examples/Function_examples.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from NGS import HiCTools as HT\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cooler\n",
    "from scipy.stats import multivariate_normal\n",
    "import matplotlib.patches as patches\n",
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fix path to pbgzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "env: PATH=/users/michael.mitter/.conda/envs/NGStest/bin/:/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/software/2020/software/anaconda3/2019.03/bin;/software/2020/software/anaconda3/2019.03/condabin;/software/2020/software/anaconda3/2019.03/library/bin;/users/michael.mitter/.vscode-server/bin/2aae1f26c72891c399f860409176fe435a154b13/bin:/users/michael.mitter/.conda/envs/gitEnv/bin/:/users/michael.mitter/.vscode-server/bin/2aae1f26c72891c399f860409176fe435a154b13/bin:/software/2020/software/anaconda3/2019.03/condabin:/users/michael.mitter/.conda/envs/gitEnv/bin/:/software/system/utils:/usr/local/bin:/usr/bin:/groups/gerlich/members/MichaelMitter/UCSC_usertools/:/groups/gerlich/members/MichaelMitter/PythonPackages/OnTAD/:/usr/local/sbin:/usr/sbin:/groups/gerlich/members/MichaelMitter/UCSC_usertools/:/groups/gerlich/members/MichaelMitter/PythonPackages/OnTAD/:/users/michael.mitter/.local/bin:/users/michael.mitter/bin\n"
    }
   ],
   "source": [
    "environment_path = f\"/users/michael.mitter/.conda/envs/NGStest/bin/\"\n",
    "\n",
    "# get pre-set PATH environment variable\n",
    "original_os_path = os.environ.get('PATH')\n",
    "os_path = original_os_path\n",
    "\n",
    "# guard PATH against multiple prepending\n",
    "if environment_path not in original_os_path:\n",
    "    os_path = environment_path + ':' + original_os_path\n",
    "%env PATH=$os_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate2dGauss(mean, variance, gridsize=10, spacing=0.2):\n",
    "    # define grid\n",
    "    x, y = np.mgrid[-gridsize:gridsize:spacing, -gridsize:gridsize:spacing]\n",
    "    # get position vector\n",
    "    pos = np.dstack((x, y))\n",
    "    # get multivariate_normal distribution with given mean and covariance matrix\n",
    "    rv1 = multivariate_normal(mean, variance)\n",
    "    # get matrix and blank out diagonal\n",
    "    rvmat1 = rv1.pdf(pos)\n",
    "    rvmat1[np.diag_indices_from(rvmat1)] = np.nan\n",
    "    return rvmat1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get chromosomal arms\n",
    "Connects to UCSC and gets chromosome sizes as well as centromeres for hg19 to compute chromosomal arms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arms  = HT.getArmsHg19()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  chrom      start        end\n0  chr1          0  125200000\n1  chr1  125200000  249250621\n2  chr2          0   93650000\n3  chr2   93650000  243199373\n4  chr3          0   90900000",
      "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>chrom</th>\n      <th>start</th>\n      <th>end</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chr1</td>\n      <td>125200000</td>\n      <td>249250621</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chr2</td>\n      <td>0</td>\n      <td>93650000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr2</td>\n      <td>93650000</td>\n      <td>243199373</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr3</td>\n      <td>0</td>\n      <td>90900000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "arms.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Expected\n",
    "Calculates the expected amount of contact frequency for each diagonal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in cooler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = cooler.Cooler(\"/groups/gerlich/experiments/Experiments_004800/004812/Sequencing_data/Pooled_FC_1_2/cooler/G2.fc_1_2.trans.1000.mcool::/resolutions/100000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected = HT.getExpected(c, arms, proc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  chrom  start        end  diag  n_valid  count.sum  balanced.sum  \\\n0  chr1      0  125200000     0     1036        0.0      0.000000   \n1  chr1      0  125200000     1      993        0.0      0.000000   \n2  chr1      0  125200000     2      979    10743.0      7.892122   \n3  chr1      0  125200000     3      972     9525.0      6.910876   \n4  chr1      0  125200000     4      960     8117.0      5.849356   \n\n   balanced.avg  \n0      0.000000  \n1      0.000000  \n2      0.008061  \n3      0.007110  \n4      0.006093  ",
      "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>chrom</th>\n      <th>start</th>\n      <th>end</th>\n      <th>diag</th>\n      <th>n_valid</th>\n      <th>count.sum</th>\n      <th>balanced.sum</th>\n      <th>balanced.avg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n      <td>0</td>\n      <td>1036</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n      <td>1</td>\n      <td>993</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n      <td>2</td>\n      <td>979</td>\n      <td>10743.0</td>\n      <td>7.892122</td>\n      <td>0.008061</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n      <td>3</td>\n      <td>972</td>\n      <td>9525.0</td>\n      <td>6.910876</td>\n      <td>0.007110</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr1</td>\n      <td>0</td>\n      <td>125200000</td>\n      <td>4</td>\n      <td>960</td>\n      <td>8117.0</td>\n      <td>5.849356</td>\n      <td>0.006093</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "expected.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- diagonal refers to the number of the specific diagonal\n",
    "- n_valid refers to numnber of valid Hi-C bins\n",
    "- count.sum refers to number reads on each diagonal\n",
    "- balanced.sum refers to the cumulative contact frequency for the specific diagonal\n",
    "- balanced.avg refers to contact frequency per bin, this is the \"expected value\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assign regions\n",
    "Constructs a region around chromosomal locations for use in pileups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1d features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      0        1        2      mid\n0  chr1    10027    10525    10276\n1  chr1   237604   237907   237755\n2  chr1   521435   521730   521582\n3  chr1   950640   951278   950959\n4  chr1  1057328  1057983  1057655",
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     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "regions = pd.read_csv(\"/groups/gerlich/experiments/Experiments_004400/004472/Datasets/Sor_clean.bed\", sep=\"\\t\", header=None)[[0, 1, 2]]\n",
    "regions.loc[:, \"mid\"] = (regions[1] + regions[2])//2\n",
    "regions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  chrom    start      end  lo  hi            region\n2  chr1   100000  1000000   1  10  chr1:0-125200000\n3  chr1   500000  1400000   5  14  chr1:0-125200000\n4  chr1   600000  1500000   6  15  chr1:0-125200000\n5  chr1   800000  1700000   8  17  chr1:0-125200000\n6  chr1  1000000  1900000  10  19  chr1:0-125200000",
      "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>chrom</th>\n      <th>start</th>\n      <th>end</th>\n      <th>lo</th>\n      <th>hi</th>\n      <th>region</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>chr1</td>\n      <td>100000</td>\n      <td>1000000</td>\n      <td>1</td>\n      <td>10</td>\n      <td>chr1:0-125200000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr1</td>\n      <td>500000</td>\n      <td>1400000</td>\n      <td>5</td>\n      <td>14</td>\n      <td>chr1:0-125200000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr1</td>\n      <td>600000</td>\n      <td>1500000</td>\n      <td>6</td>\n      <td>15</td>\n      <td>chr1:0-125200000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>chr1</td>\n      <td>800000</td>\n      <td>1700000</td>\n      <td>8</td>\n      <td>17</td>\n      <td>chr1:0-125200000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>chr1</td>\n      <td>1000000</td>\n      <td>1900000</td>\n      <td>10</td>\n      <td>19</td>\n      <td>chr1:0-125200000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "windows = HT.assignRegions(400000, 100000, regions[0], regions[\"mid\"], arms).dropna()\n",
    "windows = windows.loc[(windows[\"start\"] > 0) & (windows[\"end\"] > 0), :]\n",
    "windows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2d features\n",
    "With 2d I mean defined by the coordinates, not only being on diagonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    chr1         x1         x2   chr2         y1         y2       pos1  \\\n0  chr10  101610000  101620000  chr10  101800000  101810000  101615000   \n1  chr10  102100000  102110000  chr10  102190000  102200000  102105000   \n2  chr10  103360000  103370000  chr10  103540000  103550000  103365000   \n3  chr10  105470000  105480000  chr10  105610000  105620000  105475000   \n4  chr10  109750000  109755000  chr10  109930000  109935000  109752500   \n\n        pos2  \n0  101805000  \n1  102195000  \n2  103545000  \n3  105615000  \n4  109932500  ",
      "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>chr1</th>\n      <th>x1</th>\n      <th>x2</th>\n      <th>chr2</th>\n      <th>y1</th>\n      <th>y2</th>\n      <th>pos1</th>\n      <th>pos2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chr10</td>\n      <td>101610000</td>\n      <td>101620000</td>\n      <td>chr10</td>\n      <td>101800000</td>\n      <td>101810000</td>\n      <td>101615000</td>\n      <td>101805000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chr10</td>\n      <td>102100000</td>\n      <td>102110000</td>\n      <td>chr10</td>\n      <td>102190000</td>\n      <td>102200000</td>\n      <td>102105000</td>\n      <td>102195000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chr10</td>\n      <td>103360000</td>\n      <td>103370000</td>\n      <td>chr10</td>\n      <td>103540000</td>\n      <td>103550000</td>\n      <td>103365000</td>\n      <td>103545000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr10</td>\n      <td>105470000</td>\n      <td>105480000</td>\n      <td>chr10</td>\n      <td>105610000</td>\n      <td>105620000</td>\n      <td>105475000</td>\n      <td>105615000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr10</td>\n      <td>109750000</td>\n      <td>109755000</td>\n      <td>chr10</td>\n      <td>109930000</td>\n      <td>109935000</td>\n      <td>109752500</td>\n      <td>109932500</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "loops = pd.read_csv(\"/groups/gerlich/experiments/Experiments_004400/004472/Datasets/GSE63525_HeLa_HiCCUPS_looplist.txt\", sep=\"\\t\")[[\"chr1\", \"x1\", \"x2\", \"chr2\",\"y1\", \"y2\"]]\n",
    "loops.loc[:, \"pos1\"] = (loops[\"x1\"] + loops[\"x2\"])//2\n",
    "loops.loc[:, \"pos2\"] = (loops[\"y1\"] + loops[\"y2\"])//2\n",
    "loops.loc[:, \"chr1\"] = [\"chr\" + str(i) for i in loops[\"chr1\"]] # fix chromosome names\n",
    "loops.loc[:, \"chr2\"] = [\"chr\" + str(i) for i in loops[\"chr2\"]] # fix chromosome names\n",
    "loops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "loopWindows = HT.assignRegions2d(400000, 100000, loops[\"chr1\"], loops[\"pos1\"], loops[\"chr2\"], loops[\"pos2\"], arms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  chrom1     start1       end1 chrom2     start2       end2  \\\n0  chr10  101200000  102100000  chr10  101400000  102300000   \n1  chr10  101700000  102600000  chr10  101700000  102600000   \n2  chr10  102900000  103800000  chr10  103100000  104000000   \n3  chr10  105000000  105900000  chr10  105200000  106100000   \n4  chr10  109300000  110200000  chr10  109500000  110400000   \n\n                     region  \n0  chr10:40150000-135534747  \n1  chr10:40150000-135534747  \n2  chr10:40150000-135534747  \n3  chr10:40150000-135534747  \n4  chr10:40150000-135534747  ",
      "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>chrom1</th>\n      <th>start1</th>\n      <th>end1</th>\n      <th>chrom2</th>\n      <th>start2</th>\n      <th>end2</th>\n      <th>region</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chr10</td>\n      <td>101200000</td>\n      <td>102100000</td>\n      <td>chr10</td>\n      <td>101400000</td>\n      <td>102300000</td>\n      <td>chr10:40150000-135534747</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chr10</td>\n      <td>101700000</td>\n      <td>102600000</td>\n      <td>chr10</td>\n      <td>101700000</td>\n      <td>102600000</td>\n      <td>chr10:40150000-135534747</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chr10</td>\n      <td>102900000</td>\n      <td>103800000</td>\n      <td>chr10</td>\n      <td>103100000</td>\n      <td>104000000</td>\n      <td>chr10:40150000-135534747</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr10</td>\n      <td>105000000</td>\n      <td>105900000</td>\n      <td>chr10</td>\n      <td>105200000</td>\n      <td>106100000</td>\n      <td>chr10:40150000-135534747</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr10</td>\n      <td>109300000</td>\n      <td>110200000</td>\n      <td>chr10</td>\n      <td>109500000</td>\n      <td>110400000</td>\n      <td>chr10:40150000-135534747</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "loopWindows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do pileup obs/exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With collapsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pile = HT.doPileupObsExp(c, expected, windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[       inf,        inf, 0.98776544, 1.03853326, 1.08233088,\n        0.9807215 , 0.93241882, 0.92005139, 0.90948377],\n       [       inf,        inf,        inf, 1.0422076 , 1.07919545,\n        0.98604907, 0.9346831 , 0.91811768, 0.92366845],\n       [0.98776544,        inf,        inf,        inf, 1.08204432,\n        0.98994433, 0.93945984, 0.93774886, 0.93101527],\n       [1.03853326, 1.0422076 ,        inf,        inf,        inf,\n        1.02732275, 0.99381698, 0.97910174, 0.97732395],\n       [1.08233088, 1.07919545, 1.08204432,        inf,        inf,\n               inf, 1.08826168, 1.08474018, 1.08635626],\n       [0.9807215 , 0.98604907, 0.98994433, 1.02732275,        inf,\n               inf,        inf, 1.04320924, 1.04315134],\n       [0.93241882, 0.9346831 , 0.93945984, 0.99381698, 1.08826168,\n               inf,        inf,        inf, 0.99212708],\n       [0.92005139, 0.91811768, 0.93774886, 0.97910174, 1.08474018,\n        1.04320924,        inf,        inf,        inf],\n       [0.90948377, 0.92366845, 0.93101527, 0.97732395, 1.08635626,\n        1.04315134, 0.99212708,        inf,        inf]])"
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "pile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(9, 9)"
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "pile.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "im = ax.imshow(pile, cmap=\"bwr\")\n",
    "f.colorbar(im, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without collapsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pile = HT.doPileupObsExp(c, expected, windows, collapse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(9, 9, 40347)"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "pile.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pile now holds all 9 x 9 windows for each individual position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 5 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 4)\n",
    "for index, i in enumerate(range(80, 84)):\n",
    "    im = ax[index].imshow(pile[:, :, i], cmap=\"bwr\")\n",
    "f.colorbar(im, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pile to Frame\n",
    "Takes a non-collapsed pile and transforms it into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = HT.pileToFrame(pile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     0    1         2         3         4         5         6         7   \\\n54  inf  inf  1.442750  0.924316  1.406237  1.707031  0.759016  2.709509   \n55  inf  inf  2.875759  1.047860  2.253263  0.695243  1.654504  2.655169   \n56  inf  inf  1.086405  1.945664  2.738931  1.188719  2.409137  2.560128   \n57  inf  inf  1.086405  1.945664  2.738931  1.188719  2.409137  2.560128   \n77  inf  inf  1.339534  1.458482  0.716402  0.774567  1.448438  0.653489   \n\n          8    9   ...   71        72        73        74        75        76  \\\n54  1.086434  inf  ...  inf  1.086434  2.349782  2.228806  2.370757  1.992065   \n55  2.600023  inf  ...  inf  2.600023  2.560128  1.609995  1.770916  1.204572   \n56  1.277126  inf  ...  inf  1.277126  1.307384  1.891747  1.301658  1.198854   \n57  1.277126  inf  ...  inf  1.277126  1.307384  1.891747  1.301658  1.198854   \n77  2.547698  inf  ...  inf  2.547698  1.024557  0.000000  0.341979  1.039086   \n\n          77        78   79   80  \n54  0.467448  1.647004  inf  inf  \n55  1.580617  1.892471  inf  inf  \n56  1.569045  1.999857  inf  inf  \n57  1.569045  1.999857  inf  inf  \n77  0.217170  0.612962  inf  inf  \n\n[5 rows x 81 columns]",
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     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "frame.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do Pileup ICCF\n",
    "Same as do pileup obs/exp, just with corrected counts. Also here, one can do the pileup with and without collapsing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pile = HT.doPileupICCF(c, windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[0.00934509, 0.00972759, 0.00778578, 0.00707998, 0.00639228,\n        0.00501527, 0.00411995, 0.00358334, 0.00309482],\n       [0.00972759, 0.00930488, 0.00980669, 0.00821648, 0.0073698 ,\n        0.0058224 , 0.00477951, 0.0040582 , 0.00359841],\n       [0.00778578, 0.00980669, 0.00939655, 0.01023822, 0.00853919,\n        0.00675687, 0.00554806, 0.00479644, 0.00411368],\n       [0.00707998, 0.00821648, 0.01023822, 0.00976429, 0.01088911,\n        0.00811145, 0.00678293, 0.00577646, 0.00499521],\n       [0.00639228, 0.0073698 , 0.00853919, 0.01088911, 0.01084468,\n        0.0109444 , 0.00859185, 0.00739379, 0.00641778],\n       [0.00501527, 0.0058224 , 0.00675687, 0.00811145, 0.0109444 ,\n        0.00972693, 0.01031447, 0.00822467, 0.00711671],\n       [0.00411995, 0.00477951, 0.00554806, 0.00678293, 0.00859185,\n        0.01031447, 0.00927034, 0.00981771, 0.00783057],\n       [0.00358334, 0.0040582 , 0.00479644, 0.00577646, 0.00739379,\n        0.00822467, 0.00981771, 0.00924601, 0.00970531],\n       [0.00309482, 0.00359841, 0.00411368, 0.00499521, 0.00641778,\n        0.00711671, 0.00783057, 0.00970531, 0.00924216]])"
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "pile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(9, 9)"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "pile.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "im = ax.imshow(pile, cmap=\"magma\")\n",
    "f.colorbar(im, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sliding Diamond"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "example = generate2dGauss(mean=[0, 0], variance=[[3, 0], [0, 3]], gridsize=15)\n",
    "\n",
    "# plot figures\n",
    "f, ax = plt.subplots(1, 1)\n",
    "ax.imshow(example)\n",
    "f.set_size_inches(5, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at diamond of different sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamondDict = {}\n",
    "for param in range(2, 70, 7):\n",
    "    diamondDict[param] = patches.Rectangle((75-param//2,75-param//2),param,param,linewidth=1,edgecolor='r',facecolor='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 2160x360 with 10 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# plot figures\n",
    "f, ax = plt.subplots(1, len(diamondDict))\n",
    "for index, param in enumerate(diamondDict):\n",
    "    ax[index].imshow(example)\n",
    "    ax[index].set(title=f\"{param} bins\")\n",
    "    ax[index].add_patch(diamondDict[param])\n",
    "f.set_size_inches(30, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamondVals = {}\n",
    "for param in range(2, 70, 7):\n",
    "    diamondVals[param] = HT.slidingDiamond(example, sideLen=param)\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x360 with 10 Axes>",
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wR/sxQzcwR/sxQzcwR/sxQzcwR/u5nCHHyhbAZMTpZJPsHCIiIiIiIiIiC7A4tAAaI2Tbcs9jW+55/zZ2DtnojjvuwB133JH0ZVCPmKP9mKEbmKP9mKEbmKP9mKEbmKP9XM6wo+KQiGwTkb0isk9EPhFxv4jIp/37nxKRN2v3vSwiT4vIkyKyeyEv3hSNsbK5biMiIiIiIiIiMs2cO4dEJA3gswDeDeAQgF0ico9S6lntYdcDuMD/cyWAz/n/bHinUmp0wa7aMNFjZewcIiIiIiIiIiLzddI5dAWAfUqp/UqpMoBvALgx9JgbAdypPI8CWCEiZy3wtRor6mQynlZGRERERERERDbopDi0HsBB7f1D/m2dPkYB+GcReVxEbo37S0TkVhHZLSK7jx8/3sFlmYNjZR6bM6QW5mg/ZugG5mg/ZugG5mg/ZugG5mg/Zmi2To6yl4jb1Dwe8zal1BEROQPAd0TkeaXUQ7MerNTnAXweALZu3Rr+/EabKtUAAC/XVjVvmywtvbEymzNsuOSSS5K+hMQxR/sxQzcwR/sxQzcwR/sxQzcwR/sxQ7N1Uhw6BGCj9v4GAEc6fYxSqvHPYyJyN7wxtVnFIZsVK15x6PnaGc3bSpV6UpdDPbj88suTvgRaAMzRfszQDczRfszQDczRfszQDczRfi5n2MlY2S4AF4jIJhHJAbgZwD2hx9wD4MP+qWVvATCulDoqIstEZAgARGQZgJ8H8MwCXr8RZspecSiNGtLw3p7xC0Zkl0qlgkpl6Y0EuoY52o8ZuoE52o8ZuoE52o8ZuoE52s/lDOfsHFJKVUXkNgD3A0gD+JJSao+IfMy//3YA9wG4AcA+AAUAH/U//EwAd4tI4+/6ulJq54L/WySsUQh6d+5FAMDO8uubBSOyy/bt2wEAt9xyS7IXQj1hjvZjhm5gjvZjhm5gjvZjhm5gjvZzOcNOxsqglLoPXgFIv+127W0F4OMRH7cfwJt6vEbjRXUJzVRqUErBL4yRJWp1hVOFMl45MY1zVy9L+nKIiIiIiIiITrtOxspoDsWYEbJSlXuHbLN/dBr7jk3h/Z97GFNLcKk4ERERERERLT0sDi2AuBEyjpbZpVKr48RUCQAwOlXGw/tGE74iIiIiIiIiotOPxaEFELd8mkup7bJ3ZLLt+0REREREREQu6mjnELXXKALtq62JvJ3s8Mzh8UCGTx8eT/BqqBdbtmxJ+hKoR8zQDczRfszQDczRfszQDczRfi5nyOLQAiiWW8Wh4b4MUPN21cTtIiIzHZssBYpDxyZLCV4N9cLlF+2lghm6gTnajxm6gTnajxm6gTnaz+UMOVa2AIr+4uk8KjhzoHU6GYtDdjlVKCOPCvKoAADGZyoJXxF1q1AooFAoJH0Z1ANm6AbmaD9m6AbmaD9m6AbmaD+XM2RxaAE0Fk+/M/cS3lR5Trudp5XZZLxQwTtzL+GduZcAAGOFcsJXRN3asWMHduzYkfRlUA+YoRuYo/2YoRuYo/2YoRuYo/1czpDFoR4ppQK7hbLp1n9S7hyyy1ioU2h8poJ6XSV0NURERERERESLg8WhHpWqre6glAhSqdZYGYtDdjkV6hSqK2CyWE3oaoiIiIiIiIgWB4tDPWqMlAFAKiXQakPNRdVkh/HC7B1D4YIRERERERERkWtYHOpRsaoVh8TrHmpg55BdwmNlcbcRERERERERuYRH2feoVGmNlR3Nno3Va1cAo94oUrnKhdS2UEphfKaCvXJG4HZ2Dtlp69atSV8C9YgZuoE52o8ZuoE52o8ZuoE52s/lDFkc6pG+c2i8bx3yZ5wJPOeddlWusThki0K5hlpd4QBWBW6f4s4hK1166aVJXwL1iBm6gTnajxm6gTnajxm6gTnaz+UMOVbWo5I2VjYoZWRqM9p9LA7ZYrrsFYGWSRnLpNUtVCizOGSj8fFxjI+PJ30Z1ANm6AbmaD9m6AbmaD9m6AbmaD+XM2RxqEf66Nh5hedQ2vdo5H1ktsZi8auz+3F1dn/z9gKXilvp7rvvxt133530ZVAPmKEbmKP9mKEbmKP9mKEbmKP9XM6QxaEeBY+yDy6kZnHIHtOl6CIQi0NERERERETkOhaHeqSPlYkEj7Iv11hYsMVMJXp8jGNlRERERERE5DoWh3pUDnUOCTuHrBTXORR3OxEREREREZErWBzqUXCsLNg5xIXU9ogbH5vhWBkRERERERE5jkfZ96hUaRWAKmvOx7r1y4FDJwGwc8gmjfGxPdV1gFbgm+ZYmZXe+ta3Jn0J1CNm6AbmaD9m6AbmaD9m6AbmaD+XM2RxqEf6zqHMyvU4c+MZAFgcsk2jc+hgfQXWDuWByRIAdg7Z6qKLLkr6EqhHzNANzNF+zNANzNF+zNANzNF+LmfIsbIe6aNjuWoB1cJ48/1yjcUhWzQ6h4aliA39rW4hdg7ZaXR0FKOjo0lfBvWAGbqBOdqPGbqBOdqPGbqBOdrP5QxZHOqRXhySg4/jxV0/iLyPzNboHLoq+zIuKr3QvJ2dQ3a69957ce+99yZ9GdQDZugG5mg/ZugG5mg/ZugG5mg/lzNkcahH4YXUPK3MTvpC6my69bSYZnGIiIiIiIiIHMfiUI/CR9nrp5WxOGSPgjY+ls20nhaFEsfKiIiIiIiIyG0sDvVIX0gtIkjpnUPcOWQNvXMoo1X4iizwERERERERkeNYHOpRKdQ5pNWGAoUjMlup0soxo42VlSrMkIiIiIiIiNzGo+x7pBcVzrhwCy5dvxx4aT8AjpXZpFHI+1n1bFz7hvOBw8f825mhja655pqkL4F6xAzdwBztxwzdwBztxwzdwBzt53KGLA71SB8dW7VuIzZvWgOAxSHbFP0i39H6MNaf8zqIHINSQLWuUK3VA91EZL7NmzcnfQnUI2boBuZoP2boBuZoP2boBuZoP5cz5E+8PdLHjiqTJzF24njzfRaH7FH0c1wlBZQnTyKvLaVm95B9RkZGMDIykvRlUA+YoRuYo/2YoRuYo/2YoRuYo/1czpDFoR5VtM6hQ08/gh987zvN97mQ2h5Ff6zsiuyr2Lv7h+jLppv3sThkn507d2Lnzp1JXwb1gBm6gTnajxm6gTnajxm6gTnaz+UMWRzqkV4ACp9WVqkp1OsqicuieSpW9MXiEuoc4lJqIiIiIiIicheLQz2qVFvFn0ZdKKftp2H3kB2K2nigVxzSOocqzJCIiIiIiIjcxeJQj/TiT6NrSO86YXHIDnrnkKTAnUNERERERES0ZLA41CN96XRjoCynFxbYdWKFUrhzKMuxMiIiIiIiIloaeJR9j/SF1FuuvBrnrB7A//e1F5q3sXPIDo3uoCeqG/D777wSO3cead5XZIHPOu9617uSvgTqETN0A3O0HzN0A3O0HzN0A3O0n8sZsjjUI704dPaGDdi4ZhlymX3N23icvflqddUs4h1Xgzjvdecin20dT8jOIfts3Lgx6UugHjFDNzBH+zFDNzBH+zFDNzBH+7mcIYtDPdKLPyePHUFmpi+4kJrFIePpxZ/1mWkcOnSIC6ktd/DgQQBuv3i7jhm6gTnajxm6gTnajxm6gTnaz+UMO9o5JCLbRGSviOwTkU9E3C8i8mn//qdE5M2h+9Mi8lMRuXehLtwU5VrrtLInHvkhHnjggcDOIRaHzKePjW3JHMYDDzzAhdSWe+CBB/DAAw8kfRnUA2boBuZoP2boBuZoP2boBuZoP5cznLM4JCJpAJ8FcD2AiwF8QEQuDj3segAX+H9uBfC50P2/A+C5nq/WQPpYmfinlQWKQzWOJJkueIy9989gcYgZEhERERERkbs66Ry6AsA+pdR+pVQZwDcA3Bh6zI0A7lSeRwGsEJGzAEBENgD4RQBfWMDrNkbUaWXsOrFLMXRSGYDgWBkzJCIiIiIiIod1UhxaD+Cg9v4h/7ZOH/PXAP4jgLY/YYvIrSKyW0R2Hz9+vIPLMoPeOZRqdg61CgtLaazM1gz14k8jwz7tKHu9eLQU2JojtTBDNzBH+zFDNzBH+zFDNzBH+zFDs3VSHJKI21QnjxGR9wA4ppR6fK6/RCn1eaXUVqXU1rVr13ZwWcmr1xWq9dZ/Cr+uEFhIvZS6TmzMEAgXh7x/5rNLt3PI1hyphRm6gTnajxm6gTnajxm6gTnajxmarZPTyg4B0FdxbwBwpMPH/CqAXxKRGwD0ARgWka8ppT7U/SWbo6x1DeXSKWzbtg0AsOuB1n+epdQ5ZKuS1hk0MvR6/P62N2H7U+Pa/czQNht/7q0YnSyjWqsjk+5o7z4ZpvF6SnZjjvZjhm5gjvZjhm5gjvZzOcNOikO7AFwgIpsAHAZwM4BfCz3mHgC3icg3AFwJYFwpdRTAf/L/QETeAeD3XSkMAcGRsmxasG7dOgBALjPSvJ3FIfPpRT41sBzr1q1D/tnJ5m1cSG2XJw+O4V///T4oBYzW+/Hxd56f9CVRFxqvp2Q35mg/ZugG5mg/ZugG5mg/lzOcsziklKqKyG0A7geQBvAlpdQeEfmYf//tAO4DcAOAfQAKAD56+i7ZHBXtGPtcJoX9+/d7b6f108pYHDKdXsAbrpzE/v37uZDaYp954EWskwlAgL+4fy9+6x3nNU8SJHs0Xk83b96c8JVQL5ij/ZihG5ij/ZihG5ij/VzOsJPOISil7oNXANJvu117WwH4+Byf40EAD877Cg2mFxWy6RQeeughAEBuxdbIx5CZ9OLPyomX8NBDx5C/6B3a/ewcssmTB8dwecYb7TxaHsaLx6Zw4ZlDCV8VzVfj9dTFL7xLCXO0HzN0A3O0HzN0A3O0n8sZchlHD4JjZa3/lPpR9iwOma8cdVqZ1jlU5M4haxQrNZyYLgdue+VEIaGrISIiIiIisgOLQz3QR8b0glAuw7Eym+jFIWmeVrY0T5yz3dHx4qzbRsZnErgSIiIiIiIie7A41IPwWFmDXhxiYcF8+thYo3NIL/bpp5mR2Y5GFIKORBSMiIiIiIiIqIXFoR4ExsoyrYW3weIQCwumKwXGyrx/ciG1nY6ORXUOsThERERERETUTkcLqSmaXhzKpVN4z3veAwD4P89NNG/nziHz6aN/2U1b8Z5rzsMLY637WeCzx8iEVwh6uPK65m1HxjhWZqPG6ynZjTnajxm6gTnajxm6gTnaz+UMWRzqQSk0VrZmzRoAQD4z1bydxSHzlbSF032DK7BmzRq8Mn2qdT8ztMbxyRIAYEL1NW8LL6gmOzReT8luzNF+zNANzNF+zNANzNF+LmfI4lAPKjXVfDuXSWHv3r3+2/3aY1hYMF1gafjEUezdC+QH1zVv4mll9hifqQAANqa81q+D9RUYK1SSvCTqUuP19KKLLkr4SqgXzNF+zNANzNF+zNANzNF+LmfI4lAPKtXgWNkjjzwCABj+uetaj9EKSGQmvburdPh5PDL1Mt5+w/tbt3GszBqN4tAlmREAwMHyCkzMVKCUgoi0+1AyTOP11MUvvEsJc7QfM3QDc7QfM3QDc7SfyxlyIXUPArtqYk4r41iZ+fTij0QtpGbnkDXGCrNHyMq1OmZ44hwREREREVEsFod6EDytrPWfUi8UlTlWZrxy4LSyiKPsWeCzxthM9AgZR8uIiIiIiIjisTjUg3JorKz5NjuHrFKe8yh7dp3YYkIrDmW05ySLQ0RERERERPFYHOqB3hWUy7T2meiFIi6kNp/eGdTYS5PPap1DHCuzglIqUATq04q0YzM8sYyIiIiIiCgOF1L3oBI6yv5XfuVXAAB7T7Y6TVgcMp/eOXTRle/C1ResCYyVlWt11OsKqRQXGpusUK6hWvcWwD+mzsMVZ60GJicABDuKyA6N11OyG3O0HzN0A3O0HzN0A3O0n8sZsjjUg8BR9ukUli9f7r09Mda8nWNl5tM7wIaGh1s5ZlLN/Mq1OvpS6ciPJzPo+4ay/YMYHBoG4BWHOFZmn8bzkOzGHO3HDN3AHO3HDN3AHO3ncoYsDvWgHFpI/cwzz3hvr9qoPYZH2ZtOHxs7efAlPFN+DZdeeinyWnGoWKmhL8vikMmmitXm25vSpzBUaL0/VapGfQgZrPF6eumllyZ8JdQL5mg/ZugG5mg/ZugG5mg/lzNkcagH5dBY2e7duwEAb7/hXLE18hsAACAASURBVO0xXGZsupJW5Dv04jMYP5hpFocm/dvZAWY+vQC0oXoEqZNZAOfOuo/s0Hg9dfEL71LCHO3HDN3AHO3HDN3AHO3ncoZcSN0DfZ9QPuYo+wo7h4wXdVoZEFwszuPszTetFYDSKUFaJPI+IiIiIiIiCmJxqAfBziHttDIeZW8VvbsrJTE5crG48abCxSGt0sfOISIiIiIiongsDvVA7xzSu4WyPMreKsGj7Fu35zOtHUMs8pmvfXGI451ERERERERxWBzqgb5sWu8yYeeQXYJjZewAs1VgrEyCxSGOlREREREREcXjQuoehBdS/8pNNwEAUmmOI9lEz+gXb3wfVi7LAeBYmW3008r6L3o73rx5NfDVn826j+xwk/96SnZjjvZjhm5gjvZjhm5gjvZzOUMWh3qgj4zl0ikMDAwAAGp1FfkYMpN+lP3y4UEM5L2nRWAhdYU5mm6q3CoADQ0tw+oVQ6372DlkncbrKdmNOdqPGbqBOdqPGbqBOdrP5QxZHOpBoDiUSeHJJ58EAGzZsgXplKBWV6groFqrI5PmBJ+p9K6gvXueRjol2LJlS6hziDtrTKd3B6nRl3E0faJ1H4tD1tFfT8lezNF+zNANzNF+zNANzNF+LmfI4lAPwmNlTz7e+h8lm5ZmB1GlpqDtNiaDVGv1Zk4iwNNPeWNIs4pD3DlkPH2vUOHoS3h5PAvgzFn3kR1c/sK7lDBH+zFDNzBH+zFDNzBH+7mcIdtZelCuRR9lDwRHklhYMFc5NBqo04tDJWZoPP1EsvBpZZMsDhEREREREcVicagH4bEyHZcZ20Ev3OVDGebZOWSVmUrwKPuUCBqHz5Wr9cAuMCIiIiIiImphcagHesFgVtcJTyyzQiDD0OxfngU+q8yUW51DKb8q1J9tZTpT4d4oIiIiIiKiKCwO9aBSa3UiZEPFoaxWWKiw68RYpTadQxwNtMuMdqJcY6JsINcqDhXKHC0jIiIiIiKKwoXUPQiPlX3wgx9sva8VFnicvbnCxaFAhtw5ZJWi1hl03Xvfj81rB/GFv/ph8za9s4jMpz8XyV7M0X7M0A3M0X7M0A3M0X4uZ8jiUA/Cp5Vls9nA+w0sLJgrOFYWzJCnldlFL/4MDfQhm80GOoc4VmYX/blI9mKO9mOGbmCO9mOGbmCO9nM5QxaHehA+6WrXrl0AgMsvvzw4VsbOIWOVqq2CQS4TzDCXbhUWWBwyn178efGZJ3E4n0F/rvUSV2DnkFX05yLZiznajxm6gTnajxm6gTnaz+UMuXOoB+Gxsj179mDPnj0AgDz31VghfFpZIMMsF1LbRC8OvfTi89izZw/6tQw5VmYX/blI9mKO9mOGbmCO9mOGbmCO9nM5QxaHehAcK5PAfdlM6319cTWZpRwq8Om4kNoetboKZNQ4rWxA6xxicYiIiIiIiCgai0M9CJxW1q6wUOMPpaYqVYKjgToupLaHvoxaP75ef7vAnUNWqdYUjo7P4JnD40lfChERERGR81gc6kF455AuG+g6YeeQqfQM85l04L5gcYiFBZPpI2X92hJq/e0iO4esUasrPHd0Aq+cKOBf3v4IDp4sJH1JREREREROY3GoS0qpWaeV6QInXXFfjbHCp5Xp8jytzBr6yJjeLaSfVlYoVxf1mqh79+8ZwbSf10ylhtt/8FLCV0RERERE5DaeVtalar3VDZROCdIpwS233NK8Te8kqrCwYKzwaWW3fOCW1vvcOWQNfaysL5tqPhf/9L7nmrdzrMwejx04iZ3l1zff3/XyyQSvhnqhf10kOzFDNzBH+zFDNzBH+7mcITuHuhToOEnP/s+odxLxKHtzte0c4mll1uBYmVueOzoReP+l49Mc7SQiIiIiOo06Kg6JyDYR2Ssi+0TkExH3i4h82r//KRF5s397n4g8JiI/E5E9IvInC/0vkBS94NM4qezhhx/Gww8/DIBjZbYohYp8gQzTrcICO4fMFh4ra+QYHCtjccEGSnn7hi7NjODSzAgAbwfRi69NJXxl1A39NZXsxAzdwBztxwzdwBzt53KGcxaHRCQN4LMArgdwMYAPiMjFoYddD+AC/8+tAD7n314C8H8ppd4EYAuAbSLylgW69kRFHYH+wgsv4IUXXgAQXkjNwoKpAgups6lAhjnuHLJGsHMo08yRp5XZ5+R0GRPFKjakxrAhNda8/cDodIJXRd3SX1PJTszQDczRfszQDczRfi5n2Enn0BUA9iml9iulygC+AeDG0GNuBHCn8jwKYIWInOW/3/h1b9b/48TRXXONlbFzyA56jnkuFbdW8Cj7Vm79udZaNY6V2eHoeDHm9plFvhIiIiIioqWjk+LQegAHtfcP+bd19BgRSYvIkwCOAfiOUuonUX+JiNwqIrtFZPfx48c7vf7EVGqtGlc2E1Ec8kfNAKCyRI6yty1DIDRWFspRL/qVKkunOGRjjoHOodjTypZOccjGDBuOjEUXgY6MRReNXGZzjuRhhm5gjvZjhm5gjvZjhmbrpDgkEbeFqx2xj1FK1ZRSWwBsAHCFiFwa9ZcopT6vlNqqlNq6du3aDi4rWcGdQ3N1Di2NH0ptyxBov5B6qXYO2ZjjTLmVT2Ah9RIdK7Mxwwa9cyiTEu32pdc5ZHOO5GGGbmCO9mOGbmCO9mOGZuvkKPtDADZq728AcGS+j1FKjYnIgwC2AXhm3ldqmKixsmw227wteFrZ0ugcslE4Rz3DPHcOWWMmcJR9upkjTyuzzxG/CFRDCsv7c4BfK4obNyOz6a+pZCdm6AbmaD9m6AbmaD+XM+ykOLQLwAUisgnAYQA3A/i10GPuAXCbiHwDwJUAxpVSR0VkLYCKXxjqB3AdgD9buMtPjt5J0hgr++AHP9i8jcuM7RDYOZRN4wNahnpxqMQMjVYMjZU1nos/O9haaFyoVBf9umj+jk2UAADfKV+I37n6AuCBFwEAIywOWUn/ukh2YoZuYI72Y4ZuYI72cznDOYtDSqmqiNwG4H4AaQBfUkrtEZGP+fffDuA+ADcA2AegAOCj/oefBeAr/olnKQA7lFL3Lvy/xuKrBDpOZk/VBU4rW0IjSbYJnDrXbiF1lV0nJgsfZd+wVHcO2ezkdLn59gVnDjbfPlUoQykFkagpZiIiIiIi6kUnnUNQSt0HrwCk33a79rYC8PGIj3sKwGU9XqORoo6y/8EPfgAAuPbaawOFhgq7TowV3jkUyHCJ7hyyUfAo+3Qzx/N+7vLm7Rwrs8NYwSsOvSlzBJMvZ7Asl8Z0uYZKTWGiWMXyfndbeV2kv6aSnZihG5ij/ZihG5ij/VzOsJOF1BQhaiH1gQMHcODAAQBLd5mxbUpaR1AukwpmmA6OlXk1UDJReOdQI8elupDaZqcKFQDAWakJjL92CKsGc837TkyVkros6pL+mkp2YoZuYI72Y4ZuYI72czlDFoe6VNaOp486rSy4kJrFIVO1O8o+k06hcViSUkC1zuKQqYqxY2Wt5kiOldnhlDZWlkmnsHpZvvm+PnJGREREREQLh8WhLkWNlem4kNoOgYXUzNFa4bGyhr5sML8aC3xGq9TqmCx5i8NFvKPsVy9rdQ6NTrE4RERERER0OrA41KVKxFH2uqy2pLrMo+yNpRf5oopD+Uyr0MDikLlmKtGdQyISeH+Go2VGG/NHygAgk/Kej6u1sTJ2DhERERERnR4dLaSm2YI7h7xC0MDAQPM2nnRlh8BC6nQ6kCHA3VG20E8r68sGcxzIpZtFoUK5isE8X/ZMdarQKv6odA4DAwNYpY2V6feTHcKvqWQfZugG5mg/ZugG5mg/lzPkT0ldihoru+mmm1q3BXYOsXPIVOGdQ3qGQDBHdg6ZqxgaK9Nz7M+lgWnv7RnuHTKavm/o6OrLcNNNV+FzD77UvG18phL1YWSw8Gsq2YcZuoE52o8ZuoE52s/lDDlW1iW9UBC1kJq7auwQPso+TB81K7EDzFhxY2Xh94sVPhdNpncGrRzwjqwf7m/9DmOCxSEiIiIiotOCnUNdiuoc+u53vwsAuO6663hamSXCC6n1DIFgwajEIp+xCqHTyvQc9QXV3DlktlPazqEzJ1/Ed787juG1FzdvY+eQfcKvqWQfZugG5mg/ZugG5mg/lzNkcahL5YiF1IcOHWrepheHuKvGXOEin55h47bmY1kcMpY+VtaXC+bYpy+k5liZ0fTOoXx5DIcOlbH5nDc1b5sosjhkm/BrKtmHGbqBOdqPGbqBOdrP5Qw5VtYlvRso6rSyPIsKVphrrIw7h+wwU44fK+sLjJWxOGQyfedQJuUt+h/uzzZvm5ipLvo1EREREREtBSwOdWmuogLHyuyg7xGKLPJl2QFmOqVUYFysb9bOoVaGHCszmz5WlvGfj8u14hDHyoiIiIiITg8Wh7rEhdT2q9dV4CS5qOIQO4fMV67VUfdjzKZl1vOxn51D1hgrRHQO9WkLqTlWRkRERER0WnDnUJfKelHBLwQNDw83b8umpfk2j7I3Uzk0GphKSSBDgEU+GxTLrVwaXUN6jlxIbY+T2ljZ4NAQhgfzobGyCup1hVRKoj6cDBR+TSX7MEM3MEf7MUM3MEf7uZwhi0Ndihore9/73jfrtvBjyRxRJ87pGXq3twoLPK3MTFHH2Os5ciG1PSaLrZ1CV7/7F/GGs7wvvgO5NArlGuoKmC5XMdSXjfsUZJjwayrZhxm6gTnajxm6gTnaz+UMOVbWJb2wkI/aOZQK7qpRit1DpplrbxTAsTIbBIpDufSs+zlWZo+pUqs4NKSNkw33ce8QEREREdHpxOJQl8raIuPGjpOdO3di586dAIBUSpo7MwCgWmdxyDR6J1AuIkMgWDQqcSG1kaJOKtNz1ItDHCsz25TWOfT4jx9sZricJ5ZZK/yaSvZhhm5gjvZjhm5gjvZzOUOOlXUpapHxyMhI4DG5TApV/wfXcrUeubiakhPVORTOMM/xQONFnVSm5xgcK2OGpqrXFabKrcLPyePHIH59fbifS6ltFX5NJfswQzcwR/sxQzcwR/u5nCGrFV3qZCSJx9mbTc8wajQwfDuLQ2YqRuwc0vVpo2bFKjuHTFWo1NCYvu3PppuFIYDH2RMRERERnW4sDnVprqPsAS6lNl1HO4eYofECY2Vz7RziQmpj6SNlg33BplZ959AEi0NERERERAuOxaEuRZ10FRZYZszOIeOUa61CQScZlth1YqSo08p03Dlkh6lSq+gzlA8Vh9g5RERERER0WnHnUJeiRpJWr14deAy7TsxWqsxeSM0M7RO1c0jPsT+XinwsmWUy1DmkZ6gXhyaKXEhtk/BrKtmHGbqBOdqPGbqBOdrP5QxZHOpSVOfQe9/73sBjsunW0gx9gTWZodRBhoHiELu/jBTYOZSbnWNwITWLQ6bSj7EfzGcCGQ5rY2YcK7NL+DWV7MMM3cAc7ccM3cAc7edyhhwr6xJ3Dtmvs4XUrcICMzRT1FH2Or04VGSGxgrsHAqNlQWPsmdxiIiIiIhoobE41KVKRNfJt771LXzrW99q3p7lziGjBYtDXgEhnCELfOaL2jmk58iF1HaYLAXHyvQMh7SF1PrjyHzh11SyDzN0A3O0HzN0A3O0n8sZcqysS4GTrvwi0IkTJwKP4VH2Zos6rSycoV4cKjFDIwV2Dvmnlek5ciG1Haa1os9QPhPIcEgbK5sssnPIJuHXVLIPM3QDc7QfM3QDc7Sfyxmyc6hLnRyDnmfXidFKEQW+sMCJc8zQSMU5xsr04+1ZHDJXu6Ps9TGzKXYOEREREREtOBaHuhRYZhxTWGDnkNnK1bmPstcLfCUWh4w011H2fRwrs0JwIXU2cJ/eOTTF08qIiIiIiBYci0NdUEpF7hwKY9eJ2aJOnAsL7hxiYcFEM5VWjnqXUPM2jpVZIbxzSDcYGCtjcYiIiIiIaKFx51AXqnUF5Z9Mn04J0invyPp169YFHpflMehGizqtLJwhF1KbTz+trNElpOeYTQtSAtSV99yt1OqxJwxScvSOoKF8Bn1ahkN5LqS2Vfg1lezDDN3AHO3HDN3AHO3ncoYsDnUheIy9NN/etm1b4HHsHDJb1N6ocIZ5FviMV4wYK9NzFBH0Z9OY9otIxUqNxSEDBcfKMrhOy7Avm0ImJajWFcrVOkrVWvOEQTLbigsvx4mpMsrVemyHJpkt/HWR7MQc7ccM3cAc7edyhiwOdaHSwb4hAMhlWoWjSk2d1mui+SvNe6yMxSETBXYORYyVNW5vFIdmKrXA0ehkhnYLqUUEg30ZjBUqzcfmB1kcMt1f3r8Xf/P9fQCAt25ejTt/4woWZomIiIgMxe/SuhDsOGn9gHLXXXfhrrvuat2X5r4ak5Uqs4t87TNkcchEMxGnlYVzDC6lZo4mmgx1DoUz5Illdjl4soD/9eA+XJPdj2uy+/HI/hO47+mjSV8WdSH8XCQ7MUf7MUM3MEf7uZwhO4e6UIrYVQMAExMTgccFTytj55Bp9DGxRo7hDHM8rcx4+lhZowgUzpFLqc03Vao03x7qy8zK0Ov2mgHApdQ22LH7IOoKGJBy87btP3kVN25Zn+BVUTfCz0WyE3O0HzN0A3O0n8sZsnOoC3pRQd85FMaF1GYLLqSOHlHhWJn5Oh0ri3o8mSMwVpaf/XuLoTxPLLPJQy+OzrrtiVdOseuLiIiIyFAsDnWhk2PsAY4kmS5qIXVYPt0qKjBDM81ELKQO69OKf0UWh4yjlAoupO6bXRzSb2OBwWzjMxU8fWgMACDSKr5X6wqPHTiR5KURERERUQwWh7rQSVEhfF+FnUPG6ag4lNXGypihkaJ2DoX1sXPIaKVqvTl6m0unIjv5hgLFocqs+8kcew6Po+5PUg/kMli1rLUA/mcHxxO6KiIiIiJqhzuHuhA8yr5VPNiwYUPgcewcMltJWxLeyGquDJVSEIkfJaTFVa+ryB1g4Rz7tSJfsczikGmiuobCGQ5yrMwae460ZvH7V5yBNcv7gOOz7yM7hJ+LZCfmaD9m6AbmaD+XM2RxqAuBjhOteHDdddcFHqfvI2LnkHnKEeOB4QxTKUEmJaj6vwav1BRyGRaHTFGs6suoU0ilvGzCOXIhtdmi9g2FM9THylgcMtueI63uoIve/FZcsWk1/uKZhwAAzx5h55Btws9FshNztB8zdANztJ/LGXY0ViYi20Rkr4jsE5FPRNwvIvJp//6nROTN/u0bReT7IvKciOwRkd9Z6H+BJEQVFaLox9xzIbV5yjGnzoUFTyxjYcEknYyUAVxIbbqpUvtl1AAw3NcaTeLOIbO98NpU8+3XnzWM89Yua/6y5Mh4kfkRERERGWjOziERSQP4LIB3AzgEYJeI3KOUelZ72PUALvD/XAngc/4/qwD+vVLqCREZAvC4iHwn9LHWiSsq7NixAwBw0003AQh2DpWrPMreNFE7h8IZNu4r+EUIjgeaJW4ZdTjHPr1ziGNlxtE7gRodQuEMg2Nl3Dlkqnpd4cDodPP9vQ//Mw5kUjh39ZnYd8wrGh04Po03blie1CXSPEV9XST7MEf7MUM3MEf7uZxhJ2NlVwDYp5TaDwAi8g0ANwLQCzw3ArhTKaUAPCoiK0TkLKXUUQBHAUApNSkizwFYH/pY6wSPsm8VhwqFQuBxOR5lb7RSRHEonCEQ2jvEHI2inzymL50O56gXjkos8BlH7yRpHFkfzlAvDk1xrMxYIxPFZtF2xUAW1XIR1TKwac2yZnFo/+gUi0MWifq6SPZhjvZjhm5gjvZzOcNOxsrWAziovX/Iv21ejxGR1wG4DMBPov4SEblVRHaLyO7jx493cFnJ6eYo+8oS+IHUpgyB7sbKlkLnkE05zpRbebQbK1tqnUM2ZQgA01pxaFnMWNnQEjzK3rYcAQS6hjavWdZ6e23r7ZeOT2OpsDFDmo052o8ZuoE52o8Zmq2T4lDU9t3wjFTbx4jIIIB/APC7SqnIo0qUUp9XSm1VSm1du3ZtB5eVnLiF1GF6V9FSWEhtU4ZAqHMoHV9YyC+x4pBNORbKrSJB251DS2whtU0ZAsBkxGllYfrtE0ukc8i2HAHglROt36a9bvWyyLcPnnT3N25hNmZIszFH+zFDNzBH+zFDs3VSHDoEYKP2/gYARzp9jIhk4RWGtiul7ur+Us0RtasmCsfKzKZnks92tlicI0lmCewcyrXpHOJCaqPpY2JDcZ1DeW0h9RIpDtno4KlW4WfDqoHm2xtXtt5eSsUhIiIiIlt0snNoF4ALRGQTgMMAbgbwa6HH3APgNn8f0ZUAxpVSR0VEAHwRwHNKqb9awOtOlF4g0LuDNm3aFHicft9S6DixTVQHWDhDgEU+kwV2DmndQeEc9c6h4hIYK7PNVKm1YLqxWyic4VIcK7PRoVMzzbc3ruzHGTkvx42r+iMfQ+aL+rpI9mGO9mOGbmCO9nM5wzmLQ0qpqojcBuB+AGkAX1JK7RGRj/n33w7gPgA3ANgHoADgo/6Hvw3ArwN4WkSe9G/7f5RS9y3sv8biqtRaU3X6yNG1114beByLCmaL6gALZwgAea3IV6owR5PEnVYWznGpjZXZZiritLJwhvpYGU8rM5feFbRx1QDestXLsVytIyVAXQGvTRZRqtaQz8R3+5E5or4ukn2Yo/2YoRuYo/1czrCTziH4xZz7Qrfdrr2tAHw84uN+hOh9RFbreKxsie0csk2p2ioScDzQTp0upO7PtTIssjhknMDOoZixssBpZaUqlFLwmlPJJHpX0IaVrW6hXCaFdcN9ODJehFLAkbEiNmkLq4mIiIgoWZ3sHKKQcq31w6U+OrZ9+3Zs3769+f5SO+XKJtVaHXW/AUwEyKS8HzLDGQLM0WRxO4fCOfZl2DlkssDOIb9DaFaG2XSz4F6pKe7/MlC5WsfoVAkAkBLgzOG+QI5nr2gVi46Oc7TMFlFfF8k+zNF+zNANzNF+LmfYUecQBeljZXrhoFIJjjpk0xL5MZQ8vQMol041OxDCGQJL77Qym8TtHArnGFxIzQxNMxXoHPIWT0c9Fwf7Mjg5XQYATBargcwpea9NFJtvrx3KI5tOBXJct7yv+fbRsSLIDlHPRbIPc7QfM3QDc7Sfyxmyc6gL3Rxlz6KCWfQ88m1GyoDwWBm7Tkwyoy2XHmhzWhkXUpttqoOj7IHZo2VklqPjrYLPWcv7Z92vdw6NTLA4RERERGQSFoe6UOpw51Ceu2qMFdwb1b77IMcin7HiFlKHcSG12QILqWN2DgGhE8t4nL1x9FGxs7QuoYZ1w63bjoxxrIyIiIjIJCwOdaHjhdQZ/ZQr/kBqktI8OofyWS1HFoeMohd6+tp1Dmn3cSG1efSF1EMddg7xxDLz6J1D6yKKQ2evaN02Ms7OISIiIiKTcOdQF4rV6D0nF154YeBx+n0sKphFLxDoxZ9whgACxy2zsGAWfUSsLxOfYx87h4wW1TkU9VzUC0eTHCszjr5zqNElpOd4ptY59Noki0O2iHoukn2Yo/2YoRuYo/1czpDFoS7oXUB618lVV10VeJw+jlSq1nn0skGKlegj0MMZhu8vcpmxUQqBnUOtl7Nwjn1ZHmVvqmqt3izYibR2R0U9F4f6ss23OVZmnmMTpebbjUKQnuMZenFIeyyZLeq5SPZhjvZjhm5gjvZzOUMWh7qgdwG1Oy0nlRLkMqnmGFqpWufpOoaI6/6KwsKCuQqVzhZS59IppASoK+/kwEqtHlgYT8mZLrUyHMxn2hbQOVZmtmNaN9AZw/lZ968dbN12YqqEaq2ODJ+HRqvVFb6x61U88tIJbF47iFuv2dx2LxgRERHZi9+VdSFwfLbWOXTHHXfgjjvuCDw2H9g7xK4TU+inXOnFn6gMOZJkrplyq3tE3ysUzlFEQh1gzNEUk6VWkWdI+6Ez6rmon2TG08rMo3cDnTHkdQnpOeYyKaxelgPgFWpPTJcX/Rppfv7wH5/BD7/9TVSf/z4+/cCL+OAXfsLXT0tFvaaSXZihG5ij/VzOkMWhLgSWGc/ZdaL9QFrlN1SmCBb42meY51iZsQodHmUPBItHLPKZo9Nj7AHuHDKZUiqwc+jMiM4hIDxaxr1DJvvxvlF8/SevBm772cExfP6h/QldEREREZ1OLA51IVBYyLb/T6jfz84hcxQ7HA0Egt1hPHXOLDPzKA4FFsTzuWiM6VJnx9gDwc6iSe4cMspEsdr8xUl/Nh2bpV404t4hs33mey9G3v6FH+4PvPYSERGRG1gc6oLePZKfq+skw84hE8WdVhaF3V/m0juH+nPtCwv9HA80kl7kGdQWTkcJjJWxOGSU45PaSNlwPnZ3lL53aHSKxSFT7T8+hUf3nwTgjeVeds5KnLt6AIBXCPz200eTvDwiIiI6DVgc6kKp2l3nEOf0zVGqzGchNcfKTFXQdg4NzJFjYKyMv/U2hj5WNjRn55B2WhnHyoyiF4f0AlDY2qHWffrHkFn+6ZmR5tsrB7LIZ1K4+fJzWvezOEREROQcHjnRBb1AoO+rueSSS2Y9Vu8c0ncVUbLijrKPypCLjM2ldwDpxZ+oHPXnKjuHzKF3AOmjSFEZ6p1DPK3MLMe1LiC9ABTOcc0gi0M2+M6zrzXf3nzB63HJxhU4Y/M6/NnO5wEAP9w3iplyLfC6S2aLek0luzBDNzBH+7mcIYtDXdA7h/SRpMsvv3zWY9k5ZKa4vVHM0B6VWh2VmgIApCR4MmBkjlxIbaS4hdRRGQ5y55CxAp1DWnEonCM7h8w3UazgqUNjAAAR4Fe3XYsVA94pc+efMYh9x6ZQrtbx+Cun8PYL1iR5qTQPUa+pZBdm6AbmaD+XM+RY2TwppWI7hyqVCiqV4G+z9fu5BNcc+u6gOTPkWJmRgieVZQI7TqJy7NeLfBwrM8ZkTOdQVIbDfRwrM1XcWFk4x0BxiDuHjPTY/pOoe3V3XHr2cizLSjPDt523uvm4h18aTeLyqEtRr6lkF2boBuZoP5czWAlQYwAAIABJREFUZHFonvTRsFw6hVSq9QPp9u3bsX379sDj9c4iLjM2x0w5+rSyqAzZOWSmmXL0SBkQnWM/F4sbKbBzSOscisowOFbG4pBJ9OXSa7QCUDhHdg6Z7/FXTzXffsvmVYEM37K5VRx6/JVTsz6WzBX1mkp2YYZuYI72czlDFofmSS8O6WMscfSuFHadmKM4j6XigRPnWBwyRmAZdQd7L4ILqflcNEXczqEo+v1TpSqUUqftumh+Ol1IvYanlRnvyVfHmm9fds7KwH36+08fHke1xtdSIiIiV7A4NE+lwBHoc/9AqncOlditYIziPHIMHmXPb4RNETjGvoPnYh+PsjdS3M6hKLlMqlmUr9UVC+4GOTHdKvSsHszFPm64L4Nc2suwUK4FiryUvHpdNfcNAcBl56wI3L9ueR/OWt4HwMvvxWNTi3p9REREdPqwODRP+g8jnXQO5dk5ZKRSzGllUfSOE3YOmUMv8HTSORQoDvEHUmNMasWhZXN0DgHB0bPJkpvz3jYanSw3317TpnNIRALFoxNT5djH0uJ75WQB037hfc1gDmct75/1mDeuX958+9kjE4t2bURERHR6sTg0T6V5jCN5j2FhwUTB08rm6BzKBHcOcZTFDOGF1HNZphWQClxIbQz9SPrhOTqHAJ5YZiKlVKBzSN8rFCVwnD1Hy4yiF3suPnt55GMuPnu49fijLA4RERG5gkfZz1PgpLJQUWHLli2zHq93F5U4kmSMuJ1DURlm0ilkUoJqXaGugEpNIZeRWY+jxaV3/4QXUkflqBeQWBwyh17gGdJOI4vKMPyYKRaHjDAxU0Wl5hXNB/OZwNfGqBzXsHPIWM8eHW++ffFZXhEonGHjdoCdQzaJe00lezBDNzBH+7mcIYtD86R3DoXHyqL+R9G/SS6xc8gYcUW+uCd7Xzbd3I1SrNaQ62CkkE6vYOfQ3MWhZfnWY6Z5DLox9M4hvSso7rkYXkpNydO7f8L7hqJyXM2l1MZ64bXWDqHXrxsCMDvD169rFYdePDa5OBdGPXP5h5mlghm6gTnaz+UM+RPuPLXrHCoUCigUCoHb8hkeg26iwFiZthcqKkOAx9mbqN3Ooagc9X0209w5ZIypYvRR9nHPxeBx9tw5ZIIT+jH2oX1DUTkGTizjcfZG2actmL7gzEEAszPcsLK/+TVxdKqMk9Ps/rJB3Gsq2YMZuoE52s/lDFkcmqd2u2p27NiBHTt2BG4LdA5xrMwYM5XosbKoDIHQYnEeg26EmcBpZcEmyKgcl2ljZdMlFvhMUKur5vJbkWBGcc/FwEJqjpUZ4cS0vow62DkUlWNgrIyFBWMUKzW8cmIagPd8PG+tVxwKZ5hKCc4/Y7D5/j6eWGaFuNdUsgczdANztJ/LGbI4NE96gaeT08rYcWKmUpsOsCiBHKvM0QTtxsqiDAQWUrOoYAK9a2gwl0EqNfcuryGOlRnnRGCsrP0yaiDUOcSxMmO8fGIadf+8hY0rB9p+bTx/bas4xNEyIiIiN7A4NE/zOeUKCHacsHPIHPPNkcfZm0cvDoUXUkcJjJWxc8gIE9pY2FAHJ5UB4bEyFodMMKotlV6zLNfmkR4eZW+mA8enm2+ft3ZZ28eepxWHXh6dbvNIIiIisgWLQ/OkF3g6O8qenUMmKsaMlcXR9xLpe6coOfppZZ10DnHnkHn0zh/9FLJ2AqeVsXPICPox9p10Dq1e1nqM/rGUrP1akWfTmsE2jwQ2acWjAywOEREROYHFoXnSiwp6V1CcPIsKRipW5ztWxs4h08x3rGxZTj+tjBmaQO/8Gey0cyjPziHT6N0/4dPKovAoezPpRZ5Nc3QObVrTun8/i0NERERO4FH286Tvm8mHOk62bt066/F6V0qJu2qMUKnVUfMXK6RTgmy6lVFUhgA7wExUaDMaGJXjgFZU4M4hM0y2GSuLey4O8bQy4wSKQ8uCnUNROa7URs9OFsqo1RXSHeybotMrUBxa3Sr+RGWoF4dePVFAtVZHJs3fN5os7jWV7MEM3cAc7edyhiwOzZO+yDjcOXTppZfOenyw44SdQyYIHmMf/GY2KkMAyOs5cneUEWYCnUPBl7KoHPuz+kLqGup11dECZDp9JovxY2Vxz0W9OMSxMjOMBsbKgp1DUTlm0ymsHMjiVKECpYCT02WsHZp7HI1Or1dOtI7lPXf1QPPtqAwHchmsHcrj+GQJ1brC0fEiNq4amPU4MsOp6TJeS69Fpaaw/GSBWVkq7usi2YU52s/lDFkcmie9cyi8q2Z8fBwAsHz58uZt+olmPOXKDMU2J5VFZQiEdw4xRxMU2uwcisoxnRL0Z9OY8fMrVGqBESVafJNacSecRdxzcTCv7RziWJkR9M6hNaGdQ3E5rlqWw6mC1/nF4lDypkrV5slx2bTg7BX9zfviMjx31QCOT3of8/KJaRYcDFSs1PAX9+/FnY+8jFzdy2pa5fALl5yJT/3yG/m8s0zcc5Hswhzt53KG7AGep8AR6KHOobvvvht333134Da9+FBi55AR2p1UFpWh9ziOlZlmps1pZXE56kupC+w6SZw+FjYcGiuLy5A7h8xSrtYxPuPlmBJgRX+wAywuR31x9QkeZ5+4V7WuoY0rBwJjfnEZnquNnuldR2SGUrWG3/jKLnzxRwdQqSlcnd2Pq7P7AQD373kN7/vcjzEyXkz4Kmk+4p6LZBfmaD+XM2RxaJ5KbXYORclz55Bx5ntSGRAcSWJxyAyBk6467ABalteWUpeZY9KCY2WdZcixMrOcnG51Da1alu94VFNfSj06zaXUSXv1ZGvfUKcdQPro2SsnuJTaNJ+891n8eN+J5vvL8hks14q3B0/O4N9sfxzVGn9xSUREHhaH5qnYpnMoCk8rM0+7sbI43B1lHr0w0OlJV/puomkWFhIXXEjd6VH2XEhtklGt62dNByeVNQSOs2fnUOJePRm9b6idc7Qi0sGTMwt+TdS93S+fxNcefbX5/u9ddyHeuH453nDWMP72w1ubnWE/fXUMX330laQuk4iIDMPi0DyVqvEjSVF4Wpl5ivPM0Hscx8pMo++b6XR3UPA4exaHktZVhvlg55BSasGvizp3Ynp+x9hHPZbH2SdPL+5sXNlZcWjjqtZeooOnOFZmkj+/f2/z7XdffCZ++13nB97/vesuaL7/me/t49dDIiICwOLQvBUDp5XN/Z8vl05B/C77Sk01j1Cn5HQzVsbOIbPU6iowFrYsN//CQoFjZYnrZqwsm041xzzrijkmTe/6CR9j346+c2iUnUOJ04s7etGnHb2IdPAki0OmePLgGB47cBIAkEkJ/st7LoZIcNzz1mvOw3p/6fjJ6TLueuLQol8nERGZh0f1zFO7ZcZvfetbZz1eRJDPpJoFhWKlFvgBlRZfYJFxBxkCwaxnKvwNW9Kmy8GOk/Cek7gcgzuHmGPS2h1lH5ch4I0RNk6dmypV+ZqaIL3rZ9Wy2Z1DcTmu0R57gjuHEqcXdzaEOofiMlw7lEc+k0KpWsdEsYrxmUpgpw0lY7s2JvZLW85u7pDSc8xlUrj1ms34o3v2AAC+9uir+NBbzp1VRCKztPu6SPZgjvZzOcOOvqMWkW0A/l8AaQBfUEr9j9D94t9/A4ACgFuUUk/4930JwHsAHFNKXbqA156IUlXrHAp1nVx00UWRH9OXTbM4ZBC902Ag1HESl6FeVGCnQvLmGkeKy1HPu1BijkmbCOwc6uy5CHgLyBtHaE8WKzhzuO/0XCDNSe/6iToWOy5Hdg6ZQymFQ6e0sbLQQuq4DEUEG1cNYN+xKQDAoVMFLO9371hfmxQrNfzTMyPN9z/0lnObb4dzfN+b1+N//NPzmKnUsPe1STx3dBIXnz28aNdK89fu6yLZgznaz+UM55ypEZE0gM8CuB7AxQA+ICIXhx52PYAL/D+3Avicdt8dALYtxMWaoNCm62R0dBSjo6OzPqY/0HXCH0iTpneM6EUfID7D4CJjZpi0uZZRx+Wo7xziSVfJazdWFpdh+LE8zj5Zo1rnUNRC6rgc13DnkDFGp8rNX3wN9WVmdf+0ey42RpMABApMlIwH9x5vfm3btGYZLtu4onlfOMehvix+/pIzm+/f+9SRxbtQ6kq75yLZgznaz+UMO1m4cgWAfUqp/UqpMoBvALgx9JgbAdypPI8CWCEiZwGAUuohACcX8qKTNKMVFgZywcLCvffei3vvvXfWx/Rrj5th10ni9I6RcOdQXIb6TpsCx5ESNzlH51BsjnnmaBK9cyj8A2lchkCwIMgiX7JG59g5FJcjO4fMcehU/EgZ0P65uGEli0Mm+e5zrzXfvuGN6wJjYlE53vDGs5pvf+fZ10Bma/dcJHswR/u5nGEnxaH1AA5q7x/yb5vvY9oSkVtFZLeI7D5+/Ph8PnRRBZbgdnxCktZ14nBxyJYMC4FFxp2dVjawhMbKbMhRLwh0usgYCD5n+VxMVrVWb7tzqB29IOhy55ANOZ6Y1o6yjxgrizPcl0Eu7X0LUijXnC3W2pChXtTRiz2d0ItJhxw+scyGHOt1hQf3tq7tujec2ebRnqsvWNM8XOXFY1PMkIzHHO3HDM3WSXEoajtd+MitTh7TllLq80qprUqprWvXrp3Phy6qwDLjDgsL+uNc/QYYsCdDPYP+Dk+5GlgiGQJ25NjNEehAKEeHO05syHBCy3C4L4N0qvNFqHohacrh4pANOY5OakfZRyykjiMiS+I4exsyPDzWS3FoaXQO2ZDj8yOTzS68lQNZvGnDijk+wuuevmLTqub7P3rRzTEJwI4M2xkZL2J8poKT02V8f+8xHBidhlJL7wRk23MkZmi6Tn6qOgRgo/b+BgDhweROHmM9pVSgMDCQ7aw4tIxjZUZpt3MozjLuHDLKVKk1jjSf4pCe4xRzTNRYoVUMWDHQeVEBCHUOOVzkM51SKtg5NNh551Dj8UfHiwCA41OlWYuQaXHo3SL6DqFOrNeKQ4cdLg7Z4OGXWoWdq85fM+sUzzjXXLAWP/SLQj9+6QRuvuKc03J9NH/1usI3nziEL/3oAJ4fmcS23AQA4NNf3gXAe77+2pXn4DfevmnWCcpERN3opHNoF4ALRGSTiOQA3AzgntBj7gHwYfG8BcC4UuroAl9r4krVOup+kT6XSSGT7uQ/X2iZMYtDiWu3cyjOUuocskFg59A8xsqCu2oqbR5Jp9vYTOu//4qB+R1/HVxIzRyTMjFTRaXmfVEczGc67qZt4FJqMxzuZaxMKybpHUi0+B470FrvedV5qzv+uLdqj9114OSS7EYx0Xihgg9/6TH8x28+hedHJiMfc3hsBn9x/17c8Okf4sDo9CJfIRG5aM6fqpRSVRG5DcD98I6y/5JSao+IfMy//3YA98E7xn4fvKPsP9r4eBH5OwDvALBGRA4B+COl1BcX+l9kMQSPQJ/9TfA111wT+XEDgc4hFhaSFugcCuUYl+FS2VVji8DOoYjOobgch7VxpIkZPheTND4Tv4waiM8QCBaHXB4rM93xqWLz7aiTyoD2OeqdRscnuZQ6KXpRZ/2K2d1bc2WYS6dQrtUxPlPBVKk6r25OWhhKKex6uVUcuuJ1q2Y9Ji7HN5w1jMF8BlOlKkYmijh4cgbnrGYXX5KKlRo+8uXH8OTBseZt+UwKM6svxHBfFlelVuCZw+PN8ez9x6dx8+cfwd2/9TacPc/uP1p87V5TyQ4uZ9jRV3Cl1H3wCkD6bbdrbysAH4/52A/0coEmmS7pRYXZ/+k2b94c+XF6cYgjSckLFPlC38TGZZjPpJASoK6AcrWOaq3ececYLbypORYZx+U43N/KWy9O0OIbL+idQ7MLC3EZAsBgXts5xLGyxByf1I+xjx4pa5ejvsCaJ5YlQyk1Z+dQuwxTKcH6lf3NroXDp2Zw0bqhhb9QauvA6DRO+a+pKwayOP+MwVmPicsxnRJcds6K5mjZE6+eYnEoYZ+899lAYehj156Hj7/zvMD3O+VqHTt2H8Snvv0sipU6Xpso4bavP4G//9hV89rhR4uv3Wsq2cHlDPnT7TzMVNovox4ZGcHIyMis2/UChP45KBntOsDiMhSRwAhagTkmSi8IRI2VxeWod6hMcBwpUYGdQxGdQ3EZAsHMXT6tzHR6QWdtzEll7XJcy86hxI3PVJrdsAO5dOSIZ7sMgeCeosNj7p52ZbKfvtoqJFy2cUXgCPuGdjm++ZyV2uc6tfAXSB3b/fJJbP/Jq833/+i9F+MT178eQ33ZQIa5TAofesu5+NJHLm8Wg554dQxf/8kriVw3dW6u11Qyn8sZsjg0D3rnUNRY2c6dO7Fz585Zt+uLq6f5W+7EtcsxLsPwYwvsAEuUvoQ4aoQhLkd9rIydQ8kam2OsrN1zUR8rY5EvOXpxKK5zqF2O7BxKnn7C2PoV/3975x3nVn2m++enNr335plxwb03wGCKKQ7NdgCvSbK0TUg29W5JINm9m76fm2zYTTaXTS658RKWG0qWEjoEkmAwBo+7PbbHdXrvVaN27h+Sjn5nLB2dI50iad7v5+OPpdFpo2dUznPe93kzwpoKchoG1wtCodTmwFeZrOaMHh45HVfPCU02O8xtizCeH715Wrx945Iy3H9lnXg/nIZXzi/GV66fL97/2btnafhNghPtPZVIfFJZQzKHVDAVJXMoEnzl0CS9YZuOtHJIxaSrND7MmEw+MxmPMZA6N0PajuTzUfCmWQxPxh5IzVcakclnHny1j9pJZf51Qu2EVDlkDu1xhFGHWy+Vx9knMsfaQ4bOqproI+xnwo+9P901BpfHp8lxEeo40DyIhmZ/5ZbdyvA/b10S1rCdyReumYfKvHQAQP+4C7872KbrcRIEkbqQOaSCiRhNBZp0lViMR6k6iQQ/9p4qwMyFn1AVLpA6ElYLE5cXBGpJMpNogdRyFGbRlKtEgDd0IrWVyVHKrdNHlUOmIAmjjtEc4tdrp4llhuPy+HCqKzTNanlVnuptFGQ5UFPo19Hl9aEpwnQsQl+e3BdqCdu+qkpx9lO63YqHNocyUJ7c10JT5wiCiAkyh1TAGztqRvZKAqmpcshUBEGQGgsqqk5yuBBcMhXMJVrmkBy5VHWSEEgyh8IEUsvBm0NDk2QOmUUvZw6VxmAOleSki7epcsgcOobkJ5UpgdrKzOVMzxhcXn+lT3VBBgqy1L2fBllRFaoeOt4xosmxEcoZmXTjzROhDJMHNtWrWv/udTXiBN5zveM41ErtgQRBqIfMIRVEG58dCb46hSpOzGXa44Pb67+a4rBakG5XbvJJxmdPk6lgJpK2MpVjk3MplDoh4I05tW1l2Wk22K3+UvtJlxdOCog3BYk5lKveHMpNtyHN5v8aMunyUruuCbQPhQKkY60cqi4MmUrUVmY8JztHxdvLKtVXDQVZWpUr3m7sJHPIaN5s7BJNvuVVeVhSmRtlDSlZaTbctqJSvP/ykQ5Nj48giNmBurOqWU60E9ItW7aEXY9fdpwqTkxlLEpWTSQNZy4/SjqaypjEqL3UWJDTMTedxtknAnwgdbhpZXIaMsZQkOkQzYnBCRcq82M7sSVip2/MKd4u5aqAeKLpWJqbhrZBv6HQO+pEdsmlI7gJ/eDbymoimENyGgJAWU4abBYGj09A//g0nG6vqgsvRHzwRs6yqsiGQjQdl3LGUiNnOBHG8NrxUNXQtlWVYZeJpuG21ZV49oA/b+j1E9349u1LYaGx9gnBpMuDvrFpjDk9qF66AQKA4+0jyEyzoiQnTTIwhUh8or0Wkxkyh1TAGws5YV7ENTU1Ydfjl6V2JHORVH+FMYciaQhIJ12RyWceXp8g0ZHPggoip6NknD2ZQ6YxwgVS54WpHJLTEPC3lpE5ZB4erw8DE/6WPsaAouzwrSzRdCzJ5syhsWnMJXPIUCTTyiKYQ9E0tFktKM9LF7fVMTyFeaSjYfBGzlKZyqFoOi7lKlVOd4/C6xPEEemEvow63dh3vl+8f8vyirDLRdNwY30RirIcGJhwoW9sGkfah7EmwvQ6Ql8EQcD+i4P474Pt+PD8gMSID3FRvFWc7cD6ukJsW1WJG5eU02svwYn2WkxmqK1MBdFyTtra2tDWdumEAGk7EpkKZsLnDYWr/oqk4czlyeQzjzGnG8GcxZx0G2zWS9/G5HSkzCHzEQQh6ih7OQ0Bae7Q4ATlDhlN/7hLfB0WZjpgD/M6BKLryFcc9VLukKGMOt3ie2CazYKSCBPnomkI0MQys/D5BJzmwqPlWpGi6VicnSZmhzndPlzsn9DuQAlZPjjbL0YeLKvKjXixI5qGVgvDlsWl4v13TvZoe6CEInrHnHjwiQb8xeMf4XcH2y8xhkot4yi1jEt+1j/uwhsnuvGFpw7h9p9/gDM9FAqfyCj5XExWyBxSwWiUION3330X77777iU/z5G0I9HJqJmMO+UrhyJpOHN5yhwyjyGu4qQgQpCxnI55lDlkOmPTHnh9/i/CGXYr0myXVn/JaQhQKLXZ9IxyLWW54VvKgOg6lnFZRb3cNgn96ZhRNRRpZHY0DQGguiCUO9Q2OCmzJKEl7UNT4kXHwiyHbDC8Eh0XV4TMpVNd1FpmFO819Ym3r19YGnE5JRpevyi0/p6zfTJLEnrQOjCJHY99iD81SZ97m4WhKj8DiytysTmrG5uzurG0Mhe1RZli9l6Qk12j2PHYXjQ0Dxp56IQKlLwWkxVqK1OBxFhQNQJdWjkkCELEL2GEvoxKcqNUhuCmU+VQIiCdcqW+RzuPKodMp5+rECnOiW2yDo2zN5duzsgpjyGMOkhZXshY6h4hc8hIeBOHN3digSqHzOEkZ+AsrsiJ+7vl4opcvHemT9z27SvDZ98Q2iEIgsTE2XxZSVzb2zS/WMwAO9Exiv7xaRRHqAoktGV82oP7n9gvVgoxBuxcW4O/2FCD5VV5YoXtE0+cBwD85P6rAfgrAM/1jeOlwx3YvfcinG4fJlxe/NUTDXjlK1ehtijLnF+ImJVQ5ZAKomUORcJutSAjEM4oCDTO3kz4tr5clSPQJdlR1B5oGtHakaJBgdTm08+ZOZFaWaLBV41R5ZDx8JVD5XmRK4eiUc5VHXVT5ZChtHLmUKQwaqXUUOWQKfDVPYvK1U23Csfiihzx9mmqHDKEi/0T6AoY4zlpNqyqyY9reznpdsk29p0fiGt7hHK+/8pJXOjzt2M6bBbsvm89fnTXCqyZUxCx9RoALBaGy8py8I2ti/DSlzaJZt6o04P/8ewRsdKaIIyAKodUEC1zSI7sdBumAuOWx50e1eO3CW0Yi9IaKEcOVQ4lBHyQcX6EtjI5+PDj0SnS0Qz6uMqhEpk2CDn4AOQByhwyHL7Kp0ymrSwavDnUQ+aQofAVPnMK46scmlPEmUNDZA4ZxeluvnIofnNoSQUfSk2ZJ0bwIWfebJxbGDZHUS1Xzi/GgZYhcftUAaY/B1uGxElxAPDjO1fgukWRWwQjsag8F7++bx3u+uWHcHsFHG4dxu8OtGHXhjlaHi6hEEEQ0D/uQvvQJIYn3Zhye+H2+tA/7v8e+8KhdlgYg83KkGG3Ii/Djsr8DFTkpSdtlxA5FCqI11gInhCNOd1xXWklYoevFFFT/QVIp5VRxYl58FUi4UagR4N0NB9+BHqs5pCkcojMIcORtpXF/nkmaSsjc8hQ+AqfmjjNIaocMgfewFlUniOzpDLqi7PgsFng8vjQNeLE8KQrposwhHI+uhAyh66YV6zJNq+cV4R/f/csAODji1Q5ZAQ/fvO0eHvr0nJsX10V87ZW1uTji9fOx88CGv7bO2ewfXUV0u2X5jMS+nC8fQRPfdSCPzX1hh2WsdXhDxT/388dDbt+XoYdV80vxq4NNbhqfnFSGUVkDqlgLErm0NatWyOuyy8/SlUnpjEsqTq51FiQ05BffoTaWExjWBJIHd4cktORAqnNp2+cqxzKDm8syGkIAEVZVDlkJnyVT5nMxY5oOkorh6Ypk89A+AqfGpnMoWgaAkBpTppoKgxNujHmdKu+AEOoY3zag5YBv4ZWC8P80mzZ5ZXoaLNacFlZNk50+CuSTnWN4Yp5RfEfLBEWQRDw8cVQ6PDG+kLZ5ZVoCACravLF1+OFvgn0jjplBwcQ8XGwZVDU0WZh+NYti2WXV6LjF66Zh6f3t6J3bBo9o9N4/lA7Pr2xVpPjJSIz6fLgOy834rkD7bLL7XfLV3KNTLnx2vEuvHa8C5svK8FP7l4hmc6ayJA5pBBBEGZMK7v0S095eXnE9XPphDQh4MOMw026ktNQmnFCGpoFXzmUF+GKppyOvDk0TDqagpK2MjkNAaAgiyqHzKRrOGQOVeZFzquJpmNWmg256TaMOj1weXwYmHBReKoB+HyCaCwA0rawmUTTEPBnZtQUZOB8IG+jZWASy6ry4j9QIiJNXNXQvJKsqFUFSnQE/G0tQXPodPcomUM60jwwKX4e5qTborYGKtUw3W7Fqpp87A8YFg3NQ7h1RUV8B0tEZPcHzeLt7aurZN9PAWU6Zjis+NzVc/HD108BAP5zbzM+tWEOXTzRkZEpN+799cc42j4i+XmWw4raoiwUZTuQ5bDBbrPAygDGGARBgE8A3F4fJl1eDE+60DI4KTm/2HOmDzse+xDPPHR53FW6RkDmkEImXF64vf5AsDSbBRmOSz+EL1y4AACYO3fuJY9JT0jpRMYs+DDjcJVDchryQcajTje8PgFWC71JGw0/mao4O7w5JKdjEXfi2T9+aakooT+8ORSLhoC0coh0NBZBEMRpLABQmR/5alg0Hf3rZ2A0cKLbMTRF5pAB9I5NY9rjA+D/LJQL91eiIQDUFmWJ5lDrIJlDeqM2jFqpjnx7Go2z15cGrmpoXW1B1O+USjUEgPV1BZw5NEjmkE70jU3jrcZu8f5nr66Puo5SHXdtqMFP3zmDCZcX53rH8fHFQVw+l8xaPfB4ffjrpw5KjKGbl5bhoc1zsarqA8nrAAAgAElEQVTm0temnIaCIOBMzzh+s68ZT+9vhSAAHcNTuG/3frz05U2SeItEhMwhhfBXpsNVnADAnj17AIT/Q5HmY1C1glkMRQkzltPQZrWIV7gFwe8w8+O0CWPgjYBIJ5FyOuZn2GG1MHh9AsacHkx7vEizUR+3kUjayiJUDslpCPhNvuC43qFJN5xuL/XjG8TghEs0FnLSbbLtQ9F0BPxj0IPZKZ3DU1gZ57QeIjrNAxPi7WhjkpVo6N9O6Ioov31CH051qQujVqojhVIbR0NzyBxaH6WlDFCuIQCsrysE4B+Zvp8zoQhtefFwOzyBaWLragsUGbVKdcxJt2P76ir8v49bAQC/O9BO5pBO/OLP5yXh8N/bthT3XlEXcXk5DRljWFieg3/esRw3Li7D55866G/x7J/AP710Aj/dtVrz49cSGmWvkGhZNdHgs1Gocsg8+Oc+Fh15Q4l0NAfeHCqKUHUih8XCJKYeX4lEGIMW08qsFiaZksVPzyL0pZNrKavKj28EOuCvHArCVyQR+tHKtZTValTmzm+npZ9CqfWGN4eWVMY/qSzI4hnmkMfr02zbhJTgRDEA2FAX3RxSw9raAgSLHU53j0qG6hDa8cKhDvH2znU1mm+f3+YbJ7ow5fJqvo/ZTnP/BH7+x3Pi/a9tWSBrDKnhukWl+MndK8X7Lx3pxPtn+zTZtl6QOaQQPucklmqRfMqrSQikYcbqdeRNPtLRHPjw4aKsGMegkzlkGj6fMKM1MPYWIn7qY+cImQpG0TEcOvGv0GDyJm8O8ePVCf240B+q7Kkrlq8cUgq/nYtUOaQrPp8gqepZXBH/pLIgBVkOMSje5fHhYj9pqQf949Pic+uwWjRvw8xJt2NhoIrFJwCHW4c13T4BnO0ZE1+HaTYLPrFcWSaUGlZU52Fuif+9ddLlxbunezTfx2znR2+ehitggq+sycdXtyzQdPt3rKzE9lWV4v0fvHoKvkC1WSJC5pBChqIEGUejIIs3Fehk1Ay8PmmoOJ8hpJQ8qhwyFbfXJxp8jMVm1ALSahXKqzGW4Sm3WIKdm26LqxWMNyb4gGRCX1q5UeVzNKg64SdltQ9RxYkRXOgbF2/PK9HGHJpXEpqWdaGPDAU9aR6YwGSggqA426H5FBy+Eqmxk3KH9OBAc6hqaHl1ni5t0evrCkL746qUCG149ViXeHvL4lJdJjQyxnDHypCx8PrxLpmlCbWc7BzFGydCmVHfu2OpLnmy37plMTIDecVNPWN4/UTi6kjmkELibSuTtiNRxYkZDE26IASM2rwMO2xW9X/+ND7bXAa557ww0xHzGzivYx+ZQ4aiRUtZEL7ipHuUzCGj4M0hLSZv8AYTv21CP/jKobnF8iPQlVKZnwGHzf+52j8+TZNZdYQ3bJZWah/8vVRiDo3ILEnEygE+b0jjlrIg67jt8vsjtOEN7gT/1uWVMkvGx21cmPgfT/dSa5mG/J8958XbW5eW65Z5WJqbjgc21Yn3f/Hn8xCExKweokBqhSipHLrtttsiri8dg06mghn0joZOSksjnJTKaQhIT2b5k1zCGHo4A0DOWIimYymXVUM6GksX1/4ld7U7moYAxNYHwB9kTBgDPwI9WpixEh1nmkOCINC4Xh3xeH2SzKG6YnmDT4mGgD8HrK4oE2d6/FVJF/omsIrCxXXhBGfYLFWYN6RUR/82Q4ZTcKw9oS0NXCXPutoCmSVDqNEQkFYOHW4dhtvrgz2GC6PEpVzoGxff69JsFly3qETxump1nF+agwWl2TjbOw6n24f3zvRh6zLtW9hmG10jU5Lqry9dN1/xumo1BIAHN9Xj1x9chNPtQ2PnKPZfHMTGBAwYp3cIhfAZGQURWlmKi4tRXFwc9jHKODEfvkKkNDe8sSCnISA1lchUMJ4uLnRYLuskmo68qdBFWTWGwmfKVBdEDjOOpiEgHaFOgdTG0aairUyJjnmZdrHN1+n20XurzjQPTIr5CuW56VFbIZRoGGR+aagK6UwPTbrSixMdvDmkrHJIjY7LqkKG04nOkYS9wp2sTLo8aOQ0XFenzBxSoyEAVORliEMDptxeahHUkLcaQ9k/11xWgkyH8noLtToCkJhBbzV2yyxJKOW3H7fCG4g52FhfiOXVyqswY9GwKDsNn1xTLd5/cl+LqvWNgswhhfSOhU48yiIYC01NTWhqagr7mKTiZHw6oYOoUpVevuokQgiunIYAVQ6ZTRdXHVKeF9lYiKYjZdWYBz+NqrogsrEQTUPA/8U3SCeZQ4bg8vjQxhl80cwhJToCM8KMKQBXV85yps2CsugtZUo1BIAFpaFg5LNkDumCIAg41h4yFlYoPKFRo2NVfoY4gGPM6UHzALV7asnh1mExe29hWY4kekIONRoG2VAfai3bf3FAZklCDW+fDBk0Ny1VV8UTi443c/t491QP3DRFMC7cXh+ebWgT7993ZZ2q9WPREADu46agvdXYnZDnkmQOKaSHa0nixyfz7Nu3D/v27Qv7WLrdirwM/wet1ydQXo0J8JVDkVqS5DQEpKZSIr6gU50uzuCrlKkciqZjBZdV00WmgqEorRyKpiEAVORTBZjRtA5OiFfaqvIzkOGQD1FVoiMgDTM+T2HGuhJshQCAy8qiT7lSquHM7fH7IbSjeWASY04PAP8EVbn3UR41OjLGsKI61BJ4rJ0mXWnJxxdCJs36emVVQ4A6DcXtc7lDH1+g3CEt6B11itPfrBaGLYtKVa0fi45LK3PF772jTg9pGSd/Ot2L3sB5XElOGm5cUqZq/Vg0BICF5TliG6nHJ+D5Q+2qt6E3ZA4phDcCIuXVRINfj69EIoxBmjkU22QPvh2NNDQevnWoPI4R2nzlEAUZGws/japK4UlNJIqz0mC3+rNphifdmHR54toeEZ1zvVyQsUZTrgBgLlc5dL6PTAU9Od0dai1ZqMAcUsPC8pDJx++H0I7DraGsmhXV+brlc63kKpJoDLq2fMSd2F+uc+bI5XO5yqHmQdHcJ2Ln7ZOhlrL1dQUR40a0hDEmqVDiK5cI9Tx3IGTK3LW22tAsrl0b5oSOo6Et4dp2yRxSgCAIEiMgVmOBrzjijQrCGHhjIVLmUDRKJVk1zoR7Qac6fNUJP6lKLcXZIVNhcMKFiWkyFYxAEATJiOvaovgmXVksTMxTAIDmfmp90JvzkhHo2ky5AoB5XFbN2V4yh/SEzx1ZojDMWCn1xdlIt/u/WvaMTqOfpkFqziHOHFozR3nViVpW1/JhxjQGXSsmXR4caQuZbXzblx7UF2eJF6fHnB6aPqcBfObPTUuMC4a+iatuebuxhyJKYqRvbBp/auoV7+9cV2Po/m9dXoHsNH9G1YX+CRxsSaz3VzKHFDA06Ybb638B5qbbopbRR4I3JHqoWsFw2riKBbmsEzly00PBqdMeH41BN5jmfm2MBauFoYb7G2geoDYWIxiccGFkyj/eOtNhlQSDxwpvUFzoJ1NBb051hYwFJS1JSuG3dbqLKk70YtTpRmsgUNxmYYoyh9RgtTAsKufHoJOWWnOwJWQsrKnVbxrcmpqQOdTYOUrjszWioXlIDIRfWJYT8wVnpTDGsGl+KDh37znKHYqHkSk39p0PPYc3Gzg1bH19IfIDWWDdo04cpXbPmHjxcLtYQbe+rgD1xdpVQSshw2HFbSsqxPu/O5BYrWVkDimAn8xSIROCG41Kbl2+AoIwBl7HmsLYdazhAljbBklHoxh1usWsLofNInk9xQIfgEsVJ8ZwoV/akqRFOwRfcXK+l0w+vTnZpU/VSX1xllhx0jtGFSd6cZwLMl5QloM0W2wXu+TgJ10da6OTFy0ZnnSJ7XpWC8OqGv3MobxMOy4LmIcenyCpWCJi5/0zfeJt3rTRE34/e7j9E+r5w8keMUx8RXWepHpZb+xWC25cHKoeeuMEtZapRRAEiRlzt8FVQ6H9hqaWvXqsM6E6GJTP3ZvFtHCmgly1wo4dO2S3M4dbl98moT8jk26MBgIc0+2WiNPKomkIADUFmeLV0PahSayt1a+smwghqRoqzITFEtlYUKJjXRFnDlHlkCGc5QJq5xbLVywo0dC/nZCOZ3tpOpKeTLo84iQxC1OWV6NUR6uFYWF5Lo4GzISTnaPYfFlJ7AdLhOUQV76+eo4yY0GphkFW1RTgqY9a/fsjQ0FT9l8cRLCbfVlVHnLS7YrXVasj4M/DCQaL7zs/YJiZkcrw7SxXL1D3fMai4cz9HGgZxJjTrepvhwjx2rFO8fYtyytkloxMrDoG9/m7g+2BY+nCI1sXyX4fJqQcah0WW9czHVbcaoKGgL8leG5JFi70TWDC5cVrx7sMb2+LBFUOKaCFOymtkyk9y8vLQ15e5JGi/MloC52MGkrroLSlLFLFQjQNgRkmH413NYxzXA6J3OsQUKZjPRemSyOXjeEEl3WwuEK+6kSJhgCwqIJaWIziaNuIeGI6vzRbUYu1Uh0BYDlXcXKEKk504SBn1qxVmFejRkMAkgsmh1qHKRdDQ/acDVV98EHDSlCro38fobDk989SxUm8NPdPiNMY02wWXDFPXRh1LBoC/szTJYHPSrdXwPtn+1VvgwAGxqexh3vuYjUWYtUR8FeBBadfdwxPSd7Tieg8vb9VvH37ikpkpcVWJxOPhoC/3fMvODOIPy6zIXNIAc2cATCnMHLl0IkTJ3DixImIj9dxpkJz/wSFGRtIE3fyP09mwk40DWeu30SmgmHwJ/7RjAUlOi4uD1U9nKSME0No7AiZQ3zrSTiUaAgAi8pzYA1cNbvYP4Expzu+gyQiwleBKK2YVKrjzG0mWkBjKuD2+rD/YmhKEj/iWg41GgL+7zrFgerckSk3vb9qhCAI+HNTyKC5RmVlnVodAWDTvGIEixKOdYxggNo944IPMr5qfjHS7eraOmPRMMgNi0Pj1t+kdqSYePlop5hVs7a2QBIzoYZ4dHTYLJKKpRcScBR6ojI04cIrR0OVX/dsnCOztDzxaBjkzrXV4nCcw63DONGRGGHxZA4poKkn9MVGLrTqwIEDOHDgQMTHS3LSxHTyUacHPTSxzDD4gFM+LHMm0TScuT4FpxoHP2FjaZSsEyU6Lq7IRbCA7HzfBJxuCtvUE6fbi1PdITN1WaX8FRclGgJAut2KBVzu0LH2xPhwTUU+uhAK4VQ6JUmpjgCwdk7IrDjYMgSXx6fuAAlZDrcOYzIQKlxTmCGpgpVDjYZAMACXrzihKgUtONU1JuZVZjmsWFerrnJIrY6AP3coaNoKgj9vhYidV7iWpK0xBBnHomEQPjj5nVM9mHQlTsZJMiAIAp5taBPvf3JNVczbikdHALiT2/fLRxIrryaRebqhFdOB7xVLK3Oxsjr2yp94NQT8k5N5o2/33otxbU8ryByKwrTHiyYVJzRyMMYkAZ7HE8QhnA2c6uarTuKbsHNZWY5oKlzsn6AJHgbg9vokQarRzCElZKXZRLPX6xPIVNCZQ62hk/25JVkoyHJotu11dSGjgjcwCO2YdHnw8YVQ1cmVOmSP1BRmiOGe49MeyqvRmLclVQv65jldxf19/OEkVSlowaucsbBlcRkcNmO+wt+8NGQqvHqsy5B9piKnu0dxosP/XdRhs+CmpcZNuQKAJRW5YuX7pMtL1UMq+fjiIE4HzgfT7RbcvrLStGNZW1sgajnh8uJ5qh6KitPtxe4PmsX7D2yq12QoSrzcf2WdePvlI53oHDZ/0BGZQ1Fo6h4Tx9jPKcxEXmZ8AW7Lq0Lm0nEaQWgILo8Ph1tDz/XSOAw+wD+CMFip4BMocNMIjrUPYyJgwlXlZ2g2HWJjfejK695zdHVbT/jqgStV5ixE48p53CQWqlLQhT1n+sTxywtKs3WZ0MIYwzULQ6YF34JBxIfXJ+D146ET+5uXlsksHT83LC4T2z0PtQ6jfYjy+eLB4/VJTgBvXRFb1kks3LK8Qrwgtvd8v2TyK6GcJ/e1iLdvXFIm5sYYBWMMd64NTUj6zb4WirdQwS/fOy/e/uSaauSaGOjNGMO9V9SJ93/1/gW4vVRpK8d/7WsRp6CW56bjDhPNPZ7VcwqwIdDi7fEJ+Pkfz5p8RGQOReUD7oRxpQYjQ/ltfEAno4ZwtF1aSh9rjzDPFVxII5kK+vPH06HpHpvmF2nm9vOmAr8PQlsEQcBr3BXnqxdoW7Vw5bwi2AInokfbhunkRQeeP9Qh3r5ZxyveW7ltv3K0i1rLNOK9M73oHHECAAoy7ZL3Pj0oyHJIJlslUthmMvLKsU4xiqA4Ow3XLyqNsoZ2VOZnYHPgPVsQgF9/kBitD8lE18gU/vtgyNz7y8trTTmOnetq4LD6T/2Otg1j7zmqtFXCxxcGxLwvxoAHN9WbfETAXWurURAoWGgbnKL3WBn6xqYlpstfXzvPsMpLJXx1ywLx9rMNbaZnDyXOM5Og8P3V12owVvfq+aFwv8Ntw+gddca9TUKeV7nwsU0afSHmWypePdZF01h0xOP14aXD0nJ6rdi8oET8onS8Y0TSQkpoxwfn+sWJgTlpNtVBqtHIz3TgKm5U72/pS5KmnOsdxzunQp+FO+LIWojGpvnFKMv1hxn3j0/jxcNULh8vPp+An717Trx/19pqQ74Yf2pDaBLLkx+2YGjCpfs+UxGn24t//cMZ8f5fXl4Lu9XYr+/3b6oTb/92fyua+2nirlIEQcAPXj0lGt0ra/IlVctGUpydhrvWhaqHvvdqI6Y9FI0gx8S0B9988bh4f/uqKszncg7NIivNhoc2zxPv/8ubTVShGQafT8DDzx/DqNOfy1RXlIldGxJjZHyQTfOLcHXgO6xPAP7uuaOm5kgp+nRhjG1ljDUxxs4xxh4J8zhjjP174PFjjLE1StdNZA61DontSDYLw7UL5U9odu7ciZ07d8ouU5DlECeECALwnx82a3KsRHj6x6fxAnfFO1oZoRINAf+UkNx0f7h46+Ak3qZMBd14uqENHYEe3PxMO65bGP2KqVId8zLt2MJN8Hj07SYqs9aYaY8X/+uN0+L9HWuqFE1oUaphkF3rQx/2v/mwGS0DdPKiBS6PD9968bg4wv7ahSWYV6L8i7FaHa0Whvu4Hvx/easJ3SN0ESUefrnnPI62+b/LOKwWPKDyqrdaDYPcsLgMcwO5GGPTHjz8/DFx0g+hDK9PwDdfOI62Qf9nYF6GXZJRoYZYdQT8F0eDle8ujw9ffvoQRmkyZFQEQcDjey7gNa6l8+GtC2Oufo5HwyBfuX4+Mh3+z+AzPeN45Pnj8FBLUlgmpj340m8P4UKf//tElsOKr9+8MO7taqEjADywqU6chD027cGDTzSgh4oORFweH775wnFJZ8D3ty9Dmk3dlMBwaKUh4G8T/O4dS5Fu99syTT1j+Px/HTTtPdYWbQHGmBXAYwBuBNAOoIEx9rIgCCe5xT4BYEHg30YAvwCwUeG6EXG6vXB7fRAA8YsphOB//huCIP5IPKkL3eeWFRDxMWHGY26PD2d6xvCD106Jx3LbigoUBUazRiIzU1m70r1X1OHjwDjZX+25gKJA+XVhlgNp3NU8Bha84f9PelcT0u1WQ65AuTw+OD1e/3MtXKqfIAiizkG9+Pu8RoLA3ecen7mdtqFJ/PSdsxgLuK/zSrJw+Vz5rBOlGqbbrbh7XY1YXv3IC8cxNOnGmjkFKMi0w2phsDAGxgI6MgRua0uWwwaLxfhAtWmPFy4P99oM6OATlGkpAGK1VSQNx50e7D3fj5++EyoF/exV9YqueCvVEQA+f808vBEIZnz7ZA+++swR3LOhBtX5mUh3WJBmtSoSLtp3vZkPZ9itsBl89TccHq8PU26vKi19wgy9uNejL7COx+vDud5x7N57EY2doRDOhzbPVXRcajQEgJuWlGNhWQ6aesYw6fLinsc/whevm48V1XkoyUmDzWKBzcJgsTBo8ZLJMTFvQI7ga1Mtos6CAJfHh5EpN5p6xrD7g4s4FLhIYrUw/P1N6r4Yq9UR8H9GPvlhC7pHnegfd+GT/7EXX7xuPlbV5KMo24F0mxUWC4v5PdWs9021BF+bSpDo5/VhzOlB68Akfn+kAy8dCVVefm5zPSpV5kXFoiEA2KwW/OOti/HgE/6JLm+f7MGux/fhL6+ow8KyHORn2uGwWlRpyRgTp74mGy6PL2KVhgBA8AFeQcC0x4vBCRdOd43hqY9bJJmJ37plUczZl7HqCPif9x9sW4Yd/7EXHp+AEx2juOVn7+OvrqrHqpp8lOSk+T/TLBYwS2Qts9NsCRECGy9enyCZ9hV8/Xl9fv2GJtw42zuGFw93iO1IALBzXXVcLZ3xaBikIi8DD29dhG+/3AgAePFwB870jOHTG2uxrCoXRdlpSLdZRC0Bv55WC0OmIzlfe9HweH2YcPk/OyddHnSPOHG4bRhPfdQiTggEgO9uW6b6/TMcWugI+M9FHt25Crse3we3V8CZnnHc9G978JnL5+DyuUWozM9ATroNmQ5b0r5vqsXnE3CqexR7z/XjmYY20dgDgIc2z9Us1kArDYPMLcnG9+5Yhm88fwyAv+J+y6Pv4TMba7FtVSXqZKala42Sv5QNAM4JgnABABhjzwDYBoA3eLYBeFLwuzMfMcbyGWMVAOoUrBuRb714XFL1YRYZdiu+dsNlUZc7cuQIAGDVqlWyy21dVo7Vc/JxuHUYHp8gMaGM5me7VmHbKv1aBIK8eLgdDz9/PPqCOvJPty+NekKgVEPAf/Xl90c60D/uwvCkG998wfjfr+EfbkBJjrxpqQc/frPJ8NyBBaXZePAqZVe81ei4qiYf92yYI/Zrv3K0E69wrYh68exDl2NjFLPSCP54uhcP/ddBQ/b18NZFqC5Q9oGqRkMAsFgYfnzXCtz9y31weX3oHHHiH186EfOxymG3Mpz94S26bDte/u/7F/EvbzXpsu2/uWEBllWpC/RXqyPgP4H8150rce/u/fD4BM21fP8b12mSPac3R9uHcecv9mm2vQ11hZJsA6XEomGQ6xeV4cFN9eKI3obmITQ0xz7EoaYwA+9/4/qY1zeT5w+1x/U94Z4NNdi5LvZ2iHh0BIDl1Xn43rZl+FagxaZ9aArffUXR13mRsz/8BOzW5DeHWgYmcP2j76laZ31dAb57x7K49huvhkHuvaIWp7tH8fR+/2j2xs5RUddIbKgvxHOfvyKu/SYqrx3vwteeOSK7zFevn4+7uEDveNBKR8A/uewnd6/E3zx7BD4BGJly47E/ncdjfwoFaGc5rGj83ta495UM7Dnbh/v/s+GSn39yTRUe3rpIs/1oqWGQnetr0DPqxKOBNuK+sWn82ztn8Nv9Ldj3yBbDLmopuWxdBaCNu98e+JmSZZSsCwBgjD3EGDvAGDvQ19cXbhFTSLdb8PN7Vosjr+U4cuSI+Mcih9XC8Nin1ohjCFMFOQ2Z5jUzyrFaGL5z+xJFOSdKNQT8OSe771+PIg1HcicCSl6LRqu5oDQbu+9fr/iqlRodAeA7dywxdSyp1qh5PzXiKq6FAX9342V4kMutiIZaDQF/lsPj9641fAqMXiTK56LNwvD1mxfiS9fNV71uLDoC/ly3X927TgzcTFYSRcNbl1fg1/evi6mcPlYNg/zjrYvx1S0LxND4ZMRMHe1Whq9tWYAfbl8e1/t1vDoCwKc2zsG/37M6Kd9jzdTwng1z8OSDG5HhiK+dRQsNAf/n/g+3L8c3ti6UdCwkA3roKPccFGTa8ejdK/G3Kqtm5dBKxyDbVlXhqc9uxJwIFz0yE6xqSM/X4vq6QslnTU6aDd++fQkevXulOEFTC7TWMMhXtizALz69RsxeBIDbV1QaWu2s5K8l3NHMbBqPtIySdf0/FITHATwOAOvWrRMAf7lcsAyOcXsJbjT4Icm3W4k/E5cJHeLMtqzQfXbJNiry0rF6Tj7uvaJOlyuMlfkZePUrV+PZhlbsOduP5v4JjDo94ijCmS1yM9vitELLF0o4DYPYbf5S8KCOEq24+wxBXRj385C5xMKty0Ia8tvJy7RjZXU+dm2owaLyXM1+T54V1fl452+vwX8fbMefz/Sie8SJ4Um32Frj8wUa6ATttQOitzLFgpyOQdLsFmQ5rOJzDQaujS66liywfPB3CKehw2rB3JIsXLuwBNtXV2nSIxyJNJsVP79nNe6/shbPNbTjVPcoBidccLojtwFIiCJuuIe1fO1dsj8FGgaxWZlES78eUi0sEXS0sEtfi5bAdiyMoSzX/z5697oaRQa7Fly7sBTvff1avHi4A3vP9aNtcAqDky74fAK8ggCvV4j7tWgz6Mq3Gh2DOKyWmMvHg9o6bP5tVBVkYO2cAty5thq1RcZfzLhuUSn+/PfX4YXD7dh7bgAtAxMYnnLD5fGF3lsTnFg0DGJR2UIV1M9utSArzYby3HQsrcrFbSsqsba2QNVxa4nFwvC3N16GO9dU4YVDHTjUOoSO4SmMTvm/86jRMsuktpZ4dAxis8jryZj/cyHNZkFuuh01hZlYW1uA7aurUKVBK4tW3LGyEtdcVoJXjnZi3/kBXOyfwNCkC063Fx5fqM040dBCwyDhXpsMgNXq/+6Sm2FHVX4GVtXk4/aVlQkRYDwTi4Xhi9f6q2FePtKJhuZBtAxMYmTKLWrJf3fNUJAVaARa6hgk3W5FTpoNDpsF6XYrirMdmFeSjSvmFeGW5RXISjBzJRxXzivGu393Dd491Yv3zvThbM8YesemMenyJNxFbD00DJKVZsP1i0rhsFlw9YJifGJ5BXITNAYgEp9YXoHrFpXiDyd78Psjndi+Wv8OHx4lf+3tAPg61moAM3suIi3jULBuRP55x3L8847lShdPOjIcVty/qR73J8BIRL3ZsboaO1ZrU46ZaBRkOfC5zXPxOYVZKqnA129ehK/frF15ZqKwtrYQa2vNmSJiFtctLE25cuP8TAce2FSvOng3FUi196K8TPus1XL1nAKc+Jyo1RQAAAbCSURBVO7NZh+GZtQWZeFvbozeop+q3L2uBnfH0RaWSORl2PGZy2vxGZNGsptNXXFWyrw2S3PS8dmr5+KzV6fO54Zarl1YiuMpoKfdasHWZeXYuqzc7EMxlcfvXWf2IcRNut2K21dWmtLVoKSWsAHAAsZYPWPMAWAXgJdnLPMygHsDU8suBzAiCEKXwnUJgiAIgiAIgiAIgiAIk4haOSQIgocx9mUAbwGwAtgtCEIjY+wLgcd/CeB1ALcAOAdgEsADcuvq8psQBEEQBEEQBEEQBEEQqlHURCkIwuvwG0D8z37J3RYAfEnpuqnKpz/9abMPgYgT0jA1IB2TH9IwNSAdkx/SMDUgHZMf0jA1IB2Tn1TWMPETtpIIuz25Aq+ISyENUwPSMfkhDVMD0jH5IQ1TA9Ix+SENUwPSMflJZQ2Ta35hgtPQ0ICGhgazD4OIA9IwNSAdkx/SMDUgHZMf0jA1IB2TH9IwNSAdk59U1pDMIQ1pbGxEYyNFKiUzpGFqQDomP6RhakA6Jj+kYWpAOiY/pGFqQDomP6msIZlDBEEQBEEQBEEQBEEQsxgyhwiCIAiCIAiCIAiCIGYxZA4RBEEQBEEQBEEQBEHMYsgcIgiCIAiCIAiCIAiCmMUwQRDMPoZLYIz1AWjRYFPFAPo12M5s2We/IAhbNdgOaWjufknH5N8naWjePrXcL+mY/PskDc3bp5b7JR2Tf5+koXn71HK/pGPy75M0NG+fWu43rI4JaQ5pBWPsgCAI62ifyctsej5JR9pnIjObnk/SkfaZyMym55N0pH0mMrPp+SQdaZ+JzGx6PvXeL7WVEQRBEARBEARBEARBzGLIHCIIgiAIgiAIgiAIgpjFpLo59DjtM+mZTc8n6Uj7TGRm0/NJOtI+E5nZ9HySjrTPRGY2PZ+kI+0zkZlNz6eu+03pzCGCIAiCIAiCIAiCIAhCnlSvHCIIgiAIgiAIgiAIgiBkSDlziDH2HcZYB2PsSODfLdxj32SMnWOMNTHGbtZ4v1sD2z3HGHtEy22H2VczY+x44Pc7EPhZIWPsD4yxs4H/C/Q8Br1JdR1Jw+TXMLAv0jHJdSQNk1/DwL5IxyTXkTQkDZMF0jH5dSQNk19DgHTURUdBEFLqH4DvAPj7MD9fAuAogDQA9QDOA7BqtE9rYHtzATgC+1mi4+/YDKB4xs9+DOCRwO1HAPzIbC1IR9IwlTUkHVNDR9Iw+TUkHVNDR9KQNEyWf6Rj8utIGia/hqSjPjqmXOWQDNsAPCMIwrQgCBcBnAOwQaNtbwBwThCEC4IguAA8E9ifkWwD8JvA7d8A2G7w/o0ilXUkDePHbA0B0lELzNaRNIwfszUESEctMFtH0jB+SEPjIB2TH9IwNSAdYyRVzaEvM8aOMcZ2c6VWVQDauGXaAz/TAj23HQ4BwNuMsYOMsYcCPysTBKELAAL/l+q4f6NIZR1JwxDJqiFAOqaCjqRhiGTVECAdU0FH0jAEaZj4kI7JD2mYGpCOGmLTcmNGwRh7B0B5mIf+AcAvAHwf/ifz+wAeBfAgABZmea1Gtem57XBsEgShkzFWCuAPjLHTOu5LN2a5jqShlGTUECAdZ5KMOpKGUpJRQ4B0nEky6kgaSiENTYR0TH4dScPk1xAgHY3WMSnNIUEQblCyHGPsVwBeDdxtB1DDPVwNoFOjQ9Jz25cgCEJn4P9extiL8Je39TDGKgRB6GKMVQDo1Wv/WjGbdSQNk19DgHRECuhIGia/hgDpiBTQkTQkDRMJ0jH5dSQNk19DgHQM/G+YjinXVhZ4koLsAHAicPtlALsYY2mMsXoACwDs12i3DQAWMMbqGWMOALsC+9McxlgWYywneBvATfD/ji8DuC+w2H0Afq/H/o0ilXUkDZNfQ4B0RAroSBomv4YA6YgU0JE0JA2TCdIx+XUkDZNfQ4B0hA46JmXlUBR+zBhbBX95VzOAzwOAIAiNjLHnAJwE4AHwJUEQvFrsUBAED2PsywDegj/BfLcgCI1abDsMZQBeZIwBfv1+KwjCm4yxBgDPMcb+CkArgLt12r9RpLKOpGHyawiQjqmgI2mY/BoCpGMq6EgakobJBOmY/DqShsmvIUA6aq4jEwQ9IwAIgiAIgiAIgiAIgiCIRCbl2soIgiAIgiAIgiAIgiAI5ZA5RBAEQRAEQRAEQRAEMYshc4ggCIIgCIIgCIIgCGIWQ+YQQRAEQRAEQRAEQRDELIbMIYIgCIIgCIIgCIIgiFkMmUMEQRAEQRAEQRAEQRCzGDKHCIIgCIIgCIIgCIIgZjFkDhEEQRAEQRAEQRAEQcxi/j9XknKIOaAR0wAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, len(diamondVals), sharey=True, sharex=True)\n",
    "for index, param in enumerate(diamondVals):\n",
    "    ax[index].plot(diamondVals[param][0], diamondVals[param][1], lw=3)\n",
    "    ax[index].set(title=f\"{param} bins\")\n",
    "    ax[index].axvline(x=0, ls=\"--\", color=\"grey\")\n",
    "sbn.despine()\n",
    "f.set_size_inches(20, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load pairs\n",
    "Convenience function to load pairs data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairDir = (\"/groups/gerlich/experiments/Experiments_004700/004755/\"\n",
    "           \"Sequencing_data/exp4661/pairs/\")\n",
    "\n",
    "# Prometaphase rep1 is from experiments 4638 and rep2 is from exp4615\n",
    "pathMap = {\"G2\": pairDir,\n",
    "           \"Meta_Rep2\": \"/groups/gerlich/experiments/Experiments_004700/004755/Sequencing_data/exp4727/pairs/\"}\n",
    "barcodeMap = {\"G2\": \"G2\",\n",
    "              \"Meta_Rep2\": \"GTTTCG\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = {sample: {interType: HT.loadPairs(os.path.join(pathMap[sample], f\"{barcodeMap[sample]}.{interType}.pairs.gz\"))\n",
    "                  for interType in [\"cis\", \"trans\"]}\n",
    "         for sample in [\"G2\", \"Meta_Rep2\"]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                   readID chrom1   pos1 chrom2       pos2  \\\n0  A00700:28:H7W7NDRXX:2:2261:24117:34616   chr1  61777   chr1      62223   \n1   A00700:32:H7MWFDRXX:2:2248:9272:24126   chr1  79484   chr1      79691   \n2  A00700:28:H7W7NDRXX:2:2131:17481:25128   chr1  85502   chr1      86128   \n3  A00700:32:H7MWFDRXX:1:2251:17472:33223   chr1  86862   chr1  157378921   \n4    A00700:32:H7MWFDRXX:1:2139:3125:7467   chr1  88057   chr1      88491   \n\n  strand1 strand2 pair_type  mapq1  mapq2  n_AG_muts_phred0_1  \\\n0       +       -        UU     32     60                   0   \n1       +       -        UU     37     36                   2   \n2       +       -        UU     37     43                   0   \n3       +       -        UU     43     60                   0   \n4       +       -        UU     33     34                   0   \n\n   n_AG_muts_phred0_2  n_AG_muts_phred30_1  n_AG_muts_phred30_2  \\\n0                   0                    0                    0   \n1                   2                    2                    2   \n2                   0                    0                    0   \n3                   0                    0                    0   \n4                   0                    0                    0   \n\n   n_TC_muts_phred0_1  n_TC_muts_phred0_2  n_TC_muts_phred30_1  \\\n0                   3                   5                    3   \n1                   0                   0                    0   \n2                   4                   2                    4   \n3                   2                   7                    2   \n4                   4                   3                    4   \n\n   n_TC_muts_phred30_2  n_matches_1  n_matches_2  \n0                    5          246          250  \n1                    0          208          210  \n2                    2          250          250  \n3                    7          250          250  \n4                    3          250          250  ",
      "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>readID</th>\n      <th>chrom1</th>\n      <th>pos1</th>\n      <th>chrom2</th>\n      <th>pos2</th>\n      <th>strand1</th>\n      <th>strand2</th>\n      <th>pair_type</th>\n      <th>mapq1</th>\n      <th>mapq2</th>\n      <th>n_AG_muts_phred0_1</th>\n      <th>n_AG_muts_phred0_2</th>\n      <th>n_AG_muts_phred30_1</th>\n      <th>n_AG_muts_phred30_2</th>\n      <th>n_TC_muts_phred0_1</th>\n      <th>n_TC_muts_phred0_2</th>\n      <th>n_TC_muts_phred30_1</th>\n      <th>n_TC_muts_phred30_2</th>\n      <th>n_matches_1</th>\n      <th>n_matches_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A00700:28:H7W7NDRXX:2:2261:24117:34616</td>\n      <td>chr1</td>\n      <td>61777</td>\n      <td>chr1</td>\n      <td>62223</td>\n      <td>+</td>\n      <td>-</td>\n      <td>UU</td>\n      <td>32</td>\n      <td>60</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>246</td>\n      <td>250</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A00700:32:H7MWFDRXX:2:2248:9272:24126</td>\n      <td>chr1</td>\n      <td>79484</td>\n      <td>chr1</td>\n      <td>79691</td>\n      <td>+</td>\n      <td>-</td>\n      <td>UU</td>\n      <td>37</td>\n      <td>36</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>208</td>\n      <td>210</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A00700:28:H7W7NDRXX:2:2131:17481:25128</td>\n      <td>chr1</td>\n      <td>85502</td>\n      <td>chr1</td>\n      <td>86128</td>\n      <td>+</td>\n      <td>-</td>\n      <td>UU</td>\n      <td>37</td>\n      <td>43</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2</td>\n      <td>4</td>\n      <td>2</td>\n      <td>250</td>\n      <td>250</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A00700:32:H7MWFDRXX:1:2251:17472:33223</td>\n      <td>chr1</td>\n      <td>86862</td>\n      <td>chr1</td>\n      <td>157378921</td>\n      <td>+</td>\n      <td>-</td>\n      <td>UU</td>\n      <td>43</td>\n      <td>60</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>7</td>\n      <td>2</td>\n      <td>7</td>\n      <td>250</td>\n      <td>250</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A00700:32:H7MWFDRXX:1:2139:3125:7467</td>\n      <td>chr1</td>\n      <td>88057</td>\n      <td>chr1</td>\n      <td>88491</td>\n      <td>+</td>\n      <td>-</td>\n      <td>UU</td>\n      <td>33</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>250</td>\n      <td>250</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "source": [
    "pairs[\"G2\"][\"cis\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downsample pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairsNew = HT.downSamplePairs(pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get pairing score\n",
    "Normalized counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genome-wide pairing score\n",
    "Normalized to genome-wide median and not normalized. Moreover, pairing score with and without diagnoal removed is shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairingScoreGWNorm = HT.getPairingScore(c, windowsize=400000)\n",
    "pairingScoreGWNormWDiag = HT.getPairingScore(c, windowsize=400000, blankDiag=False)\n",
    "pairingScoreGW = HT.getPairingScore(c, windowsize=400000, norm=False)\n",
    "pairingScoreGWWDiag = HT.getPairingScore(c, windowsize=400000, norm=False, blankDiag=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  chrom    start      end  PairingScore\n0  chr1  2700000  2800000      0.023489\n1  chr1  2800000  2900000      0.021729\n2  chr1  2900000  3000000      0.021729\n3  chr1  3000000  3100000      0.021729\n4  chr1  3100000  3200000      0.021729",
      "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>chrom</th>\n      <th>start</th>\n      <th>end</th>\n      <th>PairingScore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chr1</td>\n      <td>2700000</td>\n      <td>2800000</td>\n      <td>0.023489</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chr1</td>\n      <td>2800000</td>\n      <td>2900000</td>\n      <td>0.021729</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chr1</td>\n      <td>2900000</td>\n      <td>3000000</td>\n      <td>0.021729</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>chr1</td>\n      <td>3000000</td>\n      <td>3100000</td>\n      <td>0.021729</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>chr1</td>\n      <td>3100000</td>\n      <td>3200000</td>\n      <td>0.021729</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "pairingScoreGW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
    "ax[0, 0].hist(pairingScoreGWNorm[\"PairingScore\"], bins=50, range=(-0.02, 0.02), density=True)\n",
    "ax[0, 0].set_title(\"Normalized without diagonal\")\n",
    "ax[0, 1].hist(pairingScoreGWNormWDiag[\"PairingScore\"], bins=50, range=(-0.02, 0.02), density=True)\n",
    "ax[0, 1].set_title(\"Normalized with diagonal\")\n",
    "ax[1, 0].hist(pairingScoreGW[\"PairingScore\"], bins=50, range=(-0.02, 0.02), density=True)\n",
    "ax[1, 0].set_title(\"Not normalized without diagonal\")\n",
    "ax[1, 1].hist(pairingScoreGWWDiag[\"PairingScore\"], bins=50, range=(-0.02, 0.02), density=True)\n",
    "ax[1, 1].set_title(\"Not normalized with diagonal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pairing score at specific regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions.columns = [\"chrom\", \"start\", \"end\", \"mid\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairingRegions = HT.getPairingScore(c, windowsize=400000, regions=regions, norm=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.hist(pairingScoreGW[\"PairingScore\"], bins=30, range=(0, 0.02), density=True, label=\"Genome-wide\", histtype=\"step\", lw=4)\n",
    "ax.hist(pairingRegions[\"PairingScore\"], bins=30, range=(0, 0.02), density=True, label=\"regions\", histtype=\"step\", lw=4)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get pairing score\n",
    "Obs/exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genome-wide pairing score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairingScoreGWObsExp = HT.getPairingScoreObsExp(c, expected ,windowsize=400000, norm=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(1 ,2)\n",
    "ax[0].hist(pairingScoreGWObsExp[\"PairingScore\"], bins=30,range=(-0.1, 2), density=True, histtype=\"step\", lw=4)\n",
    "ax[0].set_title(\"linear\")\n",
    "ax[1].hist(np.log2(pairingScoreGWObsExp[\"PairingScore\"]), bins=30,range=(-8, 2), density=True, histtype=\"step\", lw=4)\n",
    "ax[1].set_title(\"log2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}