factory=pd.DataFrame, @@ -182,7 +181,6 @@ class State: A converter can cast from a DataFrame to a np.ndarray (with a single datatype), for instance: - >>> import numpy as np >>> @dataclass(frozen=True) ... class CoerceStateArray(State): ... r: Optional[np.ndarray] = field(default=None, @@ -226,16 +224,16 @@ class State: Now you can access both `s.things` and `s.thing` as required by your code. The State only shows `things` in the string representation... - >>> s = FieldAliasStateWithProperty(things=["0"]) + Delta(thing="1") - >>> s + >>> u = FieldAliasStateWithProperty(things=["0"]) + Delta(thing="1") + >>> u FieldAliasStateWithProperty(things=['0', '1']) ... and exposes `things` as an attribute: - >>> s.things + >>> u.things ['0', '1'] ... but also exposes `thing`, always returning the last value. - >>> s.thing + >>> u.thing '1' """ From 402e2335dd488a365a65a2ce8f8cac6a7499d1ea Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:45:54 +0200 Subject: [PATCH 087/121] chore: remove redundant parentheses --- src/autora/state/delta.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index be836393..16538bde 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -267,7 +267,7 @@ def __add__(self, other: Union[Delta, Mapping]): updates[self_field_key] = coerced_other_value else: raise NotImplementedError( - "delta_behaviour=`%s` not implemented" % (delta_behavior) + "delta_behaviour=`%s` not implemented" % delta_behavior ) if len(other_fields_unused) > 0: From d271e2690f96d53afb2391f7b8a3d9be69cbb336 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:46:02 +0200 Subject: [PATCH 088/121] docs add docstring --- src/autora/state/delta.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 16538bde..ceea03c5 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -285,6 +285,14 @@ def __add__(self, other: Union[Delta, Mapping]): return new def update(self, **kwargs): + """ + Return a new version of the State with values updated. + + This is identical to adding a `Delta`. + + If you need to replace values, ignoring the State value aggregation rules, + use `dataclasses.replace` instead. + """ return self + Delta(**kwargs) From d4d39d8d1a1e49d7f114b5b735dff81c6576b285 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:46:17 +0200 Subject: [PATCH 089/121] docs: make obvious that first parameter is unused --- src/autora/state/delta.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index ceea03c5..9c360af1 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -499,7 +499,7 @@ def extend(a, b): @extend.register(type(None)) -def extend_none(a, b): +def extend_none(_, b): """ Examples: >>> extend(None, []) From edfae1287f14c53b41cfb0946ca5aaae57425828 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:47:25 +0200 Subject: [PATCH 090/121] docs: update parameter name to avoid shadowing --- src/autora/state/delta.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 9c360af1..c1b5cec3 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -572,22 +572,22 @@ def append(a: List[T], b: T) -> List[T]: Function to create a new list with an item appended to it. Examples: - Given a starting list `a`: - >>> a = [1, 2, 3] + Given a starting list `a_`: + >>> a_ = [1, 2, 3] ... we can append a value: - >>> append(a, 4) + >>> append(a_, 4) [1, 2, 3, 4] - `a` is unchanged - >>> a == [1, 2, 3] + `a_` is unchanged + >>> a_ == [1, 2, 3] True Why not just use `list.append`? `list.append` mutates `a` in place, which we can't allow in the AER cycle – parts of the cycle rely on purely functional code which doesn't (accidentally or intentionally) manipulate existing data. - >>> list.append(a, 4) # not what we want - >>> a + >>> list.append(a_, 4) # not what we want + >>> a_ [1, 2, 3, 4] """ return a + [b] From 4f487d5b49172c9594508bc08ea0622d445f61c8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:54:01 +0200 Subject: [PATCH 091/121] docs: update parameter name to avoid shadowing --- src/autora/state/delta.py | 42 +++++++++++++++++++-------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index c1b5cec3..d6e72979 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -612,7 +612,7 @@ def inputs_from_state(f): The `State` it operates on needs to have the metadata described in the state module: >>> @dataclass(frozen=True) - ... class S(State): + ... class U(State): ... conditions: List[int] = field(metadata={"delta": "replace"}) We indicate the inputs required by the parameter names. @@ -622,11 +622,11 @@ def inputs_from_state(f): ... new_conditions = [c + 10 for c in conditions] ... return Delta(conditions=new_conditions) - >>> experimentalist(S(conditions=[1,2,3,4])) - S(conditions=[11, 12, 13, 14]) + >>> experimentalist(U(conditions=[1,2,3,4])) + U(conditions=[11, 12, 13, 14]) - >>> experimentalist(S(conditions=[101,102,103,104])) - S(conditions=[111, 112, 113, 114]) + >>> experimentalist(U(conditions=[101,102,103,104])) + U(conditions=[111, 112, 113, 114]) If the output of the function is not a `Delta` object (or something compatible with its interface), then an error is thrown. @@ -635,7 +635,7 @@ def inputs_from_state(f): ... new_conditions = [c + 10 for c in conditions] ... return new_conditions - >>> returns_bare_conditions(S(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> returns_bare_conditions(U(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Traceback (most recent call last): ... AssertionError: Output of must be a `Delta`, @@ -646,8 +646,8 @@ def inputs_from_state(f): ... def returns_a_dictionary(conditions): ... new_conditions = [c + 10 for c in conditions] ... return {"conditions": new_conditions} - >>> returns_a_dictionary(S(conditions=[2])) - S(conditions=[12]) + >>> returns_a_dictionary(U(conditions=[2])) + U(conditions=[12]) ... as can an object which subclasses UserDict (like `Delta`) >>> class MyDelta(UserDict): @@ -656,8 +656,8 @@ def inputs_from_state(f): ... def returns_a_userdict(conditions): ... new_conditions = [c + 10 for c in conditions] ... return MyDelta(conditions=new_conditions) - >>> returns_a_userdict(S(conditions=[3])) - S(conditions=[13]) + >>> returns_a_userdict(U(conditions=[3])) + U(conditions=[13]) We recommend using the `Delta` object rather than a `UserDict` or `dict` as its functionality may be expanded in future. @@ -675,29 +675,29 @@ def inputs_from_state(f): ... return Delta(model=new_model) >>> @dataclass(frozen=True) - ... class T(State): + ... class V(State): ... variables: VariableCollection # field(metadata={"delta":... }) omitted ∴ immutable ... experiment_data: pd.DataFrame = field(metadata={"delta": "extend"}) ... model: Optional[BaseEstimator] = field(metadata={"delta": "replace"}, default=None) - >>> t = T( + >>> v = V( ... variables=VariableCollection(independent_variables=[Variable("x")], ... dependent_variables=[Variable("y")]), ... experiment_data=pd.DataFrame({"x": [0,1,2,3,4], "y": [2,3,4,5,6]}) ... ) - >>> t_prime = theorist(t) - >>> t_prime.model.coef_, t_prime.model.intercept_ + >>> v_prime = theorist(v) + >>> v_prime.model.coef_, v_prime.model.intercept_ (array([[1.]]), array([2.])) Arguments from the state can be overridden by passing them in as keyword arguments (kwargs): - >>> theorist(t, experiment_data=pd.DataFrame({"x": [0,1,2,3], "y": [12,13,14,15]}))\\ + >>> theorist(v, experiment_data=pd.DataFrame({"x": [0,1,2,3], "y": [12,13,14,15]}))\\ ... .model.intercept_ array([12.]) ... and other arguments supported by the inner function can also be passed (if and only if the inner function allows for and handles `**kwargs` arguments alongside the values from the state). - >>> theorist(t, fit_intercept=False).model.intercept_ + >>> theorist(v, fit_intercept=False).model.intercept_ 0.0 Any parameters not provided by the state must be provided by default values or by the @@ -708,8 +708,8 @@ def inputs_from_state(f): ... return Delta(conditions=new_conditions) ... then it need not be passed. - >>> experimentalist(S(conditions=[1,2,3,4])) - S(conditions=[26, 27, 28, 29]) + >>> experimentalist(U(conditions=[1,2,3,4])) + U(conditions=[26, 27, 28, 29]) If a default isn't specified: >>> @inputs_from_state @@ -718,14 +718,14 @@ def inputs_from_state(f): ... return Delta(conditions=new_conditions) ... then calling the experimentalist without it will throw an error: - >>> experimentalist(S(conditions=[1,2,3,4])) + >>> experimentalist(U(conditions=[1,2,3,4])) Traceback (most recent call last): ... TypeError: experimentalist() missing 1 required positional argument: 'offset' ... which can be fixed by passing the argument as a keyword to the wrapped function. - >>> experimentalist(S(conditions=[1,2,3,4]), offset=2) - S(conditions=[3, 4, 5, 6]) + >>> experimentalist(U(conditions=[1,2,3,4]), offset=2) + U(conditions=[3, 4, 5, 6]) """ # Get the set of parameter names from function f's signature From c5a52d44bc06d2518f65f7d561d8c18c705716e0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:55:23 +0200 Subject: [PATCH 092/121] docs: update parameter name to avoid shadowing --- src/autora/state/delta.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index d6e72979..8e90716b 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -876,7 +876,7 @@ def on_state( The `State` it operates on needs to have the metadata described in the state module: >>> @dataclass(frozen=True) - ... class St(State): + ... class W(State): ... conditions: List[int] = field(metadata={"delta": "replace"}) We indicate the inputs required by the parameter names. @@ -884,8 +884,8 @@ def on_state( ... return [c + 10 for c in conditions] >>> experimentalist = on_state(function=add_ten, output=["conditions"]) - >>> experimentalist(St(conditions=[1,2,3,4])) - St(conditions=[11, 12, 13, 14]) + >>> experimentalist(W(conditions=[1,2,3,4])) + W(conditions=[11, 12, 13, 14]) You can wrap functions which return a Delta object natively, by omitting the `output` argument: @@ -893,8 +893,8 @@ def on_state( ... def add_five(conditions): ... return Delta(conditions=[c + 5 for c in conditions]) - >>> add_five(St(conditions=[1, 2, 3, 4])) - St(conditions=[6, 7, 8, 9]) + >>> add_five(W(conditions=[1, 2, 3, 4])) + W(conditions=[6, 7, 8, 9]) If you fail to declare outputs for a function which doesn't return a Delta: >>> @on_state() @@ -902,7 +902,7 @@ def on_state( ... return [c + 5 for c in conditions] ... an exception is raised: - >>> missing_output_param(St(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> missing_output_param(W(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Traceback (most recent call last): ... AssertionError: Output of must be a `Delta`, @@ -913,8 +913,8 @@ def on_state( ... def add_six(conditions): ... return [c + 6 for c in conditions] - >>> add_six(St(conditions=[1, 2, 3, 4])) - St(conditions=[7, 8, 9, 10]) + >>> add_six(W(conditions=[1, 2, 3, 4])) + W(conditions=[7, 8, 9, 10]) """ From a44a38fd0868ef48f6da95cf02ccb50f06b76a9c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:56:38 +0200 Subject: [PATCH 093/121] docs: update parameter name to avoid shadowing --- ...Introduction to Functions and States.ipynb | 332 +++++++++--------- 1 file changed, 166 insertions(+), 166 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index e47ccd76..5a40f927 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -235,11 +235,11 @@ "2 7.171958\n", "3 3.947361\n", "4 -8.116453, experiment_data= x y\n", - "0 5.479121 23.727234\n", - "1 -1.222431 -3.425782\n", - "2 7.171958 30.108872\n", - "3 3.947361 17.792187\n", - "4 -8.116453 -30.609650, models=[])" + "0 5.479121 24.086818\n", + "1 -1.222431 -2.709502\n", + "2 7.171958 29.911578\n", + "3 3.947361 18.928439\n", + "4 -8.116453 -27.768580, models=[])" ] }, "execution_count": null, @@ -296,16 +296,16 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -3.911881\n", - "1 -4.014468\n", - "2 8.621441\n", - "3 -5.956952\n", - "4 -4.300384, experiment_data= x y\n", - "0 -3.911881 -13.395744\n", - "1 -4.014468 -13.341993\n", - "2 8.621441 35.568485\n", - "3 -5.956952 -22.891165\n", - "4 -4.300384 -14.266465, models=[LinearRegression()])" + "0 -5.213077\n", + "1 4.831915\n", + "2 -2.014685\n", + "3 4.923726\n", + "4 -4.931893, experiment_data= x y\n", + "0 -5.213077 -18.202418\n", + "1 4.831915 21.526622\n", + "2 -2.014685 -5.383766\n", + "3 4.923726 21.485098\n", + "4 -4.931893 -18.631364, models=[LinearRegression()])" ] }, "execution_count": null, @@ -377,253 +377,253 @@ " \n", " \n", " 0\n", - 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"[1.95658539] [[3.99686845]]\n" + "[2.04522595] [[4.03328388]]\n" ] } ], From fb8a3e940ce1c79d7bb7070f284faf9c12c5ea1e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 12:57:37 +0200 Subject: [PATCH 094/121] docs: update imports to avoid duplication --- src/autora/state/delta.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 8e90716b..784bfa89 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -1,13 +1,12 @@ """Classes to represent cycle state $S$ as $S_n = S_{0} + \\sum_{i=1}^n \\Delta S_{i}$.""" from __future__ import annotations -import dataclasses import inspect import logging import warnings from collections import UserDict from collections.abc import Mapping -from dataclasses import dataclass, fields, replace +from dataclasses import dataclass, fields, is_dataclass, replace from functools import singledispatch, wraps from typing import Callable, Generic, List, Optional, Protocol, Sequence, TypeVar, Union @@ -668,8 +667,8 @@ def inputs_from_state(f): >>> @inputs_from_state ... def theorist(experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs): - ... ivs = [v.name for v in variables.independent_variables] - ... dvs = [v.name for v in variables.dependent_variables] + ... ivs = [vi.name for vi in variables.independent_variables] + ... dvs = [vi.name for vi in variables.dependent_variables] ... X, y = experiment_data[ivs], experiment_data[dvs] ... new_model = LinearRegression(fit_intercept=True).set_params(**kwargs).fit(X, y) ... return Delta(model=new_model) @@ -735,10 +734,8 @@ def inputs_from_state(f): def _f(state_: S, /, **kwargs) -> S: # Get the parameters needed which are available from the state_. # All others must be provided as kwargs or default values on f. - assert dataclasses.is_dataclass(state_) - from_state = parameters_.intersection( - {i.name for i in dataclasses.fields(state_)} - ) + assert is_dataclass(state_) + from_state = parameters_.intersection({i.name for i in fields(state_)}) arguments_from_state = {k: getattr(state_, k) for k in from_state} arguments = dict(arguments_from_state, **kwargs) delta = f(**arguments) From d3ab86520cd4440a702ba5f5ecb0debb416806c8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 16 Aug 2023 13:02:43 +0200 Subject: [PATCH 095/121] docs: update random states --- ...Introduction to Functions and States.ipynb | 343 +++++++++--------- 1 file changed, 175 insertions(+), 168 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index 5a40f927..fb1bda44 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -235,11 +235,11 @@ "2 7.171958\n", "3 3.947361\n", "4 -8.116453, experiment_data= x y\n", - "0 5.479121 24.086818\n", - "1 -1.222431 -2.709502\n", - "2 7.171958 29.911578\n", - "3 3.947361 18.928439\n", - "4 -8.116453 -27.768580, models=[])" + "0 5.479121 24.372288\n", + "1 -1.222431 -1.583178\n", + "2 7.171958 30.032529\n", + "3 3.947361 16.745934\n", + "4 -8.116453 -31.388814, models=[])" ] }, "execution_count": null, @@ -296,16 +296,16 @@ "data": { "text/plain": [ "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 -5.213077\n", - "1 4.831915\n", - "2 -2.014685\n", - "3 4.923726\n", - "4 -4.931893, experiment_data= x y\n", - "0 -5.213077 -18.202418\n", - "1 4.831915 21.526622\n", - "2 -2.014685 -5.383766\n", - "3 4.923726 21.485098\n", - "4 -4.931893 -18.631364, models=[LinearRegression()])" + "0 6.159515\n", + "1 -7.713961\n", + "2 -0.655764\n", + "3 9.297426\n", + "4 2.601009, experiment_data= x y\n", + "0 6.159515 27.502964\n", + "1 -7.713961 -30.950686\n", + "2 -0.655764 -1.488309\n", + "3 9.297426 38.992089\n", + "4 2.601009 13.351848, models=[LinearRegression()])" ] }, "execution_count": null, @@ -332,8 +332,8 @@ "source": [ "s_ = s_0\n", "for i in range(10):\n", - 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"[2.04522595] [[4.03328388]]\n" + "[2.08476524] [[4.00471062]]\n" ] } ], "source": [ "print(s_.model.intercept_, s_.model.coef_)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From c9e17cf0ff9c4deb9c9728b932e183b92f308aa6 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 12:33:27 +0200 Subject: [PATCH 096/121] feat: add support for passing the full state as well in the inputs_from_state function --- src/autora/state/delta.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 784bfa89..e72252b8 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -726,6 +726,16 @@ def inputs_from_state(f): >>> experimentalist(U(conditions=[1,2,3,4]), offset=2) U(conditions=[3, 4, 5, 6]) + The state itself is passed through if the inner function requests the `state`: + >>> @inputs_from_state + ... def function_which_needs_whole_state(state, conditions): + ... print("Doing something on: ", state) + ... new_conditions = [c + 2 for c in conditions] + ... return Delta(conditions=new_conditions) + >>> function_which_needs_whole_state(U(conditions=[1,2,3,4])) + Doing something on: U(conditions=[1, 2, 3, 4]) + U(conditions=[3, 4, 5, 6]) + """ # Get the set of parameter names from function f's signature parameters_ = set(inspect.signature(f).parameters.keys()) @@ -737,6 +747,8 @@ def _f(state_: S, /, **kwargs) -> S: assert is_dataclass(state_) from_state = parameters_.intersection({i.name for i in fields(state_)}) arguments_from_state = {k: getattr(state_, k) for k in from_state} + if "state" in parameters_: + arguments_from_state["state"] = state_ arguments = dict(arguments_from_state, **kwargs) delta = f(**arguments) assert isinstance(delta, Mapping), ( From 327a21187f7b997a426269df7f690d5bd0b9cd8a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 12:34:00 +0200 Subject: [PATCH 097/121] docs: add example of using complex experimentalists which need more inputs --- src/autora/experimentalist/consensus.ipynb | 1529 ++++++++++++++++++++ 1 file changed, 1529 insertions(+) create mode 100644 src/autora/experimentalist/consensus.ipynb diff --git a/src/autora/experimentalist/consensus.ipynb b/src/autora/experimentalist/consensus.ipynb new file mode 100644 index 00000000..275a7a5c --- /dev/null +++ b/src/autora/experimentalist/consensus.ipynb @@ -0,0 +1,1529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Mixture Experimentalists\n", + "\n", + "## Introduction\n", + "\n", + "One thing the State/Delta mechanism should support is making more complex experimentalists which combine others in\n", + "clever ways.\n", + "Here we have some examples:\n", + "\n", + "- [x] A combination experimentalist which aggregates additional measures from the component experimentalists\n", + " - [x] Where the measure is passed back in the conditions array, or\n", + " - [x] Where the measure is passed back in a separate array\n", + "- [ ] A combination experimentalist where the components need the full State as they have complex arguments\n", + "\n", + "We also need to see what happens when we:\n", + "- Try to extend a dataframe with an extra data frame which has new columns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combination Experimentalist which Aggregates Measures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Returns an extended conditions array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from autora.variable import VariableCollection, Variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2\n", + "0 -3.0 -1.0\n", + "1 -2.0 0.0\n", + "2 -1.0 1.0\n", + "3 0.0 2.0\n", + "4 1.0 3.0\n", + "5 2.0 4.0\n", + "6 3.0 5.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conditions_ = pd.DataFrame({\"x1\": np.linspace(-3, 3, 7), \"x2\": np.linspace(-1, 5, 7)})\n", + "conditions_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def avoid_negative(conditions: pd.DataFrame):\n", + " downvotes = (conditions_ < 0).sum(axis=1)\n", + " with_votes = pd.DataFrame.assign(conditions, downvotes=downvotes)\n", + " with_votes_sorted = with_votes.sort_values(by=\"downvotes\", ascending=True)\n", + " return with_votes_sorted\n", + "\n", + "avoid_negative(conditions_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Avoid-even function')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " x1 x2 avoid_negative.downvotes avoid_even.downvotes downvotes\n", + "4 1.0 3.0 0 0.0 0.0\n", + "6 3.0 5.0 0 0.0 0.0\n", + "2 -1.0 1.0 1 0.0 1.0\n", + "0 -3.0 -1.0 2 0.0 2.0\n", + "3 0.0 2.0 0 2.0 2.0\n", + "5 2.0 4.0 0 2.0 2.0\n", + "1 -2.0 0.0 1 2.0 3.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding this dataframe to a State object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 avoid_negative.downvotes avoid_even.downvotes downvotes\n", + "4 1.0 3.0 0 0.0 0.0\n", + "6 3.0 5.0 0 0.0 0.0\n", + "2 -1.0 1.0 1 0.0 1.0\n", + "0 -3.0 -1.0 2 0.0 2.0\n", + "3 0.0 2.0 0 2.0 2.0\n", + "5 2.0 4.0 0 2.0 2.0\n", + "1 -2.0 0.0 1 2.0 3.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from autora.state.delta import Delta, on_state, State, inputs_from_state\n", + "from autora.state.bundled import StandardState\n", + "\n", + "s = StandardState() + Delta(conditions=downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even]))\n", + "s.conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Return a separate array of additional measures\n", + "\n", + "To ensure we don't mix up the order of return values and to facilitate updating the returned values in future without\n", + " breaking dependents functions when returning multiple objects, we return a structured object –\n", + "in this case a simple dictionary of results. (We could just as well use a `UserDict` or a `Delta` object for this\n", + "purpose – they have the same interface.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2\n", + "3 0.0 2.0\n", + "4 1.0 3.0\n", + "5 2.0 4.0\n", + "6 3.0 5.0\n", + "1 -2.0 0.0\n", + "2 -1.0 1.0\n", + "0 -3.0 -1.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def avoid_negative_separate(conditions: pd.DataFrame):\n", + " downvotes = (conditions_ < 0).sum(axis=1).sort_values(ascending=True)\n", + " conditions_sorted = pd.DataFrame(conditions, index=downvotes.index)\n", + " return {\"conditions\": conditions_sorted, \"downvotes\": downvotes}\n", + "\n", + "avoid_negative_separate(conditions_)[\"conditions\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( x1 x2\n", + " 0 -3.0 -1.0\n", + " 2 -1.0 1.0\n", + " 4 1.0 3.0\n", + " 6 3.0 5.0\n", + " 1 -2.0 0.0\n", + " 3 0.0 2.0\n", + " 5 2.0 4.0,\n", + " 0 0.0\n", + " 2 0.0\n", + " 4 0.0\n", + " 6 0.0\n", + " 1 2.0\n", + " 3 2.0\n", + " 5 2.0\n", + " dtype: float64)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def avoid_even_separate(conditions: pd.DataFrame):\n", + " downvotes = avoid_even_function(conditions_).sum(axis=1).sort_values(ascending=True)\n", + " conditions_sorted = pd.DataFrame(conditions, index=downvotes.index)\n", + " return {\"conditions\": conditions_sorted, \"downvotes\": downvotes}\n", + "\n", + "avoid_even_separate(conditions_)[\"conditions\"], avoid_even_separate(conditions_)[\"downvotes\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'conditions': x1 x2\n", + " 0 -3.0 -1.0\n", + " 1 -2.0 0.0\n", + " 2 -1.0 1.0\n", + " 3 0.0 2.0\n", + " 4 1.0 3.0\n", + " 5 2.0 4.0\n", + " 6 3.0 5.0,\n", + " 'downvotes': initial total\n", + " 0 0 0\n", + " 1 0 0\n", + " 2 0 0\n", + " 3 0 0\n", + " 4 0 0\n", + " 5 0 0\n", + " 6 0 0}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def downvote_order_separate(conditions: pd.DataFrame, experimentalists: List):\n", + " downvote_arrays = {\"initial\": pd.Series(0, index=conditions.index)}\n", + " for e in experimentalists:\n", + " downvote_arrays[e.__name__] = e(conditions)[\"downvotes\"]\n", + " combined_downvotes = pd.DataFrame(downvote_arrays)\n", + " combined_downvotes[\"total\"] = combined_downvotes.sum(axis=1)\n", + " combined_downvotes_sorted = combined_downvotes.sort_values(by=\"total\", ascending=True)\n", + " conditions_sorted = pd.DataFrame(conditions, index=combined_downvotes_sorted.index)\n", + " return {\n", + " \"conditions\": conditions_sorted,\n", + " \"downvotes\": combined_downvotes_sorted,\n", + " }\n", + "\n", + "downvote_order_separate(conditions_, experimentalists=[])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 initial avoid_even_separate avoid_negative_separate total\n", + "0 -3.0 -1.0 0 0.0 2 2.0\n", + "1 -2.0 0.0 0 2.0 1 3.0\n", + "2 -1.0 1.0 0 0.0 1 1.0\n", + "3 0.0 2.0 0 2.0 0 2.0\n", + "4 1.0 3.0 0 0.0 0 0.0\n", + "5 2.0 4.0 0 2.0 0 2.0\n", + "6 3.0 5.0 0 0.0 0 0.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = downvote_order_separate(conditions_, experimentalists=[avoid_even_separate, avoid_negative_separate])\n", + "\n", + "pd.DataFrame.join(results[\"conditions\"], results[\"downvotes\"]).sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combination Experimentalist Needing The Full State\n", + "In this case, we have at least one component-experimentalist which needs the full state.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def avoid_repeat(conditions, experiment_data: pd.DataFrame, variables: VariableCollection):\n", + " iv_column_names = [v.name for v in variables.independent_variables]\n", + " count_already_seen = pd.Series(experiment_data.groupby(iv_column_names).size(), name=\"downvotes\")\n", + " conditions = pd.DataFrame.join(conditions, count_already_seen, on=iv_column_names).fillna(0)\n", + " return {\"conditions\": conditions, \"already_seen\": count_already_seen}\n", + "\n", + "avoid_repeat(\n", + " conditions=conditions_,\n", + " experiment_data=pd.DataFrame(dict(x1=[-3, 3, -3], x2=[-1, 5, -1])),\n", + " variables=VariableCollection(independent_variables=[Variable(\"x1\"), Variable(\"x2\")])\n", + ")[\"conditions\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We wrap the `avoid_repeat` function with the usual `on_state` wrapper to make it compatible with the state mechanism.\n", + " As it already returns a dictionary, we don't need to specify the output names.\n", + " Then we can the wrapped function on the State object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jholla10/Developer/autora-core/src/autora/state/delta.py:273: UserWarning: These fields: ['already_seen'] could not be used to update StandardState, which has these fields & aliases: ['variables', 'conditions', 'experiment_data', 'models', 'model']\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x1 x2 downvotes\n", + "0 -3.0 -1.0 2.0\n", + "1 -2.0 0.0 0.0\n", + "2 -1.0 1.0 0.0\n", + "3 0.0 2.0 0.0\n", + "4 1.0 3.0 0.0\n", + "5 2.0 4.0 0.0\n", + "6 3.0 5.0 1.0, experiment_data= x1 x2\n", + "0 -3 -1\n", + "1 3 5\n", + "2 -3 -1, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avoid_repeat_state = on_state(avoid_repeat)\n", + "s = StandardState(\n", + " experiment_data=pd.DataFrame(dict(x1=[-3, 3, -3], x2=[-1, 5, -1])),\n", + " variables=VariableCollection(independent_variables=[Variable(\"x1\"), Variable(\"x2\")])\n", + ")\n", + "avoid_repeat_state(s, conditions=conditions_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The way we handle this is to write a function which operates on the State directly, passing it to\n", + "experimentalists wrapped with `on_state`, then combine their outputs.\n", + "This is easy if our conditions are returned with the downvotes in the same dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jholla10/Developer/autora-core/src/autora/state/delta.py:273: UserWarning: These fields: ['already_seen'] could not be used to update StandardState, which has these fields & aliases: ['variables', 'conditions', 'experiment_data', 'models', 'model']\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 avoid_repeat.downvotes avoid_negative.downvotes \\\n", + "4 1.0 3.0 0.0 0 \n", + "2 -1.0 1.0 0.0 1 \n", + "6 3.0 5.0 1.0 0 \n", + "3 0.0 2.0 0.0 0 \n", + "5 2.0 4.0 0.0 0 \n", + "1 -2.0 0.0 0.0 1 \n", + "0 -3.0 -1.0 2.0 2 \n", + "\n", + " avoid_even.downvotes downvotes \n", + "4 0.0 0.0 \n", + "2 0.0 1.0 \n", + "6 0.0 1.0 \n", + "3 2.0 2.0 \n", + "5 2.0 2.0 \n", + "1 2.0 3.0 \n", + "0 0.0 4.0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@on_state()\n", + "def combine_downvotes_state(state: State, conditions, experimentalists: List, num_samples: int):\n", + " # iv_column_names = [v.name for v in s.variables.independent_variables]\n", + " downvoted_conditions = []\n", + " for e in experimentalists:\n", + " new_state = e(state, conditions=conditions)\n", + " this_downvoted_conditions = new_state.conditions\n", + " this_downvoted_conditions.attrs[\"name\"] = e.__name__\n", + " downvoted_conditions.append(this_downvoted_conditions)\n", + " combined_downvotes = combine_downvotes(conditions, *downvoted_conditions)\n", + " sorted_combined_downvotes = combined_downvotes.sort_values(by=\"downvotes\", ascending=True)\n", + " filtered_sorted_combined_downvotes = sorted_combined_downvotes.iloc[:num_samples]\n", + " d = Delta(conditions=filtered_sorted_combined_downvotes)\n", + " return d\n", + "\n", + "combine_downvotes_state(\n", + " s,\n", + " conditions=conditions_,\n", + " experimentalists=[\n", + " on_state(avoid_repeat),\n", + " on_state(avoid_negative, output=[\"conditions\"]),\n", + " on_state(avoid_even, output=[\"conditions\"])\n", + " ],\n", + " num_samples=10\n", + ").conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What Happens When We Extend a Dataframe With New Columns in the State Mechanism\n", + "If we have an experiment_data field which has particular columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_0 = StandardState(\n", + " experiment_data=pd.DataFrame({\"x1\":[-10], \"x2\":[-10], \"y\":[-10]})\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and we add data with extra columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_experiment_data = pd.DataFrame({\"x1\":[5], \"x2\":[5], \"y\":[5], \"new_column\": [15]})\n", + "s_1 = s_0 + Delta(experiment_data=new_experiment_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " then the additional columns just\n", + "get added on the end, and any missing values are replaced by NaNs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_1.experiment_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From b6aa18275840048def0a54a84304014293407425 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 14:17:50 +0200 Subject: [PATCH 098/121] feat: always use combined `on_state` function in wrappers --- src/autora/state/wrapper.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index ee0dd482..11140ac5 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -2,7 +2,7 @@ so that $n$ processes $f_i$ on states $S$ can be represented as $$f_n(...(f_1(f_0(S))))$$ -These are special cases of the [autora.state.delta.inputs_from_state][] function. +These are special cases of the [autora.state.delta.on_state][] function. """ from __future__ import annotations @@ -11,7 +11,7 @@ import pandas as pd from sklearn.base import BaseEstimator -from autora.state.delta import Delta, State, inputs_from_state +from autora.state.delta import Delta, State, on_state from autora.variable import VariableCollection S = TypeVar("S") @@ -50,7 +50,7 @@ def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: """ - @inputs_from_state + @on_state() def theorist( experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs ): @@ -99,7 +99,7 @@ def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: 2 3 30 33 """ - @inputs_from_state + @on_state() def experiment_runner(conditions: pd.DataFrame, **kwargs): x = conditions y = f(x, **kwargs) @@ -146,7 +146,7 @@ def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: """ - @inputs_from_state + @on_state() def experiment_runner(conditions: pd.DataFrame, **kwargs): x = conditions experiment_data = f(x, **kwargs) From c08b580f848099cb61a2e92333f576d307ae34ab Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 14:19:56 +0200 Subject: [PATCH 099/121] feat: split inputs_from_state and delta_to_state --- src/autora/state/delta.py | 233 ++++++++++++++++++++++++++++++-------- 1 file changed, 184 insertions(+), 49 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index e72252b8..0e4e7322 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -602,7 +602,7 @@ def inputs_from_state(f): Args: f: the function which takes any arguments - Returns: the function modified to take a State object as input and return a State object + Returns: the function modified to take a State object as input. Examples: >>> from dataclasses import dataclass, field @@ -619,26 +619,13 @@ def inputs_from_state(f): >>> @inputs_from_state ... def experimentalist(conditions): ... new_conditions = [c + 10 for c in conditions] - ... return Delta(conditions=new_conditions) + ... return new_conditions >>> experimentalist(U(conditions=[1,2,3,4])) - U(conditions=[11, 12, 13, 14]) + [11, 12, 13, 14] >>> experimentalist(U(conditions=[101,102,103,104])) - U(conditions=[111, 112, 113, 114]) - - If the output of the function is not a `Delta` object (or something compatible with its - interface), then an error is thrown. - >>> @inputs_from_state - ... def returns_bare_conditions(conditions): - ... new_conditions = [c + 10 for c in conditions] - ... return new_conditions - - >>> returns_bare_conditions(U(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Traceback (most recent call last): - ... - AssertionError: Output of must be a `Delta`, - `UserDict`, or `dict`. + [111, 112, 113, 114] A dictionary can be returned and used: >>> @inputs_from_state @@ -646,20 +633,7 @@ def inputs_from_state(f): ... new_conditions = [c + 10 for c in conditions] ... return {"conditions": new_conditions} >>> returns_a_dictionary(U(conditions=[2])) - U(conditions=[12]) - - ... as can an object which subclasses UserDict (like `Delta`) - >>> class MyDelta(UserDict): - ... pass - >>> @inputs_from_state - ... def returns_a_userdict(conditions): - ... new_conditions = [c + 10 for c in conditions] - ... return MyDelta(conditions=new_conditions) - >>> returns_a_userdict(U(conditions=[3])) - U(conditions=[13]) - - We recommend using the `Delta` object rather than a `UserDict` or `dict` as its - functionality may be expanded in future. + {'conditions': [12]} >>> from autora.variable import VariableCollection, Variable >>> from sklearn.base import BaseEstimator @@ -670,8 +644,8 @@ def inputs_from_state(f): ... ivs = [vi.name for vi in variables.independent_variables] ... dvs = [vi.name for vi in variables.dependent_variables] ... X, y = experiment_data[ivs], experiment_data[dvs] - ... new_model = LinearRegression(fit_intercept=True).set_params(**kwargs).fit(X, y) - ... return Delta(model=new_model) + ... model = LinearRegression(fit_intercept=True).set_params(**kwargs).fit(X, y) + ... return model >>> @dataclass(frozen=True) ... class V(State): @@ -684,19 +658,19 @@ def inputs_from_state(f): ... dependent_variables=[Variable("y")]), ... experiment_data=pd.DataFrame({"x": [0,1,2,3,4], "y": [2,3,4,5,6]}) ... ) - >>> v_prime = theorist(v) - >>> v_prime.model.coef_, v_prime.model.intercept_ + >>> model = theorist(v) + >>> model.coef_, model.intercept_ (array([[1.]]), array([2.])) Arguments from the state can be overridden by passing them in as keyword arguments (kwargs): >>> theorist(v, experiment_data=pd.DataFrame({"x": [0,1,2,3], "y": [12,13,14,15]}))\\ - ... .model.intercept_ + ... .intercept_ array([12.]) ... and other arguments supported by the inner function can also be passed (if and only if the inner function allows for and handles `**kwargs` arguments alongside the values from the state). - >>> theorist(v, fit_intercept=False).model.intercept_ + >>> theorist(v, fit_intercept=False).intercept_ 0.0 Any parameters not provided by the state must be provided by default values or by the @@ -704,17 +678,17 @@ def inputs_from_state(f): >>> @inputs_from_state ... def experimentalist(conditions, offset=25): ... new_conditions = [c + offset for c in conditions] - ... return Delta(conditions=new_conditions) + ... return new_conditions ... then it need not be passed. >>> experimentalist(U(conditions=[1,2,3,4])) - U(conditions=[26, 27, 28, 29]) + [26, 27, 28, 29] If a default isn't specified: >>> @inputs_from_state ... def experimentalist(conditions, offset): ... new_conditions = [c + offset for c in conditions] - ... return Delta(conditions=new_conditions) + ... return new_conditions ... then calling the experimentalist without it will throw an error: >>> experimentalist(U(conditions=[1,2,3,4])) @@ -724,17 +698,17 @@ def inputs_from_state(f): ... which can be fixed by passing the argument as a keyword to the wrapped function. >>> experimentalist(U(conditions=[1,2,3,4]), offset=2) - U(conditions=[3, 4, 5, 6]) + [3, 4, 5, 6] The state itself is passed through if the inner function requests the `state`: >>> @inputs_from_state ... def function_which_needs_whole_state(state, conditions): ... print("Doing something on: ", state) ... new_conditions = [c + 2 for c in conditions] - ... return Delta(conditions=new_conditions) + ... return new_conditions >>> function_which_needs_whole_state(U(conditions=[1,2,3,4])) Doing something on: U(conditions=[1, 2, 3, 4]) - U(conditions=[3, 4, 5, 6]) + [3, 4, 5, 6] """ # Get the set of parameter names from function f's signature @@ -750,12 +724,8 @@ def _f(state_: S, /, **kwargs) -> S: if "state" in parameters_: arguments_from_state["state"] = state_ arguments = dict(arguments_from_state, **kwargs) - delta = f(**arguments) - assert isinstance(delta, Mapping), ( - "Output of %s must be a `Delta`, `UserDict`, " "or `dict`." % f - ) - new_state = state_ + delta - return new_state + result = f(**arguments) + return result return _f @@ -865,6 +835,170 @@ def inner(*args, **kwargs): return decorator +def delta_to_state(f): + """Decorator to make `f` which takes a `State` and returns a `Delta` return an updated `State`. + + This wrapper handles adding a returned Delta to an input State object. + + Args: + f: the function which returns a `Delta` object + + Returns: the function modified to return a State object + + Examples: + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> from typing import List, Optional + + The `State` it operates on needs to have the metadata described in the state module: + >>> @dataclass(frozen=True) + ... class U(State): + ... conditions: List[int] = field(metadata={"delta": "replace"}) + + We indicate the inputs required by the parameter names. + The output must be (compatible with) a `Delta` object. + >>> @delta_to_state + ... @inputs_from_state + ... def experimentalist(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return Delta(conditions=new_conditions) + + >>> experimentalist(U(conditions=[1,2,3,4])) + U(conditions=[11, 12, 13, 14]) + + >>> experimentalist(U(conditions=[101,102,103,104])) + U(conditions=[111, 112, 113, 114]) + + If the output of the function is not a `Delta` object (or something compatible with its + interface), then an error is thrown. + >>> @delta_to_state + ... @inputs_from_state + ... def returns_bare_conditions(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return new_conditions + + >>> returns_bare_conditions(U(conditions=[1])) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + AssertionError: Output of must be a `Delta`, + `UserDict`, or `dict`. + + A dictionary can be returned and used: + >>> @delta_to_state + ... @inputs_from_state + ... def returns_a_dictionary(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return {"conditions": new_conditions} + >>> returns_a_dictionary(U(conditions=[2])) + U(conditions=[12]) + + ... as can an object which subclasses UserDict (like `Delta`) + >>> class MyDelta(UserDict): + ... pass + >>> @delta_to_state + ... @inputs_from_state + ... def returns_a_userdict(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return MyDelta(conditions=new_conditions) + >>> returns_a_userdict(U(conditions=[3])) + U(conditions=[13]) + + We recommend using the `Delta` object rather than a `UserDict` or `dict` as its + functionality may be expanded in future. + + >>> from autora.variable import VariableCollection, Variable + >>> from sklearn.base import BaseEstimator + >>> from sklearn.linear_model import LinearRegression + + >>> @delta_to_state + ... @inputs_from_state + ... def theorist(experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs): + ... ivs = [vi.name for vi in variables.independent_variables] + ... dvs = [vi.name for vi in variables.dependent_variables] + ... X, y = experiment_data[ivs], experiment_data[dvs] + ... new_model = LinearRegression(fit_intercept=True).set_params(**kwargs).fit(X, y) + ... return Delta(model=new_model) + + >>> @dataclass(frozen=True) + ... class V(State): + ... variables: VariableCollection # field(metadata={"delta":... }) omitted ∴ immutable + ... experiment_data: pd.DataFrame = field(metadata={"delta": "extend"}) + ... model: Optional[BaseEstimator] = field(metadata={"delta": "replace"}, default=None) + + >>> v = V( + ... variables=VariableCollection(independent_variables=[Variable("x")], + ... dependent_variables=[Variable("y")]), + ... experiment_data=pd.DataFrame({"x": [0,1,2,3,4], "y": [2,3,4,5,6]}) + ... ) + >>> v_prime = theorist(v) + >>> v_prime.model.coef_, v_prime.model.intercept_ + (array([[1.]]), array([2.])) + + Arguments from the state can be overridden by passing them in as keyword arguments (kwargs): + >>> theorist(v, experiment_data=pd.DataFrame({"x": [0,1,2,3], "y": [12,13,14,15]}))\\ + ... .model.intercept_ + array([12.]) + + ... and other arguments supported by the inner function can also be passed + (if and only if the inner function allows for and handles `**kwargs` arguments alongside + the values from the state). + >>> theorist(v, fit_intercept=False).model.intercept_ + 0.0 + + Any parameters not provided by the state must be provided by default values or by the + caller. If the default is specified: + >>> @delta_to_state + ... @inputs_from_state + ... def experimentalist(conditions, offset=25): + ... new_conditions = [c + offset for c in conditions] + ... return Delta(conditions=new_conditions) + + ... then it need not be passed. + >>> experimentalist(U(conditions=[1,2,3,4])) + U(conditions=[26, 27, 28, 29]) + + If a default isn't specified: + >>> @delta_to_state + ... @inputs_from_state + ... def experimentalist(conditions, offset): + ... new_conditions = [c + offset for c in conditions] + ... return Delta(conditions=new_conditions) + + ... then calling the experimentalist without it will throw an error: + >>> experimentalist(U(conditions=[1,2,3,4])) + Traceback (most recent call last): + ... + TypeError: experimentalist() missing 1 required positional argument: 'offset' + + ... which can be fixed by passing the argument as a keyword to the wrapped function. + >>> experimentalist(U(conditions=[1,2,3,4]), offset=2) + U(conditions=[3, 4, 5, 6]) + + The state itself is passed through if the inner function requests the `state`: + >>> @delta_to_state + ... @inputs_from_state + ... def function_which_needs_whole_state(state, conditions): + ... print("Doing something on: ", state) + ... new_conditions = [c + 2 for c in conditions] + ... return Delta(conditions=new_conditions) + >>> function_which_needs_whole_state(U(conditions=[1,2,3,4])) + Doing something on: U(conditions=[1, 2, 3, 4]) + U(conditions=[3, 4, 5, 6]) + + """ + + @wraps(f) + def _f(state_: S, **kwargs) -> S: + delta = f(state_, **kwargs) + assert isinstance(delta, Mapping), ( + "Output of %s must be a `Delta`, `UserDict`, " "or `dict`." % f + ) + new_state = state_ + delta + return new_state + + return _f + + def on_state( function: Optional[Callable] = None, output: Optional[Sequence[str]] = None ): @@ -932,6 +1066,7 @@ def decorator(f): if output is not None: f_ = outputs_to_delta(*output)(f_) f_ = inputs_from_state(f_) + f_ = delta_to_state(f_) return f_ if function is None: From dc27c664fe4879afcdb327152043bab049e8e7f0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 14:20:33 +0200 Subject: [PATCH 100/121] docs: update Combining Experimentalists with State --- ...ombining Experimentalists with State.ipynb | 430 +++++++++++++++++- 1 file changed, 419 insertions(+), 11 deletions(-) rename src/autora/experimentalist/consensus.ipynb => docs/cycle/Combining Experimentalists with State.ipynb (86%) diff --git a/src/autora/experimentalist/consensus.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb similarity index 86% rename from src/autora/experimentalist/consensus.ipynb rename to docs/cycle/Combining Experimentalists with State.ipynb index 275a7a5c..e5a4f5c9 100644 --- a/src/autora/experimentalist/consensus.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -145,7 +145,96 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 downvotes\n", + "0 -3.0 -1.0 2.0\n", + "1 -2.0 0.0 0.0\n", + "2 -1.0 1.0 0.0\n", + "3 0.0 2.0 0.0\n", + "4 1.0 3.0 0.0\n", + "5 2.0 4.0 0.0\n", + "6 3.0 5.0 1.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def avoid_repeat(conditions, experiment_data: pd.DataFrame, variables: VariableCollection):\n", " iv_column_names = [v.name for v in variables.independent_variables]\n", @@ -1214,10 +1394,13 @@ " conditions = pd.DataFrame.join(conditions, count_already_seen, on=iv_column_names).fillna(0)\n", " return {\"conditions\": conditions, \"already_seen\": count_already_seen}\n", "\n", + "experiment_data_ = pd.DataFrame(dict(x1=[-3, 3, -3], x2=[-1, 5, -1]))\n", + "variables_ = VariableCollection(independent_variables=[Variable(\"x1\"), Variable(\"x2\")])\n", + "\n", "avoid_repeat(\n", " conditions=conditions_,\n", - " experiment_data=pd.DataFrame(dict(x1=[-3, 3, -3], x2=[-1, 5, -1])),\n", - " variables=VariableCollection(independent_variables=[Variable(\"x1\"), Variable(\"x2\")])\n", + " experiment_data=experiment_data_,\n", + " variables=variables_\n", ")[\"conditions\"]" ] }, @@ -1279,7 +1462,7 @@ "source": [ "The way we handle this is to write a function which operates on the State directly, passing it to\n", "experimentalists wrapped with `on_state`, then combine their outputs.\n", - "This is easy if our conditions are returned with the downvotes in the same dataframe:" + "This is done as follows if our conditions are returned with the downvotes in the same dataframe:" ] }, { @@ -1419,7 +1602,12 @@ ], "source": [ "@on_state()\n", - "def combine_downvotes_state(state: State, conditions, experimentalists: List, num_samples: int):\n", + "def combine_downvotes_state(\n", + " state: State,\n", + " conditions: pd.DataFrame,\n", + " experimentalists: List,\n", + " num_samples: int\n", + "):\n", " # iv_column_names = [v.name for v in s.variables.independent_variables]\n", " downvoted_conditions = []\n", " for e in experimentalists:\n", @@ -1428,9 +1616,11 @@ " this_downvoted_conditions.attrs[\"name\"] = e.__name__\n", " downvoted_conditions.append(this_downvoted_conditions)\n", " combined_downvotes = combine_downvotes(conditions, *downvoted_conditions)\n", - " sorted_combined_downvotes = combined_downvotes.sort_values(by=\"downvotes\", ascending=True)\n", - " filtered_sorted_combined_downvotes = sorted_combined_downvotes.iloc[:num_samples]\n", - " d = Delta(conditions=filtered_sorted_combined_downvotes)\n", + " combined_downvotes_sorted_filtered = combined_downvotes\\\n", + " .sort_values(by=\"downvotes\", ascending=True)\\\n", + " .iloc[:num_samples]\n", + "\n", + " d = Delta(conditions=combined_downvotes_sorted_filtered)\n", " return d\n", "\n", "combine_downvotes_state(\n", @@ -1441,10 +1631,228 @@ " on_state(avoid_negative, output=[\"conditions\"]),\n", " on_state(avoid_even, output=[\"conditions\"])\n", " ],\n", - " num_samples=10\n", + " num_samples=7\n", + ").conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Experimentalists Return Separate Conditions and Additional Measures\n", + "\n", + "If we return separate conditions and measures, then we need to split up the combined downvoted the combination function\n", + "is a little\n", + "different:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'conditions': x1 x2\n", + " 0 -3.0 -1.0\n", + " 1 -2.0 0.0\n", + " 2 -1.0 1.0\n", + " 3 0.0 2.0\n", + " 4 1.0 3.0\n", + " 5 2.0 4.0\n", + " 6 3.0 5.0,\n", + " 'downvotes': 0 2.0\n", + " 1 0.0\n", + " 2 0.0\n", + " 3 0.0\n", + " 4 0.0\n", + " 5 0.0\n", + " 6 1.0\n", + " Name: downvotes, dtype: float64}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def avoid_repeat_separate(\n", + " conditions: pd.DataFrame,\n", + " experiment_data: pd.DataFrame,\n", + " variables: VariableCollection\n", + "):\n", + " conditions_with_downvotes = avoid_repeat(\n", + " conditions=conditions,\n", + " experiment_data=experiment_data,\n", + " variables=variables\n", + " )[\"conditions\"]\n", + "\n", + " # Now we split up the results\n", + " iv_column_names = [v.name for v in variables.independent_variables]\n", + " conditions = conditions_with_downvotes[iv_column_names]\n", + " downvotes = conditions_with_downvotes[\"downvotes\"]\n", + "\n", + " return {\"conditions\": conditions, \"downvotes\": downvotes}\n", + "\n", + "avoid_repeat_separate(\n", + " conditions=conditions_,\n", + " experiment_data=experiment_data_,\n", + " variables=variables_\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the combination function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jholla10/Developer/autora-core/src/autora/state/delta.py:273: UserWarning: These fields: ['downvotes'] could not be used to update StandardState, which has these fields & aliases: ['variables', 'conditions', 'experiment_data', 'models', 'model']\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 y new_column\n", + "0 -10 -10 -10 NaN\n", + "1 5 5 5 15.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s_1.experiment_data" ] From a862fba95a4c01e402f4ec5a665b93035db23e81 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 15:07:31 +0200 Subject: [PATCH 102/121] docs: update example Notebook --- ...ombining Experimentalists with State.ipynb | 51 ++++++++++++++++--- 1 file changed, 44 insertions(+), 7 deletions(-) diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index 817cf51c..df8d9666 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -8,14 +8,51 @@ "\n", "## Introduction\n", "\n", - "One thing the State/Delta mechanism should support is making more complex experimentalists which combine others in\n", - "clever ways.\n", - "Here we have some examples:\n", + "One thing the State/Delta mechanism should support is making more complex experimentalists which combine others.\n", + "One example which have been suggested by the AER group are a \"mixture experimentalist\" which weights the outputs of\n", + "other experimentalists.\n", + "\n", + "How experimentalists are typically defined has a major impact on whether this kind of mixture experimentalist is easy\n", + " or hard to implement. Since the research group is currently (August 2023) deciding how experimentalists should\n", + " generally be defined, now seems a good time to look at the different basic options for standards & conventions.\n", + "\n", + "To help the discussion, here we've put together some examples based on some toy experimentalists.\n", + "\n", + "### Outline of the Open Question\n", + "The question has to do with whether \"additional data\" beyond the conditions are included in the same or a different\n", + "data array.\n", + " (\"Additional data\" are data which are generated by the experimentalist and potentially needed by another\n", + " experimentalist down the line, but are not the conditions themselves).\n", + "\n", + "The two competing conventions are if an experimentalist returns some extra data:\n", + "- They are included in the `conditions` array as additional columns, _or_\n", + "- They are passed as a _different_ array alongside the `conditions`.\n", + "\n", + "### Notebook Outline\n", + "\n", + "The examples are organized as follows:\n", + "\n", + "- A combination experimentalist which aggregates additional measures from the component experimentalists.\n", + " - Where the measure is passed back in the conditions array, or\n", + " - Where the measure is passed back in a separate array\n", + "- A combination experimentalist where the components need the full State as they have complex arguments\n", + "\n", + "\n", + "### Toy Experimentalists\n", + "\n", + "We're combining experimentalists which samples conditions based on whether they are downvoted (or not)\n", + "according to some criteria:\n", + "- The \"Avoid Negative\" experimentalist, which downvotes conditions which have negative values (with one downvote per\n", + "negative value in the conditions $x_i$: if both $x_1$ and $x_2$ are negative, the condition gets 2 downvotes, and so\n", + "on) and returns all the conditions in the \"preferred\" order (fewest downvotes first),\n", + "- The \"Avoid Even\" experimentalist, which downvotes conditions which are closer to even numbers more (with one downvote\n", + "per even value in the conditions and half a downvote if a condition is $1/2$ away from an even number) and returns all the conditions in the \"preferred\" order,\n", + "- The \"Avoid Repeat\" experimentalist, which downvotes conditions which have already been seen based on the number of\n", + "times a condition has been seen and returns all the conditions in the \"preferred\" order,\n", + "- The \"Combine Downvotes\" experimentalist, which sums the downvotes of the others and returns the top $n$ \"preferred\"\n", + "conditions\n", + "(with the fewest downvotes); in the case of a tie, it returns conditions the order of the original conditions list.\n", "\n", - "- [x] A combination experimentalist which aggregates additional measures from the component experimentalists\n", - " - [x] Where the measure is passed back in the conditions array, or\n", - " - [x] Where the measure is passed back in a separate array\n", - "- [ ] A combination experimentalist where the components need the full State as they have complex arguments\n", "\n", "We also need to see what happens when we:\n", "- Try to extend a dataframe with an extra data frame which has new columns." From 1980d09c7df2a02e0b9c16fad1468c6621665551 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 17 Aug 2023 16:38:32 +0200 Subject: [PATCH 103/121] docs: add more examples with chainable State-based voting. --- ...ombining Experimentalists with State.ipynb | 616 +++++++++++++++++- 1 file changed, 614 insertions(+), 2 deletions(-) diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index df8d9666..de3dc19b 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -78,7 +78,7 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import List\n", + "from typing import List, Optional\n", "\n", "import numpy as np\n", "import pandas as pd\n", @@ -1889,7 +1889,619 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "### Chained Experimentalists\n", + "We can also define experimentalists which add their vote to the existing vote, if it exists:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def combine_downvotes(a, b, *arrays):\n", + " if isinstance(b, pd.Series):\n", + " new_downvotes = b\n", + " elif isinstance(b, pd.DataFrame):\n", + " new_downvotes = b.downvotes\n", + " if \"downvotes\" in a.columns:\n", + " result = a.assign(downvotes=a.downvotes + new_downvotes)\n", + " else:\n", + " result = a.assign(downvotes=new_downvotes)\n", + " if len(arrays) == 0:\n", + " return result\n", + " else:\n", + " return combine_downvotes(result, arrays[0], *arrays[1:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we pass in some conditions with no downvotes (`conditions_`)\n", + "and then combine with a DataFrame with constant downvotes `conditions_.assign(downvotes=1)`\n", + "we get constant total downvotes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 downvotes\n", + "0 -3.0 -1.0 1\n", + "1 -2.0 0.0 2\n", + "2 -1.0 1.0 3\n", + "3 0.0 2.0 4\n", + "4 1.0 3.0 5\n", + "5 2.0 4.0 6\n", + "6 3.0 5.0 7" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combine_downvotes(\n", + " conditions_,\n", + " conditions_.assign(downvotes=1),\n", + " conditions_.assign(downvotes=[0, 1, 2, 3, 4, 5, 6]).sample(frac=1)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these, we can build functions which are aware of and add to existing downvotes if they exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x1 x2 downvotes avoid_even.downvotes\n", + "0 -3.0 -1.0 0.0 0.0\n", + "1 -2.0 0.0 2.0 2.0\n", + "2 -1.0 1.0 0.0 0.0\n", + "3 0.0 2.0 2.0 2.0\n", + "4 1.0 3.0 0.0 0.0\n", + "5 2.0 4.0 2.0 2.0\n", + "6 3.0 5.0 0.0 0.0, experiment_data= x1 x2\n", + "0 -3 -1\n", + "1 3 5\n", + "2 -3 -1, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@on_state()\n", + "def avoid_even_chainable(conditions: pd.DataFrame, variables: VariableCollection):\n", + " iv_names = [v.name for v in variables.independent_variables]\n", + " downvotes = avoid_even_function(conditions_[iv_names]).sum(axis=1)\n", + " result = combine_downvotes(conditions, downvotes)\n", + " result[\"avoid_even.downvotes\"] = downvotes\n", + " return {\"conditions\": result}\n", + "avoid_even_chainable(s, conditions=conditions_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x1 x2 downvotes avoid_negative.downvotes\n", + "0 -3.0 -1.0 2 2\n", + "1 -2.0 0.0 1 1\n", + "2 -1.0 1.0 1 1\n", + "3 0.0 2.0 0 0\n", + "4 1.0 3.0 0 0\n", + "5 2.0 4.0 0 0\n", + "6 3.0 5.0 0 0, experiment_data= x1 x2\n", + "0 -3 -1\n", + "1 3 5\n", + "2 -3 -1, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@on_state()\n", + "def avoid_negative_chainable(conditions: pd.DataFrame, variables: VariableCollection):\n", + " iv_names = [v.name for v in variables.independent_variables]\n", + " downvotes = (conditions_[iv_names] < 0).sum(axis=1)\n", + " result = combine_downvotes(conditions, downvotes)\n", + " result[\"avoid_negative.downvotes\"] = downvotes\n", + " return {\"conditions\": result}\n", + "avoid_negative_chainable(s, conditions=conditions_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x1 x2 downvotes avoid_repeat.downvotes\n", + "0 -3.0 -1.0 2.0 2.0\n", + "1 -2.0 0.0 0.0 0.0\n", + "2 -1.0 1.0 0.0 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" + ], + "text/plain": [ + " x1 x2 downvotes avoid_repeat.downvotes avoid_even.downvotes \\\n", + "4 1.0 3.0 0.0 0.0 0.0 \n", + "2 -1.0 1.0 1.0 0.0 0.0 \n", + "6 3.0 5.0 1.0 1.0 0.0 \n", + "3 0.0 2.0 2.0 0.0 2.0 \n", + "5 2.0 4.0 2.0 0.0 2.0 \n", + "1 -2.0 0.0 3.0 0.0 2.0 \n", + "0 -3.0 -1.0 4.0 2.0 0.0 \n", + "\n", + " avoid_negative.downvotes \n", + "4 0 \n", + "2 1 \n", + "6 0 \n", + "3 0 \n", + "5 0 \n", + "1 1 \n", + "0 2 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_0 = s + Delta(conditions=conditions_) # add the seed conditions\n", + "s_1 = avoid_repeat_chainable(s_0)\n", + "s_2 = avoid_even_chainable(s_1)\n", + "s_3 = avoid_negative_chainable(s_2)\n", + "s_4 = sample_downvotes(s_3, num_samples=7)\n", + "s_4.conditions" + ] }, { "cell_type": "markdown", From bcf3e5b4a2b0579298e201b67667f96492c2e63f Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:08:26 +0200 Subject: [PATCH 104/121] chore!: remove deprecated pooler and sampler submodules --- .../experimentalist/{grid_.py => grid.py} | 0 src/autora/experimentalist/pooler/grid.py | 26 --- .../experimentalist/pooler/random_pooler.py | 59 ------- .../experimentalist/sampler/random_sampler.py | 33 ---- tests/test_experimentalist_random.py | 153 ------------------ 5 files changed, 271 deletions(-) rename src/autora/experimentalist/{grid_.py => grid.py} (100%) delete mode 100644 src/autora/experimentalist/pooler/grid.py delete mode 100644 src/autora/experimentalist/pooler/random_pooler.py delete mode 100644 src/autora/experimentalist/sampler/random_sampler.py delete mode 100644 tests/test_experimentalist_random.py diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid.py similarity index 100% rename from src/autora/experimentalist/grid_.py rename to src/autora/experimentalist/grid.py diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py deleted file mode 100644 index 2a8eeb22..00000000 --- a/src/autora/experimentalist/pooler/grid.py +++ /dev/null @@ -1,26 +0,0 @@ -import logging -from itertools import product -from typing import List - -from autora.variable import IV - -_logger = logging.getLogger(__name__) -_logger.warning( - "`autora.experimentalist.pooler.grid` is deprecated. " - "Use the functions in `autora.experimentalist.grid_` instead." -) - - -def grid_pool(ivs: List[IV]): - """Creates exhaustive pool from discrete values using a Cartesian product of sets""" - # Get allowed values for each IV - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"grid_pool only supports independent variables with discrete allowed values, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Return Cartesian product of all IV values - return product(*l_iv_values) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py deleted file mode 100644 index f758abf1..00000000 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ /dev/null @@ -1,59 +0,0 @@ -import logging -import random -from typing import Iterable, List, Tuple - -import numpy as np - -from autora.utils.deprecation import deprecated_alias -from autora.variable import IV - -_logger = logging.getLogger(__name__) -_logger.warning( - "`autora.experimentalist.pooler.random_pooler` is deprecated. " - "Use the functions in `autora.experimentalist.random_` instead." -) - - -def random_pool( - ivs: List[IV], num_samples: int = 1, duplicates: bool = True -) -> Iterable: - """ - Creates combinations from lists of discrete values using random selection. - Args: - ivs: List of independent variables - num_samples: Number of samples to sample - duplicates: Boolean if duplicate value are allowed. - - """ - l_samples: List[Tuple] = [] - # Create list of pools of values sample from - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Check to ensure infinite search won't occur if duplicates not allowed - if not duplicates: - l_pool_len = [len(set(s)) for s in l_iv_values] - n_combinations = np.product(l_pool_len) - try: - assert num_samples <= n_combinations - except AssertionError: - raise AssertionError( - f"Number to sample n({num_samples}) is larger than the number " - f"of unique combinations({n_combinations})." - ) - - # Random sample from the pools until n is met - while len(l_samples) < num_samples: - l_samples.append(tuple(map(random.choice, l_iv_values))) - if not duplicates: - l_samples = [*set(l_samples)] - - return iter(l_samples) - - -random_pooler = deprecated_alias(random_pool, "random_pooler") diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py deleted file mode 100644 index 0076ddbb..00000000 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ /dev/null @@ -1,33 +0,0 @@ -import logging -import random -from typing import Iterable, Sequence, Union - -from autora.utils.deprecation import deprecated_alias - -_logger = logging.getLogger(__name__) -_logger.warning( - "`autora.experimentalist.sampler.random_sampler` is deprecated. " - "Use the functions in `autora.experimentalist.random_` instead." -) - - -def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): - """ - Uniform random sampling without replacement from a pool of conditions. - Args: - conditions: Pool of conditions - n: number of samples to collect - - Returns: Sampled pool - - """ - - if isinstance(conditions, Iterable): - conditions = list(conditions) - random.shuffle(conditions) - samples = conditions[0:num_samples] - - return samples - - -random_sampler = deprecated_alias(random_sample, "random_sampler") diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py deleted file mode 100644 index a81ad483..00000000 --- a/tests/test_experimentalist_random.py +++ /dev/null @@ -1,153 +0,0 @@ -from functools import partial - -import numpy as np -import pytest - -from autora.experimentalist.pipeline import make_pipeline -from autora.experimentalist.pooler.grid import grid_pool -from autora.experimentalist.pooler.random_pooler import random_pool -from autora.experimentalist.sampler.random_sampler import random_sample -from autora.variable import DV, IV, ValueType, VariableCollection - - -def weber_filter(values): - return filter(lambda s: s[0] <= s[1], values) - - -def test_random_pooler_experimentalist(metadata): - """ - Tests the implementation of a random pooler. - """ - num_samples = 10 - - conditions = random_pool(metadata.independent_variables, num_samples=num_samples) - - conditions = np.array(list(conditions)) - - assert conditions.shape[0] == num_samples - assert conditions.shape[1] == len(metadata.independent_variables) - for condition in conditions: - for idx, value in enumerate(condition): - assert value in metadata.independent_variables[idx].allowed_values - - -def test_random_sampler_experimentalist(metadata): - """ - Tests the implementation of the experimentalist pipeline with an exhaustive pool of discrete - values, Weber filter, random selector. Tests two different implementations of the pool function - as a callable and passing in as interator/generator. - - """ - - n_trials = 25 # Number of trails for sampler to select - - # ---Implementation 1 - Pool using Callable via partial function---- - # Set up pipeline functions with partial - pooler_callable = partial(grid_pool, ivs=metadata.independent_variables) - sampler = partial(random_sample, num_samples=n_trials) - pipeline_random_samp = make_pipeline( - [pooler_callable, weber_filter, sampler], - ) - - results = pipeline_random_samp.run() - - # ***Checks*** - # Gridsearch pool is working as expected - _, pool = pipeline_random_samp.steps[0] - pool_len = len(list(pool())) - pool_len_expected = np.prod( - [len(s.allowed_values) for s in metadata.independent_variables] - ) - assert pool_len == pool_len_expected - - # Is sampling the number of trials we expect - assert len(results) == n_trials - - # Filter is selecting where IV1 >= IV2 - assert all([s[0] <= s[1] for s in results]) - - # Is sampling randomly. Runs 10 times and checks if consecutive runs are equal. - # Assert will fail if all 9 pairs return equal. - l_results = [pipeline_random_samp.run() for s in range(10)] - assert not np.all( - [ - np.array_equal(l_results[i], l_results[i + 1]) - for i, s in enumerate(l_results) - if i < len(l_results) - 1 - ] - ) - - -def test_random_experimentalist_generator(metadata): - n_trials = 25 # Number of trails for sampler to select - - pooler_generator = grid_pool(metadata.independent_variables) - sampler = partial(random_sample, num_samples=n_trials) - pipeline_random_samp_poolgen = make_pipeline( - [pooler_generator, weber_filter, sampler] - ) - - results_poolgen = list(pipeline_random_samp_poolgen.run()) - - # Is sampling the number of trials we expect - assert len(results_poolgen) == n_trials - - # Filter is selecting where IV1 >= IV2 - assert all([s[0] <= s[1] for s in results_poolgen]) - - # This will fail - # The Generator is exhausted after the first run and the pool is not regenerated when pipeline - # is run again. The pool should be set up as a callable if the pipeline is to be rerun. - results_poolgen2 = pipeline_random_samp_poolgen.run() - assert len(results_poolgen2) == 0 - - -@pytest.fixture -def metadata(): - # Specify independent variables - iv1 = IV( - name="S1", - allowed_values=np.linspace(0, 5, 5), - units="intensity", - variable_label="Stimulus 1 Intensity", - ) - - iv2 = IV( - name="S2", - allowed_values=np.linspace(0, 5, 5), - units="intensity", - variable_label="Stimulus 2 Intensity", - ) - - iv3 = IV( - name="S3", - allowed_values=[0, 1], - units="binary", - variable_label="Stimulus 3 Binary", - ) - - # Specify dependent variable with type - # The experimentalist pipeline doesn't actually use DVs, they are just specified here for - # example. - dv1 = DV( - name="difference_detected", - value_range=(0, 1), - units="probability", - variable_label="P(difference detected)", - type=ValueType.SIGMOID, - ) - - dv2 = DV( - name="difference_detected_sample", - value_range=(0, 1), - units="response", - variable_label="difference detected", - type=ValueType.PROBABILITY_SAMPLE, - ) - # Variable collection with ivs and dvs - metadata = VariableCollection( - independent_variables=[iv1, iv2, iv3], - dependent_variables=[dv1, dv2], - ) - - return metadata From 0ba8082359271a822ddf3e1d5fdde1562de24d25 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:21:15 +0200 Subject: [PATCH 105/121] fix: ensure inputs to sample function are cast to dataFrame --- src/autora/experimentalist/random_.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 12e83e1a..6f09e04f 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -164,9 +164,18 @@ def sample( 63 163 96 196 + From a list (returns a DataFrame): + >>> sample(range(1000), num_samples=5, random_state=180) + 0 + 270 270 + 908 908 + 109 109 + 331 331 + 978 978 """ + conditions_ = pd.DataFrame(conditions) return pd.DataFrame.sample( - conditions, random_state=random_state, n=num_samples, replace=replace + conditions_, random_state=random_state, n=num_samples, replace=replace ) From 8ea071c0750290aeb7a4ab88b1c1c191dfa5ca12 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:27:14 +0200 Subject: [PATCH 106/121] test: update docstrings on aliases to fix test running --- src/autora/experimentalist/grid.py | 2 +- src/autora/experimentalist/random_.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/autora/experimentalist/grid.py b/src/autora/experimentalist/grid.py index db953654..afe96522 100644 --- a/src/autora/experimentalist/grid.py +++ b/src/autora/experimentalist/grid.py @@ -105,4 +105,4 @@ def pool(variables: VariableCollection) -> pd.DataFrame: grid_pool = pool -grid_pool.__doc__ = """Alias for pool""" +"""Alias for pool""" diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 6f09e04f..118d5450 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -131,7 +131,7 @@ def pool( random_pool = pool -random_pool.__doc__ = """Alias for `pool`""" +"""Alias for `pool`""" def sample( @@ -180,4 +180,4 @@ def sample( random_sample = sample -random_sample.__doc__ = """Alias for `sample`""" +"""Alias for `sample`""" From bfbaf975bfdb0b3605378705bce68a6243d2906e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:27:51 +0200 Subject: [PATCH 107/121] docs: update documentation to drop pooler/sampler split --- .../{pooler => }/grid/index.md | 2 +- .../{pooler => }/grid/quickstart.md | 2 +- .../pooler/random/quickstart.md | 15 ---------- .../{pooler => }/random/index.md | 0 docs/experimentalists/random/quickstart.md | 29 +++++++++++++++++++ docs/experimentalists/sampler/random/index.md | 11 ------- .../sampler/random/quickstart.md | 16 ---------- 7 files changed, 31 insertions(+), 44 deletions(-) rename docs/experimentalists/{pooler => }/grid/index.md (95%) rename docs/experimentalists/{pooler => }/grid/quickstart.md (84%) delete mode 100644 docs/experimentalists/pooler/random/quickstart.md rename docs/experimentalists/{pooler => }/random/index.md (100%) create mode 100644 docs/experimentalists/random/quickstart.md delete mode 100644 docs/experimentalists/sampler/random/index.md delete mode 100644 docs/experimentalists/sampler/random/quickstart.md diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/grid/index.md similarity index 95% rename from docs/experimentalists/pooler/grid/index.md rename to docs/experimentalists/grid/index.md index 2a56cd7c..474f78f1 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/grid/index.md @@ -24,7 +24,7 @@ This means that there are various combinations that these variables can form, th ### Example Code ```python -from autora.experimentalist.grid_ import grid_pool +from autora.experimentalist.grid import grid_pool from autora.variable import Variable, VariableCollection iv_1 = Variable(allowed_values=[1, 2, 3]) diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/grid/quickstart.md similarity index 84% rename from docs/experimentalists/pooler/grid/quickstart.md rename to docs/experimentalists/grid/quickstart.md index 444deeec..35777517 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/grid/quickstart.md @@ -10,5 +10,5 @@ You will need: you can import the grid pooler via: ```python -from autora.experimentalist.grid_ import grid_pool +from autora.experimentalist.grid import grid_pool ``` diff --git a/docs/experimentalists/pooler/random/quickstart.md b/docs/experimentalists/pooler/random/quickstart.md deleted file mode 100644 index 0687529a..00000000 --- a/docs/experimentalists/pooler/random/quickstart.md +++ /dev/null @@ -1,15 +0,0 @@ -# Quickstart Guide - -You will need: - -- `python` 3.8 or greater: [https://www.python.org/downloads/](https://www.python.org/downloads/) - - -*Random Pooler* is part of the `autora-core` package and does not need to be installed separately - -you can import the random pooler via: - -```python - -from autora.experimentalist.random_ import random_pool -``` diff --git a/docs/experimentalists/pooler/random/index.md b/docs/experimentalists/random/index.md similarity index 100% rename from docs/experimentalists/pooler/random/index.md rename to docs/experimentalists/random/index.md diff --git a/docs/experimentalists/random/quickstart.md b/docs/experimentalists/random/quickstart.md new file mode 100644 index 00000000..9f872c75 --- /dev/null +++ b/docs/experimentalists/random/quickstart.md @@ -0,0 +1,29 @@ +# Quickstart Guide + +You will need: + +- `python` 3.8 or greater: [https://www.python.org/downloads/](https://www.python.org/downloads/) + + +*Random Pooler* and *Sampler* are part of the `autora-core` package and do not need to be installed separately + +You can import and invoke the pool like this: + +```python +from autora.variable import VariableCollection, Variable +from autora.experimentalist.random_ import pool + +pool( + VariableCollection(independent_variables=[Variable(name="x", allowed_values=range(10))]), + random_state=1 +) +``` + +You can import the sampler like this: + +```python +from autora.experimentalist.random_ import sample + +sample([1, 1, 2, 2, 3, 3], num_samples=2) +``` + diff --git a/docs/experimentalists/sampler/random/index.md b/docs/experimentalists/sampler/random/index.md deleted file mode 100644 index 9d1abc22..00000000 --- a/docs/experimentalists/sampler/random/index.md +++ /dev/null @@ -1,11 +0,0 @@ -# Random Sampler - -Uniform random sampling without replacement from a pool of conditions. - -### Example Code - -```python -from autora.experimentalist.random_ import random_sample - -pool = random_sample([1, 1, 2, 2, 3, 3], num_samples=2) -``` diff --git a/docs/experimentalists/sampler/random/quickstart.md b/docs/experimentalists/sampler/random/quickstart.md deleted file mode 100644 index 5da12467..00000000 --- a/docs/experimentalists/sampler/random/quickstart.md +++ /dev/null @@ -1,16 +0,0 @@ -# Quickstart Guide - -You will need: - -- `python` 3.8 or greater: [https://www.python.org/downloads/](https://www.python.org/downloads/) - - -*Random Sampler* is part of the `autora-core` package and does not need to be installed separately - -you can import the random sampler via: - -```python -from autora.experimentalist.random_ import random_sample - -pool = random_sample([1, 1, 2, 2, 3, 3], num_samples=2) -``` From cc1d180a920efb0ce2c4a7479ba0b5ea8b33be79 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:33:49 +0200 Subject: [PATCH 108/121] refactor: move StandardState and standard wrappers into a single file --- src/autora/state/{bundled.py => standard.py} | 152 +++++++++++++++++- src/autora/state/wrapper.py | 155 ------------------- 2 files changed, 150 insertions(+), 157 deletions(-) rename src/autora/state/{bundled.py => standard.py} (51%) delete mode 100644 src/autora/state/wrapper.py diff --git a/src/autora/state/bundled.py b/src/autora/state/standard.py similarity index 51% rename from src/autora/state/bundled.py rename to src/autora/state/standard.py index 7a878907..ff46ba96 100644 --- a/src/autora/state/bundled.py +++ b/src/autora/state/standard.py @@ -1,12 +1,26 @@ +"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)` +so that $n$ processes $f_i$ on states $S$ can be represented as +$$f_n(...(f_1(f_0(S))))$$ + +These are special cases of the [autora.state.delta.on_state][] function. +""" +from __future__ import annotations + from dataclasses import dataclass, field -from typing import List, Optional +from typing import Callable, List, Optional, TypeVar import pandas as pd from sklearn.base import BaseEstimator -from autora.state.delta import State +from autora.state.delta import Delta, State, on_state from autora.variable import VariableCollection +S = TypeVar("S") +X = TypeVar("X") +Y = TypeVar("Y") +XY = TypeVar("XY") +Executor = Callable[[State], State] + @dataclass(frozen=True) class StandardState(State): @@ -173,3 +187,137 @@ def model(self): return self.models[-1] except IndexError: return None + + +def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: + """ + Convert a scikit-learn compatible estimator into a function on a `State` object. + + Supports passing additional `**kwargs` which are used to update the estimator's params + before fitting. + + Examples: + Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. + >>> from sklearn.linear_model import LinearRegression + >>> state_fn = state_fn_from_estimator(LinearRegression()) + + Define the state on which to operate (here an instance of the `StandardState`): + >>> from autora.state.standard import StandardState + >>> from autora.variable import Variable, VariableCollection + >>> import pandas as pd + >>> s = StandardState( + ... variables=VariableCollection( + ... independent_variables=[Variable("x")], + ... dependent_variables=[Variable("y")]), + ... experiment_data=pd.DataFrame({"x": [1,2,3], "y":[3,6,9]}) + ... ) + + Run the function, which fits the model and adds the result to the `StandardState` + >>> state_fn(s).model.coef_ + array([[3.]]) + + """ + + @on_state() + def theorist( + experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs + ): + ivs = [v.name for v in variables.independent_variables] + dvs = [v.name for v in variables.dependent_variables] + X, y = experiment_data[ivs], experiment_data[dvs] + new_model = estimator.set_params(**kwargs).fit(X, y) + return Delta(model=new_model) + + return theorist + + +def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: + """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ + values, with inputs and outputs as a DataFrame or Series with correct column names. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state.standard import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame (allowing the explicit inclusion of + metadata like column names). + >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(2 * c["x"] + 1, name="y") + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(c["x0"] + c["x1"], name="y") + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + """ + + @on_state() + def experiment_runner(conditions: pd.DataFrame, **kwargs): + x = conditions + y = f(x, **kwargs) + experiment_data = pd.DataFrame.merge(x, y, left_index=True, right_index=True) + return Delta(experiment_data=experiment_data) + + return experiment_runner + + +def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: + """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` + returns both $x$ and $y$ values in a complete dataframe. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state.standard import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame, allowing the explicit inclusion of + metadata like column names. + >>> def x_to_xy_fn(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=lambda df: 2 * df.x + 1) + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_xy_fn(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=c.x0 + c.x1) + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + + """ + + @on_state() + def experiment_runner(conditions: pd.DataFrame, **kwargs): + x = conditions + experiment_data = f(x, **kwargs) + return Delta(experiment_data=experiment_data) + + return experiment_runner diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py deleted file mode 100644 index 11140ac5..00000000 --- a/src/autora/state/wrapper.py +++ /dev/null @@ -1,155 +0,0 @@ -"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)` -so that $n$ processes $f_i$ on states $S$ can be represented as -$$f_n(...(f_1(f_0(S))))$$ - -These are special cases of the [autora.state.delta.on_state][] function. -""" -from __future__ import annotations - -from typing import Callable, TypeVar - -import pandas as pd -from sklearn.base import BaseEstimator - -from autora.state.delta import Delta, State, on_state -from autora.variable import VariableCollection - -S = TypeVar("S") -X = TypeVar("X") -Y = TypeVar("Y") -XY = TypeVar("XY") -Executor = Callable[[State], State] - - -def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: - """ - Convert a scikit-learn compatible estimator into a function on a `State` object. - - Supports passing additional `**kwargs` which are used to update the estimator's params - before fitting. - - Examples: - Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. - >>> from sklearn.linear_model import LinearRegression - >>> state_fn = state_fn_from_estimator(LinearRegression()) - - Define the state on which to operate (here an instance of the `StandardState`): - >>> from autora.state.bundled import StandardState - >>> from autora.variable import Variable, VariableCollection - >>> import pandas as pd - >>> s = StandardState( - ... variables=VariableCollection( - ... independent_variables=[Variable("x")], - ... dependent_variables=[Variable("y")]), - ... experiment_data=pd.DataFrame({"x": [1,2,3], "y":[3,6,9]}) - ... ) - - Run the function, which fits the model and adds the result to the `StandardState` - >>> state_fn(s).model.coef_ - array([[3.]]) - - """ - - @on_state() - def theorist( - experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs - ): - ivs = [v.name for v in variables.independent_variables] - dvs = [v.name for v in variables.dependent_variables] - X, y = experiment_data[ivs], experiment_data[dvs] - new_model = estimator.set_params(**kwargs).fit(X, y) - return Delta(model=new_model) - - return theorist - - -def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: - """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ - values, with inputs and outputs as a DataFrame or Series with correct column names. - - Examples: - The conditions are some x-values in a StandardState object: - >>> from autora.state.bundled import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) - - The function can be defined on a DataFrame (allowing the explicit inclusion of - metadata like column names). - >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(2 * c["x"] + 1, name="y") - ... return result - - We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data - x y - 0 1 3 - 1 2 5 - 2 3 7 - - We can also define functions of several variables: - >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(c["x0"] + c["x1"], name="y") - ... return result - - With the relevant variables as conditions: - >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data - x0 x1 y - 0 1 10 11 - 1 2 20 22 - 2 3 30 33 - """ - - @on_state() - def experiment_runner(conditions: pd.DataFrame, **kwargs): - x = conditions - y = f(x, **kwargs) - experiment_data = pd.DataFrame.merge(x, y, left_index=True, right_index=True) - return Delta(experiment_data=experiment_data) - - return experiment_runner - - -def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: - """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` - returns both $x$ and $y$ values in a complete dataframe. - - Examples: - The conditions are some x-values in a StandardState object: - >>> from autora.state.bundled import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) - - The function can be defined on a DataFrame, allowing the explicit inclusion of - metadata like column names. - >>> def x_to_xy_fn(c: pd.DataFrame) -> pd.Series: - ... result = c.assign(y=lambda df: 2 * df.x + 1) - ... return result - - We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data - x y - 0 1 3 - 1 2 5 - 2 3 7 - - We can also define functions of several variables: - >>> def xs_to_xy_fn(c: pd.DataFrame) -> pd.Series: - ... result = c.assign(y=c.x0 + c.x1) - ... return result - - With the relevant variables as conditions: - >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data - x0 x1 y - 0 1 10 11 - 1 2 20 22 - 2 3 30 33 - - """ - - @on_state() - def experiment_runner(conditions: pd.DataFrame, **kwargs): - x = conditions - experiment_data = f(x, **kwargs) - return Delta(experiment_data=experiment_data) - - return experiment_runner From 9bdea5b9f697f7eda87afc3ecb7bbeb84559591e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:39:23 +0200 Subject: [PATCH 109/121] chore: remove unused TypeVar --- src/autora/state/standard.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/autora/state/standard.py b/src/autora/state/standard.py index ff46ba96..4f0f2bb3 100644 --- a/src/autora/state/standard.py +++ b/src/autora/state/standard.py @@ -15,7 +15,6 @@ from autora.state.delta import Delta, State, on_state from autora.variable import VariableCollection -S = TypeVar("S") X = TypeVar("X") Y = TypeVar("Y") XY = TypeVar("XY") From 0665a0705b7181f283406dee5fe0a2e1996bdf41 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:41:16 +0200 Subject: [PATCH 110/121] chore: update pre-commit hooks --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e3649715..600c860e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/ambv/black - rev: 22.12.0 + rev: 23.7.0 hooks: - id: black - repo: https://github.com/pycqa/isort @@ -11,7 +11,7 @@ repos: - "--filter-files" - "--project=autora" - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 + rev: 6.1.0 hooks: - id: flake8 args: @@ -19,7 +19,7 @@ repos: - "--extend-ignore=E203" - "--per-file-ignores=__init__.py:F401" - repo: https://github.com/pre-commit/mirrors-mypy - rev: "v0.991" + rev: "v1.5.1" hooks: - id: mypy additional_dependencies: [types-requests,scipy,pytest] From 417f760bb7497bb1ac3cd4ac110c806f762da65c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 13:50:50 +0200 Subject: [PATCH 111/121] refactor: rename Executor to StateFunction --- src/autora/state/standard.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/autora/state/standard.py b/src/autora/state/standard.py index 4f0f2bb3..ced84446 100644 --- a/src/autora/state/standard.py +++ b/src/autora/state/standard.py @@ -18,7 +18,7 @@ X = TypeVar("X") Y = TypeVar("Y") XY = TypeVar("XY") -Executor = Callable[[State], State] +StateFunction = Callable[[State], State] @dataclass(frozen=True) @@ -188,7 +188,7 @@ def model(self): return None -def state_fn_from_estimator(estimator: BaseEstimator) -> Executor: +def state_fn_from_estimator(estimator: BaseEstimator) -> StateFunction: """ Convert a scikit-learn compatible estimator into a function on a `State` object. @@ -230,7 +230,7 @@ def theorist( return theorist -def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> Executor: +def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> StateFunction: """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ values, with inputs and outputs as a DataFrame or Series with correct column names. @@ -276,7 +276,7 @@ def experiment_runner(conditions: pd.DataFrame, **kwargs): return experiment_runner -def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> Executor: +def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` returns both $x$ and $y$ values in a complete dataframe. From e16fcd4eb7fa6526392291e93aa12db91b467e1e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 14:06:04 +0200 Subject: [PATCH 112/121] chore: remove extra newlines --- src/autora/state/delta.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py index 0e4e7322..daf43ce2 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state/delta.py @@ -241,7 +241,6 @@ def __add__(self, other: Union[Delta, Mapping]): updates = dict() other_fields_unused = list(other.keys()) for self_field in fields(self): - other_value, key = _get_value(self_field, other) if other_value is None: continue @@ -799,7 +798,6 @@ def outputs_to_delta(*output: str): """ def decorator(f): - if len(output) == 0: raise ValueError("`output` names must be specified.") From c91d2ebfc3733fcb2a1b200d57c950c3b551574c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 22 Aug 2023 14:06:09 +0200 Subject: [PATCH 113/121] chore: remove extra newlines --- tests/test_experimentalist_pipeline.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/test_experimentalist_pipeline.py b/tests/test_experimentalist_pipeline.py index a02bfa85..08daf529 100644 --- a/tests/test_experimentalist_pipeline.py +++ b/tests/test_experimentalist_pipeline.py @@ -279,7 +279,6 @@ def test_params_parser_one_level(): def test_params_parser_recurse_one(): - params = { "filter_pipeline__step1__n_samples": 100, } @@ -309,7 +308,6 @@ def test_params_parser_recurse_one_n_levels_alternative_divider(): def test_params_parser_recurse(): - params = { "pool__ivs": "%%independent_variables%%", "filter_pipeline__step1__n_samples": 100, From 7c5783e16245ba94ea7e23d093628b363fdbb563 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 23 Aug 2023 17:04:36 +0200 Subject: [PATCH 114/121] refactor: move all standard-state code into a single state.py file --- ...Introduction to Functions and States.ipynb | 6 +- ...ombining Experimentalists with State.ipynb | 2 +- ...Workflows using Functions and States.ipynb | 2 +- src/autora/experimentalist/grid.py | 2 +- src/autora/experimentalist/random_.py | 2 +- src/autora/state/history.py | 722 ------------------ src/autora/state/param.py | 143 ---- src/autora/state/protocol.py | 158 ---- src/autora/state/snapshot.py | 201 ----- src/autora/state/standard.py | 322 -------- 10 files changed, 7 insertions(+), 1553 deletions(-) delete mode 100644 src/autora/state/history.py delete mode 100644 src/autora/state/param.py delete mode 100644 src/autora/state/protocol.py delete mode 100644 src/autora/state/snapshot.py delete mode 100644 src/autora/state/standard.py diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index fb1bda44..6c8a20da 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -102,7 +102,7 @@ ], "source": [ "from autora.experimentalist.random_ import random_pool\n", - "from autora.state.delta import on_state\n", + "from autora.state import on_state\n", "\n", "experimentalist = on_state(function=random_pool, output=[\"conditions\"])\n", "s_1 = experimentalist(s_0, random_state=42)\n", @@ -144,7 +144,7 @@ } ], "source": [ - "from autora.state.delta import on_state\n", + "from autora.state import on_state\n", "import numpy as np\n", "import pandas as pd\n", "\n", @@ -197,7 +197,7 @@ } ], "source": [ - "from autora.state.delta import inputs_from_state, outputs_to_delta\n", + "from autora.state import inputs_from_state, outputs_to_delta\n", "\n", "\n", "@inputs_from_state\n", diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index de3dc19b..2ae12060 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -994,7 +994,7 @@ } ], "source": [ - "from autora.state.delta import Delta, on_state, State, inputs_from_state\n", + "from autora.state import Delta, on_state, State, inputs_from_state\n", "from autora.state.bundled import StandardState\n", "\n", "s = StandardState() + Delta(conditions=downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even]))\n", diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 7550a08c..4a5e7850 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -121,7 +121,7 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.state.delta import on_state, Delta\n", + "from autora.state import on_state, Delta\n", "\n", "def ground_truth(x: pd.Series, c=(432, -144, -3, 1)):\n", " return c[0] + c[1] * x + c[2] * x**2 + c[3] * x**3\n", diff --git a/src/autora/experimentalist/grid.py b/src/autora/experimentalist/grid.py index afe96522..f605efb5 100644 --- a/src/autora/experimentalist/grid.py +++ b/src/autora/experimentalist/grid.py @@ -17,7 +17,7 @@ def pool(variables: VariableCollection) -> pd.DataFrame: Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field Examples: - >>> from autora.state.delta import State + >>> from autora.state import State >>> from autora.variable import VariableCollection, Variable >>> from dataclasses import dataclass, field >>> import pandas as pd diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random_.py index 118d5450..b4101adf 100644 --- a/src/autora/experimentalist/random_.py +++ b/src/autora/experimentalist/random_.py @@ -24,7 +24,7 @@ def pool( Returns: the generated conditions as a dataframe Examples: - >>> from autora.state.delta import State + >>> from autora.state import State >>> from autora.variable import VariableCollection, Variable >>> from dataclasses import dataclass, field >>> import pandas as pd diff --git a/src/autora/state/history.py b/src/autora/state/history.py deleted file mode 100644 index fbb33944..00000000 --- a/src/autora/state/history.py +++ /dev/null @@ -1,722 +0,0 @@ -""" Classes for storing and passing a cycle's state as an immutable history. """ -from __future__ import annotations - -from dataclasses import dataclass -from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Union - -from numpy.typing import ArrayLike -from sklearn.base import BaseEstimator - -from autora.state.delta import Delta -from autora.state.protocol import ( - ResultKind, - SupportsControllerStateHistory, - SupportsDataKind, -) -from autora.state.snapshot import Snapshot -from autora.variable import VariableCollection - - -class History(SupportsControllerStateHistory): - """ - An immutable object for tracking the state and history of an AER cycle. - """ - - def __init__( - self, - variables: Optional[VariableCollection] = None, - params: Optional[Dict] = None, - conditions: Optional[List[ArrayLike]] = None, - observations: Optional[List[ArrayLike]] = None, - models: Optional[List[BaseEstimator]] = None, - history: Optional[Sequence[Result]] = None, - ): - """ - - Args: - variables: a single datum to be marked as "variables" - params: a single datum to be marked as "params" - conditions: an iterable of data, each to be marked as "conditions" - observations: an iterable of data, each to be marked as "observations" - models: an iterable of data, each to be marked as "models" - history: an iterable of Result objects to be used as the initial history. - - Examples: - Empty input leads to an empty state: - >>> History() - History([]) - - ... or with values for any or all of the parameters: - >>> from autora.variable import VariableCollection - >>> History(variables=VariableCollection()) # doctest: +ELLIPSIS - History([Result(data=VariableCollection(...), kind=ResultKind.VARIABLES)]) - - >>> History(params={"some": "params"}) - History([Result(data={'some': 'params'}, kind=ResultKind.PARAMS)]) - - >>> History(conditions=["a condition"]) - History([Result(data='a condition', kind=ResultKind.CONDITION)]) - - >>> History(observations=["an observation"]) - History([Result(data='an observation', kind=ResultKind.OBSERVATION)]) - - >>> from sklearn.linear_model import LinearRegression - >>> History(models=[LinearRegression()]) - History([Result(data=LinearRegression(), kind=ResultKind.MODEL)]) - - Parameters passed to the constructor are included in the history in the following order: - `history`, `variables`, `params`, `conditions`, `observations`, `models` - >>> History(models=['m1', 'm2'], conditions=['c1', 'c2'], - ... observations=['o1', 'o2'], params={'a': 'param'}, - ... variables=VariableCollection(), - ... history=[Result("from history", ResultKind.VARIABLES)] - ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - History([Result(data='from history', kind=ResultKind.VARIABLES), - Result(data=VariableCollection(...), kind=ResultKind.VARIABLES), - Result(data={'a': 'param'}, kind=ResultKind.PARAMS), - Result(data='c1', kind=ResultKind.CONDITION), - Result(data='c2', kind=ResultKind.CONDITION), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='o2', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)]) - """ - self.data: List - - if history is not None: - self.data = list(history) - else: - self.data = [] - - self.data += _init_result_list( - variables=variables, - params=params, - conditions=conditions, - observations=observations, - models=models, - ) - - def update( - self, - variables=None, - params=None, - conditions=None, - observations=None, - models=None, - history=None, - ): - """ - Create a new object with updated values. - - Examples: - The initial object is empty: - >>> h0 = History() - >>> h0 - History([]) - - We can update the variables using the `.update` method: - >>> from autora.variable import VariableCollection - >>> h1 = h0.update(variables=VariableCollection()) - >>> h1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - History([Result(data=VariableCollection(...), kind=ResultKind.VARIABLES)]) - - ... the original object is unchanged: - >>> h0 - History([]) - - We can update the variables again: - >>> h2 = h1.update(variables=VariableCollection(["some IV"])) - >>> h2._by_kind # doctest: +ELLIPSIS - Snapshot(variables=VariableCollection(independent_variables=['some IV'],...), ...) - - ... and we see that there is only ever one variables object returned. - - Params is treated the same way as variables: - >>> hp = h0.update(params={'first': 'params'}) - >>> hp - History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) - - ... where only the most recent "params" object is returned from the `.params` property. - >>> hp = hp.update(params={'second': 'params'}) - >>> hp.params - {'second': 'params'} - - ... however, the full history of the params objects remains available, if needed: - >>> hp # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), - Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) - - When we update the conditions, observations or models, a new entry is added to the - history: - >>> h3 = h0.update(models=["1st model"]) - >>> h3 # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st model', kind=ResultKind.MODEL)]) - - ... so we can see the history of all the models, for instance. - >>> h3 = h3.update(models=["2nd model"]) # doctest: +NORMALIZE_WHITESPACE - >>> h3 # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st model', kind=ResultKind.MODEL), - Result(data='2nd model', kind=ResultKind.MODEL)]) - - ... and the full history of models is available using the `.models` parameter: - >>> h3.models - ['1st model', '2nd model'] - - The same for the observations: - >>> h4 = h0.update(observations=["1st observation"]) - >>> h4 - History([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) - - >>> h4.update(observations=["2nd observation"] - ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - History([Result(data='1st observation', kind=ResultKind.OBSERVATION), - Result(data='2nd observation', kind=ResultKind.OBSERVATION)]) - - - The same for the conditions: - >>> h5 = h0.update(conditions=["1st condition"]) - >>> h5 - History([Result(data='1st condition', kind=ResultKind.CONDITION)]) - - >>> h5.update(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st condition', kind=ResultKind.CONDITION), - Result(data='2nd condition', kind=ResultKind.CONDITION)]) - - You can also update with multiple conditions, observations and models: - >>> h0.update(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='c1', kind=ResultKind.CONDITION), - Result(data='c2', kind=ResultKind.CONDITION)]) - - >>> h0.update(models=['m1', 'm2'], variables={'m': 1} - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 1}, kind=ResultKind.VARIABLES), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)]) - - >>> h0.update(models=['m1'], observations=['o1'], variables={'m': 1} - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 1}, kind=ResultKind.VARIABLES), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL)]) - - We can also update with a complete history: - >>> History().update(history=[Result(data={'m': 2}, kind=ResultKind.VARIABLES), - ... Result(data='o1', kind=ResultKind.OBSERVATION), - ... Result(data='m1', kind=ResultKind.MODEL)], - ... conditions=['c1'] - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 2}, kind=ResultKind.VARIABLES), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='c1', kind=ResultKind.CONDITION)]) - - """ - - if history is not None: - history_extension = history - else: - history_extension = [] - - history_extension += _init_result_list( - variables=variables, - params=params, - conditions=conditions, - observations=observations, - models=models, - ) - new_full_history = self.data + history_extension - - return History(history=new_full_history) - - def __add__(self, other: Delta): - """The initial object is empty: - >>> h0 = History() - >>> h0 - History([]) - - We can update the variables using the `.update` method: - >>> from autora.variable import VariableCollection - >>> h1 = h0 + Delta(variables=VariableCollection()) - >>> h1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - History([Result(data=VariableCollection(...), kind=ResultKind.VARIABLES)]) - - ... the original object is unchanged: - >>> h0 - History([]) - - We can update the variables again: - >>> h2 = h1 + Delta(variables=VariableCollection(["some IV"])) - >>> h2._by_kind # doctest: +ELLIPSIS - Snapshot(variables=VariableCollection(independent_variables=['some IV'],...), ...) - - ... and we see that there is only ever one variables object returned. - - Params is treated the same way as variables: - >>> hp = h0 + Delta(params={'first': 'params'}) - >>> hp - History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) - - ... where only the most recent "params" object is returned from the `.params` property. - >>> hp = hp + Delta(params={'second': 'params'}) - >>> hp.params - {'second': 'params'} - - ... however, the full history of the params objects remains available, if needed: - >>> hp # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), - Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) - - When we update the conditions, observations or models, a new entry is added to the - history: - >>> h3 = h0 + Delta(models=["1st model"]) - >>> h3 # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st model', kind=ResultKind.MODEL)]) - - ... so we can see the history of all the models, for instance. - >>> h3 = h3 + Delta(models=["2nd model"]) # doctest: +NORMALIZE_WHITESPACE - >>> h3 # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st model', kind=ResultKind.MODEL), - Result(data='2nd model', kind=ResultKind.MODEL)]) - - ... and the full history of models is available using the `.models` parameter: - >>> h3.models - ['1st model', '2nd model'] - - The same for the observations: - >>> h4 = h0 + Delta(observations=["1st observation"]) - >>> h4 - History([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) - - >>> h4 + Delta(observations=["2nd observation"] - ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - History([Result(data='1st observation', kind=ResultKind.OBSERVATION), - Result(data='2nd observation', kind=ResultKind.OBSERVATION)]) - - - The same for the conditions: - >>> h5 = h0 + Delta(conditions=["1st condition"]) - >>> h5 - History([Result(data='1st condition', kind=ResultKind.CONDITION)]) - - >>> h5 + Delta(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='1st condition', kind=ResultKind.CONDITION), - Result(data='2nd condition', kind=ResultKind.CONDITION)]) - - You can also update with multiple conditions, observations and models: - >>> h0 + Delta(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='c1', kind=ResultKind.CONDITION), - Result(data='c2', kind=ResultKind.CONDITION)]) - - >>> h0 + Delta(models=['m1', 'm2'], variables={'m': 1} - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 1}, kind=ResultKind.VARIABLES), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)]) - - >>> h0 + Delta(models=['m1'], observations=['o1'], variables={'m': 1} - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 1}, kind=ResultKind.VARIABLES), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL)]) - - We can also update with a complete history: - >>> History() + Delta(history=[Result(data={'m': 2}, kind=ResultKind.VARIABLES), - ... Result(data='o1', kind=ResultKind.OBSERVATION), - ... Result(data='m1', kind=ResultKind.MODEL)], - ... conditions=['c1'] - ... ) # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'m': 2}, kind=ResultKind.VARIABLES), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='c1', kind=ResultKind.CONDITION)]) - """ - return self.update(**other) - - def __repr__(self): - return f"{type(self).__name__}({self.history})" - - @property - def _by_kind(self): - return _history_to_kind(self.data) - - @property - def variables(self) -> VariableCollection: - """ - - Examples: - The initial object is empty: - >>> h = History() - - ... and returns an emtpy variables object - >>> h.variables - VariableCollection(independent_variables=[], dependent_variables=[], covariates=[]) - - We can update the variables using the `.update` method: - >>> from autora.variable import VariableCollection - >>> h = h.update(variables=VariableCollection(independent_variables=['some IV'])) - >>> h.variables # doctest: +ELLIPSIS - VariableCollection(independent_variables=['some IV'], ...) - - We can update the variables again: - >>> h = h.update(variables=VariableCollection(["some other IV"])) - >>> h.variables # doctest: +ELLIPSIS - VariableCollection(independent_variables=['some other IV'], ...) - - ... and we see that there is only ever one variables object returned.""" - return self._by_kind.variables - - @property - def params(self) -> Dict: - """ - - Returns: - - Examples: - Params is treated the same way as variables: - >>> h = History() - >>> h = h.update(params={'first': 'params'}) - >>> h.params - {'first': 'params'} - - ... where only the most recent "params" object is returned from the `.params` property. - >>> h = h.update(params={'second': 'params'}) - >>> h.params - {'second': 'params'} - - ... however, the full history of the params objects remains available, if needed: - >>> h # doctest: +NORMALIZE_WHITESPACE - History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), - Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) - """ - return self._by_kind.params - - @property - def conditions(self) -> List[ArrayLike]: - """ - Returns: - - Examples: - View the sequence of models with one conditions: - >>> h = History(conditions=[(1,2,3,)]) - >>> h.conditions - [(1, 2, 3)] - - ... or more conditions: - >>> h = h.update(conditions=[(4,5,6),(7,8,9)]) # doctest: +NORMALIZE_WHITESPACE - >>> h.conditions - [(1, 2, 3), (4, 5, 6), (7, 8, 9)] - - """ - return self._by_kind.conditions - - @property - def observations(self) -> List[ArrayLike]: - """ - - Returns: - - Examples: - The sequence of all observations is returned - >>> h = History(observations=["1st observation"]) - >>> h.observations - ['1st observation'] - - >>> h = h.update(observations=["2nd observation"]) - >>> h.observations # doctest: +ELLIPSIS - ['1st observation', '2nd observation'] - - """ - return self._by_kind.observations - - @property - def models(self) -> List[BaseEstimator]: - """ - - Returns: - - Examples: - View the sequence of models with one model: - >>> s = History(models=["1st model"]) - >>> s.models # doctest: +NORMALIZE_WHITESPACE - ['1st model'] - - ... or more models: - >>> s = s.update(models=["2nd model"]) # doctest: +NORMALIZE_WHITESPACE - >>> s.models - ['1st model', '2nd model'] - - """ - return self._by_kind.models - - @property - def history(self) -> List[Result]: - """ - - Examples: - We initialze some history: - >>> h = History(models=['m1', 'm2'], conditions=['c1', 'c2'], - ... observations=['o1', 'o2'], params={'a': 'param'}, - ... variables=VariableCollection(), - ... history=[Result("from history", ResultKind.VARIABLES)]) - - Parameters passed to the constructor are included in the history in the following order: - `history`, `variables`, `params`, `conditions`, `observations`, `models` - - >>> h.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - [Result(data='from history', kind=ResultKind.VARIABLES), - Result(data=VariableCollection(...), kind=ResultKind.VARIABLES), - Result(data={'a': 'param'}, kind=ResultKind.PARAMS), - Result(data='c1', kind=ResultKind.CONDITION), - Result(data='c2', kind=ResultKind.CONDITION), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='o2', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)] - - If we add a new value, like the params object, the updated value is added to the - end of the history: - >>> h = h.update(params={'new': 'param'}) - >>> h.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - [..., Result(data={'new': 'param'}, kind=ResultKind.PARAMS)] - - """ - return self.data - - def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]] = None) -> History: - """ - Return a copy of the object with only data belonging to the specified kinds. - - Examples: - >>> h = History(models=['m1', 'm2'], conditions=['c1', 'c2'], - ... observations=['o1', 'o2'], params={'a': 'param'}, - ... variables=VariableCollection(), - ... history=[Result("from history", ResultKind.VARIABLES)]) - - >>> h.filter_by(kind={"MODEL"}) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)]) - - >>> h.filter_by(kind={ResultKind.OBSERVATION}) # doctest: +NORMALIZE_WHITESPACE - History([Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='o2', kind=ResultKind.OBSERVATION)]) - - If we don't specify any filter criteria, we get the full history back: - >>> h.filter_by() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - History([Result(data='from history', kind=ResultKind.VARIABLES), - Result(data=VariableCollection(...), kind=ResultKind.VARIABLES), - Result(data={'a': 'param'}, kind=ResultKind.PARAMS), - Result(data='c1', kind=ResultKind.CONDITION), - Result(data='c2', kind=ResultKind.CONDITION), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='o2', kind=ResultKind.OBSERVATION), - Result(data='m1', kind=ResultKind.MODEL), - Result(data='m2', kind=ResultKind.MODEL)]) - - """ - if kind is None: - return self - else: - kind_ = {ResultKind(s) for s in kind} - filtered_history = _filter_history(self.data, kind_) - new_object = History(history=filtered_history) - return new_object - - -@dataclass(frozen=True) -class Result(SupportsDataKind): - """ - Container class for data and variables. - - Examples: - >>> Result() - Result(data=None, kind=None) - - >>> Result("a") - Result(data='a', kind=None) - - >>> Result(None, "MODEL") - Result(data=None, kind=ResultKind.MODEL) - - >>> Result(data="b") - Result(data='b', kind=None) - - >>> Result("c", "OBSERVATION") - Result(data='c', kind=ResultKind.OBSERVATION) - """ - - data: Optional[Any] = None - kind: Optional[ResultKind] = None - - def __post_init__(self): - if isinstance(self.kind, str): - object.__setattr__(self, "kind", ResultKind(self.kind)) - - -def _init_result_list( - variables: Optional[VariableCollection] = None, - params: Optional[Dict] = None, - conditions: Optional[Iterable[ArrayLike]] = None, - observations: Optional[Iterable[ArrayLike]] = None, - models: Optional[Iterable[BaseEstimator]] = None, -) -> List[Result]: - """ - Initialize a list of Result objects - - Returns: - - Args: - variables: a single datum to be marked as "variables" - params: a single datum to be marked as "params" - conditions: an iterable of data, each to be marked as "conditions" - observations: an iterable of data, each to be marked as "observations" - models: an iterable of data, each to be marked as "models" - - Examples: - Empty input leads to an empty state: - >>> _init_result_list() - [] - - ... or with values for any or all of the parameters: - >>> from autora.variable import VariableCollection - >>> _init_result_list(variables=VariableCollection()) # doctest: +ELLIPSIS - [Result(data=VariableCollection(...), kind=ResultKind.VARIABLES)] - - >>> _init_result_list(params={"some": "params"}) - [Result(data={'some': 'params'}, kind=ResultKind.PARAMS)] - - >>> _init_result_list(conditions=["a condition"]) - [Result(data='a condition', kind=ResultKind.CONDITION)] - - >>> _init_result_list(observations=["an observation"]) - [Result(data='an observation', kind=ResultKind.OBSERVATION)] - - >>> from sklearn.linear_model import LinearRegression - >>> _init_result_list(models=[LinearRegression()]) - [Result(data=LinearRegression(), kind=ResultKind.MODEL)] - - The input arguments are added to the data in the order `variables`, - `params`, `conditions`, `observations`, `models`: - >>> _init_result_list(variables=VariableCollection(), - ... params={"some": "params"}, - ... conditions=["a condition"], - ... observations=["an observation", "another observation"], - ... models=[LinearRegression()], - ... ) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - [Result(data=VariableCollection(...), kind=ResultKind.VARIABLES), - Result(data={'some': 'params'}, kind=ResultKind.PARAMS), - Result(data='a condition', kind=ResultKind.CONDITION), - Result(data='an observation', kind=ResultKind.OBSERVATION), - Result(data='another observation', kind=ResultKind.OBSERVATION), - Result(data=LinearRegression(), kind=ResultKind.MODEL)] - - """ - data = [] - - if variables is not None: - data.append(Result(variables, ResultKind.VARIABLES)) - - if params is not None: - data.append(Result(params, ResultKind.PARAMS)) - - for seq, kind in [ - (conditions, ResultKind.CONDITION), - (observations, ResultKind.OBSERVATION), - (models, ResultKind.MODEL), - ]: - if seq is not None: - for i in seq: - data.append(Result(i, kind=kind)) - - return data - - -def _history_to_kind(history: Sequence[Result]) -> Snapshot: - """ - Convert a sequence of results into a Snapshot instance: - - Examples: - History might be empty - >>> history_ = [] - >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - Snapshot(variables=VariableCollection(...), params={}, - conditions=[], observations=[], models=[]) - - ... or with values for any or all of the parameters: - >>> history_ = _init_result_list(params={"some": "params"}) - >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - Snapshot(..., params={'some': 'params'}, ...) - - >>> history_ += _init_result_list(conditions=["a condition"]) - >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - Snapshot(..., params={'some': 'params'}, conditions=['a condition'], ...) - - >>> _history_to_kind(history_).params - {'some': 'params'} - - >>> history_ += _init_result_list(observations=["an observation"]) - >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - Snapshot(..., params={'some': 'params'}, conditions=['a condition'], - observations=['an observation'], ...) - - >>> from sklearn.linear_model import LinearRegression - >>> history_ = [Result(LinearRegression(), kind=ResultKind.MODEL)] - >>> _history_to_kind(history_) # doctest: +ELLIPSIS - Snapshot(..., models=[LinearRegression()]) - - >>> from autora.variable import VariableCollection, IV - >>> variables = VariableCollection(independent_variables=[IV(name="example")]) - >>> history_ = [Result(variables, kind=ResultKind.VARIABLES)] - >>> _history_to_kind(history_) # doctest: +ELLIPSIS - Snapshot(variables=VariableCollection(independent_variables=[IV(name='example', ... - - >>> history_ = [Result({'some': 'params'}, kind=ResultKind.PARAMS)] - >>> _history_to_kind(history_) # doctest: +ELLIPSIS - Snapshot(..., params={'some': 'params'}, ...) - - """ - namespace = Snapshot( - variables=_get_last_data_with_default( - history, kind={ResultKind.VARIABLES}, default=VariableCollection() - ), - params=_get_last_data_with_default( - history, kind={ResultKind.PARAMS}, default={} - ), - observations=_list_data( - _filter_history(history, kind={ResultKind.OBSERVATION}) - ), - models=_list_data(_filter_history(history, kind={ResultKind.MODEL})), - conditions=_list_data(_filter_history(history, kind={ResultKind.CONDITION})), - ) - return namespace - - -def _list_data(data: Sequence[SupportsDataKind]): - """ - Extract the `.data` attribute of each item in a sequence, and return as a list. - - Examples: - >>> _list_data([]) - [] - - >>> _list_data([Result("a"), Result("b")]) - ['a', 'b'] - """ - return list(r.data for r in data) - - -def _filter_history(data: Iterable[SupportsDataKind], kind: Set[ResultKind]): - return filter(lambda r: r.kind in kind, data) - - -def _get_last(data: Sequence[SupportsDataKind], kind: Set[ResultKind]): - results_new_to_old = reversed(data) - last_of_kind = next(_filter_history(results_new_to_old, kind=kind)) - return last_of_kind - - -def _get_last_data_with_default(data: Sequence[SupportsDataKind], kind, default): - try: - result = _get_last(data, kind).data - except StopIteration: - result = default - return result diff --git a/src/autora/state/param.py b/src/autora/state/param.py deleted file mode 100644 index 1fca3cfc..00000000 --- a/src/autora/state/param.py +++ /dev/null @@ -1,143 +0,0 @@ -""" Functions for handling cycle-state-dependent parameters. """ -from __future__ import annotations - -import copy -import logging -from typing import Dict, Mapping - -import numpy as np - -from autora.state.protocol import SupportsControllerState -from autora.utils.deprecation import deprecate as deprecate -from autora.utils.dictionary import LazyDict - -_logger = logging.getLogger(__name__) - - -def _get_state_dependent_properties(state: SupportsControllerState): - """ - Examples: - Even with an empty data object, we can initialize the dictionary, - >>> from autora.state.snapshot import Snapshot - >>> state_dependent_properties = _get_state_dependent_properties(Snapshot()) - - ... but it will raise an exception if a value isn't yet available when we try to use it - >>> state_dependent_properties["%models[-1]%"] # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - IndexError: list index out of range - - Nevertheless, we can iterate through its keys no problem: - >>> [key for key in state_dependent_properties.keys()] # doctest: +NORMALIZE_WHITESPACE - ['%observations.ivs[-1]%', '%observations.dvs[-1]%', '%observations.ivs%', - '%observations.dvs%', '%experiment_data.conditions[-1]%', - '%experiment_data.observations[-1]%', '%experiment_data.conditions%', - '%experiment_data.observations%', '%models[-1]%', '%models%'] - - """ - - n_ivs = len(state.variables.independent_variables) - n_dvs = len(state.variables.dependent_variables) - state_dependent_property_dict = LazyDict( - { - "%observations.ivs[-1]%": deprecate( - lambda: np.array(state.observations[-1])[:, 0:n_ivs], - "%observations.ivs[-1]% is deprecated, " - "use %experiment_data.conditions[-1]% instead.", - ), - "%observations.dvs[-1]%": deprecate( - lambda: np.array(state.observations[-1])[:, n_ivs:], - "%observations.dvs[-1]% is deprecated, " - "use %experiment_data.observations[-1]% instead.", - ), - "%observations.ivs%": deprecate( - lambda: np.row_stack( - [np.empty([0, n_ivs + n_dvs])] + list(state.observations) - )[:, 0:n_ivs], - "%observations.ivs% is deprecated, use %experiment_data.conditions% instead.", - ), - "%observations.dvs%": deprecate( - lambda: np.row_stack(state.observations)[:, n_ivs:], - "%observations.dvs% is deprecated, " - "use %experiment_data.observations% instead", - ), - "%experiment_data.conditions[-1]%": lambda: np.array( - state.observations[-1] - )[:, 0:n_ivs], - "%experiment_data.observations[-1]%": lambda: np.array( - state.observations[-1] - )[:, n_ivs:], - "%experiment_data.conditions%": lambda: np.row_stack( - [np.empty([0, n_ivs + n_dvs])] + list(state.observations) - )[:, 0:n_ivs], - "%experiment_data.observations%": lambda: np.row_stack(state.observations)[ - :, n_ivs: - ], - "%models[-1]%": lambda: state.models[-1], - "%models%": lambda: state.models, - } - ) - return state_dependent_property_dict - - -def _resolve_properties(params: Dict, state_dependent_properties: Mapping): - """ - Resolve state-dependent properties inside a nested dictionary. - - In this context, a state-dependent-property is a string which is meant to be replaced by its - updated, current value before the dictionary is used. A state-dependent property might be - something like "the last theorist available" or "all the experimental results until now". - - Args: - params: a (nested) dictionary of keys and values, where some values might be - "cycle property names" - state_dependent_properties: a dictionary of "property names" and their "real values" - - Returns: a (nested) dictionary where "property names" are replaced by the "real values" - - Examples: - - >>> params_0 = {"key": "%foo%"} - >>> cycle_properties_0 = {"%foo%": 180} - >>> _resolve_properties(params_0,cycle_properties_0) - {'key': 180} - - >>> params_1 = {"key": "%bar%", "nested_dict": {"inner_key": "%foobar%"}} - >>> cycle_properties_1 = {"%bar%": 1, "%foobar%": 2} - >>> _resolve_properties(params_1,cycle_properties_1) - {'key': 1, 'nested_dict': {'inner_key': 2}} - - >>> params_2 = {"key": "baz"} - >>> _resolve_properties(params_2,cycle_properties_1) - {'key': 'baz'} - - """ - params_ = copy.copy(params) - for key, value in params_.items(): - if isinstance(value, dict): - params_[key] = _resolve_properties(value, state_dependent_properties) - elif isinstance(value, str) and ( - value in state_dependent_properties - ): # value is a key in the cycle_properties dictionary - params_[key] = state_dependent_properties[value] - else: - _logger.debug(f"leaving {params=} unchanged") - - return params_ - - -def resolve_state_params(params: Dict, state: SupportsControllerState) -> Dict: - """ - Returns the `params` attribute of the input, with `cycle properties` resolved. - - Examples: - >>> from autora.state.history import History - >>> params = {"experimentalist": {"source": "%models[-1]%"}} - >>> s = History(models=["the first model", "the second model"]) - >>> resolve_state_params(params, s) - {'experimentalist': {'source': 'the second model'}} - - """ - state_dependent_properties = _get_state_dependent_properties(state) - resolved_params = _resolve_properties(params, state_dependent_properties) - return resolved_params diff --git a/src/autora/state/protocol.py b/src/autora/state/protocol.py deleted file mode 100644 index e1a16be7..00000000 --- a/src/autora/state/protocol.py +++ /dev/null @@ -1,158 +0,0 @@ -from enum import Enum -from typing import ( - Any, - Dict, - Generic, - Mapping, - Optional, - Protocol, - Sequence, - Set, - TypeVar, - Union, - runtime_checkable, -) - -from numpy.typing import ArrayLike -from sklearn.base import BaseEstimator - -from autora.variable import VariableCollection - -State = TypeVar("State") - - -class ResultKind(str, Enum): - """ - Kinds of results which can be held in the Result object. - - Examples: - >>> ResultKind.CONDITION is ResultKind.CONDITION - True - - >>> ResultKind.CONDITION is ResultKind.VARIABLES - False - - >>> ResultKind.CONDITION == "CONDITION" - True - - >>> ResultKind.CONDITION == "VARIABLES" - False - - >>> ResultKind.CONDITION in {ResultKind.CONDITION, ResultKind.PARAMS} - True - - >>> ResultKind.VARIABLES in {ResultKind.CONDITION, ResultKind.PARAMS} - False - """ - - CONDITION = "CONDITION" - OBSERVATION = "OBSERVATION" - MODEL = "MODEL" - PARAMS = "PARAMS" - VARIABLES = "VARIABLES" - - def __repr__(self): - cls_name = self.__class__.__name__ - return f"{cls_name}.{self.name}" - - -class SupportsDataKind(Protocol): - """Object with attributes for `data` and `kind`""" - - data: Optional[Any] - kind: Optional[ResultKind] - - -class SupportsStateParamsField(Protocol): - """Support a state with a params property.""" - - params: Dict - - -class SupportsStateParamsProperty(Protocol): - """Support a state with a params property.""" - - @property - def params(self) -> Dict: - ... - - -SupportsStateParams = Union[SupportsStateParamsField, SupportsStateParamsProperty] - - -class SupportsControllerStateFields(Protocol): - """Support representing snapshots of a controller state as mutable fields.""" - - variables: VariableCollection - params: Dict - conditions: Sequence[ArrayLike] - observations: Sequence[ArrayLike] - models: Sequence[BaseEstimator] - - def update(self: State, **kwargs) -> State: - ... - - -class SupportsControllerStateProperties(Protocol): - """Support representing snapshots of a controller state as immutable properties.""" - - def update(self: State, **kwargs) -> State: - ... - - @property - def variables(self) -> VariableCollection: - ... - - @property - def params(self) -> Dict: - ... - - @property - def conditions(self) -> Sequence[ArrayLike]: - ... - - @property - def observations(self) -> Sequence[ArrayLike]: - ... - - @property - def models(self) -> Sequence[BaseEstimator]: - ... - - -SupportsControllerState = Union[ - SupportsControllerStateFields, SupportsControllerStateProperties -] - - -class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): - """Represents controller state as a linear sequence of entries.""" - - def __init__(self, history: Sequence[SupportsDataKind]): - ... - - def filter_by(self: State, kind: Optional[Set[Union[str, ResultKind]]]) -> State: - ... - - @property - def history(self) -> Sequence[SupportsDataKind]: - ... - - -class Executor(Protocol, Generic[State]): - """A Callable which, given some state, and some parameters, returns an updated state.""" - - def __call__(self, __state: State, params: Dict) -> State: - ... - - -ExecutorCollection = Mapping[str, Executor] - - -@runtime_checkable -class SupportsLoadDump(Protocol): - def dump(self, data, file) -> None: - ... - - def load(self, file) -> Any: - ... diff --git a/src/autora/state/snapshot.py b/src/autora/state/snapshot.py deleted file mode 100644 index 21be8171..00000000 --- a/src/autora/state/snapshot.py +++ /dev/null @@ -1,201 +0,0 @@ -""" Classes for storing and passing a cycle's state as an immutable snapshot. """ -from dataclasses import dataclass, field -from typing import Dict, List - -from numpy.typing import ArrayLike -from sklearn.base import BaseEstimator - -from autora.state.delta import Delta -from autora.state.protocol import SupportsControllerStateFields -from autora.variable import VariableCollection - - -@dataclass(frozen=True) -class Snapshot(SupportsControllerStateFields): - """An object passed between and updated by processing steps in the Controller.""" - - # Single values - variables: VariableCollection = field(default_factory=VariableCollection) - params: Dict = field(default_factory=dict) - - # Sequences - conditions: List[ArrayLike] = field(default_factory=list) - observations: List[ArrayLike] = field(default_factory=list) - models: List[BaseEstimator] = field(default_factory=list) - - def update( - self, - variables=None, - params=None, - conditions=None, - observations=None, - models=None, - ): - """ - Create a new object with updated values. - - Examples: - The initial object is empty: - >>> s0 = Snapshot() - >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(variables=VariableCollection(...), params={}, conditions=[], - observations=[], models=[]) - - We can update the params using the `.update` method: - >>> s0.update(params={'first': 'params'}) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., params={'first': 'params'}, ...) - - ... but the original object is unchanged: - >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., params={}, ...) - - For params, only one object is returned from the respective property: - >>> s0.update(params={'first': 'params'}).update(params={'second': 'params'}).params - {'second': 'params'} - - ... and the same applies to variables: - >>> from autora.variable import VariableCollection, IV - >>> (s0.update(variables=VariableCollection([IV("1st IV")])) - ... .update(variables=VariableCollection([IV("2nd IV")]))).variables - VariableCollection(independent_variables=[IV(name='2nd IV',...)], ...) - - When we update the conditions, observations or models, the respective list is extended: - >>> s3 = s0.update(models=["1st model"]) - >>> s3 - Snapshot(..., models=['1st model']) - - ... so we can see the history of all the models, for instance. - >>> s3.update(models=["2nd model"]) - Snapshot(..., models=['1st model', '2nd model']) - - The same applies to observations: - >>> s4 = s0.update(observations=["1st observation"]) - >>> s4 - Snapshot(..., observations=['1st observation'], ...) - - >>> s4.update(observations=["2nd observation"]) # doctest: +ELLIPSIS - Snapshot(..., observations=['1st observation', '2nd observation'], ...) - - - The same applies to conditions: - >>> s5 = s0.update(conditions=["1st condition"]) - >>> s5 - Snapshot(..., conditions=['1st condition'], ...) - - >>> s5.update(conditions=["2nd condition"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., conditions=['1st condition', '2nd condition'], ...) - - You can also update with multiple conditions, observations and models: - >>> s0.update(conditions=['c1', 'c2']) - Snapshot(..., conditions=['c1', 'c2'], ...) - - >>> s0.update(models=['m1', 'm2'], variables={'m': 1}) - Snapshot(variables={'m': 1}, ..., models=['m1', 'm2']) - - >>> s0.update(models=['m1'], observations=['o1'], variables={'m': 1}) - Snapshot(variables={'m': 1}, ..., observations=['o1'], models=['m1']) - - - Inputs to models, observations and conditions must be Lists - which can be cast to lists: - >>> s0.update(models='m1') # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - AssertionError: 'm1' must be a list, e.g. `['m1']`?) - - """ - - def _coalesce_lists(old, new): - assert isinstance( - old, List - ), f"{repr(old)} must be a list, e.g. `[{repr(old)}]`?)" - if new is not None: - assert isinstance( - new, List - ), f"{repr(new)} must be a list, e.g. `[{repr(new)}]`?)" - return old + list(new) - else: - return old - - variables_ = variables or self.variables - params_ = params or self.params - conditions_ = _coalesce_lists(self.conditions, conditions) - observations_ = _coalesce_lists(self.observations, observations) - models_ = _coalesce_lists(self.models, models) - return Snapshot(variables_, params_, conditions_, observations_, models_) - - def __add__(self, other: Delta): - """ - Add a delta to the object. - - Examples: - The initial object is empty: - >>> s0 = Snapshot() - >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(variables=VariableCollection(...), params={}, conditions=[], - observations=[], models=[]) - - We can update the params using the `+` operator: - >>> from autora.state.delta import Delta - >>> s0 + Delta(params={'first': 'params'}) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., params={'first': 'params'}, ...) - - ... but the original object is unchanged: - >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., params={}, ...) - - For params, only one object is returned from the respective property: - >>> (s0 + Delta(params={'first': 'params'}) + Delta(params={'second':'params'})).params - {'second': 'params'} - - ... and the same applies to variables: - >>> from autora.variable import VariableCollection, IV - >>> (s0 + Delta(variables=VariableCollection([IV("1st IV")])) + - ... Delta(variables=VariableCollection([IV("2nd IV")]))).variables - VariableCollection(independent_variables=[IV(name='2nd IV',...)], ...) - - When we update the conditions, observations or models, the respective list is extended: - >>> s3 = s0 + Delta(models=["1st model"]) - >>> s3 - Snapshot(..., models=['1st model']) - - ... so we can see the history of all the models, for instance. - >>> s3 + Delta(models=["2nd model"]) - Snapshot(..., models=['1st model', '2nd model']) - - The same applies to observations: - >>> s4 = s0 + Delta(observations=["1st observation"]) - >>> s4 - Snapshot(..., observations=['1st observation'], ...) - - >>> s4 + Delta(observations=["2nd observation"]) # doctest: +ELLIPSIS - Snapshot(..., observations=['1st observation', '2nd observation'], ...) - - - The same applies to conditions: - >>> s5 = s0 + Delta(conditions=["1st condition"]) - >>> s5 - Snapshot(..., conditions=['1st condition'], ...) - - >>> s5 + Delta(conditions=["2nd condition"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - Snapshot(..., conditions=['1st condition', '2nd condition'], ...) - - You can also update with multiple conditions, observations and models: - >>> s0 + Delta(conditions=['c1', 'c2']) - Snapshot(..., conditions=['c1', 'c2'], ...) - - >>> s0 + Delta(models=['m1', 'm2'], variables={'m': 1}) - Snapshot(variables={'m': 1}, ..., models=['m1', 'm2']) - - >>> s0 + Delta(models=['m1'], observations=['o1'], variables={'m': 1}) - Snapshot(variables={'m': 1}, ..., observations=['o1'], models=['m1']) - - - Inputs to models, observations and conditions must be Lists - which can be cast to lists: - >>> s0 + Delta(models='m1') # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - AssertionError: 'm1' must be a list, e.g. `['m1']`?) - """ - return self.update(**other) diff --git a/src/autora/state/standard.py b/src/autora/state/standard.py deleted file mode 100644 index ced84446..00000000 --- a/src/autora/state/standard.py +++ /dev/null @@ -1,322 +0,0 @@ -"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)` -so that $n$ processes $f_i$ on states $S$ can be represented as -$$f_n(...(f_1(f_0(S))))$$ - -These are special cases of the [autora.state.delta.on_state][] function. -""" -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Callable, List, Optional, TypeVar - -import pandas as pd -from sklearn.base import BaseEstimator - -from autora.state.delta import Delta, State, on_state -from autora.variable import VariableCollection - -X = TypeVar("X") -Y = TypeVar("Y") -XY = TypeVar("XY") -StateFunction = Callable[[State], State] - - -@dataclass(frozen=True) -class StandardState(State): - """ - Examples: - The state can be initialized emtpy - >>> from autora.state.delta import Delta - >>> from autora.variable import VariableCollection, Variable - >>> s = StandardState() - >>> s - StandardState(variables=None, conditions=None, experiment_data=None, models=[]) - - The `variables` can be updated using a `Delta`: - >>> dv1 = Delta(variables=VariableCollection(independent_variables=[Variable("1")])) - >>> s + dv1 - StandardState(variables=VariableCollection(independent_variables=[Variable(name='1',...) - - ... and are replaced by each `Delta`: - >>> dv2 = Delta(variables=VariableCollection(independent_variables=[Variable("2")])) - >>> s + dv1 + dv2 - StandardState(variables=VariableCollection(independent_variables=[Variable(name='2',...) - - The `conditions` can be updated using a `Delta`: - >>> dc1 = Delta(conditions=pd.DataFrame({"x": [1, 2, 3]})) - >>> (s + dc1).conditions - x - 0 1 - 1 2 - 2 3 - - ... and are replaced by each `Delta`: - >>> dc2 = Delta(conditions=pd.DataFrame({"x": [4, 5]})) - >>> (s + dc1 + dc2).conditions - x - 0 4 - 1 5 - - Datatypes other than `pd.DataFrame` will be coerced into a `DataFrame` if possible. - >>> import numpy as np - >>> dc3 = Delta(conditions=np.core.records.fromrecords([(8, "h"), (9, "i")], names="n,c")) - >>> (s + dc3).conditions - n c - 0 8 h - 1 9 i - - If they are passed without column names, no column names are inferred. - This is to ensure that accidental mislabeling of columns cannot occur. - Column names should usually be provided. - >>> dc4 = Delta(conditions=[(6,), (7,)]) - >>> (s + dc4).conditions - 0 - 0 6 - 1 7 - - Datatypes which are incompatible with a pd.DataFrame will throw an error: - >>> s + Delta(conditions="not compatible with pd.DataFrame") - Traceback (most recent call last): - ... - ValueError: ... - - Experiment data can be updated using a Delta: - >>> ded1 = Delta(experiment_data=pd.DataFrame({"x": [1,2,3], "y": ["a", "b", "c"]})) - >>> (s + ded1).experiment_data - x y - 0 1 a - 1 2 b - 2 3 c - - ... and are extended with each Delta: - >>> ded2 = Delta(experiment_data=pd.DataFrame({"x": [4, 5, 6], "y": ["d", "e", "f"]})) - >>> (s + ded1 + ded2).experiment_data - x y - 0 1 a - 1 2 b - 2 3 c - 3 4 d - 4 5 e - 5 6 f - - If they are passed without column names, no column names are inferred. - This is to ensure that accidental mislabeling of columns cannot occur. - >>> ded3 = Delta(experiment_data=pd.DataFrame([(7, "g"), (8, "h")])) - >>> (s + ded3).experiment_data - 0 1 - 0 7 g - 1 8 h - - If there are already data present, the column names must match. - >>> (s + ded2 + ded3).experiment_data - x y 0 1 - 0 4.0 d NaN NaN - 1 5.0 e NaN NaN - 2 6.0 f NaN NaN - 3 NaN NaN 7.0 g - 4 NaN NaN 8.0 h - - `experiment_data` other than `pd.DataFrame` will be coerced into a `DataFrame` if possible. - >>> import numpy as np - >>> ded4 = Delta( - ... experiment_data=np.core.records.fromrecords([(1, "a"), (2, "b")], names=["x", "y"])) - >>> (s + ded4).experiment_data - x y - 0 1 a - 1 2 b - - `experiment_data` which are incompatible with a pd.DataFrame will throw an error: - >>> s + Delta(experiment_data="not compatible with pd.DataFrame") - Traceback (most recent call last): - ... - ValueError: ... - - `models` can be updated using a Delta: - >>> from sklearn.dummy import DummyClassifier - >>> dm1 = Delta(models=[DummyClassifier(constant=1)]) - >>> dm2 = Delta(models=[DummyClassifier(constant=2), DummyClassifier(constant=3)]) - >>> (s + dm1).models - [DummyClassifier(constant=1)] - - >>> (s + dm1 + dm2).models - [DummyClassifier(constant=1), DummyClassifier(constant=2), DummyClassifier(constant=3)] - - The last model is available under the `model` property: - >>> (s + dm1 + dm2).model - DummyClassifier(constant=3) - - If there is no model, `None` is returned: - >>> print(s.model) - None - - `models` can also be updated using a Delta with a single `model`: - >>> dm3 = Delta(model=DummyClassifier(constant=4)) - >>> (s + dm1 + dm3).model - DummyClassifier(constant=4) - - As before, the `models` list is extended: - >>> (s + dm1 + dm3).models - [DummyClassifier(constant=1), DummyClassifier(constant=4)] - - No coercion or validation occurs with `models` or `model`: - >>> (s + dm1 + Delta(model="not a model")).models - [DummyClassifier(constant=1), 'not a model'] - - - """ - - variables: Optional[VariableCollection] = field( - default=None, metadata={"delta": "replace"} - ) - conditions: Optional[pd.DataFrame] = field( - default=None, metadata={"delta": "replace", "converter": pd.DataFrame} - ) - experiment_data: Optional[pd.DataFrame] = field( - default=None, metadata={"delta": "extend", "converter": pd.DataFrame} - ) - models: List[BaseEstimator] = field( - default_factory=list, - metadata={"delta": "extend", "aliases": {"model": lambda model: [model]}}, - ) - - @property - def model(self): - """Alias for the last model in the `models`.""" - try: - return self.models[-1] - except IndexError: - return None - - -def state_fn_from_estimator(estimator: BaseEstimator) -> StateFunction: - """ - Convert a scikit-learn compatible estimator into a function on a `State` object. - - Supports passing additional `**kwargs` which are used to update the estimator's params - before fitting. - - Examples: - Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. - >>> from sklearn.linear_model import LinearRegression - >>> state_fn = state_fn_from_estimator(LinearRegression()) - - Define the state on which to operate (here an instance of the `StandardState`): - >>> from autora.state.standard import StandardState - >>> from autora.variable import Variable, VariableCollection - >>> import pandas as pd - >>> s = StandardState( - ... variables=VariableCollection( - ... independent_variables=[Variable("x")], - ... dependent_variables=[Variable("y")]), - ... experiment_data=pd.DataFrame({"x": [1,2,3], "y":[3,6,9]}) - ... ) - - Run the function, which fits the model and adds the result to the `StandardState` - >>> state_fn(s).model.coef_ - array([[3.]]) - - """ - - @on_state() - def theorist( - experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs - ): - ivs = [v.name for v in variables.independent_variables] - dvs = [v.name for v in variables.dependent_variables] - X, y = experiment_data[ivs], experiment_data[dvs] - new_model = estimator.set_params(**kwargs).fit(X, y) - return Delta(model=new_model) - - return theorist - - -def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> StateFunction: - """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ - values, with inputs and outputs as a DataFrame or Series with correct column names. - - Examples: - The conditions are some x-values in a StandardState object: - >>> from autora.state.standard import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) - - The function can be defined on a DataFrame (allowing the explicit inclusion of - metadata like column names). - >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(2 * c["x"] + 1, name="y") - ... return result - - We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data - x y - 0 1 3 - 1 2 5 - 2 3 7 - - We can also define functions of several variables: - >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(c["x0"] + c["x1"], name="y") - ... return result - - With the relevant variables as conditions: - >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data - x0 x1 y - 0 1 10 11 - 1 2 20 22 - 2 3 30 33 - """ - - @on_state() - def experiment_runner(conditions: pd.DataFrame, **kwargs): - x = conditions - y = f(x, **kwargs) - experiment_data = pd.DataFrame.merge(x, y, left_index=True, right_index=True) - return Delta(experiment_data=experiment_data) - - return experiment_runner - - -def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: - """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` - returns both $x$ and $y$ values in a complete dataframe. - - Examples: - The conditions are some x-values in a StandardState object: - >>> from autora.state.standard import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) - - The function can be defined on a DataFrame, allowing the explicit inclusion of - metadata like column names. - >>> def x_to_xy_fn(c: pd.DataFrame) -> pd.Series: - ... result = c.assign(y=lambda df: 2 * df.x + 1) - ... return result - - We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data - x y - 0 1 3 - 1 2 5 - 2 3 7 - - We can also define functions of several variables: - >>> def xs_to_xy_fn(c: pd.DataFrame) -> pd.Series: - ... result = c.assign(y=c.x0 + c.x1) - ... return result - - With the relevant variables as conditions: - >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data - x0 x1 y - 0 1 10 11 - 1 2 20 22 - 2 3 30 33 - - """ - - @on_state() - def experiment_runner(conditions: pd.DataFrame, **kwargs): - x = conditions - experiment_data = f(x, **kwargs) - return Delta(experiment_data=experiment_data) - - return experiment_runner From 046aae4680d4887d674e4a4b461af020e694278b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 23 Aug 2023 17:05:26 +0200 Subject: [PATCH 115/121] refactor: move all standard-state code into a single state.py file --- src/autora/{state/delta.py => state.py} | 345 +++++++++++++++++++++++- 1 file changed, 336 insertions(+), 9 deletions(-) rename src/autora/{state/delta.py => state.py} (76%) diff --git a/src/autora/state/delta.py b/src/autora/state.py similarity index 76% rename from src/autora/state/delta.py rename to src/autora/state.py index 02594dce..00ee1950 100644 --- a/src/autora/state/delta.py +++ b/src/autora/state.py @@ -1,20 +1,33 @@ """Classes to represent cycle state $S$ as $S_n = S_{0} + \\sum_{i=1}^n \\Delta S_{i}$.""" + from __future__ import annotations import inspect import logging import warnings from collections import UserDict -from collections.abc import Mapping -from dataclasses import dataclass, fields, is_dataclass, replace +from dataclasses import dataclass, field, fields, is_dataclass, replace +from enum import Enum from functools import singledispatch, wraps -from typing import Callable, Generic, List, Optional, Protocol, Sequence, TypeVar, Union +from typing import ( + Callable, + Generic, + List, + Mapping, + Optional, + Protocol, + Sequence, + TypeVar, + Union, +) import numpy as np import pandas as pd +from sklearn.base import BaseEstimator -_logger = logging.getLogger(__name__) +from autora.variable import VariableCollection +_logger = logging.getLogger(__name__) T = TypeVar("T") C = TypeVar("C", covariant=True) @@ -497,7 +510,7 @@ def extend(a, b): @extend.register(type(None)) -def extend_none(_, b): +def _extend_none(_, b): """ Examples: >>> extend(None, []) @@ -510,7 +523,7 @@ def extend_none(_, b): @extend.register(list) -def extend_list(a, b): +def _extend_list(a, b): """ Examples: >>> extend([], []) @@ -523,7 +536,7 @@ def extend_list(a, b): @extend.register(pd.DataFrame) -def extend_pd_dataframe(a, b): +def _extend_pd_dataframe(a, b): """ Examples: >>> extend(pd.DataFrame({"a": []}), pd.DataFrame({"a": []})) @@ -544,7 +557,7 @@ def extend_pd_dataframe(a, b): @extend.register(np.ndarray) -def extend_np_ndarray(a, b): +def _extend_np_ndarray(a, b): """ Examples: >>> extend(np.array([(1,2,3), (4,5,6)]), np.array([(7,8,9)])) @@ -556,7 +569,7 @@ def extend_np_ndarray(a, b): @extend.register(dict) -def extend_dict(a, b): +def _extend_dict(a, b): """ Examples: >>> extend({"a": "cats"}, {"b": "dogs"}) @@ -1072,3 +1085,317 @@ def decorator(f): return decorator else: return decorator(function) + + +StateFunction = Callable[[State], State] + + +class StandardStateVariables(Enum): + CONDITIONS = "conditions" + EXPERIMENT_DATA = "experiment_data" + MODELS = "models" + VARIABLES = "variables" + + +@dataclass(frozen=True) +class StandardState(State): + """ + Examples: + The state can be initialized emtpy + >>> from autora.variable import VariableCollection, Variable + >>> s = StandardState() + >>> s + StandardState(variables=None, conditions=None, experiment_data=None, models=[]) + + The `variables` can be updated using a `Delta`: + >>> dv1 = Delta(variables=VariableCollection(independent_variables=[Variable("1")])) + >>> s + dv1 + StandardState(variables=VariableCollection(independent_variables=[Variable(name='1',...) + + ... and are replaced by each `Delta`: + >>> dv2 = Delta(variables=VariableCollection(independent_variables=[Variable("2")])) + >>> s + dv1 + dv2 + StandardState(variables=VariableCollection(independent_variables=[Variable(name='2',...) + + The `conditions` can be updated using a `Delta`: + >>> dc1 = Delta(conditions=pd.DataFrame({"x": [1, 2, 3]})) + >>> (s + dc1).conditions + x + 0 1 + 1 2 + 2 3 + + ... and are replaced by each `Delta`: + >>> dc2 = Delta(conditions=pd.DataFrame({"x": [4, 5]})) + >>> (s + dc1 + dc2).conditions + x + 0 4 + 1 5 + + Datatypes other than `pd.DataFrame` will be coerced into a `DataFrame` if possible. + >>> import numpy as np + >>> dc3 = Delta(conditions=np.core.records.fromrecords([(8, "h"), (9, "i")], names="n,c")) + >>> (s + dc3).conditions + n c + 0 8 h + 1 9 i + + If they are passed without column names, no column names are inferred. + This is to ensure that accidental mislabeling of columns cannot occur. + Column names should usually be provided. + >>> dc4 = Delta(conditions=[(6,), (7,)]) + >>> (s + dc4).conditions + 0 + 0 6 + 1 7 + + Datatypes which are incompatible with a pd.DataFrame will throw an error: + >>> s + Delta(conditions="not compatible with pd.DataFrame") + Traceback (most recent call last): + ... + ValueError: ... + + Experiment data can be updated using a Delta: + >>> ded1 = Delta(experiment_data=pd.DataFrame({"x": [1,2,3], "y": ["a", "b", "c"]})) + >>> (s + ded1).experiment_data + x y + 0 1 a + 1 2 b + 2 3 c + + ... and are extended with each Delta: + >>> ded2 = Delta(experiment_data=pd.DataFrame({"x": [4, 5, 6], "y": ["d", "e", "f"]})) + >>> (s + ded1 + ded2).experiment_data + x y + 0 1 a + 1 2 b + 2 3 c + 3 4 d + 4 5 e + 5 6 f + + If they are passed without column names, no column names are inferred. + This is to ensure that accidental mislabeling of columns cannot occur. + >>> ded3 = Delta(experiment_data=pd.DataFrame([(7, "g"), (8, "h")])) + >>> (s + ded3).experiment_data + 0 1 + 0 7 g + 1 8 h + + If there are already data present, the column names must match. + >>> (s + ded2 + ded3).experiment_data + x y 0 1 + 0 4.0 d NaN NaN + 1 5.0 e NaN NaN + 2 6.0 f NaN NaN + 3 NaN NaN 7.0 g + 4 NaN NaN 8.0 h + + `experiment_data` other than `pd.DataFrame` will be coerced into a `DataFrame` if possible. + >>> import numpy as np + >>> ded4 = Delta( + ... experiment_data=np.core.records.fromrecords([(1, "a"), (2, "b")], names=["x", "y"])) + >>> (s + ded4).experiment_data + x y + 0 1 a + 1 2 b + + `experiment_data` which are incompatible with a pd.DataFrame will throw an error: + >>> s + Delta(experiment_data="not compatible with pd.DataFrame") + Traceback (most recent call last): + ... + ValueError: ... + + `models` can be updated using a Delta: + >>> from sklearn.dummy import DummyClassifier + >>> dm1 = Delta(models=[DummyClassifier(constant=1)]) + >>> dm2 = Delta(models=[DummyClassifier(constant=2), DummyClassifier(constant=3)]) + >>> (s + dm1).models + [DummyClassifier(constant=1)] + + >>> (s + dm1 + dm2).models + [DummyClassifier(constant=1), DummyClassifier(constant=2), DummyClassifier(constant=3)] + + The last model is available under the `model` property: + >>> (s + dm1 + dm2).model + DummyClassifier(constant=3) + + If there is no model, `None` is returned: + >>> print(s.model) + None + + `models` can also be updated using a Delta with a single `model`: + >>> dm3 = Delta(model=DummyClassifier(constant=4)) + >>> (s + dm1 + dm3).model + DummyClassifier(constant=4) + + As before, the `models` list is extended: + >>> (s + dm1 + dm3).models + [DummyClassifier(constant=1), DummyClassifier(constant=4)] + + No coercion or validation occurs with `models` or `model`: + >>> (s + dm1 + Delta(model="not a model")).models + [DummyClassifier(constant=1), 'not a model'] + + """ + + variables: Optional[VariableCollection] = field( + default=None, metadata={"delta": "replace"} + ) + conditions: Optional[pd.DataFrame] = field( + default=None, metadata={"delta": "replace", "converter": pd.DataFrame} + ) + experiment_data: Optional[pd.DataFrame] = field( + default=None, metadata={"delta": "extend", "converter": pd.DataFrame} + ) + models: List[BaseEstimator] = field( + default_factory=list, + metadata={"delta": "extend", "aliases": {"model": lambda model: [model]}}, + ) + + @property + def model(self): + """Alias for the last model in the `models`.""" + try: + return self.models[-1] + except IndexError: + return None + + +X = TypeVar("X") +Y = TypeVar("Y") +XY = TypeVar("XY") + + +def state_fn_from_estimator(estimator: BaseEstimator) -> StateFunction: + """ + Convert a scikit-learn compatible estimator into a function on a `State` object. + + Supports passing additional `**kwargs` which are used to update the estimator's params + before fitting. + + Examples: + Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. + >>> from sklearn.linear_model import LinearRegression + >>> state_fn = state_fn_from_estimator(LinearRegression()) + + Define the state on which to operate (here an instance of the `StandardState`): + >>> from autora.state import StandardState + >>> from autora.variable import Variable, VariableCollection + >>> import pandas as pd + >>> s = StandardState( + ... variables=VariableCollection( + ... independent_variables=[Variable("x")], + ... dependent_variables=[Variable("y")]), + ... experiment_data=pd.DataFrame({"x": [1,2,3], "y":[3,6,9]}) + ... ) + + Run the function, which fits the model and adds the result to the `StandardState` + >>> state_fn(s).model.coef_ + array([[3.]]) + + """ + + @on_state() + def theorist( + experiment_data: pd.DataFrame, variables: VariableCollection, **kwargs + ): + ivs = [v.name for v in variables.independent_variables] + dvs = [v.name for v in variables.dependent_variables] + X, y = experiment_data[ivs], experiment_data[dvs] + new_model = estimator.set_params(**kwargs).fit(X, y) + return Delta(model=new_model) + + return theorist + + +def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> StateFunction: + """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ + values, with inputs and outputs as a DataFrame or Series with correct column names. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame (allowing the explicit inclusion of + metadata like column names). + >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(2 * c["x"] + 1, name="y") + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: + ... result = pd.Series(c["x0"] + c["x1"], name="y") + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + """ + + @on_state() + def experiment_runner(conditions: pd.DataFrame, **kwargs): + x = conditions + y = f(x, **kwargs) + experiment_data = pd.DataFrame.merge(x, y, left_index=True, right_index=True) + return Delta(experiment_data=experiment_data) + + return experiment_runner + + +def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: + """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` + returns both $x$ and $y$ values in a complete dataframe. + + Examples: + The conditions are some x-values in a StandardState object: + >>> from autora.state import StandardState + >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) + + The function can be defined on a DataFrame, allowing the explicit inclusion of + metadata like column names. + >>> def x_to_xy_fn(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=lambda df: 2 * df.x + 1) + ... return result + + We apply the wrapped function to `s` and look at the returned experiment_data: + >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data + x y + 0 1 3 + 1 2 5 + 2 3 7 + + We can also define functions of several variables: + >>> def xs_to_xy_fn(c: pd.DataFrame) -> pd.Series: + ... result = c.assign(y=c.x0 + c.x1) + ... return result + + With the relevant variables as conditions: + >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) + >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data + x0 x1 y + 0 1 10 11 + 1 2 20 22 + 2 3 30 33 + + """ + + @on_state() + def experiment_runner(conditions: pd.DataFrame, **kwargs): + x = conditions + experiment_data = f(x, **kwargs) + return Delta(experiment_data=experiment_data) + + return experiment_runner From 0cab12375698a710b734388aa48f06edcd141bfd Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 23 Aug 2023 17:18:56 +0200 Subject: [PATCH 116/121] refactor: make _extend and _append functions private --- src/autora/state.py | 48 +++++++++++++++++++++++++++------------------ 1 file changed, 29 insertions(+), 19 deletions(-) diff --git a/src/autora/state.py b/src/autora/state.py index 00ee1950..65d6c18b 100644 --- a/src/autora/state.py +++ b/src/autora/state.py @@ -269,10 +269,10 @@ def __add__(self, other: Union[Delta, Mapping]): coerced_other_value = other_value if delta_behavior == "extend": - extended_value = extend(self_value, coerced_other_value) + extended_value = _extend(self_value, coerced_other_value) updates[self_field_key] = extended_value elif delta_behavior == "append": - appended_value = append(self_value, coerced_other_value) + appended_value = _append(self_value, coerced_other_value) updates[self_field_key] = appended_value elif delta_behavior == "replace": updates[self_field_key] = coerced_other_value @@ -501,50 +501,56 @@ class Delta(UserDict, Generic[S]): @singledispatch -def extend(a, b): +def _extend(a, b): """ Function to extend supported datatypes. """ - raise NotImplementedError("`extend` not implemented for %s, %s" % (a, b)) + raise NotImplementedError("`_extend` not implemented for %s, %s" % (a, b)) -@extend.register(type(None)) +@_extend.register(type(None)) def _extend_none(_, b): """ + Implementation of `_extend` to support None-types. + Examples: - >>> extend(None, []) + >>> _extend(None, []) [] - >>> extend(None, [3]) + >>> _extend(None, [3]) [3] """ return b -@extend.register(list) +@_extend.register(list) def _extend_list(a, b): """ + Implementation of `_extend` to support Lists. + Examples: - >>> extend([], []) + >>> _extend([], []) [] - >>> extend([1,2], [3]) + >>> _extend([1,2], [3]) [1, 2, 3] """ return a + b -@extend.register(pd.DataFrame) +@_extend.register(pd.DataFrame) def _extend_pd_dataframe(a, b): """ + Implementation of `_extend` to support DataFrames. + Examples: - >>> extend(pd.DataFrame({"a": []}), pd.DataFrame({"a": []})) + >>> _extend(pd.DataFrame({"a": []}), pd.DataFrame({"a": []})) Empty DataFrame Columns: [a] Index: [] - >>> extend(pd.DataFrame({"a": [1,2,3]}), pd.DataFrame({"a": [4,5,6]})) + >>> _extend(pd.DataFrame({"a": [1,2,3]}), pd.DataFrame({"a": [4,5,6]})) a 0 1 1 2 @@ -556,11 +562,13 @@ def _extend_pd_dataframe(a, b): return pd.concat((a, b), ignore_index=True) -@extend.register(np.ndarray) +@_extend.register(np.ndarray) def _extend_np_ndarray(a, b): """ + Implementation of `_extend` to support Numpy ndarrays. + Examples: - >>> extend(np.array([(1,2,3), (4,5,6)]), np.array([(7,8,9)])) + >>> _extend(np.array([(1,2,3), (4,5,6)]), np.array([(7,8,9)])) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) @@ -568,17 +576,19 @@ def _extend_np_ndarray(a, b): return np.row_stack([a, b]) -@extend.register(dict) +@_extend.register(dict) def _extend_dict(a, b): """ + Implementation of `_extend` to support Dictionaries. + Examples: - >>> extend({"a": "cats"}, {"b": "dogs"}) + >>> _extend({"a": "cats"}, {"b": "dogs"}) {'a': 'cats', 'b': 'dogs'} """ return dict(a, **b) -def append(a: List[T], b: T) -> List[T]: +def _append(a: List[T], b: T) -> List[T]: """ Function to create a new list with an item appended to it. @@ -587,7 +597,7 @@ def append(a: List[T], b: T) -> List[T]: >>> a_ = [1, 2, 3] ... we can append a value: - >>> append(a_, 4) + >>> _append(a_, 4) [1, 2, 3, 4] `a_` is unchanged From 495631f2b1a3f80cb1a4cda1b25ca966b0746080 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 23 Aug 2023 17:27:52 +0200 Subject: [PATCH 117/121] refactor: update imports from autora.state --- docs/cycle/Basic Introduction to Functions and States.ipynb | 4 ++-- docs/cycle/Combining Experimentalists with State.ipynb | 2 +- ...ar and Cyclical Workflows using Functions and States.ipynb | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index 6c8a20da..abe64883 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -58,7 +58,7 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.state.bundled import StandardState\n", + "from autora.state import StandardState\n", "from autora.variable import VariableCollection, Variable\n", "\n", "s_0 = StandardState(\n", @@ -274,7 +274,7 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state.wrapper import state_fn_from_estimator\n", + "from autora.state import state_fn_from_estimator\n", "\n", "theorist = state_fn_from_estimator(LinearRegression(fit_intercept=True))" ] diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index 2ae12060..8640142a 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -995,7 +995,7 @@ ], "source": [ "from autora.state import Delta, on_state, State, inputs_from_state\n", - "from autora.state.bundled import StandardState\n", + "from autora.state import StandardState\n", "\n", "s = StandardState() + Delta(conditions=downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even]))\n", "s.conditions" diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 4a5e7850..808818d1 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -46,7 +46,7 @@ "import numpy as np\n", "import pandas as pd\n", "from autora.variable import VariableCollection, Variable\n", - "from autora.state.bundled import StandardState\n", + "from autora.state import StandardState\n", "\n", "s = StandardState(\n", " variables=VariableCollection(independent_variables=[Variable(\"x\", value_range=(-15,15))],\n", @@ -278,7 +278,7 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state.wrapper import state_fn_from_estimator\n", + "from autora.state import state_fn_from_estimator\n", "from sklearn.pipeline import make_pipeline as make_theorist_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", From 247511e10e20b05eecfa3df09f8fe182d35c6249 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 23 Aug 2023 17:31:15 +0200 Subject: [PATCH 118/121] refactor: update imports from autora.state --- docs/cycle/Basic Introduction to Functions and States.ipynb | 4 ++-- docs/cycle/Combining Experimentalists with State.ipynb | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index abe64883..ca2b77f1 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -21,13 +21,13 @@ "- A new state at some point $i+1$ is $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", "- The cycle state after $n$ steps is thus $$S_n = S_{0} + \\sum^{n}_{i=1} \\Delta S_{i}$$\n", "\n", - "To represent $S$ and $\\Delta S$ in code, you can use `autora.state.delta.State` and `autora.state.delta.Delta`\n", + "To represent $S$ and $\\Delta S$ in code, you can use `autora.state.State` and `autora.state.Delta`\n", "respectively. To operate on these, we define functions.\n", "\n", "- Each operation in an AER cycle (theorist, experimentalist, experiment_runner, etc.) is implemented as a\n", "function with $n$ arguments $s_j$ which are members of $S$ and $m$ others $a_k$ which are not.\n", " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", - "- There is a wrapper function $w$ (`autora.state.delta.wrap_to_use_state`) which changes the signature of $f$ to\n", + "- There is a wrapper function $w$ (`autora.state.wrap_to_use_state`) which changes the signature of $f$ to\n", "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", " $$w\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", "S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta\n", diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index 8640142a..a7d6680a 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -994,8 +994,7 @@ } ], "source": [ - "from autora.state import Delta, on_state, State, inputs_from_state\n", - "from autora.state import StandardState\n", + "from autora.state import Delta, on_state, State, StandardState, inputs_from_state\n", "\n", "s = StandardState() + Delta(conditions=downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even]))\n", "s.conditions" From e48b3877596594ce6b64ecb44d6c0e0d6230d6f2 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 24 Aug 2023 17:37:00 +0200 Subject: [PATCH 119/121] refactor: make pytest use `importlib` mode, allowing duplicate filenames --- .github/workflows/test-pytest.yml | 2 +- docs/experimentalists/random/index.md | 2 +- docs/experimentalists/random/quickstart.md | 6 +++--- src/autora/experimentalist/{random_.py => random.py} | 0 4 files changed, 5 insertions(+), 5 deletions(-) rename src/autora/experimentalist/{random_.py => random.py} (100%) diff --git a/.github/workflows/test-pytest.yml b/.github/workflows/test-pytest.yml index 3ce245e0..4c7aeab7 100644 --- a/.github/workflows/test-pytest.yml +++ b/.github/workflows/test-pytest.yml @@ -26,4 +26,4 @@ jobs: python-version: ${{ matrix.python-version }} cache: "pip" - run: pip install ".[test]" - - run: pytest --doctest-modules + - run: pytest --doctest-modules --import-mode importlib diff --git a/docs/experimentalists/random/index.md b/docs/experimentalists/random/index.md index f9370e10..774283ba 100644 --- a/docs/experimentalists/random/index.md +++ b/docs/experimentalists/random/index.md @@ -25,7 +25,7 @@ This means that there are 9 possible combinations for these variables (3x3), fro ```python -from autora.experimentalist.random_ import random_pool +from autora.experimentalist.random import random_pool pool = random_pool([1, 2, 3], [4, 5, 6], num_samples=3) ``` diff --git a/docs/experimentalists/random/quickstart.md b/docs/experimentalists/random/quickstart.md index 9f872c75..491c1528 100644 --- a/docs/experimentalists/random/quickstart.md +++ b/docs/experimentalists/random/quickstart.md @@ -11,10 +11,10 @@ You can import and invoke the pool like this: ```python from autora.variable import VariableCollection, Variable -from autora.experimentalist.random_ import pool +from autora.experimentalist.random import pool pool( - VariableCollection(independent_variables=[Variable(name="x", allowed_values=range(10))]), + VariableCollection(independent_variables=[Variable(name="x", allowed_values=range(10))]), random_state=1 ) ``` @@ -22,7 +22,7 @@ pool( You can import the sampler like this: ```python -from autora.experimentalist.random_ import sample +from autora.experimentalist.random import sample sample([1, 1, 2, 2, 3, 3], num_samples=2) ``` diff --git a/src/autora/experimentalist/random_.py b/src/autora/experimentalist/random.py similarity index 100% rename from src/autora/experimentalist/random_.py rename to src/autora/experimentalist/random.py From 1a1c897994148b25b15a2fc7c1601e31cb52dc5f Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 25 Aug 2023 17:00:25 +0200 Subject: [PATCH 120/121] refactor: rename estimator_on_state from state_fn_from_estimator --- ...Introduction to Functions and States.ipynb | 4 +- ...Workflows using Functions and States.ipynb | 6 +-- src/autora/state.py | 50 +------------------ 3 files changed, 7 insertions(+), 53 deletions(-) diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index ca2b77f1..a41bf38a 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -274,9 +274,9 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state import state_fn_from_estimator\n", + "from autora.state import estimator_on_state\n", "\n", - "theorist = state_fn_from_estimator(LinearRegression(fit_intercept=True))" + "theorist = estimator_on_state(LinearRegression(fit_intercept=True))" ] }, { diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 808818d1..f04ee104 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -268,7 +268,7 @@ "### Defining The Theorist\n", "\n", "Now we define a theorist, which does a linear regression on the polynomial of degree 5. We define a regressor and a\n", - "method to return its feature names and coefficients, and then the theorist to handle it. Here, we use a different wrapper `state_fn_from_estimator` that wraps the regressor and returns a function with the same functionality, but operating on `State` fields. In this case, we want to use the `State` field `experiment_data` and extend the `State` field `models`." + "method to return its feature names and coefficients, and then the theorist to handle it. Here, we use a different wrapper `estimator_on_state` that wraps the regressor and returns a function with the same functionality, but operating on `State` fields. In this case, we want to use the `State` field `experiment_data` and extend the `State` field `models`." ] }, { @@ -278,13 +278,13 @@ "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", - "from autora.state import state_fn_from_estimator\n", + "from autora.state import estimator_on_state\n", "from sklearn.pipeline import make_pipeline as make_theorist_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# Completely standard scikit-learn pipeline regressor\n", "regressor = make_theorist_pipeline(PolynomialFeatures(degree=5), LinearRegression())\n", - "theorist = state_fn_from_estimator(regressor)\n", + "theorist = estimator_on_state(regressor)\n", "\n", "def get_equation(r):\n", " t = r.named_steps['polynomialfeatures'].get_feature_names_out()\n", diff --git a/src/autora/state.py b/src/autora/state.py index 65d6c18b..c50cc566 100644 --- a/src/autora/state.py +++ b/src/autora/state.py @@ -1277,7 +1277,7 @@ def model(self): XY = TypeVar("XY") -def state_fn_from_estimator(estimator: BaseEstimator) -> StateFunction: +def estimator_on_state(estimator: BaseEstimator) -> StateFunction: """ Convert a scikit-learn compatible estimator into a function on a `State` object. @@ -1287,7 +1287,7 @@ def state_fn_from_estimator(estimator: BaseEstimator) -> StateFunction: Examples: Initialize a function which operates on the state, `state_fn` and runs a LinearRegression. >>> from sklearn.linear_model import LinearRegression - >>> state_fn = state_fn_from_estimator(LinearRegression()) + >>> state_fn = estimator_on_state(LinearRegression()) Define the state on which to operate (here an instance of the `StandardState`): >>> from autora.state import StandardState @@ -1319,52 +1319,6 @@ def theorist( return theorist -def state_fn_from_x_to_y_fn_df(f: Callable[[X], Y]) -> StateFunction: - """Wrapper for experiment_runner of the form $f(x) \rarrow y$, where `f` returns just the $y$ - values, with inputs and outputs as a DataFrame or Series with correct column names. - - Examples: - The conditions are some x-values in a StandardState object: - >>> from autora.state import StandardState - >>> s = StandardState(conditions=pd.DataFrame({"x": [1, 2, 3]})) - - The function can be defined on a DataFrame (allowing the explicit inclusion of - metadata like column names). - >>> def x_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(2 * c["x"] + 1, name="y") - ... return result - - We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_y_fn_df(x_to_y_fn)(s).experiment_data - x y - 0 1 3 - 1 2 5 - 2 3 7 - - We can also define functions of several variables: - >>> def xs_to_y_fn(c: pd.DataFrame) -> pd.Series: - ... result = pd.Series(c["x0"] + c["x1"], name="y") - ... return result - - With the relevant variables as conditions: - >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_y_fn_df(xs_to_y_fn)(t).experiment_data - x0 x1 y - 0 1 10 11 - 1 2 20 22 - 2 3 30 33 - """ - - @on_state() - def experiment_runner(conditions: pd.DataFrame, **kwargs): - x = conditions - y = f(x, **kwargs) - experiment_data = pd.DataFrame.merge(x, y, left_index=True, right_index=True) - return Delta(experiment_data=experiment_data) - - return experiment_runner - - def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` returns both $x$ and $y$ values in a complete dataframe. From 982a2c2270361bbd69267be28331634d34bfb9a1 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 25 Aug 2023 17:01:09 +0200 Subject: [PATCH 121/121] refactor: rename experiment_runner_on_state from state_fn_from_x_to_xy_fn_df --- src/autora/state.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/autora/state.py b/src/autora/state.py index c50cc566..ab25baac 100644 --- a/src/autora/state.py +++ b/src/autora/state.py @@ -1319,7 +1319,7 @@ def theorist( return theorist -def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: +def experiment_runner_on_state(f: Callable[[X], XY]) -> StateFunction: """Wrapper for experiment_runner of the form $f(x) \rarrow (x,y)$, where `f` returns both $x$ and $y$ values in a complete dataframe. @@ -1335,7 +1335,7 @@ def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: ... return result We apply the wrapped function to `s` and look at the returned experiment_data: - >>> state_fn_from_x_to_xy_fn_df(x_to_xy_fn)(s).experiment_data + >>> experiment_runner_on_state(x_to_xy_fn)(s).experiment_data x y 0 1 3 1 2 5 @@ -1348,7 +1348,7 @@ def state_fn_from_x_to_xy_fn_df(f: Callable[[X], XY]) -> StateFunction: With the relevant variables as conditions: >>> t = StandardState(conditions=pd.DataFrame({"x0": [1, 2, 3], "x1": [10, 20, 30]})) - >>> state_fn_from_x_to_xy_fn_df(xs_to_xy_fn)(t).experiment_data + >>> experiment_runner_on_state(xs_to_xy_fn)(t).experiment_data x0 x1 y 0 1 10 11 1 2 20 22