Notebooks/Figure5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>tool</th>\n",
       "      <th>threshold</th>\n",
       "      <th>strain</th>\n",
       "      <th>len</th>\n",
       "      <th>nTP</th>\n",
       "      <th>nFP</th>\n",
       "      <th>nTN</th>\n",
       "      <th>n_true</th>\n",
       "      <th>nFN</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>precision</th>\n",
       "      <th>specificity</th>\n",
       "      <th>f-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>10</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0       tool  threshold strain     len  nTP  nFP  nTN  n_true  \\\n",
       "0            0  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "1            1  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "2            2  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "3            3  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "4            4  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "5            5  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "6            6  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "7            7  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "8            8  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "9            9  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "10          10  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "11          11  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "12          12  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "13          13  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "14          14  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "15          15  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "16          16  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "17          17  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "18          18  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "19          19  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "20          20  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "21          21  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "22          22  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "23          23  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "24          24  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "25          25  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "26          26  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "27          27  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "28          28  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "29          29  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "30          30  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "31          31  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "32          32  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "33          33  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "34          34  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "35          35  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "36          36  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "37          37  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "38          38  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "39          39  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "40          40  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "41          41  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "42          42  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "43          43  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "44          44  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "45          45  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "46          46  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "47          47  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "48          48  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "49          49  BioGraph*         10    A_J  50-100    2  136    0      37   \n",
       "\n",
       "     nFN  sensitivity  precision  specificity   f-score  \n",
       "0   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "1   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "2   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "3   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "4   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "5   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "6   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "7   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "8   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "9   35.0     0.054054   0.014493          0.0  0.022857  \n",
       "10  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "11  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "12  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "13  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "14  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "15  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "16  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "17  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "18  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "19  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "20  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "21  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "22  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "23  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "24  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "25  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "26  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "27  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "28  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "29  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "30  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "31  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "32  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "33  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "34  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "35  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "36  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "37  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "38  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "39  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "40  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "41  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "42  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "43  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "44  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "45  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "46  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "47  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "48  35.0     0.054054   0.014493          0.0  0.022857  \n",
       "49  35.0     0.054054   0.014493          0.0  0.022857  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_all=pd.read_csv('../Data/analysis_files/df_merged_all.csv')\n",
    "df_merged_all.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>tool</th>\n",
       "      <th>threshold</th>\n",
       "      <th>strain</th>\n",
       "      <th>len</th>\n",
       "      <th>nTP</th>\n",
       "      <th>nFP</th>\n",
       "      <th>nTN</th>\n",
       "      <th>n_true</th>\n",
       "      <th>nFN</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>precision</th>\n",
       "      <th>specificity</th>\n",
       "      <th>f-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>688</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.00000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>689</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>690</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>691</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>692</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>693</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>694</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>695</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>696</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>697</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>698</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>699</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>700</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>701</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>702</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>703</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>704</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>705</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>706</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>707</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>708</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>709</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>710</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>711</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>712</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>713</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>714</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>715</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>716</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>717</td>\n",
       "      <td>BioGraph*</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.217143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088870</th>\n",
       "      <td>1088870</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088871</th>\n",
       "      <td>1088871</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088872</th>\n",
       "      <td>1088872</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088873</th>\n",
       "      <td>1088873</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088874</th>\n",
       "      <td>1088874</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088875</th>\n",
       "      <td>1088875</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088876</th>\n",
       "      <td>1088876</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088877</th>\n",
       "      <td>1088877</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088878</th>\n",
       "      <td>1088878</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088879</th>\n",
       "      <td>1088879</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088880</th>\n",
       "      <td>1088880</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088881</th>\n",
       "      <td>1088881</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088882</th>\n",
       "      <td>1088882</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088883</th>\n",
       "      <td>1088883</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088884</th>\n",
       "      <td>1088884</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088885</th>\n",
       "      <td>1088885</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088886</th>\n",
       "      <td>1088886</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088887</th>\n",
       "      <td>1088887</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088888</th>\n",
       "      <td>1088888</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088889</th>\n",
       "      <td>1088889</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088890</th>\n",
       "      <td>1088890</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088891</th>\n",
       "      <td>1088891</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088892</th>\n",
       "      <td>1088892</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088893</th>\n",
       "      <td>1088893</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088894</th>\n",
       "      <td>1088894</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088895</th>\n",
       "      <td>1088895</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088896</th>\n",
       "      <td>1088896</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088897</th>\n",
       "      <td>1088897</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088898</th>\n",
       "      <td>1088898</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088899</th>\n",
       "      <td>1088899</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>1000+</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>830</td>\n",
       "      <td>139</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.676259</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.96624</td>\n",
       "      <td>0.717557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259262 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0             tool  threshold strain     len  nTP  nFP  nTN  \\\n",
       "688             688        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "689             689        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "690             690        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "691             691        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "692             692        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "693             693        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "694             694        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "695             695        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "696             696        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "697             697        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "698             698        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "699             699        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "700             700        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "701             701        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "702             702        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "703             703        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "704             704        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "705             705        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "706             706        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "707             707        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "708             708        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "709             709        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "710             710        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "711             711        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "712             712        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "713             713        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "714             714        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "715             715        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "716             716        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "717             717        BioGraph*        100    A_J  50-100   19  119    0   \n",
       "...             ...              ...        ...    ...     ...  ...  ...  ...   \n",
       "1088870     1088870  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088871     1088871  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088872     1088872  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088873     1088873  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088874     1088874  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088875     1088875  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088876     1088876  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088877     1088877  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088878     1088878  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088879     1088879  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088880     1088880  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088881     1088881  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088882     1088882  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088883     1088883  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088884     1088884  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088885     1088885  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088886     1088886  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088887     1088887  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088888     1088888  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088889     1088889  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088890     1088890  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088891     1088891  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088892     1088892  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088893     1088893  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088894     1088894  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088895     1088895  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088896     1088896  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088897     1088897  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088898     1088898  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "1088899     1088899  manta_diploidSV        100   LP_J   1000+   94   29  830   \n",
       "\n",
       "         n_true   nFN  sensitivity  precision  specificity   f-score  \n",
       "688          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "689          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "690          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "691          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "692          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "693          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "694          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "695          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "696          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "697          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "698          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "699          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "700          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "701          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "702          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "703          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "704          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "705          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "706          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "707          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "708          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "709          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "710          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "711          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "712          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "713          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "714          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "715          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "716          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "717          37  18.0     0.513514   0.137681      0.00000  0.217143  \n",
       "...         ...   ...          ...        ...          ...       ...  \n",
       "1088870     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088871     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088872     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088873     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088874     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088875     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088876     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088877     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088878     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088879     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088880     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088881     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088882     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088883     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088884     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088885     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088886     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088887     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088888     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088889     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088890     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088891     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088892     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088893     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088894     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088895     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088896     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088897     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088898     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "1088899     139  45.0     0.676259   0.764228      0.96624  0.717557  \n",
       "\n",
       "[259262 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_100=df_merged_all[(df_merged_all['threshold'] ==100)]\n",
    "threshold_100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%store -r\n",
    "\n",
    "fig4a=sns.set_style(\"white\")\n",
    "fig4a=sns.set_context(\"talk\")\n",
    "fig4a = sns.catplot(x=\"cat\", y=\"n\",data=df_cat,kind='bar',aspect=1.5,palette=\"Greys\")\n",
    "fig4a.set(xlabel='Deletion length', ylabel='Number of deletions')\n",
    "fig4a=sns.despine()\n",
    "\n",
    "plt.savefig(\"../Figures/supplementary/mouse/supp_bar\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/supplementary/mouse/supp_bar.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot = df_cat.plot.pie(y='n', figsize=(5, 5))\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels = '50-100 bp', '100-500 bp', '500-1000 bp', '>1000 bp'\n",
    "sizes = [255, 1857, 562, 1036]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=False, startangle=90)\n",
    "ax1.axis('equal')\n",
    "#plt.show()\n",
    "\n",
    "plt.savefig(\"../Figures/supplementary/mouse/pie.png\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/supplementary/mouse/pie.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vs/.local/lib/python3.5/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/vs/.local/lib/python3.5/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4b=sns.set_style('white')\n",
    "fig4b=sns.set_context('talk')\n",
    "\n",
    "fig_order=['genomestrip','gasv','crest','popdel','indelminer','rdxplorer','mistrvar','breakdancer',\n",
    "'BioGraph*','smoove','delly','pindel','gridss','manta_diploidSV','clever']\n",
    "labels   =['GenomeSTRiP','GASV','CREST','PopDel','indelMINER','RDXplorer','MiStrVar','BreakDancer',\n",
    "'BioGraph*','LUMPY','DELLY','Pindel','GRIDSS','Manta','CLEVER']\n",
    "threshold_100[\"Tool\"] = threshold_100[\"tool\"].map(dict(zip(fig_order, labels)))\n",
    "pal = []\n",
    "for tool in fig_order:\n",
    "        pal.append(color_map[tool])\n",
    "\n",
    "fig4b = sns.catplot(x='len', y='sensitivity',data=threshold_100,hue='Tool',errwidth=2.5,aspect=1.8,kind='bar',palette=pal,hue_order=labels)\n",
    "fig4b.set_xticklabels(rotation=0)\n",
    "fig4b.set(ylim=(0, 1))\n",
    "fig4b.set(xlabel='Deletion Length (bp)', ylabel='Sensitivity')\n",
    "\n",
    "fig4b=sns.despine()\n",
    "#plt.show(fig4b)\n",
    "\n",
    "plt.savefig(\"../Figures/fig5/fig5a\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/fig5/fig5a.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.savefig(\"../Figures/fig5/fig5b.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4c=sns.set_style('white')\n",
    "fig4c=sns.set_context('talk')\n",
    "\n",
    "fig_order=['genomestrip','gasv','rdxplorer','crest','clever','breakdancer',\n",
    "'pindel','mistrvar','popdel','gridss','BioGraph*','delly','indelminer','manta_diploidSV','smoove']\n",
    "labels=['GenomeSTRiP','GASV','RDXplorer','CREST','CLEVER','BreakDancer','Pindel','MiStrVar',\n",
    "'PopDel','GRIDSS','BioGraph*','DELLY','indelMINER','Manta','LUMPY']\n",
    "\n",
    "pal = []\n",
    "for tool in fig_order:\n",
    "        pal.append(color_map[tool])\n",
    "\n",
    "fig4c = sns.catplot(x=\"len\", y=\"precision\",data=threshold_100,hue='Tool',errwidth=2.5,aspect=1.8,kind='bar',palette=pal,hue_order=labels)\n",
    "fig4c.set_xticklabels(rotation=0)\n",
    "fig4c.set(xlabel='Deletion Length (bp)', ylabel='Precision')\n",
    "fig4c=sns.despine()\n",
    "#plt.show(fig4c)\n",
    "plt.savefig(\"../Figures/fig5/fig5b\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/fig5/fig5b.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#all other figures arranged by 100 bp threshold or 100-500 bp deletion length except for this figure \n",
    "#tools arranged by 500-100bp\n",
    "\n",
    "fig4e=sns.set_style(\"white\")\n",
    "fig4e=sns.set_context(\"talk\")\n",
    "\n",
    "fig_order=['rdxplorer','gasv','indelminer','popdel','delly','breakdancer','pindel','gridss','mistrvar','smoove','BioGraph*','clever','crest','genomestrip','manta_diploidSV']\n",
    "labels=['RDXplorer','GASV','indelMINER','PopDel','DELLY','BreakDancer','Pindel','GRIDSS','MiStrVar','LUMPY','BioGraph*','CLEVER','CREST','GenomeSTRiP','Manta']\n",
    "pal = []\n",
    "for tool in fig_order:\n",
    "        pal.append(color_map[tool])\n",
    "# fig4c = sns.catplot(x=\"len\", y=\"precision\",data=threshold_100,hue='Tool',errwidth=2.5,aspect=1.8,kind='bar',palette=pal,hue_order=labels)\n",
    "fig4e = sns.catplot(x=\"len\", y=\"specificity\",data=threshold_100,hue='Tool',aspect=1.8,kind='bar',errwidth=2.5,hue_order=labels, palette=pal)\n",
    "fig4e.set_xticklabels(rotation=0)\n",
    "fig4e.set(xlabel='Deletion Length (bp)', ylabel='Specificity')\n",
    "fig4e=sns.despine()\n",
    "#plt.show(g)\n",
    "plt.savefig(\"../Figures/fig5/fig5c\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/fig5/fig5c.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Unnamed: 0</th>\n",
       "      <th>tool</th>\n",
       "      <th>threshold</th>\n",
       "      <th>strain</th>\n",
       "      <th>len</th>\n",
       "      <th>nTP</th>\n",
       "      <th>nFP</th>\n",
       "      <th>nTN</th>\n",
       "      <th>n_true</th>\n",
       "      <th>nFN</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>precision</th>\n",
       "      <th>specificity</th>\n",
       "      <th>f-score</th>\n",
       "      <th>Tool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141895</th>\n",
       "      <td>141895</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141896</th>\n",
       "      <td>141896</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141897</th>\n",
       "      <td>141897</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141898</th>\n",
       "      <td>141898</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141899</th>\n",
       "      <td>141899</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141900</th>\n",
       "      <td>141900</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141901</th>\n",
       "      <td>141901</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141902</th>\n",
       "      <td>141902</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141903</th>\n",
       "      <td>141903</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141904</th>\n",
       "      <td>141904</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141905</th>\n",
       "      <td>141905</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141906</th>\n",
       "      <td>141906</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141907</th>\n",
       "      <td>141907</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141908</th>\n",
       "      <td>141908</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141909</th>\n",
       "      <td>141909</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141910</th>\n",
       "      <td>141910</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141911</th>\n",
       "      <td>141911</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141912</th>\n",
       "      <td>141912</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141913</th>\n",
       "      <td>141913</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141914</th>\n",
       "      <td>141914</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141915</th>\n",
       "      <td>141915</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141916</th>\n",
       "      <td>141916</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141917</th>\n",
       "      <td>141917</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141918</th>\n",
       "      <td>141918</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141919</th>\n",
       "      <td>141919</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141920</th>\n",
       "      <td>141920</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141921</th>\n",
       "      <td>141921</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141922</th>\n",
       "      <td>141922</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141923</th>\n",
       "      <td>141923</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141924</th>\n",
       "      <td>141924</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>A_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>91</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>Manta</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087488</th>\n",
       "      <td>1087488</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087489</th>\n",
       "      <td>1087489</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087490</th>\n",
       "      <td>1087490</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087491</th>\n",
       "      <td>1087491</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087492</th>\n",
       "      <td>1087492</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087493</th>\n",
       "      <td>1087493</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087494</th>\n",
       "      <td>1087494</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087495</th>\n",
       "      <td>1087495</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087496</th>\n",
       "      <td>1087496</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087497</th>\n",
       "      <td>1087497</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087498</th>\n",
       "      <td>1087498</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087499</th>\n",
       "      <td>1087499</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087500</th>\n",
       "      <td>1087500</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087501</th>\n",
       "      <td>1087501</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087502</th>\n",
       "      <td>1087502</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087503</th>\n",
       "      <td>1087503</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087504</th>\n",
       "      <td>1087504</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087505</th>\n",
       "      <td>1087505</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087506</th>\n",
       "      <td>1087506</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087507</th>\n",
       "      <td>1087507</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087508</th>\n",
       "      <td>1087508</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087509</th>\n",
       "      <td>1087509</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087510</th>\n",
       "      <td>1087510</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087511</th>\n",
       "      <td>1087511</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087512</th>\n",
       "      <td>1087512</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087513</th>\n",
       "      <td>1087513</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087514</th>\n",
       "      <td>1087514</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087515</th>\n",
       "      <td>1087515</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087516</th>\n",
       "      <td>1087516</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087517</th>\n",
       "      <td>1087517</td>\n",
       "      <td>manta_diploidSV</td>\n",
       "      <td>100</td>\n",
       "      <td>LP_J</td>\n",
       "      <td>50-100</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>Manta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1170 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0             tool  threshold strain     len  nTP  nFP  nTN  \\\n",
       "141895       141895  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141896       141896  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141897       141897  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141898       141898  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141899       141899  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141900       141900  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141901       141901  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141902       141902  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141903       141903  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141904       141904  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141905       141905  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141906       141906  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141907       141907  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141908       141908  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141909       141909  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141910       141910  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141911       141911  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141912       141912  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141913       141913  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141914       141914  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141915       141915  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141916       141916  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141917       141917  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141918       141918  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141919       141919  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141920       141920  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141921       141921  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141922       141922  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141923       141923  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "141924       141924  manta_diploidSV        100    A_J  50-100   26   91   27   \n",
       "...             ...              ...        ...    ...     ...  ...  ...  ...   \n",
       "1087488     1087488  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087489     1087489  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087490     1087490  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087491     1087491  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087492     1087492  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087493     1087493  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087494     1087494  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087495     1087495  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087496     1087496  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087497     1087497  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087498     1087498  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087499     1087499  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087500     1087500  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087501     1087501  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087502     1087502  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087503     1087503  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087504     1087504  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087505     1087505  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087506     1087506  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087507     1087507  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087508     1087508  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087509     1087509  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087510     1087510  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087511     1087511  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087512     1087512  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087513     1087513  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087514     1087514  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087515     1087515  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087516     1087516  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "1087517     1087517  manta_diploidSV        100   LP_J  50-100   26   99   27   \n",
       "\n",
       "         n_true   nFN  sensitivity  precision  specificity   f-score   Tool  \n",
       "141895       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141896       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141897       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141898       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141899       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141900       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141901       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141902       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141903       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141904       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141905       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141906       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141907       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141908       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141909       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141910       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141911       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141912       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141913       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141914       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141915       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141916       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141917       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141918       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141919       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141920       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141921       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141922       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141923       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "141924       37  11.0     0.702703   0.222222     0.228814  0.337662  Manta  \n",
       "...         ...   ...          ...        ...          ...       ...    ...  \n",
       "1087488      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087489      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087490      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087491      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087492      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087493      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087494      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087495      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087496      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087497      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087498      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087499      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087500      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087501      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087502      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087503      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087504      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087505      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087506      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087507      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087508      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087509      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087510      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087511      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087512      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087513      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087514      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087515      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087516      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "1087517      35   9.0     0.742857   0.208000     0.214286  0.325000  Manta  \n",
       "\n",
       "[1170 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc[test['tool'] == 'manta_diploidSV']"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 7,
=======
   "execution_count": 17,
>>>>>>> 08b1344abb7c89817ecd3229d9513466f33800d7
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manta_diploidSV 338.55834779248414\n",
      "clever 305.4898451895248\n",
      "gridss 180.1879823575802\n",
      "delly 142.02752056291928\n",
      "smoove 123.08332666951371\n",
      "pindel 390.3524920315333\n",
      "breakdancer 0.0\n",
      "BioGraph* 198.67776915066605\n",
      "popdel 0.0\n",
      "rdxplorer 0.0\n",
      "mistrvar 227.12803068295125\n",
      "gasv 0.0\n",
      "indelminer 37.649150713126744\n",
      "crest 0.0\n",
      "genomestrip 0.0\n"
     ]
<<<<<<< HEAD
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 818.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig4b=sns.set_style('white')\n",
    "fig4b=sns.set_context('talk')\n",
    "\n",
    "fig_order=['gasv','crest','popdel','indelminer','rdxplorer','mistrvar','breakdancer','BioGraph*','smoove','delly','pindel','gridss','manta_candidateSV','genomestrip','clever']\n",
    "labels   =['GASV','CREST','PopDel','indelMINER','RDXplorer','MiStrVar','BreakDancer','BioGraph*','LUMPY','DELLY','Pindel','GRIDSS','Manta','GenomeSTRiP','CLEVER']\n",
    "threshold_100[\"Tool\"] = threshold_100[\"tool\"].map(dict(zip(fig_order, labels)))\n",
    "pal = []\n",
    "for tool in fig_order:\n",
    "        pal.append(color_map[tool])\n",
    "\n",
    "fig4b = sns.catplot(x='len', y='sensitivity',data=threshold_100,hue='Tool',errwidth=2.5,aspect=1.8,kind='bar',palette=pal,hue_order=labels)\n",
    "fig4b.set_xticklabels(rotation=0)\n",
    "fig4b.set(ylim=(0, 1))\n",
    "fig4b.set(xlabel='Deletion Length (bp)', ylabel='Sensitivity')\n",
    "\n",
    "fig4b=sns.despine()\n",
    "#plt.show(fig4b)\n",
    "\n",
    "plt.savefig(\"../figures/fig5/fig5b\", bbox_inches='tight')\n",
    "plt.savefig(\"../figures/fig5/fig5b.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 818.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4c=sns.set_style('white')\n",
    "fig4c=sns.set_context('talk')\n",
    "\n",
    "fig_order=['gasv','rdxplorer','crest','clever','breakdancer','manta_candidateSV','pindel','mistrvar','popdel','gridss','BioGraph*','delly','indelminer','genomestrip','smoove']\n",
    "labels=['GASV','RDXplorer','CREST','CLEVER','BreakDancer','Manta','Pindel','MiStrVar','PopDel','GRIDSS','BioGraph*','DELLY','indelMINER','GenomeSTRiP','LUMPY']\n",
    "\n",
    "pal = []\n",
    "for tool in fig_order:\n",
    "        pal.append(color_map[tool])\n",
    "\n",
    "fig4c = sns.catplot(x=\"len\", y=\"precision\",data=threshold_100,hue='Tool',errwidth=2.5,aspect=1.8,kind='bar',palette=pal,hue_order=labels)\n",
    "fig4c.set_xticklabels(rotation=0)\n",
    "fig4c.set(xlabel='Deletion Length (bp)', ylabel='Precision')\n",
    "fig4c=sns.despine()\n",
    "#plt.show(fig4c)\n",
    "plt.savefig(\"../figures/fig5/fig5c\", bbox_inches='tight')\n",
    "plt.savefig(\"../figures/fig5/fig5c.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 818.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#all other figures arranged by 100 bp threshold or 100-500 bp deletion length except for this figure \n",
    "#tools arranged by 500-100bp\n",
    "\n",
    "fig4e=sns.set_style(\"white\")\n",
    "fig4e=sns.set_context(\"talk\")\n",
    "\n",
    "fig_order=['rdxplorer','gasv','indelminer','popdel','delly','breakdancer','pindel','gridss','mistrvar','smoove','BioGraph*','crest','manta_candidateSV','clever','genomestrip',]\n",
    "labels=['RDXplorer','GASV','indelMINER','PopDel','DELLY','BreakDancer','Pindel','GRIDSS','MiStrVar','LUMPY','BioGraph*','CREST','Manta','CLEVER','GenomeSTRiP',]\n",
    "pal = []\n",
=======
    }
   ],
   "source": [
    "test=threshold_100.loc[threshold_100['len']=='50-100']\n",
    "fig_order=['manta_diploidSV','clever','gridss','delly','smoove','pindel','breakdancer','BioGraph*','popdel','rdxplorer','mistrvar','gasv','indelminer','crest','genomestrip',]\n",
>>>>>>> 08b1344abb7c89817ecd3229d9513466f33800d7
    "for tool in fig_order:\n",
    "    print(tool, test.loc[test['tool'] == tool,'f-score'].sum())"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 11,
=======
   "execution_count": 12,
>>>>>>> 08b1344abb7c89817ecd3229d9513466f33800d7
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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\n",
=======
      "image/png": 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yl8lT/WkUCUXlmrqX/zINGzYMp06dQlpamvjo2MuXL+PRo0eYNWuWQuIkIiIiakoqXwGQn59f4z52dnbw9fVFaWkp7t27hx07dqCwsBDKyrJX4tva2r7yy+JmzZph8eLFWLx4MZ48eYLLly9j3759CAoKgq6uLj766KOaXxTVmsITiu3btwMAEhMTAQAnT57EjRs3oKenh8mTJwMApk2bBqDiSLJKc+fORWRkJHx8fMTHgwUGBsLS0pK79YmIiIheA11dXRgZGeH+/fs17mNoaIgBAwYAAN555x20atUKS5cuRb9+/TBs2LA6x2RiYgIvLy+4uLjA2dkZp06dYkLRwBSeUHz77bcSn48fPw6g4i9DZUIhS5s2bXDgwAF8/fXX2LBhA9TU1DBkyBAsW7aMj7WIiIiIXhNHR0ccPXoUt27dgq2tba37e3p6Ys+ePdi8eTNcXFygpKRUL3Hp6+ujXbt2tUp2SD4KTyhq8jbCl59MvKxz584IDAys75CIiIiIqIZmzZqF0NBQrFixAj/88IPURujExERcu3YNEyZMkNlfWVkZ06dPx4oVK/Dzzz/D2dm5VvPHx8ejVatWUvM+efIEiYmJsLCwqN0FUa0pPKEgIiIiosbL3Nwc/v7+8PPzw4gRIzB69Gh06tQJRUVFuHHjBs6ePStevl4VT09PbN68GXv27JFKKC5cuIA///xTqk/fvn1hbGyMmJgYBAQEwMnJCba2ttDW1kZKSgqOHz+O4uJiLFiwoD4vl2RgQkFERESkQOrdusH40CGFzl9Xzs7OOHnyJAIDA3Hu3DkEBwdDQ0MDXbt2xapVqzBmzJjqY1BXx8SJE7FlyxZcvXoVffv2Fddt2rRJZp9t27bB2NgYw4YNQ35+PmJiYnDlyhVkZ2dDT08PNjY2mD59OvrV4S3gVDNKIr4BRKzyDYvXr19XcCRERERERI1Do3xTNhERERERvRmYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERERkdyYUBARERFRkxIQEACBQKDoMN4aTCiIiIiISG4hISEQCATin+7du2PIkCFYvXo1srOzJdo6OTmJ23Xt2hW9e/eGp6cnvvjiCyQkJEiNffToUQgEApw+fVqq7uLFixAIBNi9e3eDXRvVjKqiAyAiIiJqyoqLi5GVlaWw+Q0MDKCurl7ncfz8/NCmTRsUFhbi6tWrOHjwIO7du4dDhw5BSUlJ3K579+6YOnUqACAvLw8JCQkICwvD4cOH4efnh9mzZ4vbjhs3DiEhIfjqq68waNAgNGvWDABQWFiIL774Ap07d8b06dPrHDvVDRMKIiIiIgXKysrC//73P4XNP2TIELRq1arO4wwePBhdu3YFAIwfPx4AEB4ejri4ONjY2IjbGRsbw9PTU6LvJ598gnnz5sHf3x+dOnWCo6MjAEBJSQmff/45xo4di40bN+I///kPAGD79u1ISUnBgQMHoKamVufY66KsrAxlZWV1SsrqYwxF4pInIiIiIqp39vb2AIDk5ORXttXX18fGjRuhpqaGXbt2SdRZWlrCx8cHhw8fxu3bt/HgwQPs27cPY8eORa9evSTaCgQCrF27Fj/99BOGDx8Oa2trjBs3Drdu3XplDKWlpdi6dSuGDh0KKysrODs7Y9u2bSgrK6tyDldXV1hbW+PmzZsAgPLycgQGBmLEiBGwsrKCg4MDvvzyS+Tn59d4jMaITyiIiIiIqN49efIEAKCnp1ej9sbGxujduzeuXLmCvLw86Orqius+/PBDREREYNWqVdDW1kazZs2waNEimeNcuXIF4eHhmDx5MlRVVREcHIzp06fjp59+gpmZWZXzr1ixAidOnICbmxvs7e1x/fp1bNmyBc+ePcOaNWsk2sbExCAiIgITJ06Enp4ejIyMAADLly9HaGgoxo4di6lTpyIpKQkHDhzAgwcP8P3330ss/apqjMaICQURERER1VlOTg6EQiEKCwsRGxuLQ4cOwdDQEL17967xGJ06dcKlS5eQkpICS0tLcbmOjg6WL1+ODz/8EADw3//+FwYGBjLHuH//Pn766Sdxf1dXV4wYMQI7duzAunXrZPaJj4/HiRMnMGHCBKxevRoAMGnSJDRr1gxHjhzB5MmTJeJ59OgRwsPDYWFhIS67fv06QkJCsGXLFgwfPlxcbm1tDT8/P/zyyy945513qh2jsWJCQURERER15uPjI/G5S5cu+Oqrr6ClpVXjMbS1tQFAaokQAHECoaKign79+lU5hr29vcTNv5mZGQYNGoSLFy9W2efChQsAILXBe9q0aThy5AguXrwoMWa/fv2kEoHIyEgYGBigd+/eEAqF4vJevXpBRUUFsbGxEgmFrDEaKyYURERERFRnq1evhpmZGbKysnD48GE8ePCgVskEABQUFACoeCLxspKSEnz++ecwNTWFUCjEV199hS1btsgcw9zcXGZZdHQ0ioqKoKGhIVX/5MkTqKqqSi2JMjc3h6qqqnj5ViVTU1OpMZKSkpCVlYX+/fvLjOvlJKOqMRorJhREREREVGe2trbiU56GDh2K0aNH49NPP0VISAiUlWt2DtD9+/ehoqIidbMdGBgo3ox99+5d+Pv745dffsGgQYPq/TpqQlZSUl5eDiMjI6xfv15mn3+fpCVrjMaKCQURERER1SsNDQ18+OGH+PjjjxEREQE3N7dX9klNTcW1a9dga2srsSH78ePH2LFjB9zc3DBw4ED06dMHJ0+exJdffonQ0FCpG/OkpCSpsZOSktCiRYsqb+JNTExQWlqK5ORktG/fXlyenJyM0tJSmJiYvDJ+MzMzXL16Fb169Wq0x7/Ki8fGEhEREVG9c3V1hYmJCfbs2fPKtjk5Ofj4449RWlqKuXPnStR9+eWXUFNTw7JlywAAampq+Pzzz5GcnCzzLdk3btxAfHy8+HNycjJ+/fVXif0L/zZ48GAAwA8//CBRHhQUJFFfneHDh6OkpERmTMXFxcjLy3vlGI0Vn1AQERERKZCBgQGGDBmi0PkbgoqKCnx8fLBu3TpcvHhRfEOfmpqKkydPAqjYfJ2QkIDIyEjk5uZiyZIlEjfvkZGRuHDhAlauXClxrGqvXr3w7rvvYvfu3Rg1apTEvonOnTtjxowZmDJlClRUVBAcHAw1NTWpROVllpaWePfdd3Hw4EHk5OSgZ8+e+O233xAWFgYvLy8IBIJXXm+/fv0wbtw4BAQE4I8//kD//v2hrKyMR48eISIiAv7+/hgwYECtf4+NARMKIiIiIgVSV1evlzdVv4m8vLywdetW7NmzR5xQ3LlzB4sXL4aysjJ0dHRgYmICd3d3jB8/Hl26dBH3zcvLw1dffYXu3btj4sSJUmMvWrQIUVFR+OKLLxAYGCgu79evH7p3747t27fj2bNnEAgE2Lx5s8RSJlnWrFkDU1NThISE4MyZM2jVqhV8fX2rTUT+7csvv0T37t1x9OhRbNiwAerq6jA1NcW4ceMkTol62yiJRCKRooN4U1S+bfH69esKjoSIiIiIaksgEMDHxwfLly9XdChNCvdQEBERERGR3JhQEBERERGR3JhQEBERERGR3Lgpm4iIiIjeCgkJCYoOoUniEwoiIiIiIpIbEwoiIiIiIpIbEwoiIiIiIpIbEwoiIiIiIpKbQhOK4uJifPPNN3BwcICNjQ3ee+89XL58uUZ9L126hClTpqBv377o3bs3xo8fj9OnTzdwxERERERE9DKFJhRLly7FDz/8gFGjRmH58uVQVlbG7NmzcfPmzWr7RUdHY8aMGSgtLcWCBQvw0UcfQVlZGX5+fjh27Nhrip6IiIiIiJREIpFIERPfvn0b48aNw7JlyzBt2jQAQFFREdzd3dGqVSsEBwdX2XfWrFlISEjAzz//DHV1dQAVTzuGDh0Kc3NzHDhwQK6YevXqBQC4fv26XP2JiIiIiJoahT2hiIyMhJqaGsaNGycu09DQgJeXF27cuIH09PQq++bl5UFfX1+cTACAuro69PX1oaGh0aBxExEREdHbKyUlBQKBACEhIYoOpdFQ2Ivt7t27BwsLC+jo6EiU29jYQCQS4d69e2jVqpXMvn369MGuXbuwefNmjBkzBgAQEhKCR48eYdmyZQ0eOxERERFVCAkJkbj/0tDQgImJCYYOHYr3338fzZo1a5B5nZyc8OTJEwCAsrIydHV10bZtW9jb22P8+PEQCAQNMi9JU1hCkZGRgdatW0uVGxkZAUC1Tyjmzp2L5ORk7Ny5Ezt27AAAaGtrY/v27Rg4cGDDBExERETUALKzXyAu7onC5re2NoG+vladx/Hz80ObNm3w4sULXLp0CXv27EFsbCyOHDkCJSWleohUWvfu3TF16lQAFStYEhISEBYWhsOHD8PPzw+zZ89ukHlJksISisLCQqipqUmVVy5ZKioqqrKvuro62rdvD1dXV7i4uKCsrAxHjx7FwoUL8f3338PGxqbB4iYiIiKqT3FxT+DhsU1h84eGzoeDQ6c6jzN48GB07doVADBhwgT4+vrizJkzuHnzJnr27Fnn8WUxNjaGp6enRNknn3yCefPmwd/fH506dYKjo2ODzE3/UNgeCk1NTZSUlEiVVyYS1e2F+PLLL3Hx4kVs3LgRbm5uGDVqFL777jsYGRnhq6++arCYiYiIiKhm+vbtCwDiZUl37tzBzJkzYWdnBzs7O8ycORPx8fESfQICAiAQCPDw4UP4+vrCzs4O/fv3x/r162XeN8qir6+PjRs3Qk1NDbt27ZKoKywsxKZNmzB06FBYWVnB0dER3377LUpLS+vhipsuhSUURkZGMpc1ZWRkAECV+yeKi4vx448/YsiQIVBW/id8NTU1DBo0CHFxcfxLQURERKRgjx8/BgAYGBjg/v37mDx5Mh48eID3338f77//Ph48eICJEyciMTFRqq+vry/Kysrw6aefon///ggMDMTatWtrPLexsTF69+6NW7duIS8vDwBQXl6OuXPnIigoCMOGDcPKlSsxaNAg7Ny5EytXrqyfi26iFLbkydLSEvv370d+fr7Exuxbt26J62XJyspCaWkpysrKpOpKS0tRWloKBZ2ES0RERNRk5eTkQCgUorCwEDExMTh48CBatGiBXr164dNPP0VZWRkOHjwIExMTAIC7uztGjBiBzZs3IyAgQGKs9u3bi8smTZoEDQ0NHD58GDNnzkS7du1qFE+nTp1w6dIlpKSkwNLSEqdOnUJsbCwOHToEW1tbcTtTU1Ns2LABs2bNQseOHevpt9G0KOwJhaurK0pKSiReRFdcXIyQkBD07NlTvGH76dOnEplrixYtoKenh3Pnzkk8+srPz0d0dDS6dOkic28GERERETUcHx8f9O/fH46OjlixYgVMTEywc+dOqKurIyYmBi4uLuJkAqi4kXdxccGvv/4q9UXxxIkTJT5PmjQJIpEIv/76a43j0dbWBlBxjwgAZ86cQefOndGuXTsIhULxT//+/QEAsbGxcl03KfAJha2tLVxdXeHv74+MjAyYmZnhxIkTePr0KdatWydut2TJEsTGxiIhIQEAoKKighkzZmDz5s0YP348Ro0ahfLycvz4449ITU3FkiVLFHVJRERERE3W6tWrYWZmBhUVFbRq1QoWFhYAKpazv3jxQvz5ZR06dEB4eDj+/vtvtGzZUlzevn17iXaVnyv3Y9REQUEBAIhXwiQlJSExMVGcQPybUCis8dgkSWEJBQCsX78emzdvxsmTJ5GdnQ2BQIDdu3fD3t6+2n7z5s2DqakpgoKCsG3bNhQXF0MgEGDr1q1wcXF5TdETERERUSVbW1vxKU9vgvv370NFRQWmpqYAKvZQdOvWDYsWLZLZvqZLqUiaQhMKDQ0NLFmypNqnCvv375dZ7uHhAQ8Pj4YKjYjeQjnl5bhbXCxV3k1dHXrKClsBSkT0VjM0NISWlhYePnwoVffw4UNoa2ujefPmEuWPHj1CmzZtJD4DQNu2bWs0Z2pqKq5duwZbW1vo6uoCAMzMzPDgwQMMGDBAziuhqvD/QYmoybhbXAzv1FSpH1lJBhGWvMaBAAAgAElEQVQR1Q8VFRUMHDgQ586dw9OnT8XlT58+xblz5+Dg4AAVFRWJPgcPHpT4HBwcDCUlJQwaNOiV8+Xk5ODjjz9GaWkp5s6dKy4fPnw4njx5ghMnTkj1yc/Pr/YdaFQ9hT6hICIiImrqrK1NEBo6X6HzN7SFCxfi0qVLmDhxIry9vQEAhw4dgoqKChYuXCjV/tGjR5g/fz4GDBiAGzduIDw8HOPHj5dalpSamoqTJ08CqEgKEhISEBkZidzcXCxZsgSDBw8Wtx09ejROnz6NZcuWISYmBnZ2digpKcGDBw8QERGBkJAQmJubN+Bv4e3FhIKIiIhIgfT1terlTdVvss6dO+PAgQPYsGEDdu7cCQDo2bMnPv30U5lHtW7ZsgUbN26Ev78/NDQ0MGPGDHz88cdS7e7cuYPFixdDWVkZOjo6MDExgbu7O8aPH48uXbpItFVRUcGOHTuwb98+nDp1CpGRkdDR0YGZmRnmzJkjPmGUak9JxJc2iPXq1QsAcP36dQVHQkQN4UphIbxTU6XKDxkbo5+mpgIiIiKilwUEBGDr1q24du0a9PT0FB0O1RD3UBARERERkdyYUBBRoxUfH4+1a9ciPj5e0aEQERE1WdxDQUSNTnl+Doof3cUPu4IQeycBeWlP8J/3faDevhuUdfiInIiI6HXiEwoianSKH91F6ufeKPrzNwBA0Z+/IfVzbxQ/uqvgyIiIqC4WLFiAhIQE7p9oZPiEgojeSC+KyvA0U/r9EO2N/9k87a6VBU2lcrho5rzO0IiIiOglTCiI6I30NLMYu8KfSpWv9mkPpf//cwfVIszRzXi9gREREZEELnkiIiIiIiK5MaEgIiIiIiK5MaEgIiIiIiK5MaEgIiIiIiK5cVM2ERERkQw55eW4Wyx92lw3dXXoKfM7WaJKTCiIiIjeYtnZLxAX90Sq3NraBPr6WgqIqPG4W1wM79RUqfJDxsbop6kpowdR08SEgogUpqSgFNmP86XKDTvxhUZE9SUu7gk8PLZJlYeGzoeDQycFRERvq6SkJOzduxcxMTFIT0+HhoYGLC0t4ebmBi8vL6irq8PJyQmWlpbYvn17leNMmTIFsbGxMuu6d++OkJAQzJ07F1euXMGlS5egra0ts+3MmTPx+++/49KlS8jIyMDQoUOrnHPlypWYPHmyzPk1NTXRsWNHTJo0CWPHjq3Jr6LJYUJBRAqT/TgfMRvipMpHbu6ngGiIiBSjvDQHxXl3FTa/um43KKvW7YucqKgoLFy4EFpaWvD09ETnzp1RWFiIa9euYc2aNUhJScHixYtrPF7btm2xcOFCqXIDAwMAgLu7O6KjoxEVFQV3d3epdpmZmbh8+TJGjRoFDQ0NcfmoUaPg4OAg1d7GxqbK+TMzM3Hs2DF89tlnKCkpwYQJE2p8HU0FEwoiIiIiBSrOu4vU294Km9/Y5hA0DeT/Iic5ORmffPIJ2rVrh6CgILRo0UJcN2XKFDx48ADXrl2r1Zh6enrw9PSssn7o0KHQ1tZGWFiYzIQiMjISZWVl8PDwkCjv3r17teNWNf/o0aPh7OyMoKAgJhQyMKEgIiIiIrnt3bsXBQUFWLt2rUQyUalTp07o1Kl+l9dpaWlh6NChiIyMRHZ2NvT19SXqw8LCYGRkhH796ueJt6GhISwsLJCQkFAv471tmFAQERE1QRYWLRUdwmtTnp+D4kfSS4rU23eDsg73bNVVdHQ0zMzM0KNHj3obs7y8HEKhUKpcS0sLWloVhwl4eHggNDQUZ86cwXvvvSdu8+TJE9y8eRNTpkyBioqKRP8XL17IHFdfX1+q7cvKysqQlpYmlbhQBSYURERETZCurka1a/frY139m6L40V2kfi69pMj480PQ7M49W3WRl5eH9PT0ajc8y+PPP/9E//79pcpnzJiBJUuWAAAGDhwIQ0NDhIWFSSQU4eHhEIlEUsudAGDz5s3YvHmzVHloaCi6dOki/vxyQpOZmYl9+/YhIyMDkyZNqvO1vY2YUBARETVR1a3dr+u6emoa8vLyAAA6Ojr1Oq6ZmRlWr14tVd62bVvxn1VVVTFixAgcOnQI6enpaNWqFYCKhMLc3FxqozUAeHt7Y9iwYVLlpqamEp9lJTRjxoyp1cbypoQJBRERERHJRVdXFwCQny99BHhdaGtrY8CAAa9s5+7ujuDgYJw+fRrTpk1DYmIi4uPjMX/+fJnt27dvX6NxKxOa8vJy/PXXX9ixYweysrKgpqZW62tpCviaRyIiIiKSi66uLoyMjHD//n2FzN+zZ0+YmpoiLCwMQMXSJQAylzvVRmVC4+DgAB8fH2zevBlRUVEICgqqc8xvIyYURERERCQ3R0dHJCcn49atWwqZ393dHXFxcUhKSsLp06fRvXt3WFhY1Oscffv2hYODA3bu3ImCgoJ6HfttwISCiIiIiOQ2a9YsaGlpYcWKFTJPUEpMTMThw4cbbP7KpxFff/01kpKSMGrUqAaZZ9asWcjKysLRo0cbZPzGjHsoiIiIiEhu5ubm8Pf3h5+fH0aMGIHRo0ejU6dOKCoqwo0bN3D27FlMmzZN3P7Ro0fYvn271Dh2dnbijdA5OTk4efKkVBs1NTWMHDlSoqxTp06wtLREVFQUlJWVpepfdufOHZnjduzYEVZWVtVeZ//+/WFpaYnvv/8ekyZN4n6KlzChICIiIlIgdd1uMLY5pND568rZ2RknT55EYGAgzp07h+DgYGhoaKBr165YtWoVxowZI26bmJiIb7/9VmqMGTNmiBOKp0+fyjxRqVmzZjITBg8PD8THx6NPnz7i055kOXXqFE6dOiVV7uPj88qEAgCmT5+OJUuWIDQ0VOKamjomFEREREQKpKyq91Yc0duhQwesXbu22jZRUVGvHGf//v21nnvWrFmYNWtWlfWmpqY1fst1dfOPHj0ao0ePrnV8bzvuoSAiIiIiIrnxCQURERG9EYpK8vA8N1GqvGWzjtBQ01VARERUE0woiIiIqF6VFOYjL+2hVLluawuoaVb9RuXnuYk4cWORVPm79t/AxNC2XmMkovrDhIKIiIjqVV7aQ9wIXiVVbj/pCzQ3f/XG19dF1dhc0SEQvRWYUBAREVGTpKypA1FpGUQFL6TqlJpV/SSFiCQxoSCiBlPVemgAaGPw5nxLSURNl6jgBYrvSf93SsOe/40iqikmFETUYKpaDw0As4eEvOZoiF4tPj4ex48fx9ixY2FpaanocIiIGgWFHhtbXFyMb775Bg4ODrCxscF7772Hy5cv17h/aGgovLy80KNHD/Tp0weTJ0/G7du3GzBiIqI3X3x8PNauXYv4+HhFh9Jo5JSX40phIbb88AOioqKw5YcfcKWwEDnl5YoOjQDoa7dVdAhEVA2FPqFYunQpzp49Cx8fH5ibm+PEiROYPXs29u/fDzs7u2r7btq0CXv37sWoUaMwfvx4FBQUID4+HhkZGa8peiKiN0t29gvExT1BcPBO/PlnHFJSMjBp0oewtjaBvr6WosN7o90tLoZ3aipaFxVBB8BvRUWISE3FIWNj9NPUVHR4TZ6aihZKCkqR/ThfZr1+Ox2oaXPRBZGiKOx/fbdv30Z4eDiWLVuGadOmAah4+6C7uzv8/f0RHBxcZd/ffvsNu3btQkBAAFxcXF5TxEREb7a4uCfw8NiGjh0fQV8fuHbtEQ4f3obQ0PlwcOik6PAahSx3d5RraiKH/9/yxsl+nI+YDXEy6wZ+Yo2WAv3XHBERVZJ7ydO1a9ewadMmrFixAomJFZuZ8vPzce3aNeTk5Lyyf2RkJNTU1DBu3DhxmYaGBry8vHDjxg2kp6dX2TcoKAjW1tZwcXFBeXk58vNlf2NBRNQUPXvWGkKhAZ49ay2znkuiqlbUoQMy5sxBkYWFokMhImo0ap1QlJWVYeHChfDx8cGuXbtw/Phx8c2/qqoq5s+fj4MHD75ynHv37sHCwgI6OpLHstnY2EAkEuHevXtV9r18+TKsra2xceNG2Nvbo2fPnnBycsKpU6dqezlERG+dggJtPHpkhoICbQCAhUVLifr9+/cjKioK+/fvr7c5maQQNV0BAQEQCARvxHhOTk5YunRpvcVCNVPrhGLPnj04e/Ysli5ditOnT0MkEonrNDQ04OzsjAsXLrxynIyMDLRq1Uqq3MjICACqfEKRnZ2NrKwshIeH48cff8Snn36KjRs3wtjYGIsWLcK5c+dqe0lERG81XV0NlJfmoDDrCgqzriA/Nw0AkJ+bhvLSVz9RromGSFKIiOrq6tWrEAgEEAgEiIyMlNnG3d0dAoEAU6ZMkSgXCARYu3at1FiWlpYyvzz54IMP4OTkJFHm5OQknv/fP76+vuJ2lUlU5Y+VlRVcXFywadMmFBYW1uVX8FrUeg/FTz/9BE9PT0ydOhV///23VH3Hjh1x8eLFV45TWFgINTU1qXINDQ0AQFFRkcx+BQUFAICsrCwcPXoUtra2AAAXFxe4uLhg27Zt3FdBRPQvxXl3kXrbGwCgXNIagA6US/5Acd5daBr0k3vcksJ85KU9RMmL3IrPL3Lxd9If0G1tATVNvhiMXr/GePRv7otcJKQmKGx+gbEAzbSayd1/3rx5mDNnTj1GVP/U1dURFhYGV1dXifL4+Hjcv39ffP9ZEyKRCNu2bUNAQECN2nfv3h1Tp06VKjcxMZEq++KLL6CpqYm8vDxER0dj586dePz4MTZu3Fjj+BSh1gnFkydPMGPGjCrr9fT0kJ2d/cpxNDU1UVJSIlVemUhU9S+2stzU1FScTAAVf1GGDx+OoKAg5OfnSy2lIiKiCu7vZEFToxwufev+dCIv7SFuBK+ClVopXrRWg5X6I9wIXgX7SV+guTlfDEavh47RPydx7d+/H1euXEFBQYHEt8tvsoTUBMz6fpbC5t87bS96WfSSu7+qqipUVd/sU7YGDx6MCxcuIDc3F82a/ZM8hYWFwczMDNra2jUeq2vXrjh37hwSEhJqtDTL2NgYnp6eNRp7xIgR0NPTAwBMmjQJEyZMQHh4OD777DO0bNnyFb0Vp9ZLnnR0dJCVlVVlfVJSEgwNDV85jpGRkcxlTZXHvspaDgUABgYGUFdXl/lLbdmyJUQiEfLy8l45PxFRU9XBpAhzxmTAwkT2k2B5tNVXhaeVNtrqvdk3FfR2UtVUwYuiMiQ+fYGCwjIAQEFhxeeyctErelNd/XvPQ+VSoTNnzsDNzQ1WVlZwc3OTuYLl+vXrGDt2LKytreHs7IzDhw9XOc/x48fx7rvvwsbGBn379sWSJUvw/PnzGsXo7OwMJSUlnD17VlwmEolw+vRpjBw5shZXC/j4+EBHRwfbt2+vVT95VL5G4fHjxw0+V13UOqGwt7dHaGioxN6JStnZ2Th+/Dj69u37ynEsLS3x8OFDqROabt26Ja6XGbCyMrp27Yq0tDSputTUVKioqEBfn0fHERERNSVPM4uxK/wpio2cYWjeB8VGLhWfS/hyQkW4du0a1qxZA3d3dyxatAhFRUXw9fWVWC6fkJCAmTNn4u+//8aCBQswZswYBAQEyNwPu3XrVqxYsQIdO3bEsmXLMGnSJPz888/w8fGp0R4DXV1dDBkyBOHh4eKy3377DU+ePIGHh0etrk1PTw9TpkzBmTNn8ODBg1e2LykpgVAolPqpann/y54+fSqe801W64Ri7ty5ePToEXx8fPC///0PQMVfiMOHD+Pdd9/FixcvarSOztXVFSUlJTh27Ji4rLi4GCEhIejZsydat6447vDp06fiY2lf7vvs2TPExMSIy/Ly8hAREQE7Ozto8iVERERETZJOCwtY9J8FnRbtFR1Kk5aYmIiDBw9i3rx5mDp1KrZu3YoXL15I3NBv2bIFSkpKOHToEObMmYMPPvgAQUFBUjfpKSkp2L59OxYtWgR/f394e3vD19cXe/fuxV9//YUTJ07UKCZ3d3dcuXJF/FQjLCwMAoEAnTrV/j0906ZNg7a2do2eUly8eBH9+/eX+nn5HrhSdnY2hEIhHj9+jO+//x5nz55Fly5d0KFDh1rH+DrV+tm0tbU1AgICsGLFCixbtgwA8N///hcikQgtWrTA1q1ba/QvxtbWFq6urvD390dGRgbMzMxw4sQJPH36FOvWrRO3W7JkCWJjY5GQ8M9mJW9vbxw7dgwLFizAtGnToKenh+PHjyM3Nxcff/xxbS+JiIiIiOqRg4MD2rVrJ/5saWkJXV1d8dKdsrIy/Prrrxg2bJj4S2Sg4nAfBwcHiRNDz58/D5FIBBcXFwiFQnG5mZkZjIyMEBsbC29v71fGNHjwYGhpaSEiIgLe3t6IjIzE9OnT5bo+AwMDTJkyBbt378b8+fPRsWPHKtva2dlJnOhUSVaS4OzsLPG5Z8+e+O9//wslJSW54nxd5FrsOmTIEERFRSEmJgaJiYkQiURo3749HBwcoKWlVeNx1q9fj82bN+PkyZPIzs6GQCDA7t27YW9vX20/LS0tBAUFYf369Thw4AAKCwvRvXt3fPfdd6/sS0REREQNq23btlJl+vr64pcfC4VCFBYWwtzcXKqdhYWFRELx6NEjlJeXS91sV3o5yaiOhoYGXFxcEBYWBnNzcwiFwlrvn3jZtGnTEBQUhB07dsDf37/KdoaGhhgwYECNxty2bRu0tbWRkZGB7777DpmZmY1i5U2tEorCwkJERkbCwsICtra2cHR0hKOjo9yTa2hoYMmSJViyZEmVbao609zIyAjffPON3HMTEVHD0WpuLP5zXY7xbIxHgBJRxZ5XWWTtwX2V8vJyqKioYM+ePTK/qa/N/gI3NzfMmjULe/bsgZ2dHUxNTWsdT6XmzZtj8uTJCAwMxPz58+Ue52V9+vQRX88777wDV1dXrFq1Cjt37qyX8RtKrfZQqKurY8WKFbh7925DxUNERG8BFXUtlBfmoDD5Cn7YvQVRUVH4YfcWFCZfQXlh9cfVlhSU4nlCNp4nZCNwx3eIiopC4I7vUFJQ+pqiJ6KGZmhoCE1NTSQlJUnVPXz4UOKzmZkZysrKYG5ujgEDBkj9WFnV/IjqAQMGoEWLFoiNjYW7u3udr2P69OnQ0NBokBv+5s2bY+bMmYiOjsbvv/9e7+PXp1olFMrKymjTpg2PZSUiscqbxn//iMp589fUFaffRephbxQ9/Q0AUPT0N6Qe9kZxevVfSmU/zkfMhjjEbIiD8K//Xx7xVw6yH+dX24+IGg8VFRU4ODjg3LlzEid3JiYm4tdff5Vo6+LiAmVlZWzbtk1qnPLy8mpfZyBr3pUrV+LDDz+s03KnSoaGhpg4cSJCQ0Mb5GjXCRMmQFtbG7t37673setTrfdQjB49GqdOncLUqVOhrq7eEDERUSNSedP4b2a+txQQDb2J3DtkQVO1HC7mtX+RXp/mg6GhrIEe+v1r1Y9LpYjefAsWLMAvv/wCb29vTJgwAWVlZThw4AA6deokcRiPubk5fH19sXnzZjx+/BiOjo7Q0tLC48ePcebMGcybNw/jxo2r8bwjRoyo1+uYOXMmgoOD8eeff8p8+3VqaipOnjwpVW5gYIDBgwdXO7aenh68vLywf/9+JCYmVrv5W5FqnVD07NkT586dg6enJyZOnAhzc3OZG7F79+5dLwESEVHj1sGgCHMMMuTqa6xpCmNNr1r3a4xvS6amS2AswN5pexU6vyJYWloiMDAQ69atw5YtW2BsbIwFCxYgIyNDIqEAgHnz5sHc3BxBQUEICAiAkpIS2rZtC2dn5xpveG4ohoaG8Pb2xr59+2TW37lzB4sXL5Yqt7S0fGVCAQBTp05FcHAw9uzZg6+//rrO8TaEWicULx+vtXbtWqnNMSKRCEpKSrh3717doyMiIqqh8vwcFD+qWE6V/zxN/M/CO1eg3r4blHXe7BdDUdPVTKsZeln0UnQYcluwYAEWLFgg/vzvZKBSVFSUVFnv3r0REhIic8x/Gzly5CuXKf17jr59+1YZz8tkPUH4d7/qxqrqkCFZ1yzLv3+HLzM1NX3j9y/XOqF4+R0RREREb4riR3eR+nnF8jvl3NYAdKCc9AdSP/eG8eeHoNm9n2IDJCJ6S9U6oXj33XcbIg4iIqJq6RhJnsVe3T4Jd60saCqVw0Wz9vs2iIioduR6sR0REdHrpqqpghdFZXiaWQwA2LXne9z+/RqeC3Ph/43k0/MOqkWYoyvfvg0iIqoduRKKgoIC7N27F+fOnUNKSgqAivVdw4YNw8yZM6GtrV2vQRIREQHA08xi7Ap/CgBISi8S/7O4pBzSr7siIqLXodYJRVZWFiZNmoTExEQYGhqia9euACpei75t2zZERkYiODgYBgYG9R4sERFRpTbd3aCipolWXZwVHQoRUZNW64Riy5Yt+Ouvv7By5UpMmDABKioqAICysjIcOXIEa9aswdatW7FixYp6D5aIiKiSTgsLWPSfpegwiIiavFq9KRuoOP5q3LhxmDRpkjiZACrePDhx4kSMHTsW58+fr9cgiYiIiIjozVTrhOL58+fiZU6ydOvWDc+fP69TUERERERE1DjUOqFo2bJltS+tu3fvHlq2bFmnoIiIiIiIqHGodULh6OiIH3/8EYcPH0Z5ebm4vLy8HEeOHMHx48fh5ORUr0ESEREREdGbqdabsn19fXHp0iWsXr0aAQEBsLCwAAA8fPgQQqEQZmZmVb46nIiIiIiI3i61fkLRvHlzHD9+HHPmzIGBgQHi4uIQFxeH5s2bY86cOTh+/DiaN2/eELESEREREdEbRq4X2+nq6sLPzw9+fn71HQ8RERERveWuXr0KHx8fBAUFoW/fvooOh+qo1k8oiIiIiIgqhYSEQCAQQCAQIC4uTqq+uLgYffr0gUAgwNKlS6sdKyoqCpMnT0b//v1ha2uLoUOH4qOPPsLFixfFbTIyMhAQEFDtIUEvy8zMRLdu3aqdOy0tDV27dsXKlStrNCZJkuvFdmfPnkVYWJjMeg8PD4wYMQIffPBBnYMjIiIietuVFxaiNCNNYfOrGrWGsqZmncdRV1dHWFgYrK2tJcovXryI3NxcqKmpict69+6N27dvS5QFBgZi/fr16NOnD95//31oamoiKSkJly9fxunTp/HOO+8AqHiFwdatW2FiYlLtqwwqtWjRAv3798e5c+ewevVqaGhoSLU5ffo0ysvL4e7uLu/lN2m1TijOnTuHAQMGVFk/cOBAnDlzhgkFERG9keLj43H8+HGMHTsWlpaWig6HCKUZacj6cb/C5jfwmgL1duZ1Hmfw4ME4ffo0lixZAmXlfxbBhIeHo0+fPrhz5464TFlZWeLGvrS0FNu3b8egQYOwd+9eqbEzMzPljuvFixfw8PDAr7/+igsXLmDYsGFSbcLCwmBsbIzevXvLPU+lgoICaGtr13mcxqTWS55SUlLQoUOHKustLCyQkpJSp6CIiIjqk6rxPzdL+/fvR1RUFPbvV9wNHNHbyM3NDenp6YiNjRWX5efnIzo6Wuqb/6tXr0IgEODq1asAgL///ht5eXmwt7eXOXaLFi3E/UaPHg0AWLZsmXipVUhICABgypQp8PT0xO3bt+Ht7Q0bGxvs3bsXzs7O0NTUlLnCJikpCX/88QdGjhwpToTu3buHxYsXw8nJCVZWVhg4cCCWL1+OrKwsib6bNm2CQCDAo0eP4OvrC3t7+yb5pbpceyhycnKqrMvOzpZ4PwUREZGiKWvqQFRahvKcPBTk5gEACnLzUJ6TB5FIpODoiN4O7du3h5WVFcLDw8VlP//8M0pLS2U+FXhZixYtoKmpiaioKGRnZ1fZrmPHjuJDgcaPH4/169dj/fr1Ek8WhEIh3n//fVhbW2P58uWwtbWFrq4uHB0d8b///Q95eXkSY4aGhgIARo0aJS775Zdf8OzZM4wdOxarVq3CiBEjEBoairlz58qMa/78+VBSUsKnn34KDw+Paq/1bVTrJU+dO3dGVFQU5syZI1UnEokQFRUlfjcFERHRm0JU8ALF9xKhWVbxpZdmWTmK7yVCw95KwZERvT3c3d2xc+dOrFq1CmpqaggLC8OgQYOgr69fbT9lZWXMnDkT27Ztw5AhQ9C7d2/Y29tj0KBB6Natm7hdy5YtMXjwYGzatAk9evSAp6en1Fjp6elYu3YtvLy8pGKLiIjA+fPnxU85gIolWR07dpTYj+Hj4yN1r2ttbY3Fixfj5s2bsLOzk6izsbHBunXrXv0LekvV+gmFl5cXfv/9dyxduhRCoVBcLhQK8dlnn+HWrVtS/wKJiIjeFBMcXTDEtie8nar/xpSIam/kyJHIycnBL7/8gr///huXLl2q8UZnX19fbNiwAV27dsUvv/yCjRs34t1338WYMWPw119/1TgGLS0tmYnGO++8A319fYllT/fu3cNff/0l9VRB86VN6kVFRRAKhejRowcA4O7du1Jje3t71zi+t1Gtn1C89957iI2NxU8//YSTJ0/CyMgIQMURXiKRCCNHjsTEiRPrPVAiIqL6IGhnjkXj674BlYiktW7dGr169UJYWBjS0tKgpqYGJyenGvd3d3eHu7s78vLycOvWLYSEhCAsLAzz5s1DaGgo1NXVaxTDy6dHVVJXV8fw4cMREhICoVAIQ0NDcXLx76RHKBRi27ZtOH36tMQX6ACQm5srNbapqWmNr/FtJNeL7fz9/eHk5ITQ0FAkJycDqHgM5OHhAVdX13oNkIjeLDwhh4iIquPm5oavv/4aycnJcHJygpaWVq3H0NXVxcCBAzFw4ECoqanhxIkTuHXrVo1OYdKs5ghcDw8PHD16FBEREZg4cSJOnz4NOzs7tGvXTqLdRx99hLi4OMyaNQuWlpbQ0tJCaWkp5syZI3OvsKyjaJsSuRIKoOKR1siRI+szFiJ6Q5UU5iMv7SEAYN/u3bhx6w6yM9Pwtf8mBUdGRERvGldXV6xZswZxcXGYP39+ncezsrLCiRMnkJ6eDgBQUlKSe6zevXvD2NgY4QvoecQAACAASURBVOHhEAgEePr0KWbOnCnRRigUIjY2Fn5+fhKbsBMTE+We920nd0Lxb0KhEDk5OWjfvn19DUlEb4i8tIe4EbwKAJD9JP///5mAsuJCRYZFRERvIAMDA6xatQrp6elwcHCoUZ8XL14gPj5earMzAPFbsisP/al84lHdqaNVUVJSgpubG/bt24e9e/dCVVVV6gtyFRUVAJA6Ae6HH36o9XxNRa0Tip9++gk3btzAl19+KS7z9/dHYGAgAMDW1hZ79+6Frq5u/UVJRG+Mge01oK6ihN5mr17HSkT0f+zdeVxUVf/A8c+wi6i44JZCuAEJ4q4obqmJihuu2E8rM01Ne0hNfawnLdHcckmtVNIkU1MxHlfUKEsSUlPRR8E0d1IxBGQdYOb3B3FjnGEVHIHv+/XyNdxzz73nzHC83O/cs4iCmdnVwXbYGKOWX9JGjBhRpPypqamMGjWKli1b0qVLF+rWrcujR484evQop06dolevXspsT8899xy2trZs376dypUrY21tTYsWLfS6LeVlwIABBAQE8MMPP9ClSxdq1Kihs79atWq0atWK9evXk56eTu3atfn555+JiYkp0nuqSIocUGzfvl1nWtjz58+zceNG2rVrh6OjI7t372bz5s289dZbJVpRIUTp0qSlkRl7Ty/d/DndC3T9amYMqlZiDzeFEKLCM7GyKpGVqsuyqlWrsmDBAn788UeCgoKIjY3F1NQUR0dH3n33XcaM+SfgMjMzY/HixSxbtox58+aRmZnJokWLCh1QuLi40KRJE65cuZLnDFQrVqxgwYIFfP3116hUKjw9PVm/fj1du3Ytkfdb3hT5ruDmzZs6A68PHTpEtWrVCAgIwMLCApVKxcGDByWgEKKMyYy9R/wu/ZWDa02aYYTaCCGEKCt8fHzw8fEpMN+pU6eUnzt06EB0dLSybWZmxvDhwxk+fHihyuzevTvdu3fXSw8M1P87ZkjuxfcMqVevHmvXrtVLz11nAD8/P2WhvYqsyOtQPHr0iCpVqijbJ06coFOnTso0Xq6urvJISAghRKFERUXh7+9PVFSUsasihBCimIocUNjZ2XHjxg0geyB2VFQUbdu2VfanpKQog1mEEEKI/AQGBhIaGlrobxWFEEI8e4rc5alDhw5s3bqVatWqERERgUqlolu3bsr+a9euUadOyQ/uEUIIUT6Y2f7TVzw1NVXnVQghRNlT5CcUb7/9NnZ2dixdupSffvqJCRMmKKsDZmZmcvjw4UItOiKEEKJiMrGoTHpGEnfizpGekQSgbGs0WUaunRBCiKIq8hOKnMVArly5QpUqVahfv76yLy0tjQ8//FBWzxVCCJGvB4+usuf0TB48ytLZfqN7kJFrJoQQoqiKNfejqakpTk5Oeuk2Njb06tXriSslhBCiYjCz0H0VQghR9hS5y9Pj4uLi6NmzJ2fOnCnysWq1mqVLl+Lp6UmLFi0YMWIEJ06cKPJ53njjDZycnPD39y/ysUIIIYzHqbMJDV5Q4ez5xH+OhBBCGMkTX8E1Gg137twhLS2tyMfOnj2br776ioEDBzJ37lxMTEx44403ihSc/PjjjzrzGgshhCg7atRX0W6gCdXrqYxdFSGEEMVktK+EIiMj2b9/PzNmzODdd99l5MiRfPXVV9SrV49ly5YV6hxqtZpFixbx+uuvl3JthRBCCCGEEIYYLaA4dOgQ5ubmOisiWlpaMmzYME6fPs39+/cLPMeWLVtIS0uTgEIIIYQQQggjeeKAwsTEhPr162NlZVWk4y5duoSjoyOVK1fWSW/RogVarZZLly7le3xsbCzr1q3Dz8+PSpUqFbneQgghhBBCiCdXqIAiMjKS+Ph4g/tq1KhBaGgorVq1KlLBsbGx1K5dWy/dzs4OoMAnFJ988gmOjo4MGjSoSOUKIYQQQojy7fbt2zg5ObF582ZjV6VCKFRAMXLkSH7++WdlOzk5menTp3PlypViF5yWloa5ubleuqWlJQDp6el5HhsZGcl3333HnDlzUKlkIJ8QQgghhLEEBQXh5OSk869Tp06MGzeOX3/91djVy9fjdW/RogVdu3ZlwoQJ7Ny5M9/7UfGPQq1DodVqdbbVajX79+9n2LBhNGnSpFgFW1lZkZGRoZee84vLCSwM1cXf35+XXnqJtm3bFqtsIYQQQohnRUZaMkn3rhmtfJs6jphbVS44YwH8/PyoV68eWq2W2NhYdu3axbhx49i2bRtubm4lUNPSk1P3jIwM7t+/T1hYGO+99x5fffUV69ev11nIWegr1sJ2JcHOzs5gt6bY2FgAg92hAI4cOUJkZCR+fn7cvn1bZ19SUhK3b9+mVq1aRR7TIYQQQghhDEn3rnF663+MVn6blz+kuoPrE5+nW7duuLi4KNsDBw6ka9euhISE5BlQpKSkYG1t/cRlP6nH6z558mQOHDjA9OnTmTZtGjt37iyzvWKysrLIysrCwqL0VhA12ixPzs7OXLt2jeTkZJ30c+fOKfsNiYmJQaPR8Morr9CzZ0/lH2Q/turZs+cz/3hNCCGEEKK8q169OmZmZmRmZgIQERGBk5MTBw8eZPny5Xh6etK6dWslf3x8PB999BFdu3bF1dWVPn36sGXLFp1zqtVqVq1ahY+PD23atKFly5aMHj2a8PDwAuuTlZXFzJkzcXd35/jx4wXm79evHyNGjOD8+fOEhYUp6adOnWLatGl0794dV1dXunXrxsKFC/XWZJs9ezZt27blzz//5M0336RVq1Z07NiRxYsXk5WVpZNXo9GwadMmvL29cXNzw8PDgzfffJPff/9dJ09AQAB9+/bF1dUVT09PPvroI7176ZzFnr/77ju8vLxwc3Mr1gLURWG0JxReXl58+eWX7Ny5k1dffRXIbiRBQUG0bt2aOnXqANkBRGpqKo0bNwbgxRdfpEGDBnrnmzJlCj169GDYsGE0b978qb0PIYQQQggBiYmJxMXFAdk9Tr788ktUKhV9+/bVybdmzRqsrKx44403lJvhlJQUxowZw4MHDxg1ahR16tQhIiICf39/EhMTeeutt4Ds3ig7d+7E29ub4cOHk5yczK5duxg/fjw7d+7UecqQW2ZmJjNmzODHH3/k888/x8PDo1DvaeDAgWzfvp2wsDA8PT2B7KUP0tLS8PX1xdbWlsjISL7++mvu3r3L6tWr9codN24crVu35t133+WXX37hyy+/pGHDhowePVrJN3v2bIKDg+nRowcjR44kPT2diIgI/ve//9G0aVMA5s6dy969exk6dCivvPIKN27c4Ouvv+bKlSts3rxZ5wlKWFgYBw8eZPTo0VStWlWZ9Ki0FDqgOHbsGA8ePAAgNTUVlUrFoUOHiIqK0surUqmUICEv7u7ueHl5sWzZMmJjY7G3t2fPnj3ExMSwaNEiJd+sWbP49ddfiY6OBsDe3h57e3uD52zYsCG9evUq7FsSZUhUVBS7d+9m6NCheT69EkIIIYTxjB07Vmfb2tqaFStW4O7urpOemZnJN998ozNedtOmTdy5c4fg4GAaNmwIwKhRo6hatSrr169nzJgxVKtWjWrVqhEaGqrTfWfEiBH07duXwMBAFi5cqFevjIwM/Pz8CAsLY+PGjUUag5tzM3/r1i0lbcaMGTpd60eOHImDgwOffPIJMTExOuMtUlNTGTx4MBMnTgTA19eXIUOGsGvXLiWgOHHiBMHBwbz22mvMnj1bOXb8+PHKOOZTp04RFBTE6tWr6dOnj5LHzc0NPz8/fv75Z7p27aqkX79+nf379+Po6Fjo9/okCh1Q7Nu3j3379umk7dixw2DewgQUAEuWLGHlypUEBweTkJCAk5MT69evp02bNoWtlqggAgMDCQ8PJyUlBX9/f2NXRwghhBCPmT9/vvKl7/3799m+fTszZswgICBA5yZ+yJAhepPvhISE0L59eypXrqw85QDw9PRk+/btnDt3jq5du2JqaoqpqSmQ3QUoMTERjUaDq6srFy9e1KuTWq1m6tSpnDx5koCAAJ0uVoWRM74jd7ei3MFESkoKaWlptGrVCq1Wy8WLF/UGcI8cOVJnu02bNvz3v/9Vtg8fPoypqSlTpkzRKz/nqcOhQ4ewtbWlXbt2Op9P27ZtMTU15ddff9UJKDp27PjUggkoZEDxeP+1kmJpacmsWbOYNWtWnnkCAwMLda6cJxii/NAkJ6K+nn1xSH5wT3lN+184Fs+/gEnlqsasnhBCCCFycXd31+ly1KdPH3r37o2/vz979uxR0g11Xb9x4wbR0dF5dkXKfRO9Z88evvzyS65du6YzY6ih83722WekpKSwZcuWIgcTkB0wADoLMcfExLB69WpCQ0NJSEjQyZ+UlKSzbW1tja2trU5atWrVdI67desWdevWpUqVKnnW48aNG8THxxfq8wHDn0VpKlRA0b59+9KuhxB61NcvcneeLwAmj+oAlTG5cYG783ypO28bVs07GreCQgghhMhTpUqVcHd35+jRo8qNOWBwJk6NRkOXLl0YN26cwXPlLFMQHBzM7Nmz6dWrF6+//jo1a9bE1NSUL774QqdbUo4uXbrw008/sWHDBlq1alXkmY4uX74MoDx5ycrK4rXXXiMhIYHx48fTqFEjrK2tuXfvHrNnz0aj0egcn/M05UlpNBrs7OxYsmSJwf2Pz46a1/ILpcVog7KFKArvSvFYqTT0tko0dlWEEEIIUUg5MzzlDigMsbe3Jz09nU6dOuWbLyQkhIYNG7JmzRqdQciPD4bO0bp1a4YNG8bkyZOZOXMmK1aswMSk8JOc7t27F0AZkH358mWuX7/O4sWLGTx4sJIv9yxQRWVvb88vv/xCYmIiVasa7n1hb29PREQEbdu2LdXpX4vLaNPGClEUjczSmWATi6OZrFhZEqKiovD39zc4qYIQQghREhITEzl79iy1atWiZs2a+ebt06cPJ0+eJCIiQm9fXFycMjg55xv/3Isunzt3jrNnz+Z57q5du7Js2TKOHDnCf/5T+PU+Dhw4wLfffou7u7sS6OQEI7nL12q1TzQ8oHfv3mRlZbFu3Tq9fTnl9OnTh4yMDNavX6+XR61W63W1etrkCYUQFZAMchdCiGeHTR1H2rz8oVHLLwnHjh1TugjlrJQdHx/PBx98UOCicOPHj+f777/n9ddfZ+jQobi4uJCUlERUVBSHDx/mt99+w8zMjO7du3P48GGmTJlC9+7duX37Ntu3b6dJkyb5PgXx8vIiKSmJ9957jypVquiN382pe2ZmJrGxsRw/fpyTJ0/SrFkzVq1apeRr1KgR9vb2LF68mHv37mFjY0NISAiJicXvQeHh4YG3tzebNm3i+vXrdO7cmczMTCIiIvDy8mLw4MF07NiR4cOH8+mnn3LhwgU8PDwwMTHh+vXrHDx4kGXLlhX4dKc0SUAhRAXyKPUR0XejSUjJHgyWkJLAqWunaGnf0sg1E0KIisvcqnKJrFRtbCtWrFB+trKyolmzZixfvhxvb+8Cj7W2tmbr1q189tlnhISEsHv3bqpWrUqjRo2YMWOG8mTCx8eHBw8esGPHDo4fP06TJk1YunQphw4dKnBh42HDhpGUlMSiRYuoUqUKkydP1qu7hYUFtra2uLi4sGDBAgYOHKgzHsHc3JzPP/+cBQsW8MUXX2BpaUnv3r15+eWXGTRoUJE+r9yWLFmCk5MTu3fv5vjx41StWpUWLVrg6vpPu/joo49o3rw53377LcuXL8fCwoIGDRowfPhwo0+pLwGFEBVI9N1oxm8ej5nKDCs7K06oTnB883F+nv0zT3f4lhBCiPLCx8cHHx+fAvN16NAh31k5bWxsmDlzJjNnzswzj0qlYuLEicq6Djm6d++us92gQQODZb366qs6SxsUtu65NW7cmE2bNumlP17exx9/bPD4qVOnMnXqVJ00U1NTJkyYwIQJE/IsV6VS4evri6+vb771M8bMpxJQCFEBZVbJJMnJuP0thRBCCFE+yKBsIYQQQgghRLFJQCGEEEIIIYQoNgkohBBCCCGEEMUmAYUQQgghhBCi2CSgEKKcksXrhBBCCPE0SEAhRDkVGBhIaGgogYGBxq6KEEIIIcoxCSiEKGc0mYmkxYeT/OgeAMmP7pEWH45Wm2nkmgkhhBCiPJKAQogyzFC3JnXSRe5G+qJOuqizrc1KMVY1hRBCCFGOSUAhRBmWX7cmK0uNzqsQQgghRGmQlbKFKGMSElI5f/4OAHfvximvx49foWNHRyWfd9d4rCw19O6QaJR6CiGEEKJikCcUQpQx58/fYcCAtQwYsFYJLHLSkpPVSr5Gz6UzwScWx+fSjVVVIYQQAoBPP/0UJyenZ+acL774IrNnzy7R+lRk8oRCiDJMozHReRVCCCGetqCgIObMmaNsW1pa8txzz9GzZ08mTpxIlSpVjFg78TRIQCHKtaioKHbv3s3QoUNxdnY2dnVK3J9/1iEry5T792sZuypCCCGKSZOWiPr+RaOVb1H7BUysqj7xefz8/KhXrx6pqan88ssvbNiwgV9//ZUdO3YwadIkJkyYUAK1Fc8iCShEuRYYGEh4eDgpKSn4+/sbuzolLiXFmuvX7Y1dDSGEEE9Aff8id7f7Gq38uqO2YWXf8YnP061bN1xcXAAYNWoU06ZNIyQkhDNnztC6dWvMzOS2s7ySfhKiXErUaAhPS+OhJnuGo4d/bydqZMYjIYQQ4mno0KEDAHfu3DE43sHJyQl/f39CQkLo378/rq6u9O/fn59++knvXKdOnWLo0KG4ubnRq1cvtm/fnme5u3fvZsiQIbRo0YIOHTowa9YsHjx4ULJvTuiQUFGUSxfVanzv3sWyd2+qqlSE9u7Nwbt32Va3Lh2trIxdPSGEEKLcu3XrFgC2trZ55jl58iSHDh1i9OjRWFtbExgYyLRp0/jhhx+oXr06ANHR0bz++uvUrFmTqVOnkpmZyaeffkrNmjX1zrdmzRrWrl1L//79GTFiBLGxsWzZsoXz588TFBSEldwDlAoJKES5lt6oEbHSZ1OUUeV9DJAQonxJTEwkLi6OtLQ0wsLC+Oabb6hZsyZt27bl7NmzBo+5evUqBw4coGHDhkD2U41Bgwaxf/9+/u///g+A1atXo1Kp2LZtG3Xq1AGgT58+DBgwQOdct2/fZt26dcycOZNx48Yp6V27dmXUqFHs2bMHX1/jdS0rzySgqMDkZkWIZ1t5HwMkhChfxo4dq7PdqFEjFi9eTKVKlfI8xtPTUwkmAJydnbGxsVGebmRlZXH8+HFeeuklJZgAaNy4MZ6enhw7dkxJO3r0KFqtlt69exMXF6ek29vbY2dnx6+//ioBRSmRgKICk5sVIZ49msRE1BezZ3tJvndPeU0LD8fihRcwqfrkM7EIIURpmD9/Pvb29piamlK7dm0cHR0LPKZ+/fp6adWqVSMxMXtR1pwnHg4ODnr5HB0ddQKK69evo9Fo6NWrl8GycgcZomRJQFHBaNLSyIz9+ybl4UPlVX3rBmZ2dTCRvoVCGJX64kXu/v0NmkmdOlC5MiYXLnDX15e627Zh1fHJZ2IRQojS4O7urszyVFgmJobnB9JqtUUuX6PRYGpqyoYNG1CpVHr7q8oXMqVGAooKJjP2HvG7AgEwf/hAeY3fFYjtsDFYNNT/BkAIYRze8fFYaTT0/vubOiGEqGhq1KiBlZUVN27c0Nt37do1nW17e3uysrJwcHCgQYMGT6uKApk2tkIb0vR5Otevg0+z559amVFRUfj7+xMVFfXUyhSirGqUns6E2Fgc09ONXRUhhDAKU1NTPD09OXLkCPf+7gYK2YO5jx8/rpO3d+/emJiYsHbtWr3zaDQa4uPjS72+FZU8oajAmlSvylvVX3iqZcq4DSGEKJ7c42tyk7E1orybOnUqP//8M76+vowaNYqsrCy+/vprmjRpQnR0tJLPwcGBadOmsXLlSm7dukWPHj2oVKkSt27dIiQkhEmTJjF8+HAjvpPySwIKUeoyUjJJuJUMQOJfj5TXjJRMzK3/aYIy65QQQuQt9/ia3GRsTdlnUfsF6o7aZtTyn2XOzs4EBASwaNEiVq9eTd26dZk6dSqxsbE6AQXApEmTcHBwYMuWLXz66aeoVCrq169Pr1696NSpk5HeQfknAYUodQm3kglbfh6AlD8zsl/vZJCZlqUTUMjTCyGEEBWRiVVVrOzLblDo4+ODj49PvnmmTp3K1KlTddIeDwZyhIaG6qW1a9eOoKAgg+d9XL9+/ejXr1++9TFUhig+CSjEU9W+ejcsTSxpWc0DMytTUtOziPlLDUBKWpbymqUp+uwOQgghhBDi6ZOAQjxVda0aUNdqmLId85eaL/bHAKC260UNBxVqu16oMzToT/gmhBBCCCGeNRJQiGdG5ZqOOHqMN3Y1hBBCCCFEEci0sUIIIYQQQohik4BCCCGEEEIIUWwSUAghhBBCCCGKzahjKNRqNatWrSI4OJjExEScnZ3x8/PDw8Mj3+MOHz7MgQMHiIyM5K+//qJevXr06NGDyZMnU6VKladUeyGEEEIIIYRRA4rZs2dz+PBhxo4di4ODA3v27OGNN94gMDCQVq1a5Xnc+++/T+3atRk0aBD169cnOjqawMBAfv75Z3bv3o2lpeVTfBdCCCGEEEJUXEYLKCIjI9m/fz9z5szh1VdfBWDw4MF4e3uzbNkytm7dmuexq1evpkOHDjpprq6uzJo1i/379xe4uIoQQgghhBCiZBhtDMWhQ4cwNzdn+PDhSpqlpSXDhg3j9OnT3L9/P89jHw8mAHr16gXA1atXS76yQgghhBBCCIOMFlBcunQJR0dHKleurJPeokULtFotly5dKtL5Hjx4AED16tVLrI5CCCGEEEKI/BktoIiNjaV27dp66XZ2dgD5PqEwZMOGDZiamvLSSy+VSP2EEEIIIUTJiYiIwMnJiYiICGNXpcR9+umnODk5kZiYaOyqGIXRxlCkpaVhbm6ul54zoDo9Pb3Q59q7dy+7du1i4sSJ2Nvbl1gdhRBCCCFE/oKCgpgzZ45OWs2aNXF2dubNN9+kffv2JVbWyZMnCQwM5MyZMzx8+BArKyuaNGlCjx49GDlyJLa2tiVWVml78cUXCQ0NNXY1SoTRAgorKysyMjL00nMCicLO1HTq1Cnmzp1L9+7defvtt0u0jqJ4oqKi2L17N0OHDsXZ2dnY1SmT1Go18fHxeum1atUyQm2EEEKUpvSMJB48Mt4Y0FpVGmNpbvPE5/Hz86NevXpotVpiY2PZtWsX48aNY9u2bbi5udGuXTsiIyMNfqFcGCtXruSzzz7DwcGBoUOH0qBBA9LS0jh37hyff/45ISEhBAUFPfH7KE0nTpygffv2mJqa6qQfP34cT09PI9XqyRktoLCzszPYrSk2NhbAYHeox0VFRTFp0iScnJxYsWKF3i9HPD25L4YbvtzI2dMXiUu4y9KPVxq5ZmVTfHw8P/74o1764MGDn35lhBBClKoHj66y5/RMo5U/pM1Snqvh/sTn6datGy4uLsr2wIED6dq1KyEhIbi5uWFiYlLsqf0PHDjAZ599hre3N4sXL8bM7J9b2P/7v//j4cOHbN++Pd9zZGVlkZWVhYWFRbHqUBKCgoJYunQpCxcuBODWrVu8//77mJmZ0a5duzK79IHRxlA4Oztz7do1kpOTddLPnTun7M/PzZs3GT9+PDVq1OCLL77A2tq61OoqCpZzMdxzeiZ34y8CcDf+IhlZqUaumRBCCCGMoXr16piZmZGZmQnkPYbiwIEDDB48GDc3Nzw8PPj3v/9NXFycTp5Vq1ZRo0YNPvroI51gIndZkyZN0klzcnLC39+f7777Di8vL9zc3Dhz5gwAAQEBjBo1ig4dOtCiRQt8fHw4dOiQ3nlzn6NPnz64ubkxfPhw5X71cQkJCbz77ru0adOGNm3aMGfOHFJT/7kXWrp0Kf/+97/54IMPuH//PlOnTuWVV15h48aNZTaYACMGFF5eXmRkZLBz504lTa1WExQUROvWralTpw4AMTExelPBxsbGMm7cOFQqFQEBAdSoUeOp1l3kz6mzCQ1eUOHsabTmJYQQQoinLDExkbi4OOLi4oiOjub9999HpVLRt2/fPI8JCgrCz88PS0tLZs6cyeDBg9m7dy+vvPKK0g3+2rVrXL9+nZ49exb5C+SwsDCWLVvGwIED+fe//61M/rNlyxZcXFyYNm0a77zzDqamprz99tsGeweEh4ezZMkSBg0axNSpU7l//z6vvfYaN2/e1Ms7bdo00tLSmD59On379iUoKIg1a9bo5DExMUGlUgGgUqmUn8syo3V5cnd3x8vLi2XLlhEbG4u9vT179uwhJiaGRYsWKflmzZrFr7/+SnR0tJI2fvx4bt26xfjx4zl9+jSnT59W9tnb2+e7yrYofTXqq6gxsOz/53iaZNyJEEKIsm7s2LE629bW1qxYsQJ3d8PdqTIyMli2bBnOzs4EBgYqXZFcXV155513+PbbbxkzZgx//PEHAE2bNtU5XqvV8vDhQ500W1tbTEz++ULz+vXr7N+/H0dHR518ISEhWFlZKdsvv/wyPj4+bNq0ie7du+vk/f333/nuu++Uv89eXl707duXzz77TOeeFcDNzY0PP/xQ2Y6Pj2fXrl3MnJndpW327NlcvnwZf39/pkyZwurVq3n//ff5+uuvWbt2bZl9SmG0gAJgyZIlrFy5kuDgYBISEnBycmL9+vW0adMm3+OioqIA2Lhxo96+IUOGSEAhypzAwEDCw8NJSUnB39/f2NURQgghimz+/PnKbJv3799n+/btzJgxg4CAANq2bauX/8KFC/z111/861//0hnX0LdvXxYvXsyPP/7ImDFjSEpKAtB7OvHgwQO9gczff/89DRo0ULY7duyoF0wAOsFEQkICWVlZtGnThv379+vlbdOmjc6Xffb29nTp0oWffvpJL++oUaN0ttu2bcuRI0dISkrCnasyQgAAIABJREFUxsaGQYMG6QzKbtiwIZs3b+b48eNlNpgAIwcUlpaWzJo1i1mzZuWZJzAwUC8t99MKIcoqTWIi6ovZ4000f3/Donn4EO3ffU2FEEKIssTd3V1nUHafPn3o3bs3/v7+7NmzRy9/TEwMgN4Nv4mJCQ4ODsr+nEWQU1JSdPJVq1aNTZs2AXD48GG2bdumV0bu4CK3H374gc8++4xLly6hVquVdEPdjxwcHAym/fDDD6Snp+sEAvXq1dPJV7VqVSA7aLGxscHDw8NgfcryDE9g5IBCiIpMffEid319AehtaYmqalV6h4aifeyCKYQQQpRFlSpVwt3dnaNHj+oFA0XRqFEjILvrUW4WFhZ06tQJgMuXLxs81tC3/qdOnWLSpEm0a9eODz74ADs7O8zNzdm9ezf79u0rdj2BPGcc1Wq1emnlZQ0KMOKgbCHEPxqlpzMhNhbHIizoKIQQQjzrcmZ4MhRQ1K9fH8gedJ2bVqvlxo0byv5GjRrx/PPPP3FgkiMkJARLS0sCAgIYNmwY3bp1UwITQ27cuGEwrWbNmmW6m1JJkoBCCCGEEEKUuMTERM6ePUutWrWoWbOm3n5XV1dq1qzJtm3bdLodHTp0iHv37ukMjn7rrbd4+PAh77//vhKkFJepqSkqlYqsrCwl7fbt23z//fcG858+fVoZvwvZSxccP36crl27PlE9yhPp8iSEEEIIIZ7YsWPHlK5HOStlx8fH88EHHxgcm2Bubs6MGTOYM2cOY8eOpX///vz5558EBgbSrFkzRowYoeQdMGAAly9fZv369Zw/f55+/frRoEEDkpOTiYqK4sCBA9ja2hZqWtlu3bqxadMmxo8fj7e3N3/99RfffPMN9vb2BsfpNm3alHHjxjFmzBhMTU3ZunUr5ubmvPnmm0/waZUvElAIIYQRyZTBQohaVRozpM1So5ZfElasWKH8bGVlRbNmzVi+fDne3t55HuPj44OlpSUbNmxg8eLF2NjYMGDAAGbMmKHXnWj69Ol07tyZrVu3smvXLh4+fEilSpVo3LgxkyZNYuTIkVSvXr3Aenp4eODv78+GDRtYuHAhDRo0YMaMGdy5c8dgQNGxY0eaN2/OunXr+PPPP3FycmLlypU8//zzhf9wyjkJKIQQ4ilTP3pE3N/f4m3ctIkzly7x8M8/WbJypZFrJoQwBktzG56rYXithrLAx8cHHx+fAvN16NDB4A17//796d+/f6HK6tixIx07dixU3vxmBR02bBjDhg3TS586darB/IMHD2bw4MF5nm/q1KkGjy3sZ1PWSUAhhBBPWdzly4T8/ag8tlIlMDcnNjKSzNRUI9dMCCGEKDoZlF3ORUVF4e/vrzOYSAjx7GiRno5jRgYtZIYvIYQQZZQEFOVcYGAgoaGhBhcIFEIYXy2Nhi6pqdTSaIxdFSGEEKJYpMtTOfQo9RHRd7P7DSakJCivmVmyArMQQgghRGHlNw5D/EMCinIo+m404zePB8BMZYaVnRUnVCdIVaciy68IIYQQQoiSJAFFOZdZJZMkpyRjV0MIIYQQQpRTMoZCCCGEEEIIUWwSUAghhBBCCCGKTQIKIYQQQgghRLFJQCGEEEIIIYQoNgkohBBCCCGEEMUmAYUQQgghhBCi2CSgEEIIIYQQQhSbBBRCCCGEEKLYgoKCcHJyUv61aNGCrl27MmHCBHbu3El6erpO/tmzZ+vkz/2vQ4cOSr6IiAicnJw4evRonmXfvn0bJycnNm/ebHD//v37cXJyYseOHQb3T58+HXd3d27dulX0Ny4UsrCdEEIIIYQRZaRkknAr2WjlV2tYGXPrJ78l9PPzo169emRkZHD//n3CwsJ47733+Oqrr1i/fj3169dX8laqVIn58+frncPS0vKJ65Fb//79CQoKYvny5fTu3ZsaNWoo+06cOMG+ffuYPn06DRs2LNFyKxoJKIQQQgghjCjhVjJhy88brfzO092o5VTtic/TrVs3XFxclO3Jkydz4MABpk+fzrRp09i5cycqlQoAMzMzBg0a9MRlFsa8efPw9vbm448/ZsmSJQCo1Wrmz59Ps2bNeO21155KPcoz6fIkhBBCCCFKRb9+/RgxYgTnz58nLCzMKHVo2LAhkydPJjg4mIiICAA2btzI9evXmT9/Pubm5kapV3kiAYUQQgghhCg1AwcOBNAJKLRaLXFxcXr/kpKSSqUO48aNo2nTpsybN48//viDL774ghEjRtC6detSKa+ikS5PQgghhBCi1DRt2hRAZ+BzUlISHh4eenn79OnD6tWrS7wO5ubmzJ8/n5dffhlfX19sbGyYMWNGiZdTUUlAIYQQQoh8RUVFsXv3boYOHYqzs7OxqyPKGGtrawCSk5N10tauXauXt2bNmqVWjzZt2jB06FB27drFkiVLqFq1aqmVVdFIQCGEEEIIPWaVHJSfAwMDCQ8PJyUlBX9/fyPWSpRFKSkpAFSuXFlJMzU1pVOnTk+9Lq6uruzatQtXV9enXnZ5JmMohBCiAst90wjZ30T7+/sTFRWllze/faL8MTGtzKPUR5y6doqElAQAElISOHXtFI9SHxm5dqIsuXz5MgD29vZGrokoLRJQlAPyR14IUVy5bxpPXTvFmi/WEBoaypov1qDOVOvkDQwMJDQ0lMDAQCPVVjxt0XejGb95POGqcNLt0jmhOsH4zeOJvhtt7KqJMmTv3r0AeHp6GrkmorRIl6dyQB5FCyGeRM5NI0CVW1WwwIJzt86RnpGOWaaGzNh7ACQ/fKi8qm/dwPw5WQiqosiskkmSU+nMviPKtwMHDvDtt9/i7u5ulC5O4umQgKKM0mQmok66mP1zxkPlVavNNGa1hBBlXKp9KlozLan1UwHIjL1H/K5A5efcabUmyQwpQpSEag0r03m6m1HLLwnHjh3j8uXLZGZmEhsby/Hjxzl58iTNmjVj1apVOnkzMzMJDg42eJ5+/frprA1x8OBBpdtUbqNGjVJ+/uWXX5SxGrn1798fBwcHvXRRsiSgKKPUSRe5G+kLQO9WlqjUVendKhRtlv5/JiFE+WD2FP4o5vdNdCUzU51XIUTJMLc2K5GVqo1txYoVAFhYWGBra4uLiwsLFixg4MCBWFpa6uRNTU3l3XffNXieHj166AQU+/btM5ivV69eygxSx44d49ixY3p5XFxcJKB4CiSgKAcaPZfOBJ9YY1dDCFHKTCpXRq1WEx8fr7evVq1apV7+kKbPU8nMjL6NGpR6WaLwnkagKUR+fHx88PHxKXT+jz/+mI8//rjAfB06dCA6uuDxOoXJk8PX1xdfX99C5xeFIwGFEEKUIfHx8fz444966YMHDy71sptUr8pb1V8o9XJE0Rg70BRCCAkohBBCiDLOmIGmEELItLFCCCGEEEKIYpOAQgghhBBCCFFs0uVJiFKkfvSIOANT3dV2dzdCbYQQQgghSp5RAwq1Ws2qVasIDg4mMTERZ2dn/Pz88PDwKPDYe/fusXDhQsLCwtBoNHTs2JE5c+bQsKEstCSeHXGXLxPy5pt66b6hoUaojRBCCCFEyTNql6fZs2fz1VdfMXDgQObOnYuJiQlvvPEGZ86cyfe45ORkxo4dy+nTp3nzzTeZNm0aFy9eZOzYsSQkJDyl2gshhBBCCCGM9oQiMjKS/fv3M2fOHF599VUgezYKb29vli1bxtatW/M89ptvvuHGjRsEBQXxwgvZUxh26dKFAQMGsHnzZt5+++2n8RaEEEIIIYSo8Iz2hOLQoUOYm5szfPhwJc3S0pJhw4Zx+vRp7t+/n+exISEhtGzZUgkmABo3boyHhwcHDx4s1XoLIYQQQggh/mG0JxSXLl3C0dGRypUr66S3aNECrVbLpUuXqF27tt5xGo2G6OhoRo4cqbfPzc2NsLAwUlNTqVSpUqnVvaLSpCWivn9RL92yQVsj1EYIIcofmchBCFEWqbRardYYBXt7e1OnTh0CAgJ00q9cuUL//v1ZsGCBztOLHHFxcXh4ePDOO+8wceJEnX1bt27lww8/5MiRI9jb2xe5Ts7Ozmi1WqpUqVLkY58+LWg1+skqU7RaLRoD+0xNTLN/0Bg4DsCkoAdWWsPHmpgChss0UZlmV9VAM1OZqNACWo3+PhMTFWjzKC+nripVfjXF0JEmQN5HlQKtFq2B96AyNc37/Zma/n2ogc9MpUKr1ZKVZfi/rZmZCSXeNkyyz2nwffz9uy9qu4BSahvluV1AsdtGqbQLKJW2UWrXjL8/Owz9yVOpynbbKI12kV1onm3DRGWCKp//a3m3i4I+0aK3i+zzFrNtGKFdVKlShR9++KGYRwvxbDLaE4q0tDTMzc310i0tLQFIT083eFxOuoWFRZ7HpqWlFatOJiYmaPL6Y/TMUYHK1PAelQrTPPYBhQgc8inTJK/zqrIv+HkcltcfHhXZfwgM71T9c7EvIhVQvCNLmEqVfSOQx7783l+en5lKhZlZfn/KSqNtqP6+QcyjvOK0i78PfZptozy0i+wsxWkbpXXNKIW2UZrtIp/zlvW2UdLtIufYfNtG3kfm0y7yP6447eLv3cVvG896uxDiGWe0gMLKyoqMjAy99JyAISc4eFxOulqtzvNYKyurYtXp4kX97jxCCCGEEKJgt27dIiAggF9++YW7d+9iYmJCw4YN6dixI6NGjaJx48ZA9iyfe/bsUY6zsLDA3t4eHx8fXn31VUxzBX+3b9+mZ8+eOpP4BAUFMWfOHCWPpaUltra2ODs707t3bwYOHGjwPjI0NJQvv/ySq1evkpKSQq1atXB1dWXo0KF07dpVyRcXF8e6des4fvw4MTExVK5cmeeee44OHTowefJkve76wogBhZ2dncGB17GxsQAGx08A2NraYmFhoeR7/FiVSoWdnV3JVlYIIYQQopSkpmcR85f+F6VPS/2aFlSyfLJnMT/88AN+fn6Ym5szcOBAmjZtCsDVq1cJCQlh69atnDhxgmrVqgFQqVIl5s+fD0B8fDwHDhxgyZIlxMfHM3369EKV6efnR7169cjIyOD+/fuEhYXx3nvv8dVXX7F+/Xrq16+v5A0ICGDJkiW0b9+eiRMnYmVlxY0bNzhx4gQHDhxQAor4+HiGDh1KUlISQ4cOpVGjRsTHxxMdHc22bdvw9fWVgMIAowUUzs7OBAYGkpycrPOLOXfunLLfEBMTE5o1a8aFCxf09kVGRuLg4CADsoUQQghRZsT8peaL/TFGK39i//o0rl/8e6ebN2/yzjvv0LBhQzZt2kStWrV09s+cOZPNmzfrdFkzMzNj0KBByravry9eXl5s3bqVf/3rXzpPKfLSrVs3XFxclO3Jkydz4MABpk+fzrRp09i5cycqlYrMzEzWrVtHly5d2Lhxo955/vrrL+XnXbt2ERMTw44dO2jZsqVOvqSkJIPd9YURp4318vIiIyODnTt3KmlqtZqgoCBat25NnTp1AIiJieHq1as6x/bp04ezZ8/qdFH6448/CA8Px8vL6+m8ASGEEEIIwcaNG0lJScHf318vmIDsLk0TJkygatWqeZ7DwsICV1dXkpOTdW7wi6pfv36MGDGC8+fPExYWBsDDhw9JSkqiTZs2Bo+pWbOm8vPNmzcxNzfH3cDMajY2Nnl2ya/ojBZQuLu74+XlxbJly1i6dCk7duxg7NixxMTEMGPGDCXfrFmz6Nevn86xo0ePpmHDhkyYMIGAgAA2b97MuHHjsLOzU/rXCSGEEEKI0vfDDz/g4OBAixYtnug8MTExqFSqfAOPwhg4cCCAElDUrFkTKysrQkNDSUhIyPfY5557joyMDPbu3ftEdahojNblCWDJkiWsXLmS4OBgEhIScHJyYv369XlGkDlsbGwIDAxk4cKFrFu3Do1GQ4cOHZg7dy7Vq1d/SrUXQgghhKjYkpKSuH//Pr169dLbl5iYSGZmprJtY2OjzNKp1WqJi4sDICEhgaCgIM6fP0/37t2LPblOjpzxG7du3QKyu8u//vrrrF27lu7du9OuXTvatGlDly5ddBZJBhg6dCibN29m5syZfP7557Rv35527drRtWvXMrKsgHEYNaCwtLRk1qxZzJo1K888gYGBBtPr1q3L6tWrS6tqQgghhBCiAElJSQBYW1vr7RsxYgTXrl1TthctWoSPj49ynIeHh07+Hj16sHDhwieuU05dkpOTlbRp06bRqFEjvvnmG37++WeOHTvGJ598QvPmzVm2bBmNGjUCoFatWgQHB7N27VqOHj3Ktm3b2LZtG+bm5rz11ltMnDixgDVYKiajBhRCCCGEEKLsyplYJyUlRW/fokWLSE1N5ebNm3zwwQc6+6ytrVm7di1arZaYmBi++OIL4uLiSmSMQk5dHp+NydvbG29vb5KSkjh37hxBQUHs27ePSZMmsXfvXuXpSe3atZk/fz7z5s3j+vXrHD9+nA0bNrBixQpq1arFsGHDnriO5Y0EFEIIIYQQoliqVKmCnZ0dv//+u96+Vq1aARjsjm5qakqnTp2U7Xbt2uHt7c2KFSt47733nqhOly9fBsDe3t7gfhsbGzp37kznzp0xNzdnz549nDt3jnbt2unkU6lUODo64ujoSPfu3XnppZf473//KwGFAUYblC2EEEIIIcq+7t27c+PGDSIjI4t9jueffx4fHx+2b99OTMyTTaGbM6Da09OzwLyurq4ABtdGy61hw4ZUrVq1wHwVlQQUQgghhBCi2MaPH0+lSpX497//zYMHD/T2a7XaQp1n3LhxZGVlsWnTpmLX5cCBA3z77be4u7srT0BSU1M5c+aMwfw//fQTAI6OjkD2emiGum9FRkYSHx+v5BO6pMuTEEIIIYQR1a9pwcT+9QvOWIrlP4nnn3+eZcuWMX36dPr27cuAAQNwcnJCo9Fw8+ZN9u7di5mZGXZ2dgWep0ePHuzatYvJkycXOHPnsWPHuHz5MpmZmcTGxnL8+HFOnjxJs2bNWLVqlZIvNTWVUaNG0bJlS7p06ULdunV59OgRR48e5dSpU/Tq1UuZ7Sk4OJi9e/fSq1cvXF1dMTc35+rVq+zevRtLS0vefPPNJ/qsyisJKIQQQgghjKiSpekTrVT9LOjVqxf//e9/CQgI4KeffmLnzp2YmprSoEEDvLy88PX1pXHjxgWeZ9y4cXz//fd8/fXXTJ06Nd+8K1asALIXxbO1tcXFxYUFCxYwcOBAncHdVatWZcGCBfz4448EBQURGxuLqakpjo6OvPvuu4wZM0bJO3LkSKysrAgPDyc0NJSkpCSqV69O586dmThxot40syKbSlvY51DC6CIiIhg7dqzBfQcOHND5j/rbb7+xdOlSLl68iI2NDX379mX69OlUqlTwBeuPP/5g+/btREZGcvHiRdLT0/n+++9p0KCBwfw7d+7kyy+/5Pbt29SvX5+xY8fy8ssv6+W7d+8eCxcuJCwsDI1GQ8eOHZkzZw4NGzYs5CcgILuf55YtWzh37hwXLlwgJSWFLVu20KFDB72833//PWvWrOHKlSvUrFmTYcOG8eabb2JmpvtdQmJiIkuXLuXIkSOkpaXRokUL5syZg4uLS6Hq5OTkZDB93rx5+Pr66qQVpR0Utm1VNKVxLVCr1axatYrg4GASExNxdnbGz89Pb1rHvGzbto3w8HAiIyOJiYlhyJAhfPzxxwbzlkYbkOtL0Rn7WlLYcwohnn3yv7YMeuWVV2jevLlOWp06dZSfL126xKuvvkqTJk2YPXs2d+/eVf4gf/755wWe/+zZswQGBtK4cWMaN27MxYsX88y7fft2PvjgA7y8vHjttdc4deoUH374Ienp6YwbN07Jl5yczNixY0lOTlb+YGzevJmxY8fy3XffUa1atWJ8EhXTtWvX2LBhAw4ODjg5OeXZL/TYsWNMmTKFjh078v7773P58mXWrl3Lw4cPef/995V8Go2GCRMmcPnyZcaNG0f16tX55ptvGDNmDEFBQXnOkvE4T09PZXXSHO7u7jrbRWkHhW1bFVlJXgtmz57N4cOHGTt2LA4ODuzZs4c33niDwMBAZaaW/GzYsIGkpCTc3NyIjY3NM19ptAG5vhSPMa8lhT2nEKKM0IoyIzw8XNusWTPtkSNH8s03fvx4bZcuXbRJSUlK2rfffqtt1qyZ9pdffimwnIcPH2ofPXqk1Wq12k2bNmmbNWumvXXrll6+1NRUbfv27bWTJk3SSZ8+fbq2VatW2sTERCVt/fr1WicnJ+3//vc/Je3KlStaFxcX7cqVKwusk/jHo0ePtHFxcVqtVqs9cuSItlmzZtrw8HC9fP369dMOGTJEm5mZqaR98sknWmdnZ+21a9eUtP379+u1q7/++kvbtm1b7cyZMwtVp2bNmmkXLFhQYL7CtoOitK2KqKSvBefOndM2a9ZMu2nTJiUtLS1N26tXL+3o0aMLVafbt29rNRqNVqvVatu0aaOdNWuWwXyl0Qbk+lI8xryWFPachvTo0UO7evXqorxVIUQpk1meyqikpCSd5exzp//yyy8MHjxYZ0GXQYMGYW1tzcGDBws8t62tLTY2NgXmi4iIID4+ntGjR+ukv/zyyyQnJyszJwCEhITQsmVLnb6HjRs3xsPDo1B1Ev+wsbEpcKDalStXuHLlCiNHjsTU1FRJHz16NBqNhsOHDytpISEh1K5dm549eyppNWrUoG/fvhw9epSMjIxC1y0tLY309PQ89xe2HRSlbVV0JXEtOHToEObm5gwfPlxJs7S0ZNiwYZw+fbpQ0yQ+99xzhVo9tjTagFxfisdY15KinFMIUTZIQFEGzZw5kzZt2uDu7s64ceOIjo5W9kVHR5OZmanMq5zDwsICFxcXLl26VGL1yOkK9XhZzZs3x8TERNmv0WiIjo7Wywfg5ubG9evXSU1NLbF6ibx/N3Xq1KFu3bo63dguXbpE8+bN9W4G3dzcSE5O5ubNm4Uqc9euXbRs2ZIWLVowYMAAjhw5orO/KO2gsG2roiupa8GlS5dwdHTUW1W2RYsWaLXaErtulEYbkOtL6SqNa0lRzimEKBskoChDzM3N6dOnD3PnzmXdunVMmTKFyMhIRo8ezbVr1wCUvsuGpmazs7Mr0QVZYmNjlZkVcstJyykrPj4etVqdZ520Wm2+fa5F0RWlHcTGxlK7dm29fDlphWkzrVq1ws/Pj3Xr1vGf//wHtVrNW2+9xb59+5Q8RWkHhW1bFVVJXwvyagM5x5bU510abUCuL6WrNK4lT/PvlBDi6ZBB2WVI69atad26tbLds2dPXnzxRYYOHcqaNWtYvnw5aWlpQPYf3cdZWloq+0tCWloa5ubmBvdZWloqXV9yXvOqU865RMkpqB3k/sY2LS3NYL6ctML8brZv366zPWTIELy9vVm6dCn9+/dHpVIVqR0Utm1VVCV9Lcjr8875vZTU510abUCuL6WrNK4lRTmnWq0mKSlJJ49GoyE1NZW4uDid9Bo1ahTqPQkhSp4EFGWcs7MzHh4ehIeHA2BlZQVkX4Qfl56errM/ISFBZ3+NGjV0+rMWxMrKymA5OWXl/DHPec2rTrnrLUpGYdtBTl5D+XLScvLGxcWRlZWl7Le2ttbrIpN736hRo1i+fDl//PEHjRs3LlI7KGzbEv8o7rUgJ6+hsTI5v5eczzs+Pl4nn5WVFVWqVCl0HUujDcj1pXSVxrWkKOfct28fc+bM0csXEBBAQECATlruLn9CiKdLAopyoF69espNRM4jZEOP+HM/jj5z5ozePPb5rTVhiJ2dHRkZGcTHx+t0S1Cr1cTHxytl2draYmFhkWedVCpVgatniqLJ3Q4e74IQGxurMw1oXl0MctJyjh82bBh37txR9r/11lv5LjpUr149ACVwLUo7KGzbErqKcy3IyWuoDeQcm5N36tSp/Prrr8r+/NaaMKQ02oBcX0pXaVxLinJOT09PNm3apJNn5syZdO7cmcGDBxf3bQkhSpgEFOXArVu3lJk6mjVrhpmZGRcuXOCll15S8qjVai5dusSAAQOA7G8zH79IF/WPbs5CRRcuXMDT01NJv3DhAhqNRtlvYmJCs2bNuHDhgt45IiMjcXBwKNSCe6Lwcv9ucq9TcO/ePe7evauzyJSzszNnzpxBq9XqDKaMjIzE2tpamTt+6dKlOl1fClow7NatW8A/3RCK0g4K27aEruJcCyC7DQQGBpKcnKzz1OncuXPKfoBZs2aRmJio7C9qYFcabUCuL6WrNK4lRTln7dq19dqZpaUlDRs2pFOnTiX4ToUQT0IGZZchj/cXBTh16hQRERHKH9wqVarg4eFBcHAwycnJSr7g4GBSUlLw8vICoFq1anTq1EnnX1G7kXTs2BFbW1u++eYbnfRt27ZhbW1N165dlbQ+ffpw9uxZndk7/vjjD8LDw5U6iZLTtGlTGjVqxI4dO3S6KW3btg0TExOdG0wvLy/u37/P999/r6TFxcVx6NAhevbsqfRjb9OmjU57yQkoDLXLhw8f8s0339CgQQOef/55Jb2w7aAobasiKslrAWS3gYyMDHbu3KmkqdVqgoKCaN26tbJYnqurq04baNKkSZHrXhptQK4vpac0riVFOacQomwwnTdv3jxjV0IUzsSJEzl06BB37tzh6tWrfPfdd/j7+1OtWjWWL1+u9GVu3LgxgYGBHDt2DI1Gw9GjR1m1ahWdO3dmypQpBZbz6NEjAgICOHnyJCdPnuTGjRuYmZkRGRnJnTt3lG8rzczMsLa2ZvPmzVy5coWkpCS2bNlCcHAwb7/9ts63R05OThw8eJA9e/ag1WqJjIxk/vz5WFtb8/HHH8s3iEW0bt06Tp48ya+//srly5cxMTEhOjqa6OhoWrRoAWSvC7B582Z+++031Go1e/bsYdOmTYwcOZIhQ4Yo52rUqBFhYWHs2LGDjIwMfv/9dz766CMePXrEJ598ojfLzuM2btzIokWLuHv3Ljdv3iQ0NJS5c+cSFxfH4sWLcXR0VPIWth0UpW3tatPUAAAgAElEQVRVRCV9Lahbty5Xrlxh69atJCcnc/v2bRYtWsTVq1dZunQp9evXL7BOoaGhHDx4kJMnTxIREYFGoyEuLo6TJ09Sv359qlatCpROG5DrS/EZ61pS2HMa8tVXX+Hi4kKHDh1K50MRQhSZSqvVao1dCVE4W7ZsYe/evdy8eZOkpCRq1KiBp6cnU6dO1fuDf+rUKZYtW8bFixexsbGhX79+vPPOO1hbWxdYzu3bt3UWJsqtffv2BAYG6qR9++23fPnll9y+fZt69eoxZswYvfEZAHfv3mXhwoWEhYWh0Wjo0KEDc+fOLbDrjNDn5ORkMP25554jNDRU2T569Chr1qzh6tWr1KhRg6FDhzJ58mTMzHR7OyYkJLBkyRKOHj1Keno6bm5uzJ49W6c7Ql6OHz9OQEAAly9fJiEhAWtra1q2bMnEiRNp06aNXv6itIPCtq2KpjSuBenp6axcuZK9e/eSkJCAk5MT77zzTqGDt9mzZ7Nnz54865v75q802oBcX4rHmNeSwp7zcS+++CJDhgzJdwyXEOLpkoBCCCGEEEIUW1BQEHPmzOG7774zOMbt008/Zc2aNZw8eVJ5Wpnbiy++iLOzM+vWrQMgIiJC+eJg1apVBrsuent78/vvv+t90Zk7SFapVNSuXZsXXniBqVOn0rx5c44ePcqUKVOYO3euwS8n5syZw759+wgODqZRo0ZF/zAqKBmULYQQQghhRJrkRNTXjbdCuMXzL2BSWf9G39gsLCzYt2+fXkARFRXF77//nufYT09PTwYOHIhWq+XatWts3bqVkSNH8u2339KrVy9efPFFVq1aRZ8+fZQxYgCnT59mz549TJkyRYKJIpKAQgghhBDCiNTXL3J3nq/Ryq87bxtWzTsarfy8dOvWjWPHjvHo0SOdNW/27duHvb19nt24GzVqxKBBg5TtVq1aMXHiRLZt28ZHH33Ef/7zH/r168fChQtZtWoVAJmZmcybN4/nn3+eiRMnlu4bK4dklichhBBCCPHM6dWrFyqVisOHDytpWq2WAwcO0K9fv0Kfp3379gDKWkr16tXj7bff5tChQ/z8889A9mD/y5cvM2/ePIOruIv8SUAhhBBCCCGeOTY2NnTv3p39+/crab/99ht37tzRWUunIDnrIuWeaWzMmDG88MILfPjhh1y/fp01a9YwePBgOnZ89p7UlAUSUAghhBBCiGeSt7c34eHhPHjwAMju7uTk5JTvOjjp6enExcXx119/cerUKebMmQOgs8aJqakpH374Ibdv32bkyJFYWFgwa9as0n0z5ZgEFEIIIYQQ4pnUrVs3KlWqxMGDB8nMzOTQoUN4e3vne8yOHTvw8PCgU6dOvPzyy1y7dg0/Pz+9wd1ubm74+voSHx/PjBkzqFGjRmm+lXJNBmULIYQQQgijUqlUBtMtLS3p3bs3+/btw8HBgbi4uALHT7z00kv4+vqiUqmwsbGhadOmWFlZGczr6uqq8yqKR55QCCGKLCIiAicnJ4KCgkrl/E5OTsyePbtUzl0RleTn+a9//YtRo0bppL344ouMGTOmRM6fm1arZciQIUp3BSFE2ZQzvWtaWprB/ampqXlOAQvQv39/zp49y4YNG2jVqhUNGjTIt7y6devSqVMnPDw8cHNzyzOYECVHAgohypmcm/2cfy4uLrRr1w5vb29mzZrFTz/9hLHXs0xMTOTTTz8lIiLCqPXIz5gxY2jVqpWxq1Fon376KUePHi3VMk6fPs3Bgwf517/+Varl5FCpVLz11lt89913XLp06amUKYQoefXq1QPg2rVrevsSEhKIi4ujfv36eR7fqVMnatb8//buPCrKev8D+HsYh0RZBAVNHHAdjkoCgWyRskkSWIgbKi6RmIZaLil67k+7ZhqpN1NccsMwwZRFcbyJSSjiEml53ahr5sKgXFmUXVnm+f3hmbmMMygOKN18v87hj/nuz+OpM5/5bh2Rk5PzxOVO1Dq45InoLyo4OBiDBg2CIAiorKzEtWvXkJGRgX379sHT0xNffvmlzhtLn4eysjLExsZixowZcHNz08o/f/48DAz4e8fTiI2NxfDhw+Hv7//M+tiwYQP69u37XE9B8fPzg7W1NTZu3Ii1a9c+t36JqOV4eHhAIpEgISEBrq6uGsubdu3aBQAYNGhQo/XFYjH+7//+D7///vtTHRdLzw8DCqK/qH79+mlc7AMACxcuxMqVKxEXF4c5c+Zg69atrTS6x3vc1De1jhs3buDEiROtshTtrbfewubNm1FYWAhLS8vn3j8RNc3evXvRqVMnrfTIyEhMmzYN69atQ3h4OHx9fWFoaIiffvoJ6enpeOONN9R3RTQmMDDwWQ2bWgADCqIXiFgsRnR0NM6fP4/jx4/jzJkzcHFxUeeXl5dj06ZNOHz4MG7fvg1jY2N4enpi9uzZkEqlT2xfEAQkJiYiKSkJV69ehYGBAezt7REVFaX+VfvHH3/ExIkTATz8VT02NhYAYG1tjR9++AHAwzX/w4cPx2effabR/t69e5GQkICrV69CIpHAwcEB77//vsYzNKw/ZswYrF69GhcvXsRLL70Ef39/LFq0CO3bt9f/Jepw4cIFbNq0CWfOnEFlZSWsra0REhKCyMhItGnz3//NTpgwAfn5+UhMTERMTAyOHz+OmpoauLi44G9/+xt69Oih0a5CoUBMTAxOnDgBAHBzc8OiRYswadIkWFtbY+fOnVAoFPDz8wMApKamIjU1VV3/t99+02jvl19+0ft9pKenQxCEx/6KeOnSJcTExODChQuQSCTw8fHB/Pnz0bFjR3WZlJQULFy4EHFxcTh79ixSUlJQWFiIHj16YNq0aQgKCtJqd9CgQVi/fj2OHDmCsWNb7zZhomfFsHs/dPk4sVX7bwmq2YZHTZo0CTNmzEC3bt2wa9curFu3DvX19bCxscHcuXMRERHRIv1T62FAQfQCGjlyJM6ePYtjx46pv4yXl5cjLCwMt27dwogRI9CnTx8UFhYiISEBo0aNQnJyMqytrR/b7kcffYSDBw/ijTfeQGhoKGpqanDgwAFERERg3bp18PPzQ69evbBw4UKsWLECQ4YMwZAhQwDgiV9qV65cia1bt2LAgAGYM2cOKioqsGfPHkyaNAkbNmzA4MGDNcrn5uZi2rRpCA0NRXBwMHJycpCUlAQDAwN88sknzXh7mo4ePYoZM2bA1tYWERERMDMzw7lz57B27Vrk5uZqLdOpqqpCeHg4HBwcMHv2bCgUCsTHx+P999+HXC6HWCwGANy9exfjx49HcXExwsLC0LNnT5w9exaTJk1CVVWVuj0LCwt8/vnnmD9/PlxcXDB69Gid42zu+8jJyYGpqalW0KNSUFCAyZMnIyAgAG+88QYuX76M5ORkXLx4EUlJSTAyMtIov2rVKlRVVakDhJSUFMyZMwcPHjxAaGioRtl+/frB0NAQOTk5DCjoL8mgvSna9v/fvVAtNDRU679bXUJCQhASEvLEcm5ublo/iOiyf/9+rbSm1GuoqWOnJxCI6C/l9OnTgkwmE7Zu3dpomYsXLwoymUyYMWOGOu2TTz4RXnnlFSE3N1ejrEKhEJycnIQFCxZo9ZGcnKxOO3z4sCCTyYTdu3dr1K+trRWGDx8u+Pj4CEqlUhAEQcjLyxNkMpmwdu1aneOTyWQa/V29elWws7MTwsLChAcPHqjTCwoKBGdnZ8HHx0eoq6vTqG9nZyecO3dOo93IyEihX79+QkVFRaPvRiU8PFxwdHR8bJn79+8Lnp6ewrhx44Ta2lqNvLi4OEEmkwmnT5/WaFMmkwmbN2/WKLtlyxZBJpMJWVlZ6rSYmBhBJpMJ+/fv1yirSg8PD9dIf/SdPZrX3Pfh7e0thISE6Mzz8fERZDKZEBcXp5GuegdfffWVOi05OVmQyWSCt7e3UFZWpk4vKysTvL29hYEDBwrV1dVaffj7+wvBwcFPHCcRET1/3PVI9AIyNjYGAFRUVAB4uFTpwIEDGDhwIKysrFBSUqL+MzIygqOjI7Kzsx/bZlpaGtq3bw9/f3+N+mVlZfD19UV+fj6uX7+u13gzMjIgCAKmTJkCQ0NDdXrnzp0RGhqK/Px8XL58WaOOo6MjHBwcNNLc3d1RV1eH/Px8vcbxqBMnTqCoqAihoaEoKyvTeG7V0iDVciUVAwMD9ZKvhuMCHu5TUMnMzISlpaXWiSbvvvuuXmNt7vsoKSmBmZlZo/nGxsYYN26cRtq4ceNgbGyM77//Xqv82LFjYWJiov5sYmKCsLAwlJaW6jz9q0OHDiguLn7iOImI6PnjkieiF5AqkFAFFiUlJbh37x6ys7Ph4eGhs86TTl26evUqKisr4enp2WiZ4uLiRpfMPI5CoQAA9OnTRytPlZaXl4dXXnlFna5rz0eHDh0AAPfu3XvqMehy9epVAMCiRYsaLVNUVKTx2crKSmvTua5xKRQKDBgwQOu9d+zYUa/TuZr7PkQi0WOPG5ZKpRrBHgAYGhpCKpUiLy9Pq3zPnj210nr16gXgv//eDQmC0OjFV0RE1LoYUBC9gFRrTFVf7lVfFD09PREZGalXm4IgwMLCAqtXr260jK6A4FlR7UXQ5XFfjJ+Gqp358+ejb9++OstYWVk993Hp0tx+LSwsUFpa2pJDeiqlpaWwsLBotf6JiKhxDCiIXkBJSUkAoN7IbGFhAVNTU1RUVDx2huFxbG1tcf36dTg4ODxxg/XT/tKs+nX9ypUrsLGx0cj7/fffNco8T927dwcAGBkZ6f3eGmNtbY0bN25AqVRqzFIUFxejrKysRftqij59+uCnn37SGo9KXl4eampqNGYpampqkJeXp3M24o8//tBKU834PHoLbk1NDW7fvo2AgIDmPgYRET0D3ENB9AKpr69HTEwMzp49i8GDB8PZ2RnAw+VMw4YNw/nz53Ho0CGddZ+0fj0kJARKpRL/+Mc/dOY3XPrTrl07AGjyL96+vr4QiUTYtm0bamtr1el37txBSkoKrK2t0a9fyxx7+DS8vLzQsWNHbNmyReeyofv376uXlz0tHx8fFBYWQi6Xa6Rv27ZNZ/l27dq12FIuXVxdXVFZWakO4B5VUVGBhIQEjbSEhARUVFTovGwvMTER5eXl6s/l5eXYvXs3TE1Ntc6jv3z5MmprazFw4MAWeBIiImppnKEg+ou6fPmy+ki9hjdl5+fnw8vLS2tp0uzZs/Hzzz/jww8/RGBgIBwcHCCRSHDr1i1kZWWhf//+WvdCNDR06FCEhobim2++waVLl+Dj4wNzc3MUFBTg3LlzuHHjBjIyMgAA5ubmsLW1xcGDByGVStGpUycYGRnB19dXZ9s9e/bEu+++i61btyI8PByBgYGorKzEnj17UFVVhVWrVj12SY++amtrsWHDBp15AQEB6N27N2JiYhAVFYWhQ4dixIgRsLW1RVlZGf744w98//33iI2N1Xkb+JNERkZCLpdj0aJFOH/+vPrY2F9++QXm5uZa5R0dHXHq1Cls3rwZXbt2hUgk0nmng74CAgKwatUqHDt2DDKZTCvfxsYG69evx5UrV9C/f39cunQJycnJ6NmzJyZMmKBV3tzcHKNGjVIf15iSkoJbt25h2bJlWkfMHjt2DBKJ5JneAk5ERPpjQEH0FyWXyyGXy2FgYIB27dqhS5cuGDhwID7++GOdl5OZmJggMTER27dvx6FDh5CRkQGxWIwuXbrA2dkZo0aNemKfK1asgJubG/bs2YOvvvoKtbW1sLS0RL9+/TB37lyNsqtWrcLy5cvxxRdfoLq6GtbW1o0GFMDDOy5sbW2RkJCA1atXqy+2W716tdbFdi2ltrYWX375pc48W1tb9O7dG6+//jqSkpKwefNmpKWl4e7duzA1NYWNjQ0mT54MOzs7vfq2sLBAQkICYmJikJycDJFIBDc3N3z99dcYOXIk2rZtq1F+yZIlWLp0KTZt2oTKykoAaNGAQiqVwsvLC2lpaTr32XTp0gVr1qxBTEwMDh48CIlEgmHDhmHBggXqGamG5s2bhzNnziAhIQFFRUXo0aMHVq1ahWHDhmmVTUtLg6+vL2/JJiL6kxIJz3IXIBERtai7d+/C3d0dY8aMwdKlS59r37/88gvCwsIQFxen954R1U3Z8fHxTZq5OXLkCGbOnImUlJRGN74TEVHr4h4KIqI/qfv372ulbd68GQDw2muvPe/hwMnJCW+++abW7d/PiiAIiI2NRUhICIMJIqI/MS55IiL6k4qMjFRvOFcqlTh9+jQyMzPh5OTUavsJvvjii+fWl0gkwr59+55bf0REpB8GFEREf1I+Pj7Yt28fvv/+ezx48ACdO3dGREQEoqKinskmdCIi+mtQHYaxc+fO59If91AQERERUbPl5eVh27ZtOHnyJAoKCmBgYACpVAp3d3eEhYWhV69erT3E50apVCItLQ27du3CjRs3UFtbCysrKzg4OGDcuHFwdHSEr68v8vPzn9hWfHw8rK2t4efnp04TiUQwMzODi4sLPvjgA63T9x4NKBQKhUZ91aErTk5OmDVrFmxtbZv1vAwoiIiIiFqRUFcPoaq61foXtTOCqE3zZj0zMzMxe/ZsSCQSvPXWW+jTpw+AhxdWpqeno6ioCKdOnYKZmVlLDPlPb+nSpdi1axf8/Pzg7u4OsViMa9eu4fjx4wgODsbMmTNx5MgR9al8wMMT7bKzs/H5559rtPXaa6/h/v378PPzw1tvvQUvLy/U19fj3//+NxITE/HSSy9BLpfDyspKXaempgYA1JeNqgIKVf26ujr8+uuv2L17N9q2bQu5XI7OnTvr/bxc8kRERETUioSqatTkXm21/g379oLI1Fjv+jdv3sScOXMglUoRFxeHTp06aeR/9NFH2LFjB0QiUXOH+j+hqKgICQkJOk/jEwQBJSUlAKC1F+7ixYvIzs7G22+/rdWmQqEAAPTv318jv3v37liyZAn279+vcaS3KpB4lK76S5cuRWpqKqZNm/aUT/pfPOWJiIiIiPS2detWVFVV4dNPP9UKJoCHX26nTp0KU1NTddrPP/+Md955B6+++iocHR0xefJkXLx4UaNedHQ0XFxccPv2bUybNg1OTk5wd3dHTEwM6uvrNcpWVlZi+fLleP3112Fvb48333wTCQkJGmUUCgXs7OywY8cO7NixA76+vnB0dMR7772HkpIS1NXVYeXKlfD09ISTkxOio6Px4MEDredJTk7G8OHDMWDAALi5uWHBggUoKirS6EcQBJ13JIlEInTs2LFpL7YJnJ2dATxcbtbQhAkTdF4q+ihXV1cAaNLSq8fhDAURERER6S0zMxO2trYYMGBAk8qfPHkSU6dOhYODA2bNmgVBEPDtt98iPDwcSUlJ6N27t7psXV0dIiIi8Oqrr2L+/Pk4efIktm/fDqlUinHjxgF4+Kv/9OnTkZOTg9GjR0MmkyEzMxN///vfUVpaiunTp2v0n5qaCqVSiYkTJ6KoqAjbt29HdHQ0unTpgry8PERFReHChQtITU2FVCpFVFSUum5sbCzWr1+PoKAgjB49GoWFhYiPj8eFCxeQkpKCtm3bomvXrgCA7777DgEBAVoXkbakW7duAYBGsPY0VIFIhw4dmjUOBhREREREpJeKigrcuXNH51HWZWVlqKurU382NjZGmzZt8PHHH8PLywubNm1S540cORKBgYFYv369xvHU1dXVCAkJwXvvvQcAGDt2LIYPH46kpCR1QJGRkYEff/wRc+fOxdSpUwEA48ePx9SpU7FhwwaMGTMGFhYW6jaLi4tx6NAhGBsbq58hMTERrq6uiI+PVy/Nun79OlJTU9UBhUKhwIYNG/DRRx8hIiJC3d6gQYMQFhaG1NRUjB07FlZWVggJCcG+ffswaNAguLm54dVXX8XgwYPRs2fPZr3v6upqlJSUoL6+HleuXMHy5cshEokQEBDw1PVzc3PV9YcMGdKscXHJExERERHppaKiAgDQrl07rbzRo0fDw8ND/SeXy/Hrr7/ixo0bCAoKQklJifqvtrYWzs7OyMnJ0WpnzJgxGp+dnZ3VewoAICsrCxKJBOHh4eo0kUiEiRMnoqamBqdOndKoP3ToUHUwAQAODg4AgOHDh2vs8xgwYABu374NpVIJADhy5AgEQcCQIUM0xm5jYwNLS0uNsa9YsQKLFy9Gt27dcPjwYXz22WcIDAxEREQE7ty58+QX24g1a9bAw8MDXl5eeOedd1BaWoqYmJgmzw41rB8ZGYny8nJ8+umnTa7fGM5QEBEREZFe2rdvDwCoqqrSyluxYgWqq6tx8+ZNLFmyBMDDX/0BYN68eTrbMzDQ/K27Xbt2WstxzMzMUFpaqv6cn5+Pzp07awU1qtmAR/cHqJYkqZiYmAAAXn75Za30uro6VFZWwsTEBNevX4dSqWz0YlHVZmvVc4wfPx7jx4/H3bt38fPPP2P37t3IysrCnDlz8M033+hs40nGjh2LgIAA3L9/H0ePHkVKSorWO2tKfZFIBHNzc/Tq1QsSiUSvsTTEgIKIiIiI9GJiYgJLS0tcuXJFK8/JyQkAYG5urk5T3VawcOFCrbsTdHkWl3g29gW8sXTVmJVKJcRiMbZs2aLzxKrG9jGYm5vDz88Pfn5+mDBhAnJycnDr1i2twKYpunfvDk9PTwCAr68vamtrsXjxYri4uGgFRE+q35IYUBARERGR3ry9vbF3716cP3/+iUtnpFIpgIdfvlvqi621tTV+/PFHVFVVacxSXLt2TZ3fEmxsbFBfXw9bW1t069ZNrzbs7e2Rk5ODO3fu6BVQPGrWrFk4cOAANm7cqHVE7fPEPRREREREpLcpU6bAyMgIixYt0jg+VaXhHcr9+/eHVCrF9u3bUV2tfZlfw2VDTTVo0CDU1tZqHBMrCAJ27twJQ0NDeHh4PHWbugwZMgQGBgZYv369Vp5SqcS9e/cAAIWFhfj999+1yqj2cxgYGMDGxqZFxvTyyy8jMDAQqampKCwsbJE29cEZCiIiIiLSW/fu3bFq1SrMnTsXgYGBGDZsGOzs7KBUKnHz5k0cOHAAbdq0gaWlJcRiMT755BNMnToVw4YNQ0hICKysrFBQUIATJ07AxsYGK1eufKr+fX194ebmhtWrV0OhUKBPnz44evQosrKy8MEHH2ic8NQctra2mDVrFtasWYO8vDz4+PjAyMgIeXl5SE9Px/Tp0zFq1CgUFBRg1KhRcHd3h4eHBzp16oTi4mIcPHgQv/76KyZNmtRiYwKAiIgIpKWl4euvv250b8qzxoCCiIiIqBWJ2hnBsG+vVu2/ufz9/ZGWloZt27YhKysLe/fuhVgsRrdu3TB06FCMHTsWvXo9fEYPDw/s3r0b69evx86dO1FVVQUrKys4OTkhLCzsqfs2MDDAxo0bsWbNGnz33XdISkqCVCrF4sWLMX78+GY/W0PTp0+Hra0t4uPjsW7dOohEInTt2hX+/v7qJVw9evTAokWLcOzYMSQkJKC4uBiGhoaQyWRYtmwZRo4c2aJj6tu3L9zd3ZGYmIj33ntPvcn8eRIJDeehiIiIiIiIngL3UBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERERkd4YUBARERGR3lJSUmBnZwc7OztcuHBBK7+mpgaurq6ws7NDdHT0MxtHVlYW1q1b98zap8a1ae0BEBEREb3IypRKXK6pabX++xkawtSg+b8xGxoaQi6X45VXXtFIz8rKQnl5OSQSSbP7eJzjx48jPj4eM2fOfKb9kDYGFERERESt6HJNDcYWFLRa/4ldusC9bdtmtzN48GD885//xIIFC2DQIEA5ePAgXF1dcenSpWb3QX9OXPJERERERM0WFBSEO3fuICcnR51WWVmJzMxMBAcHa5StqanBl19+idDQUDg7O8PR0RHjxo3D6dOnNcopFArY2dlhx44dSExMhL+/P+zt7TFixAicP39eXS46Ohrx8fEAoF5+ZWdnp87ftm0bwsLC4ObmhgEDBiA0NBSHDh16Fq/hhcSAgoiIiIiarXv37rC3t8fBgwfVaRkZGairq0NAQIBG2YqKCuzduxeurq6YN28eZsyYgZKSEkyZMgW5ublabe/fvx87duzAmDFj8OGHHyI/Px8zZ85EbW0tAGDMmDHw8vICAHz++efqP5X4+Hj07dsXs2bNwpw5cyAWi/HBBx/g6NGjz+BNvHi45ImIiIiIWkRwcDA2bdqExYsXQyKRQC6X4/XXX4eZmZlGOTMzM/zwww8wNDRUp40ePRqBgYHYuXMnli9frlG+oKAAhw8fhomJCQCgR48eeP/995GdnQ0fHx84OTmhZ8+eyM7Oxttvv601rvT0dLRtsKxr/PjxCA0NRVxcHLy9vVvwDbyYOENBRERERC3izTffRFlZGY4fP467d+/i5MmTWsudAEAsFquDCaVSiXv37qGurg729va4fPmyVvmgoCB1MAEALi4uAIC8vLwmjathMFFaWory8nI4Ozvr7IueHmcoiIiIiKhFdO7cGS4uLpDL5fjPf/4DiUQCX19fnWVTU1Oxfft2XLt2Tb10CQC6deumVbZr164an1UzHmVlZU0aV2ZmJjZu3Ijc3FzUNDhRSyQSNak+PR4DCiIiIiJqMUFBQfjss89w8+ZN+Pr6wsjISKvM/v37ER0dDX9/f7z77rvo2LEjxGIxvvrqK52zDgaNHGsrCMITx3PmzBlMnz4dAwcOxJIlS2BpaQmJRILk5GTI5fKnf0DSwoCCiIiIiFrM0KFDsWzZMly4cAFRUVE6y6Snp0MqlSI2NlZjlmDt2rV699vYbEN6ejpeeuklbNu2TWPPRnJyst59kSYGFERERETUYjp06IDFixfjzmE3isUAAAGlSURBVJ076pOXHiUWiwE8nGFQBQL/+te/cO7cOa3lTU2lmgkpKyuDqampRl8ikQj19fXqNIVCgYyMDL36IW0MKIiIiIhaUT9DQyR26dKq/be00aNHPzbf29sbhw8fRlRUFLy9vaFQKLB792707t0bVVVVevVpb28PAFi2bBm8vLwgFosRFBSEwYMHIy4uDlOmTEFwcDCKi4uRkJAAGxsb/Pbbb3r1RZoYUBARERG1IlMDgxa5qfp/SWhoKIqKivDtt98iOzsbvXv3xsqVK3Ho0CGNi/Gehp+fHyZOnAi5XI60tDQIgoCgoCB4eHjg008/xZYtW7B8+XJ069YN8+bNQ35+PgOKFiISmrKbhYiIiIiISAfeQ0FERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHpjQEFERERERHr7fyxGggzUPJkBAAAAAElFTkSuQmCC\n",
>>>>>>> 08b1344abb7c89817ecd3229d9513466f33800d7
      "text/plain": [
       "<Figure size 823.65x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4d=sns.set_style(\"white\")\n",
    "fig4d=sns.set_context(\"talk\")\n",
    "\n",
<<<<<<< HEAD
    "fig_order=['gasv','crest','rdxplorer','popdel','clever','indelminer','mistrvar','breakdancer','pindel','manta_candidateSV','BioGraph*','delly','gridss','smoove','genomestrip']\n",
    "labels=['GASV','CREST','RDXplorer','PopDel','CLEVER','indelMINER','MiStrVar','BreakDancer','Pindel','Manta','BioGraph*','DELLY','GRIDSS','LUMPY','GenomeSTRiP']\n",
=======
    "fig_order=['gasv','crest','rdxplorer','popdel','clever','indelminer','mistrvar','breakdancer','pindel','BioGraph*','delly','gridss','smoove','genomestrip','manta_diploidSV']\n",
    "labels=['GASV','CREST','RDXplorer','PopDel','CLEVER','indelMINER','MiStrVar','BreakDancer','Pindel','BioGraph*','DELLY','GRIDSS','LUMPY','GenomeSTRiP','Manta']\n",
>>>>>>> 08b1344abb7c89817ecd3229d9513466f33800d7
    "pal = []\n",
    "for tool in fig_order:\n",
    "    pal.append(color_map[tool])\n",
    "\n",
    "fig4d = sns.catplot(x=\"len\", y=\"f-score\",data=threshold_100,hue='Tool',aspect=1.8,kind='bar',errwidth=2.5,palette=pal,hue_order=labels)\n",
    "fig4d.set_xticklabels(rotation=0)\n",
    "fig4d.set(ylim=(0, 1))\n",
    "fig4d.set(xlabel='Deletion Length (bp)', ylabel='F-score')\n",
    "fig4d=sns.despine()\n",
    "#plt.show(fig4d)\n",
    "\n",
    "plt.savefig(\"../Figures/fig5/fig5d\", bbox_inches='tight')\n",
    "plt.savefig(\"../Figures/fig5/fig5d.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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