{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating and Loading Data\n",
    "\n",
    "Damuta provides two classes for input data: `DataSet` and `SignatureSet`. \n",
    "\n",
    "`DataSet` ensures that a counts dataframe and sample annotation can be easily aligned via matching on sample ids. \n",
    "\n",
    "`SignatureSet` provides some simple methods for summarizing and understanding mutational signatures, as well for extracing damage and misrepair signatures from COSMIC-format signatures. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating Data \n",
    "\n",
    "We can simulate a dataset of mutation counts using the function `sim_parametric`. We will simulate `500` samples containing `10000` mutations each, with varying activities of `10` damage signatures, and `8` misrepair signatures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    },
    {
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       "      <th>simulated_sample_0</th>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
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       "      <td>29</td>\n",
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       "      <td>31</td>\n",
       "      <td>...</td>\n",
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       "      <td>162</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>59</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "      <td>76</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>simulated_sample_1</th>\n",
       "      <td>122</td>\n",
       "      <td>92</td>\n",
       "      <td>133</td>\n",
       "      <td>135</td>\n",
       "      <td>73</td>\n",
       "      <td>43</td>\n",
       "      <td>116</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>118</td>\n",
       "      <td>168</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>29</td>\n",
       "      <td>51</td>\n",
       "      <td>104</td>\n",
       "      <td>101</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_2</th>\n",
       "      <td>140</td>\n",
       "      <td>227</td>\n",
       "      <td>481</td>\n",
       "      <td>172</td>\n",
       "      <td>358</td>\n",
       "      <td>360</td>\n",
       "      <td>106</td>\n",
       "      <td>56</td>\n",
       "      <td>171</td>\n",
       "      <td>142</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_3</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>190</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>96</td>\n",
       "      <td>69</td>\n",
       "      <td>461</td>\n",
       "      <td>426</td>\n",
       "      <td>212</td>\n",
       "      <td>552</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_4</th>\n",
       "      <td>194</td>\n",
       "      <td>188</td>\n",
       "      <td>236</td>\n",
       "      <td>238</td>\n",
       "      <td>178</td>\n",
       "      <td>97</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
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       "      <td>134</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A[C>A]A  A[C>A]C  A[C>A]G  A[C>A]T  C[C>A]A  C[C>A]C  \\\n",
       "simulated_sample_0       55       16       63       87       20       23   \n",
       "simulated_sample_1      122       92      133      135       73       43   \n",
       "simulated_sample_2      140      227      481      172      358      360   \n",
       "simulated_sample_3       19       18       38       56        9      190   \n",
       "simulated_sample_4      194      188      236      238      178       97   \n",
       "\n",
       "                    C[C>A]G  C[C>A]T  G[C>A]A  G[C>A]C  ...  C[T>G]G  C[T>G]T  \\\n",
       "simulated_sample_0      142       29       35       31  ...      157      162   \n",
       "simulated_sample_1      116       19       17       22  ...      118      168   \n",
       "simulated_sample_2      106       56      171      142  ...       24       23   \n",
       "simulated_sample_3       31       35       75       29  ...      387       96   \n",
       "simulated_sample_4       16       54       40       79  ...       18       95   \n",
       "\n",
       "                    G[T>G]A  G[T>G]C  G[T>G]G  G[T>G]T  T[T>G]A  T[T>G]C  \\\n",
       "simulated_sample_0       42       66       59       95       49       29   \n",
       "simulated_sample_1       21        4       92       29       51      104   \n",
       "simulated_sample_2       23       13       53       55       57       10   \n",
       "simulated_sample_3       69      461      426      212      552       43   \n",
       "simulated_sample_4       15        8       34        7       22      134   \n",
       "\n",
       "                    T[T>G]G  T[T>G]T  \n",
       "simulated_sample_0       76       50  \n",
       "simulated_sample_1      101       55  \n",
       "simulated_sample_2       43        4  \n",
       "simulated_sample_3       30      203  \n",
       "simulated_sample_4       80       63  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from damuta.sim import sim_parametric\n",
    "\n",
    "counts, params = sim_parametric(S=500, N=10000, n_damage_sigs=10, n_misrepair_sigs=8, seed=1992)\n",
    "counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, lets make use of `DataSet` to organize our data for us. \n",
    "\n",
    "We will simulate some metadata annotate our 500 samples with. This is most applicable for pan-cancer data, and necessary when fitting Damuta's `HierarchicalTandemLda` model, but the annotation slot of the `DataSet` is also useful for holding clinical metadata about each sample. \n",
    "\n",
    "Note: Pan-cancer data is not required. All Damuta models can just as easily fit a dataset where all samples come from the same tissue type. \n",
    "\n",
    "The `DataSet` class at minimum acts as a container for a pandas DataFrame of mutation type counts. The metadata annotation is also be a pandas DataFrame, in [tidy format](https://en.wikipedia.org/wiki/Tidy_data) (ie. each row is a sample, each column is a feature). The DataFrame index of both the count data and annotation data is the sample id. \n",
    "\n",
    "In this example, we simulated the counts dataframe, but in principle any trinucleotide count data that can be loaded with `pd.read_csv` can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>tissue_type</th>\n",
       "      <th>tumour_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>simulated_sample_0</th>\n",
       "      <td>Breast-AdenoCA</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_1</th>\n",
       "      <td>ColoRect-AdenoCA</td>\n",
       "      <td>metastatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_2</th>\n",
       "      <td>ColoRect-AdenoCA</td>\n",
       "      <td>metastatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_3</th>\n",
       "      <td>Breast-AdenoCA</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulated_sample_4</th>\n",
       "      <td>ColoRect-AdenoCA</td>\n",
       "      <td>metastatic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         tissue_type tumour_type\n",
       "simulated_sample_0    Breast-AdenoCA     primary\n",
       "simulated_sample_1  ColoRect-AdenoCA  metastatic\n",
       "simulated_sample_2  ColoRect-AdenoCA  metastatic\n",
       "simulated_sample_3    Breast-AdenoCA     primary\n",
       "simulated_sample_4  ColoRect-AdenoCA  metastatic"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import damuta as da\n",
    "\n",
    "# pick from 3 tissues \n",
    "tissues = np.array([\"Breast-AdenoCA\", \"Kidney-RCC\", \"ColoRect-AdenoCA\"])\n",
    "\n",
    "# pick from primary or metastatic tumour\n",
    "types = np.array(['primary', 'metastatic'])\n",
    "\n",
    "\n",
    "# randomly assign tissue type to samples\n",
    "annotation = pd.DataFrame.from_dict({\"tissue_type\": tissues[np.random.choice(3,500)],\n",
    "                                     \"tumour_type\": types[np.random.choice(2,500)]\n",
    "                                     })\n",
    "annotation = annotation.set_index(counts.index)\n",
    "annotation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pair the counts and metadata with the `DataSet` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulated_data contains 500 samples\n",
      "['simulated_sample_0', 'simulated_sample_1', 'simulated_sample_2', 'simulated_sample_3', 'simulated_sample_4']\n"
     ]
    }
   ],
   "source": [
    "simulated_data = da.DataSet(counts, annotation)\n",
    "print(f\"simulated_data contains {simulated_data.n_samples} samples\")\n",
    "print(simulated_data.ids[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading signature data\n",
    "\n",
    "Lastly, let's retrieve a set of mutational signatures from the [COSMIC database](https://cancer.sanger.ac.uk/signatures/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COSMIC contains 78 signatures\n"
     ]
    }
   ],
   "source": [
    "signatures = pd.read_csv(\"https://cancer.sanger.ac.uk/signatures/documents/452/COSMIC_v3.2_SBS_GRCh37.txt\", sep='\\t', index_col=0 , header=0)\n",
    "\n",
    "COSMIC = da.SignatureSet(signatures.T)\n",
    "print(f\"COSMIC contains {COSMIC.n_sigs} signatures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every COSMIC mutational signature can be re-written as a product of a damage signautre and misrepair signature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b4c3cf129a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from damuta.plotting import *\n",
    "\n",
    "plot_damage_signatures(COSMIC.damage_signatures.loc[[\"SBS2\", \"SBS5\", \"SBS6\"]])\n",
    "plot_misrepair_signatures(COSMIC.misrepair_signatures.loc[[\"SBS2\", \"SBS5\", \"SBS6\"]])\n",
    "plot_cosmic_signatures(COSMIC.signatures.loc[[\"SBS2\", \"SBS5\", \"SBS6\"]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The COSMIC signautres are a high quality reference set, as can seen in their high degree of separation (low cosine similarity between different signatures). However there is higher similarity in the Damage and Misrepair aspects of this signature set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Mutational signature similarity</th>\n",
       "      <th>Damage signature similarity</th>\n",
       "      <th>Misrepair signature similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3003.000000</td>\n",
       "      <td>3003.000000</td>\n",
       "      <td>3003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.188203</td>\n",
       "      <td>0.339645</td>\n",
       "      <td>0.684479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.188601</td>\n",
       "      <td>0.240654</td>\n",
       "      <td>0.196205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.194228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.047759</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>0.526309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.118585</td>\n",
       "      <td>0.286935</td>\n",
       "      <td>0.703217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.276046</td>\n",
       "      <td>0.518048</td>\n",
       "      <td>0.850554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.979184</td>\n",
       "      <td>0.980951</td>\n",
       "      <td>0.999338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mutational signature similarity  Damage signature similarity  \\\n",
       "count                      3003.000000                  3003.000000   \n",
       "mean                          0.188203                     0.339645   \n",
       "std                           0.188601                     0.240654   \n",
       "min                           0.000346                     0.001997   \n",
       "25%                           0.047759                     0.137200   \n",
       "50%                           0.118585                     0.286935   \n",
       "75%                           0.276046                     0.518048   \n",
       "max                           0.979184                     0.980951   \n",
       "\n",
       "       Misrepair signature similarity  \n",
       "count                     3003.000000  \n",
       "mean                         0.684479  \n",
       "std                          0.196205  \n",
       "min                          0.194228  \n",
       "25%                          0.526309  \n",
       "50%                          0.703217  \n",
       "75%                          0.850554  \n",
       "max                          0.999338  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COSMIC.summarize_separation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DAMUTA signatures\n",
    "\n",
    "We can also initialize a signature set from just the damage and misrepair signature definitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('/home/harrigan/damuta-package/docs/examples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "DAMUTA = da.SignatureSet.from_damage_misrepair(\n",
    "    pd.read_csv('example_data/damage_signatures.csv', index_col=0), \n",
    "    pd.read_csv('example_data/misrepair_signatures.csv', index_col=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b4c3e0e3a60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_damage_signatures(DAMUTA.damage_signatures.loc[[\"D1\", \"D2\", \"D3\"]])\n",
    "plot_misrepair_signatures(DAMUTA.misrepair_signatures.loc[[\"M1\", \"M2\", \"M3\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the signature set will also contain the outer product: all (unweighted) combinations of damage and misrepair signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>A[C&gt;A]A</th>\n",
       "      <th>A[C&gt;A]C</th>\n",
       "      <th>A[C&gt;A]G</th>\n",
       "      <th>A[C&gt;A]T</th>\n",
       "      <th>C[C&gt;A]A</th>\n",
       "      <th>C[C&gt;A]C</th>\n",
       "      <th>C[C&gt;A]G</th>\n",
       "      <th>C[C&gt;A]T</th>\n",
       "      <th>G[C&gt;A]A</th>\n",
       "      <th>G[C&gt;A]C</th>\n",
       "      <th>...</th>\n",
       "      <th>C[T&gt;G]G</th>\n",
       "      <th>C[T&gt;G]T</th>\n",
       "      <th>G[T&gt;G]A</th>\n",
       "      <th>G[T&gt;G]C</th>\n",
       "      <th>G[T&gt;G]G</th>\n",
       "      <th>G[T&gt;G]T</th>\n",
       "      <th>T[T&gt;G]A</th>\n",
       "      <th>T[T&gt;G]C</th>\n",
       "      <th>T[T&gt;G]G</th>\n",
       "      <th>T[T&gt;G]T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D1_M1</th>\n",
       "      <td>0.018900</td>\n",
       "      <td>0.006087</td>\n",
       "      <td>0.049454</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>1.931383e-02</td>\n",
       "      <td>0.016475</td>\n",
       "      <td>0.032477</td>\n",
       "      <td>0.012318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1_M2</th>\n",
       "      <td>0.045850</td>\n",
       "      <td>0.014768</td>\n",
       "      <td>0.119973</td>\n",
       "      <td>0.016409</td>\n",
       "      <td>0.007516</td>\n",
       "      <td>0.008502</td>\n",
       "      <td>4.685471e-02</td>\n",
       "      <td>0.039969</td>\n",
       "      <td>0.078787</td>\n",
       "      <td>0.029884</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.009065</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>0.002140</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>0.003789</td>\n",
       "      <td>0.027079</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.005662</td>\n",
       "      <td>0.006902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1_M3</th>\n",
       "      <td>0.001538</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>1.571713e-03</td>\n",
       "      <td>0.001341</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1_M4</th>\n",
       "      <td>0.001658</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.004339</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.000308</td>\n",
       "      <td>1.694615e-03</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.001623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1_M5</th>\n",
       "      <td>0.059906</td>\n",
       "      <td>0.019295</td>\n",
       "      <td>0.156753</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.009820</td>\n",
       "      <td>0.011109</td>\n",
       "      <td>6.121874e-02</td>\n",
       "      <td>0.052222</td>\n",
       "      <td>0.102941</td>\n",
       "      <td>0.039046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.000404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D18_M2</th>\n",
       "      <td>0.036187</td>\n",
       "      <td>0.088653</td>\n",
       "      <td>0.017828</td>\n",
       "      <td>0.138128</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.006050</td>\n",
       "      <td>1.689988e-03</td>\n",
       "      <td>0.084667</td>\n",
       "      <td>0.033732</td>\n",
       "      <td>0.060501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>0.001911</td>\n",
       "      <td>0.002536</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.001977</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D18_M3</th>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.002974</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>5.668964e-05</td>\n",
       "      <td>0.002840</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D18_M4</th>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.003206</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.004996</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>6.112254e-05</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D18_M5</th>\n",
       "      <td>0.047281</td>\n",
       "      <td>0.115832</td>\n",
       "      <td>0.023294</td>\n",
       "      <td>0.180473</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>0.007904</td>\n",
       "      <td>2.208080e-03</td>\n",
       "      <td>0.110624</td>\n",
       "      <td>0.044073</td>\n",
       "      <td>0.079049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D18_M6</th>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>9.836452e-07</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         A[C>A]A   A[C>A]C   A[C>A]G   A[C>A]T   C[C>A]A   C[C>A]C  \\\n",
       "D1_M1   0.018900  0.006087  0.049454  0.006764  0.003098  0.003505   \n",
       "D1_M2   0.045850  0.014768  0.119973  0.016409  0.007516  0.008502   \n",
       "D1_M3   0.001538  0.000495  0.004024  0.000550  0.000252  0.000285   \n",
       "D1_M4   0.001658  0.000534  0.004339  0.000593  0.000272  0.000308   \n",
       "D1_M5   0.059906  0.019295  0.156753  0.021440  0.009820  0.011109   \n",
       "...          ...       ...       ...       ...       ...       ...   \n",
       "D18_M2  0.036187  0.088653  0.017828  0.138128  0.001980  0.006050   \n",
       "D18_M3  0.001214  0.002974  0.000598  0.004633  0.000066  0.000203   \n",
       "D18_M4  0.001309  0.003206  0.000645  0.004996  0.000072  0.000219   \n",
       "D18_M5  0.047281  0.115832  0.023294  0.180473  0.002587  0.007904   \n",
       "D18_M6  0.000021  0.000052  0.000010  0.000080  0.000001  0.000004   \n",
       "\n",
       "             C[C>A]G   C[C>A]T   G[C>A]A   G[C>A]C  ...   C[T>G]G   C[T>G]T  \\\n",
       "D1_M1   1.931383e-02  0.016475  0.032477  0.012318  ...  0.000178  0.001199   \n",
       "D1_M2   4.685471e-02  0.039969  0.078787  0.029884  ...  0.001342  0.009065   \n",
       "D1_M3   1.571713e-03  0.001341  0.002643  0.001002  ...  0.000005  0.000032   \n",
       "D1_M4   1.694615e-03  0.001446  0.002850  0.001081  ...  0.000316  0.002132   \n",
       "D1_M5   6.121874e-02  0.052222  0.102941  0.039046  ...  0.000079  0.000531   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "D18_M2  1.689988e-03  0.084667  0.033732  0.060501  ...  0.000455  0.001164   \n",
       "D18_M3  5.668964e-05  0.002840  0.001132  0.002029  ...  0.000002  0.000004   \n",
       "D18_M4  6.112254e-05  0.003062  0.001220  0.002188  ...  0.000107  0.000274   \n",
       "D18_M5  2.208080e-03  0.110624  0.044073  0.079049  ...  0.000027  0.000068   \n",
       "D18_M6  9.836452e-07  0.000049  0.000020  0.000035  ...  0.000117  0.000298   \n",
       "\n",
       "         G[T>G]A   G[T>G]C   G[T>G]G   G[T>G]T   T[T>G]A   T[T>G]C   T[T>G]G  \\\n",
       "D1_M1   0.000683  0.000283  0.000313  0.000501  0.003581  0.000477  0.000749   \n",
       "D1_M2   0.005168  0.002140  0.002369  0.003789  0.027079  0.003609  0.005662   \n",
       "D1_M3   0.000018  0.000008  0.000008  0.000014  0.000097  0.000013  0.000020   \n",
       "D1_M4   0.001215  0.000503  0.000557  0.000891  0.006369  0.000849  0.001332   \n",
       "D1_M5   0.000303  0.000125  0.000139  0.000222  0.001585  0.000211  0.000331   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "D18_M2  0.001911  0.002536  0.000773  0.001977  0.000882  0.001352  0.000847   \n",
       "D18_M3  0.000007  0.000009  0.000003  0.000007  0.000003  0.000005  0.000003   \n",
       "D18_M4  0.000449  0.000596  0.000182  0.000465  0.000207  0.000318  0.000199   \n",
       "D18_M5  0.000112  0.000148  0.000045  0.000116  0.000052  0.000079  0.000050   \n",
       "D18_M6  0.000489  0.000649  0.000198  0.000506  0.000226  0.000346  0.000217   \n",
       "\n",
       "         T[T>G]T  \n",
       "D1_M1   0.000913  \n",
       "D1_M2   0.006902  \n",
       "D1_M3   0.000025  \n",
       "D1_M4   0.001623  \n",
       "D1_M5   0.000404  \n",
       "...          ...  \n",
       "D18_M2  0.000382  \n",
       "D18_M3  0.000001  \n",
       "D18_M4  0.000090  \n",
       "D18_M5  0.000022  \n",
       "D18_M6  0.000098  \n",
       "\n",
       "[108 rows x 96 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAMUTA.signatures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b4c3d53c9a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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emiOPPDIzZ87MUUcdlbvuuqut5u///u9z0kknZfbs2TnqqKPy7ne/O6effnpqa2u767YAAAAAAOhCPTrsbmpqytq1azNu3Li2Y9XV1Rk3blxWr169w3PWrFmTE044od2x8ePHZ82aNUmS1tbWPPjggxk8eHCmT5+eE044Ieeff36+//3vl+w+AAAAAAAorZKF3W+++WZWrlyZb3zjG9m2bVuSZPPmzXn11Vc7fI2XX345LS0t2+24rq2tzZYtW3Z4zpYtWzJw4MC3rG9oaMhrr72W22+/PSeeeGIWLlyYD37wg/mLv/iL/PjHP+7MLQIAAAAA0EP0KcVFn3/++Vx66aXZuHFjmpqa8oEPfCD9+/fP7bffnqamplx//fWlWLZDWltbkySnn356LrnkkiTJiBEj8thjj+Ub3/hG3v/+95etNwAAAAAAdk9JdnbfcMMNOfroo/PjH/+43UMfP/jBD2bVqlUdvs6AAQNSU1Oz3cMoGxoattu9/VsDBw7cbtf379cPGDAgffr0yZFHHtmu5sgjj8wLL7zQ4d4AAAAAAOg5ShJ2P/roo7nyyivTr1+/dsff9a53ZfPmzR2+Tr9+/TJy5MjU19e3HWttbU19fX2OPfbYHZ4zZsyY7QL1lStXZsyYMW3XHDVqVH7+85+3q/nFL36Rd73rXR3uDQAAAACAnqMkYXdra2vbuJDft2nTpuy///6duta0adNy9913Z/ny5dmwYUOuu+66NDY2ZvLkyUmS2bNn56abbmqrv/jii/Pwww9n4cKF2bBhQ2655ZY88cQTmTp1alvN9OnTc9999+Xuu+/Os88+m7vuuiv/9m//lo9+9KO7eccAAAAAAJRTSWZ2f+ADH8gdd9yRz372s23HXn311dxyyy05+eSTO3Wts846Ky+99FLmz5+fQqGQESNGZMGCBW1jSTZu3Jjq6t9l9mPHjs2NN96YefPm5eabb87gwYNz6623ZujQoW01H/zgB3PdddflH//xH/M3f/M3+eM//uPMnz8/733ve/fwzgEAAAAAKIeqYrFY7OqLbtq0KdOnT0+xWMyzzz6bo48+Or/4xS8yYMCALFmyJLW1tV29ZLfbtm1bjjvuuDz66KPp379/udsBAHbghbPPTtPatTut6TdyZA799re7qaO9QzneV99LAABgV0qys/sd73hH/uVf/iXf+c538uSTT+a1117Leeedl3POOSf77rtvKZYEAAAAAKAXK0nYnSR9+vTJueeeW6rLAwAAAABAm5KE3ffcc89OX580aVIplgUAAAAAoJcqSdh9ww03tPv6zTffTGNjY/r27Zu3ve1twm4AAAAAALpUScLuRx55ZLtjv/jFL3Lddddl+vTppVgSAAAAAIBerLq7Fho8eHBmzZq13a5vAAAAAADYU90Wdie/eWjliy++2J1LAgAAAADQC5RkjMkPfvCDdl8Xi8UUCoUsWbIkY8eOLcWSAAAAAAD0YiUJu2fMmNHu66qqqhx88MH5kz/5k1x99dWlWBIAAAAAgF6sJGH3//t//68UlwUAAAAAgB3q1pndAAAAAABQCl22s3vu3Lkdrv30pz/dVcsCAAAAAEDXhd0/+9nPOlRXVVXVVUsCAAAAAECSLgy777zzzq66FAAAAAAAdIqZ3QAAAAAAVLwu29n9h/7jP/4j9913XzZu3Jjm5uZ2r33pS18q1bIAAAAAAPRCJdnZ/Z3vfCcf/ehH88wzz+R73/te3nzzzTz99NNZtWpVDjjggFIsCQAAAABAL1aSsPu2227Lpz/96dx2223p27dv/vqv/zr3339/Jk6cmHe+852lWLJXaSkWS1ILAAAAAFCpSjLG5Je//GVOPvnkJEm/fv3y2muvpaqqKpdcckn+7M/+LB//+MdLsWyvUVNVlZmFQtb/wXiYPzSkb9/Mq6vrpq4AAAAAAMqnJGH3gQcemFdffTVJcsghh+Tpp5/OsGHD8sorr6SxsbEUS/Y665ubs7apqdxtAAAAAAD0CF0adj/11FMZOnRo3ve+92XlypUZNmxYJkyYkBtuuCGrVq3KypUrc8IJJ3TlkgAAAAAA0LVh94c//OGMGjUqZ5xxRiZMmJAkufLKK9O3b9889thjOfPMM3PllVd25ZIAAAAAANC1Yfddd92VpUuX5itf+Upuu+22nHnmmTn//PNz2WWXdeUyAAAAAADQTnVXXuy9731v5s6dmx/96Ef5zGc+k+effz5Tp07Nf/tv/y3/+I//mEKh0JXLAQAAAABAki4Ou39rv/32y5QpU3LXXXflu9/9biZMmJCvf/3rOfXUU3PFFVeUYkkAAAAAAHqxkoTdv+/www/P5ZdfniuvvDL7779/HnrooVIvCQAAAABAL9OlM7v/0COPPJKlS5fmu9/9bqqrqzNx4sScd955pVwSAAAAAIBeqMvD7s2bN2f58uVZvnx5nn322Rx77LH5zGc+k4kTJ2a//fbr6uUAAAAAAKBrw+5LL7009fX1GTBgQM4999xMmTIlRxxxRFcuAQAAAAAA2+nSsLtPnz754he/mFNPPTU1NTVdeWkAAAAAAHhLXRp233bbbV15OQAAAAAA6JDqcjcAAAAAAAB7StgNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFS8igi7lyxZktNOOy2jRo3K+eefn8cff3yn9ffdd18mTJiQUaNG5ZxzzslDDz30lrXXXntthg0blq997Wtd3DUAAAAAAN2lx4fdK1asyNy5czNjxowsX748w4cPz/Tp09PQ0LDD+sceeyyzZs3Keeedl3vuuSenn356ZsyYkaeeemq72u9973v56U9/mkMOOaTUtwEAAAAAQAn1+LB70aJFueCCCzJlypQMGTIkc+bMyb777pulS5fusH7x4sU58cQTc+mll+bII4/MzJkzc9RRR+Wuu+5qV7d58+Z89rOfzY033pi+fft2x60AAAAAAFAiPTrsbmpqytq1azNu3Li2Y9XV1Rk3blxWr169w3PWrFmTE044od2x8ePHZ82aNW1ft7a25i//8i8zffr0vOc97ylJ7wAAAAAAdJ8eHXa//PLLaWlpSW1tbbvjtbW12bJlyw7P2bJlSwYOHLjT+ttvvz19+vTJxRdf3PVNAwAAAADQ7fqUu4Hu9sQTT2Tx4sVZtmxZqqqqyt0OAMBuK7a0pKqmpmT1AAAAlaRHh90DBgxITU3Ndg+jbGho2G739m8NHDhwu13fv1//k5/8JA0NDTn11FPbXm9pacnnP//5LF68OA888EAX3wUAQGlU1dSkMHNmmtev32Vt3yFDUjdvXumbAgAAKJMeHXb369cvI0eOTH19fc4444wkv5m3XV9fn6lTp+7wnDFjxmTVqlW55JJL2o6tXLkyY8aMSZKce+657WaAJ8n06dNz7rnnZvLkySW5DwDo7Tqzo9ju485pXr8+TWvXlrsNAACAsuvRYXeSTJs2LVdffXWOPvrojB49OnfccUcaGxvbgunZs2dn0KBBmTVrVpLk4osvzkUXXZSFCxfm5JNPzooVK/LEE0/k+uuvT/Kb3eIDBgxot0bfvn0zcODAHHHEEd17cwDQS3R0B7LdxwAAAOyuHh92n3XWWXnppZcyf/78FAqFjBgxIgsWLGgbS7Jx48ZUV//uOZtjx47NjTfemHnz5uXmm2/O4MGDc+utt2bo0KHlugUAIHYgAwAAUFo9PuxOkqlTp77l2JI777xzu2MTJ07MxIkTO3x9c7opl5ZiMTUdfFBqZ2oBAONzAACgt6mIsBv2VjVVVZlZKGR9c/NO64b07Zt5dXXd1BUA7B2MzwEAgN5F2A1ltr65OWubmsrdBgDslYzPAQCA3qN61yUAAAAAANCzCbsBAAAAAKh4wm4AAAAAACqesBsAAAAAgIon7AYAAAAAoOIJuwEAeqFiS0tJagEAAMqlT7kbAACg+1XV1KQwc2aa16/faV3fIUNSN29e9zQFAACwB4TdAAC9VPP69Wlau7bcbbAHii0tqaqpKVk9AABUEmE3/JeWYjE1VVUlqwcA6God3aGf2KUPAMDeT9gN/6WmqiozC4Wsb27eZe2Qvn0zr66uG7oCANg5O/QBAOA3hN3we9Y3N2dtU1O52wAAAAAAOqm63A0AAAAAAMCeEnYDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAFByxZaWktYDAAD0KXcDAADs/apqalKYOTPN69fvsrbvkCGpmzev9E0BAAB7FWE3AADdonn9+jStXVvuNgAAgL2UMSYAAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAUGbFlpaS1gMAQG/Qp9wNdMSSJUvy1a9+NYVCIcOHD88111yT0aNHv2X9fffdly9+8Yt5/vnnM3jw4Hzyk5/MySefnCRpbm7OvHnz8sMf/jC//OUv079//4wbNy6zZs3KoEGDuuuWAACgTVVNTQozZ6Z5/fpd1vYdMiR18+aVvimAnWgttqa6qmP75zpTCwB7oseH3StWrMjcuXMzZ86cHHPMMbnjjjsyffr03H///amtrd2u/rHHHsusWbNy1VVX5dRTT829996bGTNmZNmyZRk6dGhef/31/OxnP8uVV16Z4cOH55VXXskNN9yQK6+8MsuWLSvDHQIAQNK8fn2a1q4tdxsAHVJdVZ0HGx7M1uatO607qO9BOaX2lG7pCQB6/EerixYtygUXXJApU6ZkyJAhmTNnTvbdd98sXbp0h/WLFy/OiSeemEsvvTRHHnlkZs6cmaOOOip33XVXkuSAAw7IokWLctZZZ+WII47ImDFjcs0112Tt2rV54YUXuvPWAAAAoGJtbd6ahuaGnf7ZVRgOAF2pR4fdTU1NWbt2bcaNG9d2rLq6OuPGjcvq1at3eM6aNWtywgkntDs2fvz4rFmz5i3X2bZtW6qqqnLggQd2Sd8AAAAAAHSvHh12v/zyy2lpadluXEltbW22bNmyw3O2bNmSgQMHdrj+jTfeyI033pgPfehD6d+/f9c0DgAAAABAt+rRYXepNTc35xOf+ESKxWLmzJlT7nYAAAAAANhNPTrsHjBgQGpqatLQ0NDueENDw3a7t39r4MCB2+3i3lF9c3NzZs6cmRdeeCELFy60q7uHaSkWS1oPAAAAAOxd+pS7gZ3p169fRo4cmfr6+pxxxhlJktbW1tTX12fq1Kk7PGfMmDFZtWpVLrnkkrZjK1euzJgxY9q+/m3Q/eyzz2bx4sUZMGDAbve4O6FsTVXVbq/XW9RUVWVmoZD1zc27rB3St2/m1dV1Q1cAAAAAQE/Vo8PuJJk2bVquvvrqHH300Rk9enTuuOOONDY2ZvLkyUmS2bNnZ9CgQZk1a1aS5OKLL85FF12UhQsX5uSTT86KFSvyxBNP5Prrr0/ym6D74x//eH72s5/lK1/5SlpaWlIoFJIkb3/729OvX79O9SeULZ31zc1Z29RU7jZ6pM5+aOJDFgAAAAD2dj0+7D7rrLPy0ksvZf78+SkUChkxYkQWLFjQNpZk48aNqa7+3TSWsWPH5sYbb8y8efNy8803Z/Dgwbn11lszdOjQJMnmzZvzwAMPJEnOPffcdmstXrw4xx9/fKd7FMrS3XzIAgAAAADt9fiwO0mmTp36lmNL7rzzzu2OTZw4MRMnTtxh/WGHHZYnn3yyS/uDcvAhCwAAAAD8To9+QCUAAAAAPU9rsbWk9QC7oyJ2dkNndGY+tVnWAAAA0HnVVdV5sOHBbG3eusvag/oelFNqTyl5TwDCbvY6HZ1nbZZ1z+eDCwAAgJ5ra/PWNDQ3lLsNgDbCbvZK5lnvHXxwAQAAAEBHCbuBHs0HFwAAAAB0hAdUAgAAPVpra8cfataZWgAA9i52dgMAAD1adXV1li1blkKhsNO6urq6TJ48uZu6gq7XWmxNdVXH9qR1phYAegthNwDA7ym2tKSqpqZk9cDuKRQK2bRpU7nbgJKqrqrOgw0PZmvz1p3WHdT3oJxSe0q39AQAlUTYDQDwe6pqalKYOTPN69fvsrbvkCGpmzev9E0B0Gtsbd6ahuaGcrcBABVJ2A0A8Aea169P09q15W4DAACATjDgC7pAS7FYklqA3q7Y0lLSegAAAPYednZDF6ipqsrMQiHrm5t3Wjekb9/Mq6vrpq4AKp+RIgAAAHSUsBu6yPrm5qxtaip3GwB7HSNFAGhtbU11dcd/Mbmz9QDA3kHYDQAAXaTY0pKqmpour4Xerrq6OsuWLUuhUNhlbV1dXSZPntwNXQEAPY2wGyiplmIxNVVVJasHgJ6ko6N3jN2BzisUCtm0aVO52wAAejBhN1BSHZ1nnphpDsDewegdAAAoD2E3UHLmmQMAAABQap7YAQAAvVBra2tJagEAoFzs7AYAgF6oow/887A/AAAqhbAboAu0tLSkpqamy2sBoJQ88A8AgL2JsBugC9TU1OTCCy/MunXrdlo3YsSILFmypJu6AgAAAOg9hN0AXWTdunVZvXp1udsAAAAA6JU8oBIAoMyKLS0lqQUAAOhN7OzuRVqKxdRUVZWsHgDYPVU1NSnMnJnm9et3Wtd3yJDUzZvXPU0BAABUGGF3L1JTVZWZhULWNzfvsnZI376ZV1fXDV0B9CweNsruKra0pKoTPw9/WN+8fn2a1q4tRWsAAAC9grC7jDqzc7qrdlmvb27O2qamPb4OwN7Kw0bZXR3dnZ3YoQ0AlaC12Jrqqo5Pf+1sPQBdT9hdRh3daW2XNT1BOT6cgc7unO6qndYeNsrusjsbAPYe1VXVebDhwWxt3rrL2oP6HpRTak8peU8A7Jywu8zstKZS+HCGcujoLuvETmsAALre1uataWhuKHcbAHSQsBvoMB/OUA52WQMAlIYxHfQWveVnvVgspqqDv2XdmVqoJMJuAAD4PXv6sFGASmFMB71Fb/lZr6qqStP6Z1NsfGPndW/bJ/2GHN5NXUH3EnZTUp2d3WzWMwBQbh42Sjm0tramuroTuw47Wc/ebU92rRrTQSXpzM/6H9b2lp/1YuMbKb7WWO42KBO7+4XddNDuPpywo3OeE7Oe6Trleqjh3s77unPeH9i7eNhoaXQmoO1tYW51dXWWLVuWQqGwy9q6urpMnjy5G7qiUvSWXauVZE9CWd5aR3/W/ZzTW9ndL+ymg/bk4YTmPNPdPNSwNPbkfe1MsFupIXBv+bnrDd9LoHQ6Guj21jC3UChk06ZN5W6DPVSuXfq9ZddqpRDKlk53/6zbKUul6e27+4XddJjQmkrioYalsbvva0eD4EoOgZPe8XPXW76XwFvb0yBPoNv17Jgvjd19X+3S57cq5QOI7t6FXmkPi7RTFipLRYTdS5YsyVe/+tUUCoUMHz4811xzTUaPHv2W9ffdd1+++MUv5vnnn8/gwYPzyU9+MieffHLb68ViMfPnz883v/nNvPLKKxk7dmyuu+66DB48uBvuBsprd0fSwJ7qziDYSJGd29P3pzeE+sBbE+T1PHbMl8aevK8+1CkNo0FKo7t3oVfi2J3evlMWKkmPD7tXrFiRuXPnZs6cOTnmmGNyxx13ZPr06bn//vtTW1u7Xf1jjz2WWbNm5aqrrsqpp56ae++9NzNmzMiyZcsydOjQJMntt9+eO++8M5/73Ody2GGH5Ytf/GKmT5+eFStWZJ999unuW4RutScjaaBS9JaRIrvL+wPsqd4Q5FXabulK+Z6U433dkzUr5X2tJHuyq7c3jAbZ3UB/T3dLd/cu9ErZ9Q5Unh4fdi9atCgXXHBBpkyZkiSZM2dOHnzwwSxdujSXXXbZdvWLFy/OiSeemEsvvTRJMnPmzKxcuTJ33XVXrr/++hSLxSxevDhXXnllzjjjjCTJF77whYwbNy7f//7386EPfaj7bg7KxEiaHSvHbmA7kEunN+w+3pP52b3h/QHYk5ErvWG39O6+P5X2vvaG72Ul2dNdvbsTklbSjvDdDfQrcbc0O9bZud+9bU6492fvUarvZY8Ou5uamrJ27dpcfvnlbceqq6szbty4t/wf9DVr1uSSSy5pd2z8+PH5/ve/nyT51a9+lUKhkHHjxrW9fsABB+SYY47J6tWrOxx2F4vFJMm2bdtyeFNTWnaxSzZJDv+v+nbHOnBud5/XU9aspF5LvaZeO7/m0KFD2/453ZmhQ4e2O/fzn/98fvnLX+7yvD/6oz/K1Vdf3ek1/3C9PVlzd4POPQlId/d97ei5u3vejs6tpF73REd+fnb35/W3dV31/jQdfniaW1p2vuDhh+/eeXtybheuWUm9lnxNvXbbmnvS6wEHHJA33tj5DNIDDjhgt87bk3O78rz/+3//b37961/vste3v/3t+cAHPtDu2Ouvv77LNV9//fV2a+5JEFyO93V335/ufl+TPXt/SrlmV/8zsk/TPtm/ef+dnrdPcZ/dOm9H5+7ueXvSa+OrjXm9+fVdrtnYtzHb9umaNX/665/m1dZXd3re/tX755i3H9Pp9d5qzVK+Pzt6b8rxvlbSz92erNlUfDPF7Py/f6uKb6b5987bkwdbvvnCiym+sesNalX79EufQw/Z4153V7mC5915fyotJN+Tn59S/7z+YX13/6zvv//+O12vqtiR/9Mtk82bN+ekk07KN77xjRx77LFtx7/whS/kkUceyTe/+c3tzjn66KPzuc99LmeffXbbsSVLluTWW2/NypUr89hjj+WjH/1oHn744RxyyO/+hfCJT3wiVVVVmTdvXod627RpU7s54AAAAAAAlM6jjz6a/v37v+XrPXpnd092yCGH5KGHHtrlpwkAAAAAAOy5/fff+W9o9Oiwe8CAAampqUlDQ/t5XA0NDRk4cOAOzxk4cGC2bNnylvV1//XAvYaGhnY7uxsaGjJ8+PAO91ZdXZ13vOMdHa4HAAAAAKB0yvvI8F3o169fRo4cmfr6+rZjra2tqa+vbzfW5PeNGTMmq1atands5cqVGTNmTJLksMMOS11dXbtrbtu2LT/96U/f8poAAAAAAPRsPTrsTpJp06bl7rvvzvLly7Nhw4Zcd911aWxsbHtK9uzZs3PTTTe11V988cV5+OGHs3DhwmzYsCG33HJLnnjiiUydOjVJUlVVlYsvvjj/8A//kB/84Ad58sknM3v27BxyyCE544wzynKPAAAAAADsmR49xiRJzjrrrLz00kuZP39+CoVCRowYkQULFrSNJdm4cWO7p52PHTs2N954Y+bNm5ebb745gwcPzq233pqhQ4e21fzP//k/09jYmGuvvTavvPJKjjvuuCxYsCD77LNPt98fAAAAAAB7rqpYLBbL3QQAAAAAAOyJHj/GBAAAAAAAdkXYDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAEAP9KlPfSrDhg3LsGHDMnLkyIwbNy7Tpk3Lt771rbS2tiZJfvWrX7XV/OGf++67b5dr/Pb8ESNGZPPmze1ee/HFF3PUUUdl2LBh+dWvfrXduS+//HJOOumkDBs2LK+88krX3DQAAOwBYTcAAPRQJ554Yn70ox/lgQceyO23357jjz8+N9xwQy6//PK8+eabeec735kf/ehH7f587GMfy3777ZeTTjqpw+sMGjQo99xzT7tj99xzTwYNGvSW5/z1X/91hg0btru3BgAAXU7YDQAAPVS/fv1SV1eXQYMGZeTIkbniiivy5S9/OT/84Q+zfPny1NTUpK6urt2f73//+5k4cWL233//Dq8zadKkLFu2rN2xpUuXZtKkSTus//rXv57//M//zP/4H/9jT24PAAC6lLAbAAAqyAknnJDhw4fnX//1X7d77Yknnsi6dety3nnndeqap512Wn7961/nJz/5SZLkJz/5SV555ZWceuqp29WuX78+X/7yl/P5z38+1dX+dwIAgJ7Df50CAECFOeKII/L8889vd/xb3/pWjjzyyIwdO7ZT1+vbt28+/OEPZ+nSpUl+s6v7wx/+cPr27duurqmpKVdddVX+8i//Moceeuju3wAAAJSAsBsAACpMsVhMVVVVu2Ovv/56vv3tb3d6V/dvTZkyJffff38KhULuv//+TJkyZbuam266KUceeWTOPffc3VoDAABKSdgNAAAVZsOGDTnssMPaHbv//vvz+uuvv+Wc7V0ZNmxYjjjiiFx11VU58sgjM3To0O1qVq1alfvvvz9HHXVUjjrqqFxyySVJkj/5kz/J/Pnzd2tdAADoKn3K3QAAANBx9fX1eeqpp9qC5t9aunRpTjvttBx88MG7fe0pU6Zkzpw5ue6663b4+i233JLXX3+97ev/+I//yF/91V9lyZIlefe7373b6wIAQFcQdgMAQA/V1NSUQqGQ1tbWbNmyJQ8//HC+8pWv5NRTT223g/vZZ5/NI488kn/8x3/co/UuuOCCTJgwIQceeOAOX//DQPvll19Okhx55JFveQ4AAHQXYTcAAPRQDz/8cMaPH58+ffrkwAMPzPDhw/OZz3wmH/nIR1Jd/buJhEuXLs073vGOjB8/fo/W69Onzx7tDAcAgHKqKhaLxXI3AQAAAAAAe8IDKgEAAAAAqHjGmAAAwF7q2muvzb333rvD184555xcf/313dwRAACUjjEmAACwl2poaMi2bdt2+Fr//v1TW1vbzR0BAEDpCLsBAAAAAKh4ZnYDAAAAAFDxhN0AAAAAAFQ8YfduKhaL2bZtW0yBAQAAAAAoP2H3bnr11Vdz3HHH5dVXXy13KwAAAAAAvZ6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeMJuAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAAAAAKh4wm4AAAAAACqesBsAAAAAgIon7AYAAAAAoOIJuwEAAAAAqHjCbgAA6IVaW1tLUgsAAOXSp9wNAAAA3a+6ujrLli1LoVDYaV1dXV0mT57cTV0BAMDuE3YDAEAvVSgUsmnTpnK3AQAAXcIYEwAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeMJuAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAAAAAKh4wm4AAAAAACqesBsAAAAAgIon7AYAAAAAoOIJuwEAAAAAqHjCbgAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAoAdoLbaWpBYAeos+5W4AAAAASKqrqvNgw4PZ2rx1p3UH9T0op9Se0i09AUAlEXYDAABAD7G1eWsamhvK3QYAVCRjTAAAAAAAqHjCbgAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeBURdi9ZsiSnnXZaRo0alfPPPz+PP/74Tuvvu+++TJgwIaNGjco555yThx566C1rr7322gwbNixf+9rXurhrAAAAAAC6S48Pu1esWJG5c+dmxowZWb58eYYPH57p06enoaFhh/WPPfZYZs2alfPOOy/33HNPTj/99MyYMSNPPfXUdrXf+9738tOf/jSHHHJIqW8DAAAAAIAS6vFh96JFi3LBBRdkypQpGTJkSObMmZN99903S5cu3WH94sWLc+KJJ+bSSy/NkUcemZkzZ+aoo47KXXfd1a5u8+bN+exnP5sbb7wxffv27Y5bAQAAAACgRHp02N3U1JS1a9dm3Lhxbceqq6szbty4rF69eofnrFmzJieccEK7Y+PHj8+aNWvavm5tbc1f/uVfZvr06XnPe95Tkt4BAAAAAOg+PTrsfvnll9PS0pLa2tp2x2tra7Nly5YdnrNly5YMHDhwp/W33357+vTpk4svvrjrmwYAAAAAoNv1KXcD3e2JJ57I4sWLs2zZslRVVZW7HQAAAAAAukCPDrsHDBiQmpqa7R5G2dDQsN3u7d8aOHDgdru+f7/+Jz/5SRoaGnLqqae2vd7S0pLPf/7zWbx4cR544IEuvgsAAAAAAEqtR4fd/fr1y8iRI1NfX58zzjgjyW/mbdfX12fq1Kk7PGfMmDFZtWpVLrnkkrZjK1euzJgxY5Ik5557brsZ4Ekyffr0nHvuuZk8eXJJ7gMAAAAAgNLq0WF3kkybNi1XX311jj766IwePTp33HFHGhsb24Lp2bNnZ9CgQZk1a1aS5OKLL85FF12UhQsX5uSTT86KFSvyxBNP5Prrr0/ym93iAwYMaLdG3759M3DgwBxxxBHde3MAAAAAAHSJHh92n3XWWXnppZcyf/78FAqFjBgxIgsWLGgbS7Jx48ZUV//uOZtjx47NjTfemHnz5uXmm2/O4MGDc+utt2bo0KHlugUAAAAAAEqsx4fdSTJ16tS3HFty5513bnds4sSJmThxYoevb043AAAAAEBlq951CQAAAAAA9GzCbgAAAAAAKp6wGwAAoIK1traWpBYAoNJUxMxuAAAAdqy6ujrLli1LoVDYaV1dXV0mT57cTV0BAHQ/YTcAAECFKxQK2bRpU7nbAAAoK2NMAAAAAACoeMJuAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAAAAAKh4wm4AAAAAACqesBsAAAAAgIon7AYAAAAAoOIJuwEAAAAAqHjCbgAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeMJuAAAAgC7UWmwtSS0AO9en3A0AAAAA7E2qq6rzYMOD2dq8dad1B/U9KKfUntItPQH0BsJuAAAAgC62tXlrGpobyt0GQK9ijAkAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxauIsHvJkiU57bTTMmrUqJx//vl5/PHHd1p/3333ZcKECRk1alTOOeecPPTQQ+1ev+WWWzJhwoSMGTMm73vf+3LJJZfkpz/9aSlvAQAAAACAEurxYfeKFSsyd+7czJgxI8uXL8/w4cMzffr0NDQ07LD+sccey6xZs3Leeeflnnvuyemnn54ZM2bkqaeeaqsZPHhwrr322tx77735+te/nne96135H//jf+Sll17qrtsCAAAAAKAL9fiwe9GiRbngggsyZcqUDBkyJHPmzMm+++6bpUuX7rB+8eLFOfHEE3PppZfmyCOPzMyZM3PUUUflrrvuaqs555xzMm7cuPzRH/1R3vOe9+TTn/50tm3blieffLK7bgsAAAAAgC7Uo8PupqamrF27NuPGjWs7Vl1dnXHjxmX16tU7PGfNmjU54YQT2h0bP3581qxZ85Zr/PM//3MOOOCADBs2rMt6BwAA2Bu1traWtB4AYHf1KXcDO/Pyyy+npaUltbW17Y7X1tbmmWee2eE5W7ZsycCBA7er37JlS7tj//Zv/5arrroqjY2Nqaury8KFC3PwwQd37Q0AAABtWltbU13dsf02namle1VXV2fZsmUpFAq7rK2rq8vkyZO7oSsAgB4edpfS8ccfn3vuuScvv/xy7r777sycOTPf/OY3twvWAQCArtHRkFRA2vMVCoVs2rSp3G0AALTTo8PuAQMGpKamZruHUTY0NGy3e/u3Bg4cuN0u7h3V77fffjn88MNz+OGHZ8yYMTnzzDPzrW99K5dffnnX3gQAANBGSAoAQKn06N8L7NevX0aOHJn6+vq2Y62tramvr8+xxx67w3PGjBmTVatWtTu2cuXKjBkzZqdrtba2pqmpaY97BgAAAACg+/XosDtJpk2blrvvvjvLly/Phg0bct1116WxsbHt1xpnz56dm266qa3+4osvzsMPP5yFCxdmw4YNueWWW/LEE09k6tSpSZLXXnstN998c9asWZPnn38+TzzxRD796U9n8+bNmTBhQlnuEQAAAACAPdOjx5gkyVlnnZWXXnop8+fPT6FQyIgRI7JgwYK2sSQbN25s9+CasWPH5sYbb8y8efNy8803Z/Dgwbn11lszdOjQJElNTU2eeeaZLF++PC+//HIOOuigjBo1KkuWLMl73vOestwjAACwd+jsgzU9iBMAoOv0+LA7SaZOndq2M/sP3XnnndsdmzhxYiZOnLjD+n322Sdf+tKXurQ/AACApOMP4Uw8iBMAoKtVRNgNAABQKTyEEwCgPPy+HAAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAABWstdha0nqASlGyB1S++eab+fGPf5znnnsuZ599dvr375/Nmzenf//+2X///Uu1LAAAAECvUl1VnQcbHszW5q27rD2o70E5pfaUkvcEUA4lCbuff/75XHrppdm4cWOamprygQ98IP3798/tt9+epqamXH/99aVYFgAAAOig1mJrqqs6/gvfna2ne21t3pqG5oZytwFQViUJu2+44YYcffTR+Zd/+Zccf/zxbcc/+MEP5pprrinFkgAAAEAn2A0MwN6mJGH3o48+mn/6p39Kv3792h1/17velc2bN5diSQAAAKCT7AYGYG9Skt8/am1tTWvr9g872LRpk3ndAAAAQLfpzMMYPbgRoLKVZGf3Bz7wgdxxxx357Gc/23bs1VdfzS233JKTTz65FEsCAAAAbKej41qMagGofCUJuz/1qU9l+vTpOeuss9LU1JRPfvKT+cUvfpEBAwbk5ptvLsWSAAAAADtkXAtA71CSsPsd73hH/uVf/iXf+c538uSTT+a1117Leeedl3POOSf77rtvKZYEAAAAAKAXK0nYnSR9+vTJueeeW6rLAwAAAABAm5KE3ffcc89OX580aVIplgUAAAAAoJcqSdh9ww03tPv6zTffTGNjY/r27Zu3ve1twm4AAAA6rLW1NdXV1SWrBwD2DiUJux955JHtjv3iF7/Iddddl+nTp5diSQAAAPZS1dXVWbZsWQqFwi5r6+rqMnny5G7oCgDoaUo2s/sPDR48OLNmzcpf/uVf5v777++uZQEAANgLFAqFbNq0qdxtAAA9WLf+XlefPn3y4osvdueSAAAAAAD0AiXZ2f2DH/yg3dfFYjGFQiFLlizJ2LFjS7EkAAAAAAC9WEnC7hkzZrT7uqqqKgcffHD+5E/+JFdffXUplgQAAAAAoBcrSdj9//7f/yvFZQEAAAAAYIe6dWY3AAAAAACUQpft7J47d26Haz/96U931bIAAAAAANB1YffPfvazDtVVVVV11ZIAAAAAAJCkC8PuO++8s6suBQAAAEAP1lpsTXVVx6fjdrYeYHeU5AGVAAAAAOy9qquq82DDg9navHWXtQf1PSin1J5S8p4AShZ2/8d//Efuu+++bNy4Mc3Nze1e+9KXvlSqZQEAAJIkra2tqa7uxK7DTtYD9HZbm7emobmh3G0AtClJ2P2d73wnV199dcaPH58f/ehHGT9+fH7+85+noaEhH/zgB0uxJAAAQDvV1dVZtmxZCoXCLmvr6uoyefLkbugKAIBSKUnYfdttt+XTn/50Lrzwwhx77LH567/+6xx22GG59tprU1dXV4olAQAAtlMoFLJp06ZytwEAQDcoye/o/fKXv8zJJ5+cJOnXr19ee+21VFVV5ZJLLsndd99diiUBAAAAAOjFShJ2H3jggXn11VeTJIccckiefvrpJMkrr7ySxsbGUiwJAAAAAEAv1qVjTJ566qkMHTo073vf+7Jy5coMGzYsEyZMyA033JBVq1Zl5cqVOeGEE7pySQAAAAAA6Nqw+8Mf/nBGjRqVM844IxMmTEiSXHnllenbt28ee+yxnHnmmbnyyiu7ckkAAAAAKIlisZiqqqourwVKo0vD7rvuuitLly7NV77yldx2220588wzc/755+eyyy7rymUAAAAAoOSqqqrStP7ZFBvf2Hnd2/ZJvyGHd1NXwFvp0rD7ve99b9773vfmmmuuyX333Zfly5dn6tSpOfzwwzNlypR85CMfSV1dXVcuCQAAAAAlU2x8I8XXPIMOKkFJHlC53377ZcqUKbnrrrvy3e9+NxMmTMjXv/71nHrqqbniiitKsSQAAAAAAL1YScLu33f44Yfn8ssvz5VXXpn9998/Dz30UKmXBAAAAHqg1mJrSesB6N26dIzJH3rkkUeydOnSfPe73011dXUmTpyY8847r9PXWbJkSb761a+mUChk+PDhueaaazJ69Oi3rL/vvvvyxS9+Mc8//3wGDx6cT37ykzn55JOTJM3NzZk3b15++MMf5pe//GX69++fcePGZdasWRk0aNBu3ysAAACwc9VV1Xmw4cFsbd66y9qD+h6UU2pPKXlPAOw9ujzs3rx5c5YvX57ly5fn2WefzbHHHpvPfOYzmThxYvbbb79OX2/FihWZO3du5syZk2OOOSZ33HFHpk+fnvvvvz+1tbXb1T/22GOZNWtWrrrqqpx66qm59957M2PGjCxbtixDhw7N66+/np/97Ge58sorM3z48Lzyyiu54YYbcuWVV2bZsmVd8RYAAACwA62tramu7tgvGHemlsqytXlrGpobyt0GAHuhLg27L7300tTX12fAgAE599xzM2XKlBxxxBF7dM1FixblggsuyJQpU5Ikc+bMyYMPPpilS5fmsssu265+8eLFOfHEE3PppZcmSWbOnJmVK1fmrrvuyvXXX58DDjggixYtanfONddck/PPPz8vvPBCDj300D3qFwAAgB2rrq7OsmXLUigUdlpXV1eXyZMnd1NXAMDeokvD7j59+uSLX/xiTj311NTU1Ozx9ZqamrJ27dpcfvnlbceqq6szbty4rF69eofnrFmzJpdcckm7Y+PHj8/3v//9t1xn27ZtqaqqyoEHHrjHPQMAAPDWCoVCNm3aVO42AIC9UJeG3bfddltXXi4vv/xyWlpathtXUltbm2eeeWaH52zZsiUDBw7crn7Lli07rH/jjTdy44035kMf+lD69+/fNY0DAAAAANCtevUAtObm5nziE59IsVjMnDlzyt0OAAAAAAC7qcsfUNmVBgwYkJqamjQ0tH9wRUNDw3a7t39r4MCB2+3i3lF9c3NzZs6cmRdeeCF33HGHXd0AAAAAABWsR+/s7tevX0aOHJn6+vq2Y62tramvr8+xxx67w3PGjBmTVatWtTu2cuXKjBkzpu3r3wbdzz77bL72ta9lwIABJekfAAAAAIDu0aPD7iSZNm1a7r777ixfvjwbNmzIddddl8bGxrYnc8+ePTs33XRTW/3FF1+chx9+OAsXLsyGDRtyyy235IknnsjUqVOT/Cbo/vjHP54nnngiN954Y1paWlIoFFIoFNLU1FSWewQAAAAAYM/06DEmSXLWWWflpZdeyvz581MoFDJixIgsWLCgbSzJxo0bU139u8x+7NixufHGGzNv3rzcfPPNGTx4cG699dYMHTo0SbJ58+Y88MADSZJzzz233VqLFy/O8ccf3013BgAAAFBercXWVFd1bC9kZ2oByqHHh91JMnXq1Lad2X/ozjvv3O7YxIkTM3HixB3WH3bYYXnyySe7tD8AAACASlRdVZ0HGx7M1uatO607qO9BOaX2lG7pCWB3VUTYDQAAAN2ptbW13W8Rd1Ut9ERbm7emobmh3G0A7DFhNwAAAPyB6urqLFu2LIVCYad1dXV1bc+UAgDKS9gNAAAAO1AoFLJp06ZytwEAdJDfswIAAAAA9lrFYrGk9fQcdnYDAAA9mtnJAMCeqKqqStP6Z1NsfGPXtW/bJ/2GHN4NXVEKwm4AAKBHMzsZANhTxcY3UnytsdxtUGLCbgAAoMczOxkAgF3x+30AAAAAAFQ8YTcAAAAAPV5rsbUktcDewxgTAAAAAHq86qrqPNjwYLY2b91p3UF9D8optad0S09AzyLsBgAAAKAibG3emobmhnK3AfRQxpgAAAAAnWKcBAA9kZ3dAAAAQKcYJwFATyTsBgAAADrNOAkAehpjTAAAAAAAqHjCbgAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeMJuAPY6LS0tJa0HAAAAep4+5W4AALpaTU1NLrzwwqxbt26XtSNGjMiSJUu6oSuAvUNra2uqqzu+Z6az9QAAsLuE3QDsldatW5fVq1eXuw2AvU51dXWWLVuWQqGwy9q6urpMnjy5G7oCAABhN7AXamlpSU1NTcnqAaC3KxQK2bRpU7nbAIC9TrFYTFVVVcnq6T6+l+Uh7Ab2OkZY7Fxnwn0fBAAAAHSfqqqqNK1/NsXGN3Zd+7Z90m/I4d3QFbvD97I8hN3AXskIi7fW0Q8DeuMHAQDwW52ZNW4uOQBdqdj4RoqvNZa7jZLqzC7mSt7x3Bu+lz2NsBugF/JhAADsXEdnk5tLDgCd19Fdz3Y801nCbgAAgB0wm3zv0Bt26bcWW1Nd1fG+O1sPUAp2PVMKwm4AAAD2Wr1hl351VXUebHgwW5u37rL2oL4H5ZTaU0reEwCUg7AbAACAvVpv2KW/tXlrGpobyt0GAJSV31sCAAAAerzWYmtJagHYe9jZDQAA0At1dj51pc6zZu/R0XEtRrUA9F7CbgAAgF6oo7Osk8qeZ83exbgWAHZG2A0AANBL9YZZ1kDP0lpsTXVVJ36rpJP10JsVi8VUVVV1eW0lEXYDAAAA0C06Oo4mMZIGOquqqipN659NsfGNnde9bZ/0G3J4N3XVvYTdAAAAAHQb42igdIqNb6T4WmO52ygbvwcCAAAA0AO0FltLUgvQW9jZDQAAANADdHTEh/EeADsm7AYAAADoIYz4ANh9xpgAAAAAQC9VLBZLWg/dqSJ2di9ZsiRf/epXUygUMnz48FxzzTUZPXr0W9bfd999+eIXv5jnn38+gwcPzic/+cmcfPLJba//67/+a77xjW9k7dq12bp1a+65556MGDGiO24FoFdqaWlJTU1Np2s7c97u1AMAAPR2VVVVaVr/bIqNb+y69m37pN+Qw7uhK9g9PT7sXrFiRebOnZs5c+bkmGOOyR133JHp06fn/vvvT21t7Xb1jz32WGbNmpWrrroqp556au69997MmDEjy5Yty9ChQ5Mkr732WsaOHZuJEyfmM5/5THffEkCvU1NTkwsvvDDr1q3bad2IESOyZMmSTp+3o3MBAADomGLjGym+1ljuNmCP9fiwe9GiRbngggsyZcqUJMmcOXPy4IMPZunSpbnsssu2q1+8eHFOPPHEXHrppUmSmTNnZuXKlbnrrrty/fXXJ0kmTZqUJPnVr37VPTcBQNatW5fVq1d323kAAABA79KjZ3Y3NTVl7dq1GTduXNux6urqjBs37i2DjzVr1uSEE05od2z8+PFZs2ZNKVsFAAAAAKCMenTY/fLLL6elpWW7cSW1tbXZsmXLDs/ZsmVLBg4c2OF6AAAAAAAqX48OuwEAAAAAepNisViS2t6gR8/sHjBgQGpqatLQ0NDueENDw3a7t39r4MCB2+3i3lk9AAAAAEBPUVVVlab1z6bY+MbO6962T/oNObybuqoMPXpnd79+/TJy5MjU19e3HWttbU19fX2OPfbYHZ4zZsyYrFq1qt2xlStXZsyYMaVsFQAAAACgSxQb30jxtcad/9lFGN4b9eiwO0mmTZuWu+++O8uXL8+GDRty3XXXpbGxMZMnT06SzJ49OzfddFNb/cUXX5yHH344CxcuzIYNG3LLLbfkiSeeyNSpU9tqtm7dmnXr1mXDhg1Jkp///OdZt25dCoVC994cAAAAAABdokePMUmSs846Ky+99FLmz5+fQqGQESNGZMGCBW1jSTZu3Jjq6t9l9mPHjs2NN96YefPm5eabb87gwYNz6623ZujQoW01DzzwQD796U+3ff2//tf/SpL8xV/8RT72sY91050BAAAAANBVenzYnSRTp05ttzP79915553bHZs4cWImTpz4ltebPHly285wAAAAAAA6r1gspqqqqstrd1dFhN0AAAAAAPQsPe1hmsJuAAAAAKhwPW2HLb3Hbx+m2RMIuwEAAACgwvW0HbZQDsJugP/S0tKSmpqaktV3tc6sX+5eAQAAKL2etMMWykHYDfBfampqcuGFF2bdunW7rB0xYkSWLFnSDV29tY722xN6BQDoLVpbW1NdXV2yegDgrQm7AX7PunXrsnr16nK30WGV1i8AwN6uuro6y5YtS6FQ2GVtXV1dJk+e3A1dAUDvIOwGAACALlQoFLJp06ZytwEAvY7flQIoo5aWlpLWAwAA9HatxdaS1gM9h53dAGVUaXPCeWuV9oBTAADoLaqrqvNgw4PZ2rx1l7UH9T0op9SessdrFovFVFVVdXktsHPCboAyM3d77+CDCwAA6Lm2Nm9NQ3NDt61XVVWVpvXPptj4xs7r3rZP+g05vJu6gr2fsBsAuogPLgAAgN8qNr6R4muN5W6jZDq7I90OdrqDsBsAAAAA6JSO7l5P7GDv6famDy6E3QAAlFyxpSVVnZhT39l6AAC6396+e7232Js+uBB2AwAl15ngsjeGnL3h/amqqUlh5sw0r1+/y9q+Q4akbt680jcFAAC70FseNrq3fHAh7AYASq6jQWdvDTl7y/vTvH59mtauLXcbAADQYR42WlmE3QBAtxB07pz3BwAAeqa9Zddzb1Bd7gYAAAAAAGBPCbsBAAAAAKh4wm4AAAAAACqesBugC7S0tJSkFnbGzx0AAAD8jgdUAnSBmpqaXHjhhVm3bt1O60aMGJElS5Z0U1ddq6WlJTU1NSWrp/N6w88dpVNsaUlVB/8Z/f3a3T0PAACg1ITdAF1k3bp1Wb16dbnbKJmOBquJcLUz9vRDhL39547SqaqpSWHmzDSvX7/Tur5DhqRu3rw9Pg8AAKDUhN0AdJhgtev5EIFyal6/Pk1r13bbeQAAAKUk7AaAMvMhAgAAAOw5D6gEADrMQzEBSqO1tbWk9QAAPUmxWCxJvZ3dAECHeSgmQGlUV1dn2bJlKRQKu6ytq6vL5MmTu6Er6N1ai62prur4HsHO1gP0ZlVVVWla/2yKjW/suvZt+6TfkMM7dF1hNwDQKd05dqXY0pKqTjzAs7P1XamSegV6pkKhkE2bNpW7DeC/VFdV58GGB7O1eesuaw/qe1BOqT2l5D0B7E2KjW+k+Fpjl15T2A0A9FhVNTUpzJyZ5vXrd1nbd8iQ1M2bV/qm3kIl9QoAdMzW5q1paG4odxsAdJCwGwDo0ZrXr0/T2rXdtt6e7NDu7l7Zuc58L+20BwCAyifsBgD4PXZo7z06+r30fQQAgL2DsBsA4A/Yob338L0EAIDew2OCAQAAAACoeMJuAAAAAAAqnrAbANgrFVtaSlLbk3S270q9z+62J+9rb/i5AwCAnsrMbgBgr9QbHk7oYZqlsSfva2/4uQMAKJdisZiqqqqS1VP5hN0AwF6rNzycsDfcYznsyfvand+TYktLqmpqSlYPANCTVFVVpWn9syk2vrHr2rftk35DDu+GruhJhN0AAFCh7O4HAHqbYuMbKb7WWO426KGE3QDQy7S0tKSmEzs7O1sPdC+7+wEA4DeE3QDQy9TU1OTCCy/MunXrdlk7YsSILFmypBu6AgAAgD1TEWH3kiVL8tWvfjWFQiHDhw/PNddck9GjR79l/X333ZcvfvGLef755zN48OB88pOfzMknn9z2erFYzPz58/PNb34zr7zySsaOHZvrrrsugwcP7oa7AYDyW7duXVavXl3uNgAAAKDLVJe7gV1ZsWJF5s6dmxkzZmT58uUZPnx4pk+fnoaGhh3WP/bYY5k1a1bOO++83HPPPTn99NMzY8aMPPXUU201t99+e+68885cd911ufvuu/O2t70t06dPzxtv7Hq4/R9qKRZLWl/pesv705m+K/UeobdoaWkpSS3AzhQ7+e+TztYDAEBv0ON3di9atCgXXHBBpkyZkiSZM2dOHnzwwSxdujSXXXbZdvWLFy/OiSeemEsvvTRJMnPmzKxcuTJ33XVXrr/++hSLxSxevDhXXnllzjjjjCTJF77whYwbNy7f//7386EPfahT/dVUVWVmoZD1zc27rB3St2/m1dW1fd1SLKamqqpD63Smtquu8fv1u9vrnrw/u2t3e92T96ej91mOe9ydeujNOjriw3gPoCt50CQAAOy5Hh12NzU1Ze3atbn88svbjlVXV2fcuHFv+avXa9asySWXXNLu2Pjx4/P9738/SfKrX/0qhUIh48aNa3v9gAMOyDHHHJPVq1d3OOwu/tfu3G3btqX59dfT0oEwt7lv32x729vaHfvK1q15YRc7cw6tqcnlBx3U9vWehOQdWW9Ha+5ur0nS/OqrnX5/9jTM3d1e9+T96ch97uhnYHftbq/d8WFAV32IsCc/60OHDm3753Rnhg4dmm3btu3xeR09d3fPK8ealdTrjs7dk15bWlp2eW5LS0uP6LWSvpdNhx+e5l39e+vww3fvvD05twvXrKReS75mF/ZabGlJVQcfkvqHtZX0vr7a3NyhNfs2N+dtXfRzd8ABB+zytxsPOOCA3TpvT87t7vN6ypp6LU2vpV6zq3vdp2mf7N+8/07P26e4z26dt6Nzd/e8cvRa6jX16ufurc5tKr6ZYnb+379VxTfT3I3n9ZQ19VqaXku9ZiX1+ofn7r///qnaSVZUVezI/+mWyebNm3PSSSflG9/4Ro499ti241/4whfyyCOP5Jvf/OZ25xx99NH53Oc+l7PPPrvt2JIlS3Lrrbdm5cqVeeyxx/LRj340Dz/8cA455JC2mk984hOpqqrKvA7uktm0aVO7OeAAAAAAAJTOo48+mv79+7/l6z16Z3dPdsghh+Shhx7a5acJAAAAAADsuf333/lvaPTosHvAgAGpqanZ7mGUDQ0NGThw4A7PGThwYLZs2fKW9XX/NS+5oaGh3c7uhoaGDB8+vMO9VVdX5x3veEeH6wEAAAAAKJ3qcjewM/369cvIkSNTX1/fdqy1tTX19fXtxpr8vjFjxmTVqlXtjq1cuTJjxoxJkhx22GGpq6trd81t27blpz/96VteEwAAAACAnq1Hh91JMm3atNx9991Zvnx5NmzYkOuuuy6NjY2ZPHlykmT27Nm56aab2uovvvjiPPzww1m4cGE2bNiQW265JU888USmTp2aJKmqqsrFF1+cf/iHf8gPfvCDPPnkk5k9e3YOOeSQnHHGGWW5RwAAAAAA9kyPHmOSJGeddVZeeumlzJ8/P4VCISNGjMiCBQvaxpJs3Lgx1dW/y+zHjh2bG2+8MfPmzcvNN9+cwYMH59Zbb83QoUPbav7n//yfaWxszLXXXptXXnklxx13XBYsWJB99tmn2+8PAAAAAIA9V1UsFovlbgIAAAAAAPZEjx9jAgAAAAAAuyLsBgAAAACg4gm7AQAAAACoeMJuAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAAAAAKh4wm4AAAAAACqesBsAAAAAgIon7AYAAAAAoOIJuwEAAAAAqHjCbgAAAAAAKp6wGwAAAACAiifsBgAAAACg4gm7AQAAAACoeMJuAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAKAH+NSnPpVhw4Zl2LBhGTlyZMaNG5dp06blW9/6VlpbW9vq/vmf/zkXXXRRxo4dm2HDhuWVV17Z7lo///nPc+WVV+b444/P2LFj89GPfjSrVq3qUB+/+tWvMmzYsIwYMSKbN29u99qLL76Yo446KsOGDcuvfvWrdq8tW7Ys55xzTkaNGpUTTjghc+bM2Y13AQAAdp+wGwAAeogTTzwxP/rRj/LAAw/k9ttvz/HHH58bbrghl19+ed58880kSWNjY0488cRcccUVb3mdK664Ii0tLbnjjjuybNmyDB8+PFdccUUKhUKHexk0aFDuueeedsfuueeeDBo0aLvaRYsW5e///u9z2WWX5Tvf+U4WLVqU8ePHd3gtAADoCsJuAADoIfr165e6uroMGjQoI0eOzBVXXJEvf/nL+eEPf5jly5cnSS655JJcdtllOeaYY3Z4jZdeeim/+MUvctlll2X48OEZPHhwZs2alcbGxjz99NMd7mXSpElZtmxZu2NLly7NpEmT2h379a9/nXnz5uULX/hCzjnnnLz73e/O8OHDc/rpp3fu5gEAYA8JuwEAoAc74YQTMnz48Pzrv/5rh+oHDBiQP/7jP84999yT1157LW+++Wb++Z//ObW1tRk5cmSH1z3ttNPy61//Oj/5yU+SJD/5yU/yyiuv5NRTT21X93//7/9Na2trNm/enIkTJ+akk07KJz7xiWzcuLHjNwkAAF1A2A0AAD3cEUcckeeff75DtVVVVfna176Wn/3sZxk7dmxGjx6dRYsWZcGCBXn729/e4TX79u2bD3/4w1m6dGmS3+zq/vCHP5y+ffu2q/vVr36VYrGY2267LX/1V3+V+fPn59e//nWmTZuWpqamjt8kAADsIWE3AAD0cMViMVVVVR2unTNnTmpra7NkyZJ885vfzBlnnJErrrgiL774YqfWnTJlSu6///4UCoXcf//9mTJlynY1ra2taW5uzmc+85mceOKJGTNmTG6++eY8++yz+fd///dOrQcAAHuiT7kbAAAAdm7Dhg057LDDOlS7atWqPPjgg3nkkUfSv3//JMnIkSOzcuXK3HPPPbnssss6vO6wYcNyxBFH5KqrrsqRRx6ZoUOHZt26de1q6urqkiRDhgxpO3bwwQdnwIABRpkAANCt7OwGAIAerL6+Pk899VTOPPPMDtU3NjYmyXY7wauqqtLa2trp9adMmZIf//jHO9zVnSRjx45Nkvz85z9vO7Z169a8/PLLOfTQQzu9HgAA7C47uwEAoIdoampKoVBIa2trtmzZkocffjhf+cpXcuqpp2bSpElJkkKhkC1btuS5555Lkjz11FPZf//98853vjMHHXRQxowZkwMPPDCf+tSnMmPGjOyzzz65++678/zzz+eUU07pdE8XXHBBJkyYkAMPPHCHr//xH/9xTj/99Nxwww25/vrr079//9x888054ogjcvzxx+/uWwEAAJ0m7AYAgB7i4Ycfzvjx49OnT58ceOCBGT58eD7zmc/kIx/5SKqrf/NLmd/4xjfypS99qe2cCy+8MEkyd+7cTJ48OQcffHAWLFiQefPm5c/+7M/S3Nyc97znPbn11lszfPjwTvfUp0+fHHzwwTut+cIXvpC//du/zeWXX57q6uq8733vy4IFC7Z7mCUAAJRSVbFYLJa7CQAAAAAA2BNmdgMAAAAAUPGMMQEAgF7k2muvzb333rvD184555xcf/313dwRAAB0DWNMAACgF2loaMi2bdt2+Fr//v1TW1vbzR0BAEDXEHYDAAAAAFDxzOwGAAAAAKDiCbsBAAAAAKh4wu7dVCwWs23btpgCAwAAAABQfsLu3fTqq6/muOOOy6uvvlruVgAAAAAAej1hNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMA/J5iS0tJ6wEAACiNPuVuAACgJ6mqqUlh5sw0r1+/y9q+Q4akbt680jcFAADALgm7AQD+QPP69Wlau7bcbQAAANAJxpgAAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABWv7GH3kiVLctppp2XUqFE5//zz8/jjj79l7dNPP52PfexjOe200zJs2LB87Wtf267mt6/94Z85c+a01Vx00UXbvX7ttdeW4vYAAAAAAOgGZX1A5YoVKzJ37tzMmTMnxxxzTO64445Mnz49999/f2pra7erb2xszGGHHZYJEyZk7ty5O7zmt771rbS0tLR9/fTTT2fatGmZMGFCu7oLLrggH//4x9u+ftvb3tZFdwUAAAAAQHcr687uRYsW5YILLsiUKVMyZMiQzJkzJ/vuu2+WLl26w/rRo0fn6quvzoc+9KH069dvhzUHH3xw6urq2v7827/9W9797nfn/e9/f7u6fffdt11d//79u/z+AAAAAADoHmULu5uamrJ27dqMGzfud81UV2fcuHFZvXp1l63xf/7P/8mUKVNSVVXV7rV77703xx9/fM4+++zcdNNNaWxs7JI1AQAAAADofmUbY/Lyyy+npaVlu3EltbW1eeaZZ7pkje9///v5z//8z3zkIx9pd/zss8/OoYcemkMOOSRPPvlkbrzxxvz85z/Pl770pS5ZFwAAAACA7lXWmd2ltnTp0px00kkZNGhQu+N/+qd/2vb3YcOGpa6uLpdcckmee+65vPvd7+7uNgEAAAAA2ENlG2MyYMCA1NTUpKGhod3xhoaGDBw4cI+v//zzz2flypU577zzdll7zDHHJEmeffbZPV4XAAAAAIDuV7awu1+/fhk5cmTq6+vbjrW2tqa+vj7HHnvsHl9/2bJlqa2tzSmnnLLL2nXr1iVJ6urq9nhdAAAAAAC6X1nHmEybNi1XX311jj766IwePTp33HFHGhsbM3ny5CTJ7NmzM2jQoMyaNSvJbx44uWHDhra/b968OevWrct+++2Xww8/vO26ra2tWbZsWSZNmpQ+fdrf4nPPPZd77703J598cg466KA8+eSTmTt3bt73vvdl+PDh3XTnAAAAAAB0pbKG3WeddVZeeumlzJ8/P4VCISNGjMiCBQvaxphs3Lgx1dW/23z+4osvZtKkSW1fL1y4MAsXLsz73//+3HnnnW3HV65cmRdeeCFTpkzZbs2+ffumvr4+ixcvzmuvvZZ3vvOdOfPMM/Pnf/7npbtRAAAAAABKqqpYLBbL3UQl2rZtW4477rg8+uij6d+/f7nbAQC60Atnn52mtWt3Wddv5Mgc+u1vd0NHAAAA7ErZZnYDAAAAAEBXEXYDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFa/sYfeSJUty2mmnZdSoUTn//PPz+OOPv2Xt008/nY997GM57bTTMmzYsHzta1/bruaWW27JsGHD2v2ZMGFCu5o33ngjc+bMyfHHH59jjz02H/vYx7Jly5auvjUAAAAAALpJWcPuFStWZO7cuZkxY0aWL1+e4cOHZ/r06WloaNhhfWNjYw477LDMmjUrdXV1b3nd97znPfnRj37U9ufrX/96u9f/9m//Nv/2b/+WefPm5c4778yLL76Yv/iLv+jSewMAAAAAoPuUNexetGhRLrjggkyZMiVDhgzJnDlzsu+++2bp0qU7rB89enSuvvrqfOhDH0q/fv3e8ro1NTWpq6tr+3PwwQe3vfaf//mfWbp0aT71qU/lhBNOyNFHH52//du/zerVq7NmzZquvkUAAAAAALpB2cLupqamrF27NuPGjftdM9XVGTduXFavXr1H13722Wczfvz4nH766Zk1a1ZeeOGFtteeeOKJNDc3t1v3yCOPzKGHHirsBgAAAACoUH3KtfDLL7+clpaW1NbWtjteW1ubZ555ZrevO3r06MydOzd//Md/nEKhkFtvvTUXXnhh7r333vTv3z9btmxJ3759c+CBB263bqFQ2O11AQAAAAAon7KF3aVy8sknt/19+PDhOeaYY3Lqqafmvvvuy/nnn1/GzgAAAAAAKJWyjTEZMGBAampqtnsYZUNDQwYOHNhl6xx44IEZPHhwnnvuuSTJwIED09zcnFdeeWW7dXf20EsAAAAAAHqusoXd/fr1y8iRI1NfX992rLW1NfX19Tn22GO7bJ1XX301v/zlL9uC7KOPPjp9+/Ztt+4zzzyTF154IWPGjOmydQEAAAAA6D5lHWMybdq0XH311Tn66KMzevTo3HHHHWlsbMzkyZOTJLNnz86gQYMya9asJL95qOWGDRva/r558+asW7cu++23Xw4//PAkyec///mceuqpOfTQQ/Piiy/mlltuSXV1dc4+++wkyQEHHJApU6bkc5/7XN7+9renf//++Zu/+Zsce+yxwm4AAAAAgApV1rD7rLPOyksvvZT58+enUChkxIgRWbBgQdsYk40bN6a6+nebz1988cVMmjSp7euFCxdm4cKFef/7358777wzSbJp06ZcddVV2bp1aw4++OAcd9xxufvuu3PwwQe3nfdXf/VXqa6uzsc//vE0NTVl/Pjx+d//+393z00DAAAAANDlqorFYrHcTVSibdu25bjjjsujjz6a/v37l7sdAKALvXD22Wlau3aXdf1Gjsyh3/52N3QEAADArpRtZjcAAAAAAHQVYTcAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPGE3QAAAAAAVDxhNwAAAAAAFU/YDQAAAABAxRN2AwAAAABQ8YTdAAAAAABUPGE3AAAAAAAVT9gNAAAAAEDFE3YDAAAAAFDxhN0AAAAAAFQ8YTcAAAAAABVP2A0AAAAAQMUTdgMAQIUqtrSUtB4AACpJn3I3AAAA7J6qmpoUZs5M8/r1u6ztO2RI6ubNK31TAABQJsJuAACoYM3r16dp7dpytwEAAGVnjAkAAAAAABVP2A0AAAAAQMUTdgMAAAAAUPHKHnYvWbIkp512WkaNGpXzzz8/jz/++FvWPv300/nYxz6W0047LcOGDcvXvva17Wq+8pWvZMqUKTn22GNzwgkn5M///M/zzDPPtKu56KKLMmzYsHZ/rr322q6+NQAAAAAAuklZw+4VK1Zk7ty5mTFjRpYvX57hw4dn+vTpaWho2GF9Y2NjDjvssMyaNSt1dXU7rPnxj3+cCy+8MHfffXcWLVqUN998M9OnT89rr73Wru6CCy7Ij370o7Y/s2fP7vL7AwAAAACge/Qp5+KLFi3KBRdckClTpiRJ5syZkwcffDBLly7NZZddtl396NGjM3r06CTJTTfdtMNrfvWrX2339ec+97mccMIJWbt2bd73vve1Hd93333fMjAHAAAAAKCylG1nd1NTU9auXZtx48b9rpnq6owbNy6rV6/usnX+8z//M0ny9re/vd3xe++9N8cff3zOPvvs3HTTTWlsbOyyNQEAAAAA6F5l29n98ssvp6WlJbW1te2O19bWbjdje3e1trbmb//2bzN27NgMHTq07fjZZ5+dQw89NIccckiefPLJ3Hjjjfn5z3+eL33pS12yLgAA5VdsaUlVTU2X1wIAAD1TWceYlNqcOXPy9NNP5+tf/3q743/6p3/a9vdhw4alrq4ul1xySZ577rm8+93v7u42AQAogaqamhRmzkzz+vU7res7ZEjq5s3rnqYAAICSKVvYPWDAgNTU1Gz3MMqGhoYMHDhwj69//fXX58EHH8xdd92Vd7zjHTutPeaYY5Ikzz77rLAbAGAv0rx+fZrWri13GwAAQDco28zufv36ZeTIkamvr2871tramvr6+hx77LG7fd1isZjrr78+3/ve93LHHXfkj/7oj3Z5zrp165LEAysBAAAAACpUWceYTJs2LVdffXWOPvrojB49OnfccUcaGxszefLkJMns2bMzaNCgzJo1K8lvHmq5YcOGtr9v3rw569aty3777ZfDDz88yW9Gl3z729/Ol7/85ey///4pFApJkgMOOCD77rtvnnvuudx77705+eSTc9BBB+XJJ5/M3Llz8773vS/Dhw8vw7sAAAAAAMCeKmvYfdZZZ+Wll17K/PnzUygUMmLEiCxYsKBtjMnGjRtTXf27zecvvvhiJk2a1Pb1woULs3Dhwrz//e/PnXfemST5p3/6pyTJRRdd1G6tuXPnZvLkyenbt2/q6+uzePHivPbaa3nnO9+ZM888M3/+539e4rsFAAAAAKBUyv6AyqlTp2bq1Kk7fO23AfZvHXbYYXnyySd3er1dvf7Od74zd911V+eaBAAAAACgRyvbzG4AAAAAAOgqwm4AAAAAACqesBsA6LGKLS0lrQcAAGDvUfaZ3QAAb6WqpiaFmTPTvH79Lmv7DhmSunnzSt8UAAAAPZKwGwDo0ZrXr0/T2rXlbgMAAIAezhgTAAAAAAAqnrAbAAAAAICKJ+wGAAAAAKDiCbsBAAAAAKh4wm4AgF6o2NJSkloAAIBy6VPuBgAA6H5VNTUpzJyZ5vXrd1rXd8iQ1M2b1z1NAQAA7AFhNwBAL9W8fn2a1q4tdxsAAABdYrfGmLz55ptZuXJlvvGNb2Tbtm1Jks2bN+fVV1/t0uYAAAAAAKAjOr2z+/nnn8+ll16ajRs3pqmpKR/4wAfSv3//3H777Wlqasr1119fij4BAAAAAOAtdXpn9w033JCjjz46P/7xj7PPPvu0Hf/gBz+YVatWdWlzAAD8/+3dd3gU1f4/8HcSilIvTZByQUCydIIUIcHQJKRQQ7GggBRBLEgsgCDtCugXkCJXBKQXgTRKAkhRBAmKCCiItFAEAZNQkkCAlPP7g1/2suxusnvOZiaTfb+eh+chO3N25pOc+pnZWSIiIiIiInKE03d2Hzp0CGvXrkWRIkUsXq9SpQquXbvmshMjIiIiIiIiIiIiInKU03d2Z2VlISsry+r1q1evonjx4i45KSIiIiIiIiIiIiIiZzid7Pb19cXy5cstXrt9+zbmzZsHf39/l50YEREREREREREREZGjnE52jx49Gr/++iuCgoJw//59vPfee2jfvj2uXbuG9957Ly/OkYiIiIiIiIiIiIgoR04/s7tSpUrYuHEjYmJicPLkSdy5cwe9evVCly5d8Nhjj+XFORIRERERERERERER5cjpZDcAFCpUCN26dXPJCaxevRpff/01EhISYDKZMH78eDRq1MjmvqdPn8bcuXNx/PhxXL58GWPGjMGAAQOcfs979+5h+vTpiI2Nxf379+Hn54cJEyagfPnyLomJiIiIiIiIiIiIiLTldLI7Ojo6x+3du3d3+L1iY2Mxbdo0TJo0CY0bN8by5csxaNAgbNu2DeXKlbPaPy0tDVWrVkXnzp0xbdo06fecOnUq9uzZg9mzZ6NkyZKYMmUK3nzzTXzzzTcOnzsRERERERERERER5R9OJ7s/+eQTi58zMjKQlpaGwoUL4/HHH3cq2b106VL06dMHoaGhAIBJkybh+++/R0REBIYOHWq1f6NGjcx3aM+cOVPqPVNSUhAREYEZM2agVatWAB4kv4OCgnDkyBE0adLE4fMnIiIiIiIiIiIiovzB6WT3wYMHrV47f/48Jk6ciEGDBjn8Pvfv38fx48fx+uuvm1/z9PRE69atcfjwYWdPy+H3PHbsGNLT09G6dWvzPrVq1ULlypWZ7CYiIiIiIiIiIiIyKE9XvEmNGjUQFhZmddd3Tm7cuIHMzEyrx5WUK1cOiYmJUufhyHsmJiaicOHCKFWqlNU+CQkJUsclIiIiIiIiIiIiIn25JNkNPPjSyn/++cdVb0dERERERERERERE5DCnH2Oya9cui5+FEEhISMDq1avRtGlTh9+nTJky8PLyQlJSksXrSUlJKF++vLOn5fB7li9fHunp6UhOTra4uzspKQkVKlSQOi4RERERERERERER6cvpZPeIESMsfvbw8EDZsmXx7LPP4sMPP3T4fYoUKYL69esjLi4OHTt2BABkZWUhLi4O/fr1c/a0HH7PBg0aoHDhwoiLi0NAQAAAID4+Hn///Tef101ERERERERERERkUE4nu//880+XHXzgwIH48MMP0aBBAzRq1AjLly9HWloaevbsCQD44IMPULFiRYSFhQF48AWUZ8+eNf//2rVrOHHiBIoVK4bq1as79J4lS5ZEaGgopk+fjtKlS6NEiRL4z3/+Ax8fHya7iYiIiIiIiIiIiAzK6WS3KwUFBeH69euYO3cuEhISULduXSxevNj8yJErV67A0/N/jxX/559/0L17d/PPS5YswZIlS9CiRQusXLnSofcEgLFjx8LT0xNvv/027t+/Dz8/P0yYMEGboImIiIiIiIiIiIjI5RxKdk+bNs3hNxwzZoxTJ9CvXz+7jy3JTmBnq1q1Kk6ePKn0ngBQtGhRTJgwgQluIiIiIiIiIiIiogLCoWT3H3/84dCbeXh4KJ0MEREREREREREREZEMh5Ldj95hTURERERERERERESUn3jmvgsRERERERERERERUf4m9QWVv//+O7Zu3YorV64gPT3dYtsXX3zhkhMjIiIiIiIiIiIiInKU03d2x8TE4MUXX0R8fDx27NiBjIwMnD59GgcOHEDJkiXz4hyJiIiIiIiIiIiIiHLkdLJ7wYIFGDNmDBYsWIDChQvjo48+wrZt2xAYGIgnn3wyL86RiIiIiIiIiIiIiChHTie7//rrL/j7+wMAihQpgjt37sDDwwMDBgzA+vXrXX6CRERERERERERERES5cTrZXapUKdy+fRsA8MQTT+D06dMAgOTkZKSlpbn27IiIiIiIiIiIiIiIHODwF1SeOnUKderUQfPmzbF//354e3ujc+fO+OSTT3DgwAHs378frVq1ystzJSIiIiIiIiIiIiKyyeFkd9euXdGwYUN07NgRnTt3BgAMHz4chQsXxq+//opOnTph+PDheXaiRERERERERERERET2OJzsXrVqFSIiIvDVV19hwYIF6NSpE3r37o2hQ4fm5fkREREREREREREREeXK4Wd2N2vWDNOmTcO+ffswbtw4XL58Gf369UNAQAAWLlyIhISEvDxPIiIiIiIiIiIiIiK7nP6CymLFiiE0NBSrVq3C9u3b0blzZ6xZswbt2rXDsGHD8uIciYiIiIiIiIiIiIhy5HSy+2HVq1fH66+/juHDh6N48eLYs2ePq86LiIiIiIiIiIiIiMhhDj+z+1EHDx5EREQEtm/fDk9PTwQGBqJXr16uPDciIiIiIiIiIiIiIoc4ley+du0aoqKiEBUVhQsXLsDHxwfjxo1DYGAgihUrllfnSERERERERERERESUI4eT3YMHD0ZcXBzKlCmDbt26ITQ0FDVr1szLcyMiIiIiIiIiIiIicojDye5ChQphzpw5aNeuHby8vPLynIiIiIiIiIiIiIiInOJwsnvBggV5eR5EREREVICJzEx4OHHDhLP7ExERERERSX9BJRERERGRozy8vJAwciTSz5zJdd/CtWujwuzZeX9SRERERERUoOSLZPfq1avx9ddfIyEhASaTCePHj0ejRo3s7r9161bMmTMHly9fRo0aNfDee+/B39/fvN3b29tmuffffx+DBw8GALRv3x6XL1+22B4WFoahQ4e6ICIiIiIielT6mTO4f/y43qdBREREREQFlO7J7tjYWEybNg2TJk1C48aNsXz5cgwaNAjbtm1DuXLlrPb/9ddfERYWhlGjRqFdu3bYvHkzRowYgcjISNSpUwcAsG/fPosyP/zwAz766CMEBARYvP7222+jT58+5p+LFy+eBxESERERERERERERUV7z1PsEli5dij59+iA0NBS1a9fGpEmT8NhjjyEiIsLm/itWrECbNm0wePBg1KpVCyNHjkS9evWwatUq8z4VKlSw+Ldr1y60bNkS1apVs3iv4sWLW+xXrFixPI2ViIiIiIiIiIiIiPKGrsnu+/fv4/jx42jdurX5NU9PT7Ru3RqHDx+2WebIkSNo1aqVxWt+fn44cuSIzf0TExOxZ88e9OrVy2rbokWL0LJlS3Tv3h2LFy9GRkaGfDBEREREREREREREpBtdH2Ny48YNZGZmWj2upFy5coiPj7dZJjExEeXLl7faPzEx0eb+UVFRKF68ODp16mTx+iuvvIJ69eqhdOnSOHz4MGbNmoWEhASMGTNGISIiIiIiIiIiIiIi0oPuz+zOaxEREejSpQuKFi1q8frAgQPN/zeZTChcuDAmTJiAsLAwFClSROvTJCIiIiIiIiIiIiIFuj7GpEyZMvDy8kJSUpLF60lJSVZ3b2crX7681V3c9vb/5ZdfcO7cOfTu3TvXc2ncuDEyMjJw6dIlJyIgIiIiIiIiIiIiovxA12R3kSJFUL9+fcTFxZlfy8rKQlxcHHx8fGyWadKkCQ4cOGDx2v79+9GkSROrfcPDw1G/fn2YTKZcz+XEiRPw9PS0eqQKEREREREREREREeV/uia7gQePE1m/fj2ioqJw9uxZTJw4EWlpaejZsycA4IMPPsDMmTPN+7/66qvYu3cvlixZgrNnz2LevHk4duwY+vXrZ/G+qamp2LZtm827ug8fPoxly5bhzz//xF9//YVNmzZh2rRp6Nq1K0qXLp23ARMRERERERERERGRy+n+zO6goCBcv34dc+fORUJCAurWrYvFixebH0ty5coVeHr+LyfftGlTzJgxA7Nnz8asWbNQo0YNzJ8/H3Xq1LF435iYGAghEBISYnXMIkWKIDY2Fl988QXu37+PqlWrYsCAARbP8SYiIiIiIiIiIiIi49A92Q0A/fr1s7ozO9vKlSutXgsMDERgYGCO79m3b1/07dvX5rb69etj/fr1zp8oEREREVEORGYmPLy8XL4vERERERHlLl8ku4mIiIiICgIPLy8kjByJ9DNnctyvcO3aqDB7tjYnRURERETkJpjsJiIiIiJyofQzZ3D/+HG9T4OIiIiIyO3o/gWVRERERERERERERESqmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsPLF8nu1atXo3379mjYsCF69+6N3377Lcf9t27dis6dO6Nhw4bo0qUL9uzZY7F99OjR8Pb2tvg3aNAgi31u3ryJsLAwNG3aFM2aNcPYsWNx+/Ztl8dGRERERERERERERHlP92R3bGwspk2bhhEjRiAqKgomkwmDBg1CUlKSzf1//fVXhIWFoVevXoiOjkaHDh0wYsQInDp1ymK/Nm3aYN++feZ/s2bNstj+3nvv4cyZM1i6dCkWLFiAX375BR9//HGexUlEREREREREREREeUf3ZPfSpUvRp08fhIaGonbt2pg0aRIee+wxRERE2Nx/xYoVaNOmDQYPHoxatWph5MiRqFevHlatWmWxX5EiRVChQgXzv9KlS5u3nT17Fnv37sV//vMfNG7cGM2aNcO4ceMQExODa9eu5Wm8REREREREREREROR6uia779+/j+PHj6N169bm1zw9PdG6dWscPnzYZpkjR46gVatWFq/5+fnhyJEjFq/9/PPPaNWqFQICAjBhwgTcuHHDvO3w4cMoVaoUGjZsaH6tdevW8PT0zPURKkRERERERERERESU/xTS8+A3btxAZmYmypUrZ/F6uXLlEB8fb7NMYmIiypcvb7V/YmKi+ec2bdrg+eefR9WqVfHXX39h1qxZGDJkCNatWwcvLy8kJiaibNmyFu9RqFAhlC5dGgkJCS6KjoiIiIiIiIiIiIi0omuyO68EBweb/5/9BZUdO3Y03+1NRERERERERERERAWLro8xKVOmDLy8vKy+jDIpKcnq7u1s5cuXt7iLO7f9AaBatWooU6YMLly4YH6P69evW+yTkZGBW7duoUKFCjKhEBEREREREREREZGOdE12FylSBPXr10dcXJz5taysLMTFxcHHx8dmmSZNmuDAgQMWr+3fvx9NmjSxe5yrV6/i5s2b5kS2j48PkpOTcezYMfM+Bw4cQFZWFho1aqQQEREREdkiMjPzZF8iIiIiIiKibLo/xmTgwIH48MMP0aBBAzRq1AjLly9HWloaevbsCQD44IMPULFiRYSFhQEAXn31VbzyyitYsmQJ/P39ERsbi2PHjmHy5MkAgNu3b+OLL75AQEAAypcvj7/++gv/93//h+rVq6NNmzYAgFq1aqFNmzYYP348Jk2ahPT0dEyZMgXBwcGoWLGiPr8IIiKiAszDywsJI0ci/cyZHPcrXLs2Ksyerc1JERERERERUYGie7I7KCgI169fx9y5c5GQkIC6deti8eLF5seSXLlyBZ6e/7sBvWnTppgxYwZmz56NWbNmoUaNGpg/fz7q1KkDAPDy8sKpU6cQHR2NlJQUPPHEE/D19cU777yDIkWKmN9nxowZmDJlCvr37w9PT0906tQJ48aN0zZ4IiIiN5J+5gzuHz+u92kQERERERFRAaV7shsA+vXrh379+tnctnLlSqvXAgMDERgYaHP/xx57DF9//XWux/zXv/6FmTNnOneiRERERERERERERJQv6frMbiIiIiIyFj5/nYiIiIiI8qt8cWc3ERERERkDn79ORERERET5FZPdREREROQUPn+diIiIiIjyIz7GhIiIiIiIiIiIiIgMj8luIiIiIiIiIiIiIjI8JruJiIiIiIiIiIiIyPCY7CYiIiIiIiIiIiIiw2Oym4iIiIiIiIiIiIgMj8luIiIiIiIiIiIiIjI8JruJiIiIiIiIiIiIyPCY7CYiIiIiIiIiIiIiw2Oym4iIiIiIiIiIiIgMj8luIiIiIiIiIiIiIjI8JruJiIiIiIiIiIiIyPCY7CYiIiIiIiIiIiIiw2Oym4iIiIiIiIiIiIgMj8luIiIiIiIiIiIiIjI8JruJiIiIiIiIiIiIyPCY7CYiIiIiIiIiIiIiw2Oym4iIiIiIiIiIiIgMj8luIiIiIiIiIiIiIjK8fJHsXr16Ndq3b4+GDRuid+/e+O2333Lcf+vWrejcuTMaNmyILl26YM+ePeZt6enp+L//+z906dIFTZo0gZ+fHz744ANcu3bN4j3at28Pb29vi38LFy7Mk/iIiIiIiIiIiIiIKG/pnuyOjY3FtGnTMGLECERFRcFkMmHQoEFISkqyuf+vv/6KsLAw9OrVC9HR0ejQoQNGjBiBU6dOAQDu3r2LP/74A8OHD0dkZCS++OILnDt3DsOHD7d6r7fffhv79u0z/+vXr1+exkpEREREREREREREeUP3ZPfSpUvRp08fhIaGonbt2pg0aRIee+wxRERE2Nx/xYoVaNOmDQYPHoxatWph5MiRqFevHlatWgUAKFmyJJYuXYqgoCDUrFkTTZo0wfjx43H8+HH8/fffFu9VvHhxVKhQwfyvWLFieR4vEREREREREREREbmersnu+/fv4/jx42jdurX5NU9PT7Ru3RqHDx+2WebIkSNo1aqVxWt+fn44cuSI3eOkpqbCw8MDpUqVsnh90aJFaNmyJbp3747FixcjIyNDPhgiIiIiIiIiIiIi0k0hPQ9+48YNZGZmoly5chavlytXDvHx8TbLJCYmonz58lb7JyYm2tz/3r17mDFjBoKDg1GiRAnz66+88grq1auH0qVL4/Dhw5g1axYSEhIwZswYxaiIiIiIiIiIiIiISGu6JrvzWnp6Ot555x0IITBp0iSLbQMHDjT/32QyoXDhwpgwYQLCwsJQpEgRrU+ViIiIiIiIiIiIiBTo+hiTMmXKwMvLy+rLKJOSkqzu3s5Wvnx5q7u4be2fnp6OkSNH4u+//8aSJUss7uq2pXHjxsjIyMClS5ckIiEiIqL8RmRm5sm+RERERERElD/pemd3kSJFUL9+fcTFxaFjx44AgKysLMTFxaFfv342yzRp0gQHDhzAgAEDzK/t378fTZo0Mf+cnei+cOECVqxYgTJlyuR6LidOnICnp6fVI1WIiIjImDy8vJAwciTSz5zJcb/CtWujwuzZ2pwUERERERER5RndH2MycOBAfPjhh2jQoAEaNWqE5cuXIy0tDT179gQAfPDBB6hYsSLCwsIAAK+++ipeeeUVLFmyBP7+/oiNjcWxY8cwefJkAA8S3W+//Tb++OMPfPXVV8jMzERCQgIAoHTp0ihSpAgOHz6Mo0eP4tlnn0Xx4sVx+PBhTJs2DV27dkXp0qX1+UUQERGRy6WfOYP7x4/rfRpERERERESkAd2T3UFBQbh+/Trmzp2LhIQE1K1bF4sXLzY/luTKlSvw9Pzf01aaNm2KGTNmYPbs2Zg1axZq1KiB+fPno06dOgCAa9euYffu3QCAbt26WRxrxYoVaNmyJYoUKYLY2Fh88cUXuH//PqpWrYoBAwZYPMebiIiIiIiIiIiIiIxD92Q3APTr18/uY0tWrlxp9VpgYCACAwNt7l+1alWcPHkyx+PVr18f69evd/5EiYiIiIiIiIiIiChf0vULKomIiIiIiIiIiIiIXIHJbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiIiIiIiIiIyPCa7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIiIiIiIiIiIDI/JbiIiIiKih4jMzDzdn4iIiIiI8kYhvU+AiIiIyN2JzEx4eHm5fF+S4+HlhYSRI5F+5kyu+xauXRsVZs/O+5MiIiIiIqJcMdlNREREpDNHk6vumljV42JA+pkzuH/8uPL7EBERERGRdpjsJiIiIsoHmFy1jxcDiIiIiIjIEUx2ExEREVG+x4sBRERERESUG35BJREREREREREREREZHpPdRERERERERERERGR4+SLZvXr1arRv3x4NGzZE79698dtvv+W4/9atW9G5c2c0bNgQXbp0wZ49eyy2CyEwZ84c+Pn5oVGjRhgwYADOnz9vsc/NmzcRFhaGpk2bolmzZhg7dixu377t6tCIiIiIiEhRVlZWnuxLRPKyhBPt0ol9iYiIVOj+zO7Y2FhMmzYNkyZNQuPGjbF8+XIMGjQI27ZtQ7ly5az2//XXXxEWFoZRo0ahXbt22Lx5M0aMGIHIyEjUqVMHALBo0SKsXLkS06dPR9WqVTFnzhwMGjQIsbGxKFq0KADgvffeQ0JCApYuXYr09HSMHTsWH3/8MWbOnKlp/ERERFQwiMxMeHh55dn++eWYRHrw9PREZGQkEhISctyvQoUK6Nmzp0ZnReTePD088X3S97iZfjPH/f5V+F9oW66tJudERESke7J76dKl6NOnD0JDQwEAkyZNwvfff4+IiAgMHTrUav8VK1agTZs2GDx4MABg5MiR2L9/P1atWoXJkydDCIEVK1Zg+PDh6NixIwDgs88+Q+vWrbFz504EBwfj7Nmz2Lt3L8LDw9GwYUMAwLhx4zB06FB88MEHqFixokbRExERUUHh4eWFhJEjkX7mTK77Fq5dGxVmzzbkMYmysrLg6en4B0Sd3d+ehIQEXL16Vfl96H/0+ltSwXEz/SaS0pP0Pg0i+v+EEPDw8HD5vkRGomuy+/79+zh+/Dhef/1182uenp5o3bo1Dh8+bLPMkSNHMGDAAIvX/Pz8sHPnTgDApUuXkJCQgNatW5u3lyxZEo0bN8bhw4cRHByMw4cPo1SpUuZENwC0bt0anp6e+O233/D888/neu5CCABAamqqw/ESERG5s/vVqyM9MzPnnapXtxpbHSqnUtaFx7ydnu5QucLp6XjcReeqxzEL3N8yH5yryl36sufqTOLy0X1//PFH3Lp1K9dypUuXhq+vr0uOWbJkSdy7dy/HMiVLlnTZ/FzlXPP6eI/ur8ffUpbWv1e9jikrS2TB08PBc31oX2fKPby/bLlsRe8XRfH04jmWKSqKuq5dSv5+tDjeo/tr8bd0RYx6HFO13mlNNoGsV+I54+9/IO7dz3Efj6JFUKjyEy45nlE4+zvW+2/JCxf2FS9ePMd4dU1237hxA5mZmVaPKylXrhzi4+NtlklMTET58uWt9k9MTAQA80cbbb1n9j6JiYkoW7asxfZChQqhdOnSuX40Mlv28739/f0d2p+IiIgcEB8PPPOMtmWNdEyea/4qp9cxZelxTB1MmjRJ71MgIiIiojxy6NAhlChRwu523R9jYlRPPPEE9uzZk+vVBCIiIiIiIiIiIiJSV7x4zp8o0jXZXaZMGXh5eSEpyfIZX0lJSVZ3b2crX768+Q5tW/tXqFDB/NoTTzxhsY/JZDK/x/Xr1y3eIyMjA7du3TKXz42npycqVark0L5ERERERERERERElLd0/XaRIkWKoH79+oiLizO/lpWVhbi4OPj4+Ngs06RJExw4cMDitf3796NJkyYAgKpVq6JChQoW75mamoqjR4+a39PHxwfJyck4duyYeZ8DBw4gKysLjRo1clV4RERERERERERERKQR3b9Ke+DAgVi/fj2ioqJw9uxZTJw4EWlpaejZsycA4IMPPsDMmTPN+7/66qvYu3cvlixZgrNnz2LevHk4duwY+vXrBwDw8PDAq6++ii+//BK7du3CyZMn8cEHH+CJJ55Ax44dAQC1atVCmzZtMH78ePz22284dOgQpkyZguDgYFSsWFH7XwIRERERERERERERKdH9md1BQUG4fv065s6di4SEBNStWxeLFy82P5bkypUrFt+G3bRpU8yYMQOzZ8/GrFmzUKNGDcyfPx916tQx7zNkyBCkpaXh448/RnJyMp555hksXrwYRYsWNe8zY8YMTJkyBf3794enpyc6deqEcePGaRc4EREREREREREREbmMhxBC6H0SREREREREREREREQqdH+MCRERERERERERERGRKia7iYiIiIiIiIiIiMjwmOwmIiIiIiIiIiIiIsNjspuIiIhy9ffff4Nf80FERERERET5GZPdVKC4SzImr+I8e/YsPv30U6vX7969i127diE1NdVqW2pqKnbt2oX79++79Fw6dOiAGzduuPQ98xt3qa8kJzU1FWvWrEHPnj31PhUAD9rk9evX9T4NchF36X/yIk57YyWg/XjpDmMlkSOysrLw3Xff2dyWmpqKrKwsq9czMzNttlVVY8aMyZP3JX24w3jJtSUZDdulPHdpl0x25yF3WAy5SzKmIMd5584dbNiwAS+88AKCg4Oxd+9eq33WrVuHFStWoESJElbbSpQogZUrV2LDhg1W2y5evIgxY8aYf27bti1atGhh/vfss88iPj7e5nldvnzZ5sJERUH+O9py8+bNPHtvZ0RHR7t8YHxYQYvzwIEDeP/99+Hn54f//ve/aNy4sc39rl27hk8//dTmOJKSkoJPP/0UiYmJuR7v9OnT+PPPP83/Tp8+bXO/vJxQusOky136n4IapyNjJaD9eJkXY2U2d0keukP/U5DXJBcuXMCsWbPw3HPP4c0337TavmPHDoSGhuLevXtW2+7du4fQ0FDs3r3b5nsnJydjzZo15p/DwsLw5ptvmv+9/fbbSE5OtioXHR1t83iu4A71taCOIw8ryDHm5doSyJ/jJdul9tguneOO7ZLJbhdzl8WQuyRjHInTVTEC2sZ56NAhjBkzBr6+vvj444/h4+ODmJgYbNmyxWrfzZs3o3///nbfq3///oiKirJ6feXKlShfvrz551u3bmHo0KEYM2YMxowZg4YNG2LZsmXKseSmIP8dbdm3bx/eeecdPPfccza3Hzt2DK+88ordWF955RX8+eefVtuysrJw8uRJ889r167FihUrzP9Wr15ts58ZM2YMUlJSFCKyLac4tY4RUIvz2rVr+PLLL/H888/jnXfewZYtWzB16lTs3bsXEyZMsFlm2bJlSE1NtTmOlCxZErdv38bSpUuttv3yyy8IDQ01/9ynTx/06NED3bt3R/fu3dG1a1fs37/f5jE9PDyk4rPFXSZd7tL/aBmnozEC6nE6M1YCxh8vAfdIHrpD/1OQ1yR3795FdHQ0Xn75ZXTu3BmHDx/GiBEjsGfPHqt9165di8GDB+Pxxx+32lasWDEMGTIEq1evtnmc9evX49ChQ+afd+/eDU9PT5QsWRIlS5bEqVOnsHz5cqtyru5f3eXGFHcYLwvyGlqLtSWQf8ZLtsv/MeL87mFslw8UhHb5sEKaHq0AO3ToEMLDw7Ft2zbcvXsXAwYMwCeffIJatWrZ3H/z5s1444037L5f//79MX/+fLz88ssWr9uqRCNGjEC5cuUAALGxsVi2bBkmT57sgqgsXbt2DZGRkYiMjERycjKSk5Mxc+ZMBAYG2k2AOJqMef/99y22/fLLL5g2bRoiIiIAPEjG3L1719zgPTw88PXXX6N169ZW76uajHE2TtkY9YgzKSkJkZGRiIiIQGpqKoKDg7FixQq88MILCA0NtVtfL1y4AJPJZPd9vb29ceHCBavXDxw4gE8++cTitYCAAFSrVg0AUKVKFYwbN87u++7duxclS5bMMaYOHTrYfL0g/x1tuXz5MiIiIhAdHY1bt27hueees3sX19KlS/Hss8/ajdXX1xeLFy/GjBkzLLbFxMTgm2++MS8KP/vsM5QqVQpeXl4AgBs3bqBIkSLo3bu3RTlXTkYcjVPrGAG5OLdv347w8HD88ssvaNOmDT788EM899xz8PHxQZ06dXKsH3v37sXEiRPtbu/evTvGjx9vVWfXrFmDbt26Wby2YsUKVK5cGUIIrFy5EmvXrrVZX2fPnm0zYfCwhyfntjgzXsqOlYCxxkuj9j9axakSo0ycsmMloM94qTJWZrt79y62bduGDRs24Ndff0WzZs0wYsQIPP/881b7Opo8bN++vdX29evX48SJE3jppZcAPEgetmnTBsWLFwcAHD58GMuXL8dbb71lUc5V44g79D8FeU3y22+/ITw8HDExMfj3v/+NLl264PDhw5gwYQJq165ts8ypU6fsXjQGgObNm2P27Nk2t23fvh3vvvuuxWvvv/++uV3u2LED8+fPt6qvAHD79m0ULVo0x3hs9YUP06K+6llXAfcYLwvyGlrrtSWg/3jJdmnc+Z1KjHrFyXb5QG7t0hYmuxW4y2LIXZIxsnHKxqhHnO3atUNAQAA++ugj+Pr6wtPTsQ93ZGRk4Pr166hcubLN7devX0dGRobV65cuXcITTzxh/rl3794Wg0OVKlVw9epVu8cdPXp0jufl4eGBEydOWLzmDn/HbPfv38eOHTvMyYnWrVvj6tWriIqKgre3t933O3r0KIYMGWJ3e7t27WzeZRAZGWk1GVu1apW5/1m7di02bdpkMxGsMhmRiVOPGAHn43z33XcxZMgQfP7557kueh916dIlu20SACpVqoTLly9bvX7s2DEMGzbMat8qVaoAALp164ahQ4fafM9Tp06hcOHCdo9pL353mXS5S/+jdZwqMcrEKTtWAvqMlzJjZTZ3SB66Q//jDmuSLl264Pbt2wgJCcE333yDp59+GgAwc+bMHN8nOTnZZpvLlpGRYfPTBADw119/4amnnjL//NRTT1mMgSaTyW4dCAgIsHtMIYTddukuN6a4w3jpDmtordeWgD7jJdtlwZjfqcSoV5xslznPY3PCZLcCd1kMuUsyRjZO2RgB7eOsXLkyDh06hMqVK6Ny5co5Ln4e9vTTT2P//v1o0KCBze0//vijedHxME9PT/zzzz+oVKkSAGDs2LEW25OSklCokP1u6McffzRfwXaUO/wdAWDKlCnYsmULatSoga5du+Lzzz9HmTJlUL9+ffNdyPZcu3bNfBedLcWKFUNCQoLV6/Hx8XbrAAC0aNECn3/+uc1t/fv3z/FvDcDmx6Jk49QjRsD5OHv16oXVq1fjp59+Qrdu3RAUFITSpUvnWD5b0aJFcfnyZbv19vLlyzYTQ1evXrWYAH/66acWd4+ULl3a7nPQ58+f73SbBNxn0uUu/Y/WcarECDgfp+xYCegzXsqMlYD7JA/dof9xhzXJuXPnEBQUhJYtW9q9EGNLlSpVcOzYMbvt+Pfff7cbe1paGlJSUvDkk08CeHBB/GF37tyx+7iAuXPnOjyeP8wdbkwB3GO8dIc1tNZrS0Cf8ZLtMmdGmd8BbJc5MVq7zA2T3QrcZTHkLskY2ThlYwS0j3Pbtm3mj1716tULTz31FLp27Qog5ztSQ0NDMX36dDz99NNo166dxbbdu3djwYIFNgfV7HreqFEjm++7d+9e1KlTx+Y22TuB3eHvCDy4w3jIkCEYMmSI0wN12bJlce7cOfPdAY+Kj49HmTJlrF5/9Asydu3ahX/961/mnwsVKoQ7d+7YfE8/P78ck8/2yMapR4yA83FOnjwZY8eOxdatWxEREYGpU6fCz88PQohcn7fXuHFjbNy4Ec2bN7e5PTo62mbbK168OC5evGhevHfq1Mli+6VLl2z+rlXuzneXSZe79D9ax6kSI+B8nLJjJaD9eKnSLt0leegO/Y87rEl27dqFyMhITJw4EXfv3kVISAi6dOmSaxvo1KkTZs+eDV9fX4t+AwASEhIwZ84cc/t+VLVq1fDHH3/YnaseO3YMVatWtbmtadOmUv2rO9yYArjHeOkOa2it15aAPuMl22XOjDK/A9guC1K7zA2T3QrcZTHkLskY2ThlYwT0ifOZZ57BM888g3HjxiEmJgaRkZHIzMzExIkT0aVLF3Ts2BFly5a1KNO3b18cPHgQw4cPR82aNc13ZcXHx+P8+fMIDAxE3759rY7Vs2dPTJ06FSaTCW3btrXYtnv3bixatMhq8M4m+3xOd/k7fvbZZ4iIiECbNm3g7++Pbt262f1Cyke1bt0aCxYssLm/EAILFiyw+TGq8uXL49y5c/j3v/8NAFb15OzZs6hQoYLNYw4ePFhq0iUbpx4xAnJxPvbYY+jRowd69OiB8+fPIzIyEseOHcOLL76Itm3bIiAgwKpOAcBrr72G1157DSVLlsSgQYPMk6bExEQsXrwYUVFR+Prrr63KNW7cGNHR0WjZsqXN84mKirL5xSwqz8x1l0mXu/Q/WscpGyMgH6fMWAloP16qtEt3SR66Q//jDmuSihUrYvjw4Rg+fDji4uIQERGBF198ERkZGYiMjETv3r0tPjWQbciQIdi1axc6deqErl27WrTJzZs348knn7T72LOOHTti9uzZ8PPzs1nX582bZ/XRdFXucGMK4B7jpbusobVcWwL6jJdslwVnfsd2WXDaZa4EuURqaqpYt26d6Nu3r/D29hb9+vUT69atE0lJSXbLhIWFCW9vbxEYGCjeeOMN8cYbb4jOnTsLk8kk3n33XZtl1q1bJxo3biy+++47q227du0SjRs3FuvWrbPa5u3tLRITE6Xje9i5c+fEzJkzhZ+fn2jatKkYNWqU2L59u8194+LiRN26dcX06dNFQkKC+fWEhAQxbdo0UbduXbF//36rcq+//roYPXq03XP48MMPxdChQ61e1yNO2RiFyB9xCiHEmTNnxPTp00WrVq1EvXr17O4XExMjhg8fLoKCgkRgYKAYPny4iImJyfG93333Xat6HhgYKEwmk3j77bftlhs9erRISUmRjilbQf87Xrx4UcyZM0e0bdtWtGzZUphMJrF169Ycy1y4cEE888wzolevXiImJkacOHFCnDhxQsTExIjQ0FDxzDPPiPPnz1uVGz16tOjbt6/N98zKyhJ9+/a1+XswmUyax6l1jEK4Js5smZmZYteuXWL48OGifv36dvdbu3ataNCggTCZTKJZs2aiefPmwmQyiQYNGojVq1fbLBMXFydMJpOYPn26xfkmJibmWNcjIyPFvXv3lGNzdryUGSuFMNZ4adT+J1texykboxCujdPRsVII7cZLV42V+/fvF2FhYaJRo0bC29tbfPrppyI+Pt7mvikpKSI4OFj4+PiICRMmiGXLlolly5aJjz/+WPj4+IigoCC75/T5558Lf39/i79/tn/++Uf4+/uLWbNmWW1z1d/RHfofd1qTJCcni1WrVokePXoIb29vERISYne/CRMmiBYtWghvb2/h7e0tmjdvLiZMmCBu3rxp9/1TUlJEYGCg8PHxERMnTjTX9QkTJggfHx/RuXNnm3W9Xbt24vr168rxaVFfZf+OQhhrHBFC//HSXdbQQuTt2lIIfcdLtsuCNb9ju7RmxHZpi4cQeZlKd09nz55FeHg4Nm7ciFu3buH48eN2942NjcWWLVtw4cIFCCFQo0YNhISEICgoyG6ZUaNGITY21uJqy7lz53Du3Dl06tQJc+bMsSozZswYfPTRR04/7iAnWVlZ+P777xEeHo4ffvgBx44ds7nfN998g08++QQZGRkoUaIEPDw8kJKSgkKFCmHMmDF46aWXrMocOHAAAwcOxIABAyzulExKSsKiRYuwYsUKfP3112jVqpVFuaioKAQHB6NIkSKaxikTY36LE3jwXLHdu3fbvItURUxMDGJiYnD+/HkAQPXq1RESEoLg4GCb+//5559OH6N27do5fpyroP8dhRDYt28fwsPDsXv3bpQpUwadOnWy++Ulv//+O8aMGYMzZ86YrxoLIVC7dm1MnTrV5lXpixcvokePHqhZsyZee+01i6u9S5Yswblz5xAZGYnq1atblDOZTC57FpczcWoZo6vjfFhSUlKO73nt2jVs3brVYhzp3Lmz+aOPtqxevRrTpk1DZmamRV338vLC6NGj0a9fP4v9V6xY4fR59+zZM9cxx9HxUmasBIw1Xhq5/8nrOGVjzKs482qsBJwbL/NirExJScGmTZsQERGBP/74A08//TQ2b95sc7+ZM2di69atuHXrFgCgVKlSCAoKwrvvvmv3I8Gpqano06cPrl69im7duqFGjRoAHrTLTZs2oWLFitiwYYNV+2vfvj0iIiJsPn5Kljv0P+6yJgGAEydOICIiIscvbRNC4MaNGxBCoGzZsg7dNXfr1i3MnDkT27ZtMz+LvlSpUujcuTNGjRpl8aizvJSX9VXm7whwvMzLGGXjzC8xZivI4yXAdvkwo8/v2C5dIz+0y2xMduchd6lEQMFOxjwspzhlYgS0i/PSpUtOl6tduzbOnDkjVc6Z+vMok8kEDw8Phz/W4unpie3bt9t9PvOjjPx3dKS+3rx5E9HR0YiMjMSmTZty3PePP/4wx/rUU0+hbt26Oe7/22+/YfTo0YiPj7dIINesWRPTpk2z+fiL7GebufqZXI7GqUWMgPNxatFn5eTKlSvYvn27eRypUaMGAgICzB+xe5jJZEKlSpUc/kKeq1evOtUmOV4+UBD6n2yujtPZGAG5OBs2bJjrt94/KrsOaFF/HpbXY2VBTx5mc4f+xx1izGtCCPP3euRU11u0aOHU+3p4eCAyMtL8RWW5Kcg3pjzKHcZLI6+hubb8H7bLB/Lz/I7t0r6C2i6Z7Jak0pEYaTHkLskYd4jz+eefd6geZO+TXQ8cLZctu9zt27cd2v9h2fXcZDJhw4YNNp+H+ighBLp06YJNmzbhu+++c/qYRvs7Otq557U//vjDIs569erZ3O/NN990+r0nTZqUJ9/I7CxHYwTk4ty5c6dh+ixn71r38fHBpk2bUK1aNekxz2iTLncYR/SO05kYAbk4s7KypMbKatWqSdcf2fGyQYMGUmNlfhlHtEgeukP/ozLfMsqaJDQ01KnjZNeBHj16SJVzNPFsi8lkwtixYy2+jMweIQQmTZqELVu2cLzMhdHGS3dYk2i9tlRpIyrjJdulfUaa37FdWjJ6u3SE/pfJDap79+7SiyFHy2ZTncwCwPr1652qRNmmTp3qdCNr164dSpQo4ZIO88knn8SAAQMcLh8REeFUMiabbJyRkZEOn1s2Wx2mVnHK1gOZcrL1vFq1amjevDmqV6+OUqVKOVS2WbNmKFq0qNv8HVUSyK5KPterVy/H5G+2nTt3IjAwEI899phDx9q8eTPu3LmjdK4TJkyQKicbIyAXJ2CcPuvNN99EsWLFHC4zbNgw82MMZMdLlT5EdtIFaD9eGq3/0TNOZ2MEnI8zLS1Nug4A2o6XsmMlIJ9AdmXy0MPDw6G/TXJystPJw+wvmXKH/scd1iSydUCl7si2EQAIDg52uN+ZMmWK+f9a11eVCyUAx0tbssdLd1lDa7m2BPQZL9kubTPa/A5gu3yUkdulI5jsVuAOiyHAOAtilWQMYJwOUzZO2XqgUn9k28jKlSsdOla2RYsWmf9f0P+OgFoCWbasbAIZAMaNG+fw32Tbtm3m/8ueq9YxZsfmbJwvvviiYfosZy88vP766xY/u8ukyx36H0DbOFUWUTJx7tmzR7oOaD1eqoyV7pI8BNyj/3GHNYlsHZAtJ1vXnb3YcfjwYYufC/qNKdncYbws6GtoPdaWgPbjpewxZcu5S7vUen7HdmmbkdtlbpjslqRSGYy0GDLSglglGWOkDlM2Ttl6IFsur6/UZX8hyIcffmh+zR3+jtlkE8iyZWUTyCtWrLD7RWW2LFq0CBUrVlQ6V9lyKhcRZOJs1KiRU184onefJctdJl1G63/+/vtvPPbYYw49Y1nPuqeS3JCJ89G+NjcP1wE9xssOHTogPDxc6osb3SF56A79jzusSWTrgGriWeUiS06ysrKwZ88etGvXzuJ1d7kxxWjjpTOyxxB3WENrvbYE1NeXqampKFasmNXvKDMzE2lpaTYfV8F2aZve8ztnsF3alx/aZZ4RZBj9+vUTt27dcnj/wYMHi2vXrjm075kzZ8T06dNlT82mefPmiTt37ji8/4IFC8StW7eEt7e3SExMdLhckyZNxMWLF3PdLyUlRaxevVr06NHD4ffOjWyMQoh8GadsPciL+mPL7du3xfr160Xfvn2Ft7e3CA4Odsn75se/Y05++uknkZ6e7vD+Bw8eFPfu3VMqa6Q4jRSj1lTq+oULF8To0aPN2/z9/UXz5s3N/1q2bCnOnj3r8nOWoTJetm/fXly/fl3quJcvXxZZWVlOldGr/zGZTE6VfZiWcaq2S5k4VeqASlkZzv5+9JSXfWxmZqbYvXu37Km5lNHm6zLyY4yydUDLunP+/Hkxc+ZM4evrK+rVq6fJMXPiir9jSkqKyMzMtNo3IyNDpKSkKJ/jw/QaL9PS0sTOnTttxpOSkiJ27txpnme7gh7jpUqMsnVAy7ojhBDffvut6NSpk83f7e3bt0WnTp3Erl27XH5cZ+Vl/5oX8lvOJ6+4S5xau3Xrlli9erX551GjRokRI0aY/7311ltOtYeHMdmdh1QmpFpMZl2ROMxvCWRb4uLixHvvvScaN24sfH19xcSJE50+p4Icp2w9cEX9cbSe//LLL2L06NGiSZMmwmQyienTp4szZ844fby84C7JQ5UE+6NOnTolTpw4Yf536tQpV52mElfGKIRjcRqpDvznP/8RM2bMMP/cpEkTsWjRIhEZGSkiIyPF4MGDxfjx422WlU0Aap04FEIteSibQJZJHguh7cL/YVrGqTpWysSp8ruRLStb1/My2e0uyUN3uNBfkNcksnXAFXXHkbqelpYmoqKixEsvvSRMJpPo16+fWLNmjUhISLBbxh2Sh0ZJIAshxLJly8Srr75qd9/+/fuLVatWWb3uDjHK1gHVxLNMXR84cKBYv3693ffcsGGDeO211+xuN4q8TB66kur8zihrKD1yBbJ1QI+6s2jRIjFq1Cjzz02aNBFvvfWWGD16tBg9erQICAgQc+fOlXpvJrtdTGWyptVk1hWJw/yaQM529epV8d///ld07NhRtGjRQphMJhETE+P0Ils1zryMUQi1OGXrgWr9cbSeJyYmioULF4qAgADh6+srpk6dKn777TdRr149cfr0abvvb5SBTwi15OGjVBLIeZ18PnjwoOjZs6f55+y64+3tLby9vYXJZBI//vhjnp6rFgl2mThV6oDWdT0kJEQcPXrU4lwfvvPgp59+Es8//7zNsrIJOZVEnh7JQ9myWt9lLcSDc01KSpI6ptZxysYohFycetQBlXJRUVFi586dOf5zRkFMHj5Kzwv9QmgzXy/IaxLZOuCKuiOEY3X96NGjYvz48aJp06aie/fu4uuvvxZ169bNcQ4rhD7JQ9kkuUry0CgJZCGECA0NzTHxunv3bhEaGmr1umyMQmgfp2yMsnVApe7I1nVfX19x/vx5u8c8f/688PX1tbnNXZKH7rCOdocYZeuASt2Rreu9evWyWB8/ur789ttvRbdu3ez8hnLGZ3a7yKFDhxAeHo5t27bh7t27GDBgAD755BPUqlUrT8sCwJ07dxATE4OIiAgcOXIEtWvXtniOMQAkJSUhMjISERERSE1NRXBwMFasWIEXXngBoaGhDh3r2rVriIyMRGRkJJKTk5GcnIyZM2ciMDDQoWdhZjtw4AAiIiKwY8cOlChRAs8//7zDZYEHz9ratGkTwsPDrR74v337doSHh+OXX35BmzZt8OGHH+K5556Dj48P6tSp49B5uiJO1RjzKk7ZeuCK+uNsPW/Xrh0CAgLw0UcfwdfX1+HnXK1cuRLly5c3/3zr1i2MGDHC/Hyt2NhYLFu2DJMnT7Yod/HiRXz55ZeYNm0aAKBt27a4c+eOebunpyfWrFmDmjVrOnQejjhw4AA++eQTi9cCAgJQrVo1AECVKlUwbtw4m2V/+eUXTJs2DREREQCAPn364O7du+YvM/Hw8MDXX3+N1q1bu7Tsw06fPo3MzEzzz15eXnj66aet9luzZg26detm8dqKFStQuXJlCCGwcuVKrF271qXnqnWMsnGq1AGt6/qlS5fwxBNPmH/u3bu3xbMNq1SpgqtXr9o8Vz1cvnzZ/MV4ztq7d2+uzxXu0KGDzdedGQ+zCQe/hMjeeezbt8/hZwg+bPbs2Xj88cdz3GfMmDE2X9cyTpUYAbk4VeqASlkZo0ePznG7h4cHTpw4keM+d+/exbZt27Bhwwb8+uuvaNasGUaMGJHr/EW23KMuXLiAiIgIREZG4saNGzh+/LjVPr/99hvCw8MRExODf//73+jSpQsOHz6MCRMmoHbt2g4dR3bOrTpXB7SbrxfkNYlsHXBF3XGmrnfp0gW3b99GSEgIvvnmG/O8YebMmbkeZ+3atRg8eLDNPqtYsWIYMmQIVq9ejfbt27uk3I4dOzBjxgxER0dblb137x5CQ0Px4YcfWpUDgFOnTuX45d7NmzfH7NmzbW7bvHkz3njjDbtl+/fvj/nz5+Pll1+2eH3dunXYvXu3zT60RIkSWLlyJa5evWpVDnjwN/zxxx/RsmVLq2czp6am4qeffkKbNm2svk/lwoULMJlMds/V29sbFy5ccFmMKnFqHaNsHVCpO7J1PTk5GRkZGXaPmZGRgeTkZJvb1q9fjxMnTuCll14CAOzevRtt2rRB8eLFATx43v/y5cvx1ltvuaRccnIytmzZYi4XFhaGe/fumbd7enriP//5j81nJG/fvh3vvvuuxWvvv/++eW2xY8cOzJ8/3+qYgHuso2VjBLSPUzZG2TqgUndk6/pff/2Fp556yvzzU089hcKFC5t/NplMNvseRzDZrUBlsqb1ZFY2cQgYK4H87rvvYsiQIfj8889tfsFEXsap5cUA2Thl64FsOZV6XrlyZRw6dAiVK1dG5cqVHV5oGWngU0keqiSQZcvKJpCPHTuGYcOGWbxWqVIlVKlSBQDQrVs3DB061KVxah2jbJwqdUDruu7p6Yl//vkHlSpVAgCMHTvWYntSUhIKFbI/rZBNAGqdOATUkoeyCWSZ5DGglig/deqUxYTyUTmdk5ZxqsQIyMWpUgdky8rW9R9//FH6QoA7JA/1vNAPaDNfd4c1iWwdUKk7gFxdP3fuHIKCgtCyZUuH20M2rZOHsolDQC15aKQEckZGBq5fv47KlSvbPOb169dt/h5kYwS0vxggG6NsHVCpO7J1vUqVKjh27JjdPu3333+3G7/WyUPZxCGgljzUem2hkjyWXUO5w41GsnVApe7I1vW0tDSkpKTgySefBACrmzzv3LkjffMSk90KVBLIWk9mZROHgLESyL169cLq1avx008/oVu3bggKCrL4Bt28iFOPiwGyccrWA9lyKm1k27Zt5sVTr1698NRTT6Fr164Ack6WGGngU0keqiSQZcvKJpCvXr1qkcD59NNPLX5fpUuXxs2bN116rlrHKBunSh3Quq4//fTT2L9/Pxo1amTzfPbu3Ys6derY3AbIJwBVko56JA9lE8ha32UNAPPnzzdMnLIxAnJxqtQB2bIydV3l9+IuyUOtL/QD2s/X3WFNIlsHVOqObF3ftWsXIiMjMXHiRNy9exchISHo0qWLQ+1V6+Shyh22KslDIyWQs+c/DRo0sFnuxx9/tPmpP9kYAe0vBsjGKFsHVOqObF3v1KkTZs+eDV9fX4v5OQAkJCRgzpw55rXmo7ROHqrcYauSPNR6baFys5nsGsodbjSSrQMqdUe2rlerVg1//PGH3TXksWPHULVqVZvbcsNktwKVBLLWk1nZxCFgrATy5MmTMXbsWGzduhURERGYOnUq/Pz8IITI9YqQbJx6XAyQjVO2HsiWU2kjAPDMM8/gmWeewbhx4xATE4PIyEhkZmZi4sSJ6NKlCzp27IiyZctalDHSwKeSPFRJIMuWlU0gFy9eHBcvXjQPnJ06dbLYfunSJbttR/ZctY5RNk6VOqB1Xe/ZsyemTp0Kk8mEtm3bWmzbvXs3Fi1aZHUOD5NNAKokHbVOHgLyCWSt77I2WpxaJslVfjcqZWXquspd7+6SPNT6Qj+g/XzdHdYksnVApe7I1vWKFSti+PDhGD58OOLi4hAREYEXX3wRGRkZiIyMRO/evS2SAg/TOnmocoetSvLQSAnk0NBQTJ8+HU8//TTatWtnsW337t1YsGCBzfmGbIyA9hcDZGOUrQMqdUe2rg8ZMgS7du1Cp06d0LVrV3MbjI+Px+bNm/Hkk09iyJAhNt9T6+Shyh22KslDrdcWKjebya6h3OFGI9k6oFJ3ZOt6x44dMXv2bPj5+dnsC+bNm2d1I5qjmOxWoJJA1mMyK5M4BIyXQH7sscfQo0cP9OjRA+fPn0dkZCSOHTuGF198EW3btkVAQIBVEkolTj0uBqjEKVsPZMqptJGHFS9eHH369EGfPn1w9uxZhIeHY/bs2Zg0aZLVcz2NNPCpJA9VEsiyZWUTyI0bN0Z0dDRatmxp83yioqLQuHFjl56r1jECcnGq1AGt63qfPn1w4MABDBs2DDVr1jRPws+dO4dz586hU6dO6NOnj81zkU0AqiZktU4ean33cTaZBLLR4pRNksvEqfK7kS0r+zvt0aMHihYtKlXWXZKHWl/oB7Sfr7vDmkS2DqjUHZW6nq1Vq1Zo1aoVUlJSsGnTJkRERGDJkiV4+umnsXnzZqv9tU4eqtxhq5I8NFICuW/fvjh48CCGDx9uMf+Jj4/H+fPnERgYiL59+7osRpU4tY5Rtg6o1B3Zul6iRAmsXbsWM2fOxNatW3Hr1i0AQKlSpdC1a1e8++67dtdOWicPVe6wVUkear22ULnZTHYN5Q43GsnWAZW6I1vXBw8ejG+//RadOnVCt27dUKNGDQAP5oubNm1CxYoV7fYFuZL6WkuykpqaKtatW2f+1vJ+/fqJdevWiaSkJJeXffiby3v06CGWLl0q6tWr59Q3tGfL/qb0Vq1a2f028UedO3dOzJw5U/j5+YmmTZuKUaNGie3bt9vdPy0tTURGRoqXX35ZNGjQQAwbNkzUrVtXnDx50m6Z8ePHi2eeeUb07dtXrFmzRty8eVMIIUS9evVy/RZzWzIzM8WuXbvE8OHDRf369R0q40ycMjEKkT/iFEKuHjhbTqWN2JKenm7z77Fu3TrRuHFj8d1331lt27Vrl2jcuLFYt26d1ba+ffuKL7/80u7xvvjiC9G3b1+b25o2bSqOHj1qt+zRo0eFj4+PzW3vvvuu8Pb2FoGBgeKNN94Qb7zxhggMDBQmk0m8/fbbdt/z9ddft/gm6Ud9+OGHYujQoS4t++yzz4oDBw7YLXfgwAHRsmVLq9fj4uKEyWQS06dPF4mJiebXExMTxbRp00TdunXF/v37XXquWscohHycsnVAj7ouhBBbtmwRw4cPF4GBgSIwMFAMGzZMbNmyxe7+Qgjh7e1t8TtxlGw5IYQwmUxSZUePHi1SUlKkjil7vrLnqnLMyMhIce/ePU2PKRunSj2QiVOlDsiWVYnRFfbv3y/CwsJEo0aNhLe3t/j0009FfHy8y8tdvXpV/Pe//xUdO3YUfn5+Yvr06eL48eOifv36Ts97kpOTxapVq0SPHj2Et7e3CAkJybWM7FxEppxe83V3WpPI1AHZcrJtxJY//vhDTJkyxea2lJQUERwcLHx8fMSECRPEsmXLxLJly8THH38sfHx8RFBQkM0+RrbcrFmzRNu2bUVCQoLVtn/++Ue0bdtWzJo1y24sycnJYsKECaJFixbC29tbeHt7i+bNm4sJEyaY1zX2hIWFWc1/OnfuLEwmk3j33Xdtlvnmm29EkyZNxO7du6227dq1SzRp0kR88803Nsv27t1bfPXVV3bPZ8GCBaJ37952t8fExIjhw4eLoKAgERgYKIYPHy5iYmJcHqMQ8nHqEaNsHZAtJ1vXH5aVlSWSkpJEYmKiyMrKynFfIYT4/PPPhb+/v9124u/vb7OdyJYLDg4WUVFRds8nPDxcBAUF2dyWkpIiAgMDhY+Pj5g4caL59zNhwgTh4+MjOnfubPf3o/XaQmUNLYTcGko2RiH0iVMmRtk6oFJ3ZOu6EELcvHlTjB8/XjRv3tyiLxg/fry4ceOGnd9a7pjszgOyiUNny7oyeWgvcZiT/JpAzo2zi0ln49TiYoAjZBbNMvVAppxKG3GEUQa+bDLJQ5UEsmxZlQT7qlWrRP369YXJZBLNmjUTzZs3FyaTSdSvX1+sXLnS5XHqEaNKnDJ1QAjt67os2QSgHolnFbIJZD2S+o/KyMgQJ06cEOnp6bnuq3WcropRCOfi1JJKXX/YkSNHRPPmzUVcXJxU+YKYPLRHiwv9Qug7X3eXNYkQcnVAppxsG3GGlslDVyQOhXA+eZjNCAlkVTIxCqH9xQBVsnVAppzKRRYZWicPVRKHQqglD7VcW6iuoYWQW0O5w41GsnVAtpxKojxbVlaWSExMdLoPscdDCMWvuCe7MjIysHv3bpuPknB12exHO2zcuBG3bt2yerRDXktKSnLqY8lZWVn4/vvvER4ejh9++AHHjh3Lcf/sx3RERUXhzp07OT6m42GZmZk4ffo0ateubfcRFM5wJk5nYwTyT5xacaaeHz16FEOGDMHcuXPx7LPP5rp/TEwMYmJicP78eQBA9erVERISguDgYLtlRo0ahdjYWLuPaJgzZ47NcuvXr8fUqVMxe/Zsmx8xGjVqFMaOHWv3EQ+yVq9ejWnTpiEzMxMlSpSAh4cHUlJS4OXlhdGjR6Nfv34uLXvgwAEMHDgQAwYMwODBg81tISkpCYsWLcKKFSvw9ddfo1WrVjaPeeXKFWzfvt38N6lRowYCAgLMH9FzdZx6xKgSpywt67qRjBkzBh999JHTj4h6lLN9z8Mc7ZujoqIQHByMIkWKOH1+JpNJ6bnm2Xbu3Im33noLn376qd3nZNqT13G6KkZALk6VOqBSVsaECRMQERGBoKAgfPbZZ0rvdeLECURERNh9Xqaryj38eIc//vjD7uMd8orsfF2mnF7zdXdak2jJ0bp+6dIlTJ8+HaNGjULNmjUdfn8hBG7cuAEhBMqWLevw41OcLZeSkmLz0Q5BQUF49913HX48o5ZiY2OxZcsWXLhwAUII1KhRAyEhIQgKCsqx3HvvvYctW7bYfVTHrFmztDh9h8nEabQYVci2EcD5dnnr1i3MnDkT27ZtMz/HvlSpUujcuTNGjRqFf/3rXy4rl5qaij59+uDq1at2H+2wYcOGXOe4Qghcv34dAJz6/Wi1ttBrDQ3IxQgYL07ZOiBTTraN5BnldDnlK47eDaFy54+r74py5m4tZ++y3rFjhzCZTGLjxo1On5cr48zru8ll45StB6p3jsn4+OOPRf369cX777+fp8fR8g5bV/j777/F0qVLxYQJE8SECRPE0qVLxd9//51nZWXvXFYlG6eRYtSabF3PptIPuEvfI9s3a3GX9aPeeOMN0bp1azFgwACny+Z1nK6KUQi5OFXqgGxZmbp+79490aJFC7Fw4ULRpEkTkZqa6uzp6s7Ru2z/+usvMWLECHH27FkNzsr1tJiv6y2vY5StA3rUnXnz5pk/vZCfyd6Zm80o7VL2TutsRmiXesXoDu1S9u5TZ8vl1aMd8pKWd1nryV3ilKVyh7Yr+wImu11Ij4W/LD0W70Jon0DOz4t3R+VlnLL1QMvFuxDGWcDrmTzUkkqCXYgHF3Py42MEHqYaoxBycRqlDgihTwJQ5Zh6JA9l+2atx9mkpCTRoEEDsWfPHlG/fn1x5coVp46pdZyy46xMnCp1QKWsTF2PiYkRbdu2FVlZWSI4OFhEREQ4XFYI90hSCOEeF9vcYU0iWwdU6o5sXe/QoYMICwsTvr6+IjMzU5Njuku71IMe7VJrbJf5h+qjHYxwEUp1DS2EMdqXXrkCdxlHHuWp7X3kBVtkZCTu3Llj9W25eVn26NGjaNGiBQ4cOOBwmfv372Pbtm145513sGPHDty+fdupY0ZFRaFs2bKIiopyqhwAfPfdd+jRowdiY2OdKpeZmYk///zT4tujc/s48/Xr1/HDDz9g2rRpOHjwoN1v8rVHNk7ZGAFt45StByr1R7ae79y5E8WKFcPgwYNRpUoVbN++3eGyMm1EVnBwMP773/8iNjYWsbGx+PLLL3P9KNTDVPqQrKwsq7qTV2WffPJJDBgwABMnTsTEiRMxYMAApx7RsXv3buk2Ihun1jECcnGq1AEt67pKP6BH3wPI/W5V+h6VMUjrcTYmJgZPP/00nnvuOTzzzDPYuHGjw2X1iFN2nJWJU6UOyJaVrevR0dHo0qULPDw80LVrV6f7kejoaOzcuRPh4eGalAMefIz8zTffRHx8vNPHDAkJwaZNm5CVleVUWdl+Vuv+WY/5lkpZrWOUrQMqdUemrh88eBA3b97ExIkTIYTA3r178/yYKuVk22T2MbVul1rOfQD92qVR5nju0i5l24lMOQ8PD5QrVw7lypVz6jEt2VTGaK3qneoaGjBGH6JXrsBd5nePYrLbRfRY+APGWrwD2i5sjbZ4B7SNU7YeaL14B9QW8EYY+AD1RJ5KAlm2rGziWaWNyJ6r1jECzsepRzIXkKvreiQAVY6pR/JQtm/WY5yNiopC9+7dAQBdu3ZFdHS0w2X1iFO2D5GJU6UOyJaVqesJCQnYv3+/Ob4uXbrg119/xV9//eXU+Rb0JIU7XGxzhzWJbB1wReLZ2boeFRWFTp06oUSJEggKCnK639K6XcomN/Rol4D2CWQ92iVgjDmeu7VLXoSyz0jraF6Ecn257LJaX4R6FJPdLuIud/4YKYFstMV79jlrFadsPdBy8Q6oLeCNNPCp9CGAWgJZtqxMAlmvC2ZaxgjIxalHMheQq+t6JABVjqlH8lC2b9Z6nD116hROnTqFkJAQAEDnzp1x5coVHD161KFjah2nbB8iE6dKHVApK1PXN23aBJPJZP6SrSeffBLNmzd3+PfqLkkKd7jY5g5rEtk6oFJ3ZOr63bt3sX37dnM/0L17d3z33XdISUnJs2OqlAPkkxt6tEs9Esh6tEujzPHcpV0CvAiVG62T5EbqQwBehLJH9SLUo5jsdhF3ufPHKAlkoy3eAW3jlK0HWi/eAbUFvJEGPpU+RCWBrPVFKD0umOlxB6lMnFoncwG5uq5HAlB17NI6eagyBmk9zkZFRcHPzw9ly5YFABQvXhwdOnRw6HekR5yyfYhMnCp1QLasbF2Pjo42l8nWrVs3h3+v7pKkcIeLbQV9TSJbB1Trjkxd3759O0qXLo0WLVoAAOrXr49q1aohJiYmz46pUk42uaFXu9Q6gaxXuzTCHM+d2iUvQuVMjyS5UfqQbLwIZc0VF6EexWS3C7jLnT9GSiAbbfEOaBunbD3QevEOqC3gjTLwqSbyVBLIWt9dqccFM70es+BMnHokcwG5uq5HAlDlmHokD2X7Zq3H2czMTGzevBndunWzeL1r167YunUr7t+/n+vxtI5Tpg+RjVN1/JEpK1PXr1y5gjJlylg99zEgIABVq1bF+fPnczymuyQp3OFimzusSWTrgErdka3rGzduRJcuXSxe69q1q0N1XY92KZvc0KNdAtonkPVol4Ax5nju0i4BXoTKjdZJciP1IQAvQtmjehHKFia7XcBd7vwxSgLZiIv37ONqFadsPdBy8Q6oLeCNNPCpfvxcJYGs5UUovS6YaX0HqUyceiRzAbm6rkcCUOWYWicPVfpmrcfZpKQkvPDCC+jQoYPF623atMGAAQOQmJiYr+KU7UNk4lSpAyplZer6k08+iRUrVqBMmTIWrxcvXhzLli1DjRo17JYF3CdJ4Q4X29xhTSJbB1Tqjkxdv3LlCg4cOIAePXpYHfP333/HuXPnXH5MlXIqyQ092qUeCWQ92qVR5nju0i55ESrv1tGyyWMj9SEAL0LZo3oRyiZBykJCQsTKlSstXouIiBAdOnTIs7KLFy8WoaGhFq+9+uqrYt68eXbL/P333+KVV14R169ft3g9NTVV9O/fX5w7d85u2YyMDOHr6ytiY2MtXv/+++9F8+bNxb1793I83+nTp4vXX3/d4rVRo0aJjz/+OMdyJ0+eFPXr1xdJSUnmc23cuLE4cuSI3TLXrl0T8+bNszqnzMxMMX/+fHH58mW7ZVXilI1RCG3jlK0HKvVHpY3IkmkjQgjxzz//iPr164uzZ88KIR7EXbduXXHx4kWHjjtkyBAxc+ZMIYQQX331lXj55ZdzLaPy+5GpO64o26NHD7F8+XIhhBDh4eGic+fOuZbRuo2olBNCLkYh5OJUqQNa1nWVfkCPvkcI7fsf2b5Zr3FWlh5xah2j1lTruqyBAweKWbNmWbz21VdfiT59+uRJOSGEiI6OFu3atbN4LTg4WKxdu9Zumew+6tHfw5UrV0TdunVFfHx8jseU7Qu07p/1mm8ZYU0iWwdU645KXZeldbuUaZNC6NcuZec+Qmg//ynoczx3apey7US2XFpammjatKn46aefhBBCHDt2TDRq1EgkJyfneq4qvx891tEya2ghjNOHZNMyV+Au8zt7mOxWpMfCXwjjLN6FMNbC1l2SFFrSa/FulIFP9fejUne0vAilVxvRMkYh5OLUK5mrUteNQq/+R4bW42xGRoY4ceKESEtLs9p2584dceLECZGZmSkZjX1aj7N6xamV/fv3i8DAQJGSkmK1LTk5WQQFBYmDBw/aLe8uSQp3uNjmLmsSrcnU9Xnz5ok7d+5oekyVckLokzg0WgJZBud4eUOPdikEL0LlVb2TTR4brQ/hRShtMdltQEZavAuh7cLWaIv37HM2SpxaUlnAG23gk6WSQNb6IpQeF8z0uNCmEqcMJihcT6XvMdIYFBERIXr06CEyMjKstqWnp4sePXqI6Ohom8fTI07ZtiUTp0odUE0+O+v1118XS5cutbt9+fLl4o033nDZ8VxBrySF1mT651deeUXcunVLozNUp2WMsnVAj7pjMplEYmKipsdUIZvc0Ktdap1A1qtdajnHY7vMHS9C5d3aQus1NC9C5Y38Nr9jsttNcPGePxfvQmgbp2w90HrxLoQ+C3gj3cElhFpiVa9PMciQPVcjxag1mbquRwLQaH2PbN+sx/jz4osvii1bttjdHhMTI1566SWb2wp6nCp1QLasbF1v27atOHPmjN3jnTlzRvj7+9vc5i5JCtkkjh4JLm9vb6k4Vc5V6zhlY5StAyp1R7auy8aockx3aZeqZOY/erRLFVrG6C7tUg/udBFKZg1tpPaVjRehrOXlxWEmuxW4y50/Rkogc/H+gL04ZeuB1ot3IdQW8HrQOnmoF5kEstE+iaCSJHc2TiPVAT0SgCrH1CN5KNs36zHOPvvss+Kvv/6ye64XL14ULVu2tLlN6zhV+hCZOFXqgGxZ2breoEEDcf78ebvlzp8/Lxo2bGhzm7skKWTLqhxTdoGqx7lqncjTOkaV341sXff29jY/Bk2rY8qWU0luGKneqTBSHyKL7TJn7pI81KN9ySaPjdSHCMGLUPbk5UUoT/mvtqTly5ejT58+KFGihNW2kiVLom/fvli6dKlLy8bFxSEoKAipqalW21JSUhAcHIxffvnFatvJkyfRpk0bu7H4+vri+PHjNreFh4dj0KBB8PLystpWqFAhDB48GOvXr7dZduPGjRg7diwKFy5sta1w4cIYO3YsNm/ebLUtMzMTf/75J+7evWu1LS0tDX/++SeysrKstp07dw6NGze2eS4A0LBhQ5w9e9bmNtk4ZWMEtI9Tth7IllNpI4mJiShUqJDdYxYqVAjXr1+3el22jah68sknsWLFCpQpU8bi9eLFi2PZsmWoUaOGVRmV349s3VEt+8QTT+DNN99EkSJFLF739PTEG2+8gcqVK1uV0aONaB2jbJwqdUDruq4yjmjd9wDyv1vZvgeQ75v1GGfT0tJs1p1st2/fttl+AO3jVOlDZOJUqQOyZWXresWKFXH69Gm75U6ePIkKFSrY3CaEsFsuJ7LlAGD+/Pm4c+eOVFkPDw/p42rt559/Rnp6ulTZM2fO4M8//8zxX36gR4yydUC2nEpdDwgIQIsWLXL858pjypZTaZOA9u1Spd69+uqrSE5OliqrdbuUjVOPGN2hXcq2E9lyX3zxBdLS0pwuB6j9fvSodzJraBV69SFaxsn53QP2Z+OUq5MnT+L999+3u93X1xdLlixxaVlHF+/NmjWz2KbH4h1wbGG7evVqdOvWzWLbxo0bsWrVKmzYsMGqXPbCtn///lbl8uvi3VaMgPZxytYDlcW7bBvJXsBXr17d5nZ7C3jZNhIXF4cpU6Zg/fr1VmVTUlLwwgsvYNKkSVblVKj8fmTrjkrZzMxMnD59GjVq1MBjjz1msS0tLQ0XLlxAnTp14OlpeR1VjzaidYyycarUAa3ruh4JQJVjyv5uZfseQL5v1mOcrV69Og4fPgyTyWTzfQ8dOmT3d6B1nCp9iEycKnVAtqxsXff398ecOXPQpk0bFC1a1GLb3bt3MW/ePLRr187u+xotSZHbcX/++Webr585cwYJCQk5lrVVR2TLqRgwYIDN35OHhweEEPDw8MCJEyestqucq9ZxysYoWwdU6o5sXX/rrbdQsmRJqbJatkuVNgno0y5lqSS59GiXMvSI0R3apR4XoV588UU8/vjjUuWNchHq1VdfxRdffIFSpUpJHdco7UslTtkY3WV+lxMmuxXodeePURbvgPYLWyMt3gHt45StB1ov3gH5BbzWF5IAfZKHKskfrS9C6dFGtI5RNk49krmydV2PBKDKMfVIHsr2zXqMsyEhIZg9ezZ8fHyszvfPP//E3LlzMXjwYJvvqXWcKn2ITJwqdUC2rGxdHz58OL799lsEBATg5ZdfxlNPPQUAiI+Px5o1a5CZmYlhw4bZfE/APZIUgHwSR7YcIL9AXb9+PcqWLZtjOVtUzlXrRJ5sjLJ1QKXuyNb14OBglCtXTtNjypZTSYzp0S71uAilR7vUOk62y5zxItT/uKreqVycAYzTh/AiVM5U+oKcMNmtwF3u/DFSAtlIi3dA+zhl64HWi3dAfgGvx13oeiQPVZI/Wl+E0qON6HEHqUyceiRzZeu6HglAlWPqkTyU7Zv1GGcHDBiAH374AaGhoWjVqhVq1qxpjjMuLg5NmzbFgAED8kWcKn2ITJwqdUC2rGxdL1++PL755htMnDgRs2bNMi+IPDw84Ofnh48//hjly5e3+7tzlySFbBJHthwgv0CtXLmyVJwq56p1Ik82Rtk6oFJ3ZOq66l2VWrdLleSGHu1SjwSyHu1S60Qe22XOeBEqfyWQAWP1IbJ4EUoek90K3OXOHyMlkI20eNcjTtl6oPXiHZBfwOtxF7oeyUOV5I/WF6H0aCN63EEqE6ceyVzZuq5HAlDlmHokD2X7Zj3G2cKFC2PJkiVYtmwZtmzZgl9++QVCCNSoUQMjR45E//79bT4jW484VfoQmThV6oBsWZW6XqVKFSxatAi3bt3ChQsXzL+z0qVL29z/Ye6SpJBN4siWA9SSXDJUzlWPRJ6ztP5IdjaZuq56V6bW7VI2uaFXu9QjgSxLjz5EyxjdqV3yIlTO9EiSG6kP4UUo2/LykTtMditwlzt/jJRANtLiXY84ZeuBHot3QG4Br8dd6HokD1WSP1pfhNKjjehxB6lMnHokc2Xruh4JQJVj6pE8lO2b9Rhns893yJAhGDJkSI5x6R2nSoyycaokkGXKqt6hDQClS5dGo0aNHA3RrZIUepBZoDZv3tzuPDU/0jJGPb5wS7auq3xZoR7tUja5oVe71DqBrFe71DKRx3aZO16EypkRLs5k40Uo15bLlu/md4KUXLp0SQwePFiYTCbh7e0tvL29hclkEoMHDxYXL150edmEhATh5+cn/P39xcKFC8WOHTvEjh07xFdffSX8/f2Fn5+fSEhIyPG4N2/eFEePHhVHjx4VN2/edCjO+/fvi4ULF4quXbuKxo0bi0aNGomuXbuKhQsXinv37uVYrl+/fqJevXpi0KBB4pNPPhGffPKJGDRokKhXr57o16+fuH//vlW5hQsXihYtWogTJ05YbTtx4oRo0aKFWLhwoUPn7gyZOGVjFEK/OIWQqwcy5VTaiAzZNjJ58mQREhIi7t69a7UtLS1NhISEiClTptg8ZocOHcSOHTvsntP27dtF+/btbW6T/f2o1B3Zsl27dhVr1qyxe06rVq0SXbt2tXpdjzaidYwqccrWAT3qejbZ/kOlrEw5rfsfFVqPs3rRepw1Ikfr+ogRI0RKSorD7ztq1CiRmJho/tnb29viZ0fJlhNCCJPJJF1WVr9+/cStW7c0KyeE2u9Ihsq5ypbVOkY9yMRotHapR5sUQp96Z6R2qUf/bBR6tEvZdiJbTq96rnW98/b2Fr/99pu4dOlSjv9sMVIfIhun1jEabX6XGya7XUTLhb+RFu9CuMfC1l2SFCqcqeeqExKtLyTpkTxUqTt6XITSuo3odaFNNk4htEvmuuKiqdFolTzUi7P1rnnz5iIpKcnh9/f397e72NCKTNuSifO1116TrgNa1x+TySQuXLggUlJScv2XnJwsfHx8dJ8n6pGk0IPMAtVo7VLLGGXrgB51x2jtUja5oVe71DqBrFe71DLJxXaZe7vkRaicGeniDC9C5Y38OL/zEMKAnwskAHD447Fvvvkmpk+fbvPL82wJCwvD2LFj8+Qh8c5IT083f0T6woUL5o9Ih4SEoH///ihSpIjF/i1atMC2bdsc/lhJ27ZtsXr1alSpUiUvTt9hWsU5bdo0qXowYcIEzetP3bp1sX37dodiFELA398fGzduRLVq1Sy2Ofvx88uXL2PixInYt2+fzY+QP/r+2RITE9GjRw94eXnZfVxCVFRUrh9Bd5azdUe1bHp6Ol577TX8+uuvdh9BsGTJEpd/1FM2TiPFqCqv67rKOCJb1mh9j5HGIJPJhOnTpzv88diwsDBs3rzZLeK8d++edB2QrT+ffvqpVF339fV1+iOn3377LapVq8Z2aUd2fe3Ro4fm9Vy2XYaGhkqfq9btWTbGTp06SdUB2XIqbYTt0lp+GS9feeUVzJ8/H6VKlXK4jB7tUoWWMbpLu9SDyWTCjz/+6HQb1SvvI1PvAPk4jdSHAHJxMudjKaecjz18ZrckPRb+j1YkR5/NuGvXLrtfrPcoIQS+++47jBw5EuXKldN1UevsMzaTk5Pxww8/ODxQ37x5E1lZWQD0TZRrFadsPZAtp9JhCiEQEBDgULmcOPv8UtlntMo8a9UV/YDs83Zly6o8E1+FynOF83OMrpyQ5nVdVxlHtO57VMZZlb5Htm/Wa/wZPXq0VDl3iFO2DsjWH9m6vmLFCqePVbFiRaVjuku71GNOCcjVV5Vz1bo9A3IxytYBlbrjDu1Sr7m6XuPIypUrpc5X63apEqeWMbpLu9TjIpTs88X1yvvI1jvZZ8UbrQ+RidMoOR+95neOYLJbkh4Lf9kOU4/JCKBPApmLd/u0npCotBHZCYnWF5IepmXyUE/OJpCNdBdoNpkkuUyc165d03xSIVvXVcaRgrgYOnnyJLy9vQH8bzEEGGdBrPolTVrGqdKHyMT5888/O10muw7Ijl2ydb1Fixb44osvMGjQIDz++ONOlWW7zJ3Wc0qVdil7rrJltY5Rtm2pJLjcoV1qMVd35/HSKOtStsuc8SKUba6Yp8kmjwHj9CG8CGV9HFdchHIEk92S9Fj4G22RYJSBOptROkyjTEhU2ojshESPScWjtEgeqgy2Wief9Wgjenz8XCZOPSYVsnVdZTIiOwapjF0qiyF7UlNTERMTgw0bNuD48eNW35hupAUxAMTFxWHKlClYv369VX1ISUnBCy+8gEmTJqFZs2YW27SOUyVGALh37x7279+Pdu3aAQBmzpyJ+/fvm7d7eXnhnXfeQdGiRQGoLYhly6q0r/nz5+PFF190+lzZLnOmx5wSkGuXKueqR7/lbJsE5NuWSnt2h3apOhe1h+OlPn2ISoxsl/bxIlTOjHIzQ7aCnttyl4tQjmCyW5JKR6L1hESvyQig/UBtlMU7UPAnJKpX6mQmJHpMKmTvsFXpQ9zhI8sq56rXJzWcjXPGjBl44oknnCqjOqmQreuqCUB7chqDVMYu1cn+ww4ePIjw8HB8++23eOKJJ/D888/j448/trmvURbEALB8+XL06dPHZt9VsmRJ9O3bF0uXLrUaLwG5MUivu1YjIyOxZ88e87muWrUKTz/9tPnczp07hwoVKmDgwIHmMrILYtmyqneCymC7fCCnuaEec0rZdinTJlXKqsQo0yYB+XYpW84d2qUr2yTA8dJV7VKP8ZLt0j5ehMqdHjczGKUPKeg5H0Cfi1COYLJbkkpHkh8mJFosEvQYqI20eAcK9oRE9UqdzIREj0mFSvLQntz6EKDgf2Q5m5YfP1eZjMjGqfWkQqWuu3Iy4swYJFNOtX0lJCQgKioK4eHhSE1NRWBgIO7fv4/58+ejdu3adt9bdgzSY5w9efIk3n//fbvbfX19sWTJEpvbZMcgPcbZzZs3Y/DgwRavzZw50/zlNhs3bsSaNWsszlXlu9tly6q0L2e/dMsetstmLikHyLdp2XYp2yZVysom8mTaJCDftlTac0Fvl6ptEuB4CeRNu9R6vGS7tI8XoR7IiwSyynqWF6HyR84H0OcilCOY7HYh2cmIo2WNtkgAtO8wjbJ4B9xjQqKaHHN2QqLHpMKVX6zgaB9itI8sa70w0WvSJdMXaD2pUP2ImgrZMUi23KMcbV/Dhg3DwYMH0bZtW4wdOxZt2rSBl5cXvvnmm1yPITsG6ZFUS0xMRKFC9qeBhQoVsnshT3YM0nqcBYCLFy+iTp065p+LFi1qMbY0atQIkydPtiqnkqiSKavSvgICAnI9pr1nkbNd2m+XKnNK2TYt2y5l26RKWdn2LNsmAfl2KVvOHdulM+tZjpcP5EW71Hq8ZLv8H0e+u4MXodQvQqnezMCLUPkn5wPkj4vDj2KyW5HKpELLCYkekxFA+6v2Rlq8AwV/QqKaHHPFhCSvJxWqV99l+wHZNiJbVo+PZQPybUSPO0hl+gKtJxWqn7iQ7T9kxyCVsQuQa18//PADXnnlFbz44ouoUaOGU3HKjkF6jLMVK1bE6dOnUb16dZvve/LkSVSoUMHmNtkxSI9xNjk52aLtHzhwwGJ7VlaWxfZsKuOPbFnZ9vXWW285/ImUh7FdPmCvXarMKWXbtGy7VJkXat2eZdskIN+2VNqzO7RL2bkox8v/nZOr26XW4yXbZe54Ecr1F6FU1rO8CJV/cj5A/rgI9SgmuxWodCRaT0j0mIwA2neYRlq8A+4xIVG5Uic7IdFyUqGSKFfpB1QSyFpPSPT4xIUed5DK9gVaTypUrrzL9gOyY5DK2CXbvtasWYPw8HD07NkTtWrVQrdu3RAUFOTQMWXHID3GWX9/f8yZMwdt2rSxqs93797FvHnzzO3gUbJjkB7jbKVKlXD69GnUrFnTZiwnT55EpUqVrF5XWRDLlpVtX8HBwTa/MDk3bJcP2GuXKnNK2TYt2y5V5oVat2fZNgnIty2V9lzQ26XKXJTjZd61S63HS7bLnPEi1AOuvgilsp7lRaj8lfMB9LkIlRMmuxWodCRaT0j0mIwA2neYRlq8A+4xIVHpMGUmJHpMKh7laKJcpQ9RSSBrPSHR4xMXetxBKtsXaD2pULnyLtsPyI5BKmOXbPtq0qQJmjRpgrFjxyI2NhYRERGYPn06srKy8OOPP6JSpUp2v5BWdgzSY5wdPnw4vv32WwQEBODll1/GU089BQCIj4/HmjVrkJmZiWHDhtl8T9kxSI9x9rnnnsPcuXPRtm1bm3+T+fPnw9/f36qc7IJYpaxM+1K5oMx2mXO7VJlTyrZp2XapMi/Uuj3LtklAvm2ptOeC3i5V5qIcL/OuXWo9XrJd5owXoR5w9UUolfUsL0Llr5wPoP1FqNww2a1ApSPRekKix2QE0L7DNNLiHXCPCYlshyk7IdFjUgHIJcpV+hCVBLLWExI9PnGhxx2ksn2BHpMK2fYl2w/IjkEqY5dK+wKAYsWKoVevXujVqxfi4+MRHh6ORYsWYebMmWjdujUWLFhgVUZ2DNJjnC1fvjy++eYbTJw4EbNmzTJfBPHw8ICfnx8+/vhjlC9f3uZ7yo5Beoyzw4YNw7Zt29C5c2e8/PLL5nHh3LlzWL16NTIyMqz+Jlo/rzubTPtSuXjFdplzu1SZU8q2adl2qTIv1Lo9y7TJ7N+BDNVngRb0dqnaJgGOl3nRLrUeL9kuc8aLUHlzEUplPcuLUPkr5wNofxEqV4KU3b59W2zYsEG88MILon79+sJkMolly5aJlJQUl5c9fPiw+Oijj4SPj4/o1auXWLlypUhKShL16tUTp0+fduq8z549Kz799FPh6+srGjZsKF5//XWb+yUkJAg/Pz/h7+8vFi5cKHbs2CF27NghvvrqK+Hv7y/8/PxEQkKCzbKTJ08WISEh4u7du1bb0tLSREhIiJgyZYrVtg4dOogdO3bYPfft27eL9u3b29x26dIlMXjwYGEymYS3t7fw9vYWJpNJDB48WFy8eNHue06ZMkUEBQXZPdegoCCb5/r888+Lbdu22X3fmJgY0bFjR5vbGjZsKM6ePWu37JkzZ0SDBg2sXk9ISBC+vr6ibdu2YtGiRea/ycKFC4W/v7/w9fW1+TcxmUwiMTHR7vHskS3n7e0tVU6lrGwbqVu3rpg6dao4d+6cxeuOtK3XX39dNG3aVIwaNUp89913IiMjw+GyQsj1ISptRLZsgwYNxPnz5+2WO3/+vGjYsKHV67L9gBDybUS2nK+vr/jrr7/MP7ds2dLi5/j4eNG0aVOb7ynTF6i2keXLl4vIyMgc/9kq16xZM9G8efMc/z1Kth+wx9ExSLWcyhj9qIyMDLFjx44cjykzBukxzj7s5s2b4ujRo+Lo0aPi5s2bOe6bfb4yY5Ae46wQQly8eFG89tprVn+T1157zebfRI+xy9XtSxbbpWvKqbTpbM60S9k2qVJWtj0L4XybFEK+bam0Z3dql65sk0JwvMw+V9l2qcd4yXaZO9l2omfOR4i8z/vokfMRQq4PURm7ZNtXQc/5CKFPX5AbJrtdTHYy4mxZoywStO4wH5bfF+9CFPwJiZEmIyqTCpVE+aMc7QdU2ojWExKVhYlsG9F6MiKEXF+g16RCNkmeF+3ZkTHIVeVUxmhnOTsG6ZlUkyEzBukxzj7sxo0b5r/JjRs3nI45L+XlZF8G26V6Odk2LUumTaqUVUnkZcvPbVII922XWrZJIQr2eCnbLvUcL9kuHcOLUP+jZ85HCF6EepS7XYSyx0MIhc9zkF2ZmZn47rvvEB4ebvOjW64sm/1xsU2bNiE5Odnux8Vc4datW7hw4QIAoHr16ihdunSuZS5fvoyJEydi3759Nj/2lf14gIclJiaiR48e8PLysvuxlKioKLsfsZb1119/YeLEidi/f7/FubZu3RoTJ060e67du3dH4cKF7X7sKzo62ua5/uc//0FcXBwiIyNtfjQlNDQUrVq1wrhx4+ye882bN3Hx4kUAwL///W/861//koze9UwmE3788cc8eQaTM5xpI3fu3DF/5Ov3339HZmYmRo8ejdDQULsf+Tpy5AjCw8MRGxtr8RG1Nm3aYOPGjU497ztbbv2AShuRLTtlyhT8/PPPCA8Pt1lfe/fujZYtW9qsrzL9ACDfRmTLderUCWFhYQgICLB5PrGxsfj888+xY8cOq20yfYFKG6lbty727dvndNn80i71pDJG5zWtxllXcXYM0nqcJePIz+1ShUybVqEyL9SiPZNx5Pc2aaTxUqZdcrw0Btl2kp9zPoDz7aug53yyz1emfRX0nA+QP9eXTHYXIAVtQlLQF+8AJyRay+tJhUyiXIVKG9FrQuJsPyDbRvSajGi58JedVMgmySn/0zqppoJJNaKCI78vwokexfGSjIo5H9fhRaiCjcluyvcK8mQE4ITECGQmFfn9ExeyZfWYkMi2ET0nI/l54Z8fr7wTOSo/ty0iIqL8guMlGYmRcj4AL0IZAZPdRPkEJyQFU36/+i5LjwmJbBvhZISIiIiIiIj0xJyPdpjsJiIisoGTESIiIiIiIiJjYbKbiIiIiIiIiIiIiAzPU+8TICIiIiIiIiIiIiJSxWQ3ERERERERERERERkek91EREREREREREREZHhMdhMRERERERERERGR4RXS+wSIiIiIiMhx3t7eOW5/88038dZbb2l0NkRERERE+YeHEELofRJEREREROSYhIQE8/9jY2Mxd+5cbNu2zfxasWLFULx4cT1OjYiIiIhIV3yMCRERERGRgVSoUMH8r2TJkvDw8ECFChVQvHhxBAQEYO/evRb779y5E02aNEFqaiouXboEb29vxMTE4IUXXkDDhg0REhKCn3/+2aLMqVOnMHjwYPj4+KB169Z4//33cf36dS3DJCIiIiJyGpPdREREREQFQLFixRAcHIzIyEiL1yMiIhAQEIASJUqYX/vss88wcOBAREdHo0mTJhg2bBhu3LgBAEhOTkb//v1Rr149hIeHY/HixUhKSsLIkSO1DIeIiIiIyGlMdhMRERERFRC9e/fGvn378M8//wAAkpKS8MMPPyA0NNRiv5dffhkBAQGoVasWJk6ciJIlSyI8PBwAsGrVKtSrVw+jRo1CrVq1UK9ePUydOhU//fQTzp07p3lMRERERESO4hdUEhEREREVEI0aNULt2rURHR2NoUOHYtOmTahcuTKaN29usZ+Pj4/5/4UKFUKDBg0QHx8PAPjzzz/x008/WeyT7eLFi3jqqafyNggiIiIiIklMdhMRERERFSC9e/fG6tWrMXToUERGRqJnz57w8PBwuPydO3fQrl07vPfee1bbKlSo4MpTJSIiIiJyKT7GhIiIiIioAOnatSv+/vtvrFixAmfOnEGPHj2s9jly5Ij5/xkZGTh+/Dhq1qwJAKhfvz5Onz6NKlWqoHr16hb/ihUrplUYREREREROY7KbiIiIiKgAKV26NJ5//nl89tln8PX1RaVKlaz2WbNmDXbs2IGzZ89i8uTJuHXrlvm53i+99BJu3bqFUaNG4bfffsPFixexd+9ejBkzBpmZmVqHQ0RERETkMCa7iYiIiIgKmF69eiE9Pd3qiymzhYWFYeHChejWrRsOHTqEL7/8EmXLlgUAVKxYEWvXrkVWVhYGDRqELl26YOrUqShZsiQ8Pbl8ICIiIqL8y0MIIfQ+CSIiIiIicp3o6GhMmzYNe/fuRZEiRcyvX7p0CR06dEB0dDTq1q2r4xkSEREREbkev6CSiIiIiKiASEtLQ0JCAhYtWoQXXnjBItFNRERERFTQ8XOIREREREQFxOLFixEYGIjy5ctj6NChep8OEREREZGm+BgTIiIiIiIiIiIiIjI83tlNRERERERERERERIbHZDcRERERERERERERGR6T3URERERERERERERkeEx2ExEREREREREREZHhMdlNRERERERERERERIbHZDcRERERERERERERGR6T3URERERERERERERkeEx2ExEREREREREREZHhMdlNRERERERERERERIb3/wCZgmDyKvBqpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cosmic_signatures(DAMUTA.signatures.loc[[\"D1_M1\", \"D7_M4\", \"D18_M6\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Mutational signature similarity</th>\n",
       "      <th>Damage signature similarity</th>\n",
       "      <th>Misrepair signature similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5778.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.176980</td>\n",
       "      <td>0.270253</td>\n",
       "      <td>0.524924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.194173</td>\n",
       "      <td>0.209966</td>\n",
       "      <td>0.220816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.031818</td>\n",
       "      <td>0.254266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.047516</td>\n",
       "      <td>0.120673</td>\n",
       "      <td>0.320952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.109671</td>\n",
       "      <td>0.184103</td>\n",
       "      <td>0.504210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.235473</td>\n",
       "      <td>0.367171</td>\n",
       "      <td>0.643424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.999640</td>\n",
       "      <td>0.981909</td>\n",
       "      <td>0.893437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mutational signature similarity  Damage signature similarity  \\\n",
       "count                      5778.000000                   153.000000   \n",
       "mean                          0.176980                     0.270253   \n",
       "std                           0.194173                     0.209966   \n",
       "min                           0.000980                     0.031818   \n",
       "25%                           0.047516                     0.120673   \n",
       "50%                           0.109671                     0.184103   \n",
       "75%                           0.235473                     0.367171   \n",
       "max                           0.999640                     0.981909   \n",
       "\n",
       "       Misrepair signature similarity  \n",
       "count                       15.000000  \n",
       "mean                         0.524924  \n",
       "std                          0.220816  \n",
       "min                          0.254266  \n",
       "25%                          0.320952  \n",
       "50%                          0.504210  \n",
       "75%                          0.643424  \n",
       "max                          0.893437  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAMUTA.summarize_separation()"
   ]
  }
 ],
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