trans_interactions_allele1_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require([\"codemirror/keymap/sublime\", \"notebook/js/cell\", \"base/js/namespace\"],\n",
       "    function(sublime_keymap, cell, IPython) {\n",
       "        cell.Cell.options_default.cm_config.keyMap = 'sublime';    \n",
       "    \n",
       "        var cells = IPython.notebook.get_cells();    \n",
       "        for(var cl=0; cl< cells.length ; cl++){\n",
       "            cells[cl].code_mirror.setOption('keyMap', 'sublime');\n",
       "        }\n",
       "    }\n",
       ");\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require([\"codemirror/keymap/sublime\", \"notebook/js/cell\", \"base/js/namespace\"],\n",
    "    function(sublime_keymap, cell, IPython) {\n",
    "        cell.Cell.options_default.cm_config.keyMap = 'sublime';    \n",
    "    \n",
    "        var cells = IPython.notebook.get_cells();    \n",
    "        for(var cl=0; cl< cells.length ; cl++){\n",
    "            cells[cl].code_mirror.setOption('keyMap', 'sublime');\n",
    "        }\n",
    "    }\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib import gridspec\n",
    "from matplotlib import colors\n",
    "\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cooler\n",
    "\n",
    "import itertools\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cooltools used dekkerlab-fork, develop-branch, commit-af508bba9c6577b8271b716ba27c330bead15981\n",
    "from cooler.tools import split, partition\n",
    "from functools import partial\n",
    "from cooltools import expected\n",
    "\n",
    "import bioframe\n",
    "from bioframe.io import resources\n",
    "from bioframe import io\n",
    "\n",
    "from cooltools import numutils\n",
    "from cooltools.eigdecomp import cooler_cis_eig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_module import fillcolor_compartment_style, to_uscs, bar_signal, random_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import ticker\n",
    "mb_format = ticker.FuncFormatter(lambda bp, pos: f\"{int(bp/1_000_000)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cooler_names = [\n",
    "    'all-allele-spe-2percent-highcis.galGal5.mapq_30.1000.mcool',\n",
    "#     'all-allele-spe-1percent-highcis.galGal5.mapq_30.1000.mcool',\n",
    "    'MNP-DT40-1-3-16-2p-R1-T1__galGal5.galGal5.mapq_30.1000.mcool',\n",
    "#     'MNP-DT40-1-3-16-R2-T1__galGal5.galGal5.mapq_30.1000.mcool',\n",
    "    'MNP-DT40-1-3-17-2p-R1-T1__galGal5.galGal5.mapq_30.1000.mcool',\n",
    "#     'MNP-DT40-1-3-17-R1-T1__galGal5.galGal5.mapq_30.1000.mcool',\n",
    "    'MNP-DT40-WT1-R1-T1__galGal5.galGal5.mapq_30.1000.mcool',\n",
    "    'MNP-DT40-WT2-R1-T1__galGal5.galGal5.mapq_30.1000.mcool']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate some nicer sample names from coolers ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>sample</th>\n",
       "      <th>mcool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pool-2percent-highcis</td>\n",
       "      <td>all-allele-spe-2percent-highcis.galGal5.mapq_3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clone-16-2p</td>\n",
       "      <td>MNP-DT40-1-3-16-2p-R1-T1__galGal5.galGal5.mapq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clone-17-2p</td>\n",
       "      <td>MNP-DT40-1-3-17-2p-R1-T1__galGal5.galGal5.mapq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WT1</td>\n",
       "      <td>MNP-DT40-WT1-R1-T1__galGal5.galGal5.mapq_30.10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WT2</td>\n",
       "      <td>MNP-DT40-WT2-R1-T1__galGal5.galGal5.mapq_30.10...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sample                                              mcool\n",
       "0  pool-2percent-highcis  all-allele-spe-2percent-highcis.galGal5.mapq_3...\n",
       "1            clone-16-2p  MNP-DT40-1-3-16-2p-R1-T1__galGal5.galGal5.mapq...\n",
       "2            clone-17-2p  MNP-DT40-1-3-17-2p-R1-T1__galGal5.galGal5.mapq...\n",
       "3                    WT1  MNP-DT40-WT1-R1-T1__galGal5.galGal5.mapq_30.10...\n",
       "4                    WT2  MNP-DT40-WT2-R1-T1__galGal5.galGal5.mapq_30.10..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiqc learnings here, btw!...\n",
    "renamings = {\"all-allele-spe\":\"pool\",\n",
    "            \".galGal5.mapq_30.1000.mcool\":\"\",\n",
    "            \"MNP-DT40-\":\"\",\n",
    "            \"-T1__galGal5\":\"\",\n",
    "            \"-R1\":\"\",\n",
    "            \"1-3-\":\"clone-\"}\n",
    "\n",
    "sample_names = []\n",
    "for c in cooler_names:\n",
    "    for rf,rt in renamings.items():\n",
    "        c = re.sub(rf,rt,c)\n",
    "    sample_names.append(c)\n",
    "\n",
    "# sample_names\n",
    "\n",
    "# form a df of samples here:\n",
    "samples = pd.DataFrame({\"sample\":sample_names,\"mcool\":cooler_names})\n",
    "\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### read some coolers and extract raw number of interactions between allele1/2 and other chromosomes ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# READ SOME COOLERS ...\n",
    "# oldref-20200617\n",
    "cool_path = \"/home/venevs/DOTS_TESTING/alv-chicken/oldref-20200617\"\n",
    "get_cpath = lambda c,b: os.path.join(cool_path,c+f\"::/resolutions/{b}\")\n",
    "\n",
    "insertion_site = 72_500_000\n",
    "\n",
    "# we need some bad_ bins here as well ...\n",
    "# beta-globin promoter: chr1:194_569_000-194_572_000\n",
    "# beta-actin promoter: chr14:4_194_000-4_197_000\n",
    "bad_bin_regions = [(\"chr1\",9_000_000,9_500_000),\n",
    "                   (\"chr1\",32_270_000,32_770_000),\n",
    "                   (\"chr1\",194_569_534,194_570_623),\n",
    "                   (\"chr14\",4_194_000,4_197_000)]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def sum_ints_wbad(locus_idx,_clr,_bad_bins_regs):\n",
    "    result = []\n",
    "    for chrom in _clr.chromnames:\n",
    "        _res = np.sum(_clr.matrix(balance=False)[locus_idx,slice(*_clr.extent(chrom))])\n",
    "        for _breg in _bad_bins_regs:\n",
    "            if _breg[0] == chrom:\n",
    "                _res -= np.sum(_clr.matrix(balance=False)[locus_idx,slice(*_clr.extent(_breg))])\n",
    "        result.append(_res)\n",
    "    return result\n",
    "\n",
    "#         if chrom in chroms_wbad_bins.keys():\n",
    "dfs = {}\n",
    "for s in samples[\"sample\"]:\n",
    "    if \"WT\" not in s:\n",
    "        _bsize = 1_000_000\n",
    "        s_cname, = samples[\"mcool\"][samples[\"sample\"]==s]\n",
    "        _c = cooler.Cooler(get_cpath(s_cname,_bsize))\n",
    "        _df = pd.DataFrame(_c.chromsizes).reset_index().rename({\"name\":\"chrom\"},axis=1)\n",
    "        a1_idx = _c.offset(\"allele1\")\n",
    "        a2_idx = _c.offset(\"allele2\")\n",
    "        chr1_idx = slice(*_c.extent(\"chr1\"))\n",
    "        # chr1_idx = _c.offset((\"chr1\",insertion_site,insertion_site+_bsize))\n",
    "        _df[\"allele1\"] = sum_ints_wbad(a1_idx,_c,bad_bin_regions)\n",
    "        _df[\"allele2\"] = sum_ints_wbad(a2_idx,_c,bad_bin_regions)\n",
    "        _df[\"chr1\"] = sum_ints_wbad(chr1_idx,_c,bad_bin_regions)\n",
    "        # _df[\"allele1\"] = [np.sum(_c.matrix(balance=False)[a1_idx,slice(*_c.extent(chrom))]) for chrom in _c.chromnames]\n",
    "        # _df[\"allele2\"] = [np.sum(_c.matrix(balance=False)[a2_idx,slice(*_c.extent(chrom))]) for chrom in _c.chromnames]\n",
    "        # _df[\"chr1\"] = [np.sum(_c.matrix(balance=False)[chr1_idx,slice(*_c.extent(chrom))]) for chrom in _c.chromnames]\n",
    "        dfs[s] = _df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### now let's compare number of interactions per bp betweeb allele1/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/venevs/miniconda3/envs/newexp/lib/python3.7/site-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/home/venevs/miniconda3/envs/newexp/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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vZyU8EckoylsZ4xDQz933mlkW8Bcze97d36pV7r/d/Y4UxCdpTN9zqU0NNBEREZEG8MCS2HuDT7OCj8Qtky0ijZrmoImIiIg0kJk1M7OVwDZgsbu/HaLYEDNbZWZPmdkpSQ5RRDKEGmgi0ugEL5RWmNmzqY5FRJoGdz/i7n2ALsB5ZnZmrSILgQJ37w0sBmaGq8vMxphZqZmVbt++PXFBi0haUgNNRBqju4F1qQ5CRJoedy8HXgUG1jr+mbsfCj59DDgnQh3T3b3I3Yvat2+fuGBFJC2pgSYijYqZdQEGEbgAEhFJODNrb2b5wZ9zgG8D62uV6Vjt6dXoJpKIhKFFQkSksfkV8DMgL1wBMxsDjAHo2rVrksISkUasIzDTzJoRuPn9pLs/a2YTgVJ3fwa4y8yuBiqAncCNKYtWRNKaGmgi0miY2VXANndfbmaXhCvn7tOB6QBFRUVaaU1EGsTdVwF9Qxz/l2o/3w/cn8y4RCQzaYijiDQmFwBXm9lG4M9APzObndqQRERERKKnBpqINBrufr+7d3H3AuB64BV3/36KwxIRERGJmhpoIiIiIiIiaUJz0ESkUXL314DXUhyGiIiISL2oB01ERERERCRNqIEmIiIiIiKSJtRAExERERERSRMJa6CZWU8zW1ntsdvM7knU+URERERERDJdwhYJcff3gT4AZtYMKAP+J1HnExERERERyXTJGuJ4GbDB3Tcl6XwiIiIiIiIZJ1kNtOuBJ0K9YGZjzKzUzEq3b9+epHBERERERETST8IbaGbWArgamBvqdXef7u5F7l7Uvn37RIcjIiIiIiKStpLRg3YF8Dd335qEc4mIiIiIiGSsZDTQhhFmeKOIiIiIiIh8KaENNDNrDXwbmJ/I84iIiIiIiDQGCVtmH8Dd9wEnJvIcIiIiIiIijUWyVnEUERERERGROqiBJiIiIiIikibUQBMREREREUkTaqCJiIiIiIikCTXQRERERERE0oQaaCIiIiINZGYtzewdM3vXzNaa2YMhymSb2X+b2Ydm9raZFSQ/UhFJd2qgiYiIiDTcIaCfu58N9AEGmtk3apUZDXzu7l8B/h/wUJJjFJEMoAaaiIiISAN5wN7g06zgw2sV+y4wM/jzU8BlZmZJClFEMoQaaCIiIiJxYGbNzGwlsA1Y7O5v1yrSGfgEwN0rgF3AicmNUkTi5dChQ4wePZpu3bqRl5dHnz59eP7556te379/P0BXM9thZrvM7PVo6lUDTURERCQO3P2Iu/cBugDnmdmZsdRjZmPMrNTMSrdv3x7fIEUkbioqKjjllFNYunQpu3btYvLkyQwdOpSNGzcCMGbMGIDmwOnACcCPo6m3eYLiFREREWmS3L3czF4FBgJrqr1UBpwCbDaz5kAb4LMQ758OTAcoKiqqPUxSRNJE69atmTBhQtXzq666iu7du7N8+XIOHjzIM888A7DR3SvvtCyPpl71oImIiIg0kJm1N7P84M85wLeB9bWKPQOMCv58DfCKu6sBJtJIbN26lQ8++IDCwkLeeecdunXrBtA5OMRxtZkNiaYeNdBEREREGq4j8KqZrQL+SmAO2rNmNtHMrg6W+T1wopl9CPwEuC9FsYpInB0+fJgRI0YwatQoevXqxebNm1mzZg3AEaATcAcw08xOr6suDXEUERERaSB3XwX0DXH8X6r9fBC4NplxiUjiHT16lBtuuIEWLVpQUlICQE5ODllZWRw+fHiLu38BLA0Ofb4cWBepPjXQREREREREYuDujB49mq1bt7Jo0SKysrIA6N27d8ji0dSpIY4iIiIiIiIxuPXWW1m3bh0LFy4kJyen6vhFF11E165dATqaWXMzuwC4FHixrjrVgyYiIiIiIhLBghVlPPzi+2wpP0Cn/BzuHdCTvidUMG3aNLKzs+nQoUNV2WnTpjFixAiefvppzjzzzDYE9jzcBIx099qLBx1DDTQREREREZEwFqwo4/75qzlw+AgAZeUHuH/+aqYUn0WkhVgLCwsB1rt7UX3Ol9AhjmaWb2ZPmdl6M1tnZucn8nwiIiIiIiLx9PCL71c1ziodOHyEh198PyHnS3QP2q+BF9z9GjNrAbRK8PlERERERETiZkv5gXodb6iE9aCZWRvgIgJ7fuDuX7h7eaLOJyIiIiIiEm+d8nPqdbyhEjnEsTuwHfiDma0ws8fMrHUCzyciIiIiIhJX9w7oSU5WsxrHcrKace+Angk5XyIbaM2BrwG/c/e+wD7gvtqFzGyMmZWaWen27dsTGI6IiIiIiEj9DO7bmSnFZ9E5PwcDOufnMKX4LAb37ZyQ8yVyDtpmYLO7vx18/hQhGmjuPh2YDlBUVBTV5m0iIiIiIiLJMrhv54Q1yGpLWA+au/8D+MTMKvv+LgPeS9T5REREREREMl1UPWjBFRh7AQ687+5fRFn/ncCc4Ps/Am6KKUoRaZIakHtERGKm3CMiqVRnA83MBgH/CWwADOhuZj9y9+freq+7rwTqtTGbiAg0LPeIiMRKuUdEUi2aHrRfApe6+4cAZtYDeA5QohKRRFLuEZFUUO4RkZSKZg7ansokFfQRsCdB8YiIVFLuEZFUUO4RkZSKpget1MwWAU8SGIt9LfBXMysGcPf5CYxPRJou5R4RSQXlHhFJqWgaaC2BrcDFwefbgRzgOwQSlxKViCRCTLnHzFoCrwPZBHLcU+4+PuHRikhjoeseEUmpOhto7q6VF0Uk6RqQew4B/dx9r5llAX8xs+fd/a04hicijZSue0Qk1eqcg2Zmp5rZQjPbbmbbzOxpMzs1GcGJSNMVa+7xgL3Bp1nBhyc0WBFpNHTdIyKpFs0iIX8iMA67I9AJmAs8kcigRERoQO4xs2ZmthLYBix297drvT7GzErNrHT79u1xDltEMpyue0QkpaJpoLVy9z+6e0XwMZvA+GwRkUSKOfe4+xF37wN0Ac4zszNrvT7d3Yvcvah9+/YJCF1EMpiue0QkpcI20MzsBDM7AXjezO4zswIz62ZmPwMWJS9EEWlK4pl73L0ceBUYmIhYRaTxaEjuMbNTzOxVM3vPzNaa2d0hylxiZrvMbGXw8S+J+iwiktkiLRKyvNbzH1X72YH74x+OiEjDco+ZtQcOu3u5meUA3wYeim+IItIINST3VAD/5O5/M7M8YLmZLXb392qVe8Pdr4pDrCLSiEVqoF3o7mVJi0REJKChuacjMNPMmhEYJfCkuz8bn9BEpBGLOfe4+6fAp8Gf95jZOqAzULuBJiJSp0gNtP8KdvW/BrwA/MXdK5ISlYg0ZQ3KPe6+CuiboNhEpPGKy3WPmRUQyEFvh3j5fDN7F9gC/NTd14apYwwwBqBr1671DUFEMlzYOWjufiVwCYFE9T3gLTObH1z9TNlCRBJCuUdEUiEeucfMcoF5wD3uvrvWy38Durn72cB/AAsixKKFjESasIgbVbv7QQJ3kV4AMLPuwBVAiZl1cPfzEh+iiDQ1yj0ikgoNyT1mlkWgcTbH3eeHqHt3tZ8XmdmjZtbO3XfE+3OISGaLZqPq1mZWWS4L2AwMAS5MZGAi0rQp94hIKsSSe8zMgN8D69z9kTBlOgTLYWbnEbgG+yyesYskS0lJCUVFRWRnZ3PjjTdWHd+4cSNmRm5ubtVj0qRJqQs0Q0WzD9rrQEsz6wy8BNwA/MHdv0hoZCLS1Cn3iEgqxJJ7LgiW61dtGf0rzewWM7slWOYaYE1wDtpvgOvd3RP4OUQSplOnTowbN46bb7455Ovl5eXs3buXvXv38sADDyQ5urodOnSI0aNH061bN/Ly8ujTpw/PP/88AO+99x5FRUW0bduWtm3b0r9/f957L7nr/UQc4hhk7r7fzEYDj7r7vweTi4hIIin3iEgq1Dv3uPtfAKujTAlQEsc4RVKmuLgYgNLSUjZv3pziaOqvoqKCU045haVLl9K1a1cWLVrE0KFDWb16NZ06deKpp56iW7duHD16lN/+9rdcf/31rFq1KmnxRdODZmZ2PjACeK4e7xMRaQjlHhFJBeUekQbq1q0bXbp04aabbmLHjvSbZtm6dWsmTJhAQUEBxx13HFdddRXdu3dn+fLl5OfnU1BQgJnh7jRr1owPP/wwqfFFk3DuJrA54/+4+1ozOxV4NbFhiYgo94hISij3iMSoXbt2/PWvf2XTpk0sX76cPXv2MGLEiFSHVaetW7fywQcfUFhYWHUsPz+fli1bcueddzJ27NikxlNnA83dX3f3q939oeDzj9z9rmgqN7ONZrY6OBa7tKHBikhsIo21Bti/fz+33XYb7dq1o02bNlx00UUpjDagIblHRCRWyj0iscvNzaWoqIjmzZtz8sknU1JSwksvvcSePXtSHVpYhw8fZsSIEYwaNYpevXpVHS8vL2fXrl2UlJTQt29yt1etcw6amX0V+ClQUL28u/eL8hyXaglZkdSKNNa6oKCAMWPGUFFRwbp16zjhhBNYuXJlqkOOR+4REak35R6R+AkuXMrRo0dTHEloR48e5YYbbqBFixaUlBw7RbR169bccssttG/fnnXr1nHSSSclJa5oFgmZC/wn8BhwJLHhiEgiVI61rlR9rPXBgwd55pln2Lx5M8cffzwA55xzTooirUG5R6QRKSkpYcaMGaxevZphw4YxY8aMqtcee+wxpk6dyj/+8Q8uvPBCHn/8cTp16pSqUJV7ROpQUVFBRUUFR44c4ciRIxw8eJDmzZtXzeE67bTT+Pzzz7nrrru45JJLaNOmTapDPoa7M3r0aLZu3cqiRYvIysoKWe7o0aPs37+fsrKypDXQopmDVuHuv3P3d9x9eeUjyvodeMnMlpvZmFAFzGyMmZWaWen27dujDlxEYld9rPU777xDt27dGD9+PO3ateOss85i3rx5qQ4RGpZ7RKSe6hoK/fLLL9OrVy9atWrFpZdeyqZNm+pVf7hluV977TXGjh3L008/zc6dO+nevTvDhg2Ly2eKkXKPSNCCFWVcMPUVut/3HBdMfYUFK8oAmDx5Mjk5OUydOpXZs2eTk5PD5MmT+eijjxg4cCB5eXmceeaZZGdn88QTT6T4U4R26623sm7dOhYuXEhOTk7V8cWLF7NixQqOHDnC7t27+clPfkLbtm05/fTTkxZbND1oC83sNuB/gEOVB919ZxTvvdDdy8zsJGCxma1399erF3D36cB0gKKiIu0HIpJgtcdaz58/nzVr1jBkyBC2bNnCm2++yaBBgzjjjDOSmoxCaEjuEZF6ijQUOjc3l+LiYh577DG+853v8MADD3Ddddfx1ltvRV1/uGW5n332Wa699tqqyfkPPPAAnTt3ZsOGDfTo0SO+HzI6yj0iBBpn989fzYHDgY7ksvID3D9/NQATJkyoMTKnuhTfYDnGghVlPPzi+2wpP0Cn/BzuHdCTvidUMG3aNLKzs+nQoUNV2WnTptGiRQvuvPNONm/eTE5ODueddx4vvPACLVu2TFrM0TTQRgX/vbfaMQdOreuN7l4W/Hebmf0PcB6BDSBFJAVCjbXOyckhKyuLcePG0bx5cy6++GIuvfRSXnrppVQ30GLOPSJSf5GGQn/22WcUFhZy7bXXAoGLs3bt2rF+/foak+pjVX2/5sqf16xZk6oGmnKPCPDwi+9XNc4qHTh8hIdffJ/BfTunKKr6CdfInFJ8Vo28U1tlrkuVaFZx7B7iUWeSMrPWZpZX+TNwObCm4SGLSCyqj7WeN29e1Vjr3r17H1O2clJvKsWae0QkPqoPhV67di1nn3121WutW7emR48erF27tsHnGThwIE8++SSrVq3iwIEDTJw4ETNj//79Da47Fso9IgFbyg/U63g6itTITGd1NtDMLMvM7jKzp4KPO8ws9Cy6mk4G/mJm7wLvAM+5+wsNDVhEYhNurPVFF11E165dmTJlChUVFSxbtoxXX32VAQMGpDDaBuUeEWmg2kOh9+7de8wk/zZt2sRl6ez+/fvz4IMPMmTIEAoKCigoKCAvL48uXbo0uO5YKPeIBHTKz6nX8XSUqY3MaBYJ+R1wDvBo8HFO8FhEwX1Dzg4+Ct39XxsWqohEI9SE3k2bNjFt2jRWrlxJhw4dyM3NJTc3lzlz5pCVlcXTTz/NokWLaNOmDT/84Q+ZNWtWXIYtNeFfXzAAACAASURBVFBMuUdEGibUUOjc3Fx2795do9zu3bvJy8uLyzlvv/12/v73v7N161aGDBlCRUUFZ555ZlzqjoFyjwhw74Ce5GQ1q3EsJ6sZ9w7omaKI6i9TG5nRzEE7193Prvb8lWCvmIikmVjHWhcWFvLmm2/WWf+hQ4e47bbbWLJkCTt37qRHjx5MmTKFK664orJInpmtB7oCbwM3unv9lnr7knKPSJKFW3a6sLCQmTNnVpXbt28fGzZsqFrYo7pQE/IH9+0cdlnuiooKPvzwQwoLC/nkk08YM2YMd999N23btk3a565FuUcEquaZhfo+Z4p7B/SscV0EmdHIjKaBdsTMerj7BgAzOxXtCyKSlhI9obeuVd6AHsAwYCEwCfhv4Bsxnk65RyTJKodCL1mypMZQ6O9973vce++9zJs3j0GDBjFx4kR69+59TE97pFXfVj79Xzz44INVZWfPns348eO55557GD58OBs2bCAvL4+bbrqJSZMmJeHThqXcIxI0uG/njGqQ1ZapjUyLdFcdwMwuA/4AfAQY0A24yd1fjXcwRUVFXlpaGu9qRTJepJ6r9957j5EjR7JhwwZ2HzhMiw5foW3/H9GiXdeq9xvw8dRB9T5vNBvLfvTRR/Tu3Zvhw4fz85//fJ+750LV4kA7gL7uvr6+505W7lHekaYmXA/Xpk2bKCgoIDs7m+bNv7x/O23aNEaMGMGSJUu444472LRpE1//+teZMWMGBQUFNeq+YOorlIWY29E5P4dl9/VLyOcxs+XuXhTH+pJ23RMN5SiRzBZLjqqzB83dXzaz04DKvsD33f1QpPeISHxF6rnq1KkTTz31FN26deOCKUtY/8pT7Hjm3+l0c0nV+2Mda125seyLL77IgQNfXnRVbiw7d+5cBgwYQK9evfj1r38NULXsmrvvM7MNQCFQ7waaco9I/EXq4Rrct1vEodD9+/dn/frIX+VMnZBfnXKPiKRa2EVCzKxf8N9iYBDwleBjUPCYiCRJ5f5EBQUFHHfccTX2J8rPz6egoAAz45++/VVaZDWn4vNPq97bkLHWxcXFDB48mBNPPLHG8WeffZbi4mImTZrEqFGjeOSRR9iyZUuoKnYB9VpFQLlHJHESveR0pk7IB+UeEUkfkVZxvDj473dCPK5KcFwiEkH1/Ykq5efnc935Pdj24u/oculwjMCwoinFZ8V9rLW78/LLL1et8lbtrnt2raLHA/Vdh1u5RyRBEt3DleGrvin3JMChQ4cYPXo03bp1Iy8vjz59+vD8888DMGfOnKpVhXNzc2nVqhVmxvLly1MctUhqhR3i6O7jgz9OdPePq79mZt0TGpWIhFV7f6JK5eXl7Nu3j5kzZ9KtWzcGDar/nLNouDtr1qxh48aNzJkzh4qKCiZOnFj5clUDLTgHrQdQr51slXtEEqdTfk7IOWLx6uHK1An5oNyTKJGG6I8YMYIRI0ZUlZ0xYwaTJk3ia1/7WgojFkm9aPZBmxfi2FPxDkRE6hZqf6LqWrduzS233MLIkSPZtm1bQmK49dZb2bVrFw899BAjRoyo2lg2uIpjlpkNMbOWwL8Aq2JZICRIuUckzpLRwzW4b2eW3dePj6cOYtl9/TKicVaLck8cRRqiX9vMmTMZOXIkZpaCSEViE6mXOCjPzNab2X4ze9XMutVVZ9geNDPrRWByf5taY6+PB1rG+iFEJDbh9ieq7ejRo+zfv5+ysjJOOumkmM9XfaW3inc2clruF1UbXmdnZ7NmzZqqsllZWRw9ehTgY+BfgdkE9kG7vr7nVe4RSZxM7uFKNOWe5Ag1RB9g06ZNvP766zz++OMpikySLdIK1V988QXDhw+ntLSUTZs28eqrr3LJJZekOuSQErEFUaRVHHsSGHOdT2D8daU9wA8b8DlEJAbh9idavHgx7dq1o3fv3uzbt49x48bRtm1bTj/99JjPVbnS2/5DX8DRI+w+cIjl23fy9qcVHD58+JiNZUeOHMndd9/NlClTyuOw3LVyj0gCZfq+RgkUc+4xs1OAWcDJgAPT3f3XtcoY8GvgSgIr3t7o7n+LW/QZINwQfYBZs2bxrW99i+7dNZq0qahrheoLL7yQe+65h2uvvTbVoUZU2UtcqXov8WeffQZw0N3nApjZBGCHmfWKNMIo0hy0p4Gnzex8d38zTp9BRCKItD9RZc9Vhw4dqspPmzaNFi1acOedd7J582ZycnI477zzeOGFF2jZMvobvrXPu/+LCg4cPsKu//0zu5Y9UVXuuvO/EnFj2SlTpjT4d6DcIyKp0MDcUwH8k7v/zczygOVmttjd36tW5grgtODj68Dvgv82CXUN0Z81axZjx45NQWSSKpEaNgUFBdxzzz0ANGvWLEwN6al6L/Hvfvc7iGELojr3QQNuMbN17l4OYGZtgV+6+80NC19EqmvI/kQNubsU6ryV8i8cQf6FX07gNmDChMDiI6tWrYr5nFFS7hGRVKh37nH3T4FPgz/vMbN1QGegegPtu8AsDyTzt8ws38w6Bt/bqNU1RH/ZsmVs2bKFa665JkURSjoIN/w1k9TuJd67dy/AkVrF6tyCKJpFQnpXJikAd/8c6FvfgEUai0iTQb/44guuueaaqn3JXnvttajrTfT+RPU5bzhJ3stIuUdEUqFBucfMCoLl3671Umfgk2rPNwePNXqVQ/QXLlxYY4h+pZkzZzJkyBDy8uq1baY0IpGGv2aKUL3EwTlotbsA69yCKJoG2nHBu0cAmNkJRNfzJtIoVR8zvWvXLiZPnszQoUPZuHEjABdeeCGzZ8+uMRQxGonen6i+560tBXsZKfeISCrEnHvMLJfAKpD3uPvuWAMwszFmVmpmpdu3b4+1mqRbsKKMC6a+Qvf7nuOCqa+wYEVZ1RD9lStX0qFDh6o9z+bMmQPAwYMHefLJJxk1alSKo5dUqWv4a0OUlJRQVFREdnY2N954Y43XHnvsMb7yla+Qm5vLwIED2bJlS8znqd5LPG/evKpe4mBvYKvKctFuQRRNA+2XwJtmNsnMJgP/C/x7jPGLZLxISwa3aNGCe+65hwsvvLDeY6bD9U4lutcqXP35OVl0zs9J6IbXdVDukYwRqWf9vffeo6ioiLZt29K2bVv69+/Pe++9V0eNkkIx5R4zyyLQOJvj7vNDFCkDTqn2vEvw2DHcfbq7F7l7Ufv27ev9AVKhcrh8WfkBnC+H6a/Y2Rx35+DBg+zdu7fqUbn/WcuWLSkvL+eyyy5L7QeQlAjXsImXTp06MW7cOG6+ueYI5ddee42xY8fy9NNPs3PnTrp3786wYcNiPk+4XuLvfe97AC3ruwVRnQ00d58FDAG2Av8Ait39jzF/ApFGJl5jpuO1P1G4u0UbN27EzKruXubm5jJp0qSw551wdWFK9zJS7pFMEqlnvVOnTjz11FPs3LmTHTt2cPXVV3P99fXegUKSJJbcE1yh8ffAOnd/JEyxZ4CRFvANYFdjmn+WqmH6ktkiDX89dOgQBw8eBAJTSA4ePBhxPn4oxcXFDB48mBNPPLHG8WeffZZrr72WwsJCWrRowQMPPMDrr7/Ohg0bItZX317i4A2WDQS2IPqcwMJAdf4BiKrL3t3Xmtl2gvuAmFlXd/+/aN4r0pjFc8x0vPYnqrxb9OKLL3LgwLHDF8vLy2ne/Nivfjrui6TcI4kS7/13Iq1GNmTIEPLz84HA3eJmzZrx4YcfJvDTSUPFkHsuAG4AVpvZyuCxsUDXYH3/CSwisMT+hwRWdbspQeGnRKqG6Uv6i2WF6hEjRtCzZ082bdoEwIABAwD4+OOPKSgoiEtc1Rt7lT+vWbOGHj16hP0coRZzm1J8Vl0Nxz313YKozgaamV1NoLu/E7AN6AasI7A8pEiTlYgx0/HYn6i4OLC/amlpKZs3b07aeeNNuUcSKdH774TqWc/Pz2fv3r0cPXqUiRMnxuujSJzFknvc/S8EFroNK7h64+3xizS9dMrPqbEKcPXj0nQ1ZIXqyrn9iTBw4ECuv/56brnlFk477TQmTpyImbF///6w74nUSxzva6ho5qBNIrDb9Qfu3h24DHgr2hOYWTMzW2Fmz8YYo0jaSfSY6UTq1q0bXbp04aabbmLHjh2pDieSBuUekUgSNZcUwvesl5eXs2vXLkpKSujbVwuSpjHlnhjEa5i+NC7pOvS1f//+PPjggwwZMoSCggIKCgrIy8ujS5cuYd+TzF7iaBpoh939MwKrGh3n7q8C9emmu5vAnSeRRiPRY6YToV27dvz1r39l06ZNLF++nD179lRN0k5TDc09IlGL11zSunrWW7duzS233MLIkSPZtm1bg84lCaPcE4PBfTszpfisVC8uJWkmnYe+3n777fz9739n69atDBkyhIqKCs4888yw5ZO5mFs0c9DKg8vGvg7MMbNtwL5oKjezLsAgAhPjfhJzlCIpkK5jpmOVm5tLUVHgGuPkk0+mpKSEjh07smfPnnTdeybm3CNSH/GaS1rXZryVjh49yv79+ykrK+Okk06K+XySMMo9MUrH4fKSWqke+lpRUUFFRQVHjhzhyJEjHDx4kObNm1NRUcGHH35IYWEhn3zyCWPGjOHuu++mbdu2Yeu6d0DPGsM1IXG9xNE00L4LHAB+DIwA2gDRDp7/FfAz6tgtWyTdpOuY6XgKLDoWuFhMUw3JPSJRiedc0sqe9SVLltToWV+8eDHt2rWjd+/e7Nu3j3HjxtG2bVtOP/30hoYviaHcIxInyWrUhLupPnnyZB588MGqcrNnz2b8+PHcc889DB8+nA0bNpCXl8dNN93EpEmTIp4jXou5RSNiA83MmgHPuvulwFFgZrQVm9lVwDZ3X25ml0QoNwYYA9C1a9doqxdJqGROBI23cHeLli9fTn5+Pqeddhqff/45d911F5dccglt2rRJdcjHaEjuEYlWtD1etYW6EOh7QkXYnvUWLVpw5513snnzZnJycjjvvPN44YUXaNmyZaI+msRIuUckvpLRqIl0U33ChAk1VtitbtWqVfU+V7J6iSM20Nz9iJkdNbM27r6rnnVfAFxtZlcSWKb2eDOb7e7fr3WO6cB0gKKiotRP1BEhvcdMV6rv3aKePXsyduxYtm3bxvHHH8+3v/1tnnjiiRR+gvAamHtEohKuxwsCc0kre8or55JmZ2fz9MotMS2zHOtqkJJcyj0i8ZfoRk0m31QPJ5ohjnsJ7OuxmGpjsN39rkhvcvf7gfsBgj1oP63dOBNJV6keM12XWO8WDRs2LFkhxkNMuUekunjPJX34xY/iciEQaR82gP379/PTn/6UJ598ksOHD3P22Wfz+uuvN/TXIdFR7hHJIJlwU72+ommgzQ8+RJqMZE4EjUVjvFsUgnKPNEgi5pJuKV8b5nj9LgQi7cNWUFDAmDFjqKioYN26dZxwwgmsXLmy7kolXpR7RDJIut9Uj0Wdy+y7+0zgSeAtd59Z+ajPSdz9NXe/KtYgRZIt3ZcLbox3i2qLNfeY2Slm9qqZvWdma83s7sRHK+koEfvvxGuZ5Uj7sK1fv55nnnmG6dOn0759e5o1a8Y555wTc8xSP/G47pHUOHToEKNHj6Zbt27k5eXRp08fnn/++WPKVW5KvGTJkhREKfHWGPfgq7OBZmbfAVYCLwSf9zGzZxIdmEiqDe7bmWX39ePjqYNYdl+/tGmcQXL34kiVBuSeCuCf3P0MApvN3m5mZyQuUklXibiRkagLger7sL3zzjt069aN8ePH065dO8466yzmzZvXoPolerruyVzVe6Z37drF5MmTGTp0aI0e8Q0bNjB37lw6duyYukAlrtL9pnosotmoegJwHlAO4O4rgVMTGJOI1KEx3i0KYQIx5B53/9Td/xb8eQ+wDsjcLC0xS8SNjERcCNTeh23z5s2sWbOGNm3asGXLFkpKShg1ahTr1q2L+RxSLxPQdU9GitQzXen222/noYceokWLFimMVOItnW+qxyKaBtrhECsZpe3GSZKZNCyhfhrj3aIQGpx7zKwA6Au8Xev4GDMrNbPS7du3NyhISV+JupERzwuBUPuw5eTkkJWVxbhx42jRogUXX3wxl156KS+99FKD4pao6bqnkajeMw0wd+5csrOzufLKK1McmUhk0SwSstbMhgPNzOw04C7gfxMbljQ1dU2YBw1LqC1Ze3GkUINyj5nlAvOAe9x9d/XXtL1H05DMTUVjEW4ftt69ex9TtnJjeUkKXfc0ArV7pvfs2cPYsWNZvHhxqkMTqVM0PWh3AoXAIeBPwC5Ak+4lrjQsQUKIOfeYWRaBxtkcd9dqbE1YOg97qdyHbeHChTX2Ybvooovo2rUrU6ZMoaKigmXLlvHqq69WLfdfnUYfJISuezJcqJ7pCRMmcMMNN1Td9BVJZ9E00Aa5+z+7+7nBxzjg6kQHJk2bhiUIMeYeC3Q1/B5Y5+6PJDxKkTosWFHGBVNfoft9z3HB1FdYsKKsah+2lStX0qFDB3Jzc8nNzWXOnDlkZWXx9NNPs2jRItq0acMPf/hDZs2aRa9evY6pW4siJISuezJY9Z7pefPmVfVMv/zyy/zmN7+hQ4cOdOjQgU8++YShQ4fy0EMPpThikWNFM8TxfmBuFMdE4kLDEiQo1txzAXADgY1mKzePGuvui+Icn0idwu3FNqX4rIj7sBUWFvLmm2/WWX/l6INK1UcfVPYUVI4+uO222xr0WZoQXfdksMqe6SVLltTomX755Zc5fPhw1fNzzz2XRx55pGpzeJF0EraBZmZXAFcCnc3sN9VeOp7AMtYicadhCdLQ3OPufwE0YUfSQrI3ldfog9jpuiezLFhRdsz80r4nVDBt2jSys7Pp0KFDVdlp06YxYsSIGu9v1qwZbdu2JTc3N9mhi9QpUg/aFqCUQLf+8mrH9wA/TmRQ0jSFmzD/8ssvs3nzZh599FEAtm/fztChQ/n5z3/Oz3/+81SGLImh3CONRjI3ldfogwZT7skQsfZMV1d9GLBIugnbQHP3d4F3zexP7n44XDmReNGwBAHlHkmdkpISZsyYwerVqxk2bBgzZswAYM6cOfzoRz+qKnf06FEOHDhAaWkp55xzTsQ6O+XnUBaiMRbvTeU1+qDhlHsyR7J7pkWSLZpFQs4zs8Vm9oGZfWRmH5vZRwmPTBqt+k6YP/HEE6sm9Xbo0EHDEpoO5R5Jqk6dOjFu3DhuvvnmGsdHjBjB3r17qx6PPvoop556Kl/72tfqrDMZm8prUYS4U+5Jc8nsmRZJhWgWCfk9ga795cCROsqKRKRhCVIPyj2SVMXFxQCUlpayefPmsOVmzpzJyJEjo9qbLBl7sWn0Qdwp96S5ZPVMi6RKNA20Xe5+7KYqIjHQsASpB+UeSTubNm3i9ddf5/HHH4/6PfHaVF6LIiRNTLnHzB4HrgK2ufuZIV6/BHga+Dh4aL67T2xIoE3VvQN61rjZC/HvmRZJpWiGOL5qZg+b2flm9rXKR8Ijk0YplmEJJSUlFBUVkZ2dzY033lh1fM6cOVVDIXNzc2nVqhVmVmNza8loyj2SdmbNmsW3vvUtunfvntTzVo4+KCs/gPPl6IMVO5vj7hw8eLDGMMzajTMIjD7o379/UuOOl3B/B4IjKs4xs73VHg808HSx5p4ZwMA6yrzh7n2CDzXOYjS4b2emFJ9F5/wcDOicn8OU4rN0o1cajWh60L4e/Leo2jEH+sU/HGnsYhmWUDkv5MUXX+TAgS/fO2LEiBoXITNmzGDSpElRzQuRjKDcI2ln1qxZjB07NunnbeqjD8L9Hagm393jtRR+TLnH3V83s4I4xSB1iFfPtEg6qrOB5u6XJiMQaRpiGZaQiHkhkv6UeyTdLFu2jC1btnDNNdck/dxNfVGEaP8OxEOCc8/5ZvYugSX9f+rua0MVMrMxwBiArl27JjAcEUlHkTaq/r67zzazn4R63d0fSVxY0lglasJ8LPNCJD0p90iqVFRUUFFRwZEjRzhy5AgHDx6kefPmNG8e+FM5c+ZMhgwZQl5eXtJj06IIddpkZg4sBu519x31rSAJuedvQDd332tmVwILgNPCnGs6MB2gqKgouhW0RKTRiNSD1jr4b/L/EkmjlohhCamaFyIJodwjCRVqsY3BfTszefJkHnzwwapys2fPZvz48UyYMIGDBw/y5JNPMm/evJTErEURQmvXrh3AOqA3cCLwW2AOMCCG6hKae9x9d7WfF5nZo2bWLpbGpIg0bpE2qp4W/PfBcGUiMbOWwOtAdvA8T7n7+FjqEqlLquaFSPw1NPeIRBJuqw8IbOw8YcKEkO9r2bIl5eXlyQrzGMlYrj8TBVek3B+cf7bVzO4APjWzPHffU5+6Ep17zKwDsNXd3czOI7BQ22eJOJdkjpKSEmbMmMHq1asZNmwYM2bMAAILof3oRz+qKnf06FEOHDhAaWkp55xzToqilWSJZhXHWB0C+rn72UAfYKCZfSOB55MmKpXzQkQkecKt5Afw5JNPcvrpp5OXl8cZZ5zBggULQtYRabGNdDe4b2eW3dePj6cOYtl9/Zp84yyMyuGAiby+CcnMngDeBHqa2WYzG21mt5jZLcEi1wBrgnPQfgNc79FuACqNVuUCODfffHON4yNGjKixMuujjz7KqaeeqoXQmohoVnGMSTDp7A0+zQo+lIik3tJ5XoiIJE+4lfzKysr4/ve/z9NPP83AgQNZtGgR1157LRs3buSkk06qUUdTX2wjU4X7OxDcViXbzI4D2hJo+Lzm7ruSHaO7D6vj9RKgJEnhSIbQQmgSSkLvMJlZMzNbCWwDFrv724k8n8RXPO5W18eCFWVcMPUVut/3HBdMfYUFK8oAmDx5Mjk5OUydOpXZs2eTk5PD5MmTAarmhYwaNarB5xeR9FZcXMzgwYM58cQTaxzfvHkz+fn5XHHFFZgZgwYNonXr1mzYsOGYOqovqrF7+UI+nXkPm34xmH2Lf1Oj3Msvv0yvXr1o1aoVl156KZs2bUrMh5Ia6vt34KOPPgL4KrAHWENg9E7EhpJIpqlcCG3kyJGpDkWSJKYGmpndFE05dz/i7n2ALsB5ZnZmiLrGmFmpmZVu3749lnAkQcJ1u1ferX7kkUfYvXs3Dz/8MMOHD2fbtm0xnyvcJqwLVpQxYcIE3L3Go3KeSOW8kMsuu6wBn1QyRbS5R5qWoqIiTj/9dJ555hmOHDnCggULyM7Opnfv3seUvXdAT3KymgHQPPdE2px/HflnX86ZndtUldmxYwfFxcVMmjSJnTt3UlRUxHXXXZe0z9NUxfJ3YNiwYQCr3b21u3d095Hu/o94x6bcI6mkhdCanlh70Oo1gdbdy4FXgYEhXpvu7kXuXtS+ffsYw5FEiMfd6mhl8rwQSSotHCLHaNasGSNHjmT48OFkZ2czfPhwpk2bRuvWrY8pO7hvZ6YUn0Xn/Bxa9/wmp53Xj/59v0LXE1pVlZk/fz6FhYVce+21tGzZkgkTJvDuu++yfv36ZH6sJifN/w4o90jKzJo1SyOFmpiwDTQzWxXmsRo4ua6Kzay9meUHf84Bvg3or1sjUJ+71dHSvBCp1NDcI03PkiVL+NnPfsZrr73GF198wdKlS/nBD37AypUrQ5avvdhGzw4156+uXbuWs88+u+p569at6dGjB2vXhtxTWOIk1X8HlHuahmRP32goLYTWNEVaJORkAvuIfF7ruAH/G0XdHYGZZtaMQEPwSXd/NqYoJaxDhw5x2223sWTJEnbu3EmPHj2YMmUKV1xxRcKWaK1+t/rgwYO0aNGCuXPnhrxbHS1twirVNDT3SBOzcuVKLrroIoqKigA499xz+frXv86SJUvo06dPvevbu3cvtUd0tGnThj176rVqu9RTGvwdUO5pAuKx2FA8aSE0CSXSEMdngVx331TrsRF4ra6K3X2Vu/d1997ufqa7T4xTzFJNRUUFp5xyCkuXLmXXrl1MnjyZoUOHsnHjxoQt0Vrfu9XRqD4vpJI2YW2yGpR7pPGqqKjg4MGDNS5kKioqOPfcc3njjTeqctCKFSt44403Yu7Vz83NZffu3TWO7d69WxdICZYGfweUe5qAZE7fqE4LoUl9RNqoenSE14YnJhypr9atW9fYWPWqq66ie/fuLF++nIKCghpl47VEa7zvVoM2YZUvKffIghVlIXPB5MmTefDBL6cCzZ49m/Hjx1dtMH3NNdewdetW2rdvz9ixY7n88stjOn9hYSEzZ86ser5v3z42bNhAYWFhgz+bhJfqvwPKPU1b9ekbgwYNYuHChQ2evlGpcgGcyjmWlQvgAFX5K5TKhdCk6YlqHzQzuxA4zd3/YGbtCdxh+jixoUkstm7dygcffHDMhUTlEq2PP/541HWF63Y/99xzmTp1KitXrqRPnz5Vd6tvu+22BsU+uG9nNcikBuWepifWC5k77riDO+64o17nCpfjvve973Hvvfcyb948Bg0axMSJE+nduze9evVq0GeTuqXL3wHlnsanpKSEGTNmsHr1aoYNG0aXLl0AeOutt3jggQdYvnw5hw8frtqXLB7TNypFWgAnHf5/l/RT5yqOZjYe+Dlwf/BQFjA7kUFJbA4fPsyIESMYNWrUMRcSdS3RGqrrPVy3+8UXX1x1tzovL48hQ4Y06G61SCjKPU1TIlbyq+/Qovbt2zNv3jz++Z//mbZt2/L222/z5z//uUGfSzKHck/jFG7roM8//5wxY8bwhz/8gRYtWnDllVdy2WWXxWX6RqVUL4AjmSeaHrTvAX2BvwG4+xYz00D8NHP06FFuuOEGWrRoQUlJyTGvz5o1i7Fjx4Z8b7g71lOKf4jH8W61SD0p9zRB8b6QibVHrn///lpWv+lS7mmEKnvGSktL2bx5c9XxK664AoBf/OIXXHTRRTzwwANcfPHFcZm+USkNFsCRDBPNPmhfuLsDDmBmDe/rlbhyd0aPHs3WrVuZN28eWVlZNV6va4nWNN97Rpou5Z4MEGnJAc2OvwAAIABJREFU6koTJ07EzFiyZEmN46+99hozZsyocSzcBUusFzLKbxID5Z5G7OjRo1XDmkMtNvTEE09QWFjY4MWGqkuDBXAkw0TTQHvSzKYB+Wb2Q2AJ8F+JDUvq49Zbb2XdunUsXLiQnJzARUz1i6abbrop5BKtlRdNG1a+WeP4wf9bxd7VS9T1Lqmm3JMBwg0bqrRhwwbmzp1Lx44dq44tWLCA6dOnVz2fP38+06ZNA6K7kNm4cSNXXnklbdu2pUOHDtxxxx1UVFSEPL+GFkkMlHsyWOX1T1aLbE46Z8AxQ5v/8pe/MHv27BpDm7t27crll1/O8ccfzy9/+UtWrVrF1VdfTatWrbjuuuto27Yt/fv357333osppsF9OzOl+Cw65+dgQOf8HKYUn6X5ZxJWnQ00d/8F8BQwD+gJ/Iu7/0eiA5NjhZpHsWnTJqZNm8bKlSvp0KEDubm55Obm8uGHHzJu3DhGjhzJxo0bj1mitfpF04m5LQDY/8Gb7Fn5QlWZ7M2lVRdNjUmkO/4vv/wyvXr1olWrVlx66aVs2rQpNUGKck+GCLdkdaXbb7+dhx56iBYtWlQd++53v0vz5s0ZO3Ysv/rVr9iwYQM33XQTEN2FzG233cZJJ53Ep59+ysqVK1m6dCmPPvpoyPPHu0dOGj/lnszWqVMnvj38VnLP6s/+L47gfDm0ecGKMs466yzy8/Pp2LEjixcvZt68eYwaNYqzzz6bsrIyfvCDHzB79mx+8pOf8MYbb7Bz50527NjB1VdfzfXXXx9zXIP7dmbZff34eOoglt3XT40ziSiaHjTcfbG73+vuP3X3xYkOSo5VOY+irPxAjWSzYmdz3J2DBw/W2PPsV7/6FYMHD+bkk09m+PDhXHbZZTXqq37RNLToFHKympFz2jew446jfOks9i5/hq+3O1x10dQQ9bnbnQzh7vjv2LGD4uJiJk2axM6dOykqKuK6665LUZQCyj2Zbu7cuWRnZ3PllVce81r17T6OO+64Gs/rupD5+OOPGTp0KC1btqRDhw4MHDiQtWvXhoxBQ4skFso9mau4uJjXv/j/7d15eFTl2fjx701IIJBAUFBWAZGt+FKpKCoBEbEutYgIigVXWqACSmmtKxIpVnzpa3EpiqIFhIIW+FFqVQoigiAqGFBQtCwBicpSDRDWLPfvjzkTs8wkM5ntzOT+XNdcTM6cOec+YebO8zznWdpSXKdsryFv1+a33nqL888/v6TRaODAgYwcOZJPP/2UlJQUPvzwQ3bs2MHo0aNp06YNIoKqkpSUxPbt22NxSaYG8ltBE5EjInLYx+OIiBz29z4TGeEcR1G+0NTznMYlLdYgpNSuRbOGdenaqlHIa6ZBcK3d0eCvxX/x4sV06dKFwYMHU7duXbKysti8ebNNFBBllnsSw5EjR3jwwQd56qmnKry2ZMkSTp06xWOPPca4ceNo27ZtUEuAjBs3jgULFnDs2DFyc3N58803ueqqq3zua12LTKAs9yQOX12YtbiIL9e+Sa1atWjWrBmqSlFREbm5ufTt25e2bduWDBMp3WiUkZFB3bp1GTt2rN/J1owJt8oWqrYZi1wkXOMovIWm5cvLNggO6NYCzfmQ/ad1psO917F7924aNGjAyy+/zMiRI6sdN3hau8eMGRNQa3csbd26lR//+MclP9evX5927dqxdetWW/8oiiz3JIasrCxuueUW2rRpU+G166+/HvBMEgI/zK4WqN69e/PCCy/QoEEDioqKuO222xgwYIDf/d2ytpZxN8s9iWFJdi61fDQu561+hcMfLOQglNwJmzdvHu3atWPXrl3UquW5Z3Ho0CEeeugh0tLSGDlyJHl5eRw9epTZs2fTunXroOPJycnhrrvu4v3336dOnToMGjSIadOmUbt2QEsRmxoqoC6OIpIpInc4zxuLiO/FtEzEhGscRVWFppEjR5a0Gnlv+4cq0NZuf2PD1q9fzxVXXMFpp51GkyZNGDx4MN98803IcZWXn59Pw4YNy2xr2LAhR44cCfu5TGAs98Svt99+m6effpqmTZvStGlTvvrqK2688UaeeOKJkn369Onjd+ZHf4qLi7nqqqsYOHAgR48e5eDBg3z//ffcd999Yb4CU5NZ7olP3uEgRaoVXkuimCGjfouqoqq0bt2aW2+9lYkTJ6KqPPDAA1xxxRXMmDGDEydOlCn/1K9fn1GjRnHrrbeyf//+oGJyWy8iEx+qs1B1CrZgY9SFaxxFpApNlenduzdbt26lQYMGtGzZku7du/ts7a5qEcmcnBx2795Nenp6WMbGlZeWlsbhw2V7sRw+fLjC7JcmOiz3xIfCwkJOnDhRYcrqt99+my1btrBp0yY2bdpE8+bNmTFjBqNHjw7pfN999x179uxhzJgx1KlTh9NPP5077riDN954I0xXZGo6yz3xy9dwEIAkERp+v43lf59VafmnadOmfss/xcXFJQ3NwQhmzKwxXoHcQbse6A8cBc+CjYCVWKMs2HEU0S40+RNMa7e/sWFXX301gwcPpkGDBtSrV48xY8awdu3asMfapUsXNm/eXPLz0aNH2bFjB126dAn7uUxALPe4iK9ZZAEmT55MampqmSmrJ0+ezOmnn15SEGratClJSUk0atSItLS0kOJo3Lgxbdu25bnnnqOwsJC8vDxmz54dlrWKjHFY7olTX+cdR4uL0MJToEWgxWjhKYqKCtmwbrXP8s/IkSN9lpeWL19OdnY2RUVFHD58mPHjx9OoUSM6d+4cVEzBjJk1xssWqo4jvmY2i3ahyV83xM8++4zu3bvTqFGjMuuFRKK1e/Xq1SFVmvxVXq+//nq2bNnCokWLOHHiBJMmTaJr1642/ix2LPe4hL9ZZJdk55KVlVXSZcj7yMrKqnCMnJwc+vXrF/R5feW3xYsX89Zbb9GkSRPOOecckpOT+fOf/xyGK3UPf7n21KlTDBo0qGR2Oe84PhNWlnviRPkc0TA1mUPrFrDn/wZyeP1Cjm59hz3/N5CijxexZs8Jbpj1GRdP28gNsz7jZBE0atSIadOm+Swv5eXlcfPNN9OwYUPatWvHjh07eOutt6hbt25QMQbai8iY0gIZoVh+wcY7sQUbXcFbaPLezvcWmsAz1sxXIam8nJycoM7p7Ya4bNkyjh8/Xmb7woULad26NcXFxfzlL39hyJAhfPLJJyWt3b/73e/Iz88PqbX7k08+YdKkSfzjH/+oct8l2blMXfYFX+cdp3lGKvde2ZEB3VowefJkHn300ZL95s6dy8SJE8nKymLRokWMGTOGYcOG0aNHDxYsWFCtOE1YWO5xicpmkY3U5BuV5bcB3c5L+IqJv1wLkJmZybhx4xg8eHCMokt41co9IvIycC2wX1XP9fG6AE8B1wDHgNtV9eOwRl6D+MoRyUlCk97DyMgcWrJfanISN5zfosK+pw9/kfzTO5OV1c9veSnU75i3F9GIESNYt24d+fn53Hnnndx333387//+b0jHNonNFqqOY+Gcej9Q/rohZmRksOn7ZDKfeId2D/yLaW9v54v//AcIX2v39u3bufrqq3nqqafo1atXpftWt8W/X79+bNu2jePHj7Nq1Sqfk6mY6LDc4x7hmkU2GLHIb27iL9empKQwbtw4MjMzSUpK8vNuE4oQcs8soLK+a1cD7Z3HCOC50CKt2XzliIIiJa1u7QrDQd7ZdiAm+cTGzJrqCmiOT2eRRluo0WViUWjyx1sh+mLqIPTUcVCl8aW3sCQ7Nyyt3bt376Zfv35MmDCBW265pcr9Y9Hib8LPco87NM9IJddHXgl2FtlguCm/mZqnOrlHVVeLSJtKdrkOmON0n1wvIhki0kxVwz8tcQ3gLxfkHSsg+5Gfltn2m1c3BXWMcCk9ZjYcvYhMzWELVcexcE29Hw7eCtFZ416l1bjXOO2KUUiTtkG1TvkbG+ZdRHLMmDGMGjUqoGNZ4S5+We5xn3DNIhsMN+U3UzNEIfe0AL4q9fNeZ5uphmByRKTzib/xslAzxsya8PNbQVPVdFVt4OORrqoNohmk8S0WhSZ/Sld8aqXUJa3b1fz39Sf5Krdiw2CwE5vMnDmTnTt3kpWVRVpaWsmjMla4i1+We9wn2Flkw8FN+S2WPvroI15//XWf60MePHiQ/v37R3R9yJrETblHREaIyAYR2XDgwIFonjqmFixYQOfOnalfvz7t2rVjzZo1fvcNJkdEMp9UNqQC4LzzPL2Ivv/+ew4ePMhrr73GmWeeGfJ5TWKr7A7aaZU9qjqwiLQSkXdE5DMR2Soi94Q3dBOLQpM/FSo+qmjhSRpJfpnN1Rkb5l1EMj8/v8yjMla4i1+h5h4TGb5mkY30+QLJb8HOLBtv0tPT6dq1q8/1IevXr8/8+fMjuj5kTRKF3JMLtCr1c0tnWwWq+oKqdlfV7k2aNAnDqd1v+fLl3Hffffz1r3/lyJEjrF69mrPPPtvv/sGUgSJZXqrp42VNZFQ2Bm0jnilmxcdrCvj/1ngUAr9V1Y9FJB3YKCLLVTX+/kK62IBuLaJaISssLKSwsLBMN8TatWtzRcN9zN7xX4obnYUWnCRvzSsk1U3joV+UnVY7WmPDvMfyNYsjeAp1s2bN4tNPP+Xmm29m1qxZgGcK61/84hds2LCB3bt3884779CnT5+wxWUCEmruMQkikPxWnZll44E317Zv3569e/fSsGFDjh49CsDJkye57LLLSE1NJTk5mVq1ajF69GjLVaGLdO5ZCowRkQVAD+CQjT/7wcSJE3nkkUe46KKLAGjRouoyQTBloEiVlxJtSIW/8hHA22+/zejRo9mzZw89evRg1qxZtG7dOnbBJjC/FTRVbRvKgZ2k843z/IiIfI6nr7VV0OJAsFPUd+nShYIV0/jm61xISqHBWZ2ZOnMBN17Ursxxo5nIKkvGNoW1e4Wae0zNMnDgQAA2bNjA3r17S7ZnZGSQkZEBgKqSlJTE9u3bYxJjZQLNtQA//vGPAejYsSO7d+8G4MorrwRgwoQJIa0PaULPPSIyH+gDNBaRvcBEINk59vPAG3im2N+OZ5p9u+XpKCoqYsOGDfTv359zzjmHEydOMGDAAKZOnUpqqruHJsRiEqVI8lc+OnjwIAMHDmTmzJn8/Oc/Z8KECdx0002sX78+htEmroBmcRSRRnimhS1ZnU9VVwd6EmdWo27ABz5eG4FnulnOOuusQA+ZcBYsWMCjjz7Knj17aNq0KbNmzapyKvlIqe76aoFUaNySyPwV6rxTWAM2hbULhJp7jMnIyCA/P5/i4mImTZoU63DKCCbXPvzwwyW5qvz6lZ988gl9+vQJaH1IE5jq5B5VvbmK1xUYHZYAE8y+ffsoKChg4cKFrFmzhuTkZK677jomT57MY489FuvwKnXvlR3LfI8hvodU+CsfLV68mC5dupSU9bKysmjcuDHbtm2jU6dOMYk1kVW5DpqI/BJYDSwDHnX+zQr0BCKShmctkXGqWmEWpJrYz7q8YPtdR1ok+1Pb2DATqOrmHhF5WUT2i8iWyEZo4kFeXh6HDh3i2WefpVu3brEOp4xw5Npg1oc0gQm13GOC571LNnbsWJo1a0bjxo0ZP358XKwX5qb5ACJp69atJXfxgZKJXLZu3RrDqBJXIHfQ7gEuANar6mUi0gn4YyAHF5FkPJWzeaq6uPphJrbq9LuOpEh2Q6xqbJgxpVQ398wCngXmRDA2E0fq16/PqFGjaNKkCZ9//jlnnHFGrEMCQs+1wa4PaQJW7XKPqZ5GjRrRsmVLRH4Y/lf6udtFez6AWMjPz6f8jZSGDRty5MiRGEWU2AKpoJ1Q1RMigojUUdVtIlLl7Q7xfLNeAj5X1SdDjjRBubHfdaS7IdaERGbColq5J4DFYk0NVFxczLFjx8jNzXVNBS2QXOtvYqZ9+/YFvT6kCVi1co8JzR133MEzzzzDVVddVbJW2LXXXhvrsIwjLS2Nw4fLdoQ7fPgw6enpMYoosVXZxRHYKyIZwBJguYj8A9gdwPt6ArcAfUVkk/O4JoRYE1L5ftebNm0iOzubyZMnxywm64ZoXKK6uadKNXWNIQhunSE3KL32YVpaGklJSYwdO7bkdX8L3C9fvpzs7GyKioo4fPgw48ePp1GjRnTu3DmGV1OWr1wLcOxUYUTWhzQBi1juMf7XQp0wYQIXXHABHTp0oHPnznTr1o2HHnooxtEary5durB58+aSn48ePcqOHTtscqIIqfIOmqpe7zzNEpF3gIbAWwG87z18T1VrSinf7xpg/PjxMR0YWxO6Ifprla5duzYnT57EM5bbM+3+iRMnqFOnTlx1t0gE1c09AR77BeAFgO7du2s4jhkPvONdX331VS688MK4WNjYu+bhkuxcpizdxIePDeKdU+1Ykp1b5cyyY8eOZe/evaSmpnLhhRfy1ltvUbduXX+nijpvTs1aupW84wUl278/VhDQxEwTJ06MeIw1USRzT01X2cQ4A7q1YPr06UyfPj2WIdZ4/spH119/Pffeey+LFi3iZz/7GZMmTaJr1642QUiEBDSLo5eqvhupQGoqt/a7TqRuiL6msd70jxd9FuqysrJ8TmG9a9cu2rRpE4vwDZZ7wsVt410D5S3UHfj4bZLqNeRIxjlhm1k21gZ0a8HUZV+UqaBBZNaHNMGz3BNe0VoLNdFEYu3WYJdTysrKYtGiRYwZM4Zhw4bRo0cPFixYEIGrNRBYF0cTYd5+1/v37+f7778Pa7/rqroHJTpvwS437zjKD6115133K1S1zMNbyMvJyanwmlXOTLzzjnc9cOAA55xzDi1btmTMmDEV1gF0I2+hLn/L29Q/ty8iEraZZd0g0Ra6NcYf+6xXj3dtsjvvvLPCa5mZmcydO5emTZsGfDx/ZaMl2blkZWX5LR/169ePbdu2cfz4cVatWmVlowiyClqU+OtzDZHtd52fn1/y+Pbbb0lNTY2LVuVwieSSAcadnMVi3wc6isheERke65jcwI3jXQP1dd5xCg/t5+RXW6h/7uVlticCfxMwxetCt8b4Y5/16hk4cCADBgzg9NNPL7Pdu3ZrZmZmUGu3WtnI/ayCFgWVtVQAJCcnM336dPLy8vj22295+umnIzJOYtGiRZxxxhk1aq0ca62reVT1ZlVtpqrJqtpSVV+KdUxuEM/rDDXPSCV/60rqtPwRyRlNy2xPBDYxk6kp7LPuDlY2cj+roEWBW1oqZs+eza233uqKMW7RYq11xni4dbxrIO69siPHtr5D2rl9S7YlUqGupix0a4x91t3BykbuF9QkIaZ63NBSsXv3bt59911eeqlm3Uy498qOZWaMgsQq2BkTjHhdZ+iM47updew7zulxBfuOk5AzyybSxEzGVCaQz3r5JSOOHz/OXXfdxTPPPBPJ0GoMKxu5n1XQoiDSCz/706dPH9avX0/t2rU5deoUderUoW3bthE9p9vUhCUDjCnN38xc4BnvevDgQTp06EDdunW58cYbgxrvWjqngGcWyC++CF9PAH+xz549m8GDbuCVie6vTBpjQuddXsP7vGnTpmEZPx/pHBYvrGzkflZBi4JYtlQ8++yz/PKXv6RDhw7cf//9ET+fG1nLtKkpqlpjyDveNZR1hiZMmMAf/vAHBg0axNy5c8MSN1Qe+4wZM8J2HmNM9J08eZK77rqLFStW8N1339GuXTsef/xxrr766irfG+7x895yUTyJxNqtVjZyNxuDFgWx6HM9bNgw1q1bx5gxY2jVqhW7d++uUbM3GlMTRWO867x587jggguCes+wYcNo1qwZDRo0oEOHDsycObPCPm4Zq2uMCb/CwkJatWrFu+++y6FDh5g8eTI33ngjOTk5Vb63Jo2f9zfj9+TJk0lNTWXKlCnMnTuX1NTUkhl4O3bsSGpqKrm5uVx55ZWkpqaWrOVq4pdV0KJkQLcWrL2/L9se7Uen/8zjngGXkJ6eznnnncebb74Z1LH+85//ULduXYYNG1ayrfyX+sIBd3LRRReRnp7O/v37KSgo4G9/+1u4L8sY4yKRHu+6f/9+duzYwcaNG1m2bBmrVq0K6H0PPPAAOTk5HD58mKVLl/Lwww+zcePGgGK0WcWMiX/169cnKyuLNm3aUKtWLa699lratm1bIQ+U5x0/f9ttt1V4LZCGH18eeOABGjduTM+ePQPOYdFQ3bXJbO3WxGQVtCgLpRXJa/To0WVasH19qZ/bfIprf3kvO3fuZMOGDTRo0IBx48axY8eO8F+UMcYVApmZ6+TJkwwfPpzWrVsH1Uh0+PBh8vPz2bx5M+PHj6d9+/b8/Oc/DyindOnShTp16gCemSNFpML7bFYxY2qOffv28eWXX9KlS5dK93vllVfIzMz0OX4+kIaf8p544gl27txJbm4uI0aMCDiHRYP1IjClWQUtyqrbiuS1YMECMjIyuPzyHxZr9felfnL6TM4880y6du3K2WefTWZmZlyseWSMqZ5A1hiqbiPRhAkTGD16NJ06daJ27dqcffbZ9OzZM+Ccctddd1GvXj06depEs2bNuOaaawKKfdxlbfxWKOfNm0daWlrJo169eohIwPnUGBN9BQUFDB06lNtuu41OnTpVuu+cOXN83j2DwBp+yuvRowfp6enUqVOH2267LagcFmnWi8CUZhW0GAu0FQk8LdiPPPIITz75ZJnt/r68qX1GcOTIEdasWcPAgQNJSkoqGUhqjEk8gYx3rU4j0aZNm1ixYgW/+c1vymwXkYBzyvTp08vkI2/BqqrYrzn3TL8VyqFDh5Kfn1/ymD59OmeffTY/+clPAv6dGWOip7i4mFtuuYWUlBSeffZZwP+4q3Xr1pGbm8vgwYN9Du2Aqht+qhJMDos060VgSrNZHGMomFYk8LRgDx8+nJYtW5bZ7msa/+IT+aQdzqGg4HIuuugiJk6cyHvvvcfTTz8d1mswxrhLsDNzBdJItGrVKnbt2sWZZ55JSkoK+fn5FBQUUFRUxFNPPRXwuZKSksjMzGTu3Lk899xz3H333QHF7h1rAZSpUJYfZ1GTJhMwJt6oKsOHD2ffvn288cYbJCcnVzp765uzZzNw4EDS09O54YYbfE5ONH36dJ555hnef/99Vq1aRZ06dfwu15GXl8cHH3zApZdeSu3atXn11VdZvXp1UDkskmxtMlOaVdBixFcrUmW8LdjZ2dkVXrusUxPmrd9D6TagOrWUgg/m06TJZJKSkkhJSeGaa66hQ4cOYbwKY0w889dIVL6Ac3fva/nww34MGzaM7du3U1RURHp6Os8//3y1ckphYWG1x334q1Du3r2b1atX8/LLL1fruMaYyPr1r3/N559/zooVK0hN9dwVqmzc1VpneQ3v0I5LLrmE7du3Vzhu6YafXz/0OGvrXOizwtezZQoPP/ww27ZtIykpiU6dOrFkyRLXlItsbTJTmlXQYsBXK1JVVq1aRU5ODmeddRbgWbixqKiI9zduJmXQ1DKVs+KjeZybkssrH7xHamoqK1asYODAgdxxxx0RuiJjTLzx10j08JJPyzT45OYdJ+vN7Tw+8H/YtGkT4LmjtX379oCW7ti/fz8rV67k2muvLclH8+fPZ/78+UHHXFmvgzlz5tCrVy+fkwkYY6LD392r3bt3M2PGDOrUqUPTpk1L9k+9bBT1u1xW4TjeoRveoR0rV66scpbGwsJC3li7iZRe55fZ7q3wDbi/Lx999FEYrjJybG0y42UVtAjxl6TAdytSVUaMGMGQIUNKfv7Tn/5ETk4OX7S7kSPlWp9UhJX/bx4tX/ojxcXFtG7dmmnTptG/f//wXaAxJm75ayRakp1b4W48lCrgODmsdJdDL385T0R47rnnGDVqVEj5qKpeB3PmzOHBBx8M6pjGhJOIXAU8BSQBM1V1SrnXbwemArnOpmdVNbC54eNAZd0VB3Rr7XOsV88pKysM0YAfxl35G9rhr+En7arf+ozNJtow8cYqaBFQWZLqdlqhz1akGTNmMHTo0JL3+yro1KtXr2T/tLQ0Dhwr5ojUr3D+pHoNOe3GP7Jrys8ieZnGmDjlr5Fo6rIvKlTOvCor4FReMGvBu+++G1K8VfU6WLt2LV9//TWDBg0K6TzGVJeIJAF/Aa4A9gIfichSVf2s3K6vquqYqAcYBZV1V/R3V6iycVfeoR1Zf32dnlNWsnXFlyQf3e+ZVKRlis+Gn1n/bVdphc+YeBGxCpqIvAxcC+xX1XMjdR43qrRP9f19K50xqKqCjldWVhbLp6wEP4UmS0bGGF+NPZU1En2dl+H3WJXllOoUzIJRVa+D2bNnc8MNN5Cenh7yuYyppguB7aq6E0BEFgDXAeUraAmrOtPEVzbuatq0v7Nj5y5u7ns+iqKnToAWM+SaS1nwxrs+G36alCtDgU20YeJTJO+gzQKeBeZE8ByuFMpaFsEUdCo7niUjY2o2f409jw/8H7+NRNP9dDcSKs8p4Vq/J9gK5dChQzlx4gSvvfYaixYtCupcxoRZC+CrUj/vBXr42O8GEekNfAn8RlW/8rGPK8ybN4+RI0dW2H706FEeffRRHnnkkTLbfc0o7d1eGX/jrkaMGMG8Ay359tAJAA5/uJjCQ/s47crRfht/bKINkygiVkFT1dUi0iZSx3ez6iYpCK6g4+88GanJloyMqeHC1d1IgKEXnVVpTgkl53lVp0IJULduXfLy8gI+jzEx9E9gvqqeFJGRwGygr68dRWQEMAIomRws2oYOHVoy9MJr5syZPPzww/zqV7+qsH8o08T7G9pxsCiVpDRPHpGUukjtFJLqNazyrpyVgUy8s4WqI+DeKzuSmpxUZlugSSqYhQr9nSerf9WLXhtjElt1uxuVXyz6zzedx+QB/1PpuULJeV6VVSiNiQO5QKtSP7fkh8lAAFDV/6rqSefHmUDZ6QbL7vuCqnZX1e5NmjQJe7DVkZ2dzbhx41iwYAHNmjWr8Lq/xearqix5G2dy846j/NAZ1y7MAAAU20lEQVQ4syQ7t0zZJyNzKI1//jvAhnGYxBfzSULc0EpUXrC39csL5RZ7MC1QdivfGONPuLsbVfUeCC0XhaubpDEx8hHQXkTa4qmYDQF+UXoHEWmmqt84P/YHPo9uiNWXl5fHoEGDmDBhAn369PG7X3XyR2WNM7Z4s6mpYl5BU9UXgBcAunfv7r8fSxQFe1vfl+reYg+2oGO38o0xvkS7YBNqLgpHN0ljYkVVC0VkDLAMzzT7L6vqVhGZBGxQ1aXA3SLSHygEvgNuj1nAQVBVbr31Vs4991x+//vfh/34lTXOWEO0qaliXkGLB97b+q+//rrP2/rhZpUuY0yo4q1gYy3lJt6p6hvAG+W2PVLq+QPAA9GOK1RPPPEEW7duZePGjYhI2I9fVeOMlYlMTRSxMWgiMh94H+goIntFZHikzhVJgd7WN8aYcJg3bx5paWkVHiLCpEmTgjrWgG4tWHt/X3ZN+Rlr7+/r6kJOdcevGGMiZ9WqVTz22GMsXLiQjAz/y3CEIhxjWI1JNJGcxfHmSB07WiJ9W98YY8oLRxfreGUt5ca4xzfffMOQIUOYNm0a3bp1i9h54u1uvzHRYF0cKxHp2/rGGFOVaHexNsbUPL6muf/kny+xb98+7rnnHu65554y+w8bNoznn38+bOe3xhljyrIKmh/e2/qrV6+O2G19Y4ypjHWxNsZEmv81CIejVcxabYyJDFsHzYdo3dY3xhh/rIu1MSYabA1CY9ynxt9Bi/VtfWOM8cW6WBtjosHWIDTGfWp0Bc1u6xtj3Mi6WBtjosXWIDTGfWp0F0e7rW+McRvrYm2MiSab5t4Y96nRFTS7rW+MiaUl2bn0nLKStvf/i55TVrIkO5cXX3yxpIt1+bXQRo0aFeuQjTEJxtYgNMZ9anQXR7utb4yJFetibYxxC5vm3hh3qdF30Oy2vjEmVqyLtTHGGGN8qdF30Gz1emNMrFgXa2OMMcb4UqMraGC39Y0xsWFdrI0xxhjjS9x3cXz88ce5+uqry2xr3769z20LFiyIZmjGmAQWau6xLtbGmEixspEx8S3uK2i9e/dm3bp1FBV5xnJ88803FBQUkJ2dXWbb9u3b6d27dyxDNcYkkFBzj82cZoyJFCsbGRPf4r6CdsEFF1BQUMCmTZsAWLNmDZdddhkdO3Yss61du3Y0b948lqEaYxJIOHLPgG4tWHt/X3ZN+Rlr7+9rlTNjTFhY2ciY+Bb3FbSUlBR69OjB6tWrAVi9ejW9evUiMzOzzDZrITLGhJPlHmOMW1l+Mia+xX0FDeDSSy8tSThr1qyhV69e9OrVq8y2Sy+9NJYhGmMSkOUeY4xbWX4yJn4lRAWtd+/evPfee3z33XccOHCA9u3bc8kll7Bu3Tq+++47tmzZYq1Expiws9xjjHEry0/GxK+EqKBdfPHFHDp0iBdffJGePXsC0KBBA5o3b86LL75I8+bNadu2bYyjNMYkGss9xhi3svxkTPxKiApaamoq3bt358knn6RXr14l2zMzM3nyySethcgYExGWe4wxbmX5yZj4FdEKmohcJSJfiMh2Ebk/HMdckp1LzykraXv/v+g5ZSVLsnMBT1/r/fv3k5mZWbJvr1692L9/vyUhY2qYcOcef3kHLPcYYzyqyjsiUkdEXnVe/0BE2oTr3FY2MiaxiKpG5sAiScCXwBXAXuAj4GZV/czfe7p3764bNmzwe8wl2bk8sPhTjhcUlWxLTU6ytYOMcQkR2aiq3WMcQ1C5x/KOMfEtXvKOiNwFdFXVUSIyBLheVW+q6tiWo4yJb9XJUZG8g3YhsF1Vd6rqKWABcF0oB5y67IsyCQjgeEERU5d9EcphjTGJJay5x/KOMSYAgeSd64DZzvOFwOUiIqGe2HKUMYknkhW0FsBXpX7e62wrQ0RGiMgGEdlw4MCBSg/4dd7xoLYbY2qkKnOP5R1jTJgFUuYp2UdVC4FDwOm+DmY5ypiaLeaThKjqC6raXVW7N2nSpNJ9m2ekBrXdGGN8sbxjjHEzy1HG1GyRrKDlAq1K/dzS2VZt917ZkdTkpDLbUpOTuPfKjqEc1hiTWMKaeyzvGGMCEEjeKdlHRGoDDYH/hnpiy1HGJJ5IVtA+AtqLSFsRSQGGAEtDOeCAbi14fOD/0CIjFQFaZKTaIFhjTHlhzT2Wd4wxAQgk7ywFbnOeDwJWahhmarMcZUziqR2pA6tqoYiMAZYBScDLqro11OMO6NbCko4xxq9I5B7LO8aYyvjLOyIyCdigqkuBl4BXRGQ78B2eSlxYWI4yJrFErIIGoKpvAG9E8hzGGFOe5R5jTLT5yjuq+kip5yeAwdGOyxgTf2I+SYgxxhhjjDHGGA+roBljjDHGGGOMS1gFzRhjjDHGGGNcwipoxhhjjDHGGOMSVkEzxhhjjDHGGJeQMCzBETYicgDYHeDujYGDEQzHTexaE1eiXW9rVW0S6yCCUYPzjl2LO9m1BC/u8k4wEiRHuTUucG9sbo0LLLZgBZ2jXFVBC4aIbFDV7rGOIxrsWhNXTbveeJdI/192Le5k12JC4dbfuVvjAvfG5ta4wGKLBuviaIwxxhhjjDEuYRU0Y4wxxhhjjHGJeK6gvRDrAKLIrjVx1bTrjXeJ9P9l1+JOdi0mFG79nbs1LnBvbG6NCyy2iIvbMWjGGGOMMcYYk2ji+Q6aMcYYY4wxxiQUV1fQROQqEflCRLaLyP0+Xq8jIq86r38gIm2iH2X4BHC9t4vIARHZ5Dx+GYs4w0FEXhaR/SKyxc/rIiJPO7+LT0TkJ9GOMVwCuNY+InKo1P/rI9GO0VStqu9nvBCRViLyjoh8JiJbReSeWMcUChFJEpFsEXk91rGEQkQyRGShiGwTkc9F5OJYx1RdIvIb57O1RUTmi0jdWMeUSNxcNgogtvFO7vlERN4WkdZuia3UfjeIiIpIVGYCDCQuEbmxVM7+WzTiCiQ2ETnL+XuS7fyfXhOluBK/DKmqrnwAScAO4GwgBdgM/KjcPncBzzvPhwCvxjruCF/v7cCzsY41TNfbG/gJsMXP69cAbwICXAR8EOuYI3itfYDXYx2nPSr9P6zy+xkvD6AZ8BPneTrwZbxei3MN44G/xft3CJgN/NJ5ngJkxDqmal5HC2AXkOr8/Bpwe6zjSpSHm8tGAcZ2GVDPef5rN8Xm7JcOrAbWA93dEBfQHsgGGjk/n+GW3xme8V6/dp7/CMiJUmwJX4Z08x20C4HtqrpTVU8BC4Dryu1zHZ4/agALgctFRKIYYzgFcr0JQ1VXA99Vsst1wBz1WA9kiEiz6EQXXgFcq3G/hPl+quo3qvqx8/wI8DmeQnXcEZGWwM+AmbGOJRQi0hBPgeMlAFU9pap5sY0qJLWBVBGpDdQDvo5xPInEzWWjKmNT1XdU9Zjz43qgZRTiCig2xx+AJ4ATLorrV8BfVPV7AFXd76LYFGjgPG9IlL7rNaEM6eYKWgvgq1I/76ViIaJkH1UtBA4Bp0cluvAL5HoBbnBu1y4UkVbRCS0mAv19JIqLRWSziLwpIl1iHYypICE/j07Xp27AB7GNpNqmAb8HimMdSIjaAgeAvzpdhWaKSP1YB1UdqpoL/AnYA3wDHFLVf8c2qoTi5rJRsHlyOJ67HNFQZWxON7hWqvqvKMUUUFxAB6CDiKwVkfUicpWLYssChonIXuANYGx0QqtS3P/NdnMFzVT0T6CNqnYFlvNDC5mJbx8DrVX1x8AzwJIYx2NqABFJAxYB41T1cKzjCZaIXAvsV9WNsY4lDGrj6a7znKp2A44CcTnOUUQa4Wm9bgs0B+qLyLDYRmXcxvlMdAemxjoWABGpBTwJ/DbWsfhQG083xz7AzcCLIpIR04h+cDMwS1Vb4ulW+IrzuzQhcvMvMRcofYeopbPN5z5OV4qGwH+jEl34VXm9qvpfVT3p/DgTOD9KscVCIP//CUFVD6tqvvP8DSBZRBrHOCxTVkJ9HkUkGU/lbJ6qLo51PNXUE+gvIjl4ut70FZG5sQ2p2vYCe1XVeydzIZ4KWzzqB+xS1QOqWgAsBi6JcUyJxM1lo4DypIj0Ax4C+pcq08Q6tnTgXGCVk1MuApZGYaKQQH5ne4GlqlqgqrvwjBtuH+G4Ao1tOJ5xpqjq+0BdwA3ll7j/m+3mCtpHQHsRaSsiKXgGui4tt89S4Dbn+SBgpTqjA+NQlddbrv9sfzxjRxLVUuBWZyaei/B0k/km1kFFgog09Y4PEJEL8Xwv47WhIVEFko/igvNZewn4XFWfjHU81aWqD6hqS1Vtg+f/Y6WqxuWdGlX9FvhKRDo6my4HPothSKHYA1wkIvWcz9rlJPbfqmhzc9kokHJMN2AGnspZtMZSVRmbqh5S1caq2sbJKeudGDfEMi7HEjx3z3AabzsAOyMcV6Cx7cHzHUdEOuOpoB2IQmxVifsyZO1YB+CPqhaKyBhgGZ6ZZF5W1a0iMgnYoKpL8RQyXhGR7XgGCw6JXcShCfB67xaR/kAhnuu9PWYBh0hE5uNJOI2dvssTgWQAVX0eT1/ma4DtwDHgjthEGroArnUQ8GsRKQSOA0PiuKEhIfn7fsY4rOrqCdwCfCoim5xtDzp3b03sjAXmOQWhncRpzlPVD0RkIZ6u24V4Zp97IbZRJQ43l40CjG0qkAb83WmX3KOq/V0SW9QFGNcy4Kci8hlQBNyrqhFvxA0wtt/i6XL5GzwThtwejfJLTShDipUDjTHGGGOMMcYd3NzF0RhjjDHGGGNqFKugGWOMMcYYY4xLWAXNGGOMMcYYY1zCKmjGGGOMMcYY4xJWQTMmwYnIyyKyX0S2hOl4Z4nIv0XkcxH5TETahOO4xpjEEs7cIyKXicimUo8TIjIgHHEaY2oet5eNrIIWJ0Qkx7t4sYjkB7B/pfuISCsRecf5EG0VkXv87DdKRG6t4ljnicg1VcUUKhEZICI/KvXzJGfBy3Cf500RaVn6dx7nZgFXhfF4c4CpqtoZuBCI5lo2Jsos91juCcEswpR7VPUdVT1PVc8D+uKZOvvf4Ti2iV+Wnyw/hWAWLi4bWQWt5ioEfquqPwIuAkaX/oJ7qerzqjqnimOdh2e9iYCJSHXW4BsAlMSoqo+o6opqHMcvEUkFTlfVveE8biyp6mo8a+GUEJF2IvKWiGwUkTUi0imQYzmfkdqqutw5dr6qHgt/1CaBWe7xwXJPUAYBb1ruMRFg+ckHy0+Vi0TZyCpoLiMiS5wPxlYRGRHA/veKyEci8omIPBroPqr6jap+7Dw/AnwOtPDx3iwR+Z3zfJWIPCEiH4rIlyLSSzyLqk4CbnK6ndwkIvWdW8cfiki2iFznvP92EVkqIiuBt0UkTUTeFpGPReRT737Ovrc68W4WkVdE5BKgPzDVOU87EZklIoOc/S93zvWpc+46zvYcEXm01Dk6OdsvlR+6ymSLSLpz6j7AqlK/gt877/tQRM5x3jtLRJ4XkQ3O7+Haqv6fXOgFYKyqng/8Dpge4Ps6AHkistj5vU0VkaSIRWmixnJPyXkt90RWdXNPaUOA+WGNyria5aeS81p+iiz3lI1U1R4uegCnOf+mAlvwtFgA5ACNnef5zr8/dT5Mgqey/TrQO9B9Sp2zDbAHaOAjnizgd87zVcD/Oc+vAVY4z28Hni31nj8Cw5znGcCXQH1nv72lrrG295xAYzwrvgvQxXlP43K/k1nAoFLnmYWnJbUu8BXQwdk+BxhX6vc21nl+FzDTef5PoKfzPA1PywfA00DfUu99yHl+K/B6qfO+5fw+2zvXVDfWn50qPldtgC2lrvc4sKnU43PntYHO5678Y5nz+iDgEHC28/+3CBge6+uzR1g+I5Z7LPdE4nPVhjDknlLHawYcAJJjfW32iOrnyPKT5adIfK7a4NKykd1Bc5+7RWQzsB5ohedD7s9PnUc28DHQycf+le4jIml4PkjjVPVwAPEtdv7diOeD7S+u+0VkE57EVRc4y3ltuap6bykL8EcR+QRYgaeV6kw84wv+rqoHAUrt709HYJeqfun8PBvoXUXMa4EnReRuIENVC53tPYH3Sr13fql/Ly61/TVVLVbV/wA78fxe40UtIE+d8RzOozOAqi5W1XN9PK503rsX2KSqO53f2RLgJzG6DhNelnss90RaKLnH60bg/6lqQdSjN7Fk+cnyU6S5qmxUnb6uJkJEpA/QD7hYVY+JyCo8X2C/bwEeV9UZ1dlHRJLxJKB5qrq4wjt9O+n8W4T/z48AN6jqF+XO1wM4WmrTUKAJcL6qFohIDpVfb3VViFlVp4jIv/C0dq0VkSuBU8BXqnqq1Hs1gOe+fnYtVT0sIrtEZLCq/l1EBOiqqpsDePtHQIaINFHVA3j+YGyIaMAm4iz3WO6JhhBzj9fNwAMRCtG4kOUny0/R4Laykd1Bc5eGwPdOAuqEZ4BqZZYBdzotPYhICxE5I5B9nA/eS3hu3z4ZYtxHgPRSPy8DxjrnQES6+XlfQ2C/k4AuA1o721cCg0XkdOf9p/k5j9cXQBtvP2jgFuDdygIWkXaq+qmqPoHni9UJuBrP7fnSbir17/ultg8WkVoi0g7PLe0vcCkRmY8n9o4isldEhuP5AzDcaZHcClxX2TG8VLUIT7/st0XkUzx/cF6MTOQmiiz3eFjuCaNw5h7neG3w3D2p9HdsEo7lJw/LT2Hk9rKR3UFzl7eAUSLyOZ4P9frKdlbVf4tIZ+B95/ueDwyj1NSelezTAc+X9VPndjvAg6r6RjXifocfbts/DvwBmAZ8IiK1gF2Ar8Gi84B/Oh/mDcA2J+atIvIY8K6IFOHpgnA7sAB40bn1PqjUNZ4QkTuAv4tnBqSPgOeriHmck/iK8XwJ3wQWAmPL7dfI6WZwEk/Lrdce4EOgATBKVU9Ucb6YUdWb/bxUrell1TNLUdfqR2RcyHIPlnvCLQK5JwcfEzaYhGf5CctP4eb2spGoxs3dR2MiRjwzG61V1e4B7DsLz6DYhREPzBiT0Cz3GGPcyvJT7NgdNGMAVT0JVJmAjDEmnCz3GGPcyvJT7NgdNGOMMcYYY4xxCZskxBhjjDHGGGNcwipoxhhjjDHGGOMSVkEzxhhjjDHGGJewCpoxxhhjjDHGuIRV0IwxxhhjjDHGJayCZowxxhhjjDEu8f8BkkEXxlRP7wAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fx = plt.figure(figsize=(12,4),constrained_layout=True)\n",
    "\n",
    "spx = fx.add_gridspec(\n",
    "                ncols=3,\n",
    "                nrows=1,\n",
    "                width_ratios=[1,1,1],\n",
    "            )\n",
    "ax = [0]*3\n",
    "for i in range(3):\n",
    "    ax[i] = fx.add_subplot(spx[i])\n",
    "# ax2 = fx.add_subplot(spx[1])#,sharey=ax1)\n",
    "\n",
    "\n",
    "idx = 0\n",
    "for s in samples[\"sample\"]:\n",
    "    if \"WT\" not in s:\n",
    "        dd = dfs[s]\n",
    "        al1_total = dd[\"allele1\"].sum()\n",
    "        al2_total = dd[\"allele2\"].sum()\n",
    "        # pre-filter dd (by length or whatever)\n",
    "        cond1 = dd[\"length\"] >= 5_000_000\n",
    "        cond2 = ~dd[\"chrom\"].isin([\"allele1\",\"allele2\",\"chr1\"])\n",
    "        dd = dd[ cond1 & cond2 ]\n",
    "        #\n",
    "        # normalize # of contacts and plot ...\n",
    "        dd[\"allele1_rel\"] = dd[\"allele1\"]/dd[\"length\"]#/al1_sum\n",
    "        dd[\"allele2_rel\"] = dd[\"allele2\"]/dd[\"length\"]#/al2_sum\n",
    "        #\n",
    "        x=dd[\"allele2_rel\"].values\n",
    "        y=dd[\"allele1_rel\"].values\n",
    "        ax[idx].scatter(x=dd[\"allele2_rel\"].values,y=dd[\"allele1_rel\"].values)            \n",
    "        for i, txt in enumerate(dd[\"chrom\"].values):\n",
    "            dx, = np.diff(ax[idx].get_xlim())\n",
    "            dy, = np.diff(ax[idx].get_ylim())\n",
    "            dx *= 0.01\n",
    "            dy *= 0.01\n",
    "            if txt == \"chr14\":\n",
    "                txt = \"chr14*\"\n",
    "            ax[idx].annotate(txt.lstrip(\"chr\"), (x[i]+dx, y[i]+dy),fontsize=12)\n",
    "        #\n",
    "        ax[idx].set_title(s)\n",
    "        ax[idx].set_xlabel(\"allele2 interactions/bp\")\n",
    "        ax[idx].set_ylabel(\"allele1 interactions/bp\")\n",
    "        idx += 1\n",
    "\n",
    "        \n",
    "        \n",
    "plt.savefig(\"trans_scatter.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe008698690>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chrom=\"chr9\"\n",
    "f = plt.figure(figsize=(16,6))\n",
    "sp = f.add_gridspec(ncols=1,nrows=3)\n",
    "_bsize = 250_000\n",
    "balanced = False\n",
    "for i in range(3):\n",
    "    ax = f.add_subplot(sp[i])\n",
    "    s = samples[\"sample\"][i]\n",
    "    s_cname, = samples[\"mcool\"][samples[\"sample\"]==s]\n",
    "    clr = cooler.Cooler(get_cpath(s_cname,_bsize))\n",
    "\n",
    "    a1_idx = clr.offset(\"allele1\")\n",
    "    a2_idx = clr.offset(\"allele2\")\n",
    "    chr_idx = slice(*clr.extent(chrom))\n",
    "\n",
    "    al1_chr, = clr.matrix(balance=balanced)[a1_idx,chr_idx]\n",
    "    al2_chr, = clr.matrix(balance=balanced)[a2_idx,chr_idx]\n",
    "\n",
    "    x = clr.bins()[chr_idx][\"start\"].values\n",
    "    \n",
    "    al1_vis = dict(color=\"darkorange\", marker='x', markersize=8, linewidth=0, label=\"allele1\", alpha=0.999)\n",
    "    al2_vis = dict(color='steelblue', marker='x', markersize=8, linewidth=0, label=\"allele2\", alpha=0.999)\n",
    "\n",
    "    plt.plot(x, al1_chr, **al1_vis)\n",
    "    plt.plot(x, al2_chr, **al2_vis)\n",
    "    plt.gca().set_yscale(\"log\")\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim(x.min(),x.max())\n",
    "    ax.xaxis.set_major_formatter(mb_format)\n",
    "    ax.set_ylabel(\"interactions, pairs\")\n",
    "    ax.text(0.01,0.82,s,transform=ax.transAxes)\n",
    "\n",
    "ax.set_xlabel(f\"genomic positon along {chrom}, Mb\")\n",
    "plt.legend(frameon=False)\n",
    "# plt.savefig(\"supp-raw-overview-chr14.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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