diff --git a/.gitignore b/.gitignore index c24dec75..30cedf47 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ venv/ .vscode/ .ipynb_checkpoints/ __pycache__/ +application/__pycache__/ *.mp4 .env .DS_Store @@ -17,3 +18,4 @@ application/flask_session/ application/__pycache__/app.cpython-311.pyc *.pyc *.pyc +flask_session/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 82ff8018..1be48c6d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -20,7 +20,7 @@ WORKDIR /alpha-team # Install GDAL and pygraphviz RUN pip install GDAL pygraphviz - +RUN pip3 install torch --index-url https://download.pytorch.org/whl/cpu # Copy the current directory contents into the container at /AlphaTeam COPY requirements.txt . diff --git a/README.md b/README.md index ec5bf985..32fd0a51 100644 --- a/README.md +++ b/README.md @@ -34,5 +34,7 @@ the command in step 3 above. # Documentation -On the frontend of the application, you can find the documentation for the API endpoints once the frontend application -is started at `http://localhost:{chosen port}/docs` +You can view the documentation in the `docs/` directory. The documentation is generated using Sphinx. + +### To regenerate the documentation +In the root directory of the project, run `sh generate_docs.sh` to regenerate the documentation. \ No newline at end of file diff --git a/analysis/CryptoData.ipynb b/analysis/CryptoData.ipynb deleted file mode 100644 index 9c05a89f..00000000 --- a/analysis/CryptoData.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from pyvis.network import Network\n", - "import networkx as nx\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": " from \\\n0 bc1q2ar35d9ayrv0plzywlaxs8y7s8h5zkvn6fe4mj \n1 bc1q2ar35d9ayrv0plzywlaxs8y7s8h5zkvn6fe4mj \n2 bc1q2ar35d9ayrv0plzywlaxs8y7s8h5zkvn6fe4mj \n3 bc1q2ar35d9ayrv0plzywlaxs8y7s8h5zkvn6fe4mj \n4 bc1q2ar35d9ayrv0plzywlaxs8y7s8h5zkvn6fe4mj \n\n to value \n0 bc1q0fdex09zrn652502h9skl06cenfzw9c2gkna8w 2.298843e+09 \n1 bc1q2nr9fhshymd87v9ejtu6wc4n43c63patt36r28 2.298843e+09 \n2 bc1q4wa6n47akefhcfh9zcp5y7h5hnxp0ckzqwqrg6 2.298843e+09 \n3 bc1q64ezgwqtdeul3ayx7065fg46x6frj3xewh9t24 2.298843e+09 \n4 bc1qegf5d3l2jjzcdaqt8hayvnam6qwhnnnvhm7c90 2.298843e+09 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
fromtovalue
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\n
" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('../datasets/data.csv', header=None, names=['from', 'to', 'value'])\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: When cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n" - ] - } - ], - "source": [ - "net = Network(notebook=True)\n", - "net.from_nx(nx.from_pandas_edgelist(df, source='to', target='from', edge_attr='value'))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = nx.from_pandas_edgelist(df, source='to', target='from', edge_attr='value')\n", - "plt.figure(figsize=(10,10))\n", - "nx.draw(G, with_labels=False, node_size=5)\n", - "plt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "example.html\n" - ] - }, - { - "data": { - "text/plain": "", - "text/html": "\n \n " - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.show_buttons(filter_=['physics'])\n", - "net.show(\"example.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/analysis/Rail.ipynb b/analysis/Rail.ipynb deleted file mode 100644 index 45c0937b..00000000 --- a/analysis/Rail.ipynb +++ /dev/null @@ -1,770 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from src.preprocessing import *\n", - "from src.metrics import *\n", - "from src.visualisation import *\n", - "from src.NetworkGraphs import *\n", - "from math import sqrt\n", - "import networkx as nx\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "requires pygraphviz http://pygraphviz.github.io/", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "File \u001B[0;32m~/Jupyter_notebooks/GroupProject/Try/lib/python3.9/site-packages/networkx/drawing/nx_agraph.py:295\u001B[0m, in \u001B[0;36mpygraphviz_layout\u001B[0;34m(G, prog, root, args)\u001B[0m\n\u001B[1;32m 294\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 295\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpygraphviz\u001B[39;00m\n\u001B[1;32m 296\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mImportError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", - "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'pygraphviz'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[0;31mImportError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[7], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m G \u001B[38;5;241m=\u001B[39m nx\u001B[38;5;241m.\u001B[39mpetersen_graph()\n\u001B[0;32m----> 2\u001B[0m pos \u001B[38;5;241m=\u001B[39m \u001B[43mnx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnx_agraph\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgraphviz_layout\u001B[49m\u001B[43m(\u001B[49m\u001B[43mG\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 3\u001B[0m pos \u001B[38;5;241m=\u001B[39m nx\u001B[38;5;241m.\u001B[39mnx_agraph\u001B[38;5;241m.\u001B[39mgraphviz_layout(G, prog\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdot\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[0;32m~/Jupyter_notebooks/GroupProject/Try/lib/python3.9/site-packages/networkx/drawing/nx_agraph.py:251\u001B[0m, in \u001B[0;36mgraphviz_layout\u001B[0;34m(G, prog, root, args)\u001B[0m\n\u001B[1;32m 220\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mgraphviz_layout\u001B[39m(G, prog\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mneato\u001B[39m\u001B[38;5;124m\"\u001B[39m, root\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, args\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m 221\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Create node positions for G using Graphviz.\u001B[39;00m\n\u001B[1;32m 222\u001B[0m \n\u001B[1;32m 223\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 249\u001B[0m \u001B[38;5;124;03m see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.\u001B[39;00m\n\u001B[1;32m 250\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 251\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpygraphviz_layout\u001B[49m\u001B[43m(\u001B[49m\u001B[43mG\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mprog\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprog\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mroot\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mroot\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/Jupyter_notebooks/GroupProject/Try/lib/python3.9/site-packages/networkx/drawing/nx_agraph.py:297\u001B[0m, in \u001B[0;36mpygraphviz_layout\u001B[0;34m(G, prog, root, args)\u001B[0m\n\u001B[1;32m 295\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpygraphviz\u001B[39;00m\n\u001B[1;32m 296\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mImportError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[0;32m--> 297\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mImportError\u001B[39;00m(\n\u001B[1;32m 298\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrequires pygraphviz \u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhttp://pygraphviz.github.io/\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 299\u001B[0m ) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m 300\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m root \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 301\u001B[0m args \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m-Groot=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mroot\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n", - "\u001B[0;31mImportError\u001B[0m: requires pygraphviz http://pygraphviz.github.io/" - ] - } - ], - "source": [ - "G = nx.petersen_graph()\n", - "pos = nx.nx_agraph.graphviz_layout(G)\n", - "pos = nx.nx_agraph.graphviz_layout(G, prog=\"dot\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Custom_networkGraphs = NetworkGraphs('../datasets/inf-USAir97.mtx', type=\"CUSTOM\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw(Custom_networkGraphs.MultiGraph,Custom_networkGraphs.pos,node_size=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Crypto_networkGraphs = NetworkGraphs('../datasets/Dune_Eth_transaction.csv', type=\"CRYPTO\")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pos = nx.spring_layout(Crypto_networkGraphs.MultiDiGraph,k=4/sqrt(Crypto_networkGraphs.MultiDiGraph.number_of_nodes()), iterations=150, weight=None)\n", - "nx.draw(Crypto_networkGraphs.MultiDiGraph,pos, with_labels=False, node_size=5, width=0.1, arrowsize=0.1,alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Excluded 0 stations\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/admin/Jupyter_notebooks/GroupProject/src/NetworkGraphs.py:70: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " self.df = pd.read_csv(filename)\n" - ] - } - ], - "source": [ - "networkGraphs = NetworkGraphs('../datasets/Railway.csv', type=\"RAILWAY\", spatial =True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pos = nx.spring_layout(networkGraphs.DiGraph, k=2/sqrt(networkGraphs.Graph.number_of_nodes()), iterations=50)\n", - "nx.draw(networkGraphs.DiGraph,pos, with_labels=False, node_size=1, width=1, arrowsize=0.1,alpha=0.5,edge_color=networkGraphs.colors['DiGraph'])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt = static_visualisation(networkGraphs, 'Railway Network', directed=True, multi=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt = static_visualisation(networkGraphs, 'Railway Network', directed=False, multi=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MetricsDirectedUndirected
0Clustering Coefficient0.3771800.422829
1Avg. Shortest Path Length0.0000008.786388
2Diameter0.00000047.000000
3Radius0.00000024.000000
4Number of Nodes2719.0000002719.000000
5Number of Edges10772.0000006169.000000
6Density0.0014580.001669
7Transitivity0.2923700.321659
8Avg. Degree7.9235014.537698
9Avg. Clustering0.3771800.422829
10Avg. Eigenvector Centrality0.0061780.005582
11Avg. Betweenness Centrality0.0031180.002866
12Avg. Closeness Centrality0.1105250.120773
13Avg. Degree Centrality0.0029150.001669
14Avg. Page Rank0.0003680.000368
15Avg. Load Centrality0.0031180.002866
\n", - "
" - ], - "text/plain": [ - " Metrics Directed Undirected\n", - "0 Clustering Coefficient 0.377180 0.422829\n", - "1 Avg. Shortest Path Length 0.000000 8.786388\n", - "2 Diameter 0.000000 47.000000\n", - "3 Radius 0.000000 24.000000\n", - "4 Number of Nodes 2719.000000 2719.000000\n", - "5 Number of Edges 10772.000000 6169.000000\n", - "6 Density 0.001458 0.001669\n", - "7 Transitivity 0.292370 0.321659\n", - "8 Avg. Degree 7.923501 4.537698\n", - "9 Avg. Clustering 0.377180 0.422829\n", - "10 Avg. Eigenvector Centrality 0.006178 0.005582\n", - "11 Avg. Betweenness Centrality 0.003118 0.002866\n", - "12 Avg. Closeness Centrality 0.110525 0.120773\n", - "13 Avg. Degree Centrality 0.002915 0.001669\n", - "14 Avg. Page Rank 0.000368 0.000368\n", - "15 Avg. Load Centrality 0.003118 0.002866" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_metrics = compute_global_metrics(networkGraphs)\n", - "global_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<<<<<<< local \n" - ] - }, - { - "data": { - "text/html": [ - "
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NodeDegree CentralityEigenvector CentralityCloseness CentralityBetweeness CentralityLoad Centrality
06920.0080941.616408e-020.1461380.0252830.025286
113510.0055194.362783e-030.1361730.0154570.014929
26980.0022086.243377e-040.1198210.0005440.000544
39440.0036792.016231e-030.1278920.0009670.001422
42320.0051516.074115e-040.1200290.0076580.007663
.....................
271427680.0014721.452406e-070.0860940.0029260.002926
271526790.0014721.305561e-080.0792580.0021950.002195
271626570.0014721.173596e-090.0734230.0014640.001464
271726820.0014721.054935e-100.0683860.0007320.000732
271826840.0007369.407615e-120.0639920.0000000.000000
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2719 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " Node Degree Centrality Eigenvector Centrality Closeness Centrality \\\n", - "0 692 0.008094 1.616408e-02 0.146138 \n", - "1 1351 0.005519 4.362783e-03 0.136173 \n", - "2 698 0.002208 6.243377e-04 0.119821 \n", - "3 944 0.003679 2.016231e-03 0.127892 \n", - "4 232 0.005151 6.074115e-04 0.120029 \n", - "... ... ... ... ... \n", - "2714 2768 0.001472 1.452406e-07 0.086094 \n", - "2715 2679 0.001472 1.305561e-08 0.079258 \n", - "2716 2657 0.001472 1.173596e-09 0.073423 \n", - "2717 2682 0.001472 1.054935e-10 0.068386 \n", - "2718 2684 0.000736 9.407615e-12 0.063992 \n", - "\n", - " Betweeness Centrality Load Centrality \n", - "0 0.025283 0.025286 \n", - "1 0.015457 0.014929 \n", - "2 0.000544 0.000544 \n", - "3 0.000967 0.001422 \n", - "4 0.007658 0.007663 \n", - "... ... ... \n", - "2714 0.002926 0.002926 \n", - "2715 0.002195 0.002195 \n", - "2716 0.001464 0.001464 \n", - "2717 0.000732 0.000732 \n", - "2718 0.000000 0.000000 \n", - "\n", - "[2719 rows x 6 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "=======\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ">>>>>>> remote \n" - ] - } - ], - "source": [ - "directed_node_metrics = compute_node_metrics(networkGraphs, directed=True)\n", - "directed_node_metrics\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "undirected_node_metrics = compute_node_metrics(networkGraphs, directed=False)\n", - "undirected_node_metrics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# VISUALISATION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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W1vj5+Zn2eZIYnJycaN++PStXrqRv374EBwc/9WJt3t7e9OvXjwULFlC3bt187+VVHzIYDLRo0eKR7TxqGE+dOnUwNzfnyJEjHDlyhLFjxwK512PRokXs27fP9HkeLy8v9u7dS0ZGRr4egwsXLpjeL6h69erx/vvv06FDB3r06MHGjRufeS2Uq1evcujQIerXr//YHo0H8fb2Zu/evQQEBDwyScrrYYiIiKBcuXIP3e5h19/b25vw8HCaN2/+yK+Rl5cXRqOR6OjofAnrxYsXH3cqAHTo0IHVq1ezcuVKPvvsswLt8yjr169nwIAB/PTTT6bXNBpNvupl94qKiqJp06amzzMzM7l586ap9LAgCMLTEEOnBEF4Ik2aNKFOnTrMnDkTjUYD/PuU896nmidOnDDNH3hSZcqUwcPDgxkzZpCTk2Mq99mwYUOio6NZv3499erVy3ez+6QxvPPOO5w7d46xY8eiUCjo1avXU8UKuXM1cnJymDZtWr7XFQoF3bp1Y8OGDURERNy33+3bt03/zlv74EE3ghYWFvj7+/Pnn39y9erVfD0aarWa2bNn4+3tTYkSJUz7tGvXDoPBwC+//JKvrRkzZiCTyfKVSS2IFi1asHr1anbu3Mk777zzTE/FU1JS6N27NwaDgYkTJz5VGz179sRgMDB16tT73tPr9abr2KpVK2xtbfnuu+9M36957v1esba2fuCQop49e3Ljxg0WLVp033tqtdpU5Snvev63AllBE9iePXtSqVIlpk6dyvHjxx+4zZP0GigUivu2nzNnzkPX4Vi4cCE5OTmmz+fNm4der3/i7xNBEIR7iR4NQRCe2NixY+nRowdLly41Pen+66+/6Nq1K+3btyc2Npb58+dTqVKlB447L4iGDRuyevVqqlataiqlW6tWLaytrbl06dJ9k2KfNIb27dtTrFgx1q1bR9u2bXF1dX2qOOHfXo1ly5bd997333/PgQMHqFu3LkOGDKFSpUqkpKRw+vRp9u7dS0pKiqkNBwcH5s+fj62tLdbW1tStW9c0rKhhw4Z8//332NvbU7VqVQBcXV3x8fHh4sWLDBw4MN9xO3bsSNOmTZk4cSJXrlyhevXq7N69m02bNjFy5Mj75hIURJcuXViyZAn9+/fHzs6OBQsWPHafS5cusWLFCiRJMs2tWbduHZmZmfz888+0adPmieOA3Mnrw4YN47vvviMsLIxWrVqhVCqJiopi3bp1zJo1i+7du2NnZ8eMGTMYPHgw/v7+9OnTB0dHR8LDw8nOzjZ9zfz8/FizZg2jR4/G398fGxsbOnbsyDvvvMPatWt5//33OXDgAAEBARgMBi5cuMDatWvZtWsXtWvXpkaNGvTu3Zu5c+eSlpZGgwYN2LdvH5cvXy7Q+SiVSjZu3Ggq4/vWW2/RsGFDrK2tuXHjBps3b+bq1asFLpfboUMH/vjjD+zt7alUqRLHjh1j7969FCtW7IHb63Q6mjdvTs+ePbl48SJz584lMDCQTp06FewLIgiC8CBFVO1KEISXXF7Zy5CQkPveMxgMkre3t+Tt7S3p9XrJaDRK//vf/yQvLy9JpVJJNWvWlLZu3SoNGDDgvpKhPKa8bZ5ff/1VAqQPPvgg3+stWrSQAGnfvn35Xn+SGPIMHz5cAqRVq1YV+LrcW972XlFRUZJCobivvK0kSVJiYqL04YcfSp6enpJSqZSKFy8uNW/eXFq4cGG+7TZt2iRVqlRJMjMzu6/M67Zt2yRAatu2bb59Bg8eLAHS4sWL74spIyNDGjVqlOTu7i4plUqpfPny0o8//nhfCV8eUob23vK295o7d64ESJ9++umDL9I97eZ9yOVyycHBQapZs6Y0YsSI+0rp3nu8/5a3tba2fugxFi5cKPn5+UmWlpaSra2tVLVqVemzzz4zlRzOs3nzZqlBgwaSpaWlZGdnJ9WpU0f6888/Te9nZmZKffr0kRwcHCQg3/eMTqeTfvjhB6ly5cqSSqWSHB0dJT8/P2nKlCn5ys2q1Wrpk08+kYoVKyZZW1tLHTt2lK5du1ag8rZ5UlNTpa+//lqqWbOmZGNjI5mbm0uenp5S9+7dpS1btuTb9lE/o3fu3JEGDRokOTs7SzY2NlLr1q2lCxcuSF5eXtKAAQPua+PQoUPS0KFDJUdHR8nGxkbq27evlJycnK/Nh33vN27cWGrcuHGBzk8QhDeLTJLEDC5BEN5Mo0aNYvHixSQkJNy3gJkgvAnyFjwMCQkxFX0QBEEoLGKOhiAIbySNRsOKFSvo1q2bSDIEQRAE4TkQczQEQXij3Lp1i71797J+/XqSk5MZMWJEUYckCIIgCK8lkWgIgvBGOXfuHH379sXV1ZXZs2dTo0aNog5JEARBEF5LYo6GIAiCIAiCIAiFTszREARBEARBEASh0IlEQxAEQRAEQRCEQlegORpGo5H4+HhsbW2RyWTPOyZBEARBEARBEF5SkiSRkZGBu7s7cvnD+y0KlGjEx8fj6elZaMEJgiAIgiAIgvBqu3btGiVLlnzo+wVKNGxtbU2N2dnZFU5kjxEaGkqfPn1ISEhg0aJF9OzZ84UcVxAEQRAE4XnT6XScOHGCY8eOcfbsWaKjo7l58ybZ2dlotVrTdjY2NpQvXx43NzeUSiXm5uaYmZlhbm6OUqnM9/Hf9/67XXp6OmfOnCEkJISoqCgkSUImk2Fra0vJkiXx9/enTZs2tGjRAnNz8yK8OsLLLj09HU9PT1OO8DAFqjqVnp6Ovb09aWlpLyzRgNwhWz4+Ply5coWDBw8SEBDwwo4tCIIgCIJQGDZs2MDWrVs5f/48V69eJT09HbVajdFoBECpVFK2bFkqVaqEj48PFSpUoEKFCvj4+FCsWLFHDlvX6XTExMRQsWLFJ4opKyuLsLAwQkJCOHXqFMeOHSMmJgYAhUKBnZ0dZcuWpUGDBnTu3JmmTZs+coiM8GYpaG7wUicaAJmZmZQqVQqAkJAQvL29X+jxBUEQBEEQnsbFixfp2LEjUVFRyGQy3N3d8fX1pWLFiqZEokKFCnh6eqJQKB7Z1uHDh3F1dc2XUGg0GpYuXUpmZiajR49+5kQgLS2N0NBQTp06xcmTJzlx4gTXr18HwMzMDHt7eypUqEBgYCDdunWjbt26z3Q84dX12iQaAFFRUVSvXh0PDw9OnjyJo6PjC49BEARBEAShIIxGI0OHDmX58uVYWFgwffp03nnnHSwtLZ+6zV9//ZWsrCxq1KhBy5Ytyc7O5pdffkGj0aBSqejVqxelS5cuvJO46/bt24SGhhISEkJISAjHjx/n9u3bANjZ2dGvXz9++OEHbGxsCv3YwsvrtUo0APbs2UO7du0ICAhg9+7dYuygIAiCIAgvna1bt9K/f3/u3LlDnz59+Pnnn3Fzc3uiNvJuzfKGTOn1eubMmUN6ejpKpRIXFxeys7NJTU017VOpUiXKlStHeHg4xYsXp3Xr1s9cKVStVj8wOYqPjyc4OJh58+Zx4MABzM3NCQwMZPbs2VSuXPmZjim8Gl67RANgzpw5jBgxgoEDB7J48WJRalcQBEEQhJdCSkoKnTt3Jjg4mDJlyrBgwQJatGjxVG0FBwdz9OhRbGxsKFasGPb29pw+fRqdTodfSAiBQUEEBQYS6u9/375mZmYYjUYcHBxQKpVkZWXh5eVFt27dCnzflJ6ezsaNG0lLS+OTTz555LaRkZHMnj2bZcuWkZOTQ5kyZZg6dSq9e/d+qnMXXg2vZaIB8P7777NgwQK+++47xo8fX6SxCIIgCIIgfP/990yZMgW9Xs/nn3/OhAkTsLCweOr2EhIS+OOPP8jOzja9lpdgmGu1WGk0pNrbM2vUqMcmHnK5HKPRiK2tLb6+vkRFRWFjY8O7775737YGg4HDhw9z+vRpMjMzcXBwMCUaj0tSUlJS+O2335g1axbx8fE4ODjw7rvv8u233z7TtRBeTq9togHQqFEjjhw5wrp16+jevXtRhyMIgiAIwhvo7NmzdO7cmStXrtCoUSMWLlyIj4/PM7crSRKzZ8/ONzRqxIwZOKSlobW2RmZnR1ibNuzw8jK9npd4AI9NPpycnPj444+RJInbt2+zbNkyLC0tkSSJtLQ0DAYDABYWFsjlcrp160bZsmULFLter2fTpk3MmDGD4OBgVCoVTZs25ZdffhEFfV4jBc0NXsk6ZQcPHqR06dL07duXEydOFHU4giAIgiC8YSZNmoSfnx9paWksX76cgwcPFkqSAbm9ByVKlDB9bmlpSfrw4UheXqimT8c8Pp7jNWpgbW1NaMuWpDk4EBQYaNo+MCgIh7Q0AoOCTK/5hYQwYsYM/EJCMDMzY/Xq1cyePZulS5eSnZ1NcnJyviQDcqtaZWdno1arkSQJtVr92NjNzMzo1q0bQUFBnD59ml69erF3714qV65MfHx8oVwf4dXxSiYacrmcM2fOYGlpSfv27bly5UpRhyQIgiAIwhvkzJkz6PV6JEkiKiqKW7duFWr7NWrUQKlUYmNjQ+/evSn1v/8hu3IF3n8fgPbt2zNkyBCar1uH9e3bZPbrh1wuR6lUEtyoEan29g9NPm7duoXNihUMmDyZSocPm7a5N8m41969e5k1axZLly59onOoWbMmS5cu5cKFCwB06dLlifYXXn2vZKIB4ODgwLFjx8jKyqJt27akpaUVdUiCIAiCILwhtmzZwr59+yhVqhT/+9//KFmyJO+99x4RERGF0n7ZsmWxtramb9++eHp63ve+t7c39vb2QG4vwttvv02bNm0YMmQIbTZu5MgffxBWr55p+6DAQFPy0axZMxoGB9/X6/Ffeb0g3nv2kJaWhoeHx1Odi7e3N19++SWnTp1i69atT9WG8Gp6Jedo3Gv79u107tyZJk2asGPHDszMzIo6JEEQBEEQ3iCxsbEMHz6c/fv3o9PpaNmyJVOmTKF+/fpFGldISAjp6enUqlWLqKgodu7cia2tLQMGDEC+cCHyadM43KDBA+dxAPfN/1Aqldja2tK+ffsCz9nIo9Fo8PX1JTMzk8TERLHK+CvutZ6jca927drx/fffs2/fPtPEJkEQBEEQhBelTJky7Nixg+TkZD744AMOHz7MsGHDCqXtuLg4oqOjn2pff39/mjdvjqOjI3Xq1GHgwIHUqFGDBQsWEBEYiPLGDc42aACAh4fHfZWlggIDyba0RKXV4hcSQk5ODikpKRw8ePCJY7GwsODXX38lKSmJcePGPdX5CK+eVz7RABgzZgz9+/dn/vz5zJw5s6jDEQRBEAThDWRjY8PcuXOpXLlygSZOF8SxY8dYv349Bw4ceOaHqaVKlUKj0aDT6Thy5AirV6/mvffeo1+/ftjY2NzXfqi/PzpzJZYaDY2CD2Mu05iqUD2Ndu3a0b59e+bMmUNSUtIznYvwangtEg2ApUuXUrduXcaMGcOmTZuKOhxBEARBEN5QSqUSnU5XKG0lJyej0Wg4fvw4K1aseOZ2ExISANDpdFy9epVVq1ah1+tRKpV4eXnh7e1NhQoVqOzjRWWnG4Q28iPb0hIzTQ61T53CUZ7An3/+SVZW1lMdf9asWRiNRjEx/A3xys/RuJder6ds2bLcvn2boKAg/Pz8ijokQRAEQRDeMI0aNSIqKoqbN28+1f5RUVG4u7ujUqmYPXs2GRkZQG7ZW0dHR/r06UOxYsUK1JZareaPP/7A3NwcOzs7Ll++nK+3xcLCgpIlS9K3b9/8O+ruIG315eRNL6qO/QcrtZpsS0t+HjcaK4UahZUrb/cZQPHixZ/4/CZNmsS3337L7t27ad68+RPvLxS9guYGr9XMaTMzM8LCwihTpgzt2rUjNDSUkiVLFnVYgiAIgiC8QZRKJXq9/qn337lzJzk5OTg5OeXrwZDL5eTk5HD9+vUCJxoWFhZoNJoHJj1ubm60b9/+gVWtMHdEprDCziwd8p5JSxIquQ5HZQopOXaEh4c/VaIxfvx4lixZQt++fU09LMLr6bUZOpXHycmJI0eOkJaWRtu2bU1PAQRBeHPljUkWBEF4EczNzZ/pd45SqSQjI4O4uDi0Wi0ymQwbGxuaNm3Kxx9/TPXq1Qvclkwmo2rVqsC/5WprnzpF06ZNGTZs2AOTDK1Wy9xZ32HIvom1WTaHWzYmzd6OE63qYqNI46qmNDYOxWnduvVTnZ+VlRWzZ88mMTGRSZMmPVUbwqvhtUs0AKpVq8aff/7J+fPn6dWr10MXoBEE4fUXGxuLq6srNjY21K5dW8zhEgThuVOpVOTk5Dz1/nmj2u9dzdvR0ZGAgACUSuUTt1enTh1sbW1ptm8fDmlpND9wgEaNGt1XZSpP2vUz9HL8GblRg40iAyQwl+vIMlhxW+9Bw4YNGTJkyFOfH+Qu3teiRQt+/PFHUlNTn6kt4eX1WiYaAF27dmXKlCns2LGD0aNHF3U4giAUgevXr1O9enUUCgVDhw4lJiaGLl26YG9vT58+fYiLiyvqEAVBeM2cP3+eY8eOPfQmviC0d8vJttu+HYe0NJqeOMHAgQOfuj1ra2ucnJzgbkwqc/OHb3wrCIvg1jiZJZFjVOB4+A6tt+/C8o6GwKCjSJJEZGTkMyVSkNvT8ssvv6DX65+6ipXw8nttEw2AiRMn8vbbbzN79mx++eWXog5HEIQXKCEhwTRcYN++ffzyyy8kJCSwYcMGAgMDWbNmDWXLlqVMmTJMmzbtmcZTC4IgaDQaevToQbVq1VCr1cybN++J29Dr9Zw4cYK0uyt2yyUJo0yGbMKEZ17gLiAggAPNm5Pm6MjFAQPIzs4mMzMz3zaSJJF27ShZeiu2Jrbjt2vvIW0GmRGMMhlBgYFAbiL0tBPd7+Xj48Po0aM5ePAgQY9YoVx4db1WVacexs/PjzNnzrB161batWtX1OEIgvCcJSUl4ePjg0ajYf/+/dStW/e+bW7evMkff/zBokWLuHz5MiqVirp16zJ16lQaNWpUBFELgvCqmjt3Lp999hnZ2dkMHjyYb7/9FhcXlwLvr9VqOXLkCEePHkUul2MwGPALCSEwKIigwECiW7Zk0KBBz3QPJkkSy5YtIyUlBa1Wi42NDdnZ2TRr1gyZTEZkZCRJSUlkZ2djNOYOOfcLOUWzfftAJmN/s2acqVuXxo0bU6tWLWxsbJ46lntlZGRQvnx55HI5169fFyuGvyIKmhu8EYmGTqejdOnSpKamcvz4capVq1bUIQmC8JykpqZSoUIFMjMz2b17N4F3n8A9jCRJnDhxgiVLlrBixQqys7NxcnKiR48efPPNNzg7O7+gyAVBeNWEhYXRrVs3YmJi8PPzY968efj7+z9RGxqNhpkzZ6LT6ZAkKV+CEervj0qlwtzcnMaNGz9b2f7588mZOpV9/v6cqFnT9LJcLsdoND5wlxEzZuCQlkaqvT2zRo3C0tKSIUOG4Ojo+PRxPMCGDRvo3r077dq1Y9u2bYXatvB8iETjPxISEihfvjx2dnacOnWKEiVKFHVIgiAUsszMTMqXL09KSgo7d+6kadOmT7R/dnY2f/31F7/99huHDh1CoVDg7e2Nn58f1apVo06dOtSrVw8rK6vndAaCILwq3n//fX777Tfs7OyYPn06AwcOfOqn8ZcuXWL16tVIkpTv5n7PokXUrl0bLy+vZ3/SX7o0xMWZkob/ujfBAQgMCuKqpyelrl0jafBgYlq14vbt2zRu3Pi5LB3wxRdf8O233zJ+/Hi+++67Qm9fKFwi0XiAU6dOERAQQKVKlQgKCsLa2rqoQxIEoZBoNBrKlStHYmIiW7dufeqyi3liY2NZtmwZK1asIDY2Nt8TPwsLC6ytrXF2dsbLy4uKFStSq1YtGjRogLe3t+j6F4TX3LRp0xg/fjx9+/Zl9uzZz/yEf//+/WT//HO+m/ujjRrRbO1aLCwsCifo+fPJ/OILDtarR+gDel3yEpxsS0ssNBrkkmRKSkaMGIGDg0PhxPEQRqORnj178vfff7Ns2bL7FxAUXioi0XiINWvW0LdvXzp27MiGDRvEDYEgvAZ0Oh0VKlTg+vXrbNy4kY4dOxZq+3q9nmvXrhETE5Pv49KlS8TExJCenm7a1szMDEdHRypXrkzr1q3p16+fWDhUEF4je/bsoV27djRt2pTt27djZvbgtY8lSSI+Ph4PD4/73jMajWi1Ws6dO8eZM2e4cePGfcOUZDIZ7dq1o3bt2oUW+6xZs1AoFLi5uRETE4NGo8HS0hK1Wm3q0TDXarHSaDDKZGxv145Qf3/c3d0ZMGAA5o+qVlUIsrOzadiwIREREQQHBxfquQuFSyQajzBp0iS++eYbxowZw48//ljg/SRJIjo6Gm9v72cqWycIQuHR6/VUrFiR2NhY1q5dWyRlElNTU4mNjSUmJoYLFy5w7NgxgoKCSEtLA8DGxgZPT08CAgLo2bMnzZs3Fw85BOEVFBcXh6+vLx4eHoSEhDzyKf+OHTsICwtj1KhR9/VKHD16lD179uR7Le9GP7hRI075+eHu7v7Ma1X8l9FoRC6Xk5yczOrVq/Hx8SE8PBy9Xo9Ok4kRM7quX0+VyEgiKldmY/fuQO4DlAoVKtCjR49CjedBbt68iZ+fHxkZGURHR+Pq6vrcjyk8OZFoPEa3bt3466+/WLBgAUOHDn3s9pIk8dFHHzF37lyqVKnCqFGj6N27N5aWli8gWkEQHsRoNFKlShUuXLjAihUr6NOnT1GHZCJJEpcuXeLo0aMcO3aMw4cPc/HiRSB31d9ixYpRvXp12rZtS9++fcWkc0F4yel0Ojw9PVGr1YSGhlK+fPmHbnvo0CGOHz+OVqulVatW1KtXD8j9vRAaGsqJEydISkq6b+J3pUqVaNOmDYcOHUKv19OlS5fnfl6/zvgWpfYqt7TFMKDisx++x1KtIdvSkplffIFKpaJt27b4+PigUCieezwA4eHh1K9fHxcXF7He0UuqoLnBG/tIbd26dVStWpUPPvjgvqcK/yVJEiNGjGDu3LkEBASQlJTEe++9h7u7O59//jnXr19/QVELgpDHaDRSq1Ytzp8/z++///5SJRmQuxiVj48PgwYNYuHChVy4cIGUlBR27NjBhAkTqFSpEocPH2bkyJG4urpib29PjRo1TMmIIAgvlwYNGpCUlMSGDRsemWScPHmSEydOoNFokCSJ06dP53v/1KlTJCUlAbkTrh3urpkBUL16dWxtbenQocMLSTIiIiIw18ZxS+uKARUAMnLno8lkMurXr0///v3x9fV9YUkG5F6HRYsWcfXqVWbOnPnCjisUvjc20ZDL5Zw8eRJnZ2e6du3KuXPnHridJEmMHj2aOXPm0Lt3b4KCgrh58yZ79+6ldOnSTJs2DS8vL3r27ElwcDAF6CASBOEZGY1G6tWrR3h4OPPnz3+mFXNfJEdHR9q0acOUKVPYt28fGRkZhIeHM3fuXDp37sylS5fEBEhBeAm9++67hIaGMnPmTFq2bPnQ7SIjIzlw4IBpzsOIGTPw2rGDs2fPArnrZUiSZBp+HRQYSKq9PUGBgdjZ2VGhQoUXcj4AaWlpbNu2jZtaNzwsrmGrSKd2yAnkMiNaSyX7mjbl8OHDLFq0iFu3br2wuPL06dMHPz8/Jk2aJBZUfYW9sYkG5FaOCQ0NBaBNmzYkJibme1+SJMaOHcvMmTPp0aMHq1atMr3XvHlzzpw5w9WrV+natStbt24lMDCQWrVqsXz5crRa7Qs9F0F4kzRp0oSQkBBmzpzJsGHDijqcp6ZQKKhWrRrvv/8+y5cvp1u3bly6dKmowxIE4R7z5s1j6dKlDBkyhI8++uiR21pYWODm5oadnR0Ng4NxSEsj4MgR/v77bw4cOMBvv/3GrVu3TA8lQ/39mTVqFGfq1sXJyelFnA6Q+7Dmzz//zO11wYyrmjJkGyxpHHwY82w9WpWKUH9/ZDIZAQEBuLm5vbDY8shkMmbMmEFGRgYjRox44ccXCscbnWgAlCxZkj179pCYmEjHjh1Rq9VAbpIxfvx4fvrpJ7p168batWsfuL+7uzvr168nPT2dKVOmcOPGDQYMGICHhweTJk3i5s2bhRpvYmIiu3fv5scff6Rfv374+vri6+vL5MmTiYyMLNRjCcLLqGXLlhw5coQffvjhtfvj07hxYzIzM7l69WpRhyIIAnDs2DFGjBhBQEAAv/zyy2MLwXh7ezNw4EBGjBiB/tNPSXdyIigwEEmSOHz4MMnJyQ/cTyaTUalSpedxCg904MCB+x6u1ggJQ64xYLCWc6ZR7oJ+kiSRlZX1wuL6r4YNG9KxY0cWL15MampqkcUhPL03djL4fy1fvpxBgwbRrVs3Vq9ezZdffsn//vc/unTpwsaNG5+ore3btzN+/HjTcKyePXsyYsQI6tatW+A29Ho9Fy9eJDw8nPDwcM6cOcPp06dNv6QUCgU2NjZ4eHig1WqJi4tDr9fj4+ND79696dGjxwv9pSUIL8Lhw4dp3LgxgwcPZtGiRUUdTqG7fPky5cuXZ/LkyXz11VdFHY4gvNESEhIoV64cTk5OnD59+qkKNsybN8807Oi/E7//q3nz5gTeXSzveUtNTSUoKOhuxakcSlnE0WvKaizVGvRWCr797EvTtkqlkh49ejxyXsrzdPHiRSpVqkTbtm3ZunVrkcQg3E9UnXoK48aNY9q0adStW5cTJ07QoUMHtmzZ8tTtxcXFMWLECHbt2oVGo6F27dqMGjWK7t2756tFnZqaakoowsPDOXXqFOfPnycnJwfI7Yp1cHCgXLly1KlTh3bt2tG4ceN8tbvT09OZPn06K1euJC4uDoPBQMWKFRk8eDCjR48W5XiF14LRaMTNzQ0zMzPOnz//3BeQetEkSaJ48eJ4eXlx8uTJog5HEN5Yer2e0qVLk5KSwokTJ6hateoTt6HT6fjll1/IyMgAuG+djLzE40zjGgTVbsbw4R9SrFixfxu4NA8sXKBU90cf6PQYqDwRVE8+9Ory5cts3vQXH7hORvWBBnkWGM1BY23ByYZ1OVK7ORUrVqRDhw5FWmVz+PDhLFq0iAsXLuDt7V1kcQj/EonGU+rQoQPbtm2jbdu2bN++vVDa1Ol0TJkyhQULFpCcnIyrqys9evQgLi6O06dPEx8fD+ROULe2tqZ48eJUqVKFRo0a0bFjxyf+oUpNTeXHH380JR3/+9//mDBhQqGci/D8xMXFsWLFCnr16iV+kT7CsWPHaNiwIe+88w5Lliwp6nAKXZ8+fdi2bZtpDQ5BEF68xo0bc+Tu3IpOnTo9VRtxcXGsXr0aOzs71Go1Ffbvz7dORl7iYSwGydNdiVT0wK7SQGr51YbkUNhdFySgwXIofbeq3t4m4DsW7pwFzU0o/1HudhYu0Hw/6NWQfQ2KN3t4YEYDYAS5EoAzZ87gdqY97kE3YQuQDuhAsoavx05GpbLA3NwcmUzGJ5988kKrT+VJTEykTJkyVKpUiVOnTr3w4wv3E4nGMzh9+jS1atV6Lm1v3LiRiRMnEhsbi62tLaVLl6Z27dq0bNmS1q1bY2VlVajHa9SoEUFBQWzZsoX27dsXatvCs4uIiOD7779n9+7dJCUlmaqRuLu707dvXyZOnPhG/Mw9qQEDBrB8+XK2bt362n1fL1y4kPfff5/r16/j7u5e1OEIwhtn1KhRzJw585kf0kmShCRJyOVy4uLiWLlyJY6OjmRnZ5OZmYlfSAjN9u/HQq5G3hO0Tc3RK52xdqsBSSchJ7cELkoHcKoFfrNhdz3QZwNGQAb2VSHtH0ACuQoUFuBUG5ruhO3VoNI4KPMOyOR5QUFwH7i5A7rdRm+UMXPGDOpZbiLQKTh3m6FAFqgtVUwbl3v+SqWSunXr0rx586e+Hs9q6tSpfPXVVxw4cIBGjRoVWRxCLpFoCEBu96+npycZGRmEhobi4+NT1CG98YKCgpg+fTqHDh0iNTUVuVxOw4YN6d69O40bN2bfvn0sXbqU8PBw02qsn3zyCUOGDBGrSd9lNBopXrw4crmc8+fP4+joWNQhFZqLFy9SsWJFpk6dyhdffFHU4QjCG2XlypX079+ft99+m5UrVxbasGNJkti+fTv1yoHlqXfIzLHklsaekuNv4JCWnluaZ8DdbbeArOPdHbcAHYEWADJAgr3/ff0/nAOg2W7Y7A36TLD0gKpTIfMSXJwD2ruTwN2aEZvtzfLT7ljJM2ntsosz6bUoFXyVWodOExRYn1P+9QEoUaIEQ4YMKdJh2FlZWZQsWZJy5coREhIC5I4YMTMzE38bi4BINAST69evU6FCBTw8PDh16hT29vZFHdIbxWg0smXLFubMmcPJkyfJyMhAqVTSsmVLunfvTseOHR84yfDcuXP88ccfLF26lISEBCwsLKhbty6TJk2iWbNHdIu/IUJCQqhfvz59+/Zl2bJlRR1OoZEkCRcXF8qXL8+xY8eKOhxBeGNERETg5+dHlSpVCAoKej5zEnIyYasvqHMX+r293RHnVXeQSYA1SJYgSwLy/iTk/XvWPW2MeMDr9yYfyGCbEsob4aL+/oTkP4lKRLovJS1vYK3I5tRffvjuv0BQYCBh/tUwoMJMlkNJr/IMGDCg8K/HE/rkk09YsGABGRkZmJubs2bNGjIzM+nXrx8qlaqow3ujiERDyGfXrl106NCB1q1bs3nzZpH9P2dGo5Fly5axYMECzp49i1qtxtLSkk6dOvHWW2/Rtm1bbG1tC9zWoUOHWL58OWvWrEGtVuPg4EDbtm1p3bo15ubmmJubo1QqUalUqFQq078tLCxQKpVYWFiYPreysspXjOBVNmjQIJYuXcqWLVvo0KFDUYdTaN5++222bdtGSEgIvr6+RR2OILwRevbsyYYNG7h27drzHba4twncOgTA5sSOdPhyS+4kbGu41tGTErsTMHtLidyQnZsQdLWFsdNAnQgRX8Ne4/09GvcmHwoLSNTk9pIYeWSiIs3MnQYil4FRgpwhSlTqHLSW5uz8rgvhqZWo5edH06ZNsba2fn7XpIBCQkKoU6eOaVjb7NmzuXPnDs7OzrzzzjviHvUFEomGcJ8pU6bw1Vdf8cUXXzB16tSiDue1o9FomDt3LkuXLuXSpUtotVocHR1566236Nq1K82bN8fCwuKZjpGdnc2mTZtYtmwZe/bswWg0PnEbcrmcVq1asWXLlnyVy15FRqOREiVKAHDhwoXXZgjVpk2b6Nq1K5Ik4eDgQP369fnoo49o06bNK/GQwGg0vhJxCsK9evbsycaNG00VH5+buLVwfCAY1MyKHcHAi0uw2pCF3qhkX/PmhPr7U9Mtntpu1yjh6oTMyQ98R+XO29jfHCQjGLJBpgRrT6j6NSzdAPM2Q0cjFG8BK86D5224qH1sj8a9r+nT5JjlGMm2sECZGEVSlsL0O/ZlEBsbS+/evbl16xZnzpzht99+IzMzEwB7e3t69epF8eLFizjKN4NINIQHatOmDbt27WL9+vV069atqMN55aWmpvLzzz+zevVqrly5Qk5ODsWLF6dnz5689dZbBAQEPLeb+du3bxMfH49er8dgMKDX600fj/o8NDSUWbNm4eTkxO7du59b4YMXQa/X06BBA0JCQli2bBn9+/cv6pAKTWJiItu2bWPTpk3s2rULrVaLpaUllSpVYuDAgQwePPiZE9enER8fz+nTpzl37hxRUVHExcVx8+ZNkpKSyM7ORqPRoNPpMDc3x8rKCicnJ7y8vPDx8aFmzZoEBATg6+srEhHhpdOvXz9WrlyJ0Wh8vnMR9GrY6kNSphmL4/qh0SvuK32bp169erRu3Tr3E10apJwGtyZwoB24NoIq90xWz8mAsM/BrTE414OtlcDSPXeehvoGIAMzWzDqQKZA79EVfexazGQ6zEYBSZBjpSBLaUNQYCDlhtegYrtvnt91eApr1qzhwoULpKamUqpUKdLT0/O9r1KpGDBgwEuVHL2uRKIhPJDRaKRMmTLcvn2bMWPG0LZtW+rWrVsk5epeZREREQwbNoyTJ0+i1+vx9vY2JRd+fn4v/bol+/bto0+fPty5c4exY8fy7bffFnVITywiIoJmzZqRlJTE+PHjmTJlCkqlsqjDei7UajX79u1j06ZNbNy4keTkZJRKJaVKlaJr166MGjWqUIZ66PV69uzZw5EjR4iJieHatWskJiaSmpqKRqNBo9FgMBjy7WNvb4+HhwelSpWiZMmSeHh44OrqSmJiItHR0Vy6dIno6GhSUlJM+ygUCiwtLbGzs8Pd3Z1y5cpRvXp1hg8fLv7GCEXm3XffZcmSJeTk5Dz/3t5Dndl3oxpBUbnHyVtT46qnJ6WuXcu3qF+rVq2oX7/+k7VvNEBKCBSrm5to7GsOGRehbRh3Mo2c2jGdf+6Uh+wb+Nsfp3ZoKJbbtCS0cmFBhQ8BKFPCmt6DRrxUv1dDQkIeuPSAubk5NjY2+Pn54e/v/1LF/LoSiYbwULdu3SIwMND0BN7Ozo42bdrQrl072rRpg5ubW1GH+NLav38/H3/8MRcuXEClUvHBBx8wePDgV3IcfVJSEoMGDWLr1q1UqVKFQ4cO4eT05As+FYUff/yRL774AltbW/78809atmxZ1CG9MEajkZCQEDZv3sxff/3FhQsXkMvlODs706xZM0aPHo3/A1YdflA7Bw4cYNOmTRw7doyYmBjS0tJMiYRCocDV1RVPT09KlSqFh4cHHh4epmTCw8MDd3f3Ak+YzcjIICYmhpiYGKKjo4mOjuby5ctcunSJGzduYDAYUKlUdOvWjQULFmBjY/NM10l4Mhs2bGD06NEkJydTrFgxfH19adCgAZ06daJatWpvRA/U8OHDmTdvHtnZ2c9/cTpJYveePcTGxpKRkUFWVhYAY3/4ASu1mmxLS34cNw4ANzc33n///Wc7nkELl34B3zHodDp+//13EhMT84LB3iyVIZsXYnVKzT+VqvB3965IKGjatGmRl5LNycnhn3/+ISEhgfj4eOLj46l18qRplfWo5s3p2rUrXl5eL/1DvteJSDSEx9LpdCxfvpw//viDsLAwUxdkjRo16NChA+3ataNevXriB5fckoeff/45165dw9HRkdGjRzN8+PBXfk6AJEnMnz+fkSNHYmZmxvLly1/qIXUajYYWLVoQHBxMs2bNWLly5Rs/HjcmJoYtW7bw999/c/jwYYxGI7a2ttSuXZthw4bRo0cPILes8t9//01wcDDR0dGkpaWh1+sBKFmyJHXq1MHf35/atWtTuXJl3NzcXtjNpV6vJzIyku+//541a9Zgbm7O22+/zbx58wp9bSEhv/Xr1zNq1CiuX7+Op6cnbdu25Z9//iE8PJzs7Gwgdw0Fe3t7ypQpwwcffMCgQYOKOOrnI299nhd1r3PhwgX++usvFAoF5ubmpKenM+GbbzDX69GZmfHd3fLWVlZW9OvXr1CHA6nVahYvXkxycrLptS+nTEEuSUjA4U6NOOrXAO8SlvR8dzwoiq6AiEajYdasWWg0GiC396fd9u3IJYl0Jydsk5LEfUoREImG8MQiIyOZM2cOO3fuJD4+npycHAYMGMDvv//+RjzN+i+j0chPP/3EtGnTSEpKolSpUowfP56BAwc+/6ddL1hERAQ9e/bk4sWLvPXWW6xZs+al+5qfOHGCNm3akJ6eztSpUxk/fvxLF2NRu3PnDjt27GDTpk1s27aNrKwsVCoVRqPRNMG1RIkS1K1bF39/f/z8/PDz83tgeeWiEhkZyVdffcX69etRqVT06dOHuXPnFsl8lNfZmjVrGDNmDDdu3KBUqVJMmjSJ/v37m4acGI1Grly5Qnh4OGFhYYSFhXHixAkSExOpWbMmu3fvfqm+b57FwoULmTJlCvHx8ZQsWZLo6OjnVpkvKSkJMzMzHBwc2Lx5M2fOnMn3/oN6NKytralSpQpt2rQp1FgyMzNZvHgxqampKJVKBqyfg/uZBGRAqr09F7+twPksXwaW2wOdY0FRdOVjIyMj2bJlC1qt1jSfRZLBnIq+fHLuXJHF9SYTiYbwTHQ6He+//z5Llixh6NChzJ8//415YqDT6Rg3bhyLFy8mIyODqlWrMnHiRLp16/bKV2l6FLVazbhx45gzZw42NjbY2Nhgbm5uKourUqmwsrLC0tISa2trrK2tTdvZ2dnh4eFB27Ztn0sPw8SJE5k2bRqurq6sXbuWgICAQj/G60an03H48GF27tyJvb09tWvXxs/PD1dX16IOrUD++ecfvvrqK/766y8sLCx45513mD17tkg4ntHKlSsZO3YsN2/epHTp0kyaNIl+/foVaEy7wWBg1qxZTJgwAblczvfff8+IESNeQNSFT6/X8/XXXzNnzhxSU1MpV64c48ePf67rMVy9epXVq1fj4eHB7du3ycjIMFUOfNgcjYYNG9KoUaPn9rfnzp07/Prrr5ihYbjnbOwOZWBcK0NjUBHUsiH2re9QvFpvvOoMAIUlmL34HsYbN25w6NAhYmJiMBgMNDp9gMCgIHTt5bhuUJKRkSEeOhUBkWgIhWLw4MEsXryYjz76iNmzZ7/WyUZqaioffvghf/31FxqNhiZNmjBhwgRatmz5Wp/3f23bto3ly5eTnZ1NZmYm2dnZZGVlkZ2dbaoqpNVqTd3Y/2VpaYmjoyPlypWjTp06tGrVisaNGz/VE8L09HQaN25MWFgYHTp0YNmyZa/MPBKhcISFhTF58mQ2b96MpaUlAwcOZObMma/NWjAvyooVKxg7diwJCQmUKVOGyZMn07dv36e6gY2KimLQoEEEBwdTpUoVdu3a9UzFCHQ6HWq1+oUsJpuZmcmoUaNYtWoV2dnZ1KlTh88//5yOHTs+15vVixcv8vfff6PRaFCpVOTk5OQrT/6wqlMqlYr33nsPFxeX5xJXeno6c+fOpYHtLgLsD6OQGzF+AvJkSLO35Y+x/UmXXClvGUmPJiXAb+ZzieNhTp48yd69e9HnaPik9ExsD2Yi2yKh72jGpXoVmLo2i3b9p7wUiwm+aUSiIRSavJJ/Y8aM4ccff3zqm25JkkhNTeXGjRuULFkSBweHwg30KcXFxTF06FAOHDiAXq/nrbfeYty4cQWaUPsmkyQJrVZLdnY2arWaxMRE/vnnH86ePUtYWBjh4eGm8b8KhQJra2tKlChBlSpVaNiwIR06dMDb2/uh7e/bt4+uXbuiVqv56aef+Pjjj9+ohE/I7/Tp00yaNIlt27ZhaWnJ4MGD+fnnn1/rXsbCsGTJEiZMmEBiYiLe3t5MnjyZ3r17P/N1MxqN/Prrr3z22WdIksTXX3/N6NGjuXbtGrGxscTFxXHjxg3i4+O5desWycnJpKSkkJ6eTlZWFjqdjpycHNOHTCbD2dmZNm3aMGHChHwFNoxGI0eOHKFRo0ZP/TsgPj6eYcOGsXv3bnQ6He3bt2f8+PEEBAQ8998rp0+fZt++faY5L3nyejGCAgMBTP8O/c/fHgsLC3r16oWXl9dziU/KyUa7oQwWxlu5L+zF1Kuxv3lzQv1rYy7TMKjM3xTvEQwqR5C9mB4Eo9HIxo0buXjxIkPdf8J5bEq+xQYBVpwuwTs/xb+QeIR/iURDKFQ9evRg/fr1fP7553zzzTf3/WI2GAwkJiZy48YNrl+/zo0bN0z/vnbtGnFxcSQkJJiegru6uhIaGkrJkiWL4nQAOHXqFB988AFnzpxBJpPx7rvv8umnn1K+fPkii+l1c2/ycfbsWU6fPs2FCxdM8wVUKpVpkqmfnx8tWrSgdevWjBs3jnnz5lGqVCnWr1//Sq/1IRSukJAQJk2axM6dO7G2tmb16tWv1arwheW3335j4sSJ3Lp1i3LlyvHVV1/Rq1evQi9lHhMTw7vvvsuhQ4ceuo1SqcTBwQEnJyecnZ1xcXGhWLFiODk5kZaWxuLFi2nUqBFGo5HDhw8D4OjoSLNmzRg3bhxKpZJt27bh7u5Op06dnqhX8+zZs7z//vuEhIQA0LdvX8aOHUvlypWf7cQLKDY2lj///POBiwA+qBcj72+rJEnIZDJsbGzw9vamRYsWz29l7sjvMIR9gUL2bw+L9AnIksEok3Gumy/lO0VxIrUuVzXeKGy9yFaWonjx4rRs2fK59y5KksTOnTuRX/qZVuF7kd2z2KDWoMR3rMTs3zbSqlUr0dP5AolEQyh0HTt2ZOvWrQwaNAg7OzuuX7/O1atXuXbtGrdu3crXDSyXyzE3N8fS0hIbGxtcXV0pWbIkZcqUwd3dnS+//BIfHx+OHj36/H55PsT27dsZNWoUUVFRWFlZ8fHHHzNixIg3vnrRi6LX67l06RJnz541Vbc5ffo0N2/eBHL/0EqSRO/evZk/f774nSM80PHjxxk0aBCXL1/mxx9/ZOTIkUUd0kthwYIFfPnll9y+fZsKFSrw1Vdf0bNnz+e6VpLRaOSvv/4iJSUFJycnnJycTIlEsWLFsLS0fGivQZkyZUhNTSUqKgpnZ2du377Npk2bWLduHfv370ev1/PJJ5+Ykgtra2uGDx+OlZWVqciBSqUCSYK0SLDzBbmCXbt2MXLkSC5duoRKpWL48OGMHDnyhT/cMhqNREdHc/ToUZKSksjMzDT1ZFwvVYqSV6+aejHkcjkWFhbodDqsrKyoUqUKgYGBz734SE7UMs7um4fcTEWO3ohOb8QtKBHvtTG5VajMQcqBiMqV2dmzLWqjNSBDqVRSs1olMhPPUkUVjG2Txc/l+qrVan777TfUqdcYXeZnzPYbYAsYO8jIaeWM43uZqNVq5HI5tra2uLu7U7NmTZo1a0bnzp1fm6IFLxuRaAjPRfv27dmzZ48piXB0dMTNzQ1PT0/KlSuHr68vNWrUoHz58o/sml+/fj29evWiffv2bNy48YVM5Prtt9+YPHky8fHxuLi48OmnnzJs2LAXMi5YeLzU1FRT4uHu7k7Xrl3FUCnhkdLS0ujatSsHDx5k2LBhzJs3r6hDKjJz585l8uTJJCUlUbFiRb766iu6d+/+Ui/G+u233/LFF1+wbNky+vfvf9/7qamprF69mhs3buT7e5KVlUXDhg1JT08j7kos4xpGorhzErS3WZn1PeO+/I4bN27g7OzMmDFjeP/994t0qG5ycjL79u3j2rVrZGZmmnoy0h0dmXHPZPqmTZtSrVo1Ll68SK1atV7oonN5PSgRERHs2bOHKrItNA4/gnyzhDzFgBwwAun29vcM78q9fVRgoL/HcuJ8VtCwYcNCjy0yMpJd2zag12YwxHMRjmPTIAn0TgrMllUgtcFhzoT/Q1hYGGfOnOHUqVNcvHjR9PDT2toaZ2dnKleuTOPGjencuTM+Pj6FHuebRiQawkvvyy+/5JtvvmH8+PF89913z+UYRqORqVOnMmvWLO7cuYO3tzfjx4/nnXfeeW6VRQRBeHF0Oh1Dhw5l2bJlNGvWjD179rz2FWj0ej1nz57lxIkThIeHs379epKTk6lUqRJTpkzhrbfeeumvQVJSEiVLlqR+/frs37//oQ8V5s+fb1pY7t7KTF7XrnIksCGh/rVRynSo5DocZTdYuusGZcqUwdvbmwEDBjy3eQ0FlVcm/d75GQ+qMHWmbl06duxIjRo1ii7Yu3bv3o177CguZVbgn8zqdF2/gSqRkegVCsz1+vsmrBdT3qKsVQwhafWYNGnSc3lAJKVHsfH3ydjIEmkZvh/jZjl7GzTHuZs1tfr/dd8xNRoNkZGRnDlzhrCwMEJDQwkPD0etVgO5w3YdHBwoX7489evXp127djRq1Oil/7l5mRQ0NxCz6IQiM3XqVM6ePcv333+Pj48PAwcOLJR2r169yo4dOzhw4ABbt24lKysLPz8/Pv/8czp37vxSP+ETBOHJmJubs2TJEry9vZk0aRIVK1YkLCzslV/oLy4ujqNHj3L69GnOnz9PXFwct27dIisrC7VanW+oatWqVVm4cCFdunR5ZW6UOnbsiNFoZMGCBQ+9MU1MTOTOnTtYWVmRnZ1NYFAQDmlp2KWnI5ckmu3ff88E6tpkUoHmzSugQo1MKX/i3/XXwv7CrWx9zO2efWG8P//8Ezc3N2rUqEFAQAB79+4l77lu3mTvvEXnAoOCCPX3Z/fu3Xh6elKsWLFnPv6zSE5O5lhCT2xsbHB2tmB77x5szOmeb/L6vf8O9fcjOc0FCwuL59YLna1wJzLDF4XkRaMmQfxU+lP0kjmeBk/8HnBMCwsL0zpBefKGseUlH6dPnyY0NJSgoCB+/PFHzMzMsLW1xcvLCz8/P1q2bEn79u2xsbF5Luf0phA9GkKRMhqNVKpUiejoaPbt20ejRo0KvG9iYiI7duzg8OHDnD17lqtXr5Keno5WqwVy54m0aNGCCRMm0LhxYzEMRxBecytWrGDQoEE4ODgQGhpKqVKlijqkRzp27Bh79uzh3LlzREdHEx8fT0ZGBmq12rRqO+SWjPby8qJ8+fKULVuWMmXK5Pt41W6ENmzYQI8ePZgyZQpffvnlQ7fLq2y3bds2oqKi8AsJoe6BA1z38sI9NhZzrRYrjQajTMb2du0I9a+db//OZU7j5mhOcffSyBRKuLoeqk7G6NUHg8GQb2iS0Wjkh28noZLr6FTfmXI123A1w4G9e/fSvXt3Ll26RGJiItbW1tja2iKXy7G2tjatZN+oUSOqV68OwPnz59mzZw9arRaFQoHBYADyV5nKS5r+jT03+bC0tOTTTz8t0oQxKCgIuVxO1apVsbW1Zee2vwkNDUUvyQEZIDMN/zICGguLu9Wp/FEqlfj4+NCqVStsbW0LJZ6zZ88SGRnJpUuX/pPg+KNSqejfv/8zlVdOSEgwDbsKCwvj5MmTXLlyBcgtZDBw4EDmz5//yiTxL4oYOiW8MjIzM/Hy8sJoNHLq1Kn7Sp6mpaWxadMmjh49Snh4OLGxsaSnp5u6QAE8PT2pXr06VapUoUqVKlSuXJmKFSuKxb0E4Q1z8OBBOnXqhNFoZM+ePdSvX/+h2+r1enQ6nenDzMzsua7TcuvWLebNm8fGjRuJiooyDadRKBR4eHhQrlw5vL2970smnJ2dX5sHJXq9HmdnZ9zc3Dh79myBhrBmZWVhMBiwsrLCzMyM7777Dp1Oh19IiKlX4L/DeRTosTLLJlNvg7U8A1+bc9zQeuJinoyNhYGTyVVxt83iltqB4T1rk2NZltmLVgNyZBgoZn4Hv/qt2HXoNAqFAplMhpWVFRkZGTzstkmpVGJlZUVaWtoD37+3ylRQYCANg4M5EhCQL8no3bs3np6eT35hn6PMzExObxjN8RvFUefkJhv3Xnvgvutfvnx52rdv/8xzIKX0KH5fvIiEbHv0eh0jZ8zEPi2dVHt75owZQ7Vq1WjQoEGhrzOSkZHB2bNnmTNnDmvWrMHJyYkVK1bQtm3bQj3Oq0wkGsIrJTo6mqpVq+Lh4cGsWbPYtm0bwcHBxMbGkpmZiUwmw2AwUKJEifsSCl9f3xdeuUoQhJfX+fPnad26NTdu3EChUCBJkukDyPfv/1IqlVhaWuLg4EDJkiUpX7481apVo27duvj7+z9R+cy8NQB+//13Tpw4wZ07dzAajbi7u9O+fXtat25N7dq1KVmy5BszpLNPnz78+eefHD58+KknDq9bt47k5GTS0tKofORIvifceU+8Tzb0J75hCa6pS2NEQe7EZRnlHW9zU+1KpkYibzKzq3kieqOCFL0zuU/sASTkGChlEYPzkTsEBB194BoX/3Xv/Ityly+DTMb+Zs3yxZY3JyOvR0Wr1eLi4sKAAQNe3r9lEd9gcGnC6T0LCbpSjEyDNTVDwmi2fz9mej16hcLUqwG5QxqHDh36xMPAJEnit99+w8LCAnd3d1xSN2C8FcT+pOZkGm1pcmY/1Q5GENywIW6TJ1O7du3HN/qMdu3axfvvv8/Vq1cJCAjg77//FgvHIhIN4RX0+++/M2zYMNOQAS8vLwIDA7Gzs2PevHmsXLmSPn36FHGUgiC8ChITE/njjz8wGAwoFIoCfej1eq5evUpMTAyXL18mKiqKhIQEU1Iik8mwsLDA2toaNzc3SpcuTaVKlfDz8yMgIICSJUsSFRXFnDlz2LFjB9euXUOr1aJSqWjatClt2rShdevW+Pj4vDY9FE8iLCwMf39/Bg4cyKJFiwqlzXXr1nHuXCQ+1ue5mFWRETNm3e01sLv7hF2W/wbfvwZWimwCTwfhs/+SaY5BbtKRNzQoT+7Xffzs/6FKybn71H7kfW3em0TcO5zrYU/783z66acsWLAAW1tb+vfv/3IXKLm+GdQ3oVg9uLEF4+XFTD/bDTU2jP1hGlZqNdmWlvw4bpxpl4EDBz7VZPx7CwDk1roCkKMgB7nMgFFubep9HDp06LOfWwGo1Wq+++47vvvuO8zMzJg0aRITJkx4Icd+WYlEQ3ilnDhxgqZNm2JhYcHChQtp0qSJqfa1VqvFzc2NihUrcvz48SKOVBCEN4lWqyUuLo6YmBjTR3R0NFFRUcTExOQbwmlmZmZ6UOLr62vqtQgMDHzjh3EajUa8vLxQq9VcunSp0J4I5y3mdun0DuyV6bgcvk1gUBDBjRqR0Lwq11LNGTFjpmnI0uxRn2Au0/HRjF+wSc26LwnIn0DU5v6kIve1/y62l/d5tqUlOSoVVz098Y6KMvVowP0rf0+aNInoyBPs37uLfkPHEhYWhpOTExUrViyUa1OosuLg3A/gPzf3833NuZRRlkhjW9q//Tbmej06MzO+++IL0y5KpZJOnTpRpUqVJzrU4cOHOXBgP/mTPggIPUzTowc52bg+Lj8sxczMjNKlSz/jiT2ZCxcuMGzYMA4fPoyXlxebN2+mWrVqLzSGl4WoOiW8MjZt2kTPnj1xc3Nj7969VKhQId/7KpWKPn368Pvvv6PT6cTKn4IgvDAqlYoKFSrc93sJcm9yk5OTiY6OJiYmhtjYWEqUKEGrVq3w8PAogmhfXh999BHXr19n7dq1hTrsRCaT0bp169y1JzzOora+ze8Bw+nUczD1nZyYP3+uafL18Yb1GNajFn9sv8zBgCa5CUnDhvj6+pJ0M5rbqVrTJO3AoCAiGzZELpcT06oVl1u0yC00otEAmNoMCgzM93lyN3/KfjGIY7tS2JWpwcXFhStXrpgSkdwKU7XxsEwmfUsjNp2tR6bBihWzR2NnaeSE0Rc3NzccHR0L7RoVCqtSkHU199+6OyBXUkG/ERvjBbjbO6fU6/ELCTElUpIkUbZs2Sc6TFZWFk5OTliZS2Tr8icadY6cRJFqpObBU6i8vYukV7BixYocPHiQFStWMHLkSPz8/OjWrRvLly8X9yYPIXo0hCI1d+5cRowYQcWKFdm9ezclSjy4rODx48epX78+06ZNY+zYsS84SkEQBOFphYSE0KBBA3r06MGqVaue34EkiZsRf3PkfA49e/YkNTWVZcuWUatWLc6fPYEmI4FPxnzFijV/k5KSgtFoRKvVMm7cOPR6PXPmzKH8vn0EBgVxpX0t3KavxNXV1TR/JjU1lblz56JUKlFnZ+JpGc9trSNqozUODg70f+cdHM2SIekYVy6GgCaR0i4yQlNrcnN+0N3EJIBQ/zrkDsvKnTeS+yEhw4CEGSVLluS9994zrST+op/a56O7A/G74NZBiFsNKmfQpSIprJCprxGvKYHDh3ewupuA/beHKCAggBYtWjy0+ZCQEGJiYkhJSUGn05GTk5O7Mro8nTStJTKMKNGiw5IOZzdRbt9ljgQ2otz06c+95yc5OfmRc0xSUlIYP348ixYtws7Ojvnz59O7d+/nGtPLRAydEl56EyZMYNq0aQQGBrJ58+ZHVqeQJAlvb2/kcjmXL19+gVEKgiAIT8toNOLm5oZCoeD8+fNF9qRekiTu3LmDk5MTRqPRVKo0NTXVtGr4qVOnSEhIMM3BqVSp0n3t3L59GwcHB2JiYqhgdpRbR//HOfd5NGnW/MFP2HVpnAvZzrq9l/IiwS/k1H3DqP7LwsICjUaDubk5EyZMQKPRoFQqX3zRgIQDcKgDGP5dcBC5ijvyCjjq/yEtxw7zSVosrmoxyOXsb9+cY34NkMly1zEpWbIkAwYMeGjzS5cuJS4u7r7XO/3zN2X2xpLcxolbDV3Yn9yCmrahnM6og1Jlzccff/xc18rZs2cPoaGhjBkz5rErtB89epTBgwdz4cIFatSowdatW5+p3O6rQgydEl5q/fr1Y9WqVXTr1o0VK1Y8dhKcTCZj0KBBTJkyhVu3buHq6vqCIhUEQRCeVp8+fUhKSmLnzp1FOhxIJpOZhmzdux5CXpIBFKiCUV4ZVR8fH8AHtzL9cJM/4kbU3B6tVUUgL9GQ5RueBdy3Sniovz+auz0EOp2OadOmYTAY6Nu374tfG6Z4UyjTH2KWgjE3pqwcM86nWdHACWwOZiC/KiED5JKRxj0OUk57mQslZtG2XYfHDm96++23WbBgQb6SwDKMVN1xFrNsI/Zb07gZ6I61PJMMPCjvUxlJksjJyXlup3z48GFOnz6NwWDg/PnznD9/HmdnZ2rWrPnAYX8NGjQgPDycmTNnMmnSJMqUKcOIESP4/vvvxdob5JZYEIQXxmg00rRpU1auXMmHH37I6tWrC1xpo1+/fhgMBr766qvnG6QgCILwzPbs2cP69ev54IMPaN26dVGH83w8Ksm4K28R2TxBgYGmtTTyko4qkZH5kg+/kBBGzJiBX0gIarUalUpVdPN+as8hS/lvgpOutyHTkPsEW7FVujvwCy5Uqch1jSeeFlcpGz+etNTUxzZ96tQp2tot5yOvOQz3msM7HstwV11BIcutNqU2WLAvuSXZcjcqtRjJ22+/Ta9evZ55fY6HOXHiBMeOHUOj0aDX6zl58iTJyckEBQXx+++/88svv7Bnzx6Sk5Pz7adUKhk7diwXLlygZcuW/Pjjj3h4eBAdHf1c4nyViERDeGF0Oh1Vq1bl4MGDfPvtt8yePfuJuoHLlClDQEAA69evf45RCoIgCM9Ko9HQo0cPPD09+fHHH4s6nCKl1WpRKpU4OjqiVCo5U7cus0aNINS/tinpiKhc2ZR8gGRKQNpt345fSAhyuYwcdeoLjVun07F48WLWrv+L41bfkqhxwShBhs6acscuIo0AykOmgzXb2rdnXbde/J3YlXitJy6ubthYP77SmrOzMzeznbitc0aBgdJHY3l3+h9oqqhItbdnf/PmqFQqHBwcSEhIeK7nGx4ezqFDh0y9SZA7PChvbZOsrCySk5M5evQov//+O0aj8b42vLy82LJlCxs3biQ9PZ2+ffs+15hfBWLolPBCpKSkULVqVRISEvj9998ZNGjQU7UzYMAAhg0bxpw5c/j4448LOUpBEAShMHTu3Jn09HS2bdv28i5C94L4+PhQunRpvLy8MBqNzJw5k5ycHHJycjjfuPED52kEBQaaVt4ODAoivE4N5syajpmVKxYWFnTt2pXixYs/17izs7O5c+cO169fB6C0uw1ustuUPn4V5To9MmNuT8biTweTqs8dFpdpsGVL+vsMfWcUZg+pwnT79m1iY2O5fv06Fy9exJBTHxlGPro8B/lKwChhgZaMGXYkJrrh7e1Njx49nuu5Qm556rxkMDs7G71ej8+BAzQ8epTD9evnW4xw0KBBDx0WJZPJ6NKlC0OHDuXXX3/NNw/oTSR6NIRnZjQaiY2NZe3atXz55Zf07NmTevXqUbp0aRwdHbG0tMTZ2Znk5GQ2bdr01EkGQO/evalTpw6ffPIJ1apVIz4+vhDPRBAEQXhWa9euZc+ePXz22WcEBAQUdThFrnjx4qaF6/JWh69fvz4TJ06kWLFi+YZJ5Qn192dn+9amXg69pCRbb0F6ejq3b99+6Mr2hSk5ORm0t0yf25llYJTksFmWm2TI4XbrYqgNlvfsJZGWncPBgwcf2u7NmzfZs2cP//zzDzqdDoNkhl4yx/avzNz1+WQg6wR2ZukEuIXz1ltvPbdzvFflypUZMmQIH374IV26dMHb25vA4GDsUlJMQ9oUCgUGg4HMzMzHtjd8+HBycnL47LPPnnfoz0dKOOQ8/jwfR/RoCE9t1qxZTJw4EY1Gg8FgML1uZmZG8eLFKVOmDKVLl8bT05NSpUrRuHHjZy5HZ2NjQ3BwML/88gvjx4+nbNmyfPnll0ycOPFZT0cQBEF4Runp6bz77rv4+voyZcqUog7npaPX69Hr9Rw/fpzTp0+TkZHBW/dMDj9Tty5ly5alQoUKZLme5HSLmoSnVjftbyXPwkaRzubNmxk2bNhzi1OSJGxsbKhuE8bx1LpIyDBKMo6tr0ctdSgaSxVSDwiq1hBtpgUKhQJzc3McHByoUqUK9evXf2jbVatW5fjx49y8efO/R839rxWkNbZnZ2Jrbirq4W00vtBqW+bm5lSuXBlvb29iQ0KQli3jqqcnI2bMME3WP3z48GPLDpcvX54WLVqwZs0aFi5c+GKCL0wn3gNdEtSaCZ5dyM7OZseOHezdu5dTp04RExNToGZEoiE8tU2bNgHwww8/UKpUKdOHm5vbc620oFAoGDFiBJ06dWLw4MF88cUXLFmyhF27duHt7f3cjisIgiA8WuvWrdFqtaxatarAhT7eFJIkkZ2dbVq/I29V+bxJ4adbtWL06NG5Q81iV4LjftQ5oJEsOZVWGw+La7gob3MhyxcpJZH09PTntuTAnTt3+OP3OWTp6gAKAkMPY/dDOvXVR5EDafZ23GnoSLymOQO7dzT12BSETCajefPmrFq16u48ByP1Q4/lTiq3hpudi7P86kAs7Yqj02i4ceNGkawlsmrVKhLc3Mi5Z+X33AUX/bly5QobN26kS5cuj6ys9fHHH9O5c2eWL19O//79X2D0zyYqKgqnW9codugWxne7cqmujGbH4ead3GTQw8ODOnXqsHPnzse2JRIN4allZmbi4uLCmDFjiuT4ZcqUYe/evSxZsoQRI0bg6+vLiBEj3viJh4IgCEVh3rx5nDhxgm+//Zbq1as/foc3iF6vZ9asWej1enJycvALCTGtpXGlTRuutW/PoEGDsLC4O4G6dG+QKZAHDaJ5sf2cz/TlnQsrkG0GRX2Jc3UrEXnod+p3HIlOp+PChQt4eHg8coG5x4mJicHOzo64uDgiIyNpX2w9f93sRvWQEJpuP4j87nAto0xGUGAgXp7NaFuubYGSjOzsbC5fvkzx4sU5c+YM586du2cytZyAI0HIsnInli+q8D4KcidWV61a9YmSmIIwGo0cP34co9GImUyPXGmFRqNBpVLh4uJCQkIC9vb23Lx5E/3d1c7NtVqyLS1NK8FLkkR8fPxjy/e2b98eDw8Ppk6d+lImGjqdjn379rF7925OnjxJdHQ0qamp6HO0xMyAYltAngIVQyQuzbLjhvP7uNWfgIODA5s2bRKJhvB8ZWdnY2NjU6QxyGQy3n33Xdq0acMHH3zA9OnTWb16Ndu2baNatWpFGpsgCMKbIiEhgdGjR+Pv78/YsWOLOpyXjpmZGY0bN2b//v0ApqpSDYODab1x4/2LwsnkULoXZuGTyUxNwM/uJMotemQp0OrYbkL9/bgedYLTy9/icHIAWdm5Vb6eJdHYtGkTWq3WVI73uuwt9JjTbP9+5JKEEdBYWrK/WTPO1KlN2CkNNfQXCjSSQKPRsG3bNnJych44vyS6pDdV0iKJKVkGAIMxtwpUZmZmoY9UkMlkHD8WRC3z7fg7HmN6zFj0Uu71l8vlGI1GZDIZtU6eJDAoCHOdDiuNhlR7e9OEcIVCcV/Z4gdRKBR89NFHTJw4kaioKMqXL1+o5/IkUlNT+fPPPzlw4AD//PMP8fHxZGVl4Wxj4Mc+MLCTHHcnM8yV5qjM5ahQQ0dgC9BJjo1dMXyqNwUHB/R6fYHnnojJ4MJTU6vVL81K8e7u7vz999+sXbsWtVqNn58fQ4YMeWD5OUEQBKFwtWzZEoAVK1ZgZiaeYT5I7dq1Cbz7RDyvrK3xs88eufK0wu8nshsdwtUiBVknwBzM7ugZue1nzmeUJ/a2jHY2C7A3S+PKlSv3re/wJJRK5QNuniWUMj0AGgsLfh43mjD/GhglGXq9Pt/8zHtptVpCQ0OZO3cuERER5OTkoNfrTUnGfyfAl7p2DbkkUeratXzttGjR4qnP52GSLu2kv/N0AhyD0RmV2Cj+nfCcd88gSf+WGEaSSHNw4FSLFsjlckqXLs2nn35a4Dky7733HnK5nBEjRhT6uRREbGwsLVq0wNXVleHDh7Nx40YUCgVdunRh2rRprF63hT7Ni1Pd04iLtQ5780wsUCMDaAHMsYThH0OHc+DeBoDFixdz6dKlRx3WRCQawlPTaDTY2toWdRgmMpmMHj16cPHiRXr16sVvv/2Gm5sbH374IZGRkUUdniAIwmsrLi4OHx+fIp0nd+rUKb755hvOnj1bZDE8jk6nA3KrSi3/+mscxo9/9A4lO1DcqyoViqXn3vTpQSaBbWgW73ku5lxmJXbebkMbp79QXvoJKfEgPGVFqv8OA8qRzPG0uIrOWYkEpDg5YW+WSkWrf/+e3vswLz4+3pToJCQksHXrVm7fvs3ff//NwoUL823739XRgwIDSbO3M0269gsJye15OH6cixcvFk6VLX02yVs7YnWyB87K2yjleszQYyHXYH5PKd68JOiqp6dpLY+ZI0dytV073N3d6du3LxYWFgUu2+zi4kLv3r3Zv3+/6ev/Ipw+fZratWtTvnx5jhw5wpAhQzh9+jRZWVlERESwbNkyRo8eTZOWHVDUXQRyqwc3VHcB+M0ERe6wvrS0ND7//PMCz5sRiYbw1HQ63UuVaOQpVqwYf/zxB9u2baNEiRLMnTuXKlWqYGtrS926dZk2bRqpBVixVBAEQSiYr7/+mvDwcJYuXfrCj33ixAnatm2Lv78/kyZNonr16tSvX5+VK1cWaHjLiyBJEjt27EAzaxYjZsygblgY1atXf+wYfwBZ8gnMDKlIgL6OHEkO+jpmFFMm8WHUHD76eQ5lj8bQxHE3zuFvgy6tUGK2kGfStNgBLK9pkAHuN29yR+9IZHYN0zaRkZHMnz+fqVOnsmjRIpYvX86yZcv4888/TdvUOH6cj3/6KV/53qDAQNIcHExzHs7418QwE0pfjzMlIJJkJDw8nE2bNj205+RJBO1ciurOQSzk2ZzPqMi6mz1YfP1dEnTFqRocbEpw8pKgUteuMWvUKEL9/bG0tKRVq1b079//sT12kiSRnp6e77WPPvoIrVbLN99888zn8Ti7du2iYsWK+Pv7c/HiRcaNG8fVq1f59ddfqVnNF3P1f6pFaZIgYgoY7y5UuBcYcff/Snso806+zf/3v/+RmprK77//XqB4RKIhPDW9Xl/kczQepV27dpw9e5abN2/yxx9/0LVrV2JiYhg3bhzFihXD1dWVjh07sn79ejHEShAE4RmMHDmSsmXLMmbMmGcavvMkjh07RqtWrahXrx6HDx/mo48+IjU1lU8//ZSoqCj69etHiRIlGDduXIFLcT4PkiSxceNGwsLCqHfgAA5paTQ4dIhGjRoVsAE9lHqbw+q+LOsyiGlffcb3bSawKbEzjrvSkCeDfKuETAZag4LUNWU5vqgdWxaPZdOqWaSnFTzxUCgUyGUARtq47CZ+mzt6hQIjEFG5MtJ/pvbq9XoSExNNf0PT09O5cuUKWq3W1DPQbP/+fL0XMpmMsw0a8NfPPxN+twyuHD3XNaU4GliPbAsLzHU66oYcQ4ZE9+7dC2U4nptPS9ZkTSUyuxaZBmsuZ5UjOccNkOXrYckb1naiaVMsLS1RKpXk5OSYVnh/GIPBwJkzZ5g7dy6LFy/O916dOnWoWbMmCxYseObzeJgVK1bg6elJmzZtSElJ4dtvvyU2NpZvv/0WNze33I3OjIN9TSDnbiJ0YSZsKQ/uHaDeEkCeOycjidz/G435EteYmBh+/vlnGjZsiJ+fX4HikkkF6I9KT0/H3t6etLS0l2ZMvlD0bG1tGThwIHPmzCnqUApMkiQiIiLYs2cPO3fu5PDhw2i1WszNzfHw8KBFixYMHz6cGjVqFHWogiAIr5SIiAhq1qzJgAED+O233/K9l5WVxYkTJwgODuaff/7B09MTX19fKlWqhK+vL46OjgU+TlBQEJMnT2b//v1YWVkxdOhQfvzxx/tuRnfs2MEXX3zB2bNnMRgMtGzZkg8//JD27du/0HUZjh49ysGDB/NVmwpp3pyAP/7Ayuohw1X+w2g0MmvWrHxPypUyHT3PrabcvhjoCFJzyOsgkSTQSwok5GhaX8DOpexjjxG9ujXRhrrcjo/GSpFF5RPnKLfuMnJJItXenlmjRuWrlnXviuYPej2vJGy2hQU5FhbIP/+cUH9/atWqxa1bt1izZg16fe78DwU5yGQSesnctF+qvT2LPx3CJ+P+98gb/ILSaDQEBQURGxuL7vYZ5DIjAauCqRIZSXyJEthkZZnit7W1ZdSoURgMBqKjozl58iT169enXLly97Wr1Wo5duwYYWFhZGdnk5OTg7OzMx9++GG+7ZYuXcqgQYPYu3cvzZs3f+bzgdzvi59++olp06aRlJRE6dKlmTBhAv3792fJkiWo1WocHByoVasWvqUsUR1sAppbuXMtMmNBZgZNt4NVydwGL82FHz6GzcbcieAtZFB/OZTpB0D37t3ZsmUL165dw8LCokC5gZixJTw1g8HwUvdoPIhMJqNq1apUrVqV0aNHo9FoCA4OZs+ePezYsYNFixaxaNEirK2tqVixIl26dOH999/H2dm5qEMXBEF4qVWpUoVu3bqxePFiOnXqhFarJTg4mMOHDxMeHp5bTtTMDFtbWzQajWkdCQBnZ2eqVKlC5cqVTclHpUqVcHV1NQ0vOnToEJMnT+bQoUNYW1szevRofvjhh4c+7W7bti1t27YlKSmJzz77jA0bNrB7925KlCjB8OHD+eCDD56pSlNB1a9fn+LFixMcHMwlW1vC6tVDqVRSNT29wInGzZs30Why5xKoVCpUKhUl7A0o3CWkjiDbB4wk998tchMOmSShljlhZ21haicrKwuFQmEqo6vVajEYDFhZWeFtcQ639CAuWvtgp0zHY8sNU7WpvCFO9z75vzfR+O/r9vb2RHXrRoW//uJIQABWo0fTrFkzmtzd3sbGhr59+3Ly5EnOnz+PATPkUu7wqKuentilp3PV0xOt0ZxF82ZQsUpt/Pz8sLOzK9BwsweRyWSEhYWRlZUFuAESVSIjkUsS7jdvMnXy5HzbS5KEmZkZPj4++Pj4PLTdlStXcv369XzzSB4U49tvv83IkSNp27ZtgROnx52rwWBAo9FQrVo1fv31V7p162ZKoh0dHbl16xYZGRnEx8dTvOR8ipsn5O4Yvx3sq0L7u/OZcjIg5COI3wYtVdA872dTgpilUKYfR44cYcOGDQwZMgRXV9f7hoc99BxEj4bwtFQqFV9++SVffPFFUYdSaG7fvm2qKb1jxw4SEhKQy+U4OjryzjvvMGPGjKIOURAE4aWl0+lwdnYmIyMDAEtLS0qUKEG9evXo3r07HTt2NCUGqamp7N2715SIxMTEcOfOHbKzs003bXZ2dlSqVAlJkjhx4gQ2NjYMHz6cb7/99qmG06xYsYKpU6dy+fJl3Nzc2LRpE/733DA/b1qtlrNnzxISEkLnzp3x8PAo0H4JCQmcPXuWcuXKUbJkydzJy0YDrHPEkJOJYpSUO9zFGZiVu4/OqGRzykDae5/DsvpYKNmZJUuWADBw4EBkMhlnzpxh69ateLrZ0NvuW1TGZIySjMxd1tj8kYkc0JmZ8d0XEwFZvp6Lm50706JFC1auXEmN48dNr9/o2JGBAweiUqnQ6XTExMRQtmzZfBOu75WTk8PSxfPxzVlN0J1A3v95AQ5paRhlMna0a8Mp/7rI5TKsrKxRKBSoVCreeuutf4cDPYHY2FhWrlxpmvPRdf16qkREYFCacbRdPQ7WzK1ypVKZY28pUbyUL6VLl6ZmzZoPbO/atWucP3+ef/75h6ysLNP3ra2tLaNHjyYrK4uLFy9Sq1YtALZv387BgwfztfGg2/CCviaTyWjZsiWtWrXKn5ScHEb0LTPWn7FFY7TE2+oSfdxX3R0Wd49ac8CQDlHzIfsGyC1gd3busKmO5BYgsCqFsUMUfv51iY6OJiUlBTMzswLnBiLREJ6amZkZ06dPZ+TIkUUdynMhSRIXLlxgz549bNy4kYMHD/Lll1/y9ddfF3VogiAIL60TJ07w119/0bt376cahpqdnc3Bgwc5dOgQp0+fJioqCo1Gw3vvvcfUqVORy599eumpU6do3rw5arWaBQsWMGjQoGdu84W7up6kAx9SzOxWbo/GPTeHkgS3KE+6WsFVbVlcrLW4F3dm2dnK6AxmNG/enJo1a7L8l4lI2hSSc5zxtz9Bk2KHkO+TYBlwd+pitqUlP40fh1xhZur90Gq1VKlShU6dOvHjjz9Ss2ZNbt++TUJCAh999NFDk4qHkozcXFOXP6KaUunEOdpt357bmyKTsbd9c07WrouB3F4AZ2dnhg0b9tTzNv73v/+Rk5Nj+jxvqJaxmIybrYtjsy2LI4ENCa9TA72kwEwu4eRcgipVqlCzZs18IzkunNlH2IHlKIyZ3Na5cSfHCT1KitnCkA/Hs3jxYrKyshgxYsSTX5MH0dyGi7Oh+tRHb7etKsa082TqrUnR2eNhcROlXP/w7ffy7/dP3hyNvKRVaccu9XDaDPieOXPm8NFHHwEFzw1EoiE8Fb1ej1KpZNGiRQwePPg5H0wNQd0hcC2YFaycXGEzGo106dKF7du3s27dOrp27VokcQiCIAiFIz09nbp163LhwgWGDx/OjBkzHngzePbsWZycnCjp5gDKl2i48PYa3Eq4jqtF/sn3kgS3dC449TzHkeNhEL2Yssoz/JNansq2kWy71Y50qQQymYxylue4keVMusERM5mOwNNBNNpyGJkRJFnu2hn7mjWnxZpVJKdmoFAoKF68uGnhPXNzc7RaLSqV6plP57vvvjOVf/ULCTElG6n29swe9QkWci0GlPTo1Z9y5Ss89XEMBgPz588nKSnJdKzGwQex7ZqFtAVkSWCUwfZ27Tjj70dZyyhcVMlEZfviW7czzZo1M7V1df8XFI//AaVMj0wGp9NqkrTDmXpHTnC2fUf2lS+PQqGgTZs21K5d+9kuEMDJ4RC7DKr/DyoMJ/3gYOQ1/4eN4396xva3hIS9pgRCupuAyiB/UpG3TMkI/k0u8pKNzkroXBZ9diJLD6iZss2Fa/esc1LQ3EDM0RCeSt4P6AuZoxH5Xe4PzLEB0HD98z/eA8jlclatWkW9evXo3bs3Z86cwdfXt0hiEQRBEJ6dnZ0d58+fp1+/fsybN48zZ86wYcMGSpQoYdomNTWV3bt3U9fhOB7FTjDzykjkcjPTMBWZTIbBYKB06dJ07NjxxU0yv3MWMqJwVmXnfn735tHYAc76V8HFWs3ZFR05drsdzra+3JJXoZ3TD6y+2Yssgw16Y+7T7fM5/05urn4ynIbbjyCTwCiTsb1dO0L9/XFzc8PC2g4P639vJu+dY1AYSUZqaqopySimvI22qTmnzGtRZWck5lottUJOc8a/FiUtrnHh3D+4FS/x1OX1FQoF5cqVM93HhPr7c75uJd7xWE5xEmEZyI3Qfsd2QEaovz+X1aBAj5R8Gvg30ch0e5v0q/NxNs9N9mrZn0EXbIZ5qp4qW7ey7+6E8pCQkKdKNO7cucPBgwfx8vKiRuWyyG9sAUM2RHxL5j+/YKm9wsFoGZXafUtxF0ciTu4kO2Yj/lbnc2/w76kgJWshA6T8VaXyEo285CIv+WgBWDhyyf1nmrbvT45Ozv7DO578YiPK2wpPITMzkyZNmgAUeMGWp6ZLhZglYNRB4n6IW/t8j/cINjY2bNu2DWtrawICAsjMzHz8ToIgCMJLbcWKFcyfP5/Q0FBq1KjBsWPHgNye7DVr1uBsiKCWaicG7R3qeNwmNTWVO3fucOfOHVJSUtDpdERGRnL69OkXF7S1F3i+BXIVGoM5xs1AEsi3go/tZWSGDE6m1EBvMJKQasQx8xBWZll0dtuEXjLDUp4FSNx9xp2vB+HeJMPV1ZWBAwc+99NJSEhAJpMhk8lI1TsSkVmd64Ee5JgrsdJoaLd9O4GnD5OodSU0LIKNGzeyadMmTp06VeBjxMXFkZ6eTk5ODhEREfneyzZasyupDbpmZmjfUSLJQWaEZvv3m9bXMJdriU2zZcmSJWzatImlS5eyc9cett1qR7be0tTW8YZ1yba0xPxuiV/IvW/KS2yexJnQ48SeC2Lrlk2c/K0tRnV87hu621jrLqOU62lhtQyn/d4Y19pR+UpP/OV/otDeyN2uI0jOcmQf94HKn+e+1lkJzrLcpALAtiJMWAjLqkNLJViWBNvy6HUa1v7YjbRMDYeCQ6lSpcoTxw8i0XglxcXFUbFiRRo3bsyoUaPYvHkz2dnZL+TYUVFReHl5ER0dzdKlS6lXr97zPWDYONhyI7dbb/sdOD0G1AnP95iP4OXlxebNm8nIyMDPz0+svyEIgvAaGDp0KCdOnECn09GoUSMWLFiQW5I2NYquxf/CWpGFmTGLOpY78/VaKJVKDAYDer2eo0ePFsrCcgVibg8N/uBKnQh+jfuE4IYBZDlYcb6pDwlaN6Q9Mvr8sAq/kJOUtYyicbFDmMmMuJrf4rMyPzDca66pKb+QUw9MMiC3YlZeharnqXz58vTv359BgwYx8K1ASjsbuZhVifPNKmKUyZBLErUOnUEr5Vbpio2NJTw8nLS7a4QYjcZHrh6elpbGunXr2Lp1K1qtFjc3N8zMzChXrhzlShWjtGU0tmapaCQblK0VyAaAsZgMmWQ0VdNSG625cSOBq1evkpOTQ1ZWFrqsW0jIicoqy81tbuR8bEY1u3+QWxix0mgIDA7GxsYGvV5vSmAL4saNG0yb9gPxF49gIdMglxmpaHMeed7Emb0gGwn8AowEiwNalHIDZnc/9JIc/R45OZvMON2+B0xaCU61wbUJzL4CiVroWQdsykKrYCg/BNqFwdtq6HyFkOIrcX5fYvoOM8LDw59pBIdINF5Bn3zyCZcvXyYyMpKZM2fSuXNnbGxssLW1xcvLi5YtW/L555+zf/9+U43qwrB9+3aqVauGJEkcOHCAAQMGFFrb9zFoIGE/XFkFW6R/u/nU1+FQh+d33AIICAjgt99+49KlS3Tr1q1IYxEEQRAKR40aNbh27Rq+vr5MmjSJo4d208ttKfbKDNM2utTLFFPcMH2ek6MzDfnJzMwkNDT0hcbs4OiEzNyW/TVaMn3kZ6yt3JvlNwZis0ONfVo67Xdsp9+FVajkOtPNqfkBPRqDCit5Fn4hJx+QZNRGqTTD0dHxha2srlAoKF26NJ6ennhUakHCHQM6owWKFgYiK/tilMmI8yyVbx8bGxsaNmwIwJo1a9i+fTsGg4HU1NR/N8q6ikGbyZ9//klWVhbx8fFo026QnZ2NXq9Hjp6+Da3oXnwtbVx2YadIRS5poAXoZyr4p201Mh2sSG7jlBunLAcXZSKtq+gJDAzEx/oCvd1XUc0ukhK7E1Gm6LHfnk50q0qk2ttztGFD7Ozs6NOnDwEBAQW+Hq6urtgqtfSw/wmZTKJt+Hbsx6Qh7b27Qd7wpxO5c0pky8gdPkfuHJ0MvR1ZG61Rpugpv3l3bhLm2QVaHAArd5Arock2aHcWVE7/Hliu4OjxEzRt2hSQc/afSLy9vZ/gK3k/MUfjFWM0Gtm7dy/du3dn9erVJCUlERkZSUREBBEREYSHh3PixAn27t3Ld999h1wux8bGBhcXFypWrEidOnVo3bo1/v7+T1S547vvvmPSpEmUK1eO7du3U6ZMmcI9seRQOD8dMi7lrlhpUIP6JmCErtbwtxZ6uoFnXXAu+A/r8zJgwAAiIyOZPn06X3/9NZMmTSrqkARBEIRnZGNjw9mzZ/n4448pKQVjKc9CbbDEUpG7roD1gSyGbF7I/gbNOe5XD4l/ezfynlr7+fm9sLka1tbWaHLA3sYclZUDOp0Os8yLHGtYj1bb9iA3SrkTnFvw783pMnA6n8LH4XMwV2uRA5Ic0vrYElrOHxVZdHfZgK0ym7RzHsQ7zsS9QuALOR+dTsfly5exUUnosvUE3WnE4Ou/IZckyl6PQS4zYJRyr62V/hrqzBQMlvbcvHmTuLg4oqOjMVMoGPzeQJTmlsiOdGdbVDlupZQH5GRlZXF47USS1DUAuHQ5loVx18iR3qWZywHKWMZgLtch3wfmW/RUax8Os6G4lMBg/UJszdLRGiyQhW1BI+vKPxnVqetwEneLm1AeSIGc8mYcazIAec1w7MzSiI+PZ9OmTfTp0yc3C3jU2hgGHeSkotQm425xPXdzJLz3RSNLA2kzGJvLkHe8O9eiPEgncod55c25yDZY8kvcJ9QKDCUwKIhjDRtSIyEh39wjACzuXx8sOzubNm3aYG5uTkREBO7u7s/8NRVVp14x8+bNY/jw4Y9cWVKSJG7evGlKPiIiIjh79iznzp0zLZBkZmaGjY0NxYsXp1KlSjRo0IA2bdpQuXLl+9rr2bMn69evp1WrVqxdu/b5fA/c+Qf2NQFdSv6KCP3aQKP1RVZt6lEMBgOdOnVi165d/P3333ToULQ9LYIgCELhWbJkCRvXr6ZScQ1ta0BZq2g8xsUjT85dxE5jYUFQy4b807i1KbHQ6XR0796dsmUfvxJ3YZAkiczzK7CxNENWpje///47VbULcTC7jX6PGR67b6LorEfVUot8vxH53bK1eXMQIHemBvWBD+Gm1o218W8zpNRirM2yyNRbs136hq59PiyU1bkf51rMOS5uHUk9h6MYJTkymYTqgAblFj1RzctxuX4FQjPqYDRCMWUSSpkO3xJ6DsWVwIgSZ2Ui2QZrmlVIZ1e0Fy0dNnAguQVqoxX1HIJo6BiMUq5jRswo1FJeMZvcuSpy9NQLPUrj4CMoM3OQ6cidwjIw/6rrWXoLIjOqYGuWztqEXjgq7zCo5BJsh2dCFhisZeycMZzGqqVkGayZf3U4CjkUU6XybjMbrjsOJDo6mpiYGBwdHXn77bdz10T5ywUkIxi0gJGoDC/cVTf440Z/vI9dpkHQUeSdDKhaaZHLVUgyJTJDJsa9IN8Chg5yjM1lLL0+kHitJ5BbyKZ58+b4+fkVaNL+5s2b6dy5Mzt37qR169aP3FaUt31NlS9fHq1Wy5UrV564lrjRaCQuLo6IiAhTL0hYWBiXLl0y1ZRWKpXY2tri4eFB1apVCQsL49y5c4wcOZLp06cX3lOanEy4EwaaBMi6ApmxcHM3ZF7+t8yaiwKu3gQLl8I55nOQkZFB3bp1iY2N5ezZs5QvX76oQxIEQRAKgVar5eeffzYNjYL8k6YB1JYWLJg8BpWlPXZOrtSoWfuBD+yeq+tbcv+WlhtCYmIi9rIElKEfsCIqkAbV3ChbqRG39w4hIU2i0slzsFnGBXcfqkREmMbPS84g6wjS3VKosua5t9+7ktpwMrUOdZwvYTCzJtOuOZZWVqaHlY0bN8ZgMBTevcGOWhyPUVHJNhI7swyOra+H36FQ9B0U7Knegk5u2/g5ZiSZRnuclEmk5DhjIc/GDD3ZRmvM5Dk4mKVSyfofjqUGUt0uDLXBgvqOJ3BSpqA6oENam3tuh5s35lCtpgCmhQjNdVqs1BokGcjy7o7vWQTRYJRhRIbWqCJRW4LrancO3mlGW5et+I86jUwHRqUM9WIrrBVZZOjtWHp9AAEOR/CwvMHKm4PQynJ7nizl2TSsoKN+6UzIugra25D6D0i5Q95js7woZp7MTa07bqpE7M3S0BqVHMlsj2eTqVR0yUY6MxqSjiNTWBNu+SHXoiMITfPLd0kbNWp0dyjU4w0ZMoSVK1cWaN6vKG/7Grp69SqxsbFMmTLlqRYsksvllClThjJlytCxY0fT63q9nujo6Hw9IGFhYaxevRqZTMaCBQsYOnRoYZ4K3DoCwT1zh0hJ906ek0Fel+B7jV/qJANyV//cvn07tWrVol69ely7dg0rK6uiDksQBEF4RiqVitq1a3P06FHTa+F1/PC0vEG1DWHIpNyRMGkaJWiySUu7QDV3NdxNNAwGA3K53FQK99SpU6SkpNCqVavCDVRpA3erIOaulu0GpTrRv1oNZB5tkJJCSPX6jAtBfxFXpRT/lKlOjZNhlLt8GTODAYNCgUVHTW4J1CSQ3R2CI0ngrrrOW25X2Jz4FnrMIeGS6bByuZzExERiY2MZOnQojo6Oz34uTbZRRxtI0LXq1HM8To2dYZirczCsU6CoYeCyuiKZRltUcjWeFnGk5DijMVqZEoUELzeKxyVyzbMko6J/xlyeg6waEEXu0Ka7w4xkQKMth3CKSabUtWuY63RYqdVkW1iQam9PZllr3C/FI88B1CDtzR1+JpdJZOstOZVai5D0eqiN1pjLtXhbxZI3IEomk7BWZAFgpcigv/sStJIlIFFceY1SlsEUVyWgNlpyM6USdJiWW0VMewc2l8WoS+P/7J13eBTl2ofv2b7pBZKQEEIgofcQIBB6NXQQFHtBjwUFVBCP+tmOFZUiRVBRQBCUJlUQQktoSaihpUBIAoH0un1nvj8mWYiAAoKBc/a+LtjN7Mw7z8xusu/zPuUnIBGgvYgdJY3dUjDadWwr6ElieQ90Lh7oC4qhaTRC3zi4sBlUbrSq3ZljS5YglJ7B09OTpk2bcurUKUfB/F8hiiKrV6++7Y6yM6JxD/Hwww/z008/kZWVRVBQ0F8f8Dcxm80YjUa8vLzuzAm2REN+/OWfVW7Q5mM4/gkIChh44u4SR/oT4uLi6NmzJx06dCA+Pv6vD3DixIkTJ3c9ZrOZd955B71ebl8aEBBAXV8FEavfxGWjiYy6IdTLyiIuugsF3evw4LjpaPXy99bChQspLCykb9++KBQKNmzYgFqt5sUXXyQ+Ph5/f//bo8dUkAAXfoOWb1/eZsiB+IdAocR4YQ8zz76MSdJR1c520qefyhNrvZ4vXn+V1xt+jCbW7khblnpXTq7XQ26/2sxv9BwiCsfxVQiCgCRJtG7dmmHDhv3tS7FarcyfNZWCUgPdvHfT4fUDuJhMGHQ6pk55HYCuB3fSPX4X9jAFhlOuxEVHEx0XJ6t7V3aoEgVQVM5uHWliCmS186pLqNQLUUgSBp0Oi1bL/q6RXIgOoszmiUZhYuwX36IqEJEUwONA78tNgSXAaNchbVXgusmAUFC5UQksunxNmca6LD3/CE3cThCizyDTGMI5Y33skgJR4cKroxtCyizs6Egr8SXY+jtahY0fLzzCI0E/YtyiQ1yrZHfXHoR89BFNmza9bgTJYrEwZ84cevToQZs2beTLlCSHs/tn7N+/n06dOlVT//4znBGN/0LWrVvHfffd9484GSCv5twOIR4AipNhzyMwIBEUKhBFcAuD/D1UZoiCVysIfxF0deQQ4j3iZABER0cTFhbGxYs113rXiRMnTpxUx2AwcOLECU6dOkVaWhoZGRlkZ2eTm5uLRqOhcePGRERE0K1bN9q1a3dVtoBWq+Xo0aN06NABDw8PBg0axLJly0hs9CL3dV5H4zdT8SwppWvcbn7o9DjTv/yMOsFhFBcXU1xcjCRJ/P7779hsNgwGAyol/D7vYRpqj+GdZcSWokUlVsDQc6C4xfQjlTvYKnWdrGVyY5WMJWDIBNGKXgmPBC3il5zRlNi9gcpQDDgeraIWoZcZVW85w8AuCajWyx0f/bbkUbtFLpcsVxYTy9PtqrXqs2fPYrVa/1Ydh8ViYdasWZSVWQE1yWXN8A0roPnxE6SFhQECIboz9IjfiaJAQlEk4iWW0D1+Bzn9A3DZWoE1TIMqzcbFev4EpFxELdjIa+yLx9lylI1sqNNtCIPBJimwr1VyOrBxpaMYTVJkJAIiPrY8nq73HeU2N451b0GbNUcdxdZSb9nJEAT50VVlgk3I6d5V2MH+lYBwTEIAAlteYPzhaWiVZkqaehKels6OLj1I7tAci13N58tO0MwtgI7eCYQqDqBSiPxycTRZpmBSysMIW5+OuthO9O7dqBs0+NM0NY1Gw4svvohKdXl6fyNOBshzTLVazdixY2/6vfsznI7GPcLy5cspKyu7/SlM/wSSKDsZpSch+QPw6yb/7NUKmrwGKV+Bxhs6/yj/9ta791rGpqamcurUKSZNmlTTpjhx4sTJfzWiKHL27FmOHz9OamoqZ86cITs7m5ycHPLz8yktLcVsNmM2mx31h1UIgoCXlxf+/v6UlpZy6NAhli5dCsgtVvV6PV5eXoSEhNC0aVN8fHyIjY0lKiqKgQMHsnv3bodY61ljA0w99ERuSURjttBw31mSIn3JOXcckyin0Fal9FRNZG12OFPqj1Vv5kJ+EANqbUKv1ZCVdJDWrVuj0Whu/oao3cBW2YK3Ikv+TrUUOV5OrQhjd2E0jwQtZsXFUdSNzwLAoNezt08nJAQuWYM4ZuvLAM/lqO1FbCscQM9BW9CslyfmQ/x/ZUHWU9i50r7Lgn8VFRUkJSX9LW2tvXv3YjKZAFmFu8DmT8P0dBSSRFh6OsOTV9JiezKKRhKkghQOYoqC/D61SGrfntUtRlJHe4GzxjA0ghGLpAUUKLDSyWsf4a4peKuL8VCVsTm3H4n1OyFgQ6qcCiuwIwlKBgX8hkqlwk9Tgd+Io5jctOhXmsEIwjbZ2biyaY19kIC4VoHNXYnunAUBUOyTHIETxX4JvSS3CfZKKkGQoNe2bUTHxZHZN5iGLum4bDAiDJHvqLQWgrqep7aYS3h8OorGdsynNcR1jqbZpUt/2fXzVp29VatWERwcfNt1U5yOxj3Chx9+SO3atYmJialpU26e1DlQni6re5/4BFK/hujl4N8DRCtkrYB6o8HtNrfM/QdZvXo1CoWC1157raZNceLEiZP/KkRRZPr06UyfPp28vDwsFstVYql6vR4/Pz+CgoJo3749AQEB1/xXu3btahMxi8XC2bNnSU1NJSUlhZSUFE6dOsXp06cdabA6nY7nn38ejUbDqlWrcHFxQZIkMsV2RAycj3WnGo/iMnni2KUeNlHpcDSqUnqi4+IcIngFVl8KrLVwVZbjqipnR25PUjI3s3v3bsLCwhgyZMjN3SCVm9xgBcCrGfSNhx0xcqMVIMilmPrGDNxV5TwdvABzvAYXk4liT08SOkQREtwQVc+tDAkORih8mgtJC0gvaUb77nvx6VOEIEAgF1EIMOSXFbQ4fpzk5s1Zff9IKqfG2O2QkJBAx44db3gF/UrKy8vx8fHBz8+PoqIiJGMuRskVlU2OsOjMRlquOiZHFlKBGXIm1NHSZvyaN5q2TdqiTklBrSzGw2ZgTN0VbK54moxcK0q1C/maTuzL6Uo992JEpTs5Rh0+Pu4UFhYiICEhEFLXj269B4H0GAWCAV3uBjxS30LRR4SNyFGLn6nU9AIqQFwrsP3tHpxvEkS2uR6tDxy5rEsCmPR60ho2JCwtDQSBsw1DqJedhbbCgktJCR6bShF0EkIBlK92RULAvbiczrv3IAjydlKh4gtXEs+1x/XcudsvLwBkZGRw8uRJXnnllds+ttPRuAcoLCzk5MmTTJo06R9pL3dbMeXD8U8vh3VFC3g0lp0MkEVj+u8Djc91h7gXWLFiBQEBAfj5+dW0KU6cOHHyX0F2djYTJ05k48aNGAwGGjduzMSJEwkKCqrmPPj7++PmdmuptlXpU40bN77qtYqKCtLS0lCpVNSqVQtJknj22Wex2+2IoogoiuTEHiQtupSo3Xs51rMFpWJt1DoPR3F2Vf1AXPRlHYqIhESi4+I42L0dmmArpysaIyKPeUttcVVul79jATybQr89sK03UulJSixaunrvRrndjmIdSI0ljCk6DnTpwPMvvoqn9xXfv77tCOzXjhf6wfGFP+MhxWO1q8k21cUmKR2dqlokJztSjg5GtkFCwFiczfRPpjBi9KOENGxxU5dQVlbG2rVrrxAZlt9Pm1KJxmp1FHBLCjDG6BBtClyURurpM4ls354B990H4EjlEsQPGGW2MW/ePNRqNfePncTChQsx2QPo0qULSUlJjBkzhm+++Qaz2YxKpeKBR56pli6+71Q8Le0qXFVGuRNXpYMh5IPkCtSCk90bccbQELukRJQUDmfyyvc8Oi6O2N69Ha+5CcU8/8k8NFYrZlHLvi4d6Ry3l71dojCLWrrG7YYYCQ9VmSNq4qUpoWtQKoaK9jd1X7EUg9IFlH8eKVu3bh0KhYJXX3315sa/AZzF4PcAzz//PF9//TWpqamEhYXVtDk3x9aekLuj+jaVG3RcACGjasSk28358+epW7cuzz33HHPnzq1pc5w4ceLknmb16tW89dZbnD59GkmSGD58OOPGjaN79+63tFp+J7EkvsmXm0Q8lMXkWQOAywXSgiCgVCoZPHgwderUYfv27aSkpDDu888dhcs5Y+rwbaNncXNzY/jw4beuv7FzKHT/1fGjJEmknT7K76u/ocKmZ0ydpQS9cQEhH0q93Jk24VVcVBaeHTcZT0/Paw4pSRKGxA/IOraOc6Ve7Cvpwpsf/AeV3e5ImpLVxO8jKbIDAC5COZGhBhrWb0Bwo/bg11Vu7nIDzJs376o6x6rUs3JXVwJzchyRFG91EaGumYR6ldLi8W3XHTM1NRWNRkNISAhms5ny8nJ8fX2r7ZOSkoKnp2dlx67L2ErPYl/blHKrCxqFBXd1hSNlShoMRd08WXj+KUptnjTQp3HG2BAXhQG1wopOYcIiqnhs6mK8Skoo9vRkxsSJVXe28rriiY/uQmLlvfPVV2CxQZDmHAP9NqBWWGVFd7icqjU6ACZ9CSEP3Nh93T0SytKh1xbQXX8htHfv3hw7dozc3Ny/HrMSZzH4fxE///wz3bt3v7ecjK+/hvdfhwGlMNAPtLVA5QJaP3ANln/+L2HNmjUIgsCUKVNq2hQnTpw4uScpLy/nzTffZPHixRQVFVG7dm3eeustnnnmmX+sAcqtoGn6PD2LtvPbnjOObe7u7kRFRdG2bVvOnj1LkyZNABg1ahRbtmxhb/fu9F+3DoUkUeenHNoPTCT//vs5d+4cISEhKIuSIPl96LH+JiypvmZsNBr56edfkSS5RbxR1HC2d318fysksVt73FTlGCUPUlNTad/+8iq5JIoICgVxcXHs27cPm8mMt7IdNklFREIiCrucymRVqVDZ7SgkiV7bYukVux0kidjevdgptWf/2Us8mTqKg6q3qduuJy1a/HWEo2fPnqxcubKabklSZCRJkZFM+vRTR60GCBRZfbBYg1HUasqfjXylttX1Gtw0atTomseq3II5X/9jfooz8kCtb3BVVaDoA/QBm6hCsAu4K0uwqv1o7HqKRq6phLqcwVedj1XSIKIgLbgBHqWlZAbLAnoqlYrRQ3rwq6uLwzmTtyvpPfhJQkJCWPj9t8w414QOwYX09NuPsjgJYZ1Ymbp1EbqNBeMFaHoD0YfiZChLgc0doOsq8Gl31S4lJSXs3LmTESNG/PV4t4DT0bjL2bx5M4WFhfdOEbgkQv4B+PBdyCmFbfVgwbmatuqOsmLFCmrVqkVISEhNm+LEiRMn9xzx8fH07dsXo9FI165dGTduHMOHD783UoVd6+IW2A5BOIuLiwthYWEMGDDAUVBb5WSAHOno378/s1JT2SxaGLDxNxSiRJfdu5nRvj0ZGRns3buHcWE/4s4lSmPHoo+ahkbvftNm6XQ6R2QlIiGB2nFFckH6xEgaBHnxrwgb2ZreePpUX8XfNbg1dQ6lYHmhPzZaYbZruGgPBODJ2O8dHWK39O+PIECX3bvRWKy4GI0AxGzcBAic6NiMhRce57ngKRTsD8CmfAVVkxcuy2tfQeyWtRgLUlFZclHZJOwosCO34q2KaKiqivqvSMIxm8106NDhqvFuGwoV9bpMwPfktxwqbUOALhedYAa9P0lFUfye1QyNzpWh/bvSOPc3LAov9p3Qkm+phV5hQLfdTI/jO1BIkqzVodEwZswYlEoldnuc4zSCIKDT6WnSpAmCIDD22edZunQpHk1iUHWYAb/Wh8FZckQjHBgvwlsC/FVnZGvp5ZS6inOwYyC0+QQaPF5tt82bN2O325noiLjcXpyOxl3OO++8g4eHxx3zNP8WZ5dAXjyUpYLpEtgMYMySnY3X3oNp8+G/fJU/Pz+fXbt28eCDD9a0KU6cOHFyz7F+/XpGjhxJQEAAv/76q6P3/72El5cXQUFBDBkyhNq1/1xk1m63Y7VaaTX8KGezQql/NMOx2g1gtVpYkNKPNp6H6WpZQOrvOpoMmXVT9oiiyO7dux31ClcWpKf37Uvtuo1xazuAJtc4VlleSqMcC7U+Wkdtz42s798bS2hnIhIS0FU6EyadjlOdmqDQB5DYPoKIhCR6xcaiMxpRSFJl4Xt73JWl7C7sztGy5tRek8CD9etg738YD5+Ay7YWn6RJ9vMcLmnOvtLO1Wy5UoXdoNdhcHV11D2o1Wp69uz5l/f77yIIAkOHDmXpD1no1L8jNHwRWv+H0EID6u+/JygoiCatO5Oe/h1r166ltLQYEHgs6AcC4i5VanoI7O0uq3PXr1+fLVu2OLprgZyiZjKZKC4uxtvbG7VazeOPX+EMaLyhTxYM8IYXiyDfBO9PhkaroNELUHc4qPRgN4HxojwfM12ShfxMeZfHMV2EhBfkdsj1Ls8p165di5ubG1FRUXfkHjodjbsYg8HAwYMHee655257u7HbQsZiyNlcfZugga5roO5AGP/vGjHrn2TdunWIosjrr79e06Y4ceLEyT3FokWLePrppwkPD2fr1q0EBgbWtEm3RFBQEE8//fQN7Zueno7BYMDVpwK/rEuO1W6AiIQDRMfFc7JnU9oMP4hSIVGv5AfMpqlotDosFssNaVslJycTHx/vcDSuLEjX6XQMGDDg+gcLskaDjxF8jHbqLdtCnutWgsoERzQjtnMEvv6hdO99HymnT2F2P8zUyElEJCQ5zlNV8B4XHY050o1siyufp4xFnfIV9Ro0IaR+fRp5FbIjdiPpJU9gldRcqaQXkXCAmI2/OSbq23v14liHlpUK23IRf0RExA3d87+Ln58fL058G0F4Q57QA/7+7vTs2ZOWLVsCcOrUKUpLS5F7YUksuzCGYXVX07g4heTmzTncvi1vVLb+TU9Px9XVFbVajZubGw0aNKBhw4bXrZXBJ0JupNN1FYzsDCuLYZBdFjwuTADfedB3p1wTW5YKkl1e+P3d4igmJ8YH9IHQ/N9Qd6hjaJvNxrp162jbtu0du39OR+Mu5oMPPsBqtd528ZTbRuclsLE1GM9f3laro+xk/I+wYsUKvLy8aNWqVU2b4sSJEyf3DF9++SWTJ0+mXbt2/Pbbb/j43NudB2+UgwcPYrfbEZAQByspXuN2RXeieLxKSui4ez+KUbKToFdUsPmbBzlu6YbBYOCZZ54hICDguuPHx8dTUFCAUql0aIhU1TmEhITw+F9E3xWW6rojLhaoYxfRVTaDsqgUWItOEfbpJDI3fIvvoOG0GvMOtvUrSYps7xC9e3naDEcURaOw0GHXgUotkfakn0njTHoqHcM+YlQtkU9LXqe64rhAdNweh5OxMSaG4x1bYJdkJygqKoq+ffv+o40B5DS+6ql8HTt2dDwfOHAgSoXAgQP7GO63knMb6tM4OcXhSFpEBYsWLeKxxx5j8ODBuLu74+HhcWPXEDEDFFq5c9SwcOiWcPk1t4bQdaX8vP1MOT3KUiD/vA65rmODFj5dC7W7XDV0fHw8paWld3SeeWOtAJzUCN9//z3t2rW7eyexOVvk1mlCZds0XQB0+r5GTfonKSsr4/fff6d79+41bYoTJ06c3DP8+9//ZtKkSfTo0YPY2Nj/GSfj5MmTZGdnI0kSKsGGtaeKr195ztH2ND66C3ZfBYohl+sQhG3Q/721NNkeiyiKrFq1ims2CxVUZGeeZdu2bRw6dKhaag6AQqHgscceu252hN1qYecrIwk9fgEAowrOeQnsaeqP3i7IrWUF2Np/AEeHvsSB597Hf/zbVBxN4nifPngbJdo29gckfNR57OvaiWJPT+Kio+m4az9eJSX0io1l/LTpRCQk0cz9BBqFnSKrF41dTlazJSIhAY3FglGnY/+wDnj0L2aE/wq6eu8EwMfH567rPiZJEoG6izxZdwEXzHWIjot3OEpVjuTZs2f59ddfSUxMxNPT8y+vwWQykZeXB2r3y+1pQx+tjDoJ4NkC+sWDrrK5jm8kBPQBFHKXKiPgqYf3pl/TyZAkif/85z9otVoeeuih23UrrsIZ0bhLSUhIIDc3l/fff79mDRGtcq6fS93L2yylshiQ8Tz03w+JL0HeLjkc596w5mz9h9m0aRNWq9Up0ufEiRMnN8izzz7Lt99+y4gRI1iyZMkNpQLVGKIVVgWAexgEDYa6w8Cz+TULmm+EK+smUioasb+4I1bBFXc3V3r37o3vEw+RuOExIjwSUCF3d2IdCAUQs2kjWoWJfRGd+eqzt5AUGmL6RhPcqCM6FzdQueHtoXEUgF+JIAg0a9YMheLaa8unty6nfOILKIYPJi2yARxIZ210E3IihjH5i+koJAlJgA0xA0mKjESr1fLKq68iCAJNe46gLP8CSc8NwTc3nx6vfcwlYxNOSG7sb9+JqkhFdFwcGrMZr5ISYjZuJCUzDGO2lgNdOmLoWl1FXWOx4GI0IvlC1P37OVDcnj3ikzTp0If+kvSnEZ2aQJIkZs+ezTD9JyCJHCqJoE7wJTxKS0lu3pykyPYIQP3QUE4cP4aeIjJD7Kzelc2jjz56XUc7JyeHZcuW4e7uTosWLehQtwCX1PnYFa6Uid549Y0DTWW6lSRC1mooPib/vA6oAOrVhueeu+b43377LVu3buWDDz5Apbpz7oDT0bhLmTJlClqttuaLjI++A2cXwuBUuT1t2ndweBI0HAttP5P36fITxPaGtlNr1tZ/mJUrV+Lu7k70FUJMTpw4ceLk2owYMYLVq1fz1FNPMX/+fJRKZU2b9Oco1HIr9oID8r/TM+XvQe920G3VTQ/37LPPkpeXx549e9h51hODZMDFRctLL73k6LDl0eUxxOTDSBovrBYjikEmlItEBBF6r4+ld2ws5vvVnOjQjB2bztGvJJGQNkNA7YbCZkSr1WKsLNquQpKk67bHT177Lcb/+zf11+6idmhzii+cxarQULJoCYLRiKpSQM+qUstpUYJwVata91qB9FiRSOrO1ZQ//xShS9dS0ao1v/z8M2qF1ZFSdWVxd6PkNBSSRP8Nm0nOaM6grI24VFSgsdmwqFQUe3riPrgMhSRSS5NHUYGxWqrS3YQgCHTp0oXlGx5EwI4kKAlPT3O04xWwo1QoOZ95BotdQBDU7N++EoO5pUMR/Vp4enrKDkDJKXbuzCMkaBHB+iwKrLU5UBZFk9iP8Wg7kZz9M2kl/gTmXBT2CkDW+bCvVaF6441rjn3u3DkmTJhAo0aNeOutt+7YvQGno3FXYrPZiI+P5+GHH65ZgURjjuxkmHJh31NQng6WEuifUD1yofeHgck1Z2cNYDKZWLduHV26XB2OdOLEiRMnlxFFkV69erFz504mTZrEp59+etelvlwX13qyDgGAOQ9ED/BsdsvD1a5dm6FDh2K320lJSeHcuXPV2vh6NH8CQnuDaEZjN7Nd9Rsepatot+YQgghUgG6DlbZ9j9DW8whSNtiy3yTD0oyUk7WwWOQUm6rogFwXEYkxLxkOL4PWHzkiMqe2/ITprSk03nYYj9py1oJXYCgpKSmVzopYzfa2bdvKtQjXcRDDuw8ne1lt8h4cjN/PG3nr7bc5fPgw27Ztw2KxVNZvSPTcE0t6UEOaHz+BQpJocfy4nGZUOY7KZiMuOpq0sDBeFL/CTVmOzWJi3dq19OzV65ZV4O8UZrOZ/fv3U26/3Ia4KqikMxrpeTCWXRHdabn/qOM9uRgdgN1uv8oprGL9+vXk5eVhMBh4LPgXFmQ/xaqLI2jpfozO3ruJ8VmOLV/Np1/rCNTm0KTuRXSKK8bqDee7NSTkiaujGZIk8eSTT2K32/n9999v6724Fk5H4y7k888/x2w213wR+P5nZVEYgMyfIfRJiPquZm26S9i6dStGo5GXXnqppk1x4sSJk7sWi8VCZGQkR48e5ZNPPrn3OvT594GLW+XnSh0EDoJWH/ztYZVKJU2bNqVp0z+IIaj04HFZQM7uU8z6VnYau53GbaVB3jj4iuwtCRTYqa9O5uDFcOx2uaazqqVtv72baTXsCB75Ksi/CCWnod5oyo/OxvROMg03JTqcDEtxGqZ1HYg3viMPjAKbUonGasWmVJKSksKQIUP+9LrqtoqGZevIHR2D7rd9tGvXjnbtKkXizm/ggO9afoh6jDxbHc6FyJ2pMoODqZeVhQR4lZSgAHrFxpIU2Z6pZ17DjhoRFYePHObkqZNMnnx3fYbmzJlDaWkpbspSDHYXRFSkhYXRMjkZBdBl3V5K7F6OYv/ouDi+6/Q0drud8vLya45pNpvJzMyUn0saNAoL5XYPckwB2CQtAibskoI27olE++wh3+yNTqnHW12MUhBhGwSvTYHC/4NXqqfgz5s3j+3bt/Pxxx9Tr169O317nI7G3cicOXNo3LgxnSpbodUIeXshf88VGyS5DsNukv/Y/o+zc+dOlEol3bp1q2lTnDhx4uSuQRRFfvvtN3744Qfi4+PJzc3Fbrczb968e0d49kr8e4LGB6wl4NsJohbdco3GrRAZGUm9evXILwnDpd+jKIQr6i+2AmthR+ceHOnYllKbG7KDIJDWtynttu9DPcRGsD4bqSpccH410vnVuErQYrIWlSIdaAimfMSt/fg1pz+ZxiJHe9q0sDDqZWURFx2N0Whkx44d9OjR409trtsqmtIvZnDyiYFEx6ZdfiEwhvD7WxC74EfA4kipAhi54meaJ59w9J7SG41M+vQzYnv1cuwjihLu9iw5XHAXRcSaNm3K/v378VCWEKC5QJqxSWXKVKW7Jkn03bOV9H4NUP9u5Wj3VkiVvZjKysoc4+Tk5ACy2KK69DBVU3RRVNI24RDRcXFIg0F72grrQB9uYvDRDQBYh6oo7O6DVmHGTVmBsA6EAgnp008QJr4Lgny+s2fP8sorr9C0aVOm/EM6Z05H4y7j9OnTZGdn88UXX9RcaFkSYfdwsBRe3qZyk1OoTk2D5tfO+ftf4sEHH2TGjBlERkZy+vTp6xbZOXHixMl/OwkJCcyfP5/Y2FjOnz+P2WxGpVLRoUMHnn32WQYPHnx5Vftew7u1/OjRDHpsAMUf0obsZrmWQ7gz3wGenp6V+gqNMJ0eixaTPHkVQKosFG+36xAG0ZXouN0YY3TUjslHcU5EUTWFEP7YPFb+T4UZtvcH7wguFRmYn/oQeqWJhi5p9IzfjmuJHEGZUaUYLYocOHAADw+Pv3w/m933GDsWfEXCN+8R+cw7lScW+M/UEGzF7rzX9S1KbF5cNHuTYs6m2fHCq2x0MRorxf8iHdtruVkdToYkSdjt9mqFzKIoIgjCPzJ/SkneiyZ/B40aPcDRo0e5YAwmTH+aSuPkB8Co1xPbuRfprRvSZ+Ql2gmJdLLMYEb2m5SXFoMpH3S1WLniZyrKClFq3KmokD9PEQkJBO3NJsR8DqEC2FB58nygEDmdDqi9OY+TnZqxt6gTDwYuRzvYWqmhIUDKbGj8EqIo8uSTTyKKIlu3br3j96cKQbpmn7TqlJaW4unpSUlJSc3WDPyXk5ubS9++fTlx4gQ5OTnUqlXrnzfi66/h3VdhoBXGdJHbp3k2B49wcKkHriGX26z9j7No0SIef/xx7r//fn755ZeaNseJEydO/hHS09P5+uuv2bRpE2fPnsVgkCekLVu2pH///vTu3ZuuXbvi6upaw5beBh56CJb/BAM7wtcfQNkpuTC89JQc5TBehO7rwP/OtzmXTs/FmjQZwS0MVflh7L8rqFjtxu7oro5UKakWMB2ECciT0VrAjGsMthV5IhoOUiowGIw91ChiFWjXm6lo4IKUquBAt0ji2snXJggCbm5u+Pr6Vleuvg6GknxOR4UTtus47rVkMcZt2228uXgkT0WsZVUBvLQUBqTI83JlZQigKhJg1OtJa9jQEVFJimxPlNc++vXoCMbzWM6uotDsiu/DZ1Cr1eTl5bF48WKGDBly3eL324UkSRxd0J3E/EYUSXWpMMuOjY8qjyKbD+0SkojZsBEFYNDrmfr6ZC5fmYBKsCAKOu5rLdDe+DGofTiYF8TGi32xV8YAIhISiNmwAQVgUauwu6mwDVbiojSgWCchhIP1iAqbpCJvaG08B5Tgriq97GBWofGFQaeY/d1yxo0bx2effcakSZP+9j24Ud/A6WjcBRw+fJgXX3yRAwcOYLfbefPNN5k4cSKHDx8mNDSU0NDQf8wWe50AlBcvQUgIZGT8Y+e9V5k4cSIzZsxg2rRpjB8/vqbNceLEiZPbTm5uLt988w2//vorp06dcqR71K9f3+FY9OzZs2YWx+40KiXYRVl1bPE1XncJhsFp/+wCXEUWHPsAc1EqC4+Ek2MOIiIhgW7xu9jbtRN50bV54ORyVOvtCIOBPtcYYzyyI1Il910LWUF6ofyzwVuPZZqGcpsby4rGExJSn9atW1O/fn00mhu/1n0zJ2NKT6HHjDWcP3+eHj/04A23NB73gbIt4LkEBAkMKtjzRU/8vsmjxfHjJDdvzur7RzJ+2nS8SkoQEdjoEcMhn7YEu2UhqQSsKhXtWieR0yGKC1IrcnNzEUWRTp060b9//79/n/+E1BNJSLsf4OeLD2KnKsol8FK96Xhpyvg4fQqT/jMVjdWKRa3m4zengGM/2dnQCQZaehwh1+xPHc0F8qy+pBubONLWqtr8Apj0GmZOeQkbGsJ8Kxjd3Q97ytekF7hQpmiAd4c3CG3SDuuqUMxWEb1HAAqdL4JLEIJrCBn6ETRt252wsDCOHTt2W+7BjfoGztSpGmTlypVMmTKFM2fOoNVqGT9+PO3btycvL48FCxZgMBjw8/O784aUnILiI+AaQopfLRpaJDT/UO7evc7UqVM5fPgwkyZNokOHDkRFRdW0SU6cOHHytzAYDCxcuJBffvmFw4cPU1xcjCRJ1KpViyFDhtC7d2969+79jxSS1iglp6CzBuJN8MAY6NgHDr4K1uLL+0gSmHOra03daVyDodN8tEffZVjJKr5Nu9+h/q1UKrEb7fzYbT5P9h8HYmUnIo8WSOYCuZukAtkBWQdSOJCCXGC+DhBBUoBumBGdyoiLsoIW2jhqN+iFm6sryoyFUH8UaLxuyNT2z73P0baBiHYb8Tt1DDip4bFwUB4Br9WAGiQLbG0G4boc6mTlONS0QRa7q2qJG1O6EUohCTmVSlCKpNQpZ6juByySipniKwBkZmZy8eJFdDodXl43ZufNUFFRwZrVv2C3348dtdzStjJa4aEuw/K7klfXfY6AvI5vUyqpro8thxxMko4jpW2xSDrOmRoAVGsBbNDpMOj1IEnE9enKyAceQedWW44UenmhbPEmjc4ug+DBoJKjh5oH8vijGyiKIo9WCgv/E12m/ojT0fgHyc/P55dffmHz5s3Ex8eTn59PQEAAn376KZ06dWL37t2cPn3asb+Liwu1a9e+84aVHIe9jyMJai6FR6AbaiQ04CNIPAntrxVzdVKFSqXil19+oW3btvTr14/du3fTqlUrZ82GEydO7jmys7N54IEH2L9/P3a7HTc3N3r16kWfPn3o3bs3TZs2vXfa0v5djJdkYdrnTDAxCAZ/I0/mFBo4+Irc6lbQgltD2Fyp7xDQD5pNAc/GV4+3qS00fwvqjbx9NrZ6l1KXR2iz9BU679rFgV692NemDUqlkpiYgVCulmsuT3wAnb7j0KbtuKV9R6PGqUh9oDSmFT+mdKFvrS00ckuXx1wHtkFKlL3tKLaBZp2NfoPXkuu6D9UxOyVl4HGfL6qwEQ4z0rakcWrVKcIHhhMeE87hhYexGWx0GNcBlUbHxfYdear9h0R2sNIwshCrG6hmIQvKAfjCkYE6DuWMJiI6iV6xsWjMZiISEhz1GVWT715soxfbAIG9uk70uH8XSpUdwabDTVmCUhCpU3aAFQtT6NT7Afz9/fH39+fAgQO0a9cOFxeXarfQbreTnp7OgQMHsFqtPPnkk39524vyL4BowyzJ7Wx7+cbSxuMwwmwRZaKIUiUiWEB0BVEjoJasPLRqCbXP5Tk6bFW1HW554CjRcfGO7RqLxaEoHtu7N0c6tMFLmYdNUwdlVh7NWwRU1uxUEvrXWmuzZs0iLi6OadOm1YjYodPRuEMYDAZWr17Nxo0bSUxM5MKFC1RUVCBJEhqNhvbt2zNjxgxGjRqFWq1GkiQuXLhAWloaVqsVkNvf+fr63nljgwaDzg/BkIUgSGjUJuwWM8qQMXf+3P8F1KpVi3Xr1hEVFUXbtm1RqVS4uLjg7e1NaGgoLVq0oGPHjvTo0YO6df/BVS8nTpw4uQEMBgNPPPEEa9asQRAEXnnlFe6//37atWt3RxWD71psFbIIbcXZyp/LofAQ+EVD6COys5E4Ti4C77lBdkAqsuDkZ7C9H4hmqN0Vmk0G30goPwuGC5DwvPxa/Ydui5llZWWsW7eOf/3+Oy4mE9GbNrG3dWu8vb3x9/cH/0flHcPkegrjN6MJ/HYVoq+S3cteI66gIzZJTf09mbABbIMEFNMlbHYVasEu13Dkg2IdBPTJRZLgx/mPkPfBQdo840bnCW1QanVsfX0rlw5f4uC3B/Gs50np+VIEQSDn8CV+NgxiX+x0prmPwB7cm8aaQrTxgBHQAGpgCLzezMT0swaSIiMd9Sa9YmMduhMbY2KI3hGHpsKMCyYAulp2o3zVjm2QgrXNB2G269ALFZyoaEFX1x0ot4pIfquZcfFRfDQFNBG2oLccxNL0XTLLPDlw4AC5ubmYTCYsFguBgYE3dN9z9nyBRFWqkMSuwq7sK4rilYQvECQ5QgOg0AFFEgrRTtjRNATAo7RUdpi2bauWHlW13aDTUezpSXx0FEmRkaiwsO+UgR9/fM9xfldXV8LDw2nevDmNGjWicePGNGrUiEaNGl1VF5WamsrkyZNp1aoVEyZMuPkP2W3gf/AvyO3HZrOxZcsW1q5dy969ezl37hxlZWWIoohCoaBp06Y8+OCDREZGEhkZSYsWLaoJ9IBcZDVq1CiWLVtGeno6bfbto9uePahcXK4rH3/bUGqQUCEgB/REUSAzpSGho2uwve49Rps2bUhNTSUxMZGUlBRSUlI4efIkx44dY8eOHcyaNQsAjUaDq6srtWvXpmHDhrRu3ZqoqCh69OjhrH9y4sTJP4ooikyZMoVZs2ZhMpl4/PHH+eCDD/63F0REG2wfIKdNVWEtgYvbZEcDIGS07GScW+5IWcE1GNp/Jf8zFcLpLyB+jHys2ktOrwJIGg+iBYqPy8c2n3JLLeNFUWTJkiWUlpZebvVa+XCtVWu71YKmzEBAo3bs2rGDLIM/SBKt3ZNQb7BCPpjWuLC3TSd61o7DhAeawaUo1gGD5QyxM8dDkICyPDW7P47jwLRYRMEVq0FufSTZJYrPFjtqng98d5QQt7O08TJz9MxQOiXtodkoi+zAVAC1QJoGllI4UNQOkyhHG+Kio+VJuNmMV0kJMRs3sjEmhhmTJhKxIIFemdtQCBJajRnyQblOJLLzQQK1ubBNotWOoxQM8CV08JcIwCshH2Cya3HJMHHOGMKyPRtQqPVXieXp9Xpyc3NRqVSo1Wrc3NwQBIHS0lJKSkpQKBQsXboUaIhVLAQkRz1FXHQ0yc2b0zI5+XL3rAL5Qap8a0RBILl5c0fkwqukxOFYXBnpSGzfHiQRAbBJGgYMGMDs2bPZsWMHe/fu5fDhw6Snp7NhwwZ+/vlnx+I0gL+/P02aNKFp06Y0atSIZcuWIQhCjaRMVeEsBr9JRFFk//79rFq1it27d5OWlkZJSQk2mw2A0NBQoqKiiIyMpEOHDrRp0+aqUN1fjb906VIGjRuHV0kJJn9/rKmpuLu7//XBt0pBItaNPVEry9n9a1fqNzvLhXNh1H9lGf4t/O/cef9HKCkpITU11eGApKSkcOLECVJTUx3dWkDuna3T6XBzc8PPz4/AwEBCQ0Np0qQJLVq0oE2bNv/zv39OnPy3UlhYSPfu3bHb7TRt2pROnTrRt2/fO5aKOW/ePF5//XVKSkro168fU6dOpVWrVrf9PPcclhKIf1DuKmUzXHYQ/LpBn503P57NAL82APOly9tU7rI2hzEHXOpAszeg4dirW+f+CVlZWSxZsgRzZYpR7z3bSO7Zgo2tBuHn58fzzz9fbf9TW37i4g9f4fHaLGJjY6moKKfv4c2035XIubr1qH2ugEO9OtHu++V4uLshWIupWNoGV202pgoN6cmh+Bwrwj2ugh3WHgBEE0cc0Y6aiQgSrr9NiKLWuHQ6dExFMRvYD3SE04+EMTvVh3pBo6ioqKhm85X1CsWennKbXTtovjbhMyiPJ8RFaDZYyegdwqKmjxOkO8+jny1GW2jB7KNBPdNaXXcE+PXiEA6XtUONCSvXdvAEQUChUPDcc89Rq1Ytjhw5wvr16yvneVVuw7XtG75iBS2TkwGQQkHIkIvdRWDjwIGOVLDqyu3tK8cUKSurICAggEGDBlFRUUFiYiJ6vZ7HHnvsup+F9PR0tm/fzoEDBzh+/DiZmZkUFxdjMBgQRZGvvvqKcePG/dnH6ZZwdp26TZw+fZrly5ezY8cOTpw4QVFRERaLHBfz9/enU6dOdOzYkcjISCIiIvD29v7b57Tb7WwbNYoO27ezt1s3rE8//ZdqnH8HaU19BMM5AHb/Gk39Zhm4uBlYt+5zntjx1/mKTm4NSZK4dOkSKSkppKamkp6ezvnz58nMzCQzM5OcnJyrVlw0Gg1arRYXFxd8fHyoU6cOISEhhIeH06xZM9q2bfvfX6DpxMl/GaIoEh4ezvnz5wkLCyMlJcWxSqlSqXBzcyMgIIAWLVrQv39/RowYgY+Pzy2d6/fff+fJJ5/k/PnztGzZki+//JI+fa7Vluh/HEmC0tNwfi1kr5FrHfrvu/lxjDmwKQJMOVdsrFzyr2oxO0wNQ0Kh7edQd/BNDX/ixAlW/PIzWoWJof5rWJ4jpzyHhYXxwAMPOFLfdk4YTmntQA7Z/ZEkiZ6HfqfrungEkcpJ8gTcVRWMfGQcISEhAJyZN4W66i/Z/GN/yorcGZP1E0IBFCPXCHhRQjGezEDW2RjPtD/dphhmwyOuQk6bqgCpFqx/6wGOG9ug0WgwGAzY7fZq1xeRkECv2FiQJGJ79yYpMhJBtKBVWmnnc5w+3htZbXmf41kgSmJl962d2MJUeGcUX9V16+tzz3LJcmWKVJXjcNmBAPD29uall17CYDCgVquZOXMmFRUVqDBiQwcITPr0U1yMxiucCNlhqK2+yNP1vuOMoSH1x2egN5kw6HRMnTIFQRComnabjEZ0Oi2iBAqFAl9fX5599lnMZvNtWVy22WxcvHjxjkUonV2nbpEff/yRZcuWcfToUfLz8x0TPQ8PDzp06ECnTp0cKVB16tS5IzYolUouDR9OnMVC9M6dJKrVmPv3R6vVXrVvaWkpkiRVLw66SYxh0zj+7UqaNluP3aagtNAH74Byat3ZNtT/8wiCQEBAAAEBAddUGJckidLSUs6fP092djbnz593PM/OziYzM5NDhw4RGxtb7TgfHx8+/fRTxo4d+09dihMnTv4GDzzwAGfOnOHHH3/k4YcfxmazcfbsWU6cOMHx48c5ceIER44cYc2aNaxYsYJnn30WV1dXgoKCiIiI4L777mPYsGG4ubld9xwnT57kgQceIDk5mTp16rBo0SIefvhhZ+OK6yEI4NlE/tds8q2PkxsHiODVSi4c92kPvu0hcTysOyW3mF1jhb4X4cx3N+1o1K1bFxelkQq7C+V2N2ppcimw1EKVs5Zvph1m0IMvExgYiGZ/IopXP0I6fgZvVT7RcXsQRDmdJz66C66KcvoPfZygoCDH2KXqIayZl05qchs8At0o6rwd3ZYS4szRaHVmoux7yAoLhlMSkiQQR7QjoqHQKrBbROKkym2KKGI2/+aozbD7Qk6fQM4bvDBbzFgsFv647u3m5latZiNm40YAkiIjMUka9hdHEdzhGYa1H0L+t9+Sk3OBpMgIjnVoyaQZUxEKkR25PsBWWeDw8ZiFbCvoc0U0IVJ2ZrZtA0EgtlcvLkb7Y7SpmDFjBjabjWHDhmEymQDR4WQADlE+k05HUmQECkQkIN9am8SV7Wm+/QSpVyirq1Qqunfvzq+//sqiRYsIDAxk1KhRdO/enW3btnHfffeh0Whuqn3wn6FSqe6KNEino3EFubm5PProo2g0GiIiIhg1apTDqWjQoME/1m2jtLSUc+fOMbjyl6v91q3s27eP7t2rCwLl5eWxaNEiQkNDGTFixHVG+wvKz2FN28jvX4dSoowk0nCA/dqObHaLwa7IZ9BtuB4nt4YgCA5V2GbNml13P7PZTE5ODtnZ2aSlpTF16lSeeeYZ3nnnHebOnXtHo2FOnDj5e8yZM4eVK1cyfvx4Hn74YUCeIISHhxMeHs7QoUMd+5rNZpKTk0lMTCQxMZF9+/axfPlyli5dikKhwM3Njbp16xIZGUlMTAyDBg3CYDDw0EMPERsbi16v55NPPuGll15Cr9fX1CX/bxEySv53FXZZt2IdMNIT2n0OYTe/OJSwZysVdvm9PFLamuF+q9GrjLgqK/g5ZzSHDx/GtLkfipJsjp88SuekQ/TeE0tOvQBcbQbiu3XjTN++NAgKonmLltXGVrt7kpEWSd/PetD+ufYoztVmx+R5XDwcwOiWy3GNM9Ak4iQj+q0gL/h7Lu1oRNZDH5P3zWHOl9YmsNd+Di31I67gOSpcK+hXLNd/i2oo/ULJDxlPYbfI09BrJdeMHj2aU6dOEXdFilKVUrhGo2HIkCE0ad4cgIceeoglP/6IR7YnBfsKEIZKSOtAGgyiqESx1o6iAOxrlURL8tyqaqzouDhcTHKBea9t25gaOQXMNqAEtVrN2lXLUNorsKMFBEfaU9oVTgSAh7KYErsnEiqabz+BV0kJICuru7u7U9/fn7i4OL744gtGjx7NsmXLHNfapEmTW45S3u04U6f+QGBgIFqtltTU1BrptiGKIrNmzaKoqOgPOXxyEXnjxo0JCQmhrKyMZcuWUVZWhre3Ny+//PKtndBwATa14bspQxiZtsIR4vxKOZHOk6Pp/VHv23uBTu44drudn376iX//+99kZ2fTsGFDfvjhB7p06VLTpjlx4uQKDh48SFRUFB06dCA2NvaqJiE3gtFo5OjRo9Wcj5SUFEczkqqIxYsvvshbb7313ymqdy8hSXDySzg8GTnS0VJWFncNuaXhvvzsPdS2PPrW+p1AbQ7uqjKEbSCuhT1dO7O38zBe9n+DI5/YaT5Fj/4VE4qCyzUFbm5uvPrqq9ccW7SJiHYRlVaeC238ZS4DLC/IytNVgn+1QJoBgrY29NlBWl4znn0WJjy/hV0ntnDm1DTanazDy8m56EUrZRo4EQ4dLyg4Gdis2kT9yvkOwJNPPknt2rWZOnUq7Q4cqBZ1ON61KxMmTLgq00OSJOwWOydWPkjmJSOZpvrkW2rRLiGp8nhIr1Qb3xPdmQORHYhISHIocF9ZSyFgAwSkSg2MiIREem3bhs5kQoGccvbVxHFoBTNGqarb0+X6jT9eD4C7uzsrVqwgKyuLrKysW3rP7xacNRq3yE8//cRDDz3EDz/8wOOPP/6Pn1+SJA4fPszGjRsdBeZXolQqUalU1cKMerVIj7qp7Mptx4svvnjzK1WZqzj/0785/k49OpgPEEc0qUE9eeHkOLTuV6drObk3sFgszJ8/n3fffZeioiJat27NkiVLaNq0aU2b5sTJ/zzl5eXUrVsXnU7HkSNH5Hakt4mKigoOHz7saK3+7LPP0rBhw9s2vpNbxJQLu0dC0RGwlYFSD8NzQHPrqc+Hty/Cw3iAoMJv0Coq+6pWOgFl3u4smvIiY9w/ofATaGgAKQzKTnqwO7orR6KieOqpp244DTwuLp4Tu37gmZBvEbYhR2MGg9QbRF0Q8bUy+OA/KmbOKWbMqh4cNR0l6pzEklVQX17cp9BFiULtJqt9C4KjkBpwdGGyaLXERUdT9MADhIaGsm3bNgBHTYRBr2fm//0fjz322HVb0u7fv5/t27djs9nk2kallcc/+AyvklJKPD3Y/NYAOmrjSclsxF4xmnanD15deP6Huo3x06Y5ohSiIPBbTD8UfUROVzSm2OZDdVG+a6PT6bBarbz77rsUFhbe03PqG/UNnMmZf2DMmDEEBgby7rvvXnOif6c5e/Ys+/fvdzgRWq3WkbIVkZDAuM8/p0VcXLUwo9GqIDvfiEKhuGYdxx+xm8s5tv5tLGazvKHeCIK6dSI7JJgZTOSgEEGLh1o5nYx7HI1Gw7hx48jIyOC9997j9OnTtGzZkl69enHhwoWaNs+Jk/9poqKiZIXhNWtuq5MBcp/9Ll26MH78eD799FOnk3E3cOE3uSg8Lw5+K5Odge2ay+1xb5E2PR+jQcwsNAMPUWL1kMsGBoPoK3CwWxse9Z9FXi7UzQUhH4Q00M8ycqlfBG+88cYNOxlbtsDo0V1ITWlBkdVTrnsYLNc9WH5XsajkKLNmq3jooUUM+Kk1R4xHkJA45g/7guRIgUUhkNQkhLjoaIo9PTnRsiXFnp7ERUc7tiEIjrSmM2fOOJwMwFETgSRhNpv58ccfr5lyBdCxY0e8vLxo0aIFr776KkNHPkze02Mp9vRkd3RXTlY0ZXHhExyUIlAvNpPUOpLk5s0RBYHzwXX4o5MBkBkcLF+HWs3GmBgSIztwoCSKYpsv8nRatiUiIYFJn3zCpE8/JSIhodoYNpuNDh06YLfbmTNnzg3d+3sdp6NxDb788ksyMjJYvHjxP35uDw8PiouLsdvtjhZrLi4ujjZqXiUl9N6zjdbuh2igT8NXnYtKsJBa0Qi73U5eXh6iKP7pOUrP76dR8WcULgsn6+QOeWOH+Qx4/HfUWjM6VzPdpkSQkpJyVdcjJ/cebm5uvPXWW5w7d44JEyYQFxdH/fr1GTlypNyD3YkTJ/8oY8eOJTk5mdmzZ9Opk1Ov6H8DQdbLULo4hPBYWQbC35+G5eTkMG/5LjbkDqLA6oO9t4Bpmg5DdxfcVeW4xIHSDrjKNQsCImG1jDfUCODiRYiKgqefhqVLYdx9azDYXZEkkH6WnRfNChvNy1/lp5+ge/donlGOpZ5Qjxf2Q/Ic6HdGnmwadRpSg9yoeOQh1OfP47VhAz/+5z8c69IF90mTmPnKK8T26uXQlRg/bZpjoh6RkACCgEGnI7a3nNJtt9spqYwwXIunn36aYcOGoVQqqV+/Pi6vvMK8N96oTGUSsKPG5OaK5TEdSCJh6ekoJInQ9HOOc46fNo2n58/n7XffpUVyMgpAZbVS79w5Xp42k3YJSVx2SCQiEg4Qs3EjLiYTLkYjMRs2VHM4bDYbbm5u1KlTp1qNhslkqqaH8d+EM3XqOtStWxeVSkVqauot5c3eKpIkMXPmTIqLi6ttrwrZiYKANNYdoUc5FruacpsbZlGLDSVahRU7asrtbpTbPSi3e2JVeoO+LhqfJnjVbUfdevXZOGY+qsLDRMXswadBMef1/WnY9xPU2zoy44WH8apdzONzSvjqaG9sNhvdu3ZGLDhEwpE0wuu6EiZughb/R2Kaifvuu+8qJUondzdZWVm89957LFiwAI1Gw2OPPcasWbNuW6cLJ06cXJ8ff/yRxx9/nCeeeIJvv/32H2sy4uQuQLRB6lyY9g6sLJKLwQf6g1dzCHlQ1tyoOwTcQm9oOIPBwPr168nMzKSiogJ3ZQmPB32HQgBPdRknSptQavek3b/3oyuTsPsqyFw7B8m1ISrvRn/aCl0UYcIEWLwYXn0V3npL3l6w7TnOZV2ghXcG6oePIVQArsB8wL8fx1SPcOnwQhJyGvHc9Ll4m6FcDQUucLpROLZOTYmZ+avjPDabjWXLlhESEsKePXsquztdnvNUpTH98WeQmyY0bNiQBx988IbuV3l5OfPmzaO8vByFQnHVouykTz7BpbIVbWzv3o5UqqtjGzjSvqpE+MLS0+WIiyA4Wt7C5dX8Ek8Ppk98BUEQ0Ol05OXlMWfOHBISEjhw4AAXL15k8ODBNK8sbr8XcNZo/E1WrFjBqFGj+O6773jqqaf+kXNu3LiR1NRUysvLq6VtKRQKnhFF9NOno33nHXQvj4Nj7yOlf4NguohNUrAiZxRi4BAeenC03Pu7+Cj2ouOU5Z7GXJKFZCkEuxGbqGDLF73JOh6CztWIzsVEaIsztOiTjOBnQ1gAQccvoBqtYXPr/uwvaANX/Mr0bGylvXUaoqTgaFkrDpiHETNoKI0aNaK8vJzDhw+TkZHBI4888o/cMye3zsmTJ3nzzTdZvXo1Li4uTJgwgQ8++MDZ7tKJkzuIl5cXOp2OjIwMdLqbV4R28l+AzQjJH8KJj7n8/Vo5lVW5wsATstL4X7B582aSkpIua65gpJVHMsllLXn81ALcNhmJ6xKNa/Ep2iVlEdcvhuZfffWXWksrVsCLL0K7drB8OVxr2mdZ1xHN6gPwc+WG0XKtxt6ijnhpz/DAoTx2fQ/eJijUCYRP8eL1lFa08fel35crATkiUVhYyPfff39V9sQf9TPg6mLxsLAwHnrooRt21iVJ4vPPP0cQBMLDwzl+/DhWq9VRuH2lOndVS11REJAApSQhAja1GptSSVpYGC2OH3c4G4rKqfSVNSaAo4D9TFgojdJTEJA4PCCCuE6DyLxQiI+PD6IootVqefjhhwkO/uv3/W7B6WjcBoKDg1EoFKSlpd3xqEZJSQlz587FbrdXczIEQWDo0KG0atUKk8lUvdC7/AzEPYhYfJyluWPxbzqIvn37/ul5RFHkpz4vkrY9oNp2QWHHM6CY58vmoimzIdUCabpAUnEEhTYfSmyelFg9CfAU6HdgIdr1FuyDBAq7+7Im934KpfoolUoMBgNeXl6OLliiKJKVlcXBgwcRRZGRI0fevpvm5LZw4MABJk+ezM6dO/Hw8KB9+/Z07tyZmJgYOnbs6HQ8nDi5jTzzzDN8++23zJ49mxdeeKGmzXFSkxx6HU5+Vn2bUg8jC0F1Y07o0aNH2bBhAxaLhTqaLEptXlSI7tUiAHNGRlM35wyjVib9abOYjAwYOhTKy2UHo337650TQhK98dQVI42X06fsvvD4v2F9qQcNc0rplgEf7NaikASH0J4+ZTuNPV0YOkfWw/jyyy+vKdJXxZURhqlTplR7rVatWrzwwgs3HRFcv349HTp0wM/Pj8WLF3PmzJlrRkuudD6qUqaq2yERkZDo2CcsPQ0kHNdatQ9IaBUmXvxyDu7F5QAYvXVk9KlPnS05DsfJzc2NsWPH/i1NtH8ap2DfbWDatGmMGjWKhQsXXlP8bN++fbRo0eJPRZJulC1btmA2m1EqldW2e3t706pVKwRBuPoPhFsD6L8fReYvjK7dD1GhQxRFClMLOb//PJeOXCI/JZ+ScyUY8gyOMKHetfo5NG4Q2vQcw187jGZvY1ieTdnjMSzI8EMhSHiqSvBUlRDmmkawKgvNegvkg3K9RO2++Twe+C1ZxmCyNwTTescR8sLDsX76KUl9+7C7WVPMZgt2UUKjFDmdXA+1SkmDJu3+9j1zcnvo0KED27dvZ+vWrXz66ackJCQQGxvLf/7zH1QqFR4eHtSrV88hDOZ0Fp04uXW++eYbEhISePnll2nRosU1xTqd/I/QZAKkLwBL/uVtEpD5MzR47IaGCAsLc3y3G2x6DKIrIDlW5eOio1Fqdfi4u13XybDZYOxYWLsW3nkHxo+//vk2boRDv/7CG92K5UyhyoLwTzqrWFJmA0o5WAdWLQM3o5liT0/HxFshiuQWFTFg8QCC7cHULbtaTO5K8TxVlQPyB2fCxcUFk8nEpUuXCAgIuGqMP2PQoMvqYKGhoZw5c6bavaqiyuaYjRvltreC4IisVBpFUmQkSZGRKLAiIKLWumMym1AJdiRBSd26QZzLvIBZdGFnl+70io1FEASU735MyH/+g8sVWh6CINwWNfC7EWdE4y8IDg5GEATOTJ6M6vPPYcoUKh59lKysLFauXIkkSSiVSkaPHu3o7GGxWLBYLFc5IKd+PcXFQxfp8W6PatvPnz/PDz/84IhkVBWBKxQKYmJiaNOmjWNfURTJP5nPhYQLlx2JzBKM+UYksbJTlYcW9yB3fMJ88GvhR2D7QALaBaBxkXPwEz97gS3v1sZmVuJRz4OBcwYSfl94NZvOnj3L0qVLEbBhs8lqly5KE0NbldFozVxHW7vyHi6YRS1ZxrqE/fsMbsUVSAoQRKjwcmHahAnYqcr9t6MAWtUpYeizM0hMTEStVtO6dWskSeLUqVPO1qt3AZIkkZWVxZEjRzh8+DCHDx/m4MGDZGRkAPD888//z3TLcOLkTmAwGBwpEocPH76n0iWc3EaspbDCF6Q/dLj0bgP3HfrLwwsKCpg3dzY2u71S6+HyKntcdBeSIjsAoLMUEpW4mW6bT101xvffw6RJ0LOnXI/xZ9l8X30FSUkwb64F9b6hcGEzCkGiSxbsMV3e718J8OE2UAoKNvWK5pdIH1rTGrekn5nRLJsLegP++PMsz1YeIVdBRFwhzAfV05Cu1KIYMGAAmZmZ3H///X+rxun8+fMsXLjwmkXYV9oiCgIbY2IcDkH9+vXp1q0bixYtqux6JSIISl599VWWLVtGly5dSExMJDw8nNOnT1NSUoJOp6OkpISxY8fi5eUFX39N6ZtvsqlVK1J69cLT0/PW9dBqCGfq1G1i9erVjBgxglIfH9wLC+Uw5OTJ2Gy2am3VPD09GT16NIGBgSQkJLBt2zZGjBhBo+Lp2IxlrJsdhbJ8J749cmj3wlr0bl4AXLp0ie++++7yB10CykEoFVCXqgkuCZYdiQJjVec0tJ5aPOp6yI5ESz/qRNQhsF0gKt2NBaiKM4qZ03IO4feFM+S7IddsY3v+/Hn27t1LVFQUPi521v44lfQiDx5snESQdQflNldSjc05JfbCPaAlWVlZhMfG0mXXLsqbNcDn1CnKY1yp6OFGjrkOKeXhnDOFoFNYeGhwJMFtRvLxxx9jtVoZ0K8XfqbtLIlXcP/999O4ceO/85Y5uUOUlZXxwAMPsGPHDoqLi53F406c/A1OnjxJ27ZtadKkCXv37nUqdd+FGI033jb+lpBEOPIWnPoS9HXk2g3zJRCUEJMMnk1k7Y2sVRD+HCA7pm5ubgQH1eFowjayEr/HT5NHiD6DAO0lzC9pcSuuqCw+nuhwPHL1JoLSChzNW44fh5Ej5WDBqlXwZ2t8NhtMnAh+fnJRuCAAkoS03AXsJo6ZoHW2vO+/EmDWRlBJkOEJLSa68lwCvLfNhgUzG8OgSxbMjFZTL+oplIIGk11H0/0nLk/sAZNeR2yv3iRFtkctWLBKl98DFxcX7HY7Tz31FH5+frd++yWJY8eOsWXLFioqKhzbr+VkJEdH4+7ujl6vJzQ0lJ49e/Lbb7+RmJiIm5sbTZs2pX///tc9V2lpKaWlpdStezmKk5OTQ3BwMK+88gqNGjW6ZubM3YzT0biN1KtXj4fKyvjYw4P9PXvye8OG12whq9FoaNCgATabjbS0NPR6PY/7/4Cf6iQmu450QwO25vWhX9RjGM5IJO9LJjMzE6lYgjKq0vlQuCpQ+agIqhdEk8gmckSibQAqze3JdJMkidxjufi3urne7SXnDyIZ81CqtWRX+BEYFOTIJ7RarWzYsIETJ04wbNgwAtRZfLd8GzZJSWv3wzRzO4mEQIahPln25jTq9DBbfv8dSZJo7HqS0XV+5kBRJPvtD/HCCy/8o52+nNw4Bw4coGPHjkyYMIFp06bVtDlOnNzT/PTTTzzyyCP861//Yvbs2ezYsYNGjRoRFBRU06Y5QU5pPnr0KEOGDKFRo0ZYrVZOnTqF3W6vlmnwt5AksBtB5QJl6ZD9q1wkvk0Ja0UYZIX+ehguax999913eJbuILUsmFEBywjdl4lynR0GQ2E3L/as6ELXuN2cHDaczaGhjJ/2JV4lpRhVAt9+8n/0iO7Ku5/1ZOdOBV98AX+lS1xaKu/z4IPwwAN/eHH7QKznN/OfrG68b94OApydJovzScDPLRRsDxEdjgeAHVACBTp4espQ2tIWuCzGd1mZuz1X93qqTtV7MHjw4FuqJUxJSWHlypWo1epqjsaVXT6rIhnPP//8NZ2aEydO4OHhUc2BuBkCAwOpV68e+/btu6XjaxKno3Eb+fXXXxk2bBjz5s3jmWeeYdu2bezZs+e6QjHtExOJ3rGL/eEdCRBzCDuVznG/5qT6NpKTh1wVXNQH0jT/GNGpccR1iiapSyR169Xl0UcfvadXirOzs/H19UWv11Oavpnvl2+m2OpJVUFU39rbCdJmUm5z4UhZa1LKmzCqzs+EuaZjE5XsK+5EUcjrDB48uKYvxcl16N27N/v376e0tNRZKO7Eyd9k4MCBbN26lTlz5nDx4kXatGlDz549cXFxqWnT/udZsmQJaWlpaDQa1Go1CoUCg8FA48aNGTVq1HWPq5ob3HJaj90EQd5wyQS1gHl1QB8ESg1zTg7BWnqeYtEHd2UZL0+fjqpARPKFn6eMJq0inIjAi7Qa+D4LFnxHt8QtRG+Nw24E28NKDndoz8LkX5g1KxjVX6xdZmTItRv/+Q906gT7svbh4+JDuE845kuXqJg2kvgADetLG/KN9B3/SoQPY8HLeNmZ8DTLToYdKNaB1g5uVrnlbb4L/BbdhkuRw3jjww/RWK1Y1Gq29Ot3VYepayEIAi4uLrz66qu3dK9FUWT27NkUFhZW2z58xQpaHD9OcvPmrL7/fgCeeuop9uzZQ9u2bWnUqNFNn+t659fpdAwdOpRffvnltoz5T+JUBr+NDB06lHr16vH+++9jtVrp2rVrtaLtP062uuzejWd5KdEpcTTNOIWL2Uh7QyIPtV3KI9k/8lD4YroM3UH02Ti8KkqITowDhdzj+V5fya9bt64jBcCjYX/GPjQAT1UxnqoSREmJd6d3CXgyh00lj6AW7IwIWEE9vSyOo1LY6eB1AO/sGVy6dOkvz2WxW+7otTi5Nm+//TYVFRW8++67NW2KEyf3PLNmzeKpp54iKysLm83GsWPH+O6772rarNvL119D/fry412EKIp88803ZGZmOrbZbDZKPv0US1AQ3suXA3LdZUVFBWVlZdjtdmrXrn3VWGVlZSQmJvLDDz8wdepUzp49y4ULF9i/f/91FyWvSdkZ2NgGBlpkJ2MwYDeAvQLy94AhEwtaNIKZSM/9FA/wxOStwTzEjTRDGEPrbmVA+EUuHPyRl+tNJWLEQRR6UIug/NXOBWsD5swJ+ksnY98+2cn49lvZyQCYvHUynb/rTIOZDXj/xZa4TNtLg1kpfPzZDyxZIadM+RqhWC+nTSHIToZNgIkDVbR4Hl7rJ79mVsmRjwFxh1nCEqic+tiUSkdr2V7btjF+2jSGr1jBpE8+4Y0PP6wmfidJEhaLheXLl5NQuc1ut7NgwQJOnz79l7daoVDQt29fVH+4GVXCfS2Sk4lISEChULB06VJOnTp1W0X1cnNzsVqtt81xuVtxOho3yFdffcX58+dZsGABgiAwaNAgYmJi0Ol0cmEPoNPp8PT0ZG/37hR7ehLXbwClgzygFkhDQFwrIORD+Wp3fi8YwP6ePSn39eVQ//64u7tjMBgoKCio2Qu9zbjW78cLzzzKCy+Mo2+Tcuq37Cf3zq5QcKi0HUdKW1fbX6Ow0iVhG57NQrDPnl3tNUmSOJhzkBc2vEDYzDD6/3j9fEgnd47u3bvTsWNHZs6c+Zcq9E6cOLk+ly5dYu3atQQEBDhWZM1mM4WFhRgMhhq27jbywZtw7hx88klNW+Jg1apVfP7551y4cIFFixYxY8YMPvroIz788ENcZs5Ec+ECXXbvvuaxBw8exDR9OkZ/f3Y9/DDTp09n/vz5bNy4kXPnzqFUKqlVqxZHjx5l27ZtLF26FIvlxhbGysorsKs84T5vmAH0AawliKVnkCTo5r0DjcJCywPHaPX2MfYXR/Hp+DfIGvMavTrUJ7zTIyRlupCaY6fC5oqL0oBtkBKLB6TXE7hgqs2CBQsoKiq6rg3LlsFnn8m1G/XrX96et2cgBcYCMooz6JeUj94q0fTMBXwNdh44fjly8XYvgeYTXfiolwvnPAXGxcCC1jZGpOuYFwmhE+GDPrLD8Uk02FSp0MqGpIC0sDDioqMp9vQEQcCrpIQWx4/jYjKhsVpxMRqJjotz2GS1WklJSXEI8C1evJisrCz27NlzQ/e7cePGVzuOlY6hAlm7QxRFh5Dgxo0bmTZtGrP/MD+5Faoc3FatWv3tse5mnI7GDTJkyBBCQkL44IMPkCSJ1q1bExkZyaRJk7BYLNSrV4/Ro0czZMgQzvbvz8FVq2g9dy660eGUfeHOgYiObOncl2JPT3ZHdwUgMyYGt/x8ei5fzosvvsjDDz/8X9neTOPXDo13QyJHz0ChVCJJEs2aNaNfv34Mq7sVjcJWWZ4iICHAOtAVmpEmj6fQ24PZw1vR6uNW+H3oR/dvuzM3cS7pRemEeIbU9KX9TyIIAm+//TYlJSVMnz69ps1x4uSe5cKFC4iieM20j4SEBHbs2EFpaWkNWHYbkSQYUApBfvAHLYQ7gdls5scff6S4uPhP93N1dXWIxNntdoqLi7Hb7cTExKB++22kkBAS+/QBQKlUOt4jrVbLgw8+iHb6dPS5ubTeuJGSkhLKy8sdkQuLxUJubi7Z2dlYrVbS09OZN28eubm517VHkiSOHj3Kl/NX8XHSYErb/ABelxfiBMnMwZK2rLk0HLOoIzouDs+SUrrExQNw6FgqUQOeQdvsRfZk1+HshVK+PfcUs86N47PQ16n4UkeJHQotXpw/f56vv/6a8+fP/8EG+OADiI+Hn3+uLtRnNBrpW98LnahHYYf1jaDjWPi1iRyxECs/wsV62BHZhG7e3fi2k4r6EyXmRcKjOX6Y+/dwjPfSCDj/OXhGd+dAkBeaNAlBhIbn00mKjGTGxInE9upFsacnyc2byx2o1GoMej2ZwcGMnzatMrIhEqTJIOjcG1Qsrc0AcQrBunPk5eVdJQJ4LQRBYODAgdXe49jevTHo9Rh0umotb0HuGFdRUUHLli3/cuy/osrRaH89wZL/Epw1GjfB+vXrGTx4MHPmzOH55593bLfZbFeF3qqw5h5k/pJNlJoUBAQE4KoyUlihIC8vj3bt2jFw4MB/yvy7k22Vfal9OkKtSPCNgkVr4JNPMOfno62o4JwntJzojuoPsi/eCm9+GfsLbfzbOGsF/mGqnO0LFy6Qn5//1wc4ceLkmnz++eeUlpY60nGrhMLiu3YlsX17unbtSkRExD0l5FWN9AWw/2kIHAI9fr0jp0hNTSUnJ4du3bqRk5PDwoULUavVREVFERUVRVFREYmJiSQnJ3P//fcTGBjInDlzrlrVb9CgAY8++iggOwtfffUVYWFhdOrUiZKSEjZu3IjJZOKll17CdfFi+OQTxMmTWRsYyOnTpx2r3nq9Hg8PD3Jzc6ulTWm1Wnr37k3kFXUHdrudpKQk9u3bR3l5uSM1x1WvwldXRm+XH/HX5qEUbHx59lWMoisgEpGQVE37oVv8LvJCg6h99gK7ukQ7WtsCeKqKmRA6nT0fCmwZ9ioermq69B5Khw4d5Ml1WRq2MysYO20KERHw0kt/uMGSRHlFMS+90Z9FPgkoJLApq3eYsgPZnrC8ayhPfX+AWrVqcSL3BM+uf5ZDFw+RdESk+LX/Y/iWt4gUo1gacghXZWXUbhsIlQrjxpE6Zoa9jEm8fo1SVYG75AvSDNk5EJAQBKiw6fkueyxFVl969+5Nq1atyMvLIzAw8Lqd3fLz85k7d+41I/RVv49X1ot4enoybty46877bpQvvviC119/HYvFck/OYZzF4HeI0NBQTCYTGRkZN9zybt68edSrV48BAwYgCAJ2u52NGzfSsmVL6l8Zl3RSDcvMmRjffZdvOvsyJfIsdi6rhypQ0FzTHIu7hSJjERISnjpPwn3CiagTQY/6Peharysa1b1bWH+388svvzB69Gi+/fZbnn766Zo2x4mTewZRFJk2bRoffvghRUVFjBo1iubNmwNcpVKsUCgICwujf//++Pj41LDlN4m1HDY0B0OmXMw88Dhobq/DZLFYmDNnDjabjaeffhrT9OnoZ8wgLjqaQx07olAoUKvVGI1GXFxceOKJJ9i8eTPp6elXjdWzZ88/FVA0m83s3buX7t27XxWFqmqTWl5eTuvWrenfvz9z586lrKzMsY8gCEiSRL8zZ+i4fTt7e/Rga6X+FkCAOotcawAiV9ZqyvH+Xoe20XJHcuWEt3pHpiu7JCkkqZrCNYCPKo9n6n3LlvU6MiwN6BDjS5ORy/DSVkDiy9jz9lFYpCYhKIuYmKuvO2HDDNZOe5UEfzs7Q8GkhH8lXXYyRGBeJz2/9+/Pty9/d9Xn9Nf1X9D/wutovVww221oJCOCdIUO33ggHznNfDqkVDRiWc5D6PV6zGYzoigSHBxMVlYWKpWK1nv30i1+J25NyhHSQAgHUoHBUNTdk5kZExz3R6/XO1rh+vtfu9OmxWIhNTWVM2fOcOHCBcrLyykvL692bw16PRaNhrjoaE52786YMWMcjW9ulfHjx/Pdd985znWv4XQ07hAbN25k0KBBzJo1ixdeeOGGjvmziIeTPycxMZHNmzfzve170rn8xeCCC8eeP0YDvwZAZVHfmm8o9CrkWNExTuSd4GL5RSRJQqvS0sC7Aa39WxNdL5q+DfvipfOqoSv678Fut9OkSRMqKiq4cOFCTZvjxMldjyiKfPbZZ3z66acUFxfTpk0b3nvvPerUqcP+/fspKSmh9d69DmXk2F69HCJhLVq0YMSIETV9CTfHgX9B+ncg2UFQQct3ocWbf2vIwvRCfhr0E0qtEoVaQYmxBIPNAGZAB6/lzsK1MP+qyTZA56NHiY6LI7ZjR1J79UKr1VJYWIjdbsff35+WLVvSuXPnW7atoKCAxYsX07t3b9zc3Pjpp5/QarUO7YWWLVtSp04dFA0aIGRmUuzpybxX/oWbqox8qz/ylF3gshMhVT6XmDBtGp4lpRR7ejJz4su4KAxolWYsooam+07SJS4eQ4N6uJ45x67obn/o1mSjkS6Z8xV1iFq6gPb/ZyPZ0J62tbNQmOW/3YWK5lxs9TPNmjVzHHXxInz0xjr0wQt5YeZKPE1gUsHUzvDSAbmY2yZAkRZaTQlgvHY8r7322lXznZ3D29F6jA4v295r37itwDqwDlJQ1t0Ti6jh+wvP8PLEKRiNRn766SdiYmJYs2ZNtUm5I7JRKRJMLSj7wo3cjbXx/a3QEYXQ6XRMnjwZQRAozEmjoEwkvFEjJEm6ZtpicnIyK1euBC5HNDRmMy4mUzVdDZDFA2+11fGwYcPYs2fPn6bU3c3cqG/gnP3eJDExMdSvX58PPviAp556Ct2fyWhW4nQybp2IiAgOHjzImEtjmCPNoVAqRIECN9zoNbcXIxqOYOqYqeTn51OcWoynuyeLnl2EHTsapQZBELhQeoHfz/zOnqw9TNs3jQmbJ2Cz21AqlNT1qEsLvxZE1Y2iX8N+hHg56z5uFKVSyZtvvsmTTz7JypUrGTlyZE2b5MTJXYkoinz00Ud8/vnnlJSU0K5dO95//31iYmIcE5327dvz0UcfkRQZ6ei6E7NxIwBJkZH3Xq1GaSpkr5WdDJDVr898D83/fcVS9s2jr6OnILcAqfAPa6RKYAgcbj+A5uvWXZVbD9Bh+3b0RUUMPHoU1q4FICsri+PHjzNgwIBbtqkKX19fXn75ZQRBoKioiIEDBxIeHu5oVVxRUcGmTZtQtG5N55IS4qKjMUtaTFYXZKficvpM1QQ3MziY+tkZuPpUIJWBS5MKBvutZVPeQJ4JXoRSMnC2diixg56gZbNG/LDzPDqFGZ29ApPkAggICJy3NKDJwVO0zraSuVhNk0ePkZTTGJ3Kk31FXSizeyJkLKFZxkZsxalcLPKluMKdoS1qsyHhDD5GcLcAZln1+4Me8EwSHAiCvpVrgNdK/ykvvIjL2Wy8opfCjr7yxkrHgsHIxe6V/8osbnx1bjxqtRpBKZCVlUWTJk14qTKXy93dvZqjERcdTXRcPKpGVlzPGJAGw/b87nT7Tf79iY6LIykyErPZzPbt2+kc1ZGVC6dSQh06RfchIyODRx555Cqbmzdvzvr16zGbzSRFRpIUGVlNxK9qXB8fn79Vq3H27Fl8fX1v+fia5ty5cze0nzOicQts2rSJgQMHMnPmTMaNG1fT5vzXk5eXx/r16wnuEMzwFcNRCSpOjz9NZmEm/1r+LwrMBXTUdaSpqSlKhZJmzZoxt2IujXwbMWfgnOuOW24pJ/ZsLLvP7ebQxUOkFaZhtBkREKjtWpumtZrSIagDvUN709q/9T2ZQ3mnsVqtNGjQAKVSSUZGRk2b48TJXUdycjI9e/YkPz+fyMhI3n//ffr373/NlVSr1crSpUvx/eVnYjZuQiFJchGsVkt8dDQFo0YRM3AgtWrVqoEruUl2jYDcnSAowFwgRzQEJXRbA4F/r2PgjCEzKF5XfHmDFugNdJAXQHQ6He7u7rRu3ZqSkhKOHTtGRUUFPU6dovvevXJR+nPP/S0bboTi4mIqKiqw2WwkJydz5MgRRw2Gp7KQErtPNYciLC0NBIG0hg1pcfw4CklyrNY7HmvB6kkjmLPlfSJ9dvPuyI/Q2rOg+dvYw57HdDEBl9pNydv+CguOtsAsXl4MnfzJx+hNZmwC2L01iIMUfNVwHEbJDQAXrY6Th1rwdo+XsCqVZJuCCdGfIf8/WXTMvFy/IAGH/KHHk3ByNvgaoNmHocxsPpP+/ftXa9N/7P3ONG94AIUgIUdsqJYqxYzL96tAqsfx4MUEBwfj7+9/lZbMwYMH2bBhg6OWQqfTIUkSZrOZgbV/JcLzEEsuPYtX7Plq9StVzwu6+ZJjroNZ1KNTmDCJOpQKBS+//DIeV9RAiaLId999R9u2bQkLC+Orr75CFMVq+hprRo3i+eefv2ar4xvFy8uLyMhIfv/991seoyYwGo0sWrSId955h0uXLjlTp+4UDRo0wGAwkJGRcUNRDSe3h8krJlNuKmfOI5cdiG2HtjFh7QRKKaUNbQhVhrJItQiFQsH3Q7+nNLGUwMBAunbt6oguFV84we5NP9At5gk86zSrdg6baGPP7hkcPn+ObcI5ThecvqoOpG1AWzy1nkzoNMFRByJJEunp6TRs2PC64kE2m42tW7cSFRV13eLO8+fPU1hYeFu6WvwTzJkzh3HjxrFo0aJrrg45cfK/ypw5c5g4cSIeHh4sXLiQ++6774aExeLj4yn55D903r0XjcWCi9FYmQo0gYYNw+6N3zO7Gcz5si5E3EjoMB9MueAXDZ7N/vr4a5C9P5tNL22i4EwB5gozmJDzMlqC+yPu6PV6RFFk4MCB1eofc3NzWbFihaNG458gOzubtWvXkpeXd9VrPQ5to8vv8VglDRKCrIhdWV8BOJ6LggAdJRRpQGUdgmmgmnd071Gr4etMmqQAc6FcbN9wLARdbi5zdtcXLNtVhsV++fM25dOP0BotjoSsMi83Fk16jHyrrHitV1TQw2cXvxf0IV8qIJmTdI/bw5RtVoeyN8jpUmYlWJWyIJ+oEBi9ZDgrH1xZ7TovphykcFxPmj5tQ7AbLkcyrqipoM8VB3i1gpgj172nFouF6dOnYzQaadasGSNGjCA+Pp6dO3cCENFQT/Muo1ixYsVVNRbFnp78MOkJwvamER0Xx97oTiRERiKhxNfHB1c3d4qLihg0eDDh4eHVzrto0SIyMzMZ9/nnjlqYTYMGMWD16mqaajdDRUUFbm5uPPfcc8ydO/emjj169CihoaH/eJfSvLw85syZw6xZsygsLGTw4MH8+uuvTkfjTrF582buu+8+ZsyY4QjrOakZFi9ezJkzZ8gjjx3soIQSBASyySbIPYhXVa9SXlqOm5sbLcP9yE5JIN+gxmJX8eQDffFv1INfT/9K95DuaCUtu3btIiTzNerpzlCgbodv/8W4+oQiiiIn80+y9cxWpu+bTmZpJr56XwRJIJxwulo74yJqqdcVhnZ69Zp1IEajkZkzZ6JSqfD396dv375XFaj99ttvHDx4kNDQUEaMGIEoipw4cYKTJ08yZsyYW/7DdqcwmUxERUVx/PhxVq1axaBBg2raJCdOahRRFBk6dCgbNmygS5cu/Pzzz9SpU+emxji2aACrznYiIiGRXrGxIEnE9u5NUmQkAQEBDB06lICAgDt0BbeRgoOw9xEYdOKGDzl37hxr1qxBp9Ph6uqK5YSFvJV5IEHQg0EM87iI/a2P2G3uTFJwJE0+a8IDDz7wp2OKoojBYMDNze3vXtF1MRqN5OTkoNVquXjxIuvXr79qnz6Hf6Pz2n1yTQE4IlbVIxoNaJCdgXKoDX0/c7XjLaKafUUdce08nYiIiOvakhy3jJXbTuPi4oJeryeisS/W2XNpteMomUF1aHT6FFv79AKV3hFRqZeVxZ7oKP4VeZACyjBipOQj8LDIXaUyPeF0LThYV43KZqXfL4doU6CCrVthwoRq55dEkb1d6xP07iuEmOdB6amrIxkqdxh2How5kDoL3BtC4/F/eo9/+eUXAgIC6NpVlgmw2WxMnz6dmJgYmjVrRnp6Oj///DMqlQqDweCIGB3u3or4iK68+OVsh7OwMSamWi2Lq6KMEY1P02D0umrntNlsfPHFFzTfvduRPlXs6cn8f/+bUaNGUb9+/ZtWJj916hRNmzZl5syZNzyHTE1N5fXXX2f16tWEh4ezZs2aajU1d4rU1FS+/PJLfvjhBxQKBU899RQTJkygdu3azhqNO4UoiqSmpqJUKpk6darT0ahBSktLOXv2LDqdjlBVKO6SO3Mq5uCBB4EEMiq2kCfi3yahayeSIiPYm5SPXZIlS6OS4nGf8zXv9dDwUbNCWru05n7hfkzlJhoGmnFVGXER4ynf0Iazrn0IGvAtzf2asyltE8WmYpBgZIMRtM5pSHFRCWZRjV5ZypIDc/n3wQVY7VaUCiXBHsGOOpBe9XqhVqspKyujvLycnJwcfHx86NWrFyGbNqH47DM8u3fH2qABKSkpTJ06FY1Gg9lsxsXFxZG+VVZWxpo1a3jggQfQaGq2s5ZOp2Pr1q107dqVkSNHsnHjRnr37l2jNjlxUlNkZmbSuXNnzp8/z6RJk/joo49uqU4vxE8LZ+X6jF7btuFiMtFr2zaSIiMpLy+/oxPm28K6xiDaQDSBpRh+6wDaWtBhLrj+eS1cQEAANouNi/EX4RDgB8QAXuDexh3Xl99EMBcRrYgj5fleDB029C/NUSgUd/yeFRYWsmzZMux2e7VWqVemR7U4fhxBAkkAk07HiQHN2Nq2H77qAo4qWnPG1IgmPvk09ZqLWmG76hxqwUobj8N89dtv1K1b97qdlLQ+DWkekES34c/j5+dHVoaBn9snsb2t/LdZdzaehscP0yfVgldJCR6lpSgkiR7bdnAoTsm2YDP9UsHVAiYFfNEZ3u4lp3CBFYUIKbtn8cmgT6j1BycDYO+0V/HtqSWk6P/AVtl5KxworHwE0AeD6SJ4NoL2M2/oHo8aNarazyqVipdfftnxPRgaGsprr73G0aNHyczMRNu5M3O7dEZrL0CHiYRu7em9PrZarUUVJlFPHdMWtn8zhm5P/ehY1IuLi8NisTj2rUrFqipUd3d3JyIi4qYaCVTVN7Rt2/Yv9y0sLOSDDz5g9uzZBAQEMHv2bObMmUPHjh1ZtGgRw4cPv+Hz3gx79uzh888/Z82aNdSuXZs333yT559/3lFXcqN1Y05H4yZITk7m9ddfZ/v27RiNRurVq8frr79e02b9z2Kz2VixYgWSJGEymVCr1Sjs8ALPoEGNm/osD8f/jFexRKe4HfjF5KISbKgEG0fNBprtysa1BJ74Dd5rCocrErmkPMQHvj4EaeU/jIIA7spSXI2rsK1ax/zScELnp5J31Mry5vAyS2iofBK7JHvzBrsrEeYXGaCqgyiISJKEpcCCJc/C2aNnmS/OR4cORWXRn8FgwGAwsGjRIl6bNQvX/Hya/vorWyq7pdjtdofokEajcaya/Pu7LtQqGcqCBQt4/PHHMZvN5OXl4e/vXyNRR19fX7Zv306XLl0YOHAg27dvJyoq6h+3w4mTmmTFihU8+uijKJVKVq9ezbBhw255LI/AtrgojRjsLo7iaZ3JRERCAkmRkfzyyy+MGTPm7k3dDegDqVfUyBUmgEswaGtjt8oF4kr11dHZknMlbBi3gYojFdACGAVoZQ2Kfv360a5dO8jJkVXGn3uOQSMG3TX3wGq1OmowqriyiLhqMi8KAjsGdUfT10z9Zl0Z4DMaH1c7P6/eRMeOHejfvz9xm9pTsncd7evspLZHHorKBXNBAFdVBXXVp9m7d+91P2PhzSIJz30X/PxISoI5Hx+hXnM3REChAFNoF8rOpXI8QKQ5no6IhsZiplZJBSNLcaRLZblDliecmSEref/YGio0sOjkQrQlsUxpMZngTv9yfE4PLfkC5U/LqL/9GBx5Ec6vA7tRTpcSkR9BbntsKfzb9/3KxTaFQoFCoSAiIoKIiAgkSSI1NYXm4ha0aiXGPrCdHkTsOuio4bisldGF5foxjA5YTuLaDynSd6C4uJiMjAyH41hVHF6F1WrFaDTedKZBlVhfu3btrruPxWJh7ty5vPfee1itVt59910mTpyIXq/nscce48knn2TEiBG89dZbvPvuu7cl28Fut/Prr7/y+eefs3fvXpo0acL8+fN55JFHbvn37G+lTlksFkfumkajQavVotFo0Gg06HQ6x6NWq3W0ePvjc51Oh4uLCzqd7q7szmSxWPjkk0+YP38+Fy5cQKVScf/99/PMM8/QvXt3Z4FwDWGz2Vi8eDHZ2dkEa9I5Zwqt9rqrspyhQcvYu78ug5YfZ0EXP1xxZVRcOu9EG5kTKfKvBJgSJ//hnHdFJ0AFClwQQQC1IHvjKgFESUGuKGJ5T/4DbBNA+w6EKfUEqpR4KgS8FQL+agN1tCq81Dq8dR74uPhR270egb7NcPduzvzVyVwqFRBRgQZyXHPYJexi4PZsXt5eQUJ0b46173LVNavVaibq9Vg/+oDJHYtY01FgrGIAGrETep0eq9XKmDFjCA0NverYf4rs7Gy6dOlCbm4ue/fuveW2f06c3Gs888wzLFiwgGbNmrF69WrCwsL+3oCijV2749i+Y2e1yWpV21alUsm4cePw8vK6LfbfdgzZchTDlCP/rPaAyLlI9cYwZ8QcaAIvfvKiY/ezsWfZ9MomioqLEKIFNK00mK1mbDYbWq2WqKgounfvXkMXI2O1WlEqldf83t+6dSt79+69SvTtSo2L482bUS8ri5Re4USMPIjCuzXclyQXzCMrxdepU4e5cwXefhvuu0/k3X6dqa9IQKWoPq6o8kLovx/Bs9H1Dd4xmPmn1/DmW0p89Rm8Mfht8uv4UW73ACQkSSR0+w/YVUqyuj2BXlFB48RfaLMzg7hgGJAGCPBmL/m7sn4JZHhC+2fB4goPe8CDqs7sKerOUw3X4DEkjnVf/ps6K35GOetnOnevLMA4+h4kv/uHblNKaPoatP3kVt+OG+bs2bPYbDbCw8ORJIkFM/5NdokWndKKIFp59sv5eJWUIAmAC1hHqaA3mEQdApBiaMLvxSOx2WzY7fZqYyuVSgYPHkzr1q2vee7r8fbbbzN16lSHyOOVSJLE2rVrmTRpEunp6YwdO5b33nvvqlRJSZL47LPPeOONN7jvvvtYsmTJLf89MBgMLFy4kC+//JK0tDS6d+/Oa6+9RkxMzHXnuXekvW1mZiZ169Zl+vTpLFu2jDNnzlzlvf9dFAoFgiAgCAJqtRq1Wo1Go8HV1RUPDw+8vb3x9fXF39+fgIAA6tatS/369alfvz4hISG3zVnJz8/n/vvvZ9++fZjNZpo1a8brr7/Oww8/fO+JJt3j7NixA51OR2RkJEqlEpvNxqJFi8jOzkaSJIosXiiwyRN3wEtVxOMN1uE1/BDhT9bi09BP0ev1PPnxx+hKKngjXsGcSNm5uNLBqEJEpByqdJKucEhE5kXC8ubwwHH5EaBcsjPc/iT5ZhUGwcAa/U4ebDmKM8WnKS/LpPxSHoasdCqsGxElO6JVj1JrRyHY8FDZqCXCYJUCr346Vsf4U1pSgF7ajiC5gdUH0e6DWfTgoHCMkg/XE1BgZkoczIuU+FjcRHNVLMONz+DpFkpISM22561bty47duygc+fOdOnShcTERJo2bVqjNjlxcicpLi4mKiqKU6dO8cQTTzBnzpy/JeLlQKGiW/cetG7Tlnnz5gFU66Rjt9uZPXs248ePvzvTqFzqgnvYZUfDvRGEjGH5y8vJD89HEAVKCks4ufgku6bvwuJuQdFNQeMejenTpw+enp589dVXVFRU0LJlyxp3MkCOWNlsNh566CGUSiV5eXm4uro66iCupSwtt2CVV8tTO4bxcNBSwlVpCCoddFnmcDIALl4MpGtXcHWFAwfAbk8jN/9LwiregLxdjom6OFhB2eNjyDx2kEbNJLQ+ja8677598OHkV9hz2sQLL7vyXvdnURTu5dPTL+GpKqV9Qy11DfH4PZHLtiVqXDZ/jXJQGwaPzmBNLz9eLRB4WlGMWbhcIzIlDmZHQoELzDkC/9olENtZhyVCy8qzXVE/EU3D5Ey23f8Cnc2V90KSIL9SP6Oq8Hsd4BIAYz66Te+MjCiKlyfFad9AgydBoaq2+CYIAk++9AGbl7xL96GvkLd+IHu7dqL/hi1yMX4FaNbboC9syO1DmEsKScVtMZsv34cr1cLjW7a8bn2GJEmcPn2awsJCSktLq/3btGnTNf9OHDx4kFdffZUdO3bQt29fVq5ced3GMIIg8Prrr9OmTRvGjBlDZGQkq1evpkWLFjd8z3Jzc5k9ezazZ892iIcuXbq0mnr93+WmIhoge292u53Q0FBGjRrF0KFDadCgATabDavVWu3xes9v5HWz2UxxcTEFBQUUFBSQn59PXl4eBQUFFBcXY7FYrmlrlXOiVqvRarW4ubnh6enJSy+9xJNPPnnDN+bdd9/lvffe45FHHuGll14islI0yck/z4IFC7hw4QJRR47Qedcu9nRuR1ybTo7XBay4Kw2U2j3xUhXxRP3leEZMhiZy+lF2djaBgYEo5s+Xw+1TppAc04bBS4eQZSnELtmvOqcGARsSInB22uWVnNCJV+2KXqkjTG3HS1RTbKtFKrl46q/dUUqpUOJicUHSSSiVSgRBQJREJNGGRjShEi0oLAIoKpCwYpdErHYlChSoFFYe2C/y+A74z1VRGIhUuTPtUg+i1h79x9o3Xo/Tp0/TpUsXzGYzycnJNe4AOXFyu9m7dy9ffPEFmzZtwmq1MnfuXJ5++uk7cq65c+c6RL2unOQkRUby6KOP0qBBgzty3r/NhS2wcyAoddBvLzuW5LMzYyfogGPAIRBCBLz7e9N7eO+rClt37NhBbm4uo0aNqvHv3wMHDrBlyxYkSUKtVuPh4UFeXh4NGjSgXr167Nixw7Fv9feofeVWgQBtDgP9t1Buc8Gz4zvUaSXXG5SXwwMPQEICzJoFo0fLR6xYsYIBAwbgljEdQ+ZW9I/vRMgHyRdKv/BAKdjRejdEPfhyp6a1a+HVV+XssskP/crbb4kI9YbLmiZqT3Yc92Df0t081G4udT1TUAgikiSwZFsYzXamYu4D55Lv55RLc4oaxuHWdBv3uUGRHfLs4H4JSmoJPPquhLZALma3qgQu6W3saBvKhY4jUKj0jB49miZNmmA7OQvlsTcRbJX5/FUF4X4auGj6W5oqVdhsNs4e286OxDM0adKMro0k2BkDDZ+BiGmO/axWK+vWrWPYsGEOh+Snbz/mXE4JbRMO0m3rLnQKM8JoEHsLlG52R7lO5Gzd+tTLynL8zl3ZyWr6hAmsX7+eWbNm0alTp2p2rV27lqFDr64fUiqVqFQqevTowW+//QbI3SbfeustFi5cSJMmTfjiiy8YMGDADX/u09PTGT58OGfOnOGHH37g/vvv/9P9T58+zZdffsnChQtRqVQ8/fTTTJgw4aYyIu6IMvgXX3yBzWZj8ODBNGnSpEZ/8Y1GIwUFBRQWFjqckarnVz7m5uaSkZHBxYsXGTBgAGvXrr2hqMfy5ct58MEH2bNnjzPXvIb5/vvvyczMdPxyl3h6sOGz17BoglEZzzDQdS7rLt5HgbUWT9X9Ds9awRBztNpK0bWQJIl5SfN4b8d75BpykSSJQPdAGvk2YnKXyeiUOkb8PILRu4v4d7xA6jMj8ZowhRc2vkBaYRqFRjm39PN+n/Nq1KsU5h9mw+8Ps8OlE98N/e6q84miiEW0UG4pp8JSQZm5DKPNSIW1AoPVgNFqvPxoM2CymTBZTRhtRkqLSrFr7FiwcPTE7xyyXq3ErQZyZyrxKrRjCayF5vzVbRX/SY4dO+boDHLixAkCAwP/8hibzeaoW6moqMBisdCwYcMaL3h34kQURVasWMHXX39NQkIC5eXlqFQqevbsyaeffnpDRZ23SlFREfPnz8dkMlWb5MyYOBFvb29efvnlO3buv4UkwXId+HbklGI+y1cuh9PAWaA50BgmfzD5uhGg6yk3325SU1M5ePAgDzxwuXOVzWZDpVLx008/4efnx7FjxygpKbmh8f74HlXh5eXF2LFjAdDr9SgUCj7+GKZOhTFj4Kuv5PoJkNufbty4kVGjRnHhwgV++n4mL6ZOQ7fB4mgLaxMVKCOnIzR+iTlz4KOPwMUFvL3ltabhXfdBzu9ILd5mxw74+mtw1ZYzv39tVMIfUna2grQWjnpoqbBL5DXyo/8jlzgvWtErIfAP0yZpK5hXKFBUiGhEsLuAwgXsgxSYemopkEJJLmtFc5cD+OkrcHF1h/K0y+lTQ5Tw8hvQ+oObe7OuQXxcHLHbNtPANQMUWoYF78TVmgYKHQQNhujlIAisWbOG5ORkunfv7vhuKiwsxGqxEB8fj5ixlOH+q1EqKqfFlU5RVTvgqvezypHc3aULhzt1YtWqVZw+fZr169fTq1cvh11LlizhkUceYfHixTRu3Jg6deoQEBBQbQ5aUVHB1KlTmTp1Ki4uLrz//vs888wzt5SdU1FRwdNPP83y5cuJjIxEkiSsVisWi+Wqx8LCQvz9/Xn55Zf517/+dUuZOnckdWrs2LF3TXtbvV5P3bp1qVu37l/ua7Va+b//+z8++eQTAgMD2b59O82bN//TY6o+hOnp6U5Ho4ap+qKpCkHv69oJoeIcj7fKQsheidT2c/ro+6M7/Dye5UDHBX/pZFSN+1z757i/2f08vvpxuod055mIZ/DWewPyl5y/mz8Lo4yYxj7AD8N+AGD/2P3Eno1l/G/jSStMw0Uliwr51GrDo2OO8+h1zqdQKNApdOhUOmq53Lrg1pDz9TmULz9XAkFKD0aoHuGzV6ej9v+Oiv/7N6+1zWfdf1wYERjGI+2fJ6LpEyjVtyGd4yZo2bIlW7dupUePHoSGhqLVahFFuUD+eo/XWvcQBMFRz+Xu7o6fnx/BwcGEhYXRpEkT2rZtS4sWLZzOiJPbjslkYs6cOSxevJhTp05hMplwdXVl0KBBDB8+nAEDBlxXD+d24u3tTb9+/Vi7di1x0dH0io1FYzYTkZDAoY4dWbt2LYWFhYwcOfIf763/pwgC+Hbh4IXHWTdjORiBCKAr8szNAMkbkom8/9ppGnfKyUhISCA0NJRa/8/eeYdFcXZ9+J7Zwi69I6DYFcWOWLGBvScaTTQa001M7z1533x502OJiTGmmWjssfeuYEOsYEFUiiIdlrJ9Z74/ll1BTGKKLdn7urjQ2d2ZZ2aW3ef3nHN+JzAQnU7H6tWrsdls5OfnExwcjCzLfPrpp/j4+FBSUkJaWtqv7is6KYm4rVtBENhWNcl01+uRgKx69Wo812q1Issynp6e7N4N48dDaCikpMCV6zDHjx93psyEhobSIDKGdZohDO27BvU2CzwNwjCB907fw4zZ0LAhfPEFTJ9uFy7R0VBREsVPP6Sw7Ch07w5Tp0JYmCec/gTSZmCtyEIp2wWHvBqEImhSrmB7v84MGLUDjUomvBwqi4FPgSIgAKwfwRmVmi0Domiic6fbgWN4W8sRCkG5RsKznwF3+QShvmcwS0oE3DEF9MJtRfrlGo14G5yeAaH9IbjHNd+79PR0Nm/ejLkyHx85C4voR4leRMKDc5X16eyzD6Upyx7ml4zI2Usx7xpPXoP3SUtLw2azkZSURHR0NO5CGX5pb3Lx3DHOZN9JP+/zTpFhk0TMg9Vo5hkRJHtvk/IpU4iIiGBzcTF727blpZdeoo9CwcMPP0ybNm0YOHAgy5YtY9iwYQDO4uy4uLhaC22SJPHjjz/y+uuvU1hYyDPPPMNrr732lz5PPDw8WLBgAb1792b//v2o1eoaJQjVf0dERDB69Gjc3Nz+9PGulX9VH41NmzYxbtw4ysvL+d///sfzzz//m89Xq9W88cYbvPXWWzdohC6uxueff05xsT164KGooKf/Ljr5HgD3+nZv9qqJPqZiOD/XmTL1d/D94e/5OvlrEh5IQCHWdHSQZZlfTv5Ci6AWtAy6/l7WDl5ZdgefpKwkPqIbc+74mQBVAMePH3eGba1WK03fa0QG2QB4osJXKdFYq+X/ev+H2A7P3bCxAhw8eJBvvvkGhUJR68Pu936LokheXh4XLlwgOzubzMxMMjMzycnJqZE+WV2MeHt7ExwcTEREBE2aNKFly5a0b9+eli1b/uUarpycHA4dOsSJEydIS0sjIyODS5cuUVRUhF6vx2QyoVQq8fLyom7dukRFRdG5c2f69OlD8+bNXeYRtwG5ubl89tlnLFu2jOzsbCwWCyEhIYwaNYoRI0bQu3fvmyJqS0tLnR2Kr7Zi7u7uzpQpU2p1U77eGI1Gli9fbu9ZEB3tNIJRqVQUFBTQbNNKpP/OJCkmjvR+fTFYDJglMzbJhk2w0Ti4Mfc8dc/fPi6z2czJkydp3bo1OTk5CILAypUrcXd3p7S0FJvNRqdOndi/fz+VlZWA3RpVq9Xi5eFG2skUSiocNaj2Ne3qNrUR2dlOu1pHo73Sqkmib1Xkw3F/tFotCoUCg8HAwIGjeeGFSNLS4NtvYdCg2mOXZZl58+Y5a0Ec2+bOncsA6RVCX86zr7QHQvZ/WiD238a5nDq8+y58/z0YDHbRkZYGE6I/YtSbL1HjLWuthIS7Kc7PIrfETCP381hXK5BXCGjuNiD0A6XCXmMhyXDUCO0esmtDGRD23Ieu5XQWznmHrj4JnCprRPfkXYRtzkUYVnWIVSKWISq0A0zO/YjPcLmPxudqaDIZmk0B798oaL+CTz/9tKoZ35VTVwEBicfrzyRQXdPJyiqJzLzwIjqTY7FNpoV/HmPCV5CnszHv4kQqbF48c/YzvNeVYRyi4WBMRxKKe9D6wDFiExJIHTaMbj/+yJgxY1ixYgUJCQl06tTJKYbLysqIiooiJyeHn376iXHjxrFkyRLGjBlDRkZGjfTh7du38/zzz3P48GHGjh3L+++/f1NNXP4s1yV16nYXGgCXLl1i/PjxbN++nR49erBp06Zftezy9fVlxIgRzJ079waP8p/Pr4XEy8rKKCwspFGjRpjNZpYtW8aZM2fsebGChQfqfkMdTZ79yZoQiNsGvtd3kn+jwvfXislqQm/ROyMvV+OpTx/h64ofMXG5gM0LDRvunEW31pNuwCivL7IsU1RU5BQgjt/Z2dlkZWU5BUB1swqHGNFqtXh7exMSEuIUI1FRUTRt2pRz585x8uRJ0tPTyc7OJjc3l5KSEoxGI0ajsZbjiK+vL3Xr1qV+/frUrVuXsLAwdDodp06dIiUlhQsXLjgLRFUqFR4eHgQFBdGkSRPat29Pz5496dWr1y1jz/lvJSUlhY8//piNGzdSUFCAJElERkYyatQoRo4cSXR09C3xGTBv3jzOnj1ba8LryB2/9957ady48Q0d0/nz5/nxxx+d/xdF0RmdDAgIYMrHHyNkZUH9+pCRcUPGlJaWRlFRkdNIpKKiAjc3NwwGA6IoolKpahT3OseODYnqiwH2e+643o4u7dU7d4uyvZbPqNU6IxrVIxzJMTF07dqVTp268NFH6SxZEsbo0XV4773LaVJXkpOTQ0pKCv3796+x/dixfM5sfow7U39BcLo3gUn25cXNe+g1rAXz5oG/Pzz+uD2qwa477EXniitWrte0xNbyDT6ed5Sn06diXqBFPdqIZqi5VtnELj10ewWURUCwG+TqQRCpqKjAQy5A0KViu7gJ6Yy994ftKQFFkYzsAWhBGAZyPAhbqek61fodaP3GNdzRy9/D06dPp7S01Ln9/rrfYpWV7C6ORSHYGF1nKRZJjZvCiLqqD4lti4C8SMQsqZxNL93FCu4Om4+30i4y89cGLp6krQABAABJREFU0WTZWQQJdD7eTH/ueWeRv06no0WLFhiNRsaNG8dTTz3F9OnTa43RaDTSqlUrzp07x1dffUVgYCCjRo0iLS2Npk2bkpeXx6OPPsrKlSvp3Lkzn3322R/qvXGrcV1Sp/4JhIaGsnnzZj744APefPNNgoOD6dOnD3fddRd33XVXjTCSn5/fb4ZMXfw5jEYjs2bN4v7778fX15fVq1eTlZVFUFAQWVlZ+Pj44OnpyaJFiygpKUGWZZSCmfFhP14WGUpPkMyQ9Bj023ldx3srTDCq46Z0w0352+HO+KZD+OnwEqfQcEPNvZrhdG11340Y4nVHEAQCAwMJDAz8VQtdWZYpLCx0CpDqYsQRGTl06BBWa+2mWKIoOlO02rZtS926dQkPD3ema4aHhxMWFva7AsFoNJKens6pU6c4ffq0U4Ds2LGD9evX87///Q9BEAgNDeWZZ57h+eefd0U9foOysjIWLlzI+vXrKSgowGQyOfOOq//YbDbnjyRJzh9Hmt6VP2B/T3Xv3p2XXnqJESNG3PAJ+7XQp08fsrKyqrz8O/L01Gn46nTOxmMpKSk3dNxlZWWsWLGixjbH9axbty4PPPAAglLpNOG43phMJg4ePEhxcTGHDx+29zGqinw6+hFJknRVkQEgUbsPQXVrYb1GQ6mPT42IRnWhB/b3kePfoihSv149Ll70oUULbyIjO7B/v10I/BZHDh+mn/Q6FAeCfweOH4dHHwXI48k71EhxoOh7+flWixWdTiY9HebMgcDqmbnekfau3H7V7FclK1ZzBcs3JnNv+EKs/3PDx1qGvNkuCpCg0AgmJQRYoIubCuWeGXD6Mxh0xJmabHc784TCPSjOfk65Vw8oOYj3cAOsBsGAPYKxGojH7jrVF4w2NRlyb8KV9bky0a+srAx3d/ca0eeCggJnY7orm8QpBSsRidk0WHUeSxMV4nEJhcWIQm2DNmBNE7HqVWgMJpTY6JGwGwGZ7gmJHOgZw4nOUbT0PEGXzXudKVL74+J54YUXakQHL168SIsWLahfv/5VRQbYm9eeOnWKjh078uijjzqFouM9+Nprr7F7924WLFjA2LFjb7m5xfXiXxfRqE5CQgJvvvkme/bswWw2o1Qq8ff3Jzo6mlGjRvHdd99x5swZp9uHi7+HtWvXkpycTEhICA8//DAbN27kwIEDzscdPuUWi8We/7pnG4UDAkjqPpRRsb52m0SPCNCG2RtAqW5Ba8ebTFJSEs+se4o97EOLhiYKDQO8THz8ZMWvL6P9C5EkiYKCArKzs8nPzycwMNDZbffvaH70a8iyzKVLlzh16hRHjhxhzpw5nDp1Cnd3d4YNG8Znn312TcXz/3T279/PwoUL2blzJ+fOnaO8vBxJklCr1QQGBjp7N2k0GmfPJse/Hb2dHM/5rX/7+voSHx9PUFDQzT7l36WsrIyUlBQ2b95s/3zctg1kmW3x8Zzq3Zv+/fv/LWmCv4fFYuHrr7+msLCw1mMBAQE8/PDDNyT/24FOp+Onn36isrKSwMBALly48Af3YJ8KRScdrGEj7BAZkiCwbvBgp4ho2LAhbdq0Yd26ddxxxx1IksTevXu5ePFijb2WloaybNkj/PwzVO3yN7Hm7WXn5mXEu32D1WLmteWzmb9nAq+8AhERq9CfWcDdoQuRtggo1sjOqIbs3gBhwF7Q1uy1QMbP9qL8huOdm05ueIfKzC0UGzRojppQ7bLQWbcfcTgYe0GfFCiTYfdXYGyoIHj5tygbjgOk2pGRVc2g4szlKyhXM5LaAtIqYLiAFCc4e4FIMmwv6kOSvj8vvPBCjffq3LlzKSsrY/To0YSGhgKwYsUKjh49iru7O3p9OVQThM+u/QTvpAr7f0T7EB3IIggSmN2VyLKIhMD2uN50S9jnTD38+vlHEWQrLQ6cdt73invv5e677wbsAmP9+vXMnDmTkydPcuLEid8V85Ik0bNnTxITEwE4fPgwISEhNGjQgHfffZeXXnrpN19/u+BKnfoDmEwmkpKS2LVrF9u3bycxMRGDwYAgCM6u0zfyA/OfTHl5OXPmzKG8vBylUkmjRo3IyMi4ql2xIAg8PXUqPqWllPr4sPaLLxg/fvxV9uriSjZs2MDm/Zv4nM8ZqYykhfVOsjym4q42M2NyAQr1LVQw6gJZltm5cyeff/45K1asQBRFoqKieP/99xl0tSTufyClpaUsWrSINWvWcPjwYQoLC50rz/Xq1SM2NpauXbvStWtX2rZti0qluskjvnnk5+cze/bsGvUajonwkS5duPfee2nQoMF1O77NZuPzzz+/qguTRqPh4YcfvmH9pqxWK3v27GHHjh1OMwkPDw9n3cXVuLKAOzkm5nJ6lMmEu9GIDFhUKtQWSy2RIQgCd999N02bNnX+H+DLL7/EZDIhyzKlpTIGA3h6mnnyyWt09bFZMK5oia2yCHdFCYIAEkrE5k+B2pdNF9pzKeUXJoR976x3kANBmBMBno05onqEJu364ekTcHmfJccg82do9wHleankrv4CP+1+5p2PZbhtFWt/Hszk12ajUtlIyoMh+XC3BNMjwfgV7Iu7mz7vL6i2v+Nwehq4BULFOcheDlRLKa3RlA9sMhwo7UyGviFhmhzCNBfxVeootNYh9K7dzuJnQRAoKCjghx9+QK/XIwgCGo2G4OBgCgsLnfdTwIaMiKNmZsjatTjjAl1BOgaYBcxKNWmNmxCRfaFmxAkbHZIOOUXF4ZgOeCgqkLXhaLVa6tSpg7+/Pxs2bGD16tWcOHECQRDw8fHhgw8+4FF7aOma6NmzJwkJCYwePZomTZowc+ZMsrOzb4iBxI3AJTT+AhaLheTkZPr06UOrVq1ISkq62UO6bbmyk+qiRYs4depULS/4K2nVqhUjR45EmjUL67vvkhAbS8GoUYwbN+5Gn8Jtyy+//EJORSLW4nrOleDykB/INWTz9YPpuHs3uNlDdHEVsrOz+eqrr5g1axYlJSUEBQUxefJk3njjjX+Ms5Zj9XfRokXs3r3bGa2QZRk3NzdiYmLo3r07Xbp0oUuXLrU64v7bWbt2LYcOHUKSpFpdw2c89xwPPvggYWFhfyo1w9FroH79+nh6euLu7u78AeCrr5A/+IA9PXqQOWgQsixjs9kwGAzodDoaN27Mvffe+zefcU1kWebs2bPs27ePixcvXrW78tW4Y+lSWqWmYlUqUVfVb8nA8VataHL2LO4GA2aVCpXFgoB9cbzMx4fE2FiOduuGLMtYrVb69u1L9+7dr3qMRYvgySdlunQx8/XXZpRKE35+fr8eIT33PXg2psytPWWLOhOkSUetsNSok5ARkGWBlefvpk+91fgoy531DtIw2NQ0nhBtCfsN0XgpKpAr2jFkTC8qfcOpGxoEiWOh5wo2fz2BPtqfKStVM/S8mbdn96KbOYniO/2YFqRDUb+Mvu7t6N85juJvZ2A9bCNwlx6lulqK6JG34cR/a55DdXGxGigEq7+Cgo+D8NhRgWatiU1d+9f4rler1bRt29bZjmDUqFEsXLiwhkBUCFZssuO6OS6I7Pz3ix98gHvVvbd1ESh8JIhfcu9EKVh5OOIbztKLeWf61Lrk7opK9DaPqjssohaMSApPjh07xvr16ykvL3c6Mw0ZMoSnnnrqmlMSf/75Z77//nsaNmxIixYtmDdvHkeOHEGSJJ577jk+/fTTa9rP7YBLaPxFnnrqKT7//HPWr1/PwIEDb/ZwbkusViuLFi1Cp9PRtGlTZ3481PQZn/3qq86mjWBPnRo3blyNJlQmkwm9Xo+f368XQLu4OlarlaNHj7Jp0yb7RLX+WnZl7uDb8YkE1nFZN9+qGI1GFi9ezLRp0zh8+DAajYb4+HimT59+S9YP/BbFxcUsXLiQtWvXcvToUQoKCpxRzIiICHr06EHXrl3p0qULbdq0+VdHK66VxMREtmzZAlyeQKdERbG8qlGXSqWiQ4cOf+j7S5ZlfvrpJ86fPw/YP4tVKpWz1kGpVPLSl1+ivHjxqsXdRUVFBAQEXGXPfy/FxcV89dVXNcwersQRtVDabFiVStIbN6Z1SopTQBg1GrRGo/3/goDRzQ13oxEJyAkLI+zSpRrXE+zXo169eowZM6ZW74+zZ+GOO0CvhyVL4FrbqkgbumHIP0lmXhhN65xGpajdQFauSkeybQZhlQDDQOwvOx+TsYsRvdWd2acepFLlS4g2n2K5LnfeOYq0nZ/TcdjrZP4ymq7+e3nkMMzxgAufQngF5HjCvY+KrGnrhntwa+i/l7LdL1GUr6fhsPfskQuPCDj+LpyZSS3HJ0cTvkCQh4G8SmB91yEc7NjR+V1v0GgwubnVqmepOQW9LCKauZ8ky9gAo3T5Ojve5zmhofiXlKA1GOxOWGrY9GF/9um6ACLeSh1P1J+Jangq5UId5s6di8FgwGAw4OXlRceoMHbsT8NqtSIIAoIgkJmZyS+//EKHDh2YNGkS48aN+0Pph1arlZdffpnPPvuMnj17Ul5ezsmTJ50iWBAEMjIyiIiIuOZ93uq4hMafJDc3l//85z98++23DBkyhOXLl9/sId22fPLJJ+j1+lq9ETw8POh8+DAdNm8mb9IkFFOmsHjxYiIiIujUqRNHjx4lOjqaeld4kLv4a8iyTPqLL9JkyRL2DA/lPxEH+HrkzzRoevfNHpqL3+HAgQPMmDGDRYsWIUkSTZs25b///S9jHC2Eb0GsVitvvfUWX331FaWlpciyjEajcUYrunbtSufOnQkJCbnZQ/1dbDYbn3zyCR06dKBnz563TCptQkICW7du5ump02ukTzkmcgMHDqRz587XvL+VK1eSmpp61Qm8p6cnkydPxuOnny4Xd0+e/Ledyx/BarWyZcsWDhw4cNXeO9FJSQxeu7aGf5TDIcoRwVg+ejR3LF1Cq9QTpERFkVW/vvM1eq2Wj19+CfvE174XURRp3rx5rb85sxkeeADWr4d337W7PV0LhYXw2GPwRMt4ejXfBlQJiurOTFAjDckxoTe6uyF9JqL1rCpwl0Eh2l9fmOPPN18/hPkeLRqVgc4Bx2jsdpzE0njKzSJdvBPokJeOVQGPJsGbCXCwizcjHvUGwwVACQ3uhox51UYrAGpwuBhekR7l+L88DCr7eHKwNJrdxT2RUBCdlET81q1oTEYEmVpNDGtiFxpdfXdTavHnZGVLqjt/OdKkLssROwaNho9eeQURG4IgIyJxZ7sKIod+BlW29A7b2YKCAr7++mt69erF7t27iY2NJTo6mqioqD/9eVpYWMjdd9/Njh07mDZtGlOmTEEQBGw2G5mZmezbt4/S0lIev9Y3x22CS2j8AfR6PR988AE//vgjFy5cwGaz0a1bNxYtWnRNDQFdXJ2tW7eSkJBQI00qc9AgHn/88Voh/crKSjw8PG7SSP9FNGgAmZlUBgWx/pfH+GLPu3zU6780bDyZwBpWJS5uRfLy8pgzZw4zZ84kLy8PPz8/7rvvPt57770b3j/h1zAajTz33HPMnTsXvV5Pt27dGD9+PF27dqV169bXvUj570SSJARBwGq18vnnn1NRUYGXlxdt2rShR48eNz+VzVLGwhlP4rm9qEb61PRnn8XDw4Pnnnvuqi5mDrvX6pGjXbt2sWfPnqs6Mnl5eTFx4sRb4jPi2LFjbNmyBb1eX8tu2oFjFR3sk1KDVkt648ZVDlHdOBQTjYyIAHiJOsokX0BwpuJIwNZhfTnQMQarbBeVQUFBPPLIIzXev19/Da++Cv37w9y5XO5VUZoCPi1rNo4tSoaKs5wxj+GRR+DYMejZM5uv7hlDiHXf5ec9AlQCVdawFFb7txfImSB3BvGJyy9xRDwc/87NDMZgcycwoggPZQUiMjZZZHtRL75POcSCcB0IkNUA6iqr5NQfybKrFsFgOlB3FNQbjWHv43x27mmsNrB7eNlol3TkVwvqq+OYJ+zr0RmlYKXjrmSnq1eFhwfhOTlOkVFS1xdtkRGFxYpVpWJ7XB9SOrfCLLnh4eXLHYN74BvcmNNpaaxbt47Vq1dz5MgRBEHA39+fqKgoJk6cyIQJE1Cr1RQVFaFSqf7U/Pbo0aOMHDmSiooKlixZQu/evf/wPm5XXPa2v4PVamXOnDl88cUXpKWlYbFYiIyM5N133+Wee+65roV0/xaysrIAiE1IcFowFowaddXnukTGDeKVV5Dff5/trVtzZreGPp4vc8+GrxnvlcN/nvvyZo/Oxe8QEhLCG2+8wcsvv8zKlSuZPn0606ZN48svvyQ2NpapU6fSpk2bmzI2x4rd8uXLMRqNDBgwgNdff50ePa696++txqZNmzh+/HiV2409OltWVsaePXs4evQo/fv3p1WrVjdtfOWnFlFm0XA6piNg79+gNpuJTkriwtChHD9+nDZt2tRY2JFlmfnz5+Pr68vYsWMBSE5OZteuXTUm7qIoIkkSHh4ejB49+pYQGWBPtZMk6VdFBtg7cvvodAjYV7s/fvnlqkdkOngf5IXQORwNmEpqZiXmMgWtpEOkVLbjbJPGtEpJRQRiE3ZzonMkZZIWDw8Pxo8f7xQZx47B6NGgUsGePdC8ebWDm4pgcw+o0x86fw1qH5Cs6Lfei2jIID9jJp8MkGn/+ElWZvUg/1I5IUFcjhJUDyZV1TzIBhAKQS62uygJZ2qeb/UoiNAX6qTn1+izYZMFyizeRHsfIjJWxyMWaKWGAIVdoFxVYzjG0xQ4VrVtjH1/zloMR9RFqWVXdhi705/A6ly6FmmXlMTgdRvskSQRNg3uR3LHq4sMRzSp/5pNGDUa3I1GvMvKEGXZeS9lYO2QwWj6Gdlf2gUrDmUn01B1hvOmpjRt2pQGzTvSpEkTzp07h1KpJCwsjEcffZQnn3ySqKioWsf/s+l+ixYt4v777ycyMpKdO3f+o9Ki/k7+dUJj5cqVfPjhhxw6dAiTyURoaCjPPvss48ePp3Xr1re8r3FKSgpKpZLIyMga23U6HYWFhTcsd3v16tVUVFQwYMAAp5uGLMvk5eVx+PBh0tPTnd28E2JjiU1IYE9VyoHNZrutVjX/UUyejDB5MsU//oj5/Hko1nAHY5lf/gu2qeN5duKMG5Jj7eKvoVKpGD16NKNHj+bYsWN88cUXzJ07l3bt2uHl5XVNn2O/Fszu2bMny5cvv+a/0ZycHB5++GG2bNmCxWJh9OjRvPrqq7S/1iT1W5jTp09jMBjQ6/U1tjuK1h32mzcDSZKYtSoDg9Vug5wcE1NjUWd6TAybN2+uJTxPnDhBSUkJpaWl5L7zDr6zZ5PbsSNCly54eHggCAKSJGE0GhEEgQEDBtxSEyhvb++ruhTesXQprVJSkBQKRJvNWXuxLT4eh+dpa89j9PBPxN2rLq27DmN70gwsFhk3Nz9C3S7S5OxZZw3H1m59KbMFUr9Bfbp164aPjw96PYwbB4mJMG0aXNUE8eDTYCmF7CVIZel8fmgkIcaTjGiaiZvKSPemu+3Ps8DgwLWolBZkGXvfiULsTe68QBiO3bY2Hoyb3FCvNSM0lZHP2HtdCHBZDBiwR0FW218jVBVk8yPwPdAQzG8oyDMX09Yb6qmqRUGqC4ozXE6HcuyjmMuWsVX7d/54Nbd39/ZrQ+mBUpSiDVEyY5Y1gEDc1u12kSGAfB+cbhwJV7QtcpoZVP1fBDRGI3qNhvQmTZwRDUfNTHJMJxSlFmxUr+MSOG9qhkajoWvXrs66i9DQUM6dO3ddGqIuXryYu+++m3HjxjFnzpxbJqJ8K/KvmO3t37+f//znP+zatYvKykq8vb257777GD9+PLGxsbdNg6zk5GS2bt1KQECAU2hUVFSwceNGMjIy8Pb2vmFCo6ysjPT0dC5evEhISAj9+/dn+fLllJWVORsjgb0AqnD0aD7v3JmHHnropn4xu7hMq1atnAWfnnhyF2NYWraEiNX9eGTSoZs8Ohd/hDZt2jB79mw++OADfvjhB1JTU68qNK5lW0lJCUuWLCE0NJStW7f+ZnTkzJkzPPTQQ+zZsweA++67j5deeolmzZr9xTO6dfD29q7RhdhB8+bNGTVq1E1dMBEFeKThPBZdGEmuwQ8QyKpXD++yMrKq6tuUSiWlRfn4BQSBIGIwGNi0aZMzPUo7fTqa0lKGHD/OkNWrsVqtTmFVUVGBTqejdevWN+0cr4bDbcuBI+XGW6dDBMSqSMfV0nROVraiZ8gRaPcRkiTRpEkTTp48yQVTfbr4JqKoMiSxqlQkx8QgylYU1mKaNGnCxx/D++/bIxmXLsFVb33pScheUvUfGaH0EFMaHkYQQCFcLt52/Nm5qaqFL4aBvBrOxTcifMgllFjQmX24aAihUdxZ1rUdxOCQDSgEyS4OnuaywPDAWYgtVO3LKRQA8bxMyENFBHcGWgCLq57XBtiPXUg4BIVDTDj24YhoiEp47Q0ITYZLq+07DuoOLZ4BYPhw6N27N/O+eJ0Cc1UBd9WJmjUqVHEWuul2sa5wpPO+xW3disZodLa/cEQtRMDs5kZW/fpEZGdzpH17vn3kEQQkBGzYUFK7UgNnfx2AYcOGMWvWrOuS3rhv3z4mTpzIuHHjmDdv3i2/QH2z+ccKjbNnz/L222+zfv16iouLUavVjBgxgnvvvZcBAwbcMsV8V+JYqbnyj2Pfvn3s2rULg8FAWVkZZWVl7Ny5kzNnzlBeXg44unT+dSRJcrpAOX4c3XYd/3eIicrKSs6dO8d3332HWq3GarXWqMnIu+MOJk2ahE6n+8d4R9/umEwmtm3bVmObBg1PhT7LyqLnsH1Xh8cm5bga+91m+Pn58eyvFlleO5s2bWL8+PF07NiR119/nbfffrvG4wcPHuTRRx/l6NGjqFQqnnjiCZ5//vl/ZD1b9fQcx+famVGjiLnimtwUBBHfezJotmMHubt2ATJN0tMRZftvsEe6Z82aydi6K1lTdj96vbFGNGB39+703rcPz6qu3UqlEi8vL7y8vG7JIv0LFy6QkZFR4744ojgS9smqpFBgVqnYFh/PoZjLUTWFQsGwTt4EGpUQNpCV8+Zx7ty5qkcF9pV2p6ciASxWbEqRCM15OngfxktU0aDBMwQGwpEj4AzuyDLINmyygMViQaPRIO0YhChdvr6CAArkGla11f9t3qxEudqKWBW90Pd2Z+mxJ7mnYg7+/b8koHI3/sffBCA+cBvCVgnZ0XG7SmDIgcAwsPQSUKuqxEyc/Ud4C4TzVdNxGYQDQHrVa+GyyBDtdR/OiAYgDPKHvvasBPw6QKP7oXlVYYgxHzTB9mtQDVmWKTBfTrHbFhdHbEKCvdbiiWzqe17gzYvvOKNOjm8Yhyj0GVCCZqeVpltOOzMhHBG65JgYBFGJLEm0a9eOkydPYrVaCQwMJCIiAi8vrxoLrYMHD2batGksX76cUb+Ssv1nyMjIYMSIEURHR/Ptt9+6RMY18I8SGsXFxfzf//0fCxcuJDc3F4D4+HgmTJjAyJEjb7lC9ry8PACCg4Odb9YDBw6QkJCAu7s7Go2GoKAgjEYjGRkZtE5MdE7gp5aVXXV/n3zyyV8elyPsKAgCoijW+K1QKBAEodYqn9lsxmazERQURI/ERHx0OmITE8l+/30Al8i4hVCr1QwYMIA1a9bUmHSUl1Sw9JlSJs6uQ8nX3rz2UD4oXeHgfxv9+/cnNTWV++67j3feeYeVK1eyY8cOkpKSmDJlCmlpaXh6evLaa6/x1FNP3TK5+9eD6pMIx6QncuVKKmfMuDXqygSB2NhYDhw4gNFoRFk1AXf8DgsLZaT7R/jIWfRy+4GVpXdQfRU4tUcPjnfvzr1DhnA7ePwtWrSIioqKGtsc9RgiVzoayXgozVRalXh7ezN27FjCwsLANgUEgdGjR/PD52+gM6oxyRpA4lJkHRoey0BoIxGkLmBF9j1cKg7io4+K0WoT8fPrjSx7IggChxNX0jp7PMXWYDLLAyg0hjIwJBOEmmlJjloJOR4qbRo8FEYEAfKN/mhXmFGXViCtEhD7yphsAVw0PUXYQ0+jXOGPbLn8Pe+uNF6OUlRFMKRhAuY+SgxLRSoNJsIkEM+CsK1KVPQAORiE/VU76QxEAour4gFtgDP2SIgtDiRBzQlDR3KVYxk4agrkbgP9BWhyf80boQm2/75ikn05m8EebXBEkxzF4LK9Bt0ZdQK7znFEntRlMPC1ITzlPsOZrle9SztA5y5dGDBgACNGjPjN90rPnj3RaDR88803f5vQ0Ol0DB06FA8PD1asWHFdUrL+ifwjhMbSpUt55ZVXnCsd0dHRvPzyy4wdO/aWbvSUnJzMsWPH0Gq1BAYG0qZNGxo2bMi+fftotGmT8w/sWNUfa3V1fyY+HkmS0Ov1zjBycHAwk2+Q3eCyZctISUlBFEU8PT3p0KED3bt3R6lUkp+Xh27qVA7Gx9PriloSFzcfQRBo3bo1kiSxceNGOnfuzIkTJ8jPz2dPwh4WPVHCQ1/X57lZvnzyQAaiRxiyLLPj0Kf0af0QqH1v9im4uM4EBwezbt06ZsyYwQsvvEBgYCBWq5WAgAA++OADJk+efMst3PzdFBUVUVBQANijGWqTCb1Wy9GBA+l2RQ+Fm0VFRQXz5893rvA7GtEpLRZ7QXi/bvh7XkKx2ULbVcfw6lvBvBYT8fX1pXfv3gQEBODn53fb5JffcccdzJs3z1lfFJ2URKvUVGc9RvUJqUqw4eYdilYUmTBhwuX3q8KeLaDVanmoaz67j+RwsDQag6QlJCMfQQL1ASsECnh11qGuL5Oebu/ZceHCBXQ6HaIoYjEbaRyhIliZQZBPBvJBEN63T9qJB3kLCHOxz6QX2wWHR1MjpIE0HJQ9JYThEqY1KioGemItV1OpLuSxuyajXL0G2VIBslSrjkKWQR4OcryA3uxG2UdG1Onwgi8MOgL3HXNeAqx7QVHt1srJIEQCX4PR5oZZUuGlrGRrYR9OZbWk2GJfNPDUWehWUYl3WL8/dH/KnAugl01oYxMSnI5TOaGhhOXkICkUIMuIkkRKq1ZOQWK2wtRpM1i6dClTp07lsJtbjdS38vLya46curm50bdvXxITE//QOfwWEydO5MKFC+zdu5egoKC/bb//dG57obFq1SruuecewsPDefPNNxk3bhxNmza92cO6Jho2bMihQ4coLS2ltLSUs2fPIooiNputhqg41q0bderU4eigQbTfuJGkPn2wWCw888wzJCUlcfDgQSoqKn61uPN6oFAocHd3p127dvTq1atGqlfwW29xaswYDGfOuBpv3cK0bt2aZs2aodVq6dWrF7m5uRS8+y7Cgw/y3Suv86zP1zzwdT1mTTjI1NWzeUwxmwJzOkFdv7rZQ3dxAxAEgaeffppevXrxv//9jz59+jBp0qRajcr+qWg0GsxVDk7VrWMvDB16y9T1qdVqfHx8nNHxbXFxDFm/FlGiqiC8Iz8ZR3Hf6p8QiqDhlnP07n2A9uMW3HZC0Wg0kpaWhlKpdPb5iNu2zT6JhVr1GBZZQdNwLX2GTPjVVOmL9d7lzImNBKkSyTI1Ynv3Ps57PXjtWuK3bmV3v1j2RncHBIoKcvBW6lDKVlRKM26C3SRAEC4XYAtVNQ7yapxpSVD1WFUdhLAY/FeX2tOUPgeD0cbm3Dp8KeYxwTCHxl6gFKoVa1cJFhko/sy+u3KLTFKqkZV3gd9emLUYSrRQrgQvq/25ShPIPar2AQhmkH8A5oObykTFSA92xERzRh/pFBlgF7CbN2+mf//+mEym341aLliwgMLCwitME+zRDkcKVPVGfXDZgrhVaipZ9es7H6tfvz7vvPMOEydOZPPmzVRUVHD27FlkWWbDhg1Mnz6dt956i9atW+Ph4YGnpyceHh41frRaLaIoMmTIENauXUtWVtbfYmiwfft2XnrpJVq0aPGX9/Vv4rYWGseOHWPMmDE0a9aMPXv23ND0HFmWnZ7qfwRJkrhw4QJpaWmcP3++Rq6pLMsolUrCw8Odf5zJ/frx0EMPERwcDA88gCzLdKmooHlJCSqViu7du9OlSxcOHz7sTBe7EfTp04cBAwb86qQjMjKyljOWi1sLURRr3L86depQZ+1ayMxE9+qrtPjwQ46LvzBkTj9izQ+zyX0MEcZ9BHWRa4XMXfxzadeuHYsXL77Zw7hhWK1W1q9fz+HDh5FlucaK7N5evW6affDVUKvV3H333cyfP5/09DMkx8TQt3ATbkkWLkXUAQQuGuuSGNudbrv3YhqqJsrvAt5/8HvrViArK4ukpKQaheDKK4q3LyMjYqUg8whubg/V2E9hYSFKpRJfX198fHwQRZEKyYerpfpojUb6rtlCHbc88mODEEUb3f32YpUUaEQT4jbJafMqD7ucJiUhIgyTkFeBZagSBFCtsSI1EVCky/Yi7kKchdcVsgfdw4/STwA/e3+5y30tqgSLLNrdpgJ22LcFDIN6PaH5Ml9abShFBLQ2EKsuj/MT+jSgBtlcVWgtA2a76PD8pZLdTXtXf7aTEydOkJ6eTqNGjbjrrrt+897UqVOHc+fO1arRTI6J4WjXrqTFxTlrSR0kxMY6r7OjBgNwzoHUajXDhw8H4IcffrBH1XfsoF+/frzxxhu/OR6wLxQolUpkWWbOnDm8++67v/ua38PLywur1fr7T3RRg9tWaOTn5xMbG4uvry8bNmy4oSLDbDazZMkSioqKeOqpp371eVarlbNnzzrdmcrKypBlGQ8PD+rUqUNMTAzbtm2jvLwchUJBQEAA48aNo6ysjHm5uaT37cuECRNq2I0KguAs1nOgUCjo2LHjdT3nK3HVXPxDeeUV+OADTvXpw6VLlwikKxEEsZBljNaPoExyp+OlLaj+YEjdhYtbHVmW2b9/P4mJiTXqABxOTilRUWQNHsygli1v2JgOHjyIl5cXzWs0aqjJnDlzyMnJIUKTSRe/fbilWBAkaHAmEwArbmxt14+9XQbRvFlzBg4ZflsuFNSpU+caC29ltG5K4nv1Irprn1qP7tu3j2PHjqFSqbBabZjNJmQ5EEGoWVcQt20bGoMBUYZWy1LYbOyLr7oU1U4zblXF286aidXANPs2GRDrjuSbboPIbZSLJEnIsoz2qUruDvuZCO1F2GKPeAjDwCwpWaevxFsFY7xArNYTw9ADhAGgXg85jSHsNIjHgXKQFkHJZk+QgznT0ZOGhy9wIBwQIfbc5UCKkF11VQLsxf+dNh1EYzQhAG4GE3csXUaT9HTUZrMzlWn56NFOi2OHU6TRaOTEiRP4+fnRsGFD+z5lGavVSq9evUhLSyN89epa4uHVV19FFEW2b9/Orl27AGjZsiVnfX1Jycy0RzTqXa4QCg8PJz4+HoVC4dw2duxYZFnG3d2d5ORkCgsLyc3NpbCwkKKiIkpKSiguLqa0tBSdTkd5eTnl5eVUVFRgtVqd/WL+Kl5eXrUEk4vf57YUGmazmbZt22K1WtmwYQP16t24Mrb8/HwWLVpESUkJnp6eWK1WlEolRqOR06dPc/bsWXJzc6msrEQQBLy9vQkPD6dXr140bty4lh3ivn37sFgsNG3alBEjRqBQKJyvGTFihGtC7+LGMnkyTJ5MtNXKzhmfYig30pAmuOHOQpbxmNSFgqTnCRtx7Pf35cLFbYTNZuPQoUO1emZEZGcjyjIR2dn4DxlyQ8e0d+9eiouLiYyMZOTIkVdNAXKIogbaDBppzznXpt2MRqKTkuxuPUj4+gfTp++Am9/N/HeQJImMjAwaNWpUY7u3tzcjR47kl19++c00YYVCyT3j76t1H8F+j8+cOYPFYnGmX4FDd10WMckxMSTHxNRIm+uyez9ahQGxGORV9sZ4DktaaZgSASsSKhTuIdB5Di2Vp+jatStNmjRh2bJlpKens+TSWB6uNwe1ZEIp2xBlGwabiqPWMj46BOIaoAwwg7wYtIcAPeADdZNAVgGRIJ8CXTN3lPcrafPQdlAGQuPGPDqigCxfBYe/86JhXiEqc1XGhAjicPCK0fNFzJOMPzGXkAUFiECrlBSqJwK2TkmpkcqUmJjIrl270Gg0lJeX4+7uTlxcHNHR0ezYsYPTp08zYcIEWuzcSWy1DuCOepktW7bQv39/evXqha+vLxqNhvDwcL755hsiT51ClGUiT50C7HUz48aNq1UzdGXmRGBg4E0xoXAJjT/HbSc0zGYzHTt2JD8/n7Vr19KuXbu//Rg2mw2bzVbrA/nQoUNs377d+cFeWVnJjBkzsNlsiKKIv78/devWpWPHjtStW/ea8njbtGmDh4cHbdu2dW4TBIGJEyf+vSflwsUfQKlU0qVnW7av3QuIhBHGUIYyy7yKsAxvxptLQO13s4fpwsXfhlKpZPLkyXz77bfk5OQ4t1fvTbFrxQoee+yxGqutfzcnT57Ezc2N8PBwp7vfqVOnmDVrFgMHDiQyMpLTp08TEhKCr68vAb7ulJWVsaukN96qMtqPOQTf21e047ZuJTmmI24ad4YMGfKHU31vNDqdjgULFlBWVsaLL75YI4KRnZ3N0aNHCQ0NvXx/HIKjmvAYOXIkeXl5bNq0iQ4dOtCtWzdnPYogCBiNtZv9XckdS5fSKjWVlKgo1g0eTI+E3ZiGqthf1pmuu/diGyziSxnlvT3Jn/wNjRuEQs4GyFwILV8GN3+6devGxYsXSUhIoEOHDpw9e5YKmzdbi+IYsXQlYiXIS8AjzsDMYBDWYI+OVA/aHMMeIgHMAhhDQBUHuom+1AkrhUo90vaJXJAfZl2nemT5FjFtlZ6QfD0W0Q0VNmQg4676VHTywmJVUmlzp+B4CHWwGx1YlUq7iYDZjEKSEIDBa9cSt20b2+LinILDIcz0ej3r168nNTWVCxcuIEkS06ZN49kVK65aL5OdbQ+piKLobOJpNBpptm0bqqo0JEcKnMFgcHbbvhVxCY0/xy0jNPLz80lJSeH06dOkp6eTlZVFTk4OBQUFlJaWYjTa/b/NZjOyLPP1118zcODA6zKWxMREjhw5wsMPP4x27lxs773Hji5dSGjVqsbzJEmibdu2xMfH/+ljdevW7a8O14WL64Kt3A2FQuWsIwpW+HKn7U7eKl2B9udB3Dlp300eoYt/M3q9Hr1eX2Nl09ED6M+u2ouiyKRJk/j000+dTe2qRzREUbwuIsOxQi8IAgcOHCAvLw83N7ca9Qg6nY5ffvkFlUqF2WxmwIABNFHsJzMrF8dX+Zr8YbSJO4JloRp3g7Fqqd5uS+4lFIFJA27+f3h8ZrOZvXv3olar6dq1699xyrVITU1l48aNlJeXo9VqKSsrQ6vVIkkSJpOJ+fPnO7//we44Vb1Gw8GePXvQaDRYLBaSk5M5cOAAGo2GPn368v33Wnx9ZX6vjVar1FREWaZVaiqbxgwgasRxthT1J8cUTm73YPSSJw9I33FY34su7e7g1LlzNG/7HkKb/4KoZOXKlWRmZuLu7k5OTg5KUUIpmfiFLVi3nGVkVR8LQQ/K7djTsAbbIyXW5qA8C/IgsOwHdZr9uSalyCsPtSPc5xLF+ijiK7ewUw+pqQlkmvdxuo0Jvwor9x4HrRWKtWZMPt7s79IVa4WS2FcSqPDw4M1L74IsO32hNg0YUCUKJKKTkp0RHHeDoUbtRHVsNpuz4aujJkNZJURMWi0ne/VCYTI5F20dGAwG1qxZw6lTp3h+61bnGFKq5lZarfYvzaeuNy6h8ee4oUJDkiQeeughDh8+TFFRERUVFZhMJkxVb8jqqFQqAgMDCQsLo2XLloSGhtqLVevUoWXLlvTo0eO6jfP48eOUlJQwc+ZMHvvwQzyLiui+ezfFY8aQl5eHyWSisrLS3pymyv7QhYt/Gr1796Zbt27MmjWLtm3b0qJFC8xmM/HpfWn42qNID4qYh8QiL9uIwOXeKwICClGBKNwazjwu/nlYLBbmzJlDaWkpHTt2ZODAgeTl5Tkno1OmTOHEiRMEBgb+4U7lSqWSSZMmMXv27Bq2tgmxsRQWFjJr1iz69+9foznYX2Xv3r3s27ePVq1aodPpMBgM1XoSXMZhaQ6wfv16ZFlGvuJrPGFJLJ1JQq/RsC0uDrBH3+fPncXkcfEQce09BYxGI7t27eLw4cOYTCYiIyP/dqFhtVpZtWoVx48fd24zGAzOhcY9e/Y4RR9c7iitNRrttrbgPE+NaMBUmEq+FOTct2N/a9euJjj418tTqhcxp0RFOSManXz3caqyJTkmu63qeWNjQkPDSfGtR3KBltaVlSxfvpyoqCji4uJYtvB7IjUHOVrSgJKSEkDGYoOWSfvYsPUg/sZq5q8y9nqMLmA+DcfdILsUwjtA+mEv+l4oJycYAgywq77ES1MP8XGsyNcxBczIUWCPH1iwYb8+dx+3Xw8bsLGxzP+NrssYujodnnx0Ouexcf6W0Qh6jLL2cn3K1q1whV3wr+FwyNRrtRg8PXF7+20enDiRS5cukZmZidFoRJIkFi5cyNmzZy+L56obYdBqWT56tPM+/fzzz/Tt2/eG151eC15eXs4IjYtr54YKjRdffJHvv/+eJk2a0LhxY8LDw6lTp45TRFT/7evre8M6LppMJi5cuEBmZiZpaWkUFhYC9hWzPT170i85Gc2rrzqdF8rKyjh16hSpqak1VpxcuPgnIQgCbm5uPP7446hUKuffY0REBBy8BJKM29rdsNgdhw+HDEiy/bdiVCUK7e3hz+/i1mfmzJn4+fnRrVs3EhMTnWlFycnJzs9ix4R07ty5mEwmlEolUVFRhIWF0bp16989htVqZebMmU4DjtiEBNyNRmQgIjOT5JgYysvL8fX1/eMnIDsmWCKyLLN3716UxnP4uCvx8WlNRUUFe/fudT7dMfHNqlePiOzsWvagV/vuiU46SK91u51WvI7nC1hRYYKChN8XGgljkaLeZm3CWdLS0moUxl+PaE5ZWRmZmZm1th88eJDhw4c7C4jBfk0Gr11bo6bAqNE4z7N3wFZaeJxmVtbj2KiZ1++YTlx5XbPq1aNJejoaoxGRKlvgZ591Tn4pvnJkIsXFxVjb9WNAI08SEhIwm80cO3aU0ycOYzZbiQw4gbsiiEqbF45cqM67dxFgtO9BBixKUKlBGAqchaIUqAywP5Z9xhOdsRXT7g5lO7+wtx6cnwYNdPBigsSXMfZ731AhUGTTUIZdfD67DzyqPox7ZatYQzvgssWswUNDndw8LL5KVMVWRKBHQgLZ3epitNg/qx31KddKdfvak7168cLTT6MRBPz9/YmKigJgx44dpKen16itcXQNry5m3N3dCQ8P/8OLAzcKV0TjzyHI19B8oaysDB8fH3Q63Z/23i4uLiYsLIy4uDjWrVv3p/Zxvdi9ezc7duy46ge3IAiEhYUxadKkWoXcLlz8axk3DhYv5lAHLe2errhsxVhFqUmD7/21V2RduPgz5OXl8dVX9v4toijW+qy+0lLzSpo2bcq4ceN+9zjr168nKSkJsKczRSclMWTtWufK+bvvvINGo2Hs2LE0aNDgmse/ZcsWMo6u48HQqegJYGdxH/KMAeRUBmBDiZvGHYvFUiOy71iFlgQBUZbRa7WY1WpkwFeno9THBw+9HqXFUuUUNIoXP/jQLowEqJzgTmVvT6yyErOkJkBViHdQJDS6F+r0Be8WcEUd4YY1lbzx9Bm+efw5LCFhrD/XrMbkMCgoiMcff/yaz/taKS0tZebMmbUyG6pTvTAb7PfDqNU66wiik5LokbAb3SAvpHglcy9O4krb1ur7cFxXx2/HPtcNGeJ8D/3e+6pHu3COnTyPzlSVqlfVElyJkVPmAk5fCOB8sYJitww2HFhKXLYFk9Ke2iQANh8Q3gPra3DQE7I9VUzt4MmQCyZeWniK3p+P4YzlJEXeOh5NglcS4IOqefkrCbC3HnTNtm+bHQNTkhR8sE1ElBVsi4+3GwEIQtU9lBGQeKDud9R95SIU2kVNYXgAurd9mZ8zAQARG1qliUprzUWiX7sW1f8ePT09ef7552u87sCBA7z99ts0atSIoKCgGgvIAQEBlJWVIUkS7u7ujB07lvDw8F99D9xsXnrpJZYvX86ZM2du9lBuCa5VG9ywmfPYsWOxWq1MnTr1Rh3ymmnbti07duyotd3R9VqlUjlXx1y4cAH8/DP8/DNtJCubvnVjgLvkXDGUZNBHvIPvla+xlNtXddUuJzUXv86hQ4fIyMggNjYWhUKBr68vy5Ytcz7efv/+WhOe6g1OHZPO6s9xNA6VZRmLxYIgCKhUKgoKCti/fz8dOnTg/JlUUlNTa0ysk2NiGLh+PUpJQqqalBuNRn755Rcm3DOKoBMPQ/f5VOhKMBkN+Ee0qTGROrl/JUeTtnOmyAtBcMdsU+GhKGKQ31IAyqxelE/3IvxwDhal0pkvH52UhLtejwTkhIbiWVmJ2mTCV6er6rdsFxuOI7VKTbWvwFcdW3YX8Bygx5PLzktWSQRzCeRugrQvwKKzP6D2A+/m4B9NakIHks8Npf9/F9Cl+QG6DdmDCY1zHwUFBciybD9HyQbi3xPhuHjxYg2REZ2URNy2bSDLpDdpQkR2NmqTyVlsbNRonBNpB3Fbt+JuNOK9sIwTKZE0nXCSM4aWNfZZXWSkREU5IxoO5yVHPw5HepYjyhG3detVJtkyB46kY5S0CCIYjWqysyM4n1mPnDx/Mu6MhQZl0FBGKUHTLaAAVJL9/lkEKFUo0cy2UVHhyxN3msgM1DPYrQ/+7TqiU7lxV9hdvJ33NmAXErOrDn1+qj26UU9n3+cXa0GBwNqYegTFTHKes0ajIT4+nn379mHQXaKe+gx+6mIu9A0jfGEOAhCUU4T/qyVEd0kiOaYjvopi+gZuZXHuGEB0/i2pzeZadRtBQUEMGDCA1atXYzKZaoj5pKQk3n77bdavX0/nzp1riQyAXr16cezYMWJiYm7ZKAbYa1JWrlzJunXrrprS6OK3uSEzZ4db01NPPfWbfuA3Ep1OR3Z2Nq1atcLb25t27dpx6NChGs8ZMmQIbdq0cQkMFy5+BaWopM7g/ei2xeCrsC/o6SXg4iv88tUbxPbaQHCLquK+/Q9CxTnov98+QTHrQLaCW8BvHsPFvwdZlsnOzub48ePOnH2FQlFjEnqlqICa6RtXe86JEyf46KOPnKlVkiTh6emJzWbDbDZz9OgRBJsei1y7StisVqM0GjFXKzDXV5az8PtPebLBZjI3PYeycBu/5N6FVVOXJk2b07lzZ7au+pasi8WY8EZGATKk65sQ5XXSKcp9VOV4Hy1HANRWK7EJCRzsGGOf2FUV13pWVjL92WedEz5JBr+yKyIaVSkq2+LiiNu2DUGSEDdJuPW3VF1XKDN5oiuSuHjamwMlc8i19cBdC75uWQQIh/FXHOOu8MeIe+8NNqf0xeDrjlQtIuA4fnp5OZbxw2l+5k4UQw6BRwSyLHP+3DlOnT7N4MGD/9D9Xr16NRkZGTW2xyYk4F41oXOIAL1WS6mPz1WjCx2T9qEx2vOSBKBFyikavp2B0rbc+RylxYIISIJQq4t45OnTzusN9h4a7lX7kwR7i25fnY7BVdkYyTEx6PXu1N2WzeBj65jl8xirgoYTEZENUYsY7jOLd2bIqCyglOzi4qIX6JWgrHorC8DR8jYcLexAz6cvMiG2Fd4ffkFObAMKVWZ+/PFHzpnPYVXXbhD3Qaw9ohGoB0+LXWy8mqBAFTMcnFIUunbtSseOHWncuDGSqQz3ra2RbeAzSIeQCey3P1VRJBG3bRvJMR0psfmzOHcM0UnJ9N+4EZXV6ozo6TUa59+YIAgYDAasViuPPfYYKSkphIaGkpyczFtvvcW6devQarU88cQTfPrpp+Tm5rJhwwaKioowm82IokhoaOg1pTTeLMrKyvjuu++YMWMG58+fJzY2ls8+++xmD+u244bMoMeMGYO3tzdvvfXWjTjc72Kz2fj555/R6/U0bNiQxYsX03DjRp7bvJmdXbuSHBODv78/HTp0uNlDdeHilqddeEc+8r6H5yoWIABC9yOsO/UpLS/9hP5AX1btUNEj+gP88hPAUgpHXiHTrxf1U18Gnyjo8e/pOu3i6uh0OpKSkjh58qSz9sKBzWajY9IBeiXuYEf3PiTGdic2IYHUPi2I0GRgktTkxtbh686PIskCaowkxnane0Kic1J0tbQPR+3B5dSSqztVFfv7o83Jodjf4dYkY5NkdJIXZyoaUlF6lCPlfSmxeCFbyjh0KJkjhw4AAl4KC0abu/P45+IbIt95uV5AlqG4gx9+B0uwKpUkxMYiCHbh5FjRd5zDkS5d8Hv1VZKSktDpdFcda3JMR+K2bUNrMCEvBamfgCjImCQ1RbYg8oqDKbVAC/9tDHT/P86XtmX72XFszhuMqaILax94j4iAC7RvcBSjTY1FViMjUGAKJCChGF+dDvV3XzDDR0mkRyyDlrfkghTFpkudKbN4ovEKqXHffq2mQ5IkTp8+zbZt2ygqKqr1eEJsbI1aDH21FKkriU5KYtC6jc7nytgtWx1CocZxryIyqqOyWIhOSnLa5TqsWgEGrVuHQpbpsWE33dcn4CFVosI+CX+l/H1eKX8fzgusbWzhjlQZ5RVJ6eHlcLSOSLMCDXn4UuHrSW6/plRE1mUddRFSBPSDXrQLG+wF/Kc5jRH7eVRPnXJENx5Ngve2gSiLHIkdgJ/sj1KlRKVS2TueV73H/fz8AD/2eb5K3tm99ArYhTQFxCeA+wEbVa5RAjKKWrUwMlUNAAWB2IQEAOc1bNCgAW5ubgiCwNChQ1m7di1arZbHH3+cqVOnOh3gIiIieOSRRzh37hzr1693uordimRkZDBjxgy++eYbDAYDY8eOZfHixbdkgfrtwHUXGt9//z1nz55l1qxZf66A7m/k559/pry83GmXC/DJJ58giiLjdu3CrbiY+KQkDnfuXKOvhQsXLn6bF4fPZ973W+kqhdCkUVseCvsEW3okumP/Zai3Cc48z3kLhCjhXMpXmKyfEKEREKwVYCq1RzY0N74Bk4ubS25uLitWrKC8vPyqDdbsSPRM3IlnqZ7YhARmPvsEaV2aolWYcMOIj6qc+mF+lBWeRIUeN9GIZrCJ77tMosxm7/XiiHAMrlYfWF14/Frh9R1LlxKeY08xCcvJAWTqqHIQBIlL5nosuDQe+XIPZhwryRLKGuLGcfzGW89hHqnGTWHGKolszOtL8tDOyENFqtcTJMfEcKxbLOXlWjSaUmw2FXFxPdi9e3cNB6baCM5JslVWoMSGJAsk6zoQ5ZlK0+Zngb1Y/bphrv8QDU2X6FPwJuU5R8g3+VNc6U3owSJYDZphZjR9zZhsatIrG1NZzx2fch26hj6YJDeOlrfjaHm7aucOJp2OCxcuUKdOHT7//HOGDx9+VXeu8+fPs3jx1RYY5Krzt0/oHKlOZrXaObFVo8eMFhBqpkNxuWYD7OlOSmckTMaqVP2qWLEqlagtFoSq98W2+Hi67drD4sZ3MefSo5SVeXE0vC0PF83BQ6rEw3T5vSoDCIIzAnNHKihlu0hxvCNkwKCEs/oBfBl3B+FdL+Ht486Ye8Zyb2g9bDYbhYWFHDt2jP379zsjeBlk4I47EhKvJBhpoIP3ttrFBTK8Hg91XlZyJ3fSEnuamEqlYsSIETRt2rRGU0IAz8bD2Ha0khOVUQwNWkNj97O4Yx+30mp1NniM27athsi4GBZWI33PESk0mUxMmzaNnJwcZsyYgVarZfLkyUyfPv1XLaYbNWrE448/TnZ2dq3GfLcC8+fP57777sPHx4cnnniCKVOm3NJ1I7cDf0hoDB48mJYtW9KmTRu6dOlCu3btfjOtSJIknn32WVq2bMlDDz30lwf7V1GpVOTm5tba3rZtW9zeegs++ADtK6/w6J13ujpyu3BRHXMFXFwBPi1B7W/P61Z525dmbUaE2XOY8J4VxgDLQsFSjEIy4w/IAlhkqK8CqwwNhQpUbva1M9mQh7A8FPzawgBXX45/G97e3jUsWx1cnqR3R93PjH6wFts6Fcl9+1K3fhMiIiLYs2cPtqrOx8NHvMBPP/1EcXExXl5edO/SHWn7dqha0U3s0YNBa9ciyrJzRbb6hMkhBLzLypzPSY6JoVVqqnP6b1UqiU46WE2QLK3hXOQQKGCfrKpNJtyNRmITEtjboytdd+/lUK/2dJIPYLGqmJ/7ABHNuxArFXP8bCl9+g9n27ZtzmiFv78v9947mIULl9Kx48N07Kh0WoZeeb0UmLChBgTSmzShVWoq6Y2bEGTOw0upZ19pVw6Wd2dS0634yFmIxXvQLtkDq0AaJuDWF4JVOXbHuNUgFAKrQY4HWaFmv64rT2XPQJCgzuk83vj8/9jbowtb2/VFjREbIjbUyLLAqS3/IzVkCBUVFaxbt47HHnuMjRs3EhwcTExMDAaDgYULF1aN/HKaj52aYstxLas7E5lxd75HHKvuNYu47YXPlx23JDyFUsrly/1DFAoRm01yHn9bXBx9tmxHkkRmek5h7ulJhLYspEGDLHqF7cTHpwwramYxxVm/oTabESWJVS192KRuzrvHToKkYIMQT29hL6GaSwh6GVmAfaFw39AmjAnqRj2Vvdlgk8ZNqRdar2o8CkJCQoiIiGDfvn2oFArKTUXEK+MJNmpokH2RH9st54VEGS8zqKu8EN7bA+17xZCrbwGAaBWJi4tzpqhf2Ul+795ELLIKZBW/5I2mriaL+1XfI5rtcrn/xo0AaKpEk+O6Hqvq9xWTnEz39etRm0xOUaLT6di5cyePPvoo06ZNQ6PR8HsIgmB3L7zFWLx4MRMnTmTixIl88cUXt6QQuh35Q65Tfn5+Vb7QdkRRRKPR4OHhQUhICPXr16dFixbObpwfffQRX375JVu3biWuapXhZnL+/Hl+/PHHWtt79+5Nr169bsKIXLi4DZBl2D7IXkSq9gNRDYIIksVeXKr0guc1kH0JAoHpv7+7Ws7VohoGHQWfyOt1Fi5uUYxGIz/88AN5eXnObQ7HJYOfBu1MI4cqupDu+yxdunShbt26iKKIyWRi1apVnD59mpdffpmdO3cSGBhImzb2guzPPvsMLy8v2rdvT3h4OClPPEGnbdtqiIFriWi0TklBVkHxGH9Ua6z46MpqORY5fpdWLVA5+gpY1CoO9OzE4Zj2NPdMp4VnCvlEEnPP96jc/RCvcH0ymUysXr2as2fPEhYWxoQJE5AkqcbzsrOzWbVqFUVFRdUK12VEJJSChcc/+wIfXTk6H29mPfcYJtmeniIIAr6KQp548jl2LXiWXq+vQCjCnhNzH5jjlBwvakGHo8cRVgPDQIqH875PcrA4kvCNe+iw7he0ZgNCJUgBAoUDA3Fba2R3957Oib0CC4giNsmeNqVUKrFarXh5eRETE8P+/fuprKz8nXfFlQKk+v9lopMO1nCg0ms0fPzKK4CN5h6nael5ghV5d9hrY6peqxBBlC0EKgrJMQdTmBtG5sUIMjLrYTRq8Pcvpn79TOrXz8LT0z4+rVaL0WDgyklSPvkkSgmcEE/C0p+xnBwKNje6N0vgv6Pfok/LHQhbgdVQNhgm1w9mUNv3KblYgU6nY9CgQYSHhxMaGlpjv2lpaexbupBeWQeo2HkUpVVNhZvAznpWZjXNJLgcul2A1xLsZ3V6pIL2g5V8mTkFk+BDm+bRDBk95KpXNCcnh2+++QaQqzdS586lS2iVkuqswyjz8XG6na0bPJjTffogSRJDhw7Fe8ECQv/v/5zv9U+eeAKDwcB77713TQLjVmbBggVMmDCBe+65hx9++OG6WDn/07hW16k/bG+rUCjIyMjg/PnznDt3zvn7zJkzZGRk1KrIHzlyJMuXL/+VPd8gvvoK23vvsaFdOw5Wy7ETBAF3d3dkWebFF1+8iQN04eIWJvkZOPst2Iz2FKfquAXCoBT4cTl88AE8+xCETQVLLfP5GjjFxhbsDauGAff0gH67fvN1Lv6ZmM1m5syZQ2GhvQFqdNJBeifuwPOOSugLUtOnEGOurmB/qxagOnv27GHr1q2Iouhs5KZSqbDZbEiSVCMlpzovNXwfjcKEvBVYDDZZQVrj5oRnXfzViEaPhAQSevRAeuQRoqPCyT61l+i+D6JQqhGuEBdXw/Fd2qJFi6s+LkkSq1atIi0tDX9/f/Lz8+nksRmfnTrabEjBhoJtcfHVVvgv463U0StgF+0OHEKcC0h20TDzuSn0bC4Sqf8aN9Fo//tUBUCHj+DSBkorBZYf8WD8qZ9Qr7Ha/2ZXA4VQ6evO9u59iE1I4FjvVjQYmkFCcTca7c0gcnvar9rDapUiBqtUw2XK7ibVsdZ9cEQSlDbb5cJuwKzVsjU+nqOduxASGsIkj2cQZSOfn3+WEosHSiwoJBtl5wPJKK5HcVELKgwynZqdo3nLcjKMl9Boa9dziKJAgNZCXP0klhxrSKnCnRQhheaHEtkbFcFhxUlsCqhr0/D0hbGMbLObAM9i/DxKay2mpKkb02x0+lVupA12/QxLZ6M/cJriLA3p+iYk0hM30UripJ/Zp8hgyBnwN8DLCSDK8N94WNUNRogxvNXSjfXpDdDW7cOIMff/av8xm83G3LlzuXDhQg1nNYeot/f2UHIqMrLqvdyd5JhOtGvXjhEjRmAwGLDUrYt3cbFThBzt2pVXX321lmC+nbDZbLzxxht88MEHTJw4kW+//dZlAHSNXDeh8Vs7k2WZwsJCzp8/z/nz58nOzmb8+PG1VPv1RJZlsrKyiIiIQBAEJEnCWrcu6kuX0Pn6krxsGcnJyQiCwNixYwkICHBGZly4cHEFZ2bB0dftkYsr0YZB3DbwucJJzlIOW/pAySEulxFK9pzlK6MZTwOFQCBI00BvUODpTtWTZFC4w5iy63FmLm4BJEmioqLCmX7hu3ABsQmJ7I3tQs8xu/BQVi1ctXgZ2n/wl45lMpnQ6/UYjUbmz5+PzWZjyJAheHh4sGzpUu4eexe5+YWsXbvW+ZrG2jTGhf9sn/I+DUIRyIFQ9ok3x8rakKjrTf3GLUlLSwNApZCREZl0/4NkZmai1Wpp3779Xxr3tVCRuZWMDc9Q7+UsfHRl6DUazG5uVRP8Dtgn7Xar0v57N1EwMIDC2EBaJ6XAKjjUqwNr29jrKe4d2gE2doG+OyHg8tiLcs8z7/sveSD8K7yU9pQ00yYVwmrY2i2Orrv34avTofPxYuMb/RkRshrTU254l5ZT6uPD9GefrTXuIK0bEbsSahZ+O6MTNXFMiB1IgsCuYT2IGpnKtxefwSrLvDBUQ9HhpZQWm9maGsfO7D7oSkJRihaiIy/Qs4eVbn3r41W/PSjs3/mzZ88mNzcXlUrlrGkQkAhwK+H0gYtkGku41CYD2cuPjudlpn5/GJ0G6rwALd0gKQI0X4K4H6gPlINlsIitn4xGtE+vKkQPPMeUgs0K67+GVT8iHztJeZ4bacbmrDSOYI92MA2HtOKZZ0VatQJeeopfjszi5Ugrlzwh5Uu7pS1AmR8opymoiJhLcO/x2C5uQqzTG0Fx9boIBxaLhe+++65aCrnsTAd0r6xEbbViVirRe3g4xWF4eDiCIJCbm0vbvXtrRALd3NyIjo6mX79+v3ncW5XS0lLGjRvHxo0b+fDDD3n++edvWKPofwI3pY+GIAgEBQURFBREp06d/s5dXzMXLlxg7ty5+Pr6EhQUxJkzZ4iNjSV6yxaEV18lLi6O4uJiGjVqRL169W7KGF24uC3I2QSHXgZb9U6oCsAGbsHQdV5tkQGg8oIB+2H/Q5AxH5Qe4NWEMu+2aDPn4yZXWz10rIo2BfEZ8BhmwxpnlyaCACafFlTk7kWvz6Ve/aEICtWvDtdqtVJRUYFKpcLDw+NvuQQurg9bt27l+PHjSJKELMsYjUasVitPJyTiq9MxYN0m9qk6Ez3qEG6iGRS1bWf/KG5ubs6c9aZNm1JaWkqrVq0AeP6FFxAEgboRDcjPzycpKQmNm4oRdVY5m1HKw4HVIAwDH1UZ3QP20SRAR+g97/Ljjz9SUFDAk08+iSAIKJXKG1JAajab2bJlC+3zJ5NtaElOz1A67TqA2myuZQEMVbUjOgve6yoI+s+nmDwf5HB0O1LVD9DE3d0eGfJtCWN0tfIbJYUnBsEfGQWSDFZZSWqnKFY3HAYosMlKBm1fj5vZzLDjq1ksjcG/e7HT/evX0tPGJSTU6PTtXGS4InUqITbWGdGwKqoa0nWI5uDFTliMRrIu1mfKC1rOF36Oxk2ic5tsnrzPQud4Fdo67UDsctVrGBkZSbOmjWnonsHeA0mcLgrkeHYKp9yPoIo20bxud2ybA2lSvpZilR69GtQ2GHIGwnWg3g3CfuzhlfP2fSrWyCje+gGslXBwMZ7nj0I3L2xFcF5uxhL9WBbpx6Js0ph77oGXHwVPz5rjSgr1516VFUOVdvgg1l4ErrFAdpumNJUvEtza3ntCEd7/mt4vKpWKXr16sXjx4qqohlDVCTyaFz/8GLXVitJqxVenI37rVvwGFBNkK2B1wQisNi+SY2I42LGjczJuMplISkoiMjLytptPnTx5khEjRlBQUMC6desYMGDAzR7SP5Yb1hn8emM2mzlw4AC7d+/GbDbXeKxu3brcd999rnCYCxfXwldfwbuvw4Bi6KsAVIC16gcQlBAzC5pcg8HD2W8hJB48G0DK+3DifXsxucLNXkxuNYCpCCbnOiMbjhoPWYb/FMLX5VAuwflH9xEY0hn0OSCZST5dxL59++xRS6sVWZYxm81ERUUxePBgV47tLUxOTg7z58+/ahG4I/e+1MeHBa+M47GIWdDmPWj12t92fJvNhizLV/1OkGWZxMREOhdORqlP5Wrrm7LKFyGkD3T7CZQe6PV6BEG4oXadlZWVzJw50+5CJVvxUlagt2mxyuqrpCJV73S9m309utJ7yQoyDi1n/f4cpkx58lddgqofb/v27QyyPIlg03Mm5H3CyhawPi2Mk2UNAYE3P38HsQhkUeDUqBbs7tAdg80dndWTp6bOrNFw0JF+Azi7r8vAl60n85PnOAYN2Frt6LLdPKJKknjaysjKjODw+fYU5AUToNUzqGsmcX21RPeJRO3XoJZQOllwkib+TVCKSgr0BWTpssgqOsm59IUcPZXMvnwZLy896YKVCE8t9VS9ObatLRU5jfha9x8SG53l8WSIrHLj1SvsRhceVrjQEMIzQagPkk6Aup6ImMEoU+Jdn+XWe5me8xD5YhhdusDjj8NvBQEWbl3IAyse4IEcA610cFeiffs7vaHLRajXoTtdXnwEdcsJVyl4+302b97Mnj17amzrfDCRLrsP4K7Xo7ZYkNWAF1QOcUff2wMJEVkWOFzWnuOmXnh4eNCqVSuio6Px8vL6w2O4GVRWVpKYmMj27dv54osviIiIYMWKFTRp0uRmD+225JbrDH69sVgs7Nmzp5bI0Gq1PPjggzdpVC5c3IZ88AHkFMO6EJiwCGQjSOUgFYPtIriXQYNJ17avxtX+9hqOg4g7watZ7S/H04/B1K/sEY4tducby1AY1gMKZDhjgMCT70LyJSg9RIFNIK/VMgwGQ43CUg8PD9LS0lAoFH+ocZiLG8v+/ftrWW9CTaehfb16MmLC61DZBurE/63H/y0RKggCsV3awy8Xrvq47BaE0P4TaDTRue1muNN4eHgwcOBAtm7dSnl5OWVWh1Oife1QYzTWcNACGSHeRuGIQFqJKUhFh4nsdg/Nu8rXlC7i4eHB0KFDwdILVF40B8pyOxN96Q5OlTVAIVgxD1Wj+cmMIMlELjtJC598bL1LmJ35CAd69aLv6tWIsmxPqKwa2/Rnn6Xd4cOE5+Rwwj2STS1lVgzqxbyL91FhboCH0kZz8SCLUsaTnRlOQUEIjX1zeGbEfO6JL6Ld8MfR+LYAogEwWo2cKU63CwldFhdKz3E2J5F5Z3fRrk47wrzCCFJr8Cs5TWGqgZMFEBJgZFMbA/U1ZnSmUF76+V32ZPRm4uh13NHhLBlLM2mTDw1KL18Pra1azKUQ9vaHTiNB1KvZnfAs/8t6kuSLgfjLMHIkrH8GwsJ+/76+9MNLTE2bijXASlI4fP4JCFVB4Bf3QLavgq49EhG1j/wpkWG1Wjl06BDu7u4YDXqkqqXmo53acyimI20OHCNuz1bcK41QBJ7L9HgOsC8I6K1aCszBxMfH3xY9JQwGA3v37mX79u1s376dAwcOYLFYCAkJ4Z577uGTTz65bUTS7czfKjSKi4vx9/f//SdeBzw8POjYsSO7d++usd1gMDhrNly4cHENvPKKXWy88AL4BEOlHqQAoAEI0aB1g6RUp18/ogAqFWjcQKsBTy14u4ObW80vQo/6v3HMmdDkZ7CWIT9tt9e0rAKvXvCSL+h9YMHhTdzh742bEgJFmfL9d6L0/AyqGdiYzWZsNhupqam0a9eOsGv5Zndxw2nVqhVnzpy5itiQOdaprVNwnF68gAn3PYC/9w3+XjFcApUnWMu4sphaaPJoDZFxM/Hx8aFd27a4p7+Hv5CNl7ICr53leKyvRJDtUQNHgfqw5tl08CmA3olgrQCPhgB/PCddZZ+YybKMpAmHiNH0th0mtTgcW5zCnvc4FwRJRv6mEPN8DRFxmbSe/ByV9RrjMWs6oiQhAxn1wqmgDP/iYgSgKadYOPQUbgI0FVL5cvO9mEtFkr1bEtMkiaFxK+gVU4Kv5SQaUw5m3Fi1Yy9LLH6YrPb+Im5KN+p51yPCJ4IInwj8tRqel7fzaj34uiyZzvmwMkVkn1sefXNLeSUemnmZELUg28BPc4nZ9z9EudWTQ6XtEWcfYnUzK1+vAc3lBvU1Il1Km8ibpufwnHcnWIzoNH24/wlYOx7+SCLFzJkz+fidj3F/zB2rYMVoBlNT0BSDpAKv4dDWaENSRyB6/zmHPqVSyUsvvUR5eTn5+fmsWbkEq6GYSltV7la8DfdxRngEqLlui04IJ8fWnHaqX09hvdmUlZUxc+ZMNm/ezN69ezGZTAQEBNC7d2+mTZtGnz59iIyMdNVi3ED+ltQpWZaRJImPPvqIZs2aMWLEiFohaaPR+JcKro8cOcLOnTvp2bMnbdq0ueqK1IcffuhsxOdAFEUiIyO56667/vSxXbj41yLLkFsIGTlgtoBaBV3a1BQQZjOU6e2CRG8EowlMFpCky89RKcFNbRciHhrw8gAPLVR3K9k7Cc7Pha0CFSsF3u4m8U0MvOAL473AWyEQUHc7gnEPlP4fsqTnmTwILn8FK7U/WwICAnjsscdcKVS3IDqdjhkzZiBJUo3Gdqc6RyIhYpAu19h4e3szYcIEAgNvQkPHk9Pg8BVFzC1fhXb/u/FjuQoLFiwgLS2NB+vOoa72on1jlcGCLMLaQUOcoi0kOIhJ945B4/XXruPSpUu5ePEikiRhNBqx2ayMCv6ZLH09+gZuQSHKdje5KkcrqOps4eaGqHbD1rEz4vbNCEClCto93Y0jnyfhYbLYm7N7A8MgsYMvpzb6M2R3FpeGWAnsD54i+FX9OdtkUAhgELTI9UZS2e4zjDYLRquRDcfmcCplJn3rtOK/madIqlOOUgSDBEVWgUtyELnnCxlySkLYBsJwoK9daAjVPi7kKle8i81BOg71ynDawBqUoLHaq9ZKlR58MWAFQ/6vL+3a/fFrKkkSL730Ep9++inPPfccfR7sw7Alw6irgMxpIBbhTCs1SypSPZ6h/R0f/Ym7V5tTp04hiiKbN2+msLCQAFU+D9X7BvV2M+JqkIaB2BcqbB4sLZlC7OCHCQsLuyV7TKxatYrHH3+ckpIS+vXrR58+fejTpw+tWrW6rZ2xblVuWOpUTk4O8+bNIzIyEoVCwYkTJ8jNzWXs2LEEBgZSWVnJunXrSE9P55VXXvnTKlIQBEpLS1m3bh07d+4kKiqK7t27O9/shw8fdooMNzc3zGYzDRo0ID4+npCQkL96mi5c/DsRBAgNggBfOHEOjMba4Xq1GgLVEOh79X1IEpRXQnmVECkogYv5YKlmlatQgPJR8BbghYfx/LglloWRWAvzaKFR00htRi/7g6QHbRyooxBKP2JqSCK9DLOJsz6JWLOklJKSEjZu3MjgwYOxSTaskhU35V8vKnbx1/Hx8WHYsGGsXLnS2SwvNiEBW5xASkVbwP428/T0wsvL63e6YV8nJCukvF17+8VVt4zQGDlyJF999RVLcsfwYN05eKsqYFhV6uEQJckNq7ppq9UYjCYuFZbT8C8KjcjISNLT02vck/UFg3ig7nd2kQFY4hSYbWrclxlAX+U2ZzKByYTixHHn69wtkDb7CIRZILNqY1WzwGY9yumwpxStDkI2gjwYZ2E+VEUUtoB2tQHL0AVU9FzGaZMnQSojvQUD7VQyw1KSmRcsoah6nVaEurtk6q7ORx4GbLM7ibEa6AvCNmAN9msYB9IqUBSBzWKvxxAAqwBP91WyJaIuPfPz+djSA9+Tp3h97CVo98evp9FoZOLEiSxdupTp06czcNxAWn/ZGmToqgGqTAgYBuVWT066P0r08Pf/+IGugs1mw2KxcODAAXJzc7FYLCRn6BnoraZODwnPvlZEqtabTHXRhnSgYcOGt9ziTW5uLk8++SRLly5l8ODBfPnll9Sv/xsRdBc3lL8sNBxvuMOHDzu3FRYW8sMPPxAWFkZubi7l5eV4eXlhs9muuSDbYXtYWVlJRUUFly5dQhAErFYrOp2OPXv2sH//frRaLWNLSmj29ddMmDSJBh98gCiKnD9/npCQkFtSdbtwcduhVkG75mAy//5zr0QUwcfL/nM1JMkeBSmvhID/gt4E5y4yvd16ntCdoqlnI7AU425MBtHTvuSoDAO//yCaD7NTeIfnf/6QNxLU7I293DjMXWUj69Qc1hZPxmjM50TT13mz15t/4SK4+Dtp164dFouFrKVL8S4r40K9cNSiBauswMNdS5eu3Wjfvv3NcxATlTAiE1Y1sts7B3QGyQTGQrCZ4XesRK83RqPR3oTP15essjKyjfVoOfskwn4QOoMtXknX4j1c8h1HbGwsjRo1+lvSRVq1akVSUhJZWVnObeVWHww2DW6iiZMVLQh2y+PbRvfx4IwFhCVkwDzAUiUOotpCnt1eVQAo1yNXYg8TqHBGNIotbnzbU899O2BhD5BKYKI3BFVNIQRw9vFQrYGIvmYClMV8uBQm74QvusPAHhID1dXWRraAPBcECaS5IDQAwQo4SoBWA0XAIpCXwo4AJc2sEmu6tiUrz4cpaQf4Oq4OG3sJLO+/nDNnzuA+dKh9seVPUFRUxIgRI0hOTmbZsmXYmtloPas1ZtmMrwI+DsK5fCIhsM32AiNGX0X8XiM5OTns37/f+XPw4EEqKioQRZE+ffrQuHFjunTpwonyizTy3ex8nSBAmFcZje8YjHALiQxZlvn222958cUXUalU/Pzzz9x9992utKhbjL+cOlVSUsK3335LZWVljRD4lc15lEolDRo0wGazYTKZsFgsWCwWp1vM1VAqlahUKlQqFZIkkZ+f73yuIAhoNBrat29P/IMPImZnQ/36kJHxJy+FCxcublnOr4JjC5F9nkaoltsgr1wCP/9AZXE+nhb7XKXw3kCkvgZAj6co46mAfQY4Ff0997W9j7zKPERBJNgj+KadjovL6Pz88CktRefjzan3ItmqG8GkSZNunfqaX+rADndYYYbX3oDJk2/2iAD4/vvvEQSBzMwM+gZspr33IdzvN9r/CESQfgST7I52nM4umn4Fk8mEKIqoVCoOHjxIw4YNCQgIsD8oS3DoRfBqChGjQRMIkg3d+iF8c6I3FfrLUQ2FYObOkOVsL+5FH/+dGCUNPsoSGr6fgXC+Wl/vkDrg6wenT2JFpAIvzAPLCU6RsAwBYkEtwlmTgjp9jrB/7Ut09d5Asc2dcqsCf08RP1spCkC3GTzXwLEBML0DBIgw9X9AIZR6KHD/2Iba5/K5yk9V9UIBsv3cCCo3obWC7A/CQOxLr6vAVgYKCbJ9RKY+9ia+HmoaRNSlTZs2tPszuVFXwWKx0LZtWwoKCli9ejVdunQh9ttYEi/YLabWhMFAd1A8Yz8fKQBspw6hCry2viwVFRUkJyfXEBYXL9rT68LDw+ncubPzJzo6Gs/q/ro7R8DFVdgkgUqrFp0B1HW6EzJ4AbgF/C3n/3cwefJkZs+ezaRJk/jkk08uv29d3BCue+rU/PnzKS0tRaVSOVOWqofArxQagiAQHBxMQEAAHh4eeHh44Onpiaen5zVFOS5dusSPP/6I0WjEy8uL6OhoYmNj7RGV116zF69epcmPCxcu/gE0HE5ujh91rAqsko1yi5GThnM0mTud4GITDlNREXD/pRDP5fb51tahkNgdllbAyZX38+DK+5GAet71yHo2C7PNTKmx1CU6biJ7e/em58aNqM0mAk8257WZf5+N7d/GCiNkX7J/z9xkoSHLMmlpaeTk5GC1WvFVltDO+whuChN0BvYDne1pRmqMcGkThP+6A9vatWvJz89nVM9Q1q07ire3N88884w9crNrJBQlgWyF1HdB7YcsavEpP0ZrryDMO0uI27YNtcmEKElIokik9hRFkX74nShFNEr2tCnHwby8EcZNQv75BwRAQOBV3mfFxmH8t1cU4feV0SdMiXDYRlplC07sX039pn0x5h5k5aW76B7TiuC+j7FtaVvE0mOMbgz6Z7U83/V5vttihnWfYe0ioVwr4TPQBp7YzSLUgARCX5CX2CMafuUSCgkMCkAHbj/bPz/K1PB5N7jvOPzYS4LeZbw14LO//T4qFAosFgudO3emSxd7j4/dD+zm3R3vopnxNoM+B/MwUFT1GhKHgXh+DgR+edX9Xbp0ibVr13LgwAH2799PSkoKkiQ5jXLGjx/vFBa/2+NFECAkjotiN7JtbXju9U84cGAzo0Y9yqBBg2jfvj1RUVHOvjQ3i+3bt/PYY4/x5ZdXvyYubg3+dETjaj7M0UlJ9Nq7F80771A4ejTbt293pk5ptVomTpxInTp1/tRAi4uL+eqrr2jevDmDBg1ypUS5cPEvo3DfIaSSMpIuZHA87yJF+z7glW0VIMP2Jir6pcuoBQVuogmxyokqwwca1m5ITK+IXuy4fwezEt/jrd0fsvfhQzQJcHmpXw9kWcZisaBWq1m/fj2enp706NGD7d8/gsGtKUfOm3j840/w1ekw1amD26VLN3vINVlWBzabYIOPfTHrJguNH3/8kczMDCTp8le3AjOT68/GV1mCQpBqllGF9IX4zbV3JFmxHX2HGTsCqKgoJ1ydSbR3EqsLRtA+si5D1P8DfVV6lKC0i40qZNmeymN+WI3WULt+RhKgqlzDXtsgA81bwuy59m0rl8C0jxBksCFQih/v8gZ3uM+iovEZct6FCQPUnDnTlNLSWfTo0QOrUYcgqlGotby36z0+3fsp0wdO5+7wQahmzkResQLh6FGsXgKiUUYIAUEDWLC7J1X9GM0CkklDhULJmSAry+4Yhk9wC954910UkkS5Vk3AizZ+9AlgbUQfvr9zHsrfiAj9FRYsWMC4cePYs2cPLRuG4eMZwJZ9+2g+qh/1yqBMC1YNpA0QqNevDmHNeiDELqqxD1mWmTNnDi+++CIVFRVERUXRuXNnOnXqROfOnYmKivrLNRWSJPHjjz/y8ccfc/LkSWRZRqVSERUVRfv27Wnfvj0dOnSgY8eON1R8BAUF8eyzz/Laa7fg4sS/gOse0ejUqROHDx+m5a5dznSp4jFj8FqzBoBQYNy4cVRUVJCQkMDRo0exWq2/vdPfwM/PjyeeeOKWbRjowoWL64u2dXO++eYbSktLAXgqQU+AAYp8PEkZ/TypCHh6etIlcSVdN+1GBpb2ufq+OoV1QpIk5iT8j0KTnvhvO7L1wYM08K5vd8iR5d9tYObi2igpKWH27Nl06tSJ06dPYzKZsNlsXCiGcxXlgIKE2Fh774wuXeheVdN3y2CrhJ4V8GEi+LS8KUP47rvvUCqV9OzZk6iWLRktTMZg06DaZsV9rZ6c/mEc94uiq+8eFDskZ/EwfcFceBS1pQJZljiWkkqb9l0QJDNsH0TyGRMV5f2RZMgxhqIRW/Fw3dlsyBhIpp9E/aryGEmyXu6Q7tA3W2Q0VSJDrvqRRBGzWo25tRqfg2V2geEOrFwDymqmLMNHI9vM2GZ8jhIbARTzf7yBRm8iM7UOU4/k8vAIM8HB+ZSV2asUlJrLOVBhZ8N42fo8HR6ZyfdtdvHAd98hyzKiICCW29v6ydXaoJiVSgzuWrQGIwpZYlr4k5zr25UHz3xOHa8GGASB/Z060eHQIbb1jmPAeSV3fb+Cu69zPcLYsWN5//33ee3113jv3C4qFBJjxsCkLvDMfvA2gX8JRK735thjCwmP7Vnj9efOneOhhx5i+/btPPDAA3z00UfXJX1IFEUmTZrEpEmTqKys5NixYxw+fJjDhw9z6NAh5s+fj9lsZsCAAWzYsOFvP/7VkCTpprZUcHHt/KmIRm5uLuvXrycrK4unp07FV6ej1McHz8LCX02DkiQJQRBcRTouXLj4w+zbt49du3ZhMBic2xw1YYk9enCwWvOosBB/kvXPsc1g5YcQGJ4DRVd8yimwp21IyA4HTkJEJd2lvuT4XGK0PIFHH36U06dPU1paSnx8PLIsYzKZ/pJN97+RnJwcfvrpJ8xmM7IsI8sybm5ueMl5GG0izfedrlHbFxwczGOPPXazh21HfxFW1ANkaDgJun5/3Q9ZUFBAYWEhLVq0AODMmTOsXLmSyspKBEFAlmUmhM+lkft5p5UtHiBrQR4K4hr7NjkQKj71QC1aOCzeh6+ylPqm1Sjdg1Fai8nXu7O/tBOF5iCCd+cRt207SqsFVALiGBvpXRpTZPZnb2k3Hqj3A/7qEkw2NRWbPPBdXopYKTtTovRaLR+//DIAPXr0IOXgTiamTEO7yoDY0h1liv258kOTEYbfARULOXriJHPf7MfrZruDki+lKJCwIXDJTyb8PSgf78m0ac+i0XgSHR1Nt27dKC0tZfm0abRYsI+muemEWi+hqJrGyIBepcLDYkGPGyhF3KwmTqhaI9VxJ7tdKAa1SIOXX6Jjx44IZWWs+O9/OebjQ0BAAElJSUiSxDfffHPDFhpWrVrFiBEjWNdM5PtoiSXNQbABAqzYomToaRvruw1F/eST9KtqJ26z2Zg5cyavvfYaQUFBzJkzx/nYzcBsNvPtt9/y+OOPc+rUKZo3b37dj6nT6fD19WXRokWMGTPmuh/PRW2uW0RDlmUWL15MSUkJAFn16uFdVkZe48b4/kathcvD2IULF3+Wtm3bcvToUYxGI7Is1zSeuLJDbUU6L/v58rCXAUmo5GFvFR/qLM62a+uXu9H/uIkVUTKjRl1+WZ5k5Rc2gA4uUETxZ8WoZTWCIKAIlvl8+wP0k+7n4SffuuXsHW9lzGYzkiQhVeurYjKZsOIOCPRM3IW3rpzYhASOxLSloqICk8l00/O/AdgzHmfDvrztIFlAvH7NyiRJYsmSJRiNRho0aMD27dtJTU1Fr7d3Zna894On5WEZqUA1zAarQTbYm1yyGqcdqjRU4HxlQ5J0MeisXlTa6vB8wzWoTPZ0qBC3CoYFr0FCpDLRHXeHiLeA/CM0O30GOVmgq3k/KEDSQElzP4IP5yNWW5+UgG1xcVXXScDv9JN08DrHK62sfCFZEReUITjSvH6aBv3rQNl0zl0awQzr08xWT0ZvdmcNQxjEBoxKmbolID8OXlMquCd6AT8PHU9iYiKJiYnIssz5g815JPsTlFx+Tzkw27Tcx3yEYHc+kd8iouQ4rdd9An370sJsRq/X4+vra3+yjw+D/vc/sr/+mv/7v/+jUaNGbNq06cZFMyUbw7oG8/MLIUQ1zsNTD5SDrIDH9kP0cSun24skd4ymUV4eACdPnuTBBx9k7969PPHEE7z//vs1C7lvAmq1mvvvv58XX3yRhQsX8vbbf94Z61opLi4GcEU0bgP+VOpUvXr10Ol0SJJERHY2oixTLzv77x6bCxcuXACg1Wp54IEHmDdvHllZWbWMJ7y8vFAqlSgUCiRlHTIbL2Hfnl1M6qqg3klfGvAp5zmPgED/Y2ZEGYanAKPg0SR4JQE+iIXZVR4WHyy9wNjU/3E0qhmLxjSn3y/2L854/2ksXRJJWHhdevTocfMuyG2EI1XqStSiGU+lHu1IPdbVIvu7dgKFFpvNxu7du+nbt+9NGG01MhdB8ZHL/zcVwsU1UO+O63bInTt3UlRUhCRJfPLJJzXEmUNcq81m3A0GpNUCzABrnALDJg3qtWbUwy3QF+gLCmSa29LYWdKLZlVRI2WkBVKw1y0Asgp2x8dS0d2LuG3bUFotqKxWBAnkfYIzUoANqMQpMiTAqlJhUyrZFteH5JgYlBhp63mMKPeTqJQy3bxB+LiqQzhgU4iU9BTwy3sBpQoUFisNvDPoVppIA9URXrO8T0vPDZR6QOt8UFblYzU+lA5DBafjZNL2djyx+8uqeCQIIvYqbqu96NyLSn70ehD3O8fDswvs9tpN7PVXarW6hogwmUycO3eODz/8kKCgINasWXPj7JQPvQCZi7CZyrinfRkAxko4uAo6HAGzAtwsYDsqUX9SEHFxcbz//vu888471K9fn127dt1Sn0EnTpygsrLSGYm73qxfvx6A4GCXkcetzh8SGpIksXDhQs6fP+/8AHTk1lY88giu8mwXLlxcL5RKpXOV2/G5s79PHwICApgyZUqttMzu3bujUqkY176UrM+zWSYt407u5HTbTTQ/doyENhItVQKvJsjU19nFhkNojE21T3TapqbRUUpz7vOd0jJet/wfaed73VJf8rcyJpOJdvv2OSNQqV1a084zmc5+SfiqSqE+5PcMIl0/gtHx8YSFhd38Gg1rJSQ9Dlbd5W22Sjj5yXURGtnZ2ezbt4/MzEznd2t1kQGXXR31Gg2GAG80IyqQZRkBiYyu4TTsm41B0mI0aTDY3Cm2+FFoDsIsuTlfKydB9SCAaIZ2O48y/dlnSY6Jwc/Xk8cVblS++SaZdesSefo0SovFWXtxrkkj6mZfJCE2lgtDh/Loo4/ScP0bnD9SiLagjCGV66CO3bRIAORhIP9gLwY3eGmYeukV3jS/h67Qg4yljXhcN4uH+RJDaw+WZjxCekAfDoVup8ADumWBuxX2toZiivGT/TEfFpm283kacB4FgC9QevmcZKAwMIAlQ4dyRqWi/P33KS8v/9Ufi8WuuBo1asTGjRvx8/P72+/tr1LvDkynvsWNMuemGUEQcMTuiqWU4KK7khy/VvTQ7Gfo0G84evQozz//PP/5z3/QarW/vu+bwCeffELDhg258847r/uxFixYwJQpU7j//vtp3br1dT+ei7/GHxIaVqsVo9F41aJuZyjShQsXLq4DSUlJZFT1yUmOiSE5JgYPDw8m/UqDJpXKnuLi6+tLn059CE4NplWrVoQ+/Sqiry+Ri7py3LqP/YMUiOttfBALYtU66aIou9hYFHV5f/OW2rctiTrBqnvMvGAwoNfryczMxGazERAQQKNGjW7Epbit8Jo/n8Hr1iHKMnHbttFjTyJ7Yzszt/NEhKp0G6PkholicnJyiIyMvNlDBkEF0dMhdwdkzAX3eqDyBFOBvRr6b641TElJ4cSJE7/5HIe43tOzJ/XefIpWZ/ohAApBppV3GjICuRu98Vteir+1hBBFLtviw6iI8Xa+Nq9+EM3OpiNbwCorsSqUJMTGOo8hKtxQPvEEv3h41GjI5+GuwWyxVk3M7efeLjQUQRA4a3yJkd5ReISWgbrmpRH62i+XcZHIL4o2HM9oyf7MThg/dec+41x0Hl54VJrwUpho3uA0063Psvyu7eQa4eha6LoDYo5Dt6MzKPX0wrOiEhUShAMPA/+teY1ylUqa6fUEbN2Kl5dXjR+HgPX09Kz1WM+ePQkM/Gsd0/8o8zdlsGm2ju8fvdzxPFAJdAZ5PyT6h6AaWR9lextl2cuQrA3Yt28fMVe0DrgVyMzMZPHixUydOvWamzL/FS5evIgoiixbtgxvb28ef/xxmjVrdt2P6+LP8YeKwS9evMjcuXMxmy93B3YUg1vCwlBVNYNx4cKFi78bq9VKZmYmqampZGdnYzKZ0Ov1DBkyhPbtf7uJ1dXMKC4mfoxgyicwOJw9+99n5IUKdOjxFr3wULjhIUikm4udz7f8xx7lsAFyAOzoFseRzp2wSAIWSUk9P4n7n/q/63X6ty0mT0/cKiuRBQGLpyfq8nL0Wi1mtdpZAO6vKkT0ieSOO+64dRr1AeSsh913QewiCB9yXQ4hyzJfffUV+fn5tR67WhPc0aNHs3rlLzwX8V/UYtWi3xbsdRkGnNbOAKU+Pkx/trq/s8TE8B9ZljsKhUc4kiRRUVEB2COGvr6+TJkyhRMnTvDLL79gs9nQaty4/4EH8fPzY86cORQXF1OnTh26d+/OrFmRLFwIF9r7oIwtg3p2jXb53ECyCmxb15DHG0tIy1Yz8Z5lPP3uVHwlHbICsIEQBuvuGMTb699h/uP30iz0DPIjIFQ7FxnADQQ10BzIALxAzgRJKyBWyiT4+9Pm3Dl8fKp16bsF+fTTT3nhhReYNGkSTw7LpJ1hu11sbAHbKvi8u0Bop4aUiH0pMgfRL2gz7Ye9g6reoJs99KvyzDPP8NNPP5GVlXXDUs+ysrL46quvmDNnDoWFhQwYMIAXX3yR+Pj433+xi7+Fay0G/0MV2h4eHowbN46IiAhncXdCbCylPj6cCgigLCCAcy+95Gzg58KFCxd/F0qlksaNGzN8+HCmTJnCY489xp133klISMjvvlYUxVpRj/DuLxIW9zHqVs/Qe9BPbGsgMqb5cHY8tJMzz2bwTtcPazx/URRYBTAoQVkELXZvo3jvbCZ9+ilBSUvwktJITU0lJyeHgwcPsmzZMtLT0//Wa3A74ogsGTUa0hs3RhIElBaLs8YGIG7EZKZMmXJriQwAU5G9f4Tv9UvPmDp1KgUFBVd9zJHyNHjdOv6fvfMOj6L6GvA72zeb3guE0GsgEEINJaEIoYlUKVIVAQULKip2QbGCCohSpVel957QQuihtxRKes/2ne+PSRYiqIgI+Pv2fZ5JtszcuXdmduace1p4fDwA2Tcu4i1LJtfsLq20HZgPZEq2BptOwKKUYdEpsPYqRyPv07irCkpalPHL9cEYcGfAgAE8//zz+Pr6otFoaN++PcHBwQBUq1YNrVaLs7Mzg4cMxcfHB4VCwdChQ6lZsybPPjuEAQNqcPW3Y9wIDoLeJoRKt5UMUSyxZBSpKB6s5eblOvTTD8ZZY8FFkc/5jtWwagXExsA4oB+0D9uKXGZlyqaxiCaw/W4KVCj9UwAclcZLEtALvEQFndq3Jzwl5YlWMmw2G1Pe60f2vnGs+ziEXk1Xkpe1m1wriCXnUZ4FI/apKB/yFJFte9J/wEAq99n4xCoZOTk5zJo1i1GjRj26+BYgODiYSZMmkZKSwi+//EJWVhZt27bl/fffv8vt0MHj5YHS2+7bt4+9e/eWcaG6M83trAkT8PLyol27dpQrV+5fHYADBw4cPBRsVpDdziY1Z+tIxhz8EbUAuTbJDVwGPB8Pn+2QipIhgpdBKgz486vjUFH6oBVRChaebeVOxVZvAJCbm4ubmxsGgwGlUvlIXAyeCH78EfGzz9gWHk6jnTulWAHArFCw9amnONa4MRMmTHgyU5+fmwrH34Teeimo+B+QkZGBKIr4+PiUGeumTZs4fPjwPa0X4fHxdrezYo0Gk1pt/76DzwYau8fbU9yKMsh+1pX5NV/AKPNkzJgxZQS/nTt3cujQIUCyilStWhWQLIWnTp26yyq4a9cuateufVewbWIiREdD374wNTkYsUsK4idgegHUwWC1yEjYVY+6LU6hLrRg3QFFzVxI0/iTa/JA4WLBYpMTsOAGWd6e1NtwSlIivoLxuz8n7mxTFnj1IORwpj19bmmdDhlIDt+hwAnABjZP6BPdk0WLFj3RtW8sFguDBg3CLX0x3/QHze+6WmrBEQWI796IiJ9nIniGPZa+/h0mTZrExx9/TFJS0n1N+vxbiKLI559/zrvvvktMTAwLFy50uPT/y/wrFo1Sjhw5glarRafT2X/YpZaN2MhIioqKSE5OZtGiRXz77bdMmTKFFStWPNhIHDhw4OBRICubsvaZ+i9wpPMkEpt1Y2/78WjkKjZXbE9gnxcpUIOXHhAkJWNqpJyBgStp5hGHk7wIkKGV6wkOlIpniaLInDlzWL9+PQsWLGDt2rWPfnyPAbPZzLHGjfn+9dc5UK8esZGRJVEZoLBaSYiIQBRFZsyYwc6dO7mPea9HiykXEP5cyRBtMP17CAmBH3/8w9USEhKYM2cO3333HQsXLuT48eMUFRXh6+trVyjutPKAFIu0MSaGXDc3EIQy318progogrWzgMVLDoOgqEMVlC7lMJlM/PLLL2XcnKOjo6lTpw4qlYrKlSvbP1coFPd0PYyKirpLyZg1CyIjYeZMmPq1CUuLG1L17SFg+hFMg5UIL4o0Eo+Rc9bCl6cacEgbjLO6gAV7n2PuxSHsyWqNuUhOwSU3UjLKkyN4YkOGrQB6h83HmCHiedjEnWqnXqNlR6doDM5asACJQHMQPaG4s4olS5Y80UoGSPVQli9fzqGsMHKtZYvqpVmkyQwAkxbSIv0QnEMefSf/JhaLhe+++45BgwY9ViUDQBAE3n77bTZu3EhcXByNGjX6y7gnB4+GB5pSe/XVV7FYLBQUFJCfn09eXh6btVr7LEwpBoMBg8GARqOhTp06pKSkcPz4cbp06fJQOu/AgQMH/xbuPvVx95EEMH8gscbzVPKoRKRZz/urFzB6V5E9Ja4Mkes3wnnaSUWMz0asohxxu4Dtu9lc7fYb59vHUFxUyLFjRxFFuHXrJvKMXcgC2//P3g9zcnJYtGgRWVlZwO14g+uBgQTevMnp2lKkvSiKZGdnc/1JifGz6GG1P1j1gA1EK6wOBLU3hH8P/q3AXAjZCZC8ApKXwWdySE2Dzz+HF1+8Z7MVK1YkISGB3NxccnNzuXLlCgANDh+2Wy1sglAmOFuhUHCscWMSIiLKWDwA0gwBGKxqLjSqSGjbcwgC+ATVwZQmeRqkp6ezcOFCBg0aZK/70rlzZ3Jzc/92XSubDQYOhKPbszl5REV5ryTOzHwDV3creiPk+yipmSmitpjBAuJ6iOjrxMhbdWnSdwEKGZxJqUVEnXhaaPYyec0bjM6fhvfNLDzFkjioOeBsPYtXfjGzGcqrTLErG1bkZGR3QjlzGMUjRqAqKqIoQUthpAsB40Yi+w9YB2vWrInBYJDORcYBxD1dEUyZWEUZWlHJuk5GWmwDa1doU98LVO6Pu8v3hdVqfaLqCnXo0IEjR47w9NNP07hxY+bPn/9IMmE5+GMe2BasUCjw8PCgQoUK1K1bl9olDw1nZ2e7n2cparWa6tWrs27dOhITEzl//jwgXaCnT5/m559/tt90HThw4OBJpJKHlFHqVuEtpjYwUfHV2+lwbdhYza98YNjOZ7dqsC2zFbV3n0GZnk/I6g2EZH2Hj/I6pRP2VbRn6er8A64pP5TJ7vO/RHJyMrm5ufb3pfEGzkVFfPLBB/zasycgxc8EBQXRr1+/J8N9SqEFtSeIZknJADDchLxTsDMafi0P66vDrg5wcRq41oJ3P4QKFWD8+D9sNigoyB6vApKC9XslY0f37mT37o27uztOTk64uLjw3nvvMW7cOGwvvMDUV1+xT+hVOXgJ4xgNTrtM5Jg9KLZqWZQYag/uBqkq+/Lly+2WIkEQ/nYK19xcqFULMm8YOfyBF057XEhfW4dKbpsop4FKKjhttuLUxYIogE0QWMJzVJB1RBtVhyU3+mI0K2h07TDDJ8/ixPS6BLukULncFZrdOoSAZOEypELlm/A0v3GaOiyjDwB6OTgbCwk+uYH0WlFkfvopRuDdqCgCfj6MLPTfLw73sLAL5D5NuVroD0C60Zdfrg/jma4qDFOg3FPglL4Kip782mQKhYKxY8cyZ84cbt269bi7Y6dy5cocOHCAjh070qNHDyZMmHDPWj4OHg0PrVx3aGgoGo2G/v37069fP3tAllarRaFQ8NVXX5GTk4PRaGT9+vUsX76c77//nrVr15KRkfFkPGAcOHDg4C8IcQ9h64CtuKnLBp1asXHJlsEyfiOBVGyjnwVfNYaelVHLTPTwX8XgoNlUcrpIZ9+NCAJEum3jyIbJ/3MPwczMTPbt21fmvl7qXhvXogUuLi727/z9/Rk4cOATNStKg6+B3z2TZBrABvpU0N8AmwHUvtB8iWTFuHbtD60ZIE3C/b7aeWRsLDJRRJTJuPzqqzT46Seee+45xowZw+DBg+1FC3U6HRUrVizTp1LFrdLOqyy8PpCEgsYEbNrD2G+/tQeOazQabt26RU5OzgMdhrg4qdbdFyNm8/HAMFYngVIJvgrQlEgPSWZorbUhRkPGRxD9oo6xxZ8T++Vi0tNlzN86mIYfJfBcznx8TJkMK5hDutmXAo/K0H8gNqUSG6AvOTR9WUYGvsxjMDZAZZPqcNQpPsqWV7ZQfswYRnfvzi9793LZaHygcT0JeHVchUl0YkPeAPzUaSgFC9VUoBQASwHsiALTg523R8W5c+dISEjAaDSyY8eOx92dMjg7O7Ns2TImT57MpEmTCA8Pf+DfgYN/xgMFg98LURQxm812P8nz58+zevVqqlWrRpcuXZg6dSrFxcX33NbJyYkhQ4Y88jzWDhw4cPCg2EQbTp86YbSVFXZ0gozURvNxd68L5bzZEruIg+eLcFXk0djtELVdEnFV5Ev1BraDuBYu9h1MtR/mPp6B/AucOnWKtWvX3rPmkiAIVKlSBaPRiMFgYNiwYU+ef70ownIXqUgfSDlV1V6SZeNOynWDlr/dV5M5OTn89NNPZbIyhsfH03L/fpw//RTZqFF/uO2hQ4fYs2cPer2+tIM0jj9AZFwcZ6NrsLGu5H53Z1KWE2vWEBoaiqen5/2O2k5aYRoDp/zM4R9e4ose0+nb/AeslptcWQh1E0DZDWgLOVb4LAX0eRDk3Qh102c4/N11/My1ib3QAT2FtGm0gwrKazgdKmZwynzoAhEJh9lS8UOCqgRjvRCPsDGOfBUobaCxwC8M5jAR9JQvpI31AOnOPqh6PIv5jXfxqe1LQUEBDRs2RKvVcuDAgSeueN19YzVx/VYG+dv6UFMVJ8UvqX2lmi0eYZKrntMTlokNyRtl5MiRzJ49m6CgICZMmMDQoUOf2AQXrVu3Zs+ePfTt25clS5Y87u78z3C/weAP7aoQBKHMw6J69eo0b96chg0bsn379j9NeSuXyx9/JVgHDhw4+BvIBBk6tQ6j/rai8fJRJd8muCP3LYR+leBSCk28m+HrlMfZqxvYmRVFoCYVV0VJNeB1IGRB1XnzMK3egnncOBSjR6NSqf7TVt6CggK7kvH7bEp169alQ4cOGI1G1Gr1k6dkgFR1rsoIOP+N9N67KWTsK7uOTAP1v7qv5qxW611KBkjB3rrXXycqKuqe24miyObNmzl58uTvthU4FNGMQxHNgNvHOLl8eQDiWrYk0NX1gZQMmw26P3eTgzW/oNqYHxheIQ1sIFNAg6MgZIM4X7KtXG0GU4ph3EVXujx3iFEvmklPukhglZu06rACL7dsng1cwtD5c6jR7CyT/cZz82YA165Vxb9dMKSf4YImh2qCgJtJxCKAHOglLCVOXZ3p5peoK7uCMrA87vOm2vvo4uLCihUraNy4MWPHjuWnn3762+N8IpCrCAoKIrByIPrM2uS6tiUgYjS4VHnoRSEfJrNmzeLnn3/mm2++YdSoUXdZ6p4kzp8/T1xcHDVr1mTp0qX07t2b7t27P+5u/b/ioVk0/ozVq1eTnp6OzWbDZrNhsVjIz89HFEXC4+NpERfHqU6dcB43jrCwsAcZhwMHDhw8csp9U47rBVIQs0qm4tpUCMgySf76JVXMMZnhcgpk5ZKatpVA02RkYq70XUmhNVEvpbbUa7WY1GoORUVxOjIStVrNsGHDUKvV/wnFY/ny5dy4cQNRFMnPl5Sp0ll2mxcYv1Wj9Q0Dj3DwCgfPCHCtCnLN4+34vTAXwwqdpFDItWAudbsoOQ+CDLpeBl2F+2ouNzeXRYsWkZ2dbc/zr1AoeP3119Fo7j3+bdu2cfjw4d9ZhqyAjPD4I3YFrtSN6s4CfSEhIQwaNOhvDTk1FZo3h1otznO0Xks+0aXzvNsdMu/2EiXDBjYvGPYsHBChwerfSFC3QG7Q88bod0m2hgACUcd3UH/3Ub5SjcN9VDZXM2uyd18UZ8/WwPhCd8xHN3PEtQkRW/ahd1chFupxNkNcw9q8c2w63rabVHbNp/fEejQa3eiu/s6ePZvhw4ezYMECBgwY8LfG6uDByMzMpFq1anTr1o25c59sK2xSUhLt27fn+vXrXLlyhRdffJG9e/dy8uTJJ69mz3+Q+9UNHomi8Uds27aNiF697DfIY7/++oczOw4cOHDwpNFtSTcyizOZGD2R59c/z9O7bjF6ZyFTozS8MvccFdzvEEJtNrhwFDHpAIJYBMZ1YL0Kvi0xrc3FMmsv6EWcDIYyAqNCobAXVnvSOX3qFOvWrcFkvh1zUjrbXtRJQ2BM2u8mamWACL2KQPkEur8s1YE2EIouS7PMHmEQ0EGK0QjqBh5/r5Cf2Wxm5cqVXLt2DUEQ6Nu3LyEhIfdcNzk5mR07diAIAhkZGXe4HlsJjz9qDyIvTStfqnSkde+OzWZDEASGDx9+X/3Kzc1l+fLjjB/fms8+PEpj75dQmw5QTQny3+m3xm2gXAe7qsCnVeDlm3I6jqiIxpKE0SSAXEAmWMg1ueMyrhBtjpHrikBOfhXJB3NncOaSJ86qDG51CqHoVkMuZxVQ/fgpkj4KpsIHV1CJYNCoWW/szHUhiHmyF5j1g56GIxre1W9RFBk8eDArV67kyJEj1KxZ82+cDQcPwvDhw1m1ahXnz59/rPek0sxtSUlJpKamcuPGDS5evMi5c+e4efMmhYWF9vTOU6dOZcyYMWRlZREaGkqdOnXYvHnz386+5qAsj9x16u8giiK7d+/m1KlTGO+4QcodFcUdOHDwH2LNs2vsr5f1XEZ7fXu+qlcIGJj2fVVGRYxictvJqBVqqRZDjYYINRpCZg5c7QxmE4iHUXXZw/7o+RRN+4Xm+/Zh6TuQWj4BnMm4ic1mu8M3/wnmxPvUvraIHXTDhFsZlylJabLxjjgRpXBn4LsNfKOfTCUDEK88B98vRJgwlYsRHcjKyqJJ5SYP3J5SqaRv377Exsayb98++6zq7t27MZlMtG/f3r6uk5MTGRkZd537hvEJdNy4qUw63ISIcHs2qkCbjY4dO+Lj4/PH48o5zc3NkZy3OdGy10W++WYxFlMeMyftory4m1DxAPJSj7YSqxtdQGwDRU2UVDP3YFDFH9j0thuaLSGgvwTcLkInbgevtdnkV3ZFf96J1b7deSG4gDkBFgJV67mV7QMR76Netx2PvDwsSgWGJCVKUbIXaQ1GurKW4cwiw+ZBduG9kyUIgsD06dM5cuQIvXr14tChQ4+0OvX/Nw4ePMjs2bOZNm3aI1MybCXX85EjRzCZTFgsFkwm0z2rfyuVSipXrkybNm2oWbMmNWrUoE6dOoSGShMCXl5ezJ8/n/bt2zN16lReLZnMcfDv8lgUDavVyoULFzAYDPabY2RsLFcCA6Fjx8fRJQcOHDj4RzQIaEBEYATbLm/DihWzzczUQ1NZeWYlMzrNoEv1O+pleHtIS7EeLjpDQQQttWf5qXUDFrVszeCwJnQQRTKLC0kvKmDBggXUrVuXevXqcfz4cbp16/b4BvoHiHINB29WQG9RAqLdnScyNrbkPi/jzG+1qLvnFEIXoC2ADFqsfKz9/lOmzkVIN2J+/zUSjIfJllWjSUUjuNUEtc8D+dELgkCLFi1o2LAhKpUKURQ5ceIEBoMBZ2dnmjWT4i68vLzuysQlItJq/y4pUxWQWLsWJxvVo5nbPjzU1zgv9sEol/Pbb78hCAKDBg1Cq9WSnJxMSIVg0i8vZsbut1iYkc66ACvNlHlcX+xOLdconvbbgw0ZTnIDBVZQ7QT1ekAPFElJC/LCdazbU4tpA5rTs5M7cmshNmP2XekrhXUgzxJxJ4+f+w7HU5lL9s1dyIzn6BWxhFEL5oNqO+bsG+jy87H6yfj2VjbTFKCyCthkIgqFhZvGAEKdzvL1knq0f/3ex1On07FixQoiIiIYNWoU8+bN+0+4Gf7XsFqtjBo1igYNGjBixIhHtt+ePXuydetWunfvTlBQEJ6envbFy8urzHsPD4+/zF7Xrl07XnvtNcaPH090dDT16tV7RCP5/8tjUTQUCgUvvPACcXFxHDhwwP5AqrxiBYbvvvtDf1UHDhw4eJJZ1nMZ7pPdy3x2veA6PZb34PiLx6nlU6vsBk5aqFcNLBZkV7x5rn4lig0mdCopuLJvnYbMOhpHsdnEiRMnOHv27BNr7jdXG8eRzWYsohkQyrjzhIaGcvHiRSpsT0bIA9aBrQ0kW2rhv7ICscZnuWgMw2w2M3LkyDL1Jh4bJ96HTkZsa0HsApXU5yhvu4Rlx+dYFN5kGpwpFzMPvCP+sql7UZop6cqVKxQXF2MymYiNjcXFxQWj0YjZbL7LmhHqfJysp7xwWnYdmSgSnJKCCAQ5JRKiTUdpmYZrjTdYvU+BXl9I/NBuNN13jLORMKmJgQLRyC2bDTeZlJ5WLYNglQV3v22kWxW4yyRXE40AivVAJqCT4jFm1lSzJKEIVZV4Ys5U5lTGCqqpElDLDFK2pDtl+y5IiQ66wKmkurzc/jt8XG4S6rOHsYvn4upWxOUzq6iYdwaVSWBlMxU6cwY3nSEwGLKSIMAMT2m3slXxFEfOumAywR/lDahVqxYzZ85k4MCBtGzZkmHDhj3QOXHwx8ybN49jx46xadOmR5aK+uOPP+a3337jjTfe4Isvvnho7U6aNInt27fTv39/4uPj/7tZy/4jPLYnliAIREZG0rlzZ+JatCDXzY19zZsza9asMgWHHDhw4OC/gqvGFWeVc5nPFIKC8ZHj71YyyqykgGoVyK0YiNMd0pSH1okBdRshILkQGAwGjEYj+UmxnDt8220Lcz7knXnIo7kPbmyhtAqhCiMjQmYTrE0iPP5wmUxTTk5OKJVKYiMjKXTXIXaBq8UVmX+tJ78k9yVCvpT2yq8ozrvJsmXL7AXmLly4QFJS0qMfV85pbImToA2kfl4OVTsL4e5Hqe16hr2ZjfjuXC/W3WwvBbT/Q+Li4uy+5Hq9no0bN7Jnz2G2bdtRxj2kmlMiGaYAltTqz/k61RFlkFfRhW7+S6igTcdJBh62QuI3/0ZD1yk08Z9Cg+2xaG7l0Gh7HgbBgJvcRkM1dNGBR+nTfzu4vgbBey24yMG6DZQvgpAPNh2Ye8Gq1vDCYSN7ga23YNjnG/HZcYHZKUPIMnmVDRYfAxZRBlMlZfJKeiUq+15i55lozuV3wCKq0KoFCgQBmRmKrCJNdxsZHwtL6oDmoojWBCazkmdDT3I2vyYNqhfywV/U5RswYADDhw9n+PDhVKpUiT59+vDVV1+xZ88eCgoK/vF5+v9OuXLlcHV1ZeDAgcyfP5/7CO/9R6xZs4aPP/6YmJgYPvvss4fatlqtZvHixVy+fJm33nrrobbt4G4e+9SYIAgca9yYqa++SkJEBFlZWcyYMcN+Y7BarWRmZj7mXjpw4MDB/eGl9Srzvn3l9nwc9TETdk5g+No/D84NFBVolWWnbX11LjirStNHimDVk7j+TbKOfo/l7DTEzY1hXQ04OPRhDuOvKb4OsT3hwCDIOgYba1EcNJhrhqpExsbZ3aYAiouLMZvNnI+KYu+i5ehfm8am4tEA3DQF8d21seSb3XipwvfoMtezbds20vZ9xNo1v7F58+ZHOy6rEbY1RxCtCAI4K6RaGnLBhrsynxaesYyo8DNO8iJp7PkXym6fvheOvXlfu9Lr9Xc93wwGA9V3bWTMN1/x9IoVjP32WxrEH+ZqcSXSTP4YbVoCk28g2CAo5QZVnK6iK3mS+6hEUjTnWZjegr03X2dfs3bkurlxsHlHfApeYXN+MD/kqMhfBy4jgSEgzgMywbwW9CLI1wNFgAkEDVij5Dy9A+QmJCvFOnDLy0e3sZh8sxsLrj/H1eIQQHKvErJAtk4SQs0WJTLBys2cQEbOmQGFV7BZDMT1qITm1kX2CqA0QnA++BfCijog6EEhqJDbzJSrXxGtYKBL6zzmzPnr4zl9+nSWLl3K008/zY0bN/jggw9o3bo1bm5u1K5dm8GDBzNt2jQOHz6M8T9c7O9x8NRTT3H+/Hnat2/P4MGDadWqFRs2bMBsNv/hNjt27GDv3r1/Wyk5e/Ysffv2pUaNGixZsuRfsaDUrl2bL7/8ku+//55NmzY99PYd3OaxKxo5OTn2qrjh8fGM/fZb6sTGEhsbi16vJy4ujp9++omEhAQuXbr0mHvrwIEDB39OqG8oIe4hBDoH4qRwYtOlTfh86cPEfRNZdWbVn2+sNyLotFidNGRajJzPvMW2y2cxWMwl98cphMWfYF9WJKK5mOXr97PsdCWs+nRwrfG3+pmXl0dRUdGDD/TqQswmE1xbAlsaUOjdk3kH3RFFkeTy5bEJAsnly+Pj40NAQAD16tXj+eefJyYmBqc6o3h2wGB7/SQbcnZnteLXW08T7bGJWqnDWR2bRlGxnvz8fG7duvXg/fy77HoKLPn2WXo3ZQEG621XLrNNSbI+GKVCCcE94fQncOh5KEqSrDtHXoLLs+D6hruaTkpKsqf9LX0v2ix31ZEqdScOPX0a97w86sVu4hrZ2LAx7KeZuOblI8pB6CraK3QDqAVoud/MV19upf3WHRg0GuIbRlDn1Ckmf/o9h6fp2bfIi493g7xUmRDBIsC4oAh2Xq6HOUYOOkAH+s4yVqT1xdZNiegNdAFrZxlGTxVZIV6M/vYHqh88z7IbvdmU3o4zUTUocHfmWtsKxGU3JT6pAUp3E9MOvYyL1ohFcOM3fSe8v87iwPkCtCYpVa5eDqd9IUcLIqA2y5BbRahalQjNSVZs88TfHxYv/vNTp1Qq6dOnD9988w379u0jLy+PkydPMmvWLFq2bMnp06d59dVXady4MS4uLrz33nt/69L4uyQlJTFt2jRSU1P/1naLTi2i36p+/1KvHhx/f38WLVrEjh07yM/Pp3PnzgQFBTF27FiSk5PLrHv27Fk6duxIq1atqFWrFlOmTCE7O9v+vcVi4eLFi3cV9czPz6dZs2bodDo2bNjwr9ZYGz16NDExMQwZMoT09PR/bT//33ms6W0BNm7cyOnTpwF4YdKkMrnASwO6Srvo5ubGK6+88lD378CBAwf/Bg1/aojerOdM5m2XJo1cg37C/WWQysjIYPbs2ZiMekRk9noUxVqtJNAKsCe6JfERjXGWF9DM7wIBIbWpUL0xBvdIzly6TlFREd7e3vj7++Pk5IRarcZsNiOKIr/88gs2m43hw4ffHfdhygGrCbR+d/UrNjYWtVqNy6mX+DW1DS8GT8NDlc/0a6PJNHuV6Wuumxs/v/suzz77LOXKlburrStXrrBq1SoMBgMa8mjmcYAMozdVdJcJdkpiW0Z7ThfWpWrVqvTr9xeClzkfRCsISqmy8v0iilhOTsEsD0JbtAMu3138zWiVo5ZbMdmU7M9uxp6cKGpVr0yvoN+kYn5+0ZA4CSzFkBELlnxEp/IURR7hVq4v6emQlmYiMXEaouiGXj+E3GwzZw8lcuRcVdrHXKd27RXI5SWTbofjidkkpa+1CjA6BhZEOOGLL1c+vIaAJJC/9Q0010AltRSzvaoA3voIPHNAr1azKSaGPHd3uq9YhXthPtcVgbStsp2Rqq8YdHEucoMCo+jMR96DmeHyNNbmn3A1YjfBXpLwd6vYhaJCL4K9UpDLrMgEuGX0Y2HGGJ7/7Isyz+vbWcaakxDRCDkWTh6tjVxro0b1i6zf2Jdy5arjtnExc2z9WV4Nul0ArUVSdBq+ABe94PNt8HK8NL6ilUv5bvg1vra8wtLVakaOhH8632gwGDh58iRvvvkmZrOZuLi4f9bgPbDZbPz444+89dZbFBYWolAo6Nu3L6+//vpf1grbdXUXfVf1RS7IOT3qNJ5aT7KKs/By8vrT7R41pUkMFixYwLx582jSpAkbNmywf9e6dWtu3rzJDz/8wOzZs/n111+Ry+X07t2bYcOG0b17d7vioVKp7IvRaMRoNLJ3716aNm36r48jLS2N0NBQGjduzNq1ax2JBP4GT3R62ztp3rw5rVq1Yvv27WWCB4G7zG2lac2eyEqyDhw4cHAHOqWOhJsJZT6ziBbyjfm4qv96wiYrKwutVovJZEIQRfv9UWU04lSSCrzVzn00jT1EbGQkWyIicM+2EHxhM7cKdpNulASTUM8Ujss8uF7ggqurG1l5ekJDQ8nOzsZsNrNr1y7atGlDSkoK27Zto2LFikT57IdzX0PEdFLlkaxZs4YaNapRqVIVYmNjMRqNCDyFCJwrrEkTj8NEO23leE4oqYqK9r7GtWjBmDFj/rBycKVKlWjatCk7d+5E6VqOBFsfbIYkThTWJUSbTDe/NdQ+dJqAKekYMjLQjB17VxtiUSpXl72A1nAcn8ZDUVydAeW6QeiHoAv+4wMsihTv+wBr4ndotEVcvhBNtbDTKEQZNlFAJlj54evReIk5dBm2FqMLHMxuRHjCcZpvimVLaBeCr6xmVMuPCQ9+h18TuvNpj/Hk690I8clHKE4hccbTdPxmH00Me6nlfgyn5wuxWIzs2X2chCM1QayByaLi/HklNWrIMJsVWIuVHKsXhtmipOX+vUyq78XMiItAMde4hlEGGhtYZNDje1dWV9IzrqaZLhdgzCHYHwDNba7sb9mPHwt7ce10Oa67BTLCNpP4FhH0bbqcwNodybnlQvhrExDVSvL8NiN2fAaZcxaB5pLA7u3gt7aAHXUbUX33Bba98xQta+zBXHU8rfz53fP6dpax6J27iIyNIzYykl9vdqd1690IMgv16h3FbM5F6GHEtF1J3zNmkl3A1QQ2AU4EgNIK1XIEzrpGUD3/MNuOLENOY2qG6ElMVGM0wvHj8E/q+mo0Gho1akR4eLhdMH6YXLp0ieHDh7Nnzx5GjBjBe++9x/Lly5kyZQoLFy6kbdu2PD/qeWZmzeSHbj9Qwb0CTkonAE6nnab/6v6kF6UjF+TM2vYFBet+4pcKBZwcl4mbxu2h9/dBEQSBsLAwwsLCcHJy4ueff+bChQts2LCBNWvWsHfvXrZv306bNm1o3749aWlpzJ07l59++olffvkFgK+//hqdTkdGRoZ9yc7O5sUXX3wkSgaAn58fc+bMoUuXLvz444+MHDnykez3/xOP3aJRypYtWzh48CAuLi6EhoYSFBTEqlWr7sqVHBQUxNChQ/8084rVakUURRSKx65HOXDg4P8p0+OnM3bTWCzibdcAAYHTI09Ty/dPAsPvwGAwcOvWLRITE8nNySLrcjz+O27x9MHfUJgtWJRKVGYzNkFgY0wMQJkgbJCERuk2XzoPLqAW9BhFKdOKWmHDSaNCb5ZjMBppFBFOR3E85CWCyoPzOf6czK3FueJa2LjbV1pDIeWUyRTLXMkxe6K3abjTK/ftt99GEAT0en2Z50dWVhY6nQ6NRoPNZkMmk5GTk8P3301BLljQyopRYGHEtzNRZ5sp8PTAevkKLi5uZGUJpN8s4tbmd6jlvIJAj5tcywjmWNFLdA+R4iMyi/y5mlWDL7ZMJO58MwwGsFhAFK181uEV+kcuxMm5GLVWCsTesLkj5WvdoG7wCQCsNoFZE4dz61wQLu75VG14gWptLxAy+SrqHDM2L4G1Q3rStuZGdOoiLt6qSHmvm9hEgUKDC3qjiiCvW8TueordC5sic7bCWOlxW1SkoTjPiedDZqJWmDiW1oDzyppcPl6FE+caElLlHGcu1eLytarwfAQEHbEftxHxMHEH6EygtMHSOjCgJ1z9FkLyIFstEBF0EWdnLypVOkidOvEoFLefo5UqVWLgwIFcPG3kqXY2GhTHUlW/i6RmK2nR+iIvVgBhHzAfKKkA/nKH71mzezRzPt9F+wHRxG/5ia0Hk9HIjOhFJ6yijIbxh2keG2dXhHPd3IjwiWfAAMnfSRSla9FsVjDs1kwqzknBLECyK6ysDbMawIrVKqrgy85yL9LpyPvE1tRxIPUdQt7qw3uzK/LSS7BiBZSE/twXhYWFODvfbeGaOHEiU6dOfWguM1arle+++453330Xf39/Zs2aRXR0tP17i8XCypUrmThtIgabgZR2KVgFK15OXmzsvxFfZ18i50SSlCclP6iaARYFvF4F+rrCKUUTWveNBdmjyfj0d1i5ciW9evUCpEDrqKgoBgwYQP/+/e9a12azMX/+fIYOHcrw4cP5+eefH3V378no0aOZM2cOR48edRR+vE/+E5XB7+TYsWNcuHCBTp064ezsjF6v55tvvrH77wmCgEwmw2q14u7uzghA8+23MH48xc89R3JyMhcuXOD69evk5+cTFRVFo0aN/pW+OnDg4M+5fPkyvr6+/6p/7X+BIlMRXl94YbTeDjxd9Mwi+oU+mP91j5ax+GkSmHToA9zz8yjWaNAYjfYK0YDdvcqkUpVROEq5s5BeQkTEXe9dFXmMDv4eldyCTRRIyA1nR1Y0RtHpd72xUVl7kTRTAIVWF8Ljj9Aidi/7Ilva9ymKUjpzQZCj01VCoejNzZtZWK2bUKlSOHXqGa5erU5xsY1q1WKpU/sgWifJtUyFEZXMRNMjcYTtPUFxJy3HchoQvu8Iqa2DKN81FVd1AXK5JEQXGpw4lVKHBiHH7PEVAiIiAkaLhn0/d6PliTWc8a7OVb/KqLVGVBpTyWJEqTah1pqkz0teb17wFDevBgHQUDhMC2EfxgpKvPJyMHRUY2ipxlVbgEJuw2oDmXA7zavZKicj34db2V7kfONDM9NBzkVWY6byRVLTy5Ovd8FJXkwln6uE+F1FCAynWvMWREV5ELdsPS+9G86VjEqMiHZjfEIBn0fCzJJTWapUgOR2pPxAUkDGx8Kamn5ktx9JQkI7vLzcqV79N2rWrMm1a9cICQmha9duzJolY/16+PiVLA6NW4Eq8Sh9/H9B+74RmRMwFim1LWBrAqkDYb/TEfoOlrJsJZ4y075JNtevqzmRlMT69etxkWWTb9IQfiSBDge2sCGiA28mfU2fPivKXGPbq7RjnN8XBL13A8EKVqDBC/DL+acITojHSYQrTftRM3YW6XIjc1ST6Da7G1Eja3LkCNSrBxcvgqfnX/9eiouL+fbbb+ncufNd9RJ+/PFHXnrpJcxm8z92lzl79ixDhw7l0KFDvPzyy0yaNOmehQONFiOek3SECFrO2Eqya5bMAahkkqeGyWaiaiZkayFLB32dYaoP6ATQedSExrPAp9k/6u/DpqioiC+//JLw8HCio6Pvq2jinDlzGDZsGLNnz2bo0EecyOIeFBcX07BhQ9RqNQcPHvxDK6yD2/xnXKdKqV+/PvXr17e/t9lsNG3alKSkJG7cuEHXrl3x8/NDq9VSVFSEqlEjSEkhd/x4pmVlYbPZ7NYPJycnKlSoUKZ9i8XCkiVL6N69O6IoEh8fT1RUlMMfz4GDh0x8fDzbt2+nadOmtG7dGlEUOX36NN7e3gQEBJRZ9/z587i7u+Pnd3cswP8COpWOaTHTmLBzAsXmYvJN+Xy+73PaVmrLysSVIMCoiFH31VZRUREVa11gzrL+tG+yk/b7toEocrp2bYJTUuwup6XuVWWL5d3m94X0fv8+3+LG11ffQCUzordqsSFDvMOSUSo0JpcvR5XLl1FYrFhKssI4GQy0ituDoo2Za8UVSDMHYrVaAAsFuadR2+JxUqiwqFQIoo1+dd4jsMFN3D1zEORWDKKGnVltKLK6YEKNyaZmW4MObGvwFDV0Z4iZuAmXgkLc4/Iw9lDZlQwAk0XN1M2voDdpKTLqKDQ4U2BwIafIk8xCXy5aquBMAQGpt9jeqA29I5bjrspBNCsxGlR8v2EUbWrswk+eTnG+jsIiJ/Kzbz88m4txuIoF5F5144fQUVQ/e4HItXHIetigHchllKmirWxrJcD9Fr6u6QjCWeTFNmodOkv7UVvx9UnHJFdxTV+Rei7HqeR0BY38B1IyKjJ/Vn+sMi36GlcZET6BH7ZYUIiSElGqaHweCV9tBa0ZltWWPpsZIS19PAZj3hNOq1Y6Xn+9BmlpQ/Dz88NgMJCf70SfPtC0KaxZAzKZF2FHR3BFWR11f5OkZCD1v9SiIVwCf2e4tXs5hr7haDQwtFcR41scAfdO1HVzQ61Wc+PGDS4dXoJTez2iD7RaGcvzAT+Tj1uZa+wXbX/M7goywz3wOpxDmg7GGJ+h/FuDUHTpjygTCVSkgVyOTaUg1/sYorUrTz8N770HPXrA2LGwYMFf/2ZOnjyJ1Wpl69atAGWUDU9PT6xWq11YehAsFgtffvklH374ISEhIezdu5fIkt/hvdBb9LiLVs6IkpJRqhxKSqQJRHj5IKyuBQVq6fu3Y2FVGxjZA8g/C3u7gX8baL70gfr8b6DT6fjwww//1jZDhw7lwIEDjBo1irCwMBo0aPDvdO4+cXJyYvHixTRq1Ij33nvvodbt+P/OE6No/B6dTkd0dDSiKHLu3LkypiwXFxd45x3Ezz8ntXNngDIuViaTiczMTHx8fOwuVqmpqaSkpDBr1ixCQkJITEzEarXSrl27RzswBw7+h9m5cydHjhzBZDJx/vBqfJ0K2XHoKnl5ebRv3/4uRWP//v1kZmbi5+dHhw4d8PX1va/9ZJ7LRO2qxiXw37WY7Nq1i+vXr9O7d+8Hjg0b1mAYk+MmU2Quol3FdlRbsRPjhABONxfJHtT7vhWNI0eO4Ox3HbXayNGG4TTftx8fQyZB127w/etjsAkyQCAhIoJGR4/SdM8eYiMjEQShTLzb72PhblXwxfVUPsnlyxEeH0/ruF3sbt6ahIjbFuE7Z6RLhUbX/HxkpTU0zGYsMhk2QeBquRAO5Uv+1eFHjpSxlujlzmXaO/bUU2yv1dY+e34vXFQmoqOjETTdid1nlMbVNJJb18vRQHEYf91NAj1uYTBrmP+LAvXR/iBTgnNlaPQzeAVKDf04Hj56k/Idr/NOv8Nkar/l+pJJVKxxFq/ADF59MRF/35tQlAw2M2h8OXOgNkV5LiDYiJVH0tR6gOvNA3n5xWlYx8hQFNhgPVD6GFmHNJZ1YBNBXCNjn6UFzbrsR77Vxo2IhlxwCuVioREFZvQ2HdcN5ajpfIaeAauorDmLTv8V/lsbknByByqjiKIkK9TnJfJrqXA6rv1txQMktzzBpmLbl6+z5VcfGjaUPi/9zW3d6sQPP8C330Jo6O3tDDkG9ke9h1qYjv/+g8ibgND29nisreHMZVeuO79BTAx8/51I0lUbL++XzrEgCNSoUYOgoCBuHZlJmOtxZOvApaCQgbZfmMZLHIpsROPYwxxs0YQmCQfx2JZHbGQkTwkbcDPLqVucjaduHsU2KzLRiktuCjg5kd24DmFndmGzvscnn0CdOnD5MgQHg80Gf1W78sSJE4iiSM09ewiZNInU0aMp9+mngFR5HaTibSNHjiQkJOTPG7tH20OHDuX48eOMGzeODz/88C8LvxWnXOHOPG8Td4CXQfoP0nn9KRwm7YBNlaX3FfKgz0Yw7gZ1N7jVKAfXSmH83r74X+T777/n2LFj9OjRg4SEBDzvx0z1LxIWFsakSZN44403iImJoXXr1o+1P/8rPDGuU/+Es2fPsnHjRnuhPxcXF1xcXMjNzUUulxMYGIjFYuHy5csA9gevRqOhRYsWNGv2ZJkhHTh4Utm7dy9KpZLy5cvj5+dnr+AsiiJr1qzh3NlEjCbJ3dFFno+rWs/1YslaERMTQ8TvZtfnzJlDSkoKIE0u+Pn5ERMTYxcCShFtIikHUzg26xiXzl7CeNpIhykdaDDs4c2CFRQUoNFoylSlnj17Ftev38DDw4O+ffvi4+Nz13aiKGIwGP5UyPj1zK88s+IZ4LbbyzU3WLflO15u/PJ99/HD1+dy4aCSveda0zl7LeP5gh80o6k75hjF5cLIzDNhtVoJCAggICAAV1dXnJ2d2bRpE6IoEh4ezrGEw1hskiXXTZHDmG+/Q5YlUuyhxWxT4paXT767C9++8rp9v3dmkYqNjCQqbifamno4CULRndEf2LMQ/X670s/u/NwUEEBGfDwHDhwgJSUFvV5vz8uvUZgpVy6Y7r0G4OTkhM1m4/vvvyc3NxeFQkFoaChRUV0ZO/warTw+pFvDX9G2WYL62HAInwLBvX5XrhpI+Q329QSsoAmA8k9z+Upb8nZ8RlBMD/yCM8CzIeLxCZw31mfOc02wWQRCm50ksHEq7RrsLK1PiH6rBu0GA0JXoK2kWAg7pDoTts5gWaVEVWDG5KxENdOMUa9m9543CHyjN6tXr0bAhlgSy+IkFDCk/Fzkuwzkry3GxQieBsjSQoEK4spD8xRJ2Rgfe/v6qVhySGVAY3MEKSeeJ3HRcFxdb4+7oABeew08POCTT+D3HiEbRm8gtF8o6ipqdlUZS+8Jy5CVl74Tc2DjNehUH0SX6jR8O5bsTCdeq7eDl7d0sbexe/duDh06SAPNVqK89qLYaSVjsRe5ndzYEd4OTbqMZJ0P5V2S6Pj+JvzN6RS46ygqVONvyUb0BmEq6J9To8GI4CUH7+oYFs4jrVUT8mccJ7R/KDVqwLRp8NlnEBUF775bdix3JospLi5mxowZFBYW2q+3PHd3kvfuJTQ0lKKiIl599VWWLFlCYWEh0dHRDBkyhGeeeQYnpz8W400mExMnTmTSpElUr16duXPn3nVfuxODxcC2078wfMNYNBkGUt3AVqIgZU4GL/3t81x6XuuMghh3qBgLI/dKyoYAWFTQMQKih07krSHjkQmPvULBP+batWuEh4fTpEkT1q1b96fxt48Cm81Go0aN8Pf3Z/369Y+1L086/znXqX9CzZo18fDwYOnSpeTl5REeHk6rVq0A6UCcOHGCffv22dcv1a0MBgOxsbHodLq7/DcdOHBwN5cvXyY5OdkukNc/fJiG27cTF9kcpcXCiyXZZhIiIii0OmM1yNDIipEhsn/Hrxze8xsCoiRkFdrI+NlJSv7vCkXuRVzxuMIP277HT5dFD42IdtYy4nSRJHhEYHWzYvOU3FRoARkuGQ91bBs3biQlJcVe3Kuuzw2eVb/CSa/aHMxtxi+//EKbNm0ICwvDYDBw+fJlTp48SWpqKnK5nNdee+0P2+5eqzu1vWuTmJloFxantFbTO/DvVZYODGrEVwkVKDI6UxpwbTKpMeh1+KtvUatJK2IPJ9KrVy88PDzs27m4uPDbb7+RlZWFHAv14o8TGRuLrKsVWVcR1oFTFz16q4h1g0BKVCACVkTkyLFwILIJTWMPcimqNkfrNyIhoiGjQ2bgrUznwpoqVFlxGZkoIgLJ5cvb93un9cTLy4t27dqxZ88eDkdH02jnToqHDycoKIiePXtitVq5cOECWzesJrfIQvuYHmXcaWUyGWFhYezdu5eAgAA6d+6MTAZzloWQlzePDz/4ma+HKKDijT8+gM4hoHIHUxYYbsKln6isXQcf/gyBHaR1THkYj4zn6HVXrO21vND6e6oHnLc3IQXXgzFKQ2qzQKo6XyHf4k5CcVciWy5G2cZC9i1PDs5rSkv5XpyfFQEzKg9PKozpyP74eAC7kgFQLOq4WFCJar9eoUJhMVlaSeAsjcvI/Pz2zPe7bW672+iQ0tp6y2F64Dl26a5y7lw8ERERCIJAXJzkbvTBB1DySCzDzaM3segtBDcP5srlK7Qs3ItwAPAD5MAR6HAIqA+CMY2VH31G5Z5f0uubshmBfHx8sJj0NAs4hEImpei12eRUdL1GjO8GZt4cg0/2DVJVQeyoFE2/rKXouhSRlyfDsB4sLQScETErVWjMRjBqICQETf0InM1wNPs0oYQydqw0ltmz71Y0LBYLX331Fa6urvj4+KBQKOyTj3EtWxIZG8upmBh8ShQRnU7HTz/9xLfffsvKlSuZO3cuAwcOZPTo0fTt25chQ4bQuHHjMu7VR44cYciQIZw7d4533nmHd955525ffpsV1lYCv9ZQfQxjFw8lcMVJslqB1b3squ9GS+fyi0iwIb2eHAntPWCWP6yJAU1PyJwAPilgtMH6A5Bx4l2WzprE6QEdmDRq5b2v9f8IISEhLFq0iJiYGD799FPef//9x9ofmUzGsGHDePnll0lLS/ufdet9lPxPKBogFZIZNmwYCxYsoEaN24WrXF1dadasGUeOHLlnBUu9Xk/aRx9hOngQxYQJFA0cSEFBAQUFBeTm5pKdnU1YWNhdLh8OHPx/JDQ0lJSUFAwGAwaDgfqbN+OSl0e72HWYbSo0eUZaxu7lbONaFNu0eChzCXG6ikVUUrNOAypUrA4KLcg0iDINE384gPXG742qAtaa1fAq/BJZYSaNxV0crBcmuaRcBi4BOkg4noDitILKbSsT3CwYmeLPZ8IOHz5MTk4OTz311D2/DwoK4ty5cxQVFXHjxg0uu6bT09tKE4/DhMWfQLHezKkDdfksrBvhR0/SaNcurjZvTnFEBEqlEovF8qeZ7g4MP4DnF57MjLCUuLwYGaX1vu9jbzabsWj28PwII5tWd+Dt1ElUIIXXbV+TIK9PN90sMrLjqPzcyjJKBmDv35UrVwAl0Tt34qTXI64GcSaYolRkmH0wWtXom2lIM/iiyLOikunp6ruWgN43WNP0abzCPiPgxiapNkDDrzAee568Vu7sNzWj+do4BBsEl1ioABIiIjjRtCn+/v5UCQqievXqVK9eneQOHVi2aRNt27a1ryuXy6lZsyaVK7+B2Wy+56xykyZNuHLlCv369Ssz8+nmBt9OUd61/l3oKoD8DsFQpgGVN7hUvf1Z7kkMJsgyeeIZcZ1q/ufvakYQQCbYWJY+kHo+1YmMbI3LjMUcuZKKTTSQe8ODo86NqLFtCq6V0+HAQARzPjfy9HYLHoDFIufq1RDOnavBgtwBvOg/nd4Z3zEp0sbMCMlN6uq3oLaW7lhSPJZEuBCt07PKzYKHDMLVYBZN4LGRDdt0HDiQwMWL/SkuduXXX6Xj83tEm8jOd3fSbV436dC8MRkfbiLkAGbAAMJikJmA4ueg2yf0DnenW9VEonuFcuZ2eRiCg4OprTuBVl5c4jIm4GdOR1wPgW1u4K5K50RhXT7w/ZDsgV4QAkazmnKqAvI6wvkbImkzXJGXt1A5E6rnG6CwEM6dI0XjTsbWyfDys4wYARMmQMWK0pjWroWuXaU+KBQKXF1dycrKIisry943hUKB3/vvo65Th0iN5u5LQqdj0KBBDBo0iMuXLzNv3jzmz5/PTz/9RM2aNRkyZAi9evXixx9/5Msvv6RevXocOXLkjycnZXIQ5HD1F7i+gQMp+SS2vm3FWLgS+iRKsTUDet52f5MLcpSjRvJG5Ua4HB6Cq9xK/8OSheyYh4APIrqSBHZ+xdDnQBHu7ifJGJSBj+5uS+t/iQ4dOvDBBx/w4Ycf0qhRIzp06PBY+9OnTx9eeeUVFi1a9KcTSA7uj/8J16m/4vr16/zyyy9oNBoKCgpQKBQolUrUajVyuZzn3n8fl+xsijUazFotB1u1Ij48HKvVilqtZvDgwfj7+z/uYThw8NgpKChg9k/T8ROPEeEWT1DsDTQbDQjdlFBxMHw3H1s3FZlNnUg1BJPq1JnL2a4UFBTQuXPnuwL+vmr3FUXbb3stCwqB6l2q03tVb4SZM+Hzz8l6/nlmigJma8lEgQgUARkgy5Dhdt0N0y0Tok1E6aTEs4onAQ0CCG4ZTMXoiiAWsXLVSpJvZOHi4sLo0aPL9MFkMnHx4kVOnDjBpUuXEEQzzooi/FU3edr/V7Ryoz0Lj+gNtikyCkc745aXT6G7jq9fecOeU75z585cP7cbvaIc1apVu+v4fbj7Qz7a81GZzxQyBcdeOEYdvzp/euxXrlzJmTNnEEURq1UgaGkKHS9tZpL4Lq1f2kXfpssx2xQo3atC9DZwCiqz/fbt2zl8+DB19+8nZsMGZIBNB6bZvqw1f0hGng2TyYRer8dqteIkFPK073IqO1/DbFHw+baJvPHzG6Snp+Dk5IS3+QSWnR1QCBaKrVqKtmjRrSoGQWBndPQd6XUFxowZg7u7+5+O75EgirAmBIqTQeEC/m2hxUq4wwXFdmYqWUe+ZMbVYXQ4sYkGe44idLUhb1f2UVlg0bFL+TlNKtRn26srqdahHOETxrDxqy0kfJCAb3tfRq6XcvKvW/srLrZUzLoa7NyZwMWLVblwoSpGo5qQkGtMvPYOYRePsqq2jL49bfYUtu4GybCQpZGCgz+PhLkRKn7UDeNZvzkYrBqUMisn8utwNL8hWWYv0tN92LatE8OH23jzzUq/G77I1q1b8fDwQJmoxFRgovGYxnD+PNaatZGJVoQYoBpwESgpM2Fy0nBmdxodWli5lWwmopMv7dvDxIm3296/bQPeV8ZgFtVYvlQQevU0t8J8mdNxGOeS3qReve1UK5pPq6C9uOvysG0TkK0XoQvYoiDXCDn7odwvdyhWwE25Fzf9CmhwXcrc9tRTkjWjVi146y04e/b2usuWLUO3YIHdinamZUs6depE7dq1/9ZlYrVa2bFjB3PnzuXXX3/FaDSiUqn48MMPGTduXBn3SjumXNgaCV6N4OZmRP1NTus1RFy3YBQtKAQIVsL5CaAQwSrAyK9hkhfk2sAiKKnmG4Ys7yTYSrLUldx3rN6eyDKzKbWt2IAbLpKLlZNPAM83eJ6GgQ3pUr3L3f36j2Cz2ejcuTOHDh0iISHhb8fMPGx69erFxYsXOX78+GPtx5PMfy697b+JyWQiLy8PLy8vcnNz7RVy7SbRH39E/PxzTFlZqAsLy/gTe3l58dJLLz3G3jtw8GQgiiIp5+PwjW+HgBW1/A4LoVcjeOqQ9Dr7GBx/CzIPQKOfMQZ0Z+/evdSoUYPyJW4159efZ8+He0hPTUfMFbEZbcgUMspHlue5bc/dZZ1ITExkzZo197RKqlQqGjVqRFRUFPkp+VzecpnUA8nknE8kNzeHPIszKAXkPhZc/M3UDHNF43sDLNfRiDk4ywtxVltQqdQUFhZgsgoUWXQYBRcaOh9AIzfAdhDXgdA7kPy3VnDsre8J27SR7I6e5LT0YHtmOwyijiBdFs/5TWfhjQEMePWnuwSSU7dOUXdm3bvGcOrFU3+paGRnZ5OYmMiePXuo4xRPN7+1XF0agny9leN1w+j21jqMVgVKl2BknU7cVR07Ozub6dOn89JXX+Gel4dNELANVnCq66vUjHoZpXOAvZaFcG0huguf8vH+sfStugQ3cpmW+hpdujamefPmUoO5iaTHvY97zjpO5NdjY0YMr035BpfcsvdQlUqFs7MznTp14vTp01y7do2BAwfeZXV5ZKytAko3aLYEjo6BxrMlpezoG3BjA8npxfipbrAhvTOdJ65DlW3B6KlC9Z1Ub0PYAawDc2cZm2T9MCf50n7mi7hUlqwiNpuN+YPn0/eHvmhdpbidDz6YTUKCP5cuVUYut1K16iWeK55Ph1gpdkZrMCJD0qEX15FmvBUlT2aLAC/FwC9NtUyKnsSgWqOJicqmVb0dvP55W9z1+zj/5geU25rKgqABzFK8ypIlXtSq5V5m2MXFxSxZsoRbt24R4BeAcpmSGhNrUKt2LXQ6HXvdWtKiYB/0RMqelXN729jyMM7zIIO8Uhm1owf5+ZJVYdcuqFt6ORclYVofjmVjMepfjMixYfBQM3ns26xZ05/ffgth79fP06/RPGQCiGNByAS8gakgFoG4EUzrQHOHorFN0YFw5RbIzMTTyZP4eOjTB65cgYAA2L0bqleX1j1+/DghrVvb4zGOrl5NVFTUP7pccnJy2LBhAw0bNpS8JWwWKE6Bwqskpx8h0JqDojgJTPmQtgNsBqpdBScZ1FPDU07Qaj8ErpYUhAQ3CL8JQhOwjAK5IC33pDSL2bjPKfrgbXRGERuS0+SdMToATco14cCwA/9orI+b7OxswsPD8fLyIjY2Fs09LFCPivXr19OlSxeOHz/ucK3/AxyKxgMgzphB0XvvsadZM46EhyOTyYiOjr79YHXg4P8pp0+fZu/evRQUFNDEaSON3A+jlUvVqRGU0PB7qDqi7EZFKZKwq5IEysJbhex4ewcXNlxA56Oj+dvNCe0Xypxmc7h+6Dr+9f0ZGjcUpfbeLjDTp08nI0OKyxBEAVG4fesKj48n6kAs4uAIzHlX0P2aQnYHD/JauQNgMcnJvOlNRqoPBTd9KUp1wVqkAVGNc4A73rWDKN88mL3pe9GjJygoiGeeeQbd9toIxcmgDYKar0P1sSDIsFgsfPvttxQXFxOkTiXKeycn80Np5nEAP3U6mSYvUupupH6D25mbtlzaQsdFHZEiGcqmtlzUzJn8t/P/Mt320qVLOX/+PErBSO+AZVR+5wpCJticBGROIgUddcyo+QYvjX3D7nokiiIHDhxgx44d2Gw2e8ang5GNqNztKvtk4ylMO03dqj607vUOJH6G9exUar1xmgFD56OSFaI05pEv88bZWcWAAQPsFt642FiKEz7iQG5TROT2tgt1OgJv3uR07dr82rMngiCgUCgwm824uLjw8ssv33tW+FGQfxFcKktWjLyzcHICRK6Eawvh0AukFHlTXpuK2aaAHSLiWhnWrhouDvoe89nvafB+AkImiF5w8LUYmr5zd3Vpmw02bIAffoCjR8HH5wZ16+6jcuXLqFSSslwaoAxQpAQnc9mgeiuQq4WPo1XUHT+d55o+x6kTSp56CoYNg3ffLUClUpFx4wpeDeqjzTWSpvFl2eQJjBo1sowbX0pKCqtWrSKvZH9qq5qm9ZoSdz4OX19fhg4dynzFcPp/MR/rJRvqn0FWksgxyRXGfjOAQ6OncjPJDCU+66tWwZgxkJJSkv1p51OQtgvDSwKabBM2Aa71rkBCeDiKDCvy2h0IL5xIkEeq1HCJEG3rLGW5EnKAN5GCTkoQdTq+1r1Hb9snbH+3J0NfmQdICsbBg1KK2927Yft2af1xa8fRZu5Fmu7aw6WePan/00xkguzvpbEXRTCkQ+EVOPsluVZwd/IEfZp0dmQKcCoPuoqEbf8e0VxML98AIpzdOJ16jOtmkeHuhfjIwV0GShll6pJcc4OKY+EbXxjtBqqd2FMi0/ZeHRKgRwa55ULQGwrZXBmirlGmrkqAcwCHnz9MOddy9z/OJ5SEhASaN2/OoEGDmDlz5mPrh9lsJigoiAEDBvDNN988tn48yfwrisbJkycJvTMv3v8gVquVefPmkZqaiouLCyNHjvzLlHUOHPwvk5qaysaNG8nIyLAX0Owf+AshTskoBAtoA6FTohRk+ztsNhvH5xxn/1f7MeQYqN69Om0+bYOT923/+zMrz7Dl9S2MPDESjfsfz2CdPHmS48ePk5ubi06nIzU11f7dK1Om4JabS5GHEzZRdteseimCAE+3C6du/Sag9sJUZObqzqsk7Uvi5tGbpN5IRSgSUJlUOPs5U6/+SjwrOeHR9TN86tWyCyyFhYXMnDkTi8WC0WhEEC30DlhKFd0l5IKI2SYnQd+WRsM2IpPJMBWZeGbVM2y4elsovTP71JzV7/Fx9Md/eS727t3L9evX7XFk0QcWELb7BIIBKJKE38lj38LVK5hy5crRtGlT1q1bVyYu4E7kchkezgoy8/S4Kwt5udFphIJzhE04TUxXEW/vWfaA2lI8PT0ZMWIEKpWKhQsXcvnyZRQKBXK5HKNRcvl476OPkIkiNkHgkw8+KLP9E2clTvxcuoaDe8C66qRkQ3ntdfvXoghmUUlq2FlcTwwg88dc/A/f5HynelT47AP8q0vVn81mKUh5zpzbKVifew5GjoS8vDRmz55dxiLXMCGBqO3bMIhG3mkjPYanbZBcpazA6E6SIOmMMyvbrOTskTZ8/LGCRYugbVszM2bMoFy5cqxapaD+zr0MLZrD/pbNONa4MWFhYXTpIrnRHD16lG3btmEwGG6P2QaCXMq+KAgCld3r4PTqYsL6HCOk4hXMX4DSAkY5fNhWwZbUJF7wi2Pkjl5lDl337lIWq6VLgasL4PAoUn5zxXV5AS7aAmy9ZNAG8vWuXCusQi2PUxgtanTqQuQlmoxoBdluYC0QCuy+3b5l5W9MffkSrWvuJDN7H08dy2firxOZv9CFOvLBrFzqirc3HD2bxYvb+rP18lbe5m3cXN14fczrDF83nIvZF1nQaS5J02dg2bKOxtuO4qIBCq9C0VXyc87gYriBoE+V0hoDqL0pzDhE13Pn2W8A/bhUBG2AdAMxZkH6Xri5jQuXllBoyMVkU9DquhVTySRCKzWsPQeuG5AUCMCyHPKsUjB/1MTF+Kt9aHnxJYSh5yUlxBv43kma0PCoC0dfg+IbkrWty3kOj+jFMNV6TvuBoliBxWoBF9Apdbzf6n3ebP7mg13/TyDfffcdY8eOZd68eQwaNOix9ePVV19l8eLFpKamPr6JkSeYfyXrVKNGjfjiiy8YPXr0Y09B9m8hl8sZOHAgs2fPxsnJyaFkOPh/z+rVq1EoFFSvXp3ExEQaux8kw+SHzKcZlczrpNn+3ykZmecz2f7WdpL3JeNZzZOO33ekcrvK92y/Zo+aVOtSDYX6z29HdevWJTQ0FIvFwsyZM1GpVGi1Wvz8/DC++iri7Nk4jR/P1TP7sS1cS2xzqfCAgJUQbzn5ZjVFehNJ5w9TV1gFxixUokh1V6jeTQt9A6WxaAOxKP25cVrB1biRHDlkIHvBTgz5GwHQemrxruFNo4hGVGxTkYCGASyZ+w0BqlvIS6wsyp1WGq3dys1LYwia/AOLX1lM4+WNqT2lNl8kS4WgSrNPTW2t4dv7UDIAWrZsaX+9d+9e1hYWsiOsDaMvTkOz0YgxRsGAwAXMTn2BgoICatWqZbcC3Ul4/GEiY+OIjWzO0YiGgJxCs5Yzl24y6+THtOu4hhdeiGTpUtNd22ZnZ7N69Wr69u2LRqOhRo0a1K1bl4oVKxIfH8/evXs5Xbs2oYmnsSkFwuPjyxQNLE0H/DjdIspQcxzsjsHqG81lQ13M5mtwh6Kht2nJMbqS+e2zWL3zcH1nArsMzty8eZPqftF8+KEkaGdmSnUeXnsNevcuW+NBrfbFycnJblFQKBR4v/su3zZuzCrLKhrFH2N8LOiV4GyWLBkzI6TaGPVtDXh1RHny80ROn4aAAJGlS1dy86aB/C+O8nXmLLI6e7A8vA8ZlkC0Wi2nT5+mXbt2aDQagoODCQwMJCkpCau1xCdJdjv7oiiKFC3aShVtOlmpAQTXvIrSIn2X7ApOfT/g1osaRm6927K/YgWULw+b16bT3jCWg5mhmOsrCF95FFkRyFZIWeJyij05f7USk5e9Rm6xG+vf6IwgSCmBrXKQrQOyQDwJQg0Qz0FKsAe5YRWRya9Q3HI4ladupManNbhqvYypppxLi+tQY/KLuDWbQO1RCRTX3YKAwClOccJ0HPcDJuSHjvPJ5jxcX2tAuL6ITCeRCt+U47PAtvSsG8y7l46yKCmeYwPWUaVcS0jLhMkfsM1rNs+INgpFyU0pI2kDvlk7IPckFoueK2aB40Y5Gw4ZiFNBgdyGRSsyIgE+2w46MwhqJOvMOsj/Bj4JrcgteXNe6vwSjSs1lg6g71Lo3hJ+LZAUkpC+UO9jsrOzOeY0Hddbn8KNJHpUrEaKKQW6gmaPhhbKFmQ3y+a4cJwqnlUY12zcw/xFPHJu3LhBXFwcsbGxxMbG2uMiduzY8VgVjUGDBjFlyhS2bNlC55KabQ7+Pn/LolGjRg3OnTtHkyZNmDt3bpnsTv9rlOZz/192FXPg4K8wGo1MmzaNgoIC5HI5kT5HcRYy2Zr5FP369SekYCG4VIGQvlhMFvZ/uZ+jPx3FZrVRb1A9Wk5o+YeuUP+E/fv34+/vT0hIyD0nPbLSUlm+cAZZRTJUgolBPVvhVzOG/Px8LBbL3YWhLMWgvyEtxddBf12aTdTfAMvtYHWb0pPsNG9STruQelpB2gWBgnQBZ/cUPD0z8A7KwL9cGlXmXkSeY8PiJWP5dwt4fVgbQg3HaeyUgOvTSsZXe5PSEqPugsCM7guodu4j6neJQ9DcXwaZDRs2cOLECan7FjP9/OaxfF9P3uzwLQtTBlChyStERYVhMpnYsmULZ8+eRa+X/FL+qL4FNqm2g7urhkZNW7Bt27a79qtSqRBFkYEDB9pjbu5k8eLFXLx4kfe+/whZlohNgI0xnezKhkKhYOTIkY+9OFcZck+Tu+9Vph5tRg//ldRxOYMogt6qJSPNk/1LWlKh1hXqdy/iVoNTfPklbNhgw2iU0bSppFy0aPHnu1i4cCHJyclYLBb8/f0xGAzk5OTwKZ9y8VsLIXllg75nRkADc2OuzdhEzZoWfvtNTn5+LvHx8RimbKJN/HbcFTk4G4vJdXPjx3FjEOVaXnrpJZydncu4C9lsNqZMmUJBQQFQtgBjQkQEFaZepZlqP2qVgeA+KQhfSdtZgf29yqOuEE6jL3+957iOHIFOHc0cn/UcC06W5/iZesxePRytySDl3/0JNp99isNnGzJnz1AWjhxAZI3fxRJsB5ZLL8VegBqiA+AIOhrsD6egdgEZHOO6K3ziBc+7wZi50+kU/R05rtf4Yvoa6gx7ii4HodNOOBvkQkSSHqXFipvxtohzwg/CpPh8ZMiwYcPHyYcDdb+n8tezMd04Stc+WWyzYf99AtRSyVDIVWTaZBSLMpQKLS/HWXhxWw4hr0CxCtz1kPQtuJbo5nonEDWg7gpJrWSEDDQik989mWI48DLKqz+RYijH+C3hxB9JKMkOJ2XNbNiwYZklJCQEQRAoNBVSc1pNtg7YSk2fmne1+yRz/vx59uzZY1csrl69CkClSpWIjIy0L9WrV3/sk9r16tWjTp06LFq06LH240nkX7FoHDp0iPnz5/PWW28RGhrKxx9/zPjx4/+e/+N/BK1W67BmOPh/z/Xr1+3uMOHO+9EYcjhy4zmcg8wEBgaCagLJccnsHDyPtJNpBDUKos+aPgSE/bvpoP+qyKaXXzlGvPoJ29YtJOHkRTwDpAfxH94MFU6SwuRS5Y8bFUVkphy89TfwbnKd+vobkkKivw5FyVy4KJJ7zZnMGzqKfV2oWniWU5o6eL4xj0OG8XzOW3xXPJpaS87wms8n8PJEvrIaCJKLnNg1gPJOcHNVebw7HEDlVR9TwWVid04k2KcmVZq9cVd36tSpQ+3atQkODmb37t0s2icScvkq+lEa+vdYyPCzT7FiBQwYICcsLMw+QaTX639XHbw0KgDCExKIjI3lUKtILvsG2gXSQ1FRZPbsSYUKFShXrhw+Pj7odLp7HqZq1aqRnJyMoZMap4UGZDbotElyGUuIiKBVq1ZPlpIB4F6HbLE8oS6nUAlGRBGKLVqmLx5FcE4KoV1Pcjm/Er9sDODTV0Q6dBDYulVmD0K+Hzp27IjRaGTv3r34+/tz+PBhNrIRCxa7detOv3u1TcvZb3YxaNAZypffwrx5KgoKzGzb1pqVR7cQaL1FsUprL6JotMrBamLKlCnUrVuXdu3a2eN0UlNTMZluW6dKq7tHxsaSEBFB9bzzVBEvIYgiwvHbfTbLYFdmBt26urJv3z6aNWuGXC4vM66GDWHoc4V8M7sRH/WaQPedq1jTuCu9zqxE3tWGxSbj4PUIggJSqRt8gubVyyoZogi2NpJVQ8gEYYX0+U6gsGcRa7odYmi6FXOJmCHsBK998Fmzyfx4eCSTnx1PWs3DtMlvSdjuvXjmgcZaQLYayhvK7IrK2bB2seQSFhts45WDMK9eBsETBoLMzCttYIuNuzhnsvFueX8q1H2Lg4lzeDH3PGF7CxEM8N02OOgPvRNhQQQ8fwyMwBtRsKoxHCoPyZ49qSRXYDKZOHToEEeOHLEvVy5foGNd2HkhjfDwNJ5++mm7UlG5cuU/FLSdVc4kv5L8n5O/zp49S61atZDJZNSvX5+uXbsSGRlJ8+bNn8gyAtWrV7+nVdjB/fNAweDZ2dl06NCB+Ph4NmzYQExMzKPoqwMHDh4x27ZtY//+/US4HcJTmc2W9A5gFejebQg3Fp8hcWkiSp2SRi83otHLjR777NO9KCoq+kOh+F/BZgFDGqa8G+zespbGY6fill/ANSpQkWuADS16vMlksv+r9Db9yvkOUKUjFJtl5ORrcZGDRmVBozGCDLZZy6ORm4hy94TOZ+7a5blz51i2bJndUmHzEjj+SV1+iH+d0FADJlM2Go0aq9VGQUHBXTPapfy+AnjMxo3IRBEqVIBr1+77EGSmJaPaXAfXPQUwH7CByVPJZ2Pepn37DjRt2vQv23jUFORlk7UyHDVFOFkMvDfxE067hlJo0VHV6yIfPPMRCgVUGr4DdMH/eH+zl89m+NnhQNnEADMjkHQ/gweT3FYxfnxrli9fzq5dOWzd2p4mTQ7SL3/xPc/fnUoj3J4RP3/+PP6//Ub0zp0gilyqUoUqly6BIJDt6kFQ2o3bW7kDrkCKpASsrQ7ZLYaTWa0a48aNu7dgm3+Rzm2v81KXJYxfPIouPdfiq01naLk5KGRmZp4YwfFb9XitzrfULnfGXrBdFG8XQLSUZHZTlcQbgRRztPxTOYNuKTAK0oRH0rcQnCelmm7vv5VNb3YkMduPVrO+pW+FPkyIg9hOXggd3am54TJVN4E2V2qvUAku70qB7penSjFSt3TgWywgE8W7MjmVogAMVWWYPBrQ6EwyW6enEyAZHTDIYWIL2NIWPveDVzPgpAl0AnzqLeOl526g0PliMBpp27YtcXFxaDQa6tevT8OGDYmIiKBhw4ZUq1btLiXufxGj0Uj58uXp3bs3P/zww+Puzl/St29fMjMz2V6accCBnX+1MrinpycHDx7E1dWVuXPnOhQNBw7+R7ly5QoRbofxVGSz5XIHiBewHYTlny0irHdVhh0chnsF98fdzT+lVMnQ6yE7G3JyIDdXWvLyID8fCgqkpbBQ+l9UBMXF0qLXg8EgLUajtJjNknB0bxRAEIIQhFodxnAdDMz/hc8ZX/K9DLOgpEDjwlO5u5AboMYWkKlBtc6Ge5cie/aZUkGsg5AiCWT5GQjGLFB7ldmjv78/giAjNjKS1nG7We7Rg+dcFtKnxUVcbuyiaUgshVYd+RY3ZhUMtxfsi9lw29IAtyt5F+p0dNqwQcqCJJNx8ZlnuLRxI3q9Hj8/PyIjI//weBuNRnauX0iM0iaN4xxwCOTVLDR0O0ajRo+38u9diCJz9n9MVvJWuu3IotyWQr60juNwlUaEVEom53glejb+nNTscvT9eRfHekLQQ9BbL6ou2l+Pj5WE3h+kMCBmNoSqnn6Mf6M1NpvAxYvPcPDgVXr0WIWzczEJRPxOwSilrBKQn5/Pzp07AegZG4tTietccEoKJrUa97w8tHr97WxXOqA7iGtBEKXWnroEW9QrUEdN/+PZc9eq/Li6KnXqtMLDIxmFwka22ZM1aV3p4b8aUSOji/d6qvpf5M4mSl8LAsjbgtgW2IHdjSqjA1Q6VJ/FAVmc80ujvryYrE7guxk0XaC9YSvrT/Wia99gGi5W06ZVBKnP5dA37BXynGtS7moM7sEim2abqJUJOyuW7E8uZ0prBa/sNjI1SsMrlQdQ4YcFTGsqB7H494cRFfBNjg3d/COsOQy+xbe/01ihvzs08IGeNyGnxCLytY+cEZUagbMfoigyfPhwEhIS2LJlC9HR0X9a3PN/GbVazbBhw5g+fTqTJ09+tJNAD4BMJuM+5uMd/AkPfKXLZDLatm3Lb7/9RnZ29pNnCnfgwME/wmazUV6/DjdTHlumdYDLgpTdEcjWBdBzSc9/0LYk1Ofk3Bb88/LKCv75+dI6RUVlBX+D4bbwbzSCySQJ/n+FTAYqlZQlR60GjQa0WmlxcgKdTlrc3aFcOXB1vb24uUmfu7uDh4f0X6W6n5Eq+eWrqoS/mUCW6I1GZaZ8QB6Te4+iSo013Nxpgo1gjIGAdUjZZ+aXbPo7oUvsDbY2Nub+FoNr0QVChEKqKix4+DbmhPozVCqlXfjsE7uS88urYoo6yi2ND8tu9kFv1VJo0dF95Sq0JQKnDOzuMyApHAkREbz30Ud24XNDx44kuLlBfDzwJ+5nSNaj+fPnoy24iDLQjMmmRHnSjGAD8wkVRzqEc2X6dDp37kzFihXv5wD+qxT82gCKk+llM6IR9Ci2WhGyYbDbVL7sOYvhbb/mY6UK18QCjqZGU6sW3LgBQUF/3fZfMf3sdPvrzyMlJUMhSkrHzAjoFtaZpCSB0aOhRw8lS5eKrF0rYjYrqFSpErm5uWRnZ9szwf2RlaqU2MhI2pQoHbElimJkbCzmIiXelkxs9QXk4ySBSgDEZdJva1V12BfdlLH16//peMqVgwEDzCxe7IJCocFbdoXWnjtImBXGyJPTUTxtpciiQ6Uwcy99RShZaItd0fY+Bu41T1AH0NvU+P8IG5/y59N38lkaYKRf7koGzFzG02MzGD66GRO/6UZpfTVXUSRl3C2sNivtKrYjaM9xUr0UvNdiPO+0fAeT1cTk2MnMPzKDHs8OIvjLn8h8pQoa8TqGEutJab/qqWBdIYSrYEghyEsqdCMHcQBUMsDoOMi5oz6ivMp3iG1HIgCTJk1i0aJFLF26lPbt2//pcfz/wIgRI5g8eTJLlixh+PDhj7s7f4pMJsNmu4c/nYP75h+p1J9++ilr165l6dKljBo16mH1yYEDB4+bH3/E8sG7mGUt+PnWcAyoMaECBCwo0Bc5EdPWhN6qKiP0lwr+Fstf7gGF4t6Cv5PT7cXZGXx8oFIlcHG5Lfi7u0vCv4fHbcH/CfTaAuDpF57m1c8sBKgNTJyoYdAgbwRhGTWm1CCrWhaudc1s8c2X7sYlbkasQxK21gFFJQLYOrBGKuly/TK+QbkIAphFEHNOEFQ8BpPxGfaLhxgeux1dnpVqcZdZ2/AZTKK6TH/qJJ6xKxF6rYbYyBZUqFCBpKQk+zqna9emTmIip2vXLiO06nS6P60rtH79enJzc9HKIdPszf6cZnQ3rUaBDZlFelgXFBSwbds2XnjhhYdyfP8JxdpQPIpOoyopPnmwHTTeDrOaWXDXutO3bi+cL/4AQTpiOvUk5iFkEDVajPx05Cc6mDqwghV2t6lltaF5iqR0vFP1dard/JJRU6RaHJUqAVTn+PFg0tPT6dOnDzKZjPT0dH7++WcsFstdcRe/JyEigqSOHSkqKrInBUiIiCBi1SHajtqOXGZB3A7COhC7ABOh2jWooA6lu397Kle+d9a4O5HJVCiVnpw+PY6Y+kPxUWfhnKBHVWTBvEbOlMKXGdn2R7xdcspsV2q5+/1rwQPE7VbSGwbT0scZ7TAbfXee592fT8ChYQR1mMGFifW5WAx9n4Oxb0BSkuTpJwgC7hp3AA4MO8CqZqvoWr0rziqpkKVGoWFim4l8FPURMkHG5ZzLbCxfSJilLoeNCThhoxAIlMP+CpC5Bdw3g8JZcumy3QBDdbCshnk1oZ47zF5z2/3t+OEzzE2Zi6urKxMmTODDDz+kT58+f/dy+Z8kJCSEmJgYpk+fzrBhw57oOBNBEByKxj/kHykaderUwc/Pj9mzZzsUDQcO/oconjAJp6xsmmqP8ZzTCgSTEYXFgJZiXCnAV51Ph0gjPpVUuLndnvEvFfydnZ9cwf9R4+rqysKFmURFqbmd0VXg0MhDuKpcGfrbQHyKF5PRVkMhKoLX5iPvUiJwdeG2RaMLKJQWvBOzMYxXI1fYyG/rjGe/XMqpThMdlMgn10W21vDjmUN6CiMU+KjSuW68nRkqPD4ei1yOwmLhdJ06/NqzByBI0tkdbBs8mB0yGfn5+WU+DwoKsgcY34vevXtz7do19u3bx6yrIYBIN37jzhw+gYGBNGzYkPz8/Mee1c+3/RzSFh3AT7zIDzkwqjPQGfpaIKr1fJyVOri5TSru5/ZwMvsk5yUzZssY+/tStykoiQ8QwWvXFV6rKLJuncCdbvvdu3cnPz+f3NxcUlNTuXTpkt2tIzYykuidO1EZjXelFBYEAaVSibe3Nzk5dwj5haC/qEVARO4sSoptZomycQueL1ah6jQAZ2dnVPdhwtu/X1KMRo4007Gajc8vj6dh6wSa7DyI+9N5vBv9OWZb2Sx0IpS1cFgAM6CVaoGqTthIPe3DqVFDGTF0CMI4LekKH3yfOghAv37wzjtSqt2hQ+Gll2DdurL9UivU9Avtd88+K2SSGFTFswpp49K4du0aP6/6kq7u02mXCh94QqENPDaUWDIUINwCuR6cTkguZt3OS22F5Ennc06EkiBdEEFBQXTv3p2+ffvy/vtPmMvgY2bkyJF07tyZw4cP07hx48fdnT/EYdH45/xjJ8GBAwfy5ZdfcubMGWrVqvUw+uTAgYPHRFoaDOhRTM2scXwb+BW+771N7gglu3crefddZy5fhjPpYA2B1z983L3979Cxo/ddn5XOtk6JmYYuow/KgHZ49lXx7Jx6/KA4g5tZwBptwxYNakAuwDGDSNgG0IpGMIPz7mIWpQzA3TeHahFn2RJylZOFbZlCVbQHC2hSewM5Hp0ptjoDNiJjY1FZLOS5ud5WMu6Bu7s7vr6+HD16FLlcjiAIqFQqWrdu/afjFASBihUrEhISwtKlS7lw4QIWhRKV2YJVIUnMSUlJpKSk8Mwzz1C7du0HP6gPAYvVxm7xVVT5rzMgXo98I9AFKrWxUdV2DTJEcK4ATg+v4nKPZT3sr0fEg3exlEb2YHkIy+yNcG4C705Q0qPV3edGrVZz+vRpDh06hMlkKuM7nhARQfSOHTgZDETv2CEpGiLIZXIqVKxAfn4+vXv3JjExkbVr12I2m9GcLcJfTEOO5bZiW1KlWtDAmJ3+ZPXsdd9JHlJSbPj7H2TgwAtMXPouffsu43BEE4LbJaHXavC05qBRmuxVwe9ZDdsGfIdUvC8IhDPQlHhcTzZEUKsxuTixasFbvDRyLgAffAAhIdKmH30E/v6ShfVBy7RcvHgRD70zV1RVGOR6mS16kTWFGjq3kDN0VxGqaBA3lPxylIALuFaDBT5qnt5j5PNIUMvV9Ovej2bNmlG3bl3mzJnzRM/aPw46dOhASEgIM2bMeOIVDUeMxj/jH885TpgwAYVCwfz58/96ZQcOHDyR3LwJA/rbqFXJwPY4Lavogfx6Mrz4IoIAUVHSbOWuXdCly/3FRDi4P9w17ijLdwWFFrlMzvd9d/BScXmuNZyB/OkU+shimGgKRlA4U9RsCUuiBGw6EHWg6Wtk4NsLaN5pPxlJ/lyf/izFlyoQTjwv5U0n4ptrtNgqTe92X7ka17w8TEolR1qF83slw9XVFbVacrPy9vamZcuWqNVq6tSpQ7169XBxcbnv9JM7duyw1wLYGR1NcYnUF14S56FQKB77xFRhYSE//fQT+6+eJdlqwH0jUozMOkmp48S7cOknQAblHzweqRS9Wc+Nghu0d25PSQFpxsdKBfrkQNNkaJQ7gbi1ofRo9cc1qho1agRwl/ATHh+PprT6d4lQqzaq8fLxon///gwdOhRBEKhTpw6vvvoqVatWxeN4HgFtkhGcSzZpC0wt+d8cXNolU9FXTYUKFf5yfOnpubi6XmXPnp24uydRteplDh1qAghcF4P4POFtLqaHSiuXxiNt0JAlRJJT6IbJrILSQndhwGLgy5LhAL6//orVasXWpTPl56yy79fZWYoPWblScsWMipIsHA9Keno6RVYnLmT3Z2uxEn+8ecclhBZdDci+B1t7WFsOkt1gWhuIfwZcKsFYDyPlFAJPu7vTKjCU7t27I5fL+e233xyp8u+BXC5nxIgRLF26lKysrMfdnT/E4Tr1z/nHFg1XV1dq1qzJ3LlzmThx4v/bTAoOHPwXycyUXA1277CSlikHJIGwhusN4O6I11q1YO3aP8u45OCf4ufsx/JRt12Z1j+7nnxjPoJSTUu5hl8/VjOs6TPM9ge9ojwpBcXEarLZ2TAOD9ebBF4IJpJYnJD88BvtO0+D/R+jtNoQAIXFwoEGd9chqV27NoGBgWzYsIHAwEDc3Nx4+umnqVatGoIg2Oup/BXHjx/nyJEj9iDlhIgIe/xAzEYprVLhsz3IzU7Hw8vvHx6tB8dms6FyVrI/fyZfeYhlZvMBxOJUhLS94FYd3EP/8f6Grh3Kr2d/xWgxMuIITNwJarOUctUoh+8j5Yzv1g+t9tQftnH+/Hk2bdp0z3MRvWMHMiSDwM7oaACsOivh4eHIZDK0Wi2iTWTbW9vQ+ehwS3Kj8BYoO1VAymVLmeQD9AZawNFFLWjwwuW/HF9srBve3rmUV1/FRZbNc85zcPrVQKHKmUp9rkCmyJS9bzO560t4d7kFG5Tw4Td8fXIkPkVXuLVyFZMrvAmNgZKSH6WqsAi4LVmKXC4n6IOvkNVejsVmsbs9vfUWfPYZ9OwJ338PYWHwzTd/2eV7kp+fT3h8PB0PbuKtznIUbbNRyDIBMNpgyE0N254FdYGBTBVYZXD5PAQtAblNpMOWXJpHx3Ohpw1Fp6P4+/s/WEf+HzB06FDef/995s2bx+uvv/64u3NPHK5T/5yH4kU9btw4MjIyHHmGHTj4j5GZCTs2GEjLvH0rUGKimu7Gn27n8AJ4dAiCgJvGDeSSEti9Znde7puA2MuAU+9kiIzjYP4wFhdCpcMNMRvVxBKJHg02pJu8qkTJEAG9Ws1rk7/hjc8/t1sYAPbtS2DPnj28/PLL1KtXD4AaNWogk8kQBAHNffqieHl5UblyZbt1BKT4ARsgE0Wid+wgxjqWhGUv8tNPP2F+TOYxV1dX+vXpw1gPNXJAPAdidsl/scToYEwDt9CHcsG3Cm6N0WIEQbJkeOnB2QKZTuD9FrzaU0s540WwGv6wDbVajSAIZVyZwuPjeWPyZLs1w6DV2uMzAoMCy7qnCXBmxRm2v7WdI9OP4GbNY++8ylwdXAExF3vyAYqkmhb6HaBS3V/Q/rp1ArVqZtLHfxHd/NZScUcyfqZ0dJsLKTJqSSvwIwcZ2670hg46OHsY07CR7N4Nx3MrskXRDoMvVLZAuQDp+ItAvhJefQqMESXKXrlyqFCwZM80+7779pXKvBQXQ2AgBAfD7Nm3+5aZmWkPgDebzRw9evQui1BaWho5OTmYzWYiY2ORZ9lQrbegkN0WMvPMPlSxvsU470+o46mjYD4UzYSgrWD/sXUBvUlE2eBT++/Iwb3x9fWlV69ezJgx44kV5h2Kxj/noSgaAwYMwMnJiblz5z6M5hw4cPCIqFEDNvyUSjllOkJJwK4aI8EuT64p2wE0CGyAXCkJ8tWrV+fn8T/jpnLD5JaPW5PjnOmdwbSXa7OpQz2KtRpMCoW9lJuTwYCTXo+TwUDMxo13uDOZ0Gq1JCUllaki/XcpX748vXr1olKlSna/9ISICAwl7iMqmQlXRQGV1ImEhITYreBms5nCwsIHPygPgE7jRr/nc/g+5S04BIINOARpFkgRfKDqS1Cx/wO1LYoi8dfjmbRvEsHfhDBy/Yv27z6PhCwtZGmk1+UFP5zqfozl6Qy7QnkvQkJCGDJkSJnA7NKaKDLAJgjsjI7GJdmFN19/kyFDhpSpUyAIAt41pHghGWbKq5N45qulVJyVBC5AVUAAmwoWt1Jy7ZtE6gx+677Ge/gwPN09mC0FAzGKKskypANPWQ7rvu5KYlYdInxPMC/ha2i6EDzD2LRJypq8YIFARoEPp2X16ZAkI93ldrsWAQ5X9cHVycP+WWF4HYxTvr5jXNCyJXz6qfR+ypTbr61WK4sXL2bVKsndKjMzky1btjBz5ky7y05RURELFy5k5oxpFOfe5GRUffI9PCiMccZWoo8YrSp2Z7fC2dmF0UNHYwhqzPddBaxuQKCUiUp8DrJaepIT/it1O7x9X8ft/zsjR47k8uXLT+xEtSNG45/zUPycZDIZUVFRrFixglu3btGmTRuioqJo3LjxfWWqcODAweOjWscqFOlEIoqPc9pcHbloxUPz4IKmg8dD7tu5xLeNx2azcePGDU6ePMkFv1xONu2ISdTQfeVK6iQmciMgAM+cHDR6PTJRtLszJUREkJKSwq1bt+jXrx/Ozs6IovhAQaxWq5Xk5OQyD+id0VFExsaR08GDilzDzymfPB8fli9fTkZGBkVFRQQHB/Pss88+tGNyP8jlcoLV5xEbgXAYhMbgrVCgKBcFeSfB46sHajctP402v7ShwFQgfVAyrTciXnKbQoR328DBiFqcr3gBbe4G0N2jLHUJJpOJa9eusXnzZgyG21YPRYmLmg3YGBNDQsMIAjyc0TqXjQuwWWysHb6WnCs5eDvnEGi9SqAhGaXcLAU1A+JFKYtSmgZazblCOdf7D4LPyIBOnXwRsl/AuOkXxDZS9ipVppnoyzuZ6TyCvhH7UKkVUP5pAKpXh7p14dIlsKkF3sz5ms9yXmZ1tUR7XQ0PE6yfm8uB3rtpGtmaEydOoO39ApEfjC6z/0mToH176X9kpGSVOngQjMZYCgoK0Ov1nDt3DpvNhsViIS0tjR9//BEPdRGCzUCR3gURGVWcknAZ+wKWZ14j3+RKttGPYPUl9KIrtZ96H9/MLE5ln+Jy1mW+0ogMSoWblcD/GyV58oqYm/9KtQoPJ0PZ/weaN29OaGgoM2bMeCJrjDhiNP45Dy2gYunSpQwePJj9+/ezf/9+PvjgA9RqNc2bN6dNmza0bt2aiIgIlErlXzfmwIGDR0bz5jCo7XUqn4yj4mc1mfpGJq7lH2/aUQcPRkSJy8y5c+c4ffo0Gp0nlRSXuZzrwa89e5TJNBUeH0/Mxo13KRugZefOneTl5VG9enViYmL+dj8uXLhwD6uItN9LxVVwNhaglRfgkfA0RVnRGMxeKBGpWrXNgw79gcnOzsZbtwvZy7c/U2DBemMT8ipD/pbbVEZGBomJieh0OrZs2UJn83yWGp9H1GaVCQD3krx4eDsW4jrGsK+4HrUykvgjsf7mzZssX76cwsJCe+zL77EoFRyNCJfGpM/m6NGjNGjQgKwLWRydc5Qzy8/gWcWTwXsGU9ysPi59rqOtagDlHUPsIrlMeXWSodK43HM/9yI/H3SaAnIux+F5ahgqmQmjVYmmixlxObhYC6h77CQb/d7nzrjo+Hh4/XUbhw5tQ6U6xbp1XdjR62naJCba1xEAzyIzF36axZWk61y/fh0vLy96FELctX00D2kBQM0S2T4xEWrXhrffhrFjTfTpk4DFYsFisbB582ZCQ0PtgqPFYiHHKuAst9EgPoEWsfuwdRFQ+6/hpD6SugN+RYGBwlWVyQkYTu06oUzeM5kPZ32IAQO4wdTG8GOkjhNV3sS3ybsguyMfsYO/RBAERo4cyUsvvURKSgrly5f/640eIQ7XqX/OQ1M0nJ2dWblyJSDNvKxYsYKlS5cSHx/Pnj17sFqtaLVaIiMj7RaPBg0aOILHHTh4jIwcCe7uIoF7l9BjywACwjR0eqYKUOVxd83BPyAwMJDKlStjNps5mUzJg9JK6bS6DAsJEQ0B7MpGZGwsRxpGYDbnk5KSj9WqIDNTw86dO2nVqhVy+f0LUIcOHbor9qI09WrzbbHMaTKcZ/xWkGb0pZbzGXLNHmjleoICX+fChQt4enri7X13SuB/gynrP2KUJg+LrSTbFGAVZRQ518etfI8/3xgpQHv79u00bdoUpVLJnj17sNlg794WpOVp8e2lIq1IAwoDCJKrVKlF47NIiDAfIj6rNd6Nxt2laIiiyP79+zlw4ABFRUX37oBYWs1bRIYVKzKMRiNbN2zl5Mcn8fLzwlxsJvyFcCLHSxXBCzrUANfrCNo7Ejv8gOQ+1hiU7WwQ/xI0X3Bfx3DVKugWNh/NgbdIMnmjlvmyO6MlfdushLXglGXgLcUXNDvRk5YtAylVOo8cyaRy5VX4+2dhNpvR6QrIlhsYe6hsTjQBeG7hYr4IKo9Rq6UwP4fiQA9iP3mN5rNvxxkNGgTvvgu//QbPPw+ffZZJZqaB0nCh/Px8Tp08XqbvGpmBKk4Xidq/E6c8A+JmWB7xAr1f/rHEmueE7enzBKlc+fHHH/nqm68wNjeiCdZgkBn4MBrKu3ri28xRJ+NBGTBgAG+++SY///wzH3/88ePuThn+Pysaubm5nD9/nnPnztn/G41GwsLCaNCgAVWrVr2vdv4VKV+lUtG/f3/695d8Ww0GA0uWLGH58uUkJCSwc+dOrFYrTk5OtG7dmqioKKKioggLC/tbDzMHDhw8OGvXwq+/wqw+28hx9icg7P5Slzp48nF1daVZs2YsXboUhUJRYl2Q7q1yuRyr1YqAlYSICIKTkqiTmEhy+fJlJu/lcisXLhwkMVHJ7NlRfPQRVLkP/dNgMJCWloYgCGXdDkoa1xgMhOy/ys8RzyNIpeIQsKIUzJyY/y0aV1+KzUoGDx6Mu7u7tK1ogzNfQK03peJ5D4m4S+uYmTyNUxpYkgiK9UAXULS14SZeA58/roIOkJCQwNWrV8nJyWHjxo0oFApyc11YubIXlRqvo6jJDAov1Ybyt/3PZ0ZISykueRXxFEx3WSoMBgPLly/nxo0bf57xq+S4SmrGbY8Bo81Icbti/C754VPLhyZjm9i/iyeGp5Rb79wcDiH5Xx0C6yhQaHz+dOx3smNzAb2rbUWnKEanSMZqkxHjv5U8sytZbd3x2pzHz5rhhIRc5MqVQqC6NHaXueTkFNvb6dhxCzvYx2dpt9su1YME4OlVq1g2YADm4kwK63vRfHciq1evxt3dnejoaN55B8qXB5sNMjLSiIg4zIkTEYx+ahoKsZj4vAiq7jhE89g4DrdsRFqkP1391uKmzEPfRY24AfI6unGlKIS8vDzc3d2x2Wz8tmEnEyZM4MqVKwwYMICP3v+IJCGJ0RtHk5yXjOwhXpP/H3FxcaF79+6sXr36iVM0BEH4n47RsFqtJCUllVEmSv+npd3+IZYvX54aNWqgUqmYN28ekyZNuu99PBJzgkajYciQIQwZMgSA4uJiFi5cyMqVKzl8+DCbN2/GZrPh4uJC69atiY6OpnXr1tStW/e+CwU5cODg/rl1CwYPhi1rDezscoyxV8c+7i45eMiEhITw+uuvk5yczOnTp0lKSkKv16PRaMjNzUUsEeGCU1KQiSLBKSmA5FIVGRtLbGQkCRERBAX5YPo/9s46rur7++PPzy26BMUCRETsbrGxu9s5dVM3N3Mz1nPWnDVjujlnd2O3ExQETAwUVBRBui+XG5/P748PEsbm9nNz2/c+H4/7QD55PiH3fd7nnNfRCzzt1de7N3z2Gbws4CBJEk2aNKFYsWL4+/vnD5JPtW5dJHoCciEzksSpNm0Iq1+fmlbB1LdZx6H4joRuvUCLyhLqRish9hCEzwRjJtSc9VruT2RCOH229SLBZCJHAgt/IBlZeckPjFmPUcUdg9Idiuyn1Wo5fPgwtra2XLt2DYPBgCAIGI1Gbtwoj/OueC5aVGVJXAWanO3JaMNM5sqBBGadBAuTLGf7SRtwqP8x1lijl3jO0Xjy5AmJiYm/q8plVCrRGAyoDAbqhlzkcv3aoLDA2dkZHoCTtxON3mtUZJ/EvdfRzHrmuA2BYJAayo25lVGrEWp8CeqiaZTnzp0jKSmJnj175i+7dFnBty0vI0qgEECpEFGLuWQds8P5SBoBvs14XKoMLUr9yubNg1m37ioDB9YkO7ssnsUvUNc+jBPJ7Wh89QITzl0DZJ/nqQ+UaA0ltOB1/778DEyWnK/egO4Hozh0PZRq3tUAsLYGT0/YsgUGDSrB0qWtqV5dww/D5lHWdJYWJa8iLUnDMl1P24PHEVzgaRhJ6WckrZUdNuW7MK3ZtPzapE8//ZQ5c+bQtWtX9uzZQ/XqsvqVJ55cH3ud1ZdWs+PGjt98RmZ+n4cPH77yDPnfhU6n48SJE/+4dK4/Q0ZGBhEREc85E3fv3s3/G21lZYWPjw8+Pj60bNmSSpUq4ePjQ8WKFYuISoD89+ncuXP069fvd88tSK/gqmVkZODg4EB6ejr29q8/dzsjI4P169eze/durl69SlpaGqIo4uDgQKtWrfj000+pW7fuaz+vGTP/i4giVKwo989wPrwB12qutFvwzyvCM/P60Wq1XLlyhRMnTqBUCGiUBioHXiriWIxftAjH9HTSHBxYMnEilvcsGT55OK41XImKkjsxHz8Odnbw9tswefKLuzBLksT27du5e/cuJpMJKHBignwb0CjgIo7p6QAYrFUorEzQFZRtpXylHwEwuQ9GlRoMWZFgURyaboOSrfIuKBZyk8Cpxh++F9fuH2XKvoEEpev50iWbD8NA5Q9SV6ANiChAoSZQ/Sla23q0aNGCzMxMEhISOHr0aL5C1tNr+rHYu3wmdOROcms80iHaXgGCiEc6PHCQz1kuveD80Q6wZuLnKPLS2dzd3fMn455iMBgIDAzk0qVLZGZmPucE1g0Jod2xY6gNBgTIf2Z2tnY4BzpTo2sNar9dO//ZJzyJo2z8fPyHxdPjy73PlZ9IEqRnw3a9KyM6fEtgdAlatClwtB4+fMjmzZtRq9W0a9eO6pU84NqX+A3vzPHpfuSaLLBU5iJKcCG1MVU/vYljejpaJyu+/VBWr0pOdmHXruFsWh1JiUfDcLeKQgAW3P8o/92Ls4Xi2aDKkxk+UAE6RkGmnT3fT5qAgITvpQBa+J9md7MqeMz/hQYNGvDw4UMuXHBh1ixrrsn+Ch07ZtO23OdMarYQToC0HdDKRe+4gLQY9KIKQRA4m9ycGzn1mDBtLgCnT5+mTZs2zJ49m2nTpv3hd8zMq5Gbm4ujoyOzZ89m4sSXCyL83UyfPp2FCxcSFhZGtWrV3rQ5v4soijx8+DDfiSjsUMTFxeVvV6ZMmXwnovDPsmXL/qHJ/Vf1Df4RjsazpKWl8eOPPzJ16lQUCgV2dnYEBwfj4+Pzl5/bjJn/OkOGyAoxa+fFsbHdRiY/mWyOHP4PodVquX//PhUqVECr1fL990so3BqtbkhokcGsUlBi9YMVDh4O9NzQE2dvZwAuXICZM2VZ07Jl4YMPZMej8KuUk5NDQkIC27dvR6uVU2RUKhV13I2wI5QWJ06CJKE2GVAbTGAD/Fiwv5g30FQKT7uAAFZuUG063FsLWffApQm02Pen7kVMTAwj1vRnbpnz1LIEoyQnmCnzruGJrgRnTeO4HStH3G1tbUlMTMTS0jLf0fhw4SKcMtJ5pCyNz8dGhl1NYFog+VGMaQEF//7uKFgbQauC3e0bc79+e1QqFSaTiTp16tClS5cX2pmSksKyZcv4cOHCIk7g04E55KlOde7MpfoNqHinIoaKBrzaepGUlITRaOTOnQhsxDhUipu0cDxDZTsTwkkKmhT6yeeKzyjOhtQAXCy+Jy7bgbdbWiJ5DmWv/2G02ZlkZcuV7EpBYmqVNRy/V5Wp679l7KAVDHdbh+XpHMT9Co42akcpizgqnLrHed8mBNdtCIICC4WOKv7hNL9xDsf+ebbvFzjUSBYdaBhwmlNuOjrfFrHJC/JcKQEVU0GhsMC4XEEaxXCYnIF9eibZGgW/LFhChw4d2LRpE6VLl2b69He4dw/s7SEgIJppYx8RML0ZjAeSQLIBrGSn8miNdkTneGKh0JNudESvKMY7Y95HkiS+9fJiQk4OJRYuRPHee3/qHTPz+wQGBuLr60tISAj16tV70+YAEBISQqNGjZg5cyYz/j+t5v8Gtm7dyty5c7lz505+vxhLS0sqVqyY70QUjk7Y2b260MNv8a92NDIzM+nRowdnzpxh6NCh7NixA2dnZy5evGjusmnGzP+DTZvg448hOhpWVl1Giy9bUH3g/7/rsZl/J0ajkSVLlvxG/wqJpk2a4te2LRcWXSBgdgDFqxWn14Ze2Jct+C7Yswe+/RYiIqB6dZgxA9q3l9eFhoZy9OjRIqlBCoWCxvZnCEmvj7Uyh3e++RFrna6IoyFJefUDJ3huMAyFHA+fD6Hukj99D7xmCewqBRXVllgodPkF4QZRye4nPYjS13lp+lJ8vAvqn41MFWYx36cai7teAPWLG+5t3AmDwmXLHzhAvYnO/FTjJ3r06EFERAQ2NjYvTNHITszm6oarHE87Tt1LIUXSzQA6HzyYfzcOdu5MWL16hSqp5TVKjJhQI2AiO/kqI2rup6IlCBOQB94uwGLZWTl2ox1R9m1Jzs0CBKyVWYCSHJMVRQcLEmpBT9Q9Lx4+dsPX9wKVbW7Q/pujOKRnoHWyYsWk98k22WKl0NLk0jnqnr2MRQ8dwn4QkiHXUY2FygBJBdEYgPcXzcclPTv/fHrAZGmBrqQGm4Z6TF1UnNvuS+Mz5zEZslk87dP8yBBAaGh7PDy8GT78PqdPnyZwfwW+7PgVtSKvIu6H3M4WCH4SCcoabIhsh1GSZfg9raLoXOIACkHiQVYZXCfcorRRBA8PuSOgmb+EuXPnMmvWLFJTU/8RAkE6nY66detiZWVFUFDQP8Kml/Hjjz8yZswYOnfujJ+fX75j4e7u/pdPIL6qb/CPu3vx8fG0b9+e8PBwFixYwIQJExg9ejQtW7akffv2BAQEvDZvzIyZ/yWio+V0qYsX4cbWq6DA7GT8jxP/JBajLgNQoBFyMUpKRFRFUnSaVt8K2lM0ntiYhuMb8uvMX1lZcyVlGpWh57qeWLtY07Mn9OwJRiP8+CNMmgTx8XI/g8aNzwEFXa0FQSAnJ4cUQzGGlt5AMU0qtzpVptbRKwCYjivRtDVilNSoBYPsZCSRXzsBwAkJtuf9e+QRkMYDEkgmEJRQ85vnagtehKhLpaIGnCxtydA7YC1lYq/KAMAoKrif440J0wv3DbtUlePHOlOr+3J8wrfi1uWHlzoZAP1vFLhHc32hnWU7evTogSAIVKpUqahdRpG7h+9yfeN1tJZaSrYqiSpHRVj9evgGBOCYno5vQABLJk7MV/MSkGtennYFBzmtq/WpU2CSiFGUxdMiCm07iYRNloi5OhQGEGwgsxMk/1KGs23bcCK6Dd4VovJDU1qTDSAUeScAfAMCeOjmhmtkAkdqtecBntzKroqNbxbNAs8R2NSXbJMtADmiFTVOh2OVriNrjw26zhY4Hk5nhcV7vO+3AtUhI0FNCwrvAxq1oevR/SiQ75kG+LZWLoua5jL6usB0QYVV73J836Ap78+dSULuPTpfS823T1vVku3bK1C69DEwZjG982y+P/IhK0aORmql4mZWFc4/bMy7E77CYclXJBucsVOm07vkbmxU2WQabZjySzzfDh9B6ePHwZw29Zdy7tw5mjRp8o8Z0H/99dfcvXuXS5cu/WNsehGLFy9m4sSJfPDBByxevPgfm5nwj7qDd+/epW3btsTFxbF161b69OkDQOPGjdm0aRMDBw6kV69eHDx40NwI0IyZP4AoQrNm8N134OUlsqDpMYadHPamzTLzhkm7vg4LMqlsH0VV2+s4q1NYEj2+yGA2cNg0/A5WhforUJQbSMsvWtL8k+acmHqCpRWXUr5Nebr+3BVLe0tUKnjvPfmTlSVHOfz9u6DXQ926GkaNUnH27DYAbmVXo47DZTSKJ7hokhG0EgoJsvZYk9FCjaM6r6ChhyXsM8CIptDgLbg5C/zvwVO11813oOGdgouyLAV1FvAqHA9fQxNLcLMujkJ4jNhoLabzQ1EKJiyUBnydfuV0avv87Z8Otpc4vYV/3/co7tyQMIMKt1FriMqOkkMCL2FbVdnZuF5TRYs+pek4YMVzzRATbyZy+ZfLxF+Np1yHcjSe1Zjt/tu5H30flWBAiUiAr2/+gPppV/enXd8tc3LouXMn7o8e8dDNjWo3bqDIS1qoQBQKnYT9abBX6BDy7p/OFpRtIHmpK9G68gxI307r+XLUJLJCBdwfPco/59N3AsAxPR37jAwUkkS3m/4EOjeV3xffpiyeMBGJwoMegZutK1PpVAQXmzWgXHsD68r5YZfxgA9vf8+iRRMp4T0Zl9Bw0lIS0Ja0yNuL/J9TgmT54UaZdljUDcLHcBNbYTPZHhqqhZ7D92Javn1h9euj0eQiZeoY7fMjzupk5mvtSUovQXBuI8KzalA35CLSQjf6dbDmZ593aeAYjLUym8RcF5bf6U/52rnU/emnV3qPzPx5TCYTAQEBfPTRR2/aFEBOmZo3bx5ff/31P7ouY/bs2XzyySdMnTqVOXPm/KnGqn8X/5jUqZCQENq3b49Op+P48eM0bfq8rODChQuZMmUKQ4YMYd26df/oG2vGzD+J7t1BqYTdu+HIxCMk3U5iyOEhb9osM28aSYLYg2QGfIBKH0+8rjjrYt8uUqdxpVEjBnT1pcK9IeDSWC7Gzps5M+qNHP7wMDe33cSnhw9dfuiCyvL5+avYWPj6a/D3F6lSJYymTY8hCEY8raLoct0fx03pKCQJSYDLPWtzspYfE+oHo9ZYQOWPoWTrAllbkw4+6QjLz8r296Mg0qGwlFWpKk96pcvvsaoqYxQ36eDsCrlp0OkK2WeGYJ0VhiCAzmTB7axKbE/ogAVW+TURD+wVeE4SQQJLBehe8i06OqSgRmNVfbAW4HgZaFKsDHSNAJUNunQd4VvDidgbgWN5R2q/XZtSdUtx9uxZfv311zxpTdmVUKKn2879VAsPR1QoUIgiCmT/5umwXhQEFJJU8BPQWVryILcclYrdRtEt79HnRYSC20LOQwWNbmo42aZNvkNR+FhpDg5FHByQ8A0I5KFbWZxvp5DZ0xb3EzFYp+aQ5uBA4rfFuJVVhaicihhFFTmiFaUsYqlpfQ3X0vWJdlnE+SAJK6sf2LGlPn0bB/D+3PdRxh7g+tFvMJ6B2tvCefYb3gRsb2TLwJJ+cr4ecHD0u5Tbv477ddsWqS1yiE9Bm2LDDOs54A+361VkoTAJj4bRWCl1jFrwE/ZpWehLFOPu16742NwmxVCc5Xf6YRDsaNeuHS2fSq2Z+cu4cuUKtWvX5uzZszRv3vyN2pKbm0udOnX+0SlTkiTx2WefMWvWLL766is+++yzNzYW/lelTh0+fJhevXphYWHB1atXXypxNmnSJO7fv8+yZctwc3Nj1qzXI3Noxsx/mVWr4OpVuHcPtClarq69yvgHZjlbM8hFEGW6YNu3E0H753P2fhogEFa/HmH166FRGDGZTETG6fDoFIXhbB+s95cDv7OERaRQsWJFyn9QnnZzG3Pg/bMsKL2A6oOr02FRBxSqghnt0qVh5Uro3/8sAQGBiKKcjuSsScZ2d1a+k3GkUwe8O9xFlWBk2a0eDB48mBIlShS1WWkJc0/DtHA4XBekQh3IFWqo+P4rX/7FxFtMLaUiLVvExrIY6tiDKKp+jBjcHyVgqcyl6sXrfLr/Kl80tuY7X2tGBcBc36e9QeCti0WdicLMOiV3AZ91Sl6nlyBeskTSp2Lc4c2ho0vRpRup2r8q/ff0L+Kk5eTkFNLvlwcSJpRUCw9HASjy+pNICqAh6K+qMIqq/CjEQze3vGhEU27Ur4JygYTX/Nv5zeuEPOesoQTGUSJqnQ7fgADWlRzGe9ImMGVxy7NifkQjrH59wurXRy3o6VTcnxsdq2AlalFpTFS0vctlY20qH76FpV6Hx4UYEiuUp1zzkZy94k7AwbM0rXWLlecG8/a0lgQHQcOGUKFCLywszjBjxke0Hy1QKecyNezD0Z9SPudkPL0T9duOgOUb5TCtQoHYqTOlNqxmZV0PwurXxVLQYU0mYxv9wMrpY5GegCCCT+gd7rl60bX1Pi5l1+fXpi1ofeFXrL/8BjfrT0gVS/FT9GBmzV9IQEAADRo0eOX3yMyf59dff0Wj0byR+52VlUVoaChBQUEEBQVx4cIFUlNTCQsL+8c6GZMnT2bRokV8++23/5go0O/xxu/kunXrGDFiBCVKlOD69eu/2w126dKlPHjwgNmzZ1OmTBneMytBmDHzUiIi5PTiq1flSeid/XdS5506WDq8QI/UzP8subm5hN5JJ1eS34u6IaG0CjxFehdX1lZ+h+vXr5Odnc2dO43pUKkiD36cQnhWDa5dKsajuFQGuB2k8xfjYPl49g7fy3zX+dQbXY9W37QqkjfcokULIiMjiY2NBaC0RSxCXp21SaUgraUThxM70qn4QfwzRhIREfG8o/EUx2ryJ/VSwTJJBKXFK193BbWEUvAkPgdUegFF0GrCsn3pEaaAAyJ4gzpYRC3C3ENa3u+kxfMZ9c3pAeCRLjsbq+rLqlXFlOCuIn90bC3AlgrehJlqkfPQhxxpGRZWSbQd+xjrhh/m2V4QFomNjeXKlSsvsFgBagEMeV1QbEDoB/pWapZHv0+mybHQtiKSpCA21pXbp+rToMzqIopgkiSrbOVsA7upoFupwfSBGusSEuIwG1JuLuXAwQiauN6ilPE+JTKSyJRKUlZ1h5r21/MLTgQBsow2HK3VAe/TkdinZZK114bdQ3rwUeJsWtq58FnAbCrV7MKFezDeHsLCYPRosLd3Y8yYoTg4QLt28GDNJbKkCtilRRYpPBfyPjnWFpSeNgdOhMDq1fDuu2RkZZHmYIvmwWmqdPkKDw8P4uPj2RGRxLDU9Qh54R6hK/RTbmPr+aE0qH+JiMZViGxSiQljx5IeZktksgOfv92Tr776ikaNivYdMfPXce7cOerXr4/lizSyXyOiKHLnzp18pyIoKIjr16/n93Br0KAB77zzDl27ds3vlfJPQhRF3n//fVauXMmyZct4//1Xn1B507yx1ClJkpg3bx7Tp0+nUqVKXL58+ZVfNFEUqVevHleuXGH37t306NHjtdhkxsx/CaMR3N1h0SLo3x/iLsWxqeMmJsVN+scWjZn5+5EkiZUrfyAhIYGnM+dPU4SyHa1ZPvF9ciTrvK0LqT3l/1vAQZXK4NKbKD48HgQFWU+y2DNsD3GX4mgyuQlNpjbJf+eMRiOrV68mPj4eW2UmY2ctx1qnQwSeDCrJtsoD6FNyJ8WsRWwGPX654anX4XgTMBZWzFJAmc7QYv9Ld6u8rDIlbUvS0r0ZueEzmeDZnZiYhzio0sg02VPiXDyltz4BUY4WCGJBDcQDB55zNHaFQ7fTslRqTEu5cNlGAXYKmLUDhp+Bn1uAb2s1W40GPDUapjnpUSlAynXHdNiD9HKBONeCzLZ32bvxO1yrdOXO3ShSUlKwM6ZSVxPGWVMLhu1dh/vVR6AGYTD5KWOSBMl6J65Gl8M37Rp7tT2ILFMRKVli6eYJ1BzdEV+Vnk8q3ECplLd/lAqlk0D1hXxxoig7DZKU178k+iOM1ocwWT3gu0VTAIG2bdtSOaonjoqEIj04kvTFOZroR4/wPVgd1JHZyQ6Fn0R4RjWO3/2KmLmMmsYAAQAASURBVOvRtOzoyherOnP0mJJx4+DQoaL3sWNHKFk8l+691Lw7PJMDti2JzHJhYHpBV3WjSkAdeR+iomDMGLgj1+Zkf/kZgdu/pd1NufGYaMjh5gY/7P2jsTmSg/PAFMQ2AtlOXSg3dD+JiaDLySIrNY4SZb0xmUx4eXmhVqu5efMmarUaM38Pnp6e9OzZk4ULF772Y8fExPDzzz9z4cIFgoODSUtLQxAEqlSpQqNGjfI/lStXRqlUvvbzvy6MRiMjR45kw4YNrF69mhEjRrxpk4BX9w3eyGjDZDLx4YcfMn36dFq0aMGNGzf+kDerUCi4ePEibm5u9O/fn/Pnz/+F1pox8++kfXto21Z2MgB2DthJh+87mJ0MM8/RoEFDnJyK5f8e4OuL1tISVa6B8fcW0dt1O95WEVgI2rwtBNSCnubFzqLASLrRgchsL4iWC71tS9rSeWtnGmxswJ2Dd1hYaiEhK+TCZZVKhYeHBwBZJjtOtWkj1wIApbY+YcK9xZS1ikFpSmZUu6306yeScHoW3P6+qNF3loBRW3SZoIDSnX7zWktq75IQd4avzs2kviVMvuvPNUMaamU2OccNlMpzMkyCXMBtFOSBt1Eo6IdRmFRRLlJWC+CphjJqsBMEtqd4MLe2gvIT4Zs6sDXKwIy5EBqsp+4DOOUPAakPie98DseaIpIkEn92CtMz9vJF0MdUUwcw1el7xrsuoWmpALoU34f71UdyozkDiG0KXbYAjuo0GjldxqKBiX5NdjHDOIdpJedyJ9SblY0isfbJRaGQe5MEHAT3XqD6EvniVoKiPAir8spvBMgtsRxL1xtYkc2I2z/xwYIl3PtoBXsSBxKd44FJKhiYWSszGFR6CzZKHQoBHFSZqAU9d7Te6G1D2B3Sk7Ku2awZ2gFlSgDFiz9/H/ftg4NHLPjxRwWS2oEkdSmsc4tK6kqiBD4+oNVCRgbkdbO3mTiF2o+MRKdFA6BQWVLJ/h7ufR8z1GcD2maWCIIKO+0FatYwMn8+WNvY4lLaiyNHjqDX66lVqxaRkZE0bNiQw4cP8wpzsGZeA2XLls2PcL4uUlJS+Pjjj/H29mbRokWoVComT57M8ePHSU1NJTw8nNWrVzNq1CiqVav2j3YyDAYDgwcPZtOmTWzatOkf42T8Ef72iIZOp2PIkCHs3r2bgQMHsmnTpj99rLS0NMqXL48oigQFBT0nEWjGzP8q8+fDTz/B7dvywOHKuisEzgvk/Zv/nnCrmb8PSZJYsGAB2dnZ+cueRjVEQYCGEsJdMFRQId1VEFnGC/dHj7DsoSW1uTPb4vqTYnDC0yaWmFxPDEYjFhYWqFQqpkyZQsKNBPa+tZfM2Ez8vvVDqiaxf/9+JEmivNVdhtzeBOvIT3PhLbjTqALlbR9iNGowmSSuxdRl45OzfP45uLrmGXmgGmTcKLgQ63LQ4/5z15eeno5KpcLGxobLaxSUU0uki+CoAJUAv6TBkBBw3JgXwVDA2I5yKtSzBd0Kk1x3Ye3oTikhjsCFBhTJgAuQ185DegyGGbCtCnzSGoZehamBYKcHqbSsxqt3AatC82vSUTDtUSL6mEgbCTZBYHMQ6AqxLcBJAVajAH1eTCmvibjgD3iDdAeyW4Ftz0LHNAFpoDsNFiYQDkBKOXD+CPgkb6NZwFwgOu/eO+YtGyOvvnu7AlZVcygrPuaxojTOvySgUpgwShK5uXbYW2ViEBVolGJ+QzxcIP07e76LmEyfEvv4atMnfPNLV3p3y+TAnM8xaDMo3/NLNI7lizynwEDo1g2mt99PzS2LuFb/DO9cATtDgQKVTgF3SyioXrIGVKoEW7YAkOxiw7L5/fji7V/kDU+2h/hj/HRqFLaWWQxsshUqf8wNzTxat8rlSZySff4HuHHjBm3btqVBgwacO3eO6dOnExgYSLNmzZgzZ84LhWnMvD4mTJjAwYMHuXv37v/7WHID0u+ZO3cuRqORyZMnM3ny5L+l/9tfQWxsLO+++y7Hjh1j27Zt9OzZ8/d3+hv5x0U0MjIyuHz5Mu3atWPPnj1Mmzbt/+VkADg6OnL58mUMBkO+LK4ZM//rXLoEc+ZAQIDsZIiiyPEpx+m7ve+bNs3MP5SkpCRyc3OLLAvw9c1XHRKC5AZr6otGLFL0VA6/jV1aFqoDIiXOJTJu9jI+mTWLPp9tosaFC4Bc96HT6Vi4cCHh8eGMDH4Xt48HsHnsGfynHc5vsHYvx4snvq7wFjyVUJL8wULQoZD0WKqysLHIpk7VBLKzoWZNKF8evvwSDK5doNxQcKwJCGBIh+RL8GtvuV4jjzNnzrBs2TK+//57onWO2AhQTg2OSrA9BeO+AsddBY7Oo4FCfmH3j/Wg/ARYVU+27aMLELsArgQ9JMDdIKs4uSA3FATIBOEH0Bhh6DWIXgwzT4O9Xh4sK5JAaQuWhSdRTwAbQZVpQn0Jil/MczLy+oeUUoLl6YLNBWQHQ3jaY+SC/HxsCm0DcksRoRhYdgbFfvn6nKORHYuUvM9cYBpycYlYaFkesfGl+Ub8lAd4EFSlARZKIyqFREJqWXaH9EJEQWh6fbQmC/RdVPn3QgIa252ncrHrGEwqKqRPJFVbjPU3l5FbfjwPd3Ui6ewkWUUsj6ZNRHrU2sDULZ2pxTVaPcxzkApdk6UIleJFufBs9+6Ca/Xzw+OXXfIvxhxIkG9Gv0bb2RaUF9at+gW5CVswiRl8Mf0wt2/fxmg0cvHiRSRJolmzZpw7d46DBw+SkZGBr68vXbp04erVq5j5a6hbty6RkZGk5ymd/RkMBgM//vgj3t7efP755wwdOpSoqCi++uqrf6WTkZKSwtSpU/Hy8uLChQvs27fvH+dk/BFem6MhSRLx8fEEBgayfv16vvjiC4YMGUKDBg0oVqwYDg4O1KlThwsXLrBs2TJmz579Ws7r4eHBqVOnSEhIoH379mRkZLyW45ox829Ep4MOHWDDBnhaQ3t0wlFKNyhNiWovKao18z+Pi4sLU6ZMwcHBIX9ZWP36HOrUCVEQEJClTq9XrUq6gz23qlXC5CwgdAX8QcgGlUHEWqej06FD+f0dTCYTOp2eQ4cExo7dwMWYrag+zMa5VjWsj1mjDstFnabnx0djiGpcHvEt5IGqN7jPiEFxssBGpZTB0u8zWLJkGxs35hAWBqU6zqX66PX8+CgMscJ7YEiD619D7EE4P4Rffolg6tQUvL29MRgMpKamEpvdEN0J5Nn3vK7jQlLeYNYFeAsetZSw0kO3WzD+PNSOBe8k6HwXht8BpT04JoHyab3GEuR6iRPAdOC+PNB++lEU+jd6YBwI60BKBMmQZ8PTWhARhG1AG/IH7YIChO15+4JsbFe5LuTpt7ikAKlw1pgkC3KJ2+DQwg5EOrkjKSDZC6SpIDnJH6aB9C5IS0Eqlrd8KmRmynU5UVFerGIMXopIjpVrS6bOFukEOH+ajFf4PX54MIbjyR35NbkFiU2dERcLSG0gNK0ubcqeRqMyAAKauC10rLoZV+kTUsRsNj8eyPnrieiOtIaYfSCa4M5yfhz5LqWcYunCPmrHg+qZ3iQSkG4JuLiAlxds3gxAsRkzaXkjG51RB7/2lG/sCXCYnkH3tL08SCxLwpn3ORl4lTZtTrFybYN85zo7O5tHeWlYgiDQqVMnLl26xJYtW4iIiKBWrVoMHDjwtcy6mylK3bp1Abh06dLvbPk8kiSxY8cOqlWrlt/Y+fbt2yxduhTX/LDnv4fs7Gxmz55N+fLlWbZsGVOmTOHevXt07NjxTZv2HDqd7pX/P/yh1KnevXtTs2ZNfHx8SElJISoqiqioKCIiIrh//z45OTn5+2g0GqytrSlWrBgeHh5UrlyZOnXq0LFjR0qXLv3nr+4l7Nq1iwEDBtC8eXMOHz5sbuhn5n+SRo2gfn1YulT+XZukZan3UsZHj8fS3qw0Zea32bx583NfHoU7QstdpyVA5D33FRS3SJYH19vzBswGECTQWlmh12gI8PUlpG59tForEhOLY2FhQKPR06VLN9q2Lcs3xb9Bqi9BHUAF3Uvtoqb9ddI/AMdU8tOo8AODwpHVMaNJzdHQuYaOmt2/BeDwnnhmL3Dh5i0lx+s1olZYCIpuImJ7OwJi63D9Zhm0ZcdiYX2MVK2SCG6yYf52lNkg5ak24U9BRMIf0huD7QFQ5n07Pv2SlDqDcBqEwqUhTiANRq4C/wWE1Kd3qMDBEAr9LFxOL6mB7iCogEMgZRRsgwswGiQv+djCaAqaFNqAtAq+vg/DbkO5IyB2g5R64OIAgklOpUr8yZZ9bgOIu1yaJm+fpE2rQDKzbUmwyOJ4DvQbBs5q0F1XkVbch6On6rBl0zD2rx1F1A0NZRvEMW/ux8ya+xnVPa6ya3xv3v15KVvuDKekIYE0a3t2f9KbbGwwiBo8Le/RvdReBOBsUjN8nc+jUpjo+t1+/Kd0IyHDhWu51QlMa1Fw++wsGNkkmYQbayltkY6FwkBsRjkqjL1GquCIJIhYFKgJIwEnPMHvSATC9YuYPv2GrKBgHBwcSLPXsGH/LIZ7tyEmeAU+o9eiSDKhtbfkm5afMbLLajbGvkXdkBBqHLrGhRaNuN2yKgDly5dn6NChz/2fMBgMrF27lq+++oonT54wcuRIPvvsM8qWLfuS/0Vm/ggmkwkHBwe+/PJLpkyZ8sr7nTx5kmnTphEaGkrHjh2ZPXs2tWrV+usM/QvR6/WsXr2ar7/+mpSUFMaMGcMnn3zyRp0lURSJi4vj3r173L9//7mfjx8XCHX8XurUH3I0HB0dyc7OxmAwIAgClpaW2NjYUKJECTw9PalevTr16tWjWbNmL5ck/AtZvHgxkydPZuDAgWzYsMHc0M/M/xSffy73sLp+vWDZujbrKNOgDH5z/F6+oxkzeURERLBz504A7OzsSE1NRUDMGxgXBMDLFlPwtvOXCIgI1u6YHGqy+5ozNifiaXLuPJrcXKx1OtIcHFgycSJ6vZrw8KqkpzuQmWlHRkYxyqbF0CTlVzSljQUz9AoYUX0lqw4+4ev9cjaPZAOsgmu5MDOuNnUyWjH4zgZy3ZtSccke2F8JcmLRGbMQPpSwSKEgfWc/3G4PFToKmCQVu+P7cDW7JN98uwi1Ns/R+FG+JqMEyvFyCpIkyA7TU0yAXg2qr0E1s5CjoQCpKUjnIMMC7HPlS0mzAGujnPYjAQalEiMmrOT2ITywrciPVX/gkWknWzv+yCA7b35s2JoZp44x/+tIlFLefgo4OQBatQfLM0Bekz36gaG1ihMBFTkV84DT5bTgAlutVHjZGRF/gCula3I2tA0uJRPp9u5+bItloMrznCQJZqfB9NHOCMZkpL0gdY0nRypBpUoQEwOHtnxHeHQ8J0+3ITSoHvsmd8fKJYcSFom4BsSQuqkEmW42ODzOZKtLf+IbuhKZWYFpitlUC7mF1A10LazQ5lgx9IcN7BzfF2tNNgZJRVhGA6Jz3HmkcyNXtMLKUkMnx7VUtb2Tr2a15eLbNF/iT5KNES9dGhYmKKwFlWKnYOaQ6sxaH8mK999HXbo0nVctYINPDs3e30JgYCAdr+6n/rlL5HZU8/HW+Xxt/UWRLuePVaXZ/u0UJEkiNzeXSZMmYfG00cgz5OTksGLFCubMmUNWVhbjxo1j2rRpvyvJb+b38fX1xc3NjS1btiBJEunp6cTFxeV/YmNji/weExNDVFQUDRs2ZN68ebRo0eL3T/IPRBRFtmzZwueff879+/cZMmQIX331FZ6enn/L+dPT01/qSDx48KBIOm3JkiUpX748np6e+T9LlChBly5dXq+jkZ6ejq2tLffv36dUqVJYW1v/3q5/OxMnTmTx4sVMmzaNOXPmvGlzzJj5WwgIkLt/37sHT7NfYkNj2dxlM5NizXK2Zl4No9HI/Pnz8fPzIyoqisePH1OzdBa3nyhJzZIoVaoU8fHx9O7dm0rZqyD7ETTbDoICnU7HkiVLUCgU1Dh/noanTxeKguTJpgqQmWnP22934NTHJ0gJSZFTikwCooOAlADCEC3ZMd8x/6ARJfIgP32owHY3ifdVoDGBlQFKmuB0JU+cLO9jBKwVIJ3Iq1vIS+l6Wpj8tEg7y6Th580ujD4bi4UC6Ae5rcEkwd50gZqBUHmblB/JIO/8j+3htjM8sYPeKrC+LV8L/eCO3o3xWVquVEimXDKkWdqgK6XnapQBiwMaAlv3pMV336JcvkxO89mzRw475qHX6VFqlCgVSsKCt1Kn0UAkpYLw6jUImfEuU2/NIKgMlNCkIQL2ClDkDcaPJralmu117HLSUV/Jwao1JCUqObqmPzGRbui0Vqgt9Lz7zSpcSifnn1OS4PFDOxzGn8DOcg1PwjwpWX8qSUlQpw48fAh6nZYVP6wiPSONVprTBCW2wb6iK43r1+Ty+VbsWLOLgzFdIAlEZwgbX48hKzYQGN8UFylFTtOq6oRDTCYPG7uhb6lBrTSSZVLw5OJjmp3Scd63OWH1GuKoSmF4xDosD+rlZ+cHRkkg/S1Hlnh5MvLJJdwyiuZ6X7csxVtl3qP8k0rMqDgb/+7dqXLtKu7Beznyzudg0DP10XwsDhkRukLchpKUMj4h3cGeC80a0+hcEJ9rv2Z2yodYWZnIzc19pTFNRkYGCxcuZMGCBQiCwOTJk5kxY4ZZEvf/wfjx41m9ejWurq7ExcWh0+mKrLezs6NUqVJFPi1atKBbt27/ygllSZI4dOgQM2bM4Nq1a3Tr1o1Zs2ZRrVq1v+ycd+7cYePGjdy6dSvfoUhNTc1fb21tjaurK1WrVsXb27uIQ1GuXLkX/t941WLwN9ZH46+kR48e7Nu3j127dtGrV683bY4ZM38pGRlyceyuXVB4Yuf7Ct/jN9ePKn2qvDnjzPzr0Gq1WFtbk56ejrW1NWq1mrCwMEJCQnjnnXdITEykRIkSL3RedTodJpOJ48ePFymglSPgTqSlacnKsuP779/jwvkAzp7wx0KpRy3osVZqsRayuGXIZeR8f9zTi/avqPQ+DL8qK0Cldgb39vKgW51nhkGUFaSejjukE3l1DQA1QLoGWglEg6xiJAkQ0b4Crj2fYG+djUkUSNarmbXegW9CErHNlYjQeDPBcymNOl+k3KFjPHALYHxPcJwEpv5WqFvnsjusO6N++hmjKo5m1Sah8SjFvav9ePywATaOWkaMUDJjRmleMlFeFJMJPv4Yw2efkSVJODk5ERwczL3AFTiVs8S+Qn/KhQzCXp2JrUpLUq4Tzhp5sCAiy+wa9Sp2r+jFwwh3sjNsAShRLYYx01YX6X1hNCmITFLg/WEOSqXcuzcmBnx94cEDeZvLly+TG/QhVZv0wa7meGJjYzlx4gSGiEMEB7VkYclJ2B7Lga5wzLU1w1ZsYkTuz3yd+wUqTPl9SAo7e7migpwPlDimGUhztGTxhA+ZWG4l9pMz5GiSM5gWKxAEiZRJEp/WcOCd8HTqPgE9GnbSm+8ZT7TgRoLkSunisURkV+J0q1b4BgSg1qYz+pNB3Nw3Cf+r3SgjxoILPGxSGosjJmymdSOpQwOyUh4xeU4PKtSszfLlr/BsniExMZE5c+awZMkSlixZwrhx4/74QcwAcO3aNZYsWULx4sWfcyhKlSqFjY3NmzbxtREQEMC0adMIDAykefPmzJ07l8aNG/8l5zIYDOzfv58ffviBkydP4uTkRJ06dZ6LTCQnJ9O3b1+ys7Oxs7Ojffv2dOvWjU6dOuHs7PzS4/9POxqiKKLRaPj222+ZNGnSmzbHjJm/lJo1oUsXmDWrYNmlny9xYeEF3r9hlrM18/9HkiQkSXrlyFh2djYrVqygWLFiiKJIWloa48aN4/btu1y/nsKwYS3ZvPAdHmU7o8CEiJIaF6/S+NwFtvu6c5nrLD0EKknuXzGukywtmzQXnHWQYw1ZP4CTUnYuWAZSMEgeoMgkf1Y8X241T80KINvSEqtcHQpJnolX5LXnkERgG2QfhVsDa2D9yAL/Zm3QmqzQqPX0cNlDNaebADy5Wh7fLbewUsXg7nALo7okwbcqU8v9ClNn3iLxwUVidBWIjm7M7dtNuXlTwMFB/n86fjz80cyI0NBQqlatipWVFeJmBXqTGpVgRCmIBU328pyIXFEDEqTGOPDLzJEolSK1B4bg1+K0vM0JWdVL6gr3XMphnLyfSq5yJ+SoKLn3zr17effk7iqy4sOx812ab4vJZGLY4FPojWmMaL2XDg6bEQTYEtSHt1ZswmDSMJqVzGA2DrXSsI/JlEUDkM+r7WTJ6eQ2+AacI6uTLQ7t08g02VE6IL6gVsYPEnKL8d22NMonwPBrIudNzfHiAfP4mB8o+LtmZ5fOTYsqOGrTsNVqMSoE3hpWiugLW6gWcZNPhFkUG5iMdacc0rIdsS9mhVKlAZUVOXbNcOv9IwkJ8GeDvj169CA6OprLly//uQOY+Z8gLS2NoUOHcuDAAWrXrs3s2bNp3779XxKRiYmJ4aeffuKnn34iLi6OJk2aMHbsWPr06fNcz7rLly/TqlUratSowcKFCzl69Cj79+/n4sWLKBQKmjZtSteuXenWrRs+Pj5F9v3Hydv+nURFRWEymfKbQpkx819l/HiwsCjqZIiiyMlpJ+m7wyxna+b1IAjCH0q/s7GxoX379gwYMIAhQ4bQrl07rKysqF27BsM8l0HkjzR1Po9KMKIVbdGJVjQ+dwHH9HR6BoazpraSqZ01xDioWNOpNuo6Y3HKVefrnGolqBEJK9PgsQEIzlNruk++JCwgD1pdgIZyPYbeWs2ZNq0J9auN3k4lS9PmkSNZsrd4d6IrVSKnqg3H2/iiV1iiVEuYUJOFXf62JduVI/Khhku3PRk0tgZKl7pICmuuxTZk6eoRRKUPwNJSzapVvpw9K5CYCNu2QUoKNG8OJUvK/SIOH5Y7cv8e9erVw8rKCgARJRYKPSqFiHASGA/GE0pyRTkqsT++JwGpvlw7X5Mq9W/S9+NdtG52piCakaeyJW1RUqH1D/lOBsiqdfm9y+KOIyT8il3TJUVsUSqVhN9qQ/8BzcmxrUO2aEuOyYISLsl4lolCrcplFWP4vtMHqD40IDxV5Hqq7uUvEFa/Pus+Gk6ZzrHYqbU4qJIxtFblq3cZRIFTNysSay3S846IpQmaE4gHD/mY+UXsycq0pXnGWVZpRxONG8s0E5AOjOTmo2p5allRNDgTREqWE062aSj1caCNhuxHWKWfZES7ffzww+8/g5cxatQorly58qdUk8z8b2AymRg4cCABAQFs3bqV0NBQOnTo8FqdDFEUOXbsGD179qRcuXIsXLiQ7t27c+XKFQIDAxkyZMhzTsatW7do164dFStW5MCBA9SrV49PPvmE4OBgYmNjWbVqFU5OTnzxxRdUqlSJihUrMm3atOdS236P/6SjcfHiRQDc3d3fsCVmzPx1HD0q96o6c6bo8iMfHKFMozKUqGKWszXz5qhRowY2NjZYWVlRs2ZNeeHjA0hPTrHrwBlcFPfxsbmFAiMAgb5NyXayItTTnYXp1Rg2uCvrP/6KuPrdcVG6Ut6iBp+1ltOovmoBR+ybMKB3IHNSIaauXEge6waSizxbnyuCrjWwBHRjIOcHS0LnN8a9SzTZdvbM004nI80mX+bWIGq45lEb/z7dOZPTlnSjPRKy6tb4RYtQnjIVXJxtBQDUGoHB77px6BCkp0PUPSWtWgkcO9aSuXPH4eEBQ4fCr79C3bqwfr3czPrGDahXDz7+WG4+WLcuLFggH+P3yLRuBMhRjKd1KKoDJnJM1mRSmiS9E2UVD7kY2pDOY4LwbFoBZdWPEdV5nd+7yulJub1NxD/uSLdPCtJS8h2NtBtwewE0XC1r6z5DXJyCXr1cadfzXZAkfk1uwU8336VP0114lrkPiCw4NIUzt1uhM6jzzys6C5xp2gIrKyuaeuXmOz+pBldyTIVzyxTcsW7HiEuwqi7kKuA2PjyiLHOZVsSWLuoDjPh0C28r1tKy2DVKf+rH0OyjmGziAbg2twYjWq6l3byjdJm/nx4L96A9ZAnjsmHvPT4Yfpd5837/vr+MDh06UKpUKVavXv3nD2LmP80nn3yS33Svf//+r7VmMjk5me+++46KFSvSvn17oqKi+O6779i9ezdz584t+Nv7DPfv36dt27aULFmSw4cPPxeRKFWqFKNGjWLfvn0kJyfj7+9Pq1at+O6771i7du0fsvE/6Whcu3YNwBzRMPOfJSkJBg2CgwehcI1WVkIW1zdfp8+WPm/OODNmXoQhEynkAzbd70hSji0/PhzN7UwfbFVZAEQ29kacJ3AlYgBVay/Eu9t6hg4diq2tXGdQ07ImFccv4tG5nznVwo2qb+3GpWQTlvl+iH7RWAYtcuCbeT0QlygRRg3letl3uG5ZBUmw4LaiJxtzFtNk7Gmq129P3L0yiCYl7FPkR0AUGClpEUtFmztM9PyOyja3sFDk5CsUeZ+8y21jS7kLnkPlF16ikxN89BFcuCCQkKDi1Ck5ejF2rNz2oXJlmDQJkpNllbjr1yEuDqZNkycOKlaU06refVde96LEZqce5xBqzeexwyAyO9oguYDQFSwUuRxP8KVZsXP47+2O0EogyutHaH0E6swl6ZQ7CYa8g+jAai84BECGRcFJcnOhuF08hH0AjdeDyuq582u1sjOiUEC2VsuWnHlc1bchNbUYcRal2DO5B46qNBSCife2LONGXHVMJgH84NpXNQhr0ITKlStTr907oJGdn9KWj7FTFXSl15qskBQKKmc7s9sH9Eq4S0WW8QHb6VfIGoloQznE876cVrXjw5Qvmba6Aw2trtC0/WQs1TnYWWYyxm8lOyYOZ99HA9g7qSdWR3X5z92tRApqtewM/hlUKhXDhw9n8+bNRST+zZgB2Lp1K/PmzePbb7+lXbt2r+WYkiRx4cIFhg0bRpkyZfjkk09o1KgRAQEBBAYGsnnzZtq1a4ejoyOVK1fmrbfeYvny5Vy8eJHc3FweP35MmzZtsLa25vjx479ZhwFgZWVFly5dWLVqFe3atWPDhg1/yN7/ZI1Gz549OXz4MDk5Of9KRQIzZn4PHx8YMQKmTi26fG3Ltbg3daf1rNZvxjAz/9M8efKES5cu0bx5cxISElAqlTx+/BhbW1vUUUsJvZPDA50ntUMu4xsQQJx7Sco+iiHAtxkVut4l8WwJzh9qzqgr03D0cATgwIEDxMXFMWjQoFcrCs2JB6tC+vPaGCSVHXrJEguNBvZXZtlYP5LjilOXEJorz2I9KAdVBxOiJJCsd6S4hVxgnWpw5M6+iviciiDA15fbLVswrM5jStQaBsXq/OH7ExAAq1fLUcjsbFnEoWdP2bEolhdwuH4dvv8eTpyAnByoUQPeeUdOtSpcUC5JEmsXj2do8WWoFBKiCA905bgb703QuqYwCOzs7Rg5ciQO9vawrjiiOhlhgpzCBHKNyoHldenWPxSAU8dz0AT3xPeDheDwYhGJLVtg2TIIDCxYlpWVha9vBr06bsTB9jEpa52Yde9TyntFUcw+mQ1vvUNZhxgmH15E+bopjB8/HrWUzdUN3SivuoytOkvOistroKjtZMFCr4/o7r+Le5m36BIBsZRlvTCIJ1JZ1vEWmdhThavMrvkZp8Lbsco0ii/4khnM5VztpjT48DxXE9pS0y0EjZRW6L4hp5w9rQd5dwTr7/zM4sWQn/0kGuSiHeWrVPBDZGQk3t7ebNiwgSFDhrzSPmb++1y+fJmmTZvSu3dv1q9f/9x4VJIkjEYjBoMBo9H40n8XXnbp0iV++OEHrl69iqenJ2PGjOHtt9+mePHiGAwGunXrRkBAAFu2bCE5OZmLFy8SEhLClStXMBgMqNVqbG1tsbOz49y5c38482fz5s0MHjyYqKgoXFxc/neLwevUqUN6ejpRUVFv2hQzZl47I0bIxZrPpkzFBMewtftWs5ytmTdGREQE27dvx8bGBq1Wi1KppIZlAPaqdAQkPK0jyT5qR4Wd91BIEqIgoJAktA6WWHyvZ+nkDzBJtkyM/xqFUn6HTSYTCoXi9UwaRe/EcGoQSyd/QGZq3neZIOLm/Yjhn65DoRTzla6eIklwIaUhp1PbYJQ0WFhYMGbMGBwdHQHITH6A8s4irBsthj9go9EIu3fDhg0QFibXatSsKUcqBw4EjUaOfPz8M2zdCo8fQ4kS0LcvjBoFSmU84uEGlNI8lA94Qu4bctK+NYFtmqMpr0GpVFK1alU6V9dBxFKkhLPyIDtPjUvqB1JbDQp7b0BBTGZFZm8fw4rdfpBxFy6OhjYni1zX4MHg7g7PqseXLAmXVg/F/7o1Bn8VPZ13oTgqsM55GA8a1aNjtdN8uH454eFZ2NnJ9S7+i7ri53ICC4VOlux9F8hrpGhaqUTUmVDmyo0TJQXExJVmzTfvEIkHWxlMqKIKdxrG0+tWNidbtebufm8+N83Es8R9Nk0eRMWykTz7RCTAYFShUckpeyitoelWStXrSEioirJlgeB3IPMutDn9ys+0VatWAJw+ffoV3wAz/2USExOpV68exYsX59y5c/k1VuPGjWP16tUYDAbEVynQegaFQkGXLl0YO3Ys7dq1y/+ulySJ4cOHs2XLFg4dOoSfX9HeWbm5uVy9epWQkBAiIyN5//33qVChwh8+v1arxdXVlY8++ogJEya8km+g+sNn+RcQHx9PlSpmSU8z/z127ZILSKOjn1+3e9BuOq/obHYyzLwxVCoVSqWSzMxM3C0fUO3iNaqcus3llrUprkmk9MEE0CYg5A0cw6tUxePRQ+54dKZq12+pf3seEccc8p0MkIuPXxu35xP3oCR6XUHPA0GQSHjkypVzNanT8rI8MFVYgFM9yHnEzayqXM7yod/N7bgejeeBXzl+Xp5CxRrNqFChAlmhM6kl7OBh9GWs2+zCpXjxV7xX0K+f/AFZpvqXX+SIx0cfydELX195YmHKFFn59sgRWLlS/gzvsJUvWj0pOKC/3GywpfEMdrW1OJT0oUKl2qisUiBkMiAgKCzAL7dge0CQ9JB+AyxdidZ/wLUEP9AlwfGmYNJC3HEoXZDyERoKEyc+fz2SBPbeXRmV+RbBXvUpsT8JtcHE28lrWa4pzfLgFajVoFar84unYzLqcSurCk2dAqhmF449GfmOgYCIxhrISw0VsuHhYXfGzlvOtl+rkvR4PwlX3aghVcNY8hDtrp7Ed0Ago86v4VCPLmwNHsC08HloDhkQuiEXpCNfc76TAfI1BvYnej4snLOOaaMj4LE/GLIh+F0o0xncevzu8xw5ciRDhw4lMjLyTw3gzPx3MBgM9OnTB51Ox549e/KdDIBevXqxZcsWUlJSGDlyJM2aNUOlUqFWq4v8fNm/S5Ys+cKO4dOnT2f9+vVs3rz5OScDwMLCggYNGtCgQYP/17VZW1vTokUL9u7dy4QJE15pn/+ko5GVlUW5cuXetBlmzLxWYmLkmcyAAHm2szBhP4WhslJRudeLc8fNmPk7UCqV+ZEHk6TE+2QUNulaqp0Ox1qZjfA0DV8BWYOtOebdAV2CHVNmfoyloyVNZy+l6ey/yDh9KmTd58Gt6kiSAsfiqeTmaGjT/wTetSKxL5ZZYJxDVbAqAYLEr487kKRPpfiRROzTM/E6EYXgdxhN3D4iIivR0DEItYURV0MwmcEj0LfdSXxCAo8fP0aj0VCnzqulWNnbyypy48fLv0dHw6pVMHkyxMbKqVUdOuQwYcIjRPE+D6894HxqE3wv/4r6gAjekJ1hhVUPHQ1Dg8A/CLquyx9gF0baLzsl+FOwXu1ARGZXWlQ4CHv7gJinLBP+ZRFHIzVVLmQvjFYr12zY+vSDJ5txKXmTDF87lKcl1tkOI8umJRWc5b9h0dHRHDp0CHviaOMSxq2sKgSkNqWa3XXoJ0vg0hX0uRIWKlDk+YSiHpzSIrGzz2RA37M8vG6H9n490hIS0NSVW7BbN9dBWehZfTc96+2WC/2fvc689Kx8CWQAUw4aJUxt0g8pvFBE695qeLiVHP1hrs35mkqfb8fB3fGFz693796MGzeONWvWMHv2X/USm/k3MHHiRC5cuMCpU6dwc3Mrsq5169ZcuXKFgQMHsm7dOnx8fJg8efL/a4JwyZIlzJs3j4ULFzJw4MD/r/m/yebNmzl48CCffvrpK+/zn5z6zMnJMReCm/lPIYrQrJksY1u16jPrjCInp5+k385+L97ZjJm/CZVKle9oPMl1Jah5Q9IcHAjw9UXX2RJskD9vgV0HLb0tdtGmRVssHS1/87ivBY0T9E6gxjvteWfmT3y4aAldPf2ptesq9pcyAQFU9uAzAXKTIP4MUvlRlC7rQa1atQhp04Y0BwdON23F7id92P2kN57noyjxUQKcAAulHrvUY9xc14zNmzdx9OhRHj58KOf6B42WHZ0/gIcHzJ4N167J4g+bNoFKFcyvv+4gKCiIWK0jkVovVAdESALjTSVHm3ZkjdX7mPYpi8r8PoO+k1qW/e1aaGHmHd5ydGemX9cCJwMg7SZkymnIyY8TsLB4Pt0jKEhWzwKg/gpcPERse2nJ+c6SbTb9uJtYHU9PeYLEx8eHevXqoRIM1LMPY2jp9UwpPx97VSaCHwhL5IG+1TRQnJEPKR4BZkD5pklY2edSXA2zambiPfs0rvUfc8euIqQAPwPXkJ2J8YC3rEJGj0Lv19OO8U/vzdNtT+RlST2bTG7MInXH29RueoboBV3JSda+8J5aWVkxePBg1q5di9FofOE2Zv77rF69muXLl7N06VJ8fX1fuI2bmxunT59mypQpfPzxx3Tt2pWkpKQ/db5t27YxceJEPvroIya+KNT4Gjl+/DjDhw/nrbfe4uuvv37l/f5zEY2EhAQMBoPZ0TDzn2LAANnBeO+959cd+uAQbk3dcKnk8vcbZsZMIZRKJZIk4eDgQGZmJsa83g4CElIbBXQo2FaSoLhTIm496v+tNjpmLoLS8pd6hehIlBli3oy3BGoHqDUbEs6AIR3h3hq6dz4GKiv8FQqW5En2NqtTh4cPH1JhcSRCKvkz5hZKPdVtQrEknb0pb8vSkvc2wL0fIeEkdLlVMEX/B6lTx4SPTx12bwokLlGHtVLL22V/keVq/SHUoi5Pms9nyKTyPJZyKLtzDYquUpFmfk/RtDfI3/7bQdoOQj/ZfqVgKFqgcgLwT4d+zWFZDIpTLVg9qiFIvxQ5aHCwXNgOgHVprLy8USY8wEmZRj3PUIIf1ads2YJi9oyMDFIMxeT0KOULBuVPnYEDQEsQDoKQAZoDQHvZLo0/VOoCtHmIERWGxgrUShE0wIS8/ZEdFxQiiEq5HqNrJuQ1LJQkUBR2PPyev1ficXDdF4Wyh0TFJhe5/UVrnqhmkBmbQ2ZsJrVH1KH2CDlqNXLkSFasWMGRI0fo0qXLKz9bM/9u7t69y/bt29m+fTvXrl1j9OjRjB49+jf3UavVzJkzh+bNmzN06FBq167Nli1bXuqcvIhTp04xbNgwBg8ezNy5c/+0/ZIk/W4NXFhYGL169aJt27b89NNPf6hm7j8X0XjanfNpoZ4ZM/921q2DCxdg//7n12UlZHFj2w16b+r99xtmxswzODk54efnx9ixY3FxcaHdseM4pqfT7tgxLBWy9KckgckosH7521xy/fFPFXnfuXOHV9AxeTHtAsFCnn5XdBfJ1ljLM/ua4tA+FG59B5IJ2ofIg+4TzcCQhZubG3Z2dgwePJjWrVszaNAgTF1ViM4COZ0sSNY7k6h3JsNgT2mLWHpe3Ug53/ow+135vFlRsM8LQsZB4CD4tTekXX9ls5OSkli+fDndreYzzWsuH5T7Xm7a5wd3h3uTUtuVHWfL4+0NTQ6sJnRaN0Q/BZKgxiQKGE1KJEkOsOToLZH2A9ly7YNu20vUlZ4OwrfHwhYFVuJ9WpffDLtc4FAtuDIDgGvXJOrXysjfLSftLiq1CUtNLh91/Q6jPhuFQnY09EuW0GPMQKY/mINa8ZKZ/65yJELsCpIBcmuBwQKE9kXtEg6AwgY0F4yop4twDvmZ5TVqFDuCXhQABTRaA72fwIp4WFsd/BQsOzkFBlV4PrpTCMEflKkSbAfL6XpqZgfTukYf2jd/h0HDx+GWOwoO1oCjjahTpw61atXi559/fuXnaubfSWRkJHPmzKF27dpUrFiROXPmULVqVfbs2cPy5ctf+TgdO3bkypUrlCtXjpYtWzJ37txXKhK/evUqPXr0oFWrVqxZs+ZPpV6FhYXRr18/bG1t+eabb1563sjISDp27EjVqlXZvn07avUfmyz5zzkajRs3xtramo8++ojs7Ozf38GMmX8wUVEwYYKs8f6ivyM7+uygwbgGaGw1z680Y+Zvxmg0kpyczIoVK0hISEBlkBs3qAxG1Aoj0VluZGttObS+C4aWjajatF2RYm+tVovJZHrZ4QG4d+8eO3fuzJ9UesorOx625aHyZFDZo1SJIIFJFMB7jJwy9GAzeL8H9t7Q5hRYloSjDalRyY3x48dTqlQpADQaDWG957FsymRO9tmGVb+H3Ci3k58SZrDkwUTcjz9EiE2G/fqCc+c8grvLIXoLxOyBgH6ylOor4OLigo0FmCQFggCKvK7g4nEIOtyYVoMiOH0a4uPh4kU4kb2BpKySnL/bhE/3/8iX58K4uL0uiz6cSHSUB6dcWqHFEi2WJNaRC9hNopJ7mfXBqox80qed1bsCJ8BySi7q0wbQp0DaVXhyHA7XYUGLcnxQsT6iwciDzcPR5MTm212++H2Cprryy6pgIB3mzUOVbEQ4ZAUK+e9WkSdXojX4CRgXKknwLY5gDZYjQbkahM6ACGJtMFlDxjOOR346lB8wV4VYvhMXdqugwU9QuhOorMGyBHQMQ6g1l5UX5nGsYzh8rwY/Aaw9ECnavySpvqt8DyDfudFYGuSanhPg/Pll2HUdDHKNz8iRIzlw4ABPnhQq1Dfzn+DevXvMmzePunXr4u3tzTfffIOPjw+7du0iMTGRzZs306NHjz8sYFG2bFlOnz7N1KlTmT59Op07dyYxMfE399m2bRuZmZns3LnzDw38JUni5MmTtGvXjnr16hEWFsaAAQP47LPP6Nu3L5mZmUW2f/LkCe3bt6dYsWIcOHDg1STGn+E/52jY29uzZcsWIiMjee9FeSZmzPxLMBqhRQtZU9/T8/n1jy48IuVuCi2+bPH3G2fGTCHS09PZuHEjP/74I8HBwWRkyLPbYiHv+PzOxjzI9WD7xf6k2jvQtsK3aCg6GbR7925++umn/P0x6di0+jvi4uI4ePAgN2/exN/fH4PBwK+//oohz5ExGAysWLGC+Ee3X81gtT0oLJH8wcagRdqrwHTtG4y7PJEsikOFd+TtFGpo4Q/F6qI41gClvmgedYtu7/PW2E/p2KkL1tbWtGzZklatWqFUW5LwzvvgavXSmXKQIOsBXP3slUxWKpWU4wLFNGnygrzBtWGnmsr1b2FlU+CglS4NM76wo8TgIHw/PUb/GaNIUdVk5rkFmHQiP88axUcR33GKNhgEDcXLJJKjt2D56SlMPXqGjCahoLKTB+xLAD9F0cH807qG7aGQepnS9g+xtLFG3FkSD3EdtpY5+dsoTklYqbQ0aHCUxo1XEtX3bbkA5cuFCPVXgMIKoVgDsPMGQQUuDZBqzCFDdEalTMsf9CtOIadEnZRFwRRaUEVBroF8h0jqCqbcvHdOK6IathSfkyI3bGuDZaHUUoUaqnzEhIkKvpppAb2Tofzb4PMhika/MGPPckRUgIB19yyyZ1qj66pGcib/eUoiKA+Z5J4k/kBOAgCDBw9GpVKxePHiV3quZv7Z3L9/n2+//ZZ69erh5eXFV199hZeXFzt27CAxMZGtW7fSq1evIspSfwaVSsWsWbM4cuQIoaGh1KpVi3Pnzr10+9DQUPr27Zvf0PT3MJlM7Nq1iwYNGuDn50dSUhJbt24lIiKCn3/+mX379nH8+HEaN26c3xoiIyODTp06kZOTw9GjR3Fx+XPp2f85RwOgW7dujBo1ivXr1/PLL7+8aXPMmPlTdOsmy1sOHfri9bsH76bzSrOcrZk3jyRJJCcnPzcbps+TRxOAGmeuUU5xH/E29Oq+G3eLKOwCWmCM+xWdTi4+TktLIz4+ntWrVxMZfoHYs18RE5fM+p+XEhYWxokTJ0hLSwMgMzOTM3nNZHbu3ImT9jy2Z+pz81pIERtyc3OJj48vWJAVA9E7kHIToCsYHZTc8yyPUil30BAaF60/QBCgyXoo2wOO1IPsh0WO7+DgUGQGs379+gwYMADXL76ARcYXqj4B8kD8Ax2sXAHhs8BYqKv0xTGQEFB0e5Mey1INMUp55+oKkjNcsq1DrVZhoHhBQb2NGyg11KoFK1bAgZQWDPYIoITdE7KxoYXyLA5SBhYnc1EoVey8MpZz563p2y2BtEwNcbm1SXV+F6niBKTC0Y1nnA5pPKj2XEElJSMBoknI30baD491ZbC3zcDKSsdhD2eu+/sjjR4NZbtCkw3QIRi63oE+qVD9K84l1OREsh8XrtTF+DS68PSce5FrNwDu2JBuBGMLAWkuGNo7INzyITddQ2aOPWzbhpVCw6rjzzT9yGPUKLh9G/TYQaOfofIkKD8ct9bvMWTjJag5Fxv3Cth4VsdyxmcIYTvIbeEEQFa6LabOioJ7YiOrCzk5OTF9+nQWLFjA1atXX/LwzfwbCAkJwcvLiy+++IJy5cqxbds2EhMT2b59O3369MHa2vq1n7N9+/ZcuXKFChUq0LJlS2bPnv3ClCaDwYBK9Wpl1pIk0a5dO/r06YOdnR1Hjx4lLCyM/v375x+jW7duBAcHo9frqVevHv7+/vTq1Yt79+5x5MiR/1fd83+yYd9TfHx8iI6OJjQ0lGrVqr1pc8yYeWWWLoVFiyAy8sUpU6GrQglZHsLYa2P/fuPMmHkBOTk5bNq0ibi4uPwvxp47d1ItPByjSsXJDm1QOhpo0joYa0223KANMCqLEZRSixuK3qizIvCxCMXb5i52qkz2PunBHW0lANTkYMQCqdD8mLW1NY0aNeLi+VOUU10hIrsykiTQvnNP6taty6+//kpQUBAuLi6MHDkSjjWFpPNF7BZNCrYuHMCgjzaTmFiO4uPvv/wib8yDiEXgdwbsK/3+Tbk0RU7XcawJ1z6HjJsF68YjD5xdkKMGagco3RVK+kHwcHnavup08BgIlybAkxOctV1BzaRJOGpkhy5gf1McXdOo9tnP4NLw9+0BRF0OJzs05YNzm7nctxbqI0ZUvUxcr9qQejOCsLSEJk2gXDmJGzcEHj2C3nU2MaXtVEraP5aLy885wu5s6GIocADyrkNCQUxkacrcj0FxALLa27Dc5310ojwoEwQBS0tLxo0b99KBmslk4vRPP1Fp5kzUY2Jx9SK/IaGgliV2Uywhu241HIdo0efG4fF5Dv7jNPTqGYr4ThuCbKqhDIKqrVSsMgYz+Uj6C8/VoQM0bw4zZhS6R6LcHPHWLXiuLcrdlRgvjOdGUGVq+hZyJDwGQNMtAOj1eurWrYuFhQVBQUGvPCA0888iPT0db29v2rdvz4YNG/7WcxuNRr766itmzZpF27Zt2bBhAyVKlMhf36xZMzw9PVm/fv3vHuvMmTO0atWKbdu20a/fb6tTpqWlMWjQIA4fPoyFhQVHjx6lRYsXZ028qm/wn54KvXDhAiqVil69epGVlfWmzTFj5pUID4fPP5f7ZbzIyRCNIqc+OUXfnX3/fuPMmHkJVlZWdOzYsdDgUaJCVBQKwKhWk97cgSZ+QdhaFDgZnADVuBSahJxilO04BpRYSdNL5ynxcSLKk0aKn0tg/KJF9Ny5k/cWraRuSChKQco/vlarJX3eXMbM/RbL03oMkgUiAuePbWbWrFmcOXMGnU6Xn2KFc6OiRp8AxSQRn9Tb5OZocHKO5zepOhVqzILjzSC5UOREEkH3gpzqOt9Bja/BvTd0CAW1PBuORSno7QAuioLUKkM6RG+UnQwAMReufwkHfCD2MJLJQMStG1gpdUgSaDOtuH21CsW6tAJbr995OgXsur2H1p0v067REZQqE0qVCQnwirlFo0Ywc6YcSQ0NFbh1S65XuJY5mE4LfyUzx479l3vS8mY8P36dha5fR8Sugiwhm3cdAiIl3NJIr+sIS+BRw7L5TobBoKJ69eqMHz/+N2eDlUolrWfOpExsLOdvgtEE+tYgfQP39LCjCpQzqHEPucmJCw58GKZDElzwXixQefMCFKWa0ujyVeL0xTif1oROV7JJ173Y0Zg1C376qegyhUJupPjhhy/YwXsMysqjqdLoma6phSJQGo2G1atXc+nSJXMK1b8YBwcHZs2axcaNGwkODv5bz61SqZg5cyZHjx4lNDSUAQMGFFlvNBpfuTZj8eLFVKlShb59f3/M4OjoiL+/PwsWLGD//v0vdTL+CP9pR6NYsWLs2LGDe/fuMXr06D+vUmLGzN+EXg9+frBmjZxn/SIOvncQj+YeuFQ0y9ma+WcgSRI//PAD69evz5vUkdOQVHn9BNTGXLqV3I+t+pkeBE/Vg/bLErjWSn3+MnG/knq/huGYnk61GzdwTE+naUAgSp6mGMneim9AIDZpWjoePMT0WbOYPHcB5c9HFCoqFzHq8/ap+bXcK+OZ81dLCudhhAdKRQ74VwF9OogvKUqvMBIarIbTHeDJSXkkHjgQTr0sRyoPlZWsZNXiAPSKge8fwP278Onagm0K9XR40e99iq/FKCoQJYGA/c1o2e8qpdsuLVp/8DvcXjYaRWmYNmgemqNGhGRZWcmqTgb6jGzKloVPPoGQEEhMhEOHoHJluHavPJO2rmSq/3b0Rg1LlmqoNu4AT+qVgsUUSRGzsMgi4ZELJlHBuQRfRFEgJ8eSx48r07NnTywsXqJyVZjhw5GA6icgfjEob4GUCw7ZkFUSljUwYJMt4vjrZYZfkPi5oYGq6QYiTHs4NfhtFGoV3XxTSL3yiLTUGiwL+v6Fp6lbF3Jz4f4zgaxvv4WjR+VauXz0qeBfCeHeGtSqtKI75MTAZgEC5VzXhg0bMn78eD7//PP8nHcz/z5GjBhBzZo1GT9+/CupQb1uWrZsiVKplKWyC2E0Gl8pUhYZGcn+/fuZMGHCKyv8KZVKJk2aRLt27X5/41fgP+1ogCwdNnbsWDZv3syxY8fetDlmzPwmfn7QtSv07Pni9VlPsri54yY9N75kAzNm3gCCIFCiRAn0+qcKS0W/0FSCiIWgf37HvLz/qDae6EULck0axLxlYS3qcqlFHTIcbYmq7kWmow13WldEozBSNySE8YsWUTckhABfX0RBQAFoDAasdTp8AwLzT6FAwpR5H2lPOdjrAZalnzt/bjsN98I9Zaszb8GpduBfAaJ3wJ2VsLccPDldsJ9bd2i+F871hZNtIfYQ6BIg+5lZ7mex94IynUFQgMZRVsBybgjKPCWXZ9WTnvm92K9p2EzJJXe/BWmJjlRo9scbHVbIciHnJhS/mAgZgACiNyTGuJFjsuDZDIgqVWDJErkz+aqjg1i+QoWnJ2RmQmqqgvfXrWb5yQkkZ7uQo7dEq7dEFMG7eiQXrjXkvtaT9GgHbt6siiTZ5NfzXL58mTt37uTX5+Sj08FHH6FYtgwFUCEJylwCxTxQTAHnXOgdCMdLQqQTeKVCx7uw1TUbFAKzrlSizZlpSE2aorh+jT6qPTwQfEiZ8fLv//79n0+dsrWF+vXliEc+SjvIfgCm31C0TDiVL1v1zTff4Orqyrvvvmue6PyXolQqWbJkCcHBwWzevPlvP//BgwdJTExkxIgRRZabTCbOnz/PmTNnfvPd+v7773F2dmbIkCF/takv5T9do/GUhw8f4uHh8Ur5aWbMvClmz4YNG+S84JexxncN5duWp+UXLf82u8yYeRlGo5FHjx6Rm5tLTsp9Lp7dTbKhODUuXsM3IADr7Gw0RiM5lpbs/qoHVe0jqFY6F1XWzSLHScx1ZnPsYDT2ZXjb+wiWmWFgMoBY0HtDAqKyPdkb35t3FvyEY3o6aQ4OfD/xA+qEXKL1yVOoTCaMSiWn2rQBoPUpedB3uk1r7jf2xCQpKG5npL/LSpRCQTM7SYJN3w5hyNSNL7/YQjn4AOiSYG9ZOcXpKZU/gtrf/rGbmBIGJ1qCSQfHjbJT0RUkP/Ia5oHQFVKbO+AwJR1FslwEblqgQOVcCTyHgEN1sPMCW09Q/rbz0XS+D0dL3cF2IvlN7XABVpan8qdRrF8vD7ALc+WKnEp0507R5Xo9VK8u98dITDBhrU7m7NRa2FlmcuZmSwJ/8GWyaQEH3TpzvoYfRgcNtWo9xmg0kp2dTbNmzQgPD8fT05MuXbrIM65GI/j4wL17v3kdUU5woAIMDAdUCjZXg6aJKuqn2KJ4y5Y6jzsTemwLuLhgrFadn06Ax/BRdFra6bljpaVBhQowbZqcRlW9OuzcKcuL+/pCXFzehte/gutfA8/MbOc9J7oiK3T1jAcrOcp07Ngx2rdvz+rVq+U6ITP/Svr06UNQUBARERF/SuL1z9K9e3ceP35MaGhokeUHDhxg+vTphIeHU6dOHSZPnkzfvn2LpFOlpaVRtmxZJk6cyMyZM1+7beYajUI8fvwYMDfxM/PPJSQEFiyAwMCXb/Mw8CGp91Jp8blZztbMPwOtVsvWrVvZsWMH+49f5Im+LAbJAt+AABzT0zGq1aQ5OHCyTRsicyqxL747S+6O4JKp6ITPJW0LXD1qMXL0BCzbn4Qa38gD59Kdof6PmFTO5IoWZJoc0Yq23G1dgXQHe0JbNKCKSzoPm3qw5ctBbJo5iHWfD+duIy/Z0cnJwVqno+Ohw5S78IA0kxPR6facSGidL5sqSiCchEGRG+Fd5KLjF02/PT4I93fCwAGgUkE796JOBkDEMoj8g83a1I7ydTbfD+2tyVhegeQWJTia2o/53p/z/YTx3GvsiY1Ki9ANTI5yXYdKLcrF5VdnQEAfONYIjjSE9N+YqQD8+y/GWgBDZwFRDZIAkjeQG40g5mJn9/w+O3ZAvXrPL9do5LQjf3+Ie6LkwtFbONul4GCdRec6h5kuzMbVkIjfvZMcPN2KoAvVCQtzIS1Nh5WVFeXKlcNgMHD9+nU2btwo19KoVPIJn5XSbFD0V89U6HgHrpaEYtkiNep58FlDPVJqCsGNa3Gp/GbuVKwCycmorl2lr3SA80fPc3Ty0fxjSJKcGtarF6Snw9SpsjPl4CCv9/ICZ2fYtStvB58JUH74C24qBZEnhQZuFqhctWvXjrfeeovJkycTl++xmPm3MX/+fJKSkpg3b97fds4nT55w8ODB56IZAF26dOHatWscOXIEZ2dnBg8ejJeXFwsWLCA9Xa5H+vnnn9Hr9W+81YPZ0TBj5g0QFSUru8TFgVYLnTrB1q1yesLL2DNkD11WdvlTnZTNmPkrsLe3p6q7FaJYOJFd5KGbG6IgEOnlxZKJEwnLmyJXKhVoDPEU04eARZ6CisqO9m1bM2DI22g0GjnE4PMBdL4OLQ+A9zuoekcTLvYgJL0xlpaW2HWzYP/Xo2i0YQd93v+e90YMYGS5bbztto7R7j8xZmgHLrduhNbKChFQSBK+AQGAAr1kRVBGMyKzPcg1KeR0KX+5LwPZFKQtPYsxE4IGy4NgkwkCc57fRsyR06j+CHZe4LsVynSEjpcx+gWzNuljgpOqkKNXkGZ0INNkhxIDtIEtnoMwtXrmq1vMBUMGpN+A4N+eNS/m3pEj3muIbaYgSSiOIIF0B+h4mRy9BS9qB3D2rPw36kXk5EBJ21vEnZqMXVhHrNSy86VSmIio54PkDNtc+6I1WvMopiz793dl1arR/PhjTz766A5xcQoMBgMPHjzg559/Jjv8B0geD1Xy6nmUwDjgA0DulZhXAQQOBmj6CG47Q8Mf7rN9J+gUUOvb/UyLNfGD4j6SXg+pqWisrIgZepJbu25xfOpxAJ48gYED4fRpOZDyNAW/YsWC65s/X65ZwZAFiODeF5TP9C4oLP1brCHUmgOJ58Ek34uFCxdiYWHBuHHjfvPZmPnn4unpyaRJk5g/fz7R0b+TIvma2LhxIyqVioEDB75wvSAItG/fnmPHjnH16lVat27N9OnTcXNzY/LkySxdupQBAwbkNxl9U/xPOBpPZxHMjoaZfwqBgRAcDA0aQK1aMGwYtG378u1DVoRgYW+BTzefv81GM2ZeBT/Pe9irivbPcH/0CIUkUe5RNEpkJ8RC0NHQPpCx1U5QblAwdAgBjTM41pA7cf8WKhtqD95ItsYLd3d3Kg0PZMgH32Frnzf17NocqUsEOgsvFCo1Vh4dCapdn/lTp3Koc2fSHexIaFecBg5B1LYPofmlUxT/OI20fXmefVfAJu/zsgZ7NhXAyhUaqeVvzpepycbshfizzy3WaeOQTL/TBdy+IsWKFaN/aioTFi+mXshF+pXaThW7CJQKuHfDE9G6Gqr6eelZzxaPY4KUKxB75DdP08kulbKWJnaU6IvoDIpucH9HDWak9X+ho3H/vo4uXYzPLZckie+GjUA6XBvXuIVYKYs6X8EeDZEWC/T/ZCd+vifo3n0fvr4B2Nhkk5BQgiNHWvDzz2/x44+jOHasJaGhKn7Yfh/DkCC4qgUNYItcSyKBVBIyNbCgqQKtCly1YG2EaonyT/tcyFWBKhRmrc+i36M4NhVzA4MBW3tnWh+4zHs33yN8azgnZ5ykVCk4fx7c3QtsVijkNCoAkkPpqPeiT40l6PdWh4PV4WwPMD2jYpnf2BBIOgf+3nC6IxypC1kPKKZO5fziakRc3M3u3bt/89mY+ecyffp0HB0dmTp16l9+LkmSWLNmDT179sTJyel3t69RowZr167lwYMHvP/++6xZs4bo6GgmTJjwl9v6e/zrxZ3r16/P7du3cXV1pUaNGrRq1YqePXtStmzZ/G2ePHkCmB0NM/8cTpyQZ89iYkCplD+i+HI529Ofn2bkBXN+r5l/HttveFLbbh8Bqb6YUAICD93csM/IgIoiJSwSUGCiV8k9FGv8pRyteEqtuXnF0b8fpVOpVAwYMCC/O23hyF5YWBgXL14kKektnOwtKbbxFyTJRGXbCGzbZxHT1Q0XTQLtNUcQACkQFOmQccKO/Z5d6O53AKlNgRmSBBlaO2wtM1Hm/580gmiESY5gzAVj2osNPSEijW9F7oTRWExfTmp6JAnxQaiCRyAoVDzEnjKNllHR++X1gmU3boS0NNoHHYUBEipBRBIFzvv70n19LShXC27OB//4gpSdp6pPYg6c6QgKK0CEptvk4vU8Mm4tx+7SZBTAsRLtGDNxJZIgUg6oWeEuz6rOmkwSVlZx7N17lqFDhxZpTrhr1zHCdZXoaLTFRZObX6sgdoVlpvdRKCQUCgl354cs7DSZS6m1eaT2oEGDUKpVq0lAQA4XLpTj7l1vQkLqcflybSwtcymujGeIYj1KA6AHDgFtQbABu14w+K1txAzpi09ygZ0icpQjpBS0fiAHQqqmQtvRifT7Xof6SRwtE4zU/nwSpT67zZUFV1CoFLT6uhW//gpt2siRZrVgoJbdetj9GRRvBjlPmNl9AoIB+B0/Md8S7UNAKUeY/CuCypryCj3d2jdh3LhxtG7dGkcHB0gPl2VxK5r7If0bsLOzY86cObz99ttUqlSJ8ePHv5IT8Ge4ePEit27d+sPyyKVLl2bOnDl88skn3Llzhzp16vwl9v0R/vURjeTkZIxGIw4ODhw4cIAPP/wQNzc3rK2tcXd3p0OHDhw+fBiQNZHNmPkncP16wb9NJli2DFq3fvG2/qP9KdeyHM7ezn+PcWbMvCL+uzeRmx5Do5JxiHlOBhRENFSRIkPLbGCk288U06RA5Cq558RTKowCq1cP65cuXVpOr3oGSZJISEhAFEWy0lO4HxWJtTKHnuG7aPn1WaoG38T1XBKKCcBJuO/nSZqDA5db1abj0GYk6rw4Ft6ZxAx50CCKKgb9dJhqX6Rx4GpPQu43oePKe3wbGUtUzSdQ46vfuCkCQpKEtHAl+pFKHLx9cJv3Fl4qE17KXBoKiWivfgL+leBsr6L7RiyDgzWgYxaSCyi6mVAK8v0KD6qKV41IHByT5ciKlSt0Uxak7DyLmAOiAVxbyr/rEuFYc+JTI8gVZafKzfkRj5LdEATI1akYr6r+XERjx45oihVLJCYmhi1bthSR+Iy6G0kp11iyj1gjjQe2A0lg2Ktm/PplVHcLz9/Ww+UhLUqdxVGdia2tLffu3cXNLY6xY++wb99trl5NZdIkKxwdnfmOj+RJFwl5lNJV/inZAzWh1KE5BHfzJTtvqlSC/LfP9xHcdpGX2eigllMOM5pao9fpcMg1EctyOhw6yYS3N3NtXRBnvj6DhwcErfuV8qr76E1KlGIcki4O6dF2ELUFDigURJGW8Uw0iWciTHnyyMcN8F46wjEDEyZOpo13KtHra8B+LzjZGsK/ftmbZOYfyLBhw5g0aRLz5s3D3d2djz/++C+pvVmzZg1ubm60yRO2+KPY2tr+I5wM+A84Gs2bN0ev13Pq1Cmys7O5du0a69at491338XDw4OAgAAuX76Mvb39q2l3mzHzFyNJkFxoJs7JSc4J/uyz57fNjMvk9p7b9Fjf42+zz4yZV+HCueNERlxnlNd+VLr71LEPoW5IKOMXLeahmxtpDg4ENWuEpU0xBFtPsPOWc9azH7x2W2xsbPIjHLmSJUbUZBjtUPpLBTP+Twt298PRGu1ZMnECwY3aoaoyjuIjItE3OUD3FaGcu+1LrNCFg8FNuRXlQJd5u3EdfIYOHQQCA2UJ6g5D6hMVX57N5wdx+k5n0qzagsISOoTBzBXg4YHpk5lI+0GZDDYHCqIlVgJU1UVCZgTEn5QXJl1kU9A8Mm18QPcEmiUjLAGFnzy4NRqUXDpdl/p+IeRe+EhOwSo/Akb0h03NivSwKILSFq58DDnxsrJV0jm8YzZhQv471DN9D66fP4ETkKBTEpvUDKUSeLQPLo5BkiR2775OmTKPMZlMREdHs337dqSMu0iXpzKi5JeMdF+N+8lHCElyVEFyhuVW8gx9CYeEIubYWmbT2ukckyZNonPnzhgMBipUqICvry8+PqX5+mslkZFw/UlVknr2QnIAqU7eszsBBksl7x5YwZGcknT4ehvJ1pBsCY/sIFMNZzzg+4ZwuSQYBchQQ91wgUXNMnhsA7HFYWE5mOwHtRyMvDd3CeVNw8n9xRGX+y2ImOGFl/t5xp9bKYsEPJOa9rSehyQgmKJSxIXXvWjZfiMlrvZmnVcuNWc+gn33ITcJrN1e7SU3849AoVCwYMECHjx4wLhx41i1ahXlypVjzJgxr61filarZcuWLbz11ltFIoj/Vv718rYnT57Ez8+PAwcO0Llz5+fWi6LIvXv3EEWRioUrvMyYeUPExICHhywJWaWKXGzYqtWLt/256c94d/Sm+afN/14jzZh5GfFnyAz5igePk6jiGI1Ra8J0xgT+ChQ6EcucXNIcHFgycQKCoODdd9+lZMmSf6lJ9+/fZ8uWLdQ4f542p06gEozQD4yiAstDBoSnM/7+YOrlRNaEH7ib7cXdu3fp1atX/iRUdDR06wZ7dpso7/UbX/BGLbHHZrLn5gROnnfl6lUo5xBGREJdfHygRQtZyaiE/xQcFixA0w2EZ5wBvQSaRmvA620Mm5RkiSJt0isQWKc5Vg9+IW/+HIDgIw2xtMmhhu81cnM0RDscw6dvnvrctc8h9QoISrmBoLFQvcxJhTzQ7VMSfGPzF2+/V4oK1llU+tKIdWYOkjNENbegaYCW+EsHIPgdJEFJgMMPXHgnkJFJP3OheWPC6tenlfNZmhY7l197A2A6rkC3z5Kw5rUp3f4JFd+PACRil5fBziILvUlNTHJZwh42ovF7X+NTTe5lkpWVxdq1a7GysmLo0KFFolUx4eFYtGmOS1IqgghSMaAT5G5UcUHVhK+EmfhVnkKb5OtUSdIR7QALG0OP2/L+7aJgWHcI9IAnNvBjIrxdWT6OWgGmdFA6gCkTFLYFjuDDbWUpdS4OVQ8TwlMnwQW5BgOQTsgNDvEG7gLd1dBWI/fWeCpz+3RdofeO7hbQpwbS0FCEJKngmBXGQoMVL3/XzPyjSU9PZ8WKFSxatIjk5GT69+/PtGnTqFGjxp8+5saNGxk6dCiRkZF4eXm9RmtfL6/qG/zrHQ1RFLGysmL8+PF8++0f1C83Y+YNMH683Pn78GFZo/1lPPj1AbsH7mbS40l/n3FmzLyAiKAdeFdvgsKmDDHhh3G+3AsrZaFGa+OBJMi1UpOjsSbA15c7rVrSpKkv3t7eODsXSvsL+UBuVlfz9ei6i6LIsmXf45x7kQFfbUWZLX+lSS4QOcuLW9nedC1xJG8gqYCa30DV6a/l3M+SkAB798Lx43LvifR0mPF+NcZXuPFcGYokwc4MSLStwBhFJAIQbYCOGeVZbfOAEJ3ILT28ZaEm8of+DP5oEwmxZUkqtYHqb71E4tqYA7tdZWfDvjKM08reU6GBcmFitpXC+lgOcbVcqdwpgtILnhC3ogwCJoyiQGJucRiroJThCWkODiybMoV+bgfwVgeDVDA4TzfYsf7xMCrb3aVhsVvcye7H8Fkj+LLnp/hf6kLY/drMGzidZg0zsev9vIb34cOHCQ8Pp3///ri7u3Pr5k28q1VDKUkFrR/VgAWQBZICNlSexcdJM2hjO4xR2j24a3Po09+EAlh8GColgkEJehXYdwfbtqAplMMhGUF6AIIt4FqoTCjvXTY5g7IbSHl9TOjggC4nFwuVDkFpCQ61wJAEungo1QkebSs4eN4x5PuuAqUFKNTQJwW+6oe0YjdCFxF9S8iuuQqnmu+++Hma+deg1WpZs2YN8+fP5+HDhyxfvvxPy8q2bt0aURQ5c+bM6zXyNfM/00dDoVBQqlQpTp069aZNMWPm91m5kjlby5E2d+VvOhkAe4ftpetPL5PAMWPmr0YCTmI06LlzYQPx2+qg3VwC69BBqI7ri6SUiF3A4KAisFpTtk4bwp0mVdHrsqlTXoWzo40sDfrDCnBzhZ9+gftrQZ/2WqxUKBT0KHOc/je3odTmzZsJYOiiItekoaVzAA+EpkgKS9A4gedbr+W8L6JECXj3XVkB9+5d2fFwq+7G9kxbEo2QY5L7duQYFJxMKkHDAA1jJ0YinJQHuu5q+Nn6Hs1jRCYmwfFs8HsiMqvndrbfqonNkGsU7+1Dp02dKLOgDM1/eSbSqbKCdsFyL4em2+QOdO5uL1XSisx15Z3qP/HD5dHMEABJQsirLVApJIqpU1nl9C5aJ0si23jxVqlVVFBdBAlSDQ6YJHl0LkoKxjROxq9kMHZlalL37S9IMVZh5uHlXI5tQXymO47W6Vg6e4A2BrIeFLGjY8eO9O3bl61bt3L06FEOHT6M8OwcqAHIE3sSRBj2niNPnsCPoct58l0GQ6oYiL/8HddLyM0FH9lDqWwolw6anXA5F1J1hXqkKEAwAXZFtQikLpCjBiEFpNtw7Rs4FG6Hvlc892s+ZuBPx6G/FjpegG53oXcSNFgO3R+B53BAVVTuFqMc7RCU8om+3IFwOxRjFw90BoG3Jy9Fq9W++ktm5h+JtbU148aN4/r16yiVSrknzJ/g/v37nD59+oW9M/6t/OsdDYCmTZty+fJlMjMzf39jM2beJHPnYp0QjeLbub+5WfDSYKycrPDu5P03GWbGzLNkAT0xGSvToPwF7md7YiUlUkyThmm3Kj8XXRQh3LoaszM+pdqgRMa4L+c9t+/pU3wDqpO+sM0KjjaA2V9DTALsyQZtrNxo7jVRov0G8BcKioeHg9BGoprDbeztnPBs+TlCp2tQtgdYl35t530Vevc6TP8xmexwGca9mstRKFRYqESeONzBam9JhOS8VBxAIUAjK1hTQp7AbyiAhV7DHaWet62uUXaDO24L3TgceZjYrFjSden554nPiicxOxEcK0N/HThVhzFj4ND7L63hqK26wk+hI5iT+xmGc6CqtrSg6fUJEMZDJZdbWC3VUbPnFdysYlAIklw8LlqSY7BCksDJRolaoQW33qBxQFLZYTSCQqXCZFRQyt2RcxHNUEvpcKwJhE14zpZy5coxYcIEwsPDycrK4q737/zt+/xzcgbWwXKfE/0zR1BrxnusXF+R4naePLGDpqMEMvLKMpVAsgjnDJAeBdmZIB0HloHwC/lOsySB2ELA0gAKCaQLoPoMml7LJNfehsqaJ1yK9ePosUKeiVIDFs5gUxYa/wL9c8BPWSB3C4AAlSaBSS//Wqw2qq7X0JUexMngewwZMqRIkb2Zfy8XL17EZDLh5/eywqnfZu3atdjZ2dG7d+/XbNmb4z/haAwePBhRFAn8rbbKZsz8E5g2jYeCB98Yp/GyCQ+j3sjZL8/Sd2ffv9c2M2aKkA7ZlljE36O4QxKNnYMRBHkwpumrR3KWIxn3rnuxZ0VvNLZqSpSXZ2YtlToq2EahECTwGAKdwqGnTaFZXhHuroJ9XnC0MYSMkxvO/QFu3LiBySTPvmvWbUGhk5BswDBMCX6gUgp5530IpduBvTc0Wv16b9Ef4L1O66ha4z1odxFFtc8ZMtyB4u+VRHpGNUoAhtnD7pvww1cW3FuSwwchkCuJZOozMUpybcToENg3/RoZS+YTnxlH963d6bS5E6IkFp2id6oJrn7gMQhKtJKL1pE7YOvOQjEpEzuy+TAY4vxm0/lO3n7+oEkz0PXRQQQB1IpCA+ETUHJqPFZntUiCIA+040+D0goca5H04AEWFiChRsJE9YpJWJ/TQr/DsP+RXFMimp67R5lXFuKuljubl4qPBwpXqjyDVzKWzS6jVJvYmrKWlSEreWdrb1a6lWeWs8DFaAlLJWRbw4K20CUWusdBrWQ4tgiE9XLUgiBkp3kfCD+DuEiJSVDmP4sqieCQC0aTiUutqzJeasY3w7aQY3hBw0YApQpsCjXmOAGMl2DxF7DfE5KC5OVqe0p03MiWLdvYt2/f39Kbwcxfz4kTJyhZsiRVqlT5w/uKosjatWvp378/NjY2f4F1b4Z/fY0GFNRpTJo0iTlz5rxpc8yY+U08PODhQ2jUCPbsgfh48HBIw8HDAUEQ2Pv2XgzZBvpuNzsaZv5+YmNjyc3VUcL5LtanOyCIQKEx1dNvjPALVShVLo6jGzviXTMSj6pxuFZETo1BArUjlH8b6iyAO9/D5Wkg6p4/IYBNOehyC5SWr2SjwWBgwYIF2FuraF/iHO4fnkKdmIVUuhi3vihDZZvrCHUWQ+Xxf/o+/C2c60fK/T08NBiJM8r+Qbge7uTCF99AmTR5M2MxBdmLRGJFSDdBsgmaTwe7FIh1UlJ/iopYQy5qQU3XSl0ZUn0IdhZ2tPZsjUJ4Zj4x5D24v4EDX2QRXAE+PmKD0ahkXnM981rpsJYgtSJoTkLylmLQVcK5RyogP/sUvRMOUzJQpZiK1n4IeVqzCg0P1SOY+Us3etX4iWolg3CxT0IzSYcyJa8IeoUDNN9bIL2bR+zacjgp40k1OHE0ti2Dvt6CxYtmZLqB2AkUdvKvKToIy4FsNYRmw5ks2PQdeGTAAwfwnJi3X967mzEb7AwFHcYl5EBOmgXYGCEXJbYmEeUzbs5T16i9aj+12k9kaK025Ix+m0ZujYrad2ky3F4o35MPjXn1GgJsawfNd4Gq6CBy6dKlfPjhh/zwww+MGTPm+es186+hXr16VKpUiY0bN/7hfU+cOEHbtm0JDAykSZMmf4F1r5f/mRoNkHN0XV1dzXUaZv4VqPK+j4OCwM0NBg6E1Y1Ws7rRau6dukfEvgh6ru/5Zo008z+IASiDk1MtnJ1rIah6QScKRld5PJ0sL1YqBaWVElf3eEJO1kdsGwztgsDSVa6F8PkQ6i7MKz7oB41+Ae9xYOdDka8etSO08H9lJwMgNTUVjdJIRekgWyJqENOmOFIpe4Q+xajsFINUfxV4j/p/3o+/gWbbiaxkYJ5uDr0Srej9BCpp4IQONrYE0QZ01qDoLmKvhMoaObWqsy3YdQejM7j2MLHNNRc/KzBKBnbf2k3fHX3pv7M/phdEDai3HHy306Vieab1g+97fkh1l8Ps9nTCJRuW5A7jQbY3pjYKhvpswKl7QXqWKCm4n1MOZXcTkjMFkZgTyAPq40Y4pqPsuz+wvFgXOlbdgZvzIyxVOSi7SwURLUM6XP6YlIzo/GPfe3AZ17hHWCl1lLaM+z/2zjo8ivNrw/esxz0kwRIgENzd3YND0VIoLoUatNRbWqhgBYpTtLi7S9Dg7iQkxF1XZ74/ZmNIC/3aQvvbu1fK7uzIO7LJOe+Rh05Fl6P5yIRYVHYEckx+qQVYuuQ5GQCuBph7BrpEw9RkEGIFllWVnYwp+WrhFm6FvctlJyP3nIDfykGx8VB2DBQdD/16WMhUKhHJFxlCTsFSAmPNC/DY2Y1KkxcQ3q0Fhrho7ifdJzI1Ul4x6D2ouRB0PvL5eilgeAdouvspJwNgzJgxjBkzhtGjR7N3797nPy82XmuSkpK4cOHCn9a+WLp0KWXKlKFu3bp/8cheLf+JiAbAG2+8wYYNG0hNTf1PhZxs/HcQRZg6FT77DMx5nSHR6eBd18VoYiJRqBR4lvXkzcNvYu9h//yd2bDxtzAASVqJIFj/LGSAtAsEc8G1JAlESYWyn4m7e+5y7KtjDDoxSNay2FFejmSUe//Zh5Ak2FVJVkVW2MGZErA5Qy5cfpHZXEnkwa7RHLmmJ8bggygpqOR0CaPClS5+u1G2DgGXsv+/y/APY7KYKDe3HPeS7uGugN1+4KlWEqC08FMyNLGHGk/4YQYRgqPgZ28orcmLdGzMgBkpMLLxN0xqOOn5B93XByn+N9ae7sm5sLLsdFqFJ/H8+M5eDh8+TFm3ZGbNbs/Bj5uBlOew5FgMkiSgUFjf5O+yBE+1hM2/bY6jKkpwJBvqOrlg51yCR3o9Psk30eie3kZ8DIrPQdCDpAEcQWoFiraAStYm7PsQ1ivAMxNcTQIpaolYJ8j1FSQQJOhzBZZuA7WYuxgBEAV46AoXC8GiKlD3MXS+rqJMkgINJhT5ohsSMIN3GMUcrnmbuelvx7vdHGgd2JblXZbnDX5HWTBnQJ1l4PMcRVYrFouFzp07c/ToUU6cOEHFihV/d30brx8bN26ke/fuhIeHU6xYsT/eIB8pKSn4+vryxRdf/GvS6P6nIhoAvXv3xmKxcPLkyVc9FBs2nokkwbp1BZ0MAEGQuJ0t/1ISzSJxV+P4peIvXF5x+RWM0sb/NjMAa7H0PJDKAHueXksQQOknz9oFtglk8MnBuYJ5tLvyfCcjZ+OqP8gzu171ZCcjPBym/H6DBABL2GYOzWnH5ov2ROiLY5K06JR64o1e1HM+RHaZz/51TgaAWqnmx5Y/4qpzJUmEt40VCdCoEAR4112gmn9bnpwSVAKb6vZhd6b83kUJJTTwnhvc9ofT56chSRKSJLHqyiq+OppPgfr6D5DwG4IApQrdx14loMl04b5PGhViImjatCkRpg+pEPAASeNOBl5kW3QkGNxlw1yAdL0T8eo2oLCHnn6yY9HVGSZMxOimwdJRDt3mH7dgrdc3SfK/81LgdHoqUsoNxl68z4185ydJeY6J8hZgsH5glGsrFOuAhcApWQvDyx0sSigcWJVbs0TeCuya62QICFQVqtJP0Z9VVeCdNpChlseQooVkrVz8XTIZOtyFgZchQwtftjDTYKCCxcp+RJOnBSMAI5jHJrpROQ56nc1m+bwENpxfwZBBXnD7trxilalyF7A/cDIAlEolv/32GyVLlqR9+/Z/i9q0jb+XgwcPEhgY+NJOBsCaNWswGo3079//bxjZq+U/42h07NgRtVrN0aNHX/VQbNh4JkolLF8u/5ufkUPN1HW4kvtepVNh526HX41/tjuODRvgjiAMQZyrgNEgROV1RMpBFMGiKQb65xhCihdQsvVtLbeZbbhBjmQULy7/+zvEx8eza/MSTiVVJ8OSlzejQOQNv7X46eJwvDUJTP/O7oPBZYIJdA/E096Tbc0nIohyfo8CCYVoQlA75xrfmSYFWy62h/AdDHKG9Hx12kkifJ8EZzKS+GVwRaI9dZz7/G0239yct9LlvGtdstB9HsSVZGPL26hcJUas6U/tgACOHvXAu0oblsR9wPT7Y0gweuCsTkNhNd77zl2Ns4sGSgyACbNgjiP0bgTvf0cFjywSe40jKdMTs6Vg+lHOOSRaoPAxqPwhfLTSwFaFEaUACWa4Y4TU/DXt2+VoRG76FKAHLCHAbAj7Gk5F2KEDqpnltdr4twIJHCVHVoc25tD0B/QMTSKYDvxSC5wmwfTactqV1pyXIXjfHc57g3cmjD8Foy/oSay2gX20IpZCueehwUBTDiMgO331I+SBbfRMIKNaBUhPhyLBL9XlzNHRkR07diCKIh07dkSvf05Nk43XkgMHDvzpblNLliyhbdu2+Pn99/7u/2ccDYVCgbe3t61Ow8ZrjYcHjB8PPXqAvTUzyr+wEUO6PF3nUMiB2u/UZvil4XiV9XqFI7Xxv4oxMhlhqihbXgqgo2wYpiU5cTM0iF+njOeWZSu0+39E3AQBas6RhfuGD4ewsD9Mm8pIT+dyWiXKONyk3vnjvDN9OtVDQ1EKFhx8qkLtBbKugdrpd/fzuiIIAss6L+Pb5t/if+sLQLR2LAJWH4QiwQiCfOkUhZtRoYqGrnPmci+xMA5WgzxThEt68K87h2sl7Om79Tp+SUbeOazHZHVcuDmD3B62B8Dt4xTqPjpFUadMHkuwqYSeKxPf4sIF6Njdg2KlquBkJ+EbEo1mvDlXOwVJQpu4C6pOg+jdUG8l1JonO0JZSrR1fuCDretRHpJgDIgb4fKF8jxc4YXpI9AMhXeOgXsyfHBaSWWtlkWp0DEayoTD/iw5vQqQn0FPEPzlaIIAaEX58RQBvzQ4tUrFuTsCg3W3uX9xNw1bDeG3bquZ7PQhbUIu4pqaSoOQEIoqJKo7e1HcpThz66vRK8HeIjsLInDIH3aWgwmtoGMf8E0H1Nl0ZQN7aU0a8vOlAAoRlzseBzM8mgZhM8Ehywzly5OcnUzIoxDuPbosh7IvX4aEhN99DgoXLsyyZcs4f/48ly5d+n88UTb+SR49esTdu3f/VH3GtWvXCA0N5a233vobRvbq+c84GgB16tQhNDSU7OzntJ2zYeMV4+cHP/wgp1C99568TCtlY0o34RboxoCDA2gxpQUK1X/qq2njX8ERYDNKn9kwEaRiIL0J+npa7twK5EZ6Q5x67eOtqz9RvleVf3x0htQHiKjwPhZHi50HcU1NpWHIcRQKAVqdgJKDwM73Hx/XX0lZr7IM8S4CGXflBdux6pVIEL4W7ItD6Xewa76PoL7r2DbrHp/v/JSwxCKYJNApFNT1LI7/5VE4ilnQMa8g+kHCbT7aGIyYGQ4qVzk6YN1//+jlaN6T2L7KkUmBb1By9mrS06FkSSPnz5/Hok+TI1sJIG6DNKMDCoUIkpnUK1/itv9XtEt7optWErvJdkS/paPQDDVLVEtJWKuDJFBsgrs/BXFkb0f0CdC6rzyucBc40ELgm1JBLE7TcFoPgQq5G3JuLKQFCDNAqpUX1RDy/dibQRWfTvl9EnXss3G5MpB7t6/T0L0RDq5+hDRoQIqLCyca1qeXWxrnGg8gbFwYi4IXcd1b3p+InE7V+Ra0eAiSAKl2MLIDfHAKJKWJzMBtbKYLsXhiIa++I2c87npwMoJZAEtEBNeC3Ok8tzG+AVUwq9Vk16jCpvHdmXzka5IzE59+AM6fh6+/xtNTLnZRPhn+tvHacvDgQQRBoGnTpi+97dKlS/H09KRjx/+mQO9/yprp378/ZrOZIUOGkJyc/KqHY8PG7/LVV9ChA1w+mIJfLT9G3xiNd3nvVz0sG/+TPAZagNQDpcqSm7JislOxY2871I0t1PnoCEXqhObVYvxDmEwm7t27h9+1N2jttYfaIaEIIkgKiG5ViFJuKf/oeP52onfnvc6vMK1QQ+cwqDHDGtZQoa35Odt+q8LJmFoMW/4TN7XdcRANNLUHewU4tYSK78KymtDW3syYjO1It2cgmjMQsMuNFIiigJAIre9m8dHA30hNcUGnAwcHBzp06IBZ7cWFxtXIcrMjrrU3RyMaUdjtMaIELfdPJcVswWgxYrAYMFj0SGoDBsmMV5aF7U7lcjUAG3Kc/cqGfNlcIKGkwNKaUPpdBb1WpdKhxyXal+2MVqEipAgordGb3KjOQRAa1EEoUqTA5RKVCiSVAsme3C5YrsoEso72Z8WKFURGRnK+Zg2OzfmEgMlfUaFSdXCvAYA6Oo6VFSFLBcsqQ7IdFE2Hd0/l7T/MFRZUBUeLiVbxKZxtvpF1mk7E4QFAPB5YrO5GjuOjtqZ51YmAadtF7EQ5YmJnhuCVR3mvxWesaOPHuN3vEJ30CFKuQlQUdO0Kv/yCOSlJHp9a/Vc8UTb+AQ4cOEC1atVwd3d/qe1MJhMrVqygb9++aDSav2l0r5b/lKPRqVMnBg8ezJo1ayhbtiw7d+581UOyYeN3WVH/F3661JTBA0y2KIaNv4HbwCPr6+cpDy8CigEWEKyZ6lNAeATqHWZaz95HQLMSwBzgj4ta/wxR6VEYc1STrdy+fZv58+cze/Zslm/8ClHKpIrzBSwdFJjdlVzrWp4SwWG09dgKGWF/y7heCTVngX1R+XULrArTCmhxJG8dfRyYMuTXnrXp9+Uyhve6T69Jw9l/NSh3lt0gyQ6HHtiVCV2jIVsEQTIjSdny/mfAkYpNyXC0x2gV5Vu3DipUkPdRoUIFSpQowYlqDbj2dTmmVTdjp8+gZKH7DIlTcM4A+bvAak3wxlXY+ht0vSWyICCIxVXlz9S6OKIUhZjVsij3+6wm/RMDl0deB5WcRzqvwzzG1XkXT9eieTvMjeoAnqHQr1fuRxKgECUUZhGhHLlK3CqFSEWnq1TVHUCNnsLaCG5EmDBYFLg1mg7+bwBQ/4336dRlAhozlEqA3aXyIkDDQuHhdBhyAT5sBWYlPHCF1S3U/PD+Pn517EAC7jygJJuR25Hnd8FVyAXm/W7IhpYAYA/KaqDzgqEnjPzYbhYeNYsjbavErQ9LID16BHFxeK5ZA/CfNTz/ixw+fPilO4WFh4fTrl07EhISGDx48N80slfPf86yWbRoEadOnUKSJDp06MCAAQNs0Q0brx0mE8ycCWkfTUEbF4nih+9f9ZBs/OvJkR3D+u8+5C5SVYD6QAdkh+INYCeQZV1vFiDK3YF+AfyBekBxED8QEBwkwm4FAwMB17981AlZCVSbX41P9k7GZBY59PAQtxNu46wxYKe/SWxaLErdepyUerQKM7pWBozT1VR8IwmNR2Xw7wsJ/7Fug80OUMBsVdmDR035dfp92B4Ee2tDTt2F2pFawW9y+du3WbL/bWbsfkfu2ATU0sqr6IEzBqgbIZff5ASmBAGS6rjR0Wc1FwrDpZhL7N8P+Wtag4ODSbO4strwkOnpSbwt3uR24RCWpom5qUzl4mD6bti6Bpz18GlTyNZAo/tFiSndnky1nIrk4ORCaL1w8H8DjVJDkGdQ7nHc7NyYUn88iuA7CDlCg4HIlkogcpvd6qGg0YAgyBEEa1sr8YYay3gHkg8U4XG2H/cyS1DULoLWV/bSa+o6KoSc4PLlp+uKmr75Oaq33qLmtXjOtClPhZGwpgJ8fBz8U+GjELkD1R03qBYDvUJSiNBEoCiziZ10pDh3WczbpAnap1TMFTzRG8EMghpIAV0pUH0Jms9kXb9SDQyIfoDFQtElS9gMaP5YfcDGa0K7du349ddf+fTTTxHF503qyEiSxKJFi6hYsSK3bt1i9+7d/+l2xv85RwOgZs2aREdHM3jwYH777TeCgoLYvn37H29ow8bfzMOHcs1riRIS48ZJ/MIwxMJF/7Djjg0bf8xvQDkgGfgc6AKEWN+fBPYDEcBaoBeSVBqTyQ1RvApYDc+pQDhIJyHjjA7laIm0tEIElBvzl40yJTsFo9nIskvL0Jv1tF3VltjMWAo//Irdi5Us3tSckStG4RE1i/5es1C5zKejkx6l1TBWCBL2KpNsRRvj4cGvELHpLxvfa4FjgJwqlYNCB6IZUm/DwWZgSoa0G3CgiRzZSLoAxzqjsiSy+pdwMtHSd+5K7mXa8Y4rlNPIM/Th06H3WTmNJzcl6QA4OD3kQnh1Qn2h85rOXL6dQs+eeYePjo4mXpnAovRkzEC4JpFFDmewN8BbF2DXShh5Tk4/atMP0jRwZK87/qUq4NhES7q5G3M9RxHgV55iXeuwPJ/UxFPsKAPrHMl1mu9aX94FfNpBpz3Qvr0sQJQPhd6CMi4Ttx1RFLaLorhdGCXt71Pm0B2cUjJoEBJCcnIyFssTIoZ2drBkCTpXT/yadyJTC6k6uOQt11q4Z0HyFCifAB56mBgibza5s0iZXQP5skg36qr2UKfq0Fw3P38diaTLe48RCEX2+qKA74Gh8o/qLCjfBbQgmM20AtRPnKON15eFCxcyZcoUJk+eTPfu3cnIyHjmepGRkbRt25YhQ4bQo0cPrl27RuvWrf/h0f6z/CcdDZC7UOVENwRBIDg4mH79+pFkzX20YeOf5NAhqFUL6tWD+fMhMlJAgchjTSkyz15/MaEyGzZ+l6XIqVJVgHnIEYtr+T7PL+CSiSA8RiGkyQ7GPApEMvgQHAvJrTXd3eOQpAO8NKZ0iN4P+VKiLKKF4jOLU2NhDcbuGUuhHwtxKfoS0z1hdxYMj4MjWXA5/SBp9xcjIPGeewpBx8k1iq3z13J73cxwsGSCg//Lj+915txYEPOlkpnT4cIHcKgFbHuUey1IOAM7K8CBZvL1MKUiXPuEScELGdniFwYvn8mbsfCzF3x6Aoql5hnK+VOSfH3vYkLDZU87siPDSWgSjF/hvFnZU+dO8av5V/qFSjycDt8elJi3HdZuALMCuvWEyQ1Br4IKcbD0uAK3Wd6EZnbjwK0u7L7ZhyUus+HyZfoNUPC7zSGVdgUEAnPrVPqUgmY7QW0HmzbB4cNQowa4ucEYoJsIHkB7ESTQKswoBYkj9ZuQ4uJCSIMGpKWlsW/fPgwGAykpKZifEDUq4lyEwZX70+2uio73QCXJKuKuBqvTABwqLq8rqJTUbduEiNHRxHqmE3NzFGNbC4S5wGrHRiRqlWRrIaaKN1cpLz+3GqC29XxMQCay85FpvR/ewHj5WGZAbWf3gg+MjVeNIAhMmDCBrVu3sn//fho0aEB4eHju55IksXTpUsqXL8/Vq1fZtWsXixcvxsXF5RWO+p/hP6MM/nuIosjw4cNZunQpbm5uLFq0iODg4Fc9LBv/Q/z2G4wdW7CzoRNpfOi1hLEXBuFc5N/3vbLxOiECJYDwpz7Jr8b85PIchADrpsWBMMCsBVVJ5LlvFXLa1NgXGonRYmT4juG8Xaoxda+PQdC4gXMZKDWM/qEbWXntN7DuNcfMiw6QjxRhhq+TZEG333zAyZp2Ir0DQoJcuMzMgrnwudgXA10hsPMDh+LgFCiL97lWBp3ns7Z4PZEk2OwH+phnf55fhXvms1dBUIPWk9BYHW2vOdPTNZsfou5gtx2EYBCbg+KgVSOlIxiagvPgRNwGBZHoFc+hZbJy9ubelblX3pdd4UfQZel5PE3u8JSthKrDIUsHXioHxh3TUzXCwvr2UOZedWq88SmBgztRuzbYGy8TnVkaOwc7Ll2SdVgKFYL4+OeMfb0HmJ4xIah2gboroEi+zjwPlsHVExC5CFylgg+GBAZRw47kPlxL9i+wK41Gg8lkwtPTk379+sl2TVYk5tMjuXo3gVIp57Df4ojpbDJasYC4OPdcoH83KN2kG8vbzee9s9+wcvevVJi7ktBWq0mvshV+jEHpHE7p5p3ot7EbZlFHz3fWEFTzTu73ThiG7GCAfIDeQF1kx+Nb+CYRhsXF4aVNkr8/Nv41XLt2jY4dO5KVlcWmTZvw9/dn6NCh7Nq1i4EDBzJ9+nRcXV1f9TD/37yob6D6B8f0ylAoFCxYsIDhw4fTvn17OnXqRJ8+fZg1axYeHh6veng2/gfo3RvSLt1n9PfFMCOnRNiRjZ3SgiT+oa9vw8YfcB54tlBdficjv9ORu3weSOkguCO3tZVAUM0G3v5TI1EpVGy/s53NN9Yz2z2Lwsp0NPGPOHw8nJXmh6CS03gmhsC0BvBLTdnO8lLJPxtvgLDOatxVAu6CEGgds1XT45mehkMAlH0f0m5Dxj25e9ODJWBIyBdVkWRFcp0X2BXO55CUA5dKoHu5jjF/GeYsuLcQgt4BSZSNan2MHLWwOgNyobMCOop5y54kd30TdBSoWbsbcSXOEH0vDlUZFYJghu2QZQHHVmBsrOJmdGnuny+Fq52eiiEriEgqRrdoN2LxpdzMy2gtl7npBY9cZNE8gCw13PaE3rdh0a4s7NvYw5wx+Ph9Se16Gs51k2UjVCpQWgRMRgkXN3lbhQIcHeHOHShdOt/Ys6PheA85Leyp8wZMqXB+rKyyrXKQl939BTKvwV0VVDPJHZpztqsF2m8tdH7/ISWCx+YK4QEYjfLzEB8fz6KFCwh0jUPICuNuWkkMlnKU8r9GQrlktBfBJMpRDZAfu5KpcHIJzLu8kSIXN1LcO5BAqTzvWKYzeu98TFm+6E12qFMDKLv1O/xK76RJ1yMUKhZT0OnvCdI6+aXYGcznwLIZ7LNBVCg47S4y4UJ3SDkPPZILptLZeK2pUKECZ8+epXv37jRt2hR7e3vs7e3Zvn07HTp0eNXD+8f5n3A0cqhWrRqPHz9m5MiRLF68mH379rFw4UI6d+78qodm4z+IKMrtaxcsgCJF4K7ZH083E4a0LJItLgTwAEmSbI6Gjb+AlcAzZoFrAueAGsi54fOQ6zAmgjTMavRMASEJpOKQ0tOFDQu6MWRYsRc+8vHw42y6tYkvGn9BdoKaw7vj8dnzJfedTtOvysp8YgN3cp2M2bvktJR3Q+DnmuCUL4lXsZ28md4z5NW3P2/2PofkC5B6Fcp/JKdUORR/eh1RhKwISLkCaTch4wE83i4b+YaEfOlKQp5DYl9YTs1yKgXO5cGtkiw0+FexJQCywuTXF9+3elLW1KH8HZdaAIjyvznGt0In13Ok3QNMT6wfBbd+RAEUthr5OZ87bgRhF5wPqk7rc/swmjUYzDoOXPdDpTDhQyQC4Gw1sCvGgoMBPmgFH5yEvSXh2FKQSoHdTAGK1oGq33HvNKjV4OoK2dk5joYSs1kqUFJRrx4sXQrffWddIIlwdbK1qF8qeB6tXUDjCAotCAq5HsW7oVyzkvUYDmTCNiAR2JNvuxggwoLykwtUfXMJx0qVIiUlBYDqoaE0OnGMS42rcLduIMmJJtQKJUV1ETgoMlALRgo1B8pA3FaIugWlkuTHOOdRHXYBQorDfoc4um/sxhQ6Eif6oT76GYIgEFAojEiK4uybQvGy4QWjirOBMyDUBmmUvM/DyQKKEmVodeA2UldY0BDUicdA5wuGRLDzeckHy8arxMvLi/379/Phhx+SmZnJ1KlTX7r17X+F/ylHA+Toxrx58xg+fDjt2rWjS5cu9OzZk++//57ixZ/xh8mGjT/JpUtw4gQ0aAB9+8Lq35Tcj1Sysu9BJu1uSH2PR5gzzDZHw8ZfwDWgDFAU2bNIkRefI+/feSCMQbZhpwDD5GWkg+QOtzsH0qTVNyTU+5yg8CAaFm/1zCOZjCJnjsZx9EA6Z85bOB1pIV5szgzhMEKN+WjDm6Fv8ikojYD0VCXgxBDZyTALchtRgDRRbsMKyLPR1pnenIgGfUqCLgtJH4uQT9W6wKy3OR1u/giPNkD6XeiR+nTOmEIBjsXln2eGBKyIImSFQ3I+hyRiK+jnyUafaAYk2YdSO4HWG+z95KhKToTEtRJonk4nMFqMJGQl4OfkJy/Ir2QuFawZoKP1HINV4OgPhnh5Zh9AUIJXA7nrVuJZuPdL3vqByClWOdfmQL7lANlAAtS5eYZAn7tcCKuee0g7TRbDhC/ldawYVFAiRe4mZa+EoXpkZ9ULBDsVVPoagHv3wMFBvux5EQ0FZrOEVpu3v169ZB2h774DYg7DyT7W4hup4Hl3cYDgm/L1LdC+CfmcTenyeonAbuu5HgRaARtyBm9AOnWKlmfPcrpcOSL8/Wl84ghOKZnUPn4aYzMNJlGNSZJ/0iwuCIck2CGPw6OGgOcVKVcjI2eUSgkWboVqodNYbGqEiRKAAgM6vJziOfNlbbKNdqjy10bl3IdE607OgDBaXt76rIS+YyzJc1Q4h5jw+8J6HdqbZCfY5mj869BoNMyYMeNVD+OV8z/naORQpUoVIiMjGT16NIsWLWLTpk0MGzaMSZMm4ev771aXtfF6sHEjpKXJP999B6tXgwojyUevEZnYErW2Bzc338TR1/FVD9XGv56D+V5/C0yS0zTUyEWnamTnIqfG1stqg08CkiBDa0eldD9MzYeDLo2mv05iXr3i3DimI/5WUW7ctOdxjAsGi4RFlYnK4yGC72WUfjcQq1wDh3OgkA1vfek9uSPJSZGa0gDmW7uzTmmQt8yxJVxyFvBUWo12qyEm9QQh/6x9j+tgzkS4+aPsTEhPzt5b1zUmyT+6QmDJztVneGkUCnAMYOfRGxQr1p6KtfNaT0qSxIkTJ0jPTicy7BGFHDJoGeSLnSkc0u8jPlpPREwoKksqjiodzlpnWeRQ5YRo1hOVns59YzoexeuhNcRDhlXn5FnpQi2AgV2g6g+g9YD7S+HGt7LhqfOBWvNkg1s0AkpoYZG3yanjyLk2OdcK5BoX67GEjrCtWgdqfnqO6JTCgNwutq3TSsiGLCXc8oTPm8G23+CbwyB+jZxVdxWEyoDWCHtbQaWvuH17PDmp2iaTHN1QiUos5oJNotq3h7dzMvMujH+6HiUnaqOUnq32Llrg+hQwp+Y5JW2AWkoobpGjGTnBKaMR4fx5ygLauDj2jumHOthMylYX7jYuhY8mhpDkBsQa5eM4KjNQbzdDIojbBLLtHdGYTWjQPzUMtQSP4tqhVrhhcUpE1GvB5Ex8mjf7rraiWy1rNzRrBAOVPC5Jg9zqtrZ1R9vlGiTd9mQ0zUC5K9/9a2OQ77cNG/9S/ieKwf+IuLg4BgwYwMGDB1EoFIwdO5YJEybg6fkvKiC08dpRrRpcvJj33tsbegZeoGmJMLou7/rqBmbjP44EvI0kLUGYD0wBaYL8iTAy32q9wbRTgTpNJFEn4DlRKriLaz3AokPnHIPS4zGZDndRqkWctE6IkohZNGMRZUVo6SkFAZmH02UtgjAXCBgvLyuitcNoMRJntrDAG/q7KNHleEBWA1nyBCE3VUqA6jOhjLXFbvwpONQKZmbIxlsDFxihB9GQd+BDatjnBe+Ngnc+fvZlMmfJqTjKZ7cQNZlMzJ49m4yMDDw8PKhdpzYB/gGs27iOx9GPSSKJch7lSEhIwMnJiarVqnJVuErIyRBOGk/ymMcA6AQdH9Z6lxaWotRIGoedUh5nTq1JbszleUXe/gOgzGhwKS87TpHb4exQ0HpB+yt560Vuh5CeIOqfdloOgGR1LKTmTwd6ft47gglrfsBo0uDtfI4T1KNQptyu1i0LKsTDJR9o9QDOL4e2nZHT2+yQn5WzQI0EOvXyoFAhOV00NhYGDQKN8QEh57xp0sKR9euBhyvgwQqqjfiFvb/uxytsRN5Anhy3ylk+x5w0uOxouDkNIjZAZpi8TGkPXvWh/CRQlIPu3eHUKdnTeQLDoEFoOh1Fygjj67uTAAVqpUCrEvfxsNziUEw1Uo0OjLw/H8VWA6GtOhL083w8tm2DTz5Bio7O7T4lIGf1FdbewF1r4ewnVRi7ezZXwyuTYbCnsEs026t2QrfXmBvBkAQQPMDYTsEe38YEVzr81HlLbZwRjrnCuscQrIBmFqi/Bor3eOZzasPGq+JFfQObo5GP8PBwBgwYwIkTJ9Bqtbz//vu8++67/xPtx2z8tWRnQ4kSEGOdqFOpwNtbwif+GqeSyqBxtCm+2vg7MAI9gL1AjuGdE9YAHJC73lpZFaSlfrShQMShhR30c4JZqXAhn+2en1JupahXtB5eDl7MDZ1Ltjn7mes9K6KhU+oI9AgkNiMWR7Ud96tWhuh9zzaQAUqNhFpzCu54XwPoexISJPBUwGwdWPKdWI7R7qWCFTWh9Ago1qOgU3F6EKRchZYnQPn09/HSpUvs3LkztwWqiIhg/Q8gnXTaXgunyv49bG1QloM15bwkBQr2spd4CrZVEoAmajsO3ct++hwByXruYkdQ5luOzgc07mBMzmv7KppBqZXrRuyLyHUadoXh6hdgSiNScMOVbBwtqdaxKzCLImazBntNQQX2HAug+qRQbjyugO6NBoTuPI9fKqyuBOsqQGASDDsHpVMhqR4UPkS+2hsgXQlOBurWVdK9O7z3HkRGwujRoDaGczDEg45dHFm2DNhVDVIukp7tgN6kxetsUt71yIm8eAIzNaAQoMRAqPQVHO8mp8Tp48hNsVLooOMdcMinJJ6ZKedwVany1D3lkwZQ9hQo1GzVT+RSuFyv0fDECa61a03hnioU+sekFh5NWLKGdu3aYTIpefwYbu07iPMHQ6mZGQkI9GMlBrQcpREt2c/6aT1QFJKvZ6vv9pJtsuNoZBOUWSKSUi5DEWuBcgxMeeRNWnQQX9c8hiAIKBXW89EVku9jhU/Brx2Er4UbU6D8xxDQ9+nzsWHjFWLrOvUnKF68OEePHuX27dv069ePb775hhkzZvDRRx8xZswYHBwcXvUQbfxLOHhQdjLs7aFwYTlNoNjNPaiyM9E4/ncVQG28SkSgDnK9Rv7Z3HyvfwJprLxIAPrcNmB+E35qDj8Dynwz7C3soUjYs4/UfN89Pj5xn6kNBLJrPF8Fd35NmF+DAl2i9BY9V+OuohSUNC/RExosg02FZEcjf6EzACoo3uvpHSs00NFaNPx2Y2gzF659BfHHITsWKViCbRYsHbWYoi8iJL6L8vQ4jPblSPX7gAynNvhHHERliiZ9ezC3fHZhtiiwWOSJcLMZjh27TYWQa7S8sJftVZuyq1x1jKIZg2TGIJlIlQyM270bj8xMWh65zofuJbBIEpKkoO8dXz64nsGUsoHML1kSJAWSpCTWMQ1x+x4UiRC+UUFyfYEqdrLzILSQow0mCS4/rISjLout5zuTYirGubQxZFvSUYlOZItpROnvEiMco6yqFIWUOo6ZrlNX7cDaN+B+XBUa7OzD7s5zaVk8lcfpLgzZPI7zST7Mb7yFdhUPolKaUeTrPiZJcLhuIzLXuxJyJRqtCMvrgroufOkOgiO4vyVnMRU+gqy5MhHIkf+xswCLycoair+/vMhsllOn1IIKUZR18bjyFWRHAeBkl4mTXWbBFLiO+f7FCCp3WYsl4YxcK6O0gwNS3jrdyxR0MkAuEqlcGZo14ynBjskh8thHWKhU5iGnb5eh7rFTuKSnELj5EH1i11HMtzyJYY+wSGoW/GJAa2+Pn5/EpdD9DNTXQocLlbjGSvqhw0CNkpNpZ1qc2xRKEGBAwxVM3T4BhSB/NyQt+LavS3KFUCamuxDl0pTbCWt57NSF4sYDoHED3zZQ7kNwKpk33hL95R8bNv7F2CIav8PFixcZMGAA169fx8PDg08//ZShQ4eis6l12vgDgoLAYIBVq6BuXbCYzPzk8xPvPHgHnavt+bHxdyABK4AfgWjykvLzCN9eDNdOKbhIaXkLn6HHIEmwNg16xz37SPlTonJrLuoLzK+uAEGEbDcwuIJDLChNIClBpQdByo1yfF/Ni423w6jocZ5lQ3tR+EI0bAdLB4GEWt4cv9uCafs/40F86VxjGMDTKY7971bF10U2WGNTfQieHUJ4YknsNRn0q7OQoY2n8+GGedQL2E/dksdYc6oXUcmFKeYZTqMyIZTxu01h90i0KiNZRjsO3enKxzuXEqU5jovkj4eiMGr1JTae74SPIZYIrQf163yPSlCgUEhIggWdoKZvZAgDw7awqERblhevSbaQhlEwcP7QDIplpxNm50BAq+6gsFDXNYudDXbjFpKNtB3CWqhQtzZT5IlgikkEg0lH+GVfdh0qzMlaai45+JNkjsFJ7URsdhRmIQ0EETuVA2+VG8fcG5+BIFJIK1CpUHv2R22ko0sGK0uk0vyuC+f0SlBYcNy0iin6nfSIXcee4q24WL4kTgHHKF74Er0mZ+CYbMkNVFgAixYEOxBag2o/cgflHL/VqrmSc18kixPVa8axcKGOGjXg7l34/HNQWWI5dljPl6NP8WZg/6cL3nOjWAK0sCYkaTxgVxJsl56K/BRIMdv3IVSd+tTzKYkS2YNHYffrL8/shGwQNLxb8ieO6RozIGs5A6KWs7dqd9w++pxq1bwpVAhU6Zfh+mTQehMe/gA//W5OLoNGB/LE+ywCuHzgypn1KVT4Annq9gBI2+C2UyBl4u9iNik5palCePUH9D8id4YzmUz4+/vTvn07FnzZCwo1trWvtfGvw5Y69RcSEhLCoEGDuHfvHj4+Pnz55ZcMHDgQtfqPfzGEhISgVCqpU6eOXBBo4z+P9Ms89F9Mwe7LibmK37vG7CL9cTq9Nj1jdtaGjb+cy8AQ5J62IIkCtxqVxu9kNOFSMcoo76DVGuWOUD14IoogG48r0mBCIsRYCn4mIDDyvJIPjpn5zupk5K/DECSwu9mNrIB9YCdre/g6+lLMqRg3Em9wZWo6/qmQZAdmOyXKDhbcW4EwjjwD8s5luY3ss7j0MdyYSl7fW8CzrpwC9eTvWEmCzUVAH5W7yGJXhJtpsVRQ50V6LAodv2ZqGfY4g85FOlMzviZ6vZ4KoSE0DTnD8Qb1OVIziAc84DGPMWAgm2wEBBzUDhTzLUb9wPq0DGhJ1LUown/+mPYhV5nSAJbV1rG0wXC6JKxAa0rMG5p1eAqBp1LGTCKsTYXhjyFTC4X1Ggz2WhLEZ2ul5JA/VW1hTZhwAYYezZe6tnwvDx8MwZ9HhGldCHjPBJosyilg53YdxUP1BWoQchAFUDxpKfxi/XcKWN4TiCrmR/AXu9i0KYCAACdu3ICpU0ElJjEqqDlV/S/Jt+eJc83Vlmh3FZzKQPQeCFsNXdY8s2ZFOqiCbRJSsIojbY5zLbomkZFyqlZamrwv78yHzDsciJonHl5AUoMwCFLrOjMr7B3UWnuCgoJo3779s/+mJ5xFPNgMhSUTDoFpiVwEDhDhAkFjISYOnIzW88pGrl9RACKEa9z4fqAjY77ZR5BXUO5uv/32W77++msiIiJs9aA2/pW8qG+geO4nNnJp0KABd+7cYdeuXWg0GoYOHUrp0qVZuXIlFsvTv8gAHjx4QMeOHWnYsCH16tWjbNmyzJgxg8TExGeub+O/Q9KHU7CLC4cpUwAQzSJXV12l46Lfaadpw8ZfSmVgP+BJRpQDiysMxvdENC5SGsWIYEOFHpAG0lEKOhkHgHdAOggN7CC0KFwoChNdIcCaaCsIAlub+FDqXRXza8pGbE5kA+SC12IVN+Fkn05PnUBVfT/ODjnL6aGnqVW4FtMaq0n1ccNeocA7yYL7RquTEQh4QloHAVwrPP/UHiyROzDZ+VpF9/whK0ouFH4CCZAC+hVYdtLsjKeQ52SIEpjMegbqUpnoaqHMpsMM/u5rHEJX0LvmUTzHZ/BezbtsYAPb2U7NajUpG1QWQSmw7I1lXPr4Etve2saEBhOoVrgaHVp3oM2CzZQbb8f+FiW4OOwCb6SsRysWrGMRyOcXWdOHpO1wxwjnDdA/AfpdkaNHHa4YSbA838kYFiqvN/mQ7PRNDJHdsKFH894D4HeOKQE1CHMWWNo6lR98s4jygVWPKrHg3Ke5Jf1Czv80gIO1e1nO9QTEGshpU1OAcFD8IOGZloiXFE3x4nIXvZzUKY1Oxboz3fIGmy9VSpIgy2gHLlXAqRQGs5qH+pYcNy9F6ojsZDzxa1Nobib7J0du1W7Ho4xqlC0LAwfCvHmwYwds3w6L9xZB7fJ0Nz8JMPgqianpjUZhxFOTgCRJeHh4PH/iUO2MQi3vy9gUPu0AiTpItIPJDaBkPDjqgWXkBRE9gdogeUD0yKbMnhdewMkAGDp0KAALFy589nFt2PiPYIto/Ak2bNjA2LFjiY6OJigoiMmTJ9OlSxcEQSA7O5upU6fy7bffIggCI0eOxNfXl9mzZxMZGYlSqaR79+4MHTqUJk2a2KIc/zEsFhirmcc3jlNwmypHNPa9t4+EOwn02d7nVQ/Pxv8MGcAEJHE+P3mMJzPFkeqE0oAQQmhAWhUX+pxdjZQF4i4BpSTJM8vjgAQwesCZFe05+XAfldUmqmhlrYsUES4YNWzLtmN3WuofjkIJLC5UkjerNAT/ftxW+PLl8W9YVac/fNpOFlrTg5AJogekTAcnpRJ12/PgVvnZO026IHca0nrKue1P6isA009NZ9H5RSQZkijn5MMSpwiKaZQI1WaQ8fWb2G0zcaU1bK0D90zw2AyxZgg3w81pAsVSJaLc1FSc4ITerEdpVpJOOiqFivXd19O5bGdESUQhPH+u7nzUecp7l0en0snK5FG74MJ7kPlAXiF/pyHk16YOMKtFZQypt/k0Ts+DGbKjEO4M/uN5tiK6BA+t6yXqIF2bF8F4VjH+KBcY6wrugKNZy6Y5XQm76c82U0dOivVyZx+lt/JaDIsHgGWgsAaRJCUIs60rToHkXi58d20Cpx/U4+C2cNSBAzh/HhYtgkL2dyme/jUDG68oENGQOoLUXGD23tGcyvyCDKM7JT2vM7VZTQ4l/0QV+5/xsbtZMEil0IJHTVk7JOBNUNk99/rz/fcwYcJTi01qJT+9+z4GOx2CoMDNzY02bdoQGBj4jJ0AvzWEi6fABTwtFpJE2ZkG+fpOPwg6pfwMSwrgzbzrZpFgVVYL+r+9D4PFwMrLK1l15nvKFW2K0ZjCuVsbuBovEFAiAAkJvVlPl7Jd+Lntz88/Lxs2XhNsqVP/AEuWLGHChAkkJCRQpUoV3nzzTaZNm0ZkZCR169Zl8+bNeHt7565/8+ZNPvzwQw4ePEh2djb+/v4MHz6cgQMHUqhQoVd4Jjb+KmbNgnHjICBAbnwiWUR+8P6BUbdG4eht08uw8U8RAdQDIkk56sKe99vgeimFOubThNCA9GrO9D56DhwyMKxJR9BnoVKJcAiEHbIRqGihgmJdCSk2hn6b+tBSimCcq4CnWkmkUSRdFDmth80ZEGrI7QP0lHHbMbAj24JnQdgqiNoDGfdlrYucdrTW1qvf1IWkNq5MrxIMFT8rWBT7AoSnhOPn5Idaqabb2m5surXpmevl1JdkusOPX8CUZNDn+ys4PtSeL05qcf72W6Rhw7iZcJMVl1ew/c52HqU+4tCbh6jhV+Nlb4jcdmh7oCz+BwVqDaSZoLcApQZjl3SGrLTOtD/6AxXDDbx/EkwuDnQZoOWq+mn19xb+zSi5/hATQ2BaPegxYjblmr+B5w956Th1i9TlWtw1POw9+Lp0XTqlbCXhmjdbF3QiNcGFDJxYIAwhVvJCh0l2JJbnG7oEh7dB5W3gppdTISR3WP9LVyZN+oZWDw7ygfgjs7UjKdnnIXeTyuJhH8/QRvPwdJKn+YWD5DoYQgtZEzFN70LL708SeqecfKCDLSH2gPzauRyk3ch3pgpocRy8673Y9dbrZYlyQ8HWaRKQ6uLCzPHjKV26NL169UKh+J3kjhU/wtBJoNPwWfVMfqwtkW2tq0mYCh7ZYFJDliMsruZDerkq9A28hINTPO4qC0PClYS6BKIyp/KtJppaDnAoG2rbQYIFakfkHcrT3pOdvXdSq0itFztHGzZeITZH4x/kyy+/5Ouvv8ZiseDt7c2KFSto1erZqroAoigyZ84cpk+fTnh4OIIgEBwczMiRI2nRosVzt7Px+nLtGmzYIDsamcnZ1FOcpen4yhRLv4Hq0QP67e73xzuxYeMvQwJpCWS8D0IKRkmDwV2NkzmTVMGZ3dXa8saeceBZG+OpTzg37wwJMV6067MBpcqcaxTS2R5+vADOpYlKj6b3pt5ciTzGZ24CPRwlsiW4ZQCdAm6ZYFMG/PotFM9Xs6FSqDj+1nEqFaqEJfU+2kMN0ZgLRkMkCT5LgC2qIC6PvP7MSIEkScRmxOJm54YgCGjytaQNfRxK3cV16Va2G2t7rGXgpoEsu7rsmVdm5DkFHxwXmdIAopvAO64wL13LDVMpIrIjMGBgZ5OdNG/c/Klt4zPjcdG5FDj2CxO5TVbANmfK7w+Qp4DeEzmFzfsNuLwREt9HNBoY8mg2G8pC/Kgwmu/oiYvOhZ/b/ky52eXQi3oc1A6kf5SO9w/ejKs9jo8rDEf47DPIzubSe/344uosPm74MdV8q5FtyiYhK4EAtwAuTp3Lni8iMeplue4NdOUaFUnAAw+SyNZosVsqG+iiBMfTFVw3idQ8DdVXy45GBvY4kQEIPMQff8KJEApz6d2q1CxxFi/neBSClBeReEojRQC1M0HvX+fqwVOo087BrXyF3QcE2OcBbbOgaRbY+UHnyKfrcH6PcuXg5s0CiyRgXbdu3KpYkapVqxIcHPz7+zCboVQpCA/HIkClEXDDG4qnwMOfQbCA5ADMh67TN/HzwJH4usYgAqkWmHYfWiRAo8qgdMxLP1MKEJsBfULgkNWnruZbjfNDz7/4+dmw8Qqx1Wj8Q6xdu5apU6eiVquZNWsW0dHRv+tkACgUCsaMGcODBw+4d+8enTt3Zt++fbRs2ZKePXsSHx//u9vbeP1IS4Nvv4XkZDBix3GxPl/95MD3C92p+37dVz08G/+DSNIHmO6nwC44+0FNVN5ytx/7spnUG3bKKoImoKk7mYfxb9F28BlUGqthmJNHvyULdgbB5U/xc/Ll6MCjxH9s4oJfX4qHKegWDR4OHlR2cKCYRk0XB0jvAOnusLM5aAUwi2bqLa6H07dOjF5eCdGYyrOmt951g/S0MCYfn0x6ZjpDlwwlMjaSmzdvIkkSU0Km4DvNl3Jzy+Ew2YFZp2cBcCn6Eg2WNsAiWdh+ZzsV51R8rpMxqsYoftyUwaCZTZlfE7Zlwrgsf9aUrcLV0mmsLDGKXqpe1Kr+7BllLwevP+dkgKyJIJrJ7SrfAlnwLtN6vQFi14CvGepuRNEpi8Wz73Bu7DU03r4ce+sYO/rsIMAtgFXdV3Fr1C0yPs5AEATiP4xnUuNJCB4eMGcOjBxJlTHfsKX+z9QqXAuVQoWT1okAtwAwGynr8SOVql/G0TWdZFwJwx+APbTGjJJ9QktEUcBsUmCwCLSPFfHXQcDuPKPBgI6cXK4pTCSM4kzhfaZu/4C1p3piEVV5qVLvkFuDI+TWXEhgSuWTjh+gON2zoJMBcsepxwmwzxMC3oKyE17OyQAoU+aZi+tdOAWAq6vr728vijB7NiTJkSSlBL9uhsE34G4hoJes8h3bFU7fq4O/VxhFLsSgHA/qQ+CphsmR0Gw9qM7Iw1fMAeUAYDZ4m6B5zp97Ca4/vk7DRQ05+ODgy52nDRuvMbaIxp9EFEXeeOMNNm7cSIUKFVi/fj2lS5f+f+1v3LhxzJs3DycnJ+bPn0/37t3/whHb+DuJiYGiReXJrxwcSeNtFuPnZcarnBfd13TH0ceWPmXjbyRstWzQmlKRTKcRMg0YUrSsn9WDvtdXIojIhRNmgHtAvvQkcxacHQFhK3K1CnLSXEANdRZjLtabQ4cO0bx5c5RKJcsvLWfMnjGkG9J4p1AhWitj0Qlw2QAZIlTSQroEuzOgiAredgF/tTyb+ySSBFszYESSHWOdu/BlwkbsBXsaahtyUbxIhDHiqW1G1BjBgvMLePus5alahCe5MPQCVX2rAvA49TFFZxSlVuFanBp8Sq6Vi9yBdGYwGZrSOLXeDZq/+Lua9Rj21srVkQCeLVCYg11haH8NNK5/7ngPHsgCPsuXQ5Ei8jKzETaWhsUxENCSwwcy+CDme65lVUCBRARF8CCZRNzYPbQV9WsdIO5SCXTNLtLmrpktl6D8FpAkBR+bf2S2eRx5hSMSX/V4nw+0c9DuNiAEW8/peYrnyHa8IDzHf7Cm1GW9PxWHCR/+/rkebgdVvnu6rmfYMFiw4KkuWhKQNbwLDr88O72OCxdg4kQ4dw4qVcIydDjKkcMhNRVTUVC8C0ovIBakK7CnCsxfuZl5g4ZT6MNYhExkccwFwFBkZzLnfX/kSn0FSL/C2Isw20UenwLQSdBA25qt725Fq9X+/nnbsPEKsUU0/kYePnxIsWLFWL9+PaNGjeLs2bP/LycD5CjHrFmzuHDhAnZ2dvTo0YMePXoQF/ecRvY2XivWr5d1onIQsFCaO7iShjHDiCnbhFL7dNGqDRt/JekZmViiD0DcUYRkAxjhzN7a1Gp9BqE28m/8hlg7w54ruLHKHmrOBkErG4gz84pa0RVC8u/LhQsXCA0Nze24N6DKAFInpnJl+FU2Z6tpGwXBj6G8Bj5yh7o6uch6tjdMvgwlPwDlQZ4Z0QA4oYcYYzY/J61Dh4FkKZlt+m3PdDIAfjn3CxbJkttid2II2KvsKepUFC/Bq8C6EWl5+yjsUpiLwy5y+u3TeQ05inRA6BKNk08l2FYMbk5/uYv/R9gXhmK9QMink2u9zk85GdpC0PrMn3cyAEqUkKuxBwyAuDgw62FzaVgcC31/QH8zlsjkomyuFcy3b7dlU+HOuJEMgLMqjfpDThEwKIPig6LQXSpBFzuoUwa+/A5GdC/Dunc/p2jRc+S1GZZYYziBcocZIRHZgToAUrY1tcgaych/75/lYEgSpBvdeVyzBBffr0+b37rg4wOFCsk/vr5QujTUqQMdO8J7o+PJjAgle1dr7u3/leQEC5w8BCO6IK389ZlOBjo1Dp7lCh44K0suHi9cGLp2haZNITaWlOXbcB/Yifgse6RGoJoISm84fa82W652Ai8w32pBSe/7ZBntSc92+v37kvM9rA2CEqY6wA974MF0GH8ehjhoOfDpAYKCgli1ahWi+HxBTBs2/g3YHI2XZP78+ZQtW5b09HQ2b97MrFmz/tJZhwoVKvDo0SPGjh3Lli1bCAoKYt26dX+8oY1XypgxkJiY52w4kUErDqBzt6PakGoMPjkYO7ff6ZBiw8b/E0mSmL8nhfjsvJklfaaOiDtFCUy8C3eRpTXmYLW8PsEa2sjj7mKQ9E/tW9RH8nh5EHv27MZsNvPo0SOWL1+O2RrCq1CoArdG3WJTxzlUKFwbD6WsD/FmDESZZVNUyK8A/eT+JZiXCj+myMXkJ38y80bos88zp5XrsHyf52+xKyHx6N1HfF/se6pSlXHFxjG56WSq+VYrsJ/KPs/oaqVQQM050PYSPFwO20pC8pVnD+TPUHky6Hye62gBmBTu0OqU7Ji8DNITBumtGTCzv2yVjx8DW4LIOBPO41YGJNVYDmU4U6fnCeIbxjCywVFax+/PNQiUSgsBNR6B0BCfqgYyfF3pn+VBbZ18j2LL3mbQCYFjabX4sEQvBMGCnVM0N6uc4YsaKkweyKlSy+RuTNjJqudP2syCYC0SfwfZKZHgQmJ3VukTuVv2Pn5vhnDsYiAxMRAbK/+Eh8OWLfDRRzBON48vV5ZDczgFOyEWn7DR2E10IKlBN5i3BSHL+FSjLgHIUriyX2hFaCikb9gLtWrJIem7d+HIEQgLkw9w8iQx3UahFQxc7lEOqZ+A4CrvZ9fltvRZtYoKq6/x1d5v6FZzHW8vXIS5uxLJE6Se1vvZQy6cz3nPaGRdzdFWpysQBl2SHeVPDsCUMdFcu3aNKlWq0K9fP2rUqMH587a6DRv/XmyOxgtiNptp3bo1I0aMoFq1aly9epXOnTv/LcdSKBTMnDmTS5cu4eDgQK9evejevbstuvGao1SChweARA1CsSjUmNp3pvX0tiiUtq+ajb8XQZjFiOF7cC8dL8/azgbtMD3B5i25Rr60CyjnD0JpZPP/VsGdZD+iwJ8Fa3694gC4KKKo6HgJkBBFkcjISJYtW4bJJGtS6DQOdKk2kpN9t1LF0YHHkj17smXD9KskMHaQdQUIlPcpWZsLSRKEmSDMDJ9egNm78qITz3Iq8kcvVMDMxp+QMagv7SaXZX1D99wIRUxMDJ3ohOsjV3zu+eCucsdkMhEZGcnDhw9//2I6FIN2F6HSZDjUHI53lyMC/w9iYmK4d+cKksVYcCY/n68nSbA1pg0Gjd/LH+BYVzgzLC9kcH8RbDoFrvPJqLIO/S+RaEs44OcrkZ7sRJrCi8CGD6hWBo7dbI5kzDcoFaAHuYgkmmpDe7F0ex1+dtZRxQlOI/LOpSz8U2FE4gZ4owNvjKnP9sKwtZmBoPFgvgOIYBEExFLAOBAOPUNTMb8DqlBTpdABihSWOHcOPvlEjlq0by9rZeyYtZSozUNQKkTaNQqneciHOKYmoN4hX0RHh0x0jQy4f5OSG8bI79OJgpJ0Oy+MFgUO33/OD3U30K63MyVv76SQKoFCJzbh1ziQMqVF6vk9pHN/RyZGjCBNdOKzs18SGl0TyZBzrwSyTQ7ciCrPlYgqtJp6kDcbLsU1OJW0VS2xtACzfUnUPz9k5uJ34cneAqI8uJP5OrYpBfjyxA8EBQWxefNmQkJCuH//Pr/++uvLPw82bLwm2KyfF6Rjx47s37+fjz76iGPHjlGsWLG//Zjly5cnIiKC8ePHs23bNsqUKcPatWt5gbIaG68IBwdQY6QKl5gvDmHy+tK0bPmqR2XjfwGzOQWt4jCaKItsY52RVbqdbmVCJ6A4CB8LwDTgNnAfeEIYr/o06JUNdX4FhAJCcpZsFSZRJS+3OhtxcXEcPXq04D503gjBYWS2Oo9ktfaUgKo5MBOku+Sm1kgSfJoIZcLhvB7eDwGVJCtRr2ySJ0A3+VDe7vNHL3o4wtiACrTXt2NX+13cefsO0T03kL2/I4Z8bU0jIiL46aefmDFjBitWrODYsWMvdlH934Au0aByhC2F4d6iF9vuGURERGA6MQiTPkVeICHLneTrvioB7T22kJaa/HI7F82QfBHCf4NTAyDjEcSFw8cSUkVw8IOsAAuClz0A6d840OvCGpK+d6XelyG0/2EXR+wbISJg0UB0T+S6Ak4BWQgKAz/vuk/Ervas9FLgooQvmpixeIAyGIqU28NSTThD42DJVbjwExwtAnp3GFu0N1wFIQGEdeQVh1sdTToKZDo7Ymiroe20EPzHR/PlVwKJiTB+vCzCt2MH/PADNPRbShHzCvzOFEK5y5/r1SsieigKiPpJIlCYXMNeQNa9kGpVRzFwAE4+jrg5i9QbEMi66MYcN9XlfqoXsbECsbHwYP15NrgN5b3guzTQhmIsEYTBrOTUvfo0nXyYlPtOGNNUWEwKhjGPh/jzlmUpmQZHxq2YRVKNC7i03Iuq2gxUHW+Aoz9jO3yDWVkwoi0CKYkQHAWTmsNjV/imjRNvVn4zd50aNWqQlZVFuXJPpHnZsPEvwlYM/gIYjUacnZ3p27cvixcvfiVjuH79Om3btiUiIoIuXbrwyy+/2LQ3XkM+9ZrH4ITvmMIEVjqMpEoVWLYMSr6cJIANG8/FZDJx//590tPTiYiIoGXLljx48IA7IXPo5rcYRTNRNuKGAYmAB3IRaleQJDWC8CXw0XP3f+3aNWJjY2mUPAD1riikbSB2FJhyfyJmZy1Yi61VKgVvNnejcK2RCE/qEOjj+W5NXWqa71NKDa4KcM0pUbK2dZWA0x2hXj7B5BwNjnVNQdVCwegPRTRZslGWbAefNIN5NcFfCR4qaGYHUyu1IzzsLuH6Utwx1iQ6RcBOkUmW6EDOtLaAhdoupzmfWhMTGoKCgqznoKJbt3yq1b9H6h043kl+3WgbOD9H4O053Lt3j7VrVuOjDqf79bU4b89EDALDSB2KgxLqHUauN63AkRpt6dBjGCVKlHjxnccchuOdwZQGKgewL87mmzfo7JcXQSjwl76/7ISaUaLGjIDI9HLjuF6/AgsWDMu3Yndgfe672JDjXJr+I+mtttEnVsl5P5GK9hKPzTA7Bb5PhvtWnRKLB4Q3t8dpmxYPMRmFEbkg2o6CxeE6P+6UukTXnnZcu+WI2Syre2/YAKdPyx39Cnlm8V6XpbxZYTwK8lTdqTod6eK7CNaYgGSxtptVAg+Bz62pUiodCQ6BFCnjgOKrL6B166evockE330Ht25B795yr/LFi1n+cyonfwyhDLepqTpPKbc7OKVk8rVpEsOZjz/hhFOU7gEb2fZeML5emdAlEtTOkHYXHq2DuGPy+5j9YErFKGjZlGakT4zEUOszP7sBnO3akGOD8pzgixcvUq1aNU6cOEG9ei+oH2LDxj/Ei/oGqud+YiOXb7/9FoPBwPDhw1/ZGMqXL8+jR4949913mT17NocPH+aXX36hV69eNnXxv5n0dHByguhocHYuWPQtSbBunVw/WLcuDEmcQjEeMZHvuVNrJIcOPX+/Nmy8DJs2bSIqKgq9Xk9mpqxfoFQqCfQOZOdXmxjWfiNSpoT5jgJVWREpyTqbmyQbXgogI8OOK1dqU6+e9NTvDYvFQkxMDJGRkZw5c4YKxSyk1y3B9pKdCDx1h/HnpoMRDkU151LnKtTzuI5f2F6EjA3QcBNo3SHmCNxbAFo3PtQ+In8nWEmyGr0tQFon5+5X3SYwr4GWcQl6TMgdo+bXlCMgpTKciWuWweSdZpSSLIz28xF4oxPUtweVIO9TjNqH2eKPpyKc0k4XKOQZx72MkmyLDybT4oybOpGO3tvYG99WVnSW4P79+5hMJooXL/7iN8ClNHS4CXcXwr46ULgj1F4Eimf/GZUyHiGIerDoEU3ZqDNiAAmtwoh6h4iQAqZbGtZG9aLLli3oUg0U2/+I5Ar2pKSkvPi4JAnuzpGdDJB1OtJu0tm3YJpSzutsvYbj9o1onnmItciFA+4kUqbqbe5q8jc1UQIjCxyqUIOGeB27iPnuYzZXjiTelASYKKyCSe7QxwmWN4EBR+BUU+h5IBulPkt2MJzJVUE3b1ORElwCL/tsMKZQ2j+J2ES5Fa1KBV26yD8552dcXxqFMRbFEzVFySe/wa5wJ3SJW0CQi6tRgPAI+EY+lgE1J70bsqn9TiKi1Ug/g/MyCArK+wkUb2M3YSwMGMCRC87UWbIK3ebN4OhI0eMf8TM/oMZCd/N6LsZX4ST1uEkQU5jIx8JkzpatxemP6qC084Taq+HObIg9Co4loHhPKDcRBAVsLwWShUiHivS+I7fYzUkFHB0CATWPczX2KhULVQTgyhW5PqhixYov/jzYsPGaYXM0XoD58+dTqVIlatT4E2qwfzHTpk1jyJAhtGnTht69e/Pbb78xdOhQGjVqhJPTH3S7sAHIDoOTkyyy5+4u10oCnDkDb70Fn30GPXrINRcAZcuC0QipqXLN4EcfyQ1JbtyAIUPkCbARI+DLL6GkNJGJTGEKE3kZW+G/RmRkJFFRUdSqZVO4/avQ6XSkpKRgsVjImam3mMxsGriBNvX24OKaglIhId2V5E47asAIkhoeZRYlPrQQRYtGcOXKfsqXr/aUhkBmZibLluXpTyyMeBuLpAagQUgI9tlyjUKbh3vQpGXRpNRhuV1u3FHYVQlKj4EbU8GUDO51UXrUREo4jWDtSiQioESSZ9atk9Jas8RgFxNvu0K2BdZmwOI0uGGCX2v1pGLR5YQ5a/FfnYkS2fRtnN/RBxKNLlhEBe7qBDw1CQhIBJ65x3vbpyGVAumuwLH6jaj3IIQK169zrXx5NltbhyckJHD48GGaNm364jcicAgE9IdT/WCzL9SYDcV7FVglO+okwsFm/Pp4EAbRDosk4KGMoUehs6gwITSyIB0FXRcjnQtt5X6XtpTeto/QJk0ASExMfPHxZEdDxBNtWiUJDiGnvgUiNwLoaC3INqqo8cN5kgQ3+o1YBYAOA8VqRaAJM1p3YIccEmv81OGqThzNo44ROBZeidopL7rgqICKWijWDUpUh7negKOEtMOqndEiZ2gCacF18Wy9HjQuEL4edIXw9IQTJ6B+/XwH21UZNK5oSg+EO7PAnJ77kdmiwGBQIp48jDZAbpYGQCwwGSwmiC1cmNTv+lAzUEuLOurcbVNT4fZtuHVDZM1757lz3UR20Ba83juCf/wDztbtQ92LjgQFQY2dX6LynoJZVBJKDR7hT03OkY2WDLUzXSfsoUftG5CuAq0nRO2EYj2g7IdPO6F+7cCQxCpTGeAU9kZw1UO4NRUQ4JNDn7C191YALl++TMmSJW1/2238q7E5Gn/AmTNniI2N5YsvvnhtIgdly5YlPDyc9957jzlz5rBt2zaUSiW1atWiVatWNG/enNq1a6PR/Elhqf84EybIUQizGfr3hyVL5Nk+kwkiI2Xn4eOPYdAgef3Y2Dx9jJAQ6NxZbk5jseQt18+Yx0JRdjACCAPAPznfLO7/CCaTiRkzZvD5559jNBo5ceIEtWvXftXD+k9QrFgxQkMLtmKq636CGhPPohONqNSyQZ/7uFUH6QxcK12BzXe7w11wdnZm9OjRqNVqnsTZ2Rl3d3diY2MBsEhqqoeG0iAkhEdFi+JsTkUBKLtYaFnlcMFne/tj2D4xTw8i6Qzo/BBU9mDOwCLJ3wWJJ2bZAYVgQRQU2Gm0DPYQGOyaBe41wTcIVB0pOSCCHZY7NN2fjEMZSc7vtxrNFkmBiyoNt2MpKLdbEDsK8v6XASIISYAo0eTkUUiS04UqXL+e62hkZmZy6dIlHB0dqVnzOQIcz0Klg4YbIPkSHO8GN6ZA421gXxTJlIXxUBecFAZqu5wkJLkR3a5vxGtvPHeaBeLfKQyN0QSzACW4qqD67O9h6W/UTkuDM2dwd3d/sXFcnAjha8gtJ86XliafIJAkXwu2y+2KHZyzcCCLQxda4UECiXhhp8iC4q5oogCqAa7AlzyrjFNQKGi38iNWNtPTcMyc3GNni3AgC35JhbNFoZgalC1BakGB/rIKQcIj6zjsqQ4l3oKKn4NCRdOmsHLlE46GoJTTjuJPPNUTWaUU8cmOgzkgjVBAOVFOFZwCUiaM083guGcb3qMEPasWfN5dXKCWzyNqfT0KurSFzQOYXHMDqqwI9KKI6cZdTuxMYfl86OU7klqiI6eoQwpuALRjF5P4hoSWCso1LA6ujaDCp+DbBrRuz79f1Wfx29U1fLa5DwALt4GTAd4Oho3lwc/Rj7JeZXNXv3z5MpUrP6M7mg0b/yJsxeB/wIQJE9DpdPTp0+dVD+UpfvrpJ7KystizZw/du3fn0aNHTJ48mUaNGuHq6krbtm05c+bMqx7ma8W6dbBtGxgMsqOwejUEBsrOR1wcDLHM42qGP60ezuPTT+HTT2VnIqfobxjzMJnk7fMv/0qchD/hTGRK7rEMBjl68r/CsWPHqFixIhMmTCAoKAgHBwd69OhBamrqqx7af4JnTXR4qBNws0vD3tHaEekAskDYW8ApEEQode9e7voajeaZTgZARkaGtV2tbNBVDw2l3a5duKamEhgTiWKRBhbIxqpwCBTjyCvofbJ17QERhkXCnqwcbTJUCll1XJIA6xAkNWRbtCgdy6Ds9BAa75SF6pRa8G0LDddBq5N0WHyRvZM6Id2RjyOtA2EYqIeLaA+bUO2woEgExXoJyepk5GgViB4CN5qU5X7FEkgKyK6ppbbraeyVGYCcZ3z16tWXvyEAblUg+D7490e/KYjLSxpx49d6OJKAQoDyTjdo6bkX593paJNMVDh8Q47wVEMOzwCCOQUufgCiBWdnZ1q2bEn16tVf7PiF2xeY5Wc7kGltKQtIngrEWsjtVvMVTGdl2XHvQCkuntBTxfUBXlIiRu8NaDSNgPPAQeRcp2ejcXOn/exenD1WGVGCuwYI1cMdo4J1vlDyGKjHAQeeUyMCYEiQnSTr+Pv3lzvLFry+1pbEkoU8vQ4rZ4GvkB3ITaLcJWsGEA+SSoXncAWengn88ouaYsXAxwcaN4bvvpWImLYOhg6Fn36CTp2QunQl4r4JfbocoTEnZ1Dkh3EsXOVG8x9/w4kMfmYsabgAMJEpFOUx7rstnA+YCbXmgX/v33cyAASBTkFynU+LexB8W34MvrUKgcdlxPFd8++s10uyORo2/hPYHI3fQa/Xc+rUKfr37//ahi4VCgWtW7dmzZo1REZG5qY/NG7cmKNHjxIcHExSUtKrHuarY948pOL+3HxnHoGBsnZVfrvXaIT79+H776FvX3gne8pTDgPIf1h+bzlAGMVZWXgi9etDsWJyEeOFC3/7Gf6/kCSJnTt3EhQUhJ+fHyNHjmT37t3o9S/eyjM2Npb+/fvTuHFjYmJi+O2337hw4QLbt28nKiqKoUOH2jql/T9ISkri9u3b3LlzBydVKn39luOsSqF6aCgV3r8Og0B6C9nBWIU8i23M215lyctrT01NZceOHU/d34MHDzJnzhySk5IAIdfJUEgSoiBg9+U3clpIDk86Fh2Ri3s7Pvm5iIIntBLmgJQFaMDSQ0FYVglmXmvH3EWrWXMkBktwONRaCDenwsl+kHoNHIpxI6Mi2e11cotcrOeZCdI2yGpnR4arA3rRTk7nUoD0JlhGCVz+qhInqjdgVZf+SAME7B/o8T4WQ1uv3fTwXUs1z3t069z+/3eTyr7LLs08XC1hBNlfRamQjWKNwkQJ+wccr98Ag7sagkGtsMCTTYQiNsL+BmB4iZQpAO+G4Jovf78j4CAL5Bk6wfGpn5DW8C3Sx9hzzyMAQ7Yc5U7JdOWtCY0pWq8o686WIOjNOhiVfmi1L54e7F2vPvec3Nh5pRQayYF56RoGu4iczAbzNuT7v4xcZzTXyZTfQcmh0OEGaGTjvG5dSEiwfiyJYM4Gn+ZwUJHXpUrhDitLwAIl7HAAV2u+1E1kjZgEZ3BxQaFU8t7Xb/HVVwInT8pR6Xv34M0uqZyZc476X7bG6/xuKrQtwvDaoSxVvY2HObbA+UWL3kTihwCYUHMO+doosDCX4TwSfPmuVFXOxpx94WsGkGnKxMEAC3aAozXzrEQyOOqhnHfegxEVFUViYiKVKlV6qf3bsPG6YXM0foevvvoKo9HIsGHD/njl1wSNRsOAAQPYvXs3R48eJSkpiTFjxrzqYb06pkxBeBSO67wpPH78bEXiwoVh2jS4cwfmOE0kjOLMcZpI584wdSrodDAFefk09UQAHB1lZ+JHlbx8EpMJIIy0PsMJCZH/qO3cCVWr/rOn+zJcv36dVq1a0aFDB+Lj4/H29mbp0qW0a9cOd3d3unTpwtKlS5+r32KxWJg7dy6lSpVi7dq1DBw4kISEBHr1kvPVGzVqxKhRo1i3bh1Lliz5J0/tP4EkSWzYsIHFixezZs0arly5gq8mmgC7h4zdMZP2O3eizTYhGEEwIhveVsNFIi99RtLkRUJMJhO3b99+KqpRr1497O3tEa1fkEYnjlmdDDjVqQ4Z5a+AdzN5ZaV9rmMhdYSMFHuyglvAXBdrHr4SegeArwu8PxazwqGAVoJwWk5hkkxgaqYmwOEhhTXhxCelYTKZUCgU4BIEdZZCxS/h9s8Q0guFUs3V2hW5Mbks+m7aXINa7ChwqHJTpo17l0PNmmFyVyG9CbENfZj24H3u6CswIGArg4ssQtghISRAlaOX2RzThe2xHQks5oHL5bfgzFCIC3m+bPkTPHr0qIADXb9WEIV0cSiFgjPvasFMZmN7VLNEWWndyNNIZki5BKcGvtCxC1B7MVJO69QWwAIwzIA2lWD4+a9w+WEptxY1Ye2PvbmyvCIpGS5kRdmh7i0XgoeFQeUqAkYjvEy2rSAIfDbzAI8OdWT65WK862Lkq0RYkwG3W4OkAES5NXLOZRIEwM4PiveBlMugKPgcOjvD9etA/EnYVAgufQTbRPnZ2Qr8AAR2gG1JcC8DdPmK+UXkzh1BQRARgaOjY4FOTY4HtjBoRze27FTzKNWF2DkbGVFmGKsHjmPn4TQyBDXpKhA1aaiVGZQXruKEHG1ZwkAS8KI8V+lYbBJvV5/N9V+iGTZxJ1cSX9zRMFqM1FpUi8VboWi+CS8JGHgJPm30aW7k8vLlywC2iIaNfz02R+N3WLx4MVWrVqVatWp/vPJrSM2aNRk8eDCrV69m06ZNf7zBf5GJE6F4cXxnTiQzE44ehRYt8sL5Oh2Ehsr92p2cYK44nD51wxh4ajibN8OHH0LbtnAocDjtyoZxue5wnJzk5WFhMFM/nNnvhbHWdTiCIP+NA1Cr5TB94ZcU9/0nSEhIYNSoUVSqVImQkBA++ugj4uPjuXTpEpmZmWzZsoWGDRty+PBhBg0ahI+PD7Vr1+a7777j2rVrSJJEaGgoNWrUYNSoURQpUoTr16+zdOlSVKqCZV8zZ86kTJkyjBo1ips3b76iM/53IggCaWlpZGVl5S5zUqWhECSUZySrmgVIGuuPA0h1AE+42SOIne3bk+bmyKFmzXK3VygUNGnSBGVOpwMrdnZ2tG/fHoVCgYsqBXvXTCQgs6gDB6q0ZkGIE3fDY5F0PlBvNbRzh7kuCKPfJzEolJ8HtSCp9F6o/B04lYQZdyEqBSbMZKP4M4b2aiRrxEPKsS0lUBwUUSLR2nsPxVzT6dGjBwBijoS0U0movQCq/oCdysSe+LZsjuuF1ELBveklSZnjgrKlRLtCu3k3YBrubZNY81EfzL1b49X7FhXKBXI/K5AM50Zkiw5y+pAnCKUlJv08mfoXThDQcgo03QVlP5Dbjx5qCVc+h/R7PI+MjAxWrVrF/v37AbDMmo579XqoDpmwiIoCvooFJSULCShzjOoHFFSROwCMU8LdN6DRlhd5NAriVBLBr32BXVoewLUEOL4EzKg5GlGOisJ1NhbexMUVtRDO+uaue/8+lColp3q+bFmfoFDwuGwbiuypwxV9ETyV0NcRAtvKESU8IbOVXcFoRoMNUH8lZIZD2G8F9le/vtwOHI9agD1khedFyzoJ8ENNmDlT/mV94gTkCC/mtFceNgxOnQIvr7ydpqbKBXenTsmzP5Urw+TJcPQQc9ufJ1v5iGWz3+GLed8w6JvfKN8/HEUtaKQ8hgvpSMDbLCG6TCFOTqnO6q8XUurdBNo6QYBLEcbUHgNZj+Hhyj+8Xg+TH+IUn0a7exDnAHqF/CioJdnR2Lz+y9x1L1++jLOzM/7+/i93U2zYeM2w6Wg8h5CQEBo2bMjChQt5++23X/Vw/jSiKFKkSBEMBgO3bt3CK/8v4P9RsrOhTBlZcTYhAdaulZdLEly5Iv8dyo/RKDsOggARERAVBU/WN6emwsiRsgPyuk5AGY1G5syZw2effUZ2djZt27Zl1apVv/udvn79Oj/99BN79+4lNjYWi8WCr68vMTExODg4MH369D/8fiQkJFCsWDFKlChBaGgodnZ2v7u+DRlRFDl58iQHDxxEkCyMCpiDUjThGpKOtNTaulYDLAGTpGbpo4G08dqFty6R36J6E6EvRoEqXMDT05ORI0cWqPcQRZFdu3Zx+/ZtpKxo2nntoOyo2wiiPCv91WdfIGDByV7L20OH4+TiAWdHQvmJsoI2cHPzTbYP2c7wS8NxLpL3PEmSxI8//ogxO5VRxX/GSZVOxI6iFFsbgUKSyNbpEOwkktu6crNuZS7oW2I0GgkKCqJr164Fxr5nzx7K+Hty/OBWElMycXQrTNfix/FI3ZrveGBGhbrjNXCW26WmpKTg5OTE7cUVKedodXbfARLA4qHAOL8kukJVEUoOgUJN5S96/Al4uFw2IIt0kluUavLy7/ft28epU6dQq9UolUremTEDXUwMkqcCywwBlWDJN3Jr31VJBERZjL1MvlszTgXxZijsBZHW6KEpA9LvgvsLhkRv/IB06UN5lxJk7obV12HIJdhDKy5SnapcYIuuN/O2F4a5c2HNGtBoeP99ePtt2WYPC5M76L0wUbs5s/IXLt+qzYOz1yk24jeCNFBNB9n7wXMXRNYrhn+vR2SLYO9WHtpcknvYptyAAw0hOBw0jgDs2pjNopEX2KTuBSMTwD+fmqF9Mai6ExaukYvtYmLkCIZCAT17yuczbZpc6R0QILcG7N1bLsobNQrDRx+ybEZDWhy5w7mqLbjfqhrfnPkR0WImOUCL7oRBTvmzNhrgIPL7DBD0ILmCOEtAqcxnMvm2lTtnxR0FpZ1cr/MMJEnip1M/Me3UNIpdj0aQIMINQheAbyakaWBLGWi/8DAetZsA0Lt3byIjIzl+/PhL3BAbNv45XtQ3sDkaz6FBgwZcvHiR2NhYHB0dX/Vw/l9cu3aNqlWr0rFjRzZu3PjadM+y8c8gSRI7duxg3LhxPHz4kHLlyrF+/XrKli37xxvnIyUlhVmzZrFx40bKli3Lr7/+ik6ne6FtN27cSM+ePRk+fDhz5sz5M6fxP8Xjx49Zt3Ydaalp1rizyPsB3+NwRA+/ApKsdiwMBEtzgU0x3biVVQlJklCSjVnS8KyAdZMmTWjcuGDLUrPZzNy5c8lITaS950a2x3Xm7QULKBQVT3xhT34ZMhJXF1dGjBz1u53sLiy6wKFJhxh5cyT27rICtV6vZ8GCBaSlpSGIekrY3cNFnUqpk/fw3heHfVYWGpOZLJ2OHyZOzN1X7969KZ3Td/oJLl++zM1rl+hR9jbKuP1Ykq6itMprG0QdRuxwqjgGKn2RG7qUJImsFU44qDILdGYy9VCzvMwAHOy0NPc5ibfqMdj5QuFgKPk26Lzh8XZ4tBZJUJPq1o5riUU4fPREXtQFGK1S4bFwIUycSGhxE5UM89CaosCUAg7+UHUahHQHRJguwEQv+bMOd2HlLvj2K2gRA20coeQQiD0IplQIfvDHbessBqR1TghSPiG7LDAOBbNkxwzGUZ1Qvmci2+t+h/3JA/DOOzB2LJQsSbduclOMPXvkiZfBg3//cLmE/YY5fBufLBvGt82ac/pYRX6NV/FVl/NcMUCVD8E7BbKdtSTOMuCnFjDeKIR6mxdKqz4EF96DxFAwfwSff4547z71Mvdz+rIWbrWDbY9yjX9GfAmNp4BeXzC9TRDy2gDmvP7mG7n/ePxG2J8FggqpiAARJkQ7uKcvSrLoQWkxjDW8QZFhYXTcuAchn5CgKFoDJQfkOiCCwdJEgVIp5t0ShR2I2fJr57Jyzckz+PrY13x77Fua39BTOh6mWztrDTsn62hMaQBbmhQi5v0YQHb+ixYtyhtvvMFPP/30gjfEho1/lhf1DWypU88gKyuL0NBQBg4c+K93MgAqVKjA2LFj2bx5M6tXr37Vw7HxD3LlyhWaN29OcHAwaWlpbNy4kWvXrr20kwHg6urKZ599xuXLl1mzZs0LOxkA3bp1o0ePHsydO5fNmze/9LH/1/D09MTVzRUEcFSmMaToQnRKA6wjN+1GsJdnXpONbtzIKI+Hmwfly5dHpXUFFKjVaqpUqULPnj1p2rQp/v7+TzkZIKtju7u7YxJhZ3wwFtQ4J6UjAI6JGRTXhZGZlZ2bJvQ8qr1djTrj6zCv0jyMWXIhgk6nY+zYsYwdOxZJoeNOVllCU+vwW/l+zBz/Lgp1/uT9PCIjI60dsJ6mcuXKvNH3TZTVvoVGW1Cq5IJgMzo0AT1w6nwJItbB/oZyZABIubkJO2UmqSZHxG0CZEKWxo65pUaQanbmYWZh9pneg64xUGUqpN+GAw1gWym4M5tU+zqsvFqOs4fW4nN7KK08duCrfZx7M5bZ2ZF04QIMH07NtmPQdroGdVeAUxBYssGrnmyUqlyhmyBrjnR+DI7FYPhweBQFo9+TOzDdnibXL2SGy8e/8ePvPyxHgkE0FVymA0VxWEdnNBiYq3ufmU6fYn/QWr3v7w/hchMLoxG02rx/X4g7cyDuCNedl/NRw55IiNRrcpkxQYn8EKugmT24dQbJA65VK08RNVzKllCfjEF4/BDW94TTP8HsZNh1Aqa8DSNGoEiI57F3VbLCPoKsRwWbDhiuw8aN4O1dcCySJDsZOa9FEaZMgSs7kfplIf4KpvkS2ZOUxP7ozd33A1FNUlBBvIM7KYxwnUf7qnsKNDQQzQIXr1v33wKEWXIASvWeKEc6DiBHxfZl5x/Icy/Xxw0+5rMq4/hpL2RpYFgoPJwhfxYwXhapTMhMoPr86tyIu8G5c+eIiooiODj4BW+IDRuvLzZH4xl88cUXGI3G3Hzh/wI//fQT/v7+9OvXjzJlyjB27Fi2bt1qaz36HyUuLo7hw4dTpUoVTp8+zeeff05sbCxdcuV2/3lWr16Ni4sLw4YNs3Wh+gO0Wi0DBw7Er7AfdoosNAqjbNxYyzUkAegp2+eOqnT6+a2gdGBRunXrRps2bVCr1QwePJhOnTpRtmxZGjVqxIABA3L3//DhQ2bOnMncuXP55ZdfePjwIaDAJFktzRzDXxCIM/oQYB9OVlbWH963BhMbUOGNCsyvPB/RnDfjf/ToUavQ4BNUkpAUkFDGo8DikJAQZs+enauM/FzsC4NDcbAvhqrMcIT4I3K3qjaXwKkUbC8Ndxbjeqk7AnA2uRaH6zfFZK9E0EP5MzewaApRq1Yt+vbtK6s3+7WBBuugcwS0PY/ZpwMxF5ZSV70CkwU2xnTjfGp1yjveoH/h5TTxPo2vi5n09HxtZgUBinSADteh6V65Y1fTndDwBtzvBY+3wp4a8mx+DhU+AVX+7oYSZIaBmC996Fk4lURvtMeyG6R3QNor29sZje2IoCTJKm/aK/dS4eBMyElb9PeHsDDyBWVerEZDkuDql5AVCTXnEXU/krUXRnBF+grUHgQEhTM/XsNlA6hbygb68cKNSLDAqHgQj4MiKQMGrId2n8uqqKOPwphs6NsDFArGdt6AOn6vfLz83czu75CFNooUyavJeIJYvLnf25/D7Wpzv4cLgiDfCrXOgr29Hp8rcQTNvkvJR+E49s5CcgazPfy4BNLrOcJMkJpAxAofcPLLdycEOaqRAMJ2nu66BmBMem4jAaVCyUcdvmP/b9+wuBpMPCGrgU8MyVvHggWD2YC3ozdbtmzBw8OD+gVERWzY+HdiczSegSRJCIJAq1at6Nu3L8ePH/9PGEaXL19m9OjRACxcuJDOnTvj7u5OjRo1+Pjjjzl48OBLtTW18fphMBj48ccfKVGiBIsXL6Zz587ExcXxxRdfyN18XiHz588nLS2Nfv362dL3/oDY2FjmzJlDVFQU8SYf5oaPQlol5EUzBpKrtGyR1NipzQT6FwLkGf/x48fj/cTMb/5r7ufnhyiKxMfHExcXl5sGVD00lA+mTkVlltOZDjVrhllUEpnpQffu3V/ovrX6sRVFGxRlQY0FufutUqUKVatWxd7ePt+aIsqrIoIIRe9G4qJKzv1EkiSys7MLGu/Po8YsaH0aqk+Hdtcg4RTsqyu3Rq0+E869jYBscNZzP8XFmpVBJ2CfnU3t42cIDu5EixYtnn1udj4kFRrI1uSBbIzuhkHU0t57J808DpFiduZBdiAN/RPo7bWA4teD4do3YEjJd9EV4FYZFEoo1BgSE8GzOLQJherT4Eh7OSJhzoL4489wKiTw+AMhQYUKu4qDUR5wklN/loNwZgQrD4wlGy0PnSoxpL8e8gsS+vhATAzR0eBntaf/sOuUJML5d0DlCFW+A3MW1fcH8yixO5kBn4LangPODdDodfQ77cups2UwG5VEp/iRIUJ8JqTl9CDQA2NGwddfQ5wSYmvD1CD0rdoRfOczVAqjLPDYHJgJNAcUWWCOBKuifTye7KE13zCJ7qynPTv4iO9wKJNJ0w77KFFcLhQvcFtzHITVwBqwZClQC4WZ8MCTlJP2SCIIp9pRfH8U1TsOBQSwK4xQahgJTWvLrZU78nQ7ZwB9PERu5fdoVa0HFpXAlAYQlqMGbv1OB7oFcmLwCTztPdm6dSsdOnR4qrmGDRv/Rmw1Gs/h+vXrfPDBBxw+fBi9Xk+ZMmUYMWIEAwYMwM3tD0R5/iUcOXKEJUuWcPToUaKjozGZTKjVaurXr0/79u0ZNmzYa6sfYuNpdu/ezciRIwkPD6dixYps2LCBwMDAVz0sQE7hqlmzJjVr1uTw4cPPFY2zIaduHj16lPuX9tLacweHE5php8ik78TVKLCqa68Cg0mLyakqmnoz0fjUeunjrF+/nhs3CuaUj5/+E86psnGf4uLCz+NHowBGjHn3xdWqrazuuBpjupGBRwbmLhNFkbNnz3L48GECVBfp9eV6WVxOKU8GXytXnk3d5Ujy0KFD8fX1ffbOfw9zFhzrDB614d5CMOTpIxgsai6mVaHi2asot4uca96UBuv3/O7ubt++zbp166h65gwNQkIIadCAy7UqU8r+PjV8IihZ2EV2BjRuEL0fks6BQgPejWURN6/6oHGVd3b9ujwr/913ORcEQkdAxHrwaQ3JF6zdrvKFGnQ+UPZ9KDNOdlgARIvsmBRqAmsdkMxZckrPMlmkUfKA+e5DWHO/P1sKj8I17FLBKMCZM3DgAMcaTuLMGfjgA5g3T27Z3a7dMy6CxQhnrcXyJQbKN+vUQLJabMGIPY41JFTNTVyo+T1tQscReLwSEw650E6zF6NRS6qjFveMFDQCCE7IWoCuAvi7wWM3ULhhbHkV8wMBbS09ikJWB+EAeQXaLWDe4/sc/uoWmSYJD0siNcznqME5qnAJe+Q0JmmgLCrJHOAMUBsYbT2PfcAq+TuU7gEZfYbgN22B/FlGGvzyA3zwtfz+0UaI3guVvwWdJ1J6GOyrg2BKBrUX7HxsHZsCOvqAWw1o8nxHIyk7iaDZQcRnxecuGxaaV6OxtYkP99+5z+Owx5QuXZrNmzfTuXPn5+7Pho1Xja0Y/C/CbDYzbdo0fv75Zx4/foxaraZ3794MGzaMOnXq/GdmZs1mM1u2bGHlypWcPn2a+Ph43N3dmTx5MoMGDbLNrLzGSJLE999/z8SJE/Hy8mLJkiV06NDhVQ8rl6ysLIoUKYJareby5cv4+Pi86iG91sTExLBs2TICVWdo570TjWCUja5+5La0taxQEF/rCL6lG/6p/e/e+huxcYkYRBUg0GXDeipcvw7+QJycKrK7STvOW2fB3dzcGDZsGNoXTuKXWVx/MY6FHOm1qVeB5XvmvUkLxxWoDkl5s8zIKWFfff4FAI6Ojjg7O/P222+//O9ZfTwcDYbE0099ZBEVXN9aDv/94VxvWo4iE9+maPkW4Oj/zF2dPHmS/fv388706bimppLi4sLM8eMB6NKlC5XKB0HsYYjcLKc6edYF95rweCfEHpCjGhpPWRskpTjsDoPpC+TlOWQ8hKOdwKKXl5uzoGgneR+Z4VDhc0g6A9VngWMJ2FEGsqKg/TW4Ow/xxlQU+QzztAaONNxyjD7CasYda4u2QbOCJ2V1NJb4TsLVFbp2hVmzoHx5aN78iQtgzoKDb8AyPQz9GBo2hLuzSc5KZsiUr3HPFnmnEaw7ChYzuNtBQJyKpqfscZXS5PvqDMJMwIDsQwmABjl16kZ3sOwkLNuAqrwJpyR7HIvrUSrF3O5gkiec+OYbJKUb1YVR6EyyQ5W9FuyOgaAD+oBUCXACtCAMIFchXlpufSx+hdSGUOgO7C2uwKPOXGp2fAmdrG2lwLEkxJ+B0anyc+spwO0L4F7luZtJkkSqPpUua7pw5NGR3OUPp8spVGEucq1GDZ8atE9rz/eff09CQsITEUAbNl4vbI7G38CVK1f44IMPOHr0KAaDgXLlyjFy5Ej69euHi4vLqx7eX8qpU6fo168fDx48ICgoiOnTp9OmTZtXPSwbT2AwGBg6dCjLly+nUaNGHDx48LVzCmvUqMGlS5c4cuQIDRo0eNXDef1JuUp26CRMMcewF9JRKUQ5jWQAKESQlJC93BNLh3s4Ob/47534+Hh27tzJ48ePcwutq4eG0vzgQXR6vdwaVQFZv+pQCWbWRPUmwlQGJycnfHx8aNeu3Us3xxBFkXmV5lG0XlE6LpDzTO7evcvuLcsY4zc5N61FGgCCRT63GV+8Q5pFjhqrVCoCAgLo06fPSx0XgIhtcLwLOdEBvUULSGgVRvRjtNglG0hxcWHx+2/j5qSmbqsBlClTpkCKYVZWVm7KX/XQ0NyIxq0mTfDw8KBfv34Fo3OiRU7ditwMKVfBtTK4lofEC3KBt94dtj6AfkVlsTr36rJj4lEL1E7wYDmcGyNHKuqvB5UGsmMQZ32PYtZy6OEFbd0h8aR8PO+m0GQX0lq7vGspweRpEzlyoTmNOEZhIYrBtydA/uim1dH4OHMSb7wBlSrBDz9AnTqyH5GLMRlO9IFS70LV3nJ+lZ0a7M3gEYjh4nlU1WTBSCFfOY2xK4gaFcI6AUEEbQ8TBCM7GPl8RjETUiNcWLboTU6n1KJojeuM7fgrvj6xqJRigYgGqWrobEJSWHdhATELpExQFrLudx+wBaTOINwFzoDFBRTTwPIbTAB+3CMiZKSBg/1TgoF/SNpdOPUmJJ4qOLbgotD8KDgFPHOz6aemM+ngJLIt2daQpBzNmHwIkGBSc7kgXECg5NWSlDOVY+vW30/DsmHjVWPrOvU3UKlSJfbu3UtGRgbffPMNqampjBkzBh8fHwYPHsz169f/eCf/EurWrcv9+/dZtmwZsbGxtG3blpYtW/5xcaaNf4yEhASaN2/OypUrGTFiBEePHn3tnIz33nuP8+fPM2bMGGrVevn0nv85Mh7A3trYxW/HWZmKIApIokByrCtmpXxvLSolKksyd89ve+HdSpLE+vXrefToUb5uThLNTh7CLsfJAMRaoBQsaBRmOvvtZszIwYwdO5aePXv+qQ58CoWCoReG8uDAAw5+fBCAXbt2YbYIZFvsyLZokCRZZgLApFDlOhkAGo2GRo0avfRxAVDZg1tV0HqDT2t0RVuyPGYkeosGfUk7REHgUdGiZFiciEjRsXnzZpKT5ToRi8XCsWPHmDdvHmlp8qz8+Zo1WfnNN7h//DGjRo3irbfeejoFUKEE7wYYyn9Leo0N4N8H0u/LXaxcy4OzP1TOllOrinYFz/qQfAnODIbDbWUnpfoMuR5iix88WEaayZ7M75dBVCKsupXnZADEHYYNbgWMd0GAbt230IJ9lOUm/aSV0LdvwXFaW8Hevw8lS8qLnqrRiLsDXzeCX+2g80RITpZ1K+KSOCamYbp8gQwtKFuC4kqeDyG+BZqucFlVnR+ECZx0r4V0EKSDFBinJIHCAVzLpNL340Us7DqIKZ2+o4hPNGazSq6rboFco9EC6GqCwyCMQ06Leg8UZ0Hhg2zJCMA2IE3+V+oHpuJA5cZI82rwnaiib63BcnTMyeXlnQx9AhxoCmtPyZEW8o3NkAAhTzePSTekU/wzJ97d+67sZJB3DSaGgEc2pGtlJ8NR7ciYqmO4t/WeLWXKxn+K18sq+ZegUqmYNGkSkyZN4sKFC3z44YesWrWKpUuXMmDAAL766iuKFSv2qof5lzBgwAD69evHpEmTmDlzJlWqVGHQoEF8/fXXfy5/2sZfws2bN2nbti1RUVHMnTuXYcNeIvz/D7Jr1y4AZsyYwaxZs/Dz86NMmTKUKVOGUqVKERgYSGBgIAEBAb+r0QCysRwbG8uDBw+IjY2lffv2f7jNv4n9H+7n/t77mNI/QcqMRbQo0GfpaDNgJ1dOVKJmmXMUv/eIk4p61DKc4WLoSe4n6OjatetTSt9PIggCDg4OxMfn5YcXt3uItpMB1lsX9ASpmQKtQm6V6qRMQbj9MdRZ8v86L5VGxYhrI5gdOBs7DzvGjB9DREQEP/yqop3Xdmq6ngdrQyqVaEbAgoQStVpNvXr1KFKkyJ87sG8L8D0n59rH7EesPoeaLlc4f/Icle9dQiFJFIuIAMBOq6RPvwF4eHgQFhbGpk2byMzMLFCXERUczNChQ1/o0MeOHePKlSsMHToUp8rWnP+023B/rdzCVmUHCach4z7YF4HSo2WnIytCdjYc/MGYgvHsO9xN/pn0xrVocv46tE4BniiQF/X57XcASha6R6W+i2m1Pg2VozPcvCmLZHh6yitER4OvL5knwcHh/9g77/Aoqu8Pv7MtyaZXQoCQUEKvIdTQEnpHiqJiw4p0FcHuVxFUEFFUULBQpUgLvZdQQ++dkB4gPdm+M78/ZrNJCL0I+Nv3efZJdsqdO3dnk3vuOedzgOPHMW3ORrN2ISTFQE4WmAvAuwx4nAVBQBJFLAowC1DjKhgFCe/mIO205VMIIPUGRTScPVSVHctbIVpVhGcflfNGVgLtganAXhCK5U74++iQOtvyS9YAVURyyrvh6Zp/42TuTGRHVYwtH6OQLsBqELu5Yv5Kh7rXWyinynV7Pr6jT+4GSCKc+xkOvQ9WXUnFqfZqKNcdAjtA5ZdKnfpBzHASFPl4GKBJMnQ7C71PQfk8WFJdPmaCzcl78I2DbF+2HYWoeKxCXx04uF8chsZ90rBhQzZu3Eh+fj4vv/wy8+bNY968eQwbNoyxY8fi6+t7+0YecxQKBePHj+fjjz/mhRdeYNasWcybN4/333+fd999F1dX10fdxQeKxWJh/vz5zJgxgzp16tC/f39atGhx28ncv8X69evp06cPoiiyefPmxzoc6dixYxw6dIgdO3Zw+PBhTp8+zalTp9i7dy86nc6uSqRQKAgKCqJ69eqEhYXZk9gvXrzIxYsXOXv2LAkJCRiNRao8X3zxBR999NEjua+HgWdFTzIvZFK3YBeRxBJLJC5aPXUWniDT7EvlHy6wbFpPzEYnxAIVXf2XE5NVF4PBcEffQb1ej0KhsI95gr4iqZEBBHANdYzFpsxUlIQsSBZIXglX94B/0/u6N41Ww5tH3uSnmj+h9dNyWDoMQLCLPNEvNDQEKwwsN4tZyS9TsWJFmjdvfl/XlS/SB6kgnthfu7EntxV6QxRu7XKot+YYbuZ8Ig9up2KPVMoHjgIgJyfHrnYVGbsDr5xcImNj2f3223d0OUmSOHXqFPn5+fz1118MGjQIFxcXcqRAPOp/jPBJHLw+AZKWQUEC6NPh9A9g/ATKtIKQgey7Uhn3CoNZd3ItHsZjdOi2EfE5BYIxr5RRIUlFykpWK4iSErWThVbtM0lfpaRcZqZsTSxdKns21qyBiROR0q8wP/EdcDeDUonJ8gWagEtQsQz4W6H5cDAugJ7TECs1YmeYCy0TQA1o9YAzmJqC8SsV1ndc8KiXhyDAlQR/KtW6xOvjpiNaFSh3WpG2gFCo0LQX2UjYCwwpoaQMChC6gLNgQTTKtVgKDROaIIcpxQBVgXMgdbe1JYKgAqkLSGUElM2dUX62DUIb3M2TcmPi5xUZGRTrQ3dAssCV7ZC6js052ezUm3BRucgvtQv5s//g7A5wsYCXAdyKlTsJT5XzMgbVH0Rq9JcEugUyatkoWrRogb+///3324GDxwRHjsYDJiUlxS6J6+zszAcffMDw4cP/U5Pxy5cv069fPw4cOIC/vz+DBw+mSZMmRERE3LUyzeOE0Wjkr7/+Yty4cSQkJODt7Y1Op8NoNOLn58czzzxDv379HqnR8fPPPzN06FD8/Pw4cODAva/2PgZYLBYOHTrEzp07OXjwIKdPnyY5OZnc3Fx0OvmfuouLC25ubpQpU4ZKlSpRo0YNwsPD+d///se5c+e4dOkSZcqUecR38mCQRIlp9aYx4PhYvMghG0/UGjOuJh06Zxe0M/WIEuisrpglFQVWd3x7bMTFr9pt275w4QLz5s0rUc0aoHHcbjquXodCAtFXTtgVNF5ykTlnfxDUENwHGnz7QO4x+3I2P9X9CUsXC6oaJoaHfM/pHdUIn3HInuh+9OfaxGQ8TdeuXWnQ4AFMFAEkiWvrB7L7jJFj2VUYEToZl1FGhGsg+goIv3giPJUKSrkI5d9//82ZM2dof3gV4dsPs7F5R5rPmnVLxcE5c+agVqupU6cOMTExdqnwMl4q6gdb2HRSRZUqVeg/ezbCypVFJxquQvIKSF4NpgxA4nJCMsdzq3M8rxYGUUuAnzevVF6JJmNDqULhhf/B42dWIHD/VZz7GsiKANVY8Li+TJJGA1otWK2Y6obzj6ErA76sDVWr8s7UUN565gBVdB9D87mQeRi2d4elSlhmYEc5C+EXVZyjJvtpRJm+qaxNboRSaeLbV75FrRHJz3bn149e55lR8yl3KaVoQl7c61DMcJDevk5dymZA2GVjYwB5SEokdQtC0X1LyJ6QwkBwIRdI94YmodD5wE0/r7siaYUs7atLko0LpStIZhBN9kOsCJwySgxMhrlTwMkKp3ygYToE5Rc1ZVBAlhY+bw0zG6sY4T8CRbyCEydOsH79eiZMmMCoUaMeTL8dOHiIOJLBHzEnTpzg2Wef5dixY/j7+/O///2PV1555T8l67l9+3ZeeuklEhMT7XHfhauQERERNG7c+Aba+Y8fOp2OGTNmMH78eNLS0qhQoQLffvstTz/9NBaLhd9//53p06dz4sQJjEYj/v7+PP3003ZPx79Rn8JisTBq1Ch+/PFH6tevz+7du++qMveTxkcffcQ333yD0Wi8oeLQhQsXqF69Oq+99ho///zzI+jhwyE5LpkTbd6isW47sbRAqbLSUohF+ZQF5+5G8i2uLEztT5IxGEFQULNmTfr27XvT9jIzM0lISODQoUMkJCSU2j96wgRcDAZEYEfPlvh2K6Bqp0k4XZwCTf8EzYMVuTAajfzw8Q/oftShHqAn4MQ1Ug4F8aH1S5SihEWhYNwnnwAQFhbGgAEDHtzFRSvJC1uwJbEKUT6bCNqZZp8IF7QLwPXZIhlck8nEL7/8gq9pL88G/c38lAE8++4cBMkKihsEAohm5vz2LRfSzAiCUKLukoCVAE066aZANBpn3j96FMX06XCj/6XmXC7t/JmCM3PwUWciIJJr8eRQXjieYX1olv4qHi7Z5Old2XWmBTtOt+L4pTpIGoF5R57FXcxHcgUMsneoBG5AWi64ukPv3uwcOZcNW0Q++1TOvRnyUjyju44j+LOdMPhtrD4HOf3dHvbvb8R+wsn1dadcxYu0DIqjduVjeNZOxdkqoXYDUQKFAAW5Wg7vqIe3XyY1551ByAD8wDhBjZNLyerlkmTL01BgV5dCAYhg9lCi1ljlbRp5bk8TEF4SEN2VCKJFDrNSOyEU1h6xJVkDcAR4biLUfedOnow7w6KTjY2kFbKlU2887HsdNljsz5EhCkIvwS8LoVs69B8ENQ0w7A8IsBl9JgU4fQyuBihQAvMghBBq1qxJ/fr1+eCDD/5TC5MO/rs4DI3HhK1bt/LKK69w6dIlKleuzIQJE+jTp89/RhYX5Enwli1bWLp0Kbt37+bSpUvk5eUhiiIKhYIaNWrQrFkzGjduTEREBLVr134oScuSJLF3717mzp3LwoUL+fDDDxk2bNhNjxdFkcmTJzN+/HgyMzMJDQ1lypQpN42PtVgszJw5k+nTp3Py5Em70dG0aVPc3d1xc3Mr9erYsSOVKlW6r/vKycmhX79+bNy4kWeeeYZ58+bdV3tPAkuXLuWpp57i4MGDN13V7t69O6tXr+bEiRNUr179X+7hw2Nhl+84tSYXZ1c9ZqOaCmFJvPjhX1zShbI8723c3D2pXLkyYWFhBAYG3tC7lp2djbOzM2lpacyePbuUJ6OQj8Z9jtIsYVKrGf/hh6hVSrp170Fo9jTc6w0Bz5oP9N4yMjJYuXIlWYezyPkhBxQSWATGCl+ikSxIwKquXTnYuDHDhg3Dy1ac7UEgpWxg5pzFtPHehHWDQNiW83I4TzswS2qU3U6i8KxiP/7ixYucXjGEytoLXNKFkGSujN7qgmdQPQIDA4mOji4a+9xzmGLqkG/WYtmowm11Pifa1GRveGP6BP5D4M4r6Je5sKN1Rzo2bw4NG95AR1bm4MGDrFmzBqx6KmkvUtP1OJ5SDuePVWDlmqc4e7kan7z9Pxo13k/qkjIEbE9D86OE+JoCpV5E1MLwlqBXQxkdVMyGRiFQvycokmqD22swbx6/hFWl46q/0Vy8SNCC73jm7Wa0HteZ1G+mYQ2wEFIxnnrlDhPsnYhWyGa3r55mOeByADJbwFILvFXtuoJ4xTwTki1B3PKUgsTgClSsdhmFEkSrgMWsAiRMRhVungbYCFIMmINVGI84k9PCnfKVUyHGFh7VjpIhYyp3eeLP9daUjWwgcSCMn3X3D8rtyDwCZ3+ERlNgkTcMM9tkbkH6HswSZFjAeyOo14K1KugOg9YEKhFmNgCtRYWySgNmdfZjbNRYWla+e5lqBw4eNQ5D4zFjwYIFDBs2jCtXrtCwYUMmTpxI27ZtH3W3Hho6nY7ly5ezatUq4uLiSElJIT9f9h87OTlRv359mjZtSmhoKAEBASVefn5+dxWadObMGebOncusWbO4fPkyGo0Gq9XKM888w5w5c2563pgxY/j666+pXr06v/zyC23atLnjaxYaHb/++ivJycmYzWYsFgtWqxWr1Wr/Xa1W8/nnn/POO+/ckzfr0qVLdO7cmQsXLjBu3DhGjx591208icTHxxMaGsqnn37KZ599dsNjcnNzCQgIoEOHDqxYcecKTI8zVpOVOU1HEX/Im+6vLWPfuqZE9thJreanya3xPR71Bt/RIsU///zDmTNnADCbS64kh8fFEbV5M2qLGZVZzs0oNDQA1Go1zXwP0Lb76xDU8YHfo8VgYXaH2STsSrDPE98TJqCV5FAjnbMz344ZQ3BwMLVq1cLX15fg4OD79gZvXz6V2COpqBUm3vl+EooMSa7uPMV2gEs56HZKlpk157H6j9EcSvOV65ZIRddWq9VYLBa8vTwZ1Aa0dYdz+fhmPA88hZc6p0TtB763nTQChGuQ4+WF5+bNsHBhUdG+QgxXEAUt23e78euvu7l4UUFejisiSuq6rKdRhb94K/ow2gIJhRbwB/SyJDDbgYW2dvqB+R+4AhjrgsdT4FMGlIJcH1D8pTnxR3UcNFcitXw5xGAILXcZrzI5lFNfxEOrx5KsIDD/GkonkZSyoCkDPl5yiY/iOSFwnaHxOlBAUcEX2+RbECAvyxWlUmT7spbsW98USRJo0WcjUb12YjGpuHQylH+m9qVitcs89/5CQPaSS4DgHgZ5Z4uuo/YCcy4lChtezxkgcj20b3/zY+6HgsuwvDJstCLFQH5X0LYH5SZkYysXMBU5WgpUcE0LVUYIpL9/DR/tkxtm7MAB3Llt4EgG/5d4+umnefrpp/nuu+/4/PPPiYqKok2bNowePZqOHTv+K+E3/yZarZYBAwaUCH24cuUKixcvZt26dRw+fJjffvsNg8FQarVVEAS8vLzw9/enbNmyBAYG3tAYiYuLY9asWRw+fNiutz9x4kSGDh1K1apVSbQpytyI3377ja+//pqePXuybNmyu74/lUrFG2+8cUu1pytXrtC1a1c++OAD5syZw++//35HEq85OTmcOnWKo0ePMmbMGPR6PcuXL6fLDUv2/jepWLEiHh4exMbG3vQYDw8PXnvtNaZOncqOHTto2fLJXhU0XrnM7DY/kXrOg3D2U2POaa5YAnHrMBmhwhY8K3WgVID+TTCZTKUMjEIiY2PR6vUltlmKeRjNZjN+wfVlBaSHwJrha0jZn1JiMXqbujWdzOsQJHA2GAiPi+MAkJCQgFqt5vXXX8evUDHpHrh27Rpxp7MxSxrMVg15XdzwXJOH1N22Uu5ZF4T2LF0eQ9Xqddm1axfpqQGEulzgor7IIylgpXBYQ9dvwPRJLDvbrGdXgwhGXBBhDXKeAbbk58LaFt3BukJA31GDZ943cHUnrFrLNedObD5YmzKGBbQMjeF8ehUsWSEMKKfGN/wajcoc5KzPz3y9YwSv1ABXJUhxyNfpgZz7IIEQgzzB95MraKtbQ/kcm7FDycfmSFsdvl0ziVIl4izo0XrpEFzgZHIYVcolIoogBSvQ5buQccIdb1Ua7l6g2CJ7HaSuILS/zaOoBjzk+xYEwDUUZ3Mav4x5law0X8KJI1LYwe5DwRREubIrpjl71jbHwzeHp95eKldDNyTZ+q6RPRgu5UCfDCoPMOsoYWTYZ/NAOvIkP18J5crdzWNydyg0UPkVUC9m1lPRjDu6GNdEiFsBqgyKXDACZHpr+Uyh50LvYBYN+B6t5vEOJ3bg4EHi8Gg8AkRR5P3332f69Onk5eVRtWpV3n33XQYOHIiLi8uj7t6/iiiKXLlyhdOnT3P+/HkuXbpEUlISqampXL16laysLPLz8zEajZhMJkymouQ7hUJB2bJlGTBgAB9++GGJMItGjRqRmZnJxYsXS11z3bp1dOnShVq1anH48OGHbuTNnTuXt956i/z8fIYOHcqXX36Jm5sbqampnDp1yv46ceIEJ06c4Nq1a/Zz/f392blzp12F6f8D6enpfPHFF/zyyy/Url2bI0eO3PRYi8WCj48PYWFhxMXFPdEhiVeOJrK469cU5GgZrv8ejcWMDmcujahLrcl776qtP//8k8uXL5fYVlhwrsBVS1BqKijAolBgVmrYHB1trwKuVqsZ9nwL3LI3Q93PH9j9FSJaRM6tOcfKD1aSfznfptYqMaD+PKoePYcgYq++rVQqadiw4X0b2b///jvp6en2vx8vnZ5J+Q3JKHuI8mT9UDD8co1fR71DtM98cvd5saJaZ94O/oljy+rSYNshYiMjOdq4HmZJllQurBSe7uzP/3p/xPfr3kGdaSmxil+IJEGuSYHO5I/J5IQ+yxkUEl6e2QR4X0UQSiY4YwWUheMlp4XYC/INl70jdm/MRmChba5dF6ynFSS3CKTCMyk3TBw3pmhQICJYJSzxCtRVLCh8JYRCpWipqGi5ZAuFEgqVlq4hF8x7kZIJ3oUUL2Jn368kyTiU9X/V5C/vmURsiOSjghl4kUOeqxMf/FCPcmOaQIGWHq8tp2bj07IIgVRoKCvA2Q8MmYAFVJ6AKMsEl7g52ytfPoXkIPg8+YbPw4NiyYl/+N+WD0jSZZChzwDg68Pwzg5QhoHlLMwpD19XrI7lpIUjR4489jmLDhzcKY7QqScAURSZNm0aX331FcnJyXh5eTFkyBDefvttAgMDH3X3HktMJhNnz56lTp06dOnShVWrVt3wuJ49e7JhwwYKCgpKTD6PHj1Ks2bNcHd3Jz4+/l9LqDYYDDzzzDOsXLkSd3d3LBaLPZRMEAS0Wi3e3t6EhoZSr149IiMjiY6Ovq9V3CeNvLw8Jk2axDfffIPZbKZ9+/bMmTPntkpm33//PSNHjmTmzJm88sor/1JvHwKiFRa5cy1Ri+9nGQgFoMOZQ882p8Xs9XIxuDvk119/JTU1tcS2womxpLDVK/CFH0cNJctcUoLby8uL4W8MgMNj7ruGxq3YsGEDuzbtgoPgtMuA1aRg7HNfQQycaluTxbX74+npydChQ+9b5U2n05GZmUlmZibp6ek0f/ZZXK9dkyfroUCc7bgwLUKEBedFJlIiA1ndrgsvTJyNU6YZURDY0aMlWxtEoTSbqXvoKI02xJEb7UalAfEc/TOY2tsvsqM+eLwE9d1VqJUWFAqwigItf5uIE5UY0mAW1XLjqFU/CXxv4Bmw5SsU5o+USHK2GRUA9LftL0ykdgX0gAiiDwg/yIddb/AkHS6DT1AurmX0smFTzLAA2bgQCo2FwpoRfrb3f8ntFxo5hbMHoTBc6DqFKbNJxdbl3dG5P02H73uwf9Xv7HrvNLWunqee6zrGtZL43bctlU/Up4nuCnsGzuN7P+jkeotpicpdLsaILY7Lkg/W/KLxiQE6A89OgE7v37ydB8Cx9GO0/aut3cgAueL3/3aCRy/4piF8mgkchm0jt9178UkHDh5DHKFTTwAKhYLBgwczePBgNm/ezKhRoxg/fjwTJkzg2WefZdSoUdSrV+9Rd/OxQqPR2Iu0tW7d+qbHhYSEoNfrycvLs38BUlJS6NSpEwqFgsOHD/+rqk3Ozs4sW7aM2NhY+vbtS3Z2NiEhIUybNo3WrVv/pxWk7oRz587RrFkzsrKyCA8PZ/78+VQuLFl8G0aMGMGUKVMYNGgQy5cvZ+LEiU+mB8iSDwFt8LWsgf6g/9uJhFq1COo/puRM8A64PmxKgYXdLZvRauc2XBV6yACru4Icc0lVKYVCQYsWLcDJX5ZdfYgolUqCKgehK6tj4HOfo5upRVqmQNlbJKRNPNWunOKysQFbt26lbdu29+V51Gq1aLXaIjnoL76AcZ9Bh2z4vag2i0uCDikHBBME7U6jn24xG5tG03nNOhSSRLWtu8m6eIiKKX781ngEHX5bT1lVGqKkYH96TdYqenDkYBMyjnjg5JRGT781vDxyGZojFrbsf4fz1aD6LBCqUeQpgJKT9Bibx+Iv277iOePFQqQKJ/RSd9kwkPQ2A1IBQs9iBsZ1xkn5vHT4HehpK3Z3naEjFC9I1102euheVBhPKtZvCVltqngRO6kdWCUNqUkN2DynLk077yUgOp7VfUag1WZw8eM0fjqdQKKfZDOitnC6zBFOX61F2IYvafXSJ9jj6m7kIQmIguZ/yK4eySL/3P82JK+BGIvcj1XAjMG3fCbulwJTAYtOLsJgNpTYPiZWVpi6sgy+t0XdOdd1xlTeVLoRBw7+H/DfSgx4gomKiuLw4cNcuHCBjh07smDBAurXr0/btm1ZuXLlTVVj/j9y/PhxADp37nzTYwonmsnJsus8Pz+fzp07k5GRwdatWx+ZxygyMpKEhAQGDx5MfHw8o0aNsksD/38mNTWVjIwMvv/+e/bt23fHRkYhFy5cYMiQIaxbt44aNWowcuRIsrKyHlJvHzz6TD2H553jysHjcrJtOzgxoBY5A8YR2vN2AfElycvLKxF+ByCiYl+jpqz6oCuibfFVeVlERFb/USqVhIWFERUVRZ06dWzXu62z+76IioritVde5MUq63BW6Ch/Khlltoi4QqDA6oqnKgej0cjp06dvmm9yT0gSNG4MHaOR/jSW+C8oGECwqdwKevDcm0unI+vZ2LkteW5uqC0iZS+G4DZG4MdnR+CsNsphT0i0DjzMGOcf+Lvrs2z+uitrvhrEG6MX46SwICwDJx3UPAzKLBD22oyJFZSsNA3ypNom80qMHEEkWW3eg+6AH4g2YTxdnorNPq2xTgahv23fCwKZ4R5FYViFxkmBbEQIa0DIpMj4uH54bNeQuoPYzhXh0EqE93/ALHghRgtIkwWkaJs0bWENDD2yR6U7mExuLPtnGAfPjqXn0g9ISmnBhk+O0WpEeTqtXIQ+qBJZgbbvZuFj7ZoJITvo0flLrppVWNUBoPQoPTYAaevg1HdwZRuo3MC1ArT8B7zrQnfIc3PlVP1aFJjd7+apuGOu6a4RcyaGfov68cX2LyiwFJTYv7MCWATYUB7y9WpUOSrql69Plv7J+XvkwMGDxGFoPGZUrFiRlStXkpmZybBhwzh48CDdu3cnLCyMX375xV7I7P8zx44dQ61WU6tWrZseU7t2bUD2YlitVp5++mlOnDjBnDlzCA8P/7e6ekM0Gg0//fQTv/32G2fPnqVChQp24+n/K6GhoYAsf3ovKBQKfvzxR1JSUoiOjubHH38kNDSUH3744Ykw0g//eRjlgVfw8kmlcOE+uFoCF1bdfcExd3d3nn/++RuGG9X465R9bifZ//oLiKJIamoqer0eJycn+3ZuH1l7fyhUeOp24awwInWXk5ePta3NzKRBHDdHER0dzeDBg4v16R4xmWDVKnjqKahcWVYimjkPJBDVIKmK/hUWN+kECRTJItFbt+Cqz8enwErjuseoUvYiSkWxWhmCRNiBFFR5Isr1EvwAfAoMBYYBWUXtSch1JySQi9GZQPKRJ/aSBOltwPKCHNomdQNJFBD6ZxKfUU2e4E8BRQdnULlR4N6CqPrb5Al/O2AKKNtLuLsVFCXbd0c2AlyB9j6ljVZTUU0LSQKiwfodCM+0RtEnAcp3hWpDudo6i15Tt3HxSkjJnJJCQ8YF9M1cmP9tX8L6dKNClcuseOpLKtbPp//OX/Hv+TGCIPBXr78YWH9giS68EQeXJkPeFiPDLYNQ9k2DXpfgvVHgr5DvYSNymNh6A5z4Ena/BEbb3wuFGtqugS5+uE0rIDakCaemNGP0aMi5vmjhfdJkRhP6LerHmvNrbngPnS6ASoIWiWCONbOq0yp2v7abfrX6PdiOOHDwhOAwNB5TtFotU6ZMISsri2nTpmE0Gnn77bcJCgrigw8+ICUl5VF38ZFx/Phx3N3dbxlKUbduXUD2aIwYMYI1a9bw+eef06/f4/PH/tVXX2Xbtm2o1WrCw8Nl7fz/pwQFBaFSqe7b4PLx8bGrmgUGBjJ8+HBWr179gHr58Gja/E+qh59C41Tk3XLWGmjSZCbx2+Lvqq2cnBxWr15dQhwhPC6O977+mtrHTxQZGs5F3x9JkrBYLCVrvmi8wfSQV2EFBVZVAApB9uIIU6BWrxO8VH4Wb775Ji1atHgwCf7790P//rB0KVy6BJmZACQ3rsDG19phGqKCFqV9OIXvFToJhdVmhOwG/SUnrFahhB22L6wGFgEwgS4ZkpRw2a1ke1aFgrM1w+g3LaroGrnwTwCMqFyLnwyBdLoawNC68NUgVwQpEEX4F+DkTejQUwjluiM0no016iSnT7vjJ25DUJS2HbL1npgEV/BvBT0qwq/AZBVMj4fxEzH5aJH62G5wpTNC2b7MOPQdQqvlCL4RKJv+Bu23gFNRflRQEMQXtOTg5Yb2bYKAHF7lB1nNvNi4bAB1O5/iyMT5WBM38vyWEVR5d5Ytp6LwHIG3G71VYvIxJhZCcmBsrIL+Za9yKeMiR3fHwuhJkJAGHbWlvRsKFbiFFDXi5A8e1RA2wavLf6fK0ZNMmgS1a8Pbb8N1Tr57ZvnTy9EoNaW2F94DEsR7woRI0LTRENUuqnQjDhz8P8JhaDzmKBQK3njjDRITE9m6dSuVKlXim2++oWLFijz//POsXbuW48ePc+3aNe4gr/8/QXp6+m2VO3x8fFCr1XzzzTdMnTqVZ599lg9tdQIeJ5o3b86hQ4dQqVR8/fXXj7o7jwylUkm5cuW4dOnSA2mvdu3adqMlPT39Nkc/YiQJIXExKpWF3Ew3LGsVSMNAu09HxWqXWDd8xV19tz08PBAEoYR3KGrTJrR6vb28gVUtcLhjXft+JycnmjVrVtLQcK3w0CRu7YhWrAVJJTblmd1ZfPXFEgpz903z5jBxIrgXqfoVODuT5FqWdvM34fSXCXaXSlewv88KdkXSFOVlaz828u0Mib+PQJatVkJav0RiGwWQ6a4if3RfyiekEuIj516JAlzyd2HXrOlUPXaKf17fSLarErMCxkbBziYNiar8NkOeP8HBUWkMee44nYZvgz9ToZ7t75YgQJsVsHI6lpWVqFYp9aYRbp4uuUxZM4prIT+AtgLovKH8AnB3hyGjGNy7gOx3V4JLVajSH6IWEXN2JFToAZ32QdVXS1svuiQ+fGU10TU3lXR0tQPpOwFNFyP6zAKunPKj75+RRPy8AoVv7Rt+HMlHvyoaXUmelMd7wviWFoZuOkTfn6vSafEg+bl39gev2vawMboj5yx51JZrWQBc3Q2r68G13XKOSwZ4bsxFpTCSlAQzZsBbb9388bgbapepTYhnSKnthffwYTSEjoDpDcHL3Yuk3KRSxzpw8P8JRzL4E0SrVq04ePAgCQkJDB06lMWLFzN37lz7fpVKhZ+fH0FBQVSoUIGyZcve8BUQEPBQKnP/W9SvX58DB24fUqJWqzlx4gSNGze+ZeG+R01gYCAGg4GgoKBH3ZVHSlhY2AMNIVOpVCgUCvTX1Yx47BAE6JOG8LcLWnc95sUaVHoDrABlR4mIZgvJS34Jj/K3V/yTJIkVK1ZgNBpL7rBNGiXgWrgvniNyOJlaC2yRmAEBAURGRpY8R2szNLwfkiCFJMKOp9Aoij4fCcguM5BawTfSTr0z0tLS8PDwQBRFcnNzcVGDd+Ik0B5BtBlbAuBsMtJk9z4EvW1DWcAAUmaRgSEqILFXeQJ6XcMkqvl79/NIB+fR4aLEsPK90H4wHYYNI3/zbEJ8dYTN3ozWOwChQgW5gSFfwh9/oJg7l5C6dQktNnk3pCYyaPXbrD2zig1roNb3A8DJCwGoFXDzsFACfFGLRcXzCgdOFAWUSnmDk9pCvyazeOeNFvz1xaewax78/lSxMQKv2l2hTld46gbXuBEX/6JfwGcIUsmcMn2+MxeOVeJUXE0i++3A/60TqLVeN28nfh4qq5mKXiFIokTHTfGMiZUn6tMjAM5TYHIB/2sEfRvIB60/YmjVIdDhVWhnkkOohonw9GXIbwTN/wbjFTCkA6JcxyQTCIO5jZ/lhV//4cUXYerUO7zP21B9anWMFvn79UYcJfou9x8ogB/Df2TI00MezEUdOHiCcXg0nkCCg4NZvnw5ubm5bNu2jWnTpvHuu+/Su3dvKleuTF5eHrt372bu3LmMGzeO119/ne7du9OoUSPKlSuHRqPBz8+POnXq0LlzZwYNGnRTmdjHkUaNGqHT6UhISLjtsX5+fuzcufNf6NW9k56ejiiKVKlS5VF35aFw7NgxunTpQvny5XnhhRf466+/SEoqvcpXqVIlu+Tvg0KhUDwZeU0KNbRegUptRehpJRsP0uuXQZfnwqHYhszvOZ+s+NuHMQmCgMFgKGVcbY6KQkSeQHuezEUtWOgbuJjegUuo73GE2iHa0iFK2ofs0Tj+JSSvLJkTAYRqThEVFYWvr+/NziyByWQiMTHR7vVZtmwZg17ZRY3qOrp0yeD1N09xaPpuzG9tZU/zJoi2+1SKEqhsuSoSSEawTga8ZA+ExVlA19AF7x7ZuCgNoBCQKgkkDvqQwKt6tLPng5cXzJqFW7yZBhuSca0bXmRkALzzDhw/DvXqlRzfadMoW6cZS7I6kPb+VRpHd8Zr2rTb3+y5aeBtwmB7pAXBJi07AoTNEnl6V7IyPTm8oS7myy4M7iTBdx/C26GlmrplRNqJ8bKXoLjrQrSgwIIggNWixGJRkJHmzcrfu6F119Nv2GL8y2ahdrqFt/naXkhYRFSHv1nafynxufH2kKMxxWpzGtV6jEC6/oosHRvcF5z85CJ5hSFUC5LAeA32vQ4V+kD9iYACzgEiCOegc7219G/yN08/DfepkAxAhi6DMxlniM+JB7hh35EALQiVntyaPg4cPEie3GVtB2g0Glq1anVbbW6DwcDJkyc5efIkZ8+eJT4+nqSkJNLT0zl8+DA7duzgjz/+4Mcff+Ttt9/+l3p/7xQmcy9ZsoQRI0bc9Lhy5crh4eHx2HtvCiuY3yq5/UkkOTmZTz75hD/++AMnJyfCwsJYunQps2fPBmTDomPHjkRHR9OmTRtCQ0PR6/WIovjAiigqlcrH36NRiH8LAJy7m7FaVARuSmPr/rakSWUIrKvC2fPOJJD79u3LH3/8YVdcAzjRpLYcPmUwYJGUzEp8gWeC/qau+1HquB9DsF4DninZkLa8XMH6YVFtBKRthoy9IBaTCM0+Kk9w7yA3IyEhgSNHjnD06BHc3T3w8PAgOzsbJ+ckrl5rQVJyAN5HNhPCSXpplvLD9uEoi8ca5YHRQ4NKZUXR3w+ltzdJnfOQaon4hlqx5BrwUucC4KQ0Ucv/Gs8PnI5CUJSMs1KpICDgzu99wgS4fBkmTMDjzTdlud2PPoKvv4b3b1D7QZLg2KcgmqHtEqx/FCvsapPDFVaCurWFC5cq4Va5IVWeGgVDR8D73nBxIniWhSqvotfDbevCXpgBpyaCxhcqPg3VhkOVVzEdnMTGeS1p228TBzc3xNnVSJ8h/6BQgIgSpQI4/ytUu8FKvi4JjoyFlktBoeKbXd8gIDAhUrJ7BUpghSrGsljMetrM7swfFZsS6lkJuk8skrwFKLgIe18Dtbv86pIDq22yv061GPxJUzr3gXPn4DYleW6KVbTS/PfmVPSoWGL7hEhK9l0CRGhevjl5xrxS7Thw8P8Rh0fj/wHOzs40bNiQ559/nv/973/MmjWLzZs3c+LECVJTU8nOzqZhw4YMGTKEjz/++LHP9ahevTrOzs5s2rTplscFBARw7tw5tmzZ8ljfU+HqfsOGDW9z5JNBXl4eH3/8MZUrV2bWrFk89dRTXL16lSNHjpCXl8fp06cZNWoUzs7O/Pnnn/Tt2xd/f38mTpyIxWJ5YHka8AA9GlnH4NIcyIh7cAnSogUKEmH/cMg5jfXAp1hF2SjWbtGjzJJopNtPxKAqvLLzFVy8bzc7lFEqlTz//PMlCh1GeO5lc3QUOtsMMzA2jV8T3uBobh15vlzrBvlL2gry5PBhYMqBS7PAoyqc6AAjlHCoCXg3AJ8IuT7CHXD+/HkOHjyIxWIhKyuLy5fjMRoNNKxyAC+XHLoSwz/0pQMbaGA9RCXkZ0sCRK0CsZwKsUNd8o8loPg5DbqfovzcRPIrN8ViLsDLI7fE9Wr6pj+YhYsxYyBALf8E2aj68kvIzYUPPoDC0DdJkp+TfW/IK/r1x0PBNSzFI+NsydgF0S4kZJSjdr2TCEpJNjL+mg4FJ8CcC0c+gJS1XLwIxVNxbojaA0yZkH9OVnhaVZOL37/IrK+ex+toJgwXiFDGEd7hAgqPaki+jckSgxA6H4Eqr5Zuz6KDPS9DxHTQyLVbqvpWRaVQMT0CQkcWCzsqRAnntKmM2/kN2xK28cyZsxhPT0SyqWuVqEweP0v2vlmM0B74XhYX0EpxNL5cid0TuvHpK//c2WdzA3Yl7mJ/yn4WnVpUYvv0iCJj4404QIJe/r3Y/up2xkSOuefrOXDwX8JhaDhApVKxf/9+unTpwpdffsmbb76J1Wq9/YmPCKVSSf369Tl69Ogtj/vggw+wWCxERUVRu3ZtZsyY8ViG0SQmJqJQKO66dsTjhtls5ueffyYkJISvvvqKhg0bcuHCBRYvXoybW5H8TrVq1Zg0aRInTpxAp9OxZ88eBg0ahIeHB15eXnh6et7iKnfHA/No5J2Fva/Clk6wqjYsD4WVNeD4V/fe5qqasKYBnP0BVtVAcXEKolVEFCG7mSc5gjumDho6tPsQwXR3kjnOzs4MHDjQLnG7I6s1ByIaY9Jo0BoMRMbGkmd1Z8O19lxVNSRVqsmVK1dK5HZczbGQdeUiubm5N7vMvWPKhMOj5ZXzaSvgqhX+3At5F+SJqkJd4nCLxcL+/fuZOXOmfdHAYDCQaVOPAgGfq1cRT8LmzW358c9hfF8wnOX04keGcMktlE+EL+3J8HktXDHNikKZYES7KA6v4vlRgsDmjGiWpvfhUE5d8iyu6K3OWEUBRf65ByP5+1IfmGyGdmq5PaupyNho2BC6dGHWex24HKjk4pKGiGWioNKb8E0Y/B2Km2sxSdp2sHOiK58vH0PawbL89uHr+Lkuhzk/QH6MLXcBMF6FPa9w5fQhalbJgotz4PxvEP83nP4eck7KxsDWrpB90t5Vo86JZT80Y/30qnh4Z9DEtBcXvQHWOoNLAHjVwGg0kusZjeBZFZTXed4kSQ5vqjlWNixtzD4ymwBxR8vDAAC5uklEQVTXAALdblHTqDD7XoK87OMYJaFUwr6d5BUgGeRZTWF9jxHAdokw19V0rruSl166q08JgNNXT9Pqz1aI0o1lsouHTwVfDWbJkCUoFQ8gTsuBg/8Ij3dMiYN/lVWrVvHaa6/x22+/kZ6ezt9///3YVqxu3LgxM2bMuOUxXbp0IS8vj8mTJzNp0iRee+013n33Xd58800GDx5McHDwv9TbW5OUlISzs/MDCxf6t5EkieXLl/Pee+9x/vx5KleuzOrVq2nSpMkdnd+kSZM7PvZuUSqVD8a4LNcVnANK5iw4+YF/83tvs8qbciiJDUEAtVpEkqAgwhWvLjn4e16D/GuwoSV0OQLKO68n4eXlhZOTE6IoYjDIoUmxkZFExsYSa0v8VmNixrkeKM/9ilHU4O3tzeDBg1EoFFyKj0ebnsu6337DycmJSpUqUa9ePYKCgu5fctYtFDxqQNZBOQSmMBTGaiwxxjk5OezYsYNz586h0+lwcXGxXzstLY1Tp84SH1+RU6eqM/vA81S2XORD5Tgy63kTYL6GAolBwu+0D9uA+owZ0UngeLdw1tbtQu9Kzal6k+9cgwYN2LjhCvH6SvQu8w/bM1tS1fUsjXxOcWd+pZtgyobEJXBhpvx+3xtw7BM5RClqA+Sdg759wdfKszHPoBwBlqPHeGPzAKaEKnAJEhEAta3bEpCV5kPygigCnHLZvrAtzq56XD3zIK6n7H2TihU7NKSSMO573AKVWDy3oLBmoPceiGv9p+RQufrfyrLGi73AYuLc4cqsnd0ZJ2cTnV7cQEjLikjzJaQYSHj6PaqER0DicvS6Y/i0HHbjez7+Bfg1h8CSMq8Xhl9AkiQMFgNlJpYhzySHGpVIsG5U9J4e4FHxLoy84nK47SQ611nK0L9+Yt48Lc8+e+tTBy4ZyPIzy7GIFtzUbrc81h4+1QJWjFzxYOSYHTj4DyFIdxBTkpubi6enJzk5OXh43F75xMGTzSeffMK4ceNo3rw5MTExJfT4Hxdmz57NCy+8wOXLl+/YYNi6dSvvvvsuR44cQRRFevfuzfDhw4mMjLzlP4ecnBy2b9/Oli1bOHXqFL6+vvj7+9/05eXldcP2JEnCaDSi1+vR6XT2n6NHjyYuLu6ei9U9Svbu3cuoUaPYtWsXfn5+TJ06laeffvpRd8uOr68vnTp1KqHOds9sbg9pG+XfFWoIGQhNZ95TU6tXryY54Tz9tZ/gqSpWUWwjSCtA6AHmVkpUahHBIwzKdYe6/wPV3U9zCwoKSE1NvW4MRASsdDqylmqbz7EjsiWHG4cjKFWEhlamd+/e/PTTT1j1VzGILhQmJKjVapo0aUJ0dPQ93XcJ4ufDrucooc9a/ilo9Q/ffANG4yqcnU+WMBS9vb2pXj2C+fOzOXQohPx8NcHBCTSvtpMvpn+CYGtKAnJ7azEvdUHlacZTyEXIBiqUg4QkJEm66XdeFEXOnj3LymULqB67j46717M9shXNX0rHpesOeSJ+r+RflGVYLdeJHtT8AMzZcO5PmBUEzQKQQnYhiCBpkKuEF9bL+AikSyAJAhs17dhPI8zGIgPU3SuXod/9iHKHhCLGIhtw7QBBBQj8OOoNsq964eKmR5KgfOVUnvnyBEPnzWLq78FwaDSGY7NZPPUpriaWIbLHDsKjD6Dwb4KUdRjJakAArCofVAHNEHNPc6VATeCzp0rfb8JiSN8KEbeWfKo2tRpnM84CctG7kBxZKjZ0ZNF70RcUP9zFWG/EZsAK8MYgaDCRc5c9adwYDh6E0NL58XZ+P/g7r698Hat0Y8/+9WpThTQKasTuQbtRKRxruA7++9ypbeD4Njgoxf/+9z/KlSvHkCFDiIyMZMOGDZQtW/ZRd6sEDRo0AGDp0qUMHz78js5p06YN+/fvJy0tjZEjR7JixQr++ecf6tSpw8iRIxkwYADOzs4UFBSwc+dONm/ezIYNGzh8+DCiKOLk5ISXlxcmk8n+MpvNpa6jVCrx9vbG3d3drgBkMBgwGo03zRWpUaPGvQ/GI+DChQuMHTuWRYsW4erqypdffvlY1ilRqVQPLlyu6hCYtx2Wm6CfN/T76Z6bSk9PJzX9GucDKtHQ45A971laDkImmBapWXy4Dxpnkb5f6aDht/d8LVdX1xt4JhVIKAjbfB7PnFwiY2M5EBEBFpFL50/yzTfnbMeVVBAym800a9ZMfmM1glUnT7x3PieH5njUAO/64F5V9lq4BN0wqTshJ4HxRzbwEwIKJDn3260yNJ7GuM91TJqi5alWVpq2yUGHmtqaQ8RfCGXmor7k6byoVi2b7u0WUq/sUVr6xOIz42oJe0UQwOOsDl1vEambgHGHC06b/RDGfmDbf/OFBYvFwrJlyzCbBVrv3I4620KzHXvI/2E3LvdjZAC4VYKwIXDya0p0WDRC8kpAB/3Pg/V8oV0gm3jFI3EuydsESaK5uIuDqpK5XUaDhqP/q03Dy4dAgoI5WjZebI+XXzYevtkYdM6IVhUFOe44afVkpHlhuXKCSR2awCofDqwsy+ZFQ6lc9zyDv/kJJxdbPZOMvQgbJbv3SdUuE1JXIQDGMjfIR8g8JOfhtLx9boRKKJqKXJ9gPSESvt0tV0l3lUrGewuFIVKFBkV7lezBEYFo26vNWlCq4eQEqtYfz/ffQ8uWEB8v5/DfiIH1BjJ41WC7oXG9YVE8XKq4oXH8ynE+3PQhX7f//1sTyYGD63EYGg5uyBtvvEFgYCD9+/cnPDycrl27EhoaSkhIiP1nYGDgI3MTX74sF2oKCwu763MDAwOZP38+FouFb7/9lilTpvDKK6/wzjvvUK1aNfbv34/FYkGj0VC2bFmef/55XnzxRdq0aVMqvMlgMHDhwgXOnz/PpUuXSEhIICUlhStXrsga/i4uuLi44OrqiqurK25ubri7u+Pu7o6Hhweenp54eHjQvn37BzIuD5uMjAy++OILfvrpJxQKBS+//DLTpk1DoyldKfdx4IEaGkGdYLlZDsdYnAlvTYSAluBd945XuSVJ4uCmP+irGokYIqEQrLAJiJFVco751yI4M5EDNKJSn95EDIkEj/s38k+cOIEgCGi1WlQqFS4uLri7u7OnbVuabtnCzmLKdRZJjbNCh0lUI1IyV6Js2bKoBRMcmwTnfobyPaHxNHminLZBfgGoPeWE7vaxZAv1CQuDd9+FESNAo4Ef9/2Ix+VZJHuKlFPBFh1UbvgBZ37+hOEV5/Dyq65YFqrZdjmKK6lNcJGMlOl6hXVvtyWFILZkRBHiEo+vOgP/HVdQ7Lp+oEE4DzteaImbc3UavFMd4Y/Btx0ng8HAsmXLABBFiVNtq1Njy2m2NW/NxeVbGTas7q0buBN0yZSqsnd6EliRDQpnYAOwkiJvBMWEuEJljwZKcBlQQE+fpWxf1oasdB+MemfcPPOpdeWk7N1RgLaRju7HlpPf2pUkl3J4+mZjMauooz9GpC6WWF0LvnljNM83+osyh6+Rq6rKix/9QUCt8pCnAEELghosOXaFKzkcqaj7ZYNrlrwffRocegciF5fKt7kRgiDgqnbFaDXS4LOptN89kfOZ54Gi+hR+SqiUAN8fhfrrQdMDlO1BsvdJgi7ustCAwuaJkIBtnUDlDi5loP54XnwRNmyAzp3lnzfih30/4O/qT1KeLIZwvWFRwhiyglatZUC9AXSt2pVWFW+tAunAwf83HKFTDm7J7t27efbZZ8nMzESv15dYwddoNFSoUIEqVapQqVIlQkNDqVevHuHh4XesgX+vDB06lBkzZlBQUPBAchs2bNjA+++/z9WrV2nWrBkvvPACXbp0eWLzJh40BoOBH374gS+++AK9Xk9UVBTz5s3Dz8/vUXftlgQGBtKkSROWL1/+YBp8RZAnWT01EGUClZs8iVG5gNobKr0I1Ybe9PQtW7awa1csr5T9mbLOafLG4cA1sHgqmaoZSsvuO6jVPg3nZx6c4tOZM2cwGo2UL18eb29v+wLBihUrOHr06A3FH9QYMaOk+HqUUpDwVOfwdsWfKLA4kVPmFcp3nAJXdsC2HrAmu2h1+aVe0GoJKSkQHg7Z2RL+/lY++EDJ76aqLPG8QKIFGjqB30Xwsgq8csCXwbFWnPMteFrziGvWkCpBWryWxiLYosakfiC2E0jT+1HG+RqqNyR74cESaIHxQPgAaDHvjsYpKSmJWX/OwGwtXECRbC8FQ4YMeTB/11ZUgfwLJbfZQqPs2J4J/JAVlkA2RHJbolfuZ9zasXzZ/xNMFhUalcTpE03ISzOwc0VLBODZ8Ln4HbiGUJj/YmtLnCwbK4YCDZoxZpRZEqKPgDRZQBoOqmyx6JqNf4OU1ZBxAPRJcift3gOKDCBAUHvBU6lyIrjVANt7QcPJ4Hlnntovt39Jo7KNaBPaBotooe4vdXHXuOPp7MmhtEPkm+RQs2pq2DsJPDOLhVIV9qkqcg2N7sBpYC/QBChU2lV6yIpXneIQncpQvToMGlSkJrxjB0RGQqY+g8CJgVgki92TsbMCtEgsHSrldMqJNkIb1s5fe0f36cDBfwlH6JSDB0KzZs1KyI0mJSWxZ88eDh06xKlTp4iPj+fo0aPs3LkTvV5vn7CUK1eOxo0b06hRI8LDwwkPD3+gk9KVK1dSqVKlB2YItG/f/onxKvzbHD58mG7dupGSkkLt2rWZP3/+E1Pzw2w2l1C8ui/OF8vH8GsKyjg51r4w3l7tLa/k34LWrVvj7OzM2r259FX9gruqAKrK1ajPaqvi9LYev7IZaJQPVh2tWrVqN9zepUsXXF1dOXDgAHq9Hq2iAKOowooTZjSEx+0nMjaWhAoVCE5MJDYykrjwCJ5fNpOCfA8UCpFOG39D5RVK/wrOuNsmtdYVAjFNBuGacoDMMzsx5Q/EYPAmMVHFiOEmPN02sbTrt/Q5/ROq/XBZA2pJwslyDY0f0BFYCA13H0TwRc5VKLCFDM0GZQWJoGpXZQEo21BJIBdK02pB4yK7ALK7Q9gbdzxOCoUC0SYuFB4XZ0+cT2tZFl/DXqDLvX4ERXSMk/N9sg6U3F58Et9dztWhuy10SgKMwLO/sPD5SQQHysVKNSoLogTBwQn8vSKKQZ/NYM/mFrh11CO8UKxtW7sKWzVxFzcT9JK3C90kFCoJetuO6+kkX8yqk8OezkyBE+Ox6q+hiBYRrivaLqJG2SlONjIkCeLekutu3KGRAfBRq49KvL84/KL99/T8dMJ/DSc5LxlvJWyJhlYbwaUbcmJ+O9ur0DiLQa4MLgK7geq2/dZc0OdCTBgK78bsXvk7VZtWoHXtfUyZE0FMjMCq7clMPP0mFpu8cqEnA+R8kULc1e4EFQSRGJPIr6d+veP7dODg/yMOQ8PBXVG+fHn69u1L3759S+2zWCxs2bKFmJgYdu/ezbZt24iJicFikf9oBwUF0bhxYyIiIu7L+Lhw4QLx8fGMHTv29gc7uG9iY2NJTk5m7dq1dOzY8VF3566wWCwPxtDQp8Khd4tWh//cB41cwVoonauAwGio9MItGpEnss2aNSPQR8uBdfto7rML9VkLgghBulSuWMryZ+JL+LoraHHokD0X6WGhUqlopImhVsAiJFFEIVhxUhhRClacFCb4RkKjs+CRk4MC6LJ6NQBiA4GEhIq0CNjF87XmsONiL65Z6+PefS3SClhXswOzV5vwdsvEnF0ek7noX43RpCEnN4Avln/M4LyfUErgbQJxmm1R3xlQgrQYlCJIGfI8+7InBOWB2gp8AZInxDeoQCVsKlUuII53QjnwDHiXv6fxCAoKwtPDjcycAiJjY/HKySEyNhbh1ZoQNP3uG5QkuTaGhwfs2wdJSbBwIWgK5MmvmiIJ1+IqSVNAaA7okcuUd9oPOSlwaATT97zPh69Mtl9CIYCLWxK9X1/G0St1OJDUh9D4TDSeSoIbuUPnA9CuqPaLYAvVo7t8HUOBMyqnILJbgH+XHKj4HKStkZPHBQGqj4DQFzk5vx/Bwj7cVXmywWdz+igxw9pG0Hwe5BwDr7oQ1Pnux+omlHErw5BGQxi7ZSw+CljbHA5Hwpjri+8VVy87jWxkQKkwLyy5cGUjvoPrEj9eTfM31nPuiogoKXnux+9JDllpP7RUQT4beeY8zuw5w7gPxz026oUOHDyuOAwNBw8MlUpVyjNwvfGxfft2Vq5cWcr4aNSoEWFhYYiiiNlstidaF76Kvz969CiCIDBkSMnqswkJCdSoUYMKFSqwcOFC6tZ9APHUDnB3dwegRYsWj7gnd4/Var1/Q0OSYEcfWRXIPpmRIHyKXHdAMstJz6ZMyDsP7lVu22RIWF1WrOlJdfMZfKtkoMy0klChgn1/Rp7ImjVrsFqtNGrU6P76fxsOn8+mqeoaThqTfZsogSgpUFrk5X3RthSukCQiY2M5ElGPTrXX0NJ3B1qNjo7V54HKC9qB2E7NqYu1qGuV69zoMp1x3hFJvtEdf/d0qpU9y7hnxiL6KtnzW0WanriMuQk4uZacBAtNQNoLWyqrGNrewkVvOD0VKuaAwUXDtGGvM/xnm5qRAnT9tKzym0r/ezQyQM6hkSWE88jq7I1mnYlLUSE4lfsET+EevKfZ2VChAvj5QWIiVAZeF8EV2cgo9GI0AUIBAzCgGvh6g2c+BHSC0P5gyYLEX7DW/p1z+SJlPG21MWznW7oo0bbWY0l3IuVqWTbHjcbNnM2z778GmzvZcmdsrpriBk17JS69NnAhN5KpU2Gy3X4pjNeykXmQOq4HwZxHZr4nLs4GXFS2miuCEoL7yUZ33nlo/OBX+M9nnyfYM5gypJJtNfOhNzhdnx5Y6Nmw/S5Vp+hZKjymcLy7weXX/Ah2O8/BryJweUmPKMHVY/URQgSk63NorkNj0FAmpwzvvPPOg7tJBw7+ozgMDQcPlZsZH9u2bWP58uU3ND6Ko1AoEASh1M9atWoRVLzIFvDSSy9hNptJS0ujYcOGtG3blgULFpSokOzg7ik0NFJTU6lateptjn68eCCGxpnvIfOg/Lt9MmOU6yG4V4X8SxC9CZQucl2ESi9CxZtL/BqNRvbu3YvJZGFBSn+Gnp+KQpKofeIECRUrciAiAi8vL5o1a/bQPRoZGRlsT6xI7WBXnJTFDI2NCpQriwqUmZyc2BwVZQ8lsqAhQV+RIKeF9mMkS7asQGRV4KQooJb7CSq6xHNBrIRB78QH6vFoKhnwTMrj8PH6xNTtSXzfeH58YSHzgnQYJXAqnmzcHTgLNZpa+DMc3BTg1hOsq8DSVUUVr/OsbdaJtju3oO5p5nhbb7r26n9f4xF/OAZF/llaHTxKyM54srt6ktSwAulHkqh5feXqO8HbGypXhsLioqnATmAH0AOsSxUos0VYi5yDoVJBxdegY7EJbMI/cPlviFzMni92kGtugZc2m1y9G9plelRZVoyLnTnlVBXXCrmkF7hQ3s0P76upmHVm1FVeg8w42RCGYsayAC0WgH8kpmtykv4NEa2wdxCYZa+Ik0qPShDl7BVBjWK7D7y/CvrshokHbqgydr/M6CHXTMo/9hXKxCU4Zx8obTwUyxsRJdjWHPqGwrXidVBtRpa0EoLbnZdr1ygtXJhcidARCXin1SS9mJFxI3UpV8GVggkF/LZWri/jwIGDW+PIdHXwr6NSqYiOjuaHH36w14/Q6/WcP3+e5ORksrKy7FKwVqsVi8WCyWQqUYPi2LFjJdq8fPkyO3bsYPDgwVy6dInhw4ezdetWgoKCeOutt25oxDi4MwqTvFJTUx9xT+6eBxI6lbwKxNIyxqSuh4hfoGw7yD0DrhWg9XLIOgRxb8tJsddx9OhRfvzxR7Zt24ZOr8dbnYXYXUASQCFJRG3aRFhYGEOGDKFx48b2yt4PC13yLlr7bEZndcFglWeaVkmBMkaUJ/xm0Dk7szkqigMREUwZKQeqD588mdr7jmGVivonbASGg8sWI9G+G8gyebErsxnNKu5mkvu7lDFfwetYLooMiUqb5Bj8EELorHwJVG44K2TlLfywT4aFDAhcAxHOUEMDvh1BMQU07XWkWjXsa9SUr4eP5YOQIXSMz6Hf0n5YxHv/rperVB+91ZlWO2MRMsBzTS7nCqpizTx+z23So5s8da2InJi8DMiQczDW53XA6KoBrQb694clSyCqWGG7879Byko5mV3lgncVX56v8xManYG/1/YlsZ43kh+4Pl1Ao9aHqVvxGJM+eolXOvUgpMI5rp2+BsF9oN0OcA6Uc1raAVO1MPJzeR9gMt3C0MjcDzpZ5U+SQOtkQqWwyNK5kjvSMj0kpsKSnLsqKHkvuGHGpeZ7snExnCIjw2acGkTItEKPVIhKhoHugFSk8VXi+bIhCFDRL5HNH7Qkv8C7hCDYhEi5nkfx0ClFrILevXs/cWGkDhw8KhyGhoPHApVKReXKlQkKCsLLy+uu5VJffPFFFAoF77//Pt7e3kyaNIlTp07RpUsXpk2bho+PD1On3rpolIMbU+jRuHLlyiPuyd0hiuKD8WhEb4SOe0DjX7RNWwEiF4J/M/nnmSlyHodCDfUnQLlusK075J4t0VTlypXt4wkSHf3Xo2ovIhSWqxAELl26xB9//EFaWtr99ft2TJ1M+XZ9cNls4PekV8mxeCJJoEBEsKlGC5IcdnIgomg5vzB3IXrXJpSbLfKEbyrwF3ANhBgos+MancetIXz/QXyds1D1siL5wbnaVcj29ORQp06EhYXh5uaGXtQiKWVJKUU7YApI0WDuBkZf+ackYZ9cCpsgxQqDdSv4gi/4nu/5hh/IkfLZeGEjHWZ3YOKuiSw/sxyraOWtlW9htBjvaEgsaj9aem+HqiKiICBVhRBtPO063EMS+OXLkHaO7DPT4FvgU6BS0e6EoAqIogKj3gnps4/h77+he3do0EC+4RMTIOcUNJlpl4itOTCcmUdG49+wDS92m0fwoAyEKSC0kyfMGpWF8r7J6PSeuGSouHLC9p31qgndTnIusxEgQJPfoc7H9r4YjbcwNPyagL8s2SoIJV9uUiZ0zsXg7w1j/4U6OqYsyD5eMvyrO0h+IHaHN9NhwiKY+qVc++KnHKieoODnLNszFA36fvJPYRN2Y0UQoE3NWJ6utw7FlZo3v74E+aZ8JhfFmDlw4OA2OAwNB088ly5dIjY2liFDhpQoLFilShWWLFnCtm3bqFy5MkOHDqVChQps3rz5Efb2yaNwYnz16tVH3JO7Iz8/H0mSik3s74PLi+RJTiHmPNmwEFSy2k74FIgbDJIt3CioMzT9Aw6OlCtg23B1deXVV1+lWpWKVNeewkOVI0+gAdEVdrdvhtlsJjk5mVmzZrFy5UoeCrkXYPw4hKtmGsbJBs3+nHDMkpp0ox9ScUEkQSpxamxkJNmeniR1KA8rkENR9gIiSAqgO3itzcUrJ4fqm0/LJ7WDC5Obs/jpl3G9epW2f//NgAEDePXVV6lQpT5CsUrK2VY5myCqGjgPBY8Q0EnAQvlaLITdBrAAVqxkk20/1yyZ2RK/hfc2vMfvB3/n253f8ueRP3lvw3u3HRJRFFk//wtqux9HPKpCIUkojkk08ztC5fod7m58D62APyrB4jA8u2ciBAFOQJztWihYHt+bFtJOPMQ8zJ+PLwo5kiQ4PFoe0IaT4LrcELNBh1O/uehyXbCKpT1eV3N9icmYib8pmasni76zVqU3ozfuhrrjoEKvEueYTHDLKKDmc2yVxUsiCLKRY55o5milPDg5CS7NKTrAmHmLRu8BYwac/02W7S30TNiMU6Jhbj4M3l4U7mQBzppEhlyDJfnw1iX4yqYdUMJYsTHjtTd57UwSlyYXFekrbAsAC/SL7kfFihUf7H05cPAfxmFoOHjieeGFF1CpVIwePfqG+1u1asWBAweYNWsWFouFdu3a0ahRI3bu3InBUDq8xUFJCkOnMjIyHnFP7o70dDlh9r49Gjmn4fS3yNMWG+Zs2DcI9Cnye69aENgOzvxYdIy2PLRaDjnHYd+bYJEVqpRKJVWr1aay20WO5dXFtEAFBWASNXh2zAbkAmYuLi40bFiy6vP9YD3yGaf/7kXGhS1YY6pxsU0ABX5+xD/zDD6usD+nCeMvfMCBJY3Blq4hAXEdGpdo50BEBD+PHs3Ozh+xumlXsj09OVmrBnlebhx6qhFbG7QipUNZcjw9SO8YIA+XpKFym/d55513UKuLCrh5enrStecAFIICFBrQ+GAo359NTdax/X2RhoENMQIvZ7jbI1pE4LsseSJYOCG8ESvOrmDs5rEYLAYWnFjAkbQj9n2SJBGfHU/P+T2Zulf2dKamptJS/Qc6qzMW2wRelBR4qXPlKuh3Q2Y+eIvgI+dKA7IM7w7ZyNiobEOe0Y1YItHhjJhnRT/mTRAtsO81cKsCtT64Yb6D2lnLmQmfwuciRxOKBC8KK2KdvlydMzmt8XXRk3Gm6DubnAxB5VRQe2ypEKdbhk6BHBboe+MkFZ3VhUyTD9sPpmI88yscGAY7n8NqyGbZz6+Qt7AqHPsfIMtNL126lAULFtj6fNsyXjLmPDg+Ts6LMqXLxsUPaugdBt4NGS9E0jhR/obeKNwJoG8aTLfCl40g0yJ7QPCTw6nMIiRboGUijDmQazcuCtv6tpUCZ70zKoMKtzoPSC7bgYP/JziSwR080Zw7d47du3czcuRIypQpc9PjFAoFAwcOpE+fPnz33Xd89dVXREbK/4mcnJxwcnLC1dUVPz8/2rZty5QpU27a1v83jEZ5kpWbm/uIe3J3FIZ63behoXaTJ8Gi6brt3qAtV/S+6mCI7Qtl2srVwgEUKqg3DlLWwfYexJf5mHWxp0hPSwU6IaGivvmwfKhFxFOVg0ajoVq1anTv3r3EpPx+Ea/FEWZdDXtWICBxNLwqR8JsSesFssZqeFwcnVevQUA2MrIbeXAwoiFY5O+QUqlEkiTatWtHeHg4XyUkcblzZ6pVq8aqgwfx9/enVq1azMxcg1BdIKppdcrmvY/CnIEQ0AonzQ2WzQWFfLWwoVDnUwK39yKwYlMQBGb1nsXq5a0YUsYfof8ZpBgwdhMwSBJjY2UFquKJutdTuCo9IfIKTY1NaRncksvZl8kyZKEz6ygwFxBzNgaj1cg7zd+BZ07zx/fv4R+dSmRsLMfa1qaudBmXgsvgEXbng33iGvwP+Bw4iLxq3hbEc5CDO7utkWBVcIAIIonFS8rB+NPv0GQNNPzmlmICADWGfkZ2ymw+/+dTYt7rad9uMGlIzKqAIGSiclZhNRTlFp0/D1VuIoh2W0MDKMhIRFvMLpCALLMPq690psbeUwze9j1XOvri2zkP9eV5cHkRnf0EnCwmTu5ZjAttWLn1CNnZ2fj4+LBs2TKsVitVq1YlJSWFTp06lb6oPhUOjYb0rWBIgw0WeSxfagZfb7OHlJ1aOpADRtntUFhJ/GYogGl54NQQBkfJHrRnk+G4ETIk2bgYtwncbV/30JEgICHtNLB65Go6d35w0r0OHPx/wGFoOHiiefHFF1Gr1Tf1ZlyPVqvlo48+4vXXX2f//v0kJyeTnJxMUlISSUlJnDhxgqlTpzJp0iRUKsfXA7CvQr/11luPuit3xdq1crXe+zY0tOWh/W5YF4FdIhSFnGBbHEGQk8N3DoDWK+Vq4YUEdQSv2qz/ZQpXda5ICMD1YS8C81IHMmBAH8LC7mJSexPy8/PZs2cPbdu25eqVK7hdO4abvfobdPRfTzW3MyxO7Ydo+1fQYd06FJKECOhecCWxeWV8teF4iyLXrl0jJCSE7t2723OoRowYYQ9Nq1ixIpIkERYWhlKpxGAwEBERgUboBOd+AY3XzTvbK7EoRKjWB3Dsc2g4iVoBtajVYRrseg7ayWE6LkgcUmpZ3kGHtL70ynVxSqoGGdhwcUOpYyQk3tvwHofTDpOasJdkvcgzEQM5EBGBqzIfpyrf0vhujAyAuXMhEEhCTv7OAtaDpX4E5w/Jn76ztgCzSU2cMoLGhn0cc65DpOkQlO99R5fwaqJCWA9WUUCpkBAEuJIbwOFL9TGXs0BoKOqTJll5SqvmwoWbGxq3zNHIuwCx/dCbLahRoFaKiAhsyWjDVWMAHf3X47/tKkIGlFl3zV7TUIkZpRLMoorD2bU4v2QTki2IIisry+4hPX36NJ6eNyl0mX1cFmOwKV7Zw50WxMO3RUa40w2S0NWCGrNUWsRBBD7KkA2Oahp4KR0yiwTWmB5R8rmZ1cwFY44RdRO1w8hw4OAecMykHDyxnDlzhn379vHuu+/i7+9/+xOKERAQQJcupRM8ly9fTq9evTh58qSjDgewZMkSli9fzsiRIwkNDX3U3bljPvzwQyZMmECrVq0eTPiRb0PwqAa5p+T3rhXB+wbV0Z0DoMa7cHgMNLrOK6Ytx6vDv+TPX8aRmHkzCVARrVZ7k313R0pKCnv27OHc6aP08P4LJym5RLCsUrBiFJ0Rixk8qmLqbH9We5He6mXUqlXrpmNYPP+luPRxeHh4saM0UPP9W3e2eB5CYDRcmAnZJ+SQtGOfy7VKismYqjpr6PN3Nh3ndmP9xfUAqBVqkCgxubxZwbXrkZCYc0zOLShP+WJVwVtwyAUaN2526wauHIZpH0D4YFjyF1zbB/2Rcwe0gBcQAJpKIUTsX4h14jKSl85GoRBp8fxOXNz0tGAnSBrY1BaiNoDqFs+BKQcsGYR4xROfHkKI+yXyVrrhuyWDsppUjvoqoEoVfFMtXDt9jbINy3L+PDRvfpPmbuXR2PUc5J7GT6UneVEgvpuzONehEg26HsZNWSDLIvcAYiC1fRmsBhVlNOlotsjeB0U3K9GmTfRYvYKTUTW50KwKgTtSabDtMLGRkbJB5+p642uXbS8rucX2lz0a3YEYAV5uCgmLIVguHOukdMJN44aAgCiJbHlxC1P2TGHu8bmlmvTUeFJgKsCChVUr4IDt+SjuBSn+3FhNVpRzlRzYeqBUWw4cOLg9jhwNB08sgwcPxmq1MmjQoAfWZkhICAD79u17YG0+qWRnZ/PWW28RGBjIxIkTH3V37piXXnqJ8ePH06tXL9atW3fXCmY3pXWxrNGWi29+XFBnQILk1aV2HTh0hGt6V2Qtp8IApeIomPXXn+zcufPO49evQ5IkVq5cydatW7FareRnpbAqsQmJ+pIVjE/nhbE8vTeF5cx6L15sr01gUavJNPvhrDCwbds2e2L9v0b9CXBkrJx4oLZNQosn75qzYc9rrBu4jgV9FuDj5IMkSfhofXBRuVDdtzoCAtMjoMooJTMjVChtBtXtcjuSSKLtpvWystamTeTpBbKysph3bJ59DC5fvsyRI0fIz8+XT6oSDp+ugR7dIWYxPAPSVBBFZIWy0PpQvx+89BIATd7pydXUsoCEYPNGCAKgcgXjVdmLcCs0ntD3GjVPn+ZUcj2E7eC5Mh/XfD2vGP7gq9ZNoGpVAjQ5duWpixehUqUbN3fTZPD0bZB7lrx8Fb9tfgXntUacc43U3nIKH3UWGoUtvsiWkO3dOZuT+bU4r6uCZBMKEGIk3Nfk4Zato/aWE7gqC2i0/QBeOTm02bkFkPD19bVfstRzZrgK5lxAsF1HgmpLIaXo+zWgzgCWPb2MzPczWff8OuYdm8dzdZ9DpSi5lhriEULHKh2x2PKtSiV725geIYdMTY8Ak8KE6nUVu3J33fIjceDAwY1xeDQcPLH07t2b7du3ExUVxeLFi2nSpMl9t1moJnJ9nY7/j4wZM4aMjAx2796NQvH4r0mIokjnzp1Zv349b775JlOnTn2wdSjcK4PKHSwF4HODFX5JlCdFLmWg/teyvK1PuPwe2L9/P1u2bMGsz8ZJsGKUnFELZqwqBZjBqpL7arZY2bRpEykpKfTr1++OupaUlISHhwceHh4IgsCpU6fQ6XSAhFZZwFWTP8pNVlgpOwdEtUDNOqeodmQcgkXOz1CbzXbT53S1akgI/JH0Mjoxjx9++IGXX365hKrbQ8U1GPyaQuJiueI4FCs0ByDI3g6gf+3+9K/dH51JR/f53dkav5U8Ux5hvmE0r9Cc7mHdaRHcAheVCyvPrqTF988RnCPdMrdDUsheEVGwEPlMBxrNakRSbhJ6s55BDQeRlpbGunXrcHV1xVUSeSNflI00EdAD34OkA8XHQ+DzH0omdZtyEI5+whtf/MzGv6NLXlhQQvVR4F3njoapRkES++Lr0G3HMrlehAL21mxMVkIjng4/g78pmWM25SmDAVxcbtyOyQRaFxF2vUCupiV745Rsi3Vix8GKpGbt4WqeP3kGN3r0Woa4DTI7+eAt5KOkZN7SuYIq7Mluzh6a0iZyC5GxO9F3dSW/whs4z1rAnqZNOZxXG0ULkZaxOzjWpjZvVvgJQ055Nv61gfN5oeTqLLz33nsIhWNWoTccqww5RX+TJY0fQp3P7O8jg4tcVi2CWzB5z2TKuJXBT+tHWn4aChR0rtyZNRfXEH8y3n7szTxeRbk98jNiwMCBlAMQjgMHDu4Sh6Hh4IllyJAhhIeH07lzZyIjI5k0aRJDhw4t+gd1D3h5eeHm5sbZs2dvf/B/mB07djB9+nT69etHRMQtMisfEywWC40aNeLIkSN89tlnfPLJJ/f1HNyUU11h2krInQavvQyZByBlFaRtBH267BzoGW+TvJ0M+wdD5GIQBHKyruHjlI1CSiPVGIRaMGOWnBD9BaQEyPAuqmAvSRKVbrb8fAO2bNlCamoqgiDQuXNnm5qabEBkmP2RUFJ2QxoUyF1UmiTYJ6ESxVJtCUCV8+cBgQKrreIZ2ItoPpRxvRHV34GtnaHRz7CqJrSTbJWfFdDyn1ISrVqNlk0vbiI+O552s9pxKfsSvav3plf1XvY+D6gzgILPU0n6bDRfN7fa7ldAKuZZUgK/d4CXt8N7zaxMn9vevu+1mNd4f+P7vFTzJcq4lCE/P5+6J7aVdEzly28VzgKM/abIyDDnyepLl+eDPhkEEASpqMK1oAK1B7hXu+Mhqlld4s9dtZC6ghQD9IAmLfbi6rQJLvngmxdJxpkMJKlIlepGmEygcVJA2kZ++Lsy45ePRmcqHs4k8kyzeQQ8lYnUGzJ9BpGnMBOaP6uo4jjgqtQT6JpL1QZdOOzlw8EWHRj8fBSBZZvDl98SBTTV6Thx4gTTNm/GYNATl9KEaL8NtHKbTmN3JzReVoScrnZDUgIue7xMxZx3EGwDbdLnIgp+3MRu4uNWHzN131SUgpLe1XvTsGxD5h6bW8qBeLPE8eKejl8jBL5o+wXDmw6/3cfhwIGDG/D4L1M6cHALmjVrRlJSEnXr1mX48OH07duXnJyc+2ozODiYxMTE2x/4H8VgMPDKK6/g6enJvHnzHnV3bkt+fj5Vq1bl6NGj/PLLL3z66acPbzL8x25Iy4eP34IFzrChBZz4CjL2ydWTC1JgXRP4xw/UnnKhs8MfwJmfaZvxFC/5fUuioSIWSY2EQHXXk2gTDQhAUEpKiUvt3LnzjrqUmZmJ1WpFMF5Br8tjyT+LkEQThSFREkrC4+Kw6pWY1CpMajVGFzU0AckV+zaTWm2fh6ms1hLXMJvNzJ49++EXESyO0gmqvyvnaBSfIXrWLWVkFCfEK4Tzw86z+tnVTD8wHY8JHiw4vsC+33XYKDSJKVx7oS/vNn+XxJGJxA+Pp4V7BQQE3IF364PnMPjjukmohESuIZcBDQYgKSR81BlEbd/C9U+bANBeW+RCSN0IK6rAmcmykWE7pvLV8ziNNcj5J76NoesJCGx7x0Pk/1RLrl71Iq+BC9JkASEaPLV5qJQSmLNRafOwGq2kp0Ng4M3bsSeDqz0Z030cNcufLLE/yDuFyc+Psod4Vcn9mURlS6g2HNRe8kGB7TBXep0Xw5OIUk3h9XYamjdvilPZkokhWq2WiIgIW96PQK7Vk5RVQZiGatBsMYNk5drVoucsPT2dnFNz7UYGgITIyfXjbno/9QLrYbQaWfvcWpY8vYQPW37IuYxziJQ2rG9EcYnc0c1H82GrD3HTOGRtHTi4FxweDQdPPG5ubhw4cID333+f7777jnr16rFkyZJ7TgKuXLkye/fufcC9fHL46quvuHDhAsuWLXvslbdSUlKoV68e2dnZLFy4kL59+z7cC44ZAxMmQLlEGC5CVeAcRYXDMMtGh7Y8JPwjqy3ln5NXuJHrMhSiVejo5L+22Iq2HLhUO8SVhq36UqFChdt25/Tp06xYthijyUoj92NUdT2PBRWuigKCdqaiWCRPrMySCo3eQranJ1NGjiRMe4aegcvYm9WYnVktsCLnsbw3YQJagwGV2Ux4XBxnm4VRYHFDRImTkxOBt5qtPgw8akCCzUgoTAbveQq63v7U6ErRZL6fyXe7vuOl5S8xav0o1j2/jtoBtQlwDWBR/0X2Y41GI88ohtCYZF4ou5hN+X78Y3bihHgGk6mkrLNZMvP7320psz6PoUdBeaM6lmogsn7Re7/G4N0Arm4Hq1xPBYUzoecvQwawUgEDFLIn7G7o0QPh/XNovUwoVde7LKzQbStVD4dy9kQ/qlS5uVSyPUfD5Ixqi5UtV9oyVvEVU8VhgEjbGlso4ynfqCCAIBZQK+MjaHcCTNlwfjrU+oDqZWxGkjkfl8QlNMmYCrv+gpDn5DozxXImigsJNN2xF7ecAnKWeXChSSXWLdxJSKUrdO3alRNH9tLMpcjwkSRQCBCWO5X09GHs2rULJyenUuIen7T+hE+3fsrcp+YiSiLhgeHsS7157l2JcKlGMD0cBIXAMKuj1pIDB/eDw6Ph4D/D119/zYYNG8jIyKBJkyb88ssv95TAGhISQkFBwUPo4ePP8ePHGT9+PK1ataJHjx6Puju35NSpU1SvXh2dTsf69esfvpEB8OabEB8PSUFyYvJe7NWqGY48Gd4IvJYEg0bBoHOwEbsxIQgSSkGO/+9RZjke03Pti/UCEk5OztQpq6Ni6heoKCoSd/78eSRJQqfTsX37diZPnsy+fftYtGgBeqMFUYIDuY1YmPo0yavK4z/6GspFIkIBCAVglVRke3oSa6sdY5GUbLjWnu1ZbbFSNAHdHB2NKAgogC6rV/Pqmd/oG7gIkDCbzRw8ePAhDu51XP4bYipjH6DCZPDlRnvxwzthVPNRFIwtIDo0mobTGxLxWwTZhmz7fkmSmD17Nhk5esI0JpwkD6oIoXS19KSa943DmH7Oy+PpveBSUFQMXnICfAAfLdSvDa8Xq+qu9oDyPcCrLjiXkZ+RoQb0wR5IFYLhsx+g1bI7vic7VavyTLu5KJXWG+6WlCLVqm/kdOw1Kle+eTOFqlM5mZA13wu3/AK+cXsff/crBHmlMum5dxA2YX/GJQm8rKfgzBS5ennELxDQutj9ukGlF+R7qj8eso/C1i5w8B3IOgyShLu7u93zGBsZicVXgcdTuTgrjJgtVs6ePctvv/3G8aMHOZRdnwKLrMKlszqzIHUA81KeYebMmRw9epSsrKxS91TFpwruGncOph5EqVBSzf/Gn2WhOMC4zSUTw7tU7cLCfgsZWHfgbT4EBw4c3AqHoeHgP0WbNm1ITEykevXqDB48mGeeeYa8vLy7aiMkJASDwfD/rmq41WrllVdeQaPRsGLFikfdnVsSGxtLeHg4Tk5OxMbG0rbtnYebPBDGfgjl/KEp4GfbVqiIVDgh3kOREWJDgURN1+PUcTtEhV2JCLuL9olNBPyEcyyKU/FPHJz7szGppzdz7tw55s+fz9SpU5k2bRpbt26loKCAdevWUTzFon7cEQZP/olm63fhnGVEREByBaOLGlV/E4e+aMj5yDoolQoyCeG4rrDid6EZJHIgIoLVXbogAgpJwn1OPlX2nCPadwMWi4Xd29dhOfbNQxjQG1ChHzjZZKs3IidZuyJ7j87PuKumFAoFs3rP4uq7V1EICvy/9ef5Jc9jFa2Iokj58uUJCAiggksCetGFbLMnFouFY+k3F4XIcgFrnvwvVATym0Uw+e8vIKMA9h0DLy/5QEmC419CQTy03wWVXil6Rk6aiH1rDgx+G5x8b3yhWyEI9BmwVFYH3kgJQ8BkVJF0VY3KeoUXK4QiLPya6Q2mU3CloFTChskEpzcl8vQ3EzjXuAqSH7j0MTCg+Vza111HgOfVEqpfgmCbPBweC5Z82agQbjKd0JaHmqOh7ToIeR4uzoItHQnMmo2nOhcQOdmkJsIUCVNbDTqr1l5vIz8/n2ydgpDdl9GO0sFUcBllIHhnPGnGIMxm2Wi/WWHLj1t9zBfbvwBAKShR3SCIozAfA8kWLtUCVAoVTmon+tbsS3jQw80AP378OAsWLPh3Vd0cOPgXebzjIhw4uAe8vLw4duwYw4cPZ+rUqRw8eJCjR4/icjPJleuoWrUqVqsVrVaLi4sLHh4elCtXjurVq9OoUSNatWpF/fr1nwglprvhl19+IS4ujp9//hkPD49H3Z2bsnjxYp577jmCgoLYuHEjlW+1VPuwePNN+bWjn6yMVKzGA9h+zwW7KE+x/T2iV6BEQvi0WHvNQDlEorN+DTOS3iDKYwle6jwy9zzN0ZwuiGIomZlFSbfWYjkU4XH7iYyNRWM0ojUY0Lm4kO3pycHWDclu6UGCIQR/zVW8XLwZ+uZn7D94mLy8PERRpFatWvz5559YLBYKDY4DERF0WL8ejdmMIIF6pZWIqDicFUbKOKWiPJoCWXFQ+0Pwqf9wxhdAoYRel2FVPYg5CwWAnwAfz4GAVvJk2aq/db2J6/B08WTvq3s5dfUUHeZ0wO0rV74oF8i7A0+SmduYvLcnELg+jZxIT+IbVqISlchX59OtXjf0Zj1fRX/FuB3j+CVuKjWzQYlIhjO0HwhXA46jP3SRZ5u9Rhk3WWkMSZRX8V3KygbGukaQd86uoKXubKSc7i2sWf+gvIn35JYsHoNabasbYjMEpBWgb6rCWWuhvD+AGVEE88VD6LOaovXTcnJcZ66ku9Dmx6UAHPvnDIvTrCx47x1qlbfVilF7M6WOgNlSAEew91nqLsv2XjEGULbNJFC7l+7XjRAE8Gkgv0QLmnNL6Bg4Ecx56CxOJOnLMTflhVIT7peWzSTocIr8dO4FhQgtYnextUGRYtfNDI1yHuWo4l2F8TvGM+vorKIcDZMLb6wNY8yRdHZqaoPnRnvIlEbQsPLZFVTwvH3o4v2yZ88eOnXqRE5ODjNnzmTGjBkEBwff/kQHDp4gHIaGg/8kx48fZ+XKlYiiSEBAwF0ZBV27dmXz5s2cOnWKs2fPcubMGU6ePMmhQ4eYO1cuAKVUKtFqtXh5eREaGkrNmjXx8PDAZDJhMpkwm82YzWb77xaLxb7NYrFgsVioU6cO7777rl1S91GSnp7O6NGjqVWr1mNdAXzq1KmMHDmSmjVrsn79esqUKfNoO9RsFmTshc558OXfsKMvWPOhgzOsN0KMVCTLeg2EGFBGS+RbXXDFgAIJBDCEacg2eJNqDCLM9TTemhwUAvipr9HWcwVXCvpxxRxEce8DKAiPi6PL6tUoJMluYBQWQQMJZ6sLFsFCjt6bypIbSrVTKRloNzc3srOzAQGNYMRbnYVSbQWz7XLdwWmLmfCYAwi2XBQpaTGCa0VQakCXLIcG+d2/vHQpTDkg6otJ20pwYJhc1M+QDgoNRK2/62Zr+NcgcWQiS5Z35KUj6xk3wZPX/UbywZosnHJMhO84zIXuzzG18lRq1KjB4fzDVPWuygvLXmDrxQ1YgeIZ4IfKAehBryd4cjA+Lj50qBTNX2VV4B8Jogk2RIJJroZNO/ml2ihRadoJrEkNYFAPiPz77m4kaQY4C2RJnnh1ykZaDUIPcHG1wCb5eaM7KKKh08B1bNzREXP6IYK8dlPR38Kq0V8wbWlfzl9VcbB7fVy+NRTlGwkC6NNRx88CAaRouTJ7YU37rXm9GFD5+bseewAUKtyr9Sd2Ry5X0y/wYrk/OLq0PoN3/FTs+QVvVQbBRxOLhroJcA4U3SXCfK5yNlP2eDndoAhInjEPN40bYyLH8PTipxHzveFkHzjyIuRWYExuJCGkgVRA6EhAgkB9IEc/OYq/690VgL0XYmNj6dy5M/Xr12fkyJEMGzaMOnXqMHnyZF5++eV/T93NgYOHjMPQcPCfwmKx8MILL7Bo0SJcXV2ZMWMGL7/88l0ZGgqFgrZt25YKxzGZTFy8eJGzZ8/aX6dPn+bUqVNs3779pm0plUr7S6VSoVKpUCgUbNu2jZ9++gkfHx9atWrFiBEjaNWq1X3d/72yb98+9Ho9M2fOfCTXvxOmTJnCqFGjaNWqFcuXL388vC4qF4jaLBdQc/aHcl0hYaEsVdrOYEsQt2Gb9AmbwC1GD3VB2guCCOblaqaHvk6bQ1vpu2MxQk+gnW0RWJPNixVmsyilD2mm8hhEF9SCBYukIDI2FoUkIQoCm6Oi7BM0AJVKjdVqRaPRYLFY8M7fxIY/E7mqakiHTl3w9vYm+fhqCnIzwFbMziQ5kW3xwuCnQZtgQAiRJ5iMAKEwNIzCCewkaDdJ3uAUAH3SH+zYWk2wtRPoEu0Tc7mTGfIYgzzOB9+FOp/Ixs5dkJuVTjfjUXLjIWuZhY+af8s3rVV8ttsJ98+/4tVXXwXgl32/MHjN4FLnH+vmTOgGK7+2VIJksBseJtFEWkEaxrStEPEDVHgK8i/BpdmQawFzkSqe/m8nXPRGFDF6pPebl1Kvui2jMlEMH8EL09qx4tXu/FrpVV5tOxOVIJUscBgNkiTQ+pkjKDe9gae3mRydJ39tC6Nloz9Z1vUblCOLHd/RFUQLnP4WpNL5HxLg7HF/NVUMBgPZ2dmIkhJXlZ7WO7fhkmMgMjaWAxERhMfF0XbnZsRgASFBYmtdNVE/jYQzU1BIFvoFJpGbvpEt6Y3RaFqUar/H3z04n3mea7prtAttB3NXQWpDkGTvxwTGMkb4kgmtr4EELkkunPzuJN6u3vd1X3fC1q1b6dq1K02aNGHFihW4ubkRFRXFyJEjGTRoEP/88w+//vor5cqVe+h9ceDgYSNIdxAYmJubi6enJzk5OY/HP3cHDm7A/PnzefPNN8nNzeXll1/mm2++wc/P7/YnPgDy8/MxGo2o1Wq7MVFoUNyMpKQkYmJiWLZsGZs3b8ZiseDq6kr9+vUZNmwY/fv3/1f6DvDrr7/yxhtvYDQaH1wl7QdMrVq10Ol0nD59+oYrmI8F5nyIqQp+zUDhJMfdJywBY2rRMcORJ3R+2Ffqzd0UXG4eSvCYy2gyLUh+IEwpOkWSgE1g+luDVVJwoUplKiQmkVChPMGJSSRUqECVCxdAktgcHc2BiAhqup/GQ5nN3uwIJBSobHFcVtRIKNBoNEjmfCySEslmaLgq8+lxbClVF15AAEQBln7bm5aHduC/7prs0Ygp1v/CPvq2AM9qYNHJIU9Nfpe9HfdDbH9IWi57A6BkeFpxA07tDV2PgzbojppdsGAB6enp1FZvopV7DKqRkhxy5CfQ+0N3VuXmERncmtXPrmbBiQW8vOJloKQq0a8REOvXlDU57fjW/C3GYon7AG4KJVmD1mANaMX7G99naOOhVPauBJcXYN79DioxBUGAM99WoVrSeegO5vYBqJtOgtC79BLExtK1ZS4r3+3KaVU1qtY8h0oplhgvKVo2WiWVF5izOXK5HqPmfsdX/T+gadW9Jce3hwpeewXiZ8vepJuQhz+ufS+iuEfZ16ysLNavX09N93P4pU8jMaYsYZvPEhvZkvSWAfQZvwSvnBziPaHSSACB/jWe4mfLKnwwYEU2jwvqz8RUtjfe3t5IksTlnMtsuriJuUfnsuXylqILFvjBzFjIrMYbTGMME5jg8xTTh00GEaa1nsYbUW/c073cDRs2bKBnz55ERkaybNkytNqSoX8rV67ktddew2Aw8Pvvv9O7d++H3icHDu6FO7UNHIaGgyeepKQkunfvzpEjR6hWrRq//fYbkZGRtz/xMSIvL4/169ezYsUK/vnnHwoKCv7V79tnn33GhAkTHusE+LJly1KvXj3Wrl37qLtya0QzKIrFjGcegg2twSqLEkgbi0JaCifMJlHNjsxIDOudiN61GadeJgTbPqsERtEJ5QgRp0w5Hl8uxSf/LPwDXmjS6lxc+Pb99ynrlMzL5f8g1+LB2qsdOa8LK3ZWYSsKwuP2EbV5CyqLGVGlRJQEtAYDEnCsdm2W9u1LLbcj9AlcKivw3mjCX3xb72rQ7VTJatj3QtzbcGUHmLPliutDDaUNHIByPaH1sjtrU5fMoXXf43R1LdW0J1Eq5Al5wVIt6p4mNB1ErvpGE3UhlTMZZ3ip3kvMPDQDEYlLk+Wk4XhPiHoXprh247ixOap6KsYfHU+WoaTy0XN1nuNKwRU2XtxIx8odWf3caq7mJcLv7fDzO4cgwOyvBpKT6YloVdK0026adD0G7XfKeQx3itHIy87z+WboaHT+zii9rAR5p1B8jUOSin0cGyF7gQeqXlbcnApKf5aCGloulg28E+Mh+5hcTv66z10CJKcgFH2S77yvN+LEeNIPzuBoVhXO5oXQOWAdIdoENi1sRNVtcfbq3IVcDoHgYl8vo1sY031fYOm5jSTkJlBgKiBDn4GH2oNMY1Fe0xtxMGaTGxNMXzBG/J4QLhOvdSF0tGxM/dD5B4Y2Hnp/93IbVq9ezVNPPUV0dDT//PMPzs43ljPOzMykcePGVK1alTVr1jzUPjlwcK/cqW3gCJ1y8ERTmBisUCj4+uuvGTFixE0TAx9n3N3d6dOnD127dmXDhg34+vr+q0Z9SkrK4+slsKHX6wkKurNV60eK4rrnz6cB+IbDla0AmNqqUUVbUApFazwahZkmXnv5pclbaNqbaOWzA6xQYHUlz+KOpzoHU2cN/suugQ4KTxUoShUoND5c9HrGfvklmztFo/S34rswkwHMJ7u3F0dy69Fg2+EScfBRmzajLTQwzRb0Ls4lcj1cXFxQ+jfBEFoWl4TfoZ0V2plL3qM9TEeATz+7fyMDIOIn+adohrWNoPvRkgn3hSTHwLJKgJXMRjG4+FYtLfxgzITVdUASqW/MQHCT+y9KkNHSl2kVBxPoreIFjzU4O4dw4I2VHE47TOe5nRFthtmEyCKPRr4IORYvTEYTkW6RbH95O+G/hmO2mmgZFM6OlIN4/bmAL7dbmBAJs1Vb+egXDz7xyEcTIH9OoghWq5LMND9cvQqo0i4QOs8Gj7C7GycnJ2oKpzh5tQ5HY2ryv5SP2Px5e+oEH7OLSxX/OKQY8NLlQmFqyzV5m1BYdV3pJOfdhL0FHjWQ1kYgWi0oY6Si0KpCI1jte//SlbXGUiZhEe2UG4n2lVAIErkWJ55uFEfWdaWQNIJcQ6MEeWfJTPiMrRmWEpuLGxkAYza7EmLIZ4z7u0wwf8YY4zQmtE61f3HOXDtzv3dyS5YvX06/fv3o0qULCxYsuOXfW1dXV1JTU3n99dcfap8cOPg3cBgaDp5YLBYLgwYNIjQ0lPXr1/8n1Dp+/vln0tLS/vVVrOTk5FIu/McNo9FI2bL3Fxf+yGj0E2xqDVYL1ubzUe3qiSiZyLd6YJKcMFlV5IleVPW6QvMeH6I6/i6n6cjqk17UcIqjs88SlF0kpM5g2ahAtUjEYlIiWCUUoohFpeJ09erUOX4cAdBYLLTatA0MgCR7O7yXZtNGvw1BhC6rVhG1aRPnq1TB2WZkSIDe2ZnY6Eh2N2oBCLi4uBASEkKn7t3lyXvdd2Hvq5C+ueje1N7QPasoWfvSbHAPk0PHLPn3nySuUMtJ4cXzNEoggu4S+DRi8erdiOIuBg0aVHLBwckHynWHS7MRNpjtBktC8/Kc01VHREFKlsiSUyE4qc/QZ6s8CVQUm0ZPjyhaWT9YTkWGS2Xyc8uQm5tLS7UnXTzd6d34I15oMoKDKQfxmRxur8vQoIuB1z0p6RHqJtdVAXAr54/vC/cuG1xTc56TibUpo01GEhX8vu5VJr32LlaVBypTBmyRr2ntImDtqEK9ziyHwQGsVHCmeWOquSQjGNIgYjpUsIXreNVii2cCR/7+hpG9psIyvVykcjhYujuR/uk/lL/nXiO7Wg6PgezjCIh2g0irMPGiu5Lvc0rmh3gq5HApkwhqW5VyJ0HgjKo8GmUKJqup9DWANxa1wt20nwxnmNDKyvRKfzD9TD9o9D2rnl7FlANT0Kof3t+/RYsW8eyzz9K7d2/mzp1728Ww2NhYdDodnTp1emh9cuDg38JhaDh4Ynn99dfJzc0lJibmP2Fk5Obm8sUXX1CtWjU6duz4r147MTERb++HnwR5r4ii+GQbGl41wbcpeNdHG9IJFPMh5zhutT5izpw5pGek07x5c3o2by6rzVQ8SDWLheBWeub88jmSrWq4IIC6vUh6qwBmJL6GxZbYqsKMxebbqH38OBaVSjYwJAkJELUCVkmFRjTbq5RrDQZqnzghVywXBFZ36cKBiAg0ggEPrRP5enmFuESukFsoNPgONrUFpRbKREHGTuhQAO1sk7yU1ZCyBpBA6QZtVkBhxeh7QZLAqxZ41pLDeUSTrDiVf74oUXkjiOsTCW2ynj316zNv7iwGdgpDkX0A6eouLDihjvwLck9DzDbbKr6Af5sMPFX72ZUlG1auKh0v5u60X7pQDlWFgpZKZ7ZYdTjjzIns1vR66g3aeZeFqzvh0Hssfe00OMs5YYNWDKJJMQ/I3xnQ1AVqa0BZTIFMKCMbGrocJVaTFaVGeU9DVCMwi7Up7RgQsA81Flw1OUiSiNpqMzL+AkRQrgbl0t7weVc4/j8ouAADOzJ68mqWd9PD+WngVUcWObCx9B8T+TnPwbzh0La8PcdIWCGhH3tP3ZW5+CcISjlRnpI5JUK0wOfVOxLi2YER60cAcujTh7Gg6QkTG0FLZ2iulWvTvOeWxYJ0842vs/Mdxpz7HV+rjng3+DVCwNmcgqHJ9wR7BtOlRhe61Ohy43MfAPPmzWPgwIE888wz/PXXX6hUt592rV27lrJly1KnTp2H1i8HDv4tHIaGgyeSy5cvM2fOHAYMGPDIlJoeNN999x25ubmsX3/3cp33Q25uLhcuXKBFi9LKLY8L586dQ5KkJ9fQAGi9vKioWfBTwFMogNatW+Ps7FxKqlelUuHqrObF6ltR5hdNxKzdBOZWft5uZAAISg1YzSzt25eltgrp4XFxRMbGsqtlM+IaNbHX20gP9qfC+WSQJM5XqUJwYmKJUCpXr7K8+uqrJCYmkpqaSil86kH5nlBrbFGYz8YouLKFUpkj1nzY0R/6Xr33cRMEaLOq9PZ9b8HF32XDY5UziivpNNm8gYP1q5BwOZ6vf7uAVqHDKlVApVYzrKUCWq9E6lsZYfEVhO4Srio9atFCJe15Luqq4qnKJkjyJZsrJZK/9zVX01N8lZ7OSv5238ZL1zbSbd1b/NNiEMqLv8k5DbZ6EmarmTMZZzjeRMX0CFs4jwj1E6CPK8zuBi4rkSfU+ySUzkqMOUaOzT9G/Rfr39MQVYyswOWV5fGtlIUGI3+tf542XScR7Zsj5wOJIClAeq0diqa/g8oVgjrK4WQNJsrDrHaBGiNLtb1rt5Kxw0VwsglrdJdDraRuZkLi6pF2qA6Ztf+gZs2ad95h0QrHvwBTLlQeJD8uxVSypGc74h69kuGCwOiNozGJJsbEQoUciF8OH1aCcxVBKUCOFXytOfbMo+Kf29/6j8mxqJnQPsu+LczYijNTtvHad68hlhVv3scHwF9//cXLL7/Miy++yIwZM1Aq78yQXLduHR07dnRI3Dr4T+AwNBw8kfTq1QuVSsW33377qLvyQLh69SrffPMNDRs2JKKYROm/wXfffYfBYOCrr776V697Nxw7JldofiJyNG7GTSon36qOiiK2D9r8A/KbYivheUNL5u+YrRLX/zk/ENGIg40b06tXLw7FxHAgIoKzzarhq87gb31oqWup1WqCg4PJyspCq9VSrdr/tXff4TGmXQCHfzOTSe9BCBEtiN6iBtFbsMqKXtdiWV3U3eXDrtWtXnat3rtVo9foNYgSkqjpPZn2fn8MIRIEYYLnvq5cIXnLmYRkzjzPOacIRYq8YYhclaWp/173INxeCGd7pz22aNonr5mi/Ex4egjUUehGDSBu3BSOVa1Mks4UkKPSGaHSmSBDS80Sz5M4pSV3vdpR0GNWyjYdY7ka71xriVBn03f0itAXdb+YGD3iOOR3T2ZQaVs6Nh/LAJmMoKggav3tjvnSHQz3GMH/Xhlap1Qo8e/rz6PYR2z138qU0y9/Rm2Kh4LhMEECIwkin9nSZXtNTHIVwdr5w2uyFC2aodsow9wmHjNZIvGY0+1oGe43O4LieacwWVNQlTiAyWEvqHsIzByhxSOQv/1pwNNQJTUbOcPFEfpyhrr6NyNJQkYisvj7qQZIvlV8MFyfCI/2QEIQIMGNP/Wfe9HRrJkCo6K9UwpLclnl4kH0AyZ5wMSDYJUMQ86DeX7AF2x2gNIL1lWGTXEw5RRYRcPEA7Y4NlUzqcR4FupgibuSEUXHcO/HCfTt1pc5veZ82Bc7g5YsWcKPP/7IDz/8wIIFCzLcYv3Ro0dcvXqVUaNGfdL4BOFz+bpGGwvfBD8/Py5fvkzt2rWxtbU1dDiZ4vfff0etVrN69erPet+wsDAmT55M2bJlqVChwme99/u4dUtfqPlFr2i8D3UcnOkNEWdffqwpSNlA08wUI5l+m4i5UkPRokWxt7cHwNTUFLlcjkwmw9TUjAEDBlCqVCn69OmDlZUlCtNshKhd071l9uzZ6dixI/369fuwmAt0AVk6Ba5B6z7seu+iMIFqa6HYKK56NmbWoEGcr1gZmUzOy85a4H7uLNV79kfqlxfN1cnkjlyERkr95NpYrsVBGYqNUQTJ6L+2kzwg3Ez/xHb0WQccCldKeYU579Nt3K3egl3tdzP77Dys/rBi682tKdfLZ5uPqs5VSdAmIH8twexzFJThoNkBiXGm2IQ15GRgF6Zfmc7M0zO5+vTq+38t6tTBRh2NxsIIM5LQIaf/VSN94XRd9F266oKRaU6otublee9IMp49AyQd2cOmQMBMZL7ot075viwwT5Ds3pyQvi4+EII2QsIDXv0eAVDfGB7Fw8h5kK1qyocTNYnIkfN3JSPijMEhCYYfBwc5sB4IA/PV8P0YWHMdrIqADhmhOXPQstEkVM+LvR1kdkzpPJHyxcozbdq0jMX7ga5cuUKvXr3o3bv3eyUZoF/NkMlk1KtX7xNGKAifj0g0hC+Oi4sLefPmZdeuXeTOnZvRo0env8XjCxEUFMScOXOoWbMmrq7pPwn8VCZNmoRKpUqZeJ5VBQYGApAzZ04DR/KZhGyG+6sh+ZUtR3VBtqMHtChKrmwWWFpa0t39Id5NKuLp6Unnzp0ZOnQo7u7ulChRgmHDhmFjYwOAvb09nTt3oVrNemTLli1VVyZjY2OMjY1p1KgRwIdv15CbQLVXE+Xn14m6pn8l+wV1woddPz32ZaBof+zs7Khbty6tWrWiU6fO5Ha0Q66f343HiRPIw3TEro5hxe4bzL/fi81PWnInviDRaiu0OhmSBDeTZeyNfzkPY6m7MWpjExySYOhxDbIHMn29yJXfIPExuM+nTqH6RA6PZFzNcbTd2JY80/NwM+xmyjXmNJ6D5hcN8SPjMXte97Dtu6IkZzNCcgUfs6lY70+gZtQeiviPZuSBkZwIPsF7s7WlmO4GD5LzYqmIxcoqmn6DjiF7ZaeOBChyNQKzjP8fOnIgkRJ5LsON6foPvDoEEP2XQ6WwTzt7R5usH7j44t5Xf+felFZwqBHk9U597Ivk5YAMlOZQ6MeUWheAJU2XsNl7M2Ucy/CHh7698ONG+qLwFxufJLV+oKRsO/rJ4UgUjQvgaJIx5kbmyDQynv3zjP79+3Po0KFP2mFPkiQGDBiAq6srM2fOfK8kA/SJhru7Ow4ODp8oQkH4vMQcDeGLdfz4cfr378+VK1eQyWR07NiRwYMHf3EFdN27d2flypUEBQV91ifSISEhFChQgGrVqnHo0KF3n2BADRo04PTp00RHR7/74K9FmB+c7Kgven5RKNujJkw6/PKY8HNwf6V+G9F7ePz4MQcPHiQkJAS5XE7fvn0zr+vYmd5QbCRcHgVBm0BKBqU9fB8Ofn3g7gKoexRyVM+c+6VDrVbz96J5RIY/48eYGzhsvIDOx4dtVkkE3H9Gkk7/pF+OloLmt6mTzRfl4TDYAb9UlRPh3gY3mRuVL12i0qFD+NWqxYWKFRlS6yFK63zgNiTNPXU6HV23dmXN9TWUy1WOg50PYmFskfL5fXf3MeHoBJoVacb3wVNw6fcMwvRPnKsNhbxGcFFjQqXclTjS7ch7P+bNSm8ialuy6Xgr2nRYR9eay9N2GVbawveRac5t1gy2b097zXn9JpMQGcrQJvo6jvRmqGgwx6jUaCgxSr997sE6iLoCxYZBseHotDrWVe3Fg2sODPprBiZmr3WGShlgKYPQN9dMHAo8RJPVTUjUJNLdGtQS5DsC3Y/ACWeoFgx3G+qL7i12QpwXjChTgWVjbmJVyIp/p/1L/dr1M/S1/BgbN27k+++/Z/fu3e/dNSoxMZFs2bIxatQoRo8e/YkiFITMIQb2Cd+MBw8e8NNPP+Hr64tKpaJu3bqMGDGCOnXqGDq0d7px4wbFixenRYsWbNq06bPeu1evXixdupR79+6RJ89HNan85EqXLk1SUlLKFqpvgk4NeytB5MWXT8ayK+HZa0/UDntB1ZVgbPvet4iLi+P27duULfseA+LeR8gOONkJ9qtgmxq8NPonqA5VoMHJT3PP5xISEti3axvNmzdH9rx1qf+uX9l8VocWJQrUmCkSkaEDWQI9py3GKlIi2EZOp2HVWd1tNXK5HEmSQKtGdv1/OBaqhqxgt7feNzY5lrrL63LhyQU6lOzA0uZL06wSRW5wwnb3Y6Qd8HMlmPdKWZYMGQ0LNWRMjTFUda5KRt3M6cmSnF48CMzLs2zZ8f2tHkqF9sVFX2qrSjPrpWlT2LEj7TVrl73MWO8p1Mib/oqnTgeSdTEUFeeAoyccagJP9K25o8NsMM9firUTPQi6IKes9jx1LQ5g3Fqduk1xykRyOfydttZjk/8mfjv8GzdCb6R0AXuTHGY56GQSjo+dlmQJ8l2G9tEdmTtj7md57pKYmIibmxslS5ZkR3pf0HdYv3493t7e3Lp1i8KF33OeiiB8ZmJgn/DNcHFx4b///iMhIYFhw4bx77//UrduXZ48eZKmk09WM2bMGIyNjVm6dOm7D85Ed+7cYcmSJXh5eWX5JAMgPDz82/vFe6aXfiozvCyU9dJA1E2wLarv3CNX6Lea3FmsfwX5PVlaWn66JAMgT1PwugZ980KY9HLYW8TZ18ZVZz5zc3O+a90u1cdCKYS11UPs7Gx49DQaM4WasYkLCJViCKgK/zsBqzx12OS0IWfOnPptL5p4fbJUvIu+29Y7WJlY4dfTj5uhN6m7oi7mv5szqc4kBlQeoD8gyh87KRbqgm9dZ5bcfarvnPWchMTuO7sJTwjHr6ffux/orFmwYgUFw65zRzGQPFZBuJXy58bvRSgZ7A8ykFqCrD7I5EagigLT7O+8rE4HAQ+dcWszHm7f038dkp4gJT9Dp5Uhl0toVEZsGlua7wZ0xCybPUmP7nDpaCUuHC5HVKgt2XOH8jRIjlajpCqnMI5Vv/w34DYaIv2gWQDUe771VZusr7159eH5zeJ66PV3xmssN2ZotaH4+PpwVw0tLEGykRFSKgRLK8t3fx0zwZQpU3j06BH79+//oPNXrFhBpUqVvr2fdcJXTSQawlfD3NycuXPncvbsWQIDA8mRI4ehQ0oRGxvLnTt3CAwM5N69e9y7d4/bt2/j6+vLDz/88NGvtiUmJmJkZJThqei//PILRkZGnz3B+VDx8fHkzp3b0GF8XkUGgFkueHIAmj+Bug/BVwOubtDCHFqXgfonILcX3JoFRQemnUqeFUTf0g/ye3Wqt6SBDQ5Qfb3+lXBJo6/x+MTtPGs27kzN5yMTwsPDWbB1AREh8YB+GN+lagoOOms5+/gAEpJ+ovjJ9vqtYI413+teRbMXJWRwCJv8N9F1W1fGHRnHf03nUCVgpH6QIVDJ0hxLpWWaKdYAK1usJEGdgPHufRh51ob0fkaoVDBwIABKQJNkhJVtLFZmcZQM80f2vBxGu9OEhKZLsG7eBl6vpyD9L3tAACgVKrIXyA8X70DTOwQ9jmT96n+wXPKM+AgzikTdxEuxg0dzsnE0tjyRz+oRF2NJOe0F2rOaE4FVeSTpX8g4Ka9Cfat9yJvokCsswCwHlN6jT5ZjbkPQ+jRJRrImmTvhd9LE9moL2xdDFMs7lmf6julgBlvj4KjWHmcbS1RaFeEJ4WS3eHdy9TGCg4OZNGkSAwcO/KBau9DQUPbs2cPMmTMzPzhBMCCRaAhfnRs3btCyZcss04Pc39+fihUrEh+vf0KjUCgwNzfHysqKevXqMXfu3I++R+nSpXny5AmNGzemWbNmNGrU6I0D+C5fvszatWvp0KFDSreirO5dw/rUajV3797lxo0b+Pv7c+PGDa5cuYJWq+XcuXOpip+/GHal9W9FBsGprpD4FHZo9FuotiRA3WsQcVk/18K5lb6bT75277rq53dhUPpTvTWRcMQLFBaAFsrP0neu+gwexjyk+abmXHxyMdV2nAC1DnM5rHMxRnF1HISdhLJTwP7DV31aFWtFq2KtWLK7E85nO+gzgues0VCvYD3W+aftzHU68BjmfQfQ9GoyrN0AzdNZTTE2hkmTYMQIAMw1CRgbJRMdZ4O2sRzWg8JIh/y7ZKyLBqabZLzJ0UPJ5LCLhac3wdgOrcKCbduXEa+So3WxRBuuxIOTWGnj0J5TEIS+ZXJ5ztKYXciRqGl0hACrImi1xljWi0PxnY6whw5kJwGujYUizzucWbtCCX1NQkxyDFbGVshkMv67/R9RyVFpYnu19fBCd8ihzMG9hfcILRNKYdfC/FD9BxoWakjxHMXTdP36VHx8fLC2tmbMmDEfdP7atWsB8Pb2fseRgvBlEYmG8FW5cuUKcXFx1K//6Yv+Mmr06NFoNBoWL15MtWrVKFKkyHt3InmXkJAQlEolu3btYt26dcjlcqpVq0bz5s3x8vJK1X5y9OjRmJqasmjRokyN4VN5dSr4izqN1xOKe/fuodHoh6MZGRlhaWmJlZUVwcHBnDp1itq1axv4UXwEIwtIegi6pJdbqJoCmhjw9dDPQijQFY62AJe2n3xV4L2V/h2C1uonaCc81LcIekGX/HzbkARXx0K+jvpXuD+hvXf20nh1Y3RS2v3+sc9LFo00kXD9d3BqrJ+UnQl+kN1EUuq7P734DkmaBNZ+t5jD9w/zNOEpMmRISDjGQvFmPXALBWMtsH9/+okGwPDhsGQJ3LlDUfVNkhXGhD3LwT/XetBj8d/IFCAZWWVou9Sr9uyMo1LZMIL39OF+shsnp0wip/wWXrmPkbfHAy64lOP4vx54cJzjeLx4RHhwHDkSOmRYdkxgYJ2ZaLVyFAodMhnY54wErQ7M86Y7W6byksqUyFGCPbf3EKuOTTe2Sa9MXS+gLkDguEA8PDw4NeYU+fOnnRHzqR07doy1a9eydOnSD16dXr58OU2aNCFbtmzvPlgQviCiGFz4qvTp04cFCxbw9OnTLLF16syZM1SqVImePXt+0if2CoUCb29vVq9ezblz55gxYwYHDhwgNDQUnU5H/vz5adGiBYULF6Z3797069eP2bNnf7J4MlNQUBAuLi5YW1sTFxeHTqd/gmhsbIyVlRVOTk4UK1aMihUrUr9+fYoVK4ZcLkej0WBubs6IESP43//+Z+BH8ZFU0bCjECSHvfyYQ2WovgnMnfT1DpdGQO4mkKOG4eJ8F60G1pkBzydmp+piJAf3BeDa85OGkKRJotLiSlx5diXN5+TA/Xzg/GLVQW4CDu5Q79jH3zh4Mxxvm5Jo6Z7/5t1qXoPz1h4sPL8QVztXfpHVpPiQP3GJeXmqxjk3RkEhb752fDzY2PC7dWuCslfncmBZNv7yPbkLPtInNvVOQfbKbzw9va5TxQqFM3nMHeopPbkfn5s8po8wUyQhl+kDD31kx9JxPUmM0xfaW+aypEKfcmim/En52NNcdShB9b/e0qrXoSI08ANtMkkXfIjJUZ/I0DP87TeFKWGJaQ5Pb7sUGjBaZMTEIRMZMmRIhidvZyatVkuFChVQKpWcPn36g15EunnzJm5ubmzcuJFWrVp9gigFIfOJYnDhm7Rv3z5KliyZJZIMgBEjRmBubs6cOZ9uCq1arUan02FhoW+lWaFChZS5GGFhYcyaNYuNGzcyZ84cVCoVFhYWzJgx45PFk9mcnJwoUaIEJiYmlChRgipVqlC/fv13vnJpZGREjhw58PX1/fITDWMbKOoDl31efkxh/nImwpkfISZAPxAtKycaoUf1Kxa654nGq3MZ6ur0k8X9/9C/0i1p9CscpSdkagimRqZc7nMZ+4n2RGpSt3rVAatjYLgDYGwPNsWhVCbd37Y0mNhD0lMA5DIFCeb5+PXuNQKTzzG/8Xxq5qvJlaoFsVCnPlX28NFbi+e1Zqb8NLMe2t+tCU/KhYldMjnyPNOfC/pak/eQmAjxcRJVivmjfGBOYe6muXWIXWm0ttYQp8HKyQrvrd7kds9Ncq8ibG87HVPTODC9B0lvmnGkhCcH4UwvlHH3OH55IdWNk+lqAVPC0h79+nYpdGAbZMuBvQcoV6bcez2+zPT3339z6dIlTp069cEr1StWrMDW1hYvL69Mjk4QDE8kGsJXQ6fTERISwqBBgwwdCgAHDhzg0KFDjBgxIu1Aq0yUkKCv+LS0TNtZJVu2bIwfP57x48ej0WhYt24dRYoUwcjoy/mvb2RkxNWrHzAtGahSpQpbt24lISEh8+ZEGEruxnBzKiTrn0Dy7CD41oZc9fX1GUbm+tWNmACwzqJda6Kvg2VBiL8PkhaaJr9c0fAFduigaeDLeo6bf0GJMaAwzdQw9t/dz/BdkZSLtcE0OpoqwbC+OHRoDeviFQwv01JfsF1rV+bd1DI/yF/8HJCBfTnM6x3lmsKUfy78Q+//emNuZE7+QWXh8hV+362iYgjYqCBJLvHjzJpUrerNphub+K/Df5gpX9YdtdvUjoRVCko/cSLAyQG5iYTSRPPy3heHQJ7G6Yal1cLrCwEXL4KJUTIOK/+HdoOO5IbmmNZNeCXZUFD2uynEPIzHb5YfHXZ3wLGUvsOfSQ4nvj/4fO7GqWT94EnpeVetVCtYJ9AdbYlcE40CaGSajJkcNBoYdQF6vjIfY5JH6u1ShEDxnMXp17efQZOMyMhIRo8eTadOnahc+c0rRm+j0+lYuXIl3t7en3SQoCAYipgMLnw1Nm/ejEqlyhL1GZIkMXz4cKytrZk4cWK6x6hUKp49e0ZISAiBgYGEhaXzMl4GJCbqtxmkl2i8ysjIiA4dOlChQoUPus+XqGPHjmg0Gk6dOmXoUD6eTTFQvFbUHnoErowFdZR+WrXCHPz/1BeOZ0VFfgav6+AdD2ZO0MQR5lpDXVmaqdMAaOP1tRKZKEGVQLO1zXBR2VHvajQeQWAkQbtrsP+IE4OLjCOp0nL9alFs2o5Hb7L91vZ06z4A1lxdw3+3d6ckGpJNce6UXZSSQHUv152E0Qm0K9mOy08uk+BWiPFjqlN3oB2bi4KJFhz2HqPfnn4cenCIs4/Oprr+lFqTyFMqG9Vy7GfH46ZsC2muf1L/XGJ4kL49bTpUqldqxE90IGG9M5YXy1O75H9Ia+6jeBKF6T4T1JIxKRutlVZgV4ZKAyrR62KvlCQjjSpLocY2UqpSnn+PpR36WhW5JjplMrjZQf0hjgoYdky/euF9PfUqRv4BsKisjIYVGnJt/DV6V+z9xu/H5/C///2PxMREJk2a9MHXOHr0KEFBQXTq1CkTIxOErEMkGsJXY+fOnQCUKVPGsIEA27Zt4/z584wZM+aNy+kODg44Ojri7OxMgQIFcHR05NKlS+99rxeJhqifSqtp06YolcosP/k8QxIfg/b53nVfmX6Iny/Ai302EgQuh3vL4eLzmRpa9cttSllN83vQ6gm0DodmgdBUDtl4uboxAPDVwbWJsK0gHGuTKbettawWGq2G425mJCleFmbLgKp+T5l6dDJdl3eFwj/DrYzVMXn+64n3Rm/8Qvz4effPBEUHoZN0bLi+gYOBB/Hx9aHfrn7otCp0RpY0empGs43t01xnduPZxI6IJYdlDk6FnCIqtwNt2ioo1hdskyFwhr5W4c53Nbk95OXgwP5ru5FvRS6qPr2GjS4WGykG3TZIjDdBksBUmQD7PdONPTlZn2gsWwZOzf8i5JEppewuML9jL6SmoHWQofvOHBQWxCpd9V8pszwgN8LUxhSbvDZv/+LkbqhvaCA3hZZW+u+xK8gGgPRiheOVBFMugwQv/cT0dcX17/+sBiRBLlUuon2i2d1rd4a+L5/SjRs3mDNnDmPGjMHJyemDr7NixQoKFChA1aoZH84oCF8SkWgIX42ePXsik8lYs2aNQePQarWMHDkSBwcHhg1Lf4jaiRMniIuLY+TIkaxbt461a9cik8n4/ff3f/X2xdYpkWikJZfLcXR05MCBA4YO5eOpIvVzNYoOhZ2ytK/+g76uQSYHVSxsKwDrzfXzCbIyuRFYusCgcTAL/bapVE8+dfqtVkprSHr2wbcJjAykxtIanHl0Bq2kZWu2R0S+siPrqSU076Dlsm0MiXGnwaoQxN3VF+I/l17vFJ/9Phx7cIwkTRI1/63JnDNzKDK7CFZ/WNFuUzs6bu7Iw5iHBMcE45+YRIdwe/YFnyM6KZroJP21/UP9mXNGX8dlojThUJdD3Pn5DmHxYWjRcjsbdL+of3V/1DGoew8K7j+XEsMo9xpUa3WI00ZViJVbEIUNcTUsuXKyhP4AGVByXLpfF5UKjJUSlUo+ZGq7QeSxfag/RQbyuqD4S0JW9RFBRnWwbn0TKv2T0oo2w9wX6t/XjIVZILsNhIFsB0iuIMlB5wq3VBCsBqv6UGUodGwN+X+CHddyM7zYcFb+uBIrK6v3u/cn8tdff+Ho6PhRW3UTExPZsGEDnTp1yjLt2AUhs4lEQ/hqVKtWDWdnZ6ZMmYJWqzVYHNHR0QQGBgIQEZF+EeayZcsAGDJkCG3atMHb25vatWtz8ODB976fWNF4u2rVqnHu3LmUOSZfLNvi0PiSvg7DS9K/MvydCS9fk0e/EtBfBcu36gvDJU3a7VZZVfGRYPJ8C05TXq5uAKCD+2tgVxnYnBMe/vfel6+4pCLHgvTdoyQkHlpD4vPuUs/M4ccm4Pt8JqRW/QDtjiIg6eDOItZfX8+MUzMwm2hGSPTL7k9D9w1FPXc2d2fo6HUW1Dr96lKSNokEdQLmMi3N5I8xlkkMOWCO3U+h2PwXhITE47jH9NjegyJzilBrWS3+PPGnPjZJ4k7EHRwtHIlXvfw3O8lD/+r+jCrw0Bpi8lzjr/nZ6L/cndBLf5C9lh/3chVgYeMfKWt2AaNGGirUPg+A7KzFG2s0VCowiTlBkZt5aV9lBebGqTs+aXQKDoV5kk95BeIfQMGukK/t+33xnb8D01fatr7y/ZXdBplO/z5SC9vzDMQ7oijhMXIU4xUM0wzjlv8tJnWaRO38WadNta2tLUZGRh9VV7Ft2zZiY2Pp2LFjJkYmCFnLl1MRKggZMHbsWLp3786OHTv47rvvDBKDvb09//33H02aNKFw4cLcvHkzTW/0w4cPU6xYMRwcHFI+1rp1a3x9fbl161aquRfv8iLRsLW1zZT4vzbt27dn3bp1nDx5knr16hk6nI+nVUFnLxhSDbJVBlNHOPYdxNx6uRKwnpdFt7UsMj2EyMhIzp8/T506dTLvlVi5AqpvgID50D8v1J+pn7Pxgi4BkhLAuijkrPPOy0UnReMf6s+eO3to6dYSM6PXEi6ZPtFIUsBYT9ju9vJTMTpQJzwmViPDVptAx5MnUEv6LWh5Z+bFXGlOkjoJLVoCD7/WDekVg8/BrydgcImc5Pd7hjIZJh7UHychsenGppRjrZXWLLu8jMknJvM07im/1fwNNWpkyDBLlthTCJaX1sdcOhoqNYIuJuGcTApnZRw80MDyH7bwINaVpP2mmJonIZfrcyVyP3+CrtMAEujU+gRUJtOvaMjjkSQJtU6JseJlyyuVzoizUeWpne0QMrUtXP0Nqi5/59f+dc03tmNwXAg1zUEtQWwtsJFA8XxFA0AqBJVHQKlGc4lTq4kNcmfO+cWULl36ve/3ORQuXJigoCASExM/eCDoihUrqFKlCoUKFcrk6AQh6xArGsJXpVu3btja2jJ58mSDxlGnTh327t1LQkIChQsX5smTJ6k+HxwcTJ06qZ8sNX8+kOt9CwtfbJ0SiUb6vLy8UCqVHD582NChZI7iw8Fzu/69Y02wKQr1T+sTjhevFMPLrUene8Cx7yFoE6hj3nzdDNq0aROFCxemXr16dOnSJSXRzRQ5qoPHaigzSb/CYWQBJs9b+L6o27jRNHUXquRIUMeludTqa6vpuNKT1af+R5mFZXgS+4RiFg6pjgm0gU1uML8imAIvmi/F6mBXvBELLfvwyKYS0itF3j+elbg2OZ4fzupXTV+sNEx6ObMuRc+jIA+HgpefYWSkv4a5DCzTyc1i1DF039od/1B/5DI5q6+ufn45CSMdtLoOvx0CKxVkbwkyW7AxhQYWsCwn2MhA4/yE0gUuk6Q1S+kQJZNDovMOpMsV9KtBu8rCBjvYURiCt6JKljDWPSZSbUu42h6NTv+0QCfB4XBPLI11yJwaQZ2D751k9PmvD+UWlmP7re0sigHVflD1h9/W6wvCCQN1ANT3kSM9305lvlvNEC8FR33CKZ07+V23MJjChQvrV5/uZLxhwKuePn3K3r17RRG48NUTiYbw1enduzenTp3izJkzBo2jZs2a7N+/n+TkZIoWLUpIiH7LhZ+fH0lJSXh6eqY63tHRkWrVqrF79/sVOooVjbeTy+XkzJnz66jTeBNjW2h2F9p7wqLc0IaXW48SQyB4I5zqCFfHf/AtHj16xHfffUfr1q1RKpV4e3uzatUqqlatSlBQUOY8jle5+UCVlVBmIigsYMfzupTJU2BzLjg3AO78A1vzwn/Fn790/9KZkDPkkau48/wFerWkxj8+nHb5PQF9UXWVh9Dwjv7PSYCnKZjJ4J4atGgYe3Y8+XZNRcPLa7+Y5zDmpILBlQezrrot+QelXc1QaBScdiyCzh7kLXTInn9PZG1g8uWXhd2v0j2/T2hCKKcfnk75+L8FoKk33GsNK3NCQ3+QDQZ89cXTCmCzE1jLwd4mBh0yJOllNmOqAa6dB1U4UvR1gtRaQqPuwOkuxO3/DmMpAnulfqbIuafl0EkQp7HgVJQH8YXH6tv82pd957csWaNPDHSSDrc5biw5v4SLTy4CsC0OEraBRQQMOQb3GkKyA4ysKsfmZEVi65Lyb9bIyASZkRko395Jz5AKF9a3kA4ICPig89esWYNcLsfb2zszwxKELEckGsJXZ9y4cZiamjJt2jRDh0K1atU4dOgQWq2W4sWLExgYyL///gtAjRppB6u1bt2a0NBQgoODM3yPF4nGq9uwhNRe1Gns378ftVr97hO+REYWUPcQNA+Ent1eFlYDIAe78lDmj/e+rCRJLF68mCJFirBr1y4GDx5MSEgIa9euZfny5dy4cYOyZcty5MiRzHw0YGSm39ufvwuYOUKnYuDiAv3aQ9ITCPgLzvQAbRwkBMFaC0h8uXLoH+bPA3XapYN1gUfJY5aHEcfBIREckvTJA8DpZDCTIEaCA4lgL1ehJnUC82IFw3H8DKY1mMb2ttsZWmUo5XKVw0JpgUwmwzzenNn+Fanpcwf5bJDVBerCo2nQrpwDjQ+83G71Qq+z6ScfL3iaw5wc0MQBFDt5uWI1B2SdwWQeOD7fDL3LshGyzhJ0I1WbW4AoHXQN0hK4D+Y8jeHPkADuPnYkPNYenQTPduYgua8p9/5zw710YWyyu7w8NzEq1bWSNcmEJ4QDsPXmVpxnODPmwBgsJlpwM/wmGullx7N4CUZUfbn6U9sNcvU3YaYpXLryCNPmDvp/s02d9LNTGl3Wt3TOorJly4atre0HJxorVqzAy8sLe3v7TI5MELIWkWgIXx1jY2OaNm3Kxo0befDggaHDoWLFihw5cgSZTEapUqXYvn07RYsWTVO3AdCyZUt0Oh1//vlnhq//YuuU+IX1ZsOHD8fY2Jj69evj4OBAp06d2Lx585dfIJ4euRKq/AOyV7YXyeSQvbp+mN97uHPnDp6envz444/kypWLGzduMG3atJSWzR06dODiRf0r1nXq1GHOnDnpdmb6KHIF1PwPJvvB/fvw2yo0OZug0r7+60vD1r2nuXnzpv60hEc8TnOMftUgJDGEWR6mhJtBuOnLbU/xEkQC8y7ByLHQ8SwYo1/leDFqYqE7FBqsIPkHfXvZPHfzMDzfcM7/eJ6LvS7ys+XP/Hm9It2ancFSq2TNtHbsW1UPZEZY19rFvz3vYv7LeB68ut2K1JOvX3c8AXS+oBwEigOkLpb3Qz/S3A9kB0AaAFWjTyOTABWwQ99B6sVWKls5bFsL7jugxzkYW/ABuy43ovSoy2yZ0Aqvw7sxi04i//7blK3sycgrI7nw6AJbb2zFbrId4w7pu1fNPTOXPNPz4L7Ynd0Bu+m4qSOhCaFMPD6RJG1SSuyvJlAL3UlZ/XkaZUTMeA3DCgzjytUbmBrLwbEWNDij3zYnf22KYBYjk8koXLjwByUa169f58KFC2LblPBNEImG8FWaM2cOcrmc2bMz1gf/UytXrhxHjhxBqVTy6NEj6tatm+5xzs7OlCtXjm3btmX42ufOncPIyOiLmvb9uZUpU4a4uDhWr15NmTJl2LJlC61atcLe3p5mzZqxdOnSDx6YmGWZO72sa9ivgRuT4FCDDJ2q0WiYPHkyxYsX58yZM/z5558EBARQsGDBNMe6ubnx8OFDSpUqxc8//0y3bt3w9/fP3M5vNkX1KzbPXTEbhBZlqkMkSYP9o3ls2bKFDRs2oIvVouLNMeyvmZ9swyHbiJfbnmSAlQyaHwaXaJhwUsZtZ1tCXEwIyQ8lTYy41/8e6l/UWJpYImnVmJ+vxuLSMwjac4Rnfz8j56Sc5Or7E5fvTmGuz08EXHDl4b3cYGyLpVUebExtyD5kDNv2zkq13erFSsmfHlBECf9ehZi/4Opt6GatbwWbsopRl5crVpUAOUiV9HUPsjB9XgmAAqSmpCKTgdVTkMWA2Qpw80tEJVfxKDIPXvf/Q46EBjl73evTfW13fO/50mZjG1qsbwHA2KNjWXxuMf129yMsMYzAqEAar2lM/BsGAqabQKnBIcKBixcuMmnSJMzNzaHhOX0diHnuN37PsprChQtz69at9z5vxYoV2Nvb07hx+p3ABOFrIhIN4auUI0cO3N3dWbBgATExH18AmxlKly7NsWPHKF26NG3bvrk9ZJs2bXj8+HGGnvieOnWKhQsX4uXllZmhfpXkcjnt2rXj6NGjxMXF4evrS7169Th27Bjdu3fH0dERX1/fd1/oS2Fklnbatsm7V70uXbpEhQoVGDFiBCVLluTBgwf4+Pi89RxTU1MuXLhAr169WLFiBcWLF8fKyorq1aszdOhQ1q1bx927dzNttUN+Zyam8tSFwjKgmo0vGlUC/jf8uaR5kv7Jz2mSb6SsUujPl1HEuigKyY6J1eSE2phDUwVOxjFYKzQ4KOBKPgX5LexTOm3dPfiAY1uqok42Ynv3DRQxbUrTJVd5OvMae2fFUiTqFgOYhcuT+yQlRzP+8Fg6b+5M6/Wt2XN7DyYKE3Ibm9AzmwN5GsLlyeDVHHrZQLODYBUBxfZAcRN9wiBlA/Xz/+o6Cf2k7n7ACpD1A50XL5sBAJLi+bat1zVF/9tfB7Jl0C1pJQCTGMF9XOgnm8nPD3qyN3oviZpE7kbeTXX6j//9+Nav7aurGKmK5bWgeKzA3cqdkEUhlCxZ8uVJFnnfes2sqEiRIu+9oqHT6Vi1ahXe3t4f1RpXEL4UMikDP/ljYmKwsbEhOjpa9OoXvhgXLlygQoUKzJ07lz59+mTadV/8l/lUA5Zu375N4cKF8fHxeesWquTkZEqVKsWTJ08IDQ3F2Nj4jccKbzdkyBCmT5/O2bNnqVChgqHDyRxbnWF7yMs2t3UBywL6ovHXqNVqQkJCWLRoEZMnT8bMzIy5c+fSpUuX975tSEgI69at48CBA1y9epWwsDCSkvRbaWxsbHB3d6dSpUpUqFABd3d3cud+/1ewZ/35CwWML5HT5BFOJo+wVEZiLtdgJNMSsjMXlrvj2e0cQ9Vg/RPc1wu1AcrnLM+FJxeQnreJsjGxwd7MniRNEmHxYZhJChQyLcZydUojqRf/43VGVmh0RrSd25b8sZYkxFrgLvejlvEhTNsmIdWRcXhjTcptu4gt0UTKrVmy0ZNIk5w4a55QMDmQvOpHWEjJ6BRmJFgWIca+Mt8dmcPfDkk0sACtLxjvJOV7p5JbYZS9IonRd4hIimButBGDLSLJ8XwhU6MD32f6LlSy3q880CrAbcD1+fsX/xZ8gWXoR5SQl/zot5nKUaFzXwQNB4BCX5/S66x+ReJNX8vXBc7Qr2Lct9FvlUICzkI5l3L89+t/5MyZM2Pf6Cxu/fr1eHt7ExYWluEauYMHD1KnTh1OnjxJlSpVPnGEgvDpZDQ3EImG8FWzsbGhZcuWLF26NNOu2a5dO9atW4eFhQWWlpZYWFik+vPrH7O0tMTb2xs3N7d3X/y5YsWKkZSUxL179954zG+//cb48ePZtGkTLVq0yIyH9s0qWrQoiYmJ3L9//+uZ0OtbByIvgDqaFz1Xdci5qa7GpruVuHXvMffu3SMwMJBnz56h0+mQy+XUrl2bLVu2YGmZeR1/bty4wYYNGzh8+DA3btwgIiIClUoFQJ48efDz88PJySlD14qKimJWj1n6J83G+jfJRKLCs3PUDjiIqSYROaCRgZH0ypPd1/Sr0I9NNzfxNP4pv9f+nSauTbA2tcbWxBZLIzN2zWnJnfiC9HBegpWRfluQToJkrQm3kssiT0rixJQqPAvWDxkcwAxsiUZjo+CoZ3XOHa1D8ajTVJOOc86iErWWXEZhDFgWgpx1wcUbbFL/TAiPe4rN9twYvbblS4cMeY1tkNsL1FGgtGXFlZXM29uDnU5aHOQ6ImUw7QZMiAYWoK/PAH12JJGygkE29NuuAHxBt13GT+HzWIg+O8lrdpZGtSumSixeTxxeTzzS+/vI4/BHNVhYAdDC3hp7qV+/foa+x1+Kq1evUqpUKdavX8/333+foXM6d+7MqVOnCAgI+Hp+1gjfJJFoCAL6pW0TExOuXLmSadds3749R44cYejQocTFxREfH//W98+ePcPKyoorV65kuAXt2LFjmThxIpGRkek+4bt9+zbFihWjSpUqHD16NNMe27coISEBW1tbhg4dyu+//27ocN4oLCyMOXPmcPLkSeRyOTKZDLlcnurt1Y9ptVoin96jZ3l/WldQoXxeW6vRwuMo+MvXjI1XHHFycqJgwYK4ubnRqFEjypQp88kfi06n4/z58/zwww9cuXKFp0+fkiNHDgC0Wi3x8fEpb3Fxcbi5uREREcHhw4eRJcu4tf35cMKXTY1SnuiDPsnYVdySssEJXPGoz8N615geF8Jdjf6UbHI4WcAaa2MLlGa5sLcvBjYlwKEC2FciMl7NP/MmEacxp1fe+eQ89hR26LcvvdiKFLrcAeVeNcepznncKc9ZPDjOWVN3Ks26g8JIy8Wj5blytBjRz0zpc6UPtgXSNoBIJfEpnOgAz15rxWxsB98Fp6pTuRd5j4uPz9EysS2yOxJUBt1NkLcBCgPnXpwLWJNmRUPy1dd9qBvLMV6uT2x6sYBfZb9iYhKKQ9KbE4vXE480Kxg6cNvgRq48ubCvZk+ZmmUY7Tn6Pf+VZH2SJNG4cWNu3LiBv7+/vtbkLe7evUuRIkWYMmUKgwalk/0Kwhcko7mBqB4VvmolS5Zk+/btqNVqlErlu0/IgEqVKrF582b69u2boe1KQUFBlCpVin79+rFy5coM3aNVq1aMGzeOGTNm8Msvv6T5/P3799FoNAwZMuS94xdSmzp1Kmq1Osv2sw8KCmLatGksXLgQjUZD9uzZUSgU6HT6bS2SJKX686vv7ezsWHSlKjWKXyK3ZRQARgpwdsrGlDHfM8V93ud/QOjrZdzd3YmLi6NkyZIpScbMmTPR6XRIkoRGoyE5WV+HYWxszIEDB4iNiUWdrIaSwCEoH6V/cn8cD44rPagtP4hWpmFYHTUL3fVD/BoqtWzKa8OPyc+IUqv4Q+HBPyE3cWh5C3spCcJOQcQFCPeDoLWQ+BSjJBmtHJXEa82J0VjiuOMpsjDQblOgQItsB1iGx2FGMjUVR4gsZofCScu9vAUo4nQb6w4nwCI3Ht3BA4gOisbKyerdX5hnx9MmGQDGDqmSDIihgN1KCtgVBp0STFSQF+RPgVy8TDJkQDmgB2AKkuzl9q8XxeVGK3SEY88fJsPpmzwfJymUcFnqIYQL3Z+vTDw3qRqMOPHy85M8YPQJOTOqK4FkhlcfzqRx7zd49Eskk8mYPXs2JUqUYOLEiUycOPGtx0+YMIHs2bPTu3fvtx4nCF8TUQwufNVq1qyJWq3+oM4gb1KxYkWSk5MzvEqSN29e5s6dy6pVq1i3bl2GzilRogT58uVj1apV6X6+cuXKyOVyNm3alOG4hfQtX76cggULUqpUKUOHksr169fp3LkzBQoUYN68eVSuXJkbN27w+PFjQkJCePToEY8ePeLx48c8ffqUp0+f8uzZM549e0ZoaCihoaEEBARw6NAhcrvVfnnhg8YwUAHnDft4dTodISEhNGjwshNWhQoVSExMJC4ujqSkJCRJQpIkNmzYQEREBGqNWr8l6CSUV52lMbuwJRoPjnO+oTvXjh6m0p+FUtURHFAfZkGOTjxxaoOtsQV/Vh9KqE8o9mb2+s5ceVvphwLW2AyNLkLLR1g1OUA+2wTymd0nItkenZe+EJtmupQCezPzZKRsYNopnnZDV9Oo8x7KeV4kb9HH6PdIvWST1wa50Tt+3WpU4Nc17cdlSjDN/toHlej3P3UEuQpKAAuBRsCrtckScAs4D3QC2RxQ3wU2AYmADGQS2BPJJMUo1jq04LHCjtG1n7ehLQ/yh3LMZeb6DEWmL5rPP3ISeSO16H7siQwZxxoVI9uzOMZveMaQKkOokufbqT0oVKgQw4cPZ8qUKW/9PXP79m2WL1/OyJEjMTMz+4wRCoJhia1TwlftwYMH5MuXj5UrV9KhQ4dMuWZSUhLW1tbMmDGDvn37ZugcSZJo27Yt+/fv5+rVqxkqgB0+fDgzZswgNjY23e4k5cqVIyoq6q11HMLbxcTE4ODgwKhRoxg3bpyhwyEhIYFDhw6xYMECdu7cibGxMY0bN2b+/PkfV0B75Te4OR3yNId2RyEoWD/87v79TIv9fe3cuZOmTZuyZ88eGjRogFar5ebNm/j6+hIVFZXm+PJnzlJ730HQSRwsVwePgOPYxkSjQ8auPI25+GMlStYtSc/jPQlLSNuxzVhmTGLfU8itC4HyHb/HHu2GI9+B0hqtKhYF+pUVlc6Y8N125Nz3FJrCumJtCDepQvMCuYm/uIrC5R4giw+Elk9B8R4dhVRRsN0VVOHAK7+SlbZgXxEK99EPL0ylNnDo5V/zAS/GBr2oy3hBAXQE5sOFeVBmlD4/wQJIeH6sBUgLwfuJnLsJZbgQfoGcMTk5M+4MuXLnYvft3egkHc2KNEtVW7DlxhbKO5Unr82X1zUqsyQmJlK8eHEKFizIvn370q296NSpEwcPHuTu3buYmpqmcxVB+LJkNDcQKxrCV83FxQVTU1MuX76cadc0NTWldOnSnDlzJsPnyGQy5s+fj6mpKd26dUvZ6vI2rVq1Qq1WM3/+/HQ/7+npyePHjzMcg5DWpEmT0Gg0Bt02df/+febOnUujRo2ws7PDy8uLPXv20KhRI8LDw9myZcvHd+lxaQ81tkHVlTBylD7JGDEicx7AB1q6dClGRkZUr14d0K9w7Nixg5hjMfDKSIbyZ88yYMYMah88gLkmEXNdEh63j3O8ugdRxjbssm6Mf98aGBsbM+nMpHSTDACVpOJUQuK7kwzQ10QU+gHqHUXx3V0wdwbASKbhQoUyMBOoA9651tPF9leuPPOj8OgtyBpfRNcinPll/2F5veUcHHOQgF0BRAdHv7m1b9wD2OoC2ngwy/Py4zIlKK3AsUY6SQZAX1Ltfh4B2D9/mwfMf/5nOaAFVgLLoORAOOECkgNI3wNdAAeQGoDmpCkltxfjwq8XGG46nKCFQTg7O2MkN6JpkaY0L9o8zZPoFm4tvukkA8DMzIw5c+bg6+vLhg0b0nz+5s2brF69mtGjR4skQ/jmiBUN4auXK1cuSpQowf79+zPleiqVimLFiuHu7s6aNWve69x9+/bRoEED/vrrL37++ee3HqvT6cidOzc5cuRIN1HaunUrLVq04Pjx41SrVu294hD08uXLh7m5Of7+/p/1vjExMUycOJEtW7Zw+/ZtZDIZdnZ2VK1alS5dutCtWzccHR25fPkyFhYW777gFyhXrlwUKVKEQ4cOcXjGYWKfxuLv50/y7WTIDuWfnMUj/DjGRsmYJyWR8GK7iSRxsE4dzru7I9PJsLOxo9+gfgRHBFN5aWUex6effFsaWbKl3RbqFkh/WOZbhZ6Co99B8jMStSaYKZJTiqlpClJdGTKT7FBpMeRpxtzicwnzD3teIH6Cs9a1uaisxOCHgzEyeSU5CD8Hvp76FYg6x8C3GmiTwDwPFOgOxXzAKL0CYy361lIDnv/5LRagz0l06BOPcGi+Bso9lNFuiUThAFJWP5KAULmca1u30qhp0zddUXiDFi1acObMGW7evImV1cuanPbt23P8+HFu374tZmcIXw2xoiEIz7m6unLx4sVMu96iRYsIDAxk1KhR731u/fr1+fnnn/Hx8eHGjRtvPVYul9OmTRtu3ryJRqNJ9bmkpKSUJ8cbN2587zgEfRenkJAQ2rdv/9nvffr0aSZPnkxSUhJDhw7l/v37hIeHs2PHDlq3bs369eu5f/8+AwYM+OyxfQ5RUVGEhoamtDs99r9jXJx8keQjyfAIuAweT49jq4kGmYwoGxsO1q7NlOHD+eu332iwZQvOzs60/L4lPw/+GZlMxsnH+m5cbtnSbyM9ssbID0syALJXgdK/g8IMU4V+G9Wrk7plSJD8DI62AFUsro1dAfDgOLZEUTH2IJUHVU6dZDzcBb419WO8G12G8NP6JKPYaPC6AaXGviHJAH2RhQ/vTDIAjqJPMl7x73cwrH9+HlIYJP2nQ4Dlzs5w+7ZIMj7QzJkziYyMTLUN8/r166xdu5YxY8aIJEP4JolEQ/jqubu7Ex4eztOnTz/6WtHR0YwbN46uXbumnmr7HiZNmkS+fPno2LHjO7dQtWzZEpVKxbJlywD9XuBZs2bh4uLCmDFjKFCggOhg8oH++OMPtFqtQbZN5cmj3yLj4+PDlClTyJs39daTRo0a0bFjR/7++++vKpF88OABERER/PXXX4wcOZLk5GRmzpyJopkCnj8HK89ZhvEnlsSiA+4ULMisQYM4766v8JbL5SiVSrp160aJEiVSrm1naodGpyFeFY9cJsdKqX9F2SLOgnar21Elx0cWKBfqgaS0S+naRFP0Myma6id0S3JTKDURTnWmdGtHzBzMOI4HUdhw1bk+1Ue+sup4Zwkc/x7kxtDkGqzcB7XGwNP/QZnxr3WYSk8bYAXg+MrH5MAv8Mq8c50OWP/KIc/r7u3MwNy4AjW/b0OiUslm4N/+/el29y7OBQq8/9dGAPRbdX/55RdmzpzJtWvXABg3bhx58+ala9euhg1OEAxEbJ0Svnr79++nfv367N378QOjRo8ezYwZMwgICEh5svghjh49Ss2aNdm/fz916775VVatVkv27NnJly8fnTp14o8//iAsLIyCBQsyb9486tWr98ExfOvy5ctHXFwcV69eJVeuXJ/13gkJCVhYWNCzZ08WLVqU7jE6nY58+fIRFRXF9evXcXZ2/qwxfgqzZs1CpVKRkJCQ8rHyZ8/icfA4QUnO5JWCMSYZc5JSPh9lY8OsQYMoU6YM+fLlw83NLd220smaZIrOLcr9qPspHzM3Msfd150aR2vg4uFCtwPdSNAlYGn8fsMIk5KSOHxgL7UjWqGUa3m91leS9MMQj22uyc1zRUhW2RD15GVnofajd+Ba8haU+wsSAuHmNJAZQdEhcGcB9H4KoboPKNB/DDQHrqNPOrYDAwkLsyU0dAtFi+qQdQBe7PB0Ae6DPkMaBbeKISVk52kup69mWrehqVQqSpUqhaOjI7Nnz6Z06dIsWbKEHj16GDo0QchUYuuUIDzn6emJXC6nX79+DBw4kE2bNvHs2bN0j71z5w4dO3Zk7969aYo3Q0JCmD59OoMGDfqoJAOgevXquLm58ffff7/1OIVCgZeXF9evX2fIkCHY2dnh6+vL7du3RZLxkRo3bkx0dDT58+fHx8eH8PDwz3Zvc3NzHBwcuH379huPkcvlHDlyhOTkZNq3b49Wm4FtMllcgQIFUiUZAB7Hj2ObGE0Jrj8fuCcjQWmGSqkkwdSU4x4emJubExERQenSpd84u8bEyISJtSdibfLyF56TlRNNg5ui0Cl47PeYpU2W4jTFiatPrwK8uUD7NWq1mvjA/1DK9d8DnfR8FUMCrQQyGShkOqxsY4gKTZ1kKIyhkNs10MbB2e5wfTxoE/TbpK79BklPYdT/PrBAPxdwGhiAJBXm4kU1gwe7cfnyFooU0ekTotXoC8Nd0BeMA/o9X0OhSAtkZbOJJCMTGRsbM3fuXI4ePYqXlxcFChSgc+fOhg5LEAxGJBrCV8/Pzy/l1eFt27bRunVrHB0dKVKkCMOHD+fOnTsAxMfH07JlSzZu3EjDhg2pVKkSO3fuTHky8ttvv2FpaYmPj89HxySTyejevTtbtmwhIiLirccWLVoUrVbL4cOHuXXrFrVr137r8ULGzJs3jzt37lCxYkWmT59O3rx5GTduHDExMZ/l/gUKFCA4OPitx+TPn58pU6Zw/Phx/vzzz88S16dUsmTJNPvUj3t4EGVjw7XixYlS2HDQsTZTRg/nj9GjmTJiBBcqVqRt27Y0adLknddvV6IdBWz1W38UKPjB5gd00TrKc5Z+yVPJfmAT9dbWo/HKxgCUmFcC98XuLDi3gMjEyDde18rKiobdZrAmeiRT7g0mdq8lsoEgOwCSpEg5rlzt8zjmfcarvWXlcg08b4+biqQCFPraj4Gj9SsZH7AN8vHjp0ydak+pUg8pV64ccvkyatXSIX/1t3tv9CsZqS4vAROBb7tj1KdQp04dvL29CQ4O5tdff820YbGC8CUSiYbw1fvnn3/Inz8/e/bsITAwkODgYFavXo2npyeLFy/G1dWVOnXq8P3333P37l3Onz/Pvn37MDExoWnTppQvX57Zs2fz77//8uuvv2JjY5MpcXXq1AmNRvPOzlUKhQK5XE6NGjUy5b7CSy4uLhw9epSrV6/i5ubG//73P1xcXJgyZUqaV94/REREBGq1Ot3PFSxY8J1JJkD//v2pVq0av/zyC35+fh8dkyE5OzunSTTOu7sza9AgNrdqhU+HNtzzSb29UZIk9u7dS/bsrw+tS0smk7Go6SKM5cZYG1mTY2UOEsISnhdlR1PaYg/XSlwj6V4S005OQ6lQcu7ROfrv7k/J+SWptKQSS84vSXelw9zcnHiVAhOZGrbLIQxit1iy5lE79j6rz72E/Eg6OYnxpoCM8pxlADMopz1L2KNs6QdsmR/chmT46/dCYmIia9asoVGjRuTJk4cxY8ZQvHhxdu3axaRJ95HLK2XgKvWAge99byFjZs+ezcyZMzNtfpMgfKlEoiF81eLi4li/fj1du3ZF/vwlvjx58tCuXTsWLlzIw4cPWblyJRqNhj179rBkyRKKFy9OvXr1OHr0KIcOHcLW1pb+/fuTP39+evXqlWmxOTo64uXl9c7tU8nJySmxC5+Gm5sb586dw8/PDycnJ4YPH07+/PmZN28eKpXqva6l1WrZtWsXXl5eZM+enWnTpqV7XP78+UlMTMzQNfft24eVlRXe3t6fbcXlU1AoFFhZWWFmZoaxsTF2dnbUqFEjpcB79abVPHz6EAATExOsra2xs7MjOTmZ+Pj4d1wdEiMSMdpnRLdl3fhh2g8E+QYBcMqqJmEKS6ZUURBQNICwHGH47PfhVrh+krNap+Zh7EPOPjzL1FNT0Uppt6mdOXOGZzEy6mb3xa96RVT2Rhz38OBeYiHORFfkbnxB7l3LT2ykFcbmiSnJTWX1aa6eSG8KuxGUnQJyo3Q+l5YkSRw7dowffviBnDlz0r59e2JjY1mwYAFPnjxh7dq1NGrUCCMje/Tbqf5EX4uRdngcFAV2veFzQmbInj07AwYMwMgoY99fQfhaif8BwhdJkiSCgoIIDQ1FqVSm+2ZkZMSGDRtISEigS5cu6V7HzMyMDh060KFDB+Li4rC0fFkkKpPJ8PT0xNPTEz8/P+zs7N64P/xDde/enebNm3Px4kXKli2b7jEi0fh8KlSowPXr1zl8+DA9evSgb9++TJo0ieHDh9OlS5dU/z5e9+zZM/755x/mzp1LSEgIlpaWyOVy7r+huDd//vwkJSWRkJCAufmb2pjqmZubs3XrVurWrUvfvn1ZsWLFxzxMg6pSRd/9qUiRIigUCrRaLfHx8TRo0IC2bdvSunVrgoODiY2NxcTEhDp16lCvXj0ePXqEq6trulOXAe4fuc+apmvQJGnwUofgwXGO48GtIrVo1/YIMbJ4pFe2NOnQkaR5WXQuQ0Zpx9Ic7XYUo3Se/JcsWRJ740gcr8zkeo1iBDQpjKkqAS/lNhI1JlS1P4M8uw6fBVOICLXl1MjKVE0+hb+jG0kJ6bU11YGT1zu/Xvfu3WP58uUsX76cwMBA8uXLx8CBA+nUqROFChV6y5k+z98Ko+9E9QSYBGwDNqMfFy4IgvBpia5TQpYXGhrKtWvXuHr1asr769evExsbm6Hz69atm2nD+jKbWq3GycmJ3r17M378+HSPGTp0KAsWLCAuLu4zRyds376dn3/+meDgYCwsLOjduzf9+vXDxcUFePkq87x589i0aRM6nY78+fMzcuRIunXrRo4cOahduzbr169Pc21fX1/q1avHgQMHMlx306dPHxYsWMDGjRtp1apVpj7WrCQpKYnly5ezbt06Ll68SFRUFJIkkStXLho2bEjdunWpU6cOjo4v27tKksTmjpu5sfkG/ZKmYks0kVhTGhuCfwhGkUOB1jj9gno5cko5luJot6NYmVilewwAJzujCVyNJMlQHNQg3wFSU9DVAYUM8AVpB+AKyadNkCt0yNppkOopiNRmx965LErrgmCWC5wagX2ZdG8THR3Nhg0bWLZsGcePH8fKyorvv/+eLl264OHh8Z4vPEiIlQtBEDJbRnMDkWgIWUZcXBzXr19PlVRcu3YtZf6FiYkJbm5ulChRgpIlS1KiRAly5cqFWq1OedNoNKn+rlar8fDw+OguUZ9S9erVyZcv3xtfpe7Xrx/Lli3LcGIlZL5Tp04xcOBALly4gFarpWXLllSqVIm///6bW7duYWZmRv369Zk2bRoFCxZMOS9v3rwUKlSIgwcPprnmnTt3cHV15c8//3yvBgOOjo7Y29tz/fr1b2al68mTJyxatIht27YREBCQknS7ubnRsGFDPD098ff3Z9XKVZS8XhJvIqnJKc7Y1CbXsXEcO3aMHad2sDfXXiSL57/yJFBKSn68IDHqpAz7cZMx7TfwjTFIksTV0zvIf7s9Vkbx8CMQD1jA3Rn5yGf2APkgCVkY6GQgf36baLkVur/kWNgkYKSQI5fLoPxfUKhnquvHxsZy/Phxli9fztatW1GpVNStW5cuXbrw3XffvXPVSxAE4XMSiYaQ5V2+fJn169enJBWBgYGAfstSoUKFUpKJF+8LFSr0Ve53bdeuHU+ePOHQoUMAabZw9ezZkw0bNhAVFWWgCIUXnj17xoABA9i2bRtJSUnkzp2b/v37M2TIkHSf9BcvXhyFQsGVK1fSfE6lUmFqakrHjh1Zvnx5hmOYP38+P/30E1u2bOG77777mIfzxbp69SoLFy5k79693L9/H41Gg7GxMS1btqR92/aETQ8j+EQwlfpXosH0BinnzT0zl367++n/IoHJVRNuHkgmXzQ8UiqZ0rcvNWvWpHr16jg4OKS65/LlywkODqaKlS8e9scx7q2BeJAsIHS2A9mMI5AfkFJWNNTnjNColRykNmFFs9NlzL/IZKAyzs0+49ncun2PW7duERAQQEBAAI8fPwagWLFidOnShQ4dOpA7d+7P9SUVBEF4LyLRELK8Fi1asH//fjw8PFIlFG5ubt/Uq3fDhg1j69at3L59m4CAAIoUKUKNGjUYOnQoTZo0oWvXruzcuTNDHYqEz0Oj0RAREUGOHDneelzFihUJCQnh0aNH6X4+V65cFChQgBMnTrzX/e3t7cmfPz/nzp17Y83C187f35/u3bvj5+dH9+7d+eOPP1K+H5okDWuar6HpwqbY5rNNOedm2E3KLyrP2JpjcXdyZ+dgL0bvS8RMZsbaEiUY++QJDx48AKBEiRLUrFkz5U2pVLJ+/XrCQ5/wk/M0bI/E6pOKpiB7beZm2FMH5g7ux4stS8ZmSdRqdYjSdfwYswGm7wILCwsKFy6c6q1UqVKULFnym/2eCoLw5chobvD1vTwsfDGsrKwoU6YMe/bsMXQoBuXs7ExISAiSJKHRaAD9Vp1mzZpRsGDBlPa2QtZhZGT01iRDkiSmTJnC2bNnAQgMDCR//vxpjitUqNA7Z2mkx8fHh5EjR6bUeXxLNBoNU6ZMYezYseTPn58TJ05QtWrVVMcYmRrRaW+nNOcWcSjC3f53MVGYUH1pdXYeiscuAXDJRtfTp+kKPHjwgCNHjnDkyBH27NnD3LlzMTMzIyQkhJ49e7Jr62riIy2w4eVWxlerICQJdv3TEJCRTDI6hRZzEw1XT5akeIPbNBuymsGLSuDk5CQSCkEQvnri2YtgMA4ODp91GnNWlSdPHpKSkoiIiCBbNn2//WHDhrFw4UKSk5O5ffs2ZmZm77iKkFXExsbSunVrhg8fjru7OwqF4o31NwULFvygLXE+Pj5YW1u/sYHA1+rq1atUrlyZMWPGMHDgQC5evJgmyXgbmUyWkmRcD73OJA8IsTNKNZHbxcWFzp078/fff+Pv74+rqyvly5fHzs4OuVxOJaO1ZDcOR7YDZGHo379yj2S1EplJIpV6a+m6uysjA0cx9H4fek7aiFXp/tSs3YDcuXOLJEMQhG+CWNEQDEYkGnovCtVDQkIoXrw4AA8fPmTixIn8+OOP+Pn5YWtra8AIhYy6efMmzZs35+7duwwfPpxJkybh6urKokWLyJ49O1ZWVlhbW6e8Nzc3z/AsjVfJ5XJ++uknJk2axIkTJ6hWrdoneDRZh1qt5o8//mDChAm4urpy6tQpKlas+N7XiUiMoPrS6viH+gOw0B121M7BnR5dSC+Vnz17Nnfv3mX9+vXIZDJ04Zc4ctuUljnV4AqEAzEg+YKsrhJM7GgyyxKXUjb8M/9/r1zJBuocAusiH/LwBUEQvlgi0RAMxsHBgYiICCRJ+qZf3StQoAAKhYL9+/dTunRprKysUjptAVSqlJEpv4KhbdmyhY4dOyJJEjt27KBRo0YAjBkzhp49e9K3b990J07LZDI0Gs17NzoYP348f/31FxMmTGD37t2Z8hiyoosXL9KtWzeuXbvGiBEj+OWXX9JMF8+onbd2EpYQhoXSgni1fgCgWqsmMCqQYtmLpTr28ePHjB07lj59+lCmTBkADp5/Rk2Ho8hlwG30e6ZU+lUNbV0t8cbFOHjmMNtGz0h78ze0shUEQfiaia1TgsE4ODig1WqJjo42dCgGlS1bNnr06MGkSZOIjo7GwcGBsLAwQ4clZJBWq2XUqFG0bNkSe3t77ty5k5JkAHTp0gWVSoVGoyE8PJxr167h6+vL6tWrmTlzJmvWrPmgbmpGRkZ07dqVPXv2cPHixcx8SFlCcnIyv/zyCxUrVkSSJM6cOcOECRM+OMkA6FymM0EDg1jVchX1CtTD2dqZOFUct8NvpznWx8cHU1PTVNvTjOKvY68MQ5L08zMkC/2b1kvGxWh3Vl8vSu7cub+5uhlBEIQ3EV2nBIM5cOAAdevW5c6dO6lmD3yLHj58SKFChRg2bBh79+4lIiKC27fTPvkRspbw8HC8vb05ePAgjRo1YseOHZ+1cD8pKQk7OzuaNGnCxo0bP9t9P7Vz587RtWtXbt26xZgxYxg5ciTGxsaZfp8kTRK7bu+iVI5SFHJ4OWXb39+f4sWLs2jRInr27AmqaLg+ER6shQR98b4EhKpzsjKkHQCxGhs0Gg1JSUlMmTIl02MVBEHISjKaG4gVDcFgXhQ+izoNUuYxTJ8+HZlMJobzfQEuXLhAmTJlOHr0KH/++Sf//fffZ+8OZmpqyvfff8/mzZu5efPmZ733p5CUlMSIESOoVKkSJiYmnD9/nt9+++2TJBkApkamtHRrmSrJAH3rWUDfZvviCPivBNycDtuDYQDgqy8AdzAKJU5jSbzODktLS3bt2kXJkiU/SayCIAhfIpFoCAbzYiCWSDT0RowYgVKpxM/Pj6SkJEOHI7zFsmXLqFKlCtHR0Rw5coRhw4YZLJY5c+agVCr5448/DBZDZjh16hRly5ZlxowZjB8/ntOnT1OqVCmDxOLi4kK1atVYvXo1GNuCNhEkLewAwtC/B+RoqeN4kiqV3DE3N+fy5cs0bdrUIDELgiBkRaIYXDAYkWikZmdnx/Dhwxk5ciRqtdrQ4QhvMHXqVIYNG0bhwoWzREcwa2trmjRpwsqVK1EoFCgUCmQyGXK5HLlcnuE/Gxsb89NPP71zCGFmS0xM5JdffmH69OlUqFCBCxcupHRfM6T27dszYMAAwv5ZSDbN8zqNpuiTjOe5hEwGVa0PIKu6mKnTZ+Lp6YmdnZ2hQhYEQchyRI2GYDDbt2+nefPmrF27Fm9vb0OHkyUkJCTg5OREfHy8SDayoBe1NG5ubly4cMHQ4aQICwvD3d2dqKiolM5Wr75/9cd8ep8HffF1//79mTEjnY5Jn8jt27dp0qQJQUFBjB8/nkGDBn1QYfynEBoaSq5cuZgzZw69GzmA3w+giUnnSDkxda6Rzbk006dPp1+/fp89VkEQhM9N1GgIWdqZM2do27YtrVq14vvvvzd0OFmGubk5nTt3RqPRsG7dOkOHI7xm9OjRaLVatm7dauhQUsmWLRuBgYFERkYSFRVFVFQU0dHRREdHExMTQ2xsbMpbXFwccXFxxMfHEx8fT0JCAgkJCVSqVInFixd/tvqgsLAwGjdujFwu59KlSwwbNizLJBkA2bNnp169evrtUy7fQ+4maQ+SG4NJdk4c2IJaraZ58+afP1BBEIQsTCQawmd37949vLy8KFu2LCtWrPjsBbRZ3axZs2jXrh1du3bFz8/P0OEIz124cIHly5fTqlUr8ubNa+hwMt2sWbNISEhg2bJln/xeycnJtGjRgujoaHbt2kXRokU/+T0/RPv27Tl27BhBD+6BQ+W0B9iUBs8dLNt2iQoVKuDs7Pz5gxQEQcjCxNYp4bMKDw+natWqSJLEyZMnUzpPCaklJSVRu3Zt7t27x5kzZ77KJ7ZfEkmS8PT05Ny5c4SHh2NqamrokD4JFxcXlEolAQEBn+wFAEmS6NSpExs3buTQoUNUqVLlk9wnM8TGxpIjRw7++N9IBuaZAFLa7YySwpJ8AyR+/Hkko0ePNkCUgiAIn5/YOiVkOUlJSTRv3pzIyEh2794tkoy3MDU1ZevWrZiZmdG0aVPR7tZAXiTEDRo04OjRo3z//fdfbZIBMGrUKO7evcvevXs/2T3GjRvHqlWrWL58eZZOMgCsrKxo1qwZ/67aDMr0f5HeVjQh6Gk8LVq0+MzRCYIgZH0i0RA+C51OR+fOnblw4QI7duz45gf0ZUSOHDnYuXMngYGBtG/fHq1Wa+iQvhnx8fEsXryY0qVLU61aNY4dO4aFhQXLli1jzJgxaDQaQ4f4SfTs2RMrKytmzpz5Sa6/cuVKxo0bx8SJE2nTps0nuUdma9++PdeeXSZUbY8KE/AlZZZGUJQZtfofwtXVFTc3N0OHKgiCkOWIREP4LIYPH87GjRtZvXo1lSpVMnQ4X4zixYuzfv16du3ahY+Pj6HD+eoFBAQwcOBAcubMyY8//khoaCjjx48nNjaWyMhI6tevz++//06NGjUICgoydLiZTi6X07ZtW/bt25fpAwCPHTtGjx496Nq1KyNHjszUa39KDRs2hFYKchy7Td1byUjbgTCQtsPCy1XwbtuBJUuWIJPJDB2qIAhC1iNlQHR0tARI0dHRGTlcEFLodDpp6tSpEiD99ddfhg7nizV79mwJkBYuXGjoUL5KYWFhUvPmzSVAUiqVUqVKlaSjR4+me+yCBQskExMTycrKStq8efNnjvTTi46OlpRKpdS3b99Mu2ZAQIBkb28v1apVS0pOTs60634OYfFhUo4/c0iMRZKNlUlPOiPpsiFJA4sZOjRBEASDyWhuIIrBhU9Cq9WyceNGJk6cyNWrVxk6dChTpkwxdFhftH79+rFw4UL27NlDnTp1DB3OV8PPz4+WLVvy7NkzunTpwtSpU985hC8wMJDq1avz8OFDZs6cyYABAz5PsJ9JzZo1OXv2LI8fP8bGxuajrhUeHk6VKlWQy+WcOnXqixto9++lf+m1sxcqrQqAk7mhshksfpCT03caYWFhwSXrS9w2vs0gy0GYmpuSL08+mjZpKjrqCYLw1cpobiASDSFTqdVqVq1axR9//EFAQAD169dn9OjR1KhRw9ChffE0Gg1eXl74+flx6tSpLNsS9EshSRJz585l0KBBmJubs2fPnvcqTtbpdOTMmZPixYtz6NChTxjp53fp0iXKlSvHtGnTGDRo0AdfJzk5mfr16+Pv78/p06e/uNqs5ORkqiyowsWoiwDkUsD1O2D3H0Q2hrraPNywDScxW6L+hFjABFgBi39bzA8//GCw2AVBED4l0XVK+KySkpKYP38+rq6udOvWDTc3N86cOcPevXtFkpFJjIyMWLduHU5OTnh5eREeHm7okL5YsbGxtG3blp9//pnixYvz8OHD9+6AJJfLyZ07Nw8fPvxEURpOmTJlcHFxYebMmR/chECSJHr27Mnp06fZunXrF5Fk6HQ6Ll68yNSpU2nYsCG29rZcDNYnGUgw1A4stysgDGx3QY+EEIL/TiRsMvQ6B1iDqbkpdh3t6OXfiyG7hhj08QiCIBiaSDSEjxIXF8e0adMoUKAA/fr1o0qVKly5coWtW7fi7u5u6PC+OjY2NuzcuZPo6GhatmzJ1atXRTeq93Tt2jXKlSvHpk2bGDhwIJcuXcLS0vKDruXo6MjTp08zOULDS0hIIF++fAQFBeHr6/tB15gwYQIrVqzg33//pVq1apkcYea5d+8eixYtok2bNuTIkYNy5crx66+/AjB67GgaFW1Ey6ItqZ2vBl3srNnl3IQkG2OmeUCTg+CQBA6JMOK4/npJ2iQiTSPRmeh4dOKRAR+ZIAiC4RkZOgDhyxQVFcWcOXOYOXMm0dHRdO7cmREjRuDq6mro0L56+fPnZ+vWrTRo0IBSpUphbW1N5cqVqVKlClWrVqVSpUofva/+a7VixQp69uyJQqFgx44dNGrU6KOu5+TkRExMDCqVCmNj40yK0nCuXLnCwIEDOXHiBCqVCg8PDwoXLvze11mzZg2//vor//vf/2jXrt0niPTjBQcH07x5cy5evIhCoaBixYr06dOHunXrUrlyZUxMTFIdH3V1E/Kz3ZiuGkyV6UdJilexUAtD9gMymOSR+vo2JjZsnrSZwB8CyZ8//+d7YIIgCFmIqNEQ3ktoaCgzZ85kzpw5JCcn88MPPzBs2DBcXFwMHdo3Jz4+nrNnz3Ly5ElOnTrFyZMniYiIQCaTUbx4cbp168bgwYMNHeZn8fDhQzZv3kxkZCSRkZFERUURGRlJWFgYERERREREEBMTQ2JiIgUKFODUqVPkyJHjo+/7v//9j99++43g4GDy5MmTCY/k89PpdCxevJg//viDoKAgTExM6NKlC3379qVkyZLvfb2goCBcXV1p27Yt//77b5Zs+3rnzh3q1q2LTCZj5syZeHp6vjU5f/AAvJs/IT48lGcxOTAuv4xHNYeje8s9LJWWWM21wqOaB+vXr8/8ByEIgmBAGc0NxIqGkCEPHz5k6tSpLFq0CJlMRp8+fRg8eDC5cuUydGjfLAsLCzw9PfH09AT0e+IDAgI4efIkY8aMYcuWLd9MotGiRQvOnTuHUqlMeTM2NsbMzAxra2tcXV2xt7enbNmy/Prrr5nWDejFCt6TJ0++uERDpVLRu3dvNm7cSGxsLPny5WPatGl069btnV233mbz5s0AzJkzJ0smGdeuXaNevXrY2Njg6+v7zu+bvz80agRBQTmBnPoPhhR7531kMhmSt8SmeZtISEjA3Nw8E6IXBEH4sohEQ3irwMBA/vzzT5YuXYqZmRlDhgxhwIABODg4GDo04TUymYwiRYqQK1cu+vTpw5Ah30Yh6qVLlzh79iw+Pj78+eefn/XeL6ZBP3ny5LPeNzNUrVqV8+fP07BhQ/r370+DBg0yJQHbvn07derUwcrKKhOizFznzp2jQYMGODs7s2/fvgytajVvDs+eScArSVNczjce3+usvl5jkkcsC/PGsmD2ApFkCILwzRKJhpCue/fuMW7cOFatWoWdnR1jx47lp59+Env/vwD//fcfycnJtGrVytChfBbz58/H2NiYcePGffZ7v2gx/Pvvv7Nr1y7s7Oywt7dP85YzZ84slZwPGTKE8+fPZ/oMkIiICI4ePcqcOXMy7ZqZ5dixYzRp0oRixYqxe/fuDM/zuHYNtm6V8WNvFTHxalCbgs39Nx4/4jjki9a/XzPImg5dOmTSIxAEQfjyiERDSOP27dt4eHhgZGTE1KlT6dmzJxYWFoYOS8igjRs3UrFixW+ibiYmJoYVK1ZQpUoVTE1NP/v9TU1NqVKlCgEBAVy7dg21Wo1arU7TCUwul3Pjxo0PKqzObLt372bWrFm0adOG/v37Z/q1tVotzZo1y9Trfqy9e/fSokULKleuzLZt295rtcXEBLy9oV8/Y3Ycuk+rXzajKrT5jcdP8tAnGdM8jFjZdiWWxh/W0UwQBOFrIIrBhVQePnxItWrVMDU15fjx42TLls3QIQnvQa1WY2Njw+jRoxk9erShw/nk5s2bR79+/Thz5gwVKlQwdDgpEhISCAwM5MGDB0yYMIFTp04RERFh8KnYT548oWDBguTJk4fz589/cFvfN2nTpg3379/nzJkzmXrdj3Hp0iUqVqxI/fr12bBhA2ZmZu99je3bYfRoMO9fCXNjc0wUJpjITdh1ZxcaSZP6YAlyynJydtBZ8lh/WXU7giAIGSUG9gnvLSIiggYNGqDT6di/f79IMr5ARkZGuLq6snHjRtRqtaHD+aQkSWLOnDk4OTllqSQDwNzcnOLFi9O4cWOioqIoXLiwwZMM0NdlSJLE1q1bMz3JSE5OZvfu3VluNcPf3x+1Ws2qVas+KMkAGD8eOvV+wr2Ie1x6com1rdfyffHv0yYZgBw5Ib+EiCRDEAQBkWgIz8XFxdG4cWOePHnCvn37cHZ2NnRIwgeQyWQsXbqUq1evMmnSJEOH80mdOHGCGzdu0K9fP0OH8lbBwcHUqFHD0GGg0+l49OgRarWa+fPn8/jx40y9/uHDh4mLi6N58+aZet2P9SK50GjSJgUZkZgI9+7BNpPvsTO3o0fZHtia2nIi+ETKMb3OQuAM/XudTMfdyLuZErsgCMKXTiQaAiqVilatWnH9+nX27NmTUuAqfJnKlSvH8OHDGT9+PFevXjV0OJ/M/PnzMTU1ZejQoYYO5Y2CgoKIi4ujatWqhg4FuVzOvXv3qFWrFvPnzyd//vz4+PgQFhaWKdfftm0b+fPnp0SJEplyvczyonYnKSnpg87/4w8oWzEB//BrRCVFMbnuZAA6l+6ccszEA/oC8IkHQC6TU9jB8LU4giAIWYFINL5xWq2WTp06cfjwYbZt25bltqAIH+bXX3/F1dWVbt26ffAruVlZaGgo69evp06dOhgZZd2eFqtWrQLIEokG6CeZ79u3jzt37lCxYkWmT59O3rx5+eWXX4iKivqoa+/YsYOmTZtmudkZL1Y0PjTR+PdfiKnWD4VMwYyGM1JaAKu0qpcHyV55/86qR0EQhG+HSDS+YZIk0bdvXzZu3MiaNWuoXbu2oUMSMomJiQlLly7l4sWLTJs2zdDhZLqlS5ei0+mYMWOGoUN5q71792JjY5Mluk29ysXFhaNHj3L9+nVKlizJ77//jrOzMxMmTCA2NvaDrpktWzYOHz5McnJyJkf7cV6saCQmJr73ubdugVqj4YZ8Aw7mDnQo2YE4VRw9tvfg+w3fpxw3ujbct4EFLZzpWa5npsUuCILwpROJxjfs119/ZeHChSxatIiWLVsaOhwhk1WsWJEhQ4bw22+/cfPmTUOHk2l0Oh1z584lX758KZO5s6rr16/j4eGR5V7lf6FIkSL4+flx8eJFChYsyG+//UbevHmZOnUqCQkJ73Wtf//9l5s3b2a5bmcfs3Vq1CgwKbcRnaRjfev1bPLfRPF5xVl2aRmhCaEpxy10h+LDzOm56BwLmi7ItNgFQRC+dCLR+EbNnDmTCRMmMHnyZHr06GHocIRPZNy4cbi4uNCtW7c0sx2+VPv37ycoKAgfHx9Dh/JWKpWKyMhIqlWrZuhQ3qlUqVJcunSJkydP4uTkhI+PD/ny5WP27NkZXqEoXbo0EydOZNq0aRw4cOATR5xx77t1asxvyVSf2J9j909y6LCOx+V74ZHXg9I5SxMSE4Jaq0Yr6f8vvVoEnqBJYOLRiZ/scQiCIHyJRKLxDVq+fDmDBg3Cx8eHYcOGGToc4RMyMzPjn3/+wc/Pj1mzZhk6nEwxb948LCws6Nkza29R2b59O1qtNsvUZ2REpUqVuH79OgcPHsTa2poBAwZQoEABFi1alKF2yYMHD6ZWrVp06dKFyMjIzxDxu73v1qm1a2UcDz5OjXnfEVm/JTpJYm3rtRy8d5ABlQdwsvvJlGNfnQIOsOXmFhLV779FSxAE4WslEo1vzI4dO+jevTs9evT46tufCnrVqlWjf//+jB49mtu3bxs6nI8SHBzMjh07aNasWUpRbla1efNm5HI57u7uhg7lvXl6enLnzh127tyJUqmkV69euLq6sn79+reeJ5fLWbZsGfHx8fTu3ZsMzIP95N5365S9vQ5kWrAIhSLb0KgU2LcbTJ3ldbCdZEuJ+fquWr3OgpUKwk3108ABIhMj2XzjzVPDBUEQvjVZ+ze1kKmOHj1KmzZtaNasGQsWLMiy+8aFzDdx4kScnJzo0aMHOp3O0OF8ED8/P7p164ZcLmfq1KmGDued/Pz8KF26NObm5oYO5YM1btyY+/fvs2HDBpKSkvD29ubgwYNvPcfZ2ZkFCxawfv16Vq5c+ZkifbMXW6cyuqKRx1kH8ued2mSAWRSU+RdkEJ0cTbw6HtCvYjgkQqyJvkbD1sSWvR324l3CO/MfhCAIwhcq6/aFFDLVxYsXadq0KVWrVmX16tVZuiWokPksLCz4+++/qVWrFvPmzcvyQ+5e0Gg0bN26lalTp+Ln54eZmRmDBg3CycnJ0KG90+PHj/Hy8jJ0GJmidevWfPfddzg6OtK5c2f8/f2xtrZ+4/He3t7s3LmTvn37Ur16dfLly/f5gn3N+26dyldAC/GvtITWykGhT857ndUnGJM8YHI1GX+etyOwaz3cne7RvWx3qrp8OdvkhE8rIiKCW7duAaS8qPf6ezc3N6ysrAwToCB8LlIGREdHS4AUHR2dkcOFLCYgIEDKkSOHVKFCBSkmJsbQ4QgG9NNPP0kWFhbSvXv3DB3KW+l0Omn+/PmSs7OzBEh2dnbSyJEjpeTkZEOHliFXr16VAGnt2rWGDiVTHTt2TFIoFFK3bt3eeWxUVJTk4uIieXh4SBqN5jNElz6dTifZ2tpKFhYWUo8ePaRTp05JOp1O8gv2k3bc2iFpddqUYwMjAiWzNj0lBueWGIvEGGP9++dvgTZIEvr3+WbmM9hjErI+T09PCf1UlTe+2djYSGPGjJFCQ0Pfeq3Y2Fipc+fO0qxZsz5T9ILwbhnNDWSS9O5NtDExMdjY2BAdHf3WV7GErOfhw4dUq1YNU1NTjh07Rvbs2Q0dkmBAsbGxlCxZkoIFC+Lr65tlt89pNBqsrKyQJIklS5bQsWNHQ4f0Xn755RcmTJjA/fv3cXFxMXQ4mapLly4sX76c7du307Rp07cee+zYMWrWrMmECRMYNWrUZ4owreDgYJYuXcrff/9NUFAQxYoVI2/HvBzWHSa3VW5+KPcDfd37IkOGlXcfKL0G1KZgEp/qOq+uaDxo25DdHXcb6BEJWVlwcHBKm+iGDRum1CpJkpTyZ5VKxZo1a1iwQN8O+ccff2TIkCHkyZMn1bVerIxeuHCBnDlzEhISgkKh+LwPSBDSkdHcQCQaX7HQ0FBq1apFTEwMJ06cwNnZ2dAhCVnA/v37qV+/PgsWLKBXr16GDueNRo4cyZQpUzh37hxlypQxdDjvZdCgQcycOfOz/cxMTk5m3LhxKBQKcubMmebNwsIi0+6l0+lwcnJCq9Vy8+ZNHBwc3nr8qFGjmDJlCqdOnaJChQqZFseH0Gq1HDhwgCVLlrDp/iZ0TfRbohQoyGmVkxouNcg9KYwRVw6BXMPoOrCwAi8nf79iUdNFYjifkK4pU6bw66+/8vTp03f+/w8LC+Ovv/5i9uzZxMfH07VrV3x8fChUqBD+/v40atQIrVbLb7/9xo8//oivry916tT5TI9EEN5MJBrfuICAABo3bkxsbCxHjhyhaNGihg5JyEJ69uzJunXruHbtGnnz5jV0OOmKiorCxcUFZ2dnrl27Zuhw3svIkSOZNGkSz549+yyriFOmTGH48OEYGxujUqnSdHsyMzMjR44cODk5UbRoUaZNm4adnd0H3+/SpUu4u7vTokWLd3aiUqlUVKlShbi4OC5cuJCpSc/H+NfvX3rs6YGOl80RlCi5O0OLc7T+Y/dtIP+gl+cYyYxY12od005PY93368hjnef1ywoC5cqVo0CBAmzcuDHD58TExLBgwQKmT59OaGgoLVq0wNfXFxcXF/777z9y586Nq6srtWrVYvHixZ8wekHImIzmBqLr1Ffo2LFjVKlSBaVSyenTp0WSIaQxdepUTE1Ns/RsDVtbW8aMGYO/v3+WGgCXES86HalUqk9+r9DQUMaNG0eJEiVISkpCpVIREBDA1q1bmTx5Mn369KFevXrkyJGDp0+fsmLFCpo0aZLh4uj0lClThj59+rBhwwbWrl371mONjY1ZtWoVwcHBWWpuT94ceTFT6r9PSpkSK7UVsm0yJkbriDCREW4Kk6vLqJirIrmtciOXyXEwd6CaSzVO9DghkgwhXTdv3uTixYu0b9/+vc6ztrbGx8eHwMBAZs+ezfnz56latSrHjh0jT548yGQy4uLiUCqVnyhyQfhEMrPgQzC8VatWScbGxpKnp6cUERFh6HCELMzb21uqWrWqocN4q4SEBClnzpySi4uLoUN5L5MnT5YA6e7duxk+JyEhQRo3bpxUoUIFafXq1ZJOp8vQeT/99JNkZGQk3bx5M0PHL168WFIoFFKTJk0ktVqd4fjSkzdvXsna2lp6+PDhO4+dN2+eBEg7duz4qHtmlnMPz0mWv1tKTtOcpDEHxkhJ6iQpJiZGevz4sRSbFCtZ/m4ptVzbUpIkSQqLD5NG+Y6SHKc4SkFRQQaOXMjKfv31V8na2lpKTEz8qOvodLpUPwOCg4MlmUwmLVq06GNDFIRMkdHcQCQaXwmdTidNmDBBAqROnTp9Md15BMOZMWOGZGpqmuX/rSxevFgCpJUrVxo6lAybO3euBEg3btx457E6nU7auHGjlCdPHkkmk0nW1tYSIHl6ekr+/v5vPdff31+Sy+WSl5fXe8U3btw4SSaTSd26dctwQpOegIAASalUSg0bNnzndXQ6ndS4cWMpe/bs0pMnTz74npnlWdwzqeuWrtKT2PRjeRr3VEpUf9yTReHbotPpJFdXV6lLly6Zfu2JEydKZmZm4nmYkGWIROMbolKppO7du0uANHbs2I964iB8O06ePCkB0tmzZw0dylup1WrJ1dVVyp49u6FDybB//vlHAqRLly699birV6+mtMF0dHSU9uzZI0mSPhEwNTWVjIyMpG7dukm9evWS2rdvLzVt2lSqUaOGVLp0aSl//vySpaWlZGpq+kE/m3v16iUB0siRIz/oMb7wyy+/SIC0ePHidx775MkTKXv27FLTpk3Fzynhq3Pu3DkJkPbu3Zup19XpdFKhQoWkTp06Zep1BeFjiETjGxEVFSXVrVtXUiqV0rJlywwdjvAFSUxMlJRKpTRnzhxDh/JOmzZtkgBp6tSphg4lQ9atWycB0pkzZ9L9fHh4uNSvXz9JLpdLZmZm0tixY9McExkZKXl6ekoWFhaStbW1ZG9vLzk6OkouLi5S0aJFpfLly0uenp4ftRXJy8tLAqSZM2d+8DUkSZIKFy4smZmZZWg+y9atWyVAWrJkyUfdUxCymsGDB0s5cuT46C2Jrzt69KgESIcOHcrU6wrCx8jUORrR0dHY2toSHBwsuk5lIcHBwbRu3ZrHjx+zatUqqlevbuiQhC9MrVq1cHV1ZdGiRYYO5a0kSaJWrVrcuXOHoKAg5PKs3cdiz549eHt7s2fPHqpUqZLyca1Wy7///su4ceOIjY2lbt26LF26FEtLS4PFWqtWLS5cuMDixYtp06bNB13j4cOHlClThvLly7Nr1653fn/69evHpk2bOHHiBAUKFPigewpCVqLT6ShWrBheXl5MnTo1U689dOhQ/vvvP65fv57lf/YJ346YmBicnZ2JiorCxsbmjcdlKNEICQkRMxgEQRAEQRAEQUgRHBycZtDkqzKUaOh0Oh49eoSVlVWWnSQsCIIgCIIgCMKnJ0kSsbGxODk5vXWlLUOJhiAIgiAIgiAIwvsQm/0EQRAEQRAEQch0ItEQBEEQBEEQBCHTiURDEARBEARBEIRMJxINQRAEQRAEQRAynUg0BEEQBEEQBEHIdCLREARBEARBEAQh04lEQxAEQRAEQRCETPd/j/QaE1JQi9kAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt_directed = static_visualisation(networkGraphs, 'Railway Network Directed Graph', directed=True)\n", - "plt_directed.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt_undirected = static_visualisation(networkGraphs, 'Railway Network Undirected Graph', directed=False)\n", - "plt_undirected.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# plot_metrics_on_map(networkGraphs, directed_node_metrics, 'Metrics on the Map', directed=False).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Spatial and Temporal Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create frames of the graph\n", - "May take very long to create all of the frame images for 2 Days, one frame is created every 5 minute. You may change the range to create less frames and a shorter video." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib Notebook\n", - "temporal_graphs = create_temporal_subgraph(networkGraphs)\n", - "slider, plt = plot_temporal_graphs(temporal_graphs)\n", - "display(slider)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temporal_graphs = create_temporal_subgraph(networkGraphs,0, (3*24+12)*60, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "slider, plt = plot_temporal_graphs(temporal_graphs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a video of the graph with cv2 (OpenCV)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import cv2\n", - "# import os\n", - "#\n", - "# # Path to the folder containing the image frames\n", - "# frames_folder = 'frames/'\n", - "#\n", - "# # Get the list of frame filenames in the folder\n", - "# frame_filenames = os.listdir(frames_folder)\n", - "#\n", - "# # Sort the filenames in ascending order\n", - "# frame_filenames.sort(key=lambda x: int(x[:-4]))\n", - "#\n", - "# # Read the first frame to get its dimensions\n", - "# frame = cv2.imread(frames_folder + frame_filenames[0])\n", - "# height, width, layers = frame.shape\n", - "#\n", - "# # Create a VideoWriter object to write the video\n", - "# fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n", - "# video = cv2.VideoWriter('output.mp4', fourcc, 30, (width, height))\n", - "#\n", - "# # Loop through the frames and add them to the video\n", - "# i=0\n", - "# for filename in frame_filenames:\n", - "# frame = cv2.imread(frames_folder + filename)\n", - "# video.write(frame)\n", - "# print(f\"\\r{i/2764*100:.2f}%\", end=\"\")\n", - "# i+=1\n", - "#\n", - "# # Release the VideoWriter object and display a message\n", - "# video.release()\n", - "# print('\\nVideo saved as output.mp4')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SHORTEST PATH ANALYSIS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "get_shortest_path(networkGraphs, source=1136, target=1095, directed=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/application/app.py b/application/app.py index 2324f036..603fa2f4 100644 --- a/application/app.py +++ b/application/app.py @@ -14,6 +14,7 @@ from application.routes.hotspot.hotspot_routes import hotspot_routes from application.routes.metrics.global_metrics_routes import global_metrics_routes from application.routes.visualisation.visualisation_routes import visualisation_routes +from application.routes.stochastic.stochastic_routes import stochastic_routes sys.path.insert(1, '../') from src.NetworkGraphs import * @@ -40,6 +41,7 @@ app.register_blueprint(hotspot_routes) app.register_blueprint(global_metrics_routes) app.register_blueprint(visualisation_routes) +app.register_blueprint(stochastic_routes) BASE_URL = 'http://localhost:8000/api/v1' diff --git a/application/dictionary/__init__.py b/application/dictionary/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/dictionary/information.py b/application/dictionary/information.py index 17207fef..f27fdcfb 100644 --- a/application/dictionary/information.py +++ b/application/dictionary/information.py @@ -78,6 +78,7 @@ 'list_of_nodes': 'Enter a list of nodes to remove from the network for a personalized resilience analysis, ' 'allowing you to evaluate the impact of specific node deletions on the overall network stability.', + } description = { @@ -141,7 +142,8 @@ "closed loop of three nodes. By examining the number of triangles a node participates in, " "one can assess its tendency to form tightly connected groups. This metric helps identify nodes " "that contribute to the network's cohesion and community structure, revealing potential " - "subgroups or areas of high connectivity.", + "subgroups or areas of high connectivity. Triangle computation is not allow for Directed and " + "Multi graphs.", 'node_pagerank': "Node PageRank is a metric that estimates a node's relative importance within a network, " "considering both the quantity and quality of its connections. Inspired by the algorithm " @@ -322,4 +324,17 @@ "groups similar nodes using their embeddings. This combination provides a more " "comprehensive understanding of network organization, relationships between communities, " "and individual node roles, unveiling shared interests or functional clusters.", + + 'clustering_coefficient': "The Clustering Coefficient Estimator provides a robust and efficient method for " + "computing the clustering coefficient in complex networks, without the computational " + "overhead of traditional techniques. Leveraging a stochastic Monte Carlo " + "approach, our algorithm performs the metrics computation over 10," + "000 iterations, ensuring accurate estimations while significantly reducing " + "processing time. ", + + 'shortest_path': "The Shortest Path Length Estimator provides an efficient and accurate estimation of the " + "shortest path length in complex networks, without the computational burden of traditional " + "methods. Utilizing a stochastic Monte Carlo approach, our algorithm samples the metrics " + "computation over 10,000 iterations, ensuring reliable estimations while significantly " + "reducing processing time.", } diff --git a/application/routes/centrality_routes.py b/application/routes/centrality_routes.py index f85d1312..bb2b1a16 100644 --- a/application/routes/centrality_routes.py +++ b/application/routes/centrality_routes.py @@ -1,28 +1,21 @@ -from flask import Blueprint, render_template, session, request, redirect, url_for -import csv import sys -import scipy as sp -from flask_caching import Cache -import matplotlib.pyplot as plt -import re -import time -import shutil + +from flask import Blueprint, render_template, session, request sys.path.insert(1, '../') -from src.utils import * -from src.NetworkGraphs import * from src.metrics import * -from src.preprocessing import * from src.visualisation import * -from flask import g centrality_routes = Blueprint('centrality_routes', __name__) -#-------------------------------------------CENTRALITY-------------------------------------- - +# -------------------------------------------CENTRALITY-------------------------------------- @centrality_routes.route('/centrality', endpoint='centrality', methods=['GET', 'POST']) def centrality_all(): + """ + :Function: Visualise the centrality metrics + :return: the centrality page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'centralities' @@ -31,10 +24,8 @@ def centrality_all(): multi_toggle2 = True directed_toggle2 = False multi_toggle3 = True - dynamic_toggle3 = False directed_toggle3 = False multi_toggle4 = True - dynamic_toggle4 = False directed_toggle4 = False tab = 'tab1' if networkGraphs.is_spatial(): @@ -47,30 +38,35 @@ def centrality_all(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'directed_toggle') is not None or request.form.get('layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, layout=layout) + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'directed_toggle2') is not None or request.form.get('layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) layout2 = request.form.get('layout2') df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'directed_toggle3') is not None or request.form.get('layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) layout3 = request.form.get('layout3') df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'directed_toggle4') is not None or request.form.get('layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) layout4 = request.form.get('layout4') @@ -78,7 +74,8 @@ def centrality_all(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, layout=layout) + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -90,7 +87,7 @@ def centrality_all(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -100,7 +97,7 @@ def centrality_all(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -112,13 +109,22 @@ def centrality_all(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('centrality/centrality_all.html', example=df, tab=tab, method_name='All Centrality', - multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, + graph1=graph_path1, + multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, + graph2=graph_path2, + multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, + graph3=graph_path3, + multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, + graph4=graph_path4) + @centrality_routes.route('/centrality/degree', endpoint='degree', methods=['GET', 'POST']) def centrality_degree(): + """ + :Function: Degree centrality page + :return: degree centrality page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'degree_centrality' @@ -147,15 +153,20 @@ def centrality_degree(): layout4 = 'sfdp' if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -163,7 +174,9 @@ def centrality_degree(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -171,7 +184,9 @@ def centrality_degree(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -180,7 +195,8 @@ def centrality_degree(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -192,7 +208,7 @@ def centrality_degree(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -202,7 +218,7 @@ def centrality_degree(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -214,13 +230,22 @@ def centrality_degree(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('centrality/centrality_degree.html', example=df, tab=tab, method_name='Degree Centrality', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @centrality_routes.route('/centrality/eigenvector', endpoint='eigenvector', methods=['GET', 'POST']) def centrality_eigenvector(): + """ + :Function: Visualisae Eigenvector Centrality + :return: Eigenvector Centrality plot page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'eigenvector_centrality' @@ -247,17 +272,22 @@ def centrality_eigenvector(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -265,7 +295,9 @@ def centrality_eigenvector(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -273,7 +305,9 @@ def centrality_eigenvector(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -282,7 +316,8 @@ def centrality_eigenvector(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -294,7 +329,7 @@ def centrality_eigenvector(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -304,7 +339,7 @@ def centrality_eigenvector(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -315,14 +350,24 @@ def centrality_eigenvector(): else: graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 - return render_template('centrality/centrality_eigenvector.html', example=df, tab=tab, method_name='Eigenvector Centrality', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + return render_template('centrality/centrality_eigenvector.html', example=df, tab=tab, + method_name='Eigenvector Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @centrality_routes.route('/centrality/closeness', endpoint='closeness', methods=['GET', 'POST']) def centrality_closeness(): + """ + :Function: visualise closeness centrality + :return: closeness centrality page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'closeness_centrality' @@ -349,17 +394,22 @@ def centrality_closeness(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -367,7 +417,9 @@ def centrality_closeness(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -375,7 +427,9 @@ def centrality_closeness(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -384,7 +438,8 @@ def centrality_closeness(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -396,7 +451,7 @@ def centrality_closeness(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -406,7 +461,7 @@ def centrality_closeness(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -417,14 +472,24 @@ def centrality_closeness(): else: graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 - return render_template('centrality/centrality_closeness.html', example=df, tab=tab, method_name='Closeness Centrality', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + return render_template('centrality/centrality_closeness.html', example=df, tab=tab, + method_name='Closeness Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @centrality_routes.route('/centrality/betwenness', endpoint='betwenness', methods=['GET', 'POST']) def centrality_betwenness(): + """ + :Function: Visualization of the betwenness centrality + :return: betwenness centrality page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'betweenness_centrality' @@ -451,17 +516,22 @@ def centrality_betwenness(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -469,7 +539,9 @@ def centrality_betwenness(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -477,7 +549,9 @@ def centrality_betwenness(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -486,7 +560,8 @@ def centrality_betwenness(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -498,7 +573,7 @@ def centrality_betwenness(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -508,7 +583,7 @@ def centrality_betwenness(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -519,14 +594,24 @@ def centrality_betwenness(): else: graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 - return render_template('centrality/centrality_betwenness.html', example=df, tab=tab, method_name='Betwenness Centrality', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + return render_template('centrality/centrality_betwenness.html', example=df, tab=tab, + method_name='Betwenness Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @centrality_routes.route('/centrality/load', endpoint='load', methods=['GET', 'POST']) def centrality_load(): + """ + :Function: Visualize the load centrality of the network + :return: + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'load_centrality' @@ -553,17 +638,22 @@ def centrality_load(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -571,7 +661,9 @@ def centrality_load(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -579,7 +671,9 @@ def centrality_load(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -588,7 +682,8 @@ def centrality_load(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -600,7 +695,7 @@ def centrality_load(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -610,7 +705,7 @@ def centrality_load(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -622,7 +717,11 @@ def centrality_load(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('centrality/centrality_load.html', example=df, tab=tab, method_name='Load Centrality', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) diff --git a/application/routes/cluster_routes.py b/application/routes/cluster_routes.py index e2fe627f..760ed4ab 100644 --- a/application/routes/cluster_routes.py +++ b/application/routes/cluster_routes.py @@ -1,28 +1,21 @@ -from flask import Blueprint, render_template, session, request, redirect, url_for -import csv import sys -import scipy as sp -from flask_caching import Cache -import matplotlib.pyplot as plt -import re -import time -import shutil + +from flask import Blueprint, render_template, session, request sys.path.insert(1, '../') -from src.utils import * -from src.NetworkGraphs import * from src.metrics import * -from src.preprocessing import * from src.visualisation import * -from flask import g cluster_routes = Blueprint('cluster_routes', __name__) -#-------------------------------------------ML-CLUSTERING----------------------------------- - +# -------------------------------------------ML-CLUSTERING----------------------------------- @cluster_routes.route('/clustering/louvain', endpoint='clustering_louvanian', methods=['GET', 'POST']) def clustering_louvanian(): + """ + :Function: Visualise the clustering using louvain algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'louvain' @@ -33,29 +26,35 @@ def clustering_louvanian(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_louvanian.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Louvain') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Louvain') + @cluster_routes.route('/clustering/greedy_modularity', endpoint='clustering_greedy_modularity', methods=['GET', 'POST']) def clustering_greedy_modularity(): + """ + :Function: Visualise the clustering using greedy modularity algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'greedy_modularity' @@ -66,29 +65,36 @@ def clustering_greedy_modularity(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 - return render_template('cluster/clustering_greedy_modularity.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Greedy Modularity') + return render_template('cluster/clustering_greedy_modularity.html', example=df, + number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Greedy Modularity') + @cluster_routes.route('/clustering/label_propagation', endpoint='clustering_label_propagation', methods=['GET', 'POST']) def clustering_label_propagation(): + """ + :Function: Visualise the clustering using label propagation algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'label_propagation' @@ -99,29 +105,36 @@ def clustering_label_propagation(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 - return render_template('cluster/clustering_label_propagation.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Label Propagation') + return render_template('cluster/clustering_label_propagation.html', example=df, + number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Label Propagation') + @cluster_routes.route('/clustering/asyn_lpa', endpoint='clustering_asyn_lpa', methods=['GET', 'POST']) def clustering_asyn_lpa(): + """ + :Function: Visualise the clustering using asynchronous label propagation algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'asyn_lpa' @@ -132,29 +145,35 @@ def clustering_asyn_lpa(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_asyn_lpa.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Asyn Lpa') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Asyn Lpa') + @cluster_routes.route('/clustering/k_clique', endpoint='clustering_k_clique', methods=['GET', 'POST']) def clustering_k_clique(): + """ + :Function: Visualise the clustering using k clique algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'k_clique' @@ -165,29 +184,35 @@ def clustering_k_clique(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_k_clique.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='K Clique') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='K Clique') + @cluster_routes.route('/clustering/spectral', endpoint='clustering_spectral', methods=['GET', 'POST']) def clustering_spectral(): + """ + :Function: Visualise the clustering using spectral algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'spectral' @@ -198,29 +223,35 @@ def clustering_spectral(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_spectral.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='spectrals') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='spectrals') + @cluster_routes.route('/clustering/kmeans', endpoint='clustering_kmeans', methods=['GET', 'POST']) def clustering_kmeans(): + """ + :Function: Visualise the clustering using kmeans algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'kmeans' @@ -231,29 +262,35 @@ def clustering_kmeans(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_kmeans.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='KMeans') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='KMeans') + @cluster_routes.route('/clustering/agglomerative', endpoint='clustering_agglomerative', methods=['GET', 'POST']) def clustering_agglomerative(): + """ + :Function: Visualise the clustering using agglomerative algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'agglomerative' @@ -264,29 +301,35 @@ def clustering_agglomerative(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_agglomerative.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Agglomerative') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Agglomerative') + @cluster_routes.route('/clustering/dbscan', endpoint='clustering_dbscan', methods=['GET', 'POST']) def clustering_dbscan(): + """ + :Function: Visualise the clustering using dbscan algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'dbscan' @@ -297,29 +340,35 @@ def clustering_dbscan(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_dbscan.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Dbscan') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Dbscan') + @cluster_routes.route('/clustering/hierarchical', endpoint='clustering_hierarchical', methods=['GET', 'POST']) def clustering_hierarchical(): + """ + :Function: Visualise the clustering using hierarchical algorithm + :return: the clustering page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) clusterType = 'hierarchical' @@ -330,23 +379,24 @@ def clustering_hierarchical(): number_of_clusters = None if request.method == 'POST': - number_of_clusters = request.form.get('number_of_clusters', None) - multi_toggle = bool(request.form.get('multi_toggle')) - dynamic_toggle = bool(request.form.get('dynamic_toggle')) - directed_toggle = bool(request.form.get('directed_toggle')) - layout = request.form.get('layout') - number_of_clusters = int(number_of_clusters) if number_of_clusters else None - df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) - session['graph_name1'] = graph_name1 + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 else: df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 graph1 = session['graph_name1'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 return render_template('cluster/clustering_hierarchical.html', example=df, number_of_clusters=number_of_clusters, - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, method_name='Hierarchical') + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Hierarchical') diff --git a/application/routes/clusters/__init__.py b/application/routes/clusters/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/clusters/cluster_routes.py b/application/routes/clusters/cluster_routes.py index 60c65eeb..8e7ee884 100644 --- a/application/routes/clusters/cluster_routes.py +++ b/application/routes/clusters/cluster_routes.py @@ -1,8 +1,8 @@ import sys -import requests +from flask import Blueprint, render_template, session + from application.dictionary.information import * -from flask import Blueprint, render_template, session, request sys.path.insert(1, '../../') from src.metrics import * @@ -12,8 +12,14 @@ # -------------------------------------------ML-CLUSTERING----------------------------------- BASE_URL = 'http://localhost:8000/api/v1/clusters/' + @cluster_routes.route('/clustering/louvain', endpoint='clustering_louvanian', methods=['GET', 'POST']) def clustering_louvanian(): + """ + :Function: Visualise the clusters using Louvain algorithm + :return: the cluster page + + """ filename2 = session['filename2'] clusterType = 'louvain' networkGraphs = get_networkGraph(filename2) @@ -22,14 +28,20 @@ def clustering_louvanian(): else: is_spatial = 'no' - return render_template('clusters/clustering_louvanian.html', session_id=filename2, clusterType=clusterType, method_name='Louvain', is_spatial=is_spatial, - #tooltips starts from here - description=description['louvain'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_louvanian.html', session_id=filename2, clusterType=clusterType, + method_name='Louvain', is_spatial=is_spatial, + description=description['louvain'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) @cluster_routes.route('/clustering/greedy_modularity', endpoint='clustering_greedy_modularity', methods=['GET', 'POST']) def clustering_greedy_modularity(): + """ + :Function: Visualise the clusters using Greedy Modularity algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'greedy_modularity' networkGraphs = get_networkGraph(filename2) @@ -38,15 +50,20 @@ def clustering_greedy_modularity(): else: is_spatial = 'no' - return render_template('clusters/clustering_greedy_modularity.html', session_id=filename2, clusterType=clusterType, method_name='Greedy Modularity', is_spatial=is_spatial, - #tooltips starts from here - description=description['greedy_modularity'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) - + return render_template('clusters/clustering_greedy_modularity.html', session_id=filename2, clusterType=clusterType, + method_name='Greedy Modularity', is_spatial=is_spatial, + description=description['greedy_modularity'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) @cluster_routes.route('/clustering/label_propagation', endpoint='clustering_label_propagation', methods=['GET', 'POST']) def clustering_label_propagation(): + """ + :Function: Visualise the clusters using Label Propagation algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'label_propagation' networkGraphs = get_networkGraph(filename2) @@ -55,14 +72,20 @@ def clustering_label_propagation(): else: is_spatial = 'no' - return render_template('clusters/clustering_label_propagation.html', session_id=filename2, clusterType=clusterType, method_name='Label Propagation', is_spatial=is_spatial, - #tooltips starts from here - description=description['label_propagation'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_label_propagation.html', session_id=filename2, clusterType=clusterType, + method_name='Label Propagation', is_spatial=is_spatial, + description=description['label_propagation'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) @cluster_routes.route('/clustering/asyn_lpa', endpoint='clustering_asyn_lpa', methods=['GET', 'POST']) def clustering_asyn_lpa(): + """ + :Function: Visualise the clusters using Asyn Lpa algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'asyn_lpa' networkGraphs = get_networkGraph(filename2) @@ -71,13 +94,20 @@ def clustering_asyn_lpa(): else: is_spatial = 'no' - return render_template('clusters/clustering_asyn_lpa.html', session_id=filename2, clusterType=clusterType, method_name='Asyn Lpa', is_spatial=is_spatial, - #tooltips starts from here - description=description['asyn_lpa'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_asyn_lpa.html', session_id=filename2, clusterType=clusterType, + method_name='Asyn Lpa', is_spatial=is_spatial, + description=description['asyn_lpa'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + @cluster_routes.route('/clustering/k_clique', endpoint='clustering_k_clique', methods=['GET', 'POST']) def clustering_k_clique(): + """ + :Function: Visualise the clusters using K Clique algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'k_clique' networkGraphs = get_networkGraph(filename2) @@ -86,13 +116,20 @@ def clustering_k_clique(): else: is_spatial = 'no' - return render_template('clusters/clustering_k_clique.html', session_id=filename2, clusterType=clusterType, method_name='K Clique', is_spatial=is_spatial, - #tooltips starts from here - description=description['k_clique'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_k_clique.html', session_id=filename2, clusterType=clusterType, + method_name='K Clique', is_spatial=is_spatial, + description=description['k_clique'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + @cluster_routes.route('/clustering/spectral', endpoint='clustering_spectral', methods=['GET', 'POST']) def clustering_spectral(): + """ + :Function: Visualise the clusters using Spectral algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'spectral' networkGraphs = get_networkGraph(filename2) @@ -101,13 +138,20 @@ def clustering_spectral(): else: is_spatial = 'no' - return render_template('clusters/clustering_spectral.html', session_id=filename2, clusterType=clusterType, method_name='Spectral', is_spatial=is_spatial, - #tooltips starts from here - description=description['spectral'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_spectral.html', session_id=filename2, clusterType=clusterType, + method_name='Spectral', is_spatial=is_spatial, + description=description['spectral'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + @cluster_routes.route('/clustering/kmeans', endpoint='clustering_kmeans', methods=['GET', 'POST']) def clustering_kmeans(): + """ + :Function: Visualise the clusters using KMeans algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'kmeans' networkGraphs = get_networkGraph(filename2) @@ -116,13 +160,20 @@ def clustering_kmeans(): else: is_spatial = 'no' - return render_template('clusters/clustering_kmeans.html', session_id=filename2, clusterType=clusterType, method_name='KMeans', is_spatial=is_spatial, - #tooltips starts from here - description=description['kmeans'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_kmeans.html', session_id=filename2, clusterType=clusterType, + method_name='KMeans', is_spatial=is_spatial, + description=description['kmeans'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + @cluster_routes.route('/clustering/agglomerative', endpoint='clustering_agglomerative', methods=['GET', 'POST']) def clustering_agglomerative(): + """ + :Function: Visualise the clusters using Agglomerative algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'agglomerative' networkGraphs = get_networkGraph(filename2) @@ -131,14 +182,20 @@ def clustering_agglomerative(): else: is_spatial = 'no' - return render_template('clusters/clustering_agglomerative.html', session_id=filename2, clusterType=clusterType, method_name='Agglomerative', is_spatial=is_spatial, - #tooltips starts from here - description=description['agglomerative'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_agglomerative.html', session_id=filename2, clusterType=clusterType, + method_name='Agglomerative', is_spatial=is_spatial, + description=description['agglomerative'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) @cluster_routes.route('/clustering/dbscan', endpoint='clustering_dbscan', methods=['GET', 'POST']) def clustering_dbscan(): + """ + :Function: Visualise the clusters using Dbscan algorithm + :return: the clusters page + + """ filename2 = session['filename2'] clusterType = 'dbscan' networkGraphs = get_networkGraph(filename2) @@ -147,7 +204,8 @@ def clustering_dbscan(): else: is_spatial = 'no' - return render_template('clusters/clustering_dbscan.html', session_id=filename2, clusterType=clusterType, method_name='Dbscan', is_spatial=is_spatial, - #tooltips starts from here - description=description['dbscan'], tooltip_dynamic=tooltips['dynamic'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('clusters/clustering_dbscan.html', session_id=filename2, clusterType=clusterType, + method_name='Dbscan', is_spatial=is_spatial, + description=description['dbscan'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) diff --git a/application/routes/deepLearning/__init__.py b/application/routes/deepLearning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/deepLearning/cluster_embedding_routes.py b/application/routes/deepLearning/cluster_embedding_routes.py index 2055bc4e..bc680943 100644 --- a/application/routes/deepLearning/cluster_embedding_routes.py +++ b/application/routes/deepLearning/cluster_embedding_routes.py @@ -1,76 +1,119 @@ import sys -from application.dictionary.information import * from flask import Blueprint, render_template, session +from application.dictionary.information import * + sys.path.insert(1, '../../') -from src.metrics import * cluster_embedding_routes = Blueprint('cluster_embedding_routes', __name__) - -#----------------------------------------------CLUSTER-EMBEDDING------------------------------------------------------------------ +# ----------------------------------------------CLUSTER-EMBEDDING------------------------------------------------------------------ BASE_URL = 'http://localhost:8000/api/v1/deeplearning/' -@cluster_embedding_routes.route('/node2vec/clustering/embedding/kmeans', endpoint='clustering_embedding_kmeans', methods=['GET', 'POST']) + +@cluster_embedding_routes.route('/node2vec/clustering/embedding/kmeans', endpoint='clustering_embedding_kmeans', + methods=['GET', 'POST']) def clustering_embedding_kmeans(): + """ + :Function: Visualise the clusters using Kmeans algorithm + :return: the cluster page + """ filename2 = session['filename2'] clustering_alg = 'kmeans' - return render_template('deepLearning/node2vec/cluster/kmeans.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['node2vec_kmeans'], tooltip_parameters=tooltips['parameters'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('deepLearning/node2vec/cluster/kmeans.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['node2vec_kmeans'], tooltip_parameters=tooltips['parameters'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) -@cluster_embedding_routes.route('/node2vec/clustering/embedding/spectral', endpoint='clustering_embedding_spectral', methods=['GET', 'POST']) + +@cluster_embedding_routes.route('/node2vec/clustering/embedding/spectral', endpoint='clustering_embedding_spectral', + methods=['GET', 'POST']) def clustering_embedding_spectral(): + """ + :Function: Visualise the clusters using Spectral algorithm + :return: the cluster page + """ filename2 = session['filename2'] clustering_alg = 'spectral' - return render_template('deepLearning/node2vec/cluster/spectral.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['node2vec_spectral'], tooltip_parameters=tooltips['parameters'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('deepLearning/node2vec/cluster/spectral.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['node2vec_spectral'], tooltip_parameters=tooltips['parameters'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) -@cluster_embedding_routes.route('/node2vec/clustering/embedding/agglomerative', endpoint='clustering_embedding_agglomerative', methods=['GET', 'POST']) +@cluster_embedding_routes.route('/node2vec/clustering/embedding/agglomerative', + endpoint='clustering_embedding_agglomerative', methods=['GET', 'POST']) def clustering_embedding_agglomerative(): + """ + :Function: Visualise the clusters using Agglomerative algorithm + :return: the cluster page + """ filename2 = session['filename2'] clustering_alg = 'agglomerative' - return render_template('deepLearning/node2vec/cluster/agglomerative.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['node2vec_agglomerative'], tooltip_parameters=tooltips['parameters'], - tooltip_layout=tooltips['layout_dropdown'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('deepLearning/node2vec/cluster/agglomerative.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['node2vec_agglomerative'], tooltip_parameters=tooltips['parameters'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + # Dl embedding -@cluster_embedding_routes.route('/dlembedding/clustering/embedding/kmeans', endpoint='dlembedding_clustering_embedding_kmeans', methods=['GET', 'POST']) +@cluster_embedding_routes.route('/dlembedding/clustering/embedding/kmeans', + endpoint='dlembedding_clustering_embedding_kmeans', methods=['GET', 'POST']) def clustering_embedding_kmeans(): filename2 = session['filename2'] clustering_alg = 'kmeans' - return render_template('deepLearning/dlembedding/cluster/kmeans.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['dlembedding_kmeans'], tooltip_dimension=tooltips['dimension'], - tooltip_model_dropdown=tooltips['model_dropdown'], tooltip_layout=tooltips['layout_dropdown'], - tooltip_features=tooltips['features_checkbox'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('deepLearning/dlembedding/cluster/kmeans.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_kmeans'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) + -@cluster_embedding_routes.route('/dlembedding/clustering/embedding/spectral', endpoint='dlembedding_clustering_embedding_spectral', methods=['GET', 'POST']) +@cluster_embedding_routes.route('/dlembedding/clustering/embedding/spectral', + endpoint='dlembedding_clustering_embedding_spectral', methods=['GET', 'POST']) def clustering_embedding_spectral(): + """ + :Function: Visualise the clusters using Spectral algorithm + :return: the cluster page + """ filename2 = session['filename2'] clustering_alg = 'spectral' - return render_template('deepLearning/dlembedding/cluster/spectral.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['dlembedding_spectral'], tooltip_dimension=tooltips['dimension'], - tooltip_model_dropdown=tooltips['model_dropdown'], tooltip_layout=tooltips['layout_dropdown'], - tooltip_features=tooltips['features_checkbox'], tooltip_number_of_clusters=tooltips['number_of_clusters']) + return render_template('deepLearning/dlembedding/cluster/spectral.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_spectral'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) -@cluster_embedding_routes.route('/dlembedding/clustering/embedding/agglomerative', endpoint='dlembedding_clustering_embedding_agglomerative', methods=['GET', 'POST']) +@cluster_embedding_routes.route('/dlembedding/clustering/embedding/agglomerative', + endpoint='dlembedding_clustering_embedding_agglomerative', methods=['GET', 'POST']) def clustering_embedding_agglomerative(): + """ + :Function: Visualise the clusters using Agglomerative algorithm + :return: the cluster page + """ filename2 = session['filename2'] clustering_alg = 'agglomerative' - return render_template('deepLearning/dlembedding/cluster/agglomerative.html', session_id=filename2, clustering_alg=clustering_alg, - description=description['dlembedding_agglomerative'], tooltip_dimension=tooltips['dimension'], - tooltip_model_dropdown=tooltips['model_dropdown'], tooltip_layout=tooltips['layout_dropdown'], - tooltip_features=tooltips['features_checkbox'], tooltip_number_of_clusters=tooltips['number_of_clusters']) - - + return render_template('deepLearning/dlembedding/cluster/agglomerative.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_agglomerative'], + tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters']) diff --git a/application/routes/deepLearning/embedding_routes.py b/application/routes/deepLearning/embedding_routes.py index 9e4d322f..7b33712c 100644 --- a/application/routes/deepLearning/embedding_routes.py +++ b/application/routes/deepLearning/embedding_routes.py @@ -1,12 +1,10 @@ import sys from flask import Blueprint, render_template, session -import requests + from application.dictionary.information import * -from flask import Blueprint, render_template, session, request sys.path.insert(1, '../../') -from src.metrics import * embedding_routes = Blueprint('embedding_routes', __name__) @@ -16,18 +14,27 @@ @embedding_routes.route('/node2vec/embedding', endpoint='node2vec_embedding', methods=['GET', 'POST']) def embedding_visualisation(): + """ + :Function: Visualise the embedding + :return: the embedding page + """ filename2 = session['filename2'] return render_template('deepLearning/node2vec/embedding.html', session_id=filename2, - - description=description['node2vec_embedding'], tooltip_parameters=tooltips['parameters'], - tooltip_layout=tooltips['layout_dropdown']) + description=description['node2vec_embedding'], tooltip_parameters=tooltips['parameters'], + tooltip_layout=tooltips['layout_dropdown']) + @embedding_routes.route('/dlembedding/embedding', endpoint='dlembedding_embedding', methods=['GET', 'POST']) def dlembedding_embedding_visualisation(): + """ + :Function: Visualise the embedding + :return: the embedding page + """ filename2 = session['filename2'] return render_template('deepLearning/dlembedding/embedding.html', session_id=filename2, - description=description['dlembedding_embedding'], tooltip_dimension=tooltips['dimension'], - tooltip_model_dropdown=tooltips['model_dropdown'], tooltip_layout=tooltips['layout_dropdown'], - tooltip_features=tooltips['features_checkbox']) + description=description['dlembedding_embedding'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox']) diff --git a/application/routes/hotspot/__init__.py b/application/routes/hotspot/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/hotspot/hotspot_routes.py b/application/routes/hotspot/hotspot_routes.py index 1d219551..755a36da 100644 --- a/application/routes/hotspot/hotspot_routes.py +++ b/application/routes/hotspot/hotspot_routes.py @@ -1,20 +1,25 @@ import sys -import requests +from flask import Blueprint, render_template, session + from application.dictionary.information import * -from flask import Blueprint, render_template, session, request sys.path.insert(1, '../../') -from src.metrics import * hotspot_routes = Blueprint('hotspot_routes', __name__) + # -------------------------------------------HOTSPOT----------------------------------------- @hotspot_routes.route('/hotspot/density', endpoint='hotspot_density', methods=['GET', 'POST']) def hotspot_density(): + """ + :Function: Visualise the hotspot using density + :return: the hotspot page + """ filename2 = session['filename2'] hotspotType = 'density' - return render_template('hotspot/hotspot_density.html', session_id=filename2, hotspotType=hotspotType, method_name='Density', - description=description['hotspot_density']) + return render_template('hotspot/hotspot_density.html', session_id=filename2, hotspotType=hotspotType, + method_name='Density', + description=description['hotspot_density']) diff --git a/application/routes/metrics/__init__.py b/application/routes/metrics/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/metrics/centrality_routes.py b/application/routes/metrics/centrality_routes.py index 921eb58b..3190bace 100644 --- a/application/routes/metrics/centrality_routes.py +++ b/application/routes/metrics/centrality_routes.py @@ -1,9 +1,8 @@ import sys -from application.dictionary.information import * from flask import Blueprint, render_template, session -from backend.common.common import process_metric +from application.dictionary.information import * sys.path.insert(1, '../../') from src.metrics import * @@ -12,9 +11,12 @@ # -------------------------------------------CENTRALITY-------------------------------------- - @centrality_routes.route('/centrality', endpoint='centrality', methods=['GET', 'POST']) def centrality_all(): + """ + :Function: Visualise the centrality + :return: the centrality page + """ filename2 = session['filename2'] metrics = 'centralities' networkGraphs = get_networkGraph(filename2) @@ -23,15 +25,22 @@ def centrality_all(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_all.html', method_name='All Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_all.html', method_name='All Centrality', + is_spatial=is_spatial, description=description['all_centrality'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], metricsType=metrics, session_id=filename2) + @centrality_routes.route('/centrality/degree', endpoint='degree', methods=['GET', 'POST']) def centrality_degree(): + """ + :Function: Visualise the degree centrality + :return: the degree centrality page + """ filename2 = session['filename2'] metrics = 'degree_centrality' networkGraphs = get_networkGraph(filename2) @@ -40,16 +49,23 @@ def centrality_degree(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_degree.html', method_name='Degree Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_degree.html', method_name='Degree Centrality', + is_spatial=is_spatial, description=description['centrality_degree'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @centrality_routes.route('/centrality/eigenvector', endpoint='eigenvector', methods=['GET', 'POST']) def centrality_eigenvector(): + """ + :Function: Visualise the eigenvector centrality + :return: the eigenvector centrality page + """ filename2 = session['filename2'] metrics = 'eigenvector_centrality' networkGraphs = get_networkGraph(filename2) @@ -58,16 +74,23 @@ def centrality_eigenvector(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_eigenvector.html', method_name='Eigenvector Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_eigenvector.html', method_name='Eigenvector Centrality', + is_spatial=is_spatial, description=description['centrality_eigenvector'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @centrality_routes.route('/centrality/closeness', endpoint='closeness', methods=['GET', 'POST']) def centrality_closeness(): + """ + :Function: Visualise the closeness centrality + :return: the closeness centrality page + """ filename2 = session['filename2'] metrics = 'closeness_centrality' networkGraphs = get_networkGraph(filename2) @@ -76,16 +99,23 @@ def centrality_closeness(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_closeness.html', method_name='Closeness Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_closeness.html', method_name='Closeness Centrality', + is_spatial=is_spatial, description=description['centrality_closeness'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @centrality_routes.route('/centrality/betwenness', endpoint='betwenness', methods=['GET', 'POST']) def centrality_betwenness(): + """ + :Function: Visualise the betwenness centrality + :return: the betwenness centrality page + """ filename2 = session['filename2'] metrics = 'betweenness_centrality' networkGraphs = get_networkGraph(filename2) @@ -94,16 +124,23 @@ def centrality_betwenness(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_betwenness.html', method_name='Betwenness Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_betwenness.html', method_name='Betwenness Centrality', + is_spatial=is_spatial, description=description['centrality_betwenness'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @centrality_routes.route('/centrality/load', endpoint='load', methods=['GET', 'POST']) def centrality_load(): + """ + :Function: Visualise the load centrality + :return: the load centrality page + """ filename2 = session['filename2'] metrics = 'load_centrality' networkGraphs = get_networkGraph(filename2) @@ -112,10 +149,12 @@ def centrality_load(): else: is_spatial = 'no' - return render_template('metrics/centrality/centrality_load.html', method_name='Load Centrality', is_spatial=is_spatial, + return render_template('metrics/centrality/centrality_load.html', method_name='Load Centrality', + is_spatial=is_spatial, description=description['centrality_load'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], - metricsType=metrics, session_id=filename2) \ No newline at end of file + metricsType=metrics, session_id=filename2) diff --git a/application/routes/metrics/global_metrics_routes.py b/application/routes/metrics/global_metrics_routes.py index 893da46c..e5fe824e 100644 --- a/application/routes/metrics/global_metrics_routes.py +++ b/application/routes/metrics/global_metrics_routes.py @@ -1,19 +1,23 @@ import sys -from application.dictionary.information import * from flask import Blueprint, render_template, session from flask import request -from backend.common.common import process_metric + +from application.dictionary.information import * sys.path.insert(1, '../../') from src.metrics import * global_metrics_routes = Blueprint('global_metrics_routes', __name__) -# -------------------------------------------GLOBAL-METRICS----------------------------------- +# -------------------------------------------GLOBAL-METRICS----------------------------------- @global_metrics_routes.route('/global-metrics', methods=['GET', 'POST']) def globalmetrics(): + """ + :Function: Visualise the global metrics + :return: the global metrics page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) multi_toggle = False @@ -28,5 +32,5 @@ def globalmetrics(): return render_template('metrics/global_metrics.html', example=global_metrics, multi_toggle=multi_toggle, directed_toggle=directed_toggle, - #tooltips starts here - tooltip_multi = tooltips['multi'], tooltip_directed = tooltips['directed'], description = description['global_metrics']) \ No newline at end of file + tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], + description=description['global_metrics']) diff --git a/application/routes/metrics/node_routes.py b/application/routes/metrics/node_routes.py index e7c476fe..38faeae4 100644 --- a/application/routes/metrics/node_routes.py +++ b/application/routes/metrics/node_routes.py @@ -1,9 +1,8 @@ import sys -from application.dictionary.information import * from flask import Blueprint, render_template, session -from backend.common.common import process_metric +from application.dictionary.information import * sys.path.insert(1, '../../') from src.metrics import * @@ -12,9 +11,12 @@ # -------------------------------------------NODE-------------------------------------------- - @node_routes.route('/node_all', endpoint='node_all', methods=['GET', 'POST']) def node_all(): + """ + :Function: Visualise the node metrics + :return: the node metrics page + """ filename2 = session['filename2'] metrics = 'nodes' networkGraphs = get_networkGraph(filename2) @@ -26,12 +28,18 @@ def node_all(): return render_template('metrics/nodes/node_all.html', method_name='All Nodes', is_spatial=is_spatial, description=description['node_all'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], metricsType=metrics, session_id=filename2) + @node_routes.route('/node/degree', endpoint='node_degree', methods=['GET', 'POST']) def node_degree(): + """ + :Function: Visualise the node degree + :return: the node degree page + """ filename2 = session['filename2'] metrics = 'degree' networkGraphs = get_networkGraph(filename2) @@ -43,13 +51,19 @@ def node_degree(): return render_template('metrics/nodes/node_degree.html', method_name='Node Degree', is_spatial=is_spatial, description=description['node_degree'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @node_routes.route('/node/kcore', endpoint='node_kcore', methods=['GET', 'POST']) def node_kcore(): + """ + :Function: Visualise the node kcore + :return: the node kcore page + """ filename2 = session['filename2'] metrics = 'kcore' networkGraphs = get_networkGraph(filename2) @@ -61,13 +75,19 @@ def node_kcore(): return render_template('metrics/nodes/node_kcore.html', method_name='Node K Core', is_spatial=is_spatial, description=description['node_kcore'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @node_routes.route('/node/triangle', endpoint='node_triangle', methods=['GET', 'POST']) def node_triangle(): + """ + :Function: Visualise the node triangle + :return: the node triangle page + """ filename2 = session['filename2'] metrics = 'triangles' networkGraphs = get_networkGraph(filename2) @@ -79,13 +99,19 @@ def node_triangle(): return render_template('metrics/nodes/node_triangle.html', method_name='Node Triangle', is_spatial=is_spatial, description=description['node_triangle'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], metricsType=metrics, session_id=filename2) + @node_routes.route('/node/pagerank', endpoint='node_pagerank', methods=['GET', 'POST']) def node_pagerank(): + """ + :Function: Visualise the node pagerank + :return: the node pagerank page + """ filename2 = session['filename2'] metrics = 'pagerank' networkGraphs = get_networkGraph(filename2) @@ -97,7 +123,8 @@ def node_pagerank(): return render_template('metrics/nodes/node_pagerank.html', method_name='Node Page Rank', is_spatial=is_spatial, description=description['node_pagerank'], tooltip_multi=tooltips['multi'], tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], - tooltip_boxplot_tab=tooltips['boxplot_tab'], tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], tooltip_dynamic=tooltips['dynamic'], - metricsType=metrics, session_id=filename2) \ No newline at end of file + metricsType=metrics, session_id=filename2) diff --git a/application/routes/node_routes.py b/application/routes/node_routes.py index 272970ae..5ae37e94 100644 --- a/application/routes/node_routes.py +++ b/application/routes/node_routes.py @@ -1,27 +1,22 @@ -from flask import Blueprint, render_template, session, request, redirect, url_for -import csv import sys -import scipy as sp -from flask_caching import Cache -import matplotlib.pyplot as plt -import re -import time -import shutil + +from flask import Blueprint, render_template, session, request sys.path.insert(1, '../') -from src.utils import * -from src.NetworkGraphs import * from src.metrics import * -from src.preprocessing import * from src.visualisation import * -from flask import g node_routes = Blueprint('node_routes', __name__) -#-------------------------------------------NODE-------------------------------------------- + +# -------------------------------------------NODE-------------------------------------------- @node_routes.route('/node_all', endpoint='node_all', methods=['GET', 'POST']) def node_all(): + """ + :Function: Visualise the node metrics + :return: the node metrics page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'nodes' @@ -48,28 +43,33 @@ def node_all(): layout4 = 'sfdp' if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'directed_toggle') is not None or request.form.get('layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, layout=layout) + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'directed_toggle2') is not None or request.form.get('layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) layout2 = request.form.get('layout2') df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'directed_toggle3') is not None or request.form.get('layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) layout3 = request.form.get('layout3') df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'directed_toggle4') is not None or request.form.get('layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) layout4 = request.form.get('layout4') @@ -77,7 +77,8 @@ def node_all(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, layout=layout) + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -89,7 +90,7 @@ def node_all(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -99,7 +100,7 @@ def node_all(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -111,13 +112,22 @@ def node_all(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('node_all.html', example=df, tab=tab, method_name='All Nodes', - multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, + graph1=graph_path1, + multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, + graph2=graph_path2, + multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, + graph3=graph_path3, + multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, + graph4=graph_path4) + @node_routes.route('/node/degree', endpoint='node_degree', methods=['GET', 'POST']) def node_degree(): + """ + :Function: Visualize the degree of nodes + :return: the html page of the degree of nodes + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'degree' @@ -144,17 +154,22 @@ def node_degree(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -162,7 +177,9 @@ def node_degree(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -170,7 +187,9 @@ def node_degree(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -179,7 +198,8 @@ def node_degree(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -191,7 +211,7 @@ def node_degree(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -201,7 +221,7 @@ def node_degree(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -213,13 +233,22 @@ def node_degree(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('node_degree.html', example=df, tab=tab, method_name='Node Degree', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @node_routes.route('/node/kcore', endpoint='node_kcore', methods=['GET', 'POST']) def node_kcore(): + """ + :Function: Visualize the kcore of the network + :return: the html page of the kcore visualization + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'kcore' @@ -246,17 +275,22 @@ def node_kcore(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -264,7 +298,9 @@ def node_kcore(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -272,7 +308,9 @@ def node_kcore(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -281,7 +319,8 @@ def node_kcore(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -293,7 +332,7 @@ def node_kcore(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -303,7 +342,7 @@ def node_kcore(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -315,13 +354,22 @@ def node_kcore(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('node_kcore.html', example=df, tab=tab, method_name='Node K Core', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @node_routes.route('/node/triangle', endpoint='node_triangle', methods=['GET', 'POST']) def node_triangle(): + """ + :Function: Visualize the triangle of the network + :return: the html page of the triangle visualization + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'triangles' @@ -348,17 +396,22 @@ def node_triangle(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -366,7 +419,9 @@ def node_triangle(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -374,7 +429,9 @@ def node_triangle(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -383,7 +440,8 @@ def node_triangle(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -395,7 +453,7 @@ def node_triangle(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -405,7 +463,7 @@ def node_triangle(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -417,13 +475,22 @@ def node_triangle(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('node_triangle.html', example=df, tab=tab, method_name='Node Triangle', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + @node_routes.route('/node/pagerank', endpoint='node_pagerank', methods=['GET', 'POST']) def node_pagerank(): + """ + :Function: Visualize pagerank of nodes + :return: the html page for pagerank + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) metrics = 'pagerank' @@ -450,17 +517,22 @@ def node_pagerank(): layout2 = 'sfdp' layout3 = 'sfdp' layout4 = 'sfdp' - + if request.method == 'POST': - if (request.form.get('multi_toggle') is not None or request.form.get('dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get('layout') is not None): + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): multi_toggle = bool(request.form.get('multi_toggle')) dynamic_toggle = bool(request.form.get('dynamic_toggle')) directed_toggle = bool(request.form.get('directed_toggle')) layout = request.form.get('layout') - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 tab = 'tab1' - if (request.form.get('multi_toggle2') is not None or request.form.get('dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get('layout2') is not None): + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): multi_toggle2 = bool(request.form.get('multi_toggle2')) dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) directed_toggle2 = bool(request.form.get('directed_toggle2')) @@ -468,7 +540,9 @@ def node_pagerank(): df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 tab = 'tab2' - if (request.form.get('multi_toggle3') is not None or request.form.get('dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get('layout3') is not None): + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): multi_toggle3 = bool(request.form.get('multi_toggle3')) dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) directed_toggle3 = bool(request.form.get('directed_toggle3')) @@ -476,7 +550,9 @@ def node_pagerank(): df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) session['graph_name3'] = graph_name3 tab = 'tab3' - if (request.form.get('multi_toggle4') is not None or request.form.get('dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get('layout4') is not None): + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): multi_toggle4 = bool(request.form.get('multi_toggle4')) dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) directed_toggle4 = bool(request.form.get('directed_toggle4')) @@ -485,7 +561,8 @@ def node_pagerank(): session['graph_name4'] = graph_name4 tab = 'tab4' else: - df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, layout=layout) + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) session['graph_name1'] = graph_name1 df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) session['graph_name2'] = graph_name2 @@ -497,7 +574,7 @@ def node_pagerank(): graph2 = session['graph_name2'] graph3 = session['graph_name3'] graph4 = session['graph_name4'] - + if graph1 == 'no_graph.html': graph_path1 = '../static/' + graph1 else: @@ -507,7 +584,7 @@ def node_pagerank(): graph_path2 = '../static/' + graph2 else: graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 - + if graph3 == 'no_graph.html': graph_path3 = '../static/' + graph3 else: @@ -519,7 +596,11 @@ def node_pagerank(): graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 return render_template('node_pagerank.html', example=df, tab=tab, method_name='Node Page Rank', - multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, layout=layout, graph1=graph_path1, - multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, - multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, - multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4) diff --git a/application/routes/resilience/__init__.py b/application/routes/resilience/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/resilience/resilience_routes.py b/application/routes/resilience/resilience_routes.py index 5b258387..1a4b4aee 100644 --- a/application/routes/resilience/resilience_routes.py +++ b/application/routes/resilience/resilience_routes.py @@ -1,8 +1,9 @@ import sys import requests +from flask import Blueprint, render_template, session + from application.dictionary.information import * -from flask import Blueprint, render_template, session, request from src.utils import get_networkGraph sys.path.insert(1, '../../') @@ -10,10 +11,14 @@ resilience_routes = Blueprint('resilience_routes', __name__) BASE_URL = 'http://localhost:8000/api/v1' -# -------------------------------------------RESILIENCE_ANALYSIS----------------------------- +# -------------------------------------------RESILIENCE_ANALYSIS----------------------------- @resilience_routes.route('/resilience/malicious', endpoint='resilience_malicious', methods=['GET']) def resilience_analysis_malicious(): + """ + :Function: Visualise the malicious resilience analysis + :return: the malicious resilience analysis page + """ filename2 = session['filename2'] graph_path1 = '../static/no_graph.html' graph_path2 = '../static/no_graph.html' @@ -30,16 +35,20 @@ def resilience_analysis_malicious(): number_of_threshold=number_of_threshold, number_of_clusters=number_of_clusters, number_of_nodes_malicious=number_of_nodes_malicious, graph_path1=graph_path1, graph_path2=graph_path2, - tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], - tooltip_number_of_clusters=tooltips['number_of_clusters'], description=description['resilience_analysis_malicious'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analysis_malicious'], tooltip_type_of_attack=tooltips['type_of_attack'], tooltip_node_tab=tooltips['node_tab'], tooltip_threshold_tab=tooltips['threshold_tab']) @resilience_routes.route('/resilience/random', endpoint='resilience_random', methods=['GET']) -def resilience_analyisis_random(): +def resilience_analysis_random(): + """ + :Function: Visualise the random resilience analysis + :return: the random resilience analysis page + """ filename2 = session['filename2'] graph_path1 = '../static/no_graph.html' graph_path2 = '../static/no_graph.html' @@ -48,16 +57,20 @@ def resilience_analyisis_random(): number_of_edges=0, number_of_clusters=0, number_of_nodes_random=0, graph_path1=graph_path1, graph_path2=graph_path2, - tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], - tooltip_number_of_clusters=tooltips['number_of_clusters'], description=description['resilience_analyisis_random'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_random'], tooltip_node_tab=tooltips['node_tab'], tooltip_edge_tab=tooltips['edge_tab']) @resilience_routes.route('/resilience/cluster', endpoint='resilience_cluster', methods=['GET', 'POST']) -def resilience_analyisis_cluster(): +def resilience_analysis_cluster(): + """ + :Function: Visualise the cluster resilience analysis + :return: the cluster resilience analysis page + """ filename2 = session['filename2'] graph_path1 = '../static/no_graph.html' graph_path2 = '../static/no_graph.html' @@ -70,16 +83,21 @@ def resilience_analyisis_cluster(): layout3=cluster_algorithm, cluster_to_attack=number_of_clusters, number_of_cluster_to_generate=total_clusters, graph_path1=graph_path1, graph_path2=graph_path2, - tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], - tooltip_number_of_clusters=tooltips['number_of_clusters'], description=description['resilience_analyisis_cluster'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_cluster'], tooltip_type_of_cluster=tooltips['type_of_cluster'], tooltip_number_of_cluster_to_generate=tooltips['number_of_cluster_to_generate'], tooltip_number_of_cluster_to_attack=tooltips['number_of_cluster_to_attack']) + @resilience_routes.route('/resilience/custom', endpoint='resilience_custom', methods=['GET', 'POST']) -def resilience_analyisis_custom(): +def resilience_analysis_custom(): + """ + :Function: Visualise the custom resilience analysis + :return: the custom resilience analysis page + """ filename2 = session['filename2'] graph_path1 = '../static/no_graph.html' graph_path2 = '../static/no_graph.html' @@ -94,15 +112,14 @@ def resilience_analyisis_custom(): json_data = requests.get( f'{BASE_URL}/visualisation/{filename2}/plot_network/spatial?dynamic=False&layout={layout}').json() graph_input_custom = json_data['filename'] - graph_input_custom = f"../static/uploads/{filename2}/"+graph_input_custom + graph_input_custom = f"../static/uploads/{filename2}/" + graph_input_custom return render_template('resilience/resilience_analyisis_custom.html', session_id=filename2, layout3=cluster_algorithm, cluster_to_attack=number_of_clusters, number_of_cluster_to_generate=total_clusters, graph_path1=graph_path1, graph_path2=graph_path2, graph_input_custom=graph_input_custom, - tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], - tooltip_number_of_clusters=tooltips['number_of_clusters'], description=description['resilience_analyisis_custom'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_custom'], tooltip_list_of_nodes=tooltips['list_of_nodes']) - diff --git a/application/routes/stochastic/stochastic_routes.py b/application/routes/stochastic/stochastic_routes.py new file mode 100644 index 00000000..ede4c490 --- /dev/null +++ b/application/routes/stochastic/stochastic_routes.py @@ -0,0 +1,40 @@ +import sys + +from flask import Blueprint, render_template, session + +from application.dictionary.information import * + +sys.path.insert(1, '../../') +from src.metrics import * + +stochastic_routes = Blueprint('stochastic_routes', __name__) + +#-------------------------------------------------------------------------------------------------------------------------------------- +@stochastic_routes.route('/stochastic/clustering_coefficient', methods=['GET', 'POST'], endpoint='clustering_coefficient') +def clustering_coefficient(): + """ + :Function: Stochastic Analysis of the network + :return: the Clustering Coefficient page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + + return render_template('stochastic/clustering_coefficient.html', session_id=filename2, + tooltip_histogram_tab=tooltips['histogram_tab'], tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + description=description['clustering_coefficient']) + +@stochastic_routes.route('/stochastic/shortest_path', methods=['GET', 'POST'], endpoint='shortest_path') +def shortest_path(): + """ + :Function: Stochastic Analysis of the network + :return: the Shortest Path page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + + return render_template('stochastic/shortest_path.html', session_id=filename2, + tooltip_histogram_tab=tooltips['histogram_tab'], tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + description=description['shortest_path']) + diff --git a/application/routes/visualisation/__init__.py b/application/routes/visualisation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/application/routes/visualisation/visualisation_routes.py b/application/routes/visualisation/visualisation_routes.py index 14129690..4a6b8e14 100644 --- a/application/routes/visualisation/visualisation_routes.py +++ b/application/routes/visualisation/visualisation_routes.py @@ -1,19 +1,22 @@ import sys -from application.dictionary.information import * from flask import Blueprint, render_template, session -from flask import request -from backend.common.common import process_metric + +from application.dictionary.information import * sys.path.insert(1, '../../') from src.metrics import * visualisation_routes = Blueprint('visualisation_routes', __name__) -# -------------------------------------------VISUALISATION----------------------------------- +# -------------------------------------------VISUALISATION----------------------------------- @visualisation_routes.route('/visualisation', methods=['GET', 'POST'], endpoint='visualisation') def visualisation(): + """ + :Function: Visualise the network + :return: the visualisation page + """ filename2 = session['filename2'] networkGraphs = get_networkGraph(filename2) @@ -24,10 +27,9 @@ def visualisation(): else: is_spatial = 'no' - print('check spatial if yes means map, else sfdp', is_spatial) - return render_template('visualisation/visualisation.html', show_temporal=show_temporal, session_id=filename2, is_spatial=is_spatial, - #tooltips adding from here - tooltip_network_tab=tooltips['network_tab'], tooltip_temporal_tab=tooltips['temporal_tab'], tooltip_heatmap_tab=tooltips['heatmap_tab'], - tooltip_dynamic=tooltips['dynamic'], tooltip_layout_dropdown=tooltips['layout_dropdown'], description=description['visualisation']) + tooltip_network_tab=tooltips['network_tab'], tooltip_temporal_tab=tooltips['temporal_tab'], + tooltip_heatmap_tab=tooltips['heatmap_tab'], + tooltip_dynamic=tooltips['dynamic'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + description=description['visualisation']) diff --git a/application/static/js/stochastic/stochastic.js b/application/static/js/stochastic/stochastic.js new file mode 100644 index 00000000..e63df3b3 --- /dev/null +++ b/application/static/js/stochastic/stochastic.js @@ -0,0 +1,63 @@ +const BASE_URL = 'http://localhost:8000/api/v1/metrics/'; + +const stochasticClusteringCoefficientVisualisation = (data, plotType, id) => { + const plot_type = plotType; + const graph = document.getElementById(id); + $(graph).attr('src', '../static/loading.html'); + + $.ajax({ + url: BASE_URL + data.session_id + '/clustering_coefficient' + '/' + plot_type, + type: 'GET', + mode: 'no-cors', + success: function (res) { + const df_data = JSON.parse(res.data); + + const graphPath = res.filename.replace('../application/static/', ''); + if (graphPath === "no_graph.html") { + $(graph).attr('src', '../static/' + graphPath); + } + else{ + $(graph).attr('src', '../static/uploads/'+ data.session_id+ '/' + graphPath); + } + const datasetTable = document.getElementById('example-stochastic'); + createTable(datasetTable, df_data.data, df_data.columns, true); + }, + error: function (data) { + alert('An error occurred. Please try again.'); + console.log(data); + } + }); + +} + +const stochasticShortestPathVisualisation = (data, plotType, id) => { + const plot_type = plotType; + const graph = document.getElementById(id); + $(graph).attr('src', '../static/loading.html'); + + $.ajax({ + url: BASE_URL + data.session_id + '/shortest_path' + '/' + plot_type, + type: 'GET', + mode: 'no-cors', + success: function (res) { + const df_data = JSON.parse(res.data); + + const graphPath = res.filename.replace('../application/static/', ''); + if (graphPath === "no_graph.html") { + $(graph).attr('src', '../static/' + graphPath); + } + else{ + $(graph).attr('src', '../static/uploads/'+ data.session_id+ '/' + graphPath); + } + + const datasetTable = document.getElementById('example-stochastic'); + createTable(datasetTable, df_data.data, df_data.columns, true); + + }, + error: function (data) { + alert('An error occurred. Please try again.'); + console.log(data); + } + }); + +} \ No newline at end of file diff --git a/application/templates/base.html b/application/templates/base.html index d237ea29..fdc5f0f7 100644 --- a/application/templates/base.html +++ b/application/templates/base.html @@ -10,7 +10,7 @@ + integrity="sha384-GLhlTQ8iRABdZLl6O3oVMWSktQOp6b7In1Zl3/Jr59b6EGGoI1aFkw7cmDA6j6gD" crossorigin="anonymous"> @@ -28,295 +28,311 @@ -
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    + - - - - - - + + + + + - diff --git a/application/templates/stochastic/clustering_coefficient.html b/application/templates/stochastic/clustering_coefficient.html new file mode 100644 index 00000000..eec34b80 --- /dev/null +++ b/application/templates/stochastic/clustering_coefficient.html @@ -0,0 +1,34 @@ +{% extends 'base.html' %} +{% block title %}Stochastic{% endblock %} +{% block span %}Clustering Coefficient{% endblock %} +{% block content %} +{% include 'stochastic/stochastic_template.html' %} + + + + +{% endblock %} \ No newline at end of file diff --git a/application/templates/stochastic/shortest_path.html b/application/templates/stochastic/shortest_path.html new file mode 100644 index 00000000..f2f9af91 --- /dev/null +++ b/application/templates/stochastic/shortest_path.html @@ -0,0 +1,34 @@ +{% extends 'base.html' %} +{% block title %}Stochastic{% endblock %} +{% block span %}Shortest Path{% endblock %} +{% block content %} +{% include 'stochastic/stochastic_template.html' %} + + + + +{% endblock %} \ No newline at end of file diff --git a/application/templates/stochastic/stochastic_template.html b/application/templates/stochastic/stochastic_template.html new file mode 100644 index 00000000..8a3fe953 --- /dev/null +++ b/application/templates/stochastic/stochastic_template.html @@ -0,0 +1,93 @@ +
    +
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    +

    Stochastic Analysis

    +

    {{description | safe}}

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    Stochastic Information

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    + + + + + diff --git a/application/templates/visualisation/visualisation.html b/application/templates/visualisation/visualisation.html index f8079e43..87c70ede 100644 --- a/application/templates/visualisation/visualisation.html +++ b/application/templates/visualisation/visualisation.html @@ -192,9 +192,7 @@

    Visualisation

    layout1.value = layout2.value; }); } - else{ - defaultLayoutValue = "temporal"; - } + const data = { 'dynamicToggle': dynamicToggle.checked ? "True" : "False", diff --git a/backend/app.py b/backend/app.py index 235870fd..efb9b699 100644 --- a/backend/app.py +++ b/backend/app.py @@ -4,29 +4,27 @@ import time import flask -import flask_restx from flask import request, session from flask_cors import CORS from backend.clusters.clusters import cluster_bp +from backend.deepLearning.deepLearning import deepLearning_bp from backend.hotspot.density import hotspot_bp from backend.metrics.metrics import metrics_bp -from backend.resilience.resilience import resilience_bp -from backend.visualisation.visualisation import visualisation_bp -from backend.resilience.malicious import malicious_bp -from backend.resilience.random import random_bp from backend.resilience.cluster import clusters_bp from backend.resilience.custom import custom_bp -from backend.deepLearning.deepLearning import deepLearning_bp +from backend.resilience.malicious import malicious_bp +from backend.resilience.random import random_bp +from backend.resilience.resilience import resilience_bp +from backend.visualisation.visualisation import visualisation_bp from src.NetworkGraphs import NetworkGraphs from src.utils import set_networkGraph, get_networkGraph - app = flask.Flask(__name__) CORS(app, resources={r"/api/*": {"origins": "*"}}) app.config['SECRET_KEY'] = 'your_secret_key' -api_bp = flask.Blueprint("api", __name__, url_prefix="/api/v1",) +api_bp = flask.Blueprint("api", __name__, url_prefix="/api/v1", ) @api_bp.route('/') diff --git a/backend/clusters/__init__.py b/backend/clusters/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/clusters/clusters.py b/backend/clusters/clusters.py index a16c320e..ff4286d4 100644 --- a/backend/clusters/clusters.py +++ b/backend/clusters/clusters.py @@ -8,12 +8,25 @@ def get_arg_no_of_clusters(args): + """ + :Function: Get the number of clusters from the request args + :param args: number of clusters + :type: int + :return: number of clusters + :rtype: int + """ no_of_clusters = args.get('no_of_clusters', 0, type=int) return no_of_clusters @cluster_bp.route('//') def compute_clustering(session_id, clustering_alg): + """ + Compute the clustering for the network graph + :param session_id: the session id + :param clustering_alg: the clustering algorithm + :return: the jsonified response + """ dynamic_toggle = get_arg_dynamic_toggle(request.args) layout = get_arg_layout(request.args) diff --git a/backend/common/__init__.py b/backend/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/common/common.py b/backend/common/common.py index 79ae814c..8daa4705 100644 --- a/backend/common/common.py +++ b/backend/common/common.py @@ -6,6 +6,13 @@ def get_arg_multi_toggle(args): + """ + :Function: Get the multi toggle from the request args + :param args: the request args (such as multi_toggle, multi) + :type args: dict + :return: boolean value of the multi toggle or multi + :rtype: bool + """ if 'multi_toggle' in args: multi_toggle = args.get('multi_toggle', 'false') elif 'multi' in args: @@ -16,6 +23,13 @@ def get_arg_multi_toggle(args): def get_arg_directed_toggle(args): + """ + :Function: Get the directed toggle from the request args + :param args: the request args (such as directed_toggle, directed) + :type args: dict + :return: boolean value of the directed toggle or directed + :rtype: bool + """ if 'directed_toggle' in args: directed_toggle = args.get('directed_toggle', 'false') elif 'directed' in args: @@ -26,17 +40,38 @@ def get_arg_directed_toggle(args): def get_arg_dynamic_toggle(args): + """ + :Function: Get the dynamic toggle from the request args + :param args: the request args (such as dynamic_toggle, dynamic) + :type args: dict + :return: boolean value of the dynamic toggle or dynamic + :rtype: bool + """ dynamic_toggle = args.get('dynamic', 'false') dynamic_toggle = True if dynamic_toggle in ['true', 'True', True] else False return dynamic_toggle def get_arg_layout(args): + """ + :Function: Get the layout from the request args + :param args: the layout argument (default is sfdp) + :type args: dict + :return: the layout + :rtype: str + """ layout = args.get('layout', 'sfdp') return layout def extract_args(args): + """ + :Function: Extract the arguments from the request args + :param args: the request args + :type args: dict + :return: the multi toggle, directed toggle, dynamic toggle, and layout + :rtype: tuple + """ multi_toggle = get_arg_multi_toggle(args) directed_toggle = get_arg_directed_toggle(args) dynamic_toggle = get_arg_dynamic_toggle(args) @@ -46,6 +81,17 @@ def extract_args(args): def process_metric(networkGraphs, filename2, metrics): + """ + :Function: Process the network graph and metrics + :param networkGraphs: the network graph + :type networkGraphs: NetworkGraphs + :param filename2: the session id + :type filename2: str + :param metrics: the metrics + :type metrics: list + :return: the processed metrics + :rtype: tuple + """ multi_toggle = None multi_toggle2 = None multi_toggle3 = None diff --git a/backend/deepLearning/__init__.py b/backend/deepLearning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/deepLearning/deepLearning.py b/backend/deepLearning/deepLearning.py index 2d73fb31..4f57ecff 100644 --- a/backend/deepLearning/deepLearning.py +++ b/backend/deepLearning/deepLearning.py @@ -9,21 +9,49 @@ def get_arg_p(args): + """ + :Function: Get the p argument from the request args + :param args: the request args (such as p) + :type args: dict + :return: the p argument + :rtype: float + """ p = args.get('p', 1.0, type=float) return p def get_arg_q(args): + """ + :Function: Get the q argument from the request args + :param args: the request args (such as q) + :type args: dict + :return: the q argument + :rtype: float + """ q = args.get('q', 1.0, type=float) return q def get_arg_no_of_clusters(args): + """ + :Function: Get the number of clusters from the request args + :param args: number of clusters + :type: int + :return: number of clusters + :rtype: int + """ no_of_clusters = args.get('number_of_clusters', 0, type=int) return no_of_clusters def get_arg_clustering_alg(args): + """ + :Function: Get the clustering algorithm from the request args + :param args: clustering algorithm + :type: str + :return: clustering algorithm + :rtype: str + """ clustering_alg = args.get('cluster_algorithm', 'kmeans', type=str) if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: raise ValueError('Clustering algorithm not supported, please choose between kmeans, spectral and agglomerative') @@ -31,17 +59,38 @@ def get_arg_clustering_alg(args): def get_arg_features(args): + """ + :Function: Get the features from the request args + :param args: features + :type: str + :return: features + :rtype: str + """ features = args.get('features', 'degree', type=str) features = features.split(',') return features def get_arg_dimension(args): + """ + :Function: Get the dimension from the request args + :param args: dimension + :type: int + :return: dimension + :rtype: int + """ dimension = args.get('dimension', 128, type=int) return dimension def get_arg_model(args): + """ + :Function: Get the model from the request args + :param args: model type (GCN, GAT, SAGE) + :type: str + :return: model + :rtype: str + """ model = args.get('model', 'SAGE', type=str) if model not in ['GCN', 'GAT', 'SAGE']: raise ValueError('Model not supported') @@ -50,6 +99,13 @@ def get_arg_model(args): @deepLearning_bp.route('/node2vec') def node2vec(session_id): + """ + :Function: Compute the node2vec embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ q = get_arg_q(request.args) p = get_arg_p(request.args) layout = get_arg_layout(request.args) @@ -64,11 +120,18 @@ def node2vec(session_id): df_json = df.to_json(orient='split') filename = filename.replace('../application/', '') - return jsonify({'message': 'Success', 'data': df_json, 'filename': filename }) + return jsonify({'message': 'Success', 'data': df_json, 'filename': filename}) @deepLearning_bp.route('/node2vec_clusters') def node2vec_clusters(session_id): + """ + :Function: Compute the node2vec embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ clustering_alg = get_arg_clustering_alg(request.args) if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: @@ -98,6 +161,13 @@ def node2vec_clusters(session_id): @deepLearning_bp.route('/dl_embedding') def dl_embedding(session_id): + """ + :Function: Compute the deep learning embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ features = get_arg_features(request.args) model = get_arg_model(request.args) dimension = get_arg_dimension(request.args) @@ -118,6 +188,13 @@ def dl_embedding(session_id): @deepLearning_bp.route('/dl_embedding_clusters') def dl_embedding_clusters(session_id): + """ + :Function: Compute the deep learning embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ clustering_alg = get_arg_clustering_alg(request.args) if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: @@ -133,13 +210,13 @@ def dl_embedding_clusters(session_id): if layout in ['TSNE', 'PCA', 'UMAP', 'map', 'sfdp', 'twopi']: df, filename = plot_DL_embedding_cluster(networkGraphs, - clustering_alg, - model=model, - features=features, - dimension=dimension, - layout=layout, - noOfCluster=no_of_clusters, - fullPath=True) + clustering_alg, + model=model, + features=features, + dimension=dimension, + layout=layout, + noOfCluster=no_of_clusters, + fullPath=True) else: raise ValueError('Layout not supported, please choose between TSNE, PCA and UMAP, map, sfdp, twopi') df_json = df.to_json(orient='split') diff --git a/backend/hotspot/__init__.py b/backend/hotspot/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/hotspot/density.py b/backend/hotspot/density.py index cbf48a2f..60028b8f 100644 --- a/backend/hotspot/density.py +++ b/backend/hotspot/density.py @@ -8,6 +8,13 @@ @hotspot_bp.route('/density') def compute_density(session_id): + """ + :Function: Compute the density of the network + :param session_id: the session id + :type session_id: str + :return: the density of the network + :rtype: json + """ G = get_networkGraph(session_id) df, filename = plot_hotspot(G) diff --git a/backend/metrics/__init__.py b/backend/metrics/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/metrics/metrics.py b/backend/metrics/metrics.py index 0684b99a..3eb8a19d 100644 --- a/backend/metrics/metrics.py +++ b/backend/metrics/metrics.py @@ -1,4 +1,3 @@ -import pandas as pd from flask import Blueprint, request from flask_jsonpify import jsonify @@ -11,6 +10,15 @@ @metrics_bp.route('//all') def compute_all_metrics(session_id, metric): + """ + :Function: Compute all metrics for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :return: the metric values + :rtype: json + """ directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) G = get_networkGraph(session_id) @@ -20,8 +28,18 @@ def compute_all_metrics(session_id, metric): return jsonify({"message": "Success", "data": df_json, "filename": file_name}) + @metrics_bp.route('/') def compute_metrics(session_id, metric): + """ + :Function: Compute the metric for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type session_id: str + :return: the metric values + :rtype: json + """ directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) G = get_networkGraph(session_id) @@ -35,7 +53,22 @@ def compute_metrics(session_id, metric): @metrics_bp.route('//') def plot_graph(session_id, metric, plot_type): - directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) + """ + :Function: Plot the graph for the metric + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :param plot_type: the type of plot + :type plot_type: str + :return: jsonified response + :rtype: json + """ + if metric in ['shortest_path', 'clustering_coefficient']: + directed_toggle = False + multi_toggle = False + else: + directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) G = get_networkGraph(session_id) @@ -53,4 +86,4 @@ def plot_graph(session_id, metric, plot_type): data = {"message": "Success", "data": df_json, "filename": file_name} - return jsonify(data) \ No newline at end of file + return jsonify(data) diff --git a/backend/resilience/__init__.py b/backend/resilience/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/resilience/cluster.py b/backend/resilience/cluster.py index e75f7f51..1ad458eb 100644 --- a/backend/resilience/cluster.py +++ b/backend/resilience/cluster.py @@ -7,7 +7,13 @@ clusters_bp = Blueprint('resilience_clusters', __name__, url_prefix="/api/v1/resilience") + def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (cluster_algorithm, total_clusters, number_of_clusters) + :rtype: tuple + """ args = request.args cluster_algorithm = args.get('cluster_algorithm') @@ -16,8 +22,16 @@ def extract_args(): return cluster_algorithm, total_clusters, number_of_clusters + @clusters_bp.route('/clusters') def compute_clusters(session_id): + """ + :Function: Compute the clusters for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ cluster_algorithm, total_clusters, number_of_clusters = extract_args() networkGraphs = get_networkGraph(session_id) @@ -39,4 +53,3 @@ def compute_clusters(session_id): return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, "heatmap_before": heatmap_before, "heatmap_after": heatmap_after}) - diff --git a/backend/resilience/custom.py b/backend/resilience/custom.py index a10f7205..04714d52 100644 --- a/backend/resilience/custom.py +++ b/backend/resilience/custom.py @@ -9,6 +9,11 @@ def get_args_listOfNodes(): + """ + :Function: Extract the list of nodes from the request + :return: the list of nodes (list_of_nodes) + :rtype: list + """ args = request.args list_of_nodes = args.get('list_of_nodes') @@ -24,6 +29,13 @@ def get_args_listOfNodes(): @custom_bp.route('/custom') def compute_custom(session_id): + """ + :Function: Compute the custom resilience of the network + :param session_id: the session id + :type session_id: str + :return: the custom resilience of the network + :rtype: json + """ list_of_nodes = get_args_listOfNodes() networkGraphs = get_networkGraph(session_id) diff --git a/backend/resilience/malicious.py b/backend/resilience/malicious.py index c14bae3f..6d8bdd1e 100644 --- a/backend/resilience/malicious.py +++ b/backend/resilience/malicious.py @@ -9,6 +9,11 @@ def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (attack_type, number_of_nodes_malicious, number_of_threshold, operator) + :rtype: tuple + """ res_operator = { 'greater_than': '>', 'less_than': '<', @@ -21,7 +26,7 @@ def extract_args(): number_of_threshold = args.get('number_of_thresholds', None, type=int) operator = args.get('operator', None) - if (operator is not None) and (operator is not ''): + if (operator is not None) and (operator != ''): operator = res_operator[operator] if number_of_nodes_malicious == '': number_of_nodes_malicious = None @@ -33,6 +38,13 @@ def extract_args(): @malicious_bp.route('/malicious') def compute_malicious(session_id): + """ + :Function: Compute the clusters for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ attack_type, number_of_nodes_malicious, number_of_threshold, operator = extract_args() networkGraphs = get_networkGraph(session_id) diff --git a/backend/resilience/random.py b/backend/resilience/random.py index 37959187..bfb6a2e1 100644 --- a/backend/resilience/random.py +++ b/backend/resilience/random.py @@ -7,23 +7,37 @@ random_bp = Blueprint('resilience_random', __name__, url_prefix="/api/v1/resilience") + def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (number of nodes, number of edges) + :rtype: tuple + """ args = request.args number_of_nodes = args.get('number_of_nodes', None, type=int) number_of_edges = args.get('number_of_edges', None, type=int) - if number_of_nodes is '': + if number_of_nodes == '': number_of_nodes = None - if number_of_edges is '': + if number_of_edges == '': number_of_edges = None print(number_of_nodes, number_of_edges) return number_of_nodes, number_of_edges + @random_bp.route('/random') def compute_random(session_id): + """ + :Function: Compute the random resilience for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ number_of_nodes, number_of_edges = extract_args() networkGraphs = get_networkGraph(session_id) @@ -45,4 +59,3 @@ def compute_random(session_id): return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, "heatmap_before": heatmap_before, "heatmap_after": heatmap_after}) - diff --git a/backend/resilience/resilience.py b/backend/resilience/resilience.py index 8a787da2..a29f9222 100644 --- a/backend/resilience/resilience.py +++ b/backend/resilience/resilience.py @@ -11,6 +11,17 @@ @resilience_bp.route('///') def compute_metrics(session_id, metric, plot_type): + """ + :Function: Compute the metric for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :param plot_type: the type of plot to generate + :type plot_type: str + :return: the metric values + :rtype: json + """ directed_toggle = get_arg_directed_toggle(request.args) multi_toggle = get_arg_multi_toggle(request.args) layout = get_arg_layout(request.args) @@ -51,6 +62,15 @@ def compute_metrics(session_id, metric, plot_type): @resilience_bp.route('/') def visualise_cluster(session_id, cluster_type): + """ + :Function: Visualise the clusters + :param session_id: the session id + :type session_id: str + :param cluster_type: the type of cluster + :type cluster_type: str + :return: the cluster values + :rtype: json + """ layout = get_arg_layout(request.args) noOfClusters = request.args.get('noOfClusters', 0, type=int) if noOfClusters == '': @@ -73,6 +93,13 @@ def visualise_cluster(session_id, cluster_type): @resilience_bp.route('/global_metrics') def global_metrics(session_id): + """ + :Function: Compute the global metrics for the network + :param session_id: the session id + :type session_id: str + :return: the global metrics values + :rtype: json + """ directed_toggle = get_arg_directed_toggle(request.args) multi_toggle = get_arg_multi_toggle(request.args) @@ -90,6 +117,13 @@ def global_metrics(session_id): @resilience_bp.route('/visualisation') def visualisation(session_id): + """ + :Function: Visualise the network + :param session_id: the session id + :type session_id: str + :return: the network visualisation + :rtype: json + """ layout = get_arg_layout(request.args) networkGraphs = get_networkGraph(session_id) diff --git a/backend/visualisation/__init__.py b/backend/visualisation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/visualisation/visualisation.py b/backend/visualisation/visualisation.py index 00569645..94c2cbd9 100644 --- a/backend/visualisation/visualisation.py +++ b/backend/visualisation/visualisation.py @@ -10,6 +10,15 @@ @visualisation_bp.route('/plot_network/') def visualise_network(session_id, plot_type): + """ + :Function: Plot the network graph + :param session_id: the session id + :type session_id: str + :param plot_type: the type of plot + :type plot_type: str + :return: jsonified response + :rtype: json + """ dynamic_toggle = get_arg_dynamic_toggle(request.args) layout = get_arg_layout(request.args) @@ -27,6 +36,13 @@ def visualise_network(session_id, plot_type): @visualisation_bp.route('/heatmap') def visualise_heatmap(session_id): + """ + :Function: Plot the heatmap + :param session_id: the session id + :type session_id: str + :return: jsonified response + :rtype: json + """ G = get_networkGraph(session_id) file_name = plot_heatmap(G) @@ -36,6 +52,13 @@ def visualise_heatmap(session_id): @visualisation_bp.route('//dataset') def visualise_dataset(session_id): + """ + :Function: Plot the dataset + :param session_id: the session id + :type session_id: str + :return: jsonified response + :rtype: json + """ G = get_networkGraph(session_id) df = G.df.head(3000).to_json(orient='split') diff --git a/datasets/Crypto_Json_To_Csv.py b/datasets/Crypto_Json_To_Csv.py index a573284d..c37be607 100644 --- a/datasets/Crypto_Json_To_Csv.py +++ b/datasets/Crypto_Json_To_Csv.py @@ -20,5 +20,5 @@ df = df[['from', 'to', 'value','block_time']] # write to csv -df.to_csv('Dune_Eth_transaction.csv', index=False) +df.to_csv('CRYPTO.csv', index=False) diff --git a/datasets/FBI_BTC_wallets.csv b/datasets/FBI_BTC_wallets.csv index 66ad1158..0f7f9a91 100644 --- a/datasets/FBI_BTC_wallets.csv +++ b/datasets/FBI_BTC_wallets.csv @@ -135,7 +135,6 @@ bc1qskk70rkzn6jj8jpmec3dmnlkvhvf44vth6sd02 36sJyTg6CbkPmqVNh5mjupYaKRe1U7xsxi bc1q5yp6d7y5lwa8am87tlaeaamssjajcp5aruxppl 3NQ6qSinTSy5ajef83eequtYhF1XAgLMNq -1MhzNrACEWb6JFMTqjzgZaUV6Shx7Heeno bc1qrntpkcw035npnhcnslv3h90460ayq8x38k3c68 3PUmKkPxJUMemP2PY3xxN8E5GdcZ8rXxGu 13bKZmMtLoCu3o1oUiGsqyCz3yWsbaDkp8 @@ -246,4 +245,15 @@ bc1qz50nvgmcdjsrm69wwcp997eltjf9zt99n7wcdw bc1q866qx809w0ssevrxljqdweku3ftswdgyph0ly2 bc1qhz8yvfqv7c3zpfwmhhe8kpeqq73pu08glfx2jk bc1quptyse7nn7m2ljut43fssrlvkzy3gxc6ty9p3q -bc1q7p5kyqppyxe6e8t0hj36qyjsq0w36tz6vvemfl \ No newline at end of file +bc1q7p5kyqppyxe6e8t0hj36qyjsq0w36tz6vvemfl +1BK769SseNefb6fe9QuFEi8W4KGbtP8gi3  +15FcqYRbwh2JsRUyBjvZ4jJ2XAD3pycGch  +1HwSof6jnbMFpfrRRa2jvydYdopkkGB4Sn  +15emeZ7buVegqhYh9PekH7cwFEJcCeVNpS  +3MSbCJCYtx5sj1nkzD4AMEhhvvviXBc8XJ  +17Z79rZpkk8kUiJseg5aELwYKaoLnirMUn  +bc1qp2vvntdedxw4xwtyd4y3gc2t9ufk6pwz2ga4ge  +3P9WebHkiDxCi8LDXiRQp8atNEagcQeRA3  +37fnBxofDeph2fpBZxZKypNkwdXAt9nT6F  +185NxhFAmKZrdwn9rVga3kqbvDP4FkbTNw  +12283Cq1pJ3f1gXwqi6K3bRf5LZb8Bkm6g  diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/build/doctrees/application.dictionary.doctree b/docs/build/doctrees/application.dictionary.doctree new file mode 100644 index 00000000..bc856a4d Binary files /dev/null and b/docs/build/doctrees/application.dictionary.doctree differ diff --git a/docs/build/doctrees/application.doctree b/docs/build/doctrees/application.doctree new file mode 100644 index 00000000..d2b35505 Binary files /dev/null and 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    + +

    Source code for application.app

    +import sys
    +
    +import requests
    +from flask import Flask, request, render_template, session
    +from flask_cors import CORS
    +
    +from flask_session import Session
    +from application.routes.metrics.centrality_routes import centrality_routes
    +from application.routes.clusters.cluster_routes import cluster_routes
    +from application.routes.metrics.node_routes import node_routes
    +from application.routes.resilience.resilience_routes import resilience_routes
    +from application.routes.deepLearning.cluster_embedding_routes import cluster_embedding_routes
    +from application.routes.deepLearning.embedding_routes import embedding_routes
    +from application.routes.hotspot.hotspot_routes import hotspot_routes
    +from application.routes.metrics.global_metrics_routes import global_metrics_routes
    +from application.routes.visualisation.visualisation_routes import visualisation_routes
    +
    +sys.path.insert(1, '../')
    +from src.NetworkGraphs import *
    +from src.metrics import *
    +from src.visualisation import *
    +from src.utils import *
    +
    +from flask_caching import Cache
    +import shutil
    +
    +app = Flask(__name__)
    +CORS(app, resources={r"/*": {"origins": "*"}})
    +app.config['SECRET_KEY'] = 'your_secret_key'
    +app.config['SESSION_TYPE'] = 'filesystem'
    +Session(app)
    +cache = Cache(app, config={'CACHE_TYPE': 'simple'})
    +
    +app.register_blueprint(cluster_routes)
    +app.register_blueprint(centrality_routes)
    +app.register_blueprint(node_routes)
    +app.register_blueprint(resilience_routes)
    +app.register_blueprint(cluster_embedding_routes)
    +app.register_blueprint(embedding_routes)
    +app.register_blueprint(hotspot_routes)
    +app.register_blueprint(global_metrics_routes)
    +app.register_blueprint(visualisation_routes)
    +
    +BASE_URL = 'http://localhost:8000/api/v1'
    +
    +
    +# Define a custom error page for 500 Internal Server Error
    +
    [docs]@app.errorhandler(500) +def internal_server_error(e): + if cache.has('global_metrics') or 'filename' in session: + # Delete the file + filename = session['filename'] + filename2 = session['filename2'] + filepath = 'static/uploads/' + filename2 + if os.path.exists(filepath): + shutil.rmtree(filepath) + if is_saved(filename2): + delete_networkGraph(filename2) + cache.clear() + # Remove the keys from the session + session.pop('network_graphs', None) + session.pop('filename', None) + session.pop('filename2', None) + session.pop('filepath', None) + session.pop('option', None) + session.clear() + return render_template('500.html')
    + + +# Define a custom error page for 404 Not Found Error +
    [docs]@app.errorhandler(404) +def not_found_error(e): + return render_template('404.html')
    + + +
    [docs]@app.route('/') +def index(): + # Check if global_metrics is present in the cache and filename is present in the session + if cache.has('global_metrics') or 'filename' in session: + # Delete the file + filename = session['filename'] + filename2 = session['filename2'] + filepath = 'static/uploads/' + filename2 + if os.path.exists(filepath): + shutil.rmtree(filepath) + # Clear the cache + cache.clear() + if is_saved(filename2): + delete_networkGraph(filename2) + # Remove the keys from the session + session.pop('network_graphs', None) + session.pop('filename', None) + session.pop('filename2', None) + session.pop('filepath', None) + session.pop('full_path', None) + session.pop('option', None) + session.clear() + + return render_template('index.html')
    + + +
    [docs]@app.route('/index/sample-dataset') +def index_sample(): + return render_template('index_sample_dataset.html')
    + + +
    [docs]@app.route('/home') +def home(): + args = request.args + filename = args.get('filename') + filename2 = args.get('filename2') + filepath = args.get('filepath') + full_path = args.get('full_path') + option = args.get('option') + + networkGraphs = None + + if filename2 is None or filename2 == '': + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + else: + session['filename'] = filename + session['filename2'] = filename2 + session['filepath'] = filepath + session['full_path'] = full_path + session['option'] = option + + # networkGraphs = NetworkGraphs(filepath, session_folder=filepath.split('/'+filename)[0], type=option) + networkGraphs = NetworkGraphs(full_path, session_folder=filepath.split('.')[0], type=option) + set_networkGraph(networkGraphs, filename2) + + # Pass the data to the HTML template + return render_template('home.html', session_id=filename2)
    + +
    [docs]@app.route('/docs') +def docs(): + return render_template('docs.html')
    + +# -------------------------------------------MAIN-------------------------------------------- +if __name__ == '__main__': + app.run(debug=True) +
    + +
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/application/routes/centrality_routes.html b/docs/build/html/_modules/application/routes/centrality_routes.html new file mode 100644 index 00000000..1f26743e --- /dev/null +++ b/docs/build/html/_modules/application/routes/centrality_routes.html @@ -0,0 +1,1025 @@ + + + + + + + + + + + application.routes.centrality_routes — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for application.routes.centrality_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session, request
    +
    +sys.path.insert(1, '../')
    +from src.metrics import *
    +from src.visualisation import *
    +
    +centrality_routes = Blueprint('centrality_routes', __name__)
    +
    +
    +# -------------------------------------------CENTRALITY--------------------------------------
    +
    [docs]@centrality_routes.route('/centrality', endpoint='centrality', methods=['GET', 'POST']) +def centrality_all(): + """ + :Function: Visualise the centrality metrics + :return: the centrality page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'centralities' + multi_toggle = True + directed_toggle = False + multi_toggle2 = True + directed_toggle2 = False + multi_toggle3 = True + directed_toggle3 = False + multi_toggle4 = True + directed_toggle4 = False + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'directed_toggle') is not None or request.form.get('layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'directed_toggle2') is not None or request.form.get('layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'directed_toggle3') is not None or request.form.get('layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'directed_toggle4') is not None or request.form.get('layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_all.html', example=df, tab=tab, method_name='All Centrality', + multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, + graph1=graph_path1, + multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, + graph2=graph_path2, + multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, + graph3=graph_path3, + multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, + graph4=graph_path4)
    + + +
    [docs]@centrality_routes.route('/centrality/degree', endpoint='degree', methods=['GET', 'POST']) +def centrality_degree(): + """ + :Function: Degree centrality page + :return: degree centrality page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'degree_centrality' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_degree.html', example=df, tab=tab, method_name='Degree Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@centrality_routes.route('/centrality/eigenvector', endpoint='eigenvector', methods=['GET', 'POST']) +def centrality_eigenvector(): + """ + :Function: Visualisae Eigenvector Centrality + :return: Eigenvector Centrality plot page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'eigenvector_centrality' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_eigenvector.html', example=df, tab=tab, + method_name='Eigenvector Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@centrality_routes.route('/centrality/closeness', endpoint='closeness', methods=['GET', 'POST']) +def centrality_closeness(): + """ + :Function: visualise closeness centrality + :return: closeness centrality page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'closeness_centrality' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_closeness.html', example=df, tab=tab, + method_name='Closeness Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@centrality_routes.route('/centrality/betwenness', endpoint='betwenness', methods=['GET', 'POST']) +def centrality_betwenness(): + """ + :Function: Visualization of the betwenness centrality + :return: betwenness centrality page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'betweenness_centrality' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_betwenness.html', example=df, tab=tab, + method_name='Betwenness Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@centrality_routes.route('/centrality/load', endpoint='load', methods=['GET', 'POST']) +def centrality_load(): + """ + :Function: Visualize the load centrality of the network + :return: + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'load_centrality' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('centrality/centrality_load.html', example=df, tab=tab, method_name='Load Centrality', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
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    Source code for application.routes.cluster_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session, request
    +
    +sys.path.insert(1, '../')
    +from src.metrics import *
    +from src.visualisation import *
    +
    +cluster_routes = Blueprint('cluster_routes', __name__)
    +
    +
    +# -------------------------------------------ML-CLUSTERING-----------------------------------
    +
    [docs]@cluster_routes.route('/clustering/louvain', endpoint='clustering_louvanian', methods=['GET', 'POST']) +def clustering_louvanian(): + """ + :Function: Visualise the clustering using louvain algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'louvain' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_louvanian.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Louvain')
    + + +
    [docs]@cluster_routes.route('/clustering/greedy_modularity', endpoint='clustering_greedy_modularity', methods=['GET', 'POST']) +def clustering_greedy_modularity(): + """ + :Function: Visualise the clustering using greedy modularity algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'greedy_modularity' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_greedy_modularity.html', example=df, + number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Greedy Modularity')
    + + +
    [docs]@cluster_routes.route('/clustering/label_propagation', endpoint='clustering_label_propagation', methods=['GET', 'POST']) +def clustering_label_propagation(): + """ + :Function: Visualise the clustering using label propagation algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'label_propagation' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_label_propagation.html', example=df, + number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Label Propagation')
    + + +
    [docs]@cluster_routes.route('/clustering/asyn_lpa', endpoint='clustering_asyn_lpa', methods=['GET', 'POST']) +def clustering_asyn_lpa(): + """ + :Function: Visualise the clustering using asynchronous label propagation algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'asyn_lpa' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_asyn_lpa.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Asyn Lpa')
    + + +
    [docs]@cluster_routes.route('/clustering/k_clique', endpoint='clustering_k_clique', methods=['GET', 'POST']) +def clustering_k_clique(): + """ + :Function: Visualise the clustering using k clique algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'k_clique' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_k_clique.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='K Clique')
    + + +
    [docs]@cluster_routes.route('/clustering/spectral', endpoint='clustering_spectral', methods=['GET', 'POST']) +def clustering_spectral(): + """ + :Function: Visualise the clustering using spectral algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'spectral' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_spectral.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='spectrals')
    + + +
    [docs]@cluster_routes.route('/clustering/kmeans', endpoint='clustering_kmeans', methods=['GET', 'POST']) +def clustering_kmeans(): + """ + :Function: Visualise the clustering using kmeans algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'kmeans' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_kmeans.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='KMeans')
    + + +
    [docs]@cluster_routes.route('/clustering/agglomerative', endpoint='clustering_agglomerative', methods=['GET', 'POST']) +def clustering_agglomerative(): + """ + :Function: Visualise the clustering using agglomerative algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'agglomerative' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_agglomerative.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Agglomerative')
    + + +
    [docs]@cluster_routes.route('/clustering/dbscan', endpoint='clustering_dbscan', methods=['GET', 'POST']) +def clustering_dbscan(): + """ + :Function: Visualise the clustering using dbscan algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'dbscan' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_dbscan.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Dbscan')
    + + +
    [docs]@cluster_routes.route('/clustering/hierarchical', endpoint='clustering_hierarchical', methods=['GET', 'POST']) +def clustering_hierarchical(): + """ + :Function: Visualise the clustering using hierarchical algorithm + :return: the clustering page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + clusterType = 'hierarchical' + multi_toggle = True + dynamic_toggle = False + directed_toggle = False + layout = 'map' + number_of_clusters = None + + if request.method == 'POST': + number_of_clusters = request.form.get('number_of_clusters', None) + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + number_of_clusters = int(number_of_clusters) if number_of_clusters else None + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + else: + df, graph_name1 = plot_cluster(networkGraphs, clusterType, dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + graph1 = session['graph_name1'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + return render_template('cluster/clustering_hierarchical.html', example=df, number_of_clusters=number_of_clusters, + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, method_name='Hierarchical')
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/application/routes/clusters/cluster_routes.html b/docs/build/html/_modules/application/routes/clusters/cluster_routes.html new file mode 100644 index 00000000..dccf5386 --- /dev/null +++ b/docs/build/html/_modules/application/routes/clusters/cluster_routes.html @@ -0,0 +1,509 @@ + + + + + + + + + + + application.routes.clusters.cluster_routes — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for application.routes.clusters.cluster_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +from src.metrics import *
    +
    +cluster_routes = Blueprint('cluster_routes', __name__)
    +
    +# -------------------------------------------ML-CLUSTERING-----------------------------------
    +BASE_URL = 'http://localhost:8000/api/v1/clusters/'
    +
    +
    +
    [docs]@cluster_routes.route('/clustering/louvain', endpoint='clustering_louvanian', methods=['GET', 'POST']) +def clustering_louvanian(): + """ + :Function: Visualise the clusters using Louvain algorithm + :return: the cluster page + + """ + filename2 = session['filename2'] + clusterType = 'louvain' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_louvanian.html', session_id=filename2, clusterType=clusterType, + method_name='Louvain', is_spatial=is_spatial, + description=description['louvain'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/greedy_modularity', endpoint='clustering_greedy_modularity', methods=['GET', 'POST']) +def clustering_greedy_modularity(): + """ + :Function: Visualise the clusters using Greedy Modularity algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'greedy_modularity' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_greedy_modularity.html', session_id=filename2, clusterType=clusterType, + method_name='Greedy Modularity', is_spatial=is_spatial, + description=description['greedy_modularity'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/label_propagation', endpoint='clustering_label_propagation', methods=['GET', 'POST']) +def clustering_label_propagation(): + """ + :Function: Visualise the clusters using Label Propagation algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'label_propagation' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_label_propagation.html', session_id=filename2, clusterType=clusterType, + method_name='Label Propagation', is_spatial=is_spatial, + description=description['label_propagation'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/asyn_lpa', endpoint='clustering_asyn_lpa', methods=['GET', 'POST']) +def clustering_asyn_lpa(): + """ + :Function: Visualise the clusters using Asyn Lpa algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'asyn_lpa' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_asyn_lpa.html', session_id=filename2, clusterType=clusterType, + method_name='Asyn Lpa', is_spatial=is_spatial, + description=description['asyn_lpa'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/k_clique', endpoint='clustering_k_clique', methods=['GET', 'POST']) +def clustering_k_clique(): + """ + :Function: Visualise the clusters using K Clique algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'k_clique' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_k_clique.html', session_id=filename2, clusterType=clusterType, + method_name='K Clique', is_spatial=is_spatial, + description=description['k_clique'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/spectral', endpoint='clustering_spectral', methods=['GET', 'POST']) +def clustering_spectral(): + """ + :Function: Visualise the clusters using Spectral algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'spectral' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_spectral.html', session_id=filename2, clusterType=clusterType, + method_name='Spectral', is_spatial=is_spatial, + description=description['spectral'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/kmeans', endpoint='clustering_kmeans', methods=['GET', 'POST']) +def clustering_kmeans(): + """ + :Function: Visualise the clusters using KMeans algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'kmeans' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_kmeans.html', session_id=filename2, clusterType=clusterType, + method_name='KMeans', is_spatial=is_spatial, + description=description['kmeans'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/agglomerative', endpoint='clustering_agglomerative', methods=['GET', 'POST']) +def clustering_agglomerative(): + """ + :Function: Visualise the clusters using Agglomerative algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'agglomerative' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_agglomerative.html', session_id=filename2, clusterType=clusterType, + method_name='Agglomerative', is_spatial=is_spatial, + description=description['agglomerative'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_routes.route('/clustering/dbscan', endpoint='clustering_dbscan', methods=['GET', 'POST']) +def clustering_dbscan(): + """ + :Function: Visualise the clusters using Dbscan algorithm + :return: the clusters page + + """ + filename2 = session['filename2'] + clusterType = 'dbscan' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('clusters/clustering_dbscan.html', session_id=filename2, clusterType=clusterType, + method_name='Dbscan', is_spatial=is_spatial, + description=description['dbscan'], tooltip_dynamic=tooltips['dynamic'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
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    Source code for application.routes.deepLearning.cluster_embedding_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +
    +cluster_embedding_routes = Blueprint('cluster_embedding_routes', __name__)
    +
    +# ----------------------------------------------CLUSTER-EMBEDDING------------------------------------------------------------------
    +BASE_URL = 'http://localhost:8000/api/v1/deeplearning/'
    +
    +
    +@cluster_embedding_routes.route('/node2vec/clustering/embedding/kmeans', endpoint='clustering_embedding_kmeans',
    +                                methods=['GET', 'POST'])
    +def clustering_embedding_kmeans():
    +    """
    +    :Function: Visualise the clusters using Kmeans algorithm
    +    :return: the cluster page
    +    """
    +    filename2 = session['filename2']
    +    clustering_alg = 'kmeans'
    +
    +    return render_template('deepLearning/node2vec/cluster/kmeans.html', session_id=filename2,
    +                           clustering_alg=clustering_alg,
    +                           description=description['node2vec_kmeans'], tooltip_parameters=tooltips['parameters'],
    +                           tooltip_layout=tooltips['layout_dropdown'],
    +                           tooltip_number_of_clusters=tooltips['number_of_clusters'])
    +
    +
    +@cluster_embedding_routes.route('/node2vec/clustering/embedding/spectral', endpoint='clustering_embedding_spectral',
    +                                methods=['GET', 'POST'])
    +def clustering_embedding_spectral():
    +    """
    +    :Function: Visualise the clusters using Spectral algorithm
    +    :return: the cluster page
    +    """
    +    filename2 = session['filename2']
    +    clustering_alg = 'spectral'
    +
    +    return render_template('deepLearning/node2vec/cluster/spectral.html', session_id=filename2,
    +                           clustering_alg=clustering_alg,
    +                           description=description['node2vec_spectral'], tooltip_parameters=tooltips['parameters'],
    +                           tooltip_layout=tooltips['layout_dropdown'],
    +                           tooltip_number_of_clusters=tooltips['number_of_clusters'])
    +
    +
    +@cluster_embedding_routes.route('/node2vec/clustering/embedding/agglomerative',
    +                                endpoint='clustering_embedding_agglomerative', methods=['GET', 'POST'])
    +def clustering_embedding_agglomerative():
    +    """
    +    :Function: Visualise the clusters using Agglomerative algorithm
    +    :return: the cluster page
    +    """
    +    filename2 = session['filename2']
    +    clustering_alg = 'agglomerative'
    +
    +    return render_template('deepLearning/node2vec/cluster/agglomerative.html', session_id=filename2,
    +                           clustering_alg=clustering_alg,
    +                           description=description['node2vec_agglomerative'], tooltip_parameters=tooltips['parameters'],
    +                           tooltip_layout=tooltips['layout_dropdown'],
    +                           tooltip_number_of_clusters=tooltips['number_of_clusters'])
    +
    +
    +# Dl embedding
    +
    +
    [docs]@cluster_embedding_routes.route('/dlembedding/clustering/embedding/kmeans', + endpoint='dlembedding_clustering_embedding_kmeans', methods=['GET', 'POST']) +def clustering_embedding_kmeans(): + filename2 = session['filename2'] + clustering_alg = 'kmeans' + + return render_template('deepLearning/dlembedding/cluster/kmeans.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_kmeans'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_embedding_routes.route('/dlembedding/clustering/embedding/spectral', + endpoint='dlembedding_clustering_embedding_spectral', methods=['GET', 'POST']) +def clustering_embedding_spectral(): + """ + :Function: Visualise the clusters using Spectral algorithm + :return: the cluster page + """ + filename2 = session['filename2'] + clustering_alg = 'spectral' + + return render_template('deepLearning/dlembedding/cluster/spectral.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_spectral'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
    + + +
    [docs]@cluster_embedding_routes.route('/dlembedding/clustering/embedding/agglomerative', + endpoint='dlembedding_clustering_embedding_agglomerative', methods=['GET', 'POST']) +def clustering_embedding_agglomerative(): + """ + :Function: Visualise the clusters using Agglomerative algorithm + :return: the cluster page + """ + filename2 = session['filename2'] + clustering_alg = 'agglomerative' + + return render_template('deepLearning/dlembedding/cluster/agglomerative.html', session_id=filename2, + clustering_alg=clustering_alg, + description=description['dlembedding_agglomerative'], + tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'], + tooltip_number_of_clusters=tooltips['number_of_clusters'])
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/application/routes/deepLearning/embedding_routes.html b/docs/build/html/_modules/application/routes/deepLearning/embedding_routes.html new file mode 100644 index 00000000..7ad3b844 --- /dev/null +++ b/docs/build/html/_modules/application/routes/deepLearning/embedding_routes.html @@ -0,0 +1,338 @@ + + + + + + + + + + + application.routes.deepLearning.embedding_routes — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for application.routes.deepLearning.embedding_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +
    +embedding_routes = Blueprint('embedding_routes', __name__)
    +
    +# ----------------------------------------------CLUSTER-EMBEDDING------------------------------------------------------------------
    +BASE_URL = 'http://localhost:8000/api/v1/deeplearning/'
    +
    +
    +
    [docs]@embedding_routes.route('/node2vec/embedding', endpoint='node2vec_embedding', methods=['GET', 'POST']) +def embedding_visualisation(): + """ + :Function: Visualise the embedding + :return: the embedding page + """ + filename2 = session['filename2'] + + return render_template('deepLearning/node2vec/embedding.html', session_id=filename2, + description=description['node2vec_embedding'], tooltip_parameters=tooltips['parameters'], + tooltip_layout=tooltips['layout_dropdown'])
    + + +
    [docs]@embedding_routes.route('/dlembedding/embedding', endpoint='dlembedding_embedding', methods=['GET', 'POST']) +def dlembedding_embedding_visualisation(): + """ + :Function: Visualise the embedding + :return: the embedding page + """ + filename2 = session['filename2'] + + return render_template('deepLearning/dlembedding/embedding.html', session_id=filename2, + description=description['dlembedding_embedding'], tooltip_dimension=tooltips['dimension'], + tooltip_model_dropdown=tooltips['model_dropdown'], + tooltip_layout=tooltips['layout_dropdown'], + tooltip_features=tooltips['features_checkbox'])
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    Source code for application.routes.hotspot.hotspot_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +
    +hotspot_routes = Blueprint('hotspot_routes', __name__)
    +
    +
    +# -------------------------------------------HOTSPOT-----------------------------------------
    +
    +
    [docs]@hotspot_routes.route('/hotspot/density', endpoint='hotspot_density', methods=['GET', 'POST']) +def hotspot_density(): + """ + :Function: Visualise the hotspot using density + :return: the hotspot page + """ + filename2 = session['filename2'] + hotspotType = 'density' + + return render_template('hotspot/hotspot_density.html', session_id=filename2, hotspotType=hotspotType, + method_name='Density', + description=description['hotspot_density'])
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/application/routes/metrics/centrality_routes.html b/docs/build/html/_modules/application/routes/metrics/centrality_routes.html new file mode 100644 index 00000000..4090d2fb --- /dev/null +++ b/docs/build/html/_modules/application/routes/metrics/centrality_routes.html @@ -0,0 +1,458 @@ + + + + + + + + + + + application.routes.metrics.centrality_routes — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for application.routes.metrics.centrality_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +from src.metrics import *
    +
    +centrality_routes = Blueprint('centrality_routes', __name__)
    +
    +
    +# -------------------------------------------CENTRALITY--------------------------------------
    +
    [docs]@centrality_routes.route('/centrality', endpoint='centrality', methods=['GET', 'POST']) +def centrality_all(): + """ + :Function: Visualise the centrality + :return: the centrality page + """ + filename2 = session['filename2'] + metrics = 'centralities' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_all.html', method_name='All Centrality', + is_spatial=is_spatial, + description=description['all_centrality'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@centrality_routes.route('/centrality/degree', endpoint='degree', methods=['GET', 'POST']) +def centrality_degree(): + """ + :Function: Visualise the degree centrality + :return: the degree centrality page + """ + filename2 = session['filename2'] + metrics = 'degree_centrality' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_degree.html', method_name='Degree Centrality', + is_spatial=is_spatial, + description=description['centrality_degree'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@centrality_routes.route('/centrality/eigenvector', endpoint='eigenvector', methods=['GET', 'POST']) +def centrality_eigenvector(): + """ + :Function: Visualise the eigenvector centrality + :return: the eigenvector centrality page + """ + filename2 = session['filename2'] + metrics = 'eigenvector_centrality' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_eigenvector.html', method_name='Eigenvector Centrality', + is_spatial=is_spatial, + description=description['centrality_eigenvector'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@centrality_routes.route('/centrality/closeness', endpoint='closeness', methods=['GET', 'POST']) +def centrality_closeness(): + """ + :Function: Visualise the closeness centrality + :return: the closeness centrality page + """ + filename2 = session['filename2'] + metrics = 'closeness_centrality' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_closeness.html', method_name='Closeness Centrality', + is_spatial=is_spatial, + description=description['centrality_closeness'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@centrality_routes.route('/centrality/betwenness', endpoint='betwenness', methods=['GET', 'POST']) +def centrality_betwenness(): + """ + :Function: Visualise the betwenness centrality + :return: the betwenness centrality page + """ + filename2 = session['filename2'] + metrics = 'betweenness_centrality' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_betwenness.html', method_name='Betwenness Centrality', + is_spatial=is_spatial, + description=description['centrality_betwenness'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@centrality_routes.route('/centrality/load', endpoint='load', methods=['GET', 'POST']) +def centrality_load(): + """ + :Function: Visualise the load centrality + :return: the load centrality page + """ + filename2 = session['filename2'] + metrics = 'load_centrality' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/centrality/centrality_load.html', method_name='Load Centrality', + is_spatial=is_spatial, + description=description['centrality_load'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
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    Source code for application.routes.metrics.global_metrics_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +from flask import request
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +from src.metrics import *
    +
    +global_metrics_routes = Blueprint('global_metrics_routes', __name__)
    +
    +
    +# -------------------------------------------GLOBAL-METRICS-----------------------------------
    +
    [docs]@global_metrics_routes.route('/global-metrics', methods=['GET', 'POST']) +def globalmetrics(): + """ + :Function: Visualise the global metrics + :return: the global metrics page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + multi_toggle = False + directed_toggle = False + + if request.method == 'POST': + multi_toggle = bool(request.form.get('multi_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + global_metrics = compute_global_metrics(networkGraphs, directed_toggle, multi_toggle) + else: + global_metrics = compute_global_metrics(networkGraphs, directed_toggle, multi_toggle) + + return render_template('metrics/global_metrics.html', example=global_metrics, multi_toggle=multi_toggle, + directed_toggle=directed_toggle, + tooltip_multi=tooltips['multi'], tooltip_directed=tooltips['directed'], + description=description['global_metrics'])
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    Source code for application.routes.metrics.node_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +from src.metrics import *
    +
    +node_routes = Blueprint('node_routes', __name__)
    +
    +
    +# -------------------------------------------NODE--------------------------------------------
    +
    [docs]@node_routes.route('/node_all', endpoint='node_all', methods=['GET', 'POST']) +def node_all(): + """ + :Function: Visualise the node metrics + :return: the node metrics page + """ + filename2 = session['filename2'] + metrics = 'nodes' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/nodes/node_all.html', method_name='All Nodes', is_spatial=is_spatial, + description=description['node_all'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@node_routes.route('/node/degree', endpoint='node_degree', methods=['GET', 'POST']) +def node_degree(): + """ + :Function: Visualise the node degree + :return: the node degree page + """ + filename2 = session['filename2'] + metrics = 'degree' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/nodes/node_degree.html', method_name='Node Degree', is_spatial=is_spatial, + description=description['node_degree'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@node_routes.route('/node/kcore', endpoint='node_kcore', methods=['GET', 'POST']) +def node_kcore(): + """ + :Function: Visualise the node kcore + :return: the node kcore page + """ + filename2 = session['filename2'] + metrics = 'kcore' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/nodes/node_kcore.html', method_name='Node K Core', is_spatial=is_spatial, + description=description['node_kcore'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@node_routes.route('/node/triangle', endpoint='node_triangle', methods=['GET', 'POST']) +def node_triangle(): + """ + :Function: Visualise the node triangle + :return: the node triangle page + """ + filename2 = session['filename2'] + metrics = 'triangles' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/nodes/node_triangle.html', method_name='Node Triangle', is_spatial=is_spatial, + description=description['node_triangle'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
    + + +
    [docs]@node_routes.route('/node/pagerank', endpoint='node_pagerank', methods=['GET', 'POST']) +def node_pagerank(): + """ + :Function: Visualise the node pagerank + :return: the node pagerank page + """ + filename2 = session['filename2'] + metrics = 'pagerank' + networkGraphs = get_networkGraph(filename2) + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('metrics/nodes/node_pagerank.html', method_name='Node Page Rank', is_spatial=is_spatial, + description=description['node_pagerank'], tooltip_multi=tooltips['multi'], + tooltip_layout_tab=tooltips['layout_tab'], tooltip_histogram_tab=tooltips['histogram_tab'], + tooltip_boxplot_tab=tooltips['boxplot_tab'], + tooltip_violinplot_tab=tooltips['violinplot_tab'], + tooltip_directed=tooltips['directed'], tooltip_layout=tooltips['layout_dropdown'], + tooltip_dynamic=tooltips['dynamic'], + metricsType=metrics, session_id=filename2)
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    Source code for application.routes.node_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session, request
    +
    +sys.path.insert(1, '../')
    +from src.metrics import *
    +from src.visualisation import *
    +
    +node_routes = Blueprint('node_routes', __name__)
    +
    +
    +# -------------------------------------------NODE--------------------------------------------
    +
    +
    [docs]@node_routes.route('/node_all', endpoint='node_all', methods=['GET', 'POST']) +def node_all(): + """ + :Function: Visualise the node metrics + :return: the node metrics page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'nodes' + multi_toggle = True + directed_toggle = False + multi_toggle2 = True + directed_toggle2 = False + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = False + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = False + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'directed_toggle') is not None or request.form.get('layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'directed_toggle2') is not None or request.form.get('layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'directed_toggle3') is not None or request.form.get('layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'directed_toggle4') is not None or request.form.get('layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_all_metrics(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('node_all.html', example=df, tab=tab, method_name='All Nodes', + multi_toggle=multi_toggle, directed_toggle=directed_toggle, layout=layout, + graph1=graph_path1, + multi_toggle2=multi_toggle2, directed_toggle2=directed_toggle2, layout2=layout2, + graph2=graph_path2, + multi_toggle3=multi_toggle3, directed_toggle3=directed_toggle3, layout3=layout3, + graph3=graph_path3, + multi_toggle4=multi_toggle4, directed_toggle4=directed_toggle4, layout4=layout4, + graph4=graph_path4)
    + + +
    [docs]@node_routes.route('/node/degree', endpoint='node_degree', methods=['GET', 'POST']) +def node_degree(): + """ + :Function: Visualize the degree of nodes + :return: the html page of the degree of nodes + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'degree' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('node_degree.html', example=df, tab=tab, method_name='Node Degree', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@node_routes.route('/node/kcore', endpoint='node_kcore', methods=['GET', 'POST']) +def node_kcore(): + """ + :Function: Visualize the kcore of the network + :return: the html page of the kcore visualization + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'kcore' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('node_kcore.html', example=df, tab=tab, method_name='Node K Core', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@node_routes.route('/node/triangle', endpoint='node_triangle', methods=['GET', 'POST']) +def node_triangle(): + """ + :Function: Visualize the triangle of the network + :return: the html page of the triangle visualization + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'triangles' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('node_triangle.html', example=df, tab=tab, method_name='Node Triangle', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
    + + +
    [docs]@node_routes.route('/node/pagerank', endpoint='node_pagerank', methods=['GET', 'POST']) +def node_pagerank(): + """ + :Function: Visualize pagerank of nodes + :return: the html page for pagerank + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + metrics = 'pagerank' + multi_toggle = True + dynamic_toggle = False + directed_toggle = True + multi_toggle2 = True + dynamic_toggle2 = False + directed_toggle2 = True + multi_toggle3 = True + dynamic_toggle3 = False + directed_toggle3 = True + multi_toggle4 = True + dynamic_toggle4 = False + directed_toggle4 = True + tab = 'tab1' + if networkGraphs.is_spatial(): + layout = 'map' + layout2 = 'map' + layout3 = 'map' + layout4 = 'map' + else: + layout = 'sfdp' + layout2 = 'sfdp' + layout3 = 'sfdp' + layout4 = 'sfdp' + + if request.method == 'POST': + if (request.form.get('multi_toggle') is not None or request.form.get( + 'dynamic_toggle') is not None or request.form.get('directed_toggle') is not None or request.form.get( + 'layout') is not None): + multi_toggle = bool(request.form.get('multi_toggle')) + dynamic_toggle = bool(request.form.get('dynamic_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + tab = 'tab1' + if (request.form.get('multi_toggle2') is not None or request.form.get( + 'dynamic_toggle2') is not None or request.form.get('directed_toggle2') is not None or request.form.get( + 'layout2') is not None): + multi_toggle2 = bool(request.form.get('multi_toggle2')) + dynamic_toggle2 = bool(request.form.get('dynamic_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + layout2 = request.form.get('layout2') + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + tab = 'tab2' + if (request.form.get('multi_toggle3') is not None or request.form.get( + 'dynamic_toggle3') is not None or request.form.get('directed_toggle3') is not None or request.form.get( + 'layout3') is not None): + multi_toggle3 = bool(request.form.get('multi_toggle3')) + dynamic_toggle3 = bool(request.form.get('dynamic_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + layout3 = request.form.get('layout3') + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + tab = 'tab3' + if (request.form.get('multi_toggle4') is not None or request.form.get( + 'dynamic_toggle4') is not None or request.form.get('directed_toggle4') is not None or request.form.get( + 'layout4') is not None): + multi_toggle4 = bool(request.form.get('multi_toggle4')) + dynamic_toggle4 = bool(request.form.get('dynamic_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + layout4 = request.form.get('layout4') + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + tab = 'tab4' + else: + df, graph_name1 = plot_metric(networkGraphs, metrics, directed=directed_toggle, multi=multi_toggle, + dynamic=dynamic_toggle, layout=layout) + session['graph_name1'] = graph_name1 + df, graph_name2 = plot_histogram(networkGraphs, metrics, directed=directed_toggle2, multi=multi_toggle2) + session['graph_name2'] = graph_name2 + df, graph_name3 = plot_boxplot(networkGraphs, metrics, directed=directed_toggle3, multi=multi_toggle3) + session['graph_name3'] = graph_name3 + df, graph_name4 = plot_violin(networkGraphs, metrics, directed=directed_toggle4, multi=multi_toggle4) + session['graph_name4'] = graph_name4 + graph1 = session['graph_name1'] + graph2 = session['graph_name2'] + graph3 = session['graph_name3'] + graph4 = session['graph_name4'] + + if graph1 == 'no_graph.html': + graph_path1 = '../static/' + graph1 + else: + graph_path1 = '../static/uploads/' + filename2 + '/' + graph1 + + if graph2 == 'no_graph.html': + graph_path2 = '../static/' + graph2 + else: + graph_path2 = '../static/uploads/' + filename2 + '/' + graph2 + + if graph3 == 'no_graph.html': + graph_path3 = '../static/' + graph3 + else: + graph_path3 = '../static/uploads/' + filename2 + '/' + graph3 + + if graph4 == 'no_graph.html': + graph_path4 = '../static/' + graph4 + else: + graph_path4 = '../static/uploads/' + filename2 + '/' + graph4 + + return render_template('node_pagerank.html', example=df, tab=tab, method_name='Node Page Rank', + multi_toggle=multi_toggle, dynamic_toggle=dynamic_toggle, directed_toggle=directed_toggle, + layout=layout, graph1=graph_path1, + multi_toggle2=multi_toggle2, dynamic_toggle2=dynamic_toggle2, + directed_toggle2=directed_toggle2, layout2=layout2, graph2=graph_path2, + multi_toggle3=multi_toggle3, dynamic_toggle3=dynamic_toggle3, + directed_toggle3=directed_toggle3, layout3=layout3, graph3=graph_path3, + multi_toggle4=multi_toggle4, dynamic_toggle4=dynamic_toggle4, + directed_toggle4=directed_toggle4, layout4=layout4, graph4=graph_path4)
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    Source code for application.routes.resilience.resilience_routes

    +import sys
    +
    +import requests
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +from src.utils import get_networkGraph
    +
    +sys.path.insert(1, '../../')
    +
    +resilience_routes = Blueprint('resilience_routes', __name__)
    +BASE_URL = 'http://localhost:8000/api/v1'
    +
    +
    +# -------------------------------------------RESILIENCE_ANALYSIS-----------------------------
    +
    [docs]@resilience_routes.route('/resilience/malicious', endpoint='resilience_malicious', methods=['GET']) +def resilience_analysis_malicious(): + """ + :Function: Visualise the malicious resilience analysis + :return: the malicious resilience analysis page + """ + filename2 = session['filename2'] + graph_path1 = '../static/no_graph.html' + graph_path2 = '../static/no_graph.html' + + attack_types = ['degree_centrality', 'betweenness_centrality', 'closeness_centrality', 'eigenvector_centrality', + 'load_centrality', 'kcore', 'degree', 'pagerank', 'triangles'] + + number_of_nodes_malicious = None + number_of_threshold = None + number_of_clusters = None + + return render_template('resilience/resilience_analyisis_malicious.html', session_id=filename2, + attack_types=attack_types, + number_of_threshold=number_of_threshold, number_of_clusters=number_of_clusters, + number_of_nodes_malicious=number_of_nodes_malicious, graph_path1=graph_path1, + graph_path2=graph_path2, + tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], + tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analysis_malicious'], + tooltip_type_of_attack=tooltips['type_of_attack'], tooltip_node_tab=tooltips['node_tab'], + tooltip_threshold_tab=tooltips['threshold_tab'])
    + + +
    [docs]@resilience_routes.route('/resilience/random', endpoint='resilience_random', methods=['GET']) +def resilience_analysis_random(): + """ + :Function: Visualise the random resilience analysis + :return: the random resilience analysis page + """ + filename2 = session['filename2'] + graph_path1 = '../static/no_graph.html' + graph_path2 = '../static/no_graph.html' + + return render_template('resilience/resilience_analyisis_random.html', session_id=filename2, + number_of_edges=0, number_of_clusters=0, + number_of_nodes_random=0, graph_path1=graph_path1, + graph_path2=graph_path2, + tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], + tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_random'], + tooltip_node_tab=tooltips['node_tab'], + tooltip_edge_tab=tooltips['edge_tab'])
    + + +
    [docs]@resilience_routes.route('/resilience/cluster', endpoint='resilience_cluster', methods=['GET', 'POST']) +def resilience_analysis_cluster(): + """ + :Function: Visualise the cluster resilience analysis + :return: the cluster resilience analysis page + """ + filename2 = session['filename2'] + graph_path1 = '../static/no_graph.html' + graph_path2 = '../static/no_graph.html' + + cluster_algorithm = None + total_clusters = None + number_of_clusters = None + + return render_template('resilience/resilience_analyisis_cluster.html', session_id=filename2, + layout3=cluster_algorithm, cluster_to_attack=number_of_clusters, + number_of_cluster_to_generate=total_clusters, graph_path1=graph_path1, + graph_path2=graph_path2, + tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], + tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_cluster'], + tooltip_type_of_cluster=tooltips['type_of_cluster'], + tooltip_number_of_cluster_to_generate=tooltips['number_of_cluster_to_generate'], + tooltip_number_of_cluster_to_attack=tooltips['number_of_cluster_to_attack'])
    + + +
    [docs]@resilience_routes.route('/resilience/custom', endpoint='resilience_custom', methods=['GET', 'POST']) +def resilience_analysis_custom(): + """ + :Function: Visualise the custom resilience analysis + :return: the custom resilience analysis page + """ + filename2 = session['filename2'] + graph_path1 = '../static/no_graph.html' + graph_path2 = '../static/no_graph.html' + + cluster_algorithm = None + total_clusters = None + number_of_clusters = None + + NetworkGraph = get_networkGraph(filename2) + layout = 'map' if NetworkGraph.is_spatial() else 'sfdp' + + json_data = requests.get( + f'{BASE_URL}/visualisation/{filename2}/plot_network/spatial?dynamic=False&layout={layout}').json() + graph_input_custom = json_data['filename'] + graph_input_custom = f"../static/uploads/{filename2}/" + graph_input_custom + + return render_template('resilience/resilience_analyisis_custom.html', session_id=filename2, + layout3=cluster_algorithm, cluster_to_attack=number_of_clusters, + number_of_cluster_to_generate=total_clusters, graph_path1=graph_path1, + graph_path2=graph_path2, graph_input_custom=graph_input_custom, + tooltip_attack_summary=tooltips['attack_summary'], tooltip_multi=tooltips['multi'], + tooltip_directed=tooltips['directed'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + tooltip_number_of_clusters=tooltips['number_of_clusters'], + description=description['resilience_analyisis_custom'], + tooltip_list_of_nodes=tooltips['list_of_nodes'])
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    Source code for application.routes.visualisation.visualisation_routes

    +import sys
    +
    +from flask import Blueprint, render_template, session
    +
    +from application.dictionary.information import *
    +
    +sys.path.insert(1, '../../')
    +from src.metrics import *
    +
    +visualisation_routes = Blueprint('visualisation_routes', __name__)
    +
    +
    +# -------------------------------------------VISUALISATION-----------------------------------
    +
    [docs]@visualisation_routes.route('/visualisation', methods=['GET', 'POST'], endpoint='visualisation') +def visualisation(): + """ + :Function: Visualise the network + :return: the visualisation page + """ + filename2 = session['filename2'] + networkGraphs = get_networkGraph(filename2) + + show_temporal = str(networkGraphs.is_temporal()).lower() + + if networkGraphs.is_spatial(): + is_spatial = 'yes' + else: + is_spatial = 'no' + + return render_template('visualisation/visualisation.html', show_temporal=show_temporal, + session_id=filename2, is_spatial=is_spatial, + tooltip_network_tab=tooltips['network_tab'], tooltip_temporal_tab=tooltips['temporal_tab'], + tooltip_heatmap_tab=tooltips['heatmap_tab'], + tooltip_dynamic=tooltips['dynamic'], tooltip_layout_dropdown=tooltips['layout_dropdown'], + description=description['visualisation'])
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/backend/app.html b/docs/build/html/_modules/backend/app.html new file mode 100644 index 00000000..9bb05a80 --- /dev/null +++ b/docs/build/html/_modules/backend/app.html @@ -0,0 +1,230 @@ + + + + + + backend.app — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
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    +
    + +

    Source code for backend.app

    +import os
    +import re
    +import shutil
    +import time
    +
    +import flask
    +from flask import request, session
    +from flask_cors import CORS
    +
    +from backend.clusters.clusters import cluster_bp
    +from backend.deepLearning.deepLearning import deepLearning_bp
    +from backend.hotspot.density import hotspot_bp
    +from backend.metrics.metrics import metrics_bp
    +from backend.resilience.cluster import clusters_bp
    +from backend.resilience.custom import custom_bp
    +from backend.resilience.malicious import malicious_bp
    +from backend.resilience.random import random_bp
    +from backend.resilience.resilience import resilience_bp
    +from backend.visualisation.visualisation import visualisation_bp
    +from src.NetworkGraphs import NetworkGraphs
    +from src.utils import set_networkGraph, get_networkGraph
    +
    +app = flask.Flask(__name__)
    +CORS(app, resources={r"/api/*": {"origins": "*"}})
    +
    +app.config['SECRET_KEY'] = 'your_secret_key'
    +api_bp = flask.Blueprint("api", __name__, url_prefix="/api/v1", )
    +
    +
    +
    [docs]@api_bp.route('/') +def homepage(): + return {"message": "AlphaTeam Backend API"}
    + + +
    [docs]@api_bp.route('/upload', methods=['POST']) +def upload(): + data = request.form + timestamp = str(int(time.time())) + # Get the CSV file and the selected option + if 'option2' in request.form: + csv_file = request.form['csv_path'] + + option = request.form['option'] + # Save the CSV file to a folder on the server with a filename based on the selected option and file extension + if option == 'MTX': + source_file = csv_file + option + '.mtx' + filename = option + re.sub(r'\W+', '', timestamp) + '.mtx' + filename2 = option + re.sub(r'\W+', '', timestamp) + elif option == 'GTFS': + source_file = csv_file + option + '.zip' + filename = option + re.sub(r'\W+', '', timestamp) + '.zip' + filename2 = option + re.sub(r'\W+', '', timestamp) + else: + source_file = csv_file + option + '.csv' + filename = option + re.sub(r'\W+', '', timestamp) + '.csv' + filename2 = option + re.sub(r'\W+', '', timestamp) + + # Create the directory if it doesn't exist + destination_dir = '../application/static/uploads/' + filename2 + if not os.path.exists(destination_dir): + os.makedirs(destination_dir) + + destination_file = filename + shutil.copy(source_file, destination_dir + '/' + destination_file) + filepath = destination_dir + '/' + filename + # Store the filename in a session variable + session['filename'] = filename + session['filename2'] = filename2 + session['filepath'] = filepath + session['option'] = option + else: + csv_file = request.files['csv_file'] + option = request.form['option'] + # Save the CSV file to a folder on the server with a filename based on the selected option and file extension + if option == 'MTX': + filename = option + re.sub(r'\W+', '', timestamp) + 'mtx' + filename2 = option + re.sub(r'\W+', '', timestamp) + elif option == 'GTFS': + filename = option + re.sub(r'\W+', '', timestamp) + '.zip' + filename2 = option + re.sub(r'\W+', '', timestamp) + else: + filename = option + re.sub(r'\W+', '', timestamp) + '.csv' + filename2 = option + re.sub(r'\W+', '', timestamp) + + # Create the directory if it doesn't exist + destination_dir = '../application/static/uploads/' + filename2 + if not os.path.exists(destination_dir): + os.makedirs(destination_dir) + + filepath = destination_dir + '/' + filename + csv_file.save(filepath) + + # Store the filename in a session variable + session['filename'] = filename + session['filename2'] = filename2 + session['filepath'] = filepath + session['option'] = option + session['destination_dir'] = destination_dir + + networkGraphs = NetworkGraphs(filepath, session_folder=destination_dir, type=option) + set_networkGraph(networkGraphs, filename2) + # Redirect the user to the success page + return {"message": "File uploaded successfully", "filename": filename, "filename2": filename2, + "filepath": filepath, "full_path": os.getcwd() + '/' + filepath, + "option": option}
    + + +
    [docs]@api_bp.route('/get_networkGraph/<session_id>', methods=['GET']) +def retrieve_networkGraph(session_id): + networkGraphs = get_networkGraph(session_id) + return networkGraphs.to_json()
    + + +# Register the API blueprint with the app +app.register_blueprint(api_bp) +app.register_blueprint(cluster_bp) +app.register_blueprint(metrics_bp) +app.register_blueprint(hotspot_bp) +app.register_blueprint(visualisation_bp) +app.register_blueprint(resilience_bp) +app.register_blueprint(malicious_bp) +app.register_blueprint(random_bp) +app.register_blueprint(clusters_bp) +app.register_blueprint(custom_bp) +app.register_blueprint(deepLearning_bp) + +# # add documentation +# api = flask_restx.Api(app, version='1.0', title='AlphaTeam Backend API', +# description='Backend API for AlphaTeam', +# doc='/api/v1/docs/') + + +if __name__ == '__main__': + app.run() +
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    Source code for backend.clusters.clusters

    +from flask import Blueprint, request, jsonify
    +
    +from backend.common.common import get_arg_dynamic_toggle, get_arg_layout
    +from src.utils import get_networkGraph
    +from src.visualisation import plot_cluster
    +
    +cluster_bp = Blueprint('clusters', __name__, url_prefix="/api/v1/clusters")
    +
    +
    +
    [docs]def get_arg_no_of_clusters(args): + """ + :Function: Get the number of clusters from the request args + :param args: number of clusters + :type: int + :return: number of clusters + :rtype: int + """ + no_of_clusters = args.get('no_of_clusters', 0, type=int) + return no_of_clusters
    + + +
    [docs]@cluster_bp.route('/<session_id>/<clustering_alg>') +def compute_clustering(session_id, clustering_alg): + """ + Compute the clustering for the network graph + :param session_id: the session id + :param clustering_alg: the clustering algorithm + :return: the jsonified response + """ + dynamic_toggle = get_arg_dynamic_toggle(request.args) + layout = get_arg_layout(request.args) + + no_of_clusters = get_arg_no_of_clusters(request.args) + + G = get_networkGraph(session_id) + + df, filename = plot_cluster(G, clustering_alg, noOfClusters=no_of_clusters, dynamic=dynamic_toggle, layout=layout) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "filename": filename})
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    Source code for backend.common.common

    +import pandas as pd
    +import requests
    +from flask import request
    +
    +BASE_URL = 'http://localhost:8000/api/v1/metrics/'
    +
    +
    +
    [docs]def get_arg_multi_toggle(args): + """ + :Function: Get the multi toggle from the request args + :param args: the request args (such as multi_toggle, multi) + :type args: dict + :return: boolean value of the multi toggle or multi + :rtype: bool + """ + if 'multi_toggle' in args: + multi_toggle = args.get('multi_toggle', 'false') + elif 'multi' in args: + multi_toggle = args.get('multi', 'false') + else: + raise ValueError('multi_toggle or multi not found in args') + return True if multi_toggle in ['true', 'True', True] else False
    + + +
    [docs]def get_arg_directed_toggle(args): + """ + :Function: Get the directed toggle from the request args + :param args: the request args (such as directed_toggle, directed) + :type args: dict + :return: boolean value of the directed toggle or directed + :rtype: bool + """ + if 'directed_toggle' in args: + directed_toggle = args.get('directed_toggle', 'false') + elif 'directed' in args: + directed_toggle = args.get('directed', 'false') + else: + raise ValueError('directed_toggle or directed not found in args') + return True if directed_toggle in ['true', 'True', True] else False
    + + +
    [docs]def get_arg_dynamic_toggle(args): + """ + :Function: Get the dynamic toggle from the request args + :param args: the request args (such as dynamic_toggle, dynamic) + :type args: dict + :return: boolean value of the dynamic toggle or dynamic + :rtype: bool + """ + dynamic_toggle = args.get('dynamic', 'false') + dynamic_toggle = True if dynamic_toggle in ['true', 'True', True] else False + return dynamic_toggle
    + + +
    [docs]def get_arg_layout(args): + """ + :Function: Get the layout from the request args + :param args: the layout argument (default is sfdp) + :type args: dict + :return: the layout + :rtype: str + """ + layout = args.get('layout', 'sfdp') + return layout
    + + +
    [docs]def extract_args(args): + """ + :Function: Extract the arguments from the request args + :param args: the request args + :type args: dict + :return: the multi toggle, directed toggle, dynamic toggle, and layout + :rtype: tuple + """ + multi_toggle = get_arg_multi_toggle(args) + directed_toggle = get_arg_directed_toggle(args) + dynamic_toggle = get_arg_dynamic_toggle(args) + layout = get_arg_layout(args) + + return directed_toggle, multi_toggle, dynamic_toggle, layout
    + + +
    [docs]def process_metric(networkGraphs, filename2, metrics): + """ + :Function: Process the network graph and metrics + :param networkGraphs: the network graph + :type networkGraphs: NetworkGraphs + :param filename2: the session id + :type filename2: str + :param metrics: the metrics + :type metrics: list + :return: the processed metrics + :rtype: tuple + """ + multi_toggle = None + multi_toggle2 = None + multi_toggle3 = None + multi_toggle4 = None + + directed_toggle = None + directed_toggle2 = None + directed_toggle3 = None + directed_toggle4 = None + tab = 'tab1' + + layout = 'map' if networkGraphs.is_spatial() else 'sfdp' + + if request.method == 'POST': + if request.form.get('multi_toggle') is not None and request.form.get('directed_toggle') is not None \ + and request.form.get('layout') is not None: + multi_toggle = bool(request.form.get('multi_toggle')) + directed_toggle = bool(request.form.get('directed_toggle')) + layout = request.form.get('layout') + tab = 'tab1' + + if request.form.get('multi_toggle2') is not None and request.form.get('directed_toggle2') is not None: + multi_toggle2 = bool(request.form.get('multi_toggle2')) + directed_toggle2 = bool(request.form.get('directed_toggle2')) + tab = 'tab2' + + if request.form.get('multi_toggle3') is not None and request.form.get('directed_toggle3') is not None: + multi_toggle3 = bool(request.form.get('multi_toggle3')) + directed_toggle3 = bool(request.form.get('directed_toggle3')) + tab = 'tab3' + + if request.form.get('multi_toggle4') is not None and request.form.get('directed_toggle4') is not None: + multi_toggle4 = bool(request.form.get('multi_toggle4')) + directed_toggle4 = bool(request.form.get('directed_toggle4')) + tab = 'tab4' + else: + multi_toggle = True + multi_toggle2 = True + multi_toggle3 = True + multi_toggle4 = True + + directed_toggle = False + directed_toggle2 = False + directed_toggle3 = False + directed_toggle4 = False + + url_query = f'?directed={directed_toggle}&multi={multi_toggle}' + json_data = requests.get(f'{BASE_URL}{filename2}/{metrics}/all' + url_query).json() + df = pd.read_json(json_data['data'], orient='split') + graph_name1 = json_data['file'] + + url_query = f'?directed={directed_toggle2}&multi={multi_toggle2}' + json_data = requests.get(f'{BASE_URL}{filename2}/{metrics}/histogram' + url_query).json() + graph_name2 = json_data['file'] + + url_query = f'?directed={directed_toggle3}&multi={multi_toggle3}' + json_data = requests.get(f'{BASE_URL}{filename2}/{metrics}/boxplot' + url_query).json() + graph_name3 = json_data['file'] + + url_query = f'?directed={directed_toggle4}&multi={multi_toggle4}' + json_data = requests.get(f'{BASE_URL}{filename2}/{metrics}/violin' + url_query).json() + graph_name4 = json_data['file'] + + graph_names = [graph_name1, graph_name2, graph_name3, graph_name4] + graph_paths = [ + '../static/uploads/' + filename2 + '/' + graph_name if graph_name != 'no_graph.html' + else '../static/no_graph.html' + for graph_name in graph_names] + multi_toggles = [multi_toggle, multi_toggle2, multi_toggle3, multi_toggle4] + directed_toggles = [directed_toggle, directed_toggle2, directed_toggle3, directed_toggle4] + + return graph_names, graph_paths, df, tab, layout, multi_toggles, directed_toggles
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    Source code for backend.deepLearning.deepLearning

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from backend.common.common import get_arg_layout
    +from src.utils import get_networkGraph
    +from src.visualisation import plot_node2vec, plot_node2vec_cluster, plot_DL_embedding, plot_DL_embedding_cluster
    +
    +deepLearning_bp = Blueprint('deeplearning', __name__, url_prefix="/api/v1/deeplearning")
    +
    +
    +
    [docs]def get_arg_p(args): + """ + :Function: Get the p argument from the request args + :param args: the request args (such as p) + :type args: dict + :return: the p argument + :rtype: float + """ + p = args.get('p', 1.0, type=float) + return p
    + + +
    [docs]def get_arg_q(args): + """ + :Function: Get the q argument from the request args + :param args: the request args (such as q) + :type args: dict + :return: the q argument + :rtype: float + """ + q = args.get('q', 1.0, type=float) + return q
    + + +
    [docs]def get_arg_no_of_clusters(args): + """ + :Function: Get the number of clusters from the request args + :param args: number of clusters + :type: int + :return: number of clusters + :rtype: int + """ + no_of_clusters = args.get('number_of_clusters', 0, type=int) + return no_of_clusters
    + + +
    [docs]def get_arg_clustering_alg(args): + """ + :Function: Get the clustering algorithm from the request args + :param args: clustering algorithm + :type: str + :return: clustering algorithm + :rtype: str + """ + clustering_alg = args.get('cluster_algorithm', 'kmeans', type=str) + if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: + raise ValueError('Clustering algorithm not supported, please choose between kmeans, spectral and agglomerative') + return clustering_alg
    + + +
    [docs]def get_arg_features(args): + """ + :Function: Get the features from the request args + :param args: features + :type: str + :return: features + :rtype: str + """ + features = args.get('features', 'degree', type=str) + features = features.split(',') + return features
    + + +
    [docs]def get_arg_dimension(args): + """ + :Function: Get the dimension from the request args + :param args: dimension + :type: int + :return: dimension + :rtype: int + """ + dimension = args.get('dimension', 128, type=int) + return dimension
    + + +
    [docs]def get_arg_model(args): + """ + :Function: Get the model from the request args + :param args: model type (GCN, GAT, SAGE) + :type: str + :return: model + :rtype: str + """ + model = args.get('model', 'SAGE', type=str) + if model not in ['GCN', 'GAT', 'SAGE']: + raise ValueError('Model not supported') + return model
    + + +
    [docs]@deepLearning_bp.route('<session_id>/node2vec') +def node2vec(session_id): + """ + :Function: Compute the node2vec embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ + q = get_arg_q(request.args) + p = get_arg_p(request.args) + layout = get_arg_layout(request.args) + + networkGraphs = get_networkGraph(session_id) + + if layout in ['TSNE', 'PCA', 'UMAP']: + df, filename = plot_node2vec(networkGraphs, layout=layout, p=p, q=q, fullPath=True) + else: + raise ValueError('Layout not supported, please choose between TSNE, PCA and UMAP') + + df_json = df.to_json(orient='split') + filename = filename.replace('../application/', '') + + return jsonify({'message': 'Success', 'data': df_json, 'filename': filename})
    + + +
    [docs]@deepLearning_bp.route('<session_id>/node2vec_clusters') +def node2vec_clusters(session_id): + """ + :Function: Compute the node2vec embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ + clustering_alg = get_arg_clustering_alg(request.args) + + if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: + raise ValueError('Clustering algorithm not supported, please choose between kmeans, spectral and agglomerative') + + q = get_arg_q(request.args) + p = get_arg_p(request.args) + layout = get_arg_layout(request.args) + no_of_clusters = get_arg_no_of_clusters(request.args) + + networkGraphs = get_networkGraph(session_id) + + if layout in ['TSNE', 'PCA', 'UMAP', 'map', 'sfdp', 'twopi']: + df, filename = plot_node2vec_cluster(networkGraphs, + clustering_alg, + layout=layout, + p=p, + q=q, + noOfCluster=no_of_clusters, + fullPath=True) + else: + raise ValueError('Layout not supported, please choose between TSNE, PCA and UMAP, map, sfdp, twopi') + df_json = df.to_json(orient='split') + filename = filename.replace('../application/', '../') + return jsonify({'message': 'Success', 'data': df_json, 'filename': filename})
    + + +
    [docs]@deepLearning_bp.route('<session_id>/dl_embedding') +def dl_embedding(session_id): + """ + :Function: Compute the deep learning embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ + features = get_arg_features(request.args) + model = get_arg_model(request.args) + dimension = get_arg_dimension(request.args) + layout = get_arg_layout(request.args) + + networkGraphs = get_networkGraph(session_id) + + if layout in ['TSNE', 'PCA', 'UMAP']: + df, filename = plot_DL_embedding(networkGraphs, model=model, features=features, dimension=dimension, + layout=layout, fullPath=True) + else: + raise ValueError('Layout not supported, please choose between TSNE, PCA and UMAP') + + df_json = df.to_json(orient='split') + filename = filename.replace('../application/', '') + return jsonify({'message': 'Success', 'data': df_json, 'filename': filename})
    + + +
    [docs]@deepLearning_bp.route('<session_id>/dl_embedding_clusters') +def dl_embedding_clusters(session_id): + """ + :Function: Compute the deep learning embedding for the network graph + :param session_id: the session id + :type session_id: str + :return: the jsonified response + :rtype: json + """ + clustering_alg = get_arg_clustering_alg(request.args) + + if clustering_alg not in ['kmeans', 'spectral', 'agglomerative']: + raise ValueError('Clustering algorithm not supported, please choose between kmeans, spectral and agglomerative') + + features = get_arg_features(request.args) + model = get_arg_model(request.args) + dimension = get_arg_dimension(request.args) + layout = get_arg_layout(request.args) + no_of_clusters = get_arg_no_of_clusters(request.args) + + networkGraphs = get_networkGraph(session_id) + + if layout in ['TSNE', 'PCA', 'UMAP', 'map', 'sfdp', 'twopi']: + df, filename = plot_DL_embedding_cluster(networkGraphs, + clustering_alg, + model=model, + features=features, + dimension=dimension, + layout=layout, + noOfCluster=no_of_clusters, + fullPath=True) + else: + raise ValueError('Layout not supported, please choose between TSNE, PCA and UMAP, map, sfdp, twopi') + df_json = df.to_json(orient='split') + filename = filename.replace('../application/', '../') + return jsonify({'message': 'Success', 'data': df_json, 'filename': filename})
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    Source code for backend.hotspot.density

    +from flask import Blueprint, jsonify
    +
    +from src.utils import get_networkGraph
    +from src.visualisation import plot_hotspot
    +
    +hotspot_bp = Blueprint('hotspot', __name__, url_prefix="/api/v1/hotspot")
    +
    +
    +
    [docs]@hotspot_bp.route('<session_id>/density') +def compute_density(session_id): + """ + :Function: Compute the density of the network + :param session_id: the session id + :type session_id: str + :return: the density of the network + :rtype: json + """ + G = get_networkGraph(session_id) + + df, filename = plot_hotspot(G) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "filename": filename})
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    Source code for backend.metrics.metrics

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from backend.common.common import extract_args
    +from src.utils import get_networkGraph
    +from src.visualisation import *
    +
    +metrics_bp = Blueprint('metrics', __name__, url_prefix="/api/v1/metrics")
    +
    +
    +
    [docs]@metrics_bp.route('<session_id>/<metric>/all') +def compute_all_metrics(session_id, metric): + """ + :Function: Compute all metrics for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :return: the metric values + :rtype: json + """ + directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) + + G = get_networkGraph(session_id) + + dataframe, file_name = plot_all_metrics(G, metric, directed=directed_toggle, multi=multi_toggle, layout=layout) + df_json = dataframe.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "filename": file_name})
    + + +
    [docs]@metrics_bp.route('<session_id>/<metric>') +def compute_metrics(session_id, metric): + """ + :Function: Compute the metric for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type session_id: str + :return: the metric values + :rtype: json + """ + directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) + + G = get_networkGraph(session_id) + + dataframe, file_name = plot_metric(G, metric, directed=directed_toggle, multi=multi_toggle, dynamic=dynamic_toggle, + layout=layout) + df_json = dataframe.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "filename": file_name})
    + + +
    [docs]@metrics_bp.route('<session_id>/<metric>/<plot_type>') +def plot_graph(session_id, metric, plot_type): + """ + :Function: Plot the graph for the metric + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :param plot_type: the type of plot + :type plot_type: str + :return: jsonified response + :rtype: json + """ + directed_toggle, multi_toggle, dynamic_toggle, layout = extract_args(request.args) + + G = get_networkGraph(session_id) + + dataframe = None + file_name = None + + if plot_type == 'histogram': + dataframe, file_name = plot_histogram(G, metric, directed=directed_toggle, multi=multi_toggle) + elif plot_type == 'boxplot': + dataframe, file_name = plot_boxplot(G, metric, directed=directed_toggle, multi=multi_toggle) + elif plot_type == 'violin': + dataframe, file_name = plot_violin(G, metric, directed=directed_toggle, multi=multi_toggle) + + df_json = dataframe.to_json(orient='split') + + data = {"message": "Success", "data": df_json, "filename": file_name} + + return jsonify(data)
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    Source code for backend.resilience.cluster

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from src.resilience import resilience
    +from src.utils import get_networkGraph, set_networkGraph
    +from src.visualisation import *
    +
    +clusters_bp = Blueprint('resilience_clusters', __name__, url_prefix="/api/v1/resilience")
    +
    +
    +
    [docs]def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (cluster_algorithm, total_clusters, number_of_clusters) + :rtype: tuple + """ + args = request.args + + cluster_algorithm = args.get('cluster_algorithm') + total_clusters = int(args.get('total_clusters')) + number_of_clusters = int(args.get('number_of_clusters')) + + return cluster_algorithm, total_clusters, number_of_clusters
    + + +
    [docs]@clusters_bp.route('<session_id>/clusters') +def compute_clusters(session_id): + """ + :Function: Compute the clusters for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ + cluster_algorithm, total_clusters, number_of_clusters = extract_args() + + networkGraphs = get_networkGraph(session_id) + + networkGraphs2, df = resilience(networkGraphs, attack='cluster', cluster_algorithm=cluster_algorithm, + total_clusters=total_clusters, number_of_clusters=number_of_clusters) + + session_id2 = session_id + '_resilience' + set_networkGraph(networkGraphs2, session_id2) + + layout = "map" if networkGraphs.is_spatial() else "sfdp" + + before = plot_network(networkGraphs, layout=layout, dynamic=False, fullPath=True) + after = plot_network(networkGraphs2, layout=layout, dynamic=False, fullPath=True) + heatmap_before = plot_heatmap(networkGraphs, fullPath=True) + heatmap_after = plot_heatmap(networkGraphs2, fullPath=True) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, + "heatmap_before": heatmap_before, "heatmap_after": heatmap_after})
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    Source code for backend.resilience.custom

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from src.resilience import resilience
    +from src.utils import get_networkGraph, set_networkGraph
    +from src.visualisation import *
    +
    +custom_bp = Blueprint('resilience_custom', __name__, url_prefix="/api/v1/resilience")
    +
    +
    +
    [docs]def get_args_listOfNodes(): + """ + :Function: Extract the list of nodes from the request + :return: the list of nodes (list_of_nodes) + :rtype: list + """ + args = request.args + list_of_nodes = args.get('list_of_nodes') + + if list_of_nodes: + list_of_nodes = list_of_nodes.split(',') + try: + list_of_nodes = [int(i) for i in list_of_nodes] + except: + list_of_nodes = [str(i) for i in list_of_nodes] + + return list_of_nodes
    + + +
    [docs]@custom_bp.route('<session_id>/custom') +def compute_custom(session_id): + """ + :Function: Compute the custom resilience of the network + :param session_id: the session id + :type session_id: str + :return: the custom resilience of the network + :rtype: json + """ + list_of_nodes = get_args_listOfNodes() + + networkGraphs = get_networkGraph(session_id) + + networkGraphs2, df = resilience(networkGraphs, attack='custom', list_of_nodes=list_of_nodes) + + session_id2 = session_id + '_resilience' + set_networkGraph(networkGraphs2, session_id2) + + layout = "map" if networkGraphs.is_spatial() else "sfdp" + + before = plot_network(networkGraphs, layout=layout, dynamic=False, fullPath=True) + after = plot_network(networkGraphs2, layout=layout, dynamic=False, fullPath=True) + heatmap_before = plot_heatmap(networkGraphs, fullPath=True) + heatmap_after = plot_heatmap(networkGraphs2, fullPath=True) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, + "heatmap_before": heatmap_before, "heatmap_after": heatmap_after})
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/backend/resilience/malicious.html b/docs/build/html/_modules/backend/resilience/malicious.html new file mode 100644 index 00000000..c871649b --- /dev/null +++ b/docs/build/html/_modules/backend/resilience/malicious.html @@ -0,0 +1,367 @@ + + + + + + + + + + + backend.resilience.malicious — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for backend.resilience.malicious

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from src.resilience import resilience
    +from src.utils import get_networkGraph, set_networkGraph
    +from src.visualisation import *
    +
    +malicious_bp = Blueprint('resilience_malicious', __name__, url_prefix="/api/v1/resilience")
    +
    +
    +
    [docs]def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (attack_type, number_of_nodes_malicious, number_of_threshold, operator) + :rtype: tuple + """ + res_operator = { + 'greater_than': '>', + 'less_than': '<', + 'greater_than_or_equal_to': '>=', + 'less_than_or_equal_to': '<=' + } + args = request.args + attack_type = args.get('attack_type', None) + number_of_nodes_malicious = args.get('number_of_nodes_malicious', None, type=int) + number_of_threshold = args.get('number_of_thresholds', None, type=int) + operator = args.get('operator', None) + + if (operator is not None) and (operator != ''): + operator = res_operator[operator] + if number_of_nodes_malicious == '': + number_of_nodes_malicious = None + if number_of_threshold == '': + number_of_threshold = None + + return attack_type, number_of_nodes_malicious, number_of_threshold, operator
    + + +
    [docs]@malicious_bp.route('<session_id>/malicious') +def compute_malicious(session_id): + """ + :Function: Compute the clusters for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ + attack_type, number_of_nodes_malicious, number_of_threshold, operator = extract_args() + + networkGraphs = get_networkGraph(session_id) + + networkGraphs2, df = resilience(networkGraphs, attack='malicious', metric=attack_type, + number_of_nodes=number_of_nodes_malicious, threshold=number_of_threshold, + operator=operator) + + session_id2 = session_id + '_resilience' + set_networkGraph(networkGraphs2, session_id2) + + layout = "map" if networkGraphs.is_spatial() else "sfdp" + + before = plot_network(networkGraphs, layout=layout, dynamic=False, fullPath=True) + after = plot_network(networkGraphs2, layout=layout, dynamic=False, fullPath=True) + heatmap_before = plot_heatmap(networkGraphs, fullPath=True) + heatmap_after = plot_heatmap(networkGraphs2, fullPath=True) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, + "heatmap_before": heatmap_before, "heatmap_after": heatmap_after})
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    + + \ No newline at end of file diff --git a/docs/build/html/_modules/backend/resilience/random.html b/docs/build/html/_modules/backend/resilience/random.html new file mode 100644 index 00000000..88b2e6ce --- /dev/null +++ b/docs/build/html/_modules/backend/resilience/random.html @@ -0,0 +1,359 @@ + + + + + + + + + + + backend.resilience.random — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for backend.resilience.random

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from src.resilience import resilience
    +from src.utils import get_networkGraph, set_networkGraph
    +from src.visualisation import *
    +
    +random_bp = Blueprint('resilience_random', __name__, url_prefix="/api/v1/resilience")
    +
    +
    +
    [docs]def extract_args(): + """ + :Function: Extract the arguments from the request + :return: the arguments (number of nodes, number of edges) + :rtype: tuple + """ + args = request.args + + number_of_nodes = args.get('number_of_nodes', None, type=int) + number_of_edges = args.get('number_of_edges', None, type=int) + + if number_of_nodes == '': + number_of_nodes = None + if number_of_edges == '': + number_of_edges = None + + print(number_of_nodes, number_of_edges) + + return number_of_nodes, number_of_edges
    + + +
    [docs]@random_bp.route('<session_id>/random') +def compute_random(session_id): + """ + :Function: Compute the random resilience for the network + :param session_id: the session id + :type session_id: str + :return: the clusters + :rtype: json + """ + number_of_nodes, number_of_edges = extract_args() + + networkGraphs = get_networkGraph(session_id) + + networkGraphs2, df = resilience(networkGraphs, attack='random', number_of_edges=number_of_edges, + number_of_nodes=number_of_nodes) + + session_id2 = session_id + '_resilience' + set_networkGraph(networkGraphs2, session_id2) + + layout = "map" if networkGraphs.is_spatial() else "sfdp" + + before = plot_network(networkGraphs, layout=layout, dynamic=False, fullPath=True) + after = plot_network(networkGraphs2, layout=layout, dynamic=False, fullPath=True) + heatmap_before = plot_heatmap(networkGraphs, fullPath=True) + heatmap_after = plot_heatmap(networkGraphs2, fullPath=True) + + df_json = df.to_json(orient='split') + + return jsonify({"message": "Success", "data": df_json, "network_before": before, "network_after": after, + "heatmap_before": heatmap_before, "heatmap_after": heatmap_after})
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    Source code for backend.resilience.resilience

    +from flask import Blueprint
    +from flask_jsonpify import jsonify
    +
    +from backend.common.common import *
    +from src.metrics import compute_global_metrics
    +from src.utils import get_networkGraph
    +from src.visualisation import plot_metric, plot_histogram, plot_boxplot, plot_violin, plot_cluster, plot_network
    +
    +resilience_bp = Blueprint('resilience', __name__, url_prefix="/api/v1/resilience")
    +
    +
    +
    [docs]@resilience_bp.route('<session_id>/<metric>/<plot_type>/') +def compute_metrics(session_id, metric, plot_type): + """ + :Function: Compute the metric for the network + :param session_id: the session id + :type session_id: str + :param metric: the metric to compute + :type metric: str + :param plot_type: the type of plot to generate + :type plot_type: str + :return: the metric values + :rtype: json + """ + directed_toggle = get_arg_directed_toggle(request.args) + multi_toggle = get_arg_multi_toggle(request.args) + layout = get_arg_layout(request.args) + + networkGraphs = get_networkGraph(session_id) + networkGraphs2 = get_networkGraph(session_id + '_resilience') + + df = None + df1 = None + file_name = None + file_name1 = None + + if plot_type == 'layout': + df, file_name = plot_metric(networkGraphs, metric, directed=directed_toggle, multi=multi_toggle, + layout=layout, fullPath=True) + df1, file_name1 = plot_metric(networkGraphs2, metric, directed=directed_toggle, multi=multi_toggle, + layout=layout, fullPath=True) + elif plot_type == 'histogram': + df, file_name = plot_histogram(networkGraphs, metric, directed=directed_toggle, multi=multi_toggle, + fullPath=True) + df1, file_name1 = plot_histogram(networkGraphs2, metric, directed=directed_toggle, multi=multi_toggle, + fullPath=True) + elif plot_type == 'boxplot': + df, file_name = plot_boxplot(networkGraphs, metric, directed=directed_toggle, multi=multi_toggle, fullPath=True) + df1, file_name1 = plot_boxplot(networkGraphs2, metric, directed=directed_toggle, multi=multi_toggle, + fullPath=True) + elif plot_type == 'violin': + df, file_name = plot_violin(networkGraphs, metric, directed=directed_toggle, multi=multi_toggle, fullPath=True) + df1, file_name1 = plot_violin(networkGraphs2, metric, directed=directed_toggle, multi=multi_toggle, + fullPath=True) + + df_json = df.to_json(orient='split') + df_json1 = df1.to_json(orient='split') + + return jsonify({"message": "Success", "data_before": df_json, "data_after": df_json1, "network_before": file_name, + "network_after": file_name1})
    + + +
    [docs]@resilience_bp.route('<session_id>/<cluster_type>') +def visualise_cluster(session_id, cluster_type): + """ + :Function: Visualise the clusters + :param session_id: the session id + :type session_id: str + :param cluster_type: the type of cluster + :type cluster_type: str + :return: the cluster values + :rtype: json + """ + layout = get_arg_layout(request.args) + noOfClusters = request.args.get('noOfClusters', 0, type=int) + if noOfClusters == '': + noOfClusters = 0 + noOfClusters = int(noOfClusters) + + networkGraphs = get_networkGraph(session_id) + networkGraphs2 = get_networkGraph(session_id + '_resilience') + + df, file_name = plot_cluster(networkGraphs, cluster_type, noOfClusters=noOfClusters, dynamic=False, layout=layout, + fullPath=True) + df1, file_name1 = plot_cluster(networkGraphs2, cluster_type, noOfClusters=noOfClusters, dynamic=False, + layout=layout, fullPath=True) + + df_json = df.to_json(orient='split') + df_json1 = df1.to_json(orient='split') + return jsonify({"message": "Success", "data_before": df_json, "data_after": df_json1, "network_before": file_name, + "network_after": file_name1})
    + + +
    [docs]@resilience_bp.route('<session_id>/global_metrics') +def global_metrics(session_id): + """ + :Function: Compute the global metrics for the network + :param session_id: the session id + :type session_id: str + :return: the global metrics values + :rtype: json + """ + directed_toggle = get_arg_directed_toggle(request.args) + multi_toggle = get_arg_multi_toggle(request.args) + + networkGraphs = get_networkGraph(session_id) + networkGraphs2 = get_networkGraph(session_id + '_resilience') + + df1 = compute_global_metrics(networkGraphs, directed=directed_toggle, multi=multi_toggle) + df2 = compute_global_metrics(networkGraphs2, directed=directed_toggle, multi=multi_toggle) + + df_json = df1.to_json(orient='split') + df_json1 = df2.to_json(orient='split') + + return jsonify({"message": "Success", "data_before": df_json, "data_after": df_json1})
    + + +
    [docs]@resilience_bp.route('<session_id>/visualisation') +def visualisation(session_id): + """ + :Function: Visualise the network + :param session_id: the session id + :type session_id: str + :return: the network visualisation + :rtype: json + """ + layout = get_arg_layout(request.args) + + networkGraphs = get_networkGraph(session_id) + networkGraphs2 = get_networkGraph(session_id + '_resilience') + + file_name1 = plot_network(networkGraphs, fullPath=True, layout=layout) + file_name2 = plot_network(networkGraphs2, fullPath=True, layout=layout) + + return jsonify({"message": "Success", "before_frame": file_name1, "after_frame": file_name2})
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    Source code for backend.visualisation.visualisation

    +from flask import Blueprint, request
    +from flask_jsonpify import jsonify
    +
    +from backend.common.common import get_arg_dynamic_toggle, get_arg_layout
    +from src.utils import get_networkGraph
    +from src.visualisation import *
    +
    +visualisation_bp = Blueprint('visualisation', __name__, url_prefix="/api/v1/visualisation")
    +
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    [docs]@visualisation_bp.route('<session_id>/plot_network/<plot_type>') +def visualise_network(session_id, plot_type): + """ + :Function: Plot the network graph + :param session_id: the session id + :type session_id: str + :param plot_type: the type of plot + :type plot_type: str + :return: jsonified response + :rtype: json + """ + dynamic_toggle = get_arg_dynamic_toggle(request.args) + layout = get_arg_layout(request.args) + + G = get_networkGraph(session_id) + + file_name = None + + if plot_type == 'spatial': + file_name = plot_network(G, layout=layout, dynamic=dynamic_toggle) + elif plot_type == 'temporal': + file_name = plot_temporal(G, layout=layout) + + return jsonify({"message": "Success", "filename": file_name})
    + + +
    [docs]@visualisation_bp.route('<session_id>/heatmap') +def visualise_heatmap(session_id): + """ + :Function: Plot the heatmap + :param session_id: the session id + :type session_id: str + :return: jsonified response + :rtype: json + """ + G = get_networkGraph(session_id) + + file_name = plot_heatmap(G) + + return jsonify({"message": "Success", "filename": file_name})
    + + +
    [docs]@visualisation_bp.route('/<session_id>/dataset') +def visualise_dataset(session_id): + """ + :Function: Plot the dataset + :param session_id: the session id + :type session_id: str + :return: jsonified response + :rtype: json + """ + G = get_networkGraph(session_id) + + df = G.df.head(3000).to_json(orient='split') + + return jsonify({"message": "Success", "data": df})
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    Source code for scrapers.FBI_BTC_Scraper

    +import bs4
    +import requests
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    [docs]def scrape(url): + """ + :Function: Scrape the addresses from the FBI website + :param url: the URL of the website + :type url: str + :return: None + """ + res = requests.get(url) + soup = bs4.BeautifulSoup(res.text, 'html.parser') + soup.find_all('ul') + + fbi_addresses = [] + for ul in soup.find_all('ul'): + for li in ul.find_all('li'): + # scraping addresses that are 30+ characters long and have no spaces + if len(li.text) > 30 and ' ' not in li.text: + fbi_addresses.append(li.text) + + # write to file + with open('../datasets/FBI_BTC_wallets.csv', 'w') as f: + for address in fbi_addresses: + f.write(address + '\n')
    + + +url = 'https://www.fbi.gov/news/press-releases/fbi-confirms-lazarus-group-cyber-actors-responsible-for-harmonys-horizon-bridge-currency-theft' + +# uncomment to run +# scrape(url) +
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    Source code for scrapers.Get_BTCTransaction_BlockCypher

    +import pandas as pd
    +from blockcypher import get_address_full
    +
    +#Get BTC Transaction from blockcypher
    +
    +
    [docs]def scrap_btc_transaction(file_address, file_transaction): + """ + :Function: Get the transaction from the btc address + :param file_address: file where the btc address is stored + :type file_address: str + :param file_transaction: file where the transaction will be stored + :type file_address: str + :return: None + """ + with open(file_transaction, 'w') as f: + df = pd.read_csv(file_address, header=None, names=['add']) + for address in df['add']: + try: + transactions = get_address_full(address) + for transaction in transactions['txs']: + for add in transaction['addresses']: + if add != address: + f.write("%s,%s,%s\n" % (address, add, transaction['total'])) + except: + print('error', address)
    + +file_address = '' #file where the btc address is stored +file_transaction = '' #file where the transaction will be stored + +# uncomment to run +# scrap_btc_transaction(file_address, file_transaction) +
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    Source code for scrapers.Wallet_Explorer_Scrapper

    +import time
    +
    +import requests
    +from bs4 import BeautifulSoup
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    [docs]def scrape(link, nb_page, file): + """ + :Function: Scrape the addresses from the wallet explorer website + :param link: the URL of the website + :type link: str + :param nb_page: the number of pages to scrape + :type nb_page: int + :param file: the file where the addresses will be stored + :type link: str + :return: None + """ + with open(file, 'w') as f: + for i in range(1, nb_page + 1): + url = str(link) + str(i) + time.sleep(1) + page = requests.get(url) + while page.status_code != 200: + print('error: ', i) + time.sleep(10) + page = requests.get(url) + soup = BeautifulSoup(page.content, 'html.parser') + for link in soup.find_all('a'): + # only get addresses + if link.get('href').startswith('/address/'): + f.write("%s\n" % (link.get('href')[9:]))
    + + +link = 'https://www.walletexplorer.com/wallet/SilkRoadMarketplace/addresses?page=' +nb_page = 3729 +file = 'DNM_SR.csv' # file where the addresses will be stored + +# uncomment to run +# scrape(link, nb_page, file) +
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    Source code for src.DeepLearning.embedding

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Deep learning embedding module contains functions for deep learning embedding
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +import random
    +
    +import numpy as np
    +# External imports
    +import torch
    +import torch.nn.functional as F
    +from torch_geometric.nn import GCNConv, GATConv, SAGEConv
    +from torch_geometric.utils import from_networkx
    +
    +
    +# ----------------------------------------- CONSTANT ----------------------------------------- #
    +
    +# ------------------------------------------- MODEL ------------------------------------------ #
    +
    +
    +
    [docs]class GCN(torch.nn.Module): + """ + Graph Convolutional Network + """ + def __init__(self, num_features, hidden_dim, embed_dim): + super(GCN, self).__init__() + self.encoder = GCNConv(num_features, hidden_dim) + self.decoder = torch.nn.Linear(hidden_dim, embed_dim) + +
    [docs] def forward(self, data): + x, edge_index = data.x, data.edge_index + z = self.encoder(x, edge_index) + x = self.decoder(z) + return x
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]class GAT(torch.nn.Module): + """ + Graph Attention Network + """ + def __init__(self, num_features, hidden_dim, embed_dim): + super(GAT, self).__init__() + self.encoder = GATConv(num_features, hidden_dim) + self.decoder = torch.nn.Linear(hidden_dim, embed_dim) + +
    [docs] def forward(self, data): + x, edge_index = data.x, data.edge_index + z = self.encoder(x, edge_index) + x = self.decoder(z) + return x
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]class SAGE(torch.nn.Module): + """ + GraphSAGE + """ + def __init__(self, num_features, hidden_dim, embed_dim): + super(SAGE, self).__init__() + self.encoder = SAGEConv(num_features, hidden_dim) + self.decoder = torch.nn.Linear(hidden_dim, embed_dim) + +
    [docs] def forward(self, data): + x, edge_index = data.x, data.edge_index + z = self.encoder(x, edge_index) + x = self.decoder(z) + return x
    + + +# ------------------------------------------- LOSS ------------------------------------------- # + +
    [docs]def unsupervised_loss(recon_x, x): + """ + :Function: Unsupervised loss function for deep learning embedding + :param recon_x: reconstructed x + :type recon_x: torch.Tensor + :param x: original x + :type x: torch.Tensor + :return: loss + :rtype: torch.Tensor + """ + mse_loss = F.mse_loss(recon_x, x) + return mse_loss
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def contrastive_loss(embeddings, positive_pairs, negative_pairs, margin=1.0): + """ + :Function: Contrastive loss function for unsupervised learning + :param embeddings: embeddings + :type embeddings: torch.Tensor + :param positive_pairs: positive pairs + :type positive_pairs: torch.Tensor + :param negative_pairs: negative pairs + :type negative_pairs: torch.Tensor + :param margin: margin + :type margin: float + :return: loss + :rtype: torch.Tensor + """ + positive_distances = torch.norm(embeddings[positive_pairs[:, 0]] - embeddings[positive_pairs[:, 1]], dim=1) + negative_distances = torch.norm(embeddings[negative_pairs[:, 0]] - embeddings[negative_pairs[:, 1]], dim=1) + + positive_loss = torch.mean(torch.square(positive_distances)) + negative_loss = torch.mean(torch.square(torch.clamp(margin - negative_distances, min=0.0))) + + loss = 0.5 * (positive_loss + negative_loss) + return loss
    + + +# ----------------------------------------- TRAINING ----------------------------------------- # + + +
    [docs]def train(model, optimizer, data, device, proximity=False): + """ + :Function: Train model for deep learning embedding + :param model: Deep learning model + :type model: torch.nn.Module + :param optimizer: Optimizer + :type optimizer: torch.optim.Optimizer + :param data: Data + :type data: torch_geometric.data.Data + :param device: Device + :type device: torch.device + :param proximity: Proximity + :type proximity: bool + :return: loss + :rtype: float + """ + model.train() + optimizer.zero_grad() + out = model(data.to(device)) + if proximity: + loss = contrastive_loss(out, data.positive_pairs, data.negative_pairs) + else: + loss = unsupervised_loss(out[data.train_mask], data.x[data.train_mask]) + loss.backward() + optimizer.step() + return loss.item()
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def test(model, data, device, proximity=False): + """ + :Function: Test model for deep learning embedding + :param model: Deep learning model + :type model: torch.nn.Module + :param data: Data + :type data: torch_geometric.data.Data + :param device: Device + :type device: torch.device + :param proximity: Proximity + :type proximity: bool + :return: loss + :rtype: float + """ + model.eval() + out = model(data.to(device)) + if proximity: + loss = contrastive_loss(out, data.positive_pairs, data.negative_pairs) + else: + loss = unsupervised_loss(out[data.test_mask], data.x[data.test_mask]) + return loss.item()
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def train_model(model, optimizer, data, device, epochs, proximity=False): + """ + :Function: Train model for deep learning embedding + :param model: Deep learning model + :type model: torch.nn.Module + :param optimizer: Optimizer + :type optimizer: torch.optim.Optimizer + :param data: Data + :type data: torch_geometric.data.Data + :param device: Device + :type device: torch.device + :param epochs: Epochs + :type epochs: int + :param proximity: Proximity + :type proximity: bool + :return: loss + :rtype: float + """ + best_loss = float('inf') + best_weights = None + for epoch in range(1, epochs + 1): + loss = train(model, optimizer, data, device, proximity=proximity) + test_loss = test(model, data, device, proximity=proximity) + if test_loss < best_loss: + best_loss = test_loss + best_weights = model.state_dict() + if test_loss < 0.01: # early stopping + break + print('Epoch: {:03d}, Loss: {:.5f}, Test Loss: {:.5f}'.format(epoch, loss, test_loss)) + model.load_state_dict(best_weights) + return model
    + + +# ---------------------------------------- PREPROCESS ---------------------------------------- # + + +
    [docs]def generate_pairs(networkx_graph, num_negative_pairs=None): + """ + :Function: Generate positive and negative pairs + :param networkx_graph: networkx graph + :type networkx_graph: networkx.Graph + :param num_negative_pairs: number of negative pairs + :type num_negative_pairs: int + :return: positive pairs, negative pairs + :rtype: np.array, np.array + """ + nodes = list(networkx_graph.nodes()) + + positive_pairs = np.array([[u, v] for u, v in networkx_graph.edges]) + + if num_negative_pairs is None: + num_negative_pairs = len(positive_pairs) + + negative_pairs = [] + while len(negative_pairs) < num_negative_pairs: + u, v = random.sample(nodes, 2) + if not networkx_graph.has_edge(u, v): + negative_pairs.append([u, v]) + negative_pairs = np.array(negative_pairs) + + return positive_pairs, negative_pairs
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def pairs_to_indices(data, positive_pairs, negative_pairs): + """ + :Function: Convert pairs to indices + :param data: Data + :type data: torch_geometric.data.Data + :param positive_pairs: positive pairs + :type positive_pairs: np.array + :param negative_pairs: negative pairs + :type negative_pairs: np.array + :return: positive indices, negative indices + :rtype: np.array, np.array + """ + node_to_index = {node: i for i, node in enumerate(data.mapping)} + + positive_indices = np.array([[node_to_index.get(u), node_to_index.get(v)] for u, v in positive_pairs if + u in node_to_index and v in node_to_index]) + negative_indices = np.array([[node_to_index.get(u), node_to_index.get(v)] for u, v in negative_pairs if + u in node_to_index and v in node_to_index]) + + return positive_indices, negative_indices
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def preprocess_data(networkx_graph, node_features): + """ + :Function: Preprocess data for deep learning embedding + :param networkx_graph: Networkx graph + :type networkx_graph: networkx.Graph + :param node_features: Node features + :type node_features: np.array + :return: data + :rtype: torch_geometric.data.Data + """ + # Convert to torch_geometric.data.Data + data = from_networkx(networkx_graph) + + # Add node features + data.x = torch.tensor(node_features, dtype=torch.float) + + # Add train and test mask + data.train_mask = torch.zeros(data.num_nodes, dtype=torch.uint8) + data.train_mask[:data.num_nodes // 2] = 1 + data.test_mask = torch.zeros(data.num_nodes, dtype=torch.uint8) + data.test_mask[data.num_nodes // 2:] = 1 + data.mapping = {node: i for i, node in enumerate(networkx_graph.nodes)} + + return data
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def get_embeddings(model, data): + """ + :Function: Get final embeddings + :param model: Deep learning model + :type model: torch.nn.Module + :param data: Data + :type data: torch_geometric.data.Data + :return: embeddings + :rtype: np.array + """ + model.eval() + with torch.no_grad(): + x, edge_index = data.x, data.edge_index + embeddings = model.encoder(x, edge_index) + return embeddings.detach().cpu().numpy()
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    Source code for src.NetworkGraphs

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: NetworkGraphs.py contains the NetworkGraphs custom class to generalise complex network analysis
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +import scipy.io as sio
    +from pandas.api.types import is_numeric_dtype
    +
    +# Internal imports
    +from src.preprocessing import *
    +from src.visualisation import *
    +
    +"""
    +----------------------------------------------------------------------------------------
    +
    +INFORMATION ABOUT GRAPH ATTRIBUTES
    +
    +NODES:
    +- 'pos': position of the node (x, y) as a tuple, POS IS A DICTIONARY WITH LAYOUTS AS KEYS AND POSITIONS AS VALUES
    +    - pos['map'] = (lat, long)
    +    - pos['neato'] = (x, y)
    +    - pos['twopi'] = (x, y)
    +    - pos['sfdp'] = (x, y)
    +    
    +EDGES:
    +- 'weight': value of the edge (float) for value graphs, WEIGHT IS A DICTIONARY WITH GRAPH AS KEYS AND WEIGHTS AS VALUES
    +    - weight['MultiDiGraph'] = float
    +    - weight['MultiGraph'] = float
    +    - weight['DiGraph'] = float
    +    - weight['Graph'] = float
    +- 'start': time of the start of the edge (int) for temporal graphs
    +- 'end': time of the end of the edge (int) for temporal graphs
    +- 'color': color of the edge (string), COLOR IS A DICTIONARY WITH GRAPH AS KEYS AND COLORS AS VALUES
    +    - color['MultiDiGraph'] = string
    +    - color['MultiGraph'] = string
    +    - color['DiGraph'] = string
    +    - color['Graph'] = string
    +
    +GRAPHS:
    +- 'name': name of the graph (string)
    +- 'type': type of the graph (string) - 'RAILWAY', 'CRYPTO' 'MTX' or 'CUSTOM'
    +- 'temporal': True if the graph is temporal, False otherwise
    +- 'spatial': True if the graph is spatial, False otherwise
    +- 'weighted': True if the graph is weighted, False otherwise
    +
    +"""
    +
    +
    +
    [docs]class NetworkGraphs: + """ + Class containing the NetworkX graphs wrapping functionalities to generalise the use of the MultiDiGraph, + MultiGraph, DiGraph and Graph. This class store attributes of the graphs to allow effective use of the graphs. + """ + + # ----------------------------------------------------------------------------------------------------------------- + # ---------------------------------------------- CONSTRUCTOR ------------------------------------------------------ + # ----------------------------------------------------------------------------------------------------------------- + + def __init__(self, filename, type, session_folder=None, temporal=False, spatial=False, weighted=False, + attack_vector=None): + """ + Constructor of the NetworkGraphs class. It creates the NetworkX graphs and store the attributes of the graphs. + + File format accepted: + - .csv + - .mtx + - .zip (for GTFS files) + Type accepted: + - 'RAILWAY' + - 'CRYPTO' + - 'MTX' + - 'CUSTOM' + - 'GTFS' + :param filename: Path to the file containing the dataset to be loaded + :type filename: str + :param type: Type of the dataset to be loaded + :type type: str + :param session_folder: Path to the folder where the session is stored + :type session_folder: str + :param temporal: True if the graph is temporal, False otherwise + :type temporal: bool + :param spatial: True if the graph is spatial, False otherwise + :type spatial: bool + :param weighted: True if the graph is weighted, False otherwise + :type weighted: bool + :return: None + :rtype: None + + Example: + >>> graph1 = NetworkGraphs('data/RAILWAY/RAILWAY.csv', 'RAILWAY') + >>> graph2 = NetworkGraphs('data/CRYPTO/CRYPTO.csv', 'CRYPTO') + >>> graph3 = NetworkGraphs('data/MTX/MTX.mtx', 'MTX') + >>> graph4 = NetworkGraphs('data/CUSTOM/CUSTOM.csv', 'CUSTOM') + """ + + self.name = None + self.type = None + self.max_lat = None + self.min_lat = None + self.max_long = None + self.min_long = None + self.temporal = None + self.spatial = None + self.weighted = None + self.DiGraph = None + self.MultiDiGraph = None + self.Graph = None + self.MultiGraph = None + self.colors = None + self.mid_lat = None + self.mid_long = None + self.session_folder = None + self.filename = None + self.attack_vector = None + + self.set_filename(filename) + name = filename.split('/')[-1].split('.')[0] + self.set_name(name) + self.set_type(type) + self.set_session_folder(session_folder) + self.set_attack_vector(attack_vector) + + # ---------------------------------------------- RAILWAY ------------------------------------------------------- + + if type == 'RAILWAY': + self.set_spatial(True) + self.set_temporal(True) + self.set_weighted(True) + + self.DiGraph, self.MultiDiGraph = preprocess_railway(filename) + self.Graph, self.MultiGraph = self.DiGraph.to_undirected(), self.MultiDiGraph.to_undirected() + + self.colors = {'MultiDiGraph': nx.get_edge_attributes(self.MultiDiGraph, 'color').values(), + 'MultiGraph': nx.get_edge_attributes(self.MultiGraph, 'color').values(), + 'DiGraph': nx.get_edge_attributes(self.DiGraph, 'color').values(), + 'Graph': nx.get_edge_attributes(self.Graph, 'color').values()} + + self.df = pd.read_csv(filename, low_memory=False) + + # ---------------------------------------------- CRYPTO -------------------------------------------------------- + + elif type == 'CRYPTO': + self.set_spatial(False) + self.set_temporal(True) + self.set_weighted(True) + + self.DiGraph, self.MultiDiGraph = preprocess_crypto(filename) + self.Graph, self.MultiGraph = self.DiGraph.to_undirected(), self.MultiDiGraph.to_undirected() + + self.colors = None + + self.df = pd.read_csv(filename, low_memory=False) + + # ---------------------------------------------- CUSTOM -------------------------------------------------------- + + elif type == 'MTX': + self.set_spatial(False) + self.set_temporal(False) + self.set_weighted(True) + + self.DiGraph, self.MultiDiGraph = preprocess_mtx(filename) + self.Graph, self.MultiGraph = self.DiGraph.to_undirected(), self.MultiDiGraph.to_undirected() + + self.colors = None + + mtx = sio.mmread(filename) + coo = mtx.tocoo() + self.df = pd.DataFrame({'source': coo.row, 'target': coo.col, 'weight': coo.data}) + + # ---------------------------------------------- CUSTOM -------------------------------------------------------- + + elif type == 'CUSTOM': + + self.DiGraph, self.MultiDiGraph = preprocess_custom(filename) + self.Graph, self.MultiGraph = self.DiGraph.to_undirected(), self.MultiDiGraph.to_undirected() + + self.df = pd.read_csv(filename, low_memory=False) + + if 'lat1' in self.df.columns and 'lon1' in self.df.columns: + self.set_spatial(True) + + if 'start' in self.df.columns: + self.set_temporal(True) + + if 'weight' in self.df.columns: + self.set_weighted(True) + + if 'color' in self.df.columns: + self.colors = {'MultiDiGraph': nx.get_edge_attributes(self.MultiDiGraph, 'color').values(), + 'MultiGraph': nx.get_edge_attributes(self.MultiGraph, 'color').values(), + 'DiGraph': nx.get_edge_attributes(self.DiGraph, 'color').values(), + 'Graph': nx.get_edge_attributes(self.Graph, 'color').values()} + + + # ---------------------------------------------- GTFS ------------------------------------------------------- + + elif type == 'GTFS': + + self.set_spatial(True) + self.set_temporal(True) + self.set_weighted(True) + + self.DiGraph, self.MultiDiGraph = preprocess_gtfs(filename) + self.Graph, self.MultiGraph = self.DiGraph.to_undirected(), self.MultiDiGraph.to_undirected() + + self.colors = {'MultiDiGraph': nx.get_edge_attributes(self.MultiDiGraph, 'color').values(), + 'MultiGraph': nx.get_edge_attributes(self.MultiGraph, 'color').values(), + 'DiGraph': nx.get_edge_attributes(self.DiGraph, 'color').values(), + 'Graph': nx.get_edge_attributes(self.Graph, 'color').values()} + + self.df = self.DiGraph.edges(data=True) + self.df = pd.DataFrame(self.df, columns=['source', 'target', 'data']) + + # check if nan in start or end + if self.df['data'].apply(lambda x: x['start']).isnull().values.any() or self.df['data'].apply( + lambda x: x['end']).isnull().values.any(): + self.set_temporal(False) + else: + self.df['start'] = self.df['data'].apply(lambda x: x['start']) + self.df['end'] = self.df['data'].apply(lambda x: x['end']) + self.df['weight'] = self.df['data'].apply(lambda x: x['weight']) + self.df['color'] = self.df['data'].apply(lambda x: x['color']) + self.df = self.df.drop(columns=['data']) + + # ---------------------------------------------- SPATIAL ------------------------------------------------------- + + self.pos = {} + # print('start layout computation') + # self.pos['neato'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='neato') + # print('neato') + # self.pos['dot'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='dot') + # print('dot') + self.pos['twopi'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='twopi') + print('twopi') + # self.pos['fdp'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='fdp') + # print('fdp') + self.pos['sfdp'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='sfdp') + print('sfdp') + + if self.is_spatial(): + self.pos['map'] = nx.get_node_attributes(self.Graph, 'pos') + location = self.pos['map'].values() + # add space around the graph + self.set_min_long(min(location, key=lambda x: x[0])[0]) + self.set_min_lat(min(location, key=lambda x: x[1])[1]) + self.set_max_long(max(location, key=lambda x: x[0])[0]) + self.set_max_lat(max(location, key=lambda x: x[1])[1]) + delta_long = (self.get_max_long() - self.get_min_long()) * 0.05 + delta_lat = (self.get_max_lat() - self.get_min_lat()) * 0.05 + self.set_min_long(self.get_min_long() - delta_long) + self.set_min_lat(self.get_min_lat() - delta_lat) + self.set_max_long(self.get_max_long() + delta_long) + self.set_max_lat(self.get_max_lat() + delta_lat) + self.set_mid_long() + self.set_mid_lat() + + # ---------------------------------------------- TEMPORAL ------------------------------------------------------ + + if self.is_temporal(): + self.start = min(nx.get_edge_attributes(self.MultiDiGraph, 'start').values()) + self.end = max(nx.get_edge_attributes(self.MultiDiGraph, 'end').values()) + + # ---------------------------------------------- WEIGHTED ------------------------------------------------------ + + if self.is_weighted(): + self.weights = {'Graph': list(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': list(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': list(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': list(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + self.min_weight = {'Graph': min(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': min(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': min(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': min(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + + self.max_weight = {'Graph': max(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': max(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': max(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': max(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + + self.standardize_weights() + + self.clean_dataset() + + # ------------------------------------------------------------------------------------------------------------------ + # ---------------------------------------------- END CONSTRUCTOR --------------------------------------------------- + # ------------------------------------------------------------------------------------------------------------------ + + # ---------------------------------------------- ATTRIBUTES -------------------------------------------------------- + +
    [docs] def standardize_weights(self): + """ + :Function: Standardize the weights of the graph between 0 and 1 + :return: None + """ + if self.is_weighted(): + for weight in self.weights: + self.weights[weight] = [(w / self.max_weight[weight]) for w in self.weights[weight]] + else: + raise ValueError("The graph is not weighted")
    + +
    [docs] def update_attributes(self): + """ + :Function: Update the attributes of the graph + :return: None + """ + if self.colors is not None: + self.colors = {'MultiDiGraph': nx.get_edge_attributes(self.MultiDiGraph, 'color').values(), + 'MultiGraph': nx.get_edge_attributes(self.MultiGraph, 'color').values(), + 'DiGraph': nx.get_edge_attributes(self.DiGraph, 'color').values(), + 'Graph': nx.get_edge_attributes(self.Graph, 'color').values()} + + if self.is_weighted(): + self.weights = {'Graph': list(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': list(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': list(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': list(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + self.min_weight = {'Graph': min(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': min(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': min(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': min(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + + self.max_weight = {'Graph': max(nx.get_edge_attributes(self.Graph, 'weight').values()), + 'MultiGraph': max(nx.get_edge_attributes(self.MultiGraph, 'weight').values()), + 'DiGraph': max(nx.get_edge_attributes(self.DiGraph, 'weight').values()), + 'MultiDiGraph': max(nx.get_edge_attributes(self.MultiDiGraph, 'weight').values())} + + self.standardize_weights() + + self.pos = {} + self.pos['twopi'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='twopi') + self.pos['sfdp'] = nx.nx_agraph.graphviz_layout(self.Graph, prog='sfdp') + + if self.is_spatial(): + self.pos['map'] = nx.get_node_attributes(self.Graph, 'pos') + location = self.pos['map'].values() + self.set_min_long(min(location, key=lambda x: x[0])[0] - 0.5) + self.set_min_lat(min(location, key=lambda x: x[1])[1] - 0.5) + self.set_max_long(max(location, key=lambda x: x[0])[0] + 0.5) + self.set_max_lat(max(location, key=lambda x: x[1])[1] + 0.5) + self.set_mid_long() + self.set_mid_lat() + + if self.is_temporal(): + self.start = min(nx.get_edge_attributes(self.MultiDiGraph, 'start').values()) + self.end = max(nx.get_edge_attributes(self.MultiDiGraph, 'end').values())
    + + # ------------------------------------------------------------------------------------------------------------------ + # ---------------------------------------------- METHODS ----------------------------------------------------------- + # ------------------------------------------------------------------------------------------------------------------ + +
    [docs] def clean_dataset(self): + """ + :Function: Clean the dataframe by formatting the columns values and rounding the float values for display + purpose + :return: None + """ + + # if the columns is float round it to 6 decimals + for column in self.df.columns: + # if the columns is a number round it to 6 decimals + if is_numeric_dtype(self.df[column]): + self.df[column] = self.df[column].round(6) + + if self.df[column].dtype == object: + self.df[column] = self.df[column].apply( + lambda x: x[:6] + '...' + x[-6:] if len(x) > 15 and x[:2] == '0x' else x[:12])
    + + # ---------------------------------------------- SETTERS ----------------------------------------------------------- +
    [docs] def set_name(self, name): + """ + :Function: Set the name of the graph + :param name: the name of the graph + :return: None + """ + self.name = name
    + +
    [docs] def set_type(self, data_type): + """ + :Function: Set the type of the graph + Data type can be + - 'RAILWAY' + - 'CRYPTO' + - 'CUSTOM' + :param data_type: + :type data_type: str + :return: None + """ + if data_type in ['RAILWAY', 'CRYPTO', 'CUSTOM', 'MTX', 'GTFS']: + self.type = data_type + else: + raise ValueError("The type must be 'RAILWAY', 'CRYPTO', 'GTFS' or 'CUSTOM' ")
    + +
    [docs] def set_max_lat(self, max_lat): + """ + :Function: Set the maximum latitude of the graph + :param max_lat: the maximum latitude of the graph + :type max_lat: float + :return: None + """ + self.max_lat = max_lat
    + +
    [docs] def set_temporal(self, temporal): + """ + :Function: Set the temporal attribute of the graph + :param temporal: True if the graph is temporal, False otherwise + :type temporal: bool + :return: None + """ + self.temporal = temporal
    + +
    [docs] def set_min_lat(self, min_lat): + """ + :Function: Set the minimum latitude of the graph + :param min_lat: the minimum latitude of the graph + :type min_lat: float + :return: None + """ + self.min_lat = min_lat
    + +
    [docs] def set_spatial(self, spatial): + """ + :Function: Set the spatial attribute of the graph + :param spatial: True if the graph is spatial, False otherwise + :type spatial: bool + :return: None + """ + self.spatial = spatial
    + +
    [docs] def set_weighted(self, weighted): + """ + :Function: Set the weighted attribute of the graph + :param weighted: True if the graph is weighted, False otherwise + :type weighted: bool + :return: None + """ + self.weighted = weighted
    + +
    [docs] def set_min_long(self, min_long): + """ + :Function: Set the minimum longitude of the graph + :param min_long: the minimum longitude of the graph + :type min_long: float + :return: None + """ + self.min_long = min_long
    + +
    [docs] def set_max_long(self, max_long): + """ + :Function: Set the maximum longitude of the graph + :param max_long: the maximum longitude of the graph + :type max_long: float + :return: None + """ + self.max_long = max_long
    + +
    [docs] def set_mid_long(self): + """ + :Function: Set the middle longitude of the graph + :return: None + """ + self.mid_long = (self.max_long + self.min_long) / 2
    + +
    [docs] def set_mid_lat(self): + """ + :Function: Set the middle latitude of the graph + :return: None + """ + self.mid_lat = (self.max_lat + self.min_lat) / 2
    + +
    [docs] def set_session_folder(self, session_folder): + """ + :Function: Set the session folder of the graph + :param session_folder: the session folder of the graph + :type session_folder: str + :return: None + """ + self.session_folder = session_folder
    + +
    [docs] def set_filename(self, filename): + """ + :Function: Set the filename of the graph + :param filename: the filename of the graph + :type filename: str + :return: None + """ + self.filename = filename
    + +
    [docs] def set_attack_vector(self, attack_vector): + self.attack_vector = attack_vector
    + + # ---------------------------------------------- IS ----------------------------------------------------------- + +
    [docs] def is_temporal(self): + """ + :Function: Return True if the graph is temporal, False otherwise + :return: True if the graph is temporal, False otherwise + :rtype: bool + """ + return self.temporal
    + +
    [docs] def is_spatial(self): + """ + :Function: Return True if the graph is spatial, False otherwise + :return: True if the graph is spatial, False otherwise + :rtype: bool + """ + return self.spatial
    + +
    [docs] def is_weighted(self): + """ + :Function: Return True if the graph is weighted, False otherwise + :return: True if the graph is weighted, False otherwise + :rtype: bool + """ + return self.weighted
    + + # ---------------------------------------------- GETTERS ----------------------------------------------------------- + +
    [docs] def get_graph(self, graph_type): + """ + :Function: Return the graph of the specified type + Graph type can be: + - 'DiGraph' + - 'MultiDiGraph' + - 'Graph' + - 'MultiGraph' + :param graph_type: the type of the graph + :type graph_type: str + :return: the graph of the specified type + :rtype: networkx.DiGraph or networkx.MultiDiGraph or networkx.Graph or networkx.MultiGraph + """ + if graph_type == 'DiGraph': + return self.DiGraph + elif graph_type == 'MultiDiGraph': + return self.MultiDiGraph + elif graph_type == 'Graph': + return self.Graph + elif graph_type == 'MultiGraph': + return self.MultiGraph + else: + raise ValueError("The graph type must be 'DiGraph', 'MultiDiGraph', 'Graph' or 'MultiGraph'")
    + +
    [docs] def get_pos(self): + """ + :Function: Return the position of the nodes + :return: the position of the nodes + :rtype: dict + """ + if self.is_spatial(): + return self.pos + else: + raise ValueError("The graph is not spatial")
    + +
    [docs] def get_min_long(self): + """ + :Function: Return the minimum longitude of the graph + :return: the minimum longitude of the graph + :rtype: float + """ + return self.min_long
    + +
    [docs] def get_max_long(self): + """ + :Function: Return the maximum longitude of the graph + :return: the maximum longitude of the graph + :rtype: float + """ + return self.max_long
    + +
    [docs] def get_type(self): + """ + :Function: Return the type of the graph + :return: the type of the graph + :rtype: str + """ + return self.type
    + +
    [docs] def get_min_lat(self): + """ + :Function: Return the minimum latitude of the graph + :return: the minimum latitude of the graph + :rtype: float + """ + return self.min_lat
    + +
    [docs] def get_max_lat(self): + """ + :Function: Return the maximum latitude of the graph + :return: the maximum latitude of the graph + :rtype: float + """ + return self.max_lat
    + +
    [docs] def get_name(self): + """ + :Function: Return the name of the graph + :return: the name of the graph + :rtype: str + """ + return self.name
    + +
    [docs] def get_colors(self): + """ + :Function: Return the colors of the graph + :return: the colors of the graph + :rtype: dict + """ + return self.colors
    + +
    [docs] def get_weights(self, graph_type): + """ + :Function: Return the weights of the graph + :param graph_type: the type of the graph + :type graph_type: str + :return: the weights of the graph + :rtype: dict + """ + if self.is_weighted(): + return self.weights[graph_type] + else: + raise ValueError("The graph is not weighted")
    + +
    [docs] def get_start(self): + """ + :Function: Return the start of the graph + :return: the start of the graph + :rtype: str + """ + if self.is_temporal(): + return self.start + else: + raise ValueError("The graph is not temporal")
    + +
    [docs] def get_end(self): + """ + :Function: Return the end of the graph + :return: the end of the graph + :rtype: str + """ + if self.is_temporal(): + return self.end + else: + raise ValueError("The graph is not temporal")
    + +
    [docs] def get_df(self): + """ + :Function: Return the dataframe of the graph + :return: the dataframe of the graph + :rtype: pandas.DataFrame + """ + return self.df
    + +
    [docs] def get_attack_vector(self): + return self.attack_vector
    + + # ---------------------------------------------- PRINT ----------------------------------------------------------- + + def __str__(self): + """ + :Function: Return a string representation of the graph + :return: a string representation of the graph + :rtype: str + """ + return "Name: " + self.get_name() + "\nType: " + self.get_type() + "\nTemporal: " + str( + self.is_temporal()) + "\nSpatial: " + str(self.is_spatial())
    +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/deepLearning.html b/docs/build/html/_modules/src/deepLearning.html new file mode 100644 index 00000000..13f18fbe --- /dev/null +++ b/docs/build/html/_modules/src/deepLearning.html @@ -0,0 +1,241 @@ + + + + + + src.deepLearning — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.deepLearning

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Deep learning module contains functions for deep learning embeddings
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +from node2vec import Node2Vec
    +
    +# Internal imports
    +from src.DeepLearning.embedding import *
    +from src.metrics import get_metrics
    +from src.utils import memoize
    +
    +
    +# ----------------------------------------- CONSTANT ----------------------------------------- #
    +
    +
    +# ----------------------------------------- Functions ----------------------------------------- #
    +
    +@memoize
    +def node2vec_embedding(networkGraph, p=1, q=1, dimensions=64, walk_length=80, num_walks=10, workers=4):
    +    """
    +    :Function: Generate Node2Vec embedding
    +    :param networkGraph: Network graph
    +    :param p: Return hyper parameter (default: 1)
    +    :param q: Inout parameter (default: 1)
    +    :param dimensions: Dimension of the embedding (default: 64)
    +    :param walk_length: Length of the random walk (default: 80)
    +    :param num_walks: Number of random walks (default: 10)
    +    :param workers: Number of workers (default: 4)
    +    :return: model_node2vec, embeddings
    +    :rtype: node2vec.Node2Vec, numpy.ndarray
    +    """
    +    node2vec = Node2Vec(networkGraph.DiGraph,
    +                        dimensions=dimensions,
    +                        walk_length=walk_length,
    +                        num_walks=num_walks,
    +                        workers=workers,
    +                        p=p,
    +                        q=q,
    +                        seed=42)
    +    model_node2vec = node2vec.fit(window=10, min_count=1, batch_words=4)
    +    nodes = list(networkGraph.Graph.nodes())
    +    embeddings = np.array([model_node2vec.wv[str(node)] for node in nodes])
    +
    +    return model_node2vec, embeddings
    +
    +
    +# --------------------------------------------------------------------------------------------- #
    +
    +
    +
    [docs]def get_similar_nodes(networkGraph, node, model, k=10): + """ + :Function: Get similar nodes to a given node using Node2Vec embedding model + :param networkGraph: Network graph + :param node: Node + :param model: Node2Vec model + :param k: Number of similar nodes (default: 10) + :return: similar_nodes + :rtype: list + """ + similar_nodes = model.wv.most_similar(str(node), topn=k) + # similar_nodes = [int(node[0]) for node in similar_nodes] + similar_nodes = [node for node in similar_nodes if node in networkGraph.Graph.nodes()] + + return similar_nodes
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def get_DL_embedding(networkGraphs, model, features, dimension=128, epochs=200): + """ + :Function: Get deep learning embedding + :param networkGraphs: Network graphs + :param model: Deep learning model + :param features: Features + :param dimension: Dimension of the embedding + :return: embeddings + :rtype: numpy.ndarray + """ + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + if features == ['proximity']: + nodes = networkGraphs.Graph.number_of_nodes() + data_features = np.eye(nodes) + proximity = True + else: + data_features = [] + for metric in features: + proximity = False + df = get_metrics(networkGraphs, metric, directed=False, multi=False) + np_arr = np.array(df.iloc[:, 1].values) + if np.isnan(np_arr).any(): + np_arr = np.nan_to_num(np_arr) + # np_arr = (np_arr - np_arr.min()) / (np_arr.max() - np_arr.min()) + data_features.append(np_arr) + data_features = np.array(data_features).T + + data = preprocess_data(networkGraphs.Graph, data_features) + if features == ['proximity']: + positive_pairs, negative_pairs = generate_pairs(networkGraphs.Graph, num_negative_pairs=1000) + positive_indices, negative_indices = pairs_to_indices(data, positive_pairs, negative_pairs) + data.positive_pairs = positive_indices + data.negative_pairs = negative_indices + embed_dim = data.num_features + + model = get_model(model, data, dimension, embed_dim) + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + model = train_model(model, optimizer, data, device, epochs=epochs, proximity=proximity) + + embeddings = get_embeddings(model, data) + + return embeddings
    + + +# --------------------------------------------------------------------------------------------- # + + +
    [docs]def get_model(model, data, dimension, embed_dim): + """ + :Function: Get deep learning model + :param model: Deep learning model + :param data: Data + :param dimension: Dimension of the embedding + :param embed_dim: Embedding dimension + :return: model + :rtype: torch.nn.Module + """ + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + if model == "GCN": + model = GCN(data.num_features, dimension, embed_dim).to(device) + elif model == "SAGE": + model = SAGE(data.num_features, dimension, embed_dim).to(device) + elif model == "GAT": + model = GAT(data.num_features, dimension, embed_dim).to(device) + else: + raise ValueError("Model not found") + + return model
    + +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/machineLearning.html b/docs/build/html/_modules/src/machineLearning.html new file mode 100644 index 00000000..46ccb6a4 --- /dev/null +++ b/docs/build/html/_modules/src/machineLearning.html @@ -0,0 +1,609 @@ + + + + + + src.machineLearning — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
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    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.machineLearning

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Machine Learning for the NetworkX graphs analysis
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +import warnings
    +import networkx as nx
    +import networkx.algorithms.community as nx_comm
    +import numpy as np
    +import pandas as pd
    +from distinctipy import distinctipy
    +from kneed import KneeLocator
    +from sklearn.cluster import SpectralClustering, KMeans, AgglomerativeClustering, DBSCAN
    +
    +# Internal imports
    +import src.utils as utils
    +from src.utils import memoize
    +
    +warnings.filterwarnings("ignore")
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +
    [docs]def create_comm_colors(communities): + """ + :Function: Create a list of colors for the communities + :param communities: list of communities + :type communities: list + :return: list of colors + :rtype: list + """ + colors = distinctipy.get_colors(len(communities)) + np.random.shuffle(colors) + colors = [tuple([i * np.random.randint(200, 255) for i in c]) for c in colors] + # convert rgb tuple to hex + colors = [f'#{int(c[0]):02x}{int(c[1]):02x}{int(c[2]):02x}' for c in colors] + + return colors
    + + +# ---------------------------------------------------------------------------------------- +
    [docs]def create_comm_dataframe(communities, colors): + """ + :Function: Create a dataframe with the Node, communities ID and their colors + :param communities: list of communities + :type communities: list + :param colors: list of colors + :type colors: list + :return: dataframe + :rtype: pd.DataFrame + """ + df = pd.DataFrame() + for idx, community in enumerate(communities): + color = colors.pop() + for node in community: + df = pd.concat([df, pd.DataFrame({'Node': node, + 'Color': color, + 'Cluster_id': idx + }, index=[0])], ignore_index=True) + return df
    + + +# ---------------------------------------------------------------------------------------- + +@memoize +def louvain_clustering(networkGraphs, noOfClusters=0): + """ + :Function: Detect communities based on Louvain clustering with a maximum of `totalCommunities` + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: maximum number of communities + :type noOfClusters: int + :return: dataframe + :rtype: pd.DataFrame + """ + if 0 < noOfClusters: + communities = binary_search('louvain_communities', networkGraphs, noOfClusters) + else: + communities = list(nx_comm.louvain_communities(networkGraphs.Graph)) + + colors = create_comm_colors(communities) + df = create_comm_dataframe(communities, colors) + + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def greedy_modularity_clustering(networkGraphs, noOfClusters=0): + """ + :Function: Detect communities based on greedy modularity clustering with a maximum of `noOfClusters` + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: maximum number of communities + :type noOfClusters: int + :return: dataframe + :rtype: pd.DataFrame + """ + if 0 < noOfClusters: + communities = binary_search('greedy_modularity_communities', networkGraphs, noOfClusters) + else: + communities = list(nx_comm.greedy_modularity_communities(networkGraphs.Graph)) + + colors = create_comm_colors(communities) + df = create_comm_dataframe(communities, colors) + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def label_propagation_clustering(networkGraphs, noOfClusters=0): + """ + :Function: Detect communities based on label propagation + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: maximum number of communities + :type noOfClusters: int + :return: dataframe + :rtype: pd.DataFrame + """ + communities = list( + nx_comm.label_propagation_communities(networkGraphs.Graph)) + colors = create_comm_colors(communities) + df = create_comm_dataframe(communities, colors) + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def asyn_lpa_clustering(networkGraphs, noOfClusters=0): + """ + :Function: Detect communities based on asynchronous label propagation + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :return: dataframe + :rtype: pd.DataFrame + """ + communities = list(nx_comm.asyn_lpa_communities(networkGraphs.Graph)) + colors = create_comm_colors(communities) + df = create_comm_dataframe(communities, colors) + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def k_clique_clustering(networkGraphs, noOfClusters=0): + """ + :Function: Detect communities based on k-clique + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: maximum number of communities + :type noOfClusters: int + :return: dataframe + :rtype: pd.DataFrame + """ + communities = list(nx_comm.k_clique_communities(networkGraphs.Graph, 2)) + colors = create_comm_colors(communities) + df = create_comm_dataframe(communities, colors) + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def spectral_clustering(networkGraphs, noOfClusters=0, embedding=None): + """ + :Function: Detect communities based on spectral + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: number of clusters + :type noOfClusters: int + :param embedding: embedding + :type embedding: np.array + :return: dataframe + :rtype: pd.DataFrame + """ + G = networkGraphs.Graph + + if embedding is None: + if 0 < noOfClusters: + adj_mat = nx.to_numpy_array(G) + optimal_k = noOfClusters + else: + adj_mat, optimal_k = compute_clustering(G) + clustering = SpectralClustering(optimal_k, affinity='precomputed', eigen_solver='arpack', n_init=100).fit( + adj_mat) + elif noOfClusters > 0: + adj_mat = embedding + optimal_k = noOfClusters + clustering = SpectralClustering(n_clusters=optimal_k).fit(adj_mat) + else: + raise ValueError('If embedding is provided, noOfClusters must be > 0') + + df = clustering_response(G, clustering, optimal_k) + + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def compute_clustering(networkGraph, max_range=30): + """ + :Function: Compute the optimal number of clusters + :param networkGraph: NetworkGraphs + :param max_range: maximum range of clusters + :return: adjacent matrics, optimal number of clusters + :rtype: numpy array, int + """ + np.random.seed(0) + + adj_mat = nx.to_numpy_array(networkGraph) + + if max_range >= len(networkGraph.nodes()): + max_range = len(networkGraph.nodes()) - 1 + + # get optimal number of clusters + wcss = [] + for i in range(1, max_range): + kmeans = KMeans(n_clusters=i, init='k-means++', + random_state=4).fit(adj_mat) + wcss.append(kmeans.inertia_) + + # find the optimal number of clusters + optimal_k = KneeLocator(range(1, max_range), wcss, + curve='convex', direction='decreasing').elbow + if optimal_k is None: + optimal_k = 8 + print('Optimal k is : ', optimal_k) + + return adj_mat, optimal_k + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def clustering_response(networkGraph, clustering_alg, optimal_k): + """ + :Function: Create a dataframe with the Node, communities ID and their colors + :param networkGraph: NetworkGraphs + :type networkGraph: NetworkGraphs + :param clustering_alg: clustering algorithm + :type clustering_alg: KMeans + :param optimal_k: optimal number of clusters + :type optimal_k: int + :return: dataframe + :rtype: pd.DataFrame + """ + clusters = clustering_alg.labels_ + df = pd.DataFrame() + colors = create_comm_colors(list(range(optimal_k))) + for i, node in enumerate(networkGraph.nodes()): + df = pd.concat([df, pd.DataFrame({'Node': node, + 'Color': colors[clusters[i]], + 'Cluster_id': clusters[i] + }, index=[0])], ignore_index=True) + return df
    + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def kmeans_clustering(networkGraphs, noOfClusters=0, embedding=None): + """ + :Function: Detect communities based on k-means + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: number of clusters + :type noOfClusters: int + :param embedding: embedding + :type embedding: np.array + :return: dataframe + :rtype: pd.DataFrame + """ + G = networkGraphs.Graph + + if embedding is None: + if 0 >= noOfClusters: + adj_mat, optimal_k = compute_clustering(G) + + else: + optimal_k = noOfClusters + adj_mat = nx.to_numpy_array(G) + elif 0 < noOfClusters: + adj_mat = embedding + optimal_k = noOfClusters + else: + raise ValueError('If embedding is provide, noOfClusters must be greater than 0') + + clustering = KMeans(n_clusters=optimal_k, init='k-means++', + random_state=4, max_iter=10).fit(adj_mat) + df = clustering_response(G, clustering, optimal_k) + + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def agglomerative_clustering(networkGraphs, noOfClusters=0, embedding=None): + """ + :Function: Detect communities based on agglomerative + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: number of clusters + :type noOfClusters: int + :param embedding: embedding + :type embedding: np.array + :return: dataframe + :rtype: pd.DataFrame + """ + G = networkGraphs.Graph + + if embedding is None: + if 0 < noOfClusters: + optimal_k = noOfClusters + adj_mat = nx.to_numpy_array(G) + else: + adj_mat, optimal_k = compute_clustering(G) + elif 0 < noOfClusters: + adj_mat = embedding + optimal_k = noOfClusters + else: + raise ValueError('If embedding is provided, noOfClusters must be > 0') + + clustering = AgglomerativeClustering( + n_clusters=optimal_k, affinity='euclidean', linkage='ward').fit(adj_mat) + + df = clustering_response(G, clustering, optimal_k) + + return df + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def dbscan_clustering(networkGraphs, noOfClusters=0, embedding=None): + """ + :Function: Detect communities based on dbscan + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: number of clusters + :type noOfClusters: int + :param embedding: embedding + :type embedding: np.array + :return: dataframe + :rtype: pd.DataFrame + """ + G = networkGraphs.Graph + + if embedding is None: + if 0 < noOfClusters: + optimal_k = noOfClusters + adj_mat = nx.to_numpy_array(G) + else: + adj_mat, optimal_k = compute_clustering(G) + elif 0 < noOfClusters: + adj_mat = embedding + optimal_k = noOfClusters + else: + raise ValueError('If embedding is provided, noOfClusters must be > 0') + + dbscan = DBSCAN(eps=42, min_samples=2) + dbscan.fit(adj_mat) + + dbscan.labels_ = dbscan.labels_ + 1 + + # Number of clusters in labels, ignoring noise if present. + df = clustering_response(G, dbscan, len(set(dbscan.labels_))) + + return df + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def get_communities(networkGraphs, method, noOfClusters=0, embedding=None): + """ + :Function: Get communities based on the method + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param method: method to use + :type method: str + :param noOfClusters: size of the cluster + :type noOfClusters: int + :param embedding: embedding + :type embedding: np.array + :return: dataframe + """ + if method not in ['louvain', 'greedy_modularity', 'label_propagation', 'asyn_lpa', + 'k_clique', 'spectral', 'kmeans', 'agglomerative', 'hierarchical', 'dbscan']: + print(ValueError("Invalid cluster type", "please choose from the following: 'louvain', 'greedy_modularity', " + "'label_propagation', 'asyn_lpa'," + "'k_clique', 'spectral', 'kmeans' " + "'agglomerative', 'hierarchical', 'dbscan'")) + df = utils.return_nan(networkGraphs, 'Cluster') + return df + + if method == 'louvain': + return louvain_clustering(networkGraphs, noOfClusters=noOfClusters) + elif method == 'greedy_modularity': + return greedy_modularity_clustering(networkGraphs, noOfClusters=noOfClusters) + elif method == 'label_propagation': + return label_propagation_clustering(networkGraphs, noOfClusters=noOfClusters) + elif method == 'asyn_lpa': + return asyn_lpa_clustering(networkGraphs, noOfClusters=noOfClusters) + elif method == 'k_clique': + return k_clique_clustering(networkGraphs, noOfClusters=noOfClusters) + elif method == 'kmeans': + return kmeans_clustering(networkGraphs, noOfClusters=noOfClusters, embedding=embedding) + elif method == 'spectral': + return spectral_clustering(networkGraphs, noOfClusters=noOfClusters, embedding=embedding) + elif method == 'agglomerative': + return agglomerative_clustering(networkGraphs, noOfClusters=noOfClusters, embedding=embedding) + elif method == 'dbscan': + return dbscan_clustering(networkGraphs, noOfClusters=noOfClusters, embedding=embedding) + else: + return None
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def get_hotspot(networkGraphs): + """ + :Function: Get hotspot + :param networkGraphs: + :type networkGraphs: NetworkGraphs + :return: dataframe + :rtype: pd.DataFrame + """ + data = [] + for node in networkGraphs.Graph.nodes(): + temp = {'Degree': networkGraphs.Graph.degree(node), + 'Latitude': networkGraphs.pos['map'][node][1], + 'Longitude': networkGraphs.pos['map'][node][0], + 'Node': node, + 'Edges': networkGraphs.Graph.edges(node) + } + + data.append(temp) + + df = pd.DataFrame(data) + + return df
    + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def binary_search(func, networkGraphs, noOfClusters=0): + """ + :Function: Perform binary search for optimal resolution parameter + :param func: the function to use + :type func: str + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param noOfClusters: number of clusters + :type noOfClusters: int + :return: dataframe + :rtype: pd.DataFrame + """ + lower_bound, upper_bound = 0, None + step = 0.1 + tolerance = 0.0001 + function = getattr(nx_comm, func) + prev_resolution = None + communities = None + + # Perform binary search for optimal resolution parameter + for i in range(500): + if upper_bound is None: + resolution = lower_bound + step + else: + resolution = (lower_bound + upper_bound) / 2 + + communities = list(function(networkGraphs.Graph, resolution=resolution)) + num_communities = len(communities) + + # Check convergence criterion + if i > 0 and abs(resolution - prev_resolution) < tolerance: + break + + # Update bounds based on number of communities + if num_communities < noOfClusters: + if upper_bound is not None: + step /= 2 + lower_bound = resolution + elif num_communities > noOfClusters: + upper_bound = resolution + else: + break + prev_resolution = resolution + + return communities +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/metrics.html b/docs/build/html/_modules/src/metrics.html new file mode 100644 index 00000000..a9a04b01 --- /dev/null +++ b/docs/build/html/_modules/src/metrics.html @@ -0,0 +1,644 @@ + + + + + + src.metrics — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.metrics

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Compute the metrics for the network graphs
    +"""
    +
    +# -------------------------------------- IMPORTS -------------------------------------------
    +
    +# External imports
    +import networkx as nx
    +import pandas as pd
    +
    +# Internal imports
    +from src.utils import *
    +
    +
    +# ----------------------------------------------------------------------------------------
    +# --------------------------------------- GETTER -----------------------------------------
    +# ----------------------------------------------------------------------------------------
    +
    +
    [docs]def get_metrics(networkGraphs, method, directed=False, multi=False): + """ + :Function: Get the metrics for the given graph + Method: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param method: Method to compute the metrics + :type method: str + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + if method not in ['kcore', 'degree', 'triangles', 'pagerank', 'betweenness_centrality', 'closeness_centrality', + 'eigenvector_centrality', 'load_centrality', 'degree_centrality']: + raise ValueError("Method not supported, please select one of the following: kcore, degree, triangles, " + "pagerank, betweenness_centrality, closeness_centrality, eigenvector_centrality, " + "load_centrality, degree_centrality ") + + if method == 'kcore': + return compute_kcore(networkGraphs, directed=directed, multi=multi) + elif method == 'degree': + return compute_nodes_degree(networkGraphs, directed=directed, multi=multi) + elif method == 'triangles': + return compute_triangles(networkGraphs, directed=directed, multi=multi) + elif method == 'pagerank': + return compute_page_rank(networkGraphs, directed=directed, multi=multi) + elif method == 'betweenness_centrality': + return compute_betweeness_centrality(networkGraphs, directed=directed, multi=multi) + elif method == 'closeness_centrality': + return compute_closeness_centrality(networkGraphs, directed=directed, multi=multi) + elif method == 'eigenvector_centrality': + return compute_eigen_centrality(networkGraphs, directed=directed, multi=multi) + elif method == 'load_centrality': + return compute_load_centrality(networkGraphs, directed=directed, multi=multi) + elif method == 'degree_centrality': + return compute_degree_centrality(networkGraphs, directed=directed, multi=multi) + else: + raise ValueError("Method not supported")
    + + +# ---------------------------------------------------------------------------------------- +# ------------------------------------ GLOBAL METRICS ------------------------------------ +# ---------------------------------------------------------------------------------------- +@memoize +def compute_global_metrics(networkGraphs, directed=False, multi=False): + """ + :Function: Compute the global metrics for the NetworkGraphs object (all the graphs) + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values (for each graph type) + :rtype: pd.DataFrame + """ + if multi: + G = networkGraphs.MultiDiGraph if directed else networkGraphs.MultiGraph + else: + G = networkGraphs.DiGraph if directed else networkGraphs.Graph + + return compute_metrics(G) + + +# ---------------------------------------------------------------------------------------- + + +@memoize +def compute_metrics(networkx_): + """ + :Function: Compute the generals metrics for the NetworkX graph + :param networkx_: NetworkX graph + :type networkx_: nx.Graph or nx.DiGraph or nx.MultiGraph or nx.MultiDiGraph + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + + df = pd.DataFrame() + + try: + clustering_coefficient = nx.average_clustering(networkx_) + except: + clustering_coefficient = None + + try: + avg_shortest_path_length = nx.average_shortest_path_length(networkx_) + except: + avg_shortest_path_length = None + + try: + diameter = nx.diameter(networkx_) + except: + diameter = None + + try: + radius = nx.radius(networkx_) + except: + radius = None + + try: + avg_eigenvector_centrality = np.mean(list(nx.eigenvector_centrality(networkx_).values())) + except: + avg_eigenvector_centrality = None + + try: + avg_closeness_centrality = np.mean(list(nx.closeness_centrality(networkx_).values())) + except: + avg_closeness_centrality = None + + try: + avg_betweenness_centrality = np.mean(list(nx.betweenness_centrality(networkx_).values())) + except: + avg_betweenness_centrality = None + + try: + avg_degree_centrality = np.mean(list(nx.degree_centrality(networkx_).values())) + except: + avg_degree_centrality = None + + try: + avg_load_centrality = np.mean(list(nx.load_centrality(networkx_).values())) + except: + avg_load_centrality = None + + try: + avg_pagerank = np.mean(list(nx.pagerank(networkx_).values())) + except: + avg_pagerank = None + + try: + avg_clustering = np.mean(list(nx.clustering(networkx_).values())) + except: + avg_clustering = None + + try: + transitivity = nx.transitivity(networkx_) + except: + transitivity = None + + try: + avg_degree = np.mean(list(dict(networkx_.degree()).values())) + except: + avg_degree = None + + try: + density = nx.density(networkx_) + except: + density = None + + try: + efficiency_global = nx.global_efficiency(networkx_) + except: + efficiency_global = None + + try: + efficiency_local = nx.local_efficiency(networkx_) + except: + efficiency_local = None + + try: + nbr_of_isolates = nx.number_of_isolates(networkx_) + except: + nbr_of_isolates = None + + records = [ + {"Metrics": "Clustering Coefficient", "Values": clustering_coefficient}, + {"Metrics": "Avg. Shortest Path Length", "Values": avg_shortest_path_length}, + {"Metrics": "Diameter", "Values": diameter}, + {"Metrics": "Radius", "Values": radius}, + {"Metrics": "Number of Nodes", "Values": networkx_.number_of_nodes()}, + {"Metrics": "Number of Edges", "Values": networkx_.number_of_edges()}, + {"Metrics": "Global Efficiency", "Values": efficiency_global}, + {"Metrics": "Local Efficiency", "Values": efficiency_local}, + {"Metrics": "Number of Isolates", "Values": nbr_of_isolates}, + {"Metrics": "Density", "Values": density}, + {"Metrics": "Transitivity", "Values": transitivity}, + {"Metrics": "Avg. Degree", "Values": avg_degree}, + {"Metrics": "Avg. Clustering", "Values": avg_clustering}, + {"Metrics": "Avg. Eigenvector Centrality", + "Values": avg_eigenvector_centrality}, + {"Metrics": "Avg. Betweenness Centrality", + "Values": avg_betweenness_centrality}, + {"Metrics": "Avg. Closeness Centrality", "Values": avg_closeness_centrality}, + {"Metrics": "Avg. Degree Centrality", "Values": avg_degree_centrality}, + {"Metrics": "Avg. Page Rank", "Values": avg_pagerank}, + {"Metrics": "Avg. Load Centrality", "Values": avg_load_centrality}, + ] + + for record in records: + df = pd.concat([df, pd.DataFrame(record, index=[0])], ignore_index=True) + + return df + + +# ---------------------------------------------------------------------------------------- +# ------------------------------- ALL METRICS FUNCTION ----------------------------------- +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_node_centralities(networkGraphs, directed=True, multi=True): + """ + :Function: Compute all the centrality metrics for the NetworkGraphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + degree_centrality = compute_degree_centrality(networkGraphs, directed=directed, multi=multi) + eigen_centrality = compute_eigen_centrality(networkGraphs, directed=directed, multi=multi) + closeness_centrality = compute_closeness_centrality(networkGraphs, directed=directed, multi=multi) + betweenness_centrality = compute_betweeness_centrality(networkGraphs, directed=directed, multi=multi) + load_centrality = compute_load_centrality(networkGraphs, directed=directed, multi=multi) + + df = pd.merge(degree_centrality, eigen_centrality, how='inner', on='Node') + df = pd.merge(df, closeness_centrality, how='inner', on='Node') + df = pd.merge(df, betweenness_centrality, how='inner', on='Node') + df = pd.merge(df, load_centrality, how='inner', on='Node') + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_node_metrics(networkGraphs, directed=True, multi=True): + """ + :Function: Compute all the node metrics for the NetworkGraphs object + :param networkGraphs: NetworkGraphs + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + kcore = compute_kcore(networkGraphs, directed=directed, multi=multi) + triangle = compute_triangles(networkGraphs, directed=directed, multi=multi) + degree = compute_nodes_degree(networkGraphs, directed=directed, multi=multi) + pagerank = compute_page_rank(networkGraphs, directed=directed, multi=multi) + + df = pd.merge(kcore, triangle, how='inner', on='Node') + df = pd.merge(df, degree, how='inner', on='Node') + df = pd.merge(df, pagerank, how='inner', on='Node') + return df + + +# ---------------------------------------------------------------------------------------- +# ------------------------------ CENTRALITY METRICS -------------------------------------- +# ---------------------------------------------------------------------------------------- +@memoize +def compute_degree_centrality(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the degree centrality for the NetworkGraphs object + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Degree Centrality' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + try: + degree_centrality = nx.degree_centrality(G) + df = pd.DataFrame(degree_centrality.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_eigen_centrality(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the eigenvector centrality for the NetworkGraphs object + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Eigenvector Centrality' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + try: + eigen_centrality = nx.eigenvector_centrality(G) + df = pd.DataFrame(eigen_centrality.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_closeness_centrality(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the closeness centrality for the NetworkGraphs object + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Closeness Centrality' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + try: + closeness_centrality = nx.closeness_centrality(G) + df = pd.DataFrame(closeness_centrality.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_betweeness_centrality(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the betweeness centrality for the NetworkGraphs object + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Betweeness Centrality' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + try: + betweeness_centrality = nx.betweenness_centrality(G) + df = pd.DataFrame(betweeness_centrality.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_load_centrality(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the load centrality for the NetworkGraphs object + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Load Centrality' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + try: + load_centrality = nx.load_centrality(G) + df = pd.DataFrame(load_centrality.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- +# ------------------------------ NODES METRICS ------------------------------------------ +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_nodes_degree(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the node degree for the NetworkGraphs object, degree computation allows for Directed and Multi graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Degree' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + + try: + degree = nx.degree(G) + df = pd.DataFrame(degree, columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_kcore(networkGraphs, directed=True, multi=False): + """ + :Function: Compute the k-core for the NetworkGraphs object, k-core computation allows for Directed but not Multi graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + :raises: ValueError if the graph is multi + """ + metric = 'K-Core' + if multi: + print(ValueError("K-Core computation is not allowed for Multi graphs")) + df = return_nan(networkGraphs, metric) + else: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + try: + kcore = nx.core_number(G) + df = pd.DataFrame(kcore.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_triangles(networkGraphs, directed=False, multi=False): + """ + :Function: Compute the triangle for the NetworkGraphs object not allows for Directed or Multi graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + :raises: Exception if the graph is directed or multi + """ + metric = 'Triangle' + if multi or directed: + print(ValueError('Triangles computation is not allowed for directed or multi graphs')) + df = return_nan(networkGraphs, metric) + + else: + G = networkGraphs.Graph + try: + triangle = nx.triangles(G) + df = pd.DataFrame(triangle.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df + + +# ---------------------------------------------------------------------------------------- + +@memoize +def compute_page_rank(networkGraphs, directed=True, multi=True): + """ + :Function: Compute the page rank for the NetworkGraphs object allows for Directed and Multi graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param directed: Compute the metrics for the directed graph + :type directed: bool + :param multi: Compute the metrics for the multi graph + :type multi: bool + :return: Pandas dataframe with the metrics and values + :rtype: pd.DataFrame + """ + metric = 'Page Rank' + if not multi: + G = networkGraphs.Graph if not directed else networkGraphs.DiGraph + else: + G = networkGraphs.MultiGraph if not directed else networkGraphs.MultiDiGraph + + try: + page_rank = nx.pagerank(G) + df = pd.DataFrame(page_rank.items(), columns=['Node', metric]) + except: + df = return_nan(networkGraphs, metric) + + return df +
    + +
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    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/preprocessing.html b/docs/build/html/_modules/src/preprocessing.html new file mode 100644 index 00000000..2dbbfbb2 --- /dev/null +++ b/docs/build/html/_modules/src/preprocessing.html @@ -0,0 +1,460 @@ + + + + + + src.preprocessing — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
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    + +
    +
    +
    +
    + +

    Source code for src.preprocessing

    +"""
    +:Author: Alpha Team Group Project
    +:Date: March 2023
    +:Purpose: Preprocess the datasets and create custom NetworkGraphs class for general use in the project
    +"""
    +
    +# ----------------------------------------------------------------------------------------
    +
    +from random import randint
    +
    +# External imports
    +import networkx as nx
    +import pandas as pd
    +import partridge as ptg
    +import scipy.io as sio
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +
    +
    [docs]def preprocess_railway(filename_: str): + """ + :Function: Preprocess the railway dataset and create NetworkX graphs + :param filename_: Path to the file (csv) + :type filename_: str + :return: NetworkX graphs (DiGraph and MultiDiGraph) + :rtype: list of NetworkX graphs + """ + network = {} + station_id = {} + + colormap = { # Different colors for different trains + "G": "red", + "C": "yellow", + "D": "green", + "Z": "blue", + "T": "purple", + "K": "orange", + "Y": "pink", + "1": "grey", "2": "grey", "3": "grey", "4": "grey", "5": "grey", "6": "grey", "7": "grey", "8": "grey", + "9": "grey"} + + with open(filename_, 'r') as f: + excluded = 0 + prev_train = None + prev_station = None + + for line in f: + + if line.startswith("train"): # skip header + continue + + train, st_no, st_id, date, arr_time, dep_time, stay_time, mileage, lat, lon = line.split(",") + lat = float(lat) + lon = float(lon) + + try: + dep_time = int(dep_time.split(":")[0]) * 60 + int(dep_time.split(":")[1]) + except: + dep_time = int(float(dep_time) * 24 * 60) + + try: + arr_time = int(arr_time.split(":")[0]) * 60 + int(arr_time.split(":")[1]) + except: + arr_time = int(float(arr_time) * 24 * 60) + + if date == "Day 2": + arr_time += 24 * 60 + dep_time += 24 * 60 + + elif date == "Day 3": + arr_time += 48 * 60 + dep_time += 48 * 60 + + elif date == "Day 4": + arr_time += 72 * 60 + dep_time += 72 * 60 + + if train != prev_train: + prev_station = None + network[train] = [] + + station = { + "id": int(st_id), + "name": f"Station {st_id}", + "lat": lat, + "lon": lon, + "start": dep_time, + "end": None, + "from": prev_station["lat"] if prev_station else None, + "to": None, + "color": None, + } + + network[train].append(station) + + if prev_station: + prev_station["to"] = (lat, lon) + prev_station["end"] = arr_time + prev_station["color"] = colormap[train[0]] + + prev_train = train + prev_station = station + + for train in network: + for station in network[train]: + station_id[(station['lat'], station['lon'])] = station['id'] + + multi_di_graph = create_multi_DiGraph_railway(network, station_id) + di_graph = convert_to_DiGraph(multi_di_graph) + + multi_di_graph.remove_edges_from(nx.selfloop_edges(multi_di_graph)) + di_graph.remove_edges_from(nx.selfloop_edges(di_graph)) + return [di_graph, multi_di_graph]
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def create_multi_DiGraph_railway(network, station_id): + """ + :Function: Create a MultiDiGraph from the railway dataset JSON object + :param network: JSON object of the railway dataset + :type network: dict + :param station_id: Dictionary of station id per location + :type station_id: dict + :return: NetworkX MultiDiGraph + :rtype: NetworkX MultiDiGraph + """ + multi_graph = nx.MultiDiGraph() + + for stations in network: + for station in network[stations]: + multi_graph.add_node(station['id'], pos=(station['lon'], station['lat'])) + from_node = station['id'] + + if type(station['to']) is tuple: + to_node = station_id[station['to']] + multi_graph.add_edge(from_node, to_node, start=station['start'], end=station['end'], + color=station['color'], weight=1) + else: + continue + + return multi_graph
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def convert_to_DiGraph(multi_graph): + """ + :Function: Create a DiGraph from a MultiDiGraph with the same nodes and edges containing the sum of the weights, + and conserving the first edge's attributes + + :param multi_graph: MultiDiGraph to convert + :type multi_graph: networkx.MultiDiGraph + :return: NetworkX DiGraph + :rtype: networkx.DiGraph + """ + g_directed = nx.DiGraph() + for u, data in multi_graph.nodes(data=True): + g_directed.add_node(u) + for k_, v_ in data.items(): + g_directed.nodes[u][k_] = v_ + + for u, v, data in multi_graph.edges(data=True): + if g_directed.has_edge(u, v): + # if weight exists, add the new weight to the existing one + if 'weight' in g_directed.edges[u, v]: + g_directed.edges[u, v]['weight'] += data['weight'] + continue + else: + g_directed.add_edge(u, v) + for k_, v_ in data.items(): + g_directed.edges[u, v][k_] = v_ + + return g_directed
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def convert_to_undirected(g_directed): + """ + :Function: Convert a DiGraph to an undirected graph using NetworkX to_undirected() function + :param g_directed: NetworkX DiGraph to convert + :type g_directed: networkx.DiGraph + :return: NetworkX Graph + :rtype: networkx.Graph + """ + return g_directed.to_undirected()
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def preprocess_crypto(filename_: str): + """ + :Function: Preprocess the crypto dataset and return a NetworkX DiGraph and MultiDiGraph + :param filename_: Path to the crypto dataset + :type filename_: str + :return: List of NetworkX DiGraph and MultiDiGraph + :rtype: list + """ + df = pd.read_csv(filename_) + + MultiDiGraph = nx.MultiDiGraph() + MultiDiGraph.add_nodes_from(df['from'].unique()) + MultiDiGraph.add_nodes_from(df['to'].unique()) + for from_, to_, value_, time_ in df[['from', 'to', 'value', 'block_time']].values: + MultiDiGraph.add_edge(from_, to_, weight=value_, start=time_, end=time_ + 1) + + DiGraph = convert_to_DiGraph(MultiDiGraph) + + MultiDiGraph.remove_edges_from(nx.selfloop_edges(MultiDiGraph)) + DiGraph.remove_edges_from(nx.selfloop_edges(DiGraph)) + + return [DiGraph, MultiDiGraph]
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def preprocess_mtx(filename_: str): + """ + :Function: Preprocess the mtx dataset and return a NetworkX DiGraph and MultiDiGraph + :param filename_: Path to the mtx dataset + :type filename_: str + :return: List of NetworkX DiGraph and MultiDiGraph + :rtype: list + """ + mtx = sio.mmread(filename_) + + MultiDiGraph = nx.MultiDiGraph(mtx) + + # convert node labels to strings to avoid errors when using pyvis from_nx() function + temp = {} + for node in MultiDiGraph.nodes(): + temp[node] = str(node) + MultiDiGraph = nx.relabel_nodes(MultiDiGraph, temp) + temp = {} + for edge in MultiDiGraph.edges(): + temp[edge] = str(edge) + MultiDiGraph = nx.relabel_nodes(MultiDiGraph, temp) + + DiGraph = convert_to_DiGraph(MultiDiGraph) + + MultiDiGraph.remove_edges_from(nx.selfloop_edges(MultiDiGraph)) + DiGraph.remove_edges_from(nx.selfloop_edges(DiGraph)) + + return [DiGraph, MultiDiGraph]
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def preprocess_custom(filename_: str): + """ + :Function: Preprocess the custom dataset and return a NetworkX DiGraph and MultiDiGraph + Dataset must have the following columns: + - from: Source node + - to: Target node + Dataset could have the following columns: + - weight: Edge weight + - start: Start time of the edge + - end: End time of the edge + - color: Color of the edge + - lat1: Latitude of the source node + - lon1: Longitude of the source node + - lat2: Latitude of the target node + - lon2: Longitude of the target node + :param filename_: Path to the custom dataset + :type filename_: str + :return: List of NetworkX DiGraph and MultiDiGraph + :rtype: list + """ + df = pd.read_csv(filename_) + df.sort_values(by=['from', 'to'], inplace=True) + MultiDiGraph = nx.MultiDiGraph() + + # Convert labels to string to avoid errors when using pyvis from_nx() function + df['from'] = df['from'].astype(str) + df['to'] = df['to'].astype(str) + + if not 'from' in df.columns and not 'to' in df.columns: + print('No "from" and "to" columns in the dataset') + return None + + MultiDiGraph.add_nodes_from(df['from'].unique()) + MultiDiGraph.add_nodes_from(df['to'].unique()) + + for from_, to_ in df[['from', 'to']].values: + MultiDiGraph.add_edge(from_, to_, weight=1) + + if 'lat1' in df.columns and 'lon1' in df.columns: + for node, lat, lon in df[['from', 'lat1', 'lon1']].values: + MultiDiGraph.nodes[node]['pos'] = (lon, lat) + + if 'lat2' in df.columns and 'lon2' in df.columns: + for node, lat, lon in df[['to', 'lat2', 'lon2']].values: + if not 'pos' in MultiDiGraph.nodes[node]: + MultiDiGraph.nodes[node]['pos'] = (lon, lat) + + if 'weight' in df.columns: + idx = [(u, v, k) for u, v, k in MultiDiGraph.edges(keys=True)] + for i, weight_ in enumerate(df['weight'].values): + MultiDiGraph.edges[idx[i]]['weight'] = weight_ + + if 'start' in df.columns and not 'end' in df.columns: + for from_, to_, start_ in df[['from', 'to', 'start']].values: + MultiDiGraph.edges[from_, to_]['start'] = start_ + MultiDiGraph.edges[from_, to_]['end'] = start_ + + if 'start' in df.columns and 'end' in df.columns: + for from_, to_, start_, end_ in df[['from', 'to', 'start', 'end']].values: + MultiDiGraph.edges[from_, to_]['start'] = start_ + MultiDiGraph.edges[from_, to_]['end'] = end_ + + if 'color' in df.columns: + idx = [(u, v, k) for u, v, k in MultiDiGraph.edges(keys=True)] + for i, color_ in enumerate(df['color'].values): + MultiDiGraph.edges[idx[i]]['color'] = color_ + + DiGraph = convert_to_DiGraph(MultiDiGraph) + MultiDiGraph.remove_edges_from(nx.selfloop_edges(MultiDiGraph)) + DiGraph.remove_edges_from(nx.selfloop_edges(DiGraph)) + return [DiGraph, MultiDiGraph]
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def preprocess_gtfs(zip_file): + """ + :Function: Preprocess the gtfs dataset and return a NetworkX DiGraph and MultiDiGraph + :param zip_file: Path to the gtfs dataset + :type zip_file: str + :return: List of NetworkX DiGraph and MultiDiGraph + :rtype: list + """ + feed = ptg.load_feed(zip_file) + stops = feed.stops + routes = feed.routes + stop_times = feed.stop_times + + G = nx.MultiDiGraph() + + for index, stop in stops.iterrows(): + G.add_node(stop['stop_id'], pos=(stop['stop_lon'], stop['stop_lat'])) + + stop_times_grouped = stop_times.groupby('trip_id') + + for trip_id, trip_stop_times in stop_times_grouped: + trip_stop_times_sorted = trip_stop_times.sort_values('stop_sequence') + color = '#%06X' % randint(0, 0xFFFFFF) + + for i in range(len(trip_stop_times_sorted) - 1): + origin = trip_stop_times_sorted.iloc[i] + destination = trip_stop_times_sorted.iloc[i + 1] + G.add_edge(origin['stop_id'], destination['stop_id'], start=origin['departure_time'], + end=destination['arrival_time'], color=color, weight=1) + + DiGraph = convert_to_DiGraph(G) + G.remove_edges_from(nx.selfloop_edges(G)) + DiGraph.remove_edges_from(nx.selfloop_edges(DiGraph)) + + return [DiGraph, G]
    +
    + +
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    + +
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    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/resilience.html b/docs/build/html/_modules/src/resilience.html new file mode 100644 index 00000000..b4d69aff --- /dev/null +++ b/docs/build/html/_modules/src/resilience.html @@ -0,0 +1,469 @@ + + + + + + src.resilience — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.resilience

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Assess the resilience of a network using different attacks vectors
    +"""
    +
    +# -------------------------------------- IMPORT ---------------------------------------------
    +
    +# External imports
    +import random
    +
    +# Internal imports
    +import src.machineLearning as ml
    +from src.NetworkGraphs import NetworkGraphs
    +from src.metrics import *
    +from src.preprocessing import convert_to_DiGraph
    +
    +# -------------------------------------- FUNCTIONS -------------------------------------------
    +
    +
    +idx = {}
    +
    +
    +
    [docs]def resilience(networkGraph, attack, **kwargs): + """ + :Function: Compute the resilience of the networkGraph + Attack can be: + - "random" + - "malicious" + - "cluster" + - "random" + Args of the attacks: + - "random": + - "number_of_nodes": Number of nodes to be removed + - "number_of_edges": Number of edges to be removed + - "malicious": + - "metric": Metric to be used to select the nodes to be removed + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + - 'directed': True or False + - 'multi': True or False + - "number_of_nodes": Number of nodes to be removed + - "threshold": Threshold to be used to select the nodes to be removed + - "operator" can be: + - ">" + - "<" + - ">=" + - "<=" + - "cluster": + - "cluster_algorithm": Algorithm to be used to compute the clusters + - "total_clusters": Total number of clusters to be generated + - "number_of_clusters": Number of clusters to be removed + - "custom": + - "list_of_nodes": List of nodes to be removed + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param attack: Attack to be performed + :type attack: str + :param kwargs: Arguments of the attack to be performed + :type kwargs: dict + :return: NetworkGraph with the nodes removed, and a dataframe with the changed nodes/edges + :rtype: NetworkGraph, pd.DataFrame + + Example: + >>> # Random attack + >>> graph, df = resilience(networkGraph, attack='random', number_of_nodes=10) + >>> graph, df = resilience(networkGraph, attack='random', number_of_edges=10) + >>> # Malicious attack + >>> graph, df = resilience(networkGraph, attack='malicious', metric='degree', number_of_nodes=10) + >>> graph, df = resilience(networkGraph, attack='malicious', metric='degree', threshold=10, operator='>') + >>> # Cluster attack + >>> graph, df = resilience(networkGraph, attack='cluster', cluster_algorithm='spectral', total_clusters=15, number_of_clusters=2) + >>> # Custom attack + >>> graph, df = resilience(networkGraph, attack='custom', list_of_nodes=[1, 2, 3]) + """ + if attack == "random": + for key in kwargs.keys(): + if key not in ["number_of_nodes", "number_of_edges"]: + raise ValueError(f"Argument {key} not recognized") + return resilience_random(networkGraph, **kwargs) + + elif attack == "malicious": + for key in kwargs.keys(): + if key not in ["metric", "number_of_nodes", "threshold", "operator", "multi", "directed"]: + raise ValueError(f"Argument {key} not recognized") + if "metric" not in kwargs.keys(): + raise ValueError("Metric not specified") + return resilience_malicious(networkGraph, **kwargs) + + elif attack == "cluster": + for key in kwargs.keys(): + if key not in ["cluster_algorithm", "total_clusters", "number_of_clusters"]: + print(f"Argument {key} not recognized") + return 0 + + if "cluster_algorithm" not in kwargs.keys(): + raise ValueError("Cluster algorithm not specified") + return resilience_cluster(networkGraph, **kwargs) + + elif attack == "custom": + for key in kwargs.keys(): + if key not in ["list_of_nodes"]: + raise ValueError(f"Argument {key} not recognized") + return resilience_custom(networkGraph, **kwargs) + + else: + print("Attack not recognized") + return 0
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def resilience_random(networkGraph, number_of_nodes=None, number_of_edges=None): + """ + :Function: Generate the random attack to the networkGraph + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param number_of_nodes: Number of nodes to be removed + :type number_of_nodes: int + :param number_of_edges: Number of edges to be removed + :type number_of_edges: int + :return: NetworkGraph with the nodes removed + :rtype: NetworkGraph + """ + G = copy_networkGraph(networkGraph) + G.set_attack_vector('random') + df = None + + if number_of_nodes is not None: + nodes = G.MultiDiGraph.nodes() + nodes_to_remove = random.sample(sorted(nodes), number_of_nodes) + G, df = remove_nodes(G, nodes_to_remove) + + if number_of_edges is not None: + edges = G.MultiDiGraph.edges() + edges_to_remove = random.sample(sorted(edges), number_of_edges) + G, df = remove_edges(G, edges_to_remove) + + return G, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def resilience_malicious(networkGraph, metric=None, number_of_nodes=None, threshold=None, operator='>', multi=False, + directed=False): + """ + :Function: Generate the malicious attack to the networkGraph + Metrics: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + operators: + - ">" (default) + - "<" + - ">=" + - "<=" + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param metric: Metric to be used to select the nodes to be removed + :type metric: str + :param number_of_nodes: Number of nodes to be removed + :type number_of_nodes: int + :param threshold: Threshold to be used to select the nodes to be removed + :type threshold: float + :param operator: Operator to be used to select the nodes to be removed + :type operator: str + :param multi: True if the graph is multi + :type multi: bool + :param directed: True if the graph is directed + :type directed: bool + :return: NetworkGraph with the nodes removed + :rtype: NetworkGraph + """ + G = copy_networkGraph(networkGraph) + G.set_attack_vector('malicious') + + df = get_metrics(networkGraph, metric, directed=directed, multi=multi) + metric = df.columns[1] + df = df.sort_values(by=metric, ascending=False) + + nodes_to_remove = [] + if threshold: + nodes_to_remove = execute_threshold(df, metric, threshold, operator) + + if number_of_nodes: + number_of_nodes = int(number_of_nodes) + nodes_to_remove = df['Node'].values[:number_of_nodes] + + G, df = remove_nodes(G, nodes_to_remove) + return G, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def resilience_cluster(networkGraph, cluster_algorithm=None, total_clusters=0, number_of_clusters=0): + """ + :Function: Generate the cluster attack to the networkGraph + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param cluster_algorithm: Algorithm to be used to compute the clusters + :type cluster_algorithm: str + :param total_clusters: Total number of clusters to be generated + :type total_clusters: int + :param number_of_clusters: Number of clusters to be removed + :type number_of_clusters: int + :return: NetworkGraph with the nodes removed + :rtype: NetworkGraph + """ + G = copy_networkGraph(networkGraph) + G.set_attack_vector('cluster') + + if cluster_algorithm not in ['louvain', 'greedy_modularity', 'label_propagation', 'asyn_lpa', + 'k_clique', 'spectral', 'kmeans', 'agglomerative', 'hierarchical', 'dbscan']: + print(ValueError("Invalid cluster type", "please choose from the following: 'louvain', 'greedy_modularity', " + "'label_propagation', 'asyn_lpa'," + "'k_clique', 'spectral', 'kmeans' " + "'agglomerative', 'hierarchical', 'dbscan'")) + return 0 + + if 0 >= number_of_clusters > total_clusters: + print(ValueError("Invalid number of clusters", + "please choose a number of clusters smaller than the total number of clusters", + "or a positive number")) + return 0 + + clusters = ml.get_communities(networkGraph, cluster_algorithm, total_clusters) + + cluster_ids = clusters['Cluster_id'].unique() + cluster_ids = random.sample(sorted(cluster_ids), number_of_clusters) + clusters_to_remove = clusters[clusters['Cluster_id'].isin(cluster_ids)] + nodes_to_remove = [] + for cluster in clusters_to_remove.iterrows(): + nodes_to_remove.append(cluster[1]['Node']) + networkGraph, df = remove_nodes(G, nodes_to_remove) + + return networkGraph, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def resilience_custom(networkGraph, list_of_nodes=None): + """ + :Function: Generate the custom attack to the networkGraph + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param list_of_nodes: List of nodes to be removed + :type list_of_nodes: list + :return: NetworkGraph with the nodes removed + :rtype: NetworkGraph + """ + G = copy_networkGraph(networkGraph) + G.set_attack_vector('custom_node') + + G, df = remove_nodes(G, list_of_nodes) + return G, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def copy_networkGraph(networkGraph): + """ + :Function: Copy the networkGraph object + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :return: Copy of the networkGraph + :rtype: NetworkGraph + """ + if networkGraph.session_folder not in idx.keys(): + idx[networkGraph.session_folder] = 0 + else: + idx[networkGraph.session_folder] = idx[networkGraph.session_folder] + 1 + session_folder = f'{networkGraph.session_folder}/resilience{idx[networkGraph.session_folder]}' + + return NetworkGraphs(networkGraph.filename, type=networkGraph.type, session_folder=session_folder)
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def remove_nodes(networkGraph, nodes): + """ + :Function: Remove the nodes from the networkGraph + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param nodes: Nodes to be removed + :type nodes: list + :return: NetworkGraph with the nodes removed + :rtype: NetworkGraph + """ + for node in nodes: + networkGraph.MultiDiGraph.remove_node(node) + + networkGraph.DiGraph = convert_to_DiGraph(networkGraph.MultiDiGraph) + networkGraph.Graph, networkGraph.MultiGraph = networkGraph.DiGraph.to_undirected( + ), networkGraph.MultiDiGraph.to_undirected() + networkGraph.update_attributes() + + df = pd.DataFrame(nodes, columns=['Node']) + return networkGraph, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def remove_edges(networkGraph, edges): + """ + :Function: Remove the edges from the networkGraph + :param networkGraph: NetworkGraph + :type networkGraph: NetworkGraph + :param edges: Edges to be removed + :type edges: list + :return: NetworkGraph with the edges removed + :rtype: NetworkGraph + """ + for edge in edges: + networkGraph.MultiDiGraph.remove_edge(edge[0], edge[1]) + + networkGraph.DiGraph = convert_to_DiGraph(networkGraph.MultiDiGraph) + networkGraph.Graph, networkGraph.MultiGraph = networkGraph.DiGraph.to_undirected( + ), networkGraph.MultiDiGraph.to_undirected() + networkGraph.update_attributes() + + df = pd.DataFrame(edges, columns=['Source', 'Target']) + return networkGraph, df
    + + +# ------------------------------------------------------------------------------------------ + + +
    [docs]def execute_threshold(df, metric, threshold, operator='>'): + """ + :Function: Execute the threshold operation + :param df: DataFrame with the metrics + :type df: DataFrame + :param metric: Metric to be used to select the nodes to be removed + :type metric: str + :param threshold: Threshold to be used to select the nodes to be removed + :type threshold: float + :param operator: Operator to be used to select the nodes to be removed + :type operator: str + :return: DataFrame with the nodes to be removed + :rtype: DataFrame + """ + if operator == '>': + nodes_to_remove = df[df[metric] > threshold]['Node'].values + elif operator == '<': + nodes_to_remove = df[df[metric] < threshold]['Node'].values + elif operator == '>=': + nodes_to_remove = df[df[metric] >= threshold]['Node'].values + elif operator == '<=': + nodes_to_remove = df[df[metric] <= threshold]['Node'].values + else: + raise ValueError(f"Operator {operator} not supported") + + return nodes_to_remove
    +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/stochastic.html b/docs/build/html/_modules/src/stochastic.html new file mode 100644 index 00000000..7b76c7b2 --- /dev/null +++ b/docs/build/html/_modules/src/stochastic.html @@ -0,0 +1,190 @@ + + + + + + src.stochastic — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.stochastic

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Stochastic approximation algorithms for estimating graph properties Leveraging sampling techniques to improve computational efficiency
    +"""
    +
    +# ------------------------------------------------------------------------------
    +
    +import networkx as nx
    +# External import
    +import numpy as np
    +import pandas as pd
    +from tqdm import tqdm
    +
    +
    +# ------------------------------- UTILS FUNCTIONS -------------------------------------
    +
    +
    [docs]def get_random_sample(nodes): + """ + :Function: Get random sample of 2 nodes from nodes + :param nodes: list of nodes + :type nodes: list + :return: random sample of 2 nodes from nodes + :rtype: list + """ + return np.random.choice(nodes, size=2, replace=False)
    + + +# ------------------------------------------------------------------------------------- + +
    [docs]def get_random_node(nodes): + """ + :Function: Get random node from nodes + :param nodes: list of nodes + :type nodes: list + :return: random node from nodes + :rtype: list + """ + return np.random.choice(nodes, size=1, replace=False)
    + + +# -------------------------------------- FUNCTIONS ------------------------------------- + + +
    [docs]def estimate_shortest_path_length(G, iterations=10_000): + """ + :Function: Estimate shortest path length between over a iterations number of samples + :param G: Graph + :type G: networkx.classes.graph.Graph + :param iterations: Number of samples + :type iterations: int + :return: DataFrame of shortest path lengths + :rtype: pandas.core.frame.DataFrame + """ + nodes = list(G.nodes()) + lengths = [] + + for _ in tqdm(range(iterations)): + node1, node2 = get_random_sample(nodes) + try: + length = nx.shortest_path_length(G, node1, node2) + lengths.append(length) + except nx.NetworkXNoPath: + pass + + df = pd.DataFrame(lengths, columns=['Length']) + + return df
    + + +# ------------------------------------------------------------------------------------- + + +
    [docs]def estimate_clustering_coefficient(G, iterations=10_000): + """ + :Function: Estimate clustering coefficient over a iterations number of samples + :param G: Graph + :type G: networkx.classes.graph.Graph + :param iterations: Number of samples + :type iterations: int + :return: DataFrame of clustering coefficients + :rtype: pandas.core.frame.DataFrame + """ + nodes = list(G.nodes()) + coefficients = [] + + for _ in tqdm(range(iterations)): + node = get_random_node(nodes)[0] + coefficient = nx.clustering(G, node) + coefficients.append(coefficient) + + df = pd.DataFrame(coefficients, columns=['Coefficient']) + + return df
    +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/utils.html b/docs/build/html/_modules/src/utils.html new file mode 100644 index 00000000..d519ac1d --- /dev/null +++ b/docs/build/html/_modules/src/utils.html @@ -0,0 +1,253 @@ + + + + + + src.utils — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
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    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.utils

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: utils.py contains utilities functions for the project
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +from _md5 import md5
    +
    +import numpy as np
    +import pandas as pd
    +from pandas.core.dtypes.common import is_numeric_dtype
    +
    +# ----------------------------------------- CONSTANT ----------------------------------------- #
    +
    +red = "\033[0;91m"
    +green = "\033[0;92m"
    +yellow = "\033[0;93m"
    +blue = "\033[0;94m"
    +networkGraphs_cache = {}
    +
    +
    +# ----------------------------------------- Functions ----------------------------------------- #
    +
    +
    [docs]def memoize(func): + """ + :Function: Memoize the function to avoid recomputing the same value, leverage the cache + :param func: Function to memoize + :type func: function + :return: Wrapper function + :rtype: function + """ + cache = {} + + def wrapper(*args, **kwargs): + """ + :Function: Wrapper function for the memoization + :param args: arguments + :type args: list + :param kwargs: keyword arguments + :type kwargs: dict + :return: Result of the function + :rtype: any + """ + key = (func, args, tuple(sorted(kwargs.items()))) + key_hash = md5(str(key).encode('utf-8')).hexdigest() + if key_hash in cache: + print(f"{green}CACHE: Using cache for {func.__name__}, hash: {yellow}{key_hash}") + return cache[key_hash] + print(f"{blue}CACHE: Computing value for {func.__name__}, hash: {yellow}{key_hash} ") + result = func(*args, **kwargs) + cache[key_hash] = result + return result + + return wrapper
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def set_networkGraph(networkGraph, session_id): + """ + :Function: Set the network graph in the cache + :param networkGraph: Network graph + :type networkGraph: NetworkGraph + :return: None + """ + networkGraphs_cache[session_id] = networkGraph + return 0
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def get_networkGraph(session_id): + """ + :Function: Get the network graph from the cache + :param session_id: Session id + :type session_id: str + :return: Network graph + :rtype: NetworkGraph + """ + return networkGraphs_cache[session_id]
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def is_saved(session_id): + """ + :Function: Check if the network graph is saved in the cache + :param session_id: Session id + :type session_id: str + :return: True if the network graph is saved, False otherwise + :rtype: bool + """ + return session_id in networkGraphs_cache.keys()
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def delete_networkGraph(session_id): + """ + :Function: Delete the network graph from the cache + :param session_id: Session id + :type session_id: str + :return: None + """ + del networkGraphs_cache[session_id] + print(f"{red}CACHE: Deleted network graph with session id: {yellow}{session_id}") + return 0
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def return_nan(networkGraphs, column): + """ + :Function: Return a dataframe with NaN values for the given metric + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param column: Column name + :type column: str + :return: Pandas dataframe with the metric and NaN values + :rtype: pd.DataFrame + """ + df = pd.DataFrame(columns=['Node', column]) + df['Node'] = list(networkGraphs.Graph.nodes()) + df[column] = np.nan + return df
    + + +# ---------------------------------------------------------------------------------------- # + + +
    [docs]def clean_df(df): + """ + :Function: Clean the dataframe by rounding the values to 6 decimals and shortening the strings to 12 characters + :param df: Pandas dataframe to clean + :type df: pd.DataFrame + :return: Pandas dataframe cleaned + :rtype: pd.DataFrame + """ + + # if the columns is float round it to 6 decimals + for column in df.columns: + # if the columns is a number round it to 6 decimals + if is_numeric_dtype(df[column]): + df[column] = df[column].round(6) + + if df[column].dtype == object: + df[column] = df[column].apply(lambda x: x[:6] + '...' + x[-6:] if len(x) > 15 and x[:2] == '0x' else x[:12]) + + return df
    +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/visualisation.html b/docs/build/html/_modules/src/visualisation.html new file mode 100644 index 00000000..e6b6e6e9 --- /dev/null +++ b/docs/build/html/_modules/src/visualisation.html @@ -0,0 +1,815 @@ + + + + + + src.visualisation — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for src.visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: May 2023
    +Purpose: Main visualisation file wrapping sub-visualisation functionalities
    +        this file is used to generate the API calls by the user
    +"""
    +
    +# ----------------------------------------------------------------------------------------
    +
    +import pandas as pd
    +# External Imports
    +from pandas.api.types import is_numeric_dtype
    +
    +# Internal Imports
    +import src.deepLearning as dl
    +import src.machineLearning as ml
    +import src.metrics as m
    +from src import utils
    +from src.deepLearning import *
    +from src.visualisation_src.DL_visualisation import *
    +from src.visualisation_src.ML_visualisation import *
    +from src.visualisation_src.basic_network_visualisation import *
    +from src.visualisation_src.metrics_visualisation import *
    +from src.visualisation_src.temporal_visualisation import *
    +from src.visualisation_src.utils_visualisation import *
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +
    +
    [docs]def plot_network(networkGraphs, layout='map', dynamic=False, fullPath=False): + """ + :Function: Plot network on a static graph + Layouts: + - 'map' + - 'twopi' + - 'sfdp' + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param layout: Layout of the plot + :type layout: str + :param dynamic: Boolean to indicate if the plot is dynamic or not + :type dynamic: bool + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: filename + :rtype: str + """ + if not networkGraphs.is_spatial() and layout == 'map': + print(ValueError("Graph is not spatial with coordinates")) + return '../application/static/no_graph.html' + + filename = f"{'Dynamic' if dynamic else 'Static'}_{layout}.html" + if dynamic: + filename = filename.replace(f"_{layout}", "") + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + if dynamic: + dynamic_visualisation(networkGraphs, filepath) + else: + static_visualisation(networkGraphs, filepath, layout_=layout) + + return filepath if fullPath else filename
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def plot_cluster(networkGraphs, clusterType, noOfClusters=0, dynamic=False, layout='map', fullPath=False): + """ + :Function: Plot the cluster for the given network on a graph + Clusters: + - 'louvain' + - 'greedy_modularity' + - 'label_propagation' + - 'asyn_lpa' + - 'girvan_newman', + - 'k_clique' + - 'spectral' + - 'kmeans' + - 'agglomerative' + - 'dbscan' + - 'hierarchical' + Layouts: + - 'map' + - 'twopi' + - 'sfdp' + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param clusterType: Type of cluster + :type clusterType: str + :param noOfClusters: Size of the cluster + :type noOfClusters: int + :param dynamic: Boolean to indicate if the plot is dynamic or not + :type dynamic: bool + :param layout: Layout of the plot + :type layout: str + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: Cluster and filename of the plot + :rtype: pd.DataFrame, str + """ + if clusterType not in ['louvain', 'greedy_modularity', 'label_propagation', 'asyn_lpa', 'girvan_newman', + 'edge_betweenness', 'k_clique', 'spectral', 'kmeans', 'dbscan', 'hierarchical', + 'agglomerative']: + print(ValueError("Cluster type is not valid")) + df = utils.return_nan(networkGraphs, 'Cluster') + return df, 'no_graph.html' + if not networkGraphs.is_spatial() and layout == 'map' or noOfClusters >= len(networkGraphs.Graph.nodes) // 2: + print(ValueError("Graph is not spatial with coordinates, or max number of clusters is reached")) + df = utils.return_nan(networkGraphs, 'Cluster') + return df, 'no_graph.html' + + cluster = ml.get_communities(networkGraphs, clusterType, noOfClusters=noOfClusters) + if len(cluster) == 0 or cluster.isnull().values.any() or len(cluster) != len(networkGraphs.Graph.nodes): + print(ValueError("Issue with cluster or cluster not possible for this method or graph")) + return utils.return_nan(networkGraphs, 'Cluster'), 'no_graph.html' + + filename = f"{clusterType}_{'Dynamic' if dynamic else 'Static'}_{layout}.html" + if dynamic: + filename = filename.replace(f"_{layout}", "") + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath) or noOfClusters > 0: + if dynamic: + generate_dynamic_cluster(networkGraphs, cluster, filepath) + else: + generate_static_cluster(networkGraphs, cluster, filepath, clusterType, layout_=layout, nbr=noOfClusters) + + return cluster, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def plot_metric(networkGraphs, metrics, directed=True, multi=True, dynamic=False, layout='map', fullPath=False): + """ + :Function: Plot the metric for the given network on a graph + Metrics: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + Layouts: + - 'map' + - 'twopi' + - 'sfdp' + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param metrics: Metrics to be plotted + :type metrics: str + :param dynamic: Boolean to indicate if the plot is dynamic or not + :type dynamic: bool + :param layout: Layout of the plot + :type layout: str + :param directed: Boolean to indicate if the graph is directed or not + :type directed: bool + :param multi: for multi graphs + :type multi: bool + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: Dataframe with the metric and the filename of the plot + :rtype: pd.DataFrame, str + """ + df = m.get_metrics(networkGraphs, metrics, directed=directed, multi=multi) + + if df.empty or df.isnull().values.any() or not is_numeric_dtype(df[df.columns.values[1]]) or ( + not networkGraphs.is_spatial() and layout == 'map'): + print(ValueError( + 'Metric column is empty. Please select a different metric or select different layout because graphs is not spatial with coordinates ')) + return df, '../application/static/no_graph.html' + + filename = f"{metrics}_{'Directed' if directed else 'Undirected'}_{'Mutli' if multi else ''}_{'Dynamic' if dynamic else 'Static'}_{layout}.html" + if dynamic: + filename = filename.replace(f"_{layout}", "") + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + if dynamic: + generate_dynamic_metric(networkGraphs, df, filepath) + else: + generate_static_metric(networkGraphs, df, filepath, layout_=layout) + + return df, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_all_metrics(networkGraphs, metrics, directed=True, multi=True, layout='map'): + """ + :Function: Plot all the metrics for the given network on a graph + Metrics: + - 'centralities' + - 'nodes' + Layouts: + - 'map' + - 'twopi' + - 'sfdp' + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param metrics: Metrics to be plotted + :type metrics: str + :param directed: Boolean to indicate if the graph is directed or not + :type directed: bool + :param multi: for multi graphs + :type multi: bool + :param layout: Layout of the plot + :type layout: str + :return: Dataframe with all the metrics and the filename of the plot + :rtype: pd.DataFrame, str + """ + if not networkGraphs.is_spatial() and layout == 'map': + print(ValueError( + 'Metric column is empty. Please select a different metric or select different layout because graphs is ' + 'not spatial with coordinates ')) + df = utils.return_nan(networkGraphs, 'Metrics') + return df, '../application/static/no_graph.html' + + if metrics == 'centralities': + df = m.compute_node_centralities(networkGraphs, directed=directed, multi=multi) + elif metrics == 'nodes': + df = m.compute_node_metrics(networkGraphs, directed=directed, multi=multi) + else: + print(ValueError('Please select a valid metric, either "centralities" or "nodes"')) + df = utils.return_nan(networkGraphs, 'Metrics') + return df, 'no_graph.html' + + filename = f"All_{metrics}_{'Directed' if directed else 'Undirected'}_{'Multi' if multi else ''}_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_static_all_metrics(networkGraphs, df, filepath, layout_=layout) + + return df, filename
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_histogram(networkGraphs, metrics, directed=True, multi=True, fullPath=False): + """ + :Function: Plot the histogram distribution for a given metric + Metrics: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + - 'centralities' - All centralities + - 'nodes' - All node metrics + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param metrics: Metrics to be plotted + :type metrics: str + :param directed: Boolean to indicate if the graph is directed or not + :type directed: bool + :param multi: for multi graphs + :type multi: bool + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: df and filename + :rtype: pd.DataFrame, str + """ + if metrics == 'centralities': + df = m.compute_node_centralities(networkGraphs, directed=directed, multi=multi) + elif metrics == 'nodes': + df = m.compute_node_metrics(networkGraphs, directed=directed, multi=multi) + else: + df = m.get_metrics(networkGraphs, metrics, directed=directed, multi=multi) + + filename = f"{metrics}_{'Directed' if directed else 'Undirected'}_{'Mutli' if multi else ''}_Histogram.html" + filepath = get_file_path(networkGraphs, filename) + if not os.path.isfile(filepath): + generate_histogram_metric(df, filepath) + + return df, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_hotspot(networkGraphs, fullPath=False): + """ + :Function: Plot the hotspot and coldspot for the given network, only for spatial graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: Dataframe with the hotspot and coldspot and filename + :rtype: pd.DataFrame, str + """ + if not networkGraphs.is_spatial(): + print(ValueError('Graph is not spatial. Please select a spatial graph.')) + df = utils.return_nan(networkGraphs, 'Cluster') + return df, 'no_graph.html' + + df = ml.get_hotspot(networkGraphs) + + filename = f"Density_hotspot.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_hotspot(networkGraphs, df, filepath) + + return df.iloc[:, :2], filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_boxplot(networkGraphs, metrics, directed=True, multi=True, fullPath=False): + """ + :Function: Plot the boxplot for a given metric + Metrics: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + - 'centralities' - All centralities + - 'nodes' - All node metrics + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param metrics: Metrics to be plotted + :type metrics: str + :param directed: Boolean to indicate if the graph is directed or not + :type directed: bool + :param multi: for multi graphs + :type multi: bool + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: df and filename + :rtype: pd.DataFrame, str + """ + if metrics == 'centralities': + df = m.compute_node_centralities(networkGraphs, directed=directed, multi=multi) + elif metrics == 'nodes': + df = m.compute_node_metrics(networkGraphs, directed=directed, multi=multi) + else: + df = m.get_metrics(networkGraphs, metrics, directed=directed, multi=multi) + + filename = f"{metrics}_{'Directed' if directed else 'Undirected'}_{'Mutli' if multi else ''}_Boxplot.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_boxplot_metric(df, filepath) + + return df.iloc[:, :2], filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_violin(networkGraphs, metrics, directed=True, multi=True, fullPath=False): + """ + :Function: Plot the violin plot for a metric + Metrics: + - 'kcore' + - 'degree' + - 'triangles' + - 'pagerank' + - 'betweenness_centrality' + - 'closeness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + - 'degree_centrality' + - 'centralities' - All centralities + - 'nodes' - All node metrics + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param metrics: Metrics to be plotted + :type metrics: str + :param directed: Boolean to indicate if the graph is directed or not + :type directed: bool + :param multi: for multi graphs + :type multi: bool + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: df and filename + :rtype: pd.DataFrame, str + """ + if metrics == 'centralities': + df = m.compute_node_centralities(networkGraphs, directed=directed, multi=multi) + elif metrics == 'nodes': + df = m.compute_node_metrics(networkGraphs, directed=directed, multi=multi) + else: + df = m.get_metrics(networkGraphs, metrics, directed=directed, multi=multi) + + filename = f"{metrics}_{'Directed' if directed else 'Undirected'}_{'Mutli' if multi else ''}_Violin.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_violin_metric(df, filepath) + + return df.iloc[:, :2], filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_heatmap(networkGraphs, fullPath=False): + """ + :Function: Plot the heatmap for the given network + :param networkGraphs: + :type networkGraphs: NetworkGraphs + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: filename + :rtype: str + """ + filename = f"heatmap.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_heatmap(networkGraphs, filepath) + + return filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def plot_temporal(networkGraphs, layout='map'): + """ + :Function: Plot the temporal graph for the given network, only for temporal graphs + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param layout: Layout of the graph + :type layout: str + :return: filename + :rtype: str + """ + if not networkGraphs.is_temporal(): + print(ValueError('Graph is not temporal. Please select a temporal graph.')) + return '../application/static/no_graph.html' + + if not networkGraphs.is_spatial() and layout == 'map': + print(ValueError('Graph is not spatial. Please select a spatial graph.')) + return '../application/static/no_graph.html' + + filename = f"temporal_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if not os.path.isfile(filepath): + generate_temporal(networkGraphs, filepath, layout_=layout) + + return filename
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def plot_node2vec(networkGraphs, p=1, q=1, layout='TSNE', fullPath=False): + """ + :Function: Plot the Node2Vec embedding for the given network + layout: + - 'TSNE' + - 'UMAP' + - 'PCA' + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param p: p parameter for Node2Vec + :type p: float + :param q: q parameter for Node2Vec + :type q: float + :param layout: Visualisation method + :type layout: str + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: df and filename + :rtype: pd.DataFrame, str + """ + if layout not in ['TSNE', 'UMAP', 'PCA']: + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + _, emb = dl.node2vec_embedding(networkGraphs, p=p, q=q) + + filename = f"node2vec_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if layout == 'TSNE': + TSNE_visualisation(networkGraphs, emb, filepath) + elif layout == 'UMAP': + umap_visualisation(networkGraphs, emb, filepath) + elif layout == 'PCA': + PCA_visualisation(networkGraphs, emb, filepath) + + df = pd.DataFrame(emb) + + return df, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def plot_node2vec_cluster(networkGraphs, method, noOfCluster=8, p=1, q=1, layout='TSNE', fullPath=False): + """ + :Function: Plot the Node2Vec embedding with cluster on top for the given network + method: + - 'kmeans' + - 'spectral' + - 'agglomerative' + - 'dbscan' + layout: + - 'TSNE' + - 'UMAP' + - 'PCA' + - 'sfdp' + - 'twopi' + - 'map' + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param method: Clustering method + :type method: str + :param noOfCluster: Number of clusters + :type noOfCluster: int + :param p: p parameter for Node2Vec + :type p: float + :param q: q parameter for Node2Vec + :type q: float + :param layout: Visualisation method + :type layout: str + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: DataFrame, filename + :rtype: pd.DataFrame, str + """ + if layout not in ['TSNE', 'UMAP', 'PCA', 'sfdp', 'twopi', 'map']: + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + if layout == 'map' and not networkGraphs.is_spatial(): + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + if method not in ['kmeans', 'spectral', 'agglomerative', 'dbscan']: + print(ValueError('Please select a valid clustering method.')) + return '../application/static/no_graph.html' + + _, emb = dl.node2vec_embedding(networkGraphs, p=p, q=q) + clusters = ml.get_communities(networkGraphs, method=method, noOfClusters=noOfCluster, embedding=emb) + + filename = f"node2vec_{method}_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if layout == 'TSNE': + TSNE_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout == 'UMAP': + umap_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout == 'PCA': + PCA_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout in ['sfdp', 'twopi', 'map']: + generate_static_cluster(networkGraphs, clusters, filepath, method, layout_=layout, nbr=noOfCluster) + + df = pd.DataFrame(emb) + + return df, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_DL_embedding(networkGraphs, features=['proximity'], dimension=128, model='SAGE', layout='TSNE', + fullPath=False): + """ + :Function: Plot the DL embedding for the given network + features: + Format: ['feature1', 'feature2', ...] + If choose proximity just put ['proximity'] without any other features + - 'proximity' + - 'kcore' + - 'triangles' + - 'degree' + - 'pagerank' + - 'degree_centrality' + - 'closeness_centrality' + - 'betweenness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + layout: + - 'TSNE' + - 'UMAP' + - 'PCA' + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param features: Feature list + :type features: list + :param dimension: Dimension of the embedding + :type dimension: int + :param model: GNN model + :type model: str + :param layout: Visualisation method + :type layout: str + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: DataFrame, filename + :rtype: pd.DataFrame, str + """ + if layout not in ['TSNE', 'UMAP', 'PCA']: + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + if model not in ['SAGE', 'GAT', 'GCN']: + print(ValueError('Please select a valid GNN.')) + return '../application/static/no_graph.html' + + emb = get_DL_embedding(networkGraphs, model=model, features=features, dimension=dimension) + + filename = f"DLEmbedding_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if layout == 'TSNE': + TSNE_visualisation(networkGraphs, emb, filepath) + elif layout == 'UMAP': + umap_visualisation(networkGraphs, emb, filepath) + elif layout == 'PCA': + PCA_visualisation(networkGraphs, emb, filepath) + + df = pd.DataFrame(emb) + + return df, filename if not fullPath else filepath
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def plot_DL_embedding_cluster(networkGraphs, method, noOfCluster=8, features=['proximity'], dimension=128, model='SAGE', + layout='TSNE', fullPath=False): + """ + :Function: Plot the DL embeddings with clusters on top for the given network + method: + - 'kmeans' + - 'spectral' + - 'agglomerative' + - 'dbscan' + features: + Format: ['feature1', 'feature2', ...] + If choose proximity just put ['proximity'] without any other features + - 'proximity' + - 'kcore' + - 'triangles' + - 'degree' + - 'pagerank' + - 'degree_centrality' + - 'closeness_centrality' + - 'betweenness_centrality' + - 'eigenvector_centrality' + - 'load_centrality' + layout: + - 'TSNE' + - 'UMAP' + - 'PCA' + - 'sfdp' + - 'twopi' + - 'map' + :param networkGraphs: NetworkGraphs object + :type networkGraphs: NetworkGraphs + :param method: Clustering method + :type method: str + :param noOfCluster: Number of clusters + :type noOfCluster: int + :param features: Feature list + :type features: list + :param dimension: Dimension of the embedding + :type dimension: int + :param model: GNN model + :type model: str + :param layout: Visualisation method + :type layout: str + :param fullPath: Boolean to indicate if the full path is required + :type fullPath: bool + :return: DataFrame, filename + :rtype: pd.DataFrame, str + """ + if layout not in ['TSNE', 'UMAP', 'PCA', 'sfdp', 'twopi', 'map']: + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + if layout == 'map' and not networkGraphs.is_spatial(): + print(ValueError('Please select a valid visualisation method.')) + return '../application/static/no_graph.html' + + if model not in ['SAGE', 'GAT', 'GCN']: + print(ValueError('Please select a valid GNN.')) + return '../application/static/no_graph.html' + + if method not in ['kmeans', 'spectral', 'agglomerative', 'dbscan']: + print(ValueError('Please select a valid clustering method.')) + return '../application/static/no_graph.html' + + emb = get_DL_embedding(networkGraphs, model=model, features=features, dimension=dimension) + clusters = ml.get_communities(networkGraphs, method=method, noOfClusters=noOfCluster, embedding=emb) + + filename = f"DLEmbedding_{method}_{layout}.html" + filepath = get_file_path(networkGraphs, filename) + + if layout == 'TSNE': + TSNE_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout == 'UMAP': + umap_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout == 'PCA': + PCA_visualisation(networkGraphs, emb, filepath, clusters=clusters) + elif layout in ['sfdp', 'twopi', 'map']: + generate_static_cluster(networkGraphs, clusters, filepath, method, layout_=layout, nbr=noOfCluster) + + df = pd.DataFrame(emb) + + return df, filename if not fullPath else filepath
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    Source code for src.visualisation_src.DL_visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: DL_visualisation module contains functions for visualising deep learning
    +"""
    +
    +# ----------------------------------- Imports -----------------------------------
    +
    +import plotly.graph_objects as go
    +import umap.umap_ as umap
    +from sklearn.decomposition import PCA
    +from sklearn.manifold import TSNE
    +
    +
    +# ----------------------------------- Functions -----------------------------------
    +
    +
    +
    [docs]def umap_visualisation(networkGraphs, embeddings, filename, clusters=None): + """ + :Function: UMAP visualisation + :param networkGraphs: networkGraphs + :type networkGraphs: src.networkGraphs_src.networkGraphs + :param embeddings: Embeddings + :type embeddings: numpy.ndarray + :param filename: filename + :type filename: str + :param clusters: number of clusters + :type clusters: pandas.DataFrame + :return: filename + :rtype: str + """ + nodes = list(networkGraphs.Graph.nodes()) + + umap_model = umap.UMAP(n_neighbors=30, min_dist=0.3, metric='euclidean', random_state=42) + embeddings_2d = umap_model.fit_transform(embeddings) + + fig = go.Figure() + fig = get_dl_layout_update(fig, embeddings_2d, nodes, title='UMAP', clusters=clusters) + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return filename
    + + +# ---------------------------------------------------------------------------------- + + +
    [docs]def TSNE_visualisation(networkGraphs, embeddings, filename, clusters=None): + """ + :Function: TSNE visualisation + :param networkGraphs: networkGraphs + :type networkGraphs: src.networkGraphs_src.networkGraphs + :param embeddings: Embeddings + :type embeddings: numpy.ndarray + :param filename: filename + :type filename: str + :param clusters: number of clusters + :type clusters: pandas.DataFrame + :return: filename + :rtype: str + """ + nodes = list(networkGraphs.Graph.nodes()) + + tsne_model = TSNE(n_components=2, random_state=42) + embeddings_2d = tsne_model.fit_transform(embeddings) + + fig = go.Figure() + fig = get_dl_layout_update(fig, embeddings_2d, nodes, title='TSNE', clusters=clusters) + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return filename
    + + +# ---------------------------------------------------------------------------------- + + +
    [docs]def PCA_visualisation(networkGraphs, embeddings, filename, clusters=None): + """ + :Function: PCA visualisation + :param networkGraphs: networkGraphs + :type networkGraphs: src.networkGraphs_src.networkGraphs + :param embeddings: Embeddings + :type embeddings: numpy.ndarray + :param filename: + :type filename: str + :param clusters: + :type clusters: pandas.DataFrame + :return: filename + :rtype: str + """ + nodes = list(networkGraphs.Graph.nodes()) + + pca_model = PCA(n_components=2, random_state=42) + embeddings_2d = pca_model.fit_transform(embeddings) + + fig = go.Figure() + fig = get_dl_layout_update(fig, embeddings_2d, nodes, title='PCA', clusters=clusters) + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return filename
    + + +
    [docs]def get_dl_layout_update(fig, embeddings_2d, nodes, title=None, clusters=None): + """ + :Function: Get the DL layout update for the plotly plot + :param fig: Figure + :type fig: plotly.graph_objects.Figure + :param embeddings_2d: 2D embeddings + :type embeddings_2d: numpy.ndarray + :param nodes: Nodes + :type nodes: list + :param title: Title of the plot + :type title: str + :param clusters: Clusters + :type clusters: pandas.DataFrame + :return: Figure + """ + fig.add_trace(go.Scatter(x=embeddings_2d[:, 0], y=embeddings_2d[:, 1], hovertext=nodes, mode='markers')) + + if clusters is not None: + color_list = [] + for node in nodes: + metric_df = clusters[clusters['Node'] == node] + color_list.extend([metric_df['Color'].values[0]]) + fig.update_traces(marker=dict(color=color_list)) + + fig.update_layout( + title=f'{title} visualisation of node embeddings', + xaxis_title=f'{title} x', + yaxis_title=f'{title} y', + hovermode='closest', + showlegend=False, + paper_bgcolor='rgba(0,0,0,0)', + ) + fig.update_layout(margin=dict(l=0, r=0, t=40, b=0)) + + return fig
    + +# ---------------------------------------------------------------------------------- +
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    Source code for src.visualisation_src.ML_visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: ML_visualisation module contains functions for visualising machine learning
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +from pyvis import network as net
    +from tqdm import tqdm
    +
    +# Internal imports
    +from src.visualisation_src.utils_visualisation import *
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +
    +
    [docs]def generate_static_cluster(networkGraphs, df_, filename, algo, layout_='map', nbr=0): + """ + :Function: Generate static cluster + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param df_: Dataframe + :type df_: pd.DataFrame + :param filename: File name + :type filename: str + :param algo: Algorithm + :type algo: str + :param layout_: Layout + :type layout_: str + :param nbr: Number of clusters + :type nbr: int + :return: Plotly plot + :rtype: plotly.graph_objects + """ + G = networkGraphs.Graph + + if not networkGraphs.is_spatial() and layout_ == 'map': + print(ValueError('No spatial graph')) + return '../application/static/no_graph.html' + + pos = networkGraphs.pos[layout_] + + x_list = [] + y_list = [] + text_list = [] + color_list = [] + for node in tqdm(G.nodes()): + x, y = pos[node] + x_list.extend([x]) + y_list.extend([y]) + metric_df = df_[df_['Node'] == node] + node_info = f"Node: {node}<br>Cluster Id: {str(metric_df['Cluster_id'].values[0])}<br>" + text_list.extend([node_info]) + color_list.extend([metric_df['Color'].values[0]]) + + if layout_ == 'map': + node_trace = go.Scattergeo(lon=x_list, lat=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=False, size=5, color=color_list)) + else: + node_trace = go.Scatter(x=x_list, y=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=False, size=5, color=color_list)) + + edge_trace = generate_edge_trace(Graph=G, pos=pos, layout=layout_) + + layout = get_layout(networkGraphs, + title=f"{algo} {f'with {nbr} ' if nbr > 0 else ''}clusters using {layout_} layout", + layout_=layout_) + fig = go.Figure(data=[edge_trace, node_trace], + layout=layout) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + return fig
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def generate_hotspot(networkGraphs, hotspot_df, filename): + """ + :Function: Plot the hotspot for the given graph + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param hotspot_df: Hotspot dataframe + :type hotspot_df: pd.DataFrame + :param filename: Filename + :type filename: str + :return: Plotly plot + :rtype: plotly.graph_objects + """ + latitude = hotspot_df['Latitude'] + longitude = hotspot_df['Longitude'] + degree = hotspot_df['Degree'] + pos = networkGraphs.pos['map'] + G = networkGraphs.Graph + + x_list = [] + y_list = [] + for edge in tqdm(G.edges()): + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + x_list.extend([x0, x1, None]) + y_list.extend([y0, y1, None]) + + fig = go.Figure(go.Densitymapbox(lat=latitude, lon=longitude, z=degree, radius=20, hoverinfo='none')) + fig.add_scattermapbox(lat=latitude, lon=longitude, mode="markers", text=[], name='Nodes', hoverinfo='none', + marker=go.scattermapbox.Marker(size=3, color="white")) + fig.add_scattermapbox(lat=y_list, lon=x_list, text=[], mode="lines", name='Edges', hoverinfo='none', + line=dict(width=0.5, color="darkgrey")) + + fig.update_layout(mapbox_style="stamen-terrain", mapbox_center_lon=networkGraphs.mid_long, + mapbox_center_lat=networkGraphs.mid_lat, mapbox_zoom=3.5, margin={"r": 0, "t": 0, "l": 0, "b": 0}, + legend=dict(orientation="h", yanchor="bottom", y=0.1, xanchor="right", x=1, title="Show/Hide")) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return fig
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_dynamic_cluster(networkGraphs, df_, filename): + """ + :Function: Plot the metrics on the graph + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param df_: Dataframe with the metrics + :type df_: pd.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :return: Pyvis plot + :rtype: pyvis.network.Network + """ + G = networkGraphs.Graph + + for u, d in G.nodes(data=True): + metric_df = df_[df_['Node'] == u] + d['color'] = metric_df['Color'].values[0] + d['size'] = 5 + d['title'] = f"Node: {u}; Cluster Id: {str(metric_df['Cluster_id'].values[0])}" + + for u, v, d in G.edges(data=True): + d.clear() + + Net = net.Network(height="750px", width="100%", bgcolor="#E4ECF6", font_color="black", notebook=False, + cdn_resources='remote') + Net.from_nx(G) + Net.options.physics.use_force_atlas_2based( + params={'central_gravity': 0.01, 'gravity': -50, 'spring_length': 100, 'spring_strength': 0.08, 'damping': 0.4, + 'overlap': 0}) + Net.write_html(filename) + + return Net
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    Source code for src.visualisation_src.metrics_visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Metrics visualisation module contains functions for visualising metrics
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +import matplotlib as mpl
    +import networkx as nx
    +import numpy as np
    +from pyvis import network as net
    +from tqdm import tqdm
    +
    +# Internal imports
    +from src.visualisation_src.utils_visualisation import *
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    [docs]def dropStd(df_): + """ + :Function: Drop the std column from the dataframe + :param df_: Dataframe with the metrics + :type df_: pandas.DataFrame + :return: Dataframe without the std column + :rtype: pandas.DataFrame + """ + if any('std' in s for s in df_.columns): + df_.drop(columns=[col for col in df_.columns if 'std' in col], inplace=True) + + return df_
    + + +
    [docs]def generate_static_metric(networkGraphs, df_, filename, layout_='map'): # USING PLOTLY + """ + :Function: Plot the metrics on the graph + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param df_: Dataframe with the metrics (first column is the node id) (second column is the metric) + :type df_: pandas.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :param layout_: Layout to be used + :type layout_: str + :return: Plotly plot + :rtype: plotly.graph_objs._figure.Figure + """ + G = networkGraphs.Graph + + if not networkGraphs.is_spatial() and layout_ == 'map': + print(ValueError('No spatial graph')) + return '../application/static/no_graph.html' + + pos = networkGraphs.pos[layout_] + + metrics_name = df_.columns[1] + df_['std'] = (df_[metrics_name] - df_[metrics_name].min()) / (df_[metrics_name].max() - df_[metrics_name].min()) + df_['std'] = df_['std'].fillna(0.00) + size_ = 5 / df_['std'].mean() # normalise the size of the nodes + df_['std'] = df_['std'].apply(lambda x: 0.05 if x < 0.03 else x) # to avoid nodes with size 0 + df_['std'] = df_['std'].apply(lambda x: x * size_) + + x_list = [] + y_list = [] + text_list = [] + for node in tqdm(G.nodes()): + x, y = pos[node] + x_list.extend([x]) + y_list.extend([y]) + metric_df = df_[df_['Node'] == node] + node_info = f"Node: {node}<br>" + node_info += f"{metrics_name}: {str(metric_df[metrics_name].values[0])}<br>" + text_list.extend([node_info]) + + if layout_ == 'map': + node_trace = go.Scattergeo(lon=x_list, lat=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=True, + colorbar=dict(thickness=10, title=metrics_name, xanchor='left', + titleside='right'), color=df_[metrics_name], + size=df_['std'])) + else: + node_trace = go.Scatter(x=x_list, y=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=True, + colorbar=dict(thickness=10, title=metrics_name, xanchor='left', + titleside='right'), color=df_[metrics_name], + size=df_['std'])) + + edge_trace = generate_edge_trace(Graph=G, pos=pos, layout=layout_) + + layout = get_layout(networkGraphs, title=f"{metrics_name} visualisation using {layout_} layout", layout_=layout_) + fig = go.Figure(data=[edge_trace, node_trace], + layout=layout) + + steps = [] + range_ = np.arange(0.2, 2, 0.4) + for i in range_: + step = dict( + method="restyle", + label=f"Scale {round(i, 2)}", + ) + step["args"] = ["marker.size", [i * df_['std']]] + steps.append(step) + + sliders = [dict( + active=2, + currentvalue={"prefix": "Size: ", "visible": False}, + pad={"t": 10}, + steps=steps + )] + + fig.update_layout(sliders=sliders) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + df_.drop(columns=['std'], inplace=True) + return fig
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_static_all_metrics(networkGraphs, df_, filename, layout_='map'): + """ + :Function: Plot the metrics on the graph + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param df_: Dataframe with the metrics + :type df_: pandas.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :param layout_: Layout to be used + :type layout_: str + :return: Plotly plot + :rtype: plotly.graph_objs._figure.Figure + """ + G = networkGraphs.Graph + + if not networkGraphs.is_spatial() and layout_ == 'map': + print(ValueError('No spatial graph')) + return '../application/static/no_graph.html' + + pos = networkGraphs.pos[layout_] + + metrics_names = df_.columns[1:] + + x_list = [] + y_list = [] + text_list = [] + for node in G.nodes(): + x, y = pos[node] + x_list.extend([x]) + y_list.extend([y]) + metric_df = df_[df_['Node'] == node] + node_info = f"Node: {node}<br>" + for metric in metrics_names: + node_info += f"{metric}: {str(metric_df[metric].values[0])}<br>" + text_list.extend([node_info]) + + if layout_ == 'map': + node_trace = go.Scattergeo(lon=x_list, lat=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=False, size=3, color='black')) + else: + node_trace = go.Scatter(x=x_list, y=y_list, text=text_list, mode='markers', hoverinfo='text', + marker=dict(showscale=False, size=3, color='black')) + + edge_trace = generate_edge_trace(Graph=G, pos=pos, layout=layout_) + + layout = get_layout(networkGraphs, title=f"Metrics visualisation using {layout_} layout", layout_=layout_) + fig = go.Figure(data=[edge_trace, node_trace], + layout=layout) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + return fig
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_dynamic_metric(networkGraphs, df_, filename): + """ + :Function: Plot the metrics on the graph + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param df_: Dataframe with the metrics + :type df_: pandas.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :return: Pyvis plot + :rtype: pyvis.network.Network + """ + G = networkGraphs.Graph + metrics_name = df_.columns[1] + df_['std'] = df_[metrics_name].apply( + lambda x: (x - min(df_[metrics_name])) / (max(df_[metrics_name]) - min(df_[metrics_name]))) + size_ = 5 / df_['std'].mean() + + cmap = plt.cm.plasma + + for u, d in G.nodes(data=True): + metric_df = df_[df_['Node'] == u] + c = cmap(metric_df['std'].values[0]) + d['color'] = mpl.colors.rgb2hex(c) + d['size'] = metric_df['std'].values[0] * size_ + d['title'] = f"Node: {u}; {metrics_name}: {str(metric_df[metrics_name].values[0])}" + + for u, v, d in G.edges(data=True): + d.clear() + + Net = net.Network(height="750px", width="100%", bgcolor="#E4ECF6", font_color="black", notebook=True) + Net.from_nx(G) + Net.show_buttons(filter_=['physics', 'edges', 'nodes']) + Net.options.physics.use_force_atlas_2based( + params={'central_gravity': 0.01, 'gravity': -50, 'spring_length': 100, 'spring_strength': 0.08, 'damping': 0.4, + 'overlap': 0}) + Net.write_html(filename) + df_.drop(columns=['std'], inplace=True) + return Net
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_histogram_metric(df_, filename): + """ + :Function: Generate histogram of the metrics + :param df_: Dataframe with the metrics + :type df_: pd.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :return: Plotly plot + :rtype: plotly.graph_objects.Figure + """ + dropStd(df_) + + metrics_names = df_.columns[1:] + metrics = df_[metrics_names].values + title = f"Histogram: {'s' if len(metrics_names) > 1 else ''}: {', '.join(metrics_names)}" + if len(title) > 80: # if title too long write it in two lines + title = title[:80] + '-<br>-' + title[80:] + + fig = go.Figure() + for i, metric in enumerate(metrics_names): + fig.add_trace(go.Histogram(x=metrics[:, i], name=metric)) + + fig.update_layout(barmode='overlay', + title_text=title, + xaxis_title="Values", + yaxis_title="Count", + bargap=0.1, ) + fig.update_traces(opacity=0.75) + fig.update_layout(margin=dict(l=0, r=40, t=40, b=0)) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return fig
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_boxplot_metric(df_, filename): + """ + :Function: Generate boxplot of the metrics + :param df_: Dataframe with the metrics + :type df_: pd.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :return: Plotly plot + :rtype: plotly.graph_objects.Figure + """ + dropStd(df_) + + metrics_names = df_.columns[1:] + metrics = df_[metrics_names].values + title = f"Boxplot of the metric{'s' if len(metrics_names) > 1 else ''}: {', '.join(metrics_names)}" + if len(title) > 80: # if title too long write it in two lines + title = title[:80] + '-<br>-' + title[80:] + + fig = go.Figure() + for i, metric in enumerate(metrics_names): + fig.add_trace(go.Box(y=metrics[:, i], name=metric)) + + fig.update_layout(title_text=title, + xaxis_title="Metrics", + yaxis_title="Values", + ) + + fig.update_layout(margin=dict(l=0, r=40, t=40, b=0)) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return fig
    + + +# ---------------------------------------------------------------------------------------- + +
    [docs]def generate_violin_metric(df_, filename): + """ + :Function: Generate violin plot of the metrics + :param df_: Dataframe with the metrics + :type df_: pd.DataFrame + :param filename: Name of the file to be saved + :type filename: str + :return: Plotly plot + :rtype: plotly.graph_objects.Figure + """ + dropStd(df_) + + metrics_names = df_.columns[1:] + metrics = df_[metrics_names].values + title = f"Violin plot of the metric{'s' if len(metrics_names) > 1 else ''}: {', '.join(metrics_names)}" + if len(title) > 80: # if title too long write it in two lines + title = title[:80] + '-<br>-' + title[80:] + + fig = go.Figure() + for i, metric in enumerate(metrics_names): + fig.add_trace(go.Violin(y=metrics[:, i], name=metric, box_visible=True, meanline_visible=True)) + + fig.update_layout(title_text=title, + xaxis_title="Metrics", + yaxis_title="Values", + ) + fig.update_layout(margin=dict(l=0, r=40, t=40, b=0)) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return fig
    + + +# ---------------------------------------------------------------------------------------- + + +
    [docs]def generate_heatmap(networkGraph, filename): + """ + :Function: Show the heatmap of the graph + :param networkGraph: Network graph + :type networkGraph: NetworkGraph + :param filename: Name of the file to be saved + :type filename: str + :return: Plotly plot + """ + G = networkGraph.Graph + + mat = nx.to_scipy_sparse_array(G, nodelist=G.nodes(), weight=None, dtype=None, format='csc') + mat = mat.todense() + ax = [str(i) for i in G.nodes()] + + fig = go.Figure(data=go.Heatmap(z=mat, x=ax, y=ax)) + fig.update_layout(title_text="Heatmap of connections between nodes") + + fig.update_xaxes(showticklabels=False) + fig.update_yaxes(showticklabels=False) + fig.update_layout(margin=dict(l=0, r=0, t=0, b=0)) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + return fig
    +
    + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/visualisation_src/temporal_visualisation.html b/docs/build/html/_modules/src/visualisation_src/temporal_visualisation.html new file mode 100644 index 00000000..15379dd5 --- /dev/null +++ b/docs/build/html/_modules/src/visualisation_src/temporal_visualisation.html @@ -0,0 +1,232 @@ + + + + + + src.visualisation_src.temporal_visualisation — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
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    Source code for src.visualisation_src.temporal_visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Temporal visualisation module contains functions for generating temporal visualisations
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +import networkx as nx
    +import numpy as np
    +import plotly.graph_objects as go
    +from tqdm import tqdm
    +
    +# Internal imports
    +from src.visualisation_src.utils_visualisation import get_layout
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +
    +
    [docs]def generate_temporal(networkGraphs, filename, layout_='map'): + """ + :Function: Generate temporal visualisation of the network + :param networkGraphs: Network graphs + :type networkGraphs: NetworkGraphs + :param filename: File name + :type filename: str + :param layout_: Layout + :type layout_: str + :return: Plotly plot + :rtype: plotly.graph_objects + """ + if not networkGraphs.is_spatial() and layout_ == 'map': + print(ValueError('The graph is not spatial, please choose a different layout')) + return '../application/static/no_graph.html' + + G = networkGraphs.MultiDiGraph + + fig = go.Figure() + + start = networkGraphs.get_start() + end = networkGraphs.get_end() + step = (end - start) / 100 + + node_x = [] + node_y = [] + text_list = [] + for node in G.nodes(): + pos = networkGraphs.pos[layout_] + x, y = pos[node] + node_x.append(x) + node_y.append(y) + node_info = f"Node: {node}<br>" + text_list.append(node_info) + + if layout_ == 'map': + node_trace = go.Scattergeo( + lon=node_x, lat=node_y, + mode='markers', + hoverinfo='text', + marker=dict( + reversescale=True, + color='black', + size=1, + line_width=1, ), + text=text_list) + else: + node_trace = go.Scatter( + x=node_x, y=node_y, + mode='markers', + hoverinfo='text', + marker=dict( + reversescale=True, + color='black', + size=1, + line_width=1, ), + text=text_list) + + fig.add_trace(node_trace) + + for t in tqdm(np.arange(start, end + step, step)): + G2 = nx.DiGraph(((source, target, attr) for source, target, attr in G.edges(data=True) if + attr['start'] <= t and t <= attr['end'])) + + lat = [] + lon = [] + for edge in G2.edges(): + pos = networkGraphs.pos[layout_] + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + lon.extend([x0, x1, None]) + lat.extend([y0, y1, None]) + + if layout_ == 'map': + edge_trace = go.Scattergeo( + lon=lon, lat=lat, + mode='lines', + line=dict(width=1, color='red'), + opacity=0.8, + ) + else: + edge_trace = go.Scatter( + x=lon, y=lat, + mode='lines', + line=dict(width=1, color='red'), + opacity=0.8, + ) + + fig.add_trace(edge_trace) + + # Create and add slider + steps = [] + for i in range(len(fig.data)): + step = dict( + method="update", + args=[{"visible": [True] + [False] * len(fig.data)}, + {"title": "Date time " + str(i)}], # layout attribute + ) + step["args"][0]["visible"][i] = True # Toggle i'th trace to "visible" + steps.append(step) + + sliders = [dict( + active=0, + currentvalue={"prefix": "Frequency: "}, + pad={"t": 50}, + steps=steps, + )] + + fig.update_layout(sliders=sliders) + layout = get_layout(networkGraphs, layout_) + fig.update_layout(layout) + + fig.write_html(filename, full_html=False, include_plotlyjs='cdn') + + return filename
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    + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/src/visualisation_src/utils_visualisation.html b/docs/build/html/_modules/src/visualisation_src/utils_visualisation.html new file mode 100644 index 00000000..a62ef483 --- /dev/null +++ b/docs/build/html/_modules/src/visualisation_src/utils_visualisation.html @@ -0,0 +1,232 @@ + + + + + + src.visualisation_src.utils_visualisation — AlphaTeam 0.0.1 documentation + + + + + + + + + + + + + +
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    Source code for src.visualisation_src.utils_visualisation

    +"""
    +Author: Alpha Team Group Project
    +Date: March 2023
    +Purpose: Visualisation utilities module contains functions for visualising the network graphs
    +"""
    +
    +# ----------------------------------------- Imports ----------------------------------------- #
    +
    +# External imports
    +import os
    +import geopandas as gpd
    +import matplotlib.pyplot as plt
    +from plotly import graph_objects as go
    +
    +# Internal imports
    +from src.utils import memoize
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +@memoize
    +def get_layout(networkGraphs, title=None, layout_='map'):
    +    """
    +    :Function: Get the layout of the graph
    +    :param networkGraphs: Network graphs
    +    :type networkGraphs: NetworkGraphs
    +    :param title: Title of the graph
    +    :type title: str
    +    :param layout_: layout of the graph
    +    :type layout_: str
    +    :return: Plotly layout
    +    :rtype: plotly.graph_objects
    +    """
    +    if layout_ == 'map':
    +        if networkGraphs.type == 'GTFS':
    +            resolution = 50
    +        else:
    +            resolution = 110
    +        layout = go.Layout(
    +            title=f'<br>{title}',
    +            titlefont=dict(size=16, color='Black'),
    +            showlegend=False,
    +            hovermode='closest',
    +            annotations=[
    +                dict(
    +                    text="Alpha Team - 2023",
    +                    showarrow=False,
    +                    xref="paper", yref="paper",
    +                    x=0.005, y=-0.002,
    +                    font=dict(color='black')
    +                )
    +            ],
    +            margin=dict(l=0, r=0, t=0, b=0),
    +            geo=dict(
    +                scope='world',
    +                lataxis_range=[networkGraphs.min_lat, networkGraphs.max_lat],
    +                lonaxis_range=[networkGraphs.min_long, networkGraphs.max_long],
    +                center=dict(lat=networkGraphs.mid_lat, lon=networkGraphs.mid_long),
    +                showland=True,
    +                showcountries=True,
    +                resolution=resolution,
    +            ),
    +            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
    +            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
    +        )
    +
    +    else:
    +        layout = go.Layout(
    +            title=f'<br>{title}',
    +            titlefont=dict(size=16, color='Black'),
    +            showlegend=False,
    +            hovermode='closest',
    +            margin=dict(l=0, r=0, t=0, b=0),
    +            annotations=[
    +                dict(
    +                    text="Alpha Team - 2023",
    +                    showarrow=False,
    +                    xref="paper", yref="paper",
    +                    x=0.005, y=-0.002,
    +                    font=dict(color='black')
    +                )
    +            ],
    +            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
    +            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
    +        )
    +
    +    return layout
    +
    +
    +# ----------------------------------------------------------------------------------------
    +
    +@memoize
    +def generate_edge_trace(Graph, pos, layout):
    +    """
    +    :Function: Generate the edge trace for the plotly plot, leveraging the memoize decorator for cache optimization
    +    :param Graph: Network graph
    +    :type Graph: NetworkGraph
    +    :param pos: Position of the nodes
    +    :type pos: dict
    +    :param layout: Layout of the plot
    +    :type layout: str
    +    :return: Edge trace
    +    :rtype: plotly.graph_objects
    +    """
    +    x_list = []
    +    y_list = []
    +    for edge in Graph.edges():
    +        x0, y0 = pos[edge[0]]
    +        x1, y1 = pos[edge[1]]
    +        x_list.extend([x0, x1, None])
    +        y_list.extend([y0, y1, None])
    +
    +    if layout == 'map':
    +        edge_trace = go.Scattergeo(lon=x_list, lat=y_list, hoverinfo='none', mode='lines',
    +                                   line=dict(width=0.5, color='#888'))
    +    else:
    +        edge_trace = go.Scatter(x=x_list, y=y_list, hoverinfo='none', mode='lines', line=dict(width=0.5, color='#888'))
    +
    +    return edge_trace
    +
    +
    +
    [docs]def get_file_path(networkGraphs, file_name): + """ + :Function: Get the file path for the plotly plot + :param networkGraphs: Network graph + :type networkGraphs: NetworkGraph + :param file_name: Name of the file + :type file_name: str + :return: Filepath + :rtype: str + """ + folder = f"{networkGraphs.session_folder}/" + if not os.path.isdir(folder): + os.mkdir(folder) + + return f"{folder}{file_name}"
    +
    + +
    +
    + +
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    + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/application.dictionary.rst b/docs/build/html/_sources/application.dictionary.rst new file mode 100644 index 00000000..94493763 --- /dev/null +++ b/docs/build/html/_sources/application.dictionary.rst @@ -0,0 +1,21 @@ +application.dictionary package +============================== + +Submodules +---------- + +application.dictionary.information module +----------------------------------------- + +.. automodule:: application.dictionary.information + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.dictionary + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.dictionary.rst.txt b/docs/build/html/_sources/application.dictionary.rst.txt new file mode 100644 index 00000000..94493763 --- /dev/null +++ b/docs/build/html/_sources/application.dictionary.rst.txt @@ -0,0 +1,21 @@ +application.dictionary package +============================== + +Submodules +---------- + +application.dictionary.information module +----------------------------------------- + +.. automodule:: application.dictionary.information + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.dictionary + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.clusters.rst b/docs/build/html/_sources/application.routes.clusters.rst new file mode 100644 index 00000000..6f0971aa --- /dev/null +++ b/docs/build/html/_sources/application.routes.clusters.rst @@ -0,0 +1,21 @@ +application.routes.clusters package +=================================== + +Submodules +---------- + +application.routes.clusters.cluster\_routes module +-------------------------------------------------- + +.. automodule:: application.routes.clusters.cluster_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.clusters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.clusters.rst.txt b/docs/build/html/_sources/application.routes.clusters.rst.txt new file mode 100644 index 00000000..6f0971aa --- /dev/null +++ b/docs/build/html/_sources/application.routes.clusters.rst.txt @@ -0,0 +1,21 @@ +application.routes.clusters package +=================================== + +Submodules +---------- + +application.routes.clusters.cluster\_routes module +-------------------------------------------------- + +.. automodule:: application.routes.clusters.cluster_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.clusters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.deepLearning.rst b/docs/build/html/_sources/application.routes.deepLearning.rst new file mode 100644 index 00000000..0d57db1d --- /dev/null +++ b/docs/build/html/_sources/application.routes.deepLearning.rst @@ -0,0 +1,29 @@ +application.routes.deepLearning package +======================================= + +Submodules +---------- + +application.routes.deepLearning.cluster\_embedding\_routes module +----------------------------------------------------------------- + +.. automodule:: application.routes.deepLearning.cluster_embedding_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.deepLearning.embedding\_routes module +-------------------------------------------------------- + +.. automodule:: application.routes.deepLearning.embedding_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.deepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.deepLearning.rst.txt b/docs/build/html/_sources/application.routes.deepLearning.rst.txt new file mode 100644 index 00000000..0d57db1d --- /dev/null +++ b/docs/build/html/_sources/application.routes.deepLearning.rst.txt @@ -0,0 +1,29 @@ +application.routes.deepLearning package +======================================= + +Submodules +---------- + +application.routes.deepLearning.cluster\_embedding\_routes module +----------------------------------------------------------------- + +.. automodule:: application.routes.deepLearning.cluster_embedding_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.deepLearning.embedding\_routes module +-------------------------------------------------------- + +.. automodule:: application.routes.deepLearning.embedding_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.deepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.hotspot.rst b/docs/build/html/_sources/application.routes.hotspot.rst new file mode 100644 index 00000000..184f6934 --- /dev/null +++ b/docs/build/html/_sources/application.routes.hotspot.rst @@ -0,0 +1,21 @@ +application.routes.hotspot package +================================== + +Submodules +---------- + +application.routes.hotspot.hotspot\_routes module +------------------------------------------------- + +.. automodule:: application.routes.hotspot.hotspot_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.hotspot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.hotspot.rst.txt b/docs/build/html/_sources/application.routes.hotspot.rst.txt new file mode 100644 index 00000000..184f6934 --- /dev/null +++ b/docs/build/html/_sources/application.routes.hotspot.rst.txt @@ -0,0 +1,21 @@ +application.routes.hotspot package +================================== + +Submodules +---------- + +application.routes.hotspot.hotspot\_routes module +------------------------------------------------- + +.. automodule:: application.routes.hotspot.hotspot_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.hotspot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.metrics.rst b/docs/build/html/_sources/application.routes.metrics.rst new file mode 100644 index 00000000..48e84952 --- /dev/null +++ b/docs/build/html/_sources/application.routes.metrics.rst @@ -0,0 +1,37 @@ +application.routes.metrics package +================================== + +Submodules +---------- + +application.routes.metrics.centrality\_routes module +---------------------------------------------------- + +.. automodule:: application.routes.metrics.centrality_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.metrics.global\_metrics\_routes module +--------------------------------------------------------- + +.. automodule:: application.routes.metrics.global_metrics_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.metrics.node\_routes module +---------------------------------------------- + +.. automodule:: application.routes.metrics.node_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.metrics.rst.txt b/docs/build/html/_sources/application.routes.metrics.rst.txt new file mode 100644 index 00000000..48e84952 --- /dev/null +++ b/docs/build/html/_sources/application.routes.metrics.rst.txt @@ -0,0 +1,37 @@ +application.routes.metrics package +================================== + +Submodules +---------- + +application.routes.metrics.centrality\_routes module +---------------------------------------------------- + +.. automodule:: application.routes.metrics.centrality_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.metrics.global\_metrics\_routes module +--------------------------------------------------------- + +.. automodule:: application.routes.metrics.global_metrics_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.metrics.node\_routes module +---------------------------------------------- + +.. automodule:: application.routes.metrics.node_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.resilience.rst b/docs/build/html/_sources/application.routes.resilience.rst new file mode 100644 index 00000000..c5d59a0f --- /dev/null +++ b/docs/build/html/_sources/application.routes.resilience.rst @@ -0,0 +1,21 @@ +application.routes.resilience package +===================================== + +Submodules +---------- + +application.routes.resilience.resilience\_routes module +------------------------------------------------------- + +.. automodule:: application.routes.resilience.resilience_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.resilience + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.resilience.rst.txt b/docs/build/html/_sources/application.routes.resilience.rst.txt new file mode 100644 index 00000000..c5d59a0f --- /dev/null +++ b/docs/build/html/_sources/application.routes.resilience.rst.txt @@ -0,0 +1,21 @@ +application.routes.resilience package +===================================== + +Submodules +---------- + +application.routes.resilience.resilience\_routes module +------------------------------------------------------- + +.. automodule:: application.routes.resilience.resilience_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.resilience + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.rst b/docs/build/html/_sources/application.routes.rst new file mode 100644 index 00000000..0f8a4eab --- /dev/null +++ b/docs/build/html/_sources/application.routes.rst @@ -0,0 +1,37 @@ +application.routes package +========================== + +Submodules +---------- + +application.routes.centrality\_routes module +-------------------------------------------- + +.. automodule:: application.routes.centrality_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.cluster\_routes module +----------------------------------------- + +.. automodule:: application.routes.cluster_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.node\_routes module +-------------------------------------- + +.. automodule:: application.routes.node_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.rst.txt b/docs/build/html/_sources/application.routes.rst.txt new file mode 100644 index 00000000..0f8a4eab --- /dev/null +++ b/docs/build/html/_sources/application.routes.rst.txt @@ -0,0 +1,37 @@ +application.routes package +========================== + +Submodules +---------- + +application.routes.centrality\_routes module +-------------------------------------------- + +.. automodule:: application.routes.centrality_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.cluster\_routes module +----------------------------------------- + +.. automodule:: application.routes.cluster_routes + :members: + :undoc-members: + :show-inheritance: + +application.routes.node\_routes module +-------------------------------------- + +.. automodule:: application.routes.node_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.visualisation.rst b/docs/build/html/_sources/application.routes.visualisation.rst new file mode 100644 index 00000000..3f27e963 --- /dev/null +++ b/docs/build/html/_sources/application.routes.visualisation.rst @@ -0,0 +1,21 @@ +application.routes.visualisation package +======================================== + +Submodules +---------- + +application.routes.visualisation.visualisation\_routes module +------------------------------------------------------------- + +.. automodule:: application.routes.visualisation.visualisation_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.visualisation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.routes.visualisation.rst.txt b/docs/build/html/_sources/application.routes.visualisation.rst.txt new file mode 100644 index 00000000..3f27e963 --- /dev/null +++ b/docs/build/html/_sources/application.routes.visualisation.rst.txt @@ -0,0 +1,21 @@ +application.routes.visualisation package +======================================== + +Submodules +---------- + +application.routes.visualisation.visualisation\_routes module +------------------------------------------------------------- + +.. automodule:: application.routes.visualisation.visualisation_routes + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application.routes.visualisation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.rst b/docs/build/html/_sources/application.rst new file mode 100644 index 00000000..5d97a339 --- /dev/null +++ b/docs/build/html/_sources/application.rst @@ -0,0 +1,29 @@ +application package +=================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + application.routes + +Submodules +---------- + +application.app module +---------------------- + +.. automodule:: application.app + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/application.rst.txt b/docs/build/html/_sources/application.rst.txt new file mode 100644 index 00000000..5d97a339 --- /dev/null +++ b/docs/build/html/_sources/application.rst.txt @@ -0,0 +1,29 @@ +application package +=================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + application.routes + +Submodules +---------- + +application.app module +---------------------- + +.. automodule:: application.app + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: application + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.clusters.rst b/docs/build/html/_sources/backend.clusters.rst new file mode 100644 index 00000000..6a54cc11 --- /dev/null +++ b/docs/build/html/_sources/backend.clusters.rst @@ -0,0 +1,21 @@ +backend.clusters package +======================== + +Submodules +---------- + +backend.clusters.clusters module +-------------------------------- + +.. automodule:: backend.clusters.clusters + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.clusters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.clusters.rst.txt b/docs/build/html/_sources/backend.clusters.rst.txt new file mode 100644 index 00000000..6a54cc11 --- /dev/null +++ b/docs/build/html/_sources/backend.clusters.rst.txt @@ -0,0 +1,21 @@ +backend.clusters package +======================== + +Submodules +---------- + +backend.clusters.clusters module +-------------------------------- + +.. automodule:: backend.clusters.clusters + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.clusters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.common.rst b/docs/build/html/_sources/backend.common.rst new file mode 100644 index 00000000..76707ff9 --- /dev/null +++ b/docs/build/html/_sources/backend.common.rst @@ -0,0 +1,21 @@ +backend.common package +====================== + +Submodules +---------- + +backend.common.common module +---------------------------- + +.. automodule:: backend.common.common + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.common + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.common.rst.txt b/docs/build/html/_sources/backend.common.rst.txt new file mode 100644 index 00000000..76707ff9 --- /dev/null +++ b/docs/build/html/_sources/backend.common.rst.txt @@ -0,0 +1,21 @@ +backend.common package +====================== + +Submodules +---------- + +backend.common.common module +---------------------------- + +.. automodule:: backend.common.common + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.common + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.deepLearning.rst b/docs/build/html/_sources/backend.deepLearning.rst new file mode 100644 index 00000000..4ee67a56 --- /dev/null +++ b/docs/build/html/_sources/backend.deepLearning.rst @@ -0,0 +1,21 @@ +backend.deepLearning package +============================ + +Submodules +---------- + +backend.deepLearning.deepLearning module +---------------------------------------- + +.. automodule:: backend.deepLearning.deepLearning + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.deepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.deepLearning.rst.txt b/docs/build/html/_sources/backend.deepLearning.rst.txt new file mode 100644 index 00000000..4ee67a56 --- /dev/null +++ b/docs/build/html/_sources/backend.deepLearning.rst.txt @@ -0,0 +1,21 @@ +backend.deepLearning package +============================ + +Submodules +---------- + +backend.deepLearning.deepLearning module +---------------------------------------- + +.. automodule:: backend.deepLearning.deepLearning + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.deepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.hotspot.rst b/docs/build/html/_sources/backend.hotspot.rst new file mode 100644 index 00000000..7107ed22 --- /dev/null +++ b/docs/build/html/_sources/backend.hotspot.rst @@ -0,0 +1,21 @@ +backend.hotspot package +======================= + +Submodules +---------- + +backend.hotspot.density module +------------------------------ + +.. automodule:: backend.hotspot.density + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.hotspot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.hotspot.rst.txt b/docs/build/html/_sources/backend.hotspot.rst.txt new file mode 100644 index 00000000..7107ed22 --- /dev/null +++ b/docs/build/html/_sources/backend.hotspot.rst.txt @@ -0,0 +1,21 @@ +backend.hotspot package +======================= + +Submodules +---------- + +backend.hotspot.density module +------------------------------ + +.. automodule:: backend.hotspot.density + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.hotspot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.metrics.rst b/docs/build/html/_sources/backend.metrics.rst new file mode 100644 index 00000000..1bb984c5 --- /dev/null +++ b/docs/build/html/_sources/backend.metrics.rst @@ -0,0 +1,21 @@ +backend.metrics package +======================= + +Submodules +---------- + +backend.metrics.metrics module +------------------------------ + +.. automodule:: backend.metrics.metrics + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.metrics.rst.txt b/docs/build/html/_sources/backend.metrics.rst.txt new file mode 100644 index 00000000..1bb984c5 --- /dev/null +++ b/docs/build/html/_sources/backend.metrics.rst.txt @@ -0,0 +1,21 @@ +backend.metrics package +======================= + +Submodules +---------- + +backend.metrics.metrics module +------------------------------ + +.. automodule:: backend.metrics.metrics + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.resilience.rst b/docs/build/html/_sources/backend.resilience.rst new file mode 100644 index 00000000..f7514c38 --- /dev/null +++ b/docs/build/html/_sources/backend.resilience.rst @@ -0,0 +1,53 @@ +backend.resilience package +========================== + +Submodules +---------- + +backend.resilience.cluster module +--------------------------------- + +.. automodule:: backend.resilience.cluster + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.custom module +-------------------------------- + +.. automodule:: backend.resilience.custom + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.malicious module +----------------------------------- + +.. automodule:: backend.resilience.malicious + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.random module +-------------------------------- + +.. automodule:: backend.resilience.random + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.resilience module +------------------------------------ + +.. automodule:: backend.resilience.resilience + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.resilience + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.resilience.rst.txt b/docs/build/html/_sources/backend.resilience.rst.txt new file mode 100644 index 00000000..f7514c38 --- /dev/null +++ b/docs/build/html/_sources/backend.resilience.rst.txt @@ -0,0 +1,53 @@ +backend.resilience package +========================== + +Submodules +---------- + +backend.resilience.cluster module +--------------------------------- + +.. automodule:: backend.resilience.cluster + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.custom module +-------------------------------- + +.. automodule:: backend.resilience.custom + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.malicious module +----------------------------------- + +.. automodule:: backend.resilience.malicious + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.random module +-------------------------------- + +.. automodule:: backend.resilience.random + :members: + :undoc-members: + :show-inheritance: + +backend.resilience.resilience module +------------------------------------ + +.. automodule:: backend.resilience.resilience + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.resilience + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.rst b/docs/build/html/_sources/backend.rst new file mode 100644 index 00000000..85f44b61 --- /dev/null +++ b/docs/build/html/_sources/backend.rst @@ -0,0 +1,21 @@ +backend package +=============== + +Submodules +---------- + +backend.app module +------------------ + +.. automodule:: backend.app + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.rst.txt b/docs/build/html/_sources/backend.rst.txt new file mode 100644 index 00000000..85f44b61 --- /dev/null +++ b/docs/build/html/_sources/backend.rst.txt @@ -0,0 +1,21 @@ +backend package +=============== + +Submodules +---------- + +backend.app module +------------------ + +.. automodule:: backend.app + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.visualisation.rst b/docs/build/html/_sources/backend.visualisation.rst new file mode 100644 index 00000000..da99c0ad --- /dev/null +++ b/docs/build/html/_sources/backend.visualisation.rst @@ -0,0 +1,21 @@ +backend.visualisation package +============================= + +Submodules +---------- + +backend.visualisation.visualisation module +------------------------------------------ + +.. automodule:: backend.visualisation.visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.visualisation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/backend.visualisation.rst.txt b/docs/build/html/_sources/backend.visualisation.rst.txt new file mode 100644 index 00000000..da99c0ad --- /dev/null +++ b/docs/build/html/_sources/backend.visualisation.rst.txt @@ -0,0 +1,21 @@ +backend.visualisation package +============================= + +Submodules +---------- + +backend.visualisation.visualisation module +------------------------------------------ + +.. automodule:: backend.visualisation.visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: backend.visualisation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/index.rst b/docs/build/html/_sources/index.rst new file mode 100644 index 00000000..5c532f93 --- /dev/null +++ b/docs/build/html/_sources/index.rst @@ -0,0 +1,20 @@ +.. AlphaTeam documentation master file, created by + sphinx-quickstart on Wed Mar 15 19:32:54 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to AlphaTeam's documentation! +===================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 00000000..5c532f93 --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,20 @@ +.. AlphaTeam documentation master file, created by + sphinx-quickstart on Wed Mar 15 19:32:54 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to AlphaTeam's documentation! +===================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/build/html/_sources/modules.rst b/docs/build/html/_sources/modules.rst new file mode 100644 index 00000000..e9ff8ac1 --- /dev/null +++ b/docs/build/html/_sources/modules.rst @@ -0,0 +1,7 @@ +src +=== + +.. toctree:: + :maxdepth: 4 + + src diff --git a/docs/build/html/_sources/modules.rst.txt b/docs/build/html/_sources/modules.rst.txt new file mode 100644 index 00000000..e9ff8ac1 --- /dev/null +++ b/docs/build/html/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +src +=== + +.. toctree:: + :maxdepth: 4 + + src diff --git a/docs/build/html/_sources/scrapers.rst b/docs/build/html/_sources/scrapers.rst new file mode 100644 index 00000000..0a3b9573 --- /dev/null +++ b/docs/build/html/_sources/scrapers.rst @@ -0,0 +1,37 @@ +scrapers package +================ + +Submodules +---------- + +scrapers.FBI\_BTC\_Scraper module +--------------------------------- + +.. automodule:: scrapers.FBI_BTC_Scraper + :members: + :undoc-members: + :show-inheritance: + +scrapers.Get\_BTCTransaction\_BlockCypher module +------------------------------------------------ + +.. automodule:: scrapers.Get_BTCTransaction_BlockCypher + :members: + :undoc-members: + :show-inheritance: + +scrapers.Wallet\_Explorer\_Scrapper module +------------------------------------------ + +.. automodule:: scrapers.Wallet_Explorer_Scrapper + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scrapers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/scrapers.rst.txt b/docs/build/html/_sources/scrapers.rst.txt new file mode 100644 index 00000000..0a3b9573 --- /dev/null +++ b/docs/build/html/_sources/scrapers.rst.txt @@ -0,0 +1,37 @@ +scrapers package +================ + +Submodules +---------- + +scrapers.FBI\_BTC\_Scraper module +--------------------------------- + +.. automodule:: scrapers.FBI_BTC_Scraper + :members: + :undoc-members: + :show-inheritance: + +scrapers.Get\_BTCTransaction\_BlockCypher module +------------------------------------------------ + +.. automodule:: scrapers.Get_BTCTransaction_BlockCypher + :members: + :undoc-members: + :show-inheritance: + +scrapers.Wallet\_Explorer\_Scrapper module +------------------------------------------ + +.. automodule:: scrapers.Wallet_Explorer_Scrapper + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scrapers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.DeepLearning.rst b/docs/build/html/_sources/src.DeepLearning.rst new file mode 100644 index 00000000..c8a2ecf5 --- /dev/null +++ b/docs/build/html/_sources/src.DeepLearning.rst @@ -0,0 +1,21 @@ +src.DeepLearning package +======================== + +Submodules +---------- + +src.DeepLearning.embedding module +--------------------------------- + +.. automodule:: src.DeepLearning.embedding + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src.DeepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.DeepLearning.rst.txt b/docs/build/html/_sources/src.DeepLearning.rst.txt new file mode 100644 index 00000000..c8a2ecf5 --- /dev/null +++ b/docs/build/html/_sources/src.DeepLearning.rst.txt @@ -0,0 +1,21 @@ +src.DeepLearning package +======================== + +Submodules +---------- + +src.DeepLearning.embedding module +--------------------------------- + +.. automodule:: src.DeepLearning.embedding + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src.DeepLearning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.rst b/docs/build/html/_sources/src.rst new file mode 100644 index 00000000..8a53ff7f --- /dev/null +++ b/docs/build/html/_sources/src.rst @@ -0,0 +1,93 @@ +src package +=========== + +Submodules +---------- + +src.JS\_scripts module +---------------------- + +.. automodule:: src.JS_scripts + :members: + :undoc-members: + :show-inheritance: + +src.NetworkGraphs module +------------------------ + +.. automodule:: src.NetworkGraphs + :members: + :undoc-members: + :show-inheritance: + +src.deepLearning module +----------------------- + +.. automodule:: src.deepLearning + :members: + :undoc-members: + :show-inheritance: + +src.machineLearning module +-------------------------- + +.. automodule:: src.machineLearning + :members: + :undoc-members: + :show-inheritance: + +src.metrics module +------------------ + +.. automodule:: src.metrics + :members: + :undoc-members: + :show-inheritance: + +src.preprocessing module +------------------------ + +.. automodule:: src.preprocessing + :members: + :undoc-members: + :show-inheritance: + +src.resilience module +--------------------- + +.. automodule:: src.resilience + :members: + :undoc-members: + :show-inheritance: + +src.stochastic module +--------------------- + +.. automodule:: src.stochastic + :members: + :undoc-members: + :show-inheritance: + +src.utils module +---------------- + +.. automodule:: src.utils + :members: + :undoc-members: + :show-inheritance: + +src.visualisation module +------------------------ + +.. automodule:: src.visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.rst.txt b/docs/build/html/_sources/src.rst.txt new file mode 100644 index 00000000..8a53ff7f --- /dev/null +++ b/docs/build/html/_sources/src.rst.txt @@ -0,0 +1,93 @@ +src package +=========== + +Submodules +---------- + +src.JS\_scripts module +---------------------- + +.. automodule:: src.JS_scripts + :members: + :undoc-members: + :show-inheritance: + +src.NetworkGraphs module +------------------------ + +.. automodule:: src.NetworkGraphs + :members: + :undoc-members: + :show-inheritance: + +src.deepLearning module +----------------------- + +.. automodule:: src.deepLearning + :members: + :undoc-members: + :show-inheritance: + +src.machineLearning module +-------------------------- + +.. automodule:: src.machineLearning + :members: + :undoc-members: + :show-inheritance: + +src.metrics module +------------------ + +.. automodule:: src.metrics + :members: + :undoc-members: + :show-inheritance: + +src.preprocessing module +------------------------ + +.. automodule:: src.preprocessing + :members: + :undoc-members: + :show-inheritance: + +src.resilience module +--------------------- + +.. automodule:: src.resilience + :members: + :undoc-members: + :show-inheritance: + +src.stochastic module +--------------------- + +.. automodule:: src.stochastic + :members: + :undoc-members: + :show-inheritance: + +src.utils module +---------------- + +.. automodule:: src.utils + :members: + :undoc-members: + :show-inheritance: + +src.visualisation module +------------------------ + +.. automodule:: src.visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.visualisation_src.rst b/docs/build/html/_sources/src.visualisation_src.rst new file mode 100644 index 00000000..c7013d98 --- /dev/null +++ b/docs/build/html/_sources/src.visualisation_src.rst @@ -0,0 +1,61 @@ +src.visualisation\_src package +============================== + +Submodules +---------- + +src.visualisation\_src.DL\_visualisation module +----------------------------------------------- + +.. automodule:: src.visualisation_src.DL_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.ML\_visualisation module +----------------------------------------------- + +.. automodule:: src.visualisation_src.ML_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.basic\_network\_visualisation module +----------------------------------------------------------- + +.. automodule:: src.visualisation_src.basic_network_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.metrics\_visualisation module +---------------------------------------------------- + +.. automodule:: src.visualisation_src.metrics_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.temporal\_visualisation module +----------------------------------------------------- + +.. automodule:: src.visualisation_src.temporal_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.utils\_visualisation module +-------------------------------------------------- + +.. automodule:: src.visualisation_src.utils_visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src.visualisation_src + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/src.visualisation_src.rst.txt b/docs/build/html/_sources/src.visualisation_src.rst.txt new file mode 100644 index 00000000..c7013d98 --- /dev/null +++ b/docs/build/html/_sources/src.visualisation_src.rst.txt @@ -0,0 +1,61 @@ +src.visualisation\_src package +============================== + +Submodules +---------- + +src.visualisation\_src.DL\_visualisation module +----------------------------------------------- + +.. automodule:: src.visualisation_src.DL_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.ML\_visualisation module +----------------------------------------------- + +.. automodule:: src.visualisation_src.ML_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.basic\_network\_visualisation module +----------------------------------------------------------- + +.. automodule:: src.visualisation_src.basic_network_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.metrics\_visualisation module +---------------------------------------------------- + +.. automodule:: src.visualisation_src.metrics_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.temporal\_visualisation module +----------------------------------------------------- + +.. automodule:: src.visualisation_src.temporal_visualisation + :members: + :undoc-members: + :show-inheritance: + +src.visualisation\_src.utils\_visualisation module +-------------------------------------------------- + +.. automodule:: src.visualisation_src.utils_visualisation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: src.visualisation_src + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_static/alabaster.css b/docs/build/html/_static/alabaster.css new file mode 100644 index 00000000..517d0b29 --- /dev/null +++ b/docs/build/html/_static/alabaster.css @@ -0,0 +1,703 @@ +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font-family: Georgia, serif; + font-size: 17px; + background-color: #fff; + color: #000; + margin: 0; + padding: 0; +} + + +div.document { + width: 940px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; + font-size: 14px; + line-height: 1.5; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #fff; + color: #3E4349; + padding: 0 30px 0 30px; +} + +div.body > .section { + text-align: left; +} + +div.footer { + width: 940px; + margin: 20px auto 30px auto; + font-size: 14px; + color: #888; + text-align: right; +} + +div.footer a { + color: #888; +} + +p.caption { + font-family: inherit; + font-size: inherit; +} + + +div.relations { + display: none; +} + + +div.sphinxsidebar a { + color: #444; + text-decoration: none; + border-bottom: 1px dotted #999; +} + +div.sphinxsidebar a:hover { + border-bottom: 1px solid #999; +} + +div.sphinxsidebarwrapper { + padding: 18px 10px; +} + +div.sphinxsidebarwrapper p.logo { + padding: 0; + margin: -10px 0 0 0px; + text-align: center; +} + +div.sphinxsidebarwrapper h1.logo { + margin-top: -10px; + text-align: center; + margin-bottom: 5px; + text-align: left; +} + +div.sphinxsidebarwrapper h1.logo-name { + margin-top: 0px; +} + +div.sphinxsidebarwrapper p.blurb { + margin-top: 0; + font-style: normal; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + font-family: Georgia, serif; + color: #444; + font-size: 24px; + font-weight: normal; + margin: 0 0 5px 0; + padding: 0; +} + +div.sphinxsidebar h4 { + font-size: 20px; +} + +div.sphinxsidebar h3 a { + color: #444; +} + +div.sphinxsidebar p.logo a, +div.sphinxsidebar h3 a, +div.sphinxsidebar p.logo a:hover, +div.sphinxsidebar h3 a:hover { + border: none; +} + +div.sphinxsidebar p { + color: #555; + margin: 10px 0; +} + +div.sphinxsidebar ul { + margin: 10px 0; + padding: 0; + color: #000; +} + +div.sphinxsidebar ul li.toctree-l1 > a { + font-size: 120%; +} + +div.sphinxsidebar ul li.toctree-l2 > a { + font-size: 110%; +} + +div.sphinxsidebar input { + border: 1px solid #CCC; + font-family: Georgia, serif; + font-size: 1em; +} + +div.sphinxsidebar hr { + border: none; + height: 1px; + color: #AAA; + background: #AAA; + + text-align: left; + margin-left: 0; + width: 50%; +} + +div.sphinxsidebar .badge { + border-bottom: none; +} + +div.sphinxsidebar .badge:hover { + border-bottom: none; +} + +/* To address an issue with donation coming after search */ +div.sphinxsidebar h3.donation { + margin-top: 10px; +} + +/* -- body styles ----------------------------------------------------------- */ + +a { + color: #004B6B; + text-decoration: underline; +} + +a:hover { + color: #6D4100; + text-decoration: underline; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { + font-family: Georgia, serif; + font-weight: normal; + margin: 30px 0px 10px 0px; + padding: 0; +} + +div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 150%; } +div.body h4 { font-size: 130%; } +div.body h5 { font-size: 100%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #DDD; + padding: 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + color: #444; + background: #EAEAEA; +} + +div.body p, div.body dd, div.body li { + line-height: 1.4em; +} + +div.admonition { + margin: 20px 0px; + padding: 10px 30px; + background-color: #EEE; + border: 1px solid #CCC; +} + +div.admonition tt.xref, div.admonition code.xref, div.admonition a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fafafa; +} + +div.admonition p.admonition-title { + font-family: Georgia, serif; + font-weight: normal; + font-size: 24px; + margin: 0 0 10px 0; + padding: 0; + line-height: 1; +} + +div.admonition p.last { + margin-bottom: 0; +} + +div.highlight { + background-color: #fff; +} + +dt:target, .highlight { + background: #FAF3E8; +} + +div.warning { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.danger { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.error { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.caution { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.attention { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.important { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.note { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.tip { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.hint { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.seealso { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.topic { + background-color: #EEE; +} + +p.admonition-title { + display: inline; +} + +p.admonition-title:after { + content: ":"; +} + +pre, tt, code { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; + font-size: 0.9em; +} + +.hll { + background-color: #FFC; + margin: 0 -12px; + padding: 0 12px; + display: block; +} + +img.screenshot { +} + +tt.descname, tt.descclassname, code.descname, code.descclassname { + font-size: 0.95em; +} + +tt.descname, code.descname { + padding-right: 0.08em; +} + +img.screenshot { + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils { + border: 1px solid #888; + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils td, table.docutils th { + border: 1px solid #888; + padding: 0.25em 0.7em; +} + +table.field-list, table.footnote { + border: none; + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + +table.footnote { + margin: 15px 0; + width: 100%; + border: 1px solid #EEE; + background: #FDFDFD; + font-size: 0.9em; +} + +table.footnote + table.footnote { + margin-top: -15px; + border-top: none; +} + +table.field-list th { + padding: 0 0.8em 0 0; +} + +table.field-list td { + padding: 0; +} + +table.field-list p { + margin-bottom: 0.8em; +} + +/* Cloned from + * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68 + */ +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +table.footnote td.label { + width: .1px; + padding: 0.3em 0 0.3em 0.5em; +} + +table.footnote td { + padding: 0.3em 0.5em; +} + +dl { + margin-left: 0; + margin-right: 0; + margin-top: 0; + padding: 0; +} + +dl dd { + margin-left: 30px; +} + +blockquote { + margin: 0 0 0 30px; + padding: 0; +} + +ul, ol { + /* Matches the 30px from the narrow-screen "li > ul" selector below */ + margin: 10px 0 10px 30px; + padding: 0; +} + +pre { + background: #EEE; + padding: 7px 30px; + margin: 15px 0px; + line-height: 1.3em; +} + +div.viewcode-block:target { + background: #ffd; +} + +dl pre, blockquote pre, li pre { + margin-left: 0; + padding-left: 30px; +} + +tt, code { + background-color: #ecf0f3; + color: #222; + /* padding: 1px 2px; */ +} + +tt.xref, code.xref, a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fff; +} + +a.reference { + text-decoration: none; + border-bottom: 1px dotted #004B6B; +} + +/* Don't put an underline on images */ +a.image-reference, a.image-reference:hover { + border-bottom: none; +} + +a.reference:hover { + border-bottom: 1px solid #6D4100; +} + +a.footnote-reference { + text-decoration: none; + font-size: 0.7em; + vertical-align: top; + border-bottom: 1px dotted #004B6B; +} + +a.footnote-reference:hover { + border-bottom: 1px solid #6D4100; +} + +a:hover tt, a:hover code { + background: #EEE; +} + + +@media screen and (max-width: 870px) { + + div.sphinxsidebar { + display: none; + } + + div.document { + width: 100%; + + } + + div.documentwrapper { + margin-left: 0; + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + } + + div.bodywrapper { + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + margin-left: 0; + } + + ul { + margin-left: 0; + } + + li > ul { + /* Matches the 30px from the "ul, ol" selector above */ + margin-left: 30px; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .bodywrapper { + margin: 0; + } + + .footer { + width: auto; + } + + .github { + display: none; + } + + + +} + + + +@media screen and (max-width: 875px) { + + body { + margin: 0; + padding: 20px 30px; + } + + div.documentwrapper { + float: none; + background: #fff; + } + + div.sphinxsidebar { + display: block; + float: none; + width: 102.5%; + margin: 50px -30px -20px -30px; + padding: 10px 20px; + background: #333; + color: #FFF; + } + + div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, + div.sphinxsidebar h3 a { + color: #fff; + } + + div.sphinxsidebar a { + color: #AAA; + } + + div.sphinxsidebar p.logo { + display: none; + } + + div.document { + width: 100%; + margin: 0; + } + + div.footer { + display: none; + } + + div.bodywrapper { + margin: 0; + } + + div.body { + min-height: 0; + padding: 0; + } + + .rtd_doc_footer { + display: none; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .footer { + width: auto; + } + + .github { + display: none; + } +} + + +/* misc. */ + +.revsys-inline { + display: none!important; +} + +/* Make nested-list/multi-paragraph items look better in Releases changelog + * pages. Without this, docutils' magical list fuckery causes inconsistent + * formatting between different release sub-lists. + */ +div#changelog > div.section > ul > li > p:only-child { + margin-bottom: 0; +} + +/* Hide fugly table cell borders in ..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} \ No newline at end of file diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css new file mode 100644 index 00000000..7577acb1 --- /dev/null +++ b/docs/build/html/_static/basic.css @@ -0,0 +1,903 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, 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    "),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n(''),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..15541a6a Binary files /dev/null and b/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..2e8d6820 --- /dev/null +++ b/docs/build/html/_static/locales/ar/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ar\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "أقترح تحرير" + +msgid "Last updated on" +msgstr "آخر تحديث في" + +msgid "Edit this page" +msgstr "قم بتحرير هذه الصفحة" + +msgid "Launch" +msgstr "إطلاق" + +msgid "Print to PDF" +msgstr "طباعة إلى PDF" + +msgid "open issue" +msgstr "قضية مفتوحة" + +msgid "Download notebook file" +msgstr "تنزيل ملف دفتر الملاحظات" + +msgid "Toggle navigation" +msgstr "تبديل التنقل" + +msgid "Source repository" +msgstr "مستودع المصدر" + +msgid "By the" +msgstr "بواسطة" + +msgid "next page" +msgstr "الصفحة التالية" + +msgid "repository" +msgstr "مخزن" + +msgid "Sphinx Book Theme" +msgstr "موضوع كتاب أبو الهول" + +msgid "Download source file" +msgstr "تنزيل ملف المصدر" + +msgid "Contents" +msgstr "محتويات" + +msgid "By" +msgstr "بواسطة" + +msgid "Copyright" +msgstr "حقوق النشر" + +msgid "Fullscreen mode" +msgstr "وضع ملء الشاشة" + +msgid "Open an issue" +msgstr "افتح قضية" + +msgid "previous page" +msgstr "الصفحة السابقة" + +msgid "Download this page" +msgstr "قم بتنزيل هذه الصفحة" + +msgid "Theme by the" +msgstr "موضوع بواسطة" diff --git a/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..da951200 Binary files /dev/null and b/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..56ef0ebd --- /dev/null +++ b/docs/build/html/_static/locales/bg/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: bg\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "предложи редактиране" + +msgid "Last updated on" +msgstr "Последна актуализация на" + +msgid "Edit this page" +msgstr "Редактирайте тази страница" + +msgid "Launch" +msgstr "Стартиране" + +msgid "Print to PDF" +msgstr "Печат в PDF" + +msgid "open issue" +msgstr "отворен брой" + +msgid "Download notebook file" +msgstr "Изтеглете файла на бележника" + +msgid "Toggle navigation" +msgstr "Превключване на навигацията" + +msgid "Source repository" +msgstr "Хранилище на източника" + +msgid "By the" +msgstr "По" + +msgid "next page" +msgstr "Следваща страница" + +msgid "repository" +msgstr "хранилище" + +msgid "Sphinx Book Theme" +msgstr "Тема на книгата Sphinx" + +msgid "Download source file" +msgstr "Изтеглете изходния файл" + +msgid "Contents" +msgstr "Съдържание" + +msgid "By" +msgstr "От" + +msgid "Copyright" +msgstr "Авторско право" + +msgid "Fullscreen mode" +msgstr "Режим на цял екран" + +msgid "Open an issue" +msgstr "Отворете проблем" + +msgid "previous page" +msgstr "предишна страница" + +msgid "Download this page" +msgstr "Изтеглете тази страница" + +msgid "Theme by the" +msgstr "Тема от" diff --git a/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..6b96639b Binary files /dev/null and b/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..243ca31f --- /dev/null +++ b/docs/build/html/_static/locales/bn/LC_MESSAGES/booktheme.po @@ -0,0 +1,63 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: bn\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "Last updated on" +msgstr "সর্বশেষ আপডেট" + +msgid "Edit this page" +msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন" + +msgid "Launch" +msgstr "শুরু করা" + +msgid "Print to PDF" +msgstr "পিডিএফ প্রিন্ট করুন" + +msgid "open issue" +msgstr "খোলা সমস্যা" + +msgid "Download notebook file" +msgstr "নোটবুক ফাইল ডাউনলোড করুন" + +msgid "Toggle navigation" +msgstr "নেভিগেশন টগল করুন" + +msgid "Source repository" +msgstr "উত্স সংগ্রহস্থল" + +msgid "By the" +msgstr "দ্বারা" + +msgid "next page" +msgstr "পরবর্তী পৃষ্ঠা" + +msgid "Sphinx Book Theme" +msgstr "স্পিনিক্স বুক থিম" + +msgid "Download source file" +msgstr "উত্স ফাইল ডাউনলোড করুন" + +msgid "By" +msgstr "দ্বারা" + +msgid "Copyright" +msgstr "কপিরাইট" + +msgid "Open an issue" +msgstr "একটি সমস্যা খুলুন" + +msgid "previous page" +msgstr "আগের পৃষ্ঠা" + +msgid "Download this page" +msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" + +msgid "Theme by the" +msgstr "থিম দ্বারা" diff --git a/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..a4dd30e9 Binary files /dev/null and b/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..b27a13db --- /dev/null +++ b/docs/build/html/_static/locales/ca/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ca\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "suggerir edició" + +msgid "Last updated on" +msgstr "Darrera actualització el" + +msgid "Edit this page" +msgstr "Editeu aquesta pàgina" + +msgid "Launch" +msgstr "Llançament" + +msgid "Print to PDF" +msgstr "Imprimeix a PDF" + +msgid "open issue" +msgstr "número obert" + +msgid "Download notebook file" +msgstr "Descarregar fitxer de quadern" + +msgid "Toggle navigation" +msgstr "Commuta la navegació" + +msgid "Source repository" +msgstr "Dipòsit de fonts" + +msgid "By the" +msgstr "Per la" + +msgid "next page" +msgstr "pàgina següent" + +msgid "Sphinx Book Theme" +msgstr "Tema del llibre Esfinx" + +msgid "Download source file" +msgstr "Baixeu el fitxer font" + +msgid "By" +msgstr "Per" + +msgid "Copyright" +msgstr "Copyright" + +msgid "Open an issue" +msgstr "Obriu un número" + +msgid "previous page" +msgstr "Pàgina anterior" + +msgid "Download this page" +msgstr "Descarregueu aquesta pàgina" + +msgid "Theme by the" +msgstr "Tema del" diff --git a/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..c39e01a6 Binary files /dev/null and b/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..3818df97 --- /dev/null +++ b/docs/build/html/_static/locales/cs/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: cs\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "navrhnout úpravy" + +msgid "Last updated on" +msgstr "Naposledy aktualizováno" + +msgid "Edit this page" +msgstr "Upravit tuto stránku" + +msgid "Launch" +msgstr "Zahájení" + +msgid "Print to PDF" +msgstr "Tisk do PDF" + +msgid "open issue" +msgstr "otevřené číslo" + +msgid "Download notebook file" +msgstr "Stáhnout soubor poznámkového bloku" + +msgid "Toggle navigation" +msgstr "Přepnout navigaci" + +msgid "Source repository" +msgstr "Zdrojové úložiště" + +msgid "By the" +msgstr "Podle" + +msgid "next page" +msgstr "další strana" + +msgid "repository" +msgstr "úložiště" + +msgid "Sphinx Book Theme" +msgstr "Téma knihy Sfinga" + +msgid "Download source file" +msgstr "Stáhněte si zdrojový soubor" + +msgid "Contents" +msgstr "Obsah" + +msgid "By" +msgstr "Podle" + +msgid "Copyright" +msgstr "autorská práva" + +msgid "Fullscreen mode" +msgstr "Režim celé obrazovky" + +msgid "Open an issue" +msgstr "Otevřete problém" + +msgid "previous page" +msgstr "předchozí stránka" + +msgid "Download this page" +msgstr "Stáhněte si tuto stránku" + +msgid "Theme by the" +msgstr "Téma od" diff --git a/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..f43157d7 Binary files /dev/null and b/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..7f20a3bd --- /dev/null +++ b/docs/build/html/_static/locales/da/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: da\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "foreslå redigering" + +msgid "Last updated on" +msgstr "Sidst opdateret den" + +msgid "Edit this page" +msgstr "Rediger denne side" + +msgid "Launch" +msgstr "Start" + +msgid "Print to PDF" +msgstr "Udskriv til PDF" + +msgid "open issue" +msgstr "åbent nummer" + +msgid "Download notebook file" +msgstr "Download notesbog-fil" + +msgid "Toggle navigation" +msgstr "Skift navigation" + +msgid "Source repository" +msgstr "Kildelager" + +msgid "By the" +msgstr "Ved" + +msgid "next page" +msgstr "Næste side" + +msgid "repository" +msgstr "lager" + +msgid "Sphinx Book Theme" +msgstr "Sphinx bogtema" + +msgid "Download source file" +msgstr "Download kildefil" + +msgid "Contents" +msgstr "Indhold" + +msgid "By" +msgstr "Ved" + +msgid "Copyright" +msgstr "ophavsret" + +msgid "Fullscreen mode" +msgstr "Fuldskærmstilstand" + +msgid "Open an issue" +msgstr "Åbn et problem" + +msgid "previous page" +msgstr "forrige side" + +msgid "Download this page" +msgstr "Download denne side" + +msgid "Theme by the" +msgstr "Tema af" diff --git a/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..648b565c Binary files /dev/null and b/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..c0027d3a --- /dev/null +++ b/docs/build/html/_static/locales/de/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: de\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "vorschlagen zu bearbeiten" + +msgid "Last updated on" +msgstr "Zuletzt aktualisiert am" + +msgid "Edit this page" +msgstr "Bearbeite diese Seite" + +msgid "Launch" +msgstr "Starten" + +msgid "Print to PDF" +msgstr "In PDF drucken" + +msgid "open issue" +msgstr "offenes Thema" + +msgid "Download notebook file" +msgstr "Notebook-Datei herunterladen" + +msgid "Toggle navigation" +msgstr "Navigation umschalten" + +msgid "Source repository" +msgstr "Quell-Repository" + +msgid "By the" +msgstr "Bis zum" + +msgid "next page" +msgstr "Nächste Seite" + +msgid "repository" +msgstr "Repository" + +msgid "Sphinx Book Theme" +msgstr "Sphinx-Buch-Thema" + +msgid "Download source file" +msgstr "Quelldatei herunterladen" + +msgid "Contents" +msgstr "Inhalt" + +msgid "By" +msgstr "Durch" + +msgid "Copyright" +msgstr "Urheberrechte ©" + +msgid "Fullscreen mode" +msgstr "Vollbildmodus" + +msgid "Open an issue" +msgstr "Öffnen Sie ein Problem" + +msgid "previous page" +msgstr "vorherige Seite" + +msgid "Download this page" +msgstr "Laden Sie diese Seite herunter" + +msgid "Theme by the" +msgstr "Thema von der" diff --git a/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..fca6e935 Binary files /dev/null and b/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..bdeb3270 --- /dev/null +++ b/docs/build/html/_static/locales/el/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: el\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "προτείνω επεξεργασία" + +msgid "Last updated on" +msgstr "Τελευταία ενημέρωση στις" + +msgid "Edit this page" +msgstr "Επεξεργαστείτε αυτήν τη σελίδα" + +msgid "Launch" +msgstr "Εκτόξευση" + +msgid "Print to PDF" +msgstr "Εκτύπωση σε PDF" + +msgid "open issue" +msgstr "ανοιχτό ζήτημα" + +msgid "Download notebook file" +msgstr "Λήψη αρχείου σημειωματάριου" + +msgid "Toggle navigation" +msgstr "Εναλλαγή πλοήγησης" + +msgid "Source repository" +msgstr "Αποθήκη πηγής" + +msgid "By the" +msgstr "Από το" + +msgid "next page" +msgstr "επόμενη σελίδα" + +msgid "repository" +msgstr "αποθήκη" + +msgid "Sphinx Book Theme" +msgstr "Θέμα βιβλίου Sphinx" + +msgid "Download source file" +msgstr "Λήψη αρχείου προέλευσης" + +msgid "Contents" +msgstr "Περιεχόμενα" + +msgid "By" +msgstr "Με" + +msgid "Copyright" +msgstr "Πνευματική ιδιοκτησία" + +msgid "Fullscreen mode" +msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ" + +msgid "Open an issue" +msgstr "Ανοίξτε ένα ζήτημα" + +msgid "previous page" +msgstr "προηγούμενη σελίδα" + +msgid "Download this page" +msgstr "Λήψη αυτής της σελίδας" + +msgid "Theme by the" +msgstr "Θέμα από το" diff --git a/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..d1072bbe Binary files /dev/null and b/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..6749f3a3 --- /dev/null +++ b/docs/build/html/_static/locales/eo/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: eo\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "sugesti redaktadon" + +msgid "Last updated on" +msgstr "Laste ĝisdatigita la" + +msgid "Edit this page" +msgstr "Redaktu ĉi tiun paĝon" + +msgid "Launch" +msgstr "Lanĉo" + +msgid "Print to PDF" +msgstr "Presi al PDF" + +msgid "open issue" +msgstr "malferma numero" + +msgid "Download notebook file" +msgstr "Elŝutu kajeran dosieron" + +msgid "Toggle navigation" +msgstr "Ŝalti navigadon" + +msgid "Source repository" +msgstr "Fonto-deponejo" + +msgid "By the" +msgstr "Per la" + +msgid "next page" +msgstr "sekva paĝo" + +msgid "repository" +msgstr "deponejo" + +msgid "Sphinx Book Theme" +msgstr "Sfinksa Libro-Temo" + +msgid "Download source file" +msgstr "Elŝutu fontodosieron" + +msgid "Contents" +msgstr "Enhavo" + +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "Kopirajto" + +msgid "Fullscreen mode" +msgstr "Plenekrana reĝimo" + +msgid "Open an issue" +msgstr "Malfermu numeron" + +msgid "previous page" +msgstr "antaŭa paĝo" + +msgid "Download this page" +msgstr "Elŝutu ĉi tiun paĝon" + +msgid "Theme by the" +msgstr "Temo de la" diff --git a/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..ba2ee4dc Binary files /dev/null and b/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..71dde37f --- /dev/null +++ b/docs/build/html/_static/locales/es/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: es\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "sugerir editar" + +msgid "Last updated on" +msgstr "Ultima actualización en" + +msgid "Edit this page" +msgstr "Edita esta página" + +msgid "Launch" +msgstr "Lanzamiento" + +msgid "Print to PDF" +msgstr "Imprimir en PDF" + +msgid "open issue" +msgstr "Tema abierto" + +msgid "Download notebook file" +msgstr "Descargar archivo de cuaderno" + +msgid "Toggle navigation" +msgstr "Navegación de palanca" + +msgid "Source repository" +msgstr "Repositorio de origen" + +msgid "By the" +msgstr "Por el" + +msgid "next page" +msgstr "siguiente página" + +msgid "repository" +msgstr "repositorio" + +msgid "Sphinx Book Theme" +msgstr "Tema del libro de la esfinge" + +msgid "Download source file" +msgstr "Descargar archivo fuente" + +msgid "Contents" +msgstr "Contenido" + +msgid "By" +msgstr "Por" + +msgid "Copyright" +msgstr "Derechos de autor" + +msgid "Fullscreen mode" +msgstr "Modo de pantalla completa" + +msgid "Open an issue" +msgstr "Abrir un problema" + +msgid "previous page" +msgstr "pagina anterior" + +msgid "Download this page" +msgstr "Descarga esta pagina" + +msgid "Theme by the" +msgstr "Tema por el" diff --git a/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..983b8239 Binary files /dev/null and b/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..cdcd07c7 --- /dev/null +++ b/docs/build/html/_static/locales/et/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: et\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "soovita muuta" + +msgid "Last updated on" +msgstr "Viimati uuendatud" + +msgid "Edit this page" +msgstr "Muutke seda lehte" + +msgid "Launch" +msgstr "Käivitage" + +msgid "Print to PDF" +msgstr "Prindi PDF-i" + +msgid "open issue" +msgstr "avatud küsimus" + +msgid "Download notebook file" +msgstr "Laadige sülearvuti fail alla" + +msgid "Toggle navigation" +msgstr "Lülita navigeerimine sisse" + +msgid "Source repository" +msgstr "Allikahoidla" + +msgid "By the" +msgstr "Autor" + +msgid "next page" +msgstr "järgmine leht" + +msgid "repository" +msgstr "hoidla" + +msgid "Sphinx Book Theme" +msgstr "Sfinksiraamatu teema" + +msgid "Download source file" +msgstr "Laadige alla lähtefail" + +msgid "Contents" +msgstr "Sisu" + +msgid "By" +msgstr "Kõrval" + +msgid "Copyright" +msgstr "Autoriõigus" + +msgid "Fullscreen mode" +msgstr "Täisekraanirežiim" + +msgid "Open an issue" +msgstr "Avage probleem" + +msgid "previous page" +msgstr "eelmine leht" + +msgid "Download this page" +msgstr "Laadige see leht alla" + +msgid "Theme by the" +msgstr "Teema" diff --git a/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..d8ac0545 Binary files /dev/null and b/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..3c3dd089 --- /dev/null +++ b/docs/build/html/_static/locales/fi/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: fi\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "ehdottaa muokkausta" + +msgid "Last updated on" +msgstr "Viimeksi päivitetty" + +msgid "Edit this page" +msgstr "Muokkaa tätä sivua" + +msgid "Launch" +msgstr "Tuoda markkinoille" + +msgid "Print to PDF" +msgstr "Tulosta PDF-tiedostoon" + +msgid "open issue" +msgstr "avoin ongelma" + +msgid "Download notebook file" +msgstr "Lataa muistikirjatiedosto" + +msgid "Toggle navigation" +msgstr "Vaihda navigointia" + +msgid "Source repository" +msgstr "Lähteen arkisto" + +msgid "By the" +msgstr "Mukaan" + +msgid "next page" +msgstr "seuraava sivu" + +msgid "repository" +msgstr "arkisto" + +msgid "Sphinx Book Theme" +msgstr "Sphinx-kirjan teema" + +msgid "Download source file" +msgstr "Lataa lähdetiedosto" + +msgid "Contents" +msgstr "Sisällys" + +msgid "By" +msgstr "Tekijä" + +msgid "Copyright" +msgstr "Tekijänoikeus" + +msgid "Fullscreen mode" +msgstr "Koko näytön tila" + +msgid "Open an issue" +msgstr "Avaa ongelma" + +msgid "previous page" +msgstr "Edellinen sivu" + +msgid "Download this page" +msgstr "Lataa tämä sivu" + +msgid "Theme by the" +msgstr "Teeman tekijä" diff --git a/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..f663d39f Binary files /dev/null and b/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..b57d2fe7 --- /dev/null +++ b/docs/build/html/_static/locales/fr/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: fr\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "suggestion de modification" + +msgid "Last updated on" +msgstr "Dernière mise à jour le" + +msgid "Edit this page" +msgstr "Modifier cette page" + +msgid "Launch" +msgstr "lancement" + +msgid "Print to PDF" +msgstr "Imprimer au format PDF" + +msgid "open issue" +msgstr "signaler un problème" + +msgid "Download notebook file" +msgstr "Télécharger le fichier notebook" + +msgid "Toggle navigation" +msgstr "Basculer la navigation" + +msgid "Source repository" +msgstr "Dépôt source" + +msgid "By the" +msgstr "Par le" + +msgid "next page" +msgstr "page suivante" + +msgid "repository" +msgstr "dépôt" + +msgid "Sphinx Book Theme" +msgstr "Thème du livre Sphinx" + +msgid "Download source file" +msgstr "Télécharger le fichier source" + +msgid "Contents" +msgstr "Contenu" + +msgid "By" +msgstr "Par" + +msgid "Copyright" +msgstr "droits d'auteur" + +msgid "Fullscreen mode" +msgstr "Mode plein écran" + +msgid "Open an issue" +msgstr "Ouvrez un problème" + +msgid "previous page" +msgstr "page précédente" + +msgid "Download this page" +msgstr "Téléchargez cette page" + +msgid "Theme by the" +msgstr "Thème par le" diff --git a/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..eca4a1a2 Binary files /dev/null and b/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..4c425e89 --- /dev/null +++ b/docs/build/html/_static/locales/hr/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: hr\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "predloži uređivanje" + +msgid "Last updated on" +msgstr "Posljednje ažuriranje:" + +msgid "Edit this page" +msgstr "Uredite ovu stranicu" + +msgid "Launch" +msgstr "Pokrenite" + +msgid "Print to PDF" +msgstr "Ispis u PDF" + +msgid "open issue" +msgstr "otvoreno izdanje" + +msgid "Download notebook file" +msgstr "Preuzmi datoteku bilježnice" + +msgid "Toggle navigation" +msgstr "Uključi / isključi navigaciju" + +msgid "Source repository" +msgstr "Izvorno spremište" + +msgid "By the" +msgstr "Od strane" + +msgid "next page" +msgstr "sljedeća stranica" + +msgid "repository" +msgstr "spremište" + +msgid "Sphinx Book Theme" +msgstr "Tema knjige Sphinx" + +msgid "Download source file" +msgstr "Preuzmi izvornu datoteku" + +msgid "Contents" +msgstr "Sadržaj" + +msgid "By" +msgstr "Po" + +msgid "Copyright" +msgstr "Autorska prava" + +msgid "Fullscreen mode" +msgstr "Način preko cijelog zaslona" + +msgid "Open an issue" +msgstr "Otvorite izdanje" + +msgid "previous page" +msgstr "Prethodna stranica" + +msgid "Download this page" +msgstr "Preuzmite ovu stranicu" + +msgid "Theme by the" +msgstr "Tema autora" diff --git a/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..d07a06a9 Binary files /dev/null and b/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..5db2ae14 --- /dev/null +++ b/docs/build/html/_static/locales/id/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: id\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "menyarankan edit" + +msgid "Last updated on" +msgstr "Terakhir diperbarui saat" + +msgid "Edit this page" +msgstr "Edit halaman ini" + +msgid "Launch" +msgstr "Meluncurkan" + +msgid "Print to PDF" +msgstr "Cetak ke PDF" + +msgid "open issue" +msgstr "masalah terbuka" + +msgid "Download notebook file" +msgstr "Unduh file notebook" + +msgid "Toggle navigation" +msgstr "Alihkan navigasi" + +msgid "Source repository" +msgstr "Repositori sumber" + +msgid "By the" +msgstr "Oleh" + +msgid "next page" +msgstr "halaman selanjutnya" + +msgid "repository" +msgstr "gudang" + +msgid "Sphinx Book Theme" +msgstr "Tema Buku Sphinx" + +msgid "Download source file" +msgstr "Unduh file sumber" + +msgid "Contents" +msgstr "Isi" + +msgid "By" +msgstr "Oleh" + +msgid "Copyright" +msgstr "hak cipta" + +msgid "Fullscreen mode" +msgstr "Mode layar penuh" + +msgid "Open an issue" +msgstr "Buka masalah" + +msgid "previous page" +msgstr "halaman sebelumnya" + +msgid "Download this page" +msgstr "Unduh halaman ini" + +msgid "Theme by the" +msgstr "Tema oleh" diff --git a/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..53ba476e Binary files /dev/null and b/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..7d54fdef --- /dev/null +++ b/docs/build/html/_static/locales/it/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: it\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "suggerisci modifica" + +msgid "Last updated on" +msgstr "Ultimo aggiornamento il" + +msgid "Edit this page" +msgstr "Modifica questa pagina" + +msgid "Launch" +msgstr "Lanciare" + +msgid "Print to PDF" +msgstr "Stampa in PDF" + +msgid "open issue" +msgstr "questione aperta" + +msgid "Download notebook file" +msgstr "Scarica il file del taccuino" + +msgid "Toggle navigation" +msgstr "Attiva / disattiva la navigazione" + +msgid "Source repository" +msgstr "Repository di origine" + +msgid "By the" +msgstr "Dal" + +msgid "next page" +msgstr "pagina successiva" + +msgid "repository" +msgstr "repository" + +msgid "Sphinx Book Theme" +msgstr "Tema del libro della Sfinge" + +msgid "Download source file" +msgstr "Scarica il file sorgente" + +msgid "Contents" +msgstr "Contenuti" + +msgid "By" +msgstr "Di" + +msgid "Copyright" +msgstr "Diritto d'autore" + +msgid "Fullscreen mode" +msgstr "Modalità schermo intero" + +msgid "Open an issue" +msgstr "Apri un problema" + +msgid "previous page" +msgstr "pagina precedente" + +msgid "Download this page" +msgstr "Scarica questa pagina" + +msgid "Theme by the" +msgstr "Tema di" diff --git a/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..a45c6575 Binary files /dev/null and b/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..32b017cf --- /dev/null +++ b/docs/build/html/_static/locales/iw/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: iw\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "מציע לערוך" + +msgid "Last updated on" +msgstr "עודכן לאחרונה ב" + +msgid "Edit this page" +msgstr "ערוך דף זה" + +msgid "Launch" +msgstr "לְהַשִׁיק" + +msgid "Print to PDF" +msgstr "הדפס לקובץ PDF" + +msgid "open issue" +msgstr "בעיה פתוחה" + +msgid "Download notebook file" +msgstr "הורד קובץ מחברת" + +msgid "Toggle navigation" +msgstr "החלף ניווט" + +msgid "Source repository" +msgstr "מאגר המקורות" + +msgid "By the" +msgstr "דרך" + +msgid "next page" +msgstr "עמוד הבא" + +msgid "repository" +msgstr "מאגר" + +msgid "Sphinx Book Theme" +msgstr "נושא ספר ספינקס" + +msgid "Download source file" +msgstr "הורד את קובץ המקור" + +msgid "Contents" +msgstr "תוכן" + +msgid "By" +msgstr "על ידי" + +msgid "Copyright" +msgstr "זכויות יוצרים" + +msgid "Fullscreen mode" +msgstr "מצב מסך מלא" + +msgid "Open an issue" +msgstr "פתח גיליון" + +msgid "previous page" +msgstr "עמוד קודם" + +msgid "Download this page" +msgstr "הורד דף זה" + +msgid "Theme by the" +msgstr "נושא מאת" diff --git a/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..1cefd29c Binary files /dev/null and b/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..16924e19 --- /dev/null +++ b/docs/build/html/_static/locales/ja/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ja\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "編集を提案する" + +msgid "Last updated on" +msgstr "最終更新日" + +msgid "Edit this page" +msgstr "このページを編集" + +msgid "Launch" +msgstr "起動" + +msgid "Print to PDF" +msgstr "PDFに印刷" + +msgid "open issue" +msgstr "未解決の問題" + +msgid "Download notebook file" +msgstr "ノートブックファイルをダウンロード" + +msgid "Toggle navigation" +msgstr "ナビゲーションを切り替え" + +msgid "Source repository" +msgstr "ソースリポジトリ" + +msgid "By the" +msgstr "によって" + +msgid "next page" +msgstr "次のページ" + +msgid "repository" +msgstr "リポジトリ" + +msgid "Sphinx Book Theme" +msgstr "スフィンクスの本のテーマ" + +msgid "Download source file" +msgstr "ソースファイルをダウンロード" + +msgid "Contents" +msgstr "目次" + +msgid "By" +msgstr "著者" + +msgid "Copyright" +msgstr "Copyright" + +msgid "Fullscreen mode" +msgstr "全画面モード" + +msgid "Open an issue" +msgstr "問題を報告" + +msgid "previous page" +msgstr "前のページ" + +msgid "Download this page" +msgstr "このページをダウンロード" + +msgid "Theme by the" +msgstr "のテーマ" diff --git a/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..06c7ec93 Binary files /dev/null and b/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..69dd18f7 --- /dev/null +++ b/docs/build/html/_static/locales/ko/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ko\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "편집 제안" + +msgid "Last updated on" +msgstr "마지막 업데이트" + +msgid "Edit this page" +msgstr "이 페이지 편집" + +msgid "Launch" +msgstr "시작하다" + +msgid "Print to PDF" +msgstr "PDF로 인쇄" + +msgid "open issue" +msgstr "열린 문제" + +msgid "Download notebook file" +msgstr "노트북 파일 다운로드" + +msgid "Toggle navigation" +msgstr "탐색 전환" + +msgid "Source repository" +msgstr "소스 저장소" + +msgid "By the" +msgstr "에 의해" + +msgid "next page" +msgstr "다음 페이지" + +msgid "repository" +msgstr "저장소" + +msgid "Sphinx Book Theme" +msgstr "스핑크스 도서 테마" + +msgid "Download source file" +msgstr "소스 파일 다운로드" + +msgid "Contents" +msgstr "내용" + +msgid "By" +msgstr "으로" + +msgid "Copyright" +msgstr "저작권" + +msgid "Fullscreen mode" +msgstr "전체 화면으로보기" + +msgid "Open an issue" +msgstr "이슈 열기" + +msgid "previous page" +msgstr "이전 페이지" + +msgid "Download this page" +msgstr "이 페이지 다운로드" + +msgid "Theme by the" +msgstr "테마별" diff --git a/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..4468ba04 Binary files /dev/null and b/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..9f037752 --- /dev/null +++ b/docs/build/html/_static/locales/lt/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: lt\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "pasiūlyti redaguoti" + +msgid "Last updated on" +msgstr "Paskutinį kartą atnaujinta" + +msgid "Edit this page" +msgstr "Redaguoti šį puslapį" + +msgid "Launch" +msgstr "Paleiskite" + +msgid "Print to PDF" +msgstr "Spausdinti į PDF" + +msgid "open issue" +msgstr "atviras klausimas" + +msgid "Download notebook file" +msgstr "Atsisiųsti nešiojamojo kompiuterio failą" + +msgid "Toggle navigation" +msgstr "Perjungti naršymą" + +msgid "Source repository" +msgstr "Šaltinio saugykla" + +msgid "By the" +msgstr "Prie" + +msgid "next page" +msgstr "Kitas puslapis" + +msgid "repository" +msgstr "saugykla" + +msgid "Sphinx Book Theme" +msgstr "Sfinkso knygos tema" + +msgid "Download source file" +msgstr "Atsisiųsti šaltinio failą" + +msgid "Contents" +msgstr "Turinys" + +msgid "By" +msgstr "Iki" + +msgid "Copyright" +msgstr "Autorių teisės" + +msgid "Fullscreen mode" +msgstr "Pilno ekrano režimas" + +msgid "Open an issue" +msgstr "Atidarykite problemą" + +msgid "previous page" +msgstr "Ankstesnis puslapis" + +msgid "Download this page" +msgstr "Atsisiųskite šį puslapį" + +msgid "Theme by the" +msgstr "Tema" diff --git a/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..74aa4d89 Binary files /dev/null and b/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..c9633b54 --- /dev/null +++ b/docs/build/html/_static/locales/lv/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: lv\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "ieteikt rediģēt" + +msgid "Last updated on" +msgstr "Pēdējoreiz atjaunināts" + +msgid "Edit this page" +msgstr "Rediģēt šo lapu" + +msgid "Launch" +msgstr "Uzsākt" + +msgid "Print to PDF" +msgstr "Drukāt PDF formātā" + +msgid "open issue" +msgstr "atklāts jautājums" + +msgid "Download notebook file" +msgstr "Lejupielādēt piezīmju grāmatiņu" + +msgid "Toggle navigation" +msgstr "Pārslēgt navigāciju" + +msgid "Source repository" +msgstr "Avota krātuve" + +msgid "By the" +msgstr "Ar" + +msgid "next page" +msgstr "nākamā lapaspuse" + +msgid "repository" +msgstr "krātuve" + +msgid "Sphinx Book Theme" +msgstr "Sfinksa grāmatas tēma" + +msgid "Download source file" +msgstr "Lejupielādēt avota failu" + +msgid "Contents" +msgstr "Saturs" + +msgid "By" +msgstr "Autors" + +msgid "Copyright" +msgstr "Autortiesības" + +msgid "Fullscreen mode" +msgstr "Pilnekrāna režīms" + +msgid "Open an issue" +msgstr "Atveriet problēmu" + +msgid "previous page" +msgstr "iepriekšējā lapa" + +msgid "Download this page" +msgstr "Lejupielādējiet šo lapu" + +msgid "Theme by the" +msgstr "Autora tēma" diff --git a/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..2736e8fc Binary files /dev/null and b/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..9a6a41e8 --- /dev/null +++ b/docs/build/html/_static/locales/ml/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ml\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" + +msgid "Last updated on" +msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" + +msgid "Edit this page" +msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക" + +msgid "Launch" +msgstr "സമാരംഭിക്കുക" + +msgid "Print to PDF" +msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" + +msgid "open issue" +msgstr "തുറന്ന പ്രശ്നം" + +msgid "Download notebook file" +msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" + +msgid "Toggle navigation" +msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" + +msgid "Source repository" +msgstr "ഉറവിട ശേഖരം" + +msgid "By the" +msgstr "എഴുതിയത്" + +msgid "next page" +msgstr "അടുത്ത പേജ്" + +msgid "Sphinx Book Theme" +msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം" + +msgid "Download source file" +msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക" + +msgid "By" +msgstr "എഴുതിയത്" + +msgid "Copyright" +msgstr "പകർപ്പവകാശം" + +msgid "Open an issue" +msgstr "ഒരു പ്രശ്നം തുറക്കുക" + +msgid "previous page" +msgstr "മുൻപത്തെ താൾ" + +msgid "Download this page" +msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" + +msgid "Theme by the" +msgstr "പ്രമേയം" diff --git a/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..fe530100 Binary files /dev/null and b/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..ef72d8c6 --- /dev/null +++ b/docs/build/html/_static/locales/mr/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: mr\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "संपादन सुचवा" + +msgid "Last updated on" +msgstr "अखेरचे अद्यतनित" + +msgid "Edit this page" +msgstr "हे पृष्ठ संपादित करा" + +msgid "Launch" +msgstr "लाँच करा" + +msgid "Print to PDF" +msgstr "पीडीएफवर मुद्रित करा" + +msgid "open issue" +msgstr "खुला मुद्दा" + +msgid "Download notebook file" +msgstr "नोटबुक फाईल डाउनलोड करा" + +msgid "Toggle navigation" +msgstr "नेव्हिगेशन टॉगल करा" + +msgid "Source repository" +msgstr "स्त्रोत भांडार" + +msgid "By the" +msgstr "द्वारा" + +msgid "next page" +msgstr "पुढील पृष्ठ" + +msgid "Sphinx Book Theme" +msgstr "स्फिंक्स बुक थीम" + +msgid "Download source file" +msgstr "स्त्रोत फाइल डाउनलोड करा" + +msgid "By" +msgstr "द्वारा" + +msgid "Copyright" +msgstr "कॉपीराइट" + +msgid "Open an issue" +msgstr "एक मुद्दा उघडा" + +msgid "previous page" +msgstr "मागील पान" + +msgid "Download this page" +msgstr "हे पृष्ठ डाउनलोड करा" + +msgid "Theme by the" +msgstr "द्वारा थीम" diff --git a/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..f02603fa Binary files /dev/null and b/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..e29cbe2e --- /dev/null +++ b/docs/build/html/_static/locales/ms/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ms\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "cadangkan edit" + +msgid "Last updated on" +msgstr "Terakhir dikemas kini pada" + +msgid "Edit this page" +msgstr "Edit halaman ini" + +msgid "Launch" +msgstr "Lancarkan" + +msgid "Print to PDF" +msgstr "Cetak ke PDF" + +msgid "open issue" +msgstr "isu terbuka" + +msgid "Download notebook file" +msgstr "Muat turun fail buku nota" + +msgid "Toggle navigation" +msgstr "Togol navigasi" + +msgid "Source repository" +msgstr "Repositori sumber" + +msgid "By the" +msgstr "Oleh" + +msgid "next page" +msgstr "muka surat seterusnya" + +msgid "Sphinx Book Theme" +msgstr "Tema Buku Sphinx" + +msgid "Download source file" +msgstr "Muat turun fail sumber" + +msgid "By" +msgstr "Oleh" + +msgid "Copyright" +msgstr "hak cipta" + +msgid "Open an issue" +msgstr "Buka masalah" + +msgid "previous page" +msgstr "halaman sebelumnya" + +msgid "Download this page" +msgstr "Muat turun halaman ini" + +msgid "Theme by the" +msgstr "Tema oleh" diff --git a/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..e59e7ecb Binary files /dev/null and b/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..e4844d7c --- /dev/null +++ b/docs/build/html/_static/locales/nl/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: nl\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "suggereren bewerken" + +msgid "Last updated on" +msgstr "Laatst geupdate op" + +msgid "Edit this page" +msgstr "bewerk deze pagina" + +msgid "Launch" +msgstr "Lancering" + +msgid "Print to PDF" +msgstr "Afdrukken naar pdf" + +msgid "open issue" +msgstr "open probleem" + +msgid "Download notebook file" +msgstr "Download notebookbestand" + +msgid "Toggle navigation" +msgstr "Schakel navigatie" + +msgid "Source repository" +msgstr "Bronopslagplaats" + +msgid "By the" +msgstr "Door de" + +msgid "next page" +msgstr "volgende bladzijde" + +msgid "repository" +msgstr "repository" + +msgid "Sphinx Book Theme" +msgstr "Sphinx-boekthema" + +msgid "Download source file" +msgstr "Download het bronbestand" + +msgid "Contents" +msgstr "Inhoud" + +msgid "By" +msgstr "Door" + +msgid "Copyright" +msgstr "auteursrechten" + +msgid "Fullscreen mode" +msgstr "Volledig scherm" + +msgid "Open an issue" +msgstr "Open een probleem" + +msgid "previous page" +msgstr "vorige pagina" + +msgid "Download this page" +msgstr "Download deze pagina" + +msgid "Theme by the" +msgstr "Thema door de" diff --git a/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..6cd15c88 Binary files /dev/null and b/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..d079dd9b --- /dev/null +++ b/docs/build/html/_static/locales/no/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: no\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "foreslå redigering" + +msgid "Last updated on" +msgstr "Sist oppdatert den" + +msgid "Edit this page" +msgstr "Rediger denne siden" + +msgid "Launch" +msgstr "Start" + +msgid "Print to PDF" +msgstr "Skriv ut til PDF" + +msgid "open issue" +msgstr "åpent nummer" + +msgid "Download notebook file" +msgstr "Last ned notatbokfilen" + +msgid "Toggle navigation" +msgstr "Bytt navigasjon" + +msgid "Source repository" +msgstr "Kildedepot" + +msgid "By the" +msgstr "Ved" + +msgid "next page" +msgstr "neste side" + +msgid "repository" +msgstr "oppbevaringssted" + +msgid "Sphinx Book Theme" +msgstr "Sphinx boktema" + +msgid "Download source file" +msgstr "Last ned kildefilen" + +msgid "Contents" +msgstr "Innhold" + +msgid "By" +msgstr "Av" + +msgid "Copyright" +msgstr "opphavsrett" + +msgid "Fullscreen mode" +msgstr "Fullskjerm-modus" + +msgid "Open an issue" +msgstr "Åpne et problem" + +msgid "previous page" +msgstr "forrige side" + +msgid "Download this page" +msgstr "Last ned denne siden" + +msgid "Theme by the" +msgstr "Tema av" diff --git a/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..9ebb584f Binary files /dev/null and b/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..fcac51d3 --- /dev/null +++ b/docs/build/html/_static/locales/pl/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: pl\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "zaproponuj edycję" + +msgid "Last updated on" +msgstr "Ostatnia aktualizacja" + +msgid "Edit this page" +msgstr "Edytuj tę strone" + +msgid "Launch" +msgstr "Uruchomić" + +msgid "Print to PDF" +msgstr "Drukuj do PDF" + +msgid "open issue" +msgstr "otwarty problem" + +msgid "Download notebook file" +msgstr "Pobierz plik notatnika" + +msgid "Toggle navigation" +msgstr "Przełącz nawigację" + +msgid "Source repository" +msgstr "Repozytorium źródłowe" + +msgid "By the" +msgstr "Przez" + +msgid "next page" +msgstr "Następna strona" + +msgid "repository" +msgstr "magazyn" + +msgid "Sphinx Book Theme" +msgstr "Motyw książki Sphinx" + +msgid "Download source file" +msgstr "Pobierz plik źródłowy" + +msgid "Contents" +msgstr "Zawartość" + +msgid "By" +msgstr "Przez" + +msgid "Copyright" +msgstr "prawa autorskie" + +msgid "Fullscreen mode" +msgstr "Pełny ekran" + +msgid "Open an issue" +msgstr "Otwórz problem" + +msgid "previous page" +msgstr "Poprzednia strona" + +msgid "Download this page" +msgstr "Pobierz tę stronę" + +msgid "Theme by the" +msgstr "Motyw autorstwa" diff --git a/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..d0ddb872 Binary files /dev/null and b/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..1761db08 --- /dev/null +++ b/docs/build/html/_static/locales/pt/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: pt\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "sugerir edição" + +msgid "Last updated on" +msgstr "Última atualização em" + +msgid "Edit this page" +msgstr "Edite essa página" + +msgid "Launch" +msgstr "Lançamento" + +msgid "Print to PDF" +msgstr "Imprimir em PDF" + +msgid "open issue" +msgstr "questão aberta" + +msgid "Download notebook file" +msgstr "Baixar arquivo de notebook" + +msgid "Toggle navigation" +msgstr "Alternar de navegação" + +msgid "Source repository" +msgstr "Repositório fonte" + +msgid "By the" +msgstr "Pelo" + +msgid "next page" +msgstr "próxima página" + +msgid "repository" +msgstr "repositório" + +msgid "Sphinx Book Theme" +msgstr "Tema do livro Sphinx" + +msgid "Download source file" +msgstr "Baixar arquivo fonte" + +msgid "Contents" +msgstr "Conteúdo" + +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "direito autoral" + +msgid "Fullscreen mode" +msgstr "Modo tela cheia" + +msgid "Open an issue" +msgstr "Abra um problema" + +msgid "previous page" +msgstr "página anterior" + +msgid "Download this page" +msgstr "Baixe esta página" + +msgid "Theme by the" +msgstr "Tema por" diff --git a/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..3c36ab1d Binary files /dev/null and b/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..db865c8f --- /dev/null +++ b/docs/build/html/_static/locales/ro/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ro\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "sugerează editare" + +msgid "Last updated on" +msgstr "Ultima actualizare la" + +msgid "Edit this page" +msgstr "Editați această pagină" + +msgid "Launch" +msgstr "Lansa" + +msgid "Print to PDF" +msgstr "Imprimați în PDF" + +msgid "open issue" +msgstr "problema deschisă" + +msgid "Download notebook file" +msgstr "Descărcați fișierul notebook" + +msgid "Toggle navigation" +msgstr "Comutare navigare" + +msgid "Source repository" +msgstr "Depozit sursă" + +msgid "By the" +msgstr "Langa" + +msgid "next page" +msgstr "pagina următoare" + +msgid "repository" +msgstr "repertoriu" + +msgid "Sphinx Book Theme" +msgstr "Tema Sphinx Book" + +msgid "Download source file" +msgstr "Descărcați fișierul sursă" + +msgid "Contents" +msgstr "Cuprins" + +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "Drepturi de autor" + +msgid "Fullscreen mode" +msgstr "Modul ecran întreg" + +msgid "Open an issue" +msgstr "Deschideți o problemă" + +msgid "previous page" +msgstr "pagina anterioară" + +msgid "Download this page" +msgstr "Descarcă această pagină" + +msgid "Theme by the" +msgstr "Tema de" diff --git a/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..6b8ca41f Binary files /dev/null and b/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..84ab6eb5 --- /dev/null +++ b/docs/build/html/_static/locales/ru/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ru\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "предложить редактировать" + +msgid "Last updated on" +msgstr "Последнее обновление" + +msgid "Edit this page" +msgstr "Редактировать эту страницу" + +msgid "Launch" +msgstr "Запуск" + +msgid "Print to PDF" +msgstr "Распечатать в PDF" + +msgid "open issue" +msgstr "открытый вопрос" + +msgid "Download notebook file" +msgstr "Скачать файл записной книжки" + +msgid "Toggle navigation" +msgstr "Переключить навигацию" + +msgid "Source repository" +msgstr "Исходный репозиторий" + +msgid "By the" +msgstr "Посредством" + +msgid "next page" +msgstr "Следующая страница" + +msgid "repository" +msgstr "хранилище" + +msgid "Sphinx Book Theme" +msgstr "Тема книги Сфинкс" + +msgid "Download source file" +msgstr "Скачать исходный файл" + +msgid "Contents" +msgstr "Содержание" + +msgid "By" +msgstr "По" + +msgid "Copyright" +msgstr "авторское право" + +msgid "Fullscreen mode" +msgstr "Полноэкранный режим" + +msgid "Open an issue" +msgstr "Открыть вопрос" + +msgid "previous page" +msgstr "Предыдущая страница" + +msgid "Download this page" +msgstr "Загрузите эту страницу" + +msgid "Theme by the" +msgstr "Тема от" diff --git a/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..59bd0ddf Binary files /dev/null and b/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..e44878b5 --- /dev/null +++ b/docs/build/html/_static/locales/sk/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: sk\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "navrhnúť úpravu" + +msgid "Last updated on" +msgstr "Posledná aktualizácia dňa" + +msgid "Edit this page" +msgstr "Upraviť túto stránku" + +msgid "Launch" +msgstr "Spustiť" + +msgid "Print to PDF" +msgstr "Tlač do PDF" + +msgid "open issue" +msgstr "otvorené vydanie" + +msgid "Download notebook file" +msgstr "Stiahnite si zošit" + +msgid "Toggle navigation" +msgstr "Prepnúť navigáciu" + +msgid "Source repository" +msgstr "Zdrojové úložisko" + +msgid "By the" +msgstr "Podľa" + +msgid "next page" +msgstr "ďalšia strana" + +msgid "repository" +msgstr "Úložisko" + +msgid "Sphinx Book Theme" +msgstr "Téma knihy Sfinga" + +msgid "Download source file" +msgstr "Stiahnite si zdrojový súbor" + +msgid "Contents" +msgstr "Obsah" + +msgid "By" +msgstr "Autor:" + +msgid "Copyright" +msgstr "Autorské práva" + +msgid "Fullscreen mode" +msgstr "Režim celej obrazovky" + +msgid "Open an issue" +msgstr "Otvorte problém" + +msgid "previous page" +msgstr "predchádzajúca strana" + +msgid "Download this page" +msgstr "Stiahnite si túto stránku" + +msgid "Theme by the" +msgstr "Téma od" diff --git a/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..87bf26de Binary files /dev/null and b/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..228939bc --- /dev/null +++ b/docs/build/html/_static/locales/sl/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: sl\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "predlagajte urejanje" + +msgid "Last updated on" +msgstr "Nazadnje posodobljeno dne" + +msgid "Edit this page" +msgstr "Uredite to stran" + +msgid "Launch" +msgstr "Kosilo" + +msgid "Print to PDF" +msgstr "Natisni v PDF" + +msgid "open issue" +msgstr "odprto vprašanje" + +msgid "Download notebook file" +msgstr "Prenesite datoteko zvezka" + +msgid "Toggle navigation" +msgstr "Preklopi navigacijo" + +msgid "Source repository" +msgstr "Izvorno skladišče" + +msgid "By the" +msgstr "Avtor" + +msgid "next page" +msgstr "Naslednja stran" + +msgid "repository" +msgstr "odlagališče" + +msgid "Sphinx Book Theme" +msgstr "Tema knjige Sphinx" + +msgid "Download source file" +msgstr "Prenesite izvorno datoteko" + +msgid "Contents" +msgstr "Vsebina" + +msgid "By" +msgstr "Avtor" + +msgid "Copyright" +msgstr "avtorske pravice" + +msgid "Fullscreen mode" +msgstr "Celozaslonski način" + +msgid "Open an issue" +msgstr "Odprite številko" + +msgid "previous page" +msgstr "Prejšnja stran" + +msgid "Download this page" +msgstr "Prenesite to stran" + +msgid "Theme by the" +msgstr "Tema avtorja" diff --git a/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..ec740f48 Binary files /dev/null and b/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..1a712a18 --- /dev/null +++ b/docs/build/html/_static/locales/sr/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: sr\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "предложи уређивање" + +msgid "Last updated on" +msgstr "Последње ажурирање" + +msgid "Edit this page" +msgstr "Уредите ову страницу" + +msgid "Launch" +msgstr "Лансирање" + +msgid "Print to PDF" +msgstr "Испис у ПДФ" + +msgid "open issue" +msgstr "отворено издање" + +msgid "Download notebook file" +msgstr "Преузмите датотеку бележнице" + +msgid "Toggle navigation" +msgstr "Укључи / искључи навигацију" + +msgid "Source repository" +msgstr "Изворно спремиште" + +msgid "By the" +msgstr "Од" + +msgid "next page" +msgstr "Следећа страна" + +msgid "repository" +msgstr "спремиште" + +msgid "Sphinx Book Theme" +msgstr "Тема књиге Спхинк" + +msgid "Download source file" +msgstr "Преузми изворну датотеку" + +msgid "Contents" +msgstr "Садржај" + +msgid "By" +msgstr "Од стране" + +msgid "Copyright" +msgstr "Ауторско право" + +msgid "Fullscreen mode" +msgstr "Режим целог екрана" + +msgid "Open an issue" +msgstr "Отворите издање" + +msgid "previous page" +msgstr "Претходна страница" + +msgid "Download this page" +msgstr "Преузмите ову страницу" + +msgid "Theme by the" +msgstr "Тхеме би" diff --git a/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..be951bec Binary files /dev/null and b/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..7d2b56d9 --- /dev/null +++ b/docs/build/html/_static/locales/sv/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: sv\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "föreslå redigering" + +msgid "Last updated on" +msgstr "Senast uppdaterad den" + +msgid "Edit this page" +msgstr "Redigera den här sidan" + +msgid "Launch" +msgstr "Lansera" + +msgid "Print to PDF" +msgstr "Skriv ut till PDF" + +msgid "open issue" +msgstr "öppet problem" + +msgid "Download notebook file" +msgstr "Ladda ner anteckningsbokfilen" + +msgid "Toggle navigation" +msgstr "Växla navigering" + +msgid "Source repository" +msgstr "Källförvar" + +msgid "By the" +msgstr "Vid" + +msgid "next page" +msgstr "nästa sida" + +msgid "repository" +msgstr "förvar" + +msgid "Sphinx Book Theme" +msgstr "Sphinx boktema" + +msgid "Download source file" +msgstr "Ladda ner källfil" + +msgid "Contents" +msgstr "Innehåll" + +msgid "By" +msgstr "Förbi" + +msgid "Copyright" +msgstr "upphovsrätt" + +msgid "Fullscreen mode" +msgstr "Fullskärmsläge" + +msgid "Open an issue" +msgstr "Öppna ett problem" + +msgid "previous page" +msgstr "föregående sida" + +msgid "Download this page" +msgstr "Ladda ner den här sidan" + +msgid "Theme by the" +msgstr "Tema av" diff --git a/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..29f52e1f Binary files /dev/null and b/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..c75ffe19 --- /dev/null +++ b/docs/build/html/_static/locales/ta/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ta\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "திருத்த பரிந்துரைக்கவும்" + +msgid "Last updated on" +msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" + +msgid "Edit this page" +msgstr "இந்தப் பக்கத்தைத் திருத்தவும்" + +msgid "Launch" +msgstr "தொடங்க" + +msgid "Print to PDF" +msgstr "PDF இல் அச்சிடுக" + +msgid "open issue" +msgstr "திறந்த பிரச்சினை" + +msgid "Download notebook file" +msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" + +msgid "Toggle navigation" +msgstr "வழிசெலுத்தலை நிலைமாற்று" + +msgid "Source repository" +msgstr "மூல களஞ்சியம்" + +msgid "By the" +msgstr "மூலம்" + +msgid "next page" +msgstr "அடுத்த பக்கம்" + +msgid "Sphinx Book Theme" +msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்" + +msgid "Download source file" +msgstr "மூல கோப்பைப் பதிவிறக்குக" + +msgid "By" +msgstr "வழங்கியவர்" + +msgid "Copyright" +msgstr "பதிப்புரிமை" + +msgid "Open an issue" +msgstr "சிக்கலைத் திறக்கவும்" + +msgid "previous page" +msgstr "முந்தைய பக்கம்" + +msgid "Download this page" +msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" + +msgid "Theme by the" +msgstr "வழங்கிய தீம்" diff --git a/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..0a5f4b46 Binary files /dev/null and b/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..2595c035 --- /dev/null +++ b/docs/build/html/_static/locales/te/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: te\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "సవరించమని సూచించండి" + +msgid "Last updated on" +msgstr "చివరిగా నవీకరించబడింది" + +msgid "Edit this page" +msgstr "ఈ పేజీని సవరించండి" + +msgid "Launch" +msgstr "ప్రారంభించండి" + +msgid "Print to PDF" +msgstr "PDF కి ముద్రించండి" + +msgid "open issue" +msgstr "ఓపెన్ ఇష్యూ" + +msgid "Download notebook file" +msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" + +msgid "Toggle navigation" +msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" + +msgid "Source repository" +msgstr "మూల రిపోజిటరీ" + +msgid "By the" +msgstr "ద్వారా" + +msgid "next page" +msgstr "తరువాతి పేజీ" + +msgid "Sphinx Book Theme" +msgstr "సింహిక పుస్తక థీమ్" + +msgid "Download source file" +msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి" + +msgid "By" +msgstr "ద్వారా" + +msgid "Copyright" +msgstr "కాపీరైట్" + +msgid "Open an issue" +msgstr "సమస్యను తెరవండి" + +msgid "previous page" +msgstr "ముందు పేజి" + +msgid "Download this page" +msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" + +msgid "Theme by the" +msgstr "ద్వారా థీమ్" diff --git a/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..b21c6c63 Binary files /dev/null and b/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..73cd30ea --- /dev/null +++ b/docs/build/html/_static/locales/tg/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: tg\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "пешниҳод вироиш" + +msgid "Last updated on" +msgstr "Last навсозӣ дар" + +msgid "Edit this page" +msgstr "Ин саҳифаро таҳрир кунед" + +msgid "Launch" +msgstr "Оғоз" + +msgid "Print to PDF" +msgstr "Чоп ба PDF" + +msgid "open issue" +msgstr "барориши кушод" + +msgid "Download notebook file" +msgstr "Файли дафтарро зеркашӣ кунед" + +msgid "Toggle navigation" +msgstr "Гузаришро иваз кунед" + +msgid "Source repository" +msgstr "Анбори манбаъ" + +msgid "By the" +msgstr "Бо" + +msgid "next page" +msgstr "саҳифаи оянда" + +msgid "repository" +msgstr "анбор" + +msgid "Sphinx Book Theme" +msgstr "Сфинкс Мавзӯи китоб" + +msgid "Download source file" +msgstr "Файли манбаъро зеркашӣ кунед" + +msgid "Contents" +msgstr "Мундариҷа" + +msgid "By" +msgstr "Бо" + +msgid "Copyright" +msgstr "Ҳуқуқи муаллиф" + +msgid "Fullscreen mode" +msgstr "Ҳолати экрани пурра" + +msgid "Open an issue" +msgstr "Масъаларо кушоед" + +msgid "previous page" +msgstr "саҳифаи қаблӣ" + +msgid "Download this page" +msgstr "Ин саҳифаро зеркашӣ кунед" + +msgid "Theme by the" +msgstr "Мавзӯъи аз" diff --git a/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..abede98a Binary files /dev/null and b/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..0392b4ad --- /dev/null +++ b/docs/build/html/_static/locales/th/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: th\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "แนะนำแก้ไข" + +msgid "Last updated on" +msgstr "ปรับปรุงล่าสุดเมื่อ" + +msgid "Edit this page" +msgstr "แก้ไขหน้านี้" + +msgid "Launch" +msgstr "เปิด" + +msgid "Print to PDF" +msgstr "พิมพ์เป็น PDF" + +msgid "open issue" +msgstr "เปิดปัญหา" + +msgid "Download notebook file" +msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" + +msgid "Toggle navigation" +msgstr "ไม่ต้องสลับช่องทาง" + +msgid "Source repository" +msgstr "ที่เก็บซอร์ส" + +msgid "By the" +msgstr "โดย" + +msgid "next page" +msgstr "หน้าต่อไป" + +msgid "repository" +msgstr "ที่เก็บ" + +msgid "Sphinx Book Theme" +msgstr "ธีมหนังสือสฟิงซ์" + +msgid "Download source file" +msgstr "ดาวน์โหลดไฟล์ต้นฉบับ" + +msgid "Contents" +msgstr "สารบัญ" + +msgid "By" +msgstr "โดย" + +msgid "Copyright" +msgstr "ลิขสิทธิ์" + +msgid "Fullscreen mode" +msgstr "โหมดเต็มหน้าจอ" + +msgid "Open an issue" +msgstr "เปิดปัญหา" + +msgid "previous page" +msgstr "หน้าที่แล้ว" + +msgid "Download this page" +msgstr "ดาวน์โหลดหน้านี้" + +msgid "Theme by the" +msgstr "ธีมโดย" diff --git a/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..8df1b733 Binary files /dev/null and b/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..c8375b54 --- /dev/null +++ b/docs/build/html/_static/locales/tl/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: tl\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "iminumungkahi i-edit" + +msgid "Last updated on" +msgstr "Huling na-update noong" + +msgid "Edit this page" +msgstr "I-edit ang pahinang ito" + +msgid "Launch" +msgstr "Ilunsad" + +msgid "Print to PDF" +msgstr "I-print sa PDF" + +msgid "open issue" +msgstr "bukas na isyu" + +msgid "Download notebook file" +msgstr "Mag-download ng file ng notebook" + +msgid "Toggle navigation" +msgstr "I-toggle ang pag-navigate" + +msgid "Source repository" +msgstr "Pinagmulan ng imbakan" + +msgid "By the" +msgstr "Sa pamamagitan ng" + +msgid "next page" +msgstr "Susunod na pahina" + +msgid "Sphinx Book Theme" +msgstr "Tema ng Sphinx Book" + +msgid "Download source file" +msgstr "Mag-download ng file ng pinagmulan" + +msgid "By" +msgstr "Ni" + +msgid "Copyright" +msgstr "Copyright" + +msgid "Open an issue" +msgstr "Magbukas ng isyu" + +msgid "previous page" +msgstr "Nakaraang pahina" + +msgid "Download this page" +msgstr "I-download ang pahinang ito" + +msgid "Theme by the" +msgstr "Tema ng" diff --git a/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..029ae18a Binary files /dev/null and b/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..47d7bdf7 --- /dev/null +++ b/docs/build/html/_static/locales/tr/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: tr\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "düzenleme öner" + +msgid "Last updated on" +msgstr "Son güncelleme tarihi" + +msgid "Edit this page" +msgstr "Bu sayfayı düzenle" + +msgid "Launch" +msgstr "Başlatmak" + +msgid "Print to PDF" +msgstr "PDF olarak yazdır" + +msgid "open issue" +msgstr "Açık konu" + +msgid "Download notebook file" +msgstr "Defter dosyasını indirin" + +msgid "Toggle navigation" +msgstr "Gezinmeyi değiştir" + +msgid "Source repository" +msgstr "Kaynak kod deposu" + +msgid "By the" +msgstr "Tarafından" + +msgid "next page" +msgstr "sonraki Sayfa" + +msgid "repository" +msgstr "depo" + +msgid "Sphinx Book Theme" +msgstr "Sfenks Kitap Teması" + +msgid "Download source file" +msgstr "Kaynak dosyayı indirin" + +msgid "Contents" +msgstr "İçindekiler" + +msgid "By" +msgstr "Tarafından" + +msgid "Copyright" +msgstr "Telif hakkı" + +msgid "Fullscreen mode" +msgstr "Tam ekran modu" + +msgid "Open an issue" +msgstr "Bir sorunu açın" + +msgid "previous page" +msgstr "önceki sayfa" + +msgid "Download this page" +msgstr "Bu sayfayı indirin" + +msgid "Theme by the" +msgstr "Tarafından tema" diff --git a/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..16ab7890 Binary files /dev/null and b/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..e85f6f16 --- /dev/null +++ b/docs/build/html/_static/locales/uk/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: uk\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "запропонувати редагувати" + +msgid "Last updated on" +msgstr "Останнє оновлення:" + +msgid "Edit this page" +msgstr "Редагувати цю сторінку" + +msgid "Launch" +msgstr "Запуск" + +msgid "Print to PDF" +msgstr "Друк у форматі PDF" + +msgid "open issue" +msgstr "відкритий випуск" + +msgid "Download notebook file" +msgstr "Завантажте файл блокнота" + +msgid "Toggle navigation" +msgstr "Переключити навігацію" + +msgid "Source repository" +msgstr "Джерело сховища" + +msgid "By the" +msgstr "По" + +msgid "next page" +msgstr "Наступна сторінка" + +msgid "repository" +msgstr "сховище" + +msgid "Sphinx Book Theme" +msgstr "Тема книги \"Сфінкс\"" + +msgid "Download source file" +msgstr "Завантажити вихідний файл" + +msgid "Contents" +msgstr "Зміст" + +msgid "By" +msgstr "Автор" + +msgid "Copyright" +msgstr "Авторське право" + +msgid "Fullscreen mode" +msgstr "Повноекранний режим" + +msgid "Open an issue" +msgstr "Відкрийте випуск" + +msgid "previous page" +msgstr "Попередня сторінка" + +msgid "Download this page" +msgstr "Завантажте цю сторінку" + +msgid "Theme by the" +msgstr "Тема від" diff --git a/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..de8c84b9 Binary files /dev/null and b/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..0f90726c --- /dev/null +++ b/docs/build/html/_static/locales/ur/LC_MESSAGES/booktheme.po @@ -0,0 +1,66 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: ur\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "ترمیم کی تجویز کریں" + +msgid "Last updated on" +msgstr "آخری بار تازہ کاری ہوئی" + +msgid "Edit this page" +msgstr "اس صفحے میں ترمیم کریں" + +msgid "Launch" +msgstr "لانچ کریں" + +msgid "Print to PDF" +msgstr "پی ڈی ایف پرنٹ کریں" + +msgid "open issue" +msgstr "کھلا مسئلہ" + +msgid "Download notebook file" +msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" + +msgid "Toggle navigation" +msgstr "نیویگیشن ٹوگل کریں" + +msgid "Source repository" +msgstr "ماخذ ذخیرہ" + +msgid "By the" +msgstr "کی طرف" + +msgid "next page" +msgstr "اگلا صفحہ" + +msgid "Sphinx Book Theme" +msgstr "سپنکس بک تھیم" + +msgid "Download source file" +msgstr "سورس فائل ڈاؤن لوڈ کریں" + +msgid "By" +msgstr "بذریعہ" + +msgid "Copyright" +msgstr "کاپی رائٹ" + +msgid "Open an issue" +msgstr "ایک مسئلہ کھولیں" + +msgid "previous page" +msgstr "سابقہ ​​صفحہ" + +msgid "Download this page" +msgstr "اس صفحے کو ڈاؤن لوڈ کریں" + +msgid "Theme by the" +msgstr "کے ذریعہ تھیم" diff --git a/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..2bb32555 Binary files /dev/null and b/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..2cb5cf3b --- /dev/null +++ b/docs/build/html/_static/locales/vi/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: vi\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "đề nghị chỉnh sửa" + +msgid "Last updated on" +msgstr "Cập nhật lần cuối vào" + +msgid "Edit this page" +msgstr "chỉnh sửa trang này" + +msgid "Launch" +msgstr "Phóng" + +msgid "Print to PDF" +msgstr "In sang PDF" + +msgid "open issue" +msgstr "vấn đề mở" + +msgid "Download notebook file" +msgstr "Tải xuống tệp sổ tay" + +msgid "Toggle navigation" +msgstr "Chuyển đổi điều hướng thành" + +msgid "Source repository" +msgstr "Kho nguồn" + +msgid "By the" +msgstr "Bằng" + +msgid "next page" +msgstr "Trang tiếp theo" + +msgid "repository" +msgstr "kho" + +msgid "Sphinx Book Theme" +msgstr "Chủ đề sách nhân sư" + +msgid "Download source file" +msgstr "Tải xuống tệp nguồn" + +msgid "Contents" +msgstr "Nội dung" + +msgid "By" +msgstr "Bởi" + +msgid "Copyright" +msgstr "Bản quyền" + +msgid "Fullscreen mode" +msgstr "Chế độ toàn màn hình" + +msgid "Open an issue" +msgstr "Mở một vấn đề" + +msgid "previous page" +msgstr "trang trước" + +msgid "Download this page" +msgstr "Tải xuống trang này" + +msgid "Theme by the" +msgstr "Chủ đề của" diff --git a/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..0e3235d0 Binary files /dev/null and b/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..f91f3ba0 --- /dev/null +++ b/docs/build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: zh_CN\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "提出修改建议" + +msgid "Last updated on" +msgstr "上次更新时间:" + +msgid "Edit this page" +msgstr "编辑此页面" + +msgid "Launch" +msgstr "启动" + +msgid "Print to PDF" +msgstr "列印成 PDF" + +msgid "open issue" +msgstr "创建议题" + +msgid "Download notebook file" +msgstr "下载笔记本文件" + +msgid "Toggle navigation" +msgstr "显示或隐藏导航栏" + +msgid "Source repository" +msgstr "源码库" + +msgid "By the" +msgstr "作者:" + +msgid "next page" +msgstr "下一页" + +msgid "repository" +msgstr "仓库" + +msgid "Sphinx Book Theme" +msgstr "Sphinx Book 主题" + +msgid "Download source file" +msgstr "下载源文件" + +msgid "Contents" +msgstr "目录" + +msgid "By" +msgstr "作者:" + +msgid "Copyright" +msgstr "版权" + +msgid "Fullscreen mode" +msgstr "全屏模式" + +msgid "Open an issue" +msgstr "创建议题" + +msgid "previous page" +msgstr "上一页" + +msgid "Download this page" +msgstr "下载此页面" + +msgid "Theme by the" +msgstr "主题作者:" diff --git a/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo b/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo new file mode 100644 index 00000000..9116fa95 Binary files /dev/null and b/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo differ diff --git a/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po new file mode 100644 index 00000000..7833d904 --- /dev/null +++ b/docs/build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po @@ -0,0 +1,75 @@ + +msgid "" +msgstr "" +"Project-Id-Version: Sphinx-Book-Theme\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Language: zh_TW\n" +"Plural-Forms: nplurals=2; plural=(n != 1);\n" + +msgid "suggest edit" +msgstr "提出修改建議" + +msgid "Last updated on" +msgstr "最後更新時間:" + +msgid "Edit this page" +msgstr "編輯此頁面" + +msgid "Launch" +msgstr "啟動" + +msgid "Print to PDF" +msgstr "列印成 PDF" + +msgid "open issue" +msgstr "公開的問題" + +msgid "Download notebook file" +msgstr "下載 Notebook 檔案" + +msgid "Toggle navigation" +msgstr "顯示或隱藏導覽列" + +msgid "Source repository" +msgstr "來源儲存庫" + +msgid "By the" +msgstr "作者:" + +msgid "next page" +msgstr "下一頁" + +msgid "repository" +msgstr "儲存庫" + +msgid "Sphinx Book Theme" +msgstr "Sphinx Book 佈景主題" + +msgid "Download source file" +msgstr "下載原始檔" + +msgid "Contents" +msgstr "目錄" + +msgid "By" +msgstr "作者:" + +msgid "Copyright" +msgstr "Copyright" + +msgid "Fullscreen mode" +msgstr "全螢幕模式" + +msgid "Open an issue" +msgstr "開啟議題" + +msgid "previous page" +msgstr "上一頁" + +msgid "Download this page" +msgstr "下載此頁面" + +msgid "Theme by the" +msgstr "佈景主題作者:" diff --git a/docs/build/html/_static/minus.png b/docs/build/html/_static/minus.png new file mode 100644 index 00000000..d96755fd Binary files /dev/null and b/docs/build/html/_static/minus.png differ diff --git a/docs/build/html/_static/plus.png b/docs/build/html/_static/plus.png new file mode 100644 index 00000000..7107cec9 Binary files /dev/null and b/docs/build/html/_static/plus.png differ diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css new file mode 100644 index 00000000..08bec689 --- /dev/null +++ b/docs/build/html/_static/pygments.css @@ -0,0 +1,74 @@ +pre { line-height: 125%; } +td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } +span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } +.highlight .hll { background-color: #ffffcc } +.highlight { background: #f8f8f8; } +.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */ +.highlight .err { border: 1px solid #FF0000 } /* Error */ +.highlight .k { color: #008000; font-weight: bold } /* Keyword */ +.highlight .o { color: #666666 } /* Operator */ +.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */ +.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */ +.highlight .cp { color: #9C6500 } /* Comment.Preproc */ +.highlight .cpf { color: #3D7B7B; 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NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
    "},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return Cs}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(de.off(this._config.target,_s),de.on(this._config.target,_s,ys,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const 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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.2.3 (https://getbootstrap.com/)\n * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n\n return prefix;\n};\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n\n return selector;\n};\n\nconst getSelectorFromElement = element => {\n const selector = getSelector(element);\n\n if (selector) {\n return document.querySelector(selector) ? selector : null;\n }\n\n return null;\n};\n\nconst getElementFromSelector = element => {\n const selector = getSelector(element);\n return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n } // Get transition-duration of the element\n\n\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n } // If multiple durations are defined, take the first\n\n\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n\n return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object);\n }\n\n return null;\n};\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n const closedDetails = element.closest('details:not([open])');\n\n if (!closedDetails) {\n return elementIsVisible;\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n\n if (summary === null) {\n return false;\n }\n }\n\n return elementIsVisible;\n};\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n\n if (element.classList.contains('disabled')) {\n return true;\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n } // Can find the shadow root otherwise it'll return the document\n\n\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n\n if (element instanceof ShadowRoot) {\n return element;\n } // when we don't find a shadow root\n\n\n if (!element.parentNode) {\n return null;\n }\n\n return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n\n return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback();\n }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n\n index += shouldGetNext ? 1 : -1;\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n\n return fn.apply(element, [event]);\n };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n\n hydrateObj(event, {\n delegateTarget: target\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n\n return fn.apply(target, [event]);\n }\n }\n };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n\n return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n\n callable = wrapFunction(callable);\n }\n\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n if (!fn) {\n return;\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n\n let evt = new Event(event, {\n bubbles,\n cancelable: true\n });\n evt = hydrateObj(evt, args);\n\n if (defaultPrevented) {\n evt.preventDefault();\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n\n return evt;\n }\n\n};\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n\n get() {\n return value;\n }\n\n });\n }\n }\n\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n\n const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n\n instanceMap.set(key, instance);\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n\n return null;\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n\n if (value === 'false') {\n return false;\n }\n\n if (value === Number(value).toString()) {\n return Number(value);\n }\n\n if (value === '' || value === 'null') {\n return null;\n }\n\n if (typeof value !== 'string') {\n return value;\n }\n\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n\n return attributes;\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n\n static get DefaultType() {\n return {};\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n return config;\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return { ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property];\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n\n if (!element) {\n return;\n }\n\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n } // Public\n\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n } // Static\n\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n\n static get VERSION() {\n return VERSION;\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n } // Public\n\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n if (closeEvent.defaultPrevented) {\n return;\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n } // Private\n\n\n _destroyElement() {\n this._element.remove();\n\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n } // Public\n\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n\n return parents;\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling;\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n\n previous = previous.previousElementSibling;\n }\n\n return [];\n },\n\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n\n next = next.nextElementSibling;\n }\n\n return [];\n },\n\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n\n if (!element || !Swipe.isSupported()) {\n return;\n }\n\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n\n this._initEvents();\n } // Getters\n\n\n static get Default() {\n return Default$c;\n }\n\n static get DefaultType() {\n return DefaultType$c;\n }\n\n static get NAME() {\n return NAME$d;\n } // Public\n\n\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n } // Private\n\n\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n\n this._handleSwipe();\n\n execute(this._config.endCallback);\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n\n if (!direction) {\n return;\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n } // Static\n\n\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n this._addEventListeners();\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$b;\n }\n\n static get DefaultType() {\n return DefaultType$b;\n }\n\n static get NAME() {\n return NAME$c;\n } // Public\n\n\n next() {\n this._slide(ORDER_NEXT);\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n\n prev() {\n this._slide(ORDER_PREV);\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n\n this._clearInterval();\n }\n\n cycle() {\n this._clearInterval();\n\n this._updateInterval();\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n\n this.cycle();\n }\n\n to(index) {\n const items = this._getItems();\n\n if (index > items.length - 1 || index < 0) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n\n const activeIndex = this._getItemIndex(this._getActive());\n\n if (activeIndex === index) {\n return;\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n this._slide(order, items[index]);\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n } // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n\n this.pause();\n\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n\n const direction = KEY_TO_DIRECTION[event.key];\n\n if (direction) {\n event.preventDefault();\n\n this._slide(this._directionToOrder(direction));\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n\n if (!element) {\n return;\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n\n const activeElement = this._getActive();\n\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n if (nextElement === activeElement) {\n return;\n }\n\n const nextElementIndex = this._getItemIndex(nextElement);\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n\n const slideEvent = triggerEvent(EVENT_SLIDE);\n\n if (slideEvent.defaultPrevented) {\n return;\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return;\n }\n\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n\n this._setActiveIndicatorElement(nextElementIndex);\n\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n if (isCycling) {\n this.cycle();\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this);\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n\n if (slideIndex) {\n carousel.to(slideIndex);\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n carousel.prev();\n\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n\n this._initializeChildren();\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n\n if (this._config.toggle) {\n this.toggle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$a;\n }\n\n static get DefaultType() {\n return DefaultType$a;\n }\n\n static get NAME() {\n return NAME$b;\n } // Public\n\n\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n\n let activeChildren = []; // find active children\n\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n\n const dimension = this._getDimension();\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.style[dimension] = 0;\n\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n\n this._queueCallback(complete, this._element, true);\n\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n const dimension = this._getDimension();\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger);\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n\n this._element.style[dimension] = '';\n\n this._queueCallback(complete, this._element, true);\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n } // Private\n\n\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n\n config.parent = getElement(config.parent);\n return config;\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n for (const element of children) {\n const selected = getElementFromSelector(element);\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n } // Static\n\n\n static jQueryInterface(config) {\n const _config = {};\n\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n\n const selector = getSelectorFromElement(this);\n const selectorElements = SelectorEngine.find(selector);\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n } // Getters\n\n\n static get Default() {\n return Default$9;\n }\n\n static get DefaultType() {\n return DefaultType$9;\n }\n\n static get NAME() {\n return NAME$a;\n } // Public\n\n\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._createPopper(); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n this._element.focus();\n\n this._element.setAttribute('aria-expanded', true);\n\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n this._element.classList.add(CLASS_NAME_SHOW$6);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n\n this._completeHide(relatedTarget);\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n\n super.dispose();\n }\n\n update() {\n this._inNavbar = this._detectNavbar();\n\n if (this._popper) {\n this._popper.update();\n }\n } // Private\n\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n if (hideEvent.defaultPrevented) {\n return;\n } // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n if (this._popper) {\n this._popper.destroy();\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.setAttribute('aria-expanded', 'false');\n\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n\n _getConfig(config) {\n config = super._getConfig(config);\n\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n\n return config;\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n\n let referenceElement = this._element;\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n\n const popperConfig = this._getPopperConfig();\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n\n _getPlacement() {\n const parentDropdown = this._parent;\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n } // We need to trim the value because custom properties can also include spaces\n\n\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n if (!items.length) {\n return;\n } // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n\n if (!context || context._config.autoClose === false) {\n continue;\n }\n\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n };\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n\n context._completeHide(relatedTarget);\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n\n if (isInput && !isEscapeEvent) {\n return;\n }\n\n event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n\n instance._selectMenuItem(event);\n\n return;\n }\n\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n } // Public\n\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n\n hide() {\n const width = this.getWidth();\n\n this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n\n isOverflowing() {\n return this.getWidth() > 0;\n } // Private\n\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n\n this._element.style.overflow = 'hidden';\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n\n this._saveInitialAttribute(element, styleProperty);\n\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n } // Getters\n\n\n static get Default() {\n return Default$8;\n }\n\n static get DefaultType() {\n return DefaultType$8;\n }\n\n static get NAME() {\n return NAME$9;\n } // Public\n\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._append();\n\n const element = this._getElement();\n\n if (this._config.isAnimated) {\n reflow(element);\n }\n\n element.classList.add(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n\n dispose() {\n if (!this._isAppended) {\n return;\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n this._element.remove();\n\n this._isAppended = false;\n } // Private\n\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n\n this._element = backdrop;\n }\n\n return this._element;\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n\n _append() {\n if (this._isAppended) {\n return;\n }\n\n const element = this._getElement();\n\n this._config.rootElement.append(element);\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n } // Getters\n\n\n static get Default() {\n return Default$7;\n }\n\n static get DefaultType() {\n return DefaultType$7;\n }\n\n static get NAME() {\n return NAME$8;\n } // Public\n\n\n activate() {\n if (this._isActive) {\n return;\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n\n deactivate() {\n if (!this._isActive) {\n return;\n }\n\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n } // Private\n\n\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement);\n\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$6;\n }\n\n static get DefaultType() {\n return DefaultType$6;\n }\n\n static get NAME() {\n return NAME$7;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n this._isTransitioning = true;\n\n this._scrollBar.hide();\n\n document.body.classList.add(CLASS_NAME_OPEN);\n\n this._adjustDialog();\n\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = false;\n this._isTransitioning = true;\n\n this._focustrap.deactivate();\n\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY$4);\n }\n\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n }\n\n handleUpdate() {\n this._adjustDialog();\n } // Private\n\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n\n this._element.style.display = 'block';\n\n this._element.removeAttribute('aria-hidden');\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_SHOW$4);\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n\n if (this._config.keyboard) {\n event.preventDefault();\n this.hide();\n return;\n }\n\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n\n return;\n }\n\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n\n _hideModal() {\n this._element.style.display = 'none';\n\n this._element.setAttribute('aria-hidden', true);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n this._isTransitioning = false;\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n\n this._resetAdjustments();\n\n this._scrollBar.reset();\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n\n this._element.classList.add(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n\n this._element.focus();\n }\n /**\n * The following methods are used to handle overflowing modals\n */\n\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n const scrollbarWidth = this._scrollBar.getWidth();\n\n const isBodyOverflowing = scrollbarWidth > 0;\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n } // Static\n\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](relatedTarget);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n }); // avoid conflict when clicking modal toggler while another one is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$5;\n }\n\n static get DefaultType() {\n return DefaultType$5;\n }\n\n static get NAME() {\n return NAME$6;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n\n this._backdrop.show();\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n\n this._element.classList.add(CLASS_NAME_SHOW$3);\n\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n\n this._queueCallback(completeCallBack, this._element, true);\n }\n\n hide() {\n if (!this._isShown) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._focustrap.deactivate();\n\n this._element.blur();\n\n this._isShown = false;\n\n this._element.classList.add(CLASS_NAME_HIDING);\n\n this._backdrop.hide();\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n\n this._queueCallback(completeCallback, this._element, true);\n }\n\n dispose() {\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n } // Private\n\n\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n });\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n }\n\n return true;\n } // Check if a regular expression validates the attribute.\n\n\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
    '\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n } // Getters\n\n\n static get Default() {\n return Default$4;\n }\n\n static get DefaultType() {\n return DefaultType$4;\n }\n\n static get NAME() {\n return NAME$5;\n } // Public\n\n\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n\n hasContent() {\n return this.getContent().length > 0;\n }\n\n changeContent(content) {\n this._checkContent(content);\n\n this._config.content = { ...this._config.content,\n ...content\n };\n return this;\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n\n const template = templateWrapper.children[0];\n\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n\n return template;\n } // Private\n\n\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n\n this._checkContent(config.content);\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n\n if (!templateElement) {\n return;\n }\n\n content = this._resolvePossibleFunction(content);\n\n if (!content) {\n templateElement.remove();\n return;\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n\n return;\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n\n templateElement.textContent = content;\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg;\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n\n templateElement.textContent = element.textContent;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
    ' + '
    ' + '
    ' + '
    ',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n\n super(element, config); // Private\n\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null; // Protected\n\n this.tip = null;\n\n this._setListeners();\n\n if (!this._config.selector) {\n this._fixTitle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$3;\n }\n\n static get DefaultType() {\n return DefaultType$3;\n }\n\n static get NAME() {\n return NAME$4;\n } // Public\n\n\n enable() {\n this._isEnabled = true;\n }\n\n disable() {\n this._isEnabled = false;\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n\n this._activeTrigger.click = !this._activeTrigger.click;\n\n if (this._isShown()) {\n this._leave();\n\n return;\n }\n\n this._enter();\n }\n\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n\n this._disposePopper();\n\n super.dispose();\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n } // todo v6 remove this OR make it optional\n\n\n this._disposePopper();\n\n const tip = this._getTipElement();\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n const {\n container\n } = this._config;\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n if (this._isHovered === false) {\n this._leave();\n }\n\n this._isHovered = false;\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n hide() {\n if (!this._isShown()) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const tip = this._getTipElement();\n\n tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n if (!this._isHovered) {\n this._disposePopper();\n }\n\n this._element.removeAttribute('aria-describedby');\n\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n update() {\n if (this._popper) {\n this._popper.update();\n }\n } // Protected\n\n\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n\n return this.tip;\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n if (!tip) {\n return null;\n }\n\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n\n return tip;\n }\n\n setContent(content) {\n this._newContent = content;\n\n if (this._isShown()) {\n this._disposePopper();\n\n this.show();\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({ ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n\n return this._templateFactory;\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n } // Private\n\n\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg;\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n context._leave();\n });\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title');\n\n if (!title) {\n return;\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n this._element.removeAttribute('title');\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n\n this._isHovered = true;\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n this._isHovered = false;\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n\n config = { ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n\n return config;\n }\n\n _getDelegateConfig() {\n const config = {};\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key];\n }\n }\n\n config.selector = false;\n config.trigger = 'manual'; // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n\n return config;\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n\n this._popper = null;\n }\n\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
    ' + '
    ' + '

    ' + '
    ' + '
    ',\n trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n\n static get DefaultType() {\n return DefaultType$2;\n }\n\n static get NAME() {\n return NAME$3;\n } // Overrides\n\n\n _isWithContent() {\n return this._getTitle() || this._getContent();\n } // Private\n\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n } // Getters\n\n\n static get Default() {\n return Default$1;\n }\n\n static get DefaultType() {\n return DefaultType$1;\n }\n\n static get NAME() {\n return NAME$2;\n } // Public\n\n\n refresh() {\n this._initializeTargetsAndObservables();\n\n this._maybeEnableSmoothScroll();\n\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n\n dispose() {\n this._observer.disconnect();\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n\n return config;\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n } // unregister any previous listeners\n\n\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n } // Chrome 60 doesn't support `scrollTo`\n\n\n root.scrollTop = height;\n }\n });\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n } // The logic of selection\n\n\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n this._process(targetElement(entry));\n };\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n\n this._clearActiveClass(targetElement(entry));\n\n continue;\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n if (!parentScrollTop) {\n return;\n }\n\n continue;\n } // if we are scrolling up, pick the smallest offsetTop\n\n\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor);\n\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n\n this._clearActiveClass(this._config.target);\n\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n\n this._activateParents(target);\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
      and