{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with `geovar`\n",
    "\n",
    "This notebook highlights an instructive example of how to generate \"GeoVar\"-style plots using an example dataset of 5000 randomly chosen bi-allelic variants on Chromosome 22 from the new high-coverage sequencing of the [1000 Genomes Project from the New York Genome Center](http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20190425_NYGC_GATK/)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pkg_resources\n",
    "from geovar import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The `geovar` package contains example frequency tables as well as a gzipped vcf dataset to illustrate how to move from a [VCF](https://samtools.github.io/hts-specs/VCFv4.2.pdf) file and a population panel file to a full \"GeoVar\"-plot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_path = pkg_resources.resource_filename(\"geovar\", \"data\")\n",
    "\n",
    "# Filepath to the VCF File\n",
    "vcf_file = \"{}/new_1kg_nygc.chr22.biallelic_snps.filt.n5000.vcf.gz\".format(data_path)\n",
    "\n",
    "# Filepath to the population panel file\n",
    "population_panel = \"{}/integrated_call_samples_v3.20130502.1kg_superpops.panel\".format(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/aabiddanda/.pyenv/versions/3.9.1/envs/venv_geovar/lib/python3.9/site-packages/geovar/data/integrated_call_samples_v3.20130502.1kg_superpops.panel\n"
     ]
    }
   ],
   "source": [
    "print(population_panel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The population panel file is a two column file with the columns `sample` and `pop` separated by whitespace. The sample column must match the `sample` IDs in the VCF file. the `pop` column contains population labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5000it [00:02, 2059.90it/s]\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>MAC</th>\n",
       "      <th>MAF</th>\n",
       "      <th>AFR</th>\n",
       "      <th>AMR</th>\n",
       "      <th>EAS</th>\n",
       "      <th>EUR</th>\n",
       "      <th>SAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
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       "      <td>0.000759</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.008137</td>\n",
       "      <td>0.028963</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>10666881</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001104</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>10670699</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>1633</td>\n",
       "      <td>0.354538</td>\n",
       "      <td>0.228395</td>\n",
       "      <td>0.379538</td>\n",
       "      <td>0.501029</td>\n",
       "      <td>0.259709</td>\n",
       "      <td>0.447137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>10679257</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>35</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>0.025797</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CHR       SNP A1 A2   MAC       MAF       AFR       AMR       EAS       EUR  \\\n",
       "0  22  10662593  C  T     1  0.000201  0.000759  0.000000  0.000000  0.000000   \n",
       "1  22  10664208  G  A    38  0.008137  0.028963  0.000000  0.000000  0.000000   \n",
       "2  22  10666881  C  A     1  0.000218  0.000000  0.000000  0.000000  0.000000   \n",
       "3  22  10670699  T  A  1633  0.354538  0.228395  0.379538  0.501029  0.259709   \n",
       "4  22  10679257  A  T    35  0.007008  0.025797  0.001449  0.000000  0.000000   \n",
       "\n",
       "        SAS  \n",
       "0  0.000000  \n",
       "1  0.000000  \n",
       "2  0.001104  \n",
       "3  0.447137  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the population dataframe\n",
    "pop_df = read_pop_panel(population_panel)\n",
    "\n",
    "# Writing out VCF to a Frequency Table\n",
    "af_df = vcf_to_freq_table(vcf_file, pop_df=pop_df, outfile=\"{}/test.freq.csv\".format(data_path), minor_allele=True)\n",
    "\n",
    "# Print the beginning of the allele frequency table \n",
    "af_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating a GeoVar Object \n",
    "\n",
    "Once we have a frequency table, the next step is to categorize each variant by its frequency in each population. The default binning scheme is to label mutations with population allele frequencies in the interval of (0,0.05] as Rare, and those with frequency above 0.05 as common. Below we will show how to change the frequency cutoff and how to add categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aabiddanda/.pyenv/versions/3.9.1/envs/venv_geovar/lib/python3.9/site-packages/geovar/binning.py:47: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  af_df = pd.read_table(freq_mat_file, sep=r\"\\s\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeoVar\n",
      "number of variants: 5000\n",
      "number of pops: 5\n",
      "pops: AFR,AMR,EAS,EUR,SAS\n",
      "allele freq bins: (0, 0),(0, 0.05),(0.05, 1.0)\n"
     ]
    }
   ],
   "source": [
    "# Creating the GeoVar Object \n",
    "geovar_test = GeoVar()\n",
    "\n",
    "# Adding in the frequency file (all of it)\n",
    "geovar_test.add_freq_mat(freq_mat_file=\"{}/test.freq.csv\".format(data_path))\n",
    "\n",
    "# Generate a geovar binning with the binning we used in our paper\n",
    "geovar_test.geovar_binning()\n",
    "\n",
    "# Printing details about the GeoVar object \n",
    "print(geovar_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "Using the `GeoVar` object we created in the last section, we can generate a “GeoVar”-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "geovar_plot = GeoVarPlot()\n",
    "\n",
    "# Adding data directly from the geovar object itself\n",
    "geovar_plot.add_data_geovar(geovar_test)\n",
    "\n",
    "# Filter to remove very rare categories (only to speed up plotting)\n",
    "geovar_plot.filter_data()\n",
    "\n",
    "# Adding in a colormap (see code for alternative ideas beyond default)\n",
    "geovar_plot.add_cmap()\n",
    "\n",
    "# Shifting order of regional groupings to reflect OOA-demography\n",
    "geovar_plot.reorder_pops(np.array(['AFR','EUR','SAS','EAS','AMR']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+vPPO/zq1O3fynzdcy5o1q+lc2Zljjz+Bu+/5RejPTpKGJOloSljfhHgpUETkLeAznPG6T2jSw6mqQXiVyYkf3nmvbvpSaOPsnpkwIv4pYytXfBy7hqTouHnqnNinjPVcOP3tiRMnjmzunNfhhOHAoarqzw2WYRiA9wH0d4B4o8kbRgHhtcS7Bfi5iNyGY4S79fuqqtdB9MDZvqWalx+aEtfjP2dh51LWV6di1dAzARqSoiMJGi46tkvWc14N72X337+xe/uucUZLbC6Q/fpcCZIk6EiCBkiGjiRoyIZXw/tqqCoMo8jwZHiqOidsIXFwwOBeXPetUzj2sP3Yu6Ijm7ZsZ/77q5g8bQ7/eufjotGQFB1J0BCVjjZ5lBGRfXHW5bXPPK6q0U33CIhjDhnC078Yz1N/eZPTLr+fVWs3s1d5e8Ye+xXOPvHgSF50EjQkRUcSNESpw+s43r7AH4Djcdp0u61WUNXY2njX3PBD/e/Zbfev8ca0m5n//kquuOMPgegY0KOszW2KJGhIio4kaAhax81f2yfrOJ7X4YRfAQ0443k7gOOA83Cmfo3NflsyGdK/B/sP2Idps+YWtYak6EiChqh1eK1qngD8m6p+4C4N2qCq/xCROuDHwEuhKQyBfbruDcAn67cUtYak6EiChqh1eC3xyoGN7vYmoHEuziIcn5t5xYbN2wDYt2eXotaQFB1J0BC1Dq+G9wHOBGmAKuC7IjIQuJI919QlnuWrNrJ05Qa+flqz1e+i0ZAUHUnQELUOr4b3a76YMjYJx7fmcuB7OLNa8o7r7prOmaNH8JNrzmZAHyd2517l7fn3kw/hJ9c0dQVTuBqSoiMJGqLU4alXc4+bRDrhlIArVXVja9eHSa69mvDFeM1xh+9PxV4d2Fy9g7nvrmDytDnMfa9tCy5y7UVLgoak6EiChiB1tNSrmZPhJQk/hhckfl50IWlIio4kaGjJ8LL2aorIfcAPVHW7u50VVb3Gp0bDKCpaGk44CGj0MjOC5t370cJxwzCykNXwVPWrGdujI1FjGEVCqwPoIlKG44j2JFV9L3xJbWNw3+4s+OOlcctg3dq19IrJKU+SNCRFx5TZVaTKu8aqgeV/z3qqVcNzvYvtIqFVytLS0tj9ezSSBB1J0ADx6yiUMF33Az8QkXjj4xpGgeDVkI7Dma+5RkTeBbZnnlTV6NwvG0YB4NXwNgLPhCnEMIoJryvQLwlbiGEUE17beAXFmJNG8/1rrtrj+JNPPE6PLs2FASxMDUnRkQQNALPvv4WHxg3noXHDmXzuQfxu/InMeeQOamuqW7+5jXjuLBGRS3DCaDXn+mFIwLoMIxb6jTiakyfcRbqhgU2rl/HqA7dRt30bY667J9DneCrxRORG4F7gbZzQyDOBd4FuOCG8DKMgKClrT6eu+1DRozcDDhnF/qPGsqoq+BjsXqua3wHGq+oPcJzZPuD2ZN4LDAxclWEkgOq1q1hZ9TrtSoMfRfOaYj/gX+72Tr6IUz7VPf6dgHUZRiysnP86Uy46HE2naaivA2DUJTcF/hyvhrcWJzLrSmAFcDTOSvT9SeiMFsPIhX2Hj2T0FbeTqq9j0UszqF67ioNO/0bgz/Fa1XwFaBwkfxT4hYi8CjwN/ClwVSFTWVnJ1uo9e6q2bNlCZefORaMhKTqSoKGR0g4d6dxnIN0HDuO4y24lVV/L3BmTA3+OV8MbD/wEQFUfBi7GCV5yK477h7xi6LADqKqaR9NFwFXz5zFs2AFFoyEpOpKgIRtHnP895s98lO2b1gearlfDEzeUMgCq+rSqXqOqD6hq/Eue28j4y6/go+XLuXbC1SxcsIAlixdz369+yfSnp3Lt9TcWjYak6EiChmz0PfBIuvXbj7kzHg40Xa+Gt1ZE7heRowJ9ekwMHjKEl155jWVLP+TM08dw3DFHMmP6NH4/bQanjg0/BHNSNCRFRxI0tMTBZ13M+7OfYdv64BzqeXXhPh64CGey9HKceOi/V9WlgSnJkfvvf0C/c8Wesx6iJgnhh5OgISk6kh6K2VOJp6pT3FXog4DfAOOAJSLyhohcGZRQwygW2jRXU1VXqerdqnowMBLoALToCMkwjD1p85C8iBwL/AdO0JL2ONVOwzDagCfDE5Gv4BjbhUBfnCAlVwMzVXVnePIMozDxWuK9gzM17BfANFXdEJ4kwyh8vBreAar6YahKDKOI8NqraUZnGAGS917DUqkUK1d8HLcM149j2wJrBM05QzrE+vxG1q1dG7cEzhnSgV69y2PVMHVh9nN5b3hJ8auZBD+OvXqXJyIvIH6/mknRkI2i9LliGHGT1fBEpEFEerrbj4nI3tHJMozCpqUSbyfQ6OLpW0DH8OUYRnHQUhvv/4CZIvI2IMB9ItLsYLmqfjsMcYZRqLRkeN8EbuAL9w7dgbooRBlGodNSfLx1wI0AIvIRcKGqfhaVMMMoZLy6cB8cthDDKCY8DyeIyL+JyGsislFENojIHBE5PUxxhlGoePUkfRnwZ2AZcBNwM/AR8GcRsY4Vw2gjXku8m4DrVPUSVX3U/bsYp/Pl5tDUhUQxBsnIRhLyIgkaotbhdcrYAOCFZo7/FQg2mkOREVWQDCNZeDW8lcApQFPnRmNwPEsbOdIYJAOgokdv9h81lg9enRmvKCN0vBrePcD9InIYzsA6wCicsb6rvT5MRMYCvwZKgN+o6l3NXHM+cDvO2OECVb3Ia/r5TphBMoxk4XU44RERWQ9cj+NhDOB94HxVfdZLGiJSAjyIU3KuBt4SkedUdVHGNUOBHwCjVHVz41zRQiaqIBlGsvD806qqf8bp2cyVI4GlqrocQESmAWcDizKu+Q7woKpudp8ZrN/sBBJVkAwjWUS5LKgvsCpjf7V7LJNhwDAR+Yfrs3NscwmJyHgRmSsic7dUb2mzkGIMkpGNJORFEjRErSNp6/FKgaHAaByPZv8tIl2aXuQ62B2pqiO7dN7jdKsUY5CMbCQhL5KgIWodURreGqB/xn4/91gmq4HnVHWXqn4ELMExxEApxiAZ2UhCXiRBQ9Q6ojS8t4ChIjJYRNoDFwDPNblmJk5ph4j0wKl6Lg9aSDEGychGEvIiCRqi1hFZv7WqpkTkKuBFnOGEx1T1PRGZBMxV1efcc2NEZBHQANwY1oqIkUccwV9mvRhG0p456eqfNXt82PFnMOz4MyLTkYS8SIKGKHV4Njw3RNdJQE+alJSqeo2XNFR1FjCrybEfZWwrcJ37ZxgFi1cX7jcAP8eZufIJu8c9txjohtFGvJZ4E4BrVPWBMMUYRrHgtXOlkiZVRMMwcser4U0Fmh3MNgyj7Xitaq4C7hCRUcBCYFfmSVX9RdDCDKOQ8Wp4lwE1wDHuXyaKE77LMAyPmLMjw4iBNs9cEZEKEdkrDDGGUSy0xcvYlSKyEqgGtorIChH5XnjSDKNw8TqAfgvOAtV7gNfdw8cBd4lIZXMryQ3DyI7XzpXvAuNVdWrGsdki8iHwM8AMzzDagFfD64mzuqAp/wJ6BSen7SQlImwSIpAmISotJCMybRKi0raEV8NbAlwETGpy/CJgcaCK2khSIsJC/BFIkxCVFpITmTYJGrLh1fBuB6aLyPHAP9xjo4ATgPNC0GUYBY2nXk1V/RNwFLAWOMP9WwscqaozQ1NnGAVKW7yMvQ2Y+yvDCICshici3VR1U+N2S4k0XmcYhjdaKvE2iEgf17flRppf8Cru8ZIwxBlGodKS4Z0IbMrYtpXmhhEQLYVinpOx/fdI1BhGkeA1MGVDc3EMRKS7iDQEL8swChuvk6Qly/EOQH1AWgyjaGhxOEFEGt3sKfBdEanJOF2CM1H6g5C0GUbB0to4XmPsO8FZhZ5ZrawHPsaZQG0YRhto0fAaV56LyKvAuMbwWYZh+MPrzJWxNNPOE5GOQFpVrZ1nGG3Aa+fKdKC51ebfdc8ZhtEGvBreKOBvzRx/iT29jhmG0QpeDa8TkGrmeBrYOzg5hlEceDW8hTgRWptyEfBucHIMozjwaniTgFtE5Pcicqn79wfgZuCO8OSFw5iTRvP9a67a4/iTTzxOjy4VRaMBYPb9t/DQuOE8NG44k889iN+NP5E5j9xBbc2escDDIil5EaUOrw5tZ4nImcBtwH3u4fnAWar610AVGZHTb8TRnDzhLtINDWxavYxXH7iNuu3bGHPdPXFLK1jashD2BeCFELUYMVFS1p5OXfcBoKJHb/YfNZYPXp0Zr6gCJ8oY6EYeUL12FSurXqddaWRRuosSrw5t2wO34nSwDADKMs+rqi2EzWNWzn+dKRcdjqbTNNTXATDqkptiVlXYeP1Z+zHwdeBO4JfAjcAg4ALgh6EoMyJj3+EjGX3F7aTq61j00gyq167ioNPNvU6YeK1qng98V1UfwZko/ayqXgNMBE4JS1xYVFZWsrV6z167LVu2UNm5c9FoaKS0Q0c69xlI94HDOO6yW0nV1zJ3xuTInp+UvIhSh1fD6wUscrdrgC7u9gvAmEAVRcDQYQdQVTUP1d29WVTNn8ewYQcUjYZsHHH+95g/81G2b1ofyfOSkhdR6vBqeCuBfd3tpcCp7vbRwM5AFUXA+Muv4KPly7l2wtUsXLCAJYsXc9+vfsn0p6dy7fU3Fo2GbPQ98Ei69duPuTMejuR5ScmLKHV4Nbw/Aye527/GCcv8EfA48JtAFUXA4CFDeOmV11i29EPOPH0Mxx1zJDOmT+P302Zw6tjTikZDSxx81sW8P/sZtq1fE/qzkpIXUerwOoD+g4ztP4rIKpyJ00tU9flAFUXEyCOO4C+zXix6DSdd/bNmjw87/gyGHX9GZDqSkBdR6mjV8ESkDHgKuEVVlwGo6pvAmyFrM4yCpdWqpqruwulAMb+ahhEQXtt4fwLGhSnEMIoJrwPoK4HbROQ4YC6wPfOkqv4iaGGGUch4NbyLgc3ACPcvEwXM8AyjDbQULWgAsEodBkeoyTAKnpbaeB8B+wCIyCsi0iUSRYZRBLRkeNuAHu72aJqsSDAMI3daauO9DLwiIu+7+38WkWb9Z6rqiYErM4wCpiXD+ybwbWB/4ARgMbAjClGGUei0FB9vJ/AggIgcAlyvqluikWUYhY3XuZpfDVuIYRQT5nPFMGLADM8wYsAMzzBiIFLDE5GxIrJYRJaKyM0tXHeOiKiIjIxSn2FEhWfDE5FeInKDiEwWkR7usVEi4mk6mYiU4PSSngYMBy4UkeHNXLc3MAFb72cUMJ4MT0QOxxnH+w/gUqDSPXUK8FOPzzoSWKqqy91AltOAs5u57sfA3UCtx3QNI+/wujrhHuDXqjpRRLZlHH8RuMRjGn2BVRn7q4GjMi8QkcOA/qr6PyKS1buMiIwHxgNMmjSJlSs+9ighPNatXRu3BM4Z0oFevcvjlsGU2VWkylfEquGcIR1ifX5reDW8w3FKuqZ8iuP6zzci0g5nedHFrV2rqlOAKQD33/+ADhg4KAgJvkmCjiRoSJWvYFdFz1g19Opdnoi8yIbXNt5OoGszx78EeHW+uAbon7Hfzz3WyN7AgcDfReRj4P8Bz1kHi1GIeDW8Z4GJItJYfquIDMJpiz3jMY23gKEiMtiNxXAB8FzjSVWtVtUeqjpIVQcBb+CEAZvrMX3DyBu8Gt4NQDdgA05Y5tdxHNtuwYmZ1yqqmgKuwmkXvg9MV9X3RGSSiJzVRt2Gkdd4nau5FThWRE4EDsMx2Hmq+nJbHqaqs4BZTY79KMu1o9uStmHkE17DdB2iqlWq+grwSsiaDKPg8VrVnCci74rITSLSL1RFhlEEeDW8L+F0olwKfCwifxeRS0Uk2nhShlEgeDI8VV2iqhNVdRhOzISFODNWPhWRGWEKNIxCpM2TpFX1TTco5dk408jMw7RhtJE2GZ47Bneb6wDpdWATcFkoygyjgPHaq3klzgTpo4B3gceAP6hq+MHTDKMA8TpX8yZgKnC5qr4Toh7DKAq8Gt5AbRoY2jCMnGkpdsJhQJWqpoFDRSRrIqo6LwRthlGwtFTizQV646w+mIsTFag561OgJHhphlG4tGR4g3EmRTduG4YREC15ks5cQqy4IbuaXueG8zIMow14Hcf7PGRXJiLS3T1nGEYb8Gp4glPqNaWCPHRKNOak0Xz/mqv2OP7kE4/To0tF0WhIko7Z99/CQ+OG89C44Uw+9yB+N/5E5jxyB7U11ZFpiDIvWhxOEJH73E0F7hSRzGhBJTiew6oCVWQULf1GHM3JE+4i3dDAptXLePWB26jbvo0x190Tt7TAaW0c7yD3XwG+DGTGx6sH5uF4IDMM35SUtadTV6dFU9GjN/uPGssHr86MV1RItGh4jVGCROS3wAR3JbphhE712lWsrHqddqVe53jkF15dP3j1nWkYObNy/utMuehwNJ2mob4OgFGX3BSzqnDw/HMiIl8FLgQGAO0zz1koZiMI9h0+ktFX3E6qvo5FL82geu0qDjr9G3HLCgWvLtwvBv6K4/tyNM7Aelccx0eLQtIWGpWVlWyt3rO3bMuWLVR2jmZRfRI0JEkHQGmHjnTuM5DuA4dx3GW3kqqvZe6MyZE9P8q8aIt7v6tU9UJgF/ADVT0UeAqoCVRRBAwddgBVVfNoOh+gav48hg07oGg0JElHcxxx/veYP/NRtm/y6jPZH1HmhVfDGwI0uvKrwxm/A3gADy7Xk8b4y6/go+XLuXbC1SxcsIAlixdz369+yfSnp3Lt9VlDNhSchiTpaI6+Bx5Jt377MXfGw5E8L8q88Gp4n+FUM8Fxu36gu90diD9KRhsZPGQIL73yGsuWfsiZp4/huGOOZMb0afx+2gxOHXta0WhIko5sHHzWxbw/+xm2rQ9/zXWUeeG1c+V/gTHAO8B04D4ROQU4CXgpUEURMfKII/jLrBeLXkNSdJx09c+aPT7s+DMYdvwZkemIKi+8Gt5VQEd3+04gheNtbDrwkxB0GUZB43Ucb1PGdhonWIlhGDnS0gr0bl4TyTRMwzBap6USbyPNr0jIpHHVgq1AN4w20JLhfTUyFYZRZLS0An1OlEIMo5jw6tC2xfaetfEMo214HU5orb1nbTzDaANeDa9pe68MOBS4Ao+hmA3D+AKv43jNtfdeFpHlOEFL/hCoKsMocNocpqsJVcDxAegwjKIiZ8MTkQrg+8CqwNQYRpHgtVdzG7t3rgjQCdiOE77LMIw20JZJ0pmkcVahv6mqm4OVZBiFj9fOlSfCFmIYxUSbfKe5A+k9adI2VNW887tiGHHitY13KPBbdndwq9gkacPICa8l3mM4Lh8mAOtofdWCYRgt4NXwhgLnqerSMMUYRrHg1fBex4mdkDjD27BtJzdPjX8hxTlDOsQtgXVr18YtAXDyolfveH1gJSUvsuHV8C4FfiMiQ4B3cXxrfo6qvha0MK+otGNXRc+4Hv85vXqXM2DgoLhlJEIDJENHEjRkoy1VzUOBU5s5Z50rhtFGvBreIzgObe/EOlcMwzdeDa8fcLqqLgtTjGEUC14nSb8EHB6mEMMoJryWeC8A94rICBxv0k07V/4UtDDDKGS8Gt5D7r+3NHPOOlcMo414nSTtd8GsYRgZmEEZRgx4nSR9XUvnVfUXwcgxjOLAaxvv6ib7ZUAfYCewHjDDM4w24KmqqaqDm/z1A/YFXgOu9/owERkrIotFZKmI3NzM+etEZJGILBSR2SIy0PP/xDDyiJzbeKq6DrgV+LmX60WkBHgQOA0YDlwoIsObXDYfGKmqI4A/ek3bMPINv50r7YBeHq89EliqqstVtR6YBpydeYGqvqqqO9zdN3BmzBhGweG1c2Vc00M4bbwrccI0e6Evu7sCXA0c1cL1lwJ/9Zi2YeQVXjtX/thkX3G8jL1CG9p4XhGRbwAjgROynB8PjAf4z9tuD/rxhhE6XjtX2jX5K1HV3qp6kap+6vFZa4D+Gfv93GO7ISIn47Qdz1LVuix6pqjqSFUd2b5ThcfHG0ZyiHIA/S1gqIgMFpH2wAXAc5kXuE6VHsExuvURajOMSGnR8ETkNBH5WEQqmznX2T13ipcHqWoKxzHui8D7wHRVfU9EJonIWe5l/wVUADNEpEpEnsuSnGHkNa218a4C/ktVtzY9oarVInI3TvyEl7w8TFVnAbOaHPtRxvbJXtIxjHyntarmCJyV59l4BTg4ODmGURy0Znj74MRJyIYC3YOTYxjFQWuGtxqn1MvGCJrpmTQMo2VaM7z/AX4sIns4SRSRTsAk9xrDMNpAa50rPwXOBZaIyAPAB+7xL+N0vAjws/DkGUZh0qLhqep6ETkGmIxjYNJ4CmdY4Ep3srRhGG2g1SljqroCOF1EugL74xjfhxaQ0jByx3N8PNfQ3gpRi2EUDeZzxTBiwAzPMGLADM8wYsAMzzBiwAzPMGLADM8wYqAoDW/2/bfw0LjhPDRuOJPPPYjfjT+ROY/cQW1NdWQaxpw0mu9fc9Uex5984nF6dIluVX0SdCRBQ9Q6PI/jFRr9RhzNyRPuIt3QwKbVy3j1gduo276NMdfdE7c0owgoWsMrKWtPp677AFDRozf7jxrLB6/OjFeUUTQUZVWzKdVrV7Gy6nXalRbt75ARMUX7pa2c/zpTLjocTadpqHecmY265KaYVRnFQtEa3r7DRzL6ittJ1dex6KUZVK9dxUGnfyNuWUaRULRVzdIOHencZyDdBw7juMtuJVVfy9wZkyN7fmVlJVur9+xF3bJlC5WdOxeVjiRoiFpH0RpeU444/3vMn/ko2zdF485z6LADqKqah6rudrxq/jyGDTsgEg1J0ZEEDVHrMMNz6XvgkXTrtx9zZzwcyfPGX34FHy1fzrUTrmbhggUsWbyY+371S6Y/PZVrr78xEg1J0ZEEDVHrMMPL4OCzLub92c+wbX34/psGDxnCS6+8xrKlH3Lm6WM47pgjmTF9Gr+fNoNTx54W+vOTpCMJGqLWIU2L1Xzjh3feq5u+dHrcMpgwopwBAwfFqmHlio9j15AUHUnQcPdP73h74sSJI5s7ZyWeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcSAGZ5hxIAZnmHEgBmeYcRApIYnImNFZLGILBWRm5s530FEnnbPvykig6LUZxhREZnhiUgJ8CBwGjAcuFBEhje57FJgs6ruD/wSuDsqfYYRJVGWeEcCS1V1uarWA9OAs5tcczbwhLv9R+AkEZEINRpGJERpeH2BVRn7q91jzV6jqimgGugeiTrDiJDSuAXkgoiMB8YD3HrrrTU9F05f7Ce9mpqaHhUVFRv9pDF1oZ+7g9EQBEnQkQQNAekYmO1ElIa3Buifsd/PPdbcNatFpBToDHzWNCFVnQJMCUqYiMxV1WaDxEdFEjQkRUcSNIStI8qq5lvAUBEZLCLtgQuA55pc8xzwLXf7XOAVVdUINRpGJERW4qlqSkSuAl4ESoDHVPU9EZkEzFXV54BHgSdFZCmwCcc4DaPgiLSNp6qzgFlNjv0oY7sWOC9KTS6BVVt9kAQNkAwdSdAAIeoQq8kZRvTYlDHDiIGiMDwR+ZqIqIh8yd0fJCI7RaQq46+9iFwsIhvc/Q9E5NqAnt/Q5Fk3u8c/FpEeGdeNFpHn3e2wtNwqIu+JyEI37aPc46Xu8+5qcv0ZIjJfRBaIyCIRuTwADc3mh3uuh4jsEpHvNrnn2yLyjqv7XRFpOvmirRqa+yZURH7SjJYH3P3bRWSNq3mRiFyYswBVLfg/4Gngf4E73P1BwLvNXHcx8IC73R3YCPQP4Pk1WY5/DPTI2B8NPB+WFuBo4J9AB3e/B7Cvu30a8A9gGV80QcqAT4B+7n4H4ICw8sM9d4X7ruZkHOvn6urs7lcAg0P4JpYD85toqcp4D7cDN7jbQ4GtQFkuzy/4Ek9EKoBjceaBeu4lVdXPgKVAn5CkeSZALX2Ajapa56a7UVU/cc9dCPwaWIljoAB743TAfeZeX6eqviYreOBC4Hqgr4j0c4/1BLYBNa6OGlX9KNcHtPBN7ADeF5HGsbuvA9ObS0NVP3Sv75qLhoI3PJz5ny+o6hLgMxE53D2+X0ZV58GmN4nIAKAj4HNOCgDlTapWX2/LzQFq+RvQX0SWiMhDInKCm35H4GTgL8BUnI8fVd2EM7a6QkSmish/iEgQ30yz+SEi/YE+qvovnA++MZ8WAOuAj0TktyJyps/nZ/smwJlDfIGrpQGnxN8DETkM+FBV1+ekwG+1Iel/wPPAKe72NcA9tFzV3IDzgdcD4wPSkK2q+RF7VjX/ErKWEvc5dwBr3eecC/zePd8dZ75sScY9BwHXAvOBx0PMjxuAn7rbI3DGdxvPCc5E+x/glP63h/FNAO1xDP1GnGmJF7N7VXMN8B6wCxibs4awP/w4/4BuONWBFTjtqVU4VanBLRheYyaPxBnE7x3ihzYXGJqxPw74bZhamjz/XJxS7hmcEuVj929H44fZ5PoewLYQ8+Nt98Nu1FGfmT8Z140E3gnrmwAec3+UujVjeI1tvLNcrR1z0VHoVc1zgSdVdaCqDlLV/jilTP9W7kNV5wJPAhNC1Pd34Jvw+XrFbwCvhqVFRA4QkaEZhw7BKVWPAwa4eTQIuBJnvWSFiIxucv0KPxpa0DYMqFDVvhk67nR17OtW7YLQ4eWbuBe4SZ2qdrOoM9NqLl9McWwTebk6oQ1cyJ6LaZ/Bqa544W5gnoj8TFW3+dBRLiJVGfsvqOrNwI+BySKyAKcq9QLwVIhaKoD7RaQLkMKpsj0LdFK3w8XlWeDnONXL/xSRR4CdwHacEsAve+SHm/6fm1z3DE7v4xPAPSKyL1CL82PxXXKj1W9CVd/DqU62xiTgDyLy36qabosIm7liGDFQ6FVNw0gkZniGEQNmeIYRA2Z4hhEDZniGEQNmeIYRA2Z4hhEDZniGEQP/H+h4sCk//Tt8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(3,6))\n",
    "# The full plotting routine\n",
    "geovar_plot.plot_geovar(ax);\n",
    "ax.set_xticklabels(geovar_plot.poplist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want the percentages next to each value, we can run the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(4,6), sharey=True)\n",
    "\n",
    "geovar_plot.plot_geovar(ax);\n",
    "\n",
    "geovar_plot.plot_percentages(ax);\n",
    "\n",
    "# Setting the x-labels\n",
    "ax.set_xticklabels(geovar_plot.poplist);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the Binning  \n",
    "\n",
    "Suppose that we want to distinguish between \"low-frequency\" (1% < MAF < 5%) and \"rare\" variants (MAF < 1%). We can do this by generating a new binning scheme for the `GeoVar` object and then rerunning our plotting code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting new bins\n",
    "geovar_test.generate_bins([0., 0.01, 0.05])\n",
    "\n",
    "# Generating new geovar codes\n",
    "geovar_test.geovar_binning()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "geovar_plot2 = GeoVarPlot()\n",
    "\n",
    "# Adding data directly from the geovar object itself\n",
    "geovar_plot2.add_data_geovar(geovar_test)\n",
    "\n",
    "# Filter to remove very rare categories (only to speed up plotting)\n",
    "geovar_plot2.filter_data()\n",
    "\n",
    "# Adding in a colormap and a new set of labels since we have an additional category\n",
    "geovar_plot2.add_cmap(str_labels=['U','R','L','C'], lbl_colors=['black', 'black','white','white'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qbt2TulCRzbN7B/Dsu5+xpGRaIrL2SNk3s8fr9iV1BFbUzW2JypIJftOKKHrD0hK7QknKSVJWrqu280mmmbZm5vlW8kzRG5ZcaJFdoRYqq6JNCctX127QPbwrlDxFb1gmT5/Dtw89LxFZ5512SGKLEJMiya7Qpft2SzDF6jf73hVKnqI3LNkmhe/UISxom79oadayKjp0yWpUY+HXc5FEh07ZzcPKZaRm/vz5lJaWsvHGG2edebG5ytoQOalI+gUwMjo8ysyukrQHMAE4GXg3CqewMirraGafZL6bE4eSplYgacwsJ6OSK2tqkkl3UUeYXNqyZOVBzmxgYzOrAj6Lyrqa2XxCJLnphBSsmxGi+W+9oQJbO63OsEhi0/bZB0TKhdKyMtq2q2DNmg1LXxGH2tpali1bRmlpaYuRlUc584C+UeqPulm2cyRtCQwCFhBm5gIMACZtqMDWTtF3hbIlydbKJh2SGwHp0iW5ZS9JycqXHDN7B3gnrezdaPdX0d/X8iLMAVqAYemzZWdG//OURGSNqBqXiJzZ1ck5b1tDBDkneYresGQ7j2Xu3LlssskmrF69mk02yS7OR7ZT0ufPmYVZLZ27ZjeNPRfnba7Plcs0+6Sea0P+V6lIOhpYAkwF9gfuBlYBhxDi3nYndH+2BhYDLwKHmtmzOQtt5bQ6H0vSrNnA9KDNlSJ7rgFAmZl9Rpi3sgboAlSb2dtRnXlAG0KakK2jv06OtDrDYmbMnz9/g3PVxKG0rA2bdNyUlSuWF1xWS3yuPD7T50AvSYcDmxIMyDygMnLobh+lBZlLiIfbixAYysmRRLtCkg4BbgZKgbvN7Lq08z2B+4COUZ1L8t0c3XzzzfN5uwbp0KlzYrJa4nPl65lSJ8gBz6TsPxf9vTqq91JeBDrJGRZJpcCfgQMJ8wY+kPR0WmjAXwGPmtkd0S/Js0Dvhu47fX41P/rDk4VROo2c0pHmQJKzYXNa8JgjTZWwzEmeJFssuwMTU/K6DAeOAlINiwHto/0OQKMJg2pVkvXCth6bVjBtQfbN+GzTkU6cMIFt+vVb+zcbkpLTkmXVEaX/6EPo3kwD9gReM7OXo/MnA+8DRxKcvBOA7wDXAfub2bOSTjaze3JWopWRpI9lS8I/tY7pUVkqVwI/lTSd0Fo5K9ONJA2VNFLSyDXLPYODE4stgblm9gxwC1FLWFIZ0MbMPjazawl5nacBXxBa1y9E16+RlEze2xZAc3PeHgfca2ZbAYcBD0haT0czu9PMBpnZoNIK/187jdLJzC4C+kRd8v8DHojOdSaadSvpdGB4tE7oWULr5WRJWxOWBTRt+LsiIknDMgPokXK8VVSWyinAo7B2tmQ7wrBgXpFCd6hjRWGDQVdusgkTxo/HzFqEnCKWVSvpImAO8EvC92qX6NxcoL2kfYEhhLQgAHsBYwktnW2ArsTomjuBJH0sHwD9JPUhGJSfAMen1ZkKfA+4V9J2hC9A3mOSTp1f+OFfgG7dutEtgTABSckpVlmpwbQznKuVtNrMXgdeTyn/d7R7JQQ/jJl5vzsmiRkWM6uRdCahz1oK3GNmn0i6GhhpZk8DFwB3STqP4Mg9yRr5udq0XSmn75JMdyip6e8tNR5Lcx0VMrN7Y9Rxx20WJDqPJZqT8mxa2a9T9j8F9s7mnkkmLIPsU4TmQtKJvVqqLKfpaG7O24IzZfynTJs4jsmfFz5HVVKyxo4ZA4T8zZMnFXbFfzHKknSUpAslXSKpe/T3LknlKXXOlLR9dO5mST2iskMl/VBSh+jaH2zwg7UCWp1hAeixzQDK27ZtvGKRyXLqZTczuwHY3cxmRjO+pwBtASR1BRaZ2afRuRXAfEJs3MVAiZktMrOZhHVHTiO0SsMyZfynLF+aTFyWJGRJwsyora2lpKSw/9IilfUPSecS/HZIOoCQEL4us30vohEfST8h5HdeGvletgOmRwG5oQVEBEiCVmlYevXfnvJ27VhWvaTxykUgq/uWWzJm9GgmTpjA5l27FkxOEcsqIwwY/DMalfwVsJWkDtH5KUB3SXsBPwMGKvAtYDxhmkTfqG6ysUaLlFjWty6/bV06Skk7AscCn5jZw4VTL//06r89AD232bbFyOrUqROdOnUqqIxilmVmY4AxKUVD0s7PltQhCqFweMqpdZxjkroDyUT7KnLiNuseJcxUvEdSF+ANQtPxLEndzeyPhVKwMRYuXcE9z7yXiKwjBxQ+jw4kG0HOh5sDZnZbjDozgScLr03xE9ew7ATUxQg9mrCYcLdocdf1QJMZllwWIeZKRYdKH24uIllO0xHXx1JB8JADHAA8He1/xLrT9Js9W3Zsxy49OxZczqxpX/Kve27lpX8+wJvP/augU+A/HDmS0VVVa4/vv/fvRS0n37Ik9ZY0WtLHKWXnSHpd0rtRjiEkfSrpNUmvZLhHP0kXSNpY0luS3pB0d3Tux5I+rHPwRotkd472t5F0Sc7KFylxDcsE4EeSegAHEWKCQlg/sbAAerUI+u24Cwce/TMqNq5k4bw5Ta1Oa2Y2MJiwor6O281sX0IL/IKobKqZDTGz72W4x6nAcEJoy++a2XcBorhBPyTkoT9GUhtCRLpRAGY2kdDib1XENSxXAb8HvgTeNbM6p8bBwKgC6NWiWLVyZVj56DQJZrbczBanldU5sjbhGydtd0n/lfTLDLfZycxmWKAu4O9KYBYhB3QFIUD3/xKiIKYyRVLhRwuaEbEMi5k9AfQkJHc6JOXUy8D5BdCrRTBh7Ef8+/6/sHL5MjbtklzoSCcekv4KvMQ3iw+/G23fkpTeykidpbunpNGEMAqLCZER/w78Bdge2E7SHVGoVQjD2f0L9iDNkFiGRdKvgSVmNsrMalNOjQF+XBDNWgD9dtyFw044FTOjZnXhRnrKysoSybaYlJykZJnZMEJkw19HxwujRa/PESbG1XfdO2b2baJ0Imb2npkdTTAeDxL8kL8mxH1plcTtCl0BZFpCvFF0zqmH0tJSevbbjsmfjy2YjC6bbcb06SE434wZM+hcoEyFSclJQlbKOqFqoFpSG0l1ay92ByanXbI67ToIOYmWR+WlwA6Rb6UjIRPAxlG9XgQ/ZashrmER0XToNHYmrKkoKnbYYhOO+vYWHPXtLahoU/jJxz37bcuX4z5tvGKObLnllpSUlHDXX//CC889yz6Dv1vUcvItS1J7SS8DgyS9LKkdcK2k1whR+68jxFh+W9J/Aczs/bTbjI1yPW8TjSa9BmwBvBmdPw74R7Q/iTDf5aHouDchBUmrocF5LJKWEAyKAZMkpRqXUkIgpr8UTr38M2PhCh54b1rjFTeQbj16061HbwDatCnnwKN/WlB53z/iyILeP2k5+ZQVOW4PSCu+MEPVXTOU1XEX8JNoMui+GWQ8mLK/9t6StgHGNBZXqKXR2AS5MwmtlXuAywhNvzpWAV9GISQdp0VjZuPIYTp/NNz8u/xr1Lxp0LDUhfSTNBl4O2WIrtmwZmk1C/77ZCKy7h5ZxpxFhV+DtnmHZOS0ZFnH79MxETlOZmJN6Y/igdYtwtqcNN+MmX2Uf9XiUbPGmDovOXuXlKyW+ExJy3Kajrirm3cmDKNtS+gapWIEf4vjOA4Qf1ToTkISp8GEuBR9Ura+DVznOK2eaELdp1FytELJqJQ0RVLuKSPzSNwH3R7Y2czGF1KZQjOgT1fOP/FA9tllazapbMf8hUsZ9dk07hj+Ou+P/dJlNSNZ+ZYj6TpCrJVehPU+rwMXm9l6N4sCQI0hzDavMLMVKeeOBn5DGEKeClxuZo82Iv424Eozq4nuMQR4FUgNLVhtZmtznUi6gjB4shQ4KyUdCZJ+BXRMHX0ys2pJNxIiDSQ3dFcPcQ3LWKAbIZpWUbLXwL488qehPPjv9zh02K1Mm7WAjSvKOWSfHThq/2/n9aVwWc1SjgEnEb7LGxFe9vpm2N4KfEYwLGuJVkE/RIgy9y+CoRouaYaZvZVJqKT9CMn5/pXhdJdUo5Vyzc7AiQTXQz/gP5I2j3Ig7QL8DyG3dDr3Ab+T1L+pGwGKM7wuaX/gWkJIv7FEsxDrMLMmmyR39oWX212vLGi03rvDL2HUZ1M5/ap/NFq3Pnp2aRPL+bihsuLKaamy8vG/uuQHm314xRVXDKrvfBR2cizQ3cy+Sin/H+D/ETImvkxKi0XS3wnG4IiU+o8Bq8zshHrk3AZ0NrPjUsqGEFosFfUYlh8DP6y7RtIswgrpxYS4SCfXN2Ai6Q1CzN4mHeKO62N5mTDN+UVC5Li50TaPAmQqzDd9e3Rhm56bMfzZkS6rmctK8JkOAr4irE4GQFI34GZCyybTQqWBQPqM3A8IM9DrYxBpIS5TmCBpjqRXJaWGahgL7CFpsygO72rCe3Yt8EQjo7BjCO9qkxK3K7RfQbUoMJttugkAM+csdFnNXFYScqKX+DfAsWkzYu8BbjKzz6KUIOm0Z/34Qwui8vroxLoTSyFM7x8IfEIIt/AL4DlJ+5jZ+5H8a4HnCWuZfkyY7bs3sLekywkziecCZ5pZaszPRTSD+C9xwya83tBWaCU3lLkLQoT87pt3dFnNXFah5Ug6FHiC0J34T0r5MMJ6oT81cPliwgLDVDaNyutjfnTftZjZLDMbbWY1ZrbEzG4kxJH+SUqdu81s1ygY1SeEpTMnEXKb7wfsT+hBpIeF7QAUPg9wI2S1Ak8hE9x3JH03dSuUcvli0rR5TJw6l2MPrbfL7bKaiaxCypF0HCEK3PFm9kja6UOBbwNzJM0DnorKp0dGB6AK2C3tukE0HOzsQ2CHGOrVsv4csTpuBv5qZp9FOr4XBZt6g/W7YTsSumdNStx4LN2j1ZzTgbeA1wjOp7qt2XP+dY9yxJCd+O3ZR9FzixB8e+OKcn54wEB+e/ZRLqsZySqEHElnAHcAR5jZMxmqnEIYhRkYbb+IyutGggD+Chws6egozMKRwBHRfevjcWD/1Dkskg6W1FdSiaSNJJ1FSEnyWAa9f0CYL3ZTVPQFMERSBXAYMDGlbkeCf+WJBvRJhLg+lpsIzqztCdbwEEK826uB8wqiWZ55u2oSB5xyI+efeCDP33kOlRu3ZcGiZYz8eAp3DM9vb85lNUs5txGSjT2rdcOEHmpm/zWzdboPkuoGJWbUjdyY2buSfgr8ljATfQpwkpm9ST2Y2QhJMwhxcesMx+6E1dKdCfFcPgG+H+U1StVhc+AG4IAUX9CTwPcJjudJwPEpl5wIvGxmTR6iIe5w82zgcDMbKWkxMMjMxks6nDBBKNOYeiLEHW7OB9kMzRaDnJYsq7Hh5iSRtCfwN0Lc3IKswpRUSRh9OtDMmjyoVNwWSwVhaBmCM2pzwmS5T2kGHmjHac5EoUW2L7CMasJs4GZBXOft54T+JwQH1mmSegFnADMKoJfjOEVM3BbLzYQp/RD8Ks8TQvGtJPTrHMdx1hI3HstDKfsfSepNaMFMNbN59V7oOE6rJKdl3Ga2jJBetcnps2VnRv/zlERkjagaR0WHwkWmr6NvxWq6duvWeMU8MHvWrBYp6+EH03OGOUlSr2GRdAtwqZktjfbrxczOzrtmMSkrK0ss0XjF5K89KXwRyXKajoZaLDsScqNAGPmpb1y6VUUfdxynceo1LGa2X8r+kES0cRynRdDocHM0dXmWpDjrHRzHcRo3LFHKj9V4l8dxnJjEnSB3K3BpIYMBO47TcohrKAYTAs3MkPQx6wYBxsyaPHiv4zjNh7iGZR5h+bfjOE6jxI0g9/OGtkIrmQ8O+t4Qzj37zPXKH7jvXrp0rMyrrNsuP5djBnbnmIHdOXbXHpx2yCDuvOYSqhcvzKucJJ8pKVlJPlOSRIMg45IIjCbp/SgoeJORVQQ5Jz477jGYO1+u4vZn3+P0X9/Ah6+/xN3XXtrUarVaJJ0haYykxdH2ThT2I71ef0lPSFooaZmkjyRtl1Znd0kvSaqWtETS25Iam5I9FJhpZm9E9xgiyerZjonqtJX0QKTveEkHpOlxlqRMqQx+A1wnqcne79jOWEk/Jyw87AmUp54zM8+GmEab8rZs2mVzADp37c5eBx/Ja083ltfKKSDTgYuBCYQf1BOBJyXtamZjACT1IURIvJ8QU3YhYU1cdd1NotxCLwDXE4KcrQK+RVpKnFQUIkudTXjh63gb2CKt6tnAWYR8RxCM0a7AnoTQmf+Q1NXMTFIP4AIyR+R/lhBI6lAgU7S8ghM3d/NFwKWE0HzfBW4Hton2byiYdi2E2dOnUPXWq5SW+aBaU2FmT6UVXSbpdMJLOyYquwZ40cwuSKk3Ke26G4E/m9k1KWWNJQfblSjxWIo+q0hJPQJrsyw+HMVWgZBM7Wkz+0TSJIIx60KIzn87IbvinAzPukbSs4SGQJMYlrhNpVOBoWZ2KcEy3xaNBP2RkLLSSaPq7Vf56Z7bcPwefTnz+3syfdJ4fnDSGU2tlgNIKpX0E6CS0HIg6jYcAXwq6XlJcyV9IOnYlOs2JxiiryS9GeUE+m9aTqBMDAa+MLOFDeg0hGB87kwpHg3sE8W3PZgQjnJelNCswszubUDm+4SR3CYh7k/oVnyTqGk53+RReTgqPzXPehU92+3yHYZd/gdWrVzBK088xKxpUzj0+GRWYTuZkbQj8A7QjtC9+aGZjY1Ob04wNL8ELgcuIXSHHpJUHQXgruvyXwVcRIjOfwzwQtSlGl2P6F6ERH8NMRSoMrPUTG33ENbpfUoYmf0xIb3HdcBBCvmdTyC0fIamxbqdCWwpqaxQ4TAbIm6LZRahCQYhgPCe0f42FMmM3Pbt27N4UXreKFi4cCHtO3TIcMWG0bZdBVv07EOvfttx8sW/ZdWK5Tx+5415lZHkMyUlq8ByxhEi8O9BiKx/n0KqVfjmXXjKzP5kZlVm9ifgUUJy9tQ6fzWze8xslJn9khBg/rQG5FYA66VSrUNSZ+BHBL/IWsxstZmdYWZ9zGy3KGj39YRWzbbAsYRu1oPAA2m3XU5IJ9KuAb0KRlzDMoJvMtj/DfiTpFeBR2gGqQbi0K//AKqqPiI9eHjVqI/o339AweUfM+x8nrz3dubPmdV45Zgk+UxJySqkHDNbZWYTzezDqFtfxTdZJuYRovh/mnZZanL4uhzP6XU+JS2BfBrzCInN6uN/CVkwHmqgDpL2JeQxuoHQmnrGzJZE1w2StElK9U7AihR/TaLENSxDCSkPMLO6jGxjgcuA/yuIZnlm6LDTmTxpEuedcxZjRo9m/Lhx3HLTjTz6yMOcd8FFBZe/w257sVXffjx+1815u2eSz5SUrIT/TyVAW1jrTP0ASLde/QmtdIAvCV2MhupkYhQwoIHh318Aj5nZ+k21CEltCa2soVHXpoRvwprUjdKWplzyLZowGFtcw6LUfpqZPWJmZ5vZbdEixWZPn759eWnEG3wxcQJHHHYQg/fancceHc5Dwx/j4EMOTUSHI342jBFPPszcmdPzcr8knykpWYWSI+k6SYMl9Za0o6TfEZKEpbYS/gAcK2mopG0knUpIe/pngCi3z/XA2ZKOier8EvgOYcS0Pl4ldEnWy2ghaR9CBP+70s+lcTnwgpnVZTl8E/gfSQMJ/p5P0pzDgwmxqZuEuM7bWZKGAw+a2XuFVKiQDNptN/797AsFl3Pmb27KWD74sB8x+LAf5VVWUs+UpKwCyelG8EV0IyROH0NIVrZWkJk9KWkowYF7M2HOy/+mZk40s5ui1sMfCQnHPonuU5/jFjP7WtITBEdrVdrpU4HPzOyt+q6P/EDHEvxDdTxBMB6vEjJlnJhSf0tgL+Cn9d2z0MQ1LJcRMq69HY2nPwg8ZGYTG77McZoHZnZSzHr3Avc2Uuf3wO+zVOG3wKuSfmNma5PIm1mjWS7M7GPCUHRqWS1wTrSlcy5wr5nlp2mcA3HXCt0ZRZHrDdxN8GCPl/SuQk5cx3EawMw+AS4k5GEuNHMIXacmI6u1BGY2zcx+b2bfJnin2wINBtp2HCdgZvc31GXKo5zrzWx2oeU0RNZzzCNn0wmEiUHlhG6R4zjOWuKuFdqBYEyOA7YEXiIslnrSzJYXTj3HcYqRuC2WsYSp+38ChpvZ3MKp5DhOsRPXsAwwswkF1cRxnBZD3FEhNyqO48TGI8g5jpN3ij7yUE1NDVOnfJmIrL4Vq+laubLgcmbPyt9CxcYYUTWOislfJyKrb0VRrP5w8kDRG5Ykk8JDcknNW1qie0g+2b3TdNTbFZK0JoqYhaR70pZkO47j1EtDPpblhIhaEBY4NUnAGMdxio+GukJvE6KYf0iIRHWLpIyT4czs5EIo5zhOcdKQYfkZYdFUXfjJzkDhPZeO4xQ99RqWaBHTRQCSJgPHmdkGDR9IOoQQ56IUuNvMrstQ58fAlQRjNtrMjt8QmY7jJE+sUSEz2+Cl3pJKCZG4DiQkj/pA0tNm9mlKnX6E/EV7m9mCOuex4zjFRewJcpIOl/SGpHlRzpXXJR2WhazdgYlmNimKLzocOCqtzqmEZFALADIlY3Icp/kTy7BI+gXwL+ALQprKS4DJwL8kxXXcbglMSzmeHpWl0h/oL+mtKIjUIfXoM1TSSEkjFy5aGFO84zhJEbfFcjFwvpn93Mz+Fm0nEZy7l+RRnzJCCL4hhBANd0nqmF4pimg3yMwGdeyw3mnHcZqYuIalJ5kjfj9H/BSrM4AeKcdbRWWpTCfkql1tZpMJOXH74ThOURHXsEwlOF3TOYiG86mk8gHQT1IfSeWEtApPp9V5ktBaQVIXQtcoPSm34zjNnLhrhW4AbpW0C1ESbWBvwlyXs+LcwMxqJJ0JvEAYbr7HzD6RdDUw0syejs4dJOlTQma4izZ0iNtxnOSJO9z8V0lzgAsIEfohpJ78sZk9FVeYmT0LPJtW9uuUfQPOjzbHaTFIagN8DJxqZm80sS47Elwb/c1saSFkxB5uNrN/mdk+ZtY52vbJxqg0NQd9bwjnnn3meuUP3HcvXTpWZriiectJWtZtl5/LMQO7c8zA7hy7aw9OO2QQd15zCdWLF+ZVTqGeSdKVkixtm5VW50eSXoimU5ikIRnu003SA5JmSVomabSkE2KoMBSYmWpUJF0WjYAulWSZLpLUU9K/ozrzJN0SuRLqzu8saZSk6qhep5RzJZLel3RQ6j3NbCzwLgX8AfdAT05sdtxjMHe+XMXtz77H6b++gQ9ff4m7r720qdXKhnHAFinbjmnnNyZ09Rt64e4HtiPMwfpWdPyApO/Wd4EkAWcDf0s71ZaQ0fCmeq4rBZ4BNiFkPTwOOJqQhbGOu4ERwC5AB0IWxzrOBsaZ2YsZbv934HRJBQmdUvTxWJzkaFPelk27hMnQnbt2Z6+Dj+S1px9tYq2yosbM6o2iZWYPwNqBg/rYCzgrJdXwHyWdTZgAWl8XZ1fC6OZ/0uT9OpJ3dD3XHQTsAPQys2lR3f8H3C3psiij4nbACWY2XtLDwPejer0IGREH1XPvF4FOhMGSlxt43pzwFouTE7OnT6HqrVcpLSuq36a+kmZKmixpuKS+OdzjTeDHkjpHXY2jgM1o+OUcDHyRlrQ9DnsS8jqnTix9gdDS2TU6Hg0cGLU8vkfISQ1wB3C5mc3LdONo9nsVsG+WOsWiqL4VTtNS9far/HTPbaitrWX1yhUAnHjBlU2rVHzeA04CPgc2B35FyEW+Q5Yjjz8mLEeZB9QQVvwfZ2ZVDVzTC5iZg87dgPSMhvMII6bdouNfALcTJqu+BfxO0nGEd/sVSf8htGqeA84zs9T4oDMJaZPzjhsWJzbb7fIdhl3+B1atXMErTzzErGlTOPT4U5parViY2XOpx5LeJcyROpGQLysuvwW6AAcQXvIfAPdL+m4D6VMrgBXZ6hyHKCf02lZH5Ly9ltB6uQUYRRjJfZHgQP5zyuXLI93yTjaLEPeQ9EtJN0We6bVbIRTLN+3bt2fxokXrlS9cuJD2HToUnZykZQG0bVfBFj370Kvfdpx88W9ZtWI5j995Y15lJPVMZlYNfEIWM7slbU2Yt3Wqmb1iZqPN7CrC5M+G5nPNAzbNQc1ZQNe0si6EeWD1+YpuAG43s0nA/oQEg6uAx6LjVDoBBUk+GHcR4oXAO4Sm5ECCN71u+1YhFMs3/foPoKrqI8JUmW+oGvUR/fsPKDo5ScvKxDHDzufJe29n/pz8ZRVI6pkktQO2Bb7K4rKNor9r0srX0PC7NAoYIClbn+Y7wHaStkopO5DQ/fowvbKk/YFvA3XWvgRoE+2XEwxSKt8CPspSp1jEfdBzgLPNrL+ZDTGz/VK2dCvYLBk67HQmT5rEeeecxZjRoxk/bhy33HQjjz7yMOddcFHRyUlaViZ22G0vturbj8fvujlv9yzUM0m6QdK+0ZKSPYB/EoaX70up00nSQL75sdxG0kBJdf6Mz4GJwO2Sdpe0taQLCC/7vxoQ/yohZvROaTr1jOT1jo4HRlvdhJ0XCa2q+6P5KgcA1wN3RSNCqfdqR+jmDDWzmqj4TeBsSdsRGgVvptTvTYgukGkoeoOJa1jakzZjttjo07cvL414gy8mTuCIww5i8F6789ijw3lo+GMcfMihRScnaVn1ccTPhjHiyYeZO3N6Xu5XwGfaCniYMJflCcKv/nfMLHWt25GE1sWr0fFd0fFpAJHj8zBC9+HfhBGY/wV+bmb/rk9w5Bx+AkifSHd1dP/ro+NR0TYoum4NcDiwjOCYfQR4nOCoTecK4FkzS23JnE1olb1HMFCp/pXjgBfTnj9vKL3JmbGS9BdgjJndXgglNoRbb73NTj19/ZmahWDqlC8TyYuTlByAR177MLG8QjslmFfo99dc9eEVV1xR3xyOxJG0A8FgbZPe2mgCXdoCEwijWW8VQkbcUaFpwFWS9iZY6XVS2plZNl51x2l1RAtuLwT6EOaeNCW9gGsKZVQgvmH5BVBNmHW4V9o5I7vhOsdplZjZ/U2tA4CZjSfEOioYiQXTdhyn9ZD1lH5JlZI2LoQyjuO0DLKZIHeGpKnAImCxpCmS/q9wqjmOU6zE6gpJ+iUh388NfDMWPhi4TlL7TInHHMdpvcR13p5GmHjzcErZK5ImENYluGFxHGctcbtCmxPWQ6TzPuuvZXAcp5UT17CMBzLlUD6eMJPRcRxnLXG7QlcCj0bh9+om1exNWK59TAH0chyniInVYjGzJ4A9CEu1vx9ts4DdzezJgmnnOE5REjvQU7S46acF1MVxnBZCvYZFUiczm1+339BN6uo5juNAwy2WuZK2MLM5hAhYmZZBKypPDyDjOE4rpiHDsj8wP2W/8fgKTUBNTQ1Tp3yZiKzZs/IXKa05yAHoW7GarpUrE5E1omocFZM9Y25roF7DYmavp+y/log2OVBWVpZYjA8gMVkt8ZkqJn+dWOwXp2mJG/N2jaTNM5R3lpQe/9NxnFZO3Alyqqe8LbAqT7o4jtNCaHC4WVJdDlsDTpNUnXK6lLAQ8fMC6eY4TpHS2DyWulwpIkSRS+32rAK+JAo07DiOU0eDhqUucpykV4EfmdmCRLRyHKeoiTvz9hAy+FmiXCa1UaY1x3EcIL7z9lEgU7S406JzjuM4a4lrWPYmc8a0l1g/ar/jOK2cuIZlI6AmQ3ktsEn+1HEcpyUQ17CMIaRkTOd44OP8qeM4TksgrvP2auApSdsAI6Ky7xGCPP2wEIo5jlO8xA309CxwBCE14y3R1hM40sz+Uzj1HMcpRrIJ9PQ88HwBdXEcp4WQdSZEx3Gcxoi7urlc0lWSxktaEa12XrsVWknHcYqLuC2W3wAnAn8kDDFfBPwZ+JrME+ccx2nFxDUsPwZOM7O/EhYiPmVmZwNXAAcWSjnHcYqTuIalK/BptF8NdIz2nwcOyrNOjuMUOXENy1SgLqbgRODgaH9PYHm+lXIcp7iJa1j+RZgQB3AzcJWkycC9wN0F0MtxnCIm1jwWM7s0Zf+fkqYRFiaO9wlyjtM4ktoQlr+camZvNLEumwOfADub2fRCyGi0xSKpjaRHJG1dV2Zm75nZn4rJqBz0vSGce/aZ65U/cN+9dOlYWXRyWrKs2y4/l2MGdueYgd05dtcenHbIIO685hKqFy/M+Z6SvpRkGbZn0ur9n6TJ0bSKDyUNTju/taR/SZorabGkRyV1jaHCUGBmqlGRdJmktyQtldRgeh1JXSTNiHTuklLeW9Ib0T3ekNQ77brHJQ1NLYtyhd0PXBVD75xo1LCY2WqCg7ZZ5hVyWiY77jGYO1+u4vZn3+P0X9/Ah6+/xN3XXtr4hfWzG7BFyrYL4Tu9Np6QpGMJXf1rgZ2Bt4HnJPWMzm9MCB8iQq6tvYFy4N+S6n2XJAk4G/hb2qm2wBPATTH0/ztQlaH8j8AMYCDwFXBDitwfAJsBd9VzvxMay3KaK3F9LE8APyqEAo6TiTblbdm0y+Z07tqdb+81hL0OPpLR7+TegzCzuWY2q24DDgMWs26gsvOBe83sLjP7zMzOIrysp0fn9wb6AD83s7FmNpYwv2sQwdDUx65AP2CdFr6Z/drM/giMakh3SecQQpf8McPp7YD7zGwCwee5XXRN+6j+UDNbr1FgZh8DMynQex13rdBU4FdRs3AksDT1pJn9Kd+KOU4ds6dPoeqtVykti720rUGiFsQpwINmtjwqKycYgBvSqr/IN8HM2hJaOStSzq8gTBrdB3i5HpGDgS/MbGEOuu4MXExocfXLUGU0cICkFwk9izFR+XUEI9lQFo33gX0pwABM3P/UScACYKdoS8UANyxOXql6+1V+uuc21NbWsnpleI9PvODKfN3+QELLI7WL0IWQ0mZ2Wt3ZwAHR/ruEeVzXS7o4Krsuum6LBuT1IrQOsiLqeg0HzjKzGZIyGZYLgb8SMmaMAYZJ2otgzPaT9BBhWsi7hEmui1OunUkwWHmnXsMS9SunWaBPIYQ7Tn1st8t3GHb5H1i1cgWvPPEQs6ZN4dDjT8nX7U8FPjCz0dlcZGZzJR0D3EFYylILPAx8FO3XRwXrtnLicgvwppk93oBOM4Dv1x1HLa/ngWHAJYTIj/0J3aTLCctx6lge6ZZ3GvKxTCY4fpA0QlLHQiiQFO3bt2fxokXrlS9cuJD2HToUnZyWLAugbbsKtujZh179tuPki3/LqhXLefzOGzf4vtFQ61Gs79CcR1iukj7C0xWYVXdgZi+a2dbA5kAXM/sZsCUwqQGx84BNc1D3e8BJkmok1QCvROWzJF1TzzWXAG+Y2dsEv8+jZlZDMIDpfqBOwNwc9GqUhgzLEkLzEGAI0KYQCiRFv/4DqKr6iHQ/VtWoj+jff0DRyWnJsjJxzLDzefLe25k/Z1bjlRvmJGAl4UVbS5TC5kPWX/t2IGF0iLT688xsoaT9CUbm6QZkjgIGNDRyVA8HAd8mjPgMJCQNhPA+3pJeWdK2BGdy3fBZCd+8t+WELlsq3yK0tvJOQw/6MjAiSlYG8K+o5bLeVgjF8s3QYaczedIkzjvnLMaMHs34ceO45aYbefSRhznvgosav0Ezk9OSZWVih932Yqu+/Xj8rptzvkfktP0FMNzMqjNU+ROhhfALSdtJupmwlOUvKff4uaQ9o/ksPwUeA240s3ENiH4VaEeaf1JST0kDgd7R8cBoqwQws/Fm9nHdRuhFAHxuZrPT7iXgTuD8FD/Km4TUyAMII1tvptTfiOCsLkjwtoYMy88I4RKqouNxhNl6mbZmT5++fXlpxBt8MXECRxx2EIP32p3HHh3OQ8Mf4+BDDi06OS1ZVn0c8bNhjHjyYebOzHmy6BDCyEqmeR2Y2SPAucCvCN/7fYDDzGxKSrUBhCUunwG/Bq4hOFDrxcy+JkzZOCHt1NWE1sz10fGoaBsU73HWYSgw18yeSim7kjC4MpLgA7oy5dxRwFQz+28OshpFGYa4168UWi0/zGW4rNDceuttdurp688ILQRTp3xJz169W4ycpGU98tqHbLxZ98Yr5oFPnrzzwyuuuCKXF7QgSNqB0HLZJm1kpqn0eR+4ycz+UYj7xw2mvV9zNCqOUyyY2SeElk2Tj7BGDux/kuZnyif5mXHkOE6jmNn9Ta0DrF0r9IdCyvBg2o7j5B03LI7j5B03LI7j5J3YhkVSV0kXSrqjLh6EpL0lNbkzynGc5kXcvEK7EuaxnEBYFdo+OnUgYRzfcRxnLXFbLDcAN5vZzoTp0HW8QIhR4TiOs5a4hmVX4L4M5V+x/qItx3FaOXENy3Iyr87cFpiTP3Ucx2kJxDUsTwFXSGobHVsUtPf3QL2xItKRdIikcZImSrqkgXr/EwUNbjZTsh3HiU9cw3Ih38Ru2IiwSnIisJCwYKtRJJUS8j0fCmwPHCdp+wz1NgHOAd6LqZvjOM2MuHmFFgP7RLEndiEYpI/MrL4Yn5nYHZhoZpMAJA0nrLD8NK3ebwgtocKvxXccpyDEHW4eCGBmI8zsBjP7Q5ZGBUKUrWkpx9OjslQ5uwA9zGydXC8Z9BkqaaSkkQsXLcxSDcdxCk3crtBHkj6WdLGkrQqhSBRd60/ABY3VNbM7zWyQmQ3q2KFjIdRxHGcDiGtYtiU4aU8BvpT0mqRTJGUT7HQG0CPleKuorI5NCKHyXpP0JfAd4Gl34DpO8RE3Hst4M7vCzPoTJsSNIcy4/UrSYzFlfQD0k9QniiT+E1LihJrZIjPrYma9zaw3IV3BkWY2MovncRynGZD1IsQob/PZBMfrOGJmUosihZ9JmK37GSF6+CeSrpZ0ZLZ6OI7TfMkq0FO04PCEaNsGeINvIoc3ipk9CzybVvbreuoOyUY3x3GaD7EMi6QzCMZkD+Bj4B7gH1GyJMdxnHWI22K5mBAfc1iUCNtxHKde4hqWXpky1jcHampqmDrly0RkzZ61wcmyYjGiahwVk79ORFbfitWJyKmT1bVyZeMV80BR5KRpwTSUu3kXoMrMaoGdQz6kzJhZQbKpxaGsrCyx9BVAIrIqJn+dWJqMrpUrW9zn5zQ9DbVYRgLdCKuXRxISH2WyLsb6qRsdx2nFNGRY+vBNwmgPP+k4TmzqNSxpaSUNmJbJzyKpZyEUcxyneIk7QW4ysFl6oaTOfJOo2nEcB4hvWERotaRTCazInzqO47QEGhxulnRLtGvA7yQtSzldSoixUlUY1RzHKVYam8eyY/RXwHbAqpRzq4CPCBH8Hcdx1tKgYTGz/QAk/R04J4ok5ziO0yBxQ1P+vNCKOI7Tcoi9ulnSfsBxQE+gPPWcme2fZ70cxyli4sa8PQl4jhDlbQhh4tymhMDa6cGwHcdp5WST/uNMMzsOWA1cGqVbfRCoLpRyjuMUJ3ENS1+gLir/SsL8FYDbgJPyrJPjOEVOXMPyNaEbBCEA9rei/c5ARb6VchynuInrvP0vcBAwFngUuEXSgcD3gJcKpJvjOEVKXMNyJtAu2v8dUEOI1v8o8NsC6OU4ThETdx7L/JT9WkIKVMdxnIw0FEGuU9ybpBoex3Gchlos88i8ojmVulXPHkHOcZy1NGRY9ktMC8dxWhQNRZB7PUlFHMdpOcSd0t+poa3QSjpOS0DSq5L+txno0VbSVEmDCiUj7gS5eYT1QfVtzZ6DvjeEc88+c73yB+67ly4dKzNc0bzlANx2+bkcM7A7xwzszrG79uC0QwZx5zWXUL14YV7lQPF/fpK+lGQZtmdS6pwhaYykxdH2jqTD0+5zb4Z7vBtD/uFAD+ChlLKhkbFZGN2nd0y9r0s530nSvyVVSxolaee06/8o6drUMjNbCVxPAUd3485jSfe3tAF2Bk4HfpVXjZys2HGPwZx1za3Urqlh+hfjuf3KC1i2ZBHnXndHU6vW3NiNdQcZtgA+JMzFqmM6IevnBMKP7onAk5J2NbMxKfVeBn6WcpwaAK0+zgHuNbM1KWUbAS8CTwE3NnDt1UDqPzR1fd5lhFnxuxDex7uAQQCSdgUOI7yr6TwE3CBpBzPLe363uPNYMvlbXpY0iZAU/h951cqJTZvytmzaZXMAOnftzl4HH8lrTz/ayFWtDzNbp2Ut6RRgMSmGxcyeSrvsMkmnA3sCqYZlpZnFTospaTPgAOCiNJ1uis431iVZ0oC87YDhZjZe0p3A0OieZQQj839mtl5cajObL+ktQiiUvDcO4naF6qMK+G4e9HDywOzpU6h661VKy2KH2WmVKKT1PAV40MyW11OnVNJPCAtu3047vY+kOZLGS7pL0uaNiNyHsHj34xxVvlDS15KqJF0mKTUe0mhg/8iQHMw3BvB8YJSZvdrAfd8H9s1RpwbJ+RsoqRI4F5iWN22crKl6+1V+uuc21NbWsnpl+GE68YIrm1ap5s+BhCR8d6WfkLQj8A5hCUs18EMzG5tS5XngCULam96EJS0jou5SfYmpewFz0rpBcbkFGEVYCLw7cF2k+y+i89cRuklfAF8Cp0jqS+gWfUfSrYTu0OfAL8zsq5R7z4yeIe/EMiySlrDuZDkR+odLgRMKoJcTk+12+Q7DLv8Dq1au4JUnHmLWtCkcevwpTa1Wc+dU4AMzG53h3DhgINABOBq4T9IQM/sYwMyGp9QdK+lDYApwOMHgZKKCHNPkmNmfUg7HSFoMPCLpYjP72swWAcenXiPpReBS4BhgB0J36QqCkTompepyChSdIJtFiKnUEkaD3jOzBflVqTC0b9+exYsWrVe+cOFC2nfoUHRy6mjbroIteoYMuCdf/Fuu/MXRPH7njfz49AvzKqelfH5Rt+Uo4IxM581sFTAxOvxQ0m7AeYSuU6b6MyVNB/o1IHYeIeJiPngv+rsNoRWzDtFw9mozGy7pCeBxM1sl6R/AG2nVO1GgUd1YPhYzuy9te8DMni8WowLQr/8Aqqo+Ij1LbNWoj+jff0DRyamPY4adz5P33s78ObF9i7FoQZ/fSQR/x8Mx65cAbes7KakLsCXwVX11CF2ZzaK6G8rA6O968iIn8dWEbhAE3dtE++Wsv/TmW4QUPnknK+dtNGa+raTtU7dCKJZvhg47ncmTJnHeOWcxZvRoxo8bxy033cijjzzMeRdc1PgNmpmc+thht73Yqm8/Hr/r5rzetyV8fpHT9heEUZT1QqpKuk7SYEm9Je0o6XeEGM8PRecrJd0gac+ozhDg38Ac4F8NiB4V1dknTV43SQOB/lHR9pIG1k06jeScF5X1kfRj4HbgaTObmkHOjcCNKefeBE6UtB3BH/pmWv3BBJ9R3ok783ZnSVWEZtMnBO/22JS/zZ4+ffvy0og3+GLiBI447CAG77U7jz06nIeGP8bBhxxadHIa4oifDWPEkw8zd+b0vN2zhXx+QwhdlvWcthHdCHGcxwGvEOa+HGpmz0Xn1xCS+D0FjAfui+ruaWZL6hMaOW3vYX1/5GkEo1M3ae6Z6PjI6HglcCzwGiFo/dWR7sely5B0MMFA3ZpS/GfgM0L3qT9wdkr9PQl+pH/Wp/eGoPQmZ8ZK0iiCB/kPwGzSVj2b2bhCKBeHW2+9zU49ff2ZmoVg6pQv6dmrd8HlPPLah2y8WfeCywHYqXJlIs8EyX1+AL+/5qoPr7jiioJNWc+WyLfzKbCbmU1uBvo8RhiOvrbRyjkQ13nbDzjGzCY2WtNxnPUwszmSTibk5WpSwyKpLWG+S0OzfTeIuIblTcKQlRsWx8kRM3u6qXWAtWuFflNIGXENyynA3dHEm48JuYXWYmbpw1iO47RisukK7UyYMpyOR5BzHGcd4hqWvxJWdP6ODM5bx3GcVOIalq2Aw8zsi0Iq4zhOyyDuBLmXgF0LqYjjOC2HuC2W54E/StqJMCEu3Xlb3+Irx3FaIXENy+3R319mOOfOW8dx1iFuBLkNDQjlOE4rwg2G4zh5J26gp/MbOp8WjMZxnFZOXB/LWWnHbQhRzpcTloO7YXEcZy1xfSx90sskdQX+Tv1L0B3HaaXk7GMxs9mEnCZ/yJ86juO0BDbUeVsCdM2HIo7jtBziOm9/lF5E8LGcAfw330o5jlPcxHXepoevM0KYyhHABXnVyHGcoscnyDmOk3fcYDiOk3caNCySDpX0paT2Gc51iM4dWDj1HMcpRhprsZwJXG9mi9NPRKkdf0/IV+I4jrOWxgzLToTIcfUxAvh2/tRxHKcl0JjzdjNCnub6MKBz/tTJniXLV/LIax8mIqtvxerGK+VJTtfKlYnImj0rv6lYm4ssp2lpzLBMJ7RaJtRzfidgRl41yhKVlCaW3Ktrgsm9kpLTkmU5TUdjXaFngN9Iqkg/IWkjQsrHZwqhmOM4xUtjLZZrgKOB8ZJuAz6PyrcjOHYFFCRFo+M4xUuDhiVKC7kXcAfBgKjuFPACcEa0GNFxHGctjc68NbMpwGGSNgW2IRiXCWa2oNDKOY5TnMRdK0RkSD4ooC6O47QQfEq/4zh5xw2L4zh5J1HDIukQSeMkTZR0SYbz50v6VNIYSa9I6pWkfo7j5IfEDIukUuDPwKHA9sBxkrZPqzYKGGRmOxFiwHjYS8cpQpJssewOTDSzSWa2ChgOHJVawcxeNbNl0eG7hGT0juMUGUkali2BaSnH06Oy+jgFeC7TCUlDJY2UNHJp9XoLrx3HaWKapfNW0k+BQcD1mc6b2Z1mNsjMBm1cuV6oGMdxmpjY81jywAygR8rxVmRYwCjpAEJakX3NLJklvo7j5JUkWywfAP0k9ZFUDvwEeDq1gqSdgb8CR5rZnAR1cxwnjyRmWMyshrBw8QXgM+BRM/tE0tWSjoyqXQ9UAo9JqpL0dD23cxynGZNkVwgzexZ4Nq3s1yn7BySpj+M4haFZOm8dxylu3LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4jpN33LA4TjNG0omSRjS1HgCSrpd0a5y6rcaw3Hb5uRwzsDvHDOzOsbv24LRDBnHnNZdQvXhhXuUc9L0hnHv2meuVP3DfvXTpWOmymkiOpO9KelrSDEkm6aQY17STdK+kMZJWS3otQ50h0f3St21T6rxWT51PGpFfDlwDXJVSdqqk/0paIGmhpFcl7ZN23RmRzouj7R1Jh6fVuVDSnGi7IO3czpI+l1SRptIfgBMl9W3ko0s2r1BTs+MegznrmlupXVPD9C/Gc/uVF7BsySLOve6OplbNKTyVwMfA/dEWh1JgBXAbcBjQsYG6OwDzU47npuz/CChPOW4LjAUebUT+0cByM3s9pWwI8AhwNrAMOA94QdJAM5sQ1ZkOXAxMIDQeTgSelLSrmY2RtBNwNfB9QMB/JL1oZmMllQJ3AWea2fJUZcxsrqQXgdOBixpSvFUZljblbdm0y+YAdO7anb0OPpLXnm7sf+u0BFKT5Um6N+Y1S4HTomt2omHDMsfM5tVzn1SDg6QTgI2AexpR4XjgP2n3OiHtXqcDPwAOIRgSzOyptPtcFtXbExgDbAuMMbMR0T3qysYC5wIfm9nL9ej0NHAtjRiWVtMVSmf29ClUvfUqpWWtyrY6hWOkpK8kvSJpv0bqngo8b2bTGqm3DzCykTrlQDtgQaaTkkol/YTQYns7Kh4L9JfUU1IvoD/wsaQ+hDTIF2S6V8T7wJaStm5IqVb1VlW9/So/3XMbamtrWb1yBQAnXnBl0yrlFDtfEboGHxBe8p8Br0ja18z+m15ZUn9gX0Iro14kdQQ6ADMbkf9boJrQkki9fkfgHYLRqQZ+aGZjAczsM0m/BF6Kql8alT0P/AoYLOlqQsPjV2b2ZMqt6/TpDXxRn1KtyrBst8t3GHb5H1i1cgWvPPEQs6ZN4dDjT2lqtZwixszGAeNSit6R1JvQVVjPsBBaK18BzzRy6zrH6Yr6Kkg6BxgGHGBmi9NOjwMGEozT0cB9koaY2ceR3n8B/pJyr7ou1kvAeEK3qQR4S1J/M5sTna/zu6Q7dtehVXWF2rarYIuefejVbztOvvi3rFqxnMfvvDGvMtq3b8/iRYvWK1+4cCHtO3RwWc1EToF5D+iXXhiN8pwI/N3Mahq5x9eAAZtmOinpXEJr5TAzez/9vJmtMrOJZvahmV0KVBEcvZnu1Tm612nAd4AJZvaZmX1C8NvskVK9U/R3Lg3QqgxLOscMO58n772d+XNm5e2e/foPoKrqI8xsnfKqUR/Rv/+AvMlpqbKSfKYCMpDQKknnB0AX4G+N3cDMVgGfAtunn5N0PvAb4HAzezOmTiWE0ahM/Am41cy+jOq1STlXThgdq+NbwGqCn6ZBYa2WHXbbi6369uPxu27O2z2HDjudyZMmcd45ZzFm9GjGjxvHLTfdyKOPPMx5FzToSHdZBZQjqVLSQEkDCd/7ntFxz0au2z66pguQeo+68+dK+oGkfpJ2kPQ7ggG5LdPjAa+Y2aSYar9AcOCm6nMRcB1wCjBeUrdo65BS5zpJgyX1lrRjpNMQ4KEMz3cAwXjVvQQfAAMkHSnpKGAAwWFbx2Dgv2a2rCHFW7VhATjiZ8MY8eTDzJ05PS/369O3Ly+NeIMvJk7giMMOYvBeu/PYo8N5aPhjHHzIoXmR0ZJlFVDOIGBUtFUQJp2NIsznAEDSlZIs7bpno3rHArum3KOOcuB6wjDufwmG4HAzeyL1JtGksv0Jc0TichdwiKROKWVnEFoUjxBaRXVb6q9jN+BBgp/lFWA34FAzey5Npwrgz8BQM1sDYGYzCF2iO6JtmJmlOpCPi/MMSm9yFhu/u+Em63fgsYnI2qlyJT179S64nKlTvkxETkuW9ftrrvrwiiuuGJTNNZLuA7qZ2cEFUitrJA0HPjGz3zQDXQ4nGNGdGvMRtfoWi+MASBKhRXFWU+uSxv8D0kd8moqNgZ/HcDy3ruFmx6kPC033Hk2tRzpmNpV1uzlNhpnFnqbuLRbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfKOGxbHcfJOooZF0iGSxkmaKOmSDOfbSnokOv+epN5J6uc4Tn5IzLBIKgX+DBwKbA8cJ2n7tGqnAAvMbBvgRuD3SennOE7+SLLFsjsw0cwmmdkqYDhwVFqdo4D7ov1/At+TpAR1dBwnD5QlKGtLYFrK8XRgj/rqmFmNpEVAZ2BeaiVJQ4GhAJdddln1J0/eOS4bRaqrq7tUVlbOa7zmunyS7QUbICsXkpJVJM/UK+/KOLFJ0rDkDTO7E7gz1+sljTSzQXlUqVXJaonP5OSXJLtCM4AeKcdbRWUZ60gqAzoAXyeineM4eSNJw/IB0E9SH0nlwE+Ap9PqPA2cGO0fDYwwM0tQR8dx8kBiXaHIZ3Im8AJQCtxjZp9IuhoYaWZPA38DHpA0EZhPMD6FIOdulMtKVE7Sspw8IW8QOI6Tb3zmreM4eccNi+M4eafFGhZJP5BkkraNjntLWi6pKmUrl3SSpLnR8eeSzstCxpq0+10SlX8pqUtKvSGS/hPtb4i8yyR9ImlMdP0eUXlZdM/r0up/X9IoSaMlfSpp2IY8V3Sui6TVkk5Lu+ZkSWMj3T6WlD75MZOcTP8jk/TbDPJui46vlDQj0utTScfFeSYnYcysRW7AI8B/gaui497AxxnqnQTcFu3XTcbrEVNGdT3lXwJdUo6HAP/ZEHnAnsA7QNvouAvQPdo/FHgL+IJv/GZtgJnAVtFxW2DAhjxXdO706HN9PaVsq0h2h+i4EuiT4/9oEjAqTV5Vymd2JXBhtN8PWAy0aervm2/rbi2yxSKpEtiHsPYo9siSmX0NTAS2KJBqGyJvC2Cema2Mrp1nZjOjc8cBNwNTCQYIYBPCqN/XUf2VZpbVDOV6OA64ANhS0lZR2ebAEqA6klVtZpMbukkD/6NlwGeS6ibFHQs8mukeZjYhqr9pbo/iFIoWaVgIa46eN7PxwNeSdo3Kt05p3v85/SJJPYF2wJiYcirSugzHZqNklvJeBHpIGi/pdkn7RvdoBxwA/Bt4mPDiY2bzCfOCpkh6WNIJkuL+vzM+l6QewBZm9j7hZa973tHAbGCypL9LOiKGjPr+RxDWkf0kkreG0PJaD0m7ABPMbE7M53KSoqmbTIXYgP8AB0b7ZwM30HBXaC7h5V4FDM1CTn1docms3xX6dx7klUb3ugqYFd3raOCh6Hxnwlqr0pRrdgTOA0YB927gc10IXBPt70SYf1R3ToSFppcSWmFX5vo/AsoJxuoiwpqwk1i3KzSDsHRrNXBIU3/ffMvw/21qBfL+QNCJ0DyeQvB1TCN0Efo0YFjqvrSDCBPzusWUVd8LOBLol3L8I+DvGyovTcbRhFbK44TWwpfRtqzuhU2r3wVYsoHP9WH0UtfJWpX6nCn1BgFjN+R/BNwTGc9OGQxLnY/lyEifdk39vfNt3a0ldoWOBh4ws15m1tvMehBaED0auQ4zGwk8AJyzgTq8BvwM1sah+Snw6obIkzRAUr+UooGEls9goGf0rL2BMwixbiolDUmrPyXrJ/lGfn+g0sy2TJH1u0hW96hbEldWnP/RH4GLLXTpMmJhtvZIvlkG4jQTinJ1cyMcx/oBoh4nNNHj8HvgI0nXmtmSRupWSKpKOX7ezC4BfgPcIWk0oYvwPPDgBsqrBG6V1BGoIXQ3ngI2ssihG/EU8AdC9+f/SforsBxYSvjlj8N6zxXd419p9R4njOzcB9wgqTuwgmDwTqN+Gv0fmdknxItUcTXwD0l3mVltjPpOAviUfsdx8k5L7Ao5jtPEuGFxHCfvuGFxHCfvuGFxHCfvuGFxHCfvuGFxHCfvuGFxHCfv/H9JI5ZyFeGUcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(4,6), sharey=True)\n",
    "\n",
    "geovar_plot2.plot_geovar(ax);\n",
    "geovar_plot2.plot_percentages(ax);\n",
    "\n",
    "# Setting the xlabels\n",
    "ax.set_xticklabels(geovar_plot.poplist);\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the \"L\" category does not appear till the 7th category here, but it does allow for other ways to break down the frequency categories and further exploration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in from GeoVar Counts Files\n",
    "\n",
    "For some datasets like the ~92 million variants in the [NYGC 1000 Genomes Project](http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20190425_NYGC_GATK/), the full frequency table is too large to store in memory within a given jupyter notebook instance. \n",
    "\n",
    "In this case, we suggest processing the data in batches to create a geovar counts file, containing the numeric geovar code in the first column and the total count in the second column. We have included a file that we used in our analysis containing codes used in Figure 3B in [Biddanda et al 2020](https://elifesciences.org/articles/60107).   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "geovar_counts_file  = \"{}/new_1kg_nyc_hg38_filt_total.biallelic_snps.superpops_amended2.ncat3x.filt_0.geodist_cnt.txt.gz\".format(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00001 12330992\n",
      "00002 6731\n",
      "00010 14619729\n",
      "00011 342372\n",
      "00012 6324\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$geovar_counts_file\"\n",
    "\n",
    "# NOTE: the counts file is gzipped in this case!\n",
    "# NOTE: you may have to change gzcat -> zcat on linux!\n",
    "gzcat $1 | head -n5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "geovar_plot3 = GeoVarPlot()\n",
    "\n",
    "# Adding data directly from the geovar object itself\n",
    "geovar_plot3.add_text_data(geovar_counts_file, filt_unobserved=False)\n",
    "\n",
    "# Filter to remove very rare categories (only to speed up plotting)\n",
    "# geovar_plot3.filter_data()\n",
    "geovar_plot3.sort_geodist()\n",
    "\n",
    "# Adding in a colormap and a new set of labels since we have an additional category\n",
    "geovar_plot3.add_cmap(str_labels=['U','R','C'], lbl_colors=['black', 'black','white'])\n",
    "geovar_plot3.add_poplabels_manual(np.array(['AFR','EUR','SAS','EAS','AMR']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(4,6), sharey=True)\n",
    "geovar_plot3.plot_geovar(ax, pixel_thresh=100, freq_thresh=0.05, dpi=fig.dpi);\n",
    "\n",
    "# NOTE:  you can change the default fontsize parameter to make the percentages fit \n",
    "geovar_plot3.fontsize = 10\n",
    "geovar_plot3.plot_percentages(ax, pixel_thresh=100, freq_thresh=0.05, dpi=fig.dpi);\n",
    "\n",
    "# Setting the xlabels\n",
    "ax.set_xticklabels(geovar_plot3.poplist);\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}