From b9734b62870b6e2a720d2c26230377d4847e30bf Mon Sep 17 00:00:00 2001 From: Anthony Mahanna <43019056+aMahanna@users.noreply.github.com> Date: Fri, 31 Dec 2021 13:20:08 -0500 Subject: [PATCH] new: blog post preparation (#58) --- adbnx_adapter/controller.py | 16 +- examples/ArangoDB_NetworkX_Adapter.ipynb | 477 ++++-- examples/ITSM_ArangoDB_Adapter.ipynb | 54 +- .../ArangoDB_NetworkX_Adapter_output.ipynb | 1504 +++++++++++++++++ .../ITSM_ArangoDB_Adapter_output.ipynb | 667 ++++++++ examples/outputs/ITSM_EDA_output.ipynb | 768 +++++++++ .../batch_graph_pre_processing_output.ipynb | 1088 ++++++++++++ 7 files changed, 4462 insertions(+), 112 deletions(-) create mode 100644 examples/outputs/ArangoDB_NetworkX_Adapter_output.ipynb create mode 100644 examples/outputs/ITSM_ArangoDB_Adapter_output.ipynb create mode 100644 examples/outputs/ITSM_EDA_output.ipynb create mode 100644 examples/outputs/batch_graph_pre_processing_output.ipynb diff --git a/adbnx_adapter/controller.py b/adbnx_adapter/controller.py index ecf23afe..ff017df9 100644 --- a/adbnx_adapter/controller.py +++ b/adbnx_adapter/controller.py @@ -81,10 +81,14 @@ def _identify_networkx_edge( """Given a NetworkX edge, and its pair of nodes, identify what ArangoDB collection should it belong to. - NOTE: You must override this function if your NetworkX graph is NOT Homogeneous - or does NOT comply to ArangoDB standards + NOTE #1: You must override this function if your NetworkX graph is NOT + Homogeneous or does NOT comply to ArangoDB standards (i.e the edge IDs are not formatted like "{collection}/{key}"). + NOTE #2: You can accesss the ID & Collection belonging to the + **from_nx_node** & **to_nx_node** parameters via their "nx_id" & "col" + attribute keys. E.g `from_collection = from_nx_node["col"]` + :param nx_edge: The NetworkX edge object. :type nx_edge: adbnx_adapter.typings.NxData :param from_nx_node: The NetworkX node object representing the edge source. @@ -130,12 +134,16 @@ def _keyify_networkx_edge( """Given a NetworkX edge, its collection, and its pair of nodes, derive its valid ArangoDB key. - NOTE: You must override this function if you want to create custom ArangoDB _key - values for your NetworkX edges or if your NetworkX graph does NOT comply + NOTE #1: You must override this function if you want to create custom ArangoDB + _key values for your NetworkX edges or if your NetworkX graph does NOT comply to ArangoDB standards (i.e the edge IDs are not formatted like "{collection}/{key}"). For more info, see the **keyify_edges** parameter of ADBNX_Adapter.networkx_to_arangodb() + NOTE #2: You can accesss the ID & Collection belonging to the + **from_nx_node** & **to_nx_node** parameters via their "nx_id" & "col" + attribute keys. E.g `from_collection = from_nx_node["col"]` + :param nx_edge: The NetworkX edge object. :type nx_edge: adbnx_adapter.typings.NxData :param from_nx_node: The NetworkX node object representing the edge source. diff --git a/examples/ArangoDB_NetworkX_Adapter.ipynb b/examples/ArangoDB_NetworkX_Adapter.ipynb index 18f78204..3ed82969 100644 --- a/examples/ArangoDB_NetworkX_Adapter.ipynb +++ b/examples/ArangoDB_NetworkX_Adapter.ipynb @@ -34,7 +34,7 @@ "id": "bpvZS-1aeG89" }, "source": [ - "Version: 3.0.0\n", + "Version: 3.0.1\n", "\n", "Objective: Export Graphs from [ArangoDB](https://www.arangodb.com/), a multi-model Graph Database, to [NetworkX](https://networkx.github.io/), the swiss army knife for graph analysis in python, and vice-versa." ] @@ -58,10 +58,9 @@ "source": [ "%%capture\n", "!git clone -b oasis_connector --single-branch https://github.com/arangodb/interactive_tutorials.git\n", - "!git clone -b 3.0.0 --single-branch https://github.com/arangoml/networkx-adapter.git\n", - "!rsync -av networkx-adapter/examples/ ./ --exclude=.git\n", + "!git clone -b 3.0.1 --single-branch https://github.com/arangoml/networkx-adapter.git\n", "!rsync -av interactive_tutorials/ ./ --exclude=.git\n", - "!pip3 install adbnx_adapter==3.0.0\n", + "!pip3 install adbnx_adapter==3.0.1\n", "!pip3 install matplotlib\n", "!pip3 install pyArango" ] @@ -76,9 +75,9 @@ "source": [ "import json\n", "import oasis\n", - "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", + "import networkx as nx\n", "\n", "from adbnx_adapter.adapter import ADBNX_Adapter\n", "from adbnx_adapter.controller import ADBNX_Controller\n", @@ -88,115 +87,239 @@ { "cell_type": "markdown", "metadata": { - "id": "Oc__NAd1eG8-" + "id": "lRmEM5eaQxJ5" }, "source": [ - "# Create a Temporary ArangoDB Instance" + "# Understanding NetworkX" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2ekGwnJDeG8-", - "outputId": "d973c463-1f4a-43e3-a7ac-6b6e3c8e859b" + "id": "r7sRg9j2Q2ef" }, - "outputs": [], "source": [ - "# Request temporary instance from the managed ArangoDB Cloud Oasis.\n", - "con = oasis.getTempCredentials()\n", + "(referenced from [networkx.org/documentation](https://networkx.org/documentation/stable/reference/index.html))\n", "\n", - "# Connect to the db via the python-arango driver\n", - "python_arango_db_driver = oasis.connect_python_arango(con)\n", + "NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides:\n", + "* tools for the study of the structure and dynamics of social, biological, and infrastructure networks;\n", + "* a standard programming interface and graph implementation that is suitable for many applications;\n", + "* a rapid development environment for collaborative, multidisciplinary projects;\n", + "* an interface to existing numerical algorithms and code written in C, C++, and FORTRAN; and\n", + "* the ability to painlessly work with large nonstandard data sets.\n", "\n", - "# (Alternative) Connect to the db via the pyArango driver\n", - "# pyarango_db_driver = oasis.connect(con)[con['dbName']]\n", + "With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more.\n", "\n", - "print()\n", - "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", - "print(\"Username: \" + con[\"username\"])\n", - "print(\"Password: \" + con[\"password\"])\n", - "print(\"Database: \" + con[\"dbName\"])" + "\n", + "The following basic graph types are provided as Python classes:\n", + "\n", + "`Graph`\n", + "* This class implements an undirected graph. It ignores multiple edges between two nodes. It does allow self-loop edges between a node and itself.\n", + "\n", + "`DiGraph`\n", + "* Directed graphs, that is, graphs with directed edges. Provides operations common to directed graphs, (a subclass of Graph).\n", + "\n", + "`MultiGraph`\n", + "* A flexible graph class that allows multiple undirected edges between pairs of nodes. The additional flexibility leads to some degradation in performance, though usually not significant.\n", + "\n", + "`MultiDiGraph`\n", + "A directed version of a MultiGraph.\n", + "\n", + "**Note: To maximize NetworkX usability, the ArangoDB-NetworkX Adapter creates NetworkX graphs of type `MultiDiGraph`. You can to the NetworkX documentation for converting a `MultiDiGraph` to other types.**\n", + "\n", + "\n", + "However, for now, let's take a look at creating a regular `Graph`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zMpjZ7vDTATD" + }, + "outputs": [], + "source": [ + "import networkx as nx\n", + "G = nx.Graph()\n", + "G.add_edge(1, 2) # default edge data=1\n", + "G.add_edge(2, 3, weight=0.9) # specify edge data" ] }, { "cell_type": "markdown", "metadata": { - "id": "e4QfL37neG8_" + "id": "VxKM5DRqTGVn" }, "source": [ - "Feel free to use to above URL to checkout the UI!" + "Edge attributes can be anything:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T-dCKvIrTIss" + }, + "outputs": [], + "source": [ + "import math\n", + "G.add_edge('y', 'x', function=math.cos)\n", + "G.add_node(math.cos) # any hashable can be a node" ] }, { "cell_type": "markdown", "metadata": { - "id": "7y81WHO8eG8_" + "id": "ETnsO_PITKnt" }, "source": [ - "# Data Import" + "You can add many edges at one time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jvkTwbFYTL9U" + }, + "outputs": [], + "source": [ + "elist = [(1, 2), (2, 3), (1, 4), (4, 2)]\n", + "G.add_edges_from(elist)\n", + "elist = [('a', 'b', 5.0), ('b', 'c', 3.0), ('a', 'c', 1.0), ('c', 'd', 7.3)]\n", + "G.add_weighted_edges_from(elist)" ] }, { "cell_type": "markdown", "metadata": { - "id": "BM0iRYPDeG8_" + "id": "8wg37QolTZEr" }, "source": [ - "We will use an Fraud Detection example graph, explained in more detail in this [interactive notebook](https://colab.research.google.com/github/joerg84/Graph_Powered_ML_Workshop/blob/master/Fraud_Detection.ipynb)." + "### Algorithms\n", + "\n", + "A number of graph algorithms are provided with NetworkX. These include shortest path, and breadth first search, clustering and isomorphism algorithms and others.\n", + "\n", + "As an example here is code to use Dijkstra’s algorithm to find the shortest weighted path:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UMLpxD-XTjmT", + "outputId": "039592b3-7442-4581-ba88-409404ae8598" + }, + "outputs": [], + "source": [ + "G = nx.Graph()\n", + "e = [('a', 'b', 0.3), ('b', 'c', 0.9), ('a', 'c', 0.5), ('c', 'd', 1.2)]\n", + "G.add_weighted_edges_from(e)\n", + "print(nx.dijkstra_path(G, 'a', 'd'))" ] }, { "cell_type": "markdown", "metadata": { - "id": "1jWclaDdeG8_" + "id": "BFp9piLRTtjZ" }, "source": [ - "*Note the included arangorestore will only work on Linux system, if you want to run this notebook on a different OS please consider using the appropriate arangorestore from the [Download area](https://www.arangodb.com/download-major/).*" + "### Drawing\n", + "\n", + "While NetworkX is not designed as a network drawing tool, we provide a simple interface to drawing packages and some simple layout algorithms.\n", + "\n", + "The basic drawing functions essentially place the nodes on a scatterplot using the positions you provide via a dictionary or the positions are computed with a layout function. The edges are lines between those dots:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "7bgGJ3QkeG8_" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "Q7IBxct0TzzA", + "outputId": "2f272f36-0ddd-49ea-c8a2-7f993cf1ebc9" }, "outputs": [], "source": [ - "%%capture\n", - "!chmod -R 755 ./tools\n", - "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --default-replication-factor 3 --input-directory \"data/fraud_dump\"\n", - "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --default-replication-factor 3 --input-directory \"data/imdb_dump\"" + "import matplotlib.pyplot as plt\n", + "G = nx.cubical_graph()\n", + "subax1 = plt.subplot(121)\n", + "nx.draw(G) # default spring_layout\n", + "subax2 = plt.subplot(122)\n", + "nx.draw(G, pos=nx.circular_layout(G), node_color='r', edge_color='b')" ] }, { "cell_type": "markdown", "metadata": { - "id": "227hLXnPeG8_" + "id": "GdFBSsDLT3d5" + }, + "source": [ + "### Data Structure\n", + "\n", + "NetworkX uses a “dictionary of dictionaries of dictionaries” as the basic network data structure. This allows fast lookup with reasonable storage for large sparse networks. The keys are nodes so G[u] returns an adjacency dictionary keyed by neighbor to the edge attribute dictionary. A view of the adjacency data structure is provided by the dict-like object G.adj as e.g. for node, nbrsdict in G.adj.items():. The expression G[u][v] returns the edge attribute dictionary itself. A dictionary of lists would have also been possible, but not allow fast edge detection nor convenient storage of edge data.\n", + "\n", + "As an example, here is a representation of an undirected graph with the edges " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "L1Y-xc-DUAGd", + "outputId": "3d7b14a4-75cf-48c6-adb9-51828c5bdddf" }, + "outputs": [], "source": [ - "# Create Graph" + "G = nx.Graph()\n", + "G.add_edge('A', 'B')\n", + "G.add_edge('B', 'C')\n", + "print(G.adj)" ] }, { "cell_type": "markdown", "metadata": { - "id": "howeguvmeG8_" + "id": "7BC1sqmwUXjS" }, "source": [ - "The graph we will be using in the following looks as follows:" + "Graphs provide two interfaces to the edge data attributes: adjacency and edges. So G[u][v]['width'] is the same as G.edges[u, v]['width']." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "w9aZcCeoUZbz", + "outputId": "7786f795-16d1-47a7-cd8d-9873b92c2d76" + }, + "outputs": [], + "source": [ + "G = nx.Graph()\n", + "G.add_edge(1, 2, color='red', weight=0.84, size=300)\n", + "print(G[1][2]['size'])\n", + "print(G.edges[1, 2]['color'])" ] }, { "cell_type": "markdown", "metadata": { - "id": "WqRlqnJCeG8_" + "id": "Oc__NAd1eG8-" }, "source": [ - "![networkX](https://github.com/arangoml/networkx-adapter/blob/master/examples/assets/fraud_graph.jpeg?raw=1) " + "# Create a Temporary ArangoDB Oasis Instance" ] }, { @@ -206,39 +329,64 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "id": "PybHP7jpeG8_", - "outputId": "a545f1a9-3b0b-4d83-f750-02070a0f17ed" + "id": "2ekGwnJDeG8-", + "outputId": "0c0d2be0-f1c5-4d1a-fec1-a52e713c0788" }, "outputs": [], "source": [ - "edge_definitions = [\n", - " {\n", - " \"edge_collection\": \"accountHolder\",\n", - " \"from_vertex_collections\": [\"customer\"],\n", - " \"to_vertex_collections\": [\"account\"],\n", - " },\n", - " {\n", - " \"edge_collection\": \"transaction\",\n", - " \"from_vertex_collections\": [\"account\"],\n", - " \"to_vertex_collections\": [\"account\"],\n", - " },\n", - "]\n", + "# Request temporary instance from the managed ArangoDB Cloud Oasis.\n", + "con = oasis.getTempCredentials()\n", "\n", - "name = \"fraud-detection\"\n", - "python_arango_db_driver.delete_graph(name, ignore_missing=True)\n", - "fraud_graph = python_arango_db_driver.create_graph(name, edge_definitions=edge_definitions)\n", + "# Connect to the db via the python-arango driver\n", + "db = oasis.connect_python_arango(con)\n", "\n", - "print(\"Graph Setup done.\")\n", - "print(fraud_graph)" + "print('\\n--------------------')\n", + "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", + "print(\"Username: \" + con[\"username\"])\n", + "print(\"Password: \" + con[\"password\"])\n", + "print(\"Database: \" + con[\"dbName\"])\n", + "print('--------------------\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e4QfL37neG8_" + }, + "source": [ + "Feel free to use to above URL to checkout the UI!" ] }, { "cell_type": "markdown", "metadata": { - "id": "ANrsn9GreG9A" + "id": "7y81WHO8eG8_" }, "source": [ - "Feel free to visit the ArangoDB UI using the above link and login data and check the Graph!" + "# Data Import" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BM0iRYPDeG8_" + }, + "source": [ + "For demo purposes, we will be using the [ArangoDB Fraud Detection example graph](https://colab.research.google.com/github/joerg84/Graph_Powered_ML_Workshop/blob/master/Fraud_Detection.ipynb), and the [ArangoDB IMDB Dataset](https://github.com/arangodb/example-datasets/tree/master/Graphs/IMDB)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7bgGJ3QkeG8_" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!chmod -R 755 ./tools\n", + "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"networkx-adapter/examples/data/fraud_dump\" --include-system-collections true\n", + "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"networkx-adapter/examples/data/imdb_dump\" --include-system-collections true" ] }, { @@ -247,7 +395,7 @@ "id": "QfE_tKxneG9A" }, "source": [ - "# Create Adapter" + "# Instantiate the Adapter" ] }, { @@ -256,7 +404,7 @@ "id": "kGfhzPT9eG9A" }, "source": [ - "Connect the ArangoDB_Networkx_Adapter to our temp ArangoDB cluster:" + "Connect the ArangoDB-Networkx Adapter to our temporary ArangoDB cluster:" ] }, { @@ -267,7 +415,7 @@ "base_uri": "https://localhost:8080/" }, "id": "oG496kBeeG9A", - "outputId": "456d6ad6-9c1e-4301-9777-e2cff12dc9eb" + "outputId": "dd3b9aa1-0e6c-4bbf-8510-2b9f389d31be" }, "outputs": [], "source": [ @@ -293,16 +441,32 @@ "## Via ArangoDB Graph" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "oIJE69k8XERE" + }, + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Graph\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_graph_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L160-L179)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case must point to an existing ArangoDB graph in your ArangoDB instance. \n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 394 + "height": 450 }, "id": "zZ-Hu3lLVHgd", - "outputId": "0df3a807-2c29-4d3e-e48d-fb2cd2e05a75" + "outputId": "513a5f36-6a25-4a79-df48-533a9ec5c273" }, "outputs": [], "source": [ @@ -317,6 +481,8 @@ "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", "\n", "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", "print(nx_g.nodes(data=True))\n", "print(nx_g.edges(data=True))\n", "nx.draw(nx_g, with_labels=True)" @@ -331,16 +497,33 @@ "## Via ArangoDB Collections" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "wVCEdRB8YdLW" + }, + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_collections_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L130-L158)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `vertex_collections` & `edge_collections` parameters must point to existing ArangoDB collections within your ArangoDB instance." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 394 + "height": 0 }, "id": "i4XOpdRLUNlJ", - "outputId": "655a589f-2e65-4d45-808d-24cd3d8b3b28" + "outputId": "e8e233c4-7dd2-4786-8ba2-66bd34a657f4" }, "outputs": [], "source": [ @@ -356,6 +539,8 @@ "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", "\n", "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", "print(nx_g.nodes(data=True))\n", "print(nx_g.edges(data=True))\n", "nx.draw(nx_g, with_labels=True)" @@ -370,16 +555,33 @@ "## Via ArangoDB Metagraph" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "9c4OuEniY_UA" + }, + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L55-L128)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `metagraph` parameter should contain collections & associated document attributes names that exist within your ArangoDB instance." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 394 + "height": 450 }, "id": "UWX9-MsKeG9A", - "outputId": "afbf452e-f293-4cce-a3be-d3c03499b114" + "outputId": "17f0726f-7423-4384-90df-17de1171df10" }, "outputs": [], "source": [ @@ -407,6 +609,8 @@ "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", "\n", "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", "print(nx_g.nodes(data=True))\n", "print(nx_g.edges(data=True))\n", "\n", @@ -422,6 +626,25 @@ "## Via ArangoDB Metagraph with a custom controller" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "sjtWpTDbZpLq" + }, + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L55-L128)\n", + "* [`adbnx_adapter.controller._prepare_arangodb_vertex()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L20-L36)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `metagraph` parameter should contain collections & associated document attributes names that exist within your ArangoDB instance.\n", + "* We are creating a custom `ADBNX_Controller` to specify *how* to convert our ArangoDB vertices into NetworkX nodes. View the default `ADBNX_Controller` [here](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L10)." + ] + }, { "cell_type": "code", "execution_count": null, @@ -430,7 +653,7 @@ "base_uri": "https://localhost:8080/" }, "id": "QqGgOe51Vr85", - "outputId": "c5470702-b9fd-4476-d0c5-3c2a18254901" + "outputId": "4c2566b3-74f1-44df-f1c8-845419b12960" }, "outputs": [], "source": [ @@ -459,7 +682,7 @@ " In most cases, it is only required to return the ArangoDB _id of the vertex.\n", "\n", " :param vertex: The ArangoDB vertex object to (optionally) modify.\n", - " :type vertex: dict\n", + " :type vertex: adbnx_adapter.typings.Json\n", " :param col: The ArangoDB collection the vertex belongs to.\n", " :type col: str\n", " :return: The ArangoDB _id attribute of the vertex.\n", @@ -472,7 +695,7 @@ " # def _prepare_arangodb_edge(self, adb_edge: Json, col: str) -> NxId:\n", " # return super()._prepare_arangodb_edge(edge, collection)\n", "\n", - "# Instantiate the adapter\n", + "# Instantiate the custom adapter\n", "imdb_adbnx_adapter = ADBNX_Adapter(con, IMDB_ADBNX_Controller())\n", "\n", "# Create NetworkX Graph from metagraph using the custom IMDB_ArangoDB_Networx_Adapter\n", @@ -483,6 +706,8 @@ "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", "\n", "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", "print(nx_g.nodes(data=True))\n", "# print(nx_g.edges(data=True)) # (will exceed IOPub data rate)\n", "# nx.draw(nx_g, with_labels=True) # (will exceed IOPub data rate)" @@ -506,16 +731,34 @@ "## Example 1: NetworkX Grid Graph" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "2HtrFI81bitp" + }, + "source": [ + "Data source\n", + "* [NetworkX Grid Graph](https://networkx.org/documentation/stable/auto_examples/basic/plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py)\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.networkx_to_arangodb()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L181-L311)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your ArangoDB graph.\n", + "* The `edge_definitions` parameter should contain a list of valid vertex relationships along with their related edge collections. See its [official documentation](https://docs.python-arango.com/en/main/graph.html#edge-definitions) for more details.\n", + "* We are using a `batch_size` value of 1 to demo to users that this feature exists. It is not recommended to use a `batch_size` value of 1 in a real setting." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 522 + "height": 0 }, "id": "eRVbiBy4ZdE4", - "outputId": "8322de0d-fa90-4ce6-e452-2567892e8fec" + "outputId": "2b7a0834-4453-486e-e7be-b2cfe22f24fb" }, "outputs": [], "source": [ @@ -535,7 +778,7 @@ "\n", "# Create the ArangoDB graph\n", "name = \"Grid_1\"\n", - "python_arango_db_driver.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", "grid_adb_g = adbnx_adapter.networkx_to_arangodb(name, grid_nx_g, edge_definitions, batch_size=1)\n", "\n", "print('\\n--------------------')\n", @@ -557,11 +800,36 @@ "## Example 2: NetworkX Football Graph" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "1GRHJg7mcfyq" + }, + "source": [ + "Data source\n", + "* [NetworkX Football Graph](https://networkx.org/documentation/stable/auto_examples/graph/plot_football.html)\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.networkx_to_arangodb()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L181-L311)\n", + "* [`adbnx_adapter.controller._keyify_networkx_node()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L101-L121)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your ArangoDB graph.\n", + "* The `edge_definitions` parameter should contain a list of valid vertex relationships along with their related edge collections. See its [official documentation](https://docs.python-arango.com/en/main/graph.html#edge-definitions) for more details.\n", + "* We are creating a custom `ADBNX_Controller` to specify *how* to convert our NetworkX nodes into ArangoDB vertices. View the default `ADBNX_Controller` [here](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L10).\n", + " * This is a unique case where the node IDs of the NetworkX Football graphs are of type string. We need to make sure that these string **do not** contain any [invalid characters](https://www.arangodb.com/docs/stable/data-modeling-naming-conventions-document-keys.html), so we use a built-in helper method." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "dADiexlAioGH" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "dADiexlAioGH", + "outputId": "1f347187-a6ae-41ac-c7d4-4eaa697beeee" }, "outputs": [], "source": [ @@ -626,7 +894,7 @@ "\n", "# Create the ArangoDB graph\n", "name = \"Football\"\n", - "python_arango_db_driver.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", "football_adb_g = football_adbnx_adapter.networkx_to_arangodb(name, football_nx_g, edge_definitions, keyify_nodes=True)\n", "\n", "print('\\n--------------------')\n", @@ -666,14 +934,14 @@ "height": 374 }, "id": "2wmcH2hgqLQq", - "outputId": "0945594c-3a94-4c21-fb79-6a66c9b7351e" + "outputId": "510e305f-1949-4294-87e3-b61f617a8c67" }, "outputs": [], "source": [ "name = \"fraud-detection\"\n", "\n", "# Start from ArangoDB graph\n", - "original_fraud_adb_g = python_arango_db_driver.graph(name)\n", + "original_fraud_adb_g = db.graph(name)\n", "edge_definitions = original_fraud_adb_g.edge_definitions()\n", "\n", "# Create NetworkX graph from ArangoDB graph\n", @@ -715,17 +983,17 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 411 + "height": 0 }, "id": "BbPkJAEEoVjM", - "outputId": "a2830f47-f480-4945-a7e2-a5e668c2fb40" + "outputId": "0526a33c-3137-4f17-f246-fea3d7ed53e8" }, "outputs": [], "source": [ "name = \"fraud-detection\"\n", "\n", "# Start from ArangoDB graph\n", - "original_fraud_adb_g = python_arango_db_driver.graph(name) \n", + "original_fraud_adb_g = db.graph(name) \n", "\n", "# Create NetworkX graph from ArangoDB graph\n", "fraud_nx_g = adbnx_adapter.arangodb_graph_to_networkx(name)\n", @@ -778,10 +1046,14 @@ " \"\"\"Given a NetworkX edge, and its pair of nodes, identify what ArangoDB\n", " collection should it belong to.\n", "\n", - " NOTE: You must override this function if your NetworkX graph is NOT Homogeneous\n", + " NOTE #1: You must override this function if your NetworkX graph is NOT Homogeneous\n", " or does NOT comply to ArangoDB standards\n", " (i.e the edge IDs are not formatted like \"{collection}/{key}\").\n", "\n", + " NOTE #2: You can accesss the ID & Collection belonging to the\n", + " **from_nx_node** & **to_nx_node** parameters via their \"nx_id\" & \"col\"\n", + " attribute keys. E.g `from_collection = from_nx_node[\"col\"]`\n", + "\n", " :param nx_edge: The NetworkX edge object.\n", " :type nx_edge: adbnx_adapter.typings.NxData\n", " :param from_nx_node: The NetworkX node object representing the edge source.\n", @@ -794,11 +1066,12 @@ " adb_vertex_id: str = str(nx_edge[\"_id\"])\n", " return adb_vertex_id.split(\"/\")[0] + \"_new\"\n", "\n", + "\n", "fraud_adbnx_adapter = ADBNX_Adapter(con, Fraud_ADBNX_Controller())\n", "\n", "# Create a new ArangoDB graph from NetworkX graph\n", "new_name = name + \"_new\"\n", - "python_arango_db_driver.delete_graph(new_name, drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(new_name, drop_collections=True, ignore_missing=True)\n", "# Keify nodes & edges to keep the same key values as original (this is optional)\n", "new_fraud_adb_g = fraud_adbnx_adapter.networkx_to_arangodb(\n", " new_name,\n", @@ -827,10 +1100,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 467 + "height": 0 }, "id": "TFSM1Xegq9TR", - "outputId": "34f9c243-4151-44d8-94db-67a47d899ca5" + "outputId": "dcbb098b-b05d-4b6e-cc28-5fb62554891d" }, "outputs": [], "source": [ @@ -908,7 +1181,7 @@ "\n", "# Delete the Grid graph if it already exists in ArangoDB\n", "name = \"Grid_2\"\n", - "python_arango_db_driver.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", "\n", "# Create the ArangoDB graph\n", "grid_adbnx_adapter.networkx_to_arangodb(name, original_grid_nx_g, edge_definitions, keyify_nodes=True)\n", @@ -926,14 +1199,26 @@ "metadata": { "colab": { "collapsed_sections": [ + "q8KesL7xeG89", + "KS9c-vE5eG89", + "lRmEM5eaQxJ5", "Oc__NAd1eG8-", "7y81WHO8eG8_", - "227hLXnPeG8_", "QfE_tKxneG9A", "uByvwf9feG9A", - "bvzJXSHHTi3v" + "ZrEDmtqCVD0W", + "RQ4CknYfUEuz", + "umy25EsUU6Lg", + "tWU1YW9AViTA", + "bvzJXSHHTi3v", + "UafSB_3JZNwK", + "gshTlSX_ZZsS", + "5Zl4fQ1AnC_b", + "RTNNqQjpneFV", + "Qh8bYrIqnHTa", + "uV8hpastnmhg" ], - "name": "ArangoDB_NetworkxAdapter_v3.0.0.ipynb", + "name": "ArangoDB_NetworkX_Adapter_v3.ipynb", "provenance": [] }, "kernelspec": { diff --git a/examples/ITSM_ArangoDB_Adapter.ipynb b/examples/ITSM_ArangoDB_Adapter.ipynb index 117f5e87..5e5d5d8c 100644 --- a/examples/ITSM_ArangoDB_Adapter.ipynb +++ b/examples/ITSM_ArangoDB_Adapter.ipynb @@ -11,7 +11,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "vM59y6qrlvjU" + }, "source": [ "\"Open" ] @@ -89,12 +91,18 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "NhIdp2rNaos7" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NhIdp2rNaos7", + "outputId": "0928da40-11a8-4e24-8a2e-428b9f4a957f" }, "outputs": [], "source": [ + "import time\n", "import oasis\n", "con = oasis.getTempCredentials()\n", + "time.sleep(5)\n", "\n", "print()\n", "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", @@ -125,7 +133,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "o5Q1aESiatNB" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o5Q1aESiatNB", + "outputId": "d95bf0d2-9484-408b-d841-e0d5c7abc0c8" }, "outputs": [], "source": [ @@ -185,7 +197,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "3qACbcQBbLEx" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3qACbcQBbLEx", + "outputId": "61a940f3-32f2-41b0-c230-094ca37b1fb5" }, "outputs": [], "source": [ @@ -197,7 +213,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yBIgn6fGbPo1" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yBIgn6fGbPo1", + "outputId": "806a73b5-7f63-4a60-d93a-8c4c57fd9e2a" }, "outputs": [], "source": [ @@ -218,7 +238,12 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "5KL6fN4wbfW3" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 247 + }, + "id": "5KL6fN4wbfW3", + "outputId": "25a9cfcc-ceee-45cf-b40e-a80278b61cc3" }, "outputs": [], "source": [ @@ -416,7 +441,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "XH-d0z3ibzJ0" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XH-d0z3ibzJ0", + "outputId": "ee2f17b9-5f51-4448-8bcd-f864e088ae3d" }, "outputs": [], "source": [ @@ -472,8 +501,8 @@ }, "outputs": [], "source": [ - "!rm creds.dat\n", - "!touch creds.dat\n" + "# !rm creds.dat\n", + "# !touch creds.dat" ] }, { @@ -484,8 +513,8 @@ }, "outputs": [], "source": [ - "from itsm_data_load_driver import load_ITSM_data_to_ArangoDB\n", - "itsmdl = load_ITSM_data_to_ArangoDB()" + "# from itsm_data_load_driver import load_ITSM_data_to_ArangoDB\n", + "# itsmdl = load_ITSM_data_to_ArangoDB()" ] }, { @@ -496,12 +525,13 @@ }, "outputs": [], "source": [ - "#arangodump --server.endpoint --server.username --server.database --server.password --output-directory dgl_data_dump\n" + "# arangodump --server.endpoint --server.username --server.database --server.password --output-directory dgl_data_dump\n" ] } ], "metadata": { "colab": { + "collapsed_sections": [], "name": "ITSM_ArangoDB_Adapter.ipynb", "provenance": [] }, diff --git a/examples/outputs/ArangoDB_NetworkX_Adapter_output.ipynb b/examples/outputs/ArangoDB_NetworkX_Adapter_output.ipynb new file mode 100644 index 00000000..5f61ac48 --- /dev/null +++ b/examples/outputs/ArangoDB_NetworkX_Adapter_output.ipynb @@ -0,0 +1,1504 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "q8KesL7xeG89" + }, + "source": [ + "# ArangoDB NetworkX Adapter Getting Started Guide " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U1d45V4OeG89" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nx9aa3LAeG89" + }, + "source": [ + "![arangodb](https://github.com/arangoml/networkx-adapter/blob/master/examples/assets/logos/ArangoDB_logo.png?raw=1)\n", + "![networkX](https://github.com/arangoml/networkx-adapter/blob/master/examples/assets/logos/networkx_logo.svg?raw=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bpvZS-1aeG89" + }, + "source": [ + "Version: 3.0.1\n", + "\n", + "Objective: Export Graphs from [ArangoDB](https://www.arangodb.com/), a multi-model Graph Database, to [NetworkX](https://networkx.github.io/), the swiss army knife for graph analysis in python, and vice-versa." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KS9c-vE5eG89" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "fUnFAFAheG89" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!git clone -b oasis_connector --single-branch https://github.com/arangodb/interactive_tutorials.git\n", + "!git clone -b 3.0.1 --single-branch https://github.com/arangoml/networkx-adapter.git\n", + "!rsync -av interactive_tutorials/ ./ --exclude=.git\n", + "!pip3 install adbnx_adapter==3.0.1\n", + "!pip3 install matplotlib\n", + "!pip3 install pyArango" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "RpqvL4COeG8-" + }, + "outputs": [], + "source": [ + "import json\n", + "import oasis\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import networkx as nx\n", + "\n", + "from adbnx_adapter.adapter import ADBNX_Adapter\n", + "from adbnx_adapter.controller import ADBNX_Controller\n", + "from adbnx_adapter.typings import Json, ArangoMetagraph, NxId, NxData" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Understanding NetworkX" + ], + "metadata": { + "id": "lRmEM5eaQxJ5" + } + }, + { + "cell_type": "markdown", + "source": [ + "(referenced from [networkx.org/documentation](https://networkx.org/documentation/stable/reference/index.html))\n", + "\n", + "NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides:\n", + "* tools for the study of the structure and dynamics of social, biological, and infrastructure networks;\n", + "* a standard programming interface and graph implementation that is suitable for many applications;\n", + "* a rapid development environment for collaborative, multidisciplinary projects;\n", + "* an interface to existing numerical algorithms and code written in C, C++, and FORTRAN; and\n", + "* the ability to painlessly work with large nonstandard data sets.\n", + "\n", + "With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more.\n", + "\n", + "\n", + "The following basic graph types are provided as Python classes:\n", + "\n", + "`Graph`\n", + "* This class implements an undirected graph. It ignores multiple edges between two nodes. It does allow self-loop edges between a node and itself.\n", + "\n", + "`DiGraph`\n", + "* Directed graphs, that is, graphs with directed edges. Provides operations common to directed graphs, (a subclass of Graph).\n", + "\n", + "`MultiGraph`\n", + "* A flexible graph class that allows multiple undirected edges between pairs of nodes. The additional flexibility leads to some degradation in performance, though usually not significant.\n", + "\n", + "`MultiDiGraph`\n", + "A directed version of a MultiGraph.\n", + "\n", + "**Note: To maximize NetworkX usability, the ArangoDB-NetworkX Adapter creates NetworkX graphs of type `MultiDiGraph`. You can to the NetworkX documentation for converting a `MultiDiGraph` to other types.**\n", + "\n", + "\n", + "However, for now, let's take a look at creating a regular `Graph`:" + ], + "metadata": { + "id": "r7sRg9j2Q2ef" + } + }, + { + "cell_type": "code", + "source": [ + "import networkx as nx\n", + "G = nx.Graph()\n", + "G.add_edge(1, 2) # default edge data=1\n", + "G.add_edge(2, 3, weight=0.9) # specify edge data" + ], + "metadata": { + "id": "zMpjZ7vDTATD" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Edge attributes can be anything:" + ], + "metadata": { + "id": "VxKM5DRqTGVn" + } + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "G.add_edge('y', 'x', function=math.cos)\n", + "G.add_node(math.cos) # any hashable can be a node" + ], + "metadata": { + "id": "T-dCKvIrTIss" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "You can add many edges at one time:" + ], + "metadata": { + "id": "ETnsO_PITKnt" + } + }, + { + "cell_type": "code", + "source": [ + "elist = [(1, 2), (2, 3), (1, 4), (4, 2)]\n", + "G.add_edges_from(elist)\n", + "elist = [('a', 'b', 5.0), ('b', 'c', 3.0), ('a', 'c', 1.0), ('c', 'd', 7.3)]\n", + "G.add_weighted_edges_from(elist)" + ], + "metadata": { + "id": "jvkTwbFYTL9U" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Algorithms\n", + "\n", + "A number of graph algorithms are provided with NetworkX. These include shortest path, and breadth first search, clustering and isomorphism algorithms and others.\n", + "\n", + "As an example here is code to use Dijkstra’s algorithm to find the shortest weighted path:" + ], + "metadata": { + "id": "8wg37QolTZEr" + } + }, + { + "cell_type": "code", + "source": [ + "G = nx.Graph()\n", + "e = [('a', 'b', 0.3), ('b', 'c', 0.9), ('a', 'c', 0.5), ('c', 'd', 1.2)]\n", + "G.add_weighted_edges_from(e)\n", + "print(nx.dijkstra_path(G, 'a', 'd'))" + ], + "metadata": { + "id": "UMLpxD-XTjmT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b75c495-1957-482e-cd14-86d0b21762b5" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['a', 'c', 'd']\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Drawing\n", + "\n", + "While NetworkX is not designed as a network drawing tool, we provide a simple interface to drawing packages and some simple layout algorithms.\n", + "\n", + "The basic drawing functions essentially place the nodes on a scatterplot using the positions you provide via a dictionary or the positions are computed with a layout function. The edges are lines between those dots:" + ], + "metadata": { + "id": "BFp9piLRTtjZ" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "G = nx.cubical_graph()\n", + "subax1 = plt.subplot(121)\n", + "nx.draw(G) # default spring_layout\n", + "subax2 = plt.subplot(122)\n", + "nx.draw(G, pos=nx.circular_layout(G), node_color='r', edge_color='b')" + ], + "metadata": { + "id": "Q7IBxct0TzzA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "48e3ff7d-ce94-41f3-a94c-ba31f6b2b0e7" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Data Structure\n", + "\n", + "NetworkX uses a “dictionary of dictionaries of dictionaries” as the basic network data structure. This allows fast lookup with reasonable storage for large sparse networks. The keys are nodes so G[u] returns an adjacency dictionary keyed by neighbor to the edge attribute dictionary. A view of the adjacency data structure is provided by the dict-like object G.adj as e.g. for node, nbrsdict in G.adj.items():. The expression G[u][v] returns the edge attribute dictionary itself. A dictionary of lists would have also been possible, but not allow fast edge detection nor convenient storage of edge data.\n", + "\n", + "As an example, here is a representation of an undirected graph with the edges " + ], + "metadata": { + "id": "GdFBSsDLT3d5" + } + }, + { + "cell_type": "code", + "source": [ + "G = nx.Graph()\n", + "G.add_edge('A', 'B')\n", + "G.add_edge('B', 'C')\n", + "print(G.adj)" + ], + "metadata": { + "id": "L1Y-xc-DUAGd", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "11daac1a-224e-4b0b-a285-5f057c2a0bef" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'A': {'B': {}}, 'B': {'A': {}, 'C': {}}, 'C': {'B': {}}}\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Graphs provide two interfaces to the edge data attributes: adjacency and edges. So G[u][v]['width'] is the same as G.edges[u, v]['width']." + ], + "metadata": { + "id": "7BC1sqmwUXjS" + } + }, + { + "cell_type": "code", + "source": [ + "G = nx.Graph()\n", + "G.add_edge(1, 2, color='red', weight=0.84, size=300)\n", + "print(G[1][2]['size'])\n", + "print(G.edges[1, 2]['color'])" + ], + "metadata": { + "id": "w9aZcCeoUZbz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "efe27cb7-f965-49d8-83f1-24ca807fa5af" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "300\n", + "red\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oc__NAd1eG8-" + }, + "source": [ + "# Create a Temporary ArangoDB Oasis Instance" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "2ekGwnJDeG8-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c767e11b-b363-4845-9f50-ed0dd67879d4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requesting new temp credentials.\n", + "Temp database ready to use.\n", + "\n", + "--------------------\n", + "https://tutorials.arangodb.cloud:8529\n", + "Username: TUT95b328zdyqvu6icc2szy7l\n", + "Password: TUTqy7aejhetdpkpplf7nwbai\n", + "Database: TUTl7w91pj995boxn9f4etz6\n", + "--------------------\n", + "\n" + ] + } + ], + "source": [ + "# Request temporary instance from the managed ArangoDB Cloud Oasis.\n", + "con = oasis.getTempCredentials()\n", + "\n", + "# Connect to the db via the python-arango driver\n", + "db = oasis.connect_python_arango(con)\n", + "\n", + "print('\\n--------------------')\n", + "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", + "print(\"Username: \" + con[\"username\"])\n", + "print(\"Password: \" + con[\"password\"])\n", + "print(\"Database: \" + con[\"dbName\"])\n", + "print('--------------------\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e4QfL37neG8_" + }, + "source": [ + "Feel free to use to above URL to checkout the UI!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7y81WHO8eG8_" + }, + "source": [ + "# Data Import" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BM0iRYPDeG8_" + }, + "source": [ + "For demo purposes, we will be using the [ArangoDB Fraud Detection example graph](https://colab.research.google.com/github/joerg84/Graph_Powered_ML_Workshop/blob/master/Fraud_Detection.ipynb), and the [ArangoDB IMDB Dataset](https://github.com/arangodb/example-datasets/tree/master/Graphs/IMDB)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "7bgGJ3QkeG8_" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!chmod -R 755 ./tools\n", + "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"networkx-adapter/examples/data/fraud_dump\" --include-system-collections true\n", + "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"networkx-adapter/examples/data/imdb_dump\" --include-system-collections true" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QfE_tKxneG9A" + }, + "source": [ + "# Instantiate the Adapter" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kGfhzPT9eG9A" + }, + "source": [ + "Connect the ArangoDB-Networkx Adapter to our temporary ArangoDB cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "oG496kBeeG9A", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4358324c-95ea-4657-82b2-629804c6151a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Connecting to https://tutorials.arangodb.cloud:8529\n" + ] + } + ], + "source": [ + "adbnx_adapter = ADBNX_Adapter(con)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uByvwf9feG9A" + }, + "source": [ + "# ArangoDB to NetworkX\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZrEDmtqCVD0W" + }, + "source": [ + "## Via ArangoDB Graph" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Graph\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_graph_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L160-L179)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case must point to an existing ArangoDB graph in your ArangoDB instance. \n" + ], + "metadata": { + "id": "oIJE69k8XERE" + } + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "zZ-Hu3lLVHgd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "4d7ba8a2-b36e-4ce6-a88a-03a80838f74a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NetworkX: fraud-detection created\n", + "\n", + "--------------------\n", + "MultiDiGraph named 'fraud-detection' with 71 nodes and 116 edges\n", + "[('account/10000011', {'_key': '10000011', '_id': 'account/10000011', '_rev': '_dfEQim----', 'Balance': 5331, 'Status': 'active', 'account_opening_date': '2018-3-13', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000016', {'_key': '10000016', '_id': 'account/10000016', '_rev': '_dfEQim---_', 'Balance': 7630, 'Status': 'active', 'account_opening_date': '2018-10-15', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000004, 'rank': 0.003122549969702959}), ('account/10000003', {'_key': '10000003', '_id': 'account/10000003', '_rev': '_dfEQim---A', 'Balance': 1433, 'Status': 'active', 'account_opening_date': '2017-10-24', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000004, 'rank': 0.00524971354752779}), ('account/10000029', {'_key': '10000029', '_id': 'account/10000029', '_rev': '_dfEQim---B', 'Balance': 2201, 'Status': 'active', 'account_opening_date': '2017-10-25', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/10000005', {'_key': '10000005', '_id': 'account/10000005', '_rev': '_dfEQim---C', 'Balance': 4837, 'Status': 'active', 'account_opening_date': '2017-2-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000002, 'rank': 0.004550427198410034}), ('account/10000032', {'_key': '10000032', '_id': 'account/10000032', '_rev': '_dfEQim---D', 'Balance': 5817, 'Status': 'active', 'account_opening_date': '2018-9-14', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000011, 'rank': 0.0036875137593597174}), ('account/10000039', {'_key': '10000039', '_id': 'account/10000039', '_rev': '_dfEQim---E', 'Balance': 1689, 'Status': 'active', 'account_opening_date': '2017-12-26', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000028', {'_key': '10000028', '_id': 'account/10000028', '_rev': '_dfEQim---F', 'Balance': 1042, 'Status': 'active', 'account_opening_date': '2018-9-15', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000006, 'rank': 0.004198686685413122}), ('account/10000020', {'_key': '10000020', '_id': 'account/10000020', '_rev': '_dfEQim---G', 'Balance': 4104, 'Status': 'active', 'account_opening_date': '2017-5-19', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/orphan_Account_1', {'_key': 'orphan_Account_1', '_id': 'account/orphan_Account_1', '_rev': '_dfEQim---H', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/10000006', {'_key': '10000006', '_id': 'account/10000006', '_rev': '_dfEQim---I', 'Balance': 2338, 'Status': 'active', 'account_opening_date': '2017-6-21', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000002, 'rank': 0.003010563552379608}), ('account/1000053', {'_key': '1000053', '_id': 'account/1000053', '_rev': '_dfEQim---J', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10000014, 'rank': 0.003747815964743495}), ('account/10000013', {'_key': '10000013', '_id': 'account/10000013', '_rev': '_dfEQim---K', 'Balance': 3779, 'Status': 'active', 'account_opening_date': '2018-4-23', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000008, 'rank': 0.004046608693897724}), ('account/1000054', {'_key': '1000054', '_id': 'account/1000054', '_rev': '_dfEQim---L', 'rank': 0.003705498529598117}), ('account/10000012', {'_key': '10000012', '_id': 'account/10000012', '_rev': '_dfEQim---M', 'Balance': 529, 'Status': 'active', 'account_opening_date': '2018-4-15', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/1000050', {'_key': '1000050', '_id': 'account/1000050', '_rev': '_dfEQim---N', 'rank': 0.004632922820746899}), ('account/10000001', {'_key': '10000001', '_id': 'account/10000001', '_rev': '_dfEQim---O', 'Balance': 1992, 'Status': 'active', 'account_opening_date': '2017-1-23', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000008, 'rank': 0.0040132044814527035}), ('account/10000014', {'_key': '10000014', '_id': 'account/10000014', '_rev': '_dfEQim---P', 'Balance': 2912, 'Status': 'active', 'account_opening_date': '2017-12-16', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000006, 'rank': 0.003010563552379608}), ('account/10000034', {'_key': '10000034', '_id': 'account/10000034', '_rev': '_dfEQim---Q', 'Balance': 6367, 'Status': 'active', 'account_opening_date': '2017-12-5', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000012, 'rank': 0.0026350750122219324}), ('account/10000042', {'_key': '10000042', '_id': 'account/10000042', '_rev': '_dfEQim---R', 'Balance': 1819, 'Status': 'active', 'account_opening_date': '2017-3-23', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/4149551', {'_key': '4149551', '_id': 'account/4149551', '_rev': '_dfEQim---S', 'account_type': 'checking', 'customer_id': 10000001, 'rank': 0.0021126761566847563}), ('account/10000008', {'_key': '10000008', '_id': 'account/10000008', '_rev': '_dfEQim---T', 'Balance': 221, 'Status': 'active', 'account_opening_date': '2017-5-9', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.0033510124776512384}), ('account/10000040', {'_key': '10000040', '_id': 'account/10000040', '_rev': '_dfEQim---U', 'Balance': 5062, 'Status': 'active', 'account_opening_date': '2018-7-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000002', {'_key': '10000002', '_id': 'account/10000002', '_rev': '_dfEQim---V', 'Balance': 2372, 'Status': 'active', 'account_opening_date': '2018-12-12', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000005, 'rank': 0.0021126761566847563}), ('account/10000009', {'_key': '10000009', '_id': 'account/10000009', '_rev': '_dfEQim---W', 'Balance': 841, 'Status': 'active', 'account_opening_date': '2018-2-25', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10004, 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000026', {'_key': '10000026', '_id': 'account/10000026', '_rev': '_dfEQim---X', 'Balance': 5393, 'Status': 'active', 'account_opening_date': '2018-10-25', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000008, 'rank': 0.00354181369766593}), ('account/10000033', {'_key': '10000033', '_id': 'account/10000033', '_rev': '_dfEQim---Y', 'Balance': 1138, 'Status': 'active', 'account_opening_date': '2017-4-6', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000011, 'rank': 0.0026350750122219324}), ('account/10000037', {'_key': '10000037', '_id': 'account/10000037', '_rev': '_dfEQim---Z', 'Balance': 8414, 'Status': 'active', 'account_opening_date': '2018-10-17', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000014, 'rank': 0.0026350750122219324}), ('account/10000018', {'_key': '10000018', '_id': 'account/10000018', '_rev': '_dfEQim---a', 'Balance': 4064, 'Status': 'active', 'account_opening_date': '2018-11-27', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000010, 'rank': 0.004585607908666134}), ('account/10000024', {'_key': '10000024', '_id': 'account/10000024', '_rev': '_dfEQim---b', 'Balance': 5686, 'Status': 'active', 'account_opening_date': '2017-11-15', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000006, 'rank': 0.0021126761566847563}), ('account/10000017', {'_key': '10000017', '_id': 'account/10000017', '_rev': '_dfEQim---c', 'Balance': 6294, 'Status': 'active', 'account_opening_date': '2017-9-24', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10010, 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/10000010', {'_key': '10000010', '_id': 'account/10000010', '_rev': '_dfEQim---d', 'Balance': 6540, 'Status': 'active', 'account_opening_date': '2018-2-1', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000007, 'rank': 0.0035368565004318953}), ('account/10000004', {'_key': '10000004', '_id': 'account/10000004', '_rev': '_dfEQim---e', 'Balance': 7358, 'Status': 'active', 'account_opening_date': '2018-5-20', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000006, 'rank': 0.0036158403381705284}), ('account/10000023', {'_key': '10000023', '_id': 'account/10000023', '_rev': '_dfEQim---f', 'Balance': 3452, 'Status': 'active', 'account_opening_date': '2018-1-12', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000005, 'rank': 0.0035642609000205994}), ('account/1000052', {'_key': '1000052', '_id': 'account/1000052', '_rev': '_dfEQim---g', 'rank': 0.0038473859895020723}), ('account/10000025', {'_key': '10000025', '_id': 'account/10000025', '_rev': '_dfEQim---h', 'Balance': 3993, 'Status': 'active', 'account_opening_date': '2018-2-25', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.005468250252306461}), ('account/6149795', {'_key': '6149795', '_id': 'account/6149795', '_rev': '_dfEQim---i', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/1000051', {'_key': '1000051', '_id': 'account/1000051', '_rev': '_dfEQim---j', 'rank': 0.0040816692635416985}), ('account/10000019', {'_key': '10000019', '_id': 'account/10000019', '_rev': '_dfEQim---k', 'Balance': 471, 'Status': 'active', 'account_opening_date': '2017-11-19', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000009, 'rank': 0.0044366829097270966}), ('account/10000022', {'_key': '10000022', '_id': 'account/10000022', '_rev': '_dfEQim---l', 'Balance': 8148, 'Status': 'active', 'account_opening_date': '2018-5-6', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000006, 'rank': 0.0021126761566847563}), ('account/10000031', {'_key': '10000031', '_id': 'account/10000031', '_rev': '_dfEQim---m', 'Balance': 5832, 'Status': 'active', 'account_opening_date': '2018-1-28', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000002, 'rank': 0.003010563552379608}), ('account/10000021', {'_key': '10000021', '_id': 'account/10000021', '_rev': '_dfEQim---n', 'Balance': 1758, 'Status': 'active', 'account_opening_date': '2017-7-6', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000005, 'rank': 0.0034595071338117123}), ('account/10000007', {'_key': '10000007', '_id': 'account/10000007', '_rev': '_dfEQim---o', 'Balance': 1747, 'Status': 'active', 'account_opening_date': '2017-2-3', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000009, 'rank': 0.0033098594285547733}), ('account/10000035', {'_key': '10000035', '_id': 'account/10000035', '_rev': '_dfEQim---p', 'Balance': 1679, 'Status': 'active', 'account_opening_date': '2018-10-18', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000012, 'rank': 0.0026350750122219324}), ('account/10000015', {'_key': '10000015', '_id': 'account/10000015', '_rev': '_dfEQim---q', 'Balance': 6789, 'Status': 'active', 'account_opening_date': '2018-5-3', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000007, 'rank': 0.007116740569472313}), ('account/10000027', {'_key': '10000027', '_id': 'account/10000027', '_rev': '_dfEQim---r', 'Balance': 1599, 'Status': 'active', 'account_opening_date': '2018-7-12', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000002, 'rank': 0.0035642609000205994}), ('account/10000038', {'_key': '10000038', '_id': 'account/10000038', '_rev': '_dfEQim---s', 'Balance': 8320, 'Status': 'active', 'account_opening_date': '2018-5-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000014, 'rank': 0.003232583636417985}), ('account/10000044', {'_key': '10000044', '_id': 'account/10000044', '_rev': '_dfEQim---t', 'rank': 0.005929990671575069}), ('account/6149781', {'_key': '6149781', '_id': 'account/6149781', '_rev': '_dfEQim---u', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/10000043', {'_key': '10000043', '_id': 'account/10000043', '_rev': '_dfEQim---v', 'Balance': 8626, 'Status': 'active', 'account_opening_date': '2018-9-13', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10004, 'customer_id': 10000016, 'rank': 0.008981915190815926}), ('account/10000030', {'_key': '10000030', '_id': 'account/10000030', '_rev': '_dfEQim---w', 'Balance': 7199, 'Status': 'active', 'account_opening_date': '2017-7-24', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10010, 'customer_id': 10000006, 'rank': 0.005735883489251137}), ('account/10000041', {'_key': '10000041', '_id': 'account/10000041', '_rev': '_dfEQim---x', 'Balance': 8644, 'Status': 'active', 'account_opening_date': '2018-8-7', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000016, 'rank': 0.003232583636417985}), ('account/10000036', {'_key': '10000036', '_id': 'account/10000036', '_rev': '_dfEQim---y', 'Balance': 3879, 'Status': 'active', 'account_opening_date': '2017-2-9', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000013, 'rank': 0.0026350750122219324}), ('account/6149748', {'_key': '6149748', '_id': 'account/6149748', '_rev': '_dfEQim---z', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('customer/10000006', {'_key': '10000006', '_id': 'customer/10000006', '_rev': '_dfEQixG---', 'Name': 'Mahdivi Nookala', 'credit_card_number': 10000006, 'Ssn': '123-45-6786', 'Sex': 'F', 'rank': 0.013542247004806995}), ('customer/10000013', {'_key': '10000013', '_id': 'customer/10000013', '_rev': '_dfEQixG--_', 'Name': 'Petronella Brink', 'credit_card_number': 10000013, 'Ssn': '123-45-6780', 'Sex': 'F', 'rank': 0.004967293702065945}), ('customer/10000015', {'_key': '10000015', '_id': 'customer/10000015', '_rev': '_dfEQixG--A', 'Name': 'Paulo Banderas', 'credit_card_number': 10000015, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.0062342192977666855}), ('customer/10000007', {'_key': '10000007', '_id': 'customer/10000007', '_rev': '_dfEQixG--B', 'Name': 'Raj Ramachandran', 'credit_card_number': 10000007, 'Ssn': '123-45-6787', 'Sex': 'M', 'rank': 0.006640455685555935}), ('customer/10000010', {'_key': '10000010', '_id': 'customer/10000010', '_rev': '_dfEQixG--C', 'Name': 'Joanne Cadiz', 'credit_card_number': 10000010, 'Ssn': '123-45-6780', 'Sex': 'F', 'rank': 0.009605521336197853}), ('customer/10000009', {'_key': '10000009', '_id': 'customer/10000009', '_rev': '_dfEQixG--D', 'Name': 'Clint Eastwood', 'credit_card_number': 10000009, 'Ssn': '123-45-6789', 'Sex': 'M', 'rank': 0.008787025697529316}), ('customer/10000016', {'_key': '10000016', '_id': 'customer/10000016', '_rev': '_dfEQixG--E', 'Name': 'Phillip Blewitt', 'credit_card_number': 10000016, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.00887867622077465}), ('customer/10000005', {'_key': '10000005', '_id': 'customer/10000005', '_rev': '_dfEQixG--F', 'Name': 'Pieter de Bruin ', 'credit_card_number': 10000005, 'Ssn': '123-45-6785', 'Sex': 'M', 'rank': 0.004701335448771715}), ('customer/10000003', {'_key': '10000003', '_id': 'customer/10000003', '_rev': '_dfEQixG--G', 'Name': 'Sean Smith', 'credit_card_number': 10000003, 'Ssn': '123-45-6783', 'Sex': 'M', 'rank': 0.006601915694773197}), ('customer/10000004', {'_key': '10000004', '_id': 'customer/10000004', '_rev': '_dfEQixG--H', 'Name': 'Betty Blue', 'credit_card_number': 10000004, 'Ssn': '123-45-6784', 'Sex': 'F', 'rank': 0.004484817385673523}), ('customer/10000014', {'_key': '10000014', '_id': 'customer/10000014', '_rev': '_dfEQixG--I', 'Name': 'Paula Brodsky', 'credit_card_number': 10000014, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.006199253723025322}), ('customer/10000008', {'_key': '10000008', '_id': 'customer/10000008', '_rev': '_dfEQixG--J', 'Name': 'Nora Huang', 'credit_card_number': 10000008, 'Ssn': '123-45-6788', 'Sex': 'F', 'rank': 0.010254250839352608}), ('customer/10000002', {'_key': '10000002', '_id': 'customer/10000002', '_rev': '_dfEQixG--K', 'Name': 'Mary May', 'credit_card_number': 10000002, 'Ssn': '123-45-6782', 'Sex': 'F', 'rank': 0.00810169242322445}), ('customer/10000001', {'_key': '10000001', '_id': 'customer/10000001', '_rev': '_dfEQixG--L', 'Name': 'John Martin ', 'credit_card_number': 10000001, 'Ssn': '123-45-6781', 'Sex': 'M', 'rank': 0.003908450715243816}), ('customer/10000011', {'_key': '10000011', '_id': 'customer/10000011', '_rev': '_dfEQixG--M', 'Name': 'Peter Brown', 'credit_card_number': 10000011, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.0053901225328445435}), ('customer/10000012', {'_key': '10000012', '_id': 'customer/10000012', '_rev': '_dfEQixG--N', 'Name': 'Paul Bolton', 'credit_card_number': 10000012, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.004352491348981857}), ('customer/10810', {'_key': '10810', '_id': 'customer/10810', '_rev': '_dfEQixG--O', 'Name': 'Anne Onymous', 'Sex': 'F', 'Ssn': 111223333, 'rank': 0.009295775555074215})]\n", + "[('account/10000011', 'account/10000007', {'_key': '10000011100000072019-3-212:52', '_id': 'transaction/10000011100000072019-3-212:52', '_from': 'account/10000011', '_to': 'account/10000007', '_rev': '_dfEQi6S--S', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000001, 'trans_time': '12:52', 'transaction_amt': 441, 'transaction_date': '2019-3-2'}), ('account/10000011', 'account/10000023', 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Define graph name\n", + "graph_name = \"fraud-detection\"\n", + "\n", + "# Create NetworkX graph from ArangoDB graph\n", + "nx_g = adbnx_adapter.arangodb_graph_to_networkx(graph_name)\n", + "\n", + "# You can also provide valid Python-Arango AQL query options to the command above, like such:\n", + "# nx_g = adbnx_adapter.arangodb_graph_to_networkx(graph_name, ttl=1000, stream=True)\n", + "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", + "\n", + "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", + "print(nx_g.nodes(data=True))\n", + "print(nx_g.edges(data=True))\n", + "nx.draw(nx_g, with_labels=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RQ4CknYfUEuz" + }, + "source": [ + "## Via ArangoDB Collections" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_collections_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L130-L158)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `vertex_collections` & `edge_collections` parameters must point to existing ArangoDB collections within your ArangoDB instance." + ], + "metadata": { + "id": "wVCEdRB8YdLW" + } + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "i4XOpdRLUNlJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "c9d24f30-c6e0-431a-a275-f3f47179063e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NetworkX: fraud-detection created\n", + "\n", + "--------------------\n", + "MultiDiGraph named 'fraud-detection' with 88 nodes and 120 edges\n", + "[('branch/1548210', {'_key': '1548210', '_id': 'branch/1548210', '_rev': '_dfEQik2---', 'City': 'Austin', 'Country': 'US', 'Id': 10008, 'Postcode': 78704, 'bank_id': 10000000003, 'branch_id': 10008, 'branch_name': 'Bank Three Austin ', 'street_name': 'Bouldin Ave'}), ('branch/1548212', {'_key': '1548212', '_id': 'branch/1548212', '_rev': '_dfEQik2--_', 'City': 'Boston', 'Country': 'US', 'Id': 10010, 'Postcode': 2101, 'bank_id': 10000000003, 'branch_id': 10010, 'branch_name': 'Bank Three Boston', 'street_name': 'Kendall Square'}), ('branch/1548204', {'_key': '1548204', '_id': 'branch/1548204', '_rev': '_dfEQik2--A', 'City': 'Austin', 'Country': 'US', 'Id': 10002, 'Postcode': 78704, 'bank_id': 10000000001, 'branch_id': 10002, 'branch_name': 'Bank One Austin ', 'street_name': 'Bouldin Ave'}), ('branch/1548203', {'_key': '1548203', '_id': 'branch/1548203', '_rev': '_dfEQik2--B', 'City': 'Denver', 'Country': 'US', 'Id': 10001, 'Postcode': 80014, 'bank_id': 10000000001, 'branch_id': 10001, 'branch_name': 'Bank One Denver', 'street_name': 'Elm Street'}), ('branch/1548206', {'_key': '1548206', '_id': 'branch/1548206', '_rev': '_dfEQik2--C', 'City': 'Denver', 'Country': 'US', 'Id': 10004, 'Postcode': 80014, 'bank_id': 10000000002, 'branch_id': 10004, 'branch_name': 'Bank Two Denver', 'street_name': 'Elm Street'}), ('branch/1548211', {'_key': '1548211', '_id': 'branch/1548211', '_rev': '_dfEQik2--D', 'City': 'Los Angeles', 'Country': 'US', 'Id': 10009, 'Postcode': 90001, 'bank_id': 10000000003, 'branch_id': 10009, 'branch_name': 'Bank Three Los Angeles ', 'street_name': 'Hollywood Blvd'}), ('branch/1548205', {'_key': '1548205', '_id': 'branch/1548205', '_rev': '_dfEQik2--E', 'City': 'Los Angeles', 'Country': 'US', 'Id': 10003, 'Postcode': 90001, 'bank_id': 10000000001, 'branch_id': 10003, 'branch_name': 'Bank One Los Angeles ', 'street_name': 'Hollywood Blvd'}), ('branch/1548209', {'_key': '1548209', '_id': 'branch/1548209', '_rev': '_dfEQik2--F', 'City': 'Denver', 'Country': 'US', 'Id': 10007, 'Postcode': 80014, 'bank_id': 10000000003, 'branch_id': 10007, 'branch_name': 'Bank Three Denver', 'street_name': 'Elm Street'}), ('branch/1548208', {'_key': '1548208', '_id': 'branch/1548208', '_rev': '_dfEQik2--G', 'City': 'Los Angeles', 'Country': 'US', 'Id': 10006, 'Postcode': 90001, 'bank_id': 10000000002, 'branch_id': 10006, 'branch_name': 'Bank Two Los Angeles ', 'street_name': 'Hollywood Blvd'}), ('branch/1548207', {'_key': '1548207', '_id': 'branch/1548207', '_rev': '_dfEQik2--H', 'City': 'Austin', 'Country': 'US', 'Id': 10005, 'Postcode': 78704, 'bank_id': 10000000002, 'branch_id': 10005, 'branch_name': 'Bank Two Austin ', 'street_name': 'Bouldin Ave'}), ('Class/bank', {'_key': 'bank', '_id': 'Class/bank', '_rev': '_dfEQipu---', 'concrete': True, 'name': 'Bank', 'label': 'Bank'}), ('Class/branch', {'_key': 'branch', '_id': 'Class/branch', '_rev': '_dfEQipu--_', 'concrete': True, 'name': 'Branch', 'label': 'Branch Bank'}), ('Class/account', {'_key': 'account', '_id': 'Class/account', '_rev': '_dfEQipu--A', 'concrete': True, 'name': 'Account', 'label': 'Account'}), ('Class/customer', {'_key': 'customer', '_id': 'Class/customer', '_rev': '_dfEQipu--B', 'concrete': True, 'name': 'Customer', 'label': 'Customer'}), ('customer/10000006', {'_key': '10000006', '_id': 'customer/10000006', '_rev': '_dfEQixG---', 'Name': 'Mahdivi Nookala', 'credit_card_number': 10000006, 'Ssn': '123-45-6786', 'Sex': 'F', 'rank': 0.013542247004806995}), ('customer/10000013', {'_key': '10000013', '_id': 'customer/10000013', '_rev': '_dfEQixG--_', 'Name': 'Petronella Brink', 'credit_card_number': 10000013, 'Ssn': '123-45-6780', 'Sex': 'F', 'rank': 0.004967293702065945}), ('customer/10000015', {'_key': '10000015', '_id': 'customer/10000015', '_rev': '_dfEQixG--A', 'Name': 'Paulo Banderas', 'credit_card_number': 10000015, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.0062342192977666855}), ('customer/10000007', {'_key': '10000007', '_id': 'customer/10000007', '_rev': '_dfEQixG--B', 'Name': 'Raj Ramachandran', 'credit_card_number': 10000007, 'Ssn': '123-45-6787', 'Sex': 'M', 'rank': 0.006640455685555935}), ('customer/10000010', {'_key': '10000010', '_id': 'customer/10000010', '_rev': '_dfEQixG--C', 'Name': 'Joanne Cadiz', 'credit_card_number': 10000010, 'Ssn': '123-45-6780', 'Sex': 'F', 'rank': 0.009605521336197853}), ('customer/10000009', {'_key': '10000009', '_id': 'customer/10000009', '_rev': '_dfEQixG--D', 'Name': 'Clint Eastwood', 'credit_card_number': 10000009, 'Ssn': '123-45-6789', 'Sex': 'M', 'rank': 0.008787025697529316}), ('customer/10000016', {'_key': '10000016', '_id': 'customer/10000016', '_rev': '_dfEQixG--E', 'Name': 'Phillip Blewitt', 'credit_card_number': 10000016, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.00887867622077465}), ('customer/10000005', {'_key': '10000005', '_id': 'customer/10000005', '_rev': '_dfEQixG--F', 'Name': 'Pieter de Bruin ', 'credit_card_number': 10000005, 'Ssn': '123-45-6785', 'Sex': 'M', 'rank': 0.004701335448771715}), ('customer/10000003', {'_key': '10000003', '_id': 'customer/10000003', '_rev': '_dfEQixG--G', 'Name': 'Sean Smith', 'credit_card_number': 10000003, 'Ssn': '123-45-6783', 'Sex': 'M', 'rank': 0.006601915694773197}), ('customer/10000004', {'_key': '10000004', '_id': 'customer/10000004', '_rev': '_dfEQixG--H', 'Name': 'Betty Blue', 'credit_card_number': 10000004, 'Ssn': '123-45-6784', 'Sex': 'F', 'rank': 0.004484817385673523}), ('customer/10000014', {'_key': '10000014', '_id': 'customer/10000014', '_rev': '_dfEQixG--I', 'Name': 'Paula Brodsky', 'credit_card_number': 10000014, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.006199253723025322}), ('customer/10000008', {'_key': '10000008', '_id': 'customer/10000008', '_rev': '_dfEQixG--J', 'Name': 'Nora Huang', 'credit_card_number': 10000008, 'Ssn': '123-45-6788', 'Sex': 'F', 'rank': 0.010254250839352608}), ('customer/10000002', {'_key': '10000002', '_id': 'customer/10000002', '_rev': '_dfEQixG--K', 'Name': 'Mary May', 'credit_card_number': 10000002, 'Ssn': '123-45-6782', 'Sex': 'F', 'rank': 0.00810169242322445}), ('customer/10000001', {'_key': '10000001', '_id': 'customer/10000001', '_rev': '_dfEQixG--L', 'Name': 'John Martin ', 'credit_card_number': 10000001, 'Ssn': '123-45-6781', 'Sex': 'M', 'rank': 0.003908450715243816}), ('customer/10000011', {'_key': '10000011', '_id': 'customer/10000011', '_rev': '_dfEQixG--M', 'Name': 'Peter Brown', 'credit_card_number': 10000011, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.0053901225328445435}), ('customer/10000012', {'_key': '10000012', '_id': 'customer/10000012', '_rev': '_dfEQixG--N', 'Name': 'Paul Bolton', 'credit_card_number': 10000012, 'Ssn': '123-45-6780', 'Sex': 'M', 'rank': 0.004352491348981857}), ('customer/10810', {'_key': '10810', '_id': 'customer/10810', '_rev': '_dfEQixG--O', 'Name': 'Anne Onymous', 'Sex': 'F', 'Ssn': 111223333, 'rank': 0.009295775555074215}), ('bank/1548224', {'_key': '1548224', '_id': 'bank/1548224', '_rev': '_dfEQif6---', 'Id': 10000000001, 'bank_id': 10000000001, 'bank_name': 'Bank One', 'Country': 'US'}), ('bank/1548226', {'_key': '1548226', '_id': 'bank/1548226', '_rev': '_dfEQif6--_', 'Id': 10000000003, 'bank_id': 10000000003, 'bank_name': 'Bank Three', 'Country': 'US'}), ('bank/1548225', {'_key': '1548225', '_id': 'bank/1548225', '_rev': '_dfEQif6--A', 'Id': 10000000002, 'bank_id': 10000000002, 'bank_name': 'Bank Two', 'Country': 'US'}), ('account/10000011', {'_key': '10000011', '_id': 'account/10000011', '_rev': '_dfEQim----', 'Balance': 5331, 'Status': 'active', 'account_opening_date': '2018-3-13', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000016', {'_key': '10000016', '_id': 'account/10000016', '_rev': '_dfEQim---_', 'Balance': 7630, 'Status': 'active', 'account_opening_date': '2018-10-15', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000004, 'rank': 0.003122549969702959}), ('account/10000003', {'_key': '10000003', '_id': 'account/10000003', '_rev': '_dfEQim---A', 'Balance': 1433, 'Status': 'active', 'account_opening_date': '2017-10-24', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000004, 'rank': 0.00524971354752779}), ('account/10000029', {'_key': '10000029', '_id': 'account/10000029', '_rev': '_dfEQim---B', 'Balance': 2201, 'Status': 'active', 'account_opening_date': '2017-10-25', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/10000005', {'_key': '10000005', '_id': 'account/10000005', '_rev': '_dfEQim---C', 'Balance': 4837, 'Status': 'active', 'account_opening_date': '2017-2-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000002, 'rank': 0.004550427198410034}), ('account/10000032', {'_key': '10000032', '_id': 'account/10000032', '_rev': '_dfEQim---D', 'Balance': 5817, 'Status': 'active', 'account_opening_date': '2018-9-14', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000011, 'rank': 0.0036875137593597174}), ('account/10000039', {'_key': '10000039', '_id': 'account/10000039', '_rev': '_dfEQim---E', 'Balance': 1689, 'Status': 'active', 'account_opening_date': '2017-12-26', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000028', {'_key': '10000028', '_id': 'account/10000028', '_rev': '_dfEQim---F', 'Balance': 1042, 'Status': 'active', 'account_opening_date': '2018-9-15', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000006, 'rank': 0.004198686685413122}), ('account/10000020', {'_key': '10000020', '_id': 'account/10000020', '_rev': '_dfEQim---G', 'Balance': 4104, 'Status': 'active', 'account_opening_date': '2017-5-19', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/orphan_Account_1', {'_key': 'orphan_Account_1', '_id': 'account/orphan_Account_1', '_rev': '_dfEQim---H', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/10000006', {'_key': '10000006', '_id': 'account/10000006', '_rev': '_dfEQim---I', 'Balance': 2338, 'Status': 'active', 'account_opening_date': '2017-6-21', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000002, 'rank': 0.003010563552379608}), ('account/1000053', {'_key': '1000053', '_id': 'account/1000053', '_rev': '_dfEQim---J', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10000014, 'rank': 0.003747815964743495}), ('account/10000013', {'_key': '10000013', '_id': 'account/10000013', '_rev': '_dfEQim---K', 'Balance': 3779, 'Status': 'active', 'account_opening_date': '2018-4-23', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000008, 'rank': 0.004046608693897724}), ('account/1000054', {'_key': '1000054', '_id': 'account/1000054', '_rev': '_dfEQim---L', 'rank': 0.003705498529598117}), ('account/10000012', {'_key': '10000012', '_id': 'account/10000012', '_rev': '_dfEQim---M', 'Balance': 529, 'Status': 'active', 'account_opening_date': '2018-4-15', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/1000050', {'_key': '1000050', '_id': 'account/1000050', '_rev': '_dfEQim---N', 'rank': 0.004632922820746899}), ('account/10000001', {'_key': '10000001', '_id': 'account/10000001', '_rev': '_dfEQim---O', 'Balance': 1992, 'Status': 'active', 'account_opening_date': '2017-1-23', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000008, 'rank': 0.0040132044814527035}), ('account/10000014', {'_key': '10000014', '_id': 'account/10000014', '_rev': '_dfEQim---P', 'Balance': 2912, 'Status': 'active', 'account_opening_date': '2017-12-16', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10003, 'customer_id': 10000006, 'rank': 0.003010563552379608}), ('account/10000034', {'_key': '10000034', '_id': 'account/10000034', '_rev': '_dfEQim---Q', 'Balance': 6367, 'Status': 'active', 'account_opening_date': '2017-12-5', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000012, 'rank': 0.0026350750122219324}), ('account/10000042', {'_key': '10000042', '_id': 'account/10000042', '_rev': '_dfEQim---R', 'Balance': 1819, 'Status': 'active', 'account_opening_date': '2017-3-23', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/4149551', {'_key': '4149551', '_id': 'account/4149551', '_rev': '_dfEQim---S', 'account_type': 'checking', 'customer_id': 10000001, 'rank': 0.0021126761566847563}), ('account/10000008', {'_key': '10000008', '_id': 'account/10000008', '_rev': '_dfEQim---T', 'Balance': 221, 'Status': 'active', 'account_opening_date': '2017-5-9', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.0033510124776512384}), ('account/10000040', {'_key': '10000040', '_id': 'account/10000040', '_rev': '_dfEQim---U', 'Balance': 5062, 'Status': 'active', 'account_opening_date': '2018-7-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000002', {'_key': '10000002', '_id': 'account/10000002', '_rev': '_dfEQim---V', 'Balance': 2372, 'Status': 'active', 'account_opening_date': '2018-12-12', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000005, 'rank': 0.0021126761566847563}), ('account/10000009', {'_key': '10000009', '_id': 'account/10000009', '_rev': '_dfEQim---W', 'Balance': 841, 'Status': 'active', 'account_opening_date': '2018-2-25', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10004, 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000026', {'_key': '10000026', '_id': 'account/10000026', '_rev': '_dfEQim---X', 'Balance': 5393, 'Status': 'active', 'account_opening_date': '2018-10-25', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000008, 'rank': 0.00354181369766593}), ('account/10000033', {'_key': '10000033', '_id': 'account/10000033', '_rev': '_dfEQim---Y', 'Balance': 1138, 'Status': 'active', 'account_opening_date': '2017-4-6', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000011, 'rank': 0.0026350750122219324}), ('account/10000037', {'_key': '10000037', '_id': 'account/10000037', '_rev': '_dfEQim---Z', 'Balance': 8414, 'Status': 'active', 'account_opening_date': '2018-10-17', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000014, 'rank': 0.0026350750122219324}), ('account/10000018', {'_key': '10000018', '_id': 'account/10000018', '_rev': '_dfEQim---a', 'Balance': 4064, 'Status': 'active', 'account_opening_date': '2018-11-27', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000010, 'rank': 0.004585607908666134}), ('account/10000024', {'_key': '10000024', '_id': 'account/10000024', '_rev': '_dfEQim---b', 'Balance': 5686, 'Status': 'active', 'account_opening_date': '2017-11-15', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000006, 'rank': 0.0021126761566847563}), ('account/10000017', {'_key': '10000017', '_id': 'account/10000017', '_rev': '_dfEQim---c', 'Balance': 6294, 'Status': 'active', 'account_opening_date': '2017-9-24', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10010, 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/10000010', {'_key': '10000010', '_id': 'account/10000010', '_rev': '_dfEQim---d', 'Balance': 6540, 'Status': 'active', 'account_opening_date': '2018-2-1', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000007, 'rank': 0.0035368565004318953}), ('account/10000004', {'_key': '10000004', '_id': 'account/10000004', '_rev': '_dfEQim---e', 'Balance': 7358, 'Status': 'active', 'account_opening_date': '2018-5-20', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000006, 'rank': 0.0036158403381705284}), ('account/10000023', {'_key': '10000023', '_id': 'account/10000023', '_rev': '_dfEQim---f', 'Balance': 3452, 'Status': 'active', 'account_opening_date': '2018-1-12', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000005, 'rank': 0.0035642609000205994}), ('account/1000052', {'_key': '1000052', '_id': 'account/1000052', '_rev': '_dfEQim---g', 'rank': 0.0038473859895020723}), ('account/10000025', {'_key': '10000025', '_id': 'account/10000025', '_rev': '_dfEQim---h', 'Balance': 3993, 'Status': 'active', 'account_opening_date': '2018-2-25', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000010, 'rank': 0.005468250252306461}), ('account/6149795', {'_key': '6149795', '_id': 'account/6149795', '_rev': '_dfEQim---i', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/1000051', {'_key': '1000051', '_id': 'account/1000051', '_rev': '_dfEQim---j', 'rank': 0.0040816692635416985}), ('account/10000019', {'_key': '10000019', '_id': 'account/10000019', '_rev': '_dfEQim---k', 'Balance': 471, 'Status': 'active', 'account_opening_date': '2017-11-19', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10006, 'customer_id': 10000009, 'rank': 0.0044366829097270966}), ('account/10000022', {'_key': '10000022', '_id': 'account/10000022', '_rev': '_dfEQim---l', 'Balance': 8148, 'Status': 'active', 'account_opening_date': '2018-5-6', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000006, 'rank': 0.0021126761566847563}), ('account/10000031', {'_key': '10000031', '_id': 'account/10000031', '_rev': '_dfEQim---m', 'Balance': 5832, 'Status': 'active', 'account_opening_date': '2018-1-28', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000002, 'rank': 0.003010563552379608}), ('account/10000021', {'_key': '10000021', '_id': 'account/10000021', '_rev': '_dfEQim---n', 'Balance': 1758, 'Status': 'active', 'account_opening_date': '2017-7-6', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10001, 'customer_id': 10000005, 'rank': 0.0034595071338117123}), ('account/10000007', {'_key': '10000007', '_id': 'account/10000007', '_rev': '_dfEQim---o', 'Balance': 1747, 'Status': 'active', 'account_opening_date': '2017-2-3', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10007, 'customer_id': 10000009, 'rank': 0.0033098594285547733}), ('account/10000035', {'_key': '10000035', '_id': 'account/10000035', '_rev': '_dfEQim---p', 'Balance': 1679, 'Status': 'active', 'account_opening_date': '2018-10-18', 'account_type': 'checking', 'bank_id': 10000000001, 'branch_id': 10002, 'customer_id': 10000012, 'rank': 0.0026350750122219324}), ('account/10000015', {'_key': '10000015', '_id': 'account/10000015', '_rev': '_dfEQim---q', 'Balance': 6789, 'Status': 'active', 'account_opening_date': '2018-5-3', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000007, 'rank': 0.007116740569472313}), ('account/10000027', {'_key': '10000027', '_id': 'account/10000027', '_rev': '_dfEQim---r', 'Balance': 1599, 'Status': 'active', 'account_opening_date': '2018-7-12', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10008, 'customer_id': 10000002, 'rank': 0.0035642609000205994}), ('account/10000038', {'_key': '10000038', '_id': 'account/10000038', '_rev': '_dfEQim---s', 'Balance': 8320, 'Status': 'active', 'account_opening_date': '2018-5-27', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000014, 'rank': 0.003232583636417985}), ('account/10000044', {'_key': '10000044', '_id': 'account/10000044', '_rev': '_dfEQim---t', 'rank': 0.005929990671575069}), ('account/6149781', {'_key': '6149781', '_id': 'account/6149781', '_rev': '_dfEQim---u', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/10000043', {'_key': '10000043', '_id': 'account/10000043', '_rev': '_dfEQim---v', 'Balance': 8626, 'Status': 'active', 'account_opening_date': '2018-9-13', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10004, 'customer_id': 10000016, 'rank': 0.008981915190815926}), ('account/10000030', {'_key': '10000030', '_id': 'account/10000030', '_rev': '_dfEQim---w', 'Balance': 7199, 'Status': 'active', 'account_opening_date': '2017-7-24', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10010, 'customer_id': 10000006, 'rank': 0.005735883489251137}), ('account/10000041', {'_key': '10000041', '_id': 'account/10000041', '_rev': '_dfEQim---x', 'Balance': 8644, 'Status': 'active', 'account_opening_date': '2018-8-7', 'account_type': 'checking', 'bank_id': 10000000002, 'branch_id': 10005, 'customer_id': 10000016, 'rank': 0.003232583636417985}), ('account/10000036', {'_key': '10000036', '_id': 'account/10000036', '_rev': '_dfEQim---y', 'Balance': 3879, 'Status': 'active', 'account_opening_date': '2017-2-9', 'account_type': 'checking', 'bank_id': 10000000003, 'branch_id': 10009, 'customer_id': 10000013, 'rank': 0.0026350750122219324}), ('account/6149748', {'_key': '6149748', '_id': 'account/6149748', '_rev': '_dfEQim---z', 'Balance': 10, 'account_type': 'checking', 'customer_id': 10810, 'rank': 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('account/10000003', 'account/10000003', {'_key': '10000003100000032017-5-1615:52', '_id': 'transaction/10000003100000032017-5-1615:52', '_from': 'account/10000003', '_to': 'account/10000003', '_rev': '_dfEQi6S--C', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000002, 'sender_bank_id': 10000000002, 'trans_time': '15:52', 'transaction_amt': 756, 'transaction_date': '2017-5-16'}), ('account/10000003', 'account/10000028', {'_key': '10000003100000282019-7-249:32', '_id': 'transaction/10000003100000282019-7-249:32', '_from': 'account/10000003', '_to': 'account/10000028', '_rev': '_dfEQi6S--k', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000001, 'sender_bank_id': 10000000002, 'trans_time': '9:32', 'transaction_amt': 172, 'transaction_date': '2019-7-24'}), ('account/10000003', 'customer/10000004', {'_key': '1000000410000003', '_id': 'accountHolder/1000000410000003', '_from': 'account/10000003', '_to': 'customer/10000004', '_rev': '_dfEQium--I'}), ('account/10000029', 'account/10000031', {'_key': '10000029100000312017-1-1013:16', '_id': 'transaction/10000029100000312017-1-1013:16', '_from': 'account/10000029', '_to': 'account/10000031', '_rev': '_dfEQi6S--e', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '13:16', 'transaction_amt': 795, 'transaction_date': '2017-1-10'}), ('account/10000029', 'customer/10000010', {'_key': '1000001010000029', '_id': 'accountHolder/1000001010000029', '_from': 'account/10000029', '_to': 'customer/10000010', '_rev': '_dfEQium--p'}), ('account/10000005', 'account/10000013', {'_key': '10000005100000132017-3-2811:58', '_id': 'transaction/10000005100000132017-3-2811:58', '_from': 'account/10000005', '_to': 'account/10000013', '_rev': '_dfEQi6S--q', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000001, 'sender_bank_id': 10000000002, 'trans_time': '11:58', 'transaction_amt': 670, 'transaction_date': '2017-3-28'}), ('account/10000005', 'customer/10000002', {'_key': '1000000210000005', '_id': 'accountHolder/1000000210000005', '_from': 'account/10000005', '_to': 'customer/10000002', '_rev': '_dfEQium--L'}), ('account/10000032', 'account/10000037', {'_key': '10000032100000372019-6-614:26', '_id': 'transaction/10000032100000372019-6-614:26', '_from': 'account/10000032', '_to': 'account/10000037', '_rev': '_dfEQi6S--H', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '14:26', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000032', 'account/10000033', {'_key': '10000032100000332019-6-610:39', '_id': 'transaction/10000032100000332019-6-610:39', '_from': 'account/10000032', '_to': 'account/10000033', '_rev': '_dfEQi6S--T', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '10:39', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000032', 'account/10000034', {'_key': '10000032100000342019-6-614:20', '_id': 'transaction/10000032100000342019-6-614:20', '_from': 'account/10000032', '_to': 'account/10000034', '_rev': '_dfEQi6S--W', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '14:20', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000032', 'account/10000036', {'_key': '10000032100000362019-6-69:55', '_id': 'transaction/10000032100000362019-6-69:55', '_from': 'account/10000032', '_to': 'account/10000036', '_rev': '_dfEQi6S--f', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '9:55', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000032', 'account/10000035', {'_key': '10000032100000352019-6-613:42', '_id': 'transaction/10000032100000352019-6-613:42', '_from': 'account/10000032', '_to': 'account/10000035', '_rev': '_dfEQi6S--5', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '13:42', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000032', 'customer/10000011', {'_key': '1000001110000032', '_id': 'accountHolder/1000001110000032', '_from': 'account/10000032', '_to': 'customer/10000011', '_rev': '_dfEQium--B'}), ('account/10000039', 'account/10000043', {'_key': '10000039100000432019-6-611:36', '_id': 'transaction/10000039100000432019-6-611:36', '_from': 'account/10000039', '_to': 'account/10000043', '_rev': '_dfEQi6S---', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000003, 'trans_time': '11:36', 'transaction_amt': 9000, 'transaction_date': '2019-6-6'}), ('account/10000039', 'customer/10000015', {'_key': '1000001510000039', '_id': 'accountHolder/1000001510000039', '_from': 'account/10000039', '_to': 'customer/10000015', '_rev': '_dfEQium--i'}), ('account/10000028', 'customer/10000006', {'_key': '1000000610000028', '_id': 'accountHolder/1000000610000028', '_from': 'account/10000028', '_to': 'customer/10000006', '_rev': '_dfEQium--k'}), ('account/10000020', 'account/10000014', {'_key': '10000020100000142017-9-1410:46', '_id': 'transaction/10000020100000142017-9-1410:46', '_from': 'account/10000020', '_to': 'account/10000014', '_rev': '_dfEQi6S--G', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000001, 'sender_bank_id': 10000000002, 'trans_time': '10:46', 'transaction_amt': 920, 'transaction_date': '2017-9-14'}), ('account/10000020', 'customer/10000010', {'_key': '1000001010000020', '_id': 'accountHolder/1000001010000020', '_from': 'account/10000020', '_to': 'customer/10000010', '_rev': '_dfEQium--V'}), ('account/orphan_Account_1', 'customer/10810', {'_key': '6149645', '_id': 'accountHolder/6149645', '_from': 'account/orphan_Account_1', '_to': 'customer/10810', '_rev': '_dfEQium--x'}), ('account/10000006', 'account/10000008', {'_key': '10000006100000082018-9-2410:30', '_id': 'transaction/10000006100000082018-9-2410:30', '_from': 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'transaction_date': '2017-12-12'}), ('account/10000006', 'customer/10000002', {'_key': '1000000210000006', '_id': 'accountHolder/1000000210000006', '_from': 'account/10000006', '_to': 'customer/10000002', '_rev': '_dfEQium--D'}), ('account/1000053', 'account/1000054', {'_key': '3152675', '_id': 'transaction/3152675', '_from': 'account/1000053', '_to': 'account/1000054', '_rev': '_dfEQi6S--K', 'transaction_amt': 9000}), ('account/1000053', 'customer/10000014', {'_key': '1000001610000048', '_id': 'accountHolder/1000001610000048', '_from': 'account/1000053', '_to': 'customer/10000014', '_rev': '_dfEQium--l'}), ('account/10000013', 'account/10000015', {'_key': '10000013100000152017-1-312:13', '_id': 'transaction/10000013100000152017-1-312:13', '_from': 'account/10000013', '_to': 'account/10000015', '_rev': '_dfEQi6S--w', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000001, 'trans_time': '12:13', 'transaction_amt': 52, 'transaction_date': '2017-1-3'}), ('account/10000013', 'customer/10000008', {'_key': '1000000810000013', '_id': 'accountHolder/1000000810000013', '_from': 'account/10000013', '_to': 'customer/10000008', '_rev': '_dfEQium--R'}), ('account/1000054', 'account/10000032', {'_key': '3152724', '_id': 'transaction/3152724', '_from': 'account/1000054', '_to': 'account/10000032', '_rev': '_dfEQi6S--0'}), ('account/1000054', 'customer/10000016', {'_key': '1000001610000046', '_id': 'accountHolder/1000001610000046', '_from': 'account/1000054', '_to': 'customer/10000016', '_rev': '_dfEQium--S'}), ('account/10000012', 'account/10000001', {'_key': '10000012100000012018-4-79:24', '_id': 'transaction/10000012100000012018-4-79:24', '_from': 'account/10000012', '_to': 'account/10000001', '_rev': '_dfEQi6S--F', 'Transaction_type': 'EFT', 'receiver_bank_id': 10000000003, 'sender_bank_id': 10000000002, 'trans_time': '9:24', 'transaction_amt': 946, 'transaction_date': '2018-4-7'}), ('account/10000012', 'account/10000021', {'_key': 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Define collection\n", + "vertex_collections = {\"account\", \"bank\", \"branch\", \"Class\", \"customer\"}\n", + "edge_collections = {\"accountHolder\", \"Relationship\", \"transaction\"}\n", + "\n", + "# Create NetworkX graph from ArangoDB collections\n", + "nx_g = adbnx_adapter.arangodb_collections_to_networkx(\"fraud-detection\", vertex_collections, edge_collections)\n", + "\n", + "# You can also provide valid Python-Arango AQL query options to the command above, like such:\n", + "# nx_g = adbnx_adapter.arangodb_collections_to_networkx(\"fraud-detection\", vertex_collections, edge_collections, ttl=1000, stream=True)\n", + "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", + "\n", + "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", + "print(nx_g.nodes(data=True))\n", + "print(nx_g.edges(data=True))\n", + "nx.draw(nx_g, with_labels=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "umy25EsUU6Lg" + }, + "source": [ + "## Via ArangoDB Metagraph" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L55-L128)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `metagraph` parameter should contain collections & associated document attributes names that exist within your ArangoDB instance." + ], + "metadata": { + "id": "9c4OuEniY_UA" + } + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "UWX9-MsKeG9A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "869dfda0-580d-4678-f7b2-23cc413b9c92" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NetworkX: fraud-detection created\n", + "\n", + "--------------------\n", + "MultiDiGraph named 'fraud-detection' with 88 nodes and 120 edges\n", + "[('account/10000011', {'Balance': 5331, '_id': 'account/10000011', 'account_type': 'checking', 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000016', {'Balance': 7630, '_id': 'account/10000016', 'account_type': 'checking', 'customer_id': 10000004, 'rank': 0.003122549969702959}), ('account/10000003', {'Balance': 1433, '_id': 'account/10000003', 'account_type': 'checking', 'customer_id': 10000004, 'rank': 0.00524971354752779}), ('account/10000029', {'Balance': 2201, '_id': 'account/10000029', 'account_type': 'checking', 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/10000005', {'Balance': 4837, '_id': 'account/10000005', 'account_type': 'checking', 'customer_id': 10000002, 'rank': 0.004550427198410034}), ('account/10000032', {'Balance': 5817, '_id': 'account/10000032', 'account_type': 'checking', 'customer_id': 10000011, 'rank': 0.0036875137593597174}), ('account/10000039', {'Balance': 1689, '_id': 'account/10000039', 'account_type': 'checking', 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000028', {'Balance': 1042, '_id': 'account/10000028', 'account_type': 'checking', 'customer_id': 10000006, 'rank': 0.004198686685413122}), ('account/10000020', {'Balance': 4104, '_id': 'account/10000020', 'account_type': 'checking', 'customer_id': 10000010, 'rank': 0.0021126761566847563}), ('account/orphan_Account_1', {'Balance': 10, '_id': 'account/orphan_Account_1', 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/10000006', {'Balance': 2338, '_id': 'account/10000006', 'account_type': 'checking', 'customer_id': 10000002, 'rank': 0.003010563552379608}), ('account/1000053', {'Balance': 10, '_id': 'account/1000053', 'account_type': 'checking', 'customer_id': 10000014, 'rank': 0.003747815964743495}), ('account/10000013', {'Balance': 3779, '_id': 'account/10000013', 'account_type': 'checking', 'customer_id': 10000008, 'rank': 0.004046608693897724}), ('account/1000054', {'_id': 'account/1000054', 'rank': 0.003705498529598117}), ('account/10000012', {'Balance': 529, '_id': 'account/10000012', 'account_type': 'checking', 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/1000050', {'_id': 'account/1000050', 'rank': 0.004632922820746899}), ('account/10000001', {'Balance': 1992, '_id': 'account/10000001', 'account_type': 'checking', 'customer_id': 10000008, 'rank': 0.0040132044814527035}), ('account/10000014', {'Balance': 2912, '_id': 'account/10000014', 'account_type': 'checking', 'customer_id': 10000006, 'rank': 0.003010563552379608}), ('account/10000034', {'Balance': 6367, '_id': 'account/10000034', 'account_type': 'checking', 'customer_id': 10000012, 'rank': 0.0026350750122219324}), ('account/10000042', {'Balance': 1819, '_id': 'account/10000042', 'account_type': 'checking', 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/4149551', {'_id': 'account/4149551', 'account_type': 'checking', 'customer_id': 10000001, 'rank': 0.0021126761566847563}), ('account/10000008', {'Balance': 221, '_id': 'account/10000008', 'account_type': 'checking', 'customer_id': 10000010, 'rank': 0.0033510124776512384}), ('account/10000040', {'Balance': 5062, '_id': 'account/10000040', 'account_type': 'checking', 'customer_id': 10000015, 'rank': 0.003232583636417985}), ('account/10000002', {'Balance': 2372, '_id': 'account/10000002', 'account_type': 'checking', 'customer_id': 10000005, 'rank': 0.0021126761566847563}), ('account/10000009', {'Balance': 841, '_id': 'account/10000009', 'account_type': 'checking', 'customer_id': 10000009, 'rank': 0.0021126761566847563}), ('account/10000026', {'Balance': 5393, '_id': 'account/10000026', 'account_type': 'checking', 'customer_id': 10000008, 'rank': 0.00354181369766593}), ('account/10000033', {'Balance': 1138, '_id': 'account/10000033', 'account_type': 'checking', 'customer_id': 10000011, 'rank': 0.0026350750122219324}), ('account/10000037', {'Balance': 8414, '_id': 'account/10000037', 'account_type': 'checking', 'customer_id': 10000014, 'rank': 0.0026350750122219324}), ('account/10000018', {'Balance': 4064, '_id': 'account/10000018', 'account_type': 'checking', 'customer_id': 10000010, 'rank': 0.004585607908666134}), ('account/10000024', {'Balance': 5686, '_id': 'account/10000024', 'account_type': 'checking', 'customer_id': 10000006, 'rank': 0.0021126761566847563}), ('account/10000017', {'Balance': 6294, '_id': 'account/10000017', 'account_type': 'checking', 'customer_id': 10000002, 'rank': 0.0021126761566847563}), ('account/10000010', {'Balance': 6540, '_id': 'account/10000010', 'account_type': 'checking', 'customer_id': 10000007, 'rank': 0.0035368565004318953}), ('account/10000004', {'Balance': 7358, '_id': 'account/10000004', 'account_type': 'checking', 'customer_id': 10000006, 'rank': 0.0036158403381705284}), ('account/10000023', {'Balance': 3452, '_id': 'account/10000023', 'account_type': 'checking', 'customer_id': 10000005, 'rank': 0.0035642609000205994}), ('account/1000052', {'_id': 'account/1000052', 'rank': 0.0038473859895020723}), ('account/10000025', {'Balance': 3993, '_id': 'account/10000025', 'account_type': 'checking', 'customer_id': 10000010, 'rank': 0.005468250252306461}), ('account/6149795', {'Balance': 10, '_id': 'account/6149795', 'account_type': 'checking', 'customer_id': 10810, 'rank': 0.0021126761566847563}), ('account/1000051', {'_id': 'account/1000051', 'rank': 0.0040816692635416985}), ('account/10000019', {'Balance': 471, '_id': 'account/10000019', 'account_type': 'checking', 'customer_id': 10000009, 'rank': 0.0044366829097270966}), ('account/10000022', {'Balance': 8148, '_id': 'account/10000022', 'account_type': 'checking', 'customer_id': 10000006, 'rank': 0.0021126761566847563}), 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Define metagraph\n", + "fraud_detection_metagraph = {\n", + " \"vertexCollections\": {\n", + " \"account\": {\"Balance\", \"account_type\", \"customer_id\", \"rank\"},\n", + " \"bank\": {\"Country\", \"Id\", \"bank_id\", \"bank_name\"},\n", + " \"branch\": {\"City\", \"Country\", \"Id\", \"bank_id\", \"branch_id\", \"branch_name\"},\n", + " \"Class\": {\"concrete\", \"label\", \"name\"},\n", + " \"customer\": {\"Name\", \"Sex\", \"Ssn\", \"rank\"},\n", + " },\n", + " \"edgeCollections\": {\n", + " \"accountHolder\": {},\n", + " \"Relationship\": {\"label\", \"name\", \"relationshipType\"},\n", + " \"transaction\": {\"transaction_amt\", \"sender_bank_id\", \"receiver_bank_id\"},\n", + " },\n", + "}\n", + "\n", + "# Create NetworkX Graph from attributes\n", + "nx_g = adbnx_adapter.arangodb_to_networkx('fraud-detection', fraud_detection_metagraph)\n", + "\n", + "# You can also provide valid Python-Arango AQL query options to the command above, like such:\n", + "# nx_g = adbnx_adapter.arangodb_to_networkx('fraud-detection', fraud_detection_metagraph, ttl=1000, stream=True)\n", + "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", + "\n", + "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", + "print(nx_g.nodes(data=True))\n", + "print(nx_g.edges(data=True))\n", + "\n", + "nx.draw(nx_g, with_labels=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tWU1YW9AViTA" + }, + "source": [ + "## Via ArangoDB Metagraph with a custom controller" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* ArangoDB Fraud-Detection Collections\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.arangodb_to_networkx()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L55-L128)\n", + "* [`adbnx_adapter.controller._prepare_arangodb_vertex()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L20-L36)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your NetworkX graph.\n", + "* The `metagraph` parameter should contain collections & associated document attributes names that exist within your ArangoDB instance.\n", + "* We are creating a custom `ADBNX_Controller` to specify *how* to convert our ArangoDB vertices into NetworkX nodes. View the default `ADBNX_Controller` [here](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L10)." + ], + "metadata": { + "id": "sjtWpTDbZpLq" + } + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "QqGgOe51Vr85", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "211ad1a8-784a-40c2-caa1-c086b7f4917e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Connecting to https://tutorials.arangodb.cloud:8529\n", + "NetworkX: IMDBGraph created\n", + "\n", + "--------------------\n", + "MultiDiGraph named 'IMDBGraph' with 2625 nodes and 65499 edges\n", + "[('Users/1', {'Age': 35, 'Gender': 'M', '_id': 'Users/1', 'bipartite': 0}), ('Users/2', {'Age': 53, 'Gender': 'F', '_id': 'Users/2', 'bipartite': 0}), ('Users/3', {'Age': 23, 'Gender': 'M', '_id': 'Users/3', 'bipartite': 0}), ('Users/4', {'Age': 24, 'Gender': 'M', '_id': 'Users/4', 'bipartite': 0}), ('Users/5', {'Age': 33, 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1}), ('Movies/1399', {'_id': 'Movies/1399', 'bipartite': 1}), ('Movies/1400', {'_id': 'Movies/1400', 'bipartite': 1}), ('Movies/1401', {'_id': 'Movies/1401', 'bipartite': 1}), ('Movies/1402', {'_id': 'Movies/1402', 'bipartite': 1}), ('Movies/1403', {'_id': 'Movies/1403', 'bipartite': 1}), ('Movies/1404', {'_id': 'Movies/1404', 'bipartite': 1}), ('Movies/1405', {'_id': 'Movies/1405', 'bipartite': 1}), ('Movies/1406', {'_id': 'Movies/1406', 'bipartite': 1}), ('Movies/1407', {'_id': 'Movies/1407', 'bipartite': 1}), ('Movies/1408', {'_id': 'Movies/1408', 'bipartite': 1}), ('Movies/1409', {'_id': 'Movies/1409', 'bipartite': 1}), ('Movies/1410', {'_id': 'Movies/1410', 'bipartite': 1}), ('Movies/1411', {'_id': 'Movies/1411', 'bipartite': 1}), ('Movies/1412', {'_id': 'Movies/1412', 'bipartite': 1}), ('Movies/1413', {'_id': 'Movies/1413', 'bipartite': 1}), ('Movies/1414', {'_id': 'Movies/1414', 'bipartite': 1}), ('Movies/1415', {'_id': 'Movies/1415', 'bipartite': 1}), ('Movies/1416', {'_id': 'Movies/1416', 'bipartite': 1}), ('Movies/1417', {'_id': 'Movies/1417', 'bipartite': 1}), ('Movies/1418', {'_id': 'Movies/1418', 'bipartite': 1}), ('Movies/1419', {'_id': 'Movies/1419', 'bipartite': 1}), ('Movies/1420', {'_id': 'Movies/1420', 'bipartite': 1}), ('Movies/1421', {'_id': 'Movies/1421', 'bipartite': 1}), ('Movies/1422', {'_id': 'Movies/1422', 'bipartite': 1}), ('Movies/1423', {'_id': 'Movies/1423', 'bipartite': 1}), ('Movies/1424', {'_id': 'Movies/1424', 'bipartite': 1}), ('Movies/1425', {'_id': 'Movies/1425', 'bipartite': 1}), ('Movies/1426', {'_id': 'Movies/1426', 'bipartite': 1}), ('Movies/1427', {'_id': 'Movies/1427', 'bipartite': 1}), ('Movies/1428', {'_id': 'Movies/1428', 'bipartite': 1}), ('Movies/1429', {'_id': 'Movies/1429', 'bipartite': 1}), ('Movies/1430', {'_id': 'Movies/1430', 'bipartite': 1}), ('Movies/1431', {'_id': 'Movies/1431', 'bipartite': 1}), ('Movies/1432', {'_id': 'Movies/1432', 'bipartite': 1}), ('Movies/1433', {'_id': 'Movies/1433', 'bipartite': 1}), ('Movies/1434', {'_id': 'Movies/1434', 'bipartite': 1}), ('Movies/1435', {'_id': 'Movies/1435', 'bipartite': 1}), ('Movies/1436', {'_id': 'Movies/1436', 'bipartite': 1}), ('Movies/1437', {'_id': 'Movies/1437', 'bipartite': 1}), ('Movies/1438', {'_id': 'Movies/1438', 'bipartite': 1}), ('Movies/1439', {'_id': 'Movies/1439', 'bipartite': 1}), ('Movies/1440', {'_id': 'Movies/1440', 'bipartite': 1}), ('Movies/1441', {'_id': 'Movies/1441', 'bipartite': 1}), ('Movies/1442', {'_id': 'Movies/1442', 'bipartite': 1}), ('Movies/1443', {'_id': 'Movies/1443', 'bipartite': 1}), ('Movies/1444', {'_id': 'Movies/1444', 'bipartite': 1}), ('Movies/1445', {'_id': 'Movies/1445', 'bipartite': 1}), ('Movies/1446', {'_id': 'Movies/1446', 'bipartite': 1}), ('Movies/1447', {'_id': 'Movies/1447', 'bipartite': 1}), ('Movies/1448', {'_id': 'Movies/1448', 'bipartite': 1}), ('Movies/1449', {'_id': 'Movies/1449', 'bipartite': 1}), ('Movies/1450', {'_id': 'Movies/1450', 'bipartite': 1}), ('Movies/1451', {'_id': 'Movies/1451', 'bipartite': 1}), ('Movies/1452', {'_id': 'Movies/1452', 'bipartite': 1}), ('Movies/1453', {'_id': 'Movies/1453', 'bipartite': 1}), ('Movies/1454', {'_id': 'Movies/1454', 'bipartite': 1}), ('Movies/1455', {'_id': 'Movies/1455', 'bipartite': 1}), ('Movies/1456', {'_id': 'Movies/1456', 'bipartite': 1}), ('Movies/1457', {'_id': 'Movies/1457', 'bipartite': 1}), ('Movies/1458', {'_id': 'Movies/1458', 'bipartite': 1}), ('Movies/1459', {'_id': 'Movies/1459', 'bipartite': 1}), ('Movies/1460', {'_id': 'Movies/1460', 'bipartite': 1}), ('Movies/1461', {'_id': 'Movies/1461', 'bipartite': 1}), ('Movies/1462', {'_id': 'Movies/1462', 'bipartite': 1}), ('Movies/1463', {'_id': 'Movies/1463', 'bipartite': 1}), ('Movies/1464', {'_id': 'Movies/1464', 'bipartite': 1}), ('Movies/1465', {'_id': 'Movies/1465', 'bipartite': 1}), ('Movies/1466', {'_id': 'Movies/1466', 'bipartite': 1}), ('Movies/1467', {'_id': 'Movies/1467', 'bipartite': 1}), ('Movies/1468', {'_id': 'Movies/1468', 'bipartite': 1}), ('Movies/1469', {'_id': 'Movies/1469', 'bipartite': 1}), ('Movies/1470', {'_id': 'Movies/1470', 'bipartite': 1}), ('Movies/1471', {'_id': 'Movies/1471', 'bipartite': 1}), ('Movies/1472', {'_id': 'Movies/1472', 'bipartite': 1}), ('Movies/1473', {'_id': 'Movies/1473', 'bipartite': 1}), ('Movies/1474', {'_id': 'Movies/1474', 'bipartite': 1}), ('Movies/1475', {'_id': 'Movies/1475', 'bipartite': 1}), ('Movies/1476', {'_id': 'Movies/1476', 'bipartite': 1}), ('Movies/1477', {'_id': 'Movies/1477', 'bipartite': 1}), ('Movies/1478', {'_id': 'Movies/1478', 'bipartite': 1}), ('Movies/1479', {'_id': 'Movies/1479', 'bipartite': 1}), ('Movies/1480', {'_id': 'Movies/1480', 'bipartite': 1}), ('Movies/1481', {'_id': 'Movies/1481', 'bipartite': 1}), ('Movies/1482', {'_id': 'Movies/1482', 'bipartite': 1}), ('Movies/1483', {'_id': 'Movies/1483', 'bipartite': 1}), ('Movies/1484', {'_id': 'Movies/1484', 'bipartite': 1}), ('Movies/1485', {'_id': 'Movies/1485', 'bipartite': 1}), ('Movies/1486', {'_id': 'Movies/1486', 'bipartite': 1}), ('Movies/1487', {'_id': 'Movies/1487', 'bipartite': 1}), ('Movies/1488', {'_id': 'Movies/1488', 'bipartite': 1}), ('Movies/1489', {'_id': 'Movies/1489', 'bipartite': 1}), ('Movies/1490', {'_id': 'Movies/1490', 'bipartite': 1}), ('Movies/1491', {'_id': 'Movies/1491', 'bipartite': 1}), ('Movies/1492', {'_id': 'Movies/1492', 'bipartite': 1}), ('Movies/1493', {'_id': 'Movies/1493', 'bipartite': 1}), ('Movies/1494', {'_id': 'Movies/1494', 'bipartite': 1}), ('Movies/1495', {'_id': 'Movies/1495', 'bipartite': 1}), ('Movies/1496', {'_id': 'Movies/1496', 'bipartite': 1}), ('Movies/1497', {'_id': 'Movies/1497', 'bipartite': 1}), ('Movies/1498', {'_id': 'Movies/1498', 'bipartite': 1}), ('Movies/1499', {'_id': 'Movies/1499', 'bipartite': 1}), ('Movies/1500', {'_id': 'Movies/1500', 'bipartite': 1}), ('Movies/1501', {'_id': 'Movies/1501', 'bipartite': 1}), ('Movies/1502', {'_id': 'Movies/1502', 'bipartite': 1}), ('Movies/1503', {'_id': 'Movies/1503', 'bipartite': 1}), ('Movies/1504', {'_id': 'Movies/1504', 'bipartite': 1}), ('Movies/1505', {'_id': 'Movies/1505', 'bipartite': 1}), ('Movies/1506', {'_id': 'Movies/1506', 'bipartite': 1}), ('Movies/1507', {'_id': 'Movies/1507', 'bipartite': 1}), ('Movies/1508', {'_id': 'Movies/1508', 'bipartite': 1}), ('Movies/1509', {'_id': 'Movies/1509', 'bipartite': 1}), ('Movies/1510', {'_id': 'Movies/1510', 'bipartite': 1}), ('Movies/1511', {'_id': 'Movies/1511', 'bipartite': 1}), ('Movies/1512', {'_id': 'Movies/1512', 'bipartite': 1}), ('Movies/1513', {'_id': 'Movies/1513', 'bipartite': 1}), ('Movies/1514', {'_id': 'Movies/1514', 'bipartite': 1}), ('Movies/1515', {'_id': 'Movies/1515', 'bipartite': 1}), ('Movies/1516', {'_id': 'Movies/1516', 'bipartite': 1}), ('Movies/1517', {'_id': 'Movies/1517', 'bipartite': 1}), ('Movies/1518', {'_id': 'Movies/1518', 'bipartite': 1}), ('Movies/1519', {'_id': 'Movies/1519', 'bipartite': 1}), ('Movies/1520', {'_id': 'Movies/1520', 'bipartite': 1}), ('Movies/1521', {'_id': 'Movies/1521', 'bipartite': 1}), ('Movies/1522', {'_id': 'Movies/1522', 'bipartite': 1}), ('Movies/1523', {'_id': 'Movies/1523', 'bipartite': 1}), ('Movies/1524', {'_id': 'Movies/1524', 'bipartite': 1}), ('Movies/1525', {'_id': 'Movies/1525', 'bipartite': 1}), ('Movies/1526', {'_id': 'Movies/1526', 'bipartite': 1}), ('Movies/1527', {'_id': 'Movies/1527', 'bipartite': 1}), ('Movies/1528', {'_id': 'Movies/1528', 'bipartite': 1}), ('Movies/1529', {'_id': 'Movies/1529', 'bipartite': 1}), ('Movies/1530', {'_id': 'Movies/1530', 'bipartite': 1}), ('Movies/1531', {'_id': 'Movies/1531', 'bipartite': 1}), ('Movies/1532', {'_id': 'Movies/1532', 'bipartite': 1}), ('Movies/1533', {'_id': 'Movies/1533', 'bipartite': 1}), ('Movies/1534', {'_id': 'Movies/1534', 'bipartite': 1}), ('Movies/1535', {'_id': 'Movies/1535', 'bipartite': 1}), ('Movies/1536', {'_id': 'Movies/1536', 'bipartite': 1}), ('Movies/1537', {'_id': 'Movies/1537', 'bipartite': 1}), ('Movies/1538', {'_id': 'Movies/1538', 'bipartite': 1}), ('Movies/1539', {'_id': 'Movies/1539', 'bipartite': 1}), ('Movies/1540', {'_id': 'Movies/1540', 'bipartite': 1}), ('Movies/1541', {'_id': 'Movies/1541', 'bipartite': 1}), ('Movies/1542', {'_id': 'Movies/1542', 'bipartite': 1}), ('Movies/1543', {'_id': 'Movies/1543', 'bipartite': 1}), ('Movies/1544', {'_id': 'Movies/1544', 'bipartite': 1}), ('Movies/1545', {'_id': 'Movies/1545', 'bipartite': 1}), ('Movies/1546', {'_id': 'Movies/1546', 'bipartite': 1}), ('Movies/1547', {'_id': 'Movies/1547', 'bipartite': 1}), ('Movies/1548', {'_id': 'Movies/1548', 'bipartite': 1}), ('Movies/1549', {'_id': 'Movies/1549', 'bipartite': 1}), ('Movies/1550', {'_id': 'Movies/1550', 'bipartite': 1}), ('Movies/1551', {'_id': 'Movies/1551', 'bipartite': 1}), ('Movies/1552', {'_id': 'Movies/1552', 'bipartite': 1}), ('Movies/1553', {'_id': 'Movies/1553', 'bipartite': 1}), ('Movies/1554', {'_id': 'Movies/1554', 'bipartite': 1}), ('Movies/1555', {'_id': 'Movies/1555', 'bipartite': 1}), ('Movies/1556', {'_id': 'Movies/1556', 'bipartite': 1}), ('Movies/1557', {'_id': 'Movies/1557', 'bipartite': 1}), ('Movies/1558', {'_id': 'Movies/1558', 'bipartite': 1}), ('Movies/1559', {'_id': 'Movies/1559', 'bipartite': 1}), ('Movies/1560', {'_id': 'Movies/1560', 'bipartite': 1}), ('Movies/1561', {'_id': 'Movies/1561', 'bipartite': 1}), ('Movies/1562', {'_id': 'Movies/1562', 'bipartite': 1}), ('Movies/1563', {'_id': 'Movies/1563', 'bipartite': 1}), ('Movies/1564', {'_id': 'Movies/1564', 'bipartite': 1}), ('Movies/1565', {'_id': 'Movies/1565', 'bipartite': 1}), ('Movies/1566', {'_id': 'Movies/1566', 'bipartite': 1}), ('Movies/1567', {'_id': 'Movies/1567', 'bipartite': 1}), ('Movies/1568', {'_id': 'Movies/1568', 'bipartite': 1}), ('Movies/1569', {'_id': 'Movies/1569', 'bipartite': 1}), ('Movies/1570', {'_id': 'Movies/1570', 'bipartite': 1}), ('Movies/1571', {'_id': 'Movies/1571', 'bipartite': 1}), ('Movies/1572', {'_id': 'Movies/1572', 'bipartite': 1}), ('Movies/1573', {'_id': 'Movies/1573', 'bipartite': 1}), ('Movies/1574', {'_id': 'Movies/1574', 'bipartite': 1}), ('Movies/1575', {'_id': 'Movies/1575', 'bipartite': 1}), ('Movies/1576', {'_id': 'Movies/1576', 'bipartite': 1}), ('Movies/1577', {'_id': 'Movies/1577', 'bipartite': 1}), ('Movies/1578', {'_id': 'Movies/1578', 'bipartite': 1}), ('Movies/1579', {'_id': 'Movies/1579', 'bipartite': 1}), ('Movies/1580', {'_id': 'Movies/1580', 'bipartite': 1}), ('Movies/1581', {'_id': 'Movies/1581', 'bipartite': 1}), ('Movies/1582', {'_id': 'Movies/1582', 'bipartite': 1}), ('Movies/1583', {'_id': 'Movies/1583', 'bipartite': 1}), ('Movies/1584', {'_id': 'Movies/1584', 'bipartite': 1}), ('Movies/1585', {'_id': 'Movies/1585', 'bipartite': 1}), ('Movies/1586', {'_id': 'Movies/1586', 'bipartite': 1}), ('Movies/1587', {'_id': 'Movies/1587', 'bipartite': 1}), ('Movies/1588', {'_id': 'Movies/1588', 'bipartite': 1}), ('Movies/1589', {'_id': 'Movies/1589', 'bipartite': 1}), ('Movies/1590', {'_id': 'Movies/1590', 'bipartite': 1}), ('Movies/1591', {'_id': 'Movies/1591', 'bipartite': 1}), ('Movies/1592', {'_id': 'Movies/1592', 'bipartite': 1}), ('Movies/1593', {'_id': 'Movies/1593', 'bipartite': 1}), ('Movies/1594', {'_id': 'Movies/1594', 'bipartite': 1}), ('Movies/1595', {'_id': 'Movies/1595', 'bipartite': 1}), ('Movies/1596', {'_id': 'Movies/1596', 'bipartite': 1}), ('Movies/1597', {'_id': 'Movies/1597', 'bipartite': 1}), ('Movies/1598', {'_id': 'Movies/1598', 'bipartite': 1}), ('Movies/1599', {'_id': 'Movies/1599', 'bipartite': 1}), ('Movies/1600', {'_id': 'Movies/1600', 'bipartite': 1}), ('Movies/1601', {'_id': 'Movies/1601', 'bipartite': 1}), ('Movies/1602', {'_id': 'Movies/1602', 'bipartite': 1}), ('Movies/1603', {'_id': 'Movies/1603', 'bipartite': 1}), ('Movies/1604', {'_id': 'Movies/1604', 'bipartite': 1}), ('Movies/1605', {'_id': 'Movies/1605', 'bipartite': 1}), ('Movies/1606', {'_id': 'Movies/1606', 'bipartite': 1}), ('Movies/1607', {'_id': 'Movies/1607', 'bipartite': 1}), ('Movies/1608', {'_id': 'Movies/1608', 'bipartite': 1}), ('Movies/1609', {'_id': 'Movies/1609', 'bipartite': 1}), ('Movies/1610', {'_id': 'Movies/1610', 'bipartite': 1}), ('Movies/1611', {'_id': 'Movies/1611', 'bipartite': 1}), ('Movies/1612', {'_id': 'Movies/1612', 'bipartite': 1}), ('Movies/1613', {'_id': 'Movies/1613', 'bipartite': 1}), ('Movies/1614', {'_id': 'Movies/1614', 'bipartite': 1}), ('Movies/1615', {'_id': 'Movies/1615', 'bipartite': 1}), ('Movies/1616', {'_id': 'Movies/1616', 'bipartite': 1}), ('Movies/1617', {'_id': 'Movies/1617', 'bipartite': 1}), ('Movies/1618', {'_id': 'Movies/1618', 'bipartite': 1}), ('Movies/1619', {'_id': 'Movies/1619', 'bipartite': 1}), ('Movies/1620', {'_id': 'Movies/1620', 'bipartite': 1}), ('Movies/1621', {'_id': 'Movies/1621', 'bipartite': 1}), ('Movies/1622', {'_id': 'Movies/1622', 'bipartite': 1}), ('Movies/1623', {'_id': 'Movies/1623', 'bipartite': 1}), ('Movies/1624', {'_id': 'Movies/1624', 'bipartite': 1}), ('Movies/1625', {'_id': 'Movies/1625', 'bipartite': 1}), ('Movies/1626', {'_id': 'Movies/1626', 'bipartite': 1}), ('Movies/1627', {'_id': 'Movies/1627', 'bipartite': 1}), ('Movies/1628', {'_id': 'Movies/1628', 'bipartite': 1}), ('Movies/1629', {'_id': 'Movies/1629', 'bipartite': 1}), ('Movies/1630', {'_id': 'Movies/1630', 'bipartite': 1}), ('Movies/1631', {'_id': 'Movies/1631', 'bipartite': 1}), ('Movies/1632', {'_id': 'Movies/1632', 'bipartite': 1}), ('Movies/1633', {'_id': 'Movies/1633', 'bipartite': 1}), ('Movies/1634', {'_id': 'Movies/1634', 'bipartite': 1}), ('Movies/1635', {'_id': 'Movies/1635', 'bipartite': 1}), ('Movies/1636', {'_id': 'Movies/1636', 'bipartite': 1}), ('Movies/1637', {'_id': 'Movies/1637', 'bipartite': 1}), ('Movies/1638', {'_id': 'Movies/1638', 'bipartite': 1}), ('Movies/1639', {'_id': 'Movies/1639', 'bipartite': 1}), ('Movies/1640', {'_id': 'Movies/1640', 'bipartite': 1}), ('Movies/1641', {'_id': 'Movies/1641', 'bipartite': 1}), ('Movies/1642', {'_id': 'Movies/1642', 'bipartite': 1}), ('Movies/1643', {'_id': 'Movies/1643', 'bipartite': 1}), ('Movies/1644', {'_id': 'Movies/1644', 'bipartite': 1}), ('Movies/1645', {'_id': 'Movies/1645', 'bipartite': 1}), ('Movies/1646', {'_id': 'Movies/1646', 'bipartite': 1}), ('Movies/1647', {'_id': 'Movies/1647', 'bipartite': 1}), ('Movies/1648', {'_id': 'Movies/1648', 'bipartite': 1}), ('Movies/1649', {'_id': 'Movies/1649', 'bipartite': 1}), ('Movies/1650', {'_id': 'Movies/1650', 'bipartite': 1}), ('Movies/1651', {'_id': 'Movies/1651', 'bipartite': 1}), ('Movies/1652', {'_id': 'Movies/1652', 'bipartite': 1}), ('Movies/1653', {'_id': 'Movies/1653', 'bipartite': 1}), ('Movies/1654', {'_id': 'Movies/1654', 'bipartite': 1}), ('Movies/1655', {'_id': 'Movies/1655', 'bipartite': 1}), ('Movies/1656', {'_id': 'Movies/1656', 'bipartite': 1}), ('Movies/1657', {'_id': 'Movies/1657', 'bipartite': 1}), ('Movies/1658', {'_id': 'Movies/1658', 'bipartite': 1}), ('Movies/1659', {'_id': 'Movies/1659', 'bipartite': 1}), ('Movies/1660', {'_id': 'Movies/1660', 'bipartite': 1}), ('Movies/1661', {'_id': 'Movies/1661', 'bipartite': 1}), ('Movies/1662', {'_id': 'Movies/1662', 'bipartite': 1}), ('Movies/1663', {'_id': 'Movies/1663', 'bipartite': 1}), ('Movies/1664', {'_id': 'Movies/1664', 'bipartite': 1}), ('Movies/1665', {'_id': 'Movies/1665', 'bipartite': 1}), ('Movies/1666', {'_id': 'Movies/1666', 'bipartite': 1}), ('Movies/1667', {'_id': 'Movies/1667', 'bipartite': 1}), ('Movies/1668', {'_id': 'Movies/1668', 'bipartite': 1}), ('Movies/1669', {'_id': 'Movies/1669', 'bipartite': 1}), ('Movies/1670', {'_id': 'Movies/1670', 'bipartite': 1}), ('Movies/1671', {'_id': 'Movies/1671', 'bipartite': 1}), ('Movies/1672', {'_id': 'Movies/1672', 'bipartite': 1}), ('Movies/1673', {'_id': 'Movies/1673', 'bipartite': 1}), ('Movies/1674', {'_id': 'Movies/1674', 'bipartite': 1}), ('Movies/1675', {'_id': 'Movies/1675', 'bipartite': 1}), ('Movies/1676', {'_id': 'Movies/1676', 'bipartite': 1}), ('Movies/1677', {'_id': 'Movies/1677', 'bipartite': 1}), ('Movies/1678', {'_id': 'Movies/1678', 'bipartite': 1}), ('Movies/1679', {'_id': 'Movies/1679', 'bipartite': 1}), ('Movies/1680', {'_id': 'Movies/1680', 'bipartite': 1}), ('Movies/1681', {'_id': 'Movies/1681', 'bipartite': 1}), ('Movies/1682', {'_id': 'Movies/1682', 'bipartite': 1})]\n" + ] + } + ], + "source": [ + "# Define metagraph\n", + "imdb_metagraph = {\n", + " \"vertexCollections\": {\"Users\": {\"Age\", \"Gender\"}, \"Movies\": {}},\n", + " \"edgeCollections\": {\"Ratings\": {\"Rating\"}},\n", + "}\n", + "\n", + "class IMDB_ADBNX_Controller(ADBNX_Controller):\n", + " \"\"\"ArangoDB-NetworkX controller.\n", + "\n", + " Responsible for controlling how nodes & edges are handled when\n", + " transitioning from ArangoDB to NetworkX, and vice-versa.\n", + "\n", + " You can derive your own custom ADBNX_Controller, but it is not\n", + " necessary for Homogeneous graphs.\n", + " \"\"\"\n", + " # We re-define how vertex pre-insertion should be treated, specifically for the IMDB dataset.\n", + " def _prepare_arangodb_vertex(self, adb_vertex: Json, col: str) -> NxId:\n", + " \"\"\"Prepare an ArangoDB vertex before it gets inserted into the NetworkX\n", + " graph.\n", + "\n", + " Given an ArangoDB vertex, you can modify it before it gets inserted\n", + " into the NetworkX graph, and/or derive a custom node id for networkx to use.\n", + " In most cases, it is only required to return the ArangoDB _id of the vertex.\n", + "\n", + " :param vertex: The ArangoDB vertex object to (optionally) modify.\n", + " :type vertex: adbnx_adapter.typings.Json\n", + " :param col: The ArangoDB collection the vertex belongs to.\n", + " :type col: str\n", + " :return: The ArangoDB _id attribute of the vertex.\n", + " :rtype: str\n", + " \"\"\"\n", + " adb_vertex[\"bipartite\"] = 0 if col == \"Users\" else 1 # New bipartite attribute logic\n", + " return super()._prepare_arangodb_vertex(adb_vertex, col) # Return ArangoDB _id\n", + "\n", + " # We're not interested in re-defining pre-insertion handling for edges, so we leave it alone\n", + " # def _prepare_arangodb_edge(self, adb_edge: Json, col: str) -> NxId:\n", + " # return super()._prepare_arangodb_edge(edge, collection)\n", + "\n", + "# Instantiate the custom adapter\n", + "imdb_adbnx_adapter = ADBNX_Adapter(con, IMDB_ADBNX_Controller())\n", + "\n", + "# Create NetworkX Graph from metagraph using the custom IMDB_ArangoDB_Networx_Adapter\n", + "nx_g = imdb_adbnx_adapter.arangodb_to_networkx(\"IMDBGraph\", imdb_metagraph)\n", + "\n", + "# You can also provide valid Python-Arango AQL query options to the command above, like such:\n", + "# nx_g = imdb_adbnx_adapter.arangodb_to_networkx(\"IMDBGraph\", imdb_metagraph, ttl=1000, stream=True)\n", + "# See more here: https://docs.python-arango.com/en/main/specs.html#arango.aql.AQL.execute\n", + "\n", + "# Show graph data\n", + "print('\\n--------------------')\n", + "print(nx_g)\n", + "print(nx_g.nodes(data=True))\n", + "# print(nx_g.edges(data=True)) # (will exceed IOPub data rate)\n", + "# nx.draw(nx_g, with_labels=True) # (will exceed IOPub data rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bvzJXSHHTi3v" + }, + "source": [ + "# NetworkX to ArangoDB" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UafSB_3JZNwK" + }, + "source": [ + "## Example 1: NetworkX Grid Graph" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* [NetworkX Grid Graph](https://networkx.org/documentation/stable/auto_examples/basic/plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py)\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.networkx_to_arangodb()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L181-L311)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your ArangoDB graph.\n", + "* The `edge_definitions` parameter should contain a list of valid vertex relationships along with their related edge collections. See its [official documentation](https://docs.python-arango.com/en/main/graph.html#edge-definitions) for more details.\n", + "* We are using a `batch_size` value of 1 to demo to users that this feature exists. It is not recommended to use a `batch_size` value of 1 in a real setting." + ], + "metadata": { + "id": "2HtrFI81bitp" + } + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "eRVbiBy4ZdE4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "4cfbf682-9376-49de-c411-c823421e282a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ArangoDB: Grid_1 created\n", + "\n", + "--------------------\n", + "https://tutorials.arangodb.cloud:8529\n", + "Username: TUT95b328zdyqvu6icc2szy7l\n", + "Password: TUTqy7aejhetdpkpplf7nwbai\n", + "Database: TUTl7w91pj995boxn9f4etz6\n", + "--------------------\n", + "\n", + "Inspect the graph here: https://tutorials.arangodb.cloud:8529/_db/TUTl7w91pj995boxn9f4etz6/_admin/aardvark/index.html#graph/Grid_1\n", + "View the original graph here: https://networkx.org/documentation/stable/auto_examples/basic/plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Load the nx graph & draw\n", + "grid_nx_g = nx.grid_2d_graph(5, 5)\n", + "nx.draw(grid_nx_g, with_labels=True)\n", + "\n", + "# We must provide edge definitions to create the ArangoDB graph\n", + "# Since this graph is Homogeneous, we only need one edge definition.\n", + "edge_definitions = [\n", + " {\n", + " \"edge_collection\": \"to\",\n", + " \"from_vertex_collections\": [\"Grid_Node\"],\n", + " \"to_vertex_collections\": [\"Grid_Node\"],\n", + " }\n", + "]\n", + "\n", + "# Create the ArangoDB graph\n", + "name = \"Grid_1\"\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "grid_adb_g = adbnx_adapter.networkx_to_arangodb(name, grid_nx_g, edge_definitions, batch_size=1)\n", + "\n", + "print('\\n--------------------')\n", + "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", + "print(\"Username: \" + con[\"username\"])\n", + "print(\"Password: \" + con[\"password\"])\n", + "print(\"Database: \" + con[\"dbName\"])\n", + "print('--------------------\\n')\n", + "print(f\"Inspect the graph here: https://tutorials.arangodb.cloud:8529/_db/{con['dbName']}/_admin/aardvark/index.html#graph/{name}\")\n", + "print(f\"View the original graph here: https://networkx.org/documentation/stable/auto_examples/basic/plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gshTlSX_ZZsS" + }, + "source": [ + "## Example 2: NetworkX Football Graph" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data source\n", + "* [NetworkX Football Graph](https://networkx.org/documentation/stable/auto_examples/graph/plot_football.html)\n", + "\n", + "Package methods used\n", + "* [`adbnx_adapter.adapter.networkx_to_arangodb()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/adapter.py#L181-L311)\n", + "* [`adbnx_adapter.controller._keyify_networkx_node()`](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L101-L121)\n", + "\n", + "Important notes\n", + "* The `name` parameter in this case is simply for naming your ArangoDB graph.\n", + "* The `edge_definitions` parameter should contain a list of valid vertex relationships along with their related edge collections. See its [official documentation](https://docs.python-arango.com/en/main/graph.html#edge-definitions) for more details.\n", + "* We are creating a custom `ADBNX_Controller` to specify *how* to convert our NetworkX nodes into ArangoDB vertices. View the default `ADBNX_Controller` [here](https://github.com/arangoml/networkx-adapter/blob/3.0.0/adbnx_adapter/controller.py#L10).\n", + " * This is a unique case where the node IDs of the NetworkX Football graphs are of type string. We need to make sure that these string **do not** contain any [invalid characters](https://www.arangodb.com/docs/stable/data-modeling-naming-conventions-document-keys.html), so we use a built-in helper method." + ], + "metadata": { + "id": "1GRHJg7mcfyq" + } + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "dADiexlAioGH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "f7f80cb8-206d-414c-a94a-613262a7f18c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Connecting to https://tutorials.arangodb.cloud:8529\n", + "ArangoDB: Football created\n", + "\n", + "--------------------\n", + "https://tutorials.arangodb.cloud:8529\n", + "Username: TUT95b328zdyqvu6icc2szy7l\n", + "Password: TUTqy7aejhetdpkpplf7nwbai\n", + "Database: TUTl7w91pj995boxn9f4etz6\n", + "--------------------\n", + "\n", + "Inspect the graph here: https://tutorials.arangodb.cloud:8529/_db/TUTl7w91pj995boxn9f4etz6/_admin/aardvark/index.html#graph/Football\n", + "View the original graph here: https://networkx.org/documentation/stable/auto_examples/graph/plot_football.html#sphx-glr-auto-examples-graph-plot-football-py)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pKyujpKSEkpIS8vPzcTgciKKIyWRCEASqVKlCSUkJPXv25MSJEwQGBtK9mpNVYf48ktyIHTt24Ovnh5eXF5999hkKhYJXt/wGgKjx4frS0e7sLpLVjOjl6+7T7VlgRJUnAE6bGZU2gJCeb1J6YDmG4mvodDrMZjPNmjXDYDDw93ETKLuWCggIogLR06XmVAVEEthxOPrDaygqzMQzviEBHYah9AlyH+d+7Ye3ahH+cVmmmzPKyWsPMKxDIuVlBrcH759Fq9Vy9uxZ4uPj/6n9ExMTmTt37l1LTf0ZDh06xLBhw7h48eJfakdGRuaf54ERfCNGjGDChAksX76c2NhYCgsLOXLkCD179ryv/f08VUhKsZITB4BHRA1EDy/0x9bj0+hxBIUSW3EWkt2KOqIGxtR9aOIbovDyQ+GhQZIkDh06RHx8PI0aNUKpVPLdd9+Rl5eHJEl4e3tjsVjIzc3FZrO5U4AVFRVx9uxZPDw82Lt3L+DyYDUYDDz11FNERERQZBFxGApcHVN4EPb0+3hG1SLr04GoI2th/u0YADZdDiUHVxLQ5hkcJj0AuV+OQRQVqIKrEN3xGXzTtnDmzBkkSeLAgQNIEigja+ERVg3RyxfJUo69NA/JbqV456eYL/2EJDldKk67jdL9y/CMrYf+x7U4zAZEjS9b2j3LtF5JbF2/lsWLF3P48GHANdv95JNPmD5jFnlFJXgndcK//RDKf91L8Y45iN6BeNdsgfHcPhTegXjXbov+8Gq863TErs/Hu0574l7fwLnrZX/KI/V2bp/d/zPcnKn/VVq3bi0LPRmZ/zAPjOAbM2YMkiTRpUsXcnNzCQ0N5amnnrpvwVclyIvSuywXRAUhfd6hZO8SchY8D3YbyqBo/G/E11Vc+YmSvYuRbBZUfqGsWLWWx7p2ZMaMGcydOxeDwYAgCEiShJ+fH8888ww1atTgwIEDbN26lfnz5+Pt7U3btm1Zt24dACtXrmTIkCE4nU4iIiJwOBzk5OTgW6sFVlMZTrMB0dMLu7GYsjPf4zTpKwm+2zGc2ARAxLOzED00mFJ3U9VXgToigjNnztxQtToRfUNRePlhLy9FsluxFWUhKFUYf/0Bu6GIoMdfxTMmCWvhVYw/b0OSnOj2LCRi8GxUQdHYjTpUtnLW/5zNTZcUp9PpVj1v2rSJDuOXsC/1Gnlr30YZFIUgKBA1fjjLdSj8wogZvQZT+hGKts5E4R9R6Tz+jEeqjIyMzO/xPxfA/v/J/QaX3xVJolNCCIsHNwPgp59+olu3bmzZsoU333yTn376iXnz5pGZmcmpU6c4efIkFRUVVFRUULNmTRISEvj0008JCwujrKyMhIQE8vLy8PX1JTw8nIsXL9KoSTN+Pp+BOro25nRXhQbPuIaYL/+EoFQjWYyowuJxGIrQNngUTWw9Cja8h2Q1E9p/KqrgKuTMfx6l6AqGdzhcyl1BEBC9A1B4B2AtuIKg9ABJQrJbUAZVubEN2A2FNz6LOJ0OsFvwazkA32ZPIqrU5K0eR91GzShJPcClS5dIT0+nenVXSEhYRASFOgNeie1QBcVgTj+Cd2IH9EfXYTcUEtxrPN7Vm+Iwl5H92SA8qzZwCWJ9Ppr4RpQeWE71t7ZydHxntq5fy5QpUygsLKzkmZqRkcHzzz/P6dOnUalUdOzYka+//tp9jr/99hvVqlVjx44dvP7662RlZeHr68vYsWN5/fXXKSoqYsiQIRw+fBhRFElMTOTAgQOIolgpZnTy5MmkpqaiUCjYsWMH1atX58svv6RevXqAK770xRdfZOXKlVy/fp1evXoxf/58PD092b9/P4MGDSI7O/uffk5lZGT+Gg/MjO9fwcvtqnHot6J/KnOLZLfy/axX+S5sIrVq1SIlJYWFCxfStGlTLBYLn376Kc8999yt7SWJ7OxstxD88ccfiY+Px+l0YrfbiYqKwuFwsHv3bqpXr05cXBwJNavz6/kLeNdqRUDbweSt/jsOYzGi2ouwQdO5/sWLiF5+SBaT+ziChwZtva7odn7m+i4qaN6sMYsXL+att97i0KFD2Gw2fB+pj6P5YIq+/RhrwRU8YxIJ7vl38r9+BwEJp7nMVTXCUk5wr7+jqdYU/dH16A+vxnBiM54xtXHaLPy8az1h/t74+fkxadIkdz/qpzzPeUUc2V+OxafR45W8UJXaQEzn9uFdvSmm8wdR+AQj2a13XGMBWP1jOm+PHs3JkyepWbMm169fR6dztTVx4kS6dOnCvn37sFqtnDp16q736vnnn2fdunW0bt2akpISrlxxefDeXl8R4NixY3dN3A2wZcsW1q5dy6pVq/jkk0/o1asX6enpqFSupASrV6/m+++/x9vbm8cff5ypU6f+UzUfZf63kMNz/jd4YMIZ/hXUi/FnQvdaaFR/7rJoVCKdg8soyzzHoEGDqFu3LiNGjOCJJ55gxowZ+Pv7M2zYsEr7CIKAyWQiPT2d/fv3c/bsWVJSUvjoo49YuHAhffr0QRAEWrVqRYMGDWjWrBnnzp3DatRTuPF9dLsXoPAJwpp3Cb/mfVH5uzxW/Zo9ib00HySn+ziBHYcRNXIJUf0mgt1CYWEh9evXZ/fu3ZhMJkwmE0UFeZTuXwaAR1i8KzG2So22dhsElSeRw+fjndQRQanCYTIgCCL+yf3wjK1LQIfnUQZGY9fl4FW7LUVmCb1e77ZVAqRmZCH4ht2oNZiBUhvoXue0WzFnnsVZUY4xdS+C0sPlgfoPVNidpOcZEUWR1NRUzGYzERER7iobKpWKzMxMcnNz8fT0pFWrVne9XyqVirS0NAwGAwEBATRs2NC9/H7qK4KrxmKfPn1QqVS8+uqrVFRUcOzYLVXzqFGjiImJITAwkAkTJlSKJ5V58Ljf8JwzWXczqMj8u5EF3z8wqHlVOiW43PXvVwvcKSGMxeMGuwWJh4cHn3zyCZMmTWLmzJksXrzYbUs7duyYu7Brhw4dyMzMZPLkyeTn57N27VpGjx7N0KFDmTFjBg0bNiQ5OZlt27bRv39/kpOTUSqVeMYmYS8rQvT0QeEbjDIwyt0XpU8wICFZb3h82q3YSnJdgeeXDyNJEqGhobz88sv4+/tjsVh46aWXiAoPdbdxu9Cx6wtwmEqRnA6cJj2SxYRu9wIyZ/Ujc8aTVGSdw2HSI3poQABVcBV8m/dBoQ2oVPPQmHESR4URySlhyU7DK6GNe53TpEflH0bp4bXY9fk4yopQBlS28d1k16GjJCYmMnbsWAIDA2nQoAErV67kypUrvPfee0iSRNOmTUlMTGTp0qV3bWPDhg3s2LGD2NhY2rZty9GjLrXx/dZXBJcX8U1EUSQ6OrpSTcfb1/9evcf7ochoYcGBS7zy9S8MXX6SV77+hQUHLlF8o1akzH+WVceu0n/RMXafz8did97hIFdxY9mutHz6LzpWKeZV5j+DrOr8B1Ydu8qeGynB7vW2/4/sOZ/PiqNX2PHJTLp27UpQUBC7du3i448/RqlUMmrUKKKiotiyZQtBQUH07NmTFStW0KhRo7uWMbpJSkoKs2bNIiEhgVq1atG1a1fWrFlDeX46Veq34VrGBZR+4bc8PQG7sRgQEDw8ETw8kWwWCr56G9FSRkhQICqVinfeeYeAgAC+/PJLHA4Hc+bMQVSqiBi+gKJtsyv1wWkpx16cTdbsfoCAKrgKEUM/w2HSU7RlOtaCyxiOb8AjNA5lQCQIApKgQOUfTqsh4zl+vBcAZdevULZoJM6KMjxC49HW7Ux56j4AVEExWIuuYb2ejujpQ0jv8S6Bai6745r4eCqJi4sjPj4ek8nEL7/8wssvv0xAQADXr18nICCA6OhoNBoNL7zwAmlpaW7b29WrV4mKiqJJkyZs2bIFm83G559/Tr9+/cjKysLHx4eZM2f+YX1FgKysrFvXyOkkOzu7Ul7W29ffXu/xzyCnjfv3MXnyZDIyMli16s4yZL+37h/Dc+6WXP4mtyd8AOSE8f9BHljB99VXXzF79mxSU1Px9vYmLi6OwYMHM3LkyHsKtDNZpUzbceG+Ysxux2xz8u6WXwkqtXNw81oqKip47LHHyM7OdocztGnThl27dpGUlHTPdkwmE1euXOHy5ctcuXKFrKws9Ho98fHxZGdno9FoMJlMNGzYkJyscyRWi+eK3R/Dic1oqtYj5tVvKN4+G+/EtpSf24/h2EYQBLQ+fnw0/T1++eUXFixYQOfOnQkICCAgIABfX1+efPJJlixZgrP4GgBe1ZvhU68rAOrIWtiKrhE+aDp2QyHXl4+l4uppPKvWJ+ypKVhyLqAMiETpG0ze6nEAaOt2Qlu3E9tzb8XcqTw88Ht0NOXnD6PwDXbnJQUQPb2p8vw3VGSewSPsERQaHyqyXOED4QM/xF6aT+mB5SgFiao+EsZsIyqVivz8fHQ6HUajEQ8PDyIiIoiJiSEkJASLxTUbOn/+PBcuXADgueeeIz8/H7VaTdWqVYmNjcVoNFJWVuauYt+kSROSk5Px87t3fUVwOS9t3LiRlJQUPv30U9RqNc2bN3evnzt3Lj169MDLy4tp06bx1FNPVdr/j2xBfybI/6+mjfv/4L/R1rVs2TJmzpzJpUuX8PX1pXfv3nzwwQf4+/9zLw1/Zbz4ZxLGy/zreCAF38yZM5k+fTpz586la9euaLVaTp8+zYwZM3j++edRq+/+w/uz1Rkkp8M9gDskAW3TJ0hJSeHIkSNYrVbefPNN9uzZg4+PDz4+PvTs2ZMpU6YQExPjFm6XL192/+n1emJjY4mPjyc+Pp5atWqh0Who27YteXl57N69G4VCQXZ2Nj169KBBgwZs2rSJuiOeY84nr4ME2tgE+o/9gHVvP41/SBwhwUEcP36ckSNHVlLdtm3bFqfTyfbt2/n0009RqVRUK02l5HcmuUrfEEKfnEjJvi8p+vZjEETUkTUI7PLSXbc3W2+li5OsFSgU937cBEFAU7X+715vu91OgzAnu3Q6dyhG/fr1+fzzzwkNDeWNN95g27ZtnDp1Co1GQ5MmTaioqCAnJweAkpIS4uPjKSgo4OLFi1y4cAFfX18aNmzI+vXrSUtL491338VutyMIAiEhIUyfPp01a9ZQWlrqTjheUFBAREQEb7/9NoMHD6ZatWps3LjR7dgCrkr3N0Nrevbs6a73mFFgpNRko+VHLvvn3WZx8SHeXC4wYnH8sar9v20W8d86S705JixfvpyOHTuSk5PDSy+9ROfOnfnxxx//qTb/UjUXOTznP8oDF86g1+uJjIxkxYoVPPnkk3fdxmKxMGHCBNatW4fFYqF3795MeO9DOn16FIvdSdnp7zAc24Czogx1dG0Cu77szlKS+WEPAjuPwHBqC5LTSfTIJa4Ezie3gABPPdGLNSu+pF+/fjRu7PKeLCgooKzMpbZzOByufJtKJVWrVmXkyJE0btyY+Ph4IiIi7phhtG/fHoPBgMPhoG/fvu6KEpIk0aRJE6ZMmUL37t3JysoiKSmJhg0bsnfvXqKioqhevbrbM1GtVtO7d2/CwsL46KOP0Gg0BAUFUVBQgNVqRaFQkJiYSJsh49il80N/6QxF22bi0+hxDCc2IQgigV1fQlAo0e1ZhNNswLfpE/gl9wOg9NBqbEWZICgwXz6FKiCSoO5jUAVFc23GE4SHh+NTtzNXfzmI3VCIJq4RwT3GUp52EMOJTTgtRqJfduXkvFtCbK+4+jQKVWDYMYuLFy/y5ptvMnToUDSaO51g7oYkuRxucnJyyM7Odv/94/fS0lIEQaBJkyYEBgbi5eWFQqHA6XRiNpsxGAykpaWh0+lQqVQ88sgjREdHV/qbOHEi7733Hn369CEgIMCtYfijWdxf5WZNyNtnEaWlpbz66qvs2LGD8vJyIiIiGDp0KOPGuWbnt4d53A/t2rVj0KBBdzhrwf2fnyCAp1Lxb5ulGgwGIiMjWbp0Kf369XMvNxqNxMXF8dFHH3Ht2jW3OtNms/Hss89itVpZu3Yt77//fiVVZ9++fTlw8BDF+jJUIXEEdn0JjxCXPbto22wED08c+nwqss6hCoohOOUNVDds1hXZ5ynZ8wW2khw8AqPYtOILHu3UDnBd21atWrF3717Onj1L+/btWbZsGaNHj2br1q3UrBYe4m4AACAASURBVFmTb775hqpVXddszJgxbNy4Eb1eT/Xq1ZkzZw6tW7f+f7+eDwIPnHPL0aNHsVgsvxu4Pm7cONLT0zl9+jQZGRnk5OQw/BXXQGC+eobSAysI7vV3oketROkbStGW6ZX2N/12jPBnZxE5bB7myz9hOLmZ0P5TiRg6l/U7fgCguLiY69ev4+/vT9euXVm8eDEhISF0796dv/3tb/j6+uLj48P+/ftp3bo1UVFRd1Wr9e7dm7PpVwhvP4jCat3dzg1vrdxLqdlOt27dAJczxYABAzh06BC1a9cmPz+f4OBgwsLC2LFjB3q9nnXr1rkdPj7//HOcTidarZbvv//e/RLw/fxJlB9eieSw4TCWINltRL+8HL/WAyne+RnGc/uJGDKHsIEfoT/yFbbSvNuuy3G8arUkZsxavGq3pWDjNHDYSeo7lry8PC4f2EBU/3eJGrEEa+EVjL/uQVu3E4GdX3S3ca+E2ApBwNsvkOiB02j95hJWnsrjkdr1mDlz5n1lZREEAX9/fxITE9m0aRO5ublMmjSJL774gh07dnD27Fl+/vlnBEHAx8eHrl27cvbsWQIDAwkJCcFms5Gfn89vv/2GTqdDFEWCgoKIiorCy8uLsrIy0tLS+Pbbb9Hr9UybNo24uDi0Wi01a9akcf+xvLPpDGbbv07oOcpLyFv1d67N6ovuh8XuWcTtjB07FqPRyPnz59Hr9Xz77bf3LeT+DIIgMGnVD/d1fvdKbv7/xZEjR6ioqOCJJ56otFyr1dK9e3d2797tXmY2m+nVqxdqtZp169bh4XGr7uVNJ6PSoNokjFlKldGr8Qh/hKKtMyq1a0o7iF/LAcS88hXKgAhKD64AwGEuo/Cbyfg0fpyYMWsJbNabPr17Ulxc7N73q6++YuXKleTk5HDp0iVatGjBc889h06nIyEhgXfffde9bZMmTTh9+jQ6nY6nn36avn37UlFR8S+9dg8qD5yqs6ioiODgYJTKW6eWnJxMWloaFouF7777ji+++MI9qAG89dZbdOvZh4DnH6c8bT/aup1Qh7sGB/92g8ma0x97ab47ObNv874oND4AlJ8/hDapk/uNz7/hoxTsWsiCBQuoVq0aQ4YMITo6miFDhtCjRw+GDh3KkSNHWLZsGePHj2f79u2kpaVRu3btO87ll2s6lv6mJHLEEi6r1Vw4fcszUHDaEVPe47mlR4k2nGP/huX8+uuv+Pn5IYoiUVFRjBgxgjp16vDhhx/i4eGB2Wxmz549JCcn89prr2EymUhNTXUHmUdERPD000+TtXcN1Ru1olChwC+5n6tiRUIbdN99jm/jFES1Fx4hsaiCYrAVXHGHUniEV8O7liuEwLdpL8pObMJScJl8q+teRIeHYP55K+rmA/Cq1hRr/uU7b+BdEmJnzxtKcPdRHHA2dG/mGZ2M99PNWXU1k4+bdmTU048zatSo+7LXLFiw4Nbzcpst6uDXCwirlkTteg05evykO8h8yJAhREREYLVaSUtLc79gdOjQge7du5ORkeEOUA8JCcFqtXLt2jUEQXANnD6hFMe2RRKVdy0bJdkqCE55A03V+hjP7qmUBs6/zSC0ie2xlVyneOdnrqohgoBnXAOUfqGIXr7EjF2H4fgGsj57li+sZva8G83CBfPp2LEjJ0+epKCggJo1a3LlyhVq1apFrVq1WLx4Ma+//joA9erVQxAElixZQpcuXXjmmWc4fvw4drudli1bUqNGDQoKCoiNjeXQoUMcPHiQF154gXbt2rF371427zsBwJXFo1F4B+DfehDeCa6Zh/7IOvRHXRmJJKcDnA5XggRA6RfKNBbet63rn7UbFhUVERgUzOIfM+/Y1z8olLRfT1OzZk0MBgPdunWjXr16fPLJJ+6Zep6+guNXim+ppwOaQpkEChX+rZ4ma05/nBXliJ7eAHjVaIE6siYA2sR26H5YAoD50kmUgZFo63QAwKNWG3zP7WTr1q0MGTIEcNmhH3nkEQAeffRR0tLS3IW2+/bty8SJE93nNWjQIPfn1157jalTp3Lx4kW3M5fMvXngBF9QUBBFRUXY7Xa38Dty5AgA0dHR5OfnYzKZaNSokXsfSZIwWWwEAA6jDnXYI+51oocGUeOD3VjsFnxK32D3eodR5y5YCxBQpQYFwI8//ohSqaxkV/Py8iI8PJzDhw+TkpKCWq3G4XDQpk0bd67Rm6q7VceuMmnzWRzeMQiCiPUf7D2SqMQB7P9Nh4Iw+g19iyHOXA4fPkxxcTEHDx5k+/btrFu3Dp1OR6tWrdi/f79b1bN48WL69OlDkyZNKl0Hh8PB/v37MedfRePj77ZhCirXwKLwujVACSo1TuutN0xXKMWNdYKIwicYh1GHp7fLUaSkpISyaxvRmswInt44reY77t8dCbHjGoLkRPoH5cRN544S7yp4pbzF6uPb+eCDKJo2bUrbtm3dzilKpbLS381luRYVe/NUnC8VEQCbBDn7tuLbtBfp2ppkbVhBz/fX82xyVfR6PRs2bGDJkiXMmzcPp9PJ+PHjCQoKokmTJkyfPp2BAwcydepUFAoF69evp0WLFoiiyLJly3hn8hTCWxhQal3qckvuRbzrdCB6zBqMp79Ht3sB3Cgb9Y9p4Jxuz1YJvxZ98Yypg9NqonDj+9gKrqCp0QK7Lsc9S9YGhPBMHS+qVo0GoHnz5qxYsQIPDw8mTpzIrFmz3Newfv36HDhwgDNnzrhngcXFxQwZMoQ1a9ZgtVoZPnw427Zto169eowePZq9e/dSUFCAJEl06tSJn3/+maef6AFA+LMzwWYl/+uJqEJi8Qiugl9yP7c6/KbQDx90S4Nittl548s99A5x/WbtdjsOh6PS/zyrmrOOCHKlAEDCKdzmGCVdY/rOcwRUXCdKn4rGVFBpX6NHEFcqNBQWFvLB1tMIN5K2A2C/StGuU1Cu41Tacgx515AkiQsXLvDVV1+5EsvXaItVUGHXmwmxO5GcDkoPrsR04bArD67gei4dZoNb8Im3JYkXlGokm+s5dxh1KH1vhQ0BaALC3TZogLCwW5VPNBrNHd9v127MmDGDJUuWkJubiyAIGAwGioqKkPljHjjB16JFC9RqNVu2bLmrjS84OBiNRsO5c+eIiroV//b84kP8cMmAQhuI3VBA8Xefo/AJwrdJb5zmMvegBbiMFDdQaAOxl9162ILVEheBt99+G51OR0VFBQEBAZw5c4bi4mKKioqYP38+Pj4+vPTSS2RmZnLw4EEmT55MUlISCxYsIE9bjdfe/wzDr/sI6//e756vIIo4EdmSKdLOz4sff/yRatWqUVZWxpYtW1i/fj2PPfYYBw8exMPDg+vXrwPQoEEDRFFkwoQJvPHGG+72JEkiOTmZli1b8u13e/7Utb/9OkiSE0dZEQptIGEBXhi8vUlMTKSgoIBh/Vrz4Sfzcah8XOWfHLZK7WgT2+Cd2BanxUzxd5+7VETrpxDz6jduQSzZbZTs/xLTBVcibO9aLWkz9C3yDq9n2rRpqNVq+vXr5365WLRoET179sTLy4vvDp1AX5RPzJg1OMx6irfPoeLar0i2Coype9E2eBSlfwTb3nuOY4+/hvlEKk6n0233uTkTUCgUzJo1C1EU+emnn5g+fbr7RUcURdRqNR6+QQg+wRR8/Q6OMh2Sw4agUOFdq5VrJn2zbFSF8eYNxVqYicI3BEHpQen+ZVhyL4LTgTq6NqqgGNeLl6jAVnQNmy6XshObQaHElH6UwtR9TCy8ykSHFQ8PD3x8fLDb7dhsNmbPns2nn36Kv78/NpvNbXdOSkrCYrFUekkTBAGVSoXV6sqg89tvv7F+/fpK92nChAlMmDAB0ds10JvSDmI4vgHJbuX6l2MIemws2tptuBe24ix0uxeSlZfBMbWS5s2aUqtWLYxGI2vWrGHAgAEYwxtw3KAle+nfCOk1Ds/YuhjP7kZ/fAOOsmIUGl98m/dBatANg1cUHfwKObXucy5cuIATEQKrENJnIsKeZZgyTrpnogBOpwPzlV8IaPsskrGYBjUS6NKiPt988w2bNm3icL7InP2Z5O1b6b7n5WkHMP12jND+U10J2y3lZM3pD/yx/vrm2HI7FSX5lcYhcIXdxMXFMX78+Hu2dejQIaZPn84PP/xAYmIioigSEBBw37HHDzsPnODz9/dn0qRJvPTSS0iSRNeuXfH29ubs2bOUl5cjiiJKpZKYmBjOnTtHQkICOTk5nNq8lOsnD2AtuILoqSVswPuogmIo2bcUdWTNO2rQ3cS7ViuKd3yCNrEDmsBQHBkuD7F9+/ZRrVo1Bg0ahJeXF48++iiffPIJZrOZefPmkZGRQV6eyz7WuHFjevXqRefOnRk0ZgKej41Dk9gBTWKH3z3XvNXjsBVcIfpvqzCj4vtCLTkFOmy28wQFBVFcXIxOpyM2NhZRFPnqq69o0KAB5eXlJCcn43Q6eeutt9i5cyeNGzfm2LFjHDp0CEEQXAO85EQpSNile7t6Gk/vxKbLds1K8zIwXTyCpnozyk59CwoVHiFx5JzZSnl5OcePHyc0NJRJkyZht9vx9fUl8NAsLmQXuWoezngCJAnRU4tvi374NOjmyuIiCIQ8ObFSCIT+yNdYci8S8dynIAgUbniPYz95MvvdmRSl/8ysWbPYvHkzAwYMYNy4cSxatIgZM2ZwrEjJ9nPD0VZpDIKI4cQmFD5BeCW0wmHU4dfCFa/oXbst+iNfg0KFEFyVxg3r89GE12jcuDHZ2dnUr18fh8OBWq1mxIgRzJgxg+7du7Ny5UqWLFnCuHHjEAQBi1GPvdyIT5OeBLQdTNkvOyk9tIrSw2sI7PSCu2yUZLcgengS0vNNDCc2UbzzU9QR1dFUa0Jwr7/jMOrIX/MWOQuHIyiUIEkISjU+TXsR0OYZys/tR398A/aS62iiauBtykOn02EwGCoNhg6Hg+LiYnfidICKigpUKhXh4eEEBATw66+/Aq74xJvb+fv78/nnn/PBBx9QWlqKQqHgueeeY9WmHVw660oNp/9xLSg8XH+SE913n+MRWhWPYFe+V+OZ7935Xp3WCvK/moh/64FUefo9+sQ5WThuKLNnz6Z27do0b96cye9PR9v/cQp2vIc2qSOesXUBEL38Ce0zCaV/ONdmPInu+7noflgEdhsZguD6jatUBHZ5AcknFEGtxa/lAHS7FyKqvfCMrYejrJjiXfNQ+gS7c8b+fGwvnXo/zaBBg3gspReKnu/i9KysgpWsZgSFCoXGF8lmoeSAy36Xu2QUgiAi2S0gSS5nN0DU3CoXpnmkMbrdCyk/tx+vhNbYfjtC6fUr9OjR43d/53ejrKwMpVJJSEgIdrudDz/8EIPB8KfbeVh54AQfwJtvvklUVBTTp0/n2Wefxdvbm/j4eD766COSk5MJCAhwp6SyWq2EhIRwvbAYSeWqP+fbpDeFm97HWWFEHZVAcMqb9zyW5pHG+DR+nLy14xEEkRffm8yx7ze5QyaUSiWhoaH07t2bZs2a8fTTT3Pq1CkiIyOZM2cOo0ePZuTIkSxcuJCkpCS0TZ/EJij5o9B5e2k+luw0RLUXpozjeNdqhVMS8YqszqAnO7F69WpiY2Pp2bMn06dPZ+jQoaSmphIbG0taWhq+vr4UFBSgUqk4cuQIR44cISQkBIVCwSuvvEKPHj14euAgFAoFdvv9xSl5VW9G+flDFG2fjdI/gpAn3kKj9WHGmAH037+aZs2aYTQaKSkpQaFQ4OPjQ/2EaphLC7kIKHCibTsE45nvKdmzkNKDK/CMqeOuC5g9byhB3UejqVqf8rT9BHZ+EVPGSYxnd+HfcgDF381l2qbGFGychpeXF1988QUnT550pyTb+/NFZqcqcDglbopQQVRiLyumIutXBAkK829ka7HbwGmn9OBKJJuFc77N8Yqu5fbGrVGjBnq9HkEQ3Imwx40bh8FgYMKECbRp04b27duTG9uFJW8MQBUcg6BUIaq9UPgEUnEt9e7PU3wjNPGNcNosN1RqP+LbuCe6o9+gCq6CM/ciVcZ+jSn9KEXf3nKq8E5sh3diO5wWE6Yd0/FT+REfH+/OZZqcnMyRI0cwmUw4nU78/PwoKyvD4XDcuMd2cnJyKgXe30xiDi5v6Zdffhmj0YjT6XoePvjgAwiKRfT2x1legiqkKpHPfw5A4eYPsesLMF04TN7xjQBuoXBtxhMuoeu0g0KJxSlQqAgiJSWFdevWMXnyZJp264vh4yUULhkLCPg/+c6t56zaLfV87BsbKdgwFc8qSRhObkEdUxvBVkFIx2FIfrcKT/s174OtOJuCdbfyx94kZ9EIlDfs1F8ezqDgm3fxCIzEOmcQKNUgORE9NFhyzuNdpwPmyz+TPXcwoqcW/9aDMP6yA7/kp/BPfoq8lW9gNxQS/fIyACoyz1K0bSYACo0voX3eoWTPFxTvmodHQCQbNmwmODj4jj79EV27dqVbt27UqFEDb29vxo4dWylbkMzv88CFM9wPVatWZdiwYWzZsoWTJ08ycOBAMjMzsUTU5dT6+USNWELp4TXuDAwO0w11WHaaSwUUXIWwgR8iCGIl13vfwBBmTZvE8OHDmTBhApcvX2bVqlVUVFQwbNgwdxxY9erV2bZtG2FhYSxbtowpU6Zw/fp1rDY7AV1fRlu38x32kMwPexDQ6QXKTm7BaTWjCoxCQkIdWZOKq2cRPTzxCIun7PR3+Hh74+vrg0KhoKCgwO3p5XejsK1Op6Nu3bqo1WpatWrFDz/8wIULF/D09KSwsBBPT08UCgXx8fFkXMvBXOaq6YdChTrsEQK7voTh+Ebs+nwsWbcGcNHLH+/abTD+ugecEogiGi8tPh4uFfTZs2cpKHDZYFJSUkhMTOTo0aPs37+fiooKnhw4hJ+ieiGJSq4vH4tv4554J7Yj6/PBOI3FiD4hBD82Bv2PX2HJTkMVUhVbUSYeoXFY8zJAEFH5hyOUF6HRaIiJieHChQsIgoDNZiO020to6nUj/+vJWHLPU+XVb3BaTGR9NgjsVhAVIIho4hoS2OkFchaNQPTyB6cdyWJCkJyolKI7ON7b25uQkBAKCwspLy93h6nYbDZEUcTpdOIRFu9y4hFEl4pcEMDpQKENQlu3M7aia5gu/ggKFUr/MHwaPo7TrKfs5+2uRN2CAlGtwXEzqbfTQeTIpRR/+zHW/Mv4NO2FNrE99rJiFF5+lOxbQkXmWZcTiSCgUCiQJInY2FgsFgt5eXk4nU7UajVKpZKKigoUCgUWiwWlUonD4XDPBG/GJdpsNtRqNUlJSVy8eJHy8nJ3G+rqLSi7eBTJZgFR4bIFO5031NcC2nqdCer6MgB5K9/AknuR0KemYM3LoPTgKrftGKcDyVaBKIqEhITg1fUVjOXlFG6YSmC3UfjU7+Z+zsyXTlH641rsuhwkSUKyWfBr/iTG1H0EdH4Ry7VfMaUfRbKU39hDwLNqPdQxdTCdP4jDpEeyVhAxbC6i2ovinZ9hzb2I01pBYKfhFG6cBgoVSE40cQ0J6PQixtM7KE/dh1+bZzEc+aqS85Hx9Pd412mPT72ud/xub6pzrXkZiF5+bscfQYCO1fzwO7eJ9evXU1paSlJSErt37yY/P5+4uDiWLVvGxIkTMZlMjB071h3KJPPXeSBnfPdD8+bNWblyJVu3bmXXrl14eHiw+ON5dF8//45tb6rDYkavBsCSewEQ3K73fq0HEpDUgfc7BLNw+iQef/zxSqEJy5cvR6/Xk5WVhVqt5vTp02g0Gk6dOsWLL75IeHi4qxxReAPU4feuEG5KP0r4kDlIVjM5C4fjVasV3ontKTu1FSQJr5ot8arZCtP13yjLyaF27drExsZy/Phx2rVrh1arJS0tDbvdzsmTJ0lMTGTFihV8//33xMXFERcXh4eHB1u3bqVTp04sXbqUWi0602vsh5gLrmLJTnO7b3uExmMruobnI01QhcRiOn8Qp8mAKiwe5bVwRLUGJJAcFZTpr1O7dm3mzp3L+fPneeaZZ9i2bRtz5szhnXfeYcqUKa4MGg26QgFYrqdj0+XicZvT0N3wqd8VRBXGX2+4o4sKBC9fbIYCrHo9np6evPjii6SmpnLgwAHKLv2Mpn53ACSHnbxVb7oGKOmGE4JCARYT5sunKDtbFUFU4CwrdB9P9A0lOjyAS+kXUalUlJeXuwbeG4Kibt26FBcXU1ZWdkvtZChA8NSi9A/Hq3ozDMc3IfoFgM31MmLKcHlEBnYZifmyq7Yj4FKxikokm5mI4fNxmA0UbvoAuy6bgvVT8KnTwe0VKzlslB5YhjXvEogK6tatx8cffcCOHTtYu3YtxcXFZGdnu4XgzRleWFgYPj4+pKa6Xl7atm1LQEAAmzdvxuFwYLPZ3A5iTqcT/Y1retM2aLFYEC0VeMbWw3zjPCTbraoaCm0APg3vVOOVHliB7Ub5K229LpjTjyJYjMTH18RsNlNQUEjhNx/gtJrR1u2C/vAaSg+vRRAVrtJZ1y8ieHi5PIBtFeB0UHb6OxCViCo1HiFVKft5O9ywHYte/kh2K+Wpe11qYkFEkpyY04+hDAjH/NtxIp77lLxVb7pVl0hOkCQcJj263QuQbBU4ykvQ7fwEhV8YoqcWp0mPbtcCJIsJS+5FSvYsuvEydo3MmX1cx3c6EL39CegyEuw2ir79mOIdn6AOjuFqvZrYzWUcOXKE8PBwjh8/XmncOHz4MBcvXiQ9PZ2mTZvyxBNPkJCQ8Lu/CZn744GL4/szPPPMM7z55pukpKRQu3ZtOjdxhRRITnul7QRRicNYgt1QgKBQ4hlTx2XsvuF6b/zlOy7P7MvzfR7F19eX+fMrC0+VSkVxcTEZGRlkZWWxe/duWrduTUpKCoIg8Nxzz+Hp6Unjbv1QhT7CvfBr3geFxsdlJ5EkHIZC1OHVEL38EDw0+DTpiaBQUrNJa6pXr079+vVJSkrC09OT3bt38+2335Kenu4O9zh37hw6nY7t27fz008/oVQqsVqtbhXX0KFDSU6IoUebxgS1GYit8Cq+TXphK7iC5LDjVaM5Co0vgqhAFRgDgohHUAzOijIC2j+PLS8dS/4VbDYbv/zyC5GRkXz88ce89957VK9enRMnXIOlyWSioqKCle8MJ3POAPKWv4o2sb0r9+dtSJLkmklIEh5RtSg/t8898AEgKvF6pClhHYcCkJ+fz7x589yZORzlJXdcU1PGCUQPNaKXLxGDpiN6B+BZtT52fQGixheFNhCPqAQ8IqrjX78LgybMAVwxYMnJybRq1QqbzTXArl+/npo1a1JWVkbDhg1JSkpC8AlCVKlRh8W77IcCiMpbsWGeMYkofENRagPxrt0GQVQQM2YNVV5Zi7ZOO3C4nkWFNhBVkMtTM/K5T/Bt9gReN0JHPELjiBg8G4+wR/BJaMn5c7+66wHejOXctGkTJpOJDh064OPj464xeNM9PjY2lo4dO5KSkoKXlxcHDhyge/fuBAQEUKdOHby9vRk8eDCCILgdX+rVq0etuGiUnl6uPnr5ubxV/7YSTc1WCCpPCjd9UPmCS06863QgatQKJLvl/9h77/Co6u7d+7OnJjPpvVASegmEEkLvvYjIQ68CggioIEhVukBAQhUITUF6L0GkRaRDAAkQQg/pkF6nz+zzx4StecT2nPd3znN8va+LP0j23vOdzJ69vmute903xT+dxOtfs3Gp055Hjx6RkZFBQLuhduF1qxmlTzAq3yrYiu2zbp5d7NmjIFcgd3DGpUlfQLCTpIwlGJLvk3tmA4LKEffO4xEcXHCoUAdj+iNMGY9BFHEIrIlMoSL39HqyjkTg0fF9ian9eqbUqV5XtLVa4dllAoaUeGRqLTInT1BpSu/3QMp/ss++HgHcWg2lwuQDINoQ1BrkDk5o67RH4VUR377zyDu9ntwzUajL18ajyTu8+/44zn0fzbx58wgMDEQul9OsWbMyqlJz5szB0dGR0NBQQkNDiYuL+81nwz/4a/j/bcYH9gHQOXPmULt2bYYNGyb9/N+Lvy6Ne5N/aReZe+wPCad6XXBt2lei3hf/9B1qBwfMZjMdO3bE37+ss8Ds2bMJCAigSZMmGI1G6tSpQ2RkJG3btiU6Opphw4ZhMBgoOfwVtsZDUHq+uVYvLx0XKLl/DpVvZax6e1ahDqiOIfG2xDxTOmioFRpKjRo1SE1NlcpYFosFURRZvHgxly9fZtmyZTg4OEgqHp6eduZqZGQk8fHxXL16levXr5Oeno7Vau/rpG2xP3hEqwmZ1g9roZ3JqanRDNFixFrwyk4c2DMTm8WMUqnEZrNx8uRJPDw8yM/P59q1axQXF0vU6+LiYjw8PHg78ntiHmZiLckj6+gy8n/chnubd3/+XIqzydpfOsAryEAmw5j2EGSlt7FcjkvTPqizn1Dg6IjBYEClUtGrVy/27t1rnyP7N1hy0+2MSn0Rr3bPwrlBdxBFLPl29qtoMSNTOeLatB85369lwSj7TNrAgQOJi4vD39+fatWq8eDBAw4dOsTdu3ex2Ww8evSolPyiwViSby+BxZ22f17+1dE/j7UvWeuGOTedzP1zpTWlRPbFpVl/nBu9Q9HN46RueA+Fsycu4e+gf3KN34Jvx/ewnl+HSSajoKCAwsJCioqKsNls9OrVCz8/P7RaLXl5echkMjIzM3n48CHu7u44Ozsze7a9j+bg4MDYsWORyWTk5eVJZJjPP/8cJycnjhw5wvjx4+0kmHv3kZf2xzy7T6To5jHSN76PzaRDpnHDVpRdZsYNQUDpWQ65ozMyRxdshmKydk9HKZpxcHDAZDLhqFZhLXiFwrM8+ee/QeboDDI5Kv+qqHwr4dFxLLk/fI3NUIK14BUOwfXs5V0ErMU59rKr2UDeqa9ArkSfeLu07CnDmP4IY1aSPRuzmhEt9l6qTK0t/bztGWvxvbNgNlASfx4Am0mHT5/PsekKyL+4E8OLO2Qd6CE+iQAAIABJREFUmI97u1EICrXdCgxAJkc06VB4lEPpHkDJvRhe7p6JaNKDKGLKeEJow7rM+2AQG+ZOIiEhgdatW7/x8/Tz+7lHqdFo/pRQwz/4c/hbZHxBQUGcPfsz9X7Pnj24u7vz448//u55UVFRBAcHc/r0aXr37k3sMzvVWCZXoH9+C9OrZ/b/qzWSp513n88pjD2C/sUdwE4q8B2ylPLjvyGseWumTJnCli1bMBgMPHv2jI4dO5Keno6LiwsHDhzg4cOHmEwmXrx4gdVqZc2aNdSuXZtx48ahF+XklBrGvgnWomxsZiMlDy9hykrEkveSlDVDMCT+hGgxYSwtfakVcpKTk7lz5w6PHj2iQYMGrFu3jlGjRgH2xvjSpUtxc3PDYrHg6uqKh4eHpOoSHBzMyZMnOXnyJDqdDqvViouLfWDfr91w+2Je7w5eB1uZgCCA3JCPX0Agzk4aZDIZYWFhdO/eHblcTkFBAVqtllWrVhESEiKVCEtKSlAqlbg42AOYXOuOtnozqXzmX9ovkTl74zNgIeryIXh0GU/FT4/g2W0i6oBq9jU4eSIIMho3qEvFihWpVasWNWvWRKPRoHBylzIt16Z9kJX2llzCeyF39sapfjcUzl4U3YpG9/QGotVCuXFb7SUtkx6HCiEEjtmAZ5i9VJqZmUlISAjnzp3j+fPnyOVyduzYgU5nNwHu27cvkZGRCKIFhVpD+SmHqDg9Gplai6ZmC0meDaDcuK1UnB6Nz4CFyF18qDg9GvdWQyWZvMAxUQS+vxHn+l2pOD1aYrd69ZgkuQA4KmUsGtePtOQXGAwGcnNzycvLY968eXh4ePCvf/2LrVu3Mnr0aNzd3Zk8eTK1atVi27Zt5OXl8eLFC7y9vXF3d8doNFJSUkJRURH169fnk08+wcPDA1dXVx4+fEjPnj1JSUnBarXy+eefUbFGHYJmRONQMRSldxAytQZBrrQ/7EHaoCncAxCUP0vM2fTFYDVjM5uwls7wKZVKarfqQrkJ21H5BNkDpiCgDqyJYyU7SUlbtwNKz0AEhQLdk2v2zY/Nas8ma7XGp99ce78WobRkaQMEXMJ7oQ6sQcVP9uHRbhRK74oo/aogd/Yi68gSRNH2i/GYlmhrt7GzhrHPl2Yd+oKsI0swZ70AQJ/4E+mbxyOaDdhKZ/W0NVqAzYYp4zH557eBaLUT1WQynL0DuPM8g3OHduDl5SX5Sv6D//P4WwS+X2Lbtm2MHz+eEydO/OZOCuxzSdeuXePIkSPExMSg1WqZGBX9xmN1T29gzktHFEVkai2CIEMQZJhzUtG/iEO0mDEhJ9Osonbt2nz44Yd8+eWXpKWlMWbMGLy9venduzdeXl589913ZGZmsnjxYqmk5ObmhqurK8GBfr9rhVR4/WDpDlRA7uSFW+uhBIxYg1tre7aad24TgmjFlJ1MQkICAQEBODo6olAoKCkpkZzF3333XfR6vTQzVVBQwLhx4/j222/x9PSkQYMGvPPOO1SpUoUZM2bg7u7+M/PszhEAZEWvsGQlIZr0GJPiENLv42bJwyXxPJkZadSsWRNBEDh9+jSvXr3CZrOhVCrRarXMnTuXuLg47t27hyiKFBcXI5fLMWUmIljNWPWF6B5fRelV8Q1/hd+GpSQXpUykRUgwnp6eODk58fbbb7N161Y0Gk2ZjE+0lWWqaqo3w3/EKgLeW4dNX4g5285ulLv5Ys5MxJydgs1sIP+5fcNz7Ngxtm7dSm5uLhaLvRz5/Plzae5t+/btTJ48mYKCAqwmPTKrifxLu984tP+/BdEGFhPTO1f7le6ls7MzM2bM4NmzZ9StW5eBAweycuVK+vbtS5s2bahSpYqU5fv6+tKxY0dGjBjBtGnTaNGiBenp6Tx+/JgVK1ZgMBjw9fUlJCSEBQsWcPz4cR48eIDZbKaarzMqWdkZt/KT9lHugy2vF/nGpcu0rsg1rnx98CR+fn5S39vf317iNqU/BkEonX/7uddaeOMwNn0RjpUbUeGT/fgNjihzXUGpRpArUfgEow1ph8onGEHtiDnPrnxkzk0j/8K3ePWYjHfPTzG9ei6VzF9nfIJcgWgxk39pl3SOT5/ZuLcbJd1Hzo17oy5Xq8xQvEytRe7siUPFUALe/gREkV4jJrB67TqqV/THmPGEhIQEicQTExNTWlGxSnKL/+B/Hn+rUmdUVBSzZs3i1KlThIWFUVBQIAn0ymQyRowYIWndLV++HAcHB7755hu2bNmCQqmCFnbh3YLrh7Dp8jE8v03y8j6ofILthqzFOXY2XoNuOFSsS/rXE7EZCrHpi+yNdydP5CVZ2KxWbDYbBoOBEydOkJOTw/jx46XeiCAI1K5dm5SUFCpXrsyFCxc4d+4cVhGsgtxOUngDHKs2Ie9sFKIooqnWBJdGvRBkcmRqLQr3QEzpjzHaLLwozS5DQkIYNGgQly9f5tGjR8yfP5+jR49y8OBBjh8/jtFoRK1WYzQaWbVqFa1bt2bFihXMmjWLgoICypUrx5w5c9DpdOTl2ftjn3zyCVOnTiW8ZhDx8fGsX7+e9957j6y7T3BwcECGnexx+/ZtrFYrrq6uODk5oVQqMRqN5OXlodfrqVatGvv27eP69eukpqaSnp5O1KR+iDIlglKNQ8VQPDqM+Uufv0yuIuviXg7f15Oenk5aWhr169enQYMG6AxGbNYSbGYDluIc+4OuFKLVjDk7xd67VTqUsjvtv1P7VcGmK+Dl7hmAgNq7ImbsA96vB8PBPrCuUqkQRRGbzUZoaCiPHz8mMDCQtLQ0ktcMxa1pf6lc/WcglPaaZdiQY8P6i32qSl46UP3kFuWKHvDK+wk0f/PYjdFoxMnJifz8fAoKCti4cSNHjx6lT58+REREcPnyZVavXoNL7dboAkN4npFBbPwP+AVV5drVq3wVGcHdu3cZM2YM69evZ+XKlTRo0ICUlBSePn1qN0dO1iN3D3jjjNtvwalqOCV3z5D/PI5hw4axZMkSQkND8W45EKOgxFLwCt+Bi5A7e5K+6QOspcFPNOntRCa5Equ+SApOADZDMabMFwgqRzRVwim8FQ3GEkDEUpyLTCbn1a4ZaGq1QelV3t7zqxCC/tlNkMlxrNYM3eNrFMedQlCqcW87Ev2Ta/Z5UpUjhbdPSD1lQ3I8msoNMabcx6azM5+NGY8RFA6Y89JxePgdKpWK7AdX+WzXegoLC3n33XfZtm0be/fuxWAw0LJlSxo1akRxcTGhoaGcOnXqT98f/+A/x99inCEoKIiGDRty6dIlTp8+LWnVvfPOO/j4+BAZGUlJSQk9evRg1KhRNGjQgM6dO1NUVMS6deto164dDboNoiT9CYHjtyMIAi93TpcoymCfm0vbMIoKU49KpaZfHlPy8BJ5ZzfiU6clBXFnCA4OJiUlRaLxb9q0CX9/f3r16kXv3r05evSotGZRFBkxYgSVKlVi2vwIXIeslKSQXiNpSQ8C3t+I0v3XhqavKdS+g5bgqU/l9uoPfvfvdf36dd566y2uXLnC+PHjOXfuHJMnTyYiouzO+aeffmLQoEEUFhaybNkyBg0aRFFREX5+fuTn51O/fn12795NXFwcR48epUaNGly4cIGQkBBkMhkrVqwgKSmJmzdvMmLECOrWrcudO3dQq9UUFhZKPR1RFPH29mbOnDlcpCbXUnRvzBF+Ocf37+/db8hSzFlJ6M99RWGKvb+mUqkkUfCHialMXhCJMeMxKu8gHILqYUiKw2/IUvQv7pB3bjOW/JcIChUOwfXx7DIBmcoRc26aNJPmWCGEAJWBp/Fx0qhA+fLlmT9/Pv7+/ly/fp2vvvqKzNx8nOp3R+5ZHkthFkU3DuNcrwtyuQynpv1BpkT4HQNiAVDKoHugkWrCK1KzC7hToOZZtgGdRcRRLmJ89Zycm9+hy8tErVZjMplo1KiRZMJbXFxMZmYmL168IC8vj9DQUFq0aEHHjh0JDQ1l8+bNrFy5kqY9BqBt1JuYE0fIu3EEc14GgkqDploT/NqPQObojFP8YfzI58Qhe3/zs88+Izo6mpiYGGbNmsXXX3/N1KlTSXcMYvfWKPTJ96QZt5wTK6T7Njt6BbrHV/HuNR11+VpkHYnA/OI2Go0Gg8GAo6MjgiBQUqLDigxtSFs8u0wAIC1qDKLZQODYLVj1hbz8dgrWohwUbn64hL9D7qmvULj54TtkKVlHIjCll5Y/ZQqcG/ZA9+gKmhotKIo9Yv9u2ay8zkSVvpXx6DiW7GPL8Oz2EebsFAp+/Aab1YLXW1NQl6tF9rFlmF4+Re7kgcLdH8PzWwgOzjjVaYch6Z593MRqRuHmh7UkD6u+CJloRaPRoFKpCA0NZejQoaxdu5anT59SpUoVVq1aRYsWLX73u/oP/mfwtwl8ubm5tG3blsOHDyOTyXj16hUVKlQgPz9f0r/cvXs3GzduxNHRES8vL65cuUJCQgKtWrVC024MMYtGUm7Ct8id3P9y4Hu193McK4XxTv/BbBzZArlcTnZ2Np07d+bJkydMnDiRrVu3Mnz4cFatWoVWq6VChQrcv3+f5ORkvL297dqHDxNxH7IC6y/0COHPBT6f/vN5tXM6TasHcvDgQamM9SasW7eOlStXUlRUxPDhw1mxYgWZmZm4urpis9mIjIwkIiKClStXsmnTJubMmUPbtm358ccfmT59OlevXqVWrVocOHAApVJJs2bN0Gg0xMbGIpPJqFmzJhcuXKBmzZrMnj2bV69esXDhQiZNmsSuXbtwcXHBycmJZcuWsWjRIsxmM8+fP8clqA6anjMlRZO/AtFspEpSNH3bh9OpUydu377NZ599xosXL/D09KTeh+t4UKD4E+JSZaF7dAXHSg0xP7+BY9x+KlSowOzZszl06BCHDh3Czc2NXr16kVwMMa9UKCvWs3s1Kn5m6MmxYbPZMKY9xGazog6saVdfUf58zOsymyUlDpeUq5TXigQEBEj/XsverVy5Ej8/P1QqFUlJSZId1f3791EoFBQVFUmGvFqtVlJryc7OJisrC1EU8fLyQluvK8aa3RBlij8MxA7Kn22EioqKSElJ4cqVK0yZMoW2bdvi6upKSkoKL4pESio0Rx3cwN4a+MX7e53BqnKeoL95hIz46ygUClq0aIGXlxeCIJCfn2+vFjQbhaZqk99d1+9CFMtIC/7582y0rORGv9quTDj6HEGu/ONz/g2OSjni2RXU8tNy6NChv76Gf/A/jr9Nj2/9+vU8fvyY9957D1EUSUpKwmw24+/vj5ubG25ubrz//vskJSURHx9Py5Yt8fPz47PPPsPDwwO5oyuA1KT+q7AWZqNw9weV3b8N7LqgMTExmEwmIiIiMJlMLF68mE6dOnHmzBliY2Np3bo10dHRrF27lnPnzrFw5mTm9aqLo/KvfTQyAcLkyQS5yLhw4QJ+fn5ERkb+pnbfBx98gE6no3z58ixYsEBi8qWlpdGxY0eOHDnCjRs3GDx4MEVFRTg728ktN27cIDw83P6erVZkMhkGg4GcnBw2btyIj48PXl5ezJgxg08//ZSioiJWr15NRkYGVatWRaVSERUVhVqtplq1aqxbtw5XV1c2bNhARkYGw99qgzH2MKLV8sZ1/xYUgo0ZXWtwbv9Wxo4dy4sXL1i5ciWCIDB9+nTq1avH1S1zsZn/eg+l6M73pK4dSklMFHXr1mXXrl20bduWNWvWkJKSwty5c9l68Qk/KhugqNgAZIoyQQ/AigxRpkAZWAvXyvVwfHoO15TLVJJl4VaSgiLlNsVX9qA4MZfQwhu0qFWBBg0acP78ee7fv09CQgLXrl3jypUrNG7cGI1Gg4ODA1WrVqWkpISffvoJk8lE48aN2blzJzt27GDv3r1ER0dz5swZbt++TXJyMnq9ntzcXKZuioZ6vUCh+sPgImK3EZp54BauDbvj5uZGeHg4Y8eOxdXVVSqZd+rUieUzP+Li4mHs7BdMwaWd9AjxwZp8h6QlPRgZ6sSNzzpxfGpPlkwdx9ixY1Gr1fzwww+cPXuWS5cuce7cOQoLCzHcPgo28xvXk7pupEQuK7iyj5zvVv/6oP8g5gE4qhQ0dspj+KTP//jgN0AQbczqVgNbdmIZY+J/8N+Fv02Pz9fXl3PnztG6dWvGjRvH7NmzUavVZGdnl7Eo6tSpE3379kWpVJKXl8euXbtYs2YN47ZfL3vBf9stCqpSTUWzEUFtn1uyluRLv5e7eGHJy8DF4eebPS8vj23btkkBIisri86dO5fZBQ4cOJAZM2YgiiJNmjRh3Di7m3lycgobY7OhdOC24vQ3E29Emw1trZa0CqlAFTGDsOHDadGiBb169WLy5MmsXr2a6OhoQkJCypx3584dLBYLOp2OrVu38uWXXzJ27FjOnDnDxx9/zIwZM6S/W2FhIS4uds3B2NhYyevQarVSXFzMgAEDqFOnDnr9z5uGoUOHsnjxYoKCgrBarbRr145t27bh7m4XNG7VqhVdu9rnHp8+fYpWqyVVJ6eo3kCcVBmYLdY/9+wSbQiiFeHeMSat2M1sZ2fMZjM2m42uXbsyceJEqlSpQkFBAffu3cMr5QLZ5VuWVen/AwQNWcisbjXLkEcuXbrE1KlTuXXrFmariNK3Eh6VmqL2r1bmXEPKfXJOriFwTBRgFxU3WkTkNToxs2cdhjUNlo79/PPPuXNHx7Bhw4iPjyc+Pp6srCxpFMRgMKDT6XB1daVLly60atWKatWqkZ+fz/jx4+nTpw/79+/HYDCQnZ1Neno6OTk5eHl54e/vj7+/P+7u7pic/Il1bY5NKPv1L757lpzvVuIc9jYeHUZLP9c9vkbWoYVoQ9oT0HMSU0cPZMVnE+nWrRvVq1cnOzub7Oxsnjx5ws6dO3n06BFmsxlRFDmSel3SpF31UX8WjihCo9FQqVIlatSoweTJk4mKiqJy5co8evSI7du3069fP0RR5KvT91hzMRXT7yjmvXZ++DXK3j2vqzN5ZzdJPxPNRgSFUmoreHR6HwehhOnfXMGj3/z/KNuTyQTqlnOTGKr/4L8Tf5vABxAQECAFPwcHBzp16sTkyZNZsGABTk5O7Nu3j3v37nHixAnWrl3LkydP2LhxI2PGjKFm7w/J+MW15Fo3LL8wWZVrXJE7e1IS/wNO9bpQcu+cNOsF4BTamfxzm3Hs353YWDNLly7l9OnT9OjRAw8PD2QyGV5eXly9epUpU6bw5Zdf8uzZMyIjI8nOzqZKlSp88MEHGI1GFi5cSFRUFJO/WMVzx6qcf5SFwM9WPAByrCgUSrQFydQUUyhKus+3sbEMGTKEVq1a8fLlS6ZOncqaNWuoW7cuw4cPZ8OGDdKA7Pz585k+fTrdunWT3BgEQZAcxH+Jf8/4Fi5cCNjVPD755BPeeust3NzcuH79OkFBQaxfv559+/ZRvXp1bt68yaVLl2jSpEmZa1avXp2rV6/So0cPDAYD4yJ3UVS1E0az3YJIppCRum4kosVI4NgtyEo3HkVxpyi5/wPlhkYglwm0re7LuDZV0Kf587kllYSEBIYPH05QUBCJiYls3brVbrOk1yOTycg+vhHXhumoGg8AuZJXu2diTLmP/4jVqHx/Vs3JPLgQ/ZNrlB8awaxx/coEvcLCQjp37oxcLkfuXYmAAfPRpT5844PSoXyIFPR+CStyFp98RL3y7pIX3Wv90uDgYJKTkzGZTJhMJtzd3QkMDOTBgwfYbDaKi4ulLKly5crodDpcXFwoKCigU6dOFBQU8P7775OcnExSUhLPnj0jOTmZu3fv2h0jQjqhS43Gb+iyX61L4eaP7uFF3NuNlEr6xffP2QfKAbMNNl1KYs6cOXz44YfSeaIo8uLFC27evMnkyZPJz88vI22m0+no1asXFStWxGAwsHr1ajQaDffu3ePVq1eSNc/IkSOZOXMmXl5eeHl5UaF8OM/d6mEVZf97Zc9SVJj8s8OE1DOuWBesFvJ/2MqjW9F4vTOzjMjAX4FNFFh3/ikWi6WMie0/+O/C3yrwAVSoUIGYmBhatWpFnz59MBgM1KpVSxrkHTx4MDKZjKioKMkpYMWKFSxbs6HMdVzCepIdvYLi29+hDWmHR8f38ezyITmn15P343ac6na092lKoa3RAvSFRH40kCVGAz4+Phw6dIjGjRvj6elJ3759+frrrxk4cCAbNmzg+fPnXLp0qXSXf4c9e/YQFBREWFgYlSpVkoajAXKKjRy4ncrDjCIKDWauXzqPNSeFS9uXYdMXEhISwrFjx5gxYwbr16+nXr16DB48mJUrV/L+++/Ts2dPtm3bxoEDB9ixYwdBQUFcu3aNnTt3Eh8fj0ql4ty5c2zZsoWRI0dy48YNKtUKlUw/hdYf8EVMKkFuryg02sp4t/n5+bFgwQLmzp3Lpk2b2LFjB2PGjOHBgwecPn2aiRMncvfu3V8FPrBn6T/88AP+rfpx79IZtAVWqacqwWaj6OaxN+zsRT7pUI1GzoXMHDuEu3fvMmvWLEaMGIHJZOL7778nJiaG06dP4+bmxrRp02jevDmpqalMmDAB98I0hNqdQRRRuAdQfD8Gj9eBT5+PKf0haic3pnesLAU9URQ5f/48o0aNQqfT8dZbb+HdeyYxj3NwDG5QeoyNgiv7KI47hWgx2XU/O45F5qCVBItfz/AZzBaa1avJws+mcefOHbZv344oiuzduxd3d3e++uorsrKyqFKlCnv27EEURfz9/fn444+5fv06Wq2WO3fukJycjM1m4+jRo5JG6M2bN7HZbJQrVw69Xk9WVhaxsbF2OypdAcaXT0le0R9t7TZ4dBz7s9WSkzuC0gH989toqjTCqi/CmJaAU+12do1LQPSvxZoNc5k5cyZ6vR65XI5MJsPJycmeuaemStJovXv35saNG8TGxhIWFsby5culsRqtVktSUhITJkxg3bp1WCwWHBwcqFu3LuPHj5dK6POWLyTtRSKi2YRLeK8yd0H+xZ1Y8jPwemsKosVEzsnV6J/dQhRtKN0D8Ok7m8KbxzCmPpBkxZzqtMej0weAiAwbgWION3d8gSXzOYJMRu7J1egSLuDReTxyR2dyTn2FraQA794zAcj74WtML5/gM+ALbMYSco4vL2MddVr3IRa5A0qlUtLizcrKwsvLi4ULFzJ48ODff4j9g/9x/C3ILX8GP/zwA2PGjCEhIYEvvviCmJgYCgoK6NevH6NGjaJ69er0XX2GM/Ev4T/YWYo2Gy5FiSzpXpkuXbogl8sxm8307NmTwMBANm3aZLepMRpp3LgxCQkJHD9+nE6dOklEiaysLFasWMHAgQN/d55vy5YtTJgwQSot7tq1i8WLF9O2bVtUKhUnTpwgPDyctWvX4uzsjM1mY9WqVUyfPh2z2Yy7uzsTJkzAwcGBFStWsGbNGm7dusXdu3dJKZGhD26JsqKdOWn8RZapFEQsViud6gRS1ZzI9NEDGTRoECdPnqROnTpcu3atjNbg68F0i8WCRqMhKipK+tJbrVZiY2PZfPgMp621yNw3twyZCOw7cuf6XSm8fpDAsZuROThJGZ/f4CVYshLJ3vsZcpsZPz8/OnToQFpamiR67ezszJgxY8jJyeHYsWNkZGTQqFEjEhMTyc/Pp0ePHjxLfEFQwzbEHN6Jb88piGY9shc3ELOekZ6ShEwmw8PDA09PTxITE9HpdAil1jdde7zFT471yY+/iGjU4d17JsVxp8mN2YLSqzw+/eaTuX8u5pfPqDDlIIaku2QdW4o6sCbGlHgolWDTaLWEhYaQlJREeno6KpWK8uXLU7VqVX744QdJsUOr1WI2m6lUqRJvvfUWLi4uLFiwALPZjFwux2azIYoiW7Zs4aOPPsJgMGA2m9FoNGi1WoqKiuyC5aX3liBXIVqM+PSbh0OFumQeXIAh+S6CXIVc44L/qK8ouXfWbtWlccVamI1Xj0mYc9PJ2DqBShXLU7lyZUpKSoiNjSU8PFwqeVqtVjw9PalVqxYPHz4kKyuLmjVrMmnSJB49esTy5csJCwtj8ODBfPbZZ7i7u9OjRw9u3bpFYmIihYWFODo6olQqyc7Oxm/QIlQBNUlZPQjRbEDu4oPNUITcyQuVTxDeb0+l4PohCq8fxGYx2wXDXX3wHbQYuYMTqetHIuoLEW02ZI4uOFZriu7+ObROThTl/MIjT67EIbghCic3bEYd3m9PxWY2kLH1I1yb9kPh7kfWoUX4j1iNwsULq74QQ9I9HCs3BJuNnO9WIROtOJerSs+qGnbt2kVsbCzVq1eXnDJq1679l58v/+D/W/xtyC2/B1EUmT17NnPmzOHSpUuSlmbjxo2ZMWMGp0+fpk2bNuhvHsFmNf3B1X4DVjO9q2vp1q2bJAY8evRoZDIZGzZsQBAEXr16RZcuXfDx8WHEiBF8+umn7N+/nyVLlkhmtYMGDfrdoAcwZMgQDAaDpPowcOBAgoKCuH79OuXLl+fmzZuoVCoaNGjArVu3kMlkTJo0iefPn1OvXj1yc3OZP38+UVFRxMbG0r9/fxYtWkSmSzX0zd/H6h+C0WIrE/QAzKKAKFNwKv4lq+NlONbthFar5dq1a8TExFCuXDmuXr1KcXExe/bskYLwsGHD+PDDD+natSt79uxh6NCh+Pn5MXr0aO5Z/BHkv10SUvlXRV2hDgU3Dpf5uc1k4OW+eZRr+S9CQ0PJzMzkm2++oXXr1mzZsgVRFJk2bRoRERH8+OOPUvbz4MEDSkpKGDFiBMePH8fHy5PeLUNp1SSMdQNDWT+sCbkJVzHqitFqtUyZMgVvb2+SkpLQ6/WEhobSrVs3tFot331/hpfRqyi5dw594i0Kbx61iyPbrHi/PR25WoNzaGdEixHRZkW02eymxi4+BH6wlXITttl7jf61uHTpEi9evADsJsGiKJKbm0uVKlXo378/arWa6tWr07x5c8qVK0dERAQKhYK+fftKmrNxcXGS3mrdunXx9vbms8/LsAxpAAAgAElEQVQ+Y+vWrZSUlHD79m0AXBr3QR1YkwpTDqKp0QLTq+fknf8Ga0k+Kt/KBLy/EUthNvk/bqf4fgzakLK+kLrHV3CvVJdDhw5x8uRJLl26RJs2bXjvvfdISEigcePGgH2udsGCBQQE2JnIVatWZcGCBaxbtw5BELh16xaffPIJVqsVR0dHCgoKSE1NlcYz1Go1er0epdYVl0r1EBRKBLUTiCJurYfbRxt0eVLLwZAUBwj4DVxE+Y924tHxfUmKTFCoaNN7OBajnm+jVlNyOxrRpMeqt28qZDIZbm5ueHedgGvjd3BtMQjdo8uINisypQNeb00mL2Yz2ceXl9H2lDu6oK3RHJnSAZlag2uz/uiS7oFbICqVSlJn0ev1+Pv7/xP0/kvwtyt1vglnz54lKyuLDh060LBhQ0JDQ1EqlXz11VcIgsChQ4e4d+8e8gcPUFXOgHq9sCD/4wuXQmazoHp0kiNnbnDrzGG2bNnCmjVrSEhIICYmBoVCwcWLFxk4cCCjRo1i9uzZmEwm6tSpQ79+/WjevDkXLlz4w4D3Gmq1Gj8/P6KiolizZg2CILB+/XqqVq1K+/btpcygTp06dO3alWnTpjFp0iQCAwPtVjJqNTabjaSkJJo1a4afnx/1O/dFV70rNovtT7DABWRKBzzavUdIp6pUrmwX1m7cuDHXr1+nTp06RERE4OLiQmJiIv7+/kRERLBs2TK6du1K9+7def/992nZsiWVe/gg/Nv+y5yXQc7JNViLssg6tAh1+VoU3jyGS1hPAIzpj8g5tRaboYjn5/ag8/Hkzp07LF26lJiYGKkMOGfOHL788ktyc3OpVKkSaWlprF27ljFjxrB8+fIyrzls2DB27NjBnDlz8PPzY9iwYcyePZslS5ZQvnx5DAYDkZGRTJw4EZ1OR3x8PI2bNKXcx7uw5KWTefAL8mK+BkScw97++cFY6k5uLcnDnJsKoq1M/0xQqKjbrB2FMrvt0Gu5sOfPn/Pw4UPc3Nxwdnamc+fOnD17Fr1ej81mo2rVqphMJqmsCLBkyRKsViuTJk3Czc2N9PR0li1bJl33NcFJ0s3E7kphNekpjvse12YD0T+7gULrhkNQPYrvnkGudcWhXC30z29J51gKMil++pM0L/saFy9eZPr06eTk2AWlZ8+ejaOjI8nJyQCcPHmSChUq0KVLF44fPy5tRvLz8/H19eXJkycUFRVJGe5rmyyUDtImTBAEBLUWucYFuaMzSo9AaXhc5VMZS/5Lso9GIFqMaGu3QR1QA+QK5Bo3bjx4Tu3ataWNKSBl8Dabjfz8fIRT637WfhVkWEvyUDh7lZpR+2HVFUji4AA2s4G8c5vRP79l13zFPmAvytVotbB3716+/PJLRo0aRfPmzVm+fDk1atT4je/VP/g/hb99xvc625s9ezYjR44kODiYnJwc9uzZg0Kh4MCBAxw9epS+ffsik8kouHmcsY19UCuEX8la/TsEwT6zM69XKH1CfSkpKaFixYpUq1aNr7/+mujoaDQaDcuXL6dPnz5s2rSJefPmcfPmTSkAz549m7S0NJ4/f/6X3lfz5s05fdouetymTRvq1KlDWFgYu3btktwVatWqxY0bNzhw4IBkXBkbG8upU6d49eoVgwYNIjs7m5/uJ3D0dmoZ8syfgaBUs+ZiKndT7ezWxo0bc/nyZSIiIoiLiyMvL485c+ZQXFxMSEgIgYGBHD9+nLFjx1KuXLnSD+hNVxZxbdoXmZMXnt0+RjTqUTh5UHB1v3SEMfk+YqlUWEZqKlWqVGHz5s1cuHBBIrLIZDL8/f2lYO/l5cXKlSvx8fHh8OHDrF+/ntTUVI4ePcrly5c5cuQILVu2JDExkSVLlkju46mpqVgsFiZNmoQgCGi1WsLDwxGxBzSlZ3mcG3a3ky8EGQpXb2mdVp39byPXumMz6QBBCnqizYpNX8DLrFySk5Mlnc9mzZoxerSdVWkymbh27Rq3bt3Czc2N+vXrU65cOTIyMvD396dTp0506tSJYcOGcf36dfbs2YOzszMff/wxKpUKk8kk+Stu3LjxjZ+jaDYimo0UXN6FMe0hySv6Y0y5h2jSoa3d9lfHK1y8CGnTk4KCAhYsWICnpyf9+/cnJiaGW7du4eXlhUqlYvfu3cyZM0ca75k2bRrLly9nwIAByOVy6tati9FoxGQycefOHdLT02nTpg2BgXYiTVpa2s+Gta/XKoplVHcEmUKSEHNt2gfHyo3sMm6CjOJ75yi+H2P/HEry0T25zsOHD4mPj5fOHzBgAGlpaahUKhQKBY6unni0G0WFSXup+OlhFKVKO0W3ohGtZuROHhRePyidX3jjMOacVPyHRZaRT7MailGpVHTu3JkzZ86QkZFBjRo1pM/1H/zfxd8m48suNkpkjEKDBRcHBTX8XPDIfyQN3D5+/BibzcaVK1dQKBSMHz+eI0eOUKFCBXr37s3OnTvx8vJiSq8m3Ph+Cvdtgejcgu2ssF+wvJQCyOQy2lb3ZlybKnZWXtOFlC9fnhkzZiCTyRAEgalTp5Kbm0t6ejo3btzAx8eHqVOnsn37dlavXk3fvn0RBAF/f3/atGnDqVOnqFWr1p96v++++y69evUiMTGRixcv4urqikqlQq1Ws3LlSum4oKAgli1bxltvvUVRUREDBw6UNEx37tzJqFGj6DpwFLb/cPDJaLGx9EQc4cY4du/ezY0bN/Dw8KBDhw5cv36db775ho4dOzJz5kxWrVrF1atXadq0qXS++Q1zxkr3AJTuAfZemoMWl/Be5P24neK4U7iEvwOAY5VwLHnp+A5YSOb+ueif2ckcOp1OIloUFRVJ7MjXhrSpqaloNBo++eQTjEYjBoOBpKQkSbrNYDBQqVIlLBYLycnJhIeH06FDB6KioqhZsyb37t2jalV7lmur/y9u5GtK1VmOIChUyNRaCi7uwrFyI+QaV7v0lUKFIJOj9q0Koo2Sx9fRVG5IwdV9iBYzeVmvJBNbURQZOXKkNO8YFhbGoEGDqFKlitSjBruNkK+vL9evX6du3boEBARQu3ZtioqKCA4OZtasWYSEhKDT6RgxYgQbN25k9+7dwBtst5RqBIUa11ZD0T++gt+QpYiiiCEpDpXvr22yNNWbk7BtIiNGjGDUqFHcunWL+fPn061bN7p3747BYMDNzY3Lly8ze/ZsgoODyc7OZvjw4RIxauTIkSiVSlQqlTTuc/HiRebOnYsgCISHh/P06VOCg4O5desWhpR41AHVEI3Fv7ZPKYXp5VOc6nTAve0IjOkPebVzBuacFCwFmVjy0lAH1sCYWohSqZSk5ipXriyRiapUqUKN9n3Z8sWnKLwrYivOQ1OtiaTt6TtoMYJSzcttn+BYKQyVbyVEkx5BqUbmoC0jn2bOfoHZXI2jR4/SoUMHHB0dcXJyKtMD/wf/9/D/fOCLS8nnq/NP+fGxnSn2y76Ug+IlBqOJoF4zWLp5ObKCAi5evEh+fj6dO3emevXqjBw5kqKiIrZv305QUBBVq1alb9++HDx4kHfeeYf3BoSRJA/gbko+t5+/JDXjJV7eXjSqXo7Qcm4Euv2sNl+7dm3pSzxp0iTmz5+PIAhER0eTnp5O586dCQ0N5d69e3h7/5wVjB07Fq1WS/v27Tlx4gQNGjT4w/fdrVs3RFFk3rx5NGnShMaNG3PgwAHGjh3L4sWLadWqFTabjfnz57NmzRoCAgJISEhg586dXLlyhTNnzlCpUiXqhjdH7uCMpeAlGdsmYc5JxaFCXTy7T0TuaB9hyDq8GENqPKLFhMo7GI/O41B52wWks6JXcFSuICb7AbnZmZIxa0BAABkZGYwcOZJjx45Jsk39+/dHqVRKs126R1fR1iwr22QtySP37EasRdlkHVgAgoDMwQltzZZ2013AMbgBuc9i7Tv60pm81wQPm80mWR69NoRt1aoVd+7coaSkhMjISFq2bIlGo6F9+/bk5ubSrFkzxowZw6tXr4iMjCQ8PByz2UxERARt2rTBy8uLo0ePcvXqVQ4ePMjixYsxHD5iFwhROmAzluA7cBEyRxcytn7Ey+1TQLSh8g1G5uBkvx+D6iJ39iLn2FJylGpcGvVCrnGhX/d2uDSvILljNGzYEFEU8fDw4MmTJ+zZs4eOHTtSqVIlunXrxvfff8+LFy/IyspCrVZz7do1fvjhBzZt2kR4eDj9+vXj2rVrZGVloVKpmDFjBoBUmlS7eFFcmF3qkm4vHzqFdkL34EdpF2ItzrE7xZfeA7+Eys2XDz+exOG9Ozl27BgWiwUvLy9Gjhwp6bEKgsCHH37IgAED8PX1JTbWbsNUXFxMVFQUOp2OtLQ09u/fT0BAAPXr1yc0NBSTyUT58uU5fvw4lStX5u7duwgKFTnHv8RmMoAgQ+bo8sbvhO7ZTXSPLmPT5SMo7ELVjsENsJnt51my7SXX10EP7OXXxMREoqOjuXbtGitXrQCrmewjS9DWboNjlUZkH/8SlyZ9pHEXt9bDyY5ejv9w+8xj9rFlpKwahNzJQ7KOKrkXg6JDLSIjIxk2bBiCIFCvXr1feXX+g/87+H+a1bnj2gu++O4hBov1tzaBQKkSv9XMiHouBFlS+fTTT1m0aBHvvfcerVu3ZsKECYwaNQqTyYSnp6ekVL906dIygVUURUzWn1/IQSFDBNpU96ZrBTnv9+3Ct99+y6VLl1i4cCEjR46kbdu2jB49GrlczubNm+nfv/9vrvPQoUN88MEHHD58mGbNmv3h+69cuTKZmZlERETQuHFjGjVqRGRkJFarlSVLluDg4ED16tVZuXIlH3zwAe3bt+fdd9+lWbNm6PV6YmNj+S7RxLT3+mPOTcen/3wUrn7kREciKFV4vTUFgOK402hqtECQK8k7/zWG5HsEjLTbJ2VHr8DwLJapq7Yze2hnXFxcUKvVfPXVV8yePZvmzZuTnp5O06ZNOXPmjGSE+/LlS3tpT6EicOxmso8ulVid2d+tQjQbMaQ+wKv7RESTntwzG/Ab+iVpUXbhap++c1A4e5EXsxnDizgUMmjatCne3t7Uq1ePsLAwBgwYQHFxMR07dmTPnj34+fkhl8tJSUlh48aNrFixAqvVytixY5k9ezYLFiwgKiqK1atX079/f8qVK8eOHTto06YNNpuNlStXEhUVRXp6Oj4+PtSoXZf71YbwctcMNNWa4dq0LwBFt7+j6KcT+A9fiTEtocwIg6Ugk9yzG+2sTkFAW7MljtkPGd6/NyaTiYMHD2IwGGjfvj3Dhw/n6dOnfPfdd1y6dIni4mIEQcDZ2Zm2bdsybdo0RFFk586dbN68WWJwKpVKioqKEAQBR0dHateuzaNHj4iIiGD06NGM+voqu7/4yG7nI8go//EuRIuJ/Mu70T24gFVfiNzZE+f63aS+qgSbDX9bFpmHvsDX15dPP/2U1q1bc+HCBb7//nv27dtHXl4ebm5umM1munbtire3N66urjg6OrJ27VratGnDpUuX2L59Ox06dAAgMTGRVq1aERYWRnJyMvHx8XZvPkdHBJmcwIl7MVpsv9Jr/eU4Q+GNIxTePIZNX4DMwQmnel1waz4QsI8gFF4/hCDYjaGtpULyoijSuXNn7t27R0FBAb6+vgS07k+KT9Nf6eX+GQgCdK7ly8Zh4axatarMrOM/+O/Bf3Xge/HiBcHBwZjN5jLqK/A66CWgN/92X6rgyj4s+S/x7PYRYCehKO4d49DSSYSEhBAQEEBmZiaOjo7IZDIcHR05efIkXbt25cmTJxyNz/lTgVUARIuRbv5GrI/Oc/78eSIjIxkzZgxGo5HmzZtTUFCA0Wjk22+/lco9b8L333/P0KFD2bNnD+3bt//dv0/Pnj05fvy4NCPk6upK9+7d6dq1K8OHD6d69erEx8fz5MkTWrRowbNnz3BxceGnn36SAqtrl4/Iv/Ud6sAakumrKTuZjK0fUWHKQakf9Ro2QzEpKwdQfuJeZA5asqNXIMjldBw8jkaWB8yaNQuVSkV4eDhXr15FLpeXsVpxdHTEaDQik8mwWCwovCri3moohbFHpMCXdWSJ3QOx83isJXlkH4nAUpgpBY9fPvyUgkj1jNO4mXPZsWMHN2/e5J133uHAgQPSQ02pVOLp6UlQUBDNmjVj7dq1dOrUiZkzZ1K7dm3i4+MZNmwYfn5+bN68+VdGwr+HvqtOcyPd+B8OV4tYk25TrygWPz8/jhw5QsOGDRk7diw9evT41T3fokULBg8ejCAI3Lhxg4sXL/LixQsp061UqRLe3t7k5OSQnm634HkduJs2bcqsWbMYMGAAcSn5DNh0Db3518a8f7hiixHzyaV0bVybli1bsn79evLy8ujXrx8nTpxApVLx008/8emnnxIXF8fZs2cxGo0IgkDz5s1Zt24dderU+d3X2L59O6NHj6Z3796sWLGC8PBwQid8xf082V/WWbUv2obu8TWEy5tRqVSMGTOGL7/8ksLCQuRyOe7u7ixfvlwyo95/7gZTvk/9lezcn4GjUs6u98JpUNGT9evXM3bs2P9kxf/gfxj/NQXnoKAgqQ7++t/rL++/Iy4lny++e/i7QQ/sckavgx6ATaZA1rAPZ28/pkOHDmRkZKDRaGjevDmtW7dm8ODBzJ07l2nTppUGvQT05t8PelDKz1Co+e6lAw8tXvz444+cPXsWq9WKm5sbXl5enDp1igEDBtC0aVOJbv9LnD9/nnLlytGlSxcOHjxI5y5d+dcni5i49ydGbotl4t6f2PDjM3KKfw4ir/sFrq52ndFy5cpx9OhRFi1aRKtWraS5sHnz5hEUFERISAgODg6EhYVhMBgICwuTSlmKX9jlKFx8wGbBpi9EtFnJO/8NaRveIzmyL6nr7Wa2r81FAWRad+7EP2LRokWAnUFYWFiIWq3mnXfeQS6XU1xczLhx4yQG3WsCjiU7ucy1AFybD8T08hkpK/qTuX8emmpN+S3I5HJq+f9c+goLC2PTpk2MHz9eUqMJCwsjMTGR06dPs3PnTg4dOsTOnTupUaMGy5cvp02bNowdO5bo6Oi/FPQ2btzIhQ2foVL8Z/1RuWhj7+yRhIWFcejQIQYNGsTbb7/NsmXLCAoKYt68eZKiiSiKFBQUkJCQwJEjR9i/fz8+Pj5Mnz6dDRs2sHbtWtq1a4fZbCYpKQkPDw/MZjMjR47Ez88Pb29vPvjgA1JSUggt78asbjX+sh6saDZS1/IU08unvHz5klmzZjFx4kSaNm3KsmXLePbsGd7e3lgsFoYNG0ZQUBAajYbatWvj4+PDtWvXaNiwIW3atGH16tU8fPiwzPegqKiIoUOHsnDhQtRqNRs2bMDPz48DBw4Q+81C/uJyJdgsJlp4lPDy5Ut2797N/v37MZlMyOVyrFYr/fv3Z968eUybNo1Tp04xfmAPelW0lSHR/Bk4KmXM6laD6l52MtFrUs8/+O/Df03GFxQUxObNm+nQoYOkg3j//n2Kiopo2rQpq1atolGjRgCM+fYmZxJe/UF501omW3ldEvHs/gma3CfMbufPkCFDaNy4MQ8ePCAgIICPP/6YL774goM/3GTYttvoDAYKruyj5MF5rMW5yBxdcahYF7fmA1G4+b7xdVUysJ5ZTpOq/qxevRq1Wk3//v0RRZF9+/aRlJTE4MGDCQ4OZuPGjVKv7/z58wwZMoQTV+/z1fmn/PDwFSaTCeEXpJpfllZHNg6kU1gNCgsLcXZ2RhAEqZ81evRozp8/T1ZWFmazmZKSEukacrkcpVKJwWDA3d0dZav3KLh9skzGZ85OIX3rh1SYcpCSBz9ScHUfPn3nonD1RTSWkLJyQBmrGbmLF/VbdeLmmo9499132bRpEwaDAUEQePr0KTVq1MBisZCUlETVqlXRaDQIgkBCQgIVajXAufG/cK7X5S/fM6/LShuGhP3qd8uWLSM6Opr69etL4tnHjh2THDr69u3L3bt3USgUfPPNN1SqVOkNr/BmGI1GPvzwQy5evMiRI0eIzVP/YfXh3+GgkKF9fIoA3TN27dpFSUkJ8+fPZ//+/VL5MCoqin379uHl5UVRURFFRUX07NmTQYMG0a5dO9zc3N54bYvFwsOHD9m0aRO7d+8mODiYuLg4LBYLNpuNmjVr0qNHD/xa9GFjbM4fVzQEcFDIGVBdxdqJ/ZkzZw5LliyhXr16nDlzBk9PT44fP45Go6Fz586kpKSgVqsZNmwY8+fP58SJE1y5coUVK1YQERHBmjVr8PHxoaSkBJVKRadOnahSpcr/Yu+9w6I61zbe35rGwFClI6BiV8CCPRZUFMWGHY29J3aNJVGxx5KNmCixx64xGmOPIlawoNgrKipFeu9MPX+MLiWaxOzvO3vnOyf3dXk5zKxZ611l3vI8z33fbNy4kfbt2+Pm5sbLly/ZsmWL2IbNmzez5MfzGBr0+ksrMb26FKeUS6Rd+pn9+/fToEEDOnfujFarJTo6mrKyMlQqFZMmTeLgwYM8f/6cI0eOkJeXxze/XCHL3Re9IP1jpweDHr1Wg69lFjuCR5OTk4OTkxPr1q1jxIgRH93Wf/Cfw9+uuCU/P5+uXbuybt06mjRpQrVq1Zg3bx4mJiYkJyczYtQYTp+7gERpgWWztx1mbuRuNJnxCDIFxU+jsWk/Cl1+phj/B9DkpvJq/UgMmlLuOU8XVx4tW7bkypUrzJw5E41GQxufOsiqNnttPpuNXfcZKByrYlCXUvTgHCXxd7Cw7vjB9pdp9dTrM5XdM7uJ7x0+fJgxY8bg6+vL8ePHiY6OJjg4mPr167N582Y6d+4MQLFaR9Cmq2JHJPxGL/AN3eDUvRR+3rubgiKjcktBQQGAWH6/adMmZDIZMpnMqNSBUfWjX79+yGQyDh48SGlpqTEXk/4SDAaKHpxD5dkOmZUDuZG7MKv1CYJEaqxa+xNzUQlgI9fTp08f6tWrJ1IA3Nzcym2XmJiIRqPhX//6F48ePSIwMBBNTgrSf1NJXymT8rnv+2HjixcvEhwcLJL47ezs2LNnD97e3nh5eWFjYyOW2c+fP18kWH8MXr16Re/evXFxcSE6OhpLS0tqvv7sY/PNSrmEuV1q0z/Yj/Hjx9OyZUuOHDli9Mdr3pwlS5Ywd+5cZDIZvr6+WFlZcfv2bbFis02bNr876AHIZDI8PT0JDQ3l7NmzLFiwgI4dO3L79m38/PxQqVRs2bKFrJUrMXevi0XT3igqN0Cv15ezEXoz0Xq3erm1y0/069eP5s2bc/LkSerWrYufnx9+fn54eHiQnJxMxYoV6d+/Pzt27CArK4t69epRVFSEpaUlS5cuFTVkV69eLa7Gd+zYgUQi4c6dOxw4cIDly5cbQ+Gvw72jRo1iw4YNPD6zGfuOY/9cu1OvR69T45F3m/N7VnHoUGu6du1KjRo1sLa25vDhw+zbt48xY8ZQWFhIaGgoMpmM7t2788UXX2Bra8vkUaNw9fZi0LKdKD0aoTeA/gP31mAwIAgSwu8lsi/iKm08KyORSP5Z8f2N8bcJdb7BkydPAKMayZsHp0OHDnh7e9O/f38ev0hEojBFX1pA9ukNFD0xuipo89KMZpM1P8Ft6j5UdXzJjzmCrjCbkuc3yLvyE+pXj9EX5yEzt+XCzccAxMXFcf36dfLy8lCr1UTfuo/T4H9R9DSakhc3se89FxPnGkanc6UKC5+uWNQzDnqFd0/zatM4Elb15dW6kRTc+hVBIiG2QMbhX8NFhQ1XV1fKyspo2bIlVatWxcHBgXXr1qFQKESz3M59PyU3L08Mrb5rvZIbuZuMQ8vJPBpCwqq+JP8wgYLbv2LRsDP2zXshCAKOjo4YDAZMTU2NTuivc2gKhQKlUklRURFbt25l06ZNYpjxzJkzPD6xFUEioKrblqzjoSStGYxBqxbdz1We7ZBZOpAUNpTkzZ9h4lKT30KPgZuldqibDCUppwQLCwuio8u7Xej1er7++muaN2/OzJkz2bZtGzExMUZboyeX/+2w0huB5zfPzrBhw2jfvj0tWrTg2bNnNGrUSMztpaSk0LVrV3788UdiYmK4du0aly5dombNmuzYsUO8Lr+HyMhIGjduTPfu3Tlw4IDoWAEwqFll9o1pRlMXJQatGqWs/E9LJhgLrIRXd1H/upK+DZyRyWRMnz6dqlWrUrNmTWxsbESaS2hoKPXq1ePVq1cMHTqUBw8eYGdnR1xcHNWqVWPIkCFcvnz5d22nwBgKnzNnDkuWLEEikeDj48OOHTvIycnh9uM4Qk7cxdl3IIJURvGza5Ql3KXocRTqFzewzomljv4F87zL+Kq1A14VjeF0rVaLVColPDycI0eOULVqVVGJJTMzE51OZ7QnCgnhxYsX+Pr6EhYWxunTpzl8+DB6vR4rKyvmzp1LdHQ0T5484cKFCwQEBHD9+nX69OmDXC7n+++/x87Ojp49e7J+/Xp+/fVXYmNj4WkkyTtn4koWJjIJcqH8+UvRYdCqUb+I4ev2DjwP386RI0cIDAxk8ODBIh9SrVbTrFkzlEolFSpUoKSkhLKyMgoLC+nbty8XL17E3t4eX28PejSpiVaj/uCgByBIpAgyOSYejZl5KpmdV16KPNJ/8PfE3yrUmZmZiVQqpaCgAFdXVxYtWsTw4cPRaDSkpKRQqVIlLOxdMO+1EKmZFSlbJ2PQ63H9bDOZx1ZR9OgilWYcEveZENofE6eqOA74mtTdX6LNS8X1860AWGU94O6mWQiCgJubm+jY7eL9CemeA0jY9RWa9Oe4Td77wfYmfT8C8/r+qGq3RmbtRFnifdJ/WoDjoBWY2FdCE/kDyVePijyl33aqb9RVLC0tkTlWRV83gKxfv/tgAUdu5G7yrh7AoddXKKs0JPfiTkoT7uE8JAQpOhK2TGbItAWcuvGEwqQn5F8/jE0NH7JjryMVjCtBrVbL0qVLad++PbGxsdy4cYObN29y8+ZNzDtPQ1m1yb+vfi/CgEZUb9oAACAASURBVFImpaHkJRWLn4uqIQAhISEcOHCAixcvotFoWLVqFfPnz0ev1+Pp6cmIr3/4qLAbBj0KmYTgrnVF8eh79+7x9ddfExERQevWrXn69Ck3b95EIpFQr149Vq5cSUFBARMnTmTs2LHMnTu3nHJ+VFQUs2bNoqCggBUrVtCpU6dyKjoGg4G1a9eyZMkStm/fTqdOvx+WHTBgAHUaNMGhWXdRVNxSKaeWswW9GrgQfuQgo0ePRq/Xi556nTp1ws7Ojq1bt7JmzRqCgoLE4x46dIjZs2fj5ubG3bt3uXnzJqampmzfvp3169djamrKuHHjGDRokOig8S50Oh21a9dm48aN+Pr6cicxl6Er95Bv7oZBr8cgeRv0EfRaI3/vxU1U8VFUthDQ6XQ8fvwYjUaDqakpBQUFfP755xQXF7Nu3Trs7e3p0KEDx44dY968ecyYMQOlUsmsWbOYMWMGCoWCU6dO8cUXX6BUKikoKGD69Om4ubkxatQoBg0axBdffMH69ev59ttvMTMzY9iwYSxcuJC0tDQiIiI4dOgQBw8eRKVSUVpaik6nY9++fbTt1I1xK7Zx8UE8CvtK2Ng5kpWRirw4k8mDAhnSsjpP7t2kR48ezJkzh5CQEM6ePUtwcDCPHj1i586d1KtXD0EQuHLlCp06dSI/Px+VSoW3tzexsbH0mhXKuVwbSj8ijP3GLFphZU/Bhe18N6kvAwcO/NPv/YP/PP5WU5JDhw6Rl5fHgwcPaNeuncg/6tmzJ/fu3UMmk1HH/1Pk1k5IFKao6rZBl5cqKjcIf1B+rFcXl3P1zsg3rjBMTExISkoiOzublJQULh3dy7Nv+lCWcI8/c7M0camJ3MYZQRBQunuhrNLAWKYulaNyqoREImHp0qUcOnSIhw8fUrlyZY4ePcrx48cxMzPjyy+/RKVS0W3W2j/1/lK61sG0amMEiRSVZzs06S8A0BkkuAz/ln1791CYFo+17zBktq5olBUwca5O0+mb6T16GgALFy6kdevWbNu2TZTCsrCwYErH2pia/G9EvQVKtXqulrpwMu5tXvHatWusWLGCefPmMWXKFNzc3Pj2229FhY7g4GCmdmvMvjHN8K/jiMSge88w1kQmwUQmQZUTxxf1pQxqVpkbN27Qs2dPOnToQIMGDbh+/TqXLl3ihx9+QCaTceLECQwGAzt27CA4OJijR4+yaNGi9+xiWrZsSVRUFIsXL2batGm0a9dO5J2VlJSIecvLly//4aAXFxdn9DMcN5KxrasS2r8+Gz5tQF+3YlLO7qSHf3vGjRtHw4YNEQSBunXrotVqcXNzY8aMGZw5c4bZs2czf/781+EzgZ49e3L//n369OlDZmYmU6ZMoaCggGnTpvH48WNCQkI4c+YM7u7ujBs3jjt37pRrk1QqZfbs2SxdupRdV18StOkqeeaV0L82xk3dPZuCO6cAjIOgVI5ptSZIO0zHxNOPx48fi+R+Dw8POnTowPr168VVXkZGBoIgiLzVN7nsy5cv4+PjQ3R0NBYWFpibm3Pt2jXCwsJYuXIlXbp0wc/PjxkzZmBnZ8fcuXO5fPky6enprF27liFDhpCXl0evXr1ISEhg3rx51K1bF41Gg6enJ/369aNd975EPn6FsmpjFBVcyNfLkdu6gVsDNlxKpMWKs2yLk9Pp07FMnz6dn3/+mWrVqnH58mXu37+Pl5cXer0emUxGcXExMTExuLi4UFhYyOXLl+k6ZDy/pph+1KD3LgwSOaqWg0gp+8eP7++Kv9XA9wa1a9dm27ZtXLlyBYDk5GQ2bNiARqOhgq2tuN2bzlFXlPOn+5QoTI1E1tfQFBi1Ed90gm9mkkpbV9yn7cfE3etPXcDLkp+QsmM6iauDSAjtT0lcDEUPL5C0dgjxp3dgamrKxIkT6dq1K9HR0SQkJFClShUCAgKQyWQsXLiQxMQkLjzJIO/KAfRlRskqXXEe+uJc0n9eQuLqIArvnkaisibv6gEyfvkaicwEg1aNQa8jO2Ij6T/NR536lLyovST8qxeajHj0WjUIEl4JtsRYGQniNjY2pKSkiJWDUVFRRs3OmxeZ+IkLaP9Nge7fQC+R8ejMASRSKUeOHCEgIABLS0vGjRvHwYMHyc7OprS0lMqVK6NUKpk7dy4ajQZvV2vWD2qE/MRC8qJ2Y3h+lSYVTVEk36ZPTSWXZ7XDI/kMOXF3CAgIoEePHrRt25bnz58zc+ZM5s+fz8CBA2nUqBEGg4GZM2eSkpKCo6MjN2/eFJ3jPwRBEOjRowf37t3j008/pWfPnnTp0oXGjRujVqu5cuWKqEn6e3hj5puVlcWGDRvo3bs39vb24grpjT1NVFQUEydOpEqVKkRGRpKamkqNGjVYv349P/74I+Hh4QwYMEB035DL5YwbNw4zMzOqV6+Oj4+P6Hnn5+fHgQMHUKvVbN26lQYNGiAIAgqFQqyOlkqlPNE7sujYA2Mo/U/unwEBtR6eW9anZpeRGAwGPD09uXr1KseOHcPX15fIyEgSEhKYMmUKBw8eJCoqCq1Wi1arJSgoCBcXF1q2bEmXLl1Ys2aNqKITHByMh4cHZ8+eRSqVUr16dSZOnMiNB0+Yu/sC3uNW0WXFUV65d6DNuCXUrt8YBwcHBg8eTHR0NJ06deLOnTv0mhVKTsOhmHg0RpAp0P2mKyt9LbIe/iCNC7IGePX8XCyW0el0uLi4EBQUhImJCRKJhGnTplGlShXWrl2LIAh4eXlxIl73Vrfzr0Kq4NeEjxswtdo/7mf+wf8+/pYD328xZMgQnj9/jpWVFU/OHkCOFnX6CwrvnAZBYhQClsgwGN4+aAa9rlzeSG7njq4gi9LEB+hLCym6a5zlSiQSateuTaNGjfDz88PcQoUmKxFNdhIGdQna/MwPtslgMJB3aS+WTXriOnEX7lP3IbN2RFuQifPw7+g4bj4ajUY0bQXjIBsfHy++BqNclFarKbfv/Gu/gCDBIfBLXCfuQlm5HiCgqt2akrgb6NXGDlGdlUTRwwuUJtyjQodxKCrWxqbDWOSvVVWMB5CgfX2bK7YIpEaNGqxcuZImTZrw8uVL4uLiSEpKIuSzntRRP0Ypl3yESPXHQEBmXoEePXpQUlLChAkTOHLkiJgXs7Gx4fvvv8fBwUE0rwWjssfT+7couPYL0d+O56cJ7WghPKVS8VPuXLtEdHQ0ISEhBAYGEhcXx6RJkzAzM+P06dOcP3+eRYsWUVhYSGBgoKh6snr1aszMzD6q1TKZjFGjRhmpChcv8uLFC6ytrUXh5A+hoKCA7du3s3XrVvbs2UOzZs2IiooiMDCQR48eiQLafn5+KJXGqENwcDAXLlwgNTXVWLTx+DEVKlSga9euODs7k5OTg6+vr6hwA8Zq0oULF/LgwQOKi4upWbMm33zzDaWlpRQVFYm6lw4ODtSrVw9TU1PGjh1LhWoNkDftj/ov0vZKtXoSbBsjta+CQqHgyJEjREVFUadOHYYNG0ajRo0oKysT/Rdr1aqFi4sLnTt35sCBAzx//hxra2v279/PgwcPqFGjBi4uLmKud8uWLew/e42b5o3ote0BVwsrkKZ052JcDnG6Cpg16YOhxxJuqRrh06EX5ubmnDx5knHLNnNd44ZErvzT8LwBoxB3Sa3OnH5ZytKlS0lJSSEgIIC9e/cyYcIEBEHg7t275OTksGHDBmxsbLh79y4ZJ8PIvfQ21ZG+fwH5r5WD3iB5ywSKYy+/f1x1CVd+WoetnR2VKlViyZIl6F9r/27bto1PPvmEqVOnYmtry4IFC/7ajfkH/2P87Qa+N+GbpKQk8b19+/bRrFkz5syZw6tnD4hbNZD0nxcjs7JHVac1gkRq5KPp9RQ/u45BpyXv8j54R2Ra4VAZmbUTGUe+4dX60ciKjBJnubm51KhRg+HDh3Pr1i2yXjwm5+RaLLw6IFGqyDi4lLLUZ0ZB4bJiCm6doPBOOGAUz5WaWYFESklcDNrsVyjd6qJQWaAvysXU1JRt27aJD/wbI8qMjAwx51fzk0682j2H0vjbYDCQHbHRKIirKSX/+iGjUaqlg1F1/ui/UDhVpeSF0V4mdct4o7qEICH/2i9o89IoS3qIJiOe0he3KXv1mMTVQaTumglASgUvcvIKyMjIYMuWLTx79oyOHTsSHh7OixcvqKxJZPcwH/zrOKKQCsQv70rBrRO82jCahND+ZIWvE4spNDkppO75isTVA0j8diAZR74R1enfwMy7I0pTMxYtWkRoaCjdunUTZ7c///wzZWVlWFtbY2JiwpQpU6hYsSLVqlXDYDBQu3ZtTp48ScuWLblx4wbjxo2jU6dO2Nvbs2zZMsaMGYO/vz9z5syhefPm+Pv7M2/ePHbu3ImdnR1Hjx7F2dkZleqtE0FeXh4jR47E2dmZihUrMnfu3PdyrwaDgZCQEEaMGMHhw4dJSEjA1NSUOnXqMH/+fNHQOCYmhqVLl9KmTRtcXFyYP38+DRs25ODBg6SkpLBz507RfulDMDc3Z/Xq1Xz++eeo1WocHR1ZsmQJL1++pE2bNjx+/Jj09HS8vb25deuWWIEsl8vFUvmoqCiuXLlSrjhHJpNhamrKsmXLiI6OJjo6mqBPB/NGyU+bm0b88q5ieuBdaHKSSd09m4TQfsZ7emgFBokUn8Ff0aBBA0aPHk27du04efIkmzZt4tSpU9jb2xMREUF2djbXrl3D3t6e7du3s3nzZsLDwxk2bBh9+vRBEARMTEyIiIgQqRiVW3bHv1UzXmFr1DOVl6cp6AQpglSOrHJDLHsFI6lh1Jg9Elf23rYABp2G7DObSQobSkJIH5K+H0F2hFGYu1SrJ+HKCRauWoelpSW9e/cGwNvbG5VKJVYAnz9/nh07dvD1L9dROHiQF32Q4ifGyJPKsz1FD86Jx1OnPUdXkIVptcbvtSX79Hr0ZcW4+w3hwoUL7Nixg61bt4qfR0dH4+HhQVpaGnPmzPngM/IP/t/D32bge/nyJX5+fmJFYNOmTalbty4uLi54eXkREhLC9OnTmT1rFmbm5hjUpSjs3LHpYFRGsGk7HNvOk8j+9TuSwoYiyJVILe2xbG507jar1RKpyho0JcjMLBB0GmxtbTE3N6devXp8++23aDQanCtWpOLgFVi3HoTrxJ2YVm1E5qEVJIb2I3nLeNQpz1BWro8gSDBv2IWMQytIXB1E0cMLRh1BhQoQKHlxk+LiYlEUefbs2aLlStWqVUVyvrxibcqSHgJg0GooS47FecQaBLkppQl3SVwziNLXnwOo6rSh+Nk18W+zmi2M+oDPY9AXZqEtyEJqXgFBboLCpSauE3dh3caoSCGRKVCoLNFoNPj4+NC+fXtu3bqFVqvF1NSUc+fOEXlkD+sHNWJcG2NYr+TZdZyHhuIyYg3FjyIpfT3ovnFQcJ2wA5fR69DlZ4oCvW+gcKqGytqOgIAAYmNjycnJEV0orly5QkZGBunp6QiCQGBgIPn5+eTn5yORSPD29iYmJoZLly6JyvahoaG8fPmynBLMzp07qVOnDr169eLWrVuMHz+e3r174+joyOLFi+nSpYtokzNs2DBkMhnPnj3j1q1bhIeHs3nzZnFfRUVFDBw4kD179hAdHU27du2wtbVl1apVHDt2jNOnT+Pg4ICFhQWDBw8mMzOT2bNnExsbS2FhIbt376Z+/fofXc0XGBhI5cqVCQ0NFd8zNzdn8uTJPHv2jKVLl2JmZkbjxo2ZOHEicrm8XNFNzZo1OXjwIHv27GH9+vX4+Phw6tQp8XMPDw9atPZFamH7UfJbuRd3YVqlAW5TfsR1/DYsfLqCIOFelp5aXvW5ffs22dnZDBw4kL59++Lh4cHcuXO5desWFy5cQBAEHjx4gIODA4cOGYvMFi1axLVr1zA3NyclJYXatWujVqsZungT2irNQYCs36ys3oMgQSJXYuU73Pj374Qf867sR536DKchq3Cbth+ngcvKiWwbELD0akdOTg6dO3dGpVIxYsQIsrONaY8ePXqgVqtJS0sjpURAYmqJwt6d0gSj76VZ9aZos1+hyTaKChQ9OIdZ7Vbv5ecNeh1FjyKxaTucpCKBZcuWMX36dHbu3Clu4+LiwsSJE8WJyj/4z+Jvx+OrWLEiP/300+9+HhwcTI/hkz4ouWTu7Ye5t5/4t1XTXuJrqaklToNWIhGg6PYJ6ugTuHwunCVLljBjxgyCg4O5c8foMLDnxQ0kbg0QpHKsW32KdatPP9gWs+rNsO3wVpKo7NUjzGo0pUNdZ9YvO0Z4eLgYBlq/fj2rV68WE/SPHxvpFLFRJzBxqYmutBBtTjLWnwQht3FG4VQVuaMHJY+j0GS8ROFYjbJXD7HvNYecs2+JvcWxl8GgR27jgl5dgqp2K4runUWdGY/ExBRBKkNmYSTJ6/V6MexZpUoVysrKePToEWfOnKF9+/Z4enoyd+5cFixYQKWmRsqGZbM+SJTmSJTmKCt5o057jqmHj+igACCVWWHZJJDcqPKdlyBTYF61AVu2bMHW1paSkhLatm3LuXPn2LVrFy9evCArKwt/f3/mz58PGFfFycnJREREYG5ujpOTEzdu3MDb25thw4YxYcIEo0fba3Tu3JmffvoJR0dH4uPjqV+/PjqdjmnTpjF06FDWr1/P0aNH6dy5MydOnCA317gSV6lUTJ06lY0bNzJ27Fji4uLo2bMnDRs2JCoqCoPBwMmTJwkPD+fUqVOkpqbi5+dHx44duXDhAomJiTRr1gx/f3/+9a9/0bFjR6pUqfK7z+2HIAgCa9asoUmTJgQFBVGp0tsQtVwuZ+DAgQwYMIC1a9fyxRdfoFarWblyJWPHjhXVesBoUXXp0iV++eUXJk2aRFpaGs+ePcPPz4+HyfnlUgB/2B6J1MhdLchGZmmH0s1omioAkhqtsX2dX58+fTpLliwhNjZWFL7Oy8vDxsaGjh07cvPmTdFiSRAEiouLKSgowNbW1hj9EAR2bNlIyZMrGHRaih6cB8DcuyO6gkyyIzaiyUpCIlNgVrMFNu1HIUjl4iqvLOEuGQcWoivOR1XXlwodxiEIAuqUp5jVaI7MwthOmbUj5q+FJjKPhqDLzyD70j7AqHbUpk0bUWS7uLiYjRs30r17dz777DPKtHqxhkCd9hxtTjIOfeejrNaY9J8Xoy/OR19aiIXPW77uG+hL8kGvRWbpgFJTwJYt35KTkyOq8ADvcVz/wX8Wf5sV31/Bvyu5BEYCqrJue17WG4V11xm07G4c1ARBoF69ejg6OlJ8/RDoNH+yJ0CnNRaZvP5nVqcNBVd+IsjLmoyMDGbPnk3t2rUZOXIky5cvJzExkby8PMaPHy/mnMyKUrH09EVm7QR6HYJcSfGzaxg0ZUiUFuiK84zVqq8n+lIzK0zc32odOvSdj/Ow1WjzM4zq9YBJxdoIUjnqjPjX/MIT5ZqtsrDm9u3bvHjxgmbNmonC2QcPHsTLy4u7d+8Se+W08XjmNuL3BJmJWCCkK8oh4/AKktYOIWFVXzKPhhh/8L+BWdXGrF69muDgYFG8GuCnn36iT58+GAwGxo8fT5cuXWjbti2JiYno9XrRsLdq1ao4OxsrZ/PyjIajbxRq9Ho9P/30Ezqdjrlz54orvYiICMaOHQsY7XtevXpFfHy8cUXv7Iy1tTXW1taMHTuW9PR0Tp48SYsWLejSpQt16tShe/fuODo68vXXX1OhQgW2bdtGeno6+/btY8GCBZw7d46NGzfyzTff0KhRI1auXMmsWbP+/Hn5ADw8PJg8eTJTpkz54OdvXA6uXLmCRCJhw4YNeHh4MHPmzHIdqSAI9OrVi/v372NmZsZXX33FoEGDuPcsHsNHWk7ZtB0BGEjdMY3kzZ+/Dukbw4QbQ1dQs2ZNrKyssLa2Ji8vT3TAALh9+zZarZbp06cjlUo5ccL4zEVGRrJ06VLAmKN0cHDAploDNJkJmNb8BKlFBdynH8B9+gHjgCWRUqH9aNwm78Fp8L8oib9Dwc3yz+/vRSEULjXJv36IgpvHUae/LMdxtOs2HamlPRX7zaeCowvW1tb4+vry/fff4+TkhCAIlJSU8PPPP+Pq6kqlPrNQebZH4VwdVe1WOPSdj8GgR53yFH1RLrZdpiK1sKXk6dVyJr2A0T1CIkObn45SoqdFixYcOXKEsrIysU3vrtwzC8tYfyHuD+UJ/8H/Lv5PDnxgJAzPCaiNqVz6l4sxBJkJBokM0+pNGbH7LruuviQ7O5uAgACWL1+OaUk6g+qa/enAmr5/AQn/6iX+k+rUVHFzoVNTTxwdHYmLi8POzo6OHTty5coVFi9eLJb2vxn48rLSsazbGqmpJQgCRffPo81ORp3xkvzLe8FgwLxhADIrR9GHTFWnjdgGiVKFzMoBuZ27WMwjyOTIrBwwca6OfZ95YgcGxl0oLCuIqi43btzAxsY4uI0fPx6pVIq7uzvudd/PW7wLo4KLgPPIMNyn7ceu23Q+5CpbrWZNkUfo6upKhQoVAOOgdffuXQD69+/PgQMHOH78OGAsMOnVq5fYObypsrt37x5grMCNjY3l2rVrqNVq7t+/z6BBg6hYsaKYD3xTRJOQkEDFihVxc3PDxMSEzMxMcnNzyc3N5cmTJ9StW5devXqh0Wg4cOAA8fHxTJw4kVevXnHx4kXmzp1L48aN31PhaNeuHdeuXaNRo0aUlJQwa9as96gEH4sZM2bw4MEDjh079rvb2Nra4uzsjJeXF9WrVycvLw8vLy+GDx9ezlhVLpdjYWHBtm3byMzMJCu/uBw15I8qoKXmNth2noTrhB1U8B9PVvg6NDnJlCbe59avu3ny5An5+fkUFxdjMBjo168ftWvXplWrVoSGhlJQUIC/vz9VqlShT58+YntGjhwp+lNm5uZTZDDB1KMh2lxjuD/zWCg5F41hQBOnauiKcknZNoXkHyagL8qj6HEkAPpSI0Wm7NVjkjd/TsGdUyjdvVCnGcPnVs37YtW0N0UPzpOyfQqvwoZSeO9MuXNU6w1odAbRjeONQbLBYBBX3L6+vlS1M0NXnIM6I+Htd1OeYtCqkZhaknthO+beHTGv70/Ro8hyxxAkUlS1W5J/cQe92zbm7t27GAwG8vPzmTp1qjj43UnMZczOGD5ZcZbQiCccup3M2cfpHLqdzOqIJ7RYcZaxu2K4k5j7u/fsH/x7+D878MFbtQz/Oo6YyCTl1DL+KIkvQpBQotGx6NgD6gaO49KlSwQGBvLw4UOqC6noDwf/7sAqt3PDtstUKs0+hm3nyShcamFjZcknnh5s2bKF+Ph48vLy2LlzJ8OHD6dq1arMnTuXzMxMEhMTUalU+Pv74+fnR+s6bth2Ho+Jax2KHp5H5dkWhWNVZFaOWDbva9QGtbDHoClDnfYcVa2W75+KXImgUGJRzx9N9iukZlY4froCiYkKXuec5DZGAeai4hI6duyItbU1LVq0MCpiQLmO19bqfTL0uzCoS5AolEhMzNAWZJIfffC9bSQGHdGnfmHixIlYWlrStm1bMTQ2evRo7t+/j0wm4/z58xw+fJi5c+cCRr+0kJCQcisab29vceC7efMmzZs3R6PRMHv2bNzd3QFo0KABGRkZuLq6otVq2bdvHw8fPhQrJf38/OjXrx9TpkzBy8sLV1dXfv31V4KDg4mJieHp06eEhYXRvXv3cqosv3sNDAbOnTvHkSNH6NatG/7+/gwePJiXL1/+6XffhVKpJCwsjEmTJokhwt+irKwMU1NTDh48SJs2bTh79izHjx+natWqtG/fnq5du3LhwgWxU42IiCA2NpZmbfwoS3yANi8dfWlRORf736LocZRYxSxRmhsnHoIEg7oEqVSGh4cHCoVCnLS5uLhgb2+PWq0W3dJLS0uJiIhg9+7dADRs2BClUonBYOCHH37AzLk6ep2Wkuc3kJrbfrANGYe+RpdvDGcbtGXwuuo587gxF+ow4Guch39H6YubaAuyxCiEIJFi4dMVp8Hf4DZlH1bN+5F14ls0mYnljmEwwNixY8nPz6dFixbcunULMErqgfF3cGXrEjQZ8Sjs3oYkjWHgLLT5GWgyXpJ/7SB5V/Z/cDJh02EcglzJuqlGMQIHBwdkMhlnzpxh+/btpBeUErTpKqcfpVH2mnrxLkQ6xsM0gjZdZdfVl7973/7BX8d/PMfXqVMnmjRpwqJFi8q9f/jwYcaOHUtSUtJ7dix/hDfcr6zCMr47+5RdVxPQ/UUxGrUOFE2CWDJ1NCN7dhDft7cw4fsxzfj+/DPOxmagVavRv06sO/ZbiEGrBp0GTcZzyH1FLVkG3357ADMzM54+ffq7x9NoNGg0mreFCG/+l8pApyV502fo1SWYuNTAusVr/z4BpJZ2pP049z0NTwCDphSJ3Jgk12tKKXt5h8RVxvycWfVmFN55W/Rg0OuoVKkSQUFBDB8+XKx8DAgI4PZto0xaJVszrv7BNbP6ZABZx1aRGNofmY0z5nXbkh9zuNw2er0Bm5xY4uNLmT17Nl9++SW+vr4MHDiQc+fO4ezszOeff05iYiJBQUGinmNRUREajYaVK1cilUq5dOmSkVf1Onz29OlTvL29iY+PL+emsGPHDnr37s2mTZuYNWsW1apVY82aNezatYvw8HAiIyMxMzMjPDyc4uJiKlSoQGhoKIMHD/6DM/19/PLLL9jZ2dGuXTvat2/PkCFDCAkJwcfHh6FDh/LVV19hZ2f35zvCKMvXpEkTvv7663IUmDcoKysTOWcrVqygVq1aBAYGsnv3bqZPny5a+djY2JCVlcXevXuJjo4mPNFAzOVIkn+YiNTUEstmvSl5Fv2BFoA65Qk5ERvRlxUjVVlj4zcGubUTSktbPFr58eDyaSpUqED//v3ZvXs3devWJTMzk+vXr4tSZD169MDT0xNPT0/GjBmDi4sLRUVFPHz4kNzcXLRZ2WDQo6zkjbl3B7JTnpRrQ07ERuT2VXAauAyJ+FXxyAAAIABJREFUiRn51w9THHsJXVEOJc9jAJAoTJCqrLFsHEj2mU1AvffORSI3wcKnK7lRe1BnJiC3cxOFpkeFnSC0f3127tzJlClTUKlUZBWpMa3VkqzLP9Ni8lq0BoGIrd8gtXXD5nVxmMzSDpm1I1Ytgii8cwqnQSvLHbPS7LcTR4mJCo/eX9C2jgs1HMzZMmcUDevW4Ny5c+TaeaJt2gH1R9hCGQxQotGx9MQjAFGt6B/8z/AfH/iGDh3KnDlzWLhwYbk4986dO/n000//0qD3LmzNTUjJK0X/7zl2IZGbcL3IhpEYCaVqtZq8vDwObl7Nk4sXSbn7CJVXe+yr1ce9Wk0quThQr5IdfRq6stc9iVWr7hMREYGDgwNgVJP39PQkJSWFpKQkEhMTSUpKIikpiczMTCwsLJBKpXTq1AlXV1dyrGtyrcSOlIMrUDhXR5ApKEt6iMGgR5uTTOHdcGSW9uKPLX55V5SVvJGYWhrze6VFWLUwVrBa+nQj89VjnIetRmKiIvPYKvE8K45ZR/6eLzh//jw7d+6kcuXKlJSUUFJSwsSJE6lZ06jFqS0rwaJuaxQVXEQJMbuuU8X9KOwr4Tz823LX0PKdYiLXcZuxV6cQF/Gcouw0vvvuO8aOHcvmzZupV68erVq1YsOGDWRnZ7N9+3akUilubm7Url2blJQUoqOjefbsGfv372fQoEHodDpevXpFYGAgubm5ZGRkEBsbK/Ihc3Jy2Lx5M2fPniU2Npbw8HDCw8OZO3cu/v7+jBgxgl27dhEZGcno0aNZvXo1o0aN+reeFTCu9pYvX868efPE59jCwoIFCxYwbtw4Fi9eTK1atZg+fTqTJ0/+KB7hqlWr8Pb2ZvDgweJ9eIM3A98bDB8+HA8PD/r37y8ec9SoUUydOpVbt25RoUIFwsLCuHbnEVbtRmPd8TPxu+86YTh9ulx8bdN2xOs8X3lo9QYe3LjCZ599xqRJk6hYsSKrV6/m1KlTDB8+nMGDB3PgwAGCgoLw8fHh/v37hIeHk5OTQ0pKCpaWlhgMBlq0aMGl6OtYtx9D7sWdIJGiLykoJxSh15QiV9kgKEzRZCVScOsEUjMrtHnp8Jp6krx1sjHvbdCXq1bNv34YhUMVFC41EaQyih6cR68uRuFkrOyUqqwR8tOo5WxBVFQU8+fPJzc3l+yCYuS2bhgyjBGGKy+ykNu4ILVxRpv7lkepcK6BIFeSe3EnVp8MwKDXoclKxKBVY+JcA21uGq/Wj8R95mEEiZQ8NRy6nYxSJkHXdia/xsVQo01fXto1EyfQH4sSjZ6lJx7jKC1i/XIjB1Sj0eDm5sYXX3zBsGHD/tC/9PfwrivO/5/wHx/4AgMDGTduHJGRkbRu3RowdlrHjh3j1KlTNG/enEePHmFqakrv3r1ZtWrVW7K3ILBu3TpCQkLIyMjg008/FZUW0vKKOfD9MvLuRCBRmGLZpGe54+pLi8g+u5nSuBij87V3B6xbDkSQSCm8G0HhnVP86FKD7aPDsbOzJT09XSTtxsbGUrFiRZRZd5k1qjOVKtkwZsxw0uvU4YGLC5cuXSIjIwMzMzNyc43x+BUrVgDg7u5Ot27dCAoKwt3dHTc3NxwdHenatSt169YlJCREbOOuqy/5IuE+Gac34jIyjMyUpyStGYzCvjKqOr6UxpfPIZlWb0bq1snoy4pRebXH3Nu4WjWt0gCz2q0+OMsXJFI0BVkk5uvp27cv1apVY/369QA0bdoUf39//P39efXqFbb6EuQCqP+NuYRep8bfDVYWFlKvXj3Rmig0NJRJk4weiZGRkVy9epVWrVoRFhZGcnIy3bt3x8nJCZ1Ox5o1a5gwYQLh4eHExMRgMBiIjo4mMzOTM2fOoFAo0Gg0XL16lXnz5iGRSGjdujVt2rShY8eOzJw5kxo1aogegAsWLGDr1q0cPXqUpk2b/vWTegdnzpyhuLiYbt3er+pzcnIiLCyMKVOmMGfOHGrUqMH8+fMZPnz4H3ZILi4uzJ07l/Hjx3P69OlyE8PfDnwAbdq0ISoqim7duvHo0SMaN27M/v37+f7771mzZg1r1qxBo9FQ3b0tGsc6/9aUUBCgqbsFGV61CAsLY+3atQQEBKBUKjl//jx79uzhxYsXHD9+nM6dO5e7HiUlJcyYMYMDBw4ARk1U2xZ9MfPugCYnhcJbv2JWuzVFD88DAhYNAlC6eVKW9JDEVX1ROHqgqtWK0oS7SC3tEWRyDFo1LsO/FSuKM4+9pYIIchNyzm5Bk5sCCMgruGDf8yvk1kYupVWzvmSeXs9nv4YBBlwa+GIisaU04T66/HTMqjel5OnbOIf5a1PkhND+KN29sGjQ2ZhGUFqQG7mT3HM/ILN1xbr1H0cM3riqyKr48Nxg+FAq/KNQqtUxbOhQ+nZsSXx8PCYmJty7d6+cyME/+Dj8V0SqR48ejcFgEDlUGzZsYN26dWzZsgWNRkOjRo1ISkqic+fOjB07Vqx4EwSBLl26sGvXLvLz8/Hx8WHnzp106tSJgdMWc2DHRhz6L0aQK8n45WvKEu6Js6/0n5cYwzftRmHQlJJ+YCHm3h2xaNCZwrsRZP36HTbtRuBdzZ3GFvnExcVx6NAhPD09kcvlxMXFkZeXh6OjI1WrVuXp06fUr1+fgIAAnj59SlRUFCdOnGDmzJns2bOHp0+fIpVK+fHHH/nxxx/Jysqif//+DBgwAB8fn3Kd2huUlpZSvWl7mo2cz510LaWlpeVsYgS9FsNrwvq/AwED9mUpPNs2iy5dulCxYkXOnTvH06dPKS4uZsmSJVhZWXHmzBmOHDmCXC5HWtOXCu1HiTSIj4JWjebaPnJjjlJcXEy1atXIz8/n8OHDNGvWTNysZs2a/PLLL1y8eFEUiZ44cSK2trYMGzaMunXrYmpqyogRI1i4cKF4L54/f46pqSmurq4kJCTg4eHBkydPCAsLY8CAAe8NELm5uXz66acUFBSwf/9+HB0/7KX4V+Dn58egQYMYNmzYn2577do1Zs2aRWpqKsuWLaNHjx4fvP9gjDb4+Pjw5ZdfEhQURGZhGQduJHH+9lPuPIrFv21rajlZ0tfHFVtzE/H8WrduzaNHj6hWrRpFRUWo1Wq8vb2ZMGECO49f5JpFiw+Svv8MEgG+6e1Nbx83bt68yfTp07l48SJSqRRPT09mzZpFZGQku3bt4ubNm1SqVIm4uDju3r3LnTt3uHv3LjExMSQnJyMIAhZNemHTahA6dTGv1o3AadA35F8/jNTSDpvWgylLjiVtXzD2Pb9EWckbXWE2BnUJcls30g8sRmblgHXrQQgKU7S5aegKMlG6/7Gj+xsIQA1VCRWfn2Dn7j24Tv4Rw284eJqcFLJ+XWPUwhUElFUaYNvxMyRKczAYSFo3EgufrhTdP4s2Px3TKj5oC7Mw92qPaaX65VZ8+tIisk+vp+T5DQS5Ceb1/LFq0Q9BkKDJSSbrxHeo058jSGQoK9XDPtBYGazJSiT79AbUqc+QmFlh3WoQqtqtAEgI6cPZ8xfwbfG+/J67u7tYPwCIvNPRo0dz584dBEHA39+fsLAwrK2tGTx4MLt378bExASpVEpwcDAzZ87k6tWrTJs2jYcPH1KpUiW+/fZbfH19//Kz83fGf2Xgi4qKomvXrqSmpqJUKvnkk0/o06cPU6dOLbfd6tWruXDhAr/88ouxsYJAZGQkLVsaizv69etHw4YNjer1no0pdm2CRYMAAEpe3CR9XzDuMw+jL8kn6fvhuE3ZJw4kRQ8vUHD7JE4Dl1F4N4LcqN24fr4V07R7eBfdIi8vjytXrrBlyxbc3NxwcnKiSpUqbNmyheHDh+Pr68ugQYMYNWoU27ZtY/PmzURFRZGVlYWdnR179+4VVfYBHj58yN69e9m7dy+CIBAUFGRU8q9TR9xmzZo1nDp1imPHjhEReZWJq3bTKWikqPBfw9Gc87/sJFpRHz6Q5/szmMql7BvTDEd5GWvWrGHDhg34+voyePBgtmzZwrlz55DL5QQGBnLixAlKS0sZNGgQkSkGciq3A6n8TySiDKDVUKP4PlFbl1FaWoqZmRklJSUcP378PYFnOzs7Hj58iIODAyNGjBB/hOPHjyc5OZkjR47QtGlT9u3bx9mzZ/n8889JTU1FoVDQoUMHMjIyePbsGU2aNEEQBDEH+C7u379Pz5496dy5MyEhIcjl/3Ph4JiYGHr27ElcXNx7gte/e2Ve8wJnzZqFhYUFK1eu5JNPPvngtpcvX6bfuC/oOCWESy+MEYR3ix/eNSQe18qDXd8tFZ0N1Go1bm5urFmzptz1XnvqDqHn4tEJf80jTgCUcilzAmohe3mVSZMmETRsNDFZUl7maJCZWVCSn4M6/QUepPH0/i3s7OxwdXU16sDGx6PVasWJX1JmHgkNxmAQpGSdCkNflIegMBUHPjByU3Oj9qDNS0NqZk2FjuMw9fBBX1pEzoVtlDy7hl5dgkFjNGp2nbADicIoBVdw5xRF98+JIdyEkD5v74GmDJlchlwmp6SkGBPXOlg174eiYi2kSnPAqF6jzU1D6eaJXl1MxsGvUThVFW26kr4fgdTMEvve8xBkClJ3zQC9Hstmvd8b+DKPhqAvK8au23T0JQWk7ZuHafVmFFw7iNy+CqraLbFs3hd0WspSnmLiXIPM46EUP4pEUChRebXHwqsDafvm4fjpchR27iSG9kdfVsTy5csZMGCAWNjVs2dPUTTg3VDns2fPePHiBa1btyY/P5/evXvTsGFDVq9eDbwf6nz16hXe3t7iguLMmTMEBQXx+PFj0TT7/wv4r1R1tmzZEjs7Ow4dOkRcXBzXrl1j4MCBPHnyxOgM7eSEpaUlX331VTmuEFBOAsrMzEzUUCzIykBq8fbGyCwdxNdv8gNJa4eQENrfKL91ci364ry321sYixBatGnPvn37CAoKokaNGvTp04emTZsaBayVStLT0//w3N6QfGfPnl2uQq9OnTosXryYp0+fsmfPHoqLi+nYsSP16tVj+fLlPHr0iGXLlolFPwlPH9LEMp/Q/vXZMrQxof3r85lvNX5cvYDWFpnGare/AEGnYWRDa7xdrUV5rBcvXtCyZUsmTZpEbm4uLi4uTJ06lefPn5OamopOp6OoqIhlI7sw0CkdRfpDJAbde2LWJjIBCXrUcdcpPLyEJjZlzJgxA3Nzc8rKynBycmLw4MHlJJv0ej25ubkiveH69eu0adMGMzMzVqxYQVRUFEOGDOHs2bM4OzsTFhZGgQZq95pEtcGLceg7n+bT1jNp3REuxdwRhZ/frajcv38/bdu2JTg4mO++++5/ZdADYxh7+vTpHz3ogXHS1rlzZ27dusXYsWMZOHAgPXr04OHDh+9t+1zigiJgFmefZP5hxd+pB6kErj3P5vOxNG7cGFNTU7p3705BQcF7FkUT/OuxMND7deXzh+e68cu7oslJLveeAWNxRfAvt/liZySVBi3lqNCE5AoNUGs1ZFw6gKquL1YtB5Ldahr2vb6i0MSWwsJC/Pz8OHLkCKmpqfTq1Yvu3btzdP8e1C9vkbzpM1S1WmHf6yvsuk4VBz0wqhG5jFyL+7T9VBy3CVMPH8BI3bH1/5zqI0NwnbDDqFAkkVIQc+R3r/sbjmD1mQdQqCzAYMC+55e4jAxDZu1M1sk1JH07kPQDi9AV5SC3ccG0SgMEmRypmVGY4Y1yyxvUaN+Pvq28cbS3xbRqE1E7t9x1e63eYu07FImJGTJrRyyb9KTo/lkkSgu0+WmiWIAgU6B0q0vhvQjKkh6icKmB64SdqGq0QOFUFbOaLSh+HAWAzM4dE5UlK1eupEqVKtSvX5/Tp09z5coVse95F9WqVaNDhw6YmJhgb2/PtGnTuHDhwu9er127dhEQEEBAQAASiYQOHTrQqFGjD04q/y/jv6bcMmTIEHbs2EFsbCz+/v44OjoycOBAGjRowN69e7GwsGD16tVifuDPYFHBnuKCt4oe2vy3r9/kB9wm70GQ/M6M93XoyVL5tnN8U6ItCAIvX75EJpN9tFu3l5cX8+bNK5fDMx5GoHHjxjRu3JhvvvmGyMhI9u7dS6NGjVAqlURFReHi4sK9e/fw9PT8QDMFdgSPZtKa/RxOKHtd4fkHREaDHqVCxieWOawYPZrMqyOZN28eKpVKlMf6/PPP2b9/P4sWLSI4OBhHR0cqV67MxYsX+fnnn1m8eDHR0dH4+fkxvIcnWZY12fTTcVKz87FWmZD6+AYFd07TpF4d5n+3FEdHRxo3bkyvXr3o27cv/fr1Y926dXzzzTdcvHiRsLAwysrKUKlUyGQyDAYDT548obS0lLS0NMzMzMjJyWHr1q106dKFxGIJZT6B2DW0oBhAKufsY+MERCYYsB2xjtbVbCmNO4+Pjw8dO3ZEqVRy7tw5Tp06RcOGDT/qnn0Mnjx5wvnz58sN4n8Ec3PjSkKr1VJWVoZSqRQ5gebm5vj6+tK9e3cWLlxIxYoV2XX1JUtPPEIvfMxPU0CQmWDddgTO0nhOnhyBlZUVJ0+epGfPnqxatYpBgwaJW7evZMLWu9u5cCYcvaYUqbktqtqtsGzaW1wx/R70Ejkyr85kCMbjIgHzum0xr9sWQIykGJy9UHWrhSI5iqZNm9KgQQOGDx/O48ePKS4uJjg4GJecJKTDQsvZhH0sZBjYOTEAv35Gsr1l057kRx/EomGAMRz5DjRZiWSHr0edFodEENCVFmJp50iZICd971dYNuqOkPQAQWFKafxdkjaMxXXsBlK2TUFXkI04QZBISd3zJY4DvsZg0BN7cgcP9i7HoNcjmJjBb1bRCSF9RAGMzOOrceq/CInSHKnKGn1xHnbdvyDz2Cp0xbmk7piGRGmOZeNAY98kSFCnxpEUNvSdi69D5Wm8zoJESp2OQaRfO87Dhw+ZNWsWffv2JSgoiMOHy1dVA6SlpTF58mQiIyNFndk3vN0PIT4+nv3793P06Fsxbo1GQ9u2bf/CXfr747/G4xsyZAgRERFs2rSJoUONN7mgoABLS0vMzc15/PixqNj/MWjp343CG0fR5meiKy0k/+pbvpLMvALKyg3IObMZfVkxBoMeTU4KpQn3yu1DYtBRy/ntTDk9PZ3vvvsOjUbD/v37KS0tJSAg4E/b4ujoyJAhQ9i7dy+XLl363e0kEglt2rThm2++wdzcnCVLlhATE0Pt2rXZsWMHSUlJ5OR8mHD83cS+BLewoPTpVSQG3XuO3xKDFkGvpWPd/4e99w6Pqlzf/T9raiaTSZv0QgIhJLQgIITQBaV36U16FUQpIkVR6bAB6SK9C0JQkI4GpPcSiiGUdNJ7nfL+/hhYGMGtfn/nXOdsz76vKxfMzJq1pqxZ7/s+z1282DMigg2fDOb27dskJSVRtWpV9u3bJ2u+XpQ3q1Spgre3N/n5+Tx79ozdu3czePBgpkyZQvXq1Wnbti0rF89jerdwotdPQZxeQ9r3izDfOcrKf82nWbNmfPDBB9SpUwdHR0datGhBvXr1qFSpEtOmTWP69OlYLBbCw8M5c+YMOp1OZgmWlZWRn5+Pg4MDDx8+JCsri8jISG4WGEiv2Y94izMo1ba/38AsJIRCxZknuZzVvMnMrce5du0aO3fupHLlyuTn5//blPK/i7p165KTk4OXlxcuLi62gTkh4Q+3LygooKCggJKSEgICAjh48KB8344dNlG4m5sbYWFhjPhkDrN/vE/xv8l/yz2/h8Q1Q20mzKveI/3AAiS1lvvaqrzdpj3r16+nTZs2/Pzzz3z66adMnz4dq9VKVlYWERERxKXl4DVwMRU+2ouuUh1yL+0j99K+v/bmJYkXk6w/1McqFEhqLRn+TZm09ntCQ0O5f/8+SqWSo0ePcuzYMdo1qI5H2TPZkOEvQwjMT6/RrXld8q7/iF5hplGD+tgHhFF4JZL4f3XH8ryKk3V4GSlbPsIzOIzq4c1QWGyuKWZHP7TewUjCSmH0z3j2nY//uO02v02LiezTW1G5+qF0cMW17XgM9buC1YKdfw1bX/b5tcOjaR/83t+KpNQgSvKBlzFpLu+MxP+j72QN5AsP26KHl0ChxD60MbqKdVAZ3MuZBSj0Tljy0lEajFT48NuXfxO/w9h6rPwxGN09qVatGtevX2fSpEnk5ubSpUuX135k06ZNQ5Ik7ty5Q15eHtu3by/3e/h9r9nf358BAwbIJg85OTkUFhYyderUv/dd/V+O/2MDX2BgIA0bNqSwsJBOnToBtjyznTt3YjAYGD58uGyl9Vew9NOJ2FeqS8rGcaRs+gD7Kg3LPe7W4SOE1Uzy+tEkLOtNeuQ8LAVZ5baxWq2U3H9ZBggPD+fhw4e4ubmxZ88eevXq9dpywu8xa9Ysxo8fT25uLt27d5dz1f4Iy5cvp2XLlowePZqtW7eSnJyM1WolNjaWwMBAOnXqxK5du16JxunbpjGFx76idM9k6mpT6fKGD04F8aiTbqC6d4yfP2zEugH1CPNzBsDb25tt27axbds2PvvsM9q2bcvDhw/Jzs6mVatWttinmBi0Wi0tWrTg1q1bVKxYkTFjxjB27FgmTJjA3LlzUSgUCCEoKysjJyeHxo0b4+vrS1hYGOnp6ej1embNmsW5c+cIDw8nIyND3k9OTg5lZWV06dKF3NxcoqOjycnJQavV8u233xIWFiavqlP0QWgb9EFSa/88fua53mlZVBzVu4yWe0rDhw8nIiKCyMhIOSXjf4rk5GSKiorYvXs3BQUFctbfuHHj/va+AgICcHBwoE6dOnzzzTc0bNiQIxdu83TLFDKPrSJ9/1x52+yfN5G6axr5t0+Sf+ckKicPJJUGa1kxpsx4zHkZlJgtJOUUc+7cOSpVqkSDBg2wWCzs3buXrl27Mm7cOErLTFiajrK5AGHTmik0OlSOL7WGJU9vvjaNo+D2SeIWdSVt/1wSlvUh5+xOCm6flJM/ElcPIW3/HBKW9yN+SQ8SN00gObuA5NQ0rly5ws2bN9mzZw/Ozs6s23eMGxtnELe4G9ayYkqTfyVl60Til/YiccUAEr7qS+6VA/JrklNC1o0gPnIRyZm2yYyptISEI+vw05aQfWk/Xm1HQWEOlsIcKCsCi4mCG4f59cIJHB0dUSgUqCQr5tw0hKkMhd4ZlaMbppwUrCX5YDFjLS1C7eyJW5ePyf5pA/nXDoJKS9nz4GcsZlQu3qhC30Lp4Iou+CVZ6wWUWr2NOV6tKQqNjuKntzHnplF0/xe0/tWRFEpULt4U3juNsJjllWrW0VW4df0ES34maftmIyxmhMVMwvJ+FMXY2KaW3GfYleXSr18/NmzYwNy5c1Gr1bRp0waFQoEkSbIRPCBPJp2cnEhKSmLRokXlXqunp2e57fv378/Bgwc5duwYFouFkpISoqKiyqXl/BPwf4Tc8r8LI7Zd5cT91L89kQTbZLahv54Tn/Vk//79MoHmBfr370+rVq0YOHDg39pv79698fPzY/Hixa99PCcnR06FrlKlCgAZGRlUrlyZ7Oxs8vPzOXDgALt27eL8+fO0bduWPn360KZNGw4fPsyqVavYuHEjbdu2pVWrVjRu3Fh2z9+2bdsfsgdNJhPLly9nzpw5KJVK+vTpw7Jly+R8wtTUVB48eEBUVBQjRozAbDbj4OBAcXExHh4eVKxYkcuXL7N8+XJyc3NZsmSJLChv3LgxW7ZsISsri6NHj7JkyRIePHiAWq2mpKQEb29vKleuzNmzZ/H39yc7O5uioiJWrFjBpUuX2Lx5M7cScl5rRP5X8ILEE+bnjMVi4cCBAyxYsIC8vDwmT55M//79X2F+/hVMmTKFtWvXsn//fpkMcPjwYSZMmEBMTAy5ubmMGzeOI0eOYG9vz/Dhw5k2bRoKhYLNmzczatQoDh06RMuWLdHpdJSWlqJWqwkODiaoSijHL0eDQo3GsxL5Nw7bej8ValKaeB/vISvIu7gXYbVgF1gbXVBdsFrJPPwVwmpB7VaBvAt7AYFGo6FWrVoYjUbOnDkj95rVOgfcuk1H41+T4sfXSPvuS7CaQalC7eqHKf0pdhXroLB3pOTxDazFedhXbYJbx4kURv9M5uFlONRpj+vbIxAWM0X3f6Hg9nG8+i8kYXl/FBod3oOWImn1mDMTKXp8leI7JzFl2vxXNRoNZWVluHWdRvap9QhhwaXJANQegWAxo/EOpvjpTdL3fIZT0wGyeUPc/A7ogurh1nEilpJCUja+j/S8xx0SEoKjoyMPHz5EpVKhVqvJyspCpVKRn5//yneo9grG493pJK0e/OqKU6nGo8cssk9+jSkz0aYTfA6tXzW8+i8kbklPFGotSpUac3E+wmICixmXNmOxD6xD0tqhKOxtJU0kyXYMCdt9hTl4DlyMnU+ILX7s6g+gVKMyGNH4hGItzsWz15eUJj0gdfcMhLAiKdQIcyleA/+F1iuIxJXv4aJTUFRYSHFxMRUqVKBTp04sX74cPz8/WrRowZEjRzCZTBw9ehSDwcDAgQP59ddfqVy5MgMGDGDp0qXyQPb9998zbtw48vLymDFjBpMmTeLSpUtMmTKFO3fuoFQqqV+/PmvWrJGJNP8E/Edblv0eY5tXxk7191hrL2CnUvJxR5ubQ8+ePYmPjy/3+JMnTwgMDPzb+12xYgU7duzg/PlXwyrBJlru1KmTPOiBjYlYo4attOLo6MjAgQM5cuQIsbGxNG/enCVLluDj48PEiROpUaMGPj4+nD17ll9++YW+ffty4MABomPj6D7z6z80vlWr1XTo0AF7e3t8fHz44YcfOHjwIMXFxcTFxTFo0CDGjx/PokWLcHFxwcfHh+rVq1NQUIC7uzunT59m0KBB9O7dm5s3b+Li4sKGDRvIycnh7Nmz6PV6KlasyPr16+ncuTMBAQEUFxcTHR1NYWEh586dQ61Wk5KSQn5+PpIk8cMPPxAcHAzAqqhYSn4z6P1b67nfocRsYXWAbxMzAAAgAElEQVRULABKpZJ3332XS5cusXr1avbs2UOlSpVYvHixbHb9V5CTk8P69evLWZkVFRXJWZEA48aNIzc3l8ePH782g+0Fjh8/TmlpKf3798fBwYGNGzdSs8MgSlNiMWWnYM5NxbXVGIS5jOLYywhLGaVJ99H4hFJ47zSZh5dRlhaHpNbi1LAXJU9vUhx7GY1PKCgUqFQqbt26xcWLF3njjTeIj4/H1dUVpasfz/bNJmndSNK/X4DGoyIKeydcW43GZ+hKwOaDWfTgHAgrSgdXShKiX3q9Sgrsq0QgKZTlZDaWkgKspQWYc56RuGoQaXs/tzE17Qyg0WF9Pr6UldlIUdkn1iJMpegCa1MQ/RPp++eAUmXT1F6z9ZZKnt7ClJlA0jc24X1Z2hPyrv9Ixg8Lbabw2Co09+/f586dO1gsFtLS0khJSaG4uLjcoCdJktxXtQsIw5Kf+TzaSELS6lE6uuPaagwBkyPRBdZCqXfGrkJN1EZ/ND4h2AW99K3VBdZCmEpw6zKVCh/txWvA8x6+AGtZEShU2AWE4Tt2M4Y3O6Ey2sqmjnU7AoL0fV+SsKI/hffPgEKJrlJdfEetx843FPFcoK/1DcVnxNco9S4oHZxxbjYQrVcQkgQefgHMmzuXwsJCBg4cSEJCAh999JH8+oYMGUJ6ejo5OTk0aNCA6tWrc+3aNQoKCrh58yYTJ04st3rr3Lkz8fHx5OTkMGnSJMBW6Tp9+jRZWVmkp6fz448//qMGPfi/MJbo/w9epDbMOfzv+ySvwFJGS68ywvycCfNrzaRJk+jcubN8AQdkV4S/C3d3d1asWMGQIUO4ceNGueytjIwMVq1axdWrV8s9Jzo6mpo1X9Umubu7M2rUKEaNGiXbdp0+fRo/Pz/atWtHQkICNZq156PIB5jf/oRrpaVcu/mSpWenesbSkzE0D3GnmXspEwd1Z+7cuQwePJhTp07x/vvvs3DhQgIDA6lbty4rV67EYrGwbds2WrVqxZtvvsmGDRuYNm0aVquVQ4cOcejQIbRarawXCggIkNO5Hz9+zL59+1i5ciVFRUUsWrSIAwcOUL16dZ48eYK7uzuNGzdmzZo1WCwWjh49yoMHD8goKGX3j+cRKg3m3DRKE6Jxf3cmmYeXY6jbnsLonzHnpGBftSkuzQaS8eMyShPvofWuglvXT1DaObD98zEEPuvD1Ek2iYwkSUyYMIHPP/+cwMBAFi5cSKVKlRgxYgQffPDBn2r71qxZQ4cOHThz5gxdunRBpVJRWFiIu7u7XBbavXs3N2/exGAwYDAY5Ay2oUOHltvXC3bp3r17sVgsNGvWDKFQgyRhLczCqelA0vfOQulgRJhLEZKCrONr8Ow3H5eWw8k6upLUbRNtO3vem7Kv2oSSJzeo0qAVTqZMYmJiqF27Nk+fPmXfvn0EBweTpvHmSdIDHCJ64vhmZ9L2fo71WXlrPWEqxm/8LhAWktePRdLYUXj/DA41WsIfEcOEFYVGj65KA8pSYiiJu8mz7VNwatAdYbUCQnYIKvCujcrRg9wzW9F4VaYw+ifsAmrZqP8l+VgLbfINc24qqbtnonxehnV9ZxQZPy7FtfUYimIvYXp6A3NRPkqlkrKyMrmd8KKcLUkSQgi0Wi2enp6kpKRgsVioFeDGlZPrsK/SkNLkByh0jpjSn6CtUJOimIvYV2mApSgXU1YKXgMXYy3MJm3vLNTutt++0sEVYSrF25JGeoErmT/aXJGEEOTfPmkL1FWqUNg7ghBY8jNRaHUURJ/CqVEfWW4FUJoSQ/qBeViK87ALepPs01vIv3EEh7B3kJQqtL6hFN07LU8y7FRK3JxfXj/mzp3LsGHD/kcT8v/X8Y9a8cHvUhv+dGuBTq1kWF1X9s2fIBNJPvzwQ2rVqsWgQYNk492MjIy/zOj8Pbp3705YWJicOfcCixYtomfPnq8MqH/E6Pwtbt++TZMmTeRA1ePHj6MIaUZGrf4Uu1TGZBGvaP1k49u7qXxyIpUBX6xj8GBbuGfLli25du0axcXFPHjwgI8//piqVavi4OAgGwm0a9eOVatWUbt2bfR6PY8fP+bp06ckJCRgZ2dHxYoV+fXXX5k5cyZt27Zl7NixsoWYq6srn3zyCdevX8ff358GDRqQk5PDpk2b5FIggJ+fH0/wxGq1UnjvNE4Ne+H/0V7s/Gx6x6Jfz+PZ+0t8RnxNcexl0vbMwqXpQPzH77BdfJ5T2x3DWvL1xi3ye7916xZJSUm0b99eZg5fvnyZ3NxcqlatyqhRo4iNjX3tZ11cXMxXX33FlCm2ftaBAwfIycmhpKSElStX0qxZMxITEzGZTOUy9V5EIv0eLVq0QKFQ4Obmhk6no6ysDOe3h+HSfBAAz7ZOxJKfiSU/3WbpVZwvU9otBZkoDUZ0IY1x6zJVtvHSV7W5ILmH1OHy5cukpKRQu3ZtcnNzmTlzJjdu3ODJWRvrT+MZhKRU2ay9fq8HFYKktUNJ+noE1uI8LDnPysl+XgelzhFJraXo3mnMuWlIChWWvDSyTn793GzBxopOTU3Fzq8azg17gkKFtTj/ebrCI7CYcG39PkgKdCGNsBbnoXLysK3OALV7BfRVm2DOSkKhUGEuslUJ7O3ty+nLVCoVTZs2ZeDAgYwaNYqgoCASEhIwmUyo1Wo6NqgGz3tsuioRmJ49BKuV9O8+pzTlV4TVgrBYUGjsSN0+hdzz32JXsQ7mrCSE2YRTRE/URn+ub59PwqrBKOxsZLicqM2Uxt1CaTBSeDeKhEVdKbh1DMMbrREWE+bcNAx1O6B0cJH/7IPDUbv4UHjvNGpnLzx6zKIw+icSvupDysZxKPXOePaZS3bUZixx15neLhS99uVaxcfH55WWzH/x1/CPWvG9QP8GgYT5OTN+7Y/Emw2oVErKflMpU0sCSaGgMPYKGz7uT8valUmK6sKMGTNYtWoVkiSxdu1a3nrrLWbPnk3Pnj3x9/d/JZrmr0KSJC5evEjnzp3p1q0bDRo04NmzZ3zzzTdyNM9vER0dTd++ff/tPvfs2UPPnj2xWq18+eWXhLQfRpJnOCV/stLN+WUH5pwU3DpOYn90Bsvt9WRnZrBz507mzZtHfn4+Q4YMISkpiePHj/PWW2/RpEkT0tPTKUGNIqghBZ510bxTl4hAfwZ0aIFvWQL93u2EQqFgzpw5jB07VjZnTkpKYtiwYUiSxKhRo/j00085cOAAs2fP5tmzZ/IFqU2bNkiSRGZmJqnJubayWuVwecB7MYgb6nZAqbfRse38qqPQO8lejPZVImRbN3Wl+qQcW8XDhw8JDg5m27Zt9OrVq5z2rlKlSqxatYrPPvuM5cuX06BBA1q2bMnHH39cTgKxefNm6tWr98pkRKlU0q1bN0aOHMnFixdRq9XExcXJpgQvIpFeB71eT35+Pmq1Gr1ej/XOEUwuQYCEvkYLCm8eAZUdIIGpmILon3Co2fI5GcWd4ifXbaJqlQrMZaicPBBWMxkxN2jTpg03btwgKysLs9lM7dq1OX78OBUqBlFckC+nCQhTCUIgJzLYIKFQa2VtmqTR4TNsNQW3T/7hOWU1ldgGR6WqXEnarlJdREkBWp9QSpNsesXMH5eRdXwNWM1YSwvR12xJyeHlWMtKKLxzCkmtpSztMcJUSmnSryBe+nNitaC0d7KRV7CtssxmM05OTnh6enLz5k3OnDlDREQEb775Jg8fPpRZvZIk0aFDB+Z98Rn29ftQGh9NSdwtNF7BODXug33ll04oviPW/uF7VTm40HrycvZ/2JbRO29w8n7qnzopubQY+sp9wmpFoZDwHb5abjXa+VfHa8CiV7arPGkvn3asTv8GgfSPivrD4/zTCCj/O/GPW/G9QE1fJ9L2z2F1a1dqEk8FSwotQz2opMigmnjKhakt6R9QTJemtfHw8GD69Ons27ePq1evypTw/fv3s27dOnbs2PGXy5zNmzeXrdh+C6PRyIoVKxg8eDAlJSXMnz+fgQMH4ufnR0pKCkOHDsXb2xuDwcDFixc5dOgQhYWFrz1GYWEhx44do2vXrsycOZOneeIvDXq/h9neDb8JuwmJeIc9e/awceNGnJ2d6dixI0VFRXh4eHDq1CkeZpahafk+Lu+twth8AMWeNbCvHE6SyodlJ2OY8HMBHh0+pGqtusybNw93d3eMRiN9+/alTp06hISEyKngHh4ejBgxgiZNmlCvXj2bLZpSibe3NwqFgqKiIiSNjqJfz702OfzFoAcgqTUo7Z3L3RYvLtgqDYH13mb79u1YrVZ27dr1hykMHh4esqA/PDyczp07884773Dy5ElMJhOLFy9+LZ1bCMH3339PdnY2NWrUoGfPnkyfPp38/Hzi4uJe0dGBbeUZHh5OQUEBNWrUwMXFhaKiIjKe/oop7QkKvQtFd39G6eRJwMTv8B2xBoc67VG7eKPxCELlFmjbUVkxKRvGlDMTUNoZ+PX8MU6cOEF+fj7BwcGyVtLR0ZE3aoUBguwTXxO/pIct+dxqRmn/sm+psHdCF1QPv7Fb0PpXRwjrK7Kf3yPvciTCakahsUcCWSurq2ibPDiHvClva+/ijsaj4vMeGzb2tUIBVjPFDy+AQolD1WagUKJ0NKJ73l/zGfwVdr6h6EMbY18lAgCtVovZbKZx48a0bt0asJHJFi9ezI0bNygsLKRatWoolUqEEJw4cYLKlSsTosrCr/tU/MZvx7HBu6RHzpNDnP8MKgnm9G1KZmYmt3YuRBJ/n3wFgNWEW+wRWgQbX4lUA1sck0YpUcdDSenh+XSq5vo/O85/8Vr8Ywe+mzdvUlxcTOvmjYj5fjXzOldlw3v1+LipF7kXvsPooOWTTz6hqKiIsrIytm7dyvz58xk9erTcJ/D29iYyMpIlS5b8aT6bEEJ+3rZt2/jyyy9f2aZHjx7UqFEDnU7Hpk2bmDp1qqyvKi4u5sKFC8yaNUvuWzx69AhJkl4pwR0+fJgGDRpw6NAhdu3aRfWeH8ruHn+HBAJgtoLfu59g33IME/Y/IDOkI2NXRnL5lk0cG/B2f7z6z0fh/wYoVZhF+VOmxGzFIiSSzkXya4mBlUeuc+vWLZycnGR5QosWLQCbO8sLEnF6ejo3b97EZDJhZ2eHSqXCYrEQFxdHasyNv/Ue/gi1WnRmx44dnDp1Cnt7eyIiIv7t9gaDgY8++ohHjx7Rr18/xo8fT0hICBqNppzHaMeOHXFwcMDR0ZHp06ezZcsWqlevzooVK9Dr9VSqVElm1w4ZMgQhBLdv30aj0bB27VqaNGmCSqXi+vXrpKSkoFarqRJaDVNWImoXbxxqt8GSl07coq482zUdlbM3xnYfIMxlFN48gik9DpRKG6NQocRv/A4khRLH8G5I2FY37u7uxMfHU1xczNWrV3F0dOTm9WuAhKFuR/wn7MbwZkcQVnJ+3mSTAIU2RlepDkKSSFo3gtKEu2AxYynIwiHsbdukw2K2kUvMZeirNcWz7zzbZEOhxNhmDL5jNsnemZJKgzCXoc59We519A2yrdiEFbWbPwqNHfqqTW06OknC9e0RODftj+GNNlhy05E0dlSYfACVoweWgmyE2YRr2/G4BYagUqkIDg7m7NmzMk0/OTmZyZMnY7VaCQwMlKOuAPbv34/ZbKZ5WEU+aOiJRiG9FL3/hTRrrUqi+Px2rhzbR/369WkfUYM2HoWvOBn9GYSphOxTG7A8usDNlWP5YWgYH75ThZaVHCiOvUwjHxVNnPNwPr2YPePf4Z26IXKZ/b/4X4N/ZKkTYMeOHfTt25eHDx+SkZEhX/TCw8O5evUqJpMJZ2dnnJ2dMRqNLF68mNjYWDZs2MCZM2fk/ZSVlaFSqdi/fz+1a9fm2bNnZGZmkpycTPfu3WnUqBFRUVFcuHBBLqloNBquXbtGamoqK1fa2HInT56kbdu2suXZW2+9hZeXFzNmzMBgMLB9+3YUCgWJiYno9Xq++upl5M+XX37JTz/9RG5uLsHBwRgMBsLDw5k8eTIdu7zLjpmDKU19BFarjYBgtT6nUlsBCdc2Y8tF0bzAixgV6+QDJBepeLZ/BXb+1cmIu0VZcQEzlm3ApcNklCpbcz09ch4liXcR5jI07hVxbT0Gjbutp1Wa8hCXlsNZePIJRee307NLF6ZOncrOnTvp1s0WV5SVlYWDgwMnT57k/v37lJTYZtm/X9mW3jyMMJVQdP8X4h9ewqlRbxtRwmIi+8TXpBdkoXJ0R2nvjMrZ23Z/1BYKbp9AlBWTuHoI9pXrkVi7Llarlfbt29OnT5+/fO6MGDECPz8/7ty5Q+XKlQEIDQ1l0qRJPHjwADu71zuOuLi4sH37dvm21WrlwIEDzJkzh7KyMtatW0ePHj1QKBRs3boVlUpF3bp1WbRokY0FG1QZFBKuLYZhqNWG7J/WU5ocQ975byn2qIhry2E41rNpXs25aSStGYouqC5KeycQVlTWUlq3bs2ZM2eIj4/H09MTX19fYmNjMRqNtn5meFNi750m7+r3OFR/C61/DfQ13sJQqzVl6XFkHlqCKSsJlYs3TvW7kXf1e/TVmsnvKW3vrHLv2bFhLwxvdibv6kHS9s9F5eiOY/2u5VIOkm+9/D2l3b2ILrQpSoeXKxiHGi0ovGMrpdr528rJdhVrk3/tIJa8dBJXDgQhUDq6U/ToMgV3TqJVKykpsuX8ubm5UbFiRR49eoTRaMTDw4M7d+7w6NEjXFxcmDx5MjNmzODChQtkZmYSHx/Puv6tyM7JRe3qi3uXKeVYqr+HsFrRqhTMbF+NaFNFRowYwbp16wgJCaFbt25MWXeQtZfTMFv5t2VPCZvIfVhdIxaHesyePRt/f386tnqLEydOMCi8AZtHteDyzUrcvHmTnuf38umnn/Kvf/2LmjVrcurUKVq2bPmH+/8v/jr+kQOfxWJh165dnDhxgsjISLp27YriuQDa2dmZwMBAbt++Td26dTEYDBQVFVG1alWWLFnC6tWradiwIWFhYWRlZdG+fXtCQkLw8PAgNjaW1NRUKlWqxK5duwDb6u6rr75i6tSpxMTEUL9+fYYNG/ZK1tuhQ4e4cuUK0dHRNGnShEuXLslp1d26dZNfX1JS0ivZbWFhYSxZsgQnJycWLlzIjBkzuHfvHjt27OCrHQcpTX2Me5dP0AWHU/zkBun7vsQhrBWuLYdhykwkdfcMVM7e6AJfDeyE8u4NhfdO49Hzc5QGN9L2fEbetYMy6UJXqS7Gdh8gKdVkR20i4+BifIasAEDrE0LK1ok41u+Ca522mNWPmTZtGuvXr6ddu3aEhIRgZ2eHQqFgwIABJCYmolar0Wg0LFy4kDlz5qDVarFYLKQ8S0VSa9EFN8C902QAzPkZWAtzcKzbAceIHpQ8vUXa3s9Re1Yi98Jeyp7F4tSwJ8WPr+PWdjzm5Hs09FKwOjUVk8kkrzr/Dk6ePImDgwO3bt3i3LlzLFiwQDYnGD16NM7Ozq99ntlsZvfu3cybNw+9Xs+nn35Kx44dUSgUXLt2jY8++ggvLy+WLFlSLgft2qNUen1zgRKTFbXRD48es/7wtamcPAiY+tJWSqdRM2dkezYvuYbRaKR169ZyyTMkJISxY8fSsWNHbt++zYzNR8kOfAvpd5Zhf5qxOGbjH76egMmR5W4bardFWK0o7RyQzq3HarWSmZmJUqkksGlXCl2CXva2AsLKhbgC2Feu/8p9YBuErPE3WNS5Ct26dZN7th07duTp06eyt+/kyZPZvHkze/fupVmzZqSlpXH48GEGDhzItGnTCA8P5/PPPycLA9uupWKymFAqlVh+UwTTqiQsFitS+n3yLu5j5w0j9+7dY+jQoWzYsIEnT56wbds2WrcOx09/gY83n0LhVxOr1VqONPTCVFydEUNz91Jm9pkCNKdRo0Z07twZtVpNw4YNOXHiBHZ2dtSqVYvPPvuMTZs2UTuiKabKzWjw4RqGbrlC+xRHavq7lkvn+C/+Pv6Rpc7Tp0/j4eFBtWrV2Ldvn7zieIGGDRuW09UNGzaM9PR0VqxYgZeXFxERETx69Igff/yR4OBghBBMnTqV4OBgJEkiLCyMrVu3AjBo0CDOnTtXTti+ceNGZsyYUe6YFy5coFq1anKv6EUY7aVLl5g9ezaSJOHp6cmDBw9eGfg6d+6M0WjEYrFw+vRphBAUFRWxb98+0nIL0fqEUpb6mMxDSxClRTZBbfNBSCoNZRlxWE0lpO2eTtyCjuRd/UEmM+RdsbH8Mn5cSvySHpSlPEQbWBu1qy8KtRZTehxFMRdI3vA+8Ut7UvzkBpJShaRS49y4L6a0JySvH0v80l42DZOkpPDuaZ6uHsY3a1aze/du1Gq1TPYIDK2J8xutSMwpRlJpsCg0OAbWoO/gEXh6epKQkICnpydajRphMaHxDJI/g4Jbx1E5e5F/7RBJqwZRkhCNtkINNO4BlKU8xL5KBE7h7+LVZw4qR3ec3b1p3byRrAscMmQIer2ehQttQb49evTAy8sLJycnmjZtyt27dwHknu7ChQtp164dKpUKhUJBUFAQWq2W0tJS5syZg6+vL1OmTCE5+aVcpLS0VF4JrF+/nmXLlnHp0iU6d+5MSkoK7733Hh06dKB///7cuHHjlfDPWv7OhJljwfL60lni6iEUP70p3y68d5qEpb0QyXeZ3i6Urs3qyufhzz//jJubG1u2bGHZsmX88ssvhIWFsWjRIpJOf8vEFpXQqiSkPwuH+x+4QRTFXCR54zgSlvUi84eFpKWlkZmZaXNbMZl4uGMWSv5aP7ok7vbvfCtNRDjl8f7779O9e3cePXpE8+bNOXToEC4uLixatAhvb2+Sk5PZvHkzPXr04NChQ/Tu3ZsrV64wYMAA+vXrh9ls5rPPPuPaif2s7F2LKzNbM6VtNQJJQ5cVi/XxRbpW1nB2yluUnlyJn72FqKgoWrVqxbx587h9+zY1a9aUe4vN6takIC4akXQHc24qFGQS4KKlXQ0vPnynCsM94tBd3c7SmS8TaN555x1iY2NxdXUlLS2Nxo0bo9frmT59OjuOnmXs7pvoev+LvQ+KuZwG+Nbkx7tpLDsZQ8MFPzFy+1VuJeT87e/nv/iHDnw7duygX79+xMfH8/jxY5o1a1bu8YiIiHIDX6NGjfD0tPnfzZ8/n06dOpGdnc25c+cICAjg6dOnBAUFyT53KSkp5OXlUVhYiK+vL7t3736FxPACR48eBWwrw8OHD8uaPa1WK1sJhYaGUlBQwKlTp3jw4IGsHXyB9evXU7VqVRwdHTl+3CYm3rlzJ48ePSL25kVZ6wRgKc4DJBRaewru/0LmoSWoXXzQ+FfHZ8TXYLVgyirvwK8PbYL/hN0odAZKnlx/+YAElvxMPHp+jvewNZQ8vUniqkHEL+lBwqpBADg17ov/Bzsx1OkAljJcW4/Bs/8CSosLsViFrXe6ejMApjYzSHsUjUNEb3xHb0AbWIuMuBjcjS7cjr6HELaybm5uLmq1htxzu2TvxYI7pzDnpmIxlWIpKyHvwl5K46OxFGSh8Qkh78oB8q//SFnaU+w0SoZG+DF69GgeP36Mo6MjI0eORKfTYbFYMJlMsl1bWloaderUoV+/foCtzNmvXz/69euHv78/ly9fxmq10rFjR2rVqsWzZ8+4ffs2RqORmJgYatSowaBBg5g2bRpBQUFERkayZcsWoqKieOeddygqKmLWrFmEhYXh6+tLTEwMw4cPfy1DePny5dzat5KPWwXb5Dj/pu1UcOcUWcfX4tfnC+aM7kmFsngiIiKYM2cO69evJzMzk9WrVzNnzhxmzpzJiBEjOHDgANeuXcPJyYnl43vQTvMrTYOc0aoUqKTyA5zVVIqwWv92ZqopO5mMH5fg0mIo/oP+RZvWrXB2di7nlGMpyiP1yJq/TCh5ATuVgoIzWznx7QYiIiI4deoUlStX5vTp07Rr147k5GQ+/PBDzp07hyRJTJs2jbVr1zJ06FB27dqFSqWiVatWHDp0iA4dOnD9+nW+//57WrdujbtBx8imQQypqkR/bRsOd/ZRseQRhZk25nFGRgZPnz7lyZMnVKtWjfbt23P37l1W7/6REduu0nzpLxga9gb/2qiN/uBgJDXfxKkHaZy5m8DCVRvYvn37KwkhPj4+xMTE0L59e3JycsjIyGDNiTs4dJrBlaRSzFZekZzIsqR7qfT+5iLbLz79m9/Sf/GPK3WWlJQQGRnJF198wb59++jYseMrJ1vDhg35/PPP5duSJLF48WI6d+7M3bt3mThxIpUrV+bQoUN4enpSUFCAp6cnkiShVqu5desWvXv3lntVVatW/UPa+p49ewDkFO7Ro0ezbNkyJEli5MiRLFiwAA8PD3Q6HaGhoVitVp48eYLRaJRLkGvWrGHevHnlPCE7d+5s88xUqND4KG1uGwgK79v+tZQUkn3iaySNPbrK9bDkZ9kGQJ8qWHJtfUZrqa23lvnjMpAkhLDKrEgAa0kBktZA2p7PMGUmIGl0aNwqYC3Kw5SdYvvsNHZICiUONVuSeWQ5pswE7IPDAUFJcRGZjkGsOGbr8WTf/omytCdkHl2JysUHjVsFfEZ8jTk72SamLs6jV78BuDo6oEDg7uVLUcwF7IPDseSlo6/xFm7tbTPmF3mKThE9EFYLSjsHCu9GkXXqGzI0Wn5q1oTk5GRCQkK4ffs2Fy9eZN26daxbt45vv/2W9evXy9E9s2bNwsXFhdzcXJycnABbJt7EiRNRq9VcunSJ9PR0Pv30U8Amgxg9ejTR0dGMHTuWpUuXYjKZaNiwIbNmzSI8PByr1cq2bduYPn06TZo0kUNa/wi7du2S0zoCAwNpFOrH6qhYfv41HYmXKd4AJbeOkh21lS6frKR/s+qsmDaE8+fPo1ar0Wq1bNmyhfr169O2bVs5+aR79+7k5OSg0WioU6cOvXr1Ys2aNURFTcZqo8QgKZSonTwwNu2LQSojNT2NwkUn8HgAACAASURBVNsnsRTlojK44dx0APYhNg/cPwpSLUt9jMrJE13gG0hYOX3oV8oKCzGZTPLrt1gsFNw8QtGvZ/Eds4nCe2fIu7wfS34mSp0jjg26Y6jdFmtZCWl7ZyHMJuL/1R2NUkKlVHDgwAG6detGUVERCoUCT09Prl27xpYtWxg8eDD29vZs3bqVlStXMnLkSMLDw/nqq6+ws7MjJSWFq1evUqvW68v+VatWJTMzEycnJ44dOyYbfc+fP5+UlBTefPNN7t27x+3bt+kxbTkLrpWiUKXaJgi/M1B/8Z2de5qPY7dPuZKtJeR3x4uKiqJ///4kJiayZMkSPtt2kqNpehRqyZYC/G/wwp92zuH7gE3G9Wf4ff7e/6v4xwx8L5Kqj1+OxqvnLBadTSPqQgJTenR7ZdsqVaqQn59frkwVHh5O8+bNiY2NZfny5dSsWRN3d3cuXryIq6srFouFffv2YTKZmDp1KqtXryYvL4/z58+XG5Dc3d2Ji4uTb784RkxMDCdPnuTu3btyCGTt2rUBW3TIe++9R/v27dHpdGi1WlkHl5mZSV5eHgsWLChnsqzT6bBarZSWmVDqXXCK6IE5JwWHsFak7vmUrOOrsRblgCSRd2EvktqO+IcXEabS39DXbfN53zGbEOZSEtcOBasFYbXIlHRhKkZXqS6SWospK5nSxPu4d5tO1vE1WPIzSN8zC0mrQ5hNYLVgLcpFae+MQu+CJS+dD7q3RONj+7lnHVmB27szKbh2kJIn17Hmp9uYg0qVXFIrLCqmICcLSZLIeZZA04gS7mQlgRAU3jllG+AVSptbiJ0Bc14GKkc3HN/sgEeDzrzfyJsru5ayZcsW7O3t8fb25smTJ9y5c4cJEyZQo0YNunbtSrNmzVCr1VitVrm/mpGRgZOTE7m5uSQmJsqOK3FxcSQnJ8s9vRemBkIIevbsyaVLlwgMDGTjxo306tULFxcXCgoKcHNzY+/evX/KJj127BgTJkzg5MmTsgtHmJ8za/u/SWZBKd9dT+RBSj7r1UocH/9E6sNbfLt7F4cPfMfIlZMYNmwYEydO5O2335bDRmfNmiV7r9rZ2ZGenk7FihVJTEzku+++44cffkCpVMrnW15eHkKYaVAtkLWzB3I6TcOsZetw6LcApYMLRQ/OknHoX/j4hqJycCXnzHZ0FWvj2XeuHKQKoPGqjCkzkayT32BfuT4uPWdjlxNP0slNFCc+YMSIEWzfvt0WloqZvP2fow96E23XaSgdPTA9e0janllovINx8KmMb+8vSP9+IT2HjqFNvaq8//77tG/fHovFwtdff83jx49ZtmwZRqORdevWsXz5chYtWoRareb06dOUlJTw008/oVKpZKJaaGjoH34XoaGhpKSkUFpayv379zl69ChbtmyhWrVqjBw5krS0NG7evMm0jUeIfCIhqbXyqrjwbhR5Vw5gykxEodGh9qyEU0RP7PyrYxb86QAV2uBtCiZOxLXVqH97vvwexSYrcw4/eO489fq+839RHv/xpc5bCTmM2HaVRgt+YunJGK5nqShyCeLAzWSyfSOYdUP1Si1ckiQiIiK4cOFCuX3NmzeP2NhYmWW4bt06FAoF+fn5GI1GFi5ciKenJ02aNOGzzz7DbDZz586dcj3EwYMH8/TpUxYvXsz48ePx9vYGbGWsyZMnk5VlS4Qwm82yruxFXEifPn0oKCggMysbs1d1gt6bB4DGzp74eFv0jVKpRK1WI0kSTZo0wdPn1ZWmws6AOSf1+ZtV4Np6jC2Q88NvcYrogV1AmG07rY3K/SJ0U1LZyA4Jz13yEQJJY4fLW4Px7D3btj8hyDqxFpWL7X0p7J3w6DYDQ63WoFCSd+V7ktYOQ1Io0VV6E9/RG+VBVNLak3lwMc6N+6IyVgCFkvT9s8mO2oTGpwqSWkuFj/ZibDkUjb1tNXb+4E4aSTGgUOLZZ+5zz0IJSalG414ByVKGZDXTPMiFb0c0YGzrWkRFRWEwGFi7di39+vWjrKwMjUZD1apVMRqNLF26FEmSeOONNzAajbKf5gupRXR0NG+++abca/X396dixYrcv3+fYcOGoVQqee+997h//z7bt2+nevXq6PV62rZtS+3atUlMTKS0tJSioiKePHmC2Wz+w/P38uXL9O/fn3379r3Wps7ooGVk0yCW9noDo4OG1PtX8DS6MPy9fnh6ehITE8PcuXNp3749Wq0WjUbD22+/zd69e+nRoweBgYEkJiai1Wrp1q0bgwcPls2ifX19MZvNqNVqtm3bxp07d6hfvz7Nmzdn+dZ92IU2RWUwIkkK9FWbonLxoSw55vlppXwlSBVA7eyFV995WAoySf9+AYkrBpJy9RiuXafj8EZbdu/eLfdMhRDoitPJOLOD7O8+QxtzEoqy0XhWRPkwCtW9o+QdXYalOI8zkdsYOXIkZrMZpVLJzp07GTFiBPPnz+fGjRs8e/aMhw8fUlxcTIcOHejcuTNVq1YlOTmZqVOnyt+BQqGgd+/eCCFYv349zZs3L/d5azQaJEmSJ0Evcui8vb25fPkyy5YtI7lEzdlCD6TfMEHzLkeSdeobnCJ64jduO75jNmGo3Y7ih5fkbV4MULcTX9+X2/Y3S5a/lS791p/2v/hz/Eev+GyhnQ8oMVte24OX1FrKntfCz8RkML1dqDzbekFw+W1id8WKFRk2bBj5+fl8/fXXADRt2pTY2FhZS/diRj527FjOnj1LSkqKvPoCqFOnDt26dcPX15eRI0fKK4+oqCjS0tL45JNPAGSafUJCAsXFxZQ5eOEY9ja3di0Cq4XHV34i9tyPAJgVGgKm7CR132zUWU/Ra1Wkp6dz/vx5mjZtyh3nCPLj77983woFbu0/IPmb0ahdfbGWFmE1lSIpVdiHNkK80B0plSgd3UlaPdjWG7TYLg6+Y7egUGuJX9QVjdFmTqvQ6FA5e4LJGZ/hayhNeUj6/jlIWntM+Rk4Nx9E4f1fcG03Ho2rL7nnv8VqsSCp1DaNFuA/fgeF986gcvFGae+IrlJt8q8fxr3jJLJOfYMwlxG3uBsI8KwYytiB3YmMjOT62Z9QYCVj/5c0HDCJOi26kJQYBwWZtGjejG/nvM+hjdfpF7ALs1dzCgoKKC4uplGjRlSqVInly5fTt29fPDw85FxFgBs3bhAcHCz3Z7Ozs0lMTCQhIaFcKczDw4OcnByCgoJkz9X8/HyysrIICgoiJyeH2bNns3nzZiZOnMjOnTuxs7PjyJEjLFiwgOnTpzNx4kSGDBlSjrj04MEDOnXqxMaNG//UeqqkpEQ21U5PT6d169bMnDmTW7ducerUKdavX09SUhJmsxmFQoG9vT1dunTBy8uLdevWMWTIEA4cOIDRaJSp/zqdDo1GIxN1hBC8/fbbfPLJJ6yPUfFs5yeYMhNsq3lAlBU/7yGDy1tDyPllW7kgVYdarQCbybK7r43EVZoSQ8aBBeRf+R6XFkN5lvIQU2osDx48AGyZl1arFcmUwaNDLx1TkpIelHv/8fHxslwIoG/fvvTr10/25BRCUGxRUOAfgUu1rtgZXPgmOoeDI74gPupbFAqF3Fc9fvw4PXv25I033pCjtXQ6HdnZ2XTtMxDniO7g7IuksWfMtkvcScjk4fXz9OnTh8jISNS5PpSYXw461pJCcn7ZgbH9BHTB4fJEzz44HPvgcITZRHbUJjlFvd/lVlz9fnO5vmdGQSmXntgmxc92TsOU/hSVwYhzs/eetw4g49BSJHV5/1phNpFzZhvmnBQ2aPU43hvJwrmz5f1u27aNGTNmUFBQUM7MGmxkrI8//lhux/Ts2ZMFCxb8j5JL/uMg/kOx7cITETrzsAiYeuiVP4c32ginJv1euT905mGx7cITIYQQP/30k4iIiHhlv1lZWcLd3V1ER0cLIYQYN26ccHFxEceOHRNCCBEQECBOnDghhBCitLRUNGrUSIwbN04cPHhQAMLf31+4ubkJjUYjvL29xdtvvy2cnZ2FnZ2dMBqNYvny5QIQPj4+4sSJEyItLU3UathCSBqd0HgFC7VHRaHQOQq/D3aJgKmHBCAUemcRMPWQqPDRd8IxvJtQ6wzCzs5OqNVqoVAohFtIXeEU0UvoqzcXnn3mCqXBKCp8fFAoHd2FY8Pewr5qU6HQOwuFVi80PiHCo/dsETD1kFB7VBJKRw/hM2qDkNR2wqFWawGIClO+tx1bUgjn5oOEU6M+Ql+9udBWqCkUDq4CEMb2HwqFzlEAAoVKKPUuApVWGLtNF4a6HQSSwvaYpBAan1Db/0HoKtcXCnsnAQhJrRUazyCBpBAKvYtwbf+R0PrXEJJKKwARWrWaCAwMFFqtVtjb29ueI0lCrVYLBwcH4eDgIIxGo6hfv74IDAwUkiQJhcJ2XI1GI/R6vViwYIH4+uuvBSAcHR3FokWLREpKiqhbt65QKpUCkP91cnIS7dq1E++9956oVauWMBgMwt/fX7i6uoqxY8eKLl26CE9PT+Hs7CzCw8PFkSNHxMqVK4WHh4cYPny4SElJee25euHCBdGlSxfh4eEhvvjiC5GZmSkSEhJEhQoVxKZNm/7teW6xWMTWrVtFQECA0Gq1YsCAAaJPnz5Co9EIpVIp6tSpI0JCQkS9evXE6dOnhclkEpGRkcLX11fs2LFDGI1GoVQqRZUqVcRbb70lnJ2dRfXq1YVGoxEKhUI0b95cuLi4CEDo9Xr5e6o3+FOBUiU8+y2Qzwe1R0Xh2nbcK78rz34LBEq18Bm57rW/R8f6XYUuqJ4ImHpIuDTqIxR2BqHT6cSmTZvEpUuXhEajEdWqVRMGg0FoNBrh6Ogovw5Jksp97xqNRn5MqVQKpVIp7HxDhF/vz0XA5Ejb32+O7T9xn/CftF8o9c7Cp3K1cueHRqMRarVaODo6Cq1nRaF2DxQKOwehcvERbp0/lvehr95cONRqJfS+VQSSQmj9qgvf0Rvlx22vRxIqZ2+hdPIUAVMPCUPdjkJpcBOSRicUehehMvoLv3Hbhd/4HULjHiA8PD2FwWAQLi4uQq/XizVRsSJw9De287BJf1FhcqRwbNRHgCTc350pfEZ+IxR62/ckae2FfbVmwu/9bcKzz1zhPWSlqPDxDyJg+CphcDGKyMhIIYQQd+/eFXq9Xpw+fVqUlJSIDz/8UCiVSvn6NXPmTBEeHi5SU1NFWlqaiIiIEDNmzPjb1+L/RPwvLXXu3r2b8PBw9Ho9Hh4ehIeHs3r16v+lCdhgK2/OOfzgDxMYjG3ex7nRq4Ll35Ya6tWrx8WLF3FwcMDBwQGlUomdnR3+/v7k5ubK5cvExERGjhzJ2LFjiYmJYc2aNdy6dYshQ4bQpEkTbt26xerVq2VtkLe3NwcOHGDChAm0aNGCL7/8EoPBQHZ2NhkZGYwbNw4hhEy4OfaokNKWk6jw0V68By1F7eqLfZUGKLT2mPMz0PpWlWeQ5vwM7CrWpcK4LRjrd8JgMNiYimmPKU2MxpyTirZCDfzGbkGSJFxaDCX/2g/oKtbGd8Q6/CbswuWtIRTd/wUAtasPuopvYMqIR5jLKEuPQ2kwysdTGtzQeFV++bm2Hou1xBaGW5IQjaFeF5sQWQLPPnNR2jtR8ugqJfF3cHijLfbVmuE9aBmurUbL+3BpOQz/8TtQ2DsDEq6tx6D2rPT8uVcwthkrs1TVzp7cuHGDWrVqoVAo0Ov19OnTB4vFgtFo5OLFi9y/fx+tVkvnzp2Ji4sjPDwcnU4nB/dOmTKFVq1sK5HMzEwmTZqEysGFYf/6lvd3XGHghvO0+/JbgjuNIb9McPjwYU6ePImnpyd2dnYMHz6c2NhYVq5cSWRkJM+ePSMrK4tPP/2UDz/8kMjISE6cOMG6devw8vJ67fnYoEEDIiMjiYqK4smTJ1SqVIlatWrRv39/Bg0a9Mr2VquVe/fuMXnyZDw9PRkzZgzPnj3DarWSkpJCw4YN2bNnz//H3ntHRXWubdy/6QWGXqRZAHuCBbGgosYaxS62WGPXGKNRo9EYo8YWjT22GI3R2BVLorF3IWIDREVEEZDeGZg+3x8jW4kmOTnfWd+33vO+11qzgGHvPXv2lOd57vsq+Pr6EhYWRlBQEA0aNKBly5ZkZWWxePFiCgoKGDJkCBaLBavVypMnT4QoqOLiYiwWCy4uLkKZMzY2lqZNX/lVWosyAZFNHA+Uxp6xOca8hPbhVUEWI1ba21ZiIjG61PuU3D0l+Gka81IpexyNwudlX03thMTOkVq1ajFlyhTi4+MxmUxs2LCBgIAAxGIxxcXF1KtXj2nTpgmrNFdXV7y8vDh8+DBt27ZFKpXi7+9P3R5jce3xGXq93kYu+QPBRCxTIJbKQSLH2LCfEMJrMBgwGo2YzWYcg7ti0hbj0KQHvh/vxr3nZ+Sf3ogh92U0mUiC9sEV7NuMpOrU/cg9a5B7/A/5mmIJVYZ/i/fo7wCQe9XC68O1+H2y1xZwW5qPWKFGonbEYjaRnVtAaWkpWq0WrVbLovU/UPrC1id1aN4X7f2LlN2/iDKgCYbMJMCK3L0a6rpt8Bm3FXNxLkVRB1BWC0LuUR2RSAyu1Qho3plLl2xh2gcPHiQ8PJywsDAUCgULFy4U+tlgY7/PmzcPDw8P3N3d+fLLL/npp5/e+h7+b8N/rNS5cuVKli9fzoYNG+jcuTP29vbcvXuXFStWMGrUqP/Y8tlkMtmy2kz/nkdeRS1805AmNGzYkA0bNtCiRQvatm3LkCFDGDVqFElJSbRp04axY8dy6dIlHj16xLNnz2jSpAnNmjWjfv36NG/enFGjRpGQkMCqVauIi4tDpVLh5ORk6128jB+aN28ec+fOfavbR3h4dwyvjd3K6g1xChtK3olvSV01AKmzF/b121EcY9PbWc1GCi/tICcvDZFYglqlQiwWU1paSutGnqTmFvFkzWCkjp54jVyDXZ1WiOUqiq7vI//MZkRSOTK3qjg06wuAY8tB5J34ltL4C7bE6NqhwmO9DTJXX1zeG03+6e/QPryCwjMQjwELX+bD2VKcdcm3cOk4DkNWMlaDFrmn/6t+4x8g9/RH4V0bl/dGk3PsG4wv0wfs6rSm6Ppe6nQeSnl5OYmJiQQFBZGVlcXu3buZP38+HTp0oHHjxvTo0YOePXuya9cuVq9ezaVLl3Bzc2PChAn4+PjQrFkz4fHupRay+eozLiXmAAg2b6BEGRRO1bqdKX8SQ87VvZw+fZrq1avj5uZWiZEYFxfHp59+yvPnz1mxYgXdunX707DfP6Ju3bqsX7+e2NhYwQg9NTWVIUOGUFRUxM2bN4Wb2WxGJpPRu3dvRowYQXBwMEFBQXz22Wd06NCBkpIS1q1bx4gRI/D09CQrK4sffrAJzCvK7iqVColEglqt5uuvv2b48OE4Ojoyf/58VqxYwZQpU9iyZQuTJk0iJycHs9mMWq2mrKyMR5ePIXPx5sUPkxHLVahqhyJW2FFwZjOFF7bbSB0WGwtYJJGCREbGD5MRqzRI1M4UXt6FxVCOSCwGq5WSWycwlxXh0Kwv5Q8ukfDgAWaTidGjR6NWq2nfvj0Wi4WqVavi5ubGvXv3BMeZrKwsQZgeHh7O0KFDmTFjBtt37+XJLzsQq4+grttaYJz+GURSOS49ZpK1ayb+1atitVp5nlNEmdoLqZOnUKqVVwlAXTuU4ujD6NMfoPCugyqgiWDF5tRmOKmrBmAqzkHq8DIZwmJGrFALk0b7d9oJj2s16UEswZiXhtzTH7FcBWZbpmBFRqFRJMNcZkuiKIk5jjb2DJ6Dl1By+wTm0jxkzt5I7F2RaFyRqB1xaNqLwqt70L94RMHFHRhzUrBaTKSZTbzj2h+wEev8/PyE87Czs8PV1VX4+8WLF2+kibxO+Ptvxn8kgb2oqAhvb2927txJ375937qNXq9nzpw57N+/H71eT+/evVm1apUwQGzdupVly5aRn59Pq1at2LRpkxADJBKJWL9+PatXr8ZgNKIcupHsq/spuXkURCIcW39A/sl1eI/bgszZm9wTq5A4uOEcNhSzrpS84yvRv3gEFrMtSbnbZH5fFMGnH41DLpdTu3ZtVqxYgUqlIi8vD41Gg1gsFjK8GjduzNdff82QIUP4/fffKS8v55NPPuHatWvodDoGDRpE165dmTBhAnXq1EGr1fL48WPkcjmOjo48e/askqQiJSWFvXv3svWhCKNHXduXwz+FxYLhaQz5x5YRFBTE5cuXkUql7Dxxkc1XU8gUuWC1Wv/SiqkC5cm3yD7wFVVnHBE+uK/j9USHCpuzqjOPCttm7p4l2F49X9EX7xErX5kpv4aUpeFvfY3AJlTOPbES30k/Ctu+++FiQkiipKSEpKQkFAoFN27cICsri48++ojjx49jNBqxWm2p45GRkXTu3JkaNWrw4YcfsmHDBmbPnk3Pnj3x9/en9pwT6C1/p8m2opCKqZ5/m7ObvkQulyORSAQG7oMHD/jyyy8ZP378GzKZv0OFftBisdCyZUuioqK4fv06Op0Od3d32rRpQ1aWLfl+/vz5ghY1MTGRx48fV/pZXFxMYGAgtWrVwt/fn5ycHE6ePIlSqSQtLQ1fX1/WrVtH165dK83yK1C9enU2b97MqFGjqF+/PlFRURiNRrZs2cLQoUM5cOAAoz5fBm7+OLcZRsHFHVh0pbh0tDEO9S8eovCtjyk/nay9c6kybCVSjSumwiysVgtZe+ZgLitC7lYVj4gvQSQic+enWC1mAj/+kWamOB5dOExISIhQFbJarajVagwGwxuEID8/P168eCE8l5Au/bhzL47y5/F4jVyL3NNf2Db70CLKH0fhOWixQORK++5DXLt+jKp6Q3Iil6J7Ho9IKkfq4IYqsCmFl3dVIqtgMWP3TjtcO0+yvVfVjmia9KDg7BZ0qfFYyouROHri1HIQeb+uFnIRKz4XRdGHKY09jbk0H6u+HLDiMXARquoNKYk9Q9HZzVhNBry9vSkrK2Pod2fZvWcvuUeXI1Y74tRyEJrgcHKOfYPM2RtN465k7PwUS/nLgF2r1bbSlkjRNA5H07grIqkc53s/U99FxK5du/jqq6948OABe/fuBWzByU5OTvz666906NCBgIAA4T0Ctt7n2LFjK/Ee/lvxHyl13rhxA71eT8+ePf90mwpLr7t375KUlER6ejoLFiwA4Pz588yePZv9+/eTkZFBtWrVGDhwYKX9IyMjiY6O5tMtv6B9EkPxzUg8Bi7Ce9wW9H/lHm+1YPduB3wm/oDPxO2IpHIyf11P3W4jOXbsGMeOHSMtLQ1HR0cGDBhASkoK3333HQqFgrp166JQKOjVqxcLFixg+vTpTJgwgQ4dOtCmTRshR+7jjz/mgw8+oHHjxsTExLB48WJmzZqF1WpFqVQik8k4efIk/v7+SKVSAgICuHAtCpF3fURiMZm7Z1Fw+Scyf5rO85X9yD7wFebyYnKOfcPzbyPI2DFVWDVZrVbyz24ldf1QMo9+gxURgwcPZsqUKVSpUoUZowYQv/Fj9LsmkbN/Ht3f8aB9HQ9qFd6iaN8sCs5vs7E2N46i/MlLMb13HURSGWWJN/78Ov6LkDm44S3+15PN/wqOdgoiIyP55ZdfiI+PJysri06dOtGxY0ekUinPnz/ngw8+oHbt2qhUKmbOnEmDBg3QarVs27aN1atXs3v3bvrPsBkYlxvfToKqDBF6k5XnrsGsO3WX0NBQtFot169f58WLFxiNRg4fPszWrVvJzMz8yyMVFhZy9uxZlixZQu/evXFycuLy5ctIJBIsFguTJk0iISGB8+fP4+Hhwf79+7l+/Tru7u5CykXfvn3Zvn07WVlZhISEMH/+fG7evElJSQkxMTF06dKFffv2cfHiRcrKytDrbX6dycnJhIeHv3XQS0hIoKCggMGDByMWixGLxfzwww+0bNlS+Nw1adIEN6ke0UuHFZFYirm0AFNxNiKJFKXfO7aVrliM1Wy0kWDMJqROnsheMn4xG5C5+SGxc0KidkQZ2BSzthCdycLJizf4/fffOXTokFByBdtq9W0s2NTUVMF8wGg0kiitgcUKUhcfSuPPC9uZy4ttAbMvS7Rvg1PrIVgNZSi8a4NIjNTBHWXVd6g6dd+r26cHce08SdjHVJJD7omVSBzc8Bq1AURiXDtNQGJnkw/Yv2vTxZUlRlH+9DbF0YdwCO6OfYPOOIT2B5EYi06LuawIbewZgrsNwdfXl+DgYPLz8zm7/wdkrr6ALVux8Po+Cq/+THnS79jVC6Pg0k5AhH2DzlSddgC37p8CViyGcsQqDSKpHEvmI57cOCWcc79+/Thx4gRXr17FYDAwb968SpKoQYMGsWjRInJycsjNzWXBggV/asTx34b/SKkzNzcXNzc3pNJXhwsNDSUhIQG9Xs+pU6fYsmULsbGxuLjYzGk///xzBg8ezJIlS9i9ezcffvihkIG2ZMkSnJ2defbsmcCinD17Ni4uLiQXpFAYfxn7dzsIBsmOrQbbIlbeAonKAbs6LYW/HUMHkPXzbN4f+zm9xnRl6tSp9OrVS6iLP3r0iGXLljFo0CCkUimLFi2iU6dOLF68mC+//JJVq1Yhl8vx8/NDp9PRpk0b5HI52dnZhISEcP/+fQ4cOCB8eFNTU2nTpg1XrlwhLCyM1atXI5VK6T/oAzwDB4DCRtsvS7iMx4AFSFQOZP40ncyd03HpPBG38Gnk/bKawmt7cOv2Cbqnt9GnxuMzdjNWsZTyy9vZuXMngwYNonXr1lgsFnbt2sXdu3dp164dQWV3GTlyJDuM9zj//UPc6r2H05SfKb37G3kn19oYnEo7nFp9QP7pTYjEEpQ1GiESS9E9u4vueSwi6d+vGkWAQiLivfd7cPvk9yi7fQaO3qStH45Lp/HY/U0Z6m2oohJxR69n48aNjBkzBrFYTHZ2Nvfv3+fJkyfk5OTg7e3Nixcv0Gq1DB06lNTUVLZu3YpWq2X//v20yrvgjQAAIABJREFU7D6Ig+n2//ixy40Wvjn7hKKUPHx9fSksLCQ5OZmaNWsikUhYs2YNkyZNskkrRKJKekDry+Rvk8mEs7Mzzs7OFBYWIhKJqFu3Ls+ePSMuLo41a9YI0hmlUimUJp88eQLYWMbu7u4UFhYSHR0tGKGbzWays7OFspTZbMbR0RGJREJRURFJSUnUrVsXi8Ui9PhMJhOlpaWUlpZiMpmwWq2CT+3p06cFrVtFKaxu3brodDqUXjosoTocmvWh8OrPZO/9AgD7hl1wbBGBzNkbl/ZjKLr6M7k5KSj9G+P83kufWpGYssQbWHSliJX2SFQagTmsN9syMbOzszGbzdSvX5/79+8LjFs3NzcaNmyIXq/nypUrlV4bmUyGQ50WFF7ZhUVfRsnNo+jTH+Da5SN0qfGoa7VAe/8SOZFLsZqNSOxdsJQXv0x51yJROyC2c6Ls4RUQiW3XtDib0vjzlKfco+zRjZep8mqwGFEFNKX8SQxWs+26pW+06TvLn9wU+telCRcAyI1c8vIsRZTeP4/FaMScnwZWC7nHvkGstEPh6c/z+7+T+fw5z5/b+ojvqIqJfinhKYk9A0YdRVd/Rl23DTJXP1tJWSwBqQxTSS7F0YcBcOk0gYLz28g/swmlbz369+mLSV8GQP369dmwYQODBw9Gq9Uybdo0fH19hes4d+5ciouLCQqyrYojIiLesFr8b8V/ZOBzdXUlNzcXk8kkDH4VlmC+vr5kZWVRVlZGcHCwsE/FBxhstebXgz/t7e1xdXUlPT1dGPgqatXFOltMisKr5qsnoXmVwPxHWIw6Cs59T3nyLYGYYTWU8yDpGYWnjlFcXMxnn31GcnIyBw4c4OLFi9y7d4/o6Gih/FIhQB4zZgzl5eVkZ2fz6aefYrVaCQ0NxWw2Y7FYKCkpwWw2s3v37krncOXKFaxWK5cuXeLy5cu2WbaLH0WJv2P/rs1t3T6ogzBTVvoHY8xLRVW9IQDqOq0ovPLS9V8sxWIot/ULvGsh8anHg9/OMXfuXAwGA2KxGCcnJ2FmN3bsWCZMsH04zWYzhuynKOoYsXv3PZvIXVuIxN4Zh2Z9ENs7U3R9H7nHVyCSq1BUCcShxQDKn9zEXF5ik0GYjZWemy2SxYq4MB3Z5Q1klrxAbDWTvucLW3q4yWiLz/k3cO/Xnbi6unL48GGMRiMKhYJ69erh4+PD1atXuXDhgs1Y+OX1nzt3Ls2bNyc0NJQbN25w7Ngx1E9KcQwd8G89vgUJgT0m0d/bZpsWHR3N0aNHSU5OplGjRvTo0YPExERSUlIoLy8XXES8vb1RqVRotVohhxAgMDAQd3d3qlSpQm5uLlFRUbRo0YIRI0bg6+vLqlWrcHNzY+TIkdy9e5e9e/fy8OFDBgwYQPfu3ZHL5fz22298/73N9Fkul9O/f38GDRokONMcO3YMFxcXm6uP1cqNGzc4fPgwly9fJiwsjIiICNq0aUPr1q1ZsWIFbdu2pU+fPsTGxpKQkIBKpcLd3Z327duTmppKly5dqNO9ActOP0bSfjTW9qMx5Dwja88c5F41UVVviF39ttjVb4tFX0beqfUUXtwBgFjlgNTRk6Lfj+AcNhRLeSlIpFgMOkrjziO2c6Far6lY4k5w/47t+2Lu3LksWrQIhUJBSEgIBw8eFF4PBwcHdDodqnphlKc9wpSfjkOzPuhfPEbuUZ3sgwsRqzU4Nu1D6d3fcH1/JnZ1W1FweRfFUQdxeW80xtznFEcdQF4lEHNpARK1I4aMRJxaDqLk9gkMLxIRSeXIvWsh9wikJCbSFphbN4zS2DMYUuOROvvg0nmirbf5EmKZGotBh+8ne8n9ZTWmnGcYs1MQSaQ4hg6k5M6viBV2KKs3wKItpCglBpFIhJ+fH2vWrKFXr160n7qWZ4B7jxmoAkMouf0rBWc2U1qz6at+fMxxdE9ihN6/XZ1W2NVpBVYLztpUPFTPWL58i3Bew4cPZ/jwV16nc+bMEX5XKpWsXbuWtWvX/lufj//J+I8MfC1atEChUHD06NG39vjc3NxQqVTcv3//rdZe3t7eldxOtFoteXl5lbatIBA4KKVI7F0wlbxKjTaV5PzpuRX/fgRjXhpew75FYu+MISuZjO0f07FNS9ZsnkKfPn2IiIhg8+bNDBkyhNGjR9O5c2eGDRtGVlYWsbGxHD9+nISEBDw9PdmzZw+TJk2iffv2HDp0iJSUFEQiERcuXGDGjBkkJCRQu3ZtHj16RJMmTYiJiWHs2LHs2LEDpVKJxWJBp9Nhyk/HXJovnKfY7vVAVcUfAlYVWF/6GqqqN0ATHE7+6Y2YinMICGnLvadP0ev1VK9eXehRbtmyhaNHj/LkyRMeP37Mjh07+P777zl9ei9DF2whxmTT51mM5Uiwhbva12+Hff1XTXmsFjCbmN63Fa2qQO+BQ+n60QIc9t6hWGfEQSmjjpeGfl/eesMpfuxPMZx5kEXqhg8FZmCF277VZESstEMbewZt7BnUdVrh3HYkvpN+pDT2LKWxpxn3003ub57KrKWLCA8PJygoiHv37jFixAgaNWrEL7/8Qm5uLiNGjODq1atYLBbBXkwqlWIymejWqy/3aw2l7MVj5N61SV0zGLFUjrp2KM7tRwv6wpSl4bh0nkjx70cwlxVjV78tLh3HIxKLydFL2LNvP48eJGA2m7Gzs6OwsJBbt25x+/ZtPD098fPz4/Hjx8hkMvR6PXfv3sXOzs420TAaUbpXpe+cjUg1rhTlZnDh8DZKE6KxmM2kpqYil8vp2rUr+/fvFyKpLl++TL169diyZQs//fQTffv2FdjAMpmMOXPmMGbMGDQaDVevXmXdunVcvHiRevXqkZiYyI4dO9i5cydeXl6MHDmSnTt3CtUWsBkhuLq64unpyc6dOykoKMDPz0+oVERHR9OpUydkMhkjWwVSmhTD1Rw5N7LFNhKHSIxIJMaYl4apJA+lbz2bZlMqfxmHZUtdMOY+pzjmGHb121H+5Hckdk62n44eGHNT0IrUyMIm4/wihYKsdIYPH863335Leno6S5cuFc7VbDZTrVo1EhMTGThlAbvXLkRi74LUqQoy16qUP7kJIrCUFiL3qglYMRVnYzWbKHt0FY/+81FVb0jhld0ofOqgf/GIqtMPY8xPJ2P7xxT/fhjEEkQKNX6f7EUkEqNLiaX07q/IPWqg8m+M9tF1NA06Uv4khux9XyBzr4br+5NRVgtCUfVdiq7sRixX4dJ2BC+2TaLq9MNCH9wxtD9liTcourqHkSv3sWlIE8E+LCwsjC+++ILff96Bpn5bQbfnEBxOeVI0mE1/m5yBxUQbdx07v9tJnz59KuVH/h/exH9k4HNycuLLL79k4sSJWK1WOnfujJ2dHbGxsWi1WsRiMWPGjGHq1KmsX78eDw8P0tPTiY+Pp3PnzgwaNIhBgwYxePBg6tatK0gDKlZ7r6NOFQec6rcm8/hq7Ou/h8TRnaJre//03KyGckQyBWKlHebyEgqv/gxAxsNbJCU5vZHUADB+/Hi++OILgoKCaNq0KXZ2dgwdOpTTp08THh7OJ598wokTJ5BIJHTt2pVbt25Ro0YNPDw8qFGjBs+ePaNdu3Zcu3YNsK1Wu3btikgk4vz587Ru3ZqM6p0o83jn37reDk164NCkB2ZtIWVnV7Fhwwbmz5+PTCbjxYsX1KtXj+TkZNRqdaXJQ0VSeLNmzVg5JpSIlYDZhEIiQm9+1fxSSsVYrFbKkm4xoqk3y8eOo96OHVw6/Qvvv/8+Q4cO5dsvv/xLNuOktoFceZz71v8VXd+H/sUjvEauBZGInEMLKbq+F6eXRBcx0K+eI/vv36djx46ArfT2R6xcuRJfX19ycmwTn6ioKJKSkvj8888xGAw8KpbaGJliCS7txyD3qom5OJesA18ivf0rDiGvetLlSTfxGr4Ki76MjB2foA5siso/GJ2unCc6O5RKJeXl5RQXF+Pm5sY777zD7du3MRqNZGZm4uDgwM6dO+nRowcrV65k/aatSJsORO9Rj4Lz33No4xLce87EVJRHxq0zeHadTHjP3gxp7IGj5VVPdO/evZw8eZLGjRszdOhQFi1aREpKChaLBYlEgtFoZODAgfTu3RuNRsPTp0+JiIhg06ZNXLt2jTFjxpCcnMyQIUM4deoU77zz9++xgICAN+6bPHkyycnJwt+mggyurPqGjMxMJPYu2DfuirJaEIbspxRe2oExL83m96ovw3vCD+h2z8Kuflt0ybcx5qeTuWMKcu/aWM0mTEXZGLOfgtVC5o+fACKbbd1LqFQq1Go1jo6OgkFEXl4eRUVFODg4cD/p6UvXmFwKzmwGidSWSiISo6rVApmzNyKFHdq4cxRf34fFUE72oUWIxBJb1cJqAZEYs7YAsVQBZhMSN09Mxdm2fV/P1BOLMZfmYSrORebkiXO7D3Fu9yHmsiIKzv9AzqFFIFehtn9lFC919ACLGUPOM4qjDqJPvY/FUG4jpCjsqFH+GLCl0j99+pSZM2fi5eVF3/fb8/OePTxf1d92TQAsJpRVg/7y9VPJxIwN9WPxh2OZPn06I0eO5M6dO3+aG/l/+A/KGWbOnImPjw/Lly9n2LBhQhL1smXLCA0NpXnz5ixYsIDmzZuTm5uLj48PEyZMoHPnznTo0IGFCxfSt29fCgoKCA0NFZhIf0S/YF9WBYagadKdzD2zEYnEOIYORBt/XpjBvw5Nk57kHvuG1DWDkdi7CCGZx9bO5cSKqYIpsZeXl1Ae7N27N6WlpUyaNInIyEicnZ3Jy8sjISEBsBlEb9++HZPJxJkzZ+jRowd79uxhyZIlHDlyhJycHLp3705eXh6xsbFs3bqVlJQUZDIZdevWxcnJiZSntzHKXJA5e/+j66zPSASrFblnACqVGplGLbhS9O/fnzlz5rBz505iY2OJj4/nq6++IiMjg40bN/LkyRMiIyNRqVT069cNgMMTW3O3RMX524+If5RMp7atbKu4xr7MnhaJKVtHZGQkERERgvN9eHg4aWlpbNq0qVJf93U08HNiTtc6jHxLFUWbcBGXjuMEYoBjy0HkndqAU9hQxFiwanO5cnQXPXr0EPLWKqKNXodMJiMjI4OUlBQCAwNp3bo17777LmfPnuXnn3/GXmqHWKZA8ZoWUerkiabh++iex1Ua+Bya90OstEestLd9oWclo/IPRuFRneCBE5nZ2pOaNWsSFRXFggULuHDhgpC8sG3bNrRaLcOHD+fzzz/n2/WbkLT8EItvEGKxGMewoaR/NxKrxYw24SLKag1Q1AnjbGIeV5MLmdO1Dg1fnkfv3r1p2LAh3333HSdOnECr1dKgQQO+/vpr3n//fbKyslizZg1NmjThvffe4/r16/j6+vLhhx/Svn17Zs+eTefOnf+WcfpnzL0rV67g7+/PrFmzhGsPMHXqVAwGA/Pnz0eqkKP0sU1E5B418Bq+CngVbCzV2PqEIokMl47j0DTpTsb2KSj93kGXEovUwR2ZRw0sZYUCi9d2AAMDJ35GUVERJpNJyO8rK7P1rAwGAzVq1MBUVmILInZwxzF0AJoGnck9sQrt/Qu4tPsQsOn3XDqOQ14lkLQNw1H41MVzwIJKDOWKcwabVEiidsKYl4bVank1+FksSOxdkTq4YSrOEXxsJWpHHJr1Rht/DoVvPcylea+eRnEOiCWUxBwHRHiN2oBEpcH0JIqSc5vZvGQOhcmxFBcXM336dFauXMmoUaNYunQpw4cN48zTchQtBmMwW/+SjCUSgVIqERypPNes4auvvqJu3bp89dVXLFmy5M93/l+O/6hlWUWcy59h8eLFLF68+K3/Gz9+POPHv92c9XXFhZu9gja13Dlj7o9jC5texZibCiKxkOrsFv4q80qqcaXKB0tfHctiITi0DVuGhbBnzx42b95MSkoKRqORuXPncvv2bXr27CnY9/z88884OjoyadIkmjdvjoODAwMHDuTatWuMGzeO0NBQoqOjcXd3R6PRUFxcTPv27dm2bZtAvy4tLWX69Ons27eP+Ph47t+/T43Amohrd/zXL+5LWPRlFJz7HlNhJiKpnN7d32fGDFtY67p165g8eTL+/v4UFhYyatQoDAYDQUFBNGvWjEaNGnHv3j1WrVpFSEgIJ0+exFWjZFyjAOqLM5h1cC+rNn4kPNa8efNo0KABU6ZMISoqip49exIXF8epU6cYNmwYPXv2ZN++fdjbv508MqR5daaqZMglIqwWiyDbMJfmI3HwePUaOXrYaN9GPb6WLAoc1SxZsoRly5YJ27xt4JsxYwbz58+nU6dOmM1m/P39iYuLo1u3bri6uqI1mLAHjPnpFJz7Hn3mY6xGvc2oW+Na6VgSe2fhd5FUgcVoKy3nRC7lWnE6g77TU1JSgsViwdnZtq1arWb58uUMHDiQ5s2b4+XlxaF72WS9SINDX1dO4365wjAV5wq93Ap3/YW/2CZUFouFZ8+e4eHhQVlZGfXq1ePFixfcuXNHOEyVKlUYPXo0RqORNWvWYLFYcHBw4Mcff6RHjx7/sqbwbbC+zJ1ctGhRpUGvAqdOnaJ+/fq8yMpGKvnXCeEyZ2/s6ramJOY4MvdqqAJCyD+7xWaI/pLsYshORixVkO70DhLJLygUCsRiMd9++y3Lli0jKSmJzMxMdDodGqff0bzbgdK4s5gKM7FarTiEDkDm5odYpbENXiYjVrMJkVSGzNUPQ1YyppepJFaTEd3jKOxrt6C8yMbMdes2FZFUTsa2jyg4uxXn90bZtKgmA3b1bD6lWK3k/boW5w5jwWKi8PIupE5e2NV/j6LogyAS2VZ51/dhV7c1VqPOJlpXqpGWFyB/eBJUcvbs2UOnTp0oKytjzpw5Qmj1kCFDCAkJwd7envnTJ3DuBfxy9iJyZy9EDp7C9awItm1X252JbQMFY+rBgwdz+vRpDAYD27dvp0+fPoSEhPxb74X/dvyP9Oqc1DaQX48fRVItGItJT8HF7agCm75Vg/YGzEaenNhIn8glrF+/nlmzZvFOkxY4N36fZ4UGTmrtObUzmtLFP1L0LJ0+ffrYPPx698be3p4ff/yR9u1thJQNGzbQq1cvZsyYwY0bNzhz5gwmk4moqCiUSiVr1qzBzc2Njz76iPXr1zNgwACqVq3KzZs3uXLzLo4mIxKslQZmQNC2VUBVvSE+47cKv6tGrUckgs71PNk0pMmr/ZydhcxAtVrN7du3iYuL48KFC/j4+DBs2DAiIyO5du0azZo1IyUlhapVbb0+e3t7SktLKz2ur68vI0aMYNGiRWzYsIErV64wcuRIunfvzv79+5k/fz7t2rUT4pveBjuFlA7upZxNj0Xs+y5ymQyJvQvm4mxwrwYmA6LSPGRqDeGqJBJ0WcSlpQmM2opBtXbtPwa6gEajYdq0aWzdupWSkhKeP3+OWq0mOzsbiUSCh5sLJUD+bxuQewbg1mMGYoWa4ptHKXt07e/fK4Ah8wlKexVx9+JwcXEhMjKSjz76qNI2jRs3xtvbm3rN23HTpQUShz24dp2C0vfNwVrq4Ib+pdFzBfQmK5OmTqf490hEVgs9e/Zk2bJlREdHM3z4cOLi4qhRowYHDhxgx44dPHjwAB8fH4KDgzl79ix79+5l5syZgoymIrLqn2Lfvn2UlZVRq1YtTp8+TU5OTqXbtWvX8PT0JCczE7s7v6LxCKD42l5K484hlqtwaNq70vHKk2+Rf2YT5pI8xEp7IQZLrFDj3O5D8k6ttwXpmgwgluAY2h+PNh+gVSjR68oxGAyMGTOm0jFr1arFjk1f0euHOHKdfWzxVLd/RSyVo/CtB41sqzeLXkvO4UU2oopPHTT+jcna9wWmwixEEikKVx+o1ZSCs7bPldzTH5FYgkPoAEpuHrE9J6UdYqW9LV8PUAWEUPboGtr4c4AIqaM72/YcYvnRGKxl71EYfYTM7Z+AWIx7xHzsnNzIOLqS9NUDCQgIYPTI4Xz99dd069aNuXPncvLkSRYvXsyqVav44osvmD59OkePHqVLly6M6/8+CoWCcr2BkYu2kmm08vBpCh1atxQqMm9LYF+3bh3BwcH079+fESNGcPv27f8d3pv/EP8RAfv/Hwhq3ob4OzGIxGIUfu/g0nkiUnuXv9zHatTT1jGP+0c3Y7FYyDIqqdJ+OAVKL0QiEebXZI0Wox6RSIQ46wEFV/fRuLorfn5+3Lhxg6lTp3Lp0iUuXLhAeXk5fn5+tGrViu3bt1OjRg0++ugjnj17xq5duwgICKB69eocOXIEkUhEmz7DKK/emqd6NTKZHNO/efVVMgn7xjZ/I4ZEq9Uyc+ZMNm7cyKZNmxg9ejS3b98mIiKCnj17snz5ck6fPs2yZcsq0cSTkpLo0qWLwECsQE5ODnXq1OHmzZv4+/tjtVpZuHAh33//PUeOHOH48eP89NNPnDp1Skg7fx3Vq1dHJBLZHHe+38FzsRc/bVzFs7homg+bxbN7N1Cl36Jxo4bs37+fI0eO0LZtW+zs7KhZsybx8fEYDAYSExOpWbOmwIAcNWoUEydO5Pr165hMJvr27cuhQ4fw8/MjKSkJi8VCwx4fUlS3B2k/TkcV0BTHlgMx5j4n54jNXq3KEFsa++vCeqCSuD5944dUcXfl2cM4MjMz6d+/PykpKaSlpVV6ntWqVcOx5SCK/FpSEnOMssdRuIVPs61my4rQpz1AXas5pqJsXmybhOv7H6OuHYpFr8VcnIvU2Ye0dR8gsZpo1KgR/fr144cffiA1NZWIiAiOHj1KWFgYI0eOJD09nbVr1xIVFSWsPs1mM0ePHmXZsmUUFRUxY8YM+vTpQ3FxcaXBKyUrn5t5ErL0UsqMFozaEvRZyeTdPEFJbgYuLi74+vri7u4u3Dw8PHBycmLy5Mns3r2bGTNmMGLECH44cYXctGSqDFwIMhU5Rxajfx4niLjLkm4ic/VF6lQFfWo82fvn4zlkGYoqgehSYsnaOxfHFv1xbDkQ3bM75BxZis+4Lbjlx2GO/w2xWIxGo+HGjRuIRCI++OADvLy82LFjB07dZ1LmHPhvmT9YLRacy56TkZGJKqAp/BvHeH3iee3aNYaMnsDM7w6RmK2tTPx6OUBVxFzl5+ezc+dO6tati9VqZcWKFaxevZrDhw8LLkP9+vWjf//+BAcH06pVK9LT0zl06BAHDhwQDKX/Crdu3aJLly40btyYJk2a8PXXX//j5/ffjv8xK76KvL2HmcUU60yETV5BztETKP0bY0Vcuaz0B4gAq8lAiCyVc5u/Ye7cuZxN0ZFnqkqeRAYi8RuOJBWOJ1bvd3HsU4dHN/dzfd8+9Ho906dPp0OHDkLfbNasWaSnpyOTyXB1dWXRokU0bNiQGjVqkJCQQLVq1WwONT7vklQtHIPRikgq/n8x6ImZ07XOG4PeyZMnmThxIoGBgTRu3JgxY8awadMm5s2bx8aNG+nXrx9gS28fPHhwpX3ftuIDW77g5MmTmT9/Pjt37kQkEjFv3jzq169Ply5dWL9+PbNnzyYsLIzIyMhKFmFgSxXIyrL1UXp2sYl8P/30U5r5tuXHFRMwmUyMGjWK5cuX4+zszMaNG/H19aWgoIDk5GShdPfFF1+wa9cusrKyuHDhAt9++y1KpZKSkhKsViv79u2jbdu2zJkzh5ycHAYOHMicUX34cO4qJPYuFEcfpOjqbmRu1RBJZELZqwKZO6fbqOetP0Abfw77RjY3C4WnP+X5NtcYq9UqRPlU4OOPP2b37t3k5+cjKf4Rt27OaEJ6AFYytk/Botciksqxmk3IXH1w7/05dnXDbF6PRy2IVRqc3xuF3NMfVbV3saTFMXToUFasWCFovBITE/Hy8uLixYvExcWRm5vLihUriIyMFHL41Go1OTk5pKamUlBQwNixY4USmq+vLzl5+ZisIhxCeuHccgBmpRiLRE/+pcOUJ0UjsXPGo2k4PL/FvXv33ngf7Nq1C7lcjqenJ3l5eezevRuNXE71hg1IzUtB4tcAx2Z9yX7NTEId+KrMpqz6LsoajdCn3hd6riKJFMdWg2wRVgEhiORKTMU5hHUfyMaDa7h79y4HDx4kOzubatWqcfz4cbRarU069Nv3uEYsBPE/X81YzQYeHV6HnZ0dijotKhG7/lUopRImtrU9j5YtW1K9iiv2aVGsGlq5WmO1Wtm1axfTpk1j8uTJzJo1S+i/ikQiZsyYQZ06dQgPD2ft2rUMGjQIFxcXCgoK+OWXXwTnHZ1O9y+TVYKDg5k9ezY///wzW7dupU+fPpWkZP+H/wED373UQjZcTHqLvyIoqzfCYrHi7qCgSGdCIhJVSqpWSsWU63S09HdmaHANhncfz6pVqxg2aiwWk7HSYOkxYKHwe6UVgEiMSKZA0Xwgvmo1xbdshIO0tDQiIiKQSqU4OTmRn59P9erVKSoqQq/X4+HhwcSJE+nUqZNNG+Xli/ydjhgtIkR/k6xclhhF4dXdtj6eRIbMvTquXT9G7lyFkmt78FGVMmRBpLB9RkYGn3zyCTExMWzevJn09HROnz7NBx98QHR0NBKJRBj0SktL+fXXX9/Q7vzZwAcwbdo0YfVVwRLs27cvAQEB9OrVi2HDhrFlyxbCw8PZtm0bPXr0EPZt0KABEomEBg0avNFst1qt+Pv7M3WqrSf7+eefU7t2bYqLi8nNzUWn0zFw4EDu3LnD/v37hdluYGAgkydPJiwsjJCQEJo2bSqkSpeVlTFhwgQGDRpE3+5dmb38O5IeXcdjwEKbUbLZRN6pDYIRdnnyLcR2TngO/Bqpkyf5p9YD4NC0FyIR+Hi4kpubyPXr12ncuDHDhw8X9Kdnzpzh2LFjVK1aFffWg8gp1pITuRTfCT/g0LQ3Fn0ZRVEHce89G2X1huT9spqsffPQNOhE1U8PURp3lqIbBwQtp2efOeTtn8snn3yCSCSifv36lJWVVcqNLC4uRiwWs3//fnx8fASSf+2EAAAgAElEQVTxemhoKO7u7kRGRpKbm8tPP/3E5cuXCQ8PJ88ow2viDgz5mbzYOQ1V7VbI3PwourYHU1E23uO3Ya1IO9cZ2BX17I2w1C1btiCRSBg/fjxyuRwXFxdiY2Np0KABMwc0oP/QD3ELrxx7U/4khsJrezDlp9s0sUa9YDoB2BxHXmtPVPRWk1OeYzQa2XCzkFulVdE2iKBcWkxR8UV69+pJtWrVWLt2LYUXfsC5/SiQvNmP/DOIzEYamJ9wruA5rvZ+5J/7HvvWQ7H+g2NgNvBpl/qVJp5z5swRHJwqysw5OTmMHz+eR48ecerUqUpa5dfRvXt3zp07R48ePXj48KHwfXLx4kXGjRsH8I8GPoBPPvmEM2fO4OPjw4gRI7h169Zb+7b/W/H/eRDtxYsXK7kH/BV2RT1j4NYozjzIQm+yVBr0AKwSW9ZbXpkRiUhEm1ru9G7oQ97WMaSt7MuzlRFkrRvE+QWDeHrrErNmzWLi3GUgV+Peb54tnPXl7e/KpBaxDGmTCDoOGI1Op+Pu3btIJBIkEongvpGamoqbmxvr169nw4YN9OzZE5VKxb3UQgrLjZj4e+KBseAFub98i/N7o/Cbuh+f8d+jadQFqVhM53qeRAT7UsPN1veyWCxs3LiRoKAgAgMDiY+Pp1OnTty4cYOzZ8+iVCrZsGFDJeblsWPHaNmypZDwXgG1Wk15eXklS6MKODg4MHPmTL744otK9zds2JDff/+d8+fPs337dg4ePMj48eOFLMPY2Fji4uKIj49/Y4UJ8PjxY6E8ajAYePDgAX5+fuj1eho2bIiXlxeXLl3CarViZ2eHXC7Hzs6O7du3s2zZMrp164aHh40k06tXL5ycnHB0dOTMmTMC4adOFQc0tVrYdGYisU1n9hq0D64ILkBimRLHVq/OUyERU9VRxnvvvYdCoeDWrVsEBQVx8eJFGjZsyLBhwwSZillTBU2zfmAy2qj9L6H0q29LsBdLUNdphaWsCIfm/RBJpNjVDcNclCUYK1jEUqq1H4KDgwPt27fnm2++YcSIETRo0ICnT5/i4+ND27ZtkUgkJCYmUrt2bTQaDQMGDGDkyJGEh4fj6+uLvb09Go2G+vVt4bBVBszHKlMj8/RH7lFDMBQve3AFxxYRSJT2SB3c0DTpjhVbUviul6GoR48eRSqVcvXqVTw9PdHpdBgMBjp37kzLli1p374969atQyU242h4pae1mozkHFmCQ9Pe+E7eRdWp+1AFNOFfWVvF5otALOXI1XtkStyRBrQgIc+EzLs2zj0+I7nITP/+/fl5wQTKr+22EZGsb09qeQUrVpMeWfxxfl07m5CQELKysii4eQxd1J5/8RggFoHGXMLGrdt4kfdKhtK+fXs0Gg1HjhwRrltQUBABAQHExMT86aBXgaCgIKKjozl79iynTp0iLS2N69evC3KefzrwicVifvzxR2JiYtBoNCxatOjvd/pfhH95xbdkyRIuX77MyZMnhftq1qxJYGDgG/ctXLjwDa/NfwpbyOwDIXpIlxJL1p45lYxklVXfxSPiS6xW0JksXH6cw5yudTliL+f7vSfo0KEDx48fp2/fvnz88ceo1Wq8B8zn8b6lf/awAsylhTYmYFoCYqUGu6COFF3eiXbAPMA26IhEInQ6HVarFXd3dwoKCujSpQt79uwRmvISiYSaHQZXoiXrXzwi/+wWjHlpbwiqDVnJSB09BdcWkUKNX/2mvJN9jl5uXvRY/y1Wq5XDhw8jEolo1KgRU6ZMYffu3axdu1YI1Bw5cqTgBKLX6wWSSPPmzRk+fDhLly5l69atFBYW0r59ezZt2oRKpaKsrOytLM2JEyeyevVqoqOjK5UzPTw8OHfuHBMnTuTjjz9mz549jB49mtTUVFJTU+nevTvXrl2rlC5usVhISkrizp077Nu3j0WLFhEXF0dAQAAZGRm4u7tTs2ZNHB0diY6OZuLEifTt2xe9Xs+MGTNo3bo1HTt2ZPDgwYJjztChQ6latSplZWXEx8cTEhJCREQECXd+x8m+CiKLEav4TYr/n7kAWY0Gii/t4MbtMygUChITExGJRDx79oyioiI0Gg3VqlVj586dNrp9fAKIbXqyirBWQJBsgM2IQKxyeJVI//K9bDHoECvtKbqxn4xH1zEWFREVFcX06dNJTExEIpEQGhqKt7c3HTt25MaNG2g0GqKiosjIyGDUqFEMHz78DUZ1wgvbeZiVTsKU63XGqqk0H4nDK9cjqeblKthoYeGJ+2xf+RWxF0/w7rvvcvfuXerVq8fixYvp2bOnMBn46quvyMzMRCwWk3zhVf/Jaja+lAg4glhC+ZMYdE/vIHttxfdWWK2IJDLs6rai8NJO3MKnYdGVUhxzHIdmvTmdkAlOYcweVIdj+1bjXvSQYPM9Tj8XoajeyBZW+9rkRmQxIhZLeMdFRO6Vg1w/uQdPT08eP35MtWrVGDZsGJ999hmeBamU12yHXWBTGwP5j+buViuIRFisUCJ3ReQTSsvlF+hQ35uP2tW0yXfmzOGLL77g2LFjXL16lQMHDvxtwPDr8PT05Ny5c7Rr146ffvqJRo0aodHYbMx0Ot0/Jql4eHiwY8cOhg8fznfffUevXr3+dgD+34J/eeALCwtj6dKlmM1mJBIJGRkZGI1G7ty5U+m+pKQkwsLC/l+d1J/l7Uk0LpV1P39ARd5eucHI3r17mT59OtnZ2Xh4eKDRaHj48CEqnRWztoDyJzHC4FIae5aS2ycqHavg3BbkXrVw6zWb3GPfUHTD9qHW5ucgk8nR63U4OTkxYcIEbt68iZ2dHb/99hvz58+nc+fOAHz22WfEJ6Vw+mEm5tecZv5KUC2vEogxL438s1tR12yG3KsmpSo1p377je2b1jF9+nShnPX1118zatQoTp48SZ8+fVi7di0nTpzAbDbTsmVL7OzsOHnyJEOGDCEtLY28vDwCAgLo1KkTkZGRXLp0CXd3dz7++GMmTZqEvb09BQUFWK1WdDoder0enU4n3AYNGsT48eNZuHBhpft1Oh116tQhMzOT8PBwOnTowObNm4Xyr1QqJSQkhNzcXAoLCykpKREIL8eOHbORiMRiEhIShDJieno6dnZ2iEQifvnlF2EFa2dnR+3atTl79iyXL1+mZcuWGAwGsrOzcXJyQqlU0rx5c86cOYNKpaJKlSq4u7sT4C/i2HP4Y4TjH12AzC9dgHrXVhPS+n1WFz/CZDJRUFBAeno6YOvNODg4kJGRQVlZGZMmTSLN9z3uFMpJXfXv2aMBKPzeoejaXqxWK8XFxYJwvaKnGBwcTFJSEnq9HqlUilarRSaTUVRUxJIlS1i4cCEikQi5XE5CQgJl9f/cNL7iuZtLcsHNxux9/TrojBYSpTVwc3MjODiY2NhYpk2bxtmzZ8nOziYiIgIHBwdBY+fq6kq1gEBiomz9XLFCjUuHseRELsNqNtoMAWo2ffMk/oiXPV3njuMpOLOJ9E2jEUnl2DfojH1QR0AEUjlLTz3EW+csTAJ++OEHJs8Yj6JOGM413qHcBGqpCEtBKqmXD+LVoB6PExPp2rUrBQUFXLhwgZ9//pnJkycjk8koeHIPc+Jtaoa2IUHngF2dViiq1MTyh/OqgFVs++o8k5DJlaRc5nati6dKJTBu792796cyn7+CUqlk6tSpjBs3jtjYWGJiYmjSpMk/XvFVoGPHjgwdOpTffvuNESNGEBMT838lT/7BwBcSEoLRaOTu3bsEBwdz5coV2rVrR3JycqX7AgIC+O2331i+fDlpaWm4u7vz2WefCbXqP2Lt2rVs2rSJ06dP4+7uzpw5c9i0fRflOj3qWs1xbj/mb6N1jLmp5P22AUP2U6T2LphKCrl48SIKhYKcnBycnJwEgoX+4SVbPtjNo5TcjAREiDUuSO1s7Dh9ho1qbshMwljwAm38eTwHfU3W3rlYgYIzmxBLJNSrV4+nT5+ydOlSbt68SWRkJFKplEuXLpGTk4NKpWLLli34dxuHY8uOlNx5tSr+K0G1zKkKVQYvofhmJDmRS7EYytHUD8OlThiTJk1i165d2NnZCX6G33//Penp6YwbNw5HR0ciIiJYu3Yt3333HZcuXSIlJYW8vDy6devGkydPkEqlzJs3D09PT9q0aYNOp6OsrIzCQltwaLVq1VCr1SiVyjducrmcR48esWjRInx9fVEqlSgUCuH/DRs2xMHBgcjISOzt7ZFKpSQnJ6NSqahTpw4tW7bk3XffJSgoCKPRyEcffcTVq1eF9Io5c+ZgMBjYvHkz0dHR3Lx5E41Gw/Hjx994za9cuUKPHj2Ii4vDbDbTu3dvhgwZgtVq5dixY+h0OqZMmcI333yDr68vi8b1ZGhqAatPP+CQCMQvi252dVqR9+sa7Ou2RqxxJ+/kOgBiDm2iMDCQrKws8vLy6Ny5M7t376a0tJQxY8YwevRoFixYgIeHB3PmzOFgfAFXFi62OXT8m1C4VwOLGaVSSceOHTGbzVy+fJnS0lIyMjL48ccfhfy2Bw8e0KBBA2rWrImDgwPLly+na9eumEwmPDw8SHqeTvkzW8k5a88cXDtPQuZmo+UXvowYshrKyd7/Jep6bXBo2puCc99jNZSTtWcObr1nI6naAPWzs2zbtg2AcePG8fTpUxo1aoTZbObx48dotVq8vb3RarXcv3eber0m8mjTaKwGHZqQHoikctx6TBcmmRVQVgt6YxLrO/EH4XeJ0h6H5hGYtUUYMh9TfDOS8sdR/w957x0W1dWu/3+mMEPvTbqAiIrYBUUQa+zG3lti7zW2GHusUWM0FmxRg70GRI0xYgtgjV1EEVCUDtKZYdb3jwn7lahp5/zO7z3nva9rruRy9qy92LNnP2s9z/3cN5YhAzHyakTxi0fERWxjgac1G5d9gZWVFf26dSIqKoqChGiKi4tx/q1erCnIISkpiYyMDNauXcvEiRNZvHgxtWvXpri4mJo1a/Lq1SssLCyIiT6HZcNOGNi5877E59v2RoDefFej44vjv6KNO8D06dOJjo7GxMTkH98H1tbWFBUVsWbNGtq3b8/GjRspKSnB2Nj4H423ePFifv75ZwoLC1m2bBnz58//x3P7v4K/HPhUKhUBAQFcvHiRBg0acPHiRYKDg3Fycqr0byEhIdjb2xMREYGnpycXL16kffv2NGrU6J1t9qJFiyrtPKZMmcLDx09wHLaeMp1Mv9O6sg+r0KEfnJco15J+eBGm/m1w6LuYkpQHpO//nGo1avHZ1Mns2LGDkydP4u3tTVqhDp3KBHRaVE6+OA5YhjY/i9ffTZV6jHJ+2gboU0IylTFWoUNQO1XHPLAHeRf3YvXROEzyEinN0BehX716xZQpU3j+/DkFBQWYmZlJD+zy8nIevX6DqZ+FXpn+N/y+oRqdDpXjv2Sj1M6+2DnPAvSBOPP4CjKK89l2/gZ2dnYoFAppB5SWlsaFCxcwNDQkKyuLTZs2SU3WQUFBVKlShdu3bzN27FjmzJnDjBkzmD9/PllZWcjlcok1qVar8fT0JDw8nLp1Kz+k3sb+/ftZu3Ythw4dQqPRcPfuXeLi4qTX8+fP8fHx4e7du9SoUYO0tDTc3NyIjIykSpUq0jiRkZH4+vry008/sXz5cmxsbLCwsGDt2rW0b9+edu3a4eTkRLdu3d47j+DgYGJjY+nQoQN5eXkMGjSITz/9FKVSib29PcOHD+fYsWNSu8ORI0dITk5GrVajkCtR2LpDYhxWVlaUetYl9/AXaEuL6dunD7t336VHjx6sX78etVrNp59+yqZNmwB9jbq4uJglS5Zw8eJFli1bho+PD0bGJshqtUdhZvve+f4lKAwwNDKiuLCAs2fPolKpEELQrVs34uLiyMvLQ6VSYWtrixBCCvpCCCmlZmhoSF5eHoX5BVh/NI7MyHUYeTYg/fAinEZ8K52q6PFVHAevIefnHRTdv0DRoyuY+reh+NkNhBDkXz+JZWBPimx9gEvY2dnh6enJs2fPuHXrliTCDXqReQsLC33NNyIMhz6LUDr5khu9u5KiyZ+h8P4F3lw7ri8BqIz04tC1W2PfS19eKH31pJKZohDw/eXHyGQy7O3tOXHiBB06dODly5ekpqYSHh5OtWrVcHZ2lhYMffr0YdCgQSxYsIDCwkKGDx/Oli1bSEhIoEOHDhg4eGMWMvRvEWYAdDIlxkGD6DUigCNHjhAdHc3Vq1cJCwuTFt9BQUEcOHAAQDK+rmDevo20tDRkMhnjxo0jKCiIrl27otPppI3B34VKpW+ab9y4sSSIXadOnb89zv8l/C1yS/Pmzbl48SKgX3EHBwcTHBxc6d+aN29Ox44d8fLy0vetNW9O27ZtK/WMCSGYOnUqZ8+e5eeff8bOzg4hBFu3biVo4FQURmbI1cZYNO1N4cN/fa48P5vktX2kV+HDS5SmPkKnKca8SU9kCgOMPOogU6q49uAZixYtIioqCplMRmZmJnmZrxEa/Q9A7eyrZ0xaOmLo6icRDCrqL0JbiolPE0m0uTxfLyid81MYL+JO8/TpU169egXoSRoajQa5XI6Pj49EfS8pKcHe2R2dpoTyCgNJ9A3VBjYuOI/citvUQ1g2H/zBa66u4oNx9aYYaEtwdXVl1KhRhIaGEh4eTpUqVXjw4AEymYzt27fz5s0bLl68iIWFBQ0bNuTTTz+VWKX+/v68ePGCKVOm4O7uzpkzZ3jz5g15eXnk5eVRUlKCtbX1B5mdOp2O+Ph4ysrKePbsGb6+vlhZWUnpk8DAQPbu3Utubi5Dhgzh448/JicnBxsbGzp06EDTpk159OgRAMXFxezcuZOYmBi+//57Nm/ezL59+8jMzKR1a33Lg6mpKd7e3kRHR0s2Ojk5Ofz666+cPHmSDRs2sG3bNvz8/DAwMEClUqHRaCgrK5MYqkVFRUybNo2NGzdy+PBhXr9+TXZ2NjmZ6Uwf2oNerkU83DyJ6b1b4mRvy6qVK4mNjQUgIiKCI0eO8Pz5cynoAVJa/9SpU1SrVo0dO3bw5s0bzp45jZlzNZxHb5N2A5bBAyRpLNCLD7y9q5HJFbjPikBpbotMBkFVLalezRsDAwP8/PzIz8+nqKiIS5cuYW9vz5s3b6hatSrz58/nypUrGBgY8Pz5cy5fvkz9+vVp2rQpdevWpaioCAN7T0xqt8Z9VgTmgT0Q2lJKXzzEccByZAZGmDXohIGVI/bd52Ds0xRT/9ao7DxQmtth7NOEsrRnoFRRM7AloGcJVgTjRo0aVdp9KBQKmjRpgouLC40aNsDowQ8UXD+JRfAAEJC+/3Pyb+hLCZqcV6Ss64sQOspLCkg/tJCUr/uTvLo7mZHrMK3bDpcJe3Ec+jVCW6YnJP1GYjN0qYmhay10vzFQywuySb+wl5KSEqpWrcqSJUt4/Pgx169f5+XLl/j7+zN69GhpcQD6nfL06dOlNpgqVapQt25dateuTUpKCia1mutNZf8BSrTlbLn4jFmzZjFu3Dj27NnDuXPnKCgo4Pr165LwxZ/hbQZm3bp1iY2NJScnR3IB+Sfw8vKSFnKDBg3Sa9j+B+NvtTOEhISwceNGsrOzycjIoFq1ajg4ODBkyBCys7O5d+8eISEhREVFsXDhQuLj49HpdBQVFVUiN+Tm5rJ161YOHDggaWVmZGRQVFTEkhFd0Uh9NQLeYhm+r8ZX+PAiSjO73wnLKiguKaW0tBSVSoWNjQ1Vq1blzJkzkkFl/s0ICu78qD+LthTZbys863YTeLVtDMjk5N+MRFdWjHmjrlIAtv5oLE7l6SRcOIKFhQXTpk3j4MGDdOjQgadPn7J3716OHDlCz5499aovVmbcOP99JcaYrqwYmcoYmcoITVYK+bdOSQ4GJSn30WSlYFwtEIWJJZqsFIqexNK47cekxp5Cq9USHx9Py5YtMTIy4uLFi5LdjVKp5PDhw5WCV0Xf1c6dO+nRowcqlYrRo0czd+5cvvvuO9zd3cnIyODq1auVWhpev35daSd37do1zM3Nady4MZ07d+b8+fO8fPkSS8vKvYQajYZ169YRHh5Op06dJJWXkSNHSrY4x44dQ61WM3z4cNasWQPAqlWraN26NTExMSQnJ5OUlMTjx48l78OCggLkcjnu7u64ublJrz59+jBx4kTWrl1LVlYW48eP5/PPP6e0tJSxY8d+kEH89OlTGjRoILUNdOjQgenTp6PRaKhfvz7R0dHvyH/duHGDfv36cejQIerUqYMQgtjYWCZPnkxcXBzejVuhcKmN5h/0ZxoqFczqUo9M36/o2bOnRKZp164dubm5/PLLL5iamlJcXIyXlxdOTk4EBQVx9uxZunXrxqNHj4iPj8fZ2ZnRo0dz8VkeRb+NLZPJUZjboX1r96UwsUJbkI029zUo9b2sb+KOYdagEzIDFeK3lO2FyzGAXm5w1apVlJaWSuLcgMS2vXfvHm/evMHLywtve3vuPI1DFdANmcoQmVJFScp9zBp0ojT5LmqXWvrfq9BhUrsV1u0m8HLLCFSOXhQ/vY5Z3XbIDNQoraqgzUuj8OFlip/fpOQ342Rj32bYdZ9HVtQ6bDtOJufkSrKysvj888/Jz8/H0NCQkpISysvLJdeVU6dOAfrdT0Xpw9XVlYULFyKXy9HpdJiYmJJ7ORyzeh2lpvaCe+fJvbgHUVaCeeOPK31nQqsh58JOih5dluZ1VjuEBXN7M2nSJDp06CAJgDs6OjJy5EhA3/pw6dIlYmJimDx5MkOHDmXDhg1MmjSJo0ePkpqaihBC2lzcvn2b4uJiiXjm6+vL/fv3ycvLY+rUqZw6dQq5XM6wYcNYuHAhCsX7FawGDBggeS8uX778HZb2fxL+VuBr0qQJeXl5hIWFERSkN3c1NzfHycmJsLAwnJyccHJyolatWuzevZuuXbtiYGDAxx9/XElvs0Jaq3fv3hw7dkyi1xsZGdF56X5i0/76nPTkhIzfCcuW4+DqydWrpxkwYABarZbr16/jVjsAjYsPucgwsHbBcfBq/arx4HzkahPKXidgYK1X77Dvt5Tss5sovHuOoiexmDf+mLyLe3gTcwjvBo3R6XQMHjyYRYsWYWJiwq1btzA3N0cIwaEfTmNXJ5STp3+iPPIMFo27VUqBWbX4lKzTG3gTewSVgycmvsGUJN8BQG5oQvGTWHIv7tVr/RmZY1ErhAEjJ5DgpCYhIUFqpahRowZVq1Zl/fr19O7dm9LSUtzd3fHx8ZHO5evrS79+/Vi0aBGGhoYsWLCASZMmIYSgbdu2pKamYmtrS9OmTXn58iULFixg5MiRFBQU0LhxYxo3bszEiRNp1KiRJEtWkVo7efIkgwdX3q0eOnQIT09PUlNTady4MVu2bOGbb76RHOnDwsLo0qWLZOvTrFkzkpOTefHiBTY2NqSkpODu7i6l85YsWcK8efO4desW3t7efAgVzevz58/nxIkTHD58mLp16zJz5kwmT578jmhzQkICvXv35vHjx4wePZqMjAxUKhVyuZx69eq9E/SePHlCp06d2Lp1K76+vnz11Vds3ryZ5ORk3NzcuHTpEkFBQe+wkf8K3hYkyDWtT25uLqGhoZibm9OsWTPWrVuHqakpHh4e3L9/n5YtW1K7dm3q16/Prl270Gq1NGnSRHLicHZ2Rvvrv5wV9EarGShNK+uTUq4l+/QGNFkv9ASSuu0wq9+hkqO5Sq7/OwoLC3FzcyM//1+ZC3t7ewICAnjy5AmffPIJM2fORCaTUbduXaKjo2lXdp2npUWY+bWg8LG+F7Ek5R6Gbn56vVS1KSa+zSh+dgPKNVi3GU36fr0Rqkwmw6HfMt7EHCL79DfoSgtROVXHqvVIcn8KQ1eRQZEp0CGjrKwMNzc37t69K2UIKohSe/fuleacn5+PVqulevXqkn+lgYEBNWvWJEFnh7hzjrzYI1gG9aUsM5nsM99i32sBaqfq5ETvqkRSe5/LSNblfczboaV79+6Eh4ezatUqWrRoQb169aSAtHTpUr3ay+9SnY0aNWLMmDEEBASQn59Pr169eP78Oe3ataNmzZqYm5vToUMHtmzZwq1bt6Qac0JCAoWFhXTq1EnKCn0IGzZsoE6dOqxatYqPP/640obkPwl/K9VpZGREw4YNWbNmDcHBwdK/N2vWjDVr1hASEkJZWRmlpaXSDiQqKoqzZ8++M1ZoaCjff/893bt3Jy4uTrIuunXga8oL9UQLbX6m/kfxB1A7VUeuVPMm5giiXEtJ0h2EtozEuB+RyWSEh4cTGRlJ27Zt2bn5G5T2XhjYuSO0ZaSsH0jmD2swdK+L0JXjPiuCwgf6tK3SzAbr1iNBoZRsjZDJMa3VgmvnTqDVatm3bx+dOnWiQYMGnDp1Cp/ANoTOCyfGviNm7afiPPF73KYcwCKoLy5jd0gpMEM3P5xHbsZt2mEcB67EMmSgJJ+lsvPAvtd8XCfuxW3aYVzG7sC21Sf0auROfn4+R44cITIykuLiYm7evAnAuHHjSEtLIzc3l7p16zJ9+vRKfTszZszA0dGRvLw8bG1tpRpN06ZNcXNzIz09ncTERMrLy6lTpw7nz58nKyuL06dPs2jRIjp16lRJi1Mmk0lF8rKyMsrLy3nx4gWXL19m7ty5eHh4SGo29vb2TJo0idLSUsrKynByciIqKoq0tDQ6derEl19+yb59+7C2tiY1NZUrV64QHh5O586dadSoEePGjWPYsGEfFDevgFwuZ9myZXz22We0adOGNm3aEBMTw/nz56lbty4XLlyodHx8fDw7d+6kWbNmTJ06lcLCQkpKSli8eDG7du2qdHxqaipt2rShW7du7NixA19fX3bv3k1GRgabN28mPj5eWggODPRgbocaGBkofk8EfAcymV56bm6HGgwM9EAIwaRJk7CyspIC3PTp03n27BmBgYE8fPgQc3NzevbsSWJiIpGRkVy8eJHp06fj5+cn1bF69+5N0u3LlKfcQfRt4K8AACAASURBVJRreRN3DJnCALVLZWsnpYU9TsO/xaRmCOaNu2H9lkch6GX78l/qJewsLCx48eIF5eXlaDQaiU3apk0bbGxsmDBhAkIIHj16RJcuXbCxsaE88zkgWD2+F1UcHTBJukJJ4k3q+NVgduc6NHE3J+v0Br2KjdCRtm8OutJChE4fsJTmtli3HYPcyAybjpORG5qSc24rFkH9KE78l3C3QqlkwIAB+Pj4SI7zFaUHpVJZqY+1wtsvLy9PYs2WlpZy69YtCu5Hg0xG6YuHABQ9uoKRdyMM3fyQKQ2wDB5USfSi8MEFLIP6ojCxRGFsgUVQPwofXOT0L3cka7YjR47QvHlz7O3tK4muvw8DBw4kNjaWNm3aYGVlRUlJCY8fPwZAq9WiUCiYO3cua9asoXXr1kRERLBu3TpMTEywt7dnypQpH3S1qYCZmRmHDh0CoF+/fv+xKc+/3cDevHlz0tPTK/WnBAcHk56eTkhICGZmZtIOxMrKivDw8EpKHm+jTZs27Nixg86dO3Pz5k1WrFiBp5cXaXumk7ymF2n7P0eT/fIP5yNTGGDX8wuKn90gZX1/ss9uwu7jmdgG9cLZ2RljY2NKS0vZtm0b7UKDKE36Ffue81E5eiNXGaLNSqEs/RlWLT8FoCTxBnJjC17tmEjOua3YdZ2J67hdmNfvhHlgL/JijuitSWQycnJycHFxYc2aNfh3H8f252Y815iDwqCS7ud/CUKHi+INA3p05f79+1hYWPzhzichIUFKrwghePLkCXPmzMHe3p5mzZpVqssFBASwZ88ecnNzuXr1qrST8Pb2rrTjKSgo4MGDB0RFRbFlyxbmzp1LWFgYeXl5VKlSBWNjYwICAhg5ciTZ2fpaaEW6skWLFty+fRuNRsOzZ8+wsrKibdu2lJSU8Pr1a5o1a0ZMTAzdunWrtCu7ffs29erVA2D+/PmcPn26koLJhzBixAi+++47unfvzrVr1zh16hRLliyR+tzi4+OZPXs2qampODo68ujRI2bOnKmXlANmzZqFjY0N3bt35+TJk1y6dAl/f38yMzN58OAB/v7+2Nvb4+XlxcOHDxk2bNg7u8OBgR4cGBnIRzUdUCvlGCor3wuGSjlqpV6Q4MDIQEklZeXKldy7d49hw4ZV+o2pVCpGjBhBeXk5gwYN4vXr1xgbG9OgQQPUajUFBQVER0fj7OzMuHHjyM7OZsv2naSf2UTK+v4UJ8Rh1/OL99p2/RHkcjmqtPuAXnP1beFrjUZDaWkpkydP5tq1a3Tp0gW5XM7w4cPp1asXGRkZJCYmolAouHLhPJkvnmHz4jImCh3n1kxmdHMvnl84gCbrBVatR4FMjkO/9y9uyguyUTlWw6x+RzSZySgt7P+14wMMlEomTpzI3bt3sbW1pVmzZowZM4aZM2cCSDVt0NdoK/pV1Wo17du3x9fXV/+3ydC7vBib/3beLKmvE0CuMqxEUvuQy0hAcCiTJk2iZ8+euLu7k5uby+bNm5k3b540j/dh9erVTJgwgVOnTpGbmyspGFXMu2LH2LNnT9asWYNWq8Xa2hpLS0ssLS0ZNWoU6enpHxy/Ag0aNGD+/Pm8ePFCMvv9T8O/nUh1ZkEpQSvOv6PS8negVsr5eXIQd65d5YcffpDo8LVq1SJda0hGnYHIDf5eT0zqtrE4thuLedJFHl09g0qlwszMjNLSUgz92mDYpD8o388E0+alk7ptLK5TDvypg8TvjxXaUrIPfkHZa32q7c2bN7Rt21ZSJnkbr1+/pkaNGgwZMoSHDx9K7NKsrCw+/fRTunfvTv369aWmWNCTVl6/fk1ycjLLly+nrKwMLy8vkpOTpeBVUlKCm5tbpfqau7s7JSUlzJs3j4cPH2JtbU3z5s0RQnDjxg2cnZ2Jjo6uxOQEfcqsW7duXLx4EX9/f3x8fIiPj2fJkiW0bdtWOq5///60bduWoUOHAvp01dq1a4mLi/tgDeNt3Llzh06dOjF69Ghmz55Nbm4uvXv35qeffqJWrVrk5+d/0JNu8uTJXLlyhdu3bwP6B8XWrVvZuXMn+/fvZ/369fTs2fMvWQBlFZRy+OYLHr3Kf694cQUqXB9iY2MrmQd/CNevX2fVqlX88MMPmJmZodVqad68OR4eHkRGRpKXl4e8+WhUVRv8oY7thyBDIFJ+JfXgQiwsLKhZsyZCCOLi4mjcuDHPnz8nIyMDAwMDSkpK0Gg0qFQqrKysKCgooLy8nNLSUoQQ1KtXjx49erBixQqCg4OJjIwEoG2/kVy5dhPr9pN5sflTVHbulL16gttnJ9CVFZN/7QQmfi1I2z8Pq+aDKXx0GVFWjHlAdzIj1iFKC7DpNJ03Z7/hzs1rfPzxxzx9+pSmTZvSvn175s6di5GREVOnTuWLL75AqVTi6upKRkYGCoUCR0dHnj59CuiDvGXbcRQnxCFTGmD38SxyL4ejyUrBrqs+gOo0JaSs7Su5ub/cPBzrNqMw8tJrkhYn3iQragMTtp6hp0shXbt2pby8nJiYGKpXr07Dhg3p168f06ZNo0WLFgwYMEBKdV66dIkePXpIu7yuXbvy6NEjjh49SuvWrfH09MTT05Nz584BeplCT09PatWqha+vL9u2bftbfX46nY6WLVtK9fsKlZ//FPzbaXVKfnsP0/7QhPFDkMn0PlVONuY4tWsnCSnfuXNHCoJ5P+/AqtXwSpTltANfoK7ig2XIwErjFcXHkHVmAx4TvqOavSl3tWX0/XoKKXdjuHXkW7ByRRXY94NBD/QrQbdph//S/N8+VomO3Et7kOemsGPHDgoLC/n666+ZM2cO8fHx1KtXj/z8fK5du0ZcXBz5+fm8efMGU1NTxo8fT6NGjYiPj2fIkCF06NCBJ0+ecO7cOSmoVdTWrKysJNUTU1NTPvroI1q2bCkFOhsbmw8+5M+dO8fixYuJj4/n0qVLzJo1C5lMxoQJE94JeqAnQ4wdO5bExERevXqFQqHg9u3b77S63L59W1qxg74wv3XrVsLCwj7o2/g2/P39iYmJoWPHjvz444+kpKRQrVo1jh49yvz588nOzubq1as0bdoU0O9gTp8+za5duzh79ixarVZf90lIoGHDhnTr1o2mTZty7949bGxs/uTs/4KNqZpRIe86nL+NW7duMWLECKKiov5S0ANo2LAhmzZt4vLlywQEBPDzzz/z/Plzzp07h6mpKWq1mhqKVzws1yBT/n0hZ0MDJbpnerk4tVrNzZs3sbS0RKPR0L9/f2bMmMHUqVMl4XJLS0up7mxnZ0dkZKRU109MTGTFihXk5+cTFxfHgAEDaNKkCQM/bsflqzG83DICuYEhmky91JtePgxKXzzgzbUTCG0ZmRFrMPJujFXLT8k+uwnTOm0oz88i69Q6RFkRTZo0wd3dHY1GIxFHAgMDuXr1KomJiVItKyUlBdD3ysnlcoyNjbGysiIzM5Occ1sQOh3GPoEAGFcP4vXuaZSk3Eft5EPepcokNeOazcm7egBVFX1NPe/KfkxqhiByXvCkOJPJkyfz5ZdfMn36dMaOHcv9+/cl1SMHB4dKDvcVXo9eXl7Y2NiQnZ0ttYuAvryQnp6OTqdDLpdTpUoV2rZti7OzM69fvyY0NJSvv/6akpISmjdv/qffr1wu58CBA1SvXp0ePXpw7969D5pK/1/Ev92OD/TKLX3DYijWlP/tz6oUcHh00DvOBW9j8+bNrD4eS3mdruhQIJPLKXwQTW70bpxGb6v0kE8/+iVKC3usQociU/zrxlDK9JdNlBaikauRK/8ZBfp9kKH/8ct+Pc65TfNJS0ujZ8+e1KlTBzs7O8LDw6X6hUqlkuxLCgoK2L17N8HBwVJgy8nJwcLCgnr16r3DiHR3d5ca0QHWrVvH8+fPWbdu3V+a540bN5g9ezbnzp3D39+fzp07M3bsWMlAtSJ9+Ht89dVXJCcnExwczJAhQ7C2tsba2ppTp07h7OxMUVERtra25ObmVlKZuHPnDq1bt+bBgwfvaI2+D+fPn5dqZF5eXpw/f17qFTx9+jT379+nUaNGVKlShaNHj+Lt7c3QoUPp1asXXl5euLm54e3tzbFjx+jbt6/0kP/vxKtXrwgICOCrr76iV69ef/lzYWFhTJ48mUGDBrF582aePXvGwIED+eWXX7Czs6O0tJR+/frh23kE3159TdnfSKAo5WBFIUmP7yEvL2Vwl1ZsnzsCewtjnj17RuvWrdFoNO/U7qOioujduzcFBQUolUoCAwNZu3YtpaWl3L17l5s3bxIXF0d8fDxCCFQqFYatJ2DoHYBMLqfg/s/kXzuBJisFmcoItaM35k36oHb0IufnyuxJqxbDkCkN8JO94PqOBQwdOpTVq1dTXq4vQ1QE7MDAQEJCQvj666+RyWSUlpaSnZ2NSqViwYIFbNq0Sa/ckpODuaUVxVXqITTFUhtKwd2fyL20V2J15t8+IzWwC23Zu/NqPoiXm0dgaaSU+nyLiorw8PBg/vz5Ugbjl19+YciQIWRkZDBo0CDWrl1LnTp1SEhIwMrKCgcHB16+fMm+ffto3bq11FublJRE1apVuXnzJnl5ecyaNYsffviBzMxMdDodixcvrrRg/DOcOXOGrl278tlnn7Fo0aJ3XHDMDZX4OprTq8H7/f/+t+LfMvDBu1qdfwUGckHBxd3smDOM9u3bf/A4jUaDh4cH6/ee4HSyjp8fZ6AtLSR54zDse3yhZ52Va9FpinnxzWCqDP6K9CNLpBs+99L3aDKTkClVFD2JxarVcAzd65AVsZay9Geoq/igtHFBlBZi23n6O5ZHr7+fhaFrLUqS7lCW8Ry1ky+2XaZjYm5FSfYrkr79FL+Q9iTE/Yy1tbXULwj6RnO1Wo1KpSIvLw9jY2OJaGBiYoKRkRFLly6lVq1akllpdHR0Jabnh7Bt2zZiYmLYtm3bHx536dIlli5dyv3795k+fTqXL18mIiKCFy9esHfvXm7evMl3331YWm706NH4+fkxfvx4/Pz8SE9Px9/fnydPnnDq1CkKCgoYM2aMRN55G5MmTaK4uJitW7d+cPw7d+4wc+ZM4uPjWbp0Kd27d2fq1KlcuHCByMhIlixZItkePXnyBJ1Ox4wZM5g3bx4KhYJZs2axc+dO8vPzGTx4MNOmTaNnz560atWK1atX/yOT1/ehuLiY0NBQOnXq9I+p5Tqdjj179jBr1iw6dOjAvHnziIqKYsWKFZSVlZGfn0/o8HnEm/pRphV/LBL9mx4lohxkb6WTtWUolAaUJt5gSf8QFk8ZzuvXrxk0aBAzZszA1dWVb775huXLlzNkyBD69OnD5s2bOXbsGIMHD2bq1KmS4bH+NIKnT58ydepUrjx8gVm3eX+7YRz0i8Psg/OwpkDSC+3YsaNUk168eDGDBg2iV69e3L17l2rVqnHx4kVSU1MxN9fX8U6cOMGIESPw8fGhf//+7Eh1IFOrfkei7K9NSIeXYQFre9Ri2rRpXLp0CYVCgY2NDRkZGdy9e/cP6/O+vr58//33NGjQgLFjx1KrVi3GjRsHgJ+fH/v375fcUd6HAwcOMH78eLZt20bXrn8sV/c2Ro0axd5Tl+gyeyM3X+l1XEt/53IjgNDqdoxt7k0d1w9vKv634H/cneGv4p+w4+Z39uPIiskMGzbsDx+8BgYGjBkzhtPhWwgb0pjYOa2Z3bUBHvWCKbx2BJGRiAwoengZAxsXVA6e+nTLiZXSGEVPYjGuHoTrlAOY1Awl8+Qq1E4+VBm6jpKkXym4+9MfzrnwQTQ2HSfjOmEPupICSi6EkXclnKQtYwCIj/mJ0tJSXr9+Tb169Rg0aBADBgygpKSE4uJiybMuJyeH9PR0CgoK6NKlC2ZmZlKKZcuWLTg5Of2loAf6NOTb6ZW3IYTgzJkzhISEMHToUHr06EFCQgKTJk3C3t5eYtK9z+vv90hISKBatWo8f/6ctLQ0bt26RXFxMQ4ODrRo0YIDBw58UD1m4cKF/PDDD1y7du2d95KSkhgyZAht27alQ4cOPHz4kL59+6JSqVi7di1NmzbF19eXnTt3kp6ezvLly8nNzZXYn4GBgYwePZpNmzZJrQ1fffUV1apV48KFC8TGxvLpp59W8uP7pxBCMGzYMLy9vfn888//0RgV7SAbNmzg+PHjbN++HQ8PD8aMGcPTp09Zv3493t7eXNr5JS93z0CTeB3KNVK2QppLuRbEW0FR9rsaqlJFOTKUHvVZfq2EN/Z1uH37NnZ2dtSvXx8bGxuOHz/O5cuXWb16NY0aNWL79u3cvXsXAwMD6tWrx+DBg7l37x4A165do2PHjlhYWPD02nmaGr0Gbdnf+ttlOg2NDVIwyH9FQUEBpqamXLx4kSNHjnDmzBkOHjxIWFgYVatW5dSpU/j5+XH8+HGaNWtWaafatWtXLl26xPXr1wmPS+YNRv8o6CUt7wT5r7F4GUtISAgWFhYsX74cDw8PFAoFpaWl+Pr6Mnny5HfMnkH/m8jLy5MIXVZWVhJRDP6aO0OfPn04deoU48aNY8WKFfzVPU2TQZ9h13cJV5MK3uuCU6wp5+WJNWwbEUpgYIDk3PFfgUwme+91+J/Cv13gCw8Pp2HDhpiamjKjWxNsLq3GX/HqL7PjmjZtyoULF5g/fz7Lly9HCMGCBQsYOLBy7W7UqFEcPnyYrKwsqQ4zc2g3Cp/dRGbhAAolhffOY+r3AbUFmZzMk6tIWdObF1/3pyz1Mab12iP7TbzW+E8EeU1qt8bA2hmZgSEqOzfyHl6h6MZJJo7T168SEhLQaDT06dMHf39/du3axZ49ezAyMiIkJITbt2/z7bffSjd3hXrJpEmTSEpKonPnzoSFhfHo0SNGjRrF5cuX//SH8D5PPp1Ox/Hjx2ncuDFTp05l1KhRPH78mBEjRqBWq8nPz+fAgQP069ePGTNm8Pz58z9VqKiwIzp06BDdu3fH2dmZ8+fP4+fnh5mZWaW/6/ewtLRk2bJljB8/XrJQys7OZvr06dSvXx83Nzfi4+OZMGECKpWKR48eMWvWLDw8PLhz5w5DhgxBCEHv3r1p27YtCoWC2rVrc+HCBSwsLKTFwtWrV2nevLlkM2NlZcXZs2d59eqV1DP5X8HChQtJSkpi+/btfzt9mpWVxejRo+nQoQOffPLJO44ZoKftnzlzBg8PD8zNzVHkviDj2JdkbB9N9bIndKhpSytfe+q4WOhrOzLZn89DJqdEq8M0eDAn7mdz44ZeQq937948e/aMKVOmcOnSJem7c3Z2ZtWqVTx9+pQaNWrQqlUrfHx8aNeuHUuWLGHnzp2sWrWK81sW4q97ikynqSRY8T4InQ6hKSX3/A6OrZxGbm4uOTk5ODg4sHXrVsLDw0lKSiIwMJD9+/fz6tUr5HI5BQUFPHz4kM6dO9OrV69KD10XFxeEoTm3r8VQVi4oSbrDi41D/tZ3AtDYJJf1C6aTlJREmzZt+P7778nPz8fHxwcDAwNsbW3ZvXs3TZo0oVmzZpI7Cugl/Dp27ChlEyrMaCvwvsB3+fJlmjZtioWFBdbW1lJLzdSpU1m8eDFDhw790/t0b8xzVpyNB6X6g272pS/uU/L8Fs5jv8Nh8JpKtlX/W/FvFfjWrFnD5MmTmTNnDmlpaSQnJzNz6iR8Sh5zdWZLprTxoVtdZ1r52tOtrjNT2vhwdWZLNg9sWKmm5+vry9WrVwkPD2fixInv9Zizs7Pj448/JiwsDCEEOp2OkJAQ5EbmFCZcQ5PzitJX8XoJo/dAJpdLnn4O/ZYiN7bQG9f+hj/TbFSYWv1rLJUJWq2Wrl270rlzZwCqVKmCQqGgV69eHDhwAFNTUywtLSkuLpZaEzZu3MiAAQOkYJWQkIC3tzcWFhb079+fkpISLly4QNWqVRk1ahSenp7MmzdP6g36Pd4OfFqtlvDwcPz9/Vm8eDGzZ8/m7t27DBgwoFIRfMeOHbRs2ZIVK1Zw5MgRPvrooz8sklc4sru5uXHw4EGprqVWq9m+fTvjx4+nvLyc48ePf7DWOHjwYBQKBZs3b2blypVUr16dgoIC7t27x+LFi9HpdGzZsoUmTZrQsmVLhBD8+OOPxMTESP6ES5culcx4U1NTCQoK4sKFC5KhbaNGjXBxcWHPnj3SeU1MTDh58iRKpVJKqf0T7N+/n127dnH8+PG/xcQrLy9n06ZN1KhRAwMDAx4+fMjw4cORy+WEhobqdUdLSykuLuabb77hzJkzUt03NzeXmzdv0qFlMOe+mcXOkaGk/LCek+NDSFzdk+Sv9K/0Qwv/dB5CYcA3l19Su3lHHjx4wPbt23n27BndunXjk08+oVmzZkREREgB0MLCgiZNmmBubi4ZNQ8dOhQjIyNWrFhB3bp1qWeaz8Sa5QR5mP3hIrdtTQc8E09QwyATtVqNg4MDDRs2JC0tjcjISAYPHoyXlxcGBgZ4eHigVquZMWMG9erVo23btlJ/ZkVzO+jJRaZV6/6z9OZbKHtyhTp16tCyZUvS0tIICwvj5MmTVK9eHSMjI9LT0ykpKcHc3Jxx48ZJC5O+ffvy3Xff0a5dO2ksKyurPwx8iakZtG3XAYuGnem27gz9v46kQbcRFJfLsLa2xt/fn8LCQlq1avXBFocPueD8Htq8DJQWDshV+vNXuODceZH7p9fkvyM78v8JxL8JcnNzhYmJiTh48OB73y8vLxfLli0Tnp6ewtraWvTq1UtkZWUJIYRITEwUgNi1a5dwdXUVNjY2YsmSJSInJ0fUrl1byGQyoVQqhYmJifD39xdCCNG8eXPxySefCJVKJQwNDcWTJ0/EomUrhdzIXCCTC5naRBjYugv3WRHCfVaEkBtbCrmRuXCfFSEsgvoJmYFa2PddItxnRQjnMTsEMrlwnXZYOI/eLgBhXDNEmNQKFc7jvhOG7nUEIJSWjsK63XihdvUT1u0nCIugfsK4elOhtKoiAGFqaioAAYjAwECxaNEioVQqhaWlpZDJZGLMmDHCyMhI9O/fX3z77beiTZs2ws7OTsjlcmFraytMTU3Fw4cPhRBCHD16VLRo0UK6fjqdTty8eVNMmTJFODo6ikaNGon169eL9PR06Zi4uDhRv359ERYWJry8vESzZs1EVFSU0Ol07/1ONBqNcHd3FzExMUKn0wlbW1vRvn37P/ye7927J6pXry6ePXsm7OzshEajeWdMtVotLC0tRZUqVcTUqVNFeXl5pWO0Wq2YP3++kMvlomPHjuLhw4dCq9WKs2fPin79+gkLCwvRs2dPERkZ+c74iYmJwsXFRTx79kzUqFFDtG7dWlhYWAhjY2Nx4cIF6bgbN26IRo0aCYVCIc6ePfvO+YcPHy4CAgKke/CvIjY2VtjZ2Ylff/31b33u0qVLom7duiI4OFjcvn37nb9JLpcLS0tL0b9/f+Hg4CC6du0qYmNj3zvWixcvxPDhw4VlyEChMLWR7vG/8/KYFSFG7bkmXY8KZGVliYULFwoXFxdhbW0tfHx8hJGRkZDJZKJ69epi0qRJIiwsTAwcOFAoFArh5eUldu3aJUpLS6UxMvNLxOboBDF5/y3xya44MXn/LbE5OkFk5peIsrIyMXXqVGFgYCDUarVITk4WGo1GtGnTRrpXbty4IRwdHYVSqRQ1a9YU5ubmQiaTCUCo1WoBCDc3N7Ft2zaRmZkpFq9aJyyD+gqTWqHCdephIVOqBMiEzMBQyAwMhfO474Tj4K+Eyqm6kKlNhMLESpjV7yjcZhyTrgcgVq37Rnh7ewsTExNRp04dUbVqVeHm5ibGjx8vIiMjRWhoqLC3txeAMDQ0FD/88IPIysoSa9asEXK5XNjb24tp06aJO3fuiKNHj4ouXbpI18TExETk5+eL28k5YsTua8Ltk3VCrjap9J1U//yUcB+9WSgMVEIulwsTExOhVquFh4eH2LBhg6hbt64wMzMTLi4uYv78+WLE7mvCY3aE9Myy6ThFKMzthNzIXFiGDBLusyKEdfuJAoWB/ploYCgsgvoJ91kRwqb9eGFu7yKsrKxE586dxcuXL6W5AmLDhg3C29tbeHh4CCGEWLlypXB0dBRVqlQR27frz/fkyZO/9Rv478S/TeCLiooSCoXinQdVBdatWycCAgJESkqKKCkpESNHjhR9+/YVQvwr8A0fPlwUFRWJ27dvC5VKJR48eCBKSkpEzZo1hb29vcjJyZHGa968uXB1dRX16tUT+/fvF2VlZWLk4m+FXe9FAplCHwAVSuE4dN2fBj73WRFC5VRdmAd0F04jt+iDl8pImNQKFWqXWsKkZqgAhOOQtUJuZC4M7DykwIdcIeq36iqsrKyEp6enaN++vQCEv7+/UCqV+oCpVAp7e3uhUCgEIFq2bCnu3bsnzG0dxdT1+0X7BfuEQ+epQmFqLXqMnSMy80tEz549RVhY2HuvpUajEadPnxYDBgwQFhYWomPHjmL37t3is88+E0qlUrRp00ZER0f/6Xe2b98+ERwcLIQQ4vbt28LV1VXY2tqK+/fvf/Azx44dE506dRIrVqwQo0aNeuf9+/fvCy8vL/Ho0SPh6ekpnJ2dRe/evUVJSYnQ6XQiIiJC+Pn5iaCgINGtWzfRv39/MXfuXOHq6ioaNGggvvnmG5GZmfnB8//4448iNDRUJCYmitDQUGFkZCQMDAzE0aNH3zm2vLxcNG3aVJiZmYkJEyZUun90Op2YMWOG8PPzE6mpqX96rYQQIjk5WTg5OYmTJ0/+peOFECI1NVUMHDhQODs7i/Dw8PcuQmbOnClcXFyEoaGhcHJyEnfv3hVCCDFkyBAxd+5cIYQQP//8s3B2dhbLly8XDg4OIji0pbDvvUgozN4f+JyGbxJqV7/fFoBuwq7HPOk9E79WwrRee2Hs1UCoVGrRt29f0bJlS2FkZCQAoVAohK+vrxgwYIAwNTUVCoVC2NraCktLS9G2bVsRHh4u7OzsRGxsrPjxxx9Fq1athKurq1izfkfEAgAAIABJREFUZo3Iz8//4LV48eKFaNasmQgMDBQ2NjaidevW4qOPPhJFRUUiKytLeHl5iY0bN0oP+KioKOmzxcXF4urVq2LBggXSHCsWmeYBPYR5k97CpFaocJ8VIRz6ffnOdXEcuk44Dlot3D47IZxHbxdKGxdh1WpEpcBXu0kLkZOTI5KSkoStra04deqUuHfvnli6dKkICAgQZmZmQqVSiYULFwpbW1spAI8cOVK0aNFCPHz4UMyZM0e4uroKb29v4enpKdLS0oQQQigUCrHr8lPhOy9KeMyOEK5TDgq5oZkw8Wsp7HstEC6T90tzsek4WRi51hR7fkkUQgjx/fffC3Nzc/HNN9+I8vJy8euvvwo7O3vh1Otz/cL9t8BnWqetcJ12RFQZtl6gUAqn4Zv043WYLNQuNaXx7fsuFXIjc+H26XrxMjNPjB8/XnoOCKEPfK1btxZZWVmiqKhIREVFCXt7e3H37l1RUFAg+vXr9/974Pu3SXVmZWVha2v7wTTZ5s2bWbp0KS4uLqjVahYsWMDhw4crbaXnz5+PkZERderUoU6dOvz666+o1Wp69uyJlZUVISEhkpkowNChQ5kzZw4bN24kr1THC/NaGHvW10s76coxqlqf0pT7H5xzxpElklOEXGVE6ctHpO6cCOh93nTl5ZS+fIh5YE8AVA6emNZpK0mygV5y7UHsBQwMDJg9e7akTnLw4EGmTJkiiSxnZGRIqY7bD57QZuFBLId+y5EUNQ9KzDCs1RLzgB5E/fgTTZb/xBVZTXybtuV9UCqVfPTRR+zdu5cHDx5gYmIiCUYrFApmz579p87RQghWrVrF9Ol62nd4eDgDBgzgs88+e4ehmFlQyubop0w+cIu1N0vIrdGVXbEvade15zvjVii2VK9enevXr1O9enWio6Np3LgxwcHBzJgxg88//5yhQ4eSmprKvn37eP78OZGRkVy/fp3x48f/YZ/dkydPKCkpoWHDhgQGBmJjY0NAQABffvklr1+/rnSsXC5n8eLFuLu7U1paSs2aNdmzZ4/e5VsmY8WKFfTv359mzZpV6sl6HwoKCujcuTNTpkyR0tl/hLKyMlavXk3t2rVxdnbm0aNH9OvXr1Id7uXLl0ydOpVVq1bh7u7O/v37SU9Px87O7r1jvn79mqysLEaOHMlTWRWUBu//rVVYfRlVrY/rxL1YtR5F5g+r0WS9kI4pfBCNaaPuuH/0Cfb29jx9+pQ+ffrw5s0bCgoK6NOnD2fPnqVz5844OjpSu3ZtVCoVubm5DB48mIMHD9K4cWNat27NuXPnOHbsGL/88gtVq1Zl3rx5ZGRkVJrT2bNnadiwIU2bNiUlJYWtW7dy4sQJLCwsaN++PWVlZXz99ddMnDiRgoIC2rdvT926dXn58iUpKSmkpaXh4OAgudQ7Oztz+PBhGjRogMre40+FJdSO3npHF7mikofm2/D9aBCWlpa4ubnRokUL+vfvz6tXr5gzZw4xMTE8fvyYevXqER4eTmFhIXK5nPT0dLZv386VK1dYsWIFHTp0IDExkc8++4zs7Gx8fHzo1KkTxv4fsezMY4o15QihN/rVyxzKyDr9DS++7k/64UWUF+botf0FUi2uf//+nDlzhmXLlrF27Vpq166NX0gHipLuVZq/RVB/5AZqVA6eqOyrUpb+/nu68MEFTP1bY1TFmx/uZ7Bs2TJ++eWXSoIQs2fPxtraGiMjIw4ePMiwYcPw8/PDxMSEBQsW/OG1/p/Av03Hoo2NDZmZmWi12vcGv6SkJLp161aJSq5QKCSDWdAroFfA2NhYqsHIZDIaNmxI7dq1CQoKIipKbwrr6uqKZ8MWJLk+osmyc7xJuE7u5X1os18igOJnN1HZVwXArutnZEZ8BejtZgru/lTZkPI3VLQu6EqLkauNkBuaorL3wH2W3pZFaWGPyt4DszofkXNxL9bW1tjbGJCcnMyUKVNwcnLCwcGBoKAg3Nzc6NGjB+vWrePXX39l165dfL1hE8rgYai8GqHJTn2vr19ZuUBVtSHDvr/DpJAcxrX1f4e4kJ2dzfr169m4cSNt2rTh+vXrKBQKGjRowJQpU8jKymLAgAEMGjTovaoO0dHRFBQU0KlTJ3Q6Hfv27SMyMhIvLy/WrVvHtWvXUDlWY+OFBKLj9Q8xPVvMDBRmCE9rZl7VEJV1vRJF+tatWxKj08rKig0bNkiGsxYWFoSGhjJq1ChatmzJnDlzJIPWv6I88fjxY5YuXYpSqSQiIoKhQ4cybdo0Jk2axJIlSwgMDCQyMrLSWKGhoeTm5jJhwgQ+/fRTxowZw7Zt29i4cSN+fn7Mnj0bS0tLQkJCOH369Hvp5jqdjoEDB9KgQQOmTZv2p/P88ccfmThxIh4eHly5coXq1atXev/p06esXLmSQ4cO0aZNG+RyOcePH8fW1hYvLy/Cw8OZMmXKO+PK5XJ+/fVXioqKaDtoMVEXfpGsvipg0248ClOrf1l9yeQYedTByKsRhQ+isQzWBw5j7wCMPOpQ/LSIjRuXo9PpJFuypKQkioqKcHd3JyIiAjMzM9LS0lCpVMTFxQHQpUsXzM3Nkclk6HQ6ysvLpf+uXLmSJUuWYGBggEKhQKPRSP15K1fqmdW9evVCoVAgk8koLy/HyckJ2W8EnYSEBLKysqhbty5yuRyFQoFcLpf+H/SN7IMHD8bNzQ2Z2hihK6foSSxpB+djEdBDuh4vt4zAMnggKkfvP/TQBChTGkuLImNj43cIWlWqVGHHjh20aNGCxMREoqOjmTx5stSu9OOPP/LTTz9hZGREr169UKv1ovSrdhzkTrIFJb+rxRnYumLbaQqZEWuRKQ0oe51A9rkwjKrqxSAqanH+LpbIZDLc3NyYOXMms2fPRocMQ5+gSuNV8A6K4mPQZKaQGbmO7B+3IDc2l5Suci99T/GzG1g26U2JVsejV/mYmuqb7l++fImHhwegf7aC3r8yPDycr776SjqPu7v7O/fm/zT+bXZ8TZo0Qa1Wc/z48fe+7+rqSlRUFLm5udKrpKTkLyldVDz0Z86cyaJFi2jRogVv3rwhNjGLATuuIXOrS1mZhoyjyzBv3A2XCXtxm3IAI6+Gf9z39BZKX8WjyXmF+M23ufhJDCbVg9CVFKArLZKO077JQFGhki/KMdG+4ejRo0RGRlJQUMDTp08lkkpubi5hYWEcPnwYf39//LqO1Av4KlSA7I99/eRyNDoZq358iktoXzp27Mi8efPYsWMHo0ePplq1aqSkpEgkoAqNTq1Wy61bt+jUqRM//PADH330EX5+fqjV6kq75dWrVzNt2jTkcjlXr17F3Nyc2rVrY2xszLx58xi3dh99w2L48WHaeynSMqWaUq2Osw/S6BsWI7HEKnZ8aWlpkhGnm5sblpaWvHnzhnPnznHixAmOHj1Kly5dGDFiBFqtlt27d3/wu9FqtaxYsYJmzZphY2PDwoULmTRpEt26dWPy5MnIZDLmzZvHkiVLaNGihSQLpb+McgYMGMCePXto3LgxcXFx9OnTRxKQzs/PZ8yYMZKtUoWf39uYM2cOOTk5bNq06Q+Zk8+fP6dHjx6MGjWKFStWcOrUqUpB7969ewwcOJCAgADs7e25d+8eBgYGNG3alPj4eCIiIqhZsyarV6/miy++IDY2luPHj9OuXTuGDh2KRqPh4sWL/PLLL5z9WW+zpTCzxm3KAellUiOY8oLsd6y+9DqUb9kamet3lfUDgpg5cyZVqlTB0NCQ27dvY2JiwsKFC9m9eze2traSl9zLly8xNDREpVLRtGlTfY9haCiHDh3i1q1b3Lt3j/j4eF69esXjx4/55JNPJD3KU6dO0bt3b/r37y8FyApB/KdPn2JmZoaTkxNVq1alV69e1KxZk+TkZFJTU0lJSSEpKYnExEQSEhKoWrUqDRo0QKfTsXr1auwtzdBkJoNMTlnaUylgaQuy0ea8Qu3q95c8NK9e/Fn6Hfz0008UFxdz+PBhjh49yq1bt8jJyaFmzZoEBwezb98+evfuzcGDBzE0NKR9+/bY2NiQmprK69evOXjwIGlpaQwcOJBrhdaSbdqHIDe2wKR2KzSZSZVIOiXacr69kED//v1p1aoVe/bsoVq1ahhWbQDvebppclLJjFyD0tIRqzajcB69DUO3OuhlNfSQKVVo3+gJM29KNBQWFvL/yHvPsKjO9t37NzMMA8zQqxRFrAh2Yy8oYo9ijcYaW+wFjBVDEkvErtiiYidqVOwNO0iiYkOjYgVFIPTepq33w8hSgiZ5nv/e+32evc/jmA+w1qwyq1z3fV3ndZ5ZWVkV3sUf3ucymUxUzAGDju//3/iPmfFZWlryww8/MHnyZIyMjOjSpQtyuZyLFy9y5cqVT3rI/ZNGTUdHRy5cuCBaCTk4OND7ixG8jUvGrEF9QIKg0yDoNAZfPKmMkpe3KXl+A5nKBoCME8vRF+ehV5e+Zze9vE1ezAGchi5DV5hDRsRSdMX5ANh0mYhpjWYoXOqSe2031p3GoMlOpjDuAhYt+vF6hT9WTlVJykymVatWYnqqa9euJCYm8vjxY9RqNWq1milTprBq1yGepBSAXkvmsWUIeh2CTougFxCQVPL1K4dErkDVfiTNHdM5tXsjcXFxGBkZoVAoSEoySKE1adKEJk2a4OHhgVQqRa1W4+joSOPGjdm9ezdXr15l7969eHt706JFCzp16kRsbCyHDxuk1f7cu2da34/0l47wD5R3BMHQJ7TkzBMEwdCbdu7cOQYNGoSVlWGk6u3tzapVq8R0Ya9evTh27Bi+vr5IpVI2btxI79698ff3r+QPGBcXx+jRo7GxsRHFlLds2YK3t3clx4dhw4bh5ubGoEGDWLZsGV999RUAw4cPp2vXrixduhSZTMakSZMYMGAAc+bMwdPTk1WrVjF48GAsLCz4/PPP2b9/v9jSsWvXLg4fPsyNGzcqqNC8P3+B7Oxsli5dyvbt2+nfvz9Lly4lIyODlStXkp2dzdOnT7l58yaZmZnY2tpiZmbG2rVr+fHHH0XGso+PD1KpFEEQUKvVZGZmYmFhgZOTE25ubly/fh0rKytevHiBlZUVgYcfcOD4uY9ek49ZfWnzMyqwlstfrh6uTnRu0pk1a9YgkUg4dOgQaWlp7N27l1WrVmFiYsKUKVOIiIhg5syZYkM2QHp6OuvWraN379507dqVOXPm0KBBAwBRYjAgIAAbGxu++OILpFJpJfeBhIQEfH19WbhwIfv37ychIYHNmzczZswYpk2bxpYtWyqd3xdffEFERARqtZrjx49jXmBMyas7mHo0QV9ahL60AH1JAaWv7mJkVYXShLuUvn1C6dvHFD39FVV9X4oeX0NmZomuOI+s0wb2cWHMzzSs782mTZsIDAwkMzOTR48eVUhtmpmZ4ejoyIkTJwgKCqJdu3Z06tSJ8+fP07p1a9zd3UWnFIAL0TewqzWS/NsRFMQeB4kEy3ZDyT4bikWLAUhNVBQ9vvruZtIjM7c1+Hjm/MEf4XNQpyeyQ2WDJiuJVatWoVAoyM/PR5BIkZlZ8mb1QIys3mfK1GmvDAxOhRKJRIJUYYaiSi00GQmUvLpD3m+HQNCTfzOC4mc3MFt/FH9/f+RyOfXr16+QZi8qKqJ79+6UlZWxfPly1q9fz8OHD8VUZ6dOnUTm6ZYtW7Cxsfno/fi/A/8xMz6AwMBAVq9ezeLFi7G3t8fNzY0NGzbg7+/PunXruH79Or6+vpibm9OyZUtu3rxJ48aNqV69+l9ut5wyb2trS5MmTaji1RIjK0d0wvtRiVRhhk3n8WQcCyFp7WCKHl+rvCEBCm6f+Og+zGq1wHXyLlzGGdy6VQ0MTuJ2vWejzUvn7YYRZEQswbLtEJQuNUGvp3XDuiiVSn788UdRLeXGjRu8fPmSPn36MGbMGKytrUlLS+NG5AnyH0chMVJg3flrqgYcwq5XIOr0BN6GDiXrbCjKuu0+emxlah1boxPp1KkTb968obi4mAcPHjBt2jRMTU0JDw/Hx8cHGxsb9Ho9AQEBPHz4kPz8fCQSCb6+vuzatYvk5GRGjhxJaGgo6enpWFlZcfToUQ4dOsTgwYPZvn07zVq1Zdn553+pXfoxlGj0BB+Po9DYhi1bttCyZUvWr19Pamoq69ato1GjRrRt25b79+/j6OhIr169xFneZ599xueff05wcDBgqCluuPSUjvN303P5KZz7z6N/0GbMbBxFQe2ffvrpo7OvDh06cO3aNRYvXkxQUBCCIODl5YWDg0MFqyIHBwdRtHrp0qX4+fnh6OhIaGgoAwYMIDg4mO+//56pU6fSq1cvli1bxujRo/H396d9+/Z4e3vj7OyMQqHA3t6eTZs2UaVKFV6+fMnBgweJiYnhzp07HD58mKtXr9K1a1ciIiI4deoUUVFRJCcns2fPHqysrEhMTOTNmzckJiby+vVr2rVrh6mpKbVq1eLVq1dERUWxefNmlEoltra2yGQy6jpZIJd9/PH/mNVXyYtbKOu1r7iiVs2hbWvFEkSvXr3o1q0bo0ePZuXKlcTExNCkSRNWrlxJVlYWOTk5PHjwQLTFcXBwYMmSJbx69YqGDRvSrVs3evbsybhx4xgyZAg7d+4kJCSE2rVrY2FhwcKFC5kxYwYtWrQgIiKCp0+f4uPjQ0BAgHhvDxw4kM6dDYE4KiqKn376qcIhZxaWUaXjMMxqNkePlO1hO3kecxYz90aY1WyBwrUeuvxMzDzbk3V+I9r8DJBIse0xA5mFI7rCHPJi9mPiakiH5986iszckMG5dDWa5cuX06hRI6pWrYpCoaCsrIz4+HgyMjKoWbMm3377Lfv376datWro9Xru3r1LVlYWgiBw7do1kpOT8fLyErkLNTp/SWniffJjj+EweDHOX2+l7F1tUZ2RQMGdEyAISGRyVA274jxmEwrXeghlRahTn4NEirXvGCTS93ZMUqkUqUSC3L4abjMOYPSBw4SxU000WW/R5qSiyXqLXv3e8d3UoymWrQairNcem66TkAh6dkzqQm5uLpGRkeTn57Nz504AHj16hFKp5OzZszg7O7N06VIsLCxo166dOFgLDw8nJSUFa2vrCgOi/xP4j5Us+zPKe3KmTJnC1KlTAXj48KHoVp2QkCDml/8O4/feJvJRSmWFij/h9bJeOH+9Fbm1M283jca8cXfybx7BZcJ2pCYqCuLOU/T7FZyGLqP07RNyLm5Fl5OMkY0Ltr5jkb97OP4In4vCuTZlSY/RZiTgUrUab14+Q6FQiDXLciPWzZs3ExISQlpaGra2tuTk5FC3YTOe6+0peRlLldGhFd3mP0D2hZ8ofvYb+rIi5NbOWHceh4mboeaUG7WXxiZZuFQxjDZXr15Njx49mDBhAtevX8fGxoaJEycSEhLCqFGjOHXqFG/evEEqlVK3bl1iY2PZsWMH1atXp3PnzqK1i16vRyKRMGHCBOzt7dn2yynyMKU06RGCVo2xfXVsuk7C2P7v8/qCXg9v47i5amyFeu2fUVhYiL+/P9HR0cyZM4fvv/+erKwsvNv3xGfSUu6klqJRqysEXxMjKWqNhqLntzgZMpXmNR0/um2tVktubi4vXrxg7Nix2NnZMXLkSE6ePMmrV6/o0KED2dnZH/0IgoCFhQWOjo4kJCSIZr8NGzYU9UjLP3l5eaxatYrU1FQ2bNiAn5+f4TcQBM6cOcOSJUvIzMxk7ty5DBs27KOzxW7duuHl5VWhfgIGYtTkyZPRaDRUrVqVGzducOvWLYYNG8bbtwaCSmZhGY0nriX1+ApcJ1dWOVJnvCY7cjPq9FcYqWyxaj8cszoGQe/MU2uQWdhh134oeXun0bVDa16+fMmdO3eQSCQYGxszYMAA8bwsLS15/vw5SUlJ6HQ67OzsWLRoEQMHDsTa+n0/69u3b+natSuvXr2iQYMGfPvttzg7O9OlSxfOnTsnpiePHz9OcHAwT548YciQIWzatIl27doxY8YMRowYwfz58zl16hTbtm2jT58+HDlyBPNq3pXqzbnR4ajTE3AdtJDX2yZj5z8Pbe4fFNw7i0P/IFLCpmDRvC+q+hUFGdKPLMakan0sPutDbtQ+1OmvaOk/mkvLx1cYTLm7u7N48WJRPGP27Nnk5+ezZcsWQkNDmTlzJiqVioyMDGrVqkVAQACrVq3iiy++ID4+nsjISOx7zyIn/iYypRXWPqMAQzoy5afx4rup/HpYtx8OGMx+M44tw3XKHvFdkXsoiJY1DQOzJUuWoBUkxFYdRJlWT8nLWLIvheEy3jA7LkuOJz/2GKWvH6BXl6D0bI9NlwlIjU3JjQ5Hm5uK3eezkEpg16jmtK9dkUzl7+9Px44dmT59OlevXq1w3wF4enqyYcMGMSuSmppK1apVKSkp+T8mlP0fk+r8Jxg+fDh79uwRA9/u3bsZMWKEKPl0+vRpgoKCePnyJZaWlowZM0acVicmJlK9enXWbNjMzvnfIrNwQKowxaR6UyyavWfZpYRNwartl+JDXg5BU0ZhXCR6dQlvN32FxWf+yCwMTeplaa9I2/cNlo27oaCIrIwEUn+ez9DVxzC2cuScmZzU2BNI0FOlShX0ZcXI5XI2bNjA2LFjmTx5MgsWLADg1KlT3Lt3j/z8fJo2bcqePXu4lCLl4dI5mNVu/cmgB2BcpTaWbYcgVSgpiD1uuPkn7kBiZIwgCFy9cokhgweTk5ODWq0W63cpKSnEx8fj5+eHUqlk5MiRmJmZ8eLFC9avX8/Zs2cZNmwY586d4+LFi+h0OgoLC2nYsKHogXb37l2eJ7yhSGGDyrstUhMLil/coiwlnj92z8Thix8wcfNGW5BFyk/jcJm8W/Q2U//xkrSDC3GdvJu3ESE8n9ZdDHzp6em4u7vz+vVrMY2iUqmIjIwkMDCQZcuW8fz5c7pPXYqqTxC/JRUbFCj+NOMs1epBIsOkZguGhN2kbvFjzNPuVwpehYWFWFlZYW1tjZWVlejX5+fnR3x8PEOHDqVZs2aVApmVlRUZGRl88803REVFYWNjQ1lZGV26dGH69OnicRQUFLB48WLCwsKYN2+eqDCj0+k4cuQIS5cuRRAE5s+fz4ABA/7SguncuU+kKt99Z/ny5YwbNw6JRIKPj0+Fl4+dSkH3Lr5ccPX6aB3b2L4aTkM/7tVm12sm6PWY5rwi6uZ1sbZTPvI/c+YMZ8+excnJCQ8PD3788UfatGmDXC5HrVZz9uxZ9u3bxzfffEPnzp0ZPnw4lpaWjBw5kiFDhvDdd99x/PhxZs+ezfPnz5kwYQINGzYEDDXXmjVrkpmZSWBgIHFxcTg7O1OlShX8/f2RSCQsXboUY2NjRo8ezZo1axgctBFlu+GodUIFxxeFmzcFd09TUpiPrjgfuY0LMqU1mafWoCspQJP5BoWbFyUvb5Mb8470JggImjJxIGfRoh8FMT9zPXQmyo0zmThxYoWByJ8JdykpKQDUr18fiURC7dq1RXPf9PR0/P392bBhA3K53KC6IjdFV5iNokotcTsfegR+DB+r0Vo6VcPBwYJatQzbcXdzIcu0lMd5RkiMFAia9zM7hUtd7F3mAgbuQuaxEPJ+PSgG3nLoBfh63x16Wf9B9EGDIbNer6e4uPgvnd3/iqj4T91J/qf4j0p1/h1atmxJfn4+T548QafTceDAgQpSZEqlUlSpOH36NJs3b65Eljlw4jzuE37C4YsfUHr7UvToirhMnfYKXUEWpjU/q7xziQSLlv1xGrUGQRAouHsadZrBy6v0dRwAtW3kmCtN6evvD4Iez6IHhI38DFlxJmamCoYPH44gCKSmplYYGS5evFg8zrlz51agRD979gy5fTX0pQUV1F4+BpV3R2SmFkikMixa9AOtRqSgS6QyLJxrcPjwYezs7Fi4cKFIoTYxMaFRo0aMHTuWoqKiCookH0ohlacHmzZtikqlolmzZgiCQGFhoYHF6WJw+FY17ILCtR7OY0JxnbIHQasm/eiPCFo1Rua2GDvXpfhpjLiPosfXMKvTGomRHJVnOxavMwhQC4LArl27aN26NcnJyVy+fJnDhw+zdetWli9fjlwup2XLlhy5epuJY0ehRYquIJM3qwaILt6VLqNUig4ZT5VeOLYdyKxZs9iyZQsXL14kMTFRrI89f/6c2NhYUlJSROPe5s2b4+LiwvDhw+nZsyetWrWiTp062NvbI5fLcXZ2Zvfu3VSpUgW1Wk2jRo1Yu3YtwcHB6PV6fv75Zzw9PUlNTeXhw4ciw3PHjh14enqydu1alixZwv379/niiy/+ke/gh9BoNAQGBjJr1izOnj3L+PHj/5JM07e2GXrNv6aRWQ4jGYQvGFHhRWVlZcWgQYPYvn07bdq0oXnz5vTp04dZs2bh4ODAoEGD2L9/P61ateLQoUO8fv2abt26ERAQgK+vL97e3vTu3RsTExP69u2LpaUlAwcO5P79+9SuXZvNmzfz22+/4efnx+rVq1m2bJnYguPp6UmNGjWYN28eaWlpfP/99wwdOpSFuyNRtBpCmbZi0APDC15fVkxh3HkU7+5dqcIMmcqGwrjzyFQ2GKlsyTj6adKbRCbj26AFFBXkMXXqVEOgHTz4o6o+xcXFZGRkEBYWxubNm9HpdKLdU0ZGBteuXcPc3Jzg4GDUajXW1ta0aNLwXc01U9yOtqBiq0dZSjxlSe9bEz6s0ZajID2JgwcPMmDAABITEzl16hQvTmxE/je3mKJKbczqtEaT8frdCVe8n4pLSlk1ewLNe48gLS2N3NxcevToIRKEPnb//U+Iiv+r8F8V+OD9rO/ChQt4enpW+LF8fHyoX78+UqmUBg0aMGTIEK5dq1ir8+w5Go3UGKlcgVmtFmizk0WX96JHVzDzbPdRp2qJkTFGVk4oHDwwq9UCmYUdmmzD6E1415f3MOYCTZo0EVmQe/bsISAggJSUFGxsbLh06RKFhYWYmJig1WoJDQ0dRdvaAAAgAElEQVSle/fuDBw4EEtLAyll4sSJNGrUCC8vL86dO8eyZcuIOHkWqakFusKcSsf1IfJuRpC8bQJv1gzizZov0JcVoyvJF5fbudUgLS0NLy8vdu7ciSAI9O3bl71791JUVES1atXE2dzHsGvXLjp06IBKpcLY2BgnJydatmyJXq836GTWbYAgQM7VXeRdDyd58xiSfxpv+LJWLQZhpVcHih5HGX47QaDoSRTKej683TQamW1VLh0/iKOjIwqFgnnz5nH//n1GjRrF4sWLOXDgALGxseTk5GBra0vb3kMx82gi6gyW+xn+reGvICUyXYlTveY0a9YMDw8PrKysKjkvSKVSli9fzsyZM3n48KEoc/YpBAYGYmVlxdu3b+nevTv5+fls3LgRV1dXli9fzsGDB8XaXGhoKDVr1mT//v1s3bqVmJgYevbs+W9ZH6WkpNCpUyeePHnCnTt3aNas2V+fv1bLksDxdLTKxlT+r70GFDIJ3/WuT0O3ygMxvV7P+PHjKSoq4uzZsyxatIjbt2/z5MkTunfvzsmTJ6lduzYtWrQgJCSEffv2YWdnR3R0NG3btmXs2LHUqFGD5s2bY2Fhwd69e4mKimLv3r2Eh4fTtm1b/Pz86N69Ozqdjnr16jF9+nSOHTtGbGwshYWFVK9enVq1atGwY28kTfqjKSsj++JW3m76ijerBpC8ZSzZF7ciaEpRVKlJ7rXdSJXvSVEmrvXIv3UMhZuXSHrLufATKWFTKHl5m9KEe4Bh5if7dQch43rx5s0bpkyZgr29PcnJyVSvXp3MzEzWrFlD+/btcXBwYN26ddy5c4fo6GhUKhWCIFCnTh12796Ns7MzwcHBLF68mDlz5jB8+HBycnI4sWAQ5nVaUvTgIprMJPSaUvJiKhJ8zGq3qiCR+OcabcmrO+QlGOQGzc3NuX79Or///jveVczJv7aLD9XhSpMeUXD/nNhrrMlKovj5TRQudQGQKa3Q5qWLQdXw+6jZ/tNm7B0cMTMz48SJE8TFxYn3WXJyMllZ7xnB5UTF168NwTQjI4Pjx4+Ly93d3Sswq/934L8y8P3888/s2rWLESMqUopv3rxJx44dsbe3x9LSki1btpCZmVlhHUH5/gaRGBlj5tmOokdXEQS9oSnXu+NH9ytoNeRcDiNp3ZeUvLiJJu0V+qIc9CUFGKUYnLrLfdx0Oh2CIKBSqXBxcUGv11O3bl0yMzNRqVRotVqkUint2rVj6tSpzJkzh82bDaSYli1bsnPnTg4ePIivry9Dhw6lm28HTNwbUfzstwqjuA9RmvQ7+TePYN9nLm4zDlB15kGkCrMK6xgbybC2tiYqKooxY8YgCAJNmzblwIEDuLq6smnTJoyMjMjPz//oPtatW1fB+f3KlSuMGzeORo0akZKSgtzEDH1xLsXPb2Batx1Slc37B0RdIgZhszptUKfEoy3MNoxUJRIUboZ6qNzODTOVBZs2beL27duYm5vz9u1b7t+/L874tm3bRkhICPXr1yf8Xsa/5TAO76ne/wQTJkxgx44d3Lp165OWSFu2bCEyMpJffvkFMzMzxo0bx+eff05BQQEZGRlYW1vj6enJsmXLqF69ung+Fy5cwMfH59/2+rty5QrNmjWja9eunDp16h+x43788UdMTEzYMX80C3p4YoT+bwWiQcBELmVhr3oMa+leeakgMGvWLNE5XKF479/m5OTEV199xeHDh0lPT2fkyJFs2rSJhw8fkpSURFhYGHXq1OHGjRv07t2b1NRU7t27R+vWrdmwYQPZ2dk8e/aMdevWIQgCNWrUELUtyxnF1atXJzQ0lClTpmBiYsKE0KNotBrS9i9Ak/kGh0Hf4xbwC07DVyI1NUed+gyFmzcIAnK79zVohZsX+uJcFK5emClVmDftha4oB232WwrvnkJVuwUetkoWd7TFNDeBtLQ0atasiYeHBzk5OSQmJuLu7k5JSQlxcXGMGjWKuLg45s6dS7du3di1axdeXl7I5XJ+/PFHQkJCKv2WZmaGZ1ejLkWnLsO82ef8sX8eKT+NR+FsCEISmRxBr0PVwA9N5hverPmC9COLkcjk2A/4lpJXd0ha/yW5l7cze14Q6enp7Nu3D0EQRLNgXfxVeBEt7ldqoqTk+U1SwqbwZtUA0g4GY1a7FRbvehvN6hqELd6uHULqzulIFWYG5mvKM0rKNPTt21csmQDUqGHodaxduzZWVlakpKQwffp0evfuTZcuXSoQFf9P4r8u8FWrVk20GunXr1+FZV9++SW9e/cmKSmJvLw8JkyYUKmJ1MK0YllTVd+XokdXKU2MQyJXiCmPP0NfkofCpS6uk3dRNeAwKqfqkJuCPuctdmaGbfr5+bFo0SKRlTV06FACAwNRKBTUq1cPDw8PiouL+ewzQyrVwsKCHj164OfnR8eOhoB75MgRnJ2d8fb2xtLSEmtra5p4OGHfqh/6smKyTq1Bm2foodEWZJJ9aRvq9AQEdQkSqczQzqDXkXt9fwVGlpFUIp57+SzG29ubDRs20KpVKw4fPkxCQgLFxcWMGzeOy5cvU1BQUOE3cHZ2pmVLgzu1IAjcv3+ffv364e/vT1paGgVpbw0vT72Owrjz2PUMQOXlA4DE+L0prcxEhYl7Y4qfRFP0+BpKz/YVXvqO7rWZNGkS48ePp6SkBE9PT1F0AAwtD46OjvT6/HMyfj2CvuT9cWpz03i9rJeY6ix8cMEwC149kOTNYyi49347JYkPCJvUjR+WhuDg4ECVKlVEVhoYasaNGzfGwsICNzc34uLi6NWrF3PmzKlk+3Lp0iW+++47Tp48ibm5Odu2baNu3bqYmpqSkpLCjh07iImJwc7Ojl9//VVUK2ne/K9dPP4Ker2eZcuWMWTIEHbv3k1QUNA/8gq8efMmGzZsYNeuXUilUoa1dMfhYThlr2IxlkkqCUTrNWXIpdDNy4lfxrf6aNADQ8r+0qVLnD59Wnzx/RmCIPDTTz/x3XffsWPHDrKzs7l+/TqNGzdm27ZtVKlShc2bNzN+/HguXbrEt99+y7Fjx+jTpw8eHh44ODiwbds2IiIiuH7dYAD7ww8/kJCQIO5DqVRS18sbsxrNKHx0FW1+Bvb9FmBsVxWJRIpMaYVVmyGY1vgMa59RyCwckNu8b9VQeraj2txTWDbuyojPnLE3KsOpQVtsazXG3EjAXsgl/sJ+fvxmInXq1GHSpEm4u7uj1+uZN28eCQkJxMbGUlZWxty5c5kzZ47Y97pv3z7AoLqzcOFCevXqhU6nY9OmTaLBb1RUFHfu3DE08Eul5N06imWLAbhN3YeJe2NKkwyszuStX1P65iF5v/6Cac3mVJ15EPQ63qwawB97AlH/8QKhrIQWfUbyR1ICDx8+FAWzMzIyaNiwIUqlEvWr28glAjnX9pB9biOlSb9j7FAdl4lhuE7agXXHrwxONKHDSN4yFpDgOGw5Vb5aBxhqik7DV+Ie+Avrf9rB+fPnxbaT9u0NTOCysjK0Wq3YT3nq1CkyMzNFs+DZs2cDhonNmzdv+Pzzz1GpVKJgwY0bN2jdujVWVlY0bNiwAsP638F/XeADCAsL4/Lly5UeroKCAmxsbDAxMeHWrVv8/PPPlb5bx9ECxQcPtsLFEyQSci6HofTq9Bd7FZAYmyIxMqb0ze+UZKei0WhwcnIS1c/XrFmDra0ty5cvp379+pibG8gb9vb2nD17FktLS6pWrUpWVhY6nY79+/djbW3NtGnTxL3069dPvAnKMaCpKzJTc5yGrwCpjNQ9AbxZPZC0/UFIFUqMrKtgUr0JJtWbkLx1PMmbRiMxkldIfwhAVZuKM8CzZ8/SqlUrvvvuO3r37s0PP/zA0KFDmTBhAmq1mvPnz9OmTRvCw8MBg91JOdRqNU2aNKGkpEQ0jP39wkFkSiukphYIJflkHluKsZOhmC6oSyvsW+nVgaLfL1McH4Oy3gcOGDoNqRlZpKen8+DBA5YsWcK0adPEGerhw4dp2bIlLi4urDxxF9t2Qyj8/fInr5rUzAqHAcG4zfwF254z3qlvvJ/laQtzuPn0LcnJyYSFhTF58mRRFf9jNeNmzZrh4uLC/v37+frrr9FoNDx9+pQvv/ySAwcOkJWVJc7az549S1BQEEuWLGH69OkMHTqUWrVqce7cOQ4dOlTJMuZDabfRu2OZcfAeW669JKuwsrVMTk4O/v7+HD9+nNjYWJEV+ncoLCxk2LBhbNq0SZTDKykp4XZkBF2Vb/htri8z/WrT0kmK7s19yuKjKLt9hGOjvSu5oHyI0NBQ9uzZw/nz5yswNT9EXl4egwYNYufOnfz222/072+YRdSoUYOpU6eyatUqlEolixYtIjMzk+7duzNmzBhiYmL4/vvvGTt2LNu2bcPZ2ZkhQ4aIGZ/yevOwYcNEz783WcVIJBJKE+9j6tEE6QcDr38KvU7HmuVLSLx1AS97Bd2a16M45RnHDu4jPz+fx48fExERwcaNG0lISCAgIICQkBA8PT05deoUMpmMKVOm8OjRI3JycvD09CQ8PBxBEDh16pRoQzRp0iSmT5/OoEGDcHBwICAggEePHtG2bVv69euHJv0VBXHnELSGtGvx019RuHpRNfAwJq71Khyzw8BgqgYepmrgYez95yJTWpF46wIlJSXk5+czYsQIzM3NmThxIi9evODu3btER0djaetA8TuPUNep+xB0GvJvHRW3a+rRFJfxW3GbGo6xUw0yT64Ulymc65AduZmCR9fYevZWheOJijKUNHJzcyksLKRVq1YIgsC8efNISUnhyZMnJCUliSTEvXv3UrVqVU6ePElhYSGzZ88mOTmZnj17EhQURHZ2NitXrqR///6VZO3+FfxXsTrLUT59/jPKG0enTJlChw4dGDRokOh3VY7+TV1Zf7WiBp3SuxN50ftQ9v+0IahdnznkXA6j8N5ZTFzrYSSXY29jsAkq77kJDg6mU6dOmJub4+PjI343Pj6eCRMmEB4ejlwuZ9GiRSxevJgWLVpUaMgtJ4p4enoSFRXFrl27xGUdattz4Ykeu54zPn2MPWfAB8st32mESiQwcPxMtgyrWPdxdXXl4sWLqNVqAgMD2bRpE61atcLa2pobN26g0Wg4d+6c6Dp+8OBBzMzMOH36NNbW1vzxxx94enrSr18/Dh48yNChQ5HKjLAfvJiss6EUP40hL2ovVj6jKLh7psK+TWu2IOtsKEYW9hg7eoj/l8uNCZo0guAFj5HL5Rw7dox79+5RXFyMu7s7paWl6HQ6oqKiCDr9HJmLF6Y1Pz1rMvuAqGRStT4m1RtTlvQIhZPBCVsiM6Jm15HI5XJ69OiBSqXi6dOntGzZssI1LK8ZZ2VlkZubS0REBN999x3dunUjMTGROXPmsGfPHrEu26pVK1auXMmhQ4cYNWoUDx8+xMXFBZ1Ox4gRI1i/fj3h4eFs2bIF+zrNPiLt9u6Yjf5gzcVnFdyv7927x4ABA+jZsyeHDx/+aKvDpzB9+nTatWsnBh0wzGyFd76VZjI9j4+sJyYiAh8fH+yr25NkpOb29at41fi4R93evXtZsWIFUVFRn2xDuX//PgMHDsTPz4+9e/dWsmPKzMykd+/erFq1SgxoJ0+eZOTIkYwcOZLz58/z4MEDfHx88PX1JSYmhhs3blQ4h6tXr+Ln54e5uTkaSzcErR59SQHGTp92Pf8rSIyMkVs7YawwoXHjxuj1esrKyggODqZu3XfpxncyaRKJBBMTE9q3b8+vv/7KiBEjsLS0pEuXLri4uODu7k6PHj2YPXs2s2fPJisri8DAQJ4/f05WVhZ6vR4XFxfGTJ7B/dRScu+tIL1GD96q1ZiYXyE/OpycK7sQdGpkSmvs/OcYWJuf6JfVZCeTeXoNZm5epD+7wxuVFKVSybJly+jTpw+NGjXizJkzzJkzh7Zt26JBirKBH3IbA2dC6dmO4ufv04+qhu91f63afknS2sHoS4uQmiix6zuP/BuHyYzez4JjKzi4pD7btm0TM1t/Rs2aNUUnent7ewICAvj++0/bYe3bt48ePXrQo0cPwJBZa9asGWfOnGHkyH/dNxH+iwLfhwKoH8LIyEhMObm7uzNgQGXh4/Jl5esZgkiayPIysrBH4VoPuVXFh7ZcXxMMotPKum1Br0fz+g4PN03DxsYGnU5HZGQk/fv3Z/ny5QwbNoxGjRrh5+cnpjzNzMwIDg7m/PnzFBUVMWnSJCZNmiTWNMrTh2AYva5Zs4ZJkyZx79495HID0WayT02in2dS8g/UUP4MEyMZk3w+/fAbGxsTGhpKeHg448aNo7CwkMDAQORyuWhoGxISQmZmJl999ZU4wx06dCjffPONOPPu378/4/fe5sKTtE8G4XJI5QqqBhyqdCyN3CxxslNiZWXFuHHjWLRoETt27GDs2LG4urpiZGREdHQ0derUwa5/ECirYmRhj64gs9K2gL+kogNITc15mZzGixcvcHd3r6DxevPmTebOncvvv/8uymMNHDiQoUOHEhERwZEjR6hRowYZGRkEBQUxceJEjhw5wsaNGwkICGDixIk8ffq0gpqFTCZj3759LFiwgH379jHmx13ImhUhSI0+2lZQ+i4IRj5OI+pZJj5W2Rz+cTqhoaEMHjz4k9f0Y4iIiODatWvcu3evwv9XrlxJo0aNxFlWo0aNiIuLo0OHDmzZsoXHjx9z/vz5j75kytsOLl++/NE+WkEQ2LZtGwsWLGD9+vUMGTKk0jpqtZoBAwYwYMAAMegdOXKESZMmce7cOTEdnJWVxcGDBwkICBADp0QiISkpif79++Po6MiMGTM4d+4csUl52GG4vrrC7Er7/KfI+T0aY08f/jCvhZdxNvXr1yc+Pl5kNJd/ys+1TZs2SKVSbt68iYeHBz///DPu7u60aNGCzMxMrK2tiY+PR6fTER8fT9u2bXF3d+f2qwxemdbijbY+Gbc3YFK9MYV2hrKLwrsz2mc3cP48kNybEcgs7DBSfbqOqyspMIiMO1anewMXaODCyZMnKSsro2nTphQXF3Ps2DEKCwu5ePEid+/epai4BOWHHqFGCgSNIUsj6HXkRu2lOP46uuI8saauK8lHaqJEZqLC2mcU1j6jaONijPx2OP7+/hXaZz5EWloa06dPJzo6moKCAvR6/SezBGBofzh06BAnT54U/6fRaMTy0L+D/5rA978SHwYRvaaUgntnMG/c4x99VyYR0D6OxMbmO8PfMhndu3cnMDCQ169fc+HCBaKjo7l48SITJ07k9evXdOjQAUEQ6Ny5M2/evCE6Oppu3bqxaNEiZs2aRXR0dIUaV//+/dm+fTvr1q0T3Q8aulmxoEddlpx58rfGkR/CVC5lQY+6n0xRfYihQ4dy584ddu3axcSJE1m7di23b9/mypUrJCQkUFhYyMSJE0lLS+P69euEhYVx4sQJRo4cyZdffom9vf3/KEBLJNC7oQuZyQaR4W7dutG9e3dRiHz37t3I5XJq1KhBZGQk808+Iy7PIKf1MWKIoNWQcfRHbHvNxKxWSyQyI9KPLK4UYF4/f4Kf3xRSU1PR6XQEBQVx5swZ9u7dS2lpKaWlpSQnJ7N06VIyMzMZPnw4TZo0ISQkBKVSibu7O1lZWdy9e5fw8HBmzJjBpk2bsLCw+MR5GnrN3ppUJ6rAFr307x/Dcmm3s6mmzNl2ksH+Lf/2Ox/uLzo6mokTJxokut6l4MEQTGJjY/niiy/o0aMH69atY8iQITx79ozMzExatWqFq6sr8+fPR6/XV6ghlpObzpw5g6dn5dp4YWEhEyZMIC4ujujoaHGWVPG8BKZOnYq5ubkoIbd//35mzpzJuXPnaNy4sbiujY0NFy5cYNasWXz//ffcvHmTnj178uTJE0pLS7GysuL06dP89ttvWDcwvBSLn/2G1NisgtTgP4U2P5PS1w8oS33G/qe/olQYIWjKKC0tZfz48djZfdpsevXq1axevZrRo0cTERFBWFgYNWrUYPLkyURERDBu3DiuX7/OqVOncGjTn1RTSyRlWrRaDUVPY0CvJyn0XauWVoO+rAjdO7EISVkhglaNQqFArXt/NxtJ9Gi1WtIPBmEmAyHzJTduZOHo6IhGo0EQBG7fvs3r16+pWrUqarWaWrVq4eHhwdPU6588l6LH1yh+fgOHwYsxsnREKCsiae1gPqb16WBvz5hZs9i9ezfZ2dkffS7nz5+PRCLh4cOH2NjYcOzYMaZMmSIu//N33NzcGD58ONu2bfvkMf6r+K+s8f1PUR5EdG/u8Xb9UGRmVijfkTD+CjJ0BPeuT+azu5SWVqxZffPNN0RGRvL06VO6du3KihUruH//PvHx8QwaNIjr169z4cIFHjx4wDfffMOBAwfo3r07BQUFHD16tMK2JBIJoaGhLFu2rMKoaVhLdxb08MRULvtbs2iJBEzlMhb08PwkGeFjqFmzJn369OH+/fs4OTnRsWNHatSoQWhoKC9fviQ4OJg7d+6wYsUKEhMTWb58Obdv36ZWrVr06dOHl7GXGd3YyqBg/69Aq8bKVM61U4dYsGABFhYW9O3bl/bt24uK/mAgNzVr1oydO3fi16wu+pQnlLy49dFNfkx/1UBF/3AlgbSnd2ndujVbt27Fzs6OHj16UKVKFQoLCyktLUUQBJydndmwYQNRUVEsWbIEjUYDGJi81apVQyqVEhsby8aNG5k7d+4ng1454pJyidW6gZHiL9erBCNjwu7m/iP36w8xe/ZsJk+eXCG7ADBjxgwEQSAjI4O7d++KM7KIiAixybhatWrY2tpWmCmWi3X/8ssvH22dePToEZ999hkKhYI1a9YwevRoLC0txb7Q2NhYADZu3EhMTAzh4eHIZDJ2795NYGAgFy9erBD0AA4fPszTp09ZuHCh2P5QVFTE69evkclk5OXlkZubi1arxVhpCVo1VWcewsjSkYyjS9FkJSEIenQl+eT9+gslL2Pfb1ynRdCq33/0OooeXcbIxhmXcT9R5atQVIN+xMjOHalUypAhQ7h7966ojRoYGIiLiwumpqaYm5uzcOFC1Go1u3fvprS0lD179tCxY0eWLl3KrVu36NmzJ1u3bmXS6v2cTlEYnBekUkqe30DQqFHV74zzV6GGz7gtKFy9KHp0BQEJgrEZuVH74MEptM9/RVeQiTbzNfkx+0la9yWatFf06daJBQsW4O3tzd27d2natCkWFhaMHj2a0tJSiouLKSgo4O7du8THxyNoShE+4ZYuqEuQyOTITC0QNGXkXKsoCJ9zZSfqjEQUUgF3SwmbN2+mZs2a2NraYm9vj1QqrWDbVVBQgEqlwtLSkuTkZFasWFFhe46OjhXWHzZsGCdPnuT8+fPodDpKS0u5evXqJ2eU/wT/TwY+MASRXk09qDbjAI4DF/5l35eg1yPRaehbTWBE6+q4ubmJPSjlKNcSnDNnToX/Ozo68tlnn2FsbExKSgqbNm0iNzeX/fv3U7duXYqKihgzZgxnzpyhpOQ9C7NWrVpMnjy5kr3MsJbuHBzfkq71HFEYSZHqK96sUr0WGXq61nPk4PiW/1LQe/DgAcePH2f//v3I5XLat2+PVqtl/vz5ogjyixcvSExMxNfXF5lMRufOndm7dy9JSUn07duX1atXM29gWyRxR0Gn/kcBWi4RqFkQh1JhhJ+fH+7u7mi1WqKiopg/f76Y7i3Hzz//zM2bN5nj/xlZ0eGovD9OSvqY/qpprT/XAyWYZzzi2LFjLFy4kLS0NA4dOoRWq2Xx4sUIgoBMJkOpVIqB4JdffhG/XS7iXe6SMGzYMJRKJdOnT8fe3r4SU3TUqFFMnjyZzz/vxdOQfqTuDkCTkyou12QlkXYgiKS1g0ne+jVFT95TzUtexpKybSJPQ/rRqkFtVq58TzDYtm0bNWvWxMbGht69e4sKIeWsups3b4p9hDk5OfTq1QuVSsW+fftQKpWEhYWJZBcwpBo/rAN27dqVyMhIwBDUevfuTVhYWIU66Ie/iY+PD3PmzGHNmjUMGDCAqVOnkp2dTXJyMsHBwSgUCi5cuMDixYs5ceIEFhYWbN26laCgIC5fvlzJ3ikrK4tp06YRFhYmtkk4OzuTl5fHzJkzUSgUPHr0iN9//52goCD6tqiFAEiM5DgOXozc1pW0AwtJWj2IP3YHoCvJw9j5vfNF+qHveLOyn/jJvf4zhQ8vY964JzKVNTKVNXILR6r4jcbe3p6rV6/StGlTZDIZ5ubmrF+/nszMTKpVq4aZmRkajQapVIqnpyc5OTnI5XK2bt1KQEAASqWSBg0aMCogmF1x+e/cVgwofHgJI+sqSEyU4n5lKmvMm/YyiFELOpAaYd1+KKYFSUQtH0Pf1vWwKX5L2f3TONnZIJPJ+Pnnn1m0aBHnzp3Dw8ODxMREiouL2bVrF8XFxbx48YJ69erx7NkzTp8+jTo/85PPqtK7E0YWDrzdOJKU7RNROFe0yRI0ZWRELOH5ioEsGtaZ169fc+KEQdPYzMyMBQsW0KZNG6ysrLhx4wbBwcHcvXsXS0tLevbsWYmdP2/ePBYvXoyVlRUrV67Ezc2N48ePs3TpUlHDecWKFaLm57+D/xqtzv/VKCsro06dOizauJur6QouPk7F1MRErKmA4YLK5HLcpHnER6xj77oldOnSha5duzJjxgy6d+9eYZsajQYvLy82bNhAly7vi8FLly4lNTWV0NBQ1Go1dnZ2JCQkYGFhwa1btxg1ahQ6nY6MjAxatGiBn58ffn5+1KlThwYNGrBx40a6detGZmEZh++8Jf6PfPJLtcgQOHMukm6dO4KRMVfOn8ancW1+P7GdW9GfZjp+iKysLLEvsnz/eXl5REZG8u233xIXF0dsbCwTJ05kwYIFomHrhg0bKm1Lq9XSo0cPlEolkZGR2NVugqxBTyTOXhjJZJR9kJaR6rXIjY3pWMeeotijYio2JCSEhQsXMmXKlH+kXNJu3h6S9GrvTzgAACAASURBVNbwD2j8f4ag1yNLfYjmymaKiopo0qQJycnJ4gssLS2N1NRUunbtyo0bNzA1NcXCwoInT57g6urK27dvefjwIUuWLMHExIROnToRERHBsWPHMDMzQyKRIJPJKCwsZNCgQdSvX5/IyEju3buPZb9gJA41yDy1GgQ99n3moFeXkrJtAlbthqL07oQmPZG0gwtxHLoMY7uqvA0djp3/HEzcvDHSFLHVvyqd2rbk8uXLDBo0iMjISLy8vJg1axZxcXFERUURFxdHo0aNuHTpEp06GQYIv//+O8OHDyc1NRW5XI6DgwNubm6ietDr169p2rQpf/zxh6idePr0aVasWMHOnTtp3749y5YtE01dy1FcXMzUqVOJiYnh8OHDeHt7c/v2bTp37lyJZPbs2TNatWpFtWrVeP36NWVlZQiCQHR0NE2aGPzk3N3dmTJlCnv27CE+Ph53d3cePHiAiYnJRzUgtVotXbt2JSYmhhYtWmDrP5ejAT1ETUtBqyE3ag9F8dcRtBrMarfE2nccUrkhkObdOFzJAaH8u/rSIrIvbKHk1R1QF2Mkk2JsbIxcLkej0WBkZERhYaHYo2thYYGFhQWpqalidgAMrUQ2Njao1WoKyrQggLHDez3bgvvnyI7cDEiQyIwwqVofh4HBaAuyyLnwE6VJvyMxNsXysz6412uM6Z19pKWl0bRpU6Kjo9m9ezdXrlxh5cqVaLVaLCws8Pf3p3Xr1mRnZ7NmzRr69evHd999x4IFCzh48CBarZbS0lIcByxEUeMzPrQg+qeQSKBrPcdKBLr/VPw/O+MLCwvD09OT4T07ML2pGapLy5jpV5u+jZxxlxdQ9OgKbSxyiV3QhQvBg8hP/F2k7Xt4eHzUcVsul7Ns2TJmz54t2oqAQTS43CHC2NiYtm3bcuXKFeRyOW3atOHo0aMUFhby6NEjpk2bxtu3bxk6dCju7u64uLgwKiCYLzdfpU3IZdZcfMax+ylcjk/nQnwGxlW9uZ6Yj5FMgoc2ie7ucl49efCXnldarZbTp08zYMAAatSowY0bNwgJCSExMZFx48YhCALFxcVs2bKFFStWcPv2bc6fPy86InxoQVQOQRCYNm0aUqmUzMxMli5dSuLdKPaOb0e77PPkXQ9H8zwG2R+PqW2cQ0nsYXb3r8qWYc14FH2W7du3c+7cOW7dusX06dP/UdBbv349f1zZg4nxv1mq1mlob1fK5MmTqV+/PtevX+fZs2eYm5tjZmaGWm2Q84qJiSE3N5fk5GSePHmCtbU158+fB+Dy5csoFApRymz69OmYmJjQrFkz/Pz8uHv3LhYWFri7u5OXl2e4b6yqgG01JFIZKi8f1GmGHrSSl7cwsnRA1cAPiVSGsVMNzOq0pjj+Xf1FZmRQ7ygrxsjUnOd6A2kmPDyc0aNH06RJExQKheiIXa4tClC1alUADh06hK+vL507d0atVlNcXMyiRYsqKByVex1+KBjs4+NDbGwsvr6+zJs3r1LQK2fClpaWcvv2bXHGVrt2bWQyGSNHjuTs2bPk5OSQk5PD559/TmBgICEhIXzzzTfY2dnh5eVVyVfxl19+4ZtvvqFKlSpIJJIKTOdyaLVadu3aJYpEqNVqzpw5Q/CAVhXWy7m6C012MlW+Wo/L11vRFWSRF7Pf8Nu/uvNRB4RyZF/Ygr6smCpjN2HzmaH3TqfTiTT98hRrrVq18PHxwdXVVUyTg6F8UV4frVWrFmVqNXY+I3D5eiv60gJSd07j7YYRaPMzMPPsgEXL/rjNPIiRjQtJ64aQvHkMeq0afUk+Dl/8QH7scZISnvPrjVu8fPmSo0ePkpKSwvz58ykrK0Mul2NiYkKvXr04fPgwa9euxdfXlydPnpCdnY27uzv79+8XxaElEgmhE3phKv/3nqW/I9D9p+H/6sD3qb6o5Mw8lixZwg8//AAYPL08XBz4ooEtrw8uIvfEMtTXtrFn/ghsVQpRnmvt2rXk5+d/MvAB9O3bFzMzM7H37enTp6Snp4t6lwC+vr5cunRJ/Nvb25s+ffqIbuOhoaE8efKEu3fvUr/vRBQ95xCTWPBRQ1dBKhcNXRM8+nD8UTZ9+vQRvfI+xOPHj5k9ezZubm4sWWKYvb5+/Zrw8HA6d+6MVCpFpVJRWFjIzp07adOmDbVr18bZ2ZkrV65gbW1NQkJCBUWOcmzYsIFr167RpUsXdDodU6ZMQSKR0KZNG3b9tIGEs9txfHWO0si1RIWMxSX/CXOmT+Lbb7/l/v37TJo0iYsXL+Lh4VFp2x/D/v37WbFiBRcObCeop+e/LLtlIpcyo4MbD6+dJjY2luPHj1NSUkJkZCQeHh7cuHGDvLw80cqlPK1iaWlJvXr16NzZYDu1fPlyUlJSxJYWMNhfRUZGolKp+PLLL1GpVPj6+hISEkKnTp2o07SNOMv4UCBYm5dOWcoz3qz5QvwUPbqKrsjQV2jfdx4lL2+TvOkrEnfP5vI1Q0BMSUmp4GqtUqmwtbUlKChIdJPPz89n2LBhBAUFcejQIS5evEhZWRm5ubkMHjyY3NxccbAWERFRIc0JhgxJ+fWcNGlShWUHDhygbdu2TJkyhX379qFSqcRlFhYWXL9+HYlEwrhx47C3t6dOnTq0b9+e+fPnc/PmTXbs2MH169eZP39+JYnBcePGsWDBAsLCwsTa84fYuXOnKPu1fft2Dh48iCAI5Obm0tDNkEUwMTL4FBbGncPadxwyU3OkCjMsWw8SU8lFT6JR1e+MsX01pHITLNu+H9wJeh1FT6Kx8hmJkdIKoyq1ad68OXXr1iU7O5vExEQxpfz8+XNiYmJo3Lgxt2/fRqPRYGZmRo0aNTA3N0ev1xu8GRv0QFG3PVnnNhh6XfU6HL9cRmnCXbQ5BgnFwrjzlL66g22PGUiVlqAzzBzlVk6oGnUl61IYth5eeHh40Lx5c+RyOd27dyc+Pp6qVatSXFxMmzZtyM3NpXfv3owaNYrRo0dz5coVOnbsiEajITU1FScnJ4yMjNgWEsTUdq4oZP/ajO9fIdD9p+D/SlZnXFLuX/ZFrTj3GDv/eWJzdUJCgtgE27VrV3r06MH58+crjHj1ej0dOnRg4cKFYq/OxyCRSFixYgVDhgxh4MCBHDp0qJLKvq+vbyWfsHHjxtGiRQvGjx9PzZo18fHxoWbrbsQYNURi9Pe5bEEAnUTGryVOfNGmP4e2LSIgIICcnBwOHDjAzp07SU5OZsSIEVy9erWCs/eHUKlUFBQUsHr16gqjb7lcjqurKz179qRbt24sW7aMMWPGAAaXgKVLl3Lo0CH69u3LtWvXKs3YTE1NcXZ2xsfHh8TERJycnETRYZVKRa9evf6xZNf58+eZMWMGFy9exN3dnXIW/ZIz8ZRqdZXEiD+EBJAIWhz/P+7eOjqqe+vj/pzxuIckJESQkOAuxd2hOEWDa3F3p1CkxVIsuBZ3DxbcgoUQJEbcbXzeP4YcCNDe2/u+93mf9ey1slYykzkzc2bOb//23l+Jv8e4+YsY2SKMSZMmUbVqVQ4cOECTJk2Ii4vj/v375OfnA+ZqSSKRiIpAYWFheHl5AWb3CLVazbVr14iIiBAl6wq1WufPn8/ixYuLzIS/3rwUhszWBVWJ8hTruei79yvdy+DadTYmg56ch6c4vWYKzO6Ph4dHkePn5eWRmppKWFgYL1684ODBg+Is5dGjRyxatIjnz58zbNgwJBIJAwcOpEqVKphMJhITE3n+/LmY2MGMzmzTpg1Vq1YVNWUB1Go1EyZM4MKFC1y4cOEbMEphBAQEiJVav379OH78ONnZ2YwbN46QkBAsLS0JDAz8Lqz95MmTtGjRgmbNmnHz5k0+fvyITqfjzJkzJCYmsmvXLrZt20bDhmYRhMuXLyMIQhFD4pENSxJ8JxGTTkPC9i95sCZRpu3vHBCMBdlg1Iu+dSVKliU36izPnz9n6tSpTJ8+nSFDhvD69WvCw8OZMWMG27Zt48SJE/j5+WEwGJDJZFSuXJnHjx/j5uaG1EZKZMhYDNkpoPgkLCEI2NboRPplM3ox79VNbKp3wKTXYszNQK3OAyBmTS/zrE+nofuMdfzStRItWrTA2dmZNWvW0LhxY7p06cK+ffu4du0afn5+XLlyhZcvX9K5c2c0Gg2vX7/mwIEDPHr0iIULF1KtWjUqVarE4gEtUVVojqxKF3RGRA3c74UgmCu9mW3K/iMswf+G+D+X+Hbf+fC3C6B5hich29aXnpvvMKN1WU6dOsWdO3fYvHkzvXr14qeffqJly5ZFHpebm8uiRYuoX78+NWvW/MuKz8fHh6SkJHQ6HU5OTshksiLgBjCToTMyMoiNjRUXUFdX80U1a9Ys9u/fT65Gz+lniSjLVzCL5IbuID/ihpk0amGLZZnaODYzC0DHbRiIU5ufsfCpjFEi48g7E8nJBXTo0IHr16/TqlUrFixYQPPmzZFKpQwYMABPT08WLfp2gbW2tiY5OZmSJUtSt+5nayaj0ci+ffs4ffo0MpmMLl26cPv2bUaOHEm/fv04cuQIS5YsYezYsQQGBn5z3MKwt7fn3r176PV6BgwYQFhYGDqdTnQ66N+/fxHR7q+j0Ffu2LFjRaxP+tT2oaKnPRtCo7j6OgUBisxrBYMOo8lEaWsdS/s2ZlSvJSxfbsvUqVNZu3YtDRs2pEWLFgiCQFZWFk2aNGH16tW0bdsWT09PHj58SGBgID179qR27dps2rSJmJgYdDod6enpuLu7Y2FhwbVr10hKSqJSpUq0adOGtm3bsm7dOiZNmiRWsxLT96keFiVrkBG6ndznV7AKMFcQ2uR3SOQWyBzcyIu4iWXJmkhUVghKSxFN3qtXL/F7GxAQwPjx45FKpYSEhLBgwQIkEgljxoxhxowZgJmG4O7uzrNnzxg+fHgR8vDRo0dp06aNWNVrNBo6depE+fLlGTlypMgdfPv2Ld26dcPPz4+HDx/+5ef1ZWzevJm7d+8yc+ZMEZzQtGlTtmzZ8l1Yu1qtJiwsjKgos9KOwWAgKioKf39/7O3tcXR05MqVorPso0ePUrVq1SKqTm7aeFpZx7NepsBj8HpkNt/SEP7OAUFiYQsSGfrsZBTOJUiJj8YaM3Bjx44dbN68GSsrK/Ly8li1ahV169bl0KFDLF26lICAALGzEhkZiYeHB87Oztx/EIpD0yGkHl2KgPmjTAj5pOBUyJPLTUdm64xEZY3MvhiuXefycfNwSozbhzYxiuRD81ELCqytrTl16hQeHh7odDoOHDjA4sWLcXV15c8//+T+/fsEBQVx79491q9fz5QpUzh69ChKpZKPHz/i4uKCUqnkyJEjBAQEcDN0P4H6TFTVOpImc/vmWlJKBdQaDQ383fB1teFBdAZXXqdgq5JR1s2WbtU8cbL+h2jl/+H4P5X4zEnv3+O5mTDzouYee0p2rg1Lly6lV69eGI1GLl68yLJlZi+yPXv2sGPHDnJycihRogTLly9nxYoVvHv3DpPJ9N0q5eTJk3h5eVGrVi20Wi07d+4s0jqSSCQ0btyYy5cvM2DAgCKPvXbtGvfu3eNjZgE6ByNKIOv2IbSJUbj1W4XU2hFDVjLqL2xIvg61zoi8UluePtwtKjW4u7v/W+fQysqKjIwMJk+eXOS9hYWFYWtrKyabwgRUp04dli5dSnR0NPHx8d/IrX0Z6enprFu3jtzcXD58+IC9vT3FihWjY8eObNq0iXPnzrFjxw4mTZpE69at6d+/v5iswayA06FDB7EN+3VU9LQnuE910nI1/PkojoiEHLLVOuI/vEGSlUDm4/O8eP2canPiOXz4MDVr1qRq1ap4enqyZMkSMjMzsbKyonHjxhw4cABHR0cKCgqIjIwkMjKS+/fviwCPcePGceDAAX777TemTp1Kbm4uJ0+eRKEwex8ajUZCQ0PZv38/6enplCtXjjZt2iCXyzE6eONarjNGoejlJ1FaUqzHQjKubCHj8hazeLKrL45NBwOQ9/wqGReCMZmMKByLY+NXheHDh7NkyRIWLlxIly5dRARh+/btmTBhAuXLl+eXX35h5cqVLF++nF9//ZXXr19TunRp0cl98uTJIrDlyJEjDB8+HDDPznr16oWDg4NoSZWVlcXGjRuZO3euCEL6u0o9IiKC06dPU6JECWbNmsWBAwfEGXH9+vVxcnL6Lqy9oKCAtLQ0Zs+ejZWVFVu2bGHdunWoVCoOHjyIwWAQLclMJhMfP35ky5YtbNmyhdmzZzNjxgxRLmvFihW0aNGCwDpN+XB5Mw7NR5hdBnJS0aVEY+FXDauy9Ug78xvW5ZogtXMp4oAgSKRYBdQj89pOPDtNoqSTnAsvXtClSxf27NnDggULePz4MadPnyYkJARnZ2dycnKYNm0a+fn56PV6ZDIZRqORhIQE4uLikNq6oHD2RpDKsQxsQN6Tc7gH/YbcwYOM0O3oMxORWjugz0nD2rcqEoUlWXePmN+v0YBRk49RnUNuSjxGYzVmzJghqin16dOHK1euIJFI6N69O+/evRNdRZ48eSJutsG8UcjJyeHcuXNs376dcePG0aBBA25eO02N/GSO7NrHzXi9eC3ZquTYWsj48/w1wt7JuRud+S+Vhv43xv8IqnPv3r2sWrWKiIgIbGxsqFy5MjNnzqRevXr/nz3H09hMem6+8x8Rp9Fr+bV1car7ueLr64u/vz8RERHi3VqtFmtra7RaLSaTiaZNm3Lnzh1iYmK+IbH6+PiwZcsWmjVrRs2aNUlLS0MqlaLVahkyZAh//vknb9++RSaT4enpSXh4OPDZKDc4OJidB4/yOOojFuUaYVOpJcmH5qPyqYxtjY7fvPTUkyvJexGKIJODIMHuh57Y1e5K6rGlqN/cFSHKPj4+tGvXDr1eT3BwsOiU3bhxY06ePMnHjx8ZM2YMly9fJisri9WrVzNu3Oe20MiRI0UiM5grgaZNm2Jpacnjx4/R6/VcvHjxu5yuwkVg27ZtjBs3jlWrVpGTk4NCoaBSpUpotVpevnwpLqBpaWns37+fHTt2EB8fT+/evWnZsiUDBw5kwYIF/1imaPTo0ZQpUwZ/f386derE4cOHadOmDbt372bgwIHo9XoCAwPZs2cPZcuWZerUqRw7doz58+czadIk0f0bzIvszZs3WbFiBXfv3mXEiBH88ccfXLlyBZPJxJ49e9i9ezeCIODn50dycjKvX78W259qtRoXLz+se6/B+P9ixC4xGdjdzYe9IZv4888/WbJkCQMGDCA4OJhFixah1+tZs2YNvXr1KpKYpk+fTmZmJu3atePXX3/l6tXPfpRpaWn4+fnx8eNHLCwsGDRoEB8/fuTEiRNmsrRWS6VKlUhJSeHMmTP/lsB2fHw8Q4cO5dy5c2bRZakUa2tr7t+/T05ODv369eP169eUKlWKvn37snr1auLi4pgyZQrr169n2LBhHD16lJIlS+Ll5YVOp2P37t2EhobSpEkTLCwsxFaiXC5HrVZTu3Zt6tevzw8//ECrVq0YOXIkd+7c4V18CibfmuS9uomhIBupjRM2VdpgW70DAFm3D5L94ASCIMGubk/SL2yg+MgQszKQOpeMi8EUvHuEm6MNLZo35+nTpyICuBA0UricFvIfCwoKyMvLQ6PRoNVqsbe3N6NdX8egyTTb+6g8A1F/eIL70GAEQWp2QL93BF16PIJEiseQjRg1+STtm4ExLxNBYYnc2QujJh9BJkeX9E7cHBoMBhQKhcjbS01N5enTp6xevZoxY8aw7lIEb1LzyVbrsZCa2B+8ilLSVK5fOE21atWYMGECu3fv5tatWzRr1oxHjx4REhIiOmGIHTWdHtPfoD//t7dB/+sVX6FhZHBwMC1btkShUHDu3DmOHz/+/2niWx8ahVr/HyQ9wCSRcTbaQPVPuIovqQjwmXAJ5hnexo0bKVeuHLdv36Z9+/ZfH06MzMxMYmNjadu2LY8fP0alUrFz507KlSvH2bNn6dChA0ePHuXHH38UHzNgwABWngnHRLx4m8LDn+z7xxCkMpSe5ZC7eIuLmXP7iahjX4itzsKwKVUDS+fihIUsZu3atZw9exZnZ2fR58rNzY2hQ4fSvHlzdDod7du3p2PHjuj1ek6cOMHvv/9OQEAALVu2RKfTcejQIZFIbjKZGDJkCG5ubhw8eJAWLVpw9+5dTpw4QZUqVYrM986dO8ewYcNo1qwZjRo1ok6dOri7uxMXFycmBltbW06fPk27du0AMzhk1KhRjBo1ilevXhEcHEyrVq0oVqwY2dnZpKam/q1qxtcRFRVF69atRR3HKVOmsCZ4K/dTpTi1m4DKxoHGLZtyO9MaTx2sWbOGihUrMnDgQHr37l1kdiUIAvXr16d+/fq8fPmSlStXkpGRQd++fTl06BALFy6kadOmrF69mosXL2IwGPD29ubHH3/Ew8ODsLAws/dY5F0sStX62xnKX4UAuJvSadd8AH369GHHjh3MmTOHlStX8vr1a2rUqMHBgweL7OzB7Hu2adMmHj9+zMqVK7/5np84cYJmzZphaWnJhAkTiIyM5MKFCyiVSqKjo+nRowcqlYo6der8264SNjY2REdHs3r1ah49esS7d+84ffq0qB7z8OHDIv8/ceJEwsLCCA4OxtHRkWfPnrFr1y5xrYiPj2ffvn1cv36dgIAA4uLiaNCggfjj4+NDaGgoZ86cYcOGDVSoUAFbW1vWrFlD7dq1GbX/aRG5wi/Drk537Op0B0CXGguCBOknaTCpyhrnthPIf3OHxOPLOH/+PH5+fpQoUYInT55QvXp1xo4dy6tXr9i9ezdRUVHExcWhUqmYO3eu6CRhZWXF7t27Sc3VUPeXK6hzc8i4th1pWiyJOyYgs3PDrnYXPAauxWQ0kHFlGwlbxyAoLbGr1ZWM0BC8xh8wX/85KSSGmFvDBoNBRCJrtVpu374NQPXq1WncpT8b7psBUr9deYPW+Dlh2dTpQapMRpO5+/Cs+AM//fSTCIBavXo11tbW9OvXj6CgIAI7DGXZ+chPHbW/n8cXKg0tPvMK4H9d8vuvojqzsrKYM2cO69evp3PnzlhZWYltmBUrVqDRaBg3bhweHh54eHgwbtw4UbE+NDQUT09PVq5c+V3LmEIicNu2bbG2sWHXtN5o0/89IrBRpyH98hazMeXq7iTtncb110nUq18fMM8irK2tuX37Ntu3b6dVq1bihfrixQtGjx6NyWSic+fOoszSgAEDmDXLLHLdqVMnLC0tefPmDUqlktu3bxMTE8PMmTOpVasWw4cPF3dmK1asYN26dQQHBwMwc+ZMLDxKFbkw7ep0w65WF/JehJKwYxzx6/uT++wzKvR7oSzfDN9qDTl16hTz58/nzZs3jB49mqtXr9KjRw+R1zNkyBCcnJx49eoVRqORmzdvYm1tTd++fUUB7UuXLlGqVCl8fX0BWLZsGS9fvmTHjh2cOnWK6OhowsPDuXHjBm3atCE1NZX09HQGDBjAiBEj2LJlC1u3bkUul2MymfD29ubDhw8kJSWh0WhYtmwZM2fO/C4htUSJEty/f5+ff/6ZkJAQ7t69S6lSpejUqRNHjx4VKQd/F2/fvqVkyZI8fPgQW98KJJXpyGv/Pjg27IvSvz4mj/KcfJbEmkuR1P3lCoO33+X3PccZP348kZGRdOjQoYiRZmEEBgaydetWTp06RUREBIGBgVhbWzN8+HAaNmxIVFQUeXl5rF69WlSg12g0bNmyBafEB5gM/5n7uUmvZUq7yrx8+RKZTEbPnj2xsLAgIiICmUxG9erVv6scs3LlSrp3706JEiW4cOHCN4nv8OHDdO7cmYULF3LlyhVOnTqFlZUVp06dombNmnTt2pUzZ85w8+bNIijWvwqDwUDv3r2pW7cuYWFhxMXFcfbs2SKSaV+GVqtlw4YNNGrUCG9vb/bt28fGjRt58+YNQUFBlCxZkkqVKnHo0CH8/f3ZuXMnKSkpYoU7adIkAgMDOXToEA0bNuTx48eEh4ezdOlS6tevj1wuZ1SjUkXcWb6M/NdhmPQ6c3UXGoJFqZpFhC1MBh3ze9Rl2rRpaDQafvjhB06dOkVMTAxlypittHx9fXn+/LlY4Z09e5bQ0FCOHz+Ov78/v/1mtvJxtlbSqLQzEqUFTi1H4TlqByXGH8Rj4O+iW4kgkeLYbAhe4/bhOWIrcidPpNaOCIJAztOLxO2agiY/D0EQkMvl1KxZkzp16oidHJVKRZJNaSI8W5PnVhnvaaeKJD0AZAoMSIgzOfGhZCfGbzhCpUqV6NChA1euXGHu3LkIgsD+C7eZezz8H8klAmS8e8bQbm2wsbX9RrVn+/bt/6j4+fDhA4Ig/FvfvX8V/9WK7/bt26jV6iIVzZexePFi7ty5w5MnTxAEgY4dO7Jo0SIWLlwIQGJiIllZWcTHx3Px4kW6du1Kp06dROTX/v37OXv2LPdz7Jg+djiZ13eKROCk/bOxr98b1+7zRSKw3MUbhXMJMq5uQ5cSjVvfFUitHNB8jMQEtBy5gG0zBrJp0yZRSy40NJT4+Hh0Oh2jR48mJCSEgIAAfHx8iImJYcuWLRw9epQ3b94AiC28Qkkzo9FIUlISUqkUJycncnNz2b59OyEhIRgMBiIjI4mIiBBJrk5OTjhZFy9yngSJFJtq7bCp1g6jTkNe+EXSzvyG0r0Mcueiu3r4LCqbEnmTSVkpouVHamoqdnZ2yGQyfHx8RHDLpk2bGDFiBAsXLhSTz5IlSyhfvrxIcC+cyxw+fJgNGzZw584d9Ho9o0aNYteuXfj6+nLx4kVmzpwp6jb26tWLZ8+eFamWAZG0rNVqqVy5Mp06dWLZsmUcOHCgiIixTqejW7dulC5dmhUrViCRSGjevDnZ2dkiN2no0KH07NmT/v37U61atW/mTTqdjujoaEaOHMkLjSOq+qOwlMoRJBK+7g8UDvAvRSQjqT6QSp0qsWTJEmbMmEGVKlXY/5WV1wAAIABJREFUv39/EcBPdHQ0e/fuZc+ePWi1Wjp16oSPjw/79+/nzJkzlCtXjlatWtG+fXvat29PdnY2R48eZfHixbx58wZvq2IYK3VE+AeyZRKTnpbuBQzu3JxNmzYxefJkwsPDuX37NhKJhKCgIDIzMwkICGD58uX07t0bQRBISUlh8+bNPHnyhJiYGFJTU8VKNjVXw55bUTxUlCcnQkLMWyOjluxAK1Eybdo09uzZw5EjR8S5qre3N/fu3StyLr4XM2bMICcnB5lMhkaj4eTJk1hYfGsPpNVqCQkJYcmSJZhMJjw9PQkMDKR79+5IJBKxmps8eTJly5YlJyeHixcvsn79etHuq02bNixcuJD6nzavMTExREREcP78eT58+CD+REdHk+NWGftGQd/IxeU8OUfqmTUIggSlV3kcW36mbShlAtm39jNz2yX8/f2ZNm0aERER+Pv7M3PmTNauXUv//v0ZNmwYISEhbNiwgZIlS/LDDz8wbtw4YmJiSElJoXTp0vTt25fx48cjibiEYAzAJP1+IjbqNGhiwlH5VsWQl0HmrX1YlqlD9t0jZN09gnWZWjT0UnDt2jWys7OLeNQ9f/6cgPZDeBuXSMGeaRiyUzFpC8z2ZBMOIkikGPIySDm6DG3yO6wrtURoOpizCRb06WEecQiCQJ8+fWjbti0tFx0mSZB9U+flvrhK3rMrFOu58JvXH79pOIacVJxaj+HHLl34rVtFbty48V061P90/FcrvrS0NJydnYvQAr6MPXv2MGfOHFxdXXFxcWHu3Lns2rVLvF8ulzNnzpxvLGMK48cff6RmzZq8Sc3HIrDhv0UENpmM5IVfxLHZUGQ2zggSKSrPAPSCnIt3zfO2w4cPc+7cOR48eEBqaip6vR6VSkVmZiaOjo78/PPPNGvWjMqVK5Ofn8+vv/5Ko0aN6NOnDx4eHhw4cIASJUrg4uJCXl4e1tbWSCQSjEYj8+bNIzs7G51OR8uWLbGwsGDdunVMnz4dMGt+VgwozV+FRK7Eplo7JCprtKmfSOpfLfaForLd5vyBra2tSL34kkz7ZVSoUAFvb29RacLb25spU6ZQvHhxfH192bdvH5GRkWzcuJHhw4dz/PhxihcvzpQpU2jbtq0oW5WWlsa7d+9QKBTi3Oxrz0STyYSPjw/R0dE8efKEypUri6LNc+bMQafTkZqrYWNoFHUnbiLGty12rcay6cZ70ZeuUHPw2rVr3L17F2dnZ2rUqEGZMmVYvnw58fHxoudZrVq1MBqNROGO5Q99EOTKf91eFCQYJTKWnH3FwUcf+fXXX1m/fj0//vgjc+fOZePGjdSvX59q1aoRHR3Nxo0bWbRoETY2Nixfvpy3b98ycOBApk6dSsWKFdmxYwdarVZs+5lMJu7evcvYNlXg0RGMOjWY/n4nbTIaMerUZFzeyoN9q5k+fTqDBg2idOnSuLu7Y2trK5qXnj59miZNmrB8+XIaN27MixcvWLlyJT169MDLy4sLFy7QvHlznsVnM3TXA3745Qprr71H6V+faL0tJu8abL79kVpLLnIqy51dZ64XARM1bNmOX08//VvfwJ07d3Lo0CGUSiUmk4mjR49+k/Ty8vKYNm0abm5uzJkzh9TUVOLj46lRowZt2rTh1q1bxMbGsmfPHurVq8epU6do0qQJnp6erF27FgsLC0aPHk3nzp1JTExk/Pjx2NjYoFKpKFu2LF27duX48eMoFApatGjB4sWLuXnzJqm3j7KoS9VvNG+L9VhAifEH8Rq3H9cus5BZO5J5cw8Zp1Yyu20gD/atRiaT8eDBA8AsgnHx4kVxk5OQkMDDhw9p2rQptWrVYvHixWi1Wk6fPk23bt3YunUrz549Q6VSUb58eXb/vhj5i5NITJ8rmLwXoSRsH0fMyq7EBw8i5fgKYlZ2Iz54CLrk9+Q8OU/G1RCUXuWwa9gfiX9DUSxAEAQRHZ6vhzcvw8m+ewRd4ltcfpyOZWADpEpL4jcEkXF9F9mPzyCxtMVzrJnEH/vbT8RtHsGGXYcQBIE1a9aQnJxMfGomL09uIe73n4gPHkLOk3Pi69Wnf0SisiL15EpiVnXj45aRaBLMRYBz+4kgkWIV2JBrb9LJN0ho0aIFFStW5NWrVwwfPlykMxXST742fy7ctMNn+T17e3uxIwewbds2AgICcHBwoGXLlt/ISX4v/qsVn5OTk5g4vpf8vibeent7ixqDhY//8nFfWsYAovdXtlr/l0RgMYwGrMo3xpifjUmvRebwLcrRwcWdWMxiuIXPu337dj5+/IibmxsVK1ZErVbTr18/3N3defPmDS1btuTEiRPY29tjb2+PVCoVuV0KhUIkRguC8I1R7v3798nOzv6mdHdTGYvsrLLvH0fh6ovCwx9BKiPvRShGbT4KN7MvodTKHn1movj/Jm0BUpmCymVKoGvdmqFDhxY5/tcisDVr1qSgoAA//wAOhqchrz+IuwY3nOsG8FPzAdw/sBapVMq4ceOQSCRMmzYNX19fjh8/zqtXrzCZTOzevZtJkyYxcOBAdu3aRUxMjEh5CA0NpaCgQJwLeXt7s3XrVqKioli5ciVg5jYWC6xJ22XHidZaodPpMFr5AHAiPIELL5NEtNjD9eMYPmgAgwcPxs/Pj7lz5zJv3jwWLlzIxYsX8ff3RxAE7OzssLOzQ+Xhj7J2L3Smf0bMLdAZWXwmAn8XCwoKCqhYsSKLFy/GxcWF5cuX06NHDxQKBY0aNaJdu3YcPXqUdevWYWFhQZ8+fejduzcXLlxgxYoVzJgxg1KlSpGWlsaNGzdEDddx42De79vYcO0dFn7VMJlMIrkdzBUeggRfVyvI1/GxalPef4xhwaEwctQGpHote/fuZciQISgUCn777TemTJnC0qVLiYuLw83NjQbN24JfbTr0G87AHfd5+iQf58Au9Nh0G43B+KmtXvTcaAwmkMjIcyjFiEOvmZknUKG4PetDo7giq4Nep+PRk8/X6pdovvqOBUycOJEyZcpgZ2cn+lCq1Wru37/PlStXOHDgABEREVhZWdGiRQu6du3KihUrGDZsGP369RNb6VeuXOHJkycYjUYRYKbVaomPj0cul6PRaPDx8aFRo0acPHmSVatWMXToUAwGAzdu3BCv26/jX9FfVDIJJqCUizXFXJzFGVV4eDglSpRg3rx59O7dm4oVK3Lu3DkuXLjA5MmTWbE2mCaDptPpl2Pse/SUbQOXoUvJZOc8c8ekePHidOnSha1bt6JQKIi7uo9Sjh5kF69F9v1jZN35E6eWo1D5VjVf6xE3ST+/AYtSNXBo0A9dehzJh+Z/QqWm8djBh9xkM9+0ZMmSIio7LzMN50Y/gMqO3IcnyLp7BJm1Ax7DNmPSqUn+cz6CRIbSuyJ54RdQv3uIe9DvCAoVKUeXAjBp0iTGjx+PzNYZlW81PEfvRJcWR9L+WcjsimHha+4Y5L+5i2vnGTi1HUfm9V2kXwzGvd9K5I7FEQQJqadWYV++ITuuFWNCW7McXUBAAMHBwWzZskWcf8Jn8+dy5crx/PlzmjdvLnaFrl+/jq+vL5mZmeL6XKjhefLkSUqXLs2yZcvo1avXX/KsC+O/mvjq1KmDUqnk2LFj3/XJKyTeFqpLxMTE4OHh8Y+fx1ZV9G38HRHYZDIiyBToMxKKGKACxL0384X++OMP6tevL74ug8GAjY0NXl5e4tzLz8+P9+/fc+DAAcqXL0/Dhg1FwvOIESNESPvMmTPZsWMHEyZMwGg0MmfOHNEot2fPnuzevZtHjx6JO7XBgwdz6mIoJuln9QtBriTjylZ0mQmAgNzRA5cfZ4j+gXa1u5F+6Q8yroZgV7cHNlVao3n/mFnd62NpYVGEzAswaNAgunXrhr29PY0aNWL6r8FQqzeRb+4zoXN9THotMU6e2Dfoi2WJ8kgaTuZ00itGzv6FuaMHcP78eYYOHYq1tTWlS5dGoVAglUrZtm0bbdu2BcxSVXfu3GHYsGEkJCRgZWUlUkC8vb1JS0sjNzeXypXNgJzddz6QXOEnNLlGBIkRhKIEeLXeiMlk4vyLBJITsrnzLpXBX32279+/58aNG5QrVw6VSkVYWBhJSUl49lqA/h8mvcIo0OroOGMj5dJv0bt3b/bv38/y5cuZMWMGPj4+YmvN3t6e6tWrc+LECXr0MG+4BEGgZcuWNGvWjO7duxMaGorRaGTlypWMHTuW4sXNLe3Zo/qzf0M5Uu/uwehdg4A6zYj+mIyltTVKBzdyjCriMjVo9EpwLIW1YymMOg0ODfqief+I9Jv7RdUSrdZsVzN58mRmrdrMxKCuuA9aj9zBjT83/oLUxgn7H3qRlgfw7wkjFOgMzDv5wjxfMZrIef5te0v0DXyRxDm9BsdanfCyzmHAgAEsWLCA69ev8+DBA1xcXEhLS8PLy4v58+fj5OREdHQ0v/zyC1FRUUycOJERI0YA4ODgQOnSpQkKCqJGjRr4+vri4+NjJoB/JZDw4MED0fG8MApnmEajkSVLlrB582YKCgpo1aoVa9eupaKnPT09czkxdQhz910TIfuHJnVk4PRl1PV1oM8v2zGZTFhbW1OyZEmePn1KjRo1ePr0Kd7e3iiVSurWrcuMFRupOXELVyOS2HbuHpnXdqBNjUVm64J94yAGHv9Ig5dhPN4wnneRL0W5s+3bt9O3b1/sGw0kMzQEZAoKop9iUaYOgiCgS4tFZueCTbX2pJxYji75A3zCU8qdPJFI5Xj4+ZMe9YS3b98iCAJGk5mXqHDxJvX4L+bPJ+ouCvcyxK7qSrHey9ClxmLSqdHEvwIEbKq3R2ppR3zwYAwFOeJ5q1WrFnfv3gWJjJiVXUEiBZOBtHPrsC7fhJwn58FkJOX4cmS2LkhtXdB+fE3Mqm6Y9DpsanTEmJdJwolVTNw/l6kyGQ4ODnTv3l289r+M75k/X7t2jU6dOn33+xkcHMz06dPF8cqMGTNYsmQJ0dHRRYqqr+O/mvjs7OxYsGABo0aNQiaT0aJFC+RyOZcuXeLq1av06tWLRYsWUaNGDQRBYMGCBSI3559EWTdb5F/0yf+OCCx39sKqYnMyrmzBqd1EpFb2aBMisfIojVyXgyAIXL58mbVr15KQkICnpyfJycl4eHhQr149JkyYwJo1axg8eDDx8fFERkaydOlSFi5cSHh4OA8fPqR27doolUoyMzN5/tzMt1u7di3p6elMnTpVVDwBs5/fzp07SUpKwsXFBUtLSzxd7NHVGkC+o1lh3qZyK2wqt/rL929ZpjaWZT7bzZiMRtr9vIj9o5ui0Whwd3cnPj5e3FSULl1alH7afecDvbfdR1m2IcqAb40dTYABMLiW5axWRrWITJ4+eECbNm1o2LAhM2bMoFatWsjlcgYMGICTkxPNmjWjefPmNGrUiF27dnH27Fk0Gg2RkZHk5eVRoUIF0tLS0Gg0lC1bljmbDrNi3nQ06fHIHYrj0GwoKs8A1LHPST4wB5sandDEPEOb9BaLMnUoiHnBtuVz2L1mIQP69RU3KDt37iQ9PZ23b9/i6enJ6NGjOX3uAu+Or8al80yUbqXIfXGVtJMrcR+03iwK/PQCBVH3ULqXQZeZgNzBg9yn5zGq81D5VMKx5SisS9dk+7jxTB03iokTJ2IwGHB1daVz586UK1eOGzduiG7gkZGR9OjRg4iICMaMGcPDhw/R6/V4eXnx4cMH0tLSRN87FxcX0tPT2bp1K2/evKFfv37s2LGDBw+OU+mHpqTXGEKOIAOJ5Bu1F4lciQlQ+FbH278Wsb/1Jjc3F0EQMBgM7L0fS07JTy7hMjmCTIlTq9H8p2F+enOr3LpcY6zLfd8E1OyIoERdtjXnroXwcMwYHB0dSUtLEyWywLyoXr9+HblcTmJiIuHh4djb29O6dWs6d+5M8+bN/6W105fxpSZoochAIRZg+/btbN++natXr+Lq6kq/fv0YPXq0OFaRCDCsQUnxWJfnKmhX0YNmzZrxcsYMoqKi2L17t3i/XC4XxxgGg4EPSRl0GTkd24b90WWnkXJkCc7tJ6Dyq4b6w1NSjy9HOTSYq5ECScl56HVG2rZsTq1atZg4cx4A+a9vgiDgMWgdiTsnYlmqJhZ+1VB/eIJlmboIUhmOTYdgKMgm5c+FFHx4iuzRGWxrdCTVwjzn79ChA9HR0Tx9Go6gUCF3LI5NlTbkPDQbuOrSYgFI2jMNpHJQWGLhXZGCqLsoPfzJCjuAUZuPRGmJMV+LlXNxUQRcmxQFJhPWFZqR9+Iqkk9qM8a8DASlBV5j95F5fRd5L82Sc54/7yFubT/ynp7Hc/ROtKkxlFXlkn3vCMWLF+fy5cukp39rEPxX5s9/FdHR0YwdO5aJEyeKt5lMJuLj4/828f3XtTonTpzIqlWrWLRokWgpsW7dOjp16sSsWbOoXr06FStWpEKFClStWlVERv6T6FrNs8jfhUTg/FfXiVvXj7i1fcm4uh3TJ607h8YDkbv4kLhjPLG/9SLj6nYMej2NfK2ZNWsWN27cIDExkQMHDtC8eXOMRiOvXr0iICAAKysrVqxYQbFixTAYDBw7doygoCCKFy+OlZWVCNwYOHBgkdc0ffp0srOzGTJkiDjEv379Ojdu3GDLli3UrFmTPn368OeffzJ27Fj2zOqPSv6vhZq/GwYtcRe2kZ+fj1KppF27dhw+fPibf9t95wOLzrxCb5KQdedPkg7OLXJ//B9DPt8mSFDrjAxsU5fNJ28QHR3N9u3buXnzJkePHuXgwYMkJSWJwrfr1q3D09OTH374AYPBwODBgzEYDMyZMwd3d3cyMjIwGo38efEWi8cOwKpae7zG7sO2ZieS9k4j7cJGVF7lUbiXIe/FVRxbjcZrwiGc245H6RmIQ4vheI7dz/Ebj0X1joyMDKysrNi0aRP5+fm0bNkS57pdsChRHk20eX6riXmOzN4NTeyLT38/Q+VVHru63VG4+JD/5g7FflqG5+idSFTWpF/YiEQQmLJsLVlZWcTGxpKWliZC6g0GA3Z2dqJJbWZmJu/fv6d58+Z069aNevXq4e3tTUREBLGxsfj4+FCzZk2USiWtW7fGxsaGRYsWoVAouHHjBk2bNqV8+8E8eRpOxtNLoorH9yJuw0Bi1/WlIL8AnURBv+krzPNVo4n8Mq1ApvjLxybumUb8llH/Eh38dxG3YaDZxmdVN2JW9yBx1yRyHp8xd1XkShwaD8KxVGVev36Nra0twcHB3Lx5k1WrVlG6dGnu3r1LTk4OWVlZjBs3jrS0NPbs2UOXLl3+UdKD72uCdujQgaSkJPbs2cOECRPw8/PD2tqapUuXsn///v8YHXjnzh0aNWrE69evsajQglSXKuQnvMVkgqxbBzAZ9ViUrIEgSLDwrYLCvRQFbx+YZ8uCgFWVNkTLS3Dq1CnG/34AAJVvVSSWtsgd3FF5V0SbZB5FGAuykVo7oHQrhbJ4WVSe5RDkCpQeZVB/EtK2DDB3Hc6ePQuYF36T4SvollSO+wAzolThVhrvyUexKlPHTI2Sqyh494i8l6HI7FxR+ZorMcG3OvHxZlqVZZm68AllKrV1Ft1QBAsbBKnMTPIv3wRDntmFQ5BIkSgtMRn06NLiULqVonKjtgQFBfH+/XuGDRtWBK9RGD/99BMdOnQQ5QGHDx/+l9gEMJvU/vHHH2RmZoo/BQUF/xJ49T+i3NK7d+9v1NwL4/fffxdVBb6MRo0afWM0+OHDB/H3L1Xana2VtG7RlIte5UUagNzJE9du8777nBK5EsdmQ0XJLwGwzX5P++aN6N27tyheDdCqVSs8PDxITU1l8eLFhIeHExYWRlhYGEePHmXTpk28fv2a6tWrs337dvr374+VlRUTJkxgwoQJ4nE6duxIx44d6d69O69evaJ+/fokJSXx888/M2XKFDZu3EjNmjV59uwZxYoVA/iu47o69gWZoSFoU2MQBAlyJy8cmg1B6V4GMAvGBhhjiPrwjBo1arBv3z66devGihUrGDNmjHicp7GZn4io5mMrvcqTdedPTEYDgkSKPjcdk8GANultkdt0GQlYdV9AfV8NSyYNL9JykkgkVKlShSpVqjBlyhQKCgq4desWXbp04dSpU4C5HVmhQgVkMhkmk4npc+Yjc3AXPfVUvlXBaERq+Vmz0bp8UxQuRXdvJqMRvQkaDF9MYPotjh07Rrt27UTQw/nz53ny5AlJWjkKrwoUvLmDba3OqONeYFunG+oPT7Cp2gZ17HNsPgkD5Dw5i2Pz4chszRxBu3o/Eb8hiAKtjrvhkahfPmb27NmUKVOGS5cuERUVxbx58xg6dChz587ll19+QaFQMGXKFNzd3dm/fz/3799n48aN3Lhxg0OHDjF3rnkj0alTJzZv3szatWtFhZ+MjAxKVKlPXkA7LOISxcXvr8KQk4ogV5Lz4AQmE1x8V0CVBi24dfkcBuHbTVPqqdVIbZ1xaNDX7Oidk4oxP4vY33sjCBLsG/bDumJz87Hzs8m4vJmCdw8R5EqsK7XErm53BEFCbvglcsPN3nwuXWZT8PYBuS+uokl6hybhDQXvHuLaZTZGQUq+T32WtG/M+/fvWblyJSkpKbRq1UrkHx4+fJjNmzezfPnyf1ur9a8iICAAf39/kpKSuHTpEn369GHcuHHcuHGDrKwsUVzb29sbvV7Ppk2b/lJez2AwkJ+fj1qtRq1WExsbK84XTSYTGo2GEzefYvFDb9RPLmPSfTKmFgRMBv1X+AI9qhKf54wyezfSfRoRF36L33YcMt9m52rGHxgNCDIlRp2a1FOrMeo0GHIz0KXHk3F5C5rEN5iMRvKeX0HuXAKTQU/KDbMgvlar/SzibSya+FQ+VcgKMydZqa0zuowEDHkZSG2dkTsWp+DdA4yafOROXmgT3wJQu2k7Qh+fRqfTofnwGImFDbr0ePSZSVj6mxOLRCYXCwqJTCkKaceu6YVJWwCY0KaZ+Yxnly/m3TMzMGjGjBn4+fmRlpaGVqtFoTBv0r7GQezdu1dsWX9palumjHm9Gz58OLNnz6Zy5cqUK1dOtFT7uyoR/g9Jlo1qVIobb1L/I+UWpVxC3IWtNJ97/rv3FxLY5XI51apVo1q1aowZM4YhQ4ZQsmRJSpUqRVhYGFZWVmzYsIGAgADUajV169albt26eHl5YTQaOXToEM+fPycyMpINGzbw448/MnXqVIxGI4MGDWLmzJlFnrdwoL7ozCsKNPpPQ+kFOLUciWXZepgMejRxLxCkckxGIxYKGTPbBEBUGqeiA2nbtq1oI/Ps2TMSEhJwd3dHr9d/Q/hXupcGgwFt8nuUbqXQxL5A5V0RfWZikdtk9u7ILGw5fmYPO9csEiH08+fPRyqVEhUVxaBBg3jy5AlyuZymTZvi4OCApaW5NSIIAu/fm9G3gkRK7IsHqLwria8j/9V1BKUlgkRAHR2O5mMEVuUaAeYKw6ZqG7SJb9DEvcSmUgsunT3BsSubALMck4uLC2XKlOHdu3ccOXIEmUcATm1+JuPyZqKXtUNiaU/Wzb0YctPJsHbGqMlHUcyPzBt70Kd/JOXIYhAk5oXMaARMJO6ajGVgQzLj41m1alWRz+inn35Co9GIvnvAN84Yffv2BczgrMJ2rIWFBQ0bNkShUPDy5UtUKhUSiYQIoQRyzSewVu63raCvw7JsPbLvHQG5Co1ez8uEXAqBKuo4M3k4IeRn5I5eSCxtkdo6k3FtJ7qUD2AykXFtJ9aVWmBZqgbJh+ZjKMgm98k5DLkZqLwr4dhiBFk395J1cw95zy7h0rnod1Sb+AZN7HM8h21CUFqR/+omqSeWo035gMLFrFs7adfvGHRm1ZLhw4cza9YsNBoNt27dYujQoUyYMAEvLy/y8vIYPXo0pUqVYvHixSQlJdGoUSMGDRokJp0rV65w9uxZsrOz8fT0pEOHDiJxW6PREB0dzeXLl1mwYIEoxqDX63n8+DFVqlRBq9WSlZUFwJw5c9Dr9WRlZWFlZYVer0ev12M0GmnVqhVSqdRcPZlMorGqyWRCr9ezb98+Lqp9sSxd1NFeZuuMdfnGOLX++a8/NEFAkCqQlG+NUWY2WVYU80OQycmPvF30ePZu5EfeRh33AmWxkjh3mIxEaUnKsV8oiLpHzKqu8Km6K0weWq2Or8O57TgyLpmvk4I3d9FnJiFRfELZSqQoXP1QRz9BlxqNZZk66DM+YmXSiDgMQ0E2xrxMUo4sRlHMD9knfMGXoUmIFH/3HLuH+I2DMeZlkHFhI2maPKwszaCvNWvWsG3bNg4ePIi3tzdubm6indmGDRuYOHGiiIPo3r276OX4pamtTqfj3Llz/Pjjj+Tm5tKzZ0+io6Oxs7MTuy1/F/9nEl8lL/vvVkj/Mgxaepaz4YTNZyjw15Gbmyv6mX0Zvr6+ZGRk0LVrV7p27UqPHj2oV68eQUFBABw4cICxY8ei1WrR6/U4OzszZswYoqKiOHz4MPPmzaNr1678/PPPIjDm6+hT2wdVQQoj1h5FZu2IAEUIriqvcijlMt6t6knHn4LoU7s1D2SpLF26lBMnTnD16lX69+9PdnY2AwcO5PHjxzRo3IRHxTuRdOxXNB9fg9GA0jMQuasvmpjnKN1Kme1R0uPRJr8nafcUVL5Vkdk4IbF2IC54MDYB9Qh/8gKVoKdYsWIolUpmz57NoEGDRDK8QqEQFVZWrVpF8+bNcXV1JSkpCUEQEFQ2mLT5aFM/w49zn19BorJCau30+SR8UQnkvbyG3MXHbNSaHk/Gpc2Ub/kTz06ZxQ3UajU9evQQCdOvUjXIHTwQZApM2gIkKivcg34nYft4sh8cR+kZiPCpnSjIlbh2n4/KM5DcpxewLFsPQSonIzQE6ZtQ3r17R0JCAvfu3WPx4sWkp6dTpUoVnj59CpgXxS9NRwtlviQSCQaDAaPRSHZ2Nvn5+WRnZ5OcnIxcLsfS0tJsCSS3oCDxLSkXN6Fy+3xmAAAgAElEQVTPTkWiUKHPTkFm64I67hUZlzahy/g8BwXMu3VBgjE3neRD80S3AU1CFCmH5gEgtXZBlxaLKVGN+t1D7Bv0Q+7igy75PQgSHFsMJ+fRGQAyr26ncJYntXUh/WIwrl3noEmMIufBCZL/XCiqm5hPmhSjtgBdWhwKjzJYBdYnI3QbmtiXKFx8EORKLMo2QP0qlPT0dBYuXMiSJUtQKpWiWMXGjRuxt7dHpVKxdOlSLC0tcXFxwcXFhXPnzvHkyRMsLCzIzc0lNTUVV1dXnJycSExM5Pfff8fW1lYkjBf65B04YK5uJBIJMpnMTJP5pPiTlZWFVCqlRIkSogA2mJHFer2ey5cvM3r0aG7dusWzZ88wGAwEBgaKbvUBAQE0ad6Sy7f2knZmDXLH4mbQByCzdyfz+i4s/X9A5VOZ+ODBWPhVRx3zDENeOoJEhsmgR5BIsChZnbRzawFIPjgXVcnqpB5bhsLdH31uOppo8/cKk1FMTvrsVFJPLDdvXKRy7Gp3JffpBQy56bi5uWE0GomLi8OQk0rCjgnIHM1jIInKCvsGfcl7GWo2sq3Vmfx3D9GlxqBLi6P4sE1kPzhBzoMTCEorZFYOPDu9jT59+rB48WLqdhrAjb2/UXz4FlJPrRbPmfQTJQzApDVXvcVHbjcLb+vUgAmXTlMx3tvL6P49mD17Nq9fv2bjxo24uLhw+vTpIutd4Vr6V7FgwYIiHTkwbywLN5f/bvyf8uPrU9uHmW0CvuHnfD9MyAUTPumPMLy+9o0bw5fxpWRZYaTmaoi19udSbnGRy7T02APad+3Fhg0bGDBgAA0aNEAmk1GxYkXRsX3r1q1s2rSJCxcuUKNGDZo0aULt2rVFKbHvxf71v6C8t5NSHy8hk0DSnqmU0UbRupQN8oiLrG/lQp+e3Ti7f5tIqn3z5g1NmjShVq1arF27FpPJxIULFwgJCaFe0EwwmbCq0IziI7dRfGQIgkyBUZ0jil/nRdxEUFri1HYcyk86oerYF8gdPTGqc3BtMZSzrzPEzUJhK/P+/fvUqlWLV69e8eLFC4KCgjCZTCK8uGLFilSqVMmMelXnoCpdG31GInkvQtGmxqJNeIOhIBeLUjW+ey5sq7VHZueKISeV/IhbWJSqidzbPJNwcnLCZDJx9uxZwsPDefv2LSZNHoJRj8LDDPSwrtIGiVyJZcnqgAmpzecEK3fwIPPaTvRZyVhXaoHJoKPg3UPsanclLeYNw4YNY/369YSFhaHX67G2tkatVuPk5IRCoaBixYooFAoRaq1QKHB1daVkyZJIpVJxE1CoNmM0GlGr1eTm5pKfn0+WXkreqxu4dpuHbe2uKIqXRZArMRTkkHJoHjbVP89BC5MagCBXgcmIbe3uWHwCc6UcXojM0QxmMuRn4PnzbgSFBUjlIs/KjBAu/inxm5OdqnRN3AebVYRyH53GulIrlB7+ZgSxyYggkxehzijcSmJTrR3pFzYS93sf0s6uRWppj1FtRgZa+FVDWaICBoMBvV6PVCpFJpOJFS6YFXACAwOpXbs2CoWCGjVq0LZtW7p27Urp0qWpUqUKP//8M87OzgQFBbF+/XpRMUcikbB//34uXbpEq1atcHV1FX0lBw8eTFBQEBs2bKB+/frk5OQQFxeHh4cHPXr0YOHChTx+/Jht27aZW3FnzxIYGIiXlxfJyclUr16d6Ohoatasydu3b+nbt6+oXnTvwUM8ey2k+PCtGLJTMX6abUkt7ZBY2JJ1+xBxv/fGkJNK3qtrOLcbT/HhWzHpNJ+QlKD+8JiCKLOKiWvPRUik5upPlx6HJiYcQSpDZlcM586zUPlUJufBCRK2jkSXFovCMxCZvRvZ946h8jBbjCUmJpKeno5KpcKq7A/IHNzRfvysOVwYLl1mkX3vKPmvrqOJj8Cl03S0CZHY1e6KIFeSF34BgzqHJj/UEjEX7sYU/tbvC/O4BODjlhHEbzBjHCQWdiikEn75ZTl79+7FxsaGIUOGiMjn/7/i/0zFVxj/ip9j0mmQKxSo3z2kvb8lVs46zp+/wvLly//ymLm5uaLM0pdef0ajCp1tSa5EJJv/Ue+B3K8HUmU5StZsQsvqZTl58iRVq1bFYDCwYcMGDh06xLRp04iPj+fevXv88ccf3L59m+zsbHr27CnqWZYvXx6pVIrRaOTMmTNMnDiRLl260LRpU7ITXvPywAquJCbi6+vLqcMKRowYwfHjxxk9ejQXL14EoHXr1oAZhSaTyVAoFAQFBeE/YAl6RTGsyn4mJdvV7UHi7ikYC3LQpsZg0hbg3GEywv/D3n+HR1H27f/4a7ZkN8lmk2x6SIMgoZeAlFCkS+8Cht6bgIBIEekKiIpIk6Y0kSaICCgIEukREDRICy2Q3nu2Xt8/hgxEwPbcz+d33/fzO49jj2N3dnbmmrLzvt7tPCUVWUc+kT2EjAScX2gAQPxH/Rm3zMH4R823ly9fpmLFipjNZg4dOvQUT2RpbismJuYxc4MkoS9XmZLbP5F7/kusmQ+QtHp8X5mD2sWdpwM2shdirNeFjG+WYctNQ+sTim85Of/XoUMHYmNjadKkCUIISkpK0Ao5DKkLrERJ/HlcKsq9hLqQ6hC7F437Yy9f4xWEzr8iKTtmYc9NA4QclnpUJHL27FksFgt6vZ4GDRrg7OzMw4cPGThwIO+++y4XL14kICAAd3d3Hjx4QH5+vmzQcnORJInhw4ej0+mYPXs2kZGRDBkyRFEz37hxIyX5GXh3eB2tVxCSJKHWG1A7GymIO47GFKjkQV2rvkTeha+xJMmhJYe5ECQV1swHcvhKrcFekIlTuQhIvoUoKcSalQhCoDH6YEm5KUvtINB6yS0VKq0c9nKp1AitKUD2YBx28n/aR/6lb8BulT0VtRaH+XEvLYCxXheM9bpgL8wh/avFWLMeotLL/xdz0g3E1SO4ubkpXl7nzp05efIku3btonv37pw7d06ZLAQFBTF37lylrD05OZnKlSszfvx41qxZw/bt29m1a5eyb0mScHFxoUGDBjIHKjB37lyuXLnC2rVrqVWrFlOnTiUoKIi1a9eyb98+atWS9etKW2/Cw8MZMmQI3bp1o3z58ty7d4/Zs2dz5coVCgoKnuoJ8/f3p3Kn4Vx39kQNcn+w+XHERlJr8O8nq7s8XD0Uj2YD0D0yTm51OyrrFt44iz60Jt6d30DSOKFqEk3h1RMEDF6G1jNQycm6VmqIa6WGlDyII/0rufCqNEqRvv89NJ4BJJ39jhmTx6PRaNiwYQMD1v7Id0ePYUm5Teh0eVKq8fBT3jsPWa6M12EtIfXzGVizHiJpnDBUb8Ur499i3YjmOBwOZs6cyeeff86Cz/az5Zd8vDtNUn7r0fRx7YbWFKBsvxSySG0V+jcMY0ivjmW++73n9v8S/1UeXylK5WnOTGvJpDaV6F67HK0q+9Kpmg95Z76gu+oikUUXuXz8a3x8fLh+/fofVgGVenzbzt2j7/pzHL2Witnm4KmIqsYJqwNs/lXx7DmPen0nERkZSVxcHE2aNGH37t2cPHmShQsXsnr1avLy8pg9ezZZWVk0aNAALy8vzp8/T58+fTCZTLRp04b+/ftTUlLC4MGDqV27Nm3atEGj0fDw4UPi4uLQaDRs2rSJBg0aKI3py5cvRwhRJjzr4+NDt27d5Oq5/GIc1hIyv10p85V++Aopn09DWEtwlBSSf+kgqDWo9QZUOhfUBhMFV75DbTA9emBKBE/cjleXqUpxS4UKFahQoQLt27cnPDwcZ2dnKlSogEaj4eLFi0q/m8PhoLBQFtREyFpnahd33Bu9gsbghVeHCeiDqynjVru441brCW9cktCVq0K5Uetwj+qN1iuIyhVCEUIwcuRICgsLad68OTExMTKria83Lav4Y3iUJyw1dC4V66MLrl4mVyFJEsb63fFo3BeNpz+BI9cS+sY+er4rh80KCgro3LkzsbGxHDlyhIkTJ5KQkED79u0RQrB69WquX79OVlYWwcHBqFQqXnzxRSIiIvD29mbOnDlMnz6dTZs2odFomDp1KmazmdTUVPz9/RFWMxpPeTweTfvh3fkNoFSXrWwY/kmDLexW0GgpuX0BR0kBpX9rW/Yj7lqtE+l730VYSxB2KypXT4RNnlZoTOUebwPIPraBBx/1VbbtUqM1gUNXoDb6Ymo3jpA3vkT3yHsGsGY+lI2b3Yak1SPsVoSlGF2QXDSS8fVSIhq0LFOl99NPP9G/f/9n9nH9Ef5qBV+zZs04deoUWVlZCk1YKV9oVlYWcXFxNGvWjMOHD9OwYUNMJhMeHh4cOnSIjAxZk2/q1KlUrFiRtm3bUqFCBRYvXowQgqysLKxWK3m2x8VDMnlGyXPHrTZ4PnNde0EWteo34eWawUiUFb99FuwFWWjcfBSjB/J9oM5JYPl77wCPST2mdKiBpFYrpB5/BJVWT8DgZYRM3k3whM8J6jaF19vXJi8vj27dunHy5EliY2OZ0avJX46oSRI4a9WK0ft3w3+l4SuFl0HHqGbhLOtTm42DXmRl//q4Jpzl+OGvOXDgABcuXCA9PV0pMngeCgoKiM3SPcof/rHCNwCSCkmrY835VHrPXEGLFi0YPHgwJ06cQK/XK6SyCxYsoF27dmzcuJGuXbsSHx9PYmIi169f5/bt20ycOJFTp07h7OxM7dq1qV69OhaLRVH8joiIYOzYsRQVFXHr1i0GDRpElSpVmDVrliLaqQxJkujZsycHDhygRkQ4ebH7sGY+JGDgh4RM3o1/P7nR1Smgokzobbc9eoiCPqgqebFfoQuuhsbNC0nnTPaxDXRr24Lbt+UKsFmzZnHkyBGio6NZvnw5y5Ytw93dHZvNRtOmTRU+1qioKPz9/QkLC6NC+2F41WqJa/WWZP+wCYe5EJeKDf7y9XWJaExxfCzazFtYLBbmzp2rlD6DrP/n5ub2iJj4r7eGCEsxklqL2tmIvTiP7OMbAZg3bx7Hjh2jSZMmdOvWDSEEOTk5tG3blrp16zJmzBiMRiOBgYGUL18eZ2dnTp06Re3atUlLS+PIEbkSMjo6mlOnTtGjRw+lEm/z5s1oXY3YslOeGo/aYMKWl1ZmmT3vsVCqpNYiIXuxJfcuUxq2tOXKv9GF1MTJVw7ROSzFqJ2NCJucXysVZS2+K6skeDSOJmTybvz6yg/Swp8Pk7x5Ei5VmuFSuSlF8T8hbI/JtYW1hMzDK0hY1oeHKwdgTb+PS5VmOPmGKeeyfJCfUqW3adMmMjMz/9Fsf/To0SxatIirV+VWlNzcXHbv3v3Ueo0aNSI3N5f169crVGul12X9+vUKKX7Pnj2ZPHmyQrD+4osv8uuvvyrtV5mZmURERKDT6XjrrbcUKbHk5GSu/fLz3x7/7yFf1wzGNa+IXqsuI34LPEVFKIvlpiOeoLcT+el0bvYimzZtIj398e9rB3vinX3tb49J9tAq41ycTsOGDSlXrhzff/+9ktLo3zCMnSMb8nJVP3QaFfrfEX7rNSp0GhUvV/Vj58iG/5ZGD/5NDV9YWBi+vr6PPQNgw4YNZbr6/w6aN2/Ohg0bAKjXpAV39RUBcG0xgv1pHnhGvVKGZ/D3yFEZ2XnT8reZyVE7EWsNIrJRUx48eKDkNEoxYMAA7HY7ly9fplWrVjxIy6bHW2t4fefPvHnwLt/m+pMf1JC5i94nKyuLefPmUVRUhFqtpmPHjphMJubMmYOHhwcLFy6kSpUq/Pjjj1itVgICAtiwYQPdu3dn7NixpKSk0KdPH86ePcuxL7fgKC5A0upQ6V2xF+eTc2o7ALrgaojiPHRBVck8sgZ7SQFO5SrjKMqR++p8yyNsFuz5mWx9o4fSJLp8+XJq1qzJgAED6Ny5M6+99ho3b97Ezc2NxMREjh8/jkaj4cyZMyQlJdG+fXsSjn9BUV6O3P+Tl45LlaayruBfhJNPKL4vj2b7oskEBARgMBjw9fVVQqnZ2dm4ublRK9iD11qE/8nWHsO1eks0Rl8SVw0ib9vrxP4o97q9+uqrDBkyhNq1a9O6dWsGDhyoVAGWkngDNGnShJ9++gmr1YrFYmHw4MGYTCaGDBnC6NGjOXjwIJMmTWL79u3UqFGDRo0akZaWhimsKjknt2LNkrlGLWl3sRfn4RxeD2tWEoVXTyAcdgqv/Ygl44HyYFS7eiBp9bhH9VFaT1QuRnx7zUbjGYj53mWKb51HF1YbjZs3Wq8g7EW5IElkH9tA1tG1igeoD6uFvTifvJ9kYVrPdq+hMfqSf2E/yetGUfjr97hWa45/fzk1kHtqO7bcVCS1Gq13CJ7NB8scjY/g234cx7atwM3NTamibNiwoVLl+3dQWgXdt29fjEYj1atXV3rXnkRpXv29997Dy8uLTZs2KamMuXPnYrPZiIyMpLi4mOjoaGrXrk3Hjh05d+4ct27dIiEhQeFUNRgMiohscHAwAwYMwGQy0a1N0+eqPPxVuFdtyo2TB3AqSOaNlqEUnN1Z5vvfUxHqAiNQaXTknfsSYbdRcvcS+ddO0bNDa1avXs3JkyfLFFc1CHJGK2x/20PzzbtJkyZNmDBhAmvWrHnKKXheRK177XJMalOJM9Na8kn/etQM+vcUoYX/R0K0fxdhYWHk5+czZcoURfh0w4YNigDlX0VpKXLLli15qUMPkgOiOPZbMjabjYT3exA4ah1az0Cc1BKSJD1XNbj8wEVQribPOlGlD5rnjsHhQHtyDX1bRrJw4UJFcNZqtSp/0JQCK13mbOZoXKJcCWh/vCeH1YxOp6OqB1QsucmBrWuIj49HCKEUCpRWx0mShEqlkmmLHA4CAgL46KOPSElJYe7cuURHR7N+/XpsGmcCBn5A2oEPsaTEozaYMNbvTtZ3qwh5c7/M3F6c/6iP6xLYrehCa+L7qJQ998xO8n76CkdJgVy9JQTt27dnyJAhrFu3jgsXLmC324mIiGDZsmWK9EhoaCgpKSlYLBYlf9l+/k5+K3L5cy/6GZAkeLmqH5/0l8VvCwoK8PDw4NatW5QvXx6TycSNGzfw8ZFDSIqIpu2Pvfbfi2jeunWLHj16ULduXVasWEGXLl0IDAzkyJEjVKpUiTNnzigivYMGDeKbb76hT58+REREsGDBAnx8fOjfvz8XL16kTZs2XL16VWFYadOmDQ0bNmTVqlVY1XrUES3I//V7HMV5aE1B+PR4C43Rm5IHV+WqzpxktB4BeLYeScaBD/DqMIHc0ztQ6VzlnssneGgDBn5A4W8xZMdswZ6biuTkgqFGK9wbv8rDFf1RG31wj+qNW62XyY39itxT20E4ytwPASM+wckr6Pkn629cn5EjR6JSqRQJrr+DkpISUlNTn/lKS0sr8zkvLw+dTkdhYSENGzbkhRdewNfXl5SUFD7//HPGjx/P0KFDOXToEMuXL8dsNtOsWTNSUlIwm82YzWZFAqeUoGDkyJG8847sBYeFhfHBitXMjJUw2xxyK0xOMt6d36Dk/i9kfPMBQeM2A3KO70mtzCfXFTYL4sup5GSk4eLiQruBr7F56dsEjfsMtZsP1qxE0r9ajC03DX1IDXx7zsKSfp+sI2uwpt3BPyAQg5OKhIQELl++TIcOHfDy8lJ0M9977z3efvttfrqdqtQ8lJSUKDlreMxJ2iLChzEvhXNs92csXbqUnTt3KqTQ/434tzV8o0eP5r333uPOnTt4eHiUMXxnzpxh4sSJ3Lx5k0qVKrF8+XIlzt+8eXMaN27MiRMnuHTpEj169OCLHTtk7keVGkONVpjajuH+4k6YXh5LXuw+7EV5uFZrjlfb0ThrNbzVoTKW346zdOlSklNSMHuE4dV+gpJXub+4E6Y2o8m7sB/hcODdYSIZ33yAsV5Xcs9/+VQzcMY3HzKq/Yt88N5ixfDdu3eP9PR0uvcbQlFYU9zqtKfg1+MU/HJEmU3/foyGqs1o0qQpP33xId27d+fgwYNIkoTZbCY3N5dLly4xbdo0cnJylIS8m5sbZrMZm82GSqXCyckJlUrFq6t/4Ohvqc805n8G4XDgZ03hyOxXFAq6AwcOcODAAYUxo0uXLtSvX1/uzykwM2Xlbr499wtOBg/M+dkM7NKK4xveZcqcd5l/Og+z7e+PxFmrZkz5bIb36YoQgilTpnD+/HkuXbpETk4OoaGhSmFJKX55mPOHhU+SSkW7mkGMbV6xzIy1sLCQESNGcO3aNfr168e0adNYsGABM2fORAjB6dOnWbp0KefOnWPMmDGMGzeOQ4cOsWvXLpYvX87du3dZt24dhw4dwtfXl4SEBOXBWtrSEBQUhKH9ZFI0vvyZ0OdzLgw6YcGi0v+j6/qvhrNWzc6RDakZ5MHx48cZNGgQcXFxuLu7I4SgoKDgmYbrWYatpKQEX19f/Pz8nvl68jsvL6+noitPoqCggNjYWIWI4uzZs3h5eREVFUWjRo2IiopSisv+CCO3XniuqO2fQZKgTWVfunqlsWPHDr755huqVKnCuXPnGLguhrP3C557f2rSb7CgbxNefbkxDoeDhg0bEhcXx8mTJ+natStbt26lRYsWxD9IJar/JF4d8wb5Zjvm4kJOnj6LyWjAy9efmpVfoHKAG70ig3DVCEaNGsWVK1fYv3//H9J9/Tfg39bwbdiwgdWrV1O1alUWLlyoGL69e/cSHh7Oxx9/zKuvvsru3bsZO3Ys8fHxeHl50bx5c+7cucPhw4eJiIhg27m7jO3fE+eqzcsUSdxf3Ann8Bfx7jwFh7mI5E2v4915Cs4V6pKyaSJOJTmcPRXD6uM32bZzN/nnv8T3lbnoQ2tyf3En9GG1sWY+xPTyWFRaPak7ZskKzo37UnLvZ9L2vovG6E25UetJ+3IhxfGx1K5Vk4cPH5KRkYGfnx+m+l25c/k0rtVb4lbrZQp++Z78SwexZj4geNJOEt7r+tQYA7pOQZ99l2k9oxg3bhxr1qyhT58+VK5cGR8fH27fvs0rr7zCp59+CkDjxo25cOEC3bt357fffuOXX35BkiT6jJnKWZeGZWit7i/upHjBfwSdRqJK4rfEfruHBQsWMHjwYMWDi42N5euvv+brr78mEwNBbYeR4eSH3WYrozqg16gwWyx4mVO4+8t53Op2/kOKrd9DqxLM6VydExsWsmfPHoQQ1KtXj9WrVxMREcGFCxcYOXIkly5deubvMwvM7Ln0UCEmNuq1XD1zhNgdH/Pj0UPPZPQXQjBo0CA+//xzJk2axObNm9m/fz86nY6EhARl5h0TE0NCQgJOTk4UFxcTGBhIQUEBzs7ONG7cWJFkUqvVREdHs27dOnr27MnQoUMpX7c5fdef+0dEDA5rCf0ra9h+w/G3zuUfQUKgVqkQQmD/G08KJxV0DDQTLpJ48OABa9asUVhSSg2aJElPGa3nvdzd3f8Rs4sQgvv37ytG7syZM9y4cYM6deooBBONGjVS2JL+Dq48yPnH16p0UnD7px8U8eauXbuSlJREYWEhjVu+TGjzPmi8QyiygVGvxVdn49KeVXx/8CslvL548WLc3NyoV68eN27cYOXKlcxb8Rktx7/H6TvZmEtKytwLkkMOzbepFsj4VhHUCvYgKSmJ7t27ExoaymefffaUlNh/I/6x4QsLC1MEVksxePBgVq5c+Y8GMnfuXIUMttTw+fv707hxY+Lj49m/fz/btm1j2LBhrFixQnHnQU5mjxo1isGDB9O8eXOaNWvG/PnzlRvz7qapuFZvQdbhFahc3Al6bQsJ73XFr98SdIERPFw1CEdRLrqQmvhHv0vqrjkYK0fhmX2DfJ0PKT8dxFFSRLmRn6Bx9+X+4k749n2HzEPL8eowAUlSkbZ7LsGTdythzwcf98O359voylUm45tlqGwlHFq/GK1WS8OGDblwJ53+n11QxlZq+H7v8fn1W6JUOaZ/tRgnv3DcX+yKev8MEu7eVpTcS8OdIOc4bDYbFStWJDc3F4vFwpo1a7h//z6rV6/GarWSlZWFU9VWeLYchspJr+zvzwzfk+XJ3588z+SVuyjWeVK1Vl3KB/lT2d/IK3WDOByXzPwDV7HYHAqv37MgHA4kYaN9jXIc/uWhXPX2B/eJBDipIS9mM7sXTSij4JxRYGbPxYdcT8nj+p37pD9MYETvjrxSNwgvw5+LX/788880b96cQYMG8fHHHytUVaVGbe/evRw/fpzy5ctz/brcHyWEoHr16pQvX56QkBDlZTAY+O6771i1ahUALVu2JDMzk7y8PMaNG8e8efOIiYlh2rRp5OXlkZWVxfbt26lTp86jkOzfI2JQCztu8UeI++oTmg6ZSUpAo7+XkxaiTDGFsMnh6DbVAmkXIjHu7fdwadwfO9IjbYDnbMbhQHLY8Lh7nIok4+fnx6VLl7Db7cyYMaOMMft9b+y/AmazmUuXLpXx5oQQNG7cWDF0derU+ZeJof6Ta/Xkf6hdu3acPXsWtVrNSy+9xOrVq3F1dWX//v3s2LGD06dP065dO/r27Uu7du3Q6/VcuHCBiRMn8ttvv6FSqVi5ciWvvPIKdevW5aFzedxfGoJDUv0hz2tpOD+6io61UwcwduxYZsyY8T+mjftPwf+oj+/AgQO0bt36XzWWp1C9enU6derE4sWLFdmJ32v4gZw7SkhIUD6X9pD9npYLQKU3UHxb5otTGzwpvnMRld6AoyhXzpEIgT03jfSj68lSq7FbShCShCSpsOVnKuHOUj5HkPN8Kme3Mrm+Ur69x5+dmDFjBl26dAHgg8O/PDW2Z+H3pdAOawmoNGTmlzBs2DC++OILzGYzzs6yZty1a9ewWCzUqlULNzc3he6nf//+OBwOTCYTAQEBZGVlUXjlW9q3b88VVThm+5/8cYUDjQRvdahGjXIejNx6gZibWVCpNVabgysZDq5kJOGkTmbpd9exOxzyH+9PRF9lUVgnjl5LxTP9Fxq07sQPN9LlhucnpITSts/AUO0lnIze1HdJ5WbP55cAACAASURBVGr2b/Ts2ZMffvgBq1ug0lsJPFIy0IIxnI++v6noxD2ZvxVCkJ6erhi10pckSaxatYpdu3aRnZ1NUFAQISEh5OfnEx8fz8yZM6lXrx56vZ5p06YpCh579+4tE15LTU1l+fLluLm5kZeXx8mTJwkLC2PJkiWYTCbKly9PzZo1OXjwoCJzdOPGDXx9fXl4YgeW2Os4qndGpXH6wwcYgE4tkX18MyLlZ4YMGcKhQxtpMSKQc5TD6ig9x8+7rA5UOCjvVEheTiaJaVk4SXbU+Sk8/HEPG4vz+FSS8PDwwNcoUVyhGbkucp+h/YnaOCe1BJJEywi/R2Hi7oBM6Lx3715+/fVXpYH/X4nU1NQy3tzly5eJiIggKiqKnj178sEHHxAaGvq/9kAvrVr8J7ljgG+//faZ65aykWRkZLB3716WL1/O0KFD6dq1K3379uXEiRMcPnyY8ePHM3DgQN58801eX/klK04l4lD9+WO9VG5qw8VsouesYeaIzn/30P+j8T/y+DZs2PCU4bt9+zYjRozgypUrih7ZqlWrFE24JUuW8PHHH5OXl0dgYKDigXTp0gUhhELzdOjQIV588UWGDx/Ol19+icFgwNPTk/nz57Nq1SrGjh3L+vXrqV+/PitXrqRdu3Z4eXlx9OhRvLy8uHP3LnZjIKbOU8k8/DGu1ZqTdXgF7k37Y027S9GN0wSOWkfOiU04+YWT8+NWUGvx67OAtL0LcanUCK8Wg3mwYhDGhr3IPSd7cxp3P6yZD9CXj8T88Kps9JxccBTnoXI24rBZ0AdGPOJJFIRO3Ufa3ncpvnlGKT6x2+1oTEEIazH2gmzUHgFo3X3kfiirGYSDwDEbSVozDI+XBpFz+guZikrYAQl9aC0c1hKql/Oga8d2LF26lMLCQoQQSJKklPSr1Wq0WrlKcv369cycOZNatWpx6NAhHA6HUlyzaM1m9t4sYe2AFzG1HkXexa+xF+UhzIX4D12Bk6kcUvJVMk5+QfehE4i1BmNxiL+d27AVZJHz41a538xagtrghWuVphgb9ETlpEcjOdg5oiFhPkamrd3HrwmZVI+sj1Gv5eDiUYwdNpjmTRrRtm1bNBoN77zzDnO3HUMfFY3F/mfjEaiEHa/7J8i9eJAHDx7g6uqqeGihoaGEhIRw+/Zttm/fzpIlSxgxYgSSJDF37lx27tzJ999/T1DQ40IPi8XCpEmT2LhxIyNGjGDFCpl+6sMPP2TWrFk4HA5effVVDhw4QExMDL/99htLly4lPj6eRo0asWfPHpydnUlMTKRChQrKw7l///4UFRVhMwZiatKXY9fTsDsEjmccn0qSJ17mOxfpFO7EiB5t+eWXX5gwYQI2YyBBbYdh8414SuDWYTXLem/3L2O5fIDKvi5cvnyZ4cOHKwwonTt3pkuXLly7do1jx44p0Z1nhYlLc0VPetZms5nIyEhmz579L2HqsNvtxMXFlTF0WVlZSl4uKiqK+vXr/694kn+GP8odP1lA8vvc8R+hefPmXLlyhZSUFHQ6HUlJSezevZsvvviCO3fuKPRecXFxzF6+EWP32ai0+r899idzsf9X8C83fPHx8dy9e5dmzZqRl5dHz549iYyM5KOPPuLGjRu0bt2a8+fPExgYyL1797Db7YSHhz8z1Llq1Sp8fX0xm83s378fu93OnDlzWLBgAX369GHjxo0MGDCAvXv3EhcXx9tvv83nn3/Om2++SWDLgcyYOBr7I5ZyjYc/eWd3EzBsJalfzMJRlIP/4GWk7ZqH36sLSd74GlrfCjIZc9J1HCUFuNVqS8nVY1itNhw2C97dpqP18CNp7UhQqVDp3dC4++JWtwuZBz9CpXPGvWk/nHwrkPr5NFBrCZ26j4x971B44yw9evTg3r17St7JUKc9lrT72AuzcPIJw7lCXfJ/Pow17Q7OFRtQHH8eU7vXKLhyRDasBhOWxOtofcujdjFiTYnH4CRXcebk5ODv709k1Ev8fOECyQm38Ymoh0e1pjz4biM6Z2eGDezHxx9/rPCGzp8/n6ioKIKCgnBycsJoNFK+xou8/PoH3L1/n+/mD6D/1HeJLO/D21Nfx2yHwHFbkLR/PUyU+e1K1G5e2LJTKLp1Dufweng2G4jGww9bXjp55/diqNWWrKNrca3aHJ27F82k60rBx9q1a0lISKB8+fKsWbOGkSNHsmrVKmbNmoVv415YqnXk/rJXCRi2UhHmfR4erh6Kf8cJvN67NWPaVH9mLqOoqAg/Pz/q1KlDTEwMU6ZM4fjx48yfP5+pU6c+U0plxYoVTJgwgYoVK5Kfn09GRgbDhw/nnXfewcvLi8mTJ+Pq6sqCBQsQQlChQgUCAwO5fv06oaGh3Lx5E5VKxfvvv8++fftISUnh7t27XLx4kQPXclh9NgWLQ/BHBS/C4QCHFf21wzz8YTvFxcUYDAYKCgrQGb2o3GEwaWYNOjdPzAU51Arx4trhzaz+cAmDBg1Cr9dz6tSpMqQH3bt359ixY1y/fv0fCUTPmTOHy5cv89VXX/0jjysnJ4fz588rRq70uVFq5KKioqhcufIfFrL8v8ZfnRT8Ge7du0d4eDju7u6sXbv2KdLlu3fvsmPHDr744guysrIIenU+KWqfP40OPAu/r779PwHxDxEaGipcXV2Fu7u78lq3bt1T6+3bt0/Url1bCCHErVu3hI+Pjzh69KiwWCxl1pszZ47o16+fsu1du3YJJycnUVRUJBISEoROpxNVqlQRzZs3FydPnhShoaFCkiQRGRkpTp48KYQQYtCgQcLf31+sX79eTNxxSfi+MkdoTEHCf8BSofEMFIBwrdZSGGq2FYBwb9pPGGq1FYGj1glAuFRvKSSdq9AFVROuNdsISaMTIAlJ5yIkrV749l0oQqd/IwDh5P+CUBt9hVenKcK7y5sCEG71ugpD7XbyOmqtQK0VodO/EfrgagIQxcXFolatWgIQaqOPMEb1Ebrg6sKl6ktCXz5SeHV4Xajd/QRyB7IAhC64mtD6VhDuTfsL1+qthMZUTjhHNBb68nWES0SUUKvVQqVSCUCEdx4t/PotFmqDSQBC4xkoyr22RUhOLmW26ebmJnbt2iVatmwpbt26JZo1ayacnJwEIFq2bCmEEKJBgwYCEFqtVri6uoreg0cKtcEkTO1eExqPAAGSAEn4D10p1EZfgUojH5ebt7wfJ2ehcjYKQJQbvVEYo/oIrU+oCJn2tQid/s1TL11wdeHWoJfQBbwg9Hq9cHJyEmq1WkRERIi2bdsKb29vUblyZVGxYkWh0WiE1tkgQt74UoRO/0YEjlgj9GG1hUpvkI9VUglJq3/00gmtXwUhOTkLVBrhUvUl4RxcVVx5kC2EEOLatWuidevWwtPTU1SqVEns3LlTjBkzRuj1etG3b1/RsGFDkZWV9dz/QXZ2tli6dKnQarUCEAEBASI1NbXMOpcuXRJhYWHCZrOJAwcOCLVaLQwGg/D39xehoaFCo9EIg8EgWrduLRo2bKhcC7XBJExtxzzzfJUb86mQtHoR8ub+MstDpuwR7vU6CaPRKMaOHSuaNWsmAPHSSy+JuLg4Ubt2bfHNN9+IkJAQ4ePjIwIDA8X48eOFt7e3uHz5sjLmlJQU4eXlJapUqfJ3Hw1CCCGuXLkivL29RWJi4l9a3+FwiJs3b4pNmzaJkSNHiurVqwtXV1fx0ksviRkzZogDBw6I9PT0fzSW/0TMmzdPREVFiUmTJomOHTsqywcNGiRGjx4t2rdvL1xcXMTRo0dF+05dhHtUbxE6/RvhHP7iE/e+XoAkvDq8LkKnfyP8+i8VTv4vCEnnIpz8XxB+/Zcq941zSA0x5c3pIioqShgMBtGmTZsy57tXr17Cz89PGI1G0bRpUxEXF/f/i9PyL8P/aKr01VdflaEPGjFiBKmpqfTt25dy5cphNBrp37+/QgNUsWJFPvroI+bOnYuvry99+/YlKSnpqe3eu3eP0NBQpRG7Ro0a6PV6Hj58SFpaGk2aNGHu3Lk0atSIixcvlilwGDZsGMOHDyev5JG0i7VYobgCcG/SF9fqLdGVq0LJnYu4PuI/BPDpNFlh69B4BCAcNtQGT9wiOz2i6pLh3vhVhQBYbfBE4+6LysVdFl58lNdzqdRInuUKB1ovOed48uRJ5s6di5NvGGpXOazg328x+uDqWNLukH1sPfbcVGU/wVO+RB9SE2tGAnlnd1N06xy2nFSZN9FqxjOyPSaTCfdqTUFSke8ciL0gG3tBNqg02HLTSFo/RtbFUmsJmrgD1FosQu6lOnPmDN26dcNsNitNqpUqVVKqFgFatGjB3r17SXKpgLDbyInZgne3aaiN3kg6FzL2LgRkHkBAYXsxtR6tvAceK0k/Z0YqHHYKLn6NoXY79HU6KpIwfn5+pKenk52dTVpaGi1atGDSpEmYgsJRaXU4LCWk7ngb16ovETThc/TB1UGtwX/Qh4RM2SOTWJvKETR+K2oXIyV3f8YhYPWJeAoLC2nTpg3R0dGkpcll5WPHjqVVq1ZYrVbOnTvH0aNHFZ7WJ3Hjxg3GjRtHWFgY69evB2S1juRkuaBjxIgRDBs2jE6dOjFs2DDu3buHRqOhc+fOqFQqwsPDadSoEe3bt6dx48aYTCbOnTuHxWLB1dWVFZ9/jcYjANTPztdo3H0JmbLnqR5SSavHq/UI+k94i927d6PVapVinC+++IKkpCTatGmDJElkZGTQr58cBXjvvfcYMmQIVqsVu91O//79GTlyJElJSaSmpj5zDM+DzWZj6NChLFq06LmeYnFxMSdPnmTJkiV07doVX19fWrVqxeHDh6lWrRqfffYZ2dnZnDhxgnfffZdOnTr9r+QI/12xZcsWRcf0u+++K3MNtm/fzltvvUV+fj5NmjQh26pB/cij9n1lDiFT9hAyZQ8+3aajNngqpATPIjq3F+cp29287XM+++wz0tLSsFgsvP/++8p37du359atW6SlpREZGflcfdX/FPzLSapnzpyJJEn8+uuvmEwmvvrqK1577TXl++joaKKjo8nLy2PUqFFMmzaNrVu3PhUKCQ4ORqfTkZGRoRDY/h5/FD4x6p9/aLrgatgLsmTex6Bq2HKSle9cq7ck78J+LEnXcS4fCYVZlFyLgd/VGj65b7XBhKM4H+F4rOjsKJY1v1TCQaA6jxvIorbe3t4Ih07O5T1C7tk9spGQ1JjajCbr6KMGXyHIO78XjWcAbnU7IcxF5J7ZIRfk6FzxdlFj7DSCa+kl8GsMaV8uUIQgJY0W//5LSd48SR67w07i6sGAQFOhAcdPHAeHTaF/Ks3frF27lk8++UT5fPr0aW49SKGo5isImxmNZyDFt38CJNzqdibvzA45V1etLbknt+FS9SUKr3yHJfEqWq9grBn3yY3dhzUjAZXeQNHNc2T/uAVb5kNAIGl06ENqYC/MRtIbKPjleyxJN3B1dUGr1XL+/HlFxd3f35/NmzfjEAIbKqQPXkFjfKTUcPMshppt5CIjtZaUzZNlXT1LET49H+U+VBoktRZrdjJb545mw+DLOBwOpk+fzpUrV1iyZAndunVj4sSJOBwOUlJSCAkJoUqVKtSsWZNt27bRoEEDLl++TE5ODg6HAyEEeXl5uLq6YrfblbaOTz/9VGH8KM2n2mw2mjRpwsSJE8tIr7z++ut06dKFESNGEBkZiUqlYtWJ+Kcoq/4qbA6Js7lunDhxgqNHj3Ljxg2mTp1KVFQUrq6uREVFkZSUxLfffsuAAQNo1aoVgwcPZufOnbz33ns4HA6sVivz588nPj6egwcPMnTo0L+8/2XLluHh4cGwYcOUZYmJiWVyc3FxcVSrVo2oqCj69evHypUrnyI2/7+KU6dOcf/+fXr37o23tzfh4eFs375dmYx27dpVoWLT6/XkFluxibKMR9asRDIOLsOn+0w0Rp/nEp0X34rFULM1DiF4oUlnReC1d+/eigYhUOb6z507F09PT3Jzc3F3d/9fPRf/W/iXB8dLCZ3d3d1JTExk6dKlync3btzg+PHjmM1m9Ho9zs7OSnzez8+Pe/fu4XikJxYQEEDbtm2ZMmUKeXl5OBwObt++TUxMzF8aR2V/I1r1sw9PkiR8es3Gp+fbTxlPQ/WWiJICSh5cxb1WK5q274banIdBCxpJYM1OwlFSWOY3GndfnAJeoCThV4TDgTnxGiWJcrl7zqYx3L4it1688cYbVKtWDUoKsGUnk3dmJ7acVLTeQUgqDdgsOAVEKNsVdivCbkVSayhJiKPg1+8RDoE+rA5IKu4d/4IH3i/KzfmAPrQmQZN2ofEsh7CWkLLtTQKHrwZAF1qTkCl7MNRqS+HVH+CRkZYkCScnJ8XQGQwGpdEd5Jm5LbiezILjcOBW62U8Gr8q7y+kumxc7FZZyBYUcuKi+J8UAmSQ+STtBVlkHPwQjyb98O76JgEjPsEvehGSxgl7QTbCXIRXx9fReAVRUFCA2WzGYDAozfgajYa5c+eCpEYSguCJ25X9Fd++QMKyPhT+FoOwFOFSpSmBw+TWmpwftzweh8YJR3EealdPnFyNCCFIS0tj+fLl6PV6Nm7cSGJioqLuEBISgr+/P+fPn6eoqIibN2/St29fnJ2dcXZ25pNPPmHnzp2UlJQwYMAAfH19cXZ2xuFwYLfb6dixI+XLlycsLAyAS5cusW/fPkXuBWTVhxUrVuDn58f9+/f5aPU6rN7y+bTnppGydSoJH75C6o63ZaoxwJaTyv3FnRCPctjWnBRStk0j4cNXSNv5NnfOHGTO/AVcvHiRyMhI3nzzTdLT07l79y4///wzPXv2pG3btuzdu5euXbvSu3dvrFYrs2bNYv78+bz77rtoNBq6dOnCgQMHnvk/ehZu3rzJkiVLGD9+PCtXruTVV18lNDSU2rVrs23bNvz9/Vm6dCnp6enExsby0Ucf0bt37/+/0XsCmzdvpm3btoqHGx0dzebNmykpKSE3Nxer1cratWuZPn06ffv2JTG5LL+ro6SQtC8X4NFsgNIK9Tyic3tBpvJZ5fq4uMXFxUURVrbb7UyfPp3w8HCMRqNyL5dG8v4T8T8yfJ07d8ZgMCiv7t27M2fOHC5duoS7uzsdO3akR48eyvpms5np06fj7e2Nv78/aWlpLFq0CEBJ3np5eREZGQnI7r7FYqFq1ap4enrSq1cvkpOTnx7IM9Cr7h/TLDn5hOLk8zQ7gcbDD0nnAnYr2pA63DjzHQEBARTmZpP+5UIy9r6DsD/N6+nd+Q3seRkU3ThNzo9b0QdVA4edvMw0bDbZyJTy6QlzAagfz9Ccw+o8Ip4VpO9frCxX6VwwtR6JNSOB4usn0bh541KpAahUaN19yUv4DfFEYt+1ekvUOhf8ot9FpTMgLEUkb5S9bWEpxpx4jcJfjz/u05NUCq1b9+5y+XlBQQFqtZoaNWoo2y3WmZDUGiSVqgxZsrBaAIGwW7GkymTV5oe/AaD1CChTBKM2mLBmPsRQozWulRvjWrkJTqZy6AJewD2qD9gtqHSuaE3lcHoko6RSqcr0icbFxTFz5kxslhJAQlJrZeMLOIfXI2TSTlmkV6Wh6MYZkj4dD4A1/Z4yWRE2Cyq9G8UJcXhF1CcwMJCAgABmzZqFVqslOjqaI0eOoNVqcXZ2Ji8vj5iYGFxcXDCZTJw4cYKTJ09isViIi4tj1KhRtGjRgqCgIDZu3EhaWhotW7ZEpVLxzjvvcPfuXSVED3Iko7TSFiA2NpYLFy4wefJkQkNDWbt2LfElBuW4C3+Lwavj6wSN34awW8mL3ffUvQeyCoIusBLBE7fj3iSa/F+Pk5BVxKVLl6hZsyYZGRnY7Xb27NmDJEns2bOHM2fOKP1tX375JdHR0bi7u+Ps7Mzy5bJsTfv27Tl27BjFxc9n+c/MzOTgwYPMnDmT+vXrk5+fz6xZs4iLi6Ndu3Z8//33pKWlsX//fqZNm0azZs3+EVfnfztK76nt27dz7NgxDAYDzs7Ock/ylSsYjUaOHTvGuXPnuHjxIkajka5du+Lt+dhgCeEg48BS9CE1cavdTln+PKLzJwWfnbXPZqrZvn07+/fv5/vvvyc3N5d79+492te/HffJX8Y/DnWWHvyzcPHixTKfp0yRSWtr1qxZpvH8SXh5eXHq1Kkyy9zd3VmzZg1r1qx5av3BgwczePDgMss2bdqkvPc26GjfthVHg6srZe6/14oqhdYzsMx3IZN2ISHQZdwgOTkBnU6Hi4sLXbp0oXmPQcQWeHD0alKZXiatZwAVJ25RypavbX6bC6k63N3dGTVqFHv27OGDDz7gzp07bNy4kcvX4rE9Eq+0pN3F5YWGFF0/RcCgD8k8+BHmpBs8XB6NLqgq5UZvJGndKMzJN+UQqUqNJOwIh4PUnbPxaC6HIbIOfUx+7FdyDs7giaOkQA5PmgKx52eSdWwjLpUaYU6+hcZUDmO9Lnjci0GDDScnJ9asWcOYMWOw2WyKqrjD4cBHXUQWJiSNjrwLB3AUFwCCgl+OoHbzxp6XTs6P2wAovnUeAJeIRphT7zw+x94hWDMSMD+8hiXjAfkX9lMc/xP2olwktQaEwF6ch+V6DIG+JpLzUykqKuLq1avMnTuXVatWsXr1amrWrEnTl1rIfYKAc7gsWGvLz0DYbQhhR2sKxFFSKOvUPdKVs+Wng8OGvTgftYs79oIs6rTrw/m1P2OxWNi1axd2u50JEyawf/9+JWR5//59Lly4QE5ODj179qRBgwa8/PLLaDQaLl++zKRJkzhx4gQeHh60aNGCM2fOMGrUKG7evMmMGTOU9pxSgoHSiEYpNm7ciFarxWq14uvry+DBg5m06zJmm5z7dq3RWlb4BlyrNKXo0fl9ErbcNCzJt/B79R15MhBcDecXGpBTZOF+fDzjx4+noKCA3r17k56eTrt27Th48CBdunRh0aJFhISE0KhRI8aPH8/IkSM5ceIEP/zwAyD/L+vUqcPx48fp2LEjDoeD69evlwlbJiUl0aBBA5ycnAgICODUqVN4eXk9Nc7/67DZbDx48IB79+5x9+5d7t27V+Z9Wloa7u7uWCwWevToobTYBAcH8+6779K4cWO5gjMoiIULFyrb/XjzHnIepdNzYrbgsJTg03pkmX07h9cj6+haCq+ewKVKU4punMaS8UARfFZJEv7uz26FyM/PR6fT4eXlRVFRkcKf/J+M/zoh2icxrnlFTt7K+Gf0TzYLHwx9mX3iGidOnKCoqIiCggKWvf06hYWF5FsEA+asAvdy3E9O4+L3X9G/Xy9GdWzExTMxbDr5A/PmzWPWrFlERkayceNG9Ho9Q4YMYebMmah+p5OlcfNC0mgpio/FtUZrvLtNA4eDzEPLyTr6CWqjN14dJpITswV7fgbutdpgqN2OpG3TSd/3LiAXiFhSb5O0bhSSSoNrzdYU/nIUfUhNiu5cxJJ0HVt2Em51O1NyX+6zNAZXIi7mG27evMm2bdto1aoVx48fR6VS4e7uTn5+Pte+Wo1bg55IGi1OngEU3jiNMBdh1ejkwhmVRs5H2R1KcY++fN0yhk9Sa9F4BeGwlpCyeZLMDOLuh3tUb/Tl65C6dSqGup3IOrePtPS7CCGoWLFimYKGn3/+mYsXL2K3mhWPWaWTPQd7fiYPVw58VFAj4dNzFs7h9bDlJJO0diTJW6aA3Y7aYALkGbDKUsCMGTOYPHkyRUVFCCFo3bo1Li4u6PV6jh07RlRUFAcOHODIkSMUFBSwZcsWdu3axeXLl1m2bBmDBw9my5YttG/fngoVKnDmzBn27NmDp6cnbm5uaDQa3nzzTUWG5/79+0pEA+DOnTsIIXj//ffZv38/kiSRV/I4V/w8LbcnYS/IQuXsVqaHS+3mTU5uMna7nV69erFixQpiYmKU3kGAHTt2MH78eIQQ+Pr64uvry48//sjEiRMZPnw4169fJygoiCpVqiiTj3PnzuHp6am0E0ycOJHq1auTmJhI3bp1iYmJ+T9r9Ox2O4mJic81bMnJyfj7+yuh77CwMFq1aqW8L1euHJ06daJatWp88MEHZbZtNpuZMGHCMwlDQr1cuFkg5/YLf/sRe2F2GU1FU7txGKq1wLfXbLK/X0fmkdVoPQLw7TUbtcvjHF3dEM+ntg0wcOBAvvvuO8qVK4fJZGLBggXPdEb+k/BfbfhqBXvwVofKf5tSSKeWyPxhK4M3naJRo0YMGzaMdu3asWjRItLS0mjbti179uxhy4yB1K1bl86dOxNzdidrrxzg3WGZBAUFsWbNGoYMGcLChQtZtWoVderUoX379gop8bZtW+nduzc4HsmIaJzwaNKPnBOb8Wo3DpDzUbqQ6mQf/xTPlwaRE7MFYTNjMPnh7hdM/oNr6CvUxZISjz03lZA3vqT49gVyTm5DpXNFV64yhb8cRW3wQuPigSUvHf/+S9B6BZNy/wpCpabJ0Le4efYIQggcDgdnzpxBrVazceNGhgwZgru7O4UlFjSPHsBaUxD6sNoUxv0g5zZfqE/h1RiMjV7BUKMVD5bJjcoqrQ7vjq+TcPWHx+c1oBJFN8/iHF4Ph7kIU9vROIryyD23BwAnz0D6jZ/Jb+smc/78eYqLi+ndu7ciPZOQkEBUVBQm/yBysrPlEGuKrDmoC4zAt9dskrdMwZaTgj64GsJqJi9WltcJHLqC1C/kvJpK54xbpYb8/NV6TmUkodVqEUIQFhbGypUr0ev1DBw4kMjISAIDA5k3bx41atRACMFbb71FdHQ0586do2fPngwYMIADBw4QGxtLixYtADkv6unpSa1atThz5gyfffaZcg4kSeLUqVNUqFABkKMapXRVL774Imlpady7+Rvw1ysYS4urHNYSxfjZ8zPIz8uhY8eOuLi4kJiYyKeffsrixYs5c+YMJpOJsLAwzp07xwsvvMCtW7e4ePEiS5YsYf78+Wg0GurWrQtAREQE8Q9TqT/oLYJ7v41VYAYJVQAAIABJREFU0mDUa5D8jQSFB6FSqRg9ejQTJ05U+Dj/G+FwOEhOTn6uYXv48CE+Pj5lDFvTpk0ZMGAAYWFhT4W5n4XnMbn07t1bfl48Azs+36oQZgeN/fS529YHVyPgCeX1UkgSDHznU15/oo/vyYiawWBQ1O1LMXDgwD88jn93/FcbPviblELIOaAoQzZpXhbqvTyWjz/+mPT0dCZMmMCuXbu4ceMGw4cPRwjBwIEDqVSpEvv27SMrK4s+ffowdOhQ6tatqxTNmEwm4uLimDFjBpcvX6ZRI5l5pFQf8PXWlZl72gFCYGzQA0lvIPPwChylZcaSCoQDt3od0agg/dinWBHY8zOQKkYhaXWon2i1kLQ6hKUEdI+bs53D61J8Rw4/p+1ZoLR2AMSciVVUAkrL8W02G7Nnz0atVpOdnY3BzYjkeNprdpTkywlzlRqtVxAqJ2dULkYcj0K4v4fazQvvDhPJjtmCLTuJpLWjUBtMuDfuS/Gtc0jCQeymhVw5fRqHw4HFYqFLly7odDq2bdvG119/jUajwdM3gPHjxvJgebRSVFPqAWo8/HEU5/Nw1SBUegNudToAjySq7FYcxXnoAhriHtWbByv6YyspRpIkoqOj2bBhA3q9nhMnTuBwOGjRogUPHz7E4XAozEHx8fFIkkTXrl0ZMWIEs2bNokOHDmVy2d27d+eTTz7h4sWLBAQEKDlegEWLFjFt5iw2HYghu/J5Hni/SIn1G6wBNRkwfAxf795Otc7D0YS0eP6N+juUFlflntqOR7MBWFLiKboVi6lceV5++WVOnTqF0Whk7969jBw5krfekuWlLBYLp0+fJicnB1dXV+rVq4fRaFTYfypVqkTLV4aQHxxF1q8POZqsxeLIUvar16Sw7PubVHAuJqEA9k+b9pfH/O8IIQSpqanPNGr37t0jISEBT0/PMoatQYMG9OnTh7CwMEJCQv5lHKB/F/+T6JZeo2Zs84r/C6P698V/veED2fjVDPJ4LqWQ5LChVqtpXdWf7hEGhnRtyWuvvcbnn39Ou3btSEhIoF69euzcuZOaNWsSEhJCp06dSEpK4q233qJPnz5cuHABV1dXevfujcFgYOjQofTr1w9JkpgxYwbz5s2jQoUKHDp0iKKiIiW53+vFUL4w2rme9VDWBSzKQusdgk9XOU9nTblF0qZJvFzVn9fGL6VLk6MkJibSeuoqLmVpyP5xK5KT81P5S/9+cpFM1uEVqPQGAga+T8q2N9H6hJX5PvnqcRwOBy4uLlSvXp3CwkJKSkooLi4mICCAO3fuUJCfh4fVTLkxn5F5SJ4xBo39lIxDy7HlpSmzTIelBEdxPuVGb1T6+p6VV3WJiHpqmbFWa5oZ0hne/FVqfrkZLy8vkpOTkSSJ/v37s3XrVmXdcSOHccW5liKrlP7VErSPNON8ukwts11z8k00VwJI+XQCKlcPPJr2w1i/JwXx5+nVrSurVq3CZJLDn0IITp48yfvvv09qaqpCADx58mSCg4PL5KDr1avHzz8/rcK9bds2vL29y+hG3rx5k6pVq2K32znvqIBTeAOK3Lw5fC0DPKvj3XkKX2/9BFteOjpXNx78eg5VwP/H3lmHR3Vubf83FndXYkCCBookKRokuEOKQ5FSpECxQqFI0FKcAkEPnmAFUgjuFlwSLIFA3N0mo98fQ3ZJgZ72+873nvP25L6uXMnMnr33s2cmez1rPeu+7xYfHPuPYNNtGjknVwuTAZNazcl/doUlS5YgkUiwsbEhIiKCmzdv0qSJbl3ns88+QyaTYWpqSosWLQgKCmLevHl8+eWXrF27Fvvm/Tic64K4KEPH//xd0aTif+hZgQyDluM5cD/lP9ZxG3Sfb3Z29icDW0JCAiYmJpUC22effUbv3r1xd3fHzc1NKBP/p+H/trpV4bj+3yRXBv8lgQ9+cw3+mKRQ9qvHSBLvEjpY1/5+6NAhevbsiZWVFYmJiUyZMoWSkhLatm1LSEgI586dY9GiRXh4eDBnzhzWrl1LYWEhMTExHDt2jNzcXHbs2MH8+fNRKpXk5+fz7bffcvjwYQICAlCr1YSHhwuSWWX5WVgUFdA0LYJMR0Ni881oWd+ds8cPoXp6Tjf+onvUdmiMvr4+9erV4/aZXzBs2u+T1/sxWLQaRubBucisXTCp1w7U5ZTHnEepVKJSqcjJySE4OJjdu3djbW1NYWEh9vb2KJVKjPNikYgq/0MZ125F9vHlGNdujczalfwru9B38haC3p+BVqNBq1Zg8DySlPIEVj80Q09PD6lUypw5czA1NcXMzEz4MTU1JSkpCcNX6agVnpQnP6M0LgpH/xUfPb6+Y02cv95a+ZzKcr7v2YjJQ3XGovHx8ezevZtdu3ZRVFSEQqFg+/btQqnn8ePHbN++nbKysn964/tY05e5uTnmjbti0mIot7OllRzKQTcRECYD78TAy7OTsB+wpJLAtEn9dpjU163xSC3sK00qZJaOgquHSASi86tJUCvp2bNnJccUExMTXFxcOHv2LFevXuXKlSvk5OQwefJkWrduTYsWLejZsyftxobwVFYTkVTvn3r7icRiytWwOPI5wL8t+Gm1WvLy8j4a2Cp+GxgYVApsFUL4FY//N1vy/L8KZv834T/Sj+9/Gk+ePKFPnz7ExcUJz+3cuZPvvvsOpVJJWFgYHTp0IDY2lh49epCUlERycrIgvA26tm8bGxvOnTuHv78/s2bNwsfHBy8vL+zs7MjLy6OoqAhLS0tiY2ORyWTIZDLi4uKoV6+eoNCwZs0a1q9fz7179wT/t02bNtGyZUtyc3PJy8ujevXqzF64jFERaeTfPIC6MBubrjpya9nbR+Se3iDc7H9vNSRPekr+1T26tTGRCJlYhEwiEoirsbGxaLVaTpw4QatWrWjRogW3bt1i3Lhx+A2axoSvR4OJNZYthwBQ9DCSwtu/oJEXo+9cC6sO4z9wrhAhQiKmkpq/SK1ELJGgTnpM/q1DdPu8PoMGDaK0tJTXr1+zbNkypkyZQlFREYWFhcJPUVERMTExOg6RWILU3A7zgGDB9PefQauU00SWTP9Gzty7d4+zZ8/y9u1bAgMDefLkCXXq1GHLli3Y2f3GeYqPj8fHx4fQ0NC/ROSuwPjV4ZxKN/xTqvkV0KjKEYkkuo7XP4G8a3spfX4NdWEWIokMTXkJEomEzz//nJcvX5Kfn094eDgPHz7k8OHDZGdnU1JSwt69e+nRo0clvcurMQkM2/0QreTD9aiCmwcpenwGTWkBYgNj9J1rY9tTV+JM3zcTS9+2nNk0/09lEO/r8/5ZFBQUfBDM3v9bLBZXCmwVf3t4eODm5oaZmdk/P8n/cvz/EMz+u6Eq8KFbtK7wDXufSDtp0iTWr1/PP/7xD4YNGwbATz/9xK5duygpKSEsLAx/f38Axo4dS926dRkxYgQ7duxg+fLlVK9ene+//57AwECGDh0q8Jnq1avHpEmT6NevH9evX2fBggVERUVx9OhRvv/+ex49eoS+vj5z585Fo9GwaNEitFotx44dY9SoUUgkEoyMjNBrO4FyG+8/tJ35JLQatEmPKTmzhoKCAkxMTHBycuLmzZuCQ/aaNWuYM2cOQ4YMITRUpyaj8x97gVyp/ie+eVpQqyi6uot/LJzEjVQV4aeuEtihC0ZSOLR1DU+Ob8XWzJAuXbrw+vVroXOtadOmTJw4kUePHn3wOX333XdEREQQGRmJl5cXe6PeMvfoYzQiyR8rnWg1aFUKzOIvUHj/JKmpqUIGWVBQQEFBQSVekkQiwczMTOCQVpQ5R48eLez3fhb6+79NTHRcvMdJ+fRcf/mjQUQjLyb3wjbK4u+hVZYjMbbEpH57zAN0mXzCsq44jdqIzKbaB/u+j8Lbv5B/8wBaZRloRZhZWWOsJ0GhUAgqLSkpKQIHNjMzk/r16xMUFMSjR4/Izs6md+/e9O3blxYtWjB2/8OPOosXR1+gIOoQdn3nIbN0RF2cR+mr2wJfLH3fTEzqBtJ34NA/JXj8scBXVFT00TJkxd8qleqTgc3d3b3SZPS/Hf8qwey/I/5rSp1/BLFYTKtWrbh06VKlbqVVq1YRGhpKSEiIEPiuXLnC3Llz0dPTo0ePHkydOpVp06bh7OxMSkoKhoaGjB8/nq+++oqwsDAmTJhAQUEBRkZGPH/+nIsXLzJu3DiOHDnClClTsLKyomfPnmi1Wnr27MnOnTv56aefmDNnDrUaNGXl0esUH3hIYZmK/CwxBg27UvzkPOV5eXQ2yOKG2gOR+K9bkaBRoXx8gnbt2pGQkCAok1RYoIwZM4bHjx8zffp0MjMzycrKIioqiudRUVhFv+G1vhf67p9RITtWAT0JlJcrCPAw5+zqqWxbPpdRg7/A1dWV+RMmMHy4HwB31r0iNvoBDi1b0qpVK+rXr09wcDCrV68mJCQEU1NT4uPjhe7HsrIyhgwZQmZmJrdu3RLW5eobFVJ2YglB367k+us81CpVJcdpA6kYpUpF6eu7aGJOYWcCY6ZPp3///hQXFzNs2DC0Wi27du0iMDCQjRs30rhxY2JjYxk+fDjVqlVjwIABeHp68vPPPyMSiZDL5WRlZVXKQn+flRYVFWFgYIBZ56noezX9qK9C7oVtaJVynEaHItY3QpWbgiIrodJr3G2MyZGJKVdqPjrR0JSXkn99PzadJ1H8KFLXpLNkJiNHjuTAgQOcO3cOS0tLvLy8GDp0KEePHkWr1dKvXz+h29TFxYWysjKmTp1KUlY+5XZ1KHl5AwAjn+ZYtv4SkVSGIi0WQ4/PkFk6AjqqRUXQy7uym/LkZ5SnvmTL+a2org1n2+ZNTJo0iV9++YWCggJq1KjB0qVLcXFx4fDhw8KELjw8HD09PYyMjCgtLUVfX5+ysjKkUin+/v6MHj2a6tWr4+7ujpWV1X+NWer/K6xN9BnT0uvfPYz/SFRlfO+wceNG7t69W6n1HHQ3hbS0NJYtW8bkyZOxsbHh9evX2NjYkJiYyMCBAzE2NqZTp048fPiQXbt2Vdp/wYIFbN++HSsrK5RKJbNmzeL69esUFhayYMEC6tevj6urK1qtli+//JK2bdvSbdgE2k/6ibsppZSXyysFFrFGBSIRzTws+GXR14ht3LFqN6rSa/4ZtMpy2tsUETptkKCDOn36dMEt3NjYmBo1atCnTx9OnDjBnTt3kMlkNG3alICAAPz9/QkPDycttwiDWq3watSq0owy7sweTh8/TMOGDdmzZw/Tpk1j3bp1vHnzBmdnHRl75syZ6OnpERISQlhYGMeOHePAgQOAjtS9bNky8vLyaNWqFV9++SWLFy/G09OTHTt2VOqca9u2LVqtlsePH9OsbUdqdR6OwtCG7MJSctKSeHrzHKnXf2HoF72ZMmUKderUEQLd9OnTmTFjBlOmTEEikXxgtTV9+nSeP3/OnDlz+Pbbb4mKisLKyoojR47QunVrAGFd7OLFizx58gR/f3/69OnD1q1bSc0uID3xNdZdviX/2l60ynLMmvTQKdUAqdvGYdFyCEY1Az74jNL3fkd58lMdd0+txLRxd8z9epMduRZFWhxo1Oi71MawZgC5p3/GvlE7Mu+fQyKRIJFIMDAwwNbWlvT0dMrKylCr1Tg5OdGsWTNu3bqFk5MTt2/ryPDvX3eb4JHcvHEd2z5zQSQi68hCDNx8sWg5hOKYS+Sd34xZQD8MqtVHz96zkkh2+r6ZGNcNxPqzjnR1g5rqRE6ePIlEIiE1NZXnz59TXFxMjRo18PT0FNRkZs6cKWRsY8aMwc7OjlWrVlFSUiKIfI8ZM+ZPf7+rUIV/hqqM7x0CAwNZvnz5B8/L5XK6devGggULMDAwwNPTUyBVV6tWjcuXLzN//nwWLVpUycsMYOXKlezfv587d+5gb2/P+fPnWbJkCW/evBEcETp27Mgvv/zCnTt32LFjB92n/IRRt++58bYIxOIPAlrFOtG1+AKsv1iINDoCszcXyfdoi1askxWruAGZ+naofDFaLQYyCS0siohYNQNeXWPhwoVoNBoaN9aVpsrKylAoFNy5c4eUlBTatm3LmzdviI+PF9aBNBoNI0eOxNfXl/4NbBn4RYPK71nTmaxfsZS+ffui1Wq5fPkyPXr0oGfPnly+fBljY2PatWvHvHnzCAkJwc3NjYSE3zIdPT09/P392bRpE8uXL6dXr17Y2toyceJExGKdzNrVq1f54YcfuHHjBhMnTmTHjh04OTlx6tQpdu1ayMWLF3F3d0eRkcGzh3eoUUNHe8jKymLMmDG8evWKCxcuUL9+/Y9+H5KSkoiMjOTzzz+nS5cu7Nmzh+PHj7N792769OnDixcvsLW1BXSSTuHh4Zw9e5aQkBDu37+v4+d5+EHia8qTn+I0OhRVbgppu6dgVPNzZDau6Dv5kH91j2591KW2oNAC4DD4RxKWdcV11Hqm9PqcIY0dWbB0BTsNDPH7ZgWGJhY8ObSagjsHEYtE1DVX89LZGQ8PDxwcHDhy5Ajl5eXI5XKkUilSqZT09HQOHTqEsbExKSkpNGjQAFNTUzIyMli4cCFhYWHcOhuBedB4wT3EvNkAck5vwKLlEEzqBoIISp6cp+D6fkQSPcz8+2Du37fSe6dQazl35znFolgCAgIqlSJr167NoUOH8PX1FUqdFSa1GRkZREZGkp+fj6GhIcbGxnz77bds2bKlKvBV4V+KqsD3Dj4+PpSXl/PmzRs8PDyE54uLi5k7dy5Xr15l5syZDBw4sNJ+UqlU6PD8+uuv0dPTQ09PD5VKRXl5Ofr6+lSvruPIbN68mUuXLnHr1i1mzZrFrl276NmzJ0VFRfj5+RGntedK5HPkf6YdWSxGLDZA2iQYl5IYXh2cg2GjHjoJr98l8RJ0DgF1LGFecFOU6a9wV01k9+7duLi4CAG9Qk4rOjoad3d3du/ezcqVK0lMTCQsLIzg4GBkMhkPHjzAzMyM169f06BBgw+Gdvv2bZydnQkNDaVatWpoNBrCw8MZOXIkAwYM4OjRozRr1ozHjx9TWFiIm5tbpW7ICtX3Bw8esHnzZjZt2oStrS0rVqxgwoQJgsC5VqsVSpOrVq0iPDwcHx8fhg4dipeXFydOnODevXtClnnixAm++uorhgwZQlhY2Ec5Vz179kQqlQpas/b29nTu3JnOnTtTt25dtm/fjqurK5GRkQwbNgy5XI6dnR3t2rXD1tZWEP3etm0be5/J2XnlAObNBiKW6aNn74menQeKzHhkNq5Yth9D0d3jFN0/Qc7pn5Ga2WHVfgyGXr+tj6m0sGzTbqafXI1arcbQ0JD7Gx+h1WpRq9XI5TollwsXLiASiUhN1UnpaTQaYdv7PEKAkhKdbmlMTAwikQiVSsWNGze4desWSrUWy/fEjHVCxr9x90zqBGJSJxCtWkVpXBTZESvQs/PA0LNRpXM0bd6KLcOmsWLFChYuXCiMq7Cw8JPixgkJCYIVWQU0Gk2VgHUV/uX4z7Eu/jdDJBLRunVrLl68KDxX0ebfsGFD/Pz8MDY25ty5cxQWFn6wf58+fdDX16dNmzY4OjpiZWWFs7MzJ06coLi4mOLiYsHDKiAggF27dmFoaMjVq1fx8vJi3JwfCfn16Z8Leu9BoYZoSXUa+DagvcFbMjaPQlWQgawgGVKjKX95jdqq11heXcmjn8fTup4HEydOJC0tjR9++IHTp0/j6OjIixcvmD9/Ps7OzsyYMQNDQ0PGjBnDixcvMDMzY8OGDVSvXp2VK1dy8OBBOnbsSEpKitAN+j42b97MpEmTGDp0KJMnT2bRokVIJBK2bNlCSUkJkydPxsDAAD8/P65evYqjoyN5eXnCjbqgoICkpCT69u3Lvn376NKlC/fv3ycuLg5vb2+8vb1JS0sjPT2dFStW0K9fP6ysrLh16xaXL18mOjqa8+fPc/XqVZydnSkqKmL06NF88803hIeH8+OPP36SaFzhMZmQkMDGjRvJyMjg0KFDWFhYUL9+fbRaLdHR0URFRdG9e3fu3r1LTEwMFhYWjB49mpCQEBwcHEhLS+P+k3eWT7+THdO8kx0Ty/Qx/zwYxy/X4jopDKNazck6tgx1WVGlMWmkBrRt25ZXr17Rr18/LC0tEYvFggqISCTGtFYzHH0a4j/0OzrMD6Nun4mIRGJEIhH6+vrCb2NjY2HyJpfLUSqVuLm5cfr0aRQKBeY2DqjfEzNWFWYJMm/vQySR6oTG7dxRVqxLvrf2ZmYg49q1ayxfvpyDBw+Sl5dHfn6+0Dile/mnrcgqPD4LCwsF66wqVOFfharA9x7atGkjiPOCLtszMTFBJBIxduxYsrOzadOmDYMGDUKtrqyQUPEPHRwcTEZGBgqFAoVCIWzXaDQsW7YMLy8vrK2t6dq1K8HBwfj5+eHn58emxTPJeXiW5A3DSVr9BUUPIylPiyV1+wQSV39B7tnftPGKn5wnfc90cs9uInF1MKn/mMSrUgNq1KhBm2ZNMdTKybh5lITds8g8/hN39yzj1dNHZGRkEBAQwJEjR/j555/p0KEDixcvxsfHh8jISEEr8tSpU7x6pZMCE4vFeHt7s2LFCo4cOcL9+/dZtWoV0dHRVK9e/QOvxOzsbCIjIxkyZAje3t6oVCrB3kRPT48jR45w6dIlVq9eTdu2bTl//jxisRhXV1cSExPRarWcPn2aq1evsm7dOnbt2kWdOnXIzs7m9OnTfPPNN8hkMiHTysrKokGDBrRr1w43NzdGjRrF3bt3uXTpEra2tty4cYMGDRqg0Wh4/PgxLVu2/EvfCVdXV4YMGUJ+fj7Z2dlMnDgRjUbDnj17OHPmDIaGhlSvXp2aNWuyYcMG5syZQ1xcHAcPHiQ/6885iYBOc9Q8IBitUo6qoLLxq2/tmjx9+pRhw4YRHx/PnTt3KCwsZOvBkwBYtBxMSUIMWdm5PE8r4HFKEa8eXEdsaoNtdV8aBLTCxsZGmIAtXrz4g/NXrJM61qhHwY1w1KUFqEsLKLgRjnFdnYpM8ZPzlL66i6a8FK1WQ9nreyizE9Fz0llpSYwtdHJxUjE+jqYUFRUhlUqxtbVFpVIREhJSadL4r7Yiq0IV/iyqAt97CAwM5NKlS8KMtKioSHDflkqlGBkZ0a9fP4qLiwXZpwqIRCIsLCz47rvvuH79OidOnCA3N5f169cjl8tZv349x44d48qVK6SkpJCcnExcXBxGRkacvnQNAEVaLM5jtmDT8ztyz2+l4OZB7PsvwmnUBkpeXEeeGC2crzz1JVILR1wn7seixSAyr4WzYv0mbty4IbhJVHAFMzMzmTx5MvXr16d169YMGDCAR48e0aRJE5o1a8bx48dp3749N2/eZPHixWg0GgICAnjxQucp6OHhwZs3b2jcuDHLli3D1NQUjUbDy5cvGTBgAPfu3RPGtXv3brp3746JiQmLFy9mxYoVTJo0iaysLAAsLCyIjIxk1apViMViLly4AICbmxuvXr1ixIgRggXLzJkzqV+/Pvv27UOpVNKmTRt27tyJh4cH/v7+xMfHk5ycLBip2tnZcefOHU6ePImRkRHff/89ffv2ZdWqVWzfvv0vc7jUajXNmzfn0KFDNG7cGCMjI8Gup7S0FF9fX0xNTalRowbffPMNy5cvFxpjbt68SXlW4h8eP/9GmM5xQ61Eq1JQdC9CsGaCd/5ohen0aevPvXv3ePv2LU+fPkWlUhF65hGjJuv4c6Z+fbBoNQxVYSZ5Z0NJ2z0VqbkdDoOWkZORwpPkfKrVaYRWq+Xu3bvCpEahUHDu3Dlyc3MZNGgQ7du3pyg1Hpm9F2nbJ5C2fQJ69l5YvGvGEesbUXjrICkbvyRp9RfkXf4HVkHjBM83s8bdKX1xg7gVwdwPW0WHDh3o2LEjNWvWxM3NDQMDg0ply3+1FVkVqvBnUdXV+R60Wi3VqlXjwoUL1KxZk2fPntGnTx+eP3/O1KlTiY+PRy6Xs2fPHvz8/Jg/fz5DhuiI3GfPnqVr166sWbOGcePGAbrmF3d3d6HdfcuWLbRt25Zff/2VAQMGUFJSwvz583ktcmTPvDE4j9+J1FTXOJO0ZgBWHcZiXEuXoWT9sgR91zqYNelB8ZPz5F/djfP4XUK5KG3nZPxateNR5D5MTU1xcXGhd+/ezJo1C4lEgqOjI2lpaTx9+pR69ephbGxMaGjoR4Vvu3XrxpUrVxCJRAwbNgyxWIyNjQ1z5sxh48aNREVFoa+vT61atdBqtaxduxYPDw+mTJnCjBkz2L59O9HR0Rw7dowzZ84wdepUUlNTCQsLE87x8OFDgoKCkMvlxMXFMXHiRK5cuSK4mXfv3h0XFxf27duHvr4+w4YNY/DgwVhbW+Pt7c3+/fsFF+qysjL69OlDTk4O+vr6xMbGIpFI8PX1ZefOnZXI6B9DaWkpL1++JCgoiA4dOqBQKHj+/DmxsbFIpVJKS0sBXfZraGiIubk5xcXFvHjxgsDAQExMTHj9+jWenp7k5OQgl8t59uwZbjVrU5yTQbUZx4Xux4rGo5KYS4gNjFHlpaEqzEIkliCzdcei1TCkpta6js/A4RTcCEeqLGP16lV07dqV5s2bk5yahtTcDtMmvcg9s0E4fnnKc7JPrEZTWoBx3TZYtR+DMieZvIvbdFQEqZiGvvXo2LEjjx8/5uzZs1SrVg1TU1OePXtGo0aNGDVqFOfkHlx4mfWHyh+fgkgEHWrb/ykeXxWq8O9CVXPLexCJRAQGBnLx4kVq1qxJcXGxkPGdP3+etWvX0r9/fzIzM4mIiCAwMJAaNWqgUCgYPHiwcBOsgFgs5ocffiA+Pp6vv/6aLl26oNVqUSgUSCQS9PX1GTVqFD/8oiNqS4zfWwuS6SExsqj0WKP4zcpIYmJdaY1Eam7Py9cJFBUVUVJSQt++fencuTNz5sxBpVKRnZ2NSqXC21tXlqpwvv4YBg4cSGZmJmlpaSgUCvbt20ft2rWZPn06EREaU8RWAAAgAElEQVQRjBo1ih9//JERI0YQEBDAxIkTOXLkCN999x1v377lwYMHLF26VHDuXrhwIQ0aNODYsWP07NkTgIYNG7Jr1y569epF586defToERYWFkybNo0NGzZw5MgRhg4dSnh4OI0bNxaudfXq1fj6+gpBr6ioiO7du+Pk5MTRo0fZsGEDCxcuxNvbm1u3brF48WImTpyIl5cXWVlZvHjxgufPn1f6nZ6eTvXq1WndurXQ5BMTEyM0k1RQHvz8/EhJSaFr165s2bKFGjVq0KlTJ+7cuUN+fj5HjhwR3KkPHTpEi6af4TIgRNAUhd80UktiLmFYvemHnbfvUG3qYUQi6NZvEHYvjzNnzhzu3LnDur3HmX4mBaVG936YNuwk7KPvXKuSADmAzNoFu37zdd8htZLoI/MxvXEDOzs7atSowatXrwQ/wIcPH7J582amLVzNjdcS0i7uRpWfhk23aR8d4+8hT3hCzomVjHv26k+9vgpV+HehqtT5O7y/zldUVISJiYnOKubtW5o3b87YsWNZs2YNderU0dEPunenV69ehIWF0bBhQ1JSUiodTyQS0adPH8zMzFAoFHh6emJlZUVsbCxyuZySkhIeP3v5l8epLs6ppDSiKszC3r0mJiYmWFtbc+DAAfr06YNaraZGjRqIxWJBiWTAgAGEhISwbdu2D9YqAbp27cqLFy9o1KgR9vb2rFmzhviUTLx7jueRgS/H8xxJcmnD3WJzcorLkclk9O/fn4YNGzJmzBg2b95Mbm4uERERZGRkYGRkxI4dOxg/fjy5ubloNBpOnjzJmjVr0Gq1PHz4EAcHB4qLi4mOjsbWxZMxaw4haT6CTc/g24OPCL3ymjdp2SxbtowlS3T+g7m5ubRr146aNWuycOFCOnbsyNGjRzl27Bhz584VskgfHx/09fXx9PRkxowZREVF4ejoyLhx4zh37hxFRUU8fPiQJk2asGXLFpYsWYJUKuXw4cPExcXh6+urI3cnJSEWi3n9+jXu7u7Ur1+fuXPnYmDwoYDAyZMn6dKlC+Nae4Fa+Zc/X9BpKU7tWJeffvqJ169f60qqm35Fqf6/K9JoJVJq9BjHrVu3yMrKYsSIEajVavbv309hYSFv3rxh/Pjx1HW1ZHZnH6SSv0YU15OKMTWQ/dfKYFXhfw+qMr7fITAwkO+++w6tVis0t1y8eJFWrVohlUoZO3Ys3t7eLF68GBcXF0pLS3FwcCAgIIBnz57x8uVvQUyj0fCPf/yD06dPU7duXVQqndP527dv2bBhA5GRkeTm5lKt55S/PE51ST5F9yIw/awLpXFRKHOSyFPJBJ+1wYMHU7t2bVq1aiVY6QA0a9aM9u3bM3XqVCZPnsyGDRtYs2YNrVq1Eo5tampKmzZtaNmyJUs376fZVyEYD1ytI0Yj5lp8PgY+Ldl4NYENVxNo7W3LwAY2nDp1ikePHnHgwAGOHDnCiRMn8PHxoU+fPkyZMoXu3bvTsWNH8vPzBaUOlUol2ODUadEZuz5zeBSdzLHXShTqVGFMBtJ0lp9W4TogBLW5CwkJCQQFBVGjRg0yMzOpXbs2VlZW5OXlMWTIEHx8fKhVqxZjxozB3d1dEJpWqVS0b9+evn37IpPJSE1NZdCgQRw7dgyRSESvXr0YOnQo9+/fZ/ny5cTExBAYGEjnzp25cuUKY8aM4fjx4yxZsoQhQ4awdetWhg4dypw5Oq+/4cOH4+TkRGRkJPPnz2dr6ApSQ0NRlpchMTLHKmis0PqvLsgkfc90FFlv0Xfywab7NCRG5qjyM0gJHcnOa3HUd7HgzZs3DBs2jAcPH6KxqY4k9g7a8hIhE8s6uhR58lO0KgV6th5YdRiHnq0bANknViPSM0BdkIE86Snp1q78cugondu24vDhw3h4eNC2bVvhc+/Tpw8ANs9Ok3/zEGqNhtLYKKQWDjiN/JniJ+couH0EdVEOEkMzzPz7YvZZJ/Q0St4cmIdKqRCqHrGxsTg4OLB8+XK2bt1Kfn4+bdu2JTQ0VFDeqUIV/h2oWuP7CDxq1Wf4gk08iM8gMS0LfbGGGrZGrJnYH2sTXXnS0NCQw4cPs379eiIiIigvLyc4OJi9e/eyZ88e1q1bxw8//ED79u3ZtGkTrq6ufPPNN2zbtk3gVTVo0IDbt2/z0y+3mDOgdaW1oOQNw7DpOhUDNx3BOvvXFUitXLBo1p/iJ+cpfnwGPXtPip9eQmJkgU3QGMrePsHg7XVycnKoX78+o0aNIiQkhNzcXNRqNRqNBq1Wi4eHBxEREdSpU4dDhw4xY8YMGjVqxE8//SRIhO3fv5/1px6R5dIClRadL+AnIBKBRKvBPe8BLRzh1atXgo1QVlYWS5YsYcuWLUKXq729PUVFRajVajZv3sz06dNxbBFMtmsLxHoGf7y2pNWgVSvJvbANvYTbGBgYoFQqmTRpEh06dMDb21soT/8eGo2GEydOsGrVKmJiYtDT0yM9PR0HBweCgoIAOHPmDGZmZgJ3r2XLlgL1oULhZPz48fzyyy80adIEqVQq8B7fvHnD/PnzEYlE3Lt3j82bN9OiRQtGjRrF8We5GDXsiry0FD3baqTvm4m6KBu74AVITG3IPDgPfWcfrAKHIy7OJn79cJRKJVKplICAAJo1a4Z7x5Es33OS5PC5GNXwEwJf8eOzGPk0RySRkXf5H8gTo3EasV73vTmxmrJXd7ALno+eQ3XyTq7G296YuxdOEB8fT+3atRk7dizdu3enSZMmlUr18+fP5+6TZ7j2niEIHue+uI3M2gWphQPa1Gckh82l17xtzBveldxXjxg8eDDJycnCMdauXUtYWBiHDx8WRAgKCwsrrfdWoQr/06gqdb6Hx0n5fLXnHqIei9j9MJfHBfrkGbmQblCN26W2fP7jRcbsvUejoN5s3LiRJUuW0LdvX7Zs2UJCQgKRkZHcv3+f6tWrEx0dzbNnz9iwYQM7duzAy8uL69evI5VKiY2NJS4uDolEQnBwMF8086HmnMhK8k8u43cJQQ90fmsWzfr/NliRCKugsVT79iDOY7agX60+vIni9u3bvHnzhkmTJrF161bS09OFUtzEiROpXr06GRkZBAYG4ufnR25uLrdu3eKzzz6jadOmzJo1i8LCQkqcGpFq748K8R8GPdDx5VWISbRuzKZzMXTp0gVvb28uX77MyJEjBVUViUSCnp4eqampGBgYMHv2bDZt2kTt7mPI92yDSGZA3tV9ZP+qsxjKODiP4ugLlc6Vun0C5SkvsQ0ag2H9IAYOHEhiYiKzZ8+mcePGnwx6oKOn3L59mydPnpCbm0thYSEikUjwaGvcuDHXr1/n5cuXrF69mvbt23+U7zdkyBD27t3L559/jlwuR99c15AU8utTbrzK5mJsNh5dvmLqjJk4ODhw7tw55nzRCsXlzTT0ckBPIgK0GNdrh8zKWcfnq9MSVeYbOtS25+cBDYVzJSYmcvfuXUJCQniVI0fsVAujGn6VxmPiG4RY3wiRVIZF84EoM9+gkZcI241qBqDv5I1ILMGwditev9Dx4jw9Pbl8+TIpKSkEBwdjY2PD8OHDBfoJgKWRHqGDG3PzuzZ8274mg/r2pGOAL70bujB7VB86dgiipWnOJ8uboaGhQnVEX1+f+fPnc/jw4Q9I9VWowv8kqkqd7yC4DqjUaEWSD7Yr1FpAy9mnGZxRlePVYbhQPhSLxfTs2ZPZs2cjkUi4cuUKL168YPTo0bx48YKBAwfSvXt3Ll26RNeuXQX5rPz8fHJycmjbvCmNv93Cg0wNyaGjsWgxGOParT4Ywyeh1SCPv0dBZgrPnz+nRYsWnDhxAplMxhdffEGdOnXYtm0bd+7cIT4+HgcHB3Jzc6lTpw6RkZHMnDmTLl26COXXWs06YtRtFiJZ5Zt+/rV9FNwIw7LtaMya9BCeL7x7nLwLWzFvNgCr5oOJy8lHq9XSu3dvlEql4FoRFBTEyJEjuX79OtnZ2Xz//feACONiY6wCfRAbmFQ6n33wgg8u1WnURgA0gNHngxkyttlH19iEt+ZdC/+sWbO4fPkyIpEIAwMDnJ2d6dGjB02bNiU6OpqdO3dy4cIFfH198fT0/EMh5CFDhtCyZUs8fepi4NuJ5j/qgvPZZxnkF8iRmNnwXOJJcmoJFh4B1K3hyPLlywmZPx9LSxgz6RvMJTLq1fLAzccOMwMZubjxMv8hoYMbV1KxSUxMxNTUlNmzZ3Ms1wGc6iIxtUFdlP3uo1eTf3UPpS+uoy4tECYp6rJCxAY6bznx7wj05WWlwmN/f38OHjwIwN27d/niiy9YvHgxS5curXTNFYLHp06dYsHqBcTGxqLRaCgtLaVRQ99PvlcJCQn06tWrku2RRCIhIyNDUNSpQhX+p/Ffm/ENHz5cWJfRBb3nlCn/2LwR0HXnSfWhYW+WHrrOjh078Pb25tKlS0ybNg2lUklQUBDHjh1jypQpJCcns2bNGvr37y8EQYC0tDRUKhVlZWWsW7eOm9vmocrPQJWXhr5r3b90LVqtlr7+OgWV/v374+bmhkgk4sqVK9SqVQuFQkGXLl0EdZlDhw4xcOBA9u/fz8WLF3FxcSE5OZmZM2dy6dIlnIO+RPlhzwsAUitnSmIuVnquJOYi0nfcM7VWzIZLcWRnZ9O8eXMGDhyInZ0dS5cuxcPDg+vXr6PVagXlFgMbZ9RFOWSE/4D2LzaBKLWw8fLHOwgLCwuZM2cOVlZW+Pv7c+nSJapXr86SJUu4d+8eiYmJ/PzzzwwdOpSffvqJt2/f0r59e0aMGEHTpk0JCwtDqfz4eNzc3DCyduTq5UtYNB/Mx8R2lBqd3FeJXMkTm0DajpnPw4cPmTJlCr8e2k8tNwf6N6nG9mFNWP1FAwK97ZBKxMLYQRdgu3TpQn5+Pvr6+tR007nMVwQ9gJJnVyiNi8Ku/yJcvz2Iy9jt77Z8+oss/kRMb9KkCb179yYmJgb4UFmlvLycPn36MG3aNDIyMsjPz6dz586fVGIBnQDAqVOnBCWW/Px85HJ5VdCrwr8V/xWBr3Xr1lhaWlJeXv7BtsdJ+SyOfEHZX5UK00BZrU4s27Kf1q1bExsby6lTpzAwMMDExIQtW7bQs2dP9PR0FjnW1tao1WpBh/DatWsEBgbi7e2Ni4sL3wzsTtap9UgtHBDLDMiOXEvy+iEk/zyUvKt70Gp0kUiZl0px9HkUWW9JWjuQrGM/IhKL2b72R0An95WXl0dkZCSrV68mIyODDRs2sHPnTpYuXYqrqyt6enps374dc3NzYZ8bN25gb29PUlISr59Fkxw6kqS1Aym4eaDSdes71kCjKhfscxRZCWhVCvQddVmsSCxGK9Env6AAjUZDtWrVaNSokWB3VF5eTuvWrTl+/Dhxb5LAwAyZpROK9DiSfh6GMi8VrVpF9q8rSfixO8k/D6U8LQ6tRk3uhW0k/NiNpPWDKbz/K2+XduXAuoU4ODphYWGBlZUV+vr66OnpYW5uzuLFi5HJZAwbNgxHR0e++uorVqxYQWBgIDt37qx0XcbGxowbN46XL18yd+5ctmzZgqenJ8uXLycvL6/Sa/dGvUXx+VfYDViC2MDoo9+P4ifnKU99SXHMBcqSn3Mx35w7mRAeHo6fn98Hr8/OziYtLY327dvz+ec6N3aZTIafnx9SqZQff/yRO2ePUp4YTemrO8J+WkUZIokMiaEZWmU5eVd2/+H3Vk8iFro1r1+/ztatW8nM1EmUvXjxgoiICMFj8vfKKgqFgvLycmxtbZFKpZw6dYqzZ88Kx7a3tycnJ4eCggLhua+//prZs2cLIuRZWVkcP378D8dYhSr8/8bfPvC9ffuWa9euIRKJiIiI+GD7hsuvkKs+kd68Q0XQ+T1EEj0KnP0xMTHhyJEjPH78mLp169KlSxd69epVKdAePHgQZ2dnbt26BcDVq1dp0aIFzZs3Z926dcyfPx93/TJcq7mTfWIlIrEEq86TQKtF/uYBxY91N5j8q3sx9GiI6+RwXMbvxLRRV0CE/WCds8TYZTtQq9WEhoayYMECQkNDMTc3JyoqCpFIRHJysjCjNzIywtbWlrKyMq5du0Z4eDgAqvSXOI0Oxb7/IvJvhKHMTqp03SZ12ghZX0nMBUHSqgL6enoYmphz9+5dbty4QUhICM2bNycsLAyJRMKsWbNo164dDvU/R576EpmtG8Z1ApGZ21EWd5vSuNsY126JnnMtZHYe5J4LpfjxGeTx95GYWGHVYTxlsVEAlKW8oNnQ7zA1NSUvLw+FQoFGo0EsFnP48GEyMzMZNmwYGRkZFBQUkJKSwvbt2xk/fjx5eXm4u7tjaGiIiYkJ9vb2jBgxQlDwOX78ONHR0Xh5eTFx4kRUKhWvM4tZHPkCtam9EOw/BbGeITadJ5F3YRtJ64bw8NwRHsT91vjx9u1bfvjhB3x9fZk3bx5ZWVnk5OQIMnD79+8nISGB4OBg6tevj+rpGQpuhmPs0wLeGdsa122D1MyO5A3DSN02Fv138mGfghYwlOmOb2FhQUREBPXq1cPExISOHTvSq1cvZsyYAXyorGJqasq6desIDg7G0tKS/fv30717d+HYPj4+gnehhYUFqampTJo0ie7duxMUFISpqSn+/v6CHVIVqvDvwt++qzMkJIQzZ87g5+dHbGwsJ06cAHSlTis7B07qt6KspJjMIwvRs3XHst1X5Jxcg0imh6ogk/KkGGz7/IDUxJqcMxtQZL5BamqNRathGNXwQ6tSUPflTtxdXXj79i1nzpzBw8MDLy8vHB0d2bFjB4Dgk6bVaoUZtKGhISqVCoVCQY8ePXjz5g1Tpkxh5KjROH6zD2X6K7JPrMQycARFj07jMHAp2b+uRCTVw7zZAKRmNpWuNWFZVzzHb+fozN7kvnpE165dad++PadPn8bb25tXr16hp6dH165d2b17N+7u7syaNQsjIyOmTJnCN998w7x583Aet1M4dtqubzFr0hPj2q3Iv7YPVX4aFi2Hkr7vO5zHbCVl82gcBi8n/8oupBaOWLQYhDzhCdnHlmJuKCM3V6fsb2xsjEgkoqSkBAsLCzQaDaK6nSl+ehnnr7eSd3kn5amxlCc+Qd+lDg6DfyR930wM3HwpjDqEnpMPxrVaUHDrENadJwKQGT4H2z4/oFWUoriyFbVajUwmIy4ujlGjRhEYGMikSZO4fPkynTp1ErQjAezs7IiIiKB///6CF11KSgodOnSga9euLFu2THhfU1JS2LBhA1u3bsXpi/kUmrpXKiRqNepKjUmgy/iKn5zFYfBvVlciwNca7OMiOHr0qLDemJOTI0ySPDw8aN++PQMGDMDPz08Qoq7AV3vusW/pFGRWLli0GPSX/heqVFWqUAUd/vbNLbt37xZUN/z9/cnIyMDe3h6A52lFqO0KyQj/AQOPz7BsOUTYr+TZFez6zUffeR5ahZy0f0zCpH577PsvRJ70jKxfFuE4bDUG1o48fJVC1I3rHDlyBDs7Ox48eIBEIuH27dvMnDmTevXqUa1aNaZOncoPP/yAnp4ecrmc+fPnM2PGDCQSCW3atGHy5MlER0ejVqlI2filLtNUlr+zrNF5v1kGjiD/2h7Sd09BbGCCWZOemPgGCeOWK5X0nbsVzdXNlJaWcvz4cUGAGHSWNHv27GHv3r1otVq+/vprgUc3b9484NNuAhWQmtshtXAk/8puZJaOwtgqQSRGoVAQEBBAbGwsNjY2QrmrS5cuxIudeZktF86lLs4VPOAqNblIpGhVClRFOZUCfYW0m8TcDkNNKT1HjuT8+fOCeHZpaSn16tUTXm9tbV1JUNvIyKhS9yKAs7MznTp1Eux6KugEzs7O3Lx5k2mz5rAly42iCjqJY01KYi5i8llnzJr0IOfkGuSJ0cisXTD0+Ew4bgUvr9qM4zzMUJMRfgRFUS4ikQhPT0+GDRuGsbGxQPh/9eoVrq6uQtC7e/cuVlZWeHh40IC3bIuNwnzIig/f838CA6mEca2r/+X9qlCFvxv+1qXO69evC6WiRo0a4eXlxf79+4XticnJJO6egZFP80pBD8Couh8GLrURicQoMuPRKMswC+iLSCLD0N0XQ68mlDy7ghoJuUVl5OXl0bVrVw4cOMDLly85e/YsxcXFrFy5krFjx6JQKNi7dy+FhYXk5eUhEomYPn06JiYmSCQSpk3TcbJ+jTwDEimuk/Zj33cuElMrqk05hNOojaiKcsg9u4nS2CiQyDBwa0DO2U0o81LRKHUZQ9rOKbyNWE+TgOaV1oje9zgDXQYKOscEb29vunXr9pecC0zqtqHwzi/Ik56SuLIvpS9vUBoX9ZusmlbDtGnTqF69OsXFxUgkEtRqNVqtlvCjv/L43i10ORBoFGWUxd/HwP3T3YESE0tURTnCY9W7Bg91QSZd2rYiNDSUHj164Obm9kHTxZ9FhfmspaXlR7dHp5UglereN51IuAMuE/diHhBM7tlNiKR6uEzYg3XnSRQ/OffRY4iA1iN0k6F169YRFxdHUFAQv/zyC0ePHiUrK4sWLVowYMAAYZ/09HRat26NiYkJaxZ+z6iZizF3/eMy6+9hKBMzu7NPlapKFarA3zzw7dq1i6CgIMExfeDAgezatUvYHv/gOlqVAtMGnT7YV/JeFqMuzkVqaovoPT6bzqBTdyM2NjWnZcuWFBcXs2nTJlq0aIG9vT2JiYmcP39eyLJu376Nv78/Go2GsrIynQ/fuHFYWlpibm5Or169UIllyMztyLuwTRfMtKDMS6Ms4TFZh0NALMZh6CrsByymNPaGjkQnElNwIwzEEqw7T8Sm9xwOHjmKQqEgKioKjUZDTk4Onp6eODo6Cq3lIpGI/v37c+/ePSIiIoT3RqtS8M9gVKsFYiMLbHvPptrUwxh4NkZdnEfBrUNo1Eq0ahVLlizh1KlTyGQybG1tiYmJYdGiRWjKS1EV56HVqNGqFGQdW4bU1AaTOm0+eT5j7+YU3YsArRqtoozCqMMAFEUdwkFailwu59ixYwwcOJCTJ09Warr4Z+jZsycWFhY0b96cVq1avaNZfIj0QjnlKl2ZWmJqjVnjbojEEkQSKaUvb2LRYhBiPQP0bN0xrtf24yeT6lHLvx2WlpYCRzA0NJRZs2ZRq1YtpFIp33//PY8ePRIy5G7dupGUlERpaSmxsbFsWTSd2Z1rYSiT8AesC0BX3jSUSZjduRaD/d3/9HtShSr8nfG3LXWWlZVx8OBB1Go1Dg4OgK4dOz8/Xyj71Wvbi+cJ6WQemo9dcAhivff4YO/dUSQmVqiKstBqNULwUxVmIbN0AsDS3Ix7966TmlNEtMqeOD0v8kqfMPnAQ1TZBdSoq9OwvH37NgEBAdy6dQtTU1MePHiAr68vX375JcuWLSMoKAiNt4qEnxeg1ajIObkaTVkRWUeXYuQdgLqsEAN3XzL2zUBTXopIIkXPsToyCwdKn1/DpEEn8s5tRqNSUL1Ja4pfP2D79u3069eP0tJSioqKyM/Px9nZGV9fX65cuUJiYiLe3t6EhIQIsmUymYw/bvfRmaiKpHqIpHrCY6mlA8rMePSMzUFVTqlCwaNHjzh8+DDLly/H29sbsViMRqOlPDGa8nc2SyYNOmLSahgZ4Tp6SXnKc10mm5OEwTt5L5mdG+qyIjQleWQdW4qJb0dIeIK+Yw0WDO2ATCbj2bNnxMTE0KNHD+rVq8eGDRtYu3YtJiYmH+3orcCxY8cEOyGgEo/ufcjf43hUlFoBNKWFoFEjMf1tsiQ1s6OcjxuoFsor0yQSEhKYNGkSU6dOFZ7TarWkpKTg5ub20WMM9nenvosFGy+/ElRV5KrfOpMNpGK0QKC3LeNaV6/K9KpQhffwtw18x44dQyKREB0dLVAKAIKDg9m9W9fybW2sj2OncaT9upbMwyHY9ZuHWPahUoe+kzdiqT6FUUcwa9qL8uRnlL26g8WwVchEWowM9TGs+TktV15BLBYjN3ejXKXm2KNURBoQd12AvTqbbsMmkP7sNmKxmB07dmBiYkJqaiqDBw8WCMNnlh5CXZKHdYfxGPu0IPvESpxGrKPk+TXURTkUPTqjG5RYglajRqyvIymrinMxbdgJ66CvSVzZF3vvRpS+eUSnTp3w9/cnLi6OhIQEwSrJwMCAL774gq1bt3L27FlmzJjBvHnz6N+/P89LUsgzckUkrlwQ+KNmCotWw8k8NA+pQw0CmjbiwfNIJBIJjRo1olGjRoSFhTFnzhx+vvCSNwmJqAszK6n+p+2eip6tG+XJT7HpNYuswyEY1fDDIqAfJnUDSdv+DTbdpmDg2Qj528dkHV2C2NiS/pPm8mJbIfHx8Zw6dQpvb29KSkpwdXXl7t27glt7RZNNBT4V3EDXiAM6u6IKD7/09HRcvN9rYHlvYiQ2MgOxBHVRFmJrnd+cqjDrt5e+m1BpleWI9I0wM5CRnp4ubHd1dWX27NkMGvTXmlXqu1gQOrgxOcXlHH6QzIu0IgrlSswMZPg4mtL3MxesTT7uNF+FKvw3429b6ty1axdffvkl1apVw8HBQfiZMGEC+/btQ6VSUcvRFJFIhFWnCUhNrck6suijZT6RRIZt37mUxd8nad1Acs9uwqbLt8isXVGq1bzOLEJhYIlKK3qn8PIbtGIpaq2INIktKT79yLWqg5OTk3BDdXJyEkpaANqiLCQmHwr4Ss1skFrYU+3bA8KP2MCU8sRoElf2BY2a3PNb0SjKqDb1MPpi3TiGDx9OdnY22dnZREdH4+Liwq1btzhz5gynT5/GxsaGvn37IpVK8fDw4OzZs8hf30H0KZbz75B1ZBGJq/qRsnE4EiNzLFoMopGbbo2swnlh2rRpJCcnM3r0aPK0hh90QKoKMlGkxWHaWGdZZOBSu5IsV8nTyxh4vJPx0mqR2biCSISBbTWhWWP48OHUqVMHqVSKVCpFLBYTExNDWVkZjo6O1KlT509dD4CtrS3Ozs7s3QWn39wAACAASURBVLuXt2/fMnLkSGJjY7l/7Txa5YeZo0gswajm5+Rf349GKUeRnUhJzG9SaxIjcySm1pQ8vYS+WEvOg9O8fv1a2P7111+zdOlSnj7VZYgFBQUcOnToT4+3QlVl9RcNBEL8mJZeVUGvClX4BP62Gd/p06c/+nxwcHAlH7riPfc49zwDm26/lZlsun77wX56tm6Cl1oFtBoNEokUk4adyDm5mqJ7Edh0m4q+sw9ScwcSV/XDxLcDVm1H6ZbiZPpYBH5JzvYxwjEGDBhAly5diIqKwsvLi+hfd2BeL/D3p0fPsSZiPSMKog5j2qgbIokUrVqFReAIzBp1JefMRkqiL5B/dQ/W/r25/+tOlEX5gsO2XC6nTZs22NjYoNFoKCkp4eeffyYoKAh7e3tBdUOhUNB56VHiyrR/KNtVAds+czB0b4A8MZrsiJ9QlxUQdu4lmZmZgvtChTi2hYUFcvWHcy11cS5iQ9NK2fb7slyqgkxKY29R+uI6aN+V80RiOrRpKZTw3nf2NjY25sCBA6xYsYKRI0fSrFkzVq5ciY+Pzz+9noyMDC5dukS9evWYNGkSEyZMwMnJCSMjIxSpL5FIZR/dzyroa3JOriF5/RBk1i6Y1GuH/F0pF+D/tHfn8THd6wPHP2dmsk/2fZOEKGIJKRqCSNJS0aitlkiFhlJLWoq2XK3t3i6/loolsVXtUfTWfm0hCG6pEnuDEiFCZE8my8yc3x9TQy5aS+9tm3zf/+hMcs6ZOelrnjnf8yyOL4/mzs5EClKXEzF0iLFIHaBHjx6UlJTQr18/rl69iq2tLS+99JKxjk4QhN9Xja/j+y0nrxXQb9ERMmbHoi8rAEmBpFBi5tUIh84jH56q/wtZNgSHnDUTsfB/wdi/siBtDZU5l3HuMfGhwSNr/mDeGTuOzz+aQHl5ORYWFjg7O6NUKnmle0/22XWiCpWhHm7LF3iNNCSdaIvvkJ+yhPLMdNBWoddWYNc+Btvg3uiryrn59Viq8q+DXkeTwBZc+um8cSKCXq/H3t6eoqIi4yggS0tL49y6IUOGsH37dqqqqqhUu+Hc+0OUVvbcXPU+Zp4NKb96kqo7WZjXaYZj13dQWlhzbU4M6HV4v23otJ+fupyio99hHzqQ/JQlKH6pU7tbvwfgEDGUwqPfoSvJw9y7CSpHL3TFd9Bc/B73N+aSvWQEjl3HkLdzPrJeb2jMLUloC27i2CXeeA6be9ny3ch2gKEzT0xMDEOGDHngXGs0GuMQ1wMHDjzw87y8PFJTU0lJSSElJYUbN27QoUMHQkJCKC4uZsOGDSiVSuLj4xkwYADvbDjLrnM5Yjq5IPyF1dilzscV6G3HpMiGSBI49/6QOu+ux2v0ChSWduTtWvDI7RTSvd6E2sLbmDjfS0LQFd7GxKnOI6+YvEcsZe35ci5dumRssHzo0CGys7NZlDiPsABPJMDcp5kx6AGorB1xfnUC3qNX4j1mLUorB0zdDEt9ek0xSGAb3BuANSuX89NPPxEZGUmXLl1wcXFh2rRpjB8/3rAvlYqQkBDWrl2Lh4cHycnJFBcXY/NCD+wjhiGp7l19lZ5OwTHybbxGrUBSKMnf/fDzYtPqVdBWGr881K9fH41GY3yPVlZWcH43pg6emLnXxyakH6WnUwyZkO71DVmbQNmFNJCUmPsEUpC2BjP3BpRd/B7N5R+Q9TpUchV1tVerjb+5X05ODhs3bqS0tBQzMzPUarUxk7W4uJht27Yxbtw4goKC8PX1ZeHChXh7e7Ns2TKOHTtG/fr1jbP45s2bR3p6OkOHDsXS0pKRHf0xVz3YxPxxiDo6QfhzqLFLnU8iJtiXMRYmmCkVhpwFlSlWDUPI270IgJur3seqSRjWgZ2RJCg/vYeCH3fgGvMZ15OGoC3IMZQaSAos/FsbPriRKD66Eedef0NhakHe7oVU3clCoTLFskFbnCLe4JVe/diw2hDYVq1aRX5+PmfOnOHI8VOUaTRIJmaoAztj27ZPtVKK+93eMAMUShRmlljUa4V1q1cpPLSWoKAgYzsytVrN7du3OX36NE5OTsYavri4OOrWrUtKSgobNmxg4cKFLD2v57sTN6odw6pJOKbOvgDYdogh+6t4HLs+uBystLRFMrGg7PxB2g4Yw5FVM5FlmcrKSpycnJg5cyaDBw/GdUgSd7bOInfDDOP9PqeoceRuNHQ50WuKsWrUHlnWY+rih66sAJdek8nfu5TcTf+HpFBwql1b9D3aP/Sc6PV6Zs6cycCBA5EkCR8fH1544QXatGnDqVOnaN26NeHh4cyZM4dWrVphYmLCvn37+Pvf/86BAwcYPHgwR48exc/P74F93/2iZGhq/vj9XUUdnSD8eYjA9wsrMxUTuzbiuM6VPaevkX/+YLW+hyYKCTOVgrAGzmSXOrDzR8PznsMXkzX/DRwj47HwbQ4Yhn8qbZyMRfEVNy/iEDEUU/f66IpyyVn3EflHtyCb+xAYaCja/uabb7C3tycjIwMTuYp6HXtQ6hXMrfXTUaodsL6vO8v97t5jA0BXycg27kyYDWfOnKF+/foMGjQILy8vEhMTmTZtGs7Oznz55ZdYWlri5eVFy5YtCQwMRKPR0L9/f65k38akQQfsOgxEUhr+97g/dV9l4wJ6LXpNEc7dJpC75Ytqr0dhYY1tu2guVWhRKpW0bNmSI0cMfTW3bt0KgI+tCtPXP0OWIX/f1+iKczGxd8e5+/tcT4rDdcAn5G76HBNHL7T52eiryjHzaIDbgE8euly4b98+439XVlZy8eJFIiIijANh7/bh7NevH23atMHCwgIwZG0uW7aMhIQE9Ho98fHxrFy50pjV+Sh36+GMY6x+ZdlTkgxXepMiG4o6OkH4kxCB7z5jh8agUqkoLS3FytaBHu/NxdTFhR1qM8ICXPnyvXAc1WZ0Stv6K0NfHmTmdm95S2XninXzLpRnnsI6oD0lv3xqBgcH06RJE8aPH8/8+fMJCgriYI6CT7J6UnAm5ZGBD+5+uCrwKjzNjnnzfnlOqvavt7c3165dw9nZGTMzMxQKBefPn6dt27aYmJjw0Ucf8dFHH/HGnK2snPoWKgcv4zG1943B0RXdBoUKhYUNkql5tSxHWa9DrzF05vfzcCV2zBjjcmRmZiajRo1ClmW+iHuJN1aeQlOlMyawVGT/hFxp2Jfm8nHKMo7gHvx5tQQReHC5UKvVcvz4cVJSUti7dy+HDh2iQYMGhIeH895779GuXbsHhtNevXqV+fPn89VXX9GmTRtmzZplDJSPS9TRCcJflwh897lbyKzT6di4cSNDhgzh7NmzXFqqJvQ5F2N6+JOOMKrKu07+nsVU3MwwBAq9HlO3enTt8Rq93+xI165dWbVqFZGRkeh0OqKjo1Gr1bQEzPIu8/74jZipFOi0WsNE9F9IEkh6HZ0DXBnR0Z/G7p14/fVzAMakFldXVy5fvmwMfEFBQXh4ePDzzz9z/vx5APbu3YuTkxMBAQE09nUzdCO5LwiUntlrmAJg60LBgZVYNgxBUigxsfdE1lZRdvEoFn4tKDz8DbK2CpUEsd1fIjtlBQAZGRm8+OKLjBkzhuTkZP65eDbvRQ1hytItlF38Hkv/1uhK8sn711wA8vcswrHzCEzd6lU7jxYmCj7o0gD5zlVmrVtKSkoKBw4cwNvbm/DwcEaMGEFycvJDW47Jssz+/ftJSEhg3759xMbGGjNpn5aooxOEvyYR+B5CqVTSs2dPhg0bxsGDB7GysqKs7N7Uan1Z/q9s/aC8HfMwda2HU7fxKMwsKTq6kbILadiamxAZGQlgXHoECAwMJD4+noEDB6LU5NGiYV02vBfO1OU72Jp2DBcvX4KDAlmIlhDFTyTFfGg81vLly1mzZg3x8fFs376duLg4XnvtNc6dO8fVq1d59dVXmTp1Kj179mTWrFm4uLjg6enJ8OHDycrKQqlUYerfDqsm99qHWTUO487WWYasTu8mOHYeAYDC3AqHTm+Rtz0BWdZj80IvlNZOSJJE7yAv5qQYpsx37NiRadOmERcXR1RUFIMGDSIxMZE6DZphE9ABnU6HZf0XMHX25XpSHB5DE6vV+kmAStLjnHWIsVGv4+joSFhYGK+//jpLlizBxcXlkedeo9GwZs0aEhISKC8vJz4+nmXLlqFWqx+5zZO6W0cnCMJfgwh8DyHLMps2bSI/P59GjRrRvHlzvv32W4YMGcKNGzfI2L8JSfr1+0D301dqkEwtkUwtqLpzjeIft6E0t2bPtyv45GdDveEnn3zC1KlT6d69O7m5uRw8eJDJkycjSRJvvfUWDlamxLR047tPltDy1VeZ1TeWK8kd6NujW7VjqVQqKioq6N27NwMGDGD16tWcOHGCf/zjH8bJ3j169KBly5ZkZ2czbtw4Y43dhAkTsLGx4aJbC+MV33/WLv4ndbMXUTe71+7LLrgnnQNccVSbERkZSVJSErNnz6Zv374A1KtXr1pZQeeonuSqHClRKTB3csfn/S3Gn0l6La59p8GNMwTe3kv3sBaETTth/ILwa65du0ZiYiKLFy+mVatWfPrpp7z00kvG7E5BEGov8Slwn6ioKNRqNTY2NkyaNIlly5bRuHFjxowZg6mpKa6ursTGxjJoYMwT7dc+LI7Ss6lcm/kad7bPwapheySFgr9FR3DtmmHI6/Tp0/Hz8+PEiRO4u7uzb98+4+DO9evXExgYyM6dOykqKkKjMUxA+PHHH2nRosUDxzM1NWXdunUUFRUxePBgdDqdcanzrg4dOpCTk8Phw4eNy5CTJ082TDc49A2yruqB/T6Ou/fgUlNT6dq1K4sWLTIGPTCM2Ll06RJ6vZ5//etfpO7axj+G92ZsvTw880/Az/9Gl3kS55KfCXcq45+DGnNl5SQ2LJrJ66+//qtBT5ZlDh48SJ8+fQgMDKS0tJS0tDS2bt1K586dRdATBAEQBexP7c1fOr48zdmT9Xqk6+lsm9iDgIAAZFnm1KlTbNq0idmzZ1NYWEhUVBTdunUjMjISR0dH9u7dS2JiIhs2bKBu3bosWLCAHj16UFhY+MgPdI1GQ9euXalbty7R0dFMmTKF/fv3A/BZQiL/WJOC2vM5/AOaodBXcOHIHgpP7MDZ2oICp6bYhA5Cekjv0kcxpOw3wjH/HLGxsSQnJxMeXn3iwubNmxk+fDi5ublYWFhgbm5OVVUVYWFhhIeHExYWRsOGDZ8o0aS8vJzk5GQSEhIoKSlh9OjRxMbGGtvCCYIg3E8Evqd0t+OLpuq35hg8SK6q4Oaq99Dd/pnx48czffp045DU8vJyQkJC8Pb2RqlUsnv3bho3bkxUVBSvvPIKbdq0wdPTk6KiIoqLi/niiy+Ijo5+IHPxrtLSUl5++WXq1KlDWloaK7bu571le7gu2yHLcrU2YZK+Cp1exuT2T2hPbmXqnKVM3XTKMAyWRwei+1P2zbOOMXLkSDZu3EhwcDBguM+XmprK3r17SUlJITMzk/bt2xsDXbNmzZ7qauz69eskJSWxcOFCgoKCiI+PF1d2giD8JhH4nsHKI1eeqpD5gy4N2TX/Q1auXIkkSXh6erJ161aaNWsGGCYBtG7dmpkzZxIVFcW+ffvYsmULm3fupcChEdbeDXD19qOqtBCrqkLOb1vKa926MGzYMIKCgh44ZlFREREREZzXOeMQPgRJZQKPKIgHQNajkiC6kTmr5n5M495jOJmrQylJ6BX3bgv/Z8r+8d3fMXHiRNavX09RUZGxxOD8+fO0adOG8PBwwsPDCQoKqjYN/UnIssyRI0dISEhgx44dREdHM3r0aBo0aPDbGwuCICAC3zMzBL+nK2TevXs3r776KuXl5QCMHDmSmTNnolKpOH78OJ07d2bnzp0onPyYt+8iqT/dpqK8HFT3xixJei0KhQI3OY9r/1qMm2kFw4YNo1+/flhZWaHX61m1ahUTv9qO9HwvFCbmD3t51dztVGPTJAx1xk40p3bx+Zwkcqz8Hpqyb6WSmTBhAsuWLUOlUlFcXEybNm3w8/Nj27ZtXLt2zTh09WlVVFTwzTffkJCQQF5eHqNHj2bw4MHY2to+034FQah9ROD7HaRnFTx1IbNGoyEyMpLU1FQkScLZ2ZktW7bQsmVL1q1bx7ikjdz4YRfa4jy8Ri1DaXnvg/7GV/FU3bqM5/AlKG2ckWQt/kXpVJ1LIT09nfbt23PhwgV0dl5oO4yqFjB/zf0t2pSyjjVDWtPa383486qqKo4ePWps7JyWloYkScTFxZGRkUGLFi349NNP2bdvHzExMY/sqfk4srOzSUpKYsGCBTRr1oz4+Hi6dOlibLsmCILwpEQ5w1OYMmUKFy9eZOXKlWRmZtI2IIDCwkIKNNonLmS2sLBg7969LFmyhOHDh5Ofn0/r1q2JjY2l45C/oWpthnxsFyo7V0rP7semZRQAlbeuIGvvdU0xDI015We7FlQqLqDR/Jvt27ej0+lw7tkHc4XKeJdO1usemIn3KHqFkiWHs1AWXq8W6Pz9/QkLC8PR0RE/Pz9SUlLw8PBg0KBBmJg8fHTPk/j+++9JSEhg69at9O/fn5SUFAICAp55v4IgCDUy8Pn6+pKTk4NSqcTExIS2bduSlJRUbWbb76VOnTqUlJQA4KhWPnUhc1xcHF27diUkJITLly/z9fIVLFu9FqWVHfryYtTNu1B6OsUY+EpP70HdJJyC/YbuKGUXj1KwfwXagmwUppYozKxRVZagsLRF5eBF5mfdcOgST2HaGlQ2Lrj2m86d7QloLv2ALOsxsffA5TXDKCIAXeEtbq4YT8XNiyySFCzSVWJubk7Dhg1p164dx48f58svv0SpVBqD3m/5+uuvWbx4MQcPHnzozysrK1m/fj0JCQnk5OQwevRo5syZ89BOLIIgCE+rxqa/bd68mZKSErKzs3F1dWX06NF/9Ev6TSUlJeTn59OqVSscOo3A+521uMX8n6GFmFKJvrKMqtxryHodpef2Y9X43sBahYkZTq+MxXvMWpx7f4i+qpwxY8Yw67vDmJkaljgrMk/hMSQRl77TKDm1B315KZ4jl+L99mocXh5ZfRTR2VTULbogmVpi6uRNp/5DycrK4u2336ZLly5ERETg6OhI06ZNGTVq1DO975ycHKZNm4avry9Llizhgw8+4OLFi4wdO1YEPUEQfnc1NvDdZW5uTu/evTl79iwAhYWFDBw4EGdnZ3x8fJgxYwZ6veGenI+PDz/88ANgGBMkSRJnzpwBYMmSJXTv3v2B/V+5cgVJktBqtYBhKOrkyZMJCQnB2tqaTp06kZt7r8nz8uXL8fHxwdHRkenTp+Pr68vu3bsBwxJqSEgI21IO4BDUGUmhQGllh2RqiblPIFaNwyhISyZrTgySypQbywyjgWT0VNy4wO1vZ5A1ewBFR9ahbtSevKISfrpVQvGNiwCUZhzh5opxVN64gKRQoi8v5ubK9yk8uJr8XQvImhdLTvJkZL0Oq6YvoistwNyrEVbNXuL8mdM4ODjQv39/UlNTOXHiBPn5+aSnp3Py5ElefvllAE6dOsW8efOwtrYmNjbWWGx/7tw5hg8fzuHDh1Gr1djZ2XHs2DEGDBiAj48Pn332GRUVFdSvX59OnTqJe3iCIPzX1PjAV1ZWxtq1a401ZaNHj6awsJDLly+TmprK8uXLWbp0KQChoaHGETepqanUrVvXWPCdmppKaGjoYx1z9erVLF26lFu3blFZWcnnn38OwNmzZxkxYgSrVq0iOzubwsJCrl+/btxu9+7d9OrVi/U/ZD20Fk3dJJzyqyfRa4pR2XviNXwJACXpeyg5sxeFpR1IEmU/HaHwx3+xdstu1q/7hjtbZwPgHb8K+/A4bv/zY8zrPo+5XxDaO9coPLwOlaM3niO+RtZVoSu6jVJtj5nHc5T/fBzN5eMU3blFfn4+3bp1Iz09HY1GgyRJxgA175epENbW1gwcOJDCwkJiY2PJz88nOzubRo0akZSURHBwMEuWLCEgIIBevXqRmZlJWFgYmZmZXLlyhevXrzNt2rQn+hsLgiA8iRob+Lp3746dnR22trbs2rWL8ePHo9PpSE5O5uOPP8ba2hpfX1/effddVqww3CcLDQ0lNTUVgAMHDvDBBx8YHz9J4Bs8eDDPPfccFhYW9OnThxMnTgCwfv16oqKiaNeuHaampkybNq1ah5Lc3Fzc3Nw4f7OICq2eoh82kzmrL7qiWxR9/09Uti4o1YalP6dXxhrq8YDSs/sMw1sbtsNr5Nd4jvgKZLB1csVdzsfcpykAkkKJhV8LTN39Kb9yArt20Zi618e6ZTcqr5+j7EIaVo3ao68ylFeYezfBucdEtIW3KLhxGScnJ9LT01EoFOzZs4f+/fsblznvJgf7+vpibW2NQqEgPDwclUrF999/z+3bt9m8eTPHjh0jMTGRcePGcfHiRY4fP87cuXNxcHDA2tqaiRMnkpyc/PR/eEEQhN9QI5Nb4MERQ6Ghofz4449UVVXh4+Nj/D0fHx/jVVdoaCjjxo0jOzsbnU5Hnz59mDp1KleuXKGwsJDmzZs/1rHd3O6l/ltaWhqTX27cuFEtwcbS0hJHR0fjY0dHR7KzsylSGJ6zeT4Km+ejyJzVB35ZjrVp3ZP8PYtQWtog6w1dY3TFeaCrJH//cgrS1vzyvCEQWeqK0Vw6BkDml/0NgVavRWntjKlrPWRZRmnjDHdHEanMjMcCsKjXEqm8gKJDa4lo8zx79uxBqVTi6OiIVqtl79691d77pUuX2L59O3PnzkWr1VJVVcVnn33G2bNnadq0KQEBAcar6lu3blFWVsbzzz9v3F6WZXS6J++GIwiC8Lhq7BXfXXdHDCmVSo4cOYKJiQlXr141/jwzMxNPT08A/P39sbS0ZM6cOXTo0AEbGxvc3NxYuHAh7dq1e+ZWWO7u7tVq2jQaDXfu3DE+joiI4Ntvv8XG/NHfR1TWjsYrPeN7VNtj2z7GUOMn67Go0xTroK5YWFjSvnlD1A3aAlDnnTXUGbOWOu9uwMI3kNvffUzl9bMUHliJeZ2m1UYR3U+r1WFnrmTTpk106tQJExMTPD092bhxo/HcgWHA66FDh+jUqRMLFizAz88PMHwRyMjIIC4urtp0cycnJywsLDhz5gwFBQUUFBRQWFho/KIgCILw31DjA58sy2zcuJH8/HyaNGlCnz59mDRpEsXFxVy9epWZM2cSE3Nv2kJoaChz5841Lmt27Nix2uNn0bt3bzZv3syhQ4eorKxkypQp3N8/YMqUKRw4cIDTG+agKMsDQFdWiIVPc5Rqhwf2JymU+Ly/BZvnoyi/cgK3AZ9SZ+w6HLu+g3W9IN75ewLD4gahu3Ea175TDedDW0n51XTMvBrj+eYCzLwaYx8xBIeXhhlr+0xdfLEO7EzZT0coPbOPBo2akJV5laNHj5KWlsb06dMpLi5m1KhRqNVqZFnG39+frCzDvcldu3Yxd+5cOnTogFKppEuXLjg5OeHq6kpWVpZxSK5CoWDo0KGMGTOGW7duAYb+mzt27Hjmcy0IgvAoNTbwPWrE0Jw5c7CysqJu3bq0a9eO6Oho3njjDeN2oaGhFBcX06FDh4c+fhZ3j9+vXz/c3d1Rq9W4uLgY23k999xz/Pvf/8ZKW8TVRaPInPkaN1dOQKl2wK7Do0chWbfqhmX9F8hZO9mwzfJ3Kb9+gd5BXnh7e7Nw+VqKDq8jK2EAWfMGUfT9tyD/dn9Rhbma0vSdnF36Aba2tsTExDB+/HgGDBgAGGoPz549i7W1NT4+PrzyyisEBASg0Wg4d+4cZmZmhISEGPcXHh5O48aNcXNzw8nJCYBPP/0Uf39/goODsbGx4cUXX+TChQvPcpoFQRB+lWhZ9gcqKSnBzs6OjIwM47LgXc8y9kiSoHOAK0kxLY3PrTxyhRnbzlH+BA21TSSZj7o1MfYWvZ9Wq2XTpk0kJCSQkZHBiBEjGDp06K9OQxcEQfgzqLHJLX9WmzdvJiIiAlmWGTduHE2bNsXX1/eB3xvZ0Z8DGblPNfZIIesYfl8HmZPXCtifkUuVVo8h6eXXZ93Jej2mSumhQS8vL4/Fixczb948vLy8iI+Pp2fPnr9LmzJBEIT/hRq71PlntXHjRjw8PPDw8CAjI4Pk5OSHDl0N9LZjUmRDLEye7E+kryrnzq5FvDOwBwUFBaw8coV+i46w61wOOhl+O+hpae4ksWFEu2pB79SpU7z55pvUq1ePM2fOsGHDBtLS0ujbt68IeoIg/KWIpc4/uScdezQ23I+F42P44YcfcGnbE9uOb1Che/w/sYkEH3VrTEywLzqdjs2bN5OQkMD58+cZPnw4w4YNw9XV9Xd4Z4IgCH8MEfj+Ah5n7NGNpDi+SJjPm/0NbdW6Rb/Blm+/waXX3zCv0/SJjmeuUtDN6jIJU97Fw8ODGTNm0Lt3b0xNH2+skSAIwp+ZuMf3F9DMy46kmJbcKal45Nij51eYUtdZDcCyZcvYuelbXF+bgplXoyc+nqZSy54iJQEBAYwYMYLo6Ojf+y0JgiD8YcQVXw3h6+vL4sWLuXTpEhMnTkLdfTKSiz/68lLyUhZTfukYSBJWzV7Crl00kkJJSfpuStJ3YubRgJKTO5HM1Th2eguLei0pPrCCgsPrMDExQaVSMWjQIObOnftHv01BEIRnJpJbapDExEQ+/PBD3vrsa8w9ngMgd+ssJIUSj2GLcB+cQPnPxyk5udO4TcWNC6gcPPF6ezW2L/TizvYEZFnGOSyWes1aMnfuXEpKSkTQEwShxhCBrwbZtWsXwcHBFFu6U6HVoyvNR3P5GPYRQ1GYmqO0ssOmVXdKz+03bqOyccG6+ctICiVWTcPRleShLy2gXKunrEL0zBQEoeYR9/hqkMTERGbMmMG5xKnIbd9EW3gLdDqy5g6890uyHpWNs/Hh3WkPAAoTcwD0VRqU2KPTi1VwQRBqHhH4ahBXdQCQEwAAAc5JREFUV1f27NlDwPPB6ErmYxvSH0llgvfbq419OJ+ESikWBARBqHnEJ1sN4+HhwYTZqyi/fJyiIxsw921B/p7F6CvKkGU9VfnZlGee+s39mKsUuLm5cvny5f/BqxYEQfjfEYGvBnozsjXeMR9TdiENlZ0bsl7LjcVvce3Lftz+58foSvJ+cx8yMH3ieNavX4+9vT3x8fH//RcuCILwPyDKGWqo37vJtSAIQk0hrvhqqJEd/TFXPfl9PTC0PhvR0f93fkWCIAh/DiLw1VBP2+TawkTBpMiGNPOy+y+9MkEQhD+WyOqswe5OV3iSJteTIhs+dP6eIAhCTSHu8dUCj9PkOqyBMyM6+osrPUEQajwR+GqRX2ty7ag2+6NfniAIwv+ECHyCIAhCrSKSWwRBEIRaRQQ+QRAEoVYRgU8QBEGoVUTgEwRBEGoVEfgEQRCEWkUEPkEQBKFWEYFPEARBqFVE4BMEQRBqFRH4BEEQhFpFBD5BEAShVhGBTxAEQahVROATBEEQahUR+ARBEIRaRQQ+QRAEoVYRgU8QBEGoVUTgEwRBEGoVEfgEQRCEWkUEPkEQBKFWEYFPEARBqFVE4BMEQRBqFRH4BEEQhFrl/wFpId1O5kISHgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "import io\n", + "import zipfile\n", + "import urllib.request as urllib\n", + "\n", + "def get_football_graph():\n", + " url = \"http://www-personal.umich.edu/~mejn/netdata/football.zip\"\n", + " sock = urllib.urlopen(url)\n", + " s = io.BytesIO(sock.read())\n", + " sock.close()\n", + " zf = zipfile.ZipFile(s)\n", + " gml = zf.read(\"football.gml\").decode()\n", + " gml_list = gml.split(\"\\n\")[1:]\n", + " return nx.parse_gml(gml_list)\n", + "\n", + "football_nx_g = get_football_graph()\n", + "nx.draw(football_nx_g, with_labels=True)\n", + "\n", + "# We must provide edge definitions to create the ArangoDB graph\n", + "# Since this graph is Homogeneous, we only need one edge definition.\n", + "edge_definitions = [\n", + " {\n", + " \"edge_collection\": \"played\",\n", + " \"from_vertex_collections\": [\"Football_Team\"],\n", + " \"to_vertex_collections\": [\"Football_Team\"],\n", + " }\n", + "]\n", + "\n", + "class Football_ADBNX_Controller(ADBNX_Controller):\n", + " \"\"\"ArangoDB-NetworkX controller.\n", + "\n", + " Responsible for controlling how nodes & edges are handled when\n", + " transitioning from ArangoDB to NetworkX, and vice-versa.\n", + "\n", + " You can derive your own custom ADBNX_Controller, but it is not\n", + " necessary for Homogeneous graphs.\n", + " \"\"\"\n", + " def _keyify_networkx_node(self, nx_node_id: NxId, nx_node: NxData, col: str) -> str:\n", + " \"\"\"Given a NetworkX node, derive its valid ArangoDB key.\n", + "\n", + " NOTE: You must override this function if you want to create custom ArangoDB _key\n", + " values for your NetworkX nodes or if your NetworkX graph does NOT comply to\n", + " ArangoDB standards (i.e the node IDs are not formatted\n", + " like \"{collection}/{key}\"). For more info, see the **keyify_nodes**\n", + " parameter of ADBNX_Adapter.networkx_to_arangodb()\n", + "\n", + " :param nx_node: The NetworkX node object.\n", + " :type nx_node: dict\n", + " :param col: The ArangoDB collection the node belongs to.\n", + " :type col: str\n", + " :return: A valid ArangoDB _key value.\n", + " :rtype: str\n", + " \"\"\"\n", + " # Since our NetworkX Football nodes have an id of type string, we can use the existing helper function.\n", + " adb_v_key: str = self._string_to_arangodb_key_helper(str(nx_node_id))\n", + " return adb_v_key\n", + "\n", + "# Instantiate the adapter\n", + "football_adbnx_adapter = ADBNX_Adapter(con, Football_ADBNX_Controller())\n", + "\n", + "# Create the ArangoDB graph\n", + "name = \"Football\"\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "football_adb_g = football_adbnx_adapter.networkx_to_arangodb(name, football_nx_g, edge_definitions, keyify_nodes=True)\n", + "\n", + "print('\\n--------------------')\n", + "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", + "print(\"Username: \" + con[\"username\"])\n", + "print(\"Password: \" + con[\"password\"])\n", + "print(\"Database: \" + con[\"dbName\"])\n", + "print('--------------------\\n')\n", + "print(f\"Inspect the graph here: https://tutorials.arangodb.cloud:8529/_db/{con['dbName']}/_admin/aardvark/index.html#graph/{name}\")\n", + "print(f\"View the original graph here: https://networkx.org/documentation/stable/auto_examples/graph/plot_football.html#sphx-glr-auto-examples-graph-plot-football-py)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Zl4fQ1AnC_b" + }, + "source": [ + "# Full Cycles" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RTNNqQjpneFV" + }, + "source": [ + "## Example 1: ArangoDB ➡ NetworkX ➡ ArangoDB (with existing collections)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "2wmcH2hgqLQq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "440950a5-0f70-4c08-b3d9-809da09de016" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NetworkX: fraud-detection created\n", + "ArangoDB: fraud-detection created\n", + "Inspect the overwritten graph here: https://tutorials.arangodb.cloud:8529/_db/TUTl7w91pj995boxn9f4etz6/_admin/aardvark/index.html#graph/fraud-detection\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "name = \"fraud-detection\"\n", + "\n", + "# Start from ArangoDB graph\n", + "original_fraud_adb_g = db.graph(name)\n", + "edge_definitions = original_fraud_adb_g.edge_definitions()\n", + "\n", + "# Create NetworkX graph from ArangoDB graph\n", + "fraud_nx_g = adbnx_adapter.arangodb_graph_to_networkx(name)\n", + "nx.draw(fraud_nx_g, with_labels=True)\n", + "\n", + "# Modify the NetworkX graph\n", + "for _, node in fraud_nx_g.nodes(data=True):\n", + " node[\"new_vertex_data\"] = [\"new\", \"vertex\", \"data\", \"here\"]\n", + "\n", + "for _, _, edge in fraud_nx_g.edges(data=True):\n", + " edge[\"new_edge_data\"] = [\"new\", \"edge\", \"data\", \"here\"]\n", + "\n", + "# Re-use existing graph's edge definitions to overwrite existing graph\n", + "# Keify nodes & edges to keep the same key values as original (this is optional)\n", + "updated_fraud_adb_g = adbnx_adapter.networkx_to_arangodb(\n", + " name,\n", + " fraud_nx_g,\n", + " edge_definitions,\n", + " keyify_nodes=True,\n", + " keyify_edges=True,\n", + ")\n", + "\n", + "print(f\"Inspect the overwritten graph here: https://tutorials.arangodb.cloud:8529/_db/{con['dbName']}/_admin/aardvark/index.html#graph/{name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qh8bYrIqnHTa" + }, + "source": [ + "## Example 2: ArangoDB ➡ NetworkX ➡ ArangoDB (with new collections)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "BbPkJAEEoVjM", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 411 + }, + "outputId": "33cbadea-210f-485f-ef77-80cf20c74953" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NetworkX: fraud-detection created\n", + "Connecting to https://tutorials.arangodb.cloud:8529\n", + "ArangoDB: fraud-detection_new created\n", + "Inspect the new graph here: https://tutorials.arangodb.cloud:8529/_db/TUTl7w91pj995boxn9f4etz6/_admin/aardvark/index.html#graph/fraud-detection_new\n", + "View the original graph here: https://tutorials.arangodb.cloud:8529/_db/TUTl7w91pj995boxn9f4etz6/_admin/aardvark/index.html#graph/fraud-detection\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": {} + } + ], + "source": [ + "name = \"fraud-detection\"\n", + "\n", + "# Start from ArangoDB graph\n", + "original_fraud_adb_g = db.graph(name) \n", + "\n", + "# Create NetworkX graph from ArangoDB graph\n", + "fraud_nx_g = adbnx_adapter.arangodb_graph_to_networkx(name)\n", + "nx.draw(fraud_nx_g, with_labels=True)\n", + "\n", + "# We must provide edge definitions to create the ArangoDB graph\n", + "# Since this graph is Heterogeneous, we must provide multiple edge definitions.\n", + "edge_definitions = [\n", + " {\n", + " \"edge_collection\": \"accountHolder_new\",\n", + " \"from_vertex_collections\": [\"customer_new\"],\n", + " \"to_vertex_collections\": [\"account_new\"],\n", + " },\n", + " {\n", + " \"edge_collection\": \"transaction_new\",\n", + " \"from_vertex_collections\": [\"account_new\"],\n", + " \"to_vertex_collections\": [\"account_new\"],\n", + " },\n", + "]\n", + "\n", + "class Fraud_ADBNX_Controller(ADBNX_Controller):\n", + " \"\"\"ArangoDB-NetworkX controller.\n", + "\n", + " Responsible for controlling how nodes & edges are handled when\n", + " transitioning from ArangoDB to NetworkX, and vice-versa.\n", + "\n", + " You can derive your own custom ADBNX_Controller, but it is not\n", + " necessary for Homogeneous graphs.\n", + " \"\"\"\n", + " # Since we are dealing with a Heterogeneous, we must implement _identify_networkx_node().\n", + " def _identify_networkx_node(self, nx_node_id: NxId, nx_node: NxData) -> str:\n", + " \"\"\"Given a NetworkX node, identify what ArangoDB collection it should belong to.\n", + "\n", + " NOTE: You must override this function if your NetworkX graph is NOT Homogeneous\n", + " or does NOT comply to ArangoDB standards (i.e the node IDs are not formatted\n", + " like \"{collection}/{key}\").\n", + "\n", + " :param nx_node_id: The NetworkX ID of the node.\n", + " :type nx_node_id: adbnx_adapter.typings.NxId\n", + " :param nx_node: The NetworkX node object.\n", + " :type nx_node: adbnx_adapter.typings.NxData\n", + " :return: The ArangoDB collection name\n", + " :rtype: str\n", + " \"\"\"\n", + " adb_vertex_id: str = str(nx_node_id)\n", + " return adb_vertex_id.split(\"/\")[0] + \"_new\"\n", + "\n", + " # Since we are dealing with a Heterogeneous, we must implement _identify_networkx_edge().\n", + " def _identify_networkx_edge(self, nx_edge: NxData, from_nx_node: NxData, to_nx_node: NxData) -> str:\n", + " \"\"\"Given a NetworkX edge, and its pair of nodes, identify what ArangoDB\n", + " collection should it belong to.\n", + "\n", + " NOTE #1: You must override this function if your NetworkX graph is NOT Homogeneous\n", + " or does NOT comply to ArangoDB standards\n", + " (i.e the edge IDs are not formatted like \"{collection}/{key}\").\n", + "\n", + " NOTE #2: You can accesss the ID & Collection belonging to the\n", + " **from_nx_node** & **to_nx_node** parameters via their \"nx_id\" & \"col\"\n", + " attribute keys. E.g `from_collection = from_nx_node[\"col\"]`\n", + "\n", + " :param nx_edge: The NetworkX edge object.\n", + " :type nx_edge: adbnx_adapter.typings.NxData\n", + " :param from_nx_node: The NetworkX node object representing the edge source.\n", + " :type from_nx_node: adbnx_adapter.typings.NxData\n", + " :param to_nx_node: The NetworkX node object representing the edge destination.\n", + " :type to_nx_node: adbnx_adapter.typings.NxData\n", + " :return: The ArangoDB collection name\n", + " :rtype: str\n", + " \"\"\"\n", + " adb_vertex_id: str = str(nx_edge[\"_id\"])\n", + " return adb_vertex_id.split(\"/\")[0] + \"_new\"\n", + "\n", + "\n", + "fraud_adbnx_adapter = ADBNX_Adapter(con, Fraud_ADBNX_Controller())\n", + "\n", + "# Create a new ArangoDB graph from NetworkX graph\n", + "new_name = name + \"_new\"\n", + "db.delete_graph(new_name, drop_collections=True, ignore_missing=True)\n", + "# Keify nodes & edges to keep the same key values as original (this is optional)\n", + "new_fraud_adb_g = fraud_adbnx_adapter.networkx_to_arangodb(\n", + " new_name,\n", + " fraud_nx_g,\n", + " edge_definitions,\n", + " keyify_nodes=True,\n", + " keyify_edges=True,\n", + ")\n", + "\n", + "print(f\"Inspect the new graph here: https://tutorials.arangodb.cloud:8529/_db/{con['dbName']}/_admin/aardvark/index.html#graph/{new_name}\")\n", + "print(f\"View the original graph here: https://tutorials.arangodb.cloud:8529/_db/{con['dbName']}/_admin/aardvark/index.html#graph/{name}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uV8hpastnmhg" + }, + "source": [ + "## Example 3: NetworkX ➡ ArangoDB ➡ NetworkX" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "TFSM1Xegq9TR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "outputId": "51a2d8f0-dd64-46d5-c173-ae74276dcaf1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[((0, 0), {}), ((0, 1), {}), ((0, 2), {}), 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Load the nx graph\n", + "original_grid_nx_g = nx.grid_2d_graph(5, 5)\n", + "print(original_grid_nx_g.nodes(data=True))\n", + "print(original_grid_nx_g.edges(data=True))\n", + "\n", + "# We must provide edge definitions to create the ArangoDB graph\n", + "# Since this graph is Homogeneous, we only need one edge definition.\n", + "edge_definitions = [\n", + " {\n", + " \"edge_collection\": \"to_v2\",\n", + " \"from_vertex_collections\": [\"Grid_Node_v2\"],\n", + " \"to_vertex_collections\": [\"Grid_Node_v2\"],\n", + " }\n", + "]\n", + "\n", + "# Re-introduce the Grid controller class\n", + "class Grid_ADBNX_Controller(ADBNX_Controller):\n", + " \"\"\"ArangoDB-NetworkX controller.\n", + "\n", + " Responsible for controlling how nodes & edges are handled when\n", + " transitioning from ArangoDB to NetworkX, and vice-versa.\n", + "\n", + " You can derive your own custom ADBNX_Controller, but it is not\n", + " necessary for Homogeneous graphs.\n", + " \"\"\"\n", + " def _prepare_arangodb_vertex(self, adb_vertex: Json, col: str) -> NxId:\n", + " \"\"\"Prepare an ArangoDB vertex before it gets inserted into the NetworkX\n", + " graph.\n", + "\n", + " Given an ArangoDB vertex, you can modify it before it gets inserted\n", + " into the NetworkX graph, and/or derive a custom node id for networkx to use.\n", + " In most cases, it is only required to return the ArangoDB _id of the vertex.\n", + "\n", + " :param adb_vertex: The ArangoDB vertex object to (optionally) modify.\n", + " :type adb_vertex: adbnx_adapter.typings.Json\n", + " :param col: The ArangoDB collection the vertex belongs to.\n", + " :type col: str\n", + " :return: The ArangoDB _id attribute of the vertex.\n", + " :rtype: str\n", + " \"\"\"\n", + " nx_node_id = tuple(\n", + " int(n)\n", + " for n in tuple(\n", + " adb_vertex[\"_key\"],\n", + " )\n", + " )\n", + " return nx_node_id\n", + "\n", + " def _keyify_networkx_node(self, nx_node_id: NxId, nx_node: NxData, col: str) -> str:\n", + " \"\"\"Given a NetworkX node, derive its valid ArangoDB key.\n", + "\n", + " NOTE: You must override this function if you want to create custom ArangoDB _key\n", + " values for your NetworkX nodes or if your NetworkX graph does NOT comply to\n", + " ArangoDB standards (i.e the node IDs are not formatted\n", + " like \"{collection}/{key}\"). For more info, see the **keyify_nodes**\n", + " parameter of ADBNX_Adapter.networkx_to_arangodb()\n", + "\n", + " :param nx_node_id: The NetworkX node id.\n", + " :type nx_node_id: adbnx_adapter.typings.NxId\n", + " :param nx_node: The NetworkX node object.\n", + " :type nx_node: adbnx_adapter.typings.NxData\n", + " :param col: The ArangoDB collection the node belongs to.\n", + " :type col: str\n", + " :return: A valid ArangoDB _key value.\n", + " :rtype: str\n", + " \"\"\"\n", + " adb_v_key: str = self._tuple_to_arangodb_key_helper(nx_node_id) # type: ignore\n", + " return adb_v_key\n", + "\n", + "# Re-instantiate the Grid adapter class\n", + "grid_adbnx_adapter = ADBNX_Adapter(con, Grid_ADBNX_Controller())\n", + "\n", + "# Delete the Grid graph if it already exists in ArangoDB\n", + "name = \"Grid_2\"\n", + "db.delete_graph(name, drop_collections=True, ignore_missing=True)\n", + "\n", + "# Create the ArangoDB graph\n", + "grid_adbnx_adapter.networkx_to_arangodb(name, original_grid_nx_g, edge_definitions, keyify_nodes=True)\n", + "\n", + "# Create the NetworkX graph from the ArangoDB graph\n", + "new_grid_nx_g = grid_adbnx_adapter.arangodb_graph_to_networkx(name)\n", + "\n", + "# Draw the new graph\n", + "nx.draw(new_grid_nx_g, with_labels=True)\n", + "print(new_grid_nx_g.nodes(data=True))\n", + "print(new_grid_nx_g.edges(data=True))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "lRmEM5eaQxJ5", + "Oc__NAd1eG8-", + "7y81WHO8eG8_", + "QfE_tKxneG9A", + "uByvwf9feG9A", + "ZrEDmtqCVD0W", + "RQ4CknYfUEuz", + "umy25EsUU6Lg", + "tWU1YW9AViTA", + "bvzJXSHHTi3v", + "gshTlSX_ZZsS", + "RTNNqQjpneFV" + ], + "name": "ArangoDB_NetworkX_Adapter_v3.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/outputs/ITSM_ArangoDB_Adapter_output.ipynb b/examples/outputs/ITSM_ArangoDB_Adapter_output.ipynb new file mode 100644 index 00000000..3c3aee9f --- /dev/null +++ b/examples/outputs/ITSM_ArangoDB_Adapter_output.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "MjUp8oZA_0EL" + }, + "source": [ + "# Predicting IT Service Ticket Reassingnment Using RGCN (DGL)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vM59y6qrlvjU" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e6qWopH1_0EN" + }, + "source": [ + "This notebook provides the details of applying a __Graph Convolutional Network(GCN)__ to predict if an IT service ticket will be reassigned. The workflow associated with ticket resolution has a convinient graph representation. The raw data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Incident+management+process+enriched+event+log) is loaded into ArangoDB using the [ITSM_data_loader python file](files/ITSM_data_loader.py). The [DGL](https://github.com/dmlc/dgl) library is used to create the __GCN__ model that is used to predict ticket reassignment. The graph associated with this prediction task is _heterogeneous_. This means that the graph has multiple types of vertices. In this example, the _incident_ (for which the ticket is created), the _support_org_ (the organization resolving the ticket), the _customer_ (who opened the ticket) and the _vendor_ (if the ticket is associated with an external product) are the different vertices in the graph (see [working with heterographs in DGL](https://docs.dgl.ai/en/0.4.x/tutorials/hetero/1_basics.html) for more details). Each of these vertices has attributes that are utilized in predicting the _reassignment_ status of a ticket. In this work, both the graph structure associated with a particular _incident_ and the properties associated with the vertices of the graph are used in the __GCN__. In particular, the semi-supervised model described in [\"working with heterographs in DGL\"](https://docs.dgl.ai/en/0.4.x/tutorials/hetero/1_basics.html), based on work by [Kipf et al.,](https://arxiv.org/abs/1703.06103) will be used here. The details of the implementaion are as follows." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K6LQVNyO_0EQ" + }, + "source": [ + "## Install Required Libraries " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "FF_RsnbzZsAM" + }, + "outputs": [], + "source": [ + "%%capture cap_out --no-stderr\n", + "!git clone -b oasis_connector --single-branch https://github.com/arangodb/interactive_tutorials.git\n", + "!git clone -b 0.0.0.2.5.3 --single-branch https://github.com/arangoml/networkx-adapter.git\n", + "!rsync -av networkx-adapter/examples/ ./ --exclude=.git\n", + "!rsync -av interactive_tutorials/ ./ --exclude=.git\n", + "!pip3 install adbnx_adapter==0.0.0.2.5.3.post1\n", + "!pip3 install networkx\n", + "!pip3 install matplotlib\n", + "!pip3 install pyarango\n", + "!pip3 install python-arango\n", + "!pip install dgl==0.4.3.post2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Ly7pC2qEAjdL" + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('./networkx-adapter/examples')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "50Xg1Wz1_0Eb" + }, + "source": [ + "## Obtain an Oasis Connection " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "punGc278_0Ec" + }, + "source": [ + "Oasis is the __ArangoDB__ managed service database offering. We will use __Oasis__ for this work. This permits us to use __ArangoDB__ without worrying about the details of installation and set up." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "NhIdp2rNaos7", + "outputId": "0928da40-11a8-4e24-8a2e-428b9f4a957f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Credentials expired.\n", + "Requesting new temp credentials.\n", + "Temp database ready to use.\n", + "\n", + "https://d383fa0b596a.arangodb.cloud:8529\n", + "Username: TUTac5rjasxcp6gmbfa5q0lrg\n", + "Password: TUT6gmg8w1gyetzv3v6dj1iyh\n", + "Database: TUT9bax9scnr7jq4apooqhc\n" + ] + } + ], + "source": [ + "import time\n", + "import oasis\n", + "con = oasis.getTempCredentials()\n", + "time.sleep(5)\n", + "\n", + "print()\n", + "print(\"https://{}:{}\".format(con[\"hostname\"], con[\"port\"]))\n", + "print(\"Username: \" + con[\"username\"])\n", + "print(\"Password: \" + con[\"password\"])\n", + "print(\"Database: \" + con[\"dbName\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JWWsUIV0_0Ei" + }, + "source": [ + "## Load the Data into Oasis " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_HoUDu8R_0Ej" + }, + "source": [ + "The _arangorestore_ utility is used to load the database into the __Oasis__ instance." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "o5Q1aESiatNB", + "outputId": "d95bf0d2-9484-408b-d841-e0d5c7abc0c8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[0m2021-12-31T17:55:16Z [193] INFO [05c30] {restore} Connected to ArangoDB 'http+ssl://d383fa0b596a.arangodb.cloud:8529'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:16Z [193] INFO [abeb4] {restore} Database name in source dump is 'TUThjyjglmb376tll56bgsvo'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:16Z [193] INFO [9b414] {restore} # Re-creating document collection 'customer'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:16Z [193] INFO [9b414] {restore} # Re-creating document collection 'incident'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:17Z [193] INFO [9b414] {restore} # Re-creating document collection 'support_org'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:17Z [193] INFO [9b414] {restore} # Re-creating document collection 'vendor'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:17Z [193] INFO [9b414] {restore} # Re-creating edge collection 'incident_customer'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:18Z [193] INFO [9b414] {restore} # Re-creating edge collection 'incident_support_org'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:18Z [193] INFO [9b414] {restore} # Re-creating edge collection 'incident_vendor'...\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:18Z [193] INFO [6d69f] {restore} # Dispatched 7 job(s), using 2 worker(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:18Z [193] INFO [94913] {restore} # Loading data into document collection 'incident', data size: 976791 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:18Z [193] INFO [94913] {restore} # Loading data into document collection 'customer', data size: 597532 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:20Z [193] INFO [6ae09] {restore} # Successfully restored document collection 'customer'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:20Z [193] INFO [94913] {restore} # Loading data into document collection 'support_org', data size: 642293 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:21Z [193] INFO [69a73] {restore} # Still loading data into document collection 'incident', 12534592 byte(s) restored\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:21Z [193] INFO [6ae09] {restore} # Successfully restored document collection 'incident'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:21Z [193] INFO [94913] {restore} # Loading data into document collection 'vendor', data size: 568543 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:21Z [193] INFO [6ae09] {restore} # Successfully restored document collection 'support_org'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:21Z [193] INFO [94913] {restore} # Loading data into edge collection 'incident_customer', data size: 1050511 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:23Z [193] INFO [75e65] {restore} # Current restore progress: restored 3 of 7 collection(s), read 33606716 byte(s) from datafiles, sent 6 data batch(es) of 22205531 byte(s) total size, queued jobs: 2, workers: 2\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:23Z [193] INFO [6ae09] {restore} # Successfully restored document collection 'vendor'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:23Z [193] INFO [94913] {restore} # Loading data into edge collection 'incident_support_org', data size: 1058346 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:26Z [193] INFO [6ae09] {restore} # Successfully restored edge collection 'incident_customer'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:26Z [193] INFO [94913] {restore} # Loading data into edge collection 'incident_vendor', data size: 1048582 byte(s)\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:27Z [193] INFO [6ae09] {restore} # Successfully restored edge collection 'incident_support_org'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:28Z [193] INFO [75e65] {restore} # Current restore progress: restored 6 of 7 collection(s), read 48251720 byte(s) from datafiles, sent 8 data batch(es) of 41240735 byte(s) total size, queued jobs: 0, workers: 2\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:29Z [193] INFO [6ae09] {restore} # Successfully restored edge collection 'incident_vendor'\n", + "\u001b[0m\u001b[0m2021-12-31T17:55:29Z [193] INFO [a66e1] {restore} Processed 7 collection(s) in 13.085120 s, read 48251720 byte(s) from datafiles, sent 8 data batch(es) of 48251713 byte(s) total size\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!chmod -R 755 ./tools\n", + "!./tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --default-replication-factor 3 --input-directory \"data/dgl_data_dump\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZqwkRxVx_0Eo" + }, + "source": [ + "## Create the ArangoDB DGL Adapter " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b4c-5Cpl_0Ep" + }, + "source": [ + "The __ArangoDB DGL Adapter__ will create a __dgl heterograph__ from __ArangoDB__. To create the __dgl heterograph__ we will need to provide a description of the graph that we would like to create. This is done by describing the vertices and edges of the graph using a _dictionary_ data structure. The details of the vertices and edges for this example are shown below. The __ArangoDB DGL Adapter__ specifies the details of the heterograph to __DGL__ using __Networkx__." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "5C-NW4amZ815" + }, + "outputs": [], + "source": [ + "vcols = {\"incident\": {\"D_sys_mod_count\", \"D_sys_mod_count\", \"D_reopen_count\", \"urgency\", \"incident_state\",\n", + " \"u_symptom\", \"impact\", \"contact_type\", \"u_priority_confirmation\", \"cmdb_ci\",\n", + " \"rfc\", \"problem_id\", \"caused_by\", \"location\", \"knowledge\", \"resolved_by\",\n", + " \"subcategory\", \"active\", \"category\", \"priority\", \"reassigned\", \"node_id\"},\n", + " \"support_org\": {\"assigned_to\", \"assignment_group\", \"node_id\"},\n", + " \"customer\": {\"opened_by\", \"node_id\"},\n", + " \"vendor\": {\"vendor\", \"node_id\"}}\n", + "ecols = {\"incident_support_org\": {\"_from\", \"_to\"}, \"incident_customer\": {\"_from\", \"_to\"},\n", + " \"incident_vendor\": {\"_from\", \"_to\"}}\n", + "\n", + "itsm_attributes = {'vertexCollections': vcols, 'edgeCollections': ecols}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R8KCUbNi_0Eu" + }, + "source": [ + "After the graph structure has been defined, instantiate the _DGLArangoDB_Networkx_Adapter_ with connection and create the __dgl graph__ as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "3qACbcQBbLEx", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "61a940f3-32f2-41b0-c230-094ca37b1fb5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DGL backend not selected or invalid. Assuming PyTorch for now.\n", + "Using backend: pytorch\n" + ] + } + ], + "source": [ + "from adbnx_adapter.dgl_arangoDB_networkx_adapter import DGLArangoDB_Networkx_Adapter\n", + "itsmg = DGLArangoDB_Networkx_Adapter(con)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "yBIgn6fGbPo1", + "outputId": "806a73b5-7f63-4a60-d93a-8c4c57fd9e2a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Creating DGL graph...\n", + "Loading edge data...\n", + "Loading vertex data...\n", + "Creating DGL Heterograph...\n", + "done!\n" + ] + } + ], + "source": [ + "g, labels = itsmg.create_dgl_graph(\n", + " graph_name='ITSMGraph', graph_attributes=itsm_attributes)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SvY1SS3u_0E3" + }, + "source": [ + "## Visually Inspect the Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "5KL6fN4wbfW3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 247 + }, + "outputId": "25a9cfcc-ceee-45cf-b40e-a80278b61cc3" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "nx.draw_networkx(g.metagraph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6uMb3qm6_0E8" + }, + "source": [ + "## Define the GCN " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iTqlOrYA_0E8" + }, + "source": [ + "_Note_ :Node attributes have been discretized. An embedding layer is used to generate feature representations." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "UyDWiVL8bSny" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import dgl.function as fn\n", + "\n", + "\n", + "class HeteroRGCNLayer1(nn.Module):\n", + " EMBED_SIZE = 64\n", + " VOCAB_SIZE = 2386\n", + "\n", + " def __init__(self, hidden_size, G):\n", + " super(HeteroRGCNLayer1, self).__init__()\n", + " # Need an embedding layer for each node feature\n", + " self.node_embeddings = {}\n", + " #self.dropouts = {}\n", + " for ntype in G.ntypes:\n", + " # create an embedding for each feature of a node\n", + " self.node_embeddings[ntype] = {}\n", + " num_node_features = G.node_attr_schemes(ntype)['f'].shape[0]\n", + " for feature in range(num_node_features):\n", + " self.node_embeddings[ntype][feature] = nn.Embedding(\n", + " self.VOCAB_SIZE, self.EMBED_SIZE)\n", + " #self.dropouts[ntype] = nn.Dropout()\n", + " # for name in etypes:\n", + " module_layers = {}\n", + " for srctype, etype, dsttype in G.canonical_etypes:\n", + " num_features = G.node_attr_schemes(srctype)['f'].shape[0]\n", + " module_layers[etype] = nn.Linear(\n", + " num_features * self.EMBED_SIZE, hidden_size)\n", + " self.weight = nn.ModuleDict(module_layers)\n", + " self.hidden_size = hidden_size\n", + "\n", + " def forward(self, G):\n", + "\n", + " funcs = {}\n", + " for srctype, etype, dsttype in G.canonical_etypes:\n", + " # for each node compute the embedding and store it in the graph\n", + " # iterate over the features of each node and compute the embedding\n", + " the_node_embedding = self.node_embeddings[srctype]\n", + " node_feature_embeddings = []\n", + " num_features = G.node_attr_schemes(srctype)['f'].shape[0]\n", + " for feature in range(num_features):\n", + " feature_embedding_layer = the_node_embedding[feature]\n", + " node_feature_embeddings.append(feature_embedding_layer(\n", + " G.nodes[srctype].data['f'][:, feature]))\n", + " comp_node_embedding = torch.cat(node_feature_embeddings, 1)\n", + " G.nodes[srctype].data['E'] = comp_node_embedding\n", + " # Compute W_r * h\n", + " Wh = self.weight[etype](G.nodes[srctype].data['E'])\n", + " #Wh = torch.sum(Wh, dim = 1)\n", + " # Save it in graph for message passing\n", + " G.nodes[srctype].data['Wh_%s' % etype] = Wh\n", + " # Specify per-relation message passing functions: (message_func, reduce_func).\n", + " # Note that the results are saved to the same destination feature 'h', which\n", + " # hints the type wise reducer for aggregation.\n", + " funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))\n", + " # Trigger message passing of multiple types.\n", + " # The first argument is the message passing functions for each relation.\n", + " # The second one is the type wise reducer, could be \"sum\", \"max\",\n", + " # \"min\", \"mean\", \"stack\"\n", + " G.multi_update_all(funcs, 'sum')\n", + " # return G\n", + " return {ntype: G.nodes[ntype].data['h'] for ntype in G.ntypes}\n", + "\n", + "\n", + "class HeteroRGCNLayer2(nn.Module):\n", + " def __init__(self, in_size, out_size, etypes):\n", + " super(HeteroRGCNLayer2, self).__init__()\n", + " # W_r for each relation\n", + "\n", + " self.weight = nn.ModuleDict({\n", + " name: nn.Linear(in_size, out_size) for name in etypes\n", + " })\n", + "\n", + " def forward(self, G, feat_dict):\n", + " # The input is a dictionary of node features for each type\n", + " funcs = {}\n", + " for srctype, etype, dsttype in G.canonical_etypes:\n", + " # Compute W_r * h\n", + " Wh = self.weight[etype](feat_dict[srctype])\n", + " # Save it in graph for message passing\n", + " G.nodes[srctype].data['Wh2_%s' % etype] = Wh\n", + " # Specify per-relation message passing functions: (message_func, reduce_func).\n", + " # Note that the results are saved to the same destination feature 'h', which\n", + " # hints the type wise reducer for aggregation.\n", + " funcs[etype] = (fn.copy_u('Wh2_%s' %\n", + " etype, 'm'), fn.mean('m', 'h2'))\n", + " # Trigger message passing of multiple types.\n", + " # The first argument is the message passing functions for each relation.\n", + " # The second one is the type wise reducer, could be \"sum\", \"max\",\n", + " # \"min\", \"mean\", \"stack\"\n", + " G.multi_update_all(funcs, 'sum')\n", + " # return G\n", + " # return the updated node feature dictionary\n", + " return {ntype: G.nodes[ntype].data['h2'] for ntype in G.ntypes}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "Mjsfj08cboEx" + }, + "outputs": [], + "source": [ + "class HeteroRGCN(nn.Module):\n", + " def __init__(self, G, hidden_size, out_size):\n", + " super(HeteroRGCN, self).__init__()\n", + " # create layers\n", + " self.layer1 = HeteroRGCNLayer1(hidden_size, G)\n", + " self.layer2 = HeteroRGCNLayer2(hidden_size, out_size, G.etypes)\n", + "\n", + " def forward(self, G):\n", + "\n", + " h_dict = self.layer1(G)\n", + " h_dict = {k: F.leaky_relu(h) for k, h in h_dict.items()}\n", + " h_dict = self.layer2(G, h_dict)\n", + "\n", + " # get paper logits\n", + "\n", + " return h_dict['incident']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SqMjZoSO_0FE" + }, + "source": [ + "## Create Training and Test Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "z2YZAGfgbsSy" + }, + "outputs": [], + "source": [ + "training_mask = np.random.rand(len(labels)) <= 0.8\n", + "train_idx = [i for i in range(len(labels)) if training_mask[i]]\n", + "test_idx = [i for i in range(len(labels)) if not training_mask[i]]\n", + "train_idx = torch.tensor(train_idx).long()\n", + "test_idx = torch.tensor(test_idx).long()\n", + "labels = torch.tensor(labels).long()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oJIloQV-_0FI" + }, + "source": [ + "## Train and Evaluate the Model on Test Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "XH-d0z3ibzJ0", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ee2f17b9-5f51-4448-8bcd-f864e088ae3d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n", + "Wall time: 5.48 µs\n", + "Loss 0.6980, training accuracy 0.5260, test accuracy 0.5226\n", + "Loss 0.6058, training accuracy 0.6093, test accuracy 0.6120\n", + "Loss 0.5153, training accuracy 0.7644, test accuracy 0.7632\n", + "Loss 0.4469, training accuracy 0.7910, test accuracy 0.7932\n", + "Loss 0.4165, training accuracy 0.8063, test accuracy 0.8040\n", + "Loss 0.3932, training accuracy 0.8188, test accuracy 0.8167\n", + "Loss 0.3769, training accuracy 0.8254, test accuracy 0.8194\n", + "Loss 0.3648, training accuracy 0.8339, test accuracy 0.8253\n", + "Loss 0.3546, training accuracy 0.8397, test accuracy 0.8281\n", + "Loss 0.3457, training accuracy 0.8443, test accuracy 0.8291\n" + ] + } + ], + "source": [ + "%time\n", + "# Create the model. The output has three logits for three classes.\n", + "\n", + "\n", + "#model = HeteroRGCN(G, 512,64, 2)\n", + "\n", + "#opt = torch.optim.SGD(model.parameters(), lr=0.001)\n", + "# ,\n", + "model = HeteroRGCN(g, 32, 2)\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "opt = torch.optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + "model.train()\n", + "\n", + "for epoch in range(100):\n", + " opt.zero_grad()\n", + " logits = model(g)\n", + " # The loss is computed only for labeled nodes.\n", + " loss = loss_fn(logits[train_idx], labels[train_idx])\n", + " loss.backward()\n", + " opt.step()\n", + " pred_trng = torch.argmax(logits[train_idx], dim=1)\n", + " res_trng = pred_trng == labels[train_idx]\n", + " trng_acc = torch.sum(res_trng).item()/labels[train_idx].shape[0]\n", + "\n", + " pred_test = torch.argmax(logits[test_idx], dim=1)\n", + " res_test = pred_test == labels[test_idx]\n", + " test_acc = torch.sum(res_test).item()/labels[test_idx].shape[0]\n", + "\n", + " if epoch % 10 == 0:\n", + " print('Loss %.4f, training accuracy %.4f, test accuracy %.4f' %\n", + " (loss.item(), trng_acc, test_acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_B6fTCvt_0FM" + }, + "source": [ + "## Note\n", + "The implementation comes with a database dump required for this exercise. If there is a need to recreate the dump, remove the existing creds.dat file and create an empty creds.dat file. The $\\texttt{itsm_data_load_driver}$ can then be used to load the data to an __Oasis__ instance. The data load procedure can take about $15$ minutes to complete. Once, the data load is done, the _arangodump_ utility can be used to create a dump of the loaded data. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "zZucEAm4_0FM" + }, + "outputs": [], + "source": [ + "# !rm creds.dat\n", + "# !touch creds.dat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OsJyZCgz_0FQ" + }, + "outputs": [], + "source": [ + "# from itsm_data_load_driver import load_ITSM_data_to_ArangoDB\n", + "# itsmdl = load_ITSM_data_to_ArangoDB()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Bto7Q0qa_0FT" + }, + "outputs": [], + "source": [ + "# arangodump --server.endpoint --server.username --server.database --server.password --output-directory dgl_data_dump\n" + ] + } + ], + "metadata": { + "colab": { + "name": "ITSM_ArangoDB_Adapter.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/outputs/ITSM_EDA_output.ipynb b/examples/outputs/ITSM_EDA_output.ipynb new file mode 100644 index 00000000..79b1d0ff --- /dev/null +++ b/examples/outputs/ITSM_EDA_output.ipynb @@ -0,0 +1,768 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "EdwmCDNMvacj" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RjBZf-SVdrga" + }, + "source": [ + "## Install Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "baCrm4a-dpUq" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!git clone -b 0.0.0.2.5.3 https://github.com/arangoml/networkx-adapter.git\n", + "!rsync -av networkx-adapter/examples/ ./ --exclude=.git\n", + "!pip3 install adbnx-adapter==0.0.0.2.5.3.post1\n", + "!pip3 install networkx\n", + "!pip3 install matplotlib\n", + "!pip3 install pyarango\n", + "!pip3 install python-arango" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jL2HuHIqdk2W" + }, + "source": [ + "## Data Characteristics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BPqAGfABdk2X" + }, + "source": [ + "The data is an event log that was extracted from the audit system of a __ServiceNow__ platform (this is an enterprise service help desk application). The data is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Incident+management+process+enriched+event+log) (please visit the link for more details). This notebook captures the salient aspects of exploratory analysis of this dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-0zJY4eldk2a" + }, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "eAMWQcO3dk2b" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "fp = \"data/incident_event_log.csv\"\n", + "df = pd.read_csv(fp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DKTpEAXJdk2g" + }, + "source": [ + "## What are the main characteristics?\n", + "1. What does a sample of the dataset look like?\n", + "2. How many incidents are reported in this dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "4HT6rg9hdk2h", + "outputId": "ab96b0d0-e425-4881-cec1-2936f2aa7718", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 356 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
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numberincident_stateactivereassignment_countreopen_countsys_mod_countmade_slacaller_idopened_byopened_atsys_created_bysys_created_atsys_updated_bysys_updated_atcontact_typelocationcategorysubcategoryu_symptomcmdb_ciimpacturgencypriorityassignment_groupassigned_toknowledgeu_priority_confirmationnotifyproblem_idrfcvendorcaused_byclosed_coderesolved_byresolved_atclosed_at
0INC0000045NewTrue000TrueCaller 2403Opened by 829/2/2016 01:16Created by 629/2/2016 01:23Updated by 2129/2/2016 01:23PhoneLocation 143Category 55Subcategory 170Symptom 72?2 - Medium2 - Medium3 - ModerateGroup 56?TrueFalseDo Not Notify????code 5Resolved by 14929/2/2016 11:295/3/2016 12:00
1INC0000045ResolvedTrue002TrueCaller 2403Opened by 829/2/2016 01:16Created by 629/2/2016 01:23Updated by 64229/2/2016 08:53PhoneLocation 143Category 55Subcategory 170Symptom 72?2 - Medium2 - Medium3 - ModerateGroup 56?TrueFalseDo Not Notify????code 5Resolved by 14929/2/2016 11:295/3/2016 12:00
2INC0000045ResolvedTrue003TrueCaller 2403Opened by 829/2/2016 01:16Created by 629/2/2016 01:23Updated by 80429/2/2016 11:29PhoneLocation 143Category 55Subcategory 170Symptom 72?2 - Medium2 - Medium3 - ModerateGroup 56?TrueFalseDo Not Notify????code 5Resolved by 14929/2/2016 11:295/3/2016 12:00
3INC0000045ClosedFalse004TrueCaller 2403Opened by 829/2/2016 01:16Created by 629/2/2016 01:23Updated by 9085/3/2016 12:00PhoneLocation 143Category 55Subcategory 170Symptom 72?2 - Medium2 - Medium3 - ModerateGroup 56?TrueFalseDo Not Notify????code 5Resolved by 14929/2/2016 11:295/3/2016 12:00
4INC0000047NewTrue000TrueCaller 2403Opened by 39729/2/2016 04:40Created by 17129/2/2016 04:57Updated by 74629/2/2016 04:57PhoneLocation 165Category 40Subcategory 215Symptom 471?2 - Medium2 - Medium3 - ModerateGroup 70Resolver 89TrueFalseDo Not Notify????code 5Resolved by 811/3/2016 09:526/3/2016 10:00
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\n", + " " + ], + "text/plain": [ + " number incident_state ... resolved_at closed_at\n", + "0 INC0000045 New ... 29/2/2016 11:29 5/3/2016 12:00\n", + "1 INC0000045 Resolved ... 29/2/2016 11:29 5/3/2016 12:00\n", + "2 INC0000045 Resolved ... 29/2/2016 11:29 5/3/2016 12:00\n", + "3 INC0000045 Closed ... 29/2/2016 11:29 5/3/2016 12:00\n", + "4 INC0000047 New ... 1/3/2016 09:52 6/3/2016 10:00\n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "_3onCkdsdk2m", + "outputId": "56fbbaed-a5da-44ed-9949-33c6c4d9a964", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "24918" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "df['number'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQkLOWkFdk2q" + }, + "source": [ + "## List the data types of the various attributes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "wspAzvw6dk2r", + "outputId": "5ad1b0fc-86a6-4af0-b97e-793da1af48e2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "number object\n", + "incident_state object\n", + "active bool\n", + "reassignment_count int64\n", + "reopen_count int64\n", + "sys_mod_count int64\n", + "made_sla bool\n", + "caller_id object\n", + "opened_by object\n", + "opened_at object\n", + "sys_created_by object\n", + "sys_created_at object\n", + "sys_updated_by object\n", + "sys_updated_at object\n", + "contact_type object\n", + "location object\n", + "category object\n", + "subcategory object\n", + "u_symptom object\n", + "cmdb_ci object\n", + "impact object\n", + "urgency object\n", + "priority object\n", + "assignment_group object\n", + "assigned_to object\n", + "knowledge bool\n", + "u_priority_confirmation bool\n", + "notify object\n", + "problem_id object\n", + "rfc object\n", + "vendor object\n", + "caused_by object\n", + "closed_code object\n", + "resolved_by object\n", + "resolved_at object\n", + "closed_at object\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bc6SO7Jkdk2v" + }, + "source": [ + "## Convert the $\\texttt{sys_updated_at}$ attribute to be a timestamp" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "3HpATu15dk2w" + }, + "outputs": [], + "source": [ + "df['sys_updated_at'] = pd.to_datetime(df['sys_updated_at'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yGeJtQ94dk20" + }, + "source": [ + "## Machine Learning Task for this Dataset " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n3EsAxURdk20" + }, + "source": [ + "The contributors of this dataset have used this data to predict the time to resolution of the ticket. This data has been used for a classification task in this work. A [graph convolutional network for relational data(GCN)](https://arxiv.org/abs/1703.06103) will be the machine learning task for this work. We will be using a __GCN__ to predict the property of a particular node. What property would be useful to predict ? What are the characteristics of this property in the data? The cells below explore these questions. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yGxcIAlIdk21" + }, + "source": [ + "### Explore candidate list of tags\n", + "Note: For the experiment, we will pick a tag that is fairly evenly distributed in the data. This will avoild the imbalanced classs label problem." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "ofWeKiuWdk22", + "outputId": "c44dda44-49c1-4d70-83d6-cd48690a626c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "True 132497\n", + "False 9215\n", + "Name: made_sla, dtype: int64\n", + "2 - Medium 134094\n", + "1 - High 4020\n", + "3 - Low 3598\n", + "Name: urgency, dtype: int64\n", + "2 - Medium 134335\n", + "3 - Low 3886\n", + "1 - High 3491\n", + "Name: impact, dtype: int64\n", + "0 69876\n", + "1 37104\n", + "2 15097\n", + "3 8274\n", + "4 4614\n", + "5 2595\n", + "6 1447\n", + "7 985\n", + "8 574\n", + "9 365\n", + "10 285\n", + "11 174\n", + "12 108\n", + "13 61\n", + "14 45\n", + "15 21\n", + "17 16\n", + "20 16\n", + "16 13\n", + "18 13\n", + "22 9\n", + "19 8\n", + "21 3\n", + "27 3\n", + "26 2\n", + "23 2\n", + "24 1\n", + "25 1\n", + "Name: reassignment_count, dtype: int64\n" + ] + } + ], + "source": [ + "dfcc = df[['made_sla', 'urgency', 'impact', 'reassignment_count']]\n", + "for c in dfcc.columns.tolist():\n", + " print(str(dfcc[c].value_counts()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gKV5JEUUdk28" + }, + "source": [ + "A review of the level counts of the categorical variables in this dataset suggest that $\\texttt{made_sla}$ and $\\texttt{urgency}$ are both highly imbalanced. The minority levels are almost anomalies. The $\\texttt{reassignment_count}$ seems promising. We can derive a new attribute $\\texttt{reassigned}$ that captures if the ticket has been reassigned, i.e., has it been assigned to someone after the initial assignment. Such an attribute captures inefficiencies in triaging the ticket and is a useful indicator to track for an organization. A $0$ for this attribute indicates that there was no reassignment and a $1$ indicates that there was a reassignment. This attribute has a nice even spread in the data, i.e., an almost even spread of $0$ and $1$. The cells below create this attribute" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CYGI6SW5dk29" + }, + "source": [ + "## Feature Creation (reassigned):\n", + "It looks like tracking ticket reassignment can create a variable that is somewhat evenly distributed in the data. About half the tickets have the correct assignment at first. About half are reassigned to various degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "XgFfomD6dk2-", + "outputId": "e8318243-657f-4ce7-e274-98d70e7d991f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 71836\n", + "0 69876\n", + "Name: reassigned, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "df['reassigned'] = df['reassignment_count'].apply(lambda x: 0 if x == 0 else 1)\n", + "df['reassigned'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "nb44FIQZdk3B" + }, + "outputs": [], + "source": [ + "dfpp = df.loc[df.groupby(by=['number']).sys_updated_at.idxmax()]\n", + "dfpp = dfpp.reset_index()\n", + "cols = dfpp.columns.tolist()\n", + "cols.remove('index')\n", + "cols.remove('number')\n", + "dfpp = dfpp[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sFYjOPcRdk3E" + }, + "source": [ + "Now that we have characterized the data and identified the machine learning task to be performed. The next step is to transform the data to a form amenable for machine learning. " + ] + } + ], + "metadata": { + "colab": { + "name": "ITSM_EDA.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/outputs/batch_graph_pre_processing_output.ipynb b/examples/outputs/batch_graph_pre_processing_output.ipynb new file mode 100644 index 00000000..aa39ddcd --- /dev/null +++ b/examples/outputs/batch_graph_pre_processing_output.ipynb @@ -0,0 +1,1088 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "teHHQQILe_dS" + }, + "source": [ + "## Get Raw Data for Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ePOD6A7Rlxo4" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "G-oxhBfae8Ae" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!git clone -b 0.0.0.2.5.3 https://github.com/arangoml/networkx-adapter.git\n", + "!rsync -av networkx-adapter/examples/ ./ --exclude=.git\n", + "!pip3 install networkx\n", + "!pip3 install matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KzKGdXzUenuM" + }, + "source": [ + "## Preprocessing ITSM Data\n", + "The purpose of this notebook is to prepare the data in a format suitable for machine learning. The dataset consists of a few numerical and many categorical attributes. The numerical attributes are discretized. The embedding for the categorical values is developed similar to developing embeddings for words in NLP (see https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html). Each categorical value is mapped to a unique integer. The encoded data that is presented to the embedding layer is a sequence of integers, with each integer corresponding to a word, This notebook performs this mapping. It also encodes unknown values to a 'UNKNOWN' category." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "bkRP-4eBenuN" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "fp = \"data/incident_event_log.csv\"\n", + "df = pd.read_csv(fp)\n", + "df['reassigned'] = df['reassigned'] = df['reassignment_count'].apply(lambda x: 0 if x == 0 else 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YGEszr3GenuT" + }, + "source": [ + "## Discretize the numerical attributes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "d6mTJ90venuU", + "outputId": "78a4e0d0-e48d-46b5-bcf7-2d0676d81a51", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n" + ] + } + ], + "source": [ + "numeric = ['sys_mod_count', 'reopen_count']\n", + "dfn = df[numeric]\n", + "dcols = []\n", + "for col in numeric:\n", + " dlabel = 'D_' + col\n", + " labels = [dlabel +'_' + str(c) for c in range(5)]\n", + " dcols.append(dlabel)\n", + " dfn[dlabel] = pd.qcut(dfn[col].rank(method='first'),5, labels = labels, duplicates = 'drop')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "b_EF79pOenuZ", + "outputId": "8e7f396d-2726-4e02-cd9f-45490a3a0856", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
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The discretized version of these attributes is added subsequently" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "cxUt-5Enenue", + "outputId": "f6ee31c9-d5cd-435a-aa0f-d4dc15d40470", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['rfc',\n", + " 'vendor',\n", + " 'opened_by',\n", + " 'category',\n", + " 'cmdb_ci',\n", + " 'assigned_to',\n", + " 'urgency',\n", + " 'active',\n", + " 'subcategory',\n", + " 'knowledge',\n", + " 'reassignment_count',\n", + " 'impact',\n", + " 'contact_type',\n", + " 'closed_code',\n", + " 'priority',\n", + " 'incident_state',\n", + " 'location',\n", + " 'u_priority_confirmation',\n", + " 'problem_id',\n", + " 'u_symptom',\n", + " 'resolved_by',\n", + " 'assignment_group',\n", + " 'caused_by']" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "attributes = df.columns.tolist()\n", + "remove = [ 'made_sla', 'opened_at', 'resolved_at','sys_created_at', 'caller_id', 'closed_at',\\\n", + " 'notify', 'sys_updated_by','sys_created_by', 'number', 'sys_updated_at', 'reassigned' ]\n", + "exclude = remove + numeric\n", + "keep = list(set(attributes) - set(exclude)) \n", + "keep" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "DbVuZhgsenuk" + }, + "outputs": [], + "source": [ + "df_cat_vars = df[keep]\n", + "df_cat_vars = df_cat_vars.replace(to_replace = '?', value = 'UNKNOWN')\n", + "df_cat_vars = pd.concat([df_cat_vars, dfn[dcols]], axis = 1)\n", + "df['made_sla'] = df['made_sla'].map({True: 1, False: 0})\n", + "\n", + "df = df.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Vto4QKbxenuo", + "outputId": "42a6ffac-f838-4ea4-a1c0-07865deab0ed", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 71836\n", + "0 69876\n", + "Name: reassigned, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "df['reassigned'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "ogJ0oAlSenus", + "outputId": "5f97a623-9e62-4877-c57e-331c20216c79", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Num unique vals for category rfc = 182\n", + "Num unique vals for category vendor = 5\n", + "Num unique vals for category opened_by = 208\n", + "Num unique vals for category category = 59\n", + "Num unique vals for category cmdb_ci = 51\n", + "Num unique vals for category assigned_to = 235\n", + "Num unique vals for category urgency = 3\n", + "Num unique vals for category active = 2\n", + "Num unique vals for category subcategory = 255\n", + "Num unique vals for category knowledge = 2\n", + "Num unique vals for category reassignment_count = 28\n", + "Num unique vals for category impact = 3\n", + "Num unique vals for category contact_type = 5\n", + "Num unique vals for category closed_code = 18\n", + "Num unique vals for category priority = 4\n", + "Num unique vals for category incident_state = 9\n", + "Num unique vals for category location = 225\n", + "Num unique vals for category u_priority_confirmation = 2\n", + "Num unique vals for category problem_id = 253\n", + "Num unique vals for category u_symptom = 526\n", + "Num unique vals for category resolved_by = 217\n", + "Num unique vals for category assignment_group = 79\n", + "Num unique vals for category caused_by = 4\n", + "Num unique vals for category D_sys_mod_count = 5\n", + "Num unique vals for category D_reopen_count = 5\n", + "Vocab size: 2385\n" + ] + } + ], + "source": [ + "cols = df_cat_vars.columns.tolist()\n", + "vocab_size = 0\n", + "for c in cols:\n", + " print(\"Num unique vals for category \" + str(c) + \" = \" + str(df_cat_vars[c].nunique()))\n", + " vocab_size += df_cat_vars[c].nunique()\n", + "print(\"Vocab size: %s\" % vocab_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "juRDL5_lenuw" + }, + "source": [ + "## Recode the categorical values to integers" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "oVM1rplIenux" + }, + "outputs": [], + "source": [ + "UNKNOWN_VAL = 1\n", + "cat_cols = df_cat_vars.columns.tolist()\n", + "cat_int_map = {col: dict() for col in cat_cols}\n", + "int_index = 2\n", + "for c in cat_cols:\n", + " unique_col_values = df_cat_vars[c].unique().tolist()\n", + " col_int_map = cat_int_map[c]\n", + " for uv in unique_col_values:\n", + " if uv == 'UNKNOWN':\n", + " col_int_map[uv] = UNKNOWN_VAL\n", + " else:\n", + " col_int_map[uv] = int_index\n", + " int_index +=1\n", + " df_cat_vars[c] = df_cat_vars[c].map(cat_int_map[c]) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "lH6qzefOenu0" + }, + "outputs": [], + "source": [ + "combined_cat_int_map = dict()\n", + "for col in cat_int_map.keys():\n", + " for cat_val, int_map in cat_int_map[col].items():\n", + " combined_cat_int_map[cat_val] = int_map\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jH9uA2IVenu3" + }, + "source": [ + "## Write preprocessed raw data to disk" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "kWMHA7eSenu4" + }, + "outputs": [], + "source": [ + "fp_cat_int_map = \"data/category_to_integer_map.csv\"\n", + "df_map = pd.DataFrame(combined_cat_int_map, index = [0])\n", + "df_map = df_map.T\n", + "df_map = df_map.reset_index()\n", + "df_map.columns = [\"cat_value\", \"assigned_integer\"]\n", + "df_map.to_csv(fp_cat_int_map, index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "qzbT2d08enu7" + }, + "outputs": [], + "source": [ + "add_to_cat_vars = ['number','sys_updated_at', 'reassigned'] \n", + "df = pd.concat([df[add_to_cat_vars], df_cat_vars], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "wDA8_L5-enu-" + }, + "outputs": [], + "source": [ + "df['sys_updated_at'] = pd.to_datetime(df['sys_updated_at']) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "qN8WD2teenvB", + "outputId": "118a8b50-a3c8-4589-c982-93e000d4e05d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "dtype('\n", + "
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