metadata_figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8f9c30c5-48d1-4661-8d75-3dd8bd3fb592",
   "metadata": {},
   "source": [
    "#  Figure 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "a75d2919-1180-4501-8572-51bdd6c5fa81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import random\n",
    "import yaml\n",
    "import datetime\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import ncbi\n",
    "import datetime\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "4d17d139-1cff-4412-8984-1436915408f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genomes_meta = pd.read_csv(\"df_genomes_meta.csv\", index_col=0)\n",
    "df_ncbi_strepto_hq = pd.read_csv(\"df_ncbi_strepto_hq.csv\", index_col=0)\n",
    "df_ncbi_all = pd.read_csv(\"df_ncbi_all.csv\", index_col=0)\n",
    "df_isolation_src = pd.read_csv(\"df_isolation_src.csv\", index_col=0)\n",
    "df_bgcs = pd.read_csv(\"df_bgcs.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f93e7ee-656f-4d3c-8e49-afc8614a04af",
   "metadata": {},
   "source": [
    "# Panel A: Timeline of HQ Streptomyces genomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "b346a5d9-1a27-431a-9f8d-87e05802fb8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gather year information\n",
    "df_year = pd.DataFrame(index = df_ncbi_strepto_hq.index, columns=['Date', 'Year', \"Source\"])\n",
    "df_year[\"Source\"] = \"NCBI\" \n",
    "for genome_id in df_year.index:\n",
    "    date_str = df_ncbi_strepto_hq.loc[genome_id, 'date']\n",
    "    date_obj = datetime.datetime.strptime(date_str, \"%Y-%m-%d\")\n",
    "    df_year.loc[genome_id, 'Year'] = str(date_obj.year)\n",
    "    df_year.loc[genome_id, 'Date'] = date_str\n",
    "    \n",
    "\n",
    "df_internal_strepto_hq = df_genomes_meta[(df_genomes_meta.genus == \"Streptomyces\") & (df_genomes_meta.quality == \"HQ\")]\n",
    "for genome_id in df_internal_strepto_hq.index:\n",
    "    df_year.loc[genome_id, \"Date\"] = \"2023-12-01\"\n",
    "    df_year.loc[genome_id, \"Year\"] = \"2023\"\n",
    "    df_year.loc[genome_id, \"Source\"] = \"This study*\"\n",
    "    \n",
    "df_year.sort_values(by='Date', inplace=True)\n",
    "\n",
    "df = df_year.copy()\n",
    "\n",
    "# Ensure the 'year' column is of integer type\n",
    "df['Year'] = df['Year'].astype(int)\n",
    "df = df.groupby(['Year', 'Source']).size().reset_index(name='counts')\n",
    "df.sort_values('Year', inplace=True)\n",
    "df['cumulative_counts'] = df.groupby('Source')['counts'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "049c54c8-4dc0-4a3f-b0df-3e048f9c0930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar chart\n",
    "# Define colors for each source, as per your given colors\n",
    "colors = {\"NCBI\": \"#009E73\", \"This study*\": \"#F0E442\"}\n",
    "\n",
    "# Define order of sources\n",
    "sources = [\"NCBI\", \"This study*\"]\n",
    "\n",
    "# Calculate total values for each year\n",
    "total_values = df.groupby('Year')['cumulative_counts'].sum()\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# Initialize bottom to 0 for stacking, with an index matching unique years\n",
    "bottom = pd.Series([0] * len(df['Year'].unique()), index=df['Year'].unique().astype(int))\n",
    "\n",
    "for source in sources:\n",
    "    df_filtered = df[df['Source'] == source]\n",
    "    cumulative_counts = df_filtered.groupby('Year')['cumulative_counts'].sum()\n",
    "    ax.bar(cumulative_counts.index, cumulative_counts, \n",
    "           bottom=bottom.loc[cumulative_counts.index].values, \n",
    "           color=colors[source], edgecolor='white', label=source, width=0.5)\n",
    "    bottom.loc[cumulative_counts.index] += cumulative_counts\n",
    "\n",
    "# Adding the text on top of the bars\n",
    "for idx, value in total_values.items():\n",
    "    ax.text(idx, value + 5, str(int(value)), ha='center', fontsize=12)  # Adjust fontsize as needed\n",
    "\n",
    "# Set custom x-ticks to ensure all years are displayed\n",
    "years = df['Year'].unique()  # Assuming 'year' is your x-axis data\n",
    "ax.set_xticks(years)  # This sets every unique year as an x-tick\n",
    "\n",
    "# Rotate x-axis labels\n",
    "ax.set_xticklabels(years, rotation=90, ha='center')  # Set x labels with rotation\n",
    "\n",
    "# Customize font sizes\n",
    "ax.set_ylabel('Cumulative genomes', fontsize=16)\n",
    "ax.legend(fontsize=12)\n",
    "\n",
    "# Set custom font sizes for x-tick labels\n",
    "ax.tick_params(axis='x', labelsize=14)  # Change the number for your desired font size\n",
    "\n",
    "# Set custom font sizes for y-tick labels\n",
    "ax.tick_params(axis='y', labelsize=14)  # Change the number for your desired font size\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save as SVG\n",
    "plt.savefig('timeline.svg', format='svg', bbox_inches=\"tight\")\n",
    "\n",
    "# Save as PNG\n",
    "plt.savefig('timeline.png', format='png', bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d1f23a-aeb0-4b1f-8b75-6c01c4abd522",
   "metadata": {},
   "source": [
    "# Panel B: Distribution of Streptomyces HQ across countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ed375f0-020b-4f81-9906-c8239cdb2f27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing GCF_024442315.1: (500)\n",
      "Reason: Internal Server Error\n",
      "HTTP response headers: HTTPHeaderDict({'Date': 'Sun, 10 Dec 2023 22:54:38 GMT', 'Server': 'Apache', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload', 'Content-Security-Policy': 'upgrade-insecure-requests', 'Ncbi-Phid': '939B0BDC0FE721D5000034D7B1C7BB1E.1', 'Content-Type': 'application/json', 'Content-Length': '216', 'X-Datasets-Version': '15.31.2', 'Vary': 'Accept-Encoding', 'X-UA-Compatible': 'IE=Edge', 'X-XSS-Protection': '1; mode=block', 'Connection': 'close'})\n",
      "HTTP response body: {\"error\":\"Internal Server Error\",\"code\":500,\"message\":\"Internal Server Error (For more help, see the NCBI Datasets Documentation at https://www.ncbi.nlm.nih.gov/datasets/docs/) (939B0BDC0FE721D5000034D7B1C7BB1E.1)\"}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import ncbi.datasets\n",
    "\n",
    "api_instance = ncbi.datasets.GenomeApi(ncbi.datasets.ApiClient())\n",
    "\n",
    "df_isolation_src = pd.DataFrame(index=df_ncbi_strepto_hq.index, columns=['isolation_source', 'Country', 'geo_loc_name'])\n",
    "\n",
    "for genome_id in df_ncbi_strepto_hq.index:\n",
    "    try:\n",
    "        genome_summary = api_instance.assembly_descriptors_by_accessions([genome_id])\n",
    "        \n",
    "        for assembly in map(lambda d: d.assembly, genome_summary.assemblies):\n",
    "            if assembly.biosample:\n",
    "                if assembly.biosample.attributes:\n",
    "                    for attr in assembly.biosample.attributes:\n",
    "                        if 'name' in attr.to_dict().keys():\n",
    "                            attr_name = attr['name']\n",
    "                            if attr_name == 'isolation_source':\n",
    "                                if 'value' in attr.to_dict().keys():\n",
    "                                    attr_value = attr['value']\n",
    "                                    if attr_value in ['missing', 'Unknown', 'not applicable', 'unknown',\n",
    "                                                      'Missing', 'NA', 'not collected', 'Not applicable',\n",
    "                                                      'N/A', '-', 'Not collected']:\n",
    "                                        df_isolation_src.loc[genome_id, 'isolation_source'] = 'Missing'\n",
    "                                    else:\n",
    "                                        df_isolation_src.loc[genome_id, 'isolation_source'] = attr_value\n",
    "                                else:\n",
    "                                    df_isolation_src.loc[genome_id, 'isolation_source'] = 'Missing'\n",
    "\n",
    "                            if attr_name == 'geo_loc_name':\n",
    "                                if 'value' in attr.to_dict().keys():\n",
    "                                    attr_value = attr['value']\n",
    "                                    df_isolation_src.loc[genome_id, attr_name] = attr_value\n",
    "                                    if ':' in attr_value:\n",
    "                                        country = attr_value.split(\":\")[0]\n",
    "                                    else:\n",
    "                                        country = attr_value\n",
    "\n",
    "                                    if country in ['missing', 'Unknown', 'not applicable', 'unknown',\n",
    "                                                  'Missing', 'NA', 'not collected', 'Not applicable',\n",
    "                                                  'N/A', '-', 'Not collected']:\n",
    "                                        df_isolation_src.loc[genome_id, 'Country'] = 'Missing'\n",
    "                                    elif country in ['suburb of Beijing']:\n",
    "                                        df_isolation_src.loc[genome_id, 'Country'] = 'China'\n",
    "                                    else:\n",
    "                                        df_isolation_src.loc[genome_id, 'Country'] = country\n",
    "                                else:\n",
    "                                    df_isolation_src.loc[genome_id, 'Country'] = 'Missing'\n",
    "                                    df_isolation_src.loc[genome_id, 'geo_loc_name'] = 'Missing'\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing {genome_id}: {e}\")\n",
    "\n",
    "df_isolation_src.index.name = 'genome_id'\n",
    "df_isolation_src.fillna('Missing', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e5b82f5f-ef5e-47fd-b8cd-7e88e2573a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Plotting\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "# Set colors for each dataset\n",
    "colors = {\"NCBI\": \"#009E73\", \"This study*\": \"#F0E442\"}\n",
    "\n",
    "top_countries = df_isolation_src['Country_selected'].value_counts().index.tolist()[:15]\n",
    "\n",
    "# Create a DataFrame with zeros for all combinations of countries and datasets\n",
    "df_counts = pd.DataFrame(0, index=top_countries, columns=df_isolation_src['Dataset'].unique())\n",
    "\n",
    "# Fill in actual counts where available\n",
    "for dataset in df_counts.columns:\n",
    "    dataset_data = df_isolation_src[df_isolation_src['Dataset'] == dataset]\n",
    "    country_counts = dataset_data['Country_selected'].value_counts()\n",
    "    for country in top_countries:\n",
    "        if country in country_counts.index:\n",
    "            df_counts.loc[country, dataset] = country_counts[country]\n",
    "\n",
    "bar_width = 0.8\n",
    "\n",
    "for i, dataset in enumerate(df_counts.columns):\n",
    "    # Adjust the bar position for each dataset\n",
    "    current_positions = range(len(top_countries))\n",
    "    \n",
    "    ax.bar(current_positions, df_counts[dataset], bar_width, color=colors[dataset], label=dataset, \n",
    "           bottom=df_counts.iloc[:, :i].sum(axis=1))\n",
    "\n",
    "# Customize font sizes\n",
    "ax.set_ylabel('Genomes', fontsize=16)\n",
    "ax.legend(fontsize=12)\n",
    "\n",
    "# Set custom font sizes for x-tick labels\n",
    "ax.set_xticks(range(len(top_countries)))\n",
    "ax.set_xticklabels(top_countries, rotation=45, ha='center')\n",
    "ax.tick_params(axis='x', rotation=90, labelsize=14)  # Change the number for your desired font size\n",
    "\n",
    "# Set custom font sizes for y-tick labels\n",
    "ax.tick_params(axis='y', labelsize=14)  # Change the number for your desired font size\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save as SVG\n",
    "plt.savefig('country_dist.svg', format='svg', bbox_inches=\"tight\")\n",
    "\n",
    "# Save as PNG\n",
    "plt.savefig('country_dist.png', format='png', bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919c3438-3089-45d3-bd5e-c34478ad1f53",
   "metadata": {},
   "source": [
    "# Panel C: Sankey diagram with overview of different catgories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "0acca44e-0972-407d-a94c-e50c986308f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.DataFrame(columns=['genus_init','genus_gtdb', 'quality'])\n",
    "for genome_id in df_ncbi_all.index:\n",
    "    ncbi_genus = df_ncbi_all.loc[genome_id, \"genus\"]\n",
    "    if ncbi_genus == \"Streptomyces\":\n",
    "        df_raw.loc[genome_id, \"genus_init\"] = \"Streptomyces\"\n",
    "    else:\n",
    "        df_raw.loc[genome_id, \"genus_init\"] = \"non-Streptomyces\"\n",
    "\n",
    "    gtdb_genus = df_filters.loc[genome_id, \"genus\"]\n",
    "    if gtdb_genus == \"Streptomyces\":\n",
    "        df_raw.loc[genome_id, \"genus_gtdb\"] = \"Streptomyces_G\"\n",
    "    else:\n",
    "        df_raw.loc[genome_id, \"genus_gtdb\"] = \"non-Streptomyces_G\"\n",
    "    \n",
    "    df_raw.loc[genome_id, \"quality\"] = df_filters.loc[genome_id, \"quality\"]\n",
    "\n",
    "for genome_id in df_genomes_meta.index:\n",
    "    df_raw.loc[genome_id, \"genus_init\"] = \"This study*\"\n",
    "    gtdb_genus = df_genomes_meta.loc[genome_id, \"genus_gtdb\"]\n",
    "    if gtdb_genus == \"g__Streptomyces\":\n",
    "        df_raw.loc[genome_id, \"genus_gtdb\"] = \"Streptomyces_G\"\n",
    "    else:\n",
    "        df_raw.loc[genome_id, \"genus_gtdb\"] = \"non-Streptomyces_G\"\n",
    "    df_raw.loc[genome_id, \"quality\"] = df_genomes_meta.loc[genome_id, \"quality\"] + \"_I\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "31a4b82d-a22d-4544-8b30-689719ef65f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw[\"genome_id\"] = df_raw.index\n",
    "df_sankey = df_raw.groupby(['genus_init','genus_gtdb', 'quality']).count().iloc[:,:1].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "32d5629f-8a7c-447c-a67e-368ccc81b032",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = dict()\n",
    "color_dict[\"genus_init\"] = {\"Streptomyces\": \"#009E73\", \"This study*\": \"#F0E442\", \"non-Streptomyces\": \"#009E73\"}\n",
    "color_dict[\"genus_gtdb\"] = {\"Streptomyces_G\": \"#4cc9f0\", \"non-Streptomyces_G\": \"#7209b7\"}\n",
    "color_dict[\"quality\"] = {\"HQ\": \"#83c5be\", \"MQ\": \"#e9c46a\", \"LQ\": \"#e63946\", \"HQ_I\": \"#83c5be\", \"MQ_I\": \"#e9c46a\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "8bb26a87-8e4c-4810-b2b2-3298ecd13b7b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "config": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_sankey\n",
    "cat_cols=['genus_init', 'genus_gtdb', 'quality']\n",
    "value_cols='genome_id'\n",
    "\n",
    "labelList = []\n",
    "colorList = []\n",
    "for col in cat_cols:\n",
    "    labelList = labelList + list(set(df[col].values))\n",
    "    colorList = colorList + [color_dict[col][label] for label in list(set(df[col].values))]\n",
    "labelList = list(dict.fromkeys(labelList))\n",
    "\n",
    "# transform df into a source-target pair\n",
    "for i in range(len(cat_cols)-1):\n",
    "    if i==0:\n",
    "        sourceTargetDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
    "        sourceTargetDf.columns = ['source','target','count']\n",
    "    else:\n",
    "        tempDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
    "        tempDf.columns = ['source','target','count']\n",
    "        sourceTargetDf = pd.concat([sourceTargetDf,tempDf])\n",
    "    sourceTargetDf = sourceTargetDf.groupby(['source','target']).agg({'count':'sum'}).reset_index()\n",
    "        \n",
    "        \n",
    "# add index for source-target pair\n",
    "sourceTargetDf['sourceID'] = sourceTargetDf['source'].apply(lambda x: labelList.index(x))\n",
    "sourceTargetDf['targetID'] = sourceTargetDf['target'].apply(lambda x: labelList.index(x))\n",
    "    \n",
    "# sourceTargetDf['color'] = [color_quality_dict[target_id] if target_id in color_quality_dict.keys() else '#2a9d8f' for target_id in sourceTargetDf['target']]\n",
    "    \n",
    "# creating the sankey diagram\n",
    "data = dict(\n",
    "        type='sankey',\n",
    "        node = dict(\n",
    "          pad = 15,\n",
    "          thickness = 100,\n",
    "          line = dict(\n",
    "            color = \"black\",\n",
    "            width = 0\n",
    "          ),\n",
    "          # label = labelList,\n",
    "          color = colorList\n",
    "        ),\n",
    "        link = dict(\n",
    "          source = sourceTargetDf['sourceID'],\n",
    "          target = sourceTargetDf['targetID'],\n",
    "          value = sourceTargetDf['count'],\n",
    "          # color = sourceTargetDf['color'],\n",
    "        )\n",
    "      )\n",
    "    \n",
    "       \n",
    "fig_data = dict(data=[data])\n",
    "fig = go.Figure(data=fig_data)\n",
    "\n",
    "fig.update_layout(font_size=12,\n",
    "                 width=600,\n",
    "                height=600,)\n",
    "fig.write_image(\"gtdb_sankey.svg\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4299d802-5cb7-4e3a-bb1d-d46b0cc99e0b",
   "metadata": {},
   "source": [
    "# Panel D: BGC type overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "814ed62d-a3c1-4912-ba58-35d0195d1c47",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_protoclusters = pd.DataFrame(columns=[\"type\", \"count\"])\n",
    "for bgc_id in df_bgcs.index:\n",
    "    product_list = df_bgcs.loc[bgc_id, \"product\"][1:-1].split(\",\")\n",
    "    for prod in product_list:\n",
    "        type = prod.strip()[1:-1]\n",
    "        if type not in df_protoclusters.index:\n",
    "            df_protoclusters.loc[type, \"type\"] = type\n",
    "            df_protoclusters.loc[type, \"count\"] = 1\n",
    "        else:\n",
    "            df_protoclusters.loc[type, \"count\"] = df_protoclusters.loc[type, \"count\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "97bba81d-116b-487f-984f-26fc2db93961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYmNj8+3bq1cvhIaGYvv27ejbt6+8/e7du2jYsCEyMjKQkJAAExMT+bYBAwYgODgY69atw7BhwxSeLzMzE4aGiudVQkJC0K9fP3h5eWHDhg0wMzPLdx8iIiIiItJvkqTrCNSnHxWq+jWqVoeXu7i4yItOyt+tW7cgSRJ69Oih0F6tWjU0aNAAqampePHihbz95MmT2LZtG0aMGKFUcANQWTx/+eWXsLS0RFBQkFLBnds+REREREREVDAFrrC2bduGZcuW4Z9//kFKSgoqVaqENm3aYOHChXBycirKGPVeWloa1qxZgwcPHsDGxgZt27ZVOYS7fv36uHbtGvbt24d3331X3n7v3j1cunQJDRs2hJ2dnbx948aNAIDBgwfj6dOnCAkJQVxcHJycnNCzZ0/Y2toqPP+FCxdw9epVDBgwABYWFggNDcWFCxdQrlw5dOzYkcPKiYiIiIiIipjGRbcQAhMmTMDvv/+OmjVrYujQobC0tMTDhw9x6NAh3Llzh0X3Wx49egRfX1+Fth49emDt2rUKRfScOXNw9OhRDBgwAP369YOrqyuePHmCrVu3wtnZGZs2bVJ4jqioKADAjRs34O3tjcTERPk2CwsLrFixAkOGDFHqb2tri/bt2+PEiRMKzzd8+HCsWrUKxsbGRZI3ERERERFRWadx0b106VL8/vvvmDhxIn744QcYGBgobM/MzCyy4EqD0aNHw93dHfXr14eJiQmuXLmCgIAAhIaGom/fvoiMjIT03w0W9erVw4kTJzB48GBs3rxZ/hw2NjbyGc3f9PjxYwDA559/juHDh2P27NmoUKECdu3ahY8++gje3t6oW7cuGjVqpNB/1apVqF69OsLCwtCyZUvExMRg4sSJWL9+PapUqYIFCxYUxz8NERERERFRqafRPd2pqakICAhAjRo18P333ysV3IB69wQvWrRIfu9ycnKyJiHonVmzZsHd3R12dnawtLRE69atsXPnTrRv3x7Hjx/H7t275X2joqLQvn17VKhQAWfOnEFycjJu3bqFMWPGYOrUqRg8eLDCc2dnZwMAGjVqhKCgINSoUQPly5fH8OHDsWDBAmRkZODHH39U6p+dnY1NmzbB09MTFhYWaNq0KYKDg2FpaYmffvoJaWlpxfAvQ0REREREVPppVHTv378fz549g5eXF7KysrB161Z8++23+PXXX3Hjxo189xdCwM/PD9OmTcOwYcOwY8cOmJubq+yblpaGpKQkhUdpIZPJMGrUKABAZGQkACAjIwNDhgyBJEkIDg5Gs2bNUK5cOVSvXh2LFi3CkCFDsG3bNoSHh8ufx9raGgDw7rvvyq+W5+jTpw+A/x9S/mb/qlWromnTpgr9HRwc0Lp1a6SkpODq1atFnDEREREREVHZpFHRnVPAGRoaonHjxhg4cCCmT5+ODz/8ELVr14afn1+u+2ZmZsLHxweLFy/Gp59+inXr1sHIyCjX/vPnz4e1tbX8UdruE8+5lzslJQUAEB0djVu3bqF169YoV66cUv9OnToBAM6cOSNvq127NgCgfPnySv1z2lJTU9Xqn9s+REREREREVHAaFd059wQvXrwYVlZWOHXqFF68eIHDhw/jnXfeweLFi/HLL78o7ZeSkoJ+/fph7dq1mDdvHr7//nulK7Nvmz59OhITE+WPe/fuaRJqiXfy5EkA/7+Od3p6OgDgyZMnKvvntL+5RndOIX7lyhWl/jltby7Z1qZNG5iZmeHWrVt49eqV0j45V7i5zBsREREREVHR0Kjozrkn2NjYGMHBwWjZsiUsLCzQoUMHbN68GTKZDIsXL1bYJzU1FZ07d8bevXuxYsUKzJgxQ61jmZiYwMrKSuGhb65cuYLnz58rtR89ehRLliyBiYkJBgwYAABo0KABrK2tERkZiX379in0f/jwIZYtWwYA8PDwkLcPGjQIdnZ2WL9+PS5evChvT09Px+zZswEA7733nrzdwsIC3t7eSE5Oxty5cxWOsXbtWly+fBnt27dH5cqVC5U3ERERERERvabR7OU59wS3aNECjo6OCtvq16+PGjVq4MaNG3j+/Ll8qPKLFy9w9uxZ2Nrawt3dvWii1hObNm3CwoUL0blzZ7i4uMDExASXLl3Cvn37IJPJ8Ouvv6JatWoAXp9kWLx4McaOHYuePXuid+/eqFu3LuLi4rBt2zYkJSVh4sSJaNiwofz5rayssHz5cgwaNAht2rTBoEGDYGNjgwMHDuDy5cvo1auX0lJl33zzDSIiIjBv3jwcPXoULVq0QExMDHbs2AEbGxv89ttvxflPREREREREVKpJQgihbucVK1Zg3Lhx6NOnD0JCQpS2t2zZElFRUXj48CEqV64sH6b822+/wcvLC7a2toiIiICrq6vGgSYlJcHa2hqJiYl6cdX79ej5QwCWAfgHQByAVwAqAmgPYAqAVir2PAjgewAnATwDYA6gMYCxAEbmcrRIAHMBnACQAqDWf32nQvV5lWcAAgBsA/AIFStWQLdu3eDv748aNWpomCkREREREZV0+dzdW6KoX6Hqlro1qkZF982bN+Hq6gpXV1fExMQobMvIyICDgwMyMjLw/PlzGBoayovu2NhY7Nu3D/369UOFChUQERGhtOZ0USVUUvBNTUREREREJQXrk6Knbo2q0T3dNWvWRLdu3XDjxg2sWLFCYdu3336L58+fo3///irX6u7WrRtCQkKQkJAADw8PXL9+XZNDUwnj4uICSZJUPiZMmCDvl5GRgS1btsDX1xd169aFubm5fL3yZcuWISsrS+XzJyQkwM/PD66urjAxMYG9vT0GDRqEy5cvq+y/Y8cOTJo0Ce3atYO5uTkkSYK/v782UiciIiIiIlKbRle6gddXu9u2bYvHjx+jd+/eqFOnDs6ePYuwsDA4OzvjxIkTqFSpEgAoXOnOcfDgQfTp0wfly5dHeHi4fBmr/PBKt/YU5EySi4sLnj9/jsmTJytta9GiBd59910Ar5dCq1u3LiwtLdGpUyfUrl0biYmJ2LFjBx4+fIg+ffpg+/btCrPZx8fHw83NDTExMXBzc4Obmxv+/fdfbNmyBYaGhggLC0Pr1q0Vjunh4YFDhw7BysoKDg4OuHHjBmbPns3Cm4iIiIgIpb8+0QWtDC/Pce/ePcyaNQt79uxBfHw8KlWqhL59+2LWrFlwcHCQ91NVdAOKhXdYWBjq1KlTZAmVFKX9TZ3ba/u2Bw8eICQkBD4+PgrrjycnJ8PDwwNRUVHYtGkTBg8eLN/28ccf4+eff8bUqVMVZsM/fvw4OnTogNq1a+PixYuQyf5/oMaRI0dQqVIluLq6YuPGjXj//fdZdBMRERER/ae01ye6oG6NqtHs5TmcnJywevXqfPvlVpB17twZKSkpBTk06ZkqVargww8/VGo3NzfH1KlTMWzYMBw6dEih6A4ODoZMJkNAQIDCPm5ubujTpw+Cg4Nx6NAheHp6yrd16NBBe0kQEREREREVUIGKbiIASEtLw5o1a/DgwQPY2Nigbdu2aNy4sdr7GxkZAYDSHABxcXGws7ODhYWF0j7Vq1cHAISFhSkU3URERERERCURi24qsEePHimtA96jRw+sXbsWdnZ2+e6/atUqAK8n2XuTvb094uLi8PLlS6XC+/bt2wDAifiIiIiIiEgvaDR7OVGO0aNHIyIiAk+ePEFSUhJOnDiBnj17Ys+ePejbty/ymyrg999/R2hoKDp16oRevXopbOvZsyeys7OVhpefOnUKO3fuBAA8f/68SPMhIiIiIiLSBl7ppgKZNWuWws+tW7fGzp074e7ujqNHj2L37t3o3bu3yn137dqFjz/+GM7Ozli3bp3S9oCAAISGhuK7777D8ePH0aZNG/z777/YvHkz6tWrhwsXLsDAwEAreRERERERERUlXummIiOTyTBq1CgAQGRkpMo+e/fuxcCBA1GxYkWEhYWhcuXKSn2qVq2K06dPY8yYMbh9+zZ+/PFHnDhxAoGBgZgxYwaA10PQiYiIiIiISjpe6aYilXMvt6rZ6ffs2YP+/fvDzs4O4eHhqFGjRq7PU6VKFaxYsUKpPWcJsBYtWhRNwERERERERFrEK91UpE6ePAng/9fxzrFnzx54eXnBxsYG4eHhcHV11fi5s7Ky8Ndff8HQ0BADBw4sinCJiIiIiIi0ikU3aezKlSsqJzI7evQolixZAhMTEwwYMEDe/nbBXatWrTyfPyMjA6mpqQpt2dnZ8PPzw7Vr1zBp0iQ4OjoWSS5ERERERETaxOHlpLFNmzZh4cKF6Ny5M1xcXGBiYoJLly5h3759kMlk+PXXX1GtWjUAQHR0NLy8vJCWlgYPDw/8+eefSs/n4uKisPRYXFwc6tevj27duqF69epIT0/H3r17ER0djd69e2P+/PlKzxEcHIzg4GAA/7+sWHBwMGJjYwEA7du3x9ixY4v2H4KIiIiIiCgfkshvbacSIikpCdbW1khMTISVlZWuw8mXJOk6AvVp8g54ndchAMsA/AMgDsArABUBtAcwBUCrN/aIAOCZz7O6/9cvxwsAEwFEAngIwAhAAwCj/3uoGqDhDyBARftrPj4+CAoKyicOIiIiIqLSqbTWJ7qkbo3KoltLSuuburTmRURERERUmvF7fNFTt0blPd1EREREREREWsKim4iIiIiIiEhLWHQTERERERERaQmLbiIiIiIiIiItYdFNREREREREpCUsuomIiIiIiIi0hEU3ERERERERkZaw6CYiIiIiIiLSEhbdRERERERERFrCopuIiIiIiIhIS1h0ExEREREREWkJi24iIiIiIiIiLWHRTURERERERKQlLLqJiIiIiIiItIRFNxEREREREZGWsOgmIiIiIiIi0hIW3URERERERERawqKbiIiIiIiISEtYdBPlYeHChZAkCZIk4cSJEyr7JCUlYerUqXB2doaJiQmcnZ0xdepUJCUlqeyfkJAAPz8/uLq6wsTEBPb29hg0aBAuX76caxwbNmxAu3btYGFhAXNzc7Rs2RJBQUFFkSIREREREWmRJIQQug5CHUlJSbC2tkZiYiKsrKx0HU6+JEnXEahPk3dAac1LlatXr6Jp06YwNDREcnIyjh8/jjZt2ij0SU5ORvv27XHu3Dl07doVzZo1w/nz57Fnzx40adIER48ehbm5ubx/fHw83NzcEBMTAzc3N7i5ueHff//Fli1bYGhoiLCwMLRu3VrhGJ999hmWLFmCSpUqoW/fvjAyMsLu3btx+/ZtfPbZZ/juu+8KlygRERERlXpl6Xt8cVG3RjUsxpiI9EZWVhZ8fHzQuHFjvPPOO1i3bp3KfgsXLsS5c+cwbdo0LFiwQN4+e/ZsBAYGYuHChQgICFBoj4mJwdSpU7F48WJ5+/Hjx9GhQweMHj0aFy9ehEz2ehBKVFQUlixZgpo1a+LUqVOoUKECgNfFvqenJxYvXoyBAwfCzc1NG/8MRERERERUSBxeTqTCggULcP78eaxatQoGBgYq+wghsGLFClhYWGDWrFkK26ZPnw4bGxusXLkSbw4mCQ4OhkwmUyjEAcDNzQ19+vTBlStXcOjQIYX+ADB16lR5wQ0A5ubm+OqrrwAAv/76a6FyJSIiIiIi7WHRTfSWS5cuISAgADNnzkT9+vVz7RcTE4OHDx+iXbt2CkPIAcDU1BQdO3bEgwcPcOPGDXl7XFwc7OzsYGFhofR81atXBwCEhYUp9H9zW379iYiIiIioZGHRTfSGzMxM+Pr6om7duvjyyy/z7BsTEwMAqFWrlsrtOe05/QDA3t4eT58+xcuXL5X63759GwBw/fp1hf5vblPV//79+0hJSckzViIiIiIi0g0W3URv+Oabb+TDyo2MjPLsm5iYCACwtrZWuT1nMoWcfgDQs2dPZGdnKw0vP3XqFHbu3AkAeP78uUJ/APj+++8V2lNSUjB//nylWIiIiIiIqGThRGpE/zl//jzmzp0LPz8/NGvWTCvHCAgIQGhoKL777jv5bOj//vsvNm/ejHr16uHChQsK95B36NAB3t7eWLt2LerVq6cwe3lmZqZ8tsTc7jsnIiIiIiLd4pVuov/4+PigZs2a8Pf3V6t/zhXu3K4y56zT/eaV8KpVq+L06dMYM2YMbt++jR9//BEnTpxAYGAgZsyYAeD/h5TnCAoKwg8//AB7e3sEBQVh3bp1aNmyJY4cOYKsrCwYGhrCxsZG03SJiIiIiKgYaP1Kt4uLCwAgNjZW24ciKpTz588DeD0Jmio5y3Jt27YNXl5eKu/ZflNu93xXqVIFK1asUOqfU+y3aNFCoV0mk+GTTz7BJ598otAeGxuLly9folmzZvkOhSciIiIiIt0odNG9cOFCfPHFFwAgHy5LpI/GjBmjsv3w4cOIiYlB3759YW9vLz+RVKtWLTg6OiIyMhLJyckKM5i/evUKhw8fhqOjI1xdXfM9dlZWFv766y8YGhpi4MCBasW7fv16AMDQoUPV6k9ERERERMWvUEX31atXMWvWLJibmyM5ObmoYiLSCVVXnwHA19cXMTExmD59usJJJUmSMHbsWAQGBiIwMBALFiyQb5s/fz4SEhIwadIkSJIkb8/IyEBmZibMzMzkbdnZ2fDz88O1a9cwZcoUODo6Khw/KSlJPilbjiNHjmD+/PlwdnbGhAkTCpU3ERERERFpT4GL7qysLPj4+KBx48Z45513sG7duqKMi0gvTJs2DSEhIVi4cCHOnj2L5s2b4/z58wgNDUWTJk0wbdo0hf5xcXGoX78+unXrhurVqyM9PR179+5FdHQ0evfurTAjeY5BgwYhNTUVjRo1gpWVFS5evIjQ0FBUqFABwcHBsLS0LK50iYiIiIhIQwUuuhcsWIDz58/jn3/+waJFizTef9GiRZg2bRq6d++OLVu2KAzNJSpub1yMztV/t3S/xRxABIAA7N+/Gfv3RwCoBGAKzp2bDQuLt9/X1gD6YfPmSAA7ARgBaABgOXbtGg1TU1VzG3oBCMLRo+sBpKJmTSdMmjQJ06dPR8WKFdVJj4iIiIiIdKRARfelS5cQEBCAmTNnon79+hrtK4TA559/jsWLF2PYsGEICgpSOQlUWloa0tLS5D/nzARNVPyC/nvkxhrAkv8e+bEE8IeGx//ov8drN25ouDsREREREemMxkuGZWZmwtfXF3Xr1sWXX36p8b4+Pj5YvHgxPv30U6xbty7XWZfnz58Pa2tr+cPJyUnTUImIiIiIiIh0SuOi+5tvvsH58+exatUqjZYpSklJQb9+/bB27VrMmzcP33//vcIEU2+bPn06EhMT5Y979+5pGioRERERERGRTmk0vPz8+fOYO3cu/Pz80KxZM7X3S01NRefOnXH69GmsWLEi16WZ3mRiYgITExNNwiMiIiIiIiIqUTS60u3j44OaNWvC399fo4O8ePECZ8+eha2tLdzd3TXal4iIiIiIiEhfaVR0nz9/HtHR0TA1NYUkSfLHmjVrAABubm6QJAnBwcEK+zk4OGD79u1ISkqCh4cHbnAmKCIiIiIiIioDNBpentuw8MOHDyMmJgZ9+/aFvb09XFxclPp0794d27dvR79+/eDu7o6IiAjUqlWrQEETERERERER6QNJCCEK+yS+vr5Ys2YNjh8/jjZt2ihsyynAY2NjAQD79+9Hv379YGNjg/DwcLzzzjtqHSMpKQnW1tZITEyElZVVYUPWOnXWfS4pNHkHMC/dK/wnloiIiIjKGn7fLXrq1qgaz15eWF27dsWOHTuQkJAADw8PXLt2rbhDICIiIiIiIioWxV50A0Dnzp2xY8cOPH/+HJ6enoiOjtZFGERERERERERaVSTDy4sDh5drD4dhl968iIiIiIgAft/VhhI7vJyIiIiIiIiorGDRTURERERERKQlLLqJiIiIiIiItIRFNxEREREREZGWsOgmIiIiIiIi0hIW3URERERERERawqKbiIiIiIiISEtYdBMRERERERFpCYtuIiIiIiIiIi1h0U1ERERERESkJSy6iYiIiIiIiLSERTcRERERERGRlrDoJiIiIiIiItISFt1EREREREREWsKim4iIiIiIiEhLWHQTERERERERaQmLbiIiIiIiIiItYdFNVMo9ePAA33//Pbp164Zq1arB2NgYlSpVwsCBA3Hy5Eml/v7+/pAkSeXD1NRUqX9sbGyu/SVJwl9//aXQPz4+Hr///jv69u2LGjVqwMTEBHZ2dujZsyf27t2rtX8HIiIiIiJdMNR1AESkXUuXLsWCBQtQs2ZNdO3aFQ4ODoiJiUFwcDCCg4Px559/4r333lPaz8fHBy4uLgpthoa5/8po3LgxvLy8lNobNGig8PPff/+NDz/8EFWqVEGnTp1QpUoV3L9/H1u2bMGePXuwaNEi+Pn5FShXIiIiIqKSRhJCCF0HoY6kpCRYW1sjMTERVlZWug4nX5Kk6wjUp8k7gHnpnqaf2K1bt8Le3h4dOnRQaD9y5Ag6d+4MS0tLPHz4ECYmJgBeX+kOCAhAeHg4PDw88n3+2NhYVK9eHT4+PggKCsq3f1hYGFJTU9GzZ0/IZP8/2ObatWto3bo1UlJSEBsbC0dHR43yJCIiIqLclebvu7qibo3K4eVEpdyAAQOUCm4A6NChAzw9PfHs2TNcvHix2OLp1KkTevfurVBwA0Dt2rUxZMgQZGRk4NixY8UWDxERERGRNrHoJirDjIyMAKgeNn7kyBEsXLgQixcvxq5du5CWlpbncz18+BC//PIL5s+fjzVr1uD+/ftFGs/bNL1Xffny5ejTpw+qV68Oc3NzWFtbo3Hjxpg1axaePXum8hgJCQnw8/ODq6srTExMYG9vj0GDBuHy5ct5xhYREYF+/frBwcEBJiYmcHJyQv/+/XH+/Hk1/hWIiIiIqDTh8HItKa3DN5iX7hXVJ/bu3bt45513YGNjg/v378PAwADA/w8vf1vlypWxZs0adO3aVaE9Z3j52wwNDfHJJ59g0aJFSle1VXnx4gVq1aqFxMRE3L9/H7a2tnn2//LLL+X3qru7uyvcqy6EULpXvWPHjkhISEDTpk1RuXJlpKWl4cSJEzh58iSqVauGkydPolKlSvL+8fHxcHNzQ0xMDNzc3ODm5oZ///0XW7ZsgaGhIcLCwtC6dWuluObNm4eZM2fC0dERvXv3hp2dHeLi4hAZGYmZM2dixIgR+f5bEBERERW1svh9V9vUrlGFnkhMTBQARGJioq5DUcvrt4p+PJiX7mPVVl65SU9PFx07dhQAxB9//KGwbdu2bWLNmjUiNjZWpKamipiYGDFnzhxhZmYmTE1Nxblz5xT6x8XFidmzZ4tz586JpKQk8fjxYxESEiLq1q0rAIgvv/xSrZiGDRsmAIjAwEC1+m/ZskUcPnxYqf3w4cPCyMhIVKhQQbx69UrenpqaqvJ5Zs6cKQAIPz8/hfaJEycKAGLq1KkK7ceOHRMGBgaiXr16IisrS2Hb9u3bBQDh5eUlUlJSlI6VkZGhVm5ERERERU3X32GL+/tucVC3RtWblFh0l4w3ta5jZV6a5aVKVlaWGDFihAAgxo0bp/Z+v//+uwAgBg0apFb/f//9V9ja2gpjY2Px7NmzPPtOnz5dABA9evQQmZmZaseUm27dugkA4vTp0/n2PX/+vMgplN9UpUoVIZPJxIsXL5T28fLyEgBEWFiYQnvdunWFpaWleP78eeESICIiIipiuv4OW5zfd4uLujUq7+kmKkOEEBg3bhzWrVuHESNG4Ndff1V7Xx8fHxgaGiIyMlKt/pUqVUKvXr2Qnp6O06dP59ovICAA8+fPR6dOnbB161b5MPfC0OTe8F27dgFQXtosLi4OdnZ2sLCwUNonZzh9WFiYvO3ChQu4evUqunbtCgsLC4SGhmLBggVYunQp7+UmIiIiKsO4TjdRGZGdnY2xY8di9erVeP/99xEUFKTWvdY5jI2NYWlpiZSUFLX3sbOzA4Bc9wkICIC/vz88PDywY8cOmJmZqf3cubl79y4OHDiASpUqoWHDhkrbg4KCEBsbixcvXuCff/5BREQEmjZtiqlTpyr0s7e3R1xcHF6+fKlUeN++fRsAcP36dXlbVFQUAMDW1hbt27fHiRMnFPYZPnw4Vq1aBWNj40LnSERERET6g1e6icqANwvuIUOGYO3atRpfUY6JiUFCQgJcXFzU3ufUqVMAoHIff39/+Pv7w93dHbt27UK5cuU0ikeVjIwMeHt7Iy0tDQsXLlSZY1BQEAICArBkyRJERESgW7du2LNnD2xsbBT69ezZE9nZ2UqTyp06dQo7d+4EADx//lze/vjxYwDAqlWr8PTpU4SFhckLezc3N6xfvx5ff/11oXMkIiIiIv3CopuolMvOzsaYMWOwevVqDB48GOvWrcu14H7x4gUuXLig1J6QkIAxY8YAAN5//32FbadOnUJGRobSPkuWLEFkZCTq1auHxo0bK2ybPXs2AgIC0KFDhyIruLOzszF69GgcPnwY48aNg7e3t8p+EREREELgyZMn2LlzJ+7fv49mzZop5R0QEIDKlSvju+++Q/v27eHn54fhw4ejQ4cOqFevHgAo/DtmZ2fL/7tp0yZ4enrCwsICTZs2RXBwMCwtLfHTTz/lu/QaEREREZUuHF5OVMoFBgYiKCgIFhYWeOeddzB37lylPl5eXmjSpAni4+PRuHFjtGjRAg0bNoSDgwMePHiA0NBQxMfHo2vXrpgyZYrCvtOmTUN0dDTc3d3h5OSE1NRUHD9+HGfPnoWNjQ3Wrl0L6Y01KoKCghAYGAhDQ0O0atUKixYtUorHw8MDHh4eaudYkHvV7ezs0Lt3bzRq1Ai1atXCuHHjFNb3rlq1Kk6fPo3Zs2cjNDQUp06dgpOTEwIDA+Hi4oKhQ4fC3t5e3t/a2lq+X9OmTRWO5eDggNatW+PAgQO4evUqmjRponZuRERERKTfWHQTlWKva91YAMDLly8xb948lf0CAlwANAFQAcBEREWdQFTUDgDPAZgDaAhgBPbvHwsTk7evko8AsAWbNh0D8PS/NmcAnyIhwQ/Nm1d9q//reDIzM7F48eJcY1e36C7svepOTk6oW7cuTp8+jZSUFIWr7lWqVMGKFSuU9vH39wcAtGjRQt5Wu3ZtAED58uVVHienPTU1Ve3YiIiIiEj/SUIIoesg1KH2wuMlRGldfJ556R7z+n9v36u+fv36As1+XqlSJTx58gQpKSkwMTHJs29WVhbq16+Pmzdv4s6dO3B0dATw+qSGg4MDZDIZnj59ClNTU4X9GjRogMuXL+Phw4eoXLmyxjESERERFUZp/16oC+rWqLynm4j0kib3qsfHx+Py5ctK7UII+Pv7Iy4uDp6engoFd0ZGhtJV6ezsbPj5+eHatWuYNGmSvOAGAAsLC3h7eyM5OVlpCP/atWtx+fJltG/fXq2Ce926dRg/fjxatGgBExMTSJKEoKCgXPufPHkS/fr1g52dHUxMTPDOO+9g1qxZuV5VT01NxZIlS9CsWTPY2NigfPnyaNy4MebNm4fExMRcjxMREYF+/frBwcEBJiYmcHJyQv/+/bkkGhEREVEeeKVbS0rrmSTmpXvM6zV/f38EBATAwsICn376qco1uXPuVT937hyaNm2KVq1aoV69eqhUqRKePn2KI0eO4Nq1a6hUqRIiIiLkQ8QB4P79+6hfvz66deuG6tWrIz09HXv37kV0dDR69+6NLVu2KF0Vj4+PR9u2bXH9+nW4u7ujRYsWiImJwY4dO1C+fHkcPXpUPglbXlxcXHDnzh3Y2dnB3Nwcd+7cwerVq+Hr66vUd+vWrRgyZAgMDAwwcOBAVKpUCZGRkTh58iTatWuHgwcPKp1M6NChA06ePIkmTZrA3d0dkiQhPDwc58+fR/369XHq1Cmlye3mzZuHmTNnwtHREb1794adnR3i4uIQGRmJmTNnYsSIEfnmRURERLpTmr8X6oraNarQE4mJiQKASExM1HUoann9VtGPB/PSfazMqyA5+QgA+TxW/9f3mQCmC8BNAA4CMBSAhQCaCmCmAJ6qiClJAN4CqCEAUwFY/rf/cgFk5ZFLvAA+EYCTAIxExYoVhbe3t7h586baOe7fv1/ExsYKIYSYP3++ACBWr16t1C8lJUXY2dkJIyMjERUVJW/Pzs4WEydOFADE/PnzFfbZuHGjACAGDBig9HxeXl4CgFizZo1C+/bt2wUA4eXlJVJSUpT2y8jIUDs3IiIi0g1df9fT1vddXVK3RuVEakSkp4L+e6jDBsA3Gj6/JYA/NNwHeD0Z3Q//PYBHjzR/hi5duqjVLzIyEk+fPsXgwYPRvHlzebskSZg7dy5+/vln/Prrr/jiiy/kM8jfunULwOt1yN/Wq1cvBAcHy9ccz/Hll1/C0tISQUFBMDMzU9pP1SgDIiIiInqN93QTEempuLg4AED16tWVtpUvXx42Nja4c+eOvNAGgPr16wMA9uzZo7RPaGgoJElSmDn+woULuHr1Krp27QoLCwuEhoZiwYIFWLp0qUb3cmt6n3pMTAxGjRqFWrVqwczMDFWqVEHXrl0REhKi1vF69+4NSZKUJrR7U3h4OHr16gUnJyeYmZmhZs2aGDZsGO9RJyIioiLFyxNERHoqZ53w27dvK21LTExEQkICAOD69euoWbMmAODdd99Fnz59sGXLFjRv3hzu7u4AXk+SduPGDSxbtkxhKbSoqCgAgK2tLdq3b48TJ04oHGf48OFYtWoVjI2N84x15syZ8vvUK1eujDt37uTa9+TJk/D09ERGRgb69u2LgQMH4vHjx9i6dSv69esHf39/zJ49O9f9V61ahT179sDU1BRCCJV9li5dik8++QTly5fHgAEDYG9vj+vXr+Pvv//G5s2bsXv3brVHHBARERHlSZtj3J2dnYWzs3ORPBfv6S4Z90zoOlbmxbx0Hac2Xy9V8rqn+8WLF8LKykoYGRmJf/75R2HbpEmTBPD63vYNGzYobMvMzBR+fn5CkiR5HwDC29tb3L59W+XxDQwMhKurqwgLCxMvXrwQ//zzj3BzcxMAxLRp0/LNQ9371IUQomfPngKA2L59u0L7nTt3hJWVlTAzMxOvXr1Sue+9e/eEtbW1mDp1qnB2dhYmJiZKfdLT04WVlZWwsrISd+/eVdi2bds2AUB4enrmm5MQQqxdu1Z88MEHonnz5sLY2DjPvN78t87t8XY8QgiRlpYmFi9eLJo3by4sLCyEhYWFqF+/vvjoo49yjSs8PFz07dtX2NvbC2NjY1G1alXh5eUlzp07p1ZeRERU+uj6O1Fxfn8qLlq5p/vBgwf4+++/sXv3bkRHR+PRo0eoUKEC2rVrh2nTpqF169aFPglARETqsbCwwJIlSzB27Fi4ublh0KBBqFSpEo4dO4YzZ86gTp06iI6OVlhKLTU1FUOHDsXJkyexYcMGdO3aFZIk4eDBg/jkk08QGhqKEydOyK+MZ2dny/+7adMmNG3aFADQtGlTBAcHw9XVFT/99BMCAwPzXONck6vGt27dgiRJ6NGjh0J7tWrV0KBBAxw7dgwvXrxQebwxY8bA3t4ec+fOxZYtW1Q+f3x8PJKSktCuXTs4OTkpbOvVqxckSVK6rz03mlzBz+3q/I0bN7B+/XrUrVtXKZ6EhAT06NEDp06dQtu2bTF+/HgAr0c3bNy4ET///LPS870507yXl5fCTPMXL15E48aN1cqNiIiIioZGRffSpUuxYMEC1KxZE127doWDgwNiYmIQHByM4OBg/Pnnn3jvvfe0FSsREb1lzJgxcHR0xMKFC7F9+3ZkZWWhRYsWOHjwIBYsWIDo6Gj5MHQAmD9/PkJCQrB9+3b07dtX3j548GBYWlqiZ8+eCAwMxJo1awAA1tbWAICqVavKC+4cDg4OaN26NQ4cOICrV6+iSZMmRZJT/fr1ce3aNezbtw/vvvuuvP3evXu4dOkSGjZsCDs7O6X9fv/9d+zfvx/h4eEqJ3zLUbFiRdjZ2eHixYt48OABqlSpIt8WGhoKIQQ6deqkVqwrVqxArVq14OzsjG+//RbTp0/Pta+/v7/K9kmTJgEAxo4dq7RtzJgxOH36NNavX49hw4YpbMvMzFTqHxISgpkzZ8LLywsbNmxQ+ndQtQ8RERFpl0ZFd6tWrXD48GF06NBBof3IkSPo3LkzPvzwQ/Tr1y/Pqx1ERFS0evbsqXI2cm9vb8hkMjRr1kzetmvXLgCAp6enUn9PT09IkoQzZ87I23LWLi9fvrzKY+e0p6amFjR8JXPmzMHRo0cxYMAA9OvXD66urnjy5Am2bt0KZ2dnbNq0SWmfO3fuwM/PDxMmTJDfp54bSZKwdOlSeHt7o1GjRujfvz/s7e3la6r3798fc+fOVSvWwt73/erVK6xfvx7Gxsbw9vZW2Hby5Els27YN3t7eSgU3oHrWeM40T0REVPJo9Nd3wIABKts7dOgAT09P7Nu3DxcvXlSYhEeVRYsWYdq0aejevTu2bNkCc3NzTcIgIqJ8REZGIjY2Fr169ZJfrQaA9PR0AMCTJ09gaWmpsM/Tp08hhFA4cdqmTRuYmZnh1q1bePXqldJs4FevXgUAuLi4FFns9erVw4kTJzB48GBs3rxZ3m5jYyOf0fxNQgiMGTMGNjY2WLBggVrHGDp0KOzs7DB8+HCsXLlS4di+vr6wsrIqmmTysXXrViQkJGDQoEEKIxIAYOPGjQBej0J4+vQpQkJCEBcXBycnJ/Ts2RO2trYK/XNmmh8wYIB8pvkLFy6gXLly6NixI4eVExER6UiRLRlmZGQEIO+z6EII+Pn5Ydq0aRg2bBh27NjBgpuIqBCSkpKU2h4+fIixY8fC0NAQc+bMUdjWrl07AEBAQACysrLk7dnZ2Zg1axYAxavgFhYW8Pb2RnJystLV37Vr1+Ly5cto3749KleuXGQ5RUVFoX379qhQoQLOnDmD5ORk3Lp1C2PGjMHUqVMxePBghf6//PILDh48iN9++03pREJuVq9ejd69e2PYsGG4efMmUlJScPbsWVSrVg39+vXDjz/+WGT55CWn4Fc1tDxn5vgbN27A1dUVY8aMwYwZM+Dt7Q0XFxd5Uf52/5yZ5nv16oUvv/wSn3zyCZo0aYIRI0bIT7oQERFRMSqKWdvu3LkjTExMRKVKlURmZqa8/c3ZyzMyMoS3t7cAID799FORnZ2d53O+evVKJCYmyh/37t1Ta2a4kkLXM/5pa3ZAXcfKvJiXruPU5uuVY/ny5cLHx0f4+PiIZs2aCQCiXbt28rZt27bJ+86ZM0fUrl1bjBkzRkyfPl2MGDFCWFpaCgMDAxEUFKT03Hfv3hWVK1cWAET9+vXFpEmTxCeffCIaNmwoAAgXFxfx+PFjhX2ePn0q3nnnHQFAuLu7i88++0z07dtXSJIkbGxsxOXLlzXKL6/Zy9PT00WNGjVElSpVRHJystL2IUOGCAAiLCxMCCHE/fv3hYWFhfD19VXqm9vs5dHR0cLIyEj0799faVtqaqpwdnYW5ubm4sWLF0WWlyq3bt0SkiSJatWqiaysLKXttWvXFsDrmeNHjhwpbt68KRISEsS6devks9afP39e6fiFnWleCM1mZZ89e7YAVM/GrurfX5VevXrl2d/d3T3XY3Tv3l2tYxARlXW6/k6k7e9PuqCV2ctVycjIgLe3N9LS0rBw4UKFWXJzpKSkYPDgwdi9ezfmzZuHGTNm5Pu88+fPR0BAQGHDIyLSC5L05k9HAaxR2B4ZGYnIyEgAwJo1LgC8/tvSFsAhXLu2A0ACAFsAvQB8AV/fpvD1fftITgD+AfANLl8OxeXLvwGQADgDmIrY2BlwcLB9ax9bAMcBBODQoW04duwYKlSogBEjRsDf3x81atQocN5vi46Oxq1btzBgwACUK1dOaXunTp2wceNGnDlzBp6enoiJicHLly8RFBSEoKAglc8p/fePm5CQgPLly2Pfvn3IyMhQeV+7qakp2rZtiz///BPR0dH53i5VGKtWrYIQAqNGjYJMpjzwLGfm+EaNGiEoKEiex/Dhw/HixQt8+OGH+PHHH7FixQqF/oWdaR7QbFb2HD4+Pkq3GahzD7k666rnUDUDvKura77HICIi0qVCFd3Z2dkYPXo0Dh8+jHHjxilNAgO8nlync+fOOH36NFasWIExY8ao9dzTp0/H1KlT5T8nJSUpLaVCRFQ6Bf33UEen/x6aqARA0+HTFQD8AOAHaHOE8pv3nKuS055TNFauXDnXvysbN25EamoqfP8785Czj6bH0Ibs7GwEBQVBJpNh9OjRKvvk3Iv/7rvvygvuHH369MGHH34oH1L+Zv+imGlek1nZc/j6+sLDwyPffm+6f/8+pk6dismTJ2PLli149OhRnv1zmwGeiIioJCtw0S2EwLhx47Bu3TqMGDECv/76q8p+L168wNmzZ2Fra5vvjLJvMjEx4SzoRERlTIMGDWBtbY3IyEjs27cP3bp1k297+PAhli1bBgDy4q527dryK71vO3DgAB49eqS0Pee+9t9//x0ffPABqlatKt8WFhaG8PBwVKxYEfXq1SvK1BTs2bMH9+/fR/fu3VGtWjWVfWrXro2oqCiVM8ermjW+KGeaL+ys7OpSZ111IiIifVegojs7Oxtjx47F6tWr8f7778vP1qvi4OCA3377DV5eXvDw8EBERASHghERlTErVqzA0aNHAQAXL16Ut0VERAAAvLy84OXlBRMTEyxevBhjx45Fz5490bt3b9StWxdxcXHYtm0bkpKSMHHiRDRs2LDAsbRp0wYjRozAunXrUK9ePfTv3x+VKlXCtWvXsGPHDgDAjz/+qPJ2qaKS1wRqOTp16oT169fjypUrStty2t4czq2LmebfdOTIEZw6dQoGBgaoU6cOunTpkufJc3XXVX/TX3/9hdu3b8Pc3BwtW7aEm5tbUYVPRESkPZreLJ6VlSVGjRolAIghQ4YoTJz2tjcnUtu7d68wNTUVjo6O4vr165oeVu2b1EsKXU8+oK2JCnQdK/NiXrqOk3kV9D3oIwDVE2G9fsx+6zgHBPCuAOwFYCAAKwF0EMAaDWJ1FoBJLtuyBPCbANoKwFIABsLBwUF4eXmJo0ePapbkf9SdSO3x48fCyMhI2NnZibS0tFz7JSYmCjs7O2FqaiouXLggb09LSxM9e/YUAMSKFSsU9vnggw8EAPHVV18ptP/xxx8CgGjfvn2R55XbRGqVK1cW+/btU7lPbGyssLS0FB9++KG8LbeJ74TIfSK1li1bilu3bmmcExFRWaTr7w/a+q6hS+rWqBqllJWVJXx9fQUAMXjwYJGRkZFn/zeLbiGE2LdvnzAzMxOOjo7i2rVrmhyaRXcJeVPrOlbmxbx0HSfz4nvwTZrMNJ/ju+++EwDE1KlT833+bdu2CQMDA1GuXDkxcuRI8emnn4r69esLAKJXr15KJ76LeqZ5IfIvurdt2ybWrFkjYmNjRWpqqoiJiRFz5swRZmZmwtTUVJw7d06hf3Z2tujcubOoVq2aSEpKkrfnVXT/73//E6GhoeLff/8VycnJ4ty5c2LkyJECgKhRo4bKme6JiEiRrv/Oavtvsi6oW6NKQgih7lVxf39/BAQEwMLCAp9++qnKWUm9vLzkE7TkDGGLjY2Vbz948CD69OmD8uXLIzw8XH4PWn6SkpJgbW2NxMREWFlZqRuyzrw1502Jpv47gHmVBGU9L33KCSideZX19yDwZl6+eHumeUWzAfi/1VYPwFUAVwDUVeNokQDmAjgBIAVALQAjAUyF6rvEngEIALANwCNUrFgB3bp1K/BM8zkTqa1evVo+KZ06li9fjg8++ACDBg3C33//LW9ftmwZJk6ciNDQUPTo0UPe7uLigkePHuHVq1dqH2P48OHYsGEDfvrpJ0ycOFHt/YiIyqLS+jdZl9StUTW6pzuneH758iXmzZunso+Li0ues6J27twZO3bsQJ8+feDp6YmwsDDUqVNHkzCIiIhKiCCoP9N8DuV7tPPWDkCoBv3/f6Z5AMhnQnCt8fHxwUcffSRf6g4AHjx4gC+++AK+vr4KBXdBjRkzBhs2bEBkZCSLbiIiKrE0KrrzWgdVlTevcL+pc+fOSElJ0eTQREREpEeMjY1haWmp8Pe+IOuq58XOzg4A+J2CiIhKtEKt001ERESkSkxMDBISEtC4cWN5W0HWVc/LyZMnAWhvRnYiIqKiwKKbiIiICuTFixe4ffs2GjVqpNCekJAgL67ff/99eXtB1lW/desWzMzMULlyZYX2q1ev4quvvgIADB06tNC5EBERaQuLbiIiIlKg7rrq8fHxaNy4MVq0aIGGDRvCwcEBDx48QGhoKOLj49G1a1dMmTKlULEcPnwY48aNg6enJ2rWrAlLS0vExMRg165dyMjIwKxZs9CmTRu1nmvdunU4cuQIzpw5g4sXLyI9PT3PCeKSkpLg7++PLVu24NGjR6hUqRIGDhwIf39/lRPmJCQkYN68eQgODsa9e/dgZWUFd3d3BAQEoH79+gp94+PjsWXLFuzcuROXLl3CgwcPYGlpiZYtW2Ly5Mno3r27xv9WRERUMmk0e7kucfZy7eFMxMyrJCiNs3wDpTOvsv4eBMpCXr5Qb1b2JAAz8Hpm9TsAngMwB9AQwAgAYwEYqHl0FwCPALw9e/kFAN8BiALwEEAyAFv07dsaEydORLdu3dR8/tfD0O/cuQM7OzuYm5vjzp07uRbdycnJaN++Pc6dO4euXbuiWbNmOH/+PPbs2YMmTZrg6NGjMDc3l/ePj4+Hm5sbYmJi4ObmBjc3N/z777/YsmULDA0NERYWhtatW8v7//rrr/jwww9RpUoVdOrUCVWqVMH9+/exZcsWpKamYtGiRfDz81M7NyKi/JTWv126pHaNWgzLlxUJrtNdMtbB03WszIt56TpO5sX3IPMqGY+C2L9/v4iNjRVC5L/++KxZswQAMW3aNJXts2bNUmifOHGiAJTXXz927JgwMDAQ9erVE1lZWfL2gwcPip07dyq0CSFEdHS0sLa2FkZGRuLBgwdq5bV27VrxwQcfiObNmwtjY+M88xLi9XeqKVOmiGrVqgljY2NRrVo1MWXKFLW/Y/Xq1UsAULmuenZ2tvDz8xPu7u6icuXKwsTERDg4OAg3NzexYsUKkZ6ertYxtJ3XuXPnhK+vr2jYsKGoUKGCMDExETVq1BCDBw8Wp0+f1mpeRLqi69/b2v4drwvq1qh6kxKL7pLxptZ1rMyLeek6TubF9yDzKhmPwsqr6M7OzhaOjo7CwsJCvHz5UmFbamqqsLGxEVWqVBHZ2dny9ipVqgiZTCZevHih9HxeXl4CgAgLC1Mrtg8++EAAEH///bda/Z2dnQUAYWdnJ///3IrTly9fiiZNmggAomvXruKLL74QPXr0EABEkyZNlPJ928qVK4VMJhOmpqYqi+6MjAxhYmIi2rVrJ8aOHSumT58uJkyYIFxcXAQA0b17d6UTDbrIa/ny5cLBwUH069dPfPzxx+KLL74QgwcPFqampkKSJLF27Vqt5UWkK7r+vV2cv+OLC4tuHdP1G1Vbb2pdx8q8mJeu42RefA8yr5LxKKy8iu5r166JnEJKlX79+gkA4vr16/I2Q0ND4eDgoLL/lClTBAAxc+ZMtWLLuWq+bds2tfpr8wr+m+7duyesra3F1KlThbOzs8qiW4jXJybelpGRITw9PQUAsXPnTp3npSpGIYS4dOmSMDU1FQ4ODgonVYoyLyJd0fXv7eL8HV9c1K1RZUUwlJ2IiIio1IiJiQEA1KpVS+X2nPacfgBgb2+Pp0+f4uXLl0r9b9++DQC4fv16vsd+8eIFNm/eDFNTU3To0EGteLt06QJnZ+d8+wkhsGLFClhYWGDWrFkK26ZPnw4bGxusXLkSQgiV+48ZMwb29vaYO3dunscxNTVVajM0NISXlxcA4MaNG/nGCmg3L1UxAkD9+vVRt25dPH78GElJSQrbiiovIip7WHQTERERvSExMREAYG1trXJ7zmQ5Of0AoGfPnsjOzkZAQIBC31OnTmHnzp0AgOfPn+d77AkTJiAuLg4zZsyAra1tQcLPVUxMDB4+fIh27dopTAIHvC4oO3bsiAcPHqgsHn///Xfs378fK1asgJmZmcbHzs7Oxp49ewAADRo0KFgCuShMXm+7efMmrl27Bicnp1xf/zdpMy8iKj24ZBgRERFRIQUEBCA0NBTfffcdjh8/jjZt2uDff//F5s2bUa9ePVy4cAEGBnnP5D5jxgxs2LABPXr0wIwZM4o8Rk2u4L/Z586dO/Dz88OECRPg7u6u9vH8/f0BAE+fPsXBgwcRHR0NX19fdO7cuYAZqFbQvADg3LlzCA4ORkZGBu7cuYOQkBAAr2eXz01x5UVEpQeLbiIiIqI35FzhfPNK9ptyhh2/eSW0atWqOH36NGbPno3Q0FCcOnUKTk5OCAwMhIuLC4YOHQp7e/tcjxkQEID58+ejU6dO2Lp1a74FekEU5Aq+EAJjxoyBjY0NFixYoNHx3rzqL0kS/Pz8MH/+fE3DzldB8spx7tw5hTgrVqyIP/74I8+l6AqTV1BQEEaNGpVnn06dOuHgwYPyn0+ePIlvvvkGkZGRePHiBZydnTF06FBMnz5dadRBSkoKfvnlF5w5cwb//PMPrl+/DiEEbt++DRcXF7ViJKKix+HlRERERG9Qdc/2m3K7slqlShWsWLECDx48QHp6Om7evIkvvvgCV69eBQC0aNFC5fMFBATA398fHh4e2LFjR4GGb2vLL7/8goMHD+K3336DpaWlRvsKIZCVlYV79+5h2bJlWLFiBTw8PJTuldYlX19fCCGQmpqKCxcuoEePHujZsye+++67XPcpTF5NmjTB7NmzVT7q168PAOjevbu8/9atW9G+fXvs3bsX3bt3x8cff4wKFSpgzpw56Nq1K9LS0hSe//Hjx/Dz88Off/6JV69ewcbGpoD/MpoJCgqCJEl5Pt4cCZBfX0mScO/ePYVjJCQkwM/PD66urjAxMYG9vT0GDRqEy5cvl5i8/P39c+2X2zwCAJCeno4lS5agRYsWsLS0hKWlJRo0aICJEydqLTcqXrzSTURERPSGWrVqwdHREZGRkUhOTla4T/jVq1c4fPgwHB0d4erqmu9zZWVl4a+//oKhoSEGDhyotN3f3x8BAQFwd3fHrl27UK5cuSLN5U2aXsF/8OABvvjiC/j6+qJHjx4FOqZMJkPVqlUxYcIE2Nra4r333sO8efM0vmqel4KMTHibqakpGjZsiKCgIDx58gRffPEFevToket92gXNq0mTJmjSpIlSe3p6On766ScYGhrCx8cHAJCamorx48dDkiRERkaiefPmAF4X/ZMmTcLPP/+M//3vf/jyyy/lz2NnZ4d9+/ahefPmqFChAnr06IG9e/fmGVNRyDmZoMrmzZtx+fJlhZMJufW9ceMG1q9fj7p168LJyUneHh8fDzc3N8TExMDNzQ39+vXDv//+iy1btiA0NBRhYWFo3bp10SYFzfPK4ePjozSywNBQddmVkJCAHj164NSpU2jbti3Gjx8P4PUEjBs3bsTPP/9cuCSoZNDqHOpFiEuGlYwp+XUdK/NiXrqOk3nxPci8SsajsIp6Car09HSRkpKi0JaVlSUmT54sAIgpU6bkeowOHTrkuz62uopyKbTw8HABQK1HQkJCvrE9f/5cABCtWrXSaV75+f777wUAsWzZMrX6FyavHH/99ZcAILy8vORt+/fvFwDE4MGDlfonJCQIAMLZ2VlpabM3de/eXQAQt2/fLnBshZGWliZsbW2FoaGhePToUb79P/74YwFALF68WKE9Zxm9qVOnKrQfO3ZMGBgYiHr16hXrOum55TV79mwBQISHh6v9XP379xeSJIn169crbcvIyCiKcOV0/Xu7OH/HFxd1a1Re6SYiIqIyYcWKFTh69CgA4OLFi/K2iIgIAICXl5d8+adp06YhJCQECxcuxNmzZ9G8eXOcP38eoaGhaNKkCaZNm6bw3HFxcahfvz66deuG6tWrIz09HXv37kV0dDR69+6tdM9vUFAQAgMDYWhoiFatWmHRokVK8Xp4eMDDw6PI8tf0Cn7lypUxZswYlc+1ceNGpKamwtfXFwBgYmKS7/EfPnwIIPcrfgVVlCMTChJnUeS1cuVKAMDYsWPlbXFxcQCA6tWrK/UvX748bGxscOfOHdy6dQs1a9Ys8LG1adu2bYiPj4eXlxcqVqyYZ99Xr15h/fr1MDY2hre3t8K24OBgyGQypdUB3Nzc0KdPHwQHB+PQoUPw9PQs8hxU0SSvvJw8eRLbtm2Dt7c3hg0bprS9qD8rpDt8JYmIiKhUk6Sc/zsKYI3CtsjISERGRgIA1qxxAeD13xZzABEAArB//2bs3x8BoBKAKTh3bjYsLBSXpgKsAfTD5s2RAHYCMALQAMBy7No1Gqamb0+jEwsAyMzMxOLFi3ONvSiLbkmSMHbsWAQGBiIwMFBhKPT8+fORkJCASZMmQfrvH6x27dpYsWKFyuc6cOAAHj16pLQ9OjoaFSpUgIODg0J7SkoKpk6dCuD18mpFSdO8gNeve+vWrZWKmnPnzuHXX3+FoaEhunbtWix53blzBwcPHkSVKlUUhvHnTLyXs877mxITE5GQkADg9frvJbXoVnUyITdbt25FQkICBg0apDTpYFxcHOzs7GBhYaG0X85JibCwsGIruvPL68iRIzh16hQMDAxQp04ddOnSReWJqY0bNwIABg8ejKdPnyIkJARxcXFwcnJCz549i3zZQNIdFt1ERERURgT991CXNYAl/z3yYwngDw2e2/+/hyIhNHiK/2jzCr6m9uzZgy+++AIeHh6oUaMGrK2t8eDBA4SGhiI+Ph7t2rWTF6m6zGvixIl48uQJ2rVrh2rVqiEzMxPXrl3Dvn37IITAkiVLFO7JLcq83rZ69WpkZ2dj1KhRCrPWt23bFlZWVggODsbZs2fRtGlT+bavv/5a/v/qrP+uC7mdTMhNXoWsvb094uLi8PLlS6XCO+ekxPXr14sg6vypk9esWbMUfq5cuTLWrFmjcCIHAKKiogC8vpfd29tbYV4CCwsLrFixAkOGDCniDEgnime0e+Hxnu6Scc+ErmNlXsxL13EyL74HmVfJeDCvN/fzEUBe91zPfus4zwUwRQBOAjD6779T/mtXN1ZnAZioaL8ogA8F0FAANgIwFICtcHd3F7/88ota96gWT15/CMDrvzzMBGD83/8PE8AxreSlSlZWlqhWrZqQJEncunVLafuKFSsEAGFiYiKGDx8uPvvsM+Hm5iaMjY1FnTp1BACxcePGXJ9fl/d059zfPHPmzHz73rp1S0iSJKpVq6by3uzRo0cLAMLPz0+h/eTJk8LQ0FAAEN26dSuy2POSV17btm0Ta9asEbGxsSI1NVXExMSIOXPmCDMzM2FqairOnTun0L927doCgDAwMBAjR44UN2/eFAkJCWLdunXCyspKGBkZifPnzxdZ7Lr+/aat34W6pG6NqjcpseguGW9qXcfKvJiXruNkXnwPMq+S8WBeuo+VeWmWlyp79uwRAETnzp1z7bN7927h4eEhLCwshJmZmejQoYM4cuSIePfddwUAERYWluu+uiq68zuZ8LaZM2cKAGL27Nkqt9+7d09UrlxZABDt2rUTn332mRg2bJgwNjYWjRo1EgBEz549izgLZZrmleP3338XAMSgQYMU2mvVqiUAiKZNmypNiPfLL78IAGLMmDFFErsQuv+8FOdnq7ioW6NynW4iIiIiIh1Q557nnj17Ijw8HC9evEBKSgoOHz6M9u3b49KlS5DJZGjWrFlxhau2/fv34+7du+jUqZPKieDelJ2djaCgIMhkMowePVpln6pVq+L06dMYM2YMbt++jR9//BEnTpxAYGAgZsyYAQBK94FrgyZ5vcnHxweGhoby+SNy5Cxj9+677yrMOQAAffr0AfD/Q9BJv/GebiIiIiKiYhYfH4/t27ejQoUK6N+/v0b7RkZGIjY2Fr169cpz/XFd0WQCtT179uD+/fvo3r07qlWrlmu/KlWqqJzYz9/fHwDQokWLggWrAU3yepOxsTEsLS2RkpKi0F67dm1ERUWhfPnySvvktKWmphYoVipZeKWbiIiIiKiYrV27Funp6RgxYkSuS64lJSUptT18+BBjx46FoaEh5syZo+0wNabpyYSCFrIAkJWVhb/++guGhoYYOHCgxvtrojAnSWJiYpCQkKAwOR8AdOrUCQBw5coVpX1y2t7eh/QTr3QTERERERUzdYrNH3/8EevWrUP79u3h4OCAe/fuYfv27UhJScHKlStVDi338/PD06dPAfz/rO9+fn7yWb+//PJL1KlTp6jTkVPnZEKOJ0+eYMeOHbCzs0Pfvn1z7ZeRkYHMzEyYmZnJ27Kzs+Hn54dr165hypQpcHR0LLIcVMkvrxcvXuD27dto1KiRQntCQoJ8vfv3339fYdugQYPwxRdfYP369fj000/RsGFDAEB6ejpmz54NAHjvvfe0kQ4VM0kIIXQdhDqSkpJgbW2NxMREWFlZ6TqcfL11W0aJpsk7gHnpXlnPS59yAkpnXmX9PQgwr5KAeTGvkqDgeZ0C0BpAKwAn89grDMB8ABcAJACwBeAO4AsATXPZxwXAnTyeMxyAR56xFqY6aNiwIS5duoQLFy7Ii8jcLF68GH5+fpg6dWqe69Xfv38f9evXR7du3VC9enWkp6dj7969iI6ORu/evbFly5Z8C/zCyi+v2NhYVK9eHS1atEDDhg3h4OCgsKxc165dsXPnThgbGyvsFxwcjEGDBsHExASDBg2CjY0NDhw4gMuXL6NXr14ICQlRWEquMErrZ0uX1K1RWXRrSWl9UzMv3SvreelTTkDpzKusvwcB5lUSMC/mVRIwr4KeTMhRD8BVAFcA1M2j3wsAEwFEAngIwAhAAwCj/3uod8dswf8mq5NXEoAZAE7g9UmP5wDMATQEMALAWAC5Fc+RAOb+t28KgFoARgKYCnUGJpf17xq6xKJbx0rrm5p56V5Zz0ufcgJKZ15l/T0IMK+SgHkxr5KAeZXOvPQpJ6B05qUfFar6NSonUiMiIiIiIiLSEhbdRERERERERFrCopuIiIiIiIhIS1h0ExEREREREWkJi24iIiIiIiIiLWHRTURERERERKQlLLqJiIiIiIiItIRFNxEREREREZGWsOgmIiIiIiIi0hIW3URERERERERawqKbiIiIiIiISEtYdBMRERERERFpCYtuIiIiIiIiIi3RetF9+vRp9OrVCzY2NjA3N0erVq2wYcMGbR+WiIiIiIiISOcMtfnkERER6N69O4yNjTF06FBYW1tj69atGD58OGJjYzFjxgxtHp6IiIiIiIhIpyQhhNDGE2dmZqJOnTq4f/8+jh8/jqZNmwIAXrx4ATc3N1y7dg1XrlxBrVq11Hq+pKQkWFtbIzExEVZWVtoIuUhJkq4jUJ8m7wDmpXtlPS99ygkonXmV9fcgwLxKAubFvEoC5lU689KnnIDSmZd2KtSip26NqrXh5WFhYbh58yaGDRsmL7gBwNLSEl9//TUyMzOxevVqbR2eiIiIiIiISOe0VnRHREQAALp166a0Laft0KFD2jo8ERERERERkc5p7Z7umJgYAFA5fNzGxgZ2dnbyPqqkpaUhLS1N/nNiYiKA15fwqWiV1n9S5qVfmJf+KI05AcxL3zAv/cK89Avz0i+lMS99ySmnNs3vjm2tFd05RbK1tbXK7VZWVrh//36u+8+fPx8BAQFK7U5OTkUTIMnl8hLpPealX5iX/iiNOQHMS98wL/3CvPQL89IvpTEvfcvpxYsXuda9gJZnLy+M6dOnY+rUqfKfs7Oz8ezZM9ja2kLSp1kAikhSUhKcnJxw7949vZhITl3MS78wL/3CvPQL89IvpTGv0pgTwLz0DfPSL6U1L3UJIfDixQs4Ojrm2U9rRXdOpZ9zxfttOTO95cbExAQmJiYKbeXLly+y+PSVlZVVqXxDMy/9wrz0C/PSL8xLv5TGvEpjTgDz0jfMS7+U1rzUkVdNm0NrE6nl3Mut6r7thIQEPH36VO3lwoiIiIiIiIj0kdaKbnd3dwDAvn37lLbltOX0ISIiIiIiIiqNtFZ0d+7cGTVq1MCGDRtw7tw5efuLFy8wZ84cGBoawtfXV1uHL3VMTEwwe/ZspSH3+o556RfmpV+Yl35hXvqlNOZVGnMCmJe+YV76pbTmVdQkkd/85oUQHh6O7t27w8TEBO+//z6srKywdetW3L59G3PnzsVXX32lrUMTERERERER6ZxWi24AOHXqFGbPno3jx48jPT0d9evXx+TJkzF8+HBtHpaIiIiIiIhI57RedBMRERERERGVVVq7p5uIiIiIiIiorGPRTURERERERKQlLLqJiIiIiIiItIRFNxEREREREZGWsOgu4a5cuYKtW7di7dq1ug6F3pKdna2y/fnz55gyZQoaN26Mxo0b4+OPP8bjx4+LOToi/cXPln7h60VERJQ3zl5eQp0+fRrjxo3DxYsX5W1ZWVkAgMOHD6NHjx7466+/0LdvX12FWKb9+OOPmDJlCvbs2YOuXbvK21+9eoVWrVrh8uXLePOjVbNmTfzzzz+wtLTURbhF4tGjR9i6dSuio6ORkpKCFStWAACePHmC27dvo2HDhjAzM9NxlJq7cuUKoqOjkZycDG9vb12HUyTS09Nx4MABeV5ff/01gNfvz6SkJNjZ2UEmK5nnXMviZ4uvl/559uwZkpOT4eTkpOtQtC4hIQHr1q3DqlWrcPbsWV2HQ2VAafx8vXz5EtevX0dycjI6dOig63CK3PHjx7Fz506YmZlh9OjRcHR01HVIJY+gEufSpUvCwsJCWFlZic8++0z07t1byGQy+fbs7Gzh5OQkRowYocMoNZeVlaWyPSEhQUyePFk0atRINGrUSEycOFHExcUVc3SaGTBggKhUqZJS+48//igkSRINGjQQ4eHh4tSpU2LgwIFCJpOJuXPn6iDSovHzzz8LU1NTIUmSkCRJ4f146dIlIZPJxO+//67DCDV36tQp0bhxYyGTyeSPHIcOHRJmZmZi+/btOoywYLZv3y4qVqwoZDKZ0mt18uRJIZPJxPr163UYYd7K2meLr5f+eP78ufjkk0+Eg4ODkMlkwsDAQL7txIkTomfPniIqKkqHERat/fv3i6FDh8p/97/53tQXaWlpYuPGjcLPz0+MHTtWjBo1SukxevRoXYeZp4CAABEYGCji4+PlP6vzCAwM1HHkmimtn6/bt2+Lvn37CkNDQ6W8jh49KurWrSvCw8N1F6CGPvvsM2FiYiJ/PwohxN9//y0MDAzk3xErVqwo7t+/r8MoSyYW3SXQwIEDhYWFhYiJiRFCCOHv76/0x+69994TtWvX1kV4BfLDDz8ImUwm9u3bp9CempoqGjZsKP/CmfNwdXUVSUlJOoo2f66ursLLy0upvV27dkImk4nz58/L2zIzM0XVqlVFq1atijPEIhMSEiIkSRItW7YUO3bsEB999JHS+7FJkyaiV69eOopQc6X1xNbRo0eFkZGRqFatmli6dKkYPny40mv1zjvviAEDBugowvyVpc8WXy/9ER8fL2rXri0kSRLNmzcX9evXV3itUlJShJWVlZgyZYoOoyy8u3fvioCAAOHi4iL/u1yxYkXx2WefiUuXLuk6PI3ExsaKWrVqKX2/ePtR0k8m5MR47do1+c/qPEp6Xm8qrZ+vO3fuiIoVKwpDQ0MxcOBA+e/FHBkZGcLe3l588MEHOoxSM82aNRM9evRQaKtTp46oUKGCWLt2rVi0aJEwNDTUu9eqOBjq+ko7KTt06BAGDhwIV1fXXPtUq1YNe/bsKcaoCufQoUNwcHBQGH4IAMuXL8elS5dQv359LF26FObm5liwYAG2bduGH3/8EV999ZWOIs7bkydPUKNGDYW2zMxMREVFwdXVFY0aNZK3GxgYoEePHti8eXNxh1kkFi1ahGrVqiE8PBzm5uY4c+aMUp+GDRviyJEjOoiuYGbPng0AOHPmDFxdXREQEIDQ0FD5dkmS4ObmhtOnT+sqxAKZO3cuypcvj6ioKNjb2yM+Pl6pT/PmzXHq1CkdRKeesvTZ4uulP/z9/XH9+nX8+eefGDJkCAICAhAYGCjfbmZmBnd3d4SFhekwyoLJyMjAtm3bsHLlShw8eBDZ2dkwNjaGEAL9+/fHpk2bYGBgoOswNTZlyhTcuHED3t7eGD16NKpWrQpDQ/372hseHg7g9fe+N38uTUrr52v27NlISEjAoUOH0LZtWwQEBOD48ePy7YaGhujQoQMiIyN1GKVm7t69i3bt2sl/jomJwbVr1zB79myMGDECAHDkyBHs3r0bS5Ys0VWYJZL+/fYpA168eAEHB4c8+7x69Up+j7c+uHDhAtq0aaPUvnHjRkiShPXr18u/nG3cuBEuLi4ICQkpsUX3q1evlNquXLmC9PR0lXlWrFgRKSkpxRFakTt37hy8vb1hbm6ea58qVaogLi6uGKMqnNJ4YgsATpw4gUGDBsHe3j7XPk5OTggJCSnGqDRTlj5bfL30R0hICN59910MGTIk1z7Ozs44duxYMUZVOBcvXsTKlSuxfv16PHv2DEIItG7dGiNHjsSQIUNgZ2cHW1tbvSy4ASAsLAydO3fGmjVrdB1Kobi7u+f5c2lQGj9fALB37170798fbdu2zbVPtWrV9OpkwsuXL2FhYSH/+ejRo5AkCT179pS31atXDwcPHtRFeCUai+4SyMnJCZcuXcqzz5kzZ1CzZs1iiqjwStvVkMqVK+Py5csKbceOHYMkSWjVqpVS/6SkJNja2hZXeEUqOzsbRkZGefZ58uQJTExMiimiwiuNJ7YAIC0tDdbW1nn2SUxMLLGTcgFl67PF10t//Pvvvxg6dGiefUxNTZGcnFxMERVOq1atcObMGQghUK1aNYwfPx4+Pj6oVauWrkMrMtnZ2WjatKmuwyA1lLbPV45nz57BxcUl335paWnaD6aIVK5cGdeuXZP/vGfPHlhYWKB58+bytqSkJL36TlhcSu5f8jLs3Xffxb59+3I987Vp0yacOHECXl5exRtYIZS2qyEdO3bEwYMHcfjwYQBAamoqfv/9dwBA9+7dlfqfP38eVatWLdYYi0rt2rVx9OjRXLdnZmbi0KFDaNiwYTFGVTil8cQWANSoUQNRUVF59jl+/Djq1KlTTBFprix9tvh66Q9bW1vcu3cvzz7R0dGoXLlyMUVUOFFRUZAkCZ9//jlu3ryJuXPnlqqCGwDc3Nxw9epVXYdBaihtn68cFStWxI0bN/Lsc+nSJfmtA/rA3d0du3btws8//4yVK1ciODgY3bp1UxgRc+PGDb38Pa9tLLpLoBkzZqBy5cro2bMnPvjgA/mXsmXLlsHb2xvDhg2Di4sLpk6dquNI1VfaroZMmzYNBgYG6NKlC5o1a4YaNWrg3Llz6N27t9KQ5fj4eBw/fhzt27fXUbSFM3z4cPzzzz+YO3eu0rasrCz4+fnh1q1bGDlypA6iK5jSeGILAAYOHIgjR47gjz/+ULn9u+++w6VLl/IcwqdrZemzxddLf3Ts2BEhISF48OCByu1XrlzBnj170KVLl2KOrGDat2+P7OxsfPfdd6hWrRo+//xznD9/XtdhFalvv/0W4eHhJXrUnDpkMhkMDQ1x/fp1+c8GBgb5PvTp/vXS9vnK0bVrV+zYsSPXk/xHjhzBwYMH0atXr2KOrOC++uormJmZ4ZNPPsG4ceNgZGQknycHeD3yMSIiQuG+b/qPjidyo1zcvHlTtG3bVuWMlG3atBG3b9/WdYgaGTlypDA0NBSHDh0SQryeibJp06ZCJpPJZ2l/U8eOHUXLli2LO0yN7Nu3T9SsWVNIkiQMDQ1F//79xdOnT5X6zZs3T0iSJPbs2aODKAsvPT1deHh4CJlMJt555x35bPODBw8W1atXF5Ikie7du4vs7Gxdh6q2x48fi6pVqwpjY2Mxbtw48e677wqZTCZ+/vlnMWLECGFgYCCqV68unj9/rutQNfLixQv5rK9du3YVHTp0EDKZTHz++eeiffv2QiaTiWbNmolXr17pOtQ8lZXPFl8v/XHhwgVhamoqnJ2dxfr168XHH38sZDKZuHLlilixYoWoWLGiMDc3F9evX9d1qGqLiYkRX375pXB0dJTPdt2wYUOxaNEi8eDBAyFJkhg3bpyuwyywgIAA+e92T09PMXXqVL1cWsvd3V14eHiIe/fuKfyszkNflMbPlxCvlwuztrYW1tbWYt68eWLEiBFCJpOJ3bt3i5kzZ4py5coJe3t78fDhQ12HqpGHDx+KpUuXiqVLlyrVI6dPnxaTJ08Wp06d0k1wJRiL7hLu7Nmz4pdffhHz5s0TS5cu1ds38aVLl4SJiYkwMjISTZs2FZUqVRKSJIk+ffoo9X369KkwMjLSm+UGHj9+LNLS0nLdnpycrHfF29vS0tLEjBkzRIUKFRROAFlbW4svv/wyz/xLqhs3bpSqE1s5nj17JoYNGyYMDQ2Vlo8ZOnSoePbsma5DVFtZ+Gzx9dIf27dvF9bW1kImkymsqy5JkrCyshK7du3SdYgFkpWVJUJCQkS/fv2EkZGRkMlk8jWFe/fuLZKTk3UdYoGUxqW1SrPS+vk6ceKEcHFxUcgn57/Ozs7i9OnTug5RI4cOHRJnz57VdRh6SRJCCF1fbaeyYf/+/fjwww9x69YtGBgYoE+fPli+fLnSMPJvvvkGM2fORGhoqMp7AkuC27dvo3r16hrtM336dMyfP19LERUPIQSuXbuGZ8+ewcrKCnXr1pXfx5OSkoJy5crpOELNnTt3DidOnJDn1Lp1a7Rs2VLXYRVafHw8Tp8+Lc+rZcuWqFixIoDXEwyV1Mm5yupnK6/XqyQra6/Xs2fPsGbNGpw8eVLhd8aoUaNgZ2en6/AK7fHjxwgKCsLq1atx7do1SJIEc3NzDBkyBKNGjcpzFuaS5tChQ2r3LY0zguuj0vr5yszMxI4dO5Ty6tevH4yNjXUdnkYMDAwwYcIE/Pzzz7oORf/ouOinMkjdqyElebhyzZo1xaNHj9TuP336dL09mz5r1qx8+yQnJwt3d3ftB1NERo0aJf73v//pOowit3z58nz7ZGZmiiFDhhRDNAVTlj5bpQFfr9IrMjJSjBo1SlhYWAhJkoSBgYGuQ6JSas2aNXp520l+1B2d+uOPP2o5kqJTqVIlMXnyZF2HoZd4pbuEevLkCVavXo3Tp0/j+fPnKpcukiSJ6+DpiEwmQ8OGDXHo0CGUL18+z75ff/015s2bB1dXV/lEKPpEJpPhp59+wkcffaRye2pqKnr27IkjR47ozRJbpqammDJlit5ebcuNkZERNm/ejH79+qncLoTA+++/j7///rvEvlZl6bNVGvD1Kv2Sk5Px559/YtWqVXq3TnJpEBgYCEmSMHHiRFSoUAGBgYFq7SdJEr7++mstR1c0jIyMMGnSJCxZskTXoRQpBwcHREZG5rkywNKlSzF58uQS+zf5bb6+vrhw4QLOnDkDSZJ0HY5+0XHRTyqcP39e2Nrayu/5KC33IR0/flx06tRJWFpaCktLS9G5c2dx8uRJXYdVIAsWLBCSJIm2bdvmeb/brFmzhCRJwtXVVdy/f78YIyw6AwYMEAYGBmLTpk1K21JTU4WHh4eQJEl8++23OoiuYOrXry98fHx0HUaRa9++vTAzMxOHDx9W2padnS3ef/99IUmS+Oijj3QQnXrK0mdLCCEuX74sPvzwQ9GiRQvh6uoqqlevrvSoUaOGrsPMVVl7vU6ePCl++uknMXfuXL2clKswHj9+rOsQ8nTnzh1x584dkZmZqfCzOo+SLOf73rVr1+Q/l7Z71Z2cnMTEiRN1HUaRq1y5snBxccl1orSffvpJPo+Mvnjw4IGoVq2aGDt2rIiPj9d1OHqFRXcJ1KVLFyFJkvj6669FbGys/A+IPrtw4YIwMzNT+qNQrlw5cenSJV2HVyBffvmlfObujIwMpe3+/v5CkiRRo0YN+ayj+igtLU14eHgIU1NTcfDgQXl7amqq6NSpk5AkScyfP1+HEWpuyZIlws7OTq+//Kvy/Plz0aBBA1G+fHlx4cIFeXt2drYYPny4kCRJTJgwQYcRqqesfLYiIiKEqampkCRJGBkZiapVqwoXFxeVj5KsLLxe8fHx8tnlS0uho47s7Gyxa9cuMWDAAGFiYqLrcPKkqjjNmZQrr0dJHzYfEREhIiIiRGpqqsLP6jz0xaRJk8Q777xT4ldq0NT58+dF+fLlRcOGDUVCQoLCtmXLlglJkkTr1q1FUlKSbgIsAE9PT9GoUSMhk8mEqampqFu3rvDw8BCenp4Kj06dOuk61BKHRXcJZG5uLvr376/rMIrUsGHDhCRJYubMmSIuLk48fvxYfPXVV0KSJOHt7a3r8Aps/PjxQpIkMXjwYIV70AMCAoQkSaJ69eol/iy6OpKSkkSTJk2ElZWVOHPmjELBPW/ePF2Hp7Hbt2+L3r17i2rVqomffvpJnDx5UsTGxurdFRBVHjx4IJydnYWjo6O4ffu2yM7Oln/+xo8fr+vw1FYWPltt2rQRhoaGYuXKlXp/crW0v14+Pj5CkiTh6ekpgoKCxIEDB/S+0MnLzZs3xVdffSWqVq0qP9FQqVIlXYeVJx8fH+Hr6yufYyDnZ3UepFvPnz8XzZo1Ez179tTbCzG5OXz4sDAzMxPt27eXnzjJKbhbtWolEhMTdRyhZkrjSIviwqK7BLKxsRF+fn66DqNIOTk5iQ4dOii1t2/fXlSrVk0HERWN7OxsMWTIECGTyeTrmQYGBsqXgoiNjdVxhEXn0aNHokaNGsLBwUF07NhRSJIk5syZo+uwCuTtpTv09QpIbq5duybs7OxErVq1xJAhQ/Ryvd2y8NkyMzMTI0aM0HUYRaK0v162traidevWJXqCz8JKTU0Va9euFZ6envLfgcbGxmLAgAFix44den9iiEqu6tWrC0dHR/n7rly5csLFxUWvbrXJS3BwsDA0NBR9+vQRP/30k5DJZKJFixZ6vYQiac5Q1/eUk7JOnTohKipK12EUqbi4OAwdOlSpvU2bNjh9+rQOIioakiRh3bp1SEpKwsqVK3HlyhUcO3YMVatWRVhYGJydnXUdYpGpWLEi9u/fj3bt2uHo0aMICAjAzJkzdR1WgYwcObJUTwDyzjvvIDQ0FJ06dcLff/+NMWPG4Pfff9d1WBopC58tS0tLODg46DqMIlHaX69Xr16hY8eOpfL3xpkzZ7By5Ur8+eefSEpKghACLVq0QFRUFHx8fPTud0dZ88MPP+CHH37ArVu3dB1KgWVnZ8PY2BjVqlVTaBdvzfX89s/6ol+/fvj1118xbtw47Nq1C02bNsWBAwdgbW2t69CoGLHoLoEWLVqE1q1b47vvvoOfn5+uwykSGRkZsLCwUGq3sLBARkaGDiIqOoaGhtiyZQu6deuGyMhIVKlSBREREahRo4auQyuQ0aNH57ndxcUFGRkZuH37tkJfSZKwcuVKbYdXJIKCgnQdQpHIbxbbVq1a4dy5c6hSpYpCX32Z1ba0fbbe1rt3bxw5ckTXYRSZ0vx6NW3aFLGxsboOo8gkJCRg3bp1WLlyJS5evAghBKpWrYoJEybAx8cHderUgUwm03WYpIbnz5/jzp07ug6jUErLZ+vw4cO5bqtVqxb69u2Lo0eP4uuvv8b58+cVtnfs2FHb4RW5ly9f4vr160hOTkaHDh10HU6JxyXDSqDRo0fj9u3bOHz4MKpXr47GjRurPBumT0WOTCaDv78/Zs2apdAeEBCAwMBAvVkqIUevXr2U2hITE3HixAnUq1cPTk5OStslScKuXbuKI7xCKegXLUmS9O511Hel8bUqzZ+ttz19+hTt2rVD9+7d8e2336JcuXK6DkljZeX1OnjwIHr37o2IiAi0adNG1+EUmpmZGdLT01GuXDn0798fI0eOROfOnRWu5MtkMowdO1avr3RfuXIFP/30U77Lr968eVMH0RUNff0eVRrJZLJ8R8MIIVT20afXLzY2Fp9++il2796N7OxsSJKEzMxMAEBkZCTGjRuHZcuWwcPDQ7eBljAsuksgdb9Il+Qvzm+TyWRwdXWFq6urQvuNGzdw8+ZNdO/eXWmfkvzFrCDFjr68XoU5Y66PQ0iPHTuGc+fOITExEVZWVmjSpAnatWun67DUcujQoQLv6+7uXoSRFJ3S/Nl6W6dOnfD8+XOcP38e5ubmqFWrVq4nWA8ePKiDCPNXVl6vP/74AyEhIdixYweGDx+Opk2b5jo0dOTIkcUcneZkMhlkMhkmTJiAjz76CPXq1VPZR5+L7kOHDqFHjx5IS0uDoaEhKlasCEND1QM8b9++XczRFZ3SVnRnZmbi+vXr8r/JtWvXzvV1K2n8/f0LfAvK7Nmzizga7bh79y5atWqF+Ph49OvXD48ePcLx48fl77/MzEw4Ojqif//++O2333QcbcnCorsE0qTo0Zcip7R9MStoYaovr1dZcPLkSfj4+CAmJgaA4tnnWrVqYfXq1XBzc9NliGVSWfpslYYTrGXl9cq5gvXmV6a3v1zn/A4pqa/Vm2bNmoU//vgDd+/ehSRJaNasGby9vTF06FD5PAP6XnS7ubkhKioKv/32G3x8fGBgYKDrkLTC398fgYGByM7O1nUohZKQkIAvvvgCGzZsQGpqqrzdzMwMw4YNw/z582Fra6vDCAkARo0ahQ0bNiA8PBxt27ZVedJn4MCBuHbtGi5duqTDSEseFt1ULMrKFzPSD1evXkWrVq2QnJyM7t27w8PDA5UqVUJcXBwiIiKwZ88eWFhYyIfIElHZtmbNGrX7+vj4aDGSoiOEwL59+7By5UqEhIQgPT0dhoaG6NatG0aMGIFhw4bpddFdrlw5DBw4EGvXrtV1KFp1584dxMbGltjRS+pISEiAm5sbrl+/DltbW7Ro0UL+NzkqKgpPnz5FrVq1cPz4cVSoUEHX4ZZpjo6O6NixI/766y8AqkdaTJkyBUFBQUhISNBVmCWSfozXKOOePXuG5ORklffG6YvSWjzfuXMHZ86cAQC0aNFCaeZNfZQzEUirVq1gamqa58Qgb9OXiUACAgKQnp6OvXv3omvXrgrbpk2bhgMHDqB3794IDAyU/2Epie7evQsAqFKlCgwMDOQ/q6Okv1dL42erNCvtr5e+FNKakCQJ3bt3R/fu3fHs2TP88ccfWLVqFXbv3o3Q0FBIkoQzZ87gyJEjejlJUmlaHSAvzs7Oev8da86cObh+/TqmT5+Or776SmF+i9TUVHzzzTeYN28e5s6diyVLlugwUnr27BlcXFzy7ZeWlqb9YPRNsS9SRmp5/vy5+OSTT4SDg4PSesEnTpwQPXv2FFFRUTqMUPtiYmJ0HUKePvnkE2FgYKCwpvOUKVN0HVah5axdfe3aNYWf1XnoCwcHBzFs2LA8+7z//vvCwcGhmCIqmIK+ViV9/fHS+tnKz8uXL8XDhw/Fy5cvdR2KRsrq61VanTp1SowfP15YW1vLf6e4urqKb775RtehaWTUqFGiZcuWug6jyN2/f18cOnRIJCcny9uys7PFt99+K9q2bSu6dOkiQkNDdRih5qpXry46deqUZ5/OnTuL6tWrF1NEBZOzlvitW7fkP6vz0Kf1x6tVqyYGDhwo/9nf31/p+1+XLl1E7dq1izu0Eo9XukugZ8+eoW3btrh+/TqaNWsGe3t7XL16Vb69UaNGiIyMxPr169G8eXMdRqodd+7cQWBgINatW1diz5StWbMGS5cuhZmZmXxI16FDh/DDDz+gWbNmGDFihI4jLLhZs2ZBkiTY2dkp/FyaJCYm5numtnr16khMTCyegAooZ73xnAmdSsP646X5s6VKRkYGFi1ahKCgIIUZlGvUqIFRo0bBz88PxsbGOowwb2Xt9SoLWrZsiZYtW+J///sf/v77b6xcuRJHjhzBzJkzMX36dF2Hp7aFCxeiXbt2+OSTT/R2dQBVvv76awQHByMuLk7eNnfuXIWJuA4dOoRjx46hRYsWughRYw8fPsT777+fZ5/WrVvj6NGjxRRRweTM5J3bz7kRenSnb9euXbF27VpcunQJDRo0UNp+5MgRHDx4EJMnTy7+4Eo6XVf9pGzSpElCkiTx119/CSFUn0Xq06ePaNy4sQ6iK5yIiAixePFi8cMPPyhdqX/48KEYP368MDY2FpIkiapVq+ooyvx17NhRlCtXTly6dEneduHCBWFmZibc3d11FxippbScVS+NytJnKyUlRbRr107IZDJhZGQk6tSpIzw8PETdunWFsbGxkMlkws3NTaSkpOg61FyV1tdLkiRhYGBQ6kaRvOnZs2ciMDBQ9OnTR/Tp00fMmTNHJCQkqOwbExMjpk+fXrwBFpKnp6do2rSpkMlkwtLSUjRr1kx4enoqPfL7W1DS1KlTRwwePFj+c1ZWlrC3txd169YV9+7dE6dPnxbW1tZiyJAhOoxSMw4ODmL48OF59hk+fHiJH31WFty+fVtYW1sLa2trMW/ePDFixAghk8nE7t27xcyZM0W5cuWEvb29ePjwoa5DLXFYdJdAzs7Ook+fPvKfVRXdH3/8sbC1tS3u0AosLS1N9OzZU+kLyqeffiqEEGLlypXCwsJCSJIkqlSpIpYuXSrS0tJ0G3QebGxsxIgRI5Tahw0bJmxsbHQQEWli8uTJQiaTiZkzZ4rU1FSFbampqWLWrFlCJpOJyZMn6yjCsqssfbb8/f2FJEli+PDh4t69ewrbHjx4ILy9vYUkScLf319HEeavtL5e7u7uwsPDQ/665PyszkMfPH36VNSoUUPIZDIhSZLCMPL4+Hhdh1ckcvLK76FPt0YJIYS1tbX4/PPP5T+fPn1aSJIkli1bJm/z8fERLi4uugivQN577z1hbGws9u/fr3L7gQMHhLGxsXjvvfeKOTJS5cSJE8LFxUX++Xnzv87OzuL06dO6DrFE4vDyEujff//F0KFD8+xjamqK5OTkYoqo8H744Qfs2bMHjo6O8PLyghAC27Ztw9KlS2FgYID//e9/sLGxwbx58zB+/HiYmJjoOuQ8JSYmokaNGkrtNWvWRFJSkg4iIk18/fXX2LlzJ7755hv89ttvaNWqFSpWrIi4uDicPn0aT548QY0aNfD111/rOtQypyx9tv766y+0aNEC69atU9rm6OiIP/74A9HR0fjrr79K7BqupfX1ioiIyPNnfffNN9/g9u3b8PT0xPjx4wEAv/zyCw4dOoRvv/0WCxcu1HGEhafvS2jlJisrSyG3I0eOQJIkdOrUSd5WpUoVPHr0SBfhFcjs2bOxa9cudO/eHb169YK7u7v8b3JERARCQ0NRrlw5zJo1S9ehEl4P9Y+JicGOHTtw8uRJPHv2DFZWVmjdujX69etXom+J0iUW3SWQra0t7t27l2ef6OhoVK5cuZgiKryNGzfCzs4OFy9ehI2NDYDXa0vWrVsX33//PVq2bIldu3bJ7yMu6YQQKtf8NDAw0Kt7c1R58w+3JiRJwsGDB4s4Gu2oUKECTp48ic8//xx//fUXdu/eLd9mamqKUaNGYcGCBSV+aZLRo0cXaD9JkrBy5coijqZolObP1ttiY2MxZcqUPPt07twZ33//ffEEVABl6fUqTXbv3g1XV1fs27dP/voNGDAAdevWxc6dO0tF0V1aVatWDadOnZL/HBwcjMqVK6N27drytkePHqF8+fI6iK5g6tWrh3379sHX1xe7du3Crl27IEmS/HdIzZo1ERQUhPr16+s40rz98ccfBd535MiRRRiJ9hkaGqJ///7o37+/rkPRGyy6S6COHTsiJCQEDx48QJUqVZS2X7lyBXv27MGoUaN0EF3BXL9+HUOHDpUX3ABgb2+P/v37Y9WqVVi2bJneFNw5kpOT8fjxY4W2ly9fAgCePHmi8gunPixfUtArOvo2gVeFChWwcuVK/Prrr4iOjkZSUhKsrKxQp04dGBkZ6To8tQQFBRVov5JcdAOl97P1tnLlyuHJkyd59nny5EmJnwCqrLxeOTIzM3H9+nUkJibCysoKtWvXhqGhfn2dunPnDsaMGaNwwsTQ0BA9evQo0b8bCBg4cCDmzZuHwYMHw9TUFEePHsXEiRMV+ly6dEnlCJSSrG3btrh27RoiIyNx9uxZ+d/kpk2bol27dnrxHcPX11fjOIUQkCRJ74pu0pwkeCq6xLl48aJ8uOs333yD48ePY9myZbh06RKOHTuGr776Ci9fvsTZs2dRq1YtXYerFplMhlmzZsHf31+h3d/fH3PmzEFaWppefWmRyWS5/mLN+QX6NkmSkJmZqe3QqAy5c+dOgfctqeu6lqXPVu/evXH48GGcOHFC5RWcK1euoHXr1nB3d8fOnTt1EGH+ytLrlZCQgC+++AIbNmxAamqqvN3MzAzDhg3D/PnzYWtrq8MI1SeTyeDv7680XDcgIACBgYHIysrSUWRF7/79+wgPD8fDhw9VrogiSZJe3UqUlJSEbt26ya92N2zYEOHh4fKRWVevXkX9+vUxffp0zJs3T5ehljlr1qwp8L4+Pj5FGIl2paenIzg4GKdPn8bz589V/r4o6Sf3dUF/qpwypGHDhti4cSNGjhwJb29vAK+/vDRo0ABCCFhaWmLTpk16U3DnkMlkSm05X8j0qeAGXo9G0IezrlS6ldTCuTDK0mfrq6++wv79+9GyZUuMGTNG6T7G1atXIyMjo0Qv01RWXq+EhAS4ubnh+vXrsLW1RYcOHVCpUiXExcUhKioKK1aswKFDh3D8+PESf1tKWfL555/jhx9+UCgK3jwZlPP/+lR0W1lZ4cSJE7h06RIAoG7dugojFszMzLBt2za9WS6sNNGnwrmg7ty5g65du+LmzZt53kLEolsZr3SXYM+ePcOaNWuUJikYNWqU3g3FlslkGDt2LMaOHavQvnz5cqxatQonTpxQ+eFt1apVcYVIakpKSsLz589RrVo1XYdSKAcOHMCSJUvkZ2pVTbqjr1fkSH9s2bIFY8eORWJiokLxKoSAtbU1li9fjkGDBukwQgKAqVOn4vvvv8f06dPx1VdfKQz5T01NxTfffIN58+Zh8uTJWLJkiQ4jVY9MJkOXLl3QpUsXhfb9+/cjLCwM3377rcq/ydOmTSuuEAtt+fLlGD9+PLp27YoJEyZg4MCB8PX1Rffu3XH48GGsWLEC/fr1w8SJE+VrzJPuPHnyBKtXr8736qm+zB1TWg0YMADBwcHw9vbG6NGjUbVq1VwvnJXGCwOFwaKbikVBhiACKFVD3EqL0jD8cMuWLRgyZAiys7Ph7Oyc5x+N8PDwYo6u6AQEBGDOnDk8cVDCvXz5EsHBwUr3Mfbr1w+Wlpa6Do8A1KhRA9WrV8/zC3+XLl1w69Yt3Lp1qxgjKxhVI8/yI0mSXv3eb9myJeLj43Hjxg3IZDKlIfWHDh1C165dsWHDBr06sfXy5Us8e/YMjo6OCn+3Nm7ciJCQEJQrVw4ff/wxGjdurMMoNXPhwgV06tQJCQkJ+V491af34NvWrFmDNWvWICwsTNehFFj58uXRsmVL7N+/X9eh6B39GtNLeqssDLkh/REYGAgzMzNs3769wLO16wueVy35LCwsMGLECIwYMULXoVAuHj58iPfffz/PPq1bt8bRo0eLKaLCWb16ta5D0Lro6Gh4e3srnGB48wSku7s7evfuje+++06viu4vvvgCa9asQVxcnLzo/uWXX/Dxxx/Lf9//+eefOHPmjMKM5iXZZ599hmfPnmHmzJkYM2YMqlatqnJVBH0XGxuLQ4cO6TqMQsnOzkbTpk11HYZeYtFdgq1fvx5r1qzBuXPnkJiYCGtrazRp0gS+vr4YNmyYrsPTSFn4A5+X7du34/z581xjsoS4du0avL29S33Brc+uXr2KGzduoF69eqhZsyaA13/sly9fjoiICBgaGqJXr175FkIlWadOneDr65vnrLV//vknli9fXuKvjGRkZODixYswNDREw4YNcx29dOHCBZw7d07vZuq1trbOd+LCO3fuwNraupgiKpyyciL8zWWzzM3NER8fr7C9du3aOHDgQDFHVThHjhxBly5dYG5uLm+bP38+qlSpgg0bNuDRo0cYOXIkFi1ahBUrVugwUvUdP34cXl5eCAwM1HUolA83NzdcvXpV12HoJc3HF5HWpaWloVevXhg5ciQOHDiA5ORkODo6Ijk5GQcOHIC3tzd69+6tchZOKpmCg4MREBCg6zDoP3Z2diV+GaaybMKECWjQoAG8vLxQu3ZtzJkzBwAwePBgfPjhh9i4cSPWr1+PESNGYMiQITqOtuAiIiIQGxubZ5+7d++W+CsjmzdvhqOjI1q2bImmTZvCyckJGzZsUNl327ZterXcZQ4PDw/8/fffuRZoBw8exN9//w0PD4/iDayYxMfHl+j14lWpUqUK7t+/L/+5Zs2aOHnypEKfS5cuKRSv+uDBgwcKy4FdvHgR9+/fxyeffIL27dtj0KBB6Nu3b4n/vfEmY2Nj+clVKtm+/fZbhIeHY/PmzboORe/wSncJFBAQgD179qBr16745ptv0Lx5c/m2qKgozJgxA3v27MGcOXMwd+5cHUaqvo8++qhA+y1btqyII6HCEkLo/ZDl9957D3v37kVmZqbezZyvCX18rbZu3Yrff/8d9erVQ/fu3XHw4EH4+/vD3Nwcu3btwsKFC9GtWzc8ePAAM2bMwObNm7Fp0ya89957ug5dK5KTk0v0uvGnTp3C0KFDYWBggK5du8LIyEh+cvjIkSP45ZdfdB1ikZg9ezZ27dqF7t27o1evXkozzYeGhqJcuXKlbjTT3r17sXLlSoSEhCAjIwOTJ0/WdUhqa9euHY4cOSL/uV+/fpg7dy4mTJiAPn364OjRowgNDcXAgQN1GKXmUlNTYWxsLP/56NGjkCQJ3bp1k7fVqFEDISEhugivQDp16oSoqChdh0EqqBp94OnpiSFDhsDd3R1NmzZVOcJH31YFKA6cSK0EcnZ2Rvny5XH27FmVk51kZWWhSZMmSEpKKtQ6vcVJk0lb3hyWWFInzPjjjz806r98+XIcO3asxOZT1qSkpKBbt26oWLEi/ve//+n9TOy5SUxMxPPnz/VqBtHOnTvjypUruHHjBszNzfHq1SvUrl0bcXFxCAwMVJg9+dmzZ3BxcUGHDh2wa9cuHUatvrt378r/38XFBZMnT1ZZyGRlZeH+/fv44IMPIIRAdHR0MUapvkGDBiEkJATh4eFo93/t3Xtczvf/P/DH+yodHDqhiFUspw/mECUkIswwPjYjVM7GnDe2nCoWZRu2DzN0GDYbMzbnQ6NEOQ0Tk5WSHCqHMrXo8Pr94dv1065Kpa73dXU97rdbt8+u9/t13W6PPrkOz/fr/Xq+unUD8Px3HDNmDKKiojBmzBiEhoYq39e1uRHjqVOn4O3tjfj4eADPP6sKv0K9/vrrCAsLU/5/oM2SkpIQGhqKsLAwpKSkQAgBKysrjB49GitXrpQ7XpkdP34cgYGBWL9+PWxtbfHkyRO4urriwoULyr+dnZ0djh07plXvkc2bN4e9vT32798PABg4cCBiYmKQnp6ufJ1NnjwZu3btQlpampxRyywxMRFOTk6YN28ePvzwQ7njVJlLly7h4sWLWrW8oyJNFwHtb3pXFVh0ayBjY2PMmjULy5cvL3HMxx9/jC+//BLZ2dlqTFZxZb3NKTk5Gf7+/khISNDoF2xp3diLU9ihXVN/n+ruxVvxCuXm5uLOnTsAnq/7K+lKbUJCQpXne1V5eXnYtm0bzp49C+B5196RI0dq5Sx+48aNMWDAAGzYsEF57P3338eGDRuQmJiocoFk5MiROH78OO7evavuqBVSkfeOlStXYu7cuVWYquIaNmwIFxcXbN++vcjxvLw8jB07Ft999x1GjRqFzZs3Q5IkrS66ged/j5MnT6p0mu/WrZtW71f+7Nkz7Ny5E8HBwTh+/LhyC8U2bdrg008/xYABA6pFY6vc3Fz88ssvSEhIgK2tLQYNGqR1t5fPnDkTa9euxezZs2FkZITly5crL24VcnV1RVZWlsbOHo8bN07lWGJiIiIjI9GkSRO0a9euxM9k7v2sXq+yTIFb8RWlfd/IdMDrr7+u0uzj3x4+fKhV619e9sJ79OgRAgICsHbtWuTk5MDZ2RmBgYFqSld+BgYGsLa2xuTJk8s0fseOHbhw4UIVp6pcBQUFxV7hzMjIgJ+fn7Kxk4uLCxYvXgxLS0t1RyyzgoIClS/E+vr6RQq44q4/asM1yezsbPTs2RPnz59X5pUkCWvXrsWxY8dgbGwsc8LyuX//vsq/pcLHr732msp4W1tbPHz4UC3ZKoOnp6dylm3z5s1o164d2rdvrzJOT08PFhYWcHNzQ//+/dUftIwePnyIZs2aqRzX19fH5s2bYWBggNDQUBQUFGDLli0yJKxckiShe/fu6N69u9xRKsXFixcRHByM77//HhkZGRBCoHPnzvDy8sIHH3yALl26YNCgQXLHrDQ1atTQqk7lxfnkk0+wZ88efP755wCABg0aFOkZk5ycjJMnT2LGjBlyRXypsLCwEs+Vtu1edSi6L168qNyKtHv37ujcubPMiUrHwrkSCdI4GzZsECYmJuLPP/8s9vyVK1dEnTp1xMaNG9WcrPL9888/Yvny5cLc3FxIkiRatWoldu3aJXesl+rUqZOwtLQs83hvb2+hUCiqMFHlWrNmjVAoFOLw4cNFjv/zzz+ibdu2QqFQCEmSlD/29vbi8ePHMqXVbb6+vsrXTmBgoAgMDBQtW7YUCoVCLF26VO545dagQQPx/vvvFzm2ZMkSIUlSseM/+OADYWZmpo5olc7Ozk6sWbNG7hivxMbGRkyYMKHUMWPHjhWSJIkRI0aIhQsXatV7YaEmTZq89G/19ddfiyZNmqgp0atZu3at6Nixo/K93MbGRvj4+Ihr164px0iSJCZOnChjSipJdna22LNnj9izZ4/IzMwsci42NlasXr26xO+QmiApKanCP5ouIiJCjBkzRkRHR6uc8/HxEQqFosjPBx98IEPKivPz8xMRERGljomKihJ+fn5qSqQ9ONOtgVq0aAFXV1c4ODjA09MT3bt3h6WlJdLS0nDixAls2bIFffr0QfPmzREZGVnkuT169JApdfkIIRAcHAw/Pz/cvn0b1tbWCAoKwrhx4yq8fkSdHBwc8Pvvv+PWrVvFzr5pu4iICFhaWsLd3b3I8Y0bNyI2NhatW7fGV199hVq1aiEwMBC7du3Cl19+iQULFsiUWHf9/PPPaNiwIc6ePau8TfL9999HixYtsHPnTixcuFDmhOXTpEkT5ZrZQrNnzy6x4/XNmzdhbW2tjmiVLjExUe4Ir6xly5Yvvf2wcGYqLCwMderUUUesSpeUlISMjIxSx2RmZmpNn5UPPvgACoUCo0aNgre3N3r16qXVt8cDxTd8KgttbPhkbGyMgQMHFnuudevWaN26tZoTlY82raEvrx9//BE7duzA//73vyLHjx07huXLl0NfXx8eHh6oVasWfvrpJ6xbtw69e/fGkCFD5AlcTr6+vvD19S213oiKioKfn1+1ayz5yuSu+kmVJElFZhJfvCJW3LEXf7TBrl27RKtWrYRCoRBmZmZi+fLl4p9//pE7Vrls3bpV2NnZifDw8DKN37Rpk/D29q7iVJXH3t5eDBkyROV4t27dhEKhEJcuXVIey8vLE40bNxaOjo7qjFhpcnNzxZUrV8SpU6fElStXRG5urtyRyqVWrVpiypQpKsenTJkiateuLUOiVzN9+nRRp04dkZ+f/9KxmZmZonbt2mLcuHFqSFb5rly5ItasWSPS0tKKPZ+amirWrFkjrl69quZkZbdq1SohSZKIiooqdVxBQYHw9vZWfn5pG0mSXjpz88knnwhjY2M1JXo1hd8lGjVqJObNmydiY2OLHaNNM90v3n1Vnh9t/PdImqtDhw7C1dVV5fh7770nFAqFCA4OVh6Li4sThoaG4u2331ZfwFdUlvdCf39/oa+vr6ZE2oMz3Rpo8eLFWn/FuThRUVGYP38+YmJiYGBggNmzZ2PBggUwNzeXO1q5jRo1CqNGjSrz+PHjx2P8+PFVmKhypaenqzQfy8vLw7lz52Bvb4833nhDeVxPTw/9+/fXuj0b09PT4ePjg23btuGff/5RHjc2NoaHhwc+/fRT1K9fX8aEZZOdnY2GDRuqHG/QoIHWNFp80dKlSzFz5swyvQcmJiZiwoQJePfdd9WQrPKtWLEC4eHh+OCDD4o9X7duXaxcuRIXLlwo0iRJkwwfPhypqalIT08vdZwkSQgJCYGtra3WzAb/+06ypKQklWPA/+80v2XLFjRv3lxd8V5JQkICgoOD8e2332LlypX47LPP8MYbb8DT0xMeHh6wsrKSO2K5Fa6T1QX5+fnYvn07jh49ijt37uDp06cqYyRJQnh4uAzpKu67777Dt99+i4sXLyIzMxOmpqbo0KEDvLy84OHhIXe8Mrlz5w46deqkcvzYsWMwMTGBt7e38ljz5s0xYMAAnDlzRo0JX11pn8/Pnj3DiRMntPI9pMrJXfWTbhg0aJBQKBRCX19fjB07Vty6dUvuSFQKQ0NDMWfOnCLHLl26JCRJEp6enirjFyxYIAwMDNQV75WlpKQIW1tbIUmSsLS0FG+++abw9vYWb775prC0tBSSJAk7OzuRkpIid9SXKumqs6+vL2dwNJydnZ0YM2ZMqWO8vLxE06ZN1ZSIXlTaXWX//ikcu3nzZrljl0t+fr7Yt2+f+O9//ysMDAyEJElCX19f9OvXT+tmunXFkydPRNeuXYv8u/v3zL22zeDn5OSIN998U5m9Zs2aws7OTtSsWVP5uwwYMEDk5OTIHfWlDAwMxCeffFLk2K1bt4QkSWLgwIEq4z/66CNhaGiorngV0qRJE+WPJEnC3Ny8yLHCHxsbG1GzZk2hUCjE5MmT5Y6tcTjTrYHGjRuHN954o9i9W7XV3r17IUkSbGxscO/ePUyaNOmlz5EkSWP33tXT04Ovr6/WrQMrq4YNG+LKlStFjp06dQqSJMHR0VFl/OPHj1G3bl11xXtl8+bNQ3JyMvz8/PDRRx/ByMhIeS4nJwdBQUHw9fXF/PnzsXXrVhmTlk1sbKzKlk2xsbEAnnfOF8V0YR8+fLhaspVXdX9tvejevXsv7QnRqFEjjd4OrTr/vQrvOhNCwN/fH66urujZs6fKuMJO87169UKrVq3UH/QVKBQKDBgwAAMGDMD9+/fx7bffIiQkBIcPHwYAbNu2Dfn5+Rg7dmy16diu7ZYtW4bo6Gj4+/tj6tSpqFevHnx9fTF58mRERkbik08+QceOHfH999/LHbXM/Pz8cPDgQbi7uyMgIAAODg7Kc+fOnYOPjw8OHjyIpUuXYtmyZTImfbk6deootyMtdP78eQAo8nsVkiSpyHcQTfTiDjCF74nFfa+oUaMGWrduDTc3t2r5mfDK5K35qTiGhobi448/ljtGpapu66zKsqZFm3l6egp9fX1lh8rs7GzRoUMHoVAoxF9//aUyvkePHqJz587qjllhFhYW4s033yx1TL9+/YSFhYWaElVcSbNxJfV/4GtLc9StW/elnb8nTJggzM3N1ZSo/HTl79WzZ0/x7bffyh1DbWJiYsSECROEiYmJ8j2jefPmcseqkJ9//lm8++67om3btuL1119XHv/zzz9FYGCgVtzR9KLmzZsLZ2dn5eN/vwZv3bolLCwsREBAgBzxKsTGxka88cYbJfbyyMvLE23atBE2NjZqTlZ+vXr1EnXr1hVPnjxRHvPy8hIKhaLYPkDDhg0TrVq1UmfEV6Ir7/lVgTPdGsje3l6jZzYqojp06dUl8+bNw48//og+ffqgTZs2uHv3LlJTUzFw4EDY29sXGfvgwQNER0eXuC5VEz179gwdO3YsdYyDgwNOnjyppkQVt2TJErkjUAU5ODhg9+7dWLlyJczMzFTOP3r0CLt27Xrpv1Wqerq0XhgAnJyc4OTkhDVr1uDHH39EcHAwTp06JXescikoKMDIkSOV/UaMjY2L9O8wNzfHggULkJ+fj08++USumOWWnJyMt956S/lYoVAUWdPduHFjvPXWW/j222+15vdKS0uDh4dHibvX6Onp4a233sKXX36p5mTlN27cOHh6esLV1RWenp6Ij4/H1q1b8dprr6ncKZOfn4/IyEj06tVLnrAVkJiYCHNzc9y+fRt3796FJElo0KABGjVqJHc0jceiWwONHz8eAQEBuH37drX5R1ydt4eojlq3bo09e/bg/fffx8WLF6Gnp4chQ4Zg48aNKmO/+eYb5OXloV+/fjIkrRgHBwdcu3at1DHXrl0r9lYwTcOiW3tNmzYNQ4YMQa9evbBmzZoiW7BERERg5syZePTokVZd0KquTp06hZ9++gnz5s1DgwYNVM7fu3cPQUFBGD58OLp06SJDwqpRs2ZNjB07FmPHjsX169fljlMuq1atwo4dOzBlyhSsWLECX3zxBZYuXao8b2VlBRcXF+zbt09rilMAqFWrVpHi1NTUVGWipkGDBkhOTlZ3tAp7/fXX8eDBg1LHPHz4EK+//rqaElXc6NGjER4ejm+//RYXLlyAEAJ16tTBxo0bVS4q7Nu3D/fv39ea709PnjxBaGgoQkJCcPv27SLnGjVqhPHjx2Pu3LmoXbu2TAk1G4tuDTR06FCEh4eja9eumDdvHjp37gwrK6tiuwXa2NjIkJCA0rs3Vgfu7u6Ij49Heno6TE1NYWBgUOy4WbNmYdq0aTAxMVFzwopbunQp3N3dERYWVqSTaKGQkBDs378fR44cUX84qvavrUKDBw/Ghx9+iM8++wy9evWCoaEhGjRogHv37uHp06cQQuCjjz7S+P1bdeHv9fnnn+OPP/7AF198Uez5Bg0aYO/evbh9+zZ+/PFHNacrv6lTp5b7OZIkYe3atVWQpmqEhYWhU6dOWLduHYDi/53a29trbO+Yktja2hYpqNu0aYPffvsNT58+haGhIYQQCA8PL3ZXC001c+ZMfPjhh5gzZw5atmypcv7q1av44YcfSnz9aZrQ0FCMHz8e0dHRsLCwQL9+/dC4cWOVcYaGhli1ahXefvttGVKWT0JCAgYMGID4+HgIIWBtbY3XXnsNQgikpKQgJSUF/v7++P7773Hw4EE0adJE7sgaRxKimJXwJCuFQqFsVFDalxlJkpCXl6fGZFRIoVDAzMys2FtCSyJJEhISEqouFJWZv78/oqOjcfjwYbRo0QLdunWDpaUl0tLScPLkScTFxaFv375wdnYu8jxJkjSyOUhMTAwWLFiAs2fPAgAcHR0REBBQbNM7TaeLr639+/dj7dq1OHv2LDIyMmBmZgZHR0dMmzYNb775ptzxSqUrfy8bGxv07t271K3bJkyYgCNHjmjFlmgl3cZbnBe/j+Tn51dhqsplbGyMadOm4bPPPgPwvFmXv79/kd/hk08+wapVq5CTkyNXzHKbN28eQkNDcffuXejr6yMkJAQTJkxA+/bt0bt3b5w6dQoxMTGYO3cugoKC5I5bJpGRkfjss88QHh4OT09PdO/eXfmZfOLECWzZsgV9+vTB3LlzVZ774h1CmuBl392Lc/r0aTg5OVVRolf39OlTtGvXDtevX4eHhwcWLVqEFi1aFBkTFxeHZcuW4bvvvkOLFi1w8eJFGBoaypRYM7Ho1kDe3t5lfsFq6t6t1V15vrC8qKCgoJKTVJ3qVMj9W0X/fpr4pfPy5ctwcnJS+dJobGyMM2fOoHXr1jIlqxhdeG1VJ7ry9zIyMsKHH35YaufkhQsX4vPPPy+yblhTRURElGlccnIy/P39kZCQoJHvf6WxsLDAO++8gw0bNgAovuj28PDAb7/9hnv37skVs9z++usv/Pzzz/D09FTOZk+fPh3r1q1TdpQeNmwYvv32W9SsWVPOqGX24mQTUPSuhOKOvUjT/k1OmDABmzZtKvP4M2fOoF+/fnj06FEVpno1q1evxpw5c7BkyZKXLmlbunQplixZglWrVmHmzJlqSqgdWHQTVYBCoYCvry8WL14sd5QqUd0KuX8r6xfO4ri6ulZiklc3atQobNu2DQsWLMD06dMhSRLWrFmDgIAAjB49Gps3b5Y7YrlU99dWdaMrf68GDRqgb9++pb6exowZg4MHDyI9PV2NyarGo0ePEBAQgLVr1yInJwfOzs4IDAzUqm3Devfujfj4eFy/fh2GhoYqRffDhw9hb2+PHj16YPfu3fKGrQTp6em4ceMGbG1ti+07oMl8fX0rvExF0/qaKBQKfPjhh2W6y+DcuXNwd3dHVlYWnj17poZ0FePi4oLU1FTExcW99O8khEDLli1Rv359REVFqSmhduCabiJSsWLFCuTk5BRbyAUGBmpdIfdvmlY4v4oTJ06ge/fuRRoELVu2DBEREa90cYHUKz8/H/fv3y/ShfhF7N8hL2dnZ+zatQu3bt0qdm/15ORk7N69G25ubjKkqzw5OTlYvXo1goKCkJGRgZYtWyIgIEDj+woUZ8aMGRg6dCjeeecdrF+/vsi5hIQEjBs3DpmZmZgxY4ZMCStX/fr1Ub9+fbljVIivr6/cESqNs7MzPv/8c5ibm5faoO/333+Hu7s7/v77b43/TnX16lWMHDmyTBdGJElC3759tWqfeHVh0a3B7t27h59//hnXrl1DVlYWgoODATy/mpmYmIi2bdvC2NhY5pRUHbGQ0x6pqakYMWKEyvEuXboolwaQ5jp//jx8fHwQGRlZ4kwH+3fIb86cOfj111/RrVs3LFu2DO7u7mjYsCHu3r2Lw4cPY+HChfjnn3+KXXOqDYQQCA4Ohp+fH27fvg1ra2sEBQVh3LhxFV5CILe3334bH3/8MVasWAEbGxvUqlULAGBpaYkHDx5ACIFFixZp/YUS0iz79++Hq6srFi5ciLp162LSpEkqYy5cuIA+ffrg77//RmhoKDw8PGRIWnZZWVkwNTUt83gTExNkZWVVYSLtxKJbQ61btw5z585VznpIkqQsutPS0uDs7Iz169dj4sSJcsakakpXCrlTp04hLCwMFy9eRGZmJkxMTNChQwdlIxdtkJubW+z2HLVr10Zubq4MiaisLl68CBcXF+jr66Nv377Ys2cP2rVrhwYNGuD3339Heno6evbsyS0XNYCLiwu+/PJLzJo1C2PHjgWAImtQFQqFyrZv2mL37t3w8fFBXFwcTExMEBAQgFmzZsHIyEjuaK8sICAAvXr1wv/+9z+cPn0aOTk5KCgoQP/+/TFjxgyt2KqpohcFJElCeHh4JaepWllZWfjll1+KfCa3b98eQ4YMUV400XSmpqY4fPgwunXrhmnTpsHc3Bzvvvuu8vylS5fQp08fZGZmIiQkBGPGjJExbdlYWloiPj6+zOMTEhK09q6LKiVI4/z6669CkiTRuXNnsWfPHjF16lShUCiKjGnfvr0YMGCATAlJkiTh5+cnd4wqU9Lv5+vrq/JvUVvNnTtXKBQKIUmSkCRJ6OnpKf9boVCI2bNnyx2xTKrb36q6v7Ze9N///lcYGxuLq1evCiGK/u7Z2dni/fffF/Xr1xeJiYkypiydLv29hBDijz/+EFOnThWdOnUSzZo1E507dxbTpk0Tly9fljtauZ04cUJ07dpVKBQKYWRkJObOnSsePnwodyz6l8LPpfL+aNv7/65du0TdunWLfC4X/h4WFhZi586dckcsl6SkJNGoUSNhaGgoDh48KIQQ4uLFi8rfMTg4WOaEZTd8+HBRp04dcffu3ZeOvXv3rqhdu7Z477331JBMu3CmWwOtXLkSNjY2OHbsGGrVqoXz58+rjGnbti1OnDghQzoCtK/zLhW1efNmfPHFF2jZsiWWLFmCnj17wsrKCmlpaTh+/Dj8/PywZs0atG/fHp6ennLHfamtW7ciJiamyLHCq9IDBgxQGS9JksbuTatLr62oqCgMHjwYrVq1Uh4T/zdzamxsjP/97384deoUfHx8NHZ9nC79vYDnn73atFd1SQYPHox9+/ZBoVDAy8sL/v7+xe4jXB0UFBQgJSUFt2/fLvHuH02+Q0EXXmPR0dEYPnw49PT0MGnSJPTs2RMNGjRAamoqjh8/jrCwMIwYMQIREREqW3lqKltbWxw5cgQuLi4YNmwYVq1aBR8fHzx69Ahff/01xo0bJ3fEMpsyZQp27NiBoUOHYs+ePahXr16x4x48eIChQ4ciOzu72NvqdR27l2sgExMTjBkzRvnBXtLekmvWrEF2drZcMakaUygUsLe3h729fZHj8fHxSEhIKPaWPE0u5P7N2dkZd+7cQWxsLOrUqaNy/vHjx2jbti0aNmyoUsxqmoqst9S2rX+qKyMjI8yZMwcBAQEAAENDQ8yYMQMrV65Ujpk1axa2bduG1NRUuWLS/8nLy8NXX32Fbdu24dq1a8jOzlautb948SI2bNiAWbNmoXnz5jInfbnCLZrs7OxU9tstiTa9xwPPL2CtWLECq1atwoMHD0ody/dDeQ0cOBARERGIjo5GmzZtVM7HxsbC2dkZPXv2xJ49e2RIWHHnzp1D79698eTJEwDA2rVrMWXKFJlTld/kyZOxceNG1K1bF5MnT4abm5uyqeStW7cQHh6OjRs34v79+xg/fjw2btwoc2LNw5luDVRQUIAaNWqUOiY9PZ2bzlOVio+PL3ENz8GDB1WOVXS7DznExsZi4sSJxRbcwPMLX//973/LtdemXBITE+WOQBVkaWlZZG/WBg0a4K+//ioyJicnhxdXNcA///yDvn374tSpU6hXr55Ko6AmTZogNDQUFhYWpe7lrUmEEEhMTCzze4g2vccDzycngoKCYGlpibFjx6Jhw4bQ1+fXXk0UHR2N9957r9iCGwDatGmD4cOHa8XWbtu3b1c55uHhgW+++Qbu7u6wsLAodszw4cPVEa/C1q1bBxMTE6xatQrLly/H8uXLi5wXQkChUGD27Nll2i5NF/HdRwO1aNGi1L3t8vLyEBERgbZt26oxFekSXSjkXnaTj7Z8wWSTLe31n//8B3FxccrH3bp1w+7duxETE4MuXbrgzz//xPbt29GyZUsZUxLwvCHXyZMnsWLFCnz00Ufw8/MrsruDqakpXF1dcejQIa0ounXhPT4sLAwtWrTA2bNni202qa2++OILBAQE4I8//oC1tbXK+Tt37qBdu3ZYtGiR1myHlp2dDUtLy1LHWFpaasUFyBEjRpT4/eHIkSM4evRokWNCCEiSpPFFt56eHlauXInJkycjNDQU0dHRuHfvHoDnF4y7du0KLy8vNGvWTOakmotFtwYaNWoUPvzwQyxbtgwLFy4sci4/Px8ffvghbty4gfnz58uUkKq76l7ItWnTBjt37sTSpUuL/TL2999/Y+fOnWjdurUM6UhXvPXWW5g9ezbu3r2Lhg0bYv78+di1axe6desGCwsLPHr0CAUFBfDx8ZE7qs778ccf0bNnT8ybNw9A8RflmjZtigsXLqg7WoVU9/d4AHjy5AlGjx5drQpuANixYwfeeOONYgtuALC2tkb79u3xww8/aE3RbWdnhyNHjiiX2hQnPDwcdnZ26gtVQYsXL9aai/YVYW9vj08//VTuGFqJRbcGmj59Ovbs2YMlS5Zgy5YtytvIhw8fjnPnziEpKQl9+/bF+PHjZU5K9P/Fx8errAHXVFOmTMHYsWPh7OwMX19fuLq6ol69erh//76ykVpKSgr8/f3ljlqq5ORkAECjRo2gp6enfFwWNjY2VRWLymjKlCkYPnw4zM3NAQDt2rVDeHg4Pv30U9y4cQMODg6YPn063nrrLZmTUnJyMoYOHVrqGBMTE2RmZqopEb1M+/btcefOHbljVLrr169j1KhRpY5p3bo1vvvuOzUlenXvvfce/P394eXlheXLlxe5oHD37l188sknOH/+PBYtWiRjyrLx9fWVOwJpKvkap1Npnj59Knx8fISFhUWRrRNMTU3Fxx9/LJ4+fSp3RCIhxPNtMcaNGycMDAzkjlIus2bNUm5HolAohL6+vvK/JUkSM2bMkDviSxXmj4uLK/L4ZT96enoyJyfSLvXq1RNjx45VPi5uS7533nlHNG7cWN3RqAQHDhwQNWvWFOfPn5c7SqUyMjISH3/8calj5s+fL4yMjNSU6NVlZ2eLLl26CEmShKGhoWjdurVwc3MTrVu3FoaGhkKSJOHk5CSys7Pljlom0dHRws3NTdSpU0fUqVNH9O7dW5w+fVruWCQzdi/XQMnJyTAzM4OJiQmEEIiLi8PDhw9hYmKCVq1aQU9PD3///TcePXrE2SqqUhERETh//jz09fXRrVs3ODg4KM/dvXsXfn5+CA0NRW5uLho1aoRbt27JmLb8oqKiEBoaiosXL+Lx48cwMTFBhw4d4OXlBRcXF7njvZS3tzckScKKFStgZWWlfFwWoaGhVZyOqPoYNGgQYmJiEB8fD1NTU5VdRVJSUtCiRQsMGTJEq2YYq7uffvoJ06ZNw+DBg9GuXTuYmJgUO04btoYs1LJlS1hZWSEiIqLEMa6urrhz545KY0ZN9uzZMwQGBiIsLKxIz4GmTZvCy8sL8+bN04oGwpcvX4ajoyOePn1a5LixsTHOnDnDZWu6TOain4qhUCiEn59fqWNWrFihcpWdqLI8ffpUvPnmmyozpDNnzhRCCBEcHCxq164tJEkSjRo1El999ZVW3X0REREhLly4IHcM0jE3b96s8A/JKyIiQigUCtGpUydx8uRJsXDhQqFQKERWVpY4evSoaNWqlahRo4Y4d+6c3FHp/+Tk5IjRo0cr714q7k6gwmPaZO7cuUKhUIjg4OBiz2/cuFEoFAoxe/ZsNSerPI8fPxYpKSni8ePHckcpNw8PDyFJkli4cKFITU0VaWlpYsGCBUKSJDFmzBi545GMONOtgRQKBXx9fbF48eISx6xYsQILFizg3pJUJVauXIn58+fD2toaQ4YMgRACu3btQmpqKmbNmoVVq1bB3NwcS5YsweTJk7Xi6vOL9PT0MGXKFKxdu1buKGqXmJgIPz8/hIWFyR1F5xTujVxekiQp94Mm+axfvx4zZswo9nNXT08P69atw4QJE2RIRsWZNm0avv76a7zxxht45513St0yzMvLS83pKi49PR0dOnTA3bt34erqCnd3dzRq1Ai3b9/G4cOHERkZCWtra/z++++oX7++3HF1jo2NDezs7BAZGVnkuIuLC5KTk3Hz5k2ZkpHc2EhNS6WkpJS4xzDRq/rxxx9Rr149XL58WdnkydfXF61atcLq1avRuXNn7Nu3D/Xq1ZM5acVYWlrCwMBA7hhqlZycjKVLl2Lz5s3Iy8tj0S0DT0/Pat3VtrqbMmUKXF1dsX79epw+fVq57MvJyQlTp07lbaMaZseOHXBwcEB0dHS12p+7fv36OHbsGEaPHo3jx4/j+PHjkCRJuQ2mo6Mjtm7dyoJbJqmpqRgxYoTK8S5duuDs2bMyJCJNUX3ehbTcv7skHz9+vNhx+fn5SElJwQ8//AAnJyc1JCNddP36dYwYMUJZcAPPP+iHDh2KkJAQrFu3TmsLbgDo168fIiIilPtjaruoqCgsWrRIuf7excUFQUFBaNGiBbKzs7Fw4UKsW7cOz549g7W1NT755BO5I+skXujQfq1atcKaNWvkjkFlkJOTg169elWrgrtQs2bNcPr0aZw7dw5nzpxBRkYGzMzM4OjoiE6dOskd76WaNm1aoedJkoSEhIRKTlO5cnNzi92mrnbt2sjNzZUhEWmK6vdOpKVe3GJAkiTl1cuSWFtbIzAwsOqDkU568uRJsXuANmrUCMDzrY20WUBAAJydnTFp0iQEBgbCwsJC7kgVdv78efTp0wfPnj1THtuzZw/Onj2LyMhIDBkyBFevXoW1tTXmz5+PSZMmad1yACKi8nJwcEB8fLzcMapUp06dtKLI/reCgoIKXfDmiljSZiy6NcSxY8cAPH9DcXNzg7e3d7FrjPT09GBhYYGWLVtCoVCoOybpkOL+fRV+SGr7zMHo0aNhZmaGkJAQbN26FU2aNIGVlZXKlwBJkhAeHi5TyrIJCgrCs2fPsHz5cowfPx7A87WnixcvhouLC9LT07Fw4UL4+PjAyMhI5rRUktu3b+PSpUvIzMyEiYkJ2rdvr7zIRUTlFxAQgN69e2Pv3r0YOHCg3HGqzB9//IGLFy9qVQf2pKQkuSNUqa1btyImJqbIscILQAMGDFAZL0kS9u3bp5ZsJB82UtNAfn5+6NWrF3r06CF3FNJRCoUCEyZMUGkKtHHjRoSEhCAmJqbYK86Ojo7qivhKynrBSpIkjW9W2LhxY7Rs2RJHjx4tcrxXr16IjIzEypUrMWfOHJnS0cvcuHEDU6ZMKfbiTu/evbFu3TrY29vLkIxIu/n7+yMmJgaHDh1Cr1690L59+2K3DJMkCYsWLZIhYeX49/Z1JK+KTIhpw3cNenUsuolIRWldlktbB80PDfUzMDDA7NmzVZabzJs3D59//jnS0tJQt25dmdJRaVJSUtC5c2ekpqaiVatW6NGjBxo0aIDU1FScOHECV65cgZWVFc6cOYPXXntN7rhEWqU6XVwtDYtuzVLR7uS2traVnIQ0jXbfI0pEVUKbtk/RdXl5eahVq5bK8cJjLLg1l6+vL1JTU7Fhw4Zit5oKDg7GpEmT4O/vj40bN8qQkEh7FS7bI82zefPmCj9X02+jZ/FMJWHRTUQqQkND5Y6gNk+ePMH169eRlZUFFxcXueOQDjl06BAGDx5c4t7O48ePx549e3DgwAE1JyPSfq6urnJHoBJ4e3uXu5Fa4V12ml50E5WERTcR6aSkpCTMnDkT+/fvV3ZSzcvLAwCcPHkSEydOxLp169CzZ095g5YBm7Zop7S0tJfu7dy6dWsW3URUIjs7O63rAaRLF/aJCnFNNxGpmDp1aoWet27dukpOUjWSk5Ph6OiIBw8e4O2338a9e/cQHR2tXBOXl5cHa2trDB06FN98843MaUvHpi3aq3HjxujUqRN2795d4pihQ4fi7NmzSElJUV8wIiIiqlSc6SYiFevXry/z2BdvEdOWonvJkiV49OgRIiIi0LVrV/j5+SE6Olp5Xl9fHy4uLjh58qSMKcsmMTFR7ghUQf369UNYWBiCg4OV2729KCQkBHv27IG3t7f6wxGRxuPyKCLtwaKbiFSUtQFNcnIy/P39kZCQUO71WXI6dOgQhg4diq5du5Y4xsbGBr/99psaU1UMm7ZoL19fX+zduxeTJk3C6tWr4erqCisrK6SmpiIyMhJXrlxBvXr1sGTJErmjEpEGqU7Lo4h0BYtuIlLxsgY0jx49QkBAANauXYucnBw4OzurbFmlyR4+fAg7O7uXjnv69GnVhyGd9dprryEqKgpTpkzBsWPHcOXKlSLne/Xqha+//prbhRGRUnJyMrp06aKyPKqQk5MT7t+/j23btmlV0X3r1i0sW7YMR48exZ07d/Ds2TOVMS9eXCDSNiy6iajMcnJysHr1agQFBSEjIwMtW7ZEQEAAhgwZIne0crGyslI2GitJbGwsbGxs1JSIdFWzZs0QHh6OlJQUXLhwAY8fP4aJiQnat2/PYpuIVFSn5VGFbty4AScnJzx69AitW7fG06dPYWtrCyMjIyQkJCAvLw/t2rWDmZmZ3FGJKqz8HXiISOcIIbBp0yY0a9YMPj4+qFmzJjZs2IDY2FitK7gBwN3dHXv27EFsbGyx50+cOIHw8PBiO38TVRY3NzcsXrwYwPOmaoMGDcKoUaMwaNAgFtxEVKyyLo+6ffu2GlO9Gj8/P2RmZiI8PByXLl0CAIwdOxZ//vknkpKSMGjQIGRlZWHHjh0yJyWqOBbdRFSq3bt3o3Xr1pg8eTKePHmCgIAAxMfHY8KECRXqnK0JFi5cCGNjY3Tv3l35+wDAgQMHsGjRIvTv3x/16tXDRx99JHNSqs5Onz7NWyWJqFyq4/Koo0ePYsCAAUWWthVurmRtbY3t27cDABYsWCBLPqLKwNvLiahYUVFRmD9/PmJiYmBgYIDZs2djwYIFMDc3lzvaK7Ozs8OhQ4cwYsQILFy4EJIkQQiBgQMHQggBGxsb/PTTT2jYsKHcUakaa9WqFZKSkuSOQURapDouj7p//z5atmypfKyvr4/s7GzlY0NDQ7i7u5e6vSKRpmPRTUQqBg8ejH379kGhUMDLywv+/v5o3Lix3LEqlZOTE/766y/s2bMHp0+fxsOHD2FiYgInJye8/fbbMDAwkDsiVXPTp0/HtGnTcPXqVfznP/+ROw4RaQF3d3ds2bIFsbGxaNOmjcr5wuVRs2bNUn+4CqpXrx6ysrKKPP73BUl9fX1kZGSoNxhRJZJE4f0bRET/R6FQQJIk2NnZoUWLFmV6jiRJ2LdvXxUnI6o+IiMjERQUhMjISEyePBmdO3eGlZVVsdvv9ejRQ4aERKRpkpKS0L59ewDAvHnz8Oeff+L777/H3r17cerUKXzxxReoVasWLl26pDV3a/Xs2RM1a9bE/v37AQBDhgzB8ePH8fvvv6Np06ZIT09H+/btUbduXfzxxx8ypyWqGBbdRKSiImu1JUlCfn5+FaQhqp4KL24VfgyXttc9X1tEVOj06dMYMWIEbt68qXwPKfzfwuVRnTp1kjtmmQUGBsLX1xd3796FmZkZjh8/jt69e8PY2BitWrVCfHw8Hj9+jPXr12PixIlyxyWqEBbdRKTi5s2bFXqera1tJSepHP7+/hV6niRJWLRoUSWnIXrO19e31EL7RUuWLKniNESkTfLy8qrN8qjHjx/jzz//xH/+8x/UqVMHALBjxw74+vrixo0bsLW1VS7HIdJWLLqJqNorbub+xWLnxbfBwuOFMwecYSQiIiKiV8FGakRU7R07dkzl2Oeff47Dhw9jzJgxcHFxgZWVFVJTUxEZGYmtW7eiX79+mDNnjgxpSVfk5eVBX58fw0Sk2zZv3oz27dvjjTfeKHHMlStXcP78eXh6eqoxGVHl4Uw3EemcTZs2Yc6cOYiOjkbr1q1Vzl++fBldu3bF6tWrMX78eBkSki6wsrKCt7c3xo8fj+bNm8sdh4g0kC4sj1IoFPD19cXixYtLHBMYGAgfHx/efUZai0U3Eemctm3bwtnZGRs2bChxzMSJExETE4PLly+rMRnpEmtra9y7dw+SJMHFxQUTJkzAO++8AyMjI7mjEZGG0IXlUWUpugMCArBkyRLk5uaqMRlR5eF9bUSkc+Lj4zFw4MBSx9StWxcJCQlqSkS6KCUlBXv37kVwcDAOHDiAEydOYMaMGRg9ejTGjx+Pdu3ayR2RiGTG5VHPXbhwARYWFnLHIKowznQTkc6xsbGBhYUFLly4UGz36IKCAnTo0AGPHj1CcnKyDAlJ16SmpiI0NBShoaH466+/IEkSHBwcMHHiRIwcORK1a9eWOyIRaYDqsjzKzc1N+d/Hjx+HnZ0d7OzsVMbl5+cjJSUFSUlJGD58OLZt26bGlESVh0U3Eemcjz/+GEFBQRgwYAA+/fTTIjOKFy9exIIFC3Dw4EHMmzcPy5cvlzEp6aLjx48jJCQEO3fuRE5ODmrWrIkRI0Zg8uTJWrX3LhFVvuqyPOrF2+YL9xgvaZyFhQXc3NywZs0aWFlZqSsiUaVSXShCRFTN+fr6onfv3ti/fz86duyIOnXq4PXXX0edOnXg4OCAAwcOoHfv3twbmWRRr149mJubw8jICEIIPHv2DMHBwXBycsLAgQORlpYmd0Qikkl8fDzq1q1b6hhtWB5VUFCg/BFCwNfXt8ixwp+8vDykpaXhhx9+YMFNWo1FNxHpHCMjIxw+fBghISFwdXWFoaEhkpOTYWhoiJ49eyIkJASHDh1iQytSm6ysLGzatAldunRBu3bt8NVXX6Fp06bYsGEDHj58iJiYGAwbNgz79+/HlClT5I5LRDKpX78+Dhw4UOLMcEFBAQ4cOIB69eqpOVnFHTt2DF5eXnLHIKpSvL2ciIhIJjExMdi0aRO2b9+OJ0+eoHbt2vDw8MDkyZPRoUMHlfFjxozBr7/+iszMTBnSEpHcqvvyqCdPnuD69evIysqCi4uL3HGIKg2LbiIiIhm0bdsWV69ehRACHTt2xOTJk+Hh4YFatWqV+JwVK1bAx8cHBQUFakxKRJoiJycHgwYNQnh4OCRJQs2aNWFpaYm0tDRkZ2dDCIE+ffrg119/1aq7tZKSkjBz5kzs378fBQUFkCQJeXl5AICTJ09i4sSJWLduHXr27ClvUKIKYtFNREQkgzp16sDDwwOTJk2Cg4NDmZ5z69Yt3LhxA66urlWcjog0lRAC3377LTZv3ow//vgDmZmZMDU1Rbt27TBmzBh4eXkVuzOHpkpOToajoyMePHiAt99+G/fu3UN0dLRyn/G8vDxYW1tj6NCh+Oabb2ROS1QxLLqJqNpTKBRQKBS4evUqmjdvDoVCUaYvJC9eaSeqbIW3kxMR6bKxY8fi+++/x7Fjx9C1a1f4+fnB399fWXQDwLBhwxAXF4fY2FgZkxJVnL7cAYiIqlqPHj2Ut+G9+JhIToMHD4a3tzc8PT1LHLNt2zZs3LgRv/32mxqTERGpz6FDhzB06FB07dq1xDE2NjZ8HyStxqKbiKq948ePl/qYSA7Hjx9/6frE5ORkREREqCcQEZEMHj58CDs7u5eOe/r0adWHIaoi3DKMiIhIQ2VlZaFGjRpyxyAimSgUCujr6+P69evKx3p6ei/90dfXnnk1KysrxMfHlzomNjYWNjY2akpEVPm05xVJRFTFEhMTcfToURgbG2Po0KGldpEmqojk5OQijzMyMlSOAUB+fj5SUlKwY8eOMs0AEVH1pAvLo9zd3bFlyxbExsaiTZs2KudPnDiB8PBwzJo1S/3hiCoJG6kRkc4JDAzEpk2bcObMGZibmwN4fqvvwIED8c8//wAAmjVrhujoaOV5ospQ1iZ+hYQQWLlyJebOnVuFqYiI5JOUlIT27dsDAObNm4c///wT33//Pfbu3YtTp07hiy++QK1atXDp0iU0bNhQ3rBEFcSim4h0TteuXWFgYFBkbXfnzp1x5coV+Pj44N69e1i3bh0WL14MX19f2XJS9ePt7Q1JkiCEwObNm9GuXTvll80X6enpwcLCAm5ubujfv7/6gxIRqdHp06cxYsQI3Lx5U/keWfi/NjY2+Omnn9CpUye5YxJVGItuItI5DRo0wLBhw7B27VoAz/c+trW1xdy5c7Fy5UoAz293u3fvHi5fvixnVKrGmjRpgtmzZ2PGjBlyRyEiLdG0aVPMmjWr1PeN9evXIygoCDdu3FBjsleXl5eHPXv24PTp03j48CFMTEzg5OSEt99+GwYGBnLHI3olXNNNRDonIyMDZmZmysdRUVGQJAmDBg1SHuvYsSPWr18vQzrSFYmJiXJHICItk5SUhIyMjFLHZGZm4ubNm+oJVIn09fUxdOhQDB06VO4oRJWO3cuJSOdYWVkV+UJy5MgRGBoawsnJSXksJyen2jWrISKi6i8zMxOGhoZyxyCiF3Cmm4h0TufOnfHLL79g3759MDIywvbt29GzZ88iX1Ju3LgBa2trGVNSdde0adMyjZMkCQkJCVWchog0VWRkZJHHSUlJKseA/7/rwZYtW9C8eXN1xasUQgj8+uuvuHTpEm7fvo3c3FyVMZIkITg4WIZ0RK+Oa7qJSOf8/vvv6NatG549ewbg+Qf5sWPH4OLiAgD4+++/0bBhQ7z77rsIDQ2VMypVY3Z2dsXeTZGZmam8fbRhw4YwMDDgrehEOqw8ux4UNiALCwvDmDFjqjhZ5UhISMCgQYMQFxeH0soSSZKQn5+vxmRElYcz3USkczp27IiYmBhs2bIFAPDOO++gS5cuyvOXLl2Cu7s7PDw85IpIOiApKanUc3PmzEFqaiqOHDmivlBEpHEWL16s7OTt7+8PV1dX9OzZU2Vc4a4HvXr1QqtWrdQftIKmTZuGa9eu4f3338fIkSPRsGFD6OuzRKHqhTPdREREGig3Nxft2rVDv379sGrVKrnjEJEG6NWrF8aOHQtPT0+5o1SaWrVqwd3dHbt375Y7ClGVYSM1ItJ5ycnJxa6PI5JTjRo14O7uju3bt8sdhYg0xLFjx6pVwQ0AtWvXhr29vdwxiKoU790gIp0XGhoKf39/rhUjjZOdnY2HDx/KHYOINExWVhZ++eUXXLx4EZmZmTA1NUX79u3x9ttvo1atWnLHK5f+/fvj1KlTcscgqlIsuomIiDRQZGQktm3bhhYtWsgdhYg0yLZt2/DBBx8gIyOjSOMxSZJgZmaGtWvXYsSIETImLJ+goCB06dIFH330Efz9/WFsbCx3JKJKx6KbiIhIBm5ubsUez8vLw+3bt5GUlAQhBBYuXKjmZESkqfbs2YPRo0fDyMgIU6dOhYuLC6ysrJCamorIyEiEhoZi9OjRqFOnDt566y2545aJlZUVDh48CGdnZ2zYsAHNmjWDiYmJyjhJkhAeHi5DQqJXx0ZqRKTz/Pz8eHs5qZ1CUXxbFUmSYG5ujk6dOmH27Nno16+fmpMRkaZycnJCXFwczpw5U+xe3NeuXYOTkxNatWqFmJgYGRKW34ULF+Du7v7SpTTcMoy0GWe6iUjnmZqawsbGRu4YpGMKCgrkjkBEWuby5cvw9PQstuAGgJYtW2LEiBHKLTG1waxZs5CRkYEVK1bAw8MDDRs2hJ6entyxiCoVu5cTkc6bNWsWEhMT5Y5BOiwvLw9Xr15FdHQ0rl69iry8PLkjEZEGMjExgZmZWaljzMzMYGpqqp5AleD8+fN47733MG/ePDRu3JgFN1VLLLqJiIhkkp6ejokTJ8LMzAxt27ZF9+7d0bZtW5iZmWHSpElIT0+XOyIRaZBBgwZh7969Jd5mnZeXh3379mHw4MFqTlZxJiYmsLKykjsGUZXimm4i0glnzpyp0PMcHR0rOQnRc7dv30a3bt2QnJyM+vXrw8HBQdkQ6fz580hPT4etrS2ioqLQqFEjueMSkQbIyMhAnz59YGFhgaVLl8LJyUl5LiYmBosWLUJGRgaOHDny0hlxTfH+++8jOjoav//+e4m9Loi0HYtuItIJCoUCkiSV+3ls2kJVZdSoUdi2bRv8/Pzw0UcfwcjISHkuJycHQUFB8PX1hYeHB7Zu3SpjUiLSFE2bNsWzZ89w9+5dAECNGjVQt25dPHjwALm5uQCAhg0bwsDAoMjzJElCQkKC2vOWxePHj+Hu7o6mTZti5cqVaNy4sdyRiCodi24i0gne3t4VKrpDQ0OrIA0RULduXTg5OWH//v0ljunfvz/Onj2LBw8eqDEZEWkqOzu7Cn2WAdDY3iX/vpBQ0pp0Tb5wQPQy7F5ORDohLCxM7ghERTx79gwdO3YsdYyDgwNOnjyppkREpOmSkpLkjlDpCgoKUKNGjSK7iBQ3J8h5QtJmLLqJiIhk4ODggGvXrpU65tq1a3BwcFBTIiIi9auOFxKI/o1FNxHphKlTp1boeevWravkJETPLV26FO7u7ggLC4O3t7fK+ZCQEOzfvx9HjhxRfzgi0nhXr17FtWvXkJWVhTFjxsgdh4hKwTXdRKQTytMR9cX1cmykRpXF399f5Vh0dDQOHz6MFi1aoFu3brC0tERaWhpOnjyJuLg49O3bF127dsWiRYtkSExEmujs2bOYOHEiLl++rDxW+FkVGRmJ/v3744cfftCqbcNedOnSJVy6dAmenp5yRyGqNCy6iUgnRERElGlccnIy/P39kZCQAEmSWHRTpanoVjj8d0hEha5cuYIuXbpAoVBg4sSJuHbtGg4cOKB8jxBCwNbWFq6urtiyZYvMaSvGz88P/v7+fN+jaoW3lxORTnB1dS31/KNHjxAQEIC1a9ciJycHzs7OCAwMVFM60gXHjh2TOwIRabklS5YAAM6fPw97e3v4+fnhwIEDyvOSJMHZ2Rlnz56VKyIRFYNFNxHptJycHKxevRpBQUHIyMhAy5YtERAQgCFDhsgdjaqZl134ISJ6mYiICAwbNgz29vYljrGxscHBgwfVmIqIXqZi97oREWk5IQQ2bdqEZs2awcfHBzVr1sSGDRsQGxvLgpuIiDTS33//DUtLy1LH5OTk8NZsIg3DmW4i0jm7d++Gj48P4uLiYGJigoCAAMyaNQtGRkZyRyMiIirRa6+9htjY2FLHnD9/Hq+//rqaElU+Ozs79OjRQ+4YRJWKM91EpDOioqLQrVs3DBs2DImJiZg9ezZu3LiBjz/+mAU3ERFpvIEDB+Lw4cP47bffij2/fft2xMTEaPUdW15eXuyBQdUOu5cTkU4YPHgw9u3bB4VCgTFjxsDf3x+NGzeWOxYREVGZpaeno2PHjkhLS4OXlxfu3r2L/fv346uvvkJ0dDS2bdsGGxsbXLhwAaampnLHLVVMTAwWLFigbPrm6OiIgIAAODo6ypyMqPKx6CYinaBQKCBJEuzs7NCiRYsyPUeSJOzbt6+KkxEREZXdjRs3MGbMGERHR6ucc3JywrZt22BnZ6f+YOVw+fJlODk5IScnp8hxY2NjnDlzBq1bt5YpGVHVYNFNRDqhInskc39kIiLSVBcvXkRMTAwePnwIExMTODk5oXPnznLHKpNRo0Zh27ZtWLBgAaZPnw5JkrBmzRoEBARg9OjR2Lx5s9wRiSoVi24i0gk3b96s0PNsbW0rOQkREZFus7GxgZ2dHSIjI4scd3FxQXJycoU/s4k0FbuXE5FOYPFMRETVSV5eHq5fv47MzEyYmpqiefPm0NfXjq/2qampGDFihMrxLl26KNd4E1Un7F5ORERERKQl0tPTMXHiRJiZmaFt27bo3r072rZtCzMzM0yaNAnp6elyR3yp3Nxc1K5dW+V47dq1kZubK0MioqqlHZfDiIiIiIh03O3bt9GtWzckJyejfv366NGjB6ysrJCamorz589j06ZNOHLkCKKiotCoUSO54xLR/+GabiIiIiIiLVDYgMzPzw8fffQRjIyMlOdycnIQFBQEX19feHh4YOvWrTImLZ1CoYC9vT3s7e2LHI+Pj0dCQgL69eun8hzuKELajEU3EREREZEWqFu3LpycnLB///4Sx/Tv3x9nz57FgwcP1JisfLijCOka3l5ORERERKQFnj17ho4dO5Y6xsHBASdPnlRToopJTEyUOwKRWrHoJiIiIiLSAg4ODrh27VqpY65duwYHBwc1JaoY7ihCuobdy4mIiIiItMDSpUuxd+9ehIWFFXs+JCQE+/fvx7Jly9QbTA3i4+PljkBUYVzTTURERESkBfz9/REdHY3Dhw+jRYsW6NatGywtLZGWloaTJ08iLi4Offv2hbOzc5HnSZKERYsWyZT61dy8eRP+/v7YunUrnj59Knccogph0U1EREREpAUq0oAM0NwmZBERETh//jz09fXRrVu3IrfF3717F35+fggNDUVubi4aNWqEW7duyZiWqOK4ppuIiIiISAscO3ZM7giV4tmzZxgyZAgOHTpU5Pj06dOxevVqhISEYObMmcjKyoK1tTU+/vhjTJo0Saa0RK+ORTcRERERkRZwdXWVO0KlWLNmDQ4ePAhra2sMGTIEQgjs2rULX331FfT09LBq1SqYm5vj008/xeTJk2FoaCh3ZKJXwtvLiYiIiIhIbTp16oTk5GTExcXB3NwcAJCeno5WrVrh0aNH6NSpE/bt24d69erJnJSocnCmm4iIiIhIAyUnJwMAGjVqBD09PeXjsrCxsamqWK/s+vXrGDFihLLgBoD69etj6NChCAkJwbp161hwU7XCopuIiIiISAPZ2dlBkiT8+eefaN68ufLxy0iShLy8PDUkrJgnT57A2tpa5XijRo0AAO3atVN3JKIqxaKbiIiIiEgDeXp6QpIkmJqaFnlcHRTXib3wd9PXZ4lC1Qv/RRMRERERaaCwsLBSH2uzlJQUnDlzRuUYAJw9exbFtZ1ydHRUSzaiysZGakREREREpDYKhaLEGXshRInnNHGvcaKy4Ew3ERERERGpjZeXl9wRiNSKM91ERERERFri6tWr+N///oezZ88iIyOj2NlfSZKQkJAgQzoiKg5nuomIiIiItEBERAT69++Pp0+fQl9fH1ZWVsU2HeOcGpFm4Uw3EREREZEWcHZ2xrlz5/DNN9/Ay8sLenp6ckeqkKlTp1boeevWravkJETqwaKbiIiIiEgL1KxZE8OGDcOWLVvkjvJKitsurCQvNlVjIzXSVry9nIiIiIhIC9SpUweWlpZyx3hlx44dK9O45ORk+Pv7IyEhodrsT066iUU3EREREZEWeOutt3DixAm5Y7wyV1fXUs8/evQIAQEBWLt2LXJycuDs7IzAwEA1pSOqfGW/t4OIiIiIiGQTFBSEzMxMzJgxA9nZ2XLHqXQ5OTlYsWIFXn/9dXz++eews7PDzz//jJMnT6J79+5yxyOqMK7pJiIiIiLSQG5ubirHMjIycOnSJdSqVQvNmjWDqampyhhJkhAeHq6OiJVCCIHg4GD4+fnh9u3bsLa2hq+vL8aNG1eu9d9EmopFNxERERGRBqpowSlJktY0Hdu9ezd8fHwQFxcHExMTzJ8/H7NmzYKRkZHc0YgqDYtuIiIiIiJSq6ioKMyfPx8xMTEwMDDAtGnTsGDBApibm8sdjajSsegmIiIiIiK1GTx4MPbt2weFQoExY8bA398fjRs3ljsWUZVh0U1EREREpAXc3Nzg7e0NT0/PEsds27YNGzduxG+//abGZOWjUCggSRLs7OzQokWLMj1HkiTs27evipMRVQ1uGUZEREREpAWOHz+Onj17ljomOTkZERER6gn0CoQQSExMRGJiYpnGc59u0mYsuomIiIiIqomsrCzUqFFD7hilKmuhTVRdsOgmIiIiItJQycnJRR5nZGSoHAOA/Px8pKSkYMeOHbCzs1NTuoqxtbWVOwKRWnFNNxERERGRhipc/1xWQgisXLkSc+fOrcJURFQenOkmIiIiItJQnp6ekCQJQghs3rwZ7dq1Q/v27VXG6enpwcLCAm5ubujfv7/6gxJRiTjTTURERESkBZo0aYLZs2djxowZckchonJg0U1ERERERERURRRyByAiIiIiIiKqrlh0ExERERFpiaNHj2LAgAGoX78+atSoAT09PZUffX22bSLSJHxFEhERERFpgZ07d+K9995DQUEBbG1t0bJlSxbYRFqAa7qJiIiIiLRAu3btcOPGDfzyyy9wc3OTOw4RlRFvLyciIiIi0gJxcXEYMWIEC24iLcOim4iIiIhIC9SrVw81a9aUOwYRlROLbiIiIiIiLTB8+HAcPXoUeXl5ckchonLgmm4iIiIiIi2QnZ2Nvn37wsrKCqtWrYKNjY3ckYioDFh0ExERERFpgaZNmyI3Nxd37twBAJiZmcHU1FRlnCRJSEhIUHc8IioB9xggIiIiItICBQUF0NfXLzLDXdz8GefUiDQLZ7qJiIiIiIiIqggbqRERERERERFVERbdRERERERERFWEa7qJiIiIiLRIdHQ0jh49ijt37uDp06cq5yVJQnBwsAzJiKg4XNNNRERERKQF8vLyMHLkSPz8888QQkCSpCJN0wofS5KE/Px8GZMS0Yt4ezkRERERkRb4/PPPsXPnTowdOxbnzp2DEAKzZs1CdHQ0AgMDYWZmhnfffZfbhRFpGN5eTkRERESkBb777ju0adMGmzZtUh4zMzODk5MTnJycMGDAADg6OsLNzQ2TJ0+WMSkRvYgz3UREREREWiA+Ph49e/ZUPpYkCbm5ucrHrVu3xqBBg/D111/LkI6ISsKim4iIiIhICxgYGKBmzZrKx7Vr10ZaWlqRMba2tvjrr7/UHY2ISsGim4iIiIhIC7z22mu4deuW8nHLli0RGRlZpJlaTEwMLCws5IhHRCVg0U1EREREpAVcXV2LFNnvvfce4uLiMHDgQKxduxYjR45EVFQU+vfvL3NSInoRtwwjIiIiItICv//+OzZu3AgfHx+89tpryM3NxbBhw7B3717lGEdHR+zbtw9169aVMSkRvYhFNxERERGRFjt37hwSEhJga2sLR0dHKBS8mZVIk7DoJiIiIiIiIqoi3KebiIiIiEgDjRs3rkLPkyQJwcHBlZyGiCqKM91ERERERBqooreJS5KE/Pz8Sk5DRBXFmW4iIiIiIg2UmJgodwQiqgSc6SYiIiIiIiKqImxtSERERERERFRFWHQTERERERERVREW3URERERERERVhEU3ERERERERURVh0U1ERERERERURVh0ExEREREREVURFt1EREREREREVeT/AZybHX2VxKkcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_protoclusters.copy()\n",
    "\n",
    "# Sort the DataFrame by count in descending order\n",
    "df = df.sort_values(by='count', ascending=False)\n",
    "\n",
    "# Select the top 15 types\n",
    "top_15 = df.head(15)\n",
    "\n",
    "# Sum the counts of the remaining types\n",
    "others_count = df.iloc[15:]['count'].sum()\n",
    "\n",
    "# Create a new row for 'Others'\n",
    "others_row = pd.DataFrame({'type': ['Others'], 'count': [others_count]})\n",
    "\n",
    "# Concatenate the top 15 and 'Others' rows\n",
    "df_combined = pd.concat([top_15, others_row])\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Create horizontal bar chart\n",
    "bars = ax.bar(df_combined['type'], df_combined['count'], color='blue')\n",
    "\n",
    "# Set the font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# Format x-axis ticks to display as 2k, 4k, 6k, etc.\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:.0g}k'.format(x / 1000) if x > 0 else '0'))\n",
    "\n",
    "# Rotate x-axis ticks by 80 degrees\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "\n",
    "# Add counts on the bars\n",
    "for bar in bars:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), int(yval)+100, yval,\n",
    "                 va='center', ha='left', fontsize=14)\n",
    "    # else:\n",
    "    #     plt.text(bar.get_x() + bar.get_width() / 2, int(yval) - 1500, yval,\n",
    "    #              va='center', ha='left', fontsize=14, color='white')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save as SVG\n",
    "plt.savefig('bgc_protoclusters_dist.svg', format='svg', bbox_inches=\"tight\")\n",
    "\n",
    "# Save as PNG\n",
    "plt.savefig('bgc_protoclusters_dist.png', format='png', bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d451c36f-c24d-4573-87e5-26e21b3e908d",
   "metadata": {},
   "source": [
    "# Stats description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc4b27e-3b06-4cd4-82e4-f237390ff25d",
   "metadata": {},
   "source": [
    "### Taxonomic stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "540b972b-4f21-4ebf-8f53-ebd8bfa75c3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total genera: 27\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "genus_gtdb\n",
       "g__Streptomyces         885\n",
       "g__Micromonospora        36\n",
       "g__Kitasatospora         27\n",
       "g__Nocardia              21\n",
       "g__Kribbella             11\n",
       "g__Amycolatopsis          8\n",
       "g__Streptosporangium      7\n",
       "g__Microbispora           5\n",
       "g__Nonomuraea             4\n",
       "g__Kocuria                4\n",
       "g__Nocardioides           3\n",
       "g__Spirillospora          3\n",
       "g__Embleya                2\n",
       "g__Rhodococcus_C          2\n",
       "g__Micromonospora_G       2\n",
       "g__                       2\n",
       "g__Mycobacterium          1\n",
       "g__Aldersonia             1\n",
       "g__Kineococcus            1\n",
       "g__Micrococcus            1\n",
       "g__Williamsia_A           1\n",
       "g__Dactylosporangium      1\n",
       "g__Actinoplanes           1\n",
       "g__Lentzea                1\n",
       "g__Sphaerisporangium      1\n",
       "g__Micromonospora_E       1\n",
       "g__Actinoallomurus        1\n",
       "g__Actinomycetospora      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total genera:\", len(df_genomes_meta.genus_gtdb.value_counts()) -1)\n",
    "df_genomes_meta.genus_gtdb.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "fae0b6ae-ff23-4bbf-9f2b-16ec6247d28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unassigned genus: Index(['NBC_01635', 'NBC_01309'], dtype='object', name='genome_id')\n"
     ]
    }
   ],
   "source": [
    "print(\"Unassigned genus:\", df_genomes_meta[df_genomes_meta.genus_gtdb == \"g__\"].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "3c03f893-a78e-434a-97fe-7804c4553cb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total species detected: 145\n",
      "Total genomes with assigned species: (570, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "species_gtdb\n",
       "s__             462\n",
       "anulatus         39\n",
       "sp003846175      26\n",
       "albidoflavus     25\n",
       "canus            24\n",
       "               ... \n",
       "sp001280005       1\n",
       "sp900091725       1\n",
       "sp000720145       1\n",
       "xinghaiensis      1\n",
       "diastaticus       1\n",
       "Name: count, Length: 146, dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genomes_meta_1032 = df_genomes_meta.drop(index=[\"NBC_01635\", \"NBC_01309\"])\n",
    "print(\"Total species detected:\", len(df_genomes_meta_1032.species_gtdb.value_counts()) -1)\n",
    "print(\"Total genomes with assigned species:\", df_genomes_meta_1032[df_genomes_meta_1032.species_gtdb != \"s__\"].shape)\n",
    "df_genomes_meta_1032.species_gtdb.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee626400-c8d5-45c8-becb-0e0c4938ca4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genomes_meta_1032 = df_genomes_meta.drop(index=[\"NBC_01635\", \"NBC_01309\"])\n",
    "print(\"Total species detected:\", len(df_genomes_meta_1032.species_gtdb.value_counts()) -1)\n",
    "print(\"Total genomes with assigned species:\", df_genomes_meta_1032[df_genomes_meta_1032.species_gtdb != \"s__\"].shape)\n",
    "df_genomes_meta_1032.species_gtdb.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "0500587c-5d2f-4319-8d39-c2d1d5104828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>genome_id</th>\n",
       "      <th>NBC_00193</th>\n",
       "      <th>NBC_01306</th>\n",
       "      <th>NBC_01571</th>\n",
       "      <th>NBC_00201</th>\n",
       "      <th>NBC_01142</th>\n",
       "      <th>NBC_00086</th>\n",
       "      <th>NBC_00347</th>\n",
       "      <th>NBC_01381</th>\n",
       "      <th>NBC_00310</th>\n",
       "      <th>NBC_01565</th>\n",
       "      <th>...</th>\n",
       "      <th>NBC_01310</th>\n",
       "      <th>NBC_01490</th>\n",
       "      <th>NBC_01615</th>\n",
       "      <th>NBC_00405</th>\n",
       "      <th>NBC_00669</th>\n",
       "      <th>NBC_00569</th>\n",
       "      <th>NBC_01104</th>\n",
       "      <th>NBC_00536</th>\n",
       "      <th>NBC_01116</th>\n",
       "      <th>NBC_01001</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genome_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NBC_00193</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.056532</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.263022</td>\n",
       "      <td>0.230110</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122140</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.263022</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.124491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01306</th>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.167688</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170926</td>\n",
       "      <td>0.243761</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.178173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01571</th>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.159146</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.170926</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.197292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_00201</th>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.159146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.167688</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.203604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01142</th>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.167688</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.167688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.170926</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.186761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_00569</th>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.149777</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.263022</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.263022</td>\n",
       "      <td>0.219531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.203604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01104</th>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.170926</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170926</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.145727</td>\n",
       "      <td>0.186761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_00536</th>\n",
       "      <td>0.132399</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.133864</td>\n",
       "      <td>0.140258</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.136948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.295981</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.133864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01116</th>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.174408</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.243761</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.263022</td>\n",
       "      <td>0.210897</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.145727</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.178173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBC_01001</th>\n",
       "      <td>0.124491</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.125715</td>\n",
       "      <td>0.125715</td>\n",
       "      <td>0.197292</td>\n",
       "      <td>0.182269</td>\n",
       "      <td>0.098138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>0.133864</td>\n",
       "      <td>0.178173</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 462 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "genome_id  NBC_00193  NBC_01306  NBC_01571  NBC_00201  NBC_01142  NBC_00086  \\\n",
       "genome_id                                                                     \n",
       "NBC_00193   0.000000   0.178173   0.210897   0.197292   0.191731   0.056532   \n",
       "NBC_01306   0.178173   0.000000   0.219531   0.210897   0.178173   0.167688   \n",
       "NBC_01571   0.210897   0.219531   0.000000   0.159146   0.178173   0.203604   \n",
       "NBC_00201   0.197292   0.210897   0.159146   0.000000   0.174408   0.203604   \n",
       "NBC_01142   0.191731   0.178173   0.178173   0.174408   0.000000   0.191731   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "NBC_00569   0.197292   0.219531   0.182269   0.191731   0.174408   0.197292   \n",
       "NBC_01104   0.182269   0.178173   0.178173   0.174408   0.170926   0.178173   \n",
       "NBC_00536   0.132399   0.174408   0.182269   0.178173   0.174408   0.133864   \n",
       "NBC_01116   0.203604   0.174408   0.197292   0.191731   0.174408   0.210897   \n",
       "NBC_01001   0.124491   0.178173   0.197292   0.203604   0.186761   0.125715   \n",
       "\n",
       "genome_id  NBC_00347  NBC_01381  NBC_00310  NBC_01565  ...  NBC_01310  \\\n",
       "genome_id                                              ...              \n",
       "NBC_00193   0.008615   0.263022   0.230110   0.132399  ...   0.122140   \n",
       "NBC_01306   0.186761   0.203604   0.186761   0.174408  ...   0.170926   \n",
       "NBC_01571   0.219531   0.186761   0.170926   0.186761  ...   0.186761   \n",
       "NBC_00201   0.197292   0.174408   0.167688   0.191731  ...   0.191731   \n",
       "NBC_01142   0.197292   0.167688   0.182269   0.174408  ...   0.182269   \n",
       "...              ...        ...        ...        ...  ...        ...   \n",
       "NBC_00569   0.203604   0.149777   0.182269   0.182269  ...   0.219531   \n",
       "NBC_01104   0.191731   0.174408   0.186761   0.174408  ...   0.174408   \n",
       "NBC_00536   0.140258   0.210897   0.197292   0.136948  ...   0.132399   \n",
       "NBC_01116   0.203604   0.186761   0.191731   0.182269  ...   0.178173   \n",
       "NBC_01001   0.125715   0.197292   0.182269   0.098138  ...   0.115759   \n",
       "\n",
       "genome_id  NBC_01490  NBC_01615  NBC_00405  NBC_00669  NBC_00569  NBC_01104  \\\n",
       "genome_id                                                                     \n",
       "NBC_00193   0.295981   0.203604   0.263022   0.203604   0.197292   0.182269   \n",
       "NBC_01306   0.243761   0.191731   0.295981   0.191731   0.219531   0.178173   \n",
       "NBC_01571   0.295981   0.132399   1.000000   0.197292   0.182269   0.178173   \n",
       "NBC_00201   0.219531   0.132399   0.295981   0.210897   0.191731   0.174408   \n",
       "NBC_01142   0.295981   0.167688   1.000000   0.197292   0.174408   0.170926   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "NBC_00569   0.263022   0.178173   0.263022   0.219531   0.000000   0.191731   \n",
       "NBC_01104   1.000000   0.170926   0.295981   0.210897   0.191731   0.000000   \n",
       "NBC_00536   0.295981   0.174408   0.295981   0.210897   0.186761   0.174408   \n",
       "NBC_01116   0.243761   0.197292   0.263022   0.210897   0.197292   0.145727   \n",
       "NBC_01001   1.000000   0.167688   1.000000   0.203604   0.203604   0.186761   \n",
       "\n",
       "genome_id  NBC_00536  NBC_01116  NBC_01001  \n",
       "genome_id                                   \n",
       "NBC_00193   0.132399   0.203604   0.124491  \n",
       "NBC_01306   0.174408   0.174408   0.178173  \n",
       "NBC_01571   0.182269   0.197292   0.197292  \n",
       "NBC_00201   0.178173   0.191731   0.203604  \n",
       "NBC_01142   0.174408   0.174408   0.186761  \n",
       "...              ...        ...        ...  \n",
       "NBC_00569   0.186761   0.197292   0.203604  \n",
       "NBC_01104   0.174408   0.145727   0.186761  \n",
       "NBC_00536   0.000000   0.182269   0.133864  \n",
       "NBC_01116   0.182269   0.000000   0.178173  \n",
       "NBC_01001   0.133864   0.178173   0.000000  \n",
       "\n",
       "[462 rows x 462 columns]"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mash = pd.read_csv(\"df_mash.csv\", index_col=0)\n",
    "genomes_with_unassigned_species = df_genomes_meta_1032[df_genomes_meta_1032.species_gtdb == \"s__\"].index\n",
    "df_mash_novel = df_mash.loc[genomes_with_unassigned_species, genomes_with_unassigned_species]\n",
    "df_mash_novel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "e545a8ba-8a56-45cf-b8b6-b5672ca54f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from community import community_louvain\n",
    "from collections import Counter\n",
    "\n",
    "# create a graph from the dataframe\n",
    "G = nx.from_pandas_adjacency(df_mash_novel)\n",
    "\n",
    "# filter edges below the cutoff value and add singleton nodes\n",
    "filtered_G = nx.Graph()\n",
    "for u, v, w in G.edges(data=True):\n",
    "    if w['weight'] < 0.05:\n",
    "        filtered_G.add_edge(u, v, weight=w['weight'])\n",
    "\n",
    "# add nodes with no edges (singleton nodes)\n",
    "for node in G:\n",
    "    if node not in filtered_G:\n",
    "        filtered_G.add_node(node)\n",
    "\n",
    "# find communities\n",
    "partition = community_louvain.best_partition(filtered_G)\n",
    "\n",
    "# Count the abundance of each community\n",
    "community_abundance = Counter(partition.values())\n",
    "\n",
    "# Sort communities by abundance\n",
    "sorted_communities = sorted(community_abundance.items(), key=lambda item: item[1], reverse=True)\n",
    "\n",
    "# print communities\n",
    "idx = 1\n",
    "for i, com in sorted_communities:\n",
    "    list_genomes = [nodes for nodes in partition.keys() if partition[nodes] == i]\n",
    "    df_genomes_meta.loc[list_genomes, \"Mash_species\"] = \"sp_mash_{:03d}\".format(idx)\n",
    "    idx = idx + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "b97bc177-730c-422f-a08c-3c51f8f3defa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = (df_genomes_meta.Mash_species.value_counts() == 1)*1\n",
    "counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "ea30b006-bf9d-47cf-a107-d42fd9497e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mash_species\n",
       "sp_mash_001    28\n",
       "sp_mash_002    16\n",
       "sp_mash_003    13\n",
       "sp_mash_004    10\n",
       "sp_mash_005    10\n",
       "               ..\n",
       "sp_mash_132     1\n",
       "sp_mash_133     1\n",
       "sp_mash_134     1\n",
       "sp_mash_135     1\n",
       "sp_mash_244     1\n",
       "Name: count, Length: 244, dtype: int64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genomes_meta.Mash_species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "e959d1b5-dc32-4118-8ca8-17d6908f2053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = (df_genomes_meta.species_gtdb.value_counts() == 1)*1\n",
    "counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "990aa5d3-ca0e-43d9-a35b-19169462b613",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genomes_meta.to_csv(\"df_genomes_meta.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "47c68917-1fb7-49fa-b25b-e6f4a26731f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genome_id.1</th>\n",
       "      <th>genus</th>\n",
       "      <th>source</th>\n",
       "      <th>species</th>\n",
       "      <th>quality</th>\n",
       "      <th>completeness</th>\n",
       "      <th>contamination</th>\n",
       "      <th>N50</th>\n",
       "      <th>contigs</th>\n",
       "      <th>genome_len</th>\n",
       "      <th>gc</th>\n",
       "      <th>assembly_level</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genome_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GCF_000009765.2</th>\n",
       "      <td>GCF_000009765.2</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>avermitilis</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.890000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>9025608</td>\n",
       "      <td>2</td>\n",
       "      <td>9119895</td>\n",
       "      <td>0.707014</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_000010605.1</th>\n",
       "      <td>GCF_000010605.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>griseus</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.840000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8545929</td>\n",
       "      <td>1</td>\n",
       "      <td>8545929</td>\n",
       "      <td>0.722274</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_000091305.1</th>\n",
       "      <td>GCF_000091305.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>scabiei</td>\n",
       "      <td>HQ</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10148695</td>\n",
       "      <td>1</td>\n",
       "      <td>10148695</td>\n",
       "      <td>0.714528</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009-10-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_000092385.1</th>\n",
       "      <td>GCF_000092385.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>milbemycinicus</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.740000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>11936683</td>\n",
       "      <td>1</td>\n",
       "      <td>11936683</td>\n",
       "      <td>0.707546</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-05-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_000147815.2</th>\n",
       "      <td>GCF_000147815.2</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>violaceusniger_A</td>\n",
       "      <td>HQ</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>10657107</td>\n",
       "      <td>3</td>\n",
       "      <td>11138313</td>\n",
       "      <td>0.708764</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-08-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_900236475.1</th>\n",
       "      <td>GCF_900236475.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>chartreusis_D</td>\n",
       "      <td>HQ</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8983317</td>\n",
       "      <td>1</td>\n",
       "      <td>8983317</td>\n",
       "      <td>0.712257</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_900236505.1</th>\n",
       "      <td>GCF_900236505.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>chartreusis_D</td>\n",
       "      <td>HQ</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8983317</td>\n",
       "      <td>1</td>\n",
       "      <td>8983317</td>\n",
       "      <td>0.712257</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_904440635.1</th>\n",
       "      <td>GCF_904440635.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>cyaneofuscatus</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.840000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>7838151</td>\n",
       "      <td>1</td>\n",
       "      <td>7838151</td>\n",
       "      <td>0.715815</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_904440645.1</th>\n",
       "      <td>GCF_904440645.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>cyaneofuscatus</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.840000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>7837660</td>\n",
       "      <td>1</td>\n",
       "      <td>7837660</td>\n",
       "      <td>0.715844</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCF_947311705.1</th>\n",
       "      <td>GCF_947311705.1</td>\n",
       "      <td>Streptomyces</td>\n",
       "      <td>NCBI</td>\n",
       "      <td>albidoflavus</td>\n",
       "      <td>HQ</td>\n",
       "      <td>99.320458</td>\n",
       "      <td>0.572246</td>\n",
       "      <td>7047941</td>\n",
       "      <td>3</td>\n",
       "      <td>7136301</td>\n",
       "      <td>0.733530</td>\n",
       "      <td>Complete Genome</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-12-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>488 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     genome_id.1         genus source           species  \\\n",
       "genome_id                                                                 \n",
       "GCF_000009765.2  GCF_000009765.2  Streptomyces   NCBI       avermitilis   \n",
       "GCF_000010605.1  GCF_000010605.1  Streptomyces   NCBI           griseus   \n",
       "GCF_000091305.1  GCF_000091305.1  Streptomyces   NCBI           scabiei   \n",
       "GCF_000092385.1  GCF_000092385.1  Streptomyces   NCBI    milbemycinicus   \n",
       "GCF_000147815.2  GCF_000147815.2  Streptomyces   NCBI  violaceusniger_A   \n",
       "...                          ...           ...    ...               ...   \n",
       "GCF_900236475.1  GCF_900236475.1  Streptomyces   NCBI     chartreusis_D   \n",
       "GCF_900236505.1  GCF_900236505.1  Streptomyces   NCBI     chartreusis_D   \n",
       "GCF_904440635.1  GCF_904440635.1  Streptomyces   NCBI    cyaneofuscatus   \n",
       "GCF_904440645.1  GCF_904440645.1  Streptomyces   NCBI    cyaneofuscatus   \n",
       "GCF_947311705.1  GCF_947311705.1  Streptomyces   NCBI      albidoflavus   \n",
       "\n",
       "                quality  completeness  contamination       N50  contigs  \\\n",
       "genome_id                                                                 \n",
       "GCF_000009765.2      HQ     99.890000       0.790000   9025608        2   \n",
       "GCF_000010605.1      HQ     99.840000       0.000000   8545929        1   \n",
       "GCF_000091305.1      HQ    100.000000       0.000000  10148695        1   \n",
       "GCF_000092385.1      HQ     99.740000       1.960000  11936683        1   \n",
       "GCF_000147815.2      HQ    100.000000       1.050000  10657107        3   \n",
       "...                 ...           ...            ...       ...      ...   \n",
       "GCF_900236475.1      HQ    100.000000       0.000000   8983317        1   \n",
       "GCF_900236505.1      HQ    100.000000       0.000000   8983317        1   \n",
       "GCF_904440635.1      HQ     99.840000       0.450000   7838151        1   \n",
       "GCF_904440645.1      HQ     99.840000       0.450000   7837660        1   \n",
       "GCF_947311705.1      HQ     99.320458       0.572246   7047941        3   \n",
       "\n",
       "                 genome_len        gc   assembly_level  year        date  \n",
       "genome_id                                                                 \n",
       "GCF_000009765.2     9119895  0.707014  Complete Genome  2016  2016-03-24  \n",
       "GCF_000010605.1     8545929  0.722274  Complete Genome  2008  2008-04-08  \n",
       "GCF_000091305.1    10148695  0.714528  Complete Genome  2009  2009-10-06  \n",
       "GCF_000092385.1    11936683  0.707546  Complete Genome  2010  2010-05-28  \n",
       "GCF_000147815.2    11138313  0.708764  Complete Genome  2011  2011-08-23  \n",
       "...                     ...       ...              ...   ...         ...  \n",
       "GCF_900236475.1     8983317  0.712257  Complete Genome  2018  2018-01-10  \n",
       "GCF_900236505.1     8983317  0.712257  Complete Genome  2018  2018-01-10  \n",
       "GCF_904440635.1     7838151  0.715815  Complete Genome  2021  2021-01-27  \n",
       "GCF_904440645.1     7837660  0.715844  Complete Genome  2021  2021-01-27  \n",
       "GCF_947311705.1     7136301  0.733530  Complete Genome  2022  2022-12-03  \n",
       "\n",
       "[488 rows x 14 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ncbi_strepto_hq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "b9041dbf-38af-49c9-92ba-68436b74abae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw.to_csv(\"df_sankey.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "c366d6dc-4484-4275-af66-fd488e02eb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ncbi_strepto_hq.to_csv(\"df_ncbi_strepto.csv\")"
   ]
  }
 ],
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