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5_compound_clustering.ipynb
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"# 5. Compound clustering\n",
"\n",
"### Purpose of this tutorial\n",
"\n",
"Compounds with resemblant characteristics may attach to identical receptors and exhibit comparable outcomes. Utilizing this principle of resemblance, compound similarity can be harnessed to construct clusters of chemicals. By means of such clustering, a varied assortment of compounds can be opted from an extensive array of screening substances, with the intent of subsequent empirical evaluation."
],
"metadata": {
"id": "2ueOdXWI75Vl"
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"cell_type": "markdown",
"source": [
"Introduction to clustering and the Jarvis-Patrick algorithm:\n",
"\n",
"Clustering can be defined as the process of categorizing a collection of objects in a manner that items within the same cluster exhibit a greater level of resemblance to each other, relative to those in separate clusters.\n",
"\n",
"Within the realm of pharmaceutical research, compound clustering frequently relies on the chemical or structural likeness among compounds. This approach aims to uncover clusters that share attributes and to create a varied and representative subset for subsequent analysis.\n",
"\n",
"process:\n",
"1. Clustering data by similarity between neighboring points,\n",
"2. Compounds are encoded as fingerprints and similarity is described by the Tanimoto similarity\n",
"\n",
"The available algorithms for clustering are massive and the Jarvis-Patrick one is one of the most widely used algo in the pharma context. it is a deterministic algo but a downside lies in the fact that it tends to produce large heterogeneous clusters.\n",
"\n",
"Butina clustering:\n",
"\n",
"it identifies smaller but homogeneous clusters.\n",
"\n",
"### Picking diverse compounds\n",
"\n",
"process of finding representative sets of molecules"
],
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"source": [
"pip install rdkit"
],
"metadata": {
"id": "K6sZGyv5-IXf"
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"execution_count": null,
"outputs": []
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{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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"source": [
"import time\n",
"import random\n",
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"from rdkit import Chem\n",
"from rdkit import DataStructs\n",
"from rdkit.ML.Cluster import Butina\n",
"from rdkit.Chem import Draw\n",
"from rdkit.Chem import rdFingerprintGenerator"
]
},
{
"cell_type": "code",
"source": [
"# Filtered data taken from **Talktorial T002**\n",
"compound_df = pd.read_csv('compounds_lipinski.csv',\n",
" index_col=0,\n",
")\n",
"print(\"Dataframe shape:\", compound_df.shape)\n",
"compound_df.head()"
],
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" molecule_chembl_id IC50 units smiles \\\n",
"0 CHEMBL63786 0.003 nM Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1 \n",
"1 CHEMBL53711 0.006 nM CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1 \n",
"2 CHEMBL35820 0.006 nM CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC \n",
"3 CHEMBL53753 0.008 nM CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1 \n",
"4 CHEMBL66031 0.008 nM Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1 \n",
"\n",
" pIC50 ROMol \\\n",
"0 11.522879 <rdkit.Chem.rdchem.Mol object at 0x7c4034736490> \n",
"1 11.221849 <rdkit.Chem.rdchem.Mol object at 0x7c4034736500> \n",
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" molecular_weight n_hba n_hbd logp molecular_weight.1 n_hba.1 \\\n",
"0 349.021459 3 1 5.2891 349.021459 3 \n",
"1 343.043258 5 1 3.5969 343.043258 5 \n",
"2 387.058239 5 1 4.9333 387.058239 5 \n",
"3 329.027607 5 2 3.5726 329.027607 5 \n",
"4 339.011957 4 2 4.0122 339.011957 4 \n",
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"0 1 5.2891 True \n",
"1 1 3.5969 True \n",
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"import numpy as np\n",
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"\">\n",
" \n",
" </div>\n",
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" <script type=\"text/javascript\">\n",
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" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-93785ec9-5d52-4d1c-a4e2-8b19ef2767f3\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
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" <style>\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def value_plot(df, y, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
" plt.gca().spines[['top', 'right']].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = value_plot(_df_1, *['pIC50'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-2a596d28-2942-4fb4-ab41-0a20d5f55596\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-2a596d28-2942-4fb4-ab41-0a20d5f55596\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
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" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
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" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def value_plot(df, y, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
" plt.gca().spines[['top', 'right']].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = value_plot(_df_2, *['molecular_weight'], **{})\n",
"chart"
],
"text/html": [
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-30927726-0812-4727-b719-b9d63bd43d2e\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-30927726-0812-4727-b719-b9d63bd43d2e\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
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" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def value_plot(df, y, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
" plt.gca().spines[['top', 'right']].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = value_plot(_df_3, *['n_hba'], **{})\n",
"chart"
],
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" 'getCodeForChart', [\"chart-c3176503-7c62-4d22-bcba-003a092b694f\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
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" }\n",
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"data": {
"text/plain": [
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"text/html": [
"<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
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"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def histogram(df, colname, num_bins=20, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = histogram(_df_4, *['IC50'], **{})\n",
"chart"
],
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" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-29092f9f-87d1-473f-90ea-6ad364545bb2\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-29092f9f-87d1-473f-90ea-6ad364545bb2\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def histogram(df, colname, num_bins=20, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = histogram(_df_5, *['pIC50'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-bab1e90a-49be-4260-a7e4-78e4b0d2c5ee\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-bab1e90a-49be-4260-a7e4-78e4b0d2c5ee\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
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" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def histogram(df, colname, num_bins=20, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = histogram(_df_6, *['molecular_weight'], **{})\n",
"chart"
],
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" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-4fe13789-9197-42c7-8873-b6d3e4f71781\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
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" })();\n",
" </script>\n",
" <style>\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def histogram(df, colname, num_bins=20, figscale=1):\n",
" from matplotlib import pyplot as plt\n",
" df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = histogram(_df_7, *['n_hba'], **{})\n",
"chart"
],
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" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-dfe5ef77-e580-4a71-adf4-41c4d100a793\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
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" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
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"\n",
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]
},
"metadata": {}
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{
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"data": {
"text/plain": [
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],
"text/html": [
"<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
"<style>\n",
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" clear: both;\n",
" }\n",
"</style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = categorical_histogram(_df_8, *['molecule_chembl_id'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-6f582a0b-67ac-4675-ac64-e3620dccc1c9\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-6f582a0b-67ac-4675-ac64-e3620dccc1c9\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = categorical_histogram(_df_9, *['smiles'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-a1a21721-3177-480f-b894-00248b5aa50e\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-a1a21721-3177-480f-b894-00248b5aa50e\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
" plt.gca().spines[['top', 'right',]].set_visible(False)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = categorical_histogram(_df_10, *['ROMol'], **{})\n",
"chart"
],
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"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def scatter_plots(df, colname_pairs, figscale=1, alpha=.8):\n",
" from matplotlib import pyplot as plt\n",
" plt.figure(figsize=(len(colname_pairs) * 10 * figscale, 10 * figscale))\n",
" for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
" ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
" df.plot(kind='scatter', x=x_colname, y=y_colname, s=(32 * figscale), alpha=alpha, ax=ax)\n",
" ax.spines[['top', 'right',]].set_visible(False)\n",
" plt.tight_layout()\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = scatter_plots(_df_11, *[[['IC50', 'pIC50'], ['pIC50', 'molecular_weight'], ['molecular_weight', 'n_hba'], ['n_hba', 'n_hbd']]], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
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" 'getCodeForChart', [\"chart-37cd7c92-c76c-41e3-a003-980326117e67\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"<h4 class=\"colab-quickchart-section-title\">2-d categorical distributions</h4>\n",
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def heatmap(df, x_colname, y_colname, figscale=1, mpl_palette_name='viridis'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" import pandas as pd\n",
" plt.subplots(figsize=(8 * figscale, 8 * figscale))\n",
" df_2dhist = pd.DataFrame({\n",
" x_label: grp[y_colname].value_counts()\n",
" for x_label, grp in df.groupby(x_colname)\n",
" })\n",
" sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
" plt.xlabel(x_colname)\n",
" plt.ylabel(y_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = heatmap(_df_12, *['molecule_chembl_id', 'smiles'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-2b365e06-b30a-4edf-a139-b6a7ebf59531\");\n",
" async function getCodeForChartHandler(event) {\n",
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" const responseJson = chartCodeResponse.data['application/json'];\n",
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" }\n",
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" <style>\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def heatmap(df, x_colname, y_colname, figscale=1, mpl_palette_name='viridis'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" import pandas as pd\n",
" plt.subplots(figsize=(8 * figscale, 8 * figscale))\n",
" df_2dhist = pd.DataFrame({\n",
" x_label: grp[y_colname].value_counts()\n",
" for x_label, grp in df.groupby(x_colname)\n",
" })\n",
" sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
" plt.xlabel(x_colname)\n",
" plt.ylabel(y_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = heatmap(_df_13, *['smiles', 'ROMol'], **{})\n",
"chart"
],
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"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
" plt.figure(figsize=figsize)\n",
" sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
" sns.despine(top=True, right=True, bottom=True, left=True)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = violin_plot(_df_14, *['IC50', 'molecule_chembl_id'], **{'inner': 'stick'})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-bf53c686-fe6c-4794-86a0-1a905ab65ce7\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-bf53c686-fe6c-4794-86a0-1a905ab65ce7\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
" plt.figure(figsize=figsize)\n",
" sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
" sns.despine(top=True, right=True, bottom=True, left=True)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = violin_plot(_df_15, *['IC50', 'smiles'], **{'inner': 'stick'})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-b7efc00d-6ae1-4381-a51c-43f21eea7cb3\");\n",
" async function getCodeForChartHandler(event) {\n",
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" }\n",
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" </script>\n",
" <style>\n",
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" display: block;\n",
" float: left;\n",
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"\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
" plt.figure(figsize=figsize)\n",
" sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
" sns.despine(top=True, right=True, bottom=True, left=True)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = violin_plot(_df_16, *['IC50', 'ROMol'], **{'inner': 'stick'})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-23372573-8059-4a11-b15b-c3dd045d8ad5\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-23372573-8059-4a11-b15b-c3dd045d8ad5\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
" plt.figure(figsize=figsize)\n",
" sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
" sns.despine(top=True, right=True, bottom=True, left=True)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = violin_plot(_df_17, *['pIC50', 'molecule_chembl_id'], **{'inner': 'stick'})\n",
"chart"
],
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"data": {
"text/plain": [
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"<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
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"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (10 * figscale, 5.2 * figscale)\n",
" palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
" def _plot_series(series, series_name, series_index=0):\n",
" if value_colname == 'count()':\n",
" counted = (series[timelike_colname]\n",
" .value_counts()\n",
" .reset_index(name='counts')\n",
" .rename({'index': timelike_colname}, axis=1)\n",
" .sort_values(timelike_colname, ascending=True))\n",
" xs = counted[timelike_colname]\n",
" ys = counted['counts']\n",
" else:\n",
" xs = series[timelike_colname]\n",
" ys = series[value_colname]\n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
" fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
" df = df.sort_values(timelike_colname, ascending=True)\n",
" if series_colname:\n",
" for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
" _plot_series(series, series_name, i)\n",
" fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
" else:\n",
" _plot_series(df, '')\n",
" sns.despine(fig=fig, ax=ax)\n",
" plt.xlabel(timelike_colname)\n",
" plt.ylabel(value_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = time_series_multiline(_df_18, *['IC50', 'pIC50', 'molecule_chembl_id'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
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" (() => {\n",
" const chartElement = document.getElementById(\"chart-fe4df6c4-1f6f-4a3a-9df5-068fadd79be4\");\n",
" async function getCodeForChartHandler(event) {\n",
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" 'getCodeForChart', [\"chart-fe4df6c4-1f6f-4a3a-9df5-068fadd79be4\"], {});\n",
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" }\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (10 * figscale, 5.2 * figscale)\n",
" palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
" def _plot_series(series, series_name, series_index=0):\n",
" if value_colname == 'count()':\n",
" counted = (series[timelike_colname]\n",
" .value_counts()\n",
" .reset_index(name='counts')\n",
" .rename({'index': timelike_colname}, axis=1)\n",
" .sort_values(timelike_colname, ascending=True))\n",
" xs = counted[timelike_colname]\n",
" ys = counted['counts']\n",
" else:\n",
" xs = series[timelike_colname]\n",
" ys = series[value_colname]\n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
" fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
" df = df.sort_values(timelike_colname, ascending=True)\n",
" if series_colname:\n",
" for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
" _plot_series(series, series_name, i)\n",
" fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
" else:\n",
" _plot_series(df, '')\n",
" sns.despine(fig=fig, ax=ax)\n",
" plt.xlabel(timelike_colname)\n",
" plt.ylabel(value_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = time_series_multiline(_df_19, *['IC50', 'pIC50', 'smiles'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-2f04793b-2799-49e5-a18a-6545684d7fcd\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-2f04793b-2799-49e5-a18a-6545684d7fcd\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (10 * figscale, 5.2 * figscale)\n",
" palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
" def _plot_series(series, series_name, series_index=0):\n",
" if value_colname == 'count()':\n",
" counted = (series[timelike_colname]\n",
" .value_counts()\n",
" .reset_index(name='counts')\n",
" .rename({'index': timelike_colname}, axis=1)\n",
" .sort_values(timelike_colname, ascending=True))\n",
" xs = counted[timelike_colname]\n",
" ys = counted['counts']\n",
" else:\n",
" xs = series[timelike_colname]\n",
" ys = series[value_colname]\n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
" fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
" df = df.sort_values(timelike_colname, ascending=True)\n",
" if series_colname:\n",
" for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
" _plot_series(series, series_name, i)\n",
" fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
" else:\n",
" _plot_series(df, '')\n",
" sns.despine(fig=fig, ax=ax)\n",
" plt.xlabel(timelike_colname)\n",
" plt.ylabel(value_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = time_series_multiline(_df_20, *['IC50', 'pIC50', 'ROMol'], **{})\n",
"chart"
],
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"\">\n",
" \n",
" </div>\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"import numpy as np\n",
"from google.colab import autoviz\n",
"\n",
"def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
" from matplotlib import pyplot as plt\n",
" import seaborn as sns\n",
" figsize = (10 * figscale, 5.2 * figscale)\n",
" palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
" def _plot_series(series, series_name, series_index=0):\n",
" if value_colname == 'count()':\n",
" counted = (series[timelike_colname]\n",
" .value_counts()\n",
" .reset_index(name='counts')\n",
" .rename({'index': timelike_colname}, axis=1)\n",
" .sort_values(timelike_colname, ascending=True))\n",
" xs = counted[timelike_colname]\n",
" ys = counted['counts']\n",
" else:\n",
" xs = series[timelike_colname]\n",
" ys = series[value_colname]\n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
" fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
" df = df.sort_values(timelike_colname, ascending=True)\n",
" if series_colname:\n",
" for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
" _plot_series(series, series_name, i)\n",
" fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
" else:\n",
" _plot_series(df, '')\n",
" sns.despine(fig=fig, ax=ax)\n",
" plt.xlabel(timelike_colname)\n",
" plt.ylabel(value_colname)\n",
" return autoviz.MplChart.from_current_mpl_state()\n",
"\n",
"chart = time_series_multiline(_df_21, *['IC50', 'molecular_weight', 'molecule_chembl_id'], **{})\n",
"chart"
],
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"zI51kTqPyyeffML8/f1ZQ0MDY4yxv/3tb2zq1KkvZsdIn0NJQoVu377NvLy8mI2NDXNzc2M5OTmM\n",
"McbCw8PZiRMnuOX27NnDrK2tmbW1NVu2bJnUbY3y5kn0piSh7mOSlJTE7O3tmaOjI5s+fTorKSl5\n",
"cTvXBeo8Ls+ePWPLly9ntra2zNHRkU2ZMoUVFBS82B0kfQY9TEcIIUQmGpMghBAiEyUJQgghMlGS\n",
"IIQQIhMlCdIpS0tLZGVlAQAOHToEd3d3jB49Gm5ubpg9ezZyc3O55UaPHg0XFxe4uLjg0KFD3Dbu\n",
"3LkDb29vjBo1Ch4eHvjll1+6Y1cIIUqifhJEYYmJiYiNjUVqairs7OwAANeuXcODBw/g6OgIoDWJ\n",
"SJrFtxUZGYmIiAiEhYXh6NGjCAsLQ0ZGxosMnxDCA51JEIV9+OGH2LFjB5cgAMDNzQ3Tpk2Tu155\n",
"eTkyMzMRHBwMAJg/fz5KS0uRn5+v1ngJIV1HSYIoRCwWo7S0FC+//LLc5ZYsWQJHR0eEh4dzT4Mr\n",
"WquIENLzUJIgKnP+/Hnk5OTg+vXrGDJkCEJDQ7s7JEJIF1GSIAoRCoUwMzPD5cuXZS4jqR2kra2N\n",
"d955BxcuXACgeK0iQkjPQ0mCKGzTpk1Ys2YNbt++zU27ceMGTp8+jdraWlRWVnLT//nPf2Ls2LEA\n",
"gGHDhsHV1RXJyckAgJSUFJiZmWHkyJEvNH5CiPLo7iaisPDwcPTv3x+LFy9GTU0NtLS08NJLLyE2\n",
"Nha///475s+fj+bmZjDGYG1tjaSkJG7dhIQEhIWFYdu2bdDX10diYmI37gkhRFFUu4kQQohMdLmJ\n",
"EEKITJQkCCGEyERJghBCiEyUJAghhMhESYIQQohMlCQIIYTIREmCEEKITJQkCCGEyERJghBCiEyU\n",
"JAghhMj0f/Pl0pgLau2eAAAAAElFTkSuQmCC\n",
"\">\n",
" \n",
" </div>\n",
" <script></script>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-8c34ad52-3005-4d4a-b3f3-6b038ab52d42\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-8c34ad52-3005-4d4a-b3f3-6b038ab52d42\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Create molecules from SMILES and store in array\n",
"compounds = []\n",
"# .itertuples() returns a (index, column1, column2, ...) tuple per row\n",
"# we don't need index so we use _ instead\n",
"# note how we are slicing the dataframe to only the two columns we need now\n",
"for _, chembl_id, smiles in compound_df[[\"molecule_chembl_id\", \"smiles\"]].itertuples():\n",
" compounds.append((Chem.MolFromSmiles(smiles), chembl_id))\n",
"compounds[:10]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4ZjWlyZY-l8j",
"outputId": "36bcb783-ba99-4da3-cb4b-822c00bbf7d0"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[(<rdkit.Chem.rdchem.Mol at 0x796ee600b140>, 'CHEMBL63786'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b370>, 'CHEMBL53711'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b3e0>, 'CHEMBL35820'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b450>, 'CHEMBL53753'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b4c0>, 'CHEMBL66031'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b530>, 'CHEMBL176582'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b5a0>, 'CHEMBL174426'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b610>, 'CHEMBL29197'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b680>, 'CHEMBL1243316'),\n",
" (<rdkit.Chem.rdchem.Mol at 0x796ee600b6f0>, 'CHEMBL363815')]"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"# Create fingerprints for all molecules\n",
"rdkit_gen = rdFingerprintGenerator.GetRDKitFPGenerator(maxPath=5)\n",
"fingerprints = [rdkit_gen.GetFingerprint(mol) for mol, idx in compounds]\n",
"\n",
"# How many compounds/fingerprints do we have?\n",
"print(\"Number of compounds converted:\", len(fingerprints))\n",
"print(\"Fingerprint length per compound:\", len(fingerprints[0]))\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cy3gTZFA-6Ij",
"outputId": "47cbdc8d-668f-4f08-d817-5a7c16645685"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of compounds converted: 5431\n",
"Fingerprint length per compound: 2048\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"###Tanimoto similarity and distance matrix\n",
"\n",
"identification of cluster centroids"
],
"metadata": {
"id": "ElNqaygp_JeU"
}
},
{
"cell_type": "code",
"source": [
"def tanimoto_distance_matrix(fp_list):\n",
" \"\"\"Calculate distance matrix for fingerprint list\"\"\"\n",
" dissimilarity_matrix = []\n",
" # Notice how we are deliberately skipping the first and last items in the list\n",
" # because we don't need to compare them against themselves\n",
" for i in range(1, len(fp_list)):\n",
" # Compare the current fingerprint against all the previous ones in the list\n",
" similarities = DataStructs.BulkTanimotoSimilarity(fp_list[i], fp_list[:i])\n",
" # Since we need a distance matrix, calculate 1-x for every element in similarity matrix\n",
" dissimilarity_matrix.extend([1 - x for x in similarities])\n",
" return dissimilarity_matrix"
],
"metadata": {
"id": "5Ew5caga_Ot5"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Example: Calculate single similarity of two fingerprints\n",
"# NBVAL_CHECK_OUTPUT\n",
"sim = DataStructs.TanimotoSimilarity(fingerprints[0], fingerprints[1])\n",
"print(f\"Tanimoto similarity: {sim:.2f}, distance: {1-sim:.2f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Uy3jHCi4_ZsB",
"outputId": "edfb596e-7079-42fe-9a65-ceb6ca64ffad"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tanimoto similarity: 0.73, distance: 0.27\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Example: Calculate distance matrix (distance = 1-similarity)\n",
"tanimoto_distance_matrix(fingerprints)[0:5]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h3Ma9rAK_ga-",
"outputId": "af97bfa0-b38c-4e92-a4b9-32862bbe7e0a"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.26538461538461533,\n",
" 0.26996197718631176,\n",
" 0.40866873065015474,\n",
" 0.22672064777327938,\n",
" 0.050980392156862786]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# Side note: That looked like a list and not a matrix.\n",
"# But it is a triangular similarity matrix in the form of a list\n",
"n = len(fingerprints)\n",
"\n",
"# Calculate number of elements in triangular matrix via n*(n-1)/2\n",
"elem_triangular_matr = (n * (n - 1)) / 2\n",
"print(\n",
" f\"Elements in the triangular matrix ({elem_triangular_matr:.0f}) ==\",\n",
" f\"tanimoto_distance_matrix(fingerprints) ({len(tanimoto_distance_matrix(fingerprints))})\",\n",
")\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vj9yclXK_mJE",
"outputId": "f52df9d5-5ae1-4a76-b4c5-c5cb190fbede"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Elements in the triangular matrix (14745165) == tanimoto_distance_matrix(fingerprints) (14745165)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"###Clustering molecules"
],
"metadata": {
"id": "gvFY9k3T_5rJ"
}
},
{
"cell_type": "code",
"source": [
"def cluster_fingerprints(fingerprints, cutoff=0.2):\n",
" \"\"\"Cluster fingerprints\n",
" Parameters:\n",
" fingerprints\n",
" cutoff: threshold for the clustering\n",
" \"\"\"\n",
" # Calculate Tanimoto distance matrix\n",
" distance_matrix = tanimoto_distance_matrix(fingerprints)\n",
" # Now cluster the data with the implemented Butina algorithm:\n",
" clusters = Butina.ClusterData(distance_matrix, len(fingerprints), cutoff, isDistData=True)\n",
" clusters = sorted(clusters, key=len, reverse=True)\n",
" return clusters"
],
"metadata": {
"id": "5hBXcrjK_-qY"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Run the clustering procedure for the dataset\n",
"clusters = cluster_fingerprints(fingerprints, cutoff=0.3)\n",
"\n",
"# Give a short report about the numbers of clusters and their sizes\n",
"num_clust_g1 = sum(1 for c in clusters if len(c) == 1)\n",
"num_clust_g5 = sum(1 for c in clusters if len(c) > 5)\n",
"num_clust_g25 = sum(1 for c in clusters if len(c) > 25)\n",
"num_clust_g100 = sum(1 for c in clusters if len(c) > 100)\n",
"\n",
"print(\"total # clusters: \", len(clusters))\n",
"print(\"# clusters with only 1 compound: \", num_clust_g1)\n",
"print(\"# clusters with >5 compounds: \", num_clust_g5)\n",
"print(\"# clusters with >25 compounds: \", num_clust_g25)\n",
"print(\"# clusters with >100 compounds: \", num_clust_g100)\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m9cjklfhABo3",
"outputId": "a796412b-1d95-452e-9342-a4c8ab1a44ea"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total # clusters: 919\n",
"# clusters with only 1 compound: 444\n",
"# clusters with >5 compounds: 197\n",
"# clusters with >25 compounds: 34\n",
"# clusters with >100 compounds: 4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Plot the size of the clusters\n",
"fig, ax = plt.subplots(figsize=(15, 4))\n",
"ax.set_xlabel(\"Cluster index\")\n",
"ax.set_ylabel(\"Number of molecules\")\n",
"ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters], lw=5);"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 326
},
"id": "-J0cZKjkAH14",
"outputId": "338c5d4f-1a63-4c98-f49e-5cc36a4b08dc"
},
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"for cutoff in numpy.arange(0.0, 1.0, 0.2):\n",
" clusters = cluster_fingerprints(fingerprints, cutoff=cutoff)\n",
" fig, ax = plt.subplots(figsize=(15, 4))\n",
" ax.set_title(f\"Threshold: {cutoff:3.1f}\")\n",
" ax.set_xlabel(\"Cluster index\")\n",
" ax.set_ylabel(\"Number of molecules\")\n",
" ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters], lw=5)\n",
" display(fig)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "6YT79rrjAMJa",
"outputId": "107ab3ce-314e-4a42-c642-37ac14c40eb4"
},
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"cutoff = 0.2\n",
"clusters = cluster_fingerprints(fingerprints, cutoff=cutoff)\n",
"\n",
"# Plot the size of the clusters - save plot\n",
"fig, ax = plt.subplots(figsize=(15, 4))\n",
"ax.set_xlabel(\"Cluster index\")\n",
"ax.set_ylabel(\"# molecules\")\n",
"ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters])\n",
"ax.set_title(f\"Threshold: {cutoff:3.1f}\")\n",
"fig.savefig(\n",
" f\"cluster_dist_cutoff_{cutoff:4.2f}.png\",\n",
" dpi=300,\n",
" bbox_inches=\"tight\",\n",
" transparent=True,\n",
")\n",
"\n",
"print(\n",
" f\"Number of clusters: {len(clusters)} from {len(compounds)} molecules at distance cut-off {cutoff:.2f}\"\n",
")\n",
"print(\"Number of molecules in largest cluster:\", len(clusters[0]))\n",
"print(\n",
" f\"Similarity between two random points in same cluster: {DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]], fingerprints[clusters[0][1]]):.2f}\"\n",
")\n",
"print(\n",
" f\"Similarity between two random points in different cluster: {DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]], fingerprints[clusters[1][0]]):.2f}\"\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 414
},
"id": "tf_cCJ7tAzqa",
"outputId": "1a1d13fb-1593-4d2d-9957-bc5798173de7"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of clusters: 1413 from 5431 molecules at distance cut-off 0.20\n",
"Number of molecules in largest cluster: 146\n",
"Similarity between two random points in same cluster: 0.82\n",
"Similarity between two random points in different cluster: 0.22\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Ten molecules from largest cluster:\")\n",
"# Draw molecules\n",
"Draw.MolsToGridImage(\n",
" [compounds[i][0] for i in clusters[0][:10]],\n",
" legends=[compounds[i][1] for i in clusters[0][:10]],\n",
" molsPerRow=5,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "hASeID5yBDWt",
"outputId": "b0d08ea3-f375-44b2-ac81-2e66bb197e10"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ten molecules from largest cluster:\n"
]
},
{
"output_type": "execute_result",
"data": {
"image/png": 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uXMzKwg4dMCCgsqMCAgKa6egkd+iABgYIgA4OmJ9fOwGT+oJ9ld28iV5eOHcuFhfjwIH455+VHRIaGqqpoXG1e3c0NkYAbNcOs7JqJ9q6QLBixQp53zvUae7usH49KCqCtzcMGqRW4ePU1aFNG/j6a/jqK7hyBS5dgu7doX37WoyU1D9qaqCvD5qa0LAhPHsGPB506wadO4OpKVTUnVZBQaF///79nz2bHBjIMzCAly/h+nUYNw5oC9UvTPv2oKEBenpgbg7XrkFqKkyZAlZW0KBBpYfxeNC3L5SUwI0bUFAAGzfCsGFw5QrweKCrW0uhk/pJSQmMjQERFBUhMxO6dwcLCzAxgYYNKzzEzMzsRyMj7V27IDcXdHUhIgKiomD4cODxajFwUqeVlIClJQCAsjKkpkK3bvDVV6CnB5X0IjIwMPjBxsZ640bIyAB9fXjxAu7ehe++gy+jZwMtTi2VnQ2JiWBtDc+fg64u3LwJQ4dCbCwsWwbe3uDtDUOHyjtE8lmztoaICPDzg/79pXj0nTvQsycUFECjRpCaChMm/F9RPPmSZGSAnl5pLlW+QqZCmzcDjwfTpsG0aTB7Nvj4QM+e4OBQg4GSz8Xvv8PcufDzz7B9u3QHrF0L7u6grAyKitCvH/zzT4UjE+QL5uMD33wDAwfCuXPSHbB/P0yZAgIBaGiAsTH4+0PjxjUbYt1AfdxLpaTApUsAADdvQkICzJ8PN2/Cw4cweTLcuUNZO6lZQUEQEQH6+uDkJN0BXbvC1q0AAFlZ0KgRDBlS1QHks3XrFohEYGsrS9YOAPPnw7x5IBSCszO0awcLF4KPT02FSD4v3t4AAI6OUh+wdCn88AMUFMC4ceDlBeHhcP9+TQVH6i0vLwCAnj2lPmDyZHB3h5IS6NABbt4EkQieP6+x6OoQStz/c+UKrF0L588DAAwaBH//DUVFwOOBjY28IyOfu/37AQAmTJCl4OXHH8HNDYyN4eJFMDCAjRvh5s0aC5DUXYGBAADdu3M6WEsLMjIAANLTQUvrE0ZFPleJiRAcDGpq0K+fLIft3AknTsCuXbB+PURFQUwMeHjUVIikHsrNhXPngMeDESNkOWzVKti7Fy5cgAsX4NAhuHcPFi2qqRDrDErc/+PkBEuXwsCBAAB8PkyfDnv2yDsm8gXIzs65d+85jwcTJ8p45PbtrHMy+PjAL79AYCDcu1cjIZI67MmTVOCcuLdtC69fw5o1sHEjuLl92sDIZ+nECREiuLhUtZqiDIEA2GbPiYnw7bcwYgQkJ9dMgKRe8vMref8eunQBExNZDuPxYMoUUFKC69dh4UIYOxby8iA/v6airBuoHWQpRcXS8SYNDVBSgoYNoW1b6Nq17u5PcjotLb2oSFdRcSi1cqvnvLyORkS4fffdrJYtt8h2pJISKCnBqVMwZAgoKcHIkeDrC7a2NRMmqYtyc3MDApqZmBh37RoOwGmB8po1nzoo8jnz8enVqZPGmDE7AGTKsD4QiUr/UFLyCaMi9Z239+h27VImTtwK0JbL8eLlmiUlwP/Mh6QpcS9lbAyTJgEAjBwJAGBtDd7ecOUKmJjU0UToRV7e/C+qcanUCgoKnj17FhUVFRUV5e/vb2Jism7dOsNKFqjL2759+xCxb1+uPYiMjCAmBrp0gehoMDb+pKGRuu7u3buFhYV6elqamtRWiNS4xMTEwMCbqqqqvXtzHTDq3RuWLgWBgFZCE7GcnBw/v/O5ubl9+3Jt8z9oELi7g74+GBh89kufKXGvUFYWBAdDs2Z1dAI5q7h47+vX1mpqdl92ZWpGRkZMTExYWFh4eDj7Q1RUVInEWI6iomJycrKfnx+/Tt6FR0ZG3rt3T0tLa4RslX0SevWCDRtg9WpAhCVLqn48+YwEBgYCQHeOhTKEyMbb2xsRBw0a1EC2QhkJ334Lw4eXNpUkBAAAfH19c3Jy7O3tjTmPPbm4gKMj5OVBo0afNLS6iBL3CvXpAwAQEAAlJXWxN6imgsJUAwN5RyFPAQEBw4YNe/fuXZmfKygotGzZslWrVpaWlgCwZ8+eS5curVq1qu5sWZCTk8PmBCIjI729vQFg2LBhamoV7xJQOR4PFi/+lPGReiItLc3HxwcocSc1DxFfvHixf/9+AHB1da3WcylQ4kFKIWJ0dPT27dsBYCQreOBMXb10K+nPHX1+KmRiAhYW8Pw5CIV1sVrGUlUVAPJFIh6Acp0cS65p69ate//+vYqKSuvWrc3Nza2trcV/UFVVBYC0tDRnZ2c+n6+goLBq1arWrVtX93rDSUZGhuSEQHh4eFxcnEhc6AmgqakZHR0tEonq5pwAqWtiYmLOnj177ty5GzduiEQiTU3N69evDxs2TN5xEY4SEhKEQmFISMjFixcdHR23bJFtrcv796Ud0jMyQIdrocFHJSUlhX5w9+7dtLS0Bg0aqKur63zalyE1QPJ3d/v2bUNDwxs3bsj0i0tOBmVl0NaGqKjSDZJqIjZ2XikoKGhqaqp/GWn3JyDXfVvrutmzCzp1erNt2x15B/JxO1+96hoaejYtTd6ByEFqaqqysrJAIFi4cOHLly8/+piSkpKBAwcCQNOmTQFAXV39yZMntRnkpUuXNDQ0yn/oFBQUmjZtamFh0bx58wkTJujq6gLAkiVLajM2Ur/k5eVduHBh2rRpJhI9FxQVFa2srAQCAQDMnz9fJBLJO0wilfT0dH9/fw8PDxcXlyZNmoh/oXw+n8fjrVu3TqZn27ULnz5FRPz55+oGFhkZeebMmWXLlvXv379RuZIDHR0dc3NzAGjUqNGzZ8+q+2Lkk3r16pWvry87qcr/7gCgT58+hYWF0j+hlxfevIn4Kc6rkJCQY8eOzZ8/v1evXtra2uXPq5YtW7JrtFAorO6LfQEoca/M6dOnAaBHjx7yDuTjjiYn2wiFy2Ji5B2IHPz6668AwK4iP/zwQ0UPS09Pt7CwED/SwsIiIyOj1oLs3bu3QCBQVlY2NDRs3ry5kZFR+cLQ+fPnBwYGKikp8Xi8Y8eO1VpspF54/Pjxnj17hgwZInnm6OjotGrVqmHDhgAwY8YMHx8fZWVl9kEoKSmRd8jk47KzsydNmjR06NDyC+X19PT69evn7u4+cuRIgUDA4/EOHjwo/TPv2oXbtuGpU/jNN9WK0MPDQ/X/26ipqakZGRmZmZmJByAuX748ZMgQ9o365s2bar0e+RSio6PbtGnDvg0k6evrDxw4cPny5WfOnNm2bRsba//uu++kv7338sIff8QNG9DevloRrl69WuH/i6NYpt6yZUutDyv0jhw5MnHiRHZP+Pz582q93heAEvfKvHv3TlFRUVFR8d27d/KO5SNi8vJshMI+jx59geNs1tbWAMA+9sHBwZU8MiIigj3MyMgIAJydnYuLiyt/8vBwvH0bEfHKFXzxAtmd0dWrWNVx/4fNCSgoKFy7dk3yO6thw4ZdunSZOHHihg0bTp8+nZCQgIhbt24FAFVV1dDQUBleg9RVCQnIUui4OO5PEhISIrnyQV9f38TERFFiSV/z5s3Z6Kyfnx9Lub777jtpB9X8/NDdHefPx0ePuIdIpNa+fXtx+qKhoWFnZzdz5sxDhw49ffqU5VJCobBhw4YsrVdUVLx06ZKUz7xrF/r6YlgYfv899/BEIpG+vj6Px2MzgeWHRfX19V1cXG7dupWTk9OlSxcA6NixY3Z2NveXJJ9C27Zt2Zybtra25EklfsDDhw+VlJTU1NTYl8myZcukfGYvL7x2DYuKcPr0akXIFptZWFiYm5uXL4YxMDBwcXG5ePFiYWFh3759AaBFixYpKSnVesnPHSXuVejatSsAnDt3Tt6BfNyAx49thMLnubnyDqRWBQUFibP2r7/+usrH+/j48Hg8gUDADqmyKOXoUXRywuxsnDULz57FixcREefMwfx8GYLcvHkzAAwePLigoGDevHl//vnnjRs3Kvk+mjRpEgCYmpqmpqbK8DKkJmVkZORy+nAtWoQspanONW/NmjVsdMpIovGrgoKCg4PDpk2bwsPDJR9848YNTU1NABg0aFBeXl7Vzz55MiJicTFOnco9RCKdjIwMNqu2fv36qKioj456ZmZmfv311+IhBg0Njfv370vz5J+kVObu3bsA0KxZMz8/P3amaWpqfjQRRMTU1FQ2kzlgwICioiKpXqCkBN+/5x4f+Zj09HQlJSUFBYXdu3dXNNsmEonYYLaWlhZL8Xfu3CnNk584gbduISLOns09wsjISHZTce/D5oA6OjoVnVdZWVnt27cHAFtb25ycHGlf48srEaTEvQrTpk1jN4s7duyIjIyUdzhlbXr06LvDh/88flzegdQqluOy6pdt27ZJc8iyZcvYpUhBQUGyKKWwsDAiIuLs2ZT163HCBLS1RW1tXLcOjx/H5ctLE/cJE3DNGrSzky1xZ3MCZ86ckfLxeXl5nTt3BgAnJ6eqr4UiEXp54caNSLOKNSAlJeXQoUOurq4qKioCgWDNmjWyPsOiRbhlC+7aha6u3MMYPnw4AOzfv3/gwIENGzZ0dXU9dOhQenp6RY8PCQlh6yV69uz58aHQ6GjcuhWdnHDnTpw2rfSHFVeakU/l4MGDAGBmZqajo1NJGUxiYmKzZs3YDTwbjIyTYsrm3r3SGcLTp7lHOHfuXACYPXt2RkbGsWPHXrx4UfnjX7x40bhxYwCYzO4AK/f0Kf74I27ZgnPmcA+RlLN3717xpXDu3LkVPUw8mM1+ZQKBwMfHp8onf/IE2XXSy4t7hCtXrgSAiRMnFhYWnjlz5tWrV5U/PikpiS3jGTx4cJVz4/jqFf70E65di7/88kWl75S4VyY8PFxPT09yckdfX9/V1dXT0zMxMVHe0SEi/vPPPwDQr18/eQdSe7Kzs1nBpUAgUFJSknJ8WiQSsTSILVRVVVXt3bt3y5Yt2eR1jx67AVD838yZGByMmzbhgAEcR9xv3boFAE2aNJFpMVBSUhILb/78+VU8dNcuvHABc3Nx/HiUfmSCVKykpCQ4OHj58uU2NjY8Ho993tkfeDzeli1bZHq2RYswIQHfvsUff+QYj0gkYlfZ58+fJycnV30NQ0TEsLAwdgrZ29tnZmYiYkFBweXLlzcuXIjNm/93ivfti1Om4KtX+PAhLl6Mubko5bgp4WTAgAEsEQeAK1euVPLIx48fszIVlrtbWVm9ffu28ic/cgSdnTE3l/vIqEgkYi8XFBQk/VHBwcFs6cWqVas+/ojCQgwNxTNncOZMZDNXv/6Kchn/ys5GX1/87AoR+/TpI07Hr127VskjxYPZrBZLTU3t7t27lT/5+fPo6IgJCdUacW/dujUAXLhwQfpDwsLCWEX+L7/8UuGDXrzACxdw5UpMSEBE3LsXq3o7NSU1FWu95IES9wpFRkay71kHBwcXFxcLCwstia2OeDxemzZt5syZ4+fn915+M4DJyck8Hk9NTS1fptHg+oyNMbCb8lGjRkl/YGZmpqWlZYcOHTp06MCSG5b9N2/efNq0PXPmoKcnXr+Oycl46RKGhWFBAU6YgCdOoK8vIuIff6D0STibmly0aJGs7y4oKEhJSQkAxMNy+fn5jx49yj95EteswTFjsGNHtLH5b5R01y58+FDWVyFiOTk5/v7+M2fOlFwyqKqq6uTktHXr1kePHnXo0IG16Vy4cKH0T1v9Uhk2xayvry/rgVFRUazWomXLloMGDRIvKywwNUVdXXR1xUOHMDMT373D3bvx8GHMycH+/dHFBaUpsKm2L2lcrBSrk2FnkZ6eXpXzaQEBAWy1MTsnu3fvXqb2qbgYnzzBgwdx+nS0tcXff0cvL1y1inuCJa6TkbUx0dmzZ9kc5v79+xGxuLj4yZMnBw4cyFqwADt3RmVlBEBtbZw2rfTOcMcOfPyYY5SclZTghAn48CHu3o1//13br15jUlNTFRQU2NiTvr5+lff24sFsdpNWfhlofHzhiRO4eDE6O6OuLm7ahKdO4fTp3M+riIgIVhtTUFAg04E3bs0ScIQAACAASURBVNxgHwHxiEl0dLS3t3famjXo5IQ6OgiAioo4bx5mZiIiennh1asco6wODw/cvh0XLEBZ7kyqr7YS95AQ3L5dDp9YriIjI1lu5+zsnJuba2Zmxi5+fD6/WbNm5ubmKioqklWnQ4cOlXJI7KPYFGdxcWmOKJO2bdsCQEBAAOdXr1/Yuig2xuDv7y/TsXFxcTk5OS1atACApUuXPn78uPIbnpAQ1NXFDh1ku6MWzwlERETIFB6zc+dOAFBWVu7evbu5uTm73me2afPfcKmCAi5ciK9fIyIuXIh1bx2PSCSKi4s7ePBg5YOLcvf9999LtjswNTWdPn36xYsXxXnSmDFj2EWRPWzGjBlSZjb+/qWje5yrF/78808AGDlyJIdj4+PjmzVrpq+vz95Xu3btli5d+jo4GD9aBRsejg0bIgD26VPTVcgFBcgG0S5d4vJdV0+J62Sg0hZYkv79918ej8fn8/X09FjZQHh4+D///PPLL7/Y29s7OnpLzhD+/DOGhuLatThkCMcIWZ1MZQOcFdu1axe7CLZt21bc+yiuWzcEQD4fW7XCsWPRzw+XLMGLF3HSpI+fhDUqJgY3biz982dUGLZnzx5xnczP0q1vEA9mswzezMzs4MGDixcvdnZ21tXVdXDYIHleTZuG/v549Ch27MgxwuXLlwPAlClTOBwr/gh06NBB3H7+aY8epcEZGKCLC547h4sW4b17+P33cph5fv/+v3ua2l0pVCuJe2goLl+OmZn4yy/yKcl9/hw9PNDDQ8oWD+KsvU+fPmxp2r1799auXduzZ092F8ioqKiwHTr5fL6SkpJMk0FlsA9dfj6XCsABAwYoKys3atTIzc3N29u7ynnVeo3dwbPLg6mpKYfmd1evXmVjS9LcaL17h1ZWCIBjxsjwEizl4tZF9OrVq+fOnbO1tRWvR1RUVLS0tAz/+WdcsAD378egIExLw9RUXLgQV67Ev/7C7GyUa+FWQUHB06dPvb29N2zYMG7cOBsbG3F1WdOmTWVtSl1rHj9+zMZBbWxsPDw8hEJh+aQ8PT3d1taWjU6xmRApWy6uXYtjxmBhIc6axTG8cePGAcD27dtlPfD333//6quvWNjffvsta1tUhcePUV8fAQocHd+xEawKZGZment7jxs3Tk9Pz8nJSaZKMEQsKMAxY1AoxB07vqDEXfo6GUlr165lN/Dq6uqSrYQAwMFhlpkZurrixo149SqePIkREZibixMmoK8v3rghW3gikYilcbdZpbwsXrx4cfXq1W+++UZ8l2hmZjZy5Ejhnj0YEICS3diSkvDePSwoQH9/7NOnVtOstDRk02W5uThjRu29bg3r1auXeAzrJuu4LoWAgAD2VWZiYlJmu24np4n9+6O7O54+jQkJ+OBB6Viruzv+/Tfu2ydzhFZWVgBw+fJlWQ9MSEi4fPny5MmTxQ3pWXfLa7/9hmfOoOTOLbGxeOkSZmaiUIjDh9fOtGGp7GwUrytwc6u9162lxP3330vL2u7eRVna034ykydjQQHm5kpzV1Q+a5eUm5vr7++/cOFCGxsbNhQ6dOhQtlZy9erVnAMcMQLXrMGVK2VO3P/66y8+ny95OyEQCDp16rR48eKrV6/Wr/qZmLy8e+/ehf7/orrs4uKQBw8OHTq0ePHiYcOGGRgY8Hi8Hj16tGvXbuXKlRxeZfTo0QDg4eEh5ePDwlBDAwFwyxZpZ1RYznTo0CEO4dnZ2Ym/iz08PCIjI6vIjeLi8KuvsG3bWu7YUFBQMGrUKEdHR2NjY3FFuCR9fX1LS0s+ny8QCC6yVQJ1zIEDBwCgffv2e/bsec2mLz4mOzubXSC1tbXZB62Slotv3uD+/ThuHK5ejVeu4O+/c0/c2XT2Q9nroFibbXbBe/DggbSHxcQUW1iM7Nq1Q4cO5XsfRUdHe3p6uri4sEs+ALBvP2k72HxQUIDffIMXLuDatV9K4i5rnYykGTNmjB8/fuzYsQoKCtra2kOGDFmzZs3FixfTKth07+ZNFAhQRwfDwmSIkHOdDCLOmzcPAFiHmWnTplUU2H8KCtDcHAFw2LDaG3p/+xZ//x1XrMBZs+RTYV8DUlJSFBQU2B2dgYGBTGNY//zzz7p162bOnMlutNzd3cWNiT/q0SPk8VBBAf38ZIjw0aNHHM55ZvXq1QDA+kiOHj26oj0W/1NcjNbWCIAjRtTqlM68eXj0KK5Zg6dO1d6L1lLifvQospIGLy+UujftpySeHfvxR9y0CUePxgMH8GOnaVRUVCVZexnJyclHjx69evVq9fdp4jbifvToUdbdac2aNZ07dzY0NDQ1NRVfWeFDqe6GDRuCgoI4fHhq2cb4+Mfv34fn5JxNS1sbH+8WFeX86JGNUNh9yhTJjFBFRcXIyCgtLY3DO8rMzFRTU+PxeNHR0dIf5eODlpa5JiaO0kyqPH36FAC0tLRk6Gb1QWRkJI/HYw25pZwTwKys0i+sYcNqs3x46dKl4i0/FBUVzc3NXVxcFi5c6Onp6e/vn5yczB62YsUKANDR0amDe2qwdQhsZqPySrOcnBzWk0FDQ4PN9kgmrCKRKDQ0dPPmpM6dkc8vncj94QdMS8OlS3HsWMzMlPk3k5iYyM4iWQvwRCIRayzD4fA3CQksA7O2tn716lVxcXFgYCAbpJAcF7Czs9uwYcO+fftYFYejo2NWVpaUL/EFlspwqJMRKykpEYlEbNhSmqH6khIcPhwB0NDwo9e3j+NcJyMeqldWVubz+VU2DCkVFlZao8x54bZM4uJQQQG7d8fdu/HD99JnYMeOHQDQvHlzAJjFaXiAfdilLLJdtgwBUE1NhiWgS5cu5XDOM2xJK6s4LdP3tkLi84pTxZfMcnLQ2hqnTUMvL6x43KeG1EriXliIS5bg2rXo4YFv3shhddKUKZibi9nZ+MMP2LGjuIar2Mpq9owZp0+fZrtpypS1S6r+Pk179iAiFhXJMBslmbXn5+dLznmpqam1aNHCyMiIDfMwnTp1quO7Kq6Lj9+blHQpPX3m8+c2QiH7z+7+/WkHDowaNcrDw+Po0aN3797t1KkTAPTu3ZtD4r59+3b2+5X1QA+PVQCgq6sbU26f2rS0tKCgoH379i1YsGDIkCGNGzfm8/kjRoyQ9SXww/AVK8GXfk4AIyJQUxMBSsR1nDVMJBKxC8a8efNevHhRSXYoEomGDh3K4/E8R43COrZXC3sLAoFAUVGxyrusgoIC1pVIVVWVLVJ3cHDw9fWdOXMm69/HykPV1NDFBffsQU9PfPcOMzNx7lxs3x6nTpVtGIh1ixo4cKCsb4rdN7J7qgEDBsh6+OvXr1krcTU1Ncm1+Lq6umPHjj127JjkxsPh4eFs9WSnTp2krNATiUoXB4aHY1SUrNHVS9zqZMRkHbbMzUVWXt6vX5Y016Pq1MmwoXp2ssk2bnX9OiorH7az+23z5sofmJ6efv78+YULF0rTFvPjNm9GAGzRAgFw6VKOT1L39OjRg50YIGMvICY0NBQAmjRpIuW9vUiE33+PAGhvnxkbGyvNIezG4Krsa0ZZQSyrt2zbtq0MR167hsrKlzt02ClFk+gXL17s3buXe2cRLy8EQGNjBKj9+qvaWpwaGooLF6KtLfL5tbpR37p1eOQIJiTgqlW4ejUmJWFkJO7YgUOHorb2vdatxcNI4gUQTk5OHLZcqeY+TRMnYmgoZmbi1q1SPf7o0aNstZy4w/S7d+/OnDkzY8YMNjwj1rBhQxsbGzahFhISwi282rEpIYHd0l1NTz+WnHzn3bukgoLyN3ninolzZF8QwPphiZu4S0/cTdLa2trb23vTpk2TJ0+2t7dn35tlKCsrGxoayrr3W1FREasTZcNXsl2ofH0TDA07WVufP39etjfGyZUrV9hYtTRf+llZWReHDav9OYHKvX79mqWnANC1a1dpDikqKho7diz7Beno6EiuajUyMlqyZMu5cx9ZxHzzJqqpIQB+950MXYl+/PFHANiwYYMs7wkRcffu3fChdGH9+vWyHo6I6enpnTp1YhML5ubmM2fO9Pf3r6g0KCYmht3/tG/fXsoT3swMb9/G06ex0uZ1n4mXL19yrpNh3N3dZR22TEvDAQNSGza0cHJyqqibR1ZW1o0bN3777bf+/fvzeLxGjRpxGNZhQ/XsBJB1MUaEj49AIODxeP/++6/kz7OzswMDA7du3Tpu3Dhra2tWhqempmZsbBwfHy9rhIiInTuXdrYB+DwacCUmJh48eJCVyCorKxsZGXGocVq4cCEATJel6VVhIU6alNCgQeNKWpTGx8efOnVqyZIlPXr0UFRU5DBniB+WtLIBrLVr18p0bNzJk+wCeuLEiTJ/9erVK19fXw8PDxcXF1ZJqKKi8tVXX3HcpXXEiNK5LQCUoin+p1VbiTu767WwQAD87bdaetG1axEAlZQ+via1uDghOHj16tUODg6svMTY2Nja2jo5ObnqQr1y2KnGbcYKEWfPRjc3TEtDDw+UGNL6uGPHjpXJ2st48+aNt7e3m5sbKwNYsGDBhAkTAOC3WvuX5+Q3qSd3b9++zaqNDxw4IP3zszEGXV1dbqX/7969MzMzY5tTSlJUVDQwMLCwsDA1NW3UqNGUKVO6desGAPb29jL1wDpx4oR4ZM7Z2VnW8DauWwcAOjo65XdOSU5Ovn79uqen55w5cwYMGKCvr29kZHT06FFZX0Lsu+++A4AVK1ZIe0BMDOrqIgBWYx3Ip3Xs2DFxzrFgwQIpjxKJRDNnzvz666/Xr18PAI0aNVqzZk2VdeQ3brAZERw8GPPzq0jeCwsLr169ym7hOAyksSUc7CzicDiTnZ197NgxKcvrk5KS2Lx2q1atKtndIjsbT57EwECcPRunTsXjxz/PxL2oIP9BaMj27dvHjx/funVrgUDQtGlTQ0PD48eP7+OwuO/D8j5Zh+pjYmLYKTRq1CiW1RUWFj59+vTQoUNubm7W1tZstlY8ncLn831lLF3iWCcjgbXPUlRUXL169bZt28aNG2dlZSU5S8xS9m7durHxkTZt2mRWunL6IxISkMdDFRUEwJYtZY2QqZ2Z6tysjLBA35hHgSJR2Zd7l5oUeffiwd1bBgwY0KRJE/YvY2pqqqWlFRYWxm08jn313ZBxIXNWVla7du1AokWpZDYsjo1hU3Z79+6VNTZ2zrOxg2fPnsl6+IYNG1hSfuTIkRMnToh75pS5cOvr67NBfXt7e5kW6iAivn+PDRogj4cAqKlZqytiEbH2EveHDxGgtOkY192CSkpKQkJCpB20YLcKAgEePlzlY9+/fz9r1iwA+PrrrxUVFd3d3WWNLTAwEACsra1lPZCZOxeDgnD9ely8GBUV0dwc3dzQ2xvLf02Js3Yp18I+efIkNjb289uniY0sqqioBAcHV/lgNr/PNsHlfHOFiP369QOAli1bWlhYGBoaSi4nEBs7duzr169Z+cTMmTOlf/L+/fvDh87NXrJvVScSiUaOHMm+0A8ePLh+/foJEybY2tqKG2lJ4vP5hoaGb968kfVVEDE9HXv3vmtm1ke2OQE/P+Tzkc/H/9/jWl6mT58OH/oZnz17VvoDRSJRZmbmzz//DBXfOZcnFKKeHjo6BlRUEZ6Wlsa6tbDNd/h8voqKypgxY2Qdo2X36nw+X1VVtdbWpicnJ7OmtCYmJmUWM8TGZvzxBzo7l3b0Hj4c58xBoRD79KmviXvckzsnf53uu31efNg99pM3seHCC0d8t8/bM9Np1VDjWWP6St7Vs+Tgp59+4vBa1VneJxQK2Ut36NChXbt2khNELDAjIyMLCwtra2v2xaiqqirTnR73OhkJv/zyC3xY6MwoKChYW1uPGzdu69atgYGBbOwjMzOTVXA5ODjIdFZf3r37hb19PmsNtmQJhwhzc3MNDQ2///57WRsoyerVswe3T+954H8sxO9Q3vt3aS9f3PPd/8/Kcb+Oa7vCxWCFi4HHVBf2T9SwYUMnJyfWBbJdu3bSrzARCw4OBulav5f38uVL9iVjbGzMmihI0tTUbNGihYWFhZGR0aZNm0DqXVrF2DnPBsg6dOgga3gMO6XL3ARqa2vb2dnNnDnz0KFDT58+RVl3aZUQdfp0noVFaZ3M999zC7I6aitxF4lYuzHk8VBNTba94xERMTU11dbWlvXGYgsuhUJhRQ9ev25dtJ0dCgR45IiUzx8fHw8fbvJsbW1lDa+oqIidarLuqJqbi0+flvYUmjULJ0/GBg3+a6TaqdNVBweH1atX37lzp7i42MvLS6asXeyz3KfJzc2NfX0k//+So1evXvn7+3t6es6cOZN9wSkoKGRkZLAUlkObDiY1NVVZWVlBQYHtjMPo6OjY2dm5ublt2LDB19c3OjqaDc/cuXOHzQlIOcz28uVLVmwN1ZgTyMrKat26NVvbWuYLy8bGxtXV1cPDw9vb+/bt2/b29gDQrVs3WffFQMQ//ijdeVNmv/6Khw5hRgaePYuyj6N8Wm3atAEAVsbAoYMqy1Olb8GGiGFh6WwgvFu3buJK8UePHq1bt65r166SI6CGhobOzs7s9zhq1CjpM4bY2Fj4UBvq6Ogo65uqjoyMDFYuqK+v//DhQ6FQ6OHhYWNj06hRGzYsJRCgvT1u315aZjxrFu7Zg7a2mJRUm2F+AnFP7tw6sfPumb1R9y6f27Xojs+fBxYOZanVCheDlYOb7XKfNHHixJ07d967dy8/P//u3busIotDX1RWJ/Mj10WcFy5cUFNTYxc1dq/eqlUrMzMzyeaSPB4vLS2NJTq6urqRUjddqU6djFhJScmsWbPMzc0nT568e/fukJCQir6R4uLiDAwMLC2/dXPLlb4wpHPnzgDQRFd3RadO2ZxqdNlEKIeUQFavnj34d+X3x9ZMTIwUem9wi7p3+fiGH9hJtfE7q8Pu3/od2Xb8+HHxIqu3b9+2atUKAHr27Cnr1zhbTCXTuJKk0NDQBg0asAF1NTU1IyMjExOT8nPRcXFxy5YtA+l2aRVjS1pZnQyHWkGmuLh40qRJjRo16t+/P+uZU1Fi9vTpU21t7caN23h4yHAV+OabbwBgcOvW1xwd39dKeWoZtbhz6ujRCJDfsmW0nV2kLBc8RExNTWVXSsm+hwBgYmIyefLko0ePSlYpsVlsFUXFJBmHLVu2bAkAioqKAoGAw7V80KBBAPDXX39Jf0hubukuYKxJZloaCoVYUIDXr6O7O3bpgj17LhC/WXV1dXYHKf04n6TPb5+mwsLC7t27A4CdnV1BQcG6deusra0/OgquqqrKlvJ07tyZ88tt3ryZ3Zojore3d0hISOVDHYcOHQIAFRWVe/fuVfnkq1atEl8FZ1djg+lHjx7Z29vb2trOnTv3zz//vH79evLHGim8efOGjZpMmzZN1pdo1w4B0NubU3w5Ofj993j/Pnp4yHHENT09nc/ns/SlTZs2sh6emZkpEAiUlZVlnWCNiYlh42SWlpYTJkwwNjYWn6JKSkotWrQQ99acMWNGYGAguzQOHDhQylU3hw8fFl/zli1bJuv7qqasrCxHR0cAkBzc1dHR+emnjL//xjLlhyIR2toiALZqhVW2eqtT4p7cufb35uBzB+5f/veCpzsiBp3cefLX6XfP7E0IDy7M+8gq5zNnzrB6bpmuDojIMjMOy/uYU6dOsfOB/V7EFBUV27dvP3XqVE9Pz/v37xcXFxcXF7MuomZmZtJMxIlEIjZbxblOhoMHDyKaNhUB4Pz5Uj0+ISGBx+OxrRJbcq2T+fbbbwHg119/5Xa49F49e/Ao4Hj22zd+nksv/LkMEeOe3A67dTbjTYWV/bGxsawgavTo0dKXuYt/d7du3eIWKruZsba2HjFihOR5paWlJTmkLRKJRCIRK9Mtv0trRdj6HHavW77ssyZcvx5kbl4IIO0Kw6ysLFVVVZaMaWtry2UwtPYS9yfHjg1t0YJdVBYvXiz9genp6awfmaWlZVJSUlxc3L59+0aNGiXuzA8APB6vXbt28+bNYx8zPp/PoYu25Oz5yZMnZT38jz/+AIAxUm/Vw7J2AGzSpMK2u+/fvxe3jefxeE2aNBk2bNi1a9c4LNNhN9ky/cvXfZJFKWwECAAaNGhgYmLCqlkkN7gFGXetL6NHjzEAINOsHxvHMjAwqPLCtmzZMnV1dTZWynlOQCb3799nX45//vmn9EeFhCAA6upymDNDRMQbN0q7iuTmct9Hu9rOnj0r/qRLueOgpHPnzgGAvb09h5eOj483MTERF1xqamo2b95ccrBKQ0Nj+PDhp06dQsTQ0FD2Lefg4CDNhPj58+f79evH3heHTU+qLzc3t3379iYmJuJVrZWMBSYnl94EmphItS+fSCS6fPmyp6fnR+9Faw0bcX8UcPym11aWYEmDlfYpKipekqIhclJSUmJiYnXqZBi2FmXTpk0bN25s3br1hAkTduzYcffu3Y/ecObk5LBNqW1sbLKragD17NkzFRUVVifj4ODALTwOAgJKa66kybHYUAtLOTiUvyJiTk6Ouro6j8fj3tNGaq+jn5z6bcbl/asibvtd+UvaFZnigqgqu5C9fPnyzJkzt2/fvn37NgA0a9aMc+0+q8ncvHnzwYMHnZ2dFy9efOLEiYpazRQWFjo7OwNAJUtaxeLi4tTU1Fi5YKdOnbiFx8GxY6WFnOVWtH7EkSNHxJePiRMn1nx0H1F7ifvLly8BgM3/dpR6C920NPz2W1+BQGBlZVV+kxS2LYirq6v4ysfmvrntfePj4yP+fXAoSWQ9jBo2bChNVp2bmztoUDHbuDciQqrnX7lyJRs5AE6zrpcuXZLpX76+EBellF99wjRt2rRXr16TJk2SYTFlOUFByOejk1OSTBfRwsJCBwcHqLQopbi4+Pnz5+fOnevZsyfUypysGPsCUlRUlL7kw8sLdXSq0Sf34UNks+ovX6L0/S4/NbZjmrW1NXBqMcQaMizhVDKLiDNmzAAAOzs78f7wANC8efNZs2aVz3QlWy5Wvmi+qKjoxo0bs2bNYpteVZl71Rzpu+Kmp2OXLqimltOr18iKujWzPe/EbTc1NDRatWpVO0O8H5X3/l30g5tJzx+JRKK0lzKMCLKhEw0Njfv375f5q8zMTMlWKgAwY8aMatbJ5OXlscui9MOWqampbLyzf//+VX7RhYeHswfv2LGDW4TcHDqEPB42bowVnWXi5ZLsisBGRh9xqpM5cybD0XHpoEHfVSviGubn58fmuHbt2iX5c8llo6xIjw0szp49Gzj1ZGNycnIaNGjA4/GkbAqJH1vSWonIyEg20bS5qlahn9aGDQiAWlqYnl7hY+Li4k6cOMFuBdmwlzRbu9SEWiyV+bBYmE2upaamVvn4tLTSIZlhw3wq2doQEfPz8wMCAubMmdOhQweZRhAlZWVlsToZdpsu6+FBQUFqamrsy8Lc3NzNzc3b2zv9Y2dBfn7+wIED27SZZmZWIv0Wd+Hh4fBhpXavXr1kDS8vL4/N70jzL1+/DBs2jP2zl9kJKDAwkHNn/TImTkQAXLRI5gPFRSnsVjAzM1MoFHp7e3t4eLi6utrY2JTZdHoRh9eoBrZ5nr6+fpVb0z14gJs2ISJu24ay15FJWLIEV6/Gn3/G2s298vLyWP7HClSaNGkyatSoZ8+ecVjaxboGcf7KZof7+fkNHjzYxsbGw8OjkuU6iBgbG1tJy8W3b9+yFlLilg4KCgp9uSxBkI/sbJwwYSkbWg4NDRX/PC4ubufOnf3795ecNDM0NGTJqKWlZSUbPdZNIpFo3LhxAGBgYBAXF5ebm7tx40ZXV1e2N5OkBg0a2NrasvFsf7Z3oezOn0+wth4v60jNixcv2IrDyZMnl/mrjIwMf39/lgiyCg22Blr6BO5T2bULb90qXRP4118YHo6+vrh8OQ4YgI0bY4sWX5X591RTU+N2H/vttwiANV8mU1179+4FAIFAcObMmdzcXMlMXUxdXd3c3Lx169bsE8S5/srHJ6Vr162OjrJtMfHq1Sv2xevq6lpmpF/yBoOdV6zASdp9lz6dBQvwzJnSy9zly5iejnFxePIkLlmCffti+/ZdJf89eTyekpKSvBYN1mrizoaaWBXy+PHjnzx5UklhVkZG6V5JLVvW3iWeXVPZfl3lt9qpxK1bt9hR5ubmkgNpAoHA1tZ26dKl165dY7/j3NzcPn36sGwpMlK2Ei5xywglJSUOGwf07t0bALw5lifXUSKRiN0BHzx4sIb6dmVno4YGAkg7N1JGcHAwSz7EW42W+fybmJj06dPHzc1N1p611VdUVMRG+rt06VLRd1BhIUZGoq8v9u2Ljx5Vr8Lljz9w9GgsN+JYfQV579Nfx5WvMM5Key28cOTomomG+v81QNDT02P357NmzZK1BXJubq6SkpJAIJC5Mx0iIubl5bGRi4yMDOlP1/ItF8PCwjZu3NijRw/JVa1WVlbz58/39fWt6Q4Yn1Z+fv7QoUMBQEtLa/PmzYsXL2ZLhxk+n9+1a9e1a9eyErKUlBS2G4OpqemLFzLsf1wXFBYWsi9/Kyur1NRUVuQAACoqKmZmZq1atWKzK+I3LhAIOK94GT2a9V6WefV5cHAwu4QtXLjw0qVLa9eu/eabb9h0hyRdXd0OHTpwm9yuvrg4HDwY//0XN2zAsWP/a+cAgP36/dKvXz93d3cfH5+HDx+yOepevXrJuoIzJwfV1ZHH+3g36bqGrelk8zksa2fLRs3MzFjlSRl9+/bl9i3B2pdLWQ4uKSwsjPWH+PHHH318fJYtWybZ3bLMecWhieQnkZiIXbrglSv46684fvz/nVd2dmPEq13/+OMPNoTKYb/hT6JWE/dFixbxeDzJHj36+vrz598/eBDLL/ldurR06VJtth3w8PCAD1Vx0o/cBwUFsax91KhRZ86cYfl606ZNjYyMJNdpqampOTs7f/XVV+yNc7ihZOs8WPp+8eJFWQ9nXlKJxwAAIABJREFUy3anTp0q64F12dWrVwGgWbNmHDpbSWnvXgRAbh3PWB/Z/v37s6xdSUnJ2tra1dVVPCcgx5IGJi0tjY35jR8/HiU2gl2//sWQIdiyJSoqIgCuWIGnTuGUKci1FQEiIrK+bDWw2f3Dq96B3n/4bJn9JjYcEUuKiwL+3uQ5y3nFoKasM8P4IQ7W1tYLFy4MDAwUiURnz55ld1Njx46Vqfzp2rVrUI0+Zdev3wSAdu3ayXrgmzdv2PpyXV1d1sKMUVJS6tOnz7Zt22pnIVcNKSwsZDWKbG0uADRo0MDFxcXT0zOp3AUgMzPTzs7O1LRP69ZFtbmb3yeRkZHBLgFNmzZt06bNRxvqOTo6zps3b/ny5awI8Pfff5f1VfLySrcO4HZSsNW0ZfqEaGholFl6yOWpP5G4ONy2DX/+GZcuxR07sFcvXLAAjx/H8qP/0dHRLDscM2aMTDGfOIEAWIuli9Uins9RVlYuP0Kkra3du3fvBQsWeHl5Xb9+ncOSVoa1L+fzOS4ov3r1KtuVSTK28ktauTz1J5KYiL/9hlOn4rp1uGYNNmqE/frh0qV46hQmJPzfqNC1a9fYx3P7di77M1RT7SXuJ0+eZG0cpkyZcuTIkfHjx7PRBUvLXHZD06oVzpiBw4ahjw8WFODcuTh/fm03C2Pt2Nlp7erqKs0h4qz922+/LSoqOn/+vK2treQwmLq6uoWFhbGxMbtjYY2TnnLqZs3asbPGFHNZC0npJCQkTJ06la1K0dXVPX/+PPedfusYtuNMlUtzuMnMxGfPMCcHN2xA2W+UEBHt7OwAgF2ejxw5It9vpYqEhoayxSfiIUAAcHDwYx9MgQDNzdHdHc+exevX0dyc68sEBiIA6uvLsIOo1B5e9Q48vt3Pc2lq4vOjq8c/uHJs+w92K1wM1o1ocWztpPuX/333tuyKxmvXrrFP7siRIysffGJlG/369duxYwdbasJ5N4DVq9HUtHDlSqkaLEjKyMhITk5u0aIFS3D19PRcXV0PHTrEbeC/DhL32FFQULhw4ULlv5H379+PGJELgHp6WGmdUamHDx+uXbuW49abn9qrV6/Ynkfs/SoqKlpbW7u5ubHERXIe5ujRo3w+n8fjHZG6rzFz6hTrJswlvOnTp0+ePJl1jfz6669nzZp15MiRyMjI2tmHSEoscU9IQGPjqpOEkJAQ9s22fPly6V9i61ZUU8PaLbSuloKCgtYfNoPX1NSUzIbL/O5CQkLYpMpqGTdX/vdf7mNYEydOdHV1NTc35/P5HTt2XLRokWR3yzqCJe5Pn6KFBVYZ2rFjxywsvjE0LDp+vFaCk8A9cY+Li5O+PFSctc///05OERER27eLhgwpHR7Q1sY5c/Dnn/HtW2lbPn1a4nbs06dPDwwMrPLxZbJ28c8zMjJOnTo1bdo0Nk8nNn78eKhGayrWjp3d50nfxi4xMZHVyLJdiNlAo5KSEusQHx8RKiqpqbHqmpaTlfGVpTmfz6+hOsvbt7F9e8zORm55WmRkJI/HYzlxjc4JVN+aNWvYJ1RTU7Njx45jx47dtu3i8eP4+HFpA5kXL0orhf78E2/e5DRoPmECAmDN9DV6eNX7UcCJEL9DT274sF4fEbf9Xty/XlxY2fz4vXv32OjUgAEDyrRcLC4uvnXr1qJFi9jOL0znzp3Z2sHjXL+q+/RBAORw9Pz581VVVdmrr1+/vk5lUdUn2YFeyi4l+fn4zTcIgOrq+NF63dxc9Pd/8NNPP4lvCTi3G//kHj58OGXKlD/++OPRo0eVT/hs3Lixgbb29zdvhsoyNTd5MgLgxo0yByZe0sr2/aiz4ztv3yK7Pp84UfVe44h4/vx5NvstzVLamBhkDcT376/eep5aV1RUdPz48cePH1c5QuTn52fart3Q0NDj5ZbNVGLOHARADh8jcX8eHo+noaEhZX/b2peejmxRiadnZatUxTZtKgZAVVXkuks1R9wT96FDhwoEAhsbm4ULF1be9kuctc+bN6+ixxQVYVAQHjuGc+diTAwuWSKfxB0RO3bsqKCgoKWl5erq6unpWcmivdu3b7PvuDJZexnx8fEHDhwYPXq0qalpdnY2t32axNh0uaqqKo/HKz+JXF5iYiKr/GnatCkACASCb775pkuXLmxOwKq50QoXg/XfWh5bMzH43IG0l9G+f8y9enhDzKOqb1rqguBzB1YNMTq8oTrVG5W5fRt/+w3d3Tkm7gsWLIAP3dlraE7gE0pMTJRmSPLhQ1RURHV12bZAfffu3eHevd+3bi1V5z/ZsRH3i3s9ooIvS9+kD8u1XBSJRMeOHRs7dqxkk6IGDRq0atVKvN5LU1NzxIgRHJr0FRWVLpbgMJFoa2sLH7pySfPBr19YB3r2SZG+A31hIY4ahQIBeniUFiJfuYLJyXjoELq6ooYGdu16jP3KmjZtyqYca/A91Jg/wsJshMKeDx5ES7FvQGwsXr+ORUW4YQOXtWEnT56ED9WYo0aN4hJurXjyBPfsQUT84w+Ucs8o8QrO06dPV/KwwkK8exd79cLbt3HhQq59b+uD02/e2AiFtqGht6Vo4RAWhqz/1tatUmW0ZXh5eQEAu4UeO3as7MHWkshIZCXSe/dKu6RtxozSFsnSnIfx8fEhISHVChEROSfuRUVFVlZWkgUhGhoaLi4uW7duLVMEcu7cOTZCXEnWLokVgGzcKLfEPTAwULJETNwh/uLFizk5/xU5SZm1l8dhnyZJrKcYu8L9zVpiV0yctbOqJIFAIF5LlJmZefr06b1b12z/wV687d9fS0Zc8HQvLizw2TL7xf3rt0/tzs2S/TNai/bM7LPCxeBp4Jkaev7bt/H4cdy8GZ2cSn/y5Im0xxYWooVFRrduO6ytD9jZja6FTsC1hvXYMTVF6RsUeXp6Qk1u58kWp+a9f4eIsp60ERERbO1dx44d09LSWAkyADRq1MjMzEyyq0njxo379+/PsucRI0bIutwtOLh0tb2scnJy2IpYALCwsJD5+Drv78WLDTU02HVdpg70xcV46xbOnYszZiAiurkh26iVbdLt6JixcuVKoVBYN0vUpFSCOD862kYo7P/o0ZuqTjl/f+zSBd++5TjWMHbsOHHizmEzk1rz5An+9BP6+ODUqdIm7ojINvJUVVWVvIUrLi4OC3t/8CBOn462tqiiguvW4aFDOHkyzp37OSfuiLjz1SsbobD7gweROR/ZOEzSpUvYvTsmJnJsTjB69Pfi8+rMmZq6XldfeDh6eOCzZ7hyZYW765RRXIyDB6OuLrLNLUtKUPL+OiEBz5+/v3Tp0n79+rERInV19eHDh1czTo6JO6uWBgAVFRUjI6MyK2wMDAxYDaisWTsi+vkhIhYUlP5BLkQi0YMHDzZv3ty3b1/Jbn3Kyso9e/Zcu3btvn37WNY+cuRIWQfe2D5NP/4o7T9IGawdO/sAsNWEFakkay/jXeqrB/7HTm6eFnz+4N45A3y3zws6teuCp3thfh2dz2KSnj9a4WKw8TvrysshqiM4GM+cwYICtLHBoiL84QcUCKQ9M0+eRNanHwCdnWsoQPnIzcUOHRAAx46NlrJmg+09Lmupbq0Rt1w0NTXt2bOnkZGRuASZx+O1b9/e3d393r177M0GBQWxRg3lC2wq9/gxjhuHHPq/X78eqqamZmLSQken4aRJk2Q+vu6zskKB4L25eWjPnhyWa8+di2fPopcXzp6N3bvjN9/gvn1YaQPheia/pGRSZKSNULg0OjqzqCizqKiiGxF/fzx4EOfP55K45+Zi48Y5NjbeTZvecXbelFNVMidHT57g8uX45AkuXixD4i4SidgeDg0bNvTw8Jg9e7a9vX2DBg169PAWNw/h8XD6dDxyBIOD0cjoM0/cRYjLYmNthEKPuLiQrKyQrKyKvs0vXUIvL5w+nUvinp2NmppFX399UVf3sbPzbnm1UJRGeDhOmoReXjhlirSJOyLm5OCLF+jsjBcvYlISLlqE7u7Yvz82bowA2L27pziH1NbWZuMvW7ZsqU6cHBP3sLCwH374gV3qJIejWrVqpaenx8ahz58/z7J2mZZRivcxnD6dW2ifWFFRkVAo3LBhg5OTEyv4YRk8AIwZM4ZD1XJUVLq+/ssmTZDbGJC4HfuOHTsqaVL++vVr1jWfZe3Sb0p1wdO9uKjwxKYfL+6VYR2PXJzbtWiFi0HtxJmaio6OaGSEAKipidI0BBowAAHQ0BAB0Mur5kOsXfHx2KfPWXV1DWkaz1+5cgUAtLS06nIqkJiYaGlpKf6Mq6qqOjk5bd269aP9wu/fv9+oUSPnefN+jIx8L92XwJQpeO4clpTg0qUyx+bhgYqKha1axfN4oiNHZG48X9elpCCPV7olJqcFlXPnokiEP/+MXHcrqgcyi4rWxsfvefXqr9evj6ekFFVw/fD3Rz8/3LmzdAVhRASOH49SzgyxJa3sW64Ol8kgciqVYVhHTslubwDQq9dcV1fcuBEDAjAzE1+9Ku1Yu3cv7t9fV1KRGlIoEp1MTd2blHQzM/NBdvb/2LvzuBrT9w/g1znt+0qiTdo1irJk+VmzJUslMbaxjrEbwwyzYMx8rWMwYxnb2CsyobHLFgYlJCnaUypKpfV0zv374zbne75FQjlOPu+XP/RsXafu7ud67udeakjcjx9n+/YxNzfG2IveRLUcGM+HtFpaMqIXs+9/sO7dY3wQxIYNb5C4c3PmsClTWGIimz37v5NIGhmxkSPvL1iwICQkhL91DwwMFAqFQqHwrQdKsXefVSYpKemPP/7w9/fn+TonFApNTU3538abLjI/fDhbupQtXcq++OIdQ6t7eXl5ISEhEydO5K+tv/vuuxEjRrzFdXjN+NYTmTk7OyspKckOMKjyCFs9a699z5zz+1dfDFqblRgTf00OS6a/1l9rZp7ft+pq6GbG2NHf5v04xDI7+X0s0/D0KWvR4r+1T+/esTWPzC4qYsbGLyZSNDJqmM02ERERqqqqAoFAdvFRkUiUmJh4+vTpzZs3z5gxo1evXnwSJG1t7Q9/HtK4uLhhw4b16tXr77//fm1T+v2HD73v3HGLjBwdF1dQi9duX37Jpk5lRUXsLZYs7NHjxap+RCxRweYurwX+cqp5c0b0Nj8dxn75hUkkLCXlbZZIUyzbs7LCnjyJfHXlc+0au3yZVVaygAB28eKL+mr48Fq1Ew0f/t/E/QPuJsMYYxUVjP8MCgreeJKqzMxMf3//Xr16LV269MSJEzUsSJyezjQ0GBF7w8lXFM/WzMzfHz3669V9H2/fZtHRjDH2zTfs4EFmYcGIWJ8+tfrhDx7MiFjTpoyIhYXVXdD1oKjoxWiZlBT2pm/+5sxh8fFs+nT2889swQJ28OBLJiflli1bxrur1GYGlJeqy+kgExMTN2/ePHToUD7RioqKyqRJk970Ih9ai/tLvfU6TRzvIvzW67Hdv3/f0dGRr2PFaWlp9e3bd9WqVbdu3ZJIJA8ePDAzM+Pddt8oa//whW34uiD30eG1s2MvHYk4+HtmYq27nL+z27eZlhYjYq6uJ7S0tIcMGVJzx9mSErZxI+vWjclpiYb3Yf369fwFlK+vr4+PT5ViKaWtre3o6KjQE42/VGZ5+eCYGLfIyKF37+a87g725ZfswQO2ePGL1HT2bLZ/f62+S0UF09R80XXb1PSdg/4AzZr138S9xoGDrzJyJHNxYW97E1Qk27Oy/ikoSKrFKNXYWKahwdTVX4yHfu2YMbGYWVkxgYAJBExLi32o08m8b4cPMyUlJhCwBnQXfYmtmZlPKirEtXi8u3uXKSkxZWVmYMCI2Lhx4prvg5WVzM6OCYUvpg18w2FBioRX7N9//2JKoprx1UiNjIzuv9ELo3/VyzzupaWlf/zxx/nz59/iXGkH0PqZNa5uvN06TVL8zdE7rkr+/Pnz48ePf/nlly4uLtJVS3iHpeHDh3t4eDS8rJ0xFrj0s6uhfxz/47vLIb8/y3mrRSDe5bsHMi2t523bDuZLSCxevPhVR8bFMb4MwMqVDfwWOGHCBNkJWIjIwMCgU6dOkyZNWrZs2ZEjRxITa9sPXhFlV1T43b3rFhn5U0rK1szMfdlVZ4uX4h0Gf/6ZzZnzIlNVUmK1WR/w/n2mp8eaNFGADgxvac8e5uXFGjViAsEbjHeWwRuJ32ptDAWzPSsrt9YtzF988aJrn5qapGvX+b++brnLigoWFMTGjWvIPY7ewtq1vL3mnzNnXjbtaIOwLTMzv9aj9fjimJqaTE+vrGPHgEWLFtV8fFERO3GCTZ8utxlH3g/+MqGoiJ058/qDxWLxkCFDiKh58+aPHz9+0+/1XldObTDeYp0mWTk5bNQoduQIe/6c1aLppDYXzAkODp40aRJfnIWIlJWVBQLBJt4NsAE5su7LJxkPg34eH3Hwt8Inchh9tnjxLzw3VVJS0tHRqfInV1rKbt9mwcEsLIyNHs327GELFrAPuF93HSgsLPzhhx/GjRsXGBgYHR39wU7QW3/yRKLDT56sfFlXeFkJCYwxVlrKvv2WETEzsxcj4date335uHePXbnCfvmFBQfXScgfkspKtmwZW7KE/fprbSdg+1/JyYyIGRqyhvt4+F/5IlFlrUdHiUQvRtrY2obwNctrniimZUuWksIiIhpiMXs3S5fGaGpq6urq3rp1S96xyJ9EwkaNYkTMyelPnmls3769huNtbVlKCjtzpj6WzP6A8E4iz57VdhRTSUkJb2B1c3N70xH5AsZY9VfbUDORSOTtfSk5uZmh4VOJ5JMrV3RkJsaslWbN6MoVunGDTEyoS5e6jC0hIeHUqVMhISE+Pj78dUxDEnvpSGVFmaVzh/KS54amVirqmq8/p05JJBJvb+9jx45ZWFhs3rxZTU0tISEhPj6+rCzgxIl2qakkkRARff01OThQVBRJJLRiBWm+7zDhfftPWpqxioqVurqngUHNR6amkqcnPXhATZpQaemzJk26Dx7ch/d6fJWePWnmTHJ0pFOnaOrUOo1b7g4cIC0t6t+ftmwhJyfq1OlNL7BrF40ZQ4MGUWhofcSn2IqLacSI+5GRfYRCSUZGhoaGxtmzZ3m6IFVeTrdvExHt2EFKSjR8OGVm0tCh8gn4w8QYGzNmzO7du01NTa9evWppaSnviOSsooImTbr+11+9tbQ0s7KyVFRUzp492+V/U5nsbIqKIh0d2rePhELy8aGSEvL2llfI9W7wYGrblsrLSSKhpUtrdcqTJ086deqUkJDQv3//w4cPVxkzXYPaHgeyVFRUVFV7JCRQo0aUm0vR0eTu/mZXGDiQVqyg7t3rPjY7Ozs7O7tp06bV/aU/AC27DJRvAEKhcM+ePe3atUtKSurXr590e6dO9snJ7VRUyNaWHB2pSRMiovnzyd2dVqyQW7Tw3qgIBBP/XaGpZpaWdP48eXrSvXvk7LwzLi5m+fJbYrF4xYoVsn3euMREys2l1q3p5En6dwHQhiUpifz9iYicnCgp6S0S94SEoPbtVXv27EmkW/fhKTgtLdq2zbhTJ/WEhARLS0tdXV0LCwuRSBQbGxsT8yQioldkJMXEkEhE/fuToyN5eNDRo+TmJu+4PzACgWDLli0ZGRnnzp0bN27c2bNn5R2RnKmq0rp1DrduWd2+fdvc3Nze3t7V1TUnJycqKio+vuL8+UFRUZSRQUQ0cCDZ2FCnTnTkCPXqJe+465OZGS1cSAUFtHJlbU8xNjYOCwvr2LHjsWPHZsyYsWHDhlqeKHzLGD96np5ERPr6RESnT9fqlOJiGjyYbt4kItLSop49KSys3uKDemNgYHDgwAEbGxt9ff2OHTuOGzdu+fLl33zT6v59Ki6m+/fpr79o7Fjq2pWaNaPQUFJTk3fEUP+ctLRqf3DTpnThAnl738/MXGpgYKCiorJq1apNmzbxvZWVkvPn6auvyNGRbGxeNLHPmUNr1tRH4PLWvj0dP05EdOoUtW37Fhc4ePCHa9d82re/X8eBNRQ8OTA2Nk5NTS0pKfHz89PV1W3duvWMGZO2bKGbN0kioZYtycGBiMjXl+7jB/kyampqf/31l6+v79atW+UdywdBV1c3LCzMzMwsPT397t27LVu2NDEx6d+//9KlXx8+TBkZpKtL3bpRhw5ERD4+lJIi54DrG2+3UVIiE5M3OMvW1vbYsWOGhoZdu3at/VnoKvOW7t8nR0fS16dnz6h7dwoPf83xxcXk5UUXLpCVFQUHU1AQrVpFAwbQ/Pl13FUGABRCQUGBl5cXX87J2dk5KCjo8uXLR48ePXHibHl5RmGhgIgMDalvXzIxoV9+oSVLyMiowXWVIaLQUIqJoY4dqWfPNz01NzfXxMREU1MzPz9fOgc/VHfjxo0BAwaIRKL8/HyBQGBra+vu7u7hsc3FRb11a9LWJiJ68IBsbSk7m4jeLPmAj1ZsbKy3t3dBQUFeXh5/IHRzc2vXblnr1iq2tsTfIGZkkJkZ5eWRkhLp6ck74g9SUVERn6WwlpC4vz1HRzI3p27daPBgcnKq6cji4ooBA1TPnycjIyooIB0dunCBPvmEiopIRYVkllQHgI9IcXHxkCFDTp8+raGhUVFRIRaL+XZf34ctWrTw8qJOnUhJiUpLSUODxGKqrMQLnP8REhLi5+fXq1ev07V87/kRu3nz5qlTp9q1a+fm5qaHBArqSEZGxpkzZzw8PGxtbaULTkO9Qh/3t9enD332Gdna0s6dNSXuJSUlAwd6MzbH2NirsJAqK2ncOPrkEyKiN3nEAoCGRktL6+jRo76+vrdv3378+HGnTp28vb0HDRrk4PA/i1JraBARKSnRmw6Cb/CuX79ORF3w1rIW2rRp06ZNG3lHAQ2NmZnZ2LFj5R3FxwUt7m9v9mwqLaXly2nVKlqw4MXNtYqSkhIvL6/z588bGTUSie4VFhrPn081TiABAB8XxtitW7dsbW21eZcFqDXGWGxsrL6+Pl9vDgCgwUOL+9tTUqJPP6Xt20kkImNjcnSkXr2oVy/q0uW/r7PHjh17/vx5IyOjwsJnTk4/enn98tNPaDQDgP8SCAStW7eWdxQKSSAQODs7yzsKAID3Bx2S3kmXLpSaSjk5JBJRVBQtX06entSlyxUvL681a9bExMQsWrTI2tq6qKhIJBL17KmMrB0AAAAA3g66yry9f/6hDh0oN5dSUqhlS7pyhc6coTNnSFf3+3PnfuTHGBoaFhQUiMXir776agUm9AYAAACAt4XEve5lZ+ecOXP6zJkzZ86cycjIaNq06dChQ3/99Vd5xwUAAAAACgyJe/2Kjo5mjGEsPwAAAAC8IyTuAAAAAAAKAINTAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAAynL5rsXFxSEhIdHR0eXl5VZWVsOGDbO0tCSigwcP5uTkfPHFF7IHx8bGHjp0aPLkyY0bNw4NDY2JialytTlz5mhpaRFRcHBwcnKyr6+vjY2N7AHHjx+/c+dOnz59XF1diSg+Pj40NJTvUldXb968ed++fVVVVYmopKRk9erVvXv3bt++ffWwc3Nzd+/enZiYaGxs7Onp2blzZ9m9ZWVlISEhkZGRYrHYwcEhICDA0NAwJydnx44d1S81aNAgBwcHImKMnThxIjo6esKECY0bN65+ZEpKSlBQEBH5+fm1aNGCb2SMHTt27OzZsyKRyM3N7dNPP1VRUZGeUlpaun///ujoaFVV1T59+vTu3bv6ZT9C4eHh586dy8nJMTQ07Nu3b9euXYno3r17ISEhEydObNKkifRIxtjSpUs7d+7cvXv3hw8f7t+/v8qlpCUkNjY2LCzM0dFx4MCBsgekpqYGBgaamZl9+umnfMvq1asrKyuJSCgUNmnSpHv37mZmZnxXYGBgUVHRxIkTq8csEolCQ0P/+ecfInJ1dR0+fLiysjIRJSQk/PXXX9WPHzNmDP8gjLGTJ0+eP3++sLDQwsLC39/f2tqaH1NZWRkUFBQdHS2RSD755JMRI0aoqanxXRKJZOXKlbIXNDExGTt2bC1+ug1ZYWFhSEjI7du3KyoqrK2tAwIC+O9u//79RUVFkyZNkj341q1bR48enTp1qqGh4cGDB+Pi4qpcbd68efwHvm/fvvT09GHDhllZWckecPTo0Xv37nl5eTk7O9O/ZYzv0tDQsLa27tOnD/97LywsXLt2rZeXV5s2baqH/fjx4z179iQnJzdq1Khv374dOnSQ3VtSUnLw4MGbN29KJBInJ6eAgAB9ff3MzMzdu3dXv5SPj4+trS0RSSSSv//+++7du5MmTTIyMqp+ZGJi4sGDB4moyuc6fvx4REREYWGhjY3NqFGjDA0NZc/Kzc09cOBAXFycioqKu7u7r6+vtEw2YM+ePTt48GBMTExlZaWNjU2zYVM0AAAgAElEQVRAQICpqSkR7dq1SywWf/bZZ7IH37hx48SJE7NmzdLR0dm/f//Dhw+rXG3hwoVCoZCIdu7c+fjx45EjRzZr1kz2gEOHDj148EB667l169bJkyf5Lk1NTRsbG09PT169PH36dMOGDYMHD/7kk0+qh/3o0aO9e/empKSYmJgMGDDAzc1Ndu/z588PHDhw+/Ztxpizs/OwYcN0dXXT0tKq16IkU0gkEsnhw4fj4+OnTJmip6dX/cj79+8fPnyYiGQ/l0QiCQ0NvXjxokQi8fDw8Pf3V1JSkp5SVla2b9++mJgYZWXl1q1b+/v780/X4CG/qtf8ioiys7ODg4MTEhLU1NTatWs3ePBg/gHfH/beXb16tUmTJkpKSu3bt+/du7eJiYm6uvqePXsYY0OGDLGxsaly/N69e4no1q1bjDH+E3T9X0+ePOFHuri4ENGcOXOqXIGXs0WLFvEvDxw4QESOjo5ubm6Ojo5KSkrm5uYPHjxgjOXk5BDRL7/8Uj3sc+fO6enp2djYDBo0iN9Q582bJ9374MGDFi1aqKqquru7e3t7N2vWbNOmTYyxhIQEt//FgwkNDS0rK9u5cye/FBFdvny5+jeVSCQ9e/Y0MDAgoqNHj/KNYrHY39+fiNzc3Nq1a6esrNy+ffvnz5/zvbm5uS1btlRWVu7ataujoyMRffHFF2/0C2p4nj171qtXLyKysbHp3bt3q1atiOjTTz8ViUSBgYFEFBUVJXs8z7C/+eYbxhjPmWxtbWWLHC+ujLEVK1YQUZMmTSorK2WvsHDhQn6WdIu6urqJiYmbm5uLi4uenp6qqurvv//Od/Xr18/V1bV62Lm5uW5ubmpqap6ent27d1dSUnJzcyssLGSMhYeHVylX5ubmRBQTE8MYKyoq6tWrl0AgcHJy8vb2dnJyGjt2LL9mSUlJu3btNDQ0/P39R4wYoaOj4+jomJ+fz/dmZ2cTUYsWLaSXnTp1al38BhTYhQsXGjVqpKys7OHh0bt370aNGmlqah48eJAx1qdPn1atWlU5fsuWLUSUkJDAGPPx8VFTU6tSXxUVFfEj7ezsiGjhwoWyp4vFYp6XLF++nG/ZtWsXEbVs2dLNzc3BwUEoFFpZWaWkpDDG0tLSiGjjxo3Vwz516pS2tradnd2gQYOcnJyI6Pvvv5fujYuLs7Ky4nedAQMGmJqa/vnnn4yxmJiYKuWKP+8dP36c11f8UtX/ZKTBd+nShddXp06dkm4fOXKkUCjs37//mDFjmjVrZmhoGBcXJxuqvr6+jo7O//3f//Xt29fAwODGjRu1/wUpqFOnThkYGKioqHTq1MnT09PIyEhHR4dX8l26dOnQoUOV49euXUtEGRkZjLE+ffpoaGhUKVcikYgfycvP0qVLZU+vqKjgD1rr16/nWzZt2kREn3zyiZubm729vVAotLOze/ToEWPs/v37RLRz587qYR85ckRTU9PR0XHQoEH29vYCgWDZsmXSvbdv327WrJm6unqHDh0GDBjQuHHj4OBgxtiNGzeqlCuer1+8eLGoqGjz5s08zSIi2YIhVVlZ6e7uzstVRESE9BP169dPIBC0b9/ezc1NKBT27NmzvLyc783Ly7Ozs9PV1R0xYsTQoUPV1dU9PDxKS0vf9NekcJBf1Wt+xRg7evSojo6Orq5ut27devfubWBgwG+779P7TtyfPHnSuHFjCwuLe/fu8S3l5eWzZ8/mf961KVgWFhavurienp6mpqaZmZlYLJZuvH79OhFpamqOGTOGb+EF6+bNm/zLuLg4PT29QYMGsRoLVteuXadMmSKRSBhjEolk3LhxRMSrOZFI1KpVq2bNmvHSyRirrKyUjUHWjBkzGjVqVFJSEh0d3a5du59//pnXyC8tWNu2bRMKhevXr5ctWH/++ScRrVq1in/5999/E9GSJUv4l+PHj1dRUZHWbl9//TURnTt37lU/tI9BQECAUCjctm2bdMvhw4enTp1aXl5ey8T9pb8dxtgXX3yhqalJROHh4bLbbW1ttbS0VFVVpcVAXV1dWuWVlJR4eXmpqKg8ffqUvTpxX7VqlYmJCU8BGWO8yeqlhZMx1r9//08++YSXT14GTp48Kd1bUVHB/7N9+3aeh/Ev+V+H9NYbHR2N0iIrMzPTwMDAxsZG+lsoLS2dNm3akSNHWO0Sd3t7+5deWSwWq6ura2pqtmjRQnb7hQsXiEhLS+vzzz/nW3jiLk1obt++raWlNWzYMPbqxF0ikXTo0GH27NnS+mrEiBECgYCXt/LycgcHB0tLy+TkZH68SCTiR1Y3adKkpk2blpeX//PPPx4eHsuWLVu1atWrEvcNGzYoKyvz+kqauEdFRRHRf/7zH/5lbm6urq5uQEAA//LRo0d6enqdOnV69uwZ3yItqw1YSkqKjo6Ok5OT9FdQXFw8efLkEydOsNol7m3atHnplcvKyoRCoZaW1ieffCK7/dixYwKBQFNTU1oL8cQ9NTWVf3njxg11dfVx48axVyfuYrHYxcVlwYIF0i8HDx6srKzM05qSkhJra2sbG5v09HR+QA2/ypEjR1pZWYlEonPnznXu3HnlypU//fTTqxL3VatWqaqq8p+A9Nb266+/EpG0Vt+zZ4/sY8nPP/8se5c/evQoEfGn0wYM+VV951cpKSlaWlo9evTgLWhMTvXV+07cly9fTkS8eqruXQrW06dPiWjatGlEdOHCBen2OXPmNG/evH///l27duVbqhQsxlj37t35962hYIlEIunTPPv39nz16lX276928+bNr/34eXl52traP/zwg+zGEydOvLRgZWVlGRoazpw5k/9tSAvW0KFD9fT0pO0rjLEOHTo4OTnx/5uYmPTt21e6q7CwUF1dffLkya+NraFKTEwUCATjx49/6d53TNz79evXrVs3Z2dn2Z9wZGSktChK72GyiTtjbNu2bUR05coV9urEnTGWl5cn/b9EItHU1Bw5cmT1w+7fvy8UCnft2sUYy87OVlZW/uyzz156wR9++IGI0tLS+JcVFRUCgUDarM5Lcnx8/EvP/QjxH9fFixdfuvddEveMjAxpIbl+/bp0+5QpU1q2bNm9e3fpX3GVxJ0x1qFDB2dnZ1Zji3t5ebnsHYXfnHgtGhIS8qr21CpycnI0NDR+/vln2Y38PXj1xP3Ro0f6+vrz5s27dOmSbOLO+3TxRx3O2dm5U6dO/P+LFi0SCAQvfQxowL766isiioyMfOned0ncExISpOXq7t270u2jR49u27Zt+/btfXx8+JYqiTtjrFWrVu3bt2c1triXlZXJ3nr4DZ2Xdl5QDxw48NqPn5GRwRNx2Y379u17aeKenJzMb5q8Y480ce/Vq5e5ubnskfb29tJyNXnyZKFQKG1i539uP/7442tjU2jIr+o7v5o3b55QKJQ+F8nL+x6cGh4erqOj4+npWedXTklJIaLBgwc7ODjwTktExBg7ePDg0KFDmzdvnpyc/NITs7Ky7ty5w7tP1EBZWVnajam8vHzfvn2mpqb85VF4eDgRDRkypKKiIiYmJjU19VUX2bx5s0gk+vzzz2vziaZPn66mprZ48eIq20tLS9XU1GS767Vq1Yq33PC9vEcap6OjY21tnZSUVJvv2CCdP3+eMTZkyJD6uHhycnLz5s39/PwOHDhQUVHBNwYFBZmZmQUEBNC/xbIKxtjp06c1NDR4v+Ea8Ld40rMkEolAIKh+2MqVK5s0aTJs2DAiunjxYmVl5ZAhQyQSyYMHD+Li4iQSifRI3rlw2rRpvCLmleygQYP43sePHxORnp7ezZs3b926xR9gPmbh4eGNGjXq0qVLnV+ZF4yAgABLS0tpfSUWi0NCQvz8/Gqor9LT0+/du/fa+kpVVVXaL7OsrCwwMNDS0pL3nZPWV+Xl5TExMTz7f6nff/9dIBBU6cT/KlOnTtXV1f3uu++qbG/Tpo2ysvL333//4MEDIoqJiYmLixs8eDDfGx4ebmVl1aZNm6Kiolu3buXm5tbmeym68PBwS0vLKr3D6wQvNmPHjm3cuLG0XJWXlx8+fJiXq5fWSESUmJj48OHD15Yr2VtPSUlJcHCwnZ1d8+bNiSg8PFxJSWngwIFlZWV37tzhufJLrV27VlNTk7eqvtakSZNMTU35q2NZpaWl/G2nlIuLi/RO16FDB4lE8vnnnxcVFRFRcHCwioqKl5dXbb6j4kJ+Vd/5VXh4uKOjo6OjY2FhYXR0NL+Nvn/ve6xGWlqahYUFH0PzUo8fP/bz85Pdkp6eLvtldnY2H1bI+fn5TZ8+nf6tsJo3b+7j47Nly5a1a9cqKytHRESkpaX5+fldvHjx0aNHIpFIejObP3++vr5+cXFxRESEu7v7hg0bahP/nj17+GDBRo0a8dyLiJKSkrS0tH755Zc1a9aUl5cTUc+ePfft21dlMIRIJPr9999Hjx4tOw7yVf7++++DBw+GhIRUH6nj4eERFhYWGhrKb37JycnR0dGlpaV5eXlGRkYdO3Y8c+YMTyiJ6OLFi0+ePKnNR2uoeF7CfxqvMm/ePH19/RoO4EkJ/7+BgQFvdGSMpaWljRgxwsfHZ9GiRWfPnu3Xrx9j7MCBA9KRLsnJydJBNmFhYenp6ZWVlbGxsXy8o7Gxce0/yJkzZ8rKyqpXyjk5OXv37l20aBGv+Pit6/Lly+PGjeO/ejs7u8DAwNatWxNRv379Fi5c+NNPP5mZmfXp0yc8PHzXrl3Sa2ZmZvKflUgkqqysNDU13bp1a//+/WsfZAOTlpZWZeRoFampqVXqqyqJUVpammx99emnn/I8mNdX1tbWPj4++/fvX7FihVAoPHv2bE5Ojp+fX2ho6L59+xhj0ue02bNn6+joPH/+/NKlS507d+btr6+1Y8eOsLCwq1evNmvW7OTJk9ISYmhouHjx4vXr1/OnzT59+uzdu7fKYNPy8vJNmzaNGzfupYNQqwgKCgoNDT1y5Ii2tnaVXRYWFjt37vzss88cHBx69ep1+/btefPmffnll3xvUlJSo0aNhg8fHhQUxBhTUlIaO3bsxo0bq4wGa2DS0tKkowVeKiEhoUq5qjIaNSEhQbZcjR8/fvTo0fRv8WvRosWQIUP27du3ZMkSIjp+/HhBQYGvr++zZ8+kA1I53tmvqKjo4sWLvXr14oN2Xmvz5s0nTpy4cuVKixYtjh8/zrOcpKSkxo0bz507d+PGjfyZ39vbe/fu3VVuYUVFRVu2bJk8eXL1olLdjh07Tp8+febMGXV19Sq7PDw8fvnll3PnznXv3p2I7t+/Hxsbm52dXVlZqaysPHr06KtXr/7xxx+HDh3q0aPH+fPn//rrL14HNmDIr+o7v0pKSrKzs/P19f3rr78YY8rKypMmTVq3bp3sqOj34H23uPOenTUcIBQK9f9XladqVVXVNjL4mDwiSklJ4cMg/Pz8cnNz+VNaUFCQubm5u7u7lZWVWCyWLaM6OjoGBgZ6enpNmza9efMm7wP3Wpqamubm5k5OTvHx8fyNMxGVlZUVFxfHx8efPn06Kytr3759ly9frt5MtW/fvkePHs2aNeu136WwsPDzzz/v16+fj49P9b0zZsxo06aNr69vx44d+WDHwsJC6d5Vq1apqKi0atWqe/fu9vb2AQEBL22j/XjwW0jNk1To6OjIFrnqf8z29vbSIidtPMjOzi4pKbGysmrZsqWTkxNvh7h69WpKSoqfn5+JiYmmpqZsGqempmZgYKCvr29ubv706dOdO3eWlJTU8lOUlZXNmzfP1dV1xIgRVXb99ttvSkpKEyZM4F+WlpYS0cmTJ3fs2JGZmXnu3Lny8vJBgwbxFO3p06fnzp1zcXH5+uuvExISioqKfv/9d2nb2MyZM//++29eBd+9e9fMzMzPz6+GFo4G77X1lZKSUpX6it9spNTV1WXrK+mEGCkpKerq6k2aNPHz88vMzLx8+TIRBQUF2dvbOzs7W1lZlZWV8RcgnK6urrS+un79+vHjx2sTv5aWlrm5uaOj471796RTPZSVleXl5aWnp4eHh2dlZe3atSs8PHzq1KlVzv3zzz9zc3Nnzpz52u+Sl5c3c+ZMPz8/b2/v6nsrKyuPHTtmZGS0ePHi58+fZ2dnBwUF8Y7vRFRaWnrnzh1TU9OoqKhHjx59//3327Ztq2X6qLheW66UlZVrvg9qamrKlitpspKSksIrGT8/v8TERP5zDgoKatOmTYsWLaysrPLz8wsKCqTX0dPT4+XK1NT0ypUrp06dqk382tra/AXOnTt3pFMelZWVZWVlPXv27OLFi1lZWVu2bDl27Njs2bOrnLt169bi4uLq5a267OzsuXPnjh49umfPntX3fv3113Z2dr179+7cubOrq6uHhwfva8Hvd48ePbp69Wrnzp2nT59+586dgoKC9evX5+Xl1ebTKS7kV/WdX5WWlkZGRtra2kZHR6enp3/11VcbNmxYt25dbT5dXXrPXXM6duzYuHHjV+19lz5Y06ZNk+6ysbEZN25cZWWliYnJ3Llz2b8DpM6cOcOq9cGSSCRTpkxRUlKKj4+voQ9WFXyEFu/sNXr0aAMDA9m948ePV1JSKi4ult3o6urq5eVV/VLV+2BNnjxZXV09Ojo6Ly8vLy/v7NmzRLR//37p0ObS0tKdO3fOmTPn119/ffDgQUBAgLq6unRik8ePH69Zs2bu3LlbtmzJz89v1qxZjx49XvuJGio+hol3mKnuXfq4X7lyhYguXbrEGPv+++91dXVLSkpmzJhhamrKh844ODjwwV6sWh/3iIgIgUDAe+PV0MedKysrGzBggJGRUfXR68XFxcbGxjNmzJBu4W0bsl1Ft27dSv8OOQ0ICDAyMpJOFHDkyBE+Ov6l3/fatWtEtGbNmhpia9hcXV2trKxetfdd+riPHz+e75JIJObm5lOnTq2oqDA0NPzuu+8YYxEREdJSV6WPu0Qi+eyzz1RUVFJSUmro417F0qVLiejatWuMMX9/f1NTU9m9fDYJ2T7xEonE0dFR2h9aVvU+7mPHjtXU1Lx79y6vr44dO0ZEhw4d4vXVsmXLBAKBtL6NioqytrY2NTUtKSlhjDk4OHh6espev1WrVlUGVjY8dnZ20l6z1b1LH/eAgAC+q7KyslGjRl999VVxcbG2tjYfHMzzcn4zrdLHXSwWDxs2TF1dPSsrq4Y+7lUsWLBAIBDwesnLy6vKSOshQ4ZoaWnJjnuurKy0trYeNWpU9UtV7+Pu7++vq6sbHx/PyxXP5I4fPy69sT5//nzLli1z5sxZv359ampqnz59pNlF9+7dra2t+ZESiWTXrl2qqqrSIdENFfKr6peq2/zKwsKCD7SVfjpbW1s+MuR9et8t7m3bts3NzX1Vd6h3Ie0cQkS+vr6HDh06ceJEdna2r68v/dtT4qXd+wQCwcCBA8VicfUZTGvA25b4BNvNmjXLz89/9uyZdK+JiYlYLJZtTz116tStW7ekL4hrUFRU9Mcff5SVlbVu3drQ0NDQ0JC3NwwfPlzaqqqurj569OjVq1fPnDnT2tr6+vXrbm5u0pc1JiYms2bNWrly5YQJEwoLCzMzM9u1a1f7j9bAtG3bloh4Dlq3eHHiRcvX17ewsPDYsWMhISFDhw7lLytr6PnXqVMnY2PjW7duvfa75OTk9O3bNyIi4sSJE9LJraR27tyZn58/Y8YM6Rbepiv7ffkrxefPnxNReHj4//3f/0k7P3h7e/v7+0dERIjF4urfms8qLds+97Fp27Zteno670FUt6T1lUAgGDJkSHBwcFhYWF5eHn+R/dr6SiQSxcbG1v7bVamv+Msi6V4TExM+OEy6JSwsLC4urjb1lfTdkbOzM6+veN8qHx8f3qrKe7FLeym0adNm9uzZWVlZ8fHxPJgqH9PExISX1Qasbdu2Dx8+rI8OsikpKbzwKCkpDRo0KDAw8MiRI8+fP+eDfPiul1ZKQqGQd0/nWXsteXt7M8ak5SojI0MkEkn3mpiYlJaWyg6VOXjwYFJSUvVm+OrS09ODg4MLCwvt7e15ueK38n79+s2dO5cfo6WlNWHChNWrV0+bNs3ExIRPJEJElZWVFy9e7N27N29OFggEo0aN6t279/nz52v/0RQR8qvXXvkd86sq9ZVAIGjcuPH7r6/ed+L+2WefCQSCOXPmVEkUGGPveOWUlBRpb1Q/P79nz57NnDnT3Nycj8bjb21eVaB5+aiyrEAVoaGhiYmJ0i/5uiqNGjUiIt4njD+5ElFlZWVYWJilpaVsD+bVq1e3atWqW7dur/0gmpqaN27ciJTBm9zWrFnDp7iqYs+ePUlJSa8a6PPjjz8qKSnx7o8fJw8PDycnp9WrV1fpzFcnRU5NTY1nt61atbKzs5s/f/6jR494XUY1Ju4PHjx48uTJawenRkZGtmvXLjMz88qVK+7u7lX2SiSSdevWDRkyRLpyBBF17NhRRUVFWhqJKDQ0VElJia+OYWZmFh8fL/vX9+DBAwMDA14r3bhxQ3Z0IG86feniPh+JcePGSSSSuXPnyg7wpXqor3Jzc7/66itbW1veEcvU1FRdXf1Vhefq1av0uvoqJCREto9TlfpKIpFIF8SpqKg4duyYnZ2dbJ/j1atXt23btmPHjq/9ILq6ulXqK/6G5/fff1+0aBERNWvWLCsrKz8/X3rKw4cPBQKBNJgHDx7wiR2IKDs7++rVq/UxavODMn78+IqKCj5Ttez2Oi9X6enp3377rYuLi729PRFZWFgoKSm9anxqbcpVYGCg7HOsbLnq1q1beXk5X36LiEpLS0+ePOni4iI7XGHNmjU9e/asTV/zxo0bR/6v3377jYi2bdtWfaAqEW3atCknJ4ffB5WVlZs0aXLv3j3pXolEkpiYyONswJBfvfaDvGN+1a1btzt37kgfQtLT06Oiot5/ffW+B6e6uLgsWrTo+++/79Sp0+jRoxs1apSSkhISEjJ16tRRo0bV5goFBQU//vij7JaRI0fywfJDhw7lW9zd3a2trRMTE2fPni3t4c1XLZGe9eeff546dUoikcTGxgYFBXl6erZq1YoP5jt9+rTsI5SLi4uXl9ePP/6Ympq6YMGCFi1aPHz4cPny5ZaWljxF6927d9euXefMmZOWlmZnZ7d37947d+7waWW5u3fvnj59evfu3VW6m/OlE/kEXnv27Ll06VKPHj3atm1bpRzwpMHGxoY/14rF4pkzZ7q6uiopKV2/fn379u39+vWTLm/56NGjpUuXduzYsaio6OTJk0eOHFm6dCmfTeLjJBAIduzY4enp6e7uPmXKFGdn57y8vPPnz4tEIv5Srzb+/PNP/kKNc3V19fb2Tk5Olh0JNHTo0J9++qlJkyadOnXiW6ysrDIyMvhgKSK6ceMGn67r6dOnu3fv1tHRka6Wmp2dLVuqBQLBt99+u337dv6Scc6cOUeOHDly5AgR6ejoSJe+O3LkyP379/nU7FLGxsb8fYtAIOjWrdvly5d37Ngxbdo0vtjn+PHjp06d6uvrO3nyZDU1tcDAwAsXLvDVUisrK0ePHp2fnz958mRbW9ubN29u2LChV69eDX4qhhp06NBh/vz5y5YtS09P//TTTw0NDZOTkw8cOPDNN99IH89q9vTp0yr11dixY5s2bZqeni5twerUqVPTpk0TExP5ul1EJBAILCwsZOurrVu3NmrUSCKR3Llz58CBAwMHDrSzs+PPoseOHZN93HJ3d+/Vq9eiRYtycnIWLFhgZWUVHx+/fPlyvrgJEQ0YMKBDhw7Tpk1LTExs0aLFzp074+Pjg4ODpVeIioq6cOFC9b+OXbt2ZWVl8Xzozz//PH36tKenZ5s2barUV3yUha2tLb/Tjx07dvfu3X369Jk/f76JicnZs2c3bNgwYsQI/mpo6tSpmzZt8vb2njVrlr6+/tq1a8Vi8bffflubn63i6t69+4wZM9atW5ecnMzXrE1MTAwKClq2bFnfvn1rc4XHjx9XKVcTJ07U09PLzs6WlqsePXoYGhomJibyjlJEpKqqampqKluuNm7cqK+vLxaLo6OjDx06xFcF5i9DDh8+LPvs5+Hh0alTpx9++OH58+fffPONubn5vXv3li9f7uTkxGP28/NbsWLFxIkT4+LiLCwstm7dmpqayuch5S5evHjt2jXeHCBr27ZtT548uX37Nv1bzvmqFFXKFX9BYW9vb2FhQUTl5eUzZ85s164dY+zy5cu7du0KCAiQzh42fvz4JUuWjBkzZuTIkUS0ZcuWuLg4nqI1YMivZCOvj/xq5syZ27Zt69u378yZM7W0tNasWaOsrPzSJ8n69Z675nChoaGenp6NGjXS1tZ2cnKaOHFibGwsY2z69Omyc5BzYWFhDg4O9+/fZ4zNmzfPoZorV67k5eVZW1vLziC7ZMkSa2trPg8oN2nSJH9/f8bYsWPHrP/l5OQ0YMCAFStW8Alf8/Lyql+f93UuKCiYO3du8+bNVVRUTExMRo0aJZ0MmzH27NmzGTNmWFpaGhoaduzYMTQ0VPYjfPvtt+7u7tUn6g8ICLD+X1u3bq3+47p9+7a1tfXZs2f5lzk5OSNGjDA3NzcwMHB3d1+9erXsBKiRkZGenp4mJiaNGzfu0aNHSEhIrX8tDVlCQsJnn31mYWGhpaVlZWXVr18//pM5duyYg4NDlWlZ+aLKvG/3hQsXqhcJvtrlmDFjZDtr3rp1y9rampcW7u+//7a2tubdUh0dHfmv2MbGpmvXrl988YV0uvTJkydXuT7v/zpy5Ejrajw8PKTXHzVqFH9VXYVYLF6xYoWTk5O+vr6zs/Mvv/wiu1zFoUOHunTpYmxsrKGh0aZNm+3bt0t3PX78ePbs2fb29vr6+i1btly6dOnHsNbgax04cKBHjx7GxsY6OjotW7acMmUK/91NnDhx8ODBVQ4+ePCgg4MDX9l0xowZ1QtPVFRUZmamtbW1dOZgxtiCBQusra2jo6OlW+l9GpUAACAASURBVEaPHj169GjG2F9//SX97bds2dLb2/uXX37hf/JZWVnVr7948WLGWH5+/uzZsy0tLVVUVExNTT/77DO+mgmXl5f3xRdfWFhYGBkZde7cOSwsTPYjzJs3z8PDo8piwIyxIUOGVCmNfOmAKm7cuGFtbc3HfnBXr1719vZu2rSpmpqanZ3d0qVLZausxMREf3//Jk2aNG7c2MvL6+OZ033v3r1du3Y1MjLS1dV1dnaePn16YmIiY2zUqFHVe2Pv3r3bwcEhOzubMTZx4sTqv/d79+4lJSVZW1vzrsbc7Nmzra2t+d2T8/f354tO7Nu3T/p7dHZ2Hjhw4Lp16/j01cnJydWvz5fyffLkybRp08zNzVVUVJo2bTpx4kQeEpebmztx4kQzMzMjI6OuXbvKrp7LGJs+fTp/21Plo/Xt27dKuXrpZPCXLl2ytraWTn6flpY2dOhQvhBv+/btN2zYIFtiJRLJzp0727dvb2BgoKWl5eHh8fHcCpFfcfWRXzHG4uPjfXx8eIo1cODA979sKmNMwN75HQoAAAAAANS3993HHQAAAAAA3gISdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAAAUABJ3AAAAAAAFgMQdAAAAAEABIHEHAAAAAFAASNwBAAAAABQAEncAAAAAAAWAxB0AAAAAQAEgcQcAAAAAUABI3AEAAAAAFAASdwAAAAAABYDEHQAAAABAASBxBwAAAABQAEjcAQAAAKBulJSUyDuEhgyJOwAAAAC8q9zcXF9f3w4dOkgkEnnH0mAhcQcAAACAd6Wvrx8dHR0TE3Pw4EF5x9JgIXEHAAAAgHeloqIyf/58Ilq8eDEa3euJgDEm7xgAAAAAQOFVVFTY29unpKQEB4cOHTpI3uE0QEjcAQAAAKBu7Nx56I8/KgoL/W/fFgrRsaOuIXEHAAAAgLohEpG9PSUn0/79FBAg72gaHDwKAQAAAEDdUFGhb74hIlqyhNDRvc6hxR0AAAAA6oy00T0khHx85B1Nw6Is7wAAAAAAoOFQUaFVqyg6mry8SCSihARq2VLeMTUU6CoDAAAAAHXJx4eSkmjTJioupuBgwmqqdQUt7gAAAABQx0xN6elTSkujkhLS0iIVFdLVJT09cnJKKC7+XE9PT09PT1dXt02bNmPHjpV3sAoDiTsAAAAA1L25c2nGDNLTI3V1Kiujp0/p6VPS1n5858456TH+/v5I3GsPiTsAAAAA1KXCQtLQIF1d6tePkpOptJTKy6mwkAoKqKjINS/vTEFBQUFBQWFhoa2trbyDVSSYVQYAAAAA6kxKCrm50fTp9MMPJBDIO5qGBYNTAQAAAKBuMEaff055eXT/PrL2uocWdwAAAACoG1u20KRJZGxMd++SiYm8o2lw0OIOAAAAAHXg0aNH27fnE9G6dcja6wUSdwAAAPgglJaW/vPPPwkJCfIOBN7S5MmTr19vPG1a+PDh8g6lgUJXGQAAAJAPkUiUkJAQ9a/IyMjy8vI5c+asXr1a3qHBG9u1a9eYMWP09PRiY2ObNWsm73AaJkwHCQAAAO9JaWnplStXEhISIiMjo6KiYmNjKysrpXuVlJSaNWsWFxcnxwjh7WRlZc2ePZuI1q5di6y9/iBxB2iAUlJSIiMjnZ2dHRwc5B0LAMALlZWVbm5usnm5UChs1qyZjo5OeXl5RkaGSCR69OhRQUGBRCIRCtGbV5Fs3ryZMWZlZTVmzBh5x9KQIXEHaAgyMzOl75qvX7+ek5NDREuWLPnuu+/kHRoAwAvnz5+Pi4tTVVV1cnJ68uTJ48ePKysrHz16xPcqKSk5Ozu7u7u7ubmJRCI1NTX5RgtvRF9fPz8/X1lZuaSkRFNTU97hNFhI3D90hYWFqampKSkpqampWVlZP/30k7wjgg9CTk7O+fPn4+PjIyMjIyMjMzMzZffq6ekZGxvfuXNHXuHBR6u0tDQ1NTU9PT09Pb1v375NmzaVd0TwAQkJCSGiuXPnZmRk7Nq1S0lJycnJye1frVu31tLSkneM8JZmzZoVGBh47dq1jRs3fvnll/IOp8HC4NQPSFhYWFlZWWpqalpaWnJyclpaWmpq6rNnz2SPefbsmZ6enrwihA9ETk6Oi4vL48ePpVs0NTWNjIyUlZXz8vIKCgr4RjMzs/T0dDnFCA1cQkJCdnb2o0ePeI4uTdafPHkiPebo0aMDBgyQY5DwQRGLxU2bNs3Jybl16xYRPX/+3NXVFZl6Q3Ls2DEvLy9jY+Pk5GRtbW15h9MwocX9QzF48OATJ06Ul5dX2a6urm5gYKClpSUUCisqKuLj49u1ayeXCOHDcfr06cePH2toaNja2mZkZOTl5ZWUlJSUlPC9RkZG7v9ijAmwch3Utd9//33WrFmyYwqlVFVVDQwMNDU1lZWVHz58+P5jgw/WuXPncnJy7OzsXFxc5B0L1Iv+/fu3b9/+2rVrmzZtmjt3rrzDaZiQuH8Qnj59evLkyYqKilatWpWWlpaXlxcWFvK29rKysqysLOmR2dnZ8gsTPhSHDh0iomXLloWHh9+5c0dHR6dVq1bS181OTk5I1qFe7dy5s7KyskmTJjo6Ooyx8vLyoqIiXmVVVFRIqynUVyArODiYiIYNGybvQKAe/fDDD/3791++fPnnn3+ORvf6gK4yH4Rly5Z98803Xl5effr0mTFjBt+opqZmbm5uaWlpaWlpYWHRvHlzCwsLV1dXfX19+UYL8lVcXGxiYlJaWpqWlvbs2TNlZWU7Oztk6vDeREVFubu76+vrh4WFde7cWbpdVVXVzMzM3NzcwsLCwsLC3Ny8Q4cOaFsFrrKysmnTprm5uTExMc7OzvIOB+pRly5dIiIiVqxY8dVXX8k7lgYIibv8icViGxublJSUEydOtGjRIjIykifrpqamyMagusDAwOHDh3fu3PnSpUvyjgU+RiNHjty7d+9XX321cOHCHTt2SJP1Jk2aoMqCVzl58mTfvn0dHBwwR3uDd+LEiX79+hkbGyclJeno6Mg7nIYGXWXk7/DhwykpKba2tp6enkKh0MbGRt4RwQftwIEDRDR06FB5BwIfo6ysrAMHDigpKU2ZMkVPT2/WrFnyjggUA/rJfDz69u3buXPniIiIjRs3zps3T97hNDRY3UD+1q9fT0TTp0/HYhPwWsXFxSdPnhQKhb6+vvKOBT5GmzZtqqioGDx4cPPmzeUdCygMkUgUGhpKRP7+/vKOBd6Hb7/9lohWrlxZVFQk71gaGmSKchYbG3vhwgUdHZ3Ro0fLOxZQAEePHi0uLu7YsSMWlIb3r7y8/I8//iCimTNnyjsWUCSnT5/Oy8tr3ry5k5OTvGOB96FPnz6tW7d+8uTJnDlz5B1LQ4OuMnK2bt06xtiYMWMwOzvUBvrJgBwFBgY+fvy4devWXbp0kXcs8KETiUSxsbGRkZFRUVF79+4lorS0tPDw8B49esg7NHgfGGMqKiq5ubnyDqShweBUeXr27JmZmVlJScm9e/ccHBzkHQ586IqLixs3blxWVpaWloYWd3j/2rZtGxkZuWPHjrFjx8o7FpCbvDw6dYoCAuj8eTIxoeXLafNmiomhykrS0KCoKIqMpKgoUlNbd+nSf9/MKCkpicViXV3dixcvYq6hBm/v3r0jR47U0tIqLS2dNGnSxo0b5R1Rw4EWd3natm1bcXFx7969kbVDbRw9erSkpKRly5bI2uH9u3TpUmRkZKNGjQICAuQdC8hTeTklJhIRZWWRqirl59Ovv5KrKx0+TLLpWbdunezt7d3c3GxsbDZu3Jibm2thYZGWljZgwICYa9f0mzaVV/xQ36Q9ZLS1tfn8xfKOqEFB4l5nRCJSUXnxH5GINDRIIKCSEtLUfMnBT58+TUxMXLlyJRH17t37/UYKiqS0tDQ6OjoyMjIyMpL3k7l///7Zs2d79uwp79BAgZWWkobGi/8IhSQUkorKK+ur8vLy9PT0b775hoi6dOmirq7+foOFD86FC0REd+7QzJlkY0MVFZSYSLa2ZGtL7u7k5kZubtSmjZuu7n1+vJ+fX5cuXdLS0qybN99mYaHfpw9dukRYk6SBmjJlSk5ODn9Oc3R05FUH1BV0lakz06bRb7+RWExffkkVFdSmDU2YQLNm0bBhlJpKqamUlkalpXnXr/9famrq8+fPiUgoFEokEgsLi+joaENDQ3l/AnivsrMpIoJ8fenUKbK2piVLaNs2unGDVFWJiCIjX/zT1PzP1asLpGcpKytXVlbq6OhcvHjR1dVVbtGDgps+ndavJyKaNo0cHCghgdato9mz6dNPKSWF0tMpNZUyMyWpqR5paWmPHz+mf+srLS2tqKgoe3t7OX8AkJ+sLNq+nRYupP37ydKSQkLoxx+pe3datIj69XvlWeHh4UMHDbrRsqV1aio9fky9etGxYy+au6ABOXz48ODBgzU0NCoqKgQCwZUrV9q2bSvvoBoUtLjXmeJiioggiYSISEODEhMpO5vEYurUiaQPR02a6D1+HEtEenp6ZWVl5eXlJiYmaWlpo0ePPnr0KNYu+aiUlFBKChHRo0dkbExPntD69dSiBR0/Tps3//ewHj26ubi4uLu7t2jR4tdff5U2Y3gPGBB39aq2ubl8ogfFFx5ORFRSQkSkpUVXrxIRjRxJ8fHSQ4SGhnl5eY9VVVVVVFT4EIucnJyhQ4deu3ZNg7fYw8dHW5s6dSIiatmSGCNLS9LUpJUrqeYOET169EjauFFv1ChSViZjYzpzhsaMob17CTe+BuTp06eTJ08mIn19/aysrK+//hpZe51D4l6XxOIXiTsRzZ1LP/9MysrUpw9pa5OlJVlaUvPmQguL2xYWFvr6+ufOnfP09MzNzdXR0fn77793rFs3DjOsfWTOnqWyMrp5k1xcyMmJ8vMpLY0cHcnRkdzdyd2d2rYlV1cPDY1b/Pi+fft27do1LS3NtnnzUDMz7b59KSKCDAzk+ylAQfHEm2dNEybQsmWkoUG9epGTE1lYkIUFmZuThcUhc3OjJk2aJCYmurm55eTkmJiYxMTEfD9//sp16+QbP8iLjg5160ZE1KoV/fYbzZxJ9+7Rpk2vP1Fv5Eh68ICWLKHnz0lDg7KyqLT05d2zQDHNnDkzOzubty7Z29v/8MMP8o6oIWJQR6ZOZYyxyko2cyabM4cxxrZtYxMm1HQKX6HAwMBgVYcOEkNDFhHxPgKFD0NSElu1ijHGtm9nUVHsyy9ZYSFr04aFh9d0Vnh4uJmBQXabNszUlBGxrl1ZWdn7CRgakmnTXvxn6lS2fj1LSmLXrzMPj5pO2blzJxFpaGgMt7evsLJi+/a9hzjhA9e5MyNigYG1PkEiYWPHMiI2bBgrK2OpqSwpqR7jg/coLCyMiNTV1ZWVlYVC4aVLl+QdUcOEPu51JjWVLC2JiP75hzZsoPnzydGR0tNfbHwpiUQy1Nv7u+fPXZOTKT2dzM3p5k0yNn5vMYMc5edTbCx17kw3b5KKCp0/T9OnU3g4WVpSixY1nViyb5/myJEkFJKBAT15QkOHUmAgYdldeBOZmcRn9UhMpK+/pokTqXdvSkkhK6uazho7dmzHhw8npqUJ0tNJX59u3iSsn/oRy8ggS0tSV6fsbNLWrvVpIhHt2kXjxtH69aSiQsrKlJdH8+fXY6BQ/woKCpydnTMyMszMzDIyMubMmbN69Wp5B9VAyfvJoQGaP58RsZEja3d0ZiYzMWFEzMKCEbH+/ZlEUr/xwYdn9WpG9N9G0NdbupQRMTU1pq3N/PxYeXk9BgcN2pYtjIi5udXqYPHz58zRkRGxFi0YEXN3R9n7mK1ZU0nEhg592/MnT37xn0mT6igikJshQ4YIBAIrKysisrGxKS4ulndEDRZa6epYeTnt2EFENH167U4wNaX9+0lJiTIySEeHLl2iuLj6DBA+RMHBRPSi22itLFxIkydTeTmNGkVBQXTvHt28WV/BQYPG55ap5arkQi0tOnCANDUpMZEaNaKoKDp+vF7Dgw9ZSEj3tm29Ro5MfcvzpS/88eZf8RUUFAgEgtatWwcFBW3fvl0TQxfqDbrK1LHt22n8eGrfnv75501O++EHWrKE3N1pzx5q0YKUMWj4I8L7U2loUE4OaWnV+jSxmEJDydeXfvqJbGxISYliYmjx4noMFGqtpKQkIyPDzs5O3oG8Rng49exJpqaUkvJiHtJa2bKFJk0iU1P6/XcaMqQe44MPWHp6uqWlpYaGRk5OjtYb1Fwy/vyTMjNJWZl0denzz+s6QHiv7t696+Lioqqq+vDhQywRWK8+3AQxKysr9V9paWlxcXF5eXnfffedj4+PvEOrSXj4OlNTn2nTzN7stO+/J11d+uIL+vVXEospO5vGjCF39/qJET4sBw9KGBMOGPAmWTsRKSmRry8RUXo6LVxIRHTmTH2EBzXIz89PSkpKSkrKzMzMysqS/p9PfN6rV69Tp07JO8aa/PXXenv7LiNGuL5B1k5EEydSTg6NHk1Xr9KCBSQSkZfXm7wwgoYgMDCQMTZw4MC3zNqJaOxYKiggxrASUwPg7Ow8ZMiQkJCQlStX/vrrr/IOpyH7UFrcg4OD//nnn8LCQmmmXl5eXuUYJSUlY2Pju3fvGn+owzcjIiK6dOnSrFmzxMRUNTWlNz4/P5/+8x9asYIqK2n69P9ZPBoarq5du5WW6nz77W8DB756IHMNJk2iP/4gIpo4kbZsqdvYoLqKiopx48YdP368sLCwsrLypceoq6uLRCKxWLx79+6RI0e+5whrKTk52dbWVllZOS0tq3Hjt5pUdMIE2rr1f/4DHwGxWBwfH+/j4xMfH3/o0KEheOsCREQUGxvbqlUrNLrXtw+ixX379u0TJkyo8gjRuHFjCwsLy381btx4wYIFycnJo0ePDgsLE36Qc2isX7+eiMaPH/82WTsRlZe/aHRVVkafv49Eenr6pUsXNTQ0evZ828fRnj1p4UJSUqKuXes0NHi5/fv3BwcHi0QiIjIwMLC2tjY1NW3atKm1tbX0/0Q0fPjw69evT5kyxd3d3cHBQd5Rv8T69evFYvGoUaPeMmsn+u+yl+jdVxcyMzOj/nX16lV7e/vz58+rvMnaomlp1LgxqatTfDzV7eK2srFdvnw5Pz9fV1dXW1vbAOtIwL9atmzp4+Nz8ODBFStWrF27Vt7hNFzyHRvLeXl5EZGTk9OmTZuOHz9+7969kpIS2QNEIlHHjh3/n707j4t5ex8A/sxM+54W7auSshdFKDtd+7Ve4kd2oezXGq5rL7tr37dwVchSQqJFISlS0V6iZdqmpmae3x+H+Xajmhm0Oe/Xfd1Xtz6fOcc1feb5nM9znofJZCorKwPAli1bGmqqtcjIyJCUlJSUlExPTxf/VWbOxKAg9PLCS5d+3NSoxmvHjh0AMG7cuO96lYoK5HJ/0IyoOvTq1QsABg8e/OnTp9qPMTY2BoB27dpVu6A1Bvn5+eRy+vTpU/FfZf58DA3F27dx/fofN7VfSEZGhp+f37p164YMGVLtSTJppD158mS+KHXGdu78XBWd9BX5Ho8fPz5z5oybm1vPnj0Vvqr1qK6ubmpqCgCamppJSUnfOxjVXLx69YrJZMrIyHxXIETVquFTZZKTk1u1asViseTk5BYtWrRmzZpvHubh4bF+/XoVFZXCwkImkxkUFNSzZ896nqqAILG1am5rXFwcAHTt2vXBgwfivzSXC6GhoKX1g1dLqMYHERMTE4cPH/769esrV678ThLWqcbtxYsXnTp1kpOTKy0tbd269evXrxnfatiekJBgbW1dVFSkpaWVnZ09d+7c/fv31/9sASA/P79a/j35OiUlRUFBQVNT8+3bt+K/ekUF+PuDvDz07Usb1wsvOjp60qRJaWlpbDa76vd1dXWtvwgPD/fy8iouLl69evXGjRuFfGVPT/j4EVRV4f7976r3M3/+/GPHjnE4HMF3VFVVNTQ0ACArK6uoqAgAgoKCdu3a5efnZ2pq+uTJE01NTfHHo5qRsWPHXr58ef78+Xtoc+WfpKHvHNDd3R0AyL370qVLazqMx+P1798fAPT19QFAT0/v48eP9TZJDoczbtw4W1tbIyOjWh5cSkhIKCkpvaN94BpaSkrK1atXV6xY0bFjRzc3N1FPLyr6vH6dl/eDJ/b1Apu8vLyCgsK9e/d+8EjUz/F///d/guvVxo0baznywoULACArKysjIwMA5+uxzyibzR45cqS+vr6srGxNFysWiyUlJSUhIRFCGzbXOwMDAxaLBQDa2tpDhgxZt26dn59fZmam4IA7d+4wmUxVVVVy2L59+4R85Z078e1b5HJx7lzxp1dZWdmiRQsGg9GuXTsjI6Ov30WGhoajRo0KDw8vKSmxtbUFgK5du9K63RRBFt2lpaXT0tIaei7NUwMH7iUlJS1atAAAKSkpJpNZe8j74cMHkjxqYGAAAIMHD+bxePUzz7lz51aN1xUUFAwNDS0sLMzMzHR1dcme+vnz55M9Ol26dCmnTUnqV15eXkBAAAmIW7ZsKfibYjKZDAbj77//FunVDhzAV68QUZSOSDV48+aNr6/vmjVrBg8eTNarqiIZ0gCgoaHx9u3b7x2M+slycnJkZGSYTCaLxZKWls7Ozq79+ClTppDgDACUlZXrzCgoLMSsLETE1FTkcDA/HxHx40eR06A2bNgg9aVMjJSUlLq6upGRUatWrQwMDNTU1CQkJADg999/X7p0af0vglBv3rwBADk5OV9f35qO4XK5AwcOJFcGcpfl4+MjzIt7eeH794iICxaIP8PAwEAAMDc3Jx3sq91gVHvbZ2Zmkp47Q4cOraysFHaM+vrsphrE2LFjAWDEiBHBwcHR0dHJycl5P3wZ7BfWwIH7wYMHBYH48OHD6zz+wYMHLBZLkOy+efPm2o+/ehUXLkREXLEC79xBsqy5Zo1ozf4qKyvJhWn69Ok1LV8pKCi4u7vn5+eTrNaFZFTqZyoqKpo2bdqIESO+3r2urq4+aNCg1atXjx07lsViMRiMEydOCP/KBw7g7t347784cuR3zXDdunXVFqvk5OT09fWNjY0VFRXJd+7evTt8+HAAMDExqTMQpBrWhg0b4Mty+9SpU+s8vri42NLSEgDIBcTGxqb2W/oHD7B7d+Ry0c0NIyORvGe3b/8cigmJz+e3adMGAFatWvXNNB4Gg6Gtre3i4lJRUWFvbw8ATk5OIiVSU9/Dw8MDAMzMzADA09OzpsMKCws7deoEAOT6JicnFxoaWueLP3qEd+4gIl68KP4MZ86cCQCrV6/Oz8+/detWTk5O7cfHxcWRBbjZs2fX/eqxsTh3Lm7ciOvWiT9FqnGLjY39uoIIX0EBlZVRXx/btsXu3TEjA5ctw/Xr8Z9/Gnq+TUwDB+7t2rUDAPI7HxgYKMwp5KqnoqLCZDIlJCSCg4Or/rSyElNT8dEjPHsWN23CgwdxxgwMDcWFC/H6dTx1Ct+/xxkzsKxMhEleuXKFXGeLi4s1NDSsra1Hjhzp5ua2a9eua9euPXv2rOoetYiICLLWdfXqVRHGoETXqVMniS+1LBQVFe3t7RcsWHDq1KlXr16RKCQyMrJFixbkY09SUvIO+UATwoED6OeHsbE4ebL40+Pz+VpaWgwGw8zMzNTUVOWrQsVaWlpDhgwJCQkpKSmxs7MjgV1RUZH4Q1I/E5fLJe8l8oRNyD2dr169Ih0EydLpokWLyPc5HE58fPyDB9knTqCHB7q44IABuGcPbtyIO3d+DtxnzMBTp3D8eNEC99u3b5Noj8vl2tvbOzk5zZo166+//jp9+vSDBw+SkpKq3jykpqaqqanVHkFSP5aVlRVZ6wGA169f13JkZmamoaEhfFnb0tDQSEhIqOV4Ph8PH8ahQ5HNRrHXjioqKki2+suXL4U/Kzg4mKSE7dy589tH8HgYF4f37qGrK3I4iIgeHpiaKuYsqUaPfPypqqrq6OioqampKCoig4EAn/+RksI//8QPHxARV63CKnliVJ0aMnC/d+8eAJBiUm3atBFyyYfH4w0YMEBwLVNTU1u0aNHkyZMdHByMjIzs7b0FbwwAXLkSw8Jw+nR0dcXr13H5cjx7Fp2cRAvcHRwcAGDv3r1CHu/l5UVuLehe+58nPz9fSkqKwWBs3rw5Pj7+m2+egoICcmdI9kUoKio+e/ZMmBf/IakyYWFhAKCnp+fv708idSUlpap3F1UP/vjxI1mBc3JyqqioEGoAHg+Li8WfHyWic+fOwZe8l169egl/4uHDh8mKqYSEBIPBsLCwEGzjc3TcVPV6tXgx+vriqlU4ZgxGRqKnJ6al4erVogXugwcPBlFKb12/fp3BYEhKSj558kSEYSixxMTECKL2jh071nl8bGws+YgkF7FWrVpVXf/m8/kJCRUXL+LSpdinDyor499/Y0AArlwpfuB+584dALCwsBD1RG9vb5KaeObMGUTk8XivX78+e/ZsyZ9/Ys+eqKCAAKitjbNnI7lc79yJb96IOUuqcbt582a1hSpTVVWserFTUUHB85l//sHnz3/qfI5kZh7NzLxU17OjpqIhA/cRI0YAAFlROHDggPAnZmdna2trW1hYmJiYkKx3AUfHzTo62K0bjh+Py5bh9u0YEYEREWhlhdev4+3biIiLF4sQuL969YrBYCgqKrLZbCFPKyLbPAAAIABJREFU4fP5pL2rjY1NmTAj5eRgfDzSR9WiOHHiBAAYGxurqqrWkgaTlpamp6cHX3IVtLW1k5OT63zx8HAkMcy1a+LPcPHixQDg5uaWn59/8eLFxMTE2o9PTEwk8ZyLi0vdr/7qFc6ejV5e+GUFl/rZyCY8LS0tMZ6nTZw40dnZuXfv3uR0AJCWljY1NXVx8Zw8GVevxkOH0N8f//0XfX2RzUZjY4yIwGPHEEVMlXn79i2TyZSVla2lVOXXFixYQJZCcnNz6z66tBQzMoR/caqqtWvXkvgbAITce/Pw4UNpaWlBzkznzp1Pnz69dOnSPn36qKio2NufrRoOzZ2Lr1+jpyf+9puYM3RxcQGAdWLlsWzfvh0ApKSkrK2tBdmAWV27fp6coSGOGoVXruDWrfjoEU6dSjPdmyU2m03uM52cnJYtWzZz5syxY8fOGzMGu3VDS0vU00NFRTQ1xRMn8MoVLCrCKVPwJxfM3Zaa2pwCrAYL3FNSUlgsFtnxKVJYTLx8+bKgoIBcyCZPnnzs2LHAwMCEhIRqgXJqKpJPouBgnD0br1xBRIyJEeFyMWPGDACYP3++SNMTJLsvqHOLkI8PenjguXM0AhOJk5OTYPmz9iSrly9fkjQVEru3adOmzujkzBkcMABLS1H0gjSf8fl8Mtzjx4+FPysiIoKkYWzYsOHbR3C5GBWFvr64YMHnK92OHQ2zalVUhH5+GBXVAEM3hNDQUMFCqYGBgbBPRb7gcrmlpaUkKeX48eOZmZnffEYkKMefkoIDB+KqVYiIHI4I1ytXV1cAmDFjhqjTI7clv//+ex2HPnmCbm549Oh3bX78hZHtB+TXvPa8l6rOnz/PYDCYTKaGhoaSklLVtaqhQ9cMHYoeHnj9OmZmYmAgpqcjl4seHujpiTduiDY9LpdL3qWxsbGi/tFev3796NGjgQMHkssy+U0ZOXLki7178fZtrLoB+s0bDAzE0lIMCMApU2j43sy4u68GADs7u7o3K/v5oacnCrGa9p3WJycfycy8T/b7N30/MnBPex2Z8iqMy6mxJhSPx0tLSwsJCTl79ixpUEIaCoq3lfPSpUvC59j88w8CYMuWomVS5efny8vLMxiM2jMRvykiIoIsk1whtwuI5eXlSUlJvKAgPHkSPTxw6lRcvBhnzvx8wrJl2FzeVT8byZMhe1/U1dXrjKKCgoKqLln17NmTQ5Isv6isxJgYPHEC581DW1v09MRLl3DDBvEDd0GejKh7/q5fv04SKo4dO4aIlZWVMTExx48fL1y2DLt2RWnpzw8Z585F8qfetw9FSUX9MXg8/L//wxcv8OBBPHu2vkf/QYLObH1wYWfU7f/Mn8/nF+Vmp8c/e/XY38vLy83N7ffff+/atauSkhKTyRwwYIChoeHWrVvFGI4kzFhbWwtz8P37yGIhk4kilQktLCwkUV10dLSo00tMTCQ7/vfv30++U1lZmZaWxn38GC9cwG3bcP58nDcP58z5/Mb7+2+kdW9F9OLFC5IyR57HinTujh07jh49SgqXWVparlu37vr165k1f575+yMAysujSP21SIZD27ZtRZobMXv2bPiyddvd3b3OLa1YXIwtWyIAirguRjVmgYEoK8vp2/dPMe79fp5tzWs3xY8M3H283N5FP/p3p2tFeVl5aVFuRtLzgIv3z+245rXw5J+//+M+pFoFdDU1NTc3twsXLoiXC96jRw/hc2x4POzfHwHQwQGFr1hFHvwNHDhQjOkhImn5Kysra2trq6urSwLNSh2d/z3XNDHBmTM/J8ksXowiPnb4ZQnyZABg1qxZwpwiWLIi1dOHDRsWFxd37tw5d3f3Hj16ODr+Z2uEqytGReGmTShEoaNvI3ky7u7uYpx74MABAJCQkOjQoQNZmQOA5O7dEQCZTLSwwEmT0N8fV67E27dx2rQGWK969w4Fwatw//8boVuHVvN5lVe3z02NexrmdzT6nvfu6XYbRxh4DNH2GKK9cYQBi/WfqggsFqt9+/YlJSXV7vqE1L59ewA4d+6ckMevXSvyWoOnpycA9O3bV4zpIeLFixcBQFJS0sbGRl9fn+z8LrWw+N8vhrIyzp79+QK6eTPSPTwiWrVqlSBPZtu2baKezuVySSGHuLi4Og/m83HqVARAdXUUvtIsaVNQ4xO/mgm2tJIH6ULlXCFicDDKyCAA7tgh6ohUI1RcjKamCIAiVmD+6cLZbEQs5fF8Pn5sBjkzEvDjlBblpcaGK6nrPPU/Wcktl1fRuL538f8+9iQk+Xyetra2oaGhoaFheXm5r6/vgQMHJkyYQEpZi+TFixchISGKiooTJ04U5ngmE06fhk6d4OFD2L07adEi0zpP4fP5pFrl/PnzRZ0eAOzYsUNTU9PY2JjP54eHhwOAhISEnp5eQffuapKSYGgIBgZgYgJ8Pvz5JxgZgZwcfEkKpGp3+fJlACgrKwOAMWPGCHPKhAkT3r9/v2rVqqKiIgUFhVu3bvn5+Ql+6uBgbWw8xsYGyD8FBSAnB+7uMHcuXL8OysrQq5cI08MvlYiEnFtVSUlJrVu3HjlyZGhoaHR0NAAYGxt36dLlU58+hn/9BdbWIHhQ3rEjpKXBwYMQFATbtoGPD8jJiTqcmJSUIC8PAIDDgS/1wpucovyc++e2S8kq5GYk6Zp1lJCSyc9OAQB5FQ0ldW1ldZ0VbTRU1TT19fX19fVbtGjh5OT08uXLlStX7tq1S9SxgoKCXr58qa2tPXr0aCFPWbsWgoMhNBQ8PPwPHhz0dW21avh8PmnOShLWRbVr1y4pKSkrK6vCwsLIyEgAYDKZOjo6uZ066bVrB/r6YGAABgbQogWsWgUdO0J6Ooh+3f7FeXt7A0BGRgaDwRDj4nDnzp28vLwOHTqQfJvaMRhw6BBkZsKdO7B4cdaJE1IkB6YWXC6XXBWFf5cKBAUF5eTktGzZ8sOHD4MGDSI3GHXr2RNOnYIJEy5cu8bS0xs7blwtx/L5/JcvX8bExIwZM4ZUsKEam+XLISkJOnaEJUsaeir/1VVJCQCmvXmTwOGoSkr2UlZu6Bl9nx94E3DjwIrigo9Xts15/O/Bgpz03Ix31zwX3D+7/dnd80nPg3Mz3lVLQBfsiBJpHxUxbdo0ABC1Kea9e/wePQ4ymcyAgIA6D/b19QUAU1NTMdo8CXJsyPLDtWvXkpOTa8zoKC7G7Gz8+BEHD8YzZ0Qd6wdoUvtiRc2TqWr+/PlTpkyZNGmShISEiorK8OHD//rrr9u3b9f0DgwORhYLVVVRpId+YufJIOKSJUvgS43nuXPn1v2rUV6OJiYIgKNG1d/Se24uenqihwcuXNh060KQFfdrngue3jyV/S62soKbm/GuorzG3eRPnz4lhYz+/fdfUccaNmwY1NVp9WsZGdi9uysAbNq0qc6DfXx8AMDIyEiEJjhfVGuEd+HChffv33NravuUn49v3mBxMU6fjqdPizrWryk5OZk8vyXNULt27SrGizg7Owv5ZhAoLMTx419LSsrZ29vX9KQoMTHx0qVLy5Yt69Kli4SEhJGRkRhzI1taycaekydPinRuxKFDDAZDRkamWnFn/G+rafIWlZGRsbOzK6YFtRqfsjLs0gUlJfHFi4aeSg3OffhgHRk5MS6uKUU83/IjA/fzGyY/vOh1fd+yJ9f+KchJr/N4LpfbrVs3ABgyZIhIIU5eXp6cnByDwYiPjxd1kqQMvKamZsZ/CyNwudx3797dv3//1KlTHh4e06ZNIw04t2/fLuoQ+CXHhuRyTJw4Uahzjh9HAFRUrNdIKCsLZ8/GTZtw4cKmskNIjDwZAR6PJ2hPI0zfAB4Pf/8dAVBXV4SKw2LnyfD5fFJkSVpamslkZghZuyM2FkmlLWG6n3y/5GSUkMCePfHgwc9VeJum6KArIZf3vYt+9OF9XHGBUK1Dd+7cCQAqKiq193iu5v3790J2Wv3arVu3SMOKR48eCb6Zn5//8uXL69evHzhw4M8//5w0aVLPnj1JEQ+RojoBslRP3nvDhg0T6pzz5z9fr0S/CDcbhZ+yYh/5pb/5xhbtgpz010/893luHTRokKBlsqmpqZaWVnx8/HPRi9+VlZWRTQiitlgWFIMfNmwYuamrGg1Xa+esrKzMYrFuiLinVbCllclkSklJidEjc+HChWT0y5cvnz9/ftGiRQ4ODtW24ZLVENLSbsSIEWLcoFI/0JEjn784ehSPHMHISETEY8fwq5uvRqScxxscHW0dGfmwie8nbOAGTCkpKeQ22svLS/iztmzZAgBOTk5ijMjj8fr37w8AVlZWK1asmDhxor29vZ6eHovF+vpxhJSUlKiL+mQIkvxDPkrDwsKEPXPSJATAtm2xpMYNvj/Ypk2fE1VPnsQqkUFjJnw9mW8i+SfCL9WXliJJLx80qFCY2keC4FuMqthkqZ78RohUKRwfPEBp6dP29jvrus/My8u7efPm8uXLhSmL+W3btyMAtmqFAJ/rnvwy+Hw+aXPbtWvX2nugVuXm5gbCdVr9puXLl5OV2gEDBlhZWSnWnFDHYrFEXQTBKp1WSewlwu8UuV61a/ezS7k1Wq9Dbz31PxV8aXd8xF1OUUFe5vvgS7vPrvtj28S2ZKfE/AkDyF+NhoZG//79yVWrf//+NT7NqNm1a9cAoHPnzmLMU1AM3tTUlHxRlaqqqrm5ubm5eYcOHcgTP1lZWWG6tAqQVhWkNLOwN37/xePxyL7barS1tYcMGbJu3To/Pz9y35uQkEBuNkRdtaF+rHnzPn/h6oru7jhnDlZWil/Ood5c+PCh+7Nn7n5+TbpXdAMH7lil/UedhfM4HM6nT58qKyvJauutW7fEG/HDhw9KSkqCklUEk8lUU1MzMjIyNzdv1aqVjo7O8uXLJSUlGQyGj4+PSK9PcmzITh3RrrOFhWhuXqigcGzlStH+SGJbtepzla6rVz9XuW/c0tPTxc6TIVavXi3qRf/TJ3Ry+tiihVm/fv1qCtcKCwsfPny4c+fOwYMHMxgMDQ0NMTKsyFI9KcsgfMMv4rWPD4vFYjAY58+fr/r9oqKiR48e7dq1y9nZ2dLSksFgAICEhESLFi1SUlJEnSEiIqnKrKKCAI33mehPk5eXR/IBli1b9s0DysrKEhISgoKCTp065ePjU1RURBZKhey0+jUul6ulpUXuBgk5OTldXd3WrVu3adPG1NRUV1dXRkZmz5495JZP1HcO6bRK4jkrKysRPs8KC9HcnCsp+a+Hh6h/qObhdegtvz2Lz2+YnJv5/pzHpKTnD48tHUZC9m1/WJ1ZO+H6mX1Xr14V/KK9f/+eFPL/448/RI0bJkyYAKL01arm2rVrioqKpKu3vLy8np6evr6+YO87ISkpyeFw5syZQ66uwi/tky2t5PfirLhlpkpLS8eNG6epqTl8+PANGzb4+/t/qOGBHunSamTUf/fu+lrhor4ybhxu2oSbNqGrKy5ahBERuHdvEwjcy3m8zo6OACBqXNeoNHzgjl8ek+nr65OMXg6Hk5SUFBAQcOjQoeXLlzs7O/fr18/ExITJZI4ePfrff/8FgFatWokRGBGCEvKDBw/W1NSsVutGwMfHZ/PmzeQjTaTlyX79+gEA6ftzWsQc0Mro6I5t2gh5+fsBzwqfPsWlSzEiAqdMwaKi7321n6CivOx51NO9e/dOmTLFysqKxWLp6Ojo6upevnz56NGjYryg8HkyVb1794584o4fP5584nK53FevXp06dWrmzJmWlpZVn9ioqakxmUw/Pz+RhhAzT6YKkvAgKSm5cePG3bt3Ozs7t2nTptqmRjk5ue7du5MIr3379gUFBaKNkZqKDMbnQhDm5qLOkBD7N7eRCAsLI7f0vr6+lZWVnp6eCxcuHDVqVJcuXQT9lQhHR8e9e/eCqM9P/is2NpZkAE+ZMuWbVyri0qVLZNO2tLR0lCj19UmnVdIw5fDhwyLNjf/8uWPnzgDg7e0t4h+rOXgdeutd9KPMhOjgS7tuHV6DiPERd+Me3yz4kFbTKVFRUaQbwJo1a4QfiMPhkLyROvu41WTPnj3kfdjrvxvtVVVVq7Vz5nK5AwcOBABTU9OaQueqBLVuWCyWjIyMqC1ZxHPtWpC6Oo/JxIsX62E06huqrriTJjQrVjSNGmOk3F+nTp2a7qJ7owjcqya7l5SUkEXBr0lKSlpbW5ubmwPA7t27xR5u2bJlADB58uQjR46QV9bS0rK1tR0zZsySJUv27t3r5+cXHR1dWlrK5/PJrjJbW1shn4zHxcWRT1kA0NDQEKNy3KFDhwBAXl5eUPOLFIB/9OiRt7f3li1bZs6cSe5kZGVl+/fvL/42nfHjcdo0vHQJb99uwBLyyTGhV3fM89u7JCU2nHwn+31c5K0zfnuX/LOg34YRBgsnDqz6HiAfe3PmzBFjLFHzZKqKjIwkQ3fu3Lljx46kWF7Vienr65uZmVlaWs6dOxcAZGVlReq+JH6eTBXu7u4AUDVYl5CQsLS0dHZ23rVr16NHj8jbuKCgoF27dgDg4OAgVHPfL+4ePJjYo0dZmzYIgGI9FyotLdXV1Z08ebIY2QKNx99//03+slJSUqrlrkhKSmpoaJBnd5aWlqT51+XLl8Uea+bMmQAwb968S5cuSUtLm5iY2NvbjxkzZvny5YcOHfLz84uMjCwsLCQHk+VSU1NTIeMn0mmVdDlQVVUV42JC7kwUFRWr7jjicDjx8fGBgYEnTpzw8PBwcXEZMGCAlpbWsGHDSuotD/Dne/s08MaBFf7/rEyOeRJ4UtjdBf7+/uTqISiZX5OUlJSrV6++fPmSVKkSb0srQaonX7x4cevWrb/99tvatWt9fX3T07+9Fa2wsLBTp05kxDr/vsLDw6WkpEiezMiRI8Weoah27kQAlJJC0fMlqR9AcEm7cgXJbXtuLm7ahEJ3FWswHA6HrKueOnWqiX4MNYrAHRFTUlJIhuWCBQu0tbV1dHRMTU3Nzc2NjIzI+qXgc1FWVpaU8xNvIEH/woiIiNzc3Pj4+Npj69zcXLIOWtOT8WpI0EayHVavXi3eJP/44w/yWWhra1ttDe9rzs7O4oyRnIwsFkpKIgAOGSLePH+I5JjQkCv7w3yPxIffvXFgRajP4ePLR5DHzR5DtNcP0zuwetrUqVP3798fHh5eVlYWFhYmJycHQjcMr4rkycwWdxPnrVu35OTkyPNlJpOpq6trYWFhbGxc9aENg8H49OkTeRuoqam9EXqr8ffkyQjweLyFCxeamJi4uLgcPHjw6dOnNd1wpqWlkYvXuHHjhF8C79q1KwC0VFPz6NKlSPQuP/ilUKatra0Y5zYePB6PrEqqqam1bdvWyMhIVVX1mxUbmUwmg8FYv369eAPl5eUJesAJ89dUVlZGQq6xY8cK8/rz5s0TvOtWrFgh3iRHjRpFbmOGDh1qbW1NsgS/icFgCHkhbd6OHj0KACwWy9fXt+r3q24bJdURAGD58uVjx44FgB3iFjtPS0tjMplycnJFQj9WFWxpHTp0aJ2PdqOiosgTmwsXLog3Q/EsXIgAaGiINa2q8Xi8N2/enDt3btGiRb1793Z2dm6igVqTcOYMMplYj/du4tu4caNg6U1WVlZLS8vc3LxLly79+vUbM2aMi4vLokWLPDw8FixY0DjrFzWWwB0Rr127Ruonfn25Z7FY+vr6PXr0mDRpElmb19PT+/hRqEIQ1Zw7l9Whw3w7OzvhTwkNDRUy2V3QuVBCQkJCQiItrcYHprUrKiqytraW+1KWW1JSUltb29raWrDMFhAQ8OrVqxcvXpDVPtJlUzRLlvxviyF50NVAkmNC75/dHnHj+LO7528dWo2Ij6/uv7pjXpjvkdS4iG824vX19SX53KLWHSOdeu+J1I6yCkGalqOjY9X3p6SkZKdOnWbMmHHo0KFnz55VVlZWVlaSXYzGxsbC1BLh8/kkQ1TsPBkxvHz5kqwHL126VJjjU1NTBU+TzMXNkxk3btz3hCCNR05OjqqqqlSVMvYSEhL6+vr29vZ//PHHsmXLyLO7I0eOsFgsJpMp3rtu165iR8fbo0eL8AQ6ISGBXIXqzHsRXK8kJSUlJCRSxW0umJ+fb2ZmJleljYC0tLSpqamjo+PkyZNXr1596NAhf3//w4cPkwupqFlkzRJZRJCTkwsLC8vIyCB5m9U++JSUlExNTdu1a6egoMBgMMTOkzl8OKVLl/UTJkwR6ay4uDiy8+Hrx5tf16VhsVgKCgr1kycjwOPh3Ll48yaSd7q3N/J4GB+P58/j4sXo6Ii6uvpVbxqZTOY8QYYH9aNlZaGcHDIYTWDr06BBg0jIXnt/DPK8uqEn+w2NKHBHxPnz5+vp6fXt23fatGnr168/derUgwcP3r17V/UuuaKigjz1Gzx4sBjJsh07IgBevChaBgupY6Oqqvr+/ftaDsvLy/vzzz8tLS1B6BWvmhQVFXl5eYWEhGRkZNTyxzx16hS5+sfEiNLFsLQU1dQQAGVkkMlEcT8Pfgiy4h4ddFmQJyoM0htLUlLyzp07dR6cmZmZlpb2PXkyBNkftm3btq1bt1pZWf3f//3fvn37wsLCvvnQpqSkxM7ODgCsra3rXOh6+/atjIwMyZNxcHAQb3piCAoKImkSu3btqvNgUuSU9H0U72lSSUkJCUHEr2nTmCQnJ8+fP//ChQshISFpaWk1LUySfpm6urqirjVUVqKREQKgqM8XSQ9UGRmZ2ssOFhYWbt68mSRNjRkzRrQx/isrK2vx4sVXrlwJDw/PrLnX66ZNmwBAQ0OjpiSNXwefzyd7OjU0NGJjY8lTOzk5OX19fWNjY+X/Noghq4OTJ08WLyvXzg4BUIxtCA8fPiTXhzVr1ly+fHnFihX9+vX7ui6NtrZ2586db968Kcbcvl9YGPbsiS9f4qJFOGzY//r8AmDHjr319fVHjBixcePGDRs2kHtLMXrWUkJyd//cU6QxO3nyJACoqKjcvXu32jtZWlpaVVVVS0tLX1/f0dGRvPkfPHjQ0FOurnEF7kJKS0tTV1eXlpc/LWKhhocPEQC1tWt8slaTWsrAkWzOu3fvHj16dM2aNZMmTSKX4K97SfwkM2bM6d//cevW+CXNtW5BZ85k2NnxDQwQAIcO/ZmzqxunmJ30PDgzIZrP539KF+EWglQuU1RUfPbsWbUfFRQUVC2lAgDz58//zjwZMfaHffz4kbRSGjx4cJ13C3FxceTgffv2iTdD8Zw/f56sRdWUhy1YXROUagaAaLHyZHx98x0dVw0dOuH7ptzEVFRU9OzZs93AgatiYkQKu7y9EQDNzMRpsUBa1JmZmRVWuS7w+fzMzMywsLDLly+TPbUjRowg16tH9VINlsfjDRgwAAB69epF63BzuVxSyUBWVpY8+6pKXV194MCBK1euvHr16p07d0goL8YNM9lPLieH4j3zv3DhAoPBqDa9r7e0NqCwMDxzBqdPRzc3XLkS9fRw+HDcsAFv3sQPH/6zpCJ4VCtq0QhKSGTR3ciIGx0tykpiPcrKyiILZKdPn46JiXFwcOjYsaOJicnXuY5//vnn+vXrAaB3794NPevqmmTgjoi3AwNHP33aNSrqmSi1UEaPRgAUr3aZoAxc//79lyxZMnbsWFtb22o1JQkZGZnWrVuLM4ZYOBzs0AEBcPx4YU/p0KEDANibmoY6OFQI0US2ceLz+aSVoLa2dnJycmlp6datW8eMGUOqhVYlLy9va2tLfl2FaZr7TTdvplpaTrGxsRHprMTERPIE3MXFpdqP8vPzAwICyONmspOByWTKysrW/lTnZyDroDIyMiR6y8zM9PPzW7t2rZOT0zfzlUVKlq1q3DgEwKafJiOyjA8fSOOPU1lZwp9lb48AWNcOxm8rKSmxsrICgE6dOjk7Ozs4OJiYmFRN7Km6yNSuXTtxxhDLhw8fyEbGtWvX1tugjRabzTYwMCAL6srKylWj4WqL64ItrcdOnRJpiG3bRPt0qGrIkCGTJ09WVVWVkJBwcHCofUtrQwkLw4sXMSgIjYywzgx2T09PAJCSkhKjBwgljI0bb0tIyIwYMaKhJ/JtZENOTV2AioqK0tPT4+LiQkND3717x2azSdhw//79+p1mHZpq4I6Ie9LTrSMjB0dH5wmX+cDlYseOKCWFNT/IrY23t/f9+/elpKSqPcSUkpIyMTHp3bv3lClT1q1bd/z48TNnztTzDpj4eFRURAAUpkDiw4cP4UvZZjMzsyZdm4/L5ZJ2Wm3atPn48SOp+kJiUGNjYwsLC11dXcHfFJPJZLFYYnTUIv74AwFw504RH9YgRkREkP2sy5cvv3PnzqZNm0aOHEk2hlalpqbWuXPnUyJ+Kv8os2fPBgBJScmvN0Orq6sPGjRo9erVPj4+z58/J2Wd+vTpI3wHIqKkBBUUkMHAZpEmI7KQggKbyMiuUVEvhLvn4XJx1izU0RGnTCuPx9uxY0dAQICcnFy1xIaWLVva2NiMHDly4cKFO3fu9Pb29vHxqefF7wcPHpC8/7t379bnuI1TaWnp6dOnE4QoxnH06FHzHj2GPHv2QJQiYGPGIABevSryxMiWVnKzp6+v32hr5717h+TR+7ZtKMwcSUM0JSUl8R4bUrXLzs4mje3F7lzx85w/f5781Qu/n2fDhg0AYG9v/1MnJqomHLhX8vnT37yxjoycEx9fZ+y5eDFyOPjmDZ47J85YwcHB8KXUsbq6+t9//33u3LmQkJD09PRGEviePo0A6Opa95GjR4+GL80yxK5e0njk5+e3bdsWAHR0dNq3b//NDV6Ojo5LlixZu3YtSVnz9PQUdRQOB5WUEEDM7QDkEW21Dt6Kioq1LLDVs8rKShsbG2EmlpSUROpdTJw4UaQ5X7mCANjEy8l8F6+0NOvIyKExMdy6/r/17Ys5ORgejiL2nv/Mz88PvnQXNjQ0PHny5L17996+fStGddqfZO3ateQuopYkWlR8AAAgAElEQVSEeKJHD0xPx6go9Pb+XIA0Lw/rahDcbB1KS7OOjOz57FmcEFU1IyOR7IjevRvF+Jsni9Ok4pCQ+9cbRFAQXr+OiLh+PQqzOZbH45EPQV1d3ToLSPD5/Pj4+OZUw7QeLFq0CACGDx/e0BP5j48fP5Lw4MiRI8Kf1TgX3f9Ti7ppYTEYm4yN/3j9Wlta+llRkQSDYSorq/itojQA8P49eHmBgwNIiPUnJt0rSMWbdevWubq6fsfEf7A9e2DBAnB2hufPwcwMHjwAR0c4eBDmzIGSEkhOhpQUyM7+8PatV2pq6rt3754+fQoAycnJioqKkydPbujpfy8VFZU7d+60b98+Ozs7MzMTACQlJc3MzHr06GFvb29tbV21CVGbNm0mTpy4ePFiDQ2NSZMmCT/KrVtQWAhduoCpqcgzdHV1LSsrMzAwSE9Pb9euXZ8+fWxsbLp06WJmZlb7lvb6xGKxwsLC/P39dXV1O3XqVFMvBQAwMTG5ceNG7969z507Z2pqSrIAhZGeDnJyMHr0D5pxEzRPVze1vLyvquqJ7GwWgJOamva3clcAwNAQtmyB4cOhvFycgUiHEZJZsX79+to7NzWIdevWPX78+N69exMnTgwICKhaTKygANLSICUFUlNBQwOsrGDbNpg4Ebhc+PQJMjMhPx+Kihpw7g1php5eVmXl9dzcBQkJxy0s9KWlazk4JgYuXwYbG3j/HmRkRB7Lz+8mAOTn5wPAmDFjxJ3yT8fjQWoqxMdDdjYg1n08k8k8c+ZMdnZ2SEiIk5PTo0ePqj1Fz8zMjPoiNDQ0Nzf37t275NEuJYxly5YdOHAgMDBQUVFRRUVFWVlZSUmJ/FtFRUVFRYX8Z2lpqZOTE1l3Ex6fD1wuyMhAeTlISACHAwoKUFEBDEYd0d28efNycnL69Onj4uIi/HBKSkru7u5r1qxZvXp1SEiISFP9eZpw4A4AmlJS3paWyeXlkUVFnRUUsObfWmNj4PEgIQFkZUUeJTMz09fXV0JCgjRbaWzB7tu3n7+oqIDERIiLAzs7ePMGNDXh48fPP2rZkvfhw1bBKUwmk8/nd+7cudoacBOlo6Nz7969ffv2tW/f3sHBwdLSUqKG3+Dx48enpqZu2Lw5wNDQsri485fUmjrdvAkA4gSdZWVlZ86cKSwsZDAYsrKyoaGh1dqMNx4sFmvo0KHCHGljY3Pp0iXSmVxTU5PUAq/F+/fg7Q3Ll4OiIowY8SPm2jRJMhiepqYRhYWKTGavrzYjVqWqCm3aQHAwWFqKPEpcXFxQUJCsrGxaWpqGhgapv9nYMJnMc+fOdezY8f79+126dOnSpUtaWlpqaiqL1eHly3OCwxwdwdoabGzg9m0wNYX8fLhzB0pKGnDiDYwBsMrQMKeiIryw8OrHj72UlSUYjPY1X8dcXWHLFnEGSkuDiIgbHTve+vChzahR9wWP4xqnlBSIiYGcHGGPl5GR8fHxsbe3j4mJGTVq1K5du2JjY0mk/uzZMzabXfVgPT29at+hapeYmMjlcmVlZYuLi4uLi9PT02s6cs2aNSEhIV26dBH+xd+/h+vXwc0NzpwBGxsYOhTCwiA8HNTUwMGhxrP8/Py8vb3l5eWPHDlSy7LUN7m5ue3evfvx48f379/v3bu3SOf+JE07cAeAFpKSyeXl6eXlMkxmLdcvAFi8GAYNgnnzABEOH4bffwd1daGGOHGC2b27K5v9rqDg9dChAxtbsJufD3//DQDA4wEAzJ0LXl7AZAKLBTIyYGQEhoZgaKhlYLDJ0NDAyMjI0NDwyZMn48ePDwsLi4qKsra2btj5/xAdOnQQ9MGt3bJly2SHDDnF4SxJTDxqYWFS10oUeWTxzz9gZgairNF/5u/vX1hYqK+vn5aWNmzYsEYbtYvKycnp4MGDM2bMWLhwoa6u7oia4/GKCsjJgbt3oVcvePsWmsv/gO9yNz8/nsOZqqUlUfNHyNSpMHAgWFpCQQEsWABr1oCZmVAvfviwXq9eO4uLE3m8uCFDesiIsdZaL1q2bHnkyJHRo0e/fPny+fPn5JsKCgWKimBgAIaGoKcHHTtCUhKMHw/Dh4OpKbRsCVOnQm4u7NnTsHNvSBIMxjYTk3v5+W84HAaDUXsUoqcHmpoQEAAAcPcu7N8Ply9DDY95/sPbG0pLZUpKRmZlQevWFiKGOvWtWzcYMQJevBDhFDU1tbNnz9ra2gYFBbVv377qj3R1da2trW1sbKytra2trQWdsChhlJeXz5w5k8/nu7u7P3z48NGjR+T78vLyMjIy0tLSpGsEk8nMz8//9OnTgQMHTpw4IdIQb99CQADExYGNDQweDNu2gYMDREbC0qWgrAyqqqCkBCYmIZWVQWRpn8VikRp027ZtMzExEfVPpKCg4Obmtnr1anKbIerpP0VD5+r8AFFFRbdyc8tqzTUnle6CgvD+/c+1ZZychNrIUlaGmpoIgLKyyGLx4+MbXRstQUMJV9fPnZQ2bMBp0zA3t7azBB1eCwoKfvoUGxke4tKkJLKzObuuHZYBAWhnh7m5uHChOGNNmuQMX3ZHXBVjg1jjtmbNGgCQlZV98uSJ4JuVlZWxscUnTuC8eWhrizIy+PffeOoUurjg4sVYVtaA820Uwtnsh3VtLiQdTN6/x5s30cYGAbBTJ6HSlPPyUF4eGYzPbZEzMhrFDpxa3LhxY+HChQcOHLh+/XpMTEz+V/9nyP+KtDT09cXx45HPx/JybOgKhI3C1pSUE1lZd/Pyajrg/n1MSUEuF9eswX37UF0dAXDKFKE++Lp2RQBUVUWAxt5P59MnJD3u4uNRpBYdpBkISdMiWX83btzIEqXuE/W1pUuXAoCFhQWHwxkzZoy+vn61TCRCTk4uOTlZSkqKxWLFx8cL//qJiejujuHhuGoVPn+OixfjuXM4axYuXfqfEv6OjhurDqepqWlpafnixQvxqgkVFRWRRmO1n57yKuz1E3/2R7Hqn4iCgcIkhTVuz4qL8ysq+n7VFeJrHA60bw/v3oGiIrDZsGULLF9exyknT8LUqaCrCxkZMGgQ3Lr1Y+b8A/n7g5PT5y8Q4bffoKQErl+H8eNrO6u8vLx79+7Pnj0bPXr05cuX62eqjUc5nz83ISG6uHiQqupSAwMAUJKQ+OaiUmAgpKdDXBxwubBrl2ijcDhgZFSqr38zK0u/bdtH167Nq9pdshlAxOnTpx8/frxFixbz589ns9mRkZHPnz+3tj4RHPw5KZbBgLlzwc4OWreG33+HhASoNS+3+XtbWlqO2E6IRw+FhWBkBPn5oKUF2dkwbx7s21fHKVu3wooVYGwM79/DxIlw9uyPmXODKy0FY2PIyYHdu2HBgoaeTeOwPS3NTU+PCcCqaz08MBD69wdy7SkthbVr+evX17a7hs2G1q2hqAhKS8HcHOLjf+CsG5G+ffsGBQVpamrm5OQEBwf37NmzoWfU5D1//rxr1658Pv/Ro0fdu3ev+qP8/PzCwkI2m81mswsLC8vLy0eNGjV9+vRjx45NmTKFNEUSRlLS51SZo0fBxgbOnoUdO2DAAJg3D7S0gM0GNhsKCoDPf5ieHkhGfPv2bWhoqIaGRm5urqGhYXx8PGleIaSCgoKIiIiIiIg1a9YoKiqam5uTNH0NtRYju2hLyytJyynIyCnJKaslPX/YxWnKg/M7Rrrv4ZaVMBhMOWU14QcSwc++M6gfwte2WLcOAVBFBRkMtLKKCgkJrf140nNOQwMBsIEaw/0siYmJ5Fa4njv+NBIFFRWbUlL+ycg4mZV1OSenooZlqIAA9PfH/fuxVy9ExNevccoUYRt4/fsvAqC+vvh1lBs/UpGz2qaCPn0WjxmDW7diUBAWFGBGBpIeWUeO4LFjSJuOC+/Eic+P+6SksGfPQ1eu1PHQxtoaAT5XQAoPr5851hNSlUhaGiMjG3oqjcM2oUva8fk4dSoCoLIyKikV2tgM2l9Xd4DKSvT1xZkzm23XhZycHAkJCRLAaWtrN5LqcE1aeXk52WkqfA0iwaL7mzdvhB4FP31CRHz4EB0c8OFDRMRPn2prQMnn80nvGtJB4qgwZbOrmDp1KgD07t2b9d/aJ4a6mh5DtAX/7Jzc8cq2OXeOrffdvSju8Y17p7fkZ6eINJDwmkngLrzKSuzTBwGwVasQaWlpPT292vuQZ2aiuzu2bImtWonTv7CR8/b2BgBpaenIX/XD8HhW1o1PnyJr/qUPD8fHj7GyEsePx+BgNDREAJwwQajHzRMm/C9wb3ZpMv+TmZk5duzYfv36/fXXX7dv3/5ELqvfkpaGsrIIgJs31+cEm7bJkxEAzc3PAYCysnJSUm0tCTkc3LEDO3RAO7t6m2D9mTcPAdDOLoMtTNk/qgouF/v3RwDs0OEvAJCQkPD396/p4PJy1NfHvDw8darZ3ia9OXlygoUFyXgWu7kHVRXpTW5ubl5aWir8WdOnTweAyZMnizQWl/u57+TixUIdT9IKSPNvQ0ND4ZuQBAYGMhgMKSkpSUlJsqX+6dOnAQEBV65cuXjudKjP4Qfnd9w56uG7e9GtQ6tvHVrN5/NuHlz5POBCckwdi8Lf45cL3BHxwwfs0CG9XbseBgYGADB48OCa7rbz85F0mPb1xceP63WS9YaUBPk1k90R8XhWVhib/U6I9OHYWJSVRRmZz72u6lxT4PHQyAgZDGQwUF5ezH7jzY+vL7JYyGDgyZMNPZUmorgYhw0LUlPTIL0XbGxsavrUSU7GmTMREQ8fxnpvv1sfOBwcPfqBmprGuHHjGnouTQ+bjWPH3pKRkSEffAoKCrGxsdWOSUtDHx8MDcVp03D58uYcuKODAwKUmJk9cnR8UWWLDiWeFy9ekNA2ODhYpBPFWHRHRA8PBEBjY2H701VbdBeylDtpbAwApGGia12Ncq7vWxp8afeN/cvfPLmVGvcT+0/9ioE7IgYFBZHWfcrKyoqKiq++2utUUoKxsfj8OXbvjv7+6OWF7941yEx/urKyMlJYZtiwYY22N97Pczwr66PQbW7nzv2chyAtzXdwWL5r167aj+dy8dIlnDYNZ8/+7ok2I7t3IwB27BgWGHivoefSNMTExMjKygKAurq6paVlta4xOTkYFYU+PnjnDg4Zgr6+6OGBNW9ZbNoSEhJIXa9Dhw419FyanoyMDENDQwAwMjJydnYuLy9PT0/39fX19Lz/22+opfV5Y9+sWfjnn7hvH7q6NtPAPSsLWSyUkvr8SPTX++ATVkAALl2KK1fi+fO1HFVRUUGiiIVi1XCYMWMGADg7Owt5/KtX2XJyyGCgSD2Rrly5Al96xhsYGAiz6D5r1ixB1G5oaFhYSzpO/WoOm1PFs379eg8PD2VlZU9PzxYtWqSkpCQnJyP+FhLSLzX1cwX0ZctAXR0SE8HYGMaNA2Pjhp70z5GUlGRtbc1ms48fP07SuX4dBZWViixWnbu7iMpKGD4c/P3BzOzfxMTRDAbj8uXLo0aNqun4tm3h5k1IT4fMTGjEDUwawKZNr/7+21ZCQiI4OJgshFC1++eff+bMmSMjI7Ns2TIGg5GampqWlqaquvLGjd4czudjli8HExN4/hyUlWH5chBiu36T5O3tPW7cOBkZmdDQ0I4dOzb0dJqYV69e9ezZs6CggCQMZGdnA4C5+Zi3b70BoEULsLaGYcMgMxM2bgR7e9i/H5pFxeD/2rMHFi4EU1NISoLFi2HHjoaeUGM1YwaQOsvTp4OdHQQGCgouXtTQqJCRUVZWVlZWvnz58v79+42MjGJiYhSE7o4ikJqaamZmxuPxYmNjW7duXfvBlZWV3bp1KylpM3z47s2bRbjGIWKnTp2io6N1dHQyMzMPHz5Mbhhq8uDBgz59+pDKlVwu9/bt2wMGDBB+uJ+roe8cGgyPx+vXr1+1zcW2tpvJkoOMDJqb47p1uG0bxsejmVmzXXEnLl++PGnSpCIhHzv9woqLcdiw1zo6BuQuvFolRKKsDMPDMTwcZ8/GefMwJAS9vRtkso0Xn893dnYGAG1t7eTk5IaeTtMwbty4aoXV7O0PAmCLFtihAw4dinv24KFDmJiIenrNdsWdcHFxsbCwiImJaeiJNEkPHz40MDAgBf5VVVX79eu3cuU6b28U7J7g8zE9HRExOxtLShpwpj+NvT2S35zmt4n7xxI8LJ49G6dNq1pwUV9HR3AhUlJSYjAYAQEBN27cEG+cmTNnAsCkSZPqPHLz5s0g7vr31atXAUBFRQXqWnQvKSlp1aoVOQwAZpIcxEbj111xB4CMjIw//vjj48ePpqampDORiUlnXd0+BgagrQ0AUFgIYWEwYACcOAFDhwrbsIlq3j59+mRvb//27VtDQ0MlJaVbt25pamrGxsbGxHwKCekXGQkxMVBRAU5O0KYNdOsGT5+CtTVdca+uvLx88ODB9+/f79Onz7179xp6Ok1ATk7OypUry8rKTE1N9fX19fX1DQwsDAwMBYUl8/IgLQ06dABfXxg4UJwu900F2f3WbNqZ1b+EhITHjx/36tVLjH40zcHTp3DsGPj7A4sF795BI+8v1YA2boTOnUFJCa5dg4kT4e1bQcHFpRxO1qdPpLxjdHR0YWGhpaVlXFzc3bt3+/fvL+o4KSkpZmZmfD6/R48empqaZCGftE8SfKGiopKXlzds2LDy8vJbt24NHDhQ1FEQsWvXrpGRkWTR/dChQ+SG4WsLFy7cs2cPOUxHRyc2Nlal1l7X9eyXDtwpSjwJCQndu3f/9OmTqamphobGixcvysrKVFSM2ex3iMBigYUFDBwIDAbs2AEjRsDEiTRw/wY2m+3i4rJ9+3bj5pqFRlFUY3PzJjx5Ajwe9OsHrVuDvn5DT6ixWrUKxo+HvDwoK4N+/eC/xRCr2rlz55IlS3R1dTMyMrp16/bkyRNRh8rJyTE1NZWTk8vJyan9SElJydGjR58/f17UIQgfH5+RI0eqqqp27tz577//7tq1a02TKSsrk5SU5HA4vr6+w4YNE2+4n4QG7hQljqdPnw4ZMqSioiI/P5/BYJiZmdnY2HTrdqxDB5lOnYCk+SUkgJkZfPgAAEDbZlMURTW86dPh6FGAKgnc1NcuXYLx40FdHVJSoK6+geQxYGZmpoqKSkFBgRiL7mPGjLly5Uq3bt0UFBSioqKkpaUlJSUlJSUZDAaTyUTEysrKysrKoqKigoKCmTNnHjp0SLw/FiJaW1s/f/7cxMTEwcGh2rq+qqoq+eLTp09Tp05NSEgQqTlUvaGBO0WJ6dmzZ3fv3u3atau1tfU3uzpTFEVRjcvs2fDPPwAAc+bAwYMNPZtG6dMnsLKCnBw4fBhq3cEp4OnpuXjxYvEW3f38/IYPHy4vL//y5ctZs2YFBgbWdGRUVFSXLl0kJSUTExPJNjMx7Nq1a/HixXw+v/bDWCyWmpra69evW7RoId5APw8N3CmKoiiK+jW4u8PEicDlgp8fbNnS0LNpjHiTJ7POnIG+fSEgQMgNANUW3e/cuSNkDZbc3FwrK6sPHz4cOHBgzpw5ZE2dzWYXFhYK/p2fn19YWFhUVLRnz55x48Z5e3u7urru3btX7D/g48eP4+PjEVEwRLWx8vLyAMDDw8PV1VXsUX4eGrhTFEVRFPVrqKiA69eBxYIhQ2rJ2/5l+fj4bHd1vWZoqHn2rEg1sL28vBYtWkQW3e3s7EJDQ4U5a9KkSefOnXN0dAwKCmIIcZMQGxvbvn17G5sBV6/66+n9oruKaeBOURRFURT1qysoKGjbtm1GRsa+fftIV3XhCRbdlZWV2Wz27du36yz8cvPmzSFDhsjJyUVHR5Pyi8KYPz9u3z7LefNg3z6RJth80MCdoiiKoiiq2SopKVm1atWLFy8sLCxUVFRUVFTIpsyqWzNVVFTc3NxOnz7dvXv3R48eMZlMUUfZtWuXu7s7WXS3sbGJiIioZRGdzWa3bds2PT19165dCxcuFH6UuDho1w4kJSExEcRNdG/aaOBOURRFURTVbK1Zs2bfvn0FBQW1HyYhISEpKRkdHW1mZibGKGVlZa1atcrIyGjZsqWzs/OmTZukpKRqOtjFxeX48eN2dnYhISEsEXOWJkyAixfhl110p4E7RVEURVFU8xQdHd2lSxcej+fq6mphYcFms8kG0GpbMwsKCjgczrZt20Ra/65m9+7dbm5uioqKs2bNqtZBiTRRIl8EBwf3799fSkrq2bNnlpaWoo5CFt1794a7d0H0BwNNHg3cKYqiKIqimqHKyko7O7uoqCg3NzcvL6+fPRyHw9HT0yspKSkvL6/lMFlZWQ6Hs2XLlqlTp2pqaooxUGAghIfDqlVw6RIMGwaysuLOuAmSaOgJUBRFURRFUT9e7IED75KSTExM/vrrr3oYTlZW9s2bNzt37mzRokW1SotkUZ8s9pME+mfPnq1du/bhw4d2dnaiDqStDYGB0KsXxMbC4MG/VuBOV9wpiqIoiqKanVevwMYmS0Ul69Klzg4ODT2b/1m9evWmTZuMjIySk5MHDRp069YtUV8hNhbCwiAsDNTV4c8/QUnpZ0yzkfr1koMoiqIoiqKaNx4Ppk2D8nLtUaMaVdQOAEuXLlVVVU1OTpaXl799+3ZISIjw5/r4QGkpAACDAfPmwfnzP2uSjRYN3CmKoiiKopqXHTvg6VMwMICtWxt6KtUpKyu7ubkBgJFRVxOT306f1hHyxJAQ+P13aN8eXr+G9u2hY0dYvx6kpX/mXBsfmipDURRFURTVvNy8CbNmwbFjUFcjpAbBZrNHjCh98EBFUVG2qAiePIFu3eo4pbwcOnWC16/BwADS0+H0aZg4sV7m2sjQzakURVEURVFNQ0RERFBQkKDGYjdVVTUFBVBWBmVlUFWFzEzYsgU0NKCyEhITQUamoef7bcrKyr17Kz94AGpqUFQEHh5w504dp2zdmv/6taq2NqSmgrY2ODnVy0QbH7riTlEURVEU1TTs3LlzyZIlgv/8aG2tHhX1vx+vWAFubtCyJXh5wYABYGXVAFMUTmEhGBtDXh7Iy0NJCURGgrV1jQeHh4f37OlgbX3x5csRpaXw778wcmQ9zrUxoSvuFEVRFEVRTYOdnd3y5cvZXzDl5YHLBTYb2GyoqICSElBVBQBQUwM2u6EnWxslJXBzg7VroUsXWLQIOneu8cjy8nIXF5eKivL8/L+srLqZmrb8ZaN2oCvuFEVRFEVRzURAAERFwfDhsGUL/PNPI69wzmbD4cPQowd06wbXr8PQod8+bM2aNX/99ZeWllZ2drampmZsbJK6ukL9zrQRoVVlKIqiKIqimoX+/WHIEHj9Gjw9G3nUDgDKyiArC9u3Q1kZ3LsHkZEQGQlv38KHD1BW9r/DJk+ebGdnV1xcDAD79u37laN2oCvuFEVRFEVRVIPYtw9MTeHFC/jwAR49gmfP/vcjAwMTDqdYSUlJRUXl3bt3+fn5o0aNunr1asNNtlGgOe4URVEURVFUw7CwgMhIyMyEdu2Awficrs9mw6dPH0pLSz9+/AgAsrKyHTp02L9/f0NPtuHRFXeKoiiKoiiqAfj6gq0tKCnBunWwfft/flRWVsZmswsLC9lsdkZGhqOjo7KycgNNsxGhgTtFURRFURRFNQF0cypFURRFURRFNQE0cKcoiqIoiqKoJoAG7hRFURRFURTVBNDAnaIoiqIoiqKaABq4UxRFURRFUVQTQAN3iqIoiqIoimoCaOBOURRFURRFUU0ADdwpiqIoiqIoqgmggTtFURRFURRFNQE0cKcoiqIoiqKoJoAG7hRFURRFURTVBNDAnaIoiqIoiqKaABq4UxRFURRFUVQTQAN3iqIoiqIoimoCaOBOURRFURRFUU0ADdwpiqIoiqIoqgmggTtFURRFURRFNQE0cKcoiqIoiqKoJoAG7hRFURRFURTVBNDAnaIoiqIoiqKaABq4UxRFURRFUVQTQAN3iqIoiqIoimoCaOBOURRFURRFUU0ADdwpiqIoiqIoqgmggTtFURRFURRFNQE0cKcoiqIoiqKoJoAG7hRFURRFURTVBNDAnaIoiqIoiqKaABq4UxRFURRFUVQTQAN3iqIoiqIoimoCaOBOURRFURRFUU0ADdwpiqIoiqIoqgmggTtFURRFURRFNQE0cKcoiqIoiqKoJoAG7hRFURRFURTVBNDAnaIoiqIoiqKaABq4UxRFURRFUVQTQAN3iqIoiqIoimoCaOBOURRFURRFUU2AREMN/OrVq+vXr6empsrIyHTv3n3kyJESEhIFBQV79+797bffOnfuXPXgo0ePysrKTpw4sbi42MvLq9pLtW/ffvjw4QCQk5Nz4sQJNTW16dOnVz2Aw+Hs2bNHWlrazc2NfOfUqVPZ2dnk65YtW3bt2tXS0pL8Z0BAQFRU1IoVK7457YCAgHv37nE4HHNz80mTJikrK1f9aWxsrI+PT1pamrq6+oABA3r16gUA//77b0JCQrXXUVdXd3FxIV9nZ2efPn26TZs2Q4cO/eagZ86cyczM1NHRcXZ2FnwzIyPjzJkzycnJWlpa48ePt7CwqHpKZWXllStX0tLS5s+fLyMj882XbX5CQkICAwOzsrJUVFT69u07YMAAAHj37t25c+ecnZ2NjIyqHrxt27Z27doNHjw4IyPj+PHj1V6qV69eDg4OAJCUlHTlyhUjI6Nx48ZVPeDrN9uBAweKiooAgMFgaGlp2dvbm5qakh/5+vqmpKQsWLCg9vkfPnw4Pz/f1NR09OjR5DuZmZlnzpypekyHDh0GDRpU7cT37997e3sDwOjRo8mgFy9eTElJqXaY4C1UWlp6/vz5ly9fysvL29raDh8+nMFgCA57+PDhnTt3iouLTU1NnZ2dW7RoIfhRWlraxYsXk5OTtbW1+/Tp071799r/RM1DRkbGlStXEhMTGQyGlZXVhAkTlJSUAMDT09PMzKzar+2tW7diYmKWLVsGAPv27cvPz6/6U1SO6i0AACAASURBVFVVVVdXV/K1l5cXl8tdsGCBrKxs1WOOHj2am5s7depUTU1NAAgKCnr69Cn5kbKycps2bcg7EwASEhIuXrw4bdo0XV3dr6cdHx/v7e2dlZWlq6s7bty4Vq1aVf3px48fvb2937x5IyUlZWNjM2rUKGlp6ejo6Nu3b3/9UrNnzybXuvLy8gsXLuTm5rq7uzOZ31j3CQsLe/jwIQC4urrKy8uTb5aXl1+8eDEqKkpCQqJv376//fZb1VMqKysvXrwYERHBZDJ79OgxatSob75y81NRUeHn5xcWFlZYWKinpzdq1CgrKysACAwMDA8PX7VqVdWDyWVq7NixrVu3fvjwYXBwcLVXmzp1qp6eHgDcvXv3+fPnffv2tbGxqXpAaGhocHCwra2to6MjAGRnZ586dYr8SEpKSk9Pb/DgwQoKCuQ727Zt69Chw8CBA7+edlFR0dmzZ+Pi4hQVFe3t7av9bfJ4PD8/vydPnnA4HGNj4wkTJujo6JSXl+/atevrl3J0dLS1tSVfP378OCjo/9v78rimju79ScISEpYk7CBLorLITsSiiAi4IJsooLhrVYqvrYLYWvy616WoKK7UYtVaUSMiKihQxQ0FBatQRXEDVCSKgoQdkjC/P87H+7m9AfT92fq+8ub5K3fm3rn3Zs49c2bmOedcmDx5MqE2EUK//PLLmzdviEM6nf7tt98Sh2VlZSKRqLq62tLScvLkyRYWFuTGq6qqUlJSKioqDA0Nw8PD7e3tFR+g96GxsTEtLa24uLijo4PP50dERJiZmSGEjh07VltbO2/ePPLJd+7cOXnyZFRUlL6+/smTJ+/cuUNpbdGiRfAVp6amlpeXjx8/vn///uQTsrOzS0pKRo0a5eLighB69OjRiRMnoIrJZFpaWvr5+amrqyOEOjo64uPjfX19uxw1amtrDx069OjRIy6X6+Pj4+3tTa7t6OhIT0+/ceOGVCrt37//pEmT9PX1oaq6uvrIkSPl5eWGhoZjxoxxc3MjrpLJZKdPny4oKJDL5Y6OjlOmTFFVVSU3W1xcnJGRUVVVZWho6O/v7+7uTnkqjPHOnTtbWlrs7OwCAwOJ8vr6+t9++62srExTUzMoKGjo0KFdd8Y/B/zJIZfLFy5cSKPR9PX1R44cOWTIEAaD4erqWlNTU15ejhBKSkqiXOLq6urr64sxrq6uRgj16dPHmYRly5bBaTDk0On0qqoq8uVg09BotObmZigRCoXa2tpCodDV1VVfX59Goy1cuBCqoqOjNTQ0FB9bKpWGh4erq6v7+Pj4+/tramoaGBg8efKEOGHFihV0Ot3ExMTf3x+MttbWVoxxbGys8K9gsViurq4Y4wcPHhDD9pgxY7r8u3Jzc2k0GpfLHTx4MFF48eJFbW1tHo/n6+traGioqqp64sQJqGpoaEhMTCRU2MuXLz+wXz5rNDc3h4SEIIQsLS1HjRrl6upKo9GCg4Pb2tpycnIQQufPn6dcoqWlFRUVhTHOz89HCAkEArJQ7d69G07bu3cvQkhLS6ulpYV8+ZYtWxBCPB6PKOnTp4+urq5QKHR2dubxeCoqKuvWrYOqadOm9enTp+dXOHbsGJ1O53A4gYGBROGZM2cQQg4ODoTw7Nq1i3JhZ2enj48Pl8tFCGVmZkJhVFQUReqYTKaXlxfG+NGjRwKBwMjIKCgoCHSov7+/TCaDpmbNmqWqqurl5RUYGKitrc3hcO7evQttpqamgi4OCgrq27cvjUbbvHnz+3rms0dycjKTyWSz2cOHD/fx8WGz2cbGxsXFxRhjfX39mTNnUs6fN2+epqYm/La0tORyuWS5Ijq3qakJJkupqanky6urqxkMBkLo999/h5LY2Fg6nQ6dCJPPoUOHgio7ffo0QqigoEDxsXft2sVgMIRC4dixY83MzFRUVEQiEVGbk5Ojo6OjpaXl5eXl5+fH5XJv3ryJMU5PT6eIjbGxMULoxYsX9fX1iYmJYBoihDo6OhRvKpFIzMzMQBTFYjEU1tfXu7i4MBiMYcOGgfE0Y8YM4pLW1lZPT086ne7j4+Pp6Umj0QIDA6VS6Qf3z+eKR48eWVtb02g0Z2fnUaNG8fl8BoOxYcMG/K7HKecXFBQghEDPL1++HCHk/FeATGKMx40bhxAKCQmhtODr64sQmj59OhzevHkT9J5QKLSzs1NXV9fV1c3Ly4NaTU3Nb775RvGx7969a2pqampqGhwcDEbSxIkTOzs7obampmbgwIEMBsPJySkoKKhfv37ff/89xrixsZEiV7BStm3bNjD0CWPu8OHD5NsZGxubmJgQV40aNYqoSk1NVVVVNTc3Dw4ONjQ0ZLPZly9fJmqvX7+ura2tp6cXHBzM5/NVVFR+++23f6uDPkdcvXrVwMBARUXF3d191KhRBgYGGhoa8OEHBwdbW1tTzj948CBC6M6dOxhjsGspQvXmzRs4E+zy6OhoSgtgx69YsQIOT548iRCysbERCoW2trYqKiqmpqZlZWUY44aGBoQQSDgF+fn5PB6Pz+ePHTvW0dERIfT1118Ttc+ePRswYICKioqrq2tQUJCFhcWmTZug6vTp0ywWy9zcPCgoqH///nQ6nWi/rq7O3d1dTU3N19fX19dXRUXF0dGxrq4Oajs7OxctWkSj0czMzAICAtzd3e3t7RXVzp49e1RVVTU1NadNm0YUPn361MzMjM1mBwYGgkJbuXLlB3XP34f/gOG+bds26Jj29nYoKS0tnThx4qtXrz7QcN+xY0eXLf/0008IIRaLlZiYSC4PCwtjsVgIodLSUigRCoV+fn7wWyaTwUr89evXcfeGe25urp6eHtHC48ePmUzml19+CYepqakIoSVLlsjlcijpcmDDGL98+ZLJZCYkJGCMp0+fPmPGjBMnTjg5OXVpuLe0tPTr1y8sLGzUqFGE4S6VSs3Nza2treGjampqGjJkiJ6eHozl6enpPj4+iYmJcXFx/zuGe1RUFEJo69atxBCSm5v75ZdftrS0fKDhnpGR0WXLy5YtYzKZdDqdYmC5u7uDUNXX10NJnz59CIukvb196tSpNBoNpnbvNdzr6+tNTEy++uoroVBINtyTk5MRQo2NjT1cm5ycTKfTt2/fTjbcKSgvL2cwGD///DPGeMGCBb6+vsQ8BFbCsrKyMMY3btzgcDhgw2GMq6qqtLW1w8LCMMaw1DFz5kww8dvb2729vdXV1RsaGnp4ts8deXl5dDp9xIgRtbW1UFJTUzN58uSSkhL8YYb7pEmTumz57t27oKzg7yWQmJgIcrVnzx4oiY2NZTKZxAlpaWkIoR9//BF3b7hLJBJDQ8P9+/fDYVtbm4uLi7m5ORy+ePFCR0dn6NChhOh2p6wwxoMHD/b29sYY79+/f/To0bt374Ydgy4v+de//qWrq7tmzRqy4b5gwQIGg5GbmwuHUEsI6oYNGxBCBw4cgMM9e/YghEBQezE6OjoGDBigo6Nz5coVKOns7IyPj9+6dSv+YMMdvkRFuLi4sFgsdXV1on8xxi9fvmQwGGw229PTE0rAcD9+/Dgcwrq1g4MDHHZnuM+cOTMoKIgYu1evXo0QunbtGhwGBARoaWnduHGDeKnuRGvLli2qqqrPnz9/9eqVi4vLsmXLDh8+TDHcpVIpg8GgjOaA+vp6Ho/n6uoqkUgwxnV1dVZWVgKBAGwvuVxua2trZmb2/PlzjHFbW5u3t7eWlhZhhvZKvHr1Sk9Pj8/ng6GMMW5ra1uwYEF6ejr+MMOdUBGK4HK5bDbbxMSEsHDwOxFisViEXQuGOyEADx484HK5/v7+uEfDfcyYMTNnziRanj9/PkLo0aNHGOPOzk53d3d9fX1i/Ugul4Pky+Vye3v7iIgI6HSpVOrn56eqqvr69WuM8c6dO3V1de/duwdXpaenI4TWr18Ph7/88gtCaO3atYTBoCio1dXVXC73+++/FwgEZMM9JCSEzWYXFRXB482ZM4dGo92+fbu7v+6fwKc23OVyuYmJib29fZdK5yMN9yVLlvB4vMmTJ5MXpxsaGjQ0NGCwOXPmDBSSDXeM8fXr1xFCe/fuxd0b7hjjpqYm8qGFhcXIkSPh96BBg8zNzQl11gOWLl2qpaVFVqlweZeG++LFi7W1tauqqsiG+61btxBC27dvJ06Dr+XUqVPka3ft2vU/Yri/evVKVVV1/PjxXdZ+pOE+ZcoUR0fHYcOGkQ2s8vJyGo0GQgU2HP6r4Y4xPnXqFNHsew33WbNmGRoa1tXVUQz31atXa2lp9XChWCzmcrnR0dE3btzowXD/+uuv9fX1CWOdLMmFhYVkM5Ei5I6Ojl988QX8bm1tJW87gO6D6W5vRVBQEIvFqqmp6bL2Ywz3jIwMWL9gMplgfADc3d2nTp3K4XDi4uKghGK4Y4w1NDRgIOlhxZ3Sj3PmzGEwGKB1V65c+YEjzdWrVxWF6ocffujScC8oKKDT6QcPHty9ezfZcBcIBMOGDSNOa2lp0dbWJgZCa2trOzs7olYul5uamnp4eLz32T5rHD9+HCGkuHsG+EjDncPhzJs3T01NjZi5YYx37NjBYrFmzZplZmYGJRTDHWP85ZdfqqiogP3UneHe2dlJbFxjjM+fP48QOnLkCMa4tLQUIUTIbQ+QyWQCgYCsLTHG9+/fpxjuVVVVCKFjx44ptgAEQmKfGWO8c+dOhNCFCxfwu/9q48aNRC2MAoSW65VYu3YtQoiYIVPwMYZ7fX09KCuE0MWLF4nyxYsXW1paBgYGErNBiuGOMR41apSFhQXu0XCXyWRATwCkpKQQXQmUsO62dtva2sjSCHM/4gmJ9XUAj8cjBnFbW1sbGxvyJEQR48aNs7CwaGpqIhvub968YTAYs2bNIk57+vQpjUaLiYnpoam/HZ+aSvjgwYPq6uqQkBDYDv57UVFRwefzw8LCrl+/XllZCYWnTp3q6OhYtGgRQogopAA4NrBH0wMIyiZCKDc399mzZ0ABbGxsvHnzZmBgoJqamlgsLikpaWlp6bKFlpaWPXv2REZGUsjxXaK4uHjbtm3r1q2jEFhbW1sRQrAsB3ByckIIwbTnfxBXr16VSqXjx4//JxqvrKwEocrMzATVgxASiUQ6Ojpz585FCFVUVHR5YU5ODoPBANJqz7h48eKBAwe2bt0KHAMyXr58aWxs/Pbt2xs3bpSVlSleO3/+fCaTuWrVqh7af/v27YEDB+bPn09wqQlJxhj/+uuvampqPj4+lCqE0PXr1+/fv0/wXJlMJpmNLZfLEUJkcnwvQ2dn56VLlzw9PQk+5d8I0EUxMTFAdIbCZ8+e3bhxIywsjM/ndydXly9fbm1t/beU1atXr7KyskaMGAFa98KFC3w+39nZubGx8fbt22QOMQUJCQnW1tZjxox57+u0t7fPnj3b09Nz6tSplKrW1lbyw2hoaFhZWYGykslkjx8/BgoHgE6ne3t737t37713/Kxx4cIFhNA/obIkEglwk7y9vUUiEVEuEon8/f3t7e1fvHjR3t6ueGFzc/O1a9fs7e17djCg0WjE0NPZ2Xnw4EEWiwXeXPBS48aNk8lk9+/ff/jwYXeNAGGacDnrDuCHpq+vf+fOnaKioubmZqIKrPwRI0YQJfAbJEex1tvbm06nQ3lvxYULF7hcLoUd/rcAdFFwcLCtrS0hVBjj1NTUnpVVTU3N7du336usGAwG4YknlUpTUlL09PSEQiEiCZVUKi0tLaUYOerq6mRDiDIkUcZTuVwOVWKx+P79+2PHjgVa9Z9//tnW1kZ5pOPHj6enp+/cuZOsuxBCjx49ksvlZNEyNze3srL6xKL1qZ1Tnz17hhCiuAlS8PPPP8M8nkB5eTn0ImDbtm1ATQEcP34cRlawscDDRiQSLVmyBCEkEom8vLz4fL6+vj7ZcC8uLp4wYQLG+MmTJw8fPtyzZw/ZraE7NDQ0REdHl5eX5+fnr1y5MiYmBu7b2dkpkUhcXFyKi4sRQtra2lu2bCHcTwns27dPIpG810kRISSTyWbPnu3k5ETxJkEIOTg4sNnsgwcPTpkyhclkdnZ2wjT3xYsX7222VwKEis/n93DOmjVrkpKSyCUw/yEQFxe3adMm+K2iopKbmwu/Kyoq3NzcwsPDo6OjT58+DXaJSCQKDg62srKi0+lkobp8+fKECRPkcjlMUA8fPtzzUyGEWlpa5s6dO3LkyEmTJinWisXiyspKcMOQyWQODg5HjhwhJgOZmZknTpw4ceJEz/PApKQkqVQKbCICly5d+vnnn2/dutXc3JyZmUn2XISTKysr8/LyoqOjYXlPEZmZmXp6es7Ozj2/4OeL+vr6xsbGnpXVxYsXCU9iwO3bt8mHubm5hC8pQmj16tXgGlhZWWloaCgQCDw9PUUiEcjVkSNH2Gz2qFGjDh48SJYrqVQ6YcIEhNCbN2/y8vLmzZu3cOHCD3mFNWvWFBYWXrlyxdvbe//+/VAIjlwRERGwlgkLSLt376Z4blVUVJw+fTopKelDXEXXrl375MmTtLQ0xYnc4MGDc3JyysrKwIG+oKCgurpaTU0NISQWi+VyuZGREfl8IyOjt2/fNjc3U4bM3gSIymBoaNjdCZ2dnRS5qquro5xDTLYRQh4eHuvXr0fvlm9grWHevHk1NTUGBgZVVVX5+fmHDx9WU1Pr7Ox8/vw58b0nJCSIRKK2trYbN26YmpoeOXLkQ54/Ozv7wIEDf/zxh1wuz87ONjExIW6dkZHh6+sLbvouLi4ikYjizogQSkxMHDly5HtVB2yw+/v7S6VSmUymqam5fv36b775BiFUVVXFYrG0tLSIk+HPhEEQlurJcqWqqqqrqwvlvRXPnj2zsLDoYSWlurqaIlQwdBJ49eoVWVmFhYXBvw12OZ/PDw0N/emnn7Zv366qqpqfn//06dOwsLD8/Pzq6ur29nZwQkUILV26lMfjtbS0XL161dnZGfhv70VqauqxY8cKCwu1tLTOnTsHAQDKy8tVVFR+/fXXTZs2wZDt4eEhEom6dMfPzMzkcDgUn2zAlStXJBIJGNwgqDU1NQMGDACDm8fj7dq1KyIiAk6WSCTR0dETJ04kO6QCFEULIWRoaPiJRetTr7jDlKjnOCcsFovzV1CW583MzFxJgDEAvVtxZzKZ/v7+MC+sr6///fffQVgp80JVVVUul6ujo2NsbIwxPnTokFgsfu/z0+l0AwODfv366enpQaAJ9M4EzM7O/uqrr548eVJWVubh4REZGQl7keR337ZtW3h4uLm5+XtvtGnTppKSkj179ihuTWhpaW3btu3atWt9+/b18fExMTE5cODAexvsxQChIrRGl2Cz2RShoii4vn37EhIFjjgIofb29pcvX1paWhoZGQ0ePBiE6sGDB8XFxWFhYUwm08jIiGxgqampcblcDodjamra3Nx88OBB2GTsAStWrKiqqgKGuiJ27tx55syZxsbGtra2S5cuvXnzJjg4WCaTIYQkEklUVNSYMWPAF607tLe379y5c8aMGRQrQV1dHUhrb968SUlJ6ejoIKrAcbxv377GxsanTp3qci0hKysrIyNj7dq1xNfX+wD/c8/KSk1NjSJXFDnkcrlkZcXhcKAclBVCKCwsLCcnp7a2FiEkEomCgoI0NDQUF7G476Cvr5+VlQUEp/dCR0eHz+f37ds3Pz//0qVLUNja2lpSUmJiYvLHH3+8ePFi+fLle/fuJWatBBISEng8nuIKuiLu3LmzcePGpUuXUmJbAX788UctLS2hUDh8+HBbW1uyuMLSL0WE4LDLVeFeA7lczmQye7CxaDQaRa7IRirAxcWFkCsiEguoI0tLS/ifIcTHsWPH1NXVAwICYBZKVlmampqgsvr06QNBWj7k+ZlMpqmpqZ2dHcT0AA0My5aXLl1KS0sTi8Vnz559/vx5eHg45drLly/fuHEjNjb2vXfx8/PLysp69OiRVCp98eLF6NGjFy5ceO7cOYRQe3t7D2ID2owyEVVTU/tfEKoeToDgB2SQl6sRQmpqamRlBeFoEEKVlZV0Ot3MzCwsLOzNmzewqiUSiczMzAYNGmRpaQmzQaIdLS0tMK5MTU2Li4uBNfpeaGhomJmZ2dnZPXnyBAKKIITa2tpkMllRUdHZs2fFYnFaWlpJScn06dMVL7906dLx48dXrlypOOFvb2+PiYmxtbWdNWsWemewZWVlLV68uKKi4u7du46OjtOnTyf2tBctWtTY2JiQkKB4l/8W0fqUvByMMTBxCRcBCj6G497U1ITesQZhPf7evXt79+6l0+nV1dUY4wkTJgwcOBBOpnDcKyoqOBxOeHg47pHjToZEIjE3NwfuJgyx4FcEACFesmQJ+RLgNZLpXwQoHPcHDx4wmcy5c+fWvYO3t7ebm1tdXR3ByiouLl61alVcXFx6ejos8lF4YP87HHeYt4ALjiI+huP+4MED9M55IDExUVVVtba2dtWqVVpaWsDJGzJkyNixY+FkCse9pKRETU0NvOO747gXFRUxGIzvvvuO6GgIwVZXV0c4zZAB7GGI/DB37lwmk1lcXAwXwibV0aNHKeTmffv20Wg0wkdHETAQkl0mCLS0tNjY2Dg7O1PKwYc1NDS0O5Zt70B7e7uKikp3vhP44zjurq6uERERGGOxWMxgMJKTk2EVIC0tDWO8Y8cOIgoWhePe2tr6xRdfGBsbExybLjnuFEyYMIHNZoOLrbW1NTk6B8bYwcHBycmJXFJbW8tms1etWqXYFIXjLpPJYPAWi8Ugips3b0YIlZWVEbzV169fb9u2LTY2ds+ePW/evOnfv7+7uzvGGMgPFD05Z84cFov13jf6rAFRWbtz7P4YjntCQgKdTm9ra8MYjxw5cvjw4RjjQYMGgRjDOgL4/ipy3Ddu3IjehTPqjuNOAbhKg2/xihUrEEJv374laoF1ff/+ffIlQUFB9vb2ivpNkeNOQVNTk4qKCnxxMTExNBqN7FQGsxGI+gBjH+W+ampq8+bNe+8bfb5wc3MzNTXtrvZjOO4LFiwgxi8bGxtwJDUxMVm0aBHGGMwPEBtFjvs333xDp9NLS0t74LhTAHvjEC9h/vz5ampqZDI6MKwofke3b9/W1dXtMiBVR0fH+PHjdXR0/vjjDygpKSlB73waAeCeAVHgII7f+vXriUHZ0tJywoQJwJgH6g7ZuQJj7OjoCB78nwyfesXdwcGByWSC+f73Ar5bWMTy9/dns9nHjh0TiUSenp4Q0awHJpalpaWzszN05wdCW1vby8sLBNTY2JhGo5EbNzQ0pNFosF1IICEhwcvLa9CgQe9tfPfu3W1tbcnJybx3uHjxYlFREY/HI+7i5OS0cuXK9evXh4SEwP/5IVSfXgl48X9CqIgtQoRQWFiYTCZLT08/duxYcHAwrG3w+fzuHCccHR0FAgFQp7oDREPbuHEj0dElJSU5OTk8Hk8ikSieD1vSEolEIpHs3bu3ra0NQk/yeDzYBIyIiADmPfkWAQEBtra23T2Dr68vi8UC/2wKNDQ0RowYUVJSQqYVnTp1auTIkUOHDj18+PA/4any3wM1NTUnJ6ebN292dnb+7Y0TK+5GRkZDhgwRiUQpKSksFgs8Cvh8PsZYMRI/QojJZPr5+YnF4pqamg+/XWBgYHNzM8RpNjU1pQitoaEhLHwQSEpKkslkFHpVl8jLyyssLITQ/iCKixcvRgjZ2NiAvyBCSE9Pb8GCBZs3b46MjJTJZBUVFaAGWSyWvr4+RfH++eefPdOTegFAZX3gtsm/hcrKSlNTU9j2CQ0NvXLlSl5eXlFREWw76+jocDic7lQWpEPpWWVREBgYSKPRQHsAe4E8DkIiArJoPXz48MyZMxCJ7999NTabraOjA4oR/B3Jccf//PNP9I6FC9GQyXJVVlbW0dFBCfTey+Dm5iYWi8kr338XCGWFEBo/fvyJEyeys7Orq6tDQ0PRu/GxB6Hq7OyE3vlAQGYMQqg6OjrINGBFocrKyvLx8REKhampqSoqf6F/19bWBgYGnjt37uzZs0R2IBBU8gMD9QUMti1btmCMge0DqKysPHbsmK6uLupKtJqbm588efKJRetTG+4aGhqTJk3KyMggOMQAjPFHtgz6Ar5bFos1ZsyY/fv3X7x4EWQLIcTn82trawn/QjIaGhpKS0spCUooaGxsTE5OJp4TY1xWVgbkY3V1dXd397S0NMLFAdZoybz8wsLCgoKCD9kfRAgtWrTo5l/h7u7u4OBw8+ZNIo4ygebm5oSEBCsrq/9AFoD/DgwYMMDd3X337t0Ud6iPFyr4tuGbNDU1dXd337Bhw71798hC1d1sUCwWP336VJHfScaaNWsoHW1ra+vp6Xnz5k3YGT9//jyZxHLmzBkVFRUnJycIR0W+EHKpJCYmrlu3jjgfsmNQpK6wsBCihRDv2NraCtqwtbV1z549ZDsV0qzALEUul69atWr8+PEREREnTpzoxSQZArNnz3727Bllz/Tj5Uoikbx9+5YwT8PCwi5evLh///6AgADY54WxsDt384KCAk1NTQrPkoLk5GTywgEsZ4IvkJeX18OHD4mkTi9fvrx+/To55117e/uuXbsU6VVdwtXVlSLDkL0uJyeHnDCOwPr16zs7O2fOnAmHQUFBubm5sNuAELp7925RUVFwcPB77/tZY+LEiWw2Oy4ujhLG4G9RWYRcjRs3jkajzZo1C3gyUNiDyoJF/Z7HwStXrpApoBB5ELTHsGHDaDQahAQBnDx5ks1mW1tbEyUJCQl6enpd+vN0+S6wFEo8Xm1tLYyqAQEBdDodAkAD9u3bx2azwdF5+PDh2traycnJhCr75ZdfILPHh9z3MwWEOlm8eDFloeHvFarQ0NCGhoYFCxbAgIgQ0tHR4XK53QkV2N89C9WZM2fIoRcoygohRAhVZ2dnRkaGgYEB0HgwxvHx8YGBgeHh4RkZGRSm0O3bt93c3MrLywsKCshZn3R1de3sja9dywAACTNJREFU7EQikVQqhRLYKADR2rp1K0WhmZqa+vv7g8IUCAR2dnaHDh0iXKVFIlFzc/MnFq3/QObU+Pj4a9euBQQEzJ49e+jQoVKp9ObNm7m5uR++4J2dnU3OR2hoaBgZGVlRUUGj0Yh5T1hY2PHjx+l0OmFjEfQ+8HEuLy+Pj49HCDU3N6emptbV1RFO7jKZDLaDCcyfPz8vLy8qKiozMzMiIgKymRQVFUEWHoTQmjVr/Pz8hg8fPmPGjNevX2/atMnBwYGsnuLj462srChJ5iorK4FTKBaLJRJJfHw8pHc1Nzen8OC1tbVpNBoxEzh16tStW7dsbGyePn2akpLy4sWLnJwcwocMUnjCB7Nz505NTU3FhGe9DHv37h0+fLi7u3tUVJSLi4tEIrl27ZpYLO4yDWSXEIlEZLdCGxub8PDwyspKXV1d8JJBCIWFhcXGxmpqahK5Sy0tLRsaGurq6iC96J07d0CoJBIJ7PlCSFqEUENDA0Wovv32Wz6fT/FeZbFYOjo60NEQf8nCwmLmzJlGRkbZ2dlHjhxZvHgxTN7I00L0jujfr18/coMJCQlOTk7gDUlg165dR48e/fbbb93c3GpqahISEthsdmRkJEIoPz//66+/Tk9PnzZtGpPJPHny5IULFyB6YFNT07hx486fPz9o0CCBQEBIvpeXl2LCuV6DyMjIjIyMJUuWFBYWQtio+/fvp6SknD9//r1ux4DS0lJKv3///ffknRyEUGhoaExMTGVlJXAVEELgZEasCclkMpCrjo6OK1eunD9/HtK9Qe3+/fuB7wQICAgwNDRcunTptm3bFi5caGBgUFBQsHXrVj8/P8h6M3/+/J9++ikwMDAmJkZHRycxMVEuly9btoxoISUl5eXLl4pBPxISEmQyWV5eHkJo06ZNDAZj8uTJZmZmFFGEhWRHR0ew+2tqapYvX+7h4dHS0nL+/Pm0tLSlS5cSbiTLli1LT08fOXJkXFwcvKaxsTEEAevFMDAwSEpKmjVrllAojIyMNDMzq66uzszMHDp0KBBOPgTr1q0jr1tDxvGKigpiDmZgYODl5XXhwoWQkBBCiVlaWpLXGk+ePAmzpidPnhw6dMjOzo4IIlRUVEQWXQsLi+nTp2/dujUnJycuLs7R0bG6unrTpk0cDgeowzY2NlOnTt2yZUtzc7Obm9vZs2fPnj27du1agp3/+vXr33777f/+7/8oBlZmZmZpaenr168RQpBPXSgUjhgxYvHixVlZWXPmzHFzc6uoqEhMTOzXrx9Ea4Afu3btUlFR8fLyysrKOnnyZHx8PLjps9ns1atXx8TEhIeHh4eHFxYWbt++fc6cOT1sPPYCDBw4cNmyZT/88ENVVdXUqVP19PQqKiqOHz8eGxtLSfvdHSQSCUVZTZ06FXaVCdcUV1fXfv36PX78eOHChYQKogjVwYMHISZjaWnp0aNHvb29hUIhrJFfuHCBMJcRQvb29iEhIRs2bCgtLV26dKmVlVVlZWV8fLyJiQm4ig4ZMsTf33/58uU1NTX29vapqakFBQVJSUkqKiqtra1hYWFnz551cXERCARbt26FNj08PIYOHZqSkgJJzaOjozMzMzMzMxFCTCYT3PrXrl07fvx4X1/fKVOmVFVVbd68GTKII4QULSXITUZouU2bNgUGBo4ePToqKurp06cbNmzw9PSEaz8dPiUvh0BdXV1cXJytrS2kIfTw8NixY0dbW1tVVZWNjc3Ro0cp54eGhkKqo9evX9soAFLEbdiwwc3NjbiksbHR2tqanD3u8ePHAoEAopwGBwcL3mHIkCEzZ87Mz8+H0zZs2KB4i8rKSoxxbm6ur6+vtrY2k8l0dnYmc6QwxtnZ2Z6enjwez8zMbN68eUTGFoyxWCzu37//vn37KO+Vl5cn+CsU+cSA6dOnh4aGEocHDhxwcXGBe02aNInITQAYPHgwpVkiC2MvxtOnT6Oiovh8PpvNNjc3HzFiREpKCsb42rVrNjY2ijzggQMHrl69GmNcXFys2OPATY+Ojg4KCiIuqaysFAgEZKLk1atXBQIBpOUaMmQI/Nt9+/b19PSMjIwkQrwvWbJE8RaUWP6A4ODgOXPmEId3796dNm2aubk5j8dzd3ffv39/l9x3eAuBQACxbwHl5eV9+/Yl58sESKXSzZs329nZMZlMDocTEBBw69YtovbatWt+fn4cDofJZNrb2+/YsQPuCJ+PIhQ9UnoZOjo6EhMTBw0apKWlpaur6+Lisnz5csjx4eHhsXTpUsr5a9asEQqF8HvEiBGK/Q72q0AgIOddHjt2rLW1Ndk/YeDAgZBlad26dcS/7eLiEhoampKSAp1y8eJFxfZBf1ZUVEydOtXIyEhVVdXS0vK7774jN/7kyZMJEyYYGhoaGBhQBABjHBwcTE44QmDAgAGU3u/SY+fQoUMCgYAgoZaUlPj5+RkZGenr63t5eUHMbzLu3LkTFBSkr68PqenJf0vvxtWrV0NCQiDrp7W1dUREBAxDGzduJMe2B4CaOnfuHMZ4+/bt3fW7o6Mj7PUD9u/fLxAIyKHQf/jhB0gJcufOHaIfbWxsRo8evWzZMoKhLhQKKe2DSLS3t69bt87a2lpdXZ3H44WEhJBHn7a2tuXLl/fv35/D4bi6ulKGyKSkJCsrK/h2yFi0aBFFrsC5oqmpad26dS4uLhwORyAQLFiwgHytVCpdv369jY0Nh8NxcnJKSkqi6Mbk5GS41srKas2aNT1kGetNSEtL8/X11dPT09TUtLOz++qrr4DrP3/+fEiERMapU6dsbGwg1dF3332nKFT5+fn19fUCgYA8jqxdu1YgEBBZtzDGUVFRECL93LlzRCcOGDAgICDgxx9/hOwfzc3Niu3HxsZijJuamuLi4vr27auqqmpgYDBp0qTy8nKi8aampsWLF/P5fC6X+8UXXxD2YVVVVZdDEngbzpkzR7EKMtYD0tPTBw8ezOVyLSwsoqOjyck0KPDy8gI2P4HMzEwPDw8ej2dubr5gwYIuh/J/FDT80dsoSiihhBJKKKGEEkooocQ/jU/NcVdCCSWUUEIJJZRQQgkl/j+gNNyVUEIJJZRQQgkllFDiM4DScFdCCSWUUEIJJZRQQonPAErDXQkllFBCCSWUUEIJJT4D/D+My/Z6zNi14AAAAkp6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wMy4yAAB4nHu/b+09BiDgZYAAJiBWAGIlIG5gZGPIANLMTExQBjNQRAPEYOGA0EzsUBVAiQSQdiYYDVPA5gDRwOYAMwEqwA6hmeESCAGoArjRmEagmaUMdjOmOCdMHJcbEK5H9RaSf6EhwM3AqMHEyKTAxKzBxMSiwMLKwMrGwMbOwM6hwMGZwcTJlcDFncHEzZPAw5vBxMuXwMevwC+gICCYwSQolCAknMEkLMIgLJogKpbBJCbOwCeRwSQhmSAplcEkJa0gLaMgI6vBJCOnICevwSTFkSDBlSAmkCDCDLSajUNKUoKLk42bh5dPgotNUEhYVExAvIsR6C1olDEoXNyuf/Ci7eH9II5Bu8RByS1fwGzVKewH9bsiweydF3kPpjtI2oPYnMcfHEiwngcW//Rg34F3SUb7QOyN9RMO+Lbyg8VNjGMP7IhgAKvfXMBwwCTxtS2IfejLuf0W61eB1TDXae9rUL0DZqs8T7ZLPep1AMR+xnjNvqshGcxWOqvnwNkpD2afvybqcHxNK1j930WRDi/7JtiA2PuNZjuYXNgDdsOLtcsdIma+AKvR5L/mcIGdF6zX4et3h3kKk8DiDtrCjs+nQfwrXvXaYbPQXDsQ+5rEXodXpYZgN6+du8FBQYDTAcS2DEyx/5OjAVb/vpzBflasF1iNyKSi/aJLp4HZ601X7zdg1QCr9yzt27fxTzaYvf75VPtex2Aw+52w9v7V4UvA7OmWJ+xygo+B2WIArOqdcTGh4lgAAALyelRYdE1PTCByZGtpdCAyMDIzLjAzLjIAAHicfVVbjtw2EPyfU/ACS7Df7E/vw0YQeAawN7mDf4PcH67WeKkxwEQ7bEhUiSx2VfdeWl3fXv/88W9bF79eLq2N//llZvtbxhiXr61u2vPblz+u7eX90/PHzMvtr+v79ybcRPEN/n7Hfnq/ff2YofbSnmj0nCwRuO3GJozv+jiu82MuaHYT7G6FTKJQ3yClkLNriPtsT6NPzXDbILXdCjldYjDeW+I2N0Br1/YUfbByem3O4jRjg/Ta3Hq6pmVtDpI8djSjkNqNzOZB03w4zw1yFk3pas42iuYgsV2OspZkTPuMOgaRptAGSONIfHfyqVp3IqLBO+ihEdbyQEYLGpQzd9sTI02jM7M5FWOszrk7EZVG2B5LiRTSDLuPHVKxJvcQ4hh1OE2OrUZkWJO7KEGc4jlURXayk2NN5BNGwonwnsK3TqLSSMs/qVZJECQ2tsgJpMEWmcg8Np8UNLcHKpG8+0wxqgPxEB+7zHOJFD10ehmEOin5NklM7fmflh3CDMt7Gc2hsoPysajYdKv3MjzmFijHiXIkz2LnKXsfo1o/A2iUrBMCFMuxE4hLIFRj0nStbLpF0nZvP5Dw+ZQ6b+TQ3PmY427O1DAlOApK/seSs0q4fB4GBbmjloR2Fcx5X5MDTSnhE2i+dzF+t2NPIrhYkMsxt+6QXzWEfgTzIlmo39hqLnycXJIGcuRQNHwrz9v19bdOeu+tz7fr69lbucbZP2tCziaJh6ZnJyQ82tnvCMPPpkYYcXYuwphneyKMPJuQYtBjr9EKRA8tRSvQIofOoRVo8YOIWoEWRXQCrUCLJRXNCosoFdMKiysk1gq06KJUtQItxlSUEfix9KgCL85cnGvmTGhxRuDFGSVDFXhx5oOzwfoPRaAVeHGG2bUCL85cCUbgxZkrxwi8OMOlXEEe3UjHzOIsh+IQdnGWg3OcYonc6ZziSFGukyw6ZbVHY9Xzxz983F9+AkkjiORwcLi3AAABgnpUWHRTTUlMRVMgcmRraXQgMjAyMy4wMy4yAAB4nC2ROW4jQRAEv7ImCQwbdR8Q6OwAMqlHtC9Dth6/WcP1GlFXZvb5PM/b8+v+Os+vzXvL9/7et9fWvfft7899b/28b9mbX1ff+Tz//N4eTKtL7HjwcnGV4+PRy1WJBzVzOlAtS408HrTKOuPNKjQPWt54NEguEtOZEw0e4qvDOmaO04QCzJazew/zoJAC02Ue4rOMWN2AZBFFFRCztTIQr+CoS6uqWgKhGgltw5K7cJSWiHhAv6JYenxgDjUd4G4Jj7JSWeBHlrWkDVHjdw5kpiDQRORjkDjDAots3LeNdI3KhB2H1+6aueJkmIkV1eqzW0jRkiutoqeFjQPne0EqwrhCL7LpUa+YAJQiRzSio5bxH61Xco7gGiHJtYbHaTVXXHrCs1mHIUHM08oma74yaks3PnA/o99JqqYXVsX/T0dqWbByKOTnFRrsYKNCEVVdLRElqPnCH+Xc0mbC/YCfjDruv/8AgyCEold9TecAAAJrelRYdHJka2l0UEtMMSByZGtpdCAyMDIzLjAzLjIAAHice79v7T0GIOBlgAAmIFYCYlUgbmBkY8gA0sxMTFAGM1BEA8Rg4YDQTOxQFYxsDhAJNgeYSrgAhManEk0LJ4MCkIZzlWFcqDJ2iDgzFgEcboC5lo0hAeRHDJodbAMzujAHWBhDNTcDowYTI5MCE7MGExOLAgurAitbBhMbewI7RwYTB6cCJ5cCF3cGEzdPAg9vBhMvHwMvfwK/AAO/IFBWKEFIOINJWCRBRDSDSVQsQUw8g0lcQkFCkkFSikFKmkFahkFGlkFWjkFOnkFegUFBkUGcNUFRhkGUPUGQK0GEBegANlZxMVF2NjZuHl5+QS42IWERUXYONlk5eQVFGfFFjMCQgsYlg1LAoWaHZW7THEAcr6UaDuc+9IHZKq9YHQKTXMDsED4nh1zZz/YgtrW2jP2ibaZgcfmvnbbr8naAxSPlt+73F58NZi+d+mu/qZX9PhBbQyvmwMxfV8BsHfWeA/IXHcBqrk/bfSBeiQ3Mfr/i5oHUHTfAbN8eloNsibvBbDFr3oMs6m22IPbrTyoHa5arg80RSflz4KhL734Q+/IDtoNrFRUOgNiZX84cOH0rHizu+7TugI3tCzBb9qDTActPdmA1Ew4f39/pZgBmu/o27a/pWwtWM/PoWztZ8wlg9qbnDfaPZLvtQGy9YHmHDbYMYPfElUc77BHQBquJ0p7r8OvwA7B7imx3O5jcOAuxa+0rh6sNG8DsWfEsjqwiymC7CsRlHMMyRMBsy2MqjjpLssFqdPeKOv5fUQe2az3XHwfJgvk2YL8DAJhCobHZweF7AAADJnpUWHRNT0wxIHJka2l0IDIwMjMuMDMuMgAAeJx9VltuHDcQ/N9T8AJL9Ivd7E9Lsg0j8C5gK7lDfoPcH6mehThrg8hIbMxwaprVr5Iura4fb3/8/W9bl7xdLq3R//xmZvtLiejyvdVNe/n89dutvb5/evnYeb3/eXv/2dSaBr7Bz6/YT+/37x873F6bdeawINx4Oos16nRc55cCnHTXQXMCZ+4zxwanB45SjL1pJ5+DeIOzdq/XKiyjcZ+Zqr7BjXbDrjORDjiemhKywTnOxa6HusCfGc2YG1wAd8V7Bq0EUJjdcgOcD2CO8JR2pc7Tw3YhZyG1jqTQQsJ5xi6JTIjmal0jY1YUMZRzlx6uulxHny7sCsAQTd8FzlWZq/dJPKNC91SW2CGrNtfZZZoMrSS5ztwlie2BnILYj5B9DNpGNNoXpIm6UirFkSfXkF2euGp0jT7Bk6OSy6Tu2/CjvfxTDExyzmxX6YOExtbtfCTAyCZ6BAxmmOk2V1n5155TwrwYTDb1XfmFHlUlU8IUgUDCu25Hg8sr9+FIkxc0Br7acRV5dBVxwF3d6RjJuxSIHh1tbooaVbIEM7eLSqpa8ElBUZ4kOHgLHBg66TbTRr0fRGnbs6tSWslnlAcZrZP3shCHfiCEYceUGFpqKwxSZUJDDww8VzxOk7fFl6pSdIUgoIqVo5i5rWdpXUNDh6rR0SQ2KXe9rzVPicKMQA2AVJ7Td72Pg+4Y1I7G07QKKYVsO86qh1Md8FTaJTZLKzbAqhAaP1wfU8Rpx1T/Dvx8e/tFnR96/XK/vZ16LbVOWa4NPdUXD81OkWU8jlNLGctPyTSsOJXRsOapf4aVp8hxrWcp48Pwk2RZGV7k0IRWhhc/SJCV4UWRi2OZxRKSYsfOIgrh4MMsrlxka2fR5eILgoswZt3KyPNIWxlZlKUow8iZz6IMI4uyFGUYWZSlKMPIolx/wsrIoixFGUYWZUwKl5FFGRPBZWRxlsoyjD53OLejf586mcvo4qzVBDB6toHWjqLvnjrQivMiqEUZu+fh6o/QFz89kjyfSlzt+dyM9fzxjwfuL/8BfYShm9yf2ekAAAGYelRYdFNNSUxFUzEgcmRraXQgMjAyMy4wMy4yAAB4nCWRO24kMQxEr7LhDKAR+P+g4cQNbGgfQrmDjX34LWkidZfI0mPx/rjvx8f382vxWrIeX0vXWo+/z/X4/Pdc+vxZP9AXf9/3/cX3jZP//D5sMqflsBkdLOOSGerUECyi9z+1mAydFOXjwqmCQp7VrTYumsFE6kNmaUtuhSI1hFFkRlXjeuGLnXabMIcfpT3xwosmV6RtTXc95dZQ3gn7l03Nzho005WbIfksuAckF+0A9StmEVfCP1pZodSUMvHNENpHKMHl2HjuZNuJaSpB2xChKRsiZ1WKDBAyacRpNekCq0wMQXEeNLKy3VlppnLwuyR9dxab5pHIlGx3dqnrGdyDYAsp3fRgTOLs2o3qvmdEAGH6llgikCGKKBEOTUnO966sGsEhB6LeA+kGRTKgQo8IArTZzH5ATbEzjIj8nOVwBhXKr5yqGnoAshDIhfhS9YC7FawbtJ6yBWUkBCCQ9DuCFrK9rQZ/Yam4MZyynSvjhM5tITGev/8ByFqMHiW+0esAAAJ0elRYdHJka2l0UEtMMiByZGtpdCAyMDIzLjAzLjIAAHice79v7T0GIOBlgAAmIFYGYlUgbmBkY8gA0sxMTFAGM1BEA8Rg4YDQTOxQFYxsDhAJNgeYSrgAhManEk2LMoMCkMYU54SIs0O4zHB5hAAON8Bcy8aQAPIjBo1wGwN2Dexgm5khXEY4zc3AqMHEyKTAxKzBxMSiwMKqwMqWwcTGnsDOkcHEwanAyaXAxZ3BxM2TwMObwcTLx8DLn8AvkMEkIMjAIZQgJJzBJCySICKawSQqliAmnsEkLqEgIckgKcUgJc0gLcMgI6sgK6fBJCvPIK/AoKDIoKDEIM6aIMqeIMCVIMIMdAEbq7iYKDsbGzcPL78AF5uQsIgoO4f4Okag26DxyaD8dL+1A+ORjQ4gjmT+fvsP0kvA7Oqtk+y/3M4Cs93Z+B20LmiA2ddzpu671O0PZi/XVNl/Jv6JPYh9SIj1QMf+tWD2xHzpAxKt5/eC2C/Lyw9oG7LvB7F3Tpx34N9fBrCaZ1PPHFj/eq4tiL0q+/2BgG+9YPHX2/kOiq/RA7Pl/UUOzj7sBtZrGqh9UIhpFpht+4TpYFLlczDb98j1A3I3N4DZHe+2HIiskDoAYse8ajuQH/APLK6n6XGAycQRLL7G8cb+B49VwWz+jtX7n/FPBKuR6zph63rfBMz+x/HULneCA9gNH4I+2le9mAZmM4XaOei0i9mB2Gv1eh2CHwmBxYtYNjism/lqH4g9j+Oew9cjDGC/G+YzOO6V7QCb+Xv9V4fTWYpge90LxR0//lICi2vd0XDkm3oVzHa4Z+946ss8MPubi7JjsZoHWL0YAFQ4rauFR7LOAAADMXpUWHRNT0wyIHJka2l0IDIwMjMuMDMuMgAAeJx9VluOEzEQ/M8pfIFY7rf9yT5ACG1WgoU78Iu4v6h2WE9gDZO45XEqnurqdiWnktfnh0/ff5Z18cPpVEr7z3uMUb5Ja+30VHJS7h4/fLyU+5d3d68r989fLy9filiRwHfw+hP77uX56XWFyn3hOkSsR7FqZhReWm3zOr7JwFHVYRYdOOKu1DY4mThSJ5ci1X142Aan5RnPZdGwjolLRNvhrFzKuVUe1NWxISuPNjZAx4MBdCV2AoUAwZANMBLItcXgQP5VLLT1DbBfgcrdu+be1Eh4hxyJlNod6U6kcW+YvUVCM+SjFTpyzzSsSd8KTlmZs1cZwmM+NIK2klPW5hxVO/UhqX4byG6HzOqcex00RmMAvLPQTiTSRI7KPoQiU4pwZd5Brdz9KGcCpIWwY1q5CfWdUuRXBtRbYDdAe+sytvvGVYBQDppQ6eS81aqX9+Vs1U0J8ywaiY0tdFwLwOSEqmPXgfLKTldu17ISh0EPzBr1GDthmXJXgp7DmiYBG9BgpyzPaiEZbShTzkheK/w3NMuF5YFWbbMGzU12WrFOpGqIZNuFMbdd/7Hh3EH0bqKarRJ9xDZ5vxpDoP+SG2nMz98Cs0wKwWloAi0kfOsgeZ7gMK2Zcmaj1v9BMmvkMKWOU5JIdJPKTqE0u9JrQ4dopJbNoWvskITEo3oEenQWiBmb7pBZoFHVjcynpYij9XdIAU/0vZmgQ2b9vTsO7gaaBSI024C9zqqzwtF2jgfbvs9doY2I/25A+OQG+nh5+MPMr/Z+93x5OOydcxwungtymDVuih6eTLi1w3oJww+DVYw4bFQx+mGWijEOR6Qct75HM9CNv2kGWuRwyDUDLX6wJs1AiyIlxwyLJexH58oiSskUgRZXWAnNsOhS8gXBRRi+oBn49vhrBl6UOSkj8KFnUkbgRZmTMgIvypyUEXhR5lQWgRdlnDbKwIsyJ2UEXpRxfigDL86cKiPI7XngMrv9pu9prizOkk2AIEcbSK7g53pxFv29sjhLyowsFmXxa+qLn2RHpMjr2dmet82Y96//UzA//QK51Ko0R5opwAAAAaN6VFh0U01JTEVTMiByZGtpdCAyMDIzLjAzLjIAAHicJZI7biQxDESvsuEMoBbEP4mBk23A4fgQyh049uG3qM26H8hiFan7474fH1/P96a9eT/eW/bej78/z73l8/m9v0E3fd33/T6F9/txP+8/vw+eJWI1bJoZ5XjR1DJLAOJUakDq5EOme3mMF08WDR88XSJQca3JRaldw4rPg1yJnQbNgAzaLp4rigNELFoZRDk9B6ppkTDGXzLTMaiZca5SMJ2wxDWAliRGvy6fAuMgCxZWtX5MTcqCPsBqrZxF1W2eLCQgNdlLuNUjXJnBCN8rZFw0eQnVaaRcwY1ypdQZGMpICyRJ3mI23RBydA4SP0aZnE5RIZicPMRhQDIXZbsi2CnrLisMPER0reo2EqR6IVYFvMcxutxgGVA1RBR5wpiX9m0yDQR9kXVuUyHVJaT4VRgjzMLiQsJhEXdey7R11fKIOJ5AmjVCfJWAyZwLYa0dLZc+VkyP6C3BNjOhr6a64WH0rcWxTPjBIJPsNvf0fj3YBN7GWa5S2qnBWBn/NyM2nr//ALQXj8KCMBmBAAACZ3pUWHRyZGtpdFBLTDMgcmRraXQgMjAyMy4wMy4yAAB4nHu/b+09BiDgZYAAJiBWAmIVIG5gZGPIANLMTExQBjNQRAPEYOGA0EzsUBWMbA4QCTYHmEq4AITGpxJNizKDApDGFOeEiLNDuMxweYQADjfAXMvGkADyI5xGuIkBu0J2sI3MEC4jnOZmYNRgYmRSYGLWYGJiUWBhVWBly2BiY09g58hg4uBU4ORS4OLOYOLmSeDhzWDi5WPg5U/gF8hgEhBk4BBKEBLOYBIWSRARzWASFUsQE89gEpdQkJBkkJRikJJmkJZRkJHVYJKRY5CTZ5BXYJBXZBBnTRBlTxDgShBhBlrPxiouJsrOxsbNw8svwMUmJCwiys4hvogR6DBoJDIo8QUXODhEzHcAcSxchRzW7JoKZh85/dx+X3IAmJ00xdjBr5cDzI4+sM5uqoYtmL2mhGPfd90j9iC2k9bZ/Uu3zwWzK76wHMiPcd4HYvsxJR9obP8NZqdHTDzAME0RrCYo/sCB7UEr7UDsyWYPDvQx7gKL75/HftCmezKYPcWM/+Dd+C6wXq5dagenyAjtB7FfTv134IbLJjD7b9/5A63nusHsvdvXH9i8l/cAiH1pQ8MBYdavYHGVzY4HfkU4gcVneR3ff1FWH8x+1921P09iBVjNL7tjdjKLGsHsa3Ni7KNZLoHdxmMq6nDpnSnYPT2PQxzuX70Eds/GipkOEZ8e7wWxP/rvdGB7Mxes927YC4eM13lgdkfbd4c/W3+BzeETZ3H0msgMtrdllrSj05oHELe1aTtO0rEAi0cZKTvaqawCmy8GAF/MqQ5tyuo5AAADFHpUWHRNT0wzIHJka2l0IDIwMjMuMDMuMgAAeJx9Vltu2zAQ/PcpeAEL++Iu+RnHaVEUsYE27R36W/T+6CzVUA5AVDYHEjGmZl8Dn0pe365ff/0p85Lr6VQK/efbey8/lYhOryVvyuXl85dbeX57urzvPN9/3N6+F7Wijt/g85H79HZ/fd/h8lx0i1rJe7GtR6+ihTYa1/FLAU82adWigufRSWPBU/B4a0ydHAdLdWptwbNyx3mNalfBDau61AWvlht21ViZwesVB8qC53jvmfIcqQ0KqorXVSCRRN5wSqsCoqiwryJpSZSNIkx4nN0p2urdPZm6ITXEnkzrLL6KmgnhnA1UIWMQHGH1VdychTn7RsyeVdxUKaivmFmac2Df3TMiq8qx0slZnHPbLJBrB5MrZK5iZ9uZvVJDWRCSBImv8sm1XH4jo4gaMQkluQaFLbX6rhVlDPQx6qBdQnxFjT0B1lyoJpW6iNGK2sqncq6Zdo6eRRNGLZZq+14AEq5tnNoZ5V3lAKGMsiLuWvu4U/fGKyrnqWi66t0tBYT6uq4ie/8hr9qHAGUVW2VAslwofAvec0Us4asEiA1mc47RdsbEbZV/1DLHTvHOUcxg8ljK9OELGCLF+GZLI7XLQZask23wjd50jAmzLkdUGrJUwWSiEY+ohi/fnhMVmzg5jTM7xrqtIsf3DmZo3VveWvdYEnOeGvyouowWIbSgrVoEfnQrfQupaLeUCXOUuvS6YXbIopDYOLRF4GFFtZ2KSkaXDAkRKa0S+nK7fnDo3bMv99v18GzJdVhzbujhwHgodhgt47EefspYftimYcVhjobVDgs0rH74HOd6dDMewA+uZQk8xcGdLIGnPriQJfCUyKkxYaqEqdjYmUI5lQJ4asXQ8oApl1MvBE7BmHZLkMehtgSZkiUlA+TIZ0oGyJQsKRkgU7KkZIBMyZKZBciUjBniBJmSJSUDZErGTHCCTM3oTknQxx7nsTM1o5k5QadmzSYA6NEG+m9nakYPWmqeArXugU41OpIcDwXNZnxsvXx+/6uB+9NfByqezXTh+ssAAAGUelRYdFNNSUxFUzMgcmRraXQgMjAyMy4wMy4yAAB4nCWSO27kQAxEr7KhBtA0+Gn+IDixAIfjQ3TuwLEPv8WeTHgiq8hi3x/3fXx8P16L15J1vJautY7P38da+vX4WT+gi7/v+7Xr7tdxP+5/f4eOMKM456gok/OSIWmzgUfRPC8eyVR86hBzqq5IstJTBqs6WmjoZGUGKTM/ryf1L7E8eZiKGxAPJyiDiAoHiAyKmHJ2dVEklJ464E2bzWLxBJtggkloOEyqxXwQszdSpeihnjGI3B3603Tr55jh0oRNvDYpo9RWF7i49lxwanUwC4r51sIeeWJmLQndhjOxKgiVCFJ5Wg/IKEJgPGPPScLvtuIp73XggkT6Q31vg0TMy7sv1Lk2ooRm2zFS24FmcNX2YwlvlM4BBYzLxLnvoKh3kGDyQJ8OhK2102OOhN8cOGvlDplZEe1lQJBocVENjB5DnLxrCmfLBqGGEKGT5ahIHN+QVx/NLBBAjRCTHQleEGK+kGRI3w4PJCJkE2yxzaGrlOfj7z9YjotMxHB7PAAAAlR6VFh0cmRraXRQS0w0IHJka2l0IDIwMjMuMDMuMgAAeJx7v2/tPQYg4GWAACYgVgRiZSBuYGRjyADSzExMUAYzUEQDxGDhgNBM7FAVjGwOEAk2B5hKuACExqcSTYsygwKQxhTnhIizQ7jMcHmEAA43wFzLxpAA8iOcRrgJp7dQdDAyczMwajAxMikwMWswMbEosLAqsLJlMLGxJ7BzZDBxcCpwcilwcWcwcfMk8PBmMPHyMfDyJ/ALZDAJCDJwCCUICWcwCYskiIhmMImKJYiJZzCJSyhISDJISjFISTNIyyjIyGowycgpyMkzyCswiLMmiLInCHAliDAD7WZjFRcTZWdj4+bh5RfgYhMSFhFl5xCfxgh0FTTuGBRFVzY5CLTNcwBxVhqqO0wUmwpm61RyOJRZ+oPZ+g/dHY6psIPZG5M07SuibMDsz0xWdlPPHrIHsRcyLN0/i38umD2n8NF+9n1u+0Bsv0j/A5/DfoHZKxLaD7ioK4PVvCjYdmBG2Ho7EPuVyrUDrP93g8WnzmI6yOk4Dcze4sF1sM+oFqz3ygXFg7NPcO0HsdPvfjmw4fx6MFv97NEDWwW7wGxVyWUHumR5D4DYpwXzDojXfgGLn1ipd8A62AksPkFo4X7bEn0wW8fZfb8C40qwmit2hvYqzs1gdorANPtbE06B3bZJQ9Mh5YgB2D1bJ2Q4bJa5D3bPrIjFDh2eAmD2HJ7DDhOMFoH13pH55KDxsgLM7t3D4Ch5+inYnPwXHI4//djA9vYXKDh+v/8MrKbfy8BxdYs1WFwMABy5nOYHbcbIAAADBnpUWHRNT0w0IHJka2l0IDIwMjMuMDMuMgAAeJx9VluOEzEQ/M8pfIFY/XLb/bkvEEKbSLBwB34R9xfVjtaTFRaTuDXjlD3lqu5WTiWvb89ff/0p65Ln06kU+s83IspPJaLTa8mb8vjy+culPL09PL7PPF1/XN6+F9WiDWvw+Yh9eLu+vs9weSpWaSgb4SaaDseiSvM6VgpwUp3FyIHzFkQ7nE4cq2h40Srax5ANzsoVP7Moc+QCZhfa4Fq5YNad3QW4MCXZ7ed4L1Ue7E0K1yY8INS/uA7cmauMoJFAkU66A44bsHtQSDlj7zAatkFGIvOoMcQTaWOE7cRhwmnOBig1y5e6duWxQ6Yv51Y7sQwFQE2sbZHpzNmrhxCs4Wo++lZKTm/Oo0LpDrO5cofZW6TdkG7SG+WRxLq47qCtPP6GUNCH1aAPwM0EebIDp0vnjuwRkp7y4vwNom2g/XYsOCqhCaU+fJuaEPBTasVBrYOtVCEL2YoVaYDWzjHUc9dgqNZ3CU9JQGqXwMlyEamr7uQSzl2RS21oj7nI2PoWOt2i2jveH1MBIbUtAZ0pDV8Vx04BpNnYaSVpV9qJakuBNJrrFthQdahiU6aZfgGhtu9OoxTpqUNn8pu27tst0ydoT9Z8Jj/6SNAWOaCSV4qG4ptyeWffuSRZUb1iQ4II2DNwMtoh8b2WUcmcNLPTUHq8bUtp0aiteTdPhyhG2zqk6RDSGQlqg5LnIO28haZDDGN4wMTcNSTAdQN9uTx/aLu3Rvx4vTwfjVhyHP02J/Roq3godnRPxmM7miRj+NELDaMfLc8wxtHYDCOO7sU57nsUz8B3vcgy8CKHnmMZePFDhVgGXhQ5OWZYLGGMzZlFlJMpAi+uKHueYdHl5AuCizBq2DLIfalaBlmUJSkjyKFnUkaQRVmSMoIsypKUEWRRllQWQRZlVAZnkEVZkjKCLMrIdM4gizMyWjLofebynFmckaKcQRdnzSRA0CMNJmc7rFa7HWu9W6fI/c6+TL37RMvn938LuD/9BQztk5mC//8mAAABiXpUWHRTTUlMRVM0IHJka2l0IDIwMjMuMDMuMgAAeJwtkTtuIzEQRK+yoQRQRP8/EJzsAA7lQzB34NiH3yK14bwpVlV3Xx/Xdfv4ur8WryXr9lq61rr9/bmvpZ/37/UNuvjrul5v3XX9+b3ZpFK2YbNdK8dTZrAYA4T3/mQVGzpFs2o8dbIoywBnDhlPmhEcsUmb0iFcHC6DpwvD48FTqqkARJLskIymHg+I26g22xlduplVtTmYgZH7QIqmMgo8fCaxFJCamG8UM1qI4W9RiQqPmiiXBsKpFYeESfp2F0sJ3S2QznqYm2BssMTcQmjBE4muxx4DSW9EWZGnBDd5jodMIWs99ZMbg0DVTLRVMlOaeeAfaai+V+GltR+msW0VzczSOP6KaD9LbdHwEyluWOKeRPSM3R4a51KmcN9idMh9HIT836B6BkQoSraLYlSjJqCY1P4eRyIZ73J6BE4HUSMEl3rWJAusAU44Bvsm7rEXKHhfsHxifXRWyrNIk0FQl0u2pqVR6f77DzFUh5/H5biZAAACPHpUWHRyZGtpdFBLTDUgcmRraXQgMjAyMy4wMy4yAAB4nHu/b+09BiDgZYAAJiCWB2JFIG5gZGPIANLMTExQBjNQRAPEYOGA0EzsUBWMbA4QCTYHmEq4AITGpxJNizKDApDGFOeEiLNDuMxweYQADjfAXMvGkADyI5xGuAnqLYgEIzNMAzcDowYTI5MCE7MGExOLAgurAitbBhMbewI7RwYTB6cCJ5cCF3cGEzdPAg9vBhMvHwMvfwK/QAaTgCADh1CCkHAGk7BIgohoBpOoWIKYeAaTuISChCSDpBSDlDSDtIyCjCyDjJwGkzhrgih7ggBXgggz0FY2VnExUXY2Nm4eXn4BLjYhYRFRdg7xOkagu6CRxCAvc2yxg1N+rwOIw2Wf4TBneTuY7TrbyYHhvxmYvd6lxkG55oo9iK0/55j91nxtsLjvyjL773bLwOLZ3Wz7ExT6wWy73r79ngxt+0Dss5PVDiyuegRmm9smHEg/5ghWY3R0/gGrTmEw+/nMPQcq3R6D2XUObw48Vj8DZicI/T/QaB1jB2LXxIgfNJHmtQWxV9s+OnDjS/V+EFtk+qYDq7f7gtnxh6YeuKnHcADEnmjuc0Da/iVYXL1P6EBNgiNY/Cmz737Di6ZgdvDlyH3vGXeD1XyMX2wf8XgxmF21/J39oounwXa9XhDmcDCvH+wGLdUOhwifbLCag6e3O+jw24LZZQuuOBRx/gCz25WZHO+8egBmP+wVcIwxswXbVdHD7bhM8yA4HMQAC7qW4mOGaiAAAALhelRYdE1PTDUgcmRraXQgMjAyMy4wMy4yAAB4nH1VW24bMQz89yl0AQviSxQ/4zgtiiI20Ka9Q3+L3h8d7jZaBxC6iQhbO6JGnBF9Kvl8u3799afMh6+nUyntP/8RUX5Ka+30WvJDubx8/nIrz29Pl/eZ5/uP29v3IlREsAZ/H7FPb/fX9xkqz8UqkQewWpViYF2rbXuOlQyc1M5jqAPHEkxtgZMN17Q5e+E6LIR0gdNyBy6UenCh2ntrMhY4Kze8NqOuhHzdRw9Z4Dr2bTW6GBMWcAyPvsA5cOdWjc26AUg0yFfAkcB8L31YLuFOzqvSRCJBrbE7JVKVRXmBRMVu5SzVmrjkpm44zao8lLqctUbw4Exl3oVWB6dU5mx1hPhwnMjdbKxKSanN2asMHi5Z1dDovkLqjgyH3XJTJmu+OjtZufwu56gKAT0SIaq2cgb1PWnz1nhknWIoN1tBfT+TjRYoJKCjiY1loUb5lIXqHuGeOmR2ixU09upTtBGU4qK0vHQdt11THhCg56KGEiyNDEvcdoZtwKJYNBT5V1Ix7+ZjYscOIICqxdKmnFrBxlAdNgWSUYGlT1k35OiqY3sfYUubsm1XTkzM01HISEuXcgqFK25q4ptOFLR0KfvWPVyl8yaTy7ClSyH4rfQKkulNnAdu9aVLOa/TqCTSYVIg3ZyWLs1OVyIN1yEiCh+G/Kvd0dHuyInr66HJE5vz0s8vt+uH9rg3zMv9dj0aJuc4+mJOyNH+8KXo0eXQg4sdzYww+tGzFMOP1qQY42hAihFHl6Ecj72EtkAPPUMz0CSH3qAZaPKDOJqBJkVKjhkmS9xp3WYmUUqmCDS54obSFiZdSr4gOAnjumkGfrxVmoEnZU7KCHzUMykj8KTMSRmBJ2VOygg8KXNWFoEnZZiYMvCkzEk5f5QmZZiSMvDkzFllBHk0GW8zk7NsnPWQUf5RnnllK7I/SJO2ejRRfn//xcbn018abH1kjssNXQAAAX16VFh0U01JTEVTNSByZGtpdCAyMDIzLjAzLjIAAHicJZE7juQwDESvsqENuAV+RRKNTtbAhN2HUD7BxHP4LWoz+5FUFYv3676P1+d8L15L1vFeutY6/v6ca+nX+b2+QRd/7vt9H/f5+vz5PXwwR+llw7hSr6eOKZkOIFrCDcgo9JKRXrpBGc+6eMxJZNeThztPQ8eMROFJo6Y6hnlIZV7PBw0X9+kgzMmN+kvRjZpMjpZ64AWSiGZmoiZgOpw07KIRPkuh97BRJSlAHlMZrh8+sjTaVIQ7PDxiaEoGiJdVbFJBVRgTdooWrGHYIYFIzaxyt1EQaZuoNCHfz3tSWbMk9fzvYkZhAq6JJOf2ykWJnXikiNheSbIQMGoEWd5hJDSNezKt+5sJS0hP2uTOVLBa9L9AMZvkhMO2WuVC1ZdQV49eKIk7LVzNzbfP4uJOy0eYTmkUmt5pzYGXfNtEaEjtmYNV55YLD0Za1YngqPBYjvbuwZFiHwxDgvzO33/9noDk4CfZtAAAAmR6VFh0cmRraXRQS0w2IHJka2l0IDIwMjMuMDMuMgAAeJx7v2/tPQYg4GWAACYgVgJiFSBuYGRjyADSzExMUAYzUEQDxGDhgNBM7FAVjGwOEAk2B5hKuACExqcSTYsygwKQxhTnhIizQ7jMcHmEAA43wFzLxpAA8iMWGrs6drCFzBAuI5zmZmDUYGJkUmBi1mBiYlFgYVVgZctgYmNPYOfIYOLgVODkUuDizmDi5kng4c1g4uVj4OVP4BfIYBIQZOAQShASzmASFkkQEc1gEhVLEBPPYBKXUJCQZJCUYpCSZpCWYZCR5WCSkWOQk2eQV2CQV2QQZ00QZU8Q4EoQYQZaz8YqLibKzsbGzcPLL8DFJiQsIsrOIb6IEegwaBwyKPEFFzg4RMx3AHEsXIUc1uyaCmYfOf3cfl9yAJidNMXYwa+XA8yOPrDObqqGLZi9poRj33fdI/YgtpPW2f1Lt88Fsyu+sBzIj3HeB2L7MSUfaGz/DWanR0w8wDBNEawmKP7Age1BK+1A7MlmDw70Me4Ci++fx37QpnsymD3FjP/g3fgusF6uXWoHp8gI7QexX079d+CGyyYw+2/f+QOt57rB7L3b1x/YvJf3AIh9aUPDAWHWr2Bxlc2OB35FOIHFZ3kd339RVh/MftfdtT9PYgVYzS+7Y3YyixrB7GtzYuyjWS6B3cZjKupw6Z0p2D09j0Mc7l+9BHbPxoqZDhGfHu8FsT/673RgezMXrPdu2AuHjNd5YHZH23eHP1t/gc3hE2dx9JrIDLa3ZZa0o9OaBxC3tWk7TtKxAItHGSk72qmsApsvBgC9V6jDDF9LtgAAAxR6VFh0TU9MNiByZGtpdCAyMDIzLjAzLjIAAHicfVZbbtswEPz3KXgBC/viLvkZx2lRFLGBNu0d+lv0/ugs1VAOQFQ2BxIxpmZfA59KXt+uX3/9KfOS6+lUCv3n23svP5WITq8lb8rl5fOXW3l+e7q87zzff9zevhe1oo7f4POR+/R2f33f4fJcdItayXuxrUevooU2GtfxSwFPNmnVooLn0UljwVPweGtMnRwHS3VqbcGzcsd5jWpXwQ2rutQFr5YbdtVYmcHrFQfKgud475nyHKkNCqqK11UgkUTecEqrAqKosK8iaUmUjSJMeJzdKdrq3T2ZuiE1xJ5M6yy+ipoJ4ZwNVCFjEBxh9VXcnIU5+0bMnlXcVCmor5hZmnNg390zIqvKsdLJWZxz2yyQaweTK2SuYmfbmb1SQ1kQkgSJr/LJtVx+I6OIGjEJJbkGhS21+q4VZQz0MeqgXUJ8RY09AdZcqCaVuojRitrKp3KumXaOnkUTRi2WavteABKubZzaGeVd5QChjLIi7lr7uFP3xisq56louurdLQWE+rquInv/Ia/ahwBlFVtlQLJcKHwL3nNFLOGrBIgNZnOO0XbGxG2Vf9Qyx07xzlHMYPJYyvThCxgixfhmSyO1y0GWrJNt8I3edIwJsy5HVHKiKphMNOIR1fDl23OiYhMnp3Fmx1i3VeT43sEMrXvLW+seS2LOU4MfVZfRIoQWtFWLwI9upW8hFe2WMmGOUpdeN8wOWRQSG4e2CDysqLZTUcnokiEhIqVVQl9u1w8OvXv25X67Hp4tuQ5rzg09HBgPxQ6jZTzWw08Zyw/bNKw4zNGw2mGBhtUPn+Ncj27GA/jBtSyBpzi4kyXw1AcXsgSeEjk1JkyVMBUbO1Mop1IAT60YWh4w5XLqhcApGNNuCfI41JYgU7KkZIAc+UzJAJmSJSUDZEqWlAyQKVkyswCZkjFDnCBTsqRkgEzJmAlOkKkZ3SkJ+tjjPHamZjQzJ+jUrNkEAD3aQP/tTM3oQUvNU6DWPdCpRkeS46Gg2YyPrZfP7381cH/6C90gnsJyssIXAAABlHpUWHRTTUlMRVM2IHJka2l0IDIwMjMuMDMuMgAAeJwlkjtu5EAMRK+yoQbQNPhp/iA4sQCH40N07sCxD7/Fnkx4IqvIYt8f9318fD9ei9eSdbyWrrWOz9/HWvr1+Fk/oIu/7/tdd7+O+3H/+zt0hBnFOUdFmZyXDEmbDTyK5nnxSKbiU4eYU3VFkpWeMljV0UJDJyszSJn5eT2pf4nlycNU3IB4OEEZRFQ4QGRQxJSzq4siofTUAW/abBaLJ9gEE0xCw2FSLeaDmL2RKkUP9YxB5O7Qn6ZbP8cMlyZs4rVJGaW2usDFteeCU6uDWVDMtxb2yBMza0noNpyJVUGoRJDK03pARhEC4xl7ThJ+txVPea8DFyTSH+p7GyRiXt59oc61ESU0246R2g40g6u2H0t4o3QOKGBcJs59B0W9gwSTB/p0IGytnR5zJPzmwFkrd8jMimgvA4JEi4tqYPQY4uRdUzhbNgg1hAidLEdF4viGvPpoZoEAaoSY7EjwghDzhSRD+nZ4IBEhm2CLbQ5dpTwff/8BPQ6LQUPXakoAAAIoelRYdHJka2l0UEtMNyByZGtpdCAyMDIzLjAzLjIAAHice79v7T0GIOBlgAAmIJYDYgUgbmBkY8gA0sxMTFAGM1BEA8Rg4YDQTOxQFYxsDhAJNgeYSrgAhManEk2LMth6THFOiDg7hMsMl0cI4HADzLVsDAkgP2LQ7BCaEcJnZOZmYNRgYmRSYGLWYGJiUWBhVWBly2BiY09g58hg4uBU4ORS4OLOYOLmSeDhzWDi5WPg5U/gF8hgEhBk4BBKEBLOYBIWSRARzWASFUsQE89gEpdQkJBkkJRikJJmkJZhkJFlEGdNEGVPEOBKEGEG2sjGKi4mys7Gxs3Dyy/AxSYkLCLKziFexAh0CzReGOSKb651sBPpcQBxLofWOCzpbwOzf5uGOUSymILZs3e2O5g9PW8PYl/a+NZe7bcmWFypaKF97oHFYHEhh6x9HOt6wexU/uD9SfJd+0Ds1woiB75lPAKz1eJ8D9QIOYPVzImddmDTWzEw2/LPpgOhs56C2YdWPTrwoR1i17pdXw9UT6iyA6vfInRwxa/JtiC2oMzNAzcWle4HsTtyVx7gq/MEs1+f6znQ/OgfmH3YweqA3IUXYHYnM+OBs5WOB0Ds2zPZ9s8qMAOz1XT6bUNW7wGreb7muP37GcvAbO9vnA5852eC7YoLznRYU1ULdoOPx0QHnxclYDVXvx1waD7sAWYXTb3rYNsKsfffQjbHm0UQe30+ijhqeUPsFQMAKdeakbiFKuIAAAK8elRYdE1PTDcgcmRraXQgMjAyMy4wMy4yAAB4nH1V244TMQx971fkBybyNbYf6XZBCG1XgoV/4BXx/8KeYXeyImLaWJn0xIl9jt1Lq+fr7cvP3+3todvl0hr85xsR7QcDwOWp1aRdHz99vreHlw/X15WH5+/3l28tZ0y5Jz/vsR9enp9eV7A9NO3CZCFNOpuJYYMO+3PupMRxDyFjTxyBAssCxzuOxUC0UXdyDljgpD2XG0EBa9hHzpwWOG33/Nl1hEf6G6qhq3NHnoudBlKMmlgwrs61xG3QidxGRt8RhhsvgH4AnUTywNoywpapiUJSZxSFKKQIUUb1LzJvdG8bZ/bcd1c2CGDlE4uXTXqFTeVKPXSsAsdiZtOuwZQpwp6Rm61SicXNZh2cOcmppCPzWCHlQA4LSLozdpERS5/arr/aFh1HQOz3MyLVFbQo2kY3l2Gj8hTJJS7PtyMmCskEFdTcwFd8orePlSgBIPW2YY9Acl9Bo7JPPRAIuKCO7rpUOxycAqoLFmVADBorKJbXJIjUXGuTKwxaSYqKqlweEbBfVYzDlk55l3OKPZL0RHLeRFYEUFFFHVWH7kGbZngroGbFcR+ihrxTasqrNFHxJF0zo6YHTWPYKvdUNI0OeTnGgyUhXkbuOzJSR7FnPtxFlpkvkrxnX1CJg6MBsNJTNboWmRkP/EsREK5Of7zf3vW8owten++3swtSjbPZ1QKfPS1fmpytC/NVzw6FOcbZiCSHnf1GcvjZVSRHnK0Da8wNAneDUyOQMkhTwUsZ5KmwpQzKVMC4G50KVfaVMdVjvWaxTWWHu/GpuqQuGFMNSRmaS0XKEE4VIWWIJuFLGeJJ4FKGZBIyliGdFItlaEzSxDJkkwSxDPkkNSxDMUkKy/AsHamz3hwzHhd888J7km0iokQ0S6beX/90c375A29DcegHC8c7AAABbHpUWHRTTUlMRVM3IHJka2l0IDIwMjMuMDMuMgAAeJwtkTmOXDEMRK/isBvQCNwXfEziDzjsOYTyCRzP4V1SOxMeS6wieX/e9+Pz6/lavJasx2vpWuvx++9zLf3z/F7foIu/7vt+3b9+Hj5NJXvY1EzLcelskzQAISfdQC3JhsyS0h4XKsbGg2fgUePiWR7dUIR7+wYSrBCgs/K4PmiKVAYIU1QeUmLmNnYtOnPLZCqb82ZmIrSFihyFLzQzhGjLbEZXK5BXexiQT2+VhgEcc//LSaVqJyWrHRLZGASGZtEC1JOjqYEoRdy3LGaWRe0QjWk4TntpC9ksK6lOCCOSGB88uxnWO34zySbFVX4IsReG1EmiFGdwF09UUSwnNL3gjvXR1vG01D47xSr7tFf0QDCZ7B5xsvr2wWXCHIvbA6Vr7dM4Uv3PHoE9xCT813dyE9WNGgvok7zK0LomDur+To6QDc9qfscm4fH8+Qd9I3tVSDxZJgAAAkt6VFh0cmRraXRQS0w4IHJka2l0IDIwMjMuMDMuMgAAeJx7v2/tPQYg4GWAACYgVgBiJSBuYGRjyADSzExMUAYzUEQDxGDhgNBM7FAVjGwOEAk2B5hKuACExqcSTYsy2AmY4pwQcXYIlxkujxDA4QaYa9kYEkB+hNPsEJoRIQ41AMxnZIbp42Zg1GBiZFJgYtZgYmJRYGFVYGXLYGJjT2DnyGDi4FTg5FLg4s5g4uZJ4OHNYOLlY+DlT+AXyGASEGTgEEoQEs5gEhZJEBHNYBIVSxATz2ASl1CQkGSQlGKQkmaQlmGQkWWQlWOQledgEmdNEGVPEOBKEGEGWszGKi4mys7Gxs3Dyy/AxSYkLCLKziHexQh0GjS6GBTYF/Q6KEyd7wDiOCjYOjxXngZmS2bLOTAEB4LZB67GOixx4ASzp0xrsO8vsAOzuyPY7Ju+H7MHsderFe2fb7QQzF5Wu23/4+2q+0DsRXGWB+revwSzPU9WHNgYqA1W01Oy+sCV/v12IPYDzdMH1nIcAIsft/x14JTzLDBb9xTLwV69WLBexSCZg1lXmfaD2Oz3Xx7oOrUazFY4v+vAMtY2MPuM5OwD+qLcB0Dsk8IRB6pTP4DFFVdKHJB0dQCLe6lm7V+RpAtmxyy8t2/L7SVgNW7TW+wX6daB2VMZD9lreN8Gu22mgr1DlL4l2D2O3XUOOibnwO7pCF3n0Nh0Zy+IfZvrnMODN3PBeg9L/XX45V8AZnNn8DlGq7GA7fr/XtXxUvsjsPjBXWyOGtLxYHExAJgFms/6/izOAAAC73pUWHRNT0w4IHJka2l0IDIwMjMuMDMuMgAAeJx9VUmOGzEMvPsV+oAFcZFIHTNLgiAYG0gm+UOuQf6PFGVY7QGItC2iWy6xi1v5VOL6/vLt99+yL345nUpp//nOOcsvaa2d3krclKfXL18v5fn909N95/n68/L+owgXUZzB5yP20/v17b5D5bloVe7mHTfTx+w4VNu6jpMMHNfZe5sC3JjkLglOFk6ts1uRytYATXBarvhZxyAjHCDVbi3B9XIphF3tswcB64Mz3MB7W+2s5hMHeldlT3AG3LnVCX9NAOQ+pVsC9ADSygy1OEKDXLLUzEBy9Tm0USC1j9l6goSjSzlLdTf2AcAYkylFRl3OvYLbtKAnpshRhozKnEcVdR8cobfGI/UZtTlbdUIZV+yt00x9aiC98pwyVvBgOWeWT7B/+lPOs5o1uAtsJ0LRM+y4EZDmaJDIrigTpW7tFr+LmWtAmxq+GdTL53LW6jocs4FKkAmN1Ou85V8MTeTh1c3bzLyiyVZVxbuwxCH0IFMKpfCKniIdQnHIWtxnUL61n6JavDKAMUE1MmhUC2FL92HL/WT3dDR1ITv1YTGSaHxkOAN2DB3HjMtqEGMRywrFUSjBsBnrXHwbm3KGjDr1qmpTV/Gp2bBs4DGNlzIQud8jj5enyBgoq7PNTrfIBfOSDX1oXXEg25o8rm2oa+ZTYp4oxohCwPB6GwgvdcrIkiOLLSQHhVcfTTKte728fNDSm7o+XS8vh7pyrENEY0MOrcRD0UMSCY/9UD7CGofAKZYdOqZYfqiVYs1DkijWo/DQMvQgMBqGNjkIiYahzQ+CoWFoU6TgGGazxPzr2tlEKZjC0OaKKtIymy4FXxDchDGZGoYfB1DD8KbMQRmGj3wGZRjelDkow/CmzEEZhjdljszC8KaMfqcwvClzUIbhTRn9S2F4c+bIMow89iOV1W0PfcdrZ3OWxVmPworcgthvwp+MrnRtL9Foj20Vz/c/fNyf/gGod4j/WC2RZwAAAYV6VFh0U01JTEVTOCByZGtpdCAyMDIzLjAzLjIAAHicJZE5jiMxDEWvMqENyIS4kyg4aQMT2odQ3sHEffj5Uof1itRf+Hq+Xrfn5/5evJas23vpWuv29e++lv69f69v0MWf1+uNwdf9+fnzczMy8axh1BXt4xJq99kA0Vy1gaVLDyXJGT0uJYvgHEJs5jkupjkt8N3pIeOa5GJZPZjczXRcj0lt3lOBxFsDiI8yD/zj4FIDE6oO28g8esLOQ6kqIT8pooU3clLPLiBNgwegILWq2Ipzyn4+qRhJtuB0hu1HkfSWxut4p/HA9WjKnNABc+YTD5s6y3LAoZqwHMXSREtA0/L4MirDOCxzKtrYTjVDeA9VFp84Wq4y8AsNifwWwRbKezFnnzxIC/fie1Mq9xWgo165fXVLlW3k7JFIbVJweE5VgUCTUlTznKYz5cSxKWlQdDLLPp3yzEDhV0CvSo/c715Sz3Y5coq6x1Ug07bLGXbkd9t80mVAZY8Yqu2dzipw2/vPf4yBhDNSlMuMAAACYXpUWHRyZGtpdFBLTDkgcmRraXQgMjAyMy4wMy4yAAB4nHu/b+09BiDgZYAAJiBWBGJlIG5gZGNIANKMzBwMCiA5CJcJTnMwaABpZiY2BzDNwuaQAaKZGWEC7BCaGS6BEIAqYABLMDFiMQLNLGWwGzDFOWHiuNwAtwLiWha4qxlgKhEMiL/YwUYyw3wPo7kZGBkYmRiYmIGGMLCwKrCyZTCxsSewc2QwcXAmcHJlMHFxJ3DzKPDwKvDyZTDx8SfwC2QwCQgyCAglCAlnMAmLMHCLZjCJiiWIiWcwiUsoSEgqSEppMElKq7BIy2gxMTNJysrIsrDIyjHIyTPIKTCIsyaIsicI8yaIMAMdwMYqLibKzsbGwcnFDaT5+AWEhHnFpzECnQaNPAbFoGS+g1XXyw6AOJNuvzmgW1gDZltVXjpw1U4HzFbvW3Sg44YhmH1Ou+NAgs/h/SB2eaPBgWP158BslgLWA++4bcFqqtLF9i9c6gRmr/u0zvaaRQ+YXTr7lP0G1n4wO0dIyoH9pi+Y/YHxnv0PyQ9gc1YeVXaYmCUPZj8/2+gQe9EUzF4YNt0hyYEZrH6L43EH0VscYDbPkocO3+YlgdVMOM7heIi1EswOlj7jkKP00g7EdjCY48D0g8sexG5b3uYgLvIRzNZbW2Q36dkTsHoO0/v7Plk+3Qdi91R+3x+VswPMtjqnduD/zyqwekHhJ/tvfOBzALFlZk/cpxrOCWb/lZI98HhPCpg9bc2F/XPLZ4HZ+9OFDyyOPQpmS7vt3X++7C2Y3bSF80CWHo8jiL27e6bN15WvwOJiAHPVpn06LQk7AAADCXpUWHRNT0w5IHJka2l0IDIwMjMuMDMuMgAAeJx9VluO5DYM/O9T6AJtiA+J1OfOzCYIgu0BkknukN8g90eq5IzcixVitwhbKFNFskj0rfD67e3Xv/4p69K3262U+j+/MUb502qtt2+FD+Xl68+/PMrrx5eXz53X9z8eH78Xs2IN3+D+Hvvl4/3b546U13LPI8dQH+VuR7Y+qpZ61Hld32p5L/c4vEpWI3K4e80N0uizHy1CR5a7Hn2IkcoPSCeyHTVGNiMyItNlg2w83Q9tzTzKXY6mLfru9E6f8NSCcQDZxTR8g4wTWdNHVz6NZuTxIzKJrEfLaNIZe3WxNjbIUR7czebqJGwpw3Y0BXUqCCM1+yDSOxO6Qwp8KhIfhjBwuEq32CVJdPoMczdn6Mks7AISmz5by2GDoXXV0LpDskZ+1NaRGyJDqmrfIdtEouimbSa21ay7sgtr1A+4iahEiqXJNiLWKOBJNObpmU33+czy8nfJo3mGCqEjUPZtQsc83iJPVTpFtTtd64wos+ZgblDUhPo2QCk/AagQcoN/fGG+zZGyQvVQ6yMHKwS9QwA7pJ2ac+iszXjcka1d6DrbSI5hZiMItV5138QNdWdH1m7GSAaaObYh9dNppKQptKJm3nYK0WBvIqh0ywBSvI1t4jXPjvOGULQYRoRX3bWRjvP0bhUiYQ0UutvViPOOPq0a0o+ySsdc2PE09hF8emQgORhl+GDb7qanT0G3u0JUgRliu3yazXpiJoYbXKICulXI18fbd3P3nMQv74+3axLz1mvc4qXYNVMFy6/BKVjtmo6C1a8R6FhxzTnHymuYOda4JpZjyfNgchqRpwHkNLLICdnRLH5CgjSLIgaH08hiiQHBV7T/0yBwGllc0fAyzaIr5MudxVhIGUafG1VodHHWyRm6vRKq2IHRxRkt5jS6OCvzCqOLszK1MLo4ozF0msVZyZk7izOErtMszhC00NizcKVMWT4JVGhscTb9b+fSweTsV63Nz7DW2cYsMyErKmrvWWl8//y/gOfbv4COlFoJnbw0AAABlHpUWHRTTUlMRVM5IHJka2l0IDIwMjMuMDMuMgAAeJwlkjtuJDEMRK+yYQ+glvknhYYTN+Bw5hDKHTj24bekyYQnslgs6X7d92vynPIzf+bxnDrnPL5+H3Pq92PKnPy8j8/X4+P+vD/u53E/7n9/x1m9xhBvp/byGO06sxtxLTDMjEGie6a2U3oMVgXxTjnKF8qsMiDr4q5A3F1QD4RLz7FIsMqbUNmI1TdcIXCd1L3SOdZAMtZoF6HKTRQmrGvxgD5USyoXsYCxdgl8pubqEw4cUJNqpmtg7cmocS+0Y0qIpLTLOnksZerJJBILpQyVZYqcql3RwbEBAGspIkjcsOyuKl/OrupuBUWgkZLLUXTNUmvUDVGs6AzlVAME+5TlIoLovMEiSnUtKxqjdkyIUsbOxNSH8xI3y1ymcD1U9yoaJCQ7zqDYA0eR7syXCJc2gazaO2BMVkSHbcxXlugzh2xTPKyR7rZQwhPBsCTvEiUVbtE5Mt7zLaGC74ENfJcw3s1a9Yz9LfBuXGkwlF1V8asef/8BWK2IOf8nv2oAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"# Save molecules from largest cluster so other talktorials can use it\n",
"sdf_path = str(\"molecule_set_largest_cluster.sdf\")\n",
"sdf = Chem.SDWriter(sdf_path)\n",
"for index in clusters[0]:\n",
" mol, label = compounds[index]\n",
" # Add label to metadata\n",
" mol.SetProp(\"_Name\", label)\n",
" sdf.write(mol)\n",
"sdf.close()"
],
"metadata": {
"id": "Dn79VYaKBIvk"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(\"Ten molecules from second largest cluster:\")\n",
"# Draw molecules\n",
"Draw.MolsToGridImage(\n",
" [compounds[i][0] for i in clusters[1][:10]],\n",
" legends=[compounds[i][1] for i in clusters[1][:10]],\n",
" molsPerRow=5,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "UPf_zvfgBN5T",
"outputId": "633c77ad-44dc-4bc0-f442-f5707bb4a616"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ten molecules from second largest cluster:\n"
]
},
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Ten molecules from first 10 clusters:\")\n",
"# Draw molecules\n",
"Draw.MolsToGridImage(\n",
" [compounds[clusters[i][0]][0] for i in range(10)],\n",
" legends=[compounds[clusters[i][0]][1] for i in range(10)],\n",
" molsPerRow=5,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "QbWXp1BjBW7D",
"outputId": "1be717a9-8e83-4b44-80bf-3b1fbe16a3b0"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ten molecules from first 10 clusters:\n"
]
},
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"# Generate image\n",
"img = Draw.MolsToGridImage(\n",
" [compounds[clusters[i][0]][0] for i in range(0, 3)],\n",
" legends=[f\"Cluster {i}\" for i in range(1, 4)],\n",
" subImgSize=(200, 200),\n",
" useSVG=True,\n",
")\n",
"\n",
"# Patch RAW svg data: convert non-transparent to transparent background and set font size\n",
"molsvg = img.data.replace(\"opacity:1.0\", \"opacity:0.0\").replace(\"12px\", \"20px\")\n",
"\n",
"# Save altered SVG data to file\n",
"with open(\"cluster_representatives.svg\", \"w\") as f:\n",
" f.write(molsvg)"
],
"metadata": {
"id": "Jjx1Dr-IBfC0"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def intra_tanimoto(fps_clusters):\n",
" \"\"\"Function to compute Tanimoto similarity for all pairs of fingerprints in each cluster\"\"\"\n",
" intra_similarity = []\n",
" # Calculate intra similarity per cluster\n",
" for cluster in fps_clusters:\n",
" # Tanimoto distance matrix function converted to similarity matrix (1-distance)\n",
" intra_similarity.append([1 - x for x in tanimoto_distance_matrix(cluster)])\n",
" return intra_similarity"
],
"metadata": {
"id": "SnJJuPqKBnzd"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Recompute fingerprints for 10 first clusters\n",
"mol_fps_per_cluster = []\n",
"for cluster in clusters[:10]:\n",
" mol_fps_per_cluster.append([rdkit_gen.GetFingerprint(compounds[i][0]) for i in cluster])\n",
"\n",
"# Compute intra-cluster similarity\n",
"intra_sim = intra_tanimoto(mol_fps_per_cluster)"
],
"metadata": {
"id": "7uBy3IfRBp7c"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Violin plot with intra-cluster similarity\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 5))\n",
"indices = list(range(10))\n",
"ax.set_xlabel(\"Cluster index\")\n",
"ax.set_ylabel(\"Similarity\")\n",
"ax.set_xticks(indices)\n",
"ax.set_xticklabels(indices)\n",
"ax.set_yticks(numpy.arange(0.6, 1.0, 0.1))\n",
"ax.set_title(\"Intra-cluster Tanimoto similarity\", fontsize=13)\n",
"r = ax.violinplot(intra_sim, indices, showmeans=True, showmedians=True, showextrema=False)\n",
"r[\"cmeans\"].set_color(\"red\")\n",
"# mean=red, median=blue"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
},
"id": "UsXQeWg7BstU",
"outputId": "6a92d016-ae33-45aa-df22-19068efb84de"
},
"execution_count": 27,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Get the cluster center of each cluster (first molecule in each cluster)\n",
"cluster_centers = [compounds[c[0]] for c in clusters]\n",
"# How many cluster centers/clusters do we have?\n",
"print(\"Number of cluster centers:\", len(cluster_centers))\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6GilTtGTCENb",
"outputId": "2167abed-629a-4a0d-abe6-19c15ff28a38"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of cluster centers: 1413\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Sort the molecules within a cluster based on their similarity\n",
"# to the cluster center and sort the clusters based on their size\n",
"sorted_clusters = []\n",
"for cluster in clusters:\n",
" if len(cluster) <= 1:\n",
" continue # Singletons\n",
" # else:\n",
" # Compute fingerprints for each cluster element\n",
" sorted_fingerprints = [rdkit_gen.GetFingerprint(compounds[i][0]) for i in cluster]\n",
" # Similarity of all cluster members to the cluster center\n",
" similarities = DataStructs.BulkTanimotoSimilarity(\n",
" sorted_fingerprints[0], sorted_fingerprints[1:]\n",
" )\n",
" # Add index of the molecule to its similarity (centroid excluded!)\n",
" similarities = list(zip(similarities, cluster[1:]))\n",
" # Sort in descending order by similarity\n",
" similarities.sort(reverse=True)\n",
" # Save cluster size and index of molecules in clusters_sort\n",
" sorted_clusters.append((len(similarities), [i for _, i in similarities]))\n",
" # Sort in descending order by cluster size\n",
" sorted_clusters.sort(reverse=True)"
],
"metadata": {
"id": "fnYAzhu2CHia"
},
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Count selected molecules, pick cluster centers first\n",
"selected_molecules = cluster_centers.copy()\n",
"# Take 10 molecules (or a maximum of 50%) of each cluster starting with the largest one\n",
"index = 0\n",
"pending = 1000 - len(selected_molecules)\n",
"while pending > 0 and index < len(sorted_clusters):\n",
" # Take indices of sorted clusters\n",
" tmp_cluster = sorted_clusters[index][1]\n",
" # If the first cluster is > 10 big then take exactly 10 compounds\n",
" if sorted_clusters[index][0] > 10:\n",
" num_compounds = 10\n",
" # If smaller, take half of the molecules\n",
" else:\n",
" num_compounds = int(0.5 * len(tmp_cluster)) + 1\n",
" if num_compounds > pending:\n",
" num_compounds = pending\n",
" # Write picked molecules and their structures into list of lists called picked_fps\n",
" selected_molecules += [compounds[i] for i in tmp_cluster[:num_compounds]]\n",
" index += 1\n",
" pending = 1000 - len(selected_molecules)\n",
"print(\"# Selected molecules:\", len(selected_molecules))\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4_MkMFK-CLyq",
"outputId": "8cb7fcc9-7962-411a-ec31-3aca8a6091ad"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"# Selected molecules: 1413\n"
]
}
]
}
]
}