{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "E_zmG2qEhP_L" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import re\n", "\n", "from sklearn import preprocessing\n", "from sklearn.metrics import silhouette_score, silhouette_samples\n", "from sklearn.decomposition import PCA\n", "from sklearn.metrics.cluster import rand_score\n", "\n", "from scipy.cluster.hierarchy import dendrogram, linkage\n", "from scipy.cluster import hierarchy\n", "from sklearn.cluster import AgglomerativeClustering\n", "from scipy.spatial import distance_matrix\n", "from scipy.stats import pearsonr\n", "from scipy import stats\n", "from scipy.spatial.distance import squareform\n", "\n", "import mantel\n", "\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "import plotly.io as pio\n", "\n", "from matplotlib import pyplot as plt\n", "from matplotlib_venn import venn3\n", "import matplotlib.cm as cm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# This ensures Plotly output works in multiple places:\n", "# plotly_mimetype: VS Code notebook UI\n", "# notebook: \"Jupyter: Export to HTML\" command in VS Code\n", "# See https://plotly.com/python/renderers/#multiple-renderers\n", "pio.renderers.default = \"plotly_mimetype+notebook\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading and sanitizing the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "4MvdtPG2qVVN" }, "outputs": [], "source": [ "df_vd3_src_l1 = pd.read_csv('geodes_l1.csv', sep=',', decimal='.')\n", "df_vd3_src_l2 = pd.read_csv('geodes_l2.csv', sep=',', decimal='.')\n", "df_vd3_src_l3 = pd.read_csv('geodes_l3.csv', sep=',', decimal='.')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "qQ9PGq5OEkiA" }, "outputs": [], "source": [ "# helices boundaries reference for hVDR\n", "ref = np.array([[127,142], [149,152], [218,222], [226,246], [257,265], [268, 278], [298,302], [308,322], [328,338], [350,369], [379,396], [397,406], [411,413], [417,423]]).flatten()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = {'df_vd3_src_l1': df_vd3_src_l1,\n", " 'df_vd3_src_l2': df_vd3_src_l2,\n", " 'df_vd3_src_l3': df_vd3_src_l3\n", " }" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "dssp_cols_new = []\n", "dssp_cols = data['df_vd3_src_l1'].filter(regex=(\"DSSP\")).columns\n", "\n", "dssp_cols_num = dssp_cols.str.replace(\"H3\", \"H3n\")\n", "for i in range(4, 13):\n", " dssp_cols_num = dssp_cols_num.str.replace(f\"H{i}\", f\"H{i-1}\")\n", "\n", "dssp_cols_num = dssp_cols_num.str.replace(\"H13\", \"Hx\")\n", "dssp_cols_num = dssp_cols_num.str.replace(\"H14\", \"H12\")\n", "\n", "for i in range(14):\n", " if i<2:\n", " dssp_cols_new.append('DSSP start_H'+str(i+1))\n", " dssp_cols_new.append('DSSP end_H'+str(i+1))\n", " elif i == 2:\n", " dssp_cols_new.append('DSSP start_H3n')\n", " dssp_cols_new.append('DSSP end_H3n')\n", " elif i>2 and i<12:\n", " dssp_cols_new.append('DSSP start_H'+str(i))\n", " dssp_cols_new.append('DSSP end_H'+str(i))\n", " elif i == 12:\n", " dssp_cols_new.append('DSSP start_Hx')\n", " dssp_cols_new.append('DSSP end_Hx')\n", " else:\n", " dssp_cols_new.append('DSSP start_H12')\n", " dssp_cols_new.append('DSSP end_H12')\n", "\n", "dssp_rename = dict(zip(dssp_cols_num, dssp_cols_new))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "for name, df in data.items():\n", " df = df.drop(['Unnamed: 0'], axis=1)\n", " df['prot_name'] = df['prot_name'].map(lambda x: re.sub(r'.*-auto_', '', x))\n", " df['prot_name'] = df['prot_name'].map(lambda x: x.replace('.pdb', ''))\n", " df = df.astype({'prot_name': 'int32'})\n", " df = df.sort_values(by=['prot_name'], ignore_index=True).rename(columns={\"prot_name\": \"frame\"})\n", " df.columns = df.columns.str.replace(\"H3\", \"H3n\")\n", "\n", " for i in range(4, 13):\n", " df.columns = df.columns.str.replace(f\"H{i}\", f\"H{i-1}\")\n", "\n", " df.columns = df.columns.str.replace(\"H13\", \"Hx\")\n", " df.columns = df.columns.str.replace(\"H14\", \"H12\")\n", "\n", " df = df.rename(columns=dssp_rename)\n", "\n", " data.update({name: df})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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frameDist prot-H1Dist prot-H2Dist prot-H3nDist prot-H3Dist prot-H4Dist prot-H5Dist prot-H6Dist prot-H7Dist prot-H8...DSSP end_H9DSSP start_H10DSSP end_H10DSSP start_H11DSSP end_H11DSSP start_HxDSSP end_HxDSSP start_H12DSSP end_H12N_res extra helical
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" ], "text/plain": [ " frame Dist prot-H1 Dist prot-H2 Dist prot-H3n Dist prot-H3 \\\n", "0 0 17.498402 19.076990 28.664629 8.779440 \n", "1 1 17.608702 19.392324 29.129229 8.696710 \n", "2 2 17.763506 19.208082 27.356197 8.509413 \n", "3 3 18.109047 18.899027 27.416252 8.942605 \n", "4 4 18.155642 18.902773 27.108805 9.127436 \n", ".. ... ... ... ... ... \n", "96 96 17.030731 19.663542 27.178394 9.141317 \n", "97 97 17.072447 19.231226 26.651058 9.632739 \n", "98 98 17.299707 19.566305 26.433600 9.300228 \n", "99 99 17.324750 19.527970 27.074593 9.135515 \n", "100 100 16.801617 19.055742 27.846498 9.165323 \n", "\n", " Dist prot-H4 Dist prot-H5 Dist prot-H6 Dist prot-H7 Dist prot-H8 \\\n", "0 17.344360 4.633159 20.901125 16.262236 11.416581 \n", "1 17.059250 4.561357 20.784674 15.509317 11.385230 \n", "2 17.053493 4.717549 21.126010 15.380616 11.237886 \n", "3 17.735039 4.540847 20.741625 15.418817 11.483450 \n", "4 17.359402 4.440572 20.622066 15.553366 11.210203 \n", ".. ... ... ... ... ... \n", "96 16.926289 5.075649 21.175108 15.696199 11.654580 \n", "97 16.963966 5.098431 21.005108 16.012812 11.333541 \n", "98 16.397785 5.003243 20.971422 16.240135 11.341949 \n", "99 16.632240 5.063313 20.990921 15.878031 11.445569 \n", "100 16.208607 5.025341 21.491293 16.141054 11.602439 \n", "\n", " ... DSSP end_H9 DSSP start_H10 DSSP end_H10 DSSP start_H11 \\\n", "0 ... 1 0 0 0 \n", "1 ... 0 -1 0 0 \n", "2 ... 0 0 0 0 \n", "3 ... 1 0 0 0 \n", "4 ... 0 0 0 0 \n", ".. ... ... ... ... ... \n", "96 ... 0 0 0 0 \n", "97 ... 0 0 0 0 \n", "98 ... 0 0 0 0 \n", "99 ... 0 -1 0 0 \n", "100 ... 0 -1 0 0 \n", "\n", " DSSP end_H11 DSSP start_Hx DSSP end_Hx DSSP start_H12 DSSP end_H12 \\\n", "0 -1 1 -1 -1 -2 \n", "1 -2 1 -1 1 -1 \n", "2 -2 411 -413 1 -2 \n", "3 -1 1 0 -1 -2 \n", "4 -1 0 0 1 -1 \n", ".. ... ... ... ... ... \n", "96 -2 1 0 1 -2 \n", "97 -2 1 -1 -1 -2 \n", "98 0 1 0 1 -2 \n", "99 -2 1 0 1 -2 \n", "100 -2 1 0 1 -4 \n", "\n", " N_res extra helical \n", "0 6 \n", "1 6 \n", "2 6 \n", "3 6 \n", "4 3 \n", ".. ... \n", "96 6 \n", "97 6 \n", "98 3 \n", "99 6 \n", "100 6 \n", "\n", "[101 rows x 285 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transformed_data['df_vd3_src_l1']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "scaled_data = dict()\n", "scaler = preprocessing.StandardScaler()\n", "\n", "for name, df in transformed_data.items():\n", " scaled_values = scaler.fit_transform(df)\n", " scaled_df = pd.DataFrame(scaled_values, columns=transformed_data[name].columns)\n", " scaled_data.update({name:scaled_df})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PCA analysis" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "pca_data = dict()\n", "\n", "i=0\n", "for name, df in scaled_data.items():\n", " i+=1\n", " temp_pca = PCA(n_components=3).fit_transform(df)\n", " pca_df = pd.DataFrame()\n", " pca_df['X'] = temp_pca[:,0]\n", " pca_df['Y'] = temp_pca[:,1]\n", " pca_df['Z'] = temp_pca[:,2]\n", " pca_df['frame'] = data[name]['frame']\n", " pca_df['motif'] = f'L{i}'\n", " pca_data.update({name:pca_df})" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "pdList = [df for df in pca_data.values()]\n", "final_df = pd.concat(pdList)\n", "final_df = final_df.reset_index()\n", "final_df = final_df.drop(['index'], axis=1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(final_df,\n", " x = 'X',\n", " y = 'Y',\n", " facet_col='motif',\n", " opacity = 0.8,\n", " color='frame',\n", " color_continuous_scale=px.colors.sequential.Agsunset_r,\n", " title=\"VD3-SRC1 (independent)\",\n", " width=1400, height=470)\n", "\n", "fig.update_traces(marker={'size': 13})\n", "fig.update_layout(template='simple_white')\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard deviation (QCD analysis)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "stats_l1 = transformed_data['df_vd3_src_l1'].quantile(0.25).to_frame(name='Q1')\n", "stats_l1['Q3'] = transformed_data['df_vd3_src_l1'].quantile(0.75)\n", "stats_l1['IQR'] = stats_l1['Q3']-stats_l1['Q1']\n", "stats_l1['sumQ'] = stats_l1['Q3']+stats_l1['Q1']\n", "stats_l1['QCV'] = np.where(stats_l1['sumQ'] == 0, 0, stats_l1['IQR'] / stats_l1['sumQ'])\n", "stats_l1 = stats_l1.reset_index()\n", "stats_l1 = stats_l1.sort_values(by=['QCV'], ascending=False)\n", "stats_l1 = stats_l1[stats_l1['index'] != 'frame']\n", "\n", "stats_l2 = transformed_data['df_vd3_src_l2'].quantile(0.25).to_frame(name='Q1')\n", "stats_l2['Q3'] = transformed_data['df_vd3_src_l2'].quantile(0.75)\n", "stats_l2['IQR'] = stats_l2['Q3']-stats_l2['Q1']\n", "stats_l2['sumQ'] = stats_l2['Q3']+stats_l2['Q1']\n", "stats_l2['QCV'] = np.where(stats_l2['sumQ'] == 0, 0, stats_l2['IQR'] / stats_l2['sumQ'])\n", "stats_l2 = stats_l2.reset_index()\n", "stats_l2 = stats_l2.sort_values(by=['QCV'], ascending=False)\n", "stats_l2 = stats_l2[stats_l2['index'] != 'frame']\n", "\n", "stats_l3 = transformed_data['df_vd3_src_l3'].quantile(0.25).to_frame(name='Q1')\n", "stats_l3['Q3'] = transformed_data['df_vd3_src_l3'].quantile(0.75)\n", "stats_l3['IQR'] = stats_l3['Q3']-stats_l3['Q1']\n", "stats_l3['sumQ'] = stats_l3['Q3']+stats_l3['Q1']\n", "stats_l3['QCV'] = np.where(stats_l3['sumQ'] == 0, 0, stats_l3['IQR'] / stats_l3['sumQ'])\n", "stats_l3 = stats_l3.reset_index()\n", "stats_l3 = stats_l3.sort_values(by=['QCV'], ascending=False)\n", "stats_l3 = stats_l3[stats_l3['index'] != 'frame']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexQ1Q3IQRsumQQCV
156Angle H2-H113.5219227.6807374.15881511.2026590.371235
284N_res extra helical3.0000006.0000003.0000009.0000000.333333
195Angle H5-Hx6.40122612.6618966.26067019.0631220.328418
155Angle H2-H106.47311411.3759104.90279617.8490240.274681
219Angle H10-H117.56509311.0468353.48174218.6119280.187070
.....................
257DSSP end_H1-2.0000000.0000002.000000-2.000000-1.000000
281DSSP end_Hx-1.0000000.0000001.000000-1.000000-1.000000
267DSSP end_H5-1.0000000.0000001.000000-1.000000-1.000000
260DSSP start_H3n-2.0000001.0000003.000000-1.000000-3.000000
269DSSP end_H6-2.0000001.0000003.000000-1.000000-3.000000
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284 rows × 6 columns

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" ], "text/plain": [ " index Q1 Q3 IQR sumQ QCV\n", "156 Angle H2-H11 3.521922 7.680737 4.158815 11.202659 0.371235\n", "284 N_res extra helical 3.000000 6.000000 3.000000 9.000000 0.333333\n", "195 Angle H5-Hx 6.401226 12.661896 6.260670 19.063122 0.328418\n", "155 Angle H2-H10 6.473114 11.375910 4.902796 17.849024 0.274681\n", "219 Angle H10-H11 7.565093 11.046835 3.481742 18.611928 0.187070\n", ".. ... ... ... ... ... ...\n", "257 DSSP end_H1 -2.000000 0.000000 2.000000 -2.000000 -1.000000\n", "281 DSSP end_Hx -1.000000 0.000000 1.000000 -1.000000 -1.000000\n", "267 DSSP end_H5 -1.000000 0.000000 1.000000 -1.000000 -1.000000\n", "260 DSSP start_H3n -2.000000 1.000000 3.000000 -1.000000 -3.000000\n", "269 DSSP end_H6 -2.000000 1.000000 3.000000 -1.000000 -3.000000\n", "\n", "[284 rows x 6 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats_l1" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "stats_l1['motif'] = 'L1'\n", "stats_l2['motif'] = 'L2'\n", "stats_l3['motif'] = 'L3'\n", "stats_all = pd.concat([stats_l1, stats_l2, stats_l3])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "box": { "visible": true }, "hovertemplate": "motif=L1
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = stats_all\n", "\n", "fig = px.violin(df, y=\"QCV\", color='motif', box=True,\n", " points=False, color_discrete_sequence=[\"#faa920\", \"#1b9894\", \"#90c73d\"]\n", " )\n", "\n", "fig.update_layout(template='plotly_white',\n", " legend=dict(font=dict(size=14),\n", " title=\"\",\n", " orientation=\"h\",\n", " yanchor=\"top\",\n", " y=1.15,\n", " xanchor=\"left\",\n", " x=0.01),\n", " yaxis = dict(tickfont = dict(size=14)),\n", " yaxis_title=\"QCD\",\n", " width=700,\n", " height=400)\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "q3_l1 = stats_l1[\"QCV\"][stats_l1[\"QCV\"]>=stats_l1[\"QCV\"].quantile(0.75)]\n", "q3_l2 = stats_l2[\"QCV\"][stats_l2[\"QCV\"]>=stats_l2[\"QCV\"].quantile(0.75)]\n", "q3_l3 = stats_l3[\"QCV\"][stats_l3[\"QCV\"]>=stats_l3[\"QCV\"].quantile(0.75)]\n", "\n", "q1_l1 = stats_l1[\"QCV\"][stats_l1[\"QCV\"]<=stats_l1[\"QCV\"].quantile(0.25)]\n", "q1_l2 = stats_l2[\"QCV\"][stats_l2[\"QCV\"]<=stats_l2[\"QCV\"].quantile(0.25)]\n", "q1_l3 = stats_l3[\"QCV\"][stats_l3[\"QCV\"]<=stats_l3[\"QCV\"].quantile(0.25)]\n", "\n", "# can be written to xsls\n", "# stats_l1.head(q3_l1)[['index','QCV']].to_excel('stats_l1_head.xlsx')\n", "# stats_l2.head(q3_l2)[['index','QCV']].to_excel('stats_l1_head.xlsx')\n", "# stats_l3.head(q3_l3)[['index','QCV']].to_excel('stats_l1_head.xlsx')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s1_stable = set(stats_l1.tail(len(q1_l1))['index'].to_list())\n", "s2_stable = set(stats_l2.tail(len(q1_l2))['index'].to_list())\n", "s3_stable = set(stats_l3.tail(len(q1_l3))['index'].to_list())\n", "\n", "venn3([s1_stable, s2_stable, s3_stable], set_labels = ('L1', 'L2', 'L3'))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s1_flex = set(stats_l1.head(len(q3_l1))['index'].to_list())\n", "s2_flex = set(stats_l2.head(len(q3_l2))['index'].to_list())\n", "s3_flex = set(stats_l3.head(len(q3_l3))['index'].to_list())\n", "\n", "venn3([s1_flex, s2_flex, s3_flex], set_labels = ('L1', 'L2', 'L3'))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trajectory-specific feature analysis" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# extracting trajectory-specific flexible features for l1, l2, l3\n", "\n", "bot_q3_l1 = stats_l1.tail(len(q3_l1))['index'].to_list()\n", "bot_q3_l2 = stats_l2.tail(len(q3_l2))['index'].to_list()\n", "bot_q3_l3 = stats_l3.tail(len(q3_l3))['index'].to_list()\n", "\n", "tmp_l1 = [elem for elem in bot_q3_l1 if elem not in bot_q3_l2 and elem not in bot_q3_l3]\n", "subset = stats_l1[stats_l1['index'].isin(tmp_l1)][['index', 'QCV']]\n", "l1_spec_flex = [tuple(x) for x in subset.to_numpy()]\n", "\n", "tmp_l2 = [elem for elem in bot_q3_l2 if elem not in bot_q3_l1 and elem not in bot_q3_l3]\n", "subset = stats_l2[stats_l2['index'].isin(tmp_l2)][['index', 'QCV']]\n", "l2_spec_flex = [tuple(x) for x in subset.to_numpy()]\n", "\n", "\n", "tmp_l3 = [elem for elem in bot_q3_l3 if elem not in bot_q3_l2 and elem not in bot_q3_l1]\n", "subset = stats_l3[stats_l3['index'].isin(tmp_l3)][['index', 'QCV']]\n", "l3_spec_flex = [tuple(x) for x in subset.to_numpy()]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# extracting trajectory-specific stable features for l1, l2, l3\n", "\n", "top_q1_l1 = stats_l1.head(len(q3_l1))['index'].to_list()\n", "top_q1_l2 = stats_l2.head(len(q3_l2))['index'].to_list()\n", "top_q1_l3 = stats_l3.head(len(q3_l3))['index'].to_list()\n", "\n", "tmp_l1 = [elem for elem in top_q1_l1 if elem not in top_q1_l2 and elem not in top_q1_l3]\n", "subset = stats_l1[stats_l1['index'].isin(tmp_l1)][['index', 'QCV']]\n", "l1_spec_stable = [tuple(x) for x in subset.to_numpy()]\n", "\n", "tmp_l2 = [elem for elem in top_q1_l2 if elem not in top_q1_l1 and elem not in top_q1_l3]\n", "subset = stats_l2[stats_l2['index'].isin(tmp_l2)][['index', 'QCV']]\n", "l2_spec_stable = [tuple(x) for x in subset.to_numpy()]\n", "\n", "\n", "tmp_l3 = [elem for elem in top_q1_l3 if elem not in top_q1_l2 and elem not in top_q1_l1]\n", "subset = stats_l3[stats_l3['index'].isin(tmp_l3)][['index', 'QCV']]\n", "l3_spec_stable = [tuple(x) for x in subset.to_numpy()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shared features analysis" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "shared_names = sorted(list(s1_flex&s2_flex&s3_flex))\n", "shared_dict_flex = {}\n", "\n", "shared_dict_flex['names'] = sorted(list(s1_flex&s2_flex&s3_flex))\n", "shared_dict_flex['L1'] = [stats_l1[stats_l1['index']==elem]['QCV'].values[0] for elem in shared_names]\n", "shared_dict_flex['L2'] = [stats_l2[stats_l2['index']==elem]['QCV'].values[0] for elem in shared_names]\n", "shared_dict_flex['L3'] = [stats_l3[stats_l3['index']==elem]['QCV'].values[0] for elem in shared_names]\n", "\n", "shared_df_flex = pd.DataFrame.from_dict(shared_dict_flex)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=L1
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"linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "width": 1100, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "dtick": 5, "title": { "text": "frame" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Δ COMprot-COMH5, Å" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = transformed_data['df_vd3_src_l2']['Dist prot-H5']\n", "\n", "fig = px.line(df, x=df.index, y=\"Dist prot-H5\", color_discrete_sequence=['black'])\n", "\n", "fig.add_trace(go.Scatter(x=[78], y=[5.265333],\n", " text=[\"frame 78\"], textposition=\"top center\",\n", " mode='markers+text', marker=dict(size=[20], color=[\"mediumvioletred\"])))\n", "fig.add_trace(go.Scatter(x=[20], y=[4.099168],\n", " text=[\"frame 20\"], textposition=\"bottom center\",\n", " mode='markers+text', marker=dict(size=[20], color=[\"darkcyan\"])))\n", "\n", "fig.update_layout(template='plotly_white',\n", " xaxis = dict(dtick = 5),\n", " font=dict(size=18),\n", " yaxis_title=\"Δ COMprot-COMH5, Å\",\n", " xaxis_title=\"frame\",\n", " showlegend=False,\n", " height=600, width=1100)\n", "\n", "fig.update_traces(line=dict(color=\"Black\", width=1))\n", "\n", "fig.show()\n", "\n", "# frame 20 , frame 78" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hierarchical Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RMSD" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "l1 = final_df[final_df['motif']=='L1']\n", "l2 = final_df[final_df['motif']=='L2']\n", "l3 = final_df[final_df['motif']=='L3']" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# rmsd matrixes for clustering\n", "\n", "matrix_l1 = pd.read_csv('rmsd_rmsf/rmsd_matrix_l1.txt', engine ='python', sep='\\t', decimal='.', header=None)\n", "matrix_l2 = pd.read_csv('rmsd_rmsf/rmsd_matrix_l2.txt', engine ='python', sep='\\t', decimal='.', header=None)\n", "matrix_l3 = pd.read_csv('rmsd_rmsf/rmsd_matrix_l3.txt', engine ='python', sep='\\t', decimal='.', header=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Number of clusters evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Complete linkage" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.18697921565932074\n", "For n_clusters = 3 The average silhouette_score is : 0.09598350143393725\n", "For n_clusters = 4 The average silhouette_score is : 0.09508664456835145\n", "For n_clusters = 5 The average silhouette_score is : 0.09466277534683559\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l1\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L1 complex), linkage=complete, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.13425151168661328\n", "For n_clusters = 3 The average silhouette_score is : 0.12761189012326146\n", "For n_clusters = 4 The average silhouette_score is : 0.10965244879476002\n", "For n_clusters = 5 The average silhouette_score is : 0.07709885462971741\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l2\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L2 complex), linkage=complete, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.06769151826645052\n", "For n_clusters = 3 The average silhouette_score is : 0.07205251253609768\n", "For n_clusters = 4 The average silhouette_score is : 0.04997586364542923\n", "For n_clusters = 5 The average silhouette_score is : 0.04212083967509436\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l3\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L3 complex), linkage=complete, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Average linkage" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.20651395138607642\n", "For n_clusters = 3 The average silhouette_score is : 0.1158883278481784\n", "For n_clusters = 4 The average silhouette_score is : 0.1087033211661952\n", "For n_clusters = 5 The average silhouette_score is : 0.09217999894234484\n" ] }, { "data": { "image/png": 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HXL9+Xejp6UlzyN7eXmzevFmqV6vVYv78+SIzM1MIIURXV5cAIBoaGrT2OaqhoUHIZDLR09MjhBBiZGRE2NnZSe1o8+2334q5c+dKz5//WZiOubJx40axdevWScdORESvHuYv5q9RzF/MX0TTiff0IpoiNzc36bGNjQ0A4P79+1i0aBGamppQVVWlsbM4MjKCx48f49GjRzA1NdVoy8rKCpGRkQgMDMS6devg7++PkJAQqd2pjmd0TPfv3wcAtLW1QS6XQy6XS/UuLi6wsLBAW1sbPD09/7b9zs5OPHr0COvWrdMof/r0Kdzd3XUe56impiZcu3ZN626cUqmEk5PTuPLGxkZ89NFHWttTKpUYGhqCr6+vVGZoaAgvLy+0tbW9cJ+6aGxshL6+PhQKhU7HNzU1obm5GTk5OVKZEAJqtRpdXV1wdnYGoHlNR3/VY/Sa6urtt9+Gs7MzcnNzsW/fPlRUVOD+/fsa72NZWRnS09PR3t6O3377DcPDwxPOVV3oMldiYmIQHByM+vp6BAQEYNOmTRr37CAiItKG+esZ5i/mr+cxfxFNjoteRFNkaGgoPR79GrxarQYADA4OIi0tDR988MG484yNjbW2d/bsWcTHx6O4uBh5eXlISUlBaWkpVqxYMeXxjI5pdDy60NPTgxBCo2xoaEh6PHqPgaKiItjZ2WkcN2vWLJ37Gdvexo0bcfTo0XF1E4VNExOTKffzsn3qYqrjGhwcxI4dOxAfHz+ubtGiRdLjl72mo8LCwqTQlZubi/Xr12Pu3LkAnt0L5N1330VMTAwOHz4MKysrVFZWIjo6Gk+fPtUauqZjrmzYsAE9PT34/vvvUVpaCj8/P8TFxeGLL76Y8usjIqJXB/PXX5i/mL+Yv4h0x0Uvomnk4eGBjo4OODo6Tuk8d3d3uLu7Izk5Gd7e3sjNzdU5dE3G2dkZfX196Ovrk3YbW1tb8fDhQ7i4uAAA5s2bh9u3b2uc19jYKH3wu7i4YNasWejt7Z1wR83IyAgApHs8jC1/vszDwwP5+flwcHCAgYFu/wW5ubnh6tWrSEtLG1f3xhtvwMjICFVVVdK9EoaGhlBbWyvd8PRF+tSFq6sr1Go1Kioq4O/v/7fHe3h4oLW1dcrzY6yJ3mttPvnkE6SkpODWrVu4dOkSzpw5I9XdunULarUax48fh57es9s7fvPNN5O2Nx1zZbSdiIgIREREYNWqVdi7dy9DFxERvTDmL+avyTB//dUO8xe9ingje6JplJqaivPnzyMtLQ0tLS1oa2vDxYsXkZKSovX4rq4uJCcno7q6Gj09PSgpKcGdO3ekr1m/LH9/f7i6uiIsLAz19fWoqalBeHg4FAoFli9fDgBYu3Yt6urqcP78edy5cwcHDhzQ+GCdPXs29uzZg927dyM7OxtKpRL19fU4ffo0srOzAQD29vaQyWQoLCzEzz//LO04OTg44ObNm+ju7kZ/fz/UajXi4uIwMDCA0NBQ1NbWQqlU4sqVK9i6deuEQSI5ORm1tbWIjY1Fc3Mz2tvbkZmZif7+fpiZmSEmJgZ79+5FcXExWltbsW3bNjx69AjR0dEA8EJ96sLBwQERERGIiorCd999h66uLpSXl08YXpKSknDjxg3s3LkTjY2NuHPnDgoKCsbdSHUy8+fPh4mJiXQz2F9//XXS8fn4+CA6OhojIyN47733pDpHR0cMDQ3h9OnTuHv3Li5cuKARyrSZjrmSmpqKgoICdHZ2oqWlBYWFhdM234mI6NXE/MX8xfzF/EU0ES56EU2jwMBAFBYWoqSkBJ6enlixYgVOnjwp7YA9z9TUFO3t7QgODoaTkxO2b9+OuLg47NixY1rGI5PJUFBQAEtLS6xevRr+/v5YsmQJ8vLyNMa8f/9+JCYmwtPTE7///jvCw8M12jl06BD279+P9PR0ODs7Y/369SgqKpL+fLOdnR3S0tKwb98+LFiwQAoRe/bsgb6+PlxcXDBv3jz09vbC1tYWVVVVGBkZQUBAAFxdXbFr1y5YWFhIO17Pc3JyQklJCZqamuDl5QVvb28UFBRIu4aff/45goODsWXLFnh4eKCzsxNXrlyBpaUlALxQn7rKzMzEhx9+iNjYWLz11lvYtm2bxp/AHsvNzQ0VFRX46aefsGrVKri7uyM1NRW2trY692dgYIBTp04hKysLtra2CAoKmvT4sLAwNDU14f3339f4dYBly5bhxIkTOHr0KJYuXYqcnBykp6dP2tZ0zBUjIyMkJyfDzc0Nq1evhr6+Pi5evKjz6yciInoe8xfzF/MX8xfRRGTi+V8QJiIiIiIiIiIi+pfjN72IiIiIiIiIiGjG4aIXERERERERERHNOFz0IiIiIiIiIiKiGYeLXkRERERERERENONw0YuIiIiIiIiIiGYcLnoREREREREREdGMw0UvIiIiIiIiIiKacbjoRUREREREREREMw4XvYiIiIiIiIiIaMbhohcREREREREREc04XPQiIiIiIiIiIqIZh4teREREREREREQ04/wHNelwcRa5+LgAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l1\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L1 complex), linkage=average, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.26434102873072685\n", "For n_clusters = 3 The average silhouette_score is : 0.10308175531298557\n", "For n_clusters = 4 The average silhouette_score is : 0.10859475660281233\n", "For n_clusters = 5 The average silhouette_score is : 0.08609454262797464\n" ] }, { "data": { "image/png": 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uvPJK/eY3v9FZZ50lSerVq1eN2zzrrLOUkpKi6667ThdccIHOOOOMwKHm3377rYYNG6akpCTdeOONio6O1pNPPqmRI0dq5cqVGjRoUJVtzZ49W02aNNFtt92moqKiGvfXrVs33XHHHbrttts0a9YsDRs2TJI0ZMiQwDK5ubkaP368zjrrLE2ZMkWvvfaabrrpJvXs2VMTJkyQVFlQ//rXv9aqVas0a9YsdevWTV9//bUeeugh/fe//z2h6yzs3LlT9957r5555hnFxsYed/mfu/3227VgwQINGTJEd9xxh9xut1avXq0PPvhAY8eOrdW2fqm8vFzjxo1TWVmZrrrqKjVr1ky7d+/Wm2++qUOHDik5OVkvvPCCLrvsMg0cOFCzZs2SJHXo0EGStHfvXp166qmBa5Q1adJEb7/9ti699FLl5+fr2muvrbK/O++8U263W9dff73Kysrkdru1YMECLVy4MLCP/Px8ffHFF/ryyy81ZsyYk3p9ABCuqMFODDUYNdiJaN++vQoLCxUfH6/JkyfrT3/6k5o2bXrc9ajBgCAzQAOaM2eOOdrbbvny5UaS6datmykrKwvMf+SRR4wk8/XXXxtjjPH7/aZTp05m3Lhxxu/3B5YrLi427dq1M2PGjDlmhjPPPNN07979mMs8++yzRpLZtm1bYN6IESPMiBEjap23vLzcpKenmx49epiSkpLAcm+++aaRZG677baj7uOw6dOnmzZt2gSef/TRR0aSefHFF6sst2zZsmrzu3fvXuM2X331VSPJLF++vMr8goICk5KSYi6//PIq87Ozs01ycnK1+b90+HfXr18/U15eHph///33G0nmjTfeOOrrffjhh40k87//+7+BeeXl5Wbw4MEmISHB5OfnG2OM2b9/v5Fk5s+ff8wsh23bts1IMv/zP/9TZf7kyZON2+02W7ZsCcz78ccfTWJiohk+fHi11zR06FDj9XqPu781a9YYSebZZ5+t9rMRI0YYSeb5558PzCsrKzPNmjUzZ599dmDeCy+8YJxOp/noo4+qrP/EE08YSebjjz8+bo5zzjnHDBkyJPBckpkzZ85x1/v++++N0+k0v/nNb4zP56vys5//zf1y/Gr6uzHmyN/K4ffaunXrjCTz6quvHjNHfHy8mT59erX5l156qWnevLk5cOBAlfnnn3++SU5ONsXFxVX22759+8C8w3r37m0mTpx4zP0DgJ1Qg1GDUYM1TA328MMPm7lz55oXX3zRvPbaa+aaa64xUVFRplOnTiYvL++Y61KDAcHH6Y0IOTNnzpTb7Q48P/wtzdatWyVJ69ev1/fff68LL7xQOTk5OnDggA4cOKCioiKNGjVKH374ofx+/1G3n5KSoh9++EFr1qxpkLxffPGF9u3bp9mzZ1e5a8vEiRPVtWtXvfXWW7Xe56uvvqrk5GSNGTMm8PoPHDigfv36KSEhQcuXL6/z63n33Xd16NAhXXDBBVW27XK5NGjQoBPe9qxZsxQdHR14fuWVVyoqKkpLly496jpLly5Vs2bNdMEFFwTmRUdH6+qrr1ZhYaFWrlxZ59f1Sz6fT//+9781efJktW/fPjC/efPmuvDCC7Vq1Srl5+dXWefyyy8PynVGEhISqlxTxe12a+DAgYH3jFQ5xt26dVPXrl2rjMPpp58uSccdh+XLl+sf//iHHn744Vrn++c//ym/36/bbrut2nUXjvYNcW0kJydLkt555x0VFxfXal1jjP7xj39o0qRJMsZU+d2MGzdOeXl5+vLLL6usM3369GpHuqWkpOjbb7/V999/f3IvBgBshBrs+KjBTp7da7BrrrlGjz76qC688EKdffbZevjhh/Xcc8/p+++/1+OPP37MdanBgODj9EaEnNatW1d5npqaKkmBaxEc/g/k9OnTj7qNvLy8wHq/dNNNN+m9997TwIED1bFjR40dO1YXXnihsrKy6iXvjh07JEldunSptm7Xrl21atWqWu/z+++/V15entLT02v8+b59+2q9zZ9vW1Lgg/2XkpKSTmg7nTp1qvI8ISFBzZs3P+Y1Onbs2KFOnTpV+5A/fMj/4d9lMOzfv1/FxcU1jku3bt3k9/u1a9cude/ePTC/Xbt2Qdl3q1atqhUuqamp+uqrrwLPv//+e23cuDFw7YxfOtYYe71eXX311Zo2bVrg2ha1sWXLFjmdTmVmZtZ63RPRrl07zZs3Tw8++KBefPFFDRs2TL/+9a910UUXBYqxo9m/f78OHTqkp556Sk899VSNy/zyd1PTuN1xxx0688wz1blzZ/Xo0UPjx4/XtGnTjnqKBgBEAmqw46MGO3l2rsGO5sILL9Tvfvc7vffee7r55puPuhw1GBB8NL0Qco72LY756SKQh79B/J//+Z+j3or457cH/qVu3bpp06ZNevPNN7Vs2TL94x//0OOPP67bbrstcAvmYOatDYfDUeN6Pp+vynO/36/09HS9+OKLNW7naB/SJ+Lw7/eFF15Qs2bNqv08Kipy/7NR2+tiHc2JvGf8fr969uypBx98sMZlMzIyjrr9559/Xps2bdKTTz5ZrcAtKCjQ9u3blZ6erri4uNqHP4ajfQP5y/evJP3pT3/SjBkz9MYbb+jf//63rr76ai1cuFCfffbZUS9QKx15f1500UVH/T9dvyyaahq34cOHa8uWLYH9//Wvf9VDDz2kJ554QpdddtlR9w8AdkYNRg0WqsKlBjuWjIwMHTx4sE7rHg81GHB0kftfToStwxdSTEpK0ujRo+u0jfj4eJ133nk677zzVF5errPOOkt33323fv/731c5/D0YDt+1ZtOmTdW+udu0aVPg51LlN00/P7z6sF9+u9ahQwe99957ysrKOm4RcLQPwaPNP/z7TU9Pr/PvV6r8luy0004LPC8sLNSePXt0xhlnHHWdNm3a6KuvvpLf76/yTeN3330X+PmxstdGkyZNFBcXp02bNlX72XfffSen01nnoiYY+Tp06KD//Oc/GjVqVK23t3PnTlVUVNT4zfnzzz+v559/XkuWLNHkyZOPum+/368NGzYc9f/U1OTwN+yHDh2qMv9o3w737NlTPXv21B/+8Ad98sknysrK0hNPPKG77rpLUs2/xyZNmigxMVE+n++k3p+SlJaWppkzZ2rmzJkqLCzU8OHDtWDBAgouADgKajBqMGqwujHGaPv27erbt+9x900NBgQX1/RC2OnXr586dOigBx54QIWFhdV+vn///mOun5OTU+W52+1WZmamjDGqqKgIalZJ6t+/v9LT0/XEE09Uuc3x22+/rY0bN2rixImBeR06dNB3331X5TX85z//qXYb8ClTpsjn8+nOO++stj+v11vlAy8+Pr7aB+Dh+VL1D8dx48YpKSlJ99xzT42/j+P9fg976qmnqqy/aNEieb3ewJ1xanLGGWcoOztbr7zySpXX8+ijjyohIUEjRoyQpMARSjW9rhPlcrk0duxYvfHGG1WOhtq7d69eeuklDR069IRPI/ilo/1ua2PKlCnavXu3nn766Wo/KykpOeqdiyTp/PPP15IlS6o9pMrf8ZIlS6rdFennJk+eLKfTqTvuuKPatVmO9e354WL9ww8/DMzz+XzVDoHPz8+X1+utMq9nz55yOp1V/kZqeu+6XC6dffbZ+sc//qFvvvmmWoYTfX/+8r8DCQkJ6tixY5X95+Xl6bvvvgvcwhsAIh01GDUYNdixazCp5nFatGiR9u/fr/Hjxx9zXWqwStRgCCaO9ELYcTqd+utf/6oJEyaoe/fumjlzplq2bKndu3dr+fLlSkpK0v/7f//vqOuPHTtWzZo1U1ZWlpo2baqNGzfqL3/5iyZOnKjExMSg542OjtZ9992nmTNnasSIEbrgggsCt8tu27atrrvuusCyl1xyiR588EGNGzdOl156qfbt26cnnnhC3bt3r3JBzxEjRuiKK67QwoULtX79eo0dO1bR0dH6/vvv9eqrr+qRRx7ROeecI6myQF20aJHuuusudezYUenp6Tr99NPVp08fuVwu3XfffcrLy5PH49Hpp5+u9PR0LVq0SNOmTdMpp5yi888/X02aNNHOnTv11ltvKSsrS3/5y1+O+7rLy8s1atQoTZkyRZs2bdLjjz+uoUOH6te//vVR15k1a5aefPJJzZgxQ2vXrlXbtm312muv6eOPP9bDDz8cGJ/Y2FhlZmbqlVdeUefOnZWWlqYePXqoR48etRqbu+66S++++66GDh2q2bNnKyoqSk8++aTKysp0//3312pbP9ehQwelpKToiSeeUGJiouLj4zVo0KBaXY9i2rRp+vvf/67f/va3Wr58ubKysuTz+fTdd9/p73//u9555x3179+/xnW7du2qrl271vizdu3aHfUIr8M6duyoW2+9VXfeeaeGDRums846Sx6PR2vWrFGLFi20cOHCGtfr3r27Tj31VP3+97/XwYMHlZaWppdffrlacfXBBx9o7ty5Ovfcc9W5c2d5vV698MILgWLqsH79+um9997Tgw8+qBYtWqhdu3YaNGiQ7r33Xi1fvlyDBg3S5ZdfrszMTB08eFBffvml3nvvvRM6dSAzM1MjR45Uv379lJaWpi+++EKvvfaa5s6dG1hmyZIlmjlzpp599lnNmDHjuNsEALujBqMGowY7dg0mVR4Vd95556lnz56KiYnRqlWr9PLLL6tPnz664oorjrlvarBK1GAIqga/XyQi2oncLvuXt9A9fKvjX956eN26deass84yjRo1Mh6Px7Rp08ZMmTLFvP/++8fM8OSTT5rhw4cH1uvQoYO54YYbqtxCuDa3yz7RvK+88orp27ev8Xg8Ji0tzUydOtX88MMP1fL97//+r2nfvr1xu92mT58+5p133ql2u+zDnnrqKdOvXz8TGxtrEhMTTc+ePc2NN95ofvzxx8Ay2dnZZuLEiSYxMdFIqvIann76adO+fXvjcrmq3Tp7+fLlZty4cSY5OdnExMSYDh06mBkzZpgvvvjimL/fw7+7lStXmlmzZpnU1FSTkJBgpk6danJycqosW9Ptwffu3WtmzpxpGjdubNxut+nZs2eNt53+5JNPTL9+/Yzb7T7urbOPdrtsY4z58ssvzbhx40xCQoKJi4szp512mvnkk09qfE1r1qw55mv/uTfeeMNkZmaaqKioKu+HESNG1Hi79prGuLy83Nx3332me/fuxuPxmNTUVNOvXz9z++23H/eW1zWRZObMmXPCyz/zzDOB92xqaqoZMWKEeffddwM/r2n8tmzZYkaPHm08Ho9p2rSpueWWW8y7775b5f21detWc8kll5gOHTqYmJgYk5aWZk477TTz3nvvVdnWd999Z4YPH25iY2ONpCq3zt67d6+ZM2eOycjIMNHR0aZZs2Zm1KhR5qmnngosc7S/UWOMueuuu8zAgQNNSkqKiY2NNV27djV33313lVu8Hx73mt5/ABBuqMGowX6OGuyIYNdgl112mcnMzDSJiYkmOjradOzY0dx0000mPz//hF8DNRg1GILHYUwdrvQIAEexePFizZw5U2vWrDnmt2AAAAAIHmowAKiOa3oBAAAAAADAdmh6AQAAAAAAwHZoegEAAAAAAMB2uKYXAAAAAAAAbIcjvQAAAAAAAGA7NL0AAAAAAABgO1FW7NTv9+vHH39UYmKiHA6HFREAAACqMcaooKBALVq0kNNpr+8Gqb8AAECoqq8azJKm148//qiMjAwrdg0AAHBcu3btUqtWrayOEVTUXwAAINQFuwazpOmVmJgoqfLFJCUlWREBx1JUJLVoUTn9449SfLy1eQAAaCD5+fnKyMgI1Cp2Qv1lAWoqAABOSH3VYJY0vQ4fUp+UlETRFYpcriPTSUkUaACAiGPH0/+ovyxATQUAQK0EuwazpOmFEOd0SiNGHJkGAABA7VFTAQBgKZpeqC42VlqxwuoUAAAA4Y2aCgAAS/GVEwAAAAAAAGyHphcAAAAAAABsh6YXqisqkpo0qXwUFVmdBgAAIDxRUwEAYCmu6YWaHThgdQIAAIDwR00FAIBlONILAAAAAAAAtkPTCwAAAAAAALZD0wsAAAAAAAC2Q9MLAAAAAAAAtkPTCwAAAPXq3nvvlcPh0LXXXmt1FAAAEEG4eyOqczql/v2PTAMAANTRmjVr9OSTT6pXr15WR2l41FQAAFiKT19UFxsrrVlT+YiNtToNAAAIU4WFhZo6daqefvpppaamWh2n4VFTAQBgKZpeAAAAqBdz5szRxIkTNXr0aKujAACACMTpjQAAAAi6l19+WV9++aXWrFljdRQAABChONIL1RUXS23bVj6Ki61OAwAAwsyuXbt0zTXX6MUXX1RMTIzVcaxDTQUAgKU40gvVGSPt2HFkGgAAoBbWrl2rffv26ZRTTgnM8/l8+vDDD/WXv/xFZWVlcrlcFiZsINRUAABYiqYXAAAAgmrUqFH6+uuvq8ybOXOmunbtqptuuikyGl4AAMByNL0AAAAQVImJierRo0eVefHx8WrUqFG1+QAAAPWFphcAAADCi7dU2vuJZHzB33ZCaymlS/C3CwAAGhxNLwAAANS7FStWBG9jez+RVt8gVRSd/LYcTikqVnLFSlFxUvpAacBdJ79dAABgOZpeJ6Nsl7T7NslfYnWS4Cr2HpneMl2Ks8PbxCElj5OazLA6CAAAOFnGV9nwiml8AssayVdS+TCSZCqbW1FxUmIbKSVTSmonJbSVEttKcc3qNzsAAGgwduhmWKdit1T4ueRwVT7soswvdfjp9uJl30tOp7V56soYyZcjKVqK6y7FcKoCAAC24E6WYtMlf/kJLOyQ4ppLqZlSUsfKxlZiWymhjRQVU785HQ4pM/PINAAAaFA0vYLB3VJy2OhX6Za0tLXVKerOGMmXJ1Xsk6JbSI0ulJrMlFxJVicDAADBkD5QGv+WJP8JLOyUohOsaTrFxUnfftvw+wUAAJJoesFOjF/yHpR8ByVngpQ8Vmo6W4rtZnUyAAAQbO5EqxMAAIAQR9ML4c1UVF5TzVdY+YhKkdLOk9KmSLHdOZUAAAAAAIAIRdML1ZX4pHM+qpx+bZgUGwLXKzO+yubW4Ycpk+SQ5JScsZIrufI0xrSzJU9bi8MCAABIKi6WBgyonF6zpvJ0RwAA0GBoeqE6Y6TNhUemG5q/TPIVSKZE8peqsrklyRlX+YjrJMV0kzxtKh/uNpK7ub2uqwYAAMKfMdKGDUemAQBAg6JLgNBTtk2KaiJ5OkkxXaWY9j81t9pK7laS0211QgAAAAAAEOJoeiG0GCM5XFLL26SU8VanAQAAAAAAYcppdQAgwBjJu19yeKSoNKvTAAAAAACAMEbTC6HBXy6VbZGMX2pyiRR/itWJAAAAAABAGOP0RljPVySV75biekjNb5YSBlidCAAAAAAAhDmaXqjO4ZBaxh6Zrk/GX9nwSj5dyrhfciXW7/4AAAAaisMhtWlzZBoAADQoml6oLtYlfTCqYfZVkS1FN5Ga30jDCwAA2EtcnLR9u9UpAACIWFzTC9YxPslfJDWeLnnaWZ0GAAAAAADYCE0vWKdinxSVLqX+xuokAAAAAADAZmh6obpSn3T2R5WPUl/wt+8vl8q2Sf5SKe1sKbpx8PcBAABgtZISacCAykdJidVpAACIOFzTC9X5jfRN3pHpYDF+qWKv5C+UYjpJ6VdKyeOCt30AAIBQ4vdLX3xxZBoAADQoml5oOOU7Ky9W3+RaqfFULlwPAAAAAADqDU0vNAx/WeWF65vdIKVxDS8AAAAAAFC/uKYX6p8xlUd5xXSWUiZYnQYAAAAAAEQAml6oX8ZIZVulqMZS85skZ4zViQAAAAAAQASwfdNr0aJF6tWrl5KSkpSUlKTBgwfr7bfftjpW5CjfKUWlSRn3SYmDrU4DAAAAAAAihO2v6dWqVSvde++96tSpk4wxeu6553TmmWdq3bp16t69u9XxQleqOzjbMV6pySVSYlZwtgcAACJekYq0Uivlk8/qKMfkUqlGNU6SJL2vpfIpMo9476u+aqVWVscAAEQg2ze9Jk2aVOX53XffrUWLFumzzz6j6XU0cVHSZ2ODtDEjuVKCtC0AAADpM32mP+gPKlax1VGOLV7S/uY/PfmjlUnqnZGRX35JkkMOxShGiUpUghJ0qS7VFE2xOCEAIBLZvun1cz6fT6+++qqKioo0eDCn2tU7f7kkZ+XpjQAAAEHil1/FKlZHdZRDDqvjRBwjo3KVq0QlKlShSlQihxxKUIKSlazeP/3r9tO/FKVYHRkAEKEioun19ddfa/DgwSotLVVCQoKWLFmizMxMq2PZny9XimokxfezOgkAAABqycioTGUq+elfqUrll19GRm65FatYtVIr9Vd/dVd3ZSpTHdVRbgXpMhkAAJykiGh6denSRevXr1deXp5ee+01TZ8+XStXrqTxdTSlPuny1ZXTTw+SYlx12443X0obLbkSg5cNAAAgTLhLfFowobKmWvD2IJXH1rGmagB++ZWnvECDy/z0zyOPYhSjpmqqTuqkDuqg1mqtDGWotVqrsRpztB0AIGRFRNPL7XarY8eOkqR+/fppzZo1euSRR/Tkk09anCxE+Y30+cEj03XaRonkjJaSxwcvFwAAQBhx+I16rjwYmA5lOcpRqUrVUi3VRV2qNbdSlUpzCwAQdiKi6fVLfr9fZWVlVsewH1Mh+fIlX55kfFJMZynhVKtTAQAA4Di88qqZmulNvUlzCwBgG7Zvev3+97/XhAkT1Lp1axUUFOill17SihUr9M4771gdLfwZv+QvrGx0+UskOSVXshQ/REocLCWdLjm5pgMAAEAo88mnAhWom7rR8AIA2Irtm1779u3TxRdfrD179ig5OVm9evXSO++8ozFjxlgdLbyV76lseDkTJHdrKXGYFH+KFNeHuzUCAACECa+82qqtaq/2ukW3WB0HAICgsn3T629/+5vVEezHVyCZUqnpVVLyBMnTTnLwrSAAAEC42a7t6qzOelAPqpM6WR0HAICgsn3TC0FmfFL5j1LKr6T0KyWH0+pEAAAAqIN85csjj27WzTS8AAC2RNMLNTvaLbUr9knRzaXmN9DwAgAAOI7SuKPUVBbzyac92qPxGq8hGmJ1HAAA6gVNL1QXFyWtn1B9vvFVntrYeIbkbt7gsQAAAMJJWXyUzi2qoaaymJHRdm1XW7XV9bqei9cDAGyLQ3VwYoxfKtsmeVpLjaZYnQYAAAB1YGS0S7uUqETdqTvVWq2tjgQAQL2h6YXjM77Khld0cynjf6TodKsTAQAAoJaMjHZqpzzy6I/6owZpkNWRAACoV5zeiOpKK6Sr1kjGK/2pqeRxSdHNpNb/I8X3sTodAABAWIgu9en3Z6+VJC38Rz9VxFh3fa9ylWuXdilJSVqgBRqv8ZZlAQCgodD0QuWpi/5CyZcv+UukYiN9eLDyZynXSo37S/H9uY4XAABALTh9RgOW7gtMW8Evv/ZqrwpVqK7qqht0g7KUZUkWAAAaGk2vSFdxQPLmSK4kKbqVlHCqlNZZ0k/X7WpxoxQfb2lEAAAA1F6FKrRN29RYjXW5LtdFukjxoq4DAEQOml6RrGKf5CuS0q+Q0s6W3G0kh0MqKrI6GQAAAE6CkdEO7VBXddVDekjt1d7qSAAANDiaXpGqYp/kL5WaXiWlz6psdgEAAMAW9mqvkpSk23QbDS8AQMTi7o2RyF8u+fKkJpfR8AIAAPVi0aJF6tWrl5KSkpSUlKTBgwfr7bfftjpWRPDKq3zla4ZmqJ/6WR0HAADL0PSKNMZI5buk2O5Sk0toeAEAgHrRqlUr3XvvvVq7dq2++OILnX766TrzzDP17bffWh3N9vZoj9qojS7UhVZHAQDAUpzeGGm8OZIzVmp2reTiQqYAAKB+TJo0qcrzu+++W4sWLdJnn32m7t27W5QqMpSpTGfqTCUr2eooAABYiqZXJPGXSL5cqckVUuKwoy8XH195RBgAAEAQ+Hw+vfrqqyoqKtLgwYOtjtNgyuKjNMn8qkH3aX7611qtG3S/AACEIppekaR8txQ/UGp6pdVJAABABPj66681ePBglZaWKiEhQUuWLFFmZqbVsWxtn/YpWcnqLo6mAwCAa3pFCn+ZJKfU+CLJGWN1GgAAEAG6dOmi9evXa/Xq1bryyis1ffp0bdiwwepYtuSXX3nKU77yNU3TuGMjAADiSK/I4d0vedpIiSOOv2xpqTRtWuX0Cy9IMTTJAABA7bndbnXs2FGS1K9fP61Zs0aPPPKInnzySYuTNYzoUp/mTVsvSXrwhT6qiHEFdfulKlWe8lSgAklSghI0REM0QzOCuh8AAMIVTa9I4SuRGo2WnJ4TWNYnvfZa5fTixfUaCwAARA6/36+ysjKrYzQYp89o6Gt7JEkPL+4dlG0WqEAHdVBlKpNbbjVSI52m0zRIg3SKTlEbtZFD3J0bAACJpldk8JdJzmgp4VSrkwAAgAjx+9//XhMmTFDr1q1VUFCgl156SStWrNA777xjdbSwVKhC7dEexShGmcrUMA1Tf/VXL/VSrGKtjgcAQEii6RUJ/MWSM06K7Wp1EgAAECH27duniy++WHv27FFycrJ69eqld955R2PGjLE6WlgpUYl2a7eiFa0sZelSXarBGszRXAAAnACaXpHAXypFpUiuVKuTAACACPG3v/3N6ghhz8hol3ZpkAbpMl2mYRpGswsAgFqg6WV3/hLJXygl/kZyUCQBAACEixKVKEYxukbXqJ/6WR0HAICw47Q6AOqR8Uplu6SEwVKza61OAwAAgFrIV74aq7F6qZfVUQAACEsc6WVXvkKpfHfldbxa3SW54q1OBAAAgFooV7laqqWiFW11FAAAwhJNL7sxfsm7X/IVSMljpZa3SdHptdtGXJxUWHhkGgAAALVWFufSOYXjA9PH4pdfxSpW0U///PLLIYfaqE1DRAUAwJZoeoU746s8qstfIPlKKq/b5UqSml4jNblUctbhm0GHQ4rnyDAAAICT4nCoLL56uW1kVKYyFapQRSpSucrlkEOxilWykjVIg9RTPdVZndVXfS0IDgCAPdD0CjfGV3kUl69AMqWSHJIzQXJnSPGDpLheUlxPydPW6qQAAAD4Bb/82q7tcsqpRCWqj/qot3qry0//2qotpzMCABAkNL3CScVeyZcnuZIlTzsp4dTKBldsT8ndKnh3Zywrk664onL6yScljyc42wUAAIggUWU+zb3ia0nSX57sqRKPX9u0Ta3USrfoFg3SICUoweKUAADYF02vcGCMVP5DZVOr6bVS6plSdPPgNbl+yeuVnnuucvqxx2h6AQAA1IHLazTquR8kSY89lqltnu3qqZ5aqIXqpE4WpwMAwP5oeoUyYyqP7PLuk1xpUoubKxteAAAACCsHdECN1EgP6AG1VVur4wAAEBFoeoUif9lPd2AsllyJUuLIyovSJwy0OhkAAADqIF/5ulhX0vACAKAB0fQKhvJdkpzB2ZYpl+SqvDB9419JKeMkT8f6O5URAAAA9c4ll07RKVbHAAAgotD0Ohnu1lLSaZK/JHjbjGokJY+TErMkZ0zwtgsAAADLuORSrGKtjgEAQESh6XUy3C2ktn+xOgUAAABCXKxi1VqtrY4BAEBECdI5eQAAAACOpp3aqbmaWx0DAICIwpFeqC4uTtq378g0AAAAaq04Tsra10lJStK8uMutjgMAQMSh6YXqHA6pSROrUwAAAIQtv/za4tiqNk3a6nbdpYHiLtwAADQ0ml4AAABAkOUrX8lK1p/1Z3VVV6vjAAAQkbimF6orK5PmzKl8lJVZnQYAACDs5ClPncvaqMucP1NTAQBgEZpeqM7rlR5/vPLh9VqdBgAAIOyUqUy9vJlyPL6ImgoAAIvQ9AIAAACCzCmnYhRjdQwAACIaTS8AAAAgiLzyysgoXelWRwEAIKLR9AIAAACCaL/2q5maaazGWh0FAICIRtMLAAAACAIjo2xlq0QlOk/nKU1pVkcCACCiRVkdAAAAAAhnfvlVrGLt0z7FKU436AZdrIsllVgdDQCAiEbTCwAAADhBXnlV/LN/fvklSbGKVSu10i26RSM0wuKUAABAioCm18KFC/X666/ru+++U2xsrIYMGaL77rtPXbp0sTpa6IqNlbZtOzINAAAQoQp++lesYkmSQw7FKU7JStZADVSmMtX+p39t1VYeeY6sTE0FAIClbN/0WrlypebMmaMBAwbI6/Xqlltu0dixY7VhwwbFx8dbHS80OZ1S27ZWpwAAALBUvvK1X/vVVm3VXd3VRV3UQR3UXu3VSq3kkuvYG6CmAgDAUrZvei1btqzK88WLFys9PV1r167V8OHDLUoFAACAUHb4ovTjNE6P6BE55LA6EgAAqCXbN71+KS8vT5KUlsbddI6qvFy69dbK6bvvltxua/MAAAA0ACOjAhUoV7kqV7kaq7Eu1+V1b3hRUwEAYKmIanr5/X5de+21ysrKUo8ePayOE7oqKqQHHqicXrCAAg0AAISkcpUHbTsHdVBlKlOCEtRVXTVWYzVSI9VRHeu+YWoqAAAsFVFNrzlz5uibb77RqlWrrI4CAACAOnLLrRSlKFvZQdletKLVSZ00TuM0TMPUVV05nREAABuImKbX3Llz9eabb+rDDz9Uq1atrI4DAACAOspSlv6qv8onX1C2F6tYdVRHOeUMyvYAAEBosH3Tyxijq666SkuWLNGKFSvUrl07qyMBAADgJEQpSj3EpSoAAMCx2b7pNWfOHL300kt64403lJiYqOzsysPgk5OTFRsba3E6AAAAAAAA1AfbN70WLVokSRo5cmSV+c8++6xmzJhxUts2xmh32W5VmIqT2k6ocZQUq+1P09tLtss444K27XhXvNLd6UHbHgAAAAAAQE1s3/QyxtTbtr8q/EpzNs1Ria+k3vZhhZgSnz7+aXrqN1NVGuuq1fpOh1Muh0tRjii5HK7KaUUpyhmlaEe0Xuj+gpKikoIfHAAAAAAA4Ce2b3rVpxJ/iXK9uWoU3UguR+0aQ1bwG798xiev8cprvPIZX+C5T74j85wVGvV8Uznk0B7XIbl80VWaWIH/VZSindFKdCUqKSpJKVEpSo1KVVJUkhJcCUqISlC8M14JUQmVz10JahLdhIYXAACIDLGx0jffHJkGAAANiqbXSXDKqVhnrPaV7wv521obGTlV9Qgsj9Mjl8OlOGecEqMSlRyVHGhcJbStbFLFu+IDDauannucHjkcof3aAQAALOF0St27W50CAICIRdPrJPRO7K27O9wdFtf0cspZvWn105FYUU7eBgAAAAAAwF7odpwEj9OjcY3GWR0j+MrLpXvuqZy+5RbJ7bY2DwAAQDiipgIAwFIOU59Xej+K/Px8JScnKy8vT0lJXN8p5BQVSQkJldOFhVJ8vLV5AABoIHauUez82kIWNRUAACekvuoUZ9C2BAAAAAAAAIQIml4AAAAAAACwHZpeAAAACLqFCxdqwIABSkxMVHp6uiZPnqxNmzZZHQsAAEQQml4AAAAIupUrV2rOnDn67LPP9O6776qiokJjx45VUVGR1dEAAECE4O6NAAAACLply5ZVeb548WKlp6dr7dq1Gj58uEWpAABAJOFILwAAANS7vLw8SVJaWprFSQAAQKTgSC9UFxMjff75kWkAAICT4Pf7de211yorK0s9evSwOk7DoaYCAMBSNL1QncslDRhgdQoAAGATc+bM0TfffKNVq1ZZHaVhUVMBAGApml4AAACoN3PnztWbb76pDz/8UK1atbI6DgAAiCA0vVBdebn0yCOV09dcI7nd1uYBAABhxxijq666SkuWLNGKFSvUrl07qyM1PGoqAAAs5TDGmIbeaX5+vpKTk5WXl6ekpKSG3j2Op6hISkionC4slOLjrc0DAEADsXON0tCvbfbs2XrppZf0xhtvqEuXLoH5ycnJio2Nrff9hwRqKgAATkh91Sm2P9Lrww8/1P/8z/9o7dq12rNnj5YsWaLJkydbHQsAAMDWFi1aJEkaOXJklfnPPvusZsyYcVLb9vr92lSQI1/Df3dbK46iIvX8afrrQ/tkKqxpenVOTFOMy/ZlPwAA1dj+06+oqEi9e/fWJZdcorPOOsvqOAAAABGhPk8m+CRnt278arlKfN5620cwxJaW6bOfpi9fu0wlMZ6g78PpcCjK4ZTL4ZDL4VTUT//r+tn/Tm3TXRe2zgz6vgEACHW2b3pNmDBBEyZMsDoGAAAAgqTM79Wh8jI19oT2aZLRTn+V5z5j5DN++YyR1/jl/1lj0OlwyCWnXE6HXIFGlvNn0w7FuaKVGO1WUrRbyVEeJUd7lBDtVkKUW/GuaMVFRSkhyq04V7Tio6IV/9P/toxNbOiXDgBASLB90wsAAAD2EuOMUqo7RkW+CqujHFO88QWmk6M8ahyXqMToymZVcrRHSVFuJUS7A82pOFe0EqKiFfezhtXh5lVcVLRcDqeFrwYAgPBD0wsAAABhJatxK/3voF/Ja/zHX9hC8aXlkn4rSVo2fIochy9qDwAAGgRNLwAAAIQVp8OhDgmpVsc4PmdRYNLhcFgYBACAyETTC9XFxEjLlx+ZBgAAQO1RUwEAYCmaXqjO5ZJ+cXtxAAAA1BI1FQAAlrJ906uwsFCbN28OPN+2bZvWr1+vtLQ0tW7d2sJkAAAAAAAAqC+2b3p98cUXOu200wLP582bJ0maPn26Fi9efFLbLijz6+9fFaoitK+hWmtOb4W6/XOxJGnj5BnyR0VbG6gOhraJUY9mbqtjAACASFZRIT31VOX0rFlSdPjVVAAAhDPbN71GjhwpY0y9bPvbveX68yd5KvdJdro0aWxZsdY+cL0k6dq0X6nEE2dxour8MiouNzo8sh6XQwkepxLcDqXHu9Q+NYqmFwAAsFZ5uTR3buX0jBk0vQAAaGC2b3rVN69fSvQ45bJR1yvG4QxMJ8U45fY4j7F0wyquMCrzGjkcDjWKcyopxqkeTd3q2cytLo2j1blxtFokubhDEgAAAAAAEY6m10lIi3WqeaJLJRX1cySZVZyOqtPOEOofZaZHq28Lt7o0qWxytU+LljsqhAICAAAAAICQQNPrJHRu4taymc1lr5aXpKIi6brKyWWXNJfi463N8xOHpCg7HVIHAAAAAADqDU2vk2TLJszPXlO0yyFbnbsJAAAAAAAiQuhcrAkAAAAAAAAIEppeAAAACLoPP/xQkyZNUosWLeRwOPTPf/7T6kgAACDCcHojqvN4pDffPDINAABQS0VFRerdu7cuueQSnXXWWVbHsQY1FQAAlqLpheqioqSJE61OAQAAwtiECRM0YcIEq2NYi5oKAABLcXojAAAAAAAAbIcjvVBdRYX04ouV01OnStHR1uYBAAAIR9RUAABYiqYXqisvl2bOrJw+91wKNAAAgLqgpgIAwFKc3ggAAAAAAADboekFAAAAAAAA2+H0RgAAAARdYWGhNm/eHHi+bds2rV+/XmlpaWrdurWFyQAAQKSg6QUAAICg++KLL3TaaacFns+bN0+SNH36dC1evPiktl1RYfTVV+Xy+cxJbae+OUtK1f+n6S++KJU/1mVpnobQvbtb8fGcTAIACA00vQAAABB0I0eOlDH105T6+ONS3XhjjoqLQ7vpFesv1pqfpq+88oBKnMWW5qkvHo9D8fFOxcU5dNlliTrnnASrIwEAIImmFwAAAMJMRYVRfr5R48ahfURRjO9IvuRkpzyu0M57NMZIxcVGxcVGXq9RdLRD8fEOJSU5lZnpVvfu0erUKVodO0arbVv+7wUAIHTwqYTqPB7p738/Mg0AABBCYmOdatTIqZKS0D7Sq9xE66Z2iyRJ+WXR8jlCO+/ROBxSUpJTgwZFq0cPtzp2rGxytWkTpagoh9XxAAA4KppeqC4qSjr3XKtTAAAA1Cgry6O//72pfD6rk5yIyyRJcyxOcTIcDqlpU5fcbhpcAIDwQtMLAAAAYcXhcKhlS8pYAABwbFQLqM7rlZYsqZz+zW8qj/wCAABA7VBTAQBgKT55UV1ZmTRlSuV0YSEFGgAAQF1QUwEAYKnwvIUMAAAAAAAAcAw0vQAAAAAAAGA7NL0AAAAAAABgO1xY4CRs2bJFV199tYqLi62OElQxPp/e/ml6woQJKnW5LM1TF06nU+np6Vq0aJFSUlKsjgMAAAAAABoYTa+TsH//fm3fvl0xMTFyhWFj6Gh8Pl9guqioKKSbXsYYFRYWKi8vTy6XSykpKUpLS9PAgQM1dOhQJScnWx0RAAAAAABYgKZXEKSnpyvKRnfj8Xi9gen09HSVhdhrKy8vV15envLy8mSMUXx8vDIzMzV8+HANGDBAffr0UVxcnNUxAQAAAACAhUKrm4GQ4HU69XDv3oFpq/n9fhUUFCgvL09lZWWKiopSSkqKxo0bp8GDB2vAgAFq166dHA6H1VEBAACOcLulZ589Mg0AABoUTS9U43M69X5GhtUxlJeXp/3798sYo4SEBHXs2FHDhw9Xv3791KdPHyUkJFgdEQAA4Oiio6UZM6xOAQBAxKLphZCUn5+v/fv3a8yYMRo5cqROOeUUtWnThqO5AAAAAADACaHphWqcfr9O2b9fkvRlkybyN/ApjiUlJcrOztbkyZN199132+p6aQAAIIJ4vdI771ROjxsnUdMAANCg+ORFNdF+v+avWSNJOmf8eJU1YNOrvLxcO3fu1PDhw3XbbbfR8AIAAOGrrEz61a8qpwsLaXoBANDArL9KOfATr9er7du3q2/fvrr33nsVHx9vdSQAAAAAABCmaHohZOzYsUOdOnXSAw88oMaNG1sdBwAAAAAAhDGaXggJ+fn5io6O1vXXX6+MELhzJAAAAAAACG80vWA5Y4yys7N12mmnacSIEVbHAQAAAAAANkDTC5YrKChQfHy8LrnkEjkcDqvjAAAAAAAAG6DpBcvt379f/fr1U8+ePa2OAgAAAAAAbIL7JqMar9OpRT16BKYbwvDhwznKCwAAnJCysjKtWbNGfr/f6ijH5PB61ezaayVJ2atXy0SFXundq1cvpaSkWB0DAIB6EXqfvLCcz+nU0rZtG2Rfxhj5/X4lJiY2yP4AAED4++STT3TjjTeqqKjI6ign7p13rE5QTWxsrK666irNmDHD6igAANQLml6wjM/n0/bt25Wamqp27dpZHQcAAIQJr9ergoICtW/f3uooYWvHjh1KT0/XpEmTrI4CAEC9oemFapzGKDMnR5K0oVEj+evhtMOysjLt2LFDbdq00e23367evXsHfR8AAMDenE5nSF8eoSFqqtoqLy/Xrl27FB8fr+uuu06NGjWyOhIAAPWGpheqifb5tPCzzyRJ54wfr7J6uP7Erl27dMopp2jhwoVq20CnUgIAADSkhqipTpQxRgcOHFBubq66deum66+/XllZWZblAQCgIdD0QoMzxsgYo7PPPpuGFwAAQD3y+/3KyclRbm6ukpKSNGvWLM2aNYvrqQIAIgJNLzS43NxcxcXFqVu3blZHAQAAsCWv16t9+/apoKBAjRs31tSpU3XuuedSfwEAIgpNLzSooqIi5eTk6IILLlBmZqbVcQAAAGzHGKOtW7eqZcuWuvTSSzV58mS1aNHC6lgAADQ4ml5oMOXl5dq9e7fGjBmjm266KaQvPAsAABCuCgoKlJCQoEcffVTdu3e3Og4AAJZxWh0AkcHr9Wr79u3q16+f7rzzTsXExFgdCQAAwHZKSkq0Z88ede/enaPqAQARjyO9UO+MMdq2bZs6duyo++67T6mpqVZHAgAAsBVjjPbu3avCwkINGTJEf/zjHzmqHgAQ8Wh6oRqf06lnfrrIqc958gcD5uXlKT4+XnfccYcyMjJOensAAADhINg11bFs375d8fHxuvbaazVjxgx5PJ563R8AAOGApheq8TqdWtKhQ9C2d+DAAZ1++uk65ZRTgrZNAACAUBfsmupo8vPz5XK5dOedd2rs2LH1vj8AAMIFTS/UG2OMdu/erfj4eJ1//vkcYg8AABBE5eXlOnjwoA4dOqSJEydqzJgxVkcCACCk0PRCNU5j1CEvT5K0JTlZ/jo0q4wx2rlzpzwej37/+99r2LBhwY4JAAAQ0oJRU/2S3+9Xbm6ucnNz5XA4lJ6ert/85jeaOXMmXzACAPALNL1QTbTPpwdXrZIknTN+vMqiav82ycnJkdvt1h133KEJEyYEOyIAAEDIC0ZNdVhxcbH27duniooKJScna9y4cRo/fryGDh2qhISEYEUGAMBWaHqhXhw6dEjjx4+n4QUAAFBHfr9fBw8eVG5urtxut7p06aIzzzxTo0aNUosWLayOBwBAyKPphaDz+/0yxmjo0KFWRwEAAAhLOTk5OnjwoFJSUnTWWWdp0qRJGjBggKJO4mgxAAAiDZ+aCDqv16vo6Gi1bt3a6igAAABh6eDBgzrrrLN01VVXqXnz5lbHAQAgLDmtDgD78Xq9crlcSkxMtDoKAABA2KmoqJDL5dLYsWNpeAEAcBJoeiHoiouLFRsbS5EGAABQB2VlZYqJiVHbtm2tjgIAQFij6YWgy8/PV8+ePZWcnGx1FAAAgLDj9/vldDrl8XisjgIAQFjjml6oxud06qVOnQLTtV7f59PAgQODHQsAACCs1LWmKikpUVxcnFJSUuopGQAAkYGmF6rxOp36vy5d6rRueXm5oqKilJmZGeRUAAAA4aWuNVVBQYFGjRql2NjYekgFAEDk4PRGBFVhYaESEhLUtWtXq6MAAACEHZ/PJ0kaPHiwxUkAAAh/NL1QjcMYtS4oUOuCAjmMqdW6RUVFysjIUFpaWj2lAwAACA91qakOf4HYp0+f+g0HAEAEoOmFatw+nx5buVKPrVwp90/fNp6o0tJSijQAAADVvqYyxmjfvn3q3LmzMjIyGiAhAAD2RtMLQeVwOJSQkGB1DAAAgLCze/dupaWl6dZbb5XD4bA6DgAAYY+mF4KOIg0AAKB2iouLVVZWpquvvpobAgEAECQ0vRA05eXlcjgc6ty5s9VRAAAAwoYxRrt379bAgQN1zjnnWB0HAADboOmFoNm/f79atWqlESNGWB0FAAAgbJSWlsrj8eiKK65QVFSU1XEAALANml4ImpKSEg0YMECxsbFWRwEAAAgbfr9fLpdLTZs2tToKAAC2QtMLQWOMkdvttjoGAABAWCktLVVUVBR1FAAAQcbx06jG53Tq9fbtA9Mn4tChQ3I4HGrevHl9RgMAAAgbJ1JT+Xw+5eTk6Pzzz1fLli0bMh4AALZH0wvVeJ1OPVuLuwbl5OTo0KFDuvDCCzVz5sx6TAYAABA+TqSm2rNnj1q3bq25c+dyB2wAAIKMphdOSnl5uQ4ePKjLLrtM1113nVwul9WRAAAAwkZJSYnGjx+vJk2aWB0FAADboemFahzGqElJiSRpf2yszDG+dTx48KCaNWumK6+8koYXAADAzxyvpqqoqJDT6VS/fv2siAcAgO1xIXtU4/b59LcPPtDfPvhAbp/vqMsZY5SXl6cRI0YoPj6+ARMCAACEvuPVVGVlZYqNjVXr1q0tSAcAgP3R9EKdHThwQCkpKZoyZYrVUQAAAMJOWVmZ3G63GjVqZHUUAABsiaYX6sTr9So3N1fnnXeeunfvbnUcAACAsHPo0CH16tVLSUlJVkcBAMCWaHqhTn788Ue1adNGl1xyidVRAAAAwk5BQYEcDofOPPNM7toIAEA94UL2qLWSkhJVVFTosssuU2pqqtVxAAAAwsqBAweUl5enM844Q6effrrVcQAAsC2aXqiVsrIy7dq1S4MHD9bkyZOtjgMAABBW9u7dq/Lycl1xxRWaM2eOoqIoxwEAqC+c3ogTVlZWph07dqh///564IEH5Ha7rY4EAAAQVvLz83XGGWfo6quvpuEFAEA945MW1fgcDr3Vpk1g2hijffv2KT8/X6eccooeeughNW7c2OKUAAAAoe2XNZUkGWOUmJjIdbwAAGgANL1Qjdfl0hM9e0qSiouL9cMPPyglJUVz587VjBkzlJCQYHFCAACA0PfzmkqqPMorOjpamZmZFqYCACBy0PTCUfl8Pu3atUtDhw7V9ddfT4EGAABQR16vV9nZ2Zo8ebImTZpkdRwAACICTS9UZ4ySystVVlam2JgYXXvttTS8AAAAauunmkqSdpeVKTExUVdccYWcTi6rCwBAQ6DphWo8Pp9efPddSVJW795KTEy0OBEAAED4+XlNNS4rS0UOhzwej8WpAACIHBHzNdNjjz2mtm3bKiYmRoMGDdLnn39udaSwkJycrJYtW1odAwAAIKwZY+RwOLhjIwAADSgiml6vvPKK5s2bp/nz5+vLL79U7969NW7cOO3bt8/qaCGvT58+crvdVscAAAAIaz6fTy6XS/Hx8VZHAQAgYkRE0+vBBx/U5ZdfrpkzZyozM1NPPPGE4uLi9Mwzz1gdLeT16NHD6ggAAABhz+fzKSoqSnFxcVZHAQAgYti+6VVeXq61a9dq9OjRgXlOp1OjR4/Wp59+amGy8NCqVSurIwAAAIS9iooKJSUlcRF7AAAakO0/dQ8cOCCfz6emTZtWmd+0aVNlZ2dblCp8dO7c2eoIAAAAYa+0tJTrpAIA0MBs3/RC7ZWVlQWmmzVrZmESAAAA+zjjjDOsjgAAQESx/e1jGjduLJfLpb1791aZv3fvXho6R7Fn/3590Lq1RgwfLhd3GAIAAKgTn8Oh91u1UmFhoZq1aqVRo0ZZHQkAgIhi+yO93G63+vXrp/fffz8wz+/36/3339fgwYMtTBa6Svx+/es3v5HrhRckj8fqOAAAAGHJ63LpttatNb9NG117001KTEy0OhIAABHF9k0vSZo3b56efvppPffcc9q4caOuvPJKFRUVaebMmVZHC0l+v5/baQMAgKB47LHH1LZtW8XExGjQoEH6/PPPrY7UYIqLi5WTk6Pzzz+/yk2VAABAw4iIc9fOO+887d+/X7fddpuys7PVp08fLVu2rNrF7VHJ+P1KdDqloiIpLk5yOKyOBAAAwtArr7yiefPm6YknntCgQYP08MMPa9y4cdq0aZPS09OtjlevysvL9cOuXRo/fLiuvfxyUU0BANDwIuJIL0maO3euduzYobKyMq1evVqDBg2yOlLIivH7dfNdd0kJCVJxsdVxAABAmHrwwQd1+eWXa+bMmcrMzNQTTzyhuLg4PfPMM1ZHq1fGGO3YsUODevbUI3/7m2IaN6amAgDAAhHT9MKJ8Xq9cjp5WwAAgJNTXl6utWvXVjmtz+l0avTo0fr0008tTFb/Dh06pPj4eF177bVWRwEAIKLR3UAVubm5Sk1NtToGAAAIcwcOHJDP56t2OYmmTZsqOzvbolT1zxij/fv3a+zYserbt6/VcQAAiGg0vVDFoUOHlJWVZXUMAACAsJSfn6+EhARddNFFcnBdVAAALEXTCwHl5eVyOp0aMWKE1VEAAECYa9y4sVwul/bu3Vtl/t69e9WsWTOLUtW/AwcOqH///srMzLQ6CgAAEY+mFyRVNry2b9+udu3aaeDAgVbHAQAAYc7tdqtfv356//33A/P8fr/ef/99DR482MJk9csYo1NPPZWjvAAACAFRVgeA9UpKSrRz50716tVL999/vxITE62OBAAAbGDevHmaPn26+vfvr4EDB+rhhx9WUVGRZs6caXW0euH3+2WMUYsWLayOAgAARNMLkn744QcNGzZM9957r5o0aSKVlkrnnFP5Q5fL2nAAACBsnXfeedq/f79uu+02ZWdnq0+fPlq2bFm1i9vbhd/vl8vlOvIFostFTQUAgIVoekU4r9crp9Opiy66qLLhJUkxMdKrr1obDAAA2MLcuXM1d+5cq2M0CGOMHA6HnM6friBCTQUAgKW4pleEy8/PV3x8vDp27Gh1FAAAgLBWVlYmj8dz5ItEAABgKZpeEay8vFwHDhzQhAkT1KpVK6vjAAAAhLXCwkIlJSWpTZs2VkcBAACi6RWxjDHavn27+vTpoxtuuKHqHYaKiiSHo/JRVGRdSAAAgDBSUFCgIUOGKCrqpyuIUFMBAGApml4RKicnR8nJyfrjH//I3RoBAABOUmFhoTwej379619bHQUAAPyEplcE8vl8OnjwoM466yz16NHD6jgAAABhb+/everRo4cGDBhgdRQAAPATml4RKC8vTykpKZo+fbrVUQAAAMJeaWmpHA6HLrrooiN3bgQAAJbjUzkCFRUVKSMjQ82bN7c6CgAAQNg7cOCA2rRpo1GjRlkdBQAA/AxNrwhUUlKivn37Wh0DAADAFkpKSjRq1Ci53W6rowAAgJ+h6RVhvF6vnE6n+vfvb3UUAACAsFdeXq6oqCgNGjTI6igAAOAXoqwOgIZVUFCgpKQk9evX7+gLuVzSGWccmQYAAECN8vPzlZSUpF69elX/ITUVAACWoukVYbxer6Kjo5WWlnb0hWJipLfearhQAAAAYcgYo9zcXI0ZM0ZJSUnVF6CmAgDAUpzeGGF8Pp+io6O5sxAAAMBJ2r9/v5KSknTllVdaHQUAANSAzkcE8fv9ysvL09ChQ62OAgAAENb8fr8OHTqk8847T5mZmVbHAQAANaDpFUFycnKUlpammTNnHnvBoiIpPr7yUVTUMOEAAADCSG5urlJSUjRlypSjL0RNBQCApWh6RZDc3FydccYZat++/fEXLi6ufAAAAKCagwcPatiwYcrIyDj2gtRUAABYhqZXhCguLpbH49G4ceOsjgIAABD2nE6nOnToYHUMAABwDNy9MUIUFBQoNTVV/fr1szoKAABAWDLGqKCgQPv375fb7Vbr1q2tjgQAAI6BplcEiYqKUlQUQw4AAFAbfr9fOTk5ys3NVXx8vAYPHqwpU6Zo1KhRVkcDAADHQAckQpSUlKh58+ZWxwAAAAgrxhht2bJFaWlpOv/88/Wb3/xGvXv3lsPhsDoaAAA4DppeEaCwsFDGGF1yySVWRwEAAAgrBw8eVEJCgh599FEuEwEAQJih6RUB9uzZo9GjR2vSpEkntoLTKY0YcWQaAAAgwhhjdPDgQeXk5GjatGl1a3hRUwEAYCmaXjbn9/vlcDj0q1/9Ss4TLbZiY6UVK+o1FwAAQKgqKyvTrl27FBcXp4svvlhz586t24aoqQAAsBRNL5srLCxUbGys2rdvb3UUAACAkFdSUqKdO3eqb9+++t3vfqeBAwdaHQkAANQRTS8bKykpUXZ2tsaMGUPTCwAA4AQcPHhQ7du31+LFixUbG2t1HAAAcBK4uIBNlZeXa9euXRo+fLgWLlyoqKha9DeLiqQmTSofRUX1FxIAACDEFBcXa8SIEcFpeFFTAQBgKY70sqndu3crMzNT9913nxITE2u/gQMHgh8KAAAgxDkcDnXq1Cl4G6SmAgDAMhzpZUNlZWUyxmjGjBlKS0uzOg4AAEBY8Hq9kqRGjRpZnAQAAAQDTS8bOnjwoFq0aKHx48dbHQUAACAsGGO0Y8cOtWvXTr169bI6DgAACAKaXjZUUVGhZs2ayePxWB0FAAAgLOzbt0+xsbH6/e9/z5FeAADYBE0vGyotLVWzZs2sjgEAABAWsrOzVVpaqiuuuELDhg2zOg4AAAgSLmRvM3l5eYqNjdWZZ55pdRQAAICQZozRjz/+KGOM5s2bp+nTp1sdCQAABBFNLxupqKhQdna2fvWrXykrK6vuG3I6pf79j0wDAADYjDFGP/zwg6KionTTTTfp3HPPDf5OqKkAALAUTS+b8Hq92rZtmzIzMzVv3jw5HI66byw2VlqzJnjhAAAAQogxRrt27ZLb7dYf/vCH+jtCnpoKAABL0fSygYqKCm3btk1dunTRww8/rFatWlkdCQAAIGTt3r1bbrdbCxYs0MSJE62OAwAA6gnHWYe54uJibd26Vd27d9fDDz+stm3bWh0JAAAgZO3du1eSdNNNN9HwAgDA5mh6hbG8vDz98MMPOu200/TUU0+pffv2wdlwcbHUtm3lo7g4ONsEAACwWHl5uQoKCvTb3/5W55xzTv3vkJoKAABLcXpjmPL7/dq7d6/OPvts3XbbbYqJiQnexo2Rduw4Mg0AAGADe/bsUYcOHTRt2rSG2SE1FQAAluJIrzB18OBBpaam6sorrwxuwwsAAMCmysvLNWXKFMXFxVkdBQAANACaXmEqNzdXo0aNUkZGhtVRAAAAQp7X65XT6eT6pwAARBBObwxTDodDnTt3tjoGAABAyMvPz1d2draaNm0avGugAgCAkEfTK0wYY1RSUqK8vDwVFhbK4/GoadOmVscCAAAIWSUlJfrxxx/ldrs1fvx4zZ49W61atbI6FgAAaCA0vUJYRUWF8vLylJ+fL5/Pp5iYGDVp0kRnnHGG+vfvr5EjR1odEQAAIKT4/X7l5uYqNzdXTqdTPXv21Jw5czRs2DA5HA6r4wEAgAZE0yvE+Hw+5eTkKC8vTy6XS0lJSRo6dKgGDx6sU045Rd26dVN0dHT9hnA4pMzMI9MAAAAhrri4WPv371d5ebmSk5M1ceJETZw4UVlZWXK73daEoqYCAMBSNL1CRGlpqfbt26eysjI1atRIF1xwgYYPH64+ffooNTW1YcPExUnfftuw+wQAAKij8vJy7dy5U927d9fkyZM1ZswYtWzZ0upY1FQAAFiMppfFCgsLtXfvXjkcDrVr105nn322Jk6cqPT0dKujAQAAhIWKigolJibqgQceUMeOHa2OAwAAQgRNL4sUFxdrz549ioqK0qmnnqrzzjtPI0eOlMfjsToaAABAWMnLy5Pb7VZSUpLVUQAAQAih6dXAysvL9cMPP8jhcKhv37667LLLNGLECDmdTqujHVFcLA0YUDm9Zk3lofkAAAAhKDs7W16vV7Nnzw69I+WpqQAAsBRNrwbk8/m0bds2de/eXZdffrnGjh2rqKgQHAJjpA0bjkwDAACEoNzcXJWXl2vevHm6+OKLrY5THTUVAACWCsGOiz35/X5t27ZNnTp10p///GdlZGRYHQkAACCsHThwQOeee64uvvhiObg7IgAA+AWaXvWorKxMeXl5ys/PlzFG6enpuuuuu2h4AQAAnATz01FTSUlJuvDCC2l4AQCAGtH0CiKfz6eCggLl5eWprKxMbrdbKSkpmjBhgk499VQNGjRIbdq0sTomAABA2CosLNSf//xntWnTRlOnTlVmZqbVkQAAQIii6RUkhw4d0t69e5WcnKyuXbtq2LBh6tevn3r37q34+Hir4wEAAIS90tJSzZ49Wxs2bFCLFi00bdo0qyMBAIAQRtMrCIqLi7V//35NnTpVl1xyiVq1asVh9gAAAEH21Vdfaf369UpISLA6CgAACAM0vYJg586dGjdunG666SbFxMRYHefkORzS4dMwad4BAIAQ8eWXX6q8vFwpKSlWRzkx1FQAAFiKptdJKCgoUHR0tLp166Y777zTHg0vSYqLk7ZvtzoFAABAFV988YWiosKofKWmAgDAUk6rA4Srbdu26aGHHlJaWpr+9Kc/qUmTJlZHAgAAsK2ioiJ99913SkxMtDoKAAAIE2H0VZn1ioqKtHLlSr355ptas2aN9u3bpxYtWqhVq1ZWRwMAALC1jz/+WLm5uWrVqpXy8vKsjgMAAMIATa8T8N///lf/+te/tHTpUmVnZ0uS0tLS1KpVK/n9fovT1YOSEmn48MrpDz+UYmOtzQMAACLev/71L/n9frndbqujnDhqKgAALEXT6zj279+v3/72t9q9e7cSEhKUkZERKLZyc3MtTldP/H7piy+OTAMAAFho06ZNWr16tRo1amR1lNqhpgIAwFI0vY7jlVde0e7du9WhQwe5XC6r4wAAAEScJUuWKD8/X506dbI6CgAACCNcyP4Y1q5dq5dfflmJiYk0vAAAACywZs0avf7660pJSZHD4bA6DgAACCM0vY5izZo1+t3vfqeDBw+qadOmVscBAACIODk5ObrzzjtVWFjInbIBAECtcXrjT7xer7Zs2aKNGzfq22+/1TvvvKMDBw6oXbt2cjrpDQIAADSksrIyzZ8/X5s2bVK7du04ygsAANRaxDa9CgsL9d1332njxo366quv9OWXXyo3N1clJSUyxig+Pp6GFwAAQAPz+/3avXu3nn/+eb333ntq2bKloqOjrY4FAADCUMQ1vf71r3/pjTfe0MaNG1VYWKiCgoJAoysmJkZxcXFyOp0qKSnRtm3bjrmt8vJy+x5q37ix1QkAAIDNlZeXa/v27dq6dau2bNmi7777Ths2bFBeXp4KCgrUuHFjxcfHWx3z5FBTAQBgmYhrehUUFKhRo0bKysoKyvZsUYz9Uny8tH+/1SkAAIDNbN68WV9//bW2bt2qb775Rps3b1ZRUZGKi4tVVFQkSYqLi1NcXJwaN24sl8ulvLy8atspKSlRYmJiQ8evPWoqAAAsFXFNr6lTp2rq1KlWxwAAAIgoxhjdcsstOnjwoIqLi+X1eiVJUVFRSkpKUlJSUpXlKyoqVFFRUeO2PB6PfY+2BwAAQRNxTS8AAAA0PIfDoWeffTZwRNfJio+P5+L2AADgmGh6obqSEmnChMrpt9+WYmOtzQMAAGwhPj7efpeFOBZqKgAALEXTC9X5/dLKlUemAQAAUHvUVAAAWMppdQAAAAAAAAAg2Gh6AQAAAAAAwHZoegEAAAAAAMB2aHoBAAAAAADAdmh6AQAAAAAAwHa4eyNqFhdndQIAAIDwR00FAIBlaHqhuvh4qajI6hQAAADhjZoKAABLcXojAAAAAAAAbIemFwAAAAAAAGyHpheqKy2VJk6sfJSWWp0GAAAgPFFTAQBgKa7phep8Pmnp0iPTAAAAqD1qKgAALMWRXgAAAAAAALAdml4AAAAAAACwHZpeAAAAAAAAsB2aXgAAAAAAALAdml4AAAAAAACwHUvu3miMkSTl5+dbsXscT1HRken8fO42BACIGIdrk8O1ip1Qf1mAmgoAgBNSXzWYJU2vnJwcSVJGRoYVu0dttGhhdQIAABpcTk6OkpOTrY4RVNRfFqOmAgDguIJdg1nS9EpLS5Mk7dy5M6wLyvz8fGVkZGjXrl1KSkqyOg7EmIQaxiP0MCahhfEIPXl5eWrdunWgVrETu9RfEn87oYbxCD2MSehhTEIL4xF66qsGs6Tp5XRWXkosOTnZFm+wpKQkW7wOO2FMQgvjEXoYk9DCeISew7WKndit/pL42wk1jEfoYUxCD2MSWhiP0BPsGsx+FR0AAAAAAAAiHk0vAAAAAAAA2I4lTS+Px6P58+fL4/FYsfugscvrsBPGJLQwHqGHMQktjEfosfOY2Om12em12AHjEXoYk9DDmIQWxiP01NeYOIwd78kNAAAAAACAiMbpjQAAAAAAALAdml4AAAAAAACwHZpeAAAAAAAAsB2aXgAAAAAAALCdBml6HTx4UFOnTlVSUpJSUlJ06aWXqrCw8JjrPPXUUxo5cqSSkpLkcDh06NChhohazWOPPaa2bdsqJiZGgwYN0ueff37M5V999VV17dpVMTEx6tmzp5YuXdpASSNDbcbj6aef1rBhw5SamqrU1FSNHj36uOOH2qvt38hhL7/8shwOhyZPnly/ASNQbcfk0KFDmjNnjpo3by6Px6POnTvz364gqu14PPzww+rSpYtiY2OVkZGh6667TqWlpQ2U1v4+/PBDTZo0SS1atJDD4dA///nP466zYsUKnXLKKfJ4POrYsaMWL15c7zmDhRoMwUINFnqowUIPNVhooQYLLZbVYKYBjB8/3vTu3dt89tln5qOPPjIdO3Y0F1xwwTHXeeihh8zChQvNwoULjSSTm5vbEFGrePnll43b7TbPPPOM+fbbb83ll19uUlJSzN69e2tc/uOPPzYul8vcf//9ZsOGDeYPf/iDiY6ONl9//XUDJ7en2o7HhRdeaB577DGzbt06s3HjRjNjxgyTnJxsfvjhhwZObl+1HZPDtm3bZlq2bGmGDRtmzjzzzIYJGyFqOyZlZWWmf//+5owzzjCrVq0y27ZtMytWrDDr169v4OT2VNvxePHFF43H4zEvvvii2bZtm3nnnXdM8+bNzXXXXdfAye1r6dKl5tZbbzWvv/66kWSWLFlyzOW3bt1q4uLizLx588yGDRvMo48+alwul1m2bFnDBD5J1GDUYMFADRZ6qMFCDzVYaKEGCz1W1WD13vTasGGDkWTWrFkTmPf2228bh8Nhdu/efdz1ly9fblnBNXDgQDNnzpzAc5/PZ1q0aGEWLlxY4/JTpkwxEydOrDJv0KBB5oorrqjXnJGituPxS16v1yQmJprnnnuuviJGnLqMidfrNUOGDDF//etfzfTp0ym4gqy2Y7Jo0SLTvn17U15e3lARI0ptx2POnDnm9NNPrzJv3rx5Jisrq15zRqoTKbhuvPFG07179yrzzjvvPDNu3Lh6TBYc1GDUYMFCDRZ6qMFCDzVYaKEGC20NWYPV++mNn376qVJSUtS/f//AvNGjR8vpdGr16tX1vfs6Ky8v19q1azV69OjAPKfTqdGjR+vTTz+tcZ1PP/20yvKSNG7cuKMujxNXl/H4peLiYlVUVCgtLa2+YkaUuo7JHXfcofT0dF166aUNETOi1GVM/vWvf2nw4MGaM2eOmjZtqh49euiee+6Rz+drqNi2VZfxGDJkiNauXRs4/H7r1q1aunSpzjjjjAbJjOrC+bOdGiw8xinUUYOFHmqw0EMNFlqowewhWJ/tUcEMVZPs7Gylp6dX3WlUlNLS0pSdnV3fu6+zAwcOyOfzqWnTplXmN23aVN99912N62RnZ9e4fCi/znBRl/H4pZtuukktWrSo9oeDuqnLmKxatUp/+9vftH79+gZIGHnqMiZbt27VBx98oKlTp2rp0qXavHmzZs+erYqKCs2fP78hYttWXcbjwgsv1IEDBzR06FAZY+T1evXb3/5Wt9xyS0NERg2O9tmen5+vkpISxcbGWpTs+KjBqMGCgRos9FCDhR5qsNBCDWYPwarB6nyk18033yyHw3HMx4l+EAL17d5779XLL7+sJUuWKCYmxuo4EamgoEDTpk3T008/rcaNG1sdBz/x+/1KT0/XU089pX79+um8887TrbfeqieeeMLqaBFpxYoVuueee/T444/ryy+/1Ouvv6633npLd955p9XREEKowRBOqMGsRw0WmqjBQgs1mH3V+Uiv3/3ud5oxY8Yxl2nfvr2aNWumffv2VZnv9Xp18OBBNWvWrK67r3eNGzeWy+XS3r17q8zfu3fvUXM3a9asVsvjxNVlPA574IEHdO+99+q9995Tr1696jNmRKntmGzZskXbt2/XpEmTAvP8fr+kyiMPNm3apA4dOtRvaJury99J8+bNFR0dLZfLFZjXrVs3ZWdnq7y8XG63u14z21ldxuOPf/yjpk2bpssuu0yS1LNnTxUVFWnWrFm69dZb5XQ2yE2X8TNH+2xPSkqy7CgvarDqqMHqDzVY6KEGCz3UYKGFGsweglWD1XnkmjRpoq5dux7z4Xa7NXjwYB06dEhr164NrPvBBx/I7/dr0KBBdd19vXO73erXr5/ef//9wDy/36/3339fgwcPrnGdwYMHV1lekt59992jLo8TV5fxkKT7779fd955p5YtW1blmiY4ebUdk65du+rrr7/W+vXrA49f//rXOu2007R+/XplZGQ0ZHxbqsvfSVZWljZv3hwofiXpv//9r5o3b06xdZLqMh7FxcXViqrDxXDlNT/R0ELxs50arLpQHCe7oAYLPdRgoYcaLLRQg9lD0D7ba3XZ+zoaP3686du3r1m9erVZtWqV6dSpU5XbZf/www+mS5cuZvXq1YF5e/bsMevWrTNPP/20kWQ+/PBDs27dOpOTk9MQkY0xlbc59Xg8ZvHixWbDhg1m1qxZJiUlxWRnZxtjjJk2bZq5+eabA8t//PHHJioqyjzwwANm48aNZv78+dwuO4hqOx733nuvcbvd5rXXXjN79uwJPAoKCqx6CbZT2zH5Je4cFHy1HZOdO3eaxMREM3fuXLNp0ybz5ptvmvT0dHPXXXdZ9RJspbbjMX/+fJOYmGj+7//+z2zdutX8+9//Nh06dDBTpkyx6iXYTkFBgVm3bp1Zt26dkWQ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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l2\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L2 complex), linkage=average, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.13557548807226513\n", "For n_clusters = 3 The average silhouette_score is : 0.0838979121845007\n", "For n_clusters = 4 The average silhouette_score is : 0.07126432064933304\n", "For n_clusters = 5 The average silhouette_score is : 0.059033090753044234\n" ] }, { "data": { "image/png": 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/qUJCQsSWLVuEXq8vNq3o78Cix7Rr164lxtKmTRub5So66VWe6+lPP/1kczOu8OXh4SG6detmN15yT3y8sRLdeeed8vD169fRsGFDtGjRAm3atEGrVq3QpUsX1K9fv0K2vWPHDrRp0wa9evXCzp075XaPzpw5g61bt2LkyJEACtoMmj17NoQQAIA6depg1KhRyMzMlBvITktLw5AhQ/DHH38gICCg3DGZzWYMGjRIbjg5JCQEo0ePRmBgIHbs2IEDBw4gLy8P9913H1q1aoV69erhySefxLlz57B06VJ5PQ899JD8CFtUVBSioqLg7e2N1atX4+zZswCA1q1bY8SIEfIyhe0OjBs3Dr/88gsAwMfHB6NHj0ZkZCT279+P7du3w2KxYNasWWjdujU6duxY5nvS6/W466670KJFCwQFBcHb2xtpaWn49ttv8dNPP0EIgTlz5mDEiBE2jUkXunHjBm7duoX77rsPERER+M9//oPr168DAF599VU89dRT0Ol0LtmWtWnTpmHVqlUQQuCnn37CsWPH0KxZMwDAl19+KTeMHhgYiAEDBgAA3nrrLbmh+MaNG2PYsGHQaDQ4f/48fvnlFxw+fLjM/QUA7777rrz+yMhIjB07Fl5eXvjrr79w/Phx/PDDDw6tZ/ny5fjzzz9tHql64oknbM5RV5zfR44cgUajwbhx49CgQQP8/vvv8PDwcCjGQtu3b5ePa6Fbt27Znf/bb7+1+XvSpElOba+wXatCI0eOxLBhwzBr1ixYLBbk5+dj48aNmD59ukPrM5lMGDRokNy2mFqtxrhx4xAZGYmtW7fi+PHjAArO2TvvvBP33XdfievZt28fBg0ahCZNmmDDhg3ytaCwnbGIiAin3mchi8WC8+fPY+XKlfK4iIgIdOrUqdTlZs6ciX379iEnJwdvv/025s+fj9deew1AwfVh4sSJ2LJlS4nLVuT1vU2bNmjevDl+/fVXAAXnsdlshlqtLnW5H3/8Efn5+QAALy8vxMXFlWv7Jfnjjz/QoEGDEqfFx8fjsccec2g9GRkZGDBgAK5cuSKP69q1Kzp27Ij09HTs27fPZv7169fj5ZdfttnWoEGDcOnSJbz77rswm824ePEiBg8ejBMnTpTYVt2RI0cQHx+PwYMHY/v27fjpp58AFFyLAKBly5bo378/PvzwQ5w5cwZA8euvtdOnT8PPzw+zZs2CJEl45513kJqaCgCYP38+BgwYUOaxT0pKwqhRo+SeNQvfl8ViwYYNG5CcnIyLFy9iyJAhOHbsGNRqNbRaLTZu3IiWLVsiKysL27dvx3/+8x/88ssv+PPPPwEAdevWxapVq4ptLy4uDl5eXsjKykJeXh5+/PFHdO7cudQYK8OPP/6IyMhIjBkzBhcuXMDGjRsBFDye/eqrr5b6yOyhQ4fQq1cv+ZHm4cOHY8OGDTbnQGRkJLp06YLo6GgEBARACIGkpCR89NFHyMrKwrFjx7Bq1Sr5/P3rr78wa9YseXkvLy9MnjwZKpUK//3vf5Genl5iLOU5nq4gSRLq1auHu+66C7Vr10ZAQACMRiN+//13bNq0CSaTCd988w0++eQTDB8+HJIkYfz48fjXv/4FoOAx8AULFsjrK9z/ANCjRw9ERkYCKOiww7oc1qtXL3Ts2BFXr17Fu+++i8zMTOzduxezZs3CW2+9VWKse/fuRXBwMEaOHImgoCCcOHECAKBSqRAXF4e2bdsiPDwc/v7+yM3NxZEjR/B///d/EEJg7dq1eOihh9C2bVsAwObNm20e369Xrx5GjhyJ8+fPl9qWoyveR3m54jo0a9YsfPPNNwAAnU6HkSNHokGDBjh27Bg2bdoEIQRefvlltGrVCqNHj5bbVl26dKlc5ujRowd69uxZaqzff/89PD09MXbsWNSuXRtHjhwp85ydM2cOvvzyS/nvqKgoDBo0CH5+fjhx4gS++OKLcuw1x6SkpCA9PR0PPfQQ/P398f7778vllddffx1DhgxBQkJCseUK9/PgwYPRvHlzJCcnw8/PD+np6Rg4cKBcbouOjpbL1ps3b8aJEyfksuOZM2fkdgud2QepqakYPHiwfFwMBgMmTpwIX19ffPDBB0hOTobFYsHcuXPRqlWrEuO/efMmcnJyMHPmTGRlZeE///mPvD8GDRoEb29vTJs2DcnJyXI7pUV/Bxb13Xff4d5770Xz5s3x1VdfyefpTz/9hGXLluGZZ55x4sjYcuQ3HVC+66kQApMmTUJmZiaAgmvj6NGjERMTg08++aRYuZpqAMXSbTWQ0Wi0uStb0qtTp07il19+sVnOFTW92rZtK1eZzs/PF6GhofI067tA9957rzzex8dHXL16VZ62bds2m3W+8sor8rTy1PT67LPP5PFqtVqcPn1anmYymUSzZs3k6bNmzbL73orWyilr3xT69ddfbdbz3Xff2Uzv27evPG3QoEElbsOeX3/9Vbz//vvi1VdfFcuXLxfPPfeczbas7wYVvbtlXbNs69atNtOOHj3qsm0VvbPRo0cPeZr1nbAhQ4aUOH7AgAHy+JIe47l8+bLIysqS/7ZX02vGjBny+Oeff77Yem7evClu3rxpZ0/bKquqfXnPb+tzCYDYunWrQ/HYi8uRl/XnyPpcBFCsdmZZXnjhBZvP2pUrV4QQwuYO3p133unw+rZs2WITz6pVq+Rp2dnZNteD5s2by9OKXsseffRRedovv/xiM+3zzz93KBZH7lw3bNiw2HW16LJBQUHCZDKJOnXqCAAiKipKfPfddzbnftHjaH3tKe/13ZGaXkIIMXz4cJv5rl27Vua+sa6l1qBBgxLnKW9NrzNnzpT4Hrt3727zuSrLa6+9ZrP8kiVLis3z559/ysPNmzeX542JiRHZ2dnytFWrVtmsy/qxFOtzMigoSKSlpQkhhDh16pTNMqGhoXKtju3bt9u9/hY97/bv3y9P279/v820J598Up5m73vJ+vHYhg0b2nzGL126ZFPL4rPPPrPZP//973/laZ6ennLtEY1GU+oj+vXr1y/zvHOGK2p6eXl5iYsXL8rTBg4caPcaZb2tWbNmyY+bAxD33XdfiY8YCyFEamqq2LZtm1izZo146aWXxPLly8Xdd98tL9u1a1d53ueff95mO9a1jot+dl1xPM+fPy+WL1/u1KskV69eFZ999plYtWqVePHFF8Xy5cttar5MmjRJnvfs2bM2NY6OHTsmhBAiLy/PptbJRx99JC/TsmVLm31tzfpReo1GY/O4sfX+8vX1tftImBBCJCcni82bN4uVK1fK78G6RuW//vUveV7rpxm8vb1tro9Fr3HW53p530d5ufI6dOPGDaHRaOTx77zzjs22HnnkEXlay5Yt7cZRtMxe0jxqtVocOnSo2Dz2ylw3b960ia1ly5bFalCeP3/e5tHR/fv3O3XeF33svehx3rBhgzwtKSnJpgapde3Xotfykp7usK4ZHxAQYHMuZGZmyk0gABCvvvpqufbBK6+8YhPHtm3b5PmuXr1qU2PJuvZz0ff9/vvvy9Pat29vM23Tpk1CiIIanhEREfJ469+BRY/pAw88IE/Lz8+3qd0aGRlZYhyO1vQqa1qh8lxPv//+e5v1PvXUU/IyaWlpNrX2WNOrZmDSq5Klp6eLBQsWiLCwMJsPo/UrJCTE5gvbFUmvom25tGvXTp5mXU3cOhk2bNiwYvFbX9iHDx8ujy9P0uuxxx6zuw+Kvtq1a2f3vZU36VX0B1Jpr7CwsBK3UdShQ4dsvhDsvTZu3Fji/lGr1TYX899++81mud27d7tsW0Uv8taPggUEBIicnByRmZkpDAaDPP7w4cPy/MuXL5fH6/V6kZiYKB588EHx0ksviR9++KHMdhMKbdq0yeb9t2/fXkycOFH8+9//Fjt37rT7w6UkZSW9ynt+W59LTZs2dTgee3E58nJl0sv6kdlu3brJ463b9QFKTqqWpOhnt+ijH/PmzZOnSZIkJz+LXsusE905OTk20959912HYikr6eXl5WW3/bOiSS8hbBOEhT+wJEkSp0+fLjXpJUT5ru+OJr2GDRtmM58jSS/r92J9DbVW3qTXrVu3xPLly8XSpUvF1KlTbR51jYiIKPOR30LWyTwfH59SP+9ZWVk2P87nzZtnMz0zM9PmvTz22GPyNOvvqAkTJsjj8/LybJax/j4smtizvv5anzv16tUrFqt1O2zW7ZvZ+15q27atw9eGxx9/vNj2ip4fgG1SoCTW5YBly5aVOq8jXJH0Gj16tM0063bL6tatazPN3v554IEHhNlsLhaf2WwW8+bNs3nkt6RXw4YN5WWsb/qEhIQUW6f1o0SuPJ7llZ2dLSZMmFDqI/xA8Ueku3TpIk974oknhBC2NyYDAwPlR2CLfg7LelknCq3HT5s2rcT3cP369TIfCQMgHnzwQXkZ68dWi363nzt3rsRr7O28j/Jy5XWo6I260l7W38NF43Ak6dW/f/8S57FX5ioam3XCtKJYX2O0Wm2x7xLrc7xx48byeOtreUBAgDAajcXWXfSmU2mvESNGCCGc3wfW2yjpWmN9jQ8NDS3xfWs0Gpv4R40aZXefdOzYscTzregxLfrI5uLFi22mF95ErcikV3mup0WbgLFubkUIISZOnGg3XnJPfLyxkvn4+GDp0qVYsmQJTp48iYMHD2LPnj349NNP5Sr5KSkpWL9+PWbPnu2y7cbExNj8bd3TnMVikYdv3rwpD4eFhRVbT1hYmNwTmL3HscTfj44Vsu5tzpr1tspSuE1XcvX2c3Jy0L9/f1y+fLnMee3tk7CwMJtH5Yr2CFh4rFyxraL69euHevXq4ezZs7h16xY++eQTaDQauTpxixYt0LJlS3n+Rx99FEePHsXGjRuRl5eHXbt2YdeuXfL0pk2b4n//+x9q1apV6naHDh2KuXPn4vXXX0deXh6+//57m17uoqOj8eWXX7qkB1NXnN+NGze+7TjWrl2LCRMm2Iw7d+4c6tatW+L8tWvXtvn7999/R4sWLRza1sGDB216dLSuwj5kyBBMnToVRqNRjsv60TF7rPejt7c3vLy8bKZb71shBFJTU0vs/cj6umTvXHfW8uXLkZaWho0bN+Ls2bPIysrCfffdB4vFgvHjx5e5/OTJk7F48WJkZ2fj4sWLAIA+ffqgQYMG8t/2VOT1/fTp0/Kwh4cHgoKCnFre1fz9/W167XrhhRfQvn17HDt2DJcuXcLEiRNx5MiRMtdjfS5FRUWV+sjMrVu3bL5fin6Gvby84O3tLT/OYO8zbP3YbNHHFa2nFX000t45GRoaWmxcWFgYkpKSAEB+1LE0t/t9NH36dGzatEn+W6vVYsqUKaWup+h3dVXgaFmlNHXr1oVKpSo2/rXXXsPy5cvLXN76O9P62IWHhxebNzw8XH4s21p5j+eFCxfw0UcfObwsAJvP4YIFC7Bu3boylylaLpg0aRJ27twJAPjggw+wZMkSm0cbR48eLR+Lop/DstgrP9n7Lr3//vttHgmzx9HjVNJxA1z3Psrrdq9DzpxjQgjcuHGj3L0QOlvuKRqbvbKNtQMHDuDAgQMObyMqKsqm2RJrQUFBxb5LrL8v7F2TY2NjS3wkvjyfZ2f3gSPl00L2vttCQ0Nt4rc+r0JDQ232ifV8pV1bi36/FY0tNTW12DhHfwc6qjz7v+gxLut9kPtj0kshkiQhPj4e8fHxmDRpEhYtWoTY2Fj5wlP4XLmraLXaYtsvSWBgIK5duwYAuHr1arHp1uOs2zuyLmAWJkkK2Xsvhe1qAQU/4p599ll74cvPx7uS9fYB4F//+leZbV+VZs+ePTZJqDlz5mD+/PkIDg5GdnZ2scRASRw9Tq7YVlEqlQpTp07FnDlzAAD/+c9/bH5YT5w40WZ+jUaD9957Dy+99BIOHDiAU6dO4dSpU9iyZQtu3bqF48ePY/78+XL7FKVZvnw5nnrqKRw4cAC///47Tp8+jc8//xyXLl1CcnIyHnnkEezevdvp91RUec9va+XZt7erW7duePvtt+W/161bhxUrVji0bNEfQA888AAeeOCBEufdsGEDli1bVmKhz5r1ZyczMxNZWVk2+8V6P0qSBH9//xLXY32+2zvXnVX4A3DmzJlo0aKFnKiaM2cOBg4cWOa1JDAwEGPHjrVpv2XGjBlOxeDq6/vPP/8st+cFAAkJCSX+qC8qODhYHi6tzThX8PLyQvfu3XHs2DEAwC+//IK0tDSH9nehCxculNpWWUBAACRJkgvURT/DWVlZcsKrcP6SFL3OWivr3C9J4TXFmnVs9s5/a9b7IT4+vlhS3FrTpk1t/s7NzcXDDz9sM85oNGLKlCl226ADbH9IhISElBljZXD0O7Coxo0b4/fffwdQ0Jajr68vpk6dajOPdTIpIiICW7ZsQYsWLaDT6fDYY4+VmBCzPnYlHWfrtuislfd4/vnnn5g3b57deUtinfSyfo/NmjXDBx98gEaNGkGj0WD48OE2iVFrhTdA0tPTkZSUhG+++Qb/93//J0+3/v4vej4PGDCg1PbgrNs7tFbSd2lWVpZNO0fdunXDW2+9hejoaKjVarRt21ZuU8iav78/bty4AaD4cbJ3jFz1Psrrdq9DRcuws2bNKrUdzNspRztb7ikaW1JSEtq0aVPqMv/73/+wePFih7eRkJBgN+l148aNYt8ljlyT7b1P6/dTq1atUm9aFbZD5ew+sJ6/vOVTV3+3AQWfp0aNGtmNrXBflud3oKPKcz0teoyvXbtW5j4m98akVyV69913kZubi1GjRsHX19dmmpeXF1QqlfyjyJFCckXo0KEDtm7dCqCgse1r167J2fGvvvrK5k5Xhw4d5GHreH/88UcIISBJEo4dO2ZTcCq6rUK5ubmIj49Hnz59is138OBBm7u9RS/qhY2gF2U9X0nzWG8fKPiBWPSHAwCcOHHCoR+MhQWuQmPGjJF/dH788cdlLu+MitrWpEmT8MwzzyArKwu7du2S97tOp8OYMWNs5j116hSioqIQEhKCe++9Vx7ftGlTuUDgSGP2SUlJCAgIgL+/P/r06SOfAz179sTgwYMdXo8jynt+K23gwIGIjo5GcnIyAGDlypVo27YtRo8eXWzeb7/9FjqdDp07d0Zubq5N475luXbtGrZt2yZ3VmBP0X3z3nvvyZ+dnJwcm3OwefPm5b67fDuCg4Px3HPPyT/Wbty4gRUrVmDhwoVlLjtjxgw56dW4ceMyG/kFKu76furUqWINzDpaS6xevXry8MWLF2GxWBxKlpXm+++/R/369YslSXJycvDdd985vb5OnTrJ50tGRgaWL1+O+fPn28yTnJyM6OhoeHp6onnz5nLnI5s2bcLixYvlmxXvvfeezXKV9Rk+e/YsDhw4IG/vwIEDci0vAGjVqlWZ6+jQoQN+/PFHAAUdT4waNapYDU+TyYT/+7//Q7t27WzGz507V24EvE6dOrh+/Tqys7OxdetWvPnmmyXW+DKbzbh06ZL8t/W5AhTUuCq83ixcuBCLFi0q8z0o6bHHHsPOnTuxfv16AAU133x8fGw60bD+3mzdurXcCHpubq7dckrr1q3xySefACj4kbRz50506dIFALBr164Sa3kBt3c8b4f1e+zSpYtcQzolJcWmJnZRBoMBI0eOlK97Dz74oFxuat68uU3Cx8vLCy1atJA/hzdu3MDMmTOLlc3S0tLw1VdfOVVLOy0tTe4gB/inBjpQcC08evRoicu1bt0aO3bsAFDQcdOtW7fkxMDatWtLXOZ23seECRPkG3oJCQml7tuK0q5dO6jVanl/abVamwRooXPnzuHUqVM230tllY9v11133QWNRgOTyQSgoCZw//79bcoCly5dQkhISKmJmvIyGo346KOP5DLSuXPnbDpFceSabK1Dhw7y91RKSgp69uyJO+64w2YeIQS+/fZbuRF2Z/dB0W189dVXcnn42rVr+Oqrr2ziqSzr16+Xk8FGo9GmfFe7dm25xpR1uSYlJQV//vknYmNjkZeXhxdffNHu+h35TVee62nr1q1tpm/YsEGuXJGenm73mk/ui0mvSpSUlITFixfj0UcfRadOndCiRQsEBgbixo0b2Lx5s3xhBIDevXsrEuOsWbPw2WefQQiBjIwMtGnTBqNHj0ZmZibeeecdeb7AwECbR4XatGkjP8qye/du3HXXXYiIiMA333wj9x5WVL9+/RAXFyc/ejVw4EAMHjwYTZo0gcViwZ9//ok9e/YgOTkZa9eulR/nKvyCKHwk68knn8Svv/4KrVaLxMRE+UJnfUH88ssv5ZpQwcHBmDBhApo3b44ePXrg66+/BlDQg+FXX32FVq1aQaVSITk5GQcOHMBvv/2GhQsXltnzm/WdEAAYO3YsRowYgXPnzskFcVepqG35+/tj7NixePPNNwH8UyV5wIABxR6neuWVV7B+/Xp069YNdevWRVhYGG7evGnzw9ORH/cfffQRFi5ciMTERDRo0AC1atVCVlYWPvjgA6fW44jynt9K0+v1WLduHXr16oX8/HyYzWaMGTMGK1euRJcuXeDt7Y2LFy/iu+++w2+//Ya1a9eic+fO2Lp1q00V765du5ZYo+Pzzz+X78ytXbu2zKRXv3790KhRI5w6dQpAwQ/Mn376CbVr18bWrVvlH8sAbHo+q2xjx47FokWL5Hhee+01zJkzB97e3qUuFx8fjx07diA7OxuxsbEO1TZx1fW9sGfP9PR0HDlyBNu3b7dZdurUqQ4l4QCgbdu28rUyKysLp0+fLvMxlQEDBpTYQ+E999yDhQsX4qOPPsKqVauQmJiIO++8E35+frh27Ro+++wzm0RP586dHapZMGHCBCxZskS+67pgwQJ8++23aN++PbKzs/HDDz8gODhYTlbPmTMH48aNA1DwQ6ZNmzY2vTcWatiwIfr161fm9l2lb9++mDRpktx7YyGNRlPqXelC06dPx5o1a5Cbm4ubN2+iRYsWGDZsGKKiopCZmYmTJ09i165dSE1NlW8UAAXfbW+88QaAgjvt69evx7FjxzBt2jQABQnShISEYsf9t99+k39c6HQ6OQFUXRXu95SUFGzfvl3uucvb21u+edKoUSO5xsEXX3yBKVOmIDw8HJs3b5ZriRU1btw4LF68GLm5uQAKyin3338/AOC///2v3XjKezwTExNv67HTRo0ayb3nvv3221CpVPD09MT69evLfDxv4sSJctLL+rNctJY3AMybN0++EbZ//37ccccduOeeexAQEIAbN27gyJEj2LdvH2rVqmW3V7iShIaGwt/fX/7eeu6553Dt2jWYTCa88847dh+TeuCBB+SkV2pqKtq1a4fhw4fj/PnzeP/99+1ur6LeR2UIDAzEpEmT5Frgy5Ytw88//4wOHTrAw8MDFy9exA8//IAjR45g/Pjx6NWrl7xs7dq18ccffwAoqA1uMBjg4+OD2NhYDBo06LZjCwgIwIMPPij3HHv48GE0adIEAwcOhL+/P06fPo0tW7bg8uXLcvlu0aJFLk2uT5o0CXv37pV7byz8zQAUNGPgjAkTJuC5557D9evXYTKZ0LFjRwwbNgz169dHXl4eTp06hV27dsmJ8bp16zq9D8aPH49nn31WTlwPGTIEkyZNgq+vLzZu3GjTC+Gjjz7qmp3kgLfffhspKSm444478NVXX8k3WADYPDVQtBZbx44dkZCQgMOHD8vnWkkc+U1Xnutpu3btEB8fL8e7ZMkSnDt3DjExMdi8eXOxHtSpBlCgHbEaq2iDwfZe1j1lCOGahuyLNgxYWiPvr7zySqmNoPr5+RVb34kTJ4Rery82r8FgEImJiXYbCzx16pRNQ7D2XkUbdx40aFCJ81n3ZGTdCKv1Kz4+Xp7n6tWrokWLFmVu315Dn0X17t27xOWLNrZt/X7K2/ijq7dV6Pjx48XW+eWXXxabb8qUKaXuM5VKZdN7mr2G7Iv2jlXS67XXXnNo/5fVkL0Q5Tu/y+oUwdm4SmqsvOixLumc++6772x63Cnr82Ldo5Wvr69NQ7bWxo0bJ8+n1WpFSkpKme/p5MmTNo2Xl/SaMWOGzTKlXcuEEGXuo5IUPd+LWrlypc30F154ocRlCxuyL01pDdmX9/ruaCcHGo1GPPvssyU20F0a63O3aO9ezsRdeN7PnDmzzHljYmJsOikoy48//lhq4//WPVUJIcTs2bNL3X5ERIQ4fvy4zTKlNdxs73NX2vXX+txp0qSJ3e8x6/Ot6PEoei3ZsmWL8PLyKnP/Fl7XLl++bNP5RmEPXBaLRXTv3l0e37JlS5GXl2ezLetOLKw7t3Bkf9njiobsHe0IR4iSrxeZmZk2jR7rdDqxfft2IYQQe/futelNrfDl7e0tBg8ebHc7q1evLvE4REdH23QSYt0YtBDOH09X+OCDD0rcRq1atWx6aLb+DrZm/X4K99/169dLnHfBggVlvjdHjllR//73v0tcV9OmTUWrVq3sfn5GjhxZ4nJ9+vSx+btoJynleR/2yjNlcfV1KCsry+azbu9VdF9Z90Zo/erXr59DsRYqrcyVk5NTrBOeoq9bt245vO/KYn2tCAsLszlXrF+PPPKIzXKOHsv9+/fb9Phn72V9fJzdB7t37xb+/v5251WpVOLFF1+0+75LO0+LTrP3XVT0mNrrVKJVq1Y2vScLIUTnzp1LnLfoPihaxnbkN115rqcHDx4scRmtVis6dOhgd9+Qe7q95xzIKY8++ig2b96MRx55BG3btkWdOnVgMBig0+lQu3ZtDBgwAJ988olNWzJKxXnw4EGMGzcO0dHR0Ol0MBgMiIuLw6xZs3Ds2DEkJibaLNOkSRN888036Ny5MwwGA3x9fXHPPffg4MGDSEhIsLuthg0b4ujRo1i2bBk6dOiAgIAAqNVq+Pj44I477sDkyZOxZcuWYo9xvf322xg/fjzCwsLsPq4zYMAArFy5EnFxcSXWXAAK7ioePHgQq1evRteuXREcHAy1Wg0vLy80btwYY8eOxYYNGxxuY+OTTz7Bo48+ilq1akGn06F+/fpYunRpqXeEy6uithUfH4+uXbvKf0dERNjcISx0//334/HHH8fdd9+NqKgoeHh4QKfTISoqCsOGDcPu3bsxcODAMrc3cOBAPPPMM+jevTtiYmLg6ekJjUaDWrVqoV+/fvj8888xffr023pP1spzflcVXbp0wZkzZ7BmzRr069cPtWvXlvd7dHQ0hg0bhk2bNmHEiBG4ePGiXIsRKGjA3t5jhtZ38o1GIzZs2FBmLHFxcfj111+xaNEi3HnnnfD29paP26BBg7Bjxw68+uqrt/+mb9P9999v02Dpyy+/XKy9CVdw5fW98BpYt25ddOvWDYsXL8a5c+fw1FNPOf144qRJk+ThzZs3O/2+iho9ejRmzJiBNm3ayNcevV6PiIgI9OzZE6+99hpOnDiBBg0aOLzONm3a4MSJE1i8eDHatGkDX19faDQahIaGomvXrsVqWLz00kv4+uuvMWTIEERERECr1cLb2xstWrTA008/jaNHj7qk4wtHhYSE4IcffsCkSZMQGhoKvV6PFi1aYMOGDXjsscccXs/AgQNx/PhxzJ49G82aNYO3tzfUajWCgoLQvn17zJs3D/v370dMTAyEEJg4caJceycuLg5LliwBUFATYO3atXINiiNHjuCJJ56w2Zb1uWB9jgAFjz5aPybnysfvKpqXlxe2bdsm14bOz8/H4MGDsW/fPnTq1Ak7duxAhw4doNfr4efnh759++LAgQNo1qyZ3XU+9NBD+PTTT9G6dWvo9XoEBwdj3Lhx+P77720agC5aI9mZ4+kqI0eOxMcff4zmzZtDq9UiKCgII0aMwA8//FBqe0+Fitbquueee+x2mrF06VLs378fY8eORd26daHX66HValG7dm307NkTS5cuxbfffuv0e3j88cfxxhtvoGHDhtBqtQgPD8cDDzyA3bt3l1pLd/369fj3v/+N2NhYaLVaxMTE4Omnn8bq1att5it6nMrzPqzbDVPy8+Hp6YkdO3Zg48aN6Nu3L8LCwqDRaGAwGBAbG4uhQ4firbfeKtZBzdSpU7Fo0SLUq1ev3G09lcXDwwNffPEFPv74Y/Tv3x/h4eHQarXw9fVFs2bNMHPmzApr+sDDwwM7d+7ErFmzEBkZCZ1Oh0aNGuHVV1/FypUry7XODh064MSJE3j66afRqlUr+Pr6Qq1Ww9/fH61atcK0adPw9ddf4+6777aJw5l9cPfdd+P48eOYM2cO4uPj4enpCZ1Ohzp16mDMmDE4cOCA3O5uZXn99dexcuVKNGnSBHq9HrVq1cLMmTPx3XffFWsH+fPPP8fkyZMREhICvV6PO+64A//5z3/K3OeO/KYrz/W0bdu22L9/P/r06QNvb294e3ujW7du2LVrF3r06HHb+4aqF0mIKth9DxEp6qGHHpIfcZw/fz6ef/55hSMiovLIyclBVFQUbty4Aa1Wi8uXLyve86M7qApt+pRXSkoKIiIiYDKZEBwcjAsXLtj0Gvzjjz/KP+QHDBiAzz77TKlQq4ScnJwSO7n55Zdf0Lp1a7lNpQ0bNpTYziJVDnvHaeXKlTY3zi5evOhQAtAes9mMgIAAZGRkoHbt2vj999/LfGSeKt6iRYvkhvCjo6PttrdHpdu1a5fcbiFQ8KizKxPzREphm15EBKCgfZyzZ8/i5MmT8o85jUZTZrf3RFR1GQwGLFiwAHPnzoXRaMTq1avx1FNPKR0WKeiNN96Q24l74oknbBJeAOSecg0GQ5Woram0t956C+vXr8fQoUMRGxsLtVqN48eP4/XXX5cTXpGRkS5pC4nKb9y4ccjLy0PPnj0RHR2NrKws7N2716b2e2Ht0Ntx+PBhZGRkACiodcqEFxFR1cekFxEBKGjMtGh30bNmzeIdHqJqbtq0aXjttddw/vx5uTH/kmpEkPvLycmRG76vU6cOHnnkkWLzFCa9FixYwOs/ACEEDh06hEOHDpU4PSwsDJ999hk/UwozmUz44osv8MUXX5Q4vW3btnKj77ej8PPRrVs3jBgx4rbXR0REFY9JLyKyodFoEBMTg8mTJzvclhkRVV16vd6mR02quQwGQ5m9+NlLGtRUiYmJmDBhAg4cOICrV68iMzMTvr6+aNy4Mfr164eHH34YgYGBSodZ440fPx6SJOHw4cO4fv06jEYjgoKC0KJFCwwfPhzjxo1zSftVc+fOxdy5c10QMRERVRa26UVERERERERERG6HvTcSEREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeRERERERERETkdpj0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI7THoREREREREREZHbYdKLiIiIiIiIiIjcDpNeVGXs2rULkiRh8+bNSoeCdevWQZIknDt3Th6XmJiIxMRE+e+qFG91VXSfVqQzZ86gZ8+e8PPzgyRJ2Lp1a6Vst6jExEQ0bdpUkW272oQJExATE6N0GEREdJuqUpmGZbDKwTJY9cYyGJHjmPSiCiVJkkOvXbt2KR2q21q1ahXWrVtXbPzJkyexaNEim0JldZGdnY1FixY5dd6MHz8ex44dw5IlS7B+/Xq0bt26wuK7dOkSFi1ahF9++aXCtlGWTz/9FCNGjEC9evXg6emJRo0aYc6cOUhNTVUsJmfZO3eJiKhsLIMpj2WwAjWtDLZlyxb06tULERER0Ov1iIyMxNChQ3H8+HHFYnIWy2DkTjRKB0Dubf369TZ/v/fee/j666+LjY+Li8Nvv/1WmaGVaty4cRg5ciT0er3Sody2VatWITg4GBMmTLAZf/LkSSxevBiJiYnV7k5RdnY2Fi9eDAAO3aXMycnB999/jyeffBLTpk2r4OgKClyLFy9GTEwMWrRoUeHbK8mDDz6IiIgIjB07FnXq1MGxY8ewcuVKbNu2DYcPH4bBYFAkLmfYO3eJiKhsLIMpj2WwmlkGO3bsGAICAjBz5kwEBwfjypUreOedd9C2bVt8//33aN68uSJxOYNlMHInTHpRhRo7dqzN3z/88AO+/vrrYuMBVKkCl1qthlqtVjoMcpGUlBQAgL+/v8vWmZWVBS8vL5etz9U2b95crDDaqlUrjB8/Hhs2bMDkyZOVCUxhJpMJFosFOp1O6VCIiCoUy2BUFdTEMtgzzzxTbNzkyZMRGRmJ1atXY82aNQpEpTyWwUgpfLyRqhyLxYIlS5YgMjISHh4e6NatG/74449i8x08eBC9e/eGn58fPD09kZCQgP379zu0jddffx3x8fHw9PREQEAAWrdujY0bN8rTS2pP4nbj3bRpE1q1agWDwYDg4GCMHTsWFy9etJnHXvsKJT23b7FYsGLFCsTHx8PDwwNhYWGYMmUKbt26Jc8TExODEydOYPfu3fJjDImJiVi3bh2GDRsGAOjSpUuJjzh89dVX6Ny5M7y8vODj44N+/frhxIkTZe6Pwn23Z88eTJkyBUFBQfD19cV9991nE5s9165dw/3334+wsDB4eHigefPmePfdd+Xp586dQ0hICABg8eLFcuyLFi0qcX2LFi1CdHQ0AGDevHmQJMlmXx45cgR9+vSBr68vvL290a1bN/zwww8lvqfdu3fjkUceQWhoKCIjI0vc3q5du9CmTRsAwMSJE+X4ilYRP3nyJLp06QJPT0/Url0by5YtK7auvLw8LFy4EPXr14der0dUVBQee+wx5OXllboPgZLvvg4aNAiA4z9uvvrqKyQkJMDHxwe+vr5o06aNzeekqMI2Voo+8nDu3Lli++DKlSuYOHEiIiMjodfrUatWLdx7773yZ87euVsoNTUVjz76KKKioqDX61G/fn288MILsFgsxbb74osvYsWKFYiNjYVer8fJkycBlH0dICKqaVgGSyy2LMtgLIM5WwYrSWhoKDw9PR1uZoJlMCLXYU0vqnL+/e9/Q6VSYe7cuUhLS8OyZcswZswYHDx4UJ7nu+++Q58+fdCqVSssXLgQKpUKa9euRdeuXbF37160bdvW7vrffvttzJgxA0OHDsXMmTORm5uLo0eP4uDBgxg9enSFxLtu3TpMnDgRbdq0wfPPP4+rV6/i1Vdfxf79+3HkyJFy3f2aMmWKvN4ZM2YgKSkJK1euxJEjR7B//35otVqsWLEC06dPh7e3N5588kkAQFhYGGJjYzFjxgy89tpreOKJJxAXFwcA8v/r16/H+PHj0atXL7zwwgvIzs7G6tWr0alTJxw5csShqvjTpk2Dv78/Fi1ahFOnTmH16tVITk6Wv5RLkpOTg8TERPzxxx+YNm0a6tati02bNmHChAlITU3FzJkzERISgtWrV+Phhx/GoEGDMHjwYADAHXfcUeI6Bw8eDH9/f8yaNQujRo1C37594e3tDQA4ceIEOnfuDF9fXzz22GPQarV48803kZiYiN27d6Ndu3Y263rkkUcQEhKCZ555BllZWSVuLy4uDv/617/wzDPP4MEHH0Tnzp0BAB06dJDnuXXrFnr37o3Bgwdj+PDh2Lx5Mx5//HE0a9YMffr0AVBQoB4wYAD27duHBx98EHFxcTh27BheeeUVnD59ulyNwF65cgUAEBwcXOa869atw6RJkxAfH48FCxbA398fR44cwfbt28v1OSlqyJAhOHHiBKZPn46YmBhcu3YNX3/9Nc6fP4+YmBi75y5Q8GhFQkICLl68iClTpqBOnTo4cOAAFixYgMuXL2PFihU221q7di1yc3Px4IMPQq/XIzAw0OXXASIid8AymGNYBmMZzBGpqakwGo24cuUKVqxYgfT0dHTr1q3M5VgGI3IxQVSJpk6dKuyddjt37hQARFxcnMjLy5PHv/rqqwKAOHbsmBBCCIvFIho0aCB69eolLBaLPF92draoW7eu6NGjR6kx3HvvvSI+Pr7UedauXSsAiKSkJHlcQkKCSEhIcDre/Px8ERoaKpo2bSpycnLk+b744gsBQDzzzDN2t1Fo/PjxIjo6Wv577969AoDYsGGDzXzbt28vNj4+Pr7EdW7atEkAEDt37rQZn5GRIfz9/cUDDzxgM/7KlSvCz8+v2PiiCvddq1atRH5+vjx+2bJlAoD47LPP7L7fFStWCADi/fffl8fl5+eL9u3bC29vb5Geni6EECIlJUUAEAsXLiw1lkJJSUkCgFi+fLnN+IEDBwqdTif+/PNPedylS5eEj4+PuPvuu4u9p06dOgmTyVTm9n766ScBQKxdu7bYtISEBAFAvPfee/K4vLw8ER4eLoYMGSKPW79+vVCpVGLv3r02y69Zs0YAEPv37y8zjqLuv/9+oVarxenTp0udLzU1Vfj4+Ih27drZnLNCCJvPXNHzsvAzUfScKtz/hfvj1q1bJR6Pouydu88++6zw8vIq9j7mz58v1Gq1OH/+vM12fX19xbVr12zmdeQ6QETkTlgGYxmMZbDKLYM1atRIABAAhLe3t3jqqaeE2WwudRmWwYhcj483UpUzceJEm2e9C+/SnD17FgDwyy+/4MyZMxg9ejRu3LiB69ev4/r168jKykK3bt2wZ88em+q1Rfn7++Ovv/7CTz/9VCnx/vzzz7h27RoeeeQReHh4yPP169cPjRs3xpdffun0Njdt2gQ/Pz/06NFDfv/Xr19Hq1at4O3tjZ07d5b7/Xz99ddITU3FqFGjbNatVqvRrl07h9f94IMPQqvVyn8//PDD0Gg02LZtm91ltm3bhvDwcIwaNUoep9VqMWPGDGRmZmL37t3lfl9Fmc1m/O9//8PAgQNRr149eXytWrUwevRo7Nu3D+np6TbLPPDAAy5pZ8Tb29umTRWdToe2bdvK5wxQcIzj4uLQuHFjm+PQtWtXAHD6GG/cuBH//e9/MWfOHDRo0KDUeb/++mtkZGRg/vz5NucsALt3iJ1hMBig0+mwa9cuhx63KGrTpk3o3LkzAgICbPZN9+7dYTabsWfPHpv5hwwZIj+OUcjV1wEiInfAMljZWAa7fTWlDLZ27Vps374dq1atQlxcHHJycmA2m0tdhmUwItfj441U5dSpU8fm74CAAACQL8xnzpwBUND9sT1paWnyckU9/vjj+Oabb9C2bVvUr18fPXv2xOjRo9GxY8cKiTc5ORkA0KhRo2LLNm7cGPv27XN6m2fOnEFaWhpCQ0NLnH7t2jWn12m9bgDyF3tRvr6+Dq2naGLF29sbtWrVKrWNjuTkZDRo0AAqlW0+vrDKf+G+dIWUlBRkZ2eXeFzi4uJgsVhw4cIFxMfHy+Pr1q3rkm1HRkYWK7gEBATg6NGj8t9nzpzBb7/9VqygUMiZY7x3717cf//96NWrF5YsWVLm/H/++ScAoGnTpg5vwxl6vR4vvPAC5syZg7CwMNx1113o378/7rvvPoSHh5e5/JkzZ3D06FGH901Jx83V1wEiInfAMljZWAa7fTWlDNa+fXt5eOTIkfK+fPHFF+0uwzIYkesx6UVVjr27OEIIAJDvIC5fvtxuV8SF7QWUJC4uDqdOncIXX3yB7du345NPPsGqVavwzDPPyF0wuzJeZ0iSVOJyRe8KWSwWhIaGYsOGDSWux94XkSMK9+/69etL/PLTaGruZcNgMLhkPY6cMxaLBc2aNcPLL79c4rxRUVEObevXX3/FgAED0LRpU2zevLlCj5+9O5Al3dV89NFHcc8992Dr1q3YsWMHnn76aTz//PP47rvv0LJly1K3Y7FY0KNHDzz22GMlTm/YsKHN3yUdN1dfB4iI3AHLYCyDVVXVsQxmLSAgAF27dsWGDRtKTXqVF8tgRPbV3CsnVVuxsbEACu52de/evVzr8PLywogRIzBixAjk5+dj8ODBWLJkCRYsWFCsKvHtKuy15tSpU8Xu3J06dUqeDhR8IVpXry5U9O5abGwsvvnmG3Ts2LHMQoC9L0F74wv3b2hoaLn3L1BwJ6hLly7y35mZmbh8+TL69u1rd5no6GgcPXoUFovF5k7j77//Lk8vLXZnhISEwNPTE6dOnSo27ffff4dKpSpXoQZwTXyxsbH49ddf0a1bt3Kv788//0Tv3r0RGhqKbdu2lfpDpOi2AeD48eOoX7++w9srvMNetGcie3eHY2NjMWfOHMyZMwdnzpxBixYt8NJLL+H9998HUPo5mpmZeVvnJ1C51wEiInfAMhjLYCyDlV9OTg7S0tLK3DbAMhiRK7FNL6p2WrVqhdjYWLz44ovIzMwsNj0lJaXU5W/cuGHzt06nQ5MmTSCEgNFodGmsANC6dWuEhoZizZo1Nt0cf/XVV/jtt9/Qr18/eVxsbCx+//13m/fw66+/FusGfPjw4TCbzXj22WeLbc9kMtl84Xl5eZXYPbKXlxeA4l+OvXr1gq+vL5YuXVri/ihr/xZ66623bJZfvXo1TCaT3DNOSfr27YsrV67go48+snk/r7/+Ory9vZGQkAAA8PT0LDF2Z6jVavTs2ROfffaZTXX/q1evYuPGjejUqZPDjxEUZW/fOmP48OG4ePEi3n777WLTcnJy7PZcVOjKlSvo2bMnVCoVduzY4dSd5549e8LHxwfPP/88cnNzbaaVdvc8OjoaarW6WHsOq1atsvk7Ozu72HpjY2Ph4+Nj8xmxd+4OHz4c33//PXbs2FFsWmpqKkwmk90YCzlyHcjOzsbvv/+O69evl7k+IqKagGUwlsFYBiu7DFbS44/nzp3Dt99+i9atW5e6LMtgLIOR67GmF1U7KpUK//nPf9CnTx/Ex8dj4sSJqF27Ni5evIidO3fC19cX//d//2d3+Z49eyI8PBwdO3ZEWFgYfvvtN6xcuRL9+vWDj4+Py+PVarV44YUXMHHiRCQkJGDUqFFyd9kxMTGYNWuWPO+kSZPw8ssvo1evXrj//vtx7do1rFmzBvHx8TYNeiYkJGDKlCl4/vnn8csvv6Bnz57QarU4c+YMNm3ahFdffRVDhw4FUFBAXb16NZ577jnUr18foaGh6Nq1K1q0aAG1Wo0XXngBaWlp0Ov16Nq1K0JDQ7F69WqMGzcOd955J0aOHImQkBCcP38eX375JTp27IiVK1eW+b7z8/PRrVs3DB8+HKdOncKqVavQqVMnDBgwwO4yDz74IN58801MmDABhw4dQkxMDDZv3oz9+/djxYoV8vExGAxo0qQJPvroIzRs2BCBgYFo2rSp0+0fPPfcc/j666/RqVMnPPLII9BoNHjzzTeRl5eHZcuWObUua7GxsfD398eaNWvg4+MDLy8vtGvXzqn2KMaNG4ePP/4YDz30EHbu3ImOHTvCbDbj999/x8cff4wdO3aUWnDq3bs3zp49i8ceewz79u2zabckLCwMPXr0sLusr68vXnnlFUyePBlt2rTB6NGjERAQgF9//RXZ2dl49913S1zOz88Pw4YNw+uvvw5JkhAbG4svvviiWOHv9OnT8rnRpEkTaDQabNmyBVevXsXIkSPl+eydu/PmzcPnn3+O/v37Y8KECWjVqhWysrJw7NgxbN68GefOnUNwcHCp+9eR68CPP/6ILl26YOHChVi0aFGp6yMiqglYBmMZjGWwsstgzZo1Q7du3dCiRQsEBATgzJkz+O9//wuj0Yh///vfpW6bZTCWwagCKNFlJNVcjnSXvWnTJpvxRbvaLXTkyBExePBgERQUJPR6vYiOjhbDhw8X3377bakxvPnmm+Luu++Wl4uNjRXz5s0TaWlp8jzOdJftaLwfffSRaNmypdDr9SIwMFCMGTNG/PXXX8Xie//990W9evWETqcTLVq0EDt27CjWLXGht956S7Rq1UoYDAbh4+MjmjVrJh577DFx6dIleZ4rV66Ifv36CR8fHwHA5j28/fbbol69ekKtVhfr5njnzp2iV69ews/PT3h4eIjY2FgxYcIE8fPPP5e6fwv33e7du8WDDz4oAgIChLe3txgzZoy4ceOGzbwldQ9+9epVMXHiRBEcHCx0Op1o1qxZid1OHzhwQLRq1UrodLoyu8621122EEIcPnxY9OrVS3h7ewtPT0/RpUsXceDAgRLf008//VTqe7f22WefiSZNmgiNRmNzPiQkJJTYTXNJxzg/P1+88MILIj4+Xuj1ehEQECBatWolFi9ebHO+lgR/d5Fd0qukLqhL8vnnn4sOHToIg8EgfH19Rdu2bcUHH3xQaswpKSliyJAhwtPTUwQEBIgpU6aI48eP2+yD69evi6lTp4rGjRsLLy8v4efnJ9q1ayc+/vhjm3WVdu5mZGSIBQsWiPr16wudTieCg4NFhw4dxIsvvih3017acXfkOlD4GXe0W3YioqqMZTCWwayxDPYPV5fBFi5cKFq3bi0CAgKERqMRERERYuTIkeLo0aMOvweWwVgGI9eRhChHS49ERHasW7cOEydOxE8//VRmFW4iIiIicg2WwYiIimObXkRERERERERE5HaY9CIiIiIiIiIiIrfDpBcREREREREREbkdtulFRERERERERERuhzW9iIiIiIiIiIjI7TDpRUREREREREREbkejxEYtFgsuXboEHx8fSJKkRAhERERExQghkJGRgYiICKhU7nVvkOUvIiIiqqoqqgymSNLr0qVLiIqKUmLTRERERGW6cOECIiMjlQ7DpVj+IiIioqrO1WUwRZJePj4+AArejK+vrxIhkKtlZQEREQXDly4BXl7KxkNERFQO6enpiIqKkssq7oTlr0rGshEREZHDKqoMpkjSq7BKva+vLwtd7kKt/mfY15cFOyIiqtbc8fE/lr8qGctGRERETnN1GUyRpBe5IZUKSEj4Z5iIiIioJmPZiIiISHFMepFrGAzArl1KR0FERERUNbBsREREpDjediIiIiIiIiIiIrfDpBcREREREREREbkdJr3INbKygJCQgldWltLREBERESmLZSMiIiLFsU0vcp3r15WOgIiIiKjqYNmIiIhIUazpRUREREREREREbodJLyIiIiIiIiIicjtMehERERERERERkdth0ouIiIiIiIiIiNwOk15EREREREREROR22HsjuYZKBbRu/c8wERERUU3GshEREZHimPQi1zAYgJ9+UjoKIiIioqqBZSMiIiLF8bYTERERERERERG5HSa9iIiIiIiIiIjI7TDpRa6RnQ3ExBS8srOVjoaIiIhIWSwbERERKY5tepFrCAEkJ/8zTERERFSTsWxERESkONb0IiIiIiIiIiIit8OkFxERERERERERuR0mvYiIiIiIiIiIyO0w6UVERERERERERG6HSS8iIiIiIiIiInI77L2RXEOSgCZN/hkmIiIiqiAWkYss8RMAs9Kh2Cdy4dkkGgCQLfYCFg+FA7JHBS+pLVRSVY2PiIio/Jj0Itfw9AROnFA6CiIiIqoBMsUBXDQ/BovIUjoU+3QAjhj+/mMWYFIyGPvUkh9qq5fBR7pb6VCIiIhcjkmv22C0GHEw/SCMFqPSoVA1IEkSWvm0go/GR+lQiIiIqjkzzCITetQDwBrm5SOQh7PQIRZe0l1KB0NERFQhmPS6Db9k/oLZZ2Yjw5ShdChUDYRoQ/BU3afQM6in0qEQERG5CRUkNqvgFIvIgxmpMCMdaskHoerpUEk6pcMiIiKqEEx63QazMCPLnIU6HnWgkWr2rtTnmPHK2P0AgFnvd0SeQa1wRFWDRVhwLvccfDW+eKj2Q+gR2EPpkIiIiKgSSNlmRLbfCwD46/vOEJ7KlI2EEBDIgQm3YEEWJGihkYLhK/WCj6oLvKR2isRFRERUGWp2psZFVFBBJdXsjjBVkgXRZzP/HpZq/P4QQiDVlIpr+dcQ4xGDRfUWoYN/B6XDIiIiosoiBPS/ZcrDSjCKKzAjHSp4QCuFw1saDm9VB3hKraCWvBSJiYiIqDIx6UXkQkII3DDewE3TTfiofTAgZACmRU5DjCFG6dCIiIioBjGLDFiQh2DVZPiousJTugOSpFU6LCIiokrFpBfRbRJCwCiMSDWlItWYCj+tH0aEjsCo8FGI84pTOjwiIiKqYYQQMOIKfFXdEaaey3bPiIioxnL7pNeePXuwfPlyHDp0CJcvX8aWLVswcOBApcOiasokTMgx5yDHkoNsczaMoqDnTq2khY/aBxNqTcCI8BGoZ6incKRERERUE5lFOoy4Ao0UghDVw0x4ERFRjeb2Sa+srCw0b94ckyZNwuDBg5UOh6oJi7Ag15KLHEuOnOSS/v5nUBvgpfZCvFc8Gns2RowhBjEeMYj1jEWQNkjp0ImIiKgGsogcGHEREvTwVfVCiPphGCTWOCcioprN7ZNeffr0QZ8+fZQOg6o4szAj3ZSOVFMqzMIMANCr9DCoDIg1xCLOKw6xhlhEe0QjxhCDSH0ktCq2i0FERETKM4prsCADnlJrhKinwEvqyBpeREREqAFJL6osEq7WMsjD1UG+JR+pplSkm9IBAD4aH7T2aY02vm0QY4gpSHB5xMBb461wpERERFTtSBKM0QZ5uKIIYYIZaQhWPYgw9QxIEov3REREhfitSC6RZ1Bj8lfdlA6jVEIIZFuykWpKRbY5G2pJjUBNIPoH90dH/45o69sWEfoIpcMkIiIiNyA81Ug+U/FlIyMuQSfVQYj6fia8iIiIiuA3I7k9IQTSTGlIMaZAL+kRoY9AYmgi2vm1QyufVvDR+CgdIhEREZHTTOIGBCwIUT8IteSndDhERERVDpNe5LaEELhpuokbxhvwVnujd1BvjAobhVY+raBR8dQnIiKi6sss0mBGKoJU98NfGqJ0OERERFUSf/mTS+hyzfj3pAMAgPnvdEC+h1qxWCzCguvG60g1psJP44ehoUMxKmwU4r3i2agrERERVQopx4zaXQvKRhe/6wBhcF3ZSAgzjLiCANUIhKlnsnxDRERkh9snvTIzM/HHH3/IfyclJeGXX35BYGAg6tSpo2Bk7kUSAg1OpsnDSjBajLiafxXZ5mwEaYMwvtZ4DA8bjvqe9RWJh4iIiGowi4DHoTR52JXMSIVGCkSoeirb8SIiIiqF239L/vzzz+jSpYv89+zZswEA48ePx7p16xSKilwpx5yDK/lXYBZmROojMbT2UAwIGYBa+lpKh0ZERETkUkKYYUIK/KT+0ErhSodDRERUpbl90isxMRFCoZpHVLEswoK/8v6CSZgQ5xmHkWEj0TuoN7w13kqHRkRERORyZpEBIy5BL8UiSDVe6XCIiIiqPLdPepF7yrXk4nzOeYTrwzGrziz0D+rPxumJiIjILQkhYMQlCOTBT9UP4er50EphSodFRERU5TFLQNWKRVhw03gTN0030cavDRbWXYgGng2UDouIiIiowliQDQEzwtXzEagaDUlSrsMgIiKi6oRJL6oWTMKElPwUZJgy4Kf1w8TwiZheZzq81F5Kh0ZERERUoQRyoJa8EaAayoQXERGRE5j0IpdJC9C5fJ15ljxczb+KPEsewnRhGBM+BoNCBiHGEOPybRERERG5kjnYNWUjC/KgQzRUksEl6yMiIqopmPQil8gzaDB2Z0+XrU8Igb/y/kK+JR/1DPUwPGw4+gf3R6A20GXbICIiIqoowkuDpEuuKhuZoZZ8XLQuIiKimoNJL6py8i35SM5NRrA2GDNiZmBA8AB4qD2UDouIiIhIEQICElxfo56IiMjdMelFVYYQAqmmVFzLv4bm3s2xuN5iNPFuonRYRERERIoSMEKDIKXDICIiqnaY9CKX0OWasWjqQQDAojfaId/DsUZWhRDIMGfgpvEmjMIIb7U3hoQOwfyY+fDT+FVkyEREREQVRsoxI+KegrLRpf9rB2G4vQbo9VI9V4RFRERUozDpRS4hCYFmh27Kw6URQiDLnIWbppvIteTCS+WFOK849A7qjc7+nVHfUB+SJFVG2EREREQVwyJg2HNTHi4vIQQAAa0U4Zq4iIiIahAmvahSCCGQZcnCLeMt5Fhy4KnyRKwhFr0Ce+HugLvR2LMxE11ERERExQhIkKAGG7InIiJyFpNeVGGKJroMKgPqeNRBz8CeuNv/bjT1bgqVpFI6TCIiIqIqSQgBE64D0EAFg9LhEBERVTtMepHLZZgycDU3s1iiq7N/ZzT1bgq1dHttWhARERG5MyEEzLgFE65DLfkjUBoNg9RS6bCIiIiqHSa9yCFCCBiFEfmWfOSLfJv/AcCQY5HnzTJnoY5PQaKrk38nNPNuxkQXERERkQPMIg1GXIVa8kWANBzB6gnQS7FKh0VERFQtMelFZSa0AEBAQCtpoVPpoJN08Nf4o5a+FiL1kQjVhSLc5ANgMgBgQ/wGRAY1YqKLiIiIyEEWkYN8XIQKHvBT9UWwajIMqnilwyIiIqrWmPSqoS7lXUK2ORsSJFhgsUlo+Wn8UEtfC7X1tRGuC0ewNhjBuuCC//9+eam9bBuez8oCPGcAAKIN0QATXkRERFTDWTzLLg8JYUQ+LgKwwEtqhRD1I/CS2rODHyIiIhdg0qsGMlqMyDHnYFjoMDTwbIAQXQiCtEEI0Rb87632dr6g5eVVkPgiIiIiIggvDc6m9ilzvnwkQyfVRbD6AfhL/SFJ2kqIjoiIqGZg0ssFckUuNJbqsytvGm8iVBeKedHz4KNh99dEREREShDCDAEgTD0LvqoeSodDRETkdqpPpqYK0qv08FP74Wb+TaVDcY4EdAnowoQXERERkYIsyIYKntBL9ZQOhYiIyC0x6XUbmns3xztN3oFRGJUOxWkxHjGuXWFuLjBkSMHwJ58AHh6uXT8RERFRNSLlmhE+4hAA4MpHrSA8irfvJZAHleQBLaIqOzwiIqIagUmv26CSVGjs1VjpMKoGsxnYtu2fYSIiIqKazCzg9dU1ebgkFuRChxioJF0lBkZERFRzqJQOgIiIiIioJhIwQiuFKx0GERGR22LSi4iIiIhIEQIq6JUOgoiIyG0x6UVEREREVMmEMMGCPEjwVDoUIiIit8U2vYiIiIiIKpFF5CIf52GQ4hCkHqd0OERERG6LSS8iIiIiokpiFmkw4iq8Ve0RoV4KnRShdEhERERui0kvIiIiIqIKJoQFRlyAgECAagjC1U9ALXkrHRYREZFbY9KLXMPLCxAld8dNREREVNMILw3+yO8PALCIbOTjL2ilCISpZ8FPugeSJCkcIRERkftj0ouIiIiIqIIIIZCPv+CjSkAt9VPQSXWUDomIiKjGYO+NREREREQVxIIsqOCJUNVMJryIiIgqGZNe5Bq5ucCwYQWv3FyloyEiIiJSlJRrRvjIQ6g16gj0ebXgITVWOiQiIqIah0kvcg2zGdi8ueBlNisdDREREZGyzALen16G76dp8LK0hSSplY6IiIioxmHSi4iIiIioAulVDZQOgYiIqEZi0ouIiIiIqAIZpGZKh0BERFQjMelFRERERORiQgh52EOKVTASIiKimkujdABERERERO5ECAEjLigdBhERUY3Hml5ERERERC4ihBn5+BNqyVfpUIiIiGo8Jr2IiIiIiFxACIE8JEEr1UGU+kWlwyEiIqrx+HgjuYanJ5CZ+c8wERERUY1jgQQJ4eq58PTuwLIRERGRwpj0IteQJMDLS+koiIiIiBQkAEhQwYtlIyIioiqAjzcSEREREblEYY+NLGITERFVBfxGJtfIywMmTCh45eUpHQ0RERGRQiRIkFg2IiIiqgKY9CLXMJmAd98teJlMSkdDREREVOkELAAkACqWjYiIiKoAJr2IiIiIiFzAjFRopAB4SI2UDoWIiIjApBcRERERkUuYkQ4fqRvUkq/SoRARERHYe+NtM5lMEEKUPaO7MxqhlQeNgNGoaDgVRa1WQ6VirpiIiIhK5qlqqXQIRERE9DcmvW7D6dOn8cgjjyAnJ0fpUBTnYTbj27+He/fujVy1WtF4KoJKpUJkZCTef/99qN3w/REREVH5FdwEFZCgUzoUIiIi+huTXrfh5s2buHz5Mry8vGp8EsRisdgMWyRJwWhcIysrCzdu3IDBYEBwcDDat2+PQYMG1fhjTURERCURKOi5UVvmnERERFQ5mPRygaCgIGg0NXtX6q16JQoKCkJeNd4f169fx82bN2EwGNCxY0cMGDAA3bp1Q61atZQOjYiIiKosAQkq1vQiIiKqQqpvZoKqlDy1GmN69JCHqyuj0YjU1FSMGDECAwcORIsWLdiGFxEREZVJwAxABVVh0svTE7h27Z9hIiIiqnRMepFrSBLS9Xqlo7htGRkZ8PPzw4wZMxAcHKx0OERERFRNmHELGikAeqlBwQhJAkJClA2KiIiohmMVFqK/ZWdn49q1a6hbty4TXkREROQwIQTMSIeP1B0aKVDpcIiIiOhvrOlFLqExmzH55EkAwH+aNIGpGj3imJubi0uXLkGlUqF9+/aYPXu20iERERFRNWJBOlTwhJ+q/z8j8/KAwjLFyy8DblAjnoiIqLph0otcQi0E+iUnAwDWxsXBVMb8VYHRaMTFixdhsVjQpEkT3H///ejevXuN75SAiIiIHCeEgBFX4afqC0/pzn8mmEzAqlUFw8uWMelFRESkAP66pxopOzsbFy5cQKNGjTBp0iT069cPOh17WyIiIiLnFNTy8kaw6n5IkqR0OERERGSFSS+qcW7duoXr16+jW7duePbZZ9l+FxEREZWbGenQS7HwkOKVDoWIiIiKYNKLapSbN28iPT0d48aNw5w5c+Dh4aF0SERERFRNCWGBBdnwktqxlhcREVEVxKQX1Sg3b97EPffcgyeeeIKFUyIiIio3IQTykQStVAt+qr5Kh0NEREQlUCkdAFFlsVgsAIAOHTow4UVERETlVpDwOge1FIja6udhUDVVOiQiIiIqAZNeVGOkpaXB29sbLVu2VDoUIiIiqsYE8gFIqKV+At6qDkqHQ0RERHbw8UZyiXy1Gvd37SoPV0U3btxAQkICoqOjlQ6FiIiIqjUzJGiglxrYn8VgAJKS/hkmIiKiSsekF7mEkCRc8/RUOgy78vPzIUkSBgwYoHQoREREVM0JmCBJaqigtz+TSgXExFRaTERERFQcH2+kGuHSpUuoV68eEhMTlQ6FiIiIqjkTbkCPWGgRqXQoREREVAomvcglNBYLJp48iYknT0Lzd4PxVUVubi7MZjMmTpwIzypcG42IiIiqPiHMEDDBX3UvJKmUonR+PjBvXsErP7/yAiQiIiIZk17kEmqLBYPPnsXgs2ehrmJJr5SUFDRo0AD9+/dXOhQiIiKq5kxIgVYKha+qZ+kzGo3Aiy8WvIzGygmOiIiIbDDpRW7PZDKhTp060OtLaXeDiIiIXOr5559HmzZt4OPjg9DQUAwcOBCnTp1SOqzbIoQFZqTDXxoEjRSsdDhERERUBia9yO1ZLBYmvIiIiCrZ7t27MXXqVPzwww/4+uuvYTQa0bNnT2RlZSkdWrlZkAG15At/9UClQyEiIiIHsPdGcnsWiwWhoaFKh0FERFSjbN++3ebvdevWITQ0FIcOHcLdd9+tUFS3R8AICTpoUVvpUIiIiMgBrOlFbs1kMkGlUqFly5ZKh0JERFSjpaWlAQACAwMVjqT8BIxQwQCVpFM6FCIiInIAk17k1jIyMuDj44PmzZsrHQoREVGNZbFY8Oijj6Jjx45o2rSp0uGUmwVZ8JTaKB0GEREROYiPN5Jby8/Ph7e3N0JCQpQOhYiIqMaaOnUqjh8/jn379ikdSrlZRC4ANfxUvZQOhYiIiBzEpBe5RL5ajakJCfJwVWE0GhEcHAxJkpQOhYiIqEaaNm0avvjiC+zZsweRkZFKh1NuZmRALfnDS7rLsQUMBuD48X+GiYiIqNIx6UUuISQJ5318lA7DhhACOTk5aNCggdKhEBER1ThCCEyfPh1btmzBrl27ULduXaVDui0CedChDlSSh2MLqFRAfHzFBkVERESlYtKL3FZmZiY8PDwwcOBApUMhIiKqcaZOnYqNGzfis88+g4+PD65cuQIA8PPzg6Ea1nwSyIVeilE6DCIiInICG7Inl9BYLBh16hRGnToFjcWidDgQQuDKlSu488470bp1a6XDISIiqnFWr16NtLQ0JCYmolatWvLro48+Ujq0chEAdFK04wvk5wOLFhW88vMrKCoiIiIqDWt6kUuoLRaMPnMGAPBpbCxMKmXzqTdu3ICXlxemTJnC9ryIiIgUIIRQOgSXKXgvAlopwvGFjEZg8eKC4XnzAJ2uQmIjIiIi+1jTi9yO0WjEzZs3MXz4cLRr107pcIiIiKiasyALKhjgAbYTSkREVJ0w6UVuRQiB8+fPIy4uDg899JDS4RAREZEbMCMVOikSHlKc0qEQERGRE5j0Irdy48YNGAwGPP744/Dz81M6HCIiInIDFuTAS+oISVIrHQoRERE5gUkvcgtCCNy6dQu3bt3CqFGj0L59e6VDIiIiIrchQSMFKh0EEREROYkN2VO1l5OTg7/++gteXl4YPnw4Hn74YaVDIiIiIjciQYIavkqHQURERE5i0ouqLbPZjIsXL8JoNKJ169aYMWMG2rRpw94aiYiIyGWEEBCwQC35Kx0KEREROcmhpFd6errDK/T15V2wmsioVmN2p07ycGVISUmBj48Ppk+fjkGDBkHHrsCJiMjNsAxWFZggQQMNApxbzMMD+PHHf4aJiIio0jmU9PL39y+z9owQApIkwWw2uyQwql4skoQz/v6Vuk2TyYR69ephxIgRlbpdIiKiysIymPLE30kvFbydW1CtBtq0qZigiIiIyCEOJb127txZ0XEQOU0IAXUl1SojIiJSAstgyhMwA1BDJRmUDoWIiIic5FDSKyEhoaLjoGpOY7HgnqQkAMD/1a0Lk6piOwYVQiAnJwcNGzas0O0QEREpiWUw5QnkQ5K0zjdkn58PvPpqwfDMmQCbYSAiIqp05cpM7N27F2PHjkWHDh1w8eJFAMD69euxb98+lwZH1YfaYsGk337DpN9+g9piqfDtZWVlwWAw4J577qnwbREREVUVLINVPhNuwktqC40U7NyCRiPw2GMFL6OxYoIjIiKiUjmd9Prkk0/Qq1cvGAwGHD58GHl5eQCAtLQ0LF261OUBEhVlMplw6dIlNGvWDHfeeafS4RAREVUKlsEqnxD5kCDBX9Vf6VCIiIioHJxOej333HNYs2YN3n77bWi1Wnl8x44dcfjwYZcGR1SU0WjE2bNn0bBhQyxatKjMxn2JiIjcBctglc+CHKgkbxikO5QOhYiIiMrB6aTXqVOncPfddxcb7+fnh9TUVFfERFQis9mMpKQkxMXF4fXXX0dsbKzSIREREVUalsEqnwV5UEEPDZx8tJGIiIiqBKeTXuHh4fjjjz+Kjd+3bx/q1avnkqCISpKVlQVfX1+88MILiImJUTocIiKiSsUyWOWzIBs6qR4kyaG+n4iIiKiKcTrp9cADD2DmzJk4ePAgJEnCpUuXsGHDBsydOxcPP/xwRcRIBKCgppdGo0FERITSoRAREVU6lsEqn4AR3lIHpcMgIiKicnL6ttX8+fNhsVjQrVs3ZGdn4+6774Zer8fcuXMxffr0ioiRCEBB0kutVsNgMCgdChERUaVjGazyCGFGPi5ALXnDU2qldDhERERUTk4nvSRJwpNPPol58+bhjz/+QGZmJpo0aQJvb++KiI+qCaNajQV33SUPVwSLxQKtVguNho8YEBFRzcMyWOUwiVSYcA06KRqh6unlT3p5eAA7d/4zTERERJWu3NkDnU4HHx8f+Pj4sLBFsEgSjgdXbCOvQgioKyihRkREVF2wDFZx8sVfAAQCVMMRqp4OrRRa/pWp1UBioqtCIyIionJwuk0vk8mEp59+Gn5+foiJiUFMTAz8/Pzw1FNPwWg0VkSMRDKVyulTloiIyC2wDFbxBPIQop6CCPW/bi/hRURERFWC0zW9pk+fjk8//RTLli1D+/btAQDff/89Fi1ahBs3bmD16tUuD5KqPrXFgl7nzwMAdtSpA3MFJKdY04uIiGoylsEqlhBmAAIeUhwkSbr9FRqNwFtvFQw/+CCg1d7+OomIiMgpTie9Nm7ciA8//BB9+vSRx91xxx2IiorCqFGjWOCqoTQWCx4+fhwA8G1kZIUkvSwWC5NeRERUY7EMVrEE8iBBD40U5poV5ucD06YVDE+YwKQXERGRApzOTOj1esTExBQbX7duXeh0OlfERFQio9GIsDAXFUSJiIiqGZbBKpaACZKkhQb+SodCRERELuJ00mvatGl49tlnkZeXJ4/Ly8vDkiVLMK3wbhZRBcjPz0e9evWUDoOIiEgRLINVLAEjJGihhp/SoRAREZGLOPR44+DBg23+/uabbxAZGYnmzZsDAH799Vfk5+ejW7duro+QCIDZbAYAxMbGKhwJERFR5WEZrPIIGKGGHyQYlA6FiIiIXMShpJefn+0dryFDhtj8HRUV5bqIiEpw7do1hIWFoXfv3kqHQkREVGlYBqs8FuRBj2DXNGJPREREVYJDSa+1a9dWdBxEdplMJmRkZGD8+PEICgpSOhwiIqJKwzJYxRNCwIgLkCDBR9Vd6XCIiIjIhZzuvZGoMgkhkJSUhEaNGmHkyJFKh0NERERuRAgT8pAEjRSMcPVj8JPuUTokIiIicqFyJb02b96Mjz/+GOfPn0d+fr7NtMOHD7skMKpejCoVFrdpIw+7yoULFxAcHIznnnsOISEhLlsvERFRdcQymGsZcQl6qS4i1ctgUDVz7cr1euCLL/4ZJiIiokrndHbitddew8SJExEWFoYjR46gbdu2CAoKwtmzZ9GnT5+KiJGqAYtKhZ/DwvBzWBgsLkp63bp1C5IkYf78+bjjjjtcsk4iIqLqimUw17MgHwappesTXgCg0QD9+hW8NHy4goiISAlOZydWrVqFt956C6+//jp0Oh0ee+wxfP3115gxYwbS0tIqIkaqgYxGI1JSUjB48GD069dP6XCIiIgUxzKYawkhAJihlYKVDoWIiIgqiNNJr/Pnz6NDhw4AAIPBgIyMDADAuHHj8MEHH7g2Oqo21BYLul24gG4XLkBtsdzWuoQQSE5ORlxcHGbOnMlelIiIiMAymKsZ8Rc0UjB8pR4VtAEjsG5dwctorJhtEBERUamcTnqFh4fj5s2bAIA6derghx9+AAAkJSX9fceMaiKNxYJHf/0Vj/76KzS3mfS6ePEi/Pz88OSTTxbrqp2IiKimYhnMdUziFgQsCFPPq5hHGwEgPx+YOLHgVaT9NSIiIqocTie9unbtis8//xwAMHHiRMyaNQs9evTAiBEjMGjQIJcHSDXLjRs3YLFYMHfuXLT5u2F8IiIiYhnMlUy4Dl9VT/hLA5UOhYiIiCqQ061qvvXWW7D8XZNn6tSpCAoKwoEDBzBgwABMmTLF5QFSzWE0GnHjxg088MADGDJkiNLhEBERVSksg7mSBL0UzSYUiIiI3JzTSS+VSgWVVe98I0eOxMiRI10aFNVM165dQ1RUFKZMmcJCKBERUREsg7lGwaOgAmoEKh0KERERVTCHkl5Hjx51eIV33HFHuYOhmstisSArKwuTJ0+Gj4+P0uEQERFVCSyDuZ4FaVBL3vBStVM6FCIiIqpgDiW9WrRoAUmSymwkVZIkmM1mlwRGNUtWVha8vb3Ro0cF9aBERERUDbEM5nom3ISXdBf0iFU6FCIiIqpgDiW9kpKSKjoOquGys7Ph6+uLunXrKh0KERFRlcEyWEUQ8JRasCkFIiKiGsChpFd0dHRFx0HVnFGlwr/vvFMedlZ2djZatmwJjcbpZuaIiIjcFstgFUOCruI3otcDH3/8zzARERFVOmYYyCUsKhX2R0SUe3mTyYR69eq5MCIiIiKi4gQEJFRCEkqjAYYNq/jtEBERkV3OV8khqgAqlQq1atVSOgwiIiJyewJqyVfpIIiIiKgSsKYXuYTKYkH7K1cAAN+Hh8PixCOOQghYLBYEBwdXVHhEREREEMIICSqo4V/xGzOZgC1bCoYHDSqo+UVERESVyqmaXmazGXv27EFqamoFhUPVldZiwfzDhzH/8GFoLRanlrVYLFCpVPDx8amg6IiIiKo3lsFcw4hL0EnR8JLaVPzG8vKA4cMLXnl5Fb89IiIiKsappJdarUbPnj1x69atioqHaqCcnBxotVomvYiIiOxgGez2WUQeBEwIUk2EWmKZg4iIqCZwuk2vpk2b4uzZsxURC9VA+fn5uHjxIjp06IAmTZooHQ4REVGVxTLY7TEjFRopDP6qAUqHQkRERJXE6aTXc889h7lz5+KLL77A5cuXkZ6ebvMicpTZbMa5c+fQsmVLLFmyBDpdJXQfTkREVE2xDHZ7LMiEp9QCKslD6VCIiIiokjjdombfvn0BAAMGDIAkSfJ4IQQkSYLZbHZddOTWbt26haCgICxbtgyBgYFKh0NERFSlsQx2uwQMUjOlgyAiIqJK5HTSa+fOnRURB9VAeXl5iIqKQnR0tNKhEBERVXksg5WfEAICAlopXOlQiIiIqBI5nfRKSEioiDioBsrPz0etWrWUDoOIiKhaYBms/ASMkKCFBiFKh0JERESVyOk2vQBg7969GDt2LDp06ICLFy8CANavX499+/a5NDiqPkwqFVY0b44VzZvDpHLstDKZTGjcuHEFR0ZEROQ+WAYrH4E8qCR95db00umAtWsLXmy3lIiISBFOJ70++eQT9OrVCwaDAYcPH0ZeXh4AIC0tDUuXLnV5gFQ9mFUqfBsVhW+jomB2IOllNBqhVqvRokWLig+OiIjIDbAMVn4CuZCghwZhlbdRrRaYMKHgpdVW3naJiIhIVq7eG9esWYO3334bWqsv8I4dO+Lw4cMuDY7cV2ZmJry9vdG0aVOlQyEiIqoWWAYrHyHMMOHW3z03ssYVERFRTeJ0m16nTp3C3XffXWy8n58fUlNTXRETVUMqiwV3pqQAAA6HhMBSRm0vs9kMjUYDX1/fygiPiIio2mMZrHzycR46KRLh6vmVu2GTCdixo2C4Vy9A43Sxm4iIiG6T0zW9wsPD8ccffxQbv2/fPtSrV88lQVH1o7VYsPCnn7Dwp5+gtVjKnL8w6aVldX8iIiKHsAzmPJO4AQkahKsfh06KqtyN5+UB/fsXvP5+FJWIiIgql9NJrwceeAAzZ87EwYMHIUkSLl26hA0bNmDu3Ll4+OGHKyJGcjMWiwVpaWlo3bo1JElSOhwiIqJqgWUw51hEDky4iUDVWPhIPZQOh4iIiBTgdD3r+fPnw2KxoFu3bsjOzsbdd98NvV6PuXPnYvr06RURI7mZq1evIigoCNOmTVM6FCIiomqDZTDHCSGQjwvwVnVGqHoab7IRERHVUE4nvSRJwpNPPol58+bhjz/+QGZmJpo0aQJvb++KiI/cUHp6Oh599FHUrVtX6VCIiIiqDZbBHCeQAxUMCFVNh0ryUDocIiIiUojTjzdOmjQJGRkZ0Ol0aNKkCdq2bQtvb29kZWVh0qRJFREjuREhBCRJQoMGDZQOhYiIqFphGcxxZmRCLfnAQ2qkdChERESkIKeTXu+++y5ycnKKjc/JycF7773nkqDIfQkhoFKpoGEPRkRERE5hGcxxArnQSTGs5UVERFTDOZx5SE9PhxACQghkZGTAw+OfQoTZbMa2bdsQGhpaIUGS+8jNzYVer0dgYKDSoRAREVULLIM5T0BAgk7pMIiIiEhhDie9/P39IUkSJElCw4YNi02XJAmLFy92aXBUfZhUKqxu2lQetic1NRVhYWGIj4+vrNCIiIiqNZbBysOifNJLpwNWrvxnmIiIiCqdw0mvnTt3QgiBrl274pNPPrGpqaPT6RAdHY2IiIgKCZKqPrNKhW0xMWXOl5WVhcGDB/PxRiIiIgexDOY8ARN0iFI2CK0WmDpV2RiIiIhqOIczDwkJCQCApKQk1KlTh10/k9PMZjMkSULLli2VDoWIiKjaYBnMOUJYAAA6qY7CkRAREZHSnG7I/rfffsP+/fvlv9944w20aNECo0ePxq1bt1waHFUfKiHQ9Pp1NL1+HSohSpwnKysLXl5efLSRiIioHFgGK5sQAvlIhlYKg5eqrbLBmM3Arl0FL7NZ2ViIiIhqKKeTXvPmzUN6ejoA4NixY5g9ezb69u2LpKQkzJ492+UBUvWgNZvx/A8/4PkffoDWTsEuKysLgYGBqFOHd16JiIicxTJY2Yy4CJXkhQj1v+AhFW//rFLl5gJduhS8cnOVjYWIiKiGcrphpaSkJDRp0gQA8Mknn+Cee+7B0qVLcfjwYfTt29flAZL7yMvL42MZRERE5cQyWOksIgcC+ailfhI+qgSlwyEiIqIqwOmaXjqdDtnZ2QCAb775Bj179gQABAYGyncfiUqSn5+P6OhopcMgIiKqllgGK50ZGdBIQfCT+isdChEREVURTtf06tSpE2bPno2OHTvixx9/xEcffQQAOH36NCIjI10eILmHrKws6HQ6dOjQQelQiIiIqiWWwUpnRjq8pB5QSR5Kh0JERERVhNM1vVauXAmNRoPNmzdj9erVqF27NgDgq6++Qu/evV0eoKu88cYbiImJgYeHB9q1a4cff/xR6ZBqlCtXrqB58+ZITExUOhQiIqJqqbqWwSqDReRBggRfVQ+lQyEiIqIqxOmaXnXq1MEXX3xRbPwrr7zikoAqwkcffYTZs2djzZo1aNeuHVasWIFevXrh1KlTCA0NVTo8t2c0GgEAY8aMgVqtVjgaIiKi6qk6lsEqiwlXoJdi4Ssx6UVERET/cDrpdf78+VKnV8We+V5++WU88MADmDhxIgBgzZo1+PLLL/HOO+9g/vz5Ckfn/jIyMuDr64u2bRXuOpyIiKgaq45lsMpigRF+qr58tJGIiIhsOJ30iomJKbX3PbPZfFsBuVp+fj4OHTqEBQsWyONUKhW6d++O77//XsHI3ItZpcI7cXHysLVbt26hTZs2CAoKUiI0IiIit1DdymCVSYIENQKUDsOWVgssW/bPMBEREVU6p5NeR44csfnbaDTiyJEjePnll7FkyRKXBeYq169fh9lsRlhYmM34sLAw/P777wpF5X5MKhW2xMYWG3/9+nXodDqMGDFCgaiIiIjcR3Urg1UWs8gAIEEjBSodii2dDpg3T+koiIiIajSnk17NmzcvNq5169aIiIjA8uXLMXjwYJcERtVfZmYmUlNTMWXKFPTp00fpcIiIiKo1lsGKE8iBEZfhrxoAbylR6XCIiIioinE66WVPo0aN8NNPP7lqdS4THBwMtVqNq1ev2oy/evUqwsPDFYrK/aiEQGxaGgDgTz8/WCQJV65cQWJiIqZOnVrq4xhERERUflW1DFYZ8nEBPqpuqKVeBJWkUzocW2YzcPhwwfCddwLszIeIiKjSOZ30Sk9Pt/lbCIHLly9j0aJFaNCggcsCcxWdTodWrVrh22+/xcCBAwEAFosF3377LaZNm6ZscG5Eazbj5X37AABDe/dGnkYDSZLQtGlTaNmOBRER0W2rbmWwyqCTolBbvRRqyUvpUIrLzQUKO/HJzAS8qmCMREREbs7ppJe/v3+xWjtCCERFReHDDz90WWCuNHv2bIwfPx6tW7dG27ZtsWLFCmRlZcm9OVLF8fT0VDoEIiIit1Ady2AVRSAfKhjgLw2BRqpiDdgTERFRleF00mvnzp02f6tUKoSEhKB+/frQaFz2tKRLjRgxAikpKXjmmWdw5coVtGjRAtu3by/WuD25jhACFosFgYFVrFFZIiKiaqo6lsEqSo44AY0UDD91X6VDISIioirM6RJSQkJCRcRR4aZNm8bHGStRXl4edDod6tSpo3QoREREbqG6lsEqQr5IhgQddGA5g4iIiOxzKOn1+eefO7zCAQMGlDsYch+5ubkwGAyIjIxUOhQiIqJqi2Ww4oQQyBPJsCAbADvKISIiIvscSnoVNgBfFkmSYDabbycechNGoxE6nY6PNxIREd0GlsGKyxR7YBSXoJLYbigRERGVzqGkl8Viqeg4yM0YjUaEhIRAze65iYiIyo1lsAJCCBjxF7LFL0gxr4ERl6BHfaXDIiIioiquZrV6ShXGrFJh49/dpZtVKmRnZ6Nhw4YKR0VERETVlVmkIVv8gmxxBJmW/cjHOZhFJgAJOlSD5hO0WmDhwn+GiYiIqNKpHJ3xu+++Q5MmTZCenl5sWlpaGuLj47Fnzx6XBkfVh0mlwgeNGuGDRo2QYzZDkiR069ZN6bCIiIiqvZpSBhPCgmzLr7hufgfJpik4Y+qN86ZHkGJegzxxGirhAT3qwkOKRbW4b6vTAYsWFbx0OqWjISIiqpEcLjGsWLECDzzwAHx9fYtN8/Pzw5QpU/DKK6/g7rvvdmmAVL3k5+fj/PnzCAsLQ8eOHZUOh4iIqNpz9zKYWaQjXXyNW+ZPkStOwIJcSFBDDV/oEAlJYsKIiIiIysfhml6//vorevfubXd6z549cejQIZcERdWQxQKvc+egPX0ajRs2xNKlS+Hn56d0VERERNWeO5bBhBDIFadxxfQy/jDdi4ump5AjfoEaAdCjPvRSPWik4Oqd8LJYgBMnCl5sm42IiEgRDtf0unr1KrSltEeg0WiQkpLikqCoesnPz8e1pCQcOXMGAJDx/ffwCQ9XOCoiIiL34G5lMKO4hqvm5cgQO2EWGVDDG3pEQ5LcrN2rnBygadOC4cxMwMtL2XiIiIhqIIdretWuXRvHjx+3O/3o0aOoVauWS4Ki6uXChQto0qSJ/LePj4+C0RAREbkXdyqDZVr24ZxpIlItn0MSHtCjAbRShPslvIiIiKhKcDjp1bdvXzz99NPIzc0tNi0nJwcLFy5E//79XRocVX3Z2dlQqVSYNGmS0qEQERG5pepUBhNCwCJyYRRXkSvOIMtyCOmW75Bq2YrLphdw3jQT+eIc9KgHjRQASZKUDpmIiIjcmMOPNz711FP49NNP0bBhQ0ybNg2NGjUCAPz+++944403YDab8eSTT1ZYoFQ1XblyBS1btkRCQoLSoRAREbklJctgQphgRgbMSIdZpMGC9L+H//4faTCJWzDjOkziBsxIhQU5EDBBCCMETABMBeuCgBr+0KIWk11ERERUKRxOeoWFheHAgQN4+OGHsWDBAgghAACSJKFXr1544403EBYWVmGBUtVksViQmJgIjaYadB1ORERUDbm6DGYWGcgVl/9OXKVZJbHSYBZpMCEFJnETZtyEGRkQMEKIguSVkF9GCJgBSH//00CCBoAGErSQoIEKhr+H1bB+uEAgF3+/hXITMP69PSIiIiL7nCotREdHY9u2bbh16xb++OMPCCHQoEEDBAQEVFR8VIUJISCEQEREhNKhEBERuTVXlsEumZ+FznT+78SVSa6N9U9yyx7p7ySWFoChlPksEMiHQL7TsTlDDd8KXT8RERFVf+W6RRYQEIA2bdq4OhaqZrKysmAwGBAdHa10KERERDWCK8pgtdSPw6DJcVFEytGAbYIRERFR6VgvnMrtxo0baNiwIeLi4gCTCZg7t2BCKd2qExERkbI0UhAMEmtJVTitlmUjIiIihTHpReUihEBeXh769esHlUoF6HTA8uVKh0VERERUNbBsREREpDhV2bMQFZefnw+dToc77rhD6VCIiIiIiIiIiIphTS8ql7y8POj1eoSGhhaMsFiA8+cLhuvUAVTMpxIREVENxrIRERGR4pj0onJJS0tDYGDgP0mvnBygbt2C4cxMwMtLueCIiIiIlMayERERkeJ4y4mclp2djfz8fEyePBl6vV7pcIiIiIiIiIiIimHSi5x28eJFtG/fHsOGDVM6FCIiIiIiIiKiEjHpRU4RQkAIgaFDh0LL7reJiIiIiIiIqIpi0oucIoSASqXiY41EREREREREVKUx6UVOMRqNUKvVTHoRERERERERUZXGpBc55dq1a4iMjETLli2VDoWIiIiIiIiIyC6N0gFQ9WE2m5Gbm4shQ4bA09PTdqJGAzzyyD/DRERERDUZy0ZERESK4zcwOSwtLQ3+/v7o379/8Yl6PfDGG5UfFBEREVFVxLIRERGR4vh4Izns1q1baN26NWrVqqV0KEREREREREREpWJNL3KIEAIWiwWJiYn2ZgCuXy8YDg4GJKnSYiMiIiKqclg2IiIiUhyTXuQQk8kEjUaDqKiokmfIzgZCQwuGMzMBL6/KC46IiIioqmHZiIiISHF8vJEcYjQaodFo4Ofnp3QoRERERERERERlYtKLymQymfDXX38hPDwcERERSodDRERERERERFQmJr2oVCaTCWfPnkX9+vXx6quvwtfXV+mQiIiIiIiIiIjKxDa9qFTJyclo3LgxXnnlFdSrV0/pcIiIiIiIiIiIHMKaXlQqIQQmTJjAhBcRERERERERVStMepFdZrMZAODv769sIERERERERERETmLSi+zKysqCwWBAeHh42TNrNMD48QUvDZ+aJSIiqun27NmDe+65BxEREZAkCVu3blU6pMrFshEREZHi+A1Mdl27dg2dO3dG48aNy55ZrwfWravwmIiIiKh6yMrKQvPmzTFp0iQMHjzY5evPN1sgXL5WF9Jogf++88/fZotysdQAagnQqHg/n4iIbDHpRSXKysqCRqPB6NGjIUmS0uEQERFRNdOnTx/06dOnQtZ96EY65h4+gzwLE0lUoJZBhw87NYOWiS8iIrLCbwUq0eXLl9GyZUt06tTJsQWEALKyCl6iSt93JSIiomruZr4Rl3PyIKpyXS8h4JGbA4/cHJaNKtCtfCPS8k3oWSuICS8iIiqGNb2omIyMDOh0OkyePBkaR9ugyM4GvL0LhjMzAS+viguQiIiICECQTltla6Trc3KweWACAGDotgPI8zAoHJF7EULgRp4RAHBfvXA8WL+2whEREVFVxNshZMNkMuHy5cu466670LlzZ6XDISIiIiKykWE04UxGDkxCYGK9CMyJi66yyU8iIlIWa3qRzGKxICkpCQ0bNsQzzzzDwgMRERERKUYIAbMAzELAJARMFoHrefnQqCR0Dw/Eww0j0dTfW+kwiYioCmPSi2TJycmoVasWli1bhtq1WUWciIiIiG6PRQiY/36ZLFYJrMLxln+GBQoeQylsAU0IQC1JUKsAjSRBLUmI9/PGww0j0SUsgDdoiYioTEx6EQAgOzsbkiTh8ccfR1xcnNLhEBERUTWXmZmJP/74Q/47KSkJv/zyCwIDA1GnTh0FIyNnWQqTVFYJqn/+hs3fkIDCVFRh+/1qlQSNJMmJK7UKMKhV8NGo4avVwF+nQYBOC1+tBt4aNby16oL/NRp4adQ244L1WmjYYD0RETmISS8C8E9vjd27d1c6FCIiInIDP//8M7p06SL/PXv2bADA+PHjsW7dOoWiImf8mZENiwAkqaDGVWHSSvN3EsugVcNXq4afVgs/nQYBWg18dQWJKzlZpVHDW1t8nIdaxZpaRERU4Zj0IphMJgghMHr0aMd7ayQiIiIqRWJiIkRhVR+qdvItFkiShJmNotDY1/Pvmla2ySudmjWuiIioamOGg2CxWKDRaBAeHl7+lajVwNCh/wwTERER1WAWtQr7ErrLw1WZ+PvxRKOl4JVvsSDTZIavVo2R0WEI0GuVDpGIiKhcmPQiGI1GSJIE9e0kqzw8gE2bXBcUERERUTVm1OnxwqLlisYghIBRTmZZkG/5Z9hoEZD+bn/LgoKG4rUqCVqVCjpJQoyXB9qH+DPhRURE1RqTXjWY2WzG5cuXkZubi6ZNmyImJkbpkIiIiIjIARYhkG2ywCgscu0so0XAZBFQSYXz4O9E1t/JLJUK4R5ahHloEW7QIVivQ6Bei0CdFgE6jc0wG4snIiJ3wKRXDSSEwI0bN3Dz5k1ERERg4sSJGDZsGAwGg9KhEREREVEZcsxmnM/KK2hXSyXBQ61CpKceYR46hHvoEaTXIkCnRaBe83cSq2DYX6uFWsXG44mIqOZg0quGMZvNSEpKgo+PDyZNmoSJEyciNDT09leclQV4excMZ2YCXl63v04iIiKiakqfk4PNfTsAAIZuO4A8F91cvJlnxPU8IzqG+GFOXDQiPfXw1WqgYk+IRERExTDpVYOYzWacPXsWderUwQsvvICWLVsqHRIRERFRjWb5uxF5s+Xv/23+xj/j/m6DS69SYWK9WpjZuA4MGnYeREREVBomvWqInJwcXLhwAfXr18dLL72Exo0bKx0SEf0/e3ceH1V9t///mn0my2RfWMIWkH2pIIjIIqCguCC1LlgFtOJdoVqxdW0FW61WeyutVVxa19qvrVbs765b3cBq1aLiXW9Riwq4QIAEyJ7Mcj6/P0KCYQ9McmZOXs8+5sGZycyca3IS8+41Z84BADjC7sVVzBj5IpGWr39Z16CaWFOB9c1PFxojuVxNB5H3uFzyunf+63IpzedR2OdVls+rbL9XOX6vMn1eDc3O0NTiXLnYswsAgAOi9HIwy7K0Y8cOVVRUyOfzafjw4brlllvUp08fu6MBAAActi0N0abTD7Yj07L3lRS3mpab+yaXmg4Wv7fiKvCNYMOzM5SWFW4prjK8HmV4vcrweZTp9SjDt/O616N0r6fpWF0eDiQPAMDhovRyoEgkoq1bt6q2tlZZWVk68cQTdfrpp+uYY46R18smBwAAqa0o6FfvjJAaLKvd1+WWlOXzKsvvVbbPqxy/T5k+jzJayqudl92Lq8aGlue47+hBHO8UAAAb0IA4TFVVlTZv3qzu3btr3rx5Ovnkk9W7d2+7YwEAACTMsJxMPTc5yY9NGosc+D4AAKBdUXo5THV1tUpLS/XnP/9Z6byjCAAAAAAAOilKL4eJxWLKz8/v+MLL45FOOmnXMgAAQGfGbAQAgO0ovRzEGKPGxkaVlJR0/MqDQemZZzp+vQAAAMmI2QgAANtxWhgHqampUSgU0mmnnWZ3FAAAAAAAAFtRejlETU2NvvrqK40aNUqjRo2yOw4AAAAAAICtKL1SmDFGVVVVWrt2rbZt26bx48friiuukMvl6vgwtbVNp+JOT29aBgAA6MyYjQAAsB3H9EpRVVVV2rx5s0KhkCZOnKjzzz9fY8eOldttY49ZV2ffugEAAJINsxEAALai9EpBsVhMZWVlOv744zVnzhyNGjXKnr27AAAAAAAAkhSlVwqKxWIKBoOaP3++hg0bZnccAAAAAACApMMxvVJQLBaTy+WSz+ezOwoAAAAAAEBSovRKMY2Njfr66681YMAA9ejRw+44AAAAAAAASYnSK4U0NDRow4YNGj16tH7zm98oPT3d7kgAAAAAAABJiWN6pZCvvvpKRx99tG6//Xbl5eXZHac1t1uaOHHXMgAAQGfGbAQAgO0ovVLMKaecknyFlySFQtKKFXanAAAASA7MRgAA2I7SKwUYY1RdXS23262ioiK74wAAAAAAACQ9Sq8k1tDQoIqKCtXW1io9PV1HHnmkhgwZYncsAAAAAACApEfplWRisZi2b9+uHTt2yOv1qlu3bpozZ46mTJmiwYMHy+Vy2R1x72prpV69mpbXr5c4yD4AAOjMmI0AALAdpVcSsSxLa9euVVFRkWbNmqVp06bp6KOPViAQsDvawSkvtzsBAABA8mA2AgDAVpReSSQSiSg9PV1Lly7VUUcdZXccAAAAAACAlMX5k5NINBqVz+dTcXGx3VEAAAAAAABSGqVXEqmqqlJWVpYKCgrsjgIAAAAAAJDSKL2ShGVZqq2t1axZsxQMBu2OAwAAAAAAkNIovZJEeXm5cnJyNHPmTLujAAAAAAAApDwOZJ8EIpGIduzYof/6r/9St27d7I5zaNxuadSoXcsAAADtJBqN6t///rfi8bjdUfbJ1dCggQMHSpI+evddGfbkb1d9+vRRfn6+3TEAAEmG0stmxhh98cUXGjp0qC666CK74xy6UEhatcruFAAAoBN44403dOWVV6qurs7uKPvX/EbgJZfYm8PhAoGAZs+erSuuuMLuKACAJEPpZaNIJKINGzYoLy9PV111lTIyMuyOBAAAkPSi0aiqqqrUs2dPuVwuu+PAJpZlad26derbt6/OP/98u+MAAJIQpZdNtm/frq1bt2rYsGH66U9/qmHDhtkdCQAAIKX4fD5Kr04oHo9r+/btqqioULdu3XTLLbdw9nMAwF5RetmgrKxMkUhEs2fP1uWXX65wOGx3pMNXVycNGtS0vGaNlJZmbx4AAAAbBeJx3bVihSRpwaRJavR47A2U4owxqq2tVXl5uaLRqHJycjRr1iydc845GjBggN3xAABJitKrgxljVF1drUsuuUQ/+MEPnPPupDHShg27lgEAACTddddduu2221RWVqbhw4frzjvv1OjRo+2O1f6MUVF9fcsy2sYYo8bGRtXX16uurk51dXUKBoMaMGCATjvtNE2dOlXFxcV2xwQAJDlKrw5WXV2t9PR0TZ8+3TmFFwAAwF786U9/0qJFi3TPPfdozJgxWrp0qaZNm6ZPPvlEhYWFdsdDkohGoy3lVn19vWKxmCTJ7/crFAqpS5cuOuaYYzRt2jQdeeSRcnOmcADAQaL06kBVVVUqKyvT2LFj1a9fP7vjAAAAtKvbb79dF110kebNmydJuueee/TMM8/ogQce0NVXX21zOnS05o8oNhdckUhEkuTxeJSWlqbMzEwdeeSR6t+/v3r37q2ePXuqZ8+eys3N5c1iAMAhofTqAJZl6euvv1YsFtOpp56qK6+8kj/cAADA0SKRiN59911dc801Lbe53W5NnTpVb775po3J0JGi0agqKytVWVkpy7KUlpamtLQ0DRs2TIMGDVLPnj3Vq1cv9erVS8XFxfJw7DMAQAJRerWzWCymdevWqbCwUJdeeqlmzZrFLtkAAMDxysvLFY/HVVRU1Or2oqIiffzxxzalQntr3pursrJSdXV1crvdys7O1uTJkzVu3Dh961vfUu/evRUIBOyOCgDoBCi92pExRuvWrdMRRxyh//7v/+YjjQAAAHCkWCymsrIy1dfXtxyHa/z48Ro9erRGjhyp7OxsuyMCADohSq929NVXXyk3N1c33nij8wsvl0saNGjXMgAA6NTy8/Pl8Xi0efPmVrdv3ry5c5x1z+XSFxkZLctOZVmWNm/erOrqanXv3l1nnXWWjjnmGA0YMEBeL/9XAwBgL/4StZP6+npFo1H96Ec/0rBhw+yO0/7S0qQPP7Q7BQAASBJ+v18jR47Uyy+/rJkzZ0pqKkhefvllLVy40N5wHaDR49GCSZPsjtFujDEqLy/X9u3bVVRUpAsuuEBnn322cnNz7Y4GAEALSq920tDQoMzMTE2ePNnuKAAAALZYtGiR5syZo1GjRmn06NFaunSpamtrW87miNQUiUT0xRdfKCMjQxdccIHOP/98denSxe5YAADsgdKrnTQ2Nio7O1tZWVl2RwEAALDFWWedpa1bt+r6669XWVmZRowYoeeff36Pg9sjdWzfvl1bt27V4MGDdd1112nkyJF2RwIAYJ8ovdpJTU2Nxo4dK5eDj+HQSl2ddNRRTcurVjV93BEAAHR6Cxcu7BQfZ9xdIB7X7f/4hyRp0fjxavR4bE50aKLRqKqqqlRdXa1YLKZgMKjZs2frhz/8IW/uAgCSHqVXO4jH45LUuT7aaIy0Zs2uZQAAgM7MGPWoqWlZThWxWEzV1dWqqqpSJBKRx+NROBzWqFGjNGbMGI0cOVKjRo3qPG/sAgBSGqVXgsViMX3xxRfKy8vTsccea3ccAAAAYL8sy9LWrVtVVVUlt9utzMxMDR48WEcffbRGjBihoUOHcoB6AEBKovRKoMrKSm3evFm9e/fWlVdeqfz8fLsjAQAAAHtljNGOHTu0detW5ebm6vzzz9dRRx2lYcOGcdw1AIAjUHolgDFGGzZskCTNnDlTV1xxhQoLC21OBQAAAOzJGKPa2lpt2rRJaWlpOv300zV//nz16dPH7mgAACQUpVcClJeXKxgM6qc//alOPvlkjnEAAACApBOJRFReXq6amhqFQiEdc8wxuuSSSzRy5EjmVwCAI1F6JUBFRYW+973v6ZRTTrE7CgAAANAiHo9r+/bt2r59uzwej4qLi3X22Wdr6tSpGj58uNxut90RAQBoN5ReCVBcXKzzzjvP7hj2crmknj13LQMAAHRmLpc2h0Ityx3JsixVVlZq+/btisfjys7O1sknn6xp06Zp3LhxSktL69A8AADYhdLrMHm9Xh199NHq0aOH3VHslZYmrV9vdwoAAICk0Ojx6HtTpnTY+izLUlVVlbZt26Z4PK5wOKyxY8dq6tSpmjRpkoqLizssCwAAyYLS6zB89tlnysvL07Rp0+yOAgAAgE4oHo/ryy+/VDQaVWZmpo466igdf/zxmjBhgkpKSuyOBwCArSi9DsFnn32m5cuX6+mnn5bH49Ho0aPtjgQAAIBOaOvWrcrOztb8+fM1fvx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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = matrix_l3\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " ax = ax_n\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='precomputed')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels, metric='precomputed')\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='precomputed')\n", "\n", " y_lower = 10\n", "\n", "\n", " for i in range(n_clusters):\n", "\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on RMSD (L3 complex), linkage=average, metric=precomputed\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Clustering + dendrograms" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "h_cluster_l1 = AgglomerativeClustering(n_clusters=2, linkage='complete', metric='precomputed')\n", "h_cluster_l2 = AgglomerativeClustering(n_clusters=2, linkage='complete', metric='precomputed')\n", "h_cluster_l3 = AgglomerativeClustering(n_clusters=2, linkage='average', metric='precomputed')\n", "\n", "\n", "hc_l1_rmsd = h_cluster_l1.fit_predict(matrix_l1)\n", "hc_l2_rmsd = h_cluster_l2.fit_predict(matrix_l2)\n", "hc_l3_rmsd = h_cluster_l3.fit_predict(matrix_l3)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "linkage_method = linkage(squareform(matrix_l1), method='complete')\n", "dendrogram(linkage_method, color_threshold=1.3, above_threshold_color='grey')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "\n", "plt.axhline(y=2, c='grey', lw=1, linestyle='dashed')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"RMSD\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "linkage_method = linkage(squareform(matrix_l2), method='complete')\n", "dendrogram(linkage_method, color_threshold=1.1, above_threshold_color='grey')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "\n", "plt.axhline(y=2, c='grey', lw=1, linestyle='dashed')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"RMSD\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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yZd1www1p+PDhxe0v+6qqqkprr712uv7665uk22OPPdJf//rX4vvGxsZ04IEHNvs/VVVV6aijjmqSduHChenII49M1dXVTdbt3bt3WmONNYrL11xzzSbbWFqp60btuVZ3tH6y1157pUmTJhXf//Wvf021tbWpd+/eaaeddkoHHnhg2n777VOvXr3SkCFD2lSOLrG8Ot0RRxyRfvvb36aGhoY2/7+lleK8zFKv6or6aGt10c6q26yoDt5Wy+Y762+dUsev86U4r0v9W7fnvG7Nin6rLN95lrQffvhh2nPPPVNVVVUaMGBA2m677dLEiRPT5MmT08SJE9N2222XBgwYkKqqqtIee+zRarnU1fXBLPWLUu1za5ZXh85ar1qivXXCLHWElLJdq9tj2XpVlnpRStnqN6W41mat+y+trfcrpbi37WjdKGs9OGs9oRRlf0ev1+WoT5bqfqecz8vaorW6VVsI4HSicePGpU996lOpvr5+ues1NDSkbbfdNo0bN67J8quvvrrNr/33379JAf+9731vua9TTjklFQqF9KUvfam4bIlCodAkgPOZz3wmrbbaas0K8SeeeCL169cvffGLXywu69u3b7riiitSSiktXrw49e/fPx133HEt7vcBBxyQRowYUXw/fvz4tP766zcJPCxYsCBNmjQpbbXVVsVl119/ferVq1c6//zzm/y/QqGQrr322nTvvfemo446Kg0ePDgVCoXUp0+ftO+++6Zf//rXaf78+S3mZdSoUelLX/pSk21UVVWlL3/5y83W3WOPPdKoUaOK788666zUr1+/tMsuu6Rnnnmm2fqvvPJKKhQKmW9Glt7ekt964sSJaciQIemll14qfn7NNdeklVZaKa277rrp+eefLy5vqRKw/fbbpx122CEtXrw4pZTShRdemKqrq9Pee+/dZL3Gxsa00UYbpfHjxzdZ/s4776TNN988FQqFNHLkyHTIIYekqVOnptNOOy1NnTo1HXLIIWnkyJHFINHbb79dTPuHP/yhGPwaOnRo6tevX3rwwQfT0KFD06qrrpr23XfftOeee6YBAwakmpqadPfddzfZbjke7JTq4XhbLFvhy3IDmLVinyV9lgpy1kpuluOzPVoKoGfNe0e3vccee6RPfOIT6ZVXXkkffvhhmjBhQtpggw3Suuuu26SceOyxx9KgQYPSvvvu2+T/1dfXp5122qnJ77PyyiunSy+9dIX53mmnnZpURn7961+nqqqqtNpqq6UvfelL6dvf/nY65JBD0korrZT69euXHnvsseK6S46B6urqtPPOO6ef/exnrd4wtyTLNS/rfr/66qtp9dVXb3IsX3LJJem8885LdXV1adttt02bb7556tWrV1pttdXSa6+9Vky70UYbNQmMf/jhh2m77bYrfherr7568fjZa6+90sKFC5vkJUv6jz76KO2+++7NzsXevXuncePGpWHDhqVCoZD69+/fpE6QUrbyO8t2s57TWY7RlLIdp1nK8M58wL20lgI4Bx98cNpll11SdXV1qqqqSmuuuWY64YQT0t///vcV/r+f/OQnxfPulltuSZdddlnaeOONU1VVVTr77LObrLvsefX1r389rbTSSum3v/1tevTRR9O2226bhg8fnrbYYot0//33F9e78cYbU01NTTr++OOb5HvJcd9S8GXZz5ctf3/1q1+lQqGQtt9++/SrX/0qvfzyy2nevHlp8eLFad68eenll19O1113XfFc+9WvftVk20sft6eeemoqFArpiCOOSC+88EKaN29eevrpp9OkSZNSVVVVuvzyy5t8/4VCIZ1wwgnp0UcfTc8++2z66U9/mgYPHpwuvvjiNH/+/PSHP/whbbHFFqmuri499dRTTfKdpW5UzsDTGmuskS688MLi+zFjxqSRI0em119/vcl6zz77bBo2bFj67//+7+KyLHW6rA9mspyXWetlXVUfzdPDxyz5zvpbZ7nOZz2vs/zWWc7rrL9Vlu88S9qvfe1rqba2Nl1xxRWtNs5asGBBuuKKK1JdXV362te+1uSzctUHs9Qvsu5zljp01npVR+uEWeoIS77vjl6r22PZsihLvWhJvjtav8lyrU0pW90/y/1K1nvbLHWjrPXgLPWErNf5cjWiynLdyXq/U87nZe3R3s4XSxPA6UR1dXVtLuwvu+yyVFdX12TZkgO7tcK5pcK6PWmX/nzZtEsuaAsXLky9evVK5513Xov5/vrXv57WXHPN4vtBgwal73//+ymljy/IhUIh3XTTTS2m/fGPf5z69OlTfD9gwIB0wQUXNFvv6aefTlVVVU2CI1/+8pfTxhtv3Oz7WvpC3NjYmG655ZZ00EEHpX79+qWqqqo0cODAdOihh6bbb7+9GLRI6ePA09K/1YwZM1rN+7L5TimlN954I02aNCnV1NSkr371q00KsVdffTUVCp0TwBk+fHiaNm1as3Wee+65tP7666fBgwcXW0e0VAkYPHhwk15QL7/8cioUCumaa65pcburrrpqk2WHHHJIGjRoULOWN8u644470qBBg9LkyZOLy3bYYYe0+eabpzlz5qSUUvrqV7+aVl999bTZZps1ieTPmDEjrb322mmXXXYpLivXg52sFYj2WLZgz3IDmLVinyV9lgpy1kpuluOzPVq6CGfNe0e3vcYaa6Tp06cX3z/55JOpUCikn/70p83SnnbaaWmVVVZp9v+qq6vTmWeemZ5++ul02223pV122SVVVVWlI444oknLnWXzveqqqzYpT9Zff/209dZbN6vgvPPOO2nkyJFp1113LS4rFArp61//epoyZUpaZZVVUqFQSHV1dWnChAnppptuWmEPqSzXvKz7fdhhh6UhQ4akxx57LL377rtp7733Tquttlr65Cc/2aSH6kMPPZT69euXjjjiiCb5Xvr8OProo1OhUEhnn312scHB3Llz07e+9a1UKBSaXYuzpP/mN7+Zampq0iWXXJLefffdNG/evPSnP/0prbvuuunEE09MKaX0j3/8I+21116purq6yY1YlvI7y3azntNZjtEl33dHj9MsZXiWm+711luvza9Bgwa1Wh+cNWtWuvDCC9MnP/nJ4jm00UYbpWnTpqV//etfLe7z5ptvnvbff/8myxYsWJAOP/zwVCgU0tFHH11cvux5teGGG6Zvfetbxff33HNPKhQK6ayzzmq2nUMPPTSNHDmy+H611VZLAwYMSGeddVa66667mr2WBGimTZtWXLa0MWPGNGvI0pq99torbbrppsX3y56TQ4YMSfvtt1+LaT/1qU+lLbfcsvh+/fXXb7FF3g033JDq6urS7NmzU0oft4rceOON0+c+97km62WtG5Ur8FRbW5uuuuqq4r4VCoV09dVXt/idnXPOOWmllVZqku+O1umyPpjJcl5mrZd1VX00Tw8fs+Q762+d5Tqf9bzO8ltnOa+z/lZZvvMsaYcMGZJOPfXU5a6zxP/8z/+kNdZYo8myctYHO1q/yLrPWerQWetVHa0TZqkjLNnnjl6rs9SrstSLUspWv8lyrU0pW90/y/1K1nvbUtWNOlIPzlJPyHqdL2cjqo5ed7IGGcv5vKw9BHByatiwYcULz4pMnTo1DRs2rMmyQYMGpZ122ik9+uijK3wdddRRTQ6CjTbaKPXv3z+dddZZ6ZVXXkmvvvpqk9ff/va3VCgU0uWXX15ctsTSJ93cuXNToVBIt9xyS4v5vvzyy1Pv3r2L7/fee++0zTbbFN+PHDkyff3rX28x7aRJk9J6661XfD9w4MB00UUXNVvv2WefTYVCId13333FZVdeeWWLAa9lW+su8eGHH6Zrrrkm7b777qmmpiZVVVWloUOHFj9fa621mlwonnjiiVQoFNKVV17Z7H9997vfbfZbLXH33XenzTbbLK2yyirpoosuSo2NjW0K4CwdGW/rK6WU+vXr12IeU0rp3//+dxo7dmzq379/+tOf/tRiJWDpHlMppfTuu++mQqGQ/vKXvzT7f1dccUWT3zqlj4/Rc845p9X9WtrZZ5+dBg0aVHy/6qqrph/84AfF988//3wqFArp5z//eYtpBw4cWHxfrgc7WSsQWSp8WW4As1bss6TPUkHOWsnNcnx29JwsRd6zbLt///5NKkKvv/56KhQK6eabb262zz/96U9Tv379miwbPXp0mjJlSrN1p02blqqqqtK+++5b7FW6bL7r6uqK5dGSAP7SLdOXduGFFzbZ9tLHSUNDQ/rd736X9t9//1RXV5eqqqrSKquskg4//PBmD1qXyHLNy7rf6623XjrttNOK7x955JFUKBRavJ4dffTRTa57y54fgwYNarVL9Z577tnkQXHW9GuuuWaLPWT/+Mc/ppqamvTWW2+llD7uTTtu3LgmN91Zyu8s281yTqeU7RhNKdtxmqUMz3LTXV1dnYYPH54++9nPrvA1atSoVgM4S3vxxRfTaaedljbccMPiTey2226bfvSjHzVZb8CAAemyyy5rMZ/nn39+qqqqShMnTkwLFixodl7V1tamn/3sZ8X3//73v1OhUEh/+MMfmv2vH/3oR6m2trb4/j//+U869thjU01NTTrwwAOb3fitqF625AauLa644oom2176O/vwww9TofBx7/CWfO9730v9+/cvvu/Tp0+Lrbxfe+21ZvXgCy64oFmjmix1o3IGnkaNGpW+8Y1vpJQ+bjjWp0+f9Itf/KLFbZ977rlpwIABxfdZ6nRZH8xkOS+z1suy1Ee768PHcgajs1zns57XWX7rLOd11kB4lu88S9q+ffu2+H235Kc//Wnq27dvk2V5qA+2t36RdZ+z1KGz1qs6WifMUkdIKdu1Oku9Kku9KKVs9Zss19qUstX9s9yvZL23LWXdaIm21oOz1BOyPncqVyOqLNedrEHGcj4vy1JHaY/ms2VRMgcffHBcdNFFcdFFF8WHH37Y4joffvhhXHjhhfH9738/Dj744CafbbXVVvGvf/0rPvnJT67wteaaazZJ+9RTT8UZZ5wRF1xwQXz+85+Pt956K9ZZZ53ia+21146IiNVXX724bGmPPvpo/O53v4vbbrstBgwYEO+++26L+X/77bdj4MCBxfenn356/P3vf48DDjggXnzxxfjRj34Ul19+eRxzzDFx7733xksvvRR33XVXHHLIIfGb3/wmDjvssGLabbbZJn7605/Gf/7zn+KylFKcf/750bt379hkk02Ky9977712TYjXr1+/OPjgg+N///d/Y+bMmfGDH/wg1l133eLnn/rUp+KnP/1pPP/88/H+++/Hd77znairq4sbb7wxZs2aVVzvn//8Z1xyySWx+eabt7id7bffPv7+97/Hd7/73TjrrLNik002iVtvvXWFk9xWV1fHiBEj4qtf/eoKX1tttVUx3brrrhtPPfVUi/9zjTXWiLvvvjs233zz2GeffeI3v/lNs3WGDBkSM2fOLL6vq6uLI488MtZaa61m67755pux6qqrNlm2YMGCNv8OAwYMiAULFhTfL1q0KGpra4vvl/zd0v9b+hiLiJgxY0aTCfiWTFrY0u+y5ZZbxmuvvVZ8/9JLL8Whhx4aZ5xxRvz0pz+N9ddfP3bYYYfia5tttomIiNGjRxeXLfHRRx/FyiuvXHy/ZELEIUOGNNvusGHDmuxvRMTrr78eixYtik022WSFr9VXX71J2jfffDO23nrrZtvZeuuto76+Pp599tmI+Pg3/NKXvhT33HNPcZ05c+a0+Ju2ZO211465c+c2WZY1/RIfffRRvPXWWzFhwoQWP//v//7veO6554rv//nPf8Zuu+3WZJ2ampq47LLL4rzzzouf/OQnMWnSpGhsbGzx/2U5Pjt6TpYi71m2vcEGG8S9995bfP+3v/0tIiL+8pe/NNvObbfdFuutt16TZa+88krxHFjaySefHL/85S/jT3/6U+y6667xwQcfNFtn5MiR8cADD0TEx8dh//79Y86cOc3Wi/j4mGptwvnevXvH5z73ubjxxhvjrbfeiiuuuCK22GKL+NnPfhY77bRTrL322jF16tQmabJe87Ls98yZM5t8j0v+99Jl1BJjxoxpUuYube7cuTF79uzYfffdW/x89913j3/+858tftaR9O+++25svPHGzdbbeOONY+HChfHSSy9FREShUIgvfOEL8fDDDxfXyVJ+Z9lulnM6onTHaET7j9MsZfjS1/revXtH7969W61XFAqFWLx4cfH9RhttFMOHD49bbrllha9l66Gt2XDDDeOMM86IF198MR544IE45phj4l//+lcce+yxTdZb3vf7rW99K6688sr43e9+F3vuuWez9QYMGNDkWtKrV6+IiCbH3RKLFy9u8luttNJK8cMf/jD+/ve/x9tvvx2jRo2KU089NebNm9em/Rs6dGg8+uijbVr3kUceaTbR75Lfpra2Nnr37t3qxOR1dXVNfqs111wzHnvssWbrPfbYY1EoFJpMwjxw4MCYP39+k/Wy1I2W1t5rdZZjOyLikEMOicsvvzwee+yxqK6ujsmTJ8e0adOalZUvvvhi/PCHP2xSVmep0y1tyJAh8Y1vfCMeffTReOGFF+KUU06JhQsXximnnBIbbLBBfOpTn4of//jHTdJkOS+z1quy1Eez1EWz1suy/F5Z8r20jvzWWa7zWc/rrPceS/+f9pzXpTq3luS1vd95R9Nuvvnmcdlll8VHH33Uan6WfB+XXXZZbLHFFk2W56E+2N76RdZ9zlKHzlqv6midMEsdYYmOXquz1Kuy1IsistVvslxrI7LV/ZfW3vuVrPe2paobLa2t9eAs9YSsZX+W63W56pNZvq+I8j4vK1UdZYU6FPahTRoaGtKBBx6YCoWPxw3cZJNN0i677JL22muvtMsuu6RNNtmkOK7hxIkTm03Ud9ppp6VCoVBsebA8l1xySVp33XWbLX/rrbfSYYcdlqqrq9PBBx9cnBhvedH5lrrNHnDAAS1ud6+99kpbb711k2V//vOf05AhQ1JV1cdj1y4ZvmzpV6FQSFOmTGkyvuWjjz6a6urq0qqrrpomTZqUDj300GKrhVNOOaXJNnbZZZe0++67N8t3az1wVuSll15KK6+8cpP8nXXWWenaa69Nffv2TZ/+9KfTNttsk2pra1NNTU165JFHVvg/33vvvfSVr3yl2PV7eT1wPvnJTzaJPi/P0l3ujj766DRs2LDU2NjY6vr19fVpn332abF7+wEHHJD22GOPNm135513bjYHzm677ZY23HDD9MYbbyw37RtvvJE22GCDJr/Z1ltvnfbZZ5/i+4svvjgVCoUWo+Y77bRT2mKLLYrvV1tttSYtQJbXc+jiiy9u1oIkpY+H5ttpp51S37590ymnnJI++uijlNLyz42xY8c2mfPpuuuuS4VCIR177LHN1v3v//7vNHr06CbLRo8enT796U83W7cly3atXH/99Zt0tV/id7/7Xaqqqmoy4dtll13WpHXVpz71qfTJT35yhWN1fvjhh2mLLbZolscs6QuFQvrlL3+ZUvq/1j7LG1Zx6XwPHTq0xWEVl7jqqqtSr1690i677JJ+/OMfNzu+sxyfHT0nS5H3LNte0l160qRJ6dhjj00DBgxIn/zkJ9PkyZPT1772tXTHHXekP//5z+mwww5LVVVVzVrSrL322s26VS/ttttuS/37909jxowpthRb4sc//nHq3bt38dw5+eST09ChQ5uVl3fccUdaaaWVmrQKakv5vXQrzNZarXTkmpd1v5ft7v3ee++l2traFltJXnjhhWnllVdust9Lzo9Fixalfv36pd/85jct5uGHP/xhkxaAWdNvtNFGTcYZXuKqq65KVVVVTcYV/slPftKkHM1SfmfZbpZzOqVsx2hK2Y7TLGX4tGnTUv/+/dOjjz6aUkrp8MMPTyNHjmw22fELL7yQhg0b1uRa/aUvfSn17du32fxJLWlp2KG21qsWLVqU/vznPzdZNn78+Gb1hmXdfPPNqa6urlhXXGLrrbduNjb2v//97xaHNTj66KObDau7tF//+tdpnXXWSWuuuWa6+uqrVzg34XnnnZcKhUL62te+lp577rkW13nuuefSsccem6qqqtK5555bXF4oFNLw4cPTpptumjbddNPUp0+fdOaZZ7b4P775zW+m9ddfv/j+9NNPT9XV1en0009Pzz33XHr11VfTddddl4YOHdqsPvH1r3+92T5nqRtluVZnObZT+rhF6C677JL69OmTDjrooHT22WenwYMHp9ra2rTzzjunz3/+82mHHXZINTU1acCAAS1OfNuROl1bju8HH3wwfe1rXyve1ywty3mZtV6WpT6apS6atV62REd+ryz5zvpbZ7nOZz2vs/zWWc7rJTryWy3Zdke/8yxp77vvvlRXV5fWXHPN9O1vfzv95je/Sffee2965JFH0r333pt+85vfpBNPPDGtueaaqa6urkkPqJTKWx/saP0i6z4v0ZE6dNZ6VUfrhFnrCFmu1VnqVVnqRS1pT/0m67U2S90/y/1K1nvbrHWjLPXgLPWErM+dslyvy1WfzPJ9pVTe52VZ6ijtIYDTBR566KH0rW99K+26665p9OjR6ROf+EQaPXp02nXXXdO3vvWt4hwly/rwww/Tq6++usKx/9uah6222ir169cvnXHGGem5555rtXBvqZv00mMdLvHOO++knXbaKV188cXNPpszZ076yU9+kiZMmJA222yz4j6PHz8+nXTSSS3efKX08RiOe+65Zxo4cGDq06dPGjNmTIvdBO+5554mY8mm9HH3vQcffLCN30hzM2bMSNOmTUvf/va3m0zu9atf/Sptv/32aeTIkWnvvfdO99xzT7v+70svvZTuuuuu9O6777a6zlFHHZX69OlT7JK9PEvGdkzp467ZEyZMSA888MBy0yxatCgdd9xxaccdd2yy/B//+Ef64x//uMJtvv322+lzn/tcuuGGG5osf+6559LgwYNTv3790qRJk9L06dPTtddem37zm9+ka6+9Nk2fPj1NmjQp9evXL6222mpNCs0bbrghFQqFNG7cuLT33nunmpqatMsuu6Sjjz467bPPPumKK65Il156aXEiyaW7pJbrwU7WCkSWCl+WG8CsFfss6bNUkLNWcrMcnx09J0uR9yzbXrx4cfrmN7+Z+vbtmwqFQtp6663Tyy+/nN5666206aabFgO5hUIhjR8/vtk29tlnn7Ttttsud5sPPvhgGjx4cOrVq1eTfC9evDh98YtfLG732GOPTYMHD05VVVXpE5/4RNp2223TOuusk6qqqtKwYcPSK6+8Ukzb3gD8Cy+8sNzP23PNy7rfO+ywQ6vDACxrypQpaezYscX3hcLHY5wPGDAgDRgwIFVXV6eTTjqpxbRf/epX04gRI5osy5J+yY3XoYcemv73f/833XXXXWnatGlp4MCBabvttmuS9sgjj0ybb7558X2W8jvLdrOc0yllO0aXfN8dPU6zlOFZbrpvu+22dOihh6ZZs2atML9PPfVUk6EwsjSMSenjxkXL3jC15O67704rrbRSk/Pq7LPPbvagpyXz5s1LgwcPTl/5yleWu978+fPTaaedlvr27VucmLW18mDx4sXp29/+durdu3eqqvp4MtMNNtggbbLJJmmDDTZI/fv3T1VVVal3795p6tSpTdLusMMOaccdd2zy+vKXv9xsGw0NDWno0KFNHlw1NjamiRMnFsvpJWX1uuuu2+w7nDhxYpOhNVLKVjcqZ+Bpyb6fd955ac0112yxIVnv3r3Tfvvtt8JjqT11uqwPZrKcl1nrZVnqo9314WM5g9FZrvNZz+ssv3WW83pZ7Q2EZ/nOs/5ejz/+eNpjjz2KDWWXbURaU1OT9thjjxbn0ylnfTBLPTjLPi+rPXXorPWqjtYJs9YRslyrs9SrstSLWtOe+k2Wa22Wun+W+5W23NsuebV0b5u1bpSlHpylnpD1uVO5GlFlue5kDTKW83lZljpKewjg9DA/+9nP0pAhQ9Kqq666wl4hdJ2HH344nX766entt99e4bqvvfZaq/NAlMMbb7yRjjrqqLTGGmu0WBFYffXV01FHHZVmzJjRLO0Pf/jDNGLEiDRkyJB04IEHpnfeeSd9+OGHaffddy+mr66uTocffniTSSPL9WAn68PxLBW+rDeAWSv2HU2fpYJcikpuR4/PrOdklryXojxYuHBhcVLIpZfdeeed6Ve/+lV67LHHWvx/V111VSoUCisMCj/77LNp7bXXbvE7v+GGG9LWW29dPDaWfg0dOjR9/etfb9azNGsFuTVtveZl2e/LL788ffWrX11hXmbPnp369+9fHH86pY8bHiz7Ovnkk5ulnTt3blpllVXS5MmTmyzPmn7q1KnFeeGW/F5bb711mjlzZpP1jjvuuGZjXHe0/M663SzXnCU6coymlO04zVqGl+oBd3u8+uqrxdbWHTFv3rz0zDPPLLcByxIdrdt89NFH6YknnmjTNlJK6ZVXXkmHHHJI2nHHHVtthbzEm2++mX70ox+lww8/PH32s59NO++8c/rsZz+bDj/88PSjH/1ohT3BlmfOnDnprrvuavYwK6WPG+dceOGF6Zxzzkm/+93v2hTMTylb3aicgaelLV68OD3zzDPppptuStdcc0268cYb0/3335/mzp3blq8gpdT2Ol0prjtZzsss9bLl1UdHjx693Ppod334WM5gdJbr/BIdPa+z3HtkOa9b0p4HxVm+81LVCefMmZPuu+++dOONNxbLk/vuuy998MEHraYpV32wnPvcmvY8N+povSqlbHXCFWlvHWFZy7tWd0Rn1ovaU7/p6LW2o3X/rPcrKXX83jZL3ShrPTiljtcTslznU8p2vZ42bVpZ6pNZvq8laVurj/7jH/9osm6pn5dlqaO0RyGllDo+ABvd0dy5c2P69OkxY8aMOO6442Ls2LHlzhIVYubMmTFr1qyYP39+1NXVxdChQ2PYsGEd+l+vvPJKvPXWW7HBBhvE4MGDO/Q/5s2bFy+99FKstdZazebvaclrr70Wp512Wrz++usxbdq02HbbbVtcb9GiRdHY2NhkHNhFixbF3XffHW+//XaMGDGi2fjCpfLoo4/G3/72t2hoaIiRI0fGnnvuGX369Glz+rlz58bTTz/d7HcaPXr0cserLVX65f3fv//977HOOusU56eaP39+/Otf/4ohQ4as8Pd7/fXX45VXXlnuGNylPD5XpNR57yoppZg3b1707t17ufN/RHw8h9t7773XbD6ZJebOnRsvv/xyfPjhh1FXVxfDhg1rNldEV2jLNa+U+92aRYsWxYcffhh9+/Zd4TaW1dDQEP/+979j5ZVXjpVWWqldaVeU/t///nc88MADxTKltTne2qMt5fe///3vuP/++2PBggUd3m7Wc7ocx+gjjzwS9957b4fL8JRSPPvss/HSSy81yfemm24a/fv378ScUynaWzdaWkvX6iUeeeSR+Nvf/lY8p9t7bJfaq6++GqeddlrMmDGjxTrda6+9Fquttlr07ds387aynJdZ6lWt1UfvueeeeOutt0peH+3Mus2Kfq8sSvlbL09HrvOzZ8+O7373uzFlypQm87229L8767de3nndkrb8Vlm+8676vVqS9/pgR7T1GGvJnDlz4oILLmjzc6OO1qtKUSdcWpZ9zpq+XGnLve1SPLvpyP1O1nwv0ZG6UXu23dF6QkfL/q54FtFZ9cmIbPWqcj8v60wCOBQnEhs+fHi5s0IbdNffK0u+25r25ZdfjgceeCBmz54dq622Wuy4444tTvRW6rQRH19kPvroow49QCvXtpfe7uqrrx477LBDu7abZdtZ0mb9vsoty3cW8fHEsh988EFsvPHGy725nTdvXjzzzDPx5ptvxvz586N///4xYsSIGDVqVEezTnRNWdYZumu+26Ot50ZbZN3nZdPPmjUrc3Dogw8+iN69e0ddXV1x2ezZs+Pxxx+PhQsXxpgxY5ZbFmYpE7KWJ+XadqnKwcbGxuJNXFsfuJUr31mOk3Klzbrf5ch3Hs7p7vxbL6sc9cmsaUvhtddei/XXXz9uvvnm2HvvvZt9nuU4K1faUqRvSXvr/51RD85SzyhX2hUdY5257az1so6mz7rPWdKXK225t90dj++s5X85tt3WcrCc5X9LSvWcriPPrNqy7Txe81rUoX47VJQsY/DR9brr75Ul38umvfjii9OJJ55YfF9fX58+//nPN+u23bt373TKKac0+V9tSbtkbPtl0z700EPpvffea7LsiSeeSHvssUeqra1NVVVVqW/fvulzn/tcev7555vtR5Z8Z9l2lu1m3XY5853Sx/M8HXLIIem//uu/0u67755+/vOfp8WLFzdb79prr212fGZJm/VYufzyy9NGG22Uhg4dmg455JD0n//8J7311ltp3Lhxxa68/fr1S9///vebpX3xxRfThAkTittZ9jV8+PB00UUXNRvWqtzfWZa0pUjfVqUsy1LqmfnOkjbLuZF1n7OkLxQKadNNN03nnntueu2119r1v+bNm5f222+/VFVVlXr16pWOP/74lFJKP/rRj4rzsRQKhdSrV6905JFHNju3s5QJWcuTcm07a74bGxvT5ZdfnnbdddfieP5LXoMHD0677LJLuuyyy1ocD7xc+W7LcbLks2WPk85Ie8kll6wwbdb9zrLtLPucUkpVVVVlO6fLtd9Zv7Ny1Sez1slS6vh1a8k8AK29Ro0aVRziZdNNN01jxoxp8v+yHGdZrjtZtpt121nr/1nL/yz1jHKkzXqMLW/bW221Vafuc0e3nXWfs6QvV9pyb7tcx0nWfGcp/8u57azlYJYyuFzld9Z9XjZ9Q0NDm9OX85rXHgI4dNuAQE/VXX+vUj48HD16dJOJ74466qhUKBTSV77ylXTPPfek559/Pt15553poIMOSlVVVemHP/xhSdJWVVU1GZ/40UcfTX379k19+/ZNBx10UJo6dWraf//9U+/evdOgQYPSyy+/3GQ/Sr3turq6Nm07y3azbjvLd5Y13y+++GLq379/qq2tTZ/85CfTWmutlQqFQvr0pz/dbHzSZW+4s6TNut+33HJLKhQKaezYsWnvvfdOvXr1ShMnTkz7779/Gj9+fLrsssvSD37wg7TFFlukqqqqdOuttxbTPvHEE2nllVdOAwYMSJ/97GfTxIkT0zrrrJP69OmTTj311HTyySenLbfcMhUKhbTXXnulxsbGXHxnWb/vrOnbo5RlWU/Md5a0Wc6NrPucNX2hUEj9+vUrjg++3XbbpUsvvTS9//77K/xfZ555ZioUCumLX/xiOvHEE9NKK62UTj311FRVVZUOO+ywdNNNN6Vf//rXxQer5557bjFtljIha3lSrm1nzfc777yTNt9881QoFNLIkSPTIYcckqZOnZpOO+20NHXq1HTIIYekkSNHFo/FpecrK2e+sxwn5Uqbdb/Lme9lz+ntt9++S87p7vxbZ6kblSttStmuW4VCIQ0YMKDZnABLXltvvXUqFApp9OjRxWVLy3KclStt1vRZ6v9Zy9Es9Yxypc16jLVl29///vdLnu8s6UtxXnU0fbnSlnvb5TpOsuY7S/nfGdvuquc35Sr/y/WcLmv6cl7z2kMAp0JdffXVbX7tv//+3TIgUEm66++VJd9Z0vbt2zddccUVKaWPJ3jr379/Ou6441rM4wEHHJBGjBhRkrSFQtMJJj/zmc+k1VZbLf3zn/9sku6JJ55I/fr1S1/84hebLC/XtrNsN+u2y5nviRMnpiFDhqSXXnqpuOyaa65JK620Ulp33XWbtHRZ9oY7S9qs+7399tunHXbYodiy88ILL0zV1dVp7733bpK2sbExbbTRRmn8+PHFZePHj0/rr79+kwcKCxYsSJMmTUpbbbVVcdn111+fevXqlc4///xcfGdZv++s6ctVlvXEfGdJm+XcyLrPWdMXCoV07bXXpnvvvTcdddRRafDgwalQKKQ+ffqkfffdN/36179uNjnrEqNGjUpf+tKXiu+vv/76VFVV1eKkoHvssUcaNWpU8X2WMiFreVKubWfN9yGHHJIGDRqU7rjjjrQ8d9xxRxo0aFCTyXbLme8sx0m50mbd73Lmu1zndDn3uxTfWTnqk1nr71muW2eddVbq169f2mWXXdIzzzyz7NeUXnnllVQoFFqdID7LcVautFnTZ6n/Zy1Hs9QzypU26zFWrnxnSZ91n7OkL1facm+7ux7fWcr/cm67FM9vylH+l+s5Xdb05bzmtYcAToUqFArNuoot75WXgEBP1V1/ryz5zpJ20KBBxe61H330USoUCummm25qMY8//vGPU58+fUqSdumL8MKFC1OvXr3Seeed12Lar3/962nNNddssqxc286y3azbLme+hw8fnqZNm9Zs3eeeey6tv/76afDgwemhhx5KKTW/4c6SNut+Dx48uEmLkJdffjkVCoV0zTXXNEt71llnpVVXXbX4fsCAAemCCy5ott7TTz+dqqqqmlQ+v/zlL6eNN964yXrl+s6yft+l+L3KUZb1xHxnSZvl3Mi6z6X4vpe+iWtsbEy33HJLOuigg1K/fv1SVVVVGjhwYDr00EPT7bff3mRonr59+6bLL7+8+H7GjBmtlofLloVZyoSs5Um5tp0134MGDUrnnHNOs/QtOfvss9OgQYNyke8sx0m50mbd73Lmu1zndDn3u5TfWVfWJ7PW37Neq9944400adKkVFNTk7761a82GVLn1VdfTYVC2x8Atuc4K1farOmz1P+zlqNZ6hnlSptStmOsnPnOkj7LPmdNX6605dx2dz2+s5b/5dp2KZ/fpNR15X+5ntNlTV/Oa157VHX+LDuUwyqrrBI77rhjPPLIIyt8feUrXyl3dnu87vp7Zcl3lrSf+tSn4oYbboiIiL59+8aIESPi7rvvbjGPd999dwwbNqwkaZc2f/78WLRoUWy88cYtfr7JJpvEO++8U7J8Z9l2qbbbkW2XM9/vvfdeixPcjRo1Ku6///5Ya621Yuedd44///nPzdbJknZZ7d3vefPmNZnAcaWVVoqIaPF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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "linkage_method = linkage(squareform(matrix_l3), method='average')\n", "dendrogram(linkage_method, color_threshold=0.9, above_threshold_color='grey')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "\n", "plt.axhline(y=2, c='grey', lw=1, linestyle='dashed')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"RMSD\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GEODES" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Number of clusters evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Complete linkage" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.17074098635377116\n", "For n_clusters = 3 The average silhouette_score is : 0.06312625190541854\n", "For n_clusters = 4 The average silhouette_score is : 0.05675505178166501\n", "For n_clusters = 5 The average silhouette_score is : 0.06365752537038345\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l1']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L1 complex), linkage=complete, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.2551017450627907\n", "For n_clusters = 3 The average silhouette_score is : 0.07938761652846933\n", "For n_clusters = 4 The average silhouette_score is : 0.06459784053208828\n", "For n_clusters = 5 The average silhouette_score is : 0.036301085822166484\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l2']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L2 complex), linkage=complete, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.08148131331084763\n", "For n_clusters = 3 The average silhouette_score is : 0.053509747311524934\n", "For n_clusters = 4 The average silhouette_score is : 0.04506948804564061\n", "For n_clusters = 5 The average silhouette_score is : 0.03760229268605483\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l3']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L3 complex), linkage=complete, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Average linkage" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.20684957129361758\n", "For n_clusters = 3 The average silhouette_score is : 0.12385874919579154\n", "For n_clusters = 4 The average silhouette_score is : 0.11979801003595512\n", "For n_clusters = 5 The average silhouette_score is : 0.08059287077307306\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l1']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L1 complex), linkage=average, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.2551017450627907\n", "For n_clusters = 3 The average silhouette_score is : 0.12088807096577528\n", "For n_clusters = 4 The average silhouette_score is : 0.09189310892175367\n", "For n_clusters = 5 The average silhouette_score is : 0.07860503992253728\n" ] }, { "data": { "image/png": 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z1llnVRn/4sWLndO7W/Tq27dvuZyyuqJXXl6eGTt2bJXrKv0jWE2+H5R+lC0e2e12M2PGjCrnOf/8811+UDqZotc//vEPExAQUOm67rvvvgq3e02LXoWFheU+IyWPstu19LHWne1gjHvHxtLj+/fvX+H+3LRpU3Po0CGDxo3LG+uR008/3fk8PT1d7du3V1pamnr16qUePXpoyJAhatu2bZ2s+4033lCvXr00cuRIbdq0ydnv0c8//6wXX3xRU6dOlXSiz6DFixc7Owlu1aqVLrjgAmVnZzs7yM7MzNTEiRP1yy+/VHg5TU3Z7Xadd955zjuxNGvWTNOmTVOTJk30xhtv6KOPPlJBQYEuuugi9ejRQykpKbrhhhu0c+dOLV++3LmcP/3pT85L2BITE5WYmKiwsDCtWrVKv/32mySpZ8+emjJlinOekj4BZsyYoa+//lqSFB4ermnTpqlly5bavHmzNm7cKIfDoUWLFqlnz55VXppUIjAwUGeccYbS0tLUtGlThYWFKTMzU2+//bY+//xzGWN09dVXa8qUKQoODi43/5EjR3Ts2DFddNFFSkhI0D/+8Q+lp6dLku6//37deOONCggI8Mi6SluwYIEefvhhGWP0+eefa+vWreratask6dVXX3V2jN6kSRONGzdOkvToo486O4rv2LGjzj//fPn5+Wn37t36+uuv9dVXX1W7vSTpiSeecC6/ZcuWmj59ukJDQ7V3715t27ZNn3zySY2Wc/fdd+vXX391ueSs7CVfnti/t2zZIj8/P82YMUPt2rXTjz/+qKCgoBrFWGLjxo3O97XEsWPHKp3+7bffdvl/9uzZtVrf77//rv/+97/O/6dOnarzzz9fixYtksPhUGFhoZ566iktXLiwRssrLi7Weeed5+xbzNfXVzNmzFDLli314osvOi8jvP/++3X66afroosuqnA5H374oc477zx17txZ69atcx4LSvoZS0hIqDKOzz//XFlZWS6vq6RPH3c99thjFb7vpS8P+OCDD5zPo6OjNWzYsAqXNWXKFP373/92mW/QoEE1juXSSy/VQw895Px/06ZNlV4iVpO46+IcVHpbtG/fXuHh4bWavzYOHDjgcmOA3r1712i+48ePa9y4cTpw4IBz2NChQ9W/f39lZWXpww8/dJl+7dq1+utf/+r8PzU1Veedd57279+vJ554Qna7Xfv27dOECRP03XffVdgf3pYtW5SamqoJEyZo48aN+vzzzyWdON5JUvfu3XX22Wfr6aef1s8//yyp/DG+tJ9++kmRkZFatGiRbDabHn/8cWVkZEiSrr/+eo0bN67a927Hjh264IILlJeX5/K6HA6H1q1bp127dmnfvn2aOHGitm7dKl9fX/n7++upp55S9+7dlZOTo40bN+of//iHvv76a/3666+SpNatW+vhhx8ut75OnTopNDRUOTk5Kigo0GeffaaBAwdWGeOp8Nlnn6lly5a68MILtWfPHj311FOSTlwCfv/997ucQ8r68ssvNXLkSOdNG0puOFF6H2jZsqWGDBmipKQkRUdHyxijHTt26JlnnlFOTo62bt2qhx9+WNdee60kae/evVq0aJFz/tDQUF166aXy8fHRP//5T5djXGnuvJ+eYLPZlJKSojPOOEMtWrRQdHS0ioqK9OOPP2r9+vUqLi7WW2+9peeee06TJ0+WzWbTzJkz9X//93+SpKeeesqlz76S7S9Jw4cPV8uWLSWduOFG6Vxv5MiR6t+/vw4ePKgnnnhC2dnZ+uCDD7Ro0SI9+uijFcb6wQcfKCYmRlOnTlXTpk2dl437+PioU6dO6t27t+Lj4xUVFaX8/Hxt2bJF//nPf2SM0erVq/WnP/3JeZzZsGGDSxcBKSkpmjp1qnbv3q1169ZVur088Trc5Ynj0KJFi/TWW29JkgICAjR16lS1a9dOW7du1fr162WM0V//+lf16NFD06ZNc/bfunz5cmdeM3z48Gr7tfz4448VEhKi6dOnq0WLFtqyZUu1++zVV1+tV1991fl/YmKizjvvPEVGRuq7777TK6+84sZWq9yKFSucXQTYbDZNnDhR3bp1044dO7R27VoVFRVp/fr1SktL05///OeTWtcnn3yiOXPmyOFwSJL8/Px0/vnnq2PHjtq7d69H+ia9//77Xc7hJfvDtm3b9MILL1Q6n7vbobbHxrI2b96sjh07asKECfr666/12muvSTrx3emf//ynrr/++pPeJmjALCu3oZyioiKXX0wregwYMMB8/fXXLvN5oqVX7969nc2ZCwsLTWxsbIW/0Jx77rnO4eHh4ebgwYPOca+99lqlvzC409LrpZdecg739fU1P/30k3NccXGx6dq1q3P8okWLKn1tFTW5rWrblPjmm29clvPOO++4jB8zZoxz3HnnnVfhOirzzTffmH/961/m/vvvN3fffbe57bbbXNZV+vKzsr88lW5Z9uKLL7qM+/bbbz22rrK/hA8fPtw5rnRLookTJ1Y4fNy4cc7hFV1i8/vvv5ucnBzn/5X9CnXFFVc4h99xxx3llnP06FFz9OjRSra0q+p+UXN3/y69L0kyL774Yo3iqSyumjxKf45K74uSyrXOrM5dd93l8lk7cOCAMcaYoUOHOoeffvrpNV7eCy+84BLPww8/7ByXm5vrcjzo1q2bc1zZY9lVV13lHPf111+7jHv55ZerjaP0JZtl4zDG9dKsyo4ZNf3lt7Tg4GDn8LS0tErj27Jli8sy5s2b5xxXk9ZPubm5LtOMGTPmpOJ29xxUlZtvvtk57/DhwyucxhMtvYqKilxaU8TGxtb4l92//e1vLuu//fbby03z66+/Op9369bNOW1ycrLJzc11jnv44YddlvXCCy84x5Xe75s2beq85HX79u0u88TGxjpbdWzcuLHSY3zZ7bZ582bnuLKX2N5www3OcZWd+0pf3tq+fXuX48j+/ftdWlm89NJLLtvnn//8p3NcSEiI89d2Pz+/KrsBKH3pa0UtXGvLEy29QkNDzb59+5zjxo8fX+lxsPS6Fi1a5LykXZK56KKLKrxE2BhjMjIyzGuvvWYeeeQRc++995q7777bnHnmmc55hw4d6pz2jjvucFlP6ZbNZc8dnng/d+/ebe6+++5aPSpy8OBB89JLL5mHH37Y3HPPPebuu+82Xbp0ca5z9uzZzml/++03lxYaW7duNcYYU1BQYKKjo53Dn3nmGec83bt3d9nWpZU+9vv5+blcblx6e0VERJhdu3ZVGL8xxuzatcts2LDBPPjgg87XULpF5f/93/85py19xURYWJjL8afs8bj0vu7u63CXJ49DR44cMX5+fs7hjz/+uMu65s2b5xzXvXv3SuOo6LK9stP4+vqaL7/8stw0leV1R48edYmte/fu5VoJ7d692+XS0c2bN9dqvy992bvdbjcxMTHO9d18880u61qxYoXLdi/pzsDdll4TJkxwDvfx8SnXbUVBQYFLq0h3Wnp16NDBObxt27YurXEvu+yyCo+17m6HErU5NpaNPTEx0aU1eenPVm27O4D3oehVz2RlZZmlS5eauLg4lw9y6UezZs1cTqaeKHo9+uijLvP16dPHOa50E+7SxbDzzz+/XPzNmjVzjp88ebJzuDtFr2uvvbbSbVD20adPn0pfm7tFr7JfXqp6xMXFVbiOsr788kuXSxEqe1R2mZKvr69L4vrDDz+4zPfee+95bF1lvxS8/PLLznHR0dEmLy/PZGdnu3zB/+qrr5zT33333c7hgYGBZvDgwWbOnDnm3nvvNZ988km1/dOUWL9+vcvr79u3r5k1a5a58847zaZNmyr9UlGR6pILd/fv0vtSly5dahxPZXHV5OHJolfpS2aHDRvmHF66zx2p4qJqRcp+dstelrFkyRLnOJvN5ix+lj2WlS505+XluYwr2/dWRcoWvVatWuUyvqEXvXJyclymOdmilzHunYOqcvnllzvnmzJlSoXTnGzRKysry+Xy3PDwcPPJJ5/UeP7Jkye7zFvVMSUnJ8fly/mSJUtcxmdnZ7u8lmuvvdY5rvR58OKLL3YOLygocJmn9Dn3559/rvQYX3q7paSklIu1devWzvGl+1qs7NzXu3fvGh9/rrvuunLrO//888tNV7ooUJHSucaKFSuqnLYmPFH0mjZtmsu40seJ1q1bu4yrbPtcdtll5b7MGXPiC+GSJUuqvCRJOlGkKlH6h6VmzZqVW2bpLiA8+X66Kzc311x88cVVdhMgyYwYMcJlviFDhjjH/fnPfzbGuP742aRJE+eX7rKfw+oepQuFpYcvWLCgwteQnp5e7WVxksycOXOc85S+NKts/rBz506X+UoKCyfzOtzlyeNQ2R8Dq3qUPteXjaMmRa/KLlevLK8rG1vpgmld+P7772u8LaQ/uvdwt+hVOl8dPXp0tfHVtuhV9jLlsseI9957z2V8ybHW3e3gzrGxbOwlx40SU6ZMcY4bMmRItdsI3o3LG+uZ8PBwLV++XLfffru+//57ffrpp3r//ff1/PPPO5vLHz58WGvXrtXixYs9tt7k5GSX/0vfaa6k6awkHT161Pk8Li6u3HLi4uJ0+PBhSZVfjmX+d+lYidJ3myut9LqqU7JOT/L0+vPy8nT22Wfr999/r3bayrZJXFycy6VyZe8IWPJeeWJdZY0dO1YpKSn67bffdOzYMT333HPy8/NzXjqRlpam7t27O6e/6qqr9O233+qpp55SQUGB3n33Xb377rvO8V26dNF///tfNW/evMr1Tpo0Sddcc40eeOABFRQU6OOPP3a5g1xSUpJeffVVj9zB1BP7d8eOHU86jtWrV+viiy92GbZz5061bt26wulbtGjh8v+PP/6otLS0Gq3r008/dbmjY8mlzNKJO+3Nnz9fRUVFzrhKX9ZVmdLbMSwsTKGhoS7jS29bY4wyMjIUEhJSbjmlj0uV7etVKbtdtm/f7vL/2LFjFRMTox9++EGPP/54tcuTTlwyVPZ4WVbz5s2dl07v3r270unK3k20us9CWT/99JPL/2Vfb2k1iVuy7hzkrj179ujss8/Wt99+K+nEZfCvvvpqrW67Xnp/TUxMrPKSmWPHjrmcw8oeJ0JDQxUWFqbs7Gzn9BUpfWlu2csVS48re2lkZft9bGxsuWFxcXHasWOHJDkvdazKyZ7zFi5cqPXr1zv/9/f319y5c6tcTtl8oD6oaT5UldatW8vHx6fc8L/97W+6++67q52/9Hm59HsXHx9fbtr4+Hjnpd+luft+7tmzR88880yN55VcL5NeunSp1qxZU+08ZXOP2bNna9OmTZKkf//737r99ttdLm2cNm2a870o+zmsTmU5WmXn60suucTlsrjK1PR9quh9kzz3Otx1sseh2uxjxhgdOXKkwnN9TdQ2tyobW2X5U2kfffSRPvrooxqvIzEx0dk1Sm22hXTivazoNbnzHakmr622yp4zyp5jKsqRy8ZVEyXbwZ1jY1meOHbDe1H0qqdsNptSU1OVmpqq2bNna9myZWrTpo3zQ1tybb2nlL2FvM1mq3C6Jk2a6NChQ5KkgwcPlhtfeljp/mNKJ38lRZISlb2Wkn61JCkoKEh/+ctfKgtfkZGRlY5zV+n1S9L//d//Vdv3VVXef/99lyLU1Vdfreuvv14xMTHKzc0tVxioSE3fJ0+sqywfHx/Nnz9fV199tSTpH//4h5o2beocP2vWLJfp/fz89OSTT+ree+/VRx99pO3bt2v79u164YUXdOzYMW3btk3XX3+9s++Iqtx999268cYb9dFHH+nHH3/UTz/9pJdffln79+/Xrl27NG/ePL333nu1fk1lubt/l+bOtj1Zw4YN02OPPeb8f82aNVq5cmWN5i375eSyyy7TZZddVuG069at04oVKyrso6i00p+d7Oxs5eTkuGyX0tvRZrMpKiqqwuWU3t8r29er0qtXL4WHhzuLNc8++6yWL1/u/BwPHDhQAwcO1CuvvFLjoldNDBw40Fn0Onr0qN555x0NHTq03HTPPvtsuflq45///KfL/xWtw12eOgfFxMQ4n1fVL507vvjiC40bN855rGvfvr1ee+01Zx+ONVV6f92zZ4/sdnulha/o6GjZbDbnF5Oyx4mcnBxnwatk+oqUPZaXVt3nqyIlx63SSsdW2WestNLbITU1tVzhvbQuXbq4/J+fn6/LL7/cZVhRUZHmzp1bZd8vpb8gNWvWrNoYT4WanmfL6tixo3788UdJJ/qLjIiI0Pz5812mKV1MSkhI0AsvvKC0tDQFBATo2muvrfBLX+n3rqL3uXRfdKW5+37++uuvWrJkSaXTVqR00av0a+zatav+/e9/q0OHDvLz89PkyZNdCqOllfzIkpWVpR07duitt97Sf/7zH+f40jlG2f153LhxVR4/S/dXWFpF5+ucnByXvp6GDRumRx99VElJSfL19VXv3r2dfV+VFhUVpSNHjkgq/z5V9h556nW462SPQ2Xz5EWLFlXZ1+bJ5Oq1za3KxrZjx45qfwz573//q1tvvbXG6xg0aJCz6FV2fTNnzix3nCytpEBTtjhe+jtSVlZWhbloyfpK9rOSHzc8qex7VXafriqu0mq6Hdw5Npbl7rEbjQNFr3rkiSeeUH5+vi644AJFRES4jAsNDZWPj4/zC0dNEti60K9fP7344ouSTnS2fejQIWf1//XXX3f5Fapfv37O56Xj/eyzz2SMkc1m09atW12SmrLrKpGfn6/U1FSNHj263HSffvqpSzW/7EGvpBP0skpPV9E0pdcvnfjyVjapl6TvvvuuRl/mSpKhEhdeeKHzC2HZL78nq67WNXv2bN18883KycnRu+++69zuAQEBuvDCC12m3b59uxITE9WsWTOde+65zuFdunRxthCpSWf2O3bsUHR0tKKiojR69GjnPjBixAhNmDChxsupCXf3b6uNHz9eSUlJzpZDDz74oHr37q1p06aVm/btt99WQECABg4cqPz8fJeOd6tz6NAhvfbaa86bFVSm7LZ58sknnZ+dvLw8l32wW7dubv/yWx1/f3/NmzdPd911lyRp//79mj59utauXVtn65SkOXPmuBRzr7vuOr3zzjsunbi/++67Lkle586da1X0euyxx1w6sU9KSnJ+HtxVF+eglJQU5/M9e/acVHylvfDCC5o+fbrz2D1w4EC9+OKL5RLumhgwYIBznzx+/Ljuvvvuch3e7tq1S0lJSQoJCVG3bt2cNzhZv369br31Vmch9cknn3SZ71QdJ3777Td99NFHzvV99NFHLl+EevToUe0y+vXrp88++0zSiZtbXHDBBeVaDxYXF+s///mP+vTp4zL8mmuucXYC3qpVK6Wnpys3N1cvvvii/v73v1fY4stut2v//v3O/0vvK9KJL0Mlx7RbbrlFy5Ytq/Y1WOnaa6/Vpk2bnB04L1y4UOHh4S436ih9bu7Zs6ezE/T8/PxKc6GePXvqueeek3Tii+amTZs0ZMgQSSeOIxW18pJO7v08GaVf45AhQ5ytsA8fPuzS2rus4OBgTZ061dlZ+5w5c5yf727durkUfEJDQ5WWlub8HB45ckRXXnllufwvMzNTr7/+eq1agmdmZjpvwiP90cpdOpHXlLQqLatnz5564403JJ24OdSxY8ecRe/Vq1dXOM/JvI6LL77YeZ4ZNGhQldu2rvTp00e+vr7O7eXv7+9SAC2xc+dObd++3eW8Ul0OfrLOOOMM+fn5qbi4WJJ011136eyzz3Y59+/fv1/NmjWrsvhXUx06dFDTpk2d+39eXl6F2+LQoUPavHmzEhMTJZU/l37yySfq3LmzJOmOO+6otCXggAED9Pzzz0s6UazbvHmzyw21iouLdfDgwSpbgFclPDxcHTp0cLaQf+6553Trrbc68/5//etfFc7n7nZw59gI1AZFr3pkx44duvXWW3XVVVdpwIABSktLU5MmTXTkyBFt2LDBeeCWpFGjRlkS46JFi/TSSy/JGKPjx4+rV69emjZtmrKzs11aSjRp0kQzZ850/t+rVy9t2bJFkvTee+/pjDPOUEJCgt566y0VFhZWuK6xY8eqU6dOzkuvxo8frwkTJqhz585yOBz69ddf9f7772vXrl1avXq183KukhNYySVZN9xwg7755hv5+/tr8ODB6tmzpyTXS4FeffVVZ0uomJgYXXzxxerWrZuGDx+uN998U9KJOxi+/vrr6tGjh3x8fLRr1y599NFH+uGHH3TLLbdowIABVW67Dh06uPw/ffp0TZkyRTt37nQmyZ5SV+uKiorS9OnT9fe//13SH82Mx40b59LqS5Luu+8+rV27VsOGDVPr1q0VFxeno0ePunwprMkX52eeeUa33HKLBg8erHbt2ql58+bKyclxueudp4rA7u7fVgsMDNSaNWs0cuRIFRYWym6368ILL9SDDz6oIUOGKCwsTPv27dM777yjH374QatXr3YWCUo3YR86dGiFrS1efvll56+Pq1evrrboNXbsWJdkaeHChfr888/VokULvfjiiy6X9ZW+K1lduPHGG/Xmm286C6PPP/+8Nm/erHPPPVfJycnKy8tzXlJTE5XdBbFLly7O43K/fv00d+5c5+fkiy++UKdOnTR58mTFxMRo69at2rBhg/OLQkBAgB599NEKL4cqcc8998hut+vAgQN66623nHfAlE68/+vWravwrn61ibsuzkGlk/Dt27eXa/VXkZJjdFlz5szRnDlztH79ek2dOtVZgIuMjNTIkSPLtdaLjIystNViaRdffLFuv/125y/XS5cu1dtvv62+ffsqNzdXn3zyiWJiYpwF8auvvlozZsyQdOLLXK9evVzu3liiffv2Gjt2bLXr95QxY8Zo9uzZzrs3lvDz86uylU+JhQsX6pFHHlF+fr6OHj2qtLQ0nX/++UpMTFR2dra+//57vfvuu8rIyHD+GCGdOH+WFGB9fHy0du1abd26VQsWLJAkLV68WIMGDSp3Kc8PP/zg/LIbEBBQ47tt1lcl2/3w4cPauHGjjDGaPXu2wsLCnAXpDh06OFtJvvLKK5o7d67i4+O1YcMGZyuxsmbMmKFbb71V+fn5kk7kQpdccomk8q09S3P3/Rw8ePBJXXbaoUMH5/Hpsccek4+Pj0JCQrR27dpqL8+bNWuWs+hVumhbtiW5JC1ZssT5Y9vmzZt12mmn6ZxzzlF0dLSOHDmiLVu26MMPP1Tz5s1dLtuvTmxsrKKiopznxttuu02HDh1ScXGxHn/88Uovr7rsssucRa+MjAz16dNHkydP1u7duystENTl6zgVmjRpotmzZztbmq9YsUJffPGF+vXrp6CgIO3bt0+ffPKJtmzZopkzZ2rkyJHOeVu0aKFffvlF0okW58HBwQoPD1ebNm103nnnnXRs0dHRmjNnjvPOsV999ZU6d+6s8ePHKyoqSj/99JNeeOEF/f77784cctmyZW4X1318fLR48WLdcMMNkk78wPzbb79p+PDhCg8P14EDB/TFF1/o008/1YABA5yvsWPHji4t0ufNm6dXXnlFBw4ccOnKo6wlS5boxRdflMPhkN1u15AhQzR58mR16NBBBw4c0BtvvKEFCxboqquucuv1SCcu8y25U+Ivv/yivn376pxzztG2bducBTdPbQd3jo1ArVjQjxgqUdOOhy+77DKX+TzRkX3Zzt6r6uT9vvvuq7KD0sjIyHLL++6770xgYGC5aYODg83gwYOd/5ftXHb79u0unbRW9ih716fzzjuvwulK32WodAeppR+pqanOaQ4ePGjS0tKqXX9lnXCWNWrUqArnL9uRc+nXU1Xnu1W9j55eV4lt27aVW+arr75abrq5c+dWuc18fHxc7mxWWSebZe9cVdHjb3/7W422f3Udhhrj3v5d3U0RahtXRXcxK/teV7TPvfPOOyYhIaHGn5fSd5uKiIhw6WS2tBkzZjin8/f3N4cPH672NX3//femZcuWVcZxxRVXuMxT1bHMmMrvMFSd9PR0c/bZZ1e7XSSZgIAA8/nnnzvnrelxuez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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l2']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L2 complex), linkage=average, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.07561225113483713\n", "For n_clusters = 3 The average silhouette_score is : 0.05047818747143401\n", "For n_clusters = 4 The average silhouette_score is : 0.05439271415630114\n", "For n_clusters = 5 The average silhouette_score is : 0.03468291919431379\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l3']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='average', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L3 complex), linkage=average, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Ward linkage" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.062265278117204434\n", "For n_clusters = 3 The average silhouette_score is : 0.06528238390065769\n", "For n_clusters = 4 The average silhouette_score is : 0.061452285023442306\n", "For n_clusters = 5 The average silhouette_score is : 0.05879429540764299\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l1']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L1 complex), linkage=ward, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.10303208861838811\n", "For n_clusters = 3 The average silhouette_score is : 0.06044593042697582\n", "For n_clusters = 4 The average silhouette_score is : 0.06809092969456079\n", "For n_clusters = 5 The average silhouette_score is : 0.058958332839824386\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l2']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L2 complex), linkage=ward, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For n_clusters = 2 The average silhouette_score is : 0.06202684817480042\n", "For n_clusters = 3 The average silhouette_score is : 0.06887952809542888\n", "For n_clusters = 4 The average silhouette_score is : 0.05857208372391389\n", "For n_clusters = 5 The average silhouette_score is : 0.05805960557773954\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = scaled_data['df_vd3_src_l3']\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(15, 10))\n", "fig.subplots_adjust(hspace=0.3, wspace=0.1)\n", "\n", "range_n_clusters = {ax1[0]: 2, ax1[1]: 3, ax2[0]: 4, ax2[1]: 5}\n", "\n", "for ax_n, n_clusters in range_n_clusters.items():\n", "\n", " # Create a subplot with 1 row and 2 columns\n", "\n", " # The 1st subplot is the silhouette plot\n", " # The silhouette coefficient can range from -1, 1 but in this example all\n", " # lie within [-0.1, 1]\n", " ax.set_xlim([-0.1, 1])\n", "\n", " # The (n_clusters+1)*10 is for inserting blank space between silhouette\n", " # plots of individual clusters, to demarcate them clearly.\n", " ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n", "\n", " # Initialize the clusterer with n_clusters value and a random generator\n", " # seed of 10 for reproducibility.\n", " clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', metric='euclidean')\n", " cluster_labels = clusterer.fit_predict(X)\n", "\n", " # The silhouette_score gives the average value for all the samples.\n", " # This gives a perspective into the density and separation of the formed\n", " # clusters\n", " silhouette_avg = silhouette_score(X, cluster_labels)\n", " print(\n", " \"For n_clusters =\",\n", " n_clusters,\n", " \"The average silhouette_score is :\",\n", " silhouette_avg,\n", " )\n", "\n", " # Compute the silhouette scores for each sample\n", " sample_silhouette_values = silhouette_samples(X, cluster_labels)\n", "\n", " y_lower = 10\n", "\n", " ax = ax_n\n", "\n", " for i in range(n_clusters):\n", " # Aggregate the silhouette scores for samples belonging to\n", " # cluster i, and sort them\n", " ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n", "\n", " ith_cluster_silhouette_values.sort()\n", "\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", "\n", " color = cm.nipy_spectral(float(i) / n_clusters)\n", " ax.fill_betweenx(\n", " np.arange(y_lower, y_upper),\n", " 0,\n", " ith_cluster_silhouette_values,\n", " facecolor=color,\n", " edgecolor=color,\n", " alpha=0.7,\n", " )\n", "\n", " # Label the silhouette plots with their cluster numbers at the middle\n", " ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", "\n", " # Compute the new y_lower for next plot\n", " y_lower = y_upper + 10 # 10 for the 0 samples\n", "\n", " ax.set_title(f\"The silhouette plot for the {n_clusters} clusters.\")\n", "\n", " # The vertical line for average silhouette score of all the values\n", " ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax.set_yticks([]) # Clear the yaxis labels / ticks\n", " ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n", "\n", "\n", "ax1[0].set_ylabel(\"Cluster label\")\n", "ax1[1].set_ylabel(\"Cluster label\")\n", "ax2[0].set_xlabel(\"The silhouette coefficient values\")\n", "ax2[1].set_xlabel(\"The silhouette coefficient values\")\n", "\n", "plt.suptitle(\n", " \"Silhouette analysis for HCA on GEODES (L3 complex), linkage=ward, metric=euclidean\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Clustering + dendrograms" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "h_cluster_l1 = AgglomerativeClustering(n_clusters=2, linkage='complete', distance_threshold=None, metric='euclidean')\n", "h_cluster_l2 = AgglomerativeClustering(n_clusters=2, linkage='ward', distance_threshold=None, metric='euclidean')\n", "h_cluster_l3 = AgglomerativeClustering(n_clusters=2, linkage='complete', distance_threshold=None, metric='euclidean')\n", "\n", "\n", "hc_l1 = h_cluster_l1.fit_predict(scaled_data['df_vd3_src_l1'])\n", "hc_l2 = h_cluster_l2.fit_predict(scaled_data['df_vd3_src_l2'])\n", "hc_l3 = h_cluster_l3.fit_predict(scaled_data['df_vd3_src_l3'])" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "linkage_method = linkage(scaled_data['df_vd3_src_l1'], method ='complete', metric='euclidean')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "dendrogram(linkage_method, color_threshold=35, above_threshold_color='grey')\n", "\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"Dissimilarity\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "\n", "linkage_method = linkage(scaled_data['df_vd3_src_l2'], method ='ward', metric='euclidean')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "dendrogram(linkage_method, color_threshold=60, above_threshold_color='grey')\n", "\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"Dissimilarity\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(20, 7))\n", "\n", "linkage_method = linkage(scaled_data['df_vd3_src_l3'], method ='complete', metric='euclidean')\n", "\n", "pal = ['#3e338e', '#50af7c']\n", "\n", "hierarchy.set_link_color_palette(pal)\n", "dendrogram(linkage_method, color_threshold=32, above_threshold_color='grey')\n", "\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Frames\", fontsize=16, labelpad=10)\n", "plt.ylabel(\"Dissimilarity\", fontsize=16, labelpad=15)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Rand index (comparing to RMSD)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9801980198019802\n", "0.9231683168316832\n", "0.9607920792079208\n" ] } ], "source": [ "print(rand_score(hc_l1, hc_l1_rmsd))\n", "print(rand_score(hc_l2, hc_l2_rmsd))\n", "print(rand_score(hc_l3, hc_l3_rmsd))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RMSD analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RMSD (pairwise 2D-matrices) vs GEODES" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# RMSD matrices were loaded in HAC section for dendrograms\n", "\n", "# GEODES distance matrix calculation\n", "\n", "l1_dist_mult = distance_matrix(scaled_data['df_vd3_src_l1'].values, scaled_data['df_vd3_src_l1'].values)\n", "l2_dist_mult = distance_matrix(scaled_data['df_vd3_src_l2'].values, scaled_data['df_vd3_src_l2'].values)\n", "l3_dist_mult = distance_matrix(scaled_data['df_vd3_src_l3'].values, scaled_data['df_vd3_src_l3'].values)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorscale": [ [ 0, "rgb(41, 24, 107)" ], [ 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=2, cols=3, horizontal_spacing=0.06, vertical_spacing=0.07,\n", " subplot_titles=(\"L1\", \"L2\", \"L3\", \"\", \"\", \"\"))\n", "\n", "color_continuous_scale = 'haline'\n", "r_scale=True\n", "\n", "############################\n", "\n", "fig.append_trace(go.Heatmap(z=l1_dist_mult, zmin=15, zmax=35,\n", " showscale=False,\n", " #colorbar=dict(orientation='h', len=0.32, x=0.14, y=0.2, thickness=15),\n", " colorscale = color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=1, col=1)\n", "\n", "fig.append_trace(go.Heatmap(z=l2_dist_mult, zmin=15, zmax=35,\n", " showscale=False,\n", " #colorbar=dict(orientation='h', len=0.32, x=0.5, y=0.2, thickness=15),\n", " colorscale = color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=1, col=2)\n", "\n", "fig.append_trace(go.Heatmap(z=l3_dist_mult, zmin=15, zmax=35,\n", " colorbar=dict(orientation='v', len=0.5, y=0.76, thickness=15, dtick=5),\n", " colorscale=color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=1, col=3)\n", "\n", "############################\n", "\n", "fig.append_trace(go.Heatmap(z=matrix_l1.to_numpy(), zmin=0.5, zmax=1.3,\n", " showscale=False,\n", " #colorbar=dict(orientation='h', len=0.33, x=0.16, y=-0.35, thickness=15),\n", " colorscale = color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=2, col=1)\n", "\n", "fig.append_trace(go.Heatmap(z=matrix_l2.to_numpy(), zmin=0.5, zmax=1.3,\n", " showscale=False,\n", " #colorbar=dict(orientation='h', len=0.51, x=0.50, y=-0.35, thickness=15),\n", " colorscale = color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=2, col=2)\n", "\n", "fig.append_trace(go.Heatmap(z=matrix_l3.to_numpy(),zmin=0.5,zmax=1.3,\n", " colorbar=dict(orientation='v', len=0.5, y=0.23, thickness=15),\n", " colorscale=color_continuous_scale,\n", " reversescale=r_scale,\n", " ), row=2, col=3)\n", "\n", "###########################\n", "\n", "fig.update_layout(template='simple_white',\n", " title=dict(text=\"RMSD matrices vs GEODES matrices\", font=dict(size=20)),\n", " height=600, width=850)\n", "\n", "\n", "fig.update_xaxes(dtick=10)\n", "fig.update_yaxes(dtick=10)\n", "\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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L1L2L3
r0.6990110.7514120.661124
p0.0001000.0001000.000100
z12.79547113.09576014.625117
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" ], "text/plain": [ " L1 L2 L3\n", "r 0.699011 0.751412 0.661124\n", "p 0.000100 0.000100 0.000100\n", "z 12.795471 13.095760 14.625117" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mantel test for matrices\n", "\n", "mantel_df = pd.DataFrame(index=['r','p', 'z'])\n", "\n", "mantel_df['L1'] = [x for x in mantel.test(l1_dist_mult[np.triu_indices(101, k = 1)], matrix_l1.to_numpy()[np.triu_indices(101, k = 1)], method='pearson', tail='upper', perms=10000)]\n", "mantel_df['L2'] = [x for x in mantel.test(l2_dist_mult[np.triu_indices(101, k = 1)], matrix_l2.to_numpy()[np.triu_indices(101, k = 1)], method='pearson', tail='upper', perms=10000)]\n", "mantel_df['L3'] = [x for x in mantel.test(l3_dist_mult[np.triu_indices(101, k = 1)], matrix_l3.to_numpy()[np.triu_indices(101, k = 1)], method='pearson', tail='upper', perms=10000)]\n", "\n", "mantel_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RMSD (simple 1D-array form) vs GEODES" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "df_rmsd_md_l1 = pd.read_csv('rmsd_rmsf/rmsd_l1.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "df_rmsd_md_l1['Ligand'] = 'VD3'\n", "df_rmsd_md_l1['CoA'] = 'SRC1'\n", "df_rmsd_md_l1['Motif'] = 'L1'\n", "df_rmsd_md_l1['Complex'] = 'VDR-SRC1-L1'\n", "\n", "df_rmsd_md_l2 = pd.read_csv('rmsd_rmsf/rmsd_l2.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "df_rmsd_md_l2['Ligand'] = 'VD3'\n", "df_rmsd_md_l2['CoA'] = 'SRC1'\n", "df_rmsd_md_l2['Motif'] = 'L2'\n", "df_rmsd_md_l2['Complex'] = 'VDR-SRC1-L2'\n", "\n", "df_rmsd_md_l3 = pd.read_csv('rmsd_rmsf/rmsd_l3.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "df_rmsd_md_l3['Ligand'] = 'VD3'\n", "df_rmsd_md_l3['CoA'] = 'SRC1'\n", "df_rmsd_md_l3['Motif'] = 'L3'\n", "df_rmsd_md_l3['Complex'] = 'VDR-SRC1-L3'" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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frame#Prot_CAProt_BackboneProt_SidechainProt_All_HeavyLig_wrt_ProteinLig_wrt_LigandLigandCoAMotifComplex
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10.010.7010.7171.1590.8530.6000.293VD3SRC1L1VDR-SRC1-L1
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30.030.7300.7601.3300.9410.3480.308VD3SRC1L1VDR-SRC1-L1
40.040.7980.8241.3851.0170.5640.260VD3SRC1L1VDR-SRC1-L1
....................................
29989.962.0942.1862.9302.4780.8250.409VD3SRC1L3VDR-SRC1-L3
29999.972.2592.3503.0472.6211.0780.430VD3SRC1L3VDR-SRC1-L3
30009.982.1852.2432.9722.5330.8360.485VD3SRC1L3VDR-SRC1-L3
30019.992.2172.2712.9762.5540.7990.456VD3SRC1L3VDR-SRC1-L3
300210.002.2322.2742.9062.5140.7480.357VD3SRC1L3VDR-SRC1-L3
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3003 rows × 11 columns

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" ], "text/plain": [ " frame# Prot_CA Prot_Backbone Prot_Sidechain Prot_All_Heavy \\\n", "0 0.00 0.000 0.000 0.000 0.000 \n", "1 0.01 0.701 0.717 1.159 0.853 \n", "2 0.02 0.744 0.766 1.244 0.920 \n", "3 0.03 0.730 0.760 1.330 0.941 \n", "4 0.04 0.798 0.824 1.385 1.017 \n", "... ... ... ... ... ... \n", "2998 9.96 2.094 2.186 2.930 2.478 \n", "2999 9.97 2.259 2.350 3.047 2.621 \n", "3000 9.98 2.185 2.243 2.972 2.533 \n", "3001 9.99 2.217 2.271 2.976 2.554 \n", "3002 10.00 2.232 2.274 2.906 2.514 \n", "\n", " Lig_wrt_Protein Lig_wrt_Ligand Ligand CoA Motif Complex \n", "0 0.000 0.000 VD3 SRC1 L1 VDR-SRC1-L1 \n", "1 0.600 0.293 VD3 SRC1 L1 VDR-SRC1-L1 \n", "2 0.496 0.265 VD3 SRC1 L1 VDR-SRC1-L1 \n", "3 0.348 0.308 VD3 SRC1 L1 VDR-SRC1-L1 \n", "4 0.564 0.260 VD3 SRC1 L1 VDR-SRC1-L1 \n", "... ... ... ... ... ... ... \n", "2998 0.825 0.409 VD3 SRC1 L3 VDR-SRC1-L3 \n", "2999 1.078 0.430 VD3 SRC1 L3 VDR-SRC1-L3 \n", "3000 0.836 0.485 VD3 SRC1 L3 VDR-SRC1-L3 \n", "3001 0.799 0.456 VD3 SRC1 L3 VDR-SRC1-L3 \n", "3002 0.748 0.357 VD3 SRC1 L3 VDR-SRC1-L3 \n", "\n", "[3003 rows x 11 columns]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmsd_df = pd.concat([df_rmsd_md_l1, df_rmsd_md_l2,df_rmsd_md_l3], ignore_index=True)\n", "rmsd_df['frame#'] = rmsd_df['frame#'].apply(lambda x: x / 100)\n", "\n", "rmsd_df" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Complex=VDR-SRC1-L1
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Time, ns=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_colors = 11\n", "colors = px.colors.sample_colorscale(\"RdBu\", [n/(n_colors -1) for n in range(n_colors)])\n", "\n", "fig = px.line(rmsd_df, x=\"frame#\", y=\"Prot_CA\", color='Complex',\n", " labels={\"frame#\": \"Time, ns\",\n", " \"Prot_CA\": \"RMSD, Å\"},\n", " color_discrete_map={\"VDR-SRC1-L1\": '#faa920',\n", " \"VDR-SRC1-L2\": '#1b9894',\n", " \"VDR-SRC1-L3\": '#90c73d'},\n", " width=600, height=400\n", " )\n", "\n", "fig.update_layout(template='simple_white',\n", " xaxis = dict(tickmode = 'linear', dtick = 0.05),\n", " font=dict(size=16),\n", " showlegend=True,\n", " legend=dict(font=dict(size=12),\n", " title=\"\",\n", " orientation=\"v\",\n", " yanchor=\"top\",\n", " y=1,\n", " xanchor=\"right\",\n", " x=1)\n", " )\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "# distance from all the points to frame 0 (GEODES)\n", "l1_dist_tofirst = l1_dist_mult[0]\n", "l2_dist_tofirst = l2_dist_mult[0]\n", "l3_dist_tofirst = l3_dist_mult[0]\n", "\n", "# distance from all the points to frame 0 (MD RMSD, 1st row of RMSD matrix)\n", "matrix_l1_tofirst = matrix_l1.to_numpy()[0]\n", "matrix_l2_tofirst = matrix_l2.to_numpy()[0]\n", "matrix_l3_tofirst = matrix_l3.to_numpy()[0]\n", "\n", "# distance from all the points to frame 0 (MD RMSD, CA atoms but not only helices)\n", "rmsd_l1_md = df_rmsd_md_l1['Prot_CA'].to_numpy()[0::10]\n", "rmsd_l2_md = df_rmsd_md_l2['Prot_CA'].to_numpy()[0::10]\n", "rmsd_l3_md = df_rmsd_md_l3['Prot_CA'].to_numpy()[0::10]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correlation coefficient between GEODES and MD RMSD all CA atoms:\n", "PearsonRResult(statistic=np.float64(0.6706666274400139), pvalue=np.float64(1.688187566347259e-14))\n", "PearsonRResult(statistic=np.float64(0.7959886851377891), pvalue=np.float64(2.6036330118541042e-23))\n", "PearsonRResult(statistic=np.float64(0.7648681155683447), pvalue=np.float64(1.2841535631928684e-20))\n", "\n", "\n", "For helices-only correlation coefficient is higher:\n", "PearsonRResult(statistic=np.float64(0.7846428457654248), pvalue=np.float64(2.8111530349295815e-22))\n", "PearsonRResult(statistic=np.float64(0.8880309609774809), pvalue=np.float64(3.51764461349852e-35))\n", "PearsonRResult(statistic=np.float64(0.8703765703852845), pvalue=np.float64(3.1642787892189346e-32))\n" ] } ], "source": [ "# Pearson correlation\n", "\n", "print('Correlation coefficient between GEODES and MD RMSD all CA atoms:')\n", "print(pearsonr(l1_dist_tofirst, rmsd_l1_md))\n", "print(pearsonr(l2_dist_tofirst, rmsd_l2_md))\n", "print(pearsonr(l3_dist_tofirst, rmsd_l3_md))\n", "print('\\n')\n", "print('For helices-only correlation coefficient is higher:')\n", "print(pearsonr(l1_dist_tofirst, matrix_l1_tofirst))\n", "print(pearsonr(l2_dist_tofirst, matrix_l2_tofirst))\n", "print(pearsonr(l3_dist_tofirst, matrix_l3_tofirst))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Slope: 19.36834328111384 Intercept: 8.831982686820552 r_value: 0.7846428457654253\n", "Slope: 23.707018757081848 Intercept: 6.67573991076339 r_value: 0.8880309609774807\n", "Slope: 23.47880460583885 Intercept: 4.984031410237147 r_value: 0.8703765703852843\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#faa920", "width": 2 }, "mode": "lines", "name": "L1", "showlegend": true, "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=2, cols=6,\n", " subplot_titles=(\"Molecular dynamics\", \"GEODES\", \"L1\", \"L2\", \"L3\"),\n", " specs=[\n", " [{\"colspan\":3}, None, None, {\"colspan\":3}, None, None],\n", " [{\"colspan\":2}, None, {\"colspan\":2}, None, {\"colspan\":2}, None]\n", " ],\n", " horizontal_spacing=0.12, vertical_spacing=0.22)\n", "\n", "\n", "old_colors = ['#CB7053', '#608FB9', '#89A275']\n", "colors = ['#faa920', '#1b9894', '#90c73d']\n", "\n", "hel = [matrix_l1_tofirst, matrix_l2_tofirst, matrix_l3_tofirst]\n", "desc = [l1_dist_tofirst, l2_dist_tofirst, l3_dist_tofirst]\n", "\n", "\n", "for i in range(1,4):\n", " # Add traces for RMSD helices curve\n", " fig.add_trace(\n", " go.Scatter(x=np.arange(1, 101), y=hel[i-1],\n", " mode='lines', name=f\"L{i}\",\n", " #legendgroup = str(i),\n", " showlegend=True,\n", " line = dict(color=colors[i-1], width=2)),\n", " row=1, col=1,\n", " )\n", "\n", " fig.add_trace(\n", " go.Scatter(x=np.arange(1, 101), y=desc[i-1],\n", " mode='lines', name=f\"L{i}\",\n", " #legendgroup = str(i),\n", " showlegend=False,\n", " line = dict(color=colors[i-1], width=2)),\n", " row=1, col=4\n", " )\n", "\n", "\n", "for i in range(1,4):\n", " # Add traces for descr vs rmsd_hel scatter correlation\n", " next_col=2*i+1\n", " fig.add_trace(\n", " go.Scatter(y=desc[i-1], x=hel[i-1],\n", " mode='markers',\n", " #legendgroup = str(i),\n", " showlegend=False,\n", " marker_color=colors[i-1]),\n", " row=2, col=next_col-2\n", " )\n", "\n", "\n", "for i in range(1,4):\n", " next_col=2*i+1\n", " slope, intercept, r_value, p_value, std_err = stats.linregress(hel[i-1],desc[i-1])\n", "\n", " print('Slope:', slope, 'Intercept:', intercept, 'r_value:', r_value)\n", "\n", " # Add traces for descriptors curve\n", " fig.add_trace(\n", " go.Scatter(x=hel[i-1], y=slope*hel[i-1]+intercept,\n", " mode='lines', name=f\"L{i}\",\n", " #legendgroup = str(i),\n", " showlegend=False,\n", " line = dict(color=colors[i-1], width=2)),\n", " row=2, col=next_col-2,\n", " )\n", "\n", "# Set x-axis title\n", "\n", "fig.update_xaxes(title_text=\"frames\", title_font=dict(size=14), range=[0, 100], dtick=10, row=1)\n", "fig.update_xaxes(range=[0.6, 1.3], dtick=0.1, title_text=\"RMSD α-helices, Å\", showticklabels=True,\n", " title_font=dict(size=14), row=2)\n", "\n", "# Set y-axes titles\n", "\n", "fig.update_yaxes(range=[0, 1.5], dtick=0.25, title_text=\"RMSD α-helices, Å\", ticksuffix = \" \", showticklabels=True,\n", " title_font=dict(size=16), row=1, col=1)\n", "\n", "\n", "fig.update_yaxes(range=[0, 40], dtick=10, title_text=\"Distance, GEODES\", ticksuffix = \" \", showticklabels=True,\n", " title_font=dict(size=16), row=1, col=4)\n", "\n", "fig.update_yaxes(range=[14, 40], dtick=5, title_text=\"Distance, GEODES\", ticksuffix = \" \", showticklabels=True, title_font=dict(size=16), row=2)\n", "fig.update_yaxes(range=[14, 40], dtick=5, title_text=\"Distance, GEODES\", ticksuffix = \" \", showticklabels=True, title_font=dict(size=16), row=2)\n", "fig.update_yaxes(range=[14, 40], dtick=5, title_text=\"Distance, GEODES\", ticksuffix = \" \", showticklabels=True, title_font=dict(size=16), row=2)\n", "\n", "fig.update_yaxes(title_text=\"Distance, GEODES\", showticklabels=True,\n", " title_font=dict(size=16), row=2, col=1, ticksuffix = \" \")\n", "\n", "\n", "fig.update_layout(template='simple_white',\n", " legend=dict(font=dict(size=16),\n", " orientation=\"h\",\n", " yanchor=\"top\",\n", " xanchor=\"left\", y=1.2, x=0),\n", " height=600, width=1100)\n", "\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RMSF analysis" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "rmsf_vd3_src_l1 = pd.read_csv('rmsd_rmsf/rmsf_l1.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "rmsf_vd3_src_l1['Ligand'] = 'VD3'\n", "rmsf_vd3_src_l1['CoA'] = 'SRC1'\n", "rmsf_vd3_src_l1['Motif'] = 'L1'\n", "\n", "rmsf_vd3_src_l2 = pd.read_csv('rmsd_rmsf/rmsf_l2.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "rmsf_vd3_src_l2['Ligand'] = 'VD3'\n", "rmsf_vd3_src_l2['CoA'] = 'SRC1'\n", "rmsf_vd3_src_l2['Motif'] = 'L2'\n", "\n", "rmsf_vd3_src_l3 = pd.read_csv('rmsd_rmsf/rmsf_l3.dat', sep='\\s+', decimal='.').shift(periods=1, axis=\"columns\").drop(['#'], axis=1)\n", "rmsf_vd3_src_l3['Ligand'] = 'VD3'\n", "rmsf_vd3_src_l3['CoA'] = 'SRC1'\n", "rmsf_vd3_src_l3['Motif'] = 'L3'" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Residue#ChainResNameLigandContactCABackboneSidechainAll_HeavyB-factorLigandCoAMotifresn
00AASP_118No7.5727.3608.8308.15120.240VD3SRC1L1118
11ASER_119No6.1055.9906.8536.29122.885VD3SRC1L1119
22ALEU_120No4.5914.3645.3724.84122.885VD3SRC1L1120
33AARG_121No3.3693.1635.0914.46119.888VD3SRC1L1121
44APRO_122No1.6891.7412.3972.00816.120VD3SRC1L1122
..........................................
772254AGLY_423No0.6170.6600.7420.66022.238VD3SRC1L3423
773255AASN_424No0.8591.0391.7461.4270.000VD3SRC1L3424
774256AGLU_425No1.2021.4701.8651.7120.000VD3SRC1L3425
775257AILE_426No2.6332.6593.8553.2440.000VD3SRC1L3426
776258ASER_427No2.6962.6393.6242.9500.000VD3SRC1L3427
\n", "

777 rows × 13 columns

\n", "
" ], "text/plain": [ " Residue# Chain ResName LigandContact CA Backbone Sidechain \\\n", "0 0 A ASP_118 No 7.572 7.360 8.830 \n", "1 1 A SER_119 No 6.105 5.990 6.853 \n", "2 2 A LEU_120 No 4.591 4.364 5.372 \n", "3 3 A ARG_121 No 3.369 3.163 5.091 \n", "4 4 A PRO_122 No 1.689 1.741 2.397 \n", ".. ... ... ... ... ... ... ... \n", "772 254 A GLY_423 No 0.617 0.660 0.742 \n", "773 255 A ASN_424 No 0.859 1.039 1.746 \n", "774 256 A GLU_425 No 1.202 1.470 1.865 \n", "775 257 A ILE_426 No 2.633 2.659 3.855 \n", "776 258 A SER_427 No 2.696 2.639 3.624 \n", "\n", " All_Heavy B-factor Ligand CoA Motif resn \n", "0 8.151 20.240 VD3 SRC1 L1 118 \n", "1 6.291 22.885 VD3 SRC1 L1 119 \n", "2 4.841 22.885 VD3 SRC1 L1 120 \n", "3 4.461 19.888 VD3 SRC1 L1 121 \n", "4 2.008 16.120 VD3 SRC1 L1 122 \n", ".. ... ... ... ... ... ... \n", "772 0.660 22.238 VD3 SRC1 L3 423 \n", "773 1.427 0.000 VD3 SRC1 L3 424 \n", "774 1.712 0.000 VD3 SRC1 L3 425 \n", "775 3.244 0.000 VD3 SRC1 L3 426 \n", "776 2.950 0.000 VD3 SRC1 L3 427 \n", "\n", "[777 rows x 13 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.arange(118, 165)\n", "b = np.arange(216, 428)\n", "residues = np.concatenate((a,b), axis=0)\n", "residues_x3 = np. concatenate((residues, residues, residues), axis=0)\n", "\n", "resn=[str(r) for r in residues_x3]\n", "\n", "rmsf_df = pd.concat([rmsf_vd3_src_l1[:259], rmsf_vd3_src_l2[:259], rmsf_vd3_src_l3[:259]], ignore_index=True)\n", "rmsf_df['resn'] = resn\n", "rmsf_df" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Motif=L1
Residue=%{x}
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'H9':np.arange(350,370), 'H10': np.arange(379,398), \"H11\": np.arange(397,407),\n", " 'Hx':np.arange(411,414), 'H12':np.arange(417,424)}\n", "\n", "avg_hel_rmsf = []\n", "bins = [0, 0.45, 0.65, 0.85]\n", "labels = ['Low', 'Medium', 'High']\n", "\n", "for i in range(1,4):\n", " avg=[]\n", " for key, item in hel_dict.items():\n", " str_item = [str(x) for x in item]\n", " motif='L'+str(i)\n", " avg.append(rmsf_df[(rmsf_df['resn'].isin(str_item)) & (rmsf_df['Motif']==motif)].mean(numeric_only=True)['CA'])\n", " avg_hel_rmsf.append(avg)\n", "\n", "avg_hel_rmsf_l1 = pd.DataFrame({'Helix': hel_dict.keys(), 'RMSF':avg_hel_rmsf[0], 'motif':'L1', 'label': pd.cut(avg_hel_rmsf[0], bins=bins, labels=labels)})\n", "avg_hel_rmsf_l2 = pd.DataFrame({'Helix': hel_dict.keys(), 'RMSF':avg_hel_rmsf[1], 'motif':'L2', 'label': pd.cut(avg_hel_rmsf[1], bins=bins, labels=labels)})\n", "avg_hel_rmsf_l3 = pd.DataFrame({'Helix': hel_dict.keys(), 'RMSF':avg_hel_rmsf[2], 'motif':'L3', 'label': pd.cut(avg_hel_rmsf[2], bins=bins, labels=labels)})" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "#faa920" }, "name": "L1", "type": "bar", "x": [ "H1", "H2", "H3n", "H3", "H4", "H5", "H6", "H7", "H8", "H9", "H10", "H11", "Hx", "H12" ], "y": [ 0.5755625, 0.6285000000000001, 0.8622500000000001, 0.4200952380952381, 0.5433333333333333, 0.41545454545454547, 0.5038, 0.5854666666666667, 0.465, 0.7632999999999999, 0.5984736842105263, 0.6626000000000001, 0.7873333333333333, 0.6689999999999999 ] }, { "marker": { "color": "#1b9894" }, "name": "L2", "type": "bar", "x": [ "H1", "H2", "H3n", "H3", "H4", "H5", "H6", "H7", "H8", "H9", "H10", "H11", "Hx", "H12" ], "y": [ 0.5005000000000001, 0.59525, 0.8835, 0.39866666666666667, 0.4838888888888889, 0.4192727272727273, 0.513, 0.5935333333333334, 0.4310909090909091, 0.5227999999999999, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure(data=[\n", " go.Bar(name='L1', x=avg_hel_rmsf_l1['Helix'], y=avg_hel_rmsf_l1['RMSF'], marker_color='#faa920'),\n", " go.Bar(name='L2', x=avg_hel_rmsf_l2['Helix'], y=avg_hel_rmsf_l2['RMSF'], marker_color='#1b9894'),\n", " go.Bar(name='L3', x=avg_hel_rmsf_l3['Helix'], y=avg_hel_rmsf_l3['RMSF'], marker_color='#90c73d'),\n", " go.Scatter(name='Total RMSF median', x=avg_hel_rmsf_l1['Helix'],\n", " y=[0.567,0.567,0.567,0.567,0.567,0.567,0.567,0.567,0.567, 0.567,0.567,0.567,0.567,0.567],\n", " mode='lines', marker_color='black', line = dict(color='darkred', width=2, dash='dash'))\n", " ])\n", "\n", "\n", "fig.update_layout(template='simple_white',barmode='group',\n", " yaxis=dict(\n", " title='RMSF,Å',\n", " dtick=0.1,\n", " titlefont_size=16),\n", " font=dict(size=14),\n", " legend=dict(font=dict(size=16),\n", " orientation=\"h\",\n", " yanchor=\"top\",\n", " xanchor=\"right\", y=1.2, x=1),\n", " height=500, width=1000)\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [ "LTUp-k_iqGzk" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 0 }