{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "import plotly\n", "import plotly.figure_factory as ff\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "import dash_bio as dashbio" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1.92846494, ..., 6.51398624, 5.46715054,\n", " 6.31254706],\n", " [ 7.119097 , -0.30049256, -1.93627053, ..., 3.06424939, 4.12059702,\n", " -0.05375081],\n", " [ 5.52170501, 8.35458336, -0.62356781, ..., -1.75541226, -2.70175154,\n", " -4.73974628],\n", " ...,\n", " [-1.36625557, -0.31948276, -0.4058478 , ..., 1.29750689, 2.42146753,\n", " 1.76113769],\n", " [-0.30309937, 0.5055724 , 1.68077389, ..., 1.87320242, -0.89291258,\n", " 1.69391608],\n", " [-1.05175509, -0.62751865, -0.78735417, ..., -0.48581353, -1.10456434,\n", " 0.30916206]])},\n", " {'hoverinfo': 'none',\n", " 'line': {'color': '#EF553B', 'width': 6},\n", " 'marker': {'size': 0},\n", " 'mode': 'lines',\n", " 'type': 'scatter',\n", " 'x': [0, 0],\n", " 'xaxis': 'x6',\n", " 'y': [5.0, 5165.0],\n", " 'yaxis': 'y6'},\n", " {'hoverinfo': 'none',\n", " 'line': {'color': '#EF553B', 'width': 6},\n", " 'marker': {'size': 0},\n", " 'mode': 'lines',\n", " 'type': 'scatter',\n", " 'x': [5.0, 1025.0],\n", " 'xaxis': 'x8',\n", " 'y': [0, 0],\n", " 'yaxis': 'y8'}],\n", " 'layout': {'annotations': [{'showarrow': False,\n", " 'text': 'my row cluster',\n", " 'x': 0.5,\n", " 'xanchor': 'left',\n", " 'xref': 'x6',\n", " 'y': 2585.0,\n", " 'yref': 'y6'},\n", " {'showarrow': False,\n", " 'text': 'my column cluster',\n", " 'x': 515.0,\n", " 'xref': 'x8',\n", " 'y': -0.5,\n", " 'yref': 'y8'}],\n", " 'font': {'color': '#7f7f7f', 'family': 'Helvetica', 'size': 10},\n", " 'height': 1000,\n", " 'hovermode': 'closest',\n", " 'legend': {'x': 0.7, 'y': 0.7},\n", " 'paper_bgcolor': 'rgba(0,0,0,0)',\n", " 'plot_bgcolor': 'rgba(0,0,0,0)',\n", " 'showlegend': False,\n", " 'template': '...',\n", " 'width': 1000,\n", " 'xaxis': {'anchor': 'y',\n", " 'domain': [0, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis2': {'anchor': 'y4',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'scaleanchor': 'x5',\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis3': {'anchor': 'y3', 'domain': [0.75, 1.0], 'showticklabels': False},\n", " 'xaxis4': {'anchor': 'y4',\n", " 'autorange': 'reversed',\n", " 'domain': [0, 0.15833333333333333],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis5': {'anchor': 'y5',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [0.0, 1030.0],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'side': 'bottom',\n", " 'tickmode': 'array',\n", " 'ticktext': [COVID_49, COVID_99, COVID_27, COVID_77,\n", " COVID_55, COVID_46, COVID_38, COVID_71,\n", " COVID_08, COVID_57, COVID_94, COVID_95,\n", " COVID_28, COVID_92, COVID_12, COVID_48,\n", " COVID_15, COVID_16, COVID_24, COVID_39,\n", " COVID_13, COVID_10, COVID_31, COVID_62,\n", " COVID_29, COVID_14, COVID_40, COVID_01,\n", " COVID_67, COVID_101, COVID_30, COVID_65,\n", " COVID_21, COVID_33, COVID_68, COVID_85,\n", " COVID_69, COVID_09, COVID_50, COVID_82,\n", " COVID_72, COVID_05, COVID_19, COVID_66,\n", " COVID_35, COVID_97, COVID_58, COVID_91,\n", " COVID_60, COVID_63, COVID_04, COVID_36,\n", " COVID_89, COVID_07, COVID_64, COVID_103,\n", " COVID_86, COVID_96, COVID_100, COVID_83,\n", " COVID_93, COVID_70, COVID_25, COVID_42,\n", " COVID_53, COVID_37, COVID_44, COVID_76,\n", " COVID_59, COVID_90, COVID_22, COVID_06,\n", " COVID_88, COVID_26, COVID_45, COVID_23,\n", " COVID_61, COVID_78, COVID_20, COVID_54,\n", " COVID_56, COVID_18, COVID_11, COVID_51,\n", " COVID_17, COVID_03, COVID_81, COVID_43,\n", " COVID_79, COVID_47, COVID_32, COVID_34,\n", " COVID_73, COVID_80, COVID_75, COVID_98,\n", " COVID_102, COVID_84, COVID_52, COVID_74,\n", " COVID_41, COVID_02, COVID_87],\n", " 'tickvals': [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0,\n", " 75.0, 85.0, 95.0, 105.0, 115.0, 125.0,\n", " 135.0, 145.0, 155.0, 165.0, 175.0, 185.0,\n", " 195.0, 205.0, 215.0, 225.0, 235.0, 245.0,\n", " 255.0, 265.0, 275.0, 285.0, 295.0, 305.0,\n", " 315.0, 325.0, 335.0, 345.0, 355.0, 365.0,\n", " 375.0, 385.0, 395.0, 405.0, 415.0, 425.0,\n", " 435.0, 445.0, 455.0, 465.0, 475.0, 485.0,\n", " 495.0, 505.0, 515.0, 525.0, 535.0, 545.0,\n", " 555.0, 565.0, 575.0, 585.0, 595.0, 605.0,\n", " 615.0, 625.0, 635.0, 645.0, 655.0, 665.0,\n", " 675.0, 685.0, 695.0, 705.0, 715.0, 725.0,\n", " 735.0, 745.0, 755.0, 765.0, 775.0, 785.0,\n", " 795.0, 805.0, 815.0, 825.0, 835.0, 845.0,\n", " 855.0, 865.0, 875.0, 885.0, 895.0, 905.0,\n", " 915.0, 925.0, 935.0, 945.0, 955.0, 965.0,\n", " 975.0, 985.0, 995.0, 1005.0, 1015.0, 1025.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis6': {'anchor': 'y6',\n", " 'domain': [0.95, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-5, 1],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis7': {'anchor': 'y7', 'domain': [0.0, 0.25], 'showticklabels': False},\n", " 'xaxis8': {'anchor': 'y8',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 1025.0],\n", " 'scaleanchor': 'x5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis9': {'anchor': 'y9', 'domain': [0.75, 1.0], 'showticklabels': False},\n", " 'yaxis': {'anchor': 'x',\n", " 'domain': [0.9136363636363636, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis2': {'anchor': 'x2',\n", " 'domain': [0.7636363636363636, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis3': {'anchor': 'x3', 'domain': [0.75, 1.0], 'showticklabels': False},\n", " 'yaxis4': {'anchor': 'x4',\n", " 'domain': [0, 0.8636363636363635],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'scaleanchor': 'y5',\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis5': {'anchor': 'x5',\n", " 'domain': [0, 0.8636363636363635],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 5165.0],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'side': 'right',\n", " 'tickmode': 'array',\n", " 'ticktext': [P24821-4, Q71DI3, Q99497, ..., A0A0B4J2H0,\n", " H7BXV5, P0DP09],\n", " 'tickvals': [5.0, 15.0, 25.0, ..., 5145.0, 5155.0,\n", " 5165.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis6': {'anchor': 'x6',\n", " 'domain': [0, 0.8636363636363635],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 5165.0],\n", " 'scaleanchor': 'y5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis7': {'anchor': 'x7', 'domain': [0.0, 0.25], 'showticklabels': False},\n", " 'yaxis8': {'anchor': 'x8',\n", " 'domain': [0.8636363636363635, 0.9136363636363636],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-0.5, 0.5],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis9': {'anchor': 'x9', 'domain': [0.0, 0.25], 'showticklabels': False}}\n", "})\n" ] } ], "source": [ "# Clustergram class source code: https://github.com/plotly/dash-bio/blob/320d91fe06959bcbcc56215d4a3b144171330e0a/dash_bio/component_factory/_clustergram.py\n", "\n", "fig = dashbio.Clustergram(\n", " anjidf,\n", " column_labels=anjidf.columns.tolist(),\n", " row_labels=anjidf.index.tolist(),\n", " color_map=[[0.0, '#3C99B2'], [0.5, 'white'], [1.0, '#EF2D00']],\n", " display_ratio=[0.2,0.1], # Ratio of dendrogram width to heatmap width, [row, col] \n", " col_group_marker=[\n", " {'group': 0, # the dendrogram cluster number to highlight\n", " 'annotation': 'my column cluster', # annotation text\n", " 'color': '#EF553B'} \n", " ],\n", " row_group_marker=[\n", " {'group': 0, \n", " 'annotation': 'my row cluster', \n", " 'color': '#EF553B'} \n", " ],\n", "# hidden_labels=['row', 'col'],\n", " ) \n", "\n", " \n", "\n", " # TODO: Hovering over heatmap point should also show the patient metadata (e.g. mech ventilation, ICU status)\n", " \n", " # TODO: checkbox that switches math to median vs mean for log2foldchange \n", "fig.update_layout(width=1000, height=1000,\n", " font=dict(family=\"Helvetica\",\n", " size=10,\n", " color=\"#7f7f7f\"))\n", "fig.update_traces(hovertemplate='Feature: %{y}
Patient id: %{x}',\n", " name='', # removes the \"trace 2\" text on hover over heatmap, and other text from dendrogram on hover\n", " selector=None)\n", "\n", "fig.update_xaxes(showticklabels=False)\n", "fig.update_yaxes(showticklabels=False)\n", "\n", "\n", "# What all the axes do:\n", "# xaxis: unknown\n", "# xaxis2: heatmap width while being centered in middle of plot\n", "# xaxis3: unknown\n", "# xaxis4: dendrogram width \n", "# xaxis5: heatmap width, from left side, without centering. Note that heatmap width can never overlap the colored legend\n", "# xaxis6: moves xaxis location of the row group label\n", "# xaxis7: unknown\n", "# xaxis8: unknown\n", "# xaxis9: unknown\n", "# yaxis: unknown\n", "# yaxis2: dendrogram height\n", "# yaxis3: unknown\n", "# yaxis4: heatmap height centered to middle\n", "# yaxis5: heatmap height also, but different behavior when changing 'domain' key in the yaxis5 dictionary\n", "# yaxis6: unknown\n", "# yaxis7: unknown\n", "# yaxis8: moves yaxis location of the column group label\n", "# yaxis9: unknown\n", "\n", "# notes on other axes: \n", "# from: https://github.com/plotly/dash-bio/blob/master/dash_bio/component_factory/_clustergram.py\n", " # axis settings for subplots that will display group labels\n", " # axes = [\"xaxis6\", \"yaxis6\", \"xaxis8\", \"yaxis8\"]\n", "\n", "\n", "\n", "# fig.update_layout(yaxis5={'domain':[0.0, 0.99]})\n", "# fig.update_layout(yaxis2={'domain': [0.0, fig['layout']['yaxis5']['domain'][1]]})\n", "# fig.update_layout(yaxis8={'domain':[0.99, 0.8]})\n", "\n", "# Makes the dendrogram touch the heatmap\n", "fig.update_layout(yaxis2={'domain': [fig['layout']['yaxis5']['domain'][1]-0.1, 1]})\n", "# domain of yaxis2 (the vertical range of the column dendrogram) goes from [the domain start of the heatmap - 0.1, 1]\n", "\n", "\n", "fig.show()\n", "\n", "print(fig)\n", "# fig.update_traces(mode=\"markers+lines\", hovertemplate=None)\n", "# fig.update_xaxes(tickangle=180)\n", "# fig.update_yaxes(tickangle=-180)\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "# from src/db_queries/db_queries.py\n", "\n", "from sqlalchemy import create_engine, MetaData, Table, select, join\n", "import pandas as pd\n", "\n", "db_path = 'sqlite:///../../data/SQLite Database/20200525/Covid-19 Study DB.sqlite'\n", "\n", "def get_omics_data(with_metadata=False, dataset=\"proteomics\"):\n", "\n", " omics_id_dict = {\n", " \"proteomics\":1,\n", " \"lipidomics\":2,\n", " \"metabolomics\":3,\n", " \"transcriptomics\":4\n", " }\n", "\n", " omics_id = omics_id_dict[dataset]\n", "\n", " # Create an engine that connects to the Covid-19 Study DB.sqlite file: engine\n", " engine = create_engine(db_path)\n", "\n", " # Establish connection\n", " connection = engine.connect()\n", "\n", " # pull table into df\n", " query = \"SELECT * from {}_measurements\".format(dataset)\n", " omics_measurements_df = pd.read_sql_query(query, connection)\n", "\n", " # pull table into df\n", " query = \"SELECT * from {}_runs\".format(dataset)\n", " omics_runs_df = pd.read_sql_query(query, connection)\n", "\n", " # pull table into df\n", " query = \"SELECT * from rawfiles WHERE ome_id={} AND sample_ID<>-1 and keep=1\".format(omics_id)\n", " rawfiles_df = pd.read_sql_query(query, connection)\n", "\n", " # pull table into df\n", " deidentified_patient_metadata_df = pd.read_sql_query(\"SELECT * from deidentified_patient_metadata\", connection)\n", "\n", " # make sure the merge by columns are all the same type -> pandas seems sensitive to this\n", " omics_measurements_df = omics_measurements_df.astype({'replicate_id': 'int32'})\n", " omics_runs_df = omics_runs_df.astype({'replicate_id': 'int32', 'rawfile_id': 'int32'})\n", " rawfiles_df = rawfiles_df.astype({'rawfile_id': 'int32', 'sample_id': 'int32'})\n", " deidentified_patient_metadata_df = deidentified_patient_metadata_df.astype({'sample_id': 'int32'})\n", "\n", " joined_df = omics_measurements_df\\\n", " .join(omics_runs_df.set_index('replicate_id'), on='replicate_id')\\\n", " .join(rawfiles_df.set_index('rawfile_id'), on='rawfile_id')\\\n", " .join(deidentified_patient_metadata_df.set_index('sample_id'), on='sample_id')\n", " \n", "\n", " # drop samples that are missing COVID or ICU status\n", " joined_df.dropna(subset=['ICU_1','COVID'], inplace=True)\n", " \n", " # pivot to wide format\n", " wide_df = joined_df.pivot_table(index='sample_id', columns='biomolecule_id', values='normalized_abundance')\n", " wide_df.columns = [str(col) for col in wide_df.columns]\n", "\n", " query = \"SELECT * from biomolecules WHERE omics_id={}\".format(omics_id)\n", " # get biomolecule names\n", " biomolecules_df = pd.read_sql_query(query, connection)\n", "\n", " # close DB connection\n", " connection.close()\n", "\n", " # build biomolecule name dict and drop list\n", " biomolecule_name_dict = {}\n", " biomolecule_drop_list = []\n", " for index, row in biomolecules_df.iterrows():\n", " biomolecule_id = str(row['biomolecule_id'])\n", " standardized_name = row['standardized_name']\n", " biomolecule_name_dict[biomolecule_id] = standardized_name\n", "\n", " keep = row['keep']\n", " if keep!=\"1\":\n", " biomolecule_drop_list.append(biomolecule_id)\n", "\n", " # drop biomolecules\n", " wide_df.drop(biomolecule_drop_list, axis=1, inplace=True)\n", "\n", " # replace wide_df column names\n", " new_col_names = []\n", " for col in wide_df.columns:\n", " new_col_names.append(biomolecule_name_dict[str(col)])\n", " wide_df.columns = new_col_names\n", "\n", " # record quant value range\n", " quant_value_range = wide_df.shape[1]\n", "\n", " # optional return matrix with clinical metadata\n", " if with_metadata:\n", "\n", " combined_df = wide_df.join(deidentified_patient_metadata_df.set_index('sample_id'), on='sample_id')#.dropna()\n", " return combined_df, quant_value_range\n", " \n", " wide_df.index = wide_df.index.astype(int) # patient ID number should be int, not float. Could also be str.\n", "\n", " return wide_df, quant_value_range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "df, val_range = get_omics_data()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sample_id
1.037.71087828.96016529.48617726.67559519.37025525.51788326.49342930.41094531.08841030.999944...C11039M015010
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3.037.63720128.66512627.42221920.72457624.93256022.11361825.71740731.93793030.42512430.072398...C31133M02028
4.038.14523327.83007929.64929626.20359226.04000924.33278926.23218730.94593829.96209829.797982...C412249M01028
5.037.89656429.34363029.50347327.01169325.77208225.16940026.94949032.91612932.40377732.162940...C511049M0191123
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120.038.00467526.63699428.34680326.66207325.58591824.42905326.07334929.87800729.60704929.886157...NC1702484F1118028
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot a Dendrogram with a Heatmap\n", "# Example code at bottom of page https://plotly.com/python/dendrogram/\n", "\n", "# Initialize figure by creating upper dendrogram\n", "fig = ff.create_dendrogram(df, orientation='bottom')\n", "\n", "# Each line in the dendrogram has an underlying object that defines its color and metadata.\n", "# Access the object by fig['data'] to get a list of dictionaries with the data.\n", "# Within each dictionary there is a 'yaxis' key that defaults to the value 'y'\n", "# Change that value 'y' to 'y2' in each dictionary for easier subsetting later \n", "for i in range(len(fig['data'])):\n", " fig['data'][i]['yaxis'] = 'y2'\n", "\n", " \n", "# Create Side Dendrogram\n", "dendro_side = ff.create_dendrogram(df.T, orientation='right')\n", "for i in range(len(dendro_side['data'])):\n", " dendro_side['data'][i]['xaxis'] = 'x2' \n", " \n", "\n", "# Add Side Dendrogram Data to Figure\n", "for data in dendro_side['data']:\n", " fig.add_trace(data) \n", "\n", "# # Create Heatmap\n", "# dendro_leaves = dendro_side['layout']['yaxis']['ticktext']\n", "# dendro_leaves = list(map(int, dendro_leaves))\n", "\n", "# heatmap = [go.Heatmap(\n", "# x=log2foldchange.index,\n", "# y=log2foldchange.columns,\n", "# z=log2foldchange)]\n", " \n", "# for data in heatmap:\n", "# fig.add_trace(data)\n", " \n", "fig.update_layout(width=1000, height=1000)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1,\n", " 2,\n", " 3,\n", " 4,\n", " 5,\n", " 6,\n", " 7,\n", " 8,\n", " 9,\n", " 10,\n", " 11,\n", " 12,\n", " 13,\n", " 14,\n", " 15,\n", " 16,\n", " 17,\n", " 18,\n", " 19,\n", " 20,\n", " 21,\n", " 22,\n", " 23,\n", " 24,\n", " 25,\n", " 26,\n", " 27,\n", " 28,\n", " 29,\n", " 30,\n", " 31,\n", " 32,\n", " 33,\n", " 34,\n", " 35,\n", " 36,\n", " 37,\n", " 38,\n", " 39,\n", " 40,\n", " 41,\n", " 42,\n", " 43,\n", " 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"execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Heatmap({\n", " 'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n", " 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n", " 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n", " 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,\n", " 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n", " 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,\n", " 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 123,\n", " 125, 127],\n", " 'y': [A0A024R6I7;A0A0G2JRN3, A0A075B6H9, A0A075B6I0, ..., V9GY83;P12318-2;P123\n", " 18;F5GXY9;F5GX41;A0A087WXE5;H0YGT0;P31995-4;P31995-3;P31995-2;P31994-5;P3\n", " 1994-2;P31994-3;P31994-4;P31994;P31995, V9GYM3;P02652;V9GYE3;V9GYG9,\n", " X6R8F3;P80188;P80188-2],\n", " 'z': array([[ 0.12571657, 0.80495347, 0.42433079, ..., 1.44148866, -1.47676327,\n", " 0.34717476],\n", " [-0.34502228, -0.05246651, -0.87331379, ..., 1.04850139, -0.59476316,\n", " 0.31092175],\n", " [ 0.05203999, 0.5099144 , -1.63962691, ..., -2.02218312, -0.79341259,\n", " -1.69052868],\n", " ...,\n", " [-0.05728615, -0.9810408 , -0.10863098, ..., -0.50789706, 0.70972449,\n", " -0.13864868],\n", " [-0.05328745, -1.10709141, -1.10785395, ..., 1.54263822, 0.50339603,\n", " 0.20504673],\n", " [-0.26263418, 0.47780178, 0.40254618, ..., -2.63071672, 1.0124593 ,\n", " 0.53773267]])\n", "})" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "heatmap = go.Heatmap(\n", " x=log2foldchange.index.tolist(),\n", " y=log2foldchange.columns.tolist(),\n", " z=log2foldchange)\n", "heatmap" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import plotly.graph_objects as go\n", "import plotly.figure_factory as ff\n", "\n", "import numpy as np\n", "from 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'update_polars',\n", " 'update_scenes',\n", " 'update_shapes',\n", " 'update_ternaries',\n", " 'update_traces',\n", " 'update_xaxes',\n", " 'update_yaxes',\n", " 'write_html',\n", " 'write_image',\n", " 'write_json']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(fig)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method update_xaxes in module plotly.graph_objs._figure:\n", "\n", "update_xaxes(patch=None, selector=None, overwrite=False, row=None, col=None, **kwargs) method of plotly.graph_objs._figure.Figure instance\n", " Perform a property update operation on all xaxis objects\n", " that satisfy the specified selection criteria\n", " \n", " Parameters\n", " ----------\n", " patch: dict\n", " Dictionary of property updates to be applied to all\n", " xaxis objects that satisfy the selection criteria.\n", " selector: dict or None (default None)\n", " Dict to use as selection criteria.\n", " xaxis objects will be selected if they contain\n", " properties corresponding to all of the dictionary's keys, with\n", " values that exactly match the supplied values. If None\n", " (the default), all xaxis objects are selected.\n", " overwrite: bool\n", " If True, overwrite existing properties. If False, apply updates\n", " to existing properties recursively, preserving existing\n", " properties that are not specified in the update operation.\n", " row, col: int or None (default None)\n", " Subplot row and column index of xaxis objects to select.\n", " To select xaxis objects by row and column, the Figure\n", " must have been created using plotly.subplots.make_subplots.\n", " If None (the default), all xaxis objects are selected.\n", " **kwargs\n", " Additional property updates to apply to each selected\n", " xaxis object. If a property is specified in\n", " both patch and in **kwargs then the one in **kwargs\n", " takes precedence.\n", " Returns\n", " -------\n", " self\n", " Returns the Figure object that the method was called on\n", "\n" ] } ], "source": [ "help(fig.update_xaxes)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['__class__',\n", " '__contains__',\n", " '__delattr__',\n", " '__dict__',\n", " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", " '__format__',\n", " '__ge__',\n", " '__getattribute__',\n", " '__getitem__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", " '__init_subclass__',\n", " '__iter__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", " '__ne__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", " '__setitem__',\n", " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", " '__weakref__',\n", " '_add_annotation_like',\n", " 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----------\n", " dict1 : dict\n", " Dictionary of properties to be updated\n", " overwrite: bool\n", " If True, overwrite existing properties. If False, apply updates\n", " to existing properties recursively, preserving existing\n", " properties that are not specified in the update operation.\n", " kwargs :\n", " Keyword/value pair of properties to be updated\n", " \n", " Examples\n", " --------\n", " >>> import plotly.graph_objs as go\n", " >>> fig = go.Figure(data=[{'y': [1, 2, 3]}])\n", " >>> fig.update(data=[{'y': [4, 5, 6]}]) # doctest: +ELLIPSIS\n", " Figure(...)\n", " >>> fig.to_plotly_json() # doctest: +SKIP\n", " {'data': [{'type': 'scatter',\n", " 'uid': 'e86a7c7a-346a-11e8-8aa8-a0999b0c017b',\n", " 'y': array([4, 5, 6], dtype=int32)}],\n", " 'layout': {}}\n", " \n", " >>> fig = go.Figure(layout={'xaxis':\n", " ... {'color': 'green',\n", " ... 'range': [0, 1]}})\n", " >>> fig.update({'layout': {'xaxis': {'color': 'pink'}}}) # doctest: +ELLIPSIS\n", " Figure(...)\n", " >>> fig.to_plotly_json() # doctest: +SKIP\n", " {'data': [],\n", " 'layout': {'xaxis':\n", " {'color': 'pink',\n", " 'range': [0, 1]}}}\n", " \n", " Returns\n", " -------\n", " BaseFigure\n", " Updated figure\n", "\n" ] } ], "source": [ "help(fig.update)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "text", "marker": { "color": "rgb(61,153,112)" }, "mode": "lines", "type": "scatter", "x": [ 25, 25, 35, 35 ], "xaxis": "x", "y": [ 0, 87.13411548992288, 87.13411548992288, 0 ], "yaxis": "y2" }, { "hoverinfo": "text", "marker": { "color": "rgb(61,153,112)" }, "mode": "lines", "type": "scatter", "x": [ 45, 45, 55, 55 ], "xaxis": "x", "y": [ 0, 88.00085052587896, 88.00085052587896, 0 ], 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uqUseGTUzM7NWdiHQBjyQ3mh/nuzlpKoi4kFJM4A5wOPA7ct5/7MkfZvsBaS7gN0jYnE3rj8S+JmkFVMcn1vOOArjt+lbhJ8ZbW5+ZrT5+ZnRnvMzoz3TCM+MHv/U/6vvPyhWVv3/l2VmZmZmTcvT9C3CI+DNrRFGHev9v8ABdT7yWO+GDhpcdAg1LVranZnP/nf7kmeLDqGqF5cuLDqEmo4vOgBbLk5Ge6DOdqgpq9V2DTIzM7PG4mS0Z1pqS0wzMzOz3uZnRs3MzMysME5GzczMrCVIWlhyfpSk83up74tzuzh19ZrPSnpI0hxJD0s6OZWfLmlsjWunSFq9JzHXC0/Tm5mZmfUzSfsAXwX2iogFkoaSLVpPRHyn1vURsW8fh9hvPDJqZmZmLU/SBpJukjQr/fq+VH6xpPMk3SHp8c7Rz7Rj0vlpRPOPwDq5vr4j6d406tmu8gsFfxM4OSIWAETEooj4Re6eB0vaR9IVuX53k/SHdDxP0lrp+LMp7gclXdZHP6I+42TUzMzMWsUwSTM7P8DpubrzgUsjYiTwa+C8XN26wE7AfsDEVHYQsCmwJfBFYMd8XxGxbUSMINtDfr8ysYwA7q8R743A9rntSQ8BfptvIGkL4FRgj4jYCvhKjT7rjpNRMzMzawqSxkm6L/cZV9LkjYgY1fkB8tPhOwC/SceXkSWfnX4fEcsi4mHg3alsF+DyiOhIo5s359rvLuluSbOBPYAtluf7RMRSsn3qPyZpEPBR4NqSZnsAV0XEC+mal5bnXkXyM6NmZmbWFCKiHWjvre5yx2/mjlWhTVaZPfv5U2B0RDwlaQLl1ySfA2zD25PYcn4LHAe8BNwbEa+W3rJcHI3EI6NmZmZmcAdwaDr+DHBbjfa3AodKGihpXWD3VN6ZeL4gaWWg0hv23wPOlPQeAElDJJ1Ypt104ENkjwL8tkz9TcCnJK2Z+lmjRtx1xyOjZmZmZnAicJGkrwPPA5+r0f4asiny2cDfgFsAIuJlSb9I5fOAe8tdHBFTJL0b+HN6wSmAi8q065B0HXAUcGSZ+jmS/ge4RVIHMCO1bRjynuXLT9KERtmBafCQ4XX9G73M/x32SCPsql7vv8Pem75n1hy2atEh1FTve9NvudoGRYdQVSPsTT/n2bv9B7kBeZrezMzMzArjafomIWk8ZR6QbpSRW+uZeh91tOY3cIDHNnpqCcuKDqGqFQcOKToEa1JORpvHUCeeZmZm1mj8T1kzMzMzK4yTUTMzMzMrjJNRMzMza3qS1sxtBfoPSU/nzgf3Qv8bSTq0dst3XPdeSVdImtu5z72kjXshnkGSXk7HG6ftT+uSnxk1MzOzphcRLwKjIFuaEVgYEWf34i02Ils0f1JXL0jri/4eaI+IT6WyD5FtOTq3C9cPSluGNjSPjJqZmVlLk3SKpIfS54RU9j1Jx+XafF/SlyUNkHROajtbUucOSxPJ9qSfKelESe+X9BdJMyTdL2m7MrfekywpvrCzICIeiIjbK91H0lhJf5Y0iWyB+7LxV/muXYmrX3lk1MzMzFqWpDFk23+OAQYC90i6BbiQbJTzJ5IGAp8k20v+k8DmwFbA2sC9km4FxgPHR8SBqd8VgT0jYpGkzYBLgNLEbwRwf4XQKt0HYHtg84h4skr8D1fo95kuxNWvnIyamZlZU5A0DhiXK2qPiPYal+0M/C4iXk99/B7YKSJ+KulVSVsCGwD3RMQ/Je0E/CYiOoB/SLoNGA2UbvE1BDhf0lbAUuD93fw61e5zZ0Q8WS1+KiejPY2r1zkZNTMzs6aQEs9ayWepaluI/pJsn/c24OddaJ/3n8BTwOHACkC5/VTnAPstR1yvdbHd8sbVr/zMqJmZmbWyW4GDJA2TtDJwAPCXVPc74GNkLz79Odf+UEkDJb0b+DBwH/AqsEqu39WAZyIigCMpnzTeAKwq6fOdBZK2k7Rzlft0J/5yuhJXv/LIqJmZmbWsiLhH0uXAvanogoiYneoWpec0/xERnfu1XkX2zOaDZLsxnxQRz6VllAZKepBsRPV84CpJh5Elsm+WuXdIOgA4V9KpwBvAE8BXgdsr3KdL8UuqlOPVjKu/KUuMbXlImlBtC85K+8X3kbaIOKpS5eAhw+v6N3qZ/zu0gg1Q4YMDDW2dlVYvOoSaXlu8qOgQqtpstfWLDqGqpdFRdAg13bvg1l79gyxpADATODAiHu/Nvu0tHhntW/22X3xaM83MzMx6QXpxaTJwpRPRvuVktMmUjsb2VzJsVovHHZubZ9l6bogGFh1CVY0wMtqb0lT9hkXH0QqcjDaffhuNNTMzM+spv01vZmZmZoVxMmpmZmZmhXEyamZmZi1BUkfaO77z0yZptKTzalw3StK+FerWlDRN0kJJ5+fKV5T0R0mPSpojaWKu7n3pmhmSZlXqu1X4mVEzMzNrFW9ExKiSsnmUX0w+bxTZVpxTytQtAr5Nts/8iJK6syNimqTBwE2S9omIqcB/AVdExAWSNk/9tnXrmzQRj4yamZlZy5K0m6Tr0vEYSXekEcs7JG2aEsnTgUPSaOoh+esj4rWIuI0sKc2Xvx4R09LxYuABYHhnNbBqOl4NWNBnX7ABLNfIaD8v5l7P2ooOwMzMzLpsmKSZ6fiJiDiopP5RYJeIWCppLPC/EfEJSd8BRkfE8ctzU0mrk20rem4qmgDcIOkEYCVg7PL02yyWd5reywfhhebNzMzqiaRxwLhcUXtEtOfOy03T560GXCJpE7LRyxV6IaZBwOXAebnF8w8DLo6IH0jaAbhM0ojclqMtxc+MmpmZWVNIiWd7zYaVnQFMi4iDJLUB03shrHbgsYj4Ua7sC8DeABFxp6ShwFrAc71wv4bjZ0abx6I0UttWcBxmZmaNajXg6XR8VK78VWCV7nYm6bupz6+WVD0JfCS1+SDZo4/Pd7f/ZuFktElExMT06MS8gkMxMzNrVGcC35N0O5Dfn3UasHm5F5gAJM0DzgGOkjRf0uaShgOnApsDD6Rrj06X/CfwRUkPkk3hHxUtvKeup+nNzMysJUTEymXKppOm4yPiTuADuepvp/KXgG2r9NtWoUoV2j8MfLgLIbcEj4w2KUnj/YKVmZmZ1TuPjDYvr3hgZv1m6bKOokMwswblkVEzMzMzK4yTUTMzMzMrjJNRMzMzMyuMk1EzMzNrCZI60hJLnZ82SaMlnVfjulGS9q1Q93FJN+XOd0p9D5K0maQ7Jb0p6eQq/b9L0jWSZkm6R9KI5f+WjcfJqJmZmbWKNyJiVO4zLyLui4gTa1w3CiibjEbE1WQbz3w6bf35U+DLEbEUeAk4ETi7Rv/fAmZGxEjgs7y1h31LcDLafLwTk5mZWRdJ2k3Sdel4jKQ7JM1Iv24qaTBwOnBIpUXvgROA7wL/DdwbEXcARMRzEXEvsKRGGJsDN6VrHgXaJL27d75h/fPSTk0mIiYCeI1RMzOzdxgmaWY6fiIiDiqpfxTYJSKWShoL/G9EfELSd4DREXF8uU4j4nFJvwWOB96/HHE9CHwcuE3SGGADYDjw7HL01XCcjJqZmVlTkDQOGJcrao+I9tz5GxExqkoXqwGXSNoECGCFLt53ADAWWEiWSL7QrcBhInBuSpRnAzOApd3so2E5GTUzM7OmkBLP9poNKzsDmBYRB0lqI20T2gXHAQ+RbR/6E0k7VNtrXtJxwBfT6b4RsQD4XKoT8ET6tAQ/M2pmZmaWWQ14Oh0flSt/FVil3AWS3gOcBJwSEX9K1x9d7SYR8ZPcS1QLJK2enk0lXXtrRPyrB9+joTgZNTMzM8ucCXxP0u3AwFz5NGDzCi8wnQOcGRHPp/OvAqdKWkPSeyTNJ0tW/0vSfEmrlrnvB4E5kh4F9gG+0ptfqt55mt7MzMxaQkSsXKZsOmk6PiLuBD6Qq/52Kn8J2LZCn58uOX+Kt69oM7wLcd0JbFKrXbPyyGjzWuQ36s3MzKzeeWS0SXUu8dQpex66jlV+ztu6YEC9//4CVZ7ltyawZFlH0SHU1BHLig6hqv8Y+J6iQ6hq9sDXig7BmpRHRs3MzMysME5GzczMzKwwTkbNzMysJUgKSZflzgdJer5zO9Ae9n2xpIO70X6CpKfTG/oPSzosVzdZ0hG5819I+nrJ9T+WtLCncdcDJ6NmZmbWKl4DRkgals735K11RYvww7Qj1AHAzyV17vh0InB6Wn90R2A74EedF0kaDaze79H2ESejZmZm1kqmAh9Nx4cBl3dWSBoj6Q5JM9Kvm6byC9MI5sw0knqaMuenUc0/Auvk+vmOpHslPSSpXTXeIo6Ix4DXgXel83lkO0mdCfwUOD4ilqS+BwJnAaf0yk+jDjgZNTMzs1YyCThU0lBgJHB3ru5RYJeI2Br4DvC/ABFxdG4E80XgYuAgYFNgS7KtPXfM9XN+RGwbESOAYcB+1QKS9CHgsYh4Lld8NrA3MCcibs2VHw9MjohnuvWt65iXdjIzM7OmIGkcMC5X1J72q/+3iJiV9p0/DJhS0sVqwCWSNgEC6Jw2JyWvV5KNUv5d0teAyyOiA1gg6eZcP7tLOgVYEVgDmAP8oUzIX5P0RWAjssQzbyQgYDNJAyJimaT3Ap8Edqv+k2gsHhk1MzOzphAR7RExOvdpr9B0MtnI4+Ul5WcA09KI5seAobm6nwFXR8Sf87cs7TglrT8FDo6ILYFflPST98OI2BQ4BLg0XYukAamPI4DHgGNT+62BjYG5kuYBK0qaW6HvhuFk1MzMzFrNRcDpETG7pHw13nqh6ajOQknHAauUbChzK9l0/0BJ6wK7p/LOxPMFSSsDNd+wj4irgfuAI1PRMWTT9tPJ9rU/RdLaEfHHiHhPRLRFRBvwekRs3KVvXMecjJqZmVlLiYj5EXFumaozge9Juh0YmCs/Gdgy9xLTl4BryEYtZwMXALekvl8mGw2dDfweuLeLYZ0OnCRpHeAb6Z5ExALg3BRbU/Izo2ZmZtYSImLlMmXTgenp+E7gA7nqb6fyDSt0eXyF+/wX8F81YplQcn4/2QtRAG0ldedU6OMd36cReWTUzMzMzArjkdGeWSRpQpX6tn6Kw8zMzKwhORntgZIHmd+hRqJqZmb9qMa642ZWEE/Tm5mZmVlhnIyamZmZWWGcjJqZmVlLkBSSLsudD0p7zV/XC31fLKnmmqK59hMkPZ2WinpY0mG5usmSjsid/0LS10uu/7GkhWX6laQXJL0rna+bvvdOuTbPS1qzu9+xrzgZNTMzs1bxGjBC0rB0vidvLXJfhB/m9rz/uaTO7UdPBE6XtLqkHYHtgB91XiRpNLB6uQ4jIoC7gR1S0Y7AjPQrkjYFXoiIF/vg+ywXv8DUBCSNp8JWY6XrmJmZmbW4qcBHgavI9qe/HNgZQNIYsqRvGPAG8LmI+KukC4HR6fr1gPPJFqn/MbAH8ATZPvKkfr5Dtp3oMOAO4JiUJJYVEY9Jeh14F/BcRMyT1E620P0Y4PiIWJL6HgicBXwaOKhCl7eTJZ9T0q/nAJ9IdTummJD0MbL1UAcDLwKfiYhnJa0N/AZYk2zR/r2BbSLihUrfoSc8MtochkbEhHKfogMzMzOrM5PItvEcCowkG0Xs9CiwS0RsDXwH+F+AiDg6N4L5InAxWSK4KbAl8EXSyGNyfkRsm/a4HwbsVy0gSR8i2/7zuVzx2WRJ4JyIuDVXfjwwOSKeqdLlHbl4xpDtBLV+Ot+RLFkFuA3YPn3fScApqfw04OaI+BDZTlPvqxZ/T3lk1MzMzJqCpHHAuFxRe0S059tExCxJbWSjolNKulgNuETSJkAAndPmpOT1SrJRyr9L+hpweUR0AAsk3ZzrZ3dJpwArAmsAc4A/lAn5a5K+CGxElnjmjSQbbd1M0oCIWCbpvcAngd2q/yS4B9ha0krAChGxUNLjkjYmS0Z/kNoNB34raV2y0dEnUvlOpFHXiPiTpH/WuF+PeGTUzMzMmkJEtEfE6NynvULTyWQjj5eXlJ8BTEsjmh/j7Y/A/Qy4OiL+nL9laccpaf0pcHBEbEm2T33ZR+nInhndFDgEuDRdi6QBqY8jgMeAY1P7rYGNgbmS5gErSppb5ufwOjAX+DzwQCq+C9gXWAf4ayr7Mdko7pbAMbk4+3VRXiejZmZm1rslncYAACAASURBVGouAk6PiNkl5avx1gtNR3UWSjoOWKVks5tbyab7B6aRxd1TeWdC94KklYGab9hHxNXAfcCRqegYsmn76cBJwCmS1o6IP0bEeyKiLSLagNcjYuMK3d4OfBW4M53fCXwFuCv3/Gr++x6Zu/Y24FPpu+9F9ixrn3EyamZmZi0lIuZHxLllqs4EvifpdmBgrvxkYMu0DNNMSV8ie5byMWA2cAFwS+r7ZbLR0Nlkz2re28WwTgdOkrQO8I10TyJiAXBuiq07bieb/u9MRh8gm5a/I9dmAnClpL8A+ZeT/hvYS9IDwD7AM8Cr3bx/l6nKy12VL5L8ckwX9NfPqSv3GTJ0/e7/RvejjmXLig6hoQ1ogG0Ol+f/Nf3JW0X2zMqDh9VuVLCOqO//z4xfc4fajQo0m9eKDqGm3/z9Gv9B7gWShgAdEbFU0g7ABekFrj7hF5jMzMzMLO99wBXp2dXFZKsF9Bkno2ZmZmb2bxHxGNnLUv3CyaiZmfVYvT+G0QgG1/mPcKUBThmsb/gFJjMzMzMrjJNRMzMzMyuMk1EzMzNrCZJC0mW580GSnpd0XR/c6yRJD0uaJekmSRvk6s6UNEfSI5LOU1rOQ9I2kmZLmpsvb3Z+AKQOSBpP5d0ZuqKtl0IxMzNrZq8BIyQNi4g3gD15a9H3LpE0KCKWdqHpDGB0RLwu6ViydUIPkbQj8GGy7T4hW2B+V2A62Xql48h2S5pCtkXo1O7E14icjNaHoT1Zj1TScl9rZmbWYqYCHwWuItuf/nJgZ4C0l/uPgS3JcqQJEXGtpKPSNUOBlYA90t7zRwDLgKkRMT5/k4iYlju9Czi8syr1M5hs280VgGfTLk6rRsSdKZZLgQNpgWTU0/RmZmbWSiaRbeM5lGx08u5c3anAzRGxLdn2nmelBBVgB+DIiNhD0j5kieJ2EbEVtXdH+gIpqUzJ5jSyXY2eAa6PiEeA9YD5uWvmp7Km55FRMzMzawqSxpFNc3dqj4j2fJuImCWpjWxUdEpJF3sB+0s6OZ0PJVsAHuDGiHgpHY8FfhURr6c+X6ICSYcDo8mm4pG0MfBBsq05AW6UtAvwRpnL63zBr97hZNTMzMyaQko822s2hMnA2cBuwJq5cgGfiIi/5htL2g7eth+q6EKiKGks2WjrrhHxZio+CLgrIhamNlOB7YHLeCtBJR0v6MJ3aXiepjczM7NWcxFwekTMLim/Hjgh93Z7pV2IbgA+L2nF1G6N0gbp2p8D+0fEc7mqJ4Fd05v8K5CNmD4SEc8Ar0raPt3/s8C1y/8VG4eTUTMzM2spETE/Is4tU3UG2QtFsyQ9lM7LXf8nstHV+yTNBE4u0+wsYGXgSkkzJU1O5VcB/wfMBh4EHoyIP6S6Y4ELgbmpTdO/vASepm9qksZHxMSi4zAzM6sHEbFymbLpZMsqkZZ7OqZMm4uBi0vKJgIV/46NiLEVyjvK3SPV3QeMqNRns/LIaHPrydqlZmZmZn3OyaiZmZmZFcbT9GZm1mPLGmAFmgG0xM6KZg3HI6NmZmZmVhgno2ZmZmZWGCejZmZm1hIkhaTLcueDJD0v6bo+uNdJkh6WNEvSTZI2yNWdKWmOpEcknZdb13QbSbMlzc2Xp7qTJT0q6SFJD0r6bG/HXBQ/M9ocFkmaUKa8rZ/jMDMzq2evASMkDUvLOO0JPN2dDiQNioilXWg6AxgdEa9LOpZs//pDJO0IfBgYmdrdRrbw/XTgArLtTO8i26p0b2CqpC+lWMdExL8krQYc2J2465mT0SZQaS3RCgmqmZlZK5sKfJRs8fnDgMuBnQEkrQT8GNiSLEeaEBHXSjoqXTMUWAnYQ9IpwBHAMmBqRIzP3yQipuVO7wIO76xK/Qwm21Z0BeBZSesCq0bEnSmWS8kSzqnAt4DdI+Jfqe9XgEt66edROE/Tm5mZWSuZBBwqaSjZ6OTdubpTgZsjYltgd+CslKAC7AAcGRF7SNqHLFHcLiK2Ihv1rOYLpN2UUrI5DXgmfa6PiEeA9YD5uWvmA+tJWgVYJSL+b7m/cZ3zyKiZmZk1BUnjyKa5O7VHRHu+TUTMktRGNio6paSLvYD9JXVu7zkUeF86vjEiXkrHY4FfRcTrqc+XqEDS4cBosql4JG0MfBAY3tmvpF2AN8pcHmSjp/W/dloPOBk1MzOzppASz/aaDbN95c8GdgPWzJUL+ERE/DXfWNJ2ZM+b5tvVTBAljSUbbd01It5MxQcBd0XEwtRmKrA9cBlvJaik4wXpGdHXJG0UEY934bs1HE/Tm5mZWau5CDg9ImaXlF8PnJB7u33rCtffAHxe0oqp3RqlDdK1Pwf2j4jnclVPArumN/lXIBsxfSQingFelbR9uv9ngWvTNd8DfiJp1dT3qmkUuCk4GTUzM7OWEhHzI+LcMlVnkL1QNEvSQ+m83PV/IhtdvU/STODkMs3OAlYGrpQ0U9LkVH4V8H/AbOBB4MGI+EOqOxa4EJib2kxN5ReQPWd6b4rrFuD1bnzluuZp+ua2qOgAzMzM6kVErFymbDrZskqk5Z6OKdPmYuDikrKJQNnVbFL92ArlHeXukeruA0aUKQ+yl6RqvSjVkDwy2sQqLflkZmZmVi+cjJqZmZlZYTxN37cq7YxUqq2P4yAb4bdm5d9fK9rggfX/18nSjo6iQ6jqpQHLig6hqvvffKboEKxJ1f//PRpYV6fJvVOSmZmZtSpP05uZmZlZYZyMmpmZWUuQFJIuy50PkvS8pOv64F4nSXpY0ixJN0naoKR+VUlPSzo/V7aNpNmS5ko6L7fe6cWSnpD0oKS/SbpU0nq9HXNRnIyamZlZq3gNGCFpWDrfE3i6Ox1I6uojjjOA0RExkmxt0dJlmc4gWy807wKy7Uw3SZ+9c3Vfj4itgE1T39MkDe5O7PXKyaiZmZm1kqnAR9PxYcDlnRWSVpJ0kaR7Jc2QdEAqP0rSlZL+QLb7EpJOSaOYD0p6xzsiETGtc+964C5yW31K2gZ4d2dfqWxdYNWIuDOtK3opcGCZfiMifgj8A9inBz+HuuFk1MzMzFrJJOBQSUOBkcDdubpTgZsjYltgd+AsSSuluh2AIyNiD0n7kCWK26XRylqL0X+BtJuSpAHAD4Cvl7RZD5ifO5+fyip5ANisxn0bgt+mNzMzs6aQ9mvP79neHhHt+TYRMUtSG9mo6JSSLvYC9pfUub3nUOB96fjGiHgpHY8FftU58pkrLxfT4cBosj3oAb4MTImIp9Ijof9uWubyauv2lWvfkJyMmpmZWVNIiWd7zYbZvvJnA7sBa+bKBXwiIv6abyxpO7LnTfPtai7wLGks2WjrrhHxZireAdhZ0pfJ9q4fLGkhcC65qfx0vKBK91sDN9WKoRF4mt7MzMxazUXA6RExu6T8euCE3FvsW1e4/gbg85JWTO3WKG2Qrv05sH9EPNdZHhGfiYj3RUQbcDJwaUSMj4hngFclbZ/u/1ng2jL9StKJwLrAn7r1reuUR0brhKTxZNMBvSoiJvR2n2ZmZo0sIuaTjUSWOgP4ETArJYTzgP3KXP8nSaOA+yQtJpvu/1ZJs7PIRj6vTLntkxGxf43QjgUuBoaRPWM6Nd+fpG8DK5K9ELV7RCyu0V9D0PJsIyhpgpOc3tO5A1Nf/kwHDxle1/tFLvN2lj3SNA8OFajk2S3rptWGrlS7UcHqfTvQL681pugQqpr65pNFh1DTA8/c5j/IDcjT9GZmZmZWGCejZmZmZlYYPzNqZv2i3h/E8NxezwzSwKJDqGkxS4sOoapZy/5VdAhVbTF4naJDsCblkVEzMzMzK4yTUTMzMzMrjKfpzczMrCVIWhgRK3ex7YHA3yLi4V647zzgVaBzSYdbI+LEnvbbLJyMmpmZmb3TgcB1QI+T0WT3iHihuxdJGhQR9f3Acw95mt7MzMxalqQNJN0kaVb69X2SdgT2J1tofqak90uaLml0umatNNqJpC0k3ZPazZK0STfuXanPoyRdKekPwA1p16WzJD0kabakQ1K73STdKukaSQ9L+pmkAanuAkn3SZoj6b978UfW6zwyamZmZq3sfLItOS+R9HngvIg4UNJk4LqIuAqqbozxJeDciPi1pMFApaUlpknqnKa/JCJ+WCOuHYCREfGSpE8Ao4CtgLWAeyXdmtqNATYH/k62PejHgauAU9O1A4GbJI2MiFk17lkIj4yamZlZU5A0Lo0Gdn7GdeGyHYDfpOPLgJ26eds7gW9J+gawQUS8UaHd7hExKn1qJaIAN0bES+l4J+DyiOiIiGeBW4BtU909EfF4RHQAl+fi/5SkB4AZwBZkCWtd8siomZmZNYWIaAfae9pNhfKlvDWINzR3z99Iuhv4KHC9pKMj4uYu3qtsn8lrueNqSyGXxhuSNgROBraNiH9KurhM/3XDI6P1YRHQVnQQZmZmLegO4NB0/BngtnT8KrBKrt08YJt0fHBnoaSNgMcj4jxgMjCyG/cu22cZtwKHSBooaW1gF+CeVDdG0obpWdFDUvyrkiWzr0h6N7BPN2Lqdx4ZrQMRMVHShEr1ksaznP+iiYiK/ZqZmbWYFSXNz52fA5wIXCTp68DzwOdS3STgF5JOJEsUzwaukHQEkB/5PAQ4XNIS4B/A6RXunX9mdFZEfLZKn6WuIXuc4EGykdBTIuIfkjYje0xgIrAlWdJ6TUQskzQDmAM8Dtxe9adSMEV0f5M+SROc5PSuaj/T3vh5Dx4yvK53Y1y2HP8d2lsaYSvLev8dHlD55QTrgjWHrVp0CDW9vvTNokOoapc1Plh0CFWtrsFFh1DTZX+/uqX+IEvaDTg5IvYrOpae8DS9mZmZmRXG0/RmZmZmDSgipgPTCw6jx5yMmlm/aKm5sxa0ZFn9bxCzwoBKyz/Whw8OWKV2owI9suzVokOwJuVpejMzMzMrjJNRMzMzMyuMk1EzMzNrCZJC0mW580GSnpd0XR/c66S0X3znnvcbpPINJN2f9rKfI+lLuWu2SXvPz5V0nqrsQdpMnIyamZlZq3gNGCFpWDrfE3i6Ox1I6ur7NjOA0RExkmyv+DNT+TPAjhExCtgOGC/pvanuAmAcsEn67N2d2BqVk1EzMzNrJVPJtu4EOIxsP3cAJK0k6SJJ90qaIemAVH6UpCsl/QG4IZWdkkYxH5Q0sfQmETEtIl5Pp3cBw1P54ojoXPR2CCkXk7QusGpE3BnZIvCXAgf28nevS05GzczMrJVMAg6VNJRs6867c3WnAjdHxLbA7sBZklZKdTsAR0bEHpL2IUsUt4uIrXhr1LOSL5AlwQBIWl/SLOAp4PsRsQBYD8jvDjU/lTU9L+1kZmZmTUHSOLJp7k7tEdGebxMRsyS1kY2KTinpYi9gf0knp/OhwPvS8Y0R8VI6Hgv8qnPkM1deLqbDgdHArrkYngJGpun530u6ivIr4NX75nW9wsmomZmZNYWUeLbXbAiTyfaF3w1YM1cu4BMR8dd8Y0nbkT1vmm9XM1GUNJZstHXX3NR8Pt4FkuYAO5PtHz88Vz0cWNCF79LwnIzWj0WSJlSoa8ufSBpP9q+1mnq6p72ZmVkTugh4JSJmp/3dO10PnCDphIgISVtHxIwy198AfEfSbyLidUlrlI6OStoa+Dmwd0Q8lysfDrwYEW9IehfwYeCciHhG0quStid7dOCzwI9780vXKyejdSIi3vHwc6cySepQJ5lmZmbLJyLmA+eWqToD+BEwKy2rNA/Yr8z1f5I0CrhP0mKy6f5vlTQ7C1gZuDKt0PRkROwPfBD4gaQgG2E9OyJmp2uOBS4GhpE9YzqVFuBk1MzMzFpCRKxcpmw6aX/3iHgDOKZMm4vJksR82USg4kBSRIytUH4j2YtT5eruA0ZU6rNZ+W16MzMzMyuMk1EzMzMzK4yn6c3MrMeWRf2vQBN1HuNLLCk6hKpW0QpFh2BNyiOjZmZmZlYYJ6NmZmZmVhgno2ZmZtb0JE2X9B8lZV+V9NMq17RJeqgX7r2mpGmSFko6v0Kbyfl7STpL0qOSZkm6RtLqqXw3Sa9Impn7lH1zv1E4GTUzM7NWcDlwaEnZoam8ry0Cvg2cXK5S0seBhSXFNwIjImIk8Dfgm7m6v0TEqNznz30RdH9xMmpmZmat4CpgP0lDIBv1BN4L3KbMWZIekjRb0iGlF0u6W9IWufPpkraRtIak36cRzLskvWMN0Yh4LSJuI0tKS/tdGTgJ+G7JNTdExNJ0ehdv3yq0qTgZNTMzs6YXES8C9wB7p6JDgd9GtszCx4FRwFbAWOAsSeuWdDEJ+BRAqntvRNwP/DcwI41gfgu4tJuhnQH8AHi9SpvP8/bdmHYumaZ/fzfvWVecjJqZmVlTkDRO0n25z7iSJvmp+vwU/U7A5RHRERHPArcA25ZcewXwyXT8KeDK3LWXAUTEzcCaklbrYryjgI0j4poqbU4FlgK/zhWXTtP/X1fuV6+8zmhjWFSyP31bQXGYmZnVrYhoB9qrNPk9cI6kDwHDIuKBVK4u9P20pBfTNPwhvLVtaLlru7qo7Q7ANpLmkeVk60iaHhG7AUg6EtgP+EjU+0K5PeBktAGk/W//LZ+YShoPDK1y7YRKdWZmZq0kIhZKmg5cxNtfXLoVOEbSJcAawC7A13nn36+TgFOA1SJidu7azwBnSNoNeCEi/tXFeC4ALoB/P8N6XS4R3Rv4BrBrRFSbwm94TkYb31AnnGZmZl12OXA1b3+z/hqyUcoHyUY1T4mIf6QEMe8q4Fyy5zw7TQB+JWkW2XOfR5a7aRr9XBUYLOlAYK+IeLhKnOcDQ4AbJQHcFRFfSnU7S5qZa/vdiLiqSl91zcmomZmZtYz0fKZKyoJsJPTrJeXzgBG582cpyZ0i4iXggC7ct61Gfem9Nq7QbjrQpWdSG4VfYDIzMzOzwnhk1KwJNMJT7TXfDrCGtnDxG6w6ZMWiw6iqo87f//jdCzPZe80RtRsW6NVYUnQI1oQ8MmpmZj1W74loI3Aiaq3KyaiZmZmZFcbJqJmZmZkVxsmomZmZtQRJHWn7zDmSHpR0kqSquZCkNkmf7uO4di/Z3nNRWv4JScdLmispJK2Vu0aSzkt1s9JC/g3JyaiZmZm1ijfS9plbAHsC+wKn1bimDejTZDQipnVu7QnsQbZe6Q2p+nZgLPD3ksv2ATZJn3GkxfMbkZNRMzMzazkR8RxZEnd8GmVsk/QXSQ+kz46p6UTSIvOSviZpC0n3pPNZkjZJ1z7U2bekkzt3S5Q0XdL30zV/k7RzjdAOBqZ27roUETPSGqSlDgAujcxdwOqS1u3RD6UgXtqpMeX3qm8rMA4zM7OGFRGPp2n6dYDngD0jYpGkTch2ahoNjAdOjoj9ACT9GDg3In4taTAwEHh3jVsNiogxkjpHYsdWaXsocE4Xwl8PeCp3Pj+VPdOFa+uKk9EGlN+rPr9PvZmZWSuTNI5stLNTe0S017os/boCcL6kUUAH8IEK7e8ETpU0HLg6Ih5L23VWc3X69X6qDCKlkc0tgetrdUj55ZvrezHdCpyMNglJ44GhpeXet97MzFpFSjxrJZ//JmkjssTzObIRy2eBrcgeY1xU4R6/kXQ38FHgeklHA3/j7Y8+lv59/Gb6tYPqudengGsiurSo63xg/dz5cGBBF66rO05Gm8dQJ55mZmZdI2lt4GfA+RERklYD5kfEMklHkk2/A7wKrJK7biPg8Yg4Lx2PBP4CrCNpTWAhsB/wp+UI6zDgm11sO5nseddJwHbAKxHRcFP04GTUzMzMWscwSTPJpuSXApfx1vOZPwV+J+mTwDTgtVQ+C1gq6UHgYrJRz8MlLQH+AZweEUsknQ7cDTwBPNrdwCS1kY103lJSfiJwCvAeYJakKRFxNDCFbDWAuWRv33+uu/esF05GzczMrCVExMAqdY+RjXJ2+mYqXwJ8pKT598pcfx5wXpny3XLHL1DhmdH0xvx63eg3gOPK9dVovLSTmZmZmRXGI6NmZg0gGwSpX68seo1VhqxYdBgNbSXV91/Jr8fSokOwJuWRUTMz6zEnoma2vJyMmpmZmVlhnIyamZmZWWGcjJqZmVnTk7Rm2k9+pqR/SHo6dz64F/rfSNKhy3HdeyVdIWmupIcl/VHSxj2Np5HU99PS1hWd+9S3FRyHmZlZ3YqIF4FR8O+ttBdGxNm9eIuNyPaVn9TVC5TtI/p7sm1LP5XKPkS21/3cLlw/KKLx3yzzyGiDi4iJaeeleQWHYmZm1pAknSLpofQ5IZV9T9JxuTbfl/RlSQMknZPazpZ0cGoyEdg9jbSeKOn9kv4iaYak+yVtV+bWe5IlxRd2FkTEAxFxe6X7SBor6c9p56UZVeLfOJ3/UtIcSVMlDU11X5J0r6QHJV0paVgf/Fi7zCOjZmZm1rIkjQE+A4wh2wL0Hkm3ABeSjXL+RNJA4JPANunXzcn2sF8buFfSrcB44PiIODD1uyKwZ0QskrQZcAnZtp15I4D7K4RW6T4A2wObR8STVeJ/HdgUOCwiZku6GjgwfacrI+JnKc6JwFHABd3+4fUSj4yamZlZU5A0TtJ9uc+4Lly2M/D/2bvzMLuqMv373zsDSSARRQZlMiAgYoQgAWQ0KA40KiAoIA4RJWKLNPJDG+c0tIpiN4g0YKQx4MvUjDIJQSAMMgbIwKgMUREUFJEhA6Ryv3/sVeakOFV1qlLJOVV1f66rrtpn7bXXfnYVKR/X3ns9F9meb/sFqtvmO9t+FHhB0tuBPYA7bf8d2Bk4x3ab7T8DtwAT6ow7AvhfSfdRJYBb9PByujrPbbb/0FX8Zd8jtueW7btZ+kjflmXWdi7VowVv62FsfSozowOMpKOp6uYCUG7hR0REDHi2pwJTe3iYutj3v1SzhmOBnzbQv9b/A/4IfAIYDrxYp8/9wAd7EddLDfZbVLPdxtK87yxgD9v3Sfoc1Uxr02RmdOAZaXtK+1ezg4mIiGhxNwH7SBolaTSwF3Bz2XcR8CGqF59+XdP/AElDJa0D7ATMBF4AxtSMuzrwVKkh/2nqJ43TgddIOri9QdL2knbp4jw9ib8zqwF/ljQc+Hg3fVe4zIxGRETEoGX7TknnAneVplPbb22X5z1vAv5se0nZfyHVTOJswMCRtp+W9BwwVNJsqhnVk4ELJR1IlcjWzlK2n9uS9gJ+LOkbwALgceAI4DednKeh+LtZHurbwJ3AH4D7qLmj2gzqTb1jSZl1azHtv5POfjerjFi/pQtbL2nxutux/Bq9r9UsHf/At5pWr03fH8qBtvrPcN81xzc7hC49s2Rhs0Po1i//cEWf/kOWNASYBext+7G+HDuWym36iIiIiA7Ki0uPAlcnEV2xcps+YgBo7Tm96AutPnM7fMjQZofQrYVtrzQ7hH7tFZZ032kAKbfqN2p2HINBZkYHjlRiioiIiH4nM6MDhO3j4J8lziIiIiL6hcyMRkRERETTJBmNiIiIQUFSW6kdf1+pyb5CloGQNFHSFctx/JskXSdpjqQZktav2Xe1pOc6G1/STyTVW2AfSatKulLSQ6Ve/XG9jbEvJRmNiIiIwWKB7fG2xwEvA4c2cpAqKzNn+hFwlu0tgWOA79fsOx74ZL2DJE0AXtvd2LY3B7YGdpK0Rx/Eu1ySjEZERMRgdDOwCYCkI8ts6X2SjihtYyU9KOkU4B5gA0nTSp+5kr5c+m0i6deSZku6R9Kby/ijJV1YZiHPVlkSQ9I2km6UdLekayS9sU5sWwDXle0bqKoqAWD7OqpqT8uQNJQqUf1qZxdc6tffULZfLte1fmf9V5YkoxERETGoSBoG7AHMlbQN8Blge6qKR4dI2rp0fQvVDOXWwJrAerbH2X478PPS52zgf2xvBewIPFXat6aqpLQFsDHVLORw4CfAfra3Ac4AvlsnxNnAvmV7H2CMpNd3c1mHAZfZfqqbfu0/g9dSlTq9rru+K1reph942pd4AiCVsiIiYrCQNBmYXNM01fbUms+jJM0q2zdTle38AnCJ7ZfKGBcDuwCXAb+3fXvp/xiwsaSfAFcC0yWNoUpQL4GqfGgZA+BO20+Uz7Ooll58DhgHXFv6DGVp8lrrKOBkSZOoas//CVjcxXWvC3wUmNj5T2eZ/sOAc4GTWmFB/ySjA0z7Ek8RERGDTUk8p3bRZYHtZequtt8+78RLNWP/XdJWwPuBLwIfo5r57ExtLfo2qpxLwP22d+jiOGw/CXykxDca2Nf2P7o4ZGuqRw4eKZezqqRHqGZ27y59LrP97bI9Ffid7RO7imNlyW36iIiIGMxuAvYub5qvRnVb/OaOnSStCQyxfRHwLeAdtp8HnpC0d+kzops39B8G1pK0Q+k/XNLb6p2r5oWpr1Hdzu+U7Sttv8H2WNtjgfm2N7HdVl7YGt+eiEr6T2B1uk6kV6okoxERETFo2b4HmAbcCdwBnG773jpd1wNmlFvu06iSRKjebD9c0hzgVuANXZzrZWA/4AeSZgOzqJ4z7Wgi8LCk3wLrUPNcqaSbgQuA90h6QtL7G73WskTUN6ieY72nLHP1uUaPX1Fku+cHSVPyLGL/ssqI9Xv+i16JlvTiv8NYqrWrlvcPrV77vdW9buToZofQrVavTf/RNbfuvlMTPblkfrND6NZVf7gq/5D7ocyMRkRERETT5AWmQWLISl2rt+eWuK3ZIfRrQ4cMbXYI3Wpb0tq/497cJVqZWn3mdsHil5sdQrda/Q7M8dv8tdkhdGn4xq9rdggxQLV2hhIRERERA1qS0YiIiIhomiSjEREREdE0SUYjIiJiUJBkSb+o+TxM0jOSrmhSPJNK9aT2z6dL2qJOvxmSHi5LMT1YKk0NGElGIyIiYrB4CRgnaVT5/F6qUpsrjKSu3jCdBPwzGbX9OdsPdNL3oFI9aieqdUpX6aMYmi7JaERERAwmvwL2LNsHUtVoB0DSdpJulXRv+f6W0v42SXeWmck5kjYt7Z+oaf9pe9In6UVJx0i6aO/ZBAAAIABJREFUA9hB0rcl3SXpPklTVdkPmACcXY4fVWZAJ3QT/2iqpLqtnOtASXPL2D+ouZZuY+iLH2ZfSDIaERERg8l5wAGSRgJbUlVdavcQsKvtrYFvA98r7YcCPy4zkxOoSoC+Fdgf2Km0twEHlf6rAffZ3t72LcDJtre1PQ4YBXzQ9oXATMqMp+0F3cR9dqny9DBwrO22cov/B8C7gfHAtu2lSRuJoac/uBWlX64zKuloYGSz4+gPUikrIiIGi/IsZe3zlFNtT63tY3uOpLFUs6JXdRhideDMMvNpYHhpvw34RimnebHt30l6D7ANcFeZZBwFPF36twEX1Yy7m6SvAqsCawD3A5f38PIOsj1T0lrArZKupkpAZ9h+plz/2cCuwKUrKIYVol8mo8DIJFkRERFRqySeU7vtCJcBP6KqAf/6mvZjgRts71MS1hll3HPK7e49gWtKPXcBZ9r+Gq+20Hb7bfSRwCnABNt/lDSF5ZhQs/2MpHuA7YGuqk2ssBj6Wm7TR0RExGBzBnCM7bkd2ldn6QtNk9obJW0MPGb7JKpEdkvgOmA/SWuXPmtIelOdc7UnfX+VNBrYr2bfC8CYngQuaVVga+BRqkcM3iVpzfK86oHAjT2Moen668xoRERERK/YfgL4cZ1dP6S6TX8kcH1N+/7AJyS9AvyZKpF9VtI3gemShgCvAF8Eft/hXM9J+hkwF5gH3FWzexpwmqQFwA7dhH126TcCmGb7bgBJXwNuoJqpvcr2L+tcb1cxNJ16U49Z0pRm3iZv9vn7o5EjN2zposyLW7xueasbltr0A14Lvfha18hhDa8y0zStXpv+j3vUm1RrHf2hNv3o4y9p7X8oUVdu00dERERE0+Q2/SBhWntGIJZPf5hZznTF8unNXayVqT/Mzrf6z/CZB1rmfZK6psxsdgTdO+f4ZkcQvZGZ0YiIiIhomiSjEREREdE0SUYjIiIiommSjEZERMSgIOnFDp8nSTp5JZz36w30uUzSfZ3smyLpT6WG/e8kXSxpiy7GOkbS7ssT88qUZDQiIiKiAZJ6++J3l8mopI8AL3bVBzih1LDfFDgfuL6UBu041lDb37b9617GutIlGY2IiIhBT9KbJF0naU75vmFpnybpvyXdAPygzFKeIWmGpMckHV4zxick3VlmMH8qaaik44BRpe3sOucdDRwJ/Gejsdo+H5gOfLyMMU/StyXdAny0xLyfpD0k/V/NuSZKurxsv0/SbZLukXRBiaMpsrRTPyDpaHpZQzbFASIiIv5plKRZNZ/XoCrvCXAycJbtMyUdDJwE7F32bQbsbrut1HXfHNiNqpTnw5JOBTahqtS0k+1XJJ0CHGT7aEmH2R7fSUzHAv8FzO/htdxT4mi30PbOAJI+UNquBX4qaTXbL5X4zpe0JvDNck0vSfp3qoT4mB7G0CeSjPYPI5NURkREdE3SZGByTdNU21NrPi+oTQolTQImlI87AB8p27+gKg3a7gLbtQs6X2l7EbBI0tPAOsB7gG2Au0rFtFHA093EOx7YxPaXJY1t5BprD+/w+fyOHWwvlnQ18CFJFwJ7Al8F3gVsAfymxLoKcFsPz99nkoxGRETEgFASz6nddmxwuJrtlzrsW1Sz3UaVTwk40/bXenCOHYBtJM0rY6wtaYbtiQ0cuzVQW4qgY4ztzge+CDwL3GX7BVUZ6LW2D+xBrCtMnhmNiIiIgFuBA8r2QcAtPTz+OmA/SWsDSFpD0pvKvlckDe94gO1Tba9reyywM/DbRhJRSfsC7wPObSCuGcA7gENYOnt6O7CTpE3KeKtK2qyBsVaIJKMRERERcDjwGUlzgE8C/9aTg20/QPUc5vQyxrXAG8vuqcCcei8w9cCX25d2Aj4BvNv2Mw3E1QZcAexRvlOOmwScW2K9nWWfP12p1JtavZKmNPMZxmaff2Xri+sdMXKDli7K3LZkSbNDiBUstekHtjEjVm12CN1q9dr0d2ywSbND6NKU+aOaHUK3zvn9JflT0w9lZjQiIiIimiYvMPWx5VmGqQtj+3i8iIg+1ebWv7sxpMXn5//y96Yt89iQV0a80uwQYoBKMtr3+nwZprKmWURERMSAk9v0EREREdE0SUYjIiIiommSjEZERMSAV2rJv79D2xGlbGdnx7xYvq9bKhj19txHSGr9JSeaJMloREREDAbnsnRR+3YH0MDC8baftL3fcpz7CCDJaCeSjEZERMRgcCHwQUkjAEot+HWBWZKuk3SPpLmS9up4oKSxku6r2b659L9H0o6lfWKZfb1Q0kOSzlbl8HKeGyTdsJKutV/J2/T9w8LevlE/mIoDREREdMb23yTdCXwA+CXVrOj5wAJgH9vPS1oTuF3SZe68SsLTwHttL5S0KdXM6oSyb2vgbcCTwG+AnWyfJOlIYDfbf11hF9iPJRntB2wf1+wYIiIiWp2kycDkmqaptqfWfG6/Vd+ejB5MVSDue5J2BZYA6wHrAH/u5DTDgZMljQfagNqa7nfafqLEMotqnfCe1rgfdJKMRkRExIBQEs+pXXS5FPhvSe8ARtm+R9IkYC1gG9uvSJpH18Vrvgz8BdiK6nHHhTX7FtVst5E8qyF5ZjQiIiIGBdsvAjOAM1j64tLqwNMlEd0NeFM3w6wOPGV7CfBJYGgDp34BGNOroAeBJKMRERExmJxLNat5Xvl8NjBB0kzgIOChbo4/Bfi0pNupbtG/1MA5pwK/ygtM9WX6OCIiIgYN25dQPSfa/vmvwA6d9B1dvs8DxpXt3wFb1nT7WmmfQTXr2n7sYTXbPwF+0jdXMPBkZjQiIiIimiYzo4OElv6fwBiA+sNvV2rtKDtfxSUa0R9+fkta+z9BNt7o2WaH0KXd/7hus0OIASozoxERERHRNElGIyIiIqJpkoxGRERERNMkGY2IiIhBQZIl/aLm8zBJz0i6YgWec79y3gnl81hJCyTNKl+n1fTdRtJcSY9IOknlYXtJ75R0R+n/YHuJcEmTJJ1c55yjJf1U0qOS7pd0k6TtV9Q1Lq+8wBQRERGDxUvAOEmjbC8A3gv8qScDSBpme3GDfccAhwN3dNj1qO3xdQ45laqc6e3AVcAHgF8BZwIfsz1b0lDgLd2c+nTgcWBT20skbQy8tZGYmyEzowOYpKObHUNERESL+RWwZ9k+kKWVmJC0mqQzJN0l6V5Je5X2SZIukHQ5ML20fbXMYs6WdFwn5zoW+CHLlgytS9IbgdfYvs3V8hRnAXuX3WsDTwHYbrP9QBfjvBnYHvhmqRKF7cdsX9ldDM2SZHRg66q2bkRExGB0HnCApJFUi9fXzlp+A7je9rbAbsDxklYr+3YAPm373ZL2oEoUt7e9FVXCuQxJWwMb2K73CMBGJdm9UdIupW094ImaPk+UNoATgIclXSLp8yX2zrwNmGW7rYs+LSXJaERERAwIkiZLmlnzNbljH9tzgLFUs6JXddj9PuBoSbOoqimNBDYs+6613b4Y7O7Az23PL2Mus0ispCFUCeT/qxPmU8CGtrcGjgTOkfQa6i8Z7TL+McAEqlnZjwNXd/pD6IfyzGhEREQMCLanUtWB785lwI+AicDra9oF7Gv74drO5eWflzr066rSwxiq8qEzyjtIbwAuk/Rh2zOBRSXeuyU9SlXj/glg/Zox1geerLm2R4FTJf0MeEZSbdy17ge2kjSk/TZ9qxvQyWh5ZnJl36oeu5LPFxERET1zBvAP23MlTaxpvwb4kqQv2bakrW3fW+f46cC3JZ1je76kNWpnR23/A1iz/bOkGcBRtmdKWgt41nZbebFoU+Ax289KekHSO6keHfgUpZ69pD2Bq8qzpJsCbcBz9S7M9qOSZgL/Ienb5To2Bbaw/cte/bRWsAGdjAIjbU9ZmSdsX24hIiIiWpPtJ4Af19l1LHAiMKcsqzQP+GCd46+WNB6YKellqtv9X2/w9LsCx0haTJVUHlqTyH4BmAaMonrR6lel/ZPACZLmA4uBg0oyCzBJ0t41478T+BzwX8Aj5Zi/AV9pML6VbqAnoxEREREA2B5dp20G1fOhlOWePl+nzzSqJLG27Tigs7foOx4/sWb7IuCiTvrNpLq937H9gE76vyquGoc0ElsryAtMEREREdE0vZ0ZXdjk29Fjm3juiIiIiOgjvUpGy9R00+S5zIiIiIiBIc+Mtpg+XgFgbB+NExEREbFCJBltPX22AkBmkCMiIqLV5QWmiIiIGBQkWdIvaj4Pk/SMpHolO/vqnPuV804on8dKWiBpVvk6rabvNqXe/SOSTirLSyHpnZLuKP0fbJ9skjSpxD+r5muLFXUtK0pmRiMiImKweAkYJ2lUWcbpvcCfejKApGG2FzfYdwxwONUi9rUetT2+ziGnApOB26nWLv0A1VqjZwIfsz1b0lDgLTXHnG/7sJ5cQ6vJzOjAtnOzA4iIiGgxvwL2LNsHAue275C0mqQzJN0l6V5Je5X2SZIukHQ5VfUlJH21zGLOltTZi93HAj8EFnYXlKQ3Aq+xfVuptHQW0L6Y/dpUNe2x3Wb7gZ5edCtLMjqwZeY7IiJiWecBB0gaCWzJsrOW3wCut70tsBtwvKTVyr4dgE/bfrekPagSxe1tb0WVcC5D0tbABrbrPQKwUUl2b5S0S2lbj6o+fbsnShvACcDDki6R9PkSe7v9O9ymH9WDn0VLSLISERERA4KkyVS3udtNtT21to/tOZLGUs2KXtVhiPcBH5Z0VPk8EtiwbF9bU7Zzd+DntueXMZ+tHUTSEKoEclKdMJ8CNrT9N0nbAJdKehugOn1dxj9G0tklvo+X2CeWPv3+Nn2S0YiIiBgQSuI5tduOcBnwI6qE7vU17QL2tf1wbWdJ21M9b1rbz12MP4aqrOeM8g7SG4DLJH24lPxcVOK9W9KjwGZUM6Hr14yxPvBkzbU9Cpwq6WfAM5Jq4+7Xcps+IiIiBpszgGNsz+3Qfg3wpZq32Lfu5PjpwMGSVi391qjdafsftte0Pdb2WKoXkj5se6aktcpLSEjaGNgUeMz2U8AL5c15AZ8Cfln67dkeU+nfBjy3PD+AVpKZ0YiIiBhUbD8B/LjOrmOBE4E5JfmbB3ywzvFXSxoPzJT0MtXt/q83ePpdgWMkLaZKKg+tuc3/BWAaMIrqRatflfZPAidImg8sBg6y3Vby0/0l1b6w/K+2b20wlpaQZHRgm9fsACIiIlqF7dF12mYAM8r2AuDzdfpMo0oSa9uOAxoqj257Ys32RcBFnfSbSXV7v2P7AZ30f1Vc/VFu0w9s85odQERERERXMjMaERHLbenjbBERPZOZ0YiIiIhomiSjEREREdE0SUYjIiJiUJBkSb+o+TxM0jOS6lVJ6qtz7lfOO6F8HitpQU3FpNNq+m5TSow+IumkmiWm3inpjtL/QUlTSvskSSevqNhXljwzGhEREYPFS8A4SaPKm/PvBf7UkwEkDbO9uMG+Y4DDWbbkKMCjtsfXOeRUqgpSt1MtF/UBquWdzgQ+Znt2WaP0LT2JudVlZnRgW9jsACIiIlrMr4A9y/aBwLntOyStJukMSXeV2vF7lfZJki6QdDnVgvdI+mqZxZwtqbMlno6lqlvf7f8eS3oj8Brbt9k2cBawd9m9NlUZUWy32X6gpxfdypKMDmBlDbSIiIhY6jzgAEkjgS1ZdtbyG8D1trcFdgOOl7Ra2bcD8Gnb75a0B1WiuL3tragSzmWU6k0b2K73CMBGJdm9UdIupW09qpKg7Z4obVDVuX9Y0iWSPl9iHzBym77vLWx/lqOXxvZRHBEREYOKpMlUt7nbTS316v/J9hxJY6lmRa/qMMT7gA9LOqp8HglsWLavramUtDvwc9vzy5jP1g4iaQhVAjmpTphPARva/pukbYBLJb2Nqt59Ry7jHyPp7BLfx0vsE+v075eSjPax5Z2NXM5ENiIiYtAqiefUbjvCZcCPqBK619e0C9jX9sO1nSVtT/W8aW0/dzH+GKpKSjPKO0hvAC6T9OFSZWlRifduSY8Cm1HNhK5fM8b6wJM11/YocKqknwHPSKqNu1/LbfqIiIgYbM4AjrE9t0P7NcCXat5i37qT46cDB0tatfRbo3an7X/YXtP2WNtjqV5I+rDtmZLWKi8hIWljYFPgMdtPAS+UN+cFfAr4Zem3Z3tMpX8b8Nzy/ABaSZLRiIiIGFRsP2H7x3V2HQsMB+ZIuq98rnf81VSzqzMlzQKOqtevE7uW8WcDFwKH1tzm/wJwOvAI8CjVy1YAn6R6ZnQW8AvgINttZd8kSU/UfNXOrvYLuU0fERERg4Lt0XXaZgAzyvYC4PN1+kwDpnVoOw5o6NE82xNrti8CLuqk30yq2/sd2w/opP+r4uqPMjMaEREREU2TZDQiIiIimia36SNipajWcI6Bqj/8fpe+/9Gaxoxr7f9J/tNTrf87jv4pM6MRERER0TRJRiMiIiKiaZKMRkRExKAgqU3SLEn3l5ryR5ZqST0ZY4akCX0Uz3aSbpL0sKSHJJ0uaVVJU2qqQDU61jxJa5btF/sivpWltR9QiYiIiOg7C2yPB5C0NnAOsDrwnZUdiKR1gAuAA2zfVha135eqelO/JGlozfqnDUsyOkBIOpqqhu4ybE9Z+dFERES0NttPl1r2d5VS3COAU4EJwGLgSNs3SBoF/BzYAngQGNU+hqTPAv9OVbbzd8Ai24dJWgs4jaV17Y+w/ZsOIXwRONP2bSUeUy2C3/6y3RaSZpQxTrR9Utl3KbAB1f/m/7iUQO2UpK8AHyvXd4nt70gaS7Wg/i3AjsCfgL1sL5C0LfC/VOVPbwH2sD2uVI06jqqE6gjgf2z/VNJEqmT+KWB8+Tn1SJLRgWNkEs+IiIjG2X6s3KZfG/hEaXu7pM2B6ZI2o6qKNN/2lpK2BO4BkLQu8C3gHcALwPXA7DL0j4ETbN8iaUOqMqNv7XD6ccCZXYS3ObAb1Uzpw5JOtf0KcLDtZ0uSfJeki2z/rd4Akt5HVT50O0DAZZJ2Bf5Q2g+0fYik/6Oalf3/qBLvybZvlVS7qP9ngX/Y3lbSCOA3kqaXfdsB42w/3sX1dCrJaERERAwIZaZzck3T1O5mDqmSNICdgZ8A2H5I0u+BzajKd55U2udImlP6bwfc2F7KU9IFpT/A7lQzm+3neI2kMbZf6MHlXGl7EbBI0tPAOsATwOGS9il9NqBKKusmo8D7yte95fPo0v8PwOO2Z5X2u4Gxkl4LjLF9a2k/B/hgzVhbStqvfF69jPUycGdvE1FIMhoREREDREk8u0s+/0nSxkAb8DRLk9K6Q9c7vIv+Q4AdSnnRztwPbAP8spP9i2q224Bh5Zb47mXs+eU2/qse0esQ4/dt/3SZxuo2fcfxR9H1NQn4ku1rOow1keqWfq/lbfqIiIgYdGqe6zy5PK95E3BQ2bcZ1bOaD3doHwdsWYa4E3iXpNdJGkZ1m7vddOCwmnONrxPCycCnJW1f0+8Tkt7QRdirA38viejmwDu7ucxrgIMljS7jr1de3KrL9t+BFyS1j3tAh7G+IGl4GWszSat1c/6GZGY0IiIiBotRkmYBw6leUvoF8N9l3ynAaZLmln2TbC+SdCrw83J7fhZVEortP0n6HnAH1QtMDwD/KGMdDvxPOWYYVUJ7aG0gtv8i6QDgRyVBXFL6XdxF/FcDh5ZxHwZu7+pibU+X9FbgtvLIwItUz8Z29cb7Z4GfSXoJmFFzTacDY4F7ypv/zwB7d3X+Rqk/lHDrSNKURl7WabRfK+nsrfgGjLU9qbOdI0du2NK/6MVLerwSRNRo7SKHMRiMGj6i2SF0q9XLgf7xwDc3O4Qu/ejatZodQreOmXf2Sv0lSxpt+8UyM3oJcIbtS1ZmDH2t/ZrK9tHAG23/24o8Z2ZGW4zt47rv9WplWYqIiIhYeaZI2p1qEmk6cGmT4+kLe0r6GlWO+Htg0oo+YZLRiIiIiF6w3aMqSf2B7fOB81fmOZOMRkREtIC2515udggRTZG36SMiIiKiaZKMRkRERETTJBmNiIiIQUPSPpJc1ulsbxtb2o6taVtT0iuSTq5p+5Sk+yTdL+kBSa96ZlTSlHrtyxnzP8eUdEx5aWrAyDOjA8fCem/U97elrSIiIlawA4FbqBZ0n1LT/hhV6ctvlc8fpaqSBICkPYAjgPfZflLSSOCTKyPgWra/vbLPuaIlGR0gerskVERExGBRKhHtBOwGXMayyegC4EFJE2zPBPYH/g9Yt+z/GnCU7ScBbC8EftbN+Q4BJgOrAI9QJa/DgdnAxraXSFqVagH7jamqPv0PsBYwHzjE9kMdxpwGXGH7QknzgDOBD5VxP9qxf3/QX5PRurOAdYxdwXFERERE/7E3cLXt30p6VtI7bN9Ts/884ABJf6aqUvQkS5PRccDdPTzfxbZ/BiDpP4HP2v6JpNnAu4AbqBLJa2y/ImkqcKjt35UyoacA7+7mHH+1/Q5J/wocBXyuhzE2Xb9MRhudBcxC8BEREYOHpMlUM5HtptqeWvP5QODEsn1e+VybjF4NHAv8hb5Za3NcSUJfC4ymqu9OGXt/qmT0AOCUMmu7I3BBTbWwRkqbtZcPvRv4SB/EvNL1y2Q0IiIioqOSeE6tt0/S66lmGcdJMjAUsKSv1hz/sqS7gf8HvI1q1rLd/cA2wPU9CGkasLft2ZImARNL+2XA9yWtUTPmasBztsf3YHyAReV7G/00r8vb9BERETEY7AecZftNtsfa3gB4HNi5Q7//Av7d9t86tH8f+KGkNwBIGiHp8G7OOQZ4StJw4KD2xlL7/U7gx1TPf7bZfh54XNJHy/iStFXvLrV/STIaERERg8GBwCUd2i4CPl7bYPt+22d2PNj2VVQvF/1a0v1Ut8XrzUQOY+ls5beAO4BrgY4vFp0PfIJlHwc4CPhseab0fmCv7i+r/5PtZsewwkiakqWNKiNHbtjSv+jFS9qaHUK/pu67RKxQo4Y38mhbc9U8h9eS5u2xQbND6NIJd63X7BC6dcy8s5v+S5Z0CfCzkrxGAzIzGhEREdEHJM0FlgDTmx1Lf9IvH3SNiIiIaDW2397sGPqjJKMRA0Cr337sDwbyI0srw5J+8PMb2uL/Tlb91teaHUKXPvD+ui+pRyy33KaPiIiIiKZJMhoRERERTZNkNCIiIgYFSW+QdJ6kRyU9IOkqSZtJmijpihVwvimSjlqO41dIXN2cc4Kkk7rp06dx5ZnRfkDS0cDI3hybpa0iIiKqReSp1hk90/YBpW08sE4Pxhhqe4WtRShpmO3FK2r8Bs8/E5i5Ms+bZLR/GJmkMiIiYrnsBrxi+7T2BtuzoJrpA0ZLuhAYR7Wg/SdsW9I84AzgfcCvJO1r+x3luE2B82xvI+k44MPAYmC67WVmRCW9mWrR/LWA+cAhth+SNA14FtgauIeqFOmrSJoCbAS8EdgMOBJ4J7AH8CfgQ7ZfKfGeX64X4OO2H5G0FnAasGFpP8L2b8q46wJjgb9KmgocZfuDkrYDTgRGAQuAz9h+uIGfdY8kGY2IiIjBoD3J7MzWVPXonwR+A+wE3FL2LbS9M4Ck3SWNL4nsZ4Bppcb8PsDmJYF9bZ3xpwKH2v6dpO2BU4B3l32bAbs3MOv6ZqokcwvgNmBf218tC+3vCVxa+j1veztJn6JKJj9IVXr0BNu3SNoQuAZ4a+m/DbCz7QUlMW/3ELCr7cWSdge+B+zbTYw9lmQ0IiIiBgRJk4HJNU1TbTe6JtWdtp8o48yimilsT0ZrS3aeDnxG0pHA/sB2wPPAQuB0SVcCyzxPKWk0sCNwQc1SfLVlyy5o8Pb/r8rs51xgKHB1aZ9b4m13bs33E8r27sAWNed/jaQxZfsy2wvqnG914MwyA2xgeAMx9liS0YiIiBgQSuLZWfJ5P7BfF4cvqtluY9kc6aWa7YuA7wDXA3fb/htAuaX9HuAA4DCWznpC9cL4c7bHd3Lulzpprxuj7SWSXvHSBZKXdIjXdbaHADt0TDpLctrZ+Y8FbrC9j6SxwIwG4+yRvE0fERERg8H1wAhJh7Q3SNpW0rt6MojthVS3uE8Ffl7GGQ2sXurRHwGM73DM88Djkj5a+kvSVstzMd3Yv+b7bWV7OlWSTImhs8S41upUz6MCTOqr4DpKMhoREREDXplF3Ad4b1na6X5gCtUzoj11NtWMY3sN+jHAFZLmADcCX65zzEHAZyXNppql3asX523UCEl3AP9WE8vhwARJcyQ9ABzawDg/BL4v6TdUjwWsEBrIJfAkTRkIb6H3xXWMHLlhS/+iFy9ZYStlDApDWrzMYX8wkP8Wrgwjhq3S7BC6NXRIa8+/PH3rKc0OoUt394NyoDs+ddFK+WNY1g5d3fa3Vsb5eqK8TT/B9l+bHUuj8sxoRERERIPKm+tvZtlnQmM5JBmNiIiIaJDtfZodQ1dsj212DD2VZLQHlqcS0nIa24RzRkQ0rM1Lmh1Ct4a2+GsSQ9bdtNkhdOkZj+i+U0QvJBntmaZUQirVESIiIiIGnNb+v4kRERERMaBlZnQA6OrxgYGwmkBERERfkPQGqvKY21ItID+Pqkb7bxs8fgrwou0fSToGuMn2ryXNoKrnPrMv3mYvJTl/CTxemv5qe/fa8/dyzJdt39rbuHp4vnk0+HNIMjowNOXxgYiIiP5CVamhS4AzbR9Q2sYD6wANJaO1bH+7byN8lZttf7APx5sIvAislGS0J3KbPiIiIgaD3YBXbJ/W3mB7lu2bJY2WdJ2keyTNlfTPBeklfUPSw5J+Dbylpn2apK7KiyLpUkl3S7pf0uSa9g+Uc82WdF1vLkbSmyVdXca/WdLmpX0tSRdJuqt87VRKeR4KfFnSLEnvkvRYqQT1WklLJO1ajr9Z0iaS1ijxz5F0u6Qty/7O2l8vabqkeyX9FGh4zdfMjEZERMRgMA64u5N9C4F9bD8vaU3gdkmXAe+gqjW/NVXOdE8XY9RzsO1nJY0C7pJ0EdU39RnFAAAgAElEQVRE4M+AXW0/LmmNTo7dRdKssn2B7e922D8VONT27yRtD5xCtfbpj4ETbN8iaUPgGttvlXQaNbf4Jf0W2ALYqFzTLqVq0/q2H5H0E+Be23tLejdwFlWZ0//opP07wC22j5G0JzCZBiUZjYiIiAGhzD7WJkFTbTdSOkrA98rs4BJgParb97sAl9ieX8a/rIchHS6pfV3SDYBNgbWonjV9HMD2s50c2+ltekmjgR2BC7S0Al/72lu7A1vUtL9G0ph64wO7UiWj3wcOoSplelfZvzOwb4nx+jLzuXoX7bsCHyntV0r6eyfX9SpJRiMiImJAKIlnZ8nn/UBnt9UPokoSt7H9Snn5pv3F4F7VCi4vDO0O7GB7fnnJaSRV4ru89YeHAM/ZHt/Jvh1sL+gQT8d+N1Pdul8X+DbwFarnSm9qP6TO2O6ivfZ7j+SZ0YiIiBgMrgdGSDqkvUHStpLeBawOPF0S0d2AN5UuNwH7SBpVZhc/1IPzrQ78vSSimwPvLO23Ae+StFGJobPb9J2y/TzwuKSPljEkaauyezpwWM01tiesLwC1M6R3UM2uLrG9EJgFfJ4qSYXq2g8qY0ykeqP/+Qbb9wBe1+j1ZGa0f1jYzcL3Y1dSHBEREf2SbZdb5ieWJREXUpZ2opo1vVzSTKqk7KFyzD2Szi9tv2dpotaIq4FDJc0BHgZuL2M+Ux4nuFjSEOBp4L29uKSDgFMlfRMYDpwHzAYOB/6nnHcYVZJ4KHA5cGF5OetL5cWtP7bHVa7tQGBu+TwF+HkZZz7w6W7a/wM4V9I9VLf7/9Dohche3pni1iVpSl8uedTX4/WVRuIaOXLDlv5FL17S1uwQ+rUhr779Ej00kP8WrgzDhrb+3MYqLR7jX+dd2+wQunTVuG82O4Ru7fXnc/LHsB/KbfqIiIiIaJokoxERERHRNK19zyIiImKQ8IIXmh1Cl0Z5SbNDiAEqM6MRERER0TRJRiMiIiKiaXKbfmDodOmnVnz7PyIiohkkvWh7dIe2Q4H5ts/qw/PMAI6yPbMHx+wBHAusRrWw/BW2j+rFuccCO9o+p3yeAHzK9uE9Haub8+wN/Nb2A930m0JNGdJ6kowOALaPa3YMERER/ZHt05odg6RxwMnAnrYfkjSMHtR272As8HHgHICSEDecFPfA3sAVQJfJaCNymz4iIiIGLUlTJB1VtmdI+oGkOyX9VtIupX2opB9JmitpjqQvlfb3SLq3tJ8haUSd8V+U9F1JsyXdLmmdOmF8Ffiu7fbF9hfbPqUc/yZJ15XzXidpw9I+TdJJkm6V9Jik9lKnxwG7SJol6cuSJkq6ouZazyjX+Zikf86WSvpEue5Zkn4qaWhn8UvaEfgwcHzp/2ZJh0i6q/S7SNKqjf4OkoxGRERELDXM9nZUlZm+U9omAxsBW9veEjhb0khgGrC/7bdT3W3+Qp3xVgNut70VVTWkQ+r0GQfc3Uk8JwNntZ8XOKlm3xuBnYEPUiWhAEcDN9seb/uEOuNtDrwf2A74jqThkt4K7A/sVOrdt1FKe9aL3/atwGXAV8p5HgUutr1t6fcg8NlOrudVkoxGRETEgCBpsqSZNV+9udV9cfl+N0vLbe8OnGZ7MYDtZ4G3AI/b/m3pcyawa53xXqa6nd1xzEbtQLnlDvyCKvlsd6ntJeW5zXozrvVcaXuR7b9SlSJdB3gPsA1wl6RZ5fPGPYx/nKSbJc2lSmTf1mA8eWY0IiIiBgbbU4GpyznMovK9jaV5koCONYMbLT36ipfWG64ds9b9VMng7AbGq41jUc12o/HUHtMej4AzbX+tTv9G4odqlnhv27MlTQImNhhPZkYjIiIiujEdOLS8WISkNYCHgLGSNil9Pgnc2Mvxjwe+LmmzMv4QSUeWfbcCB5Ttg4BbuhnrBWBMD89/HbCfpLXL+deQ9KYenmcM8JSk4Sy9xd+QJKMRERExWKwq6YmaryO7PwSA04E/AHMkzQY+bnsh8BnggnJregnQqzfzbc+hekb1XEkPAvdRPQ8KcDjwGUlzqBLef+tmuDnA4vIi0ZcbPP8DwDeB6eU819acvzPnAV8pL3C9GfgWcEc59qFGzttOS2deBx5JU/pync2+Hm9lGjlyw5b+RS9e0tbsEPq1IWr07kx0ZiD/LVwZhg1t/ae+VmnxGJ958OLuOzXRjAnfb3YI3XrfX87LH8N+KDOjEREREdE0SUYjIiIiomla+55FRAwYrX4bXHnUYbkMHzK02SF0q23JkmaH0KVXzvqvZofQpU3f8LdmhxAD1EBPRjut2d5LY/twrIiIiIhBb0Ano31ds72PE9vlJuloYGRXffrrC1cRERExOAzoZHQQGJlkMyIiojGS2oC5NU17257Xy7GmAVfYvrBO+7uAf1At9/RF27f1YvyxwI62z+mma69ImgEcZXvmihi/J/ICU0RERAwWC0ot9faveY0c1L7YfQ98pdR4Pxr4aU+DLMYCH+/JAZJa/+HtOpKMRkRExKAlaaSkn0uaWxZw3620T5J0gaTLqRaDl6STJT0g6Upg7QaGvwnYpIx3iKS7ymL0F0latbRPk3SSpFslPSZpv3LsccAukmZJ+rKkoZKOL2PMkfT5cvxESTdIOocy6yvpUkl3S7pf0uQ+/YGtALlNHxEREYPFKEmzyvbjtvcBvghg++2SNqdKPDcrfXYAtrT9rKSPAG8B3g6sAzwAnNHN+T7E0scCLrb9MwBJ/wl8FvhJ2fdGYGdgc+Ay4EKqWdWjbH+wHDMZ+IftbSWNAH4jaXo5fjtgnO3Hy+eDS8yjgLskXWS7ZZdDSDIaERERA0JJ2GpnAqfanlrzeUG5fV5rZ0pSaPshSb8H2pPRa20/W7Z3Bc613QY8Ken6LkI5XtI3gWeokk6AcSUJfS0wGrimpv+ltpcAD0hap5Mx3wdsWTNzujqwKfAycGdNIgpwuKR9yvYGpV+S0YiIiIgVqSSeU7vtuKyuFhl+qeMpGhzzKx1fbAKmUb0wNVvSJGBizb5FDcQj4Eu2r1mmUZpYG2f5vDuwg+355UWlLlfeabY8MxoRERGD2U3AQQDl9vyGwMOd9DugPLv5RmC3Hp5nDPCUpOHt5+vGC+WYdtcAXyjHI2kzSavVOW514O8lEd0ceGcP41zpMjMaERERg9kpwGmS5gKLgUm2F9WpynYJ8G6qZ0B/C9zYw/N8C7gD+H0ZY0zX3ZkDLJY0m2pW9cdUb9jfoyq4Z4C96xx3NXCopDlUSfXtXZzjSkmvlO3bbH+0sUvpW2r1En2tRNKUVlrXsyfxjBy5YUv/ohcvaWt2CP3akH5QyrLV/9akHOjyGTlslWaH0K0lLf7f4F++sUuzQ+jS0//3VLND6NZGs6/NP+R+KDOjfaiRikh9bOxKPFdEREREn0sy2rdWakWkVitPGhEREdFTSUYjImj9xwhaXavfAofWf5xFo0Y0O4QuvfCPln4hO/qxvE0fEREREU2TmdEBpN4zq630wlVERERER0lGB5aV+sxqREREfyKpjWpZpeFUyzidCZxYqh/1dKxJwHTbTy5HPHsBxwJLSjxH2L6l7NsQOJ2qgpKBf7E9T9JGwHnAGsA9wCdtv9zbGFpBbtNHRETEYLHA9njbbwPeC/wL8J1ejjUJWLcnB0jqOAl4HbBVKVF6MFXy2e4s4Hjbb6WqPf90af8BcILtTYG/s7TcaL+VZDQiIiIGHdtPU9WxP0yVSZJObt8v6QpJE0vFpWmS7pM0V9KXS334CcDZkmZJGiVpG0k3Srpb0jWlShOSZkj6nqQbgX/rEMOLXvr25GqUcqOStgCG2b62pt/8stj9u4H2UqNnUn/h+34lt+kjIiJiULL9mKQhwNpddBsPrGd7HICk19p+TtJhwFG2Z5YSnT8B9rL9jKT9ge9SzXYCvNb2u+oNLmkf4Pslhj1L82bAc5IuBjYCfg0cDbwOeM724tLvCWC9Xl18C8nMaERERAwIkiZLmlnzNbmRw7rZ/xiwsaSfSPoA8HydPm8BxgHXSpoFfBNYv2b/+Z0NbvsS25tTzXAeW5qHAbsARwHbAhtTPRZQL9bWX1etG5kZ7d8Wdlj4fmyT4oiIiGg621OBqY32l7Qx0Eb1POZilp2kG1nG/LukrYD3A18EPsbSGc9/DgXcb3uHTk71UgOx3yTpzZLWpJrxvNf2YyXOS4F3AmcAr5U0rMyOrg/0+gWqVpFktB+zfVzt51RkioiIaIyktYDTgJNtW9I84F/Lbfv1qF4aoiSHL9u+SNKjwLQyxAvAmLL9MLCWpB1s31Zu229m+/5uYtgEeLSc/x3AKsDfqF5Mep2ktWw/Q/Wc6MzS7wZgP6o36j8N/LJPfiBNlGQ0IiIiBotR5TZ6+9JOvwD+u+z7DfA41dJP91EtmwRVYvrzkqQCfK18nwacJmkBsANVgniSpNWp8qsTgS6TUWBf4FOSXgEWAPuXF5raJB0FXFdeWrob+Fk55t+B8yT9J3Av8L89/im0mCSjERERMSjYHtrFPgMHdbL7HXX6XwRcVNM0C9i1Tr+JXZzzB1RLNdXbdy2wZZ32xyiztgNFXmCKiIiIiKbJzGhErBTVnaboraVLEbamRYtfZsSwVZodRr/m+QuaHUJEU2RmNCIillsS0YjorcyMDiwdl3oiteojIiKilSUZHUA6LvUUERER0eqSjPbMq2YeOxi7kuKIiIiIHpL0Bqoll7YFFgHzgCNs/7aZcdUq5UG/06F5S6pSoU8BpwKvoVqs/7u2zy/HvQc4nuoRzBeBSbYfWVlxL48koz3Q3cxjFp2PiIhoTWW9zkuAM20fUNrGA+sAKzwZrama1CXbl1DF2X7cZKolp64BNgE+Zft3ktYF7pZ0je3nqJLUvWw/KOlfqUqSTloBl9Ln8gJTREREDAa7Aa/YPq29wfYs2zdLGi3pOkn3SJoraS8ASWMlPSTpdEn3STpb0u6SfiPpd5LaqzRtJ+lWSfeW728p7ZMkXSDpcmC6KseXseZK2r+rgCVtBnwb+KTtJbZ/a/t3JfYnqcqYrtV+OVQzpgCr04/KhGZmNCIiIgaDcVSVjOpZCOxj+/lS/vN2SZeVfZsAHwUmA3cBHwd2Bj4MfB3YG3gI2NX2Ykm7A9+jqq4EVXWmLW0/K2lfYDywFbAmcJekm2w/1TGgUlL0HOAo23+os387qvKhj5amzwFXlYpQz1PVsu8XMjMaERERA4KkyZJm1nxNbvRQ4HuS5gC/pioBuk7Z97jtubaXUJX3vK5Ua5rL0ndFVgcukHQfcALwtpqxr7X9bNneGTjXdpvtvwA3Uj2/Ws+xwP22z6tznW+kKmX6mRIXwJeBf7G9PvBzlpY5bXmZGY2IiIgBwfZUYGonu++nqh9fz0FUt7u3sf2KpHnAyLJvUU2/JTWfl7A0jzoWuMH2PpLGAjNqjnmpZruh6h+SJlLNrL6qDKmk1wBXAt+0fXtpWwvYyvYdpdv5wNWNnKsVZGY0IiIiBoPrgRGSDmlvkLStpHdRzWw+XRLR3YA39XDs1YE/le1JXfS7Cdhf0tCSQO4K3FnbQdLrqGY2P2X7hQ77VqF6ueks2xfU7Po7sHp5xhTgvcCDPbyGpsnMaERERAx4tl2WTTpR0tFUz4nOA46gmjW9XNJMYBbVM6A98UPgTElHUiW9nbmE6hnS2VQvHH3V9p879DkUWBs4tUMZ5e8Dw6kS2NdLmlTaJ9meVZLsiyQtoUpOD+7hNTSNWr3ecX8iaUqrVjwaOXLDlv5FL17S1uwQ+rUhqfs+4LX63+r+UA601f+dPPW1HZsdQpcePf0fzQ6hW1vOu7y1f8lRV27TR0RERETT5DZ9xADQ6rNmMfANHzK02SF0awmt/e9Eq45qdghduqkf3MDastkBRK9kZjQiIiIimiYzoy2qPFw9stuO3WjVZ1gjIiIiIMloKxuZRDIiIiIGutymj4iIiEFB0osdPk+SdPJKOO/Xu9h3cKlTP6fUrN+rj845XtK/NNCvz34GZax1e3pcktGIiIiIBkjq7R3lusmopPWBbwA7296Sqp78nF6eo3bcYcB4oNtktI9NAnqcjOY2fURERAx6kt4EnEFVFvQZqrrvf5A0DXgW2Bq4R9ILwIbAxuX7ibZPKmN8AjgcWAW4A/hX4LvAKEmzqGrNH1Rz2rWBF4AXAWy/2L4taQbVAvzbAa8BDrZ9p6Q1SpwbA/OBybbnSJpClQiOBf4K7FzOuzPVgvmPAycCo4AF5foeLnFsIOlqYCPgHNv/UWI4kqWL559u+8RS7vQK2+NKn6OA0cB9wATgbEkLgB1sL2jkZ59kNCIiIgaL9qSw3RrAZWX7ZKoym2dKOhg4Cdi77NsM2N12W0n6Ngd2A8YAD0s6FdgE2B/YqZQVPQU4yPbRkg6zPb5OPLOBvwCPS7oOuNj25TX7V7O9o6RdqRLQccB/APfa3lvSu4GzqGZBAbahmmVdUCo0TbB9GPyzpv2uthdL2h34HrBvOW67MvZ84C5JV1JViPoMsD0g4A5JN1JVd3oV2xdKOgw4yvbMen06k2S0by0s/5H2hbF9NE5ERMSgIGkyMLmmaartqTWfF9Qmhe0JW/m4A/CRsv0LqhKf7S6wXbvS6pW2FwGLJD0NrAO8hyoZvKuU8RwFPN1VvCW5/QCwbTn+BP3/7Z17sFXVfcc/XxEFFKGQmupUS0NpEqsRIyGPYksT0hlbFRhJME2amCZS2qhNOmkmbdp6K1ObzNiYjklKkSBGiS+ihqC1yMuL0ApBHoLG2EpexrRjakhAENRf/1jryPZyHvex79mH8v3MnLl7r7323t+z1tnn/O56faVzCxOYb835uiWdJGk0qcXz4py+WtJYSaNy/mVNWiNHkSxLJ5ACzaGFYw9ExE9ymdyV7xHA3RGxt5B+HoeC99JwMFoiEfHZsq5VYlBrjDHGHBXkwHNBy4y9vFxhe2+PYy8Utl8ixVMCboqIv+zTTZJryUZgo6QHgBuBrjoaavv1LE9r+XrqLDIPWBMRM3NX+9o657e6D8CLvHrO0YCXofQEJmOMMcYY2ABckrffDzzUx/NXAbMknQwgaUwehwpwUNLQnidIOlXSmwtJE4HvFfZn53xTgN0RsRvozvqQNBV4NiJ+VkfPz0nDCGqMAp7O25f2yPvurHc4aWjC+nyfGZJGSDoBmAmsIw0rODm3yB4PXNDknr3CLaPGGGOMMWni0SJJf0GewNSXkyPiMUl/DayQdAxwEPgYKbhcAGyX9EiPCUxDgWvzckj7833nFo4/J2kDeQJTTusCbpS0nTTG80MNJK0BPp3HyP4DadjBTXlS0uoeeR8iDU34NdIEpm8B5MlbG3OehRGxJadfTZqgtQv4duE6i4H5fZ3AJHtadyaSuspc9H7YsNM7uqJffPkIMD3uYBr1pRjTLk48rrN91aHzvemfufqdVUtoyqLrehVXVMrlP7jl/83XYZ5N3+fJQEci7qY3xhhjjDGV4W56Y0xbyLNLOxb3Epmq0fgJVUtowYDXYjd9ICKmVq2hXTgY7VxKWSbK/vbGGGOM6WQcjHYoZS4TZYwxxhjTqXjMqDHGGGOMqQwHo8YYY4w5KpD0kqStknZIulPSiH5e59K8HFNtf6GkMwaobZykfVlf7XWcpIskfbof+r7Y4NiGXl6jK/vODzoORo0xxhhztLAvIiZGxJnAAV69pmdfuBR4JRiNiI9GxGMl6PuvrK/2OhARy+oN3ZPUr6GWEfGOOtca0p9rlYWDUWOMMcYcjawjLfKOpD/PraU7JH08p42T9LikGyTtlLRC0nBJs0h+9kty6+VwSWslTcrn/bOkb+Vz/q52M0lvkbRB0jZJGyX1yqmo2MopabGkz0taA3xO0gmSFknaJGmLpOmFU0+TdL+kJyRdVbjenvx3qqQ1kr4GPJrTPpPzrwRe3++S7SOewGSMMcaYo4rcqng+cL+kc0luS28leYg8LOlB4DlgAvC+iLhM0h3AxRFxi6TLKSxI32Ppus9ExP/m1sZVkt5Ecim6HZgdEZsknQTUcxEYnx2TANZHxMfq5Pl1YFpEvCTpGmB1RPyRpNEkf/uVOd9k4EySS9MmSffWWUB/MnBmROzK5XAJcA4pPnwE2NyyMEvAwehRwv793y91kUdJcyJiQZnXLJNO1wedr9H6Bkan64PO12h9A6dMjZdPb52nr5RdhpLmAHMKSQt6XH94IdhbB3wF+BPg7ojYm69xF3AesAzYFRG1/JuBcb2Q8d6s41jgFOAMIIBnImITQAMvecjd9C2uf2dE1GwLfxe4qDC2cxhwet5+ICJ+UnhPU4CewejGiNiVt88jlcPz+ZxlLXSUhrvpTX+Z0zpLpXS6Puh8jdY3MDpdH3S+RusbOJ2usVR9EbEgIiYVXj0D3dqY0YkRcUVEHKC5o/ILhe2XaNGIJ+lXgU8C74qINwH3kgJEQWl+tHuLtyS11tbe0+kR8Xg+1vN+9e6/txd5Bh0Ho8YYY4w5mukGZkgaIekEYCap1bQZPwfqjfk8iRTg7Zb0WtJQAEjd9KdKeguApJH9nYDUg38DrlAeJyDpnMKxd0saI2k4MANY3+Ja3cDMPAZ2JHBhCfp6hbvpjTHGGHPUEhGPSFoMbMxJCyNii6RxTU5bDMyXtA94e+Fa2yRtAXYCT5EDwIg4IGk2cH0ODvcB04A9A5Q/D/gCsD0HpN8FLsjHHgJuJk3S+lqd8aKvIpfD7cBW4Hu0DshLQ/ZjNv2h08dKdbo+6HyN1jcwOl0fdL5G6xs4na6x0/WZ9uBg1BhjjDHGVIbHjBpjjDHGmMpwMGpKIS/EO6tqHWbwaGYvVzWS/qri+/fJNi8vNn2YC4ox/aX2GZR0taRpVeupR15EfkfVOkzn4WDUGNOSkmZ9DiaVBqP1aFFmU4EjJhg9AurfZCLibyNiZeucxnQODkZNUyR9UNL2bF92t6RdkobmYydJ+m5tv0KNn5P0p4X9rmxp1q1k1bZD0nkVabtH0mYlW7g5OW1P1rxZ0kpJk5Ws5J6SdFEbNBXr9GZJF0p6WMlKbmVejqRWjgskrQC+mk+vay/XBs2fknRl3r5O0uq8/S5JS8kLWUta0kZNh9nm5Xq8Rsm95c/qlW2eoTsX+ETWXPpnU9LfSPq2pAck3ZpbzMbnutssaZ2kN+S8fan/svSNy/oW5udziaRpktZLejI/E13587k6p12Wzz1G0pfzM7Vc0n1l98oU67ZQfldKeiw/O7flfHWtGCUNkXStpEdz/ivK1FdPJ4c+g6/0Ukn6bEHztYXj8/Nn4DuSLmhyi8FgiA6311wr6QtKVpk7JE1usyZTNRHhl191X8BvAE8Ar8n7Y4AbgRl5fw7wj3l7MTCrIp3nAA8W9h8DriJZsgEMAUZWpG1M/jsc2AGMJS0qfH5OvxtYAQwFzga2VlCnv8ChyYwfLdRpF8lxZHjevxR4Jr+H2vuZ1KZyfBvJdQTSciMbc5ldBfwxsKfN9Xouyct5BGldwf8kLXS9FvhyIV+zsv3kIGmbRFqaZThpHcQns7ZVwISc560kC8FWGl+p/5I1jgNeBM4iNYpsBhaRFvCeDtyT778tv4/XAD8ATgVmAffl836JZNlY2ndPk7r9EXB8zjM6/70G+EAtDfgOcALJ0efrwLG156yNn8HFuYzGkJ519dC8GLg/l98E4IfAsDY9N7V6n5j37wA+kJ+bG3LabwE72qHHr855uevFNOOdwNKIeBYgktfuQuBTpB+LDwOXVagPgEjrwZ0s6VTgF0k/TmuARUqttvfEITu3dnOlpJl5+zTSl/8B0o8BpB+TFyLioKRH6Z3V3ECoV6dnAbdLOgU4DthVyL8sIor+yb2xlxsMNgPnKi3E/ALJM3kSyb7uyjbcvyfNbPNuL2z/Mo3LdrCYAnyjVm+SvklygHkHcKcOeWgf3wuNPeu/THZFxKNZ405gVURE4TnYWngf+yStIfloTyH9Y/Iy8OOcXiaN6nY7sETSPaTvP2hsxTgNmB8RL0J6zkrW2ExnjZ8B+4GFku4FlheO3ZHL70lJTwFvIJV3O9gV9e01bwWIiG6lXrfREfHTNmkyFeNuetOMw+zLImI9ME7SbwNDIqJTBqMvJbUGzAZui4hu0n/YTwM3S/pguwVJmkr6UXp7RJwNbCH9WB2MiFq5vky2m8s/DoP9D2I9S7rrgS9GxFmkVsZhhWOtrOLasjZcRBwkLeb8YWADqXX0d4DxwOONzxxcWQ3Si2XWrGwHi3rWhscAP41DloETI+KNvdDYs/7LpGiz+HJhv/gc1Pu8NbNuLIt6dfv7wJdILZKblcbRNrJiLNP6sa8604EUCE8mtdDO4NA/wPXOa+caj43sNavUZCrGwahpxirgvZLGAkgak9O/Svov9saqhNXhNuASUkC6VNKvAP8TETcAXwHeXIGmUcBzEfF8Hp/3tgo09KRenY4iBe0AH2pxfl/t5cqkm9QN2U0KRueShjUEcFDtHbvcW9u8RmXbyEqwDB4CLpQ0TNKJpCDqeWCXpPcAKHF2C42dwPT8PsaSJn1tIr2/i/PY0dfm9DKpV7fHAKdFxBpSz9Bo4EQaWzGuAObmgLX43TnYOl8h1/2oiLgP+DgwsXD4Pbn8xgOvI3XnV81sAElTgN0RsbtiPaaNOBg1DYmIncDfAw9K2gZ8Ph9aQhpndmtV2nqStY4Eno6IZ0g/UFuVbNkuBv6pAln3A8dK2k6ybPuPCjS8igZ12kXqvl0HPNviEjV7ua3A16OFvVzJrANOAf49Iv6b1AVZs6tbQLLDa8sEpoh4hNQdv5XU8tTINq+L+mX7TVIgUfoEpojYBCwjjbe8izSMYjfwfuAjud53ksZmNtPYCWwE7iU9O/Mi4kek8v4haczyvwAPk95fKTSo2wBuyUMItgDX5S7keaSxy9uVliyaly+zEPh+Tt8G/EFZ+lroLDISWJ6/fx4EPlE49kRO+1dgbkTsL4dR0x0AAADRSURBVFtfP3hO0gZgPvCRqsWY9mIHJtNn8kzN6RHxh1VrMcYcjqQTI2KPpBGkFrQ5OXg5YpDURZqYdm2dY7X3N5YUsP5mRPy43TqORJQ82JdHxNKqtdSQtJY0oa+d/9yaDsITmEyfkHQ9cD7we1VrMcY0ZIGkM0jjP2860gLRXrBc0mjShKt5gxWIGmPag1tGjTHGGGNMZXjMqDHGGGOMqQwHo8YYY4wxpjIcjBpjjDHGmMpwMGqMMcYYYyrDwagxxhhjjKkMB6PGGGOMMaYy/g959PzMbkumQgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Libraries\n", "import seaborn as sns\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", " \n", "# Data set\n", "url = 'https://python-graph-gallery.com/wp-content/uploads/mtcars.csv'\n", "df = pd.read_csv(url)\n", "df = df.set_index('model')\n", "del df.index.name\n", "df\n", " \n", "# Default plot\n", "sns.clustermap(df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\ben\\Anaconda3\\lib\\site-packages\\plotly\\tools.py:465: DeprecationWarning:\n", "\n", "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n", "\n" ] }, { 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" 'xaxis5': {'anchor': 'y5',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [0.0, 110.0],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': True,\n", " 'side': 'bottom',\n", " 'tickmode': 'array',\n", " 'ticktext': [mpg, qsec, cyl, vs, am, drat, gear, wt,\n", " carb, disp, hp],\n", " 'tickvals': [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0,\n", " 75.0, 85.0, 95.0, 105.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis6': {'anchor': 'y6',\n", " 'domain': [0.95, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-5, 1],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis7': {'anchor': 'y7', 'domain': [0.0, 0.25]},\n", " 'xaxis8': {'anchor': 'y8',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 105.0],\n", " 'scaleanchor': 'x5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis9': {'anchor': 'y9', 'domain': [0.75, 1.0]},\n", " 'yaxis': {'anchor': 'x',\n", " 'domain': [0.8416666666666667, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis2': {'anchor': 'x2',\n", " 'domain': [0.8416666666666667, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis3': {'anchor': 'x3', 'domain': [0.75, 1.0]},\n", " 'yaxis4': {'anchor': 'x4',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'scaleanchor': 'y5',\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis5': {'anchor': 'x5',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 315.0],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': True,\n", " 'side': 'right',\n", " 'tickmode': 'array',\n", " 'ticktext': [Maserati Bora, Chrysler Imperial, Cadillac\n", " Fleetwood, Lincoln Continental, Ford Pantera\n", " L, Duster 360, Camaro Z28, Hornet\n", " Sportabout, Pontiac Firebird, Hornet 4\n", " Drive, Valiant, Merc 450SLC, Merc 450SE,\n", " Merc 450SL, Dodge Challenger, AMC Javelin,\n", " Honda Civic, Toyota Corolla, Fiat 128, Fiat\n", " X1-9, Ferrari Dino, Lotus Europa, Merc 230,\n", " Volvo 142E, Datsun 710, Toyota Corona,\n", " Porsche 914-2, Merc 240D, Mazda RX4, Mazda\n", " RX4 Wag, Merc 280, Merc 280C],\n", " 'tickvals': [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0,\n", " 75.0, 85.0, 95.0, 105.0, 115.0, 125.0,\n", " 135.0, 145.0, 155.0, 165.0, 175.0, 185.0,\n", " 195.0, 205.0, 215.0, 225.0, 235.0, 245.0,\n", " 255.0, 265.0, 275.0, 285.0, 295.0, 305.0,\n", " 315.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis6': {'anchor': 'x6',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 315.0],\n", " 'scaleanchor': 'y5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis7': {'anchor': 'x7', 'domain': [0.0, 0.25]},\n", " 'yaxis8': {'anchor': 'x8',\n", " 'domain': [0.7916666666666666, 0.8416666666666667],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-0.5, 0.5],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis9': {'anchor': 'x9', 'domain': [0.0, 0.25]}}\n", "})\n" ] } ], "source": [ "import pandas as pd\n", "\n", "import dash_core_components as dcc\n", "import dash_bio as dashbio\n", "\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/clustergram_mtcars.tsv',\n", " sep='\t', skiprows=4).set_index('model')\n", "\n", "columns = list(df.columns.values)\n", "rows = list(df.index)\n", "\n", "clustergram = dashbio.Clustergram(\n", " data=df.loc[rows].values,\n", " row_labels=rows,\n", " column_labels=columns,\n", " color_threshold={\n", " 'row': 250,\n", " 'col': 700\n", " },\n", " height=800,\n", " width=700,\n", " color_map= [\n", " [0.0, '#636EFA'],\n", " [0.25, '#AB63FA'],\n", " [0.5, '#FFFFFF'],\n", " [0.75, '#E763FA'],\n", " [1.0, '#EF553B']\n", " ]\n", ")\n", "\n", "dcc.Graph(figure=clustergram) \n", "clustergram.show()\n", "print(clustergram)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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'showticklabels': True,\n", " 'side': 'bottom',\n", " 'tickmode': 'array',\n", " 'ticktext': [mpg, qsec, cyl, vs, am, drat, gear, wt,\n", " carb, disp, hp],\n", " 'tickvals': [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0,\n", " 75.0, 85.0, 95.0, 105.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis6': {'anchor': 'y6',\n", " 'domain': [0.95, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-5, 1],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis7': {'anchor': 'y7', 'domain': [0.0, 0.25]},\n", " 'xaxis8': {'anchor': 'y8',\n", " 'domain': [0.15833333333333333, 0.95],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 105.0],\n", " 'scaleanchor': 'x5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'xaxis9': {'anchor': 'y9', 'domain': [0.75, 1.0]},\n", " 'yaxis': {'anchor': 'x',\n", " 'domain': [0.8416666666666667, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis2': {'anchor': 'x2',\n", " 'domain': [0.8416666666666667, 1],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis3': {'anchor': 'x3', 'domain': [0.75, 1.0]},\n", " 'yaxis4': {'anchor': 'x4',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'scaleanchor': 'y5',\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis5': {'anchor': 'x5',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 315.0],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'side': 'right',\n", " 'tickmode': 'array',\n", " 'ticks': '',\n", " 'ticktext': [Maserati Bora, Chrysler Imperial, Cadillac\n", " Fleetwood, Lincoln Continental, Ford Pantera\n", " L, Duster 360, Camaro Z28, Hornet\n", " Sportabout, Pontiac Firebird, Hornet 4\n", " Drive, Valiant, Merc 450SLC, Merc 450SE,\n", " Merc 450SL, Dodge Challenger, AMC Javelin,\n", " Honda Civic, Toyota Corolla, Fiat 128, Fiat\n", " X1-9, Ferrari Dino, Lotus Europa, Merc 230,\n", " Volvo 142E, Datsun 710, Toyota Corona,\n", " Porsche 914-2, Merc 240D, Mazda RX4, Mazda\n", " RX4 Wag, Merc 280, Merc 280C],\n", " 'tickvals': [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0,\n", " 75.0, 85.0, 95.0, 105.0, 115.0, 125.0,\n", " 135.0, 145.0, 155.0, 165.0, 175.0, 185.0,\n", " 195.0, 205.0, 215.0, 225.0, 235.0, 245.0,\n", " 255.0, 265.0, 275.0, 285.0, 295.0, 305.0,\n", " 315.0],\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis6': {'anchor': 'x6',\n", " 'domain': [0, 0.7916666666666666],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [5.0, 315.0],\n", " 'scaleanchor': 'y5',\n", " 'scaleratio': 1,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis7': {'anchor': 'x7', 'domain': [0.0, 0.25]},\n", " 'yaxis8': {'anchor': 'x8',\n", " 'domain': [0.7916666666666666, 0.8416666666666667],\n", " 'fixedrange': False,\n", " 'mirror': False,\n", " 'range': [-0.5, 0.5],\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'showticklabels': False,\n", " 'type': 'linear',\n", " 'zeroline': False},\n", " 'yaxis9': {'anchor': 'x9', 'domain': [0.0, 0.25]}}\n", "})\n" ] } ], "source": [ "import pandas as pd\n", "\n", "import dash_core_components as dcc\n", "import dash_bio as dashbio\n", "\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/clustergram_mtcars.tsv',\n", " sep='\t', skiprows=4).set_index('model')\n", "\n", "columns = list(df.columns.values)\n", "rows = list(df.index)\n", "\n", "clustergram = dashbio.Clustergram(\n", " data=df.loc[rows].values,\n", " row_labels=rows,\n", " column_labels=columns,\n", " color_threshold={\n", " 'row': 250,\n", " 'col': 700\n", " },\n", " height=800,\n", " width=700,\n", " hidden_labels='row',\n", " col_group_marker=[\n", " {'group': 5, 'annotation': 'largest column cluster', 'color': '#EF553B'}\n", " ],\n", " row_group_marker=[\n", " {'group': 2, 'annotation': 'cluster 2', 'color': '#AB63FA'},\n", " {'group': 1, 'annotation': '', 'color': '#19D3F3'}\n", " ]\n", ")\n", "\n", "clustergram.show() \n", "print(clustergram)\n" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcyldisphpdratwtqsecvsamgearcarb
model
Mazda RX421.06160.01103.902.62016.460144
Mazda RX4 Wag21.06160.01103.902.87517.020144
Datsun 71022.84108.0933.852.32018.611141
Hornet 4 Drive21.46258.01103.083.21519.441031
Hornet Sportabout18.78360.01753.153.44017.020032
Valiant18.16225.01052.763.46020.221031
Duster 36014.38360.02453.213.57015.840034
Merc 240D24.44146.7623.693.19020.001042
Merc 23022.84140.8953.923.15022.901042
Merc 28019.26167.61233.923.44018.301044
Merc 280C17.86167.61233.923.44018.901044
Merc 450SE16.48275.81803.074.07017.400033
Merc 450SL17.38275.81803.073.73017.600033
Merc 450SLC15.28275.81803.073.78018.000033
Cadillac Fleetwood10.48472.02052.935.25017.980034
Lincoln Continental10.48460.02153.005.42417.820034
Chrysler Imperial14.78440.02303.235.34517.420034
Fiat 12832.4478.7664.082.20019.471141
Honda Civic30.4475.7524.931.61518.521142
Toyota Corolla33.9471.1654.221.83519.901141
Toyota Corona21.54120.1973.702.46520.011031
Dodge Challenger15.58318.01502.763.52016.870032
AMC Javelin15.28304.01503.153.43517.300032
Camaro Z2813.38350.02453.733.84015.410034
Pontiac Firebird19.28400.01753.083.84517.050032
Fiat X1-927.3479.0664.081.93518.901141
Porsche 914-226.04120.3914.432.14016.700152
Lotus Europa30.4495.11133.771.51316.901152
Ford Pantera L15.88351.02644.223.17014.500154
Ferrari Dino19.76145.01753.622.77015.500156
Maserati Bora15.08301.03353.543.57014.600158
Volvo 142E21.44121.01094.112.78018.601142
\n", "
" ], "text/plain": [ " mpg cyl disp hp drat wt qsec vs am gear \\\n", "model \n", "Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n", "Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n", "Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n", "Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n", "Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n", "Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 \n", "Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 \n", "Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 \n", "Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 \n", "Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 \n", "Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 \n", "Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 \n", "Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 \n", "Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 \n", "Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 \n", "Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 \n", "Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 \n", "Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 \n", "Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 \n", "Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 \n", "Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 \n", "Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 \n", "AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 \n", "Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 \n", "Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 \n", "Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 \n", "Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 \n", "Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 \n", "Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 \n", "Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 \n", "Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 \n", "Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 \n", "\n", " carb \n", "model \n", "Mazda RX4 4 \n", "Mazda RX4 Wag 4 \n", "Datsun 710 1 \n", "Hornet 4 Drive 1 \n", "Hornet Sportabout 2 \n", "Valiant 1 \n", "Duster 360 4 \n", "Merc 240D 2 \n", "Merc 230 2 \n", "Merc 280 4 \n", "Merc 280C 4 \n", "Merc 450SE 3 \n", "Merc 450SL 3 \n", "Merc 450SLC 3 \n", "Cadillac Fleetwood 4 \n", "Lincoln Continental 4 \n", "Chrysler Imperial 4 \n", "Fiat 128 1 \n", "Honda Civic 2 \n", "Toyota Corolla 1 \n", "Toyota Corona 1 \n", "Dodge Challenger 2 \n", "AMC Javelin 2 \n", "Camaro Z28 4 \n", "Pontiac Firebird 2 \n", "Fiat X1-9 1 \n", "Porsche 914-2 2 \n", "Lotus Europa 2 \n", "Ford Pantera L 4 \n", "Ferrari Dino 6 \n", "Maserati Bora 8 \n", "Volvo 142E 2 " ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 2 }